Saravanan666/pypi-org huggingface dataset:
Package: teacher
TeacherThis module is just a POC on how we could help newcomers better understand
some exceptions.This is just a POC, a full implementation is done by André Roberge athttps://github.com/aroberge/friendly-traceback.DemoImportingteacherin an REPL or at the top of a file enable it, like:importteachera="Foo"b="Bar"print(a*"bar")Gives:$pythontest.py
test.py:6:TypeError:can't multiply sequence by non-int of type 'str'You tried to multiply a by 'bar'whichisnotallowed,thesequenceacanonlybemultipliedbyaninteger.
Package: teacher-xai
TEACHER: Trustable Extensible Aggregator of CompreHensive ExplainersTEACHER is an open source Python Library that incorporates several state-of-the-art
explainability techniques that can be used for model interpretation and explanation.
The objective of the library is to be extensible with new tools and algorithms while
keeping compatibility with the most used machine learning models such as scikit-learn.This project was started in 2020 as the Ph.D. Thesis of Guillermo Tomás Fernández Martín,
whose advisors are José Antonio Gámez Martín and José Miguel Puerta Callejón.Website:https://xai-teacher.readthedocs.io/en/latest/InstallationDependenciesTeacher requires:* Python (>=3.9)
* scikit-learn 
* scikit-fuzzy
* matplotlib (for plotting functions)
* deap (for compatibility with the LORE algorithm)
* imblearn (for compatibility with the LORE algorithm)IMPORTANTInstall scikit-fuzzy from theirGitHubas the PyPi version
is obsolete:pipinstallgit+https://github.com/scikit-fuzzy/scikit-fuzzyUser installationIf you already have a working installation, you can install teacher withpipinstall-Uteacher-xaiThe documentation includes moredetailed instructions.UsageFor detailed instructions on how to use teacher, please refer to theAPI ReferenceSupported MethodsThe following list summarizes the models and explainers currently supportedFuzzy Factuals and Counterfactuals: Explainer obtained from a fuzzy tree that can be used for global or local explanationsLORE: Local explainer generated from a neighborhoodFLARE: Fuzzy local explainer generated from a neighborhoodMetricsThe following list summarizes the metrics and scores that can be extracted from the explainersGeneral metricsCoverage: How many instances are covered by the rules forming the explanationPrecision: How many of the instances covered by the rules forming the explanation are properly classifiedLocal neighborhood metricsFidelity: How good is the local explainer at mimicking the global classifier in the neighborhoodL-fidelity (Rule fidelity): How good is the local explainer at mimicking the global classifier in the instances of the neighborhood covered by the factual explanationCl-fidelity: How good is the local explainer at mimicking the global classifier in the instances of the neighborhood covered by the counterfactual explanation (To be implemented)Hit: Does the local explainer match the global classifier result? (To be implemented)C-hit: Does the local explainer match the global classifier result for tan instance built from the counterfactual rule? (To be implemented)References and ExamplesFuzzy Factuals and counterfactuals(Fernandez et al., 2022)Documentationhttps://xai-teacher.readthedocs.io/en/latest/Experiments:https://github.com/Kaysera/teacher-experimentsLORE (Guidotti et al., 2018)Documentation and examples:https://doi.org/10.1109/MIS.2019.2957223FLARE (Fernandez et al., 2023 preprint)
Package: teaching-toolshed
Teaching ToolshedA library of useful classes for writing scripts related to managing classes. Has helper classes for talking to various teaching APIs and for compiling gradebooks in Python.SeeTeaching Toolshed Examplesto see example scripts of how these libraries are used in my classes.Any questions or comments can be handled here on GitHub or you can email me athschafer@cs.washington.edu.Publish New VersionSeehere
Package: teachmedijkstra
teachmedijkstraThis package contains an implementation of the Dijkstra's algorithm for finding
the shortest paths in a graph and serves to educational purposes.Its main goal is to produce a LaTeX document which contains:description of the performing of the Dijkstra's algorithm in a for of a
table where each row corresponds to a vertex of the graph and each column
corresponds to one time step of the algorithm,shortest path tree (or shortest path covering) which is a subgraph of the
processed graph that contains those edges that belong the shortest paths.The main motivation to write this program was to have a tool to automatically
generate examen tests on Dijkstra's algorithm with randomly created graphs and
with the corresponding solutions which consist of two items described in the
list above.InstallationInstall the package fromPyPIutilizing thepipmodule:python -m pip install teachmedijkstraExampleWhat follows is a simple program using the packageteachmedijkstra.
It defines an undirected weighted graph with six vertices, performs the
Dijkstra's algorithm to find the shortest paths starting from the vertex "a",
and exports the result to a LaTeX file "example.tex".importteachmedijkstragraph=teachmedijkstra.UndirectedGraph()graph.addVertex("a",(0,2))graph.addVertex("b",(1,2))graph.addVertex("c",(2,2))graph.addVertex("d",(0,1))graph.addVertex("e",(1,1))graph.addVertex("f",(2,1))graph.addEdge("a","b",7)graph.addEdge("b","c",8)graph.addEdge("d","e",6)graph.addEdge("e","f",1)graph.addEdge("a","d",5)graph.addEdge("b","e",2)graph.addEdge("c","f",4)graph.addEdge("a","e",3)graph.addEdge("b","f",9)dijkstra=teachmedijkstra.Dijkstra(graph,"a")dijkstra.run()dijkstra.saveToLaTeXFile("example.tex")More ExamplesFor more examples see the following programs:example_undirected.py... example of performing the Dijkstra's algorithm on an undirected
weighted graph with 9 vertices,example_directed.py... example of performing the Dijkstra's algorithm on a directed weighted
graph with 9 vertices,example_random.py... example of creating a number of exercises on Dijkstra's algorithm with
randomly generated graphs.DocumentationThe comments in the code of the program are written with respect to produce a
nicely written documentation when usingpdoc3(see also thePyPI page of pdoc3).
In order to create the HTML document with the program documentation, install
firstpdoc3fromPyPIutilizing thepipmodule:python -m pip install pdoc3Once installed, run this command from the root directory of the project:pdoc3 --html teachmedijkstraA directory html/ that contains the documentation will be created.
Package: teachmeqmc
teachmeqmcThis package contains an implementation of the Quine-McCluskey algorithm and
serves to educational purposes.Its main goal is to produce a LaTeX document with detailed description of the
performing of the Quine-McCluskey algorithm for a given input; the input is
represented by a complete Disjunctive Normal Form (shortly DNF) which may
contain duplicate minterms.The motivation to write this program was to provide university students with
automatically generated examples for better understanding the Quine-McCluskey
algorithm and, furthermore, to randomly generate examen tests on this subject
together with their detailed solutions to make the corrections of the tests
easier.InstallationInstall the package fromPyPIutilizing thepipmodule:python -m pip install teachmeqmcExampleA simple program using this package may look like this:fromteachmeqmcimportBooleanFunctionf=BooleanFunction(4)f.addTerm((0,0,0,0))f.addTerm((0,0,0,1))f.addTerm((0,0,1,0))f.addTerm((0,0,1,1))f.addTerm((0,1,0,0))f.addTerm((1,1,0,0))f.addTerm((1,1,0,1))f.addTerm((1,0,0,1))f.addTerm((1,0,0,1))f.performQuineMcCluskey()f.show()f.saveToTextFile("output.txt")f.saveToLaTeXFile("output.tex")The program first imports the classBooleanFunction.
Then it creates a new instancefof the classBooleanFunctionwhich represents a Boolean function (a mapping from {0,1}^n to {0,1}) defined
by its complete DNF.
The parameter4gives the number of the inputs of the Boolean function.Further, by calling the methodaddTermit is defined by which minterms the
complete DNF of the Boolean function is given; for examplef.addTerm((0, 0, 1, 0))states that the minterma'b'cd'shall be a part of
the defining complete DNF.
Observe that the last two minterms are identical.
However, both will be part of the defining complete DNF and in the output
report it will be visualised that one of these terms has to be removed.Then the Quine-McCluskey algorithm is performed; this method does not
return anything, instead, it stores the result of the procedure to the
attributes ofBooleanFunction.Finally, callingshowprints the report on the processing of the Quine-McCluskey
algorithm to the terminal output, callingsaveToTextFilesaves it to a plain text file,
and callingsaveToLaTeXFilesaves it to a LaTeX file which can be further
compiled to a PDF file e.g. bypdflatex.Remark that also single parts of the report may be obtained.
For such an output, see the methods of the classBooleanFunctionin the formexport...ToLaTeXandexport...ToText.
Package: tea-cli
Tea - Powerful control versionA version controlwritten in Pythondesigned to be simple and complete, with many features. AForkofPie. With it, you can:Create repositories;Tracking files;Ignore files with.ignorefile;Get status of files;Commit of tracked files;Get all commits log;Merge all changes to the committed files;Get the changed lines between files;Creating hooks to execute scripts.To learn how to useTea, see thefull documentation here. InstallTeausing the pip package manager:pip install tea-cliLicenseThis project uses theGNU GPL v3.0license,see the license fileformore information.
Package: teacup
HTML Templating DSL for Python.==============================A simple HTML templating library that uses a DSLmini-language. I tried to design it to be similar in feelto Teacup/CoffeeKup (https://github.com/goodeggs/teacup). Ialso took inspiration from jamescasbon's template.py script(https://gist.github.com/jamescasbon/1461441).Also includes the md() function for embedding markdown, and an optionfor outputting prettified HTML.Example usage::from teacup import *with html:with head:passwith body:with p:md("This is a **test!**")with p({"class":"test"}, id="testme"):text("Another test")a("Click here", href="https://google.com")print(render())The python "with" keyword is used to nest HTML tags.
Package: teacup-visualization-back
User GuideTesting Framework forCommunicationProtocols and Web Services with visualization web back-end in PythonWhatVisualization web back-end written in Python.WhyThis project is needed so that the web front-end can interact with the server side and other Teacup projects.HowFollow the steps below:pip install teacup-visualization-backThe flask commandConfigureRun the applicationFor developers:Run the application
Package: teacv
===
teacv
===teacv is a tool for extend Python OpenCV modules with custom filters written in C++.Free software: MIT licenseDocumentation:https://teacv.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-02-12)First release on PyPI.
Package: teadropbugmaker
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: tea-encrypt
tea_encryptencrypt & decrypt string(bytes) with interleaving and padding random numbers TEAInstall$pip3installtea_encryptUsagefromtea_encryptimportEncryptStr,DecryptStrk=b"de1182b0f4203cad8d2ec629e35403d7"v=b"this is a test"encrypt_bytes=EncryptStr(v,k)original_bytes=DecryptStr(encrypt_bytes,k)
Package: teagen
Teagen (Test Equipment Automation Generator)TheTeagenpython package does the following:bundles individual yaml files into a single filepost process x- extensionsvalidates the bundled openapi.yaml filegenerates an enhanced ux python package using the bundled openapi.yaml fileGetting startedInstall the packagepip install teagenGenerate a python package from OpenAPI modelsimportteagentgen=teagen.Teagen(api_files=['./tests/api/api.yaml'])SpecificationsThis repository is based on theOpenAPI specificationwhich is a standard, language-agnostic interface to RESTful APIs.Modeling guide specific to this package
Package: tealang
tea-lang[WIP] Tea: A High-level Language and Runtime System for Automating Statistical AnalysesWhat is Tea?Tea is a domain specific programming language that automates statistical test
selection and execution. Tea is currently written in/for Python.Tea has anacademic research paper.Users provide 5 pieces of information:thedatasetof interest,thevariablesin the dataset they want to analyze,thestudy design(e.g., independent, dependent variables),theassumptionsthey make about the data based on domain knowledge(e.g.,
a variable is normally distributed), andahypothesis.Tea then "compiles" these into logical constraints to select valid
statistical tests. Tests are considered valid if and only ifallthe
assumptions they make about the data (e.g., normal distribution, equal
variance between groups, etc.) hold. Tea then finally executes the valid tests.What kinds of statistical analyses are possible with Tea?Tea currently provides a module to conduct Null Hypothesis Significance
Testing (NHST).We are actively working on expanding the kinds of analyses Tea can support. Some ideas we have: linear modeling and Bayesian inference.How can I use Tea?Tea is available on pip!pip install tealangSee community exampleshere. If you have trouble using Tea with your use case, feel free to open an issue, and we'll try to help.Step througha more guided, thorough documentation and a worked example.How can I cite Tea?For now, please cite:article{JunEtAl2019:Tea,
  title={Tea: A High-level Language and Runtime System for Automating Statistical Analysis},
  author={Jun, Eunice and Daum, Maureen and Roesch, Jared and Chasins, Sarah E. and Berger, Emery D. and Just, Rene and Reinecke, Katharina},
  journal={Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST)},
  year={2019}
}How reliable is Tea?Tea is currently a research prototype. Our constraint solver is based on
statistical texts (seeour paperfor more info).If you find any bugs, please let us know (email Eunice at emjun [at] cs.washington.edu)!I want to collaborate! Where do I begin?This is great! We're excited to have new collaborators. :)To contributecode, please seedocs and
gudielinesand open an issue or pull request.If you want to use Tea for a
project, talk about Tea's design, or anything else, please get in touch: emjun [at] cs.washington.edu!Where can I learn more about Tea?Please find more information atour website.I have ideas. I want to chat.Please reach out! We are nice :) Email Eunice at emjun [at] cs.washington.edu!FAQsBy the way, why Python?Python is a common language for data science. We hope Tea can easily integrate
into user workflows.What format should my data be in?Tea accepts data either as a CSV or a Pandas DataFrame. Tea asumes data is in"long format."
Package: tealeaf
TealeafA simple interface for readable Python HTTP clientsNo dependenciesSimple and readableLightweightA simple exampleimporttealeaf# simple POSTapi=tealeaf.Api('https://www.postman-echo.com')api.post('/post',{"message":"hello tealeaf"}).json()# Using bearer tokensapi=tealeaf.Api('https://www.postman-echo.com',credentials=tealeaf.BearerToken('my-jwt'))api.post('/post',{"message":"hello authorized tealeaf"}).json()Integrates easily withpydanticfor data validationimporttealeaffrompydanticimportBaseModelclassMessage(BaseModel):message:strclassEchoResponse(BaseModel):data:Messageurl:str# simple POSTapi=tealeaf.Api('https://www.postman-echo.com')echo=api\.post('/post',{"message":"hello tealeaf"})\.astype(EchoResponse)print(repr(echo))>>> EchoResponse(data=Message(message='hello tealeaf'), url='https://www.postman-echo.com/post')An example with custom auth algorithm:# define a custom auth handler with a `preprocess_request` methodclassCustomCredentialHandler(tealeaf.ApiCredential):def__init__(self,secret:str):super().__init__()self.__secret=secretdefpreprocess_request(self,request:tealeaf.Request):# your algorithm goes here and modifies the request objectrequest.headers['secret-key']=f'{request.data}{self.__secret}'returnrequestapi=tealeaf.Api('https://www.postman-echo.com',credentials=CustomCredentialHandler('my-super-secret'))api.post('/post',{"message":"hello custom tealeaf auth"}).json()
Package: tealinspector
tealinspectorAs a developer error like below is not descriptive,AlgodHTTPError:TransactionPool.Remember:transactionT4EVC7YANLFGBO5KJRIDEAYZBN3KKJC2B7VFRY4ZFMTSZOPTHBTQ:logicevalerror:assertfailedpc=1328.Details:pc=1328,opcodes===&&assertThis package gets the lines from the "pc".Install the dependencypipinstalltealinspectorUse in CLItealinspector--networkmainnet--application_id942781578--program_counter1328Additionally,--networkis optional. The default value ismainnet.--line_countparameter can be passed. The default value is25.OutputLine:594569&&570gtxn4Fee571globalMinTxnFee572intc10//9573*574==575&&576gtxn4NumAppArgs577intc_0//1578==579&&580gtxna4Assets0581bytec_2//"global_list_asset"582app_global_get583==584&&585gtxna4Accounts1586bytec_1//"global_list_owner"587app_global_get588==589&&590gtxna4Accounts2591bytec8//addrKQMEN76UOQEHGXPBXUMRGW3KFI7Z57IFXBXWO77HAXYKMISCZF5CAOOITI592==593&&594assert<----------AboutDeveloped byHipo.
Licensed under MIT.
Package: tealprint
tealprintPrints messages to the consoleFeaturesdifferent verbosity levels:none, error, warning, info, verbose, debugIndent messages easily under a headerSet color using thecoloredpackageExamplesIndentationfromtealprintimportTealConfig,TealLevel,TealPrint# Using push_indentation()TealConfig.level=TealLevel.verboseTealPrint.info("Header")TealPrint.push_indent(TealLevel.info)TealPrint.info("More information")TealPrint.pop_indent()# Push in header directlyTealPrint.info("Header 2",push_indent=True)TealPrint.info("Information",push_indent=True)TealPrint.verbose("Fine grained information")TealPrint.pop_indent()TealPrint.info("✅ Done")# Skips hidden levelsTealPrint.info("Header 3",push_indent=True)TealPrint.info("Saving all files...")TealPrint.push_indent(TealLevel.debug)TealPrint.debug("Saving file X")# Not displayedTealPrint.info("Couldn't save file Y")# Only indented once, since push_indent(TealLevel.debug) was usedTealPrint.pop_indent()TealPrint.info("✅ Saved all files")# Same as above but with TealLevel.debugTealConfig.level=TealLevel.debugTealPrint.info("Header 4",push_indent=True)TealPrint.info("Saving all files...")TealPrint.push_indent(TealLevel.debug)TealPrint.debug("Saving file X")TealPrint.info("Couldn't save file Y")# Will be indented twice this timeTealPrint.pop_indent()TealPrint.info("✅ Saved all files")OUTPUTHeaderMore informationHeader 2InformationFine grained information✅ DoneHeader 3Saving all files...Couldn't save file Y✅ Saved all filesHeader 4Saving all files...Saving file XCouldn't save file Y✅ Saved all filesColorfromtealprintimportTealPrintfromcoloredimportfg,attrTealPrint.info("Header",color=attr("bold"),push_indent=True)TealPrint.info("OK",color=fg("green"))OUTPUTAutomatic color for errors and warningsfromtealprintimportTealPrintTealPrint.error("Could not open file")TealPrint.warning("Missing configuration for X")OUTPUTAuthorsMatteus Magnusson,senth.wallace@gmail.com
Package: teal-ravenframework
![TEAL Logo](./logos/TEAL_LOGO_FULL.png)TEAL (Tool for Economic AnaLysis) is RAVEN plugin aimed to
contain and deploy economic analysis for RAVEN workflows.It leverages the Uncertanty Quantification, Probabilistic Risk Assesment,
Parameter Optimizzation and Data Anlysis framework  [RAVEN](https://github.com/idaholab/raven)
to deploy complex economic analyses.TEAL enables the capability to compute the NPV (Net Present Value),
IRR (Internal Rate of Return) and the PI (Profitability Index) with RAVEN.
Furthermore, it allows NPV, IRR or PI search, i.e. TEAL will compute a
multiplicative value (for example the production cost) so that the NPV, IRR or PI has
a desired value.
The plugin allows for a generic definition of cash flows, which
drivers are provided by RAVEN. Furthermore, TEAL includes flexible options to
deal with taxes, inflation, discounting and offers  capabilities to compute
a combined cash flow for components with different component lives.Instructions for installing RAVEN plugins (including TEAL)
can be found [on RAVEN’s website](https://github.com/idaholab/raven/wiki/Plugins).
Package: tealshwlibrary
Teals Homework LibraryFor TEALS homework because asgh all weve done so far has been review why ahhhhhQuickstart:shell -> pip install tealshwlibrary

from tealshwlibrary.June7 import June7
print(June7.factorial(7))Documentation:June7functiondescriptionis_prime(integer)if integer is a prime, return True, else returns Falsefactorial(integer)returns factorial of integer||integer_input()| continuously asks user for an integer input, returns the integer     |June8functiondescriptionhappy_birthday(name)requires a name parameter, prints happy birthday songpick_cards(integer, *argv)given an integer and any number of lists, returns [integer] amount of random choices from every list
Package: team4958-customs
team4958 CUSTOMSsome life-simplifying python tools for some lazy devs from some lazy devsReport Bug.Request FeatureAbout The ProjectYou probably think that some necessary boring stuff is inevitable and it will always be a part of the process.
We didn't want to agree with it so we created this library which transforms completing the most boring and pissing off tasks to few python strings.Getting StartedInstallation#Linux/macOSpython3-mpipinstallteam4958-customs#Windowspy-3-mpipinstallteam4958-customsLicenseDistributed under the MIT License. SeeLICENSEfor more information.Authorsarti404-laziest dev ever-arti404-made everything
Package: teamaker
teamakerMinimal game engine to create and playtext-editadventures.Installpipinstallvirtualenv
virtualenvvenv--python=python3sourcevenv/bin/activate
pipinstall-rrequirements.txtRunsourcevenv/bin/activate
pythongame.pyexamples/house_story.pyThen openopenme.txtwith your text editor.Note on text editorFor improved experience, your text editor should support auto-reload of externally modified files, without any prompt.We strongly suggest the (open-source) Notepad2 as modified bysheepolutioninAnd yet it hurtsexactly for this reason.Testpythongame.pyexamples/house_story.py-texamples/house_story.testNotesIconbyJacob Halton.