295 lines
9.0 KiB
Python
295 lines
9.0 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, shade_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
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plot_line, plot_barcode, strip_zeros, time_bar,\
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letter_subplot, letter_subplots
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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,
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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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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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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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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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edges = np.linspace(*limits, nbins + 1)
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centers = edges[:-1] + (edges[1] - edges[0]) / 2
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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.set_xlim(0, pdf.max())
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inset.axis('off')
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return None
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# GENERAL SETTINGS:
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with_noise = False
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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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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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# 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=3,
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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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snip=(slice(None), slice(super_grid_kwargs['ncols'] - 1)),
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big=(slice(None), -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=0.3,
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hspace=0.1,
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left=0.1,
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right=0.93,
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bottom=0.05,
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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=0.17,
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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 = [1.02, 0, 0.2, 1]
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# PLOT SETTINGS:
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colors = load_colors('../data/stage_colors.npz')
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color_factors = [0.2, -0.2]
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lw = dict(
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conv=1,
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bi=0.1,
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feat=3,
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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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)
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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,
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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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yloc = dict(
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big=0.2,
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)
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letter_snip_kwargs = dict(
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x=0.01,
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y=0.9,
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ha='left',
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va='top',
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fontsize=22,
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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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ha='left',
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va='top',
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fontsize=22,
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)
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dist_kwargs = dict(
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nbins=50,
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c='k',
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lw=1,
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)
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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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bar_time = 0.5
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bar_kwargs = dict(
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y0=0.3,
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y1=0.4,
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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, 0.525])
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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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# Get threshold-specific colors:
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factors = np.linspace(*color_factors, data['thresh_perc'].size)
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colors = 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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# 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 snippet axes:
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snip_axes = {}
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for i in range(data['thresh_perc'].size):
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subfig_specs['snip'] = (i, subfig_specs['snip'][1])
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snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
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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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time_bar(axes[0, 0], bar_time, **bar_kwargs)
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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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letter_subplots(snip_axes.keys(), **letter_snip_kwargs)
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# Prepare analysis axis:
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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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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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big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
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ylimits(data['measure_feat'], big_ax, minval=0, pad=0.01)
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big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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letter_subplot(big_subfig, 'd', **letter_big_kwargs, ref=list(snip_axes.keys())[0])
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# Plot representation snippets per threshold:
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conv_min, conv_max = ylimits(data['snip_conv'], pad=0.02)
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for i, (subfig, axes) in enumerate(snip_axes.items()):
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dist_fill_kwargs['color'] = colors['bi'][i]
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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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thresh=data['threshs'][i], ymin=conv_min, ymax=conv_max,
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fill_kwargs=dist_fill_kwargs, c=colors['conv'][i], lw=lw['conv'])
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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'][i], lw=lw['bi'])
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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'][i], lw=lw['feat'])
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# Plot kernel response distribution:
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side_distributions(axes[0, :], data['snip_conv'][:, :, i], inset_bounds,
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data['threshs'][i], ymin=conv_min, ymax=conv_max,
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fill_kwargs=dist_fill_kwargs, **dist_kwargs)
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# Plot analysis results:
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handles = big_ax.plot(data['scales'], data['measure_feat'], lw=lw['big'])
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[h.set_color(c) for h, c in zip(handles, colors['feat'])]
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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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