Saravanan666/pypi-org huggingface dataset:
Package: astrodown
astrodown[bold blue]Astrodown[/bold blue] is a toolkit to build interactive websites for data science projects.See a live example athttps://astrodown-playground.qiushiyan.dev:sparkles:Usage:$astrodown[OPTIONS]COMMAND[ARGS]...Options:--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.Report [bold red]bugs[/bold red] on [link=https://github.com/astrodown/astrodown-web/]Github[/link]Commands:check: Check for availabilities of programs...dev: [bold blue]Preivew[/bold blue] the websitedocs: [bold blue]Open[/bold blue] documentation...init: [bold blue]Create[/bold blue] an astrodown...install: [bold blue]Install[/bold blue] JavaScript...new: [bold blue]Create[/bold blue] the folder...render: [bold blue]Render[/bold blue] all Quarto...astrodown checkCheck for availabilities of programs required by astrodownUsage:$astrodowncheck[OPTIONS]Options:--help: Show this message and exit.astrodown dev[bold blue]Preivew[/bold blue] the websiteUsage:$astrodowndev[OPTIONS]Options:--package-manager [npm|yarn|pnpm]: package manager to use  [default: PackageManager.npm]--port INTEGER: port to run the website  [default: 3000]--render-quarto / --no-render-quarto: rerender quarto documents first  [default: no-render-quarto]--help: Show this message and exit.astrodown docs[bold blue]Open[/bold blue] documentation websites for relevant tools, e.g. Quarto, Python, etc.Usage:$astrodowndocs[OPTIONS]Options:--help: Show this message and exit.astrodown init[bold blue]Create[/bold blue] an astrodown project.Must have Quarto Node.js installed and avaiable in PATH variables, useastrodown checkfor health checks.Usage:$astrodowninit[OPTIONS]Options:-p, --path TEXT: path to create the project, default to the current working directory  [default: /Users/qiushi/workspace/astrodown/astrodown-python]-n, --name TEXT: name of the project-pm, --package-manager [npm|yarn|pnpm]: package manager to use, default to npm  [default: npm]-t, --template [basic|full]: template to use, default to basic  [default: basic]--help: Show this message and exit.astrodown install[bold blue]Install[/bold blue] JavaScript dependencies.Only need to be run once per project.Usage:$astrodowninstall[OPTIONS]Options:--package-manager [npm|yarn|pnpm]: package manager to use  [default: PackageManager.npm]--help: Show this message and exit.astrodown new[bold blue]Create[/bold blue] the folder structure for a new analysis, dataset, model, api, etc.Usage:$astrodownnew[OPTIONS]COMPONENT_TYPE:{analysis|dataset|model|shinyapp|api}Arguments:COMPONENT_TYPE:{analysis|dataset|model|shinyapp|api}: the type of the component to be created  [required]Options:--help: Show this message and exit.astrodown render[bold blue]Render[/bold blue] all Quarto documents.Should be run every time a Quarto document has changed. Edit _quarto.yml to include/exclude files.Usage:$astrodownrender[OPTIONS]Options:--package-manager [npm|yarn|pnpm]: package manager to use  [default: PackageManager.npm]--help: Show this message and exit.
Package: astro-drpy
drpydrpy (pronounced "doctor pi") is aDataReduction toolkit for astronomicalPhotometry and spectroscopYInstallationpip install astro-drpyNoteNo NaNs or infs should be passed into functions without being masked in advance.use_uncertaintyanduse_maskonly control the use of uncertainty and mask frames in the calculations. Whether the two frames are returned by a function depends on the custom's input.All calculations should be dimensionless to ensure best performence.Header should not convert to meta directly. Should bemeta = {'header': header}Do NOT do validation in internal functions
Package: astrodust
AstrodustA package for predicting the distribution of dust particles in protoplanetary disk based off our paper "Multi-Output Random Forest Regression to Emulatethe Earliest Stages of Planet Formation".InstallationAstrodust can be installed from PyPI via pip:pip install astrodustPretrained ModelsThe package requires two pretrained models, a random forest regression model and XGBoost classifier. These can be downloaded beforehand fromZenodoand placed in the current working directory in amodelsdirectory. Otherwise the package will prompt to automatically download them when theDustModelis instaniated.DocumentationThe documentation for the package is locatedhere. A demonstration code notebook is alsoavailable, or can be viewed onlinehere.Particle sizes for each bin for the input and output are included as a reference in ourwiki.
Package: astro-eddy
eddy - Extracting Disk DynamicsWhat Is It?eddyis a suite of Python tools to recover precise velocity profiles of protoplanetary disks from Doppler shifted line emission.eddymakes fitting of first moment maps and the inference of a rotation velocity from an annulus of spectra a breeze.InstallationThe most simple method is withpip,pip install astro-eddyThe only real dependencies for this arenumpy,scipy,matplotlib, andemcee, If you want to run the Gaussian Process method you will also needceleritewhich can be easily installed if you follow theirinstallation guide.If things have installed correctly you should be able to run theJupyter Notebookswith no errors. If something goes wrong, pleaseopen an issue.UseageFor guides on how to useeddyyou will find extensive examples in thedocuments. We shamelessly recommendbettermomentsto make the moment maps required for the fitting.CitationsIf you useeddyas part of your research, please cite theJOSS article:@article{eddy,
    doi ={10.21105/joss.01220},
    url ={https://doi.org/10.21105/joss.01220},
    year ={2019},
    month ={feb},
    publisher ={The Open Journal},
    volume ={4},
    number ={34},
    pages ={1220},
    author ={Richard Teague},
    title ={eddy},
    journal ={The Journal of Open Source Software}}A full list of citations including dependencies can be found on thecitationspage.Works UsingeddyA list of the works using (or at least citing)eddycan be found onADS.
Package: astro-elk
Welcome to the intEgratedLightKurve (a.k.a. elk) Python package!This package allows you to download, correct, analyze and visualizeintegratedlight curves from TESS FFI data using the methods outlined inWainer+2023. While originally designed for analysis of star clusters, it can be used to create integrated light curves for any given aperture.InstallationPut simply?pip install astro-elk! But we recommend creating a conda environment first to ensure everything goes smoothly! Check out the installation instructionshereto learn exactly how to installelkDocumentationAll documentation forelkcan be foundhere!Logo designThis work is based on work originally supported by the Heising Simons Foundation. We'd also like to acknowledge the absolutely splendid work ofSamantha-Lynn Martinezin designing theelklogo. You can find more of her portfoliohereand we encourage you to reach out to her for any design needs you have!
Package: astroemperor
ReddutilsThis is ReddTea's small package compiling useful self-made functions for an astrophysical context.OverviewReddutils contains many functions and classes for personal use.
Some of them may be of use for the community.DependenciesThis code makes use of:NumpypandasScipyPyAstronomy (http://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/index.html)tqdm (https://pypi.python.org/pypi/tqdm)tabulatematplotlib.pyplotipywidgetsIPython.displayMost of them come with conda, if some are missing they can be easily installed with pip.InstallationIn the console type in your work folderpipinstallreddutilsUsageCorrelatorFor a pandas dataframe or three-column table:fromreddutilsimportcorrelatorasrc
importpandasaspddf=pd.read_csv('data.csv')cor=rc.correlator()cor.display(df)ExodusUse for visualising the NASA Exoplanet Archive:fromreddutilsimportexodus#exopop = exodus.Exoplanet_Archive()exopop=exodus.Exoplanet_Archive('NasaExoplanetArchive')#exopop = exodus.Exoplanet_Archive('ExoplanetEU2')#exopop = exodus.Exoplanet_Archive('ExoplanetsOrg')exopop.display()Try out the histogram mode:exopop.display_hist()Fourier Transform VisualiserFor visualising fourier transforms and the nyquist limit.fromreddutilsimportfourierfou=fourier.fourier()fou.display()PeriodogramFor a pandas dataframe:fromreddutilsimportperiodogramasrp
importpandasaspddf=pd.read_csv('data.csv')per=rp.LSP()per.display(df)
Package: astro-forecaster
astro-forecaster (forecaster 2ish)An internally overhauled but fundamentally similar version of Forecaster by Jingjing Chen and David Kipping, originally presented inProbabilistic Forecasting of the Masses and Radii of Other Worldsand hosted athere.The model itself has not changed- no new data was included and the hyperparameter file was not regenerated. All functions were rewritten to take advantage of Numpy vectorization and some additional user features were added.Installation and basic useTo install this package withpip, runpipinstallastro-forecasterAs a basic example, here is how you would calculate the radius posterior of a 1 Jupiter mass object:importforecasterimportnumpyasnpforecasted_radius_posterior=forecaster.Mpost2R(np.ones(int(1e3)),unit='Jupiter',classify=False)See thedemo notebookfor more examples.ChangesChanges include but are not limited to:Rewriting all functions to take advantage of Numpy vectorizationIncluding the ability to input asymmetric uncertainties in mass or radiusEnabling pip installationCitationIf used, please citethe original Forecaster paperand the bibcode for this implementation eventually hosted on the Astrophysics Source Code Library (ASCL).
Package: astro-gala
Gala is a Python package for Galactic and gravitational dynamics.DocumentationThe documentation forGalais hosted onRead the docs.Installation and DependenciesThe easiest way to get Gala is to install with conda or pip. To install
with conda, use the conda-forge channel:conda install astro-gala --channel conda-forgeTo install with pip:pip install astro-galaSee theinstallation
instructionsin thedocumentationfor more information.AttributionIf you make use of this code, please cite theJOSSpaper:@article{gala,
  doi = {10.21105/joss.00388},
  url = {https://doi.org/10.21105%2Fjoss.00388},
  year = 2017,
  month = {oct},
  publisher = {The Open Journal},
  volume = {2},
  number = {18},
  author = {Adrian M. Price-Whelan},
  title = {Gala: A Python package for galactic dynamics},
  journal = {The Journal of Open Source Software}
}Please also consider citing the Zenodo DOIas a software citation:@misc{gala:zenodo,
  author       = {Adrian Price-Whelan and
                  Brigitta Sipőcz and
                  Daniel Lenz and
                  Johnny Greco and
                  Syrtis Major and
                  Sergey Koposov and
                  Semyeong Oh and
                  P. L. Lim},
  title        = {adrn/gala: v1.1},
  month        = mar,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.1},
  doi          = {10.5281/zenodo.3700827},
  url          = {https://doi.org/10.5281/zenodo.3700827}
}LicenseCopyright 2013-2020 Adrian Price-Whelan and contributors.Galais free software made available under the MIT License. For details see
theLICENSEfile.ContributorsSee theAUTHORS.rstfile for a complete list of contributors to the project.
Package: astro-gdt
The Gamma-ray Data Tools (GDT) is centralized toolkit for hard X-ray and
gamma-ray astrophysics data analysis, with a focus on providing a uniform
interface to the data provided by several different missions and instruments.The GDT Core Package (astro-gdt) contains the core components of the GDT that
can be utilized for various instruments and is a generalized version of theFermi GBM Data Tools.
Individual mission or instrument packages can be developed usingastro-gdtand released under thegdt.missionsnamespace (seeastro-gdt-fermias an example).Normal InstallationIf you don’t plan to contribute code to the project, the recommended install method is installing from PyPI using:pipinstallastro-gdtgdt-datainitThegdt-datainitis required to initialize the library after installation.Contributing Code or DocumentationIf you plan to help with the development or documentation of astro-gdt, then please visit our github site athttps://github.com/USRA-STI/gdt-core.
Package: astro-gdt-cgro
The GDT-CGRO is an extension to Gamma-ray Data Tools that adds functions specific to the cgro mission.Normal InstallationIf you don’t plan to contribute code to the project, the recommended install method is installing from PyPI using:pipinstallastro-gdt-cgrogdt-datainitThegdt-datainitis required to initialize the library after installation of astro-gdt. You do not need to
perform the initialization again if astro-gdt was already installed and initialized.  There is no harm in running
it again “just in case”.Contributing Code or DocumentationIf you plan to help with the development or documentation of astro-gdt-cgro, then please visit our github site athttps://github.com/USRA-STI/gdt-cgro.
Package: astro-gdt-fermi
The GDT-Fermi is an extension to Gamma-ray Data Tools that adds functions specific to the Fermi mission (GBM and LAT).Normal InstallationIf you don’t plan to contribute code to the project, the recommended install method is installing from PyPI using:pipinstallastro-gdt-fermigdt-datainitThegdt data initis required to initialize the library after installation of astro-gdt. You do not need to
perform the initialization again if astro-gdt was already installed and initialized.  There is no harm in running
it again “just in case”.Contributing Code or DocumentationIf you plan to help with the development or documentation of astro-gdt, then please visit our github site athttps://github.com/USRA-STI/gdt-fermi.
Package: astro-gdt-hete2
The GDT-HETE2 is an extension to Gamma-ray Data Tools that adds functions specific to the hete2 mission.Normal InstallationIf you don’t plan to contribute code to the project, the recommended install method is installing from PyPI using:pipinstallastro-gdt-hete2gdt-datainitThegdt-datainitis required to initialize the library after installation of astro-gdt. You do not need to
perform the initialization again if astro-gdt was already installed and initialized.  There is no harm in running
it again “just in case”.Contributing Code or DocumentationIf you plan to help with the development or documentation of astro-gdt-hete2, then please visit our github site athttps://github.com/USRA-STI/gdt-hete2.
Package: astro-gdt-maxi
The GDT-MAXI is an extension to Gamma-ray Data Tools that adds functions specific to the maxi mission.Normal InstallationIf you don’t plan to contribute code to the project, the recommended install method is installing from PyPI using:pipinstallastro-gdt-maxigdt-datainitThegdt-datainitis required to initialize the library after installation of astro-gdt. You do not need to
perform the initialization again if astro-gdt was already installed and initialized.  There is no harm in running
it again “just in case”.Contributing Code or DocumentationIf you plan to help with the development or documentation of astro-gdt-maxi, then please visit our github site athttps://github.com/USRA-STI/gdt-maxi.
Package: astro-gdt-swift
The GDT-Swift is an extension to Gamma-ray Data Tools that adds functions specific to the Swift mission.Normal InstallationIf you don’t plan to contribute code to the project, the recommended install method is installing from PyPI using:pipinstallastro-gdt-swiftgdt-datainitThegdt-datainitis required to initialize the library after installation of astro-gdt. You do not need to
perform the initialization again if astro-gdt was already installed and initialized.  There is no harm in running
it again “just in case”.Contributing Code or DocumentationIf you plan to help with the development or documentation of astro-gdt-swift, then please visit our github site athttps://github.com/USRA-STI/gdt-swift.
Package: astrohack
astroHack (Holography Antenna Commissioning Kit) is a Python package under development by NRAO'sCASAteam to support holography and antenna position correction measurements. It currently supports pointed and on-the-fly holographies for both ALMA and the VLA as well as antenna position corrections for the VLA. The future goal of astrohack is to support the commissioning of the Next Generation Very Large Array (ngVLA). Much of the core functionality of astroHACK is inspired by the code of the following AIPS tasks: UVHOL, HOLOG and PANEL for holography and LOCIT for the antenna position corrections. AstroHACK enables parallel execution by using Dask and efficient single-threaded performance by making use of Numba.📝 astroHACK is under active development! Breaking API changes are still happening on a regular basis, so proceed with caution.InstallingIt is recommended to use thecondaenvironment manager to create a clean, self-contained runtime where astrohack and all its dependencies can be installed:condacreate--nameastrohackpython=3.11--no-default-packages
condaactivateastrohack📝 On macOS it is required to pre-installpython-casacoreusingconda install -c conda-forge python-casacore.Making astroHACK available for download from conda-forge directly is pending, so until then the current recommendation is to sully that pristine environment by calling pipfrom within conda, like this:pipinstallastrohackTutorialsBesides the API for the user facing functions there are three tutorials that can be followed that demonstrate the capabilities of astrohack:VLA Holography tutorialHolography visualization tutorialAntenna position correction tutorial
Package: astroher
PypanA command-line utility that creates freshpythonproject from base template.Toolspython 3.6 | 3.7 | 3.8travisCIcode analysispytestblackmypypylintflake8pydocstyleUsageQuick startpipinstallpypans
✨🍰✨After please runpypantool from your shell:pypanSource codegitclonegit@github.com:vyahello/pypans.git
pipinstall-e.
pypanOr using direct release:pipinstallgit+https://github.com/vyahello/pypans@0.0.1
pypanDevelopment notesTestingGenerally,pytesttool is used to organize testing procedure.Please follow next command to run unittests:pytestCIProject has Travis CI integration using.travis.ymlfile thus code analysis (black,pylint,flake8,mypy,pydocstyle) and unittests (pytest) will be run automatically after every made change to the repository.To be able to run code analysis, please execute command below:./analyse-source-code.shRelease notesPlease checkchangelogfile to get more details about actual versions and it's release notes.MetaAuthor –Volodymyr Yahello. Please checkauthorsfile for more details.Distributed under theMITlicense. Seelicensefor more information.You can reach out me at:vyahello@gmail.comhttps://github.com/vyahellohttps://www.linkedin.com/in/volodymyr-yahello-821746127Contributingclone the repositoryconfigure Git for the first time after cloning with yournameandemailpip install -r requirements.txtto install all project dependenciespip install -r requirements-dev.txtto install all development project dependencies
Package: astroid
Professional support for astroid is available as part of theTidelift Subscription.  Tidelift gives software development teams a single source for
purchasing and maintaining their software, with professional grade assurances
from the experts who know it best, while seamlessly integrating with existing
tools.What’s this?The aim of this module is to provide a common base representation of
python source code. It is currently the library powering pylint’s capabilities.It provides a compatible representation which comes from the_astmodule.  It rebuilds the tree generated by the builtin _ast module by
recursively walking down the AST and building an extended ast. The new
node classes have additional methods and attributes for different
usages. They include some support for static inference and local name
scopes. Furthermore, astroid can also build partial trees by inspecting living
objects.InstallationExtract the tarball, jump into the created directory and run:pip install .If you want to do an editable installation, you can run:pip install -e .If you have any questions, please mail thecode-quality@python.orgmailing list for support. Seehttp://mail.python.org/mailman/listinfo/code-qualityfor subscription
information and archives.Documentationhttp://astroid.readthedocs.io/en/latest/Python Versionsastroid 2.0 is currently available for Python 3 only. If you want Python 2
support, use an older version of astroid (though note that these versions
are no longer supported).TestTests are in the ‘test’ subdirectory. To launch the whole tests suite, you can use
eithertoxorpytest:tox
pytest
Package: astroimages-file-drivers
Copyright (c) 2020 AstroImagesPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Download-URL:https://github.com/AstroImages/astroimages-file-drivers/archive/v_0.1.6.tar.gzDescription: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Package: astroimages-fits
Copyright (c) 2020 AstroImagesPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Download-URL:https://github.com/AstroImages/astroimages-fits/archive/v_0.2.4.tar.gzDescription: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6