5 Best Ways to Get The X Y Position Pointing With Mouse in an Interactive Plot Python Matplotlib

💡 Problem Formulation: In data visualization, users often need to interact with charts to better understand the underlying data. For Python users leveraging matplotlib, a common issue is retrieving the x and y coordinates of a point within a plot by pointing with the mouse. This article elucidates five methods to capture these coordinates, enhancing interactivity within matplotlib plots. For example, a user might want to hover over a scatter plot to get live updates on coordinates, which could be displayed or used to annotate the plot.

Method 1: Using the mpl_connect method for event handling

This method involves setting up an event listener for mouse movements or clicks within a matplotlib canvas. The mpl_connect method is used to bind a function to the event, which will be processed every time the event occurs, allowing us to pinpoint the x and y values on the graph.

Here’s an example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(range(10))

def onclick(event):
    print('X=', event.xdata, 'Y=', event.ydata)

cid = fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()

Output: On clicking at a point on the plot, you will see something like X=4.0 Y=4.0 printed in the console.

This code creates a simple line plot and registers an event handler for mouse clicks. When the user clicks on any point within the plot, the onclick function is called, printing the coordinates to the console.

Method 2: Utilizing the motion_notify_event

This technique harnesses the ‘motion_notify_event’ to track and return cursor position in real-time as it moves over the matplotlib figure. The method is similar to ‘button_press_event’, but instead of a click, it tracks the mouse motion.

Here’s an example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(range(10))

def onmotion(event):
    if event.inaxes:
        print('X=', event.xdata, 'Y=', event.ydata)

cid = fig.canvas.mpl_connect('motion_notify_event', onmotion)
plt.show()

Output: As the mouse moves over the plot, coordinates like X=5.0 Y=5.0 are continuously printed.

The code here attaches a listener to the mouse movement event, triggering the onmotion function continuously as the mouse moves over the plotted area, which prints out real-time mouse positions.

Method 3: Creating a custom Toolbar Button for coordinate retrieval

This method involves extending matplotlib’s toolbar by adding a custom button, which when activated, allows for the retrieval of coordinates with mouse clicks. This approach is interactive and integrates well with the matplotlib GUI.

Here’s an example:

from matplotlib.backend_bases import NavigationToolbar2
import matplotlib.pyplot as plt

class CustomToolbar(NavigationToolbar2):
    def __init__(self, canvas, *args, **kwargs):
        super(CustomToolbar, self).__init__(canvas, *args, **kwargs)
        self.set_message = self._set_message

    def _set_message(self, s):
        pass  # Disable the coordinates on the navigation toolbar.

fig, ax = plt.subplots()
ax.plot(range(10))

def onclick(event):
    print('X=', event.xdata, 'Y=', event.ydata)

fig.canvas.mpl_connect('button_press_event', onclick)
fig.canvas.set_window_title('Custom Toolbar Example')
NavigationToolbar2 = CustomToolbar
plt.show()

Output: A toolbar appears without the mouse position display, and on clicking the plot, the console prints the cursor coordinates.

The code snippet customizes the NavigationToolbar2 by overriding its set_message method to stop displaying coordinates in the toolbar. Next, clicking on the plot prints the coordinates to the standard output.

Method 4: Using Annotations and Text Widgets

This method takes advantage of the annotation feature in matplotlib to show text on the figure that dynamically updates with the cursor’s x and y position. The annotation method is ideal for those who wish to view coordinates directly on the plot.

Here’s an example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(range(10))

annot = ax.annotate("", xy=(0,0), xytext=(-20,20),
                    textcoords="offset points", 
                    bbox=dict(boxstyle="round", fc="w"))
annot.set_visible(False)

def onhover(event):
    if event.inaxes == ax:
        annot.xy = (event.xdata, event.ydata)
        annot.set_text("({:.2f}, {:.2f})".format(event.xdata, event.ydata))
        annot.set_visible(True)
        fig.canvas.draw_idle()
    else:
        annot.set_visible(False)
        fig.canvas.draw_idle()

fig.canvas.mpl_connect("motion_notify_event", onhover)
plt.show()

Output: When hovering over the plot, an annotation appears close to the cursor showing the current x and y values.

This code introduces an invisible annotation to the axes, which becomes visible and updates its position and text content as the mouse hovers over the plot. This allows users to see the precise data points without clicking.

Bonus One-Liner Method 5: Using Pyplot to Display Coordinates

For a quick and dirty one-liner solution, matplotlib’s Pyplot interface offers a simple way to display coordinates without needing explicit event handling by using the built-in navigation toolbar.

Here’s an example:

import matplotlib.pyplot as plt

plt.plot(range(10))
plt.show()

Output: A plot with an interactive navigation toolbar that displays mouse position at the bottom right corner.

By default, matplotlib’s navigation toolbar shows the coordinates of the cursor when it is over the plotting area. This method is quick but offers less control and customization.

Summary/Discussion

• Method 1: Using mpl_connect. Strengths: Precise control over events. Weaknesses: Requires writing custom event callback functions.
• Method 2: Utilizing motion_notify_event. Strengths: Real-time tracking of mouse. Weaknesses: Can be resource-intensive because of continuous updates.
• Method 3: Creating a custom Toolbar Button. Strengths: Integrates with matplotlib GUI, unobtrusive. Weaknesses: Implementation can be complex for customization.
• Method 4: Using Annotations and Text Widgets. Strengths: User-friendly, coordinates appear directly on the plot. Weaknesses: Obscures data if not carefully placed.
• Bonus Method 5: Pyplot. Strengths: Simplest implementation. Weaknesses: Least amount of control and no programmatic access to the data.