Welcome to Contourpy – A Comprehensive Guide

Contourpy is a Python library specifically designed for efficient and flexible creation of contour plots in 2D. It provides high-performance algorithms and APIs for generating contour lines, which are widely used in scientific visualization, geographic mapping, and data analysis. Built to integrate easily with popular data visualization libraries, Contourpy offers flexibility, precision, and speed for enhanced visual storytelling.

Core Features and APIs of Contourpy

Below are some of the key features and available APIs in Contourpy, alongside practical examples:

1. contour_generator

This is the central function in Contourpy, used to generate contour objects based on data input.

  from contourpy import contour_generator

  # Create a Contour Generator
  x = [1, 2, 3, 4]
  y = [1, 2, 3, 4]
  z = [
      [0.1, 0.2, 0.3, 0.4],
      [0.5, 0.6, 0.7, 0.8],
      [0.9, 1.0, 1.1, 1.2],
      [1.3, 1.4, 1.5, 1.6]
  ]
  generator = contour_generator(x, y, z)

  # Generate a single contour line
  contours = generator.contour(0.5)
  print(contours)

2. filled_contour

Creates filled contour plots by generating areas between contour levels.

  # Filled contour
  filled_contours = generator.filled_contour([0.5, 1.0])
  for filled_contour in filled_contours:
      print(filled_contour)

3. lines

Generates the vertices for each contour line at a given level.

  level = 0.7
  lines = generator.lines(level)
  for line in lines:
      print(line)

4. multi_threaded_support

Contourpy supports multithreaded processing to speed up complex visualizations, especially for large datasets.

5. Different Algorithm Support

Contourpy provides support for multiple algorithms, such as:

• Serial algorithm
• Threaded algorithm
• QuadEdge algorithm

Integrating Contourpy to Build an Application

Below is an example use case to create a Matplotlib-based application using Contourpy for rendering a dynamic contour plot.

  import numpy as np
  import matplotlib.pyplot as plt
  from contourpy import contour_generator

  # Generate data
  x = np.linspace(0, 1, 10)
  y = np.linspace(0, 1, 10)
  X, Y = np.meshgrid(x, y)
  Z = np.sin(2 * np.pi * X) * np.sin(2 * np.pi * Y)

  # Initialize contour generator
  generator = contour_generator(x, y, Z)

  # Create contour
  contours = generator.contour(0.5)

  # Plot using Matplotlib
  plt.figure()
  for contour in contours:
      plt.plot(contour[:, 0], contour[:, 1], label='Contour Level 0.5')

  plt.title('Contour Plot with Contourpy')
  plt.legend()
  plt.show()

In this example, the application visualizes the sinusoidal variations across a defined grid using Contourpy and Matplotlib jointly, which makes for a cohesive data visualization stack.

Conclusion

Contourpy is a robust library that unlocks advanced capabilities in creating 2D contour plots. Whether you are working with small datasets or large-scale visualizations, Contourpy provides the efficiency and flexibility to meet your needs. Pair it with visualization libraries like Matplotlib or Plotly for stunning results!