162 lines
6.0 KiB
Python
162 lines
6.0 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.filtertools import find_kern_specs
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from thunderhopper.filters import sosfilter
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from thunderhopper.model import convolve_kernels, process_signal
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from misc_functions import draw_noise_segment
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from IPython import embed
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# GENERAL SETTINGS:
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target_species = [
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'Chorthippus_biguttulus',
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'Chorthippus_mollis',
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'Chrysochraon_dispar',
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'Euchorthippus_declivus',
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'Gomphocerippus_rufus',
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'Omocestus_rufipes',
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'Pseudochorthippus_parallelus',
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][6]
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example_file = {
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'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
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'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
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'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
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'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
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'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
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'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
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'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
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}[target_species]
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data_paths = search_files(target_species, dir='../data/processed/')
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noise_path = '../data/processed/white_noise_sd-1.npz'
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ref_path = '../data/inv/short/ref_measures.npz'
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pre_stages = ['filt', 'env']
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stages = pre_stages + ['inv', 'conv', 'feat']
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save_path = '../data/inv/short/'
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# ANALYSIS SETTINGS:
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example_scales = np.array([0.1, 1, 10, 30, 100, 300])
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scales = np.geomspace(0.01, 10000, 500)
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scales = np.unique(np.concatenate(([0], scales, example_scales)))
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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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pure_noise = np.load(noise_path)['raw']
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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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# Normalize song component:
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song /= song[segment].std(axis=0)
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# Get normalized noise component:
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noise = draw_noise_segment(pure_noise, song.shape[0])
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noise /= noise[segment].std()
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# Prepare storage:
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shape_low = (scales.size,)
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shape_high = (scales.size, config['k_specs'].shape[0])
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measures = dict(
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measure_filt=np.zeros(shape_low, dtype=float),
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measure_env=np.zeros(shape_low, dtype=float),
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measure_inv=np.zeros(shape_low, dtype=float),
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measure_conv=np.zeros(shape_high, dtype=float),
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measure_feat=np.zeros(shape_high, dtype=float)
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)
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if save_detailed:
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# Prepare optional storage:
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shape_low = (song.shape[0], example_scales.size)
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shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
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snippets = dict(
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snip_filt=np.zeros(shape_low, dtype=float),
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snip_env=np.zeros(shape_low, dtype=float),
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snip_inv=np.zeros(shape_low, dtype=float),
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snip_conv=np.zeros(shape_high, dtype=float),
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snip_feat=np.zeros(shape_high, dtype=float)
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)
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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 and add noise:
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scaled = song * scale + noise
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# Process mixture:
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signals, rates = process_signal(config, returns=pre_stages,
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signal=scaled, rate=rate)
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# Process mixture further:
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signals['inv'] = sosfilter(signals['env'], rate, config['inv_fcut'], 'hp',
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padtype='constant', padlen=config['padlen'])
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signals['conv'] = convolve_kernels(signals['inv'], config['kernels'], config['k_specs'])
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signals['feat'] = sosfilter((signals['conv'] > config['feat_thresh']).astype(float),
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rate, config['feat_fcut'], 'lp',
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padtype='fixed', padlen=config['padlen'])
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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][i] = signals[stage][segment, :].mean(axis=0)
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else:
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measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
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# Log optional snippet data:
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if save_detailed and scale in example_scales:
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scale_ind = np.nonzero(example_scales == scale)[0][0]
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snippets[f'snip_{stage}'][:, ..., scale_ind] = 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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scales=scales,
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example_scales=example_scales,
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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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