## Summary
This PR adds a fallback logic for `is_python_notebook` to check the
`kernelspec.language` field.
Reference implementation in VS Code:
1c31e75898/extensions/ipynb/src/deserializers.ts (L20-L22)
It's also required for the kernel to provide the `language` they're
implementing based on
https://jupyter-client.readthedocs.io/en/stable/kernels.html#kernel-specs
reference although that's for the `kernel.json` file but is also
included in the notebook metadata.
Closes: #12281
## Test Plan
Add a test case for `is_python_notebook` and include the test notebook
for round trip validation.
The test notebook contains two cells, one is JavaScript (denoted via the
`vscode.languageId` metadata) and the other is Python (no metadata). The
notebook metadata only contains `kernelspec` and the `language_info` is
absent.
I also verified that this is a valid notebook by opening it in Jupyter
Lab, VS Code and using `nbformat` validator.
## Summary
This PR adds support for VS Code specific cell metadata to consider when
collecting valid code cells.
For context, Ruff only runs on valid code cells. These are the code
cells that doesn't contain cell magics. Previously, Ruff only used the
notebook's metadata to determine whether it's a Python notebook. But, in
VS Code, a notebook's preferred language might be Python but it could
still contain code cells for other languages. This can be determined
with the `metadata.vscode.languageId` field.
### References:
* https://code.visualstudio.com/docs/languages/identifiers
* e6c009a3d4/extensions/ipynb/src/serializers.ts (L104-L107)
*
e6c009a3d4/extensions/ipynb/src/serializers.ts (L117-L122)
This brings us one step closer to fixing #12281.
## Test Plan
Add test cases for `is_valid_python_code_cell` and an integration test
case which showcase running it end to end. The test notebook contains a
JavaScript code cell and a Python code cell.
## Summary
Given a statement like `colors = 6`, we currently treat the cell as an
automagic (since `colors` is an automagic) -- i.e., we assume it's
equivalent to `%colors = 6`. This PR adds some additional detection
whereby if the statement is an _assignment_, we avoid treating it as
such. I audited the list of automagics, and I believe this is safe for
all of them.
Closes https://github.com/astral-sh/ruff/issues/8526.
Closes https://github.com/astral-sh/ruff/issues/9648.
## Test Plan
`cargo test`
The example below used to panic because we tried to split at 2 bytes in
the 4-bytes character `转`.
```python
def sample_func(xx):
"""
转置 (transpose)
"""
return xx.T
```
Fixes#9145
Fixes https://github.com/astral-sh/ruff-vscode/issues/362
The second commit is a small test refactoring.
## Summary
This PR updates the logic for `is_magic_cell` to include certain cell
magics. These cell magics would contain Python code following the line
defining the command. The code could define a variable which can then be
referenced in other cells. Currently, we would ignore the cell
completely leading to undefined-name violation.
As discussed in
https://github.com/astral-sh/ruff/issues/8354#issuecomment-1832221009
## Test Plan
Add new test case to validate this scenario.
## Summary
LangChain is attempting to use Ruff over their Jupyter notebooks
(https://github.com/langchain-ai/langchain/pull/12677/files), but
running into a bunch of syntax errors, the majority of which come from
our inability to recognize automagic.
If you run this in a cell:
```jupyter
pip install requests
```
Jupyter will automatically treat that as:
```jupyter
%pip install requests
```
We need to ignore cells that use these automagics, since the parser
doesn't understand them. (I guess we could support it in the parser, but
that seems much harder?). The good news is that AFAICT Jupyter doesn't
let you mix automagics with code, so by skipping these cells, we don't
miss out on analyzing any Python code.
## Test Plan
1. `cargo test`
2. Ran over LangChain and verified that there are no more errors
relating to `pip install` automagics.
## Summary
This PR moves `ruff/jupyter` into its own `ruff_notebook` crate. Beyond
the move itself, there were a few challenges:
1. `ruff_notebook` relies on the source map abstraction. I've moved the
source map into `ruff_diagnostics`, since it doesn't have any
dependencies on its own and is used alongside diagnostics.
2. `ruff_notebook` has a couple tests for end-to-end linting and
autofixing. I had to leave these tests in `ruff` itself.
3. We had code in `ruff/jupyter` that relied on Python lexing, in order
to provide a more targeted error message in the event that a user saves
a `.py` file with a `.ipynb` extension. I removed this in order to avoid
a dependency on the parser, it felt like it wasn't worth retaining just
for that dependency.
## Test Plan
`cargo test`
