# -*- coding: UTF-8 -*-
"""
For the student example the ProbModelXML file should be:

<?xml version=“1.0” encoding=“UTF-8”?>
<ProbModelXML formatVersion=“1.0”>
    <ProbNet type="BayesianNetwork">
        <AdditionalConstraints />
        <Comment>
            Student example model from Probabilistic Graphical Models:
            Principles and Techniques by Daphne Koller
        </Comment>
        <Language>
            English
        </Language>
        <AdditionalProperties />
        <Variables>
            <Variable name="intelligence" type="FiniteState" role="Chance">
                <Comment />
                <Coordinates />
                <AdditionalProperties />
                <States>
                    <State name="smart"><AdditionalProperties /></State>
                    <State name="dumb"><AdditionalProperties /></State>
                </States>
            </Variable>
            <Variable name="difficulty" type="FiniteState" role="Chance">
                <Comment />
                <Coordinates />
                <AdditionalProperties />
                <States>
                    <State name="difficult"><AdditionalProperties /></State>
                    <State name="easy"><AdditionalProperties /></State>
                </States>
            </Variable>
            <Variable name="grade" type="FiniteState" role="Chance">
                <Comment />
                <Coordinates />
                <AdditionalProperties />
                <States>
                    <State name="grade_A"><AdditionalProperties /></State>
                    <State name="grade_B"><AdditionalProperties /></State>
                    <State name="grade_C"><AdditionalProperties /></State>
                </States>
            </Variable>
            <Variable name="recommendation_letter" type="FiniteState"
                    role="Chance">
                <Comment />
                <Coordinates />
                <AdditionalProperties />
                <States>
                    <State name="good"><AdditionalProperties /></State>
                    <State name="bad"><AdditionalProperties /></State>
                </States>
            </Variable>
            <Variable name="SAT" type="FiniteState" role="Chance">
                <Comment />
                <Coordinates />
                <AdditionalProperties />
                <States>
                    <State name="high"><AdditionalProperties /></State>
                    <State name="low"><AdditionalProperties /></State>
                </States>
            </Variable>
        </Variables>
        <Links>
            <Link var1="difficulty" var2="grade" directed=1>
                <Comment>Directed Edge from difficulty to grade</Comment>
                <Label>diff_to_grad</Label>
                <AdditionalProperties />
            </Link>
            <Link var1="intelligence" var2="grade" directed=1>
                <Comment>Directed Edge from intelligence to grade</Comment>
                <Label>intel_to_grad</Label>
                <AdditionalProperties />
            </Link>
            <Link var1="intelligence" var2="SAT" directed=1>
                <Comment>Directed Edge from intelligence to SAT</Comment>
                <Label>intel_to_sat</Label>
                <AdditionalProperties />
            </Link>
            <Link var1="grade" var2="recommendation_letter" directed=1>
                <Comment>Directed Edge from grade to
                    recommendation_letter</Comment>
                <Label>grad_to_reco</Label>
                <AdditionalProperties />
            </Link>
        </Links>
        <Potential type="Table" role="ConditionalProbability" label=string>
            <Comment>CPDs in the form of table</Comment>
            <AdditionalProperties />
            <!--
                There is no specification in the paper about
                how the tables should be represented.
            -->
        </Potential>
    </ProbNet>
    <Policies />
    <InferenceOptions />
    <Evidence>
        <EvidenceCase>
            <Finding variable=string state=string stateIndex=integer
                numericValue=number/>
        </EvidenceCase>
    </Evidence>
</ProbModelXML>
"""

import warnings
try:
    from lxml import etree
except ImportError:
    try:
        import xml.etree.ElementTree as etree
    except ImportError:
        # import xml.etree.cElementTree as etree
        # print("running with cElementTree on Python 2.5+")
        # Commented out because behaviour is different from expected
        
        warnings.warn("Failed to import ElementTree from any known place")

import networkx as nx
import numpy as np
from pgmpy.models import BayesianModel
from pgmpy.factors import TabularCPD
from pgmpy.extern import six
from pgmpy.extern.six.moves import map

# warnings.warn("Not Complete. Please use only for "
#               "reading and writing Bayesian Models.")


def generate_probmodelxml(model, encoding='utf-8', prettyprint=True):
    """
    Generate ProbModelXML lines for model.

    Parameters
    ----------
    model : Graph
        The Bayesian or Markov Model
    encoding : string (optional)
        Encoding for text data
    prettyprint: bool (optional)
        If True uses line breaks and indenting in output XML.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> s = pgmpy.readwrite.generate_ProbModelXML(G) # doctest: +SKIP
    >>> for line in pgmpy.readwrite.generate_ProbModelXML(G):  #doctest: +SKIP
    ...     print(line)
    """
    writer = ProbModelXMLWriter(G, encoding=encoding, prettyprint=prettyprint)
    for line in str(writer).splitlines():
        yield line


# @open_file(1, mode='wb')
def write_probmodelxml(model, path, encoding='utf-8', prettyprint=True):
    """
    Write model in ProbModelXML format to path.

    Parameters
    ----------
    model : A NetworkX graph
            Bayesian network or Markov network
    path : file or string
            File or filename to write.
            Filenames ending in .gz or .bz2 will be compressed.
    encoding : string (optional)
            Encoding for text data.
    prettyprint : bool (optional)
            If True use line breaks and indenting in output XML.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> pgmpy.readwrite.write_probmodelxml(G, "test.probmodelxml")
    """
    writer = ProbModelXMLWriter(model, path, encoding=encoding,
                                prettyprint=prettyprint)
    writer.dump(path)


# @open_file(0, mode='rb')
def read_probmodelxml(path):
    """
    Read model in ProbModelXML format from path.

    Parameters
    ----------
    path : file or string
        file or filename from which to read.

    Returns
    -------
    model : NetworkX Graph
            A BayesianModel or MarkovModel object depending on the
            type of model the XML represents.

    Examples
    --------
    >>> G = pgmpy.readwrite.read_probmodelxml('test.probModelXML')
    """
    reader = ProbModelXMLReader(path=path)
    return reader.make_network()


def parse_probmodelxml(string):
    """
    Read model in ProbModelXML format from string.

    Parameters
    ----------
    string : string
        String containing ProbModelXML information.
        (e.g., contents of a ProbModelXML file).

    Returns
    -------
    model : NetworkX Graph
        A BayesianModel or MarkovModel object depending on the XML given.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> linefeed = chr(10)
    >>> s = linefeed.join(pgmpy.readwrite.generate_probmodelxml(G))
    >>> H = pgmpy.readwrite.parse_probmodelxml(s)
    """
    reader = ProbModelXMLReader(string=string)
    return reader.make_network()


def get_probmodel_data(model):
    """
    Returns the model_data based on the given model.

    Parameters
    ----------
    model: BayesianModel instance
        Model to write

    Return
    ------
    model_data: dict
        dictionary containing model data of the given model.

    Examples
    --------
    >>> model_data = pgmpy.readwrite.get_model_data(model)
    >>> writer.get_model_data(model)
    """
    if not isinstance(model, BayesianModel):
        raise TypeError("Model must an instance of BayesianModel.")
    model_data = {'probnet': {'type': 'BayesianNetwork', 'Variables': {}}}

    variables = model.nodes()
    for var in variables:
        model_data['probnet']['Variables'][var] = model.node[var]

    model_data['probnet']['edges'] = {}
    edges = model.edges()
    for edge in edges:
        model_data['probnet']['edges'][str(edge)] = model.edge[edge[0]][edge[1]]

    model_data['probnet']['Potentials'] = []
    cpds = model.get_cpds()
    for cpd in cpds:
        potential_dict = {}
        potential_dict['Variables'] = {}
        if cpd.evidence is None:
            potential_dict['Variables'][cpd.variable] = []
        else:
            potential_dict['Variables'][cpd.variable] = cpd.evidence
        potential_dict['type'] = "Table"
        potential_dict['role'] = "conditionalProbability"
        potential_dict['Values'] = " ".join([str(val) for val in cpd.values.ravel().astype(float)]) + " "
        model_data['probnet']['Potentials'].append(potential_dict)

    return model_data


class ProbModelXMLWriter(object):
    """
    Class for writing models in ProbModelXML format.
    """
    def __init__(self, model_data, encoding='utf-8', prettyprint=True):
        """
        Initialize a ProbModelXMLWriter Object.

        Parameters
        ----------
        model : A BayesianModel or MarkovModel
            The model to write.
        encoding : string (optional)
            Encoding for text data
        prettyprint : bool (optional)
            If True uses line breaks and indentation in output XML.

        Examples
        --------

        Refernces
        ---------
        [1] http://leo.ugr.es/pgm2012/submissions/pgm2012_submission_43.pdf
        [2] http://www.cisiad.uned.es/techreports/ProbModelXML.pdf
        """
        # TODO: add policies, InferenceOptions, Evidence
        # TODO: add parsing of language and comments and additional properties
        self.data = model_data
        self.encoding = encoding
        self.prettyprint = prettyprint

        # Creating initial tags
        self.xml = etree.Element("ProbModelXML", attrib={'formatVersion': '1.0'})
        self.probnet = etree.SubElement(self.xml, 'ProbNet')
        self.variables = etree.SubElement(self.probnet, 'Variables')
        self.links = etree.SubElement(self.probnet, 'Links')
        self.potentials = etree.SubElement(self.probnet, 'Potentials')
        self.additional_constraints = etree.SubElement(self.probnet, 'AdditionalConstraints')

        # adding information for probnet
        self.probnet.attrib['type'] = self.data['probnet']['type']
        try:
            etree.SubElement(self.probnet, 'Language').text = self.data['probnet']['Language']
        except KeyError:
            pass
        try:
            etree.SubElement(self.probnet, 'Comment').text = self.data['probnet']['Comment']
        except KeyError:
            pass
        try:
            self._add_additional_properties(self.xml, self.data['probnet']['AdditionalProperties'])
        except KeyError:
            etree.SubElement(self.probnet, 'AdditionalProperties')
        try:
            self._add_decision_criteria(self.data['probnet']['DecisionCriteria'])
        except KeyError:
            etree.SubElement(self.probnet, 'DecisionCriteria')

        # Add Additional Constraints
        if 'AdditionalConstraints' in self.data['probnet']:
            for constraint in sorted(self.data['probnet']['AdditionalConstraints']):
                self._add_constraint(constraint)

        # Add variables
        for variable in sorted(self.data['probnet']['Variables']):
            self._add_variable(variable)

        # Add edges
        for edge in sorted(self.data['probnet']['edges']):
            self._add_link(edge)

        # Add Potentials
        for potential in self.data['probnet']['Potentials']:
            self._add_potential(potential, self.potentials)

    def __str__(self):
        """
        Return the XML as string.
        """
        if self.prettyprint:
            self.indent(self.xml)
        return etree.tostring(self.xml, encoding=self.encoding)

    @staticmethod
    def _add_additional_properties(position, properties_dict):
        """
        Sets AdditionalProperties of the ProbModelXML.
        """
        add_prop = etree.SubElement(position, 'AdditionalProperties')
        for key, value in properties_dict.items():
            etree.SubElement(add_prop, 'Property', attrib={'name': key, 'value': value})

    def _add_variable(self, variable):
        """
        Adds a node to the ProbModelXML.
        """
        # TODO: Add feature for accepting additional properties of states.
        variable_data = self.data['probnet']['Variables'][variable]
        variable_element = etree.SubElement(self.variables, 'Variable', attrib={'name': variable,
                                                                                'type': variable_data['type'],
                                                                                'role': variable_data['role']})

        try:
            etree.SubElement(variable_element, 'Comment').text = variable_data['Comment']
        except KeyError:
            pass
        try:
            etree.SubElement(variable_element, 'Coordinates', variable_data['Coordinates'])
        except KeyError:
            pass

        try:
            for key, value in sorted(variable_data['AdditionalProperties'].items()):
                etree.SubElement(variable_element, 'Property', attrib={'name': key, 'value': value})
        except KeyError:
            etree.SubElement(variable_element, 'AdditionalProperties')
        states = etree.SubElement(variable_element, 'States')
        for s in sorted(variable_data['States']):
            state = etree.SubElement(states, 'State', attrib={'name': s})
            try:
                self._add_additional_properties(state, variable_data['States'][s]['AdditionalProperties'])
            except KeyError:
                etree.SubElement(state, 'AdditionalProperties')

    def _add_link(self, edge):
        """
        Adds an edge to the ProbModelXML.
        """
        edge_data = self.data['probnet']['edges'][edge]
        if isinstance(edge, six.string_types):
           edge = eval(edge)
        link = etree.SubElement(self.links, 'Link', attrib={'var1': edge[0], 'var2': edge[1],
                                                            'directed': edge_data['directed']})
        try:
            etree.SubElement(link, 'Comment').text = edge_data['Comment']
        except KeyError:
            pass
        try:
            etree.SubElement(link, 'Label').text = edge_data['Label']
        except KeyError:
            pass
        try:
            self._add_additional_properties(link, edge_data['AdditionalProperties'])
        except KeyError:
            etree.SubElement(link, 'AdditionalProperties')

    def _add_constraint(self, constraint):
        """
        Adds constraint to the ProbModelXML.
        """
        constraint_data = self.data['probnet']['AdditionalConstraints'][constraint]
        constraint_element = etree.SubElement(
            self.additional_constraints, 'Constraint', attrib={'name': constraint})
        for argument in sorted(constraint_data):
            name = argument
            value = constraint_data[name]
            etree.SubElement(constraint_element, 'Argument', attrib={'name': name, 'value': value})

    def _add_decision_criteria(self, criteria_dict):
        """
        Adds Decision Criteria to the ProbModelXML.

        Parameters
        ----------
        criteria_dict: dict
            Dictionary containing Deecision Criteria data.
            For example: {'effectiveness': {}, 'cost': {}}

        Examples
        -------
        >>> writer = ProbModelXMLWriter(model)
        >>> writer._add_decision_criteria(criteria_dict)
        """
        decision_tag = etree.SubElement(self.xml, 'DecisionCriteria', attrib={})
        for criteria in sorted(criteria_dict):
            criteria_tag = etree.SubElement(decision_tag, 'Criterion', attrib={'name': criteria})
            self._add_additional_properties(criteria_tag, criteria_dict[criteria])

    def _add_potential(self, potential, parent_tag):
        """
        Adds Potentials to the ProbModelXML.

        Parameters
        ----------
        potential: dict
            Dictionary containing Potential data.
            For example: {'role': 'Utility',
                          'Variables': ['D0', 'D1', 'C0', 'C1'],
                          'type': 'Tree/ADD',
                          'UtilityVaribale': 'U1'}
        parent_tag: etree Element
            etree element which would contain potential tag
            For example: <Element Potentials at 0x7f315fc44b08>
                         <Element Branch at 0x7f315fc44c88>
                         <Element Branch at 0x7f315fc44d88>
                         <Element Subpotentials at 0x7f315fc44e48>

        Examples
        --------
        >>> writer = ProbModelXMLWriter(model)
        >>> writer._add_potential(potential, parent_tag)
        """
        potential_type = potential['type']
        try:
            potential_tag = etree.SubElement(parent_tag, 'Potential', attrib={
                'type': potential['type'], 'role': potential['role']})
        except KeyError:
            potential_tag = etree.SubElement(parent_tag, 'Potential', attrib={
                'type': potential['type']})
        self._add_element(potential, 'Comment', potential_tag)
        if 'AdditionalProperties' in potential:
            self._add_additional_properties(potential_tag, potential['AdditionalProperties'])
        if potential_type == "delta":
            etree.SubElement(potential_tag, 'Variable', attrib={'name': potential['Variable']})
            self._add_element(potential, 'State', potential_tag)
            self._add_element(potential, 'StateIndex', potential_tag)
            self._add_element(potential, 'NumericValue', potential_tag)
        else:
            if 'UtilityVariable' in potential:
                etree.SubElement(potential_tag, 'UtilityVariable', attrib={
                    'name': potential['UtilityVariable']})
            if 'Variables' in potential:
                variable_tag = etree.SubElement(potential_tag, 'Variables')
                for var in sorted(potential['Variables']):
                    etree.SubElement(variable_tag, 'Variable', attrib={'name': var})
                    for child in sorted(potential['Variables'][var]):
                        etree.SubElement(variable_tag, 'Variable', attrib={'name': child})
            self._add_element(potential, 'Values', potential_tag)
            if 'UncertainValues' in potential:
                value_tag = etree.SubElement(potential_tag, 'UncertainValues', attrib={})
                for value in sorted(potential['UncertainValues']):
                    try:
                        etree.SubElement(value_tag, 'Value', attrib={
                            'distribution': value['distribution'],
                            'name': value['name']}).text = value['value']
                    except KeyError:
                        etree.SubElement(value_tag, 'Value', attrib={
                            'distribution': value['distribution']}).text = value['value']
            if 'TopVariable' in potential:
                etree.SubElement(potential_tag, 'TopVariable', attrib={'name': potential['TopVariable']})
            if 'Branches' in potential:
                branches_tag = etree.SubElement(potential_tag, 'Branches')
                for branch in potential['Branches']:
                    branch_tag = etree.SubElement(branches_tag, 'Branch')
                    if 'States' in branch:
                        states_tag = etree.SubElement(branch_tag, 'States')
                        for state in sorted(branch['States']):
                            etree.SubElement(states_tag, 'State', attrib={'name': state['name']})
                    if 'Potential' in branch:
                        self._add_potential(branch['Potential'], branch_tag)
                    self._add_element(potential, 'Label', potential_tag)
                    self._add_element(potential, 'Reference', potential_tag)
                    if 'Thresholds' in branch:
                        thresholds_tag = etree.SubElement(branch_tag, 'Thresholds')
                        for threshold in branch['Thresholds']:
                            try:
                                etree.SubElement(thresholds_tag, 'Threshold', attrib={
                                    'value': threshold['value'], 'belongsTo': threshold['belongsTo']})
                            except KeyError:
                                etree.SubElement(thresholds_tag, 'Threshold', attrib={
                                    'value': threshold['value']})
            self._add_element(potential, 'Model', potential_tag)
            self._add_element(potential, 'Coefficients', potential_tag)
            self._add_element(potential, 'CovarianceMatrix', potential_tag)
            if 'Subpotentials' in potential:
                subpotentials = etree.SubElement(potential_tag, 'Subpotentials')
                for subpotential in potential['Subpotentials']:
                    self._add_potential(subpotential, subpotentials)
            if 'Potential' in potential:
                self._add_potential(potential['Potential'], potential_tag)
            if 'NumericVariables' in potential:
                numvar_tag = etree.SubElement(potential_tag, 'NumericVariables')
                for var in sorted(potential['NumericVariables']):
                    etree.SubElement(numvar_tag, 'Variable', attrib={'name': var})

    @staticmethod
    def _add_element(potential, var, potential_tag):
        """
        Helper function to add variable tag to the potential_tag

        Parameters
        ----------
        potential: dict
            Dictionary containing Potential data.
            For example: {'role': 'Utility',
                          'Variables': ['D0', 'D1', 'C0', 'C1'],
                          'type': 'Tree/ADD',
                          'UtilityVaribale': 'U1'}
        var: string
            New Element tag which needs to be added to the potential tag.
            For example: 'type'
        potential_tag: etree Element
            etree element which would contain potential tag
            For example: <Element Potentials at 0x7f315fc44b08>
                         <Element Branch at 0x7f315fc44c88>
                         <Element Branch at 0x7f315fc44d88>
                         <Element Subpotentials at 0x7f315fc44e48>

        Examples
        --------
        >>> writer = ProbModelXMLWriter(model)
        >>> writer._add_element(potential, 'State', parent_tag)
        """
        if var in potential:
            etree.SubElement(potential_tag, var).text = potential[var]

    def dump(self, stream):
        """
        Dumps the data to stream after appending header.
        """
        if self.prettyprint:
            self.indent(self.xml)
        document = etree.ElementTree(self.xml)
        header = '<?xml version="1.0" encoding="%s"?>' % self.encoding
        stream.write(header.encode(self.encoding))
        document.write(stream, encoding=self.encoding)

    def indent(self, elem, level=0):
        """
        Inplace prettyprint formatter.
        """
        i = "\n" + level*"  "
        if len(elem):
            if not elem.text or not elem.text.strip():
                elem.text = i + "  "
            if not elem.tail or not elem.tail.strip():
                elem.tail = i
            for elem in elem:
                self.indent(elem, level+1)
            if not elem.tail or not elem.tail.strip():
                elem.tail = i
        else:
            if level and (not elem.tail or not elem.tail.strip()):
                elem.tail = i

    def write_file(self, filename):
        """
        Write the xml data into the file.

        Parameters
        ----------
        filename: Name of the file.

        Examples
        -------
        >>> writer = ProbModelXMLWriter(model)
        >>> writer.write_file(test_file)
        """
        writer = self.__str__()[:-1].decode('utf-8')
        with open(filename, 'w') as fout:
            fout.write(writer)


class ProbModelXMLReader(object):
    """
    Class for reading ProbModelXML format from files or strings.
    """
    # TODO: add methods to parse policies, inferenceoption, evidence etc.
    # TODO: add reading formatVersion
    def __init__(self, path=None, string=None):
        """
        Initialize an instance of ProbModelXMLReader class.

        Parameters
        ----------
        path : file or string
            File containing ProbModelXML information.
        string : string
            String containing ProbModelXML information.

        Example
        -------
        >>> reader = ProbModelXMLReader('test.ProbModelXML')

        Structure of Probnet Object
        ---------------------------
        { probnet: { type:
                     Comment:
                     Language:
                     AdditionalProperties: { property_name1: property_value1,
                                           property_name2: property_value2,
                                                        ....
                                         }
                     Variables: { variable_name1: { type:
                                                    roles:
                                                    Comment:
                                                    Coordinates:
                                                    AdditionalProperties: { property_name1: property_value1,
                                                                            property_name2: property_value2,
                                                                                        ....
                                                                          }
                                                    states: { state1: {AdditionalProperties: {
                                                                                               ....
                                                                                               ....
                                                                                             }
                                                              state2: {AdditionalProperties: {
                                                                                               ....
                                                                                               ....
                                                                                             }
                                                                 .....
                                                            }
                                                  }
                                  variable_name2: {
                                                    ...........
                                                  }
                                      .........
                                 }
                     edges: { (var1, var2): { directed:
                                              Comment:
                                              Label:
                                              AdditionalProperties: { property_name1: property_value1,
                                                                      property_name2: property_value2,
                                                                                 .....
                                                                    }
                              (var3, var4): {
                                              .....
                                              .....
                                            }
                                   ........
                            }
                    }
        }

        References
        ----------
        [1] http://leo.ugr.es/pgm2012/submissions/pgm2012_submission_43.pdf
        [2] http://www.cisiad.uned.es/techreports/ProbModelXML.pdf
        """
        if path is not None:
            self.xml = etree.ElementTree(file=path)
        elif string is not None:
            self.xml = etree.fromstring(string)
        else:
            raise ValueError("Must specify either 'path' or 'string' as kwarg.")

        self.create_probnet()

    def create_probnet(self):
        """
        Returns a BayesianModel or MarkovModel object depending on the
        type of ProbModelXML passed to ProbModelXMLReader class.
        """
        self.probnet = {}
        # Add general properties
        probnet_elem = self.xml.find('ProbNet')
        self.probnet['type'] = probnet_elem.attrib['type']
        if probnet_elem.find('Comment') is not None:
            self.add_comment(probnet_elem.find('Comment').text)
        if probnet_elem.find('Language') is not None:
            self.add_language(probnet_elem.find('Language').text)
        if probnet_elem.find('AdditionalProperties') is not None:
            self.probnet['AdditionalProperties'] = {}
            for prop in probnet_elem.find('AdditionalProperties'):
                self.add_additional_property(self.probnet['AdditionalProperties'], prop)

        # Add additional Constraints
        self.probnet['AdditionalConstraints'] = {}
        for constraint in probnet_elem.findall('AdditionalConstraints/Constraint'):
            self.add_probnet_additionalconstraints(constraint)

        # Add Decision Criterion
        self.probnet['DecisionCriteria'] = {}
        for criterion in probnet_elem.findall('DecisionCriteria/Criterion'):
            self.add_criterion(criterion)

        # Add nodes
        self.probnet['Variables'] = {}
        for variable in probnet_elem.find('Variables'):
            self.add_node(variable)

        # Add edges
        self.probnet['edges'] = {}
        for edge in probnet_elem.findall('Links/Link'):
            self.add_edge(edge)

        # Add CPD
        self.probnet['Potentials'] = []
        for potential in probnet_elem.findall('Potentials/Potential'):
            probnet_dict = {}
            self.add_potential(potential, probnet_dict)
            self.probnet['Potentials'].append(probnet_dict)

    def add_probnet_additionalconstraints(self, constraint):
        """
        Adds Additional Constraints to the probnet dict.

        Parameters
        ----------
        criterion: <Element Constraint at AdditionalConstraints Node in XML>
            etree Element consisting Constraint tag.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_additionalconstraints(constraint)
        """
        constraint_name = constraint.attrib['name']
        self.probnet['AdditionalConstraints'][constraint_name] = {}
        for argument in constraint.findall('Argument'):
            argument_name = argument.attrib['name']
            argument_value = argument.attrib['value']
            self.probnet['AdditionalConstraints'][constraint_name][argument_name] = argument_value

    def add_criterion(self, criterion):
        """
        Adds Decision Criteria to the probnet dict.

        Parameters
        ----------
        criterion: <Element Criterion at Decision Criteria Node in XML>
            etree Element consisting DecisionCritera tag.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_criterion(criterion)
        """
        criterion_name = criterion.attrib['name']
        self.probnet['DecisionCriteria'][criterion_name] = {}
        if criterion.find('AdditionalProperties/Property') is not None:
            for prop in criterion.findall('AdditionalProperties/Property'):
                prop_name = prop.attrib['name']
                prop_value = prop.attrib['value']
                self.probnet['DecisionCriteria'][criterion_name]['AdditionalProperties'][prop_name] = prop_value

    def add_comment(self, comment):
        """
        Adds Comment to the probnet dict.

        Parameters
        ----------
        comment: string
            String consisting of comment.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_comment(comment)
        """
        self.probnet['Comment'] = comment

    def add_language(self, language):
        """
        Adds Language to the probnet dict.

        Parameters
        ----------
        comment: string
            String consisting of language.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_language(language)
        """
        self.probnet['Language'] = language

    @staticmethod
    def add_additional_property(place, prop):
        place[prop.attrib['name']] = prop.attrib['value']

    def add_node(self, variable):
        """
        Adds Variables to the probnet dict.

        Parameters
        ----------
        variable: <Element Variable at Variables Node in XML>
            etree Element consisting Variable tag.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_node(variable)
        """
        # TODO: Do some checks with variable type and roles. Right now I don't know when they are to be used.
        variable_name = variable.attrib['name']
        self.probnet['Variables'][variable_name] = {}
        self.probnet['Variables'][variable_name]['type'] = variable.attrib['type']
        self.probnet['Variables'][variable_name]['role'] = variable.attrib['role']
        if variable.find('Comment') is not None:
            self.probnet['Variables'][variable_name]['Comment'] = variable.find('Comment').text
        if variable.find('Coordinates') is not None:
            self.probnet['Variables'][variable_name]['Coordinates'] = variable.find('Coordinates').attrib
        if variable.find('AdditionalProperties/Property') is not None:
            self.probnet['Variables'][variable_name]['AdditionalProperties'] = {}
            for prop in variable.findall('AdditionalProperties/Property'):
                self.probnet['Variables'][variable_name]['AdditionalProperties'][prop.attrib['name']] = \
                    prop.attrib['value']
        if variable.find('States/State') is None:
            warnings.warn("States not available for node: " + variable_name)
        else:
            self.probnet['Variables'][variable_name]['States'] = {state.attrib['name']: {prop.attrib['name']: prop.attrib['value'] for prop in state.findall('AdditionalProperties/Property')} for state in variable.findall('States/State')}

    def add_edge(self, edge):
        """
        Adds Edges to the probnet dict.

        Parameters
        ----------
        edge: <Element Link at Links Node in XML>
            etree Element consisting Variable tag.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_edge(edge)
        """
        var1 = edge.findall('Variable')[0].attrib['name']
        var2 = edge.findall('Variable')[1].attrib['name']
        self.probnet['edges'][(var1, var2)] = {}
        self.probnet['edges'][(var1, var2)]['directed'] = edge.attrib['directed']
        # TODO: check for the case of undirected graphs if we need to add to both elements of the dic for a single edge.
        if edge.find('Comment') is not None:
            self.probnet['edges'][(var1, var2)]['Comment'] = edge.find('Comment').text
        if edge.find('Label') is not None:
            self.probnet['edges'][(var1, var2)]['Label'] = edge.find('Label').text
        if edge.find('AdditionalProperties/Property') is not None:
            self.probnet['edges'][(var1, var2)]['AdditionalProperties'] = {}
            for prop in edge.findall('AdditionalProperties/Property'):
                self.probnet['edges'][(var1, var2)]['AdditionalProperties'][prop.attrib['name']] = prop.attrib['value']

    def add_potential(self, potential, potential_dict):
        """
        Adds Potential to the potential dict.

        Parameters
        ----------
        potential: <Element Potential at Potentials node in XML>
            etree Element consisting Potential tag.
        potential_dict: dict{}
            Dictionary to parse Potential tag.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.add_potential(potential, potential_dict)
        """
        potential_type = potential.attrib['type']
        potential_dict['type'] = potential_type
        try:
            potential_dict['role'] = potential.attrib['role']
        except KeyError:
            pass
        if potential.find('Comment') is not None:
            potential_dict['Comment'] = potential.find('Comment').text
        for prop in potential.findall('AdditionalProperties/Property'):
            potential_dict['AdditionalProperties'][prop.attrib['name']] = prop.attrib['value']
        if potential_type == "delta":
            potential_dict['Variable'] = potential.find('Variable').attrib['name']
            if potential.find('State') is not None:
                potential_dict['State'] = potential.find('State').text
            if potential.find('StateIndex') is not None:
                potential_dict['StateIndex'] = potential.find('StateIndex').text
            if potential.find('NumericValue') is not None:
                potential_dict['NumericValue'] = potential.find('NumericValue').text
        else:
            if potential.find('UtilityVariable') is not None:
                potential_dict['UtilityVaribale'] = potential.find('UtilityVariable').attrib['name']
            if len(potential.findall('Variables/Variable')):
                potential_dict['Variables'] = {}
                var_list = []
                for var in potential.findall('Variables/Variable'):
                    var_list.append(var.attrib['name'])
                potential_dict['Variables'][var_list[0]] = var_list[1:]
            if potential.find('Values') is not None:
                potential_dict['Values'] = potential.find('Values').text
            if len(potential.findall('UncertainValues/Value')):
                potential_dict['UncertainValues'] = []
                for value in potential.findall('UncertainValues/Value'):
                    try:
                        potential_dict['UncertainValues'].append(
                            {'distribution': value.attrib['distribution'], 'name': value.attrib['name'],
                             'value': value.text})
                    except KeyError:
                        potential_dict['UncertainValues'].append(
                            {'distribution': value.attrib['distribution'], 'value': value.text})
            if potential.find('TopVariable') is not None:
                potential_dict['TopVariable'] = potential.find('TopVariable').attrib['name']

            if len(potential.findall('Branches/Branch')):
                potential_dict['Branches'] = []
                for branch in potential.findall('Branches/Branch'):
                    branch_dict = {}
                    if len(branch.findall('States/State')):
                        states = []
                        for state in branch.findall('States/State'):
                            states.append({'name': state.attrib['name']})
                        branch_dict['States'] = states
                    if branch.find('Potential') is not None:
                        branch_potential = {}
                        self.add_potential(branch.find('Potential'), branch_potential)
                        branch_dict['Potential'] = branch_potential
                    if branch.find('Label') is not None:
                        label = branch.find('Label').text
                        branch_dict['Label'] = label
                    if branch.find('Reference') is not None:
                        reference = branch.find('Reference').text
                        branch_dict['Reference'] = reference
                    if len(branch.findall('Thresholds/Threshold')):
                        thresholds = []
                        for threshold in branch.findall('Thresholds/Threshold'):
                            try:
                                thresholds.append({
                                    'value': threshold.attrib['value'], 'belongsTo': threshold.attrib['belongsTo']})
                            except KeyError:
                                thresholds.append({'value': threshold.attrib['value']})
                        branch_dict['Thresholds'] = thresholds
                    potential_dict['Branches'].append(branch_dict)

            if potential.find('Model') is not None:
                potential_dict['Model'] = potential.find('Model').text
            if len(potential.findall('Subpotentials/Potential')):
                potential_dict['Subpotentials'] = []
                for subpotential in potential.findall('Subpotentials/Potential'):
                    subpotential_dict = {}
                    self.add_potential(subpotential, subpotential_dict)
                    potential_dict['Subpotentials'].append(subpotential_dict)
            if potential.find('Coefficients') is not None:
                potential_dict['Coefficients'] = potential.find('Coefficients').text
            if potential.find('CovarianceMatrix') is not None:
                potential_dict['CovarianceMatrix'] = potential.find('CovarianceMatrix').text
            if potential.find('Potential') is not None:
                potential_dict['Potential'] = {}
                self.add_potential(potential.find('Potential'), potential_dict['Potential'])
            if len(potential.findall('NumericVariables/Variable')):
                potential_dict['NumericVariables'] = []
                for variable in potential.findall('NumericVariables/Variable'):
                    potential_dict['NumericVariables'].append(variable.attrib['name'])

    def get_model(self):
        """
        Returns the model instance of the ProbModel.

        Return
        ---------------
        model: an instance of BayesianModel.

        Examples
        -------
        >>> reader = ProbModelXMLReader()
        >>> reader.get_model()
        """
        if self.probnet.get('type') == "BayesianNetwork":
            model = BayesianModel(self.probnet['edges'].keys())

            tabular_cpds = []
            cpds = self.probnet['Potentials']
            for cpd in cpds:
                var = list(cpd['Variables'].keys())[0]
                states = self.probnet['Variables'][var]['States']
                evidence = cpd['Variables'][var]
                evidence_card = [len(self.probnet['Variables'][evidence_var]['States'])
                                 for evidence_var in evidence]
                arr = list(map(float, cpd['Values'].split()))
                values = np.array(arr)
                values = values.reshape((len(states), values.size//len(states)))
                tabular_cpds.append(TabularCPD(var, len(states), values, evidence, evidence_card))

            model.add_cpds(*tabular_cpds)

            variables = model.nodes()
            for var in variables:
                for prop_name, prop_value in self.probnet['Variables'][var].items():
                    model.node[var][prop_name] = prop_value

            edges = model.edges()
            for edge in edges:
                for prop_name, prop_value in self.probnet['edges'][edge].items():
                    model.edge[edge[0]][edge[1]][prop_name] = prop_value
            return model
        else:
            raise ValueError("Please specify only Bayesian Network.")
