36ac504efa
Worked those into LogHP analysis. Worked results into fig_invariance_log-hp.pdf. Put details into new fig_invariance_log-hp_species.pdf (appendix).
103 lines
3.4 KiB
Python
103 lines
3.4 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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example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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search_target = ['*', example_file][1]
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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/log_hp/'
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# ANALYSIS SETTINGS:
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add_noise = search_target == '*' or False
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save_detailed = search_target == example_file
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example_scales = np.array([0.1, 1, 10, 30, 100, 300])
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scales = np.geomspace(0.01, 10000, 1000)
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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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if add_noise:
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# Get 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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# Prepare storage:
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measure_inv = np.zeros_like(scales)
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if save_detailed:
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# Prepare optional storage:
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measure_env = np.zeros_like(scales)
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measure_log = np.zeros_like(scales)
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snip_env = np.zeros((song.shape[0], example_scales.size))
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snip_log = np.zeros((song.shape[0], example_scales.size))
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snip_inv = np.zeros((song.shape[0], example_scales.size))
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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 add_noise:
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mix += noise
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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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# Log intensity measures:
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measure_inv[i] = mix_inv[segment].std()
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if save_detailed:
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measure_env[i] = mix[segment].std()
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measure_log[i] = mix_log[segment].std()
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if scale in example_scales:
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# Log snippet data:
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save_ind = np.nonzero(example_scales == scale)[0][0]
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snip_env[:, save_ind] = mix
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snip_log[:, save_ind] = mix_log
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snip_inv[:, save_ind] = mix_inv
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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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measure_inv=measure_inv,
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)
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if save_detailed:
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archive.update(
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measure_env=measure_env,
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measure_log=measure_log,
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snip_env=snip_env,
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snip_log=snip_log,
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snip_inv=snip_inv,
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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, archive, config, overwrite=True)
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print('Done.')
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embed()
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