An extremely fast Python linter and code formatter, written in Rust.
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Dhruv Manilawala 3c99fbf808
Implement --diff for Jupyter Notebooks (#6149)
## Summary

Implement `--diff` for Jupyter Notebooks

## Test Plan

1. Use `crates/ruff/resources/test/fixtures/jupyter/isort.ipynb` as a
test case
and add a markdown cell in between the code cells to check that the diff
   outputs the correct cell index.
2. Run the command:
`cargo run --bin ruff --package ruff_cli -- check --no-cache --isolated
--select=ALL crates/ruff/resources/test/fixtures/jupyter/isort.ipynb
--fix --diff`

<details><summary>Example output:</summary>
<p>

```diff
--- /Users/dhruv/playground/ruff/notebooks/test.ipynb:cell 0
+++ /Users/dhruv/playground/ruff/notebooks/test.ipynb:cell 0
@@ -1,3 +0,0 @@
-from pathlib import Path
-import random
-import math
--- /Users/dhruv/playground/ruff/notebooks/test.ipynb:cell 4
+++ /Users/dhruv/playground/ruff/notebooks/test.ipynb:cell 4
@@ -1,5 +1,3 @@
-from typing import Any
-import collections
 # Newline should be added here
 def foo():
     pass

--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 8
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 8
@@ -1,8 +1,7 @@
 import pprint
 import tempfile
 
-from IPython import display
 import matplotlib.pyplot as plt
-
 import tensorflow as tf
-import tensorflow_datasets as tfds
+import tensorflow_datasets as tfds
+from IPython import display
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 10
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 10
@@ -1,5 +1,4 @@
 import tensorflow_models as tfm
 
 # These are not in the tfm public API for v2.9. They will be available in v2.10
-from official.vision.serving import export_saved_model_lib
-import official.core.train_lib
+from official.vision.serving import export_saved_model_lib
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 13
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 13
@@ -1,5 +1,5 @@
-exp_config = tfm.core.exp_factory.get_exp_config('resnet_imagenet')
-tfds_name = 'cifar10'
+exp_config = tfm.core.exp_factory.get_exp_config("resnet_imagenet")
+tfds_name = "cifar10"
 ds,ds_info = tfds.load(
 tfds_name,
 with_info=True)
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 15
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 15
@@ -6,12 +6,12 @@
 # Configure training and testing data
 batch_size = 128
 
-exp_config.task.train_data.input_path = ''
+exp_config.task.train_data.input_path = ""
 exp_config.task.train_data.tfds_name = tfds_name
-exp_config.task.train_data.tfds_split = 'train'
+exp_config.task.train_data.tfds_split = "train"
 exp_config.task.train_data.global_batch_size = batch_size
 
-exp_config.task.validation_data.input_path = ''
+exp_config.task.validation_data.input_path = ""
 exp_config.task.validation_data.tfds_name = tfds_name
-exp_config.task.validation_data.tfds_split = 'test'
+exp_config.task.validation_data.tfds_split = "test"
 exp_config.task.validation_data.global_batch_size = batch_size
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 17
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 17
@@ -1,16 +1,16 @@
 logical_device_names = [logical_device.name for logical_device in tf.config.list_logical_devices()]
 
-if 'GPU' in ''.join(logical_device_names):
-  print('This may be broken in Colab.')
-  device = 'GPU'
-elif 'TPU' in ''.join(logical_device_names):
-  print('This may be broken in Colab.')
-  device = 'TPU'
+if "GPU" in "".join(logical_device_names):
+  print("This may be broken in Colab.")
+  device = "GPU"
+elif "TPU" in "".join(logical_device_names):
+  print("This may be broken in Colab.")
+  device = "TPU"
 else:
-  print('Running on CPU is slow, so only train for a few steps.')
-  device = 'CPU'
+  print("Running on CPU is slow, so only train for a few steps.")
+  device = "CPU"
 
-if device=='CPU':
+if device=="CPU":
   train_steps = 20
   exp_config.trainer.steps_per_loop = 5
 else:
@@ -20,9 +20,9 @@
 exp_config.trainer.summary_interval = 100
 exp_config.trainer.checkpoint_interval = train_steps
 exp_config.trainer.validation_interval = 1000
-exp_config.trainer.validation_steps =  ds_info.splits['test'].num_examples // batch_size
+exp_config.trainer.validation_steps =  ds_info.splits["test"].num_examples // batch_size
 exp_config.trainer.train_steps = train_steps
-exp_config.trainer.optimizer_config.learning_rate.type = 'cosine'
+exp_config.trainer.optimizer_config.learning_rate.type = "cosine"
 exp_config.trainer.optimizer_config.learning_rate.cosine.decay_steps = train_steps
 exp_config.trainer.optimizer_config.learning_rate.cosine.initial_learning_rate = 0.1
 exp_config.trainer.optimizer_config.warmup.linear.warmup_steps = 100
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 21
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 21
@@ -1,14 +1,14 @@
 logical_device_names = [logical_device.name for logical_device in tf.config.list_logical_devices()]
 
 if exp_config.runtime.mixed_precision_dtype == tf.float16:
-    tf.keras.mixed_precision.set_global_policy('mixed_float16')
+    tf.keras.mixed_precision.set_global_policy("mixed_float16")
 
-if 'GPU' in ''.join(logical_device_names):
+if "GPU" in "".join(logical_device_names):
   distribution_strategy = tf.distribute.MirroredStrategy()
-elif 'TPU' in ''.join(logical_device_names):
+elif "TPU" in "".join(logical_device_names):
   tf.tpu.experimental.initialize_tpu_system()
-  tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='/device:TPU_SYSTEM:0')
+  tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu="/device:TPU_SYSTEM:0")
   distribution_strategy = tf.distribute.experimental.TPUStrategy(tpu)
 else:
-  print('Warning: this will be really slow.')
+  print("Warning: this will be really slow.")
   distribution_strategy = tf.distribute.OneDeviceStrategy(logical_device_names[0])
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 23
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 23
@@ -1,5 +1,3 @@
 with distribution_strategy.scope():
   model_dir = tempfile.mkdtemp()
   task = tfm.core.task_factory.get_task(exp_config.task, logging_dir=model_dir)
-
-#  tf.keras.utils.plot_model(task.build_model(), show_shapes=True)
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 24
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 24
@@ -1,4 +1,4 @@
 for images, labels in task.build_inputs(exp_config.task.train_data).take(1):
   print()
-  print(f'images.shape: {str(images.shape):16}  images.dtype: {images.dtype!r}')
-  print(f'labels.shape: {str(labels.shape):16}  labels.dtype: {labels.dtype!r}')
+  print(f"images.shape: {images.shape!s:16}  images.dtype: {images.dtype!r}")
+  print(f"labels.shape: {labels.shape!s:16}  labels.dtype: {labels.dtype!r}")
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 27
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 27
@@ -1 +1 @@
-plt.hist(images.numpy().flatten());
+plt.hist(images.numpy().flatten())
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 29
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 29
@@ -1,2 +1,2 @@
-label_info = ds_info.features['label']
+label_info = ds_info.features["label"]
 label_info.int2str(1)
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 31
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 31
@@ -10,9 +10,6 @@
     if predictions is None:
       plt.title(label_info.int2str(labels[i]))
     else:
-      if labels[i] == predictions[i]:
-        color = 'g'
-      else:
-        color = 'r'
+      color = "g" if labels[i] == predictions[i] else "r"
       plt.title(label_info.int2str(predictions[i]), color=color)
     plt.axis("off")
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 35
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 35
@@ -1,3 +1,3 @@
-plt.figure(figsize=(10, 10));
+plt.figure(figsize=(10, 10))
 for images, labels in task.build_inputs(exp_config.task.validation_data).take(1):
   show_batch(images, labels)
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 37
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 37
@@ -1,7 +1,7 @@
 model, eval_logs = tfm.core.train_lib.run_experiment(
     distribution_strategy=distribution_strategy,
     task=task,
-    mode='train_and_eval',
+    mode="train_and_eval",
     params=exp_config,
     model_dir=model_dir,
     run_post_eval=True)
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 38
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 38
@@ -1 +0,0 @@
-#  tf.keras.utils.plot_model(model, show_shapes=True)
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 40
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 40
@@ -1,4 +1,4 @@
 for key, value in eval_logs.items():
     if isinstance(value, tf.Tensor):
       value = value.numpy()
-    print(f'{key:20}: {value:.3f}')
+    print(f"{key:20}: {value:.3f}")
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 42
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 42
@@ -4,5 +4,5 @@
 
 show_batch(images, labels, tf.cast(predictions, tf.int32))
 
-if device=='CPU':
-  plt.suptitle('The model was only trained for a few steps, it is not expected to do well.')
+if device=="CPU":
+  plt.suptitle("The model was only trained for a few steps, it is not expected to do well.")
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 45
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 45
@@ -1,8 +1,8 @@
 # Saving and exporting the trained model
 export_saved_model_lib.export_inference_graph(
-    input_type='image_tensor',
+    input_type="image_tensor",
     batch_size=1,
     input_image_size=[32, 32],
     params=exp_config,
     checkpoint_path=tf.train.latest_checkpoint(model_dir),
-    export_dir='./export/')
+    export_dir="./export/")
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 47
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 47
@@ -1,3 +1,3 @@
 # Importing SavedModel
-imported = tf.saved_model.load('./export/')
-model_fn = imported.signatures['serving_default']
+imported = tf.saved_model.load("./export/")
+model_fn = imported.signatures["serving_default"]
--- /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 49
+++ /Users/dhruv/playground/ruff/notebooks/image_classification.ipynb:cell 49
@@ -1,10 +1,10 @@
 plt.figure(figsize=(10, 10))
-for data in tfds.load('cifar10', split='test').batch(12).take(1):
+for data in tfds.load("cifar10", split="test").batch(12).take(1):
   predictions = []
-  for image in data['image']:
-    index = tf.argmax(model_fn(image[tf.newaxis, ...])['logits'], axis=1)[0]
+  for image in data["image"]:
+    index = tf.argmax(model_fn(image[tf.newaxis, ...])["logits"], axis=1)[0]
     predictions.append(index)
-  show_batch(data['image'], data['label'], predictions)
+  show_batch(data["image"], data["label"], predictions)
 
-  if device=='CPU':
-    plt.suptitle('The model was only trained for a few steps, it is not expected to do better than random.')
+  if device=="CPU":
+    plt.suptitle("The model was only trained for a few steps, it is not expected to do better than random.")

Would fix 61 errors.
```

</p>
</details> 

resolves: #4727
2023-07-29 04:22:56 +00:00
.cargo Add Formatter benchmark (#4860) 2023-06-05 21:05:42 +02:00
.devcontainer Add devcontainer support (#4676) (#4678) 2023-05-30 14:49:51 +02:00
.github Add formatter progress tracking to CI (#5919) 2023-07-24 09:12:42 +00:00
assets Add a PNG variant of the Astral badge (#5155) 2023-06-17 03:24:32 +00:00
crates Implement --diff for Jupyter Notebooks (#6149) 2023-07-29 04:22:56 +00:00
docs Add some additional documentation around import categorization (#6107) 2023-07-26 22:39:01 +00:00
fuzz Pull in RustPython parser (#6099) 2023-07-27 09:29:11 +00:00
playground playground: Persist source and panel (#6071) 2023-07-26 07:55:59 +02:00
python/ruff Fix subprocess.run on Windows Python 3.7 (#5220) 2023-06-20 13:53:32 -04:00
scripts Implement E241 and E242 (tab/multiple ws after commas) (#6094) 2023-07-27 18:58:41 +00:00
.editorconfig markdownlint: enforce 100 char max length (#4698) 2023-05-28 22:45:56 -04:00
.gitattributes Add unreachable code rule (#5384) 2023-07-04 14:27:23 +00:00
.gitignore Read black options in format_dev script (#5827) 2023-07-17 13:29:43 +00:00
.markdownlint.yaml Fix nested lists in CONTRIBUTING.md (#5721) 2023-07-13 16:32:59 +00:00
.pre-commit-config.yaml Fix nested lists in CONTRIBUTING.md (#5721) 2023-07-13 16:32:59 +00:00
_typos.toml Fix typos found by codespell (#5607) 2023-07-08 12:33:18 +02:00
BREAKING_CHANGES.md Fix nested lists in CONTRIBUTING.md (#5721) 2023-07-13 16:32:59 +00:00
Cargo.lock Pull in RustPython parser (#6099) 2023-07-27 09:29:11 +00:00
Cargo.toml Pull in RustPython parser (#6099) 2023-07-27 09:29:11 +00:00
clippy.toml [numpy] deprecated type aliases (#2810) 2023-02-14 23:45:12 +00:00
CODE_OF_CONDUCT.md Fix nested lists in CONTRIBUTING.md (#5721) 2023-07-13 16:32:59 +00:00
CONTRIBUTING.md Pull in RustPython parser (#6099) 2023-07-27 09:29:11 +00:00
LICENSE Port Pyright's import resolver to Rust (#5381) 2023-06-27 16:15:07 +00:00
mkdocs.insiders.yml Add separate configuration for MkDocs Insiders plugins (#5544) 2023-07-05 18:40:21 -04:00
mkdocs.template.yml Add separate configuration for MkDocs Insiders plugins (#5544) 2023-07-05 18:40:21 -04:00
pyproject.toml Modify PyPA classifiers and Shields.io badge URLs (#6082) 2023-07-26 01:25:46 +00:00
README.md Modify PyPA classifiers and Shields.io badge URLs (#6082) 2023-07-26 01:25:46 +00:00
ruff.schema.json [flake8-pyi] Implement PYI049 (#6136) 2023-07-29 00:34:36 +00:00
rust-toolchain Upgrade to Rust 1.70 (#4848) 2023-06-04 17:51:47 +00:00

Ruff

Ruff image image image Actions status

Discord | Docs | Playground

An extremely fast Python linter, written in Rust.

Shows a bar chart with benchmark results.

Linting the CPython codebase from scratch.

  • 10-100x faster than existing linters
  • 🐍 Installable via pip
  • 🛠️ pyproject.toml support
  • 🤝 Python 3.11 compatibility
  • 📦 Built-in caching, to avoid re-analyzing unchanged files
  • 🔧 Autofix support, for automatic error correction (e.g., automatically remove unused imports)
  • 📏 Over 500 built-in rules
  • ⚖️ Near-parity with the built-in Flake8 rule set
  • 🔌 Native re-implementations of dozens of Flake8 plugins, like flake8-bugbear
  • ⌨️ First-party editor integrations for VS Code and more
  • 🌎 Monorepo-friendly, with hierarchical and cascading configuration

Ruff aims to be orders of magnitude faster than alternative tools while integrating more functionality behind a single, common interface.

Ruff can be used to replace Flake8 (plus dozens of plugins), isort, pydocstyle, yesqa, eradicate, pyupgrade, and autoflake, all while executing tens or hundreds of times faster than any individual tool.

Ruff is extremely actively developed and used in major open-source projects like:

...and many more.

Ruff is backed by Astral. Read the launch post, or the original project announcement.

Testimonials

Sebastián Ramírez, creator of FastAPI:

Ruff is so fast that sometimes I add an intentional bug in the code just to confirm it's actually running and checking the code.

Nick Schrock, founder of Elementl, co-creator of GraphQL:

Why is Ruff a gamechanger? Primarily because it is nearly 1000x faster. Literally. Not a typo. On our largest module (dagster itself, 250k LOC) pylint takes about 2.5 minutes, parallelized across 4 cores on my M1. Running ruff against our entire codebase takes .4 seconds.

Bryan Van de Ven, co-creator of Bokeh, original author of Conda:

Ruff is ~150-200x faster than flake8 on my machine, scanning the whole repo takes ~0.2s instead of ~20s. This is an enormous quality of life improvement for local dev. It's fast enough that I added it as an actual commit hook, which is terrific.

Timothy Crosley, creator of isort:

Just switched my first project to Ruff. Only one downside so far: it's so fast I couldn't believe it was working till I intentionally introduced some errors.

Tim Abbott, lead developer of Zulip:

This is just ridiculously fast... ruff is amazing.

Table of Contents

For more, see the documentation.

  1. Getting Started
  2. Configuration
  3. Rules
  4. Contributing
  5. Support
  6. Acknowledgements
  7. Who's Using Ruff?
  8. License

Getting Started

For more, see the documentation.

Installation

Ruff is available as ruff on PyPI:

pip install ruff

You can also install Ruff via Homebrew, Conda, and with a variety of other package managers.

Usage

To run Ruff, try any of the following:

ruff check .                        # Lint all files in the current directory (and any subdirectories)
ruff check path/to/code/            # Lint all files in `/path/to/code` (and any subdirectories)
ruff check path/to/code/*.py        # Lint all `.py` files in `/path/to/code`
ruff check path/to/code/to/file.py  # Lint `file.py`

Ruff can also be used as a pre-commit hook:

- repo: https://github.com/astral-sh/ruff-pre-commit
  # Ruff version.
  rev: v0.0.280
  hooks:
    - id: ruff

Ruff can also be used as a VS Code extension or alongside any other editor through the Ruff LSP.

Ruff can also be used as a GitHub Action via ruff-action:

name: Ruff
on: [ push, pull_request ]
jobs:
  ruff:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: chartboost/ruff-action@v1

Configuration

Ruff can be configured through a pyproject.toml, ruff.toml, or .ruff.toml file (see: Configuration, or Settings for a complete list of all configuration options).

If left unspecified, the default configuration is equivalent to:

[tool.ruff]
# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
select = ["E", "F"]
ignore = []

# Allow autofix for all enabled rules (when `--fix`) is provided.
fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
unfixable = []

# Exclude a variety of commonly ignored directories.
exclude = [
    ".bzr",
    ".direnv",
    ".eggs",
    ".git",
    ".git-rewrite",
    ".hg",
    ".mypy_cache",
    ".nox",
    ".pants.d",
    ".pytype",
    ".ruff_cache",
    ".svn",
    ".tox",
    ".venv",
    "__pypackages__",
    "_build",
    "buck-out",
    "build",
    "dist",
    "node_modules",
    "venv",
]

# Same as Black.
line-length = 88

# Allow unused variables when underscore-prefixed.
dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"

# Assume Python 3.10.
target-version = "py310"

[tool.ruff.mccabe]
# Unlike Flake8, default to a complexity level of 10.
max-complexity = 10

Some configuration options can be provided via the command-line, such as those related to rule enablement and disablement, file discovery, logging level, and more:

ruff check path/to/code/ --select F401 --select F403 --quiet

See ruff help for more on Ruff's top-level commands, or ruff help check for more on the linting command.

Rules

Ruff supports over 500 lint rules, many of which are inspired by popular tools like Flake8, isort, pyupgrade, and others. Regardless of the rule's origin, Ruff re-implements every rule in Rust as a first-party feature.

By default, Ruff enables Flake8's E and F rules. Ruff supports all rules from the F category, and a subset of the E category, omitting those stylistic rules made obsolete by the use of an autoformatter, like Black.

If you're just getting started with Ruff, the default rule set is a great place to start: it catches a wide variety of common errors (like unused imports) with zero configuration.

Beyond the defaults, Ruff re-implements some of the most popular Flake8 plugins and related code quality tools, including:

For a complete enumeration of the supported rules, see Rules.

Contributing

Contributions are welcome and highly appreciated. To get started, check out the contributing guidelines.

You can also join us on Discord.

Support

Having trouble? Check out the existing issues on GitHub, or feel free to open a new one.

You can also ask for help on Discord.

Acknowledgements

Ruff's linter draws on both the APIs and implementation details of many other tools in the Python ecosystem, especially Flake8, Pyflakes, pycodestyle, pydocstyle, pyupgrade, and isort.

In some cases, Ruff includes a "direct" Rust port of the corresponding tool. We're grateful to the maintainers of these tools for their work, and for all the value they've provided to the Python community.

Ruff's autoformatter is built on a fork of Rome's rome_formatter, and again draws on both API and implementation details from Rome, Prettier, and Black.

Ruff's import resolver is based on the import resolution algorithm from Pyright.

Ruff is also influenced by a number of tools outside the Python ecosystem, like Clippy and ESLint.

Ruff is the beneficiary of a large number of contributors.

Ruff is released under the MIT license.

Who's Using Ruff?

Ruff is used by a number of major open-source projects and companies, including:

Show Your Support

If you're using Ruff, consider adding the Ruff badge to project's README.md:

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

...or README.rst:

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

...or, as HTML:

<a href="https://github.com/astral-sh/ruff"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json" alt="Ruff" style="max-width:100%;"></a>

License

MIT