semvec/pypi-packages huggingface dataset:
oneidentity-safeguard-sessions-plugin-sdk 1.9.1
One Identity Safeguard for Privileged Sessions Plugin SDK
Requires Python: >=3.6

One Identity Safeguard for Privileged Sessions Plugin Software Development Kit
==============================================================================

The One Identity Safeguard for Privileged Sessions Plugin Software Development Kit (Plugin SDK) provides base classes
and services to enable rapid development of Python 3 plugins for the Safeguard for Privileged Sessions product.
The Plugin SDK helps implement the requirements stated in the documents

* Creating custom Authentication and Authorization plugins developer's guide,
* Creating custom Credential Store plugins developer's guide.

The documents above are part of the `One Identity technical documentation \
<https://docs.oneidentity.com/category/safeguard_sessions>`_.


The documentation for the SDK can be found here: `One Identity Safeguard Sessions Plugin SDK Documentation <https://oneidentity.github.io/safeguard-sessions-plugin-sdk/1.9.1/index.html>`_

About us
========

.. image:: https://oneidentity.github.io/safeguard-sessions-plugin-sdk/logo/logo.png
    :target: https://oneidentity.com

One Identity solutions eliminate the complexities and time-consuming processes often required to govern identities,
manage privileged accounts and control access. Our solutions enhance business agility while addressing your IAM
challenges with on-premises, cloud and hybrid environments.

Contacting us
=============

For sales or other inquiries, visit
`https://www.oneidentity.com/company/contact-us.aspx <https://www.oneidentity.com/company/contact-us.aspx>`_
or call +1-800-306-9329.

Technical support resources
===========================


Technical support is available to One Identity customers with a valid maintenance contract and customers who have trial
versions. You can access the Support Portal at `https://support.oneidentity.com/ <https://support.oneidentity.com/>`_.

The Support Portal provides self-help tools you can use to solve problems quickly and independently, 24 hours a day,
365 days a year. The Support Portal enables you to:

* Submit and manage a Service Request
* View Knowledge Base articles
* Sign up for product notifications
* Download software and technical documentation
* View how-to-videos at `www.YouTube.com/OneIdentity <https://www.youtube.com/OneIdentity>`_
* Engage in community discussions
* Chat with support engineers online
* View services to assist you with your product
mercury-cli 1.1.0
A CLI wrapper for Mercury-OCIP
Requires Python: >=3.12

# Mercury CLI

A command-line interface for managing and automating Broadworks provisioning operations.

---

## Installation

### Prerequisites

- Python 3.12 or higher  
- Access to a Broadworks provisioning server  

---

## Getting Started

Launch the CLI by running:

```bash
mercury-cli
```

On first launch, you will be prompted to authenticate with your Mercury server credentials:

- Username  
- Password  
- Server URL (e.g., https://mercury.example.com/webservice/services/ProvisioningService)  
- TLS option (defaults to yes)  

---

### Skip Authentication (Development)

For testing purposes, you can skip the login prompt via the --no-login flag:

```bash
mercury-cli --no-login
```

---

## Available Commands

### General Commands

| Command | Description |
|----------|-------------|
| `help [command]` | Display available commands or get help for a specific command |
| `sysver` | Display the current system software version |
| `clear` | Clear the terminal screen |
| `exit` | Exit the CLI |

---

### Bulk Operations

The CLI supports bulk creation and modification operations using CSV files.

#### Bulk Create

| Command | Description |
|----------|-------------|
| `bulk create hunt_group <file_path>` | Create hunt groups from CSV |
| `bulk create call_pickup <file_path>` | Create call pickup groups from CSV |
| `bulk create call_center <file_path>` | Create call centers from CSV |
| `bulk create auto_attendant <file_path>` | Create auto attendants from CSV |

#### Bulk Modify

| Command | Description |
|----------|-------------|
| `bulk modify call_center agent_list <file_path>` | Modify call center agent lists from CSV |

---

## Plugin Commands

Mercury CLI supports a plugin system for extending functionality.

| Command | Description |
|----------|-------------|
| `plugin list` | List all available plugins |

Loaded plugins automatically register their own commands and subcommands.

---

## Features

- Tab completion for commands and file paths  
- Command history with auto-suggest  
- Input validation  
- Progress indicators for long-running operations  
- Detailed error reporting for bulk operations  

---
aa-fleettracker 1.0.1
Fleet tracking Plugin for Alliance Auth (GitHub Version)
Requires Python: >=3.10
Keywords: allianceauth, fleettracker

# Fleettracker Plugin App for Alliance Auth (GitHub Version)<a name="example-plugin-app-for-alliance-auth-github-version"></a>

This is an Fleettracker Plugin app for [Alliance Auth](https://gitlab.com/allianceauth/allianceauth)

______________________________________________________________________

<!-- mdformat-toc start --slug=github --maxlevel=6 --minlevel=1 -->

- [Fleettracker Plugin App for Alliance Auth (GitHub Version)](#example-plugin-app-for-alliance-auth-github-version)
  - [Features](#features)
  - [Writing Unit Tests](#writing-unit-tests)
  - [Installing Into Your Dev AA](#installing-into-your-dev-aa)
  - [Setup Permissions](#setup-permissions)


<!-- mdformat-toc end -->

______________________________________________________________________

## Features<a name="features"></a>

- The plugin can save fleet snapshots with optional names
- Fleet detail panel with sorting by corp
- Saves characters and their ships into db


## Installing Into Your Dev AA<a name="installing-into-your-dev-aa"></a>

Once you've cloned or copied all files into place and finished renaming the app,
you're ready to install it to your dev AA instance.

Make sure you're in your venv. Then install it with pip in editable mode:

```bash
pip install aa-fleettracker
```

Add `'fleettracker',` to
INSTALLED_APPS in `settings/local.py`.


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

Finally, restart your AA server and that's it.

## Setup Permissions<a name="setup-permissions"></a>

Now it's time to set up access permissions for fleettracker module.

| ID                   | Description                       | Notes                                                                                                       |
| :------------------- | :-------------------------------- | :---------------------------------------------------------------------------------------------------------- |
| `basic_access` | Can access module | Anyone that can see previous fleets need that permission |
| `take_snapshot`      | Can take snapshot                 | Everyone with this permission can take new snapshot                                                      |
onshape-to-robot 1.8.1
Converting Onshape assembly to robot definition (URDF, SDF, MuJoCo) through Onshape API
Requires Python: >=3.9

# Onshape to Robot (URDF, SDF, MuJoCo)

<p align="center">
<img src="docs/source/_static/img/main.png" />
</p>

This tool is based on the [Onshape API](https://dev-portal.onshape.com/) to retrieve
informations from an assembly and build a robot description (URDF, SDF, MuJoCo ) suitable
for physics simulation.

* [Documentation](https://onshape-to-robot.readthedocs.io/)
* [Examples](https://github.com/rhoban/onshape-to-robot-examples)
kuva-metadata 1.1.8
Definitions of Kuva Space product metadata
Requires Python: <=3.13,>=3.10

<div align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/1d8b44f1-1999-4cfb-8744-32871056c253">
    <img alt="kuva-space-logo" src="https://github.com/user-attachments/assets/d8f47cc8-1491-4d0c-a8cf-318ea7e0afdc" width="50%">
  </picture>
</div>

# Kuva Metadata

Images taken from a satellite are complicated beasts with lot of metadata associated
to them. This repository contains the metadata definitions for the Hyperfield products. 
This metadata along with the acquired GeoTIFF images form the Kuva Space products in its 
various processing levels.

With the metadata and images, we may process products to the 
next processing levels, or do more precise processing than just with a GeoTIFF. 

# Installation

```bash
pip install kuva-metadata
``` 

This package is also included when installing the `kuva-reader`.

### Requirements

`Python 3.10` to `3.13`, preferably within a virtual environment

# Processing levels

Currently there are metadata definitions for the following processing levels of Kuva products:

- **L0**: Radiometrically corrected frames as TOA radiance
- **L1AB**: Band-aligned product formed from multiple L0 products
- **L1C**: Georeferences and orthorectified L1 product
- **L2A**: Atmospherically corrected product as BOA reflectance

# Architecture

All the metadata are defined as Pydantic models, this has several advantages:

- A very rich set of validations that are applied before data object construction
- The ability to easily (de)serialize (from)to JSON

# Contributing

Please follow the guidelines in [CONTRIBUTING.md](https://github.com/KuvaSpace/kuva-data-processing/blob/main/CONTRIBUTING.md).

Also, please follow our [Code of Conduct](https://github.com/KuvaSpace/kuva-data-processing/blob/main/CODE_OF_CONDUCT.md)
while discussing in the issues and pull requests.

# Contact information

For questions or support, please open an issue. If you have been given a support contact, 
feel free to send them an email explaining your issue.

# License

The `kuva-reader` project software is under the [MIT license](https://github.com/KuvaSpace/kuva-data-processing/blob/main/LICENSE.md).


# Status of unit tests

[![Unit tests for kuva-metadata](https://github.com/KuvaSpace/kuva-data-processing/actions/workflows/test-kuva-metadata.yml/badge.svg)](https://github.com/KuvaSpace/kuva-data-processing/actions/workflows/test-kuva-metadata.yml)
anatomic-locations 0.2.1
Anatomic location ontology navigation
Requires Python: >=3.11

# anatomic-locations

Python library for anatomic location ontology navigation: hierarchy traversal, laterality variants, and semantic search.

## Installation

```bash
pip install anatomic-locations
```

## Features

- **Hybrid Search**: Combined full-text and semantic vector search
- **Hierarchy Navigation**: Traverse parent/child relationships with tree visualization
- **Laterality Variants**: Generate left, right, bilateral variants
- **Auto-Download**: Database downloads automatically on first use
- **Flexible Lookup**: Use location IDs (RID*) or names/synonyms

## Quick Start

The database auto-downloads on first use. No setup required.

```python
from anatomic_locations import AnatomicLocationIndex

with AnatomicLocationIndex() as index:
    location = index.get("RID2772")  # Kidney
    print(location.description)       # "kidney"
    print(location.region.value)      # "Abdomen"
```

## Configuration

Override the default database path via environment variable or constructor:

```bash
# Environment variable
export ANATOMIC_DB_PATH=/path/to/custom.duckdb
```

```python
# Or specify directly
index = AnatomicLocationIndex(db_path="/path/to/custom.duckdb")
```

## CLI

The `anatomic-locations` CLI provides search, hierarchy navigation, and lookup commands. Use location IDs (e.g., `RID2772`) or names (e.g., "stomach"):

```bash
# Semantic search
anatomic-locations search "posterior cruciate ligament"

# Show full hierarchy tree
anatomic-locations hierarchy stomach
```

See the [full CLI reference](../../docs/anatomic-locations.md#cli-commands) for all commands.

## Related Packages

- **[findingmodel-ai](../findingmodel-ai/README.md)**: AI-assisted anatomic location discovery
- **[findingmodel](../findingmodel/README.md)**: Core finding model library

## Documentation

- [Anatomic Locations Guide](../../docs/anatomic-locations.md) - Full CLI reference, Python API, and configuration
oidm-common 0.2.2
Shared infrastructure for OIDM packages
Requires Python: >=3.11

# oidm-common

Internal infrastructure package for the Open Imaging Data Model (OIDM) ecosystem.

## ⚠️ Not for Direct Use

This package provides shared infrastructure for OIDM packages. **Do not install this package directly.**

Instead, use one of the user-facing packages:
- [`findingmodel`](https://pypi.org/project/findingmodel/) - Finding model index and search
- [`anatomic-locations`](https://pypi.org/project/anatomic-locations/) - Anatomic location ontology
- [`findingmodel-ai`](https://pypi.org/project/findingmodel-ai/) - AI-powered finding model tools

## Contents

- DuckDB connection management and hybrid search
- Embedding cache and providers
- Distribution utilities (manifest, download, paths)
- Shared data models (IndexCode, WebReference)

## Embedding cache

High-level helpers (`get_embedding`, `get_embeddings_batch`) use a DuckDB-backed cache by default at
`~/.cache/findingmodel/embeddings.duckdb`. Pass `cache=None` to disable caching, or pass an
`EmbeddingCache` instance to override the path.