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 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_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_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 pymc_teff_model(parm=parm):
# Set parameters
aparm = np.array([parm[i] for i in range(parm.size)])
fN_model.update_parameters(aparm)
# Calculate teff
model_teff = []
for teff_input in teff_inputs:
model_teff.append(tau_eff.lyman_ew(1215.6701*(1+teff_input[0]), teff_input[0]+0.1,
fN_model, NHI_MIN=teff_input[1], NHI_MAX=teff_input[2]))
return np.array(model_teff)
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 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 pymc_teff_model(parm=parm):
# Set parameters
aparm = np.array([parm[i] for i in range(parm.size)])
fN_model.update_parameters(aparm)
# Calculate teff
model_teff = []
for teff_input in teff_inputs:
model_teff.append(
tau_eff.lyman_ew(1215.6701 * (1 + teff_input[0]),
teff_input[0] + 0.1,
fN_model,
NHI_MIN=teff_input[1],
NHI_MAX=teff_input[2]))
return np.array(model_teff)
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 tst_fn_data(fN_model=None, model_two=None, data_list=None, outfil=None):
""" Make a matplotlib plot like the final figure from P14
See the Notebook for Bokeh plots
Taken from xastropy without actually trying to run
Parameters
----------
fN_model
model_two
data_list
outfil
"""
import matplotlib as mpl
mpl.rcParams['font.family'] = 'STIXGeneral-Regular' # Not for PDF
mpl.rcParams['lines.linewidth'] = 2
from matplotlib import pyplot as plt
#from matplotlib.backends.backend_pdf import PdfPages
# fN data
all_fN_cs = FNConstraint.load_defaults()
# Remove K12
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]
fig = plt.figure(figsize=(8, 5))
fig.clf()
main = fig.add_axes( [0.1, 0.1, 0.8, 0.8] ) # xypos, xy-size
# f(N) data
main.set_ylabel(r'$\log f(N_{\rm HI})$')
main.set_xlabel(r'$\log N_{\rm HI}$')
main.set_ylim(-25., -9)
for fN_c in fN_cs:
if fN_c.fN_dtype == 'fN':
# Length
ip = range(fN_c.data['NPT'])
val = np.where(fN_c.data['FN'][ip] > -90)[0]
if len(val) > 0:
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='o',
label=fN_c.ref,capthick=2)
main.legend(loc='lower left', numpoints=1)
# Model?
if fN_model is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = fN_model.evaluate(xplt, 2.4)
main.plot(xplt,yplt,'-',color='black')
print(xplt[0],yplt[0])
if model_two is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = model_two.evaluate(xplt, 2.4)
main.plot(xplt,yplt,'-',color='gray')
# Extras
#mpl.rcParams['lines.capthick'] = 2
inset = fig.add_axes( [0.55, 0.6, 0.25, 0.25] ) # xypos, xy-size
inset.set_ylabel('Value') # LHS
inset.xaxis.set_major_locator(plt.FixedLocator(range(5)))
inset.xaxis.set_major_formatter(plt.FixedFormatter(['',r'$\tau_{\rm eff}^{\rm Ly\alpha}$',
r'$\ell(X)_{\rm LLS}$',
r'$\lambda_{\rm mfp}^{912}$', '']))
inset.set_ylim(0., 0.6)
# tau_eff
flg_teff = 1
try:
itau = fN_dtype.index('teff') # Passes back the first one
except:
flg_teff = 0
if flg_teff:
teff=float(fN_cs[itau].data['TEFF'])
D_A = 1. - np.exp(-1. * teff)
SIGDA_LIMIT = 0.1 # Allows for systemtics and b-value uncertainty
sig_teff = np.max([fN_cs[itau].data['SIG_TEFF'], (SIGDA_LIMIT*teff)])
# Plot
inset.errorbar(1, teff, sig_teff, fmt='_', capthick=2)
# Model
if fN_model is not None:
model_teff = tau_eff.lyman_ew(1215.6701*(1+fN_cs[itau].zeval), fN_cs[itau].zeval+0.1,
fN_model, NHI_MIN=fN_cs[itau].data['NHI_MNX'][0],
NHI_MAX=fN_cs[itau].data['NHI_MNX'][1])
inset.plot(1, model_teff, 'ko')
#xdb.set_trace()
## #######
# LLS constraint
flg_LLS = 1
try:
iLLS = fN_dtype.index('LLS') # Passes back the first one
except:
#raise ValueError('fN.data: Missing LLS type')
flg_LLS = 0
if flg_LLS:
inset.errorbar(2, fN_cs[iLLS].data['LX'], yerr=fN_cs[iLLS].data['SIG_LX'],
fmt='_', capthick=2)
# Model
if fN_model != None:
lX = fN_model.calculate_lox(fN_cs[iLLS].zeval,
17.19+np.log10(fN_cs[iLLS].data['TAU_LIM']), 22.)
inset.plot(2, lX, 'ko')
## #######
# MFP constraint
flg_MFP = 1
try:
iMFP = fN_dtype.index('MFP') # Passes back the first one
except:
#raise ValueError('fN.data: Missing MFP type')
flg_MFP = 0
if flg_MFP:
inset2 = inset.twinx()
inset2.errorbar(3, fN_cs[iMFP].data['MFP'], yerr=fN_cs[iMFP].data['SIG_MFP'],
fmt='_', capthick=2)
inset2.set_xlim(0,4)
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()
def tst_fn_data(fN_model=None, model_two=None, data_list=None, outfil=None):
""" Make a plot like the final figure from P14
Parameters:
noshow: boolean (False)
Show the plot?
JXP 07 Nov 2014
"""
import matplotlib as mpl
mpl.rcParams['font.family'] = 'STIXGeneral-Regular' # Not for PDF
mpl.rcParams['lines.linewidth'] = 2
from matplotlib import pyplot as plt
#from matplotlib.backends.backend_pdf import PdfPages
# Output
#if outfil != None:
# pp = PdfPages(outfil)
#mpl.rcParams['font.family'] = 'stixgeneral' # For PDF
# fN data
#fn_file = os.environ.get('XIDL_DIR')+'IGM/fN_empirical/fn_constraints_z2.5_vanilla.fits'
#k13r13_file = os.environ.get('XIDL_DIR')+'IGM/fN_empirical/fn_constraints_K13R13_vanilla.fits'
#n12_file = os.environ.get('XIDL_DIR')+'IGM/fN_empirical/fn_constraints_N12_vanilla.fits'
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)
# 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]
fig = plt.figure(figsize=(8, 5))
fig.clf()
main = fig.add_axes( [0.1, 0.1, 0.8, 0.8] ) # xypos, xy-size
# f(N) data
main.set_ylabel(r'$\log f(N_{\rm HI})$')
main.set_xlabel(r'$\log N_{\rm HI}$')
main.set_ylim(-25., -9)
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='o',
label=fN_c.ref,capthick=2)
main.legend(loc='lower left', numpoints=1)
# Model?
#print(fN_model.param)
if fN_model is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = fN_model.evaluate(xplt, 2.4)
main.plot(xplt,yplt,'-',color='black')
print(xplt[0],yplt[0])
if model_two is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = model_two.evaluate(xplt, 2.4)
main.plot(xplt,yplt,'-',color='gray')
#xdb.set_trace()
# Extras
#mpl.rcParams['lines.capthick'] = 2
inset = fig.add_axes( [0.55, 0.6, 0.25, 0.25] ) # xypos, xy-size
inset.set_ylabel('Value') # LHS
inset.xaxis.set_major_locator(plt.FixedLocator(range(5)))
#lbl1 = r'$\tau_{\rm eff}^{\rm Ly\alpha}'
inset.xaxis.set_major_formatter(plt.FixedFormatter(['',r'$\tau_{\rm eff}^{\rm Ly\alpha}$',
r'$\ell(X)_{\rm LLS}$',
r'$\lambda_{\rm mfp}^{912}$', '']))
inset.set_ylim(0., 0.6)
## #######
# tau_eff
flg_teff = 1
try:
itau = fN_dtype.index('teff') # Passes back the first one
except:
#raise ValueError('fN.data: Missing teff type')
flg_teff = 0
#xdb.set_trace()
if flg_teff:
teff=float(fN_cs[itau].data['TEFF'])
D_A = 1. - np.exp(-1. * teff)
SIGDA_LIMIT = 0.1 # Allows for systemtics and b-value uncertainty
sig_teff = np.max([fN_cs[itau].data['SIG_TEFF'], (SIGDA_LIMIT*teff)])
# Plot
inset.errorbar(1, teff, sig_teff, fmt='_', capthick=2)
# Model
if fN_model is not None:
model_teff = pyiteff.lyman_ew(1215.6701*(1+fN_cs[itau].zeval), fN_cs[itau].zeval+0.1,
fN_model, NHI_MIN=fN_cs[itau].data['NHI_MNX'][0],
NHI_MAX=fN_cs[itau].data['NHI_MNX'][1])
inset.plot(1, model_teff, 'ko')
#xdb.set_trace()
## #######
# LLS constraint
flg_LLS = 1
try:
iLLS = fN_dtype.index('LLS') # Passes back the first one
except:
#raise ValueError('fN.data: Missing LLS type')
flg_LLS = 0
if flg_LLS:
inset.errorbar(2, fN_cs[iLLS].data['LX'], yerr=fN_cs[iLLS].data['SIG_LX'],
fmt='_', capthick=2)
# Model
if fN_model != None:
lX = fN_model.calculate_lox(fN_cs[iLLS].zeval,
17.19+np.log10(fN_cs[iLLS].data['TAU_LIM']), 22.)
inset.plot(2, lX, 'ko')
## #######
# MFP constraint
flg_MFP = 1
try:
iMFP = fN_dtype.index('MFP') # Passes back the first one
except:
#raise ValueError('fN.data: Missing MFP type')
flg_MFP = 0
if flg_MFP:
inset2 = inset.twinx()
inset2.errorbar(3, fN_cs[iMFP].data['MFP'], yerr=fN_cs[iMFP].data['SIG_MFP'],
fmt='_', capthick=2)
inset2.set_xlim(0,4)
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()
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 tst_fn_data(fN_model=None, model_two=None, data_list=None, outfil=None):
""" Make a plot like the final figure from P14
Parameters:
noshow: boolean (False)
Show the plot?
JXP 07 Nov 2014
"""
import matplotlib as mpl
mpl.rcParams['font.family'] = 'STIXGeneral-Regular' # Not for PDF
mpl.rcParams['lines.linewidth'] = 2
from matplotlib import pyplot as plt
#from matplotlib.backends.backend_pdf import PdfPages
# Output
#if outfil != None:
# pp = PdfPages(outfil)
#mpl.rcParams['font.family'] = 'stixgeneral' # For PDF
# fN data
#fn_file = os.environ.get('XIDL_DIR')+'IGM/fN_empirical/fn_constraints_z2.5_vanilla.fits'
#k13r13_file = os.environ.get('XIDL_DIR')+'IGM/fN_empirical/fn_constraints_K13R13_vanilla.fits'
#n12_file = os.environ.get('XIDL_DIR')+'IGM/fN_empirical/fn_constraints_N12_vanilla.fits'
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)
# 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]
fig = plt.figure(figsize=(8, 5))
fig.clf()
main = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # xypos, xy-size
# f(N) data
main.set_ylabel(r'$\log f(N_{\rm HI})$')
main.set_xlabel(r'$\log N_{\rm HI}$')
main.set_ylim(-25., -9)
for fN_c in fN_cs:
if fN_c.fN_dtype == 'fN':
# Length
ip = range(fN_c.data['NPT'])
#xdb.set_trace()
val = np.where(fN_c.data['FN'][ip] > -90)[0]
#xdb.set_trace()
if len(val) > 0:
#xdb.set_trace()
ipv = np.array(ip)[val]
xval = np.median(fN_c.data['BINS'][:, ipv], 0)
xerror = [
fN_c.data['BINS'][1, ipv] - xval,
xval - fN_c.data['BINS'][0, ipv]
]
yerror = [
fN_c.data['SIG_FN'][1, ipv], fN_c.data['SIG_FN'][0, ipv]
] # Inverted!
main.errorbar(xval,
fN_c.data['FN'][ipv],
xerr=xerror,
yerr=yerror,
fmt='o',
label=fN_c.ref,
capthick=2)
main.legend(loc='lower left', numpoints=1)
# Model?
#print(fN_model.param)
if fN_model is not None:
xplt = 12.01 + 0.01 * np.arange(1100)
yplt = fN_model.evaluate(xplt, 2.4)
main.plot(xplt, yplt, '-', color='black')
print(xplt[0], yplt[0])
if model_two is not None:
xplt = 12.01 + 0.01 * np.arange(1100)
yplt = model_two.evaluate(xplt, 2.4)
main.plot(xplt, yplt, '-', color='gray')
#xdb.set_trace()
# Extras
#mpl.rcParams['lines.capthick'] = 2
inset = fig.add_axes([0.55, 0.6, 0.25, 0.25]) # xypos, xy-size
inset.set_ylabel('Value') # LHS
inset.xaxis.set_major_locator(plt.FixedLocator(range(5)))
#lbl1 = r'$\tau_{\rm eff}^{\rm Ly\alpha}'
inset.xaxis.set_major_formatter(
plt.FixedFormatter([
'', r'$\tau_{\rm eff}^{\rm Ly\alpha}$', r'$\ell(X)_{\rm LLS}$',
r'$\lambda_{\rm mfp}^{912}$', ''
]))
inset.set_ylim(0., 0.6)
## #######
# tau_eff
flg_teff = 1
try:
itau = fN_dtype.index('teff') # Passes back the first one
except:
#raise ValueError('fN.data: Missing teff type')
flg_teff = 0
#xdb.set_trace()
if flg_teff:
teff = float(fN_cs[itau].data['TEFF'])
D_A = 1. - np.exp(-1. * teff)
SIGDA_LIMIT = 0.1 # Allows for systemtics and b-value uncertainty
sig_teff = np.max([fN_cs[itau].data['SIG_TEFF'], (SIGDA_LIMIT * teff)])
# Plot
inset.errorbar(1, teff, sig_teff, fmt='_', capthick=2)
# Model
if fN_model is not None:
model_teff = pyiteff.lyman_ew(
1215.6701 * (1 + fN_cs[itau].zeval),
fN_cs[itau].zeval + 0.1,
fN_model,
NHI_MIN=fN_cs[itau].data['NHI_MNX'][0],
NHI_MAX=fN_cs[itau].data['NHI_MNX'][1])
inset.plot(1, model_teff, 'ko')
#xdb.set_trace()
## #######
# LLS constraint
flg_LLS = 1
try:
iLLS = fN_dtype.index('LLS') # Passes back the first one
except:
#raise ValueError('fN.data: Missing LLS type')
flg_LLS = 0
if flg_LLS:
inset.errorbar(2,
fN_cs[iLLS].data['LX'],
yerr=fN_cs[iLLS].data['SIG_LX'],
fmt='_',
capthick=2)
# Model
if fN_model != None:
lX = fN_model.calculate_lox(
fN_cs[iLLS].zeval,
17.19 + np.log10(fN_cs[iLLS].data['TAU_LIM']), 22.)
inset.plot(2, lX, 'ko')
## #######
# MFP constraint
flg_MFP = 1
try:
iMFP = fN_dtype.index('MFP') # Passes back the first one
except:
#raise ValueError('fN.data: Missing MFP type')
flg_MFP = 0
if flg_MFP:
inset2 = inset.twinx()
inset2.errorbar(3,
fN_cs[iMFP].data['MFP'],
yerr=fN_cs[iMFP].data['SIG_MFP'],
fmt='_',
capthick=2)
inset2.set_xlim(0, 4)
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()
