49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import numpy as np
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from scipy.io import wavfile
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import urllib.request
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import zipfile
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import os
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def download_and_extract_data(url):
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if not os.path.exists('data'):
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zip_path = os.path.join('.', 'data.zip')
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urllib.request.urlretrieve(url, zip_path)
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with zipfile.ZipFile(zip_path, 'r') as zip_ref:
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zip_ref.extractall('.')
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os.remove(zip_path)
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def load_wav(file_path):
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sample_rate, data = wavfile.read(file_path)
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return sample_rate, data
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def load_all_wavs(data_dir, cut_length=None):
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wav_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith('.wav')]
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all_data = []
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for file_path in wav_files:
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_, data = load_wav(file_path)
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if cut_length is not None:
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print(cut_length)
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data = data[:cut_length]
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all_data.append(data)
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return all_data
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def compute_correlation_matrix(data):
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num_leads = len(data)
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min_length = min(len(d) for d in data)
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# Trim all leads to the minimum length
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trimmed_data = [d[:min_length] for d in data]
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corr_matrix = np.corrcoef(trimmed_data)
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np.fill_diagonal(corr_matrix, 0)
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return corr_matrix
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def split_data_by_time(data, split_ratio=0.5):
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train_data = []
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test_data = []
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for lead in data:
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split_idx = int(len(lead) * split_ratio)
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train_data.append(lead[:split_idx])
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test_data.append(lead[split_idx:])
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return train_data, test_data
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