Source code for madminer.delphes.delphes_reader

from __future__ import absolute_import, division, print_function, unicode_literals

import six
from collections import OrderedDict
import numpy as np
import logging
import os

from madminer.utils.interfaces.madminer_hdf5 import (
    save_events_to_madminer_file,
    load_madminer_settings,
    save_nuisance_setup_to_madminer_file,
)
from madminer.utils.interfaces.delphes import run_delphes
from madminer.utils.interfaces.delphes_root import parse_delphes_root_file
from madminer.utils.interfaces.hepmc import extract_weight_order
from madminer.utils.interfaces.lhe import parse_lhe_file, extract_nuisance_parameters_from_lhe_file
from madminer.sampling import combine_and_shuffle

logger = logging.getLogger(__name__)


[docs]class DelphesReader: """ Detector simulation with Delphes and simple calculation of observables. After setting up the parameter space and benchmarks and running MadGraph and Pythia, all of which is organized in the madminer.core.MadMiner class, the next steps are the simulation of detector effects and the calculation of observables. Different tools can be used for these tasks, please feel free to implement the detector simulation and analysis routine of your choice. This class provides an example implementation based on Delphes. Its workflow consists of the following steps: * Initializing the class with the filename of a MadMiner HDF5 file (the output of `madminer.core.MadMiner.save()`) * Adding one or multiple event samples produced by MadGraph and Pythia in `DelphesProcessor.add_sample()`. * Running Delphes on the samples that require it through `DelphesProcessor.run_delphes()`. * Optionally, acceptance cuts for all visible particles can be defined with `DelphesProcessor.set_acceptance()`. * Defining observables through `DelphesProcessor.add_observable()` or `DelphesProcessor.add_observable_from_function()`. A simple set of default observables is provided in `DelphesProcessor.add_default_observables()` * Optionally, cuts can be set with `DelphesProcessor.add_cut()` * Calculating the observables from the Delphes ROOT files with `DelphesProcessor.analyse_delphes_samples()` * Saving the results with `DelphesProcessor.save()` Please see the tutorial for a detailed walk-through. Parameters ---------- filename : str or None, optional Path to MadMiner file (the output of `madminer.core.MadMiner.save()`). Default value: None. """ def __init__(self, filename): # Initialize samples self.hepmc_sample_filenames = [] self.hepmc_sample_weight_labels = [] self.hepmc_sampled_from_benchmark = [] self.hepmc_is_backgrounds = [] self.lhe_sample_filenames = [] self.lhe_sample_filenames_for_weights = [] self.delphes_sample_filenames = [] self.sample_k_factors = [] self.sample_systematics = [] # Initialize observables self.observables = OrderedDict() self.observables_required = OrderedDict() self.observables_defaults = OrderedDict() # Initialize cuts self.cuts = [] self.cuts_default_pass = [] # Initialize acceptance cuts self.acceptance_pt_min_e = None self.acceptance_pt_min_mu = None self.acceptance_pt_min_a = None self.acceptance_pt_min_j = None self.acceptance_eta_max_e = None self.acceptance_eta_max_mu = None self.acceptance_eta_max_a = None self.acceptance_eta_max_j = None # Initialize samples self.reference_benchmark = None self.observations = None self.weights = None self.events_sampling_benchmark_ids = None # Initialize event summary self.signal_events_per_benchmark = None self.background_events = None # Information from .h5 file self.filename = filename (parameters, benchmarks, _, _, _, _, _, self.systematics, _, _, _, _) = load_madminer_settings( filename, include_nuisance_benchmarks=False ) self.benchmark_names_phys = list(benchmarks.keys()) self.n_benchmarks_phys = len(benchmarks) # Initialize nuisance parameters self.nuisance_parameters = OrderedDict()
[docs] def add_sample( self, hepmc_filename, sampled_from_benchmark, is_background=False, delphes_filename=None, lhe_filename=None, k_factor=1.0, weights="lhe", systematics=None, ): """ 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". systematics : None or list of str, optional List of systematics associated with this sample. Default value: None. Returns ------- None """ # Check inputs if not os.path.exists(hepmc_filename): raise ValueError("The specified hepmc file does not exist") if lhe_filename and not os.path.exists(lhe_filename): raise ValueError("The specified lhe file does not exist") if weights not in ["delphes", "lhe"]: raise ValueError("Unknown setting for weights. Has to be 'delphes' or 'lhe'.") if weights == "lhe" and lhe_filename is None: raise ValueError("With weights = 'lhe', a LHE event file has to be provided.") if self.systematics and lhe_filename is None: raise ValueError("With systematic uncertainties, a LHE event file has to be provided.") logger.debug("Adding event sample %s", hepmc_filename) 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) self.sample_systematics.append(systematics) 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)
[docs] def run_delphes(self, delphes_directory, delphes_card, initial_command=None, log_file=None): """ Runs the fast detector simulation Delphes on all HepMC samples added so far for which it hasn't been run yet. Parameters ---------- delphes_directory : str Path to the Delphes directory. delphes_card : str Path to a Delphes card. initial_command : str or None, optional Initial bash commands that have to be executed before Delphes is run (e.g. to load the correct virtual environment). Default value: None. log_file : str or None, optional Path to log file in which the Delphes output is saved. Default value: None. Returns ------- None """ if log_file is None: log_file = "./logs/delphes.log" for i, (delphes_filename, hepmc_filename) in enumerate( zip(self.delphes_sample_filenames, self.hepmc_sample_filenames) ): if delphes_filename is not None and os.path.isfile(delphes_filename): logger.debug("Delphes already run for event sample %s", hepmc_filename) continue elif delphes_filename is not None: logger.debug( "Given Delphes file %s does not exist, running Delphes again on HepMC sample at %s", delphes_filename, hepmc_filename, ) else: logger.info("Running Delphes on HepMC sample at %s", hepmc_filename) delphes_sample_filename = run_delphes( delphes_directory, delphes_card, hepmc_filename, initial_command=initial_command, log_file=log_file ) self.delphes_sample_filenames[i] = delphes_sample_filename
[docs] def set_acceptance( self, pt_min_e=None, pt_min_mu=None, pt_min_a=None, pt_min_j=None, eta_max_e=None, eta_max_mu=None, eta_max_a=None, eta_max_j=None, ): """ Sets acceptance cuts for all visible particles. These are taken into account before observables and cuts are calculated. Parameters ---------- pt_min_e : float or None, optional Minimum electron transverse momentum in GeV. None means no acceptance cut. Default value: None. pt_min_mu : float or None, optional Minimum muon transverse momentum in GeV. None means no acceptance cut. Default value: None. pt_min_a : float or None, optional Minimum photon transverse momentum in GeV. None means no acceptance cut. Default value: None. pt_min_j : float or None, optional Minimum jet transverse momentum in GeV. None means no acceptance cut. Default value: None. eta_max_e : float or None, optional Maximum absolute electron pseudorapidity. None means no acceptance cut. Default value: None. eta_max_mu : float or None, optional Maximum absolute muon pseudorapidity. None means no acceptance cut. Default value: None. eta_max_a : float or None, optional Maximum absolute photon pseudorapidity. None means no acceptance cut. Default value: None. eta_max_j : float or None, optional Maximum absolute jet pseudorapidity. None means no acceptance cut. Default value: None. Returns ------- None """ self.acceptance_pt_min_e = pt_min_e self.acceptance_pt_min_mu = pt_min_mu self.acceptance_pt_min_a = pt_min_a self.acceptance_pt_min_j = pt_min_j self.acceptance_eta_max_e = eta_max_e self.acceptance_eta_max_mu = eta_max_mu self.acceptance_eta_max_a = eta_max_a self.acceptance_eta_max_j = eta_max_j
[docs] def add_observable(self, name, definition, required=False, default=None): """ Adds an observable as a string that can be parsed by Python's `eval()` function. Parameters ---------- name : str Name of the observable. Since this name will be used in `eval()` calls for cuts, this should not contain spaces or special characters. definition : str An expression that can be parsed by Python's `eval()` function. As objects, the visible particles can be used: `e`, `mu`, `j`, `a`, and `l` provide lists of electrons, muons, jets, photons, and leptons (electrons and muons combined), in each case sorted by descending transverse momentum. `met` provides a missing ET object. `visible` and `all` provide access to the sum of all visible particles and the sum of all visible particles plus MET, respectively. All these objects are instances of `MadMinerParticle`, which inherits from scikit-hep's [LorentzVector](http://scikit-hep.org/api/math.html#vector-classes). See the link for a documentation of their properties. In addition, `MadMinerParticle` have properties `charge` and `pdg_id`, which return the charge in units of elementary charges (i.e. an electron has `e[0].charge = -1.`), and the PDG particle ID. For instance, `"abs(j[0].phi() - j[1].phi())"` defines the azimuthal angle between the two hardest jets. required : bool, optional Whether the observable is required. If True, an event will only be retained if this observable is successfully parsed. For instance, any observable involving `"j[1]"` will only be parsed if there are at least two jets passing the acceptance cuts. Default value: False. default : float or None, optional If `required=False`, this is the placeholder value for observables that cannot be parsed. None is replaced with `np.nan`. Default value: None. Returns ------- None """ self._check_python_syntax(definition) if required: logger.debug("Adding required observable %s = %s", name, definition) else: logger.debug("Adding optional observable %s = %s with default %s", name, definition, default) self.observables[name] = definition self.observables_required[name] = required self.observables_defaults[name] = default
[docs] def add_observable_from_function(self, name, fn, required=False, default=None): """ Adds an observable defined through a function. Parameters ---------- name : str Name of the observable. Since this name will be used in `eval()` calls for cuts, this should not contain spaces or special characters. fn : function A function with signature `observable(leptons, photons, jets, met)` where the input arguments are lists of MadMinerParticle instances and a float is returned. The function should raise a `RuntimeError` to signal that it is not defined. required : bool, optional Whether the observable is required. If True, an event will only be retained if this observable is successfully parsed. For instance, any observable involving `"j[1]"` will only be parsed if there are at least two jets passing the acceptance cuts. Default value: False. default : float or None, optional If `required=False`, this is the placeholder value for observables that cannot be parsed. None is replaced with `np.nan`. Default value: None. Returns ------- None """ if required: logger.debug("Adding required observable %s defined through external function", name) else: logger.debug( "Adding optional observable %s defined through external function with default %s", name, default ) self.observables[name] = fn self.observables_required[name] = required self.observables_defaults[name] = default
[docs] def add_default_observables( self, n_leptons_max=2, n_photons_max=2, n_jets_max=2, include_met=True, include_visible_sum=True, include_numbers=True, include_charge=True, ): """ Adds a set of simple standard observables: the four-momenta (parameterized as E, pT, eta, phi) of the hardest visible particles, and the missing transverse energy. Parameters ---------- n_leptons_max : int, optional Number of hardest leptons for which the four-momenta are saved. Default value: 2. n_photons_max : int, optional Number of hardest photons for which the four-momenta are saved. Default value: 2. n_jets_max : int, optional Number of hardest jets for which the four-momenta are saved. Default value: 2. include_met : bool, optional Whether the missing energy observables are stored. Default value: True. include_visible_sum : bool, optional Whether observables characterizing the sum of all particles are stored. Default value: True. include_numbers : bool, optional Whether the number of leptons, photons, and jets is saved as observable. Default value: True. include_charge : bool, optional Whether the lepton charge is saved as observable. Default value: True. Returns ------- None """ logger.debug("Adding default observables") # ETMiss if include_met: self.add_observable("et_miss", "met.pt", required=True) self.add_observable("phi_miss", "met.phi()", required=True) # Sum of visible particles if include_visible_sum: self.add_observable("e_visible", "visible.e", required=True) self.add_observable("eta_visible", "visible.eta", required=True) # Individual observed particles for n, symbol, include_this_charge in zip( [n_leptons_max, n_photons_max, n_jets_max], ["l", "a", "j"], [False, False, include_charge] ): if include_numbers: self.add_observable("n_{}s".format(symbol), "len({})".format(symbol), required=True) for i in range(n): self.add_observable( "e_{}{}".format(symbol, i + 1), "{}[{}].e".format(symbol, i), required=False, default=0.0 ) self.add_observable( "pt_{}{}".format(symbol, i + 1), "{}[{}].pt".format(symbol, i), required=False, default=0.0 ) self.add_observable( "eta_{}{}".format(symbol, i + 1), "{}[{}].eta".format(symbol, i), required=False, default=0.0 ) self.add_observable( "phi_{}{}".format(symbol, i + 1), "{}[{}].phi()".format(symbol, i), required=False, default=0.0 ) if include_this_charge and symbol == "l": self.add_observable( "charge_{}{}".format(symbol, i + 1), "{}[{}].charge".format(symbol, i), required=False, default=0.0, )
[docs] def add_cut(self, definition, pass_if_not_parsed=False): """ Adds a cut as a string that can be parsed by Python's `eval()` function and returns a bool. Parameters ---------- definition : str An expression that can be parsed by Python's `eval()` function and returns a bool: True for the event to pass this cut, False for it to be rejected. In the definition, all visible particles can be used: `e`, `mu`, `j`, `a`, and `l` provide lists of electrons, muons, jets, photons, and leptons (electrons and muons combined), in each case sorted by descending transverse momentum. `met` provides a missing ET object. `visible` and `all` provide access to the sum of all visible particles and the sum of all visible particles plus MET, respectively. All these objects are instances of `MadMinerParticle`, which inherits from scikit-hep's [LorentzVector](http://scikit-hep.org/api/math.html#vector-classes). See the link for a documentation of their properties. In addition, `MadMinerParticle` have properties `charge` and `pdg_id`, which return the charge in units of elementary charges (i.e. an electron has `e[0].charge = -1.`), and the PDG particle ID. For instance, `"len(e) >= 2"` requires at least two electrons passing the acceptance cuts, while `"mu[0].charge > 0."` specifies that the hardest muon is positively charged. pass_if_not_parsed : bool, optional Whether the cut is passed if the observable cannot be parsed. Default value: False. Returns ------- None """ self._check_python_syntax(definition) logger.debug("Adding cut %s", definition) self.cuts.append(definition) self.cuts_default_pass.append(pass_if_not_parsed)
[docs] def reset_observables(self): """ Resets all observables. """ logger.debug("Resetting observables") self.observables = OrderedDict() self.observables_required = OrderedDict() self.observables_defaults = OrderedDict()
[docs] def reset_cuts(self): """ Resets all cuts. """ logger.debug("Resetting cuts") self.cuts = [] self.cuts_default_pass = []
[docs] def analyse_delphes_samples( self, generator_truth=False, delete_delphes_files=False, reference_benchmark=None, parse_lhe_events_as_xml=True ): """ Main function that parses the Delphes samples (ROOT files), checks acceptance and cuts, and extracts the observables and weights. Parameters ---------- generator_truth : bool, optional If True, the generator truth information (as given out by Pythia) will be parsed. Detector resolution or efficiency effects will not be taken into account. delete_delphes_files : bool, optional If True, the Delphes ROOT files will be deleted after extracting the information from them. Default value: False. reference_benchmark : str or None, optional The weights at the nuisance benchmarks will be rescaled to some reference theta benchmark: `dsigma(x|theta_sampling(x),nu) -> dsigma(x|theta_ref,nu) = dsigma(x|theta_sampling(x),nu) * dsigma(x|theta_ref,0) / dsigma(x|theta_sampling(x),0)`. This sets the name of the reference benchmark. If None, the first one will be used. Default value: None. parse_lhe_events_as_xml : bool, optional Decides whether the LHE events are parsed with an XML parser (more robust, but slower) or a text parser (less robust, faster). Default value: True. Returns ------- None """ # Input if reference_benchmark is None: reference_benchmark = self.benchmark_names_phys[0] self.reference_benchmark = reference_benchmark # Reset observations self.observations = None self.weights = None self.nuisance_parameters = OrderedDict() self.events_sampling_benchmark_ids = None self.signal_events_per_benchmark = [0 for _ in range(self.n_benchmarks_phys)] self.background_events = 0 for ( delphes_file, weight_labels, is_background, sampling_benchmark, lhe_file, lhe_file_for_weights, k_factor, sample_syst_names, ) in zip( self.delphes_sample_filenames, self.hepmc_sample_weight_labels, self.hepmc_is_backgrounds, self.hepmc_sampled_from_benchmark, self.lhe_sample_filenames, self.lhe_sample_filenames_for_weights, self.sample_k_factors, self.sample_systematics, ): logger.info( "Analysing Delphes sample %s: Calculating %s observables, requiring %s selection cuts, associated with " "%s", delphes_file, len(self.observables), len(self.cuts), "no systematics" if sample_syst_names is None else "systematics" + ", ".join(list(sample_syst_names)), ) this_observations, this_weights, this_n_events = self._analyse_delphes_sample( delete_delphes_files, delphes_file, generator_truth, is_background, k_factor, lhe_file, lhe_file_for_weights, parse_lhe_events_as_xml, reference_benchmark, sampling_benchmark, weight_labels, sample_syst_names, ) # No events? if this_observations is None: continue # Store sampling id for each event if is_background: idx = -1 self.background_events += this_n_events else: idx = self.benchmark_names_phys.index(sampling_benchmark) self.signal_events_per_benchmark[idx] += this_n_events this_events_sampling_benchmark_ids = np.array([idx] * this_n_events, dtype=np.int) # First results if self.observations is None and self.weights is None: self.observations = this_observations self.weights = this_weights self.events_sampling_benchmark_ids = this_events_sampling_benchmark_ids continue # Following results: check consistency with previous results if len(self.observations) != len(this_observations): raise ValueError( "Number of observations in different Delphes files incompatible: {} vs {}".format( len(self.observations), len(this_observations) ) ) # Merge weights with previous logging.debug("Merging data extracted from this file with data from previous files") previous_reference_weights = np.copy(self.weights[reference_benchmark]) for key in self.weights: if key in this_weights: # Benchmark exists in both samples self.weights[key] = np.hstack([self.weights[key], this_weights[key]]) logging.debug(" Weights for benchmark %s exist in both", key) else: # Benchmark only in previous samples self.weights[key] = np.hstack([self.weights[key], this_weights[reference_benchmark]]) logging.debug(" Weights for benchmark %s exist only in previous files", key) for key in this_weights: if key in self.weights: continue # Benchmark only in new samples self.weights[key] = np.hstack([previous_reference_weights, this_weights[key]]) logging.debug(" Weights for benchmark %s exist only in new file", key) # Merge observations with previous (should always be the same observables) for key in self.observations: assert key in this_observations, "Observable {} not found in Delphes sample!".format(key) self.observations[key] = np.hstack([self.observations[key], this_observations[key]]) self.events_sampling_benchmark_ids = np.hstack( [self.events_sampling_benchmark_ids, this_events_sampling_benchmark_ids] ) logger.info("Analysed number of events per sampling benchmark:") for name, n_events in zip(self.benchmark_names_phys, self.signal_events_per_benchmark): if n_events > 0: logger.info(" %s from %s", n_events, name) if self.background_events > 0: logger.info(" %s from backgrounds", self.background_events)
def _analyse_delphes_sample( self, delete_delphes_files, delphes_file, generator_truth, is_background, k_factor, lhe_file, lhe_file_for_weights, parse_lhe_events_as_xml, reference_benchmark, sampling_benchmark, weight_labels, sample_syst_names, ): # Relevant systematics systematics_used = OrderedDict() if sample_syst_names is None: sample_syst_names = [] for key in sample_syst_names: systematics_used[key] = self.systematics[key] if len(systematics_used) > 0 and lhe_file_for_weights is None: raise NotImplementedError( "Systematic uncertainties are currently only supported when the weights" " are extracted from the LHE file (instead of the HepMC / Delphes ROOT" " file). Please use the keyword lhe_filename when calling add_sample()." ) # Read systematics setup from LHE file logger.debug("Extracting nuisance parameter definitions from LHE file") systematics_dict = extract_nuisance_parameters_from_lhe_file(lhe_file, systematics_used) logger.debug("systematics_dict: %s", systematics_dict) # systematics_dict has structure # {systematics_name : {nuisance_parameter_name : ((benchmark0, weight0), (benchmark1, weight1), processing)}} # Store nuisance parameters for systematics_name, nuisance_info in six.iteritems(systematics_dict): for nuisance_parameter_name, ((benchmark0, weight0), (benchmark1, weight1), _) in six.iteritems( nuisance_info ): if ( self.nuisance_parameters is not None and nuisance_parameter_name in self.nuisance_parameters and (systematics_name, benchmark0, benchmark1) != self.nuisance_parameters[nuisance_parameter_name] ): raise RuntimeError( "Inconsistent information for same nuisance parameter {}. Old: {}. New: {}.".format( nuisance_parameter_name, self.nuisance_parameters[nuisance_parameter_name], (systematics_name, benchmark0, benchmark1), ) ) self.nuisance_parameters[nuisance_parameter_name] = (systematics_name, benchmark0, benchmark1) # Calculate observables and weights in Delphes ROOT file this_observations, this_weights, cut_filter = parse_delphes_root_file( delphes_file, self.observables, self.observables_required, self.observables_defaults, self.cuts, self.cuts_default_pass, weight_labels, use_generator_truth=generator_truth, delete_delphes_sample_file=delete_delphes_files, acceptance_eta_max_a=self.acceptance_eta_max_a, acceptance_eta_max_e=self.acceptance_eta_max_e, acceptance_eta_max_mu=self.acceptance_eta_max_mu, acceptance_eta_max_j=self.acceptance_eta_max_j, acceptance_pt_min_a=self.acceptance_pt_min_a, acceptance_pt_min_e=self.acceptance_pt_min_e, acceptance_pt_min_mu=self.acceptance_pt_min_mu, acceptance_pt_min_j=self.acceptance_pt_min_j, ) # No events found? if this_observations is None: logger.warning("No remaining events in this Delphes file, skipping it") return None, None, None if this_weights is not None: logger.debug("Found weights %s in Delphes file", list(this_weights.keys())) else: logger.debug("Did not extract weights from Delphes file") # Sanity checks n_events = self._check_sample_observations(this_observations) # Find weights in LHE file if lhe_file_for_weights is not None: logger.debug("Extracting weights from LHE file") _, this_weights = parse_lhe_file( filename=lhe_file_for_weights, sampling_benchmark=sampling_benchmark, benchmark_names=self.benchmark_names_phys, observables=OrderedDict(), parse_events_as_xml=parse_lhe_events_as_xml, systematics_dict=systematics_dict, ) logger.debug("Found weights %s in LHE file", list(this_weights.keys())) # Apply cuts logger.debug("Applying Delphes-based cuts to LHE weights") for key, weights in six.iteritems(this_weights): this_weights[key] = weights[cut_filter] if this_weights is None: raise RuntimeError("Could not extract weights from Delphes ROOT file or LHE file.") # Sanity checks n_events = self._check_sample_weights(n_events, this_weights) # k factors if k_factor is not None: for key in this_weights: this_weights[key] = k_factor * this_weights[key] # Background scenario: we only have one set of weights, but these should be true for all benchmarks if is_background: logger.debug("Sample is background") benchmarks_weight = list(six.itervalues(this_weights))[0] for benchmark_name in self.benchmark_names_phys: this_weights[benchmark_name] = benchmarks_weight # Rescale nuisance parameters to reference benchmark reference_weights = this_weights[reference_benchmark] sampling_weights = this_weights[sampling_benchmark] for key in this_weights: if key not in self.benchmark_names_phys: # Only rescale nuisance benchmarks this_weights[key] = reference_weights / sampling_weights * this_weights[key] return this_observations, this_weights, n_events def _check_python_syntax(self, expression): """ Evaluates a Python expression to check for syntax errors Parameters ---------- expression : str Python expression to be evaluated. The evaluation raises either SyntaxError or NameError Returns ------- None """ try: eval(expression) except SyntaxError: raise ValueError("The provided Python expression is invalid") except NameError: pass def _check_sample_observations(self, this_observations): """ Sanity checks """ # Check number of events in observables n_events = None for key, obs in six.iteritems(this_observations): this_n_events = len(obs) if n_events is None: n_events = this_n_events logger.debug("Found %s events", n_events) if this_n_events != n_events: raise RuntimeError( "Mismatching number of events in Delphes observations for {}: {} vs {}".format( key, n_events, this_n_events ) ) if not np.issubdtype(obs.dtype, np.number): logger.warning( "Observations for observable %s have non-numeric dtype %s. This usually means something " "is wrong in the definition of the observable. Data: %s", key, obs.dtype, obs, ) return n_events def _check_sample_weights(self, n_events, this_weights): """ Sanity checks """ # Check number of events in weights for key, weights in six.iteritems(this_weights): this_n_events = len(weights) if n_events is None: n_events = this_n_events logger.debug("Found %s events", n_events) if this_n_events != n_events: raise RuntimeError( "Mismatching number of events in weights {}: {} vs {}".format(key, n_events, this_n_events) ) if not np.issubdtype(weights.dtype, np.number): logger.warning( "Weights %s have non-numeric dtype %s. This usually means something " "is wrong in the definition of the observable. Data: %s", key, weights.dtype, weights, ) return n_events
[docs] def save(self, filename_out, shuffle=True): """ Saves the observable definitions, observable values, and event weights in a MadMiner file. The parameter, benchmark, and morphing setup is copied from the file provided during initialization. Nuisance benchmarks found in the HepMC file are added. Parameters ---------- filename_out : str Path to where the results should be saved. shuffle : bool, optional If True, events are shuffled before being saved. That's important when there are multiple distinct samples (e.g. signal and background). Default value: True. Returns ------- None """ if self.observations is None or self.weights is None: logger.warning("No observations to save!") return logger.debug("Loading HDF5 data from %s and saving file to %s", self.filename, filename_out) # Save nuisance parameters and benchmarks weight_names = list(self.weights.keys()) logger.debug("Weight names: %s", weight_names) save_nuisance_setup_to_madminer_file( filename_out, weight_names, self.nuisance_parameters, reference_benchmark=self.reference_benchmark, copy_from=self.filename, ) # Save events save_events_to_madminer_file( filename_out, self.observables, self.observations, self.weights, self.events_sampling_benchmark_ids, self.signal_events_per_benchmark, self.background_events, ) if shuffle: combine_and_shuffle([filename_out], filename_out)