Fetched bunch of species-specific song snippets.
Worked those into LogHP analysis. Worked results into fig_invariance_log-hp.pdf. Put details into new fig_invariance_log-hp_species.pdf (appendix).
This commit is contained in:
j-hartling
@@ -27,58 +27,29 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
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return handles
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def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
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base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
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if pdf is None:
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pdf, axis = get_kde(data, sigma, axis)
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if base is None:
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base = pdf.min()
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if cap is None:
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cap = pdf.max()
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pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
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if which == 'x':
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transform = ax.get_xaxis_transform()
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elif which == 'y':
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transform = ax.get_yaxis_transform()
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else:
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transform = ax.transData
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rng = np.random.default_rng()
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handles = []
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for value in data:
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ind = np.nonzero(axis == value)[0][0]
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offset = base if not shifted else rng.uniform(base, pdf[ind])
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variables = (offset, value) if which=='y' else (value, offset)
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handles.extend(ax.plot(*variables, transform=transform, **kwargs))
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if add_pdf:
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variables = (pdf, axis) if which=='y' else (axis, pdf)
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pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
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return handles, pdf_handle
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return handles
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def zalpha(handles, background='w', down=1):
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twins = []
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for handle in handles:
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twin = handle.copy()
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twin.set(color=background, alpha=1)
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twin.set_zorder(handle.get_zorder() - down)
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twins.append(twin)
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return twins
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# def zalpha(handles, background='w', down=1):
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# twins = []
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# for handle in handles:
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# twin = handle.copy()
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# twin.set(color=background, alpha=1)
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# twin.set_zorder(handle.get_zorder() - down)
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# twins.append(twin)
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# return twins
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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ref_path = '../data/inv/log_hp/ref_measures.npz'
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save_path = '../figures/fig_invariance_log_hp.pdf'
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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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'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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]
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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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@@ -159,12 +130,13 @@ fs = dict(
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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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noise_colors = [(0.6,) * 3, (0.8,) * 3]
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lw = dict(
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snip=1,
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big=4,
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spec=2,
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plateau=1,
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plateau=1.5,
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legend=5,
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)
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xlabels = dict(
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big='scale $\\alpha$',
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@@ -273,7 +245,7 @@ leg_kwargs = dict(
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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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c=(0.3,) * 3,
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lw=2,
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ls='--',
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zorder=1.9,
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@@ -297,18 +269,9 @@ plateau_line_kwargs = dict(
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)
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plateau_dot_kwargs = dict(
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marker='o',
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markersize=10,
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markeredgecolor='k',
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markersize=8,
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markeredgewidth=1,
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# alpha=1,
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zorder=6,
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clip_on=False,
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# base=0,
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# cap=0.15,
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# add_pdf=True,
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)
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kde_kwargs = dict(
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sigma=0.1,
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)
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# PREPARATION:
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@@ -317,18 +280,12 @@ if compute_ratios:
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species_measures = {}
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thresh_inds = np.zeros((len(target_species),), dtype=int)
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thresh_scales = np.zeros((len(target_species),), dtype=float)
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for i, species in enumerate(target_species):
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path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0]
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species_data = load_data(path, **load_kwargs)[0]
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measure = species_data['measure_inv']
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scales = species_data['scales']
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if compute_ratios:
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measure /= ref_measures['inv']
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spec_path = search_files(species, dir='../data/inv/log_hp/condensed/')[0]
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spec_data = dict(np.load(spec_path))
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measure = spec_data['mean'].mean(axis=1)
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species_measures[species] = measure
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thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]
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thresh_scales[i] = scales[thresh_inds[i]]
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thresh_pdf, pdf_axis = get_kde(thresh_scales, axis=scales, **kde_kwargs)
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# EXECUTION:
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for data_path in data_paths:
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@@ -460,24 +417,23 @@ for data_path in data_paths:
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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_rect_kwargs)
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# Plot species-specific noise-song measures:
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# Plot species-specific noise-song invariance curves:
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for i, (species, measure) in enumerate(species_measures.items()):
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# Plot invariance curve:
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color = species_colors[species]
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ind, scale = thresh_inds[i], thresh_scales[i]
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big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
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c=color, lw=lw['spec'])
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# Indicate saturation:
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ind = thresh_inds[i]
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scale = noise_scales[ind]
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big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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handle = big_axes[2].plot(scale, 0, c=color, alpha=0.5, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
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color=color, **plateau_line_kwargs)
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big_axes[2].legend(**leg_kwargs)
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# handles = plot_dist_shifted(big_axes[2], species_threshs, axis=pdf_axis,
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# pdf=thresh_pdf, **plateau_dot_kwargs)[0]
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# [h.set_color(species_colors[s]) for h, s in zip(handles, target_species)]
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legend = big_axes[2].legend(**leg_kwargs)
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[h.set_lw(lw['legend']) for h in legend.legend_handles]
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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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