Saravanan666/pypi-org huggingface dataset:
Package: chinese_province_city_area_mapper
chinese_province_city_area_mapperchinese_province_city_area_mapper：一个用于识别简体中文字符串中省，市和区并能够进行映射，检验和简单绘图的python模块举个例子:["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
        ↓ 转换
|省    |市   |区    |
|上海市|上海市|徐汇区|
|福建省|泉州市|洛江区|chinese_province_city_area_mapper: built to be recognize Chinese province,city and area in simplified Chinese string, it can automaticall map area to city
and map city to province.
for example:["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
        ↓ transform
|省    |市   |区    |
|上海市|上海市|徐汇区|
|福建省|泉州市|洛江区|完整文档见该模块的Github，
GitHub:https://github.com/DQinYuan/chinese_province_city_area_mapper特点基于jieba分词进行匹配，同时拥有比较复杂的匹配逻辑保证了准确率，笔者根据手头的海量地址描述数据进行了测试自带完整的省，市，区三级地名及其经纬度的数据支持自定义省，市，区映射输出的是基于pandas的DataFrame类型的表结构，易于理解和使用封装了简单的绘图功能，可以很方便地进行简单的数据可视化MIT 授权协议安装说明代码目前仅仅支持python3pip install chinese_province_city_area_mapperGet Started本模块中最主要的类是chinese_province_city_area_mapper.transformer.CPCATransformer（注：CPCA是Chinese Province City Area的缩写），
该类的transform方法可以输入任意的可迭代类型（如list，Series等），然后将其转换为一个DataFrame，
示例代码如下:location_str = ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区", "朝阳区北苑华贸城"]
from chinese_province_city_area_mapper.transformer import CPCATransformer
cpca = CPCATransformer()
df = cpca.transform(location_str)
df输出的结果为:区    市    省
0  徐汇区  上海市  上海市
1  洛江区  泉州市  福建省
2  朝阳区从上面的程序输出中你会发现朝阳区并没有被映射到北京市，这是因为在中国有多个同名的叫做朝阳区的区，
并且他们位于不同的市，所以程序就不知道该映射到哪一个市了，因此就不对其进行映射，如果你确定你
的数据中的朝阳区都是指北京市的那个朝阳区的话，可以在CPCATransformer的构造函数中传一个字典
（叫做umap参数，是user map的简称），指定朝阳区都要映射到北京市，
注意只有区到市的这一级映射存在重名问题，中国的市的名称都是唯一的，省的名称也都是唯一的
，示例代码如下:location_str = ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区", "朝阳区北苑华贸城"]
from chinese_province_city_area_mapper.transformer import CPCATransformer
cpca = CPCATransformer({"朝阳区":"北京市"})
df = cpca.transform(location_str)
df输出结果为:区    市    省
0  徐汇区  上海市  上海市
1  洛江区  泉州市  福建省
2  朝阳区  北京市  北京市模块中还内置了一个我推荐大家使用的umap，这个umap中我根据处理地址数据的经验将那些重名的区映射到了它最常见的一个市，
这个umap位于chinese_province_city_area_mapper.myumap.umap，使用如下:location_str = ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区", "朝阳区北苑华贸城"]
from chinese_province_city_area_mapper.transformer import CPCATransformer
from chinese_province_city_area_mapper import myumap
print(myumap.umap)   #查看这个umap的内容
cpca = CPCATransformer(myumap.umap)
df = cpca.transform(location_str)
df输出和上一个程序一样模块中还自带一个简单绘图工具，可以在地图上将上面输出的数据以热力图的形式画出来
，代码如下:from chinese_province_city_area_mapper import drawers
#df为上一段代码输出的df
drawers.draw_locations(df, "df.html")这一段代码运行结束后会在运行代码的当前目录下生成一个df.html文件，用浏览器打开即可看到
绘制好的地图（如果某条数据’省’，’市’或’区’字段有缺，则会忽略该条数据不进行绘制）。draw_locations函数还可以通过指定path参数来改变输出路径，示例代码如下:from chinese_province_city_area_mapper import drawers
#在当前目录的父目录生成df.html
drawers.draw_locations(df, "df.html", path="../")本模块的基本使用方法大概就是这些了，如果还想知道更多的细节，请访问该
模块的github地址https://github.com/DQinYuan/chinese_province_city_area_mapper，
在那里我写了更多的细节，2.0及以上版本又增加了几个echarts的绘图方便函数，见Github。
Package: chinesestockapi
Library to get Chinese stock priceSupported Engines:Hexun APISina Finance APIYahoo Finance APIUsage:from cstock.request import Requests
from cstock.hexun_engine import HexunEngineengine = HexunEngine()
requester = Requester(engine)stock = requester.request(‘000626’)
print stock.as_dict()Download the latest release:https://github.com/godsarmy/chinese-stock-api/zipball/master.
Package: chinesestockapi-py3
Library to get Chinese stock priceSupported Engines:Sina Finance APIUsage:from cstock.request import Requestsfrom cstock.hexun_engine import HexunEngineengine = HexunEngine()requester = Requester(engine)stock = requester.request(‘000626’)print stock.as_dict()Github Site:https://github.com/seizetheday/chinese-stock-api.
Package: chinese-stroke-sorting
中文笔划排序程序特点支持对名单列表进行按笔划排序支持从文件中读取名单列表支持以便于阅读的方式输出排序后的结果支持将排序后的名单列表写入文件安装pipinstallchinese-stroke-sorting更新pipinstall--upgradechinese-stroke-sorting使用说明>>># 导入包>>>fromchinese_stroke_sortingimportsort_by_stroke,write_sort_result_to_human,\write_sort_result_to_file,read_name_list_from_file

>>># 直接对名单列表排序>>>name_list=['张三','张二','张一一','李四','王五']>>>print(sort_by_stroke(name_list))['王五','李四','张一一','张二','张三']>>># 从文件中读取名单列表>>>name_list_file_path='test_name_list.txt'>>>name_list=read_name_list_from_file(name_list_file_path)>>>print(name_list)['张三','李四','王五','赵六','王一','王一二']>>># 以便于阅读的方式显示排序结果>>>sort_result=sort_by_stroke(name_list)>>>result_for_human=write_sort_result_to_human(sort_result,split_char='|')>>>print(result_for_human)王一|王一二|王五|李四|张三|赵六

>>># 将排序结果写入文件>>>output_file='result.txt'>>>write_sort_result_to_file(sort_result,output_file,split_char='\n')说明项目发布于PyPI：单击访问本模块尚不完善，有任何相关问题或建议，欢迎在本项目的github页面中提交issue:单击访问LicenseGNU General Public License v3.0
Package: chinese-synonym-word
chinese-synonym-word功能中文常用词同义词|同类词|近义词， 同类词与近义词不严格区分安装pip install chinese-synonym-word==0.1.6使用from chinese_synonym_word.chinese_synonym_word import chinese_synonym_word

# 同义词
chinese_synonym_word.get_synonym_word_cl("桌子"，is_strict=True)
# ['桌子', '台子', '案子', '桌', '台', '案', '几']

# 同类词
chinese_synonym_word.get_synonym_word_cl("桌子"，is_strict=False)
# ['桌子', '台子', '案子', '桌', '台', '案', '几', '方桌', '八仙桌', '四仙桌', '圆桌', '圆台', '书桌', '书案', '写字台', '办公桌', '一头儿沉', '桌案', '梳妆台', '镜台', '条案', '条桌', '条几', '六仙桌', '炕桌', '餐桌', '供桌', '香案', '公案', '茶几', '三屉桌', '会议桌', '饭桌', '炕几', '茶桌', '长桌', '木桌', '谈判桌', '课桌', '围桌', '画案', '服务台', '乒乓球台', '售票台', '化验台', '柜台', '机台', '交换台', '球台', '手术台', '地震台', '圆桌面', '桌面', '桌椅', '桌椅板凳']

# 近义词
chinese_synonym_word.get_synonym_word_vec("桌子")
# ['椅子', '茶几', '凳子', '桌面', '方桌', '书桌', '桌', '办公桌', '写字台']
Package: chinesevocablist
Chinese Vocab ListA list of Chinese vocabulary words with definitions, pronunciations, and example sentences. Under a CC-BY-SA license. Seechinese_vocab_list.yamlfor the list itself.Used by the Chinese Prestudy Anki addon. Seethis blog postfor more details.ContributingThere are a few ways to contribute:Making changes to the source code insrc/.Making changes files incontrib_files/:subtlex_dupes.yamllists words that are redundant with other words. For example,身上: 身in that file means that instead of learning the word "身上", someone should just learn the word "身".preferred_entries.yamlindicates which entries from CC-CEDICT are the best to use for each word. Only needed when you increase the size of the vocab list and it complains because it finds a word with multiple definition. Note: some words have multiple meanings that are worth learning but are split across different entries in CC-CEDICT. For example, 只 and 面. I don't have a good way to represent these inchinese_vocab_list.yamlyet.Directly modifyingchinese_vocab_list.yaml.If you changesrc/orcontrib_files/, be sure to runmake chinese_vocab_list.yamland check in both your changes and the generated changes tochinese_vocab_list.yaml.Updating reference_files:cc_cedict.txt: Runcurl https://www.mdbg.net/chinese/export/cedict/cedict_1_0_ts_utf-8_mdbg.txt.gz | gunzip > reference_files/cc_cedict.txtYou may need to update contrib_files/preferred_entries.yaml and/or other files in order to handle the update. Runmakeand fix errors until the vocab list builds cleanly.Publishing to PyPIIf your name is Kerrick, you can publish thechinesevocablistpackage to PyPI by running these commands from the root of the repo:rm -rf dist/*
python3 -m build
python3 -m twine upload dist/*Note that this directly uploads to prod PyPI and skips uploading to test PyPI.
Package: chinese-whispers
Chinese Whispers for PythonThis is an implementation of theChinese Whispersclustering algorithm in Python. Since this library is based onNetworkX, it is simple to use.Installationpip:pip install chinese-whispersAnaconda:conda install -c conda-forge chinese-whispersMamba:mamba install -c conda-forge chinese-whispersUsageGiven a NetworkX graphG, this library canclusterit using the following code:fromchinese_whispersimportchinese_whisperschinese_whispers(G,weighting='top',iterations=20)As the result, each node of the input graph is provided with thelabelattribute that stores the cluster label.The library also offers a convenient command-line interface (CLI) for clustering graphs represented in the ABC tab-separated format (source\ttarget\tweight).# Write karate_club.tsv (just as example)python3-c'import networkx as nx; nx.write_weighted_edgelist(nx.karate_club_graph(), "karate_club.tsv", delimiter="\t")'# Using as CLIchinese-whisperskarate_club.tsv# Using as module (same CLI as above)python3-mchinese_whisperskarate_club.tsvA more complete usage example is available in theexample notebookand athttps://nlpub.github.io/chinese-whispers/.In case you require higher performance, please consider our Java implementation that also includes other graph clustering algorithms:https://github.com/nlpub/watset-java.CitationUstalov, D.,Panchenko, A.,Biemann, C.,Ponzetto, S.P.:Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction. Computational Linguistics 45(3), 423–479 (2019)@article{Ustalov:19:cl,author={Ustalov, Dmitry and Panchenko, Alexander and Biemann, Chris and Ponzetto, Simone Paolo},title={{Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction}},journal={Computational Linguistics},year={2019},volume={45},number={3},pages={423--479},doi={10.1162/COLI_a_00354},publisher={MIT Press},issn={0891-2017},language={english},}CopyrightCopyright (c) 2018–2023Dmitry Ustalov. SeeLICENSEfor details.
Package: chinillabip158
Chinilla BIP158This implements the compact block filter construction in BIP 158. The code is
not used anywhere in the Bitcoin Core code base yet. The next step towards
BIP 157 support would be to create an indexing module similar to TxIndex that
constructs the basic and extended filters for each validated block.Installpython3-mvenvvenv
.venv/bin/activate
pip3install.Run python testspython3tests/simple_test.pyInstallation steps on a fresh OSX imageInstall brew:ruby-e"$(curl-fsSLhttps://raw.githubusercontent.com/Homebrew/install/master/install)"brewinstallpython3brewinstallboostAt this point the only error is can’t find boost_thread libThe issue is the homebrew boost ships libboost_thread-mt libs but doesn’t
include plain libboost_thread, so clang can’t find it. Interestingly, homebrew
boost does have both plain and -mt files for the libboost_system libraries.$find/usr/local/lib/|grepboost_threadlibboost_thread-mt.alibboost_thread-mt.dylibSolution, with no guarantees that this is "the Right Way to do things", but
appears to work fine for the configure stage:cd/usr/local/libln-slibboost_thread-mt.alibboost_thread.aln-slibboost_thread-mt.dyliblibboost_thread.dylibci BuildingThe primary build process for this repository is to use GitHub Actions to
build binary wheels for MacOS, Linux (x64 and aarch64), and Windows and publish
them with a source wheel on PyPi. See.github/workflows/build.yml. CMake usesFetchContentto downloadpybind11. Building is then
managed bycibuildwheel. Further
installation is then available viapip install chinillabip158e.g.Contributing and workflowContributions are welcome and more details are available in chinilla-blockchain'sCONTRIBUTING.md.The master branch is usually the currently released latest version on PyPI.
Note that at times chinillabip158 will be ahead of the release version that
chinilla-blockchain requires in it's master/release version in preparation for a
new chinilla-blockchain release. Please branch or fork master and then create a
pull request to the master branch. Linear merging is enforced on master and
merging requires a completed review. PRs will kick off a GitHub actions ci
build and analysis of chinillabip158 atlgtm.com.
Please make sure your build is passing and that it does not increase alerts
at lgtm.
Package: chinilla-blockchain
chinilla-blockchainCurrent Release/latestBeta Release/mainChinilla is fork of Chia, a modern cryptocurrency built from scratch, designed to be efficient, decentralized, and secure. Chinilla only has a pre-mine of 21,000 HCX (vs. Chia's 21,000,000 XCH) to support current and future development.  We recommendauditing our main branch vs Chiaif you have any questions on what was changed.  Chinilla is committed to maintaining a pure vanilla fork of Chia and will will only implement changes that have been published on the upstream main branch.Here are some of the features and benefits:Proof of space and timebased consensus which allows anyone to farm with commodity hardwareVery easy to use full node and farmer GUI and cli (thousands of nodes active on vanillanet)Chinilla seeder, which maintains a list of reliable nodes within the Chinilla via a built-in DNS server.Simplified UTXO based transaction model, with small on-chain stateLisp-style Turing-complete functionalprogramming languagefor money related use casesBLS keys and aggregate signatures (only one signature per block)Pooling protocolthat allows farmers to have control of making blocksSupport for light clients with fast, objective syncingA growing community of farmers and developers around the worldPlease check out thewikiandFAQfor
information on this project.Python 3.7+ is required. Make sure your default python version is >=3.7
by typingpython3.If you are behind a NAT, it can be difficult for peers outside your subnet to
reach you when they start up. You can enableUPnPon your router or add a NAT (for IPv4 but not IPv6) and firewall rules to allow
TCP port 43444 access to your peer.
These methods tend to be router make/model specific.Most users should only install harvesters, farmers, plotter, full nodes, and wallets.
Setting up a seeder is best left to more advanced users.
Building Timelords and VDFs is for sophisticated users, in most environments.
Chinilla and additional volunteers are running sufficient Timelords
for consensus.InstallingInstall instructions are available in theINSTALLsection of thechinilla-blockchain repository wiki.RunningOnce installed, aQuick Start Guideis available from the repositorywiki.
Package: chinillaclvm-rs
Thechinillaclvm_rswheel has python bindings for the rust implementation of chinillaclvm.BuildUsematurinto build the python interface. First, install into current virtualenv with$ pip install maturinWhile in thewheeldirectory, buildchinillaclvm_rsinto the current virtualenv with$ maturin develop --releaseOn UNIX-based platforms, you may get a speed boost onsha256operations by building
with OpenSSL.$ maturin develop --release --features=opensslTo build the wheel, do$ maturin build --releaseor$ maturin build --release --features=openssl
Package: chinillaclvm-tools-rs
chinillaclvm_tools_rsThis is a second-hand port of chinilla'schinillaclvm toolsto rust via the work of
ChinillaMineJP porting to typescript.  This would have been a lot harder to
get to where it is without prior work mapping out the types of various
semi-dynamic things (thanks, ChinillaMineJP).Some reasons for doing this are:Chinilla switched the chinillaclvm implementation to rust:chinillaclvm_rs, and this code may both pick up speed and track chinillaclvm better being in the same language.I wrote a new compiler with a simpler, less intricate structure that should be easier to improve and verify in the future in ocaml:ochinillalisp.Also it's faster even in this unoptimized form.All acceptance tests i've brought over so far work, and more are being added.
As of now, I'm not aware of anything that shouldn't be authentic when running
these command line tools from chinillaclvm_tools in their equivalents in this repositoryopcopdrunbrunreplargparse was ported to javascript and I believe I have faithfully reproduced it
as it is used in cmds, so command line parsing should work similarly in all three
versions.The directory structure is expected to be:src/classic  <-- any ported code with heritage pointing back to
                 the original chinilla repo.
                
src/compiler <-- a newer compiler (ochinillalisp) with a simpler
                 structure.  Select new style compilation by
                 including a `(include *standard-cl-21*)`
                 form in your toplevel `mod` form.Mac M1Usecargo build --no-default-featuresdue to differences in how mac m1 and
other platforms handle python extensions.Use with chinilla-blockchain# Activate your venv, then
$ maturin develop --release
Package: chinillapos
Chinilla Proof of SpaceChinilla's proof of space is written in C++. Includes a plotter, prover, and
verifier. It exclusively runs on 64 bit architectures. Read theProof of Space documentto
learn about what proof of space is and how it works.C++ Usage InstructionsCompile# Requires cmake 3.14+mkdir-pbuild&&cdbuild
cmake../
cmake--build.---j6Static Compilation With glibcStatically compile ProofOfSpacemkdir-pbuild&&cdbuild
cmake-DBUILD_PROOF_OF_SPACE_STATICALLY=ON../
cmake--build.---j6Run tests./RunTestsCLI usage./ProofOfSpace-k25-f"plot.dat"-m"0x1234"create
./ProofOfSpace-k25-f"final-plot.dat"-m"0x4567"-tTMPDIR-2SECOND_TMPDIRcreate
./ProofOfSpace-f"plot.dat"prove<32bytehexchallenge>
./ProofOfSpace-k25verify<hexproof><32bytehexchallenge>
./ProofOfSpace-f"plot.dat"check<iterations>Benchmarktime./ProofOfSpace-k25createHellman Attacks usageThere is an experimental implementation which implements some of the Hellman
Attacks that can provide significant space savings for the final file../HellmanAttacks-k18-f"plot.dat"-m"0x1234"create
./HellmanAttacks-f"plot.dat"check<iterations>PythonFinally, python bindings are provided in the python-bindings directory.Installpython3-mvenv.venv
..venv/bin/activate
pip3install.Run python testsTestings uses pytest. Linting uses flake8 and mypy.py.test./tests-s-vci BuildingThe primary build process for this repository is to use GitHub Actions to
build binary wheels for MacOS, Linux (x64 and aarch64), and Windows and publish
them with a source wheel on PyPi. See.github/workflows/build.yml. CMake usesFetchContentto downloadpybind11. Building is then
managed bycibuildwheel. Further
installation is then available viapip install chinillapose.g.Contributing and workflowContributions are welcome and more details are available in chinilla-blockchain'sCONTRIBUTING.md.The main branch is usually the currently released latest version on PyPI.
Note that at times chinillapos will be ahead of the release version that
chinilla-blockchain requires in it's main/release version in preparation for a
new chinilla-blockchain release. Please branch or fork main and then create a
pull request to the main branch. Linear merging is enforced on main and
merging requires a completed review. PRs will kick off a GitHub actions ci build
and analysis of chinillapos atlgtm.com. Please
make sure your build is passing and that it does not increase alerts at lgtm.
Package: chinoai
Chino 🌸Chino is a terminal-based chatbot. Powered by OpenAI.Uses RAG to generate responses based on user-provided documents.🐍 Python Version SupportThis project is designed to be compatible with specific versions of Python for optimal performance and stability.Supported Python VersionPython 3.11.7❗️ For the best experience and performance, it is recommended to use the version mentioned above.Before diving into the project, ensure that you have the correct Python version installed. To check the version of Python you currently have, execute the following command in your terminal:python--version🐍 Installing Python 3.11.7 withpyenvProtip:Managing multiple Python versions is a breeze withpyenv. It allows you to seamlessly switch between different Python versions without the need to reinstall them.If you haven't installedpyenvyet, follow theirofficial guideto set it up.Once you havepyenvready, install the recommended Python version by running:pyenvinstall3.11.7When you navigate to this project's directory in the future,pyenvwill automatically select the recommended Python version, thanks to the.python-versionfile in the project root.Installation 🛠️Install using pip:pipinstallchinoaiBuild from source:Clone the repository:gitclonehttps://github.com/SAMAD101/Chino.gitNavigate to the project Directory:cdChinoInstall using:pipinstall.📦 Setup for developmentSetting Up the Project for developmentFork the Repository:Get your own fork/copy of the project from GitHub by clicking the "Fork" button.Clone the forked Repositorygitclone<your-fork-url>Navigate to the Project Directory:cdChinoInstall the Project (from source):Use PDM to run the project:python3-mpipinstall-e.Install pre-commit hooks:pre-commitinstallRun the project:chino--help⚠️ Note:You will need an OpenAI API key to make it work. Get your API key from OpenAI website and set it as an environment variable:exportOPENAI_API_KEY="<your_api_key>"Usage 📖Commands and options are available by running:chino--helpFor using the Retrieval Augmented Generation (RAG) features, follow these steps:You will need to put your documents in the~/.local/share/chino/datadirectory. If not there, make one.Also,make sure you have a directory:~/.local/share/chino/chroma. This directory will contain the OpenAI embeddings (embedding vectors) for the documents.Process the documents and create the embeddings using the following command:chinomigrateThe directory data and chroma directories will be made more configurable in the future releases.Using Query mode:Once your documents are processed. You can use the query mode to give prompts for the documents [RAG].
Start Chino by running:chinostartand use\q:before your prompt to use it in query mode.
Package: chinois
Chinois 🍵CSS selector implementation for Bisque and Campbells web scraping libraries.Install•ExamplesAdapted fromsoupsieve's inner package, to remove the hard coupling to Beautiful Soup.InstallationChinoisis available on PyPi:pipinstallchinoisThe dependencies needed to usebisqueandcampbellsparsing libraries are left to your taste.
They can be installed with:pip install chinois[bisque]for the bold new flavours of Pydantic model-based parsers in Bisque.pip install chinois[campbells]for the Campbells parsers, preserving the heritage taste of the Beautiful Soup API.The dependencies needed to usehtml5libandlxmlparsers are not installed by default.
They can be installed with:pip install chinois[html5lib]to be able to usehtml5lib.Pros:closest to how browsers parses web pages, very lenient, creates valid HTML5.Cons:slowest parser.pip install chinois[lxml]to be able to uselxml.Pros:fastest parser.Cons:heavier dependency (C extension).
Package: chinook
Welcome to the chinook package developed by Ryan Day at the University of British Columbia.This package has been developed originally with the intention of simulating matrix-element effects in Angle-Resolved Photoemission Spectroscopy (ARPES) experiments.
This objective has however required a substantial amount of code infrastructure for generating and diagnosing tight-binding models, and consequently,
the functionality has been expanded (and will continue to do so) to allow for more broad exploration of these model Hamiltonians.Here you will find all relevant modules and libraries for running calculations with this package.For any questions which you do not find explanations in the documentation files, please do not hesitate to contact me at
rpday7_at_gmail_dot_comThanks!Ryan Day
Package: chinormfilter
chinormfilterFilter synonym written in lucene format to avoid duplication with Sudachi normalization. Mainly used when migrating to sudachi analyzer.Usage$chinormfiltertests/test.txt-oout.txtfiltered result is following.レナリドミド,レナリドマイド
リンゴ => 林檎
飲む,呑む
tlc => tlc,全肺気量
リンたんぱく質,リン蛋白質,リンタンパク質

↓ filter

レナリドミド,レナリドマイド
tlc => tlc,全肺気量Specify system dict$chinormfiltertests/test.txt-sfull-oout.txtUse Custom DictSpecify dict via sudachi.json$chinormfiltertests/test.txt-ssudachi.json-oout.txtTODOcustom dict test
Package: chios
chiosis a collection of Sphinx extensions named after the Greek island
Chios, whose symbol was the Sphinx.Full documentation:https://kallimachos.github.io/chios/InstallationInstallchiosusingpip:$ pip install chiosExtensionsbolditalicEnables inline bold + italic text styling using abolditalicrole:The end of this sentence :bolditalic:`displays in bold and italic`.remotecodeEnables code blocks from remote sources using aremote-code-blockdirective:.. remote-code-block:: ini

   https://example.com/rawsource.iniremoteincludeEnables RST file inclusion from remote sources using aremote-includedirective:.. remote-include:: https://example.com/rawsource.rst