How-to Guide 4: Work with Timesteps, Parameters, and Variables

Timesteps and available functions

An AbstractTimestep i.e. a FixedTimestep or a VariableTimestep is a type defined within Mimi in "src/time.jl". It is used to represent and keep track of time indices when running a model.

In the run_timestep functions which the user defines, it may be useful to use any of the following functions, where t is an AbstractTimestep object:

is_first(t) # returns true or false, true if t is the first timestep to be run for the respective component
is_last(t) # returns true or false, true if t is the last timestep to be run for the respective component
gettime(t) # returns the year represented by timestep t

There are also two helper types TimestepValue and TimestepIndex that can be used with comparison operators (==, <, and >) to check whether an AbstractTimestep t during the run_timestep function corresponds with a certain year or index number. For example:

if t > TimestepValue(2020)
  # run this code only for timesteps after the year 2020

if t == TimestepIndex(3)
  # run this code only during the third timestep

See below for further discussion of the TimestepValue and TimestepIndex objects and how they should be used.

The API details for AbstractTimestep object t are as follows:

  • you may index into a variable or parameter with [t] or [t +/- x] as usual
  • to access the time value of t (currently a year) as a Number, use gettime(t)
  • useful functions for commonly used conditionals are is_first(t) and is_last(t)
  • to access the index value of t as a Number representing the position in the time array, use t.t. Users are encouraged to avoid this access, and instead use comparisons with TimestepIndex objects to check if an AbstractTimestep t corresponds with a specific index number, as described above.

Indexing into a variable or parameter's time dimension with an Integer is deprecated and will soon error. Instead, users should take advantage of the TimestepIndex and TimestepValue types. For examples we will refer back to our component definition above, and repeated below.

@defcomp MyComponentName begin
  regions = Index()

  A = Variable(index = [time])
  B = Variable(index = [time, regions])

  c = Parameter()
  d = Parameter(index = [time])
  e = Parameter(index = [time, regions])
  f = Parameter(index = [regions])

  function run_timestep(p, v, d, t)
    v.A[t] = p.c + p.d[t]
    for r in d.regions
      v.B[t, r] = p.f[r] * p.e[t, r]


TimestepIndex has one field, index, which refers to the absolute index in the parameter or variable array's time dimension. Thus, constructing a TimestepIndex is done by simply writing TimestepIndex(index::Int). Looking back at our original component example, one could modify the first line of run_timestep to always refer to the first timestep of p.d with the following. One may index into the time dimension with a single TimestepIndex, or an Array of them.

v.A[t] = p.c + p.d[TimestepIndex(1)]

TimestepValue has two fields, value and offset, referring to the value within the time dimension and an optional offset from that value. Thus, constructing a TimestepValue is done either by writing TimestepValue(value), with an implied offset of 0, or TimestepValue(value, offset = i::Int), with an explicit offset of i. One may index into the time dimension with a single TimestepValue, or an Array of them. For example, you can use a TimestepValue to keep track of a baseline year.

v.A[t] = p.c + p.d[TimestepValue(2000)]

You may also use shorthand to create arrays of TimestepIndex using Colon syntax.

TimestepIndex(1):TimestepIndex(10) # implicit step size of 1
TimestepIndex(1):2:TimestepIndex(10) # explicit step of type Int 

Both TimestepIndex and TimestepArray have methods to support addition and subtraction of integers. Note that the addition or subtraction is relative to the definition of the time dimension, so while TimestepIndex(1) + 1 == TimestepIndex(2), TimestepValue(2000) + 1 could be equivalent to TimestepValue(2001) if 2001 is the next year in the time dimension, or TimestepValue(2005) if the array has a step size of 5. Hence adding or subtracting is relative to the definition of the time dimension.

DataType specification of Parameters and Variables

By default, the Parameters and Variables defined by a user will be allocated storage arrays of type Float64 when a model is constructed. This default "number_type" can be overriden when a model is created, with the following syntax:

m = Model(Int64)    # creates a model with default number type Int64

But you can also specify individual Parameters or Variables to have different data types with the following syntax in a @defcomp macro:

@defcomp example begin
  p1 = Parameter{Bool}()                         # ScalarModelParameter that is a Bool
  p2 = Parameter{Bool}(index = [regions])        # ArrayModelParameter with one dimension whose eltype is Bool
  p3 = Parameter{Matrix{Int64}}()                # ScalarModelParameter that is a Matrix of Integers
  p4 = Parameter{Int64}(index = [time, regions]) # ArrayModelParameter with two dimensions whose eltype is Int64

If there are "index"s listed in the Parameter definition, then it will be an ArrayModelParameter whose eltype is the type specified in the curly brackets. If there are no "index"s listed, then the type specified in the curly brackets is the actual type of the parameter value, and it will be represent by Mimi as a ScalarModelParameter.

More on parameter indices

As mentioned above, a parameter can have no index (a scalar), or one or multiple of the model's indexes. A parameter can also have an index specified in the following ways:

@defcomp MyComponent begin
  p1 = Parameter(index=[4]) # an array of length 4
  p2 = Parameter{Array{Float64, 2}}() # a two dimensional array of unspecified length

In both of these cases, the parameter's values are stored of as an array (p1 is one dimensional, and p2 is two dimensional). But with respect to the model, they are considered "scalar" parameters, simply because they do not use any of the model's indices (namely 'time', or 'regions').

Updating an external parameter

When set_param! is called, it creates an external parameter by the name provided, and stores the provided scalar or array value. It is possible to later change the value associated with that parameter name using the functions described below.

update_param!(m, :ParameterName, newvalues)

Note here that newvalues must be the same type (or be able to convert to the type) of the old values stored in that parameter, and the same size as the model dimensions indicate.

Setting parameters with a dictionary

In larger models it can be beneficial to set some of the external parameters using a dictionary of values. To do this, use the following function:

set_leftover_params!(m, parameters)

Where parameters is a dictionary of type Dict{String, Any} where the keys are strings that match the names of the unset parameters in the model, and the values are the values to use for those parameters.

Using NamedArrays for setting parameters

When a user sets a parameter, Mimi checks that the size and dimensions match what it expects for that component. If the user provides a NamedArray for the values, Mimi will further check that the names of the dimensions match the expected dimensions for that parameter, and that the labels match the model's index values for those dimensions. Examples of this can be found in "test/testparameterlabels.jl".