semvec/pypi-packages huggingface dataset:
one-updater 1.0.0
One tool many packages
Requires Python: <3.14,>=3.11

# One Updater

A flexible package manager updater that helps you keep all your development tools up to date.

## Features

- Update multiple package managers with a single command
- Configure which package managers to update
- Support for virtual environments and pyenv for Python packages
- Beautiful command-line interface with rich formatting and logging
- Cross-platform support
- Extensible architecture for adding new package managers

## Supported Package Managers

- Homebrew
- pip (with virtualenv/pyenv support)
- npm
- cargo
- gem
- pipx
- pacman
- dnf
- flatpak
- pkgx
- vagrant-plugins
- micro editor plugins
- tldr pages
- go packages
- apt
- More coming soon!

## Installation

Install from PyPI:

```bash
pip install one-updater
```

To upgrade to the latest version:

```bash
pip install --upgrade one-updater
```

Use one of the pre-built executables in `one_updater/bin` e.g.:

```bash
sudo cp one-updater-linux /usr/local/bin/one-updater
sudo chmod +x /usr/local/bin/one-updater
```

## Usage

### Basic Usage

Update all enabled package managers:

```bash
one-updater update
```

Update specific package managers:

```bash
one-updater update -m homebrew -m pip
```

List configured package managers:

```bash
one-updater list-managers
```

Initialize a new configuration file:

```bash
one-updater init
```

### Configuration

The tool uses a YAML configuration file (default: `~/.config/one-updater/config.yaml`) to specify package manager settings. You can:

1. Enable/disable specific package managers
2. Configure virtualenv/pyenv for Python packages
3. Customize update commands
4. Configure logging and verbosity

Example configuration:

```yaml
verbose: true
logging:
  level: "INFO"
  format: "%(message)s"

package_managers:
  homebrew:
    enabled: true
    commands:
      update: ["brew", "update"]
      upgrade: ["brew", "upgrade"]

  pip:
    enabled: true
    virtualenv: "/path/to/virtualenv" # Optional
    pyenv: "3.11.0" # Optional
    commands:
      update: ["pip", "install", "--upgrade", "pip"]
      upgrade: ["pip", "list", "--outdated", "--format=json"]
```

## Contributing

Contributions are welcome! Feel free to:

1. Add support for new package managers
2. Improve error handling and logging
3. Add new features
4. Fix bugs
5. Improve documentation

Please ensure your changes pass the test suite and code quality checks:

```bash
# Install development dependencies
poetry install

# Run tests
poetry run pytest

# Run code formatting
poetry run black .
```

## License

MIT License
dv-utils 2.0.0rc14
Datavillage python utils for building algorithm running in confidential environment
Requires Python: <4,>=3.10.4

# Utility python library for interaction with datavillage gateway and components within the confidential environment

## About
This repository contains a set of utilities for the development of application within the confidential environment.

## Release process
The version management and git tagging must be done in accordance with the versioning practices described
in  [`dv-product` README](../dv-product/README.md#versioning-management).

## Deployment

## Code References

code references for this packages are generated with MkDocs and hosted on Github Pages for this repo:
https://datavillage-me.github.io/dv-utils/
xsltproc-py-lite 0.1.2
A lightweight Python+lxml implementation of xsltproc with limited features

# xsltproc-py-lite

[![PyPI version](https://img.shields.io/pypi/v/xsltproc-py-lite.svg)](https://pypi.org/project/xsltproc-py-lite/)
[![Python versions](https://img.shields.io/pypi/pyversions/xsltproc-py-lite.svg)](https://pypi.org/project/xsltproc-py-lite/)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

A lightweight Python reimplementation of `xsltproc`, supporting basic XSLT 1.0 transformations using `lxml`.

🔗 [View on PyPI](https://pypi.org/project/xsltproc-py-lite/)  
🔗 [GitHub Repository](https://github.com/takaakiaoki/xsltproc-py-lite)

---

## ✨ Features

- Apply XSLT 1.0 stylesheets to XML documents
- Outputs to file or standard output
- Zero-dependency CLI (except `lxml`)
- Simple and familiar `xsltproc`-like usage

---

## 📦 Installation

You can install this package from [PyPI](https://pypi.org/project/xsltproc-py-lite/):

```bash
pip install xsltproc-py-lite
```

---

## 🚀 Usage

```bash
xsltproc-py stylesheet.xsl input.xml -o output.xml
```

### Arguments
| Option           | Description                        |
| ---------------- | ---------------------------------- |
| `stylesheet.xsl` | Path to the XSLT file              |
| `input.xml`      | Path to the input XML file         |
| `-o output.xml`  | Output file path (default: stdout) |

---

## 🛠 Example
Given:

#### example.xsl
```xml
<xsl:stylesheet version="1.0"
 xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
  <xsl:template match="/">
    <hello>World</hello>
  </xsl:template>
</xsl:stylesheet>
```
#### input.xml

```xml
<root/>
```

Run:
```bash
xsltproc-py example.xsl input.xml -o result.xml
```

Output:
```xml
<hello>World</hello>
```

---

## ⚠ Limitations

* XSLT 1.0 only
* Does not support parameters (e.g., --stringparam)
* No support for chunking, profiling, or extensions

---

## 📄 License
This project is licensed under the [MIT License](LIiCENCE).

---
electroket 0.1.0
A NetKet extension for continuous space problems, with a focus on electronic structure
Requires Python: >=3.8


# electroket
<p align="center">
  <img src="docs/assets/electroket_logo.png"
       alt="Electroket – electronic-structure library with neural wave functions"
       width="300">
</p>  
electroket is an experimental extension of
[NetKet](https://www.netket.org/) that adds utilities for solving continuous
space problems with a focus on electronic structure calculations.  It reexports
NetKet so it can be used as a drop‑in replacement while providing additional
modules tailored for continuous degrees of freedom.

## Installation

```bash
pip install electroket
```

## Usage

```python
import electroket as ek

print(ek.show_version())  # prints electroket's version
```

## Library contents

electroket exposes several modules aimed at working with particles in continuous
space:

- **`hilbert`** – Hilbert spaces for particles and spinful particles in boxes or
  free space.
- **`geometry`** – simulation cells supporting periodic and open boundary
  conditions.
- **`molecule`** – helpers to define molecules and atomic properties including
  a small periodic table.
- **`operator`** – kinetic, potential and interaction operators for continuous
  systems.
- **`models`** – simple variational ansätze like Gaussian or Slater wave
  functions.
- **`sampler`** – Metropolis samplers with rules suitable for continuous
  variables.
- **`random`** – routines to generate random particle configurations.
- **`pyscf_utils`** – utilities to interface with PySCF and obtain SCF
  orbitals.

These modules are imported in ``electroket.__init__`` so they can be accessed directly
from the top-level package.
## Examples

Below is a minimal SR-VMC calculation for the H$_2$ molecule extracted from the
[Examples](Examples) directory.

```python
import netket as nk
import electroket as ek
from electroket.models import MolecularSlater

h2 = ek.Molecule(
    atoms=[
        ("H", [0.0, 0.0, -0.35]),
        ("H", [0.0, 0.0, 0.35]),
    ],
    units="angstrom",
)

ham = ek.operator.KineticEnergy(h2) + ek.operator.CoulombInteraction(h2)
model = MolecularSlater(h2)
sampler = ek.sampler.MetropolisSampler(
    h2,
    ek.sampler.GaussianRule(sigma=0.1),
)

vstate = nk.vqs.MCState(sampler, model, n_samples=500)

sr = nk.optimizer.SR(diag_shift=0.05)
opt = nk.optimizer.Sgd(learning_rate=0.01)

driver = nk.driver.VMC(
    hamiltonian=ham,
    optimizer=opt,
    variational_state=vstate,
    preconditioner=sr,
)

driver.run(n_iter=1000)
```
OSmOSE 0.2.5
OSmOSE toolkit
Requires Python: >=3.12

<br>
<br>
<div align="center">

<a href="https://github.com/Project-OSmOSE/OSEkit">
    <img src="docs/logo/osekit.png" alt="OSEkit logo" title="OSEkit" height="120" />
</a>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<a href="https://osmose.ifremer.fr/">
    <img src="docs/logo/osmose_texte_sombre.png" alt="OSmOSE logo" title="OSmOSE" height="120" />
</a>

<br>
<br>

![version](https://img.shields.io/badge/package_version-0.2.5-orange)
![PyPI status](https://img.shields.io/pypi/status/ansicolortags.svg)
![license](https://img.shields.io/github/license/mashape/apistatus.svg)
![Open Source Love](https://img.shields.io/badge/open%20source-♡-lightgrey)
![Python 3.12](https://img.shields.io/badge/python-3.12-blue.svg)

**OSEkit** is an open source suite of tools written in python and dedicated to the management and analysis of data in underwater passive acoustics.

[Presentation](#presentation) •
[Getting into it](#getting-into-it) •
[Acknowledgements](#acknowledgements)
# ㅤ

</div>

### Presentation

OSEkit is an open source suite of tools written in python and dedicated to the management and analysis of data in underwater passive acoustics. Among other key features, our toolkit has been adapted to be deployed on a cluster infrastructure; in OSmOSE, our production version runs on [DATARMOR](https://www.ifremer.fr/fr/infrastructures-de-recherche/le-supercalculateur-datarmor). Here are a few indications to help you going through our documentation. 

<br>

### Getting into it

All details to start using our toolkit and make the most out of it are given in our [documentation](https://project-osmose.github.io/OSEkit/); you will then be redirected to more specific parts of it depending on your profile and intentions:)

<br>

### Acknowledgements

- A great part of our processing codes are based on well-established python packages for data analysis including scipy ,pandas, numpy...
 
# ㅤ
<sub>© OSmOSE team, 2023-present</sub>