Modeler¶

Create and execute a module

First, you have to create an LSPModeler environment. As with the main LocalSolver object, we recommend using a “with” statement that will ensure the native memory allocated for the modeler is correctly released when the environment is discarded. LSPModeler is the main class of the Modeler library and allows you to create a module in one of two ways:

• You can load a module from an existing LSP file with the method LSPModeler.load_module().

• Or you can create an empty module with the method LSPModeler.create_module().

Both of these methods return an instance of LSPModule which will enable you to interact with the module variables and functions. To be runnable, a module must implement a model function that will build a LSModel. You can then call LSPModule.run() to start the execution of the module. After the LocalSolver model has been built, the resolution process will be started. You can also import arguments in a module with the method LSPModule.parse_arguments(). This method imports each passed argument as a global variable in the module:

with LSPModeler() as modeler:
    module = modeler.load_module("my_lsp_file.lsp")
    module.parse_arguments("lsIterationLimit=100", "lsTimeLimit=10")
    module.run()

Interacting with variables

The class LSPModule behaves like a dictionnary and you can retrieve, set or create new variables that will be exposed in your LSP code:

module = ...
print(module["lsIterationLimit"])
module["lsIterationLimit"] = 100000
module["myVariable"] = "spam, egg and cheese"
module["customData"] = -45.2
# Removing a variable, reset its value to "nil" in LSP
del module["lsIterationLimit"]

Under the hood, LSPModule overloads the special methods LSPModule.__getitem__(), LSPModule.__setitem__() and LSPModule.__delitem__().

These methods can receive or return python values that are transparently transformed to their LSP equivalent. The following python types are supported: boolean, integers, floating point numbers, strings and the special value None (which is transposed to nil in LSP).

If simple values are not sufficient for your usage, you can also create array-like or dictionary-like structures with LSPModeler.create_map(). For more information on maps you can look at the map module.

Using external functions

You can use native Python functions as LSP functions in the modeler thanks to the method LSPModeler.create_function(). The Python function must take a modeler instance as first argument, and LSP values as subsequent arguments. The function can return a value that will be converted and transmitted to the LSP caller:

def my_lsp_function(modeler, arg1, arg2):
    return arg1 + arg2

lsp_func = modeler.create_function(my_lsp_function)
result = lsp_func(1.5, 2.3)
print("result = {}".format(result)) # prints "result = 3.8"

In the snippet above we declare an LSP function that takes two LSP doubles as input, adds them together and returns the result. We can then call the function with LSPFunction.__call__() to execute the function and retrieve the result.

You can also assign the function to a variable in a module with LSPModule.__setitem__(). After doing so, the function will be callable within any function of the module.

Memory management

When you access a modeler object from the Python API, your Python program takes a new reference to that modeler object (LSPMap, LSPModule or LSPFunction are examples of references to modeler objects).

A modeler object can only be destroyed if no more Python reference points to it. In other words, a Python reference on a modeler object prevents the object from being freed even if it is no longer used in the modeler. If you have to create, access and destroy many objects in your LSP programs, you may keep in memory objects that have become inaccessible within your LSP programs.

We advise you not to rely on the destructors of python objects (__del__) to ensure a proper release of these references. Instead, use one of the 3 methods described below that are based on the with statement. They are much more explicit and safer:

1. Wait for the destruction of your LSPModeler object. All references are automatically freed when the modeler is released by using the special with statement on your LSPModeler instance. This is the simplest solution and the recommended solution if the interactions between the modeler and your LSP programs are limited to a few exchanges at the beginning/end of the resolution.

2. Release specific references at any time by using the with statement on objects LSPMap, LSPModule or LSPFunction. For instance:

   with modeler.create_map() as map:
       map.append(42)
   
   # Reference to 'map' is freed at the end of the ``with`` block
   

3. Release all references attached to a LSPReferenceScope. When you instantiate a new reference scope object by doing with LSPReferenceScope(modeler) as scope:, all references created from that point anywhere in the API will be automatically associated with that scope. At the end of the with statement, all references registred in the scope will be released and the previous scope will be restored and set as the current one.

Note that invoking any method on a released reference will throw an exception. It is therefore important not to release your references too early and to keep them alive as long as you need them.

Note also that several references can point to the same object if you query repeatedly the same key of a map or a module. At each call, a new reference is created. This is not a problem in itself, but try to minimize the number of calls to functions that return objects of type LSPMap, LSPModule or LSPFunction to reduce the number of references.