import models

import asyncio
import logging
import config

import pandas as pd
from typing import Optional, Generator

from detectors import Detector
from buckets import DataBucket
from models import ModelCache
from utils import convert_pd_timestamp_to_ms


logger = logging.getLogger('PATTERN_DETECTOR')


def resolve_model_by_pattern(pattern: str) -> models.Model:
    if pattern == 'GENERAL':
        return models.GeneralModel()
    if pattern == 'PEAK':
        return models.PeakModel()
    if pattern == 'TROUGH':
        return models.TroughModel()
    if pattern == 'DROP':
        return models.DropModel()
    if pattern == 'JUMP':
        return models.JumpModel()
    if pattern == 'CUSTOM':
        return models.CustomModel()
    raise ValueError('Unknown pattern "%s"' % pattern)

AnalyticUnitId = str
class PatternDetector(Detector):

    MIN_BUCKET_SIZE = 150

    def __init__(self, pattern_type: str, analytic_unit_id: AnalyticUnitId):
        self.analytic_unit_id = analytic_unit_id
        self.pattern_type = pattern_type
        self.model = resolve_model_by_pattern(self.pattern_type)
        self.bucket = DataBucket()

    def train(self, dataframe: pd.DataFrame, segments: list, cache: Optional[models.ModelCache]) -> models.ModelCache:
        # TODO: pass only part of dataframe that has segments
        new_cache = self.model.fit(dataframe, segments, self.analytic_unit_id, cache)
        if new_cache == None or len(new_cache) == 0:
            logging.warning('new_cache is empty with data: {}, segments: {}, cache: {}, analytic unit: {}'.format(dataframe, segments, cache, self.analytic_unit_id))
        return {
            'cache': new_cache
        }

    def detect(self, dataframe: pd.DataFrame, cache: Optional[models.ModelCache]) -> dict:
        logger.debug('Unit {} got {} data points for detection'.format(self.analytic_unit_id, len(dataframe)))
        # TODO: split and sleep (https://github.com/hastic/hastic-server/pull/124#discussion_r214085643)
              
        detected = self.model.detect(dataframe, self.analytic_unit_id, cache)

        segments = [{ 'from': segment[0], 'to': segment[1] } for segment in detected['segments']]
        newCache = detected['cache']

        last_dataframe_time = dataframe.iloc[-1]['timestamp']
        last_detection_time = convert_pd_timestamp_to_ms(last_dataframe_time)
        return {
            'cache': newCache,
            'segments': segments,
            'lastDetectionTime': last_detection_time
        }

    def recieve_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]:
        logging.debug('Start recieve_data for analytic unit {}'.format(self.analytic_unit_id))
        data_without_nan = data.dropna()

        if len(data_without_nan) == 0:
            return None

        self.bucket.receive_data(data_without_nan)
        if cache == None:
            logging.debug('Recieve_data cache is None for task {}'.format(self.analytic_unit_id))
            cache = {}
        bucket_size = max(cache.get('WINDOW_SIZE', 0) * 5, self.MIN_BUCKET_SIZE)

        res = self.detect(self.bucket.data, cache)

        if len(self.bucket.data) > bucket_size:
            excess_data = len(self.bucket.data) - bucket_size
            self.bucket.drop_data(excess_data)
        logging.debug('End recieve_data for analytic unit: {} with res: {}'.format(self.analytic_unit_id, res))
        if res:
            return res
        else:
            return None