Saravanan666/pypi-org huggingface dataset:
Package: python-dag-runner
Python DAG RunnerPython DAG Runner is a Python library for running Directed Acyclic Graphs (DAGs) efficiently. It provides a simple and intuitive interface for defining and executing complex workflows. It uses networkx library to generate directed acyclic graph from the tasks and it's dependencies.InstallationYou can install python-dag-runner library easily using pip:pipinstallpython-dag-runnerUsageYou can easily define a dag in python-dag-runner using Dag class. Furthermore, task can be defined using Task class. You can easily define dependencies of tasks using the "|=" operator.frompython_dag_runnerimportDag,Task# Create an executable functiondefprint_task_1():"""A sample function"""print("Task 1 called")defprint_task_1():"""A sample function"""print("Task 2 called")# Create Task objectstask_1=Task(name="task one",executable=print_task_1)task_2=Task(name="task two",executable=print_task_2)# Set dependencies of task using bitwise or operatortask_2|={task_1}# Create a DAG by providing name and sequence of tasksdag=Dag("My first DAG",tasks=[task_1,task_2])# Initiate execution of Dag using initialize methoddag.initiate()Example of complex dependenciesMultiple internal dependencies can be defined using dependency operator.task_2|={task_1}task_3|={task_2}task_4|={task_2}task_5|={task_2}task_6|={task_3,task_4,task_5}task_7|={task_6}task_8|={task_7}task_9|={task_7}The above steps result in the dependency graph as shown below%%{init: {'theme': 'neutral'}}%%
stateDiagram
direction LR
    task_one --> task_two
    task_two --> task_three
    task_two --> task_four
    task_two --> task_five
    task_three --> task_six
    task_four --> task_six
    task_five --> task_six
    task_six --> task_seven
    task_seven --> task_eight
    task_seven --> task_nine
Package: python-daily-med
|License MIT| | |Build Status| | |Coveralls Status| | |Codecov Status| | |Code Climate|================Python Daily Med================This library interacts with the United States National Library of Medicine's DailyMed API.Installation============* Install Python requirements ``pip install -r ./requirements``Setup=====*Usage=====* `Read The API Documentation <https://laslabs.github.io/python-daily-med>`_Import and Instantiate----------------------Importing an instantiating the Daily Med object:.. code-block:: pythonfrom daily_med import DailyMeddm = DailyMed()Structured Product Label Metadata---------------------------------`get_spls` mirrors the `/spls` interface as documented `here<https://dailymed.nlm.nih.gov/dailymed/webservices-help/v2/spls_api.cfm`_.To get an iterator of all SPLs:.. code-block:: pythonspl_metas = dm.get_spls()You can also perform an SPL search using any of the standard query parameters:.. code-block:: pythonsimvastatin_metas = dm.get_spls(drug_name='Simvastatin')Structured Product Label Documents----------------------------------Once you have a `set_id` for an SPL, you can get its document:.. code-block:: pythonspl_document = dm.get_spl('0be2e371-1f05-48d7-8f2e-f2024f3305f3')An SPL Document is basically just a dictionary representing the parsed XMLdocument.Known Issues / Road Map=======================-  SPLDocument is only Python 2 compatible. Generate a Python3 version and integratean import switch for py2/3. Downfall is that this will add another 6mb of code tothe repo & coverage will be inaccurate because not everything is testable at once.Credits=======Images------* LasLabs: `Icon <https://repo.laslabs.com/projects/TEM/repos/odoo-module_template/browse/module_name/static/description/icon.svg?raw>`_.Contributors------------* Dave Lasley <dave@laslabs.com>Maintainer----------.. image:: https://laslabs.com/logo.png:alt: LasLabs Inc.:target: https://laslabs.comThis module is maintained by LasLabs Inc... |Build Status| image:: https://api.travis-ci.org/LasLabs/python-daily-med.svg?branch=master:target: https://travis-ci.org/LasLabs/python-daily-med.. |Coveralls Status| image:: https://coveralls.io/repos/LasLabs/python-daily-med/badge.svg?branch=master:target: https://coveralls.io/r/LasLabs/python-daily-med?branch=master.. |Codecov Status| image:: https://codecov.io/gh/LasLabs/python-daily-med/branch/master/graph/badge.svg:target: https://codecov.io/gh/LasLabs/python-daily-med.. |Code Climate| image:: https://codeclimate.com/github/LasLabs/python-daily-med/badges/gpa.svg:target: https://codeclimate.com/github/LasLabs/python-daily-med.. |License MIT| image:: https://img.shields.io/badge/license-MIT-blue.svg:target: https://opensource.org/licenses/MIT:alt: License: AGPL-3
Package: python-daraja
Python DarajaDescriptionPython Wrapper for handling payment requests through the Daraja MPESA APIContributionRefer to theCONTRIBUTING GUIDE.UsageInstallationpipinstallpython-darajaInitial SetupSet the following constants first before proceedingfrompython_darajaimportpaymentpayment.SHORT_CODE="YOUR_SHORTCODE"payment.PASSKEY="YOUR PASSKEY"payment.CONSUMER_SECRET="YOUR CONSUMER SECRET"payment.CONSUMER_KEY="YOUR CONSUMER KEY"payment.ACCOUNT_TYPE="PAYBILL"# Set to TILL to use BuyGoods instead of Pay BillThe first function to call is one which would trigger an automatic STK Push on your customer's phone. There is no
simpler way of knowing whether the customer has successfully paid or not (failures such as inputting the wrong PIN or
just cancelling the request altogether). You are therefore advised to set up a simple server with an endpoint that
will acceptPOSTrequests from Daraja API with the details of the transaction.For the server, it must be secure (httpsinstead ofhttp), it must not belocalhostnor127.0.0.1:$PORT.You can use services likengrokto tunnellocalhostto live secure server.Your server's endpoint should also accept aPOSTrequest hence you may need to disable constraints such asCSRF(outside the scope of this project).If at all you must have CSRF Protection in your server, then you need to allow requests originating from the following
IP addresses (whitelisting):192.201.214.200196.201.214.206196.201.213.114196.201.214.207196.201.214.208196.201.213.44196.201.212.127196.201.212.128196.201.212.129196.201.212.136196.201.212.74196.201.212.69You can now proceed by triggering the payment process; you should expect a Python Dict object with the results of the
process. This will only tell you whether it was successful and not whether the customer has actually paid.frompython_darajaimportpaymentdetails=payment.trigger_stk_push(phone_number=2547123456,amount=1,callback_url='https://your-domain/callback/',description='Payment for services rendered',account_ref='Python Good PHP Bad and Co.')print(details)The MPESA Gateway will then send somePOSTdata to your endpoint if the customer pays, otherwise, you will not
receive any data showing that your customer hasn't paid a dime.It is advisable to save the details received from the previous method call to a DB of your choice. Some of those
details are used for other subsequent method calls especially in getting the transaction status of your payment requests.In order to query the details of a payment request made through the automatic STK Push above:-frompython_darajaimportpaymentdetails=payment.query_stk_push(checkout_request_id='ws_CO_DMZ_123212312_2342347678234')print(details)Getting a0as the Response Code or Result Code generally means that the transaction was successful, any other digit
signifies otherwise.RemarksUnfortunately as of the moment of release of this package, Safaricom has internal problems with other types of
transactions such asC2B. Maintainers of the project will work on implementing more features when the issues are addressed.
Package: python-darc
darcis designed as a swiss army knife for darkweb crawling.
It integratesrequeststo collect HTTP request and response
information, such as cookies, header fields, etc. It also bundlesseleniumto provide a fully rendered web page and screenshot
of such view.The general process ofdarccan be described as following:There are two types ofworkers:crawler– runs thedarc.crawl.crawlerto provide a
fresh view of a link and test its connectabilityloader– run thedarc.crawl.loaderto provide an
in-depth view of a link and provide more visual information
Package: python-darwin
python-darwinpython-darwinis a Python package providing an API to Darwin. Darwin is an online database for German Shepherds owned and maintained by theVDH,Vereniging Duitse Herder.InstallingUsing Pippipinstallpython-darwinFrom Sourcepipinstallgit+https://github.com/roaldnefs/python-darwin.gitUsageCommand Line OptionsThe following is the output ofdarwin --help, providing an overview of the basic command line options:usage: darwin [--version] query

Darwin Command Line Interface

positional arguments:
  query      the search query

optional arguments:
  --version  Display the version.AcknowledgementsBy default the Darwin data for this Python package is accessed throughduitseherder.nl. The copyright of the data remains with the authorVDH,Vereniging Duitse Herder.
Package: python-dashtx
This Python3 library implements support for Dash transactions.It builds on top, and is intended to be used along with python-bitcointx library.Requirementspython-bitcointx(version >= 1.0.0)Usage:With contextual switch to Dash parameters:importosimportdashtxfrombitcointximportChainParams,get_current_chain_paramsfrombitcointx.walletimport(CCoinKey,CCoinExtKey,P2WPKHCoinAddress,CCoinAddress)withChainParams('dash'):k=CCoinExtKey.from_seed(os.urandom(32))a=P2WPKHCoinAddress.from_pubkey(k.derive_path("m/0h/0h/1").pub)print('example{}address:{}'.format(get_current_chain_params().NAME,a))assertCCoinAddress(str(a))==aWith global switch to Dash parameters:fromdashtximportDashMainnetParamsfrombitcointximportselect_chain_paramsselect_chain_params('dash')# or, using the chain params class directlyselect_chain_params(DashMainnetParams)Without the switch of chain parameters:fromdashtx.walletimportP2SHDashAddressp2sh_addr=P2SHDashAddress('7UHUEqN8EwpHFJjf7U83YAUhEinxYt1MAK')p2sh_addr=P2SHDashAddress.from_scriptPubKey(p2sh_addr.to_scriptPubKey())assertstr(p2sh_addr)=='7UHUEqN8EwpHFJjf7U83YAUhEinxYt1MAK'frombitcointximportselect_chain_paramsfrombitcointx.walletimportCCoinAddressfromdashtx.walletimportP2SHDashAddressselect_chain_params('dash')p2sh_addr=CCoinAddress('7UHUEqN8EwpHFJjf7U83YAUhEinxYt1MAK')assertisinstance(p2sh_addr,P2SHDashAddress)this also works with ChainParams context manager.
Package: python-databasin
python-databasin 0.5.4python-databasinis a client library for interacting withData Basin.Install$pipinstallpython-databasinExample usageThe code snippet below will import a NetCDF dataset into Data Basin and make it public. Note that the account you use
must have import permission and the import must have complete metadata and style information.fromdatabasin.clientimportClientc=Client()c.login('user','pass')# Package must have complete metadata and style necessary for one-step importdataset=c.import_netcdf_dataset('/path/to/netcdf_with_metadata_and_style.zip')# One-step imports are private by defaultdataset.make_public()print(dataset.id)print(dataset.title)You can also upload Esri layer packages (.lpk). As with NetCDF's, layer packages for now must have the all metadata
required by Data Basin to successfully import:dataset=c.import_lpk('/path/to/lpk_with_metadata.lpk')print(dataset.id)print(dataset.title)python-databasincan be used with an API key instead of a password:fromdatabasin.clientimportClientc=Client()c.set_api_key('<username>','<api key>')my_datasets=c.list_my_datasets()
Package: python-data-converter
PythonGenerator & ConverterConverter libraryforCSV,OpenAPI,Pandas DF,URLsusing a simpleDrag & Drop UI.Recommended Python Version:3.8+and above.PythonGenerator & Converter-LIVE demoPythonGenerator & Converter-source codeGetSupportviaEmail&DiscordEULA License:Freeforstudents,solo-developers(hobby projects),NGOs, andeLearningactivitiesUnrestricted usagerequires a license (one-time payment)Payment Link:$499(managed byGUMROAD)Video PresentationThematerial explains how to use this conversion toolwritten inPythonto manipulate and convert information into different formats.The tooluses a simple UI able toconvertOpenAPIdescriptors,CSV, andDataFramesinto PythonModels definition(allows editing),Data Tables, andCharts.Product RoadmapStatusDeliveryInputOutputInfoDONEDrop 1OpenAPIJSONDB ModelDB Model(s) extractionDONEDrop 2OpenAPIYamlDB ModelDB Model(s) extractionNADrop 2Swagger UIURLDB ModelDB Model(s) extraction---------------DONEDrop 1CSVDB ModelDB ModelDONEDrop 1CSVDataTablesPaginated Data View (vanilla JS)DONEDrop 1CSVChartsData to visualisationDONEDrop 2CSVExport(with filters)CSV, PDFNADrop 2CSVRemote URL (Gdrive)DB ModelDB ModelNADrop 2CSVRemote URL (Gdrive)DataTablesPaginated Data View (vanilla JS)NADrop 2CSVRemote URL (Gdrive)ChartsData to visualisationNADrop 2CSVRemote URL (Gdrive)Export(with filters)CSV, PDF---------------DONEDrop 1Pandas DFDB ModelDB ModelDONEDrop 1Pandas DFDataTablesPaginated Data View (vanilla JS)DONEDrop 1Pandas DFChartsData to visualisationDONEDrop 2Pandas DFExport(with filters)CSV, PDF---------------DONEDrop 2DBMSRemote URLDB ModelModels IntrospectionDONEDrop 2DBMSRemote URLDataTablesPaginated Data View (vanilla JS)DONEDrop 2DBMSRemote URLChartsData to visualisationDONEDrop 2DBMSRemote URLExport(with filters)CSV, PDFBuild from sourcesNote:Recommended Python Version:3.8+and above.PKL(Pandas DF) processing will not work with a version below3.8.Step 1-Clone Sourcesfrom the putblic repository:$gitclonehttps://github.com/app-generator/devtool-python-converter.git
$cddevtool-python-converterStep 2- Install modules$# Virtualenv modules installation (Unix based systems)$virtualenvenv
$sourceenv/bin/activate
$pipinstall-rrequirements.txtStep 3- Set up environment (optional)$(Unix/Mac)exportFLASK_APP=run.py
$(Windows)setFLASK_APP=run.py
$(Powershell)$env:FLASK_APP=".\run.py"Step 4- Run theAPP$flaskrunPythonGenerator & Converter- Tool provided byAppSeed.
Package: pythondata-cpu-blackparrot
pythondata-cpu-blackparrotNon-Python  files needed for the cpu blackparrot packaged
into a Python module so they can be used with Python libraries and tools.This is useful for usage with tools likeLiteX.The data files can be found under the Python modulepythondata_cpu_blackparrot. Thepythondata_cpu_blackparrot.data_locationvalue can be used to find the files on the file
system.Example of getting the data file directly;importpythondata_cpu_blackparrotmy_data_file="abc.txt"withopen(os.path.join(pythondata_cpu_blackparrot.data_location,my_data_file))asf:print(f.read())Example of getting the data file usinglitex.data.findAPI;frompythondata_cpu_blackparrotimportdata_filemy_data_file="abc.txt"withopen(data_file(my_data_file))asf:print(f.read())The data files come fromhttps://github.com/enjoy-digital/black-parrot.gitand are imported usinggit subtreesto the directorypythondata_cpu_blackparrot/system_verilog.Installing from git repositoryManuallyYou can install the package manually, however this isnotrecommended.git clone https://github.com/litex-hub/pythondata-cpu-blackparrot.git
cd pythondata-cpu-blackparrot
sudo python setup.py installUsingpipwith git repositoryYou can usepipto install the data package directly
from github using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.gitIf you want to install for the whole system rather than just the current user,
you need to remove the--userargument and run as sudo like so;sudo pip install git+https://github.com/litex-hub/pythondata-cpu-blackparrot.gitYou can install a specific revision of the repository using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<hash>Withrequirements.txtfileAdd to your Pythonrequirements.txtfile using;-e git+https://github.com/litex-hub/pythondata-cpu-blackparrot.gitTo use a specific revision of the repository, use the following;-e https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<hash>Installing fromPyPiUsingpippip install --user pythondata-cpu-blackparrot
Package: pythondata-cpu-cv32e40p
pythondata-cpu-cv32e40pNon-Python  files needed for the cpu cv32e40p packaged
into a Python module so they can be used with Python libraries and tools.This is useful for usage with tools likeLiteX.The data files can be found under the Python modulepythondata_cpu_cv32e40p. Thepythondata_cpu_cv32e40p.data_locationvalue can be used to find the files on the file
system.Example of getting the data file directly;importpythondata_cpu_cv32e40pmy_data_file="abc.txt"withopen(os.path.join(pythondata_cpu_cv32e40p.data_location,my_data_file))asf:print(f.read())Example of getting the data file usinglitex.data.findAPI;frompythondata_cpu_cv32e40pimportdata_filemy_data_file="abc.txt"withopen(data_file(my_data_file))asf:print(f.read())The data files come fromhttps://github.com/antmicro/cv32e40pand are imported usinggit subtreesto the directorypythondata_cpu_cv32e40p/system_verilog.InstallingDirectly from git repositoryManuallyYou can install the package manually, however this isnotrecommended.git clone https://github.com/litex-hub/pythondata-cpu-cv32e40p.git
cd pythondata-cpu-cv32e40p
sudo python setup.py installUsingpipwith git repositoryYou can usepipto install the data package directly
from github using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.gitIf you want to install for the whole system rather than just the current user,
you need to remove the--userargument and run as sudo like so;sudo pip install git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.gitYou can install a specific revision of the repository using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<hash>Withrequirements.txtfileAdd to your Pythonrequirements.txtfile using;-e git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.gitTo use a specific revision of the repository, use the following;-e https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<hash>ViaPyPiUsingpippip install --user pythondata-cpu-cv32e40pWithrequirements.txtfileAdd to your Pythonrequirements.txtfile using;pythondata-cpu-cv32e40pIssues and FixesThis package is autogenerated from the sources inhttps://github.com/antmicro/cv32e40pusing thepythondata-autotoolset. Pull requests and issues on this pythondata repo may not be monitored.
Package: pythondata-cpu-cv32e40s
pythondata-cpu-cv32e40sNon-Python  files needed for the cpu cv32e40s packaged
into a Python module so they can be used with Python libraries and tools.This is useful for usage with tools likeLiteX.The data files can be found under the Python modulepythondata_cpu_cv32e40s. Thepythondata_cpu_cv32e40s.data_locationvalue can be used to find the files on the file
system.Example of getting the data file directly;importpythondata_cpu_cv32e40smy_data_file="abc.txt"withopen(os.path.join(pythondata_cpu_cv32e40s.data_location,my_data_file))asf:print(f.read())Example of getting the data file usinglitex.data.findAPI;frompythondata_cpu_cv32e40simportdata_filemy_data_file="abc.txt"withopen(data_file(my_data_file))asf:print(f.read())The data files come fromhttps://github.com/openhwgroup/cv32e40sand are imported usinggit subtreesto the directorypythondata_cpu_cv32e40s/system_verilog.InstallingDirectly from git repositoryManuallyYou can install the package manually, however this isnotrecommended.git clone https://github.com/litex-hub/pythondata-cpu-cv32e40s.git
cd pythondata-cpu-cv32e40s
sudo python setup.py installUsingpipwith git repositoryYou can usepipto install the data package directly
from github using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40s.gitIf you want to install for the whole system rather than just the current user,
you need to remove the--userargument and run as sudo like so;sudo pip install git+https://github.com/litex-hub/pythondata-cpu-cv32e40s.gitYou can install a specific revision of the repository using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40s.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40s.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40s.git@<hash>Withrequirements.txtfileAdd to your Pythonrequirements.txtfile using;-e git+https://github.com/litex-hub/pythondata-cpu-cv32e40s.gitTo use a specific revision of the repository, use the following;-e https://github.com/litex-hub/pythondata-cpu-cv32e40s.git@<hash>ViaPyPiUsingpippip install --user pythondata-cpu-cv32e40sWithrequirements.txtfileAdd to your Pythonrequirements.txtfile using;pythondata-cpu-cv32e40sIssues and FixesThis package is autogenerated from the sources inhttps://github.com/openhwgroup/cv32e40susing thepythondata-autotoolset. Pull requests and issues on this pythondata repo may not be monitored.
Package: pythondata-cpu-cv32e40x
pythondata-cpu-cv32e40xNon-Python  files needed for the cpu cv32e40x packaged
into a Python module so they can be used with Python libraries and tools.This is useful for usage with tools likeLiteX.The data files can be found under the Python modulepythondata_cpu_cv32e40x. Thepythondata_cpu_cv32e40x.data_locationvalue can be used to find the files on the file
system.Example of getting the data file directly;importpythondata_cpu_cv32e40xmy_data_file="abc.txt"withopen(os.path.join(pythondata_cpu_cv32e40x.data_location,my_data_file))asf:print(f.read())Example of getting the data file usinglitex.data.findAPI;frompythondata_cpu_cv32e40ximportdata_filemy_data_file="abc.txt"withopen(data_file(my_data_file))asf:print(f.read())The data files come fromhttps://github.com/openhwgroup/cv32e40xand are imported usinggit subtreesto the directorypythondata_cpu_cv32e40x/system_verilog.InstallingDirectly from git repositoryManuallyYou can install the package manually, however this isnotrecommended.git clone https://github.com/litex-hub/pythondata-cpu-cv32e40x.git
cd pythondata-cpu-cv32e40x
sudo python setup.py installUsingpipwith git repositoryYou can usepipto install the data package directly
from github using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40x.gitIf you want to install for the whole system rather than just the current user,
you need to remove the--userargument and run as sudo like so;sudo pip install git+https://github.com/litex-hub/pythondata-cpu-cv32e40x.gitYou can install a specific revision of the repository using;pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40x.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40x.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40x.git@<hash>Withrequirements.txtfileAdd to your Pythonrequirements.txtfile using;-e git+https://github.com/litex-hub/pythondata-cpu-cv32e40x.gitTo use a specific revision of the repository, use the following;-e https://github.com/litex-hub/pythondata-cpu-cv32e40x.git@<hash>ViaPyPiUsingpippip install --user pythondata-cpu-cv32e40xWithrequirements.txtfileAdd to your Pythonrequirements.txtfile using;pythondata-cpu-cv32e40xIssues and FixesThis package is autogenerated from the sources inhttps://github.com/openhwgroup/cv32e40xusing thepythondata-autotoolset. Pull requests and issues on this pythondata repo may not be monitored.