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modelVariables.jl
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modelVariables.jl
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# License for this file: MIT (expat)
# Copyright 2017-2018, DLR Institute of System Dynamics and Control
#
# This file is part of module
# ModiaMath.Variables (ModiaMath/Variables/_module.jl)
#
# Define ModelVariables struct and functions that holds all information about the variables in a simulation model
isArray(v::ModiaMath.AbstractVariable) = typeof(v.value) <: AbstractArray
isScalar(v::ModiaMath.AbstractVariable) = !(typeof(v.value) <: AbstractArray)
isReal(v::ModiaMath.AbstractVariable) = typeof(v) <: ModiaMath.AbstractRealVariable
valueLength(v::ModiaMath.AbstractVariable) = (isArray(v) ? length(v.value) : 1)
isUsedInAnalysis(v::ModiaMath.AbstractVariable, analysis::AnalysisType) = Int(v.analysis) <= Int(analysis)
#------------------------------- Extract variables from a hierarchical component -------------------------------
function get_ModelVariables_aux!(model, modelType, var, dict, analysis::AnalysisType)
dict[model] = true # Mark that model is going to be inspected
#println("... inspect modelType = ", modelType)
for i = 1:fieldcount(modelType)
field = getfield(model, fieldname(modelType, i))
ftype = typeof(field)
#println("... fieldname = ", fieldname(modelType,i), ", ftype = ", ftype)
if ftype <: ModiaMath.AbstractVariable
if isUsedInAnalysis(field, analysis)
# Use field since defined for the desired analysis
push!(var, field)
end
elseif ftype <: ModiaMath.AbstractComponentWithVariables && !haskey(dict, field) # Only inspect further, if model/field was not yet inspected
get_ModelVariables_aux!(field, ftype, var, dict, analysis)
end
end
end
"""
var = get_ModelVariables(model) - Return all variables defined in model component
The function returns a vector var that contains all AbstractVariables defined in (the hierarchical) model
"""
function get_ModelVariables(model::Any, analysis::AnalysisType)
var = ModiaMath.AbstractVariable[]
modelType = typeof(model)
dict = IdDict{Any,Bool}()
if modelType <: ModiaMath.AbstractComponentWithVariables
get_ModelVariables_aux!(model, modelType, var, dict, analysis)
end
#for v in var
# println(v._internal.name)
#end
return var
end
#------------------------------- Construct a variable table for printing/debugging -------------------------------
numericType(v, analysis) = (v.numericType == XD_EXP && analysis != ModiaMath.DynamicAnalysis &&
Int(v.derivative.analysis) > Int(analysis) ) ? XA : v.numericType
const staticArrays = "StaticArrays."
const lenStaticArrays = length(staticArrays)
function shortenedTypeof(value)
vtype = string(typeof(value))
if length(vtype) >= lenStaticArrays && vtype[1:lenStaticArrays ] == staticArrays
vtype = vtype[lenStaticArrays + 1:end]
end
return Symbol(vtype)
end
vecIndex(v) = (isScalar(v) || v.ivar == 0) ? v.ivar : (v.ivar:v.ivar + length(v.value) - 1)
function get_variableTable(var::Vector{ModiaMath.AbstractVariable})
v_table = DataFrames.DataFrame(name=Symbol[], ValueType=Symbol[], unit=String[],
numericType=NumericType[], vec=Symbol[], vecIndex=Any[], resultIndex=Any[],
der=Symbol[], causality=Causality[], min=Any[], max=Any[], nominal=Any[], fixed=Bool[],
start=Any[], info=Symbol[])
for v in var
if isReal(v)
unit = v.unit
numericType = v.numericType
vec = NumericTypeToVector[Int(v.numericType)]
der = typeof(v.derivative) == Nothing ? Symbol("") : instanceName(v.derivative)
vmin = v.min
vmax = v.max
vnominal = v.nominal
else
unit = Symbol("")
numericType = NoNumericType
vec = Symbol("")
der = Symbol("")
vmin = Symbol("")
vmax = Symbol("")
vnominal = Symbol("")
end
resultIndex = (isScalar(v) || v.iresult == 0) ? v.iresult : (v.iresult:v.iresult + length(v.value) - 1)
push!(v_table, [instanceName(v), shortenedTypeof(v.value), unit, numericType, vec,
vecIndex(v), resultIndex, der, v.causality, vmin, vmax, vnominal, v.fixed, v.start, Symbol(v.info)])
end
return v_table
end
function indexToString(name, A, linearIndex)
index = CartesianIndices(A)[linearIndex]
s = string(name, "[")
for i in 1:length(index)
s = string(s, index[i])
if i == length(index)
s = string(s, "]")
else
s = string(s, ",")
end
end
return s
end
function add_xName!(v::RealVariable, vnumType, xNames, ix_beg)
if vnumType == XD_EXP || vnumType == XD_IMP || vnumType == XA
name = string(instanceName(v))
elseif vnumType == LAMBDA
name = "integral(" * string(instanceName(v)) * ")"
elseif vnumType == MUE
name = "integral(" * string(instanceName(v)) * ")"
else
error("... should not occur")
end
if isScalar(v)
xNames[ix_beg] = string(name)
else
for i = 1:length(v.value)
xNames[ix_beg + i - 1] = indexToString(name, v.value, i)
end
end
end
function pushNames!(v::RealVariable, vnumType, xNames, residueNames)
if vnumType == XD_EXP || vnumType == XD_IMP || vnumType == XA || vnumType == MUE
push!(xNames, string(instanceName(v)))
elseif vnumType == LAMBDA
push!(xNames, "integral(" * string(instanceName(v)) * ")")
elseif vnumType == FD_IMP || vnumType == FC
push!(residueNames, string(instanceName(v)))
end
if vnumType == XD_EXP
if typeof(v.derivative) == Nothing
error(instanceName(v), " is defined as XD_EXP, but no variable is defined as its derivative.")
end
push!(residueNames, string(instanceName(v.derivative)) * " - derx[.]")
end
end
"""
vars = ModiaMath.ModelVariables(model::ModiaMath.AbstractComponentWithVariables;
analysis::AnalysisType = ModiaMath.DynamicAnalysis)
Return a struct that contains all the variables in `model` in a form so that
fast copying from the integrator interface to the variables can be performed,
and vice versa.
"""
mutable struct ModelVariables
# Variables used by simulator
var::Vector{ModiaMath.AbstractVariable}
# Dimensions of x and derx vector
nx::Int # nx = nx_exp + nx_imp + nx_alg + nx_lambda + nx_mue
nx_exp::Int
nx_imp::Int
nx_alg::Int
nx_lambda::Int
nx_mue::Int
# Dimensions of residue vector
nfd::Int
nfd_imp::Int # nfd_exp = nx_exp
nfc::Int
# Dimensions of auxiliary variables
nwr::Int
nwc::Int
# Info to copy variables to x/derx/residue/result-values
x_var::Vector{RealVariable} # [x_exp_var , x_imp_var , x_alg_var]
derx_var::Vector{RealVariable} # [der(x_imp_var), lambda_var, mue_var]
residue_var::Vector{RealVariable} # [fd_imp_var , fc_var]
result_var::Vector{ModiaMath.AbstractVariable} # [time, x_var, derx_var, wr_var, wc_var]
result_names::Vector{String} # Names of the (scalarized) result-vector elements
x_names::Vector{String} # Names of the (scalarized) x-vector elements
# Dimensions of the parts of the variable vectors (equivalent to the dimensions of the value vectors like "x" above)
nx_exp_var::Int
nx_imp_var::Int
nx_alg_var::Int
nx_lambda_var::Int
nx_mue_var::Int
nfd_imp_var::Int
nfc_var::Int
nwr_var::Int
nwc_var::Int
# Dummy
dummyDifferentialEquation::Bool # = true, if only dummy differential equation
function ModelVariables(model::ModiaMath.AbstractComponentWithVariables; analysis::AnalysisType=ModiaMath.DynamicAnalysis)
# Extract variables from model
var = get_ModelVariables(model, analysis)
@assert(length(var) > 0)
# Determine dimensions of the various parts of the vectors
ndim = fill(0, 12)
nvar = fill(0, 12)
for v in var
numType = Int(numericType(v, analysis))
ndim[numType] += valueLength(v)
nvar[numType] += 1
end
(nx_exp, nx_imp, nx_alg, nx_lambda, nx_mue, nderx_exp, nderx_imp, nfd_imp, nfc, nwr, nwc, dummy1) = ndim
(nx_exp_var, nx_imp_var, nx_alg_var, nx_lambda_var, nx_mue_var, nderx_exp_var, nderx_imp_var, nfd_imp_var, nfc_var, nwr_var, nwc_var, dummy2) = nvar
@assert(nx_exp >= nderx_exp)
@assert(nx_imp == nderx_imp)
#println("nx_exp = ", nx_exp, ", nx_imp = ", nx_imp, ", nx_alg = ", nx_alg, ", nx_lambda = ", nx_lambda, ", nx_mue = ", nx_mue)
nx = nx_exp + nx_imp + nx_alg + nx_lambda + nx_mue
if nx == 0
# add dummy differential equation der(x) = -x; x(0)=0
dummyDifferentialEquation = true
dummy_x = RealScalar("_dummy_x" ; fixed=true, numericType=XD_EXP)
dummy_derx = RealScalar("_dummy_derx"; numericType=DER_XD_EXP, integral=dummy_x)
pushfirst!(var, dummy_derx)
pushfirst!(var, dummy_x)
nx = 1
nx_exp = 1
nderx_exp = 1
nx_exp_var = 1
nderx_exp_var = 1
else
dummyDifferentialEquation = false
end
time = RealScalar("time"; unit="s", causality=Independent, numericType=TIME)
pushfirst!(var, time)
# println("\n... v_table = ", get_variableTable(var))
# Check dimensions
if nx_exp + nfd_imp + nfc != nx
xNames = String[]
residueNames = String[]
for i in eachindex(var)
v = var[i]
vnumType = numericType(v, analysis)
pushNames!(v, vnumType, xNames, residueNames)
end
error("The number of x-variables (= ", nx, ") is not identical to the number of equations (= ", nx_exp + nfd_imp + nfc, "):\n",
"x-variables = ", xNames, "\n",
"residues = ", residueNames)
end
# Allocate variable/index vectors to copy x-, derx-values to variables and variables to residues
x_var = Array{ModiaMath.AbstractRealVariable}(undef, nx_exp_var + nx_imp_var + nx_alg_var) # = [x_exp_var, x_imp_var, x_alg_var]
ix_exp = 1
ix_imp = nx_exp + 1
ix_alg = ix_imp + nx_imp
ix_lambda = ix_alg + nx_alg
ix_mue = ix_lambda + nx_lambda
ix_exp_var = 1
ix_imp_var = nx_exp_var + 1
ix_alg_var = ix_imp_var + nx_imp_var
ix_lambda_var = ix_alg_var + nx_alg_var
ix_mue_var = ix_lambda_var + nx_lambda_var
derx_var = Array{ModiaMath.AbstractRealVariable}(undef, nx_imp_var + nx_lambda_var + nx_mue_var) # = [der(x_imp_var), lambda_var, mue_var]
iderx_imp = 1
iderx_lambda = nx_imp + 1
iderx_mue = nx_imp + nx_lambda + 1
iderx_imp_var = 1
iderx_lambda_var = nx_imp_var + 1
iderx_mue_var = nx_imp_var + nx_lambda_var + 1
residue_var = Array{ModiaMath.AbstractRealVariable}(undef, nfd_imp_var + nfc_var) # = [fd_imp_var, fc_var]
ifd_imp = 1
ifc = nfd_imp + 1
ifd_imp_var = 1
ifc_var = nfd_imp_var + 1
result_var = Array{ModiaMath.AbstractVariable}(undef, 1 + length(x_var) + nderx_exp_var + length(derx_var) + nwr_var + nwc_var - (dummyDifferentialEquation ? 2 : 0)) # [time, x_var, derx_var, wr_var, wc_var]
result_names = Array{String}(undef, 1 + nx_exp + nx_imp + nx_alg + nderx_exp + nx_imp + nx_lambda + nx_mue + nwr + nwc - (dummyDifferentialEquation ? 2 : 0))
result_names[1] = "time"
iresult = 1
iresult_var = 1
x_names = Array{String}(undef, nx)
#println("... length(result_var) = ", length(result_var), ", length(result_names) = ", length(result_names), ", nx_exp = ", nx_exp)
for i in eachindex(var)
v = var[i]
vnumType = numericType(v, analysis)
if vnumType == FD_IMP
v.ivar = nx_exp + ifd_imp
residue_var[ifd_imp_var] = v
ifd_imp += valueLength(v)
ifd_imp_var += 1
elseif vnumType == FC
v.ivar = nx_exp + ifc
residue_var[ifc_var] = v
ifc += valueLength(v)
ifc_var += 1
else
if !(dummyDifferentialEquation && (vnumType == XD_EXP || vnumType == DER_XD_EXP))
# Determine information about result vectors
var_name = string(instanceName(v))
if typeof(v.value) == Float64
result_names[iresult] = var_name
v.iresult = iresult
iresult += 1
else
v_value = v.value
for i in eachindex(v_value)
result_names[iresult + i - 1] = indexToString(var_name, v_value, i)
end
v.iresult = iresult
iresult += length(v_value)
end
result_var[iresult_var] = v
iresult_var += 1
end
if vnumType == XD_EXP
add_xName!(v, vnumType, x_names, ix_exp)
v.ivar = ix_exp
x_var[ix_exp_var] = v
ix_exp += valueLength(v)
ix_exp_var += 1
elseif vnumType == XD_IMP
add_xName!(v, vnumType, x_names, ix_imp)
v.ivar = ix_imp
x_var[ix_imp_var] = v
ix_imp += valueLength(v)
ix_imp_var += 1
elseif vnumType == XA
add_xName!(v, vnumType, x_names, ix_alg)
v.ivar = ix_alg
x_var[ix_alg_var] = v
ix_alg += valueLength(v)
ix_alg_var += 1
elseif vnumType == LAMBDA
add_xName!(v, vnumType, x_names, ix_lambda)
v.ivar = ix_lambda
derx_var[iderx_lambda_var] = v
ix_lambda += valueLength(v)
iderx_lambda += valueLength(v)
ix_lambda_var += 1
iderx_lambda_var += 1
elseif vnumType == MUE
add_xName!(v, vnumType, x_names, ix_mue)
v.ivar = ix_mue
derx_var[iderx_mue_var] = v
ix_mue += valueLength(v)
iderx_mue += valueLength(v)
ix_mue_var += 1
iderx_mue_var += 1
end
end
end
for v in var
if v.numericType == DER_XD_IMP
v.ivar = (v.integral).ivar
derx_var[iderx_imp_var] = v
iderx_imp_var += 1
elseif v.numericType == DER_XD_EXP
v.ivar = (v.integral).ivar
end
end
new(var, nx, nx_exp, nx_imp, nx_alg, nx_lambda, nx_mue, nx_exp + nfd_imp, nfd_imp, nfc, nwr, nwc,
x_var, derx_var, residue_var, result_var, result_names, x_names,
nx_exp_var, nx_imp_var, nx_alg_var, nx_lambda_var, nx_mue_var, nx_exp_var + nfd_imp_var, nfc_var, nwr_var, nwc_var,
dummyDifferentialEquation)
end
end
"""
table = ModiaMath.get_xTable(vars::ModelVariables)
Function returns a DataFrames tables of all the variables stored in x-vector in `vars`.
"""
function get_xTable(m::ModelVariables)
x_table = DataFrames.DataFrame(x=Symbol[], name=Symbol[], fixed=Bool[], start=Union{Float64,AbstractArray}[])
for v in m.x_var
push!(x_table, [Symbol("x[", vecIndex(v), "]"), instanceName(v), v.fixed, v.start])
end
# include integral(lambda) and integral(mue)
for i in m.nx_imp_var+1:length(m.derx_var)
v = m.derx_var[i]
push!(x_table, [Symbol("x[", vecIndex(v), "]"), Symbol("integral(",instanceName(v),")"), false, 0.0])
end
return x_table
end
"""
table = ModiaMath.get_copyToVariableTable(vars::ModelVariables)
Function returns a DataFrames tables of all the variables in `vars`
that are copied from x/derx to the variables.
"""
function get_copyToVariableTable(m::ModelVariables)
copyToVariable_table = DataFrames.DataFrame(source=Symbol[], target=Symbol[])
for v in m.x_var
push!(copyToVariable_table, [Symbol("x[", vecIndex(v), "]"), instanceName(v)])
end
for v in m.derx_var
push!(copyToVariable_table, [Symbol("derx[", vecIndex(v), "]"), instanceName(v)])
end
return copyToVariable_table
end
"""
table = ModiaMath.get_copyToResidueTable(vars::ModelVariables)
Function returns a DataFrames tables of all the variables in `vars`
that are copied from variables to the residue vector.
"""
function get_copyToResidueTable(m::ModelVariables)
r_table = DataFrames.DataFrame(source=Symbol[], target=Symbol[])
x_var = m.x_var
for i = 1:m.nx_exp_var
v = x_var[i]
der_v = v.derivative
push!(r_table, [Symbol("derx[", vecIndex(v), "] - ", instanceName(der_v)), Symbol("residue[", vecIndex(v), "]") ])
end
for v in m.residue_var
push!(r_table, [instanceName(v), Symbol("residue[", vecIndex(v), "]") ])
end
return r_table
end
"""
table = ModiaMath.get_copyToResultTable(vars::ModelVariables)
Function returns a DataFrames tables of all the variables in `vars`
that are copied from variables to the result.
"""
function get_copyToResultTable(m::ModelVariables)
r_table = DataFrames.DataFrame(source=Symbol[], target=Symbol[], start=Union{Float64,AbstractArray}[])
for v in m.result_var
resultIndex = isScalar(v) ? v.iresult : (v.iresult:v.iresult + length(v.value) - 1)
push!(r_table, [instanceName(v), Symbol("result[", resultIndex, "]"), v.start])
end
return r_table
end
"""
table = print_ModelVariables(obj)
Print all the variables in `obj` in form of DataFrames tables.
`obj` can be of type ModiaMath.ModelVariables or ModiaMath.SimulationModel.
"""
function print_ModelVariables(m::ModelVariables)
variabletable = ModiaMath.get_variableTable(m.var)
x_table = ModiaMath.get_xTable(m)
copyToVariableTable = ModiaMath.get_copyToVariableTable(m)
copyToResidueTable = ModiaMath.get_copyToResidueTable(m)
copyToResultTable = ModiaMath.get_copyToResultTable(m)
print("\n\nvariables: "); show(variabletable , splitcols=true, summary=false)
print("\n\n\nx vector: "); show(x_table , splitcols=true, summary=false)
print("\n\n\ncopy to variables: "); show(copyToVariableTable, splitcols=true, summary=false)
print("\n\n\ncopy to residue vector: "); show(copyToResidueTable , splitcols=true, summary=false)
print("\n\n\ncopy to results: "); show(copyToResultTable , splitcols=true, summary=false)
print("\n")
end
"""
copy_x_and_derx_to_variables!(time::Float64, x::Vector{Float64},
derx::Vector{Float64}, vars::ModelVariables)
Copy `x`and `derx`of the integrator interface to the model variables `vars`.
"""
function copy_x_and_derx_to_variables!(time::Float64, x::Vector{Float64}, derx::Vector{Float64}, m::ModelVariables)
@assert(length(x) == m.nx)
@assert(length(derx) == m.nx)
m.var[1].value = time
for v in m.x_var
#println("... typeof(", fullName(v), ") = ", typeof(v), ", isimmutable(v) = ", isimmutable(v))
if isScalar(v)
v.value = x[ v.ivar ]
elseif isimmutable(v.value)
# v is an immutable array (e.g. SVector{3,Float64})
v.value = x[ v.ivar:v.ivar + length(v.value) - 1 ]
else
vv = v.value
for j in 1:length(vv)
vv[j] = x[ v.ivar + j - 1 ]
end
end
end
for v in m.derx_var
if isScalar(v)
v.value = derx[ v.ivar ]
elseif isimmutable(v.value)
# v is an immutable array (e.g. SVector{3,Float64})
v.value = derx[ v.ivar:v.ivar + length(v.value) - 1 ]
else
vv = v.value
for j in 1:length(vv)
vv[j] = derx[ v.ivar + j - 1 ]
end
end
end
return nothing
end
"""
copy_start_to_x!(vars::ModelVariables, x::Vector{Float64}, x_fixed::Vector{Bool}, x_nominal::Vector{Float64})
Copy `start`, `fixed` and `nominal`values of variables `vars` to `x`, `x_fixed`, and `x_nominal` vectors.
"""
function copy_start_to_x!(m::ModelVariables, x::Vector{Float64}, x_fixed::Vector{Bool}, x_nominal::Vector{Float64})
@assert(length(x) == m.nx)
@assert(length(x_fixed) == m.nx)
@assert(length(x_nominal) == m.nx)
for v in m.x_var
ibeg = v.ivar
if isScalar(v)
x[ibeg] = v.start
x_fixed[ibeg] = v.fixed
x_nominal[ibeg] = v.nominal
else
vv = v.start
for j in 1:length(vv)
k = ibeg + j - 1
x[ k ] = vv[j]
x_fixed[ k ] = v.fixed
x_nominal[ k ] = v.nominal
end
end
end
return nothing
end
copy_start_to_x!(m::ModelVariables, x::Vector{Float64}, x_fixed::Vector{Bool}) = copy_start_to_x!(m, x, x_fixed, fill(1.0,m.nx))
"""
copy_variables_to_residue!(vars::ModelVariables, x::Vector{Float64},
derx::Vector{Float64}, residue::Vector{Float64})
Copy variables `vars` to `residue` vector and include the inherent residue equations
of `XD_EXP` variables (`residue = der(v) - derx`).
"""
function copy_variables_to_residue!(m::ModelVariables, x::Vector{Float64}, derx::Vector{Float64}, residue::Vector{Float64})
@assert(length(x) == m.nx)
@assert(length(derx) == m.nx)
@assert(length(residue) == m.nx)
# If dummy equations present, compute derivative of dummy equation
if m.dummyDifferentialEquation
# der(x) = -x
m.var[3].value = -m.var[2].value
end
# Generate residues of explicitly solvable derivatives
x_var = m.x_var
for i = 1:m.nx_exp_var
v = x_var[i]
if isScalar(v)
residue[v.ivar] = derx[v.ivar] - v.derivative.value
else
der_vv = v.derivative.value
for j in 1:length(der_vv)
k = v.ivar + j - 1
residue[ k ] = derx[ k ] - der_vv[j]
end
end
end
# Copy residue variables of fd_imp and fc to residue vector
for v in m.residue_var
if isScalar(v)
residue[ v.ivar ] = v.value
else
vv = v.value
for j in 1:length(vv)
residue[ v.ivar + j - 1 ] = vv[j]
end
end
end
return nothing
end
function get_variableValueTable(m::ModelVariables)
v_table = DataFrames.DataFrame(simulator=Symbol[], variable=Symbol[], value=Any[])
for v in m.x_var
push!(v_table, [Symbol("x[", vecIndex(v), "]"), instanceName(v), v.value])
end
for v in m.derx_var
push!(v_table, [Symbol("derx[", vecIndex(v), "]"), instanceName(v), v.value])
end
return v_table
end
function get_residueValueTable(m::ModelVariables, derx_integrator::Vector{Float64})
r_table = DataFrames.DataFrame(simulator=Symbol[], variable=Symbol[], value=Any[])
x_var = m.x_var
for i = 1:m.nx_exp_var
v = x_var[i]
der_v = v.derivative
value = derx_integrator[ vecIndex(v) ] - der_v.value
push!(r_table, [Symbol("residue[", vecIndex(v), "]"), Symbol("derx[", vecIndex(v), "] - ", instanceName(der_v)), value ])
end
for v in m.residue_var
push!(r_table, [Symbol("residue[", vecIndex(v), "]"), instanceName(v), v.value ])
end
return r_table
end