308 lines
8.9 KiB
Python
308 lines
8.9 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 misc_functions import get_saturation
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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,\
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letter_subplot, letter_subplots
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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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def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
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if measures.ndim == 1:
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ax.plot(scales, measures, **kwargs)[0]
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return measures
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median_measure = np.median(measures, axis=1)
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spread_measure = [np.percentile(measures, 25, axis=1),
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np.percentile(measures, 75, axis=1)]
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ax.plot(scales, median_measure, **kwargs)[0]
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ax.fill_between(scales, *spread_measure, **fill_kwargs)
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return median_measure
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def show_saturation(ax, scales, measures, high=0.95, **kwargs):
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high_ind = get_saturation(measures, high=high)[1]
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return ax.plot(scales[high_ind], 0, transform=ax.get_xaxis_transform(),
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marker='o', ms=10, zorder=6, clip_on=False, **kwargs)
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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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ref_path = '../data/inv/full/ref_measures.npz'
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save_path = '../figures/fig_invariance_full.pdf'
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stages = ['filt', 'env', 'log', 'inv', 'conv', '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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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 20/2.54),
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)
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super_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,
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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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height_ratios=[3, 2]
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)
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subfig_specs = dict(
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snip=(0, 0),
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big=(1, 0),
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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=0.08,
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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=3,
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wspace=0.2,
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hspace=0,
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left=snip_grid_kwargs['left'],
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right=0.96,
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bottom=0.2,
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top=0.95
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)
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# PLOT SETTINGS:
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fs = dict(
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lab_norm=16,
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lab_tex=20,
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letter=22,
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tit_norm=16,
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tit_tex=20,
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bar=16,
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)
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colors = load_colors('../data/stage_colors.npz')
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lw = dict(
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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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big='scale $\\alpha$',
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)
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ylabels = dict(
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filt='$x_{\\text{filt}}$',
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env='$x_{\\text{env}}$',
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log='$x_{\\text{db}}$',
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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=['intensity', 'rel. intensity', 'norm. intensity']
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)
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xlab_big_kwargs = dict(
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y=0,
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fontsize=fs['lab_norm'],
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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=fs['lab_tex'],
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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=-0.12,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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)
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yloc = dict(
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filt=3000,
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env=1000,
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log=50,
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inv=20,
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conv=2,
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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=fs['tit_norm'],
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)
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letter_snip_kwargs = dict(
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x=0,
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yref=0.5,
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ha='left',
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va='center',
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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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y=1,
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ha='left',
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va='bottom',
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fontsize=fs['letter'],
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)
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bar_time = 5
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bar_kwargs = dict(
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dur=bar_time,
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y0=-0.25,
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y1=-0.1,
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xshift=1,
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color='k',
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lw=0,
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clip_on=False,
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text_pos=(-0.1, 0.5),
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text_str=f'${bar_time}\\,\\text{{s}}$',
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text_kwargs=dict(
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fontsize=fs['bar'],
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ha='right',
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va='center',
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)
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)
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# PREPARATION:
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ref_data = dict(np.load(ref_path))
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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_filt'].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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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
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hide_axis(ax, 'bottom')
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if i == 0:
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title = title_subplot(ax, f'$\\alpha={strip_zeros(data["example_scales"][j])}$',
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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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snip_axes[i, j] = ax
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time_bar(snip_axes[-1, -1], **bar_kwargs)
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letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
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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_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i in range(big_grid.ncols):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(data['scales'][0], data['scales'][-1])
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ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
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ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
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big_axes[i] = ax
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letter_subplots(big_axes, 'bc', **letter_big_kwargs)
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# # Plot filtered snippets:
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# plot_snippets(snip_axes[0, :], 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[1, :], 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[2, :], t_full, data['snip_log'],
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# c=colors['log'], lw=lw['log'])
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# # Plot invariant snippets:
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# plot_snippets(snip_axes[3, :], 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[4, :], t_full, data['snip_conv'],
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# c=colors['conv'], lw=lw['conv'])
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# # Plot feature snippets:
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# plot_snippets(snip_axes[5, :], 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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scales_rel = data['scales'] - data['scales'][0]
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scales_rel /= scales_rel[-1]
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for stage in stages:
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measure = data[f'measure_{stage}']
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# Plot unmodified intensity measures:
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curve = plot_curves(big_axes[0], data['scales'], measure, c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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if stage in ['log', 'inv', 'conv', 'feat']:
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show_saturation(big_axes[0], data['scales'], curve, c=colors[stage])
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# # Relate to pure-noise reference:
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# norm_measure = measure / ref_data[stage]
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# # Plot noise-related intensity measures:
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# big_axes[1].plot(data['scales'], norm_measure, c=colors[stage], lw=lw['big'])
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# Normalize measure to [0, 1]:
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min_measure = measure.min(axis=0)
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max_measure = measure.max(axis=0)
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norm_measure = (measure - min_measure) / (max_measure - min_measure)
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# Plot normalized intensity measures:
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curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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if stage in ['log', 'inv', 'conv', 'feat']:
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show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
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# # Plot over relative scales:
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# plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
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# fill_kwargs=dict(color=colors[stage], alpha=0.25))
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# scales_rel = curve - curve.min()
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# scales_rel /= scales_rel.max()
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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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