Seriously, no idea. Wild amount of changes. Good luck.
This commit is contained in:
j-hartling
@@ -58,19 +58,19 @@ def side_distributions(axes, snippets, inset_bounds, thresh, nbins=1000,
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = search_files(target, incl='noise', dir='../data/inv/thresh_lp/')
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example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
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data_path = search_files(example_file, incl='noise', dir='../data/inv/thresh_lp/')[0]
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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 = None#'../figures/fig_invariance_thresh_lp_single.pdf'
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save_path = '../figures/fig_invariance_thresh_lp_single.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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figsize=(32/2.54, 16/2.54),
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figsize=(32/2.54, 32/2.54),
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)
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super_grid_kwargs = dict(
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nrows=None,
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@@ -140,6 +140,8 @@ lw = dict(
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bi=0.1,
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feat=3,
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big=4,
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kern=2.5,
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plateau=1.5,
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)
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xlabels = dict(
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alpha='scale $\\alpha$',
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@@ -216,6 +218,10 @@ letter_big_kwargs = dict(
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va='top',
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fontsize=fs['letter'],
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)
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kern_kwargs = dict(
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c='k',
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lw=lw['kern'],
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)
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dist_kwargs = dict(
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c='k',
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lw=1,
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@@ -257,171 +263,198 @@ plateau_settings = dict(
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last=True,
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condense=None,
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)
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plateau_line_kwargs = dict(
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lw=lw['plateau'],
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ls='--',
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zorder=1,
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)
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plateau_dot_kwargs = dict(
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marker='o',
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markersize=8,
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markeredgewidth=1,
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clip_on=False,
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)
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zoom_rel = np.array([0.5, 0.515])
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# SUBSET SETTINGS:
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kern_specs = np.array([
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[1, 0.008],
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[2, 0.004],
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[3, 0.002],
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])[np.array([1])]
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zoom_rel = np.array([0.5, 0.515])
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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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print(f'Processing {data_path}')
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# Load invariance data:
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noise_data, config = load_data(data_path, **load_kwargs)
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pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)
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# Load invariance data:
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noise_data, config = load_data(data_path, **load_kwargs)
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pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)
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# Unpack shared variables:
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scales = noise_data['scales']
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plot_scales = noise_data['example_scales']
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thresh_rel = noise_data['thresh_rel']
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thresh_abs = noise_data['thresh_abs']
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# Unpack shared variables:
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scales = noise_data['scales']
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plot_scales = noise_data['example_scales']
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thresh_rel = noise_data['thresh_rel']
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thresh_abs = noise_data['thresh_abs']
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# Reduce to kernel subset and crop to zoom frame:
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t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
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zoom_abs = zoom_rel * t_full[-1]
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zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
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kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
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noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
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noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
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noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
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noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
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noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
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pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
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thresh_abs = thresh_abs[:, kern_ind]
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t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
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if exclude_zero:
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# Reduce to nonzero scales:
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nonzero_inds = scales > 0
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scales = scales[nonzero_inds]
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noise_data['measure_inv'] = noise_data['measure_inv'][nonzero_inds]
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noise_data['measure_feat'] = noise_data['measure_feat'][nonzero_inds, :]
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pure_data['measure_feat'] = pure_data['measure_feat'][nonzero_inds, :]
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# Reduce to kernel subset and crop to zoom frame:
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t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
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zoom_abs = zoom_rel * t_full[-1]
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zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
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kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
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noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
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noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
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noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
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noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
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noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
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pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
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config['kernels'] = config['kernels'][:, kern_ind]
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thresh_abs = thresh_abs[:, kern_ind]
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t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
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# Get threshold-specific colors:
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factors = np.linspace(*shade_factors, thresh_rel.size)
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shaded = dict(
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conv=shade_colors(colors['conv'], factors),
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bi=shade_colors(colors['bi'], factors),
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feat=shade_colors(colors['feat'], factors),
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)
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if exclude_zero:
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# Exclude zero scale:
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inds = scales > 0
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scales = scales[inds]
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noise_data['measure_inv'] = noise_data['measure_inv'][inds]
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noise_data['measure_feat'] = noise_data['measure_feat'][inds, :]
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pure_data['measure_feat'] = pure_data['measure_feat'][inds, :]
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# Adjust grid parameters to loaded data:
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super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + input_rows
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input_grid_kwargs['ncols'] = plot_scales.size
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snip_grid_kwargs['ncols'] = plot_scales.size
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# Get threshold-specific colors:
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factors = np.linspace(*shade_factors, thresh_rel.size)
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shaded = dict(
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conv=shade_colors(colors['conv'], factors),
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bi=shade_colors(colors['bi'], factors),
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feat=shade_colors(colors['feat'], factors),
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)
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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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# Adjust grid parameters to loaded data:
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super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + input_rows
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input_grid_kwargs['ncols'] = plot_scales.size
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snip_grid_kwargs['ncols'] = plot_scales.size
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# Prepare input snippet axes:
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input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
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input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
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input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
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input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
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ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
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for ax, scale in zip(input_axes, plot_scales):
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title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
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letter_subplot(input_subfig, 'a', **letter_snip_kwargs)
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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 snippet axes:
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snip_subfigs, snip_axes = [], []
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for i in range(thresh_rel.size):
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subfig_spec = subfig_specs['snip'].copy()
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subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
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snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
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axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
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[hide_axis(ax, 'left') for ax in axes[1:, 1]]
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super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
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snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
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for (ax1, ax2), stage in zip(axes[:, :2], stages):
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ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
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ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
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ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
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if i == thresh_rel.size - 1:
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axes[-1, -1].set_xlim(t_full[0], t_full[-1])
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time_bar(axes[-1, -1], **bar_kwargs)
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snip_subfigs.append(snip_subfig)
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snip_axes.append(axes)
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letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
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# Prepare input snippet axes:
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input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
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input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
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input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
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input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
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ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
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for ax, scale in zip(input_axes, plot_scales):
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title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
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letter_subplot(input_subfig, 'a', **letter_snip_kwargs)
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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# Prepare snippet axes:
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snip_subfigs, snip_axes = [], []
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for i in range(thresh_rel.size):
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subfig_spec = subfig_specs['snip'].copy()
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subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
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snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
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axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
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[hide_axis(ax, 'left') for ax in axes[1:, 1]]
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super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
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snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
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for (ax1, ax2), stage in zip(axes[:, :2], stages):
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ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
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ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
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ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
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if i == thresh_rel.size - 1:
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axes[-1, -1].set_xlim(t_full[0], t_full[-1])
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time_bar(axes[-1, -1], **bar_kwargs)
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snip_subfigs.append(snip_subfig)
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snip_axes.append(axes)
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letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
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alpha_ax = big_subfig.add_subplot(big_grid[0, 0])
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alpha_ax.set_xlim(scales[0], scales[-1])
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alpha_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
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ylimits(pure_data['measure_feat'], alpha_ax, minval=0, pad=ypad['big'])
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alpha_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs)
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ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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sigma_ax = big_subfig.add_subplot(big_grid[1, 0])
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sigma_ax.set_xlim(noise_data['measure_inv'].min(), noise_data['measure_inv'].max())
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# sigma_ax.set_xscale('log')
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sigma_ax.set_xlim(scales[0], scales[-1])
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sigma_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
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ylimits(pure_data['measure_feat'], sigma_ax, minval=0, pad=ypad['big'])
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sigma_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs)
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ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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alpha_ax = big_subfig.add_subplot(big_grid[0, 0])
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alpha_ax.set_xlim(scales[0], scales[-1])
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alpha_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
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ylimits(pure_data['measure_feat'], alpha_ax, minval=0, pad=ypad['big'])
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alpha_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs)
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ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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# Plot intensity-adapted snippets:
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plot_snippets(input_axes, t_full, noise_data['snip_inv'],
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ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
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ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])
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sigma_ax = big_subfig.add_subplot(big_grid[1, 0])
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sigma_ax.set_xlim(noise_data['measure_inv'].min(), noise_data['measure_inv'].max())
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sigma_ax.set_xlim(scales[0], scales[-1])
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sigma_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
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ylimits(pure_data['measure_feat'], sigma_ax, minval=0, pad=ypad['big'])
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sigma_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs)
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ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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# Plot representation snippets per threshold:
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for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
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dist_fill_kwargs['color'] = shaded['bi'][i]
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# Plot intensity-adapted snippets:
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plot_snippets(input_axes, t_full, noise_data['snip_inv'],
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ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
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ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])
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# Plot kernel response snippets:
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plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
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ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
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ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
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# Indicate kernel waveform over 1st intensity-adapted snippet:
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input_axes[0].plot(config['k_times'] + 0.5 * t_full[-1], config['kernels'], **kern_kwargs)
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# Plot kernel response distributions:
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side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
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thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
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side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
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thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
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# Plot representation snippets per threshold:
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for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
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dist_fill_kwargs['color'] = shaded['bi'][i]
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# Plot binary snippets:
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plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
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color=shaded['bi'][i], lw=lw['bi'])
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# Plot kernel response snippets:
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plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
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ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
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ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
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# Plot feature snippets:
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handles = plot_snippets(axes[2, :], t_full, noise_data['snip_feat'][:, :, i],
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ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
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[set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]
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# Plot kernel response distributions:
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side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
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thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
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side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
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thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
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# Get threshold-specific saturation:
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for i in range(thresh_rel.size):
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ind = get_saturation(noise_data['measure_feat'][:, i], **plateau_settings)[1]
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# Plot binary snippets:
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plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
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color=shaded['bi'][i], lw=lw['bi'])
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# Plot analysis results:
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for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
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# Plot pure-song analysis results:
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handles = ax.plot(x, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
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[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
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# Plot feature snippets:
|
||||
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 noise-song analysis results:
|
||||
handles = ax.plot(x, noise_data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
# Get saturation:
|
||||
saturation_inds = []
|
||||
for i in range(thresh_rel.size):
|
||||
ind = get_saturation(noise_data['measure_feat'][:, i], **plateau_settings)[1]
|
||||
saturation_inds.append(ind)
|
||||
|
||||
# Add proxy legend:
|
||||
if ax == alpha_ax:
|
||||
h1 = ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
|
||||
h2 = ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
|
||||
ax.legend(handles=[h1, h2], **leg_kwargs)
|
||||
# Plot analysis results:
|
||||
for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
|
||||
# Plot pure-song analysis results:
|
||||
handles = ax.plot(x, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
# Plot noise-song analysis results:
|
||||
handles = ax.plot(x, noise_data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Indicate threshold-specific saturation:
|
||||
for i, ind in enumerate(saturation_inds):
|
||||
color = shaded['feat'][i]
|
||||
ax.plot(x[ind], 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=ax.get_xaxis_transform())
|
||||
ax.plot(x[ind], 0, mfc=color, mec='k', alpha=0.75, zorder=6,
|
||||
**plateau_dot_kwargs, transform=ax.get_xaxis_transform())
|
||||
ax.vlines(x[ind], ax.get_ylim()[0], noise_data['measure_feat'][ind, i],
|
||||
color=color, **plateau_line_kwargs)
|
||||
|
||||
# Add proxy legend:
|
||||
if ax == alpha_ax:
|
||||
h1 = ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
|
||||
h2 = ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
|
||||
ax.legend(handles=[h1, h2], **leg_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
Reference in New Issue
Block a user