# Copyright (c) 2017, MD2K Center of Excellence
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

import ast
import json
import uuid
from datetime import datetime
from typing import List

from pytz import timezone

from cerebralcortex.kernel.DataStoreEngine.Metadata.Metadata import Metadata
from cerebralcortex.kernel.DataStoreEngine.dataset import DataSet
from cerebralcortex.kernel.datatypes.datastream import DataStream, DataPoint


class LoadData:
    def get_stream(self, stream_id: uuid, start_time: datetime = None, end_time: datetime = None,
                   data_type=DataSet.COMPLETE) -> DataStream:

        """
        :param stream_id:
        :param start_time:
        :param end_time:
        :param data_type: this parameter accepts only three types (i.e., all, data, metadata)
        :return: spark dataframe
        """
        start_time = str(start_time)
        end_time = str(end_time)

        where_clause = "identifier='" + stream_id + "'"

        if stream_id == 'None':
            raise Exception("Identifier cannot be null.")

        if start_time != 'None':
            where_clause += " and start_time>=cast('" + start_time + "' as timestamp)"

        if end_time != 'None':
            where_clause += " and start_time<=cast('" + end_time + "' as timestamp)"

        if data_type == DataSet.COMPLETE:
            datapoints = self.map_dataframe_to_datapoint(
                self.load_data_from_cassandra(self.datapointTable, where_clause))
            stream = self.map_datapoint_and_metadata_to_datastream(stream_id, datapoints)
        elif data_type == DataSet.ONLY_DATA:
            return self.map_dataframe_to_datapoint(self.load_data_from_cassandra(self.datapointTable, where_clause))
        elif data_type == DataSet.ONLY_METADATA:
            datapoints = []
            stream = self.map_datapoint_and_metadata_to_datastream(stream_id, datapoints)
        else:
            raise ValueError("Invalid type parameter.")

        return stream

    def deprecated_get_annotation_stream(self, annotation_stream_id: uuid, input_stream_id: uuid, annotation: str,
                                         start_time: datetime = None, end_time: datetime = None,
                                         data_type=DataSet.COMPLETE) -> DataStream:
        """
        This method is not optimized. It will send n (depending on the number of windows) number of queries to Cassandra to retrieve data.
        In future, this will be deleted after verifying performance of other filtering method(s).
        :param annotation_stream_id:
        :param input_stream_id:
        :param annotation:
        :param start_time:
        :param end_time:
        :param data_type:
        :return:
        """
        datapointsList = []
        start_time = str(start_time)
        end_time = str(end_time)

        where_clause = "identifier='" + annotation_stream_id + "'"

        if annotation_stream_id == 'None':
            raise Exception("Stream identifier cannot be null.")

        if input_stream_id == 'None':
            raise Exception("Input stream identifier cannot be null.")

        if start_time != 'None':
            where_clause += " and start_time>=cast('" + start_time + "' as timestamp)"

        if end_time != 'None':
            where_clause += " and start_time<=cast('" + end_time + "' as timestamp)"

        if annotation != 'None':
            where_clause += " and sample='{" + annotation + "}'"

        annotation_stream = self.load_data_from_cassandra(self.datapointTable, where_clause)
        rows = annotation_stream.collect()

        for row in rows:
            localtz = timezone(self.CC_obj.time_zone)
            start_time = localtz.localize(row["start_time"])
            end_time = localtz.localize(row["end_time"])

            dp = self.get_stream(input_stream_id, start_time=start_time,
                                 end_time=end_time, data_type=DataSet.ONLY_DATA)
            datapointsList.append(dp)

        if data_type == DataSet.COMPLETE:
            annotation_stream = self.map_datapoint_and_metadata_to_datastream(annotation_stream_id, datapointsList)
        elif data_type == DataSet.ONLY_DATA:
            return datapointsList
        elif data_type == DataSet.ONLY_METADATA:
            datapoints = []
            annotation_stream = self.map_datapoint_and_metadata_to_datastream(annotation_stream_id, datapoints)
        else:
            raise ValueError("Invalid type parameter.")

        return annotation_stream

    def get_annotation_stream(self, input_stream_id: uuid, annotation_stream_id: uuid, annotation: str,
                              start_time: datetime = None, end_time: datetime = None) -> List[DataPoint]:
        """

        :param input_stream_id:
        :param annotation_stream_id:
        :param annotation:
        :param start_time:
        :param end_time:
        :return:
        """
        datapoints_list = []

        annotation_stream_dps = self.get_stream(annotation_stream_id, start_time=start_time,
                                                end_time=end_time, data_type=DataSet.ONLY_DATA)

        data_stream_dps = self.get_stream(input_stream_id, start_time=start_time,
                                          end_time=end_time, data_type=DataSet.ONLY_DATA)

        for dp in self.map_annotation_stream_to_data_stream(annotation_stream_dps, data_stream_dps, annotation):
            datapoints_list = datapoints_list + dp

        return datapoints_list

    @classmethod
    def map_annotation_stream_to_data_stream(self, annotation_stream_dps: List[DataPoint],
                                             data_stream_dps: List[DataPoint], annotation: str) -> List[DataPoint]:
        """
        Map annotation stream to data stream.
        :param annotation_stream_dps:
        :param data_stream_dps:
        :param annotation:
        :rtype: List[DataPoint]
        """
        filtered_datapoints = []
        tmp = 0
        for annotation_dp in annotation_stream_dps:
            if annotation_dp.sample == annotation:
                for index, datastream_dp in enumerate(data_stream_dps[tmp:], start=0):
                    if datastream_dp.start_time >= annotation_dp.start_time:
                        if (annotation_dp.start_time <= datastream_dp.start_time) and (
                                    annotation_dp.end_time >= datastream_dp.end_time):
                            filtered_datapoints.append(datastream_dp)
                            if (tmp + index + 1) == len(data_stream_dps):
                                tmp = index + tmp + 1
                                yield filtered_datapoints
                                filtered_datapoints = []
                        else:
                            tmp = tmp + index
                            yield filtered_datapoints
                            filtered_datapoints = []
                            break
        yield filtered_datapoints

    def map_dataframe_to_datapoint(self, dataframe: object) -> list:
        """
        Converts a PySpark DataFrame into a list of datapoint objects
        :param dataframe:
        :return: list of datapoint objects
        """
        datapointsList = []
        rows = dataframe.collect()

        for row in rows:
            localtz = timezone(self.CC_obj.time_zone)
            start_time = localtz.localize(row["start_time"])
            if row["end_time"] != None:
                end_time = localtz.localize(row["end_time"])
            else:
                end_time = ""

            # ast.literal_eval is used to convert strings into json, list, dict. it returns string if ast.literal_eval fails
            try:
                smple = ast.literal_eval(row["sample"])
            except:
                smple = row["sample"]

            dp = DataPoint(start_time, end_time, smple)
            datapointsList.append(dp)
        return datapointsList

    def map_datapoint_and_metadata_to_datastream(self, stream_id: int, data: list) -> DataStream:
        """
        This method will map the datapoint and metadata to datastream object
        :param stream_id:
        :param data: list
        :return: datastream object
        """