Saravanan666/pypi-org huggingface dataset:
Package: cosmic-profiles
CosmicProfiles is a Cython package for Point Cloud ProfilingThe CosmicProfiles projectThis repository provides shape and density profile analysis tools for cosmological simulations (and beyond). Its features includeoverall halo shape determination, i.e. major, intermediate, minor axis vectors and shape quantities such as intermediate-to-major axis ratio or sphericityhalo shape profile determinationiterative shell-based shape profile determination algorithm for high-resolution halositerative ellipsoid-based shape profile determination algorithm for lower-resolution halosuser can choose between reduced shape tensor and non-reduced shape tensorsupports‘direct’ datasets (i.e. index catalogue provided by user) andFoF / SUBFIND halo cataloguesGadget-style I, II and HDF5 snapshot filesall functionalities available for dark matter halos, gas particle halos and star particle halosin addition, allows for velocity dispersion tensor eigenaxes determinationhalo density profile estimation using direct binning and kernel-based approachesuser can choose between direct binning into spherical shells anddirect binning into ellipsoidal shellsdensity profile fitting assuming eitherNFW,Hernquist 1990,Einastooralpha-beta-gammaprofile modelconcentration-mass relationship of halos easy to calculatemock halo generator: ellipsoidal or spherical, compatible with the 4 density profile modelseasy to interface withpynbodyto work with halos identified in a cosmological simulation (see example scripts)easy to interface withnbodykitto harness large-scale structure capabilities (see example scripts)various profile plotting and 3D point cloud plotting toolsefficient caching capabilities to accelerate look-upsDocumentationThe documentation for CosmicProfiles is hosted onRead the Docs.Installation and DependenciesThe easiest way to get CosmicProfiles is to install it with conda using the conda-forge channel:conda install cosmic_profiles --channel conda-forgeAlternatively, you can use pip:pip install cosmic-profilesSee theinstallation
instructionsin thedocumentationfor more information.LicenseCopyright 2020-2023 Tibor Dome.CosmicProfiles is free software made available under the MIT License.Contributions are welcome. Please raise an issue or open a PR.
Package: cosmic-ray
Cosmic Ray: mutation testing for Python“Four human beings – changed by space-born cosmic rays into something more than merely human.”—The Fantastic FourCosmic Ray is a mutation testing tool for Python 3.It makes small changes to your source code, running your test suite for each
one. Here’s how the mutations look:Read the full documentation at readthedocs.ContributingThe easiest way to contribute is to use Cosmic Ray and submit reports for defects or any other issues you come across.
Please see CONTRIBUTING.rst for more details.
Package: cosmic-ray-spor-filter
Spor-based filter for Cosmic Ray mutation testing.The filter scans a Cosmic Ray session, looking for spor anchors attached to files in the session. If it finds an anchor
with the metadata{mutate: False}, then that mutation is marked as “skipped”.ExampleAssume you’ve got a directory structure like this:project_root/
    .spor/
       ...anchors...
    cr-session.sqlite
    src/
       ...project source...If you run this command:cosmic-ray-spor-filter cr-session.sqlitewill look at each job scheduled incr-session.sqlite. If the file referenced in job has an anchor in the.spordirectoryandif that anchor has{mutate: False}in its metadata, then that job will be skipped.
Package: cosmic-shapes
The cosmic_shapes projectThis repository provides shape analysis tools for cosmological simulations (and beyond). Its features includeiterative shell-based shape determination algorithm for high-resolution halos/galaxiesiterative ellipsoid-based shape determination algorithm for low-resolution halos/galaxiesoverall point cloud shape determination, shape profiles determination, velocity dispersion tensor eigenaxes determinationwrapper to the Amiga Halo Finder (AHF) viapynbodyto identify halos in a cosmological simulationmock point cloud generatormock log-normal universe generatorexample scripts.The documentation can be foundhere.Contributions are welcome. Please raise a PR. Comments/recommendations/complaints can be senthere.
Package: cosmic-toolkit
Cosmic ToolkitCosmic Toolkit is an opinionated toolkit to speed up the development of complex apps.There aren't many examples of how to implement Domain-Driven Design in Python so this
package aims to make DDD adoption in Python applications simple and straightfoward.This toolkit is inspired byCosmic Python.Cosmic
Pythonand Domain-Driven Design help to decouple business logic from application
concerns.It's highly recommended that you read theCosmic Pythonbook and view the samplerepository.
In particular, the branch with the code for Chapter 13 is relevant for how to implement
these patterns.Currently in Alpha, so proceed with caution!I'm learning DDD as I use
this package in my own projects. While this package is in Alpha, expect many breaking
changes.Installationpip install cosmic-toolkitExample usageThere's a fairly complete example of how to use this package in./test/test_message_bus.py.You can also see a thorough domain model example with an aggregate root in./test/test_models.py.When you're using DDD principles, it's recommended that you start by building out your
domain models. This is where your business logic needs to reside. Thus, the domain
models can be written w/out specific knowledge of your application and domain models
should not depend on any application concerns.
Package: cosmic-variance
Package to calculate cosmic variance in pencil-beam surveysPython package based on the IDL code released with the Cosmic Variance Cookbook of Moster et al. (2010)The code is based on galaxy stellar mass bins (as described inhttps://arxiv.org/pdf/1001.1737.pdf), scaled to dark matter cosmic variance (as described inhttps://arxiv.org/pdf/astro-ph/0109130.pdf).This is significantly more useful than dark matter - only variance, since the empirical galaxy variance is significantly higher.Free software: MIT licenseInstall and UseTo install the package, simply run:pipinstallcosmic-varianceThen in your script/notebook, import the package as:importcosmic_varianceascvThe main use of the package is through the get_cv function, which takes in a rectangular survey geometry with side lengths side1 and side2 (in degrees), and an array of redshift bin edges, and returns a pandas dataframe with the cosmic variance for 0.5 dex galaxy stellar mass bins for each redshift bin.importcosmic_varianceascvimportnumpyasnp# Example of using the main function, get_cv to calculate# cosmic variance for a single JWST pointing#### these arguments are required ####side1=2.2/60.# /60 to convert from arcmin to degreesside2=2*2.2/60.# /60 to convert from arcmin to degreeszarray=np.array([7,8,9,11,13])# redshift bin edges, if dz is given, this array will be the center of the redshift bins#### these arguments are optional ####name='JWST'# name of the survey, if provided, the output file will be saved as dfs/{name}.csv along with a meta file.# Default is None, in which case the output will not be savedacc='low'# accuracy of the calculation, 'low' or 'high, low is default, faster and sufficient for almost all applicationsverbose=False# if True, will print out the progress of the calculation, default is False#If you want to use a different cosmology, you can specify it by the following in the get_cv call# OmegaM = 0.308, OmegaL = 0.692, OmegaBaryon = 0.022/(0.678)**2 sigma8 = 0.82, ns = 0.96, h = 0.678cv_df=cv.get_cv(side1,side2,zarray,name=name,acc=acc,verbose=verbose)This will calculate the cosmic variance for a 2.2 arcmin x 4.4 arcmin survey in redshifts bin [7, 8], [8,9], [9,11], [11,13] and save the output.
Package: cosmid
There’s a wealth of publicly available genomics resources; assemblies, database dumps, sample data etc. Problem is, they are spread across various FTP servers all over the world. Cosmid can help you find, clone and manage resources, including a quick way to keep up-to-date on the latest releases.# Clone dependencies from './cosmid.yaml'
$ cosmid clone
# Clone a specific resource + target verion (optional)
$ cosmid clone <package>#<version>

# Walk through setting up a new 'cosmid.yaml' file
$ cosmid init
Package: cos_migrate_tool
.. image:: https://travis-ci.org/tencentyun/cos_migrate_tool.svg?branch=master:target: https://travis-ci.org/tencentyun/cos_migrate_tool.. image:: https://badge.fury.io/py/cos_migrate_tool.svg:target: https://badge.fury.io/py/cos_migrate_tool.. image:: https://img.shields.io/github/release/tencentyun/cos_migrate_tool.svg:target: https://github.com/tencentyun/cos_migrate_tool.. image:: https://img.shields.io/pypi/dm/cos_migrate_tool.svg:target: https://pypi.python.org/pypi/cos_migrate_toolcos_migrate_tool##########################.. warn::发现第三方sdk存在bug，可能会导致迁移数据不完整，正在添加更强的校验方式来避免第三方sdk bug带来的影响，新版本会在 01.25号之前release，建议暂时不要使用该工具A simple tool for migrating data between object storage services.INSTALL-----------use pip ::pip install -U cos_migrate_toolHOW TO USE---------------as following ::cos_migrate_tool -hcos_migrate_tool -c /path/to/your/confISSUE---------------send emails to liuchang0812#gmail.com or post an issueTODO---------------1. add overwrite option2. add retry option3. add more loggingLICENSE----------MIT
Package: cos-migrate-tool-for-restore
.. image:: https://travis-ci.org/tencentyun/cos_migrate_tool.svg?branch=master:target: https://travis-ci.org/tencentyun/cos_migrate_tool.. image:: https://badge.fury.io/py/cos_migrate_tool.svg:target: https://badge.fury.io/py/cos_migrate_tool.. image:: https://img.shields.io/github/release/tencentyun/cos_migrate_tool.svg:target: https://github.com/tencentyun/cos_migrate_tool.. image:: https://img.shields.io/pypi/dm/cos_migrate_tool.svg:target: https://pypi.python.org/pypi/cos_migrate_toolcos_migrate_tool_for_restore##########################A simple tool for migrating data between object storage services.INSTALL-----------use pip ::pip install -U cos_migrate_tool_for_restoreHOW TO USE---------------as following ::cos_migrate_tool_for_restore -hcos_migrate_tool_for_restore -c /path/to/your/confISSUE---------------send emails to liuchang0812#gmail.com or post an issueTODO---------------1. add overwrite option2. add retry option3. add more loggingLICENSE----------MIT
Package: cos_migrate_tool_test
cos_migrate_toolA simple tool for migrating data between object storage services.INSTALLuse pippip install -U cos_migrate_toolHOW TO USEas followingcos_migrate_tool -h
cos_migrate_tool -c /path/to/your/confISSUEsend emails to liuchang0812#gmail.com or post an issueTODOadd overwrite optionadd retry optionadd more loggingLICENSEMIT
Package: cosmo
CosmoCosmo is an asynchronous python webserver that utilizes raw sockets to send and receive data, without using ASGI/WSGI.BenchmarkingExamplesSimple ServerfromcosmoimportApp,Request,Responseapp=App("0.0.0.0",8080)@app.route("/","text/html")asyncdefindex(request:Request):returnResponse(f"<h1>{request.address}</h1>")app.serve()Using Custom HeadersfromcosmoimportApp,Request,Responseapp=App("0.0.0.0",8080)@app.route("/","text/html")asyncdefindex(request:Request):headers={"x-custom-header":"custom"}content="<h1>Custom Headers: </h1>\n<ul>"forheaderinheaders.keys():content+=f"<li>{header}:{headers[header]}</li>\n"content+="</ul>"returnResponse(content,headers)app.serve()Returning an HTML PagefromcosmoimportApp,html_page,Request,Responseapp=App("0.0.0.0",8080)@app.route("/","text/html")asyncdefindex(request:Request):returnResponse(html_page("path/to/page.html"))app.serve()Using Routes from a different fileInrouter.py:fromcosmoimportRequest,Response,Routerrouter=Router()@router.route("/")asyncdefindex():returnResponse("<h1>Hi</h1>")Inapp.py:fromcosmoimportApp,Request,Responsefromrouterimportrouterapp=App("0.0.0.0",8080)app.import_router(router)app.serve()DocsComing soon...
Package: cosmoboost
CosmoBoostCosmoBoost is a python package for Lorentz boosting anisotropic temperature and polarization maps in healpix format. The calculations are done in spherical harmonic space based on therelativistic Doppler and aberration kernel formalismdeveloped inYasini & Pierpaoli (2017)andDai & Chluba (2014), following up on the original idea byChallinor & van Leeuwen (2002).Currently the supported radiation types are:Cosmic Microwave Background (CMB)Kinetic Sunyaev Zeldovich (kSZ)Thermal Sunyaev Zeldovich (tSZ)See thetutorial.ipynbnotebook for an overview of the features through a set of examples.Dependenciespython 3numpyscipyhealpy(needed for running the tutorial)InstallationYou can install CosmoBoost from pypi usingpip install cosmoboostAlternatively, you can clone the repository by runninggit clone https://github.com/syasini/CosmoBoost.gitthen move to the CosmoBoost directorycd CosmoBoostand runpython setup.py install --useror use pippip install [-e] .the-eargument will install the package in editable mode which is suitable for developement. If you want to modify the code use this option.Example SessionCosmoBoost has a simple and user friendly interface. Simply import the package usingimport cosmoboost as cbThen load the default boosting parameters dictionary (beta = 0.00123,d=1,s=0,lmax= 1000, etc.)pars = cb.DEFAULT_PARSInstantiate the kernel objectkernel = cb.Kernel(pars)Now simply boost a set of alm's (recommended) usingalm_boosted = cb.boost_alm(alm_rest, kernel)or boost the power spectrum Cl directlyCl_boosted = cb.boost_Cl(Cl_rest, kernel)See the tutorial for a comprehensive example.AcknowledgementIf you find the contents of this repository useful for your research, please consider citing the following papers:@article{Yasini:2017jqg,
     author         = "Yasini, Siavash and Pierpaoli, Elena",
     title          = "{Generalized Doppler and aberration kernel for
                       frequency-dependent cosmological observables}",
     journal        = "Phys. Rev.",
     volume         = "D96",
     year           = "2017",
     number         = "10",
     pages          = "103502",
     doi            = "10.1103/PhysRevD.96.103502",
     eprint         = "1709.08298",
     archivePrefix  = "arXiv",
     primaryClass   = "astro-ph.CO",
     SLACcitation   = "%%CITATION = ARXIV:1709.08298;%%"
 }@article{Dai:2014swa,
      author         = "Dai, Liang and Chluba, Jens",
      title          = "{New operator approach to the CMB aberration kernels in
                        harmonic space}",
      journal        = "Phys. Rev.",
      volume         = "D89",
      year           = "2014",
      number         = "12",
      pages          = "123504",
      doi            = "10.1103/PhysRevD.89.123504",
      eprint         = "1403.6117",
      archivePrefix  = "arXiv",
      primaryClass   = "astro-ph.CO",
      SLACcitation   = "%%CITATION = ARXIV:1403.6117;%%"
}The bibtex entries are copied frominspirehep.net.
Package: cosmocalc
The following values are calculated:NameValueUnitszInput redshiftH0Hubble constantWROmega(radiation)WKOmega curvaturve = 1-Omega(total)WMOmega matterWVOmega vacuumDTTTime from z to nowGyrageAge of UniverseGyrzageAge of Universe at redshift zGyrDCMRComoving radial distanceGyr Mpc cmVCMComoving volume within redshiftGpc3DAAngular size distanceGyr Mpc cmDLLuminosity distanceGyr Mpc cmPSPlate scale - distance per arcseckpc cm
Package: cosmodiscovery
Cosmo Network Discovery APIDocumentation for The OpenSource Cosmo Network Discovery APIThis is the main implemtation written in python. For other implementations seeImplemtationsand for a description on the API and Protocol seeAPI DocsInstallationYou can Install it with Pip and PyPipython3 -m pip install cosmodiscoveryor with Pip and Github without PyPipython3 -m pip install git+https://github.com/SamHDev/CosmoDiscovery.gitUsageImport the libaryimportcosmodiscoveryasdiscoveryA Discovery Serverserver=discovery.DiscoveryServer()server.listen()Yep! Thats it!If you want a customschemeorportyou can do it like this:server=discovery.DiscoveryServer(port=12892,scheme="_discovery._cosmo.home_device"))Sending Custom Data with the discovery reply.clsis aDiscoveryRequestMessageObject@server.discovery_callbackdefon_msg(cls):cls.reply(data={})A Discovery ClientCreate a DiscoveryClient Object like thisserver=discovery.DiscoveryServer()Or with Paramaters like soclient=discovery.DiscoveryClient(port=12892,scheme="_discovery._cosmo.home_device"))One Created you can run thisresults=client.discovery()This function returns a list ofDiscoveryResultobjects. If the list is empty, then no devices were found.If you wish to add arguments such as custom data to send within the Discovery Request or shorten the discovey reply wait time
you can do so like this:results = discovery(timeout=1, data={})Again! Its that simpleFurther DocumentationFor Further Documentation see docs.mdWritten forCosmoHomebySamHDev. Licensed underGNU General Public License v3.0as perLicense File
Package: cosmoglobe
cosmoglobeis a python package that interfaces theCosmoglobe Sky ModelwithCommanderoutputs for the purpose of producing astrophysical sky maps.FeaturesSee thedocumentationfor a more comprehensive guide.Initialize the Cosmoglobe Sky Model (this downloads and caches a ~800 MB file with the sky model data)importcosmoglobemodel=cosmoglobe.sky_model(nside=256)Simulate the sky at 150 GHz in units of MJy/sr, smoothed to 40 arcmin with a gaussian beam:importastropy.unitsasuemission=model(150*u.GHz,fwhm=40*u.arcmin,output_unit="MJy/sr")Integrate over a bandpass:importnumpyasnpimporthealpyashpimportmatplotlib.pyplotasplt# Reading in WMAP K-band bandpass profile.bandpass_frequencies,bandpass_weights=np.loadtxt(wmap_bandpass.txt,unpack=True)# The units of the detector must be specified even if the bandpass is pre-normalized.bandpass_weights*=u.Unit("K_RJ")# Specify K_RJ or K_CMBbandpass_frequencies*=u.GHzmodel.remove_dipole()# Remove the dipole from the CMB componentemission=model(freqs=bandpass_frequencies,weights=bandpass_weights,fwhm=0.8*u.deg,output_unit="mK_RJ",)hp.mollview(emission[0],hist="norm")# Plotting the intensityplt.show()Installationcosmoglobecan be installed via pippipinstallcosmoglobeFundingThis work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 776282 (COMPET-4; BeyondPlanck), 772253 (ERC; bits2cosmology) and 819478 (ERC; Cosmoglobe).LicenseGNU GPLv3
Package: cosmoHammer
=======================================================Cosmological parameter estimation with the MCMC Hammer=======================================================.. image:: https://badge.fury.io/py/cosmoHammer.png:target: http://badge.fury.io/py/cosmoHammer.. image:: https://cosmo-gitlab.phys.ethz.ch/cosmo/CosmoHammer/badges/master/build.svg:target: https://cosmo-gitlab.phys.ethz.ch/cosmo/CosmoHammer/commits/master.. image:: https://cosmo-gitlab.phys.ethz.ch/cosmo/CosmoHammer/badges/master/coverage.svg:target: https://cosmo-gitlab.phys.ethz.ch/cosmo/CosmoHammer/commits/master.. image:: http://img.shields.io/badge/arXiv-1212.1721-orange.svg?style=flat:target: http://arxiv.org/abs/1212.1721.. raw:: html<script type="text/javascript">// if loaded from obsolete readthedocs we redirectif (String(window.location).indexOf("readthedocs") !== -1) {window.location.replace('http://cosmo-docs.phys.ethz.ch/cosmoHammer#redirected');}if (window.location.hash == "#redirected") {// modify shown url without reloadingwindow.location.hash = "";// insert message$('#cosmological-parameter-estimation-with-the-mcmc-hammer').prepend('<br><h3 style="padding: 1em; background: #eeeeee; color: red">You were redirected from the outdated documentation at readthedocs.org. Please update your bookmarks.</h3><br/>') ;}</script>CosmoHammer is a framework which embeds `emcee<http://arxiv.org/abs/1202.3665>`_ , an implementation by Foreman-Mackey et al.(2012) of the `Affine Invariant Markov chain Monte Carlo (MCMC) Ensemblesampler <http://msp.berkeley.edu/camcos/2010/5-1/p04.xhtml>`_ by Goodman andWeare (2010).It gives the user the possibility to plug in modules for the computation of anydesired likelihood. The major goal of the software is to reduce the complexitywhen one wants to extend or replace the existing computation by modules whichfit the user's needs as well as to provide the possibility to easily use largescale computing environments.We published a `paper <http://arxiv.org/abs/1212.1721>`_ in the `Astronomy andComputing Journal <http://authors.elsevier.com/sd/article/S221313371300022X>`_which discusses the advantages and performance of our framework.This project has been realized in collaboration with the `Institute of 4DTechnologies <https://www.fhnw.ch/en/about-fhnw/schools/school-of-engineering/institutes/institute-of-4d-technologies>`_ of the`University of Applied Sciences and Arts Northwest Switzerland<http://www.fhnw.ch>`_ (Fachhochschule Nordwestschweiz - FHNW).The development is coordinated on `our gitlab instance<https://cosmo-gitlab.phys.ethz.ch/cosmo/CosmoHammer>`_ and contributions are welcome. Thedocumentation of **CosmoHammer** is available at `our documentation server<http://cosmo-docs.phys.ethz.ch/cosmoHammer/>`_ and the package is distributedover `PyPI <https://pypi.python.org/pypi/CosmoHammer>`_.For all public modules such as PyCamb, WMAP, Planck and more, see thecosmoHammerPlugins project at https://cosmo-gitlab.phys.ethz.ch/cosmo/CosmoHammerPlugins.Contact-------Please contact `Uwe Schmitt <mailto:uwe.schmitt@id.ethz.ch>`_ per Email in case of any question.