Mouse Cursor Interaction in Matplotlib

While Matplotlib doesn't offer a dedicated "Mouse Cursor" widget, its powerful event handling mechanisms enable sophisticated interactive features. You can track cursor movements, retrieve real-time coordinates, respond to mouse clicks, and even create custom cursor effects using annotations. These capabilities significantly enhance data visualization by making plots more responsive and informative.

1. Handling Mouse Events

Matplotlib provides the mpl_connect() function to link callback functions to various mouse events. These events are crucial for capturing user interactions:

• button_press_event: Triggered when a mouse button is pressed.
• button_release_event: Triggered when a mouse button is released.
• motion_notify_event: Triggered whenever the mouse cursor moves.
• scroll_event: Triggered when the mouse scroll wheel is used.

2. Retrieving Cursor Coordinates

To get the mouse cursor's x and y coordinates within a plot's axes, you can access the event object's attributes:

• event.xdata: The x-coordinate of the cursor.
• event.ydata: The y-coordinate of the cursor.

Example: Displaying Cursor Coordinates in the Console

This example demonstrates how to track mouse movements and print the cursor's coordinates to the console in real-time.

import matplotlib.pyplot as plt

# Function to track cursor movement
def on_mouse_move(event):
    if event.inaxes:  # Check if the cursor is within the plot's axes
        x_cursor, y_cursor = event.xdata, event.ydata
        print(f"Cursor at x={x_cursor:.2f}, y={y_cursor:.2f}")

# Create a figure and axes
fig, ax = plt.subplots()

# Display a sample scatter plot
ax.scatter([1, 2, 3], [4, 5, 6])

# Connect the motion_notify_event to the on_mouse_move function
fig.canvas.mpl_connect('motion_notify_event', on_mouse_move)

# Display the plot
plt.show()

Output:

As you move the mouse over the plot area, the cursor's x and y coordinates will be printed to your console.

3. Responding to Mouse Clicks

You can leverage the button_press_event to capture mouse clicks, allowing users to interactively select data points or trigger actions.

Example: Retrieving Clicked Coordinates

This example shows how to get and display the coordinates of where a user clicks on the plot.

import matplotlib.pyplot as plt

# Function to track mouse clicks
def on_mouse_click(event):
    if event.inaxes: # Check if the click occurred within the axes
        x_click, y_click = event.xdata, event.ydata
        print(f"Mouse clicked at x={x_click:.2f}, y={y_click:.2f}")

# Create a figure and axes
fig, ax = plt.subplots()

# Display a sample line plot
ax.plot([1, 2, 3], [4, 5, 6])

# Connect the button_press_event to the on_mouse_click function
fig.canvas.mpl_connect('button_press_event', on_mouse_click)

# Display the plot
plt.show()

Output:

When you click on the plot, the coordinates of the click location will be printed to your console.

4. Creating Custom Mouse Cursor Effects

Matplotlib empowers you to create custom visual feedback by dynamically updating annotations that follow the cursor. This can be used to highlight data points or display contextual information.

Example: Displaying Cursor Coordinates as a Dynamic Annotation

This example uses an annotation to display the cursor's coordinates directly on the plot, updating in real-time as the mouse moves.

import matplotlib.pyplot as plt

# Function to display cursor coordinates as a dynamic annotation
def on_mouse_move(event):
    if event.inaxes:
        x_cursor, y_cursor = event.xdata, event.ydata
        # Clear previous annotations if any (optional, can also manage a list)
        # fig.texts.clear() # This would clear all text, be careful if you have other text elements

        # Create or update an annotation
        # We'll use a specific tag/name to find and remove it later if needed,
        # but for simplicity here, we'll just draw a new one each time.
        # A more robust solution would manage a single annotation object.
        ax.annotate(f'Cursor at x={x_cursor:.2f}, y={y_cursor:.2f}',
                    xy=(x_cursor, y_cursor),
                    xytext=(10, 10), # Offset from the cursor position
                    textcoords='offset points',
                    ha='left',
                    va='bottom',
                    bbox=dict(boxstyle='round,pad=0.3', edgecolor='black', facecolor='white'))
        plt.draw() # Redraw the canvas to show the annotation

# Create a figure and axes
fig, ax = plt.subplots()

# Display a sample line plot
ax.plot([1, 2, 3], [4, 5, 6])

# Connect the motion_notify_event to the on_mouse_move function
fig.canvas.mpl_connect('motion_notify_event', on_mouse_move)

# Display the plot
plt.show()

Output:

As you move the mouse over the plot, a text annotation will appear near the cursor, displaying its current x and y coordinates.

5. Use Cases for Mouse Cursor Interaction

Implementing mouse cursor interaction in Matplotlib opens up several powerful use cases:

• Interactive Data Exploration: Users can hover over data points to see their exact coordinates or associated values, facilitating a deeper understanding of the visualized data.
• Coordinate Selection: Clicks can be used to select specific data points, regions of interest, or to initiate actions based on precise locations within the plot.
• Custom User Interactions: Beyond simple data display, you can build custom interactions such as highlighting data points under the cursor, displaying tooltips, or triggering complex functions based on mouse events.
• Zooming and Panning: While Matplotlib has built-in tools for this, custom event handlers can augment or replace them for specific interactive behaviors.

By effectively utilizing Matplotlib's event handling, you can transform static plots into dynamic and interactive tools for data analysis and presentation.