Saravanan666/pypi-org huggingface dataset:
Package: pystm32flash
pystm32flashpython bindings of stm32flash serial/i2c flash libit should work on windows, linux and macos.how to install it:pip install pystm32flashhow to use it:get device information:import pystm32flash as stm32f
stm32f.api.set_device(b'/dev/ttyS0')
stm32f.api.run_it()or:import pystm32flash as stm32f
stm32f.api.set_device(b'/dev/i2c-0')
stm32f.api.run_it()write and verify:import pystm32flash as stm32f
#show help:
stm32f.api.show_help()
stm32f.api.set_arg(b'-w', b'test.hex')
stm32f.api.set_arg(b'-v', b'')
stm32f.api.set_device(b'/dev/ttyS0')
stm32f.api.run_it()write and verify and start execution:import pystm32flash as stm32f
#show help:
stm32f.api.show_help()
stm32f.api.set_arg(b'-w', b'test.hex')
stm32f.api.set_arg(b'-v', b'')
stm32f.api.set_arg(b'-g', b'0x0')
stm32f.api.set_device(b'/dev/ttyS0')
stm32f.api.run_it()read flash to file:import pystm32flash as stm32f
#show help:
stm32f.api.show_help()
stm32f.api.set_arg(b'-r', b'test.bin')
stm32f.api.set_device(b'/dev/ttyS0')
stm32f.api.run_it()any problem?make an issue:https://github.com/Jiangshan00001/pystm32flash/issuesc code is mostly from:https://github.com/ARMinARM/stm32flash
Package: pystmark
pystmarkPostmark APIlibrary for python 2.7, 3.6 and pypy.
Built on top of therequestslibrary.Web Framework IntegrationFlask-PystmarkDocumentationThe full Sphinx-compiled documentation is available here:https://readthedocs.org/docs/pystmark/en/latest/Example Usagefrompystmarkimport(Message,send,send_with_template,send_batch,send_batch_with_templates,UnauthorizedError)API_KEY='my_api_key'SENDER='me@example.com'# Send a single messagemessage=Message(sender=SENDER,to='you@example.com',subject='Hi',text='A message',tag='greeting')response=send(message,api_key=API_KEY)# Send a template messagemodel={'user_name':'John Smith','company':{'name':'ACME'}message=Message(sender=SENDER,to='you@example.com',template_id=11111,template_model=model,tag='welcome',)response=send_with_template(message,api_key=API_KEY)# Send multiple messagesmessages=[Message(sender=SENDER,to='you@example.com',subject='Hi',text='A message',tag='greeting',message_stream='broadcasts',)]response=send_batch(messages,api_key=API_KEY)# Send multiple messages with templatesmessages=[Message(sender=SENDER,to='you@example.com',template_id=11111,template_model=model,tag='greeting',message_stream='broadcasts',)]response=send_batch_with_templates(messages,api_key=API_KEY)# Check API response errortry:response.raise_for_status()exceptUnauthorizedError:print'Use your real API key'# Check for errors in each message when sending batch emails:forminresponse.messages:ifm.error_code>0:printm.messageContributionFork this repoMake your changes and write a test for themAdd yourself to theAUTHORS.mdfile and submit a pull requestPlease run the tests with./setup.py test --with-integration, with at least python2.7,
before you make a pull request. Requirements for running the tests are intests/requirements.txt.
The other versions will be handled bytravis-ci.The pep8 tests may fail if using pypy due tothis bug,
so that test is disabled if pypy is detected.Copyright and Licensepystmark is licensed under the MIT license. See theLICENSEfile for full details.
Package: pystoch
PyStochPython based Stochastic Gravitational Wave AnalysisPrerequisites Packagesconfigparser: >= 3.5.0matplotlib:   >= 1.5.1numpy:        >= 1.14.2h5py:         >= 2.7.1healpy:       >= 1.11.0six:          >= 1.12.0VersionBetaPlease refer documentation for installing PyStoch and TutorialsAuthorsDeveloped at IUCAA Stochastic Gravitational Wave GroupDeveloper: Anirban Ani,Jishnu Suresh, Sudhagar SLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsIUCAA, Pune.NRTT Grant
Package: pyStock
#  pystock[![Build Status](https://travis-ci.org/llazzaro/pystock.svg)](https://travis-ci.org/llazzaro/pystock) [![Coverage Status](https://coveralls.io/repos/llazzaro/pystock/badge.svg?branch=master&service=github)](https://coveralls.io/github/llazzaro/pystock?branch=master) [![Code Health](https://landscape.io/github/llazzaro/pystock/master/landscape.svg?style=flat)](https://landscape.io/github/llazzaro/pystock/master)pystock is intuitive relational model implemented with SQLAlchemy.To get started check## Table of contents[Quick start](#quick-start)[Bugs and feature requests](#bugs-and-feature-requests)[Documentation](#documentation)[Creators](#creators)## Quick startTwo options are available:Clone the repo:git clone https://github.com/llazzaro/pystock.git.Install with pip: pip install pystock## Bugs and feature requestsHave a bug or a feature request? [please open a new issue](https://github.com/llazzaro/pystock/issues/new).## Documentationpystock’s documentation, included in this repo in the root directory, is built with  and publicly hosted on read the docs. The docs may also be run locally.### Running documentation locally## CreatorsLeonardo Lazzaro<https://www.lazzaro.com.ar>your name!
Package: pystock0
pystockA small python library for stock market analysis. Especially for portfolio optimization.Installationpipinstallpystock0Note: You will need to callpip install pystock0to install the library. However, you can import the library asimport pystock. The library is still in development, so, a lot of changes will be made to the code.After installation, you can import the library as follows:importpystockUsageThe end goal of the library is to provide a simple interface for portfolio optimization. The library is still in development, so the interface is not yet stable. For now, this is how you can use the library to optimize a portfolio of stocks.frompystock.portfolioimportPortfoliofrompystock.modelsimportModel# Creating the benchmark and stocksbenchmark_dir="Data/GSPC.csv"benchmark_name="S&P"stock_dirs=["Data/AAPL.csv","Data/MSFT.csv","Data/GOOGL.csv","Data/TSLA.csv"]stock_names=["AAPL","MSFT","GOOGL","TSLA"]# Setting the frequency to monthlyfrequency="M"# Creating a Portfolio objectpt=Portfolio(benchmark_dir,benchmark_name,stock_dirs,stock_names)start_date="2012-01-01"end_date="2022-12-20"# Loading the datapt.load_benchmark(columns=["Close"],start_date=start_date,end_date=end_date,frequency=frequency,)pt.load_all(columns=["Close"],start_date=start_date,end_date=end_date,frequency=frequency,)# Creating a Model object and adding the portfoliomodel=Model(frequency=frequency,risk_free_rate=0.33)model.add_portfolio(pt,weights="equal")# Optimizing the portfolio using CAPMrisk=0.5model_="capm"res=model.optimize_portfolio(risk=risk,model=model_)print(res)Optimized successfully.Expected return: 1.1159%Risk:            0.5000%Expected weights:--------------------AAPL      :  47.40%MSFT      :   0.00%GOOGL     :  35.83%TSLA      :  16.77%{'weights': array([0.474 , 0.    , 0.3583, 0.1677]), 'expected_return': 1.115892062822632, 'variance': 0.5000278422222152, 'std': 0.707126468336616}More ExamplesFor more examples, please refer to the notebookWorking_With_pystock.ipynb. Also have a look atDownloading_Data.ipynb. Please also have a look atWorking_With_frontier.ipynbto see how to use thefrontiermodule to plot efficient frontiers.DocumentationThe documentation is available athttps://hari31416.github.io/pystock/.
Package: pystockdb
pystockdbDatabase for stocks based onpony.orm.
This package provides an create, sync and update tool.At the moment we are only support a few stocks.
If you want to have more, please contributepytickersymbols.
Database for stocks this package provides an create, sync and update tool.installpipinstallpystockdbdatabase schemaquick startIn all samples we use sqlite but you are free to use other providers.
For more information's please readConnecting to the Database.Install sqlite stock db:importloggingfrompystockdb.tools.createimportCreateAndFillDataBaselogger=logging.getLogger('test')config={'max_history':1,'indices':['DAX'],'currencies':['EUR'],'create':True,'db_args':{'provider':'sqlite','filename':'demo.sqlite','create_db':True},}create=CreateAndFillDataBase(config,logger)create.build()Install mysql stock db:For mysql you need an additional package:pip install pymysqlimportloggingfrompystockdb.tools.createimportCreateAndFillDataBaselogger=logging.getLogger('test')config={'max_history':1,'indices':['DAX'],'currencies':['EUR'],'create':True,'db_args':{'provider':'mysql','host':'127.0.0.1','user':'root','passwd':'test','db':'test',},}create=CreateAndFillDataBase(config,logger)create.build()Update sqlite stock db:importloggingfrompystockdb.toolsimportALL_SYMBOLSfrompystockdb.tools.updateimportUpdateDataBaseStockslogger=logging.getLogger('test')config={'symbols':[ALL_SYMBOLS],'prices':True,# update prices'fundamentals':True,# update fundamental stock data'db_args':{'provider':'sqlite','filename':'demo.sqlite','create_db':False},}update=UpdateDataBaseStocks(config,logger)update.build()Sync sqlite stock db:importloggingfrompystockdb.tools.syncimportSyncDataBaseStockslogger=logging.getLogger('test')config={'max_history':1,'indices':['CAC 40'],# add new index to existing database'currencies':['EUR'],'db_args':{'provider':'sqlite','filename':'demo.sqlite',},}sync=SyncDataBaseStocks(config,logger)sync.build()Getting database objects:importdatetimefrompony.ormimportdb_sessionfrompystockdb.db.schema.stocksimportPrice,Stockfrompystockdb.tools.baseimportDBBase# connect to databasearguments={'db_args':{'provider':'sqlite','filename':'test.sqlite','create_db':False}}# Read https://docs.ponyorm.org/api_reference.html for other provider settingsDBBase(arguments,None)now=datetime.datetime.now()last_week=now-datetime.timedelta(days=7)withdb_session:# get ifx stock objectstock=Stock.select((lambdas:'IFX.F'ins.price_item.symbols.name)).first()# select ifx.f prices of the last weekprices=Price.select(lambdap:p.symbol.name=='IFX.F'andp.date>=last_weekandp.date<=now)issue trackerhttps://github.com/portfolioplus/pystockdb/issuese
Package: pystockfish
This integrates the Stockfish chess engine with python.  It allows the engine to be usedby invoking an easy-to-use Engine class with synchronization handled automatically.>>> from pystockfish import *
>>> deep = Engine(depth=20)
>>> deep.setposition(['e2e4'])
>>> deep.bestmove()
{'info': 'info depth 10 seldepth 13 score cp -40
nodes 33303 nps 951514 time 35 multipv 1 pv b8c6 g1f3 g8f6
b1c3 e7e5 f1b5 f8d6 e1g1 e8g8 d2d4 e5d4 f3d4 a7a6',
'ponder': 'g1f3',
'move': 'b8c6'}
>>> deep.setposition(['e2e4','e7e5'])
>>> deep.bestmove()
{'info': 'info depth 10 seldepth 2 score cp 40
nodes 4230 nps 1057500 time 4 multipv 1 pv g1f3 g8f6
b1c3 b8c6 f1b5 f8d6 e1g1 e8g8 d2d4 e5d4 f3d4 a7a6',
'ponder': 'g8f6',
'move': 'g1f3'}The package also implements a Match class to play chess two engines against one another.
Package: pystoi
Python implementation of STOIImplementation of the classical and extended Short Term Objective Intelligibility measuresIntelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive noise, single/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech intelligibility.Description taken fromCees Taal's websiteInstallpip install pystoiorpip3 install pystoiUsageimport soundfile as sf
from pystoi import stoi

clean, fs = sf.read('path/to/clean/audio')
denoised, fs = sf.read('path/to/denoised/audio')

# Clean and den should have the same length, and be 1D
d = stoi(clean, denoised, fs, extended=False)Running the Octave testssudoaptupdatesudoaptinstalloctaveoctave-signalpipinstalloct2pypython-mpytesttests/test_python_octave.py
python-mpytesttests/test_stoi_octave.pyMatlab code & TestingAll the Matlab code in this repo is taken from or adapted from the code availablehere(STOI – Short-Time Objective Intelligibility Measure – ) written by Cees Taal.Thanks to Cees Taal who open-sourced his Matlab implementation and enabled thorough testing of this python code.If you want to run the tests, you will need Matlab,matlab.engine(install instructionshere) andmatlab_wrapper(install withpip install matlab_wrapper).
The tests can only be ran under Python 2.7 asmatlab.engineandmatlab_wrapperare only compatible with Python2.7
Tests are passing at relative and absolute tolerance of1e-3, which is enough for the considered application (all the variability is coming from the resampling method when signals are not natively sampled at 10kHz).Very big thanks to @gauss256 who translated all the matlab scripts to Octave, and wrote all the tests for it!ContributeAny contribution are welcome~, specially to improve the execution speed of the code~ (thank you Przemek Pobrotyn for a 4x speed-up!) :Improve the resampling method to match Matlab's resampling intests/.This can be considered a solved issue thanks to @gauss256 !Write tests for Python 3 (withtransplantfor example)References[1] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'A Short-Time
Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech',
ICASSP 2010, Texas, Dallas.[2] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'An Algorithm for
Intelligibility Prediction of Time-Frequency Weighted Noisy Speech',
IEEE Transactions on Audio, Speech, and Language Processing, 2011.[3] J. Jensen and C. H. Taal, 'An Algorithm for Predicting the
Intelligibility of Speech Masked by Modulated Noise Maskers',
IEEE Transactions on Audio, Speech and Language Processing, 2016.
Package: pystol
The open source, self-hosted and cloud-native fault injection platformRunning PystolThe easiest way to execute Pystol in a Kubernetes-based cluster is:pip install pystol # Install the operator using a pypi package
pystol -b # Print the Pystol banner
pystol -v # Print the installed version
pystol deploy # Install Pystol in the cluster
# Run the pingtest action.
pystol run --namespace pystol \
           --collection actions \
           --role pingtest
pystol --help # Run the help for further informationDocumentationPlease refer to theofficial documentationwebsite for any information related to the project.CI dashboardPystol usesGitHub actionsandbadgesto run all the CI
tasks, the result of running these
tasks is represented using badges.In particular we embrace the use of
CI dashboard as information radiators.We created thebadgeboad projectto show the value of any set of badges as a dashboard.For more information you can open theCI dashboarddirectly or go to theproject page in GitHub.Container imagesAll pystol official container images are hosted in Quay.io under
thePystol organization.There you will find two repositories:The Pystolstaging repository.
Here you will find all the container images from the upstream end-to-end jobs from the GitHub
Actions jobs.The Pystolstable repository.
Here you will find all the container images from the stable branches.LicensePystol is open source software
licensed under theApache license.
Package: pystoned
pyStoNEDpyStoNED is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. It also facilitates efficiency measurement using the conventional data envelopement analysis (DEA) and free disposable hull (FDH) approaches. The pyStoNED package allows practitioners to estimate these models in an open access environment under a GPL-3.0 License.InstallationThepyStoNEDpackage is now avaiable on PyPI and the latest development version can be installed from the Github repositorypyStoNED. Please feel free to download and test it. We welcome any bug reports and feedback.PyPIpip install pystonedGitHubpip install -U git+https://github.com/ds2010/pyStoNEDAuthorsSheng Dai, PhD, Turku School of Economics, University of Turku.Yu-Hsueh Fang, Computer Engineer, Institute of Manufacturing Information and Systems, National Cheng Kung University.Chia-Yen Lee, Professor, College of Management, National Taiwan University.Timo Kuosmanen, Professor, Turku School of Economics, University of Turku.CitationIf you usepyStoNEDfor published work, we encourage you to cite our following paper and other relatedworks. We appreciate it.Dai S, Fang YH, Lee CY, Kuosmanen T. (2021). pyStoNED: A Python Package for Convex Regression and Frontier Estimation. arXiv preprint arXiv:2109.12962.
Package: pystoned2
[pyStoNED2]pyStoNED2 is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. It also facilitates efficiency measurement using the conventional data envelopement analysis (DEA) and free disposable hull (FDH) approaches. The pyStoNED2 package allows practitioners to estimate these models in an open access environment under a GPL-3.0 License.InstallationThepyStoNED2package is now avaiable on PyPI and the latest development version can be installed from the Github repositorypyStoNED2. Please feel free to download and test it. We welcome any bug reports and feedback.PyPIpip install pystonedGitHubpip install -U git+https://github.com/advancehs/pyStoNED2Contribute在tests添加相应的单元测试使用python -m pytest来运行所有单元测试，确保所有单测都是通过的
Package: pystonkslib
pystonkslibStonks are cool - and if you want to do your own analysis, you have to pull all the data.Installationpipinstallpystonkslib(venv)PSC:\Users\bub\Development\pystonkslib>pslib--help
usage:usage:pslib[-h][-g][-c]optionalarguments:-h,--helpshowthishelpmessageandexit-g,--get-symbolsGetupdatedstocksymbols.-c,--get-chunked-dataGetchunkeddata.

optionalarguments:-h,--helpshowthishelpmessageandexit-g,--get-symbolsGetupdatedstocksymbols.-c,--get-chunked-dataGetchunkeddata.
Package: pystools
项目地址：https://pypi.org/project/pystools/安装sourcevenv/bin/activate
pipinstall-Upystools-ihttps://pypi.org/simple--trusted-hostpypi.org本项目图像处理使用的是Pillow-SIMD# must remove existed pillow first.
$ pip uninstall pillow
# install SSE4 version
$ pip install pillow-simd
# install AVX2 version
$ CC="cc -mavx2" pip install -U --force-reinstall pillow-simd安装，之前需要先安装扩展库(详见：https://pillow.readthedocs.io/en/latest/installation.html#external-libraries)# mac：
brew install libjpeg libtiff little-cms2 openjpeg webp

# linux
# Debian or Ubuntu:
sudo apt-get install python3-dev python3-setuptools

# Fedora:
sudo dnf install python3-devel redhat-rpm-config

# Alpine:
sudo apk add python3-dev py3-setuptools
Package: pystooq
PyStooqThis package provides simple interface for downloading
time daily series fromStooqwebsite.Please note that author of this package isnotaffiliated with
Stooq in any way.UsageIn order to download Stooq time series of prices for tickersPKOandTPEfor the period from 1st April 2020 to 31st October 2022 use the snippet below:frompystooqimportStooqDataFetcherfromdatetimeimportdatefetcher=StooqDataFetcher()data_df=fetcher.get_data(tickers=["PKO","TPE"],start=date(2020,4,1),end=date(2022,10,31))Format of the output:ticker          PKO                                             TPE                               
variable       open     high      low    close        volume   open   high    low  close    volume
date                                                                                              
2020-04-01  20.8531  20.9742  20.4063  20.6669  3.176696e+06  1.100  1.113  1.082  1.091   4377953
2020-04-02  20.7600  20.8904  19.7546  20.3225  4.157717e+06  1.095  1.138  1.086  1.130   8213427
2020-04-03  20.2480  20.3411  19.6895  20.3411  4.003517e+06  1.140  1.165  1.114  1.160   8037788
2020-04-06  20.8997  21.2255  20.6111  21.1138  3.749039e+06  1.180  1.210  1.175  1.188   9852203
2020-04-07  21.5327  22.7709  21.4582  21.7934  7.634852e+06  1.210  1.248  1.168  1.168  13587105The returned data is apandas.DataFramewith
the following indexes:date index on rows (data sorted in ascending order)two-levelpd.MultiIndexon columns:first level is the tickersecond level is the variable (open, high, low, close, volume)All the available variables are downloaded for each ticker.
Package: pystopwatch
PyStopwatch is a simple GUI stopwatch application.InstallationPyStopwatch is published onPyPIand can be installed from there:pip install pystopwatchUsageTo get started right away, run the following command:pystopwatchYou can run PyStopwatch as a package if running it as a script doesn’t work:python -m pystopwatchAdjust the time interval[ms] and run the application:pystopwatch --interval=1Import the module and use it:from pystopwatch import Stopwatch
sw = Stopwatch()
sw.run()
Package: py-stopwatch
Stopwatch class for timing portions of python code.Free software: MIT licenseDocumentation:https://py-stopwatch.readthedocs.io.FeaturesTick-based stopwatchSupport for Pause/ResumeSupport for multiple named-ticksUtility functions for time between different ticksNo third party requirements.UsagefromstopwatchimportStopwatcht=Stopwatch()t.start()print("Started ..")time.sleep(0.24)print(f"t.tick():{t.tick():.4f}seconds")time.sleep(0.48)print(f"t.tick():{t.tick():.4f}seconds")time.sleep(0.16)print(f"t.tick('Named Tick-1'):{t.tick('Named Tick-1'):.4f}seconds")t.pause()print("Paused ..")time.sleep(0.12)t.resume()print("Resumed ..")print(f"t.last():{t.last():.4f}seconds")time.sleep(0.12)print(f"t.tick():{t.tick():.4f}seconds")time.sleep(0.12)print(f"t.tick('Named Tick-2'):{t.tick('Named Tick-2'):.4f}seconds")t.stop()print("Timer stopped.")print("---")print(f"Total pause:{t.time_paused:.2f}seconds.")print(f"Total runtime:{t.time_active:.2f}seconds.")print(f"Total time:{t.time_total:.2f}seconds.")tij=t.time_elapsed(start_name='Named Tick-1',end_name='Named Tick-2')print(f"Time between 'Named Tick-1' and 'Named Tick-2':{tij:.4f}")History0.0.1 (2021-04-13)First release on PyPI.
Package: pystopwatch2
pystopwatchMulti-functional Simple Stopwatch for Python.Multiple Stopwatch by TagsManage elapsed times by tagsExample of use : profiling python codes(latency by functions, ...)Install$ pip install pystopwatch2Usagefrompystopwatch2importPyStopwatchw=PyStopwatch()w.start(tag='a')time.sleep(1)w.pause('a')e=w.get_elapsed('a')print(e)# 1.0xxxw.start(tag='b')time.sleep(0.5)w.pause('b')print(w)"""a: state=ClockState.PAUSE elapsed=1.0027 prev_time=1548382910.66398311b: state=ClockState.PAUSE elapsed=0.5051 prev_time=1548382911.66670585"""e.clear('a')e.clear('b')
Package: pystopwords
pystopwords简介中文停用词大全，支持Python接口, 可选择百度，哈工大，中科院等公开停用词典。目前只专注于中文，未来考虑加入多语言支持。安装pipinstallpystopwords使用方法frompystopwordsimportstopwordsstopwords函数返回一个停用词set，有两个参数：langs: string，支持的语言，目前仅支持中文(zh)source: string, 停用词来源，目前支持baidu: 百度停用词表hit: 哈工大停用词表ict: 中科院计算所停用词表scu: 四川大学机器智能实验室停用词库cn: 广为流传未知来源的中文停用词表marimo: Marimo multi-lingual stopwords collection 内的中文停用词iso: Stopwords ISO 内的中文停用词all: 上述所有停用词并集默认参数是stopwords(langs='zh', source='all')frompystopwordsimportstopwordsimportjieba# 默认的参数为：# all_stopwords = stopwords(langs='zh', source='all')all_stopwords=stopwords()# 可以选择不同的来源baidu_stopwords=stopwords(source='baidu')hit_stopwords=stopwords(source='hit')word_list=jieba.lcut('我想找一个简单好用的停用词典')word_list_drop_stopwords=[wordforwordinword_listifwordnotinall_stopwords]print(word_list_drop_stopwords)# Stdout: ['想', '找', '简单', '好用', '停用', '词典']来源说明名称来源来源url个数备注ict中科院计算所1207网络上大部分很多链接失效，而且一共1207个，不是网传的1208个baidu百度1429hit哈工大767scu四川大学机器智能实验室976cn未知来源746marimokoheiwhttps://github.com/koheiw/marimo387原始文件有更细致的分类体系isostopwords-isohttps://github.com/stopwords-iso/stopwords-iso794原始文件支持很多语言