Seriously, no idea. Wild amount of changes. Good luck.
This commit is contained in:
j-hartling
@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
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from itertools import product
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from thunderhopper.filetools import search_files
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from thunderhopper.modeltools import load_data
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from misc_functions import shorten_species, get_kde, get_saturation
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from misc_functions import shorten_species, get_saturation
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from color_functions import load_colors
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from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, hide_ticks,\
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plot_line, strip_zeros, time_bar, zoom_inset,\
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@@ -27,18 +27,9 @@ 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 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_DJN_32-40s724ms-48s779ms'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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data_path = search_files(target, excl='noise', dir='../data/inv/log_hp/')[0]
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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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@@ -56,6 +47,7 @@ load_kwargs = dict(
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keywords=['scales', 'snip', 'measure']
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)
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compute_ratios = True
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exclude_zero = True
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show_diag = True
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show_plateaus = True
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@@ -275,169 +267,180 @@ plateau_dot_kwargs = dict(
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)
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# PREPARATION:
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if compute_ratios:
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ref_measures = dict(np.load(ref_path))
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species_measures = {}
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thresh_inds = np.zeros((len(target_species),), dtype=int)
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for i, species in enumerate(target_species):
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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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measure = spec_data['mean_inv'].mean(axis=-1)
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if exclude_zero:
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measure = measure[spec_data['scales'] > 0]
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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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# EXECUTION:
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for data_path in data_paths:
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print(f'Processing {data_path}')
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print(f'Processing {data_path}')
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# Load invariance data:
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pure_data, config = load_data(data_path, **load_kwargs)
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noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
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pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
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t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
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# Load invariance data:
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pure_data, config = load_data(data_path, **load_kwargs)
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noise_data, _ = load_data(data_path.replace('pure', 'noise'), **load_kwargs)
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pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
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t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
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# Prepare overall graph:
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fig = plt.figure(**fig_kwargs)
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super_grid = fig.add_gridspec(**super_grid_kwargs)
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fig.canvas.draw()
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if compute_ratios:
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# Relate pure-song measures to near-zero scale:
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pure_data['measure_env'] /= pure_data['measure_env'][1]
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pure_data['measure_log'] /= pure_data['measure_log'][1]
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pure_data['measure_inv'] /= pure_data['measure_inv'][1]
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# Relate noise-song measures to zero scale:
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noise_data['measure_env'] /= noise_data['measure_env'][0]
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noise_data['measure_log'] /= noise_data['measure_log'][0]
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noise_data['measure_inv'] /= noise_data['measure_inv'][0]
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# Prepare pure-song snippet axes:
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pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
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pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
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pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
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for ax, stage in zip(pure_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=pure_subfig.transSubfigure)
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for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
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pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
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pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
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pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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pure_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(pure_inset, 'bottom', ticks=False)
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if exclude_zero:
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# Exclude zero scales:
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inds = pure_scales > 0
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pure_scales = pure_scales[inds]
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pure_data['measure_env'] = pure_data['measure_env'][inds]
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pure_data['measure_log'] = pure_data['measure_log'][inds]
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pure_data['measure_inv'] = pure_data['measure_inv'][inds]
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inds = noise_scales > 0
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noise_scales = noise_scales[inds]
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noise_data['measure_env'] = noise_data['measure_env'][inds]
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noise_data['measure_log'] = noise_data['measure_log'][inds]
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noise_data['measure_inv'] = noise_data['measure_inv'][inds]
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# Prepare noise-song snippet axes:
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noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
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noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
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noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
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for ax, stage in zip(noise_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
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noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
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noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
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noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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noise_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(noise_inset, 'bottom', ticks=False)
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# Prepare overall graph:
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fig = plt.figure(**fig_kwargs)
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super_grid = fig.add_gridspec(**super_grid_kwargs)
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fig.canvas.draw()
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_aspect(**anchor_kwargs)
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if i > 0:
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hide_ticks(ax, 'left')
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big_axes[i] = ax
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ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
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letter_subplots(big_axes, 'cde', **letter_big_kwargs)
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# Prepare pure-song snippet axes:
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pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
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pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
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pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
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for ax, stage in zip(pure_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=pure_subfig.transSubfigure)
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for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
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pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
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pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
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pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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pure_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(pure_inset, 'bottom', ticks=False)
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# Plot pure-song envelope snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
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ymin=0, c=colors['env'], lw=lw['snip'])[0]
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zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
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# Prepare noise-song snippet axes:
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noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
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noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
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noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
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for ax, stage in zip(noise_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
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noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
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noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
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noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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noise_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(noise_inset, 'bottom', ticks=False)
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# Plot pure-song logarithmic snippets:
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plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
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c=colors['log'], lw=lw['snip'])
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_aspect(**anchor_kwargs)
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if i > 0:
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hide_ticks(ax, 'left')
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big_axes[i] = ax
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ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
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letter_subplots(big_axes, 'cde', **letter_big_kwargs)
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# Plot pure-song invariant snippets:
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plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
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c=colors['inv'], lw=lw['snip'])
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# Plot pure-song envelope snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
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ymin=0, c=colors['env'], lw=lw['snip'])[0]
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zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot noise-song envelope snippets:
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ymin, ymax = pure_axes[0, 0].get_ylim()
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handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
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ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
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zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot pure-song logarithmic snippets:
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plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
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c=colors['log'], lw=lw['snip'])
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# Plot noise-song logarithmic snippets:
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ymin, ymax = pure_axes[1, 0].get_ylim()
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plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
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ymin, ymax, c=colors['log'], lw=lw['snip'])
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# Plot pure-song invariant snippets:
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plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
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c=colors['inv'], lw=lw['snip'])
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# Plot noise-song invariant snippets:
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ymin, ymax = pure_axes[2, 0].get_ylim()
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plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
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ymin, ymax, c=colors['inv'], lw=lw['snip'])
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# Plot noise-song envelope snippets:
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ymin, ymax = pure_axes[0, 0].get_ylim()
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handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
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ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
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zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
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# Indicate time scale:
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time_bar(noise_axes[-1, -1], **bar_kwargs)
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# Plot noise-song logarithmic snippets:
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ymin, ymax = pure_axes[1, 0].get_ylim()
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plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
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ymin, ymax, c=colors['log'], lw=lw['snip'])
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if compute_ratios:
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# Relate pure-song measures to zero scale:
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pure_data['measure_env'] /= ref_measures['env']
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pure_data['measure_log'] /= ref_measures['log']
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pure_data['measure_inv'] /= ref_measures['inv']
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# Relate noise-song measures to zero scale:
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noise_data['measure_env'] /= ref_measures['env']
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noise_data['measure_log'] /= ref_measures['log']
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noise_data['measure_inv'] /= ref_measures['inv']
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# Plot noise-song invariant snippets:
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ymin, ymax = pure_axes[2, 0].get_ylim()
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plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
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ymin, ymax, c=colors['inv'], lw=lw['snip'])
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# Plot pure-song measures (ideal):
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big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
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big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
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big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])
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# Indicate time scale:
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time_bar(noise_axes[-1, -1], **bar_kwargs)
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# Plot noise-song measures (limited):
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big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
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big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
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big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])
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# Plot pure-song measures (ideal):
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big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
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big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
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big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])
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if show_diag:
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# Indicate diagonal:
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big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
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big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
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# Plot noise-song measures (limited):
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big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
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big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
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big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])
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if show_plateaus:
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# Indicate low and high plateaus of noise invariance curve:
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low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
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big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
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fc=noise_colors[0], **plateau_rect_kwargs)
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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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if show_diag:
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# Indicate diagonal:
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big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
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big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
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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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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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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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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 show_plateaus:
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# Indicate low and high plateaus of noise invariance curve:
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low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
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big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
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fc=noise_colors[0], **plateau_rect_kwargs)
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big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
|
||||
fc=noise_colors[1], **plateau_rect_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path, bbox_inches='tight')
|
||||
plt.show()
|
||||
# Plot species-specific noise-song invariance curves:
|
||||
for i, (species, measure) in enumerate(species_measures.items()):
|
||||
# Plot invariance curve:
|
||||
color = species_colors[species]
|
||||
big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
|
||||
c=color, lw=lw['spec'])
|
||||
# Indicate saturation:
|
||||
ind = thresh_inds[i]
|
||||
scale = noise_scales[ind]
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
legend = big_axes[2].legend(**leg_kwargs)
|
||||
[h.set_lw(lw['legend']) for h in legend.legend_handles]
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
Reference in New Issue
Block a user