iqm.benchmarks.utils.bootstrap_counts

iqm.benchmarks.utils.bootstrap_counts#

iqm.benchmarks.utils.bootstrap_counts(original_counts: dict[str, int], num_bootstrap_samples: int = 100, rgen: Generator | None = None, include_original_counts: bool = False) → list[dict[str, int]]#

Returns num_bootstrap_samples resampled copies of the original_counts.

Parameters:

  • original_counts (dict[str, int]) – The original counts to bootstrap from.

  • num_bootstrap_samples (int) – The number of bootstrapping samples to generate.

  • rgen (Generator | None) – Random number generator to use. If None, use Numpy’s default_rng().

  • include_original_counts (bool) – Whether to include the original counts in the returned bootstrapped count samples.

Returns:

Bootstrapped counts.

Return type:

list[dict[str, int]]

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