Annotated Cursor in Matplotlib

An annotated cursor in data visualization combines cursor tracking with dynamic annotations or tooltips, displaying contextual information as the cursor moves across a plot. This feature significantly enhances interactive data exploration, allowing users to gain deeper insights by easily identifying and understanding specific data points.

In Matplotlib, an annotated cursor enables users to:

• Track cursor position dynamically: See the precise (x, y) coordinates of the mouse pointer in real-time.
• Display data values, labels, or coordinates: Automatically show relevant information associated with the data points under the cursor.
• Interactively explore data points: Click on points to mark them or reveal further details, facilitating a more engaging analysis.

Features of an Annotated Cursor

1. Interactive Cursor Tracking: The cursor's movement across the plot is continuously monitored. As the mouse pointer moves, the corresponding x and y coordinates are displayed and updated dynamically.

2. Annotation Capability: Specific data points can be marked with text labels, markers, or custom annotations. This helps in visually highlighting important data points and providing immediate context.

3. Customization: The appearance and behavior of the annotated cursor can be tailored. This includes modifying:

   • Marker style and color
   • Text content and formatting (e.g., font size, decimal places)
   • Annotation position relative to the data point
   • Arrow style connecting the annotation to the point
   • The events that trigger or remove annotations (e.g., hover, click)

Implementing an Annotated Cursor in Matplotlib

Matplotlib's event handling system is key to creating an annotated cursor. Cursor movements are captured using the motion_notify_event, and mouse clicks using the button_press_event.

Core Concepts: Event Handling

• motion_notify_event: This event is triggered whenever the mouse pointer moves. We can connect a function to this event to update the annotation based on the current cursor position.
• button_press_event: This event fires when a mouse button is pressed. It can be used to add persistent annotations or to clear existing ones.

Example: Annotating Data Points on Cursor Hover and Removing on Click

This example demonstrates how to create an annotated cursor that displays the (x, y) coordinates of the nearest data point when the cursor hovers over a line plot. Clicking anywhere on the plot will remove any existing annotations.

import matplotlib.pyplot as plt
import numpy as np

# Generate sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create a plot
fig, ax = plt.subplots()
line, = ax.plot(x, y, label='Sine Wave')

# Store current annotations to manage them
current_annotation = None

# Function to annotate points on cursor hover
def annotate_point(event):
    global current_annotation
    # Remove previous annotation if it exists
    if current_annotation:
        current_annotation.remove()
        current_annotation = None

    # Only process events within the axes
    if event.inaxes == ax:
        x_cursor, y_cursor = event.xdata, event.ydata
        # Find the index of the nearest data point to the cursor
        index = np.argmin(np.abs(x - x_cursor))
        
        # Create and display the annotation
        current_annotation = ax.annotate(
            f'({x[index]:.2f}, {y[index]:.2f})',  # Text to display
            xy=(x[index], y[index]),             # Point to annotate
            xytext=(x[index] + 0.5, y[index] + 0.5), # Position of the text
            arrowprops=dict(facecolor='black', arrowstyle='->'), # Arrow properties
            fontsize=8, 
            color='black',
            bbox=dict(boxstyle='round,pad=0.3', fc='white', alpha=0.7) # Optional: background for text
        )
        fig.canvas.draw_idle() # Redraw the canvas efficiently

# Function to remove all annotations on mouse click
def remove_all_annotations(event):
    global current_annotation
    if event.inaxes == ax:
        if current_annotation:
            current_annotation.remove()
            current_annotation = None
            fig.canvas.draw_idle()

# Connect events to their respective handler functions
# 'motion_notify_event' triggers annotation on hover
plt.connect('motion_notify_event', annotate_point)
# 'button_press_event' triggers removal of annotations on click
plt.connect('button_press_event', remove_all_annotations)

# Set plot title and labels for clarity
ax.set_title("Annotated Cursor Example")
ax.set_xlabel("X-axis")
ax.set_ylabel("Y-axis")
ax.legend()
ax.grid(True)

# Display the plot
plt.show()

Explanation:

1. Data Generation: We create a simple sine wave using NumPy.
2. Plot Creation: A Matplotlib figure and axes are set up, and the data is plotted.
3. annotate_point Function:
   • This function is called whenever the mouse moves (motion_notify_event).
   • It checks if the cursor is within the plot axes (event.inaxes == ax).
   • event.xdata and event.ydata provide the cursor's coordinates in data space.
   • np.argmin(np.abs(x - x_cursor)) finds the index of the data point whose x-value is closest to the cursor's x-position.
   • ax.annotate() is used to draw the annotation with text, arrow, and positioning details.
   • fig.canvas.draw_idle() is called to efficiently update the plot without excessive redrawing.
   • A global variable current_annotation is used to keep track of the latest annotation so it can be removed before a new one is drawn.
4. remove_all_annotations Function:
   • This function is triggered by a mouse click (button_press_event).
   • It checks if the click occurred within the axes.
   • If an annotation exists (current_annotation), it's removed using .remove(), and fig.canvas.draw_idle() updates the display.
5. Event Connection: plt.connect() links the Matplotlib events to our custom functions.
6. Display: plt.show() renders the plot and makes it interactive.

Output:

The plot will display a sine wave. As you move your mouse over the line, a small text box with an arrow will appear near the nearest data point, showing its (x, y) coordinates. Clicking anywhere on the plot will clear this annotation.

Adding Annotations Dynamically

Annotations are crucial for providing immediate context to data points. They can be added dynamically based on user interaction, such as hovering or clicking, to highlight specific values, labels, or categories.

The previous example already covers dynamically adding annotations on mouse hover. The core mechanism involves:

1. Capturing Mouse Events: Using motion_notify_event for hover effects or button_press_event for click-based annotations.
2. Identifying Data Points: Determining which data point is under or closest to the cursor using its event.xdata and event.ydata.
3. Using ax.annotate(): This powerful Matplotlib function allows for flexible placement and styling of text and arrows pointing to data.

Use Cases of Annotated Cursor

The annotated cursor is a versatile tool in data visualization, with several practical applications:

1. Data Highlighting: Visually emphasizes specific data points or ranges of data as the user interacts with the plot, drawing attention to patterns or outliers.
2. Informational Markers: Provides supplementary details for key data points, such as names, categories, timestamps, or calculated values, directly within the visualization.
3. Interactive Exploration: Enables users to intuitively explore datasets by moving the cursor, revealing information without cluttering the initial plot with static labels. This is especially useful for dense scatter plots or time series data.
4. Debugging and Verification: Helps users verify the exact values of plotted data points during development or analysis.
5. Educational Tools: Makes complex datasets more approachable by providing on-demand explanations for different parts of a graph.

Conclusion

Matplotlib's event handling system empowers developers to create sophisticated interactive features like the annotated cursor. By dynamically tracking cursor movements and displaying relevant information, this technique significantly enhances user engagement and understanding of plotted data. Users can effectively:

• Track cursor position dynamically for precise data point identification.
• Display context-rich annotations on hover or click.
• Remove annotations to maintain a clean view or clear specific points.
• Explore data interactively, leading to deeper insights and a more intuitive analytical experience.