Source code for yastn._split_combine_dict

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""" yastn.split_data_and_meta() and yastn.combine_data_and_meta() """
from __future__ import annotations

__all__ = ['split_data_and_meta', 'combine_data_and_meta']


[docs] def split_data_and_meta(d: dict, squeeze=False) -> tuple[tuple['numpy.array' | 'torch.tensor'], dict]: """ Split a dictionary generated by `to_dict` methods into a tuple containing data array, and a dictionary `meta` with remaining information. Meta is a copy of `d`, where data arrays are replaced with their position in the data tuple. Parameters ---------- d: dict A result of ``to_dict`` method squeeze: bool If True, and there is a single data tensor, it is unpacked from the tuple Return: data, meta """ data = [] # list of datas meta = _split_data_and_meta(d, data) data = data[0] if squeeze and len(data) == 1 else tuple(data) return data, meta
def _split_data_and_meta(d, data): meta = {} for k in sorted(d): if k == "data": data.append(d[k]) meta[k] = len(data) - 1 elif isinstance(d[k], dict): meta[k] = _split_data_and_meta(d[k], data) else: meta[k] = d[k] return meta
[docs] def combine_data_and_meta(data: tuple['numpy.array' | 'torch.tensor'], meta: dict) -> dict: """ Reverse :meth:`yastn.split_data_and_meta`. """ d = {} if not isinstance(data, (list, tuple)): data = [data] for k in sorted(meta): if k == "data": d[k] = data[meta[k]] elif isinstance(meta[k], dict): d[k] = combine_data_and_meta(data, meta[k]) else: d[k] = meta[k] return d