Again, numerous changes.
Mostly figure polishing and fixing. Crucial fix to "short" invariance analysis.
This commit is contained in:
j-hartling
@@ -39,17 +39,20 @@ def plot_bi_snippets(axes, time, binary, **kwargs):
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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=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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def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
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limits=None, fill_kwargs={}, **kwargs):
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if limits is None:
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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), **fill_kwargs)
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handle = inset.plot(pdf, centers, **kwargs)[0]
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set_clip_box(handle, inset, bounds=[[-0.05, 0], [1.05, 1]])
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handle = inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), **fill_kwargs)
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set_clip_box(handle, inset, bounds=[[-0.05, 0], [1.05, 1]])
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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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@@ -99,7 +102,7 @@ snip_grid_kwargs = dict(
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right=0.93,
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bottom=0.15,
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top=0.95,
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height_ratios=[2, 1, 1]
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height_ratios=[4, 1, 2]
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)
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input_grid_kwargs = dict(
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nrows=1,
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@@ -115,10 +118,10 @@ big_grid_kwargs = dict(
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nrows=2,
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ncols=1,
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wspace=0,
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hspace=0.3,
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hspace=0.15,
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left=0.17,
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right=0.96,
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bottom=0.1,
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bottom=0.05,
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top=0.99
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)
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dist_inset_bounds = [1.02, 0, 0.2, 1]
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@@ -140,6 +143,7 @@ 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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thresh=1.5,
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kern=2.5,
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plateau=1.5,
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)
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@@ -155,16 +159,16 @@ ylabels = dict(
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big='$\\mu_f$',
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)
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xlab_alpha_kwargs = dict(
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y=-0.15,
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y=0.5,
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fontsize=fs['lab_norm'],
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ha='center',
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va='top',
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va='bottom',
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)
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xlab_sigma_kwargs = dict(
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y=-0.12,
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y=0,
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fontsize=fs['lab_tex'],
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ha=xlab_alpha_kwargs['ha'],
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va=xlab_alpha_kwargs['va'],
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va='bottom',
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)
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ylab_snip_kwargs = dict(
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x=0.08,
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@@ -212,8 +216,8 @@ letter_snip_kwargs = dict(
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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x=0,
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yref=letter_snip_kwargs['y'],
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xref=0,
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y=1,
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ha='left',
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va='top',
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fontsize=fs['letter'],
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@@ -230,6 +234,12 @@ dist_fill_kwargs = dict(
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color=colors['bi'],
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lw=0.1,
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)
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thresh_kwargs = dict(
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color='k',
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lw=lw['thresh'],
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ls='--',
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zorder=3,
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)
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bar_time = 0.1
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bar_kwargs = dict(
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dur=bar_time,
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@@ -353,9 +363,11 @@ for i in range(thresh_rel.size):
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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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low_box = axes[-1, 0].get_position()
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high_box = axes[0, 0].get_position()
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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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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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@@ -376,17 +388,18 @@ 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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xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs, transform=big_subfig)
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ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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letter_subplot(alpha_ax, 'e', ref=big_subfig, **letter_big_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_xlim(scales[0], scales[-1])
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sigma_ax.set_xlim(1, noise_data['measure_inv'].max())
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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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xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs, transform=big_subfig)
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ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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letter_subplot(sigma_ax, 'f', ref=big_subfig, **letter_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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@@ -403,13 +416,18 @@ for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
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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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ylim_zoom = ylimits(noise_data['snip_conv'][:, 0], axes[0, 0],
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pad=ypad['conv'], maxval=thresh_abs[-1])
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# Indicate absolute threshold value:
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handle = axes[0, 0].axhline(thresh_abs[i], **thresh_kwargs)
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set_clip_box(handle, axes[0, 0], bounds=[[0, 0], [1, 1.05]])
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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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thresh_abs[i], nbins=50, limits=ylim_zoom, 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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thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
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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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@@ -444,7 +462,7 @@ for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
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ax.plot(x[ind], 0, mfc=color, mec='k', alpha=0.75, zorder=6,
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**plateau_dot_kwargs, transform=ax.get_xaxis_transform())
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ax.vlines(x[ind], ax.get_ylim()[0], noise_data['measure_feat'][ind, i],
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color=color, **plateau_line_kwargs)
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color=color, **plateau_line_kwargs)
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# Add proxy legend:
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if ax == alpha_ax:
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