qlat_utils.g_mk_jk

qlat_utils.g_mk_jk(data_list, jk_idx_list, *, avg=None, jk_type, all_jk_idx, get_all_jk_idx, n_rand_sample, rng_state, jk_blocking_func, is_normalizing_rand_sample, is_apply_rand_sample_jk_idx_blocking_shift, is_use_old_rand_alg, is_hash_jk_idx, jk_idx_hash_size, eps, **_kwargs)

Perform (randomized) Super-Jackknife for the Jackknife data set.

–

Parameters:

• data_list – initial un-jackknifed data.

• jk_idx_list – should be list of indices that names the jk_arr.

• jk_type – [ "rjk", "super", ]

• eps – Error scaling factor.

Returns:

(randomized) Super-Jackknife data set.

Note that::

len(data_list) == len(jk_idx_list) jk_idx_list = [(job_tag, traj,) for traj in traj_list]

We can set eps to be factor len(data_list) larger.