semvec/pypi-packages huggingface dataset:
wnet 0.9.7
Tools for calculation of Wasserstein metric between distributions based on Network Flow algorithm
Requires Python: >=3.9
Keywords: Wasserstein, Optimal Transport, Network Flow, Earth Mover's Distance

# wnet

Wasserstein Network (wnet) is a Python/C++ library for working with Wasserstein distances. It uses the Min Cost Flow algorithm as implemented by the [LEMON library](https://lemon.cs.elte.hu/trac/lemon), exposed to Python via the [pylmcf module](https://github.com/michalsta/pylmcf), enabling efficient computation and manipulation of Wasserstein distances between multidimensional distributions.

## Features
- Wasserstein and Truncated Wasserstein distance calculations between multidimensional distributions
- Calculation of derivatives with respect to deltas in flow or position (in progress)
- Python and C++ integration
- Support for distribution mixtures, and efficient recalculation of distance with changed mixture proportions

## Installation

You can install the Python package using pip:

```bash
pip install wnet
```

## Usage

Simple usage:
```python
import numpy as np
from wnet import WassersteinDistance, Distribution
from wnet.distances import DistanceMetric

positions1 = np.array(
    [[0, 1, 5, 10],
     [0, 0, 0, 3]]
)
intensities1 = np.array([10, 5, 5, 5])

positions2 = np.array(
    [[1, 10],
     [0, 0]]
)
intensities2 = np.array([20, 5])

S1 = Distribution(positions1, intensities1)
S2 = Distribution(positions2, intensities2)

print(WassersteinDistance(S1, S2, DistanceMetric.L1))
# 45
```

## Licence
MIT Licence

## Related Projects

- [pylmcf](https://github.com/michalsta/pylmcf) - Python bindings for Min Cost Flow algorithms from LEMON library.
- [wnetalign](https://github.com/michalsta/wnetalign) - Alignment of MS/NMR spectra using Truncated Wasserstein Distance
legend-pydataobj 2.0.0a2
LEGEND Python Data Objects
Requires Python: >=3.10

# legend-pydataobj

[![PyPI](https://img.shields.io/pypi/v/legend-pydataobj?logo=pypi)](https://pypi.org/project/legend-pydataobj/)
![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/legend-exp/legend-pydataobj?logo=git)
[![GitHub Workflow Status](https://img.shields.io/github/checks-status/legend-exp/legend-pydataobj/main?label=main%20branch&logo=github)](https://github.com/legend-exp/legend-pydataobj/actions)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Codecov](https://img.shields.io/codecov/c/github/legend-exp/legend-pydataobj?logo=codecov)](https://app.codecov.io/gh/legend-exp/legend-pydataobj)
![GitHub issues](https://img.shields.io/github/issues/legend-exp/legend-pydataobj?logo=github)
![GitHub pull requests](https://img.shields.io/github/issues-pr/legend-exp/legend-pydataobj?logo=github)
![License](https://img.shields.io/github/license/legend-exp/legend-pydataobj)
[![Read the Docs](https://img.shields.io/readthedocs/legend-pydataobj?logo=readthedocs)](https://legend-pydataobj.readthedocs.io)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10592107.svg)](https://doi.org/10.5281/zenodo.10592107)

This package provides a Python implementation of the LEGEND Data Objects (LGDO)
and I/O to HDF5, including [Numba](https://numba.pydata.org/)-accelerated custom
compression algorithms for particle detector signals. More documentation is
available in the
[LEGEND data format specification](https://legend-exp.github.io/legend-data-format-specs).

If you are using this software,
[consider citing](https://doi.org/10.5281/zenodo.10592107)!
mirth-connect-mcp 0.1.1
FastMCP server for NextGen Connect (Mirth) API
Requires Python: >=3.13
Keywords: mcp, fastmcp, mirth, nextgen-connect, openapi

# mirth_connect_mcp

MCP server for NextGen Connect (Mirth) that exposes API operations as domain-grouped tools.

## Requirements

- Python 3.13+
- Reachable NextGen Connect API endpoint
- Credentials with API access

## Install

Recommended (global command available in PATH):

```bash
uv tool install mirth_connect_mcp
```

Alternative:

```bash
pip install mirth_connect_mcp
```

After install, the command is:

```bash
mirth-connect-mcp
```

## Environment variables

Required:

- `MIRTH_BASE_URL` (example: `https://localhost:8443/api`)
- `MIRTH_USERNAME`
- `MIRTH_PASSWORD`

Optional:

- `MIRTH_VERIFY_SSL` (`true` by default; set `false` for local self-signed certs)
- `MIRTH_TIMEOUT_SECONDS` (`30` by default)
- `MIRTH_OPENAPI_PATH` (defaults to bundled OpenAPI spec)

## Add to MCP clients

Use `stdio` transport for all client configs below.

### VS Code (MCP)

Open your VS Code MCP config (`mcp.json`) and add:

```json
{
	"servers": {
		"mirthNextgen": {
			"type": "stdio",
			"command": "mirth-connect-mcp",
			"env": {
				"MIRTH_BASE_URL": "https://localhost:8443/api",
				"MIRTH_USERNAME": "admin",
				"MIRTH_PASSWORD": "admin",
				"MIRTH_VERIFY_SSL": "false"
			}
		}
	}
}
```

### Cline

In Cline MCP settings, add this server entry:

```json
{
	"mcpServers": {
		"mirthNextgen": {
			"command": "mirth-connect-mcp",
			"args": [],
			"env": {
				"MIRTH_BASE_URL": "https://localhost:8443/api",
				"MIRTH_USERNAME": "admin",
				"MIRTH_PASSWORD": "admin",
				"MIRTH_VERIFY_SSL": "false"
			}
		}
	}
}
```

### Claude Desktop

In `claude_desktop_config.json`, add:

```json
{
	"mcpServers": {
		"mirthNextgen": {
			"command": "mirth-connect-mcp",
			"env": {
				"MIRTH_BASE_URL": "https://localhost:8443/api",
				"MIRTH_USERNAME": "admin",
				"MIRTH_PASSWORD": "admin",
				"MIRTH_VERIFY_SSL": "false"
			}
		}
	}
}
```

### Gemini CLI

In your Gemini CLI MCP config, add:

```json
{
	"mcpServers": {
		"mirthNextgen": {
			"command": "mirth-connect-mcp",
			"env": {
				"MIRTH_BASE_URL": "https://localhost:8443/api",
				"MIRTH_USERNAME": "admin",
				"MIRTH_PASSWORD": "admin",
				"MIRTH_VERIFY_SSL": "false"
			}
		}
	}
}
```

## Tool model

Built-in tools:

- `list_domains`
- `list_actions`
- One dispatch tool per OpenAPI domain/tag

Domain tool request envelope:

```json
{
	"action": "operationId",
	"path_params": {},
	"query": {},
	"body": {},
	"headers_override": {}
}
```