How to have a fast crosshair mouse cursor for subplots in matplotlib? - python-3.x

Question

In this video of backtrader's matplotlib implementation https://youtu.be/m6b4Ti4P2HA?t=2008 I can see that a default and very fast and CPU saving crosshair mouse cursor seems to exist in matplotlib.
I would like to have the same kind of mouse cursor for a simple multi subplot plot in matplotlib like this:
import numpy as np
import matplotlib
matplotlib.use('QT5Agg')
matplotlib.rcParams['figure.figsize'] = (20.0, 22.0)
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
ax1.plot(np.array(np.random.rand(100)))
ax2.plot(np.array(np.random.rand(100)))
plt.show()
So, if I am with my mouse in the lower subplot, I want to see directly and very precisely, which value of x/y in the lower plot corresponds to which value pair in the upper plot.
I have found other solutions to do this but they seem to be very slow compared to the implementation in the video.

Answer 1

You can create a crosshair cursor via mplcursors. sel.extras.append() takes care that the old cursor is removed when a new is drawn. With sel.annotation.set_text you can adapt the popup annotation shown. To leave out the annotation, use sel.annotation.set_visible(False). To find the corresponding y-value in the other subplot, np.interp with the data extracted from the curve can be used.
import numpy as np
import matplotlib.pyplot as plt
import mplcursors
def crosshair(sel):
x, y2 = sel.target
y1 = np.interp( sel.target[0], plot1.get_xdata(), plot1.get_ydata() )
sel.annotation.set_text(f'x: {x:.2f}\ny1: {y1:.2f}\ny2: {y2:.2f}')
# sel.annotation.set_visible(False)
hline1 = ax1.axhline(y1, color='k', ls=':')
vline1 = ax1.axvline(x, color='k', ls=':')
vline2 = ax2.axvline(x, color='k', ls=':')
hline2 = ax2.axhline(y2, color='k', ls=':')
sel.extras.append(hline1)
sel.extras.append(vline1)
sel.extras.append(hline2)
sel.extras.append(vline2)
fig = plt.figure(figsize=(15, 10))
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
plot1, = ax1.plot(np.array(np.random.uniform(-1, 1, 100).cumsum()))
plot2, = ax2.plot(np.array(np.random.uniform(-1, 1, 100).cumsum()))
cursor = mplcursors.cursor(plot2, hover=True)
cursor.connect('add', crosshair)
plt.show()
Here is an alternative implementation that stores the data in global variables and moves the lines (instead of deleting and recreating them):
import numpy as np
import matplotlib.pyplot as plt
import mplcursors
def crosshair(sel):
x = sel.target[0]
y1 = np.interp(x, plot1x, plot1y)
y2 = np.interp(x, plot2x, plot2y)
sel.annotation.set_visible(False)
hline1.set_ydata([y1])
vline1.set_xdata([x])
hline2.set_ydata([y2])
vline2.set_xdata([x])
hline1.set_visible(True)
vline1.set_visible(True)
hline2.set_visible(True)
vline2.set_visible(True)
fig = plt.figure(figsize=(15, 10))
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
plot1, = ax1.plot(np.array(np.random.uniform(-1, 1, 100).cumsum()))
plot2, = ax2.plot(np.array(np.random.uniform(-1, 1, 100).cumsum()))
plot1x = plot1.get_xdata()
plot1y = plot1.get_ydata()
plot2x = plot2.get_xdata()
plot2y = plot2.get_ydata()
hline1 = ax1.axhline(plot1y[0], color='k', ls=':', visible=False)
vline1 = ax1.axvline(plot1x[0], color='k', ls=':', visible=False)
hline2 = ax2.axhline(plot2y[0], color='k', ls=':', visible=False)
vline2 = ax2.axvline(plot2x[0], color='k', ls=':', visible=False)
cursor = mplcursors.cursor([plot1, plot2], hover=True)
cursor.connect('add', crosshair)
plt.show()

Answer 2

Sorry for the late answer, but I was horrified by how much code was suggested above, when there is this one-liner on matplotlib to do a simple crosshair accross different axes. It won't show your labels but it's CPU-light.
from matplotlib.widgets import MultiCursor
cursor = MultiCursor(fig.canvas, (ax[0], ax[1]), color='r',lw=0.5, horizOn=True, vertOn=True)