Fetched bunch of species-specific song snippets.
Worked those into LogHP analysis. Worked results into fig_invariance_log-hp.pdf. Put details into new fig_invariance_log-hp_species.pdf (appendix).
This commit is contained in:
j-hartling
136
python/condense_inv_data_log-hp.py
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136
python/condense_inv_data_log-hp.py
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@@ -0,0 +1,136 @@
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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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@@ -27,58 +27,29 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
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return handles
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def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
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base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
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if pdf is None:
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pdf, axis = get_kde(data, sigma, axis)
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if base is None:
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base = pdf.min()
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if cap is None:
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cap = pdf.max()
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pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
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if which == 'x':
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transform = ax.get_xaxis_transform()
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elif which == 'y':
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transform = ax.get_yaxis_transform()
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else:
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transform = ax.transData
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rng = np.random.default_rng()
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handles = []
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for value in data:
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ind = np.nonzero(axis == value)[0][0]
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offset = base if not shifted else rng.uniform(base, pdf[ind])
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variables = (offset, value) if which=='y' else (value, offset)
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handles.extend(ax.plot(*variables, transform=transform, **kwargs))
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if add_pdf:
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variables = (pdf, axis) if which=='y' else (axis, pdf)
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pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
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return handles, pdf_handle
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return handles
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def zalpha(handles, background='w', down=1):
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twins = []
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for handle in handles:
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twin = handle.copy()
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twin.set(color=background, alpha=1)
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twin.set_zorder(handle.get_zorder() - down)
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twins.append(twin)
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return twins
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# def zalpha(handles, background='w', down=1):
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# twins = []
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# for handle in handles:
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# twin = handle.copy()
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# twin.set(color=background, alpha=1)
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# twin.set_zorder(handle.get_zorder() - down)
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# twins.append(twin)
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# return twins
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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ref_path = '../data/inv/log_hp/ref_measures.npz'
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save_path = '../figures/fig_invariance_log_hp.pdf'
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target_species = [
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'Omocestus_rufipes',
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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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]
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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@@ -159,12 +130,13 @@ fs = dict(
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)
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colors = load_colors('../data/stage_colors.npz')
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species_colors = load_colors('../data/species_colors.npz')
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noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)]
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noise_colors = [(0.6,) * 3, (0.8,) * 3]
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lw = dict(
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snip=1,
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big=4,
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spec=2,
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plateau=1,
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plateau=1.5,
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legend=5,
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)
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xlabels = dict(
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big='scale $\\alpha$',
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@@ -273,7 +245,7 @@ leg_kwargs = dict(
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columnspacing=1,
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)
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diag_kwargs = dict(
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c=(0.75, 0.75, 0.75),
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c=(0.3,) * 3,
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lw=2,
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ls='--',
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zorder=1.9,
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@@ -297,18 +269,9 @@ plateau_line_kwargs = dict(
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)
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plateau_dot_kwargs = dict(
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marker='o',
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markersize=10,
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markeredgecolor='k',
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markersize=8,
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markeredgewidth=1,
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# alpha=1,
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zorder=6,
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clip_on=False,
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# base=0,
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# cap=0.15,
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# add_pdf=True,
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)
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kde_kwargs = dict(
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sigma=0.1,
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)
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# PREPARATION:
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@@ -317,18 +280,12 @@ if compute_ratios:
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species_measures = {}
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thresh_inds = np.zeros((len(target_species),), dtype=int)
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thresh_scales = np.zeros((len(target_species),), dtype=float)
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for i, species in enumerate(target_species):
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path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0]
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species_data = load_data(path, **load_kwargs)[0]
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measure = species_data['measure_inv']
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scales = species_data['scales']
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if compute_ratios:
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measure /= ref_measures['inv']
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spec_path = search_files(species, dir='../data/inv/log_hp/condensed/')[0]
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spec_data = dict(np.load(spec_path))
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measure = spec_data['mean'].mean(axis=1)
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species_measures[species] = measure
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thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]
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thresh_scales[i] = scales[thresh_inds[i]]
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thresh_pdf, pdf_axis = get_kde(thresh_scales, axis=scales, **kde_kwargs)
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# EXECUTION:
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for data_path in data_paths:
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@@ -460,24 +417,23 @@ for data_path in data_paths:
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big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
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fc=noise_colors[1], **plateau_rect_kwargs)
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# Plot species-specific noise-song measures:
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# Plot species-specific noise-song invariance curves:
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for i, (species, measure) in enumerate(species_measures.items()):
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# Plot invariance curve:
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color = species_colors[species]
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ind, scale = thresh_inds[i], thresh_scales[i]
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big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
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c=color, lw=lw['spec'])
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# Indicate saturation:
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ind = thresh_inds[i]
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scale = noise_scales[ind]
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big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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handle = big_axes[2].plot(scale, 0, c=color, alpha=0.5, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
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color=color, **plateau_line_kwargs)
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big_axes[2].legend(**leg_kwargs)
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# handles = plot_dist_shifted(big_axes[2], species_threshs, axis=pdf_axis,
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# pdf=thresh_pdf, **plateau_dot_kwargs)[0]
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# [h.set_color(species_colors[s]) for h, s in zip(handles, target_species)]
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legend = big_axes[2].legend(**leg_kwargs)
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[h.set_lw(lw['legend']) for h in legend.legend_handles]
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if save_path is not None:
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fig.savefig(save_path, bbox_inches='tight')
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108
python/fig_invariance_log-hp_species.py
Normal file
108
python/fig_invariance_log-hp_species.py
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@@ -0,0 +1,108 @@
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import plotstyle_plt
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import numpy as np
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import matplotlib.pyplot as plt
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from thunderhopper.filetools import search_files
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from plot_functions import ylabel, super_xlabel, letter_subplots, title_subplot
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from color_functions import load_colors
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from misc_functions import shorten_species
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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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data_path = '../data/inv/log_hp/condensed/'
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save_path = '../figures/fig_invariance_log-hp_species.pdf'
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 16/2.54),
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nrows=1,
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ncols=len(target_species),
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sharex=True,
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sharey=True,
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gridspec_kw=dict(
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wspace=0.4,
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hspace=0,
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left=0.07,
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right=0.98,
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bottom=0.1,
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top=0.95,
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)
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)
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# PLOT SETTINGS:
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colors = load_colors('../data/species_colors.npz')
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line_kwargs = dict(
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lw=2,
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)
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fill_kwargs = dict(
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alpha=0.3,
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zorder=1,
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)
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xlab = 'scale $\\alpha$'
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ylab = '$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$'
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xlab_kwargs = dict(
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y=0,
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fontsize=16,
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ha='center',
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va='bottom',
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)
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ylab_kwargs = dict(
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x=0,
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fontsize=20,
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ha='center',
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va='top',
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)
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title_kwargs = dict(
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x=0.5,
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yref=0.99,
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ha='center',
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va='top',
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fontsize=16,
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fontstyle='italic',
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)
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letter_kwargs = dict(
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x=0.005,
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y=0.99,
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fontsize=22,
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ha='left',
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va='top',
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)
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# Prepare graph:
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fig, axes = plt.subplots(**fig_kwargs)
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axes[0].set_ylim(0.9, 20)
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axes[0].set_xscale('log')
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axes[0].set_yscale('log')
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super_xlabel(xlab, fig, axes[0], axes[-1], **xlab_kwargs)
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ylabel(axes[0], ylab, **ylab_kwargs, transform=fig.transFigure)
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# letter_subplots(axes, **letter_kwargs)
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# Run through species:
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for species, ax in zip(target_species, axes):
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title_subplot(ax, shorten_species(species), ref=fig, **title_kwargs)
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color = colors[species]
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# Load species data:
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path = search_files(species, dir=data_path)[0]
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data = dict(np.load(path))
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scales = data['scales']
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means = data['mean']
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sds = data['sd']
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# Plot recording-specific traces:
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for mean, sd in zip(means.T, sds.T):
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ax.plot(scales, mean, c=color, **line_kwargs)
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ax.fill_between(scales, mean - sd, mean + sd, color=color, **fill_kwargs)
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# Save graph:
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fig.savefig(save_path)
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plt.show()
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@@ -2,6 +2,7 @@ import string
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox
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from misc_functions import get_kde
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def hide_ticks(ax, side='bottom', ticks=True):
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axis = 'x' if side in ['top', 'bottom'] else 'y'
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@@ -298,3 +299,33 @@ def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
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def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
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artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
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return None
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def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
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base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
|
||||
if pdf is None:
|
||||
pdf, axis = get_kde(data, sigma, axis)
|
||||
if base is None:
|
||||
base = pdf.min()
|
||||
if cap is None:
|
||||
cap = pdf.max()
|
||||
pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
|
||||
|
||||
if which == 'x':
|
||||
transform = ax.get_xaxis_transform()
|
||||
elif which == 'y':
|
||||
transform = ax.get_yaxis_transform()
|
||||
else:
|
||||
transform = ax.transData
|
||||
|
||||
rng = np.random.default_rng()
|
||||
handles = []
|
||||
for value in data:
|
||||
ind = np.nonzero(axis == value)[0][0]
|
||||
offset = base if not shifted else rng.uniform(base, pdf[ind])
|
||||
variables = (offset, value) if which=='y' else (value, offset)
|
||||
handles.extend(ax.plot(*variables, transform=transform, **kwargs))
|
||||
if add_pdf:
|
||||
variables = (pdf, axis) if which=='y' else (axis, pdf)
|
||||
pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
|
||||
return handles, pdf_handle
|
||||
return handles
|
||||
|
||||
@@ -5,16 +5,17 @@ from thunderhopper.filters import decibel, sosfilter
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, excl='noise', dir='../data/processed/')
|
||||
example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
|
||||
search_target = ['*', example_file][1]
|
||||
data_paths = search_files(search_target, excl='noise', dir='../data/processed/')
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
save_path = '../data/inv/log_hp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
add_noise = target == '*' or False
|
||||
save_snippets = target == 'Omocestus_rufipes'
|
||||
add_noise = search_target == '*' or False
|
||||
save_detailed = search_target == example_file
|
||||
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
|
||||
scales = np.geomspace(0.1, 10000, 500)
|
||||
scales = np.geomspace(0.01, 10000, 1000)
|
||||
scales = np.unique(np.concatenate((scales, example_scales)))
|
||||
|
||||
# PREPARATION:
|
||||
@@ -35,47 +36,67 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
|
||||
# Normalize song component:
|
||||
song /= song[segment].std()
|
||||
|
||||
# Rescale song component:
|
||||
mix = song[:, None] * scales[None, :]
|
||||
|
||||
if add_noise:
|
||||
# Add normalized noise component:
|
||||
# Get normalized noise component:
|
||||
noise = pure_noise[:song.shape[0]]
|
||||
noise /= noise[segment].std()
|
||||
mix += noise[:, None]
|
||||
|
||||
# Process mixture:
|
||||
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
|
||||
padtype='even', padlen=config['padlen'])
|
||||
mix_log = decibel(mix, ref=1)
|
||||
mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
|
||||
padtype='constant', padlen=config['padlen'])
|
||||
# Prepare storage:
|
||||
measure_inv = np.zeros_like(scales)
|
||||
if save_detailed:
|
||||
# Prepare optional storage:
|
||||
measure_env = np.zeros_like(scales)
|
||||
measure_log = np.zeros_like(scales)
|
||||
snip_env = np.zeros((song.shape[0], example_scales.size))
|
||||
snip_log = np.zeros((song.shape[0], example_scales.size))
|
||||
snip_inv = np.zeros((song.shape[0], example_scales.size))
|
||||
|
||||
# Get intensity measure per stage:
|
||||
measure_env = mix[segment, :].std(axis=0)
|
||||
measure_log = mix_log[segment, :].std(axis=0)
|
||||
measure_inv = mix_inv[segment, :].std(axis=0)
|
||||
# Execute piecewise:
|
||||
for i, scale in enumerate(scales):
|
||||
|
||||
# Get scaled mixture:
|
||||
mix = song * scale
|
||||
if add_noise:
|
||||
mix += noise
|
||||
|
||||
# Process mixture:
|
||||
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
|
||||
padtype='even', padlen=config['padlen'])
|
||||
mix_log = decibel(mix, ref=1)
|
||||
mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
|
||||
padtype='constant', padlen=config['padlen'])
|
||||
|
||||
# Log intensity measures:
|
||||
measure_inv[i] = mix_inv[segment].std()
|
||||
if save_detailed:
|
||||
measure_env[i] = mix[segment].std()
|
||||
measure_log[i] = mix_log[segment].std()
|
||||
if scale in example_scales:
|
||||
# Log snippet data:
|
||||
save_ind = np.nonzero(example_scales == scale)[0][0]
|
||||
snip_env[:, save_ind] = mix
|
||||
snip_log[:, save_ind] = mix_log
|
||||
snip_inv[:, save_ind] = mix_inv
|
||||
|
||||
# Save analysis results:
|
||||
save_inds = np.nonzero(np.isin(scales, example_scales))[0]
|
||||
if save_path is not None:
|
||||
data = dict(
|
||||
archive = dict(
|
||||
scales=scales,
|
||||
example_scales=example_scales,
|
||||
measure_env=measure_env,
|
||||
measure_log=measure_log,
|
||||
measure_inv=measure_inv,
|
||||
)
|
||||
if save_snippets:
|
||||
data.update(
|
||||
snip_env=mix[:, save_inds],
|
||||
snip_log=mix_log[:, save_inds],
|
||||
snip_inv=mix_inv[:, save_inds],
|
||||
)
|
||||
if save_detailed:
|
||||
archive.update(
|
||||
measure_env=measure_env,
|
||||
measure_log=measure_log,
|
||||
snip_env=snip_env,
|
||||
snip_log=snip_log,
|
||||
snip_inv=snip_inv,
|
||||
)
|
||||
file_name = save_path + name
|
||||
if add_noise:
|
||||
file_name += '_noise'
|
||||
save_data(file_name, data, config, overwrite=True)
|
||||
save_data(file_name, archive, config, overwrite=True)
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
@@ -7,9 +7,10 @@ from IPython import embed
|
||||
## SETTINGS:
|
||||
|
||||
# General:
|
||||
search_target = '*'
|
||||
input_folder = '../data/raw/'
|
||||
output_folder = '../data/processed/'
|
||||
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat', 'norm']
|
||||
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
if False:
|
||||
# Overwrites edited:
|
||||
stages.append('songs')
|
||||
@@ -30,7 +31,7 @@ config.update({
|
||||
'rate_ratio': None,
|
||||
'env_fcut': 250,
|
||||
'db_ref': 1,
|
||||
'inv_fcut': 5,
|
||||
'inv_fcut': 10,
|
||||
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
|
||||
'feat_fcut': 0.5,
|
||||
'label_channels': 0,
|
||||
@@ -40,7 +41,7 @@ config.update({
|
||||
## PREPARATION:
|
||||
|
||||
# Fetch WAV recording files:
|
||||
input_paths = search_files(ext='wav', dir=input_folder)
|
||||
input_paths = search_files(search_target, ext='wav', dir=input_folder)
|
||||
path_names = crop_paths(input_paths)
|
||||
|
||||
# PROCESSING:
|
||||
|
||||
@@ -6,11 +6,12 @@ from IPython import embed
|
||||
|
||||
# Settings:
|
||||
species = [
|
||||
'Omocestus_rufipes',
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
file_name = '../data/species_colors.npz'
|
||||
|
||||
Reference in New Issue
Block a user