semvec/pypi-packages huggingface dataset:
textarium 0.1.10
Textarium is a Python package for text analysis
License: MIT
Requires Python: >=3.9,<4.0

# textarium: easy-to-use Python package for text analysis.
[![PyPI Latest Release](https://img.shields.io/pypi/v/textarium)](https://pypi.org/project/textarium/)
[![License](https://img.shields.io/pypi/l/textarium)](https://github.com/6b656b/textarium/blob/main/LICENSE)
[![Activity](https://img.shields.io/github/commit-activity/m/6b656b/textarium)](https://github.com/6b656b/textarium/pulse)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

## What is it?

**textarium** is a Python package that provides flexible text analysis functions designed to 
make text analysis intuitive and easy. It aims to be the high-level tool for
preparing text-data and complex analysis or NLP modeling.

## Installation
Binary installer for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/textarium).

```sh
# Type this in your command-line
pip install textarium
```

## Getting started

```py
from textarium import Text
import nltk
nltk.download('wordnet')

s = "This a text example. You can preprocess and analyze it with this package."
text = Text(s, lang='en')
text.prepare()

print(text.prepared_text)
```
```py
from textarium import Corpus
import nltk
nltk.download('wordnet')

txts = [
    "Hello! My name is Mr.Parker.",
    "I have a website https://parker.com.",
    "It has about 5000 visitors per day.",
    "I track it with a simple html-block like this:",
    "<div>Google.Analytics</div>",
]
c = Corpus(txts, lang='en')
c.info()

c = c.filter(condition=lambda x: len(x.split() > 5), attribute="raw_text")
c.info()

c.prepare()

print(c)
```

## Documentation
The official documentation is hosted on Github.io: https://6b656b.github.io/textarium
## License
[MIT](LICENSE)
pvsvg 0.1.3
an implementation of visjs from networkx with export svg
License: MIT
Requires Python: >=3.8,<4.0

# pvsvg

## Introduction

a python wrapper of the [`vis.js`](https://visjs.github.io/vis-network/docs/network/)
network visualization library with an additional ability to export static SVG.

Inspired by the excellent python library [`pyvis`](https://pyvis.readthedocs.io/en/latest/tutorial.html)
but I required a function to export the resulting network as a static SVG.

The code for exporting the SVG is taken from [`justinharrell/vis-svg`](https://github.com/justinharrell/vis-svg).

## Installation

You can install this via pip:

``` bash
pip install pvsvg
```

## Usage

This library exposes a single class ( `Network` ) which takes as input
a [networkx `Graph`](https://networkx.org/documentation/stable/reference/classes/graph.html).

All the configuration for node and edge level attributes (like color, size, mass)
are passed transparently to `vis.js` so you can configure your resulting network
via the attributes of your networkx nodes and edges respectively.

You can see
[node level configuration](https://visjs.github.io/vis-network/docs/network/nodes.html)
and
[edge level configuration](https://visjs.github.io/vis-network/docs/network/edges.html)
on the `vis.js` documentation.

### Exporting as SVG

The resulting HTML file will have some configuration buttons built-in.
One of these is the `export-svg` button which will generate the static
SVG and write it to a file (`network.svg`)

Keep in mind that it will write the SVG of whatever is within the `Canvas`
element at the time.
Essentially the view you have within the boundaries of the `Canvas` is exactly
what will be written to the SVG.

> **Note:** Currently the node borders are not being written to the SVG. Besides
> that everything else should appear.

### Example

``` python3
from pvsvg import Network
import networkx as nx

# generate some random graph
g = nx.fast_gnp_random_graph(15, 0.2)

# set node level attributes
for n in g.nodes(data=True):
    id = n[0]
    if id % 2 == 0:
        n[1]["color"] = "red"
    else:
        n[1]["color"] = "blue"

    if id % 3 == 0:
        n[1]["shape"] = "square"
    else:
        n[1]["shape"] = "dot"

    n[1]["label"] = f"Node {id}"
    n[1]["title"] = f"Node {id} has color {n[1]['color']} and shape {n[1]['shape']}"
    n[1]["size"] = (id + 1)

# set edge level attributes
for e in g.edges(data=True):
    e[2]["color"] = {
        "color": "black",
        "inherit": False,
    }

# Instantiate the network
n = Network(g, width=1200)

# Draw the network as an html
n.draw("test.html")
```
gitlab-scripts 1.0.1
Helper and convenience scripts to help with common GitLab actions.
License: MIT
Requires Python: >=3.8

Project moved:

GitLab: [https://gitlab.com/1-sfm/gitlabsolute](https://gitlab.com/1-sfm/gitlabsolute)

PyPi: [https://pypi.org/project/gitlab-scripts/](https://pypi.org/project/gitlab-scripts/)