139 lines
4.8 KiB
Python
139 lines
4.8 KiB
Python
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 search_files, crop_paths
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from thunderhopper.filters import sosfilter
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from thunderhopper.filtertools import find_kern_specs, pdf_proportion
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from IPython import embed
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# GENERAL SETTINGS:
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target = ['Omocestus_rufipes', '*'][0]
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data_paths = search_files(target, dir='../data/processed/')
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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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save_snippets = True
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example_scales = np.array([0, 1, 10, 50])
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scales = np.geomspace(0.01, 1000, 100)
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scales = np.unique(np.concatenate((scales, example_scales)))
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thresh_percent = np.array([0.6, 0.75, 0.999])
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thresholds = pdf_proportion(thresh_percent, sd=1, mu=0)
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plot_results = False
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kernels = np.array([
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[1, 0.008],
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[2, 0.004],
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[3, 0.002],
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])
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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 + '_subset'
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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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conv, rate = data['conv'], data['conv_rate']
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# Get song segment to be analyzed:
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time = np.arange(conv.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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# Reduce to kernel subset:
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kern_inds = find_kern_specs(config['k_specs'], kerns=kernels)
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config['kernels'] = config['kernels'][:, kern_inds]
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config['k_specs'] = config['k_specs'][kern_inds]
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config['k_props'] = [config['k_props'][i] for i in kern_inds]
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conv = conv[:, kern_inds]
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# Normalize kernel responses:
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conv /= conv[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=(conv.shape[0], 1))
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noise /= noise[segment].std()
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if save_snippets:
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# Prepare snippet storage:
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shape = conv.shape + (example_scales.size, thresh_percent.size)
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snip_conv = np.zeros(shape, dtype=float)
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snip_bi = np.zeros(shape, dtype=float)
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snip_feat = np.zeros(shape, dtype=float)
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# Prepare measure storage:
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shape = (scales.size, conv.shape[1], thresh_percent.size)
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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 analysis:
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for i, thresh in enumerate(thresholds):
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print('\nSimulating threshold ', thresh_percent[i])
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for j, scale in enumerate(scales):
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print('Simulating scale ', scale)
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# Rescale conv component:
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scaled_conv = conv * 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 > thresh).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 save_snippets and scale in example_scales:
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scale_ind = np.nonzero(example_scales == scale)[0][0]
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snip_conv[:, :, scale_ind, i] = scaled_conv
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snip_bi[:, :, scale_ind, i] = scaled_bi
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snip_feat[:, :, scale_ind, i] = scaled_feat
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# Get intensity measure per stage:
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measure_feat[j, :, i] = scaled_feat[segment, :].mean(axis=0)
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# measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
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if plot_results:
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fig, axes = plt.subplots(thresh_percent.size, kernels.shape[0],
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figsize=(16, 9), layout='constrained',
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sharex=True, sharey=True, squeeze=True)
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axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
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linscale=0.25)
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axes[0, 0].set_ylim(0, 1)
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for i, thresh in enumerate(thresh_percent):
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for j, kernel in enumerate(kernels):
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ax = axes[i, j]
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ax.plot(scales, measure_feat[:, j, i], 'k')
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if i == 0:
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ax.set_title(f'Kernel {kernel}')
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if j == 0:
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ax.set_ylabel(f'{100 * thresh}%')
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plt.show()
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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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# measure_conv=measure_conv,
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measure_feat=measure_feat,
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thresh_perc=thresh_percent,
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threshs=thresholds,
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)
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if save_snippets:
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data.update(dict(
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snip_conv=snip_conv,
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snip_bi=snip_bi,
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snip_feat=snip_feat,
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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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