Dive into NumPy! Start organizing, rearranging, and sampling your data.

Python Data Analysis and Visualization

From Python to Numpy

## Problem Description


>*Blue noise* refers to sample sets that have random and yet uniform distributions with absence of any spectral bias. Such noise is very useful in a variety of graphics applications like rendering, dithering, stippling, etc.


Many different methods have been proposed to achieve such noise, but the most simple is certainly the **DART method**.


# Problem Description


>*Blue noise* refers to sample sets that have random and yet uniform distributions with absence of any spectral bias. Such noise is very useful in a variety of graphics applications like rendering, dithering, stippling, etc.


Many different methods have been proposed to achieve such noise, but the most simple is certainly the **DART method**.


The case study discussed in this lesson is based on a study related to the Starry Night painting called "Blue Noise Sampling". Let's learn what is it about!

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Coding Example: Blue Noise Sampling

Problem Description