cihai 0.7.4

Library for CJK (chinese, japanese, korean) language data.

cihai - Python library for CJK (chinese, japanese, korean) data

This project is under active development. Follow our progress and check back for updates!

Usage

Python library only

$ pip install --user cihai

from cihai.core import Cihai
from cihai.bootstrap import bootstrap_unihan

c = Cihai()
if not c.is_bootstrapped:  # download and install Unihan to db
    bootstrap_unihan(c.metadata)
    c.reflect_db()         # automap new table created during bootstrap

query = c.lookup_char('好')
glyph = query.first()
print("lookup for 好: %s" % glyph.kDefinition)
# lookup for 好: good, excellent, fine; well

query = c.reverse_char('good')
print('matches for "good": %s ' % ', '.join([glph.char for glph in query]))
# matches for "good": 㑘, 㑤, 㓛, 㘬, 㙉, 㚃, 㚒, 㚥, 㛦, 㜴, 㜺, 㝖, 㤛, 㦝, ...

See API documentation and /examples.

CLI front-end (cihai-cli)

$ pip install --user cihai-cli

# character lookup
$ cihai info 好
char: 好
kCantonese: hou2 hou3
kDefinition: good, excellent, fine; well
kHangul: 호
kJapaneseOn: KOU
kKorean: HO
kMandarin: hǎo
kTang: '*xɑ̀u *xɑ̌u'
kTotalStrokes: '6'
kVietnamese: háo
ucn: U+597D

# reverse lookup
$ cihai reverse library
char: 圕
kCangjie: WLGA
kCantonese: syu1
kCihaiT: '308.302'
kDefinition: library
kMandarin: tú
kTotalStrokes: '13'
ucn: U+5715
--------

No library, just data

All datasets that cihai uses have stand-alone tools to export their data. No library required.

• unihan-tabular - UNIHAN data exports for csv, yaml and json.

Quick links

• Usage
• Datasets a full list of current and future data sets
• Python API
• 2017 roadmap

Python support	Python 2.7, >= 3.5, pypy
Source	https://github.com/cihai/cihai
Docs	https://cihai.git-pull.com
Changelog	https://cihai.git-pull.com/en/latest/history.html
API	https://cihai.git-pull.com/en/latest/api.html
Issues	https://github.com/cihai/cihai/issues
Travis	https://travis-ci.org/cihai/cihai
Test coverage	https://codecov.io/gh/cihai/cihai
pypi	https://pypi.python.org/pypi/cihai
OpenHub	https://www.openhub.net/p/cihai
License	MIT
git repo	$ git clone https://github.com/cihai/cihai.git
install stable	$ pip install cihai
install dev	$ git clone https://github.com/cihai/cihai.git cihai $ cd ./cihai $ virtualenv .env $ source .env/bin/activate $ pip install -e .
tests	$ python setup.py test