text:
"""
File: pylinex/nonlinear/NLFitter.py
Author: Keith Tauscher
Date: 14 Jan 2018

Description: File containing class which analyzes MCMC chains in order to infer
             things about the parameter distributions they describe.
"""
from __future__ import division
import os, re, gc, h5py
import numpy as np
from scipy.special import erfinv
import matplotlib.pyplot as pl
from distpy import TransformList, TransformSet, GaussianDistribution,\
    DistributionSet, JumpingDistributionSet
from ..util import sequence_types, real_numerical_types, int_types,\
    univariate_histogram, bivariate_histogram, triangle_plot
from ..model import CompoundModel
from ..loglikelihood import load_loglikelihood_from_hdf5_group,\
    GaussianLoglikelihood
from .BurnRule import BurnRule

try:
    # this runs with no issues in python 2 but raises error in python 3
    basestring
except:
    # this try/except allows for python 2/3 compatible string type checking
    basestring = str

class NLFitter(object):
    """
    Class which analyzes MCMC chains in order to infer things about the
    parameter distributions they describe.
    """
    def __init__(self, file_name, burn_rule=None, chunk_slice=slice(None)):
        """
        Initializes an NLFitter to analyze the chain in the hdf5 file at
        file_name.

        file_name: the hdf5 file in which a chain was saved by a Sampler object
        burn_rule: if None, none of the chain is excluded as a burn-in
                   otherwise, must be a BurnRule object describing minimum
                              number of checkpoints to read, desired fraction
                              of the chain to read, and the thinning factor
                              describing the stride with which to read.
        chunk_slice: integer or slice object that can specifies which chunks to
                     load. By default, slice(None) is used, which causes all
                     chunks to load.
        """
        self.file_name = file_name
        self.burn_rule = burn_rule
        self.chunk_slice = chunk_slice

    @property
    def chunk_slice(self):
        """
        Property storing a boolean describing whether all chunks (as opposed to
        just the last one) should be loaded.
        """
        if not hasattr(self, '_chunk_slice'):
            raise AttributeError("chunk_slice referenced before it was set.")
        return self._chunk_slice

    @chunk_slice.setter
    def chunk_slice(self, value):
        """
        Setter for the chunk_slice property.

        value: integer or slice object that specifies which chunks to load
        """
        if (type(value) in int_types) or isinstance(value, slice):
            self._chunk_slice = value
        else:
            raise TypeError("chunk_slice was set to neither an integer nor " +\
                "a slice.")

    def __enter__(self):
        """
        Enters a with statement by binding the variable to this object.
        """
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        """
        Exits a with statement by closing the relevant hdf5 file.

        exc_type: type of exception thrown in with-statement (if any)
        exc_value: the exception thrown in with-statement (if any)
        traceback: traceback of the exception thrown in with-statement (if any)
        """
        self.close()

    @property
    def burn_rule(self):
        """
        Property storing the BurnRule object describing how the chain will be
        trimmed.
        """
        if not hasattr(self, '_burn_rule'):
            raise AttributeError("burn_rule was referenced before it was set.")
        return self._burn_rule

    @burn_rule.setter
    def burn_rule(self, value):
        """
        Setter for the BurnRule describing how the chain is trimmed.

        value: if None, a BurnRule that never burns any of the chain is set
               otherwise, value must be a BurnRule object
        """
        if type(value) is type(None):
            self._burn_rule =\
                BurnRule(min_checkpoints=1, desired_fraction=1.)
        elif isinstance(value, BurnRule):
            self._burn_rule = value
        else:
            raise TypeError("burn_rule was sent to something other than a " +\
                "BurnRule object.")

    @property
    def thin(self):
        """
        Property storing the factor by which the chain is thinned while being
        read.
        """
        if not hasattr(self, '_thin'):
            self._thin = self.burn_rule.thin
        return self._thin

    @property
    def burn_end(self):
        """
        Property storing whether the chain is burned from the end (if True) or
        from the start (if False, the usual case). This property is taken from
        the burn_rule.
        """
        if not hasattr(self, '_burn_end'):
            self._burn_end = self.burn_rule.burn_end
        return self._burn_end

    @property
    def file_name(self):
        """
        Property storing the string name of the file with the chain to be read.
        """
        if not hasattr(self, '_file_name'):
            raise AttributeError("file_name referenced before it was set.")
        return self._file_name

    @file_name.setter
    def file_name(self, value):
        """
        Setter for the name of the file with the chain to be read.

        value: string file name
        """
        if isinstance(value, basestring):
            self._file_name = value
        else:
            raise TypeError("file_name was set to a non-string.")

    @property
    def file(self):
        """
        Property storing the hdf5 file with information to be read.
        """
        if not hasattr(self, '_file'):
            self._file = h5py.File(self.file_name, 'r')
        return self._file

    def close(self):
        """
        Closes the file at the heart of this fitter.
        """
        self.file.close()

    @property
    def loglikelihood(self):
        """
        Property storing the Loglikelihood object which is explored in the
        chain being analyzed.
        """
        if not hasattr(self, '_loglikelihood'):
            self._loglikelihood =\
                load_loglikelihood_from_hdf5_group(self.file['loglikelihood'])
        return self._loglikelihood

    @property
    def use_ensemble_sampler(self):
        """
        Property storing whether emcee's ensemble sampler was used.
        """
        if not hasattr(self, '_use_ensemble_sampler'):
            self._use_ensemble_sampler =\
                self.file.attrs['use_ensemble_sampler']
        return self._use_ensemble_sampler

    @property
    def error(self):
        """
        Property storing the error vector used in the loglikelihood explored by
        the Sampler underlying this NLFitter.
        """
        if not hasattr(self, '_error'):
            self._error = self.loglikelihood.error
        return self._error

    @property
    def data(self):
        """
        Property storing the data vector used in the loglikelihood explored by
        the Sampler underlying this NLFitter.
        """
        if not hasattr(self, '_data'):
            self._data = self.loglikelihood.data
        return self._data

    @property
    def model(self):
        """
        Property storing the model being used for the fit being done by this
        fitter.
        """
        return self.loglikelihood.model

    @property
    def num_chunks(self):
        """
        Property storing the number of chunks stored in the file this NLFitter
        around which this NLFitter is based.
        """
        if not hasattr(self, '_num_chunks'):
            self._num_chunks = self.file.attrs['max_chunk_index'] + 1
        return self._num_chunks

    @property
    def chunks_to_load(self):
        """
        Property storing the chunk numbers to load for this NLFitter.
        """
        if not hasattr(self, '_chunks_to_load'):
            self._chunks_to_load = np.arange(self.num_chunks)[self.chunk_slice]
            if type(self._chunks_to_load) in int_types:
                self._chunks_to_load = np.array([self._chunks_to_load])
        return self._chunks_to_load

    @property
    def num_chunks_to_load(self):
        """
        Property storing the number of chunks to load for this NLFitter.
        """
        if not hasattr(self, '_num_chunks_to_load'):
            self._num_chunks_to_load = len(self.chunks_to_load)
        return self._num_chunks_to_load

    @property
    def num_checkpoints(self):
        """
        Property storing a sequence of length num_chunks containing the number
        of checkpoints in each chunk of this NLFitter's file.
        """
        if not hasattr(self, '_num_checkpoints'):
            self._num_checkpoints = []
            for ichunk in self.chunks_to_load:
                self._num_checkpoints.append(1 + self.file[('checkpoints/' +\
                    'chunk{:d}').format(ichunk)].attrs['max_checkpoint_index'])
        return self._num_checkpoints

    @property
    def chunk_summary_string(self):
        """
        Property storing a string which summarizes the chunks of the Fitter.
        """
        if not hasattr(self, '_chunk_summary'):
            summary = ''
            for ichunk in range(self.num_chunks):
                summary = '{0!s}Chunk #{1:d} has {2:d} checkpoints\n'.format(\
                    summary, 1 + ichunk, 1 + self.file[('checkpoints/' +\
                    'chunk{:d}').format(ichunk)].attrs['max_checkpoint_index'])
            self._chunk_summary = summary[:-1]
        return self._chunk_summary

    @staticmethod
    def chunk_summary(file_name):
        """
        Finds a string which summarizes the chunks of the Fitter which is
        assumed to located in an hdf5 file at the given file name.

        file_name: path to an hdf5 file which could be used to initialize an
                   NLFitter

        returns: multi-line string summary of the chunks given by the sampler
                 hdf5 file assumed to be located at file_name
        """
        if not os.path.exists(file_name):
            return "No sampler has been run at {!s}.".format(file_name)
        with h5py.File(file_name, 'r') as hdf5_file:
            summary = ''
            for ichunk in range(1 + hdf5_file.attrs['max_chunk_index']):
                summary = '{0!s}Chunk #{1:d} has {2:d} checkpoints\n'.format(\
                    summary, 1 + ichunk, 1 + hdf5_file[('checkpoints/' +\
                    'chunk{:d}').format(ichunk)].attrs['max_checkpoint_index'])
        return summary[:-1]

    @property
    def total_num_checkpoints(self):
        """
        Property storing the integer total number of checkpoints to be
        considered by the BurnRule of this NLFitter.
        """
        if not hasattr(self, '_total_num_checkpoints'):
            self._total_num_checkpoints = sum(self.num_checkpoints)
        return self._total_num_checkpoints

    @property
    def total_num_checkpoints_to_load(self):
        """
        Property storing the integer number of checkpoints to load from the end
        of the chunks under consideration.
        """
        if not hasattr(self, '_total_num_checkpoints_to_load'):
            self._total_num_checkpoints_to_load =\
                len(self.burn_rule(self.total_num_checkpoints))
        return self._total_num_checkpoints_to_load

    @property
    def checkpoints_to_load(self):
        """
        Property storing a sequence of 1D numpy.ndarrays of (specified by the
        BurnRule) of checkpoints which should be loaded into the final chain.
        """
        if not hasattr(self, '_checkpoints_to_load'):
            left_to_load = self.total_num_checkpoints_to_load
            if self.burn_end:
                answer = []
                for iichunk in range(self.num_chunks_to_load):
                    ichunk = self.chunks_to_load[iichunk]
                    this_num_checkpoints = self.num_checkpoints[iichunk]
                    if left_to_load == 0:
                        answer.append(np.array([]))
                    elif left_to_load > this_num_checkpoints:
                        left_to_load -= this_num_checkpoints
                        answer.append(np.arange(this_num_checkpoints))
                    else:
                        answer.append(\
                            np.arange(this_num_checkpoints)[:left_to_load])
                        left_to_load = 0
                self._checkpoints_to_load = [array for array in answer]
            else:
                backwards_answer = []
                for iichunk in range(self.num_chunks_to_load-1, -1, -1):
                    ichunk = self.chunks_to_load[iichunk]
                    this_num_checkpoints = self.num_checkpoints[iichunk]
                    if left_to_load == 0:
                        backwards_answer.append(np.array([]))
                    elif left_to_load > this_num_checkpoints:
                        left_to_load -= this_num_checkpoints
                        backwards_answer.append(\
                            np.arange(this_num_checkpoints))
                    else:
                        backwards_answer.append(\
                            np.arange(this_num_checkpoints)[-left_to_load:])
                        left_to_load = 0
                self._checkpoints_to_load = backwards_answer[-1::-1]
        return self._checkpoints_to_load

    def _load_checkpoints(self):
        """
        Loads the desired checkpoints of the chain, lnprobability, and
        acceptance fraction.
        """
        (chain_chunks, lnprobability_chunks, acceptance_fraction_chunks) =\
            ([], [], [])
        for (iichunk, ichunk) in enumerate(self.chunks_to_load):
            these_checkpoints_to_load = self.checkpoints_to_load[iichunk]
            if these_checkpoints_to_load.size != 0:
                checkpoints_group =\
                    self.file['checkpoints/chunk{:d}'.format(ichunk)]
                (chain_chunk, lnprobability_chunk,\
                    acceptance_fraction_chunk) = ([], [], [])
                for checkpoint_index in these_checkpoints_to_load:
                    checkpoint_group =\
                        checkpoints_group['{}'.format(checkpoint_index)]
                    chain_chunk.append(\
                        checkpoint_group['chain'][:,::self.thin,:])
                    lnprobability_chunk.append(\
                        checkpoint_group['lnprobability'][:,::self.thin])
                    acceptance_fraction_chunk.append(\
                        checkpoint_group['acceptance_fraction'][:])
                chain_chunks.append(np.concatenate(chain_chunk, axis=1))
                lnprobability_chunks.append(\
                    np.concatenate(lnprobability_chunk, axis=1))
                acceptance_fraction_chunks.append(\
                    np.stack(acceptance_fraction_chunk, axis=1))
                del chain_chunk, lnprobability_chunk, acceptance_fraction_chunk
                gc.collect()
        self._guess_distribution_set = DistributionSet.load_from_hdf5_group(\
            self.file['guess_distribution_sets/chunk{:d}'.format(ichunk)])
        if 'chunk{:d}'.format(ichunk) in\
            self.file['jumping_distribution_sets']:
            self._jumping_distribution_set =\
                JumpingDistributionSet.load_from_hdf5_group(\
                self.file['jumping_distribution_sets/chunk{:d}'.format(\
                ichunk)])
        else:
            self._jumping_distribution_set = None
        if 'chunk{:d}'.format(ichunk) in self.file['prior_distribution_sets']:
            self._prior_distribution_set =\
                DistributionSet.load_from_hdf5_group(\
                self.file['prior_distribution_sets/chunk{:d}'.format(ichunk)])
        else:
            self._prior_distribution_set = None
        self._chain = np.concatenate(chain_chunks, axis=1)
        self._lnprobability = np.concatenate(lnprobability_chunks, axis=1)
        self._acceptance_fraction =\
            np.concatenate(acceptance_fraction_chunks, axis=1)
        del chain_chunks, lnprobability_chunks, acceptance_fraction_chunks

    @property
    def prior_distribution_set(self):
        """
        Property storing the prior distribution(s) used in the sampler
        underlying this fitter.
        """
        if not hasattr(self, '_prior_distribution_set'):
            self._load_checkpoints()
        return self._prior_distribution_set

    @property
    def guess_distribution_set(self):
        """
        Property storing the DistributionSet used to initialize the last
        starting point of walkers.
        """
        if not hasattr(self, '_guess_distribution_set'):
            self._load_checkpoints()
        return self._guess_distribution_set

    @property
    def jumping_distribution_set(self):
        """
        Property storing the JumpingDistributionSet used in the final chunk
        included in the chain loaded by this Fitter.
        """
        if not hasattr(self, '_jumping_distribution_set'):
            self._load_checkpoints()
        return self._jumping_distribution_set

    def guess_distribution_set_triangle_plot(self, ndraw, parameters=None,\
        in_transformed_space=True, figsize=(8, 8), fig=None, show=False,\
        kwargs_1D={}, kwargs_2D={}, fontsize=28, nbins=100,\
        plot_type='contour', reference_value_mean=None,\
        reference_value_covariance=None, contour_confidence_levels=0.95,\
        parameter_renamer=(lambda x: x), tick_label_format_string='{x:.3g}',\
        num_ticks=3, minor_ticks_per_major_tick=1, xlabel_rotation=0,\
        xlabelpad=None, ylabel_rotation=90, ylabelpad=None):
        """
        Makes a triangle plot out of ndraw samples from the distribution which
        last generated new walker positions.

        ndraw: integer number of samples to draw to plot in the triangle plot
        parameters: sequence of string parameter names to include in the plot
        figsize: the size of the figure on which to put the triangle plot
        show: if True, matplotlib.pyplot.show is called before this function
                       returns
        kwargs_1D: keyword arguments to pass on to univariate_histogram
                   function
        kwargs_2D: keyword arguments to pass on to bivariate_histogram function
        fontsize: the size of the label fonts
        nbins: the number of bins for each sample
        plot_type: 'contourf', 'contour', or 'histogram'
        reference_value_mean: reference values to place on plots, if there are
                              any
        reference_value_covariance: if not None, used (along with
                                    reference_value_mean) to plot reference
                                    ellipses in each bivariate histogram
        contour_confidence_levels: the confidence level of the contour in the
                                   bivariate histograms. Only used if plot_type
                                   is 'contour' or 'contourf'. Can be single
                                   number or sequence of numbers
        parameter_renamer: function that renames parameters for labeling
        tick_label_format_string: format string that can be called using
                                  tick_label_format_string.format(x=loc) where
                                  loc is the location of the tick in data
                                  coordinates
        num_ticks: integer number of ticks per panel, default: 3
        minor_ticks_per_major_tick: integer number of minor ticks per major
                                    tick
        xlabel_rotation: rotation of x-label in degrees, default: 0
        xlabelpad: pad size for xlabel, default: None
        ylabel_rotation: rotation of y-label in degrees, default: 90
        ylabelpad: pad size for ylabel, default: None
        """
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        parameters = [self.parameters[index] for index in parameter_indices]
        return self.guess_distribution_set.triangle_plot(ndraw,\
            parameters=parameters, in_transformed_space=in_transformed_space,\
            figsize=figsize, fig=fig, show=show, kwargs_1D=kwargs_1D,\
            kwargs_2D=kwargs_2D, fontsize=fontsize, nbins=nbins,\
            plot_type=plot_type, reference_value_mean=reference_value_mean,\
            reference_value_covariance=reference_value_covariance,\
            contour_confidence_levels=contour_confidence_levels,\
            parameter_renamer=parameter_renamer,\
            tick_label_format_string=tick_label_format_string,\
            num_ticks=num_ticks,\
            minor_ticks_per_major_tick=minor_ticks_per_major_tick,\
            xlabel_rotation=xlabel_rotation, xlabelpad=xlabelpad,\
            ylabel_rotation=ylabel_rotation, ylabelpad=ylabelpad)

    def jumping_distribution_set_triangle_plot(self, ndraw, source=None,\
        parameters=None, in_transformed_space=True, figsize=(8, 8), fig=None,\
        show=False, kwargs_1D={}, kwargs_2D={}, fontsize=28, nbins=100,\
        plot_type='contour', reference_value_mean=None,\
        reference_value_covariance=None, contour_confidence_levels=0.95,\
        parameter_renamer=(lambda x: x), tick_label_format_string='{x:.3g}',\
        num_ticks=3, minor_ticks_per_major_tick=1, xlabel_rotation=0,\
        xlabelpad=None, ylabel_rotation=90, ylabelpad=None):
        """
        Makes a triangle plot out of ndraw samples from the proposal
        distribution.

        ndraw: integer number of samples to draw to plot in the triangle plot
        source: dictionary with parameters as keys and source parameters as
                values. If None (default), the source is assumed to be the
                maximum probability parameters
        parameters: sequence of string parameter names to include in the plot
        figsize: the size of the figure on which to put the triangle plot
        show: if True, matplotlib.pyplot.show is called before this function
                       returns
        kwargs_1D: keyword arguments to pass on to univariate_histogram
                   function
        kwargs_2D: keyword arguments to pass on to bivariate_histogram function
        fontsize: the size of the label fonts
        nbins: the number of bins for each sample
        plot_type: 'contourf', 'contour', or 'histogram'
        reference_value_mean: reference values to place on plots, if there are
                              any
        reference_value_covariance: if not None, used (along with
                                    reference_value_mean) to plot reference
                                    ellipses in each bivariate histogram
        contour_confidence_levels: the confidence level of the contour in the
                                   bivariate histograms. Only used if plot_type
                                   is 'contour' or 'contourf'. Can be single
                                   number or sequence of numbers
        parameter_renamer: function that renames parameters for labeling
        tick_label_format_string: format string that can be called using
                                  tick_label_format_string.format(x=loc) where
                                  loc is the location of the tick in data
                                  coordinates
        num_ticks: integer number of ticks per panel, default: 3
        minor_ticks_per_major_tick: integer number of minor ticks per major
                                    tick
        xlabel_rotation: rotation of x-label in degrees, default: 0
        xlabelpad: pad size for xlabel, default: None
        ylabel_rotation: rotation of y-label in degrees, default: 90
        ylabelpad: pad size for ylabel, default: None
        """
        if type(source) is type(None):
            source =\
                dict(zip(self.parameters, self.maximum_probability_parameters))
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        parameters = [self.parameters[index] for index in parameter_indices]
        return self.jumping_distribution_set.triangle_plot(source, ndraw,\
            parameters=parameters, in_transformed_space=in_transformed_space,\
            figsize=figsize, fig=fig, show=show, kwargs_1D=kwargs_1D,\
            kwargs_2D=kwargs_2D, fontsize=fontsize, nbins=nbins,\
            plot_type=plot_type, reference_value_mean=reference_value_mean,\
            reference_value_covariance=reference_value_covariance,\
            contour_confidence_levels=contour_confidence_levels,\
            parameter_renamer=parameter_renamer,\
            tick_label_format_string=tick_label_format_string,\
            num_ticks=num_ticks,\
            minor_ticks_per_major_tick=minor_ticks_per_major_tick,\
            xlabel_rotation=xlabel_rotation, xlabelpad=xlabelpad,\
            ylabel_rotation=ylabel_rotation, ylabelpad=ylabelpad)

    @property
    def chain(self):
        """
        Property storing the MCMC chain in a numpy.ndarray of shape
        (nwalkers, nsteps, ndim)
        """
        if not hasattr(self, '_chain'):
            self._load_checkpoints()
        return self._chain

    @property
    def lnprobability(self):
        """
        Property storing the log_probability in a numpy.ndarray of shape
        (nwalkers, nsteps)
        """
        if not hasattr(self, '_lnprobability'):
            self._load_checkpoints()
        return self._lnprobability

    @property
    def lnprior(self):
        """
        Property storing the log of the prior probability at each sampling
        point. Note that depending on the distributions included in the
        prior_distribution_set, this may or may not be normalized, although in
        either case, the normalization is consistent throughout the run.
        """
        if not hasattr(self, '_lnprior'):
            (nwalkers, nsteps) = self.lnprobability.shape
            self._lnprior = np.zeros((nwalkers, nsteps))
            if type(self.prior_distribution_set) is not type(None):
                for iwalker in range(nwalkers):
                    for istep in range(nsteps):
                        values = dict(zip(self.parameters,\
                            self.chain[iwalker,istep,:]))
                        self._lnprior[iwalker,istep] =\
                            self.prior_distribution_set.log_value(values)
        return self._lnprior

    @property
    def lnlikelihood(self):
        """
        Property storing the values of the loglikelihood from each sampled
        point. This is equal to the log of the posterior minus the log of the
        prior. Note that the lnlikelihood property (this one) contains
        numerical values at sampled points whereas the loglikelihood property
        contains the Loglikelihood object used to evaluate points.
        """
        if not hasattr(self, '_lnlikelihood'):
            self._lnlikelihood = self.lnprobability - self.lnprior
        return self._lnlikelihood

    @property
    def maximum_probability_multi_index(self):
        """
        Property storing a tuple of the form (iwalker, istep) where iwalker and
        istep describe the maximizing index of the lnprobability array.
        """
        if not hasattr(self, '_maximum_probability_multi_index'):
            self._maximum_probability_multi_index = np.unravel_index(\
                np.argmax(self.lnprobability), self.lnprobability.shape)
        return self._maximum_probability_multi_index

    @property
    def maximum_prior_multi_index(self):
        """
        Property storing a tuple of the form (iwalker, istep) where iwalker and
        istep describe the maximizing index of the lnprior array.
        """
        if not hasattr(self, '_maximum_prior_multi_index'):
            self._maximum_prior_multi_index = np.unravel_index(\
                np.argmax(self.lnprior), self.lnprior.shape)
        return self._maximum_prior_multi_index

    @property
    def maximum_likelihood_multi_index(self):
        """
        Property storing a tuple of the form (iwalker, istep) where iwalker and
        istep describe the maximizing index of the lnlikelihood array.
        """
        if not hasattr(self, '_maximum_likelihood_multi_index'):
            self._maximum_likelihood_multi_index = np.unravel_index(\
                np.argmax(self.lnlikelihood), self.lnlikelihood.shape)
        return self._maximum_likelihood_multi_index

    @property
    def maximum_probability_parameters(self):
        """
        Property storing the maximum probability set of parameters found by the
        MCMC sampler upon which this NLFitter is based.
        """
        if not hasattr(self, '_maximum_probability_parameters'):
            self._maximum_probability_parameters =\
                self.chain[self.maximum_probability_multi_index]
        return self._maximum_probability_parameters

    @property
    def maximum_probability_parameter_dictionary(self):
        """
        Property storing the maximum probability parameters in the form of a
        dictionary whose keys are the parameter names and whose values are the
        maximum probability values.
        """
        if not hasattr(self, '_maximum_probability_parameter_dictionary'):
            self._maximum_probability_parameter_dictionary =\
                {name: value for (name, value) in\
                zip(self.parameters, self.maximum_probability_parameters)}
        return self._maximum_probability_parameter_dictionary

    @property
    def maximum_prior_parameters(self):
        """
        Property storing the maximum prior set of parameters found by the
        MCMC sampler upon which this NLFitter is based.
        """
        if not hasattr(self, '_maximum_prior_parameters'):
            self._maximum_prior_parameters =\
                self.chain[self.maximum_prior_multi_index]
        return self._maximum_prior_parameters

    @property
    def maximum_prior_parameter_dictionary(self):
        """
        Property storing the maximum prior parameters in the form of a
        dictionary whose keys are the parameter names and whose values are the
        maximum prior values.
        """
        if not hasattr(self, '_maximum_prior_parameter_dictionary'):
            self._maximum_prior_parameter_dictionary =\
                {name: value for (name, value) in\
                zip(self.parameters, self.maximum_prior_parameters)}
        return self._maximum_prior_parameter_dictionary

    @property
    def maximum_likelihood_parameters(self):
        """
        Property storing the maximum likelihood set of parameters found by the
        MCMC sampler upon which this NLFitter is based.
        """
        if not hasattr(self, '_maximum_likelihood_parameters'):
            self._maximum_likelihood_parameters =\
                self.chain[self.maximum_likelihood_multi_index]
        return self._maximum_likelihood_parameters

    @property
    def maximum_likelihood_parameter_dictionary(self):
        """
        Property storing the maximum likelihood parameters in the form of a
        dictionary whose keys are the parameter names and whose values are the
        maximum likelihood values.
        """
        if not hasattr(self, '_maximum_likelihood_parameter_dictionary'):
            self._maximum_likelihood_parameter_dictionary =\
                {name: value for (name, value) in\
                zip(self.parameters, self.maximum_likelihood_parameters)}
        return self._maximum_likelihood_parameter_dictionary

    @property
    def acceptance_fraction(self):
        """
        Property storing the acceptance fraction per walker per checkpoint in
        an array of the form (nwalkers, ncheckpoints).
        """
        if not hasattr(self, '_acceptance_fraction'):
            self._load_checkpoints()
        return self._acceptance_fraction

    @property
    def parameters(self):
        """
        Property storing the sequence of names of the parameters being fit by
        this fitter.
        """
        if not hasattr(self, '_parameters'):
            group = self.file['parameters']
            iparameter = 0
            parameters = []
            while '{:d}'.format(iparameter) in group.attrs:
                parameters.append(group.attrs['{:d}'.format(iparameter)])
                iparameter += 1
            self._parameters = parameters
        return self._parameters

    @property
    def transform_set(self):
        """
        Property storing the set of Transforms used in the last loaded chunk's
        JumpingDistributionSet.
        """
        if not hasattr(self, '_transform_set'):
            if type(self.jumping_distribution_set) is type(None):
                self._transform_set = TransformSet({parameter: None\
                    for parameter in self.parameters})
            else:
                self._transform_set =\
                    self.jumping_distribution_set.transform_set
        return self._transform_set

    @property
    def transform_list(self):
        """
        Property storing the list of Transforms used in the last loaded chunk's
        JumpingDistributionSet.
        """
        if not hasattr(self, '_transform_list'):
            self._transform_list = self.transform_set[self.parameters]
        return self._transform_list

    @property
    def num_parameters(self):
        """
        Property storing the integer number of parameters being fit by this
        fitter.
        """
        if not hasattr(self, '_num_parameters'):
            self._num_parameters = len(self.parameters)
        return self._num_parameters

    @property
    def nwalkers(self):
        """
        Property storing the integer number of walkers used in the MCMC chain
        being analyzed by this fitter.
        """
        if not hasattr(self, '_nwalkers'):
            self._nwalkers = self.chain.shape[0]
        return self._nwalkers

    @property
    def nsteps(self):
        """
        Property storing the integer number of steps in the burned MCMC chain.
        """
        if not hasattr(self, '_nsteps'):
            self._nsteps = self.chain.shape[1]
        return self._nsteps

    @property
    def nsamples(self):
        """
        Property storing the total number of samples in the burned chain.
        """
        if not hasattr(self, '_nsamples'):
            self._nsamples = (self.nwalkers * self.nsteps)
        return self._nsamples

    @property
    def flattened_chain(self):
        """
        Property storing the flattened chain (i.e. the chain with walker
        information lost) in a 2D numpy.ndarray of shape (nsamples, ndim)
        """
        if not hasattr(self, '_flattened_chain'):
            self._flattened_chain = np.swapaxes(self.chain, 0, 1)
            self._flattened_chain =\
                np.concatenate(self._flattened_chain, axis=0)
        return self._flattened_chain

    @property
    def flattened_lnprobability(self):
        """
        Property storing the flattened log_probability (i.e. the
        log_probabilitywith walker information lost) in a 1D numpy.ndarray of
        length nsamples.
        """
        if not hasattr(self, '_flattened_lnprobability'):
            self._flattened_lnprobability =\
                np.concatenate(self.lnprobability.T, axis=0)
        return self._flattened_probability

    @property
    def flattened_lnprior(self):
        """
        Property storing a flattened version of the lnprior array.
        """
        if not hasattr(self, '_flattened_lnprior'):
            self._flattened_lnprior = np.concatenate(self.lnprior.T, axis=0)
        return self._flattened_lnprior

    @property
    def flattened_lnlikelihood(self):
        """
        Property storing a flattened version of the lnlikelihood array
        """
        if not hasattr(self, '_flattened_lnlikelihood'):
            self._flattened_lnlikelihood =\
                np.concatenate(self.lnlikelihood.T, axis=0)
        return self._flattened_lnlikelihood

    @property
    def parameter_mean(self):
        """
        Property storing the mean parameter vector (of length ndim) of the
        flattened chain.
        """
        if not hasattr(self, '_parameter_mean'):
            self._parameter_mean = np.mean(self.flattened_chain, axis=0)
        return self._parameter_mean

    @property
    def parameter_covariance(self):
        """
        Property storing the parameter covariance matrix described by the
        flattened chain in a 2D numpy.ndarray of shape (ndim, ndim). This
        covariance applies in untransformed space.
        """
        if not hasattr(self, '_parameter_covariance'):
            if self.num_parameters == 1:
                self._parameter_covariance =\
                    np.array([[np.var(self.flattened_chain[:,0])]])
            else:
                self._parameter_covariance =\
                    np.cov(self.flattened_chain, rowvar=False)
        return self._parameter_covariance

    @property
    def parameter_correlation(self):
        """
        Property storing the parameter correlation matrix described by the
        flattened chain in a 2D numpy.ndarray of shape (ndim, ndim) with 1's on
        the diagonal.
        """
        if not hasattr(self, '_parameter_correlation'):
            covariance = self.parameter_covariance
            variances = np.diag(covariance)
            self._parameter_correlation = covariance /\
                np.sqrt(variances[np.newaxis,:] * variances[:,np.newaxis])
        return self._parameter_correlation

    @property
    def approximate_gaussian_posterior(self):
        """
        Property storing a Gaussian distribution (in untransformed space) with
        the same mean and covariance as the chain.
        """
        if not hasattr(self, '_approximate_gaussian_posterior'):
            self._approximate_gaussian_posterior = DistributionSet([(\
                GaussianDistribution(self.parameter_mean,\
                self.parameter_covariance), self.parameters, None)])
        return self._approximate_gaussian_posterior

    @property
    def transformed_parameter_mean(self):
        """
        Property storing the mean of transformed parameters (the transformation
        is that which defines the proposal distribution).
        """
        if not hasattr(self, '_transformed_parameter_mean'):
            self._transformed_parameter_mean =\
                np.mean(self.transform_list(self.flattened_chain), axis=0)
        return self._transformed_parameter_mean

    @property
    def transformed_parameter_covariance(self):
        """
        Property storing the parameter covariance matrix described by the
        flattened chain in a 2D numpy.ndarray of shape (ndim, ndim). This
        covariance applies in the same space as the proposal distribution,
        which may be transformed.
        """
        if not hasattr(self, '_transformed_parameter_covariance'):
            if self.num_parameters == 1:
                self._transformed_parameter_covariance = np.array(\
                    [[np.var(self.transform_list(self.flattened_chain)[:,0])]])
            else:
                self._transformed_parameter_covariance = np.cov(\
                    self.transform_list(self.flattened_chain), rowvar=False)
        return self._transformed_parameter_covariance

    @property
    def transformed_parameter_correlation(self):
        """
        Property storing the parameter correlation matrix described by the
        flattened chain in a 2D numpy.ndarray of shape (ndim, ndim) with 1's on
        the diagonal.
        """
        if not hasattr(self, '_transformed_parameter_correlation'):
            covariance = self.transformed_parameter_covariance
            variances = np.diag(covariance)
            self._transformed_parameter_correlation = covariance /\
                np.sqrt(variances[np.newaxis,:] * variances[:,np.newaxis])
        return self._transformed_parameter_correlation

    @property
    def approximate_transformed_gaussian_posterior(self):
        """
        Property storing a Gaussian distribution (in untransformed space) with
        the same transformed mean and covariance as the chain.
        """
        if not hasattr(self, '_approximate_transformed_gaussian_posterior'):
            self._approximate_transformed_gaussian_posterior =\
                DistributionSet([(GaussianDistribution(\
                self.transformed_parameter_mean,\
                self.transformed_parameter_covariance), self.parameters,\
                self.transform_list)])
        return self._approximate_transformed_gaussian_posterior

    @property
    def mean_deviance(self):
        """
        Property storing the mean value of -2 lnL = delta^T C^{-1} delta where
        delta is the weighted bias vector.
        """
        if not hasattr(self, '_mean_deviance'):
            self._mean_deviance =\
                -2 * np.mean(self.flattened_lnprobability)
        return self._mean_deviance

    @property
    def deviance_at_mean(self):
        """
        Property storing the value of -2lnL = delta^T C^{-1} delta where delta
        is the weighted bias vector evaluated at the mean parameter vector.
        """
        if not hasattr(self, '_deviance_at_mean'):
            self._deviance_at_mean =\
                -2 * self.loglikelihood(self.parameter_mean)
        return self._deviance_at_mean

    @property
    def effective_number_of_parameters(self):
        """
        Property storing the effective number of parameters in the model, as
        measured by the difference between the mean deviance and the deviance
        at the mean. In the Gaussian limit, this is the same as the true number
        of parameters.
        """
        if not hasattr(self, '_effective_number_of_parameters'):
            self._effective_number_of_parameters =\
                self.mean_deviance - self.deviance_at_mean
        return self._effective_number_of_parameters

    @property
    def half_variance_of_deviance(self):
        """
        Property storing half of the variance of the deviance. This is another
        measure of the effective number of parameters.
        """
        if not hasattr(self, '_half_variance_of_deviance'):
            self._half_variance_of_deviance =\
                (np.var(self.flattened_lnprobability) / 2.)
        return self._half_variance_of_deviance

    @property
    def akaike_information_criterion(self):
        """
        Property storing Akaike's information criterion given by -2ln(L_max)+2p
        where L is the likelihood and p is the number of parameters.
        """
        if not hasattr(self, '_akaike_information_criterion'):
            self._akaike_information_criterion = self.deviance_at_mean +\
                (2 * self.num_parameters)
        return self._akaike_information_criterion

    @property
    def AIC(self):
        """
        Alias for the akaike_information_criterion property.
        """
        return self.akaike_information_criterion

    @property
    def bayesian_information_criterion(self):
        """
        Property storing the Bayesian information criterion, given by
        -2ln(L_max)+p*ln(k) where L is the likelihood, p is the number of
        parameters, and k is the number of data channels.
        """
        if not hasattr(self, '_bayesian_information_criterion'):
            self._bayesian_information_criterion = self.deviance_at_mean +\
                (self.num_parameters * np.log(self.num_channels))
        return self._bayesian_information_criterion

    @property
    def BIC(self):
        """
        Alias for the bayesian_information_criterion property.
        """
        return self.bayesian_information_criterion

    @property
    def bayesian_predictive_information_criterion(self):
        """
        Property storing the Bayesian predictive information criterion, which
        is not yet implemented.
        """
        # TODO
        raise NotImplementedError("BPIC not implemented yet because it " +\
            "requires taking derivatives of the (at this point completely " +\
            "generalized) model.")

    @property
    def BPIC(self):
        """
        Alias for the bayesian_predictive_information_criterion property.
        """
        return self.bayesian_predictive_information_criterion

    @property
    def deviance_information_criterion(self):
        """
        Property storing the deviance information criterion given by the
        deviance at the mean plus twice the effective number of parameters.
        """
        if not hasattr(self, '_deviance_information_criterion'):
            self._deviance_information_criterion = self.deviance_at_mean +\
                (2 * self.effective_number_of_parameters)
        return self._deviance_information_criterion

    @property
    def DIC(self):
        """
        Alias for the deviance_information_criterion property.
        """
        return self.deviance_information_criterion

    @property
    def modified_deviance_information_criterion(self):
        """
        Property storing the deviance information criterion where the effective
        number of parameters is given by half the variance of the deviance.
        """
        if not hasattr(self, '_modified_deviance_information_criterion'):
            self._modified_deviance_information_criterion =\
                (self.deviance_at_mean + (2 * self.half_variance_of_deviance))
        return self._modified_deviance_information_criterion

    @property
    def DIC2(self):
        """
        Alias for the modified_deviance_information_criterion property.
        """
        return self.modified_deviance_information_criterion

    @property
    def rescaling_factors(self):
        """
        Property storing the rescaling factors (R-values) of Gelman Rubin et al
        for all parameters in a 1D numpy.ndarray of shape (num_parameters,)
        """
        if not hasattr(self, '_rescaling_factors'):
            half_nsteps = (self.nsteps // 2)
            first_half = self.chain[:,:half_nsteps,:]
            second_half = self.chain[:,-half_nsteps:,:]
            split_chains = np.concatenate((first_half, second_half), axis=0)
            averaged_over_chain_steps = np.mean(split_chains, axis=1)
            between_chain_variance = half_nsteps *\
                np.var(averaged_over_chain_steps, axis=0, ddof=1)
            averaged_over_chains_and_steps =\
                np.mean(averaged_over_chain_steps, axis=0)
            vared_over_chain_steps = np.var(split_chains, axis=1, ddof=1)
            within_chain_variance = np.mean(vared_over_chain_steps, axis=0)
            self._rescaling_factors =\
                (between_chain_variance / within_chain_variance)
            self._rescaling_factors =\
                np.sqrt(1. + ((self._rescaling_factors - 1.) / half_nsteps))
        return self._rescaling_factors

    @property
    def effective_sample_sizes(self):
        """
        Property storing the effective sample size when computed with each of
        the parameters.
        """
        if not hasattr(self, '_effective_sample_size'):
            half_nsteps = (self.nsteps // 2)
            first_half = self.chain[:,:half_nsteps,:]
            second_half = self.chain[:,-half_nsteps:,:]
            split_chains = np.concatenate((first_half, second_half), axis=0)
            # TODO define correlations (rho(t) from equation 11.7 of Gelman)
            raise NotImplementedError("effective_sample_size property not " +\
                "yet implemented.")
            self._effective_sample_size =\
                (self.nsamples / (1 + (2 * np.sum(correlations))))
        return self._effective_sample_size

    def get_parameter_indices(self, parameters=None):
        """
        Gets the indices associated with the given parameters input.

        parameters: if None, all parameter indices are returned
                    if string, it is interpreted as a regular expression with
                               which to search through the parameter list
                    if sequence of ints, describes the very indices of the
                                         parameters to be returned
                    if sequence of strings, describes parameters
                                            whose indices should be returned

        returns: numpy.array of parameter indices
        """
        if type(parameters) is type(None):
            # return indices of all parameters
            return np.arange(self.num_parameters)
        elif isinstance(parameters, basestring):
            # assume that this is a regex for searching through parameters
            parameter_indices = []
            for (iparameter, parameter) in enumerate(self.parameters):
                if re.search(parameters, parameter):
                    parameter_indices.append(iparameter)
            return np.array(parameter_indices)
        elif type(parameters) in sequence_types:
            # parameters must be either a list of individual
            # string parameters or a list of indices
            if all([isinstance(value, basestring) for value in parameters]):
                return np.array(\
                    [self.parameters.index(value) for value in parameters])
            elif all([(type(value) in int_types) for value in parameters]):
                return np.array(parameters)
            else:
                raise TypeError("parameters was set to a sequence, but the " +\
                    "elements of that sequence were neither all strings or " +\
                    "all ints.")
        else:
            raise TypeError("parameters was set to something other than " +\
                "None, a string, a sequence of strings, and a sequence of " +\
                "ints")

    def sample(self, number, parameters=None, random=np.random):
        """
        Takes number samples from the chain associated with this fitter.

        number: the number of samples in the returned array
        parameters: if None, sample all parameters
                    if string, sample all parameters which match this regular
                               expression
                    if list of indices, sample parameters with these indices
                    if list of strings, sample these parameters
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: array of the form (number, nparameters)
        """
        index_sample = random.randint(0, high=self.nsamples, size=number)
        full_parameter_sample = self.flattened_chain[index_sample,:]
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        return full_parameter_sample[:,parameter_indices]

    def subbasis_parameter_mean(self, parameters):
        """
        Finds a subset of the parameter mean.

        parameters: if None, full parameter mean is returned
                    if string, parameter mean is returned for all parameters
                               matching the regular expression given by
                               parameters
                    if list of ints, the mean of the parameters with these
                                     indices is returned
                    if list of strings, mean of these parameters is returned

        returns: 1D numpy.ndarray of parameter values
        """
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        return self.parameter_mean[parameter_indices]

    def subbasis_parameter_covariance(self, parameters):
        """
        Finds a subset of the parameter covariance

        parameters: if None, full parameter covariance is returned
                    if string, parameter covariance is returned for all
                               parameters matching the regular expression given
                               by parameters
                    if list of ints, the covariance of the parameters with
                                     these indices is returned
                    if list of strings, the covariance of these parameters is
                                        returned

        returns: symmetric 2D numpy.ndarray of parameter covariances
        """
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        return\
            self.parameter_covariance[:,parameter_indices][parameter_indices,:]

    def reconstructions(self, number, parameters=None, model=None,\
        random=np.random):
        """
        Computes reconstructions of a quantity by applying model to a sample of
        parameters from this fitter's chain.

        number: the number of reconstructions to create
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), the full model in the loglikelihood is used
               otherwise, the model with which to create reconstructions from
                          parameters
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: 2D numpy.ndarray of shape (number, nchannels) containing
                 reconstructions created from the given model and parameters
        """
        parameter_sample =\
            self.sample(number, parameters=parameters, random=random)
        if type(model) is type(None):
            model = self.model
        return np.array([model(args) for args in parameter_sample])

    def reconstruction_RMS_by_channel(self, number, parameters=None,\
        model=None, random=np.random):
        """
        Computes the root-mean-square deviations from the mean in each channel
        of the given number of reconstructions made using the given model and
        parameters.

        number: the number of reconstructions to create
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), the full model in the loglikelihood is used
               otherwise, the model with which to create reconstructions from
                          parameters
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: 1D numpy.ndarray of shape (num_channels,) with RMS values
        """
        reconstructions = self.reconstructions(number, parameters=parameters,\
            model=model, random=random)
        centered_reconstructions = reconstructions -\
            np.mean(reconstructions, axis=0, keepdims=True)
        return np.sqrt(np.mean(np.power(centered_reconstructions, 2), axis=0))

    def reconstruction_RMS(self, number, parameters=None, model=None,\
        x_slice=slice(None), random=np.random):
        """
        Computes the root-mean-square deviation from the mean of the given
        number of reconstructions made using the given model and parameters.

        number: the number of reconstructions to create
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), the full model in the loglikelihood is used
               otherwise, the model with which to create reconstructions from
                          parameters
        x_slice: slice with which to trim x_values to include in final RMS'ing
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: single number containing the RMS of the
                 reconstruction_RMS_by_channel error
        """
        rms_by_channel = self.reconstruction_RMS_by_channel(number,\
            parameters=parameters, model=model, random=random)
        return np.sqrt(np.mean(np.power(rms_by_channel[x_slice], 2)))

    def bias(self, number, parameters=None, model=None, true_curve=None,\
        random=np.random):
        """
        Computes differences between a true_curve and reconstructions of the
        quantity made by applying model to a sample of parameters from this
        fitter's chain.

        number: the number of reconstructions to create and compare to data
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), full likelihood's model is used
               otherwise, the model with which to create reconstructions from
                          parameters
        true_curve: if None (default), model and parameters must also be None.
                                       true_curve is replaced with the
                                       loglikelihood's data curve.
                    Otherwise, curve to subtract from all reconstructions to
                               compute bias
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: 2D numpy.ndarray of (number, nchannels) containing differences
                 between true_curve and reconstructions created by applying
                 model to parameters
        """
        reconstructions = self.reconstructions(number, parameters=parameters,\
            model=model, random=random)
        if type(true_curve) is type(None):
            if (type(parameters) is type(None)) and\
                (type(model) is type(None)):
                true_curve = self.data
            else:
                raise NotImplementedError("The bias cannot be computed if " +\
                    "no true_curve is given unless the full " +\
                    "loglikelihood's model and all parameters are used, in " +\
                    "which case, if true_curve is None, it is replaced the " +\
                    "loglikelihood's data vector.")
        return reconstructions - true_curve

    def reconstruction_confidence_intervals(self, number, probabilities,\
        parameters=None, model=None, reconstructions=None,\
        return_reconstructions=False, random=np.random):
        """
        Computes confidence intervals on reconstructions of quantities created
        by applying the given model to the given parameters from this fitter's
        chain.

        number: the number of curves to use in calculating the confidence
                intervals
        probabilities: the confidence levels of the bands to compute
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), full likelihood's model is used
               otherwise, the model with which to create reconstructions from
                          parameters
        reconstructions: reconstructions to use, if they have already been
                         calculated. In most cases, this should remain None
                         (its default value)
        return_reconstructions: if True (default: False), returns
                                reconstructions as well as intervals.
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: list of tuples of the form (band_min, band_max) representing
                 confidence_intervals with the given confidence levels, or
                 single such tuple. If return_reconstructions is True, the
                 return value is
                 (value_when_return_reconstructions_is_False, reconstructions)
                 where reconstructions has shape (number, num_channels)
        """
        if type(reconstructions) is type(None):
            reconstructions = self.reconstructions(number,\
                parameters=parameters, model=model, random=random)
        sorted_channel_values = np.sort(reconstructions, axis=0)
        single_input = (type(probabilities) in real_numerical_types)
        if single_input:
            probabilities = [probabilities]
        if type(probabilities) in sequence_types:
            probabilities = np.array(probabilities)
        numbers_to_exclude =\
            np.around(number * (1 - probabilities)).astype(int)
        numbers_to_exclude_from_left = (numbers_to_exclude // 2)
        numbers_to_exclude_from_right =\
            (numbers_to_exclude - numbers_to_exclude_from_left)
        left_bands = sorted_channel_values[numbers_to_exclude_from_left,:]
        right_bands =\
            sorted_channel_values[number-1-numbers_to_exclude_from_right,:]
        if single_input:
            return_value = (left_bands[0], right_bands[0])
        else:
            return_value = [(left_bands[index], right_bands[index])\
                for index in range(len(probabilities))]
        if return_reconstructions:
            return_value = (return_value, reconstructions)
        return return_value

    def bias_confidence_intervals(self, number, probabilities,\
        parameters=None, model=None, true_curve=None, reconstructions=None,\
        return_biases=False, random=np.random):
        """
        Computes confidence intervals on differences between reconstructions of
        quantities created by applying the given model to the given parameters
        from this fitter's chain and the given true curve.

        number: the number of curves to use in calculating the confidence
                intervals
        probabilities: the confidence levels of the bands to compute
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), full likelihood's model is used
               otherwise, the model with which to create reconstructions from
                          parameters
        true_curve: curve to subtract from all reconstructions to compute bias
        reconstructions: reconstructions to use, if they have already been
                         calculated. In most cases, this should remain None
                         (its default value)
        return_biases: if True (default: False), returns biases as well as
                       intervals.
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random

        returns: list of tuples of the form (band_min, band_max) representing
                 confidence_intervals with the given confidence levels. If
                 return_biases is True, the return value is
                 (value_when_return_biases_is_False, biases) where
                 reconstructions has shape (number, num_channels)
        """
        (intervals, reconstructions) =\
            self.reconstruction_confidence_intervals(number, probabilities,\
            parameters=parameters, model=model,\
            reconstructions=reconstructions, return_reconstructions=True,\
            random=random)
        if type(true_curve) is type(None):
            if (type(parameters) is type(None)) and\
                (type(model) is type(None)):
                true_curve = self.data
            else:
                raise NotImplementedError("The bias cannot be computed if " +\
                    "no true_curve is given unless the full " +\
                    "loglikelihood's model and all parameters are used, in " +\
                    "which case, if true_curve is None, it is replaced the " +\
                    "loglikelihood's data vector.")
        if type(probabilities) in real_numerical_types:
            return_value =\
                (intervals[0] - true_curve, intervals[1] - true_curve)
        else:
            return_value =\
                [(interval[0] - true_curve, interval[1] - true_curve)\
                for interval in intervals]
        if return_biases:
            return_value =\
                (return_value, reconstructions - true_curve[np.newaxis,:])
        return return_value

    def plot_maximum_probability_reconstruction(self, parameters=None,\
        model=None, true_curve=None, subtract_truth=False, x_values=None,\
        ax=None, xlabel=None, ylabel=None, title=None, fontsize=28,\
        scale_factor=1., show=False):
        """
        Plots maximum probability reconstruction with the given model and
        parameters.

        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), full model is used
               otherwise, the model with which to create reconstructions from
                          parameters
        true_curve: true form of the quantity being reconstructed
        subtract_truth: if True, true_curve is subtracted from all plotted
                                 curve(s)
        x_values: the array to use as the x_values of all plots.
                  If None, x_values start at 0 and increment up in steps of 1
        ax: the Axes instance on which to plot the confidence intervals
        xlabel: string to place on x axis
        ylabel: string to place on y axis
        title: title to place on the Axes with this plot
        scale_factor: factor by which to multiply reconstructions before
                      plotting
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: None if show is True, otherwise Axes instance with plot
        """
        if type(ax) is type(None):
            fig = pl.figure()
            ax = fig.add_subplot(111)
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        maximum_probability_parameter_subset =\
            self.maximum_probability_parameters[parameter_indices]
        if (type(parameters) is type(None)) and\
            (type(model) is type(None)) and (type(true_curve) is type(None)):
            true_curve = self.data * scale_factor
        if type(model) is type(None):
            model = self.model
        curve = model(maximum_probability_parameter_subset)
        if type(x_values) is type(None):
            x_values = np.arange(len(curve))
            to_plot = curve * scale_factor
        if subtract_truth:
            if type(true_curve) is type(None):
                raise NotImplementedError("Cannot subtract truth if true " +\
                    "curve is None.")
            else:
                to_plot = to_plot - true_curve
        ax.plot(x_values, to_plot, color='r', label='Max probability')
        if type(true_curve) is not type(None):
            if subtract_truth:
                ax.plot(x_values, np.zeros_like(true_curve), color='k',\
                    label='input')
            else:
                ax.plot(x_values, true_curve, color='k', label='input')
        ax.legend(fontsize=fontsize)
        if type(title) is type(None):
            title = 'Maximum probability reconstruction{!s}'.format(\
                ' bias' if subtract_truth else '')
        ax.set_title(title, size=fontsize)
        if type(xlabel) is not type(None):
            ax.set_xlabel(xlabel, size=fontsize)
        if type(ylabel) is not type(None):
            ax.set_ylabel(ylabel, size=fontsize)
        ax.tick_params(labelsize=fontsize, width=2.5, length=7.5,\
            which='major')
        ax.tick_params(width=1.5, length=4.5, which='minor')
        if show:
            pl.show()
        else:
            return ax

    def plot_reconstructions(self, number, parameters=None, model=None,\
        true_curve=None, reconstructions=None, subtract_truth=False,\
        x_values=None, ax=None, xlabel=None, ylabel=None, title=None,\
        fontsize=28, scale_factor=1., matplotlib_function='plot',\
        return_reconstructions=False, breakpoints=None, random=np.random,\
        show=False, **kwargs):
        """
        Plots a given number of reconstructions using given parameters
        evaluated by the given model.

        number: the number of reconstructions to create
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), the full model in the loglikelihood is used
               otherwise, the model with which to create reconstructions from
                          parameters
        true_curve: true form of the quantity being reconstructed
        reconstructions: reconstructions to use, if they have already been
                         calculated. In most cases, this should remain None
                         (its default value)
        subtract_truth: if True, true_curve is subtracted from all plotted
                                 curve(s)
        x_values: the array to use as the x_values of all plots.
                  If None, x_values start at 0 and increment up in steps of 1
        ax: the Axes instance on which to plot the confidence intervals
        xlabel: string to place on x axis
        ylabel: string to place on y axis
        title: title to place on the Axes with this plot
        scale_factor: factor by which to multiply reconstructions before
                      plotting
        matplotlib_function: either 'plot' or 'scatter'
        return_reconstructions: allows user to access reconstructions used
        breakpoints: either None (default), integer index of a breakpoint (the
                     first point of the next segment) or list of such integers
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns
        **kwargs: extra keyword arguments are passed to the
                  matplotlib.pyplot.plot function

        returns: (reconstructions if return_reconstructions else None) if show
                 is True, otherwise
                 ((ax, reconstructions) if return_reconstructions else None)
                 where ax is an Axes instance with plot
        """
        if type(reconstructions) is type(None):
            reconstructions = self.reconstructions(number,\
                parameters=parameters, model=model, random=random)
        if type(ax) is type(None):
            fig = pl.figure()
            ax = fig.add_subplot(111)
        if (type(parameters) is type(None)) and\
            (type(model) is type(None)) and (type(true_curve) is type(None)):
            true_curve = self.data * scale_factor
        num_channels = reconstructions.shape[-1]
        if type(x_values) is type(None):
            x_values = np.arange(num_channels)
        to_plot = reconstructions * scale_factor
        if subtract_truth:
            if type(true_curve) is type(None):
                raise NotImplementedError("Cannot subtract truth if true " +\
                    "curve is None.")
            else:
                to_plot = to_plot - true_curve[np.newaxis,:]
        random_what = ('biases' if subtract_truth else 'reconstructions')
        kwargs_copy = {parameter: kwargs[parameter] for parameter in kwargs}
        if 'label' not in kwargs_copy:
            kwargs_copy['label'] = '{0:d} {1!s}'.format(number, random_what)
        if matplotlib_function == 'plot':
            if type(breakpoints) is type(None):
                breakpoints = num_channels
            if type(breakpoints) in int_types:
                breakpoints = (breakpoints % num_channels)
                if breakpoints == 0:
                    breakpoints = np.array([0, num_channels])
                else:
                    breakpoints = np.array([0, breakpoints, num_channels])
            elif type(breakpoints) in sequence_types:
                breakpoints = np.sort(np.mod(breakpoints, num_channels))
                if breakpoints[0] != 0:
                    breakpoints = np.concatenate([[0], breakpoints])
                breakpoints = np.concatenate([breakpoints, [num_channels]])
            for (isegment, (start, end)) in\
                enumerate(zip(breakpoints[:-1], breakpoints[1:])):
                ax.plot(x_values[start:end],\
                    reconstructions[0,start:end] * scale_factor, **kwargs_copy)
                if (isegment == 0) and ('label' in kwargs_copy):
                    del kwargs_copy['label']
                ax.plot(x_values[start:end],\
                    reconstructions[1:,start:end].T * scale_factor,\
                    **kwargs_copy)
        elif matplotlib_function == 'scatter':
            for index in range(number):
                ax.scatter(x_values, to_plot[index,:], **kwargs_copy)
                if (index == 0) and ('label' in kwargs_copy):
                    del kwargs_copy['label']
        else:
            raise RuntimeError("matplotlib_function was neither 'plot' or " +\
                "'scatter'")
        if type(true_curve) is not type(None):
            if subtract_truth:
                ax.plot(x_values, np.zeros_like(true_curve), color='k',\
                    label='input')
            elif matplotlib_function == 'plot':
                for (isegment, (start, end)) in\
                    enumerate(zip(breakpoints[:-1], breakpoints[1:])):
                    ax.plot(x_values[start:end], true_curve[start:end],\
                        color='k',\
                        label=('input' if (isegment == 0) else None))
            elif matplotlib_function == 'scatter':
                ax.scatter(x_values, true_curve, color='k', label='input')
            else:
                raise RuntimeError("matplotlib_function was neither 'plot' " +\
                    "or 'scatter'")
        ax.legend(fontsize=fontsize)
        ax.set_xlim((x_values[0], x_values[-1]))
        if type(title) is type(None):
            title = '{0:d} random {1!s}'.format(number, random_what)
        ax.set_title(title, size=fontsize)
        if type(xlabel) is not type(None):
            ax.set_xlabel(xlabel, size=fontsize)
        if type(ylabel) is not type(None):
            ax.set_ylabel(ylabel, size=fontsize)
        ax.tick_params(labelsize=fontsize, width=2.5, length=7.5,\
            which='major')
        ax.tick_params(width=1.5, length=4.5, which='minor')
        if show:
            pl.show()
            if return_reconstructions:
                return reconstructions
        elif return_reconstructions:
            return (ax, reconstructions)
        else:
            return ax

    def plot_reconstruction_confidence_intervals(self, number, probabilities,\
        parameters=None, model=None, reconstructions=None, true_curve=None,\
        subtract_truth=False, x_values=None, ax=None,\
        matplotlib_function='fill_between', figsize=(12,9), alphas=None,\
        xlabel=None, ylabel=None, title=None, fontsize=28, scale_factor=1.,\
        show=False, return_reconstructions=False, breakpoints=None,\
        error_expansion_factors=1., random=np.random, **kwargs):
        """
        Plots reconstruction confidence intervals with the given model,
        parameters, and confidence levels.

        number: number of curves used to compute of confidence intervals
        probabilities: either single number between 0 and 1 (exclusive), or
                       sequence of such numbers
        parameters: if None (default), all parameters are used
                    if string, parameter which match the regular expression
                               given by parameters are used
                    if list of ints, the parameters with these indices are used
                    if list of strings, these parameters are used
        model: if None (default), full model is used
               otherwise, the model with which to create reconstructions from
                          parameters
        reconstructions: reconstructions to use, if they have already been
                         calculated. In most cases, this should remain None
                         (its default value)
        true_curve: true form of the quantity being reconstructed
        x_values: the array to use as the x_values of all plots.
                  If None, x_values start at 0 and increment up in steps of 1
        matplotlib_function: either 'errorbar' or 'fill_between' (default)
        ax: the Axes instance on which to plot the confidence intervals
        figsize: size of figure to make is ax is None
        alphas: the alpha values with which to fill in each interval (must be
                sequence of values between 0 and 1 of length greater than or
                equal to the length of probabilities.
        xlabel: string to place on x axis
        ylabel: string to place on y axis
        title: title to place on the Axes with this plot
        fontsize: size of label and title font
        scale_factor: factor by which to multiply reconstructions before
                      plotting (is not applied to true_curve, if given)
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns
        return_reconstructions: allows user to access reconstructions used
        breakpoints: either None (default), integer index of a breakpoint (the
                     first point of the next segment) or list of such integers
        error_expansion_factors: factors by which to multiply error bars.
                                 Should either be a constant or an array of
                                 len(breakpoints)+1, which is equal to 1 when
                                 breakpoints isn't given. It should be a single
                                 number, not an array, if matplotlib_function
                                 is 'fill_between'
        random: random number generator from which to draw indices using
                random.randint(0, high=self.nsamples, size=number),
                default is numpy.random
        **kwargs: extra keyword arguments to pass to matplotlib_function

        returns: (reconstructions if return_reconstructions else None) if show
                 is True, otherwise
                 ((ax, reconstructions) if return_reconstructions else None)
                 where ax is an Axes instance with plot
        """
        available_matplotlib_functions = ['fill_between', 'errorbar']
        if matplotlib_function not in available_matplotlib_functions:
            raise ValueError(("The given matplotlib function, '{0!s}', was " +\
                "not in the list of implemented matplotlib functions of " +\
                "the plot_reconstruction_confidence_intervals function, " +\
                "{1}.").format(matplotlib_function,\
                available_matplotlib_functions))
        (intervals, reconstructions) =\
            self.reconstruction_confidence_intervals(number, probabilities,\
            parameters=parameters, model=model,\
            reconstructions=reconstructions, return_reconstructions=True,\
            random=random)
        mean_reconstruction = np.mean(reconstructions, axis=0)
        if type(probabilities) in real_numerical_types:
            probabilities = [probabilities]
            intervals = [intervals]
            alphas = [0.3 if (type(alphas) is type(None)) else alphas]
        if type(ax) is type(None):
            fig = pl.figure(figsize=figsize)
            ax = fig.add_subplot(111)
        num_channels = intervals[0][0].shape[0]
        if type(x_values) is type(None):
            x_values = np.arange(num_channels)
        if matplotlib_function == 'fill_between':
            if type(breakpoints) is type(None):
                breakpoints = num_channels
            if type(breakpoints) in int_types:
                breakpoints = (breakpoints % num_channels)
                if breakpoints == 0:
                    breakpoints = np.array([0, num_channels])
                else:
                    breakpoints = np.array([0, breakpoints, num_channels])
            elif type(breakpoints) in sequence_types:
                breakpoints = np.sort(np.mod(breakpoints, num_channels))
                if breakpoints[0] != 0:
                    breakpoints = np.concatenate([[0], breakpoints])
                breakpoints = np.concatenate([breakpoints, [num_channels]])
            error_expansion_factors =\
                error_expansion_factors * np.ones((len(breakpoints) - 1,))
        for (iinterval, interval) in enumerate(intervals):
            pconfidence = int(round(probabilities[iinterval] * 100))
            confidence_label = '{:d}% confidence'.format(pconfidence)
            if matplotlib_function == 'fill_between':
                for (isegment, (start, end, error_expansion_factor)) in\
                    enumerate(zip(breakpoints[:-1], breakpoints[1:],\
                    error_expansion_factors)):
                    center =\
                        (interval[0][start:end] + interval[1][start:end]) / 2
                    half_width =\
                        (interval[1][start:end] - interval[0][start:end]) / 2
                    ax.fill_between(x_values[start:end], ((center -\
                        (error_expansion_factor * half_width)) *\
                        scale_factor) -\
                        (true_curve[start:end] if subtract_truth else 0),\
                        ((center + (error_expansion_factor * half_width)) *\
                        scale_factor) -\
                        (true_curve[start:end] if subtract_truth else 0),\
                        alpha=alphas[iinterval],\
                        label=(confidence_label if (isegment == 0) else None),\
                        **kwargs)
            elif matplotlib_function == 'errorbar':
                error = [((endpoint - mean_reconstruction) * scale_factor *\
                    sign * error_expansion_factors)\
                    for (sign, endpoint) in zip((-1, 1), interval)]
                ax.errorbar(x_values, (mean_reconstruction * scale_factor) -\
                    (true_curve if subtract_truth else 0), yerr=error,\
                    alpha=alphas[iinterval], label=confidence_label, **kwargs)
            else:
                raise ValueError("This error should never happen!")
        if type(true_curve) is not type(None):
            if subtract_truth:
                ax.plot(x_values, np.zeros_like(x_values), linewidth=2,\
                    color='k', label='input')
            else:
                if matplotlib_function == 'fill_between':
                    for (isegment, (start, end)) in\
                        enumerate(zip(breakpoints[:-1], breakpoints[1:])):
                        ax.plot(x_values[start:end], true_curve[start:end],\
                            linewidth=2, color='k',\
                            label=('input' if (isegment == 0) else None))
                elif matplotlib_function == 'errorbar':
                    ax.scatter(x_values, true_curve, color='k')
                else:
                    raise ValueError("This error should never happen!")
        ax.set_xlim((x_values[0], x_values[-1]))
        ax.legend(fontsize=fontsize)
        if type(title) is type(None):
            title = 'Reconstruction confidence intervals'
        ax.set_title(title, size=fontsize)
        if type(xlabel) is not type(None):
            ax.set_xlabel(xlabel, size=fontsize)
        if type(ylabel) is not type(None):
            ax.set_ylabel(ylabel, size=fontsize)
        ax.tick_params(labelsize=fontsize, width=2.5, length=7.5)
        if show:
            pl.show()
            if return_reconstructions:
                return reconstructions
        elif return_reconstructions:
            return (ax, reconstructions)
        else:
            return ax

    def plot_chain(self, parameters=None, apply_transforms_to_chain=True,\
        apply_transforms_to_reference_value=True, walkers=None, thin=1,\
        figsize=12, show=False, parameter_renamer=(lambda x: x),\
        reference_value_mean=None, reference_value_covariance=None,\
        confidence_level=0.95, maximum_aspect_ratio=2, fontsize=12):
        """
        Plots the chain of this MCMC.

        parameters: if None, the chain is plotted for all parameters
                    if string, the chain is plotted for all parameters which
                               match this regular expression
                    if sequence of ints, describes the indices of the
                                         parameters to be plotted
                    if sequence of strings, describes parameters to be plotted
        apply_transforms_to_chain: if True, chains are plotted in transformed
                                   space, instead of in untransformed space
        apply_transforms_to_reference_value: if True, reference values are
                                              assumed to be in untransformed
                                              space and they are transformed
                                              before being plotted
        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        thin: factor by which to thin the chain
        figsize: either a 2-tuple (width, height) or the size of the vertical
                 dimension of the figure (then the aspect ratio will be
                 determined by the maximum_aspect_ratio argument)
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns
        parameter_renamer: a callable which is used to rename parameters for
                           plotting purposes. The default value is
                           (lambda x: x), which simply give parameters the same
                           name with which they are internally represented
        reference_value_mean: either None or a 1D array containing reference
                              values.
        reference_value_covariance: either None, a 2D array of rank
                                    len(parameter_indices), or a tuple of
                                    length greater than 1 of the form
                                    (model, error)
                                    or (model, error, fisher_kwargs) to use for
                                    estimating the covariance matrix under the
                                    Fisher matrix formalism
        confidence_level: confidence to use when plotting reference interval
                          (only used if reference_value_mean and
                          reference_value_covariance are known)
        maximum_aspect_ratio: if figsize is an integer, this is the maximum
                              aspect ratio allowed
        fontsize: font size to use for axis and tick labels

        returns: if show is True, None
                 otherwise, the matplotlib Figure object containing plots
        """
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        num_parameter_plots = len(parameter_indices)
        if type(thin) is type(None):
            thin = 1
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        trimmed_chain = self.chain[walkers,:,:]
        if apply_transforms_to_chain:
            trimmed_chain = self.transform_list(trimmed_chain)
        trimmed_chain = trimmed_chain[:,::thin,:][:,:,parameter_indices]
        (reference_value_mean, reference_value_covariance) =\
            self.process_reference_values(parameter_indices,\
            apply_transforms_to_reference_value, reference_value_mean,\
            reference_value_covariance)
        steps = np.arange(0, self.nsteps, thin)
        number_of_rows = np.arange(\
            int(np.ceil(np.sqrt(num_parameter_plots / maximum_aspect_ratio))),\
            int(np.ceil(np.sqrt(num_parameter_plots))) + 1)
        number_of_columns =\
            np.ceil(num_parameter_plots / number_of_rows).astype(int)
        index = np.argmin(number_of_rows * number_of_columns)
        number_of_rows = number_of_rows[index]
        number_of_columns = number_of_columns[index]
        if type(figsize) in real_numerical_types:
            figsize = ((figsize * number_of_columns) / number_of_rows, figsize)
        fig = pl.figure(figsize=figsize)
        if type(self.prior_distribution_set) is type(None):
            minima = {parameter: -np.inf for parameter in self.parameters}
            maxima = {parameter: +np.inf for parameter in self.parameters}
        else:
            minima = self.prior_distribution_set.minimum
            minima = {parameter: ((-np.inf) if (type(minima[parameter])\
                is type(None)) else minima[parameter]) for parameter in minima}
            maxima = self.prior_distribution_set.maximum
            maxima = {parameter: ((+np.inf) if (type(maxima[parameter])\
                is type(None)) else maxima[parameter]) for parameter in maxima}
            if apply_transforms_to_chain:
                minima = self.transform_set(minima)
                maxima = self.transform_set(maxima)
        for index in range(num_parameter_plots):
            parameter_index = parameter_indices[index]
            parameter_name = self.parameters[parameter_index]
            renamed_parameter = parameter_renamer(parameter_name)
            ax = fig.add_subplot(number_of_rows, number_of_columns, index + 1)
            ax.plot(steps, trimmed_chain[:,:,index].T, linewidth=1)
            if type(reference_value_mean) is not type(None):
                this_mean = reference_value_mean[index]
                if type(this_mean) is not type(None):
                    ax.plot([steps[0], steps[-1]], [this_mean] * 2,\
                        linewidth=2, color='k', linestyle='-')
                if type(reference_value_covariance) is not type(None):
                    this_variance = reference_value_covariance[index,index]
                    if type(this_variance) is not type(None):
                        variance_expansion_for_1D_confidence =\
                            (2 * np.power(erfinv(confidence_level), 2))
                        this_error = np.sqrt(this_variance *\
                            variance_expansion_for_1D_confidence)
                        lower_bound =\
                            max(this_mean - this_error, minima[parameter_name])
                        upper_bound =\
                            min(this_mean + this_error, maxima[parameter_name])
                        ax.plot([steps[0], steps[-1]], [lower_bound] * 2,\
                            color='k', linewidth=2, linestyle='--')
                        ax.plot([steps[0], steps[-1]], [upper_bound] * 2,\
                            color='k', linewidth=2, linestyle='--')
            ax.set_xlabel('Step #', size=fontsize)
            ax.set_ylabel(renamed_parameter, size=fontsize)
            ax.tick_params(labelsize=fontsize, width=2.5, length=7.5,\
                which='major')
            ax.tick_params(labelsize=fontsize, width=1.5, length=4.5,\
                which='minor')
            ax.set_xlim((steps[0], steps[-1]))
        fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95,\
            wspace=0.25, hspace=0.25)
        if show:
            pl.show()
        else:
            return fig

    def get_parameter_index(self, parameter):
        """
        Gets the integer index associated with the given parameter or index.

        parameter: either a string name of a parameter of a valid parameter
                   index

        returns: the integer index associated with the given parameter
        """
        if isinstance(parameter, basestring):
            return self.parameters.index(parameter)
        elif type(parameter) in int_types:
            if (parameter >= 0) and (parameter < self.num_parameters):
                return parameter
            else:
                raise ValueError(("parameter was {0}, even though it " +\
                    "should be 0<=parameter<{1}").format(parameter,\
                    self.num_parameters))
        else:
            raise TypeError("parameter must be a string name of a " +\
                "parameter or a valid index of a parameter.")

    def process_reference_values(self, parameter_indices,\
        apply_transforms_to_reference_value, reference_value_mean,\
        reference_value_covariance):
        """
        Processes the given reference values, performing a Fisher matrix
        calculation if desired.

        parameter_indices: array of integer indices (like those returned by
                           get_parameter_indices method)
        apply_transforms_to_reference_value: boolean determining whether
                                             transforms should be applied to
                                             the reference values
        reference_value_mean: either None or a 1D array containing reference
                              values.
        reference_value_covariance: either None, a 2D array of rank
                                    len(parameter_indices), or a tuple of
                                    length greater than 1 of the form
                                    (model, error)
                                    or (model, error, fisher_kwargs) to use for
                                    estimating the covariance matrix under the
                                    Fisher matrix formalism

        returns: (reference_value_mean, reference_value_covariance)
                 If reference_value_mean was not given as None, then it is
                 returned (after being transformed if
                 apply_transforms_to_reference_value is True). If
                 reference_value_covariance was not given as None, then it is
                 returned (in transformed space if
                 apply_transforms_to_reference_value is True) as a 2D array.
        """
        if (type(reference_value_covariance) is not type(None)) and\
            (type(reference_value_mean) is not type(None)):
            if isinstance(reference_value_covariance, np.ndarray):
                if reference_value_covariance.shape !=\
                    ((len(reference_value_mean),) * 2):
                    raise ValueError("reference_value_covariance was a " +\
                        "numpy.ndarray but it was not square with rank " +\
                        "given by the number of parameters in the triangle " +\
                        "plot.")
                else:
                    transform_list = self.transform_list[parameter_indices]
                    reference_value_covariance =\
                        transform_list.transform_covariance(\
                        reference_value_covariance, reference_value_mean,\
                        axis=(0, 1))
            else:
                (model, error, fisher_kwargs) =\
                    (reference_value_covariance[0],\
                    reference_value_covariance[1],\
                    reference_value_covariance[2:])
                if len(fisher_kwargs) == 0:
                    fisher_kwargs = {}
                else:
                    fisher_kwargs = fisher_kwargs[0]
                if apply_transforms_to_reference_value:
                    fisher_kwargs['transform_list'] =\
                        self.transform_list[parameter_indices]
                likelihood = GaussianLoglikelihood(\
                    model(reference_value_mean), error, model)
                try:
                    reference_value_covariance =\
                        likelihood.parameter_covariance_fisher_formalism(\
                        reference_value_mean, **fisher_kwargs)
                except np.linalg.LinAlgError:
                    reference_value_covariance = None
        if (type(reference_value_mean) is not type(None)) and\
            apply_transforms_to_reference_value:
            reference_value_mean = [(None\
                if (type(reference) is type(None)) else transform(reference))\
                for (transform, reference) in\
                zip(self.transform_list[parameter_indices],\
                reference_value_mean)]
        return (reference_value_mean, reference_value_covariance)

    def triangle_plot(self, parameters=None, walkers=None, thin=1,\
        scale_factors=None, figsize=(12, 12), fig=None, show=False,\
        kwargs_1D={}, kwargs_2D={}, fontsize=28, nbins=100,\
        plot_type='contour', plot_limits=None, parameter_renamer=None,\
        reference_value_mean=None, reference_value_covariance=None,\
        contour_confidence_levels=0.95, apply_transforms_to_chain=True,\
        apply_transforms_to_reference_value=True,\
        apply_transforms_to_plot_limits=True,\
        tick_label_format_string='{x:.3g}', num_ticks=3,\
        minor_ticks_per_major_tick=1, xlabel_rotation=0, xlabelpad=None,\
        ylabel_rotation=90, ylabelpad=None):
        """
        Makes a triangle plot.

        parameters: key describing parameters to appear in subplots
        walkers: either None or indices of walkers to include chain samples
        thin: integer stride with which to thin chain samples
        scale_factors: scale factors to apply to the parameters (after
                       transformation if it is applied). By default,
                       scale_factors is None and no samples are scaled after
                       transformation
        figsize: size of figure on which the triangle plot will be placed
        fig: existing figure on which to place triangle plot, if applicable.
             Default: None
        show: if True, matplotlib.pyplot.show is called before this function
              returns
        kwargs_1D: kwargs to pass on to univariate_histogram function
        kwargs_2D: kwargs to pass on to bivariate_histogram function
        fontsize: the size of the label/tick fonts
        nbins: the number of bins in all dimensions in all subplots
        plot_type: 'contourf', 'contour', or 'histogram'
        plot_limits: if not None, a of 2-tuples of the form (low, high)
                                  representing the desired axis limits for each
                                  variable in the space of the plot (i.e. in
                                  untransformed space if
                                  apply_transforms_to_chain is False and in
                                  transformed space if
                                  apply_transforms_to_chain is True)
                     if None (default), bins are used to decide plot limits
        parameter_renamer: function to apply to each parameter name to
                           determine the label which will correspond to it
        reference_value_mean: either None or a 1D array containing reference
                              values. (Should be given in untransformed space
                              regardless of the value of apply_transforms.)
        reference_value_covariance: either None, a 2D array of rank
                                    num_parameters, or a tuple of length
                                    greater than 1 of the form (model, error)
                                    or (model, error, fisher_kwargs) to use for
                                    estimating the covariance matrix under the
                                    Fisher matrix formalism (error should
                                    always be 1 sigma ; confidence level will
                                    be computed automatically using the
                                    contour_confidence_levels argument). This
                                    covariance will be used to plot ellipses.
                                    (Should be given in untransformed space
                                    regardless of the value of
                                    apply_transforms.)
        contour_confidence_levels: the confidence level of the contour in the
                                   bivariate histograms. Only used for main
                                   plots if plot_type is 'contour' or
                                   'contourf'. No matter the value of
                                   plot_type, this argument is used to compute
                                   Fisher matrix ellipses if
                                   reference_value_covariance is given as a
                                   tuple.
        apply_transforms_to_chain: if True (default), transforms are applied to
                                                      chain before
                                                      histogram-ing
        apply_transforms_to_reference_value: if True (default), reference value
                                                                is given in
                                                                untransformed
                                                                space
        apply_transforms_to_plot_limits: if True (default), if plot limits are
                                                            given, then
                                                            transforms are
                                                            applied to them
                                                            before plotting
        tick_label_format_string: format string that can be called using
                                  tick_label_format_string.format(x=loc) where
                                  loc is the location of the tick in data
                                  coordinates
        num_ticks: integer number of major ticks, default: 3
        minor_ticks_per_major_tick: integer number of minor ticks per major
                                    tick
        xlabel_rotation: rotation of x-label in degrees, default: 0
        xlabelpad: pad size for xlabel, default: None
        ylabel_rotation: rotation of y-label in degrees, default: 90
        ylabelpad: pad size for ylabel, default: None
        """
        parameter_indices = self.get_parameter_indices(parameters=parameters)
        labels = [self.parameters[parameter_index]\
            for parameter_index in parameter_indices]
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        if type(parameter_renamer) is not type(None):
            labels = [parameter_renamer(label) for label in labels]
        if type(self.prior_distribution_set) is type(None):
            minima = np.array([-np.inf] * len(parameter_indices))
            maxima = np.array([+np.inf] * len(parameter_indices))
        else:
            (minima, maxima) = ([], [])
            for parameter_index in parameter_indices:
                parameter_name = self.parameters[parameter_index]
                if type(self.prior_distribution_set.minimum[parameter_name])\
                    is type(None):
                    minima.append(-np.inf)
                else:
                    minima.append(\
                        self.prior_distribution_set.minimum[parameter_name])
                if type(self.prior_distribution_set.maximum[parameter_name])\
                    is type(None):
                    maxima.append(+np.inf)
                else:
                    maxima.append(\
                        self.prior_distribution_set.maximum[parameter_name])
            minima = np.array(minima)
            maxima = np.array(maxima)
        if apply_transforms_to_chain:
            samples = [self.transform_list[parameter_index](\
                self.chain[:,:,parameter_index])[walkers,:][:,::thin].flatten(\
                ) for parameter_index in parameter_indices]
            minima = np.array([self.transform_list[parameter_index](\
                minima[index]) for (index, parameter_index) in\
                enumerate(parameter_indices)])
            maxima = np.array([self.transform_list[parameter_index](\
                maxima[index]) for (index, parameter_index) in\
                enumerate(parameter_indices)])
        else:
            samples = [\
                self.chain[:,:,parameter_index][walkers,:][:,::thin].flatten()\
                for parameter_index in parameter_indices]
        if type(scale_factors) is not type(None):
            samples = [(sample * scale_factor)\
                for (sample, scale_factor) in zip(samples, scale_factors)]
        num_samples = len(samples)
        (reference_value_mean, reference_value_covariance) =\
            self.process_reference_values(parameter_indices,\
            apply_transforms_to_reference_value, reference_value_mean,\
            reference_value_covariance)
        if apply_transforms_to_plot_limits and\
            (type(plot_limits) is not type(None)):
            plot_limits = [(transform(limits[0]), transform(limits[1]))\
                for (transform, limits) in zip(self.transform_list, plot_limits)]
        return triangle_plot(samples, labels, figsize=figsize, show=show,\
            kwargs_1D=kwargs_1D, kwargs_2D=kwargs_2D, fontsize=fontsize,\
            nbins=nbins, plot_type=plot_type, plot_limits=plot_limits,\
            reference_value_mean=reference_value_mean,\
            reference_value_covariance=reference_value_covariance,\
            contour_confidence_levels=contour_confidence_levels, fig=fig,\
            minima=minima, maxima=maxima,\
            tick_label_format_string=tick_label_format_string,\
            num_ticks=num_ticks,\
            minor_ticks_per_major_tick=minor_ticks_per_major_tick,\
            xlabel_rotation=xlabel_rotation, xlabelpad=xlabelpad,\
            ylabel_rotation=ylabel_rotation, ylabelpad=ylabelpad)

    def univariate_confidence_intervals(self, parameters,\
        confidence_level=0.95, walkers=None, thin=1,\
        apply_transforms_to_chain=False, interval_type='central',\
        include_mean=False):
        """
        Computes the central, left, or right confidence intervals of the given
        confidence level for samples of one or more parameters of this fitter.

        parameters: integer index, string name, or sequence of such identifiers
        confidence_level: confidence level of the interval(s), default: 0.95
        walkers: the walkers to include when collecting sample
        thin: the stride of the sample in the chain of each walker
        apply_transforms_to_chain: if True, transforms are applied before
                                   computing confidence intervals
        interval_type: 'central', 'left', or 'right' specifying which way to
                       compute the confidence interval from each sample
        include_mean: if True, mean is included in the intervals

        returns: numpy.ndarray of shape ((3 if include_mean else 2,) if
                 parameters is string or int else
                 (len(parameters), (3 if include_mean else 2))) where the axis
                 of shape 2 or 3 contains either (left, right) or
                 (left, mean, right) and the first axis (if present) indexes
                 the parameters for which confidence intervals were computed
        """
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        originally_single_parameter = (isinstance(parameters, basestring) or\
            (type(parameters) in int_types))
        if originally_single_parameter:
            parameters = [parameters]
        parameter_indices = self.get_parameter_indices(parameters)
        endpoints = []
        for parameter_index in parameter_indices:
            sample =\
                self.chain[:,:,parameter_index][walkers,:][:,::thin].flatten()
            if apply_transforms_to_chain:
                sample = self.transform_list[parameter_index](sample)
            sample = np.sort(sample)
            if interval_type == 'central':
                half_of_one_minus_confidence_level = (1 - confidence_level) / 2
                left_index = int(np.floor(\
                    half_of_one_minus_confidence_level * (len(sample) - 1)))
                right_index = int(np.ceil((1 -\
                    half_of_one_minus_confidence_level) * (len(sample) - 1)))
            elif interval_type == 'left':
                left_index = 0
                right_index =\
                    int(np.ceil(confidence_level * (len(sample) - 1)))
            elif interval_type == 'right':
                left_index =\
                    int(np.floor((1 - confidence_level) * (len(sample) - 1)))
                right_index = len(sample) - 1
            else:
                raise ValueError("interval_type must be one of ['central', " +\
                    "'left', 'right'].")
            if include_mean:
                these_endpoints =\
                    (sample[left_index], np.mean(sample), sample[right_index])
            else:
                these_endpoints = (sample[left_index], sample[right_index])
            endpoints.append(these_endpoints)
        endpoints = np.array(endpoints)
        if originally_single_parameter:
            return endpoints[0]
        else:
            return endpoints

    def plot_univariate_histogram(self, parameter_index, walkers=None, thin=1,\
        ax=None, show=False, reference_value=None,\
        apply_transforms_to_chain=True,\
        apply_transforms_to_reference_value=True, fontsize=28,\
        matplotlib_function='fill_between', show_intervals=False, bins=None,\
        xlabel='', ylabel='', title='', **kwargs):
        """
        Plots a 1D histogram of the given parameter.

        parameter_index: either a string parameter name or a valid integer
                         index of the parameter
        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        thin: factor by which to thin the chain
        ax: if None, new Figure and Axes are created
            otherwise, this Axes object is plotted on
        show: if True, matplotlib.pyplot.show is called before this function
                       returns
        reference_value: a point at which to plot a dashed reference line
                         (Should always be given in untransformed_space,
                         regardless of the value of apply_transforms.)
        apply_transforms: if True, plots are made in transformed space
        fontsize: the size of the tick label font
        matplotlib_function: either 'fill_between' or 'plot'
        bins: bins to pass to numpy.histogram: default, None
        xlabel: string for labeling x axis
        ylabel: string for labeling y axis
        title: string for top of plot
        kwargs: keyword arguments to pass on to matplotlib.Axes.plot or
                matplotlib.Axes.fill_between

        returns: None if show is True, otherwise Axes instance with plot
        """
        parameter_index = self.get_parameter_index(parameter_index)
        if type(thin) is type(None):
            thin = 1
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        if apply_transforms_to_chain:
            sample = self.transform_list[parameter_index](\
                self.chain[:,:,parameter_index])[walkers,:][:,::thin].flatten()
        if apply_transforms_to_reference_value and\
            (type(reference_value) is not type(None)):
            reference_value =\
                self.transform_list[parameter_index](reference_value)
        else:
            sample =\
                self.chain[:,:,parameter_index][walkers,:][:,::thin].flatten()
        return univariate_histogram(sample, reference_value=reference_value,\
            bins=bins, matplotlib_function=matplotlib_function,\
            show_intervals=show_intervals, xlabel=xlabel, ylabel=ylabel,\
            title=title, fontsize=fontsize, ax=ax, show=show, **kwargs)

    def plot_bivariate_histogram(self, parameter_index1, parameter_index2,\
        walkers=None, thin=1, ax=None, show=False, reference_value_mean=None,\
        reference_value_covariance=None, apply_transforms_to_chain=True,\
        apply_transforms_to_reference_value=True, fontsize=28,\
        bins=None, matplotlib_function='imshow', xlabel='', ylabel='',\
        contour_confidence_levels=0.95, title='', **kwargs):
        """
        Plots a 2D histogram of the given parameters.

        parameter_index1: either a string parameter name or a valid integer
                          index of the parameter (x axis)
        parameter_index2: either a string parameter name or a valid integer
                          index of the parameter (y axis)
        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        thin: factor by which to thin the chain
        ax: if None, new Figure and Axes are created
            otherwise, this Axes object is plotted on
        show: if True, matplotlib.pyplot.show is called before this function
                       returns
        reference_value: a point at which to plot a dashed reference line
                         (Should always be given in untransformed_space,
                         regardless of the value of apply_transforms.)
        reference_value_covariance: if not None, used to plot reference ellipse
                                    (Should always be given in untransformed
                                    space, regardless of the value of
                                    apply_transforms.)
        apply_transforms: if True, plots are made in transformed space
        fontsize: the size of the tick label font
        bins: bins to pass to numpy.histogram2d, default: None
        xlabel: string for labeling x axis
        ylabel: string for labeling y axis
        title: string for top of plot
        matplotlib_function: function to use in plotting. One of ['imshow',
                             'contour', 'contourf']. default: 'imshow'
        contour_confidence_levels: the confidence level of the contour in the
                                   bivariate histograms. Only used if
                                   matplotlib_function is 'contour' or
                                   'contourf'. Can be single number or sequence
                                   of numbers
        kwargs: keyword arguments to pass on to matplotlib.Axes.imshow (any but
                'origin', 'extent', or 'aspect') or matplotlib.Axes.contour or
                matplotlib.Axes.contourf (any)

        returns: None if show is True, otherwise Axes instance with plot
        """
        parameter_index1 = self.get_parameter_index(parameter_index1)
        parameter_index2 = self.get_parameter_index(parameter_index2)
        if type(thin) is type(None):
            thin = 1
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        if type(self.prior_distribution_set) is type(None):
            minima = np.array([-np.inf, -np.inf])
            maxima = np.array([+np.inf, +np.inf])
        else:
            (minima, maxima) = ([], [])
            for parameter_index in [parameter_index1, parameter_index2]:
                parameter_name = self.parameters[parameter_index]
                if type(self.prior_distribution_set.minimum[parameter_name])\
                    is type(None):
                    minima.append(-np.inf)
                else:
                    minima.append(\
                        self.prior_distribution_set.minimum[parameter_name])
                if type(self.prior_distribution_set.maximum[parameter_name])\
                    is type(None):
                    maxima.append(+np.inf)
                else:
                    maxima.append(\
                        self.prior_distribution_set.maximum[parameter_name])
            minima = np.array(minima)
            maxima = np.array(maxima)
        if apply_transforms_to_chain:
            xsample = self.transform_list[parameter_index1](self.chain[\
                :,:,parameter_index1])[walkers,:][:,::thin].flatten()
            ysample = self.transform_list[parameter_index2](self.chain[\
                :,:,parameter_index2])[walkers,:][:,::thin].flatten()
            minima = [self.transform_list[parameter_index1](minima[0]),\
                self.transform_list[parameter_index2](minima[1])]
            maxima = [self.transform_list[parameter_index1](maxima[0]),\
                self.transform_list[parameter_index2](maxima[1])]
        else:
            xsample =\
                self.chain[:,:,parameter_index1][walkers,:][:,::thin].flatten()
            ysample =\
                self.chain[:,:,parameter_index2][walkers,:][:,::thin].flatten()
        if apply_transforms_to_reference_value:
            transform_list = TransformList(\
                self.transform_list[parameter_index1],\
                self.transform_list[parameter_index2])
            if type(reference_value_covariance) is not type(None):
                reference_value_covariance =\
                    transform_list.transform_covariance(\
                    reference_value_covariance, reference_value_mean,\
                    axis=(0, 1))
            if type(reference_value_mean) is not type(None):
                reference_value_mean = [None if\
                    (type(reference) is type(None)) else transform(reference)\
                    for (transform, reference) in\
                    zip(transform_list, reference_value_mean)]
        return bivariate_histogram(xsample, ysample,\
            reference_value_mean=reference_value_mean,\
            reference_value_covariance=reference_value_covariance, bins=bins,\
            matplotlib_function=matplotlib_function, xlabel=xlabel,\
            ylabel=ylabel, title=title, fontsize=fontsize, ax=ax, show=show,\
            contour_confidence_levels=contour_confidence_levels,\
            minima=minima, maxima=maxima, **kwargs)

    def plot_lnprobability(self, walkers=None, log_scale=False,\
        which='posterior', fontsize=24, ax=None, show=False):
        """
        Plots the log probability values accessed by this MCMC chain.

        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        log_scale: if False (default), everything is plotted on linear scale
                   if True, difference from maximum likelihood is plotted on a
                            log scale (on y-axis)
        which: one of 'posterior' (default), 'likelihood', or 'prior'
        fontsize: size of fonts for labels and title
        ax: Axes instance on which to plot the log_probability chain
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: None if show is True, otherwise Axes instance with plot
        """
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        if which == 'posterior':
            trimmed_lnprobability = self.lnprobability[walkers,:]
        elif which == 'likelihood':
            trimmed_lnprobability = self.lnlikelihood[walkers,:]
        elif which == 'prior':
            trimmed_lnprobability = self.lnprior[walkers,:]
        else:
            raise ValueError("'which' argument of plot_lnprobability was " +\
                "neither 'posterior', 'likelihood', and 'prior'.")
        steps = np.arange(self.nsteps)
        if type(ax) is type(None):
            fig = pl.figure()
            ax = fig.add_subplot(111)
        if log_scale:
            ax.semilogy(steps,\
                (np.max(trimmed_lnprobability) - trimmed_lnprobability).T)
            ax.set_ylabel('$\ln(p_{max})-\ln(p)$', size=fontsize)
        else:
            ax.plot(steps, trimmed_lnprobability.T)
            ax.set_ylabel('$\ln(p)$', size=fontsize)
        ax.set_xlim((steps[0], steps[-1]))
        ax.set_title('Log {!s}'.format(which), size=fontsize)
        ax.set_xlabel('Step number', size=fontsize)
        ax.tick_params(labelsize=fontsize, width=2.5, length=7.5,\
            which='major')
        ax.tick_params(labelsize=fontsize, width=1.5, length=4.5,\
            which='minor')
        if show:
            pl.show()
        else:
            return ax

    def plot_lnprobability_both_types(self, walkers=None,\
        which='posterior', fontsize=24, fig=None, show=False):
        """
        Plots both types of lnprobability plots on a single matplotlib.Figure

        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        log_scale: if False (default), everything is plotted on linear scale
                   if True, difference from maximum likelihood is plotted on a
                            log scale (on y-axis)
        title: string title to put on top of Figure
        fig: Figure instance on which to plot the log_probability chain
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: matplotlib.Figure if show is False, otherwise None
        """
        if type(fig) is type(None):
            fig = pl.figure(figsize=(14,14))
        ax = fig.add_subplot(211)
        self.plot_lnprobability(walkers=walkers, log_scale=False,\
            which=which, fontsize=fontsize, ax=ax, show=False)
        ax.set_xlabel('')
        ax.set_xticklabels([])
        ax = fig.add_subplot(212)
        self.plot_lnprobability(walkers=walkers, log_scale=True,\
            which=which, fontsize=fontsize, ax=ax, show=False)
        ax.set_title('')
        fig.subplots_adjust(hspace=0)
        if show:
            pl.show()
        else:
            return fig

    def plot_covariance_matrix(self, normalize_by_variances=False, fig=None,\
        ax=None, show=False, transform_chain=True, **imshow_kwargs):
        """
        Plots a covariance (or correlation) matrix of the parameters.

        normalize_by_variances: if True, correlation matrix is plotted
                                otherwise, covariance matrix is plotted
        fig: matplotlib.Figure object containing axes, or None if one should be
             created
        ax: matplotlib.axes.Axes object
        show: if True, matplotlib.pyplot.show is called before this function
                       returns
        imshow_kwargs: keyword arguments to pass on to the
                       matplotlib.axes.Axes.imshow function. Common keywords
                       include 'norm' and 'cmap'. imshow_kwargs should not
                       include 'extent'. It is worked out internally

        returns: None if show is True, otherwise Axes instance with plot
        """
        if type(ax) is type(None):
            fig = pl.figure()
            ax = fig.add_subplot(111)
        if transform_chain:
            if normalize_by_variances:
                to_plot = self.transformed_parameter_correlation
            else:
                to_plot = self.transformed_parameter_covariance
        elif normalize_by_variances:
            to_plot = self.parameter_correlation
        else:
            to_plot = self.parameter_covariance
        low = 0.5
        high = self.num_parameters + 0.5
        image =\
            ax.imshow(to_plot, extent=[low, high, high, low], **imshow_kwargs)
        pl.colorbar(image, orientation='vertical')
        if show:
            pl.show()
        else:
            return ax

    def plot_rescaling_factors(self, log_scale=False, ax=None, fontsize=20,\
        show=False):
        """
        Plots the rescaling factors for each of the parameters. These values
        should be close to 1.

        ax: Axes instance on which to plot the acceptance fraction
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: None if show is True, otherwise Axes instance with plot
        """
        if type(ax) is type(None):
            fig = pl.figure(figsize=(12,9))
            ax = fig.add_subplot(111)
        x_values = np.arange(self.num_parameters) + 1
        to_subtract = (1 if log_scale else 0)
        x_ones = np.ones(self.num_parameters)
        if not log_scale:
            ax.plot(x_values, x_ones, color='r', linestyle='-')
        ax.plot(x_values, x_ones * (1.1 - to_subtract), color='r',\
            linestyle='--')
        ax.scatter(x_values, self.rescaling_factors - to_subtract, color='k')
        if log_scale:
            ax.set_yscale('log')
        subtract_string = (' - 1' if log_scale else '')
        ax.set_title('Rescaling factors{!s}'.format(subtract_string),\
            size=fontsize)
        ax.set_xlabel('Parameter number', size=fontsize)
        ax.set_ylabel('Rescaling factor{0!s}, $R{0!s}$'.format(\
            subtract_string), size=fontsize)
        ylim = ax.get_ylim()
        ax.set_ylim((\
            min(ylim[0], np.min(self.rescaling_factors - to_subtract)),\
            max(ylim[1], 1.15 - to_subtract)))
        ax.set_xlim((x_values[0] - 0.5, x_values[-1] + 0.5))
        ax.tick_params(labelsize=fontsize, width=2.5, length=7.5,\
            which='major')
        ax.tick_params(labelsize=fontsize, width=1.5, length=4.5,\
            which='minor')
        if show:
            pl.show()
        else:
            return ax

    def plot_rescaling_factors_both_types(self, fontsize=20, fig=None,\
        show=False):
        """
        Plots both types of lnprobability plots on a single matplotlib.Figure

        fontsize: size of font for labels
        fig: Figure instance on which to plot the log_probability chain
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: matplotlib.Figure if show is False, otherwise None
        """
        if type(fig) is type(None):
            fig = pl.figure(figsize=(14,14))
        ax = fig.add_subplot(211)
        self.plot_rescaling_factors(log_scale=False, fontsize=fontsize, ax=ax,\
            show=False)
        ax = fig.add_subplot(212)
        self.plot_rescaling_factors(log_scale=True, fontsize=fontsize, ax=ax,\
            show=False)
        if show:
            pl.show()
        else:
            return fig

    def plot_acceptance_fraction(self, walkers=None, average=False, ax=None,\
        log_scale=False, fontsize=20, show=False):
        """
        Plots the fraction of MCMC steps which are accepted by walker.

        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        average: if True, the acceptance fraction is averaged across the given
                          walkers before being plotted
                 if False, all of the given walkers' acceptance fractions are
                           plotted
        ax: Axes instance on which to plot the acceptance fraction
        log_scale: if True, the acceptance fraction is shown in a semilogy plot
        fontsize: size of fonts for labels and title
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: None if show is True, otherwise Axes instance with plot
        """
        if type(walkers) is type(None):
            walkers = np.arange(self.nwalkers)
        elif type(walkers) in int_types:
            walkers = np.arange(walkers)
        trimmed_acceptance_fraction = self.acceptance_fraction[walkers,:]
        steps = np.arange(trimmed_acceptance_fraction.shape[1]) + 1
        if type(ax) is type(None):
            fig = pl.figure(figsize=(12,9))
            ax = fig.add_subplot(111)
        if average:
            title = '$f_{acc}$ averaged across walkers'
            trimmed_acceptance_fraction =\
               np.mean(trimmed_acceptance_fraction, axis=0)
        else:
            title = '$f_{acc}$ by walker'
        if len(steps) == 1:
            if trimmed_acceptance_fraction.ndim == 1:
                ax.scatter(steps, trimmed_acceptance_fraction)
            else:
                for index in range(len(trimmed_acceptance_fraction)):
                    ax.scatter(steps, trimmed_acceptance_fraction[index])
        else:
            ax.plot(steps, trimmed_acceptance_fraction.T)
        if log_scale:
            ylim = (1e-3, 1)
            ax.set_yscale('log')
        else:
            ylim = (0, 1)
        loaded_so_far = 0
        ax.plot([1, 1], ylim, color='k', linestyle='--')
        for these_checkpoints_to_load in self.checkpoints_to_load:
            loaded_so_far += len(these_checkpoints_to_load)
            if loaded_so_far == 0:
                ax.plot([1, 1], ylim, color='k', linestyle='--')
            elif loaded_so_far == self.total_num_checkpoints_to_load:
                ax.plot([loaded_so_far] * 2, ylim, color='k', linestyle='--')
            else:
                ax.plot([0.5 + loaded_so_far] * 2, ylim, color='k',\
                    linestyle='--')
        ax.set_title(title, size=fontsize)
        ax.set_xlabel('Checkpoint #', size=fontsize)
        ax.set_ylabel('$f_{acc}$', size=fontsize)
        ax.set_xlim((steps[0] - 0.5, steps[-1] + 0.5))
        ax.set_ylim(ylim)
        ax.tick_params(labelsize=fontsize, width=2.5, length=7.5,\
            which='major')
        ax.tick_params(labelsize=fontsize, width=1.5, length=4.5,\
            which='minor')
        if show:
            pl.show()
        else:
            return ax

    def plot_acceptance_fraction_four_types(self, walkers=None, fig=None,\
        fontsize=20, show=False):
        """
        Plots the acceptance fraction in four formats on a single figure. The
        top (bottom) figures show on a linear (log) scale. The left (right)
        figures show all walkers (all walkers averaged).

        walkers: if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        fig: None or Figure instance on which to plot the acceptance fraction
        fontsize: size of fonts for labels and title
        show: if True, matplotlib.pyplot.show() is called before this function
                       returns

        returns: Figure if show is False, None if show is True
        """
        if type(fig) is type(None):
            fig = pl.figure(figsize=(20, 16))
        ax = fig.add_subplot(221)
        self.plot_acceptance_fraction(walkers=walkers, average=False, ax=ax,\
            log_scale=False, fontsize=fontsize, show=False)
        ax = fig.add_subplot(222)
        self.plot_acceptance_fraction(walkers=walkers, average=True, ax=ax,\
            log_scale=False, fontsize=fontsize, show=False)
        ax = fig.add_subplot(223)
        self.plot_acceptance_fraction(walkers=walkers, average=False, ax=ax,\
            log_scale=True, fontsize=fontsize, show=False)
        ax = fig.add_subplot(224)
        self.plot_acceptance_fraction(walkers=walkers, average=True, ax=ax,\
            log_scale=True, fontsize=fontsize, show=False)
        if show:
            pl.show()
        else:
            return fig

    def plot_diagnostics(self, walkers=None, fontsize=20, show=False):
        """
        Plots diagnostic plots for the sample from this object. This function
        calls NLFitter.plot_rescaling_factors_both_types,
        NLFitter.plot_acceptance_fraction_four_types, and
        NLFitter.plot_lnprobability_both_types functions.

        walkers: Walkers to show for convergence + acceptance fraction figures
                 if None, all walkers are shown.
                 if int, describes the number of walkers shown in the plot
                 if sequence, describes which walkers are shown in the plot
        fontsize: size of fonts for labels and title
        show: if True, matplotlib.pyplot.show() is called before this function
              returns

        returns: if show is True, nothing is returned
                 otherwise, returns tuple of form (convergence_figure,
                            acceptance_fraction_figure, log_probability_figure)
                            or (convergence_figure, acceptance_fraction_figure,
                            log_probability_figure, log_likelihood_figure,
                            log_prior_figure) depending on whether any priors
                            are used by the Sampler sourcing this NLFitter or
                            not
        """
        convergence_figure = self.plot_rescaling_factors_both_types(\
            fontsize=fontsize, fig=None, show=False)
        acceptance_fraction_figure = self.plot_acceptance_fraction_four_types(\
            walkers=walkers, fig=None, fontsize=fontsize, show=False)
        log_posterior_figure = self.plot_lnprobability_both_types(\
            which='posterior', walkers=walkers, fontsize=fontsize, fig=None,\
            show=False)
        if type(self.prior_distribution_set) is not type(None):
            log_likelihood_figure = self.plot_lnprobability_both_types(\
                which='likelihood', walkers=walkers, fontsize=fontsize,\
                fig=None, show=False)
            log_prior_figure = self.plot_lnprobability_both_types(\
                which='prior', walkers=walkers, fontsize=fontsize, fig=None,\
                show=False)
        if show:
            pl.show()
        else:
            return_value = (convergence_figure, acceptance_fraction_figure,\
                log_posterior_figure)
            if type(self.prior_distribution_set) is not type(None):
                return_value = return_value +\
                    (log_likelihood_figure, log_prior_figure)
            return return_value

ext: py

max_stars_repo_name: CU-NESS/pylinex

max_issues_repo_name: CU-NESS/pylinex

max_forks_repo_name: CU-NESS/pylinex

reason: import numpy,from scipy