36ac504efa
Worked those into LogHP analysis. Worked results into fig_invariance_log-hp.pdf. Put details into new fig_invariance_log-hp_species.pdf (appendix).
109 lines
2.4 KiB
Python
109 lines
2.4 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 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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