import logging as log

import operator
import pandas as pd
import numpy as np
from typing import Optional, List

from analytic_types import ModelCache, AnalyticUnitId
from analytic_types.detector import DetectionResult, ProcessingResult
from analytic_types.segment import Segment
from detectors import ProcessingDetector
from time import time
import utils


logger = log.getLogger('THRESHOLD_DETECTOR')


class ThresholdDetector(ProcessingDetector):

    WINDOW_SIZE = 3

    def __init__(self, analytic_unit_id: AnalyticUnitId):
        super().__init__(analytic_unit_id)

    def train(self, dataframe: pd.DataFrame, threshold: dict, cache: Optional[ModelCache]) -> ModelCache:
        time_step = utils.find_interval(dataframe)
        return {
            'cache': {
                'value': threshold['value'],
                'condition': threshold['condition'],
                'timeStep': time_step
            }
        }

    def detect(self, dataframe: pd.DataFrame, cache: ModelCache) -> DetectionResult:
        if cache is None or cache == {}:
            raise ValueError('Threshold detector error: cannot detect before learning')
        if len(dataframe) == 0:
            return None

        value = cache['value']
        condition = cache['condition']

        segments = []
        for index, row in dataframe.iterrows():
            current_value = row['value']
            current_timestamp = utils.convert_pd_timestamp_to_ms(row['timestamp'])
            segment = Segment(current_timestamp, current_timestamp)
            # TODO: merge segments
            if pd.isnull(current_value):
                if condition == 'NO_DATA':
                    segment.message = 'NO_DATA detected'
                    segments.append(segment)
                continue

            comparators = {
                '>': operator.gt,
                '<': operator.lt,
                '=': operator.eq,
                '>=': operator.ge,
                '<=': operator.le
            }

            assert condition in comparators.keys(), f'condition {condition} not allowed'

            if comparators[condition](current_value, value):
                segment.message = f"{current_value} {condition} threshold's value {value}"
                segments.append(segment)

        last_entry = dataframe.iloc[-1]
        last_detection_time = utils.convert_pd_timestamp_to_ms(last_entry['timestamp'])
        return DetectionResult(cache, segments, last_detection_time)


    def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[DetectionResult]:
        result = self.detect(data, cache)
        return result if result else None

    def get_window_size(self, cache: Optional[ModelCache]) -> int:
        return self.WINDOW_SIZE

    def concat_detection_results(self, detections: List[DetectionResult]) -> DetectionResult:
        result = DetectionResult()
        time_step = detections[0].cache['timeStep']
        for detection in detections:
            result.segments.extend(detection.segments)
            result.last_detection_time = detection.last_detection_time
            result.cache = detection.cache
        result.segments = utils.merge_intersecting_segments(result.segments, time_step)
        return result

    def process_data(self, dataframe: pd.DataFrame, cache: ModelCache) -> ProcessingResult:
        data = dataframe['value']
        value = self.get_value_from_cache(cache, 'value', required = True)
        condition = self.get_value_from_cache(cache, 'condition', required = True)

        if condition == 'NO_DATA':
            return ProcessingResult()

        data.values[:] = value
        timestamps = utils.convert_series_to_timestamp_list(dataframe.timestamp)
        result_series = list(zip(timestamps, data.values.tolist()))

        if condition in ['>', '>=', '=']:
            return ProcessingResult(upper_bound = result_series)

        if condition in ['<', '<=']:
            return ProcessingResult(lower_bound = result_series)

        raise ValueError(f'{condition} condition not supported')