semvec/pypi-packages huggingface dataset:
lsst-daf-butler 30.0.4rc1
An abstraction layer for reading and writing astronomical data to datastores.
Requires Python: >=3.11.0
Keywords: lsst

# lsst-daf-butler

[![pypi](https://img.shields.io/pypi/v/lsst-daf-butler.svg)](https://pypi.org/project/lsst-daf-butler/)
[![codecov](https://codecov.io/gh/lsst/daf_butler/branch/main/graph/badge.svg?token=2BUBL8R9RH)](https://codecov.io/gh/lsst/daf_butler)

LSST Data Access framework described in [arXiv:2206.14941](https://arxiv.org/abs/2206.14941).
Please cite [the SPIE paper](https://doi.org/10.1117/12.2629569) when using this software.

This is a **Python 3 only** package (we assume Python 3.11 or higher).

* SPIE paper from 2024: [Converting Rubin Observatory's data butler to a client/server architecture](https://doi.org/10.1117/12.3019130) ([Tech note](https://dmtn-288.lsst.io))
* SPIE Paper from 2022: [The Vera C. Rubin Observatory Data Butler and Pipeline Execution System](https://doi.org/10.1117/12.2629569) ([arXiv](https://arxiv.org/abs/2206.14941)) \[Primary reference]
* ADASS paper from 2019: [Abstracting the Storage and Retrieval of Image Data at the LSST](https://ui.adsabs.harvard.edu/abs/2019ASPC..523..653J/abstract).
* Early design note: [DMTN-056](https://dmtn-056.lsst.io)

PyPI: [lsst-daf-butler](https://pypi.org/project/lsst-daf-butler/)

This software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license).
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.

## Arrow Memory Leaks

From version 18 of arrow we have seen significant memory leaks when accessing parquet files using the default memory allocator.
If you see such leaks the workaround is to set the `ARROW_DEFAULT_MEMORY_POOL` environment variable to `jemalloc` following the advice from [apache/arrow#45882](https://github.com/apache/arrow/issues/45882).
For EUPS users this variable is automatically set.
semanticapi-mcp 0.1.2
MCP server for Semantic API — search and discover APIs using natural language
Requires Python: >=3.10
Keywords: api-discovery, chatgpt, claude, llm, mcp, semantic-api

# Semantic API MCP Server

<!-- mcp-name: io.github.peter-j-thompson/semanticapi -->

An [MCP (Model Context Protocol)](https://modelcontextprotocol.io) server that lets Claude, ChatGPT, and other LLM agents search and discover APIs using natural language via [Semantic API](https://semanticapi.dev). Ask for any API capability in plain English and get back endpoint details, parameters, auth info, and code snippets.

## Install

```bash
pip install semanticapi-mcp
```

Or run directly with uvx:

```bash
uvx semanticapi-mcp
```

## Configuration

### Get an API Key

Sign up at [semanticapi.dev](https://semanticapi.dev) to get your API key.

### Environment Variables

| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `SEMANTIC_API_KEY` | Yes | — | Your Semantic API key |
| `SEMANTIC_API_URL` | No | `https://semanticapi.dev` | API base URL override |

### Claude Desktop

Add to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "semanticapi": {
      "command": "uvx",
      "args": ["semanticapi-mcp"],
      "env": {
        "SEMANTIC_API_KEY": "your-api-key-here"
      }
    }
  }
}
```

Or if installed with pip:

```json
{
  "mcpServers": {
    "semanticapi": {
      "command": "semanticapi-mcp",
      "env": {
        "SEMANTIC_API_KEY": "your-api-key-here"
      }
    }
  }
}
```

## Tools

### `semantic_query`

Search for an API capability using natural language.

**Inputs:**
- `query` (string, required) — What you want to do, e.g. "send an email with Gmail"
- `auto_discover` (boolean, optional, default: true) — Auto-discover new APIs if needed

**Example:** "Find me an API to convert currencies in real-time"

### `semantic_discover`

Deep discovery of a specific provider/API by name and intent.

**Inputs:**
- `provider_name` (string, required) — API provider name, e.g. "stripe", "twilio"
- `user_intent` (string, optional) — What you want to do with this API

**Example:** Discover Stripe's capabilities for "process a refund"

### `semantic_discover_url`

Analyze any API from its documentation URL.

**Inputs:**
- `url` (string, required) — URL of the API documentation
- `user_intent` (string, optional) — What you want to do with this API

**Example:** Analyze `https://docs.example.com/api` to generate a provider config

## Related

- **[Semantic API](https://semanticapi.dev)** — The hosted API service
- **[semanticapi-engine](https://github.com/peter-j-thompson/semanticapi-engine)** — Open source engine (AGPL-3.0)
- **[semantic-api-skill](https://github.com/peter-j-thompson/semantic-api-skill)** — Agent framework skill package

<!-- mcp-name: io.github.peter-j-thompson/semanticapi -->

## License

MIT
autonomous-app 0.3.79
Containerized application framework built on Flask with additional libraries and tools for rapid development of web applications.
Requires Python: >=3.12

# Autonomous

:warning: :warning: :warning: WiP :warning: :warning: :warning:

![Tests](https://github.com/Sallenmoore/autonomous/actions/workflows/tests.yml/badge.svg)

A local, containerized, service based application library built on top of Flask.
A self-contained containerized Python applications with minimal dependencies using built in libraries for many different kinds of tasks.

- **[pypi](https://test.pypi.org/project/autonomous)**
- **[github](https://github.com/Sallenmoore/autonomous)**

## Features

- Fully containerized, service based Python application framework
- All services are localized to a virtual intranet
- Container based MongoDB database
- Model ORM API
- File storage locally or with services such as Cloudinary or S3 
- Separate service for long running tasks
- Built-in Authentication with Google or Github
- Auto-Generated Documentation Pages

## Dependencies

- **Languages**
  - [Python 3.11](/Dev/language/python)
- **Frameworks**
  - [Flask](https://flask.palletsprojects.com/en/2.1.x/)
- **Containers**
  - [Docker](https://docs.docker.com/)
  - [Docker Compose](https://github.com/compose-spec/compose-spec/blob/master/spec.md)
- **Server**
  - [nginx](https://docs.nginx.com/nginx/)
  - [gunicorn](https://docs.gunicorn.org/en/stable/configure.html)
- **Networking and Serialization**
  - [requests](https://requests.readthedocs.io/en/latest/)
- **Database**
  - [pymongo](https://pymongo.readthedocs.io/en/stable/api/pymongo/index.html)
- **Testing**
  - [pytest](/Dev/tools/pytest)
  - [coverage](https://coverage.readthedocs.io/en/6.4.1/cmd.html)
- **Documentation** - Coming Soon
  - [pdoc](https://pdoc.dev/docs/pdoc/doc.html)
---

## Developer Notes

### TODO

- Setup/fix template app generator
- Add type hints
- Switch to less verbose html preprocessor
- 100% testing coverage

### Issue Tracking

- None

## Processes

### Generate app

TDB

### Tests

```sh
make tests
```

### package

1. Update version in `/src/autonomous/__init__.py`
2. `make package`
connic-composer-sdk 0.1.2
The Connic Composer SDK for building agents and enterprise-level tools.
Requires Python: >=3.10

# Connic Composer SDK

<div align="center">

**Build production-ready AI agents with code.**

Define agents in YAML. Extend them with Python. Deploy anywhere.

[![PyPI version](https://img.shields.io/pypi/v/connic-composer-sdk.svg)](https://pypi.org/project/connic-composer-sdk/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[Documentation](https://connic.co/docs/v1/composer/overview) • [Quick Start](https://connic.co/docs/v1/quickstart) • [Dashboard](https://connic.co/projects)

</div>

---

## Installation

```bash
pip install connic-composer-sdk
```

## Quick Start

```bash
# Initialize a new project
connic init my-agents
cd my-agents

# Validate your project
connic dev

# Test with hot reload
connic login
connic test

# Deploy
connic deploy
```

## Documentation

For detailed guides and configuration options, see the full documentation:

| Topic | Link |
|-------|------|
| **Overview** | [Getting Started](https://connic.co/docs/v1/composer/overview) |
| **Agent Configuration** | [Agent YAML Reference](https://connic.co/docs/v1/composer/agent-configuration) |
| **Custom Tools** | [Writing Python Tools](https://connic.co/docs/v1/composer/tools) |
| **Testing** | [Local Development & Testing](https://connic.co/docs/v1/composer/testing) |
| **Deployment** | [Deploying Agents](https://connic.co/docs/v1/composer/deployment) |
| **Knowledge & RAG** | [Built-in RAG](https://connic.co/docs/v1/composer/knowledge) |
| **Middleware** | [Request/Response Hooks](https://connic.co/docs/v1/composer/middleware) |

## CLI Commands

| Command | Description |
|---------|-------------|
| `connic init [name]` | Initialize a new project |
| `connic dev` | Validate project locally |
| `connic test [env]` | Start hot-reload test session |
| `connic deploy` | Deploy to production |
| `connic tools` | List available tools |
| `connic login` | Save credentials |

## Getting Help

- 📖 [Full Documentation](https://connic.co/docs/v1/composer/overview)
- 🐛 [Issue Tracker](https://github.com/connic-org/connic-composer-sdk/issues)
- 📧 [support@connic.co](mailto:support@connic.co)

## License

MIT License - see [LICENSE](LICENSE) file.

---

<div align="center">

**Built with ❤️ by the Connic team**

[Website](https://connic.co) • [Documentation](https://connic.co/docs) • [Projects](https://connic.co/projects)

</div>
hydroserverpy 1.9.0b1
A Python client for managing HydroServer data
Requires Python: <4,>=3.9

# HydroServer Python Client

The hydroserverpy Python package provides an interface for managing HydroServer data and metadata, loading observations, and performing data quality control. This guide will go over how to install the package and connect to a HydroServer instance. Full hydroserverpy documentation and examples can be found [here](https://hydroserver2.github.io/hydroserver/how-to/hydroserverpy/hydroserverpy-examples.html).

## Installation

You can install the package via pip:

```bash
pip install hydroserverpy
```

## Connecting to HydroServer

To connect to HydroServer, you need to initialize the client with the instance of HydroServer you're using and your user credentials if you want to access and modify your own data. If you don't provide authentication credentials you can read public data, but you will not be able to create or modify any data.

### Example: Anonymous User

```python
from hydroserverpy import HydroServer

# Initialize HydroServer connection.
hs_api = HydroServer(
    host='https://playground.hydroserver.org'
)
```

### Example: Basic Authentication

```python
from hydroserverpy import HydroServer

# Initialize HydroServer connection with credentials.
hs_api = HydroServer(
    host='https://playground.hydroserver.org',
    email='user@example.com',
    password='******'
)
```

## Funding and Acknowledgements

Funding for this project was provided by the National Oceanic & Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama (NA22NWS4320003). Utah State University is a founding member of CIROH and receives funding under subaward from the University of Alabama. Additional funding and support have been provided by the State of Utah Division of Water Rights, the World Meteorological Organization, and the Utah Water Research laboratory at Utah State University.
spectracles 0.6.2
Unified spectrospatial models: glasses for your spectra.
Requires Python: >=3.10

<div id="top"></div>

<!-- PROJECT SHIELDS -->
<div align="center">

[![Tests](https://img.shields.io/github/actions/workflow/status/TomHilder/spectracles/tests.yml?branch=main&label=tests&style=flat-square)](https://github.com/TomHilder/spectracles/actions/workflows/tests.yml)
[![Coverage](https://img.shields.io/codecov/c/github/TomHilder/spectracles?style=flat-square)](https://codecov.io/gh/TomHilder/spectracles)
[![PyPI](https://img.shields.io/pypi/v/spectracles?style=flat-square)](https://pypi.org/project/spectracles/)
[![Python](https://img.shields.io/pypi/pyversions/spectracles?style=flat-square)](https://pypi.org/project/spectracles/)
[![Docs](https://img.shields.io/github/actions/workflow/status/TomHilder/spectracles/docs.yml?branch=main&label=docs&style=flat-square)](https://tomhilder.github.io/spectracles/)

</div>

<!-- PROJECT LOGO -->
<br />
<div align="center">
  <a href="https://github.com/TomHilder/spectracles">
    <img src="https://raw.githubusercontent.com/TomHilder/spectracles/main/logo.png" alt="spectracles" width="420">
  </a>

  <p align="center">
    Unified spectrospatial models for integral field spectroscopy in JAX
  </p>
</div>

## Glasses for your spectra

Spectracles is a Python library for inferring properties of IFU/IFS spectra as continuous functions of sky position.

It can also be used as a general-purpose statistical model library that extends [`equinox`](https://github.com/patrick-kidger/equinox) to allow for composable models that may have *coupled* parameters. It also implements some other nice features that are a bit awkward in `equinox` out of the box, like easily updating model parameters between fixed and varying.

## Installation

From PyPI with `pip`:

```sh
pip install spectracles
```

Or with `uv` (recommended):

```sh
uv add spectracles
```

From source:

```sh
git clone git@github.com:TomHilder/spectracles.git
cd spectracles
pip install -e .
```

**Note:** `fftw` must be installed or the dependency `jax-finufft` will fail to build.

## Features

- **Parameter sharing** - Couple parameters across model components
- **Declarative optimization schedules** - Specify which parameters are free/fixed per phase
- **Glob patterns** - Use wildcards like `"gp.kernel.*"` to match parameters
- **JAX integration** - Built on equinox, fully compatible with JAX transformations
- **Rich output** - Pretty-printed model trees and gradient diagnostics

## Documentation

Full documentation: [tomhilder.github.io/spectracles](https://tomhilder.github.io/spectracles/)

## Citation

Coming soon.

## License

MIT