def main(): parser = argparse.ArgumentParser() parser.add_argument('log_path', metavar='LOG-PATH') args = parser.parse_args() data = get_interarrival_times(args.log_path) find_best_fit(data)
def main(): data = get_interarrival_times() fig, ax = plt.subplots() ax.hist(data, bins=max(data) - min(data), normed=True) ax.set_xlabel('Interarrival times (ms)') ax.set_ylabel('Frequency') ax.grid() fig.savefig('hist_interarrival_times.png', dpi=300, bbox_inches='tight', pad_inches=0.2)
def main(): parser = argparse.ArgumentParser() parser.add_argument('log_path', metavar='LOG-PATH') args = parser.parse_args() data = get_interarrival_times(args.log_path) fig, ax = plt.subplots() bins_num = max(data) - min(data) if bins_num > 50: bins_num = 50 ax.hist(data, bins=bins_num, normed=True) ax.set_xlabel('Interarrival times (ms)') ax.set_ylabel('Frequency') ax.grid() fig.savefig('hist_interarrival_times.png', dpi=300, bbox_inches='tight', pad_inches=0.2)
def train_lambda(log_path): data = get_interarrival_times(log_path) return float(len(data) - 1) / sum(data)
def main(): data = get_interarrival_times() find_best_fit(data)