How to Implement Gradient Colors on a Horizontal Broken Bar Plot Using Matplotlib in Python

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What will you learn?

In this tutorial, you will learn how to enhance your horizontal broken bar plots created with Matplotlib by incorporating gradient colors. By applying gradient color schemes, you can elevate the visual appeal of your data visualizations and make them more informative.

Introduction to the Problem and Solution

When working with Matplotlib to create various types of graphs, it’s essential to consider the aesthetic aspect of your charts. While standard solid colors may suffice for some plots, they can oversimplify the information presented in broken barh (horizontal) plots. To address this challenge, implementing gradient color schemes can add depth and improve the visual appeal of your visualizations.

To tackle this issue, we will delve into how gradients function within Matplotlib’s plotting capabilities. By leveraging existing functionalities within Matplotlib and creatively manipulating its API, we can simulate gradient effects on our broken barh plots. This process involves understanding how colors are handled by Matplotlib and utilizing that knowledge to craft custom gradients tailored to our specific visualization needs.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import BrokenBarHCollection

def gradient_fill(xranges, yrange, orientation='horizontal', cmap='viridis', **kwargs):
    fig, ax = plt.subplots()
    collection = BrokenBarHCollection(xranges, yrange,
                                       facecolors=plt.get_cmap(cmap)(np.linspace(0, 1 , len(xranges))),
                                       **kwargs)
    ax.add_collection(collection)
    ax.set_ylim(yrange[0], yrange[1] + xranges[-1][1])
    ax.set_xlim(0, sum([xr[1] for xr in xranges]))

    if orientation == 'vertical':
        # Rotate plot for vertical orientation
        plt.xticks(rotation=90)

gradient_fill([(10*i ,5) for i in range(10)], [5, 15], cmap='cool')
plt.show()

# Copyright PHD

Explanation

The gradient_fill function allows you to specify parameters such as xranges, yrange, orientation, and a colormap (cmap) name. By using BrokenBarHCollection from matplotlib.collections, we can create broken horizontal bars with each segment displaying a progressively changing color extracted from the specified colormap. This technique visually creates a gradient effect across different segments of the plot.

  • Setting up: Begin by creating figure and axis objects using plt.subplots().
  • Creating Colorful Bars: The crucial step involves creating a BrokenBarHCollection instance where each segment is assigned varying shades from the chosen colormap. This results in an array of colors that transition smoothly across the segments.
  • Adjustment & Display: Finally, adjust the axes limits accordingly before displaying the plot with horizontal gradients applied to the bars.

This approach offers flexibility in customization through colormaps or orientations to meet specific visualization requirements while achieving visually appealing effects not directly supported by high-level APIs.

  1. How do I change the direction of the gradient?

  2. To change the direction of the gradient, modify the value passed into the orientation parameter; setting it to ‘vertical’ rotates your plot and alters the perceived direction of gradation.

  3. Can I apply this method to other types of plots?

  4. While designed for broken barh plots in this context, similar concepts may be creatively adapted for other plot types based on specific visualization needs.

  5. What are colormaps?

  6. Colormaps are sequential arrays defining color spectrums used extensively in data visualization applications, particularly when representing scalar data effectively.

  7. Where can I find more colormaps?

  8. Refer to Matplotlib documentation which lists numerous pre-defined colormaps catering to diverse aesthetic requirements ranging from sequential patterns to diverging scales.

  9. Is there performance overhead when using gradients?

  10. There might be minor computational overhead due to rendering complex visuals; however, this is typically negligible considering modern computing capabilities.

Conclusion

By incorporating gradients into horizontal broken bar charts using Matplotlib, you can elevate both aesthetics and data insights simultaneously. While exploring these advanced techniques may require stepping beyond conventional API usage, the resulting flexibility and visual engagement justify delving into these creative avenues for enhanced plotting experiences. Happy plotting!

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