def make_doc_sections():
return [{
'name' : latex_to_rst(section.name()),
'groups': make_doc_groups(section)
} for section in eos.Observables().sections()]
#!/usr/bin/env python3
# vim: set sw=4 sts=4 et tw=120 :
import eos
observables = eos.Observables()
for section in observables.sections():
section_title = section.name()
print('\section{' + section_title + '}\n')
for group in section:
group_title = group.name()
print('\subsection{' + group_title + '}\n')
print(r'{%')
print(r'\captionsetup{type=table}')
print(r'\renewcommand{\arraystretch}{1.1}')
print(r'\newcolumntype{L}[1]{>{\raggedright\arraybackslash}p{#1}}')
print(r'\begin{center}')
print(r'\begin{longtable}{@{} p{.5\textwidth} p{.5\textwidth} @{}}')
print(r'\toprule')
print(r' \textbf{Qualified Name} & \textbf{Description} \\')
print(r'\midrule')
for qn, entry in group:
latex = entry.latex()
if 0 == len(latex):
continue
print(r' \verb|{0}| & {1} \\'.format(str(qn),
def plot(self):
xvalues = []
xticklabels = []
yvalues = []
yerrors = []
idx = 0
for constraint in self.constraints:
if constraint['type'] == 'Gaussian':
sigma_hi = np.sqrt(float(constraint['sigma-stat']['hi'])**2 + float(constraint['sigma-sys']['hi'])**2) / width
sigma_lo = np.sqrt(float(constraint['sigma-stat']['lo'])**2 + float(constraint['sigma-sys']['lo'])**2) / width
latex = '$' + eos.Observables()[constraint['observable']].latex() + '$'
xvalues.append(idx)
xticklabels.append(latex)
yvalues.append(constraint['mean'])
yerrors.append([sigma_hi, sigma_lo])
idx = idx + 1
elif constraint['type'] == 'MultivariateGaussian(Covariance)':
dim = constraint['dim']
observables = constraint['observables']
kinematics = constraint['kinematics']
options = constraint['options']
covariance = np.array(constraint['covariance'])
means = constraint['means']
for i in range(0, dim):
latex = '$' + eos.Observables()[observables[i]].latex() + '$'
sigma = np.sqrt(covariance[i, i])
xvalues.append(idx)
xticklabels.append(latex)
yvalues.append(means[i])
yerrors.append([sigma, sigma])
idx = idx + 1
elif constraint['type'] == 'MultivariateGaussian':
dim = constraint['dim']
observables = constraint['observables']
sigma_stat_hi = np.array(constraint['sigma-stat-hi'])
sigma_stat_lo = np.array(constraint['sigma-stat-lo'])
sigma_sys = np.array(constraint['sigma-sys'])
sigma = np.sqrt(np.power(sigma_sys, 2) + 0.25 * np.power(sigma_stat_hi + sigma_stat_lo, 2))
means = constraint['means']
for i in range(0, dim):
latex = '$' + eos.Observables()[observables[i]].latex() + '$'
xvalues.append(idx)
xticklabels.append(latex)
yvalues.append(means[i])
yerrors.append(sigma[i])
idx = idx + 1
else:
raise ValueError('type of constraint presently not supported')
xvalues = np.array(xvalues)
yvalues = np.array(yvalues)
yerrors = np.array(yerrors)
self.plotter.ax.tick_params(axis='x', which='minor', bottom=False)
plt.xticks(xvalues, xticklabels, rotation=self.rotation)
plt.errorbar(x=xvalues, y=yvalues, xerr=None, yerr=yerrors.T,
color=self.color, elinewidth=1.0, fmt='_', linestyle='none', label=self.label)
plt.margins(0.2)
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=0.15)