5411a309f7
Added complete "rect-lp" analysis except figure. Added multiple appendix figs. Overhauled normalization options across all condense scripts. Co-authored-by: Copilot <copilot@github.com>
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import numpy as np
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from thunderhopper.filetools import search_files, crop_paths
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from thunderhopper.model import configuration, process_signal
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from thunderhopper.modeltools import load_data
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from IPython import embed
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## SETTINGS:
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# General:
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search_target = '*'
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mode = ['song', 'noise'][1]
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input_folder = f'../data/field/raw/{mode}/'
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output_folder = f'../data/field/processed/{mode}/'
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stages = ['raw', 'norm']
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if True:
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# Overwrites edited:
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stages.append('songs')
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# Interactivity:
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reload_saved = False
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gui = True
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# Processing:
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env_rate = 96000.0
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feat_rate = 96000.0
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sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
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types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
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6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
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config = configuration(env_rate, feat_rate, types=types, sigmas=sigmas)
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config.update({
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'channel': None,
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'rate_ratio': None,
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'env_fcut': 250,
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'db_ref': 1,
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'inv_fcut': 10,
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'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
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'feat_fcut': 0.5,
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'label_channels': np.array([0]),
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'label_thresh': 0.5,
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})
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## PREPARATION:
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# Fetch WAV recording files:
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input_paths = search_files(search_target, ext='wav', dir=input_folder)
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path_names = crop_paths(input_paths)
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# PROCESSING:
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# Run processing pipeline:
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for path, name in zip(input_paths, path_names):
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print('Processing:', name)
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# Fetch and store representations:
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save = None if output_folder is None else output_folder + f'{name}.npz'
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process_signal(config, stages, path, save=save, label_edit=gui)
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# Cross-control:
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if reload_saved:
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data, params = load_data(save, stages, ['songs'])
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embed()
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print('Done.')
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