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Controls#

The Controls class details control parameters for the RAT run. Note that many of these are specific to certain procedures: the relevant procedure is listed in square brackets before the field description.

pydantic model RATapi.Controls#

The full set of controls parameters for all five procedures that are required for the compiled RAT code.

Fields:

  • adaptPCR (bool)

  • boundHandling (RATapi.utils.enums.BoundHandling)

  • calcSldDuringFit (bool)

  • crossoverProbability (float)

  • display (RATapi.utils.enums.Display)

  • fWeight (float)

  • funcTolerance (float)

  • jumpProbability (float)

  • maxFuncEvals (int)

  • maxIterations (int)

  • nChains (int)

  • nLive (int)

  • nMCMC (int)

  • nSamples (int)

  • nsTolerance (float)

  • numGenerations (int)

  • pUnitGamma (float)

  • parallel (RATapi.utils.enums.Parallel)

  • populationSize (int)

  • procedure (RATapi.utils.enums.Procedures)

  • propScale (float)

  • resampleMinAngle (float)

  • resampleNPoints (int)

  • strategy (RATapi.utils.enums.Strategies)

  • targetValue (float)

  • updateFreq (int)

  • updatePlotFreq (int)

  • xTolerance (float)

field adaptPCR: bool = True#

[DREAM] Whether the crossover probability for differential evolution should be adapted during the run.

field boundHandling: BoundHandling = BoundHandling.Reflect#

[DREAM] How steps past the space boundaries should be handled. Can be ‘off’, ‘reflect’, ‘bound’, or ‘fold’.

field calcSldDuringFit: bool = False#

Whether SLD will be calculated during fit (for live plotting etc.)

field crossoverProbability: float = 0.8#

[DE] The probability of exchange of parameters between individuals at any iteration.

Constraints:

  • gt = 0.0

  • lt = 1.0

field display: Display = Display.Iter#

How much RAT should print to the terminal. Can be ‘off’, ‘iter’, ‘notify’, or ‘final’.

field fWeight: float = 0.5#

[DE] The step size for how different mutations are to their parents.

Constraints:

  • gt = 0.0

field funcTolerance: float = 1e-06#

[SIMPLEX] The termination tolerance for change in chi-squared.

Constraints:

  • gt = 0.0

field jumpProbability: float = 0.5#

[DREAM] The probability range for the size of jumps in sampling. Larger values mean more variable jumps.

Constraints:

  • gt = 0.0

  • lt = 1.0

field maxFuncEvals: int = 10000#

[SIMPLEX] The maximum number of function evaluations before the algorithm terminates.

Constraints:

  • gt = 0

field maxIterations: int = 1000#

[SIMPLEX] The maximum number of iterations before the algorithm terminates.

Constraints:

  • gt = 0

field nChains: int = 10#

[DREAM] The number of Markov chains to use in the algorithm.

Constraints:

  • gt = 0

field nLive: int = 150#

[NS] The number of points to sample.

Constraints:

  • ge = 1

field nMCMC: int = 0#

[NS] If non-zero, an MCMC process with nMCMC chains will be used instead of MultiNest.

Constraints:

  • ge = 0

field nSamples: int = 20000#

[DREAM] The number of samples in the initial population for each chain.

Constraints:

  • ge = 0

field nsTolerance: float = 0.1#

[NS] The tolerance threshold for when the algorithm should terminate.

Constraints:

  • ge = 0.0

field numGenerations: int = 500#

[DE] The maximum number of iterations before the algorithm terminates.

Constraints:

  • ge = 1

field pUnitGamma: float = 0.2#

[DREAM] The probability that the scaling-down factor of jumps will be ignored and a larger jump will be taken.

Constraints:

  • gt = 0.0

  • lt = 1.0

field parallel: Parallel = Parallel.Single#

How the calculation should be parallelised. Can be ‘single’, ‘contrasts’ or ‘points’.

field populationSize: int = 20#

[DE] The number of candidate solutions that exist at any time.

Constraints:

  • ge = 1

field procedure: Procedures = Procedures.Calculate#

Which procedure RAT should execute. Can be ‘calculate’, ‘simplex’, ‘de’, ‘ns’, or ‘dream’.

field propScale: float = 0.1#

[NS] A scaling factor for the ellipsoid generated by MultiNest.

Constraints:

  • gt = 0.0

  • lt = 1.0

field resampleMinAngle: float = 0.9#

The upper threshold on the angle between three sampled points for resampling, in units of radians over pi.

Constraints:

  • gt = 0

  • le = 1

field resampleNPoints: int = 50#

The number of initial points to use for resampling.

Constraints:

  • gt = 0

field strategy: Strategies = Strategies.RandomWithPerVectorDither#

[DE] The algorithm used to generate new candidates.

field targetValue: float = 1.0#

[DE] The value of chi-squared at which the algorithm will terminate.

Constraints:

  • ge = 1.0

field updateFreq: int = 1#

[SIMPLEX, DE] Number of iterations between printing progress updates to the terminal.

field updatePlotFreq: int = 20#

[SIMPLEX, DE] Number of iterations between updates to live plots.

field xTolerance: float = 1e-06#

[SIMPLEX] The termination tolerance for step size.

Constraints:

  • gt = 0.0

delete_IPC()#

Delete the inter-process communication file.

initialise_IPC()#

Setup the inter-process communication file.

classmethod load(path)#

Load a controls object from file.

Parameters:

path (str or Path) – The path to the controls object file.

Return type:

Controls

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.

  • context (Any) – The context.

Return type:

None

save(path, filename='controls')#

Save a controls object to a JSON file.

Parameters:

  • path (str or Path) – The directory in which the controls object will be written.

  • filename (str) – The name for the JSON file containing the controls object.

sendStopEvent()#

Sends the stop event via the inter-process communication file.

Warning

UserWarning

Raised if we try to delete an IPC file that was not initialised.

serialize()#

Filter fields so only those applying to the chosen procedure are serialized.

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