def add_hepmc_sample(self, filename, sampled_from_benchmark):
"""
Adds simulated events in the HepMC format.
Parameters
----------
filename : str
Path to the HepMC event file (with extension '.hepmc' or '.hepmc.gz').
sampled_from_benchmark : str
Name of the benchmark that was used for sampling in this event file (the keyword `sample_benchmark`
of `madminer.core.MadMiner.run()`).
Returns
-------
None
"""
logging.debug("Adding HepMC sample at %s", filename)
self.hepmc_sample_filenames.append(filename)
self.hepmc_sample_weight_labels.append(extract_weight_order(filename, sampled_from_benchmark))
def add_sample(
self,
hepmc_filename,
sampled_from_benchmark,
is_background=False,
delphes_filename=None,
lhe_filename=None,
k_factor=1.0,
weights="lhe",
):
"""
Adds a sample of simulated events. A HepMC file (from Pythia) has to be provided always, since some relevant
information is only stored in this file. The user can optionally provide a Delphes file, in this case
run_delphes() does not have to be called.
By default, the weights are read out from the Delphes file and their names from the HepMC file. There are some
issues with current MadGraph versions that lead to Pythia not storing the weights. As work-around, MadMiner
supports reading weights from the LHE file (the observables still come from the Delphes file). To enable this,
use weights="lhe".
Parameters
----------
hepmc_filename : str
Path to the HepMC event file (with extension '.hepmc' or '.hepmc.gz').
sampled_from_benchmark : str
Name of the benchmark that was used for sampling in this event file (the keyword `sample_benchmark`
of `madminer.core.MadMiner.run()`).
is_background : bool, optional
Whether the sample is a background sample (i.e. without benchmark reweighting).
delphes_filename : str or None, optional
Path to the Delphes event file (with extension '.root'). If None, the user has to call run_delphes(), which
will create this file. Default value: None.
lhe_filename : None or str, optional
Path to the LHE event file (with extension '.lhe' or '.lhe.gz'). This is only needed if weights is "lhe".
k_factor : float, optional
Multiplies the cross sections found in the sample. Default value: 1.
weights : {"delphes", "lhe"}, optional
If "delphes", the weights are read out from the Delphes ROOT file, and their names are taken from the
HepMC file. If "lhe" (and lhe_filename is not None), the weights are taken from the LHE file (and matched
with the observables from the Delphes ROOT file). The "delphes" behaviour is generally better as it
minimizes the risk of mismatching observables and weights, but for some MadGraph and Delphes versions
there are issues with weights not being saved in the HepMC and Delphes ROOT files. In this case, setting
weights to "lhe" and providing the unweighted LHE file from MadGraph may be an easy fix. Default value:
"lhe".
Returns
-------
None
"""
logger.debug("Adding event sample %s", hepmc_filename)
# Check inputs
assert weights in ["delphes", "lhe"], "Unknown setting for weights: %s. Has to be 'delphes' or 'lhe'."
if self.systematics is not None:
if lhe_filename is None:
raise ValueError("With systematic uncertainties, a LHE event file has to be provided.")
if weights == "lhe":
if lhe_filename is None:
raise ValueError("With weights = 'lhe', a LHE event file has to be provided.")
self.hepmc_sample_filenames.append(hepmc_filename)
self.hepmc_sampled_from_benchmark.append(sampled_from_benchmark)
self.hepmc_is_backgrounds.append(is_background)
self.sample_k_factors.append(k_factor)
self.delphes_sample_filenames.append(delphes_filename)
self.lhe_sample_filenames.append(lhe_filename)
if weights == "lhe" and lhe_filename is not None:
self.hepmc_sample_weight_labels.append(None)
self.lhe_sample_filenames_for_weights.append(lhe_filename)
else:
self.hepmc_sample_weight_labels.append(extract_weight_order(hepmc_filename, sampled_from_benchmark))
self.lhe_sample_filenames_for_weights.append(None)