def set_fn_model(flg=0):
""" Load up f(N) model
Parameters
----------
flg : int, optional
* 0 = Default model
* 1 = Gamma
Returns
-------
sfN_model : FNModel
"""
if flg==0: # I may choose to pickle a few of these
sfN_model = FNModel.default_model(use_mcmc=True) # Hermite Spline
elif flg==1:
sfN_model = FNModel('Gamma')
elif flg==2:
tfN_model = FNModel.default_model()
NHI_pivots = [11., 15., 17.0, 20.0, 21.5, 22.]
param = []
for NHI in NHI_pivots:
imin = np.argmin(np.abs(NHI-tfN_model.pivots))
param.append(tfN_model.param['sply'][imin])
# Init
sfN_model = FNModel('Hspline', zmnx=(0.5,5.5), pivots=NHI_pivots,
param=dict(sply=np.array(param)))
else:
raise ValueError('mcmc.set_model: Not ready for this type of fN model {:d}'.format(flg))
#
return sfN_model
def set_fn_model(flg=0):
""" Load up f(N) model
Parameters
----------
flg : int, optional
* 0 = Default model
* 1 = Gamma
Returns
-------
sfN_model : FNModel
"""
if flg == 0: # I may choose to pickle a few of these
sfN_model = FNModel.default_model(use_mcmc=True) # Hermite Spline
elif flg == 1:
sfN_model = FNModel('Gamma')
elif flg == 2:
tfN_model = FNModel.default_model()
NHI_pivots = [11., 15., 17.0, 20.0, 21.5, 22.]
param = []
for NHI in NHI_pivots:
imin = np.argmin(np.abs(NHI - tfN_model.pivots))
param.append(tfN_model.param['sply'][imin])
# Init
sfN_model = FNModel('Hspline',
zmnx=(0.5, 5.5),
pivots=NHI_pivots,
param=dict(sply=np.array(param)))
else:
raise ValueError(
'mcmc.set_model: Not ready for this type of fN model {:d}'.format(
flg))
#
return sfN_model
def test_lya():
# f(N)
fN_model = FNModel.default_model()
# tau_eff
lamb = 1215.6701 * (1 + 2.4)
teff = pyteff.lyman_ew(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
#np.testing.assert_allclose(teff, 0.19821452949713764)
np.testing.assert_allclose(teff, 0.19827, atol=1e-3) # scipy 0.19
# Change cosmology
cosmo = FlatLambdaCDM(H0=60, Om0=0.2)
fN_model2 = FNModel.default_model(cosmo=cosmo)
teff2 = pyteff.lyman_ew(lamb, 2.5, fN_model2, NHI_MIN=12., NHI_MAX=17.)
np.testing.assert_allclose(teff2, 0.2381, atol=3e-4)
def set_fn_model(flg=0, use_p14=False):
""" Load up f(N) model
Parameters
----------
flg : int, optional
* 0 = Default model
* 1 = Gamma
Returns
-------
sfN_model : FNModel
"""
if flg == 0: # I may choose to pickle a few of these
sfN_model = FNModel.default_model(use_mcmc=True) # Hermite Spline
elif flg == 1:
sfN_model = FNModel('Gamma')
elif flg == 2:
tfN_model = FNModel.default_model()
# GGG analysis
pdb.set_trace() # BEING DEVELOPED IN GGG/LLS/Analysis/py
zpivot = 4.4
#NHI_pivots = [11., 15., 17.0, 20.0, 21.5, 22.]
NHI_pivots = [11., 15., 17.0, 18.0, 20.0, 21.5, 22.]
if use_p14:
param = []
for NHI in NHI_pivots:
imin = np.argmin(np.abs(NHI - tfN_model.pivots))
# Adjust for zpivot
adj_parm = tfN_model.param['sply'][imin] + np.log10(
((1 + zpivot) / (1 + tfN_model.zpivot))**tfN_model.gamma)
param.append(adj_parm)
else:
#param = [-8.45, -13.959, -18.06, -21.000, -23.735, -24.88]
if len(param) != len(NHI_pivots):
raise ValueError("Incorrect number of parameters")
# Init
sfN_model = FNModel('Hspline',
zmnx=(0.5, 5.5),
pivots=NHI_pivots,
zpivot=zpivot,
param=dict(sply=np.array(param)))
else:
raise ValueError(
'mcmc.set_model: Not ready for this type of fN model {:d}'.format(
flg))
#
return sfN_model
def test_mfp():
fN_default = FNModel.default_model()
z = 2.44
mfp = fN_default.mfp(z)
# Test
assert mfp.unit == u.Mpc
np.testing.assert_allclose(mfp.value, 257.10258545808983)
def test_rhoHI():
fN_default = FNModel.default_model()
z = 2.44
rho_HI = fN_default.calculate_rhoHI(z, (20.3, 22.))
# Test
assert rho_HI.unit == u.solMass / u.Mpc**3
np.testing.assert_allclose(rho_HI.value, 83553755.13025708)
def test_rhoHI():
fN_default = FNModel.default_model()
z=2.44
rho_HI = fN_default.calculate_rhoHI(z, (20.3, 22.))
# Test
assert rho_HI.unit == u.solMass/u.Mpc**3
np.testing.assert_allclose(rho_HI.value, 83553755.13025708)
def test_mfp():
fN_default = FNModel.default_model()
z=2.44
mfp = fN_default.mfp(z)
# Test
assert mfp.unit == u.Mpc
np.testing.assert_allclose(mfp.value, 257.53517867221353)
def test_teff():
fN_default = FNModel.default_model()
zval, teff_LL = pyteff.lyman_limit(fN_default, 0.5, 2.45)
#
np.testing.assert_allclose(zval[0], 0.5)
#np.testing.assert_allclose(teff_LL[0], 1.8176161746504436) scipy 0.16
np.testing.assert_allclose(teff_LL[0], 1.8190744845274058) # scipy 0.17
def test_teff():
fN_default = FNModel.default_model()
zval,teff_LL = pyteff.lyman_limit(fN_default, 0.5, 2.45)
#
np.testing.assert_allclose(zval[0], 0.5)
#np.testing.assert_allclose(teff_LL[0], 1.8176161746504436) scipy 0.16
np.testing.assert_allclose(teff_LL[0], 1.8190744845274058) # scipy 0.17
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
fN_model = FNModel.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(pyift.map_lymanew, 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_lyx():
# f(N)
fN_model = FNModel.default_model()
# tau_eff
lamb = 917.*(1+2.4)
teff = pyteff.lyman_ew(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.234694549996041)
def test_lyx():
# f(N)
fN_model = FNModel.default_model()
# tau_eff
lamb = 917. * (1 + 2.4)
teff = pyteff.lyman_ew(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.234765, rtol=2e-4) # scipy 0.19
def test_lya():
# f(N)
fN_model = FNModel.default_model()
# tau_eff
lamb = 1215.6701*(1+2.4)
teff = pyteff.lyman_ew(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.19821452949713764)
def test_lya():
# f(N)
fN_model = FNModel.default_model()
# tau_eff
lamb = 1215.6701 * (1 + 2.4)
teff = pyteff.lyman_ew(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
#np.testing.assert_allclose(teff, 0.19821452949713764)
np.testing.assert_allclose(teff, 0.1981831995528795) # scipy 0.17
def teff_LL(outfil='Figures/teff_LL.pdf'):
""" Plot teff_LL from z=3.5 down
"""
# z
zem = 3.5
z912 = 3.
# f(N)
fnmodel = FNModel.default_model()
fnmodel.zmnx = (0.5, 4) # extend default range
# Calculate
zval, teff_LL = lyman_limit(fnmodel, z912, zem)
# Start the plot
xmnx = (3.5, 3)
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., 2)
ax.set_ylabel(r'$\tau_{\rm eff}^{\rm LL}$')
ax.set_xlabel('z')
lw = 2.
# Data
ax.plot(zval, teff_LL, 'b', linewidth=lw) #, label='SDSS QSOs (z=4)')
# Label
csz = 17
ax.text(0.10,
0.80,
'Source at z=3.5',
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 sawtooth(outfil='Figures/sawtooth.pdf', all_tau=None):
""" Sawtooth opacity
"""
# Load fN
fN_model = FNModel.default_model()
fN_model.zmnx = (2.,4.1) # extrapolate a bit
# teff
zem = 4
wave = np.arange(4500., 6200., 10)
# Calculate
if all_tau is None:
all_tau = np.zeros_like(wave)
for qq,iwave in enumerate(wave):
all_tau[qq] = lyman_ew(iwave, zem, fN_model)
# Flux attenuation
flux = np.exp(-all_tau)
# Start the plot
xmnx = (4500, 6200)
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., 1.1)
ax.set_ylabel('IGM Transmission')
ax.set_xlabel('Observed wavelength (z=4 source)')
lw = 2.
ax.plot(wave, flux, 'b', linewidth=lw)
# Label
csz = 17
ax.text(0.10, 0.90, 'f(N) from Prochaska+14', color='blue',
transform=ax.transAxes, size=csz, ha='left')
ax.text(0.10, 0.80, 'Ignores Lyman continuum opacity', color='blue',
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()
return all_tau
def dteff(outfil='Figures/dteff.pdf'):
""" Differential teff (Lya)
"""
# Load fN
fN_model = FNModel.default_model()
# teff
cumul = []
iwave = 1215.67 * (1+2.5)
zem = 2.6
teff_alpha = lyman_ew(iwave, zem, fN_model, cumul=cumul)
print('teff = {:g}'.format(teff_alpha))
dteff = cumul[1] - np.roll(cumul[1],1)
dteff[0] = dteff[1] # Fix first value
# Start the plot
xmnx = (12, 22)
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(ymnx)
ax.set_ylabel(r'$d\tau_{\rm eff, \alpha}/d\log N$')
ax.set_xlabel(r'$\log \, N_{\rm HI}$')
lw = 2.
ax.plot(cumul[0], dteff, 'k', linewidth=lw)
# Label
csz = 17
ax.text(0.60, 0.90, 'f(N) from Prochaska+14', color='blue',
transform=ax.transAxes, size=csz, ha='left')
ax.text(0.60, 0.80, 'z=2.5', color='blue',
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 teff_LL(outfil='Figures/teff_LL.pdf'):
""" Plot teff_LL from z=3.5 down
"""
# z
zem = 3.5
z912 = 3.
# f(N)
fnmodel = FNModel.default_model()
fnmodel.zmnx = (0.5,4) # extend default range
# Calculate
zval, teff_LL = lyman_limit(fnmodel, z912, zem)
# Start the plot
xmnx = (3.5, 3)
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., 2)
ax.set_ylabel(r'$\tau_{\rm eff}^{\rm LL}$')
ax.set_xlabel('z')
lw = 2.
# Data
ax.plot(zval, teff_LL, 'b', linewidth=lw)#, label='SDSS QSOs (z=4)')
# Label
csz = 17
ax.text(0.10, 0.80, 'Source at z=3.5',
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 test_mk_mock():
# Quasar
zem = 2.5
# Spectral properties
s2n = 10.
sampling = 2.
R = 2000.
# Resultant wavelength array (using constant dwv instead of constant dv)
disp = 4000/R/sampling # Angstrom
wave = np.arange(3800., 1300*(1+zem), disp)*u.AA
# f(N)
fN_model = FNModel.default_model(cosmo=Planck15)
#
mock_spec, HI_comps, _ = mk_mock(wave, zem, fN_model, s2n=s2n,
fwhm=sampling, seed=11223)
np.testing.assert_allclose(mock_spec.flux[300].value, 21.77, rtol=0.05) # scipy 0.17
def drho_dNHI(outfil='Figures/drho_dNHI.pdf'):
""" Differential contribution to rho_HI
"""
# Wavelength array for my 'perfect' instrument
fnmodel = FNModel.default_model()
rhoHI, cumul, lgNHI = fnmodel.calculate_rhoHI(2.5, (12., 22.5), cumul=True)
cumul = cumul/cumul[-1] / (lgNHI[1]-lgNHI[0]) # dlogN
diff = cumul - np.roll(cumul,1)
diff[0] = diff[1]
# Start the plot
xmnx = (16., 22.5)
ymnx = (0., 1.0)
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(ymnx)
ax.set_ylabel(r'Normalized $d\rho_{\rm HI} / d\log N_{\rm HI}$')
ax.set_xlabel(r'$\log N_{\rm HI}$')
ax.plot(lgNHI, diff, 'b')
# Legend
#legend = plt.legend(loc='lower left', scatterpoints=1, borderpad=0.3,
# handletextpad=0.3, fontsize='large', numpoints=1)
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 drho_dNHI(outfil='Figures/drho_dNHI.pdf'):
""" Differential contribution to rho_HI
"""
# Wavelength array for my 'perfect' instrument
fnmodel = FNModel.default_model()
rhoHI, cumul, lgNHI = fnmodel.calculate_rhoHI(2.5, (12., 22.5), cumul=True)
cumul = cumul / cumul[-1] / (lgNHI[1] - lgNHI[0]) # dlogN
diff = cumul - np.roll(cumul, 1)
diff[0] = diff[1]
# Start the plot
xmnx = (16., 22.5)
ymnx = (0., 1.0)
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(ymnx)
ax.set_ylabel(r'Normalized $d\rho_{\rm HI} / d\log N_{\rm HI}$')
ax.set_xlabel(r'$\log N_{\rm HI}$')
ax.plot(lgNHI, diff, 'b')
# Legend
#legend = plt.legend(loc='lower left', scatterpoints=1, borderpad=0.3,
# handletextpad=0.3, fontsize='large', numpoints=1)
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 test_parallel():
import multiprocessing
from linetools.lists.linelist import LineList
# f(N)
fN_model = FNModel.default_model()
# Lines
HI = LineList('HI')
tst_wv = HI._data['wrest']
#
adict = []
for wrest in tst_wv:
tdict = dict(ilambda=wrest.value*(1+2.4), zem=2.5, fN_model=fN_model, wrest=copy.deepcopy(tst_wv))
adict.append(tdict)
pool = multiprocessing.Pool(2) # initialize thread pool N threads
ateff = pool.map(pyteff.map_lymanew, adict)
# Test
np.testing.assert_allclose(ateff[-3:],
np.array([0.23440858789182742,
0.20263221240650739, 0.21927057420866358]))
def test_mk_mock():
# Quasar
zem = 2.5
# Spectral properties
s2n = 10.
sampling = 2.
R = 2000.
# Resultant wavelength array (using constant dwv instead of constant dv)
disp = 4000 / R / sampling # Angstrom
wave = np.arange(3800., 1300 * (1 + zem), disp) * u.AA
# f(N)
fN_model = FNModel.default_model(cosmo=Planck15)
#
mock_spec, HI_comps, _ = mk_mock(wave,
zem,
fN_model,
s2n=s2n,
fwhm=sampling,
seed=11223)
np.testing.assert_allclose(mock_spec.flux[300].value, 21.77,
rtol=0.05) # scipy 0.17
def init_fNHI(slls=False, mix=False, high=False):
""" Generate the interpolator for log NHI
Returns
-------
fNHI : scipy.interpolate.interp1d function
"""
from pyigm.fN.fnmodel import FNModel
# f(N)
fN_model = FNModel.default_model()
# Integrate on NHI
if slls:
lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
19.5,
NHI_max=20.3,
cumul=True)
elif high:
lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
21.2,
NHI_max=22.5,
cumul=True)
elif mix:
lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
19.5,
NHI_max=22.5,
cumul=True)
else:
lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
20.3,
NHI_max=22.5,
cumul=True)
# Interpolator
cum_lX /= cum_lX[-1] # Normalize
fNHI = interpolate.interp1d(cum_lX,
lX_NHI,
bounds_error=False,
fill_value=lX_NHI[0])
return fNHI
def test_parallel():
import multiprocessing
from linetools.lists.linelist import LineList
# f(N)
fN_model = FNModel.default_model()
# Lines
HI = LineList('HI')
tst_wv = u.Quantity(HI._data['wrest'])
#
adict = []
for wrest in tst_wv:
tdict = dict(ilambda=wrest.value * (1 + 2.4),
zem=2.5,
fN_model=fN_model,
wrest=copy.deepcopy(tst_wv))
adict.append(tdict)
pool = multiprocessing.Pool(2) # initialize thread pool N threads
ateff = pool.map(pyteff.map_lymanew, adict)
# Test
np.testing.assert_allclose(ateff[-3:],
np.array([0.238569, 0.206972, 0.225049]),
atol=3e-4)
def get_telfer_spec(zqso=0., igm=False, fN_gamma=None,
LL_flatten=True, nproc=6):
"""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?
nproc : int, optional
For multi-processing with IGM
Returns
-------
telfer_spec : XSpectrum1D
Spectrum
"""
# Read
telfer = ascii.read(
pyigm_path+'/data/quasar/telfer_hst_comp01_rq.ascii', comment='#')
scale = telfer['flux'][(telfer['wrest'] == 1450.)]
telfer_spec = XSpectrum1D.from_tuple((np.array(telfer['wrest'])*(1+zqso),
np.array(telfer['flux'])/scale[0])) # Observer frame
# IGM?
if igm is True:
"""The following concept is rather experimental.
Use at your own risk.
"""
import multiprocessing
fN_model = FNModel.default_model()
# Expanding range of zmnx (risky)
fN_model.zmnx = (0.,5.5)
if fN_gamma is not None:
fN_model.gamma = fN_gamma
# Setup inputs
#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
HI = LineList('HI')
twrest = HI._data['wrest']
# Parallel
if LL_flatten:
igm_wv = np.where((telfer['wrest'] > 900.) & (telfer['wrest'] < 1220.))[0]
else:
igm_wv = np.where(telfer['wrest'] < 1220.)[0]
adict = []
for wrest in telfer_spec.wavelength[igm_wv].value:
tdict = dict(ilambda=wrest, zem=zqso, fN_model=fN_model,
wrest=twrest.copy())
adict.append(tdict)
# Run
if nproc > 1:
pool = multiprocessing.Pool(nproc) # initialize thread pool N threads
ateff = pool.map(pyift.map_lymanew, adict)
else:
ateff = map(pyift.map_lymanew, adict)
# Apply
new_flux = telfer_spec.flux.value
new_flux[igm_wv] *= np.exp(-1.*np.array(ateff))
# Flatten?
if LL_flatten:
wv_LL = np.where(np.abs(telfer_spec.wavelength/(1+zqso)-914.*u.AA)<3.*u.AA)[0]
f_LL = np.median(new_flux[wv_LL])
wv_low = np.where(telfer_spec.wavelength/(1+zqso)<911.7*u.AA)[0]
new_flux[wv_low] = f_LL
# Regenerate spectrum
telfer_spec = XSpectrum1D.from_tuple(
(np.array(telfer['wrest'])*(1+zqso), new_flux))
# Return
return telfer_spec
def obs_sawtooth(outfil='Figures/obs_sawtooth.pdf', all_tau=None, scl=1.):
""" Sawtooth opacity
"""
# SDSS
hdu = fits.open(
'/Users/xavier/paper/LLS/taueff/Analysis/stack_DR7_z3.92_z4.02.fits')
sdss_fx = hdu[0].data
sdss_wave = hdu[2].data
# Telfer
telfer = pyicq.get_telfer_spec()
i1450 = np.argmin(np.abs(telfer.wavelength.value - 1450.))
nrm = np.median(telfer.flux[i1450 - 5:i1450 + 5])
telfer.flux = telfer.flux / nrm
trebin = telfer.rebin(sdss_wave * u.AA)
# Load fN
fN_model = FNModel.default_model()
fN_model.zmnx = (2., 4.1) # extrapolate a bit
# teff
zem = 4.
wave = np.arange(4500., 6200., 10)
# Calculate
if all_tau is None:
all_tau = np.zeros_like(wave)
for qq, iwave in enumerate(wave):
all_tau[qq] = lyman_ew(iwave, zem, fN_model)
# Flux attenuation
trans = np.exp(-all_tau)
ftrans = interp1d(wave, trans, fill_value=1., bounds_error=False)
# Start the plot
xmnx = (4500, 6200)
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., 1.5)
ax.set_ylabel('Relative Flux')
ax.set_xlabel('Observed wavelength')
lw = 2.
# Data
ax.plot(sdss_wave * (1 + zem),
sdss_fx,
'k',
linewidth=lw,
label='SDSS QSOs (z=4)')
# Model
model = trebin.flux * ftrans(sdss_wave * (1 + zem)) * scl
ax.plot(sdss_wave * (1 + zem),
model,
'r',
linewidth=lw,
label='IGM+Telfer model')
# Label
csz = 17
#ax.text(0.10, 0.10, 'SDSS quasar stack at z=4', color='black',
# transform=ax.transAxes, size=csz, ha='left')
# Legend
legend = plt.legend(loc='upper left',
scatterpoints=1,
borderpad=0.3,
handletextpad=0.3,
fontsize='large',
numpoints=1)
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()
return all_tau
def sawtooth(outfil='Figures/sawtooth.pdf', all_tau=None):
""" Sawtooth opacity
"""
# Load fN
fN_model = FNModel.default_model()
fN_model.zmnx = (2., 4.1) # extrapolate a bit
# teff
zem = 4
wave = np.arange(4500., 6200., 10)
# Calculate
if all_tau is None:
all_tau = np.zeros_like(wave)
for qq, iwave in enumerate(wave):
all_tau[qq] = lyman_ew(iwave, zem, fN_model)
# Flux attenuation
flux = np.exp(-all_tau)
# Start the plot
xmnx = (4500, 6200)
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., 1.1)
ax.set_ylabel('IGM Transmission')
ax.set_xlabel('Observed wavelength (z=4 source)')
lw = 2.
ax.plot(wave, flux, 'b', linewidth=lw)
# Label
csz = 17
ax.text(0.10,
0.90,
'f(N) from Prochaska+14',
color='blue',
transform=ax.transAxes,
size=csz,
ha='left')
ax.text(0.10,
0.80,
'Ignores Lyman continuum opacity',
color='blue',
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()
return all_tau
def dteff(outfil='Figures/dteff.pdf'):
""" Differential teff (Lya)
"""
# Load fN
fN_model = FNModel.default_model()
# teff
cumul = []
iwave = 1215.67 * (1 + 2.5)
zem = 2.6
teff_alpha = lyman_ew(iwave, zem, fN_model, cumul=cumul)
print('teff = {:g}'.format(teff_alpha))
dteff = cumul[1] - np.roll(cumul[1], 1)
dteff[0] = dteff[1] # Fix first value
# Start the plot
xmnx = (12, 22)
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(ymnx)
ax.set_ylabel(r'$d\tau_{\rm eff, \alpha}/d\log N$')
ax.set_xlabel(r'$\log \, N_{\rm HI}$')
lw = 2.
ax.plot(cumul[0], dteff, 'k', linewidth=lw)
# Label
csz = 17
ax.text(0.60,
0.90,
'f(N) from Prochaska+14',
color='blue',
transform=ax.transAxes,
size=csz,
ha='left')
ax.text(0.60,
0.80,
'z=2.5',
color='blue',
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 redshift(outfil='Figures/redshift.pdf'):
""" Series of plots illustrating redshift in the Lya forest
"""
lrest = np.array([900., 1250]) # Ang
zem = 3.
toff = 0.15
yqso = 0.7
sqso = 35
# QSO lines
qsolya = AbsLine(1215.6700 * u.AA)
qsolya.attrib['N'] = 10.**(16.0) / u.cm**2
qsolya.attrib['b'] = 40 * u.km / u.s
qsolya.attrib['z'] = zem
qsolyb = AbsLine('HI 1025')
qsolyb.attrib['N'] = qsolya.attrib['N']
qsolyb.attrib['b'] = qsolya.attrib['b']
qsolyb.attrib['z'] = qsolya.attrib['z']
def tick_function(z, X):
V = X * (1 + z)
return ["{:d}".format(int(round(x))) for x in V]
def add_lines(axi, z):
wvtwo = (1 + z) * 1215.67 / (1 + zem)
axi.scatter([wvtwo], [yqso],
marker='o',
facecolor='none',
edgecolor='green',
s=sqso * 5)
axi.text(wvtwo,
yqso - 1.7 * toff,
'HI Gas (z={:0.1f})'.format(z),
color='green',
ha='center')
#
twolya = copy.deepcopy(qsolya)
twolya.attrib['z'] = z
twolyb = copy.deepcopy(qsolyb)
twolyb.attrib['z'] = z
return [twolya, twolyb]
# Telfer
telfer = pyicq.get_telfer_spec()
# Start the plot
pp = PdfPages(outfil)
scl = 1.0
fig = plt.figure(figsize=(8.0 * scl, 5.0 * scl))
plt.clf()
gs = gridspec.GridSpec(5, 1)
jet = cm = plt.get_cmap('jet')
# Loop
for qq in range(9):
# Cartoon
ax0 = plt.subplot(gs[0, 0])
ax0.set_xlim(lrest)
ax0.set_ylim(0., 1.)
ax0.set_frame_on(False)
ax0.axes.get_yaxis().set_visible(False)
ax0.axes.get_xaxis().set_visible(False)
# QSO
ax0.scatter([1215.67], [yqso], marker='o', facecolor='blue', s=sqso)
ax0.text(1215.67,
yqso + toff,
'Quasar (z=3)',
color='blue',
ha='center')
# Redshifted light
if qq > 0:
light = np.linspace(1215.67, lrest[0], 20)
ax0.scatter(light, [yqso] * len(light),
marker='_',
s=40,
cmap=cm,
c=1. / light)
# Gas at QSO
if qq > 1:
ax0.scatter([1215.67], [yqso],
marker='o',
facecolor='none',
edgecolor='green',
s=sqso * 5)
ax0.text(1215.67,
yqso - 1.7 * toff,
'HI Gas (z=3)',
color='green',
ha='center')
# Spectrum
ax = plt.subplot(gs[1:, 0])
ax.set_xlim(lrest)
#ax.set_ylim(ymnx)
ax.set_ylabel('Relative Flux')
ax.set_xlabel("Rest Wavelength")
if qq < 3:
tsty = 'k'
else:
tsty = 'b:'
ax.plot(telfer.wavelength, telfer.flux, tsty)
# Observer frame axis
if qq > 0:
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
xtcks = ax.get_xticks()
ax2.set_xticks(xtcks)
ax2.set_xticklabels(tick_function(zem, xtcks))
ax2.set_xlabel('Observed Wavelength (Angstroms)')
# Absorption lines
abslines = []
if (qq > 2) and (qq != 8):
# Lya at zem
abslines.append(qsolya)
if qq > 3: # Lyb at zem
abslines.append(qsolyb)
# Gas at z=2.8
if qq > 4:
zadd = 2.8
abslines += add_lines(ax0, zadd)
# Gas at z=2.5
if qq > 5:
zadd = 2.5
abslines += add_lines(ax0, zadd)
if qq > 6:
zadd = 2.2
abslines += add_lines(ax0, zadd)
#
abs_model = ltav.voigt_from_abslines(telfer.wavelength * (1 + zem),
abslines)
#ax.plot(telfer.wavelength, telfer.flux*abs_model.flux, 'k')
ax.plot(telfer.wavelength, telfer.flux.value * abs_model.flux, 'k')
# Final plot
if qq == 8:
nlin = 100
dotwv = np.linspace(900, 1215., nlin)
ax0.scatter(dotwv, [yqso] * nlin,
marker='o',
facecolor='none',
edgecolor='green',
s=sqso * 5)
# Mock spectrum
fN_model = FNModel.default_model()
gdp = np.where(telfer.wavelength > 900. * u.AA)[0]
mock_spec, HI_comps, misc = pyimock.mk_mock(
telfer.wavelength[gdp] * (1 + zem),
zem,
fN_model,
s2n=100.,
fwhm=3,
add_conti=False)
ax.plot(telfer.wavelength[gdp],
telfer.flux[gdp].value * mock_spec.flux, 'k')
# Layout and save
plt.tight_layout(pad=0.2, h_pad=0.0, w_pad=0.4)
plt.subplots_adjust(hspace=0)
pp.savefig(bbox_inches='tight', transparent=True)
plt.close()
# Finish
print('Writing {:s}'.format(outfil))
pp.close()
return mock_spec
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
from astropy.cosmology import Planck15
from lyacolore import DLA
#Import pyigm modules
from pyigm.fN.fnmodel import FNModel
fN_default = FNModel.default_model(cosmo=Planck15)
fN_default.zmnx = (0.5, 5)
fN_cosmo = fN_default.cosmo
use_pyigm = True
include_RSDs = True
#basedir = '/global/cscratch1/sd/jfarr/LyaSkewers/CoLoRe_GAUSS/test_DLA_sample/'
basedir = '/global/projecta/projectdirs/desi/mocks/lya_forest/develop/london/v9.0/v9.0.0/'
#basedir = '/global/projecta/projectdirs/desi/mocks/lya_forest/develop/saclay/v4.4/v4.4.0/'
qso_z_buffer = 0.00
save_plot = True
ver = 'london_v9.0.0'
f_filename = 'f_NHI_{}.pdf'.format(ver)
dndz_filename = 'dndz_{}.pdf'.format(ver)
#Open a DLA master file and extract the relevant data.
h = fits.open(basedir + '/master_DLA.fits')
if include_RSDs:
dla_z = h['DLACAT'].data['Z_DLA_RSD']
dla_z_qso = h['DLACAT'].data['Z_QSO_RSD']
else:
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
fN_model = FNModel.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(pyift.map_lymanew, 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
if outfil != None:
plt.savefig(outfil,bbox_inches='tight')
else:
plt.show()
## #################################
## #################################
## TESTING
## #################################
if __name__ == '__main__':
# Read a dataset
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 = fn_data_from_fits([fn_file, k13r13_file, n12_file])
#ascii_file = xa_path+'/igm/fN/asciidatan12'
#ascii_data = fN_data_from_ascii_file(ascii_file)
#all_fN_cs.append(ascii_data)
print(all_fN_cs)
for fN_c in all_fN_cs: print(fN_c)
# Plot with model
fnmodel = FNModel.default_model()
tst_fn_data(fN_model=fnmodel)
xdb.set_trace()
print('fN.data: All done testing..')
if verbose:
print('Calculating tau_eff from Becker+13')
tau0 = 0.751
beta = 2.90
C = -0.132
z0 = 3.5
teff[np.where(highz)] = tau0 * ((1+z[highz])/(1+z0))**beta + C
badz = z>zmax
if np.sum(badz) > 0:
raise ValueError('teff_obs: Not ready for z>{:f}'.format(zmax))
# Return
if flg_float:
return teff[0]
else:
return teff
if __name__ == '__main__':
import profile, pstats
fN_model = FNModel.default_model()
fN_model.zmnx = (0.,5.)
zem=3.
# Profile
outfil = 'tmp.prof'
profile.run('print lyman_ew(900.*(1+zem), zem, fN_model)', outfil)
# Stats
stats = pstats.Stats(outfil)
stats.strip_dirs()
stats.sort_stats('tottime')
stats.print_stats()
inset2.set_ylim(0,350)
inset2.set_ylabel('(Mpc)')
# Model
if fN_model is not None:
#fN_model.zmnx = (0.1, 20.) # Reset for MFP calculation
mfp = fN_model.mfp(fN_cs[iMFP].zeval)
inset2.plot(3, mfp, 'ko')
# Show
if outfil != None:
plt.savefig(outfil,bbox_inches='tight')
else:
plt.show()
if __name__ == '__main__':
flg_fig = 0
#flg_fig += 2**0 # Without model
flg_fig += 2**1 # With model
# Without model
if (flg_fig % 2**1) >= 2**0:
tst_fn_data()
# With model
if (flg_fig % 2**2) >= 2**1:
fN_default = FNModel.default_model()
tst_fn_data(fN_model=fN_default)
def test_default():
fN_default = FNModel.default_model()
#
assert fN_default.mtype == 'Hspline'
assert fN_default.zmnx == (0.5, 3)
def test_lx():
fN_default = FNModel.default_model()
lX = fN_default.calculate_lox(2.4, 17.19 + np.log10(2.), 23.)
np.testing.assert_allclose(lX, 0.36298679339713974)
def obs_sawtooth(outfil='Figures/obs_sawtooth.pdf', all_tau=None, scl=1.):
""" Sawtooth opacity
"""
# SDSS
hdu = fits.open('/Users/xavier/paper/LLS/taueff/Analysis/stack_DR7_z3.92_z4.02.fits')
sdss_fx = hdu[0].data
sdss_wave = hdu[2].data
# Telfer
telfer = pyicq.get_telfer_spec()
i1450 = np.argmin(np.abs(telfer.wavelength.value - 1450.))
nrm = np.median(telfer.flux[i1450-5:i1450+5])
telfer.flux = telfer.flux / nrm
trebin = telfer.rebin(sdss_wave*u.AA)
# Load fN
fN_model = FNModel.default_model()
fN_model.zmnx = (2.,4.1) # extrapolate a bit
# teff
zem = 4.
wave = np.arange(4500., 6200., 10)
# Calculate
if all_tau is None:
all_tau = np.zeros_like(wave)
for qq,iwave in enumerate(wave):
all_tau[qq] = lyman_ew(iwave, zem, fN_model)
# Flux attenuation
trans = np.exp(-all_tau)
ftrans = interp1d(wave, trans, fill_value=1.,
bounds_error=False)
# Start the plot
xmnx = (4500, 6200)
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., 1.5)
ax.set_ylabel('Relative Flux')
ax.set_xlabel('Observed wavelength')
lw = 2.
# Data
ax.plot(sdss_wave*(1+zem), sdss_fx, 'k', linewidth=lw, label='SDSS QSOs (z=4)')
# Model
model = trebin.flux * ftrans(sdss_wave*(1+zem)) * scl
ax.plot(sdss_wave*(1+zem), model, 'r', linewidth=lw, label='IGM+Telfer model')
# Label
csz = 17
#ax.text(0.10, 0.10, 'SDSS quasar stack at z=4', color='black',
# transform=ax.transAxes, size=csz, ha='left')
# Legend
legend = plt.legend(loc='upper left', scatterpoints=1, borderpad=0.3,
handletextpad=0.3, fontsize='large', numpoints=1)
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()
return all_tau
def test_lx():
fN_default = FNModel.default_model()
lX = fN_default.calculate_lox(2.4, 17.19+np.log10(2.), 23.)
np.testing.assert_allclose(lX, 0.36298679339713974)
def test_init_I14():
# Class
fN_I14 = FNModel('Gamma')
# Mtype
assert fN_I14.mtype == 'Gamma'
assert fN_I14.zmnx == (0., 10.)
def redshift(outfil='Figures/redshift.pdf'):
""" Series of plots illustrating redshift in the Lya forest
"""
lrest = np.array([900., 1250]) # Ang
zem = 3.
toff = 0.15
yqso = 0.7
sqso = 35
# QSO lines
qsolya = AbsLine(1215.6700*u.AA)
qsolya.attrib['N'] = 10.**(16.0)/u.cm**2
qsolya.attrib['b'] = 40 * u.km/u.s
qsolya.attrib['z'] = zem
qsolyb = AbsLine('HI 1025')
qsolyb.attrib['N'] = qsolya.attrib['N']
qsolyb.attrib['b'] = qsolya.attrib['b']
qsolyb.attrib['z'] = qsolya.attrib['z']
def tick_function(z, X):
V = X*(1+z)
return ["{:d}".format(int(round(x))) for x in V]
def add_lines(axi,z):
wvtwo = (1+z)*1215.67/(1+zem)
axi.scatter([wvtwo], [yqso], marker='o', facecolor='none',
edgecolor='green', s=sqso*5)
axi.text(wvtwo, yqso-1.7*toff, 'HI Gas (z={:0.1f})'.format(z),
color='green', ha='center')
#
twolya = copy.deepcopy(qsolya)
twolya.attrib['z'] = z
twolyb = copy.deepcopy(qsolyb)
twolyb.attrib['z'] = z
return [twolya, twolyb]
# Telfer
telfer = pyicq.get_telfer_spec()
# Start the plot
pp = PdfPages(outfil)
scl = 1.0
fig = plt.figure(figsize=(8.0*scl, 5.0*scl))
plt.clf()
gs = gridspec.GridSpec(5,1)
jet = cm = plt.get_cmap('jet')
# Loop
for qq in range(9):
# Cartoon
ax0 = plt.subplot(gs[0,0])
ax0.set_xlim(lrest)
ax0.set_ylim(0.,1.)
ax0.set_frame_on(False)
ax0.axes.get_yaxis().set_visible(False)
ax0.axes.get_xaxis().set_visible(False)
# QSO
ax0.scatter([1215.67], [yqso], marker='o', facecolor='blue', s=sqso)
ax0.text(1215.67, yqso+toff, 'Quasar (z=3)', color='blue', ha='center')
# Redshifted light
if qq > 0:
light = np.linspace(1215.67, lrest[0],20)
ax0.scatter(light, [yqso]*len(light), marker='_', s=40, cmap=cm, c=1./light)
# Gas at QSO
if qq > 1:
ax0.scatter([1215.67], [yqso], marker='o', facecolor='none',
edgecolor='green', s=sqso*5)
ax0.text(1215.67, yqso-1.7*toff, 'HI Gas (z=3)', color='green', ha='center')
# Spectrum
ax = plt.subplot(gs[1:,0])
ax.set_xlim(lrest)
#ax.set_ylim(ymnx)
ax.set_ylabel('Relative Flux')
ax.set_xlabel("Rest Wavelength")
if qq < 3:
tsty = 'k'
else:
tsty = 'b:'
ax.plot(telfer.wavelength, telfer.flux, tsty)
# Observer frame axis
if qq > 0:
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
xtcks = ax.get_xticks()
ax2.set_xticks(xtcks)
ax2.set_xticklabels(tick_function(zem, xtcks))
ax2.set_xlabel('Observed Wavelength (Angstroms)')
# Absorption lines
abslines = []
if (qq > 2) and (qq != 8):
# Lya at zem
abslines.append(qsolya)
if qq > 3: # Lyb at zem
abslines.append(qsolyb)
# Gas at z=2.8
if qq > 4:
zadd = 2.8
abslines += add_lines(ax0, zadd)
# Gas at z=2.5
if qq > 5:
zadd = 2.5
abslines += add_lines(ax0, zadd)
if qq > 6:
zadd = 2.2
abslines += add_lines(ax0, zadd)
#
abs_model = ltav.voigt_from_abslines(telfer.wavelength*(1+zem), abslines)
#ax.plot(telfer.wavelength, telfer.flux*abs_model.flux, 'k')
ax.plot(telfer.wavelength, telfer.flux.value*abs_model.flux, 'k')
# Final plot
if qq == 8:
nlin = 100
dotwv = np.linspace(900,1215.,nlin)
ax0.scatter(dotwv, [yqso]*nlin, marker='o', facecolor='none',
edgecolor='green', s=sqso*5)
# Mock spectrum
fN_model = FNModel.default_model()
gdp = np.where(telfer.wavelength > 900.*u.AA)[0]
mock_spec, HI_comps, misc = pyimock.mk_mock(
telfer.wavelength[gdp]*(1+zem),
zem, fN_model, s2n=100., fwhm=3, add_conti=False)
ax.plot(telfer.wavelength[gdp],
telfer.flux[gdp].value*mock_spec.flux, 'k')
# Layout and save
plt.tight_layout(pad=0.2,h_pad=0.0,w_pad=0.4)
plt.subplots_adjust(hspace=0)
pp.savefig(bbox_inches='tight', transparent=True)
plt.close()
# Finish
print('Writing {:s}'.format(outfil))
pp.close()
return mock_spec
def test_teff():
fN_default = FNModel.default_model()
zval, teff_LL = pyteff.lyman_limit(fN_default, 0.5, 2.45)
#
np.testing.assert_allclose(zval[0], 0.5)
np.testing.assert_allclose(teff_LL[0], 1.8197, rtol=1e-4) # scipy 0.19
def test_default():
fN_default = FNModel.default_model()
#
assert fN_default.mtype == 'Hspline'
assert fN_default.zmnx == (0.5, 3)