semvec/pypi-packages huggingface dataset:
ClientShowBusinessTg 0.1
async upload archive
Requires Python: >=3.11


# AioShowBusiness File Upload

This script demonstrates how to upload a file using the ClientShowBusinessTg client.

## Usage

```python
import asyncio
from ClientShowBusinessTg import ClientShowBusinessTg

client = ClientShowBusinessTg()

asyncio.run(client.upload_archive(file_path="file.zip", user_id=9999999, token="token"))
```

## Requirements
- 2 requests per second rate limit
- Maximum file size: **20MB**
- Supported formats: **.ZIP** / **.RAR**

## Installation
Ensure you have the necessary dependencies installed:
```
pip install ClientShowBusinessTg
```

## Notes
- Replace `file.zip` with your desired file.
- Update `user_id` and `token` with valid values for authentication.
AioShowBusiness 1.0
async upload archive
Requires Python: >=3.11


# AioShowBusiness File Upload

This script demonstrates how to upload a file using the AioShowBusiness client.

## Usage

```python
import asyncio

client = AioShowBusiness()

asyncio.run(client.upload_archive(file_path="file.zip", user_id=9999999, token="token"))
```

## Requirements
- 2 requests per second rate limit
- Maximum file size: **20MB**
- Supported formats: **.ZIP** / **.RAR**

## Installation
Ensure you have the necessary dependencies installed:
```
pip install aioshowbusiness
```

## Notes
- Replace `file.zip` with your desired file.
- Update `user_id` and `token` with valid values for authentication.
pvrecorderdemo 1.2.8
Recorder library for Picovoice.
Requires Python: >=3.9
Keywords: Audio Recorder

# PvRecorder Demo for Python

This project contains a Python command-line demo for PvRecorder that demonstrates how to use PvRecorder to record audio to a WAV file.

## PvRecorder

PvRecorder is an easy-to-use, cross-platform audio recorder designed for real-time speech audio processing. It allows developers access to an audio device's input stream, broken up into data frames of a given size.

## Compatibility

- Python 3.9+
- Runs on Linux (x86_64), macOS (x86_64 and arm64), Windows (x86_64 and arm64), and Raspberry Pi (Zero, 3, 4, 5).

## Installation

```console
pip3 install pvrecorderdemo
```

## Usage

In the following instructions, we will refer to  `{AUDIO_DEVICE_INDEX}` as the index of the audio device to use, and `{OUTPUT_WAV_PATH}` as the path to save the audio data in `wav` format.

`{AUDIO_DEVICE_INDEX}` defaults to -1 and `{OUTPUT_WAV_PATH}` can be empty if you wish to not save any data.

To show the available audio devices run:

```console
pv_recorder_demo --show_audio_devices
```

To run PvRecorder run:

```console
pv_recorder_demo --audio_device_index {AUDIO_DEVICE_INDEX} --output_wav_path {OUTPUT_WAV_PATH}
```
DomainScout 1.4
A desktop GUI client to view discovered domains using known disposable emails in WHOIS contact records.

# Domain Scout 

## Description

Domain Scout is the primary GUI to view vulnerable domains found by CT Domain Data (see https://derekrgreene.com/DomainScout).
CT Domain Data identifies disposable email addresses used as contact methods in WHOIS records. Domains are collected using Certstream Server Go which streams Certificate Transparency logs continuously to a websocket connection. Domains are extracted from the data stream and WHOIS queries are subsequently made to identify contact email addresses which are compared against a list of 15k+ known disposable email domains. If a disposable email address is found, the domain and associated data is added to the database and displayed in Domain Scout.

## Usage

To install the application, run:

`pip install DomainScout`

Run the application

`domainscout`
linguistics_robin 0.5.9
A Python 3 linguistics collection library.

# Linguistics Robin

Linguistics Robin is a Python linguistics collection that stemmed from a phonetics only library (which is why there is currently more phonetic tooling). Right now, the following algorithms are implemented and supported:

 * Soundex
 * Metaphone
 * Double-Metaphone
 * Refined Soundex
 * Fuzzy Soundex
 * Lein
 * Matching Rating Approach
 * New York State Identification and Intelligence System (NYSIIS)
 * Caverphone
 * Caverphone 2
 
In addition, the following distance metrics:

 * Hamming
 * Levenshtein

More will be added in the future. Please refer to the issues list for algorithms slated for the future.

Pull requests are always welcome to assist with the addition of new algorithms.

## Installation

The module is available in PyPI, just use `pip install linguistics_robin`.


## Usage

```python
>>> from linguistics_robin import Soundex
>>> soundex = Soundex()
>>> soundex.phonetics('Rupert')
'R163'
>>> soundex.phonetics('Robert')
'R163'
>>> soundex.sounds_like('Robert', 'Rupert')
True
```

The same API applies to every algorithm, e.g:

```python
>>> from linguistics_robin import Metaphone
>>> metaphone = Metaphone()
>>> metaphone.phonetics('discrimination')
'TSKRMNXN'
```

You can also use the `distance(word1, word2, metric='levenshtein')` method to find the distance between 2 phonetic representations.

```python
>>> from linguistics_robin import RefinedSoundex
>>> rs = RefinedSoundex()
>>> rs.distance('Rupert', 'Robert')
0
>>> rs.distance('assign', 'assist', metric='hamming')
2
```
sigcoeff 1.0.1
Sparse signature coefficient computations via kernels

<h1 align='center'>sigcoeff</h1>
<h2 align='center'>Sparse Signature Coefficient Recovery via Kernels</h2>

This package provides an example implementation for computing deep coefficients of the path signature transform using
signature kernels on both CPU and GPU, to accompany the paper
_[Sparse Signature Coefficient Recovery via Kernels](https://arxiv.org/abs/2412.08579)_. This method allows for 
parallelised computation of coefficients, with a time complexity independent of their depth.

---

## Installation

```bash
pip install git+https://github.com/daniil-shmelev/sigcoeff.git
```

## How to use the package

To run the below, see `./examples/example_usage.py`

```python
import torch
import sigcoeff

torch.manual_seed(42)

# Underlying path
len_x = 1000
dim = 10
X = torch.rand((len_x, dim), device="cpu")

# Target multi-index
multi_index = [1, 5, 2, 6, 3]

# Algorithm parameters
M = 2
dyadic_order = 3

#############################################
# Serial CPU computation
#############################################
coeff = sigcoeff.coeff(X, multi_index, scaling_depth=M, dyadic_order=dyadic_order, parallel=False)

#############################################
# Parallel CPU computation
#############################################
coeff = sigcoeff.coeff(X, multi_index, scaling_depth=M, dyadic_order=dyadic_order, parallel=True)

#############################################
# Parallel GPU computation
#############################################
coeff = sigcoeff.coeff(X.cuda(), multi_index, scaling_depth=M, dyadic_order=dyadic_order)

#############################################
# Varying dyadic orders for the two dimensions of the PDE grid
#############################################
coeff = sigcoeff.coeff(X.cuda(), multi_index, scaling_depth=M, dyadic_order=(3, 2))

#############################################
# Extraction of the entire grid of coefficients. I.e. all coefficients given by multi_index[:i],
# evaluated at all time points up to len_x. (CUDA only)
#############################################
coeff_grid = sigcoeff.coeff(X.cuda(), multi_index, scaling_depth=M, dyadic_order=dyadic_order, full=True)
```

## Citation

```bibtex
@article{shmelev2024sparsesig,
  title={Sparse Signature Coefficient Recovery via Kernels},
  author={Shmelev, Daniil and Cristopher, Salvi},
  journal={arXiv preprint arXiv:2412.08579},
  year={2024}
}
```

<!-- 
-->
pvrecorder 1.2.7
Recorder library for Picovoice.
Requires Python: >=3.9
Keywords: Audio Recorder

# PvRecorder Binding for Python

## PvRecorder

PvRecorder is an easy-to-use, cross-platform audio recorder designed for real-time speech audio processing. It allows developers access to an audio device's input stream, broken up into data frames of a given size.

## Compatibility

- Python 3.9+
- Runs on Linux (x86_64), macOS (x86_64 and arm64), Windows (x86_64 and arm64), and Raspberry Pi (Zero, 3, 4, 5).

## Installation

pip3 install pvrecorder

## Usage

Initialize and begin recording:

```python
from pvrecorder import PvRecorder

recorder = PvRecorder(frame_length=512)
recorder.start()
```

(or)

Use `get_available_devices()` to get a list of available devices and then initialize the instance based on the index of a device:

```python
from pvrecorder import PvRecorder

devices = PvRecorder.get_available_devices()

recorder = PvRecorder(frame_length=512, device_index=0)
recorder.start()
```

Read frames of audio:

```python
while recorder.is_recording:
    frame = recorder.read()
    # process audio frame
```

To stop recording, call `stop()` on the instance:

```python
recorder.stop()
```

Once you are done, free the resources acquired by PvRecorder. You do not have to call `stop()` before `delete()`:

```python
recorder.delete()
```

## Demos

[pvrecorderdemo](https://pypi.org/project/pvrecorderdemo/) provides command-line utilities for recording audio to a file.
reten-service-core 0.1.0
Core library providing common components and utilities for Reten microservices
Requires Python: >=3.10

# Reten Service Core Library

Core library providing common components and utilities for Reten microservices.

## Features

- **Authentication**: API Key and JWT authentication
- **Logging**: Structured logging configuration
- **Monitoring**: Metrics and distributed tracing
- **Storage**: Database clients (BigQuery, Redis)
- **Testing**: Common fixtures and mocks
- **Utilities**: Common helper functions

## Installation

```bash
pip install reten-service-core
```

For development:
```bash
pip install reten-service-core[dev]
```

## Quick Start

```python
from fastapi import FastAPI
from reten_service_core import APIKeyAuth
from reten_service_core.logging import configure_logging
from reten_service_core.monitoring import metrics

# Configure logging
configure_logging()

# Create FastAPI app with auth
app = FastAPI()
app.include_middleware(APIKeyAuth)

# Add metrics
@app.get("/")
@metrics.track_latency()
async def root():
    return {"message": "Hello World"}
```

## Development

1. Clone the repository:
   ```bash
   git clone git@github.com:reten/service-core.git
   cd service-core
   ```

2. Create and activate virtual environment:
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. Install development dependencies:
   ```bash
   pip install -e ".[dev]"
   ```

4. Run tests:
   ```bash
   pytest
   ```

## Contributing

We welcome contributions to the Reten Service Core library! Whether it's:

- 🐛 Bug fixes
- ✨ New features
- 📚 Documentation improvements
- 🧪 Additional tests
- 🔧 Infrastructure updates

### Quick Start

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run quality checks:
   ```bash
   # Run tests
   pytest

   # Run linting
   ruff check .
   ruff format .
   ```
5. Commit your changes (using [Conventional Commits](https://www.conventionalcommits.org/))
6. Push to your fork
7. Open a Pull Request

### Development Environment

See the [Development](#development) section above for basic setup instructions.

### Detailed Guidelines

For detailed information about:
- Code style and standards
- Testing requirements
- Pull request process
- Development environment setup
- Commit message conventions

Please read our [Contributing Guide](CONTRIBUTING.md).

### Need Help?

- 📖 Check the [examples](examples/) directory for usage examples
- 🤔 Open an issue for questions or problems
- 💬 Contact the maintainers at dev@reten.ai

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

### What does this mean?

- ✅ Commercial use
- ✅ Modification
- ✅ Distribution
- ✅ Private use
- ⚠️ No liability
- ⚠️ No warranty

The MIT License is a permissive license that allows you to do anything you want with the code as long as you include the original copyright and license notice in any copy of the software/source.