Saravanan666/pypi-org huggingface dataset:
Package: openmindat
OpenMindat Python PackageThis is a test version of the OpenMindat Python package, designed to facilitate querying and retrieving data on minerals and geomaterials from the Mindat API. It provides classes for detailed queries based on various attributes like IMA status, keywords, and specific geomaterial properties.GitHub Reposity:OpenMindat Python PackageGet StartedInstall via Pipfoo@bar:~$pipinstallopenmindatImport the Package in PythonimportopenmindatUse Cases1. Perform Detailed Queries on GeomaterialsfromopenmindatimportGeomaterialRetrievergr=GeomaterialRetriever()gr.density_min(2.0).density_max(5.0).crystal_system("Hexagonal")gr.elements_exc("Au,Ag")gr.saveto("/path/to/geomaterials_data")2. Retrieve IMA-Approved MineralsfromopenmindatimportMineralsIMARetrievermir=MineralsIMARetriever()mir.ima(1).fields("id,name,ima_formula,ima_year")mir.saveto("/path/to/minerals_data")3. Search Geomaterials Using KeywordsfromopenmindatimportGeomaterialSearchRetrievergsr=GeomaterialSearchRetriever()gsr.geomaterials_search("quartz, green, hexagonal")gsr.save()4. Retrieve LocalitiesfromopenmindatimportLocalitiesRetrieverlr=LocalitiesRetriever()lr.country("France").txt("mine")lr.save()DocumentationGitHub Wiki: For comprehensive documentation, visit ourGitHub Wiki.Built-in Help:To explore detailed class and method documentation within the OpenMindat package, use Python's built-inhelp()function. This provides direct access to docstrings, showcasing usage examples and parameter details. Example:fromopenmindatimportGeomaterialRetrieverhelp(GeomaterialRetriever)Pressqto exit the help interface.Contact UsFor further assistance or feedback, feel free to contact the development team atjiyinz@uidaho.edu.LicenseApacheAuthorJiyin ZhangAcknowledgmentsThis work is supported by NSF, Award #2126315.
Package: openMINDS
openMINDS Python libraryopenMINDS Python is a small library to support the creation and use of openMINDS metadata models and schemas in your Python application, with import and export in JSON-LD format.Installationpip install openMINDSUsagefromdatetimeimportdatefromopenmindsimportCollection,IRIimportopenminds.latest.coreasomcore# Create an empty metadata collectioncollection=Collection()# Create some metadatamgm=omcore.Organization(full_name="Metro-Goldwyn-Mayer Studios, Inc.",short_name="MGM",homepage=IRI("https://www.mgm.com"))stan=omcore.Person(given_name="Stan",family_name="Laurel",affiliations=omcore.Affiliation(member_of=mgm,start_date=date(1942,1,1)))ollie=omcore.Person(given_name="Oliver",family_name="Hardy",affiliations=omcore.Affiliation(member_of=mgm,start_date=date(1942,1,1)))# Add the metadata to the collectioncollection.add(stan,ollie,mgm)# Check the metadata are validfailures=collection.validate()# Save the collection in a single JSON-LD filecollection.save("my_collection.jsonld")# Save each node in the collection to a separate filecollection.save("my_collection",individual_files=True)# creates files within the 'my_collection' directory# Load a collection from filenew_collection=Collection()new_collection.load("my_collection.jsonld")LicenseThis work is licensed under the MIT License.Getting helpIn case of questions aboutopenMINDS, please contact us viahttps://ebrains.eu/support/.
If you find a bug in the Python library or would like to suggest an enhancement or new feature,
please open a ticket in theopenMINDS Python library issue tracker.If you identify a problem or would like to suggest changes to the openMINDS specification itself,
please open a ticket in theopenMINDS issue tracker.DevelopmentContributions are welcome, please see ourcontribution guidelines.This repository has two main branches:pipelineandmain.
Thepipelinecontains the code for building the Python package from the openMINDS schemas.
The build process is triggered by changes to the schemas, and runs as a GitHub action.
The resultant package is copied to themainbranch, and at intervals is published on PyPI.AcknowledgementsThis open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270, No. 785907 and No. 945539 (Human Brain Project SGA1, SGA2 and SGA3).
Package: openmined.threepio
Threepio Python ClientThreepio is a multi-language library for translating commands between PyTorch, TensorFlow, and TensorFlow.js
This section of the code-base is for thePython Client.InstallationUse the package managerpipto install threepio.pipinstallopenmined.threepioUsageimporttensorflowastffrompythreepioimportThreepio,Command# Create a threepio translating from pytorch -> tensorflow pythonthreepio=Threepio("torch","tf",tf)# Create your command to translateargs=[tf.constant([[1,0],[0,1]]),tf.constant([[0,1],[1,0]])]kwargs={}cmd=Command("add",args,kwargs)# Translate the commandtranslated_cmd=threepio.translate(cmd,lookup_command=True)...# When you're ready, execute the translationtranslated_cmd.execute_routine()# -> [[1, 1], [1, 1]]Command SupportSupport for commands is currently limited. Officially supported commands are listed in ourtests.
Package: openml
OpenML-PythonA python interface forOpenML, an online platform for open science collaboration in machine learning.
It can be used to download or upload OpenML data such as datasets and machine learning experiment results.GeneralDocumentation.Contribution guidelines.Citing OpenML-PythonIf you use OpenML-Python in a scientific publication, we would appreciate a reference to the
following paper:Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank HutterOpenML-Python: an extensible Python API for OpenMLJournal of Machine Learning Research, 22(100):1−5, 2021Bibtex entry:@article{JMLR:v22:19-920,author={Matthias Feurer and Jan N. van Rijn and Arlind Kadra and Pieter Gijsbers and Neeratyoy Mallik and Sahithya Ravi and Andreas Müller and Joaquin Vanschoren and Frank Hutter},title={OpenML-Python: an extensible Python API for OpenML},journal={Journal of Machine Learning Research},year={2021},volume={22},number={100},pages={1--5},url={http://jmlr.org/papers/v22/19-920.html}}Contributors ✨Thanks goes to these wonderful people (emoji key):a-moadel📖💡Neeratyoy Mallik💻📖💡This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: openmlhub
OpenMLHub:  Machine Learning Model Tracking and ValidationOverviewWelcome to the OpenMLHub Client – your go-to solution for tracking and validating machine learning models for scientific research. This project facilitates seamless integration with OpenMLHub, enabling you to manage and assess your models efficiently.FeaturesModel Tracking: Easily track the performance of your machine learning models over time.Validation Support: Streamline the validation process for scientific research purposes.OpenMLHub Integration: Connect and collaborate with the OpenMLHub community effortlessly.User-Friendly Interface: Intuitive design for a smooth user experience.Scalable Architecture: Built for scalability to accommodate various project sizes.Getting StartedFollow these steps to get started with the OpenMLHub Client:Installation:pip install openmlhubConfiguration:Obtain your OpenMLHub API key.Set up your configuration file with the API key.Usage:importosfromopenmlhubimportmake_loggerfromopenmlhub.configimportOpenMLHubConf('<user_id>','api_key')api_key=os.environ['OPENMLHUB_API_KEY']logger=make_logger(OpenMLHubConf('<user_id>',api_key),'<model_id>')logger.log()ExamplesTBCContributingWe welcome contributions to enhance the OpenMLHub Client. To contribute:Fork the repository.Create a new branch:git checkout -b feature/your-feature.Commit your changes:git commit -m 'Add your feature'.Push to the branch:git push origin feature/your-feature.Open a pull request.LicenseThis project is licensed under theMIT License.ContactFor any inquiries or support, contact us atsupport@openmlhubclient.com.Happy modeling and tracking! 🚀
Package: openml-keras
Keras extension for OpenML pythonKeras extension foropenml-python API.Installation Instructions:pip install openml-kerasPyPi linkhttps://pypi.org/project/openml-keras/UsageImport openML librariesimportopenmlimportopenml_kerasCreate  and compile a keras modelmodel=keras.models.Sequential([keras.layers.BatchNormalization(),keras.layers.Dense(units=1024,activation=keras.activations.relu),keras.layers.Dropout(rate=0.4),keras.layers.Dense(units=2,activation=keras.activations.softmax),])# We will compile using the Adam optimizer while targeting accuracy.model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])Download the task from openML and run the model on task.task=openml.tasks.get_task(3573)run=openml.runs.run_model_on_task(model,task,avoid_duplicate_runs=False)run.publish()print('URL for run:%s/run/%d'%(openml.config.server,run.run_id))Note: The input layer of the network should be compatible with OpenML data output shape. Please check examples for more information.
Package: openml-pytorch
Pytorch extension for OpenML pythonPytorch extension foropenml-python API.Installation Instructions:pip install openml-pytorchPyPi linkhttps://pypi.org/project/openml-pytorch/UsageImport openML librariesimportopenmlimportopenml_pytorchimportopenml_pytorch.layersimportopenml_pytorch.configCreate a torch modelmodel=torch.nn.Sequential(processing_net,features_net,results_net)Download the task from openML and run the model on task.task=openml.tasks.get_task(3573)run=openml.runs.run_model_on_task(model,task,avoid_duplicate_runs=False)run.publish()print('URL for run:%s/run/%d'%(openml.config.server,run.run_id))Note: The input layer of the network should be compatible with OpenML data output shape. Please check examples for more information.
Package: openml-speed-dating-pipeline-steps
OpenML Speed Dating Pipeline StepsThis contains all the steps needed to reproduce the OpenML pipeline stepsFree software: MIT licenseDocumentation:https://openml-speed-dating-pipeline-steps.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-12-17)First release on PyPI.
Package: openmltelemetry
llm-traceguardllm-traceguardis an open-source Python library that provides Open Telemetry integration with Large Language Models (LLMs). It is designed to facilitate tracing and safeguarding applications that leverage LLMs, ensuring better monitoring and reliability.FeaturesEasy integration with Open Telemetry for LLM applications.Real-time tracing and monitoring of LLM-based systems.Enhanced safeguards and insights for your LLM applications.InstallationTo installllm-traceguard, simply use pip:pipinstallllm-traceguardUsage 🚀Here's a basic example of how to usellm-traceguardin your project:importllm_traceguardllm_traceguard.instrument()Requirements 📋Python 3.8 or higheropentelemetry-apiopentelemetry-sdkContributing 👐Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.License 📄llm-traceguardis licensed under the Apache-2.0 License. SeeLICENSEfor more details.Contact 📧For support or any questions, feel free to contact us atsupport@whylabs.ai.
Package: openml-tensorflow
Tensorflow extension for OpenML pythonTensorflow extension foropenml-python API.Installation Instructions:pip install openml-tensorflowPyPi linkhttps://pypi.org/project/openml-keras/UsageImport openML librariesimportopenmlimportopenml_tensorflowCreate  and compile a keras modelmodel=tensorflow.keras.models.Sequential([tensorflow.keras.layers.BatchNormalization(),tensorflow.keras.layers.Dense(units=1024,activation=tensorflow.keras.activations.relu),tensorflow.keras.layers.Dropout(rate=0.4),tensorflow.keras.layers.Dense(units=2,activation=tensorflow.keras.activations.softmax),])# We will compile using the Adam optimizer while targeting accuracy.model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])Download the task from openML and run the model on task.task=openml.tasks.get_task(3573)run=openml.runs.run_model_on_task(model,task,avoid_duplicate_runs=False)run.publish()print('URL for run:%s/run/%d'%(openml.config.server,run.run_id))This library is currently under development, please report any bugs or feature reuest in issues section.
Package: openmm
This program sets up and runs an OpenMM simulation in a user-friendly
way.  It is roughly equivalent to togrompp/mdrunin Gromacs,dynamicin Tinker, andpmemdin Amber.To install this package, runpython setup.py install. The only dependencies
are python and OpenMM.For the short help, runopenmm -h. To get help on all available options,
runopenmm –help-all.You need to provide coordinates and specify a force field.
Coordinates may come from a .pdb file.  Built-in protein force fields
and water models are shown in the help text, and you may add your own
OpenMM force field XML files.  Alternatively, you may provide AMBER
inpcrd/prmtop files.You may provide simulation options through the command line; all
options are given in the help text.  Every time this program executes,
it makes a ‘configuration file’ which you may use as input for future
runs using the–configargument.  The command line options take
priority over those in the configuration file.  This provides a
convenient way to customize your default options.Some nice features include:1) Extensive validation of options and checking dependencies /
conflicts between options.2) Reports your simulation progress, including speed in ns/day and
time left.3) You may use units from simtk.unit on the command line when
specifying options with physical units.Suggestions are provided for typos on the command line.5) Restart files that contain state information (coordinates,
velocities, and unit cell vectors) from the previous run.This program is provided as an option for users to use OpenMM without
needing to write a Python script.  To take advantage of the full
flexibility of OpenMM as a domain-specific language for molecular
dynamics simulation, check out the OpenMM Script Builder located athttp://builder.openmm.org.
Package: openmm-mdanalysis-reporter
openmm-mdanalysis-reporterLatest releaseStatusCommunityMDAnalysis based reporter for OpenMMopenmm-mdanalysis-reporter is bound by aCode of Conduct.Currently a WIP but it should workThis is anOpenMMReporter class that usesMDAnalysisfor output. This means it can use anyformat supported by MDAnalysis.
It also supports MDAnalysisselection strings.The reporter is calledMDAReporter, once this package is installed it can be imported as:frommdareporterimportMDAReporterInstallationPip:pipinstallgit+https://github.com/sef43/openmm-mdanalysis-reporterFrom source:gitclonehttps://github.com/sef43/openmm-mdanalysis-reporter.gitcdopenmm-mdanalysis-reporter
pipinstall.UsageIt is used like the existing OpenMM reporters, where the file format is read from the suffix of the output file by MDAnalysis, e.g. to output in XYZ format:frommdareporterimportMDAReporter...simulation.reporters.append(MDAReporter('traj.xyz',100))...Additionally it supports theMDAnalysis selection syntax# using MDAnalysis selection string to output just Carbon atomssimulation.reporters.append(MDAReporter('traj.xyz',100,selection='name C'))ExampleFull example of using MDAReporter to output just the protein (script and data file incdexample):fromopenmm.appimport*fromopenmmimport*fromopenmm.unitimport*fromsysimportstdoutfrommdareporterimportMDAReporterpdb=PDBFile('villin.pdb')forcefield=ForceField('amber14-all.xml','amber14/tip3pfb.xml')system=forcefield.createSystem(pdb.topology,nonbondedMethod=PME,nonbondedCutoff=1*nanometer,constraints=HBonds)integrator=LangevinMiddleIntegrator(300*kelvin,1/picosecond,0.004*picoseconds)simulation=Simulation(pdb.topology,system,integrator)simulation.context.setPositions(pdb.positions)simulation.minimizeEnergy(maxIterations=100)# output just protein in xyz formatsimulation.reporters.append(MDAReporter('traj.xyz',100,enforcePeriodicBox=False,selection="protein"))simulation.reporters.append(StateDataReporter(stdout,100,step=True,potentialEnergy=True,temperature=True))simulation.step(1000)Testingtestsuite can be run usingpytestcd mdareporter/tests
pytestorpytest --pyargs mdareporter.testsBenchmarksA benchmarking script which writes trajectory snapshots every 10 steps for 1000 steps can be found atexample/benchmark.pyThe output on a M2 Macbook is:OpenMM PDBReporter time =  7.989748208987294 s
OpenMM DCDReporter time =  5.6639587499958 s
MDAReporter format DCD time =  3.5682871250028256 s
MDAReporter format NCDF time =  3.609358207992045 s
MDAReporter format PDB time =  11.491382707987214 s
MDAReporter format TRR time =  4.7894440419913735 s
MDAReporter format XTC time =  4.086603666975861 s
MDAReporter format XYZ time =  5.835725833981996 sExcluding MDAReporter using PDB format the MDAReporter formats are all as fast, or faster than the OpenMM formats.AcknowledgementsProject based on theMDAnalysis Cookiecutterversion 0.1.
Please citeMDAnalysiswhen using openmm-mdanalysis-reporter in published work.
Package: openmnglab
OpenMNGLab🚧 under heavy construction, expect every new version to break the API 🚧A Python framework formicroneurographyanalysis.
The core idea is to build up an execution plan of pre-defined (implemented) "function modules". The plan can then be executed and
results retrieved.InstallationCurrently, the package requires Python v3.11 at minimum.
The package is distributed via PyPI as "openmnglab".QuickstartFor a quickstart see thequickstart.ipynbCitingThe repository in general can be cited using the Zenodo-DOI10.5281/zenodo.8331924.
On theZenodo project pageyou will find additional DOIs to cite specific versions of the library.
Package: openmoc
Welcome to the OpenMOC repository! OpenMOC is a simulation tool for
solving for the flux, power distribution, and multiplication factor
within a nuclear reactor. The code employs the deterministic method
of characteristics, with support for both fixed source and eigenvalue
calculations. The OpenMOC project aims to provide a simple-to-use
Python package bound to a back-end of source code written in C/C++
and CUDA. It includes support for constructive solid geometry and 2D
ray tracing for fully heterogeneous multi-group calculations.
Development of OpenMOC began at MIT in 2012 and is spearheaded by
several graduate students in theNuclear Science & Engineering Department.Complete documentation on OpenMOC is hosted athttps://mit-crpg.github.io/OpenMOC/. If you would like to
contribute to the OpenMOC project, pleasecontactthe
development team.For a guided example, see a demonstrationIPython Notebook.InstallationDetailedinstallation instructionscan be found in the
User’s Guide.TroubleshootingJoin the OpenMOCusers groupto ask questions and discuss
methods and simulation workflows.Citing OpenMOCPlease cite OpenMOC in your publications if it helps your research:@article{openmoc2014,
  author ={Boyd, William and Shaner, Samuel and Li, Lulu and Forget, Benoit and Smith, Kord},
  journal ={Annals of Nuclear Energy},
  title ={The OpenMOC Method of Characteristics Neutral Particle Transport Code},
  volume ={68},
  pages ={43--52},
  year ={2014}}LicenseOpenMOC is approved for distribution under the MIT/Xlicense.
Package: openmodule
OpenModule V2[TOC]QuickstartYou can install this library via pip:pipinstallopenmoduleDevelopment with Feature BranchesDuring development it might be necessary to install a version of openmodule, where no pip package exists.
Below you can find how to install a certain openmodule branch for your application with pip.OpenmoduleBash command:pipinstall"git+https://gitlab.com/arivo-public/device-python/openmodule@<branch>#egg=openmodule"requirements.txt:git+https://gitlab.com/arivo-public/device-python/openmodule@<branch>#egg=openmoduleOpenmodule TestBash command:pipinstall"git+https://gitlab.com/arivo-public/device-python/openmodule@<branch>#egg=openmodule-test&subdirectory=openmodule_test"requirements.txt:git+https://gitlab.com/arivo-public/device-python/openmodule@<branch>#egg=openmodule-test&subdirectory=openmodule_testOpenmodule CommandsBash command:pipinstall"git+https://gitlab.com/arivo-public/device-python/openmodule@<branch>#egg=openmodule-commands&subdirectory=openmodule_commandsrequirements.txt:git+https://gitlab.com/arivo-public/device-python/openmodule@<branch>#egg=openmodule-commands&subdirectory=openmodule_commandsLocal DevelopmentSometimes you want to test local changes of the Openmodule library in device services and therefore you can do a local
installation of the library. We use theeditable installsof Pip for this.Openmodulebash command:pipinstall-e<path_to_openmodule_root>Openmodule Testbash command:pipinstall-e<path_to_openmodule_root>/openmodule_test/Openmodule Commandsbash command:pipinstall-e<path_to_openmodule_root>/openmodule_commands/ChangesBreaking ChangesKnown IssuesDocumentationOpenmoduleGetting StartedCoding StandardSettingsRPCHealthDatabaseEventlogPackage ReaderAnonymizationConnection Status ListenerSettings ProviderAccess ServiceCSV ExportTranslationsUtilsDeprecated FeaturesTestingFile CleanupOpenmodule CommandsOpenmodule Commands
Package: openmol
OpenMOLOpenMOL is a Python package to convert between popular molecular dynamics data file formats. It exposes a pythonic way to easily manipulate MOL2 and LAMMPS data files.FeaturesConvert AMBER PARM7 and restart files into LAMMPS data files.Read, manipulate and write MOL2 files.Save data files into portable openmol json format without losing any properties.Build complex mol2 systems using Discovery Studio and import intotleap.Extensible.InstallationYou can installopenmoldirectly fromPyPIusingpip.pipinstallopenmolUsage ExamplesAMBER to LAMMPSConvert AMBERprmtopandrestartfiles to a LAMMPSdatafile.fromopenmolimportamber_parm7asamberfromopenmolimportlammps_fullaslammps# read amber parm filesp=amber.read('system.prmtop','system.rst7')# calculate necessary lammps items and writep=lammps.build(p)lmp=lammps.Writer(p,'system.data')lmp.write()Fix VMD MOL2TheSUBSTRUCTUREsection of the MOL2 files created by VMD are not always properly formated. Use the following to fix the format.fromopenmolimporttripos_mol2asmol2# read vmd output mol2 filep=mol2.read('vmd_saved.mol2')# calculate necessary mol2 residue infop=mol2.build(p)# write the fixed mol2 filemol2.Writer(p,'fixed_vmd_saved.mol2').write()Fix DSV mol2 residue namesDiscovery Studio Visualizer generated MOL2 files can contain digits as suffixes in the residue names. Update the residue names for all atoms as below.fromopenmolimporttripos_mol2asmol2p=mol2.read('dsv_output.mol2')# set all atom's resname to FeOfori,atominenumerate(p.atom_name):p.atom_resname[i]='FeO'p=mol2.build(p)# write the fixed mol2 filemol2.Writer(p,'fixed_dsv_output.mol2').write()Current ImplementationsParserReaderWriterAmberYesxLAMMPSxYesMOL2YesYesContributions welcome!For the latest development version you can clone theGit repository.gitclonehttps://github.com/akhlakm/OpenMOLcdOpenMOL
pipinstall-e.LicenseGNU General Public License v3.0 (GPLv3.0)
Package: open-moldyn
open-moldynOpen Molecular Dynamics tools in Python 3read the doc onhttps://open-moldyn.readthedocs.io/en/latest/Installation instructionsThe package is on PyPI, to install use :pip install open-moldynTo update the package use :pip install -U open-moldynTo run open-moldyn's GUI use the script:moldyn-guiIf it does not work in your terminal,
maybe your python installation is not in the PATH.