juliette hypotheses

hypotheses.py

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


fig, axes = plt.subplots(6, 1, sharex=True, sharey=True, figsize=[4, 6])
np.random.seed(1)

hypotheses = (
    ('No Similarity', np.random.randn(5)/1e1),
    ('Inductive bias', np.ones(5) * .5),
    ('Sound-specific similarity', [0, .8, .8, .8, .8]),
    ('Speech-specific similarity', [0, 0, .8, .8, .8]),
    ('Phoneme-specific similarity', [0, 0, 0, .8, .8]),
    ('Language-specific similarity', [0, 0, 0, 0, .8]),
)
for ax, (title, y) in zip(axes, hypotheses):
    ax.set_title(title)
    sns.barplot(x=np.arange(5), y=y, palette='tab10', ax=ax)
    sns.despine(ax=ax, right=True, top=True)
    ax.axhline(0, color='k', ls=':')
    ax.set_yticks([])
    ax.set_ylabel('R', rotation=0, labelpad=10)
    ax.set_xticklabels(['Init', 'Noise', 'Bengali', 'English', 'Dutch'])
    
fig.tight_layout()