from isatools.model import OntologyAnnotation, OntologySource, FactorValue, Characteristic
from isatools.create.model import StudyDesign, NonTreatment, Treatment, StudyCell, StudyArm, SampleAndAssayPlan
from isatools.create.constants import (
    SCREEN,
    INTERVENTIONS,
    BASE_FACTORS,
    SAMPLE,
    ORGANISM_PART,
    DEFAULT_SOURCE_TYPE,
    DATA_FILE,
    DEFAULT_EXTENSION
)
from collections import OrderedDict

AGENT = 'agent'

EVENT_TYPE_SAMPLING = 'sampling'
EVENT_TYPE_ASSAY = 'assay'


def _map_ontology_annotation(annotation, expand_strings=False):
    """
    converts an input annotation into an OntologyAnnotation. If the input is a string it is kept as it is and not
    cast as an OntologyAnnotation
    :param annotation: str/dict
    :return: str/OntologyAnnotation
    """
    if isinstance(annotation, dict):
        res = OntologyAnnotation(
            term=annotation['term']
        )
        if annotation.get('iri', None):
            res.term_accession = annotation['iri']
        source = annotation.get('source', None)
        if source:
            if isinstance(source, str):
                res.term_source = OntologySource(name=source)
            elif isinstance(source, dict):
                res.term_source = OntologySource(**source)
        return res
    else:
        return OntologyAnnotation(term=annotation) if expand_strings else annotation


def _reverse_map_ontology_annotation(onto_annotation, compress_strings=False):
    """
    converts an OntologyAnnotation into its serializable form (i.e. a dict with string keys)
    no comments or ids are serialized by this special serializer
    if the input is a string the string itself is returned.
    :param onto_annotation: str or OntologyAnnotation
    :param compress_strings Bool - if true compress to strings onto_annotation objects missing a term_accession
    :return: dict or string - to be serialized as JSON
    """
    if isinstance(onto_annotation, OntologyAnnotation):
        if not onto_annotation.term_accession and compress_strings:
            return onto_annotation.term
        res = dict(term=onto_annotation.term, iri=onto_annotation.term_accession or None)
        if onto_annotation.term_source:
            if isinstance(onto_annotation.term_source, str):
                res['source'] = onto_annotation.term_source
            elif isinstance(onto_annotation.term_source, OntologySource):
                res['source'] = dict(
                    name=onto_annotation.term_source.name,
                    file=onto_annotation.term_source.file,
                    version=onto_annotation.term_source.version,
                    description=onto_annotation.term_source.description
                )
        else:
            res['source'] = None
        return res
    else:
        return onto_annotation


def assay_template_to_ordered_dict(assay_template):
    """
    deserializes a JSON plain dictionary into an OrderedDictionary to be consumed by
        isatools.create.models.AssayGraph.generate_assay_plan_from_dict() to generate a full AssayGraph
    :param assay_template: dict
    :return: OrderedDict.
    """
    res = OrderedDict()
    res['measurement_type'] = _map_ontology_annotation(assay_template['measurement_type'], expand_strings=True)
    res['technology_type'] = _map_ontology_annotation(assay_template['technology_type'], expand_strings=True)
    for name, nodes in assay_template['workflow']:
        prepared_nodes = None
        if isinstance(nodes, list):
            # "nodes" represent a list of ProductNodes
            prepared_nodes = [
                {
                    key: _map_ontology_annotation(
                        value, expand_strings=True if key in ['characteristics_category'] else False
                    ) for key, value in el.items()
                } for el in nodes
            ]
        if isinstance(nodes, dict):
            # "nodes" represent a ProtocolNode
            prepared_nodes = {}
            for candidate_param_name, param_values in nodes.items():
                # if it is a special key (e.g."#replicates") leave it alone
                if candidate_param_name[0] == '#' and not isinstance(param_values, list):
                    prepared_nodes[candidate_param_name] = param_values
                else:
                    # this is really a parameter name
                    param_name = _map_ontology_annotation(candidate_param_name, expand_strings=True)
                    prepared_nodes[param_name] = [
                        _map_ontology_annotation(param_value) for param_value in param_values
                    ]
        res[_map_ontology_annotation(name)] = prepared_nodes
    return res


def assay_ordered_dict_to_template(assay_ord_dict):
    """
    Serializes an OrderedDict compatible with isatools.create.models.AssayGraph.generate_assay_plan_from_dict() into a
        JSON representation that can be consumed by external applications (e.g. Datascriptor and other client
        applications)
    :param assay_ord_dict: OrderedDictionary
    :return: dict, can be directly serialized to JSON
    """
    res = dict()
    res['measurement_type'] = _reverse_map_ontology_annotation(assay_ord_dict.get('measurement_type', None),
                                                               compress_strings=True)
    res['technology_type'] = _reverse_map_ontology_annotation(assay_ord_dict.get('technology_type', None),
                                                              compress_strings=True)
    res['workflow'] = []
    for name, nodes in assay_ord_dict.items():
        if name in {'measurement_type', 'technology_type'}:
            continue
        if isinstance(nodes, dict):
            # "nodes" represent a ProtocolNode
            serialized_nodes = {}
            for node_prop, prop_values in nodes.items():
                if isinstance(prop_values, list):
                    serialized_nodes[_reverse_map_ontology_annotation(node_prop, compress_strings=True)] = [
                        _reverse_map_ontology_annotation(prop_value) for prop_value in prop_values
                    ]
                else:
                    serialized_nodes[node_prop] = prop_values
        elif isinstance(nodes, list):
            # "nodes" represent a list of ProductNodes
            serialized_nodes = [
                {
                    key: _reverse_map_ontology_annotation(
                        val, compress_strings=True if key in ['characteristics_category'] else False
                    ) for key, val in node.items()
                } for node in nodes
            ]
        else:
            serialized_nodes = {}
        res['workflow'].append([
            _reverse_map_ontology_annotation(name),
            serialized_nodes
        ])
    return res


def _generate_element(datascriptor_element_dict):
    """
    Generates elements (Treatment and NonTreatment) from their description as Datascriptor dicts
    :param datascriptor_element_dict: dict
    :return: isatools.create.models.Element
    """
    if 'agent' in datascriptor_element_dict:
        agent = FactorValue(
            factor_name=BASE_FACTORS[0],
            value=_map_ontology_annotation(datascriptor_element_dict['agent']),
            unit=_map_ontology_annotation(datascriptor_element_dict.get('agentUnit', None))
        )
        intensity = FactorValue(
            factor_name=BASE_FACTORS[1],
            value=_map_ontology_annotation(datascriptor_element_dict.get('intensity', None)),
            unit=_map_ontology_annotation(datascriptor_element_dict.get('intensityUnit', None))
        )
        duration = FactorValue(
            factor_name=BASE_FACTORS[2],
            value=datascriptor_element_dict.get('duration', 0),
            unit=_map_ontology_annotation(datascriptor_element_dict.get('durationUnit', 's'))
        )
        intervention_type = datascriptor_element_dict.get('interventionType', '')
        element = Treatment(
            element_type=_map_ontology_annotation(intervention_type) if intervention_type
            else INTERVENTIONS['UNSPECIFIED'],
            factor_values=[agent, intensity, duration]
        )
    else:
        element = NonTreatment(
            element_type=datascriptor_element_dict.get('name', SCREEN),
            duration_value=datascriptor_element_dict['duration'],
            duration_unit=_map_ontology_annotation(datascriptor_element_dict['durationUnit'])
        )
    return element


def _generate_sample_dict_from_config(datascriptor_sample_type_config, arm_name, epoch_no):
    return dict(
        node_type=SAMPLE,
        characteristics_category=_map_ontology_annotation(
            datascriptor_sample_type_config.get('characteristicCategory', ORGANISM_PART),
            expand_strings=True
        ),
        characteristics_value=_map_ontology_annotation(datascriptor_sample_type_config['sampleType']),
        size=datascriptor_sample_type_config['sampleTypeSizes'][arm_name][epoch_no],
        is_input_to_next_protocols=datascriptor_sample_type_config.get('isAssayInput', True)
    )


def _generate_characteristics_from_observational_factor(observational_factor_dict):
    category = _map_ontology_annotation(observational_factor_dict['name'], expand_strings=True)
    value = _map_ontology_annotation(
        observational_factor_dict['value'], expand_strings=True
    ) if observational_factor_dict['isQuantitative'] is False else observational_factor_dict['value']
    unit = _map_ontology_annotation(
        observational_factor_dict['unit'], expand_strings=True
    ) if observational_factor_dict['isQuantitative'] is True else None
    return Characteristic(category=category, value=value, unit=unit)


def generate_assay_ord_dict_from_config(datascriptor_assay_config, arm_name, epoch_no):
    res = OrderedDict()
    res['id'], res['name'] = datascriptor_assay_config['id'], datascriptor_assay_config['name']
    res['measurement_type'] = _map_ontology_annotation(
        datascriptor_assay_config['measurement_type'], expand_strings=True
    )
    res['technology_type'] = _map_ontology_annotation(
        datascriptor_assay_config['technology_type'], expand_strings=True
    )
    res['selected_sample_types'] = list(map(
        _map_ontology_annotation,
        datascriptor_assay_config['selectedSampleTypes'][arm_name][epoch_no]
    ))
    for name, node in datascriptor_assay_config['workflow']:
        prepared_nodes = None
        assert isinstance(node, dict)
        if '#replicates' in node:
            # this is a ProtocolNode
            prepared_nodes = {}
            for candidate_param_name, param in node.items():
                # if it is a special key (e.g."#replicates") leave it alone
                if candidate_param_name[0] == '#':
                    prepared_nodes[candidate_param_name] = param['value']
                else:
                    if not param["values"]:
                        raise ValueError('Missing values for Protocol Param {}'.format(
                            candidate_param_name['term'] if isinstance(
                                candidate_param_name, dict
                            ) else candidate_param_name
                        ))
                    # this is really a parameter name
                    param_name = _map_ontology_annotation(candidate_param_name, expand_strings=True)
                    prepared_nodes[param_name] = [
                        _map_ontology_annotation(param_value) for param_value in param["values"]
                    ]
        elif 'node_type' in node:
            # this is a product node
            extension = node['extension']['value'] if 'extension' in node else DEFAULT_EXTENSION \
                if node['node_type'] == DATA_FILE else None
            if "characteristics_value" in node:
                if not node["characteristics_value"]['values']:
                    raise ValueError('Missing values for Characteristic {}'.format(
                        node['characteristics_category']['term'] if isinstance(
                            node['characteristics_category'], dict
                        ) else node['characteristics_category']
                    ))
                prepared_nodes = [
                    dict(
                        node_type=node['node_type'],
                        characteristics_category=_map_ontology_annotation(node['characteristics_category'],
                                                                          expand_strings=True),
                        characteristics_value=_map_ontology_annotation(value),
                        size=node.get('size', 1),
                        is_input_to_next_protocols=node['is_input_to_next_protocols']['value'],
                        extension=extension
                    ) for value in node["characteristics_value"]["values"]
                ]
            else:
                prepared_nodes = [dict(
                    node_type=node['node_type'],
                    size=node.get('size', 1),
                    is_input_to_next_protocols=node['is_input_to_next_protocols']['value'],
                    extension=extension
                )]

        res[_map_ontology_annotation(name)] = prepared_nodes
    return res


def generate_study_design(datascriptor_study_config):
    """
    This function takes a study design configuration as produced from the Datascriptor application
    and outputs a StudyDesign object
    :param datascriptor_study_config: dict
    :return: isatools.create.StudyDesign
    """
    study_design_config = datascriptor_study_config['design']
    arms = []
    for arm_ix, arm_dict in enumerate(study_design_config['arms']['selected']):
        arm_map = OrderedDict()
        for epoch_ix, epoch_dict in enumerate(arm_dict['epochs']):
            element_ids = epoch_dict.get('elements', [])
            elements = [
                _generate_element(element_dict) for element_dict in
                filter(
                    lambda el: el['id'] in element_ids,
                    study_design_config['elements']
                )
            ]
            cell_name = 'A{}E{}'.format(arm_ix, epoch_ix)
            cell = StudyCell(name=cell_name, elements=elements)
            sample_type_dicts = [
                _generate_sample_dict_from_config(
                    ds_sample_config, arm_dict['name'], epoch_ix
                ) for ds_sample_config in study_design_config['samplePlan']
                if ds_sample_config['selectedCells'][arm_dict['name']][epoch_ix] and
                ds_sample_config['sampleTypeSizes'][arm_dict['name']][epoch_ix]
            ]
            assay_ord_dicts = [
                generate_assay_ord_dict_from_config(
                    ds_assay_config, arm_dict['name'], epoch_ix
                ) for ds_assay_config in study_design_config['assayPlan']
                if ds_assay_config['selectedCells'][arm_dict['name']][epoch_ix] is True
            ]
            sa_plan_name = 'SAP_A{}E{}'.format(arm_ix, epoch_ix)
            # TODO this method will probably need some rework to bind a sample type to a specific assay plan
            sa_plan = SampleAndAssayPlan.from_sample_and_assay_plan_dict(
                sa_plan_name, sample_type_dicts, *assay_ord_dicts
            )
            arm_map[cell] = sa_plan
        source_type = Characteristic(
            category=DEFAULT_SOURCE_TYPE.category,
            value=_map_ontology_annotation(
                arm_dict.get('subjectType', None) or study_design_config.get('subjectType', None)
            )
        )
        arm = StudyArm(
            name=arm_dict['name'],
            # should we generate a Characteristic if subjectType is an OntologyAnnotation?
            source_type=source_type,
            source_characteristics=[
                _generate_characteristics_from_observational_factor(
                    obs_factor_dict
                ) for obs_factor_dict in arm_dict.get('observationalFactors', [])
            ],
            group_size=arm_dict.get('size', 10),
            arm_map=arm_map
        )
        arms.append(arm)
    return StudyDesign(
        identifier=datascriptor_study_config.get('_id', None),
        name=datascriptor_study_config['name'],
        description=datascriptor_study_config.get('description', None),
        design_type=_map_ontology_annotation(study_design_config['designType']),
        study_arms=arms
    )
