def plot_model(pars):
""" Plot the observed values and errors, along with the predicted
model values.
"""
import matplotlib.pyplot as pl
from barak.plot import draw_arrows, puttext
fig = pl.figure(figsize=(6.4, 3.4))
ax = fig.add_subplot(111)
ms = 6
ipot = [get_ionization_energy(t) for t in tr_plot]
for i,tr in enumerate(tr_plot):
if use_ipot:
ind = ipot[i]
else:
ind = i
if tr in obs:
colour = 'k' if tr in trans else 'w'
fmt = 'o' + colour
val, siglo, sighi = obs[tr]
if siglo == 0:
draw_arrows(ind, val, direction='up', ax=ax,lw=1)
ax.plot(ind, val, fmt,ms=ms)
elif sighi == 0:
draw_arrows(ind, val, direction='down', ax=ax,lw=1)
ax.plot(ind, val, fmt,ms=ms)
else:
ax.plot([ind, ind], [val-siglo, val+sighi], 'k',lw=1)
ax.plot(ind, val, fmt,ms=ms)
ax.text(ind, val + 0.8, tr,
fontsize=10, ha='center')
else:
puttext(ind, 0.02, tr, ax=ax, xcoord='data',
fontsize=10, ha='center')
puttext(0.9,0.1, 'Model', ax, color='r', ha='right')
xvals = list(range(len(tr_plot)))
#print np.asarray(pars).shape
for par in pars:
Nmodel = model(par, for_plot=True)
if use_ipot:
if len(pars) == 1:
ax.plot(ipot, Nmodel, 'r.-', lw=1, zorder=0)
else:
ax.plot(ipot, Nmodel, 'r-', lw=0.2, zorder=0)
else:
if len(pars) == 1:
ax.plot(xvals, Nmodel, 'r.-', lw=1, zorder=0)
else:
ax.plot(xvals, Nmodel, 'r-', lw=0.2, zorder=0)
if use_ipot:
ax.set_xlabel('Ionization potential (eV)')
ax.set_xlim(ipot[0]-1, ipot[-1] + 1)
else:
ax.set_xlim(-0.5, xvals[-1] + 0.5)
ax.set_xticks([])
ax.set_ylabel(r'$\log_{10}\ N$')
fig.tight_layout()
#return fig, ax
return fig
def plot_model(pars):
""" Plot the observed values and errors, along with the predicted
model values.
"""
import matplotlib.pyplot as pl
from barak.plot import draw_arrows, puttext
fig = pl.figure(figsize=(6.4, 3.4))
ax = fig.add_subplot(111)
ms = 6
ipot = [get_ionization_energy(t) for t in tr_plot]
for i, tr in enumerate(tr_plot):
if use_ipot:
ind = ipot[i]
else:
ind = i
if tr in obs:
colour = 'k' if tr in trans else 'w'
fmt = 'o' + colour
val, siglo, sighi = obs[tr]
if siglo == 0:
draw_arrows(ind, val, direction='up', ax=ax, lw=1)
ax.plot(ind, val, fmt, ms=ms)
elif sighi == 0:
draw_arrows(ind, val, direction='down', ax=ax, lw=1)
ax.plot(ind, val, fmt, ms=ms)
else:
ax.plot([ind, ind], [val - siglo, val + sighi], 'k', lw=1)
ax.plot(ind, val, fmt, ms=ms)
ax.text(ind, val + 0.8, tr, fontsize=10, ha='center')
else:
puttext(ind,
0.02,
tr,
ax=ax,
xcoord='data',
fontsize=10,
ha='center')
puttext(0.9, 0.1, 'Model', ax, color='r', ha='right')
xvals = list(range(len(tr_plot)))
#print np.asarray(pars).shape
for par in pars:
Nmodel = model(par, for_plot=True)
if use_ipot:
if len(pars) == 1:
ax.plot(ipot, Nmodel, 'r.-', lw=1, zorder=0)
else:
ax.plot(ipot, Nmodel, 'r-', lw=0.2, zorder=0)
else:
if len(pars) == 1:
ax.plot(xvals, Nmodel, 'r.-', lw=1, zorder=0)
else:
ax.plot(xvals, Nmodel, 'r-', lw=0.2, zorder=0)
if use_ipot:
ax.set_xlabel('Ionization potential (eV)')
ax.set_xlim(ipot[0] - 1, ipot[-1] + 1)
else:
ax.set_xlim(-0.5, xvals[-1] + 0.5)
ax.set_xticks([])
ax.set_ylabel(r'$\log_{10}\ N$')
fig.tight_layout()
#return fig, ax
return fig
)
names = ('nH', 'aUV','T','NH','D','Pk')
for i in range(6):
ax = fig.add_subplot(12,1,i+7)
k = names[i]
v = V[k]
c0 = v['limit'] == 'none'
v1 = v[c0]
errplot(dv[c0], v1['val'], (v1['val']-v1['erlo'],
v1['val']+v1['erhi']), c='k',
ax=ax, lw=0.5, ms=6)
c0 = v['limit'] == 'upper'
if c0.any():
v1 = v[c0]
draw_arrows(dv[c0], v1['val'], ax=ax, direction='down', c='k',lw=0.5)
errplot(dv[c0], v1['val'], 0, ax=ax, lw=0.5, ms=6, c='k')
c0 = v['limit'] == 'lower'
if c0.any():
v1 = v[c0]
if k in ('D', 'NH'):
# tweak so lower limit arrow still shows
draw_arrows(dv[c0], v1['val']+0.55, ax=ax,direction='up',c='k',
ms=0.6,capsize=4,lw=0.5)
else:
draw_arrows(dv[c0], v1['val'], ax=ax,direction='up',c='k',
ms=0.6,capsize=4,lw=0.5)
errplot(dv[c0], v1['val'], 0, ax=ax, lw=0.5, ms=6, c='k')
c0 = v['strong_prior']
v1 = v[c0]
errplot(dv[c0], v1['val'], (v1['val']-v1['erlo'],v1['val']+v1['erhi']),
fig = pl.figure(figsize=(4.3, 6))
fig.subplots_adjust(right=0.97, top=0.92,bottom=0.09)
ax = pl.subplot(111)
plot_trans_vs_U(iZ, iNHI, M, trans1, ax)
ax.set_ylim(11.1, 16.3)
# observed values
val,sig = obs['MgII'][:2]
ax.plot(nHbest, val, 'o', mec=COLORS['Mg'], mew=2, mfc='w', ms=6)
val,sig = obs['MgI'][:2]
ax.plot(nHbest, val, 'o', color=COLORS['Mg'], ms=9, mew=2, mec='w')
val,sig = obs['NI'][:2]
ax.plot(nHbest, val, 'o', color=COLORS['N'], ms=9, mew=2, mec='w')
draw_arrows(nHbest, val, ax=ax, c=COLORS['N'],ms=1.5,capsize=5,
lw=2, direction='down', zorder=6)
val,sig = obs['NII'][:2]
ax.plot(nHbest, val, 'o',mec=COLORS['N'], mew=2, mfc='w', ms=6)
val,siglo,sighi = obs['OI']
ax.plot(nHbest, val, 'o', color=COLORS['O'], ms=9, mew=2, mec='w')
val,sig = obs['FeII'][:2]
ax.plot(nHbest-0.2, val, 'o', color=COLORS['Fe'], ms=9, mew=2, mec='w')
#pl.savefig('cloudy1.pdf')
#pl.savefig('cloudy1.png')
if 0:
trans2 = ('HIII CaII SiII SiIII SiIV CI CII CIII').split()
fig = pl.figure(figsize=(4.3, 6))
fig.subplots_adjust(right=0.97, top=0.92,bottom=0.09)
ax = pl.subplot(111)
# ax.plot(x, y, '-k',zorder=1)
# x = 2 * [np.log10(10**c6_NHI + 2*10**c6_NH2)]
# y = [np.log10(2*10**c6_NH2lo / (10**c6_NHI + 2*10**c6_NH2lo)),
# np.log10(2*10**c6_NH2hi / (10**c6_NHI + 2*10**c6_NH2hi))]
# ax.plot(x, y, '-k',zorder=1)
else:
ax.plot(F.MWplane[0], np.log10(F.MWplane[1]), 'cv', ms=5, label='$\mathrm{In MW plane}$')
ax.plot(F.IVC[0], np.log10(F.IVC[1] ), 'b^', ms=5, label= '$\mathrm{IVC}$')
ax.plot(F.s01[0], np.log10(F.s01[1] ), 'mp', ms=7)
ax.plot(F.r01[0], np.log10(F.r01[1] ), 'mp', ms=7)
ax.plot(F.r99[0], np.log10(F.r99[1] ), 'mp', ms=7, label='$\mathrm{HVC}$')
ax.plot(F.SMC[0], np.log10(F.SMC[1] ), 'rD', ms=4.5, label='$\mathrm{SMC}$')
ax.plot(F.LMC[0], np.log10(F.LMC[1] ), 'gs', ms=5, label='$\mathrm{LMC}$')
ax.plot(F.DLA[0], np.log10(F.DLA[1] ), 'ok', ms=6, mfc='none', label='\mathrm{z>1.5 DLA}$')
draw_arrows(F.p02[0], np.log10(F.p02[1]), ax, ms=1.5, capsize=4)
ax.plot(F.p02[0], np.log10(F.p02[1] ), 'ok', ms=6, mfc='w')
ax.plot(F.C5[0], np.log10(F.C5[1] ), '*k', ms=12)
ax.plot(F.C6[0], np.log10(F.C6[1] ), '*k', ms=12,label='$\mathrm{z=0.56 sub-DLA}$')
#col = ax.scatter(F.Clo[0], np.log10(F.Clo[1] ), marker='o', c='w',s=40,
# label='z=0.56 sub-DLA (lower limit)')
#col.set_linestyles([(0, (2, 1))])
if 0:#xaxis != 'NH2':
bins = np.arange(-9.1, 2.01, 0.6)
y,_ = np.histogram(np.log10(np.concatenate(
[np.atleast_1d(F[k][1]) for k in F])), bins)
b = np.repeat(bins, 2)
Y = np.concatenate([[0], np.repeat(y,2), [0]])
Y = 0.2 * Y / Y.max()
import matplotlib.transforms as mtransforms
