import selfies as sf点进入selfies，可以看到inti中有如下方法：
1、"encoder",
>>> import selfies as sf
>>> sf.encoder("C=CF")
'[C][=C][F]'
2、"decoder",
>>> import selfies as sf
>>> sf.decoder('[C][=C][F]')
'C=CF'
3、"get_preset_constraints",
返回给定名称的预设语义约束。
4、"get_semantic_robust_alphabet",
返回在当前语义约束下受 :mod:`selfies` 约束的所有 SELFIES 符号的子集。
5、"get_semantic_constraints",
语义约束，可以查看SELFIES里面存在的语义约束，我觉得就是化合价，SELFIES里面定义了如下语义约束
default_constraints = sf.get_semantic_constraints()
default_constraints
{'H': 1,
'F': 1,
'Cl': 1,
'Br': 1,
'I': 1,
'O': 2,
'O+1': 3,
'O-1': 1,
'N': 3,
'N+1': 4,
'N-1': 2,
'C': 4,
'C+1': 5,
'C-1': 3,
'S': 6,
'S+1': 7,
'S-1': 5,
'P': 7,
'P+1': 8,
'P-1': 6,
'?': 8}
6、"set_semantic_constraints",
更新:mod: ' selfie '操作的语义约束。
7、"len_selfies",
>>> import selfies as sf
>>> sf.len_selfies("[C][=C][F].[C]")
5
8、"split_selfies",
将SELFIES有序的拆分为多个独立的token
>>> import selfies as sf
>>> list(sf.split_selfies("[C][=C][F].[C]"))
['[C]', '[=C]', '[F]', '.', '[C]']
"""
9、"get_alphabet_from_selfies",
获得单个/多个SELFIES的词库，有点像去重的"split_selfies"方法 + 可以同时处理多个SELFIES
>>> import selfies as sf
>>> selfies_list = ["[C][F][O]", "[C].[O]", "[F][F]"]
>>> alphabet = sf.get_alphabet_from_selfies(selfies_list)
>>> sorted(list(alphabet))
['[C]', '[F]', '[O]']
10、"selfies_to_encoding",
Converts a SELFIES string into its label (integer)and/or one-hot encoding.
>>> import selfies as sf
>>> sf.selfies_to_encoding("[C][F]", {"[C]": 0, "[F]": 1})
([0, 1], [[1, 0], [0, 1]])
11、"batch_selfies_to_flat_hot",
将 多个SELFIES转为one-hot encodings
>>> import selfies as sf
>>> batch = ["[C]", "[C][C]"]
>>> vocab_stoi = {"[nop]": 0, "[C]": 1}
>>> sf.batch_selfies_to_flat_hot(batch, vocab_stoi, 2)
[[0, 1, 1, 0], [0, 1, 0, 1]]
12、"encoding_to_selfies",
>>> import selfies as sf
>>> one_hot = [[0, 1, 0], [0, 0, 1], [1, 0, 0]]
>>> vocab_itos = {0: "[nop]", 1: "[C]", 2: "[F]"}
>>> sf.encoding_to_selfies(one_hot, vocab_itos, enc_type="one_hot")
'[C][F][nop]'
13、"batch_flat_hot_to_selfies",
>>> import selfies as sf
>>> batch = [[0, 1, 1, 0], [0, 1, 0, 1]]
>>> vocab_itos = {0: "[nop]", 1: "[C]"}
>>> sf.batch_flat_hot_to_selfies(batch, vocab_itos)
['[C][nop]', '[C][C]']
14、"EncoderError",
Exception raised by :func:`selfies.encoder`.
15、"DecoderError"
Exception raised by :func:`selfies.decoder`.