Made fig_invariance_cross_species_thresh__appendix.pdf.
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j-hartling
247
python/fig_inv_cross_spec-thresh_appendix.py
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247
python/fig_inv_cross_spec-thresh_appendix.py
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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.modeltools import load_data
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from thunderhopper.filetools import search_files
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from thunderhopper.filtertools import find_kern_specs
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from misc_functions import shorten_species, x_dist, y_dist, get_saturation
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from color_functions import load_colors
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from plot_functions import reorder_by_sd, ylabel, super_xlabel, super_ylabel,\
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title_subplot, assign_colors, strip_zeros, hide_axis,\
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hide_ticks
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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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example_files = {
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'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
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'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
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'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
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'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
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'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
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'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
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'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
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}
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search_path = '../data/inv/full/'
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save_path = '../figures/fig_invariance_cross_species_thresh_appendix.pdf'
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# ANALYSIS SETTINGS:
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exclude_zero = True
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thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])
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# SUBSET SETTINGS:
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types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
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# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
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sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
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# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
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kernels = None
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reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 32/2.54),
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nrows=thresh_rel.size,
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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=0.2,
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hspace=0.75,
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left=0.1,
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right=0.95,
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bottom=0.08,
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top=0.98,
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)
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)
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inset_x_bounds = [0, -0.5, 1, 0.4]
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inset_y_bounds = [1.01, 0, 0.1, 1]
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# PLOT SETTINGS:
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fs = dict(
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lab_norm=16,
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lab_tex=20,
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letter=22,
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tit_norm=16,
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tit_tex=20,
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bar=16,
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)
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lw = dict(
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swarm=1,
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single=3,
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dist=2,
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)
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base_color = load_colors('../data/stage_colors.npz')['feat']
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kern_colors = load_colors('../data/feat_colors_subset.npz')
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median_kwargs = dict(
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c='k',
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lw=lw['single'],
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ls='--',
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zorder=3
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)
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xlab = 'scale $\\alpha$'
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xlab_kwargs = dict(
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y=0,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom'
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)
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ylab = '$\\mu_{f_i}$'
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ylab_super_kwargs = dict(
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x=0,
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fontsize=fs['lab_norm'],
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ha='left',
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va='center'
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)
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ylab_ax_kwargs = dict(
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x=0.03,
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fontsize=fs['lab_norm'],
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ha='center',
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va='top'
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)
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yloc = 0.5
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title_kwargs = dict(
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x=0.5,
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yref=1,
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fontsize=fs['tit_norm'],
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ha='center',
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va='top',
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fontstyle='italic'
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)
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plateau_settings = dict(
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low=0.05,
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high=0.95,
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first=True,
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last=True,
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condense=None,
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)
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plateau_dot_kwargs = dict(
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marker='o',
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mfc=base_color,
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mec='k',
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ms=8,
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mew=1,
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clip_on=False,
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zorder=6
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)
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x_dist_kwargs = dict(
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line_kwargs = dict(
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c=base_color,
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lw=lw['dist'],
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),
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fill_kwargs = dict(
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color=base_color,
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alpha=1,
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),
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nbins=100,
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log=True,
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)
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y_dist_kwargs = dict(
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line_kwargs = dict(
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c=base_color,
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lw=lw['dist'],
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),
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fill_kwargs = dict(
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color=base_color,
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alpha=1,
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),
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edges=np.linspace(0, 1, 101),
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log=False,
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)
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# EXECUTION:
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# Prepare graph:
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fig, axes = plt.subplots(**fig_kwargs)
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axes[0, 0].set_ylim(0, 1)
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axes[0, 0].yaxis.set_major_locator(plt.MultipleLocator(yloc))
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super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
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super_ylabel(ylab, fig, axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
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for ax, species in zip(axes[0, :], target_species):
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title_subplot(ax, shorten_species(species), ref=fig, **title_kwargs)
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for ax, thresh in zip(axes[:, 0], thresh_rel):
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title = f'$\\Theta_i\\,=\\,{strip_zeros(thresh)}\\,\\cdot\\,\\sigma_{{\\eta_i}}$'
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ylabel(ax, title, transform=fig.transFigure, **ylab_ax_kwargs)
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for ax in axes[-1, :]:
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hide_ticks(ax, 'bottom')
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# Run through species:
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for i, species in enumerate(target_species):
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print(f'Processing {species}...')
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# Load invariance data:
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path = search_files(example_files[species], dir=search_path)[0]
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data, config = load_data(path, ['scales', 'measure_feat', 'thresh_rel'])
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scales, measure = data['scales'], data['measure_feat']
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# Reduce data:
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if exclude_zero:
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inds = np.nonzero(scales > 0)[0]
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scales, measure = scales[inds], measure[inds, ...]
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if reduce_kernels:
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kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
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measure = measure[:, kern_inds, :]
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config['kernels'] = config['kernels'][:, kern_inds]
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config['k_specs'] = config['k_specs'][kern_inds, :]
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if i == 0:
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# Update settings:
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x_dist_kwargs['edges'] = np.geomspace(scales[scales > 0][0], scales[-1],
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x_dist_kwargs['nbins'] + 1)
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symlog_kwargs = dict(linthresh=scales[scales > 0][0], linscale=0.5)
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# Run through thresholds:
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for j in range(thresh_rel.size):
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ax = axes[j, i]
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# Plot swarm of feature-specific intensity curves:
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handles = ax.plot(scales, measure[:, :, j], lw=lw['swarm'])
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assign_colors(handles, config['k_specs'][:, 0], kern_colors)
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reorder_by_sd(handles, measure[:, :, j])
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# Plot single compressed intensity curve:
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compressed = np.median(measure[:, :, j], axis=1)
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ax.plot(scales, compressed, **median_kwargs)
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# Plot distribution of saturation levels:
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inset = ax.inset_axes(inset_y_bounds)
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inset.set_ylim(0, 1)
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inset.axis('off')
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y_dist(inset, measure[-1, :, j], **y_dist_kwargs)
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# Plot distribution of saturation points:
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crit_inds = np.array(get_saturation(measure[:, :, j], **plateau_settings)[1])
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if np.isnan(crit_inds).sum():
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print(f'WARNING: No saturation points found for {species} at threshold {thresh_rel[j]}')
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crit_inds = crit_inds[~np.isnan(crit_inds)].astype(int)
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crit_scales = scales[crit_inds]
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inset = ax.inset_axes(inset_x_bounds)
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inset.set_xlim(scales[0], scales[-1])
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inset.set_xscale('symlog', **symlog_kwargs)
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hide_axis(inset, 'left')
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if j < thresh_rel.size - 1:
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hide_ticks(inset, 'bottom')
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x_dist(inset, crit_scales, **x_dist_kwargs)
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if j > 0:
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# Plot single saturation point:
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crit_ind = get_saturation(compressed, **plateau_settings)[1]
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crit_scale = scales[crit_ind]
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inset.plot(crit_scale, 0, **plateau_dot_kwargs)
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# Posthocs:
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axes[0, 0].set_xscale('symlog', **symlog_kwargs)
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axes[0, 0].set_xlim(scales[0], scales[-1])
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if save_path is not None:
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fig.savefig(save_path)
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print('Done.')
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plt.show()
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