deepdog/deepdog/indexify/__init__.py

59 lines
1.7 KiB
Python

"""
Probably should just include a way to handle the indexify function I reuse so much.
All about breaking an integer into a tuple of values from lists, which is useful because of how we do CHTC runs.
"""
import itertools
import typing
import logging
import math
_logger = logging.getLogger(__name__)
# from https://stackoverflow.com/questions/5228158/cartesian-product-of-a-dictionary-of-lists
def _dict_product(dicts):
"""
>>> list(dict_product(dict(number=[1,2], character='ab')))
[{'character': 'a', 'number': 1},
{'character': 'a', 'number': 2},
{'character': 'b', 'number': 1},
{'character': 'b', 'number': 2}]
"""
return list(dict(zip(dicts.keys(), x)) for x in itertools.product(*dicts.values()))
class Indexifier:
"""
The order of keys is very important, but collections.OrderedDict is no longer needed in python 3.7.
I think it's okay to rely on that.
"""
def __init__(self, list_dict: typing.Dict[str, typing.Sequence]):
self.dict = list_dict
self.product_dict = _dict_product(self.dict)
def indexify(self, n: int) -> typing.Dict[str, typing.Any]:
return self.product_dict[n]
def _indexify_indices(self, n: int) -> typing.Sequence[int]:
"""
legacy indexify from old scripts, copypast.
could be used like
>>> ret = {}
>>> for k, i in zip(self.dict.keys(), self._indexify_indices):
>>> ret[k] = self.dict[k][i]
>>> return ret
"""
weights = [len(v) for v in self.dict.values()]
N = math.prod(weights)
curr_n = n
curr_N = N
out = []
for w in weights[:-1]:
# print(f"current: {curr_N}, {curr_n}, {curr_n // w}")
curr_N = curr_N // w # should be int division anyway
out.append(curr_n // curr_N)
curr_n = curr_n % curr_N
return out