Saravanan666/pypi-org huggingface dataset:
Package: airflow-workflows
WorkflowsWorkflows are a cleaner way of implementing DAGs using a Django-inspired class-based syntax.Simple ExampleLet's create a single Airflow DAG, whose name is a camelcased version of the class name, and whose operator dependencies are in the order they are defined.There is an option to override the defaultdependenciesmethod implementation to customise the dependency chain for your use case.importworkflowsclassExampleWorkflow(workflows.Workflow):classMeta:schedule_interval='0 9 * * *'do_something_useful=workflows.PythonOperator(python_callable=lambda**kwargs:print('something useful'),)something_else=workflows.PythonOperator(python_callable=lambda**kwargs:print('Something not useful'),)globals()[ExampleWorkflow.DAG.dag_id]=ExampleWorkflow.DAGDynamic DAG ExampleLet's create (in this case three) DAGs, created dynamically and based on theExampleWorkflowclass as implemented above. In other words, they will share the same DAG metadata (so schedule in this case).importworkflowsworkflow_names=['Test1','Test2','Test3',]forworkflowinworkflow_names:WorkflowClass=workflows.create_workflow(workflow,base=ExampleWorkflow,)globals()[WorkflowClass.DAG.dag_id]=WorkflowClass.DAG
Package: airflow-yeedu-operator
Airflow Yeedu OperatorInstallationTo use the Yeedu Operator in your Airflow environment, install it using the following command:pip3installairflow-yeedu-operatorDAG: Yeedu Job ExecutionOverviewTheYeeduJobRunOperatorin this DAG facilitates the submission and monitoring of jobs using the Yeedu API in Apache Airflow. This DAG enables users to execute Yeedu jobs and handle their completion status and logs seamlessly within their Airflow environment.PrerequisitesBefore using this DAG, ensure you have:Access to the Yeedu API.Proper configuration of Airflow with required connections and variables (if applicable).UsageDAG InitializationImport the necessary modules and instantiate the DAG with required arguments and schedule interval.fromdatetimeimportdatetime,timedeltafromairflowimportDAGfromyeedu.operators.yeeduimportYeeduJobRunOperator# Define DAG argumentsdefault_args={'owner':'airflow','depends_on_past':False,'start_date':datetime(2023,1,1),'retries':1,'retry_delay':timedelta(minutes=5),}# Instantiate DAGdag=DAG('yeedu_job_execution',default_args=default_args,description='DAG to execute jobs using Yeedu API',schedule_interval='@once',catchup=False,)Task InitializationCreate tasks usingYeeduJobRunOperatorto perform various Yeedu API operations.Define YeeduJobRunOperator taskssubmit_job_task=YeeduJobRunOperator(task_id='submit_yeedu_job',job_conf_id='your_job_config_id',# Replace with your job config IDtoken='your_yeedu_api_token',# Replace with your Yeedu API tokenhostname='yeedu.example.com',# Replace with your Yeedu API hostnameworkspace_id=123,# Replace with your Yeedu workspace IDdag=dag,)ExecutionTo execute this DAG:Ensure all required configurations (job config ID, API token, hostname, workspace ID) are correctly provided in the task definitions.Place the DAG file in the appropriate Airflow DAGs folder.Trigger the DAG manually or based on the defined schedule interval.Monitor the Airflow UI for task execution and logs.
Package: airflux
AirfluxAirflux is averysimple installer and tmux session manager for Airflow.It exists for those of us who deploy Airflow a lot; maybe you're developing an operator and need to test it by hand on Airflow 1.3, or you manage a collection of Airflow pipelines and need a quick way to start and stop local versions of them.Getting startedInstall airflux:pip install airfluxAirflux uses constraints files to install Airflow for your version of Python. This command will list the versions of Airflow with available constraints files.airflux versionsYou will probably want to make a directory for your deployment. The script doesn't do this for you.mkdir my-airflow-deployment && cd my-airflow-deploymentTo download the constraints file and create a local Python environment for Airflow, specify the version number toairflux new:airflux new 2.3.3After the installation is complete, start Airflux in a tmux session:airflux startOnce inside the tmux session, you can exit it and shut down Airflow:airflux stopTo install Airflow into your current directory
Package: airfoils
Airfoilsis a smallPythonlibrary for object-oriented airfoil modelling. The library provides tools to easily instantiate airfoil objects and to query geometric information. An airfoil object is defined by upper and a lower surface coordinates.Airfoil nomenclature. Image in the public domain, viaWikimedia Commons.FeaturesAirfoil generation with aNACA-4series definitionImport from filesFull support for airfoils from theUIUC Airfoil Coordinates DatabaseInterpolation or computation of airfoil geometry parametersUpper and lower surface coordinatesCamber line coordinatesChord line coordinates (TODO)Thickness distribution (TODO)Maximum thickness (TODO)Linear interpolation between two different airfoils (MorphAirfoil)Plotting of airfoilsExample>>>fromairfoilsimportAirfoil>>>foil=Airfoil.NACA4('4812')>>>foil.plot()>>>foil.y_upper(x=0.5)array(0.13085448)>>>foil.y_lower(x=[0.2,0.6,0.85])array([0.00217557,0.02562315,0.01451318])>>>foil.camber_line(x=0.5)0.07789290253609385Installationpip install airfoilsDocumentationhttps://airfoils.readthedocs.io/LicenseLicense:Apache-2.0
Package: airframe
AirFrame===============================.. image:: https://badge.fury.io/py/airframe.png:target: http://badge.fury.io/py/airframe.. image:: https://pypip.in/d/airframe/badge.png:target: https://crate.io/packages/airframe?version=latestDownload images from an authenticated Flickr account (or local filesystem) andpush them wirelessly to a Toshiba FlashAir Wifi SD card mounted in a digitalphoto frame.* Free software: ASL2 license* Documentation: http://documentup.com/virantha/airframe* Source: http://github.com/virantha/airframe* API docs: http://virantha.github.com/airframe/htmlFeatures--------* Authenticates to Flickr to get your private photos* Only downloads photos with specified tags* Alternativly, can sync files from a local directory* Caches and syncs the photos to the Wifi SD cardInstallation------------.. code-block:: bash$ pip install airframeUsage-----First, go to Flickr and get a private key at http://www.flickr.com/services/api/misc.api_keys.htmlThen, create a directory from where you will start airframe, and create a file called flickr_api.yaml:.. code-block:: yamlkey: "YOUR_API_KEY"secret: "YOUR_API_SECRET"Then, setup your FlashAir card as described in `this post's<http://virantha.com/2014/01/09/hacking-together-a-wifi-photo-frame-with-a-toshiba-flashair-sd-card-wireless-photo-uploads>`__"Enabling the FlashAir" section.Now, you're ready to sync some photos!  Just run:.. code-block:: bash$ airframe -n 100 -t photoframe YOUR_AIRFRAME_IPThis will download and sync the 100 most recent photos tagged with "photoframe" to yourAirFrame... warning:: Any other image files in the FlashAir upload directory will be deleted, so make sure you backup anything you want to keep from your SD card.The image files from Flickr will be cached in a sub-directory called``.airframe`` in the location you invoked airframe from, so as long as you rerunfrom the same directory, the script will only download new files from Flickr.  If you want toredownload all the files from scratch, just ``rm .airframe`` these files.The script will also only upload new images to the FlashAir card, and ignore any files that arealready present on the card.  If you want to force a clean upload, do the following:.. code-block:: bash$ airframe -n 100 -t photoframe -f YOUR_AIRFRAME_IPThis will delete all images already on the card, and upload every image again.Alternatively, you can sync files directly from your local computer by pointingthe script at a directory of ``.jpg`` files:.. code-block:: bash$ airframe -l /path/to/photos YOUR_AIRFRAME_IPNote: other flags are ignored in this mode... :changelog:History-------0.1.0 (2014-01-10)++++++++++++++++++* First release on PyPI.Todo list=========- Add tests- Add docstrings- Travis integration- Use docopt instead of argparse- Better error handling
Package: airfrans
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes SolutionsThe AirfRANS dataset makes available numerical resolutions of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations over the NACA 4 and 5 digits series of airfoils and in a subsonic flight regime setup. Readthedocs documentation is availablehere.FeaturesAccess to 1000 simulations.Reynolds number between 2 and 6 million.Angle of attack between -5° and 15°.Airfoil drawn in the NACA 4 and 5 digits series.Four machine learning tasks representing different challenges.InstallationInstall withpip install airfransUsageDownloading the datasetFrom python:import airfrans as af

af.dataset.download(root = PATH_TO_SAVING_DIRECTORY, unzip = True)You can also directly download a ready-to-use version of the dataset in thePyTorch Geometric libraryFinally, you can directly download the dataset in the raw OpenFOAM versionhere, or in the more friendly pre-processed versionhere.Loading the datasetFrom python:import airfrans as af

dataset, dataname = af.dataset.load(root = PATH_TO_DATASET, task = TASK, train = True)The tasks are the one presented in theassociated paper. You can choose between'full','scarce','reynolds' and'aoa'.
The dataset loaded in this case is the same as the one you can directly access via thePyTorch Geometric library. If you want more flexibility about the sampling of each simulation for the inputs or targets, please feel free to build a custom loader with the help of the'Simulation'class presented in the following. We highly recommend to handle those data with a Gemetric Deep Learning library such asPyTorch GeometricorDeep Graph Library.Simulation classThe'Simulation'class is an object to facilitate the manipulation of AirfRANS simulations. Given the root folder of where the directories of the simulations have been saved and the name of a simulation you can easily manipulate it.import airfrans as af

name = 'airFoil2D_SST_57.872_7.314_5.454_3.799_13.179'
simulation = af.Simulation(root = PATH_TO_DATASET, name = name)See the documentation for more details about this object.LicenseThis project is licensed under theODbL-1.0 License.ReferenceThe original paper accepted at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks can be foundhereand the preprinthere.Disclaimer: An important update correcting an inconsistency in the Machine Learning experiments proposed in the main part of the NeurIPS version of the paper has been done. Please refer to theArXiv versionfor the up to date version.Please cite this paper if you use this dataset in your own work.@inproceedings{
bonnet2022airfrans,
title={Airf{RANS}: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier{\textendash}Stokes Solutions},
author={Florent Bonnet and Jocelyn Ahmed Mazari and Paola Cinnella and Patrick Gallinari},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://arxiv.org/abs/2212.07564}
  }
Package: airfs
airfs: A Python library for cloud and remote file Systemsairfs brings standard Python I/O to various storages (like cloud objects storage, remote
file-systems, ...) by providing:Abstract classes of Cloud objects with the completeio.RawIOBaseandio.BufferedIOBasestandard interfaces.Features equivalent to the standard library (io,os,os.path,shutil)
for seamlessly managing storage objects and local files.These functions are source agnostic and always provide the same interface for all files
from storage or local file systems.Buffered storage objects also support the following features:Buffered asynchronous writing of any object size.Buffered asynchronous preloading in reading mode.Write or read lock depending on memory usage limitation.Maximization of bandwidth using parallels connections.For more information, refer to thedocumentation.Supported storageairfs is compatible with the following storage services:Alibaba Cloud OSSAWS S3 /MinIOGitHub (Read Only)Microsoft Azure Blobs StorageMicrosoft Azure Files StorageOpenStack Swift Object Storeairfs can also access any publicly accessible file via HTTP/HTTPS (Read only)."airfs" is a fork of the unmaintained "Pycosio"
project by its main developer.
Package: airgoodies
AirgoodiesAirgoodies is a project that contains various APIs to interact with external services inside Apache Airflow using
minimum configuration (seeairgoodies.{module}.variables.jsonfor each module).Current version matrix:Airgoodies VersionApache Airflow VersionPython VersionProject tag0.0.42.7.23.11v0.0.40.0.32.7.23.11v0.0.30.0.22.7.23.11v0.0.20.0.1-alpha2.7.23.11v0.0.1-alphaProvided goodies for version0.0.4:ModuleDescriptionDependency Versionsairgoodies.commandAPI for dynamic task configuration through YAMLpyyaml==6.0.1airgoodies.awsAPI for easy interaction with AWSpandas==2.1.1apache-airflow-providers-amazon===8.7.1airgoodies.mongoAPI for easy interaction with MongoDBpymongo==4.5.0airgoodies.xcomAPI for managing variables through XComNoneInstallationAdd the following requirement in yourrequirements.txt# requirements.txt
airgoodies=0.0.4Example usageFor the official documentation, seehereFor an example of how to use this project, seehereBuilding the projectTo build the project:$python3setup.pysdistbdist_wheelLicenseThis project is available under the MIT License.AuthorStavros Grigoriou (stav121)
Package: airgpt
airgptThis repository containsairgpt: a command line tool empowered by AI and LLM. It calls OpenAI to get the appropriate command based on user prompts, and executes the command to get the results.InstallationSign up withOpenAIto get an API key. Set up the environment vairableOPENAI_API_KEYin a terminal:exportOPENAI_API_KEY=<your_openai_api_key>Make sure you have Python 3.10+ installed. Installairgptwith the following command:pip3installairgptWhen the installation is complete, run the following command to start theairgpt:airgptDeveloping the Python source code locallyThe following steps could be used to developairgptlocally:Clone the repository from GitHub:gitclonehttps://github.com/kp-enterprise/airgpt.gitCreate a virtual environment and install the required libraries:cdairgpt
python3-mvenvvenvsourcevenv/bin/activate
pip3install-irequirements.txtRun the following command to startairgpt:python3airgpt.py
Package: airgram
A python wrapper for making calls to theAirgram API, which enables you to send push notifications to your mobile devices.Since it is a very shallow wrapper, you can refer to theofficial api referencefor details on the functions.ExamplesAt the time of writing (2015-08-20) airgram is using wrong certificates (see), which are intended for herokuapp.com. Because of this cert verification needs to be turned off.Using as a guestfromairgramimportAirgramag=Airgram(verify_certs=False)# Send a message to a userag.send_as_guest("your@email.com","Test message from Airgram API","http://example.com")Using with an authenticated airgram servicefromairgramimportAirgramag=Airgram(key="MY_SERVICE_KEY",secret="MY_SERVICE_SECRET",verify_certs=False)# Subscribe an email to the serviceag.subscribe("your@email.com")# Send a message to a subscriberag.send("your@email.com","Hello, how are you?")# Send a message to ALL subscribersag.broadcast("Airgram for python is awesome",url="https://github.com/the01/python-airgram")History0.1.3 (2015-08-25)BugFixadded MANIFEST.in (fix install problem)0.1.2 (2015-08-21)BugFixCorrect wrong api url0.1.1 (2015-08-21)Add module loggerAdd class loggerFunctions throw AirgramException on failure0.1.0 (2015-07-30)First release on PyPI.
Package: airgym
XPlane Gym EnvironmentThis project provides an OpenAI Gym environment for training reinforcement learning agents on an XPlane simulator. The environment allows agents to control an aircraft and receive rewards based on how well they perform a task, such as flying a certain trajectory or landing safely.InstallationTo install the package, run the following command:pipinstallairgymUsage/ExamplesTo use the environment in your Python code, you can import it as follows:importairgymimportgym# If XPlane is running on the same machine, you can use the default address and port.# Or, set ip address and port according to your configuration.env=gym.make('AirGym-v1')episods=0forepisodinrange(episods):obs=env.reset()done=Falsewhilenotdone:actions=env.action_space.sample()obs,reward,done,info=env.step(action)env.close()
Package: airhttprunner
HttpRunnerHttpRunneris a simple & elegant, yet powerful HTTP(S) testing framework. Enjoy! ✨ 🚀 ✨Design PhilosophyConvention over configurationROI mattersEmbrace open source, leveragerequests,pytest,pydantic,allureandlocust.Key FeaturesInherit all powerful features ofrequests, just have fun to handle HTTP(S) in human way.Define testcase in YAML or JSON format, run withpytestin concise and elegant manner.Record and generate testcases withHARsupport.Supportsvariables/extract/validate/hooksmechanisms to create extremely complex test scenarios.Withdebugtalk.pyplugin, any function can be used in any part of your testcase.Withjmespath, extract and validate json response has never been easier.Withpytest, hundreds of plugins are readily available.Withallure, test report can be pretty nice and powerful.With reuse oflocust, you can run performance test without extra work.CLI command supported, perfect combination withCI/CD.SponsorsThank you to all our sponsors! ✨🍰✨ (become a sponsor)金牌赞助商（Gold Sponsor）霍格沃兹测试学院是业界领先的测试开发技术高端教育品牌，隶属于测吧（北京）科技有限公司。学院课程均由 BAT 一线测试大咖执教，提供实战驱动的接口自动化测试、移动自动化测试、性能测试、持续集成与 DevOps 等技术培训，以及测试开发优秀人才内推服务。点击学习!霍格沃兹测试学院是 HttpRunner 的首家金牌赞助商。开源服务赞助商（Open Source Sponsor）HttpRunner is in Sentry Sponsored plan.Subscribe关注 HttpRunner 的微信公众号，第一时间获得最新资讯。
Package: airignis
AirignisThis package aims to provide a C# familiar way of handling events in addition to the possibility to schedule the execution of periodic events on different time bases (year, month, weak, day, hour, minute, second) called auto events.
When using this package, please keep in mind that the timing precision of the auto event executions mainly depends on the real-time ability of your operating system. Therefore, it is not intended for applications requiring a high level of timing precision.Installation$pipinstallairignisHow to use this package?Create an Event and add subscriber's callbacksfromairignisimportEventdeffoo(data):print(f"doing something with the data:{data}")# creating an Event objectmy_event=Event()# adding the foo() function as subscriber callbackmy_event+=fooAfter importing the Event class an object can be created. Following, one or multiple callback functions can be added with
the += operator.Invoke the Event and pass arguments to the callbackmy_event.invoke(data)The type and number of arguments of the subscriber callbacks must match with the data template accepted by the Event.Remove a subscriber's callback# removing the foo() function from the list of subscriber callbacksmy_event-=fooSchedule an AutoEvent and set the function to be executed at each due timeThis example shows a minimalistic usage of the AutoEvent class. Please refer to the package documentation for a
detailed showcase of the functionalities.fromairignisimportAutoEvent,duetimefromdatetimeimportdatetime,timedeltadefsay_hello(name:str):print(f"Hello{name}")rate=2rate_unit='second'exec_time=20# Specify the target due timetest_due_time=duetime(rate=rate,rate_unit=rate_unit)# Defining the stop date time of the auto event. Should stop after 20 secondsstop_datetime=datetime.now(test_due_time.timezone)+timedelta(seconds=exec_time)auto_event=AutoEvent(say_hello,test_due_time,stop_datetime,'world')#  starting the auto event handlerauto_event.start()#  Interrupting the auto event handlerauto_event.stop()License© 2022 Subvael
Package: airkupofrod
Airflow KubernetesPodOperatorFromDeploymentOrairkupofrodfor short, is a tiny package which does one thing - takes a deployment in your kubernetes cluster and
turns allows you to use its pod template as aKubernetesPodOperatorobject. It does this by providing theKubernetesPodOperatorFromDeploymentoperator.airkupofrodsupports 1.10.9<=airflow<2Installation and usageEnsure your airflow image has the python packageairkupofrodinstalledpipinstallairkupofrodThen in your dags:fromairkupofrod.operatorimportKubernetesPodOperatorFromDeploymentmy_kupofrod_task=KubernetesPodOperatorFromDeployment(deployment_labels={"app":"my-app"},# deployment labels to lookup bydeployment_fields={"metadata.name":"my-app-deploy-template"},# deployment fields to lookup bydeployment_namespace="some-ns",# where the deployment livesnamespace="default",# where the pod will be deployedtask_id="my-kupofrod-task",dag=dag,in_cluster=True,)You will also need to make sure that a service account attached to your airflow pods
has the a role capable of listing deployments bound to it. Seerole-bindingfor an example of this.This is in addition to the role bindings necessary for theKubernetesPodOperatorto work which can be seen in theairflow helm chartDevelopingSkaffoldis used to test and develop inside kubernetes.After ensuring you have:SkaffoldHelmSome type of k8s cluster availableRun:skaffolddev--force=false--cleanup=false--status-check=false--port-forwardThen navigate tohttp://localhost:8080and enable and trigger a run of the test deployments dag.
Package: airlabs
Project Portfolio:https://github.com/calebyhan/CalebHanairlabsPython wrapper forAirLabs.InstallationUse the package managerpipto install airlabs.pipinstallairlabsUsageimportairlabs# Gets airline information about American Airlinesairlabs.airlines(iata_code="AA")# Gets information about real-time flightsairlabs.flights()DocumentationDocumentation found inhttps://airlabs.readthedocs.io/en/latest/.ContributingPull requests are welcome. For major changes, please open an issue firstto discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT