Saravanan666/pypi-org huggingface dataset:
Package: pieorg
PieOrgA python CLI app to organise every story you read on the internet!See the whole documentation on Github:https://github.com/yashprakash13/PieOrgFeatures:Insert a story with the following options:TitleAuthorLink(url)Category/Folder to create2 different genresFandom ( if it's a fanfiction, enter the fandom, otherwise enter misc)The type of story (a novel or a short story)A Love meter for the story (on a scale of 1 to 5, 5 being the most loved story)If the story is completeView your stories from the database with the following options:View all storiesView stories which are completeView stories which belong to a particular category/folderView stories which are written by a particular authorView stories of a particular genreView all categories/folders you haveView stories with a specific minimum love metreView stories of a particular type(novel or short story)View stories of a genre and love metreView stories of a description/keyword (upon search)Edit a story in the database:Edit the description of an existing storyEdit the status(complete or not) of an existing story
Package: piepd
ContextThis tool is showcased in the projectIotaWorkshopSummaryThis is a high level e-paper libaray. It supports QR code displays and allows easy lines of text writing along with right text align. Ideally if used this libaray should be imported to use the helpful classEPD.NoteThis works for all waveshare screens when you import it. Simply change the line from,from waveshare_epd import epd1in54_V2To whatever screen you have. For instance the 5.83 inch BW screenfrom waveshare_epd import epd5in83Or the color version (bc)from waveshare_epd import epd5in8bcInstallationThis project depends onPillowbut there are a variety of ways of installing it so I did not mark it as a dependency in the pypi package, you can install it using,pip install pillowSimply install this package from the pypi repopip install piepdCommand Line InterfaceThere are a few example displays that can be run through the cli,epd occipied #Displays text saying the tools is occupied
epd --help #To see altenative displaysImplementation Examplefrom epd import EPD
epd = EPD()
epd.setFontSize(21)
epd.drawText("Hello world",draw=False) #Draws this text on the screen
epd.drawText("Left",draw=False) #Does not display yet until draw=True

#The following line will right align the text and put it on the previous line
epd.drawText("Right",right=True,overlap=True,draw=True)

#Once you are completly done using the dislplay run
epd.close()WiringBoard pin nameBoard pinRPi pin nameVCC13v3GND6GNDDIN19GPIO10, MOSICLK11GPIO11, SCKLCS24GPIO8, CE0DC22GPIO25RST11GPIO17BUSY18GPIO24
Package: piepline
PiePlineNeural networks training pipeline based on PyTorch. Designed to standardize training process and accelerate experiments.Core is about 2K lines, covered by tests, that you don't need to write againFlexible and customizable training processCheckpoints management and train process resuming (source and target device independent)Metrics processing and visualization by builtin (tensorboard,Matplotlib) or custom monitorsTraining best practices (e.g. learning rate decaying and hard negative mining)Metrics logging and comparison (DVC compatible)Getting started:Documentation| Stable:| Latest:|See the full documentation thereRead getting started guideSee the examplesMNIST classification -notebook,file,Kaggle kernelSegmentation -notebook,fileResume training process -filePiePline short overview:importtorchfromneural_pipeline.builtin.monitors.tensorboardimportTensorboardMonitorfromneural_pipeline.monitoringimportLogMonitorfromneural_pipelineimportDataProducer,TrainConfig,TrainStage,\ValidationStage,Trainer,FileStructManagerfromsomethigimportMyNet,MyDatasetfsm=FileStructManager(base_dir='data',is_continue=False)model=MyNet().cuda()train_dataset=DataProducer([MyDataset()],batch_size=4,num_workers=2)validation_dataset=DataProducer([MyDataset()],batch_size=4,num_workers=2)train_config=TrainConfig(model,[TrainStage(train_dataset),ValidationStage(validation_dataset)],torch.nn.NLLLoss(),torch.optim.SGD(model.parameters(),lr=1e-4,momentum=0.5))trainer=Trainer(train_config,fsm,torch.device('cuda:0')).set_epoch_num(50)trainer.monitor_hub.add_monitor(TensorboardMonitor(fsm,is_continue=False))\.add_monitor(LogMonitor(fsm))trainer.train()This example of training MyNet on MyDataset with vizualisation in Tensorflow and with metrics logging for further experiments comparison.Installation:pip install pieplineForbuiltinmodule using install:pip install tensorboardX matplotlibInstall latest version before it's published on PyPipip install -U git+https://github.com/PiePline/piepline
Package: pie-python-bitcoinrpc
=================
pie-python-bitcoinrpcAuthServiceProxy is an improved version of python-jsonrpc.It includes the following generic improvements:HTTP connections persist for the life of the AuthServiceProxy objectsends protocol 'version', per JSON-RPC 1.1sends proper, incrementing 'id'uses standard Python json libcan optionally log all RPC calls and resultsJSON-2.0 batch supportIt also includes the following bitcoin-specific details:sends Basic HTTP authentication headersparses all JSON numbers that look like floats as Decimal,
and serializes Decimal values to JSON-RPC connections.Installationchange the first line of setup.py to point to the directory of your installation of python 2.*run setup.pyNote: This will only install bitcoinrpc. If you also want to install jsonrpc to preserve
backwards compatibility, you have to replace 'bitcoinrpc' with 'jsonrpc' in setup.py and run it again.Or simply install the library using pip::pip install pie-python-bitcoinrpcExample.. code:: pythonfrom bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException

# rpc_user and rpc_password are set in the bitcoin.conf file
rpc_connection = AuthServiceProxy("http://%s:%s@127.0.0.1:8332"%(rpc_user, rpc_password))
best_block_hash = rpc_connection.getbestblockhash()
print(rpc_connection.getblock(best_block_hash))

# batch support : print timestamps of blocks 0 to 99 in 2 RPC round-trips:
commands = [ [ "getblockhash", height] for height in range(100) ]
block_hashes = rpc_connection.batch_(commands)
blocks = rpc_connection.batch_([ [ "getblock", h ] for h in block_hashes ])
block_times = [ block["time"] for block in blocks ]
print(block_times)Logging all RPC calls to stderr.. code:: pythonfrom bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException
import logging

logging.basicConfig()
logging.getLogger("BitcoinRPC").setLevel(logging.DEBUG)

rpc_connection = AuthServiceProxy("http://%s:%s@127.0.0.1:8332"%(rpc_user, rpc_password))
print(rpc_connection.getinfo())Produces output on stderr likeDEBUG:BitcoinRPC:-1-> getinfo []
DEBUG:BitcoinRPC:<-1- {"connections": 8, ...etc }Socket timeouts under heavy loadPass the timeout argument to prevent "socket timed out" exceptions:.. code:: pythonrpc_connection = AuthServiceProxy(
    "http://%s:%s@%s:%s"%(rpc_user, rpc_password, rpc_host, rpc_port),
    timeout=120)
Package: pieqt
## pieqt Project------pieqt is a module that helps you start with python qt(PySide2) with minimal effort.Many examples of PySide2 that I've seen are cumbersome, have many classes etc...With pieqt, you just declare an app variable that holds your GUI app, and then add widgets and layouts.Finally you run it, and thats it!!!You have a GUI app up and running.**************Example*******************----------------------------------------from pieqt import*app = App()app.addWidget(QPushButton("Press me!"))app.run()---------------------------------------**************Example******************
Package: piera
PieraPiera is a lightweight, pure-PythonHieraparser. It was built to help bridge the gap between Puppet/Hiera and Python system scripts. Piera is currently not feature complete, lacking some less-used interoplation and loading features (feel free to contribute!)Why?Piera was built atBraintreeto help us bridge a gap of emerging Python system scripts, and a historical storage of Puppet/Hiera data.Install ItPyPipip install pieraManualgitclonegit@github.com:b1naryth1ef/piera.gitcdpiera
pythonsetup.pyinstallUsageimportpierah=piera.Hiera("my_hiera.yaml")# You can use piera to simply interact with your structured Hiera data# key: 'value'asserth.get("key")=="value"# key_alias: '%{alias('key')}'asserth.get("key_alias")=="value"# key_hiera: 'OHAI %{hiera('key_alias')}'asserth.get("key_hiera")=="OHAI value"# Give piera contextasserth.get("my_context_based_key",name='test01',environment='qa')=="context is great!"RequirementsPython 2.7+
Package: pier-cli
pier-clipier-cli is a development tool that provides an easy way to deploy your local images to Kubernetes. No need to write any YAML. Just build your image and deploy.PrerequisitesThe following resources need to be installed and running:DockerKubernetesInstallationInstallation using pip(3):pipinstall--userpier-cliCommandsImagesCreate imageCreate an image based on the Dockfile in the current folder➜piercreateimage{IMAGE_TAG}Pull imagePull an existing image➜pierpullimage{IMAGE_TAG}Search imagesSearch for images on Docker Hub➜piersaerchimage{SEARCH_TERM}List imagesList available images➜piergetimagesDelete imageDelete your images once they are not needed anymore➜pierdeleteimage
Selectimage:[0]-your_image:0.1[1]-another_image:latest
➜0DeploymentsCreate deploymentDeploy an image to your Kubernetes cluster➜piercreatedeployment{DEPLOYMENT_NAME}Selectimage:[0]-your_image:0.1[1]-another_image:latest
➜0List deploymentsList current deployments➜piergetdeploymentsDelete deploymentDelete your deployments once they are not needed anymore➜pierdeletedeployment
Selectdeployment:[0]-your-application[1]-another-application
➜1To doCreate k8s loadbalancer to expose docker portsCommand to create configmapsPossibility to edit deployments...
Package: pier-ds-utils
Data Science UtilsA toolkit for day-to-day DS tasks such as using custom transformers or estimators.Found a bug or have a feature request?Open an issue!Usageimportpier_ds_utilsasds# Transformersdiscrete_categorizer=ds.transformer.CustomDiscreteCategorizer(...)interval_categorizer=ds.transformer.CustomIntervalCategorizer(...)interval_categorizer_by_category=ds.transformer.CustomIntervalCategorizerByCategory(...)# Estimatorsglm_wrapper=ds.estimator.GLMWrapper(...)predict_proba_selector=ds.estimator.PredictProbaSelector(...)Installationpipinstallpier-ds-utils# orpoetryaddpier-ds-utilsFor a specificversion:pipinstallpier-ds-utils@_version_# orpoetryaddpier-ds-utils@_version_ContributingContributions are welcome! Please read thecontributing guidelinesfirst.
Package: pierskarsenbarg-pulumi-ngrok
ngrok Resource ProviderThe ngrok Resource Provider lets you managengrokresources.InstallingThis package is available for several languages/platforms:Node.js (JavaScript/TypeScript)To use from JavaScript or TypeScript in Node.js, install using eithernpm:npminstall@pierskarsenbarg/ngrokoryarn:yarnadd@pierskarsenbarg/ngrokPythonTo use from Python, install usingpip:pipinstallpierskarsenbarg-pulumi-ngrokGoTo use from Go, usego getto grab the latest version of the library:gogetgithub.com/pierskarsenbarg/pulumi-ngrok/sdk/go/....NETTo use from .NET, install usingdotnet add package:dotnetaddpackagePiersKarsenbarg.NgrokConfigurationThe following configuration points are available for thengrokprovider:ngrok:apiKey(environment:NGROK_API_KEY) - the API key forngrokngrok:apiBaseUrl(environment:NGROK_API_BASE_URL) - The API URL used to talk with ngrok. The default ishttps://api.ngrok.com.- optionalTodoAdd Java language supportAdd YAML language support
Package: pierskarsenbarg-pulumi-sdm
StrongDM Resource ProviderThe StrongDM Resource Provider lets you manageStrongDMresources.InstallingThis package is available for several languages/platforms:Node.js (JavaScript/TypeScript)To use from JavaScript or TypeScript in Node.js, install using eithernpm:npminstall@pierskarsenbarg/sdmoryarn:yarnadd@pierskarsenbarg/sdmPythonTo use from Python, install usingpip:pipinstallpierskarsenbarg-pulumi-sdmGoTo use from Go, usego getto grab the latest version of the library:gogetgithub.com/pierskarsenbarg/pulumi-sdm/sdk/go/....NETTo use from .NET, install usingdotnet add package:dotnetaddpackagePiersKarsenbarg.SdmConfigurationThe following configuration points are available for thesdmprovider:sdm:apiAccessKey(environment:SDM_API_ACCESS_KEY) - the API key forStrong DMsdm:apiSecretKey(environment:SDM_API_SECRET_KEY) - the corresponding secret key for the above API keyTodoAdd Java language supportAdd YAML language support
Package: piesparrow
Version - 0.1.2, Release Date - 13-Nov-22Website|Release Notes|Docs|Examples|GitHubpiesparrow is a stupidly simple python package to create interactive HTML frontends and dashboards. It is built on top of the javascript charting library Billboard.js while harnessing the power of custom light weight Sparrow CSS framework for styling and data processing capabilities of Python through Pandas.piesparrow - Code in Python, Present in HTMLJust import pieSparrow into your python script and perform data processingthrough Pandas as usual. When you are ready to visualize, call the relatedfunctions from pieSparrow to design your interactive dashboards and compose the HTML files by running your parent script in Python.import piesparrow as ps

ps.init(filename = 'hello_world', title = 'Hello World !')

ps.row (
    ps.h1('Hello World')
)Terminal:
$ python helloWorld.pymyFolder
|- helloWorld.py
|- hello_world.htmlLight and Dark Themes with multiple color optionspiesparrow have built in five color themes with both light and dark options, themes can also be customized using external CSS.Simple but flexible layoutsWith the power of flexible grids having intuitive rows and columns structure, you can shape your dashboards how ever you want.ps.row (
    ps.colsm(ps.h1('This is small column.')) +
    ps.colmd(ps.h1('This is medium column.'))
)Typography at your serviceWrite what you want the way you want, four heading sizes and optionality of strong text made simple.ps.row (
    ps.h1('This is H1 Text') +
    ps.h2('This is H2 Text') +
    ps.h3('This is H3 Text') +
    ps.h4('This is H4 Text') +
    ps.p('This is paragraph text') +
    ps.bold('This is bold text') +
    ps.link('https://piesparrow.com','This is a link')
)Built in ChartspieSparrow comes with the capability of charts through javascript out of the box, currently Bar, Line, Spline, Area, Pie and Donut charts are available alongside Gauge and KPI visual cards. We keep adding more chart type with new releases.data = pd.read_csv('mock_data.csv')
ps.row (
    ps.chart(
        title = 'myChart',
        df = data,
        columns = ['Month','Data 1'],
        xcolumn = 'Month',
        type = 'bar'
    )
)See theWebsite,DocsorExamplesto learn more.copyrights © 2022 | developed byDaniyal M, released under MIT License.
Package: piestore
piestoreis A Python API for RESTful Store Services.ObjectivesInstallingInstall and update usingpip:pipinstall-UpiestoreA Simple ExamplefrompiestoreimportAppapp=App()def_say(interjection='I like',noun='Pies'):returnapp.say(interjection,noun)LinksWebsite:Documentation:License:https://www.gnu.org/licenses/agpl.htmlReleases:https://pypi.org/project/piestore/Code: _To be released soon…_Issue tracker:Test status:Test coverage:
Package: pieterraform
PieTerraformThis is a wrapper for terraform to facilitate python developer to call terraform.
It is designed to invoke terraform commands in"Functional Chaining"styleNote: Onlypython>=3.7is supported.FeaturesBuilder pattern. One line code can call multiple terraform commandsTerraform commands' arguments are encapsulated as functions so code completion supportedNo need to worry about the sequence of argumentsUsageMake sure haveterraform in $PATHterraformversionInstallpipinstallpieterraformQuick startfrompieterraformimportTerraform# suppose you have terraform files in ./tfTerraform().workdir('./tf').init().run()# 'terraform init'.plan().run()# 'terraform plan'.apply().run()# 'terraform apply'# suppose you have terraform files in ./tf/prodTerraform().workdir('./tf').init().dir('prod').run().plan().dir('prod').run().apply().dir('prod').run()# suppose you have terraform files in ./tf/prodTerraform().workdir().init().dir('tf/prod').run().plan().dir('tf/prod').run().apply().dir('tf/prod').run()JustONE LINEcode!With ParamersfrompieterraformimportTerraform# suppose you have terraform files in ./tfTerraform().workdir('./tf')# 'terraform init -no-color -upgrade=false'.init().no_upgrade().no_color().run()# 'terraform plan -state mystate.json -no-color'.plan().state_file('mystate.json').no_color().out('myplan').run()# 'terraform apply myplan'.apply().use_plan('myplan').run()# 'terraform destroy -auto-approve -state mystate.json'.destroy().auto_approve().state('mystate.json').run()With Custome LogBy default it prints log in screen.
But you can cusomize it to use any loggerimportloggingfrompieterraformimportTerraform# output log to filelogFormatter=logging.Formatter('%(asctime)s[%(levelname)-5.5s]%(message)s')log_file='log.txt'f_handler=logging.FileHandler(log_file)f_handler.setFormatter(logFormatter)f_handler.setLevel(logging.DEBUG)logger=logging.getLogger(__name__)logger.setLevel(logging.DEBUG)logger.addHandler(f_handler)# suppose you have terraform files in ./tfTerraform(logger=logger)# this will output log to file 'log.txt'.workdir('./tf').init().run().plan().run().apply().run()Traditional calls rather than functions chainfrompieterraformimportTerraformtf=Terraform().workdir('./tf')initer=tf.init()initer.no_upgrade()initer.no_color().run()planer=tf.plan()planer.state_file('mystate.json')planer.no_color()planer.out('myplan').run()applyer=tf.apply()applyer.use_plan('myplan')applyer.run()Check resultfrompieterraformimportTerraforma_run=Terraform().workdir('./tf').init().run().plan().run().apply().run()forrina_run.results:print(r.output)print(r.command)Source CodeThis project is fully using docker as dev environment.Prerequisitiondocker: ">= 17.06"docker-compose: ">= 1.26"No pythonneeded.BuildmakeInstall to localmakeinstallRun testmaketestDistributionmakedistDevelopmentStart dev dockermake docker-devthis will start a container named pieterraform-devenvUse VSCodeOpen your vscode, attach to above container to do remote development
Package: pie_time
Desk clock for your Raspberry Pi.AboutPieTime lets you turn your Raspberry Pi into a desk clock. It’s written in
Python using PyGame framework.PieTime comes with three modules that allow displaying the clock, weather and
set of pictures. These modules (also known ascards) are highly configurable,
so you can tweak them to better suit your needs.Additionally, an API is provided for users who wish to write their own cards.
Read on for an example of a card.FeaturesWith PieTime you can:Choose any of the three builtin cards.Configure display aspects (e.g. text color) of the cards.Define card visibility intervals.Set up screen blanking period for power saving.Use the card API to create your own cards.Author, License and AttributionsPieTime has been created and is developed byTomek Wójcik.PieTime is licensed under the MIT License.PieTime uses the following 3rd party code and resources:Open Sans font by Steve Matteson, (Apache License, Version 2.0),Linea Weather 1.0 font by Dario Ferrando (CC BY 4.0),PT Mono font by Paratype (SIL OFL, Version 1.1).
Package: pietoolbelt
PiePline toolbeltInstallation:pip install pietoolbeltInstall latest version before it's published on PyPipip install -U git+https://github.com/PiePline/pietoolbeltFunctionalaugmentationsaugmentations.detection- predefined augmentations for detection taskaugmentations.segmentation- predefined augmentations for segmentation taskdatasetsdatasets.stratification- stratification by histogramdatasets.utils- set of datasets constructors thatlosseslosses.common- losses utilslosses.regression- regression losseslosses.segmentation- losses for single and multi-class segmentationlosses.detection- losses for detection taskmetrics-metrics.common- common utils for metricscpu- metrics, that calculates bynumpymetrics.cpu.classification- classification metricsmetrics.cpu.detection- detection metricsmetrics.cpu.regression- regression metricsmetrics.cpu.segmentation- segmentation metricstorch- metrics, that calculates bytorchmetrics.torch.classification- classification metricsmetrics.torch.detection- detection metricsmetrics.torch.regression- regression metricsmetrics.torch.segmentation- segmentation metricsmodels- models zoo and constructorsdecoders.unet- UNet decoder, that automatically constructs by encoderencoders.common- basic interfaces for encodersencoders.inception- InceptionV3 encoderencoders.mobile_net- MobileNetV2 encoderencoders.resnet- ResNet encodersalbunet- albunet modelutils- models utilsweights_storage- pretrained weights storagesteps- some training process stepsregression.train- train step for regression taskregression.bagging- bagging step for regression tasksegmentation.bagging- bagging step for segmentation tasksegmentation.inference- inference for segmentation modelsegmentation.predict- predict step for segmentation taskstratification- dataset stratification classimg_matcher- image comparison and matching tool based on descriptorsmask_composer- mask composer tools that can effectively combine masks for regular, instance or multiclass segmentationtrain_config- some predefined train configs forPiePlinetta- test time augmentationsutils- some utilsviz- image visualisation tools
Package: pie-torch
PIE-torch: Poisson Image Editing in PytorchFast, n-dimensional Poisson image editing.2 implementations, including:using Green Function Convolution, as described inFast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolutionusing a Discrete Sine Transform, followingOpenCV's implementationRecommendations:For blending images with consistent boundaries, useblend, the Green Function Convolution implementation.For images with inconsistent boundaries, useblend_dst_numpy.Main interface:blend: primary entrypoint, blends source image into target image at specified coordinates.blend_dst_numpy: entrypoint for DST-based blending (currently only available in NumPy).CachedPoissonBlender: callsblendbut caches the Green function, so should be faster if you're repeatedly
blending source patches of equal size, as you will only need to construct the Green function once.blend_numpy: A NumPy implementation ofblend.blend_wide[_numpy]: Wrappers ofblendmethods which allow for blending over entire image to more smoothly integrate the source region.Why use it?It's faster than any available alternative (OpenCV'sseamlessClone, or manual solvers using iterative methods).It's flexible, working on n-dimensional images, with no explicit limitations on data types
(unlikeseamlessClone, which only operates on 8-bit 3-channel images).You using it makes me feel like I'm contributing to the world.InstallationUsing pip... coming soon ...ManuallyClone PIE-torch repository and install the package locally:gitclonehttps://github.com/matt-baugh/pytorch-poisson-image-editing.git
pipinstall-e./pytorch-poisson-image-editingYou can check the installation by running the tests:cdpytorch-poisson-image-editing/test
pytestUsageExample of blending normal images using mixed gradients:frompietorchimportblendtarget:torch.Tensor=...# 3 x N x M image to be blended intosource:torch.Tensor=...# 3 x H x W image to be blendedmask:torch.Tensor=...# H x W mask of which pixels from source to be includedcorner:torch.Tensor=...# [y, x] coordinate of location in target for source to be blendedresult=blend(target,source,mask,corner,True,channels_dim=0)A wide variety of full examples are given inthe examples notebook.