Julia language: a concise tutorial
  • Introduction
  • Language core
    • 1 - Getting started
    • 2 - Data types
    • 3 - Control flow
    • 4 - Functions
    • 5 - Custom structures
    • 6 - Input - Output
    • 7 - Managing run-time errors (exceptions)
    • 8 - Interfacing Julia with other languages
    • 9 - Metaprogramming
    • 10 - Performance (parallelisation, debugging, profiling..)
    • 11 - Developing Julia packages
  • Useful packages
    • Plotting
    • DataFrames
    • JuMP
    • SymPy
    • Weave
    • LAJuliaUtils
    • IndexedTables
    • Pipe
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  • SymPy
  • Other Mathematical packages
  1. Useful packages

SymPy

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Last updated 4 years ago

SymPy

is a wrapper to the Python SymPy library for symbolic computation: solve equations (or system of equations), simplify them, find derivates or integrals...

An overview of its capabilities can be found on the following notebook:

Some additional notes to that notebook:

  • You can plot a function that includes symbols, e.g.: plot(2x,0,1) plots y=2x in the [0,1] range

  • For the infinity symbol use either oo or Inf (eventually with + or -)

Other Mathematical packages

  • Numerical integration of definite integrals (univariate): (: quadgk(x->2x,0,2))

While an updated, expanded and revised version of this chapter is available in "Chapter 10 - Mathematical Libraries" of , this tutorial remains in active development.

SymPy
http://nbviewer.jupyter.org/github/sylvaticus/juliatutorial/blob/master/assets/Symbolic computation.ipynb
QuadGK Package
Antonello Lobianco (2019), "Julia Quick Syntax Reference", Apress