This tutorial walks through the steps to download, run, and view the output of an existing model. There are several existing models publically available on Github, and for the purposes of this tutorial we will use The Climate Framework for Uncertainty, Negotiation and Distribution (FUND), available on Github here.
Working through the following tutorial will require:
- Julia v1.4.0 or higher
- Mimi v0.10.0 or higher
- connection of your julia installation with the central Mimi registry of Mimi models
If you have not yet prepared these, go back to the first tutorial to set up your system.
The first step in this process is downloading the FUND model, which is now made easy with the Mimi registry. Assuming you have already done the one-time run of the following command to connect your julia installation with the central Mimi registry of Mimi models, as instructed in the first tutorial,
pkg> registry add https://github.com/mimiframework/MimiRegistry.git
you simply need to add the FUND model in the Pkg REPL with:
pkg> add MimiFUND
The next step is to run FUND. If you wish to first get more acquainted with the model itself, take a look at the provided online documentation.
Now open a julia REPL and type the following command to load the MimiFUND package into the current environment:
Now we can access the public API of FUND, including the function
MimiFUND.get_model. This function returns a copy of the default FUND model. Here we will first get the model, and then use the
run function to run it.
m = MimiFUND.get_model() run(m)
These steps should be relatively consistent across models, where a repository for
ModelX should contain a primary file
ModelX.jl which exports, at minimum, a function named something like
construct_model which returns a version of the model, and can allow for model customization within the call.
In the MimiFUND package, the function
get_model has the signature
get_model(; nsteps = default_nsteps, datadir = default_datadir, params = default_params)
Thus there are no required arguments, although the user can input
nsteps to define the number of timesteps (years in this case) the model runs for,
datadir to define the location of the input data, and
params, a dictionary definining the parameters of the model. For example, if you wish to run only the first 200 timesteps, you may use:
using MimiFUND m = MimiFUND.get_model(nsteps = 200) run(m)
After the model has been run, you may access the results (the calculated variable values in each component) in a few different ways.
Start off by importing the Mimi package to your space with
First of all, you may use the
getindex syntax as follows:
m[:ComponentName, :VariableName] # returns the whole array of values m[:ComponentName, :VariableName] # returns just the 100th value
Indexing into a model with the name of the component and variable will return an array with values from each timestep. You may index into this array to get one value (as in the second line, which returns just the 100th value). Note that if the requested variable is two-dimensional, then a 2-D array will be returned. For example, try taking a look at the
income variable of the
socioeconomic component of FUND using the code below:
m[:socioeconomic, :income] m[:socioeconomic, :income]
You may also get data in the form of a dataframe, which will display the corresponding index labels rather than just a raw array. The syntax for this uses
getdataframe as follows:
getdataframe(m, :ComponentName=>:Variable) # request one variable from one component getdataframe(m, :ComponentName=>(:Variable1, :Variable2)) # request multiple variables from the same component getdataframe(m, :Component1=>:Var1, :Component2=>:Var2) # request variables from different components
Try doing this for the
income variable of the
socioeconomic component using:
getdataframe(m, :socioeconomic=>:income) # request one variable from one component getdataframe(m, :socioeconomic=>:income)[1:16,:] # results for all regions in first year (1950)
After running the FUND model, you may also explore the results using plots and graphs.
If you wish to explore the results graphically, use the explorer UI. This functionality is described in more detail in the second how-to guide, How-to Guide 2: View and Explore Model Results. For now, however, you don't need this level of detail and can simply follow the steps below.
To explore all variables and parameters of FUND in a dynamic UI app window, use the
explore function called with the model as the required first argument. The menu on the left hand side will list each element in a label formatted as
Alternatively, in order to view just one parameter or variable, call the function
explore as below to return a plot object and automatically display the plot in a viewer, assuming
explore is the last command executed. This call will return the type
VegaLite.VLSpec, which you may interact with using the API described in the VegaLite.jl documentation. For example, VegaLite.jl plots can be saved as PNG, SVG, PDF and EPS files. You may save a plot using the
Note that saving an interactive plot in a non-interactive file format, such as .pdf or .svg will result in a warning
WARN Can not resolve event source: window, but the plot will be saved as a static image. If you wish to preserve interactive capabilities, you may save it using the .vegalite file extension. If you then open this file in Jupyter lab, the interactive aspects will be preserved.
p = Mimi.plot(m, :mycomponent, :myvariable) save("MyFilePath.svg", p)
More specifically for our tutorial use of FUND, try:
p = Mimi.plot(m, :socioeconomic, :income) save("MyFilePath.svg", p)
You're done! Now feel free to move on to the next tutorial, which will go into depth on how to modify an existing model such as FUND.