7e1aa8721a
Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB. Co-authored-by: Copilot <copilot@github.com>
110 lines
3.6 KiB
Python
110 lines
3.6 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 sosfilter
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from misc_functions import draw_noise_segment
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from IPython import embed
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# GENERAL SETTINGS:
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example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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search_target = ['*', example_file][0]
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data_paths = search_files(search_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/rect_lp/'
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# ANALYSIS SETTINGS:
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mode = ['pure', 'noise'][1]
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example_scales = np.array([0.1, 0.3, 1, 3, 10])
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scales = np.geomspace(0.01, 100, 1000)
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scales = np.unique(np.concatenate(([0], scales, example_scales)))
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cutoffs = np.array([np.nan, 2500, 250, 25])
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# PREPARATION:
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if mode == 'noise':
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pure_noise = np.load(noise_path)['raw']
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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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save_detailed = example_file in name
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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='raw')
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song, rate = data['raw'], 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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if mode == 'noise':
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# Get normalized noise component:
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noise = draw_noise_segment(pure_noise, song.shape[0])
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noise /= noise[segment].std()
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# Prepare storage:
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measure_filt = np.zeros_like(scales)
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measure_env = np.zeros((scales.size, len(cutoffs)), dtype=float)
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if save_detailed:
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# Prepare optional storage:
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shape = (song.shape[0], example_scales.size)
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snip_raw = np.zeros(shape)
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snip_filt = np.zeros(shape)
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snip_env = np.zeros(shape + (len(cutoffs),))
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# Execute piecewise:
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for i, scale in enumerate(scales):
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# Get scaled mixture:
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mix = song * scale
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if mode == 'noise':
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mix += noise
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# Process mixture:
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mix = sosfilter(mix, rate, config['bp_fcut'], 'bp',
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padtype='fixed', padlen=config['padlen'])
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mix_rect = np.abs(mix)
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# Store non-envelope results:
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measure_filt[i] = mix[segment].std()
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if save_detailed and scale in example_scales:
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scale_ind = np.nonzero(example_scales == scale)[0][0]
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snip_raw[:, scale_ind] = mix
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snip_filt[:, scale_ind] = mix
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# Process piecewise again:
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for j, cutoff in enumerate(cutoffs):
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if np.isnan(cutoff):
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mix_env = mix_rect
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else:
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mix_env = sosfilter(mix_rect, rate, cutoff, 'lp',
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padtype='even', padlen=config['padlen'])
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# Store envelope results:
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measure_env[i, j] = mix_env[segment].std()
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if save_detailed and scale in example_scales:
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snip_env[:, scale_ind, j] = mix_env
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# Save analysis results:
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if save_path is not None:
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archive = dict(
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scales=scales,
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example_scales=example_scales,
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cutoffs=cutoffs,
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measure_filt=measure_filt,
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measure_env=measure_env,
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)
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if save_detailed:
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archive.update(
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snip_raw=snip_raw,
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snip_filt=snip_filt,
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snip_env=snip_env,
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)
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save_name = save_path + name + '_' + mode
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save_data(save_name, archive, config, overwrite=True)
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print('Done.')
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embed()
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