scippuncertainty.mc.driver.MCResult

class scippuncertainty.mc.driver.MCResult(*, data, n_samples, samples)

Result of a Monte-Carlo error estimation.

Behaves like a dict of strings to data arrays but has additional properties that encode the number of samples that were computed and lists of those samples.

__init__(*, data, n_samples, samples)

Methods

__init__(*, data, n_samples, samples)
assemble(results)	Instantiate from results of individual accumulators.
clear()
copy()
fromkeys([value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

Attributes

n_samples	Number of samples.
samples	Recorded samples for each accumulator.

classmethod assemble(results)

Instantiate from results of individual accumulators.

Return type:

MCResult

property n_samples: int

Number of samples.

property samples: Mapping[str, tuple[DataArray, ...] | None]

Recorded samples for each accumulator.