36ac504efa
Worked those into LogHP analysis. Worked results into fig_invariance_log-hp.pdf. Put details into new fig_invariance_log-hp_species.pdf (appendix).
137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from thunderhopper.filetools import search_files, crop_paths
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from thunderhopper.modeltools import load_data, save_data
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from misc_functions import shorten_species
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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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]
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sources = [
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'BM04',
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'BM93',
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'DJN',
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'GBC',
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'FTN'
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]
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search_path = '../data/inv/log_hp/'
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ref_path = '../data/inv/log_hp/ref_measures.npz'
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save_path = '../data/inv/log_hp/condensed/'
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# ANALYSIS SETTINGS:
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compute_ratios = True
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plot_overview = True
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# PREPARATION:
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if compute_ratios:
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ref_measure = np.load(ref_path)['inv']
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if plot_overview:
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fig, axes = plt.subplots(3, len(target_species), figsize=(16, 9),
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sharex=True, sharey=True, layout='constrained')
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axes[0, 0].set_ylabel('songs')
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axes[1, 0].set_ylabel('recordings\n(mean ± SD)')
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axes[2, 0].set_ylabel('total\n(mean ± SEM)')
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# EXECUTION:
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for i, species in enumerate(target_species):
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print(f'Processing {species}')
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if plot_overview:
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axes[0, i].set_title(shorten_species(species))
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# Fetch all species-specific song files:
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all_paths = search_files(species, incl='noise', ext='npz', dir=search_path)
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# Separate by source:
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sorted_paths = {}
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for source in sources:
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# Check for any source-specific song files:
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source_paths = [path for path in all_paths if source in path]
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if not source_paths:
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continue
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# Separate by recording:
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sorted_paths[source] = [[]]
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for path, name in zip(source_paths, crop_paths(source_paths)):
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# Find numerical ID behind source tag:
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id_ind = name.find(source) + len(source) + 1
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# Check if ID is followed by sub-ID:
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sub_id = name[id_ind:].split('-')[1]
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if 's' in sub_id:
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# Single (time stamp in next spot):
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sorted_paths[source][0].append(path)
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continue
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sub_id = int(sub_id)
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# Multiple (sub-ID in next spot):
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if sub_id > len(sorted_paths[source]):
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# Open new recording-specific slot:
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sorted_paths[source].append([])
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sorted_paths[source][sub_id - 1].append(path)
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# Re-sort song files only by recording (discarding source separation):
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sorted_paths = [path for paths in sorted_paths.values() for path in paths]
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# Condense across song files per recording:
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for j, rec_paths in enumerate(sorted_paths):
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for k, path in enumerate(rec_paths):
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# Load invariance data:
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data, _ = load_data(path, ['scales', 'measure_inv'])
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scales, measure = data['scales'], data['measure_inv']
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# Relate to noise:
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if compute_ratios:
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measure /= ref_measure
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if k == 0:
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# Prepare song file-specific storage:
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file_data = np.zeros((scales.size, len(rec_paths)), dtype=float)
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if j == 0:
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# Prepare recording-specific storage:
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rec_mean = np.zeros((scales.size, len(sorted_paths)), dtype=float)
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rec_sd = np.zeros((scales.size, len(sorted_paths)), dtype=float)
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# Log song file data:
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file_data[:, k] = measure
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if plot_overview:
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axes[0, i].plot(scales, measure, c='k', alpha=0.5)
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# Get recording statistics:
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rec_mean[:, j] = file_data.mean(axis=1)
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rec_sd[:, j] = file_data.std(axis=1)
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if plot_overview:
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axes[1, i].plot(scales, rec_mean[:, j], c='k')
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axes[1, i].fill_between(scales, rec_mean[:, j] - rec_sd[:, j],
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rec_mean[:, j] + rec_sd[:, j], color='k', alpha=0.2)
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# Save condensed recording data for current species:
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np.savez(save_path + species, scales=scales, mean=rec_mean, sd=rec_sd)
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if plot_overview:
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spec_mean = rec_mean.mean(axis=1)
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spec_sd = rec_mean.std(axis=1)
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axes[2, i].plot(scales, spec_mean, c='k')
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axes[2, i].fill_between(scales, spec_mean - spec_sd, spec_mean + spec_sd,
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color='k', alpha=0.2)
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print('Done.')
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if plot_overview:
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axes[0, 0].set_xlim(scales[0], scales[-1])
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axes[0, 0].set_xscale('log')
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axes[0, 0].set_yscale('log')
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plt.show()
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