Creating a Scatterplot with Custom Color Map Using (n,3) Array

Creating a Scatterplot using a (n,3) array where n is the number of data points in dataset as the ‘color’ parameter in plt.scatter()

Introduction

In this blog post, we will explore how to create a scatterplot using a custom color map by utilizing an (n,3) array as the c parameter in the plt.scatter() function. We’ll dive into the details of creating and manipulating this array to achieve our desired visualization.

Understanding the Problem

The original code snippet attempts to recreate a scatterplot with a black-to-red fade effect using a custom color map. However, it encounters an issue where the colors are not being applied correctly due to a misunderstanding about how to utilize the colors parameter in plt.scatter().

Problem Analysis

Upon reviewing the provided code snippet, we notice that there are several issues:

The colors array is defined as a (5,5) matrix instead of an (n,3) array where n is the number of data points.
The c parameter in plt.scatter() expects a 1D array or a colormap object, but the provided code uses a 2D array.

Solution

To solve this problem, we need to redefine the colors array as an (n,3) array where each row represents an RGB value. We will also adjust the scaling of the color values to achieve the desired black-to-red fade effect.

Recreating the Scatterplot with Custom Color Map

Here’s a step-by-step guide on how to recreate the scatterplot using the custom color map:

Step 1: Import Necessary Libraries and Define Data

import matplotlib.pyplot as plt
import numpy as np

First, we import the necessary libraries. Then, we define our data points.

limits = [-2.25, 2.25, -2.25, 2.25]  # [xmin,xmax,ymin,ymax]

x = np.repeat(np.linspace(0, 1, 5), 5)
y = np.tile(np.linspace(0, 1, 5), 5)
rows, cols = 5, 5

In this code snippet:

We define our x- and y-axis limits.
We create x values by repeating the range of numbers from 0 to 1 five times. This is done using np.repeat().
Similarly, we create y values by tiling the range of numbers from 0 to 1 five times.

Step 2: Create Custom Color Array

Next, we need to redefine our colors array as an (n,3) array representing RGB values. Here’s how to do it:

# Define custom color map with black-to-red fade effect
colors = np.array([np.linspace(0, 1, rows*cols),
                   np.linspace(0, 0.3, rows*cols),
                   np.linspace(0, 0, rows*cols)]).T

# Scale the colors to match our data points
sizes = np.arange(1, rows*cols + 1)

# Combine x and y values with custom color array
plt.scatter(x, y, s=sizes, c=colors)

Here’s what’s happening in this code snippet:

We define two separate arrays representing the green and blue components of our custom color map (np.linspace(0, 0.3, rows*cols)).
We scale the sizes of our scatterplot by creating an array sizes using np.arange().
Finally, we create our scatterplot with combined x and y values using the plt.scatter() function.

Step 3: Customize Plot

After creating the scatterplot, we can customize it further to suit your needs. Here’s how to do it:

# Add title and labels
plt.xlabel("y")
plt.ylabel("x")

# Set x- and y-axis limits
plt.xlim(limits[0], limits[1])
plt.ylim(limits[2], limits[3])

# Display the plot
plt.title('Custom Scatterplot')
plt.show()

This code snippet:

Adds labels for our x- and y-axis using plt.xlabel() and plt.ylabel().
Sets our x- and y-axis limits using plt.xlim() and plt.ylim().
Displays our scatterplot with a custom title.

Conclusion

In this blog post, we explored how to create a scatterplot using a custom color map by utilizing an (n,3) array as the c parameter in the plt.scatter() function. We dove into the details of creating and manipulating this array to achieve our desired visualization. By following these steps, you should now be able to recreate the desired black-to-red fade effect in your scatterplot.

Last modified on 2024-07-31