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>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
import numpy as np
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from thunderhopper.filetools import search_files
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from thunderhopper.model import 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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stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
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noise_path = search_files('merged_noise', dir='../data/field/processed/noise/')[0]
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save_path = '../data/inv/field/ref_measures.npz'
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channels = np.array([0, 1, 2, 3, 4, 5, 6, 7])
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# PROCESSING:
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# Load pure-noise starter representation:
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noise_data, config = load_data(noise_path, stages[0])
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# Accumulate channels in time-major order:
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starter = noise_data[stages[0]][:, channels].ravel(order='F')
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# Get song segment to be analyzed:
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time = np.arange(starter.shape[0]) / config['rate']
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start, end = noise_data['songs_0'].ravel()
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segment = (time >= start) & (time <= end)
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# Run pipeline:
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data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]
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# Get measures:
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measures = {}
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for stage in stages:
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if stage == 'feat':
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measures[stage] = data[stage][segment, :].mean(axis=0)
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else:
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measures[stage] = data[stage][segment, ...].std(axis=0)
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# Save results:
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np.savez(save_path, **measures)
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print('Done.')
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embed()
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