Made fig_invariance_rect_lp.pdf and corresponding appendix figure.

Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB.

Co-authored-by: Copilot <copilot@github.com>

python/fig_invariance_rect-lp_appendix.py

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+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data
+from plot_functions import ylabel, super_xlabel, super_ylabel, title_subplot, time_bar
+from color_functions import load_colors
+from misc_functions import shorten_species
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    # 'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+data_path = '../data/inv/rect_lp/condensed/'
+save_path = '../figures/fig_invariance_rect-lp_appendix.pdf'
+
+# ANALYSIS SETTINGS:
+relate_to_noise = True
+exclude_zero = True
+cutoffs = np.array([np.nan, 2500, 250, 25])
+search_kwargs = dict(
+    incl=['noise', 'norm-base' if relate_to_noise else 'unnormed'],
+    dir=data_path,
+)
+
+# GRAPH SETTINGS:
+fig_kwargs = dict(
+    figsize=(32/2.54, 16/2.54),
+    nrows=cutoffs.size,
+    ncols=len(target_species),
+    sharex=True,
+    sharey=True,
+    gridspec_kw=dict(
+        wspace=0.4,
+        hspace=0.2,
+        left=0.12,
+        right=0.98,
+        bottom=0.1,
+        top=0.95,
+    )
+)
+
+# PLOT SETTINGS:
+species_colors = load_colors('../data/species_colors.npz')
+line_kwargs = dict(
+    lw=2,
+    alpha=0.5,
+    zorder=2,
+)
+fill_kwargs = dict(
+    alpha=0.3,
+    zorder=1,
+)
+mean_kwargs = dict(
+    lw=2,
+    alpha=1,
+    zorder=3,
+    ls='--'
+)
+mean_colors = {
+    'Chorthippus_biguttulus': (1,) * 3,
+    'Chorthippus_mollis': (0,) * 3,
+    'Chrysochraon_dispar': (0,) * 3,
+    'Euchorthippus_declivus': (0,) * 3,
+    'Gomphocerippus_rufus': (0,) * 3,
+    'Omocestus_rufipes': (0,) * 3,
+    'Pseudochorthippus_parallelus': (1,) * 3,
+}
+xlab = 'scale $\\alpha$'
+ylabs = ['$\\text{unfiltered}$'] + [f'${int(cutoff)}\\,\\text{{Hz}}$' for cutoff in cutoffs[1:]]
+super_ylab = '$\\sigma_{\\text{env}}\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else '$\\sigma_{\\text{env}}$'
+xlab_kwargs = dict(
+    y=0,
+    fontsize=16,
+    ha='center',
+    va='bottom',
+)
+ylab_kwargs = dict(
+    x=0.05,
+    fontsize=16,
+    ha='center',
+    va='top',
+)
+ylab_super_kwargs = dict(
+    x=0,
+    fontsize=20,
+    ha='left',
+    va='center',
+)
+title_kwargs = dict(
+    x=0.5,
+    yref=0.99,
+    ha='center',
+    va='top',
+    fontsize=16,
+    fontstyle='italic',
+)
+letter_kwargs = dict(
+    x=0.005,
+    y=0.99,
+    fontsize=22,
+    ha='left',
+    va='top',
+)
+
+# Prepare graph:
+fig, axes = plt.subplots(**fig_kwargs)
+[ylabel(ax, lab, transform=fig.transFigure, **ylab_kwargs) for ax, lab in zip(axes[:, 0], ylabs)]
+super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
+super_ylabel(super_ylab, fig, axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
+
+# Run through species:
+for i, (species, spec_axes) in enumerate(zip(target_species, axes.T)):
+    title_subplot(spec_axes[0], shorten_species(species), ref=fig, **title_kwargs)
+    
+    # Load species data:
+    path = search_files(species, **search_kwargs)[0]
+    data, config = load_data(path, files=['scales', 'mean_env', 'sd_env'])
+    scales = data['scales']
+    means = data['mean_env']
+    sds = data['sd_env']
+
+    if exclude_zero:
+        # Exclude zero scale:
+        inds = scales > 0
+        scales = scales[inds]
+        means = means[inds, ...]
+        sds = sds[inds, ...]
+
+    # Run through cutoffs:
+    for j, ax in enumerate(spec_axes):
+        # Plot recording-specific traces:
+        for k in range(means.shape[-1]):
+            ax.plot(scales, means[:, j, k], c=species_colors[species], **line_kwargs)
+            spread = (means[:, j, k] - sds[:, j, k], means[:, j, k] + sds[:, j, k])
+            ax.fill_between(scales, *spread, color=species_colors[species], **fill_kwargs)
+        # Plot cutoff-specific mean trace:
+        ax.plot(scales, means[:, j, :].mean(axis=-1), c=mean_colors[species], **mean_kwargs)
+
+# Posthocs:
+sylog_kwargs = dict(linthresh=scales[scales > 0][0], linscale=0.5)
+axes[0, 0].set_xscale('symlog', **sylog_kwargs)
+axes[0, 0].set_yscale('symlog', **sylog_kwargs)
+axes[0, 0].set_xlim(scales[0], scales[-1])
+axes[0, 0].set_ylim(0.9, scales[-1])
+axes[0, 0].xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
+
+# Save graph:
+fig.savefig(save_path)
+plt.show()  
+
+