Noise Based Gradient Pattern using Python

 

import numpy as np

import matplotlib.pyplot as plt

from scipy.ndimage  import gaussian_filter

np.random.seed(42)

noise=np.random.rand(100,100)

smooth_noise=gaussian_filter(noise,sigma=10)

fig,ax=plt.subplots(figsize=(6,6))

ax.imshow(smooth_noise,cmap='viridis',interpolation='bilinear')

ax.set_xticks([])

ax.set_yticks([])

ax.set_frame_on(False)

plt.title("Noise based gradient pattern")

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Import Required Libraries

import numpy as np

import matplotlib.pyplot as plt

from scipy.ndimage import gaussian_filter

numpy: Used to generate random noise.

matplotlib.pyplot: Used to display the pattern.

gaussian_filter: Applies a blur effect to smooth the noise into a gradient.

 2. Generate Random Noise

np.random.seed(42)  # For reproducibility

noise = np.random.rand(100, 100)  # 100x100 grid of random values

np.random.seed(42): Ensures that the random values are consistent each time the code runs.

np.random.rand(100, 100): Creates a 100×100 grid of random values between 0 and 1, forming a random noise pattern.

 3. Apply Gaussian Blur to Create Gradient Effect

smooth_noise = gaussian_filter(noise, sigma=10)  # Adjust sigma for smoothness

gaussian_filter(): Applies a Gaussian blur to smooth out the noise.

sigma=10: Controls the amount of blurring.

Higher values (e.g., sigma=20) → Smoother, more blended gradient.

Lower values (e.g., sigma=5) → Sharper, more detailed noise.

 4. Create the Figure and Axis

fig, ax = plt.subplots(figsize=(6, 6))

Creates a figure (fig) and an axis (ax) with a 6x6 aspect ratio for a square plot.

 5. Display the Gradient Pattern

ax.imshow(smooth_noise, cmap='viridis', interpolation='bilinear')

ax.imshow(smooth_noise): Displays the smoothed noise as an image.

cmap='viridis': Uses the "viridis" colormap for a smooth color transition.

Other options: "plasma", "magma", "gray", etc.

interpolation='bilinear': Ensures smooth transitions between pixels.

 6. Remove Axes for a Clean Look

ax.set_xticks([])

ax.set_yticks([])

ax.set_frame_on(False)

ax.set_xticks([]) and ax.set_yticks([]): Hides axis labels for a cleaner visualization.

ax.set_frame_on(False): Removes the surrounding box, making the gradient the main focus.

 7. Display the Pattern

plt.show()

plt.show(): Displays the final gradient pattern.