CorruptionFigure.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script for creating Figure 7B
JL, June 27 2020

For "A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives"
by Alexander Mathis, Steffen Schneider, Jessy Lauer, and Mackenzie Weygandt Mathis

"""

import matplotlib.pyplot as plt
import numpy as np
import pickle


with open('ResultsCorruption/data_corruption_experiments.pickle', 'rb') as file:
    data = pickle.load(file)

bpts = ['snout', 'leftear', 'rightear', 'tailbase']
n_bpts = len(bpts)
mags = sorted(data['Magnitude'].unique())
test_error = data[~data['Training set']]

cmap = plt.cm.get_cmap('Blues', 7)
colors = cmap(range(2, 7))

for frac in sorted(test_error['Training size'].unique()):

    fig, axes = plt.subplots(2, 4, figsize=(5.32, 3.14), dpi=100)
    for i, mag in enumerate(mags):
        d = test_error[((test_error['Training size'] == frac) &
                        (test_error['Magnitude'] == mag))]
        for (bpt, corrupt), temp in d.groupby(['Bodyparts', 'Corruption']):
            n = bpts.index(bpt)
            if corrupt == 'swap':
                n += 4
            sorted_errors = np.sort(temp['Error'])
            frac_invalid = (sorted_errors == 0).sum() / len(sorted_errors)
            sorted_errors = sorted_errors[sorted_errors != 0]
            x = np.concatenate([sorted_errors, sorted_errors[[-1]]])
            y = np.linspace(0, 1 - frac_invalid, len(x))
            axes.flat[n].step(x, y, label=mag, color=colors[i], lw=2, where='mid')
            if corrupt!='swap':
                axes.flat[n].set_title(bpt)

            if corrupt == 'default':
                axes.flat[n + 4].step(x, y, label=mag, color=colors[i], lw=2)
    for n, ax in enumerate(axes.flat):
        ax.tick_params(axis='both', direction='in')

        # ax.set_box_aspect(1)
        # For matplotlib < 3.3.0, uncomment the line below:
        #ax.set_aspect(20, adjustable='datalim')
        ax.set_xlim(0, 20)
        ax.set_ylim(0, 1)
        ax.spines['left'].set_position(('outward', 10))
        ax.spines['bottom'].set_position(('outward', 10))
        ax.spines['right'].set_visible(False)
        ax.spines['top'].set_visible(False)
        if n < 4:
            ax.set_xticks([])
            ax.set_xticklabels([])
            ax.spines['bottom'].set_visible(False)
        if n % 4 != 0:
            ax.spines['left'].set_visible(False)
            ax.set_yticks([])
            ax.set_yticklabels([])
    patches = [plt.plot([0, 0], [0, 0], color=color, label=f'{label}%', lw=2)[0]
               for color, label in zip(colors, mags)]

    fig.legend(handles=patches, loc='center', frameon=False,
               bbox_to_anchor=(0, 0.45, 1, 0.1),
               ncol=5, borderaxespad=0.)
    plt.savefig('ResultsCorruption/LabelingCorruptionimpact_'+str(frac)+'Percent.png')