Replace pylint with ruff (#756)

Ruff is a code linting and formatting tool that have the ability to
improve the code quality of our node and seems to be better than
previously used black & pylint.

This PR makes initial transition towards this tool.

.github/workflows/ci.yml

@@ -310,15 +310,14 @@ jobs:
             mv .venv/Scripts .venv/bin    # venv is placed under `Scripts` on Windows
           fi
           source .venv/bin/activate
-          pip3 install maturin black pylint pytest
+          pip3 install maturin ruff pytest
           maturin build -m apis/python/node/Cargo.toml
           pip3 install target/wheels/*
           dora new test_python_project --lang python --internal-create-with-path-dependencies
           cd test_python_project
 
           # Check Compliancy
-          black . --check
-          pylint --disable=C,R **/*.py
+          ruff check .
           pip install -e ./*/
           pytest
 

.github/workflows/node-hub-ci-cd.yml

@@ -86,7 +86,7 @@ jobs:
         run: |
           curl -sSL https://install.python-poetry.org | python3 -
           echo "$HOME/.local/bin" >> $GITHUB_PATH
-          pip install black pylint pytest
+          pip install ruff pytest
 
       - name: Set up Rust
         if: runner.os == 'Linux' || github.event_name == 'workflow_dispatch' || (github.event_name == 'release' && startsWith(github.ref, 'refs/tags/'))

.github/workflows/node_hub_test.sh

@@ -43,8 +43,7 @@ else
             if [ -f "$dir/pyproject.toml" ]; then
             echo "Running linting and tests for Python project in $dir..."
             pip install .
-            poetry run black --check .
-            poetry run pylint --disable=C,R  --ignored-modules=cv2,pyrealsense2 **/*.py
+            ruff check .
             poetry run pytest
             fi
         fi 

binaries/cli/src/template/python/__node-name__/README.md

@@ -10,16 +10,16 @@ pip install -e .
 
 ## Contribution Guide
 
-- Format with [black](https://github.com/psf/black):
+- Format with [ruff](https://docs.astral.sh/ruff/):
 
 ```bash
-black . # Format
+ruff check . --fix
 ```
 
-- Lint with [pylint](https://github.com/pylint-dev/pylint):
+- Lint with ruff:
 
 ```bash
-pylint --disable=C,R --ignored-modules=cv2 . # Lint
+ruff check .
 ```
 
 - Test with [pytest](https://github.com/pytest-dev/pytest)

binaries/cli/src/template/python/__node-name__/pyproject.toml

@@ -12,13 +12,12 @@ packages = [{ include = "__node_name__" }]
 [tool.poetry.dependencies]
 dora-rs = "^0.3.6"
 numpy = "< 2.0.0"
-pyarrow = ">= 5.0.0"
+pyarrow = ">= 15.0.0"
 python = "^3.7"
 
 [tool.poetry.dev-dependencies]
-pytest = ">= 8.3.4"
-pylint = ">= 3.3.2"
-black = ">= 24.10"
+pytest = ">= 6.3.4"
+ruff = ">= 0.9.1"
 
 [tool.poetry.scripts]
 __node-name__ = "__node_name__.main:main"

binaries/cli/src/template/python/mod.rs

@@ -77,9 +77,10 @@ fn create_custom_node(
         .with_context(|| format!("failed to write `{}`", node_path.display()))?;
 
     // tests/tests___node_name__.py
-    let node_path = root
-        .join("tests")
-        .join(format!("test_{}.py", name.replace(" ", "_")));
+    let node_path = root.join("tests").join(format!(
+        "test_{}.py",
+        name.replace(" ", "_").replace("-", "_")
+    ));
     let file = replace_space(_TEST_PY, &name);
     fs::write(&node_path, file)
         .with_context(|| format!("failed to write `{}`", node_path.display()))?;
@@ -90,8 +91,8 @@ fn create_custom_node(
     );
     println!("   cd {}", Path::new(".").join(&root).display());
     println!("   pip install -e . # Install",);
-    println!("   black . # Format");
-    println!("   pylint  --disable=C,R . # Lint",);
+    println!("   ruff check . --fix # Format");
+    println!("   ruff check . # Lint",);
     println!("   pytest . # Test");
 
     Ok(())

examples/piper/dummy_inference_2.py

@@ -1,7 +1,6 @@
 from dora import Node
 
 
-import numpy as np
 import h5py
 
 f = h5py.File("data/episode_0.hdf5", "r")

examples/piper/replay.py

@@ -1,7 +1,6 @@
 from dora import Node
 
 
-import numpy as np
 import h5py
 import os
 

examples/python-operator-dataflow/plot.py

@@ -1,6 +1,5 @@
 import os
 import cv2
-import time
 
 from dora import DoraStatus
 from utils import LABELS

node-hub/dora-argotranslate/dora_argotranslate/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-argotranslate/dora_argotranslate/main.py

@@ -2,10 +2,10 @@ import os
 
 os.environ["ARGOS_DEVICE_TYPE"] = "auto"
 
-from dora import Node
-import pyarrow as pa
 import argostranslate.package
 import argostranslate.translate
+import pyarrow as pa
+from dora import Node
 
 from_code = os.getenv("SOURCE_LANGUAGE", "fr")
 to_code = os.getenv("TARGET_LANGUAGE", "en")
@@ -15,8 +15,8 @@ argostranslate.package.update_package_index()
 available_packages = argostranslate.package.get_available_packages()
 package_to_install = next(
     filter(
-        lambda x: x.from_code == from_code and x.to_code == to_code, available_packages
-    )
+        lambda x: x.from_code == from_code and x.to_code == to_code, available_packages,
+    ),
 )
 argostranslate.package.install_from_path(package_to_install.download())
 

node-hub/dora-distil-whisper/dora_distil_whisper/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-distil-whisper/dora_distil_whisper/main.py

@@ -47,7 +47,8 @@ def load_model():
 
 
 def load_model_mlx():
-    from lightning_whisper_mlx import LightningWhisperMLX  # noqa
+    # noqa: disable: import-error
+    from lightning_whisper_mlx import LightningWhisperMLX
 
     whisper = LightningWhisperMLX(model="distil-large-v3", batch_size=12, quant=None)
     return whisper
@@ -78,7 +79,8 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
     if sum(1 for char in text if "\u4e00" <= char <= "\u9fff") / len(text) > 0.5:
         # Chinese text processing
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(text) // 2)
+            min_repeat_length,
+            min(max_repeat_length, len(text) // 2),
         ):
             for i in range(len(text) - repeat_length * 2 + 1):
                 chunk1 = text[i : i + repeat_length]
@@ -90,7 +92,8 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
         # Non-Chinese (space-separated) text processing
         words = text.split()
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(words) // 2)
+            min_repeat_length,
+            min(max_repeat_length, len(words) // 2),
         ):
             for i in range(len(words) - repeat_length * 2 + 1):
                 chunk1 = " ".join(words[i : i + repeat_length])

node-hub/dora-distil-whisper/pyproject.toml

@@ -28,8 +28,7 @@ dora-distil-whisper = "dora_distil_whisper.main:main"
 
 [tool.poetry.dev-dependencies]
 pytest = ">= 6.3.4"
-pylint = ">= 3.3.2"
-black = ">= 22.10"
+ruff = ">= 0.9.1"
 
 [build-system]
 requires = ["poetry-core>=1.8.0"]

node-hub/dora-distil-whisper/tests/test_distil_whisper.py

@@ -2,7 +2,6 @@ import pytest
 
 
 def test_import_main():
-
     from dora_distil_whisper.main import main
 
     # Check that everything is working, and catch dora Runtime Exception as we're not running in a dora dataflow.

node-hub/dora-echo/dora_echo/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-echo/dora_echo/main.py

@@ -1,12 +1,12 @@
 import argparse
 import os
+
 from dora import Node
 
 RUNNER_CI = True if os.getenv("CI") == "true" else False
 
 
 def main():
-
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(description="Simple arrow sender")
 
@@ -20,7 +20,7 @@ def main():
     args = parser.parse_args()
 
     node = Node(
-        args.name
+        args.name,
     )  # provide the name to connect to the dataflow if dynamic node
 
     for event in node:

node-hub/dora-internvl/dora_internvl/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-internvl/dora_internvl/main.py

@@ -1,9 +1,10 @@
 import os
-from dora import Node
+
 import numpy as np
 import pyarrow as pa
 import torch
 import torchvision.transforms as T
+from dora import Node
 from PIL import Image
 from torchvision.transforms.functional import InterpolationMode
 from transformers import AutoModel, AutoTokenizer
@@ -20,7 +21,7 @@ def build_transform(input_size):
             T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
             T.ToTensor(),
             T.Normalize(mean=MEAN, std=STD),
-        ]
+        ],
     )
     return transform
 
@@ -42,7 +43,7 @@ def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_
 
 
 def dynamic_preprocess(
-    image, min_num=1, max_num=12, image_size=448, use_thumbnail=False
+    image, min_num=1, max_num=12, image_size=448, use_thumbnail=False,
 ):
     orig_width, orig_height = image.size
     aspect_ratio = orig_width / orig_height
@@ -59,7 +60,7 @@ def dynamic_preprocess(
 
     # find the closest aspect ratio to the target
     target_aspect_ratio = find_closest_aspect_ratio(
-        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size,
     )
 
     # calculate the target width and height
@@ -91,7 +92,7 @@ def load_image(image_array: np.array, input_size=448, max_num=12):
     image = Image.fromarray(image_array).convert("RGB")
     transform = build_transform(input_size=input_size)
     images = dynamic_preprocess(
-        image, image_size=input_size, use_thumbnail=True, max_num=max_num
+        image, image_size=input_size, use_thumbnail=True, max_num=max_num,
     )
     pixel_values = [transform(image) for image in images]
     pixel_values = torch.stack(pixel_values)
@@ -116,7 +117,7 @@ def main():
         .to(device)
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_path, trust_remote_code=True, use_fast=False
+        model_path, trust_remote_code=True, use_fast=False,
     )
 
     node = Node()
@@ -138,10 +139,7 @@ def main():
                 width = metadata["width"]
                 height = metadata["height"]
 
-                if encoding == "bgr8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding == "rgb8":
+                if encoding == "bgr8" or encoding == "rgb8":
                     channels = 3
                     storage_type = np.uint8
                 else:
@@ -168,7 +166,7 @@ def main():
                     )
                     generation_config = dict(max_new_tokens=1024, do_sample=True)
                     response = model.chat(
-                        tokenizer, pixel_values, question, generation_config
+                        tokenizer, pixel_values, question, generation_config,
                     )
                     node.send_output(
                         "text",

node-hub/dora-keyboard/dora_keyboard/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-keyboard/dora_keyboard/main.py

@@ -1,7 +1,7 @@
-from pynput import keyboard
-from pynput.keyboard import Events
 import pyarrow as pa
 from dora import Node
+from pynput import keyboard
+from pynput.keyboard import Events
 
 
 def main():

node-hub/dora-microphone/dora_microphone/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-microphone/dora_microphone/main.py

@@ -1,9 +1,9 @@
-import sounddevice as sd
-import numpy as np
-import pyarrow as pa
-import time as tm
 import os
+import time as tm
 
+import numpy as np
+import pyarrow as pa
+import sounddevice as sd
 from dora import Node
 
 MAX_DURATION = float(os.getenv("MAX_DURATION", "0.1"))
@@ -19,7 +19,6 @@ def main():
     always_none = node.next(timeout=0.001) is None
     finished = False
 
-    # pylint: disable=unused-argument
     def callback(indata, frames, time, status):
         nonlocal buffer, node, start_recording_time, finished
 
@@ -36,7 +35,7 @@ def main():
 
     # Start recording
     with sd.InputStream(
-        callback=callback, dtype=np.int16, channels=1, samplerate=SAMPLE_RATE
+        callback=callback, dtype=np.int16, channels=1, samplerate=SAMPLE_RATE,
     ):
         while not finished:
-            sd.sleep(int(1000))
+            sd.sleep(1000)

node-hub/dora-openai-server/dora_openai_server/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-openai-server/dora_openai_server/main.py

@@ -1,11 +1,12 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
+import ast
+import asyncio
 from typing import List, Optional
+
+import pyarrow as pa
 import uvicorn
 from dora import Node
-import asyncio
-import pyarrow as pa
-import ast
+from fastapi import FastAPI
+from pydantic import BaseModel
 
 DORA_RESPONSE_TIMEOUT = 10
 app = FastAPI()
@@ -55,13 +56,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
         print("Passing input as string")
     if isinstance(data, list):
         data = pa.array(data)  # initialize pyarrow array
-    elif isinstance(data, str):
-        data = pa.array([data])
-    elif isinstance(data, int):
-        data = pa.array([data])
-    elif isinstance(data, float):
-        data = pa.array([data])
-    elif isinstance(data, dict):
+    elif isinstance(data, str) or isinstance(data, int) or isinstance(data, float) or isinstance(data, dict):
         data = pa.array([data])
     else:
         data = pa.array(data)  # initialize pyarrow array
@@ -73,12 +68,10 @@ async def create_chat_completion(request: ChatCompletionRequest):
         if event["type"] == "ERROR":
             response_str = "No response received. Err: " + event["value"][0].as_py()
             break
-        elif event["type"] == "INPUT" and event["id"] == "v1/chat/completions":
+        if event["type"] == "INPUT" and event["id"] == "v1/chat/completions":
             response = event["value"]
             response_str = response[0].as_py() if response else "No response received"
             break
-        else:
-            pass
 
     return ChatCompletionResponse(
         id="chatcmpl-1234",
@@ -90,7 +83,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
                 "index": 0,
                 "message": {"role": "assistant", "content": response_str},
                 "finish_reason": "stop",
-            }
+            },
         ],
         usage={
             "prompt_tokens": len(data),
@@ -110,7 +103,7 @@ async def list_models():
                 "object": "model",
                 "created": 1677610602,
                 "owned_by": "openai",
-            }
+            },
         ],
     }
 

node-hub/dora-openai-server/pyproject.toml

@@ -28,3 +28,6 @@ dora-openai-server = "dora_openai_server.main:main"
 [build-system]
 requires = ["poetry-core>=1.8.0"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.ruff.lint]
+extend-select = ["I"]

node-hub/dora-opus/dora_opus/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-opus/dora_opus/main.py

@@ -1,10 +1,10 @@
 import os
 from pathlib import Path
-from dora import Node
-import pyarrow as pa
-import numpy as np
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
+import numpy as np
+import pyarrow as pa
+from dora import Node
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 from_code = os.getenv("SOURCE_LANGUAGE", "zh")
 to_code = os.getenv("TARGET_LANGUAGE", "en")
@@ -29,7 +29,7 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
     if sum(1 for char in text if "\u4e00" <= char <= "\u9fff") / len(text) > 0.5:
         # Chinese text processing
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(text) // 2)
+            min_repeat_length, min(max_repeat_length, len(text) // 2),
         ):
             for i in range(len(text) - repeat_length * 2 + 1):
                 chunk1 = text[i : i + repeat_length]
@@ -41,7 +41,7 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
         # Non-Chinese (space-separated) text processing
         words = text.split()
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(words) // 2)
+            min_repeat_length, min(max_repeat_length, len(words) // 2),
         ):
             for i in range(len(words) - repeat_length * 2 + 1):
                 chunk1 = " ".join(words[i : i + repeat_length])

node-hub/dora-outtetts/README.md

@@ -10,16 +10,16 @@ pip install -e .
 
 ## Contribution Guide
 
-- Format with [black](https://github.com/psf/black):
+- Format with [ruff](https://docs.astral.sh/ruff/):
 
 ```bash
-black . # Format
+ruff check . --fix
 ```
 
-- Lint with [pylint](https://github.com/pylint-dev/pylint):
+- Lint with ruff:
 
 ```bash
-pylint --disable=C,R --ignored-modules=cv2 . # Lint
+ruff check .
 ```
 
 - Test with [pytest](https://github.com/pytest-dev/pytest)

node-hub/dora-outtetts/dora_outtetts/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-outtetts/dora_outtetts/__main__.py

@@ -1,5 +1,4 @@
 from .main import main
 
-
 if __name__ == "__main__":
     main()

node-hub/dora-outtetts/dora_outtetts/main.py

@@ -1,10 +1,11 @@
-from dora import Node
-import outetts
 import argparse  # Add argparse import
-import pathlib
 import os
-import torch
+import pathlib
+
+import outetts
 import pyarrow as pa
+import torch
+from dora import Node
 
 PATH_SPEAKER = os.getenv("PATH_SPEAKER", "speaker.json")
 
@@ -45,7 +46,6 @@ def create_speaker(interface, path):
     interface.save_speaker(speaker, "speaker.json")
 
     print("saved speaker.json")
-    return
 
 
 def main(arg_list: list[str] | None = None):
@@ -85,7 +85,7 @@ def main(arg_list: list[str] | None = None):
                     f"""Node received:
                 id: {event["id"]},
                 value: {event["value"]},
-                metadata: {event["metadata"]}"""
+                metadata: {event["metadata"]}""",
                 )
 
             elif event["id"] == "text":

node-hub/dora-outtetts/dora_outtetts/tests/test_main.py

@@ -1,7 +1,5 @@
 import pytest
-
-from dora_outtetts.main import load_interface
-from dora_outtetts.main import main
+from dora_outtetts.main import load_interface, main
 
 
 def test_import_main():

node-hub/dora-outtetts/pyproject.toml

@@ -21,8 +21,7 @@ outetts = "^0.2.3"
 
 [tool.poetry.dev-dependencies]
 pytest = ">= 6.3.4"
-pylint = ">= 3.3.2"
-black = ">= 22.10"
+ruff = ">= 0.9.1"
 
 [tool.poetry.scripts]
 dora-outtetts = "dora_outtetts.main:main"

node-hub/dora-parler/dora_parler/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-parler/dora_parler/main.py

@@ -1,19 +1,19 @@
-from threading import Thread
-from dora import Node
 import os
+import time
 from pathlib import Path
+from threading import Thread
+
 import numpy as np
-import torch
-import time
 import pyaudio
-
+import torch
+from dora import Node
 from parler_tts import ParlerTTSForConditionalGeneration, ParlerTTSStreamer
 from transformers import (
-    AutoTokenizer,
     AutoFeatureExtractor,
-    set_seed,
+    AutoTokenizer,
     StoppingCriteria,
     StoppingCriteriaList,
+    set_seed,
 )
 
 device = "cuda:0"  # if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
@@ -31,7 +31,7 @@ if bool(os.getenv("USE_MODELSCOPE_HUB") in ["True", "true"]):
         MODEL_NAME_OR_PATH = snapshot_download(MODEL_NAME_OR_PATH)
 
 model = ParlerTTSForConditionalGeneration.from_pretrained(
-    MODEL_NAME_OR_PATH, torch_dtype=torch_dtype, low_cpu_mem_usage=True
+    MODEL_NAME_OR_PATH, torch_dtype=torch_dtype, low_cpu_mem_usage=True,
 ).to(device)
 model.generation_config.cache_implementation = "static"
 model.forward = torch.compile(model.forward, mode="default")
@@ -58,7 +58,6 @@ stream = p.open(format=pyaudio.paInt16, channels=1, rate=sampling_rate, output=T
 
 
 def play_audio(audio_array):
-
     if np.issubdtype(audio_array.dtype, np.floating):
         max_val = np.max(np.abs(audio_array))
         audio_array = (audio_array / max_val) * 32767
@@ -73,7 +72,7 @@ class InterruptStoppingCriteria(StoppingCriteria):
         self.stop_signal = False
 
     def __call__(
-        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs,
     ) -> bool:
         return self.stop_signal
 
@@ -109,7 +108,6 @@ def generate_base(
     thread.start()
 
     for new_audio in streamer:
-
         current_time = time.time()
 
         print(f"Time between iterations: {round(current_time - prev_time, 2)} seconds")
@@ -127,7 +125,7 @@ def generate_base(
             if event["id"] == "stop":
                 stopping_criteria.stop()
                 break
-            elif event["id"] == "text":
+            if event["id"] == "text":
                 stopping_criteria.stop()
 
                 text = event["value"][0].as_py()

node-hub/dora-piper/dora_piper/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-piper/dora_piper/main.py

@@ -1,16 +1,16 @@
-from piper_sdk import C_PiperInterface
-from dora import Node
-import pyarrow as pa
-import numpy as np
 import os
 import time
 
+import numpy as np
+import pyarrow as pa
+from dora import Node
+from piper_sdk import C_PiperInterface
+
 TEACH_MODE = os.getenv("TEACH_MODE", "False") in ["True", "true"]
 
 
 def enable_fun(piper: C_PiperInterface):
-    """
-    使能机械臂并检测使能状态,尝试5s,如果使能超时则退出程序
+    """使能机械臂并检测使能状态,尝试5s,如果使能超时则退出程序
     """
     enable_flag = False
     # 设置超时时间（秒）
@@ -144,7 +144,6 @@ def main():
                 )
 
         elif event["type"] == "STOP":
-
             if not TEACH_MODE:
                 piper.MotionCtrl_2(0x01, 0x01, 50, 0x00)
                 piper.JointCtrl(0, 0, 0, 0, 0, 0)

node-hub/dora-pyaudio/README.md

@@ -22,16 +22,16 @@ pip install -e .
 
 ## Contribution Guide
 
-- Format with [black](https://github.com/psf/black):
+- Format with [ruff](https://docs.astral.sh/ruff/):
 
 ```bash
-black . # Format
+ruff check . --fix
 ```
 
-- Lint with [pylint](https://github.com/pylint-dev/pylint):
+- Lint with ruff:
 
 ```bash
-pylint --disable=C,R --ignored-modules=cv2 . # Lint
+ruff check .
 ```
 
 - Test with [pytest](https://github.com/pytest-dev/pytest)

node-hub/dora-pyaudio/dora_pyaudio/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-pyaudio/dora_pyaudio/__main__.py

@@ -1,5 +1,4 @@
 from .main import main
 
-
 if __name__ == "__main__":
     main()

node-hub/dora-pyaudio/dora_pyaudio/main.py

@@ -12,17 +12,16 @@ p = pyaudio.PyAudio()
 
 
 def play_audio(
-    audio_array: pa.array, sample_rate: int, stream: pyaudio.Stream = None
+    audio_array: pa.array, sample_rate: int, stream: pyaudio.Stream = None,
 ) -> pyaudio.Stream:
     """Play audio using pyaudio and replace stream if already exists"""
-
     if np.issubdtype(audio_array.dtype, np.floating):
         max_val = np.max(np.abs(audio_array))
         audio_array = (audio_array / max_val) * 32767
         audio_array = audio_array.astype(np.int16)
     if stream is None:
         stream = p.open(
-            format=pyaudio.paInt16, channels=1, rate=sample_rate, output=True
+            format=pyaudio.paInt16, channels=1, rate=sample_rate, output=True,
         )
     stream.write(audio_array.tobytes())
     return stream

node-hub/dora-pyaudio/pyproject.toml

@@ -18,8 +18,8 @@ pyaudio = ">= 0.1.0"
 
 [tool.poetry.dev-dependencies]
 pytest = ">= 6.3.4"
-pylint = ">= 2.5.2"
-black = ">= 22.10"
+ruff = ">= 0.9.1"
+
 
 [tool.poetry.scripts]
 dora-pyaudio = "dora_pyaudio.main:main"

node-hub/dora-pyorbbecksdk/dora_pyorbbecksdk/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-pyorbbecksdk/dora_pyorbbecksdk/main.py

@@ -13,21 +13,28 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 # ******************************************************************************
-import cv2
 import os
+
+import cv2
 import numpy as np
+import pyarrow as pa
+from dora import Node
 
 try:
-    from pyorbbecsdk import Context
-    from pyorbbecsdk import Config
-    from pyorbbecsdk import OBError
-    from pyorbbecsdk import OBSensorType, OBFormat
-    from pyorbbecsdk import Pipeline, FrameSet
-    from pyorbbecsdk import VideoStreamProfile
-    from pyorbbecsdk import VideoFrame
+    from pyorbbecsdk import (
+        Config,
+        Context,
+        FrameSet,
+        OBError,
+        OBFormat,
+        OBSensorType,
+        Pipeline,
+        VideoFrame,
+        VideoStreamProfile,
+    )
 except ImportError as err:
     print(
-        "Please install pyorbbecsdk first by following the instruction at: https://github.com/orbbec/pyorbbecsdk"
+        "Please install pyorbbecsdk first by following the instruction at: https://github.com/orbbec/pyorbbecsdk",
     )
     raise err
 
@@ -42,7 +49,7 @@ class TemporalFilter:
             result = frame
         else:
             result = cv2.addWeighted(
-                frame, self.alpha, self.previous_frame, 1 - self.alpha, 0
+                frame, self.alpha, self.previous_frame, 1 - self.alpha, 0,
             )
         self.previous_frame = result
         return result
@@ -115,14 +122,11 @@ def frame_to_bgr_image(frame: VideoFrame):
         image = np.resize(data, (height, width, 2))
         image = cv2.cvtColor(image, cv2.COLOR_YUV2BGR_UYVY)
     else:
-        print("Unsupported color format: {}".format(color_format))
+        print(f"Unsupported color format: {color_format}")
         return None
     return image
 
 
-from dora import Node
-import pyarrow as pa
-
 ESC_KEY = 27
 MIN_DEPTH_METERS = 0.01
 MAX_DEPTH_METERS = 15.0
@@ -141,7 +145,7 @@ def main():
     profile_list = pipeline.get_stream_profile_list(OBSensorType.COLOR_SENSOR)
     try:
         color_profile: VideoStreamProfile = profile_list.get_video_stream_profile(
-            640, 480, OBFormat.RGB, 30
+            640, 480, OBFormat.RGB, 30,
         )
     except OBError as e:
         print(e)
@@ -150,7 +154,7 @@ def main():
     profile_list = pipeline.get_stream_profile_list(OBSensorType.DEPTH_SENSOR)
     try:
         depth_profile: VideoStreamProfile = profile_list.get_video_stream_profile(
-            640, 480, OBFormat.Y16, 30
+            640, 480, OBFormat.Y16, 30,
         )
     except OBError as e:
         print(e)
@@ -200,7 +204,7 @@ def main():
             node.send_output("depth", storage)
             # Covert to Image
             depth_image = cv2.normalize(
-                depth_data, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U
+                depth_data, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U,
             )
             # Send Depth Image
             depth_image = cv2.applyColorMap(depth_image, cv2.COLORMAP_JET)

node-hub/dora-pyrealsense/dora_pyrealsense/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-pyrealsense/dora_pyrealsense/main.py

@@ -4,11 +4,10 @@ import time
 import cv2
 import numpy as np
 import pyarrow as pa
-
+import pyrealsense2 as rs
 from dora import Node
 
 RUNNER_CI = True if os.getenv("CI") == "true" else False
-import pyrealsense2 as rs
 
 
 def main():
@@ -26,7 +25,7 @@ def main():
     serials = [device.get_info(rs.camera_info.serial_number) for device in devices]
     if DEVICE_SERIAL and (DEVICE_SERIAL in serials):
         raise ConnectionError(
-            f"Device with serial {DEVICE_SERIAL} not found within: {serials}."
+            f"Device with serial {DEVICE_SERIAL} not found within: {serials}.",
         )
 
     pipeline = rs.pipeline()
@@ -53,7 +52,6 @@ def main():
     pa.array([])  # initialize pyarrow array
 
     for event in node:
-
         # Run this example in the CI for 10 seconds only.
         if RUNNER_CI and time.time() - start_time > 10:
             break
@@ -116,7 +114,7 @@ def main():
                 # metadata["principal_point"] = [int(rgb_intr.ppx), int(rgb_intr.ppy)]
                 node.send_output("image", storage, metadata)
                 node.send_output(
-                    "depth", pa.array(scaled_depth_image.ravel()), metadata
+                    "depth", pa.array(scaled_depth_image.ravel()), metadata,
                 )
 
         elif event_type == "ERROR":

node-hub/dora-pyrealsense/tests/test_dora_pyrealsense.py

@@ -2,7 +2,6 @@ import pytest
 
 
 def test_import_main():
-
     from dora_pyrealsense.main import main
 
     # Check that everything is working, and catch dora Runtime Exception as we're not running in a dora dataflow.

node-hub/dora-qwenvl/dora_qwenvl/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-qwenvl/dora_qwenvl/main.py

@@ -1,13 +1,14 @@
 import os
-from dora import Node
-import torch
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-from qwen_vl_utils import process_vision_info
+from pathlib import Path
+
+import cv2
 import numpy as np
 import pyarrow as pa
+import torch
+from dora import Node
 from PIL import Image
-from pathlib import Path
-import cv2
+from qwen_vl_utils import process_vision_info
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
 
 DEFAULT_PATH = "Qwen/Qwen2-VL-2B-Instruct"
 
@@ -27,7 +28,7 @@ ADAPTER_PATH = os.getenv("ADAPTER_PATH", "")
 
 # Check if flash_attn is installed
 try:
-    import flash_attn as _
+    import flash_attn as _  # noqa
 
     model = Qwen2VLForConditionalGeneration.from_pretrained(
         MODEL_NAME_OR_PATH,
@@ -52,10 +53,8 @@ processor = AutoProcessor.from_pretrained(MODEL_NAME_OR_PATH)
 
 
 def generate(frames: dict, question):
+    """Generate the response to the question given the image using Qwen2 model.
     """
-    Generate the response to the question given the image using Qwen2 model.
-    """
-
     messages = [
         {
             "role": "user",
@@ -69,12 +68,12 @@ def generate(frames: dict, question):
             + [
                 {"type": "text", "text": question},
             ],
-        }
+        },
     ]
 
     # Preparation for inference
     text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
+        messages, tokenize=False, add_generation_prompt=True,
     )
     image_inputs, video_inputs = process_vision_info(messages)
     inputs = processor(
@@ -118,7 +117,6 @@ def main():
         event_type = event["type"]
 
         if event_type == "INPUT":
-
             event_id = event["id"]
 
             if "image" in event_id:
@@ -128,13 +126,7 @@ def main():
                 width = metadata["width"]
                 height = metadata["height"]
 
-                if encoding == "bgr8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding == "rgb8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"]:
+                if encoding == "bgr8" or encoding == "rgb8" or encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"]:
                     channels = 3
                     storage_type = np.uint8
                 else:

node-hub/dora-rdt-1b/dora_rdt_1b/RoboticsDiffusionTransformer

@@ -1 +1 @@
-Subproject commit 198374ea8c4a2ec2ddae86c35448d21aa9756f37
+Subproject commit b2889e65cfe62571ced3ce88f00e7d80b41fee69

node-hub/dora-rdt-1b/dora_rdt_1b/__init__.py

@@ -7,7 +7,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-rdt-1b/dora_rdt_1b/main.py

@@ -1,21 +1,22 @@
 # install dependencies as shown in the README here https://github.com/alik-git/RoboticsDiffusionTransformer?tab=readme-ov-file#installation
-import yaml
-import torch
+import os
+from pathlib import Path
+
+import cv2
 import numpy as np
+import pyarrow as pa
+import torch
+import yaml
+from dora import Node
 from PIL import Image
 
 from dora_rdt_1b.RoboticsDiffusionTransformer.configs.state_vec import (
     STATE_VEC_IDX_MAPPING,
 )
-from dora import Node
-import cv2
-import pyarrow as pa
-import os
-from pathlib import Path
 
 VISION_DEFAULT_PATH = "robotics-diffusion-transformer/rdt-1b"
 ROBOTIC_MODEL_NAME_OR_PATH = os.getenv(
-    "ROBOTIC_MODEL_NAME_OR_PATH", VISION_DEFAULT_PATH
+    "ROBOTIC_MODEL_NAME_OR_PATH", VISION_DEFAULT_PATH,
 )
 LANGUAGE_EMBEDDING_PATH = os.getenv("LANGUAGE_EMBEDDING", "lang_embed.pt")
 
@@ -33,7 +34,7 @@ config_path = (
     file_path / "RoboticsDiffusionTransformer/configs/base.yaml"
 )  # default config
 
-with open(config_path, "r", encoding="utf-8") as fp:
+with open(config_path, encoding="utf-8") as fp:
     config = yaml.safe_load(fp)
 
 
@@ -74,7 +75,7 @@ def get_language_embeddings():
     )
 
     return lang_embeddings.unsqueeze(
-        0
+        0,
     )  # Size:  (B, L_lang, D) or None, language condition tokens (variable length),    dimension D is assumed to be the same as the hidden size.
 
 
@@ -82,14 +83,13 @@ def expand2square(pil_img, background_color):
     width, height = pil_img.size
     if width == height:
         return pil_img
-    elif width > height:
+    if width > height:
         result = Image.new(pil_img.mode, (width, width), background_color)
         result.paste(pil_img, (0, (width - height) // 2))
         return result
-    else:
-        result = Image.new(pil_img.mode, (height, height), background_color)
-        result.paste(pil_img, ((height - width) // 2, 0))
-        return result
+    result = Image.new(pil_img.mode, (height, height), background_color)
+    result.paste(pil_img, ((height - width) // 2, 0))
+    return result
 
 
 def process_image(rgbs_lst, image_processor, vision_encoder):
@@ -156,12 +156,12 @@ def get_states(proprio):
 
     B, N = 1, 1  # batch size and state history size
     states = torch.zeros(
-        (B, N, config["model"]["state_token_dim"]), device=DEVICE, dtype=DTYPE
+        (B, N, config["model"]["state_token_dim"]), device=DEVICE, dtype=DTYPE,
     )
     # suppose you do not have proprio
     # it's kind of tricky, I strongly suggest adding proprio as input and further fine-tuning
     proprio = torch.tensor(proprio, device=DEVICE, dtype=DTYPE).reshape(
-        (1, 1, -1)
+        (1, 1, -1),
     )  # B, N = 1, 1  # batch size and state history size
 
     # if you have proprio, you can do like this
@@ -170,19 +170,19 @@ def get_states(proprio):
     states[:, :, STATE_INDICES] = proprio
 
     state_elem_mask = torch.zeros(
-        (1, config["model"]["state_token_dim"]), device=DEVICE, dtype=torch.bool
+        (1, config["model"]["state_token_dim"]), device=DEVICE, dtype=torch.bool,
     )
 
     state_elem_mask[:, STATE_INDICES] = True
-    states, state_elem_mask = states.to(DEVICE, dtype=DTYPE), state_elem_mask.to(
-        DEVICE, dtype=DTYPE
+    states, state_elem_mask = (
+        states.to(DEVICE, dtype=DTYPE),
+        state_elem_mask.to(DEVICE, dtype=DTYPE),
     )
     states = states[:, -1:, :]  # only use the last state
     return states, state_elem_mask, STATE_INDICES
 
 
 def main():
-
     rdt = get_policy()
     lang_embeddings = get_language_embeddings()
     vision_encoder, image_processor = get_vision_model()
@@ -195,11 +195,9 @@ def main():
     frames = {}
     joints = {}
     with torch.no_grad():
-
         for event in node:
             event_type = event["type"]
             if event_type == "INPUT":
-
                 event_id = event["id"]
 
                 if "image" in event_id:
@@ -207,13 +205,7 @@ def main():
                     metadata = event["metadata"]
                     encoding = metadata["encoding"]
 
-                    if encoding == "bgr8":
-                        channels = 3
-                        storage_type = np.uint8
-                    elif encoding == "rgb8":
-                        channels = 3
-                        storage_type = np.uint8
-                    elif encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"]:
+                    if encoding == "bgr8" or encoding == "rgb8" or encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"]:
                         channels = 3
                         storage_type = np.uint8
                     else:
@@ -243,13 +235,13 @@ def main():
                     else:
                         raise RuntimeError(f"Unsupported image encoding: {encoding}")
                     frames[f"last_{event_id}"] = frames.get(
-                        event_id, Image.fromarray(frame)
+                        event_id, Image.fromarray(frame),
                     )
                     frames[event_id] = Image.fromarray(frame)
                 elif "jointstate" in event_id:
                     joints[event_id] = event["value"].to_numpy()
 
-                elif "tick" == event_id:
+                elif event_id == "tick":
                     ## Wait for all images
                     if len(frames.keys()) < 6:
                         continue
@@ -270,7 +262,7 @@ def main():
                         ],
                     ]
                     image_embeds = process_image(
-                        rgbs_lst, image_processor, vision_encoder
+                        rgbs_lst, image_processor, vision_encoder,
                     )
 
                     ## Embed states
@@ -278,26 +270,26 @@ def main():
                         [
                             joints["jointstate_left"],
                             joints["jointstate_right"],
-                        ]
+                        ],
                     )
                     states, state_elem_mask, state_indices = get_states(proprio=proprio)
 
                     actions = rdt.predict_action(
                         lang_tokens=lang_embeddings,
                         lang_attn_mask=torch.ones(
-                            lang_embeddings.shape[:2], dtype=torch.bool, device=DEVICE
+                            lang_embeddings.shape[:2], dtype=torch.bool, device=DEVICE,
                         ),
                         img_tokens=image_embeds,
                         state_tokens=states,  # how can I get this?
                         action_mask=state_elem_mask.unsqueeze(1),  # how can I get this?
                         ctrl_freqs=torch.tensor(
-                            [25.0], device=DEVICE
+                            [25.0], device=DEVICE,
                         ),  # would this default work?
                     )  # (1, chunk_size, 128)
 
                     # select the meaning action via STATE_INDICES
                     action = actions[
-                        :, :, state_indices
+                        :, :, state_indices,
                     ]  # (1, chunk_size, len(STATE_INDICES)) = (1, chunk_size, 7+ 1)
                     action = action.detach().float().to("cpu").numpy()
                     node.send_output("action", pa.array(action.ravel()))

node-hub/dora-rdt-1b/pyproject.toml

@@ -29,12 +29,9 @@ huggingface_hub = "0.23.5"
 # flash_attn = "^2.6.1" # Install using: pip install -U flash-attn --no-build-isolation
 
 
-[tool.pylint.MASTER]
-ignore-paths = '^dora_rdt_1b/RoboticsDiffusionTransformer.*$'
-
 [tool.poetry.dev-dependencies]
 pytest = "^8.3.4"
-pylint = "^3.3.2"
+ruff = ">= 0.9.1"
 
 [tool.black]
 extend-exclude = 'dora_rdt_1b/RoboticsDiffusionTransformer'
@@ -46,3 +43,6 @@ dora-rdt-1b = "dora_rdt_1b.main:main"
 [build-system]
 requires = ["poetry-core>=1.8.0"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+exclude = ["dora_rdt_1b/RoboticsDiffusionTransformer"]

node-hub/dora-rdt-1b/tests/test_dora_rdt_1b.py

@@ -1,10 +1,10 @@
+import os
+
+import numpy as np
 import pytest
 import torch
-import numpy as np
 from PIL import Image
 from torchvision import transforms
-import os
-
 
 CI = os.environ.get("CI")
 
@@ -20,8 +20,8 @@ def test_import_main():
     # Check that everything is working, and catch dora Runtime Exception as we're not running in a dora dataflow.
     # with pytest.raises(RuntimeError):
     # main()
-    import dora_rdt_1b.RoboticsDiffusionTransformer as _
-    import dora_rdt_1b as _
+    import dora_rdt_1b.RoboticsDiffusionTransformer as _  # noqa
+    import dora_rdt_1b as _  # noqa
 
 
 def test_download_policy():
@@ -44,7 +44,6 @@ def test_download_vision_model():
 
 
 def test_download_language_embeddings():
-
     ## in the future we should add this test within CI
     if CI:
         return
@@ -55,7 +54,6 @@ def test_download_language_embeddings():
 
 
 def test_load_dummy_image():
-
     from dora_rdt_1b.main import config
 
     # Load pretrained model (in HF style)
@@ -85,7 +83,7 @@ def test_load_dummy_image():
     # image pre-processing
     # The background image used for padding
     background_color = np.array(
-        [int(x * 255) for x in image_processor.image_mean], dtype=np.uint8
+        [int(x * 255) for x in image_processor.image_mean], dtype=np.uint8,
     ).reshape((1, 1, 3))
     background_image = (
         np.ones(
@@ -119,21 +117,20 @@ def test_load_dummy_image():
                     width, height = pil_img.size
                     if width == height:
                         return pil_img
-                    elif width > height:
+                    if width > height:
                         result = Image.new(
-                            pil_img.mode, (width, width), background_color
+                            pil_img.mode, (width, width), background_color,
                         )
                         result.paste(pil_img, (0, (width - height) // 2))
                         return result
-                    else:
-                        result = Image.new(
-                            pil_img.mode, (height, height), background_color
-                        )
-                        result.paste(pil_img, ((height - width) // 2, 0))
-                        return result
+                    result = Image.new(
+                        pil_img.mode, (height, height), background_color,
+                    )
+                    result.paste(pil_img, ((height - width) // 2, 0))
+                    return result
 
                 image = expand2square(
-                    image, tuple(int(x * 255) for x in image_processor.image_mean)
+                    image, tuple(int(x * 255) for x in image_processor.image_mean),
                 )
             image = image_processor.preprocess(image, return_tensors="pt")[
                 "pixel_values"
@@ -144,7 +141,7 @@ def test_load_dummy_image():
     # encode images
     image_embeds = vision_encoder(image_tensor).detach()
     pytest.image_embeds = image_embeds.reshape(
-        -1, vision_encoder.hidden_size
+        -1, vision_encoder.hidden_size,
     ).unsqueeze(0)
 
 
@@ -159,7 +156,7 @@ def test_dummy_states():
     # it's kind of tricky, I strongly suggest adding proprio as input and further fine-tuning
     B, N = 1, 1  # batch size and state history size
     states = torch.zeros(
-        (B, N, config["model"]["state_token_dim"]), device=DEVICE, dtype=DTYPE
+        (B, N, config["model"]["state_token_dim"]), device=DEVICE, dtype=DTYPE,
     )
 
     # if you have proprio, you can do like this
@@ -168,7 +165,7 @@ def test_dummy_states():
     # states[:, :, STATE_INDICES] = proprio
 
     state_elem_mask = torch.zeros(
-        (B, config["model"]["state_token_dim"]), device=DEVICE, dtype=torch.bool
+        (B, config["model"]["state_token_dim"]), device=DEVICE, dtype=torch.bool,
     )
     from dora_rdt_1b.RoboticsDiffusionTransformer.configs.state_vec import (
         STATE_VEC_IDX_MAPPING,
@@ -187,8 +184,9 @@ def test_dummy_states():
     ]
 
     state_elem_mask[:, STATE_INDICES] = True
-    states, state_elem_mask = states.to(DEVICE, dtype=DTYPE), state_elem_mask.to(
-        DEVICE, dtype=DTYPE
+    states, state_elem_mask = (
+        states.to(DEVICE, dtype=DTYPE),
+        state_elem_mask.to(DEVICE, dtype=DTYPE),
     )
     states = states[:, -1:, :]  # only use the last state
     pytest.states = states
@@ -211,7 +209,7 @@ def test_dummy_input():
     actions = rdt.predict_action(
         lang_tokens=lang_embeddings,
         lang_attn_mask=torch.ones(
-            lang_embeddings.shape[:2], dtype=torch.bool, device=DEVICE
+            lang_embeddings.shape[:2], dtype=torch.bool, device=DEVICE,
         ),
         img_tokens=image_embeds,
         state_tokens=states,  # how can I get this?
@@ -221,6 +219,6 @@ def test_dummy_input():
 
     # select the meaning action via STATE_INDICES
     action = actions[
-        :, :, STATE_INDICES
+        :, :, STATE_INDICES,
     ]  # (1, chunk_size, len(STATE_INDICES)) = (1, chunk_size, 7+ 1)
     print(action)

node-hub/dora-ugv/dora_ugv/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-ugv/dora_ugv/main.py

@@ -3,14 +3,15 @@ try:
     from ugv_sdk_py import hunter_robot
 except ImportError as err:
     print(
-        "Please install ugv_sdk_py first by following the instruction at: https://github.com/westonrobot/ugv_sdk/tree/main?tab=readme-ov-file#build-the-package-as-a-python-package"
+        "Please install ugv_sdk_py first by following the instruction at: https://github.com/westonrobot/ugv_sdk/tree/main?tab=readme-ov-file#build-the-package-as-a-python-package",
     )
     raise err
 
-from dora import Node
-import pyarrow as pa
 import os
 
+import pyarrow as pa
+from dora import Node
+
 
 def main():
     # Create an instance of HunterRobot
@@ -41,7 +42,7 @@ def main():
                         [
                             state.motion_state.linear_velocity,
                             state.motion_state.angular_velocity,
-                        ]
+                        ],
                     ),
                 )
 

node-hub/dora-vad/dora_vad/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-vad/dora_vad/main.py

@@ -1,9 +1,10 @@
-from dora import Node
-import pyarrow as pa
-import numpy as np
 import os
-from silero_vad import load_silero_vad, get_speech_timestamps
+
+import numpy as np
+import pyarrow as pa
 import torch
+from dora import Node
+from silero_vad import get_speech_timestamps, load_silero_vad
 
 model = load_silero_vad()
 MIN_SILENCE_DURATION_MS = int(os.getenv("MIN_SILENCE_DURATION_MS", "200"))
@@ -38,14 +39,12 @@ def main():
                 len(speech_timestamps) > 0
                 and len(last_audios) > MIN_AUDIO_SAMPLING_DURAION_S
             ):
-
                 # Check if the audio is not cut at the end. And only return if there is a long time spent
                 if speech_timestamps[-1]["end"] == len(audio):
                     continue
-                else:
-                    audio = audio[0 : speech_timestamps[-1]["end"]]
-                    node.send_output("audio", pa.array(audio))
-                    last_audios = [audio[speech_timestamps[-1]["end"] :]]
+                audio = audio[0 : speech_timestamps[-1]["end"]]
+                node.send_output("audio", pa.array(audio))
+                last_audios = [audio[speech_timestamps[-1]["end"] :]]
 
             # If there is no sound for too long return the audio
             elif len(last_audios) > 75:

node-hub/dora-yolo/dora_yolo/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-yolo/dora_yolo/main.py

@@ -3,15 +3,14 @@ import os
 
 import numpy as np
 import pyarrow as pa
-from ultralytics import YOLO
-
 from dora import Node
+from ultralytics import YOLO
 
 
 def main():
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(
-        description="UltraLytics YOLO: This node is used to perform object detection using the UltraLytics YOLO model."
+        description="UltraLytics YOLO: This node is used to perform object detection using the UltraLytics YOLO model.",
     )
 
     parser.add_argument(
@@ -52,10 +51,7 @@ def main():
                 width = metadata["width"]
                 height = metadata["height"]
 
-                if encoding == "bgr8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding == "rgb8":
+                if encoding == "bgr8" or encoding == "rgb8":
                     channels = 3
                     storage_type = np.uint8
                 else:

node-hub/llama-factory-recorder/llama_factory_recorder/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/llama-factory-recorder/llama_factory_recorder/main.py

@@ -1,11 +1,12 @@
-import os
 import json
-from dora import Node
+import os
+from pathlib import Path
+
+import cv2
 import numpy as np
 import pyarrow as pa
+from dora import Node
 from PIL import Image
-from pathlib import Path
-import cv2
 
 DEFAULT_QUESTION = os.getenv(
     "DEFAULT_QUESTION",
@@ -14,13 +15,14 @@ DEFAULT_QUESTION = os.getenv(
 
 
 def write_dict_to_json(file_path, key: str, new_data):
-    """
-    Writes a dictionary to a JSON file. If the file already contains a list of entries,
+    """Writes a dictionary to a JSON file. If the file already contains a list of entries,
     the new data will be appended to that list. Otherwise, it will create a new list.
 
-    Parameters:
+    Parameters
+    ----------
     - file_path: str, the path to the JSON file.
     - new_data: dict, the dictionary to add to the JSON file.
+
     """
     try:
         # Open the JSON file and load its content
@@ -43,22 +45,22 @@ def write_dict_to_json(file_path, key: str, new_data):
 
 
 def save_image_and_add_to_json(
-    frame_dict: dict, root_path, llama_root_path, jsonl_file, messages
+    frame_dict: dict, root_path, llama_root_path, jsonl_file, messages,
 ):
-    """
-    Saves an image from a NumPy array and adds a new JSON object as a line to a JSONL file.
+    """Saves an image from a NumPy array and adds a new JSON object as a line to a JSONL file.
     The function generates a sequential numeric image filename starting from 0 and
     follows the provided template structure.
 
-    Parameters:
+    Parameters
+    ----------
     - image_array: numpy.ndarray, the image data as a NumPy array.
     - root_path: str, the root directory where the image will be saved.
     - jsonl_file: str, the path to the JSONL file.
     - messages: list of dicts, each containing 'content' and 'role'.
 
     The image is saved as a PNG file, and the JSONL entry includes the 'messages' and 'images' keys.
-    """
 
+    """
     # Create the root directory if it doesn't exist
     os.makedirs(llama_root_path / root_path, exist_ok=True)
 
@@ -68,7 +70,7 @@ def save_image_and_add_to_json(
             name
             for name in os.listdir(llama_root_path / root_path)
             if os.path.isfile(os.path.join(llama_root_path / root_path, name))
-        ]
+        ],
     )
     image_paths = []
     for event_id, data in frame_dict.items():
@@ -94,9 +96,9 @@ def main():
     pa.array([])  # initialize pyarrow array
     node = Node()
 
-    assert os.getenv(
-        "LLAMA_FACTORY_ROOT_PATH"
-    ), "LLAMA_FACTORY_ROOT_PATH is not set, Either git clone the repo or set the environment variable"
+    assert os.getenv("LLAMA_FACTORY_ROOT_PATH"), (
+        "LLAMA_FACTORY_ROOT_PATH is not set, Either git clone the repo or set the environment variable"
+    )
     llama_factory_root_path = Path(os.getenv("LLAMA_FACTORY_ROOT_PATH")) / "data"
 
     entry_name = os.getenv("ENTRY_NAME", "dora_demo")
@@ -141,13 +143,7 @@ def main():
                 width = metadata["width"]
                 height = metadata["height"]
 
-                if encoding == "bgr8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding == "rgb8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding == "jpeg":
+                if encoding == "bgr8" or encoding == "rgb8" or encoding == "jpeg":
                     channels = 3
                     storage_type = np.uint8
                 else:

node-hub/opencv-plot/opencv_plot/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/opencv-plot/opencv_plot/main.py

@@ -4,7 +4,6 @@ import os
 import cv2
 import numpy as np
 import pyarrow as pa
-
 from dora import Node
 
 RUNNER_CI = True if os.getenv("CI") == "true" else False
@@ -76,10 +75,9 @@ def yuv420p_to_bgr_opencv(yuv_array, width, height):
 
 
 def main():
-
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(
-        description="OpenCV Plotter: This node is used to plot text and bounding boxes on an image."
+        description="OpenCV Plotter: This node is used to plot text and bounding boxes on an image.",
     )
 
     parser.add_argument(
@@ -118,7 +116,7 @@ def main():
             plot_height = int(plot_height)
 
     node = Node(
-        args.name
+        args.name,
     )  # provide the name to connect to the dataflow if dynamic node
     plot = Plot()
 
@@ -168,7 +166,6 @@ def main():
                     plot.frame = cv2.imdecode(storage, cv2.IMREAD_COLOR)
 
                 elif encoding == "yuv420":
-
                     storage = storage.to_numpy()
 
                     # Convert back to BGR results in more saturated image.
@@ -201,7 +198,7 @@ def main():
                                 y + h / 2,
                             )
                             for [x, y, w, h] in original_bbox
-                        ]
+                        ],
                     )
                 else:
                     raise RuntimeError(f"Unsupported bbox format: {bbox_format}")
@@ -210,7 +207,7 @@ def main():
                     "bbox": bbox,
                     "conf": arrow_bbox["conf"].values.to_numpy(),
                     "labels": arrow_bbox["labels"].values.to_numpy(
-                        zero_copy_only=False
+                        zero_copy_only=False,
                     ),
                 }
             elif event_id == "text":

node-hub/opencv-video-capture/opencv_video_capture/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/opencv-video-capture/opencv_video_capture/main.py

@@ -5,7 +5,6 @@ import time
 import cv2
 import numpy as np
 import pyarrow as pa
-
 from dora import Node
 
 RUNNER_CI = True if os.getenv("CI") == "true" else False
@@ -16,7 +15,7 @@ FLIP = os.getenv("FLIP", "")
 def main():
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(
-        description="OpenCV Video Capture: This node is used to capture video from a camera."
+        description="OpenCV Video Capture: This node is used to capture video from a camera.",
     )
 
     parser.add_argument(
@@ -77,7 +76,6 @@ def main():
     pa.array([])  # initialize pyarrow array
 
     for event in node:
-
         # Run this example in the CI for 10 seconds only.
         if RUNNER_CI and time.time() - start_time > 10:
             break
@@ -95,7 +93,7 @@ def main():
                     cv2.putText(
                         frame,
                         f"Error: no frame for camera at path {video_capture_path}.",
-                        (int(30), int(30)),
+                        (30, 30),
                         cv2.FONT_HERSHEY_SIMPLEX,
                         0.50,
                         (255, 255, 255),

node-hub/pyarrow-assert/pyarrow_assert/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/pyarrow-assert/pyarrow_assert/main.py

@@ -1,6 +1,6 @@
 import argparse
-import os
 import ast
+import os
 
 import pyarrow as pa
 from dora import Node
@@ -9,7 +9,6 @@ RUNNER_CI = True if os.getenv("CI") == "true" else False
 
 
 def main():
-
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(description="Simple arrow sender")
 
@@ -33,18 +32,14 @@ def main():
     data = os.getenv("DATA", args.data)
 
     node = Node(
-        args.name
+        args.name,
     )  # provide the name to connect to the dataflow if dynamic node
 
     data = ast.literal_eval(data)
 
     if isinstance(data, list):
         data = pa.array(data)  # initialize pyarrow array
-    elif isinstance(data, str):
-        data = pa.array([data])
-    elif isinstance(data, int):
-        data = pa.array([data])
-    elif isinstance(data, float):
+    elif isinstance(data, str) or isinstance(data, int) or isinstance(data, float):
         data = pa.array([data])
     else:
         data = pa.array(data)  # initialize pyarrow array

node-hub/pyarrow-sender/pyarrow_sender/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/pyarrow-sender/pyarrow_sender/main.py

@@ -1,16 +1,14 @@
 import argparse
-import os
 import ast
+import os
 
 import pyarrow as pa
-
 from dora import Node
 
 RUNNER_CI = True if os.getenv("CI") == "true" else False
 
 
 def main():
-
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(description="Simple arrow sender")
 
@@ -34,29 +32,24 @@ def main():
     data = os.getenv("DATA", args.data)
 
     node = Node(
-        args.name
+        args.name,
     )  # provide the name to connect to the dataflow if dynamic node
 
     if data is None:
         raise ValueError(
-            "No data provided. Please specify `DATA` environment argument or as `--data` argument"
+            "No data provided. Please specify `DATA` environment argument or as `--data` argument",
         )
+    try:
+        data = ast.literal_eval(data)
+    except ValueError:
+        print("Passing input as string")
+    if isinstance(data, list):
+        data = pa.array(data)  # initialize pyarrow array
+    elif isinstance(data, str) or isinstance(data, int) or isinstance(data, float):
+        data = pa.array([data])
     else:
-        try:
-            data = ast.literal_eval(data)
-        except ValueError:
-            print("Passing input as string")
-        if isinstance(data, list):
-            data = pa.array(data)  # initialize pyarrow array
-        elif isinstance(data, str):
-            data = pa.array([data])
-        elif isinstance(data, int):
-            data = pa.array([data])
-        elif isinstance(data, float):
-            data = pa.array([data])
-        else:
-            data = pa.array(data)  # initialize pyarrow array
-        node.send_output("data", data)
+        data = pa.array(data)  # initialize pyarrow array
+    node.send_output("data", data)
 
 
 if __name__ == "__main__":

node-hub/terminal-input/terminal_input/__init__.py

@@ -5,7 +5,7 @@ readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.m
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/terminal-input/terminal_input/main.py

@@ -1,16 +1,15 @@
 import argparse
-import os
 import ast
+import os
 import time
-import pyarrow as pa
 
+import pyarrow as pa
 from dora import Node
 
 RUNNER_CI = True if os.getenv("CI") == "true" else False
 
 
 def main():
-
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
     parser = argparse.ArgumentParser(description="Simple arrow sender")
 
@@ -36,7 +35,7 @@ def main():
     while True:
         try:
             node = Node(
-                args.name
+                args.name,
             )  # provide the name to connect to the dataflow if dynamic node
         except RuntimeError as err:
             if err != last_err:
@@ -58,13 +57,7 @@ def main():
                     print("Passing input as string")
                 if isinstance(data, list):
                     data = pa.array(data)  # initialize pyarrow array
-                elif isinstance(data, str):
-                    data = pa.array([data])
-                elif isinstance(data, int):
-                    data = pa.array([data])
-                elif isinstance(data, float):
-                    data = pa.array([data])
-                elif isinstance(data, dict):
+                elif isinstance(data, str) or isinstance(data, int) or isinstance(data, float) or isinstance(data, dict):
                     data = pa.array([data])
                 else:
                     data = pa.array(data)  # initialize pyarrow array
@@ -82,13 +75,7 @@ def main():
                 print("Passing input as string")
             if isinstance(data, list):
                 data = pa.array(data)  # initialize pyarrow array
-            elif isinstance(data, str):
-                data = pa.array([data])
-            elif isinstance(data, int):
-                data = pa.array([data])
-            elif isinstance(data, float):
-                data = pa.array([data])
-            elif isinstance(data, dict):
+            elif isinstance(data, str) or isinstance(data, int) or isinstance(data, float) or isinstance(data, dict):
                 data = pa.array([data])
             else:
                 data = pa.array(data)  # initialize pyarrow array