perceptual_matplotlib_centering.py

def get_perceptual_axis_center_in_figure_space(fig, ax):
    
    xs = ax.get_xlim()
    if ax.get_xscale() == 'log':
        xs = np.log10(xs)
        c_x = sum(xs) / 2
        c_x = 10**c_x
    else:
        c_x = sum(xs) / 2
    
    ys = ax.get_ylim()
    if ax.get_yscale() == 'log':
        ys = np.log10(ys)
        c_y = sum(ys) / 2
        c_y = 10**c_y
    else:
        c_y = sum(ys) / 2
            
    return np.array(fig.transFigure.inverted().transform(ax.transData.transform([c_x, c_y])))


def perceptual_center(axes, fig):
    centers = [get_perceptual_axis_center_in_figure_space(fig, ax) for ax in axes]
    figure_p_center = np.mean(np.array(centers), axis=0)
    return figure_p_center
  
    
def perceptual_bbox_inches(axes, f):
    figure_p_center = perceptual_center(axes, f)
    bbox = np.array(f.bbox_inches)
    perceptual_center_inches = f.transFigure.transform(figure_p_center) / f.dpi
    delta = np.abs(perceptual_center_inches - bbox).max(axis=0)
    perceptual_bbox = Bbox(np.array([
        perceptual_center_inches + delta,
        perceptual_center_inches - delta,
    ]))
    return perceptual_bbox


'''
plt.rc('text', usetex=True)
plt.rc('text.latex', unicode=True)
plt.rc('font', family='serif')
'''