def output(self):
basename = os.path.splitext(self.input()["merged"].basename)[0]
return law.SiblingFileCollection({
"x": self.local_target(basename + ".x.0"),
"y": self.local_target(basename + ".y.0"),
"meta": self.local_target(basename + ".meta"),
"dc": self.local_target(basename + ".dc"),
})
def output(self):
collection = {
"evaluations":
self.local_target("evaluations_{}.pdf".format(self.branch)),
"convergence":
self.local_target("convergence_{}.pdf".format(self.branch))
}
if self.has_fitted_model():
collection["objective"] = self.local_target(
"objective_{}.pdf".format(self.branch))
return law.SiblingFileCollection(collection)
def output(self):
return law.SiblingFileCollection([
self.local_target("eta_phi_{}.png".format(i))
for i in range(self.n_events)
])
def cascade_output(self):
n_trees = self.config_inst.get_aux("get_file_merging")(
self.config_inst, "trees", self.dataset)
return law.SiblingFileCollection([
self.wlcg_target("tree_{}.root".format(i)) for i in range(n_trees)
])
def cascade_output(self):
return law.SiblingFileCollection(
self.local_target("tuple_{}Of{}_n{}.root".format(
i + 1, self.n_merged_files, self.n_events))
for i in range(self.n_merged_files)
)