semvec/pypi-packages huggingface dataset:
nostr-sdk 0.44.2
Nostr protocol implementation, Relay, RelayPool, high-level client library, NWC client and more.
Requires Python: >=3.9

# Nostr SDK

## Description

Nostr protocol implementation, Relay, RelayPool, high-level client library, NWC client and more.

## Documentation

Learn more about `rust-nostr` at <https://rust-nostr.org>.

### Supported platforms

The following OS and architectures are supported:

| OS            | x86_64 | aarch64 | armv7 | i686 | riscv64 |
|---------------|--------|---------|-------|------|---------|
| Android       | ❌      | ❌       | ❌     | ❌    | ❌       |
| Linux (GLIBC) | ✅      | ✅       | ✅     | ✅    | ✅*      |
| Linux (MUSL)  | ✅      | ✅       | ✅     | ✅    | ✅*      |
| FreeBSD       | ✅*     | ❌       | ❌     | ❌    | ❌       |
| macOS         | ✅      | ✅       | ❌     | ❌    | ❌       |
| Windows       | ✅      | ✅       | ❌     | ✅    | ❌       |

Are you interested in other platforms? Open an issue [here](https://github.com/rust-nostr/nostr-sdk-ffi).

<small>* PyPI currently doesn't allow uploading this wheel</small>

## State

**This library is in an ALPHA state**, things that are implemented generally work but the API will change in breaking ways.

## Donations

`rust-nostr` is free and open-source. This means we do not earn any revenue by selling it. Instead, we rely on your financial support. If you actively use any of the `rust-nostr` libs/software/services, then please [donate](https://rust-nostr.org/donate).

## License

This project is distributed under the MIT software license - see the [LICENSE](https://rust-nostr.org/license) file for details
eventdisplay-ml 1.0.0
A machine learning toolkit for Eventdisplay
Requires Python: >=3.12

# Machine learning for Eventdisplay

[![LICENSE](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://github.com/Eventdisplay/Eventdisplay-ML/blob/main/LICENSE)
[![release](https://img.shields.io/github/v/release/eventdisplay/eventdisplay-ml)](https://github.com/Eventdisplay/Eventdisplay-ML/releases)
[![pypi](https://badge.fury.io/py/eventdisplay-ml.svg)](https://badge.fury.io/py/eventdisplay-ml)
[![DOI](https://zenodo.org/badge/1120034687.svg)](https://doi.org/10.5281/zenodo.18117884)

Toolkit to interface and run machine learning methods together with the Eventdisplay software package for gamma-ray astronomy data analysis.

Provides examples on how to use e.g., scikit-learn or XGBoost regression trees to estimate event direction, energies, and gamma/hadron separators.

Introduces a Python environment and a scripts directory to support training and inference.

Input is provided through the `mscw` output (`data` trees).

## Direction and energy reconstruction using XGBoost

Stereo analysis methods implemented in Eventdisplay provide direction / energies per event resp telescope image. The machine learner implemented Eventdisplay-ML uses XGB Boost regression trees. Features are all estimators (e.g. DispBDT or intersection method results) plus additional features (mostly image parameters) to get a better estimator for directions and energies.

Output is a single ROOT tree called `StereoAnalysis` with the same number of events as the input tree.

## Gamma/hadron separation using XGBoost

Gamma/hadron separation is performed using XGB Boost classification trees. Features are image parameters and stereo reconstruction parameters provided by Eventdisplay.
Training is performed in overlapping energy bins to account for energy dependence of the classification.
The zenith angle dependence is accounted for by including the zenith angle as a binned feature in the training.

Output is a single ROOT tree called `Classification` with the same number of events as the input tree. It contains the classification prediction (`Gamma_Prediction`) and boolean flags (e.g. `Is_Gamma_75` for 75% signal efficiency cut).

## Generative AI disclosure

Generative AI tools (including Claude, ChatGPT, and Gemini) were used to assist with code development, debugging, and documentation drafting. All AI-assisted outputs were reviewed, validated, and, where necessary, modified by the authors to ensure accuracy and reliability.

## Citing this Software

Please cite this software if it is used for a publication, see the [Zenodo record](https://doi.org/10.5281/zenodo.18117884) and [CITATION.cff](CITATION.cff) for details.
pdm-backend 2.4.7
The build backend used by PDM that supports latest packaging standards
Requires Python: >=3.9
Keywords: packaging, PEP 517, build

# PDM-Backend

The build backend used by [PDM] that supports latest packaging standards.

[![PyPI](https://img.shields.io/pypi/v/pdm-backend?label=PyPI)](https://pypi.org/project/pdm-backend)
[![Tests](https://github.com/pdm-project/pdm-backend/actions/workflows/ci.yml/badge.svg)](https://github.com/pdm-project/pdm-backend/actions/workflows/ci.yml)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/pdm-project/pdm-backend/main.svg)](https://results.pre-commit.ci/latest/github/pdm-project/pdm-backend/main)
[![pdm-managed](https://img.shields.io/endpoint?url=https%3A%2F%2Fcdn.jsdelivr.net%2Fgh%2Fpdm-project%2F.github%2Fbadge.json)](https://pdm-project.org)

This is the backend for [PDM] projects that is fully-compatible with [PEP 517] spec, but you can also use it alone.
It reads the metadata of [PEP 621] format and coverts it to [Core metadata].

[pep 517]: https://www.python.org/dev/peps/pep-0517/
[pep 621]: https://www.python.org/dev/peps/pep-0621/
[Core metadata]: https://packaging.python.org/specifications/core-metadata/
[PDM]: https://pdm-project.org

## Links

- [Documentation](https://backend.pdm-project.org)
- [Changelog](https://github.com/pdm-project/pdm-backend/releases)
- [PDM Documentation][PDM]
- [PyPI](https://pypi.org/project/pdm-backend)
- [Discord](https://discord.gg/Phn8smztpv)

> **NOTE**
> This project has been renamed from `pdm-pep517` and the old project lives in the [legacy] branch.

[legacy]: https://github.com/pdm-project/pdm-backend/tree/legacy

## Sponsors

<p align="center">
    <a href="https://cdn.jsdelivr.net/gh/pdm-project/sponsors/sponsors.svg">
        <img src="https://cdn.jsdelivr.net/gh/pdm-project/sponsors/sponsors.svg"/>
    </a>
</p>

## License

This project is licensed under [MIT license](/LICENSE).
e3series 26.21.0
Python wrapper and utilities for the E3.series COM interface.
Requires Python: >=3.9
Keywords: CAE, E3.series, E3.series COM interface, ECAD, Electrical CAD, Electrical CAD scripting, Electrical cabinet, Electrical engineering, Formboard, Harness, Zuken

# Python wrapper for the E3.series API

python e3series is a wrapper library for the E3.series API.
The library enhances the automatic code completion and static program verification for python programs that automate E3.series.

This library requires a working instance of the software Zuken E3.series.

## Getting Started

Install the library via pip:
```
pip install e3series
```

Use the library:
```python
import e3series as e3

app = e3.Application()
app.PutInfo(0, "hello, world!")
```

The documentation is currently optimised for use with VSCode and may appear poorly formatted in other IDEs.

For more samples you can visit the git repository [https://github.com/Zuken-E3-GmbH/E3SeriesPythonExamples](https://github.com/Zuken-E3-GmbH/E3SeriesPythonExamples).

The Changelog can also be found on github: [Link](https://github.com/Zuken-E3-GmbH/E3SeriesPythonExamples/tree/main/Changelog)
xnat-dashboard 0.0.2a7
Dashboard showing the users and projects in a XNAT instance.
Requires Python: >=3.10
Keywords: XNAT, Dashboard, nicegui

# XNAT-Dashboard

## environment variables
- SERVER: _URL of the XNAT instance, i.e. https://localhost_
- UPDATE_INTERVAL= _Data refresh interval in hours_
- TITLE= _Title shown on the top right of every page_
- PORT= _port on which the web page is available_
- DEFAULT_ROUTE= _redirect to this page when accessing the root page_
- PRIMARY_COLOR= _color for the header/footer_
- SECONDARY_COLOR= _color for the buttons_
- LOGO= _base64 png with the logo, i.e. 'data:image/png;base64,...'_
rana-process-sdk 0.12
The Software Development Kit for creating Rana processes.
Requires Python: >=3.12

# rana-process-sdk

The SDK (Software Development Kit) for creating your own [Rana Water Intelligence](https://ranawaterintelligence.com) processes.


## Development on this project itself

See [nens-meta's documentation](https://nens-meta.readthedocs.io) for an explanation of the config files like `.editorconfig` and tools like `pre-commit` and `uv`.

The standard commands apply:

    $ uv sync
    $ uv run pytest
    $ uv run python  # You can also activate the virtualenv in .venv
    $ pre-commit run --all  # You can also do 'pre-commit install'
    $ uv add some-dependency

`uv` replaces pip/venv/requirement.txt/pip-tools. `pre-commit` does basic formatting.

The tests and pre-commit are also run automatically [on "github actions"](https://github.com/nens/rana-process-sdk/actions) for "main" and for pull requests. So don't just make a branch, but turn it into a pull request right away. On your pull request page, you also automatically get the feedback from the automated tests.

If you need a new dependency (like `requests`), add it in
`pyproject.toml` in `dependencies`:

    $ uv add requests

## Release

Make sure you have [zest.releaser](https://zestreleaser.readthedocs.io/en/latest/) installed.

    uv run fullrelease

When you created a tag, it will be uploaded automatically [to pypi](https://pypi.org/project/rana-process-sdk/) by a Github Action.