How to make a circular color gradient in Python?

A circular color gradient in Python is a visual representation of a continuous transition of colors in a circular shape. It is often used in data visualization to represent magnitude, direction, or other quantities that vary over a circular domain. The color gradient can be created by mapping a scalar value to a color on a color map, with the scalar value being a function of the position in the circular domain. The result is a smooth transition of colors that can be represented as an image, typically with a circular shape. The process of creating a circular color gradient in Python involves mapping scalar values to colors using a color map, generating a 2D matrix that represents the color gradient, and displaying the matrix using a library such as matplotlib.

6 Ways To Make A Circular Color Gradient In Python?

1. Using the matplotlib library – imshow function:

You can use the matplotlib library to create a circular color gradient in Python. Here’s an example that generates a circular color gradient and displays it using matplotlib:

import numpy as np

import matplotlib.pyplot as plt
def gradient(x):
    return np.sin(3 * np.pi * x)
X, Y = np.meshgrid(np.linspace(0, 1, 256), np.linspace(0, 1, 256))
R = np.sqrt((X - 0.5)**2 + (Y - 0.5)**2)
Z = gradient(R)
plt.imshow(Z, cmap='inferno', origin='lower', extent=[0, 1, 0, 1])
plt.axis('equal')
plt.axis('off')
plt.show()

This code generates a 256×256 matrix Z that represents the circular color gradient, and displays it using the imshow function from matplotlib. The gradient function takes as input a scalar value x and returns the corresponding gradient value, in this case determined by the sine function. The R matrix is a measure of the distance from each point in the matrix to the center of the image. By combining R and the gradient function, you can generate a circular color gradient.

2. Using Matplotlib’s pcolormesh function:

import numpy as np
import matplotlib.pyplot as plt

def gradient(x, y):
return np.sin(np.pi * np.sqrt(x**2 + y**2))

X, Y = np.meshgrid(np.linspace(-1, 1, 256), np.linspace(-1, 1, 256))
Z = gradient(X, Y)

plt.pcolormesh(X, Y, Z, cmap='inferno')
plt.axis('equal')
plt.axis('off')
plt.show()

In this method, you define a gradient function that maps 2D coordinates to colors, and use it to generate a 2D matrix Z that represents the color gradient. Then, you display the matrix using the pcolormesh function from matplotlib.

Read more:- How to convert a column in text output in Python?

3. Using Matplotlib’s Circle and Rectangle functions:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Rectangle

def gradient(x, y, cx, cy, r):
return np.sin(np.pi * np.sqrt((x - cx)**2 + (y - cy)**2) / r)

X, Y = np.meshgrid(np.linspace(-1, 1, 256), np.linspace(-1, 1, 256))
Z = gradient(X, Y, 0, 0, 1)

fig, ax = plt.subplots()
ax.imshow(Z, cmap='inferno', origin='lower', extent=[-1, 1, -1, 1])
circle = Circle((0, 0), 1, color='none', ec='k')
ax.add_patch(circle)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.axis('equal')
ax.axis

4. Using Pyplot’s scatter function

import numpy as np
import matplotlib.pyplot as plt

def gradient(x, y):
return np.sin(np.pi * np.sqrt(x**2 + y**2))

N = 256 x = np.linspace(-1, 1, N)
y = np.linspace(-1, 1, N)
X, Y = np.meshgrid(x, y)
Z = gradient(X, Y)

fig, ax = plt.subplots()
ax.scatter(X, Y, c=Z, cmap='inferno', s=20)
ax.axis('equal')
ax.axis('off')
plt.show() I

The scatter function from matplotlib to plot points at each location in the 2D grid, and color the points based on the value of the gradient function. The result is a smooth representation of the color gradient, with circular shape.

Read more:- What Are Natural Language Processing (NLP) And Text Mining?

5. Using OpenCV

import cv2
import numpy as np
import matplotlib.pyplot as plt

def gradient(x, y, cx, cy, r):
return np.sin(np.pi * np.sqrt((x - cx)**2 + (y - cy)**2) / r)

N = 256
x = np.linspace(-1, 1, N)
y = np.linspace(-1, 1, N)
X, Y = np.meshgrid(x, y)
Z = gradient(X, Y, 0, 0, 1)

image = (Z * 255).astype(np.uint8)
mask = np.zeros_like(image)
cv2.circle(mask, (N//2, N//2), N//2, (255, 255, 255), -1)
image = cv2.bitwise_and(image, mask)

plt.imshow(image, cmap='inferno')
plt.axis('equal')
plt.axis('off')
plt.show()

Use the cv2.circle function from the OpenCV library to create a circular mask that is applied to the 2D grid representing the color gradient.

6. Using the numpy library:

import numpy as np
import matplotlib.pyplot as plt

N = 256
cx, cy = N//2, N//2
r = N//2
y, x = np.ogrid[-cy:N-cy, -cx:N-cx]
mask = x*x + y*y <= r*r

Z = np.sin(np.pi * np.sqrt(x**2 + y**2) / r)
Z = np.ma.array(Z, mask=np.logical_not(mask))

plt.imshow(Z, cmap='inferno')
plt.axis('equal')
plt.axis('off')
plt.show()

These are the ways to create a circular color gradient in Python. You can use any of these methods, or a combination of them, to generate the desired color gradient depending on your needs and preferences.