Saravanan666/pypi-org huggingface dataset:
Package: planning-poker-jira
This application extends the Planning Poker app with the ability to import stories and their description from Jira and
export the estimated amount of story points back to the Jira instance.This extension also serves as an example on how to add custom behaviour to/extend the functionality of the Planning
Poker app.Features⬇️Import storiesfrom your backlog and use them to poker.⬆️Export story pointsback to the Jira backend hassle-free.📋 Easily managemultiple Jira backends.🔒Securely safe your passwordin an encrypted database field.QuickstartYou’ll need an existing system with the Planning Poker app installed. See itsdocumentationif you haven’t already.Install the Planning Poker Jira app.$ pip install planning-poker-jiraAdd the app and its dependencies to the list of your installed apps.INSTALLED_APPS=[...'planning_poker','encrypted_fields','planning_poker_jira']Add encryption keys to your settings.
This is used to encrypt your passwords before they are stored in the database. If you don’t already have this
defined, it’s probably easiest to take yourSECRET_KEYand convert it to hex since that should be kept secret
anyways. Seeencrypted fields docsfor more
information on this setting.FIELD_ENCRYPTION_KEYS=[SECRET_KEY.encode().hex()[:64]]Seeconfigurationfor more
ways to customize the application to fit your needs.Run the migrations.$ python manage.py migrateYou can now start your server.$ python manage.py runserver 0.0.0.0:8000
Package: planning-sat
planning_satAI Planning as SatisfiabilityAboutThis library implements Planning as Satisfiability that can be used to solve STRIPS-like AI Planning Problems using PDDL.
When passed the domain and problem representations in PDDL, the library returns Satisfiability and the model if it is satisfiable and None otherwise.DocumentationIf you are interested in understanding the details, please read my post
atTBDInstallationpip install planning_sator, you can clone this repositoryExamplePDDL for representing planning domain and problemThe Dock-Worker Robots Domain and Problem are provided in thedomaindirectory.
There are also Simple Domain and Problem in the same directory.
You can create your PDDL files, or you can download them from the internet.Use as a CLI ScriptYou can execute the script directly by passing it the required arguments which are:path to the domain filepath to the problem filelength of planexample:python3 davis_putnam.py -d domain/simple-domain.pddl -p domain/simple-problem.pddl -l 1 -fIncluding the library in your projectIf you want to include the library in your project, you can install it with pip.
The steps are simple:Create an encoder object which will encode the Planning Problem to a boolean (propositional) formula in CNF (Conjunctive Normal Form)Create a DPLL object which will runDavis–Putnam–Logemann–Lovelandalgorithm over the encoded problem to determine whether it issatisfiableor notimportplanning_sat.encoderasencoderfromplanning_sat.davis_putnamimportDavisPutnamdomain_file="domain/simple-domain.pddl"problem_file="domain/simple-problem.pddl"encoder=encoder.PlanningProblemEncoder(domain_file,problem_file,length=1)davis_putnam=DavisPutnam()satisfiable,model=davis_putnam(encoder.propositional_formulas)pddlpypddlpy included in this repo is the work of Hernán M. Foffani, it is copied fromhere.IssuesPlease report issues if you found bugs or raise a Pull Request.
Package: planqk-quantum
PlanQK Quantum SDKThe PlanQK Quantum SDK is for developing quantum circuits usingQiskitto be run on
quantum devices provided by thePlanQK Platform.
This library is anextensionfor Qiskit.
This means that you are able to seamlessly integrate and reuse your existing Qiskit code, leveraging the power and
familiarity of a framework you are already accustomed to.Getting StartedCheck out the following guides on how to get started with PlanQK:PlanQK Quickstart GuideUsing the PlanQK Quantum SDKInstallationThe package is released on PyPI and can be installed viapip:pipinstall--upgradeplanqk-quantumDevelopmentTo create a new Conda environment, run:condaenvcreate-fenvironment.ymlThen, to activate the environment:condaactivateplanqk-quantumTo install the package in development mode, run:pipinstall-e.Release ProcessThe SDK is released toPyPi.
The release numbers follow theSemantic Versioningapproach resulting in a version number
in the formatmajor.minor.patch.
The version number is automatically increased by the CI/CD pipeline based on yourcommit message format.Production ReleaseIf you push to themainbranch and the commit message contains, for example, the prefixfeat:,fix:orperf:a new release will be created automatically.
You can use the keywordBREAKING CHANGEin the commit message to trigger a major release.
Usechore:or omit the prefix if you do not want a new release to be created.Warning:This release will be public and affects all services using the SDK in production.Staging / Testing ReleaseIf you want to create a release only for the testing environment (pre-release), perform the following steps:Create a new branch frommainand name itdev(make sure you delete an olddevbranch).
This branch is used for pre-releases and its commits are not automatically released.In thedevbranch opensetup.pyfile and increase the version number and add the suffixrcXto it, whereXis the release candidate number.
If the version number is for instance12.1.0, then the new version number should be12.1.1rc1.Push your changes to thedevbranch.Git to theGitHub repositoryand click an onReleases.Click onDraft a new release.Click onChoose a tag.
Enter the new version number prefixed byvin theTag versionfield, e.g.,v12.1.1rc1.Select thedevbranch in theTargetfield.Enter as title the tag name and add a description for the release.SelectThis is a pre-releaseand click onPublish release.Do not forget to merge your changes back to the main branch once you want to release these changes to production.
Package: planqk-service-sdk
PlanQK Service SDKInstallationThe package is published on PyPI and can be installed viapip:pipinstall--upgradeplanqk-service-sdkUsagefromplanqk.service.clientimportPlanqkServiceClientconsumer_key="..."consumer_secret="..."service_endpoint="..."client=PlanqkServiceClient(service_endpoint,consumer_key,consumer_secret)# prepare your input data and parametersdata={"input":{"a":1,"b":2}}params={"param1":"value1","param2":"value2"}# start the executionjob=client.start_execution(data=data,params=params)# wait for the resultresult=client.get_result(job.id)DevelopmentTo create a new Conda environment, run:condaenvcreate-fenvironment.ymlThen, to activate the environment:condaactivateplanqk-service-sdkTo install the package in development mode, run:pipinstall-e.
Package: planship
planship-pythonWelcome to the Python client for thePlanship API.Planshipenables developers to build subscription logic for product pricing based on any combination of features, seats, and usage.Installation and basic usageInstallplanshipwith pip, or another package manager of your choice like Poetrypip install planship#orpoetry add planshipImport and instantiate thePlanshipclass, and start making calls to the Planship APIfromplanshipimportPlanshipplanship=Planship("clicker-demo",# Planship product slug"273N1SQ3GQFZ8JSFKIOK",# Planship API client ID"GDSfzPD2NEM5PEzIl1JoXFRJNZm3uAhX"# Planship API client secret)# List product plansplans=planship.list_plans()# Create a customer with a name and emailcustomer=planship.create_customer({"name":"Darth Vader","email:":"vader@empire.gov"})# Subscribe the customer to a plan with the slug "medium"subscription=planship.create_subscription(customer.id,"medium")# Retrieve entitlements for the customerentitlements=planship.get_entitlements(customer.id)# Report 11 units of usage for the "api-call" metering ID for the customerplanship.report_usage(customer.id,"api-call",11)The complete reference for thePlanshipclass can be foundhere.LinksPlanship documentationSign up for PlanshipPlanship Python client class referencePlanship Python client at PyPI
Package: plantain
PlantainA commandline tool for managing the lifecycle of Mandrill templates.In short,plantainallows the user to manage their Mandril email
templates from the commandline, perhaps as part of their configuration
management system (eg Salt, Ansible, cfengine, Chef, Puppet, etc).Install$ pip install plantainor:$ sudo python setup.py installNotesNote:Plantainused to be calledbanana, until it turned out
that someone else already hadbananain the cheese shop! For
backward comparabilityplantaininstalls aplantainand for
legacy support, abananabinary.Setupplantainexpects each template to consist of three files: an HTML
file (.html) for the HTML fork of the email to be sent, a text file
(.txt) for the text fork, and a configfile (.cfg) for the metadata
aspects of the template (sending address, Subject: header etc). eg:account_expired.cfg
account_expired.html
account_expired.txtA sample configfile might read:from_name = YourCompany
from_email = robot@yourcompany.com
subject = Account ExpirationThe keys in the configfile match the keys used by the Mandrill API:https://mandrillapp.com/api/docs/Note that for fields which are lists, such as labels, you must put a
comma after the first item if there’s only one item in the list for it
to be recognised as a list (otherwise the list is the list of characters
in the token):labels = foo,Usage$ plantain -h
usage: plantain [-h] -k KEY -t TEMPLATE -a ACTION [-p] [-q] [-v]

Manage and deploy Mandrill templates.

optional arguments:
  -h, --help            show this help message and exit
  -k KEY, --key KEY     Mandrill API key.
  -t TEMPLATE, --template TEMPLATE
                        Template to manipulate.
  -a ACTION, --action ACTION
                        Action to perform: ['addinfo', 'update',
                        'publish', 'delete', 'list', 'time_series']
  -p, --publish         Auto-publish (for add and update).
  -q, --quiet           Suppress normal output.
  -v, --verbose         Output results and operations.ExamplesExample call to add a new template to Mandrill called “foo_bar”:$ plantain -k AbbbcdeDeAdBeeFGaFFPA -t foo_bar -a addNote that this will not “publish” the template. To do that:$ plantain -k AbbbcdeDeAdBeeFGaFFPA -t foo_bar -a publishOr you can publish at the same time you add or update the template:$ plantain -k AbbbcdeDeAdBeeFGaFFPA -t foo_bar -a update -p
Package: plantcv
PlantCV: Plant phenotyping using computer visionPlease use, cite, andcontribute toPlantCV!
If you have questions, please submit them via theGitHub issues page.
Follow us on twitter@plantcv.Introduction to PlantCVPlantCV is an open-source image analysis software package targeted for plant phenotyping. PlantCV provides a common
programming and documentation interface to a collection of image analysis techniques that are integrated from a variety
of source packages and algorithms. PlantCV utilizes a modular architecture that enables flexibility in the design of
analysis workflows and rapid assimilation and integration of new methods. For more information about the project,
links to recorded presentations, and publications using PlantCV, please visit our homepage:https://plantcv.danforthcenter.org/.Quick LinksDocumentationInteractive DocumentationInstallation InstructionsUpdating/ChangelogPublic Image DatasetsContribution GuideCode of ConductDownloadsGitHubPyPIConda-forgeDockerZenodoCiting PlantCVIf you use PlantCV, please cite thePlantCV publicationsrelevant to your work. To see how others have used PlantCV in their research, check out our list ofpublications using PlantCV.Issues with PlantCVPlease file any PlantCV suggestions/issues/bugs via ourGitHub issues page. Please check to see if any related
issues have already been filed.ContributorsAFis-245💻Adam Dimech💻📖⚠️🤔Alexander Kutschera💻📖⚠️Alexandria Pokorny💻📖⚠️Andy Lin💻Cesar Lizarraga💻📖⚠️Collin Luebbert💻David Peery💻📖⚠️🤔Dhiraj Srivastava💻📖⚠️Dominik Schneider💻📖⚠️🤔Eric Platon💻📖⚠️Fabian Dubois💻Fabio Barbero💻Garrot Yoan📖GrantKonkel💻📖Haley Schuhl💻📖🚧✅⚠️📢🤔💬Hudanyun Sheng💻📖⚠️🤔Jake💻📖Jeffrey Berry💻📖⚠️🤔JoeDuenwald📖Jorge Gutierrez💻📖⚠️📢🤔💬Josh Sumner💻📖⚠️✅🤔Katie Murphy📖✅📢🤔📣Malia Gehan📆💻📖🔍🧑‍🏫📢✅🤔💬⚠️Malinda💻Mark Wilson💻📖⚠️Max💻📖🤔Noah Fahlgren📆💻📖⚠️🔍🧑‍🏫📢✅🤔💬Sanazjd💻📖⚠️🤔SethPolydore💻📖⚠️Steen Hoyer💻📖🤔Stephan Summerer💻Steven Wu💻Stylopidae1793📖adrianethompson💻📖✅annacasto💻📖⚠️✅📢🤔bganglia💻📖⚠️jgerardhodge💻📖⚠️jmgordon1223📖jwheeler5💻📖⚠️kbgilbert🎨lacostag📖lchavez037📖leowlima📖mtwatso2-eng💻scallen81💻sdkenney42📖typelogic💻wurDevTim💻📖⚠️zeeuqsze📖✅📢🧑‍🏫
Package: plant-disease-classification
plant-disease-classificationA Python module that classifies disease on plants. You can classify plant images from Python or
from the command line with a simple classification library.Built using Deep Learning and Convolutional Networks models trainedhere.This also provides a simple plant disease classification command line tool that lets you do face recognition on a folder of images from the command line.InstallationYou can install plant-disease-classification using:$ pip install plant-disease-classificationGetting the codeThe code is hosted athttps://github.com/abdullahselek/plant-disease-classificationCheck out the latest development version anonymously with:$ git clone git://github.com/abdullahselek/plant-disease-classification.git
$ cd plant-disease-classificationTo install dependencies, run either:$ pip install -r requirements.txtTo install the minimal dependencies for production use (i.e., what is installed
withpip installplant-disease-classification) run:$ pip install -r requirements.txtUsageClassification using Pythonfromplant_disease_classificationimportapi# Classification with image pathresult=api.classify(image_path="YOUR_IMAGE_PATH")# Classification with image dataresult=api.classify(image_data=YOUR_IMAGE_DATA)Classification via CLIpython-mplant_disease_classification-ptestdata/4507d7a208e839e34466ba7d7bdb144e.jpgpython-mplant_disease_classification-dBASE64_ENCODED_IMAGE_DATA
Package: plantextract
Plant Extract Document processor================================A package for interacting with and creating [SunSpec](http://sunspec.org)Plant Extract Documents and the standard blocks contained therein.Installation------------Using pip::pip install plantextractUsage-------Typical `parse` usage looks like this::#!/usr/bin/env pythonfrom pedparser import ModelIDValues, PointIDValuesimport plantextract.pedparser...this_dir, this_filename = os.path.split(__file__)ped_file = os.path.join(this_dir, 'examples', 'ped-kitchen-sink.xml')parser = pedparser.PlantExtractParser()parser.parse(ped_file=ped_file)parser.ped.plant               # the Plant blockparser.ped.plant.name          # the Plant's name attributeparser.ped.plant.location.city # the Plant's location city attribute# get ENERGY Points that are from an INVERTER_SINGLE_PHASE Modelpoints = parser.match_model_points(model_id=ModelIDValues.INVERTER_SINGLE_PHASE,point_ids=[PointIDValues.ENERGY])print('Retrieved point:', points[0].id, points[0].value())print('Point scale factor:', points[0].sf)print('Point value:', points[0].value())  # another way to get Point value# get ENERGY Points that are from an INVERTER_SINGLE_PHASE Model, but only# if the Device containing the Model has a logger ID of '11:22:33:44:55:66'points = parser.match_model_points(model_id=ModelIDValues.INVERTER_SINGLE_PHASE,logger_id='11:22:33:44:55:66',point_ids=[PointIDValues.ENERGY, PointIDValues.POWER])print('Retrieved point:', points[0].id, points[0].value())# ...etc...# ...or one can directly interact with the sunSpecData blockprint parser.ped.sunSpecData.d   # the DeviceRecords list in the sunSpecDataprint parser.ped.sunSpecData.d[0].m # the Models list in the Zero'th Deviceprint parser.ped.sunSpecData.d[0].m[0].p[0].id # the ID of the Zero'th Point# ...etc...Typical `create` usage looks like this:#!/usr/bin/env pythonfrom plantextract.ped import PlantExtract, Plant, Locationfrom plantextract.ped import NamePlate, Array, Equipmentped = PlantExtract(Plant(uuid4(),activation_date="2013-03-02",location=Location(latitude=1.1, longitude=2.2,city="Redwood City",state_province="CA"),name_plate=NamePlate(props=[Property('installedDCCapacity', 'float', '6.5'),Property('installedACCapacity', 'float', '6.4')]),design_elements=DesignElements(props=[Property('plantType', 'string', 'commercial')]),array=Array(props=[Property('description','string','Carport')], array_id=1),equipment=Equipment(props=[Property('Mn', 'string', 'MeterManuf'),Property('Md', 'string', 'MeterModel'),Property('uncertainty', 'float', '0.5')], equipment_type='meter')))Requires [PyXB](http://pyxb.sourceforge.net) 1.2.3
Package: plantgateway
plantgatewayBluetooth LE to mqtt gateway for Xiaomi Mi plant sensors. For more details see thedocumentation overview.Use caseFor many setups the Xiaomi Mi plant sensors are too far away from your
home server to connect directly via Bluetooth LE.
In such a scenario the plantgatway will poll the data from a list of
Xiaomi Mi plant sensors via Bluetooth LE usingmiflora.
The data is then published via mqtt to your home automation server.The plantgateway is intended to be run on a small Linux machine (e.g.Raspberry Pior aC.H.I.P) that has both Bluetooth LE and WiFi.installation & updateinstallpython 3.4(or above)
andpipsudo apt-get install python3-pip build-essential libglib2.0-dev libyaml-devinstall the plant gateway from pypi:sudo pip install --upgrade plantgatewayor if you have multiple python and pip installations:sudo pip3 install --upgrade plantgatewayTo update your installation just run pip again.If you have problems with the PyYaml installation, update your pip version
withsudo pip3 install --upgrade pipand try again.configurationCopy theplantgw.yaml(in this repository) to your home directory and
rename it to ".plantgw.yaml".
Then change this file to match your requirements.executionAfter the installation with pip you can simply run the tool from the command line:plantgatewayThere are no command line parameters and there is no interaction required.
You probably want to add the script to your cron tab to be executed
in regular intervals (e.q. every hour).integration in home automationHomeAssistantIf you enable theMQTT discoveryfeature by setting thediscovery_prefixparameter in
the config file, all configured sensors are automatically available in HomeAssistant.
To monitor the state of your plants, you can use the"plant" component.fhemTo check your plants in the home automation toolfhem,
you can use thegardenermodule.
The installation is explained on the github page of the module.If you haven't done so, you need to configure your MQTT server in fhem with
aMQTTmodule.
For each sensor you have, set up aMQTT_Deviceand make it auto subscribe to the topic
you configured in the plantgateway:define <plant_name> MQTT_Device
attr <plant_name> autoSubscribeReadings <prefix_in_config>/<plant alias>/+After that configure the gardener to match your requirementsSecurityA remark on security:
Before running your MQTT server on the internet make sure that you enable
SSL/TLS encryption and client authentication.Problem analysisIn case you have any problem with plantgateway, please check:Is you configuration file a valid YAML file?Does your Bluetooth dongle support Bluetooh Low Energy? Check withsudo hcitool lescan, this should list all Low Energy devices.If you have connection issues, please try a system updatesudo apt update; sudo apt dist-upgrade. This fixes these issues usually.If all this does not help, please file a bug ticket in github.LicenseUnless stated otherwise all software in this repository is licensed under the Apache License 2.0http://www.apache.org/licenses/LICENSE-2.0
Package: plantilla-programas-python
﻿# Plantilla de programas para Python![pipy](https://badge.fury.io/py/plantilla-programas-python.svg)
[![codecov](https://codecov.io/gh/BREKIADATA-SL/plantilla-programas-python/branch/main/graph/badge.svg?token=2T1J6LQTJE)](https://codecov.io/gh/BREKIADATA-SL/plantilla-programas-python)El objetivo de este repositorio es servir de referencia a las distintas herramientas que se están desarrollado dentro de la organización para estandar las siguientes funcionalidades en cada uno de los entornos de trabajo:Empaquetamiento con PipyEjecución del Análisis estático del código en integración continuaEjecución de la Generación automatica de Documentación en integración continuaEjecución de pruebas unitarias en integración continua## Paquete PythonEste repositorio está configurado en los ficheros setup.py y setup.cfg para compilar toda la lógica y empaquetarlas dentro del sistema de PIPY.De forma que podremos hacer uso de:pip3 install plantilla-programas-python## Ejecutar Integración continua en localTox es una herramienta de automatización para python, sus comandos son los siguientes:### Ejecutar Tests, Lint y Compilar la documentacióntox### Ejecutar teststox -e py38### Ejecutar Linttox -e lint### Compilar la documentacióntox -e docs## Integración continua
Github está configurado con dos distintas comprobaciones.### ToxSe ejecutará cada vez que se haga una Pull request y realizara el comando Tox completo. Indicandote si todo a ido bien.### Publicación del paquete en PythonSe ejecutará cada vez que se haga una Pull Request a la rama “Main”.Solamente pasará si el paquete no existe previamente en el repositorio de paquetes, por lo tanto cuando estemos seguros de que todo está finalizado deberemos hacer uso de los comandos:bumpversion minorDentro de la version actual del paquete, incrementa en 1 la subversionbumversion major (Incrementa la version)Incrementa la version del paquete## Documentación
Al estar trabajando sobre repositorios privados, no podemos utilizar readthedocs para alojar la nuestra.Pero igualmente estára accesible para los desarrolladores bajo el directorio:/docs/_build/html![docs](https://github.com/BREKIADATA-SL/plantilla-programas-python/raw/main/imagenes/docs.png)
Package: plantlabeller
A pixel-level label tools for plant pictures taken by drones.Implement Python version >= 3.6.5, QGIS version >=3.4RUNpip3 install plantlabellerto install from terminalNeed to modify matplotlibrc file (if crashed)ADD:backend: TkAggGDAL instllation instruction:RUNpip3 install GDAL==2.4.2if failed with error: > gdal-config not foundgo to step 2.RUNbrew install gdal, go to step 1if failed with "gcc" go to step 3RUNbrew reinstall gcc, go to step 1
Package: plantower
Plantower Particulate Sensor Python interfaceA basic python interface for interacting with the plantower PM sensors.  This code has been tested with the following devices:Plantower PMS5003Plantower PMS7003Plantower PMSA003It may work with other sensors from the plantower range, if you try it please do let us know either way.  If there are any problems with it please either raise an issue or fix it and issue a pull request.The following persons have contributed to this library:Philip J. BasfordFlorentin M. J. BulotSimon J. CoxSteven J. J. Ossontserunis