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>
108 lines
3.4 KiB
Python
108 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.filtertools import find_kern_specs
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from thunderhopper.model import process_signal
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from IPython import embed
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# GENERAL SETTINGS:
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mode = ['song', 'noise'][0]
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example_file = dict(
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song='Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms',
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noise='merged_noise'
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)[mode]
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search_path = f'../data/field/processed/{mode}/'
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data_paths = search_files('*', ext='npz', dir=search_path)
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ref_path = '../data/inv/field/ref_measures.npz'
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stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
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save_path = f'../data/inv/field/{mode}/'
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# ANALYSIS SETTINGS:
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distances = np.load('../data/field/recording_distances.npy')[::-1]
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thresh_rel = 0.5
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# SUBSET SETTINGS:
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kernels = np.array([
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[1, 0.002],
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[-1, 0.002],
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[2, 0.004],
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[-2, 0.004],
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[3, 0.032],
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[-3, 0.032]
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])
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kernels = None
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types = None#np.array([-1])
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sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
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# PREPARATION:
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if thresh_rel is not None:
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# Get threshold values from pure-noise response SD:
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thresh_abs = np.load(ref_path)['conv'] * thresh_rel
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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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save_detailed = example_file in name
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print(f'Processing {name}')
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# Get song recording (prior to anything):
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data, config = load_data(data_path, files='raw')
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song, rate = data['raw'], config['rate']
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if thresh_rel is not None:
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# Set kernel-specific thresholds:
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config['feat_thresh'] = thresh_abs
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# Reduce to kernel subset:
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if any(var is not None for var in [kernels, types, sigmas]):
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kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
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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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config['feat_thresh'] = config['feat_thresh'][kern_inds]
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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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# Prepare storage:
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measures = {}
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if save_detailed:
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snippets = {}
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# Process snippet:
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signals, rates = process_signal(config, returns=stages, signal=song, rate=rate)
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for stage in stages:
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# Sort largest to smallest distance:
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signals[stage] = signals[stage][..., ::-1]
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# Store results:
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for stage in stages:
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# Log intensity measures:
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mkey = f'measure_{stage}'
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if stage == 'feat':
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measures[mkey] = signals[stage][segment, ...].mean(axis=0)
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else:
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measures[mkey] = signals[stage][segment, ...].std(axis=0)
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if measures[mkey].ndim == 2:
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# Make shape (distances, kernels):
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measures[mkey] = np.moveaxis(measures[mkey], 1, 0)
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# Log optional snippet data:
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if save_detailed:
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snippets[f'snip_{stage}'] = signals[stage]
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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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distances=distances,
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)
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data.update(measures)
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if save_detailed:
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data.update(snippets)
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save_data(save_path + name, data, config, overwrite=True)
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print('Done.')
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embed()
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