Finished (:D) fig_invariance_thresh_lp_single.pdf.
Added/modified few plot functions. Cleaned up simulation/plotting scripts regarding Thresh-LP.
This commit is contained in:
j-hartling
@@ -1,271 +0,0 @@
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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 color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel,\
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plot_line, plot_barcode, 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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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 = search_files(target, excl='noise', dir='../data/inv/thresh_lp/')
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stages = ['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 = '../figures/fig_invariance_thresh_lp.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=0.07,
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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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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=0.07,
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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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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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conv=(0, slice(None)),
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bi=(1, slice(None)),
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feat=(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 = dict(
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conv=0.5,
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feat=2
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)
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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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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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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['conv'].shape[0]) / config['env_rate']
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# Reduce snippet data to kernel subset:
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pure_data['conv'] = pure_data['conv'][:, kernel_ind]
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pure_data['bi'] = pure_data['bi'][:, kernel_ind]
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pure_data['feat'] = pure_data['feat'][:, kernel_ind]
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noise_data['conv'] = noise_data['conv'][:, kernel_ind]
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noise_data['bi'] = noise_data['bi'][:, kernel_ind]
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noise_data['feat'] = noise_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'] = 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['conv']], 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['conv']], 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 kernel response snippets:
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plot_snippets(pure_axes[snip_specs['conv']], t_full, pure_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, pure_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, pure_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(noise_data['scales'], noise_data['measure_conv'],
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c=colors['conv'], lw=lw_analysis)
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lower, upper = noise_data['spread_conv']
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analysis_ax.fill_between(noise_data['scales'], lower, upper,
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color=colors['conv'], **spread_kwargs)
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analysis_ax.plot(noise_data['scales'], noise_data['measure_feat'],
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c=colors['feat'], lw=lw_analysis)
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lower, upper = noise_data['spread_feat']
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analysis_ax.fill_between(noise_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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@@ -1,73 +1,71 @@
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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 thunderhopper.filtertools import find_kern_specs
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from color_functions import load_colors, shade_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel, super_xlabel,\
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plot_line, plot_barcode, strip_zeros, time_bar,\
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letter_subplot, letter_subplots, title_subplot
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from plot_functions import shift_subplot, hide_axis, ylimits, xlabel, ylabel,\
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super_ylabel, plot_line, plot_barcode, strip_zeros,\
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time_bar, letter_subplot, letter_subplots, title_subplot,\
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set_clip_box
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from IPython import embed
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def add_snip_axes(fig, grid_kwargs, snip_specs):
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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((len(snip_specs), grid.ncols), dtype=object)
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for i, specs in enumerate(snip_specs.values()):
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for j in range(grid.ncols):
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axes[i, j] = fig.add_subplot(grid[specs, j])
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[hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
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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,
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, ypad=0.05,
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thresh=None, fill_kwargs={}, **kwargs):
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=ypad)
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handles = []
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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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handles.append(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
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if thresh is not None:
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ax.fill_between(time, thresh, snippet, where=(snippet > thresh), **fill_kwargs)
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return None
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return handles
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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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def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
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ymin=None, ymax=None, fill_kwargs={}, **kwargs):
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limits = np.array([snippets.min(), snippets.max()]) * 1.05
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def side_distributions(axes, snippets, inset_bounds, thresh, nbins=1000,
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fill_kwargs={}, **kwargs):
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limits = np.array([snippets.min(), snippets.max()]) * 1.1
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edges = np.linspace(*limits, nbins + 1)
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centers = edges[:-1] + (edges[1] - edges[0]) / 2
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insets = []
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for ax, snippet in zip(axes, snippets.T):
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pdf, _ = np.histogram(snippet, edges, density=True)
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inset = ax.inset_axes(inset_bounds)
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inset.plot(pdf, centers, **kwargs)
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inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh),
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**fill_kwargs)
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ylimits(centers, inset, minval=ymin, maxval=ymax, pad=0)
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inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), **fill_kwargs)
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inset.set_xlim(0, pdf.max())
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inset.set_ylim(ax.get_ylim())
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inset.axis('off')
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return None
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insets.append(inset)
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return insets
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# GENERAL SETTINGS:
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with_noise = True
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target = 'Omocestus_rufipes'
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search_kwargs = dict(
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incl=['subset', 'noise'] if with_noise else 'subset',
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excl=None if with_noise else 'noise',
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dir='../data/inv/thresh_lp/'
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)
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data_paths = search_files(target, **search_kwargs)
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data_paths = search_files(target, incl='noise', dir='../data/inv/thresh_lp/')
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stages = ['conv', 'bi', 'feat']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure', 'thresh']
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)
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save_path = '../figures/fig_invariance_thresh_lp_single.pdf'
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if with_noise and save_path is not None:
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save_path = save_path.replace('.pdf', '_noise.pdf')
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exclude_zero = True
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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@@ -83,19 +81,34 @@ super_grid_kwargs = dict(
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bottom=0,
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top=1
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)
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input_rows = 1
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snip_rows = 2
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subfig_specs = dict(
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snip=(slice(None), slice(super_grid_kwargs['ncols'] - 1)),
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input=(slice(input_rows), slice(-1)),
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snip=[np.array([input_rows, input_rows + snip_rows]), slice(-1)],
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big=(slice(None), -1),
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)
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snip_col_shift = -0.07
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snip_grid_kwargs = dict(
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nrows=len(stages) + 1,
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nrows=len(stages),
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ncols=None,
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wspace=0.3,
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hspace=0,
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left=0.17,
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left=0.25,
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right=0.93,
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bottom=0.15,
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top=0.9
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top=0.95,
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height_ratios=[2, 1, 1]
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)
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input_grid_kwargs = dict(
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nrows=1,
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ncols=None,
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wspace=snip_grid_kwargs['wspace'],
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hspace=0,
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left=snip_grid_kwargs['left'],
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right=snip_grid_kwargs['right'],
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bottom=0.15,
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top=0.75,
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)
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big_grid_kwargs = dict(
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nrows=1,
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@@ -107,12 +120,7 @@ big_grid_kwargs = dict(
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bottom=0.1,
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top=0.99
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)
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snip_specs = dict(
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conv=slice(0, -2),
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bi=-2,
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feat=-1
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)
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inset_bounds = [1.02, 0, 0.2, 1]
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dist_inset_bounds = [1.02, 0, 0.2, 1]
|
||||
|
||||
# PLOT SETTINGS:
|
||||
fs = dict(
|
||||
@@ -124,29 +132,24 @@ fs = dict(
|
||||
bar=16,
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
color_factors = [0.2, -0.2]
|
||||
shade_factors = [0.2, -0.2]
|
||||
lw = dict(
|
||||
conv=1,
|
||||
inv=1.5,
|
||||
conv=1.5,
|
||||
bi=0.1,
|
||||
feat=3,
|
||||
big=4,
|
||||
)
|
||||
xlabels = dict(
|
||||
snip='time [s]',
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
bi='$b_i$',
|
||||
feat='$f_i$',
|
||||
big='$\\mu_f$',
|
||||
)
|
||||
xlab_snip_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
@@ -157,7 +160,7 @@ ylab_snip_kwargs = dict(
|
||||
x=0.08,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='right',
|
||||
ha='center',
|
||||
va='center',
|
||||
)
|
||||
ylab_super_kwargs = dict(
|
||||
@@ -168,14 +171,20 @@ ylab_super_kwargs = dict(
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ypad = dict(
|
||||
inv=0.05,
|
||||
conv=0.05,
|
||||
big=0.075
|
||||
)
|
||||
yloc = dict(
|
||||
conv=100,
|
||||
bi=1,
|
||||
feat=1,
|
||||
inv=(2, 200),
|
||||
conv=(0.02, 2),
|
||||
bi=(1, 1),
|
||||
feat=(1, 1),
|
||||
big=0.2,
|
||||
)
|
||||
title_kwargs = dict(
|
||||
@@ -200,7 +209,6 @@ letter_big_kwargs = dict(
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
dist_kwargs = dict(
|
||||
nbins=50,
|
||||
c='k',
|
||||
lw=1,
|
||||
)
|
||||
@@ -211,8 +219,8 @@ dist_fill_kwargs = dict(
|
||||
bar_time = 0.1
|
||||
bar_kwargs = dict(
|
||||
dur=bar_time,
|
||||
y0=-0.25,
|
||||
y1=-0.1,
|
||||
y0=-0.5,
|
||||
y1=-0.35,
|
||||
xshift=1,
|
||||
color='k',
|
||||
lw=0,
|
||||
@@ -225,7 +233,16 @@ bar_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
kernel = np.array([
|
||||
leg_kwargs = dict(
|
||||
ncols=2,
|
||||
loc='center',
|
||||
bbox_to_anchor=(0, 0.95, 1, 0.05),
|
||||
frameon=False,
|
||||
fontsize=fs['tit_norm'],
|
||||
handlelength=1.5,
|
||||
columnspacing=1,
|
||||
)
|
||||
kern_specs = np.array([
|
||||
[1, 0.008],
|
||||
[2, 0.004],
|
||||
[3, 0.002],
|
||||
@@ -238,96 +255,138 @@ for data_path in data_paths:
|
||||
print(f'Processing {data_path}')
|
||||
|
||||
# Load invariance data:
|
||||
data, config = load_data(data_path, **load_kwargs)
|
||||
t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
|
||||
noise_data, config = load_data(data_path, **load_kwargs)
|
||||
pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)
|
||||
|
||||
# Unpack shared variables:
|
||||
scales = noise_data['scales']
|
||||
plot_scales = noise_data['example_scales']
|
||||
thresh_rel = noise_data['thresh_rel']
|
||||
thresh_abs = noise_data['thresh_abs']
|
||||
|
||||
# Reduce to kernel subset and crop to zoom frame:
|
||||
t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
|
||||
zoom_abs = zoom_rel * t_full[-1]
|
||||
zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
|
||||
kern_ind = find_kern_specs(config['k_specs'], kerns=kernel)[0]
|
||||
|
||||
# Reduce to kernel subset and crop time to zoom frame:
|
||||
data['snip_conv'] = data['snip_conv'][zoom_inds, kern_ind, ...]
|
||||
data['snip_bi'] = data['snip_bi'][zoom_inds, kern_ind, ...]
|
||||
data['snip_feat'] = data['snip_feat'][zoom_inds, kern_ind, ...]
|
||||
data['measure_feat'] = data['measure_feat'][:, kern_ind, :]
|
||||
t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
|
||||
kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
|
||||
noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
|
||||
noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
|
||||
noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
|
||||
noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
|
||||
noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
|
||||
pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
|
||||
thresh_abs = thresh_abs[:, kern_ind]
|
||||
t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
|
||||
if exclude_zero:
|
||||
# Reduce to nonzero scales:
|
||||
nonzero_inds = scales > 0
|
||||
scales = scales[nonzero_inds]
|
||||
noise_data['measure_feat'] = noise_data['measure_feat'][nonzero_inds, :]
|
||||
pure_data['measure_feat'] = pure_data['measure_feat'][nonzero_inds, :]
|
||||
|
||||
# Get threshold-specific colors:
|
||||
factors = np.linspace(*color_factors, data['threshs'].size)
|
||||
colors = dict(
|
||||
factors = np.linspace(*shade_factors, thresh_rel.size)
|
||||
shaded = dict(
|
||||
conv=shade_colors(colors['conv'], factors),
|
||||
bi=shade_colors(colors['bi'], factors),
|
||||
feat=shade_colors(colors['feat'], factors),
|
||||
)
|
||||
|
||||
# Adjust grid parameters:
|
||||
super_grid_kwargs['nrows'] = data['threshs'].size
|
||||
snip_grid_kwargs['ncols'] = data['example_scales'].size
|
||||
# Adjust grid parameters to loaded data:
|
||||
super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + 1
|
||||
input_grid_kwargs['ncols'] = plot_scales.size
|
||||
snip_grid_kwargs['ncols'] = plot_scales.size
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
|
||||
# Prepare input snippet axes:
|
||||
input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
|
||||
input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
|
||||
input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
|
||||
input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
|
||||
ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
|
||||
for ax, scale in zip(input_axes, plot_scales):
|
||||
title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
|
||||
letter_subplot(input_subfig, 'a', **letter_snip_kwargs)
|
||||
|
||||
# Prepare snippet axes:
|
||||
snip_axes = {}
|
||||
for i in range(data['threshs'].size):
|
||||
subfig_specs['snip'] = (i, subfig_specs['snip'][1])
|
||||
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
|
||||
axes = add_snip_axes(snip_subfig, snip_grid_kwargs, snip_specs)
|
||||
snip_axes[snip_subfig] = axes
|
||||
super_ylabel(f'{strip_zeros(100 * data["thresh_perc"][i])}%',
|
||||
snip_subfigs, snip_axes = [], []
|
||||
for i in range(thresh_rel.size):
|
||||
subfig_spec = subfig_specs['snip'].copy()
|
||||
subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
|
||||
snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
|
||||
axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
|
||||
[hide_axis(ax, 'left') for ax in axes[1:, 1]]
|
||||
super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
|
||||
snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
|
||||
for ax, stage in zip(axes[:, 0], stages):
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
|
||||
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
|
||||
transform=snip_subfig.transSubfigure)
|
||||
if i == 0:
|
||||
for ax, scale in zip(axes[0, :], data['example_scales']):
|
||||
title = f'$\\alpha={strip_zeros(scale)}$'
|
||||
title_subplot(ax, title, **title_kwargs, ref=fig)
|
||||
elif i == data['threshs'].size - 1:
|
||||
for (ax1, ax2), stage in zip(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], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
|
||||
# for ax, scale in zip(axes[0, :], plot_scales):
|
||||
# title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=snip_subfig, **title_kwargs)
|
||||
if i == thresh_rel.size - 1:
|
||||
axes[-1, -1].set_xlim(t_full[0], t_full[-1])
|
||||
time_bar(axes[-1, -1], **bar_kwargs)
|
||||
letter_subplots(snip_axes.keys(), **letter_snip_kwargs)
|
||||
snip_subfigs.append(snip_subfig)
|
||||
snip_axes.append(axes)
|
||||
letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
|
||||
|
||||
# Prepare analysis axis:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_ax = big_subfig.add_subplot(big_grid[0, 0])
|
||||
xlabel(big_ax, xlabels['big'], **xlab_big_kwargs,
|
||||
transform=big_subfig.transSubfigure)
|
||||
ylabel(big_ax, ylabels['big'], **ylab_big_kwargs,
|
||||
transform=big_subfig.transSubfigure)
|
||||
big_ax.set_xlim(data['scales'].min(), data['scales'].max())
|
||||
big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
|
||||
ylimits(data['measure_feat'], big_ax, minval=0, pad=0.01)
|
||||
big_ax.set_xlim(scales[0], scales[-1])
|
||||
big_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
ylimits(noise_data['measure_feat'], big_ax, minval=0, pad=ypad['big'])
|
||||
big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
|
||||
letter_subplot(big_subfig, 'd', **letter_big_kwargs, ref=list(snip_axes.keys())[0])
|
||||
xlabel(big_ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
|
||||
ylabel(big_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
letter_subplot(big_subfig, 'e', **letter_big_kwargs, ref=input_subfig)
|
||||
|
||||
# Plot intensity-adapted snippets:
|
||||
plot_snippets(input_axes, t_full, noise_data['snip_inv'],
|
||||
ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
|
||||
ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])
|
||||
|
||||
# Plot representation snippets per threshold:
|
||||
conv_min, conv_max = ylimits(data['snip_conv'], pad=0.02)
|
||||
for i, (subfig, axes) in enumerate(snip_axes.items()):
|
||||
dist_fill_kwargs['color'] = colors['bi'][i]
|
||||
for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
|
||||
dist_fill_kwargs['color'] = shaded['bi'][i]
|
||||
|
||||
# Plot kernel response snippets:
|
||||
plot_snippets(axes[0, :], t_full, data['snip_conv'][:, :, i],
|
||||
thresh=data['threshs'][i], ymin=conv_min, ymax=conv_max,
|
||||
fill_kwargs=dist_fill_kwargs, c=colors['conv'][i], lw=lw['conv'])
|
||||
plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
|
||||
ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
|
||||
ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
|
||||
|
||||
# Plot kernel response distributions:
|
||||
side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
|
||||
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
|
||||
side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
|
||||
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
|
||||
|
||||
# Plot binary snippets:
|
||||
plot_bi_snippets(axes[1, :], t_full, data['snip_bi'][:, :, i],
|
||||
color=colors['bi'][i], lw=lw['bi'])
|
||||
plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
|
||||
color=shaded['bi'][i], lw=lw['bi'])
|
||||
|
||||
# Plot feature snippets:
|
||||
plot_snippets(axes[2, :], t_full, data['snip_feat'][:, :, i],
|
||||
ymin=0, ymax=1, c=colors['feat'][i], lw=lw['feat'])
|
||||
handles = plot_snippets(axes[2, :], t_full, noise_data['snip_feat'][:, :, i],
|
||||
ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
|
||||
[set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]
|
||||
|
||||
# Plot kernel response distribution:
|
||||
side_distributions(axes[0, :], data['snip_conv'][:, :, i], inset_bounds,
|
||||
data['threshs'][i], ymin=conv_min, ymax=conv_max,
|
||||
fill_kwargs=dist_fill_kwargs, **dist_kwargs)
|
||||
# Plot pure-song analysis results:
|
||||
handles = big_ax.plot(scales, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Plot analysis results:
|
||||
handles = big_ax.plot(data['scales'], data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, colors['feat'])]
|
||||
# Plot noise-song analysis results:
|
||||
handles = big_ax.plot(scales, noise_data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Add proxy legend:
|
||||
h1 = big_ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
|
||||
h2 = big_ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
|
||||
big_ax.legend(handles=[h1, h2], **leg_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
@@ -1,160 +0,0 @@
|
||||
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 thunderhopper.filtertools import find_kern_specs
|
||||
from color_functions import load_colors, shade_colors
|
||||
from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
|
||||
plot_line, plot_barcode, strip_zeros, time_bar,\
|
||||
letter_subplot, letter_subplots, hide_ticks,\
|
||||
super_xlabel, super_ylabel, assign_colors
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
search_kwargs = dict(
|
||||
incl='subset',
|
||||
excl='noise',
|
||||
dir='../data/inv/thresh_lp/'
|
||||
)
|
||||
pure_paths = search_files(target, **search_kwargs)
|
||||
load_kwargs = dict(
|
||||
keywords=['scales', 'measure', 'thresh']
|
||||
)
|
||||
save_path = None#'../figures/fig_invariance_thresh_lp_subset.pdf'
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1
|
||||
)
|
||||
grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.1,
|
||||
left=0.15,
|
||||
right=0.95,
|
||||
bottom=0.1,
|
||||
top=0.85
|
||||
)
|
||||
inset_bounds = [0.2, 1.01, 0.6, 0.4]
|
||||
|
||||
# PLOT SETTINGS:
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
color_factors = [-0.5, 0.5]
|
||||
lw = dict(
|
||||
one=3,
|
||||
kern=3,
|
||||
all=1,
|
||||
)
|
||||
ax_labels = dict(
|
||||
x='scale $\\alpha$',
|
||||
y='$\\mu_f$',
|
||||
)
|
||||
xlab_kwargs = dict(
|
||||
y=0.005,
|
||||
fontsize=16,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=20,
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
yloc = 0.2
|
||||
|
||||
# EXECUTION:
|
||||
for pure_path in pure_paths:
|
||||
print(f'Processing {pure_path}')
|
||||
noise_path = pure_path.replace('.npz', '_noise.npz')
|
||||
|
||||
# Load kernel invariance data:
|
||||
pure_data, config = load_data(pure_path, **load_kwargs)
|
||||
noise_data, _ = load_data(noise_path, **load_kwargs)
|
||||
scales = pure_data['scales']
|
||||
|
||||
# Adjust grid parameters:
|
||||
n_columns = config['k_specs'].shape[0] + 1
|
||||
super_grid_kwargs['ncols'] = n_columns
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
|
||||
# Prepare axes:
|
||||
all_axes = np.zeros((grid_kwargs['nrows'], n_columns), dtype=object)
|
||||
subfigs = []
|
||||
for i in range(n_columns):
|
||||
subfig = fig.add_subfigure(super_grid[0, i])
|
||||
grid = subfig.add_gridspec(**grid_kwargs)
|
||||
subfigs.append(subfig)
|
||||
for j in range(grid_kwargs['nrows']):
|
||||
ax = subfig.add_subplot(grid[j, 0])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
|
||||
if i > 0:
|
||||
hide_ticks(ax, side='left')
|
||||
all_axes[j, i] = ax
|
||||
hide_ticks(all_axes[0, i], side='bottom')
|
||||
super_xlabel(ax_labels['x'], fig, all_axes[-1, 0], all_axes[-1, -1], **xlab_kwargs)
|
||||
super_ylabel(ax_labels['y'], fig, all_axes[0, 0], all_axes[1, 0], **ylab_kwargs)
|
||||
|
||||
# Plot kernel-specific results:
|
||||
in_min, in_high = ylimits(config['kernels'], pad=0.05)
|
||||
for i in range(config['k_specs'].shape[0]):
|
||||
pure_ax, noise_ax = all_axes[:, i]
|
||||
# Plot results of pure-song analysis:
|
||||
pure_ax.plot(scales, pure_data['measure_feat'][:, i, :],
|
||||
c=colors['feat'], lw=lw['one'])
|
||||
# Plot results of noise-song analysis:
|
||||
noise_ax.plot(scales, noise_data['measure_feat'][:, i, :],
|
||||
c=colors['feat'], lw=lw['one'])
|
||||
# Indicate kernel waveform:
|
||||
inset = pure_ax.inset_axes(inset_bounds)
|
||||
inset.plot(config['k_times'], config['kernels'][:, i], c='k', lw=lw['kern'])
|
||||
inset.set_xlim(config['k_times'][0], config['k_times'][-1])
|
||||
inset.set_ylim(in_min, in_high)
|
||||
inset.axis('off')
|
||||
|
||||
# Load population invariance data:
|
||||
pure_data, config = load_data(pure_path.replace('_subset', ''), **load_kwargs)
|
||||
noise_data, _ = load_data(noise_path.replace('_subset', ''), **load_kwargs)
|
||||
scales = pure_data['scales']
|
||||
|
||||
# Get kernel type-specific colors:
|
||||
types, ind = np.unique(config['k_specs'][:, 0], return_index=True)
|
||||
types = types[np.argsort(ind)].astype(int)
|
||||
factors = np.linspace(*color_factors, types.size)
|
||||
kern_colors = shade_colors(colors['feat'], factors)
|
||||
kern_colors = dict(zip(types.astype(str), kern_colors))
|
||||
|
||||
# Plot population-wide results:
|
||||
pure_ax, noise_ax = all_axes[:, -1]
|
||||
handles = pure_ax.plot(scales, pure_data['measure_feat'], c='k', lw=lw['all'])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors)
|
||||
|
||||
handles = noise_ax.plot(scales, noise_data['measure_feat'], c='k', lw=lw['all'])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors)
|
||||
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
@@ -1,7 +1,7 @@
|
||||
import string
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.transforms import BboxTransformTo
|
||||
from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox
|
||||
|
||||
def hide_ticks(ax, side='bottom', ticks=True):
|
||||
axis = 'x' if side in ['top', 'bottom'] else 'y'
|
||||
@@ -33,6 +33,14 @@ def get_trans_artist(artist):
|
||||
return BboxTransformTo(artist.get_tightbbox(renderer))
|
||||
raise ValueError('Artist does not have a bounding box to use as transform.')
|
||||
|
||||
def shift_subplot(ax, dx=0, dy=0, ref=None):
|
||||
if ref is not None:
|
||||
transform = get_trans_artist(ref) + get_trans_artist(ax.figure).inverted()
|
||||
dx, dy = transform.transform((dx, dy)) - transform.transform((0, 0))
|
||||
pos = ax.get_position()
|
||||
ax.set_position([pos.x0 + dx, pos.y0 + dy, pos.width, pos.height])
|
||||
return None
|
||||
|
||||
def title_subplot(artist, title, x=0.5, y=1.0, xref=None, yref=None, ref=None,
|
||||
ha='center', va='bottom', fontsize=16, fontweight='normal', **kwargs):
|
||||
trans_artist = get_trans_artist(artist)
|
||||
@@ -68,6 +76,10 @@ def letter_subplots(artists, labels=None, x=None, y=None, xref=None, yref=None,
|
||||
return handles
|
||||
|
||||
def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):
|
||||
if minval is not None and maxval is not None:
|
||||
if ax is not None:
|
||||
return ax.set_xlim([minval, maxval])
|
||||
return [minval, maxval]
|
||||
limits = [minval, maxval]
|
||||
if minval is None:
|
||||
limits[0] = time[0]
|
||||
@@ -83,6 +95,10 @@ def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):
|
||||
return limits
|
||||
|
||||
def ylimits(signal, ax=None, minval=None, maxval=None, pad=0.05):
|
||||
if minval is not None and maxval is not None:
|
||||
if ax is not None:
|
||||
return ax.set_ylim([minval, maxval])
|
||||
return [minval, maxval]
|
||||
limits = [minval, maxval]
|
||||
if minval is None:
|
||||
limits[0] = signal.min()
|
||||
@@ -267,3 +283,7 @@ def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
|
||||
visibility = low_left, up_left, low_right, up_right
|
||||
[l.set_visible(v) for l, v in zip(elements.connectors, visibility)]
|
||||
return inset_handle, elements.rectangle, elements.connectors
|
||||
|
||||
def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
|
||||
artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
|
||||
return None
|
||||
|
||||
@@ -1,86 +1,151 @@
|
||||
import glob
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from thunderhopper.filetools import crop_paths
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
from thunderhopper.filters import sosfilter
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from thunderhopper.model import convolve_kernels
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_paths = glob.glob(f'../data/processed/{target}*.npz')
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, dir='../data/processed/')
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
save_path = '../data/inv/thresh_lp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
add_noise = False
|
||||
thresh_percent = 90
|
||||
example_scales = np.array([0, 1, 10, 50])
|
||||
scales = np.geomspace(0.01, 100, 100)
|
||||
add_noise = True
|
||||
save_snippets = add_noise and True
|
||||
plot_results = False
|
||||
example_scales = np.array([0, 1, 10, 30, 100])
|
||||
scales = np.geomspace(0.01, 10000, 100)
|
||||
scales = np.unique(np.concatenate((scales, example_scales)))
|
||||
thresh_rel = np.array([0.5, 1, 3])
|
||||
kern_specs = np.array([
|
||||
[1, 0.008],
|
||||
[2, 0.004],
|
||||
[3, 0.002],
|
||||
])
|
||||
|
||||
# PREPARATION:
|
||||
pure_noise = np.load(noise_path)['inv']
|
||||
|
||||
# EXECUTION:
|
||||
for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
print(f'Processing {name}')
|
||||
save_name = save_path + name
|
||||
|
||||
# Get pure-song kernel responses:
|
||||
data, config = load_data(data_path, files='conv')
|
||||
song, rate = data['conv'], data['conv_rate']
|
||||
# Get adapted envelope (prior to convolution):
|
||||
data, config = load_data(data_path, files='inv')
|
||||
song, rate = data['inv'], data['inv_rate']
|
||||
|
||||
# Get song segment to be analyzed:
|
||||
time = np.arange(song.shape[0]) / rate
|
||||
start, end = data['songs_0'].ravel()
|
||||
segment = (time >= start) & (time <= end)
|
||||
|
||||
|
||||
# Reduce to kernel subset:
|
||||
kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['k_props'] = [config['k_props'][i] for i in kern_inds]
|
||||
|
||||
# Normalize song component:
|
||||
song /= song[segment, :].std(axis=0)
|
||||
song /= song[segment].std()
|
||||
|
||||
if add_noise:
|
||||
# Get normalized noise:
|
||||
rng = np.random.default_rng()
|
||||
noise = rng.normal(size=(song.shape[0], 1))
|
||||
noise /= noise[segment].std()
|
||||
# Get normalized noise component:
|
||||
noise = pure_noise[:song.shape[0]]
|
||||
noise /= noise[segment].std()
|
||||
|
||||
# Prepare noise-bound threshold:
|
||||
threshold = np.percentile(noise, thresh_percent, axis=0)
|
||||
else:
|
||||
# Reuse threshold from previous noise run:
|
||||
threshold = np.load(save_name + '_noise.npz')['thresh']
|
||||
# Define kernel-specific threshold values based on pure-noise response SD:
|
||||
ref_conv = convolve_kernels(noise, config['kernels'], config['k_specs'])
|
||||
thresh_abs = ref_conv[segment, :].std(axis=0, keepdims=True) * thresh_rel[:, None]
|
||||
|
||||
# Prepare measure storage:
|
||||
shape = (scales.size, song.shape[1])
|
||||
# measure_conv = np.zeros(shape, dtype=float)
|
||||
shape = (scales.size, kern_specs.shape[0], thresh_rel.size)
|
||||
measure_feat = np.zeros(shape, dtype=float)
|
||||
if save_snippets:
|
||||
# Prepare snippet storage:
|
||||
snip_inv = np.zeros((song.size, example_scales.size), dtype=float)
|
||||
shape = (song.size, kern_specs.shape[0], example_scales.size, thresh_rel.size)
|
||||
snip_conv = np.zeros(shape[:-1], dtype=float)
|
||||
snip_bi = np.zeros(shape, dtype=float)
|
||||
snip_feat = np.zeros(shape, dtype=float)
|
||||
|
||||
# Execute piecewise:
|
||||
for i, scale in enumerate(scales):
|
||||
print('Simulating scale', scale)
|
||||
|
||||
# Rescale song component:
|
||||
scaled_conv = song * scale
|
||||
scaled_song = song * scale
|
||||
if add_noise:
|
||||
# Add noise:
|
||||
scaled_conv += noise
|
||||
scaled_song += noise
|
||||
|
||||
# Process mixture:
|
||||
scaled_bi = (scaled_conv > threshold).astype(float)
|
||||
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
|
||||
padtype='fixed', padlen=config['padlen'])
|
||||
scaled_conv = convolve_kernels(scaled_song, config['kernels'], config['k_specs'])
|
||||
|
||||
# Log threshold-independent snippet data:
|
||||
if save_snippets and scale in example_scales:
|
||||
save_ind = np.nonzero(example_scales == scale)[0][0]
|
||||
snip_inv[:, save_ind] = scaled_song
|
||||
snip_conv[:, :, save_ind] = scaled_conv
|
||||
|
||||
# Get intensity measure per stage:
|
||||
# measure_conv[i] = scaled_conv[segment, :].std(axis=0)
|
||||
measure_feat[i] = scaled_feat[segment, :].mean(axis=0)
|
||||
# Execute piecewise again:
|
||||
for j, thresholds in enumerate(thresh_abs):
|
||||
|
||||
# Process mixture further:
|
||||
scaled_bi = (scaled_conv > thresholds).astype(float)
|
||||
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
|
||||
padtype='fixed', padlen=config['padlen'])
|
||||
|
||||
# Log threshold-dependent snippet data:
|
||||
if save_snippets and scale in example_scales:
|
||||
snip_bi[:, :, save_ind, j] = scaled_bi
|
||||
snip_feat[:, :, save_ind, j] = scaled_feat
|
||||
|
||||
# Log intensity measure:
|
||||
measure_feat[i, :, j] = scaled_feat[segment, :].mean(axis=0)
|
||||
|
||||
# Overview plot:
|
||||
if plot_results:
|
||||
fig, axes = plt.subplots(thresh_rel.size, kern_specs.shape[0],
|
||||
figsize=(16, 9), layout='constrained',
|
||||
sharex=True, sharey=True, squeeze=True)
|
||||
axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
|
||||
linscale=0.25)
|
||||
axes[0, 0].set_ylim(0, 1)
|
||||
for i, thresh in enumerate(thresh_rel):
|
||||
for j, kernel in enumerate(kern_specs):
|
||||
ax = axes[i, j]
|
||||
ax.plot(scales, measure_feat[:, j, i], 'k')
|
||||
if i == 0:
|
||||
ax.set_title(f'Kernel {kernel}')
|
||||
if j == 0:
|
||||
ax.set_ylabel(f'{thresh} * SD')
|
||||
plt.show()
|
||||
|
||||
# Save analysis results:
|
||||
if save_path is not None:
|
||||
data = dict(
|
||||
scales=scales,
|
||||
# measure_conv=measure_conv,
|
||||
example_scales=example_scales,
|
||||
measure_feat=measure_feat,
|
||||
thresh=threshold,
|
||||
thresh_perc=thresh_percent,
|
||||
thresh_rel=thresh_rel,
|
||||
thresh_abs=thresh_abs,
|
||||
)
|
||||
if save_snippets:
|
||||
data.update(dict(
|
||||
snip_inv=snip_inv,
|
||||
snip_conv=snip_conv,
|
||||
snip_bi=snip_bi,
|
||||
snip_feat=snip_feat,
|
||||
))
|
||||
if add_noise:
|
||||
save_name += '_noise'
|
||||
else:
|
||||
save_name += '_pure'
|
||||
save_data(save_name, data, config, overwrite=True)
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
@@ -1,138 +0,0 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
from thunderhopper.filters import sosfilter
|
||||
from thunderhopper.filtertools import find_kern_specs, pdf_proportion
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, dir='../data/processed/')
|
||||
save_path = '../data/inv/thresh_lp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
add_noise = False
|
||||
save_snippets = True
|
||||
example_scales = np.array([0, 1, 10, 50])
|
||||
scales = np.geomspace(0.01, 1000, 100)
|
||||
scales = np.unique(np.concatenate((scales, example_scales)))
|
||||
thresh_percent = np.array([0.6, 0.75, 0.999])
|
||||
thresholds = pdf_proportion(thresh_percent, sd=1, mu=0)
|
||||
plot_results = False
|
||||
kernels = np.array([
|
||||
[1, 0.008],
|
||||
[2, 0.004],
|
||||
[3, 0.002],
|
||||
])
|
||||
|
||||
# EXECUTION:
|
||||
for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
print(f'Processing {name}')
|
||||
save_name = save_path + name + '_subset'
|
||||
|
||||
# Get pure-song kernel responses:
|
||||
data, config = load_data(data_path, files='conv')
|
||||
conv, rate = data['conv'], data['conv_rate']
|
||||
|
||||
# Get song segment to be analyzed:
|
||||
time = np.arange(conv.shape[0]) / rate
|
||||
start, end = data['songs_0'].ravel()
|
||||
segment = (time >= start) & (time <= end)
|
||||
|
||||
# Reduce to kernel subset:
|
||||
kern_inds = find_kern_specs(config['k_specs'], kerns=kernels)
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
config['k_specs'] = config['k_specs'][kern_inds]
|
||||
config['k_props'] = [config['k_props'][i] for i in kern_inds]
|
||||
conv = conv[:, kern_inds]
|
||||
|
||||
# Normalize kernel responses:
|
||||
conv /= conv[segment, :].std(axis=0)
|
||||
|
||||
if add_noise:
|
||||
# Get normalized noise:
|
||||
rng = np.random.default_rng()
|
||||
noise = rng.normal(size=(conv.shape[0], 1))
|
||||
noise /= noise[segment].std()
|
||||
|
||||
if save_snippets:
|
||||
# Prepare snippet storage:
|
||||
shape = conv.shape + (example_scales.size, thresh_percent.size)
|
||||
snip_conv = np.zeros(shape, dtype=float)
|
||||
snip_bi = np.zeros(shape, dtype=float)
|
||||
snip_feat = np.zeros(shape, dtype=float)
|
||||
|
||||
# Prepare measure storage:
|
||||
shape = (scales.size, conv.shape[1], thresh_percent.size)
|
||||
# measure_conv = np.zeros(shape, dtype=float)
|
||||
measure_feat = np.zeros(shape, dtype=float)
|
||||
|
||||
# Execute piecewise analysis:
|
||||
for i, thresh in enumerate(thresholds):
|
||||
print('\nSimulating threshold ', thresh_percent[i])
|
||||
|
||||
for j, scale in enumerate(scales):
|
||||
print('Simulating scale ', scale)
|
||||
|
||||
# Rescale conv component:
|
||||
scaled_conv = conv * scale
|
||||
if add_noise:
|
||||
# Add noise:
|
||||
scaled_conv += noise
|
||||
|
||||
# Process mixture:
|
||||
scaled_bi = (scaled_conv > thresh).astype(float)
|
||||
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
|
||||
padtype='fixed', padlen=config['padlen'])
|
||||
|
||||
# Log snippet data:
|
||||
if save_snippets and scale in example_scales:
|
||||
scale_ind = np.nonzero(example_scales == scale)[0][0]
|
||||
snip_conv[:, :, scale_ind, i] = scaled_conv
|
||||
snip_bi[:, :, scale_ind, i] = scaled_bi
|
||||
snip_feat[:, :, scale_ind, i] = scaled_feat
|
||||
|
||||
# Get intensity measure per stage:
|
||||
measure_feat[j, :, i] = scaled_feat[segment, :].mean(axis=0)
|
||||
# measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
|
||||
|
||||
if plot_results:
|
||||
fig, axes = plt.subplots(thresh_percent.size, kernels.shape[0],
|
||||
figsize=(16, 9), layout='constrained',
|
||||
sharex=True, sharey=True, squeeze=True)
|
||||
axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
|
||||
linscale=0.25)
|
||||
axes[0, 0].set_ylim(0, 1)
|
||||
|
||||
for i, thresh in enumerate(thresh_percent):
|
||||
for j, kernel in enumerate(kernels):
|
||||
ax = axes[i, j]
|
||||
ax.plot(scales, measure_feat[:, j, i], 'k')
|
||||
if i == 0:
|
||||
ax.set_title(f'Kernel {kernel}')
|
||||
if j == 0:
|
||||
ax.set_ylabel(f'{100 * thresh}%')
|
||||
plt.show()
|
||||
|
||||
# Save analysis results:
|
||||
if save_path is not None:
|
||||
data = dict(
|
||||
scales=scales,
|
||||
example_scales=example_scales,
|
||||
# measure_conv=measure_conv,
|
||||
measure_feat=measure_feat,
|
||||
thresh_perc=thresh_percent,
|
||||
threshs=thresholds,
|
||||
)
|
||||
if save_snippets:
|
||||
data.update(dict(
|
||||
snip_conv=snip_conv,
|
||||
snip_bi=snip_bi,
|
||||
snip_feat=snip_feat,
|
||||
))
|
||||
if add_noise:
|
||||
save_name += '_noise'
|
||||
save_data(save_name, data, config, overwrite=True)
|
||||
print('Done.')
|
||||
embed()
|
||||
Reference in New Issue
Block a user