semvec/pypi-packages huggingface dataset:
chipiq-mcp 0.0.9.2
MCP server for the ChipIQ tools
Requires Python: >=3.13

# chipiq-mcp

## Note: May need to use `uv cache clean` to ensure the latest `chipiq` package is picked up when starting the MCP-server.

## To configure the server using the PyPi chipiq-mcp package, use:
```
{
	"inputs": [],
	"servers": {
		"chipiq-mcp-server": {
			"type": "stdio",
			"command": "uvx",
			"args": [
				"chipiq-mcp>=0.0.9"
			]
		}
	}
}
```

## To configure the server using a local development chipiq-mcp package, use:
```
{
	"inputs": [],
	"servers": {
		"chipiq-mcp-server": {
			"type": "stdio",
			"command": "uv",
			"args": [
				"tool",
				"run",
				"~/workspace/chipiq-mcp"
			]
		}
	}
}
```
hats-import 0.8.0
Utility for ingesting large survey data into HATS structure
Requires Python: >=3.11

<img src="https://github.com/astronomy-commons/lsdb/blob/main/docs/lincc-logo.png?raw=true" width="300" height="100">

# hats-import

[![Template](https://img.shields.io/badge/Template-LINCC%20Frameworks%20Python%20Project%20Template-brightgreen)](https://lincc-ppt.readthedocs.io/en/stable/)

[![PyPI](https://img.shields.io/pypi/v/hats-import?color=blue&logo=pypi&logoColor=white)](https://pypi.org/project/hats-import/)
[![Conda](https://img.shields.io/conda/vn/conda-forge/hats-import.svg?color=blue&logo=condaforge&logoColor=white)](https://anaconda.org/conda-forge/hats-import)

[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/astronomy-commons/hats-import/smoke-test.yml)](https://github.com/astronomy-commons/hats-import/actions/workflows/smoke-test.yml)
[![codecov](https://codecov.io/gh/astronomy-commons/hats-import/branch/main/graph/badge.svg)](https://codecov.io/gh/astronomy-commons/hats-import)
[![Read the Docs](https://img.shields.io/readthedocs/hats-import)](https://hats-import.readthedocs.io/)

## HATS import - Utility for ingesting large survey data into HATS structure.

Check out our [ReadTheDocs site](https://hats-import.readthedocs.io/en/stable/)
for more information on partitioning, installation, and contributing.

See related projects:

* HATS ([on GitHub](https://github.com/astronomy-commons/hats))
  ([on ReadTheDocs](https://hats.readthedocs.io/en/stable/))
* LSDB ([on GitHub](https://github.com/astronomy-commons/lsdb)) 
  ([on ReadTheDocs](https://lsdb.readthedocs.io/en/stable/))

## Contributing

[![GitHub issue custom search in repo](https://img.shields.io/github/issues-search/astronomy-commons/hats-import?color=purple&label=Good%20first%20issues&query=is%3Aopen%20label%3A%22good%20first%20issue%22)](https://github.com/astronomy-commons/hats-import/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)

See the [contribution guide](https://hats-import.readthedocs.io/en/stable/guide/contributing.html)
for complete installation instructions and contribution best practices.

## Citation

If you use HATS / HATS-import in your work, please cite the conference proceedings: 
["Using LSDB to enable large-scale catalog distribution, cross-matching, and analytics"](https://ui.adsabs.harvard.edu/abs/2025arXiv250102103C). 

If you use Rubin Data Preview 1 (DP1) with HATS, please also cite: ["Variability-finding in Rubin Data Preview 1 with LSDB"](https://ui.adsabs.harvard.edu/abs/2025arXiv250623955M).

Find full citation information [here](./CITATION.bib).

## Acknowledgements

This project is supported by Schmidt Sciences.

This project is based upon work supported by the National Science Foundation
under Grant No. AST-2003196.

This project acknowledges support from the DIRAC Institute in the Department of 
Astronomy at the University of Washington. The DIRAC Institute is supported 
through generous gifts from the Charles and Lisa Simonyi Fund for Arts and 
Sciences, and the Washington Research Foundation.
MiLoMerge 1.0.1
A package to take a given histogram and merge bins such that the seperability loss is minimized.
Requires Python: >=3.8

# MiLoMerge

[![arXiv](https://img.shields.io/badge/arXiv-2601.10822-b31b1b.svg)](https://arxiv.org/abs/2601.10822)
[![Homepage](https://img.shields.io/badge/Homepage-spin.pha.jhu.edu-blue.svg)](https://spin.pha.jhu.edu/)

![Python](https://img.shields.io/badge/python->=%203.8-blue.svg)
[![PyPI version](https://badge.fury.io/py/MiLoMerge.svg)](https://badge.fury.io/py/MiLoMerge)
[![Code style: black](https://img.shields.io/badge/code%20style-black-orange)](https://black.readthedocs.io/en/stable/)

A package to merge bins together in such a way that the separability between distributions is minimally lossless.
The ROC and LOC curves defined in the paper above are also included as functions in this package.
It is fully Pythonic, with full interoperability with Numpy.

This package is derived from the findings of [Maximizing Returns: Optimizing Experimental Observables at the LHC](https://arxiv.org/abs/2601.10822), which should
be cited should the package be used.

## Installation

### Through pip

MiLoMerge is available in pip, and soon to conda, and can be installed as below:

```bash
pip install MiLoMerge
```

### Manual Installation

Download the .tar.gz installation available
at [https://spin.pha.jhu.edu/](https://spin.pha.jhu.edu/)
and run the following command in the MiLoMerge directory:

```bash
pip install .
```

## Getting Started

To use MiLoMerge, import the package within your file, and 
generate distributions to merge. The documentation,
alongside useful examples and tutorials,
is available at the [homepage](https://spin.pha.jhu.edu/MiLoMerge/).