Saravanan666/pypi-org huggingface dataset:
Package: planbee
PlanBee 🐝If Plan A fails, dont worry there is always PlanBee! PlanBee is a Job Shop Scheduling algorithm module buzzing with features. Feed PlanBee with your tasks and resources and it quickly finds a solution. It employs the high-speed computation power of NumPy to achieve fast results. With PlanBee, Plan B becomes your Plan A!Features 🚀Define your own tasks and resources.Specify available windows for each resource.Indicate priority, duration, and necessary resources for each task.Solve your scheduling problems with a single function call!Get a detailed summary and visualization of the scheduling solution.Installation 🛠️pipinstallplanbeeUsage 🐍First, import the necessary modules:fromPlanBeeimportResource,Task,SchedulerThen, define your resources:resource1=Resource(id=1,available_windows=[(0,10),(15,20)])resource2=Resource(id=2,available_windows=[(5,20)])And your tasks:task1=Task(id=1,duration=5,priority=1,resources=[resource1,resource2],resource_count=1)task2=Task(id=2,duration=3,priority=2,resources=[resource1],predecessors=[task1],resource_count=1)Finally, use the Scheduler to solve:scheduler=Scheduler(tasks=[task1,task2])result=scheduler.schedule()Visualization 📊PlanBee provides a function to plot your schedule:result.plot_resource_plan()Contributions 💡Contributions are always welcome! SeeCONTRIBUTING.mdfor ways to get started.License 📄This project is licensed under the terms of theMIT license.
Package: planck
PlanckPhysical constants and unit conversion.Planck makes it easy to convert units and to access physical constant values in multiple units.HelpSeedocumentationfor more details.InstallationInstall usingpip install planckFor more installation options,
see theInstallsection in the documentation.A Basic Examplefromplanckimportunitsprint(1*units["m"]["ft"])#> 3.280839895013124To get started with more examples, jump into theQuickstart.Okube CompanyOkubeis dedicated to building open source frameworks, known as thekubes, empowering businesses to build, deploy and operate highly scalable data platforms and AI models.
Package: planck-2020-hillipop
HiLLiPoP: High-L Likelihood Polarized for PlanckHillipopis a multifrequency CMB likelihood for Planck data. The likelihood is a spectrum-based
Gaussian approximation for cross-correlation spectra from Planck 100, 143 and 217GHz split-frequency
maps, with semi-analytic estimates of the Cl covariance matrix based on the data. The cross-spectra
are debiased from the effects of the mask and the beam leakage usingXpol(a generalization to polarization of the algorithm
presented inTristram et al. 2005) before being compared
to the model, which includes CMB and foreground residuals. They cover the multipoles from ℓ=30
to ℓ=2500.The model consists of a linear combination of the CMB power spectrum and several foregrounds
residuals. These are:Galactic dust (estimated directly from the 353 GHz channel);the cosmic infrared background (as measured inPlanck Collaboration XXX
2014);thermal Sunyaev-Zeldovich emission (based on the Planck measurement reported inPlanck
Collaboration XXI 2014);kinetic Sunyaev-Zeldovich emission, including homogeneous and patchy reionization components fromShaw et al. (2012)andBattaglia et
al. (2013);a tSZ-CIB correlation consistent with both models above; andunresolved point sources as a Poisson-like power spectrum.HiLLiPoP has been used as an alternative to the public Planck likelihood in the 2013 and 2015 Planck
releases [Planck Collaboration XV 2014;Planck Collaboration XI
2016], and is described in detail inCouchot et
al. (2017).Likelihoods available arehillipop.TT,hillipop.EE,hillipop.TE,hillipop.TTTE, andhillipop.TTTEEE.It is interfaced with thecobayaMCMC sampler.Likelihood versionsPlanck 2020 (v4.1.0, PR4)InstallThe easiest way to install theHillipoplikelihood isviapippipinstallplanck-2020-hillipop[--user]If you plan to dig into the code, it is better to clone this repository to some locationgitclonehttps://github.com/planck-npipe/hillipop.git/where/to/cloneThen you can install theHillipoplikelihood and its dependenciesviapipinstall-e/where/to/cloneThe-eoption allow the developer to make changes within theHillipopdirectory without having
to reinstall at every changes. If you plan to just use the likelihood and do not develop it, you can
remove the-eoption.Installing Hillipop likelihood dataTheexamples/hillipop_example.yamlfile is a good starting point to
know the different nuisance parameters used byhillipoplikelihoods.You should use thecobaya-installbinary to automatically download the data needed by theHillipoplikelihoodcobaya-install/where/to/clone/examples/hillipop_example.yaml-p/where/to/put/packagesData and code such asCAMBwill be downloaded and installed within
the/where/to/put/packagesdirectory. For more details, you can have a look tocobayadocumentation.RequirementsPython >= 3.5numpyastropy
Package: planck-2020-lollipop
LoLLiPoP: Low-L Likelihood Polarized for PlanckLollipopis a Planck low-l polarization likelihood based on cross-power-spectra for which the
bias is zero when the noise is uncorrelated between maps. It uses the approximation presented inHamimeche & Lewis (2008), modified as described inMangilli et
al. (2015)to apply to cross-power spectra.  This version is
based on the Planck PR4 data. Cross-spectra are computed on the CMB maps from Commander component
separation applied on each detset-split Planck frequency maps.It was previously applied and described inPlanck Collaboration Int. XLVII (2016)for investigating the
reionization history,Tristram et al. (2020)for estimating constraints on the
tensor-to-scalar ratio.It is interfaced with thecobayaMCMC sampler.RequirementsPython >= 3.5numpyastropyInstallThe easiest way to install theLollipoplikelihood isviapippipinstallplanck-2020-lollipop[--user]If you plan to dig into the code, it is better to clone this repository to some locationgitclonehttps://github.com/planck-npipe/lollipop.git/where/to/cloneThen you can install theLollipoplikelihoods and its dependenciesviapipinstall-e/where/to/cloneThe-eoption allow the developer to make changes within theLollipopdirectory without having
to reinstall at every changes. If you plan to just use the likelihood and do not develop it, you can
remove the-eoption.Installing Lollipop likelihood dataYou should use thecobaya-installbinary to automatically download the data needed by thelollipop.lowlEorlollipop.lowlBorlollipop.lowlEBlikelihoodscobaya-install/where/to/clone/examples/test_lollipop.yaml-p/where/to/put/packagesData and code such asCAMBwill be downloaded and installed within
the/where/to/put/packagesdirectory. For more details, you can have a look tocobayadocumentation.Likelihood versionslowlElowlBlowlEB
Package: plancton
[![Build Status](https://travis-ci.org/mconcas/plancton.svg?branch=master)](https://travis-ci.org/mconcas/plancton)
[![PyPI version](https://badge.fury.io/py/plancton.svg)](https://badge.fury.io/py/plancton)Plancton continuously deploys pilot Docker containers running any application
you want based on the amount of available system resources.Main featuresUpgrade pilot jobs to pilot containers.Plancton is meant to run “pilot”
containers: your container starts and tries to fetch something to do. When the
container exits, Plancton will replace it with a brand new one. An example of
application easy to containerize is
[WorkQueue from cctools](https://github.com/cooperative-computing-lab/cctools)).Meant for clusters.Pilot applications are containerized and deployed on
a cluster of nodes, each one of them running a Plancton instance. Plancton
instances are totally independent, therefore it naturally scales.Monitoring.Sends monitoring data to [InfluxDB](https://www.influxdata.com/),
easy to plot via [Grafana](http://grafana.org/).Containers for the masses.Plancton brings the features of Docker
containers (environment consistency, isolation, sandboxing) to disposable
cluster applications. Plancton is not a replacement to
[Apache Mesos](http://mesos.apache.org/) or [Kubernetes](http://kubernetes.io/)
but it is a very simple and lightweight alternative when you don’t need all
the extra features they offer.Instant gratification[Docker](https://www.docker.com) is required, and a recent Linux operating
system.Install the latest version withpip:pip install planctonIf you want to install from the master branch (use at your own risk):pip install git+https://github.com/mconcas/planctonPlancton can be run as root or as any user with Docker privileges:planctonctl startConfigureThe configuration file is located under/etc/plancton/config.yamland it can
be modified while Plancton is running. By default it starts with an empty
configuration running dummybusyboxcontainers.You can get configurations with:plancton-bootstrap <gh-user/gh-repo:branch>and they’ll be downloaded to the correct place. An example dry run configuration
can be obtained with:plancton-bootstrap <mconcas/plancton-conf:dryrun>CreditsCredits for the name go to G.
Package: planedict
PlaneDictPlaneDict is a dict-like class which represents built-in dict class
as a ‘plane’ structure, i.e. path-value pairs. Path is a tuple of keys,
value is a value which allows in built-in dict.Supported methods:native:__check_path__(self, path)overridden:__init__(self, seq=None, _factory=dict, **kwargs)__getitem__(self, path)__setitem__(self, path, value)__delitem__(self, path)__iter__(self)__len__(self)__repr__(self)get(self, key, default=None, stddict=False)inherited:clear(self)pop(self, key, default=<object object>)popitem(self)setdefault(self, key, default=None)update(*args, **kwds)__contains__(self, key)__eq__(self, other)__ne__(self, other)keys(self)iterkeys(self)values(self)itervalues(self)items(self)iteritems(self)Installation$ pip install planedictNotesConstructor have _factory argument, it expects dict-like class (dict by default).
OrderedDict is useful to use.After removing value by path, if higher dicts will become
empty, they will be removed.get method has stddict parameter. If it’s True then method will return
built-in dict, it will return PlaneDict object else.If PlaneDict object was passed to update method, it’s a ‘soft’
update, i.e. the intersecting values will be overridden and the new
values will be added.
If standard dict passed to update method, it works as a
standard update method.__check_path__ method takes a sequence of keys.
For example: __check_path__((1, [2, [3]], 4, (5, 6), (i for i in [7, 8])))
returns (1, 2, 3, 4, (5, 6), 7, 8). As you can see from the
example above, tuple is not unfold, because tuple can be
a key of dict. So if you want to get a single-key tuple,
you should do this:d[('key',),]orpath='key',d[path,]See examples.Examplesd=PlaneDict({'key1':{'key2':'val2','key3':'val3'},'key4':{'key5':{'key6':'val6'}}})>>>len(d)3>>>d['key4','key5','key6']'val6'>>>path=['key1','key2','key10']>>>d[path]=1>>>d[path]1>>>deld['key4','key5','key6']>>>d{'key1':{'key3':'val3','key2':'val2'}}>>>list(d)[('key1','key3'),('key1','key2'),('key4','key5','key6')]>>>d.get('key1',stddict=True){'key3':'val3','key2':'val2'}>>>d.get(('key1','key2'))'val2'>>>d.clear()>>>printd{}>>>d.pop(['key4','key5','key6'],default=None)'val6'>>>d.pop(['key4','key5','key6'],default=None)None>>>d.popitem()(('key1','key3'),'val3')>>>d.setdefault(['key1','key2'],default=None)'val2'>>>d.setdefault(['key1','key7','key8','key9'],default=None)>>>d['key1']{'key3':'val3','key2':'val2','key7':{'key8':{'key9':None}}}>>>update={'key1':{'key10':'val10'}}>>>d.update(PlaneDict(update))>>>d{'key1':{'key2':'val2','key3':'val3','key10':'val10'},'key4':{'key5':{'key6':'val6'}}}>>>d.update(update)>>>d{'key1':{'key10':'val10'},'key4':{'key5':{'key6':'val6'}}}>>>d.keys()[('key1','key3'),('key1','key2'),('key4','key5','key6')]>>>d.values()['val3','val2','val6']>>>d.items()[(('key1','key3'),'val3'),(('key1','key2'),'val2'),(('key4','key5','key6'),'val6')]>>>['key1','key2']indTrue>>>['key1','missed_key']indFalse>>>d==PlaneDict({'key1':{'key2':'val2','key3':'val3'},'key4':{'key5':{'key6':'val6'}}})True>>>d=={'key1':{'key2':'val2','key3':'val3'},'key4':{'key5':{'key6':'val6'}}}FalseLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: planefigures
planefiguresplanefiguresis a python package based on theturtle library. It takes the pain out of drawing plain shapes.InstallationpipinstallplanefiguresPython 3 is supported.ExampleTry out this little peace of code and be amazed.importplanefiguresasfiguresimportturtlefigures.color('red','yellow')figures.begin_fill()forninrange(8):figures.polygon(8,80)turtle.right(360/8)figures.end_fill()Or better still ...python-mplanefigures.exampleand enjoy.Road mapAt this stage, onlyregular polygonsare supported. The aim however, is to support regular as well as irregular shapes.CircleScalene triangleIsosceles triangleRight triangleObtuse triangleRectangleParallelogramTrapezium
Package: planegeometry
planegeometry packagePython implementation of algorithms and data structures
from plane geometry is presented.Problems, algorithms, and data structuresGeometric objects: points, segments, polygons, rectangles, triangles, circlesData structures: edges, graphs, triangle collections, AVL trees,
quadtrees, connected planar mapsPoint in polygon problem: the winding number method, the crossing number methodPolygon orientationBounding boxLine segment intersection problem:
brute force O(n^2), Shamos-Hoey, Bentley-OttmannClosest pair of points problem: brute force search O(n^2),
sweep line technique, divide and conquer approachFinding two furthest points: brute force search O(n^2), rotating calipersConvex hulls: Graham scan O(n log n), Jarvis march (gift wrapping) O(n h),
quickhull (divide and conquer approach)Delaunay triangulations: naive approach O(n^4), edge flipping, Bowyer-WatsonConvex polygons: recognition, fan triangulation O(n)Monotone polygons: in progress (recognition, triangulation)Connected planar maps based on the doubly connected edge list (DCEL):
constructors, map overlay procedure.Voronoi diagrams: from Delaunay triangulationDownloadTo install an official release dopython3 -m pip install planegeometryTo get the git version dogit clone https://github.com/ufkapano/planegeometry.gitUsageSee doc/quickstart.txt and other doc/*.txt files.ContributorsAndrzej Kapanowski (project leader)Marcin Permus (convex hull, rotating calipers)Wojciech Chrobak (sweep line technique, quadtree, closest pair problem)Monika Wiech (Delaunay triangulation)Anna Sarnavska (planar maps overlay)Gabriela Mazur (monotone polygons)Mateusz Malczewski (Voronoi diagrams)Maciej Mularski (range searching)EOF
Package: planekit
Getting StartedPlanekit helps you create powerful apps forfixed-wingUAVs. These apps run on a UAV’s Companion Computer, and augment the autopilot by performing tasks that are both computationally intensive and require a low-latency link.This documentation provides everything you need to get started with Planekit, including an overview of the API, quick start, guide material, a number of demos and examples, and API Reference.Install Planekitpip install planekitStart simulation anywhere or connect UAV:Plankit does not include any built-in simulation. Therefore, you must connect the planekit still or an arduplane installed UAV.A simple example of starting a still with Mission planarPlanekit supports other alternatives. (Mavproxy etc.)First connectionYou can see the first connection example below.import time

import planekit

vehicle = planekit.Connection("tcp:127.0.0.1:5762")
vehicle.wait_heartbeat()
print("connection is successful")See Docshttps://plane-kit.com/
Package: plane-kitasddsa
Getting StartedPlanekit helps you create powerful apps forfixed-wingUAVs. These apps run on a UAV’s Companion Computer, and augment the autopilot by performing tasks that are both computationally intensive and require a low-latency link.This documentation provides everything you need to get started with Planekit, including an overview of the API, quick start, guide material, a number of demos and examples, and API Reference.Install Planekitpip install planekitStart simulation anywhere or connect UAV:Plankit does not include any built-in simulation. Therefore, you must connect the planekit still or an arduplane installed UAV.A simple example of starting a still with Mission planarPlanekit supports other alternatives. (Mavproxy etc.)First connectionYou can see the first connection example below.import time

import planekit

vehicle = planekit.Connection("tcp:127.0.0.1:5762")
vehicle.wait_heartbeat()
print("connection is successful")See Docshttps://plane-kit.com/
Package: planes
Aboutplanes is a hierarchical surface framework for PyGame.The planes module introduces the “Plane” class which extends PyGame’s
“Surface” functionality, offering a hierarchy of mouse-sensitive,
draggable Surfaces that can be used as sprites, windows, icons or other
interactive elements.In addition, the planes.gui module uses planes to provide basic GUI
elements like container, label, text box, option list and button.PrerequisitesPython >= 2.6http://www.python.orgPyGame >= 1.9.1http://pygame.org/InstallationUnzip the file, then at the command line runpython setup.py installExamplesA showcase python script will be installed along with planes. Runplanes_interactive.pyDocumentationAPI documentation is included and will be installed during install.Linksplanes on Launchpad:https://launchpad.net/planesplanes on Freecode:http://freecode.com/projects/planesplanes on ohloh:https://www.ohloh.net/p/planesplanes on the Python Package Index:http://pypi.python.org/pypi/planesplanes on pygame.org:http://pygame.org/project-planes-2392-4051.htmlAuthorFlorian Berger
Package: planesections
PlaneSectionsA lightweight beam bending library built onOpenSeesPy.
The goal of PlaneSections is to make easy-to-use beam anayses, which can be used to quickly document structural calculations.
Being built on OpenSees, the structural results are reliable, and there is lots of room to build more complex models.The core classes and API are complete, but development is still in progress. Expect some sytax changes before final release, however deprication warnings
will be given for breaking changes.NOTE:Units for force and length must be applied to the beam in aconsistent unit base for FEM.
PlaneSections has been design with Metric units in mind in terms of scaling diagrams. For those using imperial units, results will work with a consitent unit basis, but the scale of the digrams may be off.ContentsInstallationDocumentationDemoWork StatusInstallationPackage is installable through pippip install planesectionsDocumentationAll major functions and classes are documented on read-the-docs:https://planesections.readthedocs.io/en/latest/DemoThe PlaneSections can plot representations that can be used in documentation.
It's also possible to get the output bening moment, shear force, rotation, and deflection diagrams.importplanesectionsasps# Define node locations, and support conditionsL=5beam=ps.newEulerBeam2D(L)# Define beam and fixitiespinned=[1,1,0]beam.setFixity(L*0.1,pinned,label='1')beam.setFixity(L*0.9,pinned)# Define loadsPz=-1beam.addVerticalLoad(0,Pz,label='A')beam.addVerticalLoad(L*0.5,2*Pz,label='B')beam.addVerticalLoad(L,Pz,label='C')beam.addDistLoadVertical(0,L,Pz)beam.addDistLoadVertical(1,L*0.3,5*Pz)# Plot the beam diagramps.plotBeamDiagram(beam)# Run the analysisanalysis=ps.OpenSeesAnalyzer2D(beam)analysis.runAnalysis()# Plot the SFD and BMDps.plotShear2D(beam)ps.plotMoment2D(beam)Further ExamplesSee the examples folder forStatusBefore the first beta release, the following will be completed.Analysis and API:fancy plots (distributed Load)Better deflection plots (distributed Load)Infacstructure:pip packagesetup on pypi: complete, package is installable with "pip install planesections"Add requirementsDocument codeFinish all class docstrings.Add Sphinx docs.Add read the docs website.ExamplesFinish landing page with code examples and figures.Compare simple cases with analytic equations.Compare complex cases with sap/rfem/etc.Post 1.0 releaseAnalysis and API:plot labelsFuture work:3D beamsTimoshenko beamsNonlinear beams analysisOne-way Slabs?Two-way Slabs?Orthtropic Slabs?Basic simple frame analysis?Basic Nonlinear frame analysis?
Package: planet
Planet SDK for PythonThePlanetSoftware Development Kit (SDK) for Python
provides both a Python API and a command-line interface (CLI)
to make use ofthe Planet APIs.
Everything you need to get started is found in ouronline documentation.Version 2.0 includes support for the core workflows of the following APIs:Data- Search for imagery from Planet's data catalog.Orders- Process and download or deliver imagery.Subscriptions- Set up a search to auto-process and deliver imagery.After the initial 2.0 release there will be additional work to support the
remaining Planet APIs:basemaps,tasking) andanalytics.Versions and StabilityThe default branch (main) of this repo is for thePlanet SDK for
Python,
a complete rewrite and upgrade from the originalPlanet Python
Client. If you
are looking for the source code to that library see thev1branch.The Planet SDK for Python is in 'pre-release' stages, working towards a solid
beta release in December. Upcoming milestones are tracked in thePlanet SDK
for Python Milestones.Installation and Quick StartThe main installation path and first steps are found in theQuick Start Guideof the documentation.Installing from sourceThis option enables you to get all the latest changes, but things might also be a bit less stable.
To install you mustclonetheplanet-client-pythonrepository
to your local computer. After you have the repo local just navigate to the root
directory, where this readme lives.Then you can install locally with pip:$pipinstall.DocumentationDocumentation is currentlyhosted onlineIt should be considered 'in progress', with many updates to come. It can also
be built and hosted locally (seeCONTRIBUTING.md) or can be
read from source in thedocsdirectory.AuthenticationPlanet's APIs require an account for use. To get started you need toGet a Planet Account.DevelopmentTo contribute or develop with this library, seeCONTRIBUTING.md.