paper_2025/python/fig_invariance_full.py

import plotstyle_plt
import glob
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from thunderhopper.modeltools import load_data
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
                           plot_line, plot_barcode, strip_zeros, time_bar, super_xlabel
from IPython import embed

def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
    ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
    for i, ax in enumerate(axes):
        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
    return None

def plot_bi_snippets(axes, time, snippets, **kwargs):
    for i, ax in enumerate(axes):
        plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
    return None

# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/inv/full/{target}*.npz')
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
load_kwargs = dict(
    files=stages,
    keywords=['scales', 'snip', 'measure']
)
save_path = '../figures/fig_invariance_full.pdf'

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
    nrows=1,
    ncols=3,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1
)
subfig_specs = dict(
    snip=(slice(None), slice(0, -1)),
    big=(slice(None), -1),
)
snip_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.1,
    hspace=0.4,
    left=0.15,
    right=0.95,
    bottom=0.08,
    top=0.95
)
big_grid_kwargs = dict(
    nrows=1,
    ncols=1,
    wspace=0,
    hspace=0,
    left=0.2,
    right=0.96,
    bottom=0.08,
    top=0.95
)

# PLOT SETTINGS:
fs = dict(
    lab_norm=16,
    lab_tex=20,
    letter=22,
    tit_norm=16,
    tit_tex=20,
    bar=16,
)
colors = load_colors('../data/stage_colors.npz')
colors['raw'] = "#000000"
lw = dict(
    raw=0.25,
    filt=0.25,
    env=0.25,
    log=0.25,
    inv=0.25,
    conv=0.25,
    bi=0,
    feat=1,
    big=3
)
xlabels = dict(
    snip='time [s]',
    big='scale $\\alpha$',
)
ylabels = dict(
    raw='$x$',
    filt='$x_{\\text{filt}}$',
    env='$x_{\\text{env}}$',
    log='$x_{\\text{log}}$',
    inv='$x_{\\text{inv}}$',
    conv='$c_i$',
    bi='$b_i$',
    feat='$f_i$',
    big='norm. intensity measure'
)
xlab_snip_kwargs = dict(
    y=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
xlab_big_kwargs = dict(
    y=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
ylab_snip_kwargs = dict(
    x=0,
    fontsize=fs['lab_tex'],
    rotation=0,
    ha='left',
    va='center'
)
ylab_big_kwargs = dict(
    x=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='top',
)
yloc = dict(
    raw=500,
    filt=500,
    env=250,
    log=25,
    inv=10,
    conv=1,
    feat=1,
)
title_kwargs = dict(
    x=0.5,
    yref=1,
    ha='center',
    va='top',
    fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
    x=0.02,
    y=1,
    ha='left',
    va='top',
    fontsize=fs['letter'],
    fontweight='bold'
)
letter_big_kwargs = dict(
    x=0,
    y=1,
    ha='left',
    va='top',
    fontsize=fs['letter'],
    fontweight='bold'
)
bar_time = 5
bar_kwargs = dict(
    dur=bar_time,
    y0=-0.25,
    y1=-0.1,
    xshift=1,
    color='k',
    lw=0,
    clip_on=False,
    text_pos=(-0.1, 0.5),
    text_str=f'${bar_time}\\,\\text{{s}}$',
    text_kwargs=dict(
        fontsize=fs['bar'],
        ha='right',
        va='center',
    )
)


# EXECUTION:
for data_path in data_paths:
    print(f'Processing {data_path}')

    # Load invariance data:
    data, config = load_data(data_path, **load_kwargs)
    t_full = np.arange(data['snip_raw'].shape[0]) / config['rate']

    # Adjust grid parameters:
    snip_grid_kwargs['ncols'] = data['example_scales'].size

    # Prepare overall graph:
    fig = plt.figure(**fig_kwargs)
    super_grid = fig.add_gridspec(**super_grid_kwargs)

    # Prepare stage-specific snippet axes:
    snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
    snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
    snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
    for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
        ax = snip_subfig.add_subplot(snip_grid[i, j])
        ax.set_xlim(t_full[0], t_full[-1])
        hide_axis(ax, 'bottom')
        if i == 0:
            title = f'$\\alpha={strip_zeros(data["example_scales"][j])}$'
            title_subplot(ax, title, ref=snip_subfig, **title_kwargs)
        if j == 0:
            ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
        else:
            hide_axis(ax, 'left')
        if stages[i] != 'bi':
            ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
        snip_axes[i, j] = ax
    time_bar(snip_axes[-1, -1], **bar_kwargs)

    # Prepare single analysis axis:
    big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
    big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
    big_ax = big_subfig.add_subplot(big_grid[0, 0])
    big_ax.set_xlim(data['scales'].min(), data['scales'].max())
    big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
    big_ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
    xlabel(big_ax, xlabels['big'], **xlab_big_kwargs, transform=big_subfig.transSubfigure)
    ylabel(big_ax, ylabels['big'], **ylab_big_kwargs, transform=big_subfig.transSubfigure)

    # Plot raw snippets:
    plot_snippets(snip_axes[0, :], t_full, data['snip_raw'],
                  c=colors['raw'], lw=lw['raw'])

    # Plot filtered snippets:
    plot_snippets(snip_axes[1, :], t_full, data['snip_filt'],
                  c=colors['filt'], lw=lw['filt'])

    # Plot envelope snippets:
    plot_snippets(snip_axes[2, :], t_full, data['snip_env'],
                  ymin=0, c=colors['env'], lw=lw['env'])

    # Plot logarithmic snippets:
    plot_snippets(snip_axes[3, :], t_full, data['snip_log'],
                  ymax=0, c=colors['log'], lw=lw['log'])

    # Plot invariant snippets:
    plot_snippets(snip_axes[4, :], t_full, data['snip_inv'],
                  c=colors['inv'], lw=lw['inv'])

    # Plot kernel response snippets:
    plot_snippets(snip_axes[5, :], t_full, data['snip_conv'],
                  c=colors['conv'], lw=lw['conv'])

    # Plot binary snippets:
    plot_bi_snippets(snip_axes[6, :], t_full, data['snip_bi'],
                     color=colors['bi'], lw=lw['bi'])

    # Plot feature snippets:
    plot_snippets(snip_axes[7, :], t_full, data['snip_feat'],
                  ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])

    # Analysis results:
    for stage in stages:
        key = f'measure_{stage}'
        if stage == 'bi':
            continue
        # Min-max normalization:
        base_ind = np.argmin(data['scales'])
        data[key] -= data[key][base_ind, ...]
        data[key] /= data[key].max(axis=0)

        # Condense measure:
        if stage in ['conv', 'feat']:
            data[key] = np.nanmedian(data[key], axis=1)

        # Plot measure over scales:
        big_ax.plot(data['scales'], data[key],
                    c=colors[stage], lw=lw['big'])

    if save_path is not None:
        fig.savefig(save_path)
    plt.show()

print('Done.')
embed()