semvec/pypi-packages huggingface dataset:
openobd 1.26.1rc2
Jifeline Networks openOBD Python Client Library
Requires Python: >=3.11
Keywords: openOBD, Protocol, gRPC, Jifeline, Remote, Diagnostics, OBD, On-board, Automotive, Car, Vehicle, CAN, K-line, DoIP

openOBD Client by Jifeline Networks for Python 3

# Release notes:
- **1.26.x : 2025-11**
  - *Feature*: New logging service to allow log output to ticket workspace
- **1.25.3 : 2025-11**
  - *Feature*: Serve functions and compositions of functions to the ticket dashboard
- **1.25.x-alpha : 2025-09**
  - *Feature*: Serve functions and compositions of functions to the ticket dashboard
- **1.25.x : 2025-09**
  - *Feature*: Starting functions from the ticket dashboard
- **1.24.x : 2025-08**
  - *Feature*: Support for TP2.0 communication
  - *Feature*: Option to link functions to products and vehicles
- **1.23.x : 2025-07**
  - *Feature*: Support for DoIP communication
  - *Feature*: Reading the vehicle's voltage
- **1.22.x : 2025-07**
  - *Feature*: A list of ECUs detected in the vehicle can now be requested
- **1.20.x : 2025-06**
  - *Feature*: Added support for showing images in the user interface
- **1.19.x : 2025-06**
  - *Feature*: Added translations to the user interface
- **1.18.x : 2025-06**  
  - *Feature*: Support for function calls
  - *Feature*: Terminal 15 support
  - *Feature*: Session transfer
    - Passing the openOBD session to another function executor
 - **1.17.x : 2024-12**  
    *Feature*: ISO14230 K-line support with Slow/Fast initialization  
    - Supported bitrates are 9600, 10k4 and 15k6
    - Supported protocol is KWP2000
 - **1.16.x : 2024-09**  
   First production release
 - **1.11.x** :  
   Beta release

---------------------------------------------------------------
rich-typography 0.2.0
Large letter typography for Rich and Textual
Requires Python: >=3.9

[![Test](https://github.com/mtkalms/rich-typography/actions/workflows/python-package.yml/badge.svg)](https://github.com/mtkalms/rich-typography/actions/workflows/python-package.yml)
[![Ruff](https://github.com/mtkalms/rich-typography/actions/workflows/ruff-format.yml/badge.svg)](https://github.com/mtkalms/rich-typography/actions/workflows/ruff-format.yml)
[![Documentation](https://github.com/mtkalms/rich-typography/actions/workflows/gh-pages.yml/badge.svg)](https://github.com/mtkalms/rich-typography/actions/workflows/gh-pages.yml)

# rich-typography
Large letter typography for [Rich](https://github.com/Textualize/rich) and [Textual](https://github.com/Textualize/textual).

![rich-typography example](https://github.com/mtkalms/rich-typography/raw/main/images/preview.svg)

## Installation

Install with `uv` or your favorite PYPI package manager:

```bash
uv add rich-typography
```

## Usage

```python
from rich.console import Console
from rich_typography import Typography

console = Console()
text = Typography.from_markup("Hello from [purple]rich-typography[/purple]")
console.print(text)
```

![rich-typography usage](https://github.com/mtkalms/rich-typography/raw/main/images/usage.svg)

### Custom Markdown

Rich allows you to create your own Markdown renderable and overwrite how specific markdown blocks are rendered.
With that we can modify [this example](https://rich.readthedocs.io/en/latest/markdown.html) to render H1 headings as `Typography` instead of `Text`.

```python
from rich.markdown import Markdown as RichMarkdown, Heading as RichHeading
from rich.console import Console, RenderResult
from rich_typography import Typography


MARKDOWN = """
# This is an h1

Rich can do a pretty *decent* job of rendering markdown.

## This is an h2

1. This is a list item
2. This is another list item
"""


class Heading(RichHeading):
    def __rich_console__(self, *args, **kwargs) -> RenderResult:
        self.text.style = "red"
        if self.tag == "h1":
            text = Typography.from_text(self.text)
            text.justify = "center"
            yield text
        else:
            yield from super().__rich_console__(*args, **kwargs)


class Markdown(RichMarkdown):
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.elements["heading_open"] = Heading



output = Markdown(MARKDOWN)
```

![markdown example](https://github.com/mtkalms/rich-typography/raw/main/images/markdown.svg)

## Similar Projects

- [rich-pyfiglet](https://github.com/edward-jazzhands/rich-pyfiglet)
- [textual-pyfiglet](https://github.com/edward-jazzhands/textual-pyfiglet)
sentry-kafka-management 0.1.6
Libraries used by Sentry for kafka management
Requires Python: >=3.11

# sentry-kafka-management
Libraries for managing our Kafka clusters/consumers, as well as Taskbroker.


### Usage
- `make install-dev` to install the development environment
- `make tests` to run the unit tests
- `make typecheck` to run Python type checking
- `make lint` to lint the code base and apply auto generated changes
- `make build` to build the wheels for the project and package the release

### Local kafka
- `devservices up` to spin up a local kafka node
- `docker exec kafka-kafka-1 kafka-topics --bootstrap-server localhost:9092 --topic test-topic --create` to create a test topic for the cluster
  - TODO: find a way to auto-create a test topic on the local kafka

### Docker

Build the image:

```bash
docker build -t sentry-kafka-management:local .
```

Show CLI help (router):

```bash
docker run --rm sentry-kafka-management:local --help
```

Run a script against your local devservices kafka via the unified CLI router:

```bash
docker run --rm \
  -v "$PWD/devservices/kafka-conf:/config" \
  --network "devservices" \
  sentry-kafka-management:local \
  list-topics \
  -c /config/local.yml \
  -n <cluster-name>
```
pyoperon 0.6.0
Python bindings for the Operon symbolic regression library
Requires Python: >=3.9

# pyoperon

[![License](https://img.shields.io/github/license/heal-research/pyoperon)](https://github.com/heal-research/pyoperon/blob/master/LICENSE)
[![Build-linux](https://github.com/heal-research/pyoperon/actions/workflows/build-linux.yml/badge.svg?branch=main)](https://github.com/heal-research/pyoperon/actions/workflows/build-linux.yml)
[![Build-macos](https://github.com/heal-research/pyoperon/actions/workflows/build-macos.yml/badge.svg?branch=main)](https://github.com/heal-research/pyoperon/actions/workflows/build-linux.yml)
[![Matrix chat](https://matrix.to/img/matrix-badge.svg)](https://matrix.to/#/#operon:matrix.org)

**pyoperon** is the python bindings library of [**Operon**](https://github.com/heal-research/operon), a modern C++ framework for symbolic regression developed by [Heal-Research](https://github.com/heal-research) at the University of Applied Sciences Upper Austria.

A scikit-learn regressor is also available:
```python
from pyoperon.sklearn import SymbolicRegressor
```

The [example](https://github.com/heal-research/pyoperon/tree/main/example) folder contains sample code for using either the Python bindings directly or the **pyoperon.sklearn** module.

# Installation

New releases are published on [github](https://github.com/heal-research/pyoperon/releases/) and on [PyPI](https://pypi.org/project/pyoperon/).

Most of the time `pip install pyoperon` should be enough.

## Building from source

### Conda/Mamba

1. Clone the repository
```
git clone https://github.com/heal-research/pyoperon.git
cd pyoperon
```

2. Install and activate the environment (replace micromamba with your actual program)
```
micromamba env create -f environment.yml
micromamba activate pyoperon
```

3. Install the dependencies
```
export CC=${CONDA_PREFIX}/bin/clang
export CXX=${CONDA_PREFIX}/bin/clang++
python script/dependencies.py
```

4. Install `pyoperon`
```
pip install .
```

### Nix

Use this [environment](https://github.com/foolnotion/poetryenv) created with [poetry2nix](https://github.com/nix-community/poetry2nix)

```
nix develop github:foolnotion/poetryenv --no-write-lock-file
```

This will install operon and dependencies. Modify the flake file if you need additional python libraries (see https://github.com/nix-community/poetry2nix#how-to-guides)


# Contributing

See the [CONTRIBUTING](CONTRIBUTING.md) document.