192 lines
4.9 KiB
Python
192 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 plot_functions import ylabel, ylimits, super_xlabel, title_subplot, time_bar
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from color_functions import load_colors, shade_colors
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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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data_path = '../data/inv/thresh_lp/condensed/'
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save_path = '../figures/fig_invariance_thresh-lp_appendix.pdf'
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# ANALYSIS SETTINGS:
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exclude_zero = True
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# SUBSET SETTINGS:
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thresh_rel = np.array([0.5, 1, 3])[0]
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kern_specs = np.array([
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[1, 0.008],
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[2, 0.004],
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[3, 0.002],
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])[np.array([0, 1, 2])]
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n_kernels = kern_specs.shape[0]
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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=n_kernels,
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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=0.2,
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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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species_colors = load_colors('../data/species_colors.npz')
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kern_shades = [0, 0.75]
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kern_colors = shade_colors((0., 0., 0.), np.linspace(*kern_shades, n_kernels))
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line_kwargs = dict(
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lw=2,
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alpha=0.5,
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zorder=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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mean_kwargs = dict(
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# c=(0.5,) * 3,
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lw=2,
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alpha=1,
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zorder=3,
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ls='--'
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)
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mean_colors = {
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'Chorthippus_biguttulus': (1,) * 3,
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'Chorthippus_mollis': (0,) * 3,
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'Chrysochraon_dispar': (0,) * 3,
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'Euchorthippus_declivus': (0,) * 3,
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'Gomphocerippus_rufus': (0,) * 3,
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'Omocestus_rufipes': (0,) * 3,
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'Pseudochorthippus_parallelus': (1,) * 3,
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}
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kern_kwargs = dict(
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lw=2,
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)
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inset_bounds = [0.05, 0.6, 0.3, 0.25]
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kern_bar_time = 0.05
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kern_bar_kwargs = dict(
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dur=kern_bar_time,
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y0=0.1,
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y1=0.2,
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color='k',
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lw=0,
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clip_on=False,
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text_pos=(0.5, -1),
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text_str=f'${int(kern_bar_time * 1000)}\\,\\text{{ms}}$',
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text_kwargs=dict(
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fontsize=12,
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ha='center',
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va='top',
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)
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)
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xlab = 'scale $\\alpha$'
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ylabs = [f'$\\mu_{{f_{i}}}$' for i in range(1, n_kernels + 1)]
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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, 0].set_xscale('log')
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axes[0, 0].set_ylim(0, 1)
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axes[0, 0].yaxis.set_major_locator(plt.MultipleLocator(0.5))
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super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
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insets = []
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for ax, ylab in zip(axes[:, 0], ylabs):
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ylabel(ax, ylab, **ylab_kwargs, transform=fig.transFigure)
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insets.append(ax.inset_axes(inset_bounds))
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# Run through species:
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for i, (species, spec_axes) in enumerate(zip(target_species, axes.T)):
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title_subplot(spec_axes[0], shorten_species(species), ref=fig, **title_kwargs)
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# Load species data:
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path = search_files(species, dir=data_path)[0]
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data, config = load_data(path, files=['scales', 'mean_feat', 'sd_feat', 'thresh_rel'])
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scales = data['scales']
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means = data['mean_feat']
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sds = data['sd_feat']
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# Reduce to single threshold:
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ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
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means = means[:, :, ind, :]
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sds = sds[:, :, ind, :]
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if exclude_zero:
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# Exclude zero scale:
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inds = scales > 0
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scales = scales[inds]
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means = means[inds, :, :]
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sds = sds[inds, :, :]
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# Run through kernels:
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for j, (ax, inset) in enumerate(zip(spec_axes, insets)):
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if i == 0:
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# Indicate kernel waveform:
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inset.plot(config['k_times'], config['kernels'][:, j],
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c=kern_colors[j], **kern_kwargs)
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inset.set_xlim(config['k_times'][[0, -1]])
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ylimits(config['kernels'], inset, pad=0.05)
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inset.set_title(rf'$k_{{{j+1}}}$', fontsize=15)
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if j == 0:
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time_bar(inset, **kern_bar_kwargs)
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inset.axis('off')
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# Plot recording-specific traces:
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for k in range(means.shape[-1]):
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ax.plot(scales, means[:, j, k], c=species_colors[species], **line_kwargs)
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spread = (means[:, j, k] - sds[:, j, k], means[:, j, k] + sds[:, j, k])
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ax.fill_between(scales, *spread, color=species_colors[species], **fill_kwargs)
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# Plot kernel-specific mean trace:
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ax.plot(scales, means[:, j, :].mean(axis=-1), c=mean_colors[species], **mean_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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