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main.tex

@@ -763,6 +763,15 @@ the signal for reliable song recognition.
 \end{figure}
 \FloatBarrier
 
+\begin{figure}[!ht]
+    \centering
+    \includegraphics[width=\textwidth]{figures/fig_features_cross_species.pdf}
+    \caption{\textbf{Inter- and intraspecific feature variability.}
+                     }
+    \label{fig:cross_species}
+\end{figure}
+\FloatBarrier
+
 \begin{figure}[!ht]
     \centering
     \includegraphics[width=\textwidth]{figures/fig_invariance_field.pdf}

python/collect_inv_data_full.py

@@ -9,9 +9,9 @@ target_species = [
     # 'Chorthippus_mollis',
     # 'Chrysochraon_dispar',
     # 'Euchorthippus_declivus',
-    # 'Gomphocerippus_rufus',
+    'Gomphocerippus_rufus',
     'Omocestus_rufipes',
-    # 'Pseudochorthippus_parallelus',
+    'Pseudochorthippus_parallelus',
 ]
 stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
 search_path = '../data/inv/full/'

python/fig_features_cross_species.py

@@ -0,0 +1,425 @@
+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from itertools import product
+from scipy.stats import ttest_ind
+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
+from color_functions import load_colors
+from plot_functions import hide_ticks, xlabel, ylabel
+from IPython import embed
+
+
+# GENERAL SETTINGS:
+cross_species = [
+    # 'Chorthippus_biguttulus',
+    # 'Chorthippus_mollis',
+    # 'Chrysochraon_dispar',
+    # 'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+n_spec = len(cross_species)
+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'
+}
+in_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+][5]
+save_path = '../figures/fig_features_cross_species.pdf'
+
+
+# ANALYSIS SETTINGS:
+thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
+single_spec_file = True # Only use example files for cross-species comparison
+equalize_spec_files = False # Prune to minimum available across species
+n_song = n_spec#None # Limit to n first songs of in-species dataset (None for all)
+color_kern_types = True
+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 = [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 = [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),
+)
+spec_grid_kwargs = dict(
+    wspace=0.1,
+    hspace=0.1,
+    left=0.07,
+    right=0.93,
+    bottom=0.07,
+    top=0.93,
+)
+song_grid_kwargs = dict(
+    wspace=0.1,
+    hspace=0.1,
+    left=spec_grid_kwargs['left'],
+    right=spec_grid_kwargs['right'],
+    bottom=spec_grid_kwargs['bottom'],
+    top=spec_grid_kwargs['top'],
+)
+
+# PLOT SETTINGS:
+kern_colors = load_colors('../data/feat_colors_all.npz')
+fs = dict(
+    lab_norm=16,
+    lab_tex=20,
+    letter=22,
+    tit_norm=16,
+    tit_tex=20,
+    bar=16,
+)
+label_song_prefix = ['song ', ''][0]
+xlab_low_kwargs = dict(
+    y=0.01,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='bottom',
+    fontstyle='italic',   
+)
+xlab_up_kwargs = dict(
+    y=0.99,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='top',
+)
+ylab_low_kwargs = dict(
+    x=0.01,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='top',
+    fontstyle='italic',
+)
+ylab_up_kwargs = dict(
+    x=0.99,
+    rotation=270,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='top',
+)
+loc = 0.5
+dot_spec_kwargs = dict(
+    ls='none',
+    marker='o',
+    mec='w',
+    ms=6,
+    mew=0.5,
+    alpha=1,
+    zorder=2,
+)
+dot_song_kwargs = dict(
+    ls='none',
+    marker='o',
+    mec='w',
+    ms=6,
+    mew=0.5,
+    alpha=1,
+    zorder=2, 
+)
+if not color_kern_types:
+    dot_spec_kwargs['mfc'] = 'k'
+    dot_song_kwargs['mfc'] = 'k'
+diag_fig_kwargs = dict(
+    c='k',
+    lw=1,
+    ls='-'
+)
+diag_ax_kwargs = dict(
+    c='k',
+    lw=1,
+    ls='-',
+    zorder=3,
+)
+text_spec_kwargs = dict(
+    x=0.05,
+    y=0.95,
+    ha='left',
+    va='top',
+    fontsize=16,
+)
+text_song_kwargs = dict(
+    x=0,
+    y=0.95,
+    ha='left',
+    va='top',
+    fontsize=16,
+)
+text_spec_prefix = '$\\rho\\,=\\,$'
+text_song_prefix = ['$\\rho\\,=\\,$', ''][0]
+if test_regression:
+    test_ax_side = 0.15
+    test_ax_bounds = [
+        song_grid_kwargs['right'] - test_ax_side,
+        spec_grid_kwargs['bottom'],
+        test_ax_side,
+        test_ax_side
+    ]
+    xlab_test = '$\\rho$'
+    ylab_test = '$\\text{PDF}_{\\rho}$'
+    xloc_test = 0.5
+    yloc_test = 10
+    ylab_test_kwargs = dict(
+        x=-0.3,
+        fontsize=fs['lab_norm'],
+        ha='center',
+        va='bottom',
+    )
+    nbins = 10
+    spec_color = 'darkorchid'
+    song_color = 'goldenrod'
+    bar_kwargs = dict(
+        width=0.95,
+        alpha=0.75,
+    )
+    mean_kwargs = dict(
+        ls='--',
+        lw=1,
+    )
+    leg_kwargs = dict(
+        ncols=1,
+        loc='lower center',
+        bbox_to_anchor=(0, 1.05, 1, 0.5),
+        frameon=False,
+        prop=dict(
+            size=14,
+        ),
+        borderpad=0,
+        borderaxespad=0,
+        handlelength=1,
+        columnspacing=1,
+        handletextpad=0.5,
+        labelspacing=0.1
+    )
+
+
+# EXECUTION:
+
+# Gather cross-species data:
+spec_data, min_files = {}, np.inf
+for species in cross_species:
+    # Load species data:
+    if single_spec_file:
+        path = search_files(example_files[species], dir='../data/inv/full/')[0]
+    else:
+        path = search_files(species, dir='../data/inv/full/collected/')[0]
+    data, config = load_data(path, ['measure_feat', 'thresh_rel'])
+    # Reduce to single threshold, last scale, optional kernel subset:
+    thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
+    kern_inds = slice(None)
+    if reduce_kernels:
+        kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
+        config['kernels'] = config['kernels'][:, kern_inds]
+        config['k_specs'] = config['k_specs'][kern_inds, :]
+    spec_data[species] = data['measure_feat'][-1, kern_inds, thresh_ind, ...]
+    if single_spec_file:
+        # Ensure consistent shape (features, files=1):
+        spec_data[species] = spec_data[species][..., None]
+    # Update number of possible comparisons:
+    min_files = min(min_files, spec_data[species].shape[-1])
+
+if equalize_spec_files and not single_spec_file:
+    # Equalize number of files across species:
+    spec_data = {spec: feat[:, :min_files] for spec, feat in spec_data.items()}
+
+# Gather in-species data:
+paths = search_files(in_species, dir='../data/inv/full/')
+if n_song is not None:
+    paths = paths[:n_song]
+else:
+    n_song = len(paths)
+song_data = np.zeros((n_song, config['k_specs'].shape[0]))
+for i, path in enumerate(paths):
+    # Load song-specific dataset:
+    data, config = load_data(path, ['measure_feat', 'thresh_rel'])
+    # Reduce to single threshold, last scale, optional kernel subset:
+    thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
+    kern_inds = slice(None)
+    if reduce_kernels:
+        kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
+        config['kernels'] = config['kernels'][:, kern_inds]
+        config['k_specs'] = config['k_specs'][kern_inds, :]
+    song_data[i] = data['measure_feat'][-1, kern_inds, thresh_ind]
+
+# Prepare graph:
+fig = plt.figure(**fig_kwargs)
+spec_grid = fig.add_gridspec(nrows=n_spec, ncols=n_spec, **spec_grid_kwargs)
+song_grid = fig.add_gridspec(nrows=n_song, ncols=n_song, **song_grid_kwargs)
+# fig.add_artist(plt.Line2D([0, 1], [1, 0], **diag_fig_kwargs))
+
+# Prepare cross-species axes:
+spec_labels = [shorten_species(spec) for spec in cross_species]
+spec_axes = np.zeros((n_spec, n_spec), dtype=object)
+for i, j in product(range(n_spec), range(n_spec)):
+    if i <= j:
+        continue
+    # Lower trianglular:
+    ax = fig.add_subplot(spec_grid[i, j])
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.yaxis.set_major_locator(plt.MultipleLocator(loc))
+    ax.xaxis.set_major_locator(plt.MultipleLocator(loc))
+    # Indicate subplot diagonal:
+    ax.plot([0, 1], [0, 1], **diag_ax_kwargs)
+    # Adjust:
+    if j > 0:
+        hide_ticks(ax, 'left')
+    if i < n_spec - 1:
+        hide_ticks(ax, 'bottom')
+    if j == 0:
+        ylabel(ax, spec_labels[i], transform=fig.transFigure, **ylab_low_kwargs)
+    if i == n_spec - 1:
+        xlabel(ax, spec_labels[j], transform=fig.transFigure, **xlab_low_kwargs)
+    spec_axes[i, j] = ax
+
+# Prepare in-species axes:
+song_labels = [label_song_prefix + f'{i}' for i in range(1, n_song + 1)]
+song_axes = np.zeros((n_song, n_song), dtype=object)
+for i, j in product(range(n_song), range(n_song)):
+    if i >= j:
+        continue
+    # Upper triangular:
+    ax = fig.add_subplot(song_grid[i, j])
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.yaxis.set_major_locator(plt.MultipleLocator(loc))
+    ax.xaxis.set_major_locator(plt.MultipleLocator(loc))
+    ax.spines[['top', 'right']].set_visible(True)
+    ax.spines[['bottom', 'left']].set_visible(False)
+    ax.yaxis.set_label_position('right')
+    ax.yaxis.tick_right()
+    ax.xaxis.set_label_position('top')
+    ax.xaxis.tick_top()
+    # Indicate subplot diagonal:
+    ax.plot([0, 1], [0, 1], **diag_ax_kwargs)
+    # Adjust:
+    if i > 0:
+        hide_ticks(ax, 'top')
+    if j < n_song - 1:
+        hide_ticks(ax, 'right')
+    if i == 0:
+        xlabel(ax, song_labels[j], transform=fig.transFigure, **xlab_up_kwargs)
+    if j == n_song - 1:
+        ylabel(ax, song_labels[i], transform=fig.transFigure, **ylab_up_kwargs)
+    song_axes[i, j] = ax
+
+# Remember coefficients:
+if calculate_regression:
+    spec_regs, song_regs = [], []
+
+# Plot cross-species comparisons:
+for x, y in product(range(n_spec), range(n_spec)):
+    if x >= y:
+        continue
+    # Get axis and data:
+    ax = spec_axes[y, x]
+    x_spec = spec_data[cross_species[x]]
+    y_spec = spec_data[cross_species[y]]
+    x_reg, y_reg = [], []
+    # Plot features of species 1 against species 2 (all files cross-wise):
+    for i, j in product(range(x_spec.shape[-1]), range(y_spec.shape[-1])):
+        if color_kern_types:
+            for k, (f1, f2) in enumerate(zip(x_spec[:, i], y_spec[:, j])):
+                c = kern_colors[str(int(config['k_specs'][k, 0]))]
+                ax.plot(f1, f2, mfc=c, **dot_spec_kwargs)
+        else:
+            ax.plot(x_spec[:, i], y_spec[:, j], **dot_spec_kwargs)
+        # Accumulate value pairs:
+        if calculate_regression:
+            x_reg.append(x_spec[:, i])
+            y_reg.append(y_spec[:, j])
+    # Get regression coefficient:
+    if calculate_regression:
+        rho = np.corrcoef(np.concatenate(x_reg), np.concatenate(y_reg))[0, 1]
+        ax.text(s=text_spec_prefix + f'{rho:.2f}', transform=ax.transAxes, **text_spec_kwargs)
+        spec_regs.append(rho)
+    # print('\nAxis position: ', (y, x))
+    # print(cross_species[x], '(x) vs.', cross_species[y], '(y)')
+
+# Plot in-species comparisons:
+for x, y in product(range(n_song), range(n_song)):
+    if x <= y:
+        continue
+    # Get axis and data:
+    ax = song_axes[y, x]
+    x_song = song_data[x]
+    y_song = song_data[y]
+    # Plot features of song 1 against song 2:
+    if color_kern_types:
+        for i, (f1, f2) in enumerate(zip(x_song, y_song)):
+            c = kern_colors[str(int(config['k_specs'][i, 0]))]
+            ax.plot(f1, f2, mfc=c, **dot_song_kwargs)
+    else:
+        ax.plot(x_song, y_song, **dot_song_kwargs)
+    # Get regression coefficient:
+    if calculate_regression:
+        rho = np.corrcoef(x_song, y_song)[0, 1]
+        ax.text(s=text_song_prefix + f'{rho:.2f}', transform=ax.transAxes, **text_song_kwargs)
+        song_regs.append(rho)
+    # print('\nAxis position: ', (y, x))
+    # print(f'Song {song_labels[x]} (x) vs. Song {song_labels[y]} (y)')
+
+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.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)
+    # 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)
+plt.show()
+
+
+
+
+

python/save_inv_data_full.py

@@ -12,11 +12,11 @@ target_species = [
     'Chorthippus_biguttulus',
     'Chorthippus_mollis',
     'Chrysochraon_dispar',
-    'Euchorthippus_declivus',
+    # 'Euchorthippus_declivus',
-    'Gomphocerippus_rufus',
+    # 'Gomphocerippus_rufus',
-    'Omocestus_rufipes',
+    # 'Omocestus_rufipes',
-    'Pseudochorthippus_parallelus',
+    # 'Pseudochorthippus_parallelus',
-][4]
+][2]
 example_file = {
     'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
     'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',