Made fig_invariance_cross_species_thresh__appendix.pdf.

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main.fls

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 INPUT ./figures/fig_kernel_sd_perc_field_appendix.pdf
 INPUT ./figures/fig_kernel_sd_perc_field_appendix.pdf
+INPUT ./figures/fig_invariance_cross_species_thresh_appendix.pdf
+INPUT ./figures/fig_invariance_cross_species_thresh_appendix.pdf
+INPUT ./figures/fig_invariance_cross_species_thresh_appendix.pdf
+INPUT ./figures/fig_invariance_cross_species_thresh_appendix.pdf
+INPUT ./figures/fig_invariance_cross_species_thresh_appendix.pdf
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main.tex

@@ -1021,4 +1021,14 @@ initiation of one behavior over another is categorical (e.g. approach/stay)
 \end{figure}
 \FloatBarrier
 
+
+\begin{figure}[!ht]
+    \centering
+    \includegraphics[width=\textwidth]{figures/fig_invariance_cross_species_thresh_appendix.pdf}
+    \caption{\textbf{}
+                     }
+    \label{}
+\end{figure}
+\FloatBarrier
+
 \end{document}

python/fig_features_cross_species.py

@@ -54,9 +54,9 @@ calculate_regression = True
 test_regression = True
 
 # SUBSET SETTINGS:
-types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
+types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
 # types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
-sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
+sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
 # sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
 kernels = None
 reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
@@ -84,7 +84,7 @@ song_grid_kwargs = dict(
 )
 
 # PLOT SETTINGS:
-kern_colors = load_colors('../data/feat_colors_all.npz')
+kern_colors = load_colors('../data/feat_colors_subset.npz')
 fs = dict(
     lab_norm=16,
     lab_tex=20,
@@ -178,16 +178,44 @@ if test_regression:
         test_ax_side,
         test_ax_side
     ]
-    xlab_test = '$\\rho$'
+    ylab_test = '$\\rho$'
-    ylab_test = '$\\text{PDF}_{\\rho}$'
+    yloc_test = 0.5
-    xloc_test = 0.5
-    yloc_test = 10
     ylab_test_kwargs = dict(
         x=-0.3,
         fontsize=fs['lab_norm'],
         ha='center',
         va='bottom',
     )
+    boxplot_kwargs = dict(
+        positions=[0, 1],
+        widths=0.9,
+        tick_labels=['inter', 'intra'],
+        zorder=1,
+        medianprops=dict(
+            color='k',
+            lw=1,
+        ),
+        boxprops=dict(
+            color='k',
+            lw=1,
+        ),
+    )
+    boxplot_kwargs.update(
+        capprops=boxplot_kwargs['boxprops'],
+        whiskerprops=boxplot_kwargs['boxprops'],
+    )
+    boxplot_dot_kwargs = dict(
+        ls='none',
+        marker='o',
+        ms=4,
+        mec='k',
+        mfc='w',
+        mew=1.5,
+        alpha=0.5,
+        zorder=2,
+    )
+
+
     nbins = 10
     spec_color = 'darkorchid'
     song_color = 'goldenrod'
@@ -385,35 +413,22 @@ for x, y in product(range(n_song), range(n_song)):
 if test_regression:
     # Add test result subplot:
     test_ax = fig.add_subplot(test_ax_bounds)
-    test_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc_test))
+    test_ax.set_xlim(-0.6, 1.6)
+    test_ax.set_ylim(0, 1)
     test_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc_test))
-    xlabel(test_ax, xlab_test, transform=fig.transFigure, **xlab_low_kwargs)
     ylabel(test_ax, ylab_test, **ylab_test_kwargs)
+
+    # Show boxplots of correlation coefficients:
+    test_ax.boxplot([spec_regs, song_regs], **boxplot_kwargs)
+
+    # Show underlying datapoints:
+    test_ax.plot(np.zeros(len(spec_regs)), spec_regs, **boxplot_dot_kwargs)
+    test_ax.plot(np.ones(len(song_regs)), song_regs, **boxplot_dot_kwargs)
+
     # Perform t-test:
     test = ttest_ind(spec_regs, song_regs, equal_var=False)
     t, p = test.pvalue, test.statistic
     print(f'\nT-test result: t={t}, p={p}')
-    # Calculate histograms:
-    limits = np.array([min(spec_regs + song_regs), max(spec_regs + song_regs)])
-    limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
-    edges = np.linspace(*limits, nbins + 1)
-    centers = edges[:-1] + (edges[1] - edges[0]) / 2
-    spec_hist, _ = np.histogram(spec_regs, bins=edges, density=True)
-    song_hist, _ = np.histogram(song_regs, bins=edges, density=True)
-    # Plot histograms:
-    bar_kwargs['width'] *= (centers[1] - centers[0])
-    test_ax.bar(centers, spec_hist, color=spec_color, label='inter-species', **bar_kwargs)
-    test_ax.bar(centers, song_hist, color=song_color, label='intra-species', **bar_kwargs)
-    # Indicate means:
-    test_ax.axvline(np.mean(spec_regs), color=spec_color, **mean_kwargs)
-    test_ax.axvline(np.mean(song_regs), color=song_color, **mean_kwargs)
-    # Add legend:
-    test_ax.legend(**leg_kwargs)
-    # Posthocs:
-    test_ax.set_ylim(0, max(spec_hist.max(), song_hist.max()) * 1.05)
-    test_ax.set_xlim(min(0, max(-1, limits[0])),
-                     min(1, limits[1]))
-    
 
 if save_path is not None:
     fig.savefig(save_path)

python/fig_inv_cross_spec-thresh_appendix.py

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

python/fig_invariance_field.py

@@ -5,11 +5,12 @@ from itertools import product
 from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data
 from thunderhopper.filtertools import find_kern_specs
-from misc_functions import get_saturation
+from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
+                            divide_by_zero, x_dist, y_dist
 from color_functions import load_colors
-from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel,\
+from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
-                           ylabel, title_subplot, plot_line, time_bar,\
+                           plot_line, strip_zeros, time_bar, assign_colors,\
-                           assign_colors, letter_subplot, letter_subplots
+                           letter_subplot, letter_subplots, hide_ticks
 from IPython import embed
 
 def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
@@ -17,25 +18,16 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
     handles = []
     for i, ax in enumerate(axes):
         handles.append(plot_line(ax, time, snippets[:, ..., i],
-                                 ymin=ymin, ymax=ymax, **kwargs))
+                       ymin=ymin, ymax=ymax, **kwargs))
     return handles
 
-def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
+def plot_curves(ax, scales, measures, **kwargs):
     if measures.ndim == 1:
-        ax.plot(scales, measures, **kwargs)[0]
+        handles = ax.plot(scales, measures, **kwargs)
-        return measures
+        return handles, measures
-    median_measure = np.median(measures, axis=1)
+    median_measure = np.nanmedian(measures, axis=1)
-    spread_measure = [np.percentile(measures, 25, axis=1),
+    line_handle = ax.plot(scales, median_measure, **kwargs)[0]
-                      np.percentile(measures, 75, axis=1)]
+    return line_handle, median_measure
-    ax.plot(scales, median_measure, **kwargs)[0]
-    ax.fill_between(scales, *spread_measure, **fill_kwargs)
-    return median_measure
-
-def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
-    for stage in stages:
-        key = f'{keyword}_{stage}'
-        data[key] = data[key][:, inds, ...]
-    return data
 
 def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']):
     half_offset = int((nin - nout) / 2)
@@ -64,19 +56,12 @@ save_path = '../figures/fig_invariance_field.pdf'
 offset_distance = 10 # centimeter
 
 # SUBSET SETTINGS:
-types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
+types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
-sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
 # types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
+sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
 # sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
-kernels = np.array([
-    [1, 0.002],
-    [-1, 0.002],
-    [2, 0.004],
-    [-2, 0.004],
-    [3, 0.032],
-    [-3, 0.032]
-])
 kernels = None
+reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
 
 # GRAPH SETTINGS:
 fig_kwargs = dict(

python/fig_invariance_field_backup.py

@@ -0,0 +1,433 @@
+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from itertools import product
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data
+from thunderhopper.filtertools import find_kern_specs
+from misc_functions import get_saturation
+from color_functions import load_colors
+from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel,\
+                           ylabel, title_subplot, plot_line, time_bar,\
+                           assign_colors, letter_subplot, letter_subplots
+from IPython import embed
+
+def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
+    ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
+    handles = []
+    for i, ax in enumerate(axes):
+        handles.append(plot_line(ax, time, snippets[:, ..., i],
+                                 ymin=ymin, ymax=ymax, **kwargs))
+    return handles
+
+def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
+    if measures.ndim == 1:
+        ax.plot(scales, measures, **kwargs)[0]
+        return measures
+    median_measure = np.median(measures, axis=1)
+    spread_measure = [np.percentile(measures, 25, axis=1),
+                      np.percentile(measures, 75, axis=1)]
+    ax.plot(scales, median_measure, **kwargs)[0]
+    ax.fill_between(scales, *spread_measure, **fill_kwargs)
+    return median_measure
+
+def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
+    for stage in stages:
+        key = f'{keyword}_{stage}'
+        data[key] = data[key][:, inds, ...]
+    return data
+
+def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']):
+    half_offset = int((nin - nout) / 2)
+    segment = np.arange(half_offset, half_offset + nout)
+    for stage in stages:
+        key = f'snip_{stage}'
+        snippets[key] = snippets[key][segment, ...]
+    return snippets
+
+
+# GENERAL SETTINGS:
+search_target = 'Pseudochorthippus_parallelus'
+stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
+song_example = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
+noise_example = 'merged_noise'
+song_path = '../data/inv/field/song/'
+noise_path = '../data/inv/field/noise/'
+raw_path = search_files(search_target, incl='unnormed', dir=song_path + 'condensed/')[0]
+base_path = search_files(search_target, incl='base', dir=song_path + 'condensed/')[0]
+range_path = search_files(search_target, incl='range', dir=song_path + 'condensed/')[0] 
+song_snip_path = search_files(song_example, dir=song_path)[0]
+noise_snip_path = search_files(noise_example, dir=noise_path)[0]
+save_path = '../figures/fig_invariance_field.pdf'
+
+# ANALYSIS SETTINGS:
+offset_distance = 10 # centimeter
+
+# SUBSET SETTINGS:
+types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
+sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
+# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
+# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
+kernels = np.array([
+    [1, 0.002],
+    [-1, 0.002],
+    [2, 0.004],
+    [-2, 0.004],
+    [3, 0.032],
+    [-3, 0.032]
+])
+kernels = None
+
+# GRAPH SETTINGS:
+fig_kwargs = dict(
+    figsize=(32/2.54, 32/2.54),
+)
+super_grid_kwargs = dict(
+    nrows=2,
+    ncols=1,
+    wspace=0,
+    hspace=0,
+    left=0,
+    right=1,
+    bottom=0,
+    top=1,
+    height_ratios=[3, 2]
+)
+subfig_specs = dict(
+    snip=(0, 0),
+    big=(1, 0),
+)
+snip_grid_kwargs = dict(
+    nrows=len(stages),
+    ncols=None,
+    wspace=0.1,
+    hspace=0.4,
+    left=0.11,
+    right=0.98,
+    bottom=0.08,
+    top=0.95
+)
+big_grid_kwargs = dict(
+    nrows=1,
+    ncols=3,
+    wspace=0.4,
+    hspace=0,
+    left=snip_grid_kwargs['left'],
+    right=snip_grid_kwargs['right'],
+    bottom=0.13,
+    top=0.98
+)
+
+# PLOT SETTINGS:
+fs = dict(
+    lab_norm=16,
+    lab_tex=20,
+    letter=22,
+    tit_norm=16,
+    tit_tex=20,
+    bar=16,
+)
+colors = load_colors('../data/stage_colors.npz')
+conv_colors = load_colors('../data/conv_colors_all.npz')
+feat_colors = load_colors('../data/feat_colors_all.npz')
+lw = dict(
+    filt=0.25,
+    env=0.25,
+    log=0.25,
+    inv=0.25,
+    conv=0.25,
+    feat=1,
+    big=3,
+    plateau=1.5,
+)
+xlabels = dict(
+    big='distance [cm]',
+)
+ylabels = dict(
+    filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
+    env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
+    log='$x_{\\text{log}}$\n$[\\text{dB}]$',
+    inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
+    conv='$c_i$\n$[\\text{dB}]$',
+    feat='$f_i$',
+    big=['measure', 'rel. measure', 'norm. measure']
+)
+xlab_big_kwargs = dict(
+    y=0,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='bottom',
+)
+ylab_snip_kwargs = dict(
+    x=0,
+    fontsize=fs['lab_tex'],
+    rotation=0,
+    ha='left',
+    va='center'
+)
+ylab_big_kwargs = dict(
+    x=-0.2,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='bottom',
+)
+yloc = dict(
+    filt=0.03,
+    env=0.01,
+    log=50,
+    inv=20,
+    conv=1,
+    feat=1,
+)
+title_kwargs = dict(
+    x=0.5,
+    yref=1,
+    ha='center',
+    va='top',
+    fontsize=fs['tit_norm'],
+)
+letter_snip_kwargs = dict(
+    x=0,
+    yref=0.5,
+    ha='left',
+    va='center',
+    fontsize=fs['letter'],
+)
+letter_big_kwargs = dict(
+    x=0,
+    y=1,
+    ha='left',
+    va='bottom',
+    fontsize=fs['letter'],
+)
+song_bar_time = 1
+song_bar_kwargs = dict(
+    dur=song_bar_time,
+    y0=-0.25,
+    y1=-0.1,
+    xshift=1,
+    color='k',
+    lw=0,
+    clip_on=False,
+    text_pos=(-0.1, 0.5),
+    text_str=f'${song_bar_time}\\,\\text{{s}}$',
+    text_kwargs=dict(
+        fontsize=fs['bar'],
+        ha='right',
+        va='center',
+    )
+)
+noise_bar_time = 0.5
+noise_bar_kwargs = song_bar_kwargs.copy()
+noise_bar_kwargs['dur'] = noise_bar_time
+noise_bar_kwargs['text_str'] = f'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$'
+plateau_settings = dict(
+    low=0.05,
+    high=0.95,
+    first=True,
+    last=True,
+    condense=None,
+)
+plateau_line_kwargs = dict(
+    lw=lw['plateau'],
+    ls='--',
+    zorder=1,
+)
+plateau_dot_kwargs = dict(
+    marker='o',
+    markersize=8,
+    markeredgewidth=1,
+    clip_on=False,
+)
+
+# EXECUTION:
+
+# Load raw (unnormed) invariance data:
+data, config = load_data(raw_path, files='distances', keywords='mean')
+dists = data['distances'] + offset_distance
+
+# Load snippet data:
+song_snip, _ = load_data(song_snip_path, keywords='snip')
+t_song = np.arange(song_snip['snip_filt'].shape[0]) / config['rate']
+noise_snip, _ = load_data(noise_snip_path, keywords='snip')
+noise_snip = crop_noise_snippets(noise_snip, noise_snip['snip_filt'].shape[0], t_song.size)
+t_noise = np.arange(noise_snip['snip_filt'].shape[0]) / config['rate']
+snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists]
+
+# Optional kernel subset:
+reduce_kernels = False
+if any(var is not None for var in [kernels, types, sigmas]):
+    kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
+    data = reduce_kernel_set(data, kern_inds, keyword='mean')
+    song_snip = reduce_kernel_set(song_snip, kern_inds, keyword='snip')
+    noise_snip = reduce_kernel_set(noise_snip, kern_inds, keyword='snip')
+    config['k_specs'] = config['k_specs'][kern_inds, :]
+    config['kernels'] = config['kernels'][:, kern_inds]
+    reduce_kernels = True
+
+# Adjust grid parameters:
+snip_grid_kwargs['ncols'] = len(snip_dists)
+
+# Prepare overall graph:
+fig = plt.figure(**fig_kwargs)
+super_grid = fig.add_gridspec(**super_grid_kwargs)
+
+# Prepare stage-specific snippet axes:
+snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
+snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
+snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
+for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
+    ax = snip_subfig.add_subplot(snip_grid[i, j])
+    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
+    hide_axis(ax, 'bottom')
+    if i == 0:
+        title = title_subplot(ax, snip_dists[j], ref=snip_subfig, **title_kwargs)
+    if j == 0:
+        ax.set_xlim(t_noise[0], t_noise[-1])
+        ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
+    else:
+        ax.set_xlim(t_song[0], t_song[-1])
+        hide_axis(ax, 'left')
+    snip_axes[i, j] = ax
+time_bar(snip_axes[-1, -1], **song_bar_kwargs)
+# time_bar(snip_axes[-1, 0], **noise_bar_kwargs)
+letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
+
+# Prepare analysis axes:
+big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
+big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
+big_axes = np.zeros((big_grid.ncols,), dtype=object)
+for i in range(big_grid.ncols):
+    ax = big_subfig.add_subplot(big_grid[0, i])
+    ax.set_xlim(dists[0], 0)
+    # ax.set_xscale('symlog', linthresh=offset_distance, linscale=0.5)
+    ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
+    ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
+    # if i < (big_grid.ncols - 1):
+    #     ax.set_ylim(scales[0], scales[-1])
+    # else:
+    #     ax.set_ylim(0, 1)
+    big_axes[i] = ax
+super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
+letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
+
+if True:
+    # Plot filtered snippets:
+    plot_snippets(snip_axes[0, 1:], t_song, song_snip['snip_filt'],
+                  c=colors['filt'], lw=lw['filt'])
+    plot_line(snip_axes[0, 0], t_noise, noise_snip['snip_filt'][:, 0],
+              *snip_axes[0, 1].get_ylim(), c=colors['filt'], lw=lw['filt'])
+
+    # Plot envelope snippets:
+    plot_snippets(snip_axes[1, 1:], t_song, song_snip['snip_env'],
+                  ymin=0, c=colors['env'], lw=lw['env'])
+    plot_line(snip_axes[1, 0], t_noise, noise_snip['snip_env'][:, 0],
+              *snip_axes[1, 1].get_ylim(), c=colors['env'], lw=lw['env'])
+
+    # Plot logarithmic snippets:
+    plot_snippets(snip_axes[2, 1:], t_song, song_snip['snip_log'],
+                  c=colors['log'], lw=lw['log'])
+    plot_line(snip_axes[2, 0], t_noise, noise_snip['snip_log'][:, 0],
+              *snip_axes[2, 1].get_ylim(), c=colors['log'], lw=lw['log'])
+
+    # Plot invariant snippets:
+    plot_snippets(snip_axes[3, 1:], t_song, song_snip['snip_inv'],
+                  c=colors['inv'], lw=lw['inv'])
+    plot_line(snip_axes[3, 0], t_noise, noise_snip['snip_inv'][:, 0],
+              *snip_axes[3, 1].get_ylim(), c=colors['inv'], lw=lw['inv'])
+
+    # Plot kernel response snippets:
+    all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_snip['snip_conv'],
+                                 c=colors['conv'], lw=lw['conv'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+        reorder_by_sd(handles, song_snip['snip_conv'][..., i])
+    handles = plot_line(snip_axes[4, 0], t_noise, noise_snip['snip_conv'][:, 0],
+                        *snip_axes[4, 1].get_ylim(), c=colors['conv'], lw=lw['conv'])
+    assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+    reorder_by_sd(handles, noise_snip['snip_conv'][:, 0])
+
+    # Plot feature snippets:
+    all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_snip['snip_feat'],
+                                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+        reorder_by_sd(handles, song_snip['snip_feat'][..., i])
+    handles = plot_line(snip_axes[5, 0], t_noise, noise_snip['snip_feat'][:, 0],
+                        ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+    reorder_by_sd(handles, noise_snip['snip_feat'][:, 0])
+del song_snip, noise_snip
+
+# Remember saturation points:
+crit_inds, crit_dists = {}, {}
+
+# Unnormed measures:
+for stage in stages:
+    # Plot average intensity measure across recordings:
+    curve = plot_curves(big_axes[0], dists, data[f'mean_{stage}'],
+                        c=colors[stage], lw=lw['big'],
+                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
+    # # Indicate saturation point:
+    # if stage in ['log', 'inv', 'conv', 'feat']:
+    #     ind = get_saturation(curve, **plateau_settings)[1]
+    #     dist = dists[ind]
+    #     big_axes[0].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+    #                      transform=big_axes[0].get_xaxis_transform()) 
+    #     big_axes[0].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
+    #                      transform=big_axes[0].get_xaxis_transform())
+    #     big_axes[0].vlines(dist, big_axes[0].get_ylim()[0], curve[ind],
+    #                        color=colors[stage], **plateau_line_kwargs)
+    #     # Log saturation point:
+    #     crit_inds[stage] = ind
+    #     crit_dists[stage] = dist
+del data
+
+# Noise baseline-related measures:
+data, _ = load_data(base_path, files='scales', keywords='mean')
+if reduce_kernels:
+    data = reduce_kernel_set(data, kern_inds, keyword='mean')
+for stage in stages:
+    # Plot average intensity measure across recordings:
+    curve = plot_curves(big_axes[1], dists, data[f'mean_{stage}'],
+                        c=colors[stage], lw=lw['big'],
+                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
+    # Indicate saturation point:
+    # if stage in ['log', 'inv', 'conv', 'feat']:
+    #     ind, dist = crit_inds[stage], crit_dists[stage]
+    #     big_axes[1].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+    #                      transform=big_axes[1].get_xaxis_transform()) 
+    #     big_axes[1].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
+    #                      transform=big_axes[1].get_xaxis_transform())
+    #     big_axes[1].vlines(dist, big_axes[1].get_ylim()[0], curve[ind],
+    #                        color=colors[stage], **plateau_line_kwargs)
+del data
+
+# Min-max normalized measures:
+data, _ = load_data(range_path, files='scales', keywords='mean')
+if reduce_kernels:
+    data = reduce_kernel_set(data, kern_inds, keyword='mean')
+for stage in stages:
+    # Plot average intensity measure across recordings:
+    curve = plot_curves(big_axes[2], dists, data[f'mean_{stage}'],
+                        c=colors[stage], lw=lw['big'],
+                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
+
+    # # Indicate saturation point:
+    # if stage in ['log', 'inv', 'conv', 'feat']:
+    #     ind, dist = crit_inds[stage], crit_dists[stage]
+    #     big_axes[2].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+    #                      transform=big_axes[2].get_xaxis_transform()) 
+    #     big_axes[2].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
+    #                      transform=big_axes[2].get_xaxis_transform())
+    #     big_axes[2].vlines(dist, big_axes[2].get_ylim()[0], curve[ind],
+    #                        color=colors[stage], **plateau_line_kwargs)
+del data
+
+# Save graph:
+if save_path is not None:
+    fig.savefig(save_path)
+plt.show()
+
+print('Done.')
+embed()

python/misc_functions.py

@@ -112,17 +112,18 @@ def get_thresholds(data=None, path=None, perc=None, factor=None,
     factors = data['factors'][inds]
     return data['sds'] * factors, factors, data['percs'][inds, :]
 
-def y_dist(ax, values, nbins=50, limits=None, log=False, cap=0.01, density=True,
+def y_dist(ax, values, edges=None, nbins=50, limits=None, log=False, cap=0.01,
-           line_kwargs={}, fill_kwargs={}):
+           density=True, line_kwargs={}, fill_kwargs={}):
     # Get distribution:
-    if limits is None:
+    if edges is None:
-        limits = np.array([np.nanmin(values), np.nanmax(values)])
+        if limits is None:
-        limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
+            limits = np.array([np.nanmin(values), np.nanmax(values)])
-    if log:
+            limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
-        limits[0] = max(limits[0], cap)
+        if log:
-        edges = np.geomspace(*limits, nbins + 1)
+            limits[0] = max(limits[0], cap)
-    else:
+            edges = np.geomspace(*limits, nbins + 1)
-        edges = np.linspace(*limits, nbins + 1)
+        else:
+            edges = np.linspace(*limits, nbins + 1)
     centers = edges[:-1] + np.diff(edges) / 2
     pdf, _ = np.histogram(values, bins=edges, density=density)
 
@@ -132,17 +133,18 @@ def y_dist(ax, values, nbins=50, limits=None, log=False, cap=0.01, density=True,
     ax.set_xlim(0, pdf.max() * 1.05)
     return pdf, centers, line_handle, fill_handle
 
-def x_dist(ax, values, nbins=50, limits=None, log=False, cap=0.01, density=True,
+def x_dist(ax, values, edges=None, nbins=50, limits=None, log=False, cap=0.01,
-           line_kwargs={}, fill_kwargs={}):
+           density=True, line_kwargs={}, fill_kwargs={}):
     # Get distribution:
-    if limits is None:
+    if edges is None:
-        limits = np.array([np.nanmin(values), np.nanmax(values)])
+        if limits is None:
-        limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
+            limits = np.array([np.nanmin(values), np.nanmax(values)])
-    if log:
+            limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
-        limits[0] = max(limits[0], cap)
+        if log:
-        edges = np.geomspace(*limits, nbins + 1)
+            limits[0] = max(limits[0], cap)
-    else:
+            edges = np.geomspace(*limits, nbins + 1)
-        edges = np.linspace(*limits, nbins + 1)
+        else:
+            edges = np.linspace(*limits, nbins + 1)
     centers = edges[:-1] + np.diff(edges) / 2
     pdf, _ = np.histogram(values, bins=edges, density=density)
 

python/save_inv_data_full.py

@@ -11,12 +11,12 @@ from IPython import embed
 target_species = [
     'Chorthippus_biguttulus',
     'Chorthippus_mollis',
-    'Chrysochraon_dispar',
+    # 'Chrysochraon_dispar',
     # 'Euchorthippus_declivus',
     # 'Gomphocerippus_rufus',
     # 'Omocestus_rufipes',
     # 'Pseudochorthippus_parallelus',
-][2]
+][1]
 example_file = {
     'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
     'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',

python/save_kernel_colors.py

@@ -3,20 +3,29 @@ from color_functions import load_colors, shade_colors
 
 # Settings:
 stages = ['conv', 'bi', 'feat']
-mode = ['subset', 'all'][1]
+mode = ['subset', 'all'][0]
 if mode == 'subset':
     kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
-    shade_factors = np.linspace(-0.6, 0.2, kern_types.size)
+    shade_factors = dict(
+        conv=[-0.6, 0.25],
+        bi=[-0.6, 0.25],
+        feat=[-0.5, 0.5]
+    )
 elif mode == 'all':
     kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
-    shade_factors = np.linspace(-0.6, 0.6, kern_types.size)
+    shade_factors = dict(
+        conv=[-0.75, 0.25],
+        bi=[-0.75, 0.25],
+        feat=[-0.5, 0.5]
+    )
 
 # Main colors:
 stage_colors = load_colors('../data/stage_colors.npz')
 
 # Execution:
 for stage in stages:
-    colors = shade_colors(stage_colors[stage], shade_factors)
+    factors = np.linspace(*shade_factors[stage], kern_types.size)
+    colors = shade_colors(stage_colors[stage], factors)
     colors = {str(k): c for k, c in zip(kern_types, colors)}
     print(f'\n{stage} colors:')
     print(colors)