simplified interface for declaring parameters

Author: ChrisRackauckas

README.md

@@ -28,7 +28,7 @@ and parameters. Therefore we label them as follows:
 using ModelingToolkit
 
 # Define some variables
-@parameters t() σ() ρ() β()
+@parameters t σ ρ β
 @variables x(t) y(t) z(t)
 @derivatives D'~t
 ```
@@ -80,8 +80,8 @@ state of the previous ODE. This is the nonlinear system defined by where the
 derivatives are zero. We use (unknown) variables for our nonlinear system.
 
 ```julia
-@variables x() y() z()
-@parameters σ() ρ() β()
+@variables x y z
+@parameters σ ρ β
 
 # Define a nonlinear system
 eqs = [0 ~ σ*(y-x),
@@ -166,12 +166,14 @@ including and excluding empty parentheses. When in call format, variables are
 aliased to the given call, allowing implicit use of dependents for convenience.
 
 ```julia
-@parameters t() α() σ
+@dependent_parameters t() α() σ
 @variables w x(t) y() z(t, α, x)
 
 expr = x + y^α + σ(3) * (z - t) - w(t - 1)
 ```
 
+`@parameters` is a simplified tool for defining constant parameters, meaning
+`@parameters t`  is the same as `@dependent_parameters t()`
 
 ### Constants
 
@@ -274,7 +276,7 @@ is accessible via a function-based interface. This means that all macros are
 syntactic sugar in some form. For example, the variable construction:
 
 ```julia
-@parameters t() σ ρ() β()
+@parameters t σ ρ β
 @variables x(t) y(t) z(t)
 @derivatives D'~t
 ```
@@ -297,8 +299,8 @@ D = Differential(t)
 The system building functions can handle intermediate calculations. For example,
 
 ```julia
-@variables x() y() z()
-@parameters σ() ρ() β()
+@variables x y z
+@parameters σ ρ β
 a = y - x
 eqs = [0 ~ σ*a,
        0 ~ x*(ρ-z)-y,

src/variables.jl

@@ -1,4 +1,4 @@
-export Variable, @variables, @parameters
+export Variable, @variables, @parameters, @dependent_parameters
 
 
 struct Variable <: Function
@@ -29,7 +29,7 @@ Base.convert(::Type{Expr}, c::Constant) = c.value
 
 
 # Build variables more easily
-function _parse_vars(macroname, known, x)
+function _parse_vars(macroname, known, x, add_call = true)
     ex = Expr(:block)
     var_names = Symbol[]
     # if parsing things in the form of
@@ -50,7 +50,11 @@ function _parse_vars(macroname, known, x)
             expr = :($var_name = $Variable($(Meta.quot(var_name)); known = $known)($(_var.args[2:end]...)))
         else
             var_name = _var
-            expr = :($var_name = $Variable($(Meta.quot(var_name)); known = $known))
+            if add_call
+                expr = :($var_name = $Variable($(Meta.quot(var_name)); known = $known)())
+            else
+                expr = :($var_name = $Variable($(Meta.quot(var_name)); known = $known))
+            end
         end
 
         push!(var_names, var_name)
@@ -65,3 +69,6 @@ end
 macro parameters(xs...)
     esc(_parse_vars(:parameters, true, xs))
 end
+macro dependent_parameters(xs...)
+    esc(_parse_vars(:parameters, true, xs, false))
+end

test/derivatives.jl

@@ -2,7 +2,7 @@ using ModelingToolkit
 using Test
 
 # Derivatives
-@parameters t() σ() ρ() β()
+@parameters t σ ρ β
 @variables x(t) y(t) z(t)
 @derivatives D'~t D2''~t Dx'~x
 

test/simplify.jl

@@ -1,7 +1,7 @@
 using ModelingToolkit
 using Test
 
-@parameters t()
+@parameters t
 @variables x(t) y(t) z(t)
 
 null_op = 0*t

test/system_construction.jl

@@ -2,7 +2,7 @@ using ModelingToolkit
 using Test
 
 # Define some variables
-@parameters t() σ() ρ() β()
+@parameters t σ ρ β
 @variables x(t) y(t) z(t)
 @derivatives D'~t
 
@@ -60,7 +60,7 @@ fwt(FW, u, p, 0.2, 0.1)
         du ≈ [11, -3, -7]
     end
 
-    @parameters σ
+    @dependent_parameters σ
     eqs = [D(x) ~ σ(t-1)*(y-x),
            D(y) ~ x*(ρ-z)-y,
            D(z) ~ x*y - β*z]
@@ -127,7 +127,7 @@ test_nlsys_inference("standard", ns, (x, y, z), (σ, ρ, β))
 end
 
 @derivatives D'~t
-@parameters A() B() C()
+@parameters A B C
 _x = y / C
 eqs = [D(x) ~ -A*x,
        D(y) ~ A*x - B*_x]
@@ -140,8 +140,8 @@ de = ODESystem(eqs)
 end
 
 # Now nonlinear system with only variables
-@variables x() y() z()
-@parameters σ() ρ() β()
+@variables x y z
+@parameters σ ρ β
 
 # Define a nonlinear system
 eqs = [0 ~ σ*(y-x),

test/variable_parsing.jl

@@ -1,7 +1,7 @@
 using ModelingToolkit
 using Test
 
-@parameters t()
+@parameters t
 @variables x(t) y(t) # test multi-arg
 @variables z(t) # test single-arg
 x1 = Variable(:x)(t)
@@ -12,10 +12,11 @@ z1 = Variable(:z)(t)
 @test isequal(z1, z)
 
 @parameters begin
-    t()
-    s()
-    σ
+    t
+    s
 end
+@dependent_parameters σ
+
 t1 = Variable(:t; known = true)()
 s1 = Variable(:s; known = true)()
 σ1 = Variable(:σ; known = true)