semvec/pypi-packages huggingface dataset:
exbw 0.1.1
Wrapper for bitwarden cli client.
Requires Python: >=3.8
Keywords: bitwarden

# exbw

Wrapper for bitwarden cli client.

Retrieve specified item field of bitwarden.
**NOTICE: Require Bitwarden CLI Installed**

## Install

`pip install exbw`

## Usage

- `ITEM` vault item name
- `FIELD` item field name (default: password)

`python3 -m exbw ITEM [-f FIELD]`
but-info-maths 0.0.10
Outils pour l'enseignement des maths en BUT d'informatique
Requires Python: >=3.9

# Bibliothèque `but-info-maths`

Cette bibilithèque propose des outils pour l'enseignement des mathématiques en
BUT d'informatique. Les fonctionnalités d'affichage des expressions
mathématiques sont prévues pour l'environnement Jupyter Notebook.

## Installation

### Depuis la console système

```bash
pip install but-info-maths
```

### Depuis une cellule de cahier Jupyter

```python
!pip install but-info-maths
```

*Remarque.* Cette dernière commande fonctionne notamment dans les Jupyter
Notebook de Google Colab.

## Documentation

La documentation est accessible à cette adresse
[mbensussan.yj.fr/projets_info/but-info-maths/](https://mbensussan.yj.fr/projets_info/but-info-maths/).
wizard-template 0.4.0
A template for a python project containing a package, tests, docs, and CI/CD.
Requires Python: >=3.10

# The Wizard Template for Python Projects

<p align="center">
<a href="https://github.com/fschuch/wizard-template"><img src="https://raw.githubusercontent.com/fschuch/wizard-template/refs/heads/main/docs/logo.png" alt="Wizard template logo" width="320"></a>
</p>
<p align="center">
    <em>Let the wizard do the heavy lifting so you can focus on your craft</em>
</p>

______________________________________________________________________

- CI/CD:
  [![CI](https://github.com/fschuch/wizard-template/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/fschuch/wizard-template/actions/workflows/ci.yaml)
  [![Docs](https://github.com/fschuch/wizard-template/actions/workflows/docs.yaml/badge.svg?branch=main)](https://docs.fschuch.com/wizard-template)
  [![CodeQL](https://github.com/fschuch/wizard-template/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/fschuch/wizard-template/actions/workflows/github-code-scanning/codeql)
  [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=fschuch_wizard-template&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=fschuch_wizard-template)

- Meta:
  [![Wizard Template](https://img.shields.io/badge/Wizard-Template-%23447CAA)](https://github.com/fschuch/wizard-template)
  [![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://mypy-lang.org/)
  [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch)
  [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
  ![GitHub License](https://img.shields.io/github/license/fschuch/wizard-template?color=blue)
  [![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver)

______________________________________________________________________

## Overview

This is a general-purpose template that aims to provide a magical start to any Python project. It includes the initial configuration of quality assurance tools, documentation, and automated actions to deploy a Python package.

Check out the documentation for more details on how to use the template and its features: <https://docs.fschuch.com/wizard-template>.

## Copyright and License

© 2023 [Felipe N. Schuch](https://github.com/fschuch).
All content is under [MIT License](https://github.com/fschuch/wizard-template/blob/main/LICENSE).
veriphi-core 0.1.3.post1
Python bindings for veriphi-core routines
Requires Python: >=3.10

# Veriphi Core Python Bindings

## Overview
Veriphi Core exposes cryptographic guardrail primitives for orchestrating agent authorisation graphs. The Python bindings wrap the Rust implementation via PyO3 so you can generate keys, prepare conditional envelopes, and obfuscate packets from Python without managing the underlying Rust toolchain.

## Installation
Once published, install directly from PyPI:

```bash
pip install veriphi-core
```

For local development inside the repository, install in editable mode with maturin:

```bash
pip install maturin
maturin develop --release
```

## Quickstart

```python
import numpy as np
from veriphi_core import interface

example_data = np.random.rand(1000)   # Generate some test data
byte_data    = example_data.tobytes() # Input data should be bytes

public_data, private_data = interface.setup_node(byte_data, 0, 1000, True) # Setup the encryption for conditions between 0, 1000

class_0, class_1 = interface.distribute_data(public_data, 'E', 2) # Distribute packets to 2 different classes

party_c0 = interface.encrypt_node(class_0,"class0_member")

party_c1 = interface.encrypt_node(class_1,"class1_member") # Each party independently processes the public data for their class

decrypted = interface.decrypt_node(private_data, 500, True, party_c0, party_c1) # recover the packet with the private data, a valid condition, and data from each class

recovered_example = np.frombuffer(decrypted, dtype=np.float64)

print(np.all(example_data == recovered_example))


```

Refer to [`notebooks/py_start.ipynb`](https://github.com/Veriphi-labs/veriphi/blob/main/notebooks/py_start.ipynb) in the full repository for a complete walkthrough.

## Supported Platforms
- macOS universal2 (`x86_64`/`arm64`), Python 3.10+
- manylinux2014 `x86_64` and `aarch64`, Python 3.10+

The project also ships a source distribution so unsupported platforms can build from source if a compatible Rust toolchain is available.

## Building Release Wheels Locally
1. **macOS universal build**
   ```bash
   rustup target add aarch64-apple-darwin x86_64-apple-darwin
   maturin build --release --strip --target universal2-apple-darwin -m rust/veriphi-core-py/Cargo.toml --out dist
   ```
2. **Linux manylinux (run from the repo root)**
   ```bash
   docker run --rm --platform=linux/arm64 -v "$ROOT":/io \
      ghcr.io/pyo3/maturin:latest build --release --strip \
      -m /io/rust/veriphi-core-py/Cargo.toml --out /io/dist --manylinux 2014
   
   docker run --rm --platform=linux/amd64 -v "$ROOT":/io \
      ghcr.io/pyo3/maturin:latest build --release --strip \
      -m /io/rust/veriphi-core-py/Cargo.toml --out /io/dist --manylinux 2014
   ```
4. **Source distribution**
   ```bash
   maturin sdist -m rust/veriphi-core-py/Cargo.toml --out dist
   ```

After each build, install the resulting wheel in a clean environment and run `pytest` to validate the artefact.
biopb 0.1.4
Python bindings of BioPB: open protobuf/gRPC protocols for biomedical image processing
Requires Python: >=3.8

# BioPB
A place for collecting protobuf/gRPC definitions for bio-imaging data. Currently it has only two packages

1. `biopb.ome` Microscopy data representation modeled after [OME-XML](https://ome-model.readthedocs.io/en/stable/ome-xml/index.html).
2. `biopb.image` Image processing protocols. Current focus is single-cell segmentation, designed originally for the [Lacss](https://github.com/jiyuuchc/lacss/) project.


## Documentation
[Documentation](https://buf.build/jiyuuchc/biopb/)

## Python binding
A python binding of schema is included in this repo. The package additionally implements some utility functions for data conversion between _numpy_ <--> _protobuf_.

``` sh
pip install biopb
```

## Related project
* [`napari-biopb`](https://github.com/biopb/napari-biopb) is a [napari](https://napari.org) widget and a `biopb.image` client, allowing users to perform 2D/3D single-cell segmentation within the Napari environement.
* [`trackmate-lacss`](https://github.com/biopb/TrackMate-Lacss) is a [`FIJI`](https://imagej.net/software/fiji/) plugin and a `biopb.image` client, designed as a cell detector/segmentor for [`trackmate`](https://imagej.net/plugins/trackmate/index). It works with any `biopb.image` servers.
* [`biopb-server`](https://github.com/biopb/biopb-server) implement ready-to-deploy biopb servers (as Docker containers).
infoworkssdk 5.1.2
A package to work with Infoworks via SDK. This library is compatible with Infoworks v6.2.0 onwards. Code can be found in https://github.com/Infoworks/InfoworksPythonSDK branch: release/sdk-5.1

# Infoworks Python SDK

## Introduction
The Infoworks Python library provides convenient access to the Infoworks v3 APIs from
applications written in the Python language. 

It includes pre-defined set of functions performing various actions.

<br>
Supports Infoworks version 5.3 onwards

## Table of Contents
- [Introduction](#introduction)
- [Documentation](#documentation)
- [Installation](#installation)
- [Usage](#usage)
- [Example](#example)
- [Authors](#authors)

![Infoworks SDK Usage GIF](/infoworks_sdk.gif?raw=true)

## Documentation

https://infoworks.github.io/infoworks-python-sdk/

## Installation

You don't need this source code unless you want to modify the package. If you just want to use the package, just run:
```sh
pip install infoworkssdk
```
## Requirements

Python 3.4+ (PyPy supported)

## Usage

The library needs to be configured with your user's refresh token key which is available in your Infoworks UI. Set refresh_token to its value.

### Steps to get refresh token:

https://docs.infoworks.io/infoworks-5.5.0/developer-resources/rest-api#using-refresh-token

```python
from infoworks.sdk.client import InfoworksClientSDK
# Your refresh token here
protocol="https"
host="<your infoworks hostname>"
port="443"
refresh_token = "<your_refresh_token>"
# Initialise the client
iwx_client = InfoworksClientSDK()
iwx_client.initialize_client_with_defaults(protocol=protocol, ip=host, port=port, refresh_token=refresh_token)
```
## Example

Create Oracle Source
```
src_create_response = iwx_client.create_source(source_config={
            "name": "iwx_sdk_srcname",
            "type": "rdbms",
            "sub_type": "oracle",
            "data_lake_path": "/iw/sources/iwx_sdk_srcname",
            "environment_id": "",
            "storage_id": "",
            "is_source_ingested": True
        })
```

## Support

If you need any support or assistance, please contact customer-engineering@infoworks.io
quadrosdesaude 0.1.6
Ferramentas para processar dados do Datasus (Sistema Público de Saúde).
Requires Python: >=3.10

# QUADROS DE SAUDE - DATASUS

Este conjunto de ferramentas te ajudará a obter, tratar e utilizar os dados do Datasus - Dados abertos do Ministério da Saúde do Brasil.








Para dúvidas e contato: [André Quadros](https://www.linkedin.com/in/andrequadros/)

> *Este projeto está licenciado sob a Licença MIT. Veja o arquivo [LICENSE.md](LICENSE.md) para detalhes.*
mujopy 0.0.1
Pythonic wrappers for MuJoCo models and Graph representations.
Requires Python: >=3.10
Keywords: mujoco, robotics, graph

# MuJoPy

## Table of Contents
- [Project Motivation](#project-motivation)
- [Installation](#installation)
- [File Descriptions](#file-descriptions)
- [Usage](#usage)
- [Licensing](#licensing)

## Project Motivation
MuJoPy provides pythonic access to MuJoCo models by wrapping the low-level structs of the model in dataclasses and by this deriving a navigable graph of bodies, joints and geoms. By this, downstream usage of robot models, e.g. feature extraction is faster without the need of custom parsing logic for the MuJoCo models.

With `RobotGraph()` the package also exposes an example of using the MuJoPy wrapper by providing a graph representation alongside of a feature matrix of the model.
## Installation
Install the package together with its MuJoCo dependency:

```bash
pip install mujopy
```

For development work inside this repository, install in editable mode with the testing extras:

```bash
pip install -e ".[test]"
```

## File Descriptions
- `mujopy/src/mujopy_model/`: Core wrappers (`Body`, `Joint`, `Geom`, `MuJoPyModel`) that expose MuJoCo fields as Python properties.
- `mujopy/src/robot_graph/`: Graph utilities built on top of NetworkX, including the `RobotGraph` and feature extraction pipeline.
- `tests/`: PyTest-based unit tests and fixtures covering the main APIs.
- `pyproject.toml`: Packaging metadata, dependencies, and tool configuration.

## Usage
```python
from pathlib import Path
from mujopy import MuJoPyModel, RobotGraph

# Load a MuJoCo XML and register the default property packs
model = MuJoPyModel(
    xml_path=str(Path("path/to/model.xml")),
    include_world_body=True,
    include_free_joints=False,
)
model.register_default_properties()

# Inspect bodies, joints, and geoms
trunk = model.body(0)
print(trunk.name, trunk.children_ids, trunk.primitive_geoms)

# Build a graph with feature vectors
graph = RobotGraph(
    xml_path=Path("path/to/model.xml"),
    feature_config_path=Path("path/to/feature_config.yml"),
)
print(graph.number_of_nodes(), graph.feature_matrix.shape)
```

Each wrapper exposes the underlying MuJoCo struct through the `mujoco_view` attribute—use it when you need direct access to raw MuJoCo fields:

```python
body_view = trunk.mujoco_view  # mujoco.MjDataView for the body
print(body_view.pos)
```

### Registering Custom Properties
Extend the core wrappers with your own read-only properties by registering callables on `MuJoPyModel`:

```python
from mujopy import MuJoPyModel

def _body_is_not_root(body: Body) -> bool:
        return int(np.asarray(body.mujoco_view.parentid).item()) != body.id

model = MuJoPyModel(xml_path="path/to/model.xml")
MuJoPyModel.register_body_property("_body_is_not_root", is_not_root)
model.register_default_properties()

print(model.body(0).is_not_root)
```

## Licensing
This project is distributed under the MIT License—see the license text in `pyproject.toml` for details.