Saravanan666/pypi-org huggingface dataset:
Package: tentaclio-s3
tentaclio-s3A package containing all the dependencies for the s3 tentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)
Package: tentaclio-snowflake
tentaclio-snowflakeA package containing all the dependencies for the snowflake tentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)Configuring access to SnowflakeYour connection url should be in the following format:snowflake://<username>:<password>@<host>
Package: tentakel
AboutTentakel is a program for executing the same command on many hosts in parallel
using various remote methods. It can make use of several sets of hosts that are
defined in a configuration file as groups.It also supports an interactive mode that can be used for repeated commands.The author uses tentakel to simultaneously install software on many
workstations or ask and set parameters on a linux compute cluster.  With the
power of format strings tentakel can also be used for monitoring purposes.Supported remote methods are ssh(1) and rsh(1).  Both must be configured to
allow for password-less logins. Password-protected keyfiles for ssh can be
used with ssh-agent(1).A plugin mechanism allows users to implement their own remote methods in
addition to the builtin ones.For more information on available options please refer to the manpage
tentakel(1).The project homepage is:https://github.com/sfermigier/tentakelThe current maintainer is:Stefane Fermigiersf@fermigier.comThe original authors were:Sebastian Starkcran@users.sourceforge.netMarlon Berlinimaginat@users.sourceforge.netThis software contains the Toy Parser Generator (tpg.py)
written by Christophe Delord.
Package: tentalog
tentalogConfigure your logging with a YAML file. A simple and light weight logging configuration is provided by default.Getting StartedTo install tentalog in your environment you can simply usepip install tentalogLogging with TentacleThe Tentacle is the single unit of tentalog, managing a single logger. To start logging with tentacle you can do something like this:from tentalog import Tentacle

tentacle = Tentacle(name='mylogger')

logger = tentacle.logger
logger.info("Hello World")This code will use the tentalog default configuration, that contains the configuration of two logging handlers, one for the console and one for a time based file rotation of 15 days. If the "mylogger" logger is not found in configuration, the "root" logger will be used and a warning message will be showed. If the name of the logger is specified, the "root" logger will be used. If you want to use a custom configuration you can just initialize your Tentacle in this way:tentacle = Tentacle(path='myconf.yaml')VersioningWe useSemVerfor versioning.
Package: tentamaker
TentaMakerThis Python package provides a system for building exams (tentor) at Chalmers.Based on a pool of questions and solutions, the system generates exams both in
the form of PDF files for printing and PNG files for inclusion in online exam
systems such as Inspera or Canvas.DocumentationFirst, install the package by the following command:$ pip install tentamakerThen, create a directory where you want to create and store the generated exams
and enter the directory, for example:$ mkdir exams
$ cd examsThen, run the following command to create the initial structure for your exams:$ init-examThis will populate the current directory with a number of directories where the
output will be stored. This will also create a directory namedconfigcontaining the configuration of the exam.To create an exam, you first need to build a pool of questions. Edit the fileconfig/pool.texto build the pool of questions. Each question should be
entered in LaTeX in the following format% X.y.z
Formulation of question.
% Answer
Solution to question.
%See the fileconfig/pool.texfor examples of how to write questions and
solutions.Each question has a label in the formatX.y.zwhereXis a capital letter
(A,B,C, ...) denoting which part of the exam that the question belongs
to. The numberydenotes the number of the question within the partXand
the numberzdenotes a variant of the question. Each question can have many
variants.When generating exams, thelast variantof each question is selected, unless
the randomization option is set (see below).The next step is to edit the configuration fileconf/conf.tomlto set the title
of the course, the course code, name of examiner etc.Once this is done, you may proceed to generate the exam by running the following
command:$ make-exam yyyy-mm-yyHere,yyyy-mm-yyis the date of the exam. This will generate PDF files in the
directorypdfand snapshots of the questions in the directorypng.The commandmake-examsupports a number of optional command-line arguments
described below.Add--randomizeto randomize the selection of questions. The randomization
uses the seed specified inconf/conf.toml. If the seed is missing, the seed
will use a "non-deterministic" seed from the operating system.Add--verboseto see more detailed output. This is useful for example if the
process hangs at "Building PDF file..." to see what might have gone wrong when
running LaTeX (pdflatex).Add--no-snapshotsto skip generation of PNG snapshots. This is useful during
the creation of an exam (not creating PNG files until the exam is final).Authors (in order of appearance)Anders LoggLicenseThis project is licensed under theMIT license.Copyright is held by the individual authors as listed at the top of
each source file.
Package: tentapp
python-tent-client==================A command-line client for talking to [Tent](http://tent.io/) servers such as [tent.is](https://tent.is/).Current Status--------------We're in the "move fast and break things" phase right now.What works:* Discovery of the tent server's API root using link headers* Registering an app with the server and requesting permissions using OAuth* Auth keys are saved to a local config file for use next time* API methods (both public ones and auth-requiring ones)It should be possible to register a notification URL using this code, but since it's not necessarily running a webserver it won't be able to be notified of incoming posts.  In that case it can poll for new updates.What needs attention:* Some API methods have not been written yet.  Most of them should be quick to write by starting with copies of the methods that are already done.* Write tests* Error handling.  Should add a few exception types and also pay closer attention to errors from the `requests` module.* Documentation* Find elegant ways to deal with the JSON we get back from the Tent server.  Maybe add some classes representing posts, profiles, etc.* Package this up as a real Python module that can be installed in the usual wayDependencies------------* [requests](http://docs.python-requests.org/en/latest/#)Installation of dependencies:```# get requests using pippip install requests# you might already have an old version of requests that's missing the link headers feature.  If so:pip install --update requests```Quick Start-----------Run `example.py` and it will launch your browser, have the Tent server ask you to approve the app, and then post a hello world message.  The OAuth process is awkward when run from a command line, so read the prompt when it asks you to copy and paste things from your browser.  You only have to do this once; after that your auth details are saved in 'auth.cfg' and reused in future runs.`example_dashboard.py` will get your latest posts and followings' posts and print them to the shell.Here's a quick overview:```import tentapptentapp.debug = False  # Turn this on if you want to see verbose debugging info while# the app is running.  Defaults to False.# "entity" is the Tent version of a username.  It's a full URL.entityUrl = 'https://pythonclienttest.tent.is'app = tentapp.TentApp(entityUrl)# Get your profile info as a JSON-style Python dictionaryprint app.getProfile()# Send the user to their tent server to approve this app# and save the resulting keys in a local fileapp.authorizeFromCommandLine('keystore.js')# Post a new post.  For now you need to supply the JSON dictionary yourself.app.putPost(yourPostJsonHere)```You'll need to work directly with the JSON types described here: http://tent.io/docs/app-server
Package: tentativa-descricao
Shapiro-Francia normality testDescription:The statistical test of Shapiro-Francia considers the squared correlation between the ordered sample values and the (approximated) expected ordered quantiles from the standard normal distribution. The p-value is computed from the formula given by Royston (1993).This function performs the Shapiro-Francia test for the composite hypothesis of normality, according to Thode Jr. (2002).Installpip install sfranciaUsagefromsfranciaimportshapiroFranciaimportpandasaspddf=pd.read_csv('fake_table.csv')# any array, series or list of numeric valuesshapiroFrancia(df['column'])References:Royston, P. (1993). A pocket-calculator algorithm for the Shapiro-Francia test for non-normality: an application to medicine. Statistics in Medicine, 12, 181-184.Thode Jr., H. C. (2002). Testing for Normality. Marcel Dekker, New York.
Package: tenuki
tenukiA set of development tools for go/baduk/wéiqí projects.InstallationUsepipor your favorite package manager to installtenuki:pipinstalltenukiRankRankclass is a representation of a player's rank.fromtenuki.rankimportRankrank_1=Rank.parse("1d")rank_2=Rank.parse("1k")assertrank_1>rank_2assertrank_1.diff(rank_2)==1assertrank_1+8==Rank.parse("9d")EGFGoR <-> RankConvert between GoR and Rank:fromtenuki.rankimportRankfromtenuki.egfimportgor_to_rank,rank_to_gorassertgor_to_rank(2100)==Rank.parse("1d")assertrank_to_gor(Rank.parse("1d"))==2100
Package: tenv
===============================tenv - Typed Environment Variables===============================tenv - Typed Environment Variables* Free software: ISC licenseCredits---------This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
Package: tenvs
tenvs基于OpenAI Gym的程序化交易环境模拟器, 旨在为沪深A股基于增强学习的交易算法提供方便使用, 接近真实市场的交易环境基于tenvs的RL算法baselines repo:tbaseFeatures自动从tushare下载数据，已经下载的数据不会重复下载(默认目录"/tmp/tenvs")撮合规则:基于最高，最低价成交对交易量不作限制下单按照A股的规则，买卖按照1手100股为基本交易单位有拆分时，会根据复权因子对持仓进行相应的倍增, 以保持与真实市场一致step() 比OpenAI gym多返回一个名为rewards的list, 包含每支股票的reward, 以方便Multi-Agent算法实现安装指南支持: MacOS/Linux/Windows, python 3.5+, 推荐使用 python3.7安装方式一(直接使用):pip install tenvs安装方式二(开发者模式)git clone https://github.com/tradingAI/tenvs
cd tenvs
pip install -e .使用设置 tushare token(Tushare token注册申请):export TUSHARE_TOKEN=YOUR_TOKENExamples场景实现actionobservationreward使用例子单支股票, 全仓操作, 每日先卖再买simple.py[scaled_sell_price, scaled_buy_price市场信息+部分账户信息可参数选择simple_test.py多支股票平均分仓, 每日先卖再买average.py[scaled_sell_price, scaled_buy_price] * n市场信息+部分账户信息可参数选择average_test.py多支股票, 支持仓位控制, 每日先卖再买multi_vol.py[scaled_sell_price, scaled_sell_volume, scaled_buy_price, scaled_buy_volume] * n市场信息+部分账户信息可参数选择multi_vol_test.py场景:单支股票, 全仓操作多支股票, 均匀分仓操作多支股票，支持仓位控制reward functions:simple: 盈利=1,否则=-1daily_return: 每日的收益率daily_return_add_count_rate: 收益率 + 成交统计信息daily_return_add_price_bound: 收益率 - 最高最低价与买卖价差MSEdaily_return_with_chl_penalty: 收益率 - [close,high,low]与买卖价格相应惩罚ContributionFork this repoAdd or change code &&Please add tests for changesTeststep1. 设置docker-compose需要的环境变量: BAZEL_USER_ROOT, OUTPUT_DIR, TUSHARE_TOKEN默认使用docker hub 镜像阿里云镜像:registry.cn-hangzhou.aliyuncs.com/tradingai/tenvs:lateststep2.docker-compose upSend pull request扩展Scenario可以参考average.py的写法定义action定义observation定义rewardTODO List(欢迎一起完善)测试: 提升 unit test 覆盖率(coveralls.io)场景增加增加的reward函数(Blog post在不同的算法上的表现性能比较)增加observation中信息(因子挖掘)线上交流方式QQ群: 477860214
Package: tenyksservice
# Tenyks ServiceThis is a python abstraction for creating a
[Tenyks](https://github.com/kyleterry/tenyks) service. This is supposed to
bootstrap all the necessary coroutines and connections needed to communicate
with the IRC bot.There will be two branches that follow Tenyks. Version 1.x will follow the version
1.x of Tenyks, which currently uses Redis for Pub/Sub. Version 2.x will follow
version 2.x of Tenyks, which is intended to use ZeroMQ as the Pub/Sub
mechanism.The Primary Tenyks README has more information on how to create a service.## InstallPypi:pip install tenyksserviceGit:git clone https://github.com/kyleterry/tenyks-service; cd tenyks-service;
./setup.py install### For ContributingGit:git clone https://github.com/kyleterry/tenyks-service; cd tenyks-service;
./setup.py develop## Generating settingstenyks-service-mkconfig <servicename> > <servicename>_settings.pyThis will also generate a UUID for your service. [Please don’t lose
this](http://d315f714-26fh-4346-96a8-11a864cdfcb5.com/) or you will have to
keep purging all the old services registered with Tenyks.Then, I would setSERVICE_DESCRIPTIONto something.## UsageSee [examples](./examples/hello-service).See the [Tenyks
README](https://github.com/kyleterry/tenyks#lets-make-a-service) section on
making a service.See [tenyks-contrib](https://github.com/kyleterry/tenyks-contrib). A repo full
of services.
Package: tenzir
Tenzir PythonThe Python package of Tenzir provides a flexible control plane to integrate Tenzir
with other security tools.NoteThe Python effort is still highly experimental and subject to rapid change.
Please do not consider it for production use.UsageTo get started, clone the Tenzir repository and install the Python package viaPoetry:gitclonehttps://github.com/tenzir/tenzir.gitcdtenzir/python
poetryinstall-EmoduleDevelopmentWe recommend that you work with an editable installation, which is the default
forpoetry install.Unit TestsRun the unit tests via pytest:poetryrunpytestIntegration TestsRun the integrations tests via Docker Compose and pytest:./docker-poetry-run.shpytest-vPackagingThe following instructions concern maintainers who want to publish the Python
package to PyPI.NoteOur releasing scripts and CI run these steps automatically. You do not need to
intervene anywhere. The instructions below merely document the steps taken.Bump the versionPrior to releasing a new version, bump the version, e.g.:poetryversion2.3.1This updates thepyproject.tomlfile.Publish to Test PyPIAdd a Test PyPi repository:poetryconfigrepositories.test-pypihttps://test.pypi.org/legacy/Get the token fromhttps://test.pypi.org/manage/account/token/.Store the token:poetryconfigpypi-token.test-pypipypi-XXXXXXXXPublish:poetrypublish--build-rtest-pypiPublish to PyPIGet the token fromhttps://pypi.org/manage/account/token/.Store the token:poetryconfigpypi-token.pypipypi-XXXXXXXXPublishpoetrypublish--build
Package: teobresums
TEOBResumS: Effective-one-body model for compact binary waveformsPre-requisitesC compiler (e.g. GCC)GSL libraryUsageTo start, checkout theWiki.Python extension$ python
>>> import EOBRun_moduleExample scripts for using the python module can be found inPython/Examples/here.ReferencesPlease, consult the complete list ofreferences on the Wikiand cite papers according to what you use.DevelopersPlease contact Sebastiano Bernuzzi and Alessandro Nagar if you wish to be included in the list of developers.A short description of the code can be found inhere. Please, take a moment to understand the data structures and how the parameters are managed.Seeherefor a description of the workflow of this repo.If you want to start a new branch or development make sure toTalk to usRun the benchmarkbeforeapplying any changeRun the benchmarkafteryou applied your changes and before pushing them; compare and make sure you did not break anything.ContactYou can get in contact with TEOBResumS developers and fellow users through our mailing list. To subscribe please click on the badge below.
Package: teocomp
TeoCompTeoComp is a library for teaching Automata Theory, Languages and Theory of Computing.
TeoComp providesJupyter Notebooks(in Portuguese) for Theory of Computing content that can be run through the Google Colab cloud infrastructure.LicenseTeoComp is available byMIT License.Requirements:You must install:graphvizpandasipywidgetsxmltodictInstallTo install TeoComp from Github, run the following command:pipinstallgit+https://github.com/daviromero/teocomp.gitTo install TeoComp from PyPi repository, run the following command:pipinstallteocomp
Package: teo-files
What is this ? 🧐A library for make many operations for files Move Delete Name Filter ETCInstalationpip install teo-filespy -m pip install teo-filesContribute1.- Make a fork2.- Use the dev branch3.- Write the changelog4.- Make the pull request with the description5.- Accepted the pull request and add New ReleaseCreditsMade with :heart: in Ecuador by TeoDev1611
Package: teomim
Author:Paraknowledge Corp <research.paraknowledge.ai>Description:Digital twin for generating medical historiesDocumentation:https://zeroknowledgediscovery.github.io/teomim/Example:https://github.com/zeroknowledgediscovery/teomim/blob/main/examples/example.ipynbUsage:from teomim import teomim
P=teomim(outfile='out100.csv',num_patients=500)
P.set_modelpath('adrd')
P.generate()
P.evaluate()
P.quality()
Package: tepe
异物检测AD-TEPEA concise and unified toolkit forAnomalyDetection'sTraining,Evalutation,Prediction andExport (AD-TEPE).The supported algorithms are as follows,anomaly detection:CFARD环境安装可以直接运行下列命令，本地conda环境配置和服务器docker使用详见：环境安装## step1 克隆仓库到ad-tepe文件夹下gitclone-banomaly_detectiongit@10.106.249.57:SD/tepe.gitad-tepe## step2 转到ad-tepe目录下cdad-tepe## step3 安装相关依赖pipinstall-rrequirements.txt## step4 将tepe依赖安装到环境中pipinstall-v-e.# or "python setup.py develop"异物检测数据集格式参考公开数据集MVTec格式data_root
├── scene1
│   ├── train
│   │   └── good
│   │       ├── 000000000001.jpg
│   │       ├── 000000000002.jpg
│   │       ...
│   │       └── 000000001111.jpg
│   └── test
│       ├── good
│       │   ├── 000000002222.jpg
│       │   ├── 000000002223.jpg
│       │   ...
│       │   └── 000000004444.jpg
│       ├── bad1
│       │   ├── 000000003333.jpg
│       │   ├── 000000003334.jpg
│       │   ...
│       │   └── 000000005555.jpg
│       ├── bad2
│       ...
├── scene2
│   ├── train
│   │   └── good
│   │       │
│   │       ...
│   └── test
│       ├── good
│       │   │
│       │   ...
│       ├── bad1
│       │   │
│       │   ...
│       ├── bad2
│       ...
└── scene3
    │
    ...快速使用1. 创建配置文件参考configs/anomaly_detection目录下的配置文件，复制/创建一个自己需要的config.py文件，它既可以当作配置文件，也可以当作程序运行的入口。它里面包含一个任务配置类，这个类需要继承一个默认的配置类，默认配置类在tepe/tasks/*/task.py中实现。以cfa检测算法为例：## config.pyfromtepe.tasks.cfaimportCFAConfigclassTaskConfig(CFAConfig):def__init__(self):super(TaskConfig,self).__init__()self.task_name='cfa_eguo'# 任务名称，输出文件夹以这个命名self.data_root='/home/zepei/DATA/yiwushuju/image'# 数据集路径self.scene='eguo'# 数据集中场景名称self.max_epoch=30# 最大迭代轮数self.input_size=[320,512]# 输入分辨率self.batch_size=4self.keep_ratio=False2. 调用配置文件有两种调用方式：方法一：使用命令行# 训练 (train)tepetrain-t{config.py}# 验证 (evaluate)tepeeval-t{config.py}-w{trained_weights}# 预测 (predict)tepepredict-t{config.py}-s{img|fold|video}-w{trained_weights}# 导出 (export)tepeexport-t{config.py}-w{trained_weights}方法二：直接运行config.py只需要在config.py中实现main入口，去实例化你的任务类，## config.pyfromtepe.tasks.cfaimportCFAConfigclassTaskConfig(CFAConfig):...# 运行此py文件入口if__name__=='__main__':task=TaskConfig()# 训练task.train()# 评估task.eval()# 导出task.export()# 预测task.weights=""# 模型文件source=""# 图片/文件/视频/等view_img=True# 是否可视化结果save_img=True# 是否保存预测结果task.predict(source=source,view_img=view_img,save_img=save_img)更新日志：[2022/09/22]新的功能: 可以通过http请求来进行存图片,训练,导出,预测等操作[2022/09/14]实现了异常检测(anomaly detection)知识蒸馏算法rd的训练train和导出export
Package: tepet
tepetTEmporalPErformanceTool25-cent term for a 5-cent concept!What is thistepet is a small utility to help you understand and track execution time of your code. You could write it anytime, but why do this every other day?UsageCan be used as a context managerfromtepetimportPerfTimerwithPerfTimer():print('doing stuff...')# Output:## > 2020 May 31 20:11:49 +0000 ==== started# > doing stuff...# > 2020 May 31 20:11:49 +0000 ==== elapsed 0.00000 secondsand a function decoratorfromtepetimportPerfTimer@PerfTimer()defwork():print("working...")work()# Output:## > 2020 May 31 20:14:25 +0000 ==== started# > working...# > 2020 May 31 20:14:25 +0000 ==== elapsed 0.00001 secondsAlso, can be used in production environments for naive tracking:fromtepetimportTimerdefprinter(seconds):print(f'code block 1:{seconds:.5f}elapsed')withTimer(printer):...# Output:# > code block 1: 0.00000 elapsed
Package: tepezza
TepezzaScraperMotivation/use caseGet a list of doctors and their metadata fromwww.tepezza.com/ted-specialist-finder, which is unfortunately ReCaptcha-protected.Mechanism of actionA user enters zip codes and the program uses tshark to monitor the JSON sent back.RequirementsAll third-party python packages that pip requires for the package downloadtsharkPython 3.8+ConfigurationSeedefault_config.inifor the default; otherwise pass custom args to the constructor ofTepezzaApi. Available fields are inTepezzaApi._SEARCH_FOR_KEYS.
Package: tephi
Tephigram plotting in PythonCIHealthMetaPackageRepoWelcome to Tephi!InstallationcondaTephi is available onconda-forge, and can be easily installed withconda:condainstall-cconda-forgetephior alternatively withmamba:mambainstall-cconda-forgetephiFor more information see thefeedstock.pipTephi is available onPyPI:pipinstalltephiCheck out ourPyPI Download Stats!DevelopersIf you simply can't wait for the next release to play with the latest hot features, then you can easily
install themaindevelopment branch from GitHub:pipinstallgit+https://github.com/SciTools/tephi@mainAlternatively, to configure a full developer environment, first clone the GeoVista GitHub repository:gitclonegit@github.com:SciTools/tephi.gitChange to the root directory:cdtephiCreate thetephi-devconda development environment:mambaenvcreate--filerequirements/dev.ymlNow activate the environment and install themaindevelopment branch of tephi:condaactivatetephi-dev
pipinstall--no-deps--editable....and you're all set up!DocumentationThedocumentationis built bySphinxand hosted onRead the Docs.SupportNeed help? 😢Why not check out ourexisting GitHub issues. See something similar?
Well, give it a thumbs up to raise its priority, and feel free to chip in on the conversation. Otherwise, don't hesitate to
create anew GitHub issueinstead.However, if you'd rather have a natter, then head on over to ourGitHub Discussions.LicenseTephi is distributed under the terms of theBSD-3-Clauselicense.#ShowYourStripesGraphics and Lead Scientist:Ed Hawkins, National Centre for Atmospheric Science, University of Reading.Data: Berkeley Earth, NOAA, UK Met Office, MeteoSwiss, DWD, SMHI, UoR, Meteo France & ZAMG.#ShowYourStripesis distributed under aCreative Commons Attribution 4.0 International License