def apply_function(self):
weight_scale = self.params.weight_scale.m_as('dimensionless')
livetime_s = self.params.livetime.m_as('sec')
scale = weight_scale * livetime_s
for container in self.data:
vectorizer.scale(scale,
container['weights'],
out=container['weights'])
def compute_function(self):
for container in self.data:
vectorizer.assign(vals=container["weights"],
out=container["manual_variance"])
vectorizer.scale(
vals=container["manual_variance"],
scale=self.variance_scale,
out=container["manual_variance"],
)
if self.divide_n:
vectorizer.scale(
vals=container["manual_variance"],
scale=self.expected_total_mc /
self.total_mc[container.name],
out=container["manual_variance"],
)
if self.variance_floor is not None:
apply_floor(self.variance_floor,
out=container["manual_variance"])
def apply_function(self):
scale = self.params.atm_muon_scale.m_as('dimensionless')
for container in self.data:
vectorizer.scale(scale,
container['count'],
out=container['weights'])
def apply_function(self):
# reset weights
for container in self.data:
vectorizer.scale(1.,
container['initial_weights'],
out=container['weights'])