def pymc_teff_model(parm=parm):
# Set parameters
fN_model.upd_param(parm)
# Calculate teff
model_teff = tau_eff.ew_teff_lyman(1215.6701*(1+teff_input[0]), teff_input[0]+0.1,
fN_model, NHI_MIN=teff_input[1], NHI_MAX=teff_input[2])
return model_teff
def pymc_teff_model(parm=parm):
# Set parameters
fN_model.param = parm
fN_model.model = scii.PchipInterpolator(fN_model.pivots, fN_model.param)
# Calculate teff
model_teff = tau_eff.ew_teff_lyman(1215.6701*(1+teff_input[0]), teff_input[0]+0.1,
fN_model, NHI_MIN=teff_input[1], NHI_MAX=teff_input[2])
return model_teff
def pymc_teff_model(parm=parm):
# Set parameters
fN_model.upd_param(parm)
# Calculate teff
model_teff = tau_eff.ew_teff_lyman(1215.6701 * (1 + teff_input[0]),
teff_input[0] + 0.1,
fN_model,
NHI_MIN=teff_input[1],
NHI_MAX=teff_input[2])
return model_teff
def test_lya():
# f(N)
fN_model = xifm.default_model()
#fN_model = xifm.default_model(recalc=True)
# tau_eff
lamb = 1215.6701 * (1 + 2.4)
teff = xit.ew_teff_lyman(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.19821448846)
def test_lya():
# f(N)
fN_model = xifm.default_model()
#fN_model = xifm.default_model(recalc=True)
# tau_eff
lamb = 1215.6701*(1+2.4)
teff = xit.ew_teff_lyman(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.19821448846)
def 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.eval(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.eval(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 != None:
model_teff = tau_eff.ew_teff_lyman(
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.calc_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 != 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}$')
main.set_ylim(-25., -9)
for fN_c in fN_cs:
if fN_c.fN_dtype == 'fN':
# Length
ip = range(fN_c.data['NPT'])
#xdb.set_trace()
val = np.where(fN_c.data['FN'][ip] > -90)[0]
#xdb.set_trace()
if len(val) > 0:
#xdb.set_trace()
ipv = np.array(ip)[val]
xval = np.median(fN_c.data['BINS'][:,ipv],0)
xerror = [ fN_c.data['BINS'][1,ipv]-xval, xval-fN_c.data['BINS'][0,ipv] ]
yerror = [ fN_c.data['SIG_FN'][1,ipv], fN_c.data['SIG_FN'][0,ipv] ] # Inverted!
main.errorbar(xval, fN_c.data['FN'][ipv], xerr=xerror, yerr=yerror, fmt='o',
label=fN_c.ref,capthick=2)
main.legend(loc='lower left', numpoints=1)
# Model?
#print(fN_model.param)
if fN_model is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = fN_model.eval(xplt, 2.4)
main.plot(xplt,yplt,'-',color='black')
print(xplt[0],yplt[0])
if model_two is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = model_two.eval(xplt, 2.4)
main.plot(xplt,yplt,'-',color='gray')
#xdb.set_trace()
# Extras
#mpl.rcParams['lines.capthick'] = 2
inset = fig.add_axes( [0.55, 0.6, 0.25, 0.25] ) # xypos, xy-size
inset.set_ylabel('Value') # LHS
inset.xaxis.set_major_locator(plt.FixedLocator(range(5)))
#lbl1 = r'$\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 != None:
model_teff = tau_eff.ew_teff_lyman(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.calc_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 != 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()
