text:
# License for this file: MIT (expat)
# Copyright 2020-2021, DLR Institute of System Dynamics and Control

using  OrderedCollections: OrderedDict, OrderedSet
using  DataFrames

#=
fieldnames(typeof(integrator)) = (:sol, :u, :du, :k, :t, :dt, :f, :p, :uprev, :uprev2, :duprev, :tprev, :alg,
                                  :dtcache, :dtchangeable, :dtpropose, :tdir, :eigen_est, :EEst, :qold, :q11,
                                  :erracc, :dtacc, :success_iter, :iter, :saveiter, :saveiter_dense, :cache,
                                  :callback_cache, :kshortsize, :force_stepfail, :last_stepfail, :just_hit_tstop,
                                  :do_error_check, :event_last_time, :vector_event_last_time, :last_event_error,
                                  :accept_step, :isout, :reeval_fsal, :u_modified, :reinitialize, :isdae, :opts,
                                  :destats, :initializealg, :fsalfirst, :fsallast)
=#

"""
    baseType(T)

Return the base type of a type T, according to the following definition.

```julia
baseType(::Type{T})                                           where {T}     = T
baseType(::Type{Unitful.Quantity{T,D,U}})                     where {T,D,U} = T
baseType(::Type{Measurements.Measurement{T}})                 where {T}     = T
baseType(::Type{MonteCarloMeasurements.Particles{T,N}})       where {T,N}   = T
baseType(::Type{MonteCarloMeasurements.StaticParticles{T,N}}) where {T,N}   = T
```

# Examples
```
baseType(Float32)                # Float32
baseType(Measurement{Float64})   # Float64
```
"""
baseType(::Type{T})                                           where {T}     = T
baseType(::Type{Unitful.Quantity{T,D,U}})                     where {T,D,U} = T
baseType(::Type{Measurements.Measurement{T}})                 where {T}     = T
baseType(::Type{MonteCarloMeasurements.Particles{T,N}})       where {T,N}   = T
baseType(::Type{MonteCarloMeasurements.StaticParticles{T,N}}) where {T,N}   = T

isQuantity(              ::Type{T}) where {T} = T <: Unitful.Quantity         || T <: MonteCarloMeasurements.AbstractParticles && baseType(T) <: Unitful.Quantity
isMeasurements(          ::Type{T}) where {T} = T <: Measurements.Measurement || T <: Unitful.Quantity && baseType(T) <: Measurements.Measurement
isMonteCarloMeasurements(::Type{T}) where {T} = T <: MonteCarloMeasurements.AbstractParticles

Base.floatmax(::Type{Unitful.Quantity{T,D,U}})                     where {T,D,U} = Base.floatmax(T)
Base.floatmax(::Type{Measurements.Measurement{T}})                 where {T}     = Base.floatmax(T)
Base.floatmax(::Type{MonteCarloMeasurements.Particles{T,N}})       where {T,N}   = Base.floatmax(T)
Base.floatmax(::Type{MonteCarloMeasurements.StaticParticles{T,N}}) where {T,N}   = Base.floatmax(T)

"""
    str = measurementToString(v)

Return variable `v::Measurements.Measurement{FloatType}` or a vector of such variables
in form of a string will the full number of significant digits.
"""
measurementToString(v::Measurements.Measurement{FloatType}) where {FloatType} =
       string(Measurements.value(v)) * " ± " * string(Measurements.uncertainty(v))

function measurementToString(v::Vector{Measurements.Measurement{FloatType}}) where {FloatType}
    str = string(typeof(v[1])) * "["
    for (i,vi) in enumerate(v)
        if i > 1
            str = str * ", "
        end
        str = str * measurementToString(vi)
    end
    str = str * "]"
    return str
end

#= No longer needed
function get_x_start!(FloatType, equationInfo, parameters)
    x_start = []
    startIndex = 1
    for xe_info in equationInfo.x_info
        xe_info.startIndex = startIndex

        # Determine init/start value in m.parameters
        xe_value = get_value(parameters, xe_info.x_name)
        if ismissing(xe_value)
            error("Missing start/init value ", xe_info.x_name)
        end
        if hasParticles(xe_value)
            len = 1
            push!(x_start, xe_value)
        else
            len = length(xe_value)
            push!(x_start, xe_value...)
        end
        if len != xe_info.length
            printstyled("Model error: ", bold=true, color=:red)
            printstyled("Length of ", xe_info.x_name, " shall be changed from ",
                        xe_info.length, " to $len\n",
                        "This is currently not support in Modia.", bold=true, color=:red)
            return false
        end
        startIndex += xe_info.length
    end
    equationInfo.nx = startIndex - 1

    # Temporarily remove units from x_start
    # (TODO: should first transform to the var_unit units and then remove)
    converted_x_start = convert(Vector{FloatType}, [stripUnit(v) for v in x_start])  # stripUnit.(x_start) does not work for MonteCarloMeasurements
    x_start2 = deepcopy(converted_x_start)
    return x_start2
end
=#

const BasicSimulationKeywordArguments = OrderedSet{Symbol}(
        [:merge, :tolerance, :startTime, :stopTime, :interval, :interp_points, :dtmax, :adaptive, :nlinearMinForDAE,
         :log, :logStates, :logEvents, :logProgress, :logTiming, :logParameters, :logEvaluatedParameters,
         :requiredFinalStates, :requiredFinalStates_rtol, :requiredFinalStates_atol, :useRecursiveFactorizationUptoSize])
const RegisteredExtraSimulateKeywordArguments = OrderedSet{Symbol}()

function registerExtraSimulateKeywordArguments(keys)::Nothing
    for key in keys
        if key in BasicSimulationKeywordArguments
            @error "registerExtraSimulateKeywordArguments:\nKeyword $key is ignored, since standard keyword of simulate!(..) (use another keyword)\n\n"
        end
        push!(RegisteredExtraSimulateKeywordArguments, key)
    end
    return nothing
end

"""
    convertTimeVariable(TimeType, t)

If `t` has a unit, it is transformed to u"s", the unit is stripped off,
converted to `TimeType` and returned. Otherwise `t` is converted to `TimeType` and returned.

# Example
```julia
convertTimeVariable(Float32, 2.0u"hr")  # = 7200.0f0
```
"""
convertTimeVariable(TimeType, t) = typeof(t) <: Unitful.AbstractQuantity ? convert(TimeType, stripUnit(t)) : convert(TimeType, t)

struct SimulationOptions{FloatType,TimeType}
    merge::OrderedDict{Symbol,Any}
    tolerance::Float64
    startTime::TimeType   # u"s"
    stopTime::TimeType    # u"s"
    interval::TimeType    # u"s"
    desiredResultTimeUnit
    interp_points::Int
    dtmax::Float64
    adaptive::Bool
    nlinearMinForDAE::Int
    log::Bool
    logStates::Bool
    logEvents::Bool
    logProgress::Bool
    logTiming::Bool
    logParameters::Bool
    logEvaluatedParameters::Bool
    requiredFinalStates::Union{Missing, Vector{FloatType}}
    requiredFinalStates_rtol::Float64
    requiredFinalStates_atol::Float64
    useRecursiveFactorizationUptoSize::Int
    extra_kwargs::OrderedDict{Symbol,Any}

    function SimulationOptions{FloatType,TimeType}(merge, errorMessagePrefix=""; _dummy=false, kwargs...) where {FloatType,TimeType}
        success   = true
        adaptive  = get(kwargs, :adaptive, true)
        tolerance = _dummy ? Inf : get(kwargs, :tolerance, 1e-6)
        if tolerance <= 0.0
            printstyled(errorMessagePrefix, "tolerance (= $(tolerance)) must be > 0\n\n", bold=true, color=:red)
            success = false
        elseif tolerance < 100*eps(FloatType) && adaptive
            newTolerance = max(tolerance, 100*eps(FloatType))
            printstyled("Warning from Modia.simulate!(..):\n"*
                        "tolerance (= $(tolerance)) is too small for FloatType = $FloatType (eps(FloatType) = $(eps(FloatType))).\n" *
                        "tolerance changed to $newTolerance.\n\n", bold=true, color=:red)
            tolerance = newTolerance
        end
        startTime   = convertTimeVariable(TimeType, get(kwargs, :startTime, 0.0) )
        rawStopTime = get(kwargs, :stopTime, startTime)
        stopTime    = convertTimeVariable(TimeType, rawStopTime)
        interval    = convertTimeVariable(TimeType, get(kwargs, :interval , (stopTime - startTime)/500.0) )
        dtmax       = convert(Float64, get(kwargs, :dtmax, 100*getValue(interval)))
        desiredResultTimeUnit = unit(rawStopTime)
        interp_points = get(kwargs, :interp_points, 0)
        if interp_points < 0
            printstyled(errorMessagePrefix, "interp_points (= $(interp_points)) must be > 0\n\n", bold=true, color=:red)
            success = false
        elseif interp_points == 1
            # DifferentialEquations.jl crashes
            interp_points = 2
        end
        nlinearMinForDAE = max(1, get(kwargs, :nlinearMinForDAE, 10))   # >= 10
        adaptive         = get(kwargs, :adaptive     , true)
        log              = get(kwargs, :log          , false)
        logStates        = get(kwargs, :logStates    , false)
        logEvents        = get(kwargs, :logEvents    , false)
        logProgress      = get(kwargs, :logProgress  , false)
        logTiming        = get(kwargs, :logTiming    , false)
        logParameters    = get(kwargs, :logParameters, false)
        logEvaluatedParameters   = get(kwargs, :logEvaluatedParameters  , false)
        requiredFinalStates      = get(kwargs, :requiredFinalStates     , missing)
        if isnothing(requiredFinalStates)
            requiredFinalStates = missing
        end
        requiredFinalStates_rtol = get(kwargs, :requiredFinalStates_rtol, 1e-3)
        requiredFinalStates_atol = get(kwargs, :requiredFinalStates_atol, 0.0)
        useRecursiveFactorizationUptoSize = get(kwargs, :useRecursiveFactorizationUptoSize, 0)
        extra_kwargs = OrderedDict{Symbol,Any}()
        for option in kwargs
            key = option.first
            if key in BasicSimulationKeywordArguments
                nothing;
            elseif key in RegisteredExtraSimulateKeywordArguments
                extra_kwargs[key] = option.second
            else
                printstyled(errorMessagePrefix, "simulate!(..., $key=$(option.second)): $key is unknown.\n\n", bold=true, color=:red)
                success = false
            end
        end

#        obj = new(isnothing(merge) ? NamedTuple() : merge, tolerance, startTime, stopTime, interval, desiredResultTimeUnit, interp_points,
        obj = new(ismissing(merge) || isnothing(merge) ? OrderedDict{Symbol,Any}() : merge, tolerance, startTime, stopTime, interval, desiredResultTimeUnit, interp_points,
                  dtmax, adaptive, nlinearMinForDAE, log, logStates, logEvents, logProgress, logTiming, logParameters, logEvaluatedParameters,
                  requiredFinalStates, requiredFinalStates_rtol, requiredFinalStates_atol, useRecursiveFactorizationUptoSize, extra_kwargs)
        return success ? obj : nothing
    end

    SimulationOptions{FloatType,TimeType}() where {FloatType,TimeType} =
        SimulationOptions{FloatType,TimeType}(OrderedDict{Symbol,Any}(); _dummy=true)
end

"""
    @enum ResultStore RESULT_VARS RESULT_X RESULT_DER_X RESULT_ZERO

SimulationModel field name where result is stored:

- RESULT_VARS : stored in `result_vars`
- RESULT_X    : stored in `result_x` (return argument of DifferentialEquations.solve(...))
- RESULT_DER_X: stored in `result_der_x`
- RESULT_ZERO : value is zero
"""
@enum ResultStore RESULT_VARS RESULT_X RESULT_DER_X RESULT_ZERO

struct ResultInfo
    store::ResultStore  # Location where variable is stored
    index::Int          # Index, depending on store-type
                        #   store = RESULT_VARS : result_vars[ti][index]
                        #         = RESULT_X    : result_x[ti][ibeg:iend]
                        #                           ibeg = equationInfo.x_info[index].startIndex
                        #                           iend = ibeg + equationInfo.x_info[index].length-1
                        #         = RESULT_DER_X: result_der_x[ti][ibeg:iend]
                        #                           ibeg = equationInfo.x_info[index].startIndex
                        #                           iend = ibeg + equationInfo.x_info[index].length-1
                        #         = RESULT_ZERO : not stored (value is zero; index = 0)
    negate::Bool        # = true, if result must be negated

    ResultInfo(store, index=0, negate=false) = new(store, index, negate)
end

struct LinearEquationsCopyInfoForDAEMode
    ileq::Int           # Index of LinearEquations()
    index::Vector{Int}  # Copy: leq.x[i]                = dae_der_x[index[i]]
                        #       dae_residuals[index[i]] = leq.residuals[i]

    LinearEquationsCopyInfoForDAEMode(ileq) = new(ileq, Int[])
end

"""
    simulationModel = SimulationModel{FloatType,TimeType}(
            modelModule, modelName, getDerivatives!, equationInfo, x_startValues,
            parameters, variableNames;
            vSolvedWithInitValuesAndUnit::OrderedDict{String,Any}(),
            vEliminated::Vector{Int}=Int[],
            vProperty::Vector{Int}=Int[],
            var_name::Function = v->nothing)

# Arguments

- `modelModule`: Module in which `@instantiateModel` is invoked (it is used for `Core.eval(modelModule, ...)`),
  that is evaluation of expressions in the environment of the user.
- `modelName::String`: Name of the model
- `getDerivatives::Function`: Function that is used to evaluate the model equations,
  typically generated with [`Modia.generate_getDerivatives!`].
- `equationInfo::Modia.EquationInfo`: Information about the states and the equations.
- `x_startValues`:: Deprecated (is no longer used).
- `parameters`: A hierarchical NamedTuple of (key, value) pairs defining the parameter and init/start values.
- variableNames: A vector of variable names. A name can be a Symbol or a String.
"""
mutable struct SimulationModel{FloatType,TimeType}
    modelModule::Module
    modelName::String
    buildDict::OrderedDict{String,Any}
    timer::TimerOutputs.TimerOutput
    cpuFirst::UInt64                  # cpu time of start of simulation
    cpuLast::UInt64                   # Last time from time_ns()
    options::SimulationOptions{FloatType,TimeType}
    getDerivatives!::Function
    equationInfo::Modia.EquationInfo
    linearEquations::Vector{Modia.LinearEquations{FloatType}}
    eventHandler::EventHandler{FloatType,TimeType}
    vSolvedWithInitValuesAndUnit::OrderedDict{String,Any}   # Dictionary of (names, init values with units) for all explicitly solved variables with init-values defined

    parameters::OrderedDict{Symbol,Any}
    evaluatedParameters::OrderedDict{Symbol,Any}
    previous::AbstractVector                # previous[i] is the value of previous(...., i)
    nextPrevious::AbstractVector            # nextPrevious[i] is the current value of the variable identified by previous(...., i)
    previous_names::Vector{String}          # previous_names[i] is the name of previous-variable i
    previous_dict::OrderedDict{String,Int}  # previous_dict[name] is the index of previous-variable name

    pre::AbstractVector
    nextPre::AbstractVector
    pre_names::Vector{String}
    pre_dict::OrderedDict{String,Int}

    hold::AbstractVector
    nextHold::AbstractVector
    hold_names::Vector{String}
    hold_dict::OrderedDict{String,Int}

    isInitial::Bool
    solve_leq::Bool                         # = true, if linear equations 0 = A*x-b shall be solved
                                            # = false, if leq.x is provided by DAE solver and leq.residuals is used by the DAE solver.
    odeMode::Bool                           # = false: copy der(x) into linear equation systems that have leq.odeMode=false and do not solve these equation systems
    storeResult::Bool
    storeInit::Bool
    time::TimeType
    nGetDerivatives::Int                        # Number of getDerivatives! calls
    nf::Int                                     # Number of getDerivatives! calls from integrator (without zero-crossing calls)
    x_vec::Vector{Vector{FloatType}}            # x_vec[i] holds the actual values of state vector element equationInfo.x_info[equationInfo.nxFixedLength+i]
    x_start::Vector{FloatType}                  # States x before first event iteration (before initialization)
    x_init::Vector{FloatType}                   # States x after initialization (and before integrator is started)
    der_x::Vector{FloatType}                    # Derivatives of states x or x_init
    odeIntegrator::Bool                         # = true , if ODE integrator used
                                                # = false, if DAE integrator used

    daeCopyInfo::Vector{LinearEquationsCopyInfoForDAEMode}  # Info to efficiently copy between DAE and linear equation systems
    algorithmName::Union{String,Missing}        # Name of integration algorithm as string (used in default-heading of plot)
    addEventPointsDueToDEBug::Bool              # = true, if event points are explicitly stored for Sundials integrators, due to bug in DifferentialEquations
                                                #         (https://github.com/SciML/Sundials.jl/issues/309)
    result_info::OrderedDict{String,ResultInfo} # key  : Full path name of result variables
                                                # value: Storage location and index into the storage.
    result_vars::AbstractVector                 # result_vars[ti][j] is result of variable with index j at time instant ti
    init_vars::AbstractVector                 #
    result_x::Union{Any,Missing}                # Return value of DifferentialEquations.solve(..) (is a struct)
    result_der_x::Vector{Vector{FloatType}}     # result_der_x[ti][j] is der_x[j] at time instant ti
    success::Bool                               # = true, if after first outputs!(..) call and no error was triggered
                                                # = false, either before first outputs!(..) call or at first outputs!(..) after init!(..) and
                                                #          an error was triggered and simulate!(..) should be returned with nothing.
    unitless::Bool                              # = true, if simulation is performed without units.

    function SimulationModel{FloatType,TimeType}(modelModule, modelName, buildDict, getDerivatives!, equationInfo, x_startValues,
                                        previousVars, preVars, holdVars,
                                        parameterDefinition, variableNames;
                                        unitless=true,
                                        nz::Int = 0,
                                        nAfter::Int = 0,
                                        vSolvedWithInitValuesAndUnit::AbstractDict = OrderedDict{String,Any}(),
                                        vEliminated::Vector{Int} = Int[],
                                        vProperty::Vector{Int}   = Int[],
                                        var_name::Function       = v -> nothing) where {FloatType,TimeType}
        # Construct result dictionary
        result_info = OrderedDict{String, ResultInfo}()
        vSolvedWithInitValuesAndUnit2 = OrderedDict{String,Any}( [(string(key),vSolvedWithInitValuesAndUnit[key]) for key in keys(vSolvedWithInitValuesAndUnit)] )
            # Store x and der_x
            for (xe_index, xe_info) in enumerate(equationInfo.x_info)
                result_info[xe_info.x_name]     = ResultInfo(RESULT_X    , xe_index)
                result_info[xe_info.der_x_name] = ResultInfo(RESULT_DER_X, xe_index)
            end

            # Store variables
            for (i, name) in enumerate(variableNames)
                str_name = string(name)
                if !haskey(result_info, str_name)
                    result_info[str_name] = ResultInfo(RESULT_VARS, i)
                end
            end

            # Store eliminated variables
            for v in vEliminated
                name = var_name(v)
                if ModiaBase.isZero(vProperty, v)
                    result_info[name] = ResultInfo(RESULT_ZERO)
                elseif ModiaBase.isAlias(vProperty, v)
                    name2 = var_name( ModiaBase.alias(vProperty, v) )
                    result_info[name] = result_info[name2]
                else # negated alias
                    name2 = var_name( ModiaBase.negAlias(vProperty, v) )
                    info2 = result_info[name2]
                    result_info[name] = ResultInfo(info2.store, info2.index, true)
                end
            end

            # Build previous-arrays
            previous       = Vector{Any}(missing, length(previousVars))
            previous_names = string.(previousVars)
            previous_dict  = OrderedDict{String,Int}(zip(previous_names, 1:length(previousVars)))

            # Build pre-arrays
            pre       = Vector{Any}(missing, length(preVars))
            pre_names = string.(preVars)
            pre_dict  = OrderedDict{String,Int}(zip(pre_names, 1:length(preVars)))

            # Build hold-arrays
            hold       = Vector{Any}(missing, length(holdVars))
            hold_names = string.(holdVars)
            hold_dict  = OrderedDict{String,Int}(zip(hold_names, 1:length(holdVars)))

        # Construct parameter values that are copied into the code
        #parameterValues = [eval(p) for p in values(parameters)]
        #@show typeof(parameterValues)
        #@show parameterValues
        parameters = deepcopy(parameterDefinition)

        # Determine x_start and previous values
        evaluatedParameters = propagateEvaluateAndInstantiate!(FloatType, unitless, modelModule, parameters, equationInfo, previous_dict, previous, pre_dict, pre, hold_dict, hold)
        if isnothing(evaluatedParameters)
            return nothing
        end
        x_start      = initialStateVector(equationInfo, FloatType)
        nx           = length(x_start)
        nextPrevious = deepcopy(previous)
        nextPre      = deepcopy(pre)
        nextHold     = deepcopy(hold)

        # Provide storage for x_vec
        x_vec = [zeros(FloatType, equationInfo.x_info[i].length) for i in equationInfo.nxFixedLength+1:length(equationInfo.x_info)]

        # Construct data structure for linear equations
        linearEquations = Modia.LinearEquations{FloatType}[]
        for leq in equationInfo.linearEquations
            push!(linearEquations, Modia.LinearEquations{FloatType}(leq...))
        end

        # Initialize execution flags
        eventHandler = EventHandler{FloatType,TimeType}(nz=nz, nAfter=nAfter)
        eventHandler.initial = true
        isInitial   = true
        storeResult = false
        storeInit = false
        solve_leq   = true
        nGetDerivatives = 0
        nf = 0

        new(modelModule, modelName, buildDict, TimerOutputs.TimerOutput(), UInt64(0), UInt64(0), SimulationOptions{FloatType,TimeType}(), getDerivatives!,
            equationInfo, linearEquations, eventHandler,
            vSolvedWithInitValuesAndUnit2, parameters, evaluatedParameters,
            previous, nextPrevious, previous_names, previous_dict,
            pre, nextPre, pre_names, pre_dict,
            hold, nextHold, hold_names, hold_dict,
            isInitial, solve_leq, true, storeResult, storeInit, convert(TimeType, 0), nGetDerivatives, nf,
            x_vec, x_start, zeros(FloatType,nx), zeros(FloatType,nx), true, LinearEquationsCopyInfoForDAEMode[],
            missing, false, result_info, Tuple[], Tuple[], missing, Vector{FloatType}[], false, unitless)
    end

    function SimulationModel{FloatType,TimeType}(m::SimulationModel) where {FloatType,TimeType}
        # Construct data structure for linear equations
        linearEquations = Modia.LinearEquations{FloatType}[]
        for leq in m.equationInfo.linearEquations
            push!(linearEquations, Modia.LinearEquations{FloatType}(leq...))
        end

        # Initialize execution flags
        eventHandler = EventHandler{FloatType,TimeType}()
        eventHandler.initial = true
        isInitial   = true
        storeResult = false
        storeInit = false
        solve_leq   = true
        nGetDerivatives = 0
        nf = 0
        nx = m.equationInfo.nx

         new(m.modelModule, m.modelName, deepcopy(m.buildDict), TimerOutputs.TimerOutput(), UInt64(0), UInt64(0), m.options, m.getDerivatives!, m.equationInfo, linearEquations,
            eventHandler,
            m.vSolvedWithInitValuesAndUnit, deepcopy(m.parameters), deepcopy(m.evaluatedParameters),
            deepcopy(m.previous), deepcopy(m.nextPrevious), m.previous_names, m.previous_dict,
            deepcopy(m.pre), deepcopy(m.nextPre), m.pre_names, m.pre_dict,
            deepcopy(m.hold), deepcopy(m.nextHold), m.hold_names, m.hold_dict,
            isInitial, solve_leq, true, storeResult, convert(TimeType, 0), nGetDerivatives, nf, deepcopy(m.x_vec),
            convert(Vector{FloatType}, m.x_start), zeros(FloatType,nx), zeros(FloatType,nx), true, LinearEquationsCopyInfoForDAEMode[],
            missing, false, m.result_info, Tuple[], missing, Vector{FloatType}[], false, m.unitless)
    end
end

# Default constructors
SimulationModel{FloatType}(args...; kwargs...) where {FloatType} = SimulationModel{FloatType,FloatType}(args...; kwargs...)

SimulationModel{Measurements.Measurement{T},}(args...; kwargs...) where {T} = SimulationModel{Measurements.Measurement{T},T}(args...; kwargs...)
SimulationModel{MonteCarloMeasurements.Particles{T,N}}(args...; kwargs...) where {T,N,} = SimulationModel{MonteCarloMeasurements.Particles{T,N},T}(args...; kwargs...)
SimulationModel{MonteCarloMeasurements.StaticParticles{T,N}}(args...; kwargs...) where {T,N} = SimulationModel{MonteCarloMeasurements.StaticParticles{T,N},T}(args...; kwargs...)

timeType(m::SimulationModel{FloatType,TimeType}) where {FloatType,TimeType} = TimeType

positive(m::SimulationModel, args...; kwargs...) = Modia.positive!(m.eventHandler, args...; kwargs...)
negative(m::SimulationModel, args...; kwargs...) = Modia.negative!(m.eventHandler, args...; kwargs...)
change(  m::SimulationModel, args...; kwargs...) = Modia.change!(  m.eventHandler, args...; kwargs...)
edge(    m::SimulationModel, args...; kwargs...) = Modia.edge!(    m.eventHandler, args...; kwargs...)
after(   m::SimulationModel, args...; kwargs...) = Modia.after!(   m.eventHandler, args...; kwargs...)
pre(     m::SimulationModel, i)                  = m.pre[i]

function emptyResult!(m::SimulationModel)::Nothing
    empty!(m.result_vars)
    empty!(m.result_der_x)
    m.result_x = missing
    return nothing
end

function get_xinfo(m::SimulationModel, x_index::Int)::Tuple{Int, Int, String}
    xinfo = m.equationInfo.x_info[x_index]
    ibeg  = xinfo.startIndex
    iend  = ibeg + xinfo.length-1
    return (ibeg, iend, xinfo.unit)
end

"""
    v_zero = reinit(instantiatedModel, _x, j, v_new, _leqMode; v_threshold=0.01)

Re-initialize state j with v_new, that is set x[i] = v_new, with i = instantiatedModel.equationInfo.x_info[j].startIndex.
If v_new <= v_threshold, set x[i] to the floating point number that is closest to zero, so that x[i] > 0 and return
v_zero = true. Otherwise return v_zero = false.

An error is triggered if the function is not called during an event phase
or if leqMode >= 0 (which means that reinit is called inside the for-loop
to solve a linear equation system - this is not yet supported).

# Implementation notes

At the beginning of getDerivatives!(..), assignments of _x to appropriate variables are performed.
Therefore, setting _x[i] afterwards via reinit, has no immediate effect on this model evaluation.
With reinit, instantiatedModel.eventHandler.newEventIteration = true is set, to force a new
event iteration. At the next event iteration, the new value v_new is copied from _x and therefore
has then an effect.
"""
function reinit(m::SimulationModel, x, j, v_new, leqMode; v_threshold=0.01)
    if leqMode >= 0
        error("reinit(..) of model ", m.modelName, " is called when solving a linear equation system (this is not supported)")
    elseif !isEvent(m)
        error("reinit(..) of model ", m.modelName, " is called outside of an event phase (this is not allowed)")
    end

    eh = m.eventHandler
    eh.restart = max(eh.restart, Restart)
    eh.newEventIteration = true

    i = m.equationInfo.x_info[j].startIndex
    if v_new <= v_threshold
        x[i] = nextfloat(convert(typeof(v_new), 0))
        if eh.logEvents
            println("        State ", m.equationInfo.x_info[j].x_name, " reinitialized to ", x[i], " (reinit returns true)")
        end
        return true
    else
        x[i] = v_new
        if eh.logEvents
            println("        State ", m.equationInfo.x_info[j].x_name, " reinitialized to ", v_new, " (reinit returns false)")
        end
        return false
    end
end

"""
    floatType = getFloatType(simulationModel::SimulationModel)

Return the floating point type with which `simulationModel` is parameterized
(for example returns: `Float64, Float32, DoubleFloat, Measurements.Measurement{Float64}`).
"""
getFloatType(m::SimulationModel{FloatType,TimeType}) where {FloatType,TimeType} = FloatType

"""
    hasParticles(value)

Return true, if `value` is of type `MonteCarloMeasurements.StaticParticles` or
`MonteCarloMeasurements.Particles`.
"""
hasParticles(value) = typeof(value) <: MonteCarloMeasurements.StaticParticles ||
                      typeof(value) <: MonteCarloMeasurements.Particles

"""
    get_value(obj, name::String)

Return the value identified by `name` from the potentially hierarchically
dictionary `obj`. If `name` is not in `obj`, the function returns `missing`.

# Examples
```julia
s1 = Map(a = 1, b = 2, c = 3)
s2 = Map(v1 = s1, v2 = (d = 4, e = 5))
s3 = Map(v3 = s2, v4 = s1)

@show get_value(s3, "v3.v1.b")   # returns 2
@show get_value(s3, "v3.v2.e")   # returns 5
@show get_value(s3, "v3.v1.e")   # returns missing
```
"""
function get_value(obj::OrderedDict, name::String)
    if length(name) == 0 || length(obj) == 0
        return missing
    end
    j = findnext('.', name, 1)
    if isnothing(j)
        key = Symbol(name)
        return haskey(obj,key) ? obj[key] : missing
    elseif j == 1
        return missing
    else
        key = Symbol(name[1:j-1])
        if haskey(obj,key) && typeof(obj[key]) <: OrderedDict && length(name) > j
            get_value(obj[key], name[j+1:end])
        else
            return missing
        end
    end
end

"""
    appendName(path::String, name::Symbol)

Return `path` appended with `.` and `string(name)`.
"""
appendName(path, key) = path == "" ? string(key) : path * "." * string(key)

"""
    get_names(obj)

Return `Vector{String}` containing all the names present in `obj`

# Examples
```julia
s1 = Map(a = 1, b = 2, c = 3)
s2 = Map(v1 = s1, v2 = (d = 4, e = 5))
s3 = Map(v3 = s2, v4 = s1)

@show get_names(s3)
```
"""
function get_names(obj) # ::NamedTuple)
    names = String[]
    get_names!(obj, names, "")
    return names
end
function get_names!(obj #= ::NamedTuple =# , names::Vector{String}, path::String)::Nothing
    for (key,value) in obj # zip(keys(obj), obj)
        if key != :_class
            name = appendName(path, key)
            if typeof(value) <: OrderedDict
                get_names!(value, names, name)
            else
                push!(names, name)
            end
        end
    end
    return nothing
end

"""
    get_lastValue(model::SimulationModel, name::String; unit=true)

Return the last stored value of variable `name` from `model`.
If `unit=true` return the value with its unit, otherwise with stripped unit.

If `name` is not known or no result values yet available, an info message is printed
and the function returns `nothing`.
"""
function get_lastValue(m::SimulationModel{FloatType,TimeType}, name::String; unit::Bool=true) where {FloatType,TimeType}
    if haskey(m.result_info, name)
        # Time varying variable stored in m.result_xxx
        resInfo = m.result_info[name]
        if ismissing(m.result_x) || length(m.result_x.t) == 0
            @info "get_lastValue(model,\"$name\"): No results yet available."
            return nothing
        end

        if resInfo.store == RESULT_VARS
            value = m.result_vars[end][resInfo.index]
            if !unit
                value = stripUnit(value)
            end

        elseif resInfo.store == RESULT_X
            (ibeg,iend,xunit) = get_xinfo(m, resInfo.index)
            if ibeg==iend
                value = m.result_x[end][ibeg]
            else
                value = m.result_x[end][ibeg:iend]
            end
            if unit && !m.unitless && xunit != ""
                value = value*uparse(xunit)
            end

        elseif resInfo.store == RESULT_DER_X
            (ibeg,iend,xunit) = get_x_indices(m, resInfo.index)
            if ibeg==iend
                value = m.result_der_x[end][ibeg]
            else
                value = m.result_der_x[end][ibeg:iend]
            end
            if unit && !m.unitless
                if xunit == ""
                    value = value/u"s"
                else
                    value = value*(uparse(xunit)/u"s")
                end
            end

        elseif resInfo.store == RESULT_ZERO
            # Type, size and unit is not known (needs to be fixed)
            value = convert(FloatType, 0)

        else
            error("Bug in get_lastValue(...), name = $name, resInfo.store = $resInfo.store.")
        end

        if resInfo.negate
            value = -value
        end

    else
        # Parameter stored in m.evaluatedParameters
        value = get_value(m.evaluatedParameters, name)
        if ismissing(value)
            @info "get_lastValue: $name is not known and is ignored."
            return nothing;
        end
    end

    return value
end

"""
    get_extraSimulateKeywordArgumentsDict(instantiateModel)

Return the dictionary, in which extra keyword arguments of the last
`simulate!(instantiatedModel,...)` call are stored.
"""
get_extraSimulateKeywordArgumentsDict(m::SimulationModel) = m.options.extra_kwargs

"""
    terminateEventIteration!(instantiatedModel)

Return true, if event iteration shall be terminated and simulation started.

Return false, if event iteration shall be continued. Reasons for continuing are:

1. pre != nextPre

2. positive(..), negative(..), change(..), edge(..) trigger a new iteration.

3. triggerEventAfterInitial = true

Note

- isInitial:
  When simulation shall be started, isInitial = true. When neither (1) nor (2) hold, isInitial = false
  and isInitial remains false for the rest of the simulation.

- firstInitialOfAllSegments:
  When simulation shall be started, firstInitialOfAllSegments = true. After the first iteration,
  this flag is set to false. This flag can be used to initialize devices (e.g. renderer), that should be
  initialized once and not several times.

- firstEventIteration:
  This flag is true during the first iteration at an event.

- firstEventIterationDirectlyAfterInitial:
  This flag is true during the first iteration directly at the time event after initialization
  (at simulation startTime).
"""
function terminateEventIteration!(m::SimulationModel)::Bool
    h = m.eventHandler
    h.firstInitialOfAllSegments = false
    h.firstEventIteration       = false
    h.firstEventIterationDirectlyAfterInitial = false

    # pre-iteration
    pre_iteration = false
    for i = 1:length(m.pre)
        if m.pre[i] != m.nextPre[i]
            if m.options.logEvents
                println("        ", m.pre_names[i], " changed from ", m.pre[i], " to ", m.nextPre[i])
            end
            m.pre[i] = m.nextPre[i]
            pre_iteration = true
        end
    end

    if pre_iteration || h.newEventIteration
        h.newEventIteration = false
        return false   # continue event iteration
    end

    if h.restart == Terminate || h.restart == FullRestart
        h.initial = false
        return true

    elseif h.triggerEventDirectlyAfterInitial
        h.triggerEventDirectlyAfterInitial = false
        if h.initial
            h.firstEventIterationDirectlyAfterInitial = true
        end
        h.initial = false
        return false # continue event iteration
    end

    h.initial = false
    return true
end

function eventIteration!(m::SimulationModel, x, t_event)::Nothing
    eh = m.eventHandler

    # Initialize event iteration
    initEventIteration!(eh, t_event)

    # Perform event iteration
	iter_max = 20
    iter     = 0
	success  = false
    eh.event = true
    while !success && iter <= iter_max
        iter += 1
        #Base.invokelatest(m.getDerivatives!, m.der_x, x, m, t_event)
        invokelatest_getDerivatives_without_der_x!(x, m, t_event)
        eh.firstInitialOfAllSegments = false
        success = terminateEventIteration!(m)

        if eh.firstEventIterationDirectlyAfterInitial
            iter = 0  # reset iteration counter, since new event
            if m.options.logEvents
                println("\n      Time event at time = ", eh.time, " s")
            end
            eh.nTimeEvents += 1
            eh.nextEventTime = m.options.stopTime
        end
    end
    eh.event = false

    if !success
        error("Maximum number of event iterations (= $iter_max) reached")
    end
    return nothing
end

"""
    get_xNames(instantiatedModel)

Return the names of the elements of the x-vector in a Vector{String}.
"""
get_xNames(m::SimulationModel) = Modia.get_xNames(m.equationInfo)

"""
    isInitial(instantiatedModel)

Return true, if **initialization phase** of simulation.
"""
isInitial(m::SimulationModel) = m.eventHandler.initial
initial(  m::SimulationModel) = m.eventHandler.initial

"""
    isTerminal(instantiatedModel)

Return true, if **terminal phase** of simulation.
"""
isTerminal(m::SimulationModel) = m.eventHandler.terminal
terminal(  m::SimulationModel) = m.eventHandler.terminal

"""
    isEvent(instantiatedModel)

Return true, if **event phase** of simulation (including initialization).
"""
isEvent(m::SimulationModel) = m.eventHandler.event

"""
    isFirstEventIteration(instantiatedModel)

Return true, if **event phase** of simulation (including initialization) and
during the first iteration of the event iteration.
"""
isFirstEventIteration(m::SimulationModel) = m.eventHandler.firstEventIteration

"""
    isFirstEventIterationDirectlyAfterInitial(instantiatedModel)

Return true, if first iteration directly after initialization where initial=true
(so at the startTime of the simulation).
"""
isFirstEventIterationDirectlyAfterInitial(m::SimulationModel) = m.eventHandler.firstEventIterationDirectlyAfterInitial

"""
    isAfterSimulationStart(instantiatedModel)

Return true, if **after start of simulation** (returns false during initialization).
"""
isAfterSimulationStart(m::SimulationModel) = m.eventHandler.afterSimulationStart

"""
    isZeroCrossing(instantiatedModel)

Return true, if **event indicators (zero crossings) shall be computed**.
"""
isZeroCrossing(m::SimulationModel) = m.eventHandler.crossing

"""
    storeResults(instantiatedModel)

Return true, if **results shall be stored**.
"""
storeResults(m::SimulationModel) = m.storeResult

isFirstInitialOfAllSegments(m::SimulationModel) = m.eventHandler.firstInitialOfAllSegments
isTerminalOfAllSegments(m::SimulationModel)     = m.eventHandler.isTerminalOfAllSegments

"""
    setNextEvent!(instantiatedModel, nextEventTime)

At an event instant, set the next time event to `nextEventTime`.
"""
setNextEvent!(m::SimulationModel{FloatType,TimeType}, nextEventTime) where {FloatType,TimeType} =
        setNextEvent!(m.eventHandler, convert(TimeType,nextEventTime))

"""
    tCurrent = getTime(instantiatedModel)

Return current simulation time.
"""
getTime(m::SimulationModel) = m.time

"""
    zStartIndex = addZeroCrossings(instantiatedModel, nz)

Add nz new zero crossing functions and return the start index with respect to
instantiatedModel.eventHandler.z.
"""
function addZeroCrossings(m::SimulationModel, nz::Int)::Int
    eh = m.eventHandler
    zStartIndex = eh.nz + 1
    eh.nz += nz
    resize!(eh.z, eh.nz)
    resize!(eh.zPositive, eh.nz)
    return zStartIndex
end

get_xe(x, xe_info) = xe_info.length == 1 ? x[xe_info.startIndex] : x[xe_info.startIndex:xe_info.startIndex + xe_info.length-1]

#function set_xe!(x, xe_info, value)::Nothing
#    if xe_info.length == 1
#        x[xe_info.startIndex] = value
#    else
#        x[xe_info.startIndex:xe_info.startIndex + xe_info.length-1] = value
#    end
#    return nothing
#end
import Printf

invokelatest_getDerivatives_without_der_x!(x, m, t) = TimerOutputs.@timeit m.timer "Modia getDerivatives!" begin
    if m.options.logProgress && m.cpuLast != UInt64(0)
        cpuNew = time_ns()
        if (cpuNew - m.cpuLast) * 1e-9 > 5.0
            m.cpuLast = cpuNew
            Printf.@printf("      progress: integrated up to time = %.3g s (in cpu-time = %.3g s)\n", t, (cpuNew-m.cpuFirst)*1e-9)
        end
    end
    if length(m.x_vec) > 0
        # copy vector-valued x-elements from x to m.x_vec
        eqInfo = m.equationInfo
        x_vec  = m.x_vec
        j      = 0
        for i in eqInfo.nxFixedLength+1:length(eqInfo.x_info)
            j += 1
            xe = eqInfo.x_info[i]
            x_vec[j] .= x[xe.startIndex:(xe.startIndex+xe.length-1)]
        end
    end
    empty!(m.der_x)
    Base.invokelatest(m.getDerivatives!, x, m, t)

    @assert(length(m.der_x) == m.equationInfo.nx)
end

invokelatest_getDerivatives!(der_x, x, m, t) = begin
    invokelatest_getDerivatives_without_der_x!(x, m, t)
    der_x .= m.der_x
end

"""
    success = init!(simulationModel)

Initialize `simulationModel::SimulationModel` at `startTime`. In particular:

- Empty result data structure.

- Merge parameter and init/start values into simulationModel.

- Construct x_start.

- Call simulationModel.getDerivatives! once with isInitial(simulationModel) = true to
  compute and store all variables in the result data structure at `startTime`
  and initialize simulationModel.linearEquations.

- Check whether explicitly solved variables that have init-values defined,
  have the required value after initialization (-> otherwise error).

If initialization is successful return true, otherwise false.
"""
function init!(m::SimulationModel{FloatType,TimeType})::Bool where {FloatType,TimeType}
    emptyResult!(m)
    eh = m.eventHandler
    reinitEventHandler(eh, m.options.stopTime, m.options.logEvents)
    eh.firstInitialOfAllSegments = true

	# Apply updates from merge Map and propagate/instantiate/evaluate the resulting evaluatedParameters
    if length(m.options.merge) > 0
        m.parameters = mergeModels(m.parameters, m.options.merge)
        m.evaluatedParameters = propagateEvaluateAndInstantiate!(FloatType, m.unitless, m.modelModule, m.parameters, m.equationInfo, m.previous_dict, m.previous, m.pre_dict, m.pre, m.hold_dict, m.hold)
        if isnothing(m.evaluatedParameters)
            return false
        end

        # Resize linear equation systems if dimensions of vector valued tearing variables changed
        resizeLinearEquations!(m, m.options.log)

        # Resize state vector memory
        m.x_start = updateEquationInfo!(m.equationInfo, FloatType)
        nx = length(m.x_start)
        resize!(m.x_init, nx)
        resize!(m.der_x, nx)
        eqInfo = m.equationInfo
        m.x_vec = [zeros(FloatType, eqInfo.x_info[i].length) for i in eqInfo.nxFixedLength+1:length(eqInfo.x_info)]
    end

    # Initialize auxiliary arrays for event iteration
    m.x_init .= 0
    m.der_x  .= 0

    # Log parameters
    if m.options.logParameters
        parameters = m.parameters
        @showModel parameters
    end
    if m.options.logEvaluatedParameters
        evaluatedParameters = m.evaluatedParameters
        @showModel evaluatedParameters
    end

    if m.options.logStates
        # List init/start values
        x_table = DataFrames.DataFrame(state=String[], init=Any[], unit=String[], nominal=Float64[])
        for xe_info in m.equationInfo.x_info
            xe_init = get_xe(m.x_start, xe_info)
            if hasParticles(xe_init)
                xe_init = string(minimum(xe_init)) * " .. " * string(maximum(xe_init))
            end
            push!(x_table, (xe_info.x_name, xe_init, xe_info.unit, xe_info.nominal))
        end
        show(stdout, x_table; allrows=true, allcols=true, rowlabel = Symbol("#"), summary=false, eltypes=false)
        println("\n")
    end

    # Initialize model, linearEquations and compute and store all variables at the initial time
    if m.options.log
        println("      Initialization at time = ", m.options.startTime, " s")
    end

    # Perform initial event iteration
    m.nGetDerivatives = 0
    m.nf = 0
    m.isInitial   = true
    eh.initial    = true
#    m.storeResult = true
#    m.getDerivatives!(m.der_x, m.x_start, m, startTime)
#    Base.invokelatest(m.getDerivatives!, m.der_x, m.x_start, m, startTime)
    for i in eachindex(m.x_init)
        m.x_init[i] = deepcopy(m.x_start[i])
    end
    eventIteration!(m, m.x_init, m.options.startTime)
    m.success     = false   # is set to true at the first outputs! call.
    eh.initial    = false
    m.isInitial   = false
    m.storeResult = false
    eh.afterSimulationStart = true
    return true
end

"""
    success = check_vSolvedWithInitValuesAndUnit(m::SimulationModel)

Check that m.vSolvedWithInitValuesAndUnit is consistent to calculated values.
If this is not the case, print an error message and return false.
"""
function check_vSolvedWithInitValuesAndUnit(m::SimulationModel)::Bool
    # Check vSolvedWithInitValuesAndUnit
    if length(m.vSolvedWithInitValuesAndUnit) > 0
        names = String[]
        valuesBeforeInit = Any[]
        valuesAfterInit  = Any[]
        tolerance = m.options.tolerance

        for (name, valueBefore) in m.vSolvedWithInitValuesAndUnit
            valueAfterInit  = get_lastValue(m, name, unit=false)
            valueBeforeInit = stripUnit.(valueBefore)
            if !isnothing(valueAfterInit) && abs(valueBeforeInit - valueAfterInit) >= max(abs(valueBeforeInit),abs(valueAfterInit),0.01*tolerance)*tolerance
                push!(names, name)
                push!(valuesBeforeInit, valueBeforeInit)
                push!(valuesAfterInit , valueAfterInit)
            end
        end

        if length(names) > 0
            v_table = DataFrames.DataFrame(name=names, beforeInit=valuesBeforeInit, afterInit=valuesAfterInit)
            #show(stderr, v_table; allrows=true, allcols=true, summary=false, eltypes=false)
            #print("\n\n")
            ioTemp = IOBuffer();
            show(ioTemp, v_table; allrows=true, allcols=true, rowlabel = Symbol("#"), summary=false, eltypes=false)
            str = String(take!(ioTemp))
            close(ioTemp)
            printstyled("Model error: ", bold=true, color=:red)
            printstyled("The following variables are explicitly solved for, have init-values defined\n",
                        "and after initialization the init-values are not respected\n",
                        "(remove the init-values in the model or change them to start-values):\n",
                        str, bold=true, color=:red)
            println("\n")
            return false
        end
    end
    return true
end

"""
    terminate!(m::SimulationModel, x, time)

Terminate model.
"""
function terminate!(m::SimulationModel, x, t)::Nothing
    #println("... terminate! called at time = $t")
    eh = m.eventHandler
    eh.terminal = true
    invokelatest_getDerivatives_without_der_x!(x, m, t)
    eh.terminal = false
    return nothing
end

approxTime(t1,t2) = eps(typeof(t1))

"""
    outputs!(x, t, integrator)

DifferentialEquations FunctionCallingCallback function for `SimulationModel`
that is used to store results at communication points.
"""
function outputs!(x, t, integrator)::Nothing
    m = integrator.p
    m.storeResult = true
    #println("... Store result at time = $t")
    if m.odeMode
        invokelatest_getDerivatives_without_der_x!(x, m, t)
    else
        if t==m.options.startTime
            m.der_x .= integrator.du   # Since IDA gives an error for integrator(t, Val{1]}) at the initial time instant
        else
            integrator(m.der_x, t, Val{1})  # Compute derx
        end

        # Copy derx to linearEquations
        for copyInfo in m.daeCopyInfo
            leq = m.linearEquations[ copyInfo.ileq ]
            for i in 1:length(copyInfo.index)
                leq.x[i] = m.der_x[ copyInfo.index[i] ]
            end
        end
        m.solve_leq = false
        invokelatest_getDerivatives_without_der_x!(x, m, t)
        m.solve_leq = true
    end
    m.storeResult = false
    if !m.success
        m.result_x = integrator.sol
        m.success = check_vSolvedWithInitValuesAndUnit(m)
        if !m.success
            DifferentialEquations.terminate!(integrator)
        end
    elseif m.eventHandler.restart == Terminate
        DifferentialEquations.terminate!(integrator)
    end
    return nothing
end

#=
"""
    affect_outputs!(integrator)

DifferentialEquations PresetTimeCallback function for `SimulationModel`
that is used to store results at communication points.
"""
function affect_outputs!(integrator)::Nothing
    m = integrator.p
    m.storeResult = true
    m.solve_leq = false
    getDerivatives!2(m.der_x, integrator.u, m, integrator.t)
    m.solve_leq = true
    m.storeResult = false
    if m.eventHandler.restart == Terminate
        DifferentialEquations.terminate!(integrator)
    end
    return nothing
end
=#

"""
    derivatives!(derx, x, m, t)

DifferentialEquations callback function to get the derivatives.
"""
derivatives!(der_x, x, m, t) = begin
                                    m.nf += 1
                                    invokelatest_getDerivatives!(der_x, x, m, t)
                               end

"""
    DAEresidualsForODE!(residuals, derx, x, m, t)

DifferentialEquations callback function for DAE integrator for ODE model
"""
function DAEresidualsForODE!(residuals, derx, x, m, t)::Nothing
    m.nf += 1

    # Copy derx to linearEquations
    for copyInfo in m.daeCopyInfo
        leq = m.linearEquations[ copyInfo.ileq ]
        for i in 1:length(copyInfo.index)
            leq.x[i] = derx[ copyInfo.index[i] ]
        end
    end

    m.solve_leq = false
    invokelatest_getDerivatives_without_der_x!(x, m, t)
    m.solve_leq = true
    residuals .= m.der_x .- derx

    # Get residuals from linearEquations
    for copyInfo in m.daeCopyInfo
        leq = m.linearEquations[ copyInfo.ileq ]
        for i in 1:length(copyInfo.index)
            residuals[  copyInfo.index[i] ] = leq.residuals[i]
        end
    end
    return nothing
end

"""
    affectEvent!(integrator, stateEvent, eventIndex)

Called when a time event (stateEvent=false) or state event (stateEvent=true) is triggered.
In case of stateEvent, eventIndex is the index of the crossing function that triggered the event.
"""
function affectEvent!(integrator, stateEvent::Bool, eventIndex::Int)::Nothing
    m  = integrator.p
    eh = m.eventHandler
    time = integrator.t
    #println("... begin affect: time = ", time, ", nextEventTime = ", eh.nextEventTime)

    if m.addEventPointsDueToDEBug
        push!(integrator.sol.t, deepcopy(integrator.t))
        push!(integrator.sol.u, deepcopy(integrator.u))
    end

    # Compute and store outputs before processing the event
    sol_t = integrator.sol.t
    sol_x = integrator.sol.u
    ilast = length(sol_t)
    for i = length(m.result_vars)+1:ilast    # -1
        outputs!(sol_x[i], sol_t[i], integrator)
    end
    # A better solution is needed.
    #if sol_t[ilast] == sol_t[ilast-1]
    #    # Continuous time instant is present twice because "saveat" and "DiscreteCallback" occur at the same time instant
    #    # Do not compute the model again
    #    push!(m.result_vars , m.result_vars[end])
    #    push!(m.result_der_x, m.result_der_x[end])
    #else
    #    outputs!(sol_x[ilast], sol_t[ilast], integrator)
    #end

    if stateEvent
        # State event
        if eh.logEvents
            println("\n      State event (zero-crossing) at time = ", time, " s (due to z[$eventIndex])")
        end
        eh.nStateEvents += 1
    else
        # Time event
        if eh.logEvents
            println("\n      Time event at time = ", time, " s")
        end
        eh.nTimeEvents += 1
    end

    # Event iteration
    eventIteration!(m, integrator.u, time)

    if eh.restart == Restart || eh.restart == FullRestart
        eh.nRestartEvents += 1
    end
    if eh.logEvents
        if eh.restart == Restart
            println("        restart = ", eh.restart)
        else
            printstyled("        restart = ", eh.restart, "\n", color=:red)
        end
    end

    # Compute outputs and store them after the event occurred
    if m.addEventPointsDueToDEBug
        push!(integrator.sol.t, deepcopy(integrator.t))
        push!(integrator.sol.u, deepcopy(integrator.u))
    end

    outputs!(integrator.u, time, integrator)
    if eh.restart == Terminate
        DifferentialEquations.terminate!(integrator)
        return nothing
    end

    # Adapt step size
    if eh.restart != NoRestart && supertype(typeof(integrator.alg)) == DifferentialEquations.OrdinaryDiffEq.OrdinaryDiffEqAdaptiveAlgorithm
        DifferentialEquations.auto_dt_reset!(integrator)
        DifferentialEquations.set_proposed_dt!(integrator, integrator.dt)
    end

    # Set next time event
    if abs(eh.nextEventTime - m.options.stopTime) < 1e-10
        eh.nextEventTime = m.options.stopTime
    end
    #println("... end affect: time ", time,", nextEventTime = ", eh.nextEventTime)
    if eh.nextEventTime <= m.options.stopTime
        DifferentialEquations.add_tstop!(integrator, eh.nextEventTime)
    end

    return nothing
end

"""
    zeroCrossings!(z, x, t, integrator)

Called by integrator to compute zero crossings
"""
function zeroCrossings!(z, x, t, integrator)::Nothing
    m = integrator.p
    eh = m.eventHandler
    eh.nZeroCrossings += 1
    eh.crossing = true
    m.solve_leq = false # has only an effect for DAE integrators
    invokelatest_getDerivatives_without_der_x!(x, m, t)
    m.solve_leq = true
    eh.crossing = false
    for i = 1:eh.nz
        z[i] = eh.z[i]
    end
    #println("... t = $t, z = $z")
    #println("... time = ", t, ", z = ", z)
    return nothing
end

"""
    affectStateEvent!(integrator, event_index)

Called by integrator when a state event is triggered
"""
affectStateEvent!(integrator, event_index) = affectEvent!(integrator, true, event_index)

"""
    timeEventCondition!(u, t, integrator)

Called by integrator to check if a time event occurred
"""
function timeEventCondition!(u, t, integrator)::Bool
    m  = integrator.p
    eh = m.eventHandler
    if t >= eh.nextEventTime
        eh.nextEventTime = m.options.stopTime
        return true
    end
    return false
end

"""
    affectTimeEvent!(integrator)

Called by integrator when a time event is triggered
"""
affectTimeEvent!(integrator) = affectEvent!(integrator, false, 0)

"""
    leq = initLinearEquationsIteration!(m::SimulationModel, leq_index::Int)

Initialize iteration over linear equations system of index `leq_index` and return a reference to it
that can be used in the while-loop to construct and solve the linear equation system.
"""
initLinearEquationsIteration!(m::SimulationModel, leq_index::Int) = begin
    leq      = m.linearEquations[leq_index]
    leq.mode = -3
    return leq
end

"""
    resizeLinearEquations!(instantiatedModel)

Inspect all linear equations inside the instantiatedModel and resize the
internal storage, of the length of vector-valued elements of the iteration variables
has changed due to changed parameter values.
"""
function resizeLinearEquations!(m::SimulationModel{FloatType}, log::Bool)::Nothing where {FloatType}
    for (i,leq) in enumerate(m.linearEquations)
        nx_vec = length(leq.x_names) - leq.nx_fixedLength
        if nx_vec > 0
            j = 0
            for i = leq.nx_fixedLength+1:length(leq.x_names)
                # Length  of element is determined by start or init value
                xi_name  = leq.x_names[i]
                xi_param = get_value(m.evaluatedParameters, xi_name)
                if ismissing(xi_param)
                    @error "resizeLinearEquations!(instantiatedModel,$i): $xi_name is not part of the evaluated parameters."
                end
                leq.x_lengths[i] = length(xi_param)
                j += 1
                if length(leq.x_vec[j]) != leq.x_lengths[i]
                    if log
                        println("      Modia: Resize linear equations vector $xi_name from ", length(leq.x_vec[j]), " to ", leq.x_lengths[i] )
                    end
                    resize!(leq.x_vec[j], leq.x_lengths[i])
                end
            end
            nx = sum(leq.x_lengths)
            if length(leq.x) != nx
                # Resize internal arrays
                resize!(leq.x        , nx)
                resize!(leq.b        , nx)
                resize!(leq.pivots   , nx)
                resize!(leq.residuals, nx)
                leq.A = zeros(FloatType,nx,nx)
                leq.useRecursiveFactorization = nx <= leq.useRecursiveFactorizationUptoSize
            end
        end
    end

    return nothing
end

"""
    addToResult!(simulationModel, der_x, variableValues...)

Add `variableValues...` to `simulationModel::SimulationModel`.
It is assumed that the first variable in `variableValues` is `time`.
"""
function addToResult!(m::SimulationModel, variableValues...)::Nothing
    push!(m.result_vars , variableValues)
    push!(m.result_der_x, deepcopy(m.der_x))
    return nothing
end

function addToInit!(m::SimulationModel, variableValues...)::Nothing
    push!(m.init_vars, variableValues)
    return nothing
end

"""
    code = generate_getDerivatives!(AST, equationInfo, parameters, variables, functionName;
                                    hasUnits=false)

Return the code of the `getDerivatives!` function as `Expr` using the
Symbol `functionName` as function name. By `eval(code)` or
`fc = @RuntimeGeneratedFunction(code)` the function is compiled and can afterwards be called.

# Arguments

- `AST::Vector{Expr}`: Abstract Syntax Tree of the equations as vector of `Expr`.

- `equationInfo::Modia.EquationInfo`: Data structure returned by `Modia.getSortedAndSolvedAST
            holding information about the states.

- `parameters`: Vector of parameter names (as vector of symbols)

- `variables`: Vector of variable names (as vector of symbols). The first entry is expected to be time, so `variables[1] = :time`.

- `functionName::Function`: The name of the function that shall be generated.

# Optional Arguments

- pre:Vector{Symbol}: pre-variable names

- `hasUnits::Bool`: = true, if variables have units. Note, the units of the state vector are defined in equationinfo.
"""
function generate_getDerivatives!(FloatType, TimeType, AST::Vector{Expr}, equationInfo::Modia.EquationInfo,
                                  parameters, variables, previousVars, preVars, holdVars, functionName::Symbol;
                                  pre::Vector{Symbol} = Symbol[], hasUnits=false)

    # Generate code to copy x to struct and struct to der_x
    x_info     = equationInfo.x_info
    code_x     = Expr[]
    code_der_x = Expr[]
    #code_p     = Expr[]

    if length(x_info) == 1 && x_info[1].x_name == "_dummy_x" && x_info[1].der_x_name == "der(_dummy_x)"
        # Explicitly solved pure algebraic variables. Introduce dummy equation
        push!(code_der_x, :( Modia.appendVariable!(_m.der_x, -_x[1]) ))
    else
        i1 = 1
        for i in 1:equationInfo.nxFixedLength
            xe = x_info[i]
            x_name = xe.x_name_julia
            # x_name     = Meta.parse("m."*xe.x_name)
            # der_x_name = Meta.parse("m."*replace(xe.der_x_name, r"der\(.*\)" => s"var\"\g<0>\""))
            if xe.scalar
                # x-element is a scalar
                if !hasUnits || xe.unit == ""
                    push!(code_x, :( $x_name = _x[$i1] ) )
                else
                    x_unit = xe.unit
                    push!(code_x, :( $x_name = _x[$i1]*@u_str($x_unit)) )
                end
                i1 += 1
            else
                # x-element is a static vector
                i2 = i1 + xe.length - 1
                v_length = xe.length
                x_elements = Expr[]
                for i in i1:i2
                    push!(x_elements, :( _x[$i] ))
                end
                if !hasUnits || xe.unit == ""
                    push!(code_x, :( $x_name = Modia.SVector{$v_length,_FloatType}($(x_elements...)) ))
                else
                    x_unit = xe.unit
                    push!(code_x, :( $x_name = Modia.SVector{$v_length,_FloatType}($(x_elements...))*@u_str($x_unit)) )
                end
                i1 = i2 + 1
            end
        end

        i1 = 0
        for i in equationInfo.nxFixedLength+1:length(equationInfo.x_info)
            # x-element is a dynamic vector (length can change before initialization)
            xe     = x_info[i]
            x_name = xe.x_name_julia
            i1    += 1
            if !hasUnits || xe.unit == ""
                push!(code_x, :( $x_name = _m.x_vec[$i1]) )
            else
                x_unit = xe.unit
                push!(code_x, :( $x_name = _m.x_vec[$i1]*@u_str($x_unit)) )
            end
        end

        for xe in equationInfo.x_info
            der_x_name = xe.der_x_name_julia
            if hasUnits
                push!(code_der_x, :( Modia.appendVariable!(_m.der_x, Modia.stripUnit( $der_x_name )) ))
            else
                push!(code_der_x, :( Modia.appendVariable!(_m.der_x, $der_x_name) ))
            end
        end
    end

    #for (i,pi) in enumerate(parameters)
    #    push!(code_p, :( $pi = _m.p[$i] ) )
    #end

    timeName = variables[1]
    if hasUnits
        code_time = :( $timeName = _time*upreferred(u"s") )
    else
        code_time = :( $timeName = _time )
    end

    # Code for previous
    code_previous = []
    if length(previousVars) > 0
        code_previous2 = Expr[]
        for (i, value) in enumerate(previousVars)
            previousName = previousVars[i]
            push!(code_previous2, :( _m.nextPrevious[$i] = $previousName ))
        end
        code_previous3 = quote
             if Modia.isFirstEventIteration(_m) && !Modia.isInitial(_m)
                 $(code_previous2...)
             end
        end
        push!(code_previous, code_previous3)
    end

    # Code for pre
    code_pre = Expr[]
    for (i, value) in enumerate(preVars)
        preName = preVars[i]
        push!(code_pre, :( _m.nextPre[$i] = $preName ))
    end

    # Code for deepcopy of vectors with pre-allocated memory
    code_copy = Expr[]
    for leq_tuple in equationInfo.linearEquations
        x_vec_julia_names = leq_tuple[2]
        for name in x_vec_julia_names
            push!(code_copy, :( $name = deepcopy($name) ))
        end
    end

    # Generate code of the function
    # temporarily removed: _m.time = $TimeType(Modia.getValue(_time))
    code = quote
                function $functionName(_x, _m::Modia.SimulationModel{$FloatType,$TimeType}, _time::$TimeType)::Nothing
                    _FloatType = $FloatType
                    _TimeType = $TimeType
                    _m.time = _time
                    _m.nGetDerivatives += 1
                    instantiatedModel = _m
                    _p = _m.evaluatedParameters
                    _leq_mode = nothing
                    $code_time
                    $(code_x...)
                    $(AST...)
                    $(code_der_x...)
                    $(code_previous...)
                    $(code_pre...)

                    if _m.storeResult
                        Modia.TimerOutputs.@timeit _m.timer "Modia addToResult!" begin
                            $(code_copy...)
                            Modia.addToResult!(_m, $(variables...))
                        end
                    end
                    if _m.storeInit
                        Modia.addToInit!(_m, $(variables...))
                    end

                    return nothing
                end
            end
    return code
end

max_stars_repo_name: johhell/EPSphasor

max_issues_repo_name: johhell/EPSphasor

max_forks_repo_name: johhell/EPSphasor


text:
import ArgParse
include("../benchmark/CUTest/run_cutest.jl")

"""
Defines parses and args.
# Returns
A dictionary with the values of the command-line arguments.
"""
function parse_command_line()
  arg_parse = ArgParse.ArgParseSettings()

  help_method = "The optimization method to use, must be `one-phase` or `ipopt`."

  ArgParse.@add_arg_table! arg_parse begin
    "--method"
    help = help_method
    arg_type = String
    required = true

    "--output_dir"
    help = "The directory for output files."
    arg_type = String
    required = true

    "--linear_solver"
    help = "The linear solver type to use, must be ':julia' or 'HSL'"
    arg_type = Symbol
    required = true

    "--kkt_solver"
    help = "The kkt solver type to use, when selecting one-phase it must be ':schur' or ':symmetric' or ':clever_symmetric'"
    arg_type = Symbol

    "--max_it"
    help = "The maximum number of iterations to run"
    arg_type = Int64
    default = 3000

    "--max_time"
    help = "The maximum time to run in seconds"
    arg_type = Float64
    default =  60.0^2

    "--tol_opt"
    help = "The tolerance for optimality"
    arg_type = Float64
    default =  1e-6

    "--verbosity"
    help =
      "The verbosity level for printing. Values between 1 and 5 " *
      "print generic information on the solve process, i.e., a table of the " *
      "iteration statistics. Values greater than 5 provide information " *
      "useful for developers."
    arg_type = Int64
    default = 2

    "--min_nvar"
    help = "The minimum number of variables for CUTEst model"
    arg_type = Int64
    default =  10

    "--max_nvar"
    help = "The maximum number of variables for CUTEst model"
    arg_type = Int64
    default =  10000

    "--min_ncon"
    help = "The minimum number of constraints for CUTEst model"
    arg_type = Int64
    default = 10

    "--max_ncon"
    help = "The maximum constraints of variables for CUTEst model"
    arg_type = Int64
    default =  10000

  end

  return ArgParse.parse_args(arg_parse)
end

function main()
  parsed_args = parse_command_line()

  if parsed_args["method"] == "one-phase"
    my_par = OnePhase.Class_parameters()
    my_par.term.max_time = 60.0 * 60
    my_par.term.max_it = 3000
    if parsed_args["linear_solver"] ==  Symbol(":julia") || parsed_args["linear_solver"] ==  Symbol(":HSL")
      my_par.kkt.linear_solver_type = parsed_args["linear_solver"]
      if parsed_args["linear_solver"] ==  Symbol(":HSL")
        OnePhase.setUSE_HSL(true)
        OnePhase.loadHSL("../src/linear_system_solvers/")
      end
    else
     error("Unknown linear solver type")
    end

    if parsed_args["kkt_solver"] ==  Symbol(":schur") || parsed_args["kkt_solver"] ==  Symbol(":symmetric") || parsed_args["kkt_solver"] ==  Symbol(":clever_symmetric")
      my_par.kkt.kkt_solver_type = parsed_args["kkt_solver"]
    else
     error("Unknown kkt solver type")
    end

    my_par.term.max_it = parsed_args["max_it"]
    my_par.term.max_time = parsed_args["max_time"]
    my_par.term.tol_opt = parsed_args["tol_opt"]
    my_par.output_level = parsed_args["verbosity"]
    min_ncon = parsed_args["min_ncon"]
    max_ncon = parsed_args["max_ncon"]
    min_nvar = parsed_args["min_nvar"]
    max_nvar = parsed_args["max_nvar"]

    folder_name = parsed_args["output_dir"]
    println("pwd: ", pwd())
    # if_mkdir("../benchmark/results/$folder_name")
    if_mkdir("$folder_name")
    run_cutest_problems_using_our_solver(folder_name, my_par, min_ncon, max_ncon, min_nvar, max_nvar)
  elseif parsed_args["method"] == "ipopt"
    linear_solver = ""
    if parsed_args["linear_solver"] ==  Symbol(":HSL")
      linear_solver = "ma97"
    end
    max_it = parsed_args["max_it"]
    max_time = parsed_args["max_time"]
    tol_opt = parsed_args["tol_opt"]
    output_level = parsed_args["verbosity"]
    min_ncon = parsed_args["min_ncon"]
    max_ncon = parsed_args["max_ncon"]
    min_nvar = parsed_args["min_nvar"]
    max_nvar = parsed_args["max_nvar"]

    folder_name = parsed_args["output_dir"]
    # if_mkdir("../benchmark/results/$folder_name")
    if_mkdir("$folder_name")
    run_cutest_problems_on_solver(folder_name, output_level, tol_opt, max_it, max_time, linear_solver, min_ncon, max_ncon, min_nvar, max_nvar)
  else
    error("`method` arg must be either `one-phase` or `ipopt`.")
  end
end

main()

ext: jl

max_stars_repo_name: ohinder/OnePhase

max_issues_repo_name: ohinder/OnePhase

max_forks_repo_name: ohinder/OnePhase


text:
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.

struct AuthorizationV1beta1Api <: SwaggerApi
    client::Swagger.Client
end

"""

create a LocalSubjectAccessReview
Param: namespace::String (required)
Param: body::IoK8sApiAuthorizationV1beta1LocalSubjectAccessReview (required)
Param: dryRun::String
Param: fieldManager::String
Param: pretty::String
Return: IoK8sApiAuthorizationV1beta1LocalSubjectAccessReview
"""
function createAuthorizationV1beta1NamespacedLocalSubjectAccessReview(_api::AuthorizationV1beta1Api, namespace::String, body; dryRun=nothing, fieldManager=nothing, pretty=nothing, _mediaType=nothing)
    _ctx = Swagger.Ctx(_api.client, "POST", IoK8sApiAuthorizationV1beta1LocalSubjectAccessReview, "/apis/authorization.k8s.io/v1beta1/namespaces/{namespace}/localsubjectaccessreviews", ["BearerToken"], body)
    Swagger.set_param(_ctx.path, "namespace", namespace)  # type String
    Swagger.set_param(_ctx.query, "dryRun", dryRun)  # type String
    Swagger.set_param(_ctx.query, "fieldManager", fieldManager)  # type String
    Swagger.set_param(_ctx.query, "pretty", pretty)  # type String
    Swagger.set_header_accept(_ctx, ["application/json", "application/yaml", "application/vnd.kubernetes.protobuf"])
    Swagger.set_header_content_type(_ctx, (_mediaType === nothing) ? ["*/*"] : [_mediaType])
    Swagger.exec(_ctx)
end

"""

create a SelfSubjectAccessReview
Param: body::IoK8sApiAuthorizationV1beta1SelfSubjectAccessReview (required)
Param: dryRun::String
Param: fieldManager::String
Param: pretty::String
Return: IoK8sApiAuthorizationV1beta1SelfSubjectAccessReview
"""
function createAuthorizationV1beta1SelfSubjectAccessReview(_api::AuthorizationV1beta1Api, body; dryRun=nothing, fieldManager=nothing, pretty=nothing, _mediaType=nothing)
    _ctx = Swagger.Ctx(_api.client, "POST", IoK8sApiAuthorizationV1beta1SelfSubjectAccessReview, "/apis/authorization.k8s.io/v1beta1/selfsubjectaccessreviews", ["BearerToken"], body)
    Swagger.set_param(_ctx.query, "dryRun", dryRun)  # type String
    Swagger.set_param(_ctx.query, "fieldManager", fieldManager)  # type String
    Swagger.set_param(_ctx.query, "pretty", pretty)  # type String
    Swagger.set_header_accept(_ctx, ["application/json", "application/yaml", "application/vnd.kubernetes.protobuf"])
    Swagger.set_header_content_type(_ctx, (_mediaType === nothing) ? ["*/*"] : [_mediaType])
    Swagger.exec(_ctx)
end

"""

create a SelfSubjectRulesReview
Param: body::IoK8sApiAuthorizationV1beta1SelfSubjectRulesReview (required)
Param: dryRun::String
Param: fieldManager::String
Param: pretty::String
Return: IoK8sApiAuthorizationV1beta1SelfSubjectRulesReview
"""
function createAuthorizationV1beta1SelfSubjectRulesReview(_api::AuthorizationV1beta1Api, body; dryRun=nothing, fieldManager=nothing, pretty=nothing, _mediaType=nothing)
    _ctx = Swagger.Ctx(_api.client, "POST", IoK8sApiAuthorizationV1beta1SelfSubjectRulesReview, "/apis/authorization.k8s.io/v1beta1/selfsubjectrulesreviews", ["BearerToken"], body)
    Swagger.set_param(_ctx.query, "dryRun", dryRun)  # type String
    Swagger.set_param(_ctx.query, "fieldManager", fieldManager)  # type String
    Swagger.set_param(_ctx.query, "pretty", pretty)  # type String
    Swagger.set_header_accept(_ctx, ["application/json", "application/yaml", "application/vnd.kubernetes.protobuf"])
    Swagger.set_header_content_type(_ctx, (_mediaType === nothing) ? ["*/*"] : [_mediaType])
    Swagger.exec(_ctx)
end

"""

create a SubjectAccessReview
Param: body::IoK8sApiAuthorizationV1beta1SubjectAccessReview (required)
Param: dryRun::String
Param: fieldManager::String
Param: pretty::String
Return: IoK8sApiAuthorizationV1beta1SubjectAccessReview
"""
function createAuthorizationV1beta1SubjectAccessReview(_api::AuthorizationV1beta1Api, body; dryRun=nothing, fieldManager=nothing, pretty=nothing, _mediaType=nothing)
    _ctx = Swagger.Ctx(_api.client, "POST", IoK8sApiAuthorizationV1beta1SubjectAccessReview, "/apis/authorization.k8s.io/v1beta1/subjectaccessreviews", ["BearerToken"], body)
    Swagger.set_param(_ctx.query, "dryRun", dryRun)  # type String
    Swagger.set_param(_ctx.query, "fieldManager", fieldManager)  # type String
    Swagger.set_param(_ctx.query, "pretty", pretty)  # type String
    Swagger.set_header_accept(_ctx, ["application/json", "application/yaml", "application/vnd.kubernetes.protobuf"])
    Swagger.set_header_content_type(_ctx, (_mediaType === nothing) ? ["*/*"] : [_mediaType])
    Swagger.exec(_ctx)
end

"""

get available resources
Return: IoK8sApimachineryPkgApisMetaV1APIResourceList
"""
function getAuthorizationV1beta1APIResources(_api::AuthorizationV1beta1Api; _mediaType=nothing)
    _ctx = Swagger.Ctx(_api.client, "GET", IoK8sApimachineryPkgApisMetaV1APIResourceList, "/apis/authorization.k8s.io/v1beta1/", ["BearerToken"])
    Swagger.set_header_accept(_ctx, ["application/json", "application/yaml", "application/vnd.kubernetes.protobuf"])
    Swagger.set_header_content_type(_ctx, (_mediaType === nothing) ? ["application/json", "application/yaml", "application/vnd.kubernetes.protobuf"] : [_mediaType])
    Swagger.exec(_ctx)
end

export createAuthorizationV1beta1NamespacedLocalSubjectAccessReview, createAuthorizationV1beta1SelfSubjectAccessReview, createAuthorizationV1beta1SelfSubjectRulesReview, createAuthorizationV1beta1SubjectAccessReview, getAuthorizationV1beta1APIResources


text:
@testset "API" begin

    #########
    # SETUP #
    #########

    using Evolutionary: AbstractOptimizer, AbstractOptimizerState, Options, value!,
                        f_calls, NonDifferentiable, BoxConstraints, PenaltyConstraints
    import Evolutionary: value, population_size, default_options, initial_state, update_state!

    # objectvive function
    dimension = 5
    individual = ones(dimension)
    minval = 1.0
    func = x->sum(x)+1
    objfun = NonDifferentiable(func, individual)

    # state
    mutable struct TestOptimizerState <: AbstractOptimizerState
        individual
        fitness
    end
    Evolutionary.value(state::TestOptimizerState) = state.fitness
    Evolutionary.minimizer(state::TestOptimizerState) = state.individual
    st = TestOptimizerState(individual, minval);

    # optimizer
    struct TestOptimizer <: AbstractOptimizer end
    Evolutionary.population_size(method::TestOptimizer) = 5
    Evolutionary.default_options(method::TestOptimizer) = Dict(:iterations=>10, :abstol=>1e-10)
    Evolutionary.initial_state(method, options, d, population) = TestOptimizerState(population[end], value(d, population[end]))
    function Evolutionary.update_state!(d, constraints, state::TestOptimizerState, population::AbstractVector, method, itr)
        i = rand(1:population_size(method))
        state.individual = population[i]
        state.fitness = value!(d, state.individual)
        return false
    end
    mthd = TestOptimizer()
    cnstr= Evolutionary.NoConstraints()

    # options
    opts = Evolutionary.Options(store_trace=true; default_options(mthd)...)
    show(IOBuffer(), opts)

    # population
    ppl_size = 7
    ppl = [rand(dimension) for i in 1:ppl_size]

    #########
    # TRACE #
    #########
    @test value(st) == minval
    val = value!(objfun, rand(dimension))
    @test Evolutionary.abschange(objfun, st) == val - minval
    @test Evolutionary.relchange(objfun, st) == (val - minval)/val

    tr = Evolutionary.OptimizationTrace{typeof(value(objfun)), typeof(mthd)}()
    @test !Evolutionary.trace!(tr, 1, objfun, st, ppl, mthd, opts)
    @test tr[1].iteration == 1
    @test tr[1].value == val
    @test tr[1].metadata["time"] <= time()
    @test !update_state!(objfun, cnstr, st, ppl, mthd, 10)
    @test !Evolutionary.trace!(tr, 2, objfun, st, ppl, mthd, opts)
    @test tr[2].iteration == 2
    @test tr[2].value >= 1
    @test tr[2].metadata["time"] >= tr[1].metadata["time"]
    show(IOBuffer(), tr)

    # callback on state
    opts = Evolutionary.Options(store_trace=false, callback=(s->s.iteration == 2))
    tr = Evolutionary.OptimizationTrace{typeof(value(objfun)), typeof(mthd)}()
    @test Evolutionary.trace!(tr, 1, objfun, st, ppl, mthd, opts) == false
    @test Evolutionary.trace!(tr, 2, objfun, st, ppl, mthd, opts) == true

    # callback on trace
    opts = Evolutionary.Options(store_trace=true, callback=(tr->length(tr)>1))
    tr = Evolutionary.OptimizationTrace{typeof(value(objfun)), typeof(mthd)}()
    @test Evolutionary.trace!(tr, 1, objfun, st, ppl, mthd, opts) == false
    @test Evolutionary.trace!(tr, 2, objfun, st, ppl, mthd, opts) == true

    # custom trace
    function Evolutionary.trace!(record::Dict{String,Any}, objfun, state::TestOptimizerState, population, method, options)
        record["ppl_size"] = length(population)
    end
    tr = Evolutionary.OptimizationTrace{typeof(value(objfun)), typeof(mthd)}()
    @test !Evolutionary.trace!(tr, 1, objfun, st, ppl, mthd, opts)
    @test tr[1].iteration == 1
    @test tr[1].metadata["ppl_size"] == length(ppl)

    ##########
    # RESULT #
    ##########
    res = Evolutionary.EvolutionaryOptimizationResults(
        mthd, individual, value(objfun),
        opts.iterations, opts.show_trace, opts.store_trace,
        opts.abstol, tr, f_calls(objfun), 1.0, 1.0
    );
    @test summary(res) == summary(mthd)
    @test Evolutionary.minimizer(res) == individual
    @test minimum(res) == value(objfun)
    @test Evolutionary.iterations(res) == opts.iterations
    @test Evolutionary.iteration_limit_reached(res) == opts.show_trace
    @test Evolutionary.tol(res) == opts.abstol
    @test Evolutionary.converged(res) == opts.store_trace
    @test f_calls(res) == f_calls(objfun)
    @test Evolutionary.time_run(res) == 1.0
    @test Evolutionary.time_limit(res) == 1.0
    @test length(Evolutionary.trace(res)) >= 1
    show(IOBuffer(), res)

    ##############
    # POPULATION #
    ##############
    pop = Evolutionary.initial_population(mthd, BitVector(ones(dimension)))
    @test length(pop) == population_size(mthd)
    @test size(first(pop)) == (dimension,)

    @test_throws AssertionError Evolutionary.initial_population(mthd, fill(BitVector(ones(dimension)),4))
    pop = Evolutionary.initial_population(mthd, fill(BitVector(ones(dimension)),5))
    @test length(pop) == population_size(mthd)
    @test size(first(pop)) == (dimension,)

    pop = Evolutionary.initial_population(mthd, BitMatrix(ones(dimension,6)))
    @test length(pop) == population_size(mthd)
    @test size(first(pop)) == (dimension,6)

    pop = Evolutionary.initial_population(mthd, (()->rand(Bool,dimension)))
    @test length(pop) == population_size(mthd)
    @test size(first(pop)) == (dimension,)

    lb = [0,1,2,-1]
    ub = [0,3,2,1]
    cb = Evolutionary.ConstraintBounds(lb,ub,[],[])
    pop = Evolutionary.initial_population(mthd, cb)
    @test length(pop) == population_size(mthd)
    @test map(i->i[1],pop) == fill(ub[1], population_size(mthd))
    @test all(map(i->lb[2] <= i[2] <= ub[2],pop))
    @test map(i->i[3],pop) == fill(ub[3], population_size(mthd))
    @test all(map(i->lb[4] <= i[4] <= ub[4],pop))

    ###########
    # OPTIONS #
    ###########
    dopts = default_options(mthd)
    opts = Options(;store_trace=true, dopts...)
    @test opts.abstol == dopts[:abstol]

    #############
    # OBJECTIVE #
    #############
    objfun= NonDifferentiable(sum, BitVector(ones(dimension)))
    @test value(objfun) == 0.0
    @test 0 <= value(objfun,rand(Bool,dimension)) <= dimension
    @test f_calls(objfun) == 1
    @test 0 <= value!(objfun,rand(Bool,dimension)) <= dimension
    @test f_calls(objfun) == 2
    v = value!(objfun,rand(Bool,dimension))
    @test 0 <= v <= dimension
    @test value(objfun) == v

    ###############
    # CONSTRAINTS #
    ###############
    c = Evolutionary.NoConstraints()
    @test isfeasible(c, 1)
    @test isa(c.bounds, Evolutionary.ConstraintBounds)
    @test value(c, 1) === nothing
    @test apply!(c, 1) == 1
    @test penalty(c, 1) == 0

    c = BoxConstraints(lb, ub)
    @test isfeasible(c, [0, 1, 2, 0])
    @test value(c, [1,4,2,-2]) === nothing
    @test apply!(c, [1,4,2,-2]) == [0,3,2,-1]
    @test penalty(c, [1,4,2,-2]) == 0

    con_c!(x) = [sum(x)]
    cb = Evolutionary.ConstraintBounds(fill(0, 3), fill(1, 3), [1], [1])
    c = PenaltyConstraints(1, cb, con_c!)
    objfun = NonDifferentiable(sum, zeros(3))
    x, y = [0, 1, 0], [0, -1, 3]
    @test isfeasible(c, x)    # feasible
    @test !isfeasible(c, y) # not feasible
    @test apply!(c, x) == x
    @test apply!(c, y) == y
    @test value(c, x) == [sum(x)]
    @test value(c, y) == [sum(y)]
    @test penalty(c, x) == 0.0 # c penalty
    @test penalty(c, y) == 1+2^2+1.0 # c penalty
    @test penalty!([1, 2], c, [x,y]) == [1, 2 + penalty(c, y)]

    ############
    # OPTIMIZE #
    ############
    opts = Options(;store_trace=true, iterations=7, abstol=-1.0, reltol=-1.0)
    res =  Evolutionary.optimize(sum, (()->BitVector(rand(Bool,dimension))), mthd, opts)
    @test Evolutionary.minimum(res) <= dimension
    @test length(Evolutionary.minimizer(res)) == dimension
    @test Evolutionary.iterations(res) == 7
    @test !Evolutionary.converged(res)
    @test length(Evolutionary.trace(res)) > 1

end