Source code for madminer.core.madminer

from __future__ import absolute_import, division, print_function, unicode_literals
import six

import os
import logging
from collections import OrderedDict
import tempfile

from madminer.utils.morphing import PhysicsMorpher
from madminer.utils.interfaces.madminer_hdf5 import save_madminer_settings, load_madminer_settings
from madminer.utils.interfaces.mg_cards import export_param_card, export_reweight_card, export_run_card
from madminer.utils.interfaces.mg import generate_mg_process, setup_mg_with_scripts, run_mg, create_master_script
from madminer.utils.interfaces.mg import setup_mg_reweighting_with_scripts, run_mg_reweighting
from madminer.utils.various import create_missing_folders, format_benchmark, copy_file

logger = logging.getLogger(__name__)


[docs]class MadMiner: """ The central class to manage parameter spaces, benchmarks, and the generation of events through MadGraph and Pythia. An instance of this class is the starting point of most MadMiner applications. It is typically used in four steps: * Defining the parameter space through `MadMiner.add_parameter` * Defining the benchmarks, i.e. the points at which the squared matrix elements will be evaluated in MadGraph, with `MadMiner.add_benchmark()` or, if operator morphing is used, with `MadMiner.set_benchmarks_from_morphing()` * Saving this setup with `MadMiner.save()` (it can be loaded in a new instance with `MadMiner.load()`) * Running MadGraph and Pythia with the appropriate settings with `MadMiner.run()` or `MadMiner.run_multiple()` (the latter allows the user to combine runs from multiple run cards and sampling points) Please see the tutorial for a hands-on introduction to its methods. """ def __init__(self): self.parameters = OrderedDict() self.benchmarks = OrderedDict() self.default_benchmark = None self.morpher = None self.export_morphing = False self.systematics = OrderedDict()
[docs] def add_parameter( self, lha_block, lha_id, parameter_name=None, param_card_transform=None, morphing_max_power=2, parameter_range=(0.0, 1.0), ): """ Adds an individual parameter. Parameters ---------- lha_block : str The name of the LHA block as used in the param_card. Case-sensitive. lha_id : int The LHA id as used in the param_card. parameter_name : str or None An internal name for the parameter. If None, a the default 'benchmark_i' is used. morphing_max_power : int or tuple of int The maximal power with which this parameter contributes to the squared matrix element of the process of interest. If a tuple is given, gives this maximal power for each of several operator configurations. Typically at tree level, this maximal number is 2 for parameters that affect one vertex (e.g. only production or only decay of a particle), and 4 for parameters that affect two vertices (e.g. production and decay). Default value: 2. param_card_transform : None or str Represents a one-parameter function mapping the parameter (`"theta"`) to the value that should be written in the parameter cards. This str is parsed by Python's `eval()` function, and `"theta"` is parsed as the parameter value. Default value: None. parameter_range : tuple of float The range of parameter values of primary interest. Only affects the basis optimization. Default value: (0., 1.). Returns ------- None """ # Default names if parameter_name is None: parameter_name = "parameter_" + str(len(self.parameters)) # Check and sanitize input assert isinstance(parameter_name, six.string_types), "Parameter name is not a string: {}".format(parameter_name) assert isinstance(lha_block, six.string_types), "LHA block is not a string: {}".format(lha_block) assert isinstance(lha_id, int), "LHA id is not an integer: {}".format(lha_id) parameter_name = parameter_name.replace(" ", "_") parameter_name = parameter_name.replace("-", "_") assert parameter_name not in self.parameters, "Parameter name exists already: {}".format(parameter_name) if isinstance(morphing_max_power, int): morphing_max_power = (morphing_max_power,) # Add parameter self.parameters[parameter_name] = (lha_block, lha_id, morphing_max_power, parameter_range, param_card_transform) # After manually adding parameters, the morphing information is not accurate anymore self.morpher = None logger.info( "Added parameter %s (LHA: %s %s, maximal power in squared ME: %s, range: %s)", parameter_name, lha_block, lha_id, morphing_max_power, parameter_range, ) # Reset benchmarks if len(self.benchmarks) > 0: logger.warning("Resetting benchmarks and morphing") self.benchmarks = OrderedDict() self.default_benchmark = None self.morpher = None self.export_morphing = False
[docs] def set_parameters(self, parameters=None): """ Manually sets all parameters, overwriting previously added parameters. Parameters ---------- parameters : dict or list or None, optional If parameters is None, resets parameters. If parameters is an dict, the keys should be str and give the parameter names, and the values are tuples of the form (LHA_block, LHA_ID, morphing_max_power, param_min, param_max) or of the form (LHA_block, LHA_ID). If parameters is a list, the items should be tuples of the form (LHA_block, LHA_ID). Default value: None. Returns ------- None """ if parameters is None: parameters = OrderedDict() self.parameters = OrderedDict() if isinstance(parameters, dict): for key, values in six.iteritems(parameters): if len(values) == 5: self.add_parameter( lha_block=values[0], lha_id=values[1], parameter_name=key, parameter_range=[values[3], values[4]], morphing_max_power=values[2], ) elif len(values) == 2: self.add_parameter(lha_block=values[0], lha_id=values[1], parameter_name=key) else: raise ValueError("Parameter properties has unexpected length: {0}".format(values)) else: for values in parameters: assert len(values) == 2, "Parameter list entry does not have length 2: {0}".format(values) self.add_parameter(values[0], values[1]) # After manually adding parameters, the morphing information is not accurate anymore if len(self.benchmarks) > 0: logger.warning("Resetting benchmarks and morphing") self.benchmarks = OrderedDict() self.default_benchmark = None self.morpher = None self.export_morphing = False
[docs] def add_benchmark(self, parameter_values, benchmark_name=None, verbose=True): """ Manually adds an individual benchmark, that is, a parameter point that will be evaluated by MadGraph. If this command is called before Parameters ---------- parameter_values : dict The keys of this dict should be the parameter names and the values the corresponding parameter values. benchmark_name : str or None, optional Name of benchmark. If None, a default name is used. Default value: None. verbose : bool, optional If True, prints output about each benchmark. Default value: True. Returns ------- None Raises ------ RuntimeError If a benchmark with the same name already exists, if parameter_values is not a dict, or if a key of parameter_values does not correspond to a defined parameter. """ # Default names if benchmark_name is None: benchmark_name = "benchmark_" + str(len(self.benchmarks)) # Check input if not isinstance(parameter_values, dict): raise RuntimeError("Parameter values are not a dict: {}".format(parameter_values)) for key, value in six.iteritems(parameter_values): if key not in self.parameters: raise RuntimeError("Unknown parameter: {0}".format(key)) if benchmark_name in self.benchmarks: raise RuntimeError("Benchmark name {} exists already".format(benchmark_name)) # Add benchmark self.benchmarks[benchmark_name] = parameter_values # If first benchmark, this will be the default for sampling if len(self.benchmarks) == 1: self.default_benchmark = benchmark_name if verbose: logger.info("Added benchmark %s: %s)", benchmark_name, format_benchmark(parameter_values)) else: logger.debug("Added benchmark %s: %s)", benchmark_name, format_benchmark(parameter_values))
[docs] def set_benchmarks(self, benchmarks=None, verbose=True): """ Manually sets all benchmarks, that is, parameter points that will be evaluated by MadGraph. Calling this function overwrites all previously defined benchmarks. Parameters ---------- benchmarks : dict or list or None, optional Specifies all benchmarks. If None, all benchmarks are reset. If dict, the keys are the benchmark names and the values are dicts of the form {parameter_name:value}. If list, the entries are dicts {parameter_name:value} (and the benchmark names are chosen automatically). Default value: None. verbose : bool, optional If True, prints output about each benchmark. Default value: True. Returns ------- None """ if benchmarks is None: benchmarks = OrderedDict() self.benchmarks = OrderedDict() self.default_benchmark = None if isinstance(benchmarks, dict): for name, values in six.iteritems(benchmarks): self.add_benchmark(values, name, verbose=verbose) else: for values in benchmarks: self.add_benchmark(values) # After manually adding benchmarks, the morphing information is not accurate anymore if self.morpher is not None: logger.warning("Reset morphing") self.morpher = None self.export_morphing = False
[docs] def set_morphing( self, max_overall_power=4, n_bases=1, include_existing_benchmarks=True, n_trials=100, n_test_thetas=100 ): """ Sets up the morphing environment. Sets benchmarks, i.e. parameter points that will be evaluated by MadGraph, for a morphing algorithm, and calculates all information required for morphing. Morphing is a technique that allows MadMax to infer the full probability distribution `p(x_i | theta)` for each simulated event `x_i` and any `theta`, not just the benchmarks. The morphing basis is optimized with respect to the expected mean squared morphing weights over the parameter region of interest. If keep_existing_benchmarks=True, benchmarks defined previously will be incorporated in the morphing basis and only the remaining basis points will be optimized. Note that any subsequent call to `set_benchmarks` or `add_benchmark` will overwrite the morphing setup. The correct order is therefore to manually define benchmarks first, using `set_benchmarks` or `add_benchmark`, and then to create the morphing setup and complete the basis by calling `set_benchmarks_from_morphing(keep_existing_benchmarks=True)`. Parameters ---------- max_overall_power : int or tuple of int, optional The maximal sum of powers of all parameters contributing to the squared matrix element. If a tuple is given, gives the maximal sum of powers for each of several operator configurations (see `add_parameter`). Typically, if parameters can affect the couplings at n vertices, this number is 2n. Default value: 4. n_bases : int, optional The number of morphing bases generated. If n_bases > 1, multiple bases are combined, and the weights for each basis are reduced by a factor 1 / n_bases. Currently only the default choice of 1 is fully implemented. Do not use any other value for now. Default value: 1. include_existing_benchmarks : bool, optional If True, the previously defined benchmarks are included in the morphing basis. In that case, the number of free parameters in the optimization routine is reduced. If False, the existing benchmarks will still be simulated, but are not part of the morphing routine. Default value: True. n_trials : int, optional Number of random basis configurations tested in the optimization procedure. A larger number will increase the run time of the optimization, but lead to better results. Default value: 100. n_test_thetas : int, optional Number of random parameter points used to evaluate the expected mean squared morphing weights. A larger number will increase the run time of the optimization, but lead to better results. Default value: 100. Returns ------- None """ logger.info("Optimizing basis for morphing") if isinstance(max_overall_power, int): max_overall_power = (max_overall_power,) morpher = PhysicsMorpher(parameters_from_madminer=self.parameters) morpher.find_components(max_overall_power) if include_existing_benchmarks: n_predefined_benchmarks = len(self.benchmarks) basis = morpher.optimize_basis( n_bases=n_bases, fixed_benchmarks_from_madminer=self.benchmarks, n_trials=n_trials, n_test_thetas=n_test_thetas, ) else: n_predefined_benchmarks = 0 basis = morpher.optimize_basis( n_bases=n_bases, fixed_benchmarks_from_madminer=None, n_trials=n_trials, n_test_thetas=n_test_thetas ) basis.update(self.benchmarks) self.set_benchmarks(basis, verbose=False) self.morpher = morpher self.export_morphing = True logger.info( "Set up morphing with %s parameters, %s morphing components, %s predefined basis points, and %s " "new basis points", morpher.n_parameters, morpher.n_components, n_predefined_benchmarks, morpher.n_components - n_predefined_benchmarks, )
[docs] def reset_systematics(self): self.systematics = OrderedDict()
[docs] def add_systematics( self, effect, systematic_name=None, norm_variation=1.1, scale="mu", scale_variations=(0.5, 1.0, 2.0), pdf_variation="CT10", ): """ Parameters ---------- effect : {"norm", "scale", "pdf"} Type of the nuisance parameter. If "norm", it will affect the overall normalization of one or multiple samples in the process. If "scale", the nuisance parameter effect will be determined by varying factorization or regularization scales (depending on scale_variation and scales). If "pdf", the effect of the nuisance parameters will be determined by varying the PDF used. systematic_name : None or str, optional scale : {"mu", "mur", "muf"}, optional If type is "scale", this sets whether only the regularization scale ("mur"), only the factorization scale ("muf"), or both simulatenously ("mu") are varied. Default value: "mu". norm_variation : float, optional If type is "norm", this sets the relative effect of the nuisance parameter on the cross section at the "plus 1 sigma" variation. 1.1 corresponds to a 10% increase, 0.9 to a 10% decrease relative to the nominal cross section. Default value: 1.1. scale_variations : tuple of float, optional If type is "scale", this sets how the regularization and / or factorization scales are varied. A tuple like (0.5,1.,2.) specifies the factors with which they are varied. Default value: (0.5,1.,2.0). pdf_variation : str, optional If type is "pdf", defines the PDF set for the variation. The option is passed along to the `--pdf` option of MadGraph's systematics module. See https://cp3.irmp.ucl.ac.be/projects/madgraph/wiki/Systematics for a list. The option "CT10" would, as an example, run over all the eigenvectors of the CTEQ10 set. Default value: "CT10". Returns ------- None """ # Default name if systematic_name is None: i = 0 while "{}_{}".format(effect, i) in list(six.iterkeys(self.systematics)): i += 1 systematic_name = "{}_{}".format(type, i) systematic_name = systematic_name.replace(" ", "_") systematic_name = systematic_name.replace("-", "_") if effect == "pdf": self.systematics[systematic_name] = ("pdf", pdf_variation) elif effect == "scale": scale_variation_string = ",".join([str(factor) for factor in scale_variations]) assert scale in ["mu", "mur", "muf"] self.systematics[systematic_name] = ("scale", scale, scale_variation_string) elif effect == "norm": self.systematics[systematic_name] = ("norm", norm_variation) else: raise ValueError("Unknown systematic type {}, has to be one of 'norm', 'scale', or 'pdf'!".format(type))
[docs] def load(self, filename, disable_morphing=False): """ Loads MadMiner setup from a file. All parameters, benchmarks, and morphing settings are overwritten. See `save` for more details. Parameters ---------- filename : str Path to the MadMiner file. disable_morphing : bool, optional If True, the morphing setup is not loaded from the file. Default value: False. Returns ------- None """ # Load data ( self.parameters, self.benchmarks, _, morphing_components, morphing_matrix, _, _, self.systematics, _, _, _, _, ) = load_madminer_settings(filename, include_nuisance_benchmarks=False) logger.info("Found %s parameters:", len(self.parameters)) for key, values in six.iteritems(self.parameters): logger.info( " %s (LHA: %s %s, maximal power in squared ME: %s, range: %s)", key, values[0], values[1], values[2], values[3], ) logger.info("Found %s benchmarks:", len(self.benchmarks)) for key, values in six.iteritems(self.benchmarks): logger.info(" %s: %s", key, format_benchmark(values)) if self.default_benchmark is None: self.default_benchmark = key # Morphing self.morpher = None self.export_morphing = False if morphing_matrix is not None and morphing_components is not None and not disable_morphing: self.morpher = PhysicsMorpher(self.parameters) self.morpher.set_components(morphing_components) self.morpher.set_basis(self.benchmarks, morphing_matrix=morphing_matrix) self.export_morphing = True logger.info("Found morphing setup with %s components", len(morphing_components)) else: logger.info("Did not find morphing setup.") # Systematics setup if len(self.systematics) == 0: logger.info("Did not find systematics setup.") else: logger.info("Found systematics setup with %s nuisance parameter groups", len(self.systematics)) for key, value in six.iteritems(self.systematics): logger.debug(" %s: %s", key, " / ".join(str(x) for x in value))
[docs] def save(self, filename): """ Saves MadMiner setup into a file. The file format follows the HDF5 standard. The saved information includes: * the parameter definitions, * the benchmark points, * the systematics setup (if defined), and * the morphing setup (if defined). This file is an important input to later stages in the analysis chain, including the processing of generated events, extraction of training samples, and calculation of Fisher information matrices. In these downstream tasks, additional information will be written to the MadMiner file, including the observations and event weights. Parameters ---------- filename : str Path to the MadMiner file. Returns ------- None """ create_missing_folders([os.path.dirname(filename)]) if self.morpher is not None: logger.info("Saving setup (including morphing) to %s", filename) save_madminer_settings( filename=filename, parameters=self.parameters, benchmarks=self.benchmarks, morphing_components=self.morpher.components, morphing_matrix=self.morpher.morphing_matrix, systematics=self.systematics, overwrite_existing_files=True, ) else: logger.info("Saving setup (without morphing) to %s", filename) save_madminer_settings( filename=filename, parameters=self.parameters, benchmarks=self.benchmarks, systematics=self.systematics, overwrite_existing_files=True, )
def _export_cards( self, param_card_template_file, mg_process_directory, sample_benchmark=None, param_card_filename=None, reweight_card_filename=None, include_param_card=True, benchmarks=None, ): """ Writes out a param_card and reweight_card for MadGraph. Instead of this low-level function, it is recommended to use `run` or `run_multiple`. Parameters ---------- param_card_template_file : str Path to a param_card.dat of the used model. mg_process_directory : str Path to the directory of the MG process. sample_benchmark : str or None, optional Name of the benchmark used for sampling. If None, the very first defined benchmark is used. Default value: None. param_card_filename : str or None, optional Output filename for the generated param card. If None, a default filename in the MG process folder is used. Default value: None. reweight_card_filename : str or None, optional str or None. Output filename for the generated reweight card. If None, a default filename in the MG process folder is used. Default value: None. include_param_card : bool, optional If False, no param card is exported, only a reweight card benchmarks : None or OrderedDict, optional If None, uses all benchmarks. Otherwise uses these benchmarks. Returns ------- None """ if param_card_filename is None or reweight_card_filename is None: logger.info("Creating param and reweight cards in %s", mg_process_directory) else: logger.info("Creating param and reweight cards in %s, %s", param_card_filename, reweight_card_filename) if benchmarks is None: benchmarks = self.benchmarks # Check status assert self.default_benchmark is not None assert len(self.benchmarks) > 0 # Default benchmark if sample_benchmark is None: sample_benchmark = self.default_benchmark # Export param card if include_param_card: export_param_card( benchmark=benchmarks[sample_benchmark], parameters=self.parameters, param_card_template_file=param_card_template_file, mg_process_directory=mg_process_directory, param_card_filename=param_card_filename, ) # Export reweight card export_reweight_card( sample_benchmark=sample_benchmark, benchmarks=benchmarks, parameters=self.parameters, mg_process_directory=mg_process_directory, reweight_card_filename=reweight_card_filename, )
[docs] def run( self, mg_directory, proc_card_file, param_card_template_file, run_card_file=None, mg_process_directory=None, pythia8_card_file=None, configuration_file=None, sample_benchmark=None, is_background=False, only_prepare_script=False, ufo_model_directory=None, log_directory=None, temp_directory=None, initial_command=None, python2_override=False, systematics=None, ): """ High-level function that creates the the MadGraph process, all required cards, and prepares or runs the event generation for one combination of cards. If `only_prepare_scripts=True`, the event generation is not run directly, but a bash script is created in `<process_folder>/madminer/run.sh` that will start the event generation with the correct settings. High-level function that creates the the MadGraph process, all required cards, and prepares or runs the event generation for multiple combinations of run_cards or importance samplings (`sample_benchmarks`). If `only_prepare_scripts=True`, the event generation is not run directly, but a bash script is created in `<process_folder>/madminer/run.sh` that will start the event generation with the correct settings. Parameters ---------- mg_directory : str Path to the MadGraph 5 base directory. proc_card_file : str Path to the process card that tells MadGraph how to generate the process. param_card_template_file : str Path to a param card that will be used as template to create the appropriate param cards for these runs. run_card_file : str Paths to the MadGraph run card. If None, the default run_card is used. mg_process_directory : str or None, optional Path to the MG process directory. If None, MadMiner uses ./MG_process. Default value: None. pythia8_card_file : str or None, optional Path to the MadGraph Pythia8 card. If None, the card present in the process folder is used. Default value: None. configuration_file : str, optional Path to the MadGraph me5_configuration card. If None, the card present in the process folder is used. Default value: None. sample_benchmark : list of str or None, optional Lists the names of benchmarks that should be used to sample events. A different sampling does not change the expected differential cross sections, but will change which regions of phase space have many events (small variance) or few events (high variance). If None, the benchmark added first is used. Default value: None. is_background : bool, optional Should be True for background processes, i.e. process in which the differential cross section does not depend on the parameters (i.e. is the same for all benchmarks). In this case, no reweighting is run, which can substantially speed up the event generation. Default value: False. only_prepare_script : bool, optional If True, the event generation is not started, but instead a run.sh script is created in the process directory. Default value: False. ufo_model_directory : str or None, optional Path to an UFO model directory that should be used, but is not yet installed in mg_directory/models. The model will be copied to the MadGraph model directory before the process directory is generated. (Default value = None. log_directory : str or None, optional Directory for log files with the MadGraph output. If None, ./logs is used. Default value: None. temp_directory : str or None, optional Path to a temporary directory. If None, a system default is used. Default value: None. initial_command : str or None, optional Initial shell commands that have to be executed before MG is run (e.g. to load a virtual environment). Default value: None. python2_override : bool, optional If True, MadMiner explicitly calls "python2" instead of relying on the system Python version to be Python 2.6 or Python 2.7. If you use systematics, make sure that the python interface of LHAPDF was compiled with the Python version you are using. Default: False. systematics : None or list of str, optional If list of str, defines which systematics are used for this run. Returns ------- None """ if sample_benchmark is None: sample_benchmark = self.default_benchmark self.run_multiple( mg_directory=mg_directory, proc_card_file=proc_card_file, param_card_template_file=param_card_template_file, run_card_files=[run_card_file], mg_process_directory=mg_process_directory, pythia8_card_file=pythia8_card_file, configuration_file=configuration_file, sample_benchmarks=[sample_benchmark], is_background=is_background, only_prepare_script=only_prepare_script, ufo_model_directory=ufo_model_directory, log_directory=log_directory, temp_directory=temp_directory, initial_command=initial_command, python2_override=python2_override, systematics=systematics, )
[docs] def run_multiple( self, mg_directory, proc_card_file, param_card_template_file, run_card_files, mg_process_directory=None, pythia8_card_file=None, configuration_file=None, sample_benchmarks=None, is_background=False, only_prepare_script=False, ufo_model_directory=None, log_directory=None, temp_directory=None, initial_command=None, python2_override=False, systematics=None, ): """ High-level function that creates the the MadGraph process, all required cards, and prepares or runs the event generation for multiple combinations of run_cards or importance samplings (`sample_benchmarks`). If `only_prepare_scripts=True`, the event generation is not run directly, but a bash script is created in `<process_folder>/madminer/run.sh` that will start the event generation with the correct settings. Parameters ---------- mg_directory : str Path to the MadGraph 5 base directory. proc_card_file : str Path to the process card that tells MadGraph how to generate the process. param_card_template_file : str Path to a param card that will be used as template to create the appropriate param cards for these runs. run_card_files : list of str Paths to the MadGraph run card. mg_process_directory : str or None, optional Path to the MG process directory. If None, MadMiner uses ./MG_process. Default value: None. pythia8_card_file : str, optional Path to the MadGraph Pythia8 card. If None, the card present in the process folder is used. Default value: None. configuration_file : str, optional Path to the MadGraph me5_configuration card. If None, the card present in the process folder is used. Default value: None. sample_benchmarks : list of str or None, optional Lists the names of benchmarks that should be used to sample events. A different sampling does not change the expected differential cross sections, but will change which regions of phase space have many events (small variance) or few events (high variance). If None, a run is started for each of the benchmarks, which should map out all regions of phase space well. Default value: None. is_background : bool, optional Should be True for background processes, i.e. process in which the differential cross section does not depend on the parameters (i.e. is the same for all benchmarks). In this case, no reweighting is run, which can substantially speed up the event generation. Default value: False. only_prepare_script : bool, optional If True, the event generation is not started, but instead a run.sh script is created in the process directory. Default value: False. ufo_model_directory : str or None, optional Path to an UFO model directory that should be used, but is not yet installed in mg_directory/models. The model will be copied to the MadGraph model directory before the process directory is generated. (Default value = None) log_directory : str or None, optional Directory for log files with the MadGraph output. If None, ./logs is used. Default value: None. temp_directory : str or None, optional Path to a temporary directory. If None, a system default is used. Default value: None. initial_command : str or None, optional Initial shell commands that have to be executed before MG is run (e.g. to load a virtual environment). If not specified and `python2_override` is True, it adds the user-installed Python2 binaries to the PATH. Default value: None. python2_override : bool, optional If True, MadMiner explicitly calls "python2" instead of relying on the system Python version to be Python 2.6 or Python 2.7. If you use systematics, make sure that the python interface of LHAPDF was compiled with the Python version you are using. Default: False. systematics : None or list of str, optional If list of str, defines which systematics are used for these runs. Returns ------- None """ # Defaults if mg_process_directory is None: mg_process_directory = "./MG_process" if temp_directory is None: temp_directory = tempfile.gettempdir() if log_directory is None: log_directory = "./logs" if sample_benchmarks is None: sample_benchmarks = [benchmark for benchmark in self.benchmarks] # Gives 'python2_override' full power if 'initial_command' is empty. # (Reference: https://github.com/diana-hep/madminer/issues/422) if python2_override and initial_command is None: logger.info("Adding Python2.7 bin folder to PATH") binary_path = os.popen("command -v python2.7").read().strip() binary_folder = os.path.dirname(os.path.realpath(binary_path)) initial_command = "export PATH={}:$PATH".format(binary_folder) # Generate process folder log_file_generate = log_directory + "/generate.log" generate_mg_process( mg_directory, temp_directory, proc_card_file, mg_process_directory, ufo_model_directory=ufo_model_directory, initial_command=initial_command, log_file=log_file_generate, explicit_python_call=python2_override, ) # Make MadMiner folders create_missing_folders( [ mg_process_directory + "/madminer", mg_process_directory + "/madminer/cards", mg_process_directory + "/madminer/scripts", ] ) # Systematics if systematics is None: systematics_used = self.systematics else: systematics_used = OrderedDict() for key in systematics: systematics_used[key] = self.systematics[key] # Loop over settings i = 0 mg_scripts = [] for run_card_file in run_card_files: for sample_benchmark in sample_benchmarks: # Files script_file = "madminer/scripts/run_{}.sh".format(i) log_file_run = "run_{}.log".format(i) mg_commands_filename = "madminer/cards/mg_commands_{}.dat".format(i) param_card_file = "madminer/cards/param_card_{}.dat".format(i) reweight_card_file = "madminer/cards/reweight_card_{}.dat".format(i) new_pythia8_card_file = None if pythia8_card_file is not None: new_pythia8_card_file = "madminer/cards/pythia8_card_{}.dat".format(i) new_run_card_file = None if run_card_file is not None: new_run_card_file = "madminer/cards/run_card_{}.dat".format(i) new_configuration_file = None if configuration_file is not None: new_configuration_file = "madminer/cards/me5_configuration_{}.txt".format(i) logger.info("Run %s", i) logger.info(" Sampling from benchmark: %s", sample_benchmark) logger.info(" Original run card: %s", run_card_file) logger.info(" Original Pythia8 card: %s", pythia8_card_file) logger.info(" Original config card: %s", configuration_file) logger.info(" Copied run card: %s", new_run_card_file) logger.info(" Copied Pythia8 card: %s", new_pythia8_card_file) logger.info(" Copied config card: %s", new_configuration_file) logger.info(" Param card: %s", param_card_file) logger.info(" Reweight card: %s", reweight_card_file) logger.info(" Log file: %s", log_file_run) # Check input if run_card_file is None and self._check_pdf_or_scale_variation(systematics_used): logger.warning( "Warning: No run card given, but PDF or scale variation set up. The correct systematics" " settings are not set automatically. Make sure to set them correctly!" ) # Create param and reweight cards self._export_cards( param_card_template_file, mg_process_directory, sample_benchmark=sample_benchmark, param_card_filename=mg_process_directory + "/" + param_card_file, reweight_card_filename=mg_process_directory + "/" + reweight_card_file, ) # Create run card if run_card_file is not None: export_run_card( template_filename=run_card_file, run_card_filename=mg_process_directory + "/" + new_run_card_file, systematics=systematics_used, ) # Copy Pythia card if pythia8_card_file is not None: copy_file(pythia8_card_file, mg_process_directory + "/" + new_pythia8_card_file) # Copy Configuration card if configuration_file is not None: copy_file(configuration_file, mg_process_directory + "/" + new_configuration_file) # Run MG and Pythia if only_prepare_script: mg_script = setup_mg_with_scripts( mg_process_directory, proc_card_filename_from_mgprocdir=mg_commands_filename, run_card_file_from_mgprocdir=new_run_card_file, param_card_file_from_mgprocdir=param_card_file, reweight_card_file_from_mgprocdir=reweight_card_file, pythia8_card_file_from_mgprocdir=new_pythia8_card_file, configuration_file_from_mgprocdir=new_configuration_file, is_background=is_background, script_file_from_mgprocdir=script_file, initial_command=initial_command, log_dir=log_directory, log_file_from_logdir=log_file_run, explicit_python_call=python2_override, ) mg_scripts.append(mg_script) else: run_mg( mg_directory, mg_process_directory, mg_process_directory + "/" + mg_commands_filename, mg_process_directory + "/" + new_run_card_file, mg_process_directory + "/" + param_card_file, mg_process_directory + "/" + reweight_card_file, None if new_pythia8_card_file is None else mg_process_directory + "/" + new_pythia8_card_file, None if new_configuration_file is None else mg_process_directory + "/" + new_configuration_file, is_background=is_background, initial_command=initial_command, log_file=log_directory + "/" + log_file_run, explicit_python_call=python2_override, ) i += 1 n_runs_total = i # Master shell script if only_prepare_script: master_script_filename = "{}/madminer/run.sh".format(mg_process_directory) create_master_script(log_directory, master_script_filename, mg_directory, mg_process_directory, mg_scripts) logger.info( "To generate events, please run:\n\n %s [MG_directory] [MG_process_directory] [log_dir]\n\n", master_script_filename, ) else: expected_event_files = [ mg_process_directory + "/Events/run_{:02d}".format(i + 1) for i in range(n_runs_total) ] expected_event_files = "\n".join(expected_event_files) logger.info( "Finished running MadGraph! Please check that events were succesfully generated in the following " "folders:\n\n%s\n\n", expected_event_files, )
[docs] def reweight_existing_sample( self, mg_process_directory, run_name, param_card_template_file, sample_benchmark, reweight_benchmarks=None, only_prepare_script=False, log_directory=None, temp_directory=None, initial_command=None, ): """ High-level function that adds the weights required for MadMiner to an existing sample. If `only_prepare_scripts=True`, the event generation is not run directly, but a bash script is created in `<process_folder>/madminer/run.sh` that will start the event generation with the correct settings. Currently does not support adding systematics. Parameters ---------- mg_process_directory : str Path to the MG process directory. If None, MadMiner uses ./MG_process. run_name : str Run name. param_card_template_file : str Path to a param card that will be used as template to create the appropriate param cards for these runs. sample_benchmark : str The name of the benchmark used to generate this sample. reweight_benchmarks : list of str or None Lists the names of benchmarks to which the sample should be reweighted. If None, all benchmarks (except sample_benchmarks) are used. only_prepare_script : bool, optional If True, the event generation is not started, but instead a run.sh script is created in the process directory. Default value: False. log_directory : str or None, optional Directory for log files with the MadGraph output. If None, ./logs is used. Default value: None. initial_command : str or None, optional Initial shell commands that have to be executed before MG is run (e.g. to load a virtual environment). Default value: None. Returns ------- None """ # TODO: check that we don't reweight to benchmarks that already have weights in the LHE file # TODO: add systematics # Defaults if log_directory is None: log_directory = "./logs" # Make MadMiner folders create_missing_folders( [ mg_process_directory + "/madminer", mg_process_directory + "/madminer/cards", mg_process_directory + "/madminer/scripts", ] ) # Files script_file = "madminer/scripts/run_reweight.sh" log_file_run = "reweight.log" reweight_card_file = "/madminer/cards/reweight_card_reweight.dat" # Missing benchmarks missing_benchmarks = OrderedDict() for benchmark in reweight_benchmarks: missing_benchmarks[benchmark] = self.benchmarks[benchmark] # Inform user logger.info("Reweighting setup") logger.info(" Originally sampled from benchmark: %s", sample_benchmark) logger.info(" Now reweighting to benchmarks: %s", reweight_benchmarks) logger.info(" Reweight card: %s", reweight_card_file) logger.info(" Log file: %s", log_file_run) # Create param and reweight cards self._export_cards( param_card_template_file, mg_process_directory, sample_benchmark=sample_benchmark, reweight_card_filename=mg_process_directory + "/" + reweight_card_file, include_param_card=False, benchmarks=missing_benchmarks, ) # Run reweighting if only_prepare_script: call_instruction = setup_mg_reweighting_with_scripts( mg_process_directory, run_name=run_name, reweight_card_file_from_mgprocdir=reweight_card_file, script_file_from_mgprocdir=script_file, initial_command=initial_command, log_dir=log_directory, log_file_from_logdir=log_file_run, ) logger.info("To generate events, please run:\n\n %s \n\n", call_instruction) else: run_mg_reweighting( mg_process_directory, run_name=run_name, reweight_card_file=mg_process_directory + "/" + reweight_card_file, initial_command=initial_command, log_file=log_directory + "/" + log_file_run, ) logger.info( "Finished running reweighting! Please check that events were succesfully reweighted in the following " "folder:\n\n %s/Events/%s \n\n", mg_process_directory, run_name, )
def _check_pdf_or_scale_variation(self, systematics): for value in six.itervalues(systematics): if value[0] in ["pdf", "scale"]: return True return False