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EncartaLabs

NumPy

( Duration: 3 Days )

NumPy is an acronym for "Numeric Python" or "Numerical Python". It is an extension module for Python, mostly written in C. This makes sure that the precompiled mathematical and numerical functions and functionalities of Numpy guarantee great execution speed. Furthermore, NumPy enriches the programming language Python with powerful data structures, implementing multi-dimensional arrays and matrices. These data structures guarantee efficient calculations with matrices and arrays and supplies a large library of high-level mathematical functions to operate on these matrices and arrays.

This Numpy training course provides an overview of the capabilities of NumPy, including multidimensional arrays, linear algebra capabilities, and other high-level operations.

  • Intermediate level Python.

COURSE AGENDA

1

Introduction

  • What is NumPy?
  • Creation in NumPy
  • Reshaping in NumPy
  • Indexing in NumPy
  • Broadcasting in NumPy
  • NumPy Vectorization
  • Readability vs. Speed
2

Anatomy of an Array

  • Introduction
  • Memory layout
  • Views and Copies
  • Coding Example: How to find if one vector is view of the other?
  • Solution Review
3

Code Vectorization

  • Introduction
  • Uniform Vectorization
  • Coding Example: Game of life (Python approach)
  • Coding Example: Game of life (NumPy approach)
  • Coding Example: Reaction-Diffusion
  • Temporal Vectorization
  • Coding Example: The Mandelbrot Set (Python approach)
  • Coding Example: The Mandelbrot Set (NumPy approach)
  • Coding Example: Minkowski-Bouligand Dimension
  • Spatial Vectorization
  • Coding Example: Implement the behavior of Boids (Python approach)
  • Coding Example: Implement the behavior of Boids (NumPy approach)
4

Problem Vectorization

  • Introduction
  • Coding Example: Find shortest path in a maze
  • Coding Example: Find shortest path in a maze (Breadth-First approach)
  • Coding Example: Find shortest path in a maze (Bellman-Ford approach)
  • Coding Example: Fluid Dynamics
  • Coding Example: Blue Noise Sampling
  • Coding Example: Blue Noise Sampling using DART method
  • Coding Example: Blue Noise Sampling using Bridson method
5

Custom Vectorization

  • Typed list
  • Coding Example: Modifying the list
  • Memory-aware Array: Glumpy
  • Memory-aware Array: Array Subclass in GPUData class
    • 6

    Beyond NumPy

    • Back to Python
    • NumPy & co
    • Scipy & co

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    Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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