Expression Manipulation

Symbolics.jl provides functionality for easily manipulating expressions. Most of the functionality comes by the expression objects obeying the standard mathematical semantics. For example, if one has A a matrix of symbolic expressions wrapped in Num, then A^2 calculates the expressions for the squared matrix. In that sense, it is encouraged that one uses standard Julia for performing a lot of the manipulation on the IR, as, for example, calculating the sparse form of the matrix via sparse(A) is valid, legible, and easily understandable to all Julia programmers.

Functionality Inherited From SymbolicUtils.jl

SymbolicUtils.substitute — Function

substitute(expr, s)

Performs the substitution on expr according to rule(s) s.

Examples

julia> @variables t x y z(t)
4-element Vector{Num}:
    t
    x
    y
 z(t)
julia> ex = x + y + sin(z)
(x + y) + sin(z(t))
julia> substitute(ex, Dict([x => z, sin(z) => z^2]))
(z(t) + y) + (z(t) ^ 2)

source

SymbolicUtils.simplify — Function

simplify(x; expand=false,
            threaded=false,
            thread_subtree_cutoff=100,
            rewriter=nothing)

Simplify an expression (x) by applying rewriter until there are no changes. expand=true applies expand in the beginning of each fixpoint iteration.

Additional Manipulation Functions

Other additional manipulation functions are given below.

Symbolics.get_variables — Function

get_variables(O) -> Vector{Union{Sym, Term}}

Returns the variables in the expression. Note that the returned variables are not wrapped in the Num type.

Examples

julia> @variables t x y z(t)
4-element Vector{Num}:
    t
    x
    y
 z(t)

julia> ex = x + y + sin(z)
(x + y) + sin(z(t))

julia> Symbolics.get_variables(ex)
3-element Vector{Any}:
 x
 y
 z(t)

source

Symbolics.tosymbol — Function

tosymbol(x::Union{Num,Symbolic}; states=nothing, escape=true) -> Symbol

Convert x to a symbol. states are the states of a system, and escape means if the target has escapes like val"y(t)". If escape is false then it will only output y instead of y(t).

Examples

julia> @variables t z(t)
2-element Vector{Num}:
    t
 z(t)

julia> Symbolics.tosymbol(z)
Symbol("z(t)")

julia>  Symbolics.tosymbol(z; escape=false)
:z

source

Symbolics.diff2term — Function

diff2term(x::Term) -> Symbolic
diff2term(x) -> x

Convert a differential variable to a Term. Note that it only takes a Term not a Num.

julia> @variables x t u(x, t); Dt = Differential(t); Dx = Differential(x);

julia> Symbolics.diff2term(Symbolics.value(Dx(Dt(u))))
uˍtx(x, t)

source

Symbolics.solve_for — Function

solve_for(eq, var; simplify, check) -> Any

Solve equation(s) eqs for a set of variables vars.

Assumes length(eqs) == length(vars)

Currently only works if all equations are linear. check if the expr is linear w.r.t vars.

Examples

julia> @variables x y
2-element Vector{Num}:
 x
 y

julia> Symbolics.solve_for(x + y ~ 0, x)
-y

julia> Symbolics.solve_for([x + y ~ 0, x - y ~ 2], [x, y])
2-element Vector{Float64}:
  1.0
 -1.0

source