semvec/pypi-packages huggingface dataset:
pyssh-connect 1.2.2
Basic wrapper for paramiko SSHClient
License: MIT
Requires Python: >=3.6

# pyssh-connect

[![PySSH_Connect Version](https://img.shields.io/pypi/v/pyssh_connect?style=for-the-badge)](https://pypi.org/project/pyssh-connect/)
[![PySSH_Connect Status](https://img.shields.io/pypi/status/pyssh_connect?style=for-the-badge)](https://pypi.org/project/pyssh-connect/)
[![PySSH_Connect Workflow Status](https://img.shields.io/github/workflow/status/degagne/pyssh_connect/PyPI?style=for-the-badge)](https://github.com/degagne/pyssh_connect/actions/workflows/python-publish.yml)
[![PySSH_Connect Python Versions](https://img.shields.io/pypi/pyversions/pyssh_connect?style=for-the-badge)](https://pypi.org/project/pyssh-connect/)

[![PySSH_Connect Release](https://img.shields.io/github/release/degagne/pyssh_connect?style=for-the-badge)](https://github.com/degagne/pyssh_connect/releases/)
[![PySSH_Connect License](https://img.shields.io/github/license/degagne/pyssh_connect?style=for-the-badge)](https://github.com/degagne/pyssh_connect/blob/master/LICENSE)

The `pyssh-connect` provides a command-line interface (CLI) for the fast and easy creation of SSH terminal connections for remote command execution in Python applications.

## Installation

`pyssh_connect` is available using pip:

```bash
pip install pyssh-connect
```

For additional details visit [PyPI](https://pypi.org/)

## Documentation

For documentation, please visit [pyssh-connect](https://pyssh-connect.readthedocs.io/en/latest/index.html)

## License

`pyssh_connect` is licensed under MIT license. See the [LICENSE](https://github.com/degagne/pyssh_connect/blob/master/LICENSE) for more information
delphin-latex 1.0.1
LaTeX exporters for DELPH-IN data.
License: MIT
Requires Python: >=3.6
Keywords: latex delph-in linguistics

# delphin-latex

LaTeX exporters for DELPH-IN data.

Currently only one exporter is available:

* `delphin.codecs.dmrstikz` -- export DMRS for rendering with
  [`tikz-dependency`][]

Contributions of other exporters are welcome!


# Example

Here is an image of the PDF produced for the DMRS for "The chef whose
soup spilled quit":

![DMRS rendering for "The chef whose soup spilled quit."](https://raw.githubusercontent.com/delph-in/delphin-latex/master/images/dmrs-tikz-pdf.png)


# Installation and Requirements

This package is a plugin for [PyDelphin][]. Install it with `pip`:

``` console
$ pip install delphin-latex
```

It depends on the `delphin.dmrs` and `delphin.predicate` modules in
PyDelphin version `1.0.0`. For rendering, [LaTeX][] and the
[`tikz-dependency`] package are required.

# Usage

The `delphin.codecs.dmrstikz` module implements the output functions
of PyDelphin's [Codec API][]:

* [`dump()`][] - serialize list and write to a file
* [`dumps()`][] - serialize list and return a string
* [`encode()`][] - serialize a single instance and return a string

The [`dump()`][] and [`dumps()`][] functions insert the LaTeX preamble
and postamble so the result is a full document, while [`encode()`][]
only serializes the individual `dependency` environment.

# Related

For visually presenting MRSs, DMRSs, and derivation trees, you may
also be interested in [delphin-viz][] which can save visualizations as
PNG or SVG files.

[delphin-viz]: https://github.com/delph-in/delphin-viz
[Codec API]: https://pydelphin.readthedocs.io/en/latest/api/delphin.codecs.html
[`dump()`]: https://pydelphin.readthedocs.io/en/latest/api/delphin.codecs.html#dump
[`dumps()`]: https://pydelphin.readthedocs.io/en/latest/api/delphin.codecs.html#dumps
[`encode()`]: https://pydelphin.readthedocs.io/en/latest/api/delphin.codecs.html#encode
[LaTeX]: https://www.latex-project.org/
[PyDelphin]: https://github.com/delph-in/pydelphin/
[`tikz-dependency`]: https://ctan.org/pkg/tikz-dependency
GraphHierarchy 1.8
A module calculating quantities related to a network metric known as trophic coherence but now generalised to all networks, see Moutsinas, G., Shuaib, C., Guo, W., & Jarvis, S. (2019). Graph hierarchy and spread of infections. arXiv preprint arXiv:1908.04358 for more details.
License: MIT
Keywords: Graph Hierarchy,Trophic Coherence,Hierarchical Coherence,Trophic Levels,Hierarchical Levels,Influence Centrality,Democracy Coefficient

**GraphHierarchy** is a python package that calculates the hierarchical level of nodes in a network, the associated hierarchical differences for the edges and the hierarchical coherence of the network. 
Hierarchical analysis is the mathematical generalisation of the trophic analysis of networks. Trophic levels and hence trophic coherence can be defined only on networks with well defined sources, known as basal nodes. 
Trophic coherence, a measure of a network’s hierarchical organisation, has been shown to be linked to a network’s structural and dynamical properties. Thus trophic analysis of networks had been restricted to the ecological domain, until now.  
Graph Hierarchy is a python package that implements this mathematical generalisation that allows for analysis of all network structures via the trophic approach.
 
See Moutsinas, G., Shuaib, C., Guo, W., & Jarvis, S. (2019). Graph hierarchy and spread of infections. arXiv preprint arXiv:1908.04358 for more details.

.. _GitHub: https://github.com/shuaib7860/GraphHierarchy