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from argparse import ArgumentParser
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from tqdm import tqdm
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from anticipation.vocab import MIDI_TIME_OFFSET, MIDI_START_OFFSET, TIME_RESOLUTION, SEPARATOR
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from anticipation.ops import max_time
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plt.rcParams['font.family'] = 'serif'
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plt.rcParams['font.serif'] = ['Computer Modern']
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plt.rcParams['font.size'] = 16
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def loghist(filename, data, title, xlabel):
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sns.set_style('whitegrid')
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plt.clf()
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plt.figure(figsize=(10,4))
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plt.xscale('log')
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plt.xlabel(xlabel)
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plt.ylabel('Density')
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plt.grid(True, which='both', linestyle='-', linewidth=0.5)
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density = sns.kdeplot(data, bw_adjust=1.0)
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plt.tight_layout()
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fig = density.get_figure()
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fig.savefig(filename, dpi=300)
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if __name__ == '__main__':
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parser = ArgumentParser(description='calculate statistics of a tokenized MIDI dataset')
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parser.add_argument('-f', '--filename',
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help='file containing a tokenized MIDI dataset')
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parser.add_argument('-i', '--interarrival',
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action='store_true',
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help='request interarrival-time enocoding (default to arrival-time encoding)')
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args = parser.parse_args()
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print(f'Calculating statistics for {args.filename}')
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time_lengths = []
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token_counts = []
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with open(args.filename, 'r') as f:
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for i,line in tqdm(list(enumerate(f))):
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if i % 10 != 0: continue
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tokens = [int(token) for token in line.split()]
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if args.interarrival:
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time_lengths.append(sum(t-MIDI_TIME_OFFSET for t in tokens if t < MIDI_START_OFFSET))
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token_counts.append(len(tokens))
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else:
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if SEPARATOR in tokens:
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continue
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time_lengths.append(max_time(tokens[1:], seconds=False))
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token_counts.append(len(tokens[1:]))
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tokens_per_second = [TIME_RESOLUTION*tokens/float(time) for (tokens, time) in zip(token_counts, time_lengths)]
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print('Total tokens:', sum(token_counts))
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print(f'Total time: {float(sum(time_lengths))/(3600*TIME_RESOLUTION)} hours')
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print('Mean tokens-per-second:', TIME_RESOLUTION*sum(token_counts)/float(sum(time_lengths)))
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print('Std tokens-per-second:', np.std(tokens_per_second))
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print(np.mean(tokens_per_second))
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loghist('output/tokens_per_second.png',
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tokens_per_second,
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'Distribution of Tokens per Second',
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'Tokens per Second (log10 scale)')
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