cb = plt.colorbar(sc)
cb.set_label('Effective number of samples')
plt.xlim(-25., 25.)
plt.ylim(-25., 25.)
plt.tight_layout()
#plt.show()
plt.savefig("effectiveNSamples.pdf")
# ## 2. Plot cross section over parameter space
# This is not strictly necessary, but we can also plot the cross section as a function of parameter space:
# In[7]:
thetas_benchmarks, xsecs_benchmarks, xsec_errors_benchmarks = sampler.cross_sections(
theta=sampling.benchmarks(list(sampler.benchmarks.keys())))
thetas_morphing, xsecs_morphing, xsec_errors_morphing = sampler.cross_sections(
theta=sampling.random_morphing_points(1000, [('gaussian', 0.,
4.), ('gaussian', 0., 4.)]))
# In[8]:
cmin, cmax = 0., 2.5 * np.mean(xsecs_morphing)
fig = plt.figure(figsize=(5, 4))
sc = plt.scatter(thetas_morphing[:, 0],
thetas_morphing[:, 1],
c=xsecs_morphing,
s=40.,
mode="rate",
include_xsec=True,
resolutions=resolutions,
luminosity=uselumi)
np.save('/home/rates/grid.npy', theta_grid)
np.save('/home/rates/rate.npy',
[p_values_expected_xsec, best_fit_expected_xsec])
sa_rates = SampleAugmenter(h5_file, include_nuisance_parameters=False)
xs_grid = []
neff_grid = []
n_test = 10000
for theta_element in theta_grid:
_, xs, _ = sa_rates.cross_sections(
theta=sampling.morphing_point(theta_element))
_, _, neff = sa_rates.sample_train_plain(
theta=sampling.morphing_point(theta_element), n_samples=n_test)
xs_grid.append(xs)
neff_grid.append(neff / float(n_test))
neff_grid = np.array(neff_grid)
xsgrid = np.array(xs_grid)
np.save('/home/rates/neff_grid.npy', neff_grid)
np.save('/home/rates/xs_grid.npy', xs_grid)
for bool_xsec in xsec:
# histogram + save
_, p_values_expected_histo, best_fit_expected_histo = limits.expected_limits(
theta_true=theta_true,
theta_ranges=theta_ranges,
