semvec/pypi-packages huggingface dataset:
dqtk 0.1.0
tbd
Requires Python: <4.0,>=3.9

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dqkit 0.1.0
tbd
Requires Python: <4.0,>=3.9

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request-forwarder-client 1.1.0
A client for consuming request forwarder server requests
Requires Python: >=3.6

# Request Forwarder Client

A lightweight client for receiving and executing requests from request forwarder server.

Mandatory OS variable:
```sh
export REQUEST_FORWARDER_TOKEN=<your_token>
```

Optional OS variables:
```sh
# MQTT broker address (default: 37.157.254.65)
export REQUEST_FORWARDER_BROKER=<your custom server ip/host>

# Mode of operation (default: 'exec')
# 'exec': Execute the received command
# Any other value: Just print the received command without executing it
export REQUEST_FORWARDER_MODE=<anything except 'exec' will just print the request>

# Override the scheme (http/https) of URLs in commands
export REQUEST_FORWARDER_OVERRIDE_SCHEME=<http/https>

# Redirect requests to a different URL
# Example: https://newhost:9090
export REQUEST_FORWARDER_REDIRECT_URL=<url>

# Override the port in URLs
export REQUEST_FORWARDER_OVERRIDE_PORT=<port>

# Override the path in URLs
export REQUEST_FORWARDER_OVERRIDE_PATH=<path>

# Ignore certificate checks (add -k flag to curl commands)
# Values: true, false (default: false)
export REQUEST_FORWARDER_CERTIFICATE_CHECK_IGNORE=<true|false>
```

## Installation
```sh
pip install request-forwarder-client
```

## License
This project is licensed under the Apache 2.0 license. See the [LICENSE](LICENSE) file for details.
astrocal 0.1.0
Package to calibrate astro images and analyse them
Requires Python: >=3.9

# Pipeline for light-curve

## Calibration

### Level 1
1. Create master files
    1. $m_d$ : Dark (darkflat)
    2. $m_d^f$ : Dark for flats
    3. $m_f$ : Flat
2. Create dead pixels maps:
    1. Hot pixels map from $m_d$ (mask, or set them to median)
    2. Dead pixels map from $m_f$ (mask, or set them to local gaussian)
3. Calibrate images : $s=\frac{s-m_d}{m_f-m_d^f}$

### Level 2
1. Clean $s$ with dead pixels maps (local gaussian *or mask*)
2. Find sources in $s$ (coarse) ([photutils](https://photutils.readthedocs.io/en/stable/detection.html) or [SExtractor](https://astromatic-wrapper.readthedocs.io/en/latest/))
3. Plate-solve and update WCS ([astroquery](https://astroquery.readthedocs.io/en/latest/astrometry_net/astrometry_net.html) or [SCAMP](https://astromatic-wrapper.readthedocs.io/en/latest/))
4. Calibrate photometry
    1. [Search stars in SDSS](https://astroquery.readthedocs.io/en/latest/api/astroquery.sdss.SDSSClass.html#astroquery.sdss.SDSSClass.query_region) (once for all frames)
    3. RANSAC fit to find ZP and slope
    4. Calibrate pixel values with RANSAC (to Jy)

### *Level 3*
1. [Clean cosmic rays](https://www.astropy.org/ccd-reduction-and-photometry-guide/v/dev/notebooks/08-03-Cosmic-ray-removal.html) (local gaussian *or mask*)
2. [Remove sky](https://photutils.readthedocs.io/en/stable/background.html)

### *PSF*
1. Co-add images ([reproject](https://reproject.readthedocs.io/en/stable/mosaicking.html) or [SWarp](https://astromatic-wrapper.readthedocs.io/en/latest/))
2. Build PSF ([photutils](https://photutils.readthedocs.io/en/stable/epsf.html) or [PSFEx](https://astromatic-wrapper.readthedocs.io/en/latest/))

## Light-curve

1. For all $s$:
    1. Find sources in $s$ (fine) ([photutils](https://photutils.readthedocs.io/en/stable/detection.html) or [SExtractor](https://astromatic-wrapper.readthedocs.io/en/latest/))
    2. Aperture *and PSF photometry* ([photutils](https://photutils.readthedocs.io/en/stable/psf.html) or [SExtractor](https://astromatic-wrapper.readthedocs.io/en/latest/))
2. Match sources in different catalogs by DBSCAN clustering
3. Find moving sources
    1. Calculate dispersion of RA/DEC (MAD + threshold)
    2. Linear regression for RA/DEC
    3. Plot RA/DEC for moving sources
4. Find variable and stable sources
    1. Calculate dispersion of flux/mag (MAD + threshold)
    2. Plot flux/mag
5. Identify target: Match target RA/DEC to global catalog
databunkerpro 0.1.1
Python client library for DatabunkerPro API
Requires Python: >=3.7

# DatabunkerPro Python Client

A Python client library for interacting with the DatabunkerPro API. This library provides a simple and intuitive interface for managing user data, tokens, and other DatabunkerPro features.

## Installation

You can install the package using pip:

```bash
pip install databunkerpro
```

Or install directly from GitHub:

```bash
pip install git+https://github.com/securitybunker/databunkerpro-python.git
```

## Quick Start

```python
from databunkerpro import DatabunkerproAPI

# Initialize the client
api = DatabunkerproAPI(
    base_url="https://pro.databunker.org",
    x_bunker_token="your-api-token",
    x_bunker_tenant="your-tenant-name"
)

# Create a new user
user_data = {
    "email": "user@example.com",
    "name": "John Doe",
    "phone": "+1234567890"
}
result = api.create_user(user_data)
print(f"Created user with token: {result['token']}")

# Get user information
user = api.get_user("email", "user@example.com")
print(f"User profile: {user['profile']}")

# Update user information
update_data = {
    "name": "John Updated",
    "phone": "+0987654321"
}
api.update_user("email", "user@example.com", update_data)

# Create a token for sensitive data
token_result = api.create_token("creditcard", "4111111111111111")
print(f"Created token in base format (credit card): {token_result['tokenbase']}")
print(f"Created token in uuid format: {token_result['tokenuuid']}")
```

## Features

- User Management (create, read, update, delete)
- Token Management
- System Statistics
- Type hints and comprehensive documentation
- Error handling and validation

## Development

To set up the development environment:

1. Clone the repository:
```bash
git clone https://github.com/securitybunker/databunkerpro-python.git
cd databunkerpro-python
```

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

3. Run tests:
```bash
pytest
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License

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

## Support

If you encounter any issues or have questions, please [open an issue](https://github.com/securitybunker/databunkerpro-python/issues) on GitHub.

## API Documentation

For detailed API documentation, please visit the [DatabunkerPro API Documentation](https://databunker.org/databunker-pro-docs/introduction/).
bbuddy 0.0.2
collection of helpful bash tools
Requires Python: >=3.8

## bbuddy

The finest bash buddy on the internet

bbuddy is a graphical tool for typing your long commands.

Special thanks to [xdotool](https://www.semicomplete.com/projects/xdotool/), which does the heavy lifting.
xdotool is available on github [here](https://github.com/jordansissel/xdotool).

Also included in this distribution are the command line tools 'fffind', 'ppgrep', 'dbdump'.
cc-tpm-tools 0.1.1
CLI and Python tools for CC-TPM.
Requires Python: >=3.12

# CC-TPM Repository Tools

This package contains CLI tools for managing [CC-TPM](https://github.com/Deleranax/tpm) repositories.

Install `cc-tpm-tools` with your favorite Python3 package manager (e.g., with `pip install cc-tpm-tools`).

## Tools
### CC-TPM Repository Control Utility 
The main tool is the *CC-TPM Repository Control Utility*, a command-line tool for managing self-hosted TPM repositories.

See `cctpm --help` or [TPM on GitHub](https://github.com/Deleranax/tpm) for more information.
drf-iam 0.2.11
IAM-style roles and permissions for Django Rest Framework
Requires Python: >=3.6

# DRF-IAM (Django REST Framework - Identity and Access Management)

[![PyPI version](https://badge.fury.io/py/drf-iam.svg)](https://badge.fury.io/py/drf-iam)
[![Documentation Status](https://readthedocs.org/projects/drf-iam/badge/?version=latest)](https://drf-iam.readthedocs.io/en/latest/?badge=latest)

A powerful Django application that provides Role-Based Access Control (RBAC) for Django REST Framework applications. Easily manage permissions and roles across your API endpoints.

## Features

- 🔒 Role-Based Access Control (RBAC)
- 🎯 Custom policy names for viewsets
- 🔄 Seamless integration with Django's User model
- ⚡ Easy to set up and configure
- 📚 Comprehensive documentation

## Installation

```bash
pip install drf-iam
```

## Quick Start

1. Add `drf_iam` to your `INSTALLED_APPS` in `settings.py`:

```python
INSTALLED_APPS = [
    ...
    'drf_iam',
]
```

2. Add the Role relationship to your User model:

```python
from django.contrib.auth.models import AbstractUser
from drf_iam.models import Role

class User(AbstractUser):
    role = models.ForeignKey(Role, on_delete=models.SET_NULL, null=True)
```

3. Run migrations:

```bash
python manage.py makemigrations
python manage.py migrate
```

4. Use in your viewsets:

```python
from rest_framework import viewsets
from drf_iam.permissions import IAMPermission

class UserViewSet(viewsets.ModelViewSet):
    permission_classes = [IAMPermission]
    iam_policy_name = "users"  # Optional: Custom policy name for this viewset
```

## Documentation

For detailed documentation, visit [https://drf-iam.readthedocs.io/](https://drf-iam.readthedocs.io/)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.