161 lines
4.9 KiB
Python
161 lines
4.9 KiB
Python
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 thunderhopper.modeltools import load_data
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from thunderhopper.filtertools import find_kern_specs
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from color_functions import load_colors, shade_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
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plot_line, plot_barcode, strip_zeros, time_bar,\
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letter_subplot, letter_subplots, hide_ticks,\
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super_xlabel, super_ylabel, assign_colors
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from IPython import embed
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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search_kwargs = dict(
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incl='subset',
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excl='noise',
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dir='../data/inv/thresh_lp/'
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)
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pure_paths = search_files(target, **search_kwargs)
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load_kwargs = dict(
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keywords=['scales', 'measure', 'thresh']
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)
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save_path = None#'../figures/fig_invariance_thresh_lp_subset.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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)
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super_grid_kwargs = dict(
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nrows=1,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0,
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right=1,
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bottom=0,
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top=1
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)
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grid_kwargs = dict(
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nrows=2,
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ncols=1,
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wspace=0,
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hspace=0.1,
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left=0.15,
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right=0.95,
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bottom=0.1,
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top=0.85
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)
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inset_bounds = [0.2, 1.01, 0.6, 0.4]
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# PLOT SETTINGS:
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colors = load_colors('../data/stage_colors.npz')
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color_factors = [-0.5, 0.5]
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lw = dict(
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one=3,
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kern=3,
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all=1,
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)
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ax_labels = dict(
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x='scale $\\alpha$',
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y='$\\mu_f$',
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)
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xlab_kwargs = dict(
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y=0.005,
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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='left',
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va='center',
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)
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yloc = 0.2
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# EXECUTION:
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for pure_path in pure_paths:
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print(f'Processing {pure_path}')
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noise_path = pure_path.replace('.npz', '_noise.npz')
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# Load kernel invariance data:
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pure_data, config = load_data(pure_path, **load_kwargs)
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noise_data, _ = load_data(noise_path, **load_kwargs)
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scales = pure_data['scales']
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# Adjust grid parameters:
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n_columns = config['k_specs'].shape[0] + 1
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super_grid_kwargs['ncols'] = n_columns
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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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# Prepare axes:
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all_axes = np.zeros((grid_kwargs['nrows'], n_columns), dtype=object)
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subfigs = []
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for i in range(n_columns):
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subfig = fig.add_subfigure(super_grid[0, i])
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grid = subfig.add_gridspec(**grid_kwargs)
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subfigs.append(subfig)
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for j in range(grid_kwargs['nrows']):
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ax = subfig.add_subplot(grid[j, 0])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(0, 1)
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
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if i > 0:
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hide_ticks(ax, side='left')
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all_axes[j, i] = ax
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hide_ticks(all_axes[0, i], side='bottom')
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super_xlabel(ax_labels['x'], fig, all_axes[-1, 0], all_axes[-1, -1], **xlab_kwargs)
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super_ylabel(ax_labels['y'], fig, all_axes[0, 0], all_axes[1, 0], **ylab_kwargs)
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# Plot kernel-specific results:
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in_min, in_high = ylimits(config['kernels'], pad=0.05)
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for i in range(config['k_specs'].shape[0]):
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pure_ax, noise_ax = all_axes[:, i]
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# Plot results of pure-song analysis:
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pure_ax.plot(scales, pure_data['measure_feat'][:, i, :],
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c=colors['feat'], lw=lw['one'])
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# Plot results of noise-song analysis:
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noise_ax.plot(scales, noise_data['measure_feat'][:, i, :],
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c=colors['feat'], lw=lw['one'])
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# Indicate kernel waveform:
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inset = pure_ax.inset_axes(inset_bounds)
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inset.plot(config['k_times'], config['kernels'][:, i], c='k', lw=lw['kern'])
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inset.set_xlim(config['k_times'][0], config['k_times'][-1])
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inset.set_ylim(in_min, in_high)
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inset.axis('off')
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# Load population invariance data:
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pure_data, config = load_data(pure_path.replace('_subset', ''), **load_kwargs)
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noise_data, _ = load_data(noise_path.replace('_subset', ''), **load_kwargs)
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scales = pure_data['scales']
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# Get kernel type-specific colors:
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types, ind = np.unique(config['k_specs'][:, 0], return_index=True)
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types = types[np.argsort(ind)].astype(int)
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factors = np.linspace(*color_factors, types.size)
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kern_colors = shade_colors(colors['feat'], factors)
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kern_colors = dict(zip(types.astype(str), kern_colors))
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# Plot population-wide results:
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pure_ax, noise_ax = all_axes[:, -1]
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handles = pure_ax.plot(scales, pure_data['measure_feat'], c='k', lw=lw['all'])
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assign_colors(handles, config['k_specs'][:, 0], kern_colors)
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handles = noise_ax.plot(scales, noise_data['measure_feat'], c='k', lw=lw['all'])
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assign_colors(handles, config['k_specs'][:, 0], kern_colors)
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if save_path is not None:
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fig.savefig(save_path)
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plt.show()
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print('Done.')
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embed()
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