def _make_histo_data(self, thetas, n_samples, test_split=0.2):
sampler = SampleAugmenter(
self.madminer_filename,
include_nuisance_parameters=self.include_nuisance_parameters)
x, theta, _ = sampler.sample_train_plain(
theta=sampling.morphing_points(thetas),
n_samples=n_samples,
test_split=test_split,
filename=None,
folder=None,
)
return theta, x
def _make_sampled_histo_data(self,
summary_function,
thetas,
n_toys_per_theta,
test_split=0.2,
histo_theta_batchsize=100):
sampler = SampleAugmenter(
self.madminer_filename,
include_nuisance_parameters=self.include_nuisance_parameters)
all_summary_stats, all_theta = None, None
if n_toys_per_theta is None:
n_toys_per_theta = 10000
n_thetas = len(thetas)
n_batches = (n_thetas - 1) // histo_theta_batchsize + 1
for i_batch in range(n_batches):
logger.debug("Generating histogram data for batch %s / %s",
i_batch + 1, n_batches)
theta_batch = thetas[i_batch *
histo_theta_batchsize:(i_batch + 1) *
histo_theta_batchsize]
logger.debug(
"Theta data: indices %s to %s, shape %s",
i_batch * histo_theta_batchsize,
(i_batch + 1) * histo_theta_batchsize,
theta_batch.shape,
)
x, theta, _ = sampler.sample_train_plain(
theta=sampling.morphing_points(theta_batch),
n_samples=n_toys_per_theta * len(theta_batch),
test_split=test_split,
filename=None,
folder=None,
suppress_logging=True,
)
summary_stats = summary_function(x)
logger.debug("Output: x has shape %s, summary_stats %s, theta %s",
x.shape, summary_stats.shape, theta.shape)
if all_theta is None or all_summary_stats is None:
all_theta = theta
all_summary_stats = summary_stats
else:
all_theta = np.concatenate((all_theta, theta), 0)
all_summary_stats = np.concatenate(
(all_summary_stats, summary_stats), 0)
return all_theta, all_summary_stats
def _make_sampled_histo_data(self, summary_function, thetas, n_toys_per_theta, test_split=0.2):
sampler = SampleAugmenter(self.madminer_filename, include_nuisance_parameters=self.include_nuisance_parameters)
if n_toys_per_theta is None:
n_toys_per_theta = 100000
with less_logging():
x, theta, _ = sampler.sample_train_plain(
theta=sampling.morphing_points(thetas),
n_samples=n_toys_per_theta * len(thetas),
test_split=test_split,
filename=None,
folder=None,
)
summary_stats = summary_function(x)
summary_stats = summary_stats.reshape((len(thetas), n_toys_per_theta, -1))
return summary_stats
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,
mode="histo",
hist_vars=[unicode(str(asymptotic['hist_vars']))],
