Source code for hepstats.utils.fit.diverse

from contextlib import ExitStack, contextmanager

import numpy as np


[docs] def get_ndims(dataset): """Return the number of dimensions in the dataset""" return len(dataset.obs)
[docs] def get_value(value): return np.array(value)
[docs] def eval_pdf(model, x, params=None, allow_extended=False): """Compute pdf of model at a given point x and for given parameters values""" if params is None: params = {} def pdf(model, x): if model.is_extended and allow_extended: ret = model.ext_pdf(x) else: ret = model.pdf(x) return get_value(ret) with ExitStack() as stack: for param in model.get_params(): if param in params: value = params[param]["value"] stack.enter_context(param.set_value(value)) return pdf(model, x)
[docs] def pll(minimizer, loss, pois) -> float: """Compute minimum profile likelihood for fixed given parameters values.""" with ExitStack() as stack: for p in pois: param = p.parameter stack.enter_context(param.set_value(p.value)) param.floating = False if any(param_loss.floating for param_loss in loss.get_params()): minimum = minimizer.minimize(loss=loss) value = minimum.fmin else: value = get_value(loss.value()) for p in pois: p.parameter.floating = True return value
[docs] @contextmanager def set_values(params, values): old_values = [p.value() for p in params] for p, v in zip(params, values): p.set_value(v) yield for p, v in zip(params, old_values): p.set_value(v)
[docs] def array2dataset(dataset_cls, obs, array, weights=None): """ dataset_cls: only used to get the class in which array/weights will be converted. """ if hasattr(dataset_cls, "from_numpy"): return dataset_cls.from_numpy(obs, array=array, weights=weights) else: return dataset_cls(obs, array=array, weights=weights)
[docs] def get_nevents(dataset): """Returns the number of events in the dataset""" return get_value(dataset.nevents)