Saravanan666/pypi-org huggingface dataset:
Package: atooms
Atoomsatoomsis a high-level Python framework for simulations of interacting particles, such as molecular dynamics or Monte Carlo.This is the core package: it provides a consistent interface to the basic objects of particle-based simulations. [Feature packages](Feature packages) are built on top of it and implement complex simulation methods and analysis tools.Quick startHere is a small example for setting up a mixture of two types of particles, A and B, in a periodic elongated cell. The number density is set to unity.fromatooms.systemimportSystemsystem=System(N=64)system.replicate(times=4,axis=0)system.composition={'A':128,'B':128}system.density=1.0Particles in the central part of the cell get a random displacement and are folded back into the simulation cellimportnumpyforpinsystem.particle:ifabs(p.position[0])<system.cell.side[0]/4:p.position+=0.5*(numpy.random.random()-0.5)p.fold(system.cell)system.show('ovito')Simulation data are stored in trajectory files, which are easy to manipulate and convert with atooms. Here, we write the system species and positions in a single-frame trajectory file using thexyz format.fromatooms.trajectoryimportTrajectoryXYZwithTrajectoryXYZ('input.xyz','w')asth:th.variables=['species','position']# actually, this is the defaultth.write(system)The trajectory file can now be used to start a simulation using one the availablesimulation backendsor your own code.FeaturesFocus on a simple and expressive interfaceAPI refined over the years towards consistencyModular and extensible design via namespace packagesSemantic versioning - for what is worthEasy to interface: in-house codes and custom formats are first-class citizensSupport for efficient simulation backends, with a focus on GPU codesDocumentationCheck out thetutorialfor more examples and thepublic APIfor more details.Org-mode and jupyter notebooks are available underdocs. You can run them interactively onBinder.InstallationFrom the python package indexpip install atoomsFrom the code repositorygit clone https://framagit.org/atooms/atooms.git
cd atooms
make installContributingYou are welcome to contribute to this project! Please have a look atthese guidelines.Feature packagesAtooms is modular: it is easy to add new functionalities, and just those you actually need.Feature packages are available from theatooms main repository. They are installed in theatoomsnamespace to prevent name clashing. If you want to add your own feature package to the atooms namespace, structure it this wayatooms/your_package
atooms/your_package/__init__.pywhere__init__.pycontainsfrompkgutilimportextend_path__path__=extend_path(__path__,__name__)Installyour_packageand you are ready to goimportatooms.your_packageAuthorsDaniele Coslovich:https://www.units.it/daniele.coslovich/Thanks go to Francesco Turci and Geert Kapteijns for their contributions.
Package: atooms.active
activeactiveis a simulation backend foratooms, a high-level framework for particle-based simulations. This backend implements active matter systems, e.g. the Vicsek model as described inVicseket al.(1995)and inGrégoireandChaté (2004).Quick startHere we have a simulation of an active matter system based on the Vicsek model. First of all, we need to setup a 2Datoomssystem.importnumpyimportatoomsfromatooms.systemimportSystemn=200dim=2system=System(N=n)system.cell=[32.foriinrange(dim)]forpinsystem.particle:p.orientation=numpy.random.uniform(-numpy.pi,numpy.pi)p.position=[L*numpy.random.uniform()foriinrange(dim)]p.fold(system.cell)Having done so, we can run a simple Vicsek model simulation as follows. We need to provide the system sizen, its noise amplitudeeta, velocityv0, and densityrho.fromatooms.active.vicsekimportVicsekfromatooms.active.neighborsimportVicsekNeighborsfromatooms.simulationimportSimulationeta=0.4v0=0.5rho=0.5backend=Vicsek(system,eta,v0,noise='vectorial')neighbors=VicsekNeighbors(system,method='kdtree')bck.neighbors=neighborsSimulation(backend).run(10)This simulation employs the so-called 'vectorial' noise implementation (discussed in Grégoire and Chaté) and thescipy-based kD-tree neighbor search algorithm.A basic API is also available. We can run the same simulation as followsfromatooms.active.apiimportvmvm('input.xyz',file_out='output.xyz',npart=2048,eta=0.4,rho=2.0,nsteps=1000,config_number=10)or, from the command lineapi.pyinput.xyz--file-outoutput.xyz--npart2048--eta0.4--rho2.0--nsteps1000--config-number10FeaturesSeamless integration withatoomsframeworkVarious nearest neighbors algorithms availableEasy extension to new variations of the Vicsek modelSupport for user-provided codeDependenciesgfortran 11.3.0python 3.10argh  0.28.1atooms 3.17.0f2py-jit 0.9.2numpyscipy (optional)DocumentationCheck out thetutorialfor more detailed examples and thepublic APIfor more detail.InstallationFrom the Python Package Indexpip install atooms.active[full]to get all optional dependencies (i.e.scipy). A lightweight installation can be performed as wellpip install atooms.activeFrom the code repositorygit clone https://framagit.org/atooms/active.git
cd active
make installAuthorsIacopo Ricci:https://iricci.frama.io/Huge thanks to Prof. Daniele Coslovich for his careful supervision and for his profound insights.
Package: atooms-database
Atooms databaseA database of interaction models and trajectory samples for classical molecular dynamics and Monte Carlo simulations.Quick startList the available modelsfromatoomsimportdatabaseformodelindatabase.models:print(model["name"])Gaussian core
Bernu-Hiwatari-Hansen-Pastore
Kob-Andersen
Lennard-Jones
Dellavalle-Gazzillo-Frattini-Pastore
Roux-Barrat-Hansen
Coslovich-Pastore
Grigera-Cavagna-Giardina-Parisi
harmonic spheres
WahnstromPrint all the available samplesforsampleindatabase.samples:print(sample["path"])coslovich_pastore/0_488db481cdac35e599922a26129c3e35.xyz
kob_andersen/0_8f4a9fe755e5c1966c10b50c9a53e6bf.xyz
grigera_cavagna_giardina_parisi/0_0ac97fa8c69c320e48bd1fca80855e8a.xyz
lennard_jones/0_5cc3b80bc415fa5c262e83410ca65779.xyz
lennard_jones/0_13ce47602b259f7802e89e23ffd57f19.xyz
bernu_hiwatari_hansen_pastore/0_f61d7e58b9656cf9640f6e5754441930.xyzGet a local copy of a Lennard-Jones fluid samplelocal_file=database.samples.copy("lennard_jones/0_5cc3b80bc415fa5c262e83410ca65779.xyz")Thelocal_filecan then be used to start a simulation or further analysis usingatoomspackages.DocumentationCheck out thedocumentationfor full details.InstallationClone the code repository and install from sourcegit clone https://framagit.org/atooms/database.git
cd database
pip install .Installatooms-databasewith pippip install atooms-databaseContributingContributions to the project are welcome. If you wish to contribute, check outthese guidelines.AuthorsDaniele Coslovich:https://www.units.it/daniele.coslovich/
Package: atooms-dynamics
DynamicsNewtonian and stochastic dynamics backends for atooms.Quick startRun a molecular dynamics simulation of a Lennard-Jones system from an existing xyz filefromatooms.trajectoryimportTrajectoryfromatooms.simulationimportSimulationfromatooms.dynamics.netwonianimportVelocityVerlet# Start from the last frame of input.xyztrajectory=Trajectory('input.xyz')system=trajectory[-1]system.interaction=Interaction('lennard_jones')backend=VelocityVerlet(system,timestep=0.002)sim=Simulation(backend,steps=200)sim.run()Do the same via the API, storing configurations inoutput.xyz,fromatooms.dynamics.apiimportmdmd('input.xyz','output.xyz',method='velocity-verlet',model='lennard_jones',dt=0.002,nsteps=200,config_number=20)or from the command linemd.py--methodvelocity-verlet-n200--dt0.002--config-number20input.xyzoutput.xyzFeaturesIntegration algorithms (work in progress)Netwonian dynamicsvelocity-VerletNose-PoincaréBerendsen thermostat/barostatevent-drivenStochastic dynamicsoverdamped Langevin dynamicsDocumentationCheck out thetutorialfor more examples and thepublic APIfor full details.Org-mode and jupyter notebooks are available underdocs/. You can run the tutorial interactively onBinder.InstallationFrom pypipip install atooms-dynamicsYou can clone the code repository and install from sourcegit clone https://framagit.org/atooms/dynamics.git
cd dynamics
make installContributingContributions to the project are welcome. If you wish to contribute, check outthese guidelines.AuthorsDaniele Coslovich:https://www.units.it/daniele.coslovich/
Package: atooms-landscape
Energy landscapeEnergy landscape analysis tools built with atooms.Quick startFind the minimum of the potential energy surface (PES) of a Lennard-Jones trimerfromatooms.landscape.surfacesimportpotential_energyfromatooms.landscapeimportconjugate_gradientfromatooms.systemimportParticle,Cell,Systemfromatooms.models.f90importInteractionsystem=System()system.particle=[Particle(species=1,position=[0.,0.,0.]),Particle(species=1,position=[0.5,1.,-.1]),Particle(species=1,position=[1.1,0.1,0.1])]system.cell=Cell([10.,10.,10.])system.interaction=Interaction('lennard_jones')result=conjugate_gradient(system)distance=sum(system.particle[0].distance(system.particle[1])**2)**0.5assertabs(distance-1.12246)<1e-6FeaturesOptimization methods to findlocal minimaof the PESSteepest descentConjugate gradientsl-BFGSOptimization methods to findstationary pointsof the PESEigenvector-followingSquare-force minimizationNormal modesanalysisInstallationFrom pypipip install atooms-landscapeYou can clone the code repository and install from sourcegit clone https://framagit.org/atooms/landscape.git
cd landscape
make installContributingContributions to the project are welcome. If you wish to contribute, check outthese guidelines.AuthorsDaniele Coslovich:https://www.units.it/daniele.coslovich/
Package: atooms-models
Atooms modelsA database of interaction models for classical molecular dynamics and Monte Carlo simulations.Quick startShow all the available modelsfromatoomsimportmodelsmodels.available()Pretty print the full databasefrompprintimportpprintpprint(models.database)Select the Lennard-Jones one-component system and inspect the parametersmodel=models.database["lennard_jones"]print(model["potential"])print(model["cutoff"])Look for a fluid Lennard-Jones sample and get a local copyforsampleinmodel["samples"]:ifsample["state"]=='fluid':local_file=models.copy(sample)breakThelocal_filecan then be used to start a simulation or further analysis usingatoomspackages.InstallationClone the code repository and install from sourcegit clone https://framagit.org/atooms/models.git
cd sample
make installInstallatooms-modelswith pippip install atooms-modelsContributingContributions to the project are welcome. If you wish to contribute, check outthese guidelines.AuthorsDaniele Coslovich:https://www.units.it/daniele.coslovich/
Package: atooms-pp
PostprocessingA Python package to compute static and dynamic correlation functions from simulations of interacting particles, such as molecular dynamics or Monte Carlo simulations. Based onatooms.Quick startPostprocessing works on trajectories. Any trajectory format recognized byatoomscan be processed, for instance most "xyz" files
should work fine. If you use a custom trajectory format, it is easy toadd it.As an example, we compute the structure factor S(k) from the filetrajectory.xyzin thedata/folder.From the command linepp.py--norigins0.2msddata/trajectory.xyzWe just used 20% of the available time frames to compute the averages using the--noriginsflag. Without it,atooms-ppapplies an heuristics to determine the number of time frames required to achieve a reasonable data quality. The results of the calculation are stored in the filedata/trajectory.xyz.pp.sk.From PythonThe same calculation can be done from Python:fromatooms.trajectoryimportTrajectoryimportatooms.postprocessingasppwithTrajectory('data/trajectory.xyz')ast:p=pp.StructureFactor(t)p.do()FeaturesAvailable correlation and distribution functionsReal spaceradial distribution functionmean square displacementvelocity auto-correlation functionself overlap functionscollective overlap functionsdynamic susceptibility of the self overlap functionnon-Gaussian parameterbond-angle distributionFourier spacestructure factorspectral densityself intermediate scattering functionscollective intermediate scattering functionsfour-point dynamic susceptibilityDocumentationCheck out thetutorialfor more examples and thepublic APIfor full details.Org-mode and jupyter notebooks are available underdocs/. You can run the tutorial interactively onBinder.Requirementsnumpyatooms[optional]argh(only needed when usingpp.py)[optional]tqdm(enable progress bars)[optional]argcomplete(enable tab-completion forpp.py)[optional] fortran compiler for more efficient executionInstallationInstall withpippip install atooms-ppOr clone the project repositorygit clone https://framagit.org/atooms/postprocessing.git
cd postprocessing
make installContributingContributions to the project are welcome. If you wish to contribute, check outthese guidelines.AuthorsDaniele Coslovich:https://www.units.it/daniele.coslovich/
Package: atooms-pt
Parallel
temperingis a
simulation method that accelerates sampling of configuration space in
systems with rugged energy landscapes. Applications range from the
simulation of biomolecules to studies of phase transitions in condensed
matter or spin glasses. The key idea is to perform Monte Carlo or
molecular dynamics simulations of independent replicas of the system of
interest at different state conditions (say, temperatures). During the
simulation, the states of pairs of replicas are exchanged in a way that
respects detailed balance. Through these exchanges replicas can overcome
energy barriers and sample the configuration space more efficiently.Since replicas evolve independently between exchanges, the algorithm is
straightforward to parallelize. atooms-pt relies onmpi4pyto distribute groups of
replicas to multiple CPUs and/or GPUs and it builds on theatoomsframework to decouple
the algorithm from the underlying simulation backend. The scalability is
excellent up to several tens of CPUs or GPUs. The preferred simulation
backend isRUMD, a molecular dynamics code running
entirely on GPUs that is very efficient even on small system sizes, say
of a few hundreds particles.Quick start>From the command line:pt.py--steps10-T1.0,0.9,0.8-e50000-idata/kalj.xyz.gz/tmp/output_dirThis will run 10 steps of parallel tempering simulation for three
replicas, starting from the configuration indata/kalj.xyz.gzand
writing output to/tmp/output_dir. The replicas are simulated at
temperatures 1.0, 0.9 and 0.8 and temperature exchanges are attempted
every 50000 steps of the underlying simulation backend.The same simulation can be ran from python:fromatooms.backends.rumdimportRumdfromatooms.simulationimportSimulationfromatooms.parallel_temperingimportParallelTemperingtemperatures=[1.0,0.9,0.8]# Create backends and wrap them as simulation instancesbackend=[Rumd(integrator='nvt',temperature=T)forTintemperatures]sim=[Simulation(s)forsinbackend]pt=ParallelTempering(sim,params=temperatures,output_path='/tmp/output_dir',steps=10,exchange_interval=50000)pt.run()The current implementation targets the RUMD molecular dynamics package,
but any atooms simulation backend (for instance, LAMMPS) should work
just fine.Requirementsnumpyatoomsmpi4pyRUMD(for multi-GPU)Installation>From the python package indexpip install atooms-pt>From the code repositorygit clone https://gitlab.info-ufr.univ-montp2.fr/atooms/parallel_tempering.git
python setup.py installAcknowledgmentsThis code was developed in the context of PRACE (“Partnership for
Advanced Computing in Europe”) project 2010PA1751 “Multi-GPU parallel
tempering simulations”.AuthorsDaniele Coslovich:http://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/
Package: atooms-voronoi
SampleSample packageQuick startimportsampleInstallationThe easiest way to install sample is with pippip install sampleAlternately, you can clone the code repository and install from sourcegit clone git@framagit.org:coslo/sample.git
cd sample
make installFeaturesFeature 1Feature 2AuthorsDaniele Coslovich:http://www-dft.ts.infn.it/~coslovich/
Package: atoot
atootis a Python library providing an easy way to create Mastodon API applications.Key featuresPython 3 libraryAsynchronous networking with asyncio and aiohttpEvery API method is implementedhttps://docs.joinmastodon.org/methods/Client as a context managerResults paginationRequirementsaiohttppytest (for tests)Getting startedInstallpip install atootUseBasic usage example:importatootimportasyncioasyncdefmastodon_bot():instance="botsin.space"access_token="YOUR_APPLICATION_ACCESS_TOKEN"client=awaitatoot.MastodonAPI.create(instance,access_token=access_token)resp=awaitclient.verify_account_credentials()print(resp)awaitclient.close()asyncio.run(mastodon_bot())Using client as a context manager, get 5 pages of home timeline and all notifications:asyncdefmastodon_bot():instance="botsin.space"access_token="YOUR_APPLICATION_ACCESS_TOKEN"asyncwithatoot.client(instance,access_token=access_token)asc:home=awaitc.get_n_pages(c.home_timeline(limit=20),n=5)print("Home timeline:",home)notifs=awaitc.get_all(c.get_notifications())print("Notifications:",notifs)LicenseMIT
Package: atoparser
AtoparserAtoparser is a zero dependency Atop log processing library written in Python. The library supports reading binary C data
directly from compressed or uncompressed Atop log files, without the need to install Atop or call a subprocess.
The converted data contains structured Python objects, that can then be used for JSON, CSV, or other types of output,
storage, and analysis.For full information on the amazing performance monitoring software that creates these files, known as "Atop", refer to:Atop - The one stop shop for all your topsTable Of ContentsCompatibilityGetting StartedInstallationHow TosRead an Atop log with the example JSON commandIterate over the C structs as Python objectsConvert the C structs into JSON compatible objectsContributeAdvanced GuidesCompatibilitySupports Python 3.10+Supports Atop 1.26 and 2.3 through 2.10.Getting StartedInstallationInstall Atoparser via pip:pipinstallatoparserOr via git clone:gitclone<pathtofork>cdatoparser
pipinstall.Or build and install from wheel:# Build locally.gitclone<pathtofork>cdatoparser
makewheel# Push dist/atoparser*.tar.gz to environment where it will be installed.pipinstalldist/atoparser*.tar.gzHow TosRead an Atop log with the example JSON command:atoparser~/atop.log-PCPU--prettyIterate over the C structs as Python objects:importatoparserwithopen(file,'rb')asraw_file:header=atoparser.get_header(raw_file)forrecord,sstat,tstatinatoparser.generate_statistics(raw_file,header):total_cycles=record.interval*sstat.cpu.nrcpu*header.hertzusage=1-sstat.cpu.all.itime/total_cyclesprint(f'CPU usage was{usage:.02%}')Convert the C structs into JSON compatible objects:importjsonimportatoparserwithopen(file,'rb')asraw_file:header=atoparser.get_header(raw_file)print(json.dumps(atoparser.struct_to_dict(header),indent=2))ContributeRefer to theContributing Guidefor information on how to contribute to this project.Advanced GuidesRefer toAdvanced How Tosfor more advanced topics, such as adding a new Atop version.
Package: atopile-omegaconf
OmegaConfDescriptionProjectCode qualityDocs and supportOmegaConf is a hierarchical configuration system, with support for merging configurations from multiple sources (YAML config files, dataclasses/objects and CLI arguments)
providing a consistent API regardless of how the configuration was created.ReleasesStable (2.3)OmegaConf 2.3 is the current stable version.What's newDocumentationSource codeInstall withpip install --upgrade omegaconfPrevious release (2.2)What's newDocumentationSlidesSource codeInstall withpip install omegaconf==2.1Previous release (2.1)What's newDocumentationSlidesSource codeInstall withpip install omegaconf==2.1Previous release (2.0)What's newDocumentationSlidesSource codeInstall withpip install omegaconf==2.0.6Live tutorialRun the live tutorial:
Package: atosl
atosl: atosl is short for atos for linux. It is partial replacement for Mac OS X’s atos tool which can be used for converting macho binary addresses into symbols. This version does not depend on any existing library.I wrote this tool when I was developinghttp://egg.iowith my friends, and we can not find a usable atos tool for our Linux server. So we decided to develop our own atos. And we use atosl instead of atos to avoid conflict with apple’s existing atos tool. In order to avoid reinventing wheels we decide to make it open source.sample macho file can be reached athttps://github.com/renoqiu/atosl/tree/master/test/res## INSTALLpip install atosl## USAGEimport atosl
atosl.symbolicate(arch_str, macho_path, addresses_tuple)## EXAMPLEatosl.symbolicate(‘armv7s’, ‘test/res/CrashTest3Dwarf.fat’, (“0x00006ed7”, ))
Package: atos-osiris
Bull S. A. S.## Development LeadPierre FUMERY (Atos) <pierre.fumery@atos.net>Yves VINTER (Atos) <yves.vinter@atos.net>Francois WAESELYNCK (Atos) <francois.waeselynck@atos.net>Elisabet BIALUNGANA LOUREIRO (Atos) <elisabet.bialunganaloureiro@atos.net>Nabil MAMORI (Atos) <nabil.mamori@atos.net>Grégory NATTER (Atos) <gregory.natter@atos.net>## ContributorsNone yet.## Thanks toJérémie LallemandLicense: Other/Proprietary License
Description: All the documentation is available on the official [Osiris Wiki](https://git.atosone.com/osiris/osiris/-/wikis/home).# ChangelogAll notable changes to this project will be documented in this file.The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).## [Unreleased]## [0.0.6] - 2020-08-27### Addedosiris system showosiris system selectAllosiris system unselectAllosiris object listosiris object createosiris object deleteSphinx documentation (with make docs)### ChangedCorrect includes problem with wheels### RemovedNothing## [0.0.5] - 2020-08-05### AddedNothing### ChangedCorrect includes problem with wheels### RemovedNothing## [0.0.4] - 2020-08-05### Added
Nothing### ChangedInclude alm plugins files into package### RemovedNothing## [0.0.3] - 2020-08-05### Added
Nothing### ChangedInclude json plugins files into package### RemovedNothing## [0.0.2] - 2020-08-05### AddedPython projet skeleton (setuptools, Makefile, bumpversion)CLI with ArgparseAutomatic creation of folders in user homedirosiris system setosiris system getosiris system addosiris system removeosiris system listosiris object introspectosiris system introspectVerbose option### ChangedSystems data moved in user homedirFlake8 compliance (with CI check)### Removed
Nothing## [0.0.1] - 2020-07-20### AddedSkeleton (nothing operationnal)### Changed
Nothing### Removed
NothingKeywords: osiris,backup,restore,openstack
Platform: any
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Framework :: Flake8
Classifier: Framework :: Pytest
Classifier: Framework :: Setuptools Plugin
Classifier: Framework :: tox
Classifier: Intended Audience :: System Administrators
Classifier: License :: Other/Proprietary License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: System
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Package: atpcl
Atypical Tracker (atpcl)Atypical One Time TrackerPackage structure.
├── LICENSE
├── README.md
├── setup.py
├── src
│   └── atpcl_tracker
│       ├── __init__.py
│       ├── atpcl-tracker.py
│       └── services
│           ├── api
│           ├── core
│           └── utils
└── venvInstallationpip install atpclUsageWork In Progress