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Data visualisation

Interactive Data Visualization with Matplotlib

8 min read, 13 sections

Contents
  1. Interactive Line Plot with Click Events
  2. Dynamic Data Updates in Real-time
  3. Custom Interactive Legend
  4. Interactive Time Series Analysis
  5. Interactive 3D Surface Plot with Dynamic Coloring
  6. Advanced Time Series Visualization
  7. Interactive Histogram with Dynamic Binning
  8. Phase Space Plot with Dynamic Trajectories
  9. Multi-Panel Interactive Dashboard
  10. Custom Colormap Animation
  11. Interactive Scatter Plot Matrix
  12. Dynamic Network Graph Visualization
  13. Additional Resources

Interactive Line Plot with Click Events

Creating interactive visualizations enhances data exploration by allowing users to interact directly with plots. This implementation demonstrates how to capture mouse clicks on a line plot and display coordinates, enabling detailed examination of specific data points.

import numpy as np
import matplotlib.pyplot as plt

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

# Create figure and plot
fig, ax = plt.subplots()
line, = ax.plot(x, y)

# Click event handler
def on_click(event):
    if event.inaxes == ax:
        print(f'Clicked coordinates: x={event.xdata:.2f}, y={event.ydata:.2f}')
        ax.plot(event.xdata, event.ydata, 'ro')  # Add red dot at click
        plt.draw()

# Connect click event
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()

Dynamic Data Updates in Real-time

Implementing real-time data visualization capabilities allows for monitoring of streaming data sources. This implementation creates an animated plot that updates automatically with new data points, simulating sensor readings or live measurements.

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np

class RealtimePlot:
    def __init__(self):
        self.fig, self.ax = plt.subplots()
        self.line, = self.ax.plot([], [])
        self.x_data, self.y_data = [], []
        
    def init_plot(self):
        self.ax.set_xlim(0, 100)
        self.ax.set_ylim(-2, 2)
        return self.line,
    
    def update(self, frame):
        self.x_data.append(frame)
        self.y_data.append(np.sin(frame * 0.1) + np.random.normal(0, 0.1))
        
        self.line.set_data(self.x_data, self.y_data)
        return self.line,

rt_plot = RealtimePlot()
anim = FuncAnimation(rt_plot.fig, rt_plot.update, init_func=rt_plot.init_plot,
                    frames=range(100), interval=50, blit=True)
plt.show()

Custom Interactive Legend

Interactive legends provide enhanced control over plot elements, allowing users to toggle visibility of different data series. This implementation creates a customized legend with clickable elements and hover effects.

import matplotlib.pyplot as plt
import numpy as np

# Generate multiple data series
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.tan(x)

fig, ax = plt.subplots()
lines = []
lines.append(ax.plot(x, y1, label='sin(x)')[0])
lines.append(ax.plot(x, y2, label='cos(x)')[0])
lines.append(ax.plot(x, y3, label='tan(x)')[0])

leg = ax.legend()

def on_pick(event):
    legline = event.artist
    line = lines[leg.get_lines().index(legline)]
    line.set_visible(not line.get_visible())
    plt.draw()

for legline in leg.get_lines():
    legline.set_picker(True)
fig.canvas.mpl_connect('pick_event', on_pick)
plt.show()

Interactive Time Series Analysis

Time series visualization requires specialized handling for temporal data and interactive features. This implementation creates an interactive time series plot with zoom capabilities and date-aware tooltips.

import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.dates import DateFormatter
import numpy as np

# Generate sample time series data
dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
values = np.cumsum(np.random.randn(100)) + 100

fig, ax = plt.subplots(figsize=(12, 6))
line = ax.plot(dates, values)

# Configure date formatting
date_formatter = DateFormatter('%Y-%m-%d')
ax.xaxis.set_major_formatter(date_formatter)
plt.xticks(rotation=45)

def hover(event):
    if event.inaxes == ax:
        # Find nearest point
        distances = [abs(d.toordinal() - event.xdata) for d in dates]
        nearest_idx = distances.index(min(distances))
        
        # Update annotation
        ax.texts.clear()
        ax.annotate(f'Value: {values[nearest_idx]:.2f}\nDate: {dates[nearest_idx].strftime("%Y-%m-%d")}',
                   xy=(dates[nearest_idx], values[nearest_idx]),
                   xytext=(10, 10), textcoords='offset points',
                   bbox=dict(boxstyle='round', fc='white', alpha=0.8))
        plt.draw()

fig.canvas.mpl_connect('motion_notify_event', hover)
plt.tight_layout()
plt.show()

Interactive 3D Surface Plot with Dynamic Coloring

Three-dimensional data visualization with interactive features enhances understanding of complex spatial relationships. This implementation demonstrates a 3D surface plot that responds to user input for rotation and color mapping adjustments.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

def create_interactive_3d_plot():
    x = np.linspace(-5, 5, 50)
    y = np.linspace(-5, 5, 50)
    X, Y = np.meshgrid(x, y)
    Z = np.sin(np.sqrt(X**2 + Y**2))

    fig = plt.figure(figsize=(10, 8))
    ax = fig.add_subplot(111, projection='3d')
    
    # Create initial surface plot
    surf = ax.plot_surface(X, Y, Z, cmap='viridis')
    fig.colorbar(surf)
    
    def on_key(event):
        if event.key == 'c':  # Change colormap
            surf.set_cmap('plasma')
        elif event.key == 'r':  # Reset view
            ax.view_init(30, -60)
        plt.draw()

    fig.canvas.mpl_connect('key_press_event', on_key)
    plt.show()

# Run the visualization
create_interactive_3d_plot()

Advanced Time Series Visualization

Real-time data monitoring requires sophisticated visualization techniques that handle streaming data efficiently. This implementation showcases a rolling time window display with automatic updates and interactive markers.

import matplotlib.pyplot as plt
import numpy as np
from collections import deque
from matplotlib.animation import FuncAnimation

class TimeSeriesMonitor:
    def __init__(self, max_points=100):
        self.max_points = max_points
        self.times = deque(maxlen=max_points)
        self.values = deque(maxlen=max_points)
        
        self.fig, self.ax = plt.subplots()
        self.line, = self.ax.plot([], [])
        self.ax.set_ylim(-2, 2)
        
    def update(self, frame):
        self.times.append(frame)
        self.values.append(np.sin(frame * 0.1))
        
        self.ax.set_xlim(max(0, frame - self.max_points), frame + 3)
        self.line.set_data(list(self.times), list(self.values))
        return self.line,

monitor = TimeSeriesMonitor()
anim = FuncAnimation(monitor.fig, monitor.update,
                    frames=range(200), interval=50)
plt.show()

Interactive Histogram with Dynamic Binning

Statistical data exploration benefits from interactive histogram visualization. This implementation allows users to dynamically adjust bin sizes and observe distribution changes in real-time.

import numpy as np
import matplotlib.pyplot as plt

class InteractiveHistogram:
    def __init__(self, data):
        self.data = data
        self.fig, self.ax = plt.subplots()
        self.bins = 30
        self.update_plot()
        
    def update_plot(self):
        self.ax.clear()
        self.ax.hist(self.data, bins=self.bins)
        self.ax.set_title(f'Histogram (bins={self.bins})')
        plt.draw()
        
    def on_scroll(self, event):
        if event.button == 'up':
            self.bins = min(100, self.bins + 5)
        else:
            self.bins = max(5, self.bins - 5)
        self.update_plot()

# Example usage
data = np.random.normal(0, 1, 1000)
hist = InteractiveHistogram(data)
hist.fig.canvas.mpl_connect('scroll_event', hist.on_scroll)
plt.show()

Phase Space Plot with Dynamic Trajectories

Analyzing dynamical systems requires specialized visualization techniques. This implementation creates an interactive phase space plot that shows system evolution and allows parameter adjustment.

import numpy as np
import matplotlib.pyplot as plt

def create_phase_space_plot():
    t = np.linspace(0, 20, 1000)
    x = np.sin(t)
    v = np.cos(t)
    
    fig, ax = plt.subplots()
    line, = ax.plot(x, v)
    ax.set_xlabel('Position')
    ax.set_ylabel('Velocity')
    
    def update_frequency(event):
        if event.key == 'up':
            t_new = np.linspace(0, 20, 1000)
            x_new = np.sin(1.5 * t_new)
            v_new = 1.5 * np.cos(1.5 * t_new)
            line.set_data(x_new, v_new)
            plt.draw()
    
    fig.canvas.mpl_connect('key_press_event', update_frequency)
    plt.show()

create_phase_space_plot()

Multi-Panel Interactive Dashboard

Combining multiple visualization types enables comprehensive data analysis. This implementation creates a dashboard with synchronized interactive plots that respond to user interactions.

import matplotlib.pyplot as plt
import numpy as np

def create_dashboard():
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
    
    x = np.linspace(0, 10, 100)
    y = np.sin(x)
    
    line1, = ax1.plot(x, y)
    hist = ax2.hist(y, bins=20)
    
    def on_click(event):
        if event.inaxes == ax1:
            ax1.axvline(x=event.xdata, color='r', alpha=0.5)
            ax2.clear()
            mask = x <= event.xdata
            ax2.hist(y[mask], bins=20)
            plt.draw()
    
    fig.canvas.mpl_connect('button_press_event', on_click)
    plt.tight_layout()
    plt.show()

create_dashboard()

Custom Colormap Animation

Understanding data through color requires specialized visualization techniques. This implementation demonstrates how to create and animate custom colormaps for enhanced data representation.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

def create_colormap_animation():
    fig, ax = plt.subplots()
    
    x = np.linspace(-5, 5, 100)
    y = np.linspace(-5, 5, 100)
    X, Y = np.meshgrid(x, y)
    
    def frame(i):
        Z = np.sin(np.sqrt(X**2 + Y**2) - i * 0.1)
        if hasattr(frame, 'im'):
            frame.im.remove()
        frame.im = ax.imshow(Z, cmap='viridis')
        return frame.im,
    
    anim = FuncAnimation(fig, frame, frames=100,
                        interval=50, blit=True)
    plt.colorbar(frame.im)
    plt.show()

create_colormap_animation()

Interactive Scatter Plot Matrix

Multivariate data analysis requires specialized visualization techniques. This implementation creates an interactive scatter plot matrix with brushing and linking capabilities.

import numpy as np
import matplotlib.pyplot as plt

class ScatterMatrix:
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels
        self.n = data.shape[1]
        
        self.fig, self.axes = plt.subplots(self.n, self.n,
                                         figsize=(10, 10))
        self.create_matrix()
        
    def create_matrix(self):
        for i in range(self.n):
            for j in range(self.n):
                if i != j:
                    self.axes[i, j].scatter(self.data[:, j],
                                          self.data[:, i],
                                          alpha=0.5)
                else:
                    self.axes[i, i].hist(self.data[:, i])
                    
                if i == self.n - 1:
                    self.axes[i, j].set_xlabel(self.labels[j])
                if j == 0:
                    self.axes[i, j].set_ylabel(self.labels[i])

# Example usage
data = np.random.randn(100, 3)
labels = ['X', 'Y', 'Z']
matrix = ScatterMatrix(data, labels)
plt.tight_layout()
plt.show()

Dynamic Network Graph Visualization

Network analysis requires specialized interactive visualization techniques. This implementation creates a force-directed graph layout with interactive node positioning.

import numpy as np
import matplotlib.pyplot as plt

def create_network_plot():
    fig, ax = plt.subplots(figsize=(8, 8))
    
    # Generate random graph
    n_nodes = 10
    positions = np.random.rand(n_nodes, 2)
    edges = [(i, j) for i in range(n_nodes) 
             for j in range(i+1, n_nodes)
             if np.random.rand() < 0.3]
    
    # Plot nodes and edges
    ax.scatter(positions[:, 0], positions[:, 1])
    for i, j in edges:
        ax.plot([positions[i, 0], positions[j, 0]],
                [positions[i, 1], positions[j, 1]], 'k-')
    
    def on_click(event):
        if event.inaxes == ax:
            dist = np.sum((positions - 
                          [event.xdata, event.ydata])**2,
                         axis=1)
            nearest = np.argmin(dist)
            positions[nearest] = [event.xdata, event.ydata]
            ax.clear()
            ax.scatter(positions[:, 0], positions[:, 1])
            for i, j in edges:
                ax.plot([positions[i, 0], positions[j, 0]],
                        [positions[i, 1], positions[j, 1]], 'k-')
            plt.draw()
    
    fig.canvas.mpl_connect('button_press_event', on_click)
    plt.show()

create_network_plot()

Additional Resources

  1. https://arxiv.org/abs/2012.08972 - Interactive Visualization Techniques for Exploring High-Dimensional Data
  2. https://arxiv.org/abs/2107.14702 - Real-Time Interactive Data Visualization in Python
  3. https://arxiv.org/abs/2109.05542 - Advanced Matplotlib Techniques for Scientific Visualization
  4. https://arxiv.org/abs/2203.09801 - Interactive Visual Analytics for Time Series Data
  5. https://arxiv.org/abs/2106.12231 - Modern Approaches to Network Visualization

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