Saravanan666/pypi-org huggingface dataset:
Package: tenhoulog
tenhoulogインストールpipinstalltenhoulogGameResult objectGameResultオブジェクトは1試合の結果を保持します。文字列から生成>>>log_str=""">>> L1000 | 00:30 | 四般南喰赤－ | A(+45.0) B(+9.0) C(-20.0) D(-34.0)>>> L1000 | 00:30 | 四般南喰赤－ | D(+85.0) B(+1.0) C(-10.0) E(-74.0)>>> """>>>fromdatetimeimportdate>>>print(GameResult.parse_str(log_str,date(2020,8,15)))[GameResult(lobby='L1000',playernum=4,player1='A',player1ptr=45.0,player1shuugi=None,player2='B',player2ptr=9.0,player2shuugi=None,player3='C',player3ptr=-20.0,player3shuugi=None,player4='D',player4ptr=-34.0,player4shuugi=None,starttime=datetime.datetime(2020,8,15,0,30)),GameResult(lobby='L1000',playernum=4,player1='D',player1ptr=85.0,player1shuugi=None,player2='B',player2ptr=1.0,player2shuugi=None,player3='C',player3ptr=-10.0,player3shuugi=None,player4='E',player4ptr=-74.0,player4shuugi=None,starttime=datetime.datetime(2020,8,15,0,30))]nodocchi.moeのAPIからfetch>>>results=fetch_player_log("ASAPIN")>>>results[:2][GameResult(lobby=None,playernum=4,player1='KAZ2000',player1ptr=50.0,player1shuugi=None,player2='ASAPIN',player2ptr=7.0,player2shuugi=None,player3='くに＠けん',player3ptr=-18.0,player3shuugi=None,player4='NoName',player4ptr=-39.0,player4shuugi=None,starttime=datetime.datetime(2009,6,16,19,46,tzinfo=datetime.timezone.utc)),GameResult(lobby=None,playernum=4,player1='ダイナマイト四国',player1ptr=58.0,player1shuugi=None,player2='ASAPIN',player2ptr=20.0,player2shuugi=None,player3='Del９',player3ptr=-34.0,player3shuugi=None,player4='M*Do',player4ptr=-44.0,player4shuugi=None,starttime=datetime.datetime(2009,6,16,20,14,tzinfo=datetime.timezone.utc))]>>>results=fetch_lobby_log("C0000")>>>results[:2]...ResultBook objectResultBookは複数試合の結果を保持している集計用クラスです。例第９期天鳳名人戦の結果集計fromtenhoulogimport*fromtenhoulog.utilsimportdf2tableresults=fetch_lobby_log("C0011")players=["タケオしゃん","Ⓟ醍醐大","就活生@川村軍団","Ⓟ木原浩一","おかもと","Ⓢ福地誠","Ⓟ渋川難波","Ⓟ小林剛","独歩","Ⓟ松ヶ瀬隆弥","Ⓟ中嶋隼也","お知らせ",]book=ResultBook.from_results(results,players)JST=timezone(timedelta(hours=+9),"JST")meijin_book=book.filter_by_period((datetime(2019,8,6,tzinfo=JST),datetime(2020,6,11,tzinfo=JST)))print(meijin_book.aggregate(4).sort_values("得点",ascending=False))名前回数得点順位分布平均順位祝儀1Ⓢ福地誠40336.212-9-12-72.35000002Ⓟ醍醐大40226.711-9-12-82.42500005就活生@川村軍団40195.510-11-10-92.45000008おかもと40187.411-9-12-82.42500004Ⓟ木原浩一36143.911-8-9-82.38888900Ⓟ松ヶ瀬隆弥3697.67-13-9-72.44444409お知らせ7091.620-17-17-162.41428603タケオしゃん3953.210-13-5-112.43589706独歩362.38-14-6-82.38888907Ⓟ中嶋隼也24-45.95-6-7-62.583333011Ⓟ渋川難波24-50.68-3-5-82.541667010Ⓟ小林剛43-755.910-5-11-172.8139530
Package: tenji
TenjiAsyncronous Python client for scraping data from MyFigureCollection (MFC).UsageimportasynciofromtenjiimportMfcClientasyncdefmain():client=MfcClient()profile=awaitclient.get_profile("syntack")print(profile.status)if__name__=="__main__":asyncio.run(main())Authenticated RequestsThere is some session support but as of right now the goal is to focus on non-authenticated requests.NotesThe client currently relies on an English locale.
Package: tenkan
tenkanCommand line tool to convert HTTP RSS/Atom feeds to gemini format.InstallationpipinstalltenkanUsageAdd a feed# Any valid RSS/Atom feedtenkanaddfeednameurlUpdate content of feed listtenkanupdateDelete feedtenkandeletefeednameList subscripted feedstenkanlistOptionsA debug mode is avaible via --debug option.
If you want to use your configuration or feeds file in another place than default one, you can use --config and --feedsfile options.Configurationtenkan searches for a configuration file at the following location:$XDG_CONFIG_HOME/tenkan/tenkan.confExample configThis can be found in tenkan.conf.example.[tenkan]gemini_path=/usr/local/gemini/gemini_url=gemini://foo.bar/feeds/# will purge feed folders having more than defined element count# purge_feed_folder_after = 100[filters]# authors we don't want to read# authors_blacklist = foo, bar# blacklist of article titles, if provided, it won't be processed# titles_blacklist = foo, bar# blacklist of article links, if provided, it won't be processed# links_blacklist = foo/bar.com, bar/foo, bla[formatting]# maximum article title size, 120 chars if not provided# title_size = 120# feeds with a truncated content# will be fetched and converted using readability# truncated_feeds = foo, barTodolistAdd a edit commandAdd a --feedname option to update command, to update a single feedRewrite configuration checksadd configuration option to log output into a logfileImprove testsRefactor needed parts like write_article(not sure if relevant) migrate images too, for gemini clients that can handle itDevelopmentI recommend using pre-commit. The pre-commit configuration I use is located in .pre-commit-config.yamlfile.Run pre-commit command before every pull request and fix the warnings or errors it produces.
Package: tenkichecker
tenkitenkiは、日本の都道府県の名前を入力すると、その都道府県の今日と明日の天気をスクレイピングして表示するPythonパッケージです。Yahoo!天気のサイトから情報を取得します。インストールpipを使ってインストールできます。pip install tenkichecker使い方tenkiモジュールをインポートして、kisyou関数に都道府県の名前を渡します。すると、その都道府県の今日と明日の天気がリストとして返されます。
また、その県で発令されている注意報を表示するfrom tenkichecker import Weatherweather = Weather("任意の県名")weather.kisyou()[["今日", "晴れ", "25℃", "17℃", "10%"], ["明日", "曇り", "23℃", "18℃", "20%"]]['注意報', '乾燥注意報', '低温注意報']ライセンスこのパッケージはMITライセンスのもとで公開されています。作者gotouyamatos2122100@stu.musashino-u.ac.jp
Package: tenks
Tenks is a utility that manages virtual bare metal clusters for development and
testing purposes. It offers:Declarative definition of clusters using Ansible’s familiar YAML
syntaxAutomated creation and deletion of virtualised bare metal nodes across any
number of hypervisorsIn-built management of Open vSwitch bridges for physical networksRegistration of nodes and ports with OpenStack Ironic, and creation of
OpenStack Nova flavorsSimple cluster reconfiguration and tear-downTo get started with Tenks, see the documentation (linked below).License: Apache License, Version 2.0Documentation:https://docs.openstack.org/tenks/Source:https://opendev.org/openstack/tenks/Bugs:https://storyboard.openstack.org/#!/project/openstack/tenksKnown issues/limitations: refer to the documentation.
Package: tennet-py
TenneT-pyPython client for ingesting the TenneT System & transmission data API which can be foundhere.
It is a public API for which no API key is needed.The library currently supports four data items:Measurement dataImbalance priceBalance delta with pricesAvailable capacityData can be retrieved as raw text or a Pandas DataFrame.The client currently has two methods:base_request: retrieves specified data in csv or xml text formatquery_df: retrieves specified data and returnes a Pandas DataFrameInstallationpip install tennet-pyExample UsagesfromtennetimportTenneTClient,DataType,OutputTypeimportpandasaspdstart=pd.Timestamp("2021-01-01")end=pd.Timestamp("2021-01-31")# initiate the client, you can specify a default output to not always specify it per callclient=TenneTClient(default_output=OutputType.CSV)# retrieve data as text in default output (in this case csv)data=client.base_request(DataType.settlementprices,d_from=start,d_to=end)# retrieve data as xmldata=client.base_request(DataType.settlementprices,d_from=start,d_to=end,output_type=OutputType.XML)# retrieve same data as a dataframedf=client.query_df(DataType.settlementprices,d_from=start,d_to=end)NetztransparenzAlso supported is a limited number of endpoints of the german TenneT from the Netztransparenz platform. First register and get oauth credentials as explained on the documentation pagehereThen can be used as follows:fromtennetimportNetztransparenzClientimportpandasaspdclient=NetztransparenzClient(oauth_client_id,oauth_client_secret)df=client.query_imbalance(start=pd.Timestamp('2023-12-22',tz='europe/amsterdam'),end=pd.Timestamp('2023-12-22 23:59',tz='europe/amsterdam'))DisclaimerThis is an unoffical package which is not supported or endorsed in any way by TenneT TSO.
Package: tennga
Tennga一个简单的源管理器用法：fromtenngaimporttenngatennga.init()#在文件夹下初始化源srcftennga.listsrc()#打印所有源tennga.add(url)#添加源tennga.remove(url或者数字)#移除list出来的源tennga.update()#更新与使用源 下载所有源文件到srcs下面tennga.find(文件夹，文件名，uuid)#从srcs的源中查找文件tennga.install(文件夹，文件名)#从源搜索并下载文件，携带一个aop。自动将文件下载到srcf/内容1中。tennga.uninstall(文件夹，文件名)tennga.freeze(文件夹)#freeze会返回已经安装的所有文件信息tennga.getfile(文件夹，文件名)#获得文件目录tennga.getfiles(文件夹)#获得虚文件下所有文件目录源格式 utf-8 json{"目录1":[{"name":"文件1","urls":["http://www.baidu.com","block://块名1/文件名"]"uuid":"f0645603-2fc6-4007-82e6-b731b8920b5b"},],"目录2":[],"blocks":[{"name":"块名1","url":"http://www.baidu.com/xx.block",}]}结构1. 文件存储源文件存储在: srcf/srcs/全部源文件文件存储在虚目录下: srcf/目录1/文件1/文件内容.txtblock存储在: srcf/blocks/xx.block2. 特殊文件uuid.lock所有已经安装的文件下面会有这不仅仅是文件的特殊标记，也存储了所有文件的下载顺序，包括文件顺序，block文件顺序，以及文件综合顺序。这其实是个json。需要文件顺序的，比如小说软件，需要知道多个文件的顺序，则可以读取这个文件。block文件以tar归档的文件。不能包含文件夹，是多个文件被裸归档的。扩展名为:*.blockblock链接：block://block名/文件名.扩展名，可以直接访问block中的文件。其中，程序会先从blocks目录中查找block块，如果没有，会尝试从源中下载对应block。然后再从block中获取文件。通常用于应对过于散碎的批量文件，比如一个源中全是图片，下载很多次不如一次下完。因为程序会主动检查本地是否存在对应block,所以不会重新下载block。如果压缩错了重新修改上传了，也不会被重新下载。因此建议如果修改，请使用新的block，并修改源中的链接，等待用户自己下次更新源以及更新文件源制作器fromtenngaimportsrcmakesrcmake.openSourceFile(name)srcmake.addFile(srcfile,floder,filename)srcmake.addLink(srcfile,floder,filename,link)srcmake.addKV(srcfile,floder,filename,key,value)srcmake.addBlock(srcfile,blockname)srcmake.addBlockLink(srcfile,blockname,link)srcmake.save(name,srcfile)小说源制作方案可以将对应网站的小说对应页面作为源，下载。分为：xx.yaml 中存储xpath，用于分解页面其他的全是 xx.html，都是小说网站的页面方案：小说阅读器通过yaml中xpath解析对应离线的html页面，就成了小说。并且可以离线使用。预计功能（v2.0.0）提供一个源管理器和制作器的UI使用：python -m tennga可以导出两个文件:tennga.html #这是tennga管理器页面，放在static目录下即可tennga.py #这是flask的蓝图，可以直接注册使用可以使用iframe导入tennga集成到flask软件中使用。
Package: tennisim
tennisimSimulate tennis points, games, sets and matches.Small pure python package (no dependencies outside of standard lib) to simulate tennis using points-based modelling i.e. given a probability of a server winning a given point, simulate the outcome of:pointsgamessetstiebreaksmatcheswith the ability to alter various parameters to gain some intuition behind how the tennis scoring system impacts matches. Using this we can answer various questions like:what effect does removing the second serve have on match duration?if the probability of winning a point on serve is reflective of skill, then how do rule alterations affect the likelihood that the more skillful player will actually end up winning the match (not just a given point)?Installingpip install tennisimPoints-based ModellingPoints-based modelling is a popular model for modelling tennis matches where predictions for games, sets and matches depends on modelling every constituent point. This can lead to a wealth of data that can be used to generate in-play match odds as we can output distributions e.g. for a given starting point, if we simulate 1000 outcomes how many show that player 1 wins the next set?For more background I wrotethis article on Towards Data Science.ExampleSuppose we want to simulate a game of tennis. We define the probability that the server will win a given point:fromtennisim.simimportsim_gamep=0.7sim_game(p)This will simulate 1 game of tennis where the probability that the server will win any given point is0.7. It will return a tuple containing:boolean result for if the server won the gamelist of tuples for the score progression of the gameWe can then take this further and simulate 1,000 groups of 100 games to generate a distribution of results. This can be interesting when looking at how changes in the probability p or the length of a game impacts the servers win probability for the game.importnumpyasnpfromtennisim.simimportsim_game# set params for simulationgames=100sims=1000probabs=np.linspace(0.5,0.8,4)results={}# for each serve win probabilityforpinprobabs:# we now need to generate simsmeans=[]game_lengths=[]foriinrange(0,sims):g_results=[sim_game(p)forxinrange(games)]# get mean resultmean_res=np.mean([x[0]forxing_results])# get mean game lengthmean_length=np.mean([len(x[1])forxing_results])# join to results holdersmeans.append(mean_res)game_lengths.append(mean_length)# add data to probab dictresults[p]=(means,game_lengths)HelpFor more info see thedocumentationLicensetennisimis free and open source software, distributed under the terms of theMIT license.
Package: tennis-new-library
th2 gRPC generator template library (1.4.0)This is the template project for creating custom gRPC libraries. It contains an example of proto messages and services.The tool generates code from.protofiles and uploads built packages (.protofiles and generated code) to the specified repositories.How to transform the templateCreate a directory with the same name as the project name (use underscores instead of dashes) undersrc/main/protodirectory (remove other files and directories if they exist).Place your custom.protofiles in the created directory. Pay attention to both thepackagespecifier and to theimportstatements.Editrelease_versionandvcs_urlproperties ingradle.propertiesfile.EditrootProject.namevariable insettings.gradlefile. This will be the name of the Java package.Editpackage_info.jsonfile in order to specify its name and its version for Python package (create the file in case it's absent).Edit parameters ofsetup.pyinsetupfunction invocation such as:author,author_email,url. Do not edit the other's parameters.EditREADME.mdfile according to the new project.Note that the name of the created directory undersrc/main/protodirectory is used in Python (it's a package name).How to maintain a projectPerform the necessary changes.Update the package version of Java ingradle.propertiesfile.Update the package version of Python inpackage_info.jsonfile.Commit everything.How to run projectJavaIf you wish to manually create and publish a package for Java, run the following command:gradle --no-daemon clean build publish artifactoryPublish \
       -Purl=${URL} \ 
       -Puser=${USER} \
       -Ppassword=${PASSWORD}URL,USERandPASSWORDare parameters for publishing.PythonIf you wish to manually create and publish a package for Python:Generate services withGradle:gradle --no-daemon clean generateProtoYou can find the generated files by following path:src/gen/main/services/pythonGenerate code from.protofiles and publish everything usingtwine:pip install -r requirements.txt
pip install twine
python setup.py generate
python setup.py sdist
twine upload --repository-url ${PYPI_REPOSITORY_URL} --username ${PYPI_USER} --password ${PYPI_PASSWORD} dist/*PYPI_REPOSITORY_URL,PYPI_USERandPYPI_PASSWORDare parameters for publishing.Release notes1.4.0Implement stubs creation for Python
Package: tennisrank
tennis-rankComputes tennis player ranks using pagerank algorithm on match graphInstallUsing pip:pipinstalltennisrankRun tests:make lint
make testExamplefromtennisrank.utilsimportload_atpfromtennisrank.modelimportPlayer,PlayerRankfromtennisrank.algorithmimportTennisRankmatches=load_atp(2021)ranker=TennisRank(matches)# Get top 3 on all surfacestop=3forsurfacein['all','hard','clay','grass']:print(f'Top{top}on{surface}surface (unit weight):')ranks=ranker.get_ranks(surface=surface,point_diff=False)forplayer_rankinranks[:top]:print('-',player_rank)print(f'{len(ranks)-top}more...')print()# Get rank of particular playerranker.get_rank('Nuvak Djokovich')# fuzzy matchingCreates the following output:Top 3 on all surface (unit weight):
- PlayerRank(player=Player(id=104925, name='Novak Djokovic'), rank=1, surface='all')
- PlayerRank(player=Player(id=126774, name='Stefanos Tsitsipas'), rank=2, surface='all')
- PlayerRank(player=Player(id=106421, name='Daniil Medvedev'), rank=3, surface='all')
299 more...

Top 3 on hard surface (unit weight):
- PlayerRank(player=Player(id=106421, name='Daniil Medvedev'), rank=1, surface='hard')
- PlayerRank(player=Player(id=104925, name='Novak Djokovic'), rank=2, surface='hard')
- PlayerRank(player=Player(id=100644, name='Alexander Zverev'), rank=3, surface='hard')
251 more...

Top 3 on clay surface (unit weight):
- PlayerRank(player=Player(id=104745, name='Rafael Nadal'), rank=1, surface='clay')
- PlayerRank(player=Player(id=104925, name='Novak Djokovic'), rank=2, surface='clay')
- PlayerRank(player=Player(id=126774, name='Stefanos Tsitsipas'), rank=3, surface='clay')
190 more...

Top 3 on grass surface (unit weight):
- PlayerRank(player=Player(id=104925, name='Novak Djokovic'), rank=1, surface='grass')
- PlayerRank(player=Player(id=200000, name='Felix Auger Aliassime'), rank=2, surface='grass')
- PlayerRank(player=Player(id=126610, name='Matteo Berrettini'), rank=3, surface='grass')
155 more...Publish new versionFor developersVia Github ActionsUpdate version number in pyproject.tomlMerge to main (via PR, direct push to main or other way)Create new release on GithubDirect to PyPIBuild and upload:python-mbuild
twineuploaddist/*
Package: tennyson
Collection of helpful functions by Tennyson T BardwellImportant Noteshas personal info committedDevelopment### Helpful Commands ###`bash sudo pip install .     # install package python3 setup.py test  # test package python3 setup.py build # builds package python3 setup.py sdist bdist_wheel && twine upload dist/* # distributes package `### TODO ###graceful handling of email errors if no network
Package: tenor-dl
tenor-dlSimple CLI script to download gifs from tenor.com.Installationpip3installtenor-dlUsageusage:tenor-dl[-h][-oOUTPUT][-u]URL

simplescripttodownloadgifsfromtenor.com

positionalarguments:URLtenor.comURLtodownloadthegiffrom

optionalarguments:-h,--helpshowthishelpmessageandexit-oOUTPUT,--outputOUTPUToutputpathoruse"-"forSTDOUT-u,--urlonlyonlyprintdirectlinktogifandexitExample Usagetenor-dlhttps://tenor.com/view/rage-work-pc-stressed-pissed-gif-15071896
Package: tenor.py
TENOR파이썬용 Gif 라이브러리입니다.https://pypi.org/projects/tenorhttps://github.com/cord0318/python_tenorDownloadpipinstalltenorHow To Use# 비동기importtenorimportasyncioasyncdefmain():print(awaittenor.asyncSearchGif("gif 이름","가져올 수","다운로드 확인","다운로드 경로"))asyncio.get_event_loop().run_until_complete(main())# 동기importtenorprint(tenor.searchGif("gif 이름","가져올 수","다운로드 확인","다운로드 경로"))Tip이 모듈을 사용하여 디스코드 봇을 만들수 있습니다!
Package: tensap
tensap (Tensor Approximation Package)A Python package for the approximation of functions and tensors.tensap features low-rank tensors (including canonical, tensor-train and tree-based tensor formats or tree tensor networks), sparse tensors, polynomials, and allows the plug-in of other approximation tools. It provides different approximation methods based on interpolation, least-squares projection or statistical learning.See the tutorials folder and its subfolders for examples of use.Install from PyPi:pip install tensapInstall from conda:conda config --add channels conda-forge
conda config --set channel_priority strict
conda install tensap
Package: tensify
TensifyAn easy to use python package to convert your tenses...Features :
1.Super-Simple
2.Easy
3.Minimalistic
4.Super-Fast
5.Open-Source
6.New Concept
7.Trusted
8.FreeUsage :import tensify
tensify.PAST("I get it")
# As simple as thatOutput :I got itContributors :1.Dev Shah (Contact : devshah1032@gmail.com)
2.Haard Majmudar (Contact : haardmajmudar2827@gmail.com)License :MIT License
Copyright (c) 2021 Dev Shah
Package: tensiometer
Tensiometer:utilities to understand concordance and discordance of posterior distributionsAuthor:Marco Raveri and Cyrille DouxHomepage:https://tensiometer.readthedocs.ioSource:https://github.com/mraveri/tensiometerReferences:https://arxiv.org/abs/2105.03324(non-Gaussian metrics),https://arxiv.org/abs/1806.04649andhttps://arxiv.org/abs/1912.04880(Gaussian)DescriptionThe tensiometer package is a collection of tools to test the level of
agreement/disagreement between different posterior distributions.The best way to get up to speed is to read through the worked examplefull worked examplethat you canrun online! There’s also a documented example ofnon-Gaussian tension estimates between DES Y1 and Planck 18.InstallationThe tensiometer package is available on PyPI and can be easily installed with:pip install tensiometerAlternatively one can download the source code from github:git clone https://github.com/mraveri/tensiometer.gitand install it locally with the shortcut:make installYou can test that the code is working properly by using:make testDependencies
Package: tension
tensionThetensionpackage is a Python package for building and FORCE training chaotic recurrent neural networks.tensionis built to integrate seamlessly with TensorFlow/Keras, a widely used software package for training artificial neural networks.Code and documentationInstallation guide, contributing guide, and API reference are available on thedocumentation websiteExample notebooks for reproducing the experiments in the associated software report can also be found on the documentation website and in theexamplesfolder. These notebooks can be run either in Google Colab or in a local jupyter notebook.Documentation for TensorFlow/Keras, on whichtensionis based and interoperable with, can be found at theTensorFlow guide.Dependenciestensiondepends on Python >= 3.7,tensorflow>=2.5,numpyandmatplotlib.InstallationLocal installationWe recommend installingtensioninto a conda environmentconda create -n tension python=3.7followed byconda activate tensionFrom GitHubClone this repo usinggit clone https://github.com/zhenruiliao/tension.gitChange into thetensiondirectory and install usingpipcd tension/
pip install -e .From PyPIpip install tensionGoogle ColabTo quickly get started withtension, the package can also be installed in Google Colab using the following commands!git clone https://github.com/zhenruiliao/tension.git tension
!pip install -e tensionThe runtime must be restarted for the package to become importableContributingBug reports, feature requests, and pull requests are welcome and encouraged! Use the Issues and Pull requests tabs to open new issues or pull requests. Always be kind and respectful.Licensetensionis provided under theMIT License. TensorFlow is provided under theApache 2.0 license.AcknowledgmentsBased on work by David Sussillo and Larry Abbotthttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2756108/With thanks to James Priestley for the package name.
Package: tensionflow
TensionflowA Tensorflow framework for working with audio data.FeaturesTODOUsageTODOInstallpip install tensionflowDocumentationSeehttps://tensionflow.readthedocs.org/en/latest/Developmentpip install poetrycd tensionflowpoetry installRunTo run cli entrypoint:poetry run tensionflow --helpTestspoetry run toxDockerTo run with dockerdocker build -t tensionflow .docker run tensionflow:latest tensionflow --help
Package: tensonent
TensoNentTensoNent is a python module containing collection of tensor decomposition methods.Website:https://github.com/TensoNentInstallationDependencies:Python >= 3.7Numpy >= 1.17.4PyTest >= 5.3.2PyLint >= 2.4.4If you already have working installations of dependencies, you can install tensonent usingpip:$ pip install tensonentTo update TensoNent to the latest version, add--upgradeflag to the command above.DevelopmentWe welcome new contributors of all experience level. If you want to contribute to TensoNent,
be sure to review thecontributing guideline.Important linksOfficial source code repository:https://github.com/TensoNent/tensonentDownload releases:https://pypi.org/project/tensonent/Issue tracker:https://github.com/TensoNent/tensonent/issuesSource codeYou can check the latest sources with the command below:$ git clone https://github.com/TensoNent/tensonent.gitAuthorsMilad Sadeghi.DM-Initial work-EverLookNeverSeeSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under theMIT License- see theLICENSEfile for details.CommunicationEmail address:TensoNent@Gmail.comTelegram group:https://t.me/TensoNent
Package: tensor-analysis
Tensor analysis package.This is a separate version of what_we_commited_to_sympy.Consists of:Arraypy class - N-dimentional arrays.TensorArray class - N-dimentional array with contravariant and covariant indicies.Classes declared in arraypy.py file.There is also modules:
1) tensor_methods.py       - Tensor algebra.tensor_fields.py        - operation in tensor fields like diff, rot, lie, etc…riemannian_geometry.py  - tensor operations about some riemannian geometryhelper_functions.py     - some help functions.