324 lines
11 KiB
Python
324 lines
11 KiB
Python
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
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from plot_functions import hide_axis, xlimits, ylimits, xlabel, ylabel, super_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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def side_distributions(axes, snippets, inset_bounds, thresh,
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ymin=None, ymax=None):
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bins = np.linspace(snippets.min(), snippets.max(), 50)
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centers = bins[:-1] + (bins[1] - bins[0]) / 2
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for ax, snippet in zip(axes, snippets.T):
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inset = ax.inset_axes(inset_bounds)
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inset.axis('off')
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pdf, _ = np.histogram(snippet, bins, density=True)
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inset.plot(pdf, centers, c='k', lw=1)
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inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh),
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color=colors['bi'], lw=0)
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inset.set_xlim(0, pdf.max())
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ylimits(centers, inset, minval=ymin, maxval=ymax, pad=0)
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return None
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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' if not with_noise else 'subset_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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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'
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if with_noise and save_path is not None:
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save_path += '_noise'
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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=None,
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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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snip_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=0.11,
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hspace=0.1,
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left=0.1,
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right=0.95,
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bottom=0.01,
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top=0.85
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)
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big_grid_kwargs = dict(
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nrows=1,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0.15,
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right=0.96,
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bottom=0.1,
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top=0.99
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)
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inset_bounds = [1, 0, 0.1, 1]
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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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big='scale $\\alpha$',
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)
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xlab_big_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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big='$\\mu_f$',
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)
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ylab_snip_kwargs = dict(
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x=0.08,
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fontsize=20,
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rotation=0,
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ha='right',
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va='center',
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)
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ylab_super_kwargs = dict(
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x=0.005,
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fontsize=16,
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ha='left',
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va='center',
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)
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ylab_big_kwargs = dict(
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x=0.02,
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fontsize=20,
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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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kernel = np.array([
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[2, 0.008],
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[4, 0.008],
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])[:1]
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zoom_rel = np.array([0.5, 0.55])
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# EXECUTION:
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for data_path in data_paths:
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print(f'Processing {data_path}')
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# Load invariance data:
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data, config = load_data(data_path, **load_kwargs)
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t_full = np.arange(data['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=kernel)[0]
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# Reduce to kernel subset and crop time to zoom frame:
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data['snip_conv'] = data['snip_conv'][zoom_inds, kern_ind, ...]
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data['snip_bi'] = data['snip_bi'][zoom_inds, kern_ind, ...]
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data['snip_feat'] = data['snip_feat'][zoom_inds, kern_ind, ...]
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data['measure_conv'] = data['measure_conv'][:, kern_ind, :]
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data['measure_feat'] = data['measure_feat'][:, kern_ind, :]
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data['threshs'] = data['threshs'][:, kern_ind]
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t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
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# Adjust grid parameters:
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super_grid_kwargs['nrows'] = data['thresh_perc'].size
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snip_grid_kwargs['ncols'] = data['example_scales'].size
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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 analysis axis:
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big_subfig = fig.add_subfigure(super_grid[slice(None), 1])
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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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xlabel(big_ax, xlabels['big'], **xlab_big_kwargs,
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transform=big_subfig.transSubfigure)
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ylabel(big_ax, ylabels['big'], **ylab_big_kwargs,
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transform=big_subfig.transSubfigure)
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big_ax.set_xlim(data['scales'].min(), data['scales'].max())
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ylimits(data['measure_feat'], big_ax, minval=0, pad=0.05)
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big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
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# Prepare snippet axes:
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snip_axes = {}
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for i in range(data['thresh_perc'].size):
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snip_subfig = fig.add_subfigure(super_grid[i, 0])
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axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
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snip_axes[snip_subfig] = axes
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super_ylabel(f'{data["thresh_perc"][i]}%', snip_subfig,
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axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
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for ax, stage in zip(axes[:, 0], stages):
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=snip_subfig.transSubfigure)
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if i == 0:
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for ax, scale in zip(axes[0, :], data['example_scales']):
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ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
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# Plot representation snippets per threshold:
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for i, (subfig, axes) in enumerate(snip_axes.items()):
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# Plot kernel response snippets:
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plot_snippets(axes[0, :], t_full, data['snip_conv'][:, :, i],
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c=colors['conv'], lw=0.5)
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# Plot binary snippets:
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plot_bi_snippets(axes[1, :], t_full, data['snip_bi'][:, :, i],
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color=colors['bi'], lw=0)
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# Plot feature snippets:
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plot_snippets(axes[2, :], t_full, data['snip_feat'][:, :, i],
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ymin=0, ymax=1, c=colors['feat'], lw=2)
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# Plot kernel response distribution:
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side_distributions(axes[0, :], data['snip_conv'][:, :, i],
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inset_bounds, data['threshs'][i])
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# Plot analysis results:
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big_ax.plot(data['scales'], data['measure_feat'],
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c=colors['feat'], lw=3)
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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 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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