269 lines
6.9 KiB
Python
269 lines
6.9 KiB
Python
import plotstyle_plt
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import glob
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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.modeltools import load_data
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from color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
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plot_line, plot_barcode, strip_zeros, time_bar, super_xlabel
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from IPython import embed
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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 i, ax in enumerate(axes):
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plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
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return None
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def plot_bi_snippets(axes, time, snippets, **kwargs):
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for i, ax in enumerate(axes):
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plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
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return None
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = glob.glob(f'../data/inv/full/{target}*.npz')
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stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure']
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)
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save_path = '../figures/fig_invariance_full.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=1,
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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(0, -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=0.1,
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hspace=0.4,
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left=0.15,
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right=0.95,
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bottom=0.08,
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top=0.95
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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=0.2,
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right=0.96,
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bottom=0.08,
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top=0.95
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)
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# PLOT SETTINGS:
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colors = load_colors('../data/stage_colors.npz')
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colors['raw'] = "#000000"
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lw = dict(
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raw=0.25,
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filt=0.25,
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env=0.25,
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log=0.25,
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inv=0.25,
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conv=0.25,
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bi=0,
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feat=1,
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big=3
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)
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xlabels = dict(
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snip='time [s]',
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big='scale $\\alpha$',
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)
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ylabels = dict(
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raw='$x$',
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filt='$x_{\\text{filt}}$',
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env='$x_{\\text{env}}$',
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log='$x_{\\text{log}}$',
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inv='$x_{\\text{inv}}$',
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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='norm. intensity measure'
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)
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xlab_snip_kwargs = dict(
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y=0,
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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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xlab_big_kwargs = dict(
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y=0,
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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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ylab_snip_kwargs = dict(
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x=0,
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fontsize=20,
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rotation=0,
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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=16,
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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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raw=500,
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filt=500,
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env=250,
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log=25,
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inv=10,
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conv=1,
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feat=1,
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)
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title_kwargs = dict(
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x=0.5,
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yref=1,
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ha='center',
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va='top',
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fontsize=16,
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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_big_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=0.8,
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y1=0.9,
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color='k',
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lw=0,
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)
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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_raw'].shape[0]) / config['rate']
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# Adjust grid parameters:
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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 stage-specific snippet axes:
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snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
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snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
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snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
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for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
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ax = snip_subfig.add_subplot(snip_grid[i, j])
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ax.set_xlim(t_full[0], t_full[-1])
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hide_axis(ax, 'bottom')
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if i == 0:
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title = f'$\\alpha={strip_zeros(data["example_scales"][j])}$'
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title_subplot(ax, title, ref=snip_subfig, **title_kwargs)
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if j == 0:
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ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
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else:
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hide_axis(ax, 'left')
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if stages[i] != 'bi':
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
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snip_axes[i, j] = ax
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super_xlabel(xlabels['snip'], snip_subfig, snip_axes[-1, 0], snip_axes[-1, -1], **xlab_snip_kwargs)
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time_bar(snip_axes[0, 0], bar_time, **bar_kwargs)
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# Prepare single 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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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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big_ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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xlabel(big_ax, xlabels['big'], **xlab_big_kwargs, transform=big_subfig.transSubfigure)
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ylabel(big_ax, ylabels['big'], **ylab_big_kwargs, transform=big_subfig.transSubfigure)
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# Plot raw snippets:
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plot_snippets(snip_axes[0, :], t_full, data['snip_raw'],
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c=colors['raw'], lw=lw['raw'])
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# Plot filtered snippets:
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plot_snippets(snip_axes[1, :], t_full, data['snip_filt'],
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c=colors['filt'], lw=lw['filt'])
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# Plot envelope snippets:
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plot_snippets(snip_axes[2, :], t_full, data['snip_env'],
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ymin=0, c=colors['env'], lw=lw['env'])
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# Plot logarithmic snippets:
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plot_snippets(snip_axes[3, :], t_full, data['snip_log'],
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ymax=0, c=colors['log'], lw=lw['log'])
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# Plot invariant snippets:
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plot_snippets(snip_axes[4, :], t_full, data['snip_inv'],
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c=colors['inv'], lw=lw['inv'])
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# Plot kernel response snippets:
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plot_snippets(snip_axes[5, :], t_full, data['snip_conv'],
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c=colors['conv'], lw=lw['conv'])
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# Plot binary snippets:
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plot_bi_snippets(snip_axes[6, :], t_full, data['snip_bi'],
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color=colors['bi'], lw=lw['bi'])
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# Plot feature snippets:
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plot_snippets(snip_axes[7, :], t_full, data['snip_feat'],
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ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
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# Analysis results:
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for stage in stages:
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key = f'measure_{stage}'
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if stage == 'bi':
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continue
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# Min-max normalization:
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base_ind = np.argmin(data['scales'])
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data[key] -= data[key][base_ind, ...]
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data[key] /= data[key].max(axis=0)
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# Condense measure:
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if stage in ['conv', 'feat']:
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data[key] = np.nanmedian(data[key], axis=1)
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# Plot measure over scales:
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big_ax.plot(data['scales'], data[key],
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c=colors[stage], lw=lw['big'])
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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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