mcsas3.McHat.McHat

class mcsas3.McHat.McHat(loadFromFile: Path | None = None, resultIndex: int = 1, **kwargs: dict)

Bases: object

The hat sits on top of the McCore. It takes care of parallel processing of each repetition.

__init__(loadFromFile: Path | None = None, resultIndex: int = 1, **kwargs: dict) → None

Methods

__init__([loadFromFile, resultIndex])
fillFitParameterLimits(measData)
load(filename[, path])
run(measData, filename[, resultIndex])	runs the full sequence: multiple repetitions of optimizations, to be parallelized.
runOnce(measData, filename[, repetition, ...])	runs the full sequence: multiple repetitions of optimizations, to be parallelized.
store(filename[, path])	stores the settings in an output file (HDF5)

Attributes

loadKeys
nCores
nRep	kwargs accepts all parameters from McModel and McOpt.
storeKeys

nRep = 10

kwargs accepts all parameters from McModel and McOpt.

run(measData: dict, filename: Path, resultIndex: int = 1) → None

runs the full sequence: multiple repetitions of optimizations, to be parallelized. This probably needs to be taken out of core, and into a new parent

runOnce(measData: dict, filename: Path, repetition: int = 0, bufferStdIO: bool = False, resultIndex: int = 1) → None

runs the full sequence: multiple repetitions of optimizations, to be parallelized. This probably needs to be taken out of core, and into a new parent

store(filename: Path, path: PurePosixPath | None = None) → None

stores the settings in an output file (HDF5)