Made fig_invariance_cross_species_thresh__appendix.pdf.
This commit is contained in:
j-hartling
@@ -54,9 +54,9 @@ calculate_regression = True
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test_regression = True
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# SUBSET SETTINGS:
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types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
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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, 0.032])
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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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@@ -84,7 +84,7 @@ song_grid_kwargs = dict(
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)
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# PLOT SETTINGS:
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kern_colors = load_colors('../data/feat_colors_all.npz')
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kern_colors = load_colors('../data/feat_colors_subset.npz')
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fs = dict(
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lab_norm=16,
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lab_tex=20,
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@@ -178,16 +178,44 @@ if test_regression:
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test_ax_side,
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test_ax_side
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]
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xlab_test = '$\\rho$'
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ylab_test = '$\\text{PDF}_{\\rho}$'
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xloc_test = 0.5
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yloc_test = 10
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ylab_test = '$\\rho$'
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yloc_test = 0.5
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ylab_test_kwargs = dict(
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x=-0.3,
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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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boxplot_kwargs = dict(
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positions=[0, 1],
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widths=0.9,
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tick_labels=['inter', 'intra'],
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zorder=1,
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medianprops=dict(
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color='k',
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lw=1,
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),
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boxprops=dict(
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color='k',
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lw=1,
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),
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)
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boxplot_kwargs.update(
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capprops=boxplot_kwargs['boxprops'],
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whiskerprops=boxplot_kwargs['boxprops'],
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)
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boxplot_dot_kwargs = dict(
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ls='none',
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marker='o',
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ms=4,
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mec='k',
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mfc='w',
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mew=1.5,
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alpha=0.5,
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zorder=2,
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)
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nbins = 10
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spec_color = 'darkorchid'
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song_color = 'goldenrod'
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@@ -385,35 +413,22 @@ for x, y in product(range(n_song), range(n_song)):
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if test_regression:
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# Add test result subplot:
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test_ax = fig.add_subplot(test_ax_bounds)
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test_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc_test))
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test_ax.set_xlim(-0.6, 1.6)
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test_ax.set_ylim(0, 1)
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test_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc_test))
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xlabel(test_ax, xlab_test, transform=fig.transFigure, **xlab_low_kwargs)
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ylabel(test_ax, ylab_test, **ylab_test_kwargs)
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# Show boxplots of correlation coefficients:
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test_ax.boxplot([spec_regs, song_regs], **boxplot_kwargs)
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# Show underlying datapoints:
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test_ax.plot(np.zeros(len(spec_regs)), spec_regs, **boxplot_dot_kwargs)
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test_ax.plot(np.ones(len(song_regs)), song_regs, **boxplot_dot_kwargs)
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# Perform t-test:
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test = ttest_ind(spec_regs, song_regs, equal_var=False)
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t, p = test.pvalue, test.statistic
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print(f'\nT-test result: t={t}, p={p}')
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# Calculate histograms:
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limits = np.array([min(spec_regs + song_regs), max(spec_regs + song_regs)])
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limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
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edges = np.linspace(*limits, nbins + 1)
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centers = edges[:-1] + (edges[1] - edges[0]) / 2
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spec_hist, _ = np.histogram(spec_regs, bins=edges, density=True)
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song_hist, _ = np.histogram(song_regs, bins=edges, density=True)
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# Plot histograms:
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bar_kwargs['width'] *= (centers[1] - centers[0])
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test_ax.bar(centers, spec_hist, color=spec_color, label='inter-species', **bar_kwargs)
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test_ax.bar(centers, song_hist, color=song_color, label='intra-species', **bar_kwargs)
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# Indicate means:
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test_ax.axvline(np.mean(spec_regs), color=spec_color, **mean_kwargs)
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test_ax.axvline(np.mean(song_regs), color=song_color, **mean_kwargs)
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# Add legend:
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test_ax.legend(**leg_kwargs)
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# Posthocs:
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test_ax.set_ylim(0, max(spec_hist.max(), song_hist.max()) * 1.05)
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test_ax.set_xlim(min(0, max(-1, limits[0])),
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min(1, limits[1]))
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if save_path is not None:
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fig.savefig(save_path)
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247
python/fig_inv_cross_spec-thresh_appendix.py
Normal file
247
python/fig_inv_cross_spec-thresh_appendix.py
Normal file
@@ -0,0 +1,247 @@
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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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@@ -5,11 +5,12 @@ from itertools import product
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from thunderhopper.filetools import search_files
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from thunderhopper.modeltools import load_data
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from thunderhopper.filtertools import find_kern_specs
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from misc_functions import get_saturation
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from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
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divide_by_zero, x_dist, y_dist
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from color_functions import load_colors
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from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel,\
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ylabel, title_subplot, plot_line, time_bar,\
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assign_colors, letter_subplot, letter_subplots
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from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
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plot_line, strip_zeros, time_bar, assign_colors,\
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letter_subplot, letter_subplots, hide_ticks
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from IPython import embed
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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@@ -17,25 +18,16 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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handles = []
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for i, ax in enumerate(axes):
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handles.append(plot_line(ax, time, snippets[:, ..., i],
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ymin=ymin, ymax=ymax, **kwargs))
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ymin=ymin, ymax=ymax, **kwargs))
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return handles
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def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
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def plot_curves(ax, scales, measures, **kwargs):
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if measures.ndim == 1:
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ax.plot(scales, measures, **kwargs)[0]
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return measures
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median_measure = np.median(measures, axis=1)
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spread_measure = [np.percentile(measures, 25, axis=1),
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np.percentile(measures, 75, axis=1)]
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ax.plot(scales, median_measure, **kwargs)[0]
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ax.fill_between(scales, *spread_measure, **fill_kwargs)
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return median_measure
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def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
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for stage in stages:
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key = f'{keyword}_{stage}'
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data[key] = data[key][:, inds, ...]
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return data
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handles = ax.plot(scales, measures, **kwargs)
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return handles, measures
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median_measure = np.nanmedian(measures, axis=1)
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line_handle = ax.plot(scales, median_measure, **kwargs)[0]
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return line_handle, median_measure
|
||||
|
||||
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])
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
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 = 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(
|
||||
|
||||
433
python/fig_invariance_field_backup.py
Normal file
433
python/fig_invariance_field_backup.py
Normal file
@@ -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()
|
||||
@@ -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,
|
||||
line_kwargs={}, fill_kwargs={}):
|
||||
def y_dist(ax, values, edges=None, nbins=50, limits=None, log=False, cap=0.01,
|
||||
density=True, line_kwargs={}, fill_kwargs={}):
|
||||
# Get distribution:
|
||||
if limits is None:
|
||||
limits = np.array([np.nanmin(values), np.nanmax(values)])
|
||||
limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
|
||||
if log:
|
||||
limits[0] = max(limits[0], cap)
|
||||
edges = np.geomspace(*limits, nbins + 1)
|
||||
else:
|
||||
edges = np.linspace(*limits, nbins + 1)
|
||||
if edges is None:
|
||||
if limits is None:
|
||||
limits = np.array([np.nanmin(values), np.nanmax(values)])
|
||||
limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
|
||||
if log:
|
||||
limits[0] = max(limits[0], cap)
|
||||
edges = np.geomspace(*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,
|
||||
line_kwargs={}, fill_kwargs={}):
|
||||
def x_dist(ax, values, edges=None, nbins=50, limits=None, log=False, cap=0.01,
|
||||
density=True, line_kwargs={}, fill_kwargs={}):
|
||||
# Get distribution:
|
||||
if limits is None:
|
||||
limits = np.array([np.nanmin(values), np.nanmax(values)])
|
||||
limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
|
||||
if log:
|
||||
limits[0] = max(limits[0], cap)
|
||||
edges = np.geomspace(*limits, nbins + 1)
|
||||
else:
|
||||
edges = np.linspace(*limits, nbins + 1)
|
||||
if edges is None:
|
||||
if limits is None:
|
||||
limits = np.array([np.nanmin(values), np.nanmax(values)])
|
||||
limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
|
||||
if log:
|
||||
limits[0] = max(limits[0], cap)
|
||||
edges = np.geomspace(*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)
|
||||
|
||||
|
||||
@@ -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',
|
||||
|
||||
@@ -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)
|
||||
|
||||
Reference in New Issue
Block a user