def test_dxdz():
# Default cosmology (Vanilla)
Xz = cosm_xz(3.)
np.isclose(Xz, 7.68841320742732)
dxdz = cosm_xz(3., flg_return=1)
np.isclose(dxdz, 3.5847294011983)
# Open
copen = LambdaCDM(H0=70., Om0=0.3, Ode0=0.)
dxdz = cosm_xz(3., cosmo=copen, flg_return=1)
np.isclose(dxdz, 2.90092902756)
def lX(z, extrap=False, calc_lz=False):
""" Returns l(X) from a preferred data source
Currently Sanchez-Ramirez+16 with smoothing and interpolation
Parameters
----------
z : float or ndarray
Redshift
extrap : bool, optional
If True, take closest value (i.e. extrapolate constant from each edge)
calc_lz : bool, optional
Calculate l(z) instead
Returns
-------
lX : float or ndarray
Float if z is a float
Returns 0 if beyond the interpolated range
"""
# Input
if isinstance(z, float):
z = np.array([z])
flg_float = True
else:
flg_float = False
# Sanchez-Ramirez et al. 2016 (Vanilla cosmology: 0.3, 0.7, 70)
cosmo = cosmology.core.FlatLambdaCDM(70., 0.3)
tab7_fil = pyigm_path + '/data/DLA/XQ-100/sramirez16_table7.dat'
sr16_tab7 = Table.read(tab7_fil, format='ascii')
# Smooth
lxmed = medfilt(sr16_tab7['lx'], 21)
# Interpolate and evaluate
flX = interpolate.interp1d(sr16_tab7['z'],
lxmed,
bounds_error=False,
fill_value=0.)
eval = flX(z)
# Extrapolate?
if extrap:
# Low
zmin = np.min(sr16_tab7['z'])
lowz = z <= zmin
eval[lowz] = lxmed[0]
# High
zmax = np.max(sr16_tab7['z'])
hiz = z >= zmax
eval[hiz] = lxmed[-1]
# l(z)?
if calc_lz:
dXdz = pyigmu.cosm_xz(z, cosmo=cosmo, flg_return=1)
eval = eval * dXdz
# Return
if flg_float:
return eval[0]
else:
return eval
def __find_dXtot__(self, zbins, calc_Dz=False):
""" Calculate DX in zbins
Parameters
----------
zbins : list
calc_Dz : bool, optional
Return Dztot instead of DXtot
Returns
-------
dXtot : ndarray
dX for the full survey
"""
# get z, g(z)
z, gz = self.calculate_gz()
dz = z[1] - z[0]
#
if not calc_Dz:
dXdz = pyigmu.cosm_xz(z, cosmo=self.cosmo, flg_return=1)
else:
dXdz = 1.
dXtot = np.zeros(len(zbins) - 1)
for kk in range(len(zbins) - 1):
# the indices of values within the redshift range
idx = np.where((z >= zbins[kk]) & (z < zbins[kk + 1]))
dXtot[kk] = np.sum((gz * dz * dXdz)[idx])
return dXtot
def dXdz(outfil='Figures/dXdz.pdf'):
""" Plot dXdz vs. z
"""
# z
zval = np.linspace(1., 5, 100)
# dX/dz
dXdz = pyiu.cosm_xz(zval, cosmo=Planck15, flg_return=1)
# Start the plot
xmnx = (1., 5)
pp = PdfPages(outfil)
fig = plt.figure(figsize=(8.0, 5.0))
plt.clf()
gs = gridspec.GridSpec(1, 1)
# Lya line
ax = plt.subplot(gs[0])
#ax.xaxis.set_minor_locator(plt.MultipleLocator(0.5))
#ax.xaxis.set_major_locator(plt.MultipleLocator(20.))
#ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
#ax.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.set_xlim(xmnx)
ax.set_ylim(0., 5)
ax.set_ylabel('dX/dz')
ax.set_xlabel('z')
lw = 2.
# Data
ax.plot(zval, dXdz, 'k', linewidth=lw) #, label='SDSS QSOs (z=4)')
# Label
csz = 17
#ax.text(0.10, 0.80, 'HST/WFC3: QSO spectrum (z~2)', color='black',
# transform=ax.transAxes, size=csz, ha='left')
xputils.set_fontsize(ax, 17.)
# Layout and save
print('Writing {:s}'.format(outfil))
plt.tight_layout(pad=0.2, h_pad=0.0, w_pad=0.4)
plt.subplots_adjust(hspace=0)
pp.savefig(bbox_inches='tight')
plt.close()
# Finish
pp.close()
def dXdz(outfil='Figures/dXdz.pdf'):
""" Plot dXdz vs. z
"""
# z
zval = np.linspace(1., 5, 100)
# dX/dz
dXdz = pyiu.cosm_xz(zval, cosmo=Planck15, flg_return=1)
# Start the plot
xmnx = (1., 5)
pp = PdfPages(outfil)
fig = plt.figure(figsize=(8.0, 5.0))
plt.clf()
gs = gridspec.GridSpec(1,1)
# Lya line
ax = plt.subplot(gs[0])
#ax.xaxis.set_minor_locator(plt.MultipleLocator(0.5))
#ax.xaxis.set_major_locator(plt.MultipleLocator(20.))
#ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
#ax.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.set_xlim(xmnx)
ax.set_ylim(0., 5)
ax.set_ylabel('dX/dz')
ax.set_xlabel('z')
lw = 2.
# Data
ax.plot(zval, dXdz, 'k', linewidth=lw)#, label='SDSS QSOs (z=4)')
# Label
csz = 17
#ax.text(0.10, 0.80, 'HST/WFC3: QSO spectrum (z~2)', color='black',
# transform=ax.transAxes, size=csz, ha='left')
xputils.set_fontsize(ax, 17.)
# Layout and save
print('Writing {:s}'.format(outfil))
plt.tight_layout(pad=0.2,h_pad=0.0,w_pad=0.4)
plt.subplots_adjust(hspace=0)
pp.savefig(bbox_inches='tight')
plt.close()
# Finish
pp.close()
def evaluate(self, NHI, z, vel_array=None, cosmo=None):
""" Evaluate the f(N,X) model at a set of NHI values
Parameters
----------
NHI : array
log NHI values
z : float or array
Redshift for evaluation
vel_array : ndarray, optional
Velocities relative to z
cosmo : astropy.cosmology, optional
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
"""
# 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.to('km/s').value)
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.mtype == 'Hspline':
# Evaluate without z dependence
log_fNX = self.model.__call__(NHI)
# Evaluate
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)
#
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.mtype == 'Gamma':
# Setup the parameters
Nl, Nu, Nc, bval = [
self.param['common'][key]
for key in ['Nl', 'Nu', 'Nc', 'bval']
]
# gNHI
Bi = self.param['Bi']
ncomp = len(Bi)
log_gN = np.zeros((lenNHI, ncomp))
beta = [self.param[itype]['beta'] for itype in ['LAF', 'DLA']]
for kk in range(ncomp):
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
itypes = ['LAF', 'DLA']
for kk in range(ncomp):
if kk == 0: # LyaF
zcuts = self.param['LAF']['zcuts']
gamma = self.param['LAF']['gamma']
else: # DLA
zcuts = self.param['DLA']['zcuts']
gamma = self.param['DLA']['gamma']
zcuts = [0] + zcuts + [999.]
Aval = self.param[itypes[kk]]['Aval']
# 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!)
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])
# dX/dz
if cosmo is None:
cosmo = self.cosmo
dXdz = pyigmu.cosm_xz(z_val, cosmo=cosmo, flg_return=1)
# Final steps
if flg_1D == 1:
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))
elif self.mtype == 'PowerLaw':
log_fNX = self.param['B'] + self.param['beta'] * NHI
#
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:
lgNHI_grid = np.outer(log_fNX, np.ones(len(z_val)))
lenfX = len(log_fNX)
#
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)
else:
raise ValueError(
'fN.model: Not ready for this model type {:%s}'.format(
self.mtype))
# Return
if (lenNHI + lenz) == 2:
return log_fNX.flatten()[0] # scalar
else:
return log_fNX
def lyman_limit(fN_model, z912, zem, N_eval=5000, cosmo=None, debug=False):
""" Calculate teff_LL
Effective opacity from LL absorption at z912 from zem
Parameters
----------
fN_model : FNModel
f(N) model
z912 : float
Redshift for evaluation
zem : float
Redshift of source
cosmo : astropy.cosmology, optional
Cosmological model to adopt (as needed)
N_eval : int, optional
Discretization parameter (5000)
debug : bool, optional
Returns
-------
zval : array
teff_LL : array
z values and Effective opacity from LL absorption from z912 to zem
"""
if not isinstance(fN_model,FNModel):
raise IOError("Improper model")
# 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 = pyigmu.cosm_xz(zval, cosmo=cosmo, flg_return=1)
# Evaluate f(N,X)
velo = (zval-zem)/(1+zem) * (const.c.cgs.value/1e5) # Kludge for eval [km/s]
log_fnX = fN_model.evaluate(lgNval, zem, cosmo=cosmo, 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 = ltaa.photo_cross(1, 1, teff_engy)
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 is False:
#; Sum in N first
N_summed = np.sum(intg * np.outer(Nval, np.ones(N_eval)), 0) * dlgN * np.log(10.)
# Sum in z
teff_LL = (np.cumsum(N_summed[::-1]))[::-1] * dz
# Debug
if debug is True:
pdb.set_trace()
# Return
return zval, teff_LL
def lyman_ew(ilambda, zem, fN_model, NHI_MIN=11.5, NHI_MAX=22.0, N_eval=5000,
bval=24., cosmo=None, debug=False, cumul=None,
verbose=False, EW_spline=None, wrest=None):
""" tau effective from HI Lyman series absorption
Parameters
----------
ilambda : float
Observed wavelength (Ang)
zem : float
Emission redshift of the source [sets which Lyman lines are included]
fN_model : FNModel
NHI_MIN : float, optional
-- Minimum log HI column for integration [default = 11.5]
NHI_MAX : float, optional
-- Maximum log HI column for integration [default = 22.0]
N_eval : int, optional
Number of NHI evaluations
bval : float
-- Characteristics Doppler parameter for the Lya forest
-- [Options: 24, 35 km/s]
cosmo : astropy.cosmology (None)
-- Cosmological model to adopt (as needed)
cumul : List of cumulative sums
-- Recorded only if cumul is not None
EW_spline : spline, optional
Speeds up execution if input
HI : LineList, optional
HI line list. Speeds up execution
Returns
-------
teff : float
Total effective opacity of all lines contributing
ToDo:
1. Parallelize the Lyman loop
"""
# Cosmology
if cosmo is None:
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
# Lambda
if not isinstance(ilambda, float):
raise ValueError('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 is None:
if int(bval) == 24:
EW_FIL = pyigm_path+'/data/fN/EW_SPLINE_b24.yml'
with open(EW_FIL, 'r') as infile:
EW_spline = yaml.load(infile) # dict from mk_ew_lyman_spline
else:
raise ValueError('tau_eff: Not ready for this bvalue %g' % bval)
# Lines
if wrest is None:
HI = LineList('HI')
wrest = HI._data['wrest']
# 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 cumul is not 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
# dxdz
dxdz = pyigmu.cosm_xz(zeval, cosmo=cosmo, flg_return=1)
# Get EW values (could pack these all together)
idx = np.where(EW_spline['wrest']*u.AA == 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[0]], der=0)
# dz
dz = ((restEW*u.AA) * (1+zeval) / line).value
# Evaluate f(N,X) at zeval
log_fnX = fN_model.evaluate(lgNval, zeval, cosmo=cosmo).flatten()
# Sum
intgrnd = 10.**(log_fnX) * dxdz * dz * Nval
teff_lyman[qq] = np.sum(intgrnd) * dlgN * np.log(10.)
if cumul is not None:
cumul.append(np.cumsum(intgrnd) * dlgN * np.log(10.))
# Debug
if debug:
try:
from xastropy.xutils import xdebug as xdb
except ImportError:
break
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()
# Return
return np.sum(teff_lyman)
def monte_HIcomp( zmnx, fN_model, NHI_mnx=None, dz=0.001, cosmo=None,
rstate=None, seed=None):
""" Generate a Monte Carlo draw of HI components (z,N,b)
Parameters
----------
zmnx : tuple (float,float)
Redshift range for analysis.
Should correspond to Lya
fN_model : fN_Model class
NHI_mnx : tuple, optional (float,float)
Range of logNHI for linelist
dz : float, optional
Step size for z discretization
cosmo : astropy Cosmology, optional
rstate : RandomState, optional
For random number generation
seed : int, optional
Seed for random numbers
Returns:
-----------
HI_comps : list
List of HI components drawn for the given sightline
"""
# Init
# NHI range
if NHI_mnx is None:
NHI_mnx = (12., 22.)
# seed
if rstate is None:
if seed is None:
seed = 12345
rstate = np.random.RandomState(seed)
# Check fN_model type
if fN_model.fN_mtype != 'Hspline':
raise ValueError('monte_HIlines: Can only handle Hspline so far.')
# Calculate lX at pivot
lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
NHI_mnx[0],NHI_max=NHI_mnx[1], cumul=True)
# Interpolator for NHI distribution (assumed independent of redshift)
# Uses lowest NHI value for the first bit (kludgy but ok)
invfN = interpolate.interp1d(cum_lX/lX,lX_NHI,bounds_error=False,fill_value=lX_NHI[0])#, kind='quadratic')
# z evaluation
zeval = np.arange(zmnx[0], zmnx[1]+dz, dz)
# Cosmology
if cosmo is None:
print('Using a Flat LCDM cosmology: h=0.7, Om=0.3')
cosmo = cosmology.core.FlatLambdaCDM(70., 0.3)
# dXdz
dXdz = pyigmu.cosm_xz(zeval, cosmo=cosmo, flg_return=1)
# Generate loz
loz = lX * dXdz * ( (1+zeval)/(1+fN_model.zpivot) )**fN_model.gamma
# Calculate average number of lines for analysis
sum_loz = np.cumsum(loz*dz)
# Interpolator
# Uses lowest NHI value for the first bit (kludgy but ok)
invz = interpolate.interp1d(sum_loz/sum_loz[-1],zeval, bounds_error=False, fill_value=zeval[0])
# Assume Gaussian stats for number of lines
nlines = int(np.round(sum_loz[-1] + np.sqrt(sum_loz[-1])*rstate.randn(1)))
# NHI values
randNHI = rstate.random_sample(nlines)
lgNHI = invfN(randNHI)
# z values
randz = rstate.random_sample(nlines)
zval = invz(randz)
# b values
randb = rstate.random_sample(nlines)
bval = dopp_val(randb)
# Pack em up as a QTable
HI_comps = QTable([zval, lgNHI, bval], names=('z','lgNHI','bval'))
return HI_comps
def evaluate(self, NHI, z, vel_array=None, cosmo=None):
""" Evaluate the f(N,X) model at a set of NHI values
Parameters
----------
NHI : array
log NHI values
z : float or array
Redshift for evaluation
vel_array : ndarray, optional
Velocities relative to z
cosmo : astropy.cosmology, optional
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
"""
# 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.to('km/s').value)
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.mtype == 'Hspline':
# Evaluate without z dependence
log_fNX = self.model.__call__(NHI)
# Evaluate
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)
#
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.mtype == 'Gamma':
# Setup the parameters
Nl, Nu, Nc, bval = [self.param['common'][key]
for key in ['Nl', 'Nu', 'Nc', 'bval']]
# gNHI
Bi = self.param['Bi']
ncomp = len(Bi)
log_gN = np.zeros((lenNHI, ncomp))
beta = [self.param[itype]['beta'] for itype in ['LAF', 'DLA']]
for kk in range(ncomp):
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
itypes = ['LAF', 'DLA']
for kk in range(ncomp):
if kk == 0: # LyaF
zcuts = self.param['LAF']['zcuts']
gamma = self.param['LAF']['gamma']
else: # DLA
zcuts = self.param['DLA']['zcuts']
gamma = self.param['DLA']['gamma']
zcuts = [0] + zcuts + [999.]
Aval = self.param[itypes[kk]]['Aval']
# 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!)
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] )
# dX/dz
if cosmo is None:
cosmo = self.cosmo
dXdz = pyigmu.cosm_xz(z_val, cosmo=cosmo, flg_return=1)
# Final steps
if flg_1D == 1:
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))
elif self.mtype == 'PowerLaw':
log_fNX = self.param['B'] + self.param['beta'] * NHI
#
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:
lgNHI_grid = np.outer(log_fNX, np.ones(len(z_val)))
lenfX = len(log_fNX)
#
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)
else:
raise ValueError('fN.model: Not ready for this model type {:%s}'.format(self.mtype))
# Return
if (lenNHI + lenz) == 2:
return log_fNX.flatten()[0] # scalar
else:
return log_fNX
def monte_HIcomp( zmnx, fN_model, NHI_mnx=None, dz=0.001, cosmo=None,
rstate=None, seed=None):
""" Generate a Monte Carlo draw of HI components (z,N,b)
Parameters
----------
zmnx : tuple (float,float)
Redshift range for analysis.
Should correspond to Lya
fN_model : fN_Model class
NHI_mnx : tuple, optional (float,float)
Range of logNHI for linelist
dz : float, optional
Step size for z discretization
cosmo : astropy Cosmology, optional
rstate : RandomState, optional
For random number generation
seed : int, optional
Seed for random numbers
Returns:
-----------
HI_comps : list
List of HI components drawn for the given sightline
"""
# Init
# NHI range
if NHI_mnx is None:
NHI_mnx = (12., 22.)
# seed
if rstate is None:
if seed is None:
seed = 12345
rstate = np.random.RandomState(seed)
# Check fN_model type
if fN_model.fN_mtype != 'Hspline':
raise ValueError('monte_HIlines: Can only handle Hspline so far.')
# Calculate lX at pivot
lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
NHI_mnx[0],NHI_max=NHI_mnx[1], cumul=True)
# Interpolator for NHI distribution (assumed independent of redshift)
# Uses lowest NHI value for the first bit (kludgy but ok)
invfN = interpolate.interp1d(cum_lX/lX,lX_NHI,bounds_error=False,fill_value=lX_NHI[0])#, kind='quadratic')
# z evaluation
zeval = np.arange(zmnx[0], zmnx[1]+dz, dz)
# Cosmology
if cosmo is None:
print('Using a Flat LCDM cosmology: h=0.7, Om=0.3')
cosmo = cosmology.core.FlatLambdaCDM(70., 0.3)
# dXdz
dXdz = pyigmu.cosm_xz(zeval, cosmo=cosmo, flg_return=1)
# Generate loz
loz = lX * dXdz * ( (1+zeval)/(1+fN_model.zpivot) )**fN_model.gamma
# Calculate average number of lines for analysis
sum_loz = np.cumsum(loz*dz)
# Interpolator
# Uses lowest NHI value for the first bit (kludgy but ok)
invz = interpolate.interp1d(sum_loz/sum_loz[-1],zeval, bounds_error=False, fill_value=zeval[0])
# Assume Gaussian stats for number of lines
nlines = int(np.round(sum_loz[-1] + np.sqrt(sum_loz[-1])*rstate.randn(1)))
# NHI values
randNHI = rstate.random_sample(nlines)
lgNHI = invfN(randNHI)
# z values
randz = rstate.random_sample(nlines)
zval = invz(randz)
# b values
randb = rstate.random_sample(nlines)
bval = dopp_val(randb)
# Pack em up as a QTable
HI_comps = QTable([zval, lgNHI, bval], names=('z','lgNHI','bval'))
return HI_comps
def lyman_limit(fN_model, z912, zem, N_eval=5000, cosmo=None, debug=False):
""" Calculate teff_LL
Effective opacity from LL absorption at z912 from zem
Parameters
----------
fN_model : FNModel
f(N) model
z912 : float
Redshift for evaluation
zem : float
Redshift of source
cosmo : astropy.cosmology, optional
Cosmological model to adopt (as needed)
N_eval : int, optional
Discretization parameter (5000)
debug : bool, optional
Returns
-------
zval : array
teff_LL : array
z values and Effective opacity from LL absorption from z912 to zem
"""
if not isinstance(fN_model,FNModel):
raise IOError("Improper model")
# 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 = pyigmu.cosm_xz(zval, cosmo=cosmo, flg_return=1)
# Evaluate f(N,X)
velo = (zval-zem)/(1+zem) * (const.c.cgs.value/1e5) # Kludge for eval [km/s]
log_fnX = fN_model.evaluate(lgNval, zem, cosmo=cosmo, 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 = ltaa.photo_cross(1, 1, teff_engy)
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 is False:
#; Sum in N first
N_summed = np.sum(intg * np.outer(Nval, np.ones(N_eval)), 0) * dlgN * np.log(10.)
# Sum in z
teff_LL = (np.cumsum(N_summed[::-1]))[::-1] * dz
# Debug
if debug is True:
pdb.set_trace()
# Return
return zval, teff_LL
def lyman_ew(ilambda, zem, fN_model, NHI_MIN=11.5, NHI_MAX=22.0, N_eval=5000,
bval=24., cosmo=None, debug=False, cumul=None,
verbose=False, EW_spline=None, wrest=None):
""" tau effective from HI Lyman series absorption
Parameters
----------
ilambda : float
Observed wavelength (Ang)
zem : float
Emission redshift of the source [sets which Lyman lines are included]
fN_model : FNModel
NHI_MIN : float, optional
-- Minimum log HI column for integration [default = 11.5]
NHI_MAX : float, optional
-- Maximum log HI column for integration [default = 22.0]
N_eval : int, optional
Number of NHI evaluations
bval : float
-- Characteristics Doppler parameter for the Lya forest
-- [Options: 24, 35 km/s]
cosmo : astropy.cosmology (None)
-- Cosmological model to adopt (as needed)
cumul : List of cumulative sums
-- Recorded only if cumul is not None
EW_spline : spline, optional
Speeds up execution if input
HI : LineList, optional
HI line list. Speeds up execution
Returns
-------
teff : float
Total effective opacity of all lines contributing
ToDo:
1. Parallelize the Lyman loop
"""
# Cosmology
if cosmo is None:
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
# Lambda
if not isinstance(ilambda, float):
raise ValueError('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 is None:
if int(bval) == 24:
EW_FIL = pyigm_path+'/data/fN/EW_SPLINE_b24.yml'
with open(EW_FIL, 'r') as infile:
EW_spline = yaml.load(infile) # dict from mk_ew_lyman_spline
else:
raise ValueError('tau_eff: Not ready for this bvalue %g' % bval)
# Lines
if wrest is None:
HI = LineList('HI')
wrest = HI._data['wrest']
# 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 cumul is not 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
# dxdz
dxdz = pyigmu.cosm_xz(zeval, cosmo=cosmo, flg_return=1)
# Get EW values (could pack these all together)
idx = np.where(EW_spline['wrest']*u.AA == 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.evaluate(lgNval, zeval, cosmo=cosmo).flatten()
# Sum
intgrnd = 10.**(log_fnX) * dxdz * dz * Nval
teff_lyman[qq] = np.sum(intgrnd) * dlgN * np.log(10.)
if cumul is not None:
cumul.append(np.cumsum(intgrnd) * dlgN * np.log(10.))
# Debug
if debug:
try:
from xastropy.xutils import xdebug as xdb
except ImportError:
break
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()
# Return
return np.sum(teff_lyman)