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symbolic save idxs, new observed #392

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70e387d
symbolic save idxs, new observed
xtalax Feb 15, 2023
875658b
more solutions
xtalax Feb 17, 2023
4e1611e
handle case
xtalax Feb 21, 2023
659a3e4
passing
xtalax Feb 21, 2023
c432843
passing
xtalax Feb 27, 2023
406dcd4
Merge branch 'SciML:master' into ordinarydiff
xtalax Feb 27, 2023
82df76c
fix nonlinear
xtalax Feb 27, 2023
bb17abc
why this fail
xtalax Mar 9, 2023
1d20e97
odae passing!!
xtalax Mar 10, 2023
92d307a
ode passing
xtalax Mar 13, 2023
59dd393
add comment
xtalax Mar 21, 2023
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2 changes: 1 addition & 1 deletion src/SciMLBase.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -811,7 +811,7 @@ export step!, deleteat!, addat!, get_tmp_cache,
set_reltol!, get_du, get_du!, get_dt, get_proposed_dt, set_proposed_dt!,
u_modified!, savevalues!, reinit!, auto_dt_reset!, set_t!,
set_u!, check_error, change_t_via_interpolation!, addsteps!,
isdiscrete, reeval_internals_due_to_modification!
isdiscrete, reeval_internals_due_to_modification!, is_dense_output

export ContinuousCallback, DiscreteCallback, CallbackSet, VectorContinuousCallback

Expand Down
171 changes: 98 additions & 73 deletions src/solutions/dae_solutions.jl
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43 changes: 31 additions & 12 deletions src/solutions/nonlinear_solutions.jl
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Expand Up @@ -13,12 +13,14 @@ or the steady state solution to a differential equation defined by a SteadyState
- `original`: if the solver is wrapped from an alternative solver ecosystem, such as
NLsolve.jl, then this is the original return from said solver library.
- `retcode`: the return code from the solver. Used to determine whether the solver solved
successfully or whether it exited due to an error. For more details, see
successfully or whether it exited due to an error. For more details, see
[the return code documentation](https://docs.sciml.ai/SciMLBase/stable/interfaces/Solutions/#retcodes).
- `left`: if the solver is bracketing method, this is the final left bracket value.
- `right`: if the solver is bracketing method, this is the final right bracket value.
- `sym_map`: Map of symbols to their index in the solution
"""
struct NonlinearSolution{T, N, uType, R, P, A, O, uType2} <: AbstractNonlinearSolution{T, N}
struct NonlinearSolution{T, N, uType, R, P, A, O, uType2, MType, DI} <:
AbstractNonlinearSolution{T, N}
u::uType
resid::R
prob::P
Expand All @@ -27,6 +29,8 @@ struct NonlinearSolution{T, N, uType, R, P, A, O, uType2} <: AbstractNonlinearSo
original::O
left::uType2
right::uType2
sym_map::MType
dep_idxs::DI
end

function Base.show(io::IO,
Expand All @@ -49,17 +53,25 @@ function build_solution(prob::AbstractNonlinearProblem,
original = nothing,
left = nothing,
right = nothing,
sym_map = nothing,
dep_idxs = Ref{Vector{Union{Int, Nothing}}}(Union{Int, Nothing}[nothing]),
kwargs...)
T = eltype(eltype(u))
N = ndims(u)

if isnothing(sym_map)
sym_map = default_sym_map(prob)
end

NonlinearSolution{T, N, typeof(u), typeof(resid),
typeof(prob), typeof(alg), typeof(original), typeof(left)}(u, resid,
prob, alg,
retcode,
original,
left,
right)
typeof(prob), typeof(alg), typeof(original), typeof(left),
typeof(sym_map), typeof(dep_idxs)}(u, resid,
prob, alg,
retcode,
original, left,
right,
sym_map,
dep_idxs)
end

function sensitivity_solution(sol::AbstractNonlinearSolution, u)
Expand All @@ -68,8 +80,15 @@ function sensitivity_solution(sol::AbstractNonlinearSolution, u)

NonlinearSolution{T, N, typeof(u), typeof(sol.resid),
typeof(sol.prob), typeof(sol.alg),
typeof(sol.original), typeof(sol.left)}(u, sol.resid, sol.prob,
sol.alg, sol.retcode,
sol.original, sol.left,
sol.right)
typeof(sol.original), typeof(sol.left), typeof(sol.sym_map),
typeof(dep_idxs)}(u,
sol.resid,
sol.prob,
sol.alg,
sol.retcode,
sol.original,
sol.left,
sol.right,
sol.sym_map,
sol.dep_idxs)
end
56 changes: 42 additions & 14 deletions src/solutions/ode_solutions.jl
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Expand Up @@ -21,13 +21,14 @@ https://docs.sciml.ai/DiffEqDocs/stable/basics/solution/
- `alg`: the algorithm type used by the solver.
- `destats`: statistics of the solver, such as the number of function evaluations required,
number of Jacobians computed, and more.
- `sym_map`: Map of symbols to their index in the solution
- `retcode`: the return code from the solver. Used to determine whether the solver solved
successfully, whether it terminated early due to a user-defined callback, or whether it
exited due to an error. For more details, see
successfully, whether it terminated early due to a user-defined callback, or whether it
exited due to an error. For more details, see
[the return code documentation](https://docs.sciml.ai/SciMLBase/stable/interfaces/Solutions/#retcodes).
"""
struct ODESolution{T, N, uType, uType2, DType, tType, rateType, P, A, IType, DE,
AC <: Union{Nothing, Vector{Int}}} <:
AC <: Union{Nothing, Vector{Int}}, MType, DI} <:
AbstractODESolution{T, N, uType}
u::uType
u_analytic::uType2
Expand All @@ -39,17 +40,31 @@ struct ODESolution{T, N, uType, uType2, DType, tType, rateType, P, A, IType, DE,
interp::IType
dense::Bool
tslocation::Int
sym_map::MType
destats::DE
alg_choice::AC
dep_idxs::DI
retcode::ReturnCode.T
end
function ODESolution{T, N}(u, u_analytic, errors, t, k, prob, alg, interp, dense,
tslocation, destats, alg_choice, retcode) where {T, N}
tslocation, destats, alg_choice, retcode,
sym_map = nothing,
dep_idxs = nothing) where {T, N}
if isnothing(dep_idxs)
dep_idxs = Ref{Vector{Union{Int, Nothing}}}(Union{Int, Nothing}[nothing])
end
if isnothing(sym_map)
sym_map = default_sym_map(prob)
end
return ODESolution{T, N, typeof(u), typeof(u_analytic), typeof(errors), typeof(t),
typeof(k), typeof(prob), typeof(alg), typeof(interp),
typeof(destats),
typeof(alg_choice)}(u, u_analytic, errors, t, k, prob, alg, interp,
dense, tslocation, destats, alg_choice, retcode)
typeof(alg_choice),
typeof(sym_map), typeof(dep_idxs)}(u, u_analytic, errors, t, k, prob,
alg, interp,
dense, tslocation, sym_map,
destats, alg_choice, dep_idxs,
retcode)
end

function (sol::AbstractODESolution)(t, ::Type{deriv} = Val{0}; idxs = nothing,
Expand Down Expand Up @@ -161,9 +176,12 @@ function build_solution(prob::Union{AbstractODEProblem, AbstractDDEProblem},
k = nothing,
alg_choice = nothing,
interp = LinearInterpolation(t, u),
sym_map = nothing, dep_idxs = nothing,
retcode = ReturnCode.Default, destats = nothing, kwargs...)
T = eltype(eltype(u))

if isnothing(sym_map)
sym_map = default_sym_map(prob)
end
if prob.u0 === nothing
N = 2
else
Expand All @@ -190,7 +208,9 @@ function build_solution(prob::Union{AbstractODEProblem, AbstractDDEProblem},
0,
destats,
alg_choice,
retcode)
retcode,
sym_map,
dep_idxs)
if calculate_error
calculate_solution_errors!(sol; timeseries_errors = timeseries_errors,
dense_errors = dense_errors)
Expand All @@ -208,7 +228,7 @@ function build_solution(prob::Union{AbstractODEProblem, AbstractDDEProblem},
0,
destats,
alg_choice,
retcode)
retcode, sym_map, dep_idxs)
end
end

Expand Down Expand Up @@ -264,7 +284,9 @@ function build_solution(sol::ODESolution{T, N}, u_analytic, errors) where {T, N}
sol.tslocation,
sol.destats,
sol.alg_choice,
sol.retcode)
sol.retcode,
sol.sym_map,
sol.dep_idxs)
end

function solution_new_retcode(sol::ODESolution{T, N}, retcode) where {T, N}
Expand All @@ -280,7 +302,9 @@ function solution_new_retcode(sol::ODESolution{T, N}, retcode) where {T, N}
sol.tslocation,
sol.destats,
sol.alg_choice,
retcode)
retcode,
sol.sym_map,
sol.dep_idxs)
end

function solution_new_tslocation(sol::ODESolution{T, N}, tslocation) where {T, N}
Expand All @@ -296,7 +320,9 @@ function solution_new_tslocation(sol::ODESolution{T, N}, tslocation) where {T, N
tslocation,
sol.destats,
sol.alg_choice,
sol.retcode)
sol.retcode,
sol.sym_map,
sol.dep_idxs)
end

function solution_slice(sol::ODESolution{T, N}, I) where {T, N}
Expand All @@ -312,7 +338,9 @@ function solution_slice(sol::ODESolution{T, N}, I) where {T, N}
sol.tslocation,
sol.destats,
sol.alg_choice,
sol.retcode)
sol.retcode,
sol.sym_map,
sol.dep_idxs)
end

function sensitivity_solution(sol::ODESolution, u, t)
Expand All @@ -330,5 +358,5 @@ function sensitivity_solution(sol::ODESolution, u, t)
nothing, sol.prob,
sol.alg, interp,
sol.dense, sol.tslocation,
sol.destats, sol.alg_choice, sol.retcode)
sol.destats, sol.alg_choice, sol.retcode, sol.sym_map, sol.dep_idxs)
end
29 changes: 19 additions & 10 deletions src/solutions/optimization_solutions.jl
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Expand Up @@ -15,8 +15,10 @@ Representation of the solution to a non-linear optimization defined by an Optimi
- `original`: if the solver is wrapped from an alternative solver ecosystem, such as
Optim.jl, then this is the original return from said solver library.
- `solve_time`: Solve time from the solver in Seconds
- `sym_map`: Map of symbols to their index in the solution
"""
struct OptimizationSolution{T, N, uType, C <: AbstractOptimizationCache, A, OV, O, S} <:
struct OptimizationSolution{T, N, uType, C <: AbstractOptimizationCache, A, OV, O, S, MType
} <:
AbstractOptimizationSolution{T, N}
u::uType # minimizer
cache::C # optimization cache
Expand All @@ -25,13 +27,15 @@ struct OptimizationSolution{T, N, uType, C <: AbstractOptimizationCache, A, OV,
retcode::ReturnCode.T
original::O # original output of the optimizer
solve_time::S # [s] solve time from the solver
sym_map::MType
end

function build_solution(cache::AbstractOptimizationCache,
alg, u, objective;
retcode = ReturnCode.Default,
original = nothing,
solve_time = nothing,
sym_map = nothing,
kwargs...)
T = eltype(eltype(u))
N = ndims(u)
Expand All @@ -40,12 +44,13 @@ function build_solution(cache::AbstractOptimizationCache,
retcode = symbol_to_ReturnCode(retcode)

OptimizationSolution{T, N, typeof(u), typeof(cache), typeof(alg),
typeof(objective), typeof(original), typeof(solve_time)}(u, cache,
alg,
objective,
retcode,
original,
solve_time)
typeof(objective), typeof(original), typeof(solve_time),
typeof(sym_map)}(u, cache,
alg,
objective,
retcode,
original,
solve_time, sym_map)
end

function Base.show(io::IO,
Expand Down Expand Up @@ -75,6 +80,7 @@ function build_solution(prob::AbstractOptimizationProblem,
alg, u, objective;
retcode = ReturnCode.Default,
original = nothing,
sym_map = default_sym_map(prob),
kwargs...)
T = eltype(eltype(u))
N = ndims(u)
Expand All @@ -89,9 +95,12 @@ function build_solution(prob::AbstractOptimizationProblem,
retcode = symbol_to_ReturnCode(retcode)

OptimizationSolution{T, N, typeof(u), typeof(cache), typeof(alg),
typeof(objective), typeof(original)}(u, cache, alg, objective,
retcode,
original)
typeof(objective), typeof(original), typeof(sym_map)}(u, cache,
alg,
objective,
retcode,
original,
sym_map)
end

get_p(sol::OptimizationSolution) = sol.cache.p
Expand Down