semvec/pypi-packages huggingface dataset:
graphsync 1.1.1
Python package for creating and manipulating graphs and networks
Requires Python: >=3.7
Keywords: Networks, Graph Theory, Mathematics, network, graph, discrete mathematics, math

graphsync
=========

graphsync is a Python package for the creation, manipulation,
and study of the structure, dynamics, and functions of complex networks.

- **Website (including documentation):** https://networkx.org
- **Mailing list:** https://groups.google.com/forum/#!forum/networkx-discuss
- **Source:** https://github.com/miltonkaufman/graphsync
- **Bug reports:** https://github.com/miltonkaufman/graphsync/issues
- **Tutorial:** https://networkx.org/documentation/latest/tutorial.html
- **GitHub Discussions:** https://github.com/miltonkaufman/graphsync/discussions

Simple example
--------------

Find the shortest path between two nodes in an undirected graph:

.. code:: python

   >>> import graphsync as nx
   >>> G = nx.Graph()
   >>> G.add_edge('A', 'B', weight=4)
   >>> G.add_edge('B', 'D', weight=2)
   >>> G.add_edge('A', 'C', weight=3)
   >>> G.add_edge('C', 'D', weight=4)
   >>> nx.shortest_path(G, 'A', 'D', weight='weight')
   ['A', 'B', 'D']

Install
-------

Install the latest version of graphsync::

   $ pip install graphsync

Install with all optional dependencies::

   $ pip install graphsync[all]

For additional details, please see `INSTALL.rst`.

Bugs
----

Please report any bugs that you find `here <https://github.com/miltonkaufman/graphsync/issues>`_.
Or, even better, fork the repository on `GitHub <https://github.com/miltonkaufman/graphsync>`_
and create a pull request (PR). We welcome all changes, big or small, and we
will help you make the PR if you are new to `git`

License
-------

Released under the 3-Clause BSD license (see `LICENSE.txt`)::

   Copyright (C) 2004-2021 graphsync Developers
   Milton Kaufman <miltonkaufman1@protonmail.com>
mcp-server-google-patents 0.1.6
MCP server for Google Patents via SerpApi
Requires Python: >=3.10

# Google Patents MCP Server

This is a Model Context Protocol (MCP) server that provides access to Google Patents data using SerpApi.

## Configuration

You need a SerpApi API key to use this server. Set the `SERPAPI_API_KEY` environment variable.

## Tools

- `search_patents(query: str, num: int = 10, page: int = 1)`: Search for patents.
- `get_patent_details(patent_id: str)`: Get details of a specific patent.

## Installation

```bash
pip install mcp-server-google-patents
```

## Usage

Run the server:

```bash
mcp-server-google-patents
```
circulation 0.2.0
Closed loop blood circulation model in FEniCS
Requires Python: >=3.11

# Cardiac circulation models

This package provides a set of models for the circulation of blood in the heart and the rest of the body.

## Install

```
python -m pip install circulation
```


## Documentation

See https://computationalphysiology.github.io/circulation

# Usage

```python
import matplotlib.pyplot as plt
import circulation

# Create a model
model = circulation.regazzoni2020.Regazzoni2020()

# Solve the model for 5 seconds with a  time step of 1 ms and an evaluation time step of 10 ms
results = model.solve(T=5.0, dt=1e-3, dt_eval=0.01)

# Print some information about the pressure, flows and volumes inside the model
model.print_info()

# Plot the pressure-volume loop
fig, ax = plt.subplots()
ax.plot(results["V_LV"], results["p_LV"])
ax.set_xlabel("V [mL]")
ax.set_ylabel("p [mmHg]")
plt.show()
```
![_](https://raw.githubusercontent.com/ComputationalPhysiology/circulation/main/docs/_static/regazzoni.png)

See more examples in the [documentation](https://computationalphysiology.github.io/circulation).

# License

MIT
pydataio 1.1.1
A scalable framework for data input and output operations in Spark applications
Requires Python: >=3.11

# PyData I/O

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Spark](https://img.shields.io/badge/Spark-3.5.2-blue)](https://spark.apache.org/releases/spark-release-3-5-2.html)
[![Python](https://img.shields.io/badge/python-3.11-red)](https://www.python.org/)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)][contributing]

Data I/O is an open source project that provides a flexible and scalable framework for data input and output operations in Spark applications. It offers a set of powerful tools and abstractions to simplify and streamline data processing pipelines.

## Features

- Easy-to-use API for defining data processors and transformations
- Seamless integration with popular data storage systems and formats
- Support for batch and streaming data processing
- Extensible architecture for custom data processors and pipelines
- Scalable and fault-tolerant processing using Apache Spark
- Open to make use of python ML models ecosystem (sklearn, xgboost, pytorch...)

## Getting Started
To get started with PyData I/O, please refer to the [documentation][gettingstarted] for installation instructions, usage examples, and API references.

## Issues and Support
If you encounter any issues or require support, please create a new issue on the [GitHub repository][issues].

## Contribution
Contributions to Data I/O are welcome! To contribute, please follow the guidelines outlined in [our contribution guide][contributing].

## License
This project is licensed under the Apache License 2.0 license. See the [LICENSE][license] file for more information.

[gettingstarted]: https://amadeusitgroup.github.io/PyDataIO/getting-started.html
[documentation]: https://amadeusitgroup.github.io/PyDataIO/
[contributing]: CONTRIBUTING.md
[codeofconduct]: CODE_OF_CONDUCT.md
[license]: LICENSE
[repository]: https://github.com/AmadeusITGroup/PyDataIO
[issues]: https://github.com/AmadeusITGroup/PyDataIO/issues
mms-lid 0.1.2
A Robust Language Identification Tool using MMS-1B
Requires Python: >=3.8

# mms-LID

MMS-LID는 Meta의 MMS-1B 모델을 기반으로 한 강력한 다국어 식별(LID) 도구입니다.

## 설치 방법 (Installation)

```bash
pip install mms-lid
setuseragent 0.7.0
Monkey patches for setting user-agent header.
Requires Python: >=3.10

# Monkey patches for setting user-agent header.

```python
# Importing hooks automatically handles monkey patching
from setuseragent import hooks
# We can set to a specific value
hooks.set_user_agent("my-new-user-agent")
# Or we can set it with a package version
hooks.set_distribution('my-package')
# or using meta package name
hooks.set_distribution(__package__)
```

If using Django, can optionally set the site name as part of the package

```python
# in settings.py

INSTALLED_APPS = [
    ...,
    "setuseragent.django",
    "django.contrib.sites",
]

# Optionally can configure a specific package to lookup.
# otherwise defaults to the value of DJANGO_SETTINGS_MODULE
USER_AGENT_DISTRIBUTION = __package__
```