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
424
python/fig_invariance_rect-lp.py
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424
python/fig_invariance_rect-lp.py
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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(
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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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),
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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,
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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,
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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 = {}
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for i, species in enumerate(target_species):
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spec_path = search_files(species, incl=['noise', 'norm-base'], dir='../data/inv/rect_lp/condensed/')[0]
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spec_data = dict(np.load(spec_path))
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measure = spec_data['mean_env'].mean(axis=-1)
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if exclude_zero:
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measure = measure[spec_data['scales'] > 0, :]
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species_measures[species] = measure
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# EXECUTION:
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print(f'Processing {data_path}')
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# Load invariance data:
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pure_data, config = load_data(data_path, **load_kwargs)
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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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cutoff_ind = np.nonzero(pure_data['cutoffs'] == snip_cutoff)[0][0]
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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_filt'] /= noise_data['measure_filt'][0]
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noise_data['measure_env'] /= noise_data['measure_env'][0]
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if exclude_zero:
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# Exclude zero scales:
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inds = pure_scales > 0
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pure_scales = pure_scales[inds]
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pure_data['measure_filt'] = pure_data['measure_filt'][inds]
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pure_data['measure_env'] = pure_data['measure_env'][inds]
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inds = noise_scales > 0
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noise_scales = noise_scales[inds]
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noise_data['measure_filt'] = noise_data['measure_filt'][inds]
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noise_data['measure_env'] = noise_data['measure_env'][inds]
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symlog_kwargs = dict(linthresh=pure_scales[pure_scales > 0][0], linscale=0.5)
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# Prepare overall graph:
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fig = plt.figure(**fig_kwargs)
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super_grid = fig.add_gridspec(**super_grid_kwargs)
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fig.canvas.draw()
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# Prepare pure-song snippet axes:
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pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
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pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
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pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
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for (ax1, ax2), stage in zip(pure_axes[:, :2], stages):
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ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
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ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
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ylabel(ax1, ylabels[stage], **ylab_snip_kwargs, transform=pure_subfig.transSubfigure)
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for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
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pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
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# Prepare noise-song snippet axes:
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noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
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noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
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noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
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for (ax1, ax2), stage in zip(noise_axes[:, :2], stages):
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ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
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ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
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ylabel(ax1, ylabels[stage], **ylab_snip_kwargs, transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
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noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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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', **symlog_kwargs)
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ax.set_yscale('symlog', **symlog_kwargs)
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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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pos_equal = ax.get_position().bounds
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else:
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pos_auto = list(ax.get_position().bounds)
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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(0.1, 100)
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big_axes[i] = ax
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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_subfig.transSubfigure, **ylab_pure_kwargs)
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ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_noise_kwargs,
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x=(big_subfig.transSubfigure + big_axes[0].transAxes.inverted()).transform((ylab_pure_kwargs['x'], 0))[0])
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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 filtered snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_filt'],
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c=colors['filt'], lw=lw['snip'], ypad=ypad['filt'])
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# Plot pure-song envelope snippets:
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plot_snippets(pure_axes[1, :], t_full, pure_data['snip_env'][..., cutoff_ind],
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ymin=0, c=colors['env'], lw=lw['snip'], ypad=ypad['env'])
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# Plot noise-song filtered snippets:
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handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_filt'], ypad=ypad['filt'],
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*pure_axes[0, 0].get_ylim(), c=colors['filt'], lw=lw['snip'])
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# Plot noise-song envelope snippets:
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plot_snippets(noise_axes[1, :], t_full, noise_data['snip_env'][..., cutoff_ind],
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*pure_axes[1, 0].get_ylim(), c=colors['env'], lw=lw['snip'])
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# Zoom into first filtered snippet:
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# ylim_zoom = np.array(noise_axes[0, -1].get_ylim()) * ylim_zoom_factor
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# noise_axes[0, 0].set_ylim(*ylim_zoom)
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ylim_zoom = ylimits(noise_data['snip_filt'][:, 0], noise_axes[0, 0], pad=ypad['filt'])
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pure_axes[0, 0].set_ylim(*ylim_zoom)
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# Zoom into first envelope snippet:
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# ylim_zoom = np.array(noise_axes[1, -1].get_ylim()) * ylim_zoom_factor
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# noise_axes[1, 0].set_ylim(*ylim_zoom)
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ylim_zoom = ylimits(noise_data['snip_env'][:, 0, cutoff_ind], noise_axes[1, 0], minval=0, pad=ypad['env'])
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pure_axes[1, 0].set_ylim(*ylim_zoom)
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# Indicate time scale:
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time_bar(noise_axes[-1, -1], **bar_kwargs)
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# Plot pure-song measures (ideal):
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big_axes[0].plot(pure_scales, pure_data['measure_filt'], c=colors['filt'], lw=lw['big'])
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handles = big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
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[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
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# Plot noise-song measures (limited):
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big_axes[1].plot(noise_scales, noise_data['measure_filt'], c=colors['filt'], lw=lw['big'])
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handles = big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
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[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
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# Add proxy legend:
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proxy_handles = []
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for i, cutoff in enumerate(pure_data['cutoffs']):
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label = '$\\text{unfiltered}$' if np.isnan(cutoff) else f'${int(cutoff)}\\,\\text{{Hz}}$'
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proxy_handles.append(big_axes[0].plot([], [], c=colors['env'], lw=lw['big'],
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ls=ls_env[i], label=label)[0])
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big_axes[0].legend(handles=proxy_handles, **cutoff_leg_kwargs)
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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)
|
||||
|
||||
# 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()
|
||||
Reference in New Issue
Block a user