python-ottype 20.5.1

Python窝

用于操作转换（OT）的python库。基本思想遵循https://github.com/ottypes/docs的规范。在

安装

pip install ottype

OT操作

跳过

对象类型：int

从当前位置跳过n字符表示为n

^{pr2}$

插入

对象类型：str

在当前位置插入一个字符串s表示为s

assertapply('asdf',['qwer'])=='qwerasdf'assertapply('asdf',[2,'qwer'])=='asqwerdf'

删除

对象类型：dict

删除当前位置的字符串s表示为{'d': s}

assertapply('asdf',[{'d':'as'}])=='df'assertapply('asdf',[1,{'d':'sd'}])=='af'

支持的功能

OT=Union[int,str,dict]

check(ots: List[OT], *, check_unoptimized: bool = True) -> bool

检查列表是否只包含有效的OTs。如果check_unoptimized是{}，则只接受OTs的规范化列表。在

assertcheck(['a',4,'b'])assertnotcheck(['a','b'])# is not normalizedassertnotcheck([3])# is not normalized

apply(doc: str, ots: List[OT]) -> str

将OTs列表应用于字符串。在

assertapply('abcde',[2,'qq',{'d':'c'},1,'w'])=='abqqdwe'

inverse_apply(doc: str, ots: List[OT]) -> str

将OTs列表反向应用于字符串。在

assertinverse_apply(apply(doc,ots),ots)==doc

normalize(ots: List[OT]) -> List[OT]

规范化OTs列表：合并连续的OTs并修剪最后的跳过操作。在

assertnormalize([1,2,'as','df',{'d':'qw'},{'d':'er'},3]) \
        ==[3,'asdf',{'d':'qwer'}]

transform(ots1: List[OT], ots2: List[OT]) -> List[OT]

使用以下属性转换OTs列表：

assertapply(apply(doc,ots1),transform(ots2,ots1,'left')) \
        ==apply(apply(doc,ots2),transform(ots1,ots2,'right'))

compose(ots1: List[OT], ots2: List[OT]) -> List[OT]

使用属性组合两个OTs列表：

assertapply(apply(doc,ots1),ots2)==apply(doc,compose(ots1,ots2))

基准测试（python3.7）

apply函数

=== baseline (extra.old_ottype) ===
Doc Length :   100, OT Length :   5, Performance :   5.80 ms/loop (  1.00x )
Doc Length :   100, OT Length :  10, Performance :   8.87 ms/loop (  1.00x )
Doc Length :   100, OT Length :  20, Performance :  16.47 ms/loop (  1.00x )
Doc Length :   100, OT Length :  50, Performance :  39.13 ms/loop (  1.00x )
Doc Length :   100, OT Length : 100, Performance :  73.84 ms/loop (  1.00x )
Doc Length :  1000, OT Length :   5, Performance :   4.70 ms/loop (  1.00x )
Doc Length :  1000, OT Length :  10, Performance :   7.46 ms/loop (  1.00x )
Doc Length :  1000, OT Length :  20, Performance :  14.55 ms/loop (  1.00x )
Doc Length :  1000, OT Length :  50, Performance :  50.22 ms/loop (  1.00x )
Doc Length :  1000, OT Length : 100, Performance :  83.51 ms/loop (  1.00x )
Doc Length : 10000, OT Length :   5, Performance :   7.43 ms/loop (  1.00x )
Doc Length : 10000, OT Length :  10, Performance :  13.05 ms/loop (  1.00x )
Doc Length : 10000, OT Length :  20, Performance :  18.66 ms/loop (  1.00x )
Doc Length : 10000, OT Length :  50, Performance :  41.28 ms/loop (  1.00x )
Doc Length : 10000, OT Length : 100, Performance :  97.67 ms/loop (  1.00x )
=== python ===
Doc Length :   100, OT Length :   5, Performance :   6.77 ms/loop (  0.86x )
Doc Length :   100, OT Length :  10, Performance :  15.48 ms/loop (  0.57x )
Doc Length :   100, OT Length :  20, Performance :  27.14 ms/loop (  0.61x )
Doc Length :   100, OT Length :  50, Performance :  54.14 ms/loop (  0.72x )
Doc Length :   100, OT Length : 100, Performance :  84.28 ms/loop (  0.88x )
Doc Length :  1000, OT Length :   5, Performance :   4.23 ms/loop (  1.11x )
Doc Length :  1000, OT Length :  10, Performance :   8.74 ms/loop (  0.85x )
Doc Length :  1000, OT Length :  20, Performance :  18.65 ms/loop (  0.78x )
Doc Length :  1000, OT Length :  50, Performance :  37.61 ms/loop (  1.34x )
Doc Length :  1000, OT Length : 100, Performance :  82.60 ms/loop (  1.01x )
Doc Length : 10000, OT Length :   5, Performance :   8.86 ms/loop (  0.84x )
Doc Length : 10000, OT Length :  10, Performance :  13.19 ms/loop (  0.99x )
Doc Length : 10000, OT Length :  20, Performance :  20.43 ms/loop (  0.91x )
Doc Length : 10000, OT Length :  50, Performance :  48.91 ms/loop (  0.84x )
Doc Length : 10000, OT Length : 100, Performance : 102.81 ms/loop (  0.95x )
=== cython ===
Doc Length :   100, OT Length :   5, Performance :   0.77 ms/loop (  7.55x )
Doc Length :   100, OT Length :  10, Performance :   1.36 ms/loop (  6.53x )
Doc Length :   100, OT Length :  20, Performance :   2.34 ms/loop (  7.04x )
Doc Length :   100, OT Length :  50, Performance :   4.74 ms/loop (  8.25x )
Doc Length :   100, OT Length : 100, Performance :   9.73 ms/loop (  7.59x )
Doc Length :  1000, OT Length :   5, Performance :   0.70 ms/loop (  6.75x )
Doc Length :  1000, OT Length :  10, Performance :   1.61 ms/loop (  4.64x )
Doc Length :  1000, OT Length :  20, Performance :   2.47 ms/loop (  5.88x )
Doc Length :  1000, OT Length :  50, Performance :   5.52 ms/loop (  9.10x )
Doc Length :  1000, OT Length : 100, Performance :   9.02 ms/loop (  9.26x )
Doc Length : 10000, OT Length :   5, Performance :   2.20 ms/loop (  3.38x )
Doc Length : 10000, OT Length :  10, Performance :   2.57 ms/loop (  5.07x )
Doc Length : 10000, OT Length :  20, Performance :   2.95 ms/loop (  6.33x )
Doc Length : 10000, OT Length :  50, Performance :   5.97 ms/loop (  6.92x )
Doc Length : 10000, OT Length : 100, Performance :  10.92 ms/loop (  8.94x )

inverse_apply函数

=== baseline (extra.old_ottype) ===
Doc Length :   100, OT Length :   5, Performance :   8.26 ms/loop (  1.00x )
Doc Length :   100, OT Length :  10, Performance :  15.00 ms/loop (  1.00x )
Doc Length :   100, OT Length :  20, Performance :  27.50 ms/loop (  1.00x )
Doc Length :   100, OT Length :  50, Performance :  56.86 ms/loop (  1.00x )
Doc Length :   100, OT Length : 100, Performance : 129.10 ms/loop (  1.00x )
Doc Length :  1000, OT Length :   5, Performance :   6.35 ms/loop (  1.00x )
Doc Length :  1000, OT Length :  10, Performance :  16.14 ms/loop (  1.00x )
Doc Length :  1000, OT Length :  20, Performance :  22.65 ms/loop (  1.00x )
Doc Length :  1000, OT Length :  50, Performance :  67.26 ms/loop (  1.00x )
Doc Length :  1000, OT Length : 100, Performance : 113.74 ms/loop (  1.00x )
Doc Length : 10000, OT Length :   5, Performance :   8.79 ms/loop (  1.00x )
Doc Length : 10000, OT Length :  10, Performance :  10.16 ms/loop (  1.00x )
Doc Length : 10000, OT Length :  20, Performance :  22.46 ms/loop (  1.00x )
Doc Length : 10000, OT Length :  50, Performance :  54.26 ms/loop (  1.00x )
Doc Length : 10000, OT Length : 100, Performance : 129.20 ms/loop (  1.00x )
=== python ===
Doc Length :   100, OT Length :   5, Performance :   6.05 ms/loop (  1.37x )
Doc Length :   100, OT Length :  10, Performance :  11.30 ms/loop (  1.33x )
Doc Length :   100, OT Length :  20, Performance :  21.16 ms/loop (  1.30x )
Doc Length :   100, OT Length :  50, Performance :  44.43 ms/loop (  1.28x )
Doc Length :   100, OT Length : 100, Performance : 101.05 ms/loop (  1.28x )
Doc Length :  1000, OT Length :   5, Performance :  10.06 ms/loop (  0.63x )
Doc Length :  1000, OT Length :  10, Performance :  12.38 ms/loop (  1.30x )
Doc Length :  1000, OT Length :  20, Performance :  24.55 ms/loop (  0.92x )
Doc Length :  1000, OT Length :  50, Performance :  42.64 ms/loop (  1.58x )
Doc Length :  1000, OT Length : 100, Performance :  96.43 ms/loop (  1.18x )
Doc Length : 10000, OT Length :   5, Performance :   9.42 ms/loop (  0.93x )
Doc Length : 10000, OT Length :  10, Performance :  11.89 ms/loop (  0.85x )
Doc Length : 10000, OT Length :  20, Performance :  25.74 ms/loop (  0.87x )
Doc Length : 10000, OT Length :  50, Performance :  58.58 ms/loop (  0.93x )
Doc Length : 10000, OT Length : 100, Performance :  97.37 ms/loop (  1.33x )
=== cython ===
Doc Length :   100, OT Length :   5, Performance :   1.12 ms/loop (  7.37x )
Doc Length :   100, OT Length :  10, Performance :   1.69 ms/loop (  8.90x )
Doc Length :   100, OT Length :  20, Performance :   2.80 ms/loop (  9.82x )
Doc Length :   100, OT Length :  50, Performance :   5.49 ms/loop ( 10.35x )
Doc Length :   100, OT Length : 100, Performance :  12.22 ms/loop ( 10.56x )
Doc Length :  1000, OT Length :   5, Performance :   1.36 ms/loop (  4.68x )
Doc Length :  1000, OT Length :  10, Performance :   2.25 ms/loop (  7.19x )
Doc Length :  1000, OT Length :  20, Performance :   2.98 ms/loop (  7.61x )
Doc Length :  1000, OT Length :  50, Performance :   6.26 ms/loop ( 10.75x )
Doc Length :  1000, OT Length : 100, Performance :  11.14 ms/loop ( 10.21x )
Doc Length : 10000, OT Length :   5, Performance :   2.35 ms/loop (  3.75x )
Doc Length : 10000, OT Length :  10, Performance :   3.62 ms/loop (  2.81x )
Doc Length : 10000, OT Length :  20, Performance :   4.50 ms/loop (  4.99x )
Doc Length : 10000, OT Length :  50, Performance :   7.07 ms/loop (  7.68x )
Doc Length : 10000, OT Length : 100, Performance :  14.20 ms/loop (  9.10x )

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