Allows to write multi-dimensional equations in Julia using an easy and compact syntax:
@meq nTrees!(r in reg, sp in species, dc in diameterClass[2-end], y in years) = nTrees_(r, sp, dc, y)*(1-mortRate_(r, sp, dc, y-1) - promotionRate_(r, sp, dc, y-1))) + promotionRate_(r, sp, dc-1, y-1)
It is somehow similar to Algebraic modeling language (AML) like GAMS or Julia/JuMP, but outside the domain of optimisation.
Due to the fact that the functions to access the data are dynamically created at run time, and would not be available to you with a normal
import <package>, you have instead to include the file in your program:
Define each group of variables with their associated data source. At the moment MultiDimEquations support only DataFrame in long format, i.e. in the format parameter|dim1|dim2|...|value
df = wsv""" reg prod var value us banana production 10 us banana transfCoef 0.6 us banana trValues 2 us apples production 7 us apples transfCoef 0.7 us apples trValues 5 us juice production NA us juice transfCoef NA us juice trValues NA eu banana production 5 eu banana transfCoef 0.7 eu banana trValues 1 eu apples production 8 eu apples transfCoef 0.8 eu apples trValues 4 eu juice production NA eu juice transfCoef NA eu juice trValues NA """ variables = vcat(unique(dropna(df[:var])),["consumption"]) defVars(variables,df;dfName="df",varNameCol="var", valueCol="value")
Each time you run
defVars(), access functions are automatically created for each variable in the form of
variable_(dim1,dim2,...) to access the data and
variable!(value,dim1,dim2,..) to store the value.
For more info type
?defVars once you installed and loaded the package.
These are simple Julia Arrays..
products = ["banana","apples","juice"] primPr = products[1:2] secPr = [products] reg = ["us","eu"]
The @meq macro adds a bit of convenience transforming at parse time (so, without adding run-time overheads) your equation from
par1!(d1 in DIM1, d2 in DIM2, dfix3) = par2_(d1,d2)+par3_(d1,d2) to
[par1!(par2_(d1,d2)+par3_(d1,d2), d1,d2,dfix3) for d1 in dim1, d2 in dim2].
# equivalent to [production!(sum(trValues_(r,pp) * transfCoef_(r,pp) for pp in primPr), r, sp) for r in reg, sp in secPr] @meq production!(r in reg, sp in secPr) = sum(trValues_(r,pp) * transfCoef_(r,pp) for pp in primPr) @meq consumption!(r in reg, pp in primPr) = production_(r,pp) - trValues_(r,pp) @meq consumption!(r in reg, sp in secPr) = production_(r, sp)
defVars() with the
@meq macro your data is kept in a single
IndexedTable where one column is used to keep the variable names.
An alternative (and faster) approach is to define your variables as each one being a separate
IndexedTable (the package LAJuliaUtils has some useful functions for such approach).
You can still use @meq to provide some convenience:
par1[d1 in DIM1, d2 in DIM2, dfix3] = par2[d1,d2]+par3[d1,d2] ==>
[par1[d1,d2,dfix3] = par2[d1,d2]+par3[d1,d2] for d1 in dim1, d2 in dim2].
For more info on the @meq macro type
IndexedTablesas backend, this package provides a reasonable fast implementation.
var = ...assignments are supported.
The development of this package was supported by the French National Research Agency through the Laboratory of Excellence ARBRE, a part of the “Investissements d'Avenir” Program (ANR 11 – LABX-0002-01).
almost 2 years ago