semvec/pypi-packages huggingface dataset:
mqtty 0.1.1
Bridges MQTT topics to a pseudo-terminal device

# mqtty

`mqtty` bridges an MQTT topic to a local pseudoterminal (PTY), allowing you to interact with serial ports over MQTT as if they were local TTY devices.

This is useful for debugging, automation, or tunneling serial communication through a networked MQTT broker.

## Usage

```bash
mqtty mqtt://<host>/<topic> [--pts-only | --wrap-picocom]
```

You must choose one of the two modes:

* `--pts-only`, `-p`:
  Print the path to the PTS device and keep it open. Useful for tools or scripts that want to use the virtual serial port.

* `--wrap-picocom`, `-w`:
  Launch `picocom --quiet` attached to the PTS device. This allows interactive use of a remote serial port over MQTT.

The `<topic>` segment of the URI is used as the base path for MQTT messages:

* `.../device_serial_input` receives data from your local PTY (sent to the device)
* `.../device_serial_output` sends incoming data from the device to your PTY

## Example

```bash
mqtty mqtt://broker.local/mydevice --wrap-picocom
```

This opens a local PTS device, bridges it to `mydevice/device_serial_input` and `mydevice/device_serial_output`, and launches `picocom` on the virtual port.

## License

MIT
profile-kernel 0.1.2
CLI tool for analyzing Nsight Compute kernel output

# profile-kernel

A CLI tool to profile CUDA kernels using NVIDIA Nsight Compute, extract source-level performance information, and automatically highlight performance bottlenecks (e.g. excessive memory access or warp stalls).

## Features

- Parses Nsight Compute CSV output
- Annotates source lines with Access and Stall warnings
- Filters to only show lines with specific warnings
- Exports as a clean, annotated CSV

## Installation

```bash
pip install profile-kernel
```

## Usage

```bash
profile-kernel \
  --ncu_path /path/to/ncu \
  --exe_path /path/to/python \
  --filepath my_kernel.py \
  --output_filename output \
  [--filter access|stall|warning]  # optional
```

## Example Output

The output CSV will contain columns like:

```
#, Warning Type, Warning Info, Address, SASS, ...
```

## License

MIT License © 2025 Arjun Menon

Permission is hereby granted, free of charge, to any person obtaining a copy  
of this software and associated documentation files (the "Software"), to deal  
in the Software without restriction, including without limitation the rights  
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell  
copies of the Software, and to permit persons to whom the Software is  
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all  
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR  
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,  
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE  
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER  
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  
SOFTWARE.
deforum-flux 0.1.3
Flux backend for Deforum
Requires Python: >=3.12

# Deforum Flux Backend

Flux backend for Deforum using Black Forest Labs Flux. This package includes code from [Black Forest Labs Flux](https://github.com/black-forest-labs/flux) to enable PyPI installation.

## Installation

Install PyTorch with CUDA 12.8 support first:

```bash
# Install PyTorch with CUDA 12.8 (required for RTX 50 series)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

# Then install deforum-flux
pip install deforum-flux

# Optional: For TensorRT acceleration
pip install deforum-flux[tensorrt]
```

**Note:** RTX 50 series cards require CUDA 12.8.

## Development Installation

```bash
# Clone the repository
git clone https://github.com/deforum/flux.git
cd flux

# Install PyTorch with CUDA 12.8
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

# Install in development mode
pip install -e .

# Optional: Install with TensorRT support
pip install -e .[tensorrt]
```

## Publish
```bash
python -m build
python -m twine upload dist/*
```

## License

This package includes code from multiple sources:

- **Deforum Flux Backend** (wrapper code): MIT License
- **Black Forest Labs Flux** (core implementation): Apache 2.0 License
- **FLUX.1-schnell model**: Apache 2.0 License (commercial use allowed)
- **FLUX.1-dev model**: Non-commercial license (no commercial use)

⚠️ **Important**: If you use the FLUX.1-dev model, you are bound by its non-commercial license terms. See `src/flux/LICENSE-FLUX1-dev.md` for details.

For commercial applications, use only the FLUX.1-schnell model.
paulstretch 0.1.3
A modern Python implementation of Paul Nasca's extreme time stretch algorithm with minimal dependencies.
Requires Python: >=3.9

# Paulstretch

A modern Python implementation of [Paul Nasca](https://github.com/paulnasca)'s [extreme sound stretch algorithm](https://www.paulnasca.com/algorithms-created-by-me#h.4c6i2abbt3xk). It allows for extremely high stretch factors like 10 or 1000 without introducing the unpleasant grainy artifacts of other time stretch algorithms. It does smear all the transients though.

There was no modern and well maintained python package for this algorithm available on the package index, so I decided to make one. This is a fun DSP algorithm with a permissive license, so everyone should enjoy it!

### Installation

If you want to use this package in your own project, install it from the [python package index](https://pypi.org/project/paulstretch/):

`pip install paulstretch`

If you want to work with the [github repository](https://github.com/MoritzSchaller/paulstretch/), install the latest version of the [uv package manager](https://docs.astral.sh/uv/). A virtual environment will automatically be installed when you run or build the project with uv.

### Run Tests

The project uses pytest as its testing framework. Run them like this:

`uv run pytest`

### Build & Publish Package

To build and publish this package on the [python package index](https://pypi.org), this is what you (or rather I) should do:

1. Increment Version if appropriate

    The package uses semantic versioning, so bump the version depending on the extent of changes.

    `uv version --bump patch` or 

    `uv version --bump minor` or 

    `uv version --bump major`


2. Build both the source distribution and the wheels

    `uv build`


3. Publish the package:

    `uv publish`

    Enter your pypi credentials when prompted.
simple-infer 0.1.0
Minimal, hackable batch inference library for LLMs
Requires Python: >=3.12

# simple-infer

Minimal, hackable batch inference library for LLMs. No batch endpoints needed.

## Installation

```bash
pip install simple-infer
```

## Usage

```python
from simple_infer import infer

conversations = [
    [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is 2+2?"},
    ]
]

results = infer(conversations, model="gpt-4.1-nano", max_concurrent=32)
```

## Features

- **Simple**: Two main functions - `infer()` and `call_llm()`
- **Fast**: Async batch processing with configurable concurrency
- **Reliable**: Built-in retries with exponential backoff
- **Hackable**: Clean, readable code you can modify

## Development

### Setup
```bash
# Clone and install dependencies
uv sync --extra test --extra docs

# Copy environment template and add your OpenAI API key
cp .env.example .env
# Edit .env and add: OPENAI_API_KEY=your-key-here
```

### Testing
```bash
# Run tests (requires OPENAI_API_KEY in environment)
export OPENAI_API_KEY=your-key-here
uv run pytest tests/ -v

# Or use .env file
uv run pytest tests/ -v
```
apicruds 0.0.1
apicrud is a library that allows you to generate data (dataset) and also to do crud (based on requests)
Requires Python: >=3.9

# apicrud


## Description 
apicrud est une librairie python qui permet de générer de données et de se connecter aux api pour créer, lire, mettre à jour , supprimer,
s'authentifier et de se deconnecter.


#
ovcharuk-cacher 0.1.1
A module for caching function results that contain unique characters in string.
Requires Python: >=3.9

# ovcharuk-cacher

`ovcharuk-cacher` is a Python package that counts the number of **unique characters** in a string or text file.

## Features

- Counts unique characters in a string or file
- Uses' functools.cache to cache reated calls for the same input.
- Provides a command-line interface (CLI).

## Installation

Install packet [PyPi](https://pypi.org/):

# bach
pip install cacher    


How it's work:
- Option 1: Using via CLI:
ovcharuk-cacher-cli --string "aabbcde"

Conclusion:
Number of unique characters occurring only once: 3

Using with file:
ovcharuk-cacher-cli --file path/to/file.txt

- Option 2: Use as a Python module:
python -m ovcharuk_cacher --string "aabbcde"


- Testing:
pytest
python-sane 2.9.2
This is the python-sane package
Requires Python: >=3.8

Python SANE module 2.9.2
========================

.. image:: https://github.com/python-pillow/Sane/workflows/Test/badge.svg
    :target: https://github.com/python-pillow/Sane/actions

Python SANE has been split from Python-Pillow as of version 2.7.0.


The SANE module provides an interface to the SANE scanner and frame grabber
interface for Linux.  This module was contributed by Andrew Kuchling formerly
maintained by Ralph Heinkel. It is currently maintained by Sandro Mani.

Build
-----

To build this module, make sure the sane development package is installed. Then, type::

	python3 setup.py build

In order to install the module type::

	python3 -m pip install .


For some basic documentation please look at the file sanedoc.txt
The example.py script gives a basic example on how to use the software.

Documentation
-------------

Available at https://python-sane.readthedocs.io/en/latest/