Saravanan666/pypi-org huggingface dataset:
Package: teams-logger
Python logging handler for Microsoft Teams webhook integration with both simple and dictionary configurations.Installationpipinstallteams-loggerExamplesSimple configurationimportloggingfromteams_loggerimportTeamsHandlerth=TeamsHandler(url='YOUR_WEB_HOOK_URL',level=logging.INFO)logging.basicConfig(handlers=[th])logging.warning('warn message')Simple configuration and non blocking handlerimportloggingfromteams_loggerimportTeamsQueueHandlerth=TeamsQueueHandler(url='YOUR_WEB_HOOK_URL',level=logging.INFO)logging.basicConfig(handlers=[th])logging.info("info message")Simple configuration and Card Formatterimportloggingfromteams_loggerimportTeamsHandler,Office365CardFormatterlogger=logging.getLogger(__name__)logger.setLevel(logging.DEBUG)th=TeamsHandler(url='YOUR_WEB_HOOK_URL',level=logging.INFO)th.setLevel(logging.DEBUG)logger.addHandler(th)cf=Office365CardFormatter(facts=["name","levelname","lineno"])th.setFormatter(cf)logger.debug('debug message')logger.info('info message')logger.warning('warning message')logger.error('error message')logger.critical('critical message')try:2/0exceptZeroDivisionErrorase:logger.error('Oops !',exc_info=True)Dictionary configuration and Card Formatterimportloggingimportlogging.configfromteams_loggerimportTeamsHandler,Office365CardFormatterurl='YOUR_WEB_HOOK_URL'logging_dict={'version':1,'disable_existing_loggers':False,'formatters':{'teamscard':{'()':Office365CardFormatter,'facts':["name","levelname","lineno"],},},'handlers':{'msteams':{'level':logging.INFO,'class':'teams_logger.TeamsHandler','url':url,'formatter':'teamscard',},},'loggers':{__name__:{'handlers':['msteams'],'level':logging.DEBUG,}},}logging.config.dictConfig(logging_dict)logger=logging.getLogger(__name__)logger.info('Info message')
Package: teams-report-converter
Teams Attendance Report ConverterThis tool is a simple script that converts basic attendance reports generated from MS Teams events into calculated attendance spreadsheets.InstalationYou can install this converter fromPyPI:pipinstallteams-report-converterHow to useThis converter can be used as a command line application as well as a package imported by your own python application.Using as command line application:convert-teams-report -f <path-to-original-csv> -s <datetime-event-start> -e <datetime-event-end> -tz <timezone-event> -o <path-to-resulting-spreadsheet>In this scenario the converter uses the following parameters:-f: path of attendance report generated by MS Teams for a live event (usually called "AttendeeReport.csv")-s: datetime of event start in the format "%Y-%m-%d %H:%M:%S" (you must use double quotes)-e: datetime of event end in the format "%Y-%m-%d %H:%M:%S" (you must use double quotes)-o: path of resulting spreadsheet-tz: timezone of event start and event end (for reference useTZ database name)Note that this is the unique scenario that outputs a ready to use resulting spreadsheet.Using as package imported by your python applicationYou can still use the converter as a package only for processing the original report e apply your own calculations. In this scenario, the Converter class outputs three dataframes that you can handle:fromteams_report_converterimportConverterconverter=Converter(report_content='AttendeeReport.csv',event_start='2021-11-03 15:00:00',event_end='2021-11-03 17:00:00',local_tz='America/Sao_Paulo')From this point, you can callconverter.datafor accessing the original data. Callingconverter.sessionsoutputs another dataframe listing all joins and lefts paired by sessions andconverter.attendanceoutputs the dataframe that contains the total of valid minutes accumulated by each participant.How the tool calculates attendanceThe below table shows how the timestamps from original data are processed in different scenarios:Join TimestampLeft TimestampTruncated JoinedTruncated LeftAttendance Calculationbefore event startno recordset to event startset to event end[event end] - [event start]before event startbefore event startset to left timestampleft timestamp[set to zero]before event startbetween event start and event endset to event startleft timestamp[left timestamp] - [event start]before event startafter event endset to event startset to event end[event end] - [event start]after event startno recordjoin timestampset to event end[event end] - [join timestamp]after event startbefore event endjoin timestampleft timestamp[left timestamp] - [join timestamp]after event startafter event endjoin timestampset to event end[event end] - [join timestamp]after event endno recordjoin timestampset to join timestamp[set to zero]after event endafter event endjoin timestampset to join timestamp[set to zero]
Package: teamsupport
Python TeamSupport Client=========================|Build Status| |Latest Version|Python library for interfacing with the TeamSupport XML API, using `demands <https://github.com/yola/demands>`__.Free software: MIT licenseFeatures---------  Client methods parse response content in order to return LXML Elementobjects-  Converting Python dictionaries into XML for POST and PUT calls-  Simple models for Tickets and Actions.Usage-----::from teamsupport import Ticket, initinit(<org_id>, <auth_key>[, default_ticket_type=<>, default_ticket_status=<>])# Get ticket with given ID/Number.ticket = Ticket(<ticket_number_or_ticket_id>)# Create new ticket.ticket = Ticket.create(contact_email, contact_first_name, contact_last_name,ticket_name, ticket_text)# Get Ticket description.descr = ticket.get_description()# Update ticket description.ticket.set_decription('New description')To run integration tests please set correct values in `teamsupport/config.py`and run: `nosetests integration_tests`.. |Build Status| image:: https://img.shields.io/travis/yola/teamsupport-python.svg?style=flat-square:target: https://travis-ci.org/yola/teamsupport-python.. |Latest Version| image:: https://img.shields.io/pypi/v/teamsupport.svg?style=flat-square:target: https://warehouse.python.org/project/teamsupportRunning Tests-------------Set correct data in teamsupport/config.py::nosetestsChangelog=========0.5.0 (2018-07-05)------------------- Switched to JSON API.- Set correct ticket status when creating a ticket- Added ability to modify default ticket type and status when callingteamsupport.init()0.4.1 (2018-02-08)------------------- Convert input data to str when composing XML.0.4.0 (2016-10-21)----------------- Switched to Demands == 4.0.00.3.0 (2015-12-01)------------------- Automatically convert DateTime fields to datetime type for Ticket model.0.2.2 (2015-11-27)------------------- Add Ticket.search() and Ticket.update() methods.0.2.1 (2015-11-27)------------------- Fix README.rst formatting.0.2.0 (2015-11-26)-------------------  Add `create` class method to the Ticket model; Add `Contact` model;-  Remove User model.-  Change models constructors to not require `client` param.0.1.3 (2015-09-13)-------------------  Fix bug in `update_ticket` method0.1.2 (2015-09-12)-------------------  Bump version to fix PyPI upload issue0.1.1 (2015-09-12)-------------------  Add `User` model and `TeamSupportService.get_user` method.0.1.0 (2015-09-10)-------------------  First release on PyPI.
Package: teamtalk-py
Teamtalk.pyteamtalk.py is a simple but powerful pythonic library for making bots for theTeamTalk5 Conferencing SystemInstallingPython 3.8 or higher is requiredFrom PyPIpipinstallteamtalk.pyFrom sourcegitclonehttps://github.com/JessicaTegner/teamtalk.pycdteamtalk.py
poetryinstallUsageimportteamtalkbot=teamtalk.TeamTalkBot()@bot.eventasyncdefon_ready():test_server=teamtalk.TeamTalkServerInfo("localhost",10335,10335,"user","pass")awaitbot.add_server(test_server)@bot.eventasyncdefon_message(message):ifmessage.content.lower()=="ping":message.reply("pong")bot.run()DocumentationYou can find the full documentationhereContributingSo you want to contribute to teamtalk.py? Great! There are many ways to contribute to this project, and all contributions are welcome.
If you have found a bug, have a feature request or want to help improve documentation pleaseopen an issue_
You can also donate to the projects maintainer (JessicaTegner) to help support the development of teamtalk.py:LicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: teamtime
Note: As of 10/13 I asked in the snapcraft forum to have the ownership of the snap changed to joesecurity . Thanks for checking roadmr.teamTimeteamTime is a tool to aid the problem of keeping track of time for a globally distributed team.You will need to put the name of your teammates in staff.csv using the format name, timezone, city. Take a look athttps://raw.githubusercontent.com/joemcmanus/teamTime/master/example.csvAlice,US/Eastern,New York New York
Bob,US/Central,Chicago Illinois
Charlie,Africa/Abidjan, Abidjan
Doug,America/Tijuana, Tijuana Mexico
Ed,America/Winnipeg, Winnipeg
Frank,Asia/Dubai,DubaiQuestions/Feedback/Feature Requests? Please let me know.InstallationSnapTo install teamTime as a snap, type:sudo snap install teamtimepipTo install teamTime with pip, type:pip install teamtimeTo install and run from sourceInstall the requirements:pip install -r requirements.txt
python -m teamtime.teamtimeNoteTo avoid typing the path to the CSV file you might want to make an alias:alias teamtime='teamtime --src=/home/foo/staff.csv'Usageteamtime --help
usage: teamtime [-h] [--name NAME] [--src SRC] [--map]

Time Table

optional arguments:
  -h, --help   show this help message and exit
  --name NAME  Optional name to search for
  --comp COMP  Compare times, use --name and --comp together
  --src SRC    Optional src file, defaults to staff.csv
  --map        Draw mapTo simply print a table of your team runteamtime+---------+------------------+
|  Person |    Local Time    |
+---------+------------------+
|  Alice  | 2019-09-25 12:16 |
|   Bob   | 2019-09-25 11:16 |
| Charlie | 2019-09-25 16:16 |
|   Doug  | 2019-09-25 09:17 |
|    Ed   | 2019-09-25 11:17 |
|  Frank  | 2019-09-25 20:17 |
|  now()  | 2019-09-25 10:16 |
+---------+------------------+To search for just Bob runteamtime --name=Bob+--------+------------------+
| Person |    Local Time    |
+--------+------------------+
| Bob    | 2019-10-02 15:37 |
+--------+------------------+To convert a local time to another time in a person's time zone use --comp. This helps when you are trying to figure out when to schedule a call.$ teamtime --name=andy --comp=15:00
+--------+------------------+---------------------+
| Person |    Their Time    |      Your Time      |
+--------+------------------+---------------------+
|  Andy  | 2019-10-18 07:30 | 2019-10-17 15:00:00 |
+--------+------------------+---------------------+To create a map runteamtime --map
Package: teamtrees
teamtreesGet the amount of trees planted by TeamTrees!How to install$pipinstallteamtreesUsageYou can either use the cliteamtrees:$teamtrees--recent>>recent_donations.csv
$teamtrees--top>>top_donations.csv
$teamtrees# Get current amount of trees plantedOr through Python:importteamtreesprint(teamtrees.get_trees_count())
Package: teamverify
TeamVerify : A Team Analysis tool for Competitive PokemonTeamVerify is a Competitive Pokemon teambuilding companion tool based on automated reasoning.InstallationBefore installing TeamVerify, you need to have Java installed. The reasoner underneath TeamVerify was written in Java, so the program will crash if it tries to run without Java.TeamVerify is listed on the PyPI repository. Therefore, it is an easy install for anyone already running Python. Ensure you have a working distribution of Python 3.6 or higher then run the following command in a terminal window:pip install teamverifyTeamVerify also needs to run a first-time setup script to populate its knowledge base. Once TeamVerify is installed simply run this command in a terminal window.teamverify-ftsWe also advise that you re-run this command whenever Smogon implements a major tier shift. TeamVerify's knowledge base only contains a subset of all Pokemon, so it may fall out-of-date as Pokemon shift into and out of OU.Usageteamverify {txt/pokepaste} {outputfile}Once installed, teamVerify can be invoked with the above command to the command line. TeamVerify works on one OU team at a time and produces console output as well as output to a text file. {txt/pokepaste} is a mandatory argument and should be the filename of a .txt file containing the team or a link to pokepast.es. {outputfile} is also a mandatory argument and should be the name of the output file you want created (output will have .txt appended to it, so don't include a file extension).
Package: teamwork
teamworkteamwork is a Python library designed to aid in the study of medical care team collaboration.Specifically, the library takes EHR data as input and does the following:identifies the members of medical care teams per patient visitbuilds a network graph for each care team based on their common patients in the previous 90 daysThe resulting care team network graphs can be used to compute the cumulative collaborative experience of care teams and other metrics.InstallationUse the package managerpipto install teamwork.pipinstall-ihttps://test.pypi.org/simple/teamworkUsageimportteamworkimportpandasaspd# read medical notes (3 columns) into pandas DataFramenotes_df=pd.read_csv("notes_file.csv",parse_dates=[2])# enforce column namesnotes_df.columns=['discharge_id','dr','date']# set a 90 day window to find collaboration among care teamsWINDOW=90# identify care teams within 2 day incrementsSTEP=2# the study runner is a generatorget_care_dates=teamwork.TeamworkStudyRunner(notes_df,WINDOW,STEP)# gather care teams into a listcare_team_list=[care_teamforcare_dateinget_care_datesforcare_teamincare_date]# do something with the care team network graphforcare_teamincare_team_list:do_something(care_team.G)A sample study can be foundhereContributorsContributorRoleHenry PhilofskyConcept, SMEGrant DeLongDeveloperLicenseMIT
Package: teanglann
DescriptionThis is a library designed to return a random word in Irish
fromhttp://www.teanglann.ieto help learns of Irish improve
their vocabulary.This tool can be used by learners as inspiration to learn a
new random word in Irish. Even learning just one word a day can
make a big difference!AboutThis script uses Python 3 with BeautifulSoup and requests to
get information from teanglann.ie and parse the data.
Package: teaplatfrom
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: teapots-teal
TeaL, short forTeacups Listener, is an event listener and dispatcher for
interactive applications requiring ping, push and callback events from
external sources.The website and documentation are accessible onteal.teapots.fr.The code and documentation contents is licensed underCeCILL2.1; seeLICENSE.txtfor the full license text as present oncecill.info.
All rights are reserved on the project’s logo as present in TeaL’s
documentation, repository and archives by Thomas Touhey.
Package: teapy
TeapyBlazingly fast datadict library in PythonTeapy is a high-performance data dictionary library implemented in Rust, designed for blazingly fast operations. It offers the following features:Lazy evaluationHandling of NaN valuesMulti-threaded processingSupport for any dimensionalitySetupInstall the latest teapy version with:pip install teapyBasic UsageCreating Expressions# Expressions can be created in various waysimportnumpyasnpimportpandasaspdimportpolarsasplimportteapyastpe1=tp.Expr([1,2,3])# Create from liste2=tp.Expr((1,2,3))# Create from tuplee3=tp.Expr(np.array([1,2,3]),'e3')# Create from numpy.ndarray, name is e3e4=tp.Expr(pd.Series([1,2,3]))# Create from pandas.Seriese5=tp.Expr(pl.Series([1,2,3]))# Create from polars.SeriesCreating DataDicts# DataDicts can be created in different waysdd1=tp.DataDict({'a':[1,2],'b':[2,3]},c=[3,4])# Create from dictionarydd2=tp.DataDict([tp.Expr([1,2],'a'),tp.Expr([2,3],'b')])# Create from list of expressionsdd3=tp.DataDict(a=[1,2],b=[2,3],c=np.array([3,6,2]))# Create by specifying key-value pairsEvaluating Expressions and DataDicts# Evaluating Expressionse=tp.Expr([1,2,3]).mean()e.eval()# Execute the expressione.view# View the memory of the arraye.eview()# Execute the expression and view the memory of the arraye.value()# Execute the expression and copy the memory of the array to a new numpy.ndarray# Evaluating DataDictsdd=tp.DataDict({'a':[1,2]*10,'b':[2,3]*10},c=[3,4])dd=dd.select([dd['a'].ts_mean(3).alias('d'),dd['b'].ts_std(4).alias('e')])dd.eval(['d','e'])# Evaluate specific keys in paralleldd.eval()# Or evaluate all expressions in parallelprint(dd['d'])
Package: tea-python
tea介绍tea：TEA算法由剑桥大学计算机实验室的David Wheeler和Roger Needham于1994年发明。它是一种分组密码算法，其明文密文块为64比特，密钥长度为128比特。TEA算法利用不断增加的Delta(黄金分割率)值作为变化，使得每轮的加密是不同，该加密算法的迭代次数可以改变，本算法的迭代次数为32轮。本软件包已上传到 pypi, 可通过 pip 进行安装：pip install tea-python示例代码：importteaif__name__=='__main__':key="1122334455667788".encode()origin=bytearray()foriinrange(16):origin.append(i)print('原数据:  ',origin.hex())encrypt_data=tea.encrypt(origin,key)print('加密数据:',encrypt_data.hex())decrypt_data=tea.decrypt(encrypt_data,key)print('解密数据:',decrypt_data.hex())
Package: teareduce
teareduceUtilities for astronomical data reductionInstalling the codeIn order to keep your Python installation clean, it is highly recommended to
first build a specific Python 3virtual enviromentCreating and activating the Python virtual environment$python3-mvenvvenv_teareduce
$.venv_teareduce/bin/activate(venv_teareduce)$Installing the packageThe latest stable version is available via dePyPI repository:(venv_teareduce)$pipinstallteareduceNote: This command can also be employed in a Windows terminal opened through theCMD.exe prompticon available in Anaconda Navigator.The latest development version is available throughGitHub:(venv_teareduce)$pipinstallgit+https://github.com/nicocardiel/teareduce.git@main#egg=teareduceTesting the installation(venv_teareduce)$pipshowteareduce(venv_teareduce)$ipython
In[1]:importteareduceastea
In[2]:print(tea.__version__)0.2.1
Package: tearex
TeaRex AI Python SDKThis package provides a Python SDK for thetearex.aiRecommendation Engine API.
If you don't have a tearex.ai account, you can request one here:tearex.ai.DocumentationFor more information about the API, please refer to thedocumentation.InstallationTo install the SDK, run the following command:pipinstalltearex-aiUsageImport the SDK:importtearexInitialize the SDKProvide the url of the API and your API KE as an environment variableTEAREX_URLandTEAREX_API_KEY, or pass them as an argument to theinitfunction:trx=tearex.Client(uri='YOUR_TEAREX_API_URL',apiKey='YOUR_TEAREX_API_KEY')Exampleimporttearextrx=tearex.Client()# Define the entities where Alice is a new user and Kale is an existing product.alice={"id":"user-1","label":"User","properties":{"name":"Alice"}}kale={"id":"product-1","label":"User","properties":{"name":"Kale"}}# Create Alice since she is a new usertrx.create_entity(alice)# Alice purchases Kale, so we create the "Purchased" eventtrx.create_event(alice,"Purchased",kale)# Recommend Alice the next "Product" to purchasetrx.recommend(alice,"Product")
Package: teas
teasis a package of utilities to analyze spectra from the James Webb Space Telescope. It provides tools to align and process JWST data products, extract 1D spectra from 3D integral-field-unit data cubes from NIRSpec and MIRI MRS, and create images of observed regions with NIRCam observations.Features:Extract 1D spectra from 3D IFU data from NIRSpec and MIRI MRSCreate images of spectrally imaged regions using NIRCam dataAlign and reproject the WCS of data cubes and imagesFind the field of view of NIRSpec or MIRI MRS data products within NIRCam imagesFit the continuum using either an anchors-and-splines fitting or  linear regressionStitch together spectraShift, scale, and normalize spectrum fluxInstallationIt is recommended to installteasin a new environment to avoid
version conflicts with other packages. To do this, run:condacreate--nameteas-envpython=3.9Then, activate the environment and install the package. The latest released version can be installed with pip:condaactivateteas-envpipinstallteas--upgrade
Package: teaspoon
DescriptionThe emerging field of topological signal processing brings methods from Topological Data Analysis (TDA) to create new tools for signal processing by incorporating aspects of shape.
This python package, teaspoon for tsp or topological signal processing, brings together available software for computing persistent homology, the main workhorse of TDA, with modules that expand the functionality of teaspoon as a state-of-the-art topological signal processing tool.
These modules include methods for incorporating tools from machine learning, complex networks, information, and parameter selection along with a dynamical systems library to streamline the creation and benchmarking of new methods.
All code is open source with up to date documentation, making the code easy to use, in particular for signal processing experts with limited experience in topological methods.Full documentation of this package is availablehere. The full documentation includes information about installation, module documentation with examples, contributing, the license, and citing teaspoon.The code is a compilation of work done byElizabeth MunchandFiras Khasawnehalong with their students and collaborators.  People who have contributed to teaspoon include:Audun MyersMelih YesilliSarah TymochkoDanielle BarnesSunia TanweerMax ChumleyIsmail GuzelWe gratefully acknowledge the support of the National Science Foundation, which has helped make this work possible.InstallationTo install this package, both boost and CMake must be installed as system dependencies.  For boost see forunixandwindows.  For mac, you can runbrew install boostif using homebrew as a package manager.  For CMake seehere.The teaspoon package is available through pip install with version details foundhere.
The package can be installed using the following pip installation:``pip install teaspoon``To install the most up-to-date version of the code, you can clone the repo and then run::pip install .from the main directory.  Note that the master branch will correspond to the version available in pypi, and the test_release branch may have new features.Please reference the requirements page in thedocumentationfor more details on other required installations.ContactsLiz Munch:muncheli@msu.edu.Firas Khasawneh:khasawn3@egr.msu.edu.
Package: teasysnmp
teasysnmpExtended easysnmp with helpers to work with snmp tablesteasysnmp TableThe teasysnmp extensions vastly depends on a custom teasysnmp data
structure that is referred to as the“teasysnmp table”in all
documentations and documentation strings as well as in the extended
function calls.To keep things simple and optimized, the datastructure is a simple
tuple with 2 elements - the snmp table entry oid - dictionary with
definition of fields- field_name: tuple of (field_index str, easysnmp typestr)An example is given below:ifTableEntry=(".1.3.6.1.2.1.2.2.1",{'Index':('1','INTEGER'),'Descr':('2','OCTETSTR'),'Type':('3','INTEGER'),'MTU':('4','INTEGER'),})The possible easysnmp typestr are:Easysnmp TypestrOBJECTIDOCTETSTRINTEGERINTEGER32UNSIGNED32NETADDRIPADDRCOUNTERGAUGETICKSOPAQUECOUNTER64NULLUINTEGERNOTIFBITSTRAPSpecial Thanks ToContributors ofeasysnmp