path: root/python-udkanbun.spec

%global _empty_manifest_terminate_build 0
Name:		python-udkanbun
Version:	3.3.7
Release:	1
Summary:	Tokenizer POS-tagger and Dependency-parser for Classical Chinese
License:	MIT
URL:		https://github.com/KoichiYasuoka/UD-Kanbun
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b8/fa/1725e688be150918a02652c9551543bd248c312840771913dee1a3ed6249/udkanbun-3.3.7.tar.gz
BuildArch:	noarch


%description
[![Current PyPI packages](https://badge.fury.io/py/udkanbun.svg)](https://pypi.org/project/udkanbun/)

# UD-Kanbun

Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on [Universal Dependencies](https://universaldependencies.org/format.html).

## Basic usage

```py
>>> import udkanbun
>>> lzh=udkanbun.load()
>>> s=lzh("不入虎穴不得虎子")
>>> print(s)
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	Case=Loc	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> t=s[1]
>>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc)
1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No

>>> print(s.kaeriten())
不㆑入㆓虎穴㆒不㆑得㆓虎子㆒

>>> print(s.to_tree())
不 <════╗   advmod
入 ═══╗═╝═╗ root
虎 <╗ ║   ║ nmod
穴 ═╝<╝   ║ obj
不 <════╗ ║ advmod
得 ═══╗═╝<╝ parataxis
虎 <╗ ║     nmod
子 ═╝<╝     obj

>>> f=open("trial.svg","w")
>>> f.write(s.to_svg())
>>> f.close()
```
![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UD-Kanbun/master/trial.png)
`udkanbun.load()` has three options `udkanbun.load(MeCab=True,Danku=False)`.  By default, the UD-Kanbun pipeline uses [MeCab](https://taku910.github.io/mecab/) for tokenizer and POS-tagger, then uses [UDPipe](http://ufal.mff.cuni.cz/udpipe) for dependency-parser. With the option `MeCab=False` the pipeline uses UDPipe for all through the processing. With the option `Danku=True` the pipeline tries to segment sentences automatically.

`udkanbun.UDKanbunEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth". `to_tree(kaeriten=True,Japanese=True)` is convenient for Japanese users.

You can simply use `udkanbun` on the command line:
```sh
echo 不入虎穴不得虎子 | udkanbun
```

## Usage via spaCy

If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline.

```py
>>> import udkanbun.spacy
>>> lzh=udkanbun.spacy.load()
>>> d=lzh("不入虎穴不得虎子")
>>> print(type(d))
<class 'spacy.tokens.doc.Doc'>
>>> print(udkanbun.spacy.to_conllu(d))
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	_	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	_	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	_	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> t=d[0]
>>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_)
1 不 不 ADV v,副詞,否定,無界 2 advmod  not
```

## Installation for Linux

Tar-ball is available for Linux, and is installed by default when you use `pip`:
```sh
pip install udkanbun
```

## Installation for Cygwin

Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then:
```sh
pip3.7 install udkanbun
```
Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`.

## Installation for Jupyter Notebook (Google Colaboratory)

```py
!pip install udkanbun
```

Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UD-Kanbun/blob/master/udkanbun.ipynb) for Google Colaboratory.

## Author

Koichi Yasuoka (安岡孝一)

## References

* Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28.
* 安岡孝一: [四書を学んだMeCab＋UDPipeはセンター試験の漢文を読めるのか](http://hdl.handle.net/2433/237383), 東洋学へのコンピュータ利用, 第30回研究セミナー (2019年3月8日), pp.3-110.
* 安岡孝一: [漢文の依存文法解析と返り点の関係について](http://hdl.handle.net/2433/235609), 日本漢字学会第1回研究大会予稿集 (2018年12月1日), pp.33-48.

%package -n python3-udkanbun
Summary:	Tokenizer POS-tagger and Dependency-parser for Classical Chinese
Provides:	python-udkanbun
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-udkanbun
[![Current PyPI packages](https://badge.fury.io/py/udkanbun.svg)](https://pypi.org/project/udkanbun/)

# UD-Kanbun

Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on [Universal Dependencies](https://universaldependencies.org/format.html).

## Basic usage

```py
>>> import udkanbun
>>> lzh=udkanbun.load()
>>> s=lzh("不入虎穴不得虎子")
>>> print(s)
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	Case=Loc	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> t=s[1]
>>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc)
1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No

>>> print(s.kaeriten())
不㆑入㆓虎穴㆒不㆑得㆓虎子㆒

>>> print(s.to_tree())
不 <════╗   advmod
入 ═══╗═╝═╗ root
虎 <╗ ║   ║ nmod
穴 ═╝<╝   ║ obj
不 <════╗ ║ advmod
得 ═══╗═╝<╝ parataxis
虎 <╗ ║     nmod
子 ═╝<╝     obj

>>> f=open("trial.svg","w")
>>> f.write(s.to_svg())
>>> f.close()
```
![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UD-Kanbun/master/trial.png)
`udkanbun.load()` has three options `udkanbun.load(MeCab=True,Danku=False)`.  By default, the UD-Kanbun pipeline uses [MeCab](https://taku910.github.io/mecab/) for tokenizer and POS-tagger, then uses [UDPipe](http://ufal.mff.cuni.cz/udpipe) for dependency-parser. With the option `MeCab=False` the pipeline uses UDPipe for all through the processing. With the option `Danku=True` the pipeline tries to segment sentences automatically.

`udkanbun.UDKanbunEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth". `to_tree(kaeriten=True,Japanese=True)` is convenient for Japanese users.

You can simply use `udkanbun` on the command line:
```sh
echo 不入虎穴不得虎子 | udkanbun
```

## Usage via spaCy

If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline.

```py
>>> import udkanbun.spacy
>>> lzh=udkanbun.spacy.load()
>>> d=lzh("不入虎穴不得虎子")
>>> print(type(d))
<class 'spacy.tokens.doc.Doc'>
>>> print(udkanbun.spacy.to_conllu(d))
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	_	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	_	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	_	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> t=d[0]
>>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_)
1 不 不 ADV v,副詞,否定,無界 2 advmod  not
```

## Installation for Linux

Tar-ball is available for Linux, and is installed by default when you use `pip`:
```sh
pip install udkanbun
```

## Installation for Cygwin

Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then:
```sh
pip3.7 install udkanbun
```
Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`.

## Installation for Jupyter Notebook (Google Colaboratory)

```py
!pip install udkanbun
```

Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UD-Kanbun/blob/master/udkanbun.ipynb) for Google Colaboratory.

## Author

Koichi Yasuoka (安岡孝一)

## References

* Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28.
* 安岡孝一: [四書を学んだMeCab＋UDPipeはセンター試験の漢文を読めるのか](http://hdl.handle.net/2433/237383), 東洋学へのコンピュータ利用, 第30回研究セミナー (2019年3月8日), pp.3-110.
* 安岡孝一: [漢文の依存文法解析と返り点の関係について](http://hdl.handle.net/2433/235609), 日本漢字学会第1回研究大会予稿集 (2018年12月1日), pp.33-48.

%package help
Summary:	Development documents and examples for udkanbun
Provides:	python3-udkanbun-doc
%description help
[![Current PyPI packages](https://badge.fury.io/py/udkanbun.svg)](https://pypi.org/project/udkanbun/)

# UD-Kanbun

Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on [Universal Dependencies](https://universaldependencies.org/format.html).

## Basic usage

```py
>>> import udkanbun
>>> lzh=udkanbun.load()
>>> s=lzh("不入虎穴不得虎子")
>>> print(s)
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	Case=Loc	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> t=s[1]
>>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc)
1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No

>>> print(s.kaeriten())
不㆑入㆓虎穴㆒不㆑得㆓虎子㆒

>>> print(s.to_tree())
不 <════╗   advmod
入 ═══╗═╝═╗ root
虎 <╗ ║   ║ nmod
穴 ═╝<╝   ║ obj
不 <════╗ ║ advmod
得 ═══╗═╝<╝ parataxis
虎 <╗ ║     nmod
子 ═╝<╝     obj

>>> f=open("trial.svg","w")
>>> f.write(s.to_svg())
>>> f.close()
```
![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UD-Kanbun/master/trial.png)
`udkanbun.load()` has three options `udkanbun.load(MeCab=True,Danku=False)`.  By default, the UD-Kanbun pipeline uses [MeCab](https://taku910.github.io/mecab/) for tokenizer and POS-tagger, then uses [UDPipe](http://ufal.mff.cuni.cz/udpipe) for dependency-parser. With the option `MeCab=False` the pipeline uses UDPipe for all through the processing. With the option `Danku=True` the pipeline tries to segment sentences automatically.

`udkanbun.UDKanbunEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth". `to_tree(kaeriten=True,Japanese=True)` is convenient for Japanese users.

You can simply use `udkanbun` on the command line:
```sh
echo 不入虎穴不得虎子 | udkanbun
```

## Usage via spaCy

If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline.

```py
>>> import udkanbun.spacy
>>> lzh=udkanbun.spacy.load()
>>> d=lzh("不入虎穴不得虎子")
>>> print(type(d))
<class 'spacy.tokens.doc.Doc'>
>>> print(udkanbun.spacy.to_conllu(d))
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	_	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	_	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	_	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> t=d[0]
>>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_)
1 不 不 ADV v,副詞,否定,無界 2 advmod  not
```

## Installation for Linux

Tar-ball is available for Linux, and is installed by default when you use `pip`:
```sh
pip install udkanbun
```

## Installation for Cygwin

Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then:
```sh
pip3.7 install udkanbun
```
Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`.

## Installation for Jupyter Notebook (Google Colaboratory)

```py
!pip install udkanbun
```

Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UD-Kanbun/blob/master/udkanbun.ipynb) for Google Colaboratory.

## Author

Koichi Yasuoka (安岡孝一)

## References

* Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28.
* 安岡孝一: [四書を学んだMeCab＋UDPipeはセンター試験の漢文を読めるのか](http://hdl.handle.net/2433/237383), 東洋学へのコンピュータ利用, 第30回研究セミナー (2019年3月8日), pp.3-110.
* 安岡孝一: [漢文の依存文法解析と返り点の関係について](http://hdl.handle.net/2433/235609), 日本漢字学会第1回研究大会予稿集 (2018年12月1日), pp.33-48.

%prep
%autosetup -n udkanbun-3.3.7

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-udkanbun -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.7-1
- Package Spec generated