Cross-checked and polished remainders of fig_invariance_thresh_lp_species.pdf.
Added misc_functions.py for anything not plot-related.
This commit is contained in:
j-hartling
@@ -4,10 +4,11 @@ import matplotlib.pyplot as plt
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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 misc_functions import shorten_species, get_saturation
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from color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, hide_ticks,\
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from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, hide_ticks,\
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plot_line, strip_zeros, time_bar, zoom_inset,\
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letter_subplot, title_subplot
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letter_subplot, letter_subplots, title_subplot
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from IPython import embed
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def add_snip_axes(fig, grid_kwargs):
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@@ -30,7 +31,14 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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species_paths = search_files('*', incl='noise', dir='../data/inv/log_hp/')
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target_species = [
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'Omocestus_rufipes',
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'Chorthippus_biguttulus',
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'Chorthippus_mollis',
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'Chrysochraon_dispar',
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'Gomphocerippus_rufus',
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'Pseudochorthippus_parallelus',
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]
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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@@ -39,28 +47,29 @@ load_kwargs = dict(
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save_path = '../figures/fig_invariance_log_hp.pdf'
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compute_ratios = True
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show_diag = True
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show_noise = True
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show_plateaus = True
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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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)
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snip_rows = 1
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big_rows = 1
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# snip_rows = 1
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# big_rows = 1
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super_grid_kwargs = dict(
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nrows=2 * snip_rows + big_rows,
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nrows=3,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0,
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right=1,
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bottom=0,
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top=1
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top=1,
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height_ratios=[1, 1, 1]
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)
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subfig_specs = dict(
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pure=(slice(0, snip_rows), slice(None)),
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noise=(slice(snip_rows, 2 * snip_rows), slice(None)),
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big=(slice(-big_rows, None), slice(None)),
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pure=(0, slice(None)),
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noise=(1, slice(None)),
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big=(2, slice(None)),
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)
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block_height = 0.8
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edge_padding = 0.08
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@@ -112,6 +121,8 @@ fs = dict(
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bar=16,
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)
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colors = load_colors('../data/stage_colors.npz')
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species_colors = load_colors('../data/species_colors.npz')
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noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)]
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lw_snippets = 1
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lw_big = 3
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xlabels = dict(
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@@ -206,15 +217,34 @@ bar_kwargs = dict(
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va='center',
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)
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)
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leg_kwargs = dict(
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ncols=2,
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loc='upper right',
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bbox_to_anchor=(0, 0.6, 1, 0.4),
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frameon=False,
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prop=dict(
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size=12,
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style='italic',
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),
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borderpad=0,
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borderaxespad=0,
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handlelength=1,
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columnspacing=1,
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)
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diag_kwargs = dict(
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c=(0.75, 0.75, 0.75),
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lw=2,
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ls='--',
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zorder=1.9,
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)
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noise_rel_thresh = 0.95
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noise_kwargs = dict(
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fc=(0.9, 0.9, 0.9),
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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_kwargs = dict(
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ec='none',
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lw=0,
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zorder=1.5,
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@@ -225,13 +255,13 @@ if compute_ratios:
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ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
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ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
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species_measures = []
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for species_path in species_paths:
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species_measure = load_data(species_path, **load_kwargs)[0]['measure_inv']
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species_measures = {}
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for species in target_species:
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path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0]
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measure = load_data(path, **load_kwargs)[0]['measure_inv']
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if compute_ratios:
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species_measure /= ref_measures['inv']
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species_measures.append(species_measure)
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species_measures = np.array(species_measures).T
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measure /= ref_measures['inv']
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species_measures[species] = measure
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# EXECUTION:
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for data_path in data_paths:
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@@ -291,14 +321,10 @@ for data_path in data_paths:
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_aspect(**anchor_kwargs)
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ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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if i == 0:
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hide_ticks(ax, 'bottom')
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letter_subplot(ax, 'c', **letter_big_kwargs)
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else:
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xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
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letter_subplot(ax, 'd', **letter_big_kwargs)
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big_axes[i] = ax
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ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
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letter_subplots(big_axes, 'cde', **letter_big_kwargs)
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# Plot pure-song envelope snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
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@@ -352,25 +378,26 @@ for data_path in data_paths:
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big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big)
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big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big)
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# Plot species measures:
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big_axes[2].plot(noise_scales, species_measures, 'k', lw=lw_big)
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if show_diag:
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# Indicate diagonal:
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big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
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big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
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if show_noise:
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# Indicate noise floor:
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if compute_ratios:
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span_measure = noise_data['measure_inv'][-1] - ref_measures['inv']
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thresh_measure = ref_measures['inv'] + noise_rel_thresh * span_measure
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else:
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span_measure = noise_data['measure_inv'][-1] - noise_data['measure_inv'][0]
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thresh_measure = noise_data['measure_inv'][0] + noise_rel_thresh * span_measure
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thresh_ind = np.nonzero(noise_data['measure_inv'] < thresh_measure)[0][-1]
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thresh_scale = noise_scales[thresh_ind]
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big_axes[1].axvspan(noise_scales[0], thresh_scale, **noise_kwargs)
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if show_plateaus:
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# Indicate low and high plateaus of noise invariance curve:
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low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
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big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
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fc=noise_colors[0], **plateau_kwargs)
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big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
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fc=noise_colors[1], **plateau_kwargs)
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# Plot species-specific noise-song measures:
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for species, measure in species_measures.items():
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label = shorten_species(species)
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big_axes[2].plot(noise_scales, measure, label=label,
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c=species_colors[species], lw=lw_big)
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big_axes[2].legend(**leg_kwargs)
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if save_path is not None:
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fig.savefig(save_path, bbox_inches='tight')
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