Merge pull request #194 from SciML/update

Update for removal of pins

Author: ChrisRackauckas

Project.toml

@@ -1,7 +1,7 @@
 name = "DataDrivenDiffEq"
 uuid = "2445eb08-9709-466a-b3fc-47e12bd697a2"
 authors = ["Julius Martensen <[email protected]>"]
-version = "0.5.4"
+version = "0.5.5"
 
 [deps]
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"

src/DataDrivenDiffEq.jl

@@ -10,7 +10,7 @@ using FiniteDifferences, DataInterpolations
 using Compat
 using DocStringExtensions
 
-abstract type AbstractKoopmanOperator end;
+abstract type AbstractKoopmanOperator <: Function end;
 
 include("./optimizers/Optimize.jl")
 using .Optimize
@@ -85,6 +85,6 @@ export burst_sampling, subsample
 
 include("./basis_generators.jl")
 export chebyshev_basis, monomial_basis, polynomial_basis
-export sin_basis, cos_basis, fourier_basis 
+export sin_basis, cos_basis, fourier_basis
 
 end # module

src/basis.jl

@@ -12,7 +12,7 @@ It can be called with the typical DiffEq signature, meaning out of place with `f
 or in place with `f(du, u, p, t)`.
 If `linear_independent` is set to `true`, a linear independent basis is created from all atom function in `f`.
 If `simplify_eqs` is set to `true`, `simplify` is called on `f`.
-Additional keyworded arguments include `name`, which can be used to name the basis, `pins` used for connections and
+Additional keyworded arguments include `name`, which can be used to name the basis, and
 `observed` for defining observeables.
 
 # Fields
@@ -48,7 +48,6 @@ mutable struct Basis <: ModelingToolkit.AbstractSystem
     states::Vector
     """Parameters"""
     ps::Vector
-    pins::Vector
     observed::Vector
     """Independent variable"""
     iv::Num
@@ -63,7 +62,7 @@ end
 function Basis(eqs::AbstractVector, states::AbstractVector; parameters::AbstractArray = [], iv = nothing,
 
     simplify = false, linear_independent = false, name = gensym(:Basis), eval_expression = false,
-    pins = [], observed = [],
+    observed = [],
     kwargs...)
 
     eqs = simplify ? ModelingToolkit.simplify.(eqs) : eqs
@@ -80,7 +79,7 @@ function Basis(eqs::AbstractVector, states::AbstractVector; parameters::Abstract
     f_(u,p,t) = f_oop(u,p,t)
     f_(du, u, p, t) = f_iip(du, u, p, t)
 
-    return Basis(eqs, value.(states), value.(parameters), pins, observed, value(iv), f_, name, Basis[])
+    return Basis(eqs, value.(states), value.(parameters), observed, value(iv), f_, name, Basis[])
 end
 
 function Basis(f::Function, states::AbstractVector; parameters::AbstractArray = [], iv = nothing, kwargs...)
@@ -239,9 +238,8 @@ function Base.merge(x::Basis, y::Basis; eval_expression = false)
     b =  unique(vcat([xi.rhs  for xi ∈ equations(x)], [xi.rhs  for xi ∈ equations(y)]))
     vs = unique(vcat(x.states, y.states))
     ps = unique(vcat(x.ps, y.ps))
-    pins = unique(vcat(x.pins, y.pins))
     observed = unique(vcat(x.observed, y.observed))
-    return Basis(Num.(b), vs, parameters = ps, pins = pins, observed = observed, eval_expression = eval_expression)
+    return Basis(Num.(b), vs, parameters = ps, observed = observed, eval_expression = eval_expression)
 end
 
 """

src/system_conversions.jl

@@ -1,10 +1,10 @@
 import ModelingToolkit.SymbolicUtils.FnType
 
 function _generate_deqs(x::Basis, states, iv, p)
-    @assert length(x) == length(states)  
+    @assert length(x) == length(states)
     # Create new variables with time dependency
     ∂t = Differential(iv)
-    dvs = [Num(Sym{FnType{Tuple{Any}, Real}}(_get_name(xi))(value(iv))) for xi in states] 
+    dvs = [Num(Sym{FnType{Tuple{Any}, Real}}(_get_name(xi))(value(iv))) for xi in states]
     dvsdt = ∂t.(dvs)
     # Adapt equations
     eqs = dvsdt .~ x(dvs,p,iv)
@@ -16,17 +16,16 @@ $(SIGNATURES)
 
 Convert a given `Basis` or `SparseIdentificationResult` into an `ODESystem`. For details, see ModelingToolkit.jl.
 """
-function ModelingToolkit.ODESystem(x::Basis, iv = nothing, dvs = Num[], ps = Num[]; pins = Num[], observed = Num[], systems = ODESystem[],kwargs...)
+function ModelingToolkit.ODESystem(x::Basis, iv = nothing, dvs = Num[], ps = Num[]; observed = Num[], systems = ODESystem[],kwargs...)
     iv = isnothing(iv) ? independent_variable(x) : iv
     dvs = isempty(dvs) ? variables(x) : dvs
     ps = isempty(ps) ? parameters(x) : ps
     eqs, dvs = _generate_deqs(x, dvs, iv, ps)
-    pins = isempty(pins) ? x.pins : pins
     observed = isempty(observed) ? x.observed : observed
     systems = isempty(systems) ? x.systems : systems
     return ODESystem(
         eqs, iv, dvs, ps,
-        pins = pins, observed = observed, systems = systems, kwargs...)
+        observed = observed, systems = systems, kwargs...)
 end
 
 function ModelingToolkit.ODESystem(b::SparseIdentificationResult, iv = nothing, dvs = Num[], ps = Num[]; kwargs...)
@@ -46,7 +45,7 @@ function _generate_deqs(x::Basis, states, iv, p, controls)
     states_ = _remove_controls(states, controls)
     @assert length(x) == length(states_)
     ∂t = Differential(iv)
-    dvs = [Num(Sym{FnType{Tuple{Any}, Real}}(_get_name(xi))(value(iv))) for xi in states_] 
+    dvs = [Num(Sym{FnType{Tuple{Any}, Real}}(_get_name(xi))(value(iv))) for xi in states_]
     input_states = _create_input_vec(states, dvs, controls)
     dvsdt = ∂t.(dvs)
     # Adapt equations
@@ -77,20 +76,19 @@ $(SIGNATURES)
 
 Convert a given `Basis` or `SparseIdentificationResult` into a `ControlSystem`. For details, see ModelingToolkit.jl.
 """
-function ModelingToolkit.ControlSystem(loss, x::Basis, controls, iv = nothing, dvs = nothing, ps = nothing; 
-    pins = Num[], observed = Num[], systems = ODESystem[], kwargs...)
+function ModelingToolkit.ControlSystem(loss, x::Basis, controls, iv = nothing, dvs = nothing, ps = nothing;
+    observed = Num[], systems = ODESystem[], kwargs...)
     iv = isnothing(iv) ? independent_variable(x) : iv
     dvs = isnothing(dvs) ? variables(x) : dvs
     ps = isnothing(ps) ? parameters(x) : ps
     eqs, dvs, input_states = _generate_deqs(x, dvs, iv, ps, controls)
     #return input_states
     subs = [(xi => is) for (xi, is) in zip(variables(x), input_states)]
     loss = substitute.(loss, (subs,))[1]
-    pins = isempty(pins) ? x.pins : pins
     observed = isempty(observed) ? x.observed : observed
     systems = isempty(systems) ? x.systems : systems
     return ControlSystem(loss, eqs, iv, dvs, controls, ps,
-        pins = pins, observed = observed, systems = systems, kwargs...)
+                         observed = observed, systems = systems, kwargs...)
 end
 
 function ModelingToolkit.ControlSystem(loss, b::SparseIdentificationResult, controls, iv = nothing, dvs = Num[], ps = Num[]; kwargs...)