270 lines
8.6 KiB
Python
270 lines
8.6 KiB
Python
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 color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel,\
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plot_line, strip_zeros, time_bar
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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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[hide_axis(ax, 'left') for ax in axes.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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for ax, snippet in zip(axes, snippets.T):
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plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
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return None
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'measure']
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)
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save_path = '../figures/fig_invariance_log_hp.pdf'
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 16/2.54),
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)
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super_grid_kwargs = dict(
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nrows=2,
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ncols=2,
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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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)
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subfig_specs = dict(
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pure=(0, 0),
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noise=(1, 0),
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analysis=(slice(None), 1)
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)
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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=0.05,
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hspace=0.1,
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left=0.13,
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right=0.95,
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bottom=0.15,
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top=0.9
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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=0.05,
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hspace=0.1,
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left=0.13,
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right=0.95,
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bottom=0.15,
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top=0.9
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)
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analysis_grid_kwargs = dict(
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nrows=1,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0.15,
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right=0.96,
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bottom=0.1,
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top=0.95
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)
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snip_specs = dict(
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env=(0, slice(None)),
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log=(1, slice(None)),
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inv=(2, slice(None))
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)
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# PLOT SETTINGS:
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colors = load_colors('../data/stage_colors.npz')
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lw_snippets = 0.5
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lw_analysis = 3
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xlabels = dict(
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analysis='scale $\\alpha$',
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)
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xlab_analysis_kwargs = dict(
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y=0.01,
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fontsize=16,
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ha='center',
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va='bottom',
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)
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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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analysis='ratio $\\text{SD}_{\\alpha}\\,/\\,\\text{SD}_{\\min[\\alpha]}$',
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# analysis='ratio $\\sigma_{\\alpha}\\,/\\,\\sigma_{\\min[\\alpha]}$',
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)
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ylab_snip_kwargs = dict(
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x=0.01,
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fontsize=20,
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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_analysis_kwargs = dict(
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x=0.02,
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fontsize=16,
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ha='center',
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va='top',
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)
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xloc = dict(
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analysis=200,
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)
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letter_snip_kwargs = dict(
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x=0.02,
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y=1,
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ha='left',
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va='top',
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fontsize=22,
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fontweight='bold'
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)
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letter_analysis_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='top',
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fontsize=22,
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fontweight='bold'
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)
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indicate_unsaturated = False
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unsaturated_proportion = 0.85
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unsaturated_kwargs = dict(
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color=3 * (0.85,),
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zorder=0,
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lw=0
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)
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bar_time = 5
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bar_kwargs = dict(
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y0=0.5,
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y1=0.6,
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color='k',
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lw = 0,
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)
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# EXECUTION:
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for data_path in data_paths:
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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('.npz', '_noise.npz'), **load_kwargs)
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t_full = np.arange(pure_data['env'].shape[0]) / config['env_rate']
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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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# Prepare pure-song snippet axes:
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pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
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pure_grid_kwargs['nrows' if pure_grid_kwargs['nrows'] is None else 'ncols'] = pure_data['example_scales'].size
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pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
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for ax, stage in zip(pure_axes[:, 0], stages):
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=pure_subfig.transSubfigure)
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for ax, scale in zip(pure_axes[snip_specs['env']], pure_data['example_scales']):
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ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
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pure_subfig.text(s='a', **letter_snip_kwargs)
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# Prepare noise-song snippet axes:
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noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
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noise_grid_kwargs['nrows' if noise_grid_kwargs['nrows'] is None else 'ncols'] = noise_data['example_scales'].size
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noise_grid = noise_subfig.add_gridspec(**noise_grid_kwargs)
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noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
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for ax, stage in zip(noise_axes[:, 0], stages):
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[snip_specs['env']], noise_data['example_scales']):
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ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
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noise_subfig.text(s='b', **letter_snip_kwargs)
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# Prepare analysis axis:
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analysis_subfig = fig.add_subfigure(super_grid[subfig_specs['analysis']])
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analysis_grid = analysis_subfig.add_gridspec(**analysis_grid_kwargs)
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analysis_ax = analysis_subfig.add_subplot(analysis_grid[0, 0])
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analysis_ax.set_xlim(noise_data['scales'].min(), noise_data['scales'].max())
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analysis_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['analysis']))
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xlabel(analysis_ax, xlabels['analysis'], **xlab_analysis_kwargs,
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transform=analysis_subfig.transSubfigure)
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analysis_ax.set_yscale('log')
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ylabel(analysis_ax, ylabels['analysis'], **ylab_analysis_kwargs,
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transform=analysis_subfig.transSubfigure)
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analysis_subfig.text(s='c', **letter_analysis_kwargs)
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# Plot pure-song envelope snippets:
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plot_snippets(pure_axes[snip_specs['env']], t_full, pure_data['env'],
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ymin=0, c=colors['env'], lw=lw_snippets)
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# Plot pure-song logarithmic snippets:
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plot_snippets(pure_axes[snip_specs['log']], t_full, pure_data['log'],
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ymax=None, c=colors['log'], lw=lw_snippets)
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# Plot pure-song invariant snippets:
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plot_snippets(pure_axes[snip_specs['inv']], t_full, pure_data['inv'],
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c=colors['inv'], lw=lw_snippets)
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# Indicate time scale:
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time_bar(pure_axes[snip_specs['env']][0], bar_time, **bar_kwargs)
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# Plot noise-song envelope snippets:
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plot_snippets(noise_axes[snip_specs['env']], t_full, noise_data['env'],
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ymin=0, c=colors['env'], lw=lw_snippets)
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# Plot noise-song logarithmic snippets:
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plot_snippets(noise_axes[snip_specs['log']], t_full, noise_data['log'],
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ymax=None, c=colors['log'], lw=lw_snippets)
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# Plot noise-song invariant snippets:
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plot_snippets(noise_axes[snip_specs['inv']], t_full, noise_data['inv'],
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c=colors['inv'], lw=lw_snippets)
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# Indicate time scale:
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time_bar(noise_axes[snip_specs['env']][0], bar_time, **bar_kwargs)
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# Plot pure-song SD ratios (ideal):
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base_ind = np.argmin(pure_data['scales'])
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# measure_env = pure_data['measure_env'] / pure_data['measure_env'][base_ind]
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# measure_log = pure_data['measure_log'] / pure_data['measure_log'][base_ind]
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measure_inv = pure_data['measure_inv'] / pure_data['measure_inv'][base_ind]
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# analysis_ax.plot(pure_data['scales'], measure_env, c=colors['env'], lw=lw_analysis, ls='--')
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# analysis_ax.plot(pure_data['scales'], measure_log, c=colors['log'], lw=lw_analysis, ls='--')
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analysis_ax.plot(pure_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis, ls='--')
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if indicate_unsaturated:
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# Indicate influence of noise floor:
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limit = noise_data['limit'] * unsaturated_proportion
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thresh_ind = np.nonzero(noise_data['measure_inv'] <= limit)[0][-1]
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analysis_ax.axvspan(0, noise_data['scales'][thresh_ind], **unsaturated_kwargs)
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# Plot noise-song SD ratios (limited):
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base_ind = np.argmin(noise_data['scales'])
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measure_env = noise_data['measure_env'] / noise_data['measure_env'][base_ind]
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measure_log = noise_data['measure_log'] / noise_data['measure_log'][base_ind]
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measure_inv = noise_data['measure_inv'] / noise_data['measure_inv'][base_ind]
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analysis_ax.plot(noise_data['scales'], measure_env, c=colors['env'], lw=lw_analysis)
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analysis_ax.plot(noise_data['scales'], measure_log, c=colors['log'], lw=lw_analysis)
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analysis_ax.plot(noise_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis)
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analysis_ax.set_ylim(0.1, measure_env.max())
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if save_path is not None:
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fig.savefig(save_path)
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plt.show()
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print('Done.')
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embed()
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