Made fig_invariance_rect_lp.pdf and corresponding appendix figure.
Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB. Co-authored-by: Copilot <copilot@github.com>
This commit is contained in:
j-hartling
@@ -33,7 +33,7 @@ normalization = [
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'max',
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'base',
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'range',
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][3]
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][0]
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suffix = dict(
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none='_unnormed',
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min='_norm-min',
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@@ -7,8 +7,8 @@ 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, super_xlabel, ylabel, hide_ticks,\
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plot_line, strip_zeros, time_bar, zoom_inset,\
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letter_subplot, letter_subplots, title_subplot
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plot_line, strip_zeros, time_bar, zoom_inset, shift_subplot,\
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letter_subplot, letter_subplots, title_subplot, color_axis
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from IPython import embed
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def add_snip_axes(fig, grid_kwargs):
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@@ -27,10 +27,10 @@ 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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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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data_path = search_files(target, excl='noise', dir='../data/inv/log_hp/')[0]
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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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'Chorthippus_biguttulus',
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@@ -46,7 +46,7 @@ load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure']
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)
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compute_ratios = True
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relate_to_noise = True
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exclude_zero = True
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show_diag = True
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show_plateaus = True
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@@ -79,7 +79,7 @@ pure_grid_kwargs = dict(
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wspace=0.1,
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hspace=0.15,
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left=0.11,
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right=0.95,
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right=0.98,
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bottom=1 - block_height - edge_padding,
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top=1 - edge_padding,
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height_ratios=[1, 2, 1]
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@@ -95,14 +95,15 @@ noise_grid_kwargs = dict(
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top=edge_padding + block_height,
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height_ratios=[1, 2, 1]
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)
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big_col_shift = -0.12
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big_grid_kwargs = dict(
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nrows=1,
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ncols=3,
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wspace=0.3,
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wspace=0.25,
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hspace=0,
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left=pure_grid_kwargs['left'],
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left=pure_grid_kwargs['left'] - big_col_shift,
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right=pure_grid_kwargs['right'],
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bottom=0.05,
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bottom=0.03,
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top=1
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)
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anchor_kwargs = dict(
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@@ -137,7 +138,9 @@ ylabels = dict(
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env='$x_{\\text{env}}$',
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log='$x_{\\text{dB}}$',
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inv='$x_{\\text{adapt}}$',
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big='$\\sigma_x\\,/\\,\\sigma_{\\eta}$',
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big_pure='$\\sigma_x$',
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big_log='$\\sigma_x\\,[\\text{dB}]$',
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big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
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)
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xlab_big_kwargs = dict(
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y=0,
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@@ -145,6 +148,18 @@ xlab_big_kwargs = dict(
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ha='center',
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va='bottom',
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)
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ylab_big_left_kwargs = dict(
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x=-0.2,
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fontsize=fs['lab_tex'],
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ha='center',
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va='bottom'
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)
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ylab_big_right_kwargs = dict(
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x=1.2,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top'
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)
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ylab_snip_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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@@ -152,12 +167,6 @@ ylab_snip_kwargs = dict(
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ha='left',
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va='center',
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)
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ylab_big_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top',
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)
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yloc = dict(
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env=1000,
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log=40,
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@@ -233,7 +242,7 @@ leg_kwargs = dict(
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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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handlelength=0.5,
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columnspacing=1,
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)
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diag_kwargs = dict(
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@@ -287,11 +296,7 @@ noise_data, _ = load_data(data_path.replace('pure', 'noise'), **load_kwargs)
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pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
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t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
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if compute_ratios:
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# Relate pure-song measures to near-zero scale:
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pure_data['measure_env'] /= pure_data['measure_env'][1]
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pure_data['measure_log'] /= pure_data['measure_log'][1]
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pure_data['measure_inv'] /= pure_data['measure_inv'][1]
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if relate_to_noise:
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# Relate noise-song measures to zero scale:
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noise_data['measure_env'] /= noise_data['measure_env'][0]
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noise_data['measure_log'] /= noise_data['measure_log'][0]
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@@ -354,8 +359,10 @@ big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(scales[0], scales[-1])
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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.xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
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ax.set_aspect(**anchor_kwargs)
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if i in [0, 1]:
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ax.set_ylim(scales[0], scales[-1])
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@@ -365,10 +372,19 @@ for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
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ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
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ax.set_ylim(0.9, 30)
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if i == 1:
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hide_ticks(ax, 'left')
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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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shift_subplot(big_axes[0], dx=big_col_shift)
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ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_left_kwargs)
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ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_left_kwargs)
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big_twin = big_axes[0].twinx()
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hide_axis(big_twin, 'left')
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big_twin.spines['right'].set_visible(True)
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big_twin.set_position(big_axes[0].get_position().bounds)
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big_twin.set_ylim(scales[0], scales[-1])
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big_twin.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ylabel(big_twin, ylabels['big_log'], transform=big_twin.transAxes, **ylab_big_right_kwargs)
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color_axis(big_axes[0], colors['env'], side='left')
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color_axis(big_twin, colors['log'], side='right')
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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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@@ -65,7 +65,7 @@ mean_colors = {
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'Pseudochorthippus_parallelus': (0,) * 3,
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}
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xlab = 'scale $\\alpha$'
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ylab = '$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$'
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ylab = '$\\sigma_{\\text{adapt}}\\,/\\,\\sigma_{\\eta}$'
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xlab_kwargs = dict(
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y=0,
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fontsize=16,
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424
python/fig_invariance_rect-lp.py
Normal file
424
python/fig_invariance_rect-lp.py
Normal file
@@ -0,0 +1,424 @@
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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 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
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from color_functions import load_colors
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from plot_functions import hide_axis, shift_subplot, shift_subplot, ylimits,\
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super_xlabel, ylabel, hide_ticks,\
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plot_line, strip_zeros, time_bar,\
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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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grid = fig.add_gridspec(**grid_kwargs)
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axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
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for i, j in product(range(grid.nrows), range(grid.ncols)):
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axes[i, j] = fig.add_subplot(grid[i, j])
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if j == 0:
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shift_subplot(axes[i, j], dx=snip_col_shift)
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[hide_axis(ax, 'left') for ax in axes[:, 2:].flatten()]
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[hide_axis(ax, 'bottom') for ax in axes.flatten()]
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return axes
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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handles = []
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for ax, snippet in zip(axes, snippets.T):
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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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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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data_path = search_files(target, excl='noise', dir='../data/inv/rect_lp/')[0]
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save_path = '../figures/fig_invariance_rect_lp.pdf'
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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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stages = ['filt', 'env']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'cutoff', 'snip', 'measure']
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)
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# ANALYSIS SETTINGS:
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relate_to_noise = True
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exclude_zero = True
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show_diag = True
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snip_cutoff = np.array([np.nan, 2500, 250, 25])[2]
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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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super_grid_kwargs = dict(
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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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height_ratios=[1, 1, 1]
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)
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subfig_specs = dict(
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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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snip_col_shift = -0.05
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pure_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=0.1,
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hspace=0.15,
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left=0.08 - snip_col_shift,
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right=0.95,
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bottom=1 - block_height - edge_padding,
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top=1 - edge_padding,
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height_ratios=[1, 1]
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)
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noise_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=pure_grid_kwargs['wspace'],
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hspace=pure_grid_kwargs['hspace'],
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=edge_padding,
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top=edge_padding + block_height,
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height_ratios=[1, 1]
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)
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big_col_shift = -0.05
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big_grid_kwargs = dict(
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nrows=1,
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ncols=3,
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wspace=0.25,
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hspace=0,
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left=pure_grid_kwargs['left'] + snip_col_shift - big_col_shift,
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right=pure_grid_kwargs['right'],
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bottom=0.04,
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top=1
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)
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anchor_kwargs = dict(
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aspect='equal',
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adjustable='box',
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anchor=(0.5, 0.5)
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)
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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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colors = load_colors('../data/stage_colors.npz')
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colors['raw'] = (0., 0., 0.,)
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species_colors = load_colors('../data/species_colors.npz')
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lw = dict(
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snip=0.5,
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big=3,
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spec=2,
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legend=5,
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)
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dash_cycle = 6 # points
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ls_env = [
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(0, np.array((0.2, 0.8)) * dash_cycle),
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(0, np.array((0.6, 0.1, 0.2, 0.1)) * dash_cycle),
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(0, np.array((0.5, 0.5)) * dash_cycle),
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'solid',
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] # [np.nan, 2500, 250, 25]
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xlabels = dict(
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big='scale $\\alpha$',
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)
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ylabels = dict(
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raw='$x$',
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filt='$x_{\\text{filt}}$',
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env='$x_{\\text{env}}$',
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big_pure='$\\sigma_x$',
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big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
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)
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xlab_big_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_snip_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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rotation=0,
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ha='left',
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va='center',
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)
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ylab_pure_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top',
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)
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ylab_noise_kwargs = dict(
|
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y=0.5,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top',
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)
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ylim_zoom_factor = 0.03
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yloc = dict(
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filt=(3, 100),
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env=(0.5, 30),
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)
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ypad = dict(
|
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filt=0.05,
|
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env=0.05,
|
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)
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title_kwargs = dict(
|
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x=0.5,
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y=1,
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ha='center',
|
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va='bottom',
|
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fontsize=fs['tit_norm'],
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)
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letter_snip_kwargs = dict(
|
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x=0,
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yref=0.5,
|
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ha='left',
|
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va='center',
|
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fontsize=fs['letter'],
|
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)
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letter_big_kwargs = dict(
|
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x=0,
|
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y=1,
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ha='left',
|
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va='bottom',
|
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fontsize=fs['letter'],
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)
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bar_time = 5
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bar_kwargs = dict(
|
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dur=bar_time,
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y0=-0.2,
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y1=-0.1,
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xshift=1,
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color='k',
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lw=0,
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clip_on=False,
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text_pos=(-0.1, 0.5),
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text_str=f'${bar_time}\\,\\text{{s}}$',
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text_kwargs=dict(
|
||||
fontsize=fs['bar'],
|
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ha='right',
|
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va='center',
|
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)
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)
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cutoff_leg_kwargs = dict(
|
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ncols=1,
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loc='upper left',
|
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bbox_to_anchor=(0.05, 0.5, 0.5, 0.5),
|
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frameon=False,
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prop=dict(
|
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size=14,
|
||||
),
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borderpad=0,
|
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borderaxespad=0,
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handletextpad=0.3
|
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)
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cutoff_leg_kwargs['handlelength'] = 2 * dash_cycle * lw['big'] / cutoff_leg_kwargs['prop']['size']
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spec_leg_kwargs = dict(
|
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ncols=2,
|
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loc='lower center',
|
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bbox_to_anchor=(0, 0, 1, 0.5),
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frameon=False,
|
||||
prop=dict(
|
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size=13,
|
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style='italic',
|
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),
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borderpad=0,
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borderaxespad=0,
|
||||
handlelength=0.75,
|
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handletextpad=0.5,
|
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columnspacing=1,
|
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)
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diag_kwargs = dict(
|
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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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)
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# PREPARATION:
|
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species_measures = {}
|
||||
for i, species in enumerate(target_species):
|
||||
spec_path = search_files(species, incl=['noise', 'norm-base'], dir='../data/inv/rect_lp/condensed/')[0]
|
||||
spec_data = dict(np.load(spec_path))
|
||||
measure = spec_data['mean_env'].mean(axis=-1)
|
||||
if exclude_zero:
|
||||
measure = measure[spec_data['scales'] > 0, :]
|
||||
species_measures[species] = measure
|
||||
|
||||
# EXECUTION:
|
||||
print(f'Processing {data_path}')
|
||||
|
||||
# Load invariance data:
|
||||
pure_data, config = load_data(data_path, **load_kwargs)
|
||||
noise_data, _ = load_data(data_path.replace('pure', 'noise'), **load_kwargs)
|
||||
pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
|
||||
t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
|
||||
cutoff_ind = np.nonzero(pure_data['cutoffs'] == snip_cutoff)[0][0]
|
||||
|
||||
if relate_to_noise:
|
||||
# Relate noise-song measures to zero scale:
|
||||
noise_data['measure_filt'] /= noise_data['measure_filt'][0]
|
||||
noise_data['measure_env'] /= noise_data['measure_env'][0]
|
||||
|
||||
if exclude_zero:
|
||||
# Exclude zero scales:
|
||||
inds = pure_scales > 0
|
||||
pure_scales = pure_scales[inds]
|
||||
pure_data['measure_filt'] = pure_data['measure_filt'][inds]
|
||||
pure_data['measure_env'] = pure_data['measure_env'][inds]
|
||||
inds = noise_scales > 0
|
||||
noise_scales = noise_scales[inds]
|
||||
noise_data['measure_filt'] = noise_data['measure_filt'][inds]
|
||||
noise_data['measure_env'] = noise_data['measure_env'][inds]
|
||||
symlog_kwargs = dict(linthresh=pure_scales[pure_scales > 0][0], linscale=0.5)
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
fig.canvas.draw()
|
||||
|
||||
# Prepare pure-song snippet axes:
|
||||
pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
|
||||
pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
|
||||
pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
|
||||
for (ax1, ax2), stage in zip(pure_axes[:, :2], stages):
|
||||
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
|
||||
ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
|
||||
ylabel(ax1, ylabels[stage], **ylab_snip_kwargs, transform=pure_subfig.transSubfigure)
|
||||
for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
|
||||
pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
|
||||
letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
|
||||
|
||||
# Prepare noise-song snippet axes:
|
||||
noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
|
||||
noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
|
||||
noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
|
||||
for (ax1, ax2), stage in zip(noise_axes[:, :2], stages):
|
||||
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
|
||||
ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
|
||||
ylabel(ax1, ylabels[stage], **ylab_snip_kwargs, transform=noise_subfig.transSubfigure)
|
||||
for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
|
||||
noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
|
||||
letter_subplot(noise_subfig, 'b', ref=noise_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, scales in enumerate([pure_scales, noise_scales, noise_scales]):
|
||||
ax = big_subfig.add_subplot(big_grid[0, i])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', **symlog_kwargs)
|
||||
ax.set_yscale('symlog', **symlog_kwargs)
|
||||
ax.set_aspect(**anchor_kwargs)
|
||||
if i in [0, 1]:
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
pos_equal = ax.get_position().bounds
|
||||
else:
|
||||
pos_auto = list(ax.get_position().bounds)
|
||||
ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
|
||||
ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
|
||||
ax.set_ylim(0.1, 100)
|
||||
big_axes[i] = ax
|
||||
shift_subplot(big_axes[0], dx=big_col_shift)
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_subfig.transSubfigure, **ylab_pure_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_noise_kwargs,
|
||||
x=(big_subfig.transSubfigure + big_axes[0].transAxes.inverted()).transform((ylab_pure_kwargs['x'], 0))[0])
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'cde', **letter_big_kwargs)
|
||||
|
||||
# Plot pure-song filtered snippets:
|
||||
handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_filt'],
|
||||
c=colors['filt'], lw=lw['snip'], ypad=ypad['filt'])
|
||||
|
||||
# Plot pure-song envelope snippets:
|
||||
plot_snippets(pure_axes[1, :], t_full, pure_data['snip_env'][..., cutoff_ind],
|
||||
ymin=0, c=colors['env'], lw=lw['snip'], ypad=ypad['env'])
|
||||
|
||||
# Plot noise-song filtered snippets:
|
||||
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_filt'], ypad=ypad['filt'],
|
||||
*pure_axes[0, 0].get_ylim(), c=colors['filt'], lw=lw['snip'])
|
||||
|
||||
# Plot noise-song envelope snippets:
|
||||
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_env'][..., cutoff_ind],
|
||||
*pure_axes[1, 0].get_ylim(), c=colors['env'], lw=lw['snip'])
|
||||
|
||||
# Zoom into first filtered snippet:
|
||||
# ylim_zoom = np.array(noise_axes[0, -1].get_ylim()) * ylim_zoom_factor
|
||||
# noise_axes[0, 0].set_ylim(*ylim_zoom)
|
||||
ylim_zoom = ylimits(noise_data['snip_filt'][:, 0], noise_axes[0, 0], pad=ypad['filt'])
|
||||
pure_axes[0, 0].set_ylim(*ylim_zoom)
|
||||
|
||||
# Zoom into first envelope snippet:
|
||||
# ylim_zoom = np.array(noise_axes[1, -1].get_ylim()) * ylim_zoom_factor
|
||||
# noise_axes[1, 0].set_ylim(*ylim_zoom)
|
||||
ylim_zoom = ylimits(noise_data['snip_env'][:, 0, cutoff_ind], noise_axes[1, 0], minval=0, pad=ypad['env'])
|
||||
pure_axes[1, 0].set_ylim(*ylim_zoom)
|
||||
|
||||
# Indicate time scale:
|
||||
time_bar(noise_axes[-1, -1], **bar_kwargs)
|
||||
|
||||
# Plot pure-song measures (ideal):
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_filt'], c=colors['filt'], lw=lw['big'])
|
||||
handles = big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
|
||||
|
||||
# Plot noise-song measures (limited):
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_filt'], c=colors['filt'], lw=lw['big'])
|
||||
handles = big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
|
||||
|
||||
# Add proxy legend:
|
||||
proxy_handles = []
|
||||
for i, cutoff in enumerate(pure_data['cutoffs']):
|
||||
label = '$\\text{unfiltered}$' if np.isnan(cutoff) else f'${int(cutoff)}\\,\\text{{Hz}}$'
|
||||
proxy_handles.append(big_axes[0].plot([], [], c=colors['env'], lw=lw['big'],
|
||||
ls=ls_env[i], label=label)[0])
|
||||
big_axes[0].legend(handles=proxy_handles, **cutoff_leg_kwargs)
|
||||
|
||||
if show_diag:
|
||||
# Indicate diagonal:
|
||||
big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
|
||||
big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
|
||||
|
||||
# Plot species-specific noise-song invariance curves:
|
||||
leg_handles = []
|
||||
for i, (species, measure) in enumerate(species_measures.items()):
|
||||
handles = big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
|
||||
c=species_colors[species], lw=lw['spec'])
|
||||
[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
|
||||
leg_handles.append(handles[-1])
|
||||
legend = big_axes[2].legend(handles=leg_handles, **spec_leg_kwargs)
|
||||
[h.set_lw(lw['legend']) for h in legend.legend_handles]
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
159
python/fig_invariance_rect-lp_appendix.py
Normal file
159
python/fig_invariance_rect-lp_appendix.py
Normal file
@@ -0,0 +1,159 @@
|
||||
import plotstyle_plt
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from plot_functions import ylabel, super_xlabel, super_ylabel, title_subplot, time_bar
|
||||
from color_functions import load_colors
|
||||
from misc_functions import shorten_species
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
# 'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
data_path = '../data/inv/rect_lp/condensed/'
|
||||
save_path = '../figures/fig_invariance_rect-lp_appendix.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
relate_to_noise = True
|
||||
exclude_zero = True
|
||||
cutoffs = np.array([np.nan, 2500, 250, 25])
|
||||
search_kwargs = dict(
|
||||
incl=['noise', 'norm-base' if relate_to_noise else 'unnormed'],
|
||||
dir=data_path,
|
||||
)
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
nrows=cutoffs.size,
|
||||
ncols=len(target_species),
|
||||
sharex=True,
|
||||
sharey=True,
|
||||
gridspec_kw=dict(
|
||||
wspace=0.4,
|
||||
hspace=0.2,
|
||||
left=0.12,
|
||||
right=0.98,
|
||||
bottom=0.1,
|
||||
top=0.95,
|
||||
)
|
||||
)
|
||||
|
||||
# PLOT SETTINGS:
|
||||
species_colors = load_colors('../data/species_colors.npz')
|
||||
line_kwargs = dict(
|
||||
lw=2,
|
||||
alpha=0.5,
|
||||
zorder=2,
|
||||
)
|
||||
fill_kwargs = dict(
|
||||
alpha=0.3,
|
||||
zorder=1,
|
||||
)
|
||||
mean_kwargs = dict(
|
||||
lw=2,
|
||||
alpha=1,
|
||||
zorder=3,
|
||||
ls='--'
|
||||
)
|
||||
mean_colors = {
|
||||
'Chorthippus_biguttulus': (1,) * 3,
|
||||
'Chorthippus_mollis': (0,) * 3,
|
||||
'Chrysochraon_dispar': (0,) * 3,
|
||||
'Euchorthippus_declivus': (0,) * 3,
|
||||
'Gomphocerippus_rufus': (0,) * 3,
|
||||
'Omocestus_rufipes': (0,) * 3,
|
||||
'Pseudochorthippus_parallelus': (1,) * 3,
|
||||
}
|
||||
xlab = 'scale $\\alpha$'
|
||||
ylabs = ['$\\text{unfiltered}$'] + [f'${int(cutoff)}\\,\\text{{Hz}}$' for cutoff in cutoffs[1:]]
|
||||
super_ylab = '$\\sigma_{\\text{env}}\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else '$\\sigma_{\\text{env}}$'
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=16,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_kwargs = dict(
|
||||
x=0.05,
|
||||
fontsize=16,
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ylab_super_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=20,
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
title_kwargs = dict(
|
||||
x=0.5,
|
||||
yref=0.99,
|
||||
ha='center',
|
||||
va='top',
|
||||
fontsize=16,
|
||||
fontstyle='italic',
|
||||
)
|
||||
letter_kwargs = dict(
|
||||
x=0.005,
|
||||
y=0.99,
|
||||
fontsize=22,
|
||||
ha='left',
|
||||
va='top',
|
||||
)
|
||||
|
||||
# Prepare graph:
|
||||
fig, axes = plt.subplots(**fig_kwargs)
|
||||
[ylabel(ax, lab, transform=fig.transFigure, **ylab_kwargs) for ax, lab in zip(axes[:, 0], ylabs)]
|
||||
super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
|
||||
super_ylabel(super_ylab, fig, axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
|
||||
|
||||
# Run through species:
|
||||
for i, (species, spec_axes) in enumerate(zip(target_species, axes.T)):
|
||||
title_subplot(spec_axes[0], shorten_species(species), ref=fig, **title_kwargs)
|
||||
|
||||
# Load species data:
|
||||
path = search_files(species, **search_kwargs)[0]
|
||||
data, config = load_data(path, files=['scales', 'mean_env', 'sd_env'])
|
||||
scales = data['scales']
|
||||
means = data['mean_env']
|
||||
sds = data['sd_env']
|
||||
|
||||
if exclude_zero:
|
||||
# Exclude zero scale:
|
||||
inds = scales > 0
|
||||
scales = scales[inds]
|
||||
means = means[inds, ...]
|
||||
sds = sds[inds, ...]
|
||||
|
||||
# Run through cutoffs:
|
||||
for j, ax in enumerate(spec_axes):
|
||||
# Plot recording-specific traces:
|
||||
for k in range(means.shape[-1]):
|
||||
ax.plot(scales, means[:, j, k], c=species_colors[species], **line_kwargs)
|
||||
spread = (means[:, j, k] - sds[:, j, k], means[:, j, k] + sds[:, j, k])
|
||||
ax.fill_between(scales, *spread, color=species_colors[species], **fill_kwargs)
|
||||
# Plot cutoff-specific mean trace:
|
||||
ax.plot(scales, means[:, j, :].mean(axis=-1), c=mean_colors[species], **mean_kwargs)
|
||||
|
||||
# Posthocs:
|
||||
sylog_kwargs = dict(linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
axes[0, 0].set_xscale('symlog', **sylog_kwargs)
|
||||
axes[0, 0].set_yscale('symlog', **sylog_kwargs)
|
||||
axes[0, 0].set_xlim(scales[0], scales[-1])
|
||||
axes[0, 0].set_ylim(0.9, scales[-1])
|
||||
axes[0, 0].xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
|
||||
|
||||
# Save graph:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
|
||||
|
||||
@@ -98,7 +98,7 @@ snip_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.3,
|
||||
hspace=0,
|
||||
left=0.25,
|
||||
left=0.2 - snip_col_shift,
|
||||
right=0.93,
|
||||
bottom=0.15,
|
||||
top=0.95,
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
import plotstyle_plt
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
from scipy.stats import norm
|
||||
from plot_functions import xlabel, ylabel
|
||||
from IPython import embed
|
||||
|
||||
@@ -26,10 +25,22 @@ fig_kwargs = dict(
|
||||
)
|
||||
)
|
||||
line_kwargs = dict(
|
||||
color='black',
|
||||
c='black',
|
||||
lw=1,
|
||||
alpha=0.5,
|
||||
)
|
||||
fit_kwargs = dict(
|
||||
c='red',
|
||||
lw=3,
|
||||
ls='--',
|
||||
)
|
||||
grid_line_kwargs = dict(
|
||||
visible=True,
|
||||
which='major',
|
||||
axis='both',
|
||||
color='k',
|
||||
lw=0.5,
|
||||
)
|
||||
xlab = '$\\text{multiple of }\\sigma_{k_i}$'
|
||||
ylab = '$P\\,(c_i > \\Theta_i)$'
|
||||
xlab_kwargs = dict(
|
||||
@@ -50,15 +61,20 @@ data = dict(np.load(thresh_path))
|
||||
factors = data['factors']
|
||||
perc = data['percs']
|
||||
|
||||
# Get Gaussian CDF for reference:
|
||||
fit = norm.cdf(factors, loc=0, scale=1)[::-1]
|
||||
|
||||
# Prepare graph:
|
||||
fig, ax = plt.subplots(**fig_kwargs)
|
||||
ax.grid(**grid_line_kwargs)
|
||||
ax.set_xlim(factors[0], factors[-1])
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_ylim(-0.01, 1.01)
|
||||
ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
|
||||
xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
|
||||
|
||||
# Plotting:
|
||||
ax.plot(factors, perc, **line_kwargs)
|
||||
ax.plot(factors, fit, **fit_kwargs)
|
||||
|
||||
# Save figure:
|
||||
fig.savefig(save_path)
|
||||
|
||||
@@ -300,6 +300,12 @@ def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
|
||||
artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
|
||||
return None
|
||||
|
||||
def color_axis(ax, color, axis='y', side='left'):
|
||||
ax.spines[side].set_color(color)
|
||||
ax.tick_params(colors=color, axis=axis, which='both')
|
||||
ax.yaxis.label.set_color(color)
|
||||
return None
|
||||
|
||||
def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
|
||||
base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
|
||||
if pdf is None:
|
||||
|
||||
@@ -7,16 +7,17 @@ from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
|
||||
data_paths = search_files('*', excl='noise', dir='../data/processed/')
|
||||
search_target = ['*', example_file][0]
|
||||
data_paths = search_files(search_target, excl='noise', dir='../data/processed/')
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
save_path = '../data/inv/rect_lp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
mode = ['pure', 'noise'][1]
|
||||
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
|
||||
scales = np.geomspace(0.01, 10000, 1000)
|
||||
example_scales = np.array([0.1, 0.3, 1, 3, 10])
|
||||
scales = np.geomspace(0.01, 100, 1000)
|
||||
scales = np.unique(np.concatenate(([0], scales, example_scales)))
|
||||
cutoffs = np.array([np.nan, 125, 250, 500])
|
||||
cutoffs = np.array([np.nan, 2500, 250, 25])
|
||||
|
||||
# PREPARATION:
|
||||
if mode == 'noise':
|
||||
|
||||
Reference in New Issue
Block a user