131 lines
2.8 KiB
Python
131 lines
2.8 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 xlabel, super_ylabel
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from color_functions import load_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/log_hp/saturation/'
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save_path = '../figures/fig_saturation_log-hp_appendix.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=len(target_species),
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ncols=1,
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sharex=True,
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sharey=False,
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gridspec_kw=dict(
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wspace=0,
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hspace=0.3,
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left=0.09,
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right=0.99,
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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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bar_kwargs = dict(
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ec='w',
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)
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mean_kwargs = dict(
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c='k',
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lw=3,
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ls='--'
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)
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xlab = 'scale $\\alpha$'
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ylab = '$\\text{PDF}_{\\alpha}$'
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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=0.005,
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fontsize=16,
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ha='left',
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va='center',
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)
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leg_x = fig_kwargs['gridspec_kw']['left']
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leg_y = fig_kwargs['gridspec_kw']['top']
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leg_box = [
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leg_x,
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leg_y,
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fig_kwargs['gridspec_kw']['right'] - leg_x,
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1 - leg_y
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]
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leg_kwargs = dict(
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ncols=len(target_species),
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loc='upper center',
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bbox_to_anchor=leg_box,
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frameon=False,
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prop=dict(
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size=15,
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style='italic',
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),
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borderpad=0,
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borderaxespad=0,
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handlelength=1,
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columnspacing=1,
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)
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text_kwargs = dict(
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x=1,
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y=1,
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fontsize=14,
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ha='right',
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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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xlabel(axes[-1], xlab, **xlab_kwargs, transform=fig.transFigure)
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super_ylabel(ylab, fig, axes[0], axes[-1], **ylab_kwargs)
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# Run through species:
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handles = []
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for species, ax in zip(target_species, axes):
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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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hist = data['hist']
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bins = data['bins']
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n_songs = data['crit_scales'].size
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# Plot distribution of saturation points:
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handles.append(ax.bar(bins, hist, width=bins[1] - bins[0], fc=color, **bar_kwargs))
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ax.set_ylim(0, hist.max() * 1.05)
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# Indicate mean of distribution:
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ax.axvline(data['crit_scales'].mean(), **mean_kwargs)
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# Indicate number of songs:
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ax.text(**text_kwargs, s=f'n = {n_songs}', transform=ax.transAxes)
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# Posthocs:
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labels = [shorten_species(species) for species in target_species]
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fig.legend(handles, labels, **leg_kwargs)
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ax.set_xlim(0, bins[-1])
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# Save graph:
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fig.savefig(save_path)
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plt.show()
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