The power of PySD, and its motivation for existence, is its ability to tie in to other models and analysis packages in the Python environment. In this section we’ll discuss how those connections happen.
Replacing model components with more complex objects¶
In the last section we saw that a parameter could take on a single value, or a series of values over time, with PySD linearly interpolating between the supplied time-series values. Behind the scenes, PySD is translating that constant or time-series into a function that then goes on to replace the original component in the model. For instance, in the teacup example, the room temperature was originally a function defined through parsing the model file as something similar to:
def room_temperature(): return 75
However, when we made the room temperature something that varied with time, PySD replaced this function with something like:
def room_temperature(): return np.interp(t, series.index, series.values)
This drew on the internal state of the system, namely the time t, and the time-series data series that that we wanted to variable to represent. This process of substitution is available to the user, and we can replace functions ourselves, if we are careful.
Because PySD assumes that all components in a model are represented as functions taking no arguments, any component that we wish to modify must be replaced with a function taking no arguments. As the state of the system and all auxiliary or flow methods are public, our replacement function can call these methods as part of its internal structure.
In our teacup example, suppose we didn’t know the functional form for calculating the heat lost to the room, but instead had a lot of data of teacup temperatures and heat flow rates. We could use a regression model (here a support vector regression from Scikit-Learn) in place of the analytic function:
from sklearn.svm import SVR regression = SVR() regression.fit(X_training, Y_training)
Once the regression model is fit, we write a wrapper function for its predict method that accesses the input components of the model and formats the prediction for PySD:
def new_heatflow_function(): """ Replaces the original flowrate equation with a regression model""" tea_temp = model.components.teacup_temperature() room_temp = model.components.room_temperature() return regression.predict([room_temp, tea_temp])
In order to substitute this function directly for the heat_loss_to_room model component using the
If you want to replace a subscripted variable, you need to ensure that the output from the new function is the same as the previous one. You can check the current coordinates and dimensions of a component by using
get_coords(variable_name) as it is explained in basic usage.
Alternatively, you can also set a model component directly:
model.components.heat_loss_to_room = new_heatflow_function
However, this will only accept the python name of the model component. While for the
set_component() method, the original name can be also used.
Splitting Vensim views in separate Python files (modules)¶
In order to replicate the Vensim views in translated models, the user can set the split_views argument to True in the
The option to split the model in views is particularly interesting for large models with tens of views. Translating those models into a single file may make the resulting Python model difficult to read and maintain.
In a Vensim model with three separate views (e.g. view_1, view_2 and view_3), setting split_views to True would create the following tree inside the directory where the .mdl model is located:
Often, modelers wish to organise views further. To that end, a common practice is to include a particular character in the View name to indicate that what comes after it is the name of the subview. For instance, we could name one view as ENERGY.Supply and another one as ENERGY.Demand. In that particular case, setting the subview_sep kwarg equal to [“.”], as in the code below, would name the translated views as demand.py and supply.py and place them inside the ENERGY folder:
read_vensim("many_views_model.mdl", split_views=True, subview_sep=["."])
If macros are present, they will be self-contained in files named as the macro itself. The macro inner variables will be placed inside the module that corresponds with the view in which they were defined.
Starting simulations from an end-state of another simulation¶
The current state of a model can be saved in a pickle file using the
import pysd model1 = pysd.read_vensim("my_model.mdl") model1.run(final_time=50) model1.export("final_state.pic")
Then the exported data can be used in another session:
import pysd model2 = pysd.load("my_model.py") model2 = run(initial_condition="final_state.pic", return_timestamps=[55, 60])
the new simulation will have initial time equal to 50 with the saved values from the previous one.
You can set the exact final time of the simulation using the final_time argument. If you want to avoid returning the dataframe of the stocks you can use return_timestamps=:
The changes done with params arguments are not ported to the new model (model2) object that you initialize with final_state.pic. If you want to keep them, you need to call run with the same params values as in the original model (model1).
Exported data is saved and loaded using pickle, this data can be incompatible with future versions of PySD or xarray. In order to prevent data losses save always the source code.