<commit_before>"""
.. module:: likelihood_class
   :synopsis: Definition of the major likelihoods
.. moduleauthor:: <NAME> <<EMAIL>>
.. moduleauthor:: <NAME> <<EMAIL>>

Contains the definition of the base likelihood class :class:`Likelihood`, with
basic functions, as well as more specific likelihood classes that may be reused
to implement new ones.

"""
import os
import numpy as np
import math
import warnings
import re

import io_mp


class Likelihood(object):
    """
    General class that all likelihoods will inherit from.

    """

    def __init__(self, path, data, command_line):
        """
        It copies the content of self.path from the initialization routine of
        the :class:`Data <data.Data>` class, and defines a handful of useful
        methods, that every likelihood might need.

        If the nuisance parameters required to compute this likelihood are not
        defined (either fixed or varying), the code will stop.

        Parameters
        ----------
        data : class
            Initialized instance of :class:`Data <data.Data>`
        command_line : NameSpace
            NameSpace containing the command line arguments

        """

        self.name = self.__class__.__name__
        self.folder = os.path.abspath(os.path.join(
            data.path['MontePython'], 'likelihoods', self.name))
        if not data.log_flag:
            path = os.path.join(command_line.folder, 'log.param')

        # Define some default fields
        self.data_directory = ''

        # Recover the values potentially read in the input.param file.
        if hasattr(data, self.name):
            exec("attributes = [e for e in dir(data.%s) if e.find('__') == -1]" % self.name)
            for elem in attributes:
                exec("setattr(self, elem, getattr(data.%s, elem))" % self.name)

        # Read values from the data file
        self.read_from_file(path, data, command_line)

        # Default state
        self.need_update = True

        # Check if the nuisance parameters are defined
        error_flag = False
        try:
            for nuisance in self.use_nuisance:
                if nuisance not in data.get_mcmc_parameters(['nuisance']):
                    error_flag = True
                    warnings.warn(
                        nuisance + " must be defined, either fixed or " +
                        "varying, for %s likelihood" % self.name)
            self.nuisance = self.use_nuisance
        except AttributeError:
            self.use_nuisance = []
            self.nuisance = []

        # If at least one is missing, raise an exception.
        if error_flag:
            raise io_mp.LikelihoodError(
                "Check your nuisance parameter list for your set of"
                "experiments")

        # Append to the log.param the value used (WARNING: so far no comparison
        # is done to ensure that the experiments share the same parameters)
        if data.log_flag:
            io_mp.log_likelihood_parameters(self, command_line)

    def loglkl(self, cosmo, data):
        """
        Placeholder to remind that this function needs to be defined for a
        new likelihood.

        Raises
        ------
        NotImplementedError

        """
        raise NotImplementedError(
            'Must implement method loglkl() in your likelihood')

    def read_from_file(self, path, data, command_line):
        """
        Extract the information from the log.param concerning this likelihood.

        If the log.param is used, check that at least one item for each
        likelihood is recovered. Otherwise, it means the log.param does not
        contain information on the likelihood. This happens when the first run
        fails early, before calling the likelihoods, and the program did not
        log the information. This check might not be completely secure, but it
        is better than nothing.

        .. warning::

            This checks relies on the fact that a likelihood should always have
            at least **one** line of code written in the likelihood.data file.
            This should be always true, but in case a run fails with the error
            message described below, think about it.

        .. warning::

            As of version 2.0.2, you can specify likelihood options in the
            parameter file. They have complete priority over the ones specified
            in the `likelihood.data` file, and it will be reflected in the
            `log.param` file.

        """

        # Counting how many lines are read.
        counter = 0

        self.path = path
        self.dictionary = {}
        if os.path.isfile(path):
            data_file = open(path, 'r')
            for line in data_file:
                if line.find('#') == -1:
                    if line.find(self.name+'.') != -1:
                        # Recover the name and value from the .data file
                        regexp = re.match(
                            "%s\.(.*) = (.*)" % self.name,
                            line)
                        name, value = (elem.strip() for elem in regexp.groups())
                        # If this name was already defined in the parameter
                        # file, be sure to take this value instead
                        try:
                            value = getattr(self, name)
                        except AttributeError:
                            exec(line.replace(self.name+'.', 'self.'))
                        counter += 1
                        self.dictionary[name] = value
            data_file.seek(0)
            data_file.close()

        # Checking that at least one line was read, exiting otherwise
        if counter == 0:<commit_msg>Scheme to specify likelihood parameters from param file now working<commit_after>"""
.. module:: likelihood_class
   :synopsis: Definition of the major likelihoods
.. moduleauthor:: <NAME> <<EMAIL>>
.. moduleauthor:: <NAME> <<EMAIL>>

Contains the definition of the base likelihood class :class:`Likelihood`, with
basic functions, as well as more specific likelihood classes that may be reused
to implement new ones.

"""
import os
import numpy as np
import math
import warnings
import re

import io_mp


class Likelihood(object):
    """
    General class that all likelihoods will inherit from.

    """

    def __init__(self, path, data, command_line):
        """
        It copies the content of self.path from the initialization routine of
        the :class:`Data <data.Data>` class, and defines a handful of useful
        methods, that every likelihood might need.

        If the nuisance parameters required to compute this likelihood are not
        defined (either fixed or varying), the code will stop.

        Parameters
        ----------
        data : class
            Initialized instance of :class:`Data <data.Data>`
        command_line : NameSpace
            NameSpace containing the command line arguments

        """

        self.name = self.__class__.__name__
        self.folder = os.path.abspath(os.path.join(
            data.path['MontePython'], 'likelihoods', self.name))
        if not data.log_flag:
            path = os.path.join(command_line.folder, 'log.param')

        # Define some default fields
        self.data_directory = ''

        # Store all the default fields stored, for the method read_file.
        self.default_values = ['data_directory']

        # Recover the values potentially read in the input.param file.
        if hasattr(data, self.name):
            exec("attributes = [e for e in dir(data.%s) if e.find('__') == -1]" % self.name)
            for elem in attributes:
                exec("setattr(self, elem, getattr(data.%s, elem))" % self.name)

        # Read values from the data file
        self.read_from_file(path, data, command_line)

        # Default state
        self.need_update = True

        # Check if the nuisance parameters are defined
        error_flag = False
        try:
            for nuisance in self.use_nuisance:
                if nuisance not in data.get_mcmc_parameters(['nuisance']):
                    error_flag = True
                    warnings.warn(
                        nuisance + " must be defined, either fixed or " +
                        "varying, for %s likelihood" % self.name)
            self.nuisance = self.use_nuisance
        except AttributeError:
            self.use_nuisance = []
            self.nuisance = []

        # If at least one is missing, raise an exception.
        if error_flag:
            raise io_mp.LikelihoodError(
                "Check your nuisance parameter list for your set of"
                "experiments")

        # Append to the log.param the value used (WARNING: so far no comparison
        # is done to ensure that the experiments share the same parameters)
        if data.log_flag:
            io_mp.log_likelihood_parameters(self, command_line)

    def loglkl(self, cosmo, data):
        """
        Placeholder to remind that this function needs to be defined for a
        new likelihood.

        Raises
        ------
        NotImplementedError

        """
        raise NotImplementedError(
            'Must implement method loglkl() in your likelihood')

    def read_from_file(self, path, data, command_line):
        """
        Extract the information from the log.param concerning this likelihood.

        If the log.param is used, check that at least one item for each
        likelihood is recovered. Otherwise, it means the log.param does not
        contain information on the likelihood. This happens when the first run
        fails early, before calling the likelihoods, and the program did not
        log the information. This check might not be completely secure, but it
        is better than nothing.

        .. warning::

            This checks relies on the fact that a likelihood should always have
            at least **one** line of code written in the likelihood.data file.
            This should be always true, but in case a run fails with the error
            message described below, think about it.

        .. warning::

            As of version 2.0.2, you can specify likelihood options in the
            parameter file. They have complete priority over the ones specified
            in the `likelihood.data` file, and it will be reflected in the
            `log.param` file.

        """

        # Counting how many lines are read.
        counter = 0

        self.path = path
        self.dictionary = {}
        if os.path.isfile(path):
            data_file = open(path, 'r')
            for line in data_file:
                if line.find('#') == -1:
                    if line.find(self.name+'.') != -1:
                        # Recover the name and value from the .data file
                        regexp = re.match(
                            "%s\.(.*) = (.*)" % self.name,
                            line)
                        name, value = (elem.strip() for elem in regexp.groups())
                        # If this name was already defined in the parameter
                        # file, be sure to take this value instead. Beware,
                        # there are a few parameters which are always
                        # predefined, such as data_directory, which should be
                        # ignored in this check.
                        is_ignored = False
                        if name not in self.default_values:
                            try:
                                value = getattr(self, name)
                                is_ignored = True
                            except AttributeError:
                                pass
                        if not is_ignored:
                            exec(line.replace(self.name+'.', 'self.'))
                        value = getattr(self, name)
                        counter += 1
                        self.dictionary[name] = value
            data_file.seek(0)
            data_file.close()

        # Checking that at least one line was read, exiting otherwise
        if counter == 0:
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{'dir': 'git-commits-cleaned', 'id': '2623482', 'n_tokens_mistral': 3044, 'n_tokens_neox': 2888, 'n_words': 1176}