Saravanan666/pypi-org huggingface dataset:
Package: test-earlybirds
===============Test Earlybirds===============.. image:: https://img.shields.io/pypi/v/test_earlybirds.svg:target: https://pypi.python.org/pypi/test_earlybirds.. image:: https://img.shields.io/travis/simonmeoni/test_earlybirds.svg:target: https://travis-ci.org/simonmeoni/test_earlybirds.. image:: https://readthedocs.org/projects/test-earlybirds/badge/?version=latest:target: https://test-earlybirds.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/simonmeoni/test_earlybirds/shield.svg:target: https://pyup.io/repos/github/simonmeoni/test_earlybirds/:alt: Updatestechnical test for Earlybirds* Free software: Apache Software License 2.0* Documentation: https://test-earlybirds.readthedocs.io.Install--------To install the package : `pip3 install test-earlybirds`CLI--------To launch the application with CLI, enter the following commands :test_ealybirds --k 5 \--output ./output/path/to/csv/result/file \--learn ./output/path/to/csv/learning/file \--evaluation ./output/path/to/csv/evaluation/filehere is a description of arguments :- k: the number of the neighbors- output: the output path- learn: the input path of the train set- evaluation: the input path of the evaluation set- user_limit: limit the number of user to evaluateFeatures--------* I tried to implement this method from this following article : https://www.itm-conferences.org/articles/itmconf/pdf/2017/04/itmconf_ita2017_04008.pdfCredits-------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=======History=======0.1.0 (2019-04-14)------------------* First release on PyPI.
Package: testeasydubins
testeasydubinsInstallationYou can install the testeasydubins fromPyPI:pip install testeasydubinsThe testeasydubins is supported on Python 2.7, as well as Python 3.4 and above.How to useTo call the testeasydubins in your own Python code, by importing from thetesteasydubinspackage:>>> from testeasydubins import dubin_path
Package: tested
testedGeneral python testing utilsTo install:pip install testedA little tour...validate_codecLet's start withvalidate_codec, a function to test encoder/decoder pairs.>>>fromtestedimportvalidate_codecpickle.dumps/pickle.loadsis the default encoder/decoder pair.
You can pickle lists, and datetime objects>>>validate_codec([1,2,3])True>>>fromdatetimeimportdatetime>>>validate_codec(datetime.now())TrueBut you can't pickle a lambda function>>>validate_codec(lambdax:x)False>>>fromfunctoolsimportpartial>>>importjson>>>validate_jsonability=partial(validate_codec,coder=json.dumps,decoder=json.loads)You can jsonize lists and dicts>>>assertvalidate_jsonability([1,2,3])>>>assertvalidate_jsonability({'a':1,'b':{'c':[1,2,3]}})You can't jsonize datetime objects>>>fromdatetimeimportdatetime>>>validate_jsonability(datetime.now())FalseSeevalidate_codecdocsfor more examples.
Package: teste-do-noia
libpythonproMÃ³dulo para exemplificar construÃ§Ã£o de projetos PythonLink para o acesso doGooglePara instalar Windows:python3 -m venv .venv.venv\Scripts\activatepip install -r requirements-dev.txtPara conferir qualidade de cÃ³digo:flake8
Package: testelibpythonpro
teste-libpythonproteste dos comandos do gitlink do curso:PythonProbusca de dados x implementadasuportada versão 3 de pythonPara instalar:
'''console
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements-dev.txt
'''Para conferir qualidade de código:
'''console
flake8
'''Tópicos a serem abordados:GitVirtualenvPip
Package: teste-libpythonpro-pypi
# libpythonpro
Módulo para exemplificar a construção de projetos Python no curso PytoolsNesse curso é ensinado como contribuir com projetos de código abertoSuportada versão 3 de Python
Escrito em Python 3.10
[![Updates](https://pyup.io/repos/github/juaoantonio/libpythonpro/shield.svg)](https://pyup.io/repos/github/juaoantonio/libpythonpro/)
[![Python 3](https://pyup.io/repos/github/juaoantonio/libpythonpro/python-3-shield.svg)](https://pyup.io/repos/github/juaoantonio/libpythonpro/)Para instalar:`console $ python-mvenv .venv $ source .venv/bin/activate `Uso padrão:`console $ pip install-rrequirements.txt `—Se o uso for para desenvolvimento:`console $ pip install-rrequirements-dev.txt`Para conferir a qualidade do código:`console $ flake8 `
Package: test-engine
Disclaimer: This repository is under active development and is not yet suitable for public use. We are currently working on an alpha release and will remove this disclaimer at the time of release.EngineThis is the source code repository for a framework for creating and runningSeldonianalgorithms.InstallationTo run this code as a developer, create a virtual environment. Then install the package locally, e.g.python setup.py developorpip install -e .If you want to view the parse tree graphs using the built-in tools this library provides, installgraphvizonto your system. The Seldonian library uses a Python API for graphviz, and the API requires that graphviz be installed system-wide. This should resolve mysterious error messages like "dot" not found. "dot" is a command line program included with graphviz for rendering the graphs from code.InterfaceThis library is in its infancy, but a lite command line interface exists for parsing string expressions into parse trees. To use this interface, edit the file:interface.py. Modifyconstraint_stranddeltato values of interest. Then run (make sure you have activated theseldoconda environment first) from the command line:python interface.pyAs a quick test, if you use the following variables:constraint_str = 'abs((Mean_Error | [M]) - (Mean_Error | [F])) - 0.1'
delta = 0.05The resulting diagram should matchthis one, although the actual intervals may be different due to the random seeding of the base variable intervals.TestingTo run the unit tests, from the command line do:pytestThis will automatically run all tests intests/. Currently, there is only one test file in this folder, calledtest_parse_tree.py. Pytest will find that file and run all tests within that file.To get more introspection into the tests see thePytest documentation.VersioningThe naming of versions of this software adheres tosemantic versioning. Pre-release versions use a major version of "0", e.g. "0.0.1" is the very first pre-release version.
Package: tester-flask
Class for autotests flask python appsInstallpipinstalltester_flaskUsage in testsfromflaskimportFlaskfromtester_flaskimportTestFlaskapp=Flask(__name__)@app.route('/',methods=['GET','POST'])defmain_page():return"Flask OK"@app.route('/redirect',methods=['POST'])defredirect_page():returnredirect(url_for('main_page'))classTestFlaskApp(TestFlask):defsetUp(self):super(TestFlaskApp,self).setUp()TestFlask.set_up(self,app)deftest_app(self):assertself.simple_view('main_page').status_code==200assertself.param_post('main_page',{'hello':1},{'one':1}).status_code==200response=self.simple_post('redirect_page',{'one':1},follow=False)assertself.final_url(response)==self.get_url('main_page')Developmentgitclonegit@github.com:vb64/test.helper.flask.gitcdtest.helper.flaskWith Python 3makesetupPYTHON_BIN="/path/to/python3make testsWith Python 2makesetup2PYTHON_BIN="/path/to/python2make tests2
Package: tester-sc-pr2
🍰 PromptLayerThe first platform built forprompt engineersPromptLayeris the first platform that allows you to track, manage, and share your GPT prompt engineering. PromptLayer acts a middleware between your code and OpenAI’s python library.PromptLayer records all your OpenAI API requests, allowing you to search and explore request history in the PromptLayer dashboard.This repo contains the Python wrapper library for PromptLayer.Quickstart ⚡Install PromptLayerpipinstallpromptlayerInstalling PromptLayer LocallyUsepip install .to install locally.Using PromptLayerTo get started, create an account by clicking “Log in” onPromptLayer. Once logged in, click the button to create an API key and save this in a secure location (Guide to Using Env Vars).Once you have that all set up,install PromptLayer usingpip.In the Python file where you use OpenAI APIs, add the following. This allows us to keep track of your requests without needing any other code changes.importpromptlayerpromptlayer.api_key="<YOUR PromptLayer API KEY pl_xxxxxx>"openai=promptlayer.openaiYou can then useopenaias you would if you had imported it directly.💡 Your OpenAI API Key is **never** sent to our servers. All OpenAI requests are made locally from your machine, PromptLayer just logs the request.Adding PromptLayer tags:pl_tagsPromptLayer allows you to add tags through thepl_tagsargument. This allows you to track and group requests in the dashboard.Tags are not required but we recommend them!openai.Completion.create(engine="text-ada-001",prompt="My name is",pl_tags=["name-guessing","pipeline-2"])After making your first few requests, you should be able to see them in the PromptLayer dashboard!Using the REST APIThis Python library is a wrapper over PromptLayer's REST API. If you use another language, like Javascript, just interact directly with the API.Here is an example request below:import requests
request_response = requests.post(
  "https://api.promptlayer.com/track-request",
  json={
    "function_name": "openai.Completion.create",
    "args": [],
    "kwargs": {"engine": "text-ada-001", "prompt": "My name is"},
    "tags": ["hello", "world"],
    "request_response": {"id": "cmpl-6TEeJCRVlqQSQqhD8CYKd1HdCcFxM", "object": "text_completion", "created": 1672425843, "model": "text-ada-001", "choices": [{"text": " advocacy\"\n\nMy name is advocacy.", "index": 0, "logprobs": None, "finish_reason": "stop"}]},
    "request_start_time": 1673987077.463504,
    "request_end_time": 1673987077.463504,
    "api_key": "pl_<YOUR API KEY>",
  },
)ContributingWe welcome contributions to our open source project, including new features, infrastructure improvements, and better documentation. For more information or any questions, contact us athello@magniv.io.
Package: testery
Testery CLITo install you must have Python 3 and pip installed, then run:pip install testeryUpgrade viapip install testery --upgradeCreate Test RunStarts a test run.testerycreate-test-run--token<yourTesteryApiToken>--project<projectKeyFromTestery>--build-id<uniqueBuildIdOfYourChoice>--environment<environmentToBeTested>--wait-for-resultsWhen set,--fail-on-failure, will return an exit code of 1 if there are test failures.Output FormatsteamcityprettyjsonappveyoroctopusCreate Test EnvironmentCreates an environment where tests may be run.testerycreate-environment--token"<your_Testery_Api_Token>"--key"<key>"--name"<name>"--variable"KEY1=FOO1"--variable"KEY2=FOO2"--variable"secure:KEY3=SECRET"--pipeline-stage"<pipeline_stage_name>"Update Test EnvironmentUpdates an environment where tests are run with some new variables or pipeline stage.testeryupdate-environment--token"<your_Testery_Api_Token>"--key"<key>"--name"<name>"--variable"KEY1=FOO1"--variable"KEY2=FOO2"--pipeline-stage"<pipeline_stage_name>"Add environment variables from .env fileTakes in a .env file and adds or overwrites variables in target environment.testeryadd-env-vars-from-file--token"<your_Testery_Api_Token>"--environment-key"<key>"--env-file"<path/to/file/.env>"--overwriteUpdate Test StageUpdates or creates a pipeline stage where tests are run with some new variables.testeryupdate-pipeline-stage--token"<your_Testery_Api_Token>"--name"<name>"--variable"KEY1=FOO1"--variable"KEY2=FOO2"--create-if-not-existsAdd Pipeline Stage variables from .env fileTakes in a .env file and adds or overwrites variables in target Pipeline.testeryadd-stage-vars-from-file--token"<your_Testery_Api_Token>"--name"<name>"--env-file"<path/to/file/.env>"--overwrite
Package: test-esmf-loader
The Aspect Model Loader as part of the Python SDK provided by theEclipse Semantic Modeling Framework.An Aspect of the Meta ModelTheesmf-aspect-model-loaderpackage provides the Python implementation for the SAMM Aspect Meta Model, or SAMM.
Each Meta Model element and each Characteristic class is represented by an interface with a corresponding
implementation.UsageAn Aspect of the Meta Model can be created as follows using the providedAspectInstantiator.aspect_loader = AspectLoader()
aspect = aspect_loader.load_aspect_model("absolute/path/to/turtle.ttl");oraspect_loader = AspectLoader()
aspect = aspect_loader.load_aspect_model_from_multiple_files(["list/of/absolute/paths/to/turtles.ttl"], "aspect_urn");Automatic DeploymentAGitHub action called 'Release'has been set up for theesmf-aspect-model-loader. This action checks the code quality by running tests, thestatic type checker MyPyand
thecode formatter 'Black'.Set Up SAMM Aspect Meta Model for developmentIn order to download the SAMM sources, it is required to runpoetry installonce in theesmf-aspect-model-loadermodule. There are two possibilities to download the SAMM files and extract the Turtle sources for the Meta Model.Possibility 1 (downloading a release)Thedownload_samm_releasescript may be executed withpoetry run download-samm-releaseIt downloads a release JAR-file from GitHub and extracts the SAMM Files.
The version is specified in the python script.Link to all Releases:https://github.com/eclipse-esmf/esmf-semantic-aspect-meta-model/releasesPossibility 2 (downloading from the repository)It may happen that there is no .jar file that is up to date with the changes of the SAMM.
This script is an alternative to thedownload_samm_release.pyand extracts the files from the repository
directly instead of using the newest release.The script uses the GitHub API and downloads the files from themainbranch. If the script is run in a
pipeline, it uses a GitHub token to authorize. If the script is run locally, the API is called without a token.
This may cause problems because unauthorized API calls are limited.This script can be executed withpoetry run download-samm-branchto download and start working with the Aspect Model Loader.
Package: testevoo
FlightPrice PackageO pacote Flight Price Ã© uma ferramenta que permite buscar os melhores preÃ§os de voos por meio de web scraping em diferentes sites de companhias aÃ©reas e agÃªncias de viagens. Com este pacote, vocÃª pode automatizar a busca por preÃ§os competitivos para suas viagens.InstalaÃ§Ã£o
Para instalar o pacote Flight Price, vocÃª pode usar o pip. Abra um terminal e execute o seguinte comando: pip install flight-priceUso
import FlightPrice as flp#utilizando a biblioteca.
scraper = FlightScraper('SÃ£o paulo','Rio de janeiro','17/01/2024','17/02/2024')
voos = scraper.buscar_voos()#Exibindo informaÃ§Ãµes dos voos.
for c,k in var.items():
print(var[c])ContribuiÃ§Ã£o
ContribuiÃ§Ãµes sÃ£o bem-vindas! Se vocÃª quiser melhorar o pacote Flight Price, sinta-se Ã  vontade para abrir uma pull request no repositÃ³rio do GitHub.Aviso Legal
O pacote Flight Price foi desenvolvido apenas para fins educacionais e de demonstraÃ§Ã£o. O web scraping pode violar os termos de serviÃ§o de alguns sites. Certifique-se de estar em conformidade com as polÃ­ticas de uso dos sites que vocÃª deseja fazer scraping.LicenÃ§a
Este projeto Ã© licenciado sob a LicenÃ§a MIT - consulte o arquivo LICENSE para obter mais detalhes.
Package: testExamAnalyzerINF219v23Copy
Exam analyzerExam analyzer is a tool that extract data from json files, and the creates visualizations with descriptive texts.Getting startedTo run the exam analyzer you need following libraries:matlab - pip install matlabflask - pip install Flasklatex and pypandocs - go to the followng linkhttps://www.tug.org/mactex/mactex-download.html, and donwload the package. After it is downloaded run  the command line:  pip install pandoc pypandocrunning the websiteIf everthing is donwloaded and updated, you should be able to run the local webpage. When you import the package at run the app the terminal should show up and you can be able to go to the link in the terminal.
Package: test-example-package
<REPOSITORY_NAME>The function of this test package template is to make it easier to build and deploy new packages onhttps://pypi.org/It does not pretend to fit the demadnd of all type of packages but make some steps easier to fullfit using this package as template. And afther importing the repository. Adapt is for each usecase.Instalation protocolUse the content of this repository in your own git/github environment. With your own repository name and self builded package contentClone the github repository.$ git clone https://github.com/<GITHUB_USERNAME>/<REPOSITORY_NAME>.gitEnter the project folder.$ cd <REPOSITORY_NAME>how deploy package on pipyBefore execute the deploy command. There must be prerequisites:It the__init__.pyTher must be defined:__doc__description of the module information insite """ .... """ on the header of the docuemntAn defined release version of the package__version__An defined author of the package__author__An defined author email of the package__email__If there are pip dependencies that the package need to have installed to work. They must be listed on the 'requirement.txt' file.The Project need to be part of an "git" environment connected to an github repository. If it is part of it. Some information is used as metadata of the pip project. Such as:The package name will be the repository name of the projectThe webside will be directly the github repository.The bugtracker web will be the issues part of the repoitory on github.There must be an definedREADME.mdon the root of the project.If this information is not correct. The build process wil be interupted and the deploy will not happenYou must be registered onhttps://pypi.org/with a defined "username" and "password"Execute the command:$ deploy.batThis command will create and temporary virtual environment. Install the requirement packages on it. Build the new release. And deploy it onhttps://pypi.org/(will ask username and password) and as last will destroy the builded virtual environment asking if you will delete it or not.Now you have deployed your package on PIPY so you can use it with PIP installationsHow to test it (on VSCode):Change<YOUR_PACKAGE_NAME>your your deployed package name insidebuild.bat(on the last line)...
python -m venv env
call .\env\Scripts\activate
pip install <YOUR_PACKAGE_NAME>Build the virtual environment on the repository with the package content by running:$ build.batAfter that you can use method located in the tests directory .\tests\init.py$ cd tests
$ python __init__.py
Package: test-executor
Test ExecutorIntroductionThis module allows you to execute tests in serial and parallel mode.
A test's flow defined by this module is as follows:setup- general configurations for starting th testpre_test- this should be defined specifically per test class if neededtest- the test logic itselfpost_test- same as for pre_testcleanup- cleans what is needed after the test's flow is doneMain one can create a general test class that will implement the "setup" and "cleanup" steps, let other test classes
inherit from it and then implement any specific logic for pre_test and/or post_test if needed.Testing ExampleTests executionTest execution can be done as follows:# Load the tests needed from a list of paths
loaded_tests = TestLoader.load_tests(test_paths)

# Creating a new test executor with the required concurrency level
test_executor = TestExecutor(concurrency_level=concurrency)

# Starting the execution and getting the results
test_executor.Logger.info("*" * 32 + " Start Execution " + "*" * 32)
results = test_executor.execute(loaded_tests, listener=listener)
test_executor.Logger.info("*" * 32 + " End Execution " + "*" * 32)Tests implementationThe following is an example for a basic class implementation, as you can also see
a fully configured logger is available during the execution:from test_executor.abstract_test.abstract_test import AbstractTest
class ExampleTest(AbstractTest):
    def setup(self):
        self.logger.info(f"Setup 1")

    def cleanup(self):
        self.logger.info(f"Cleanup 1")

    def test_1(self):
        self.logger.info(f"Test 1")

    def test_2(self):
        self.logger.info(f"Test 2")The logger available is configured separately for each test, so that each
test will have their own logs. Logs will be saved be default under a "logs"
folder in the current working directory. Each execution that contains 1 test
or more will generate a logs folder in the following format:{DAY}-{MONTH}-{YEAR}_{HOUR}-{MINUTE}-{SECOND}-{MICROSECONDS}Under this folder each test will create its own test folder in the following
format:{TEST_NUMBER}_{TEST_CLASS_NAME}.{TEST_METHOD_NAME}
Package: testextension
testextensiondescriptionRequirementsJupyterLab >= 3.0InstallpipinstalltestextensionContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the testextension directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstalltestextension
Package: test-F
dotnet贊助我們發展更多功能 (金額由你決定)QuickstartRefer to ourOfficial Documentation.這是什麼?FinMind是超過 50 種金融開源數據50 datasets。
包含技術面 : 台股股價 daily、即時報價、即時最佳五檔、PER、PBR、每5秒委託成交統計。基本面 : 綜合損益表、現金流量表、資產負債表、股利政策表、除權除息結果表、月營收。籌碼面 : 外資持股、股權分散表、融資融券、三大法人買賣、借券成交明細。消息面 : 台股相關新聞。衍生性商品 : 期貨、選擇權 daily data、即時報價、交易明細。國際市場 : 美股股價 daily、minute、美國債券殖利率、貨幣發行量(美國)、黃金價格、原油價格、G8 央行利率、G8 匯率、資料每天更新。你不需收集資料，就可進行分析。What is this?FinMindis open source of more than50 datasets, includingTaiwan stock trade data daily, Taiwan stock trade data (5 seconds) (2019-05-29 ~ now, more than 30 million data in total), Financial Statements, Balance Sheet, Cash Flows Statement, Month Revenue, Holding Shares Per, Institutional Investors Buy Sell. Taiwan Futures Trade Detail, Taiwan Option Trade Detail.US stock price daily, minute (2019-06-01 ~ now, more than 80 million data in total), oil price, gold price,G7exchange rate, interest rate.US Government Bonds Yield.The datasets are automatically updated daily.
You can analyze financial data without having to collect the data by yourself.LicenseLicense Detail資料來源:證交所,櫃買中心,公開資訊觀測站,期交所。本專案提供的所有內容均用於教育、非商業用途。資料僅供參考，使用者依本資料交易發生交易損失需自行負責，本專案不對資料內容錯誤、更新延誤或傳輸中斷負任何責任。Project of ContentsDataset線上 APITaiwan Stock DataDocumentOtherOpen UP Summit Slide (PPT)Median SharingHistoryUpdateContactSolicit partners who are interested in joint development.徵求有興趣共同開發的夥伴。Email:FinMind.TW@gmail.com每週日早上零點至早上七點為維護時間，不提供服務。NoteAPI Request 上限：600 / 小時。Limit amount of request, 600 / hour.至FinMind官網註冊並驗證信箱後，API 的 Request 加上user_id與password參數可以提高使用上限到 1500/hr。user_id與password參數的使用方法，可參考線上 API。
Package: testfarm
TestFarm is a continuous integration platform.The typical workflow is:* Several clients,usually running on different platforms/configurations,monitor periodically for updatesat the project version control system (VCS).* When a client detects any changes, it starts an execution.* Execution progress and results are reported to the TestFarm server.* The server collects such data into a public web site.* Developers can take a look at that web site to see whethertheir changes had unexpected impacts over other platforms or configurations.