82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import numpy as np
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from thunderhopper.modeltools import load_data, save_data
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from thunderhopper.filetools import search_files, crop_paths
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from thunderhopper.filters import decibel, sosfilter
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from IPython import embed
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# GENERAL SETTINGS:
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target = ['Omocestus_rufipes', '*'][0]
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data_paths = search_files(target, excl='noise', dir='../data/processed/')
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noise_path = '../data/processed/white_noise_sd-1.npz'
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save_path = '../data/inv/log_hp/'
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# ANALYSIS SETTINGS:
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add_noise = target == '*' or False
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save_snippets = target == 'Omocestus_rufipes'
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example_scales = np.array([0.1, 1, 10, 30, 100, 300])
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scales = np.geomspace(0.1, 10000, 500)
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scales = np.unique(np.concatenate((scales, example_scales)))
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# PREPARATION:
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pure_noise = np.load(noise_path)['filt']
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# EXECUTION:
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for data_path, name in zip(data_paths, crop_paths(data_paths)):
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print(f'Processing {name}')
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# Get filtered song (prior to envelope extraction):
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data, config = load_data(data_path, files='filt')
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song, rate = data['filt'], config['rate']
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# Get song segment to be analyzed:
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time = np.arange(song.shape[0]) / rate
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start, end = data['songs_0'].ravel()
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segment = (time >= start) & (time <= end)
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# Normalize song component:
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song /= song[segment].std()
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# Rescale song component:
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mix = song[:, None] * scales[None, :]
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if add_noise:
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# Add normalized noise component:
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noise = pure_noise[:song.shape[0]]
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noise /= noise[segment].std()
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mix += noise[:, None]
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# Process mixture:
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mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
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padtype='even', padlen=config['padlen'])
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mix_log = decibel(mix, ref=1)
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mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
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padtype='constant', padlen=config['padlen'])
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# Get intensity measure per stage:
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measure_env = mix[segment, :].std(axis=0)
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measure_log = mix_log[segment, :].std(axis=0)
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measure_inv = mix_inv[segment, :].std(axis=0)
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# Save analysis results:
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save_inds = np.nonzero(np.isin(scales, example_scales))[0]
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if save_path is not None:
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data = dict(
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scales=scales,
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example_scales=example_scales,
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measure_env=measure_env,
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measure_log=measure_log,
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measure_inv=measure_inv,
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)
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if save_snippets:
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data.update(
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snip_env=mix[:, save_inds],
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snip_log=mix_log[:, save_inds],
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snip_inv=mix_inv[:, save_inds],
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)
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file_name = save_path + name
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if add_noise:
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file_name += '_noise'
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save_data(file_name, data, config, overwrite=True)
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print('Done.')
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embed()
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