| from argparse import ArgumentParser
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| from tqdm import tqdm
|
|
|
| import numpy as np
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| import matplotlib.pyplot as plt
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| import seaborn as sns
|
|
|
| from anticipation.vocab import MIDI_TIME_OFFSET, MIDI_START_OFFSET, TIME_RESOLUTION, SEPARATOR
|
| from anticipation.ops import max_time
|
|
|
| plt.rcParams['font.family'] = 'serif'
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| plt.rcParams['font.serif'] = ['Computer Modern']
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| plt.rcParams['font.size'] = 16
|
|
|
| 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')
|
| 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}')
|
| time_lengths = []
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| token_counts = []
|
| with open(args.filename, 'r') as f:
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| for i,line in tqdm(list(enumerate(f))):
|
| if i % 10 != 0: continue
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| tokens = [int(token) for token in line.split()]
|
|
|
| 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
|
| time_lengths.append(max_time(tokens[1:], seconds=False))
|
| token_counts.append(len(tokens[1:]))
|
|
|
| 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)')
|
|
|
