245 lines
7.1 KiB
Python
245 lines
7.1 KiB
Python
import plotstyle_plt
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import glob
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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.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, plot_line, plot_barcode, strip_zeros
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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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def plot_bi_snippets(axes, time, binary, **kwargs):
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for ax, binary in zip(axes, binary.T):
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plot_barcode(ax, time, binary[:, None], **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 = glob.glob(f'../data/processed/{target}*.npz')
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stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'measure', 'spread']
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)
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save_path = None#'../figures/fig_invariance_full.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=len(stages),
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ncols=3,
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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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**{stage: (slice(0, -1), i) for i, stage in enumerate(stages)},
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big=(slice(None), -1)
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)
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stage_grid_kwargs = dict(
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nrows=1,
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ncols=None,
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wspace=0.05,
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hspace=0,
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left=0.07,
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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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big_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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# PLOT SETTINGS:
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colors = load_colors('../data/stage_colors.npz')
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lw_snippets = dict(
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raw=0.25,
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filt=0.25,
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env=0.5,
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log=0.5,
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inv=0.5,
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conv=0.5,
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bi=0.01,
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feat=2
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)
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lw_big = 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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conv='$c_i$',
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bi='$b_i$',
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feat='$f_i$',
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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=10,
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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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# bar_time = 5
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# bar_kwargs = dict(
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# y0=0.7,
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# y1=0.8,
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# color='k',
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# lw=0,
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# )
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# spread_kwargs = dict(
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# alpha=0.3,
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# lw=0,
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# zorder=0
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# )
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# kernel_ind = 0
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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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data, config = load_data(data_path, **load_kwargs)
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t_full = np.arange(data['conv'].shape[0]) / config['env_rate']
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# Reduce snippet data to kernel subset:
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data['conv'] = data['conv'][:, kernel_ind]
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data['bi'] = data['bi'][:, kernel_ind]
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data['feat'] = data['feat'][:, kernel_ind]
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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'] = 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['conv']], 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 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(data['scales'].min(), 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 kernel response snippets:
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plot_snippets(pure_axes[snip_specs['conv']], t_full, data['conv'],
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c=colors['conv'], lw=lw_snippets['conv'])
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# Plot pure-song binary snippets:
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plot_bi_snippets(pure_axes[snip_specs['bi']], t_full, data['bi'],
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color=colors['bi'], lw=0)
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# Plot pure-song feature snippets:
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plot_snippets(pure_axes[snip_specs['feat']], t_full, data['feat'],
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ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
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# Indicate time scale:
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time_bar(pure_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
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# # Plot noise-song kernel response snippets:
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# plot_snippets(noise_axes[snip_specs['conv']], t_full, noise_data['conv'],
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# c=colors['conv'], lw=lw_snippets['conv'])
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# # Plot noise-song binary snippets:
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# plot_bi_snippets(noise_axes[snip_specs['bi']], t_full, noise_data['bi'],
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# color=colors['bi'], lw=0)
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# # Plot noise-song feature snippets:
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# plot_snippets(noise_axes[snip_specs['feat']], t_full, noise_data['feat'],
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# ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
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# # Indicate time scale:
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# time_bar(noise_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
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# Plot noise-song SD ratios (limited):
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analysis_ax.plot(data['scales'], data['measure_conv'],
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c=colors['conv'], lw=lw_analysis)
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lower, upper = data['spread_conv']
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analysis_ax.fill_between(data['scales'], lower, upper,
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color=colors['conv'], **spread_kwargs)
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analysis_ax.plot(data['scales'], data['measure_feat'],
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c=colors['feat'], lw=lw_analysis)
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lower, upper = data['spread_feat']
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analysis_ax.fill_between(data['scales'], lower, upper,
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color=colors['feat'], **spread_kwargs)
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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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