Saravanan666/pypi-org huggingface dataset:
Package: gwaxion
gwaxionThis is a package to facilitate computations related to gravitational waves from ultralight-boson condensates around black holes.
The code was primarilty developed in writingarXiv:1810.03812, but it is of broad applicability.The primary objects in this package are:BlackHoleBosonBosonCloudTheir use, as well as other features, is demonstrated in a set of Jupyter notebooks in theexamples/directory.Computations of boson cloud evolution and gravitational-wave emission rely on results fromarXiv:1411.0686andarXiv:1706.06311.Installationpip install gwaxionUsageHere is a barebones example to get some properties of a given black-hole--boson system, for a BH with massM = 50 Msunand dimensionless spinchi = 0.9, and a boson with mass such thatalpha = 0.2.importgwaxion# create a black-hole--boson object (scalar boson by default)bhb=gwaxion.BlackHoleBoson.from_parameters(m_bh=50,chi_bh=0.9,alpha=0.2)# get the fastest-growing boson level# (l, m, nr, growth rate in Hz)bhb.max_growth_rate()# > (1, 1, 0, 4.175501554995195e-06)# get the mass of the corresponding cloud after superradiant growth# as a fraction of the original BH masscloud=bhb.best_cloud()cloud.mass/cloud.bhb_initial.bh.mass# > 0.066For more examples see theexamples/directory.CreditThis code was developed byMaximiliano Isiwith important contributions byRichard Brito.If you make use of this code for your own publications, please cite:@article{Isi:2018pzk,
    author = "Isi, Maximiliano and Sun, Ling and Brito, Richard and Melatos, Andrew",
    title = "{Directed searches for gravitational waves from ultralight bosons}",
    eprint = "1810.03812",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    reportNumber = "LIGO-P1800270",
    doi = "10.1103/PhysRevD.99.084042",
    journal = "Phys. Rev. D",
    volume = "99",
    number = "8",
    pages = "084042",
    year = "2019",
    note = "[Erratum: Phys.Rev.D 102, 049901 (2020)]"
}(This might be replaced by a dedicated publication at a later date.)
Package: gwaxion-maxisi
gwaxionThis is a package to facilitate computations related to gravitational waves from ultralight-boson condensates around black holes.
The code was primarilty developed in writingarXiv:1810.03812, but it is of broad applicability.The primary objects in this package are:BlackHoleBosonBosonCloudTheir use, as well as other features, is demonstrated in a set of Jupyter notebooks in theexamples/directory.Computations of boson cloud evolution and gravitational-wave emission rely on results fromarXiv:1411.0686andarXiv:1706.06311.Installationpip install gwaxionUsageHere is a barebones example to get some properties of a given black-hole--boson system, for a BH with massM = 50 Msunand dimensionless spinchi = 0.9, and a boson with mass such thatalpha = 0.2.importgwaxion# create a black-hole--boson object (scalar boson by default)bhb=gwaxion.BlackHoleBoson.from_parameters(m_bh=50,chi_bh=0.9,alpha=0.2)# get the fastest-growing boson level# (l, m, nr, growth rate in Hz)bhb.max_growth_rate()# > (1, 1, 0, 4.175501554995195e-06)# get the mass of the corresponding cloud after superradiant growth# as a fraction of the original BH masscloud=bhb.best_cloud()cloud.mass/cloud.bhb_initial.bh.mass# > 0.066For more examples see theexamples/directory.CreditThis code was developed byMaximiliano Isiwith important contributions byRichard Brito.If you make use of this code for your own publications, please cite:@article{Isi:2018pzk,
    author = "Isi, Maximiliano and Sun, Ling and Brito, Richard and Melatos, Andrew",
    title = "{Directed searches for gravitational waves from ultralight bosons}",
    eprint = "1810.03812",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    reportNumber = "LIGO-P1800270",
    doi = "10.1103/PhysRevD.99.084042",
    journal = "Phys. Rev. D",
    volume = "99",
    number = "8",
    pages = "084042",
    year = "2019",
    note = "[Erratum: Phys.Rev.D 102, 049901 (2020)]"
}(This might be replaced by a dedicated publication at a later date.)
Package: gwbench
gwbenchAcknowledgmentWe request that any academic report, publication, or other academic
disclosure of results derived from the use of this software acknowledge
the use of the software by an appropriate acknowledgment or citation.The gwbench software can be cited fromarXiv:2010.15202, with INSPIRE BibTeX entry:@article{Borhanian:2020ypi,
    author = "Borhanian, Ssohrab",
    title = "{gwbench: a novel Fisher information package for gravitational-wave benchmarking}",
    eprint = "2010.15202",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    month = "10",
    year = "2020"
}Installation via pippip install gwbenchInstallation from sourceClone the gwbench repository and enter itClone this repository and follow the next steps.git clone https://gitlab.com/sborhanian/gwbench.git
cd gwbenchUsing condaSource Oasis Conda -do this first; only on LIGO clusters neededsource /cvmfs/oasis.opensciencegrid.org/ligo/sw/conda/etc/profile.d/conda.sh  
which condaThe last line should print/cvmfs/oasis.opensciencegrid.org/ligo/sw/conda/condabin/condaor similar.Setup conda virtual environmentconda create -y --name gwbench python=3.7  
conda activate gwbench  
conda install -y -c conda-forge --file requirements_conda.txtUsingpython -m venvand pipReplace~/gwbenchwith the appropriate path of choice in the following instructions:python3 -m venv ~/gwbench
source ~/gwbench/bin/activate
pip install -r requirements_pip.txtUsing pip or condaInstall while the virtual environment is active:pip install .Uninstallpip uninstall gwbenchTestcd example_scripts  
python quick_start.py
Package: gwcat
Gravitational Wave CatalogAn importable edition of the LIGO/Virgo gravitational wave event catalogue.Free software: ISC licenseDocumentation:https://gwcat.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-09-12)First release on PyPI.
Package: gwcelery
GWCeleryGWCelery is a simple and reliable package for annotating and orchestrating
LIGO/Virgo alerts, built from widely used open source components.See thequick start installation instructions,
the fulldocumentation, or thecontributing guide.FeaturesEasy installation with pipLightning fast distributed task queue powered byCeleryandRedisTasks are defined bysmall, self-contained Python functionsLightweight test suiteusing mocks of external servicesContinuous integrationOne environment variable to switch from playground to production GraceDB serverBrowser-based monitoring console
Package: gwcloud-python
GWCloudis a service used to handle both the submission ofBilbyjobs to a supercomputer queue and the obtaining of the results produced by these jobs.
While there is a web interface for this service, which is recommended for beginners, this package can be used to allow Bilby job submission and manipulation from Python scripts.Check out thedocumentationfor more information.InstallationThe gwcloud-python package can be installed withpip install gwcloud-pythonExample>>> from gwcloud_python import GWCloud
>>> gwc = GWCloud(token='<user_api_token_here>')
>>> job = gwc.get_official_job_list()[0]
>>> job.save_corner_plot_files()

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Package: gwcosmo
Welcome to gwcosmoGwcosmo: a package to estimate cosmological parameters using gravitational-wave observations.InstallationThe recommended method for installing gwcosmo is using pip. We recommend setting up a virtual environment first. Once you have done this and activated your virtual environment, simply runpip install gwcosmoNote that gwcosmo requires Python version 3.8 or higher.DocumentationGwcosmo's documentation is available athttps://lscsoft.docs.ligo.org/gwcosmo/.Citing gwcosmoIf you use gwcosmo in a scientific publication, please cite the following publications, and include the following statement in your manuscript: "This work makes use of gwcosmo which is available athttps://git.ligo.org/lscsoft/gwcosmo".@ARTICLE{2023arXiv230802281G,author={{Gray}, Rachel and {Beirnaert}, Freija and {Karathanasis}, Christos and {Revenu}, Beno{\^\i}t and {Turski}, Cezary and {Chen}, Anson and {Baker}, Tessa and {Vallejo}, Sergio and {Enea Romano}, Antonio and {Ghosh}, Tathagata and {Ghosh}, Archisman and {Leyde}, Konstantin and {Mastrogiovanni}, Simone and {More}, Surhud},title="{Joint cosmological and gravitational-wave population inference using dark sirens and galaxy catalogues}",journal={arXiv e-prints},keywords={Astrophysics - Cosmology and Nongalactic Astrophysics},year=2023,month=aug,eid={arXiv:2308.02281},pages={arXiv:2308.02281},doi={10.48550/arXiv.2308.02281},archivePrefix={arXiv},eprint={2308.02281},primaryClass={astro-ph.CO},adsurl={https://ui.adsabs.harvard.edu/abs/2023arXiv230802281G},adsnote={Provided by the SAO/NASA Astrophysics Data System}}@article{10.1093/mnras/stac366,author={Gray, R and Messenger, C and Veitch, J},title="{A pixelated approach to galaxy catalogue incompleteness: improving the dark siren measurement of the Hubble constant}",journal={Monthly Notices of the Royal Astronomical Society},volume={512},number={1},pages={1127-1140},year={2022},month={02},issn={0035-8711},doi={10.1093/mnras/stac366},url={https://doi.org/10.1093/mnras/stac366},eprint={https://academic.oup.com/mnras/article-pdf/512/1/1127/45303118/stac366.pdf}}@article{PhysRevD.101.122001,title={Cosmological inference using gravitational wave standard sirens: A mock data analysis},author={Gray, Rachel and Hernandez, Ignacio Maga\~na and Qi, Hong and Sur, Ankan and Brady, Patrick R. and Chen, Hsin-Yu and Farr, Will M. and Fishbach, Maya and Gair, Jonathan R. and Ghosh, Archisman and Holz, Daniel E. and Mastrogiovanni, Simone and Messenger, Christopher and Steer, Dani\`ele A. and Veitch, John},journal={Phys. Rev. D},volume={101},issue={12},pages={122001},numpages={22},year={2020},month={Jun},publisher={American Physical Society},doi={10.1103/PhysRevD.101.122001},url={https://link.aps.org/doi/10.1103/PhysRevD.101.122001}}
Package: gwcs
Generalized World Coordinate System (GWCS) is anAstropyaffiliated package providing tools for managing the World Coordinate System of astronomical data.GWCS takes a general approach to the problem of expressing transformations between pixel and world coordinates. It supports a data model which includes the entire transformation pipeline from input coordinates (detector by default) to world coordinates. It is tightly integrated withAstropy.Transforms are instances ofastropy.Model. They can be chained, joined or combined with arithmetic operators using the flexible framework of compound models inastropy.modeling.Celestial coordinates are instances ofastropy.SkyCoordand are transformed to other standard celestial frames usingastropy.coordinates.Time coordinates are represented byastropy.Timeand can be further manipulated using the tools inastropy.timeSpectral coordinates areastropy.Quantityobjects and can be converted to other units using the tools inastropy.units.For complete features and usage examples see thedocumentationsite.NoteBeginning with version 0.9 GWCS requires Python 3.5 and above.InstallationTo install:pip install gwcs  # Make sure pip >= 9.0.1 is used.To clone from github and install the master branch:git clone https://github.com/spacetelescope/gwcs.git
cd gwcs
python setup.py installContributing Code, Documentation, or FeedbackWe welcome feedback and contributions to the project. Contributions of
code, documentation, or general feedback are all appreciated. Please
follow thecontributing guidelinesto submit an
issue or a pull request.We strive to provide a welcoming community to all of our users by
abiding to theCode of Conduct.Citing GWCSIf you use GWCS, please cite the package via its Zenodo record.
Package: gwdatafind
GWDataFindThe GWDataFind data discovery client.The GWDataFind service allows users to query for the location of
Gravitational-Wave Frame (GWF) files containing data from the current
gravitational-wave detectors.InstallationThe simplest installation is viapip:python-mpipinstallgwdatafindThis package is also available as a Conda package:condainstall-cconda-forgegwdatafindBasic UsageTo find the URLs of allH1_Rfiles for the LIGO-Hanford observatory in
a given GPS interval:>>>fromgwdatafindimportfind_urls>>>find_urls('H','H1_R',1198800018,1198800618)To utillise connection pooling, create aSession:>>>fromgwdatafindimport(find_urls,Session)>>>withSession()assess:...forifoin("H","L"):...urls[ifo]=find_urls(...ifo,...f"{ifo}1_R",...1198800018,...1198800618,...session=sess,...)## On the command-lineGWDataFindcanalsobeexecutedviathecommand-lineclient,forfulldetailsrun```bash$python-mgwdatafind--helpFor more documentation, seehttps://gwdatafind.readthedocs.io/.
Package: gwdatafind-server
GWDataFind ServerThis module defines a Flask Application that serves data URLs based on the contents of a diskcache.
A link to the documentation page can be foundhere.RequirementsPython >= 3.4Flask >= 1.0.0Apache + Gunicorn configurationFor a detailed description of how to set up and configure a datafind server,
please seehere.Client APIA detailed explination of the API can be foundhere.
Package: gwdetchar
GWDetChar is a python package for for gravitational-wave detector
characterisation and data quality.https://gwdetchar.readthedocs.ioInstallationGWDetChar is best installed withconda:condainstall-cconda-forgegwdetcharbut can also be installed withpip:python-mpipinstallgwdetcharContributingAll code should follow the Python Style Guide outlined inPEP 0008;
users can use theflake8package to check their code for style issues
before submitting.Seethe contributions guidefor the recommended procedure for
proposing additions/changes.
Package: gw-dsl-parser
gw-dsl-parser-pyInstallpip install gw-dsl-parserDeveloppip install ./

python ./scripts/download_wasm.pyExamplefromgw_dsl_parserimportget_sql_from_payloadpayload={"workflow":[{"type":"view","query":[{"op":"aggregate","groupBy":[],"measures":[{"field":"*","agg":"count","asFieldKey":"count"}]}]}]}table_name="test_table"sql=get_sql_from_payload(table_name,payload)
Package: gwebsocket
==========gwebsocket==========`gwebsocket`_ is a WebSocket library for the gevent_ networking library.::from gevent import pywsgifrom gwebsocket.handler import WebSocketHandlerdef websocket_app(environ, start_response):if environ["PATH_INFO"] == '/echo':ws = environ["wsgi.websocket"]message = ws.receive()ws.send(message)server = pywsgi.WSGIServer(("", 8000), websocket_app,handler_class=WebSocketHandler)server.serve_forever()Installation------------The easiest way to install gwebsocket is directly from PyPi_ using pip orsetuptools by running the commands below::$ pip install gwebsocketGunicorn Worker^^^^^^^^^^^^^^^Using Gunicorn it is even more easy to start a server. Only the`websocket_app` from the previous example is required to start the server.Start Gunicorn using the following command and worker class to enable WebSocketfuntionality for the application.::gunicorn -k "gwebsocket.gunicorn.GWebSocketWorker" wsgi:websocket_appPerformance^^^^^^^^^^^`gwebsocket`_ is pretty fast, but can be accelerated further byinstalling `wsaccel <https://github.com/methane/wsaccel>`_ and `ujson` or `simplejson`::$ pip install wsaccel ujson`gwebsocket`_ automatically detects ``wsaccell`` and uses the Cythonimplementation for UTF8 validation and later also frame masking and demasking.Get in touch^^^^^^^^^^^^Issues can be createdat `Bitbucket <https://bitbucket.org/btubbs/gwebsocket/issues?status=new&status=open>`_.Acknowledgements^^^^^^^^^^^^^^^^gwebsocket is based on `Jeffrey Gelens`_' `gevent-websocket`_.gwebsocket omits gevent-websocket's WAMP features and WebSocketApplication miniframework, in favor of providing just a minimal library for use in otherframeworks.  gwebsocket also makes it possible to do cleanup on close withoutbuilding your app as a WebSocketApplication... _gwebsocket: http://www.bitbucket.org/btubbs/gwebsocket/.. _gevent: http://www.gevent.org/.. _Jeffrey Gelens: http://www.gelens.org/.. _PyPi: http://pypi.python.org/pypi/gevent-websocket/.. _repository: http://www.bitbucket.org/Jeffrey/gevent-websocket/.. _RFC6455: http://datatracker.ietf.org/doc/rfc6455/?include_text=1
Package: gw-eccentricity
Defining eccentricity for gravitational wave astronomyAboutgw_eccentricityprovides methods to measure eccentricity and mean anomaly
from gravitational waveforms.These methods are described in the following paper:[1] Md Arif Shaikh, Vijay Varma, Harald Pfeiffer, Antoni Ramos-Buades and Maarten van de Meent,
"Defining eccentricity for gravitational wave astronomy", (2023).arXiv:2302.11257Please seeCreditsbelow for citing this work.
This package lives onGitHub, is compatible withpython3, and is tested every week. You can see the current build status of
the main branch at the top of this page.InstallationPyPIgw_eccentricityis available throughPyPI:pipinstallgw_eccentricityCondagw_eccentricitycan be installed usingcondaalso:condainstall-cconda-forgegw_eccentricityFrom sourcegitclonegit@github.com:vijayvarma392/gw_eccentricity.gitcdgw_eccentricity
pythonsetup.pyinstallIf you do not have root permissions, replace the last step withpython setup.py install --userDependenciesAll of these can be installed through pip or conda.numpyscipyh5pylalsuiteUsageSee the example notebookherefor a demo.Making contributionsSee thisREADMEfor instructions on how to make contributions to this package.Please report bugs by raising an issue on ourGitHubrepository.CreditsThe main contributors to this code areMd Arif Shaikh,Vijay
Varma, andHarald Pfeiffer. You can find the full list of contributorshere.If you find this package useful in your work, please cite reference [1] and
this package. You can use the following bibtex keys:@article{Shaikh:2023ypz,author="Shaikh, Md Arif and Varma, Vijay and Pfeiffer, Harald P. and Ramos-Buades, Antoni and van de Meent, Maarten",title="{Defining eccentricity for gravitational wave astronomy}",eprint="2302.11257",archivePrefix="arXiv",primaryClass="gr-qc",month="2",year="2023",Note="{\href{https://pypi.org/project/gw_eccentricity}{pypi.org/project/gw\_eccentricity}}",}Image at the top shows an eccentric gravitational wave signal (SXS:BBH:2558) fromarXiv:2209.03390.
Package: gwemopt
gwemoptGravitational-wave Electromagnetic OptimizationThe code currently can:interact with gracedb, download the skymaps, read them etc.read telescope configuration files with location, FOV, limiting magnitude, exposure times, etc.create the tiling based on telescope configuration (MOC, Shaon's method).generate exposure time as a function of tile, which accounts for number of hours available.perform scheduling to include most of the requested exposurestest the efficiency of the tiling, exposure time method choices, for a given lightcurve.Current planned improvements / open questions:Include moon and sky brightness when schedulingHow to improve scheduling when multiple (and different numbers of exposures are expected)How to use WAW when inclination is not immediately availableHow to include distance estimates as function of sky location in PEMRelated repositories:https://github.com/JavedRANA/emgwfollowuphttps://github.com/shaonghosh/sky_tiling.githttps://github.com/omsharansalafia/wawhttps://github.com/manleongchan/optimizationcodeReferences:Rana et al:http://iopscience.iop.org/article/10.3847/1538-4357/838/2/108/metaSalafia et al:https://arxiv.org/abs/1704.05851Ghosh et al:https://www.aanda.org/articles/aa/abs/2016/08/aa27712-15/aa27712-15.htmlChan et al:http://iopscience.iop.org/article/10.3847/1538-4357/834/1/84/metaCoughlin and Stubbs:https://link.springer.com/article/10.1007/s10686-016-9503-4Setting up the environmentIf you want the latest version, we recommend creating a clean environment:conda create -n gwemopt python=3.11
git clone git@github.com:skyportal/gwemopt.git
pip install -e gwemopt
pre-commit installor if you just want the latest version on Github:pip install gwemoptIf you run into dependency issues, you can try installing dependencies via conda:conda install numpy scipy matplotlib astropy h5py shapely
conda install -c astropy astroquery
conda install -c conda-forge voeventlib astropy-healpix python-ligo-lw ligo-segments ligo.skymap ffmpegAnd then runpip install -e gwemoptagain.UsageOnce installed, You can use gwemopt via the command line:gwemopt-run ....where ... corresponds to the various arguments.
Package: gwent
Gravitational Wave dEtector desigN Toolkit.Generates strain sensitivity curves and Waterfall plots for various gravitational wave detector designs.Free software: MIT licenseDocumentation:https://gwent.readthedocs.io.FeaturesCalculates the sensitivity curves for various designs of pulsar timing arrays, space-based detectors, and ground-based detectors.
This includes:NANOGravSKALISAaLIGOVoyagerand more!Calculates the strain from coalescing black hole binaries. It contains functionality for different source descriptions:Slowly-evolving sources, ie. BHBs early in their inspiral where they appear to not change in frequency.Rapidly-evolving sources, ie. BHBs in the final stages of coalescence.Uses a fully Pythonic implementation of the phenomenological modelIMRPhenomDto accurately represent the inspiral, merger, and ringdown of the BHB.Calculates the matched-filtered signal-to-noise ratio (SNR) to help assess the detectability of any BHB source configuration by any represented gravitational wave detector.Includes robust plotting methods to represent these SNRs.Getting Startedgwentis available on the Python Package Inventory, so the preferred method to installgwentis to install it withpip, as it will always install the most recent stable release.$pipinstallgwentREADME Figure and DataIf you are looking for quick data, we conveniently place the figure above in thedatafolder on the Github repo. There you can also find the raw data used for this figure in.npzformat. To load this data, simply usenp.load(filename), and the data can be accessed by the kwargs'mass','redshift', and'snr'. E.g.,importnumpyasnpimportgwentfromgwent.snrplotimportPlot_SNRloaded_file=np.load(filename)Plot_SNR('M',load_file['mass'],'z',load_file['redshift'],load_file['snr'])PublicationThis work and methodology is available onarXiv. If you usegwent, please cite this work using the following:@ARTICLE{Kaiser:2021,
  doi ={10.1088/1361-6382/abd4f6},
  url ={https://doi.org/10.1088/1361-6382/abd4f6},
  year = 2021,
  month ={jan},
  publisher ={{IOP}Publishing},
  volume ={38},
  number ={5},
  pages ={055009},
  author ={A R Kaiser and S T McWilliams},
  title ={Sensitivity of present and future detectors across the black-hole binary gravitational wave spectrum},
  journal ={Classical and Quantum Gravity},
  eprint ={2010.02135},
  adsurl ={https://ui.adsabs.harvard.edu/abs/2020arXiv201002135K},
  adsnote ={Provided by the SAO/NASA Astrophysics Data System}}CreditsWe utilize and include within the package a specific commit ofpygwincfound athttps://git.ligo.org/gwinc/pygwincto create many of the ground-based gravitational wave detector sensitivity curves. At the time of creation, there is nopygwincavailability on PyPI, so we explicitly include the necessary portions of the code within.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: gwf
A flexible, pragmatic workflow tool.gwfis a flexible, pragmatic workflow tool for building and running large,
scientific workflows. It runs on Python 3.5+ and is developed at the
Bioinformatics Research Centre (BiRC), Aarhus University.ExamplesTo get a feeling for what agwfworkflow looks like, have a look at a fewexamples.Getting startedTo quickly get started writing workflows ingwfyou can read theUser’s Guide.ExtendingWe don’t have the backend you need to run your workflow on your cluster?
See theWriting Backendssection to roll your own.ContributingWe aim to makegwfa community developed project. Learn how tocontribute.