Holiday syncing :)
This commit is contained in:
j-hartling
@@ -5,6 +5,7 @@ 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 misc_functions import get_saturation
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from color_functions import load_colors, shade_colors
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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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@@ -64,7 +65,7 @@ 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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save_path = None#'../figures/fig_invariance_thresh_lp_single.pdf'
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exclude_zero = True
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# GRAPH SETTINGS:
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@@ -79,7 +80,7 @@ super_grid_kwargs = dict(
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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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top=1,
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)
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input_rows = 1
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snip_rows = 2
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@@ -111,10 +112,10 @@ input_grid_kwargs = dict(
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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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nrows=2,
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ncols=1,
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wspace=0,
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hspace=0,
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hspace=0.3,
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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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@@ -141,7 +142,8 @@ lw = dict(
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big=4,
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)
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xlabels = dict(
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big='scale $\\alpha$',
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alpha='scale $\\alpha$',
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sigma='$\\sigma_{\\text{adapt}}$',
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)
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ylabels = dict(
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inv='$x_{\\text{adapt}}$',
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@@ -150,11 +152,17 @@ ylabels = dict(
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feat='$f_i$',
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big='$\\mu_f$',
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)
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xlab_big_kwargs = dict(
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y=0,
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xlab_alpha_kwargs = dict(
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y=-0.15,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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va='top',
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)
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xlab_sigma_kwargs = dict(
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y=-0.12,
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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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)
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ylab_snip_kwargs = dict(
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x=0.08,
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@@ -178,7 +186,7 @@ ylab_big_kwargs = dict(
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ypad = dict(
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inv=0.05,
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conv=0.05,
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big=0.075
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big=0.1
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)
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yloc = dict(
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inv=(2, 200),
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@@ -242,6 +250,13 @@ leg_kwargs = dict(
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handlelength=1.5,
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columnspacing=1,
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)
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plateau_settings = dict(
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low=0.05,
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high=0.95,
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first=True,
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last=True,
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condense=None,
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)
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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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@@ -281,6 +296,7 @@ for data_path in data_paths:
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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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@@ -293,7 +309,7 @@ for data_path in data_paths:
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)
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# Adjust grid parameters to loaded data:
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super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + 1
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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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@@ -325,8 +341,6 @@ for data_path in data_paths:
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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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# for ax, scale in zip(axes[0, :], plot_scales):
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# title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=snip_subfig, **title_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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@@ -334,17 +348,27 @@ for data_path in data_paths:
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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 analysis axis:
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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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big_ax = big_subfig.add_subplot(big_grid[0, 0])
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big_ax.set_xlim(scales[0], scales[-1])
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big_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
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ylimits(noise_data['measure_feat'], big_ax, minval=0, pad=ypad['big'])
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big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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xlabel(big_ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
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ylabel(big_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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letter_subplot(big_subfig, 'e', **letter_big_kwargs, ref=input_subfig)
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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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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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# Plot intensity-adapted snippets:
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plot_snippets(input_axes, t_full, noise_data['snip_inv'],
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@@ -375,18 +399,25 @@ for data_path in data_paths:
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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 pure-song analysis results:
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handles = big_ax.plot(scales, 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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# 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 noise-song analysis results:
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handles = big_ax.plot(scales, noise_data['measure_feat'], lw=lw['big'])
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[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
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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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# Add proxy legend:
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h1 = big_ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
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h2 = big_ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
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big_ax.legend(handles=[h1, h2], **leg_kwargs)
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# Plot noise-song analysis results:
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handles = ax.plot(x, noise_data['measure_feat'], lw=lw['big'])
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[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
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# Add proxy legend:
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if ax == alpha_ax:
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h1 = ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
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h2 = ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
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ax.legend(handles=[h1, h2], **leg_kwargs)
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if save_path is not None:
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fig.savefig(save_path)
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