Saravanan666/pypi-org huggingface dataset:
Package: daggerpy
DAGGERDAG tools to process numerical topography and landscape evolution modelsFree software: GNU General Public License v3Documentation:https://dagger.readthedocs.io. (WIP - will be online at publication time)FeaturesTODOReferencesTODO add and format the referencesCreditsMain developer: Boris Gailleton (boris.gailleton@univ-rennes.fr)Project partly funded byt projects within the SUBITOP ITN, University of Edinburgh, GFZ Postdam, ERC Feasible, Université de RennesThis package formatting was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.Some I/O operations are usinglibnpy(MIT), a header-only c++ library to write/load simplenumpyarrays.History0.0.11 (05/01/2023)Minor improvements and cleaningsMinor bug fixes in the installation (related to pep 517 and 518)Start of some refactoring prior to version  0.10.0.10 (13/11/2023)Loads of minor bug fixes and random additions on the experimental side0.0.9 (23/10/2023)A lot of minor fixes for the graph, classic connector and graphfloodMajors additions behind teh scenes for the next big refactoring (experimental features in)0.0.8 (01/08/2023)Fixing bugs in the connectorsAdding tools for hydrogeomorphometrics in graphfloodAdding some standalone models to trackscape0.0.7 (01/08/2023)Hotfixes0.0.6 (01/08/2023)Adding quick river and drainage divide extraction tools0.0.4 (20/07/2023)Hot fix (still developping the CI/CD toolchain)0.0.3 (20/07/2023)Fixing compilation for conda-forge on MacOS and WindowsAdding a quick topo function as quick test0 (2023-07-20)First release on PyPI.
Package: dagging
Python package implementing the dagging methodFree software: 3-clause BSD licenseExamplefromdaggingimportDaggingClassifierfromsklearn.datasetsimportload_iris# Load Iris from from scikit-learn.X,y=load_iris(True)model=DaggingClassifier(n_estimators=50,voting='hard',random_state=0)# Train the model.model.fit(X,y)# Accuracyprint(model.score(X,y))DependenciesThe dependency requirements are based on the last scikit-learn release:scipynumpyscikit-learnInstallationdagging is currently available on the PyPi’s repository and you can
install it viapip:pip install -U daggingIf you prefer, you can clone it and run the setup.py file. Use the following
commands to get a copy from GitHub and install all dependencies:git clone https://github.com/chkoar/dagging.git
cd dagging
pip install .Or install using pip and GitHub:pip install -U git+https://github.com/chkoar/dagging.git
Package: dag-json
dag-jsonPython implemention of theIPLDDAG-JSON codec. Uses theCIDclass frommultiformats. Passes all of IPLD'sDAG-JSON cross-codec test fixtures.Install fromPyPIwithpip install dag-json.License: This project is placed in the public domain.UsageChangelogRelease instructionsUsageThedag_jsonmodule has two functions,encodeanddecode.encodetakes any IPLD-compatible native Python object -int,float,str,bool,list,bytes, ormultiformats.CID- and returns it as DAG-JSON encodedbytes.decodetakes DAG-JSON encodedbytesand returns the corresponding native Python object.Here's example usage:>>>fromdag_jsonimportdecode,encode>>>frommultiformatsimportCID>>>>>>encoded=encode({'foo':'bar','data':b'hello world','link':CID.decode('QmUGhP2X8xo9dsj45vqx1H6i5WqPqLqmLQsHTTxd3ke8mp'),})>>>encodedb'{"data":{"/":{"bytes":"aGVsbG8gd29ybGQ"}},"foo":"bar","link":{"/":"QmUGhP2X8xo9dsj45vqx1H6i5WqPqLqmLQsHTTxd3ke8mp"}}'>>>>>>repr(decode(encoded)){'data':b'hello world','foo':'bar','link':CID('base58btc',0,'dag-pb','12205822d187bd40b04cc8ae7437888ebf844efac1729e098c8816d585d0fcc42b5b'),}Changelog0.1 - 2023-04-23Initial release!Release instructionsHere's how to package, test, and ship a new release.Run the unit tests.sourcelocal/bin/activate.csh
python3-munittestdiscoverBump the version number inpyproject.toml.git grepthe old version number to make sure it only appears in the changelog. Change the current changelog entry inREADME.mdfor this new version fromunreleasedto the current date.git commit -am 'release vX.Y'Upload totest.pypi.orgfor testing.python3-mbuild
setenvverX.Y
twineupload-rpypitestdist/dag-json-$ver*Install from test.pypi.org.cd/tmp
python3-mvenvlocalsourcelocal/bin/activate.csh
pip3uninstalldag-json# make sure we force pip to use the uploaded versionpip3install--upgradepip
pip3install-ihttps://test.pypi.org/simple--extra-index-urlhttps://pypi.org/simpledag-json==$verdeactivateRun the example code aboveto test that the code loads and runs.Tag the release in git. In the tag message editor, delete the generated comments at bottom, leave the first line blank (to omit the release "title" in github), put### Notable changeson the second line, then copy and paste this version's changelog contents below it.gittag-av$ver--cleanup=verbatim
gitpush&&gitpush--tagsClick here to draft a new release on GitHub.EntervX.Yin theTag versionbox. LeaveRelease titleempty. Copy### Notable changesand the changelog contents into the description text box.Upload topypi.org!twineuploaddist/dag-json-$ver.tar.gzdist/dag-json-$ver-py3-none-any.whl
Package: dagknows
DagKnows ToolsWelcome to the CLI tools for DagKnows.  With this you can manage all your dags, nodes, executions and sessions without ever leaving the comfort of your terminal.Getting StartedInstall the cli with:pip install dagknows --force-reinstall -USetup your accountDagKnows uses the auth token associated with your organization and account.  You can either use an existing token or login and create a new one.To use an existing token simply do:dk config initHere you will be prompted for your org's username and password.Get goingFor a full list of commands and help descriptions do:dk --help
Package: daglib
⚗️ Daglib - Lightweight DAG composition frameworkDaglib is a lightweight, embeddable parallel task execution library used for turning pure Python functions into executable task graphs.InstallationCorepipinstalldaglibWith visualizations enabledpipinstall'daglib[graphviz]'# static visualizations# orpipinstall'daglib[ipycytoscape]'# interactive visulizationsCreate your first DAGimportdaglibdag=daglib.Dag()@dag.task()deftask_1a():return"Hello"@dag.task()deftask_1b():return"world!"@dag.task()deftask_2(task_1a,task_1b):returnf"{task_1a},{task_1b}"dag.run()'Hello, world!'Beyond the "Hello, world!" exampleFor a more involved example, we will create a small pipeline that takes data from four source tables and creates a single reporting table. The data is driver-level information from the current 2022 Formula 1 season. The output will be a pivot table for team-level metrics.Source TablesTeam - Team of driverPoints - Current total Driver's World Championship points for each driver for the seasonWins - Current number of wins for each driver for the seasonPodiums - Current number of times the driver finished in the top 3 for the seasonimportpandasaspdimportdaglib# Ignore. Used to render the DataFrame correctly in the READMEpd.set_option("display.notebook_repr_html",False)dag=daglib.Dag()@dag.task()defteam():returnpd.DataFrame(dict(driver=["Max","Charles","Lewis","Sergio","Carlos","George"],team=["Red Bull","Ferrari","Mercedes","Red Bull","Ferrari","Mercedes"],)).set_index("driver")@dag.task()defpoints():returnpd.DataFrame(dict(driver=["Max","Charles","Lewis","Sergio","Carlos","George"],points=[258,178,146,173,156,158])).set_index("driver")@dag.task()defwins():returnpd.DataFrame(dict(driver=["Max","Charles","Lewis","Sergio","Carlos","George"],wins=[8,3,0,1,1,0])).set_index("driver")@dag.task()defpodiums():returnpd.DataFrame(dict(driver=["Max","Charles","Lewis","Sergio","Carlos","George"],podiums=[10,5,6,6,6,5])).set_index("driver")@dag.task()defdriver_metrics(team,points,wins,podiums):returnteam.join(points).join(wins).join(podiums)@dag.task()defteam_metrics(driver_metrics):returndriver_metrics.groupby("team").sum().sort_values("points",ascending=False)dag.run()points  wins  podiums
team
Red Bull     431     9       16
Ferrari      334     4       11
Mercedes     304     0       11Task Graph VisualizationThe DAG we created above will create a task graph that looks like the following
Package: dagline
OverviewDagline is a package for executing multiple tasks of a DAG flow in parallel. The type of task can be python function and Windows bat script.System RequirementsPython 3.10+Installationpython -m pip install daglineUse-casesCreate a folder where you put the dag files locally. You can name your folder/py whatever you want:dags\└──dag_example.pyDefine the DAG, opendag_example.pyand enter the following content:fromdagline.models.dagimportDAGfromdagline.models.operators.winbatimportWinbatOperatorfromdagline.models.operators.pythonimportPythonOperatordefread_excel():'''Do your works here'''print('call this func read_excel')defread_csv():'''Do your works here'''print('call this func read_csv')defload_to_db():'''Do your works here'''print('call this func load_to_db')task1=PythonOperator(task_id='read_excel',python_callable=read_excel)task2=PythonOperator(task_id='read_csv',python_callable=read_csv)task3=PythonOperator(task_id='load_to_db',python_callable=load_to_db)task4=WinbatOperator(task_id='housekeep',bat_command=r"C:\xxx\housekeep.bat")'''DAG graph, child task : [parent tasks]'''tasks_flow={task1:[],task2:[],task3:[task1,task2],task4:[task3]}withDAG(dag_id='dag_example',tasks_flow=tasks_flow,logfile="C:\xxx\dag_example.log",retry_cnt=3,retry_delay=10#seconds)asdag:passRun a DAG in parallel or run a DAG from some specified taskspython -m dagline dags run <dag_files_home> <dag_id> < --start_with_task_ids >dag_files_home                     The folder of the DAG filesdag_id                                    The id of the dag--start_with_task_ids            A list of the task ids, if it was provided, the DAG will start from these tasks, not from the beginning of the DAG>python-mdaglinedagsrunC:\xxx\dags_tasks\dagsdag_example1>python-mdaglinedagsrunC:\xxx\dags_tasks\dagsdag_example1--start_with_task_idsread_csvRun a task of the DAGpython -m dagline tasks run <dag_files_home> <dag_id> <task_id>dag_files_home                     The folder of the DAG filesdag_id                                    The id of the dagtask_id                                   The id of the task, only runs this task in the DAG>python-mdaglinetasksrunC:\xxx\dags_tasks\dagsdag_exampleread_csvVisualize  a DAG on the html pagepython -m dagline dags show <dag_files_home> <dag_id>dag_files_home                     The folder of the DAG filesdag_id                                    The id of the dag>python-mdaglinedagsshowC:\xxx\dags_tasks\dagsdag_example
Package: dagmar-adform-api-wrapper
Adform API wrapper 📦Adform is a digital advertising platform.There's no Python API wrapper that I could find to communicate with Adform's API.This is an attempt to create an API wrapper I can use at work.Installationpipenv install adform_api_wrapperGetting Startedimportosfromadform_api_wrapperimportAdformApiADFORM_CLIENT_ID=os.getenv('ADFORM_CLIENT_ID')ADFORM_CLIENT_SECRET=os.getenv('ADFORM_CLIENT_SECRET')adform_api=AdformApi(ADFORM_CLIENT_ID,ADFORM_CLIENT_SECRET)# https://api.adform.com/v1/help/buyer/campaigns#!/Campaigns/get_v1_buyer_campaignsall_campaigns_response=adform_api.get('/buyer/campaigns')print(all_campaigns_response.json())FeaturesAutomatic fetching of authentication tokenAutomatic refereshing of authentication token when it expiresRe-using of internal session object between API calls
Package: dagmc-bounding-box
FeaturesFinds the bounding box of a DAGMC geometry file.The bounding box is a pair of coordinates that define the upper right and lower left corner of the geometry.This which is particularly useful when assigning a regular mesh tally over the entire DAGMC geometry.InstallationUsing Pippipinstalldagmc_bounding_boxUsing Condacondainstall-cfusion-energy-cconda-forgedagmc_bounding_boxUsageFind the bounding boxfromdagmc_bounding_boximportDagmcBoundingBoxmy_corners=DagmcBoundingBox("dagmc.h5m").corners()print(my_corners)>>>((-100,-100,-100),(100,100,100))Extend the bounding boxfromdagmc_bounding_boximportDagmcBoundingBoxmy_corners=DagmcBoundingBox("dagmc.h5m").corners(extend=(10,5,2)print(my_corners)>>>((-110,-105,-102),(110,105,102))
Package: dagmc-h5m-file-inspector
dagmc-h5m-file-inspectorA minimal Python package that finds the volume ids and the material tags in a
DAGMC h5m file.Installation (Conda)The dagmc-h5m-file-inspector package can be installed with a single conda
install terminal command.condainstall-cfusion-energy-cfusion-energy-cconda-forgedagmc_h5m_file_inspectorInstallation (Conda + Pip)The dagmc-h5m-file-inspector package requires pymoab which can be installed
alongside Moab with a conda install command. Moab is not avialable on pip,
however it can be installed with Conda.condainstall-cconda-forgemoab

pipinstalldagmc-h5m-file-inspectorPython API UsageFinding the volume IDs in a DAGMC h5m file.importdagmc_h5m_file_inspectorasdidi.get_volumes_from_h5m("dagmc.h5m")>>>[1,2]Finding the material tags in a DAGMC h5m file.importdagmc_h5m_file_inspectorasdidi.get_materials_from_h5m("dagmc.h5m")>>>['steel','graveyard']Finding the volume IDs with their materials present in a DAGMC h5m file.importdagmc_h5m_file_inspectorasdidi.get_volumes_and_materials_from_h5m("dagmc.h5m")>>>{1:'steel',2:'graveyard'}Command line tool usageThe options for the command line tool can be obtained withinspect-dagmc-h5m-file --helpPrint the volume IDs present in a dagmc h5m file to the terminalinspect-dagmc-h5m-file -i dagmc.h5m -v
>>> Volume IDs =[1, 2]Print the material tags present in a dagmc h5m file to the terminalinspect-dagmc-h5m-file-idagmc.h5m-m
>>>Materialtags=['steel','graveyard']Print the volume IDs and materials present in a dagmc h5m file to the terminalinspect-dagmc-h5m-file-idagmc.h5m-b
>>>VolumeIDsandmaterialtags={1:'steel',2:'graveyard'}Write the volume IDs and materials present in a dagmc h5m file to a txt fileinspect-dagmc-h5m-file-idagmc.h5m-b-ooutput.txt
>>>writingfileoutput.txtAknowledgementsThis package is based on aPython scriptby @gonuke
Package: dagobah-messenger
### dagobah-messenger ###
JSON instant messenger with presence control and AES encryption.### Install ###pip install dagobah-messenger### How to start ###Run server:python3 -m dagobah-messenger -m serverRun any number (max 5) of GUI or console clients:python3 -m dagobah-messenger  # run console client
python3 -m dagobah-messenger -m clnt_gui  # run GUI client
python3 -m dagobah-messenger  # run console client (default)
python3 -m dagobah-messenger -m clnt_cli  # run console clientP.S. Use PyCharm to avoid module not found issue with pycryptodome.Thx a lot to Leo Orlov and GeekBrains.
Ilia Kruglov, 31072018.
Package: dagon
Dagon - An Asynchronous Task Graph Execution EngineDagonis a job execution system designed for speed, flexibility,
expressiveness, and correctness with two overarching goals in mind:It should beeasyto usecorrectly.It should behardto useincorrectly.Dagon itself can be used as a development tool, a CI tool, an application
framework, or a library for creating declarative task-graph programs and
pipelines.For more information, refer to thedocs/subdirectory.
Package: dagpy
DAGpy is a data science collaboration tool based on iPython notebooks enabling data science teams to:easily collaborate by branching out of others’ notebooksminimize code duplicationgive a clean overview of the projectcache intermediate outputs so team members can use them without re-evaluationautomate the process of code execution upon data changes or on scheduleprovide a clean interface to the data visualization dashboard designers and developersDAGpy manages a DAG (directed acyclic graph) of blocks of code, with
each block being a sequence of iPython notebook cells, together with
their outputs. It is designed to work seamlessly with popular VC systems
like git and can be run locally or as a server application.GitHub:github.com/ibestvina/dagpy/.Author: Ivan BestvinaExample projectTo play around with the example project, you can:view the project DAG:dagpy viewrun all the blocks:dagpy execute-aadd blocks through flows (with block B as a parent) and run them automatically:dagpy makeflow B-rcommit the changes:dagpy submitflow dagpy_flow.ipynbexplore other DAGpy options withdagpy-hPlease note that notebook execution time includes a significant overhead
of over a second, because a kernel must be started for each one. In
future, we plan on adding support for non-notebook plane python blocks.
These would also be edited through a flow notebook view, but would be
saved as .py scripts, and executed without noticable overhead.Dependencies:python 3jupyterdillnetworkx, matplotlib (for DAG view)
Package: dagred3
example-project-pythonExample python projectDetailsThis project is a pure python project using modern tooling. It uses aMakefileas a command registry, with the following commands:make: list available commandsmake develop: install and build this library and its dependencies usingpipmake build: build the library usingsetuptoolsmake lint: perform static analysis of this library withflake8andblackmake format: autoformat this library usingblackmake annotate: run type checking usingmypymake test: run automated tests withpytestmake coverage: run automated tests withpytestand collect coverage informationmake dist: package library for distribution
Package: dag-schedule-graph
DAG Schedule GraphAirflow plugin for visualising DAG schedules within 24 hour window of a day.Each bubble indicates the number of DAGs that will run at that instant. Bubble radius is relative to the DAG count.Installpipinstalldag-schedule-graphTrying it out using Docker# Start the servicesdocker-composeup# Access the webserveropenhttp://localhost:8082/dag-schedule-graph/# Cleanup containers, networks and volumesdocker-composedown-vDevelopment# Create virtual environment using condacondacreate-ndag-schedule-graphpython=3.7.9# Activate the environmentcondaactivatedag-schedule-graph# Load environemnt variablessource.env# Create Postgres database and usercreateuserairflow_rbac
createdb-Oairflow_rbacairflow_rbac# Install plugin and all dependenciespipinstall-e'.[dev]'# Running testspytesttests# Initialize Airflowairflowinitdb# Create Airflow userairflowcreate_user-uadmin-eadmin@gmail.com-padmin-fadmin-ladmin-rAdmin# Build static assetsnpmrunbuild# Start Airflow Webserverairflowwebserver# Access webserveropenhttp://localhost:8080/dag-schedule-graph/
Package: dagsesh
Dagsesh: Airflow DAG Session ManagerOverviewPrerequisitesGetting Started(macOS Users) Upgrading GNU MakeCreating the Local EnvironmentLocal Environment MaintenanceHelpRunning the Test HarnessUsingdagseshPlugin in your Project's Test SuiteFAQsOverviewAn Apache Airflow session context manager that overrides the defaultAIRFLOW_HOMEpath with a random, ephemeral alternate.Why is this useful? As per theAirflow configuration docs:The first time you run Airflow, it will create a file calledairflow.cfgin your$AIRFLOW_HOMEdirectory (~/airflowby default).Dagsesh delays the creation ofAIRFLOW_HOMEusing a lazy-loading facility whilst injecting it with a random alternate. Great if you want to create a pristine Airflow environment for repeatable testing.Dagsesh can be used as a convenientpytestplugin to prime an Airflow environment for testing.topPrerequisitesGNU makePython 3 Interpreter.We recommend installing pyenvDockertopGetting StartedMakesteris used as the Integrated Developer Platform.(macOS Users) Upgrading GNU MakeFollowthese notesto getGNU make.Creating the Local EnvironmentGet the code and change into the top levelgitproject directory:git clone git@github.com:loum/dagsesh.git && cd dagseshNOTE:Run all commands from the top-level directory of thegitrepository.For first-time setup, get theMakester project:git submodule update --initInitialise the environment:make init-devLocal Environment MaintenanceKeepMakester projectup-to-date with:git submodule update --remote --mergetopHelpThere should be amaketarget to get most things done. Check the help for more information:make helptopRunning the Test HarnessWe usepytest. To run the tests:make teststopUsingdagseshPlugin in your Project's Test SuiteAdd thedagseshpackage as a dependency to your project's development environment so that the plugin is installed and visible in yourPYTHONPATH.dagseshtakes care of the distribution's entry point so thatpytestautomatically finds the plugin module.  Nothing else needs to be done.NOTE:SeeMaking your plugin installable by othersfor more information.top
Package: dagshub
What is DagsHub?DagsHubis a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:Version code, data, and modelsin one place. Use the free provided DagsHub storage or connect it to your cloud storageTrack Experimentsusing Git, DVC or MLflow, to provide a fully reproducible environmentVisualizepipelines, data, and notebooks in and interactive, diff-able, and dynamic wayLabelyour data directly on the platform using Label StudioShareyour work with your team membersStream and uploadyour data in an intuitive and easy way, while preserving versioning and structure.DagsHub is built firmly around open, standard formats for your project. In particular:GitDVCMLflowLabel StudioStandard data formats like YAML, JSON, CSVTherefore, you can work with DagsHub regardless of your chosen programming language or frameworks.DagsHub Client API & CLIThis client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.For more details on the different functions of the client, check out the docs segments:Installation & SetupData StreamingData UploadExperiment TrackingAutologgingData EngineSome functionality is supported only in Python.To read about some of the awesome use cases for Direct Data Access, check out
therelevant doc page.InstallationpipinstalldagshubDirect Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:dagshubloginQuickstart for Data StreamingThe easiest way to start using DagsHub is via the Python Hooks method. To do this:Your DagsHub project,Copy the following 2 lines of code into your Python code which accesses your data:fromdagshub.streamingimportinstall_hooksinstall_hooks()That’s it! You now have streaming access to all your project files.🤩 Check out this colab to see an example of this Data Streaming work end to end:Next StepsYou can dive into the expandeddocumentation, to learn more about data streaming, data upload and
experiment tracking with DagsHubAnalyticsTo improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set theDAGSHUB_DISABLE_ANALYTICSenvironment variable to any value.Made with 🐶 byDagsHub.