from datetime import timedelta
import numpy as np
import scipy.stats as stats

from util.data_prepare import COL_VALUE, DAY_OF_WEEK_NAME, resample_with_offset
from six.moves import zip

class SubSequence:
    def __init__(self, values, start_time, duration) -> None:
        self.values = values
        self.length = len(values)
        self.start_time = start_time
        self.duration = duration

        self.silhouette = float('nan')
        self.has_anomaly = False

    def __str__(self):
        return (
            f'start_time={self.start_time} {DAY_OF_WEEK_NAME[self.start_time.dayofweek]} duration={self.duration}, ' +  
            f'silhouette={self.silhouette:7.3f} {"has-anomaly" if self.has_anomaly else ""}, ' +
            f'values=[({self.length}) min={np.min(self.values):.3f} max={np.max(self.values):.3f} mean={np.mean(self.values):.3f} med={np.median(self.values):.3f}]')

    def hour(self):
        return self.start_time.hour

    def dayofweek(self):
        return self.start_time.dayofweek

def df_to_hour_sequences(df, hourDelta=1, offset=0):
    assert hourDelta==1 or hourDelta==2 or hourDelta==3 or hourDelta==4,  f'hourDelta={hourDelta} is NOT supported'
    assert offset>=0 and offset<hourDelta, f'Invalid offset({offset}), should be [0, hourDelta({hourDelta})).'
    dfre = resample_with_offset(df, f'{hourDelta}h', offset)
    subsequence_list = [SubSequence(
                np.array(x[1][COL_VALUE]), 
                duration=timedelta(hours=hourDelta),
                start_time=x[0]) for x in dfre]

    return subsequence_list[1:] # ignore beginning / ending partial block

def df_to_day_sequences(df, offset=0):
    dfre = resample_with_offset(df, '24h', offset)
    subsequence_list = [SubSequence(
                np.array(x[1][COL_VALUE]), 
                duration=timedelta(days=1),
                start_time=x[0]) for x in dfre]

    return subsequence_list[1:] # ignore beginning / ending partial day

def df_to_half_day_sequences(df, start_hour):
    dfre = resample_with_offset(df, '12h', start_hour)
    subsequence_list = [SubSequence(
                np.array(x[1][COL_VALUE]), 
                duration=timedelta(hours=12),
                start_time=x[0]) for x in dfre]

    return subsequence_list[1:] # ignore beginning / ending partial day

def pack_sub_values_with_filter(subs, percentile_threshold):
    sub_len = len(subs[0].values)
    if len(subs[-1].values) != sub_len:
        subs = subs[:-1]
        
    if percentile_threshold > 0:
        scores = [sub.silhouette for sub in subs]
        percentiles=[stats.percentileofscore(scores, sub.silhouette) for sub in subs]
        subs = [sub for sub, percent in zip(subs, percentiles) if percent>=percentile_threshold]
    else:
        percentiles = []

    cnt_subs = len(subs)
    sub_values = np.empty((cnt_subs, sub_len))   
    for i, sub in enumerate(subs):
        sub_values[i] = sub.values
    return sub_values, percentiles