The SymPy tutorial (1.3) in Julia

Here the tutorial for SymPy 1.3 is re-expressed using Julia commands and SymPy.jl. It attempts to show the similarities and differences in using SymPy under Python and Julia.

The SymPy package for Julia allows Julia users to interact with python's SymPy module in a mostly seamless manner, thanks to the power of the PyCall package for Julia. The following pages reexpress the SymPy tutorial also illustrating the associated Julia commands. There are some changes, but mostly modest ones. To create these pages, the .rst files were downloaded and modfied. There is only hand sychronization available with new versions of the SymPy tutorial for Python.

• Introduction
• • What is Symbolic Computation?
  • A More Interesting Example
  • The Power of Symbolic Computation
  • Why SymPy?
• Gotchas
• • Symbols
  • Equals signs
  • Two Final Notes: ^ and /
  • Further Reading
• Basic Operations
• • Substitution
  • Converting Strings to SymPy Expressions
  • evalf
  • N with Julia
  • lambdify
• Simplification
• • simplify
  • Polynomial/Rational Function Simplification
  • Trigonometric Simplification
  • Powers
  • powsimp
  • Exponentials and logarithms
  • expand_log
  • logcombine
  • Special Functions
  • rewrite
  • hyperexpand
• Calculus
• • Derivatives
  • Integrals
  • Limits
  • Series Expansion
  • Finite differences
• Solvers
• • A Note about Equations
  • Solving Equations Algebraically
  • Solving Differential Equations
  • Julia alternative interface
• Matrices
• • Basic Operations
  • Basic Methods
  • Matrix Constructors
  • Advanced Methods
  • Nullspace
  • Columnspace
  • Eigenvalues, Eigenvectors, and Diagonalization
  • Possible Issues
  • Zero Testing
• Advanced Expression Manipulation
• • Understanding Expression Trees
  • Recursing through an Expression Tree
  • Prevent expression evaluation