bgt_cmp: Ready to test
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code/expt/bgt_cmp.py
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250
code/expt/bgt_cmp.py
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#!/usr/bin/env python3
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import sys
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sys.path.insert(1, 'code/lib')
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import argparse
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from datetime import datetime
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from geopy.distance import distance
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import lmdk_bgt
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import lmdk_lib
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import numpy as np
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from matplotlib import pyplot as plt
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import time
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def main(args):
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# The data files
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data_files = {
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'T-drive': '/home/manos/Cloud/Data/T-drive/Results.zip',
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'Geolife': '/home/manos/Cloud/Data/Geolife/Results.zip'
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}
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# Data related info
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data_info = {
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'T-drive': {
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'uid': 2,
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'lmdks': {
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0: {'dist': 0, 'per': 1000}, # 0.0%
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20: {'dist': 2095, 'per': 30}, # 19.6%
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40: {'dist': 2790, 'per': 30}, # 40.2%
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60: {'dist': 3590, 'per': 30}, # 59.9%
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80: {'dist': 4825, 'per': 30}, # 79.4%
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100: {'dist': 10350, 'per': 30} # 100.0%
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}
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},
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'Geolife': {
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'uid': 97,
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'lmdks': {
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0: {'dist': 0, 'per': 100000}, # 0.0%
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20: {'dist': 205, 'per': 30}, # 19.8%
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40: {'dist': 450, 'per': 30}, # 41.7%
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60: {'dist': 725, 'per': 30}, # 59.2%
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80: {'dist': 855, 'per': 30}, # 82.1%
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100: {'dist': 50000, 'per': 30} # 100.0%
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}
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}
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}
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# The data sets
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data_sets = {}
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# Load data sets
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for df in data_files:
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args.res = data_files[df]
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data_sets[df] = lmdk_lib.load_data(args, 'usrs_data')
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# Geo-I configuration
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# epsilon = level/radius
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# Radius is in meters
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bgt_conf = [
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{'epsilon': 1},
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# {'label': 'ln(2)/200', 'epsilon': 0.0035, 'level': 0.69314718056, 'radius': 200},
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# {'label': 'ln(4)/200', 'epsilon': 0.0069, 'level': 1.38629436112, 'radius': 200},
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# {'label': 'ln(6)/200', 'epsilon': 0.0090, 'level': 1.79175946923, 'radius': 200}
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]
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# Number of methods
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n = 6
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# Width of bars
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bar_width = 1/(n + 1)
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# The x axis
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x_i = np.arange(len(list(data_info.values())[0]['lmdks']))
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x_margin = bar_width*(n/2 + 1)
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for d in data_sets:
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# d = 'T-drive'
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# d = 'Geolife'
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print('\n##############################', d, '\n')
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args.res = data_files[d]
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data = data_sets[d]
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# Truncate trajectory according to arguments
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seq = data[data[:,0]==data_info[d]['uid'], :][:args.time]
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# Initialize plot
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lmdk_lib.plot_init()
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# The x axis
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plt.xticks(x_i, np.array([key for key in data_info[d]['lmdks']]).astype(int))
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plt.xlabel('Landmarks percentage') # Set x axis label.
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plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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# The y axis
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plt.ylabel('Mean absolute error (m)') # Set y axis label.
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plt.yscale('log')
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plt.ylim(1, 100000)
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# Bar offset
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x_offset = -(bar_width/2)*(n - 1)
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mae_u = np.zeros(len(data_info[d]['lmdks']))
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mae_s = np.zeros(len(data_info[d]['lmdks']))
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mae_a = np.zeros(len(data_info[d]['lmdks']))
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mae_r = np.zeros(len(data_info[d]['lmdks']))
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mae_d = np.zeros(len(data_info[d]['lmdks']))
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mae_i = np.zeros(len(data_info[d]['lmdks']))
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for i, lmdk in enumerate(data_info[d]['lmdks']):
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# Find landmarks
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args.dist = data_info[d]['lmdks'][lmdk]['dist']
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args.per = data_info[d]['lmdks'][lmdk]['per']
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lmdks = lmdk_lib.find_lmdks(seq, args)[:args.time]
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# Print stats
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lmdk_lib.lmdks_stats(args, lmdks)
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# # Find long enough sequences
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# usrs = np.unique(data[:,0])
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# for usr_i, usr in enumerate(usrs):
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# traj = data[data[:,0]==usr, :]
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# if(len(traj)) >= 1000 and len(traj) < 2000:
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# print(usr, len(traj))
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for bgt in bgt_conf:
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for _ in range(args.iter):
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# Skip
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rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, bgt['epsilon'])
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mae_s[i] += lmdk_bgt.mae(seq, rls_data_s)/args.iter
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# Uniform
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rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, bgt['epsilon'])
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mae_u[i] += lmdk_bgt.mae(seq, rls_data_u)/args.iter
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# Adaptive
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rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, bgt['epsilon'], .5, .5)
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mae_a[i] += lmdk_bgt.mae(seq, rls_data_a)/args.iter
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# Sample
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rls_data_r, _, _ = lmdk_bgt.sample(seq, lmdks, bgt['epsilon'])
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mae_r[i] += lmdk_bgt.mae(seq, rls_data_r)/args.iter
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# Discount
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rls_data_d, _, _ = lmdk_bgt.discount(seq, lmdks, bgt['epsilon'])
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mae_d[i] += lmdk_bgt.mae(seq, rls_data_d)/args.iter
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# Incremental
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rls_data_i, _, _ = lmdk_bgt.incremental(seq, lmdks, bgt['epsilon'])
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mae_i[i] += lmdk_bgt.mae(seq, rls_data_i)/args.iter
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# print(
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# '\nEpsilon : %f\n'
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# 'Sampled : %d%% (%d/%d)\n'
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# 'Landmarks: %d%% (%d/%d)\n'
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# %(bgt['epsilon'], 100*(len(seq) - skipped)/len(seq), len(seq) - skipped, len(seq), 100*len(lmdks)/len(seq), len(lmdks), len(seq))
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# )
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# s, l = lmdk_lib.simplify_data(seq, lmdks)
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# # Validate the process
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# lmdk_bgt.validate_bgts(s, l, bgt['epsilon'], bgts)
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# # Analysis
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# lmdk_bgt.utility_analysis(seq, lmdks, rls_data, bgt['epsilon'])
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plt.bar(
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x_i + x_offset,
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mae_s,
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bar_width,
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label='Skip',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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# Plot bars
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plt.bar(
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x_i + x_offset,
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mae_u,
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bar_width,
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label='Uniform',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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plt.bar(
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x_i + x_offset,
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mae_a,
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bar_width,
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label='Adaptive',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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plt.bar(
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x_i + x_offset,
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mae_r,
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bar_width,
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label='Sample',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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plt.bar(
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x_i + x_offset,
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mae_d,
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bar_width,
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label='Discount',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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plt.bar(
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x_i + x_offset,
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mae_i,
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bar_width,
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label='Incremental',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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path = str('rslt/bgt_cmp/' + d)
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# Plot legend
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lmdk_lib.plot_legend()
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# Show plot
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plt.show()
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# Save plot
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# lmdk_lib.save_plot(path + '.pdf')
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print('[OK]', flush=True)
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def parse_args():
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'''
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Parse arguments.
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Optional:
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dist - The coordinates distance threshold in meters.
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per - The timestaps period threshold in mimutes.
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time - The total timestamps.
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iter - The total iterations.
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'''
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# Create argument parser.
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parser = argparse.ArgumentParser()
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# Mandatory arguments.
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# Optional arguments.
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parser.add_argument('-l', '--dist', help='The coordinates distance threshold in meters.', type=int, default=200)
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parser.add_argument('-p', '--per', help='The timestaps period threshold in mimutes.', type=int, default=30)
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parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/T-drive/Results.zip')
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parser.add_argument('-t', '--time', help='The total timestamps.', type=int, default=1000)
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parser.add_argument('-i', '--iter', help='The total iterations.', type=int, default=1)
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# Parse arguments.
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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try:
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start_time = time.time()
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main(parse_args())
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end_time = time.time()
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print('##############################')
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print('Time : %.4fs' % (end_time - start_time))
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print('##############################')
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except KeyboardInterrupt:
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print('Interrupted by user.')
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exit()
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