#!/usr/bin/env python3

import sys
sys.path.insert(1, '../lib')
import argparse
import gdp
import lmdk_lib
import math
from matplotlib import pyplot as plt
import numpy as np
import os
import time


def main(args):
  # Number of timestamps
  seq = lmdk_lib.get_seq(1, args.time)
  # Distribution type
  dist_type = np.array(range(0, 4))
  # Number of landmarks
  lmdk_n = np.array(range(0, args.time + 1, int(args.time/5)))

  markers = [
    '^', # Symmetric
    'v', # Skewed
    'D', # Bimodal
    's'  # Uniform
  ]

  # Initialize plot
  lmdk_lib.plot_init()
  # The x axis
  x_i = np.arange(len(lmdk_n))
  plt.xticks(x_i, ((lmdk_n/len(seq))*100).astype(int))
  plt.xlabel('Landmarks (%)')  # Set x axis label.
  plt.xlim(x_i.min(), x_i.max())
  # The y axis
  plt.ylabel('Normalized average distance')  # Set y axis label.
  plt.yscale('log')
  plt.ylim(.001, 1)
  # Logging
  print('Average distance', end='', flush=True)
  for d_i, d in enumerate(dist_type):
    avg_dist = np.zeros(len(lmdk_n))
    # Logging
    print('.', end='', flush=True)
    for i, n in enumerate(lmdk_n):
      for r in range(args.iter):
        # Generate landmarks
        lmdks = lmdk_lib.get_lmdks(seq, n, d)
        # Calculate average distance
        avg_cur = 0
        for t in seq:
          t_prv, t_nxt = gdp.get_limits(t, seq, lmdks)
          avg_cur += (abs(t - t_prv) - 1 + abs(t - t_nxt) - 1 )/len(seq)
        # Normalized average based on repetitions
        avg_dist[i] += avg_cur/args.iter
    # Rescaling (min-max normalization)
    # https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization)
    avg_dist = (avg_dist - avg_dist.min())/(avg_dist.max() - avg_dist.min())
    # Normalize for log scale
    if avg_dist[len(avg_dist) - 1] == 0:
      avg_dist[len(avg_dist) - 1] = .001
    # Set label
    label = lmdk_lib.dist_type_to_str(d_i)
    if d_i == 1:
      label = 'Skewed'
    # Plot line
    plt.plot(
      x_i,
      avg_dist,
      label=label,
      marker=markers[d_i],
      markersize=lmdk_lib.marker_size,
      markeredgewidth=0,
      linewidth=lmdk_lib.line_width
    )
  # Plot legend
  lmdk_lib.plot_legend()
  # Show plot
  # plt.show()
  # Save plot
  lmdk_lib.save_plot(str('../../rslt/avg_dist/' + 'avg-dist' + '.pdf'))
  print(' [OK]', flush=True)


'''
  Parse arguments.

  Optional:
    iter - The total iterations.
    time - The time limit of the sequence.
'''
def parse_args():
  # Create argument parser.
  parser = argparse.ArgumentParser()

  # Mandatory arguments.

  # Optional arguments.
  parser.add_argument('-i', '--iter', help='The total iterations.', type=int, default=1)
  parser.add_argument('-t', '--time', help='The time limit of the sequence.', type=int, default=100)

  # Parse arguments.
  args = parser.parse_args()

  return args


if __name__ == '__main__':
  try:
    args = parse_args()
    start_time = time.time()
    main(args)
    end_time = time.time()
    print('##############################')
    print('Time elapsed: %s' % (time.strftime('%H:%M:%S', time.gmtime(end_time - start_time))))
    print('##############################')
  except KeyboardInterrupt:
    print('Interrupted by user.')
    exit()