#!/usr/bin/env python3

import sys
sys.path.insert(1, 'lib')
import argparse
import ast
import csv
from datetime import datetime
from geopy.distance import distance
import io
import itertools
import lmdk_lib
import numpy as np
import os
import matplotlib.pyplot as plt
import time
import zipfile


# https://cloud.delkappa.com/s/oNWLmM6jrpjK8Ff
# HUE data format
# Header size
hdr = 1
# Date
dt = 0
# Hour
hr = 1
# Energy consumption in kWh
kwh = 2
# Timestamp format
tim_fmt = "%Y-%m-%d %H"


def main(args):
  '''
    Load data
  '''
  # Get consumption data [timestamp, consumption] from previous parsing
  cons_data = lmdk_lib.load_data(args, 'cons')
  if cons_data.size == 0:
    # Consumption [dt, hr, kwh]
    cons = []
    try:
      print('Extracting %s... ' %(os.path.abspath(args.arc)), end='', flush=True)
      with zipfile.ZipFile(args.arc, 'r') as arc:
        print('[OK]')
        with io.TextIOWrapper(arc.open(args.dat), newline='\n') as dat:
          try:
            print('Finding consumption... ', end='', flush=True)
            # Get the consumption by skipping the header
            cons_l = list(csv.reader(dat, delimiter=','))[hdr:]
            # Check each line
            for c in cons_l:
              # Add valid consumption
              if c[kwh] != '' and float(c[kwh]) > 0:
                cons.append([datetime.strptime(c[dt] + ' ' + c[hr], tim_fmt).timestamp(), c[kwh]])
            print('[OK]')
          except Exception as e:
            print('[Error: %s]' %(e))
    except Exception as e:
      print('[Error: %s]' %(e))
    # Save to results
    lmdk_lib.save_data(args, np.array(cons, np.float32), 'cons')
  '''
    Find thresholds for goals.
    0.2: 0.30
    0.4: 0.23
    0.6: 0.15
    0.8: 0.13
  '''
  # {goal: theta}
  lmdk = {}
  # Percentage of landmarks
  goal = [.2, .4, .6, .8]
  # Find max consumpton to start from
  theta = max(cons_data[:, 1])
  cons_data_cur = np.copy(cons_data)
  # Find thetas for each goal
  for g in goal:
    print('Looking for %.1f... ' %(g), end='', flush=True)
    while theta > 0:
      # Reduce threshold gradually
      theta -= .01
      # Find data below the current theta
      cons_data_cur = cons_data_cur[cons_data_cur[:, 1] < theta]
      # Calculate the percentage of landmarks
      diff = (len(cons_data) - len(cons_data_cur))/len(cons_data)
      # Check if it is close enough to what we need
      if abs(diff - g)/g < .05:
        print('%.2f' %(theta))
        lmdk[g] = theta
        # Continue with the next goal
        break



'''
  Parse arguments.

  Optional:
    arc - The data archive file.
    dat - The consumption data file.
    res - The results archive file.
'''
def parse_args():
  # Create argument parser.
  parser = argparse.ArgumentParser()

  # Mandatory arguments.

  # Optional arguments.
  parser.add_argument('-a', '--arc', help='The data archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Data.zip')
  parser.add_argument('-d', '--dat', help='The consumption data file.', type=str, default='Residential_26.csv')
  parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Results.zip')

  # Parse arguments.
  args = parser.parse_args()

  return args


if __name__ == '__main__':
  try:
    start_time = time.time()
    main(parse_args())
    end_time = time.time()
    print('##############################')
    print('Time   : %.4fs' % (end_time - start_time))
    print('##############################')
  except KeyboardInterrupt:
    print('Interrupted by user.')
    exit()