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
    BUCKET_WINDOW_SIZE_FACTOR = 5
    DEFAULT_WINDOW_SIZE = 1

    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 consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]:
        logging.debug('Start consume_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('consume_data cache is None for task {}'.format(self.analytic_unit_id))
            cache = {}
        bucket_size = max(cache.get('WINDOW_SIZE', 0) * self.BUCKET_WINDOW_SIZE_FACTOR, 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 consume_data for analytic unit: {} with res: {}'.format(self.analytic_unit_id, res))
        if res:
            return res
        else:
            return None

    def get_window_size(self, cache: Optional[ModelCache]) -> int:
        if cache is None: return self.DEFAULT_WINDOW_SIZE
        return cache.get('WINDOW_SIZE', self.DEFAULT_WINDOW_SIZE)