Learn to create an interactive Matplotlib Polygon Selector widget to define regions of interest for data analysis and visualization in your AI/ML projects.

Polygon Selector for Matplotlib

Matplotlib does not provide a built-in, dedicated Polygon Selector widget. However, its robust event-handling capabilities allow for the interactive creation and manipulation of polygons on plots. This enables users to select specific regions of interest, filter data within those regions, and customize the visual appearance and behavior of the selected polygons. By leveraging mouse events and Matplotlib's Path class, developers can implement custom polygon selection tools for enhanced interactive data exploration.

Key Concepts

1. Event Handling in Matplotlib

Matplotlib's event handling system captures user interactions such as mouse clicks, movements, and releases. This is crucial for implementing interactive selection tools like the polygon selector, allowing users to precisely define regions on a plot.

2. `matplotlib.patches.Polygon`

The Polygon class from matplotlib.patches is used to represent shapes defined by a series of connected line segments. It's fundamental for drawing the polygon on the plot. Furthermore, the Path object associated with a Polygon can be used to efficiently determine if data points lie within the defined polygon.

3. Use Cases and Extensions

Region Selection: Focus analysis on specific areas of interest within a plot.
Data Filtering: Extract and analyze subsets of data that fall within the drawn polygon.
Integration with Callbacks: Trigger custom functions or actions in response to polygon selection events.
Dynamic Visualization: Visually highlight data points that are inside the selected polygon, aiding in data interpretation.

Implementing a Polygon Selector

This section outlines the steps to create a basic Polygon Selector in Matplotlib.

Step 1: Import Required Libraries

import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import numpy as np

Step 2: Define the Polygon Selector Class

The PolygonSelector class will manage the drawing process and store the vertices of the polygon.

class PolygonSelector:
    def __init__(self, ax):
        self.ax = ax
        self.points = []
        self.polygon_patch = None  # Renamed for clarity
        self.cid_click = ax.figure.canvas.mpl_connect('button_press_event', self.on_click)
        self.cid_key = ax.figure.canvas.mpl_connect('key_press_event', self.on_key_press)

    def on_click(self, event):
        """Handles mouse clicks to add vertices to the polygon."""
        if event.inaxes == self.ax:
            if event.button == 1:  # Left mouse button
                self.points.append((event.xdata, event.ydata))
                self.update_polygon_display()

    def on_key_press(self, event):
        """Handles key presses, specifically 'enter' to finalize the polygon."""
        if event.key == 'enter':
            print("Polygon finalized. Vertices:", self.points)
            self.finalize_polygon()
            self.reset_selector()

    def update_polygon_display(self):
        """Updates or creates the polygon patch on the axes."""
        if self.polygon_patch:
            self.polygon_patch.remove()  # Remove the previous visual representation

        if len(self.points) > 1:  # Need at least two points to draw a line
            # Create a temporary line or polygon for visual feedback during drawing
            if len(self.points) == 1:
                # Draw a single point if only one click
                self.polygon_patch = Polygon([self.points[0]], closed=False, edgecolor='red', alpha=0.5)
            else:
                # Draw a line connecting the points
                self.polygon_patch = Polygon(self.points, closed=False, edgecolor='red', alpha=0.5)
            self.ax.add_patch(self.polygon_patch)
            self.ax.figure.canvas.draw()

    def finalize_polygon(self):
        """Creates the final closed polygon once the user is done."""
        if len(self.points) > 2:
            if self.polygon_patch:
                self.polygon_patch.remove()
            self.polygon_patch = Polygon(self.points, edgecolor='red', facecolor='none', closed=True)
            self.ax.add_patch(self.polygon_patch)
            self.ax.figure.canvas.draw()

    def reset_selector(self):
        """Resets the selector to allow drawing a new polygon."""
        self.points = []
        if self.polygon_patch:
            self.polygon_patch.remove()
            self.polygon_patch = None
            self.ax.figure.canvas.draw()

Step 3: Create a Plot and Initialize the Selector

This demonstrates how to set up a basic scatter plot and then instantiate the PolygonSelector.

# Create a scatter plot with random data
np.random.seed(42)
x_data = np.random.rand(50)
y_data = np.random.rand(50)

fig, ax = plt.subplots()
ax.scatter(x_data, y_data, picker=5) # picker=5 adds a tolerance for picking points
ax.set_title("Click to draw polygon, press Enter to finalize")

# Initialize the PolygonSelector
polygon_selector = PolygonSelector(ax)

plt.show()

How to Use:

Click on the plot to add vertices for your polygon.
Press the Enter key to finalize the polygon. The selected points will be printed to the console.
You can then call reset_selector() to draw a new polygon.

Highlighting Points Inside a Polygon

A common use case is to highlight data points that fall within the selected polygon. This requires adding a method to the PolygonSelector class.

Example: Highlighting Points Within a Selected Polygon

This enhanced version of the PolygonSelector includes a highlight_points_inside_polygon method.

import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import numpy as np

class PolygonSelector:
    def __init__(self, ax, scatter_collection_index=0):
        self.ax = ax
        self.scatter_collection_index = scatter_collection_index # Index of the scatter plot to interact with
        self.points = []
        self.polygon_patch = None
        self.highlighted_scatter_artists = [] # To keep track of highlighted points
        self.cid_click = ax.figure.canvas.mpl_connect('button_press_event', self.on_click)
        self.cid_key = ax.figure.canvas.mpl_connect('key_press_event', self.on_key_press)

    def on_click(self, event):
        """Handles mouse clicks to add vertices to the polygon."""
        if event.inaxes == self.ax:
            if event.button == 1:  # Left mouse button
                self.points.append((event.xdata, event.ydata))
                self.update_polygon_display()

    def on_key_press(self, event):
        """Handles key presses, specifically 'enter' to finalize and highlight."""
        if event.key == 'enter':
            self.highlight_points_inside_polygon()
            self.reset_selector()

    def update_polygon_display(self):
        """Updates or creates the polygon patch on the axes."""
        if self.polygon_patch:
            self.polygon_patch.remove()

        if len(self.points) > 1:
            if len(self.points) == 1:
                self.polygon_patch = Polygon([self.points[0]], closed=False, edgecolor='red', alpha=0.5)
            else:
                self.polygon_patch = Polygon(self.points, closed=False, edgecolor='red', alpha=0.5)
            self.ax.add_patch(self.polygon_patch)
            self.ax.figure.canvas.draw()

    def reset_selector(self):
        """Resets the selector to allow drawing a new polygon."""
        self.points = []
        if self.polygon_patch:
            self.polygon_patch.remove()
            self.polygon_patch = None
        # Reset any previously highlighted points
        if self.highlighted_scatter_artists:
            for artist in self.highlighted_scatter_artists:
                artist.set_markersize(5) # Reset to default size
            self.highlighted_scatter_artists = []
        self.ax.figure.canvas.draw()

    def highlight_points_inside_polygon(self):
        """Highlights data points that fall within the current polygon."""
        if not self.polygon_patch or len(self.points) < 3:
            print("Cannot highlight: Polygon not defined or not closed.")
            return

        # Get the scatter plot data
        try:
            scatter_collection = self.ax.collections[self.scatter_collection_index]
            xs, ys = scatter_collection.get_offsets().T
        except IndexError:
            print(f"Error: Scatter plot at index {self.scatter_collection_index} not found.")
            return

        # Create a Path object from the polygon patch
        path = self.polygon_patch.get_path()

        # Check which points are inside the polygon
        # Use contains_points which is efficient for this
        points_inside_mask = path.contains_points(np.column_stack((xs, ys)))

        # Reset previously highlighted points
        if self.highlighted_scatter_artists:
            for artist in self.highlighted_scatter_artists:
                artist.set_markersize(5)
            self.highlighted_scatter_artists = []

        # Highlight the points that are inside
        if np.any(points_inside_mask):
            # Note: plot returns a list of artists; we store the scatter plot artist
            highlighted_data_x = xs[points_inside_mask]
            highlighted_data_y = ys[points_inside_mask]

            # Re-plot these points with a larger marker size and different color
            # It's better to modify the existing collection or plot new points carefully
            # For simplicity, we'll plot new points here
            new_scatter_artist = self.ax.scatter(highlighted_data_x, highlighted_data_y,
                                                 s=100,  # Larger size
                                                 edgecolors='yellow', # Outline
                                                 facecolors='none', # Transparent fill
                                                 lw=2) # Line width for outline
            self.highlighted_scatter_artists.append(new_scatter_artist)

            print(f"Highlighted {len(highlighted_data_x)} points inside the polygon.")
        else:
            print("No points found inside the polygon.")

        self.ax.figure.canvas.draw()


# Create a scatter plot with random data
np.random.seed(42)
x_data = np.random.rand(50)
y_data = np.random.rand(50)

fig, ax = plt.subplots()
scatter_plot = ax.scatter(x_data, y_data, s=25) # s is marker size
ax.set_title("Draw polygon, press Enter to highlight points inside")

# Initialize the PolygonSelector, specifying the index of the scatter plot (usually 0 for the first one)
polygon_selector = PolygonSelector(ax, scatter_collection_index=0)

plt.show()

How to Use:

Click on the plot to define the vertices of your polygon.
Press the Enter key. The points within the drawn polygon will be highlighted with a larger marker and an outline.
The selector will then reset, allowing you to draw another polygon.

Further Use Cases

The PolygonSelector can be extended for various advanced applications:

Region Selection: Users can visually delineate areas of interest on complex plots (e.g., heatmaps, contour plots) to focus their analysis.
Data Filtering: The list of points within a selected polygon can be directly used to filter a dataset for further statistical analysis or manipulation.
Integration with Callbacks: The selection process can trigger custom functions. For example, clicking Enter could update a separate table with the data points inside the polygon, or trigger a different type of visualization for the selected subset.
Dynamic Visualization: Beyond static highlighting, points inside the polygon could be animated, recolored, or have their labels shown, enhancing interactive exploration.

Conclusion

By leveraging Matplotlib's powerful event handling, it's feasible to create sophisticated interactive tools like a Polygon Selector. This allows users to:

Interactively draw polygons on plots with mouse clicks.
Select and analyze specific data regions by defining arbitrary polygonal boundaries.
Dynamically highlight data points located within a selected polygon.
Customize the appearance and behavior of the polygon and the selection process.

These capabilities significantly enhance the interactivity and utility of Matplotlib plots for data exploration and analysis.

Matplotlib Polygon Selector for Data Region Selection