import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import MultiCursor

# Fixing random state for reproducibility
np.random.seed(19680801)

# some random data
x = np.random.randn(1000)
y = np.random.randn(1000)


def scatter_hist(x, y, ax, ax_histx, ax_histy):
    # no labels
    ax_histx.tick_params(axis="x", labelbottom=False)
    ax_histy.tick_params(axis="y", labelleft=False)

    # the scatter plot:
    ax.scatter(x, y)

    # now determine nice limits by hand:
    binwidth = 0.25
    xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
    lim = (int(xymax/binwidth) + 1) * binwidth

    bins = np.arange(-lim, lim + binwidth, binwidth)
    ax_histx.hist(x, bins=bins, label="X")
    ax_histy.hist(y, bins=bins, orientation='horizontal', label="Y")
    
# Create a Figure, which doesn't have to be square.
fig = plt.figure(layout='constrained')
# Create the main axes, leaving 25% of the figure space at the top and on the
# right to position marginals.
ax = fig.add_gridspec().subplots()
# The main axes' aspect can be fixed.

#ax.set(aspect=1)
# Create marginal axes, which have 25% of the size of the main axes.  Note that
# the inset axes are positioned *outside* (on the right and the top) of the
# main axes, by specifying axes coordinates greater than 1.  Axes coordinates
# less than 0 would likewise specify positions on the left and the bottom of
# the main axes.
ax_histx = ax.inset_axes([0, -0.25, 1, 0.12], sharex=ax)
ax_histy = ax.inset_axes([-0.25, 0, 0.12, 1], sharey=ax)
ax_plus = ax.inset_axes([1.05, 0, 0.02, 1])
# Draw the scatter plot and marginals.
scatter_hist(x, y, ax, ax_histx, ax_histy)


multi1 = MultiCursor(None, (ax, ax_histx), useblit=False, horizOn=False, vertOn=True, color='r', lw=1)
multi2 = MultiCursor(None, (ax_histy, ax), useblit=False, horizOn=True, vertOn=False, color='r', lw=1)

#ax.set_aspect(0.5)

plt.show()