Source code for threeML.utils.fitted_objects.fitted_source_handler

from builtins import map, object, zip

__author__ = "grburgess"

import functools
import itertools

import numpy as np
from astromodels import use_astromodels_memoization

from threeML.config import threeML_config
from threeML.io.logging import setup_logger
from threeML.utils.progress_bar import tqdm

log = setup_logger(__name__)


[docs]class GenericFittedSourceHandler(object): def __init__( self, analysis_result, new_function, parameter_names, parameters, confidence_level, equal_tailed, *independent_variable_range ): """ A generic 3ML fitted source post-processor. This should be sub-classed in general :param analysis_result: a 3ML analysis result :param new_function: the function to use the fitted values to compute new values :param parameter_names: a list of parameter names :param parameters: astromodels parameter dictionary :param confidence_level: the confidence level to compute error :param independent_variable_range: the range(s) of independent values to compute the new function over """ # bind the class properties self._analysis_results = analysis_result self._analysis = analysis_result self._independent_variable_range = independent_variable_range self._cl = confidence_level self._equal_tailed = equal_tailed self._function = new_function self._parameter_names = parameter_names self._parameters = parameters # if only 1-D then we must place into its own tuple to # keep from confusing itertools if len(self._independent_variable_range) == 1: self._independent_variable_range = ( self._independent_variable_range[0],) # figure out the output shape of the best fit and errors self._out_shape = tuple(map(len, self._independent_variable_range)) # construct the propagated function self._build_propagated_function() # fold the function through its independent values self._evaluate() def __add__(self, other): """ The basics of adding are handled in the VariatesContainer :param other: another fitted source handler :return: a VariatesContainer with the summed values """ # assure that the shapes will be the same if other._out_shape != self._out_shape: log.error("cannot sum together arrays with different shapes!") raise RuntimeError() # this will get the value container for the other values return self.values + other.values def __radd__(self, other): if other == 0: return self else: return self.values + other.values def _transform(self, value): """ dummy transform to be overridden in a subclass :param value: :return: transformed value """ return value
[docs] def update_tag(self, tag, value): pass
def _build_propagated_function(self): """ builds a propagated function using RandomVariates propagation :return: """ arguments = {} # first test a parameters to check the number of samples for par in list(self._parameters.values()): if par.free: test_par = par break else: log.error("There are no free parameters in the model!") raise RuntimeError() test_variate = self._analysis_results.get_variates(test_par.path) if len(test_variate) > threeML_config.point_source.max_number_samples: choices = np.random.choice(range(len(test_variate)), size=threeML_config.point_source.max_number_samples) # because we might be using composite functions, # we have to keep track of parameter names in a non-elegant way for par, name in zip(list(self._parameters.values()), self._parameter_names): if par.free: this_variate = self._analysis_results.get_variates(par.path) # Do not use more than 1000 values (would make computation too slow for nothing) if len(this_variate) > threeML_config.point_source.max_number_samples: this_variate = this_variate[choices] arguments[name] = this_variate else: # use the fixed value rather than a variate arguments[name] = par.value # create the propagtor self._propagated_function = self._analysis_results.propagate( self._function, **arguments ) def _evaluate(self): """ calculate the best or mean fit of the new function or quantity :return: """ # if there are independent variables if self._independent_variable_range: variates = [] # scroll through the independent variables n_iterations = np.product(self._out_shape) with use_astromodels_memoization(False): variables = list(itertools.product(*self._independent_variable_range)) if len(variables) > 1: for v in tqdm(variables, desc="Propagating errors"): variates.append(self._propagated_function(*v)) else: for v in variables: variates.append(self._propagated_function(*v)) # otherwise just evaluate else: variates = self._propagated_function() # create a variates container self._propagated_variates = VariatesContainer( variates, self._out_shape, self._cl, self._transform, self._equal_tailed ) @property def values(self): """ :return: The VariatesContainer """ return self._propagated_variates @property def samples(self): """ :return: the raw samples of the variates """ return self._propagated_variates.samples @property def median(self): """ :return: the median of the variates """ return self._propagated_variates.median @property def average(self): """ :return: the average of the variates """ return self._propagated_variates.average @property def upper_error(self): """ :return: the upper error of the variates """ return self._propagated_variates.upper_error @property def lower_error(self): """ :return: the lower error of the variates """ return self._propagated_variates.lower_error
[docs]def transform(method): """ A wrapper to call the _transform method for outputs of Variates container class :param method: :return: """ @functools.wraps(method) def wrapped(instance, *args, **kwargs): return instance._transform(method(instance, *args, **kwargs)) return wrapped
[docs]class VariatesContainer(object): def __init__(self, values, out_shape, cl, transform, equal_tailed=True): """ A container to store an *List* of RandomVariates and transform their outputs to the appropriate shape. This cannot be done with normal numpy array operations because an array of RandomVariates becomes a normal ndarray. Therefore, we calculate the averages, errors, etc, and transform those. Additionally, any unit association must be done post calculation as well because the numpy array constructor sees a unit array as a regular array and again loses the RandomVariates properties. Therefore, the transform method is used which applies a function to the output properties, e.g., a unit association and or conversion. :param values: a flat List of RandomVariates :param out_shape: the array shape for the output variables :param cl: the confidence level to calculate error intervals on :param transform: a method to transform the outputs :param equal_tailed: whether to use equal-tailed error intervals or not """ self._values = values # type: list self._out_shape = out_shape # type: tuple self._cl = cl # type: float self._equal_tailed = equal_tailed # type: bool self._transform = transform # type: callable # calculate mean and median and transform them into the provided # output shape self._average = np.array([val.average for val in self._values]) self._average = self._average.reshape(self._out_shape) self._median = np.array([val.median for val in self._values]) self._median = self._median.reshape(self._out_shape) # construct the error intervals upper_error = [] lower_error = [] # if equal tailed errors requested if equal_tailed: for val in self._values: error = val.equal_tail_interval(self._cl) upper_error.append(error[1]) lower_error.append(error[0]) else: # else use the hdp for val in self._values: error = val.highest_posterior_density_interval(self._cl) upper_error.append(error[1]) lower_error.append(error[0]) # reshape the errors into the output shape self._upper_error = np.array(upper_error).reshape(self._out_shape) self._lower_error = np.array(lower_error).reshape(self._out_shape) samples = [] for val in self._values: samples.append(val.samples) n_samples = len(samples[0]) samples_shape = list(self._out_shape) + [n_samples] self._samples_shape = tuple(samples_shape) self._samples = np.array(samples).reshape(samples_shape) @property def values(self): """ :return: the list of of RandomVariates """ return self._values @property @transform def samples(self): """ :return: the transformed raw samples """ return self._samples @property @transform def average(self): """ :return: the transformed average """ return self._average @property @transform def median(self): """ :return: the transformed median """ return self._median @property @transform def upper_error(self): """ :return: the transformed upper error """ return self._upper_error @property @transform def lower_error(self): """ :return: the transformed lower error """ return self._lower_error def __add__(self, other): """ :param other: :return: """ assert ( other._out_shape == self._out_shape ), "cannot sum together arrays with different shapes!" # this will get the value container for the other values other_values = other.values summed_values = [v + vo for v, vo in zip(self._values, other_values)] return VariatesContainer( summed_values, self._out_shape, self._cl, self._transform, self._equal_tailed, ) def __radd__(self, other): if other == 0: return self else: other_values = other.values summed_values = [v + vo for v, vo in zip(self._values, other_values)] return VariatesContainer( summed_values, self._out_shape, self._cl, self._transform, self._equal_tailed, )