132 lines
4.4 KiB
Python
132 lines
4.4 KiB
Python
import glob
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import numpy as np
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import matplotlib.pyplot as plt
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from thunderhopper.modeltools import load_data, save_data
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from thunderhopper.filetools import crop_paths
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from thunderhopper.filters import sosfilter
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from IPython import embed
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = glob.glob(f'../data/processed/{target}*.npz')
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save_path = '../data/inv/thresh_lp/'
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# ANALYSIS SETTINGS:
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add_noise = False
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thresh_percent = 90
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example_scales = np.array([0, 0.5, 1, 10, 50])
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scales = np.geomspace(0.01, 50, 100)
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scales = np.unique(np.concatenate((scales, example_scales)))
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plot_results = True
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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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save_name = save_path + name
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# Get pure-song kernel responses:
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data, config = load_data(data_path, files='conv')
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song, rate = data['conv'], data['conv_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(axis=0)
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if add_noise:
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# Get normalized noise:
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rng = np.random.default_rng()
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noise = rng.normal(size=(song.shape[0], 1))
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noise /= noise[segment].std()
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# Prepare noise-bound threshold:
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threshold = np.percentile(noise, thresh_percent, axis=0)
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else:
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# Reuse threshold from previous noise run:
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threshold = np.load(save_name + '_noise.npz')['thresh']
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# Prepare snippet storage:
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shape = song.shape + (example_scales.size,)
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conv = np.zeros(shape, dtype=float)
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bi = np.zeros(shape, dtype=float)
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feat = np.zeros(shape, dtype=float)
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# Prepare measure storage:
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shape = (scales.size, song.shape[1])
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measure_conv = np.zeros(shape, dtype=float)
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measure_feat = np.zeros(shape, dtype=float)
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# Execute piecewise:
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for i, scale in enumerate(scales):
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print('Simulating scale ', scale)
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# Rescale song component:
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scaled_conv = song * scale
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if add_noise:
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# Add noise:
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scaled_conv += noise
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# Process mixture:
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scaled_bi = (scaled_conv > threshold).astype(float)
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scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
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padtype='fixed', padlen=config['padlen'])
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# Log snippet data:
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if scale in example_scales:
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scale_ind = np.nonzero(example_scales == scale)[0][0]
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conv[:, :, scale_ind] = scaled_conv
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bi[:, :, scale_ind] = scaled_bi
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feat[:, :, scale_ind] = scaled_feat
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# Get "intensity measure" per stage:
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measure_conv[i] = scaled_conv[segment, :].std(axis=0)
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measure_feat[i] = scaled_feat[segment, :].mean(axis=0)
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# # Relate to smallest scale:
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# base_ind = np.argmin(scales)
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# measure_conv /= measure_conv[base_ind, :]
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if plot_results:
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fig, (ax1, ax2) = plt.subplots(2, 1)
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ax1.plot(scales, measure_conv)
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ax1.plot(scales, measure_conv.mean(axis=1), c='k')
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ax1.plot(scales, np.median(measure_conv, axis=1), c='k', ls='--')
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ax2.plot(scales, measure_feat)
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ax2.plot(scales, np.nanmean(measure_feat, axis=1), c='k')
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ax2.plot(scales, np.nanmedian(measure_feat, axis=1), c='k', ls='--')
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plt.show()
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# Condense measures across kernels:
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spread_conv = np.zeros((2, scales.size))
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spread_conv[0] = np.nanpercentile(measure_conv, 25, axis=1)
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spread_conv[1] = np.nanpercentile(measure_conv, 75, axis=1)
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measure_conv = np.nanmedian(measure_conv, axis=1)
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spread_feat = np.zeros((2, scales.size))
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spread_feat[0] = np.nanpercentile(measure_feat, 25, axis=1)
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spread_feat[1] = np.nanpercentile(measure_feat, 75, axis=1)
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measure_feat = np.nanmedian(measure_feat, axis=1)
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# Save analysis results:
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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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conv=conv,
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bi=bi,
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feat=feat,
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measure_conv=measure_conv,
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spread_conv=spread_conv,
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measure_feat=measure_feat,
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spread_feat=spread_feat,
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thresh=threshold,
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thresh_perc=thresh_percent,
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)
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if add_noise:
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save_name += '_noise'
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save_data(save_name, data, config, overwrite=True)
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print('Done.')
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embed()
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