semvec/pypi-packages huggingface dataset:
antimony 3.1.1
Python bindings for libAntimony
License: MIT

# libAntimony
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 <table style="width:100%">
  <tr>
    <td><img alt="PyPI - Downloads", src="https://img.shields.io/pypi/dm/antimony"></td>
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    <td><img alt="Funding", src="https://img.shields.io/badge/Funding-NIBIB%20(EB028887)-blue"></td>    
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</table> 

# Summary
Antimony is a human-readable, human-writable modular model definition language, letting you create SBML models with a simple text interface, i.e.

```
   S1 -> S2; k1*S1;
   S2 -> S3; k2*S2;
   S1 = 10
   k1 = 0.3
   k2 = 0.1
```

For a working online example, see [MakeSBML](https://sys-bio.github.io/makesbml/)

Antimony is free to use in any application: a simple API is provided to allow Antimony strings to be converted to and from SBML, so the application's users can interact with their models in an easy-to-comprehend format.  C/C++ applications can use the library directly, and Python applications can use the Python bindings.

Documentation can be found:
* [In the doc/ subdirectory](https://github.com/sys-bio/antimony/blob/develop/doc/AntimonyTutorial.md) 
* As part of the [Tellurium Antimony documentation](https://tellurium.readthedocs.io/en/latest/antimony.html)

Since Antimony is used within Tellurium, the general [Tellurium documentation](https://tellurium.readthedocs.io/en/latest/index.html) may also be of interest.

Python bindings are available via pypi, and can be installed with pip:

```pip install antimony```

Other binaries are available from https://github.com/sys-bio/antimony/releases



Antimony depends on the libSBML and SBMLNetwork libraries.  The currently-used versions of each will always be available from https://github.com/sys-bio/libroadrunner-deps/.

Antimony has been supported by NIH/NIGMS Grants GM081070, GM123032, and NIBIB EB028887.
objectgraph 1.0.5
A basic graph library
License: MIT
Requires Python: >=3.10

Introduction
------------

Objectgraph is a library that provides a class representing
basic graphs with nodes and edges between nodes.

Nodes can be arbitrary objects with an "identifier" attribute
(which should be hashable). Edges can have arbitrary attributes.

The model for edges is slighly non-standard: There can be multiple
edges between nodes, but all edges with the same attributes are
collapsed into one edge.

There is `documentation at readthedocs <https://objectgraph.readthedocs.io>`_

Historic
........

Objectgraph is a complete rewrite of the ObjectGraph class in
`altgraph <https://pypi.org/project/altgraph/>`_,
using lessons learned in that project but with a complete new
Python 3 code base and full test coverage.
useful-moonshine-onnx 20251121
Speech recognition for live transcription and voice commands with the Moonshine ONNX models.
License: MIT
Requires Python: >=3.8

# useful-moonshine-onnx

Moonshine is a family of speech-to-text models optimized for fast and accurate automatic speech recognition (ASR) on resource-constrained devices. This package contains inference code for using Moonshine models with the ONNX runtime. For more information, please refer to the [project repo on GitHub](https://github.com/moonshine-ai/moonshine).