xsimlab.Model

class xsimlab.Model(processes)

An immutable collection of process units that together form a computational model.

This collection is ordered such that the computational flow is consistent with process inter-dependencies.

Ordering doesn’t need to be explicitly provided ; it is dynamically computed using the processes interfaces.

Processes interfaces are also used for automatically retrieving the model inputs, i.e., all the variables that require setting a value before running the model.

__init__(processes)

Parameters

processes (dict) – Dictionnary with process names as keys and classes (decorated with process()) as values.

Raises

NoteAProcessClassError – If values in processes are not classes decorated with process().

Methods

__init__(processes)	param processes Dictionnary with process names as keys and classes (decorated with
clone()	Clone the Model.
drop_processes(keys)	Drop processe(s) from this model.
execute(stage, runtime_context[, hooks, …])	Run one stage of a simulation.
get(k[,d])
items()
keys()
update_cache(var_key)	Update the model’s cache for a given model variable.
update_processes(processes)	Add or replace processe(s) in this model.
update_state(input_vars[, validate, …])	Update the model’s state (only input variables) with new values.
validate([p_names])	Run the variable validators of all or some of the processes in the model.
values()
visualize([show_only_variable, show_inputs, …])	Render the model as a graph using dot (require graphviz).

Attributes

active
all_vars	Returns all variables in the model as a list of (process_name, var_name) tuples (or an empty list).
all_vars_dict	Returns all variables in the model as a dictionary of lists of variable names grouped by process.
cache	Returns a mapping of model variables and some of their (meta)data cached for fastpath access.
dependent_processes	Returns a dictionary where keys are process names and values are lists of the names of dependent processes.
index_vars	Returns all index variables in the model as a list of (process_name, var_name) tuples (or an empty list).
index_vars_dict	Returns all index variables in the model as a dictionary of lists of variable names grouped by process.
input_vars	Returns all variables that require setting a value before running the model.
input_vars_dict	Returns all variables that require setting a value before running the model.
state	Returns a mapping of model variables and their current value.