semvec/pypi-packages huggingface dataset:
nestipy-cli 0.9.0
Nestipy is a Python framework built on top of FastAPI that follows the modular architecture of NestJS
Requires Python: >=3.11

<p align="center">
  <a target="_blank"><img src="https://raw.githubusercontent.com/nestipy/nestipy/release-v1/nestipy.png" width="200" alt="Nestipy Logo" /></a></p>
<p align="center">
    <a href="https://pypi.org/project/nestipy">
        <img src="https://img.shields.io/pypi/v/nestipy?color=%2334D058&label=pypi%20package" alt="Version">
    </a>
    <a href="https://pypi.org/project/nestipy">
        <img src="https://img.shields.io/pypi/pyversions/nestipy.svg?color=%2334D058" alt="Python">
    </a>
    <a href="https://github.com/tsiresymila1/nestipy/blob/main/LICENSE">
        <img src="https://img.shields.io/github/license/tsiresymila1/nestipy" alt="License">
    </a>
</p>

## Description

<p>Nestipy is a Python framework built on top of FastAPI that follows the modular architecture of NestJS</p>
<p>Under the hood, Nestipy makes use of <a href="https://fastapi.tiangolo.com/" target="_blank">FastAPI</a>, but also provides compatibility with a wide range of other libraries, like <a href="https://fastapi.tiangolo.com/" target="_blank">Blacksheep</a>, allowing for easy use of the myriad of third-party plugins which are available.</p>

## Getting started

```cmd
    pip install nestipy-cli
    nestipy new my_app
    cd my_app
    nestipy start --dev
```

```
    ├── src
    │    ├── __init__.py
    ├── app_module.py
    ├── app_controller.py
    ├── app_service.py
    ├── main.py
    ├── pyproject.toml
    ├── uv.lock
    ├── README.md
    
       
```

## Documentation

View full documentation from [here](https://nestipy.vercel.app).

## Support

Nestipy is an MIT-licensed open source project. It can grow thanks to the sponsors and support from the amazing backers.
If you'd like to join them, please [read more here].

## Stay in touch

- Author - [Tsiresy Mila](https://tsiresymila.vercel.app)

## License

Nestipy is [MIT licensed](LICENSE).
dans 1.1.3
A package for scraping data from basketball-reference.com and stats.nba.com to provide opponent-adjusted statistics.
Requires Python: >=3.10

# Defense Adjusted NBA Stats

This is a package that collects data from [basketball-reference.com](https://www.basketball-reference.com) and [stats.nba.com](https://www.stats.nba.com) and provides endpoints that adjust a player's statistics depending on their opponents defensive strength.

## Why adjust stats?

Numbers at face-value don't always tell the full story. For example, if player A scores 30 points on 50% from the field, and player B scores 27 points on 47% from the field, we could come to the conclusion that player A had a better scoring game.

However, what if player B faced one of the best defensive teams in the league, and player A faced a mediocre defensive team? How does that change how we view those performances?

This package attempts to provide a more even playing field with statistics by using the opponent team's defensive to adjust a player scoring and efficiency. For more details about how stats are "adjusted", see the methodology [here](https://github.com/oscarg617/dans/blob/main/docs/data_formats.md).

Stats from basketball-reference for this package go back to the 1970-71 season, and stats from nba-stats only go back to the 1996-97 season.

Read the full documentation for this API [here](https://github.com/oscarg617/dans/tree/main/docs/).

## Installing
### Via `pip`
Install using the following command:

```
pip install dans
```

### Via GitHub
Or, you can clone this repo to a Git repository on your local machine.


## License & Terms of Use

## API  Package

The `dans` package is Open Source with an [MIT License](https://github.com/oscarg617/dans/blob/main/LICENSE).

## NBA.com

NBA.com has a [Terms of Use](https://www.nba.com/termsofuse) regarding the use of the NBA’s digital platforms.
pro-timer-ajay-2026 0.0.1
A professional function timer and profiler decorator
Requires Python: >=3.7

# Pro-Timer
A professional-grade function profiler using decorators.

### Installation
```bash
pip install .
```

### How to use:
```python
from pro_timer.profiler import time_wrapper

@time_wrapper
def my_function():
    print("Doing work...")
```

---

### Create a `demo.py` (To see it in action)
If you want to see your library work like a "real user" would see it, create a file called `demo.py` in your root folder:

```python
import logging
from pro_timer.profiler import time_wrapper

# Setup logging so we can see the output in the console
logging.basicConfig(level=logging.INFO)

@time_wrapper
def calculate_something():
    sum(range(1000000))
    print("Calculation finished!")

if __name__ == "__main__":
    calculate_something()
uwacan 2.3.0
Underwater Acoustic Analysis tools
Requires Python: >=3.11

# Underwater Acoustic Analysis Toolbox
Welcome to the Underwater Acoustic Analysis toolbox for python.
This contains implementations of some analysis methods for underwater acoustics, with a focus on monitoring of noise for environmental impact.

- **Documentation**: https://underwater-acoustic-analysis.readthedocs.io
- **Source code**: https://github.com/CarlAndersson/underwater-acoustic-analysis

## Issues?
It you have issues, feel free to open an issue on the [issue tracker](https://github.com/CarlAndersson/underwater-acoustic-analysis/issues).
You can use this not only for bug reports, but also for general questions about how to use the code.

## Installation
The package is available on the [python package index](https://pypi.org/project/uwacan/). The easiest way is to install from there.
```
pip install uwacan
```
```
uv add uwacan
```
For Conda, refer to [this page](https://www.anaconda.com/docs/tools/working-with-conda/packages/pip-install) on how to install from PyPI.
For other python environment managers, refer to their specific documentation.
running 0.4.0
User-friendly running pace calculator
Requires Python: >=3.10

# 🏃‍♀️ running 🏃
[![image](https://img.shields.io/pypi/v/running.svg)](https://pypi.org/project/running/)
[![image](https://img.shields.io/pypi/pyversions/running.svg)](https://pypi.org/project/running/)

A flexible but user-friendly running pace calculator as a command line tool.

# Installation
```
$ pip install running
```

# Usage
Call `running` with two of the three arguments `pace`, `distance` or `time`, and it will calculate the missing one for you.

```
$ running --pace 4:30/km --distance marathon 
Elapsed time: 3:09:53 [H:]MM:SS
```

You can also just use the first letter of the argument names:
```
$ running -p 4:30 -d marathon
```

Note that distance unit for the pace was omitted, defaulting to kilometer. This can be changed with the `unit` (`-u`) parameter as follows:
```
$ running -p 8:00 -d 10km -u miles
Elapsed time: 49:43 [H:]MM:SS
```

You can also directly specify the units for the pace:
```
$ running -p 6min/mile -t 1hour
Travelled distance: 16.09 km
$ running -d half-marathon -t 1:45:00
Required pace: 04:59 /km
```

You can also view split times with `--splits` (`-s`):
```
$ running -p 4:30 -d marathon --splits
Elapsed time: 3:09:53 [H:]MM:SS

   1 km  04:30
   2 km  09:00
   3 km  13:30
  ...
  42 km  3:09:00
42.195 km  3:09:53
```

Predict race times for other distances with `--predict` (`-r`), using the Riegel formula:
```
$ running -d 10km -t 45:00 --predict
Required pace: 04:30 /km

Race predictions:
          1500m    06:01   (04:01 /km)
         1 mile    06:29   (04:02 /km)
             5K    21:35   (04:19 /km)
            10K    45:00   (04:30 /km)
  Half-marathon  1:39:17   (04:42 /km)
       Marathon  3:27:01   (04:54 /km)
```

The tool is quite flexible in terms of the valid expressions for the arguments, see `running --help` for more examples.

**Ever wondered how fast Usain Bolt would run a marathon?**
```
$ running -p bolt -d marathon
Elapsed time: 1:07:22 [H:]MM:SS
```
typerdrive 0.8.1
Develop API-Connected Typer Apps at Lightspeed
Requires Python: >=3.12

[![Latest Version](https://img.shields.io/pypi/v/typerdrive?label=pypi-version&logo=python&style=plastic)](https://pypi.org/project/typerdrive/)
[![Python Versions](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fdusktreader%2Ftyperdrive%2Fmain%2Fpyproject.toml&style=plastic&logo=python&label=python-versions)](https://www.python.org/)
[![Build Status](https://github.com/dusktreader/typerdrive/actions/workflows/main.yml/badge.svg)](https://github.com/dusktreader/typerdrive/actions/workflows/main.yml)
[![Documentation Status](https://github.com/dusktreader/typerdrive/actions/workflows/docs.yml/badge.svg)](https://dusktreader.github.io/typerdrive/)


![typerdrive-logo](https://github.com/dusktreader/typerdrive/blob/main/docs/source/images/logo-dark.png)

_Boost your command-line workflows to lightspeed_


## Features

- [Settings management & commands](https://dusktreader.github.io/typerdrive/features/settings)
- [Cache management & commands](https://dusktreader.github.io/typerdrive/features/cache)
- [Logging management & commands](https://dusktreader.github.io/typerdrive/features/logging.md)
- [User-friendly error handling](https://dusktreader.github.io/typerdrive/features/exceptions)
- [Powered Up API clients](https://dusktreader.github.io/typerdrive/features/client)


## Super-quick Start

Requires: Python 3.12 to 3.14

Install through pip:

```bash
pip install typerdrive
```


## Documentation

The complete documentation can be found at the
[typerdrive home page](https://dusktreader.github.io/typerdrive)


## Demo

To see the features in action, you can run the demo in an isolated environment with `uv`:

```
uvx --from=typerdrive[demo] typerdrive-demo
```
pipecat-plugins-faseeh 0.1.0
Pipecat plugin for Faseeh Arabic text-to-speech
Requires Python: >=3.10
Keywords: pipecat, tts, arabic, faseeh, voice, ai

# pipecat-plugins-faseeh

Pipecat plugin for [Faseeh](https://faseeh.ai) Arabic text-to-speech.

Build real-time Arabic conversational AI agents using [Pipecat](https://github.com/pipecat-ai/pipecat) with Faseeh's high-quality Arabic TTS voices.

## Installation

```bash
pip install pipecat-plugins-faseeh
```

## Configuration

Set your Faseeh API key as an environment variable:

```bash
export FASEEH_API_KEY="your-api-key"
```

Or pass it directly:

```python
tts = FaseehTTSService(api_key="your-api-key", aiohttp_session=session)
```

Get your API key at [app.faseeh.ai](https://app.faseeh.ai).

### Parameters

| Parameter   | Type   | Default                          | Description                        |
|-------------|--------|----------------------------------|------------------------------------|
| `api_key`   | str    | env `FASEEH_API_KEY`             | Faseeh API key                     |
| `voice_id`  | str    | `ar-hijazi-female-2`             | Voice identifier                   |
| `model`     | str    | `faseeh-v1-preview`              | TTS model                          |
| `stability` | float  | 0.5                              | Voice consistency (0.0-1.0)        |
| `speed`     | float  | 1.0                              | Speech rate (0.7-1.2)              |
| `base_url`  | str    | `https://api.faseeh.ai/api/v1`   | API base URL                       |

## Usage

```python
import aiohttp
from pipecat.pipeline.pipeline import Pipeline
from pipecat_plugins_faseeh import FaseehTTSService

async with aiohttp.ClientSession() as session:
    tts = FaseehTTSService(
        api_key="your-api-key",
        aiohttp_session=session,
        voice_id="ar-hijazi-female-2",
        model="faseeh-v1-preview",
        params=FaseehTTSService.InputParams(stability=0.5, speed=1.0),
    )

    # Add to your Pipecat pipeline
    pipeline = Pipeline([..., tts, ...])
```

### Runtime settings update

Change voice, model, speed, or stability mid-conversation:

```python
from pipecat.frames.frames import TTSUpdateSettingsFrame

await task.queue_frame(TTSUpdateSettingsFrame(settings={"voice_id": "ar-emirati-male-1"}))
await task.queue_frame(TTSUpdateSettingsFrame(settings={"speed": 1.1, "stability": 0.8}))
```

## Running the example

```bash
# Install dependencies
pip install pipecat-plugins-faseeh "pipecat-ai[daily,openai,deepgram,silero]" python-dotenv loguru

# Copy and fill in your API keys
cp .env.example .env

# Run
python examples/faseeh_tts_example.py
```

The example auto-creates a Daily room and logs the URL — open it in your browser to talk to the bot.

## Available voices

See the full voice library at [docs.faseeh.ai](https://docs.faseeh.ai).

## Pipecat compatibility

Tested with `pipecat-ai >= 0.0.100`.

## License

Apache 2.0

## Maintained by

[Actualize](https://actualize.ae)