Python-interoperability for Typescript-Interfaces. Transpiles TypeScript-Interface-definitions to Python TypedDicts, plus support for run-time type-checking of JSON-data.
ts2python is open source software under the Apache 2.0 License
Copyright 2021-2023 Eckhart Arnold arnold@badw.de, Bavarian Academy of Sciences and Humanities
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
The complete source-code of ts2python can be downloaded from the git-repository.
When processing JSON data, as for example, form a JSON-RPC call, with Python, it would be helpful to have Python-definitions of the JSON-structures at hand, in order to solicit IDE-Support, static type checking and, potentially to enable structural validation at runtime.
There exist different technologies for defining the structure of JSON-data. Next to JSON-schema, a de facto very popular technology for defining JSON-obejcts are Typescript-Interfaces. For example, the language server protocol defines the structure of the JSON-data exchanged between client and server with Typescript-Interfaces.
In order to enable structural validation on the Python-side, ts2python transpiles the typescript-interface definitions to Python-data structure definitions, primarily, TypedDicts, but with some postprocessing it can also be adjusted to other popular models for records or data structures in Python, e.g. pydantic-Classes and the like.
ts2python aims to support translation of TypeScript-Interfaces on two different tiers:
-
Tier 1: Transpilation of passive data-structures, that is, Typescript-definition-files that contain only data definitions and no function definitions and, in particular, only "passive" Typescript-Interface that define data-structures but do not contain any methods.
-
Tier 2: Tanspilation of active data-structures, function- and method-definitions, i.e. Translation of (almost) any Typescript-definition-file.
Presently, Tier 1 support, i.e. transpilation of passive data structures works quite well. So, for example, all Interfaces from the language server protocol V3.16 can be transpiled to Python Typed-Dicts.
Tier 2 support is still very much work in progress. I am using "vscode.d.ts"-definition file as test-case. Some things work, but there are still some unsupported constructs, and the Python code emitted for features that go beyond Tier 1 may not even be valid Python all the time! Please, keep that in mind.
The documentation presently only covers Tier 1 support.
ts2python can be installed from the command line with the command:
$ pip install ts2python
ts2python requires the parsing-expression-grammar-framework
DHParser
which will automatically be installed as a dependency by
the pip-command. ts2python requires at least Python Version 3.8
to run. (If there is any interest, I might backport it to Python 3.6.)
However, the Python-code it produces is backwards compatible
down to Python 3.6, if the
typing extensions
have been installed.
In order to generate TypedDict-classes from Typescript-Interfaces,
run ts2python on the Typescript-Interface definitions:
$ ts2python interfaces.ts
This generates a .py-file in same directory as the source file that contains the TypedDict-classes and can simpy be imported in Python-Code:
from interfaces import *
Json-data which adheres to a specific structure (no matter whether defined on the typescript side via interfaces or on the Python-side via TypedDicts) can easily be interchanged and deserialized:
import json
request_msg: RequestMessage = json.loads(input_data)
The root-type (RootMessage in the above example) can
be arbitrarily complex and deeply nested.
Calling ts2python from another Python-script:
The ts2python-Parser can, of course, also be accessed directly from Python with the followin imports and function calls:
from ts2python.ts2pthonParser import process_file
...
process_file("SOURCE.ts", "DESTINATION.py")
Or, use:
from ts2pthon.ts2pythonParser import compile_src, serialize_result
...
result, errors = compile_src(DOCUMENT)
if errors:
for e in errors: print(e)
else:
print(serialize_result(result))
ts2python ships support for runtime-type validation. While type errors can be detected by static type checkers, runtime type validation can be useful when processing data from an outside source which cannot statically be checked, like, for example, json-data stemming from an RPC-call. ts2python runtime-type validation can be invoked via dedicated functions or via decorator as in this example:
from ts2python.json_validation import TypedDict, type_check
class Position(TypedDict, total=True):
line: int
character: int
class Range(TypedDict, total=True):
start: Position
end: Position
@type_check
def middle_line(rng: Range) -> Position:
line = (rng['start']['line'] + rng['end']['line']) // 2
character = 0
return Position(line=line, character=character)
data = {'start': {'line': 1, 'character': 1},
'end': {'line': 8, 'character': 17}}
assert middle_line(data) == {'line': 4, 'character': 0}
malformed_data = {'start': 1, 'end': 8}
middle_line(malformed_data) # <- TypeError raised by @type_check
With the type decorator the last call fails with a TypeError:
TypeError: Parameter "rng" of function "middle_line" failed the type-check, because:
Type error(s) in dictionary of type <class '__main__.Range'>:
Field start: '1' is not of <class '__main__.Position'>, but of type <class 'int'>
Field end: '8' is not of <class '__main__.Position'>, but of type <class 'int'>
Both the call and the return types can be validated.
The output ts2python is somewhat more liberal than what strict typecheckers like mypy or pylance seem to allow. In particular class definitions inside TypedDicts are considered illegal by the specification und thus marked as an error by some type-checkers. Use the command-line switch "-a toplevel" to generate Python-code that is more acceptable to type checkers.
See ts2python.readthedocs.io for the comprehensive documentation of ts2python
The git-repository of ts2python contains unit-tests as well as doctests. After cloning ts2python from the git-repository with:
$ git clone https://github.com/jecki/ts2python
the unit tests can be found in the tests subdirectory.
Both the unit and the doctests can be run by changing to the
tests-sub-directory and calling the runner.py-skript therein.
$ cd tests
$ python runner.py
It is also possible to run the tests with pytest or nose, in case you have either of theses testing-frameworks installed.
For a demonstration how the TypeScript-Interfaces are transpiled
to Python-code, run the demo-3-16.sh-script (or demo-3-16.bat on Windows)
in the "demo"-sub-directory or the ts2python-repository.
(Presently, this does not work with the LSP 3.17 specs, because the specs contain typescript bugs at a few places and thus would need to be corrected by hand after downloading.)
Or, run the tst_ts2python_grammar.py in the ts2python-directory
and look up the grammar-test-reports in the "REPORT"-sub-directory
of the "test_grammar"-subdirectory.