A python library for Operational Transformation (OT). Basic idea follows the spec at https://github.com/ottypes/docs.
Supported Python versions : CPython 3.9 - 3.12, PyPy 3.9 - 3.10
pip install python-ottypeBasic usage:
import ottype
assert ottype.apply('asdf', [3, '123', {'d':'f'}]) == 'asd123'Object type : int
Skip n characters from the current position is represented as n
assert apply('asdf', [3]) == 'asdf'Object type : str
Insert a string s at the current position is represented as s
assert apply('asdf', ['qwer']) == 'qwerasdf'
assert apply('asdf', [2, 'qwer']) == 'asqwerdf'Object type : dict
Delete a string s at the current position is represented as {'d': s}
assert apply('asdf', [{'d': 'as'}]) == 'df'
assert apply('asdf', [1, {'d': 'sd'}]) == 'af'OT = int | str | dict[str, str]Check the sequence if it only contains valid OTs. If check_unoptimized is True, only normalized sequence of OTs is accepted.
assert check(['a', 4, 'b'])
assert not check(['a', 'b']) # is not normalized
assert not check([3]) # is not normalizedApply a sequence of OTs to a string.
assert apply('abcde', [2, 'qq', {'d': 'c'}, 1, 'w']) == 'abqqdwe'Inversely apply a sequence of OTs to a string.
assert inverse_apply(apply(doc, ots), ots) == docNormalize a sequence of OTs : merge consecutive OTs and trim the last skip operation.
assert normalize([1, 2, 'as', 'df', {'d': 'qw'}, {'d': 'er'}, 3]) \
== [3, 'asdf', {'d': 'qwer'}]Transform a sequence of OTs with the property:
assert apply(apply(doc, ots1), transform(ots2, ots1, 'left')) \
== apply(apply(doc, ots2), transform(ots1, ots2, 'right'))Compose two sequences of OTs with the property:
assert apply(apply(doc, ots1), ots2) == apply(doc, compose(ots1, ots2))| len(doc) | len(ots) | python (Kops/s) | cython (Kops/s) |
|---|---|---|---|
| 100 | 5 | 289.58 ( 1.00x) | 663.47 ( 2.29x) |
| 100 | 10 | 206.50 ( 1.00x) | 513.00 ( 2.48x) |
| 100 | 20 | 114.52 ( 1.00x) | 298.82 ( 2.61x) |
| 100 | 50 | 48.37 ( 1.00x) | 127.90 ( 2.64x) |
| 100 | 100 | 26.96 ( 1.00x) | 71.84 ( 2.66x) |
| 1000 | 5 | 412.41 ( 1.00x) | 974.43 ( 2.36x) |
| 1000 | 10 | 220.19 ( 1.00x) | 493.53 ( 2.24x) |
| 1000 | 20 | 128.66 ( 1.00x) | 317.18 ( 2.47x) |
| 1000 | 50 | 37.56 ( 1.00x) | 98.56 ( 2.62x) |
| 1000 | 100 | 23.43 ( 1.00x) | 64.25 ( 2.74x) |
| 10000 | 5 | 203.14 ( 1.00x) | 322.31 ( 1.59x) |
| 10000 | 10 | 142.25 ( 1.00x) | 279.79 ( 1.97x) |
| 10000 | 20 | 95.63 ( 1.00x) | 202.97 ( 2.12x) |
| 10000 | 50 | 48.58 ( 1.00x) | 122.09 ( 2.51x) |
| 10000 | 100 | 21.17 ( 1.00x) | 57.59 ( 2.72x) |
| len(doc) | len(ots) | python (Kops/s) | cython (Kops/s) |
|---|---|---|---|
| 100 | 5 | 194.12 ( 1.00x) | 408.56 ( 2.10x) |
| 100 | 10 | 113.27 ( 1.00x) | 269.10 ( 2.38x) |
| 100 | 20 | 60.99 ( 1.00x) | 150.49 ( 2.47x) |
| 100 | 50 | 26.35 ( 1.00x) | 64.27 ( 2.44x) |
| 100 | 100 | 16.62 ( 1.00x) | 42.87 ( 2.58x) |
| 1000 | 5 | 309.57 ( 1.00x) | 565.47 ( 1.83x) |
| 1000 | 10 | 153.62 ( 1.00x) | 351.55 ( 2.29x) |
| 1000 | 20 | 69.48 ( 1.00x) | 162.52 ( 2.34x) |
| 1000 | 50 | 37.12 ( 1.00x) | 91.88 ( 2.48x) |
| 1000 | 100 | 17.06 ( 1.00x) | 42.51 ( 2.49x) |
| 10000 | 5 | 145.11 ( 1.00x) | 257.81 ( 1.78x) |
| 10000 | 10 | 96.66 ( 1.00x) | 198.97 ( 2.06x) |
| 10000 | 20 | 65.18 ( 1.00x) | 140.90 ( 2.16x) |
| 10000 | 50 | 27.40 ( 1.00x) | 67.74 ( 2.47x) |
| 10000 | 100 | 13.53 ( 1.00x) | 32.23 ( 2.38x) |