run#

SemiGrandCanonicalCalculator.run( self: SemiGrandCanonicalCalculator, state: IsingState, sampling_functions: StateSamplingFunctionMap, json_sampling_functions: jsonStateSamplingFunctionMap, completion_check_params: CompletionCheckParams, event_generator: SemiGrandCanonicalEventGenerator, sample_period: int = 1, method_log: MethodLog | None = None, random_engine: RandomNumberEngine = None, write_status_f: Callable[[SemiGrandCanonicalCalculator, MethodLog], None] | None = None, ) → None#

Run a semi-grand canonical calculation at a single thermodynamic state

Notes

On completion, results can be obtained from the data attribute.

Parameters:

state (IsingState) – Initial Monte Carlo state, including configuration and conditions. Is modified by the method.
sampling_functions (StateSamplingFunctionMap) – The sampling functions to use
json_sampling_functions (jsonStateSamplingFunctionMap) – The JSON sampling functions to use
completion_check_params (CompletionCheckParams) – Controls when the run finishes
event_generator (jsonStateSamplingFunctionMap) – An event generator which proposes new events and applies accepted events.
sample_period (int = 1) – Number of passes per sample. One pass is one Monte Carlo step per site with variable occupation.
method_log (MethodLog) – Method log, for writing status updates. If None, default writes to a “status.json” file in the current working directory every 10 minutes.
random_engine (RandomNumberEngine) – Random number engine. Default constructs a new engine.
write_status_f (Optional[Callable] = None) –
Function with signature
```
def f(
    mc_calculator: SemiGrandCanonicalCalculator,
    method_log: MethodLog,
) -> None:
    ...
```
accepting self as the first argument, that writes status updates, after a new sample has been taken and due according to method_log.log_frequency(). Default uses default_write_status(), which writes the current completion check results to method_log.logfile_path() and prints a summary of the current state and sampled data to stdout.