From Workspace and To Workspace
In this demo, we will describe the features of using the From Workspace and To Workspace blocks. Our goal is to show how we can feed data into the model or read it from it. To do this, a simple model is implemented, shown in the figure below.
Next, we will connect the library necessary to form the WorkspaceArray data structure.
First, let's create a DataFrame structure with columns of timestamps and data.
After that, we create a WorkspaceArray object, the first parameter of which is the internal database name, and the second is data from the DataFrame structure.
using DataFrames
# Задаем данные:
t = [1.0, 2.0, 4.0];
d = rand(Float64, size(t));
df = DataFrame(time=t, value=d)
wa = WorkspaceArray(string(rand()), df)
Let's move on to launching our model.
function run_model( name_model, path_to_folder )
Path = path_to_folder * "/" * name_model * ".engee"
if name_model in [m.name for m in engee.get_all_models()] # Проверка условия загрузки модели в ядро
model = engee.open( name_model ) # Открыть модель
model_output = engee.run( model, verbose=true ); # Запустить модель
else
model = engee.load( Path, force=true ) # Загрузить модель
model_output = engee.run( model, verbose=true ); # Запустить модель
engee.close( name_model, force=true ); # Закрыть модель
end
return model_output
end
run_model("from_to_workspace", @__DIR__)
At the output, we will also get the WorkspaceArray structure. Let's view its fields and compare them with the input data.
dump(out)
data = collect(out)
using Plots
plot(df.time,df.value) # Входные данные
plot!(data.time,data.value) # Выходные данные
As we can see from the comparison, as a result, the data that we submitted to the model was doubled.
Conclusion
We have demonstrated the options for transferring data from the workspace to the model and back. These features of the environment greatly simplify your interaction with the models.