two slope scale for Matplotlib

demo.py

import itertools
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.scale as mscale


def factory(break_point, slope_ratio):
    def forward(values):
        out = np.array(values, copy=True)
        out -= break_point
        out[out>0] *= slope_ratio
        out += break_point

        return out

    def inverse(values):
        out = np.array(values, copy=True)
        out -= break_point
        out[out>0] /= slope_ratio
        out += break_point

        return out


    return forward, inverse

def demo_plot(ax):
    ax.plot(x,x, '-o', label='linear')
    ax.plot(x,x*x, '-o', label='quadratic')
    ax.plot(x,np.sin(x*5), '-o', label='sin')


x = np.linspace(0, 10, 50)


fig, axs = plt.subplots(2, 2, layout='constrained', figsize=(10, 10))

y_ratio = 1/10
x_ratio = 1/5

for ((x_break, y_break), ax) in zip(itertools.product([1, 5], [5, 50]), axs.flat):

    fcx = mscale.FuncScale(None, factory(x_break, x_ratio))
    fcy = mscale.FuncScale(None, factory(y_break, y_ratio))

    ax.axvline(x_break, color='k')
    ax.set_xscale(fcx)
    ax.axhline(y_break, color='k')
    ax.set_yscale(fcy)
    demo_plot(ax)
    ax.set_title(f'{x_break=}, {y_break=}, {x_ratio=}, {y_ratio=}')




fig, axs = plt.subplots(2, 2, layout='constrained', figsize=(10, 10))

y_break = 50
x_break = 5

for ((x_ratio, y_ratio), ax) in zip(itertools.product([5, 1/5], [5, 1/5]), axs.flat):

    fcx = mscale.FuncScale(None, factory(x_break, x_ratio))
    fcy = mscale.FuncScale(None, factory(y_break, y_ratio))

    ax.axvline(x_break, color='k')
    ax.set_xscale(fcx)
    ax.axhline(y_break, color='k')
    ax.set_yscale(fcy)
    demo_plot(ax)
    ax.set_title(f'{x_break=}, {y_break=}, {x_ratio=}, {y_ratio=}')

plt.show()