semvec/pypi-packages huggingface dataset:
vndb-dl 0.1.0
Download visual novel metadata and screenshots from vndb.org

# vndb_dl

vndb_dl is a command-line utility that allows to download VN metadata and screenshots from vndb.org.

## Usage

vndb_dl can be used from the command line. You can either specify an id to use with the -i option:

```shell
$ vndb_dl -i 7
```

or an vndb.org url:

```shell
$ vndb_dl -u https://vndb.org/v7
```

Bulk downloading is supported with ranges:

```shell
$ vndb_dl -i 7-12, 14 # Will download vns with id [7; 12] and vn with id 14
```

By default, vn_dl will output metadata about each vn in a metadata.txt and metadata.json file. To disable either or both of these, set their flags to false:

```shell
$ vndb_dl -i 7 -j False -p False
```

By default, vndb_dl will output into the working directory. You can change the directory with the directory flag:
```shell
$ vndb_dl -i 7-12 -d download_folder
```

This covers full usage of vndb_dl.

```shell
$ vndb_dl --help
usage: vndb_dl [-h] [-j JSON] [-p PLAIN] [-i ID [ID ...]] [-u URL [URL ...]]
               [-d DIRECTORY]

Download visual novel information from vndb.org.

optional arguments:
  -h, --help            show this help message and exit
  -j JSON, --json JSON  Parse the visual novel metadata as a JSON file in the
                        visual novel folder ([True]/False)
  -p PLAIN, --plain PLAIN
                        Parse the visual novel metadata as a plain text file
                        in the visual novel folder ([True]/False)
  -i ID [ID ...], --id ID [ID ...]
                        vndb id of the visual novel; where in vndb.org/v###,
                        the ### is the vn's id. --id accepts both list of
                        numbers (eg. 5 6 7) and ranges (eg. 5-7) as well as
                        both (eg. 5-7 9 11). Commas and spaces are accepted
                        (eg 5-7, 40 49, 51), as long as each character is
                        separated by a space (57,59 will give you vn n.5759).
  -u URL [URL ...], --url URL [URL ...]
                        vndb url of the visual novel
  -d DIRECTORY, --directory DIRECTORY
                        Source directory where the data for each visual novel
                        will be stored
```

## For developers

Any contributions are very appreciated. Please submit a pull request if you find improvements.

### Documentation

All documentation can be found in sphinx format under the `docs` folder.

### Unit tests

Tests are located in the `tests` folder.
korhal 0.1.2
KOrean Rpc-based Application for Handy Application for Language-processing
Requires Python: >=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
Keywords: korean analysis tagger tokenizer

# Korhal
[![GitHub tag (latest SemVer)](https://img.shields.io/github/tag/ingtranet/korhal.svg?style=flat-square)](https://github.com/ingtranet/korhal)
[![PyPI](https://img.shields.io/pypi/v/korhal.svg?style=flat-square)](https://pypi.org/project/korhal/)
[![Travis (.com) branch](https://img.shields.io/travis/com/ingtranet/korhal/master.svg?style=flat-square)](https://travis-ci.com/ingtranet/korhal)
[![Codacy branch grade](https://img.shields.io/codacy/grade/b8ad24518efb4bec9a68b32eeb994d78/master.svg?style=flat-square)](https://app.codacy.com/project/cookieshake/korhal/dashboard)

Korhal(KOrean Rpc-based Handy Application for Language-processing) is a python wrapper for several korean Part-Of-Speech taggers.

## How to install

``` sh
pip install korhal
```

## Available taggers

- KOMORAN with `korhal.komoran`
- Hannanum with `korhal.hannanum`
- Open-source Korean Text Processor with `korhal.openkoreantext`

## How to use

``` python
from korhal.komoran import tokenize

result = tokenize("집에 가서 잠을 자고 싶다")
# result => Token(text=집,pos=NNG), Token(text=에,pos=JKB), Token(text=가,pos=VV), Token(text=아서,pos=EC), Token(text=잠,pos=NNG), Token(text=을,pos=JKO), Token(text=자,pos=VV), Token(text=고,pos=EC), Token(text=싶,pos=VX), Token(text=다,pos=EC)]
print(result.text) # => 집
print(result.pos) # => NNG

nouns = [token.text for token in result if token.pos.startswith('N')]
```

## Asynchronous methods

With `korhal.aio`, you can use asynchronous methods. The performance of multi-core systems can be slightly improved when performing extensive processing.

``` python
from korhal.aio.opentextkorean import tokenize
 
texts = ['달디단 맛있는 케이크가 있었다', '솜사탕 같이 귀여운 구름']
futures = [tokenize(text) for text in texts]
results = [f.result() for f in futures]
```

## Thanks to

- [KOMORAN](http://www.shineware.co.kr/products/komoran/)
- [Hannanum](http://semanticweb.kaist.ac.kr/hannanum/index.html)
- [Open-source Korean Text Processor](https://github.com/open-korean-text/open-korean-text)
- [KoalaNLP](https://koalanlp.github.io/KoalaNLP-core/)