Saravanan666/pypi-org huggingface dataset:
Package: tamplar
# TamplarTamplar is service generator. This project can to help you control your development. We think, you should development
with isolated environment. Therefore we creates docker-compose for local debugging and testing.Now, we use [this](https://github.com/U-Company/python-service-layout) python layout.# Installingpip install tamplarIf you want local environments, you need a docker. Please install:sudo apt-get install docker.ioWe don’t use docker-compose as console util. We build docker-compose from source and we use docker API into python code.# Usagetamplar can work with:pippublishing package to pypi registrypublishing docker to docker registrydocker-compose for local development# Console running### InitGo to empty directory and init your first projecttamplar initAfter that, you get [FastAPI](https://fastapi.tiangolo.com/) service with healthcheck methods. FastAPI is ASGI framework
instead of [Flask](https://flask.palletsprojects.com/).If you want reinitialize, tamplar ask you about cleaning repo. If you works with JetBrains IDE, while initialization we
ignore.ideafiles.### DepsWe use default excellent library for logging. This is [loguru](https://github.com/Delgan/loguru). Loguru is a very useful
wrapper over standart python [logger](https://docs.python.org/3.8/library/logging.html). After initilize you can install
all dependencies:tamplar depsThis command usespipandrequirements.### Upload (Not implemented)You can upload your package to concrete pypi registry and docker registrytamplar upload –docker=True –pypi=True –namespace=<your-namespace from .pypirc>After that, it built your docker image, pypi registry and push to the registry with latest version frominfo.py.
Registry is selected by namespace parameter.### Validate (Not implemented)This command checks struct of project by [python-service-layout](https://github.com/U-Company/python-service-layout).tamplar validate### Run fullThis command build and run docker with services with environment (dependencies of services)tamplar run –mode=fullWe implement execute docker-compose by python library docker-compose internal API. After run, it run docker-compose file
or you get status code error### Run envThis command build and run docker WITHOUT services with environment (dependencies of services)tamplar run –mode=envWe implement execute docker-compose by python library docker-compose internal API. After run, it run docker-compose file
or you get status code error### CleanClean is command for cleaning your building files:tamplar clean# DependencyIf you want to add new service as dependency, you need to go to thedeployment/docker-compose.*.yml. After that, you
need new service to your docker-compose files.# TestTamplar have a lot of good tests. But we have not any integration tests, because it is very difficult emulate python’s
environment. Therefore we change integration tests to unit tests mixed integration tests (we creates and deletes files)
while tests:make testIf you want to execute test from IDE, you need to gotests/__init__.pyand changedeep=2.
Package: tamr-toolbox
tamr-toolboxThe Tamr Toolbox is a Python library created to provide a simple interface for common interactions with Tamr and common data workflows that include Tamr. The more specializedTamr Python Clientlibrary is used for direct interactions with Tamr in both the development of the Tamr Toolbox and in the recommended use of the Tamr Toolbox.ResourcesReleases:https://github.com/Datatamer/tamr-toolbox/releasesDocumentation Home:https://datatamer.github.io/tamr-toolbox/latest/Installation:https://datatamer.github.io/tamr-toolbox/latest/installation.htmlExamples:https://datatamer.github.io/tamr-toolbox/latest/examples.htmlCommunity ContributionsContributions to the Tamr Toolbox are currently limited to Tamr employees only.
Tamr does not routinely review or merge community submitted pull requests.If you are a community member that would like to report a bug or a feature request, you may
file a Github issue in the tamr-toolbox repository.
Package: tamr-unify-client
Python ClientProgrammatically 💻 interact with Tamr using Python 🐍Quick links:Docs|Contributing|LicenseInstallpipinstalltamr-unify-clientFeatures🐍 Python objects/methods/functions instead of raw HTTP requests🤖 Automate operational workflowsContinuous MasteringContinuous Categorization🚀 Kick-off synchronous/asynchronous operationsRefresh datasets in your pipelineTrain Tamr's machine learning modelsGenerate predictions from trained models🔒 Authenticate with TamrFor more see theofficial docs.MaintainersSamuel Kalish
Package: tamu-d3m
Common code for D3M projectThis package provides a core package for D3M project with common code available.
It contains standard interfaces, reference implementations, and utility implementations.InstallationThis package works with Python 3.6 and pip 19+. You need to have the following packages installed on the system (for Debian/Ubuntu):libyaml-devYou can install latest stable version fromPyPI:$ pip3 install d3mTo install latest development version:$ pip3 install -e git+https://gitlab.com/datadrivendiscovery/d3m.git@devel#egg=d3mWhen cloning a repository, clone it recursively to get also git submodules:$ git clone --recursive https://gitlab.com/datadrivendiscovery/d3m.gitChangelogSeeHISTORY.mdfor summary of changes to this package.DocumentationDocumentation for the package is available athttps://datadrivendiscovery.org/.ContributingSeeCODE_STYLE.mdfor our coding style and contribution guide. Please ensure any merge requests you open follow this guide.Repository structuremasterbranch contains latest stable release of the package.develbranch is a staging branch for the next release.Releases aretagged.About Data Driven Discovery ProgramDARPA Data Driven Discovery (D3M) Program is researching ways to get machines to build
machine learning pipelines automatically. It is split into three layers:
TA1 (primitives), TA2 (systems which combine primitives automatically into pipelines
and executes them), and TA3 (end-users interfaces).
Package: tana2tree
Tanagra Parsertana2treeis a Python module for converting a Tanagra tree description into a more usable format.Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. Tanagra supports several standard data mining tasks such as Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression, factor analysis, clustering, classification and association rule learning.Clickhereto vist the Tanagra website. This is where you can find the downloads.InstallationDependenciesPython >= 3.6time modulere moduleUser Installationpip install tana2treeUsing tana2treeCopy results from TanagraSave results to text filePass text file in program.import tana2tree as t2t

def main():
    # get tanagra description as input
    input_file = "example/tanagra-output.txt"

    # build the tree, returns root node
    tree = t2t.Tanagra_Parser()
    print("Tree: ")
    print(tree.parse(input_file))A full example is given with all available methodshere.
Package: tanabesugano
TanabeSuganoA python-based Eigensolver for Tanabe-Sugano- & Energy-Correlation-Diagrams based on the original three proposed studies ofYukito Tanabe and Satoru Suganofor d2-d8transition metal ions:On the Absorption Spectra of Complex Ions. IYukito Tanabe, Satoru SuganoJournal of the Physical Society of Japan, 9, 753-766 (1954)DOI:10.1143/JPSJ.9.753https://journals.jps.jp/doi/10.1143/JPSJ.9.753On the Absorption Spectra of Complex Ions IIYukito Tanabe, Satoru SuganoJournal of the Physical Society of Japan, 9, 766-779 (1954)DOI:10.1143/JPSJ.9.766https://journals.jps.jp/doi/10.1143/JPSJ.9.766On the Absorption Spectra of Complex Ions, III The Calculation of the Crystalline Field StrengthYukito Tanabe, Satoru SuganoJournal of the Physical Society of Japan, 11, 864-877 (1956)DOI:10.1143/JPSJ.11.864https://journals.jps.jp/doi/10.1143/JPSJ.11.864It provides:Tanabe-Sugano- & Energy-Correlation-Diagrams plotted viamatplotlibTanabe-Sugano- & Energy-Correlation-Diagrams exported astxt-fileAtomic-Termsymbols and theirEigen-Energiesfor a given 10Dq and oxidation state as exported table viaprettytableSet-up individuallyC/B-ratiosWorking with Slater-Condon-ParametersF2, F4instead of Racah-ParametersB, CExport of theTanabe-Sugano-Diagramas ahtml-file viaplotlyfor interactive useTheTanabeSugano-application can be installed and run:#viaPyPipip install TanabeSugano#viapip+gitpip git+https://github.com/Anselmoo/TanabeSugano.git#locallypython setup.py installtanabesugano#forplotly-exportpip install TanabeSugano[plotly]The options for theTanabeSugano-application are:tanabesugano --helpusage: __main__.py [-h] [-d D] [-Dq DQ] [-cut CUT] [-B B B] [-C C C] [-n N][-ndisp] [-ntxt] [-slater]optional arguments:-h, --help     show this help message and exit-d D           Number of unpaired electrons (default d5)-Dq DQ         10Dq crystal field splitting (default 10Dq = 8065 cm-)-cut CUT       10Dq crystal field splitting (default 10Dq = 8065 cm-)-B B B         Racah Parameter B and the corresponding reduction (default B = 860 cm- * 1.)-C C C         Racah Parameter C and the corresponding reduction (default C = 4.477*860 cm- * 1.)-n N           Number of roots (default nroots = 500)-ndisp         Plot TS-diagram (default = on)-ntxt          Save TS-diagram and dd energies (default = on)-slater        Using Slater-Condon F2,F4 parameter instead Racah-Parameter B,C (default = off)-v, --version  Print version number and exit-html          Save TS-diagram and dd energies (default = on)Reference-Examplefor d6forB = 860 cm-andC = 3850 cm-as regularmatplotlib-plot:Reference-Examplefor d6forF2= 1065 cm-andF4= 5120 cm-as interactiveplotly-plot:Test the TS-diagrams interactively in Google Colab
Package: tanaod
PyPI:https://pypi.org/project/tanaod/โปรแกรมนี้ใช้สำหรับเขียนโปรแกรมแบบ OOPวิธีติดตั้งเปิด CMD / Terminalpipinstalltanaodวิธีใช้[STEP 1] - เปิด IDLE ขึ้นมาแล้วพิมพ์…fromtanaodimportStudent,Tesla,SpecialStudent,Teacher#day0print('---------day0---------')allstudent=[]teacher1=Teacher('ada lovelace')teacher2=Teacher('bill gate')print(teacher1.students)#day1print('--------day1--------')st1=Student('Albert','Einstein')allstudent.append(st1)print(st1.fullname)teacher2.Addstudent(st1)#day2print('--------day2--------')st2=Student('steve','jobs')allstudent.append(st2)print(st2.fullname)teacher2.Addstudent(st2)#day3print('--------day3--------')foriinrange(3):st1.Coding()st2.Coding()st1.Showexp()st2.Showexp()#day4print('-------day4---------')stp1=SpecialStudent('thomas','Edison','Hitler')allstudent.append(stp1)teacher1.Addstudent(st1)print(stp1.fullname)stp1.exp=20stp1.Coding()stp1.Showexp()#day5print('-------day5---------')print('กลับบ้านยังไง')print(allstudent)forstinallstudent:print('ผม{}กลับด้วย{}ครับ'.format(st.name,st.vehicle))ifisinstance(st,SpecialStudent):st.vehicle.SelfDriving(st)#day6print('---------day6--------')teacher1.CheckStudent()teacher2.CheckStudent()print('รวมพลังของนักเรียน2คน',st1+st2)พัฒนาโดย: ตนุภัทร สถืตวรรธนะ FB:https://www.facebook.com/tanupat.satitvattana/IG: tan_aod , a0d14
Package: tanchan
Tan-chanA utility library which extends Tanjun.InstallationYou can install Tan-chan from PyPI using the following command in any Python 3.9 or above environment.python -m pip install -U tanchanThe commands provided intanchan.componentsrequire the 3rd party library Hikari-Yuyo to work.
The feature flagtanchan[yuyo]should be used to ensure that a compatible version of Yuyo is
installed (i.e.pip install -U tanchan[yuyo]).Quick UsageFor usage see thedocumentationand theusage guide.SupportHikari's support guildprovides for support for Tan-chan.ContributingBefore contributing you should read through thecontributing guidelinesand
thecode of conduct.
Package: tanco
tancoA test-driven teaching environment for programmers.This is a work in progress. There are videos about it on:https://www.youtube.com/@tangentstreamIf your goal is to work through a coding challenge,
detailed setup instructions are here:https://tangentcode.com/setupInstallation# eventually:# pip install tanco# but for now, this is still very alpha stage, so:gitclonehttps://github.com/tangentcode/tanco.gitcdtanco
pipinstall-e.Using the Clienttancologincd/path/to/your/project
tancoinit
tancotest# keep doing this until it passesgitcommit# once the test passestanconext# to fetch the next testinspecting the databaseTanco (both the client and server) creates a sqlite database in~/.tanco.sdb.You can override this location by setting theTANCO_SDBenvironment
variable to a different path.You can inspect the database with thesqlite3command-line tool.sqlite3~/.tanco.sdb
.schemaRunning the ServerFirst set up a private key, then run quart or hypercorn.Private KeyThetanco logincommand lets the command-line client
log into the server in a multi-user setup.In this setup, the server uses a private key to sign
ajson web token.To set up the private key, do this:cd/path/to/tanco-server
ssh-keygen-trsa-b4096-mPEM-ftanco_auth_key.pemThis will also create a public key intanco_auth_key.pem.pub.
This is not currently used for anything.Running the serverYou can run the development server like so:bash QUART_APP=tanco.app:app quart run # --reloadOr (on platforms that support it) use hypercorn:hypercorntanco.app:app# --reload
Package: tandems
Calculate yearly average efficiencies for multijunction tandem solar cellsRandom sampling of multijunction photovoltaic efficiencies. Jose M. RipaldaThe main file is tandems.pyTested with Python 2.7 and 3.6SMARTS 2.9.5 is required only to generate a new set of random spectra.File "data/lat40.npy" can be used instead of SMARTS to load a set of averaged spectra.Clone or download fromhttps://github.com/Ripalda/Tandemsto obtain full set of spectra (about 600 MB).genBins.py imports tandems.py to generate sets of proxy spectraProject Organization.
├── LICENSE
├── README.md
└── dataUSAGE EXAMPLEimporttandemstandems.docs()eff=tandems.effs(junctions=4,bins=6,concentration=500)#    Include as many or as few options as needed.eff.findGaps()eff.plot()# Figures saved to PNG files.eff.save()# Data saved for later reuse/replotting. Path and file name set in eff.name, some parameters and timestamp are appended to filenameeff2=tandems.deepcopy(eff)eff2.__init__(junctions=4,bins=8,concentration=1,R=4e-5)# Change input parameters but keep previously found set of optimal gap combinations.eff2.recalculate()# Recalculate efficiencies for previously found set of optimal gap combinations.eff2.compare(eff)# Compares efficiencies in two datasets by doing eff2 - eff. Plots difference and saves PNG files.# eff = tandems.load('/path/and file name here') # Load previusly saved data# eff.results()# eff.plot()# The .npy files with the spectra used to calculate the yearly average efficiency have been generated with genBins.py
Package: tangelokit
This package allows you to generate structured data ## UsageImport theGenerateDataStructureclass from the package: You can
create an instance of GenerateDataStructure and pass in the necessary
data as follows:fromtangelokitimportGenerateDataStructureresult=GenerateDataStructure(credit_line={"uid":"1","created_at":"2023-08-30","balance":23,"todayToPay":{"value":497819.23,"total":582662.65,"date":"2023-08-30","last_quota":448773.98}},credit_request={"code":"22","approve_amount":2},contact={"cc":"22"},disbursements=[{"uid":"23232","uuid":"32323432","start_date":"2020-02-02","created_at":"2020-01-02","principal":23232,"days_late":222,"status":"disbursed","quotas":[],"resumen":{"last_quota":2323,"total":784,"value":4565}},{"uid":"23232","uuid":"32323432","start_date":"2020-02-02","created_at":"2020-01-02","principal":23232,"days_late":222,"status":"disbursed","quotas":[],"resumen":{"last_quota":2323,"total":784,"value":4565}}],product={"uuid":"22","period":{"type":"month","holidays":"holidays"}})Before getting result, you have to validate erros in data structure
making:result.errorsValidate if there are errors, if there are no errors the response is [][{"credit_line_schema":["'created_at' is a required property"]},{"disbursement_schema":["'start_date' is a required property","'created_at' is a required property"]}]GET RESPONSE:result.response{"total":1568,"value":9130,"moratorium_amount":4646,"quotas_in_arrears":0,"days_late":222,"product_uuid":"22","product_name":"","disbursements":[{"uid":"23232","uuid":"32323432","start_date":"2020-02-02","created_at":"2020-01-02","principal":23232,"days_late":222,"status":"","quotas":[],"resumen":{"last_quota":2323,"total":784,"value":4565},"quotas_in_arrears":0,"expiration_date":"N/A","payment_date":"2023-08-30","moratorium_amount":2323,"periods":{"periodicity":"month"},"todayToPay":{"value":497819.23,"total":582662.65,"date":"2023-08-30","last_quota":448773.98}},{"uid":"23232","uuid":"32323432","start_date":"2020-02-02","created_at":"2020-01-02","principal":23232,"days_late":222,"status":"","quotas":[],"resumen":{"last_quota":2323,"total":784,"value":4565},"quotas_in_arrears":0,"expiration_date":"N/A","payment_date":"2023-08-30","moratorium_amount":2323,"periods":{"periodicity":"month"},"todayToPay":{"value":497819.23,"total":582662.65,"date":"2023-08-30","last_quota":448773.98}}],"periods":{"periodicity":"month"},"payment_date":"2023-08-30","created_at":"2023-08-30","amount_disbursed":23,"total_disbursement":46464.0,"credit_request":{"code":"22","approve_amount":2,"contact":{"cc":"22"}}}
Package: tangent-tokenauth
# PythonAuthenticationLibA library for authenticating against the UserService## Installationpip install tangent-tokenauth## UsageAdd to installed apps:```INSTALLED_APPS = (...'tokenauth',)```Add to middleware:```MIDDLEWARE_CLASSES = ('django.contrib.sessions.middleware.SessionMiddleware','django.middleware.common.CommonMiddleware','django.middleware.csrf.CsrfViewMiddleware','django.contrib.auth.middleware.AuthenticationMiddleware','django.contrib.auth.middleware.SessionAuthenticationMiddleware','django.contrib.messages.middleware.MessageMiddleware','django.middleware.clickjacking.XFrameOptionsMiddleware','django.middleware.security.SecurityMiddleware','tokenauth.middleware.TokenAuthMiddleware',)```**Add authentication backends**```AUTHENTICATION_BACKENDS = ('django.contrib.auth.backends.ModelBackend',## this will allow you to authenticate against the userservice with a token'tokenauth.authbackends.TokenAuthBackend'## this will allow you to authenticate against the userservice with a username and password'tokenauth.authbackends.UserServiceAuthBackend')```**Notes:** It is advisable to include `UserServiceAuthBackend` after `ModelBackend`.Since `UserServiceAuthBackend` will sync the user returned from the UserService, it will be preferable to login using the local synced user rather than hitting the UserService each time.From the frontend this will be transparent.**Add userservice url**USERSERVICE_BASE_URL = "http://example.com"**Profit**$$You can now authenticate against the UserService:from django.contrib.auth.models import User# TokenAuthBackenduser.authenticate(token)# UserServiceAuthBackenduser.authenticate(username, password)
Package: tangerine
```_____                           _|_   _|_ _ _ __   __ _  ___ _ __(_)_ __   ___| |/ _` | '_ \ / _` |/ _ \ '__| | '_ \ / _ \| | (_| | | | | (_| |  __/ |  | | | | |  __/|_|\__,_|_| |_|\__, |\___|_|  |_|_| |_|\___||___/```[![Latest PyPI version](https://img.shields.io/pypi/v/tangerine.svg)](https://pypi.python.org/pypi/tangerine)[![CircleCI](https://circleci.com/gh/kevinjqiu/tangerine.svg?style=svg)](https://circleci.com/gh/kevinjqiu/tangerine)[![codecov](https://codecov.io/gh/kevinjqiu/tangerine/branch/master/graph/badge.svg)](https://codecov.io/gh/kevinjqiu/tangerine)API and scraper for the Tangerine Bank (Canada).Install=======pip install tangerineUsage=====Authentication--------------```pythonfrom tangerine import InteractiveSecretProvider, TangerineClientsecret_provider = InteractiveSecretProvider()client = TangerineClient(secret_provider)with client.login():...```Using `InteractiveSecretProvider` will prompt user for username/account #, security challenge questions and PIN number.The call to `client.login()` will initiate the login process. After the login is successful, subsequent calls to the API will be authenticated.If `client.login()` is used as a context manager (i.e., `with client.login():`), logout will be automatically initiated after the code block exitsor any exception is raised.List accounts-------------With an active session, use `client.list_accounts()`:```pythonwith client.login():accounts = client.list_accounts()```List transactions-----------------```pythonwith client.login():accounts = client.list_accounts()start_date = datetime.date(2017, 10, 1)end_date = datetime.date(2017, 11, 1)client.list_transactions([acct['number'] for acct in accounts], start_date, end_date)```Download statements-------------------```pythonwith client.login():accounts = client.list_accounts()start_date = datetime.date(2017, 10, 1)end_date = datetime.date(2017, 11, 1)client.download_ofx(account[0], start_date, end_date)```Contribution============Requirements:* Python 3* [pipenv](https://github.com/kennethreitz/pipenv)* basic knowledge of Python and gitTo contribute to the development, fork this repo to your github account, and clone it.```$ git clone https://YOUR-CLONE-URL tangerine$ cd tangerine$ pipenv install  # this creates the virtualenv and installs the dependencies$ pipenv shell    # this activates the virtualenv$ git checkout -b branch-for-feature-or-bug-fixHACK...HACK...HACK$ git commit ...$ git push```and send a PR :)Licence=======MITChangelog=========See [CHANGELOG.md](CHANGELOG.md)Authors=======tangerine was written by Kevin J. Qiu <kevin@idempotent.ca> with [contributors](https://github.com/kevinjqiu/tangerine/graphs/contributors).
Package: tangiball
Tangiball PackageA small analysis package designed for identification and examination of peaks in a voltage time-seriesMIT LicenseCopyright (c) 2022 Joseph David StarkeyPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.