Saravanan666/pypi-org huggingface dataset:
Package: astrool
.. astroolastrool is an open source pure Python package dedicated to problems arising in life ofday to day Astronomy, such as orbit propagation, night sky plotting,analemma plotting, conversion between position and velocity vectors and classicalorbital elements and orbit plotting.It is released under the MIT license.Requirements============astrool requires the following Python packages:* NumPy, for basic numerical routines* Astropy, for physical units and time handling* ephem, for the planetary ephemerides using SPICE kernels* matplotlib* poliastroTesting=======|codecov|If installed correctly, the tests can be run using pytest::$ python -c "import astrool.testing; astrool.testing.test()"Running unit tests for astrool[...]OK$Problems========If the installation fails or you find something that doesn't work as expected,please open an issue in the `issue tracker`_... _`issue tracker`: https://github.com/shreyasbapat/astrool/issuesContributing============.. image:: https://img.shields.io/waffle/label/astrool/astrool/1%20-%20Ready.svg?style=flat-square:target: https://waffle.io/astrool/astrool:alt: 'Stories in Ready'astrool is a community project, hence all contributions are more thanwelcome! For more information, head to `CONTRIBUTING.rst`_... _`CONTRIBUTING.rst`: https://github.com/shreyasbapat/astrool/blob/master/CONTRIBUTING.rstSupport=======|mailing|Release announcements and general discussion take place on our `mailing list`_.Feel free to join!.. _`mailing list`: https://groups.io/g/astrool-devhttps://groups.io/g/astrool-devLicense=======|license|astrool is released under the MIT license, hence allowing commercialuse of the library. Please refer to the COPYING file.FAQ===What's up with the name?------------------------astrool is made up of astro + tools. So basically, we are trying to come up with an aggregated software.What's the future of the project?---------------------------------astrool is a very new project and we aim to develop it further so that it can become onepackage for all in basic astronomy.
Package: astro-p3
P3 - fourier PSFP3 is a collection of libraries for Adaptive Optics (AO) Point Spread Function (PSF) modeling, simulation, fitting and reconstruction.The list of libraries is:aoSystem--> The main purpose of this library is to model an AO PSF. It is an analytical simulator based on a Fourier appoach.
Supported wavefront sensors are Shack-Hartmann and Pyramid.
Several kind of AO are supported: single conjugate AO (SCAO), single laser AO (SLAO), laser tomography AO (LTAO), ground layer AO (GLAO), multi conjugate AO (MCAO), but only one kind of guide star can be used (natural or laser).
InTIPTOPit is used to compute the so-called high order PSF.
Examples and tests can be found inp3/aoSystem/testing.
Examples of parameters file can be found inp3/aoSystem/parFiles.deepLooppsfFitting--> This library can be used to fit parameters on a PSF model (aoSystem/fourierModel or psfao21).
Examples and tests can be found inp3/testing.psfao21--> This library can be used to model with a small number of parameters the AO PSF. Its main purpose is PSF fitting.
Examples and tests can be found inp3/testing.psfr--> The main purpose of this library is to reconstruct PSF from telemetry data or from image fitting.
Examples and tests can be found inp3/testing.telemetry--> This library is used to manage telemetry data for PSFR.
Examples and tests can be found inp3/testing.
Package: astropalmerio
astropalmerioThis python package regroups useful data analysis code that I’ve developped throughout my work.
These are mainly aimed towards astronomy-related topics but some modules (like Monte Carlo errors) may be useful to a wider audience.
This repository provides an easy way to version, package and distribute this code to colleagues, but anyone is welcome to use it.
If you have any questions, you can contact me atjesse.palmerio@obspm.fr.InstallationTo install astropalmerio using pip:pipinstallastropalmerio
Package: astropandas
astropandasTools to expand on pandas functionality for astronomical applications.
The code is publically hosted on github:https://github.com/jlvdb/yet_another_wizz.gitInstallationThe yet_another_wizz package can be installed directly with pip::pip install astropandasFeaturesdirectly read and write FITS table data, seeastropandas.read_fitsandastropandas.to_fitsmatch based on sky coordinates, seeastropandas.matchExamplesReading and writing FITS table files:importastropandasasapddata=apd.read_fits("myfile.fits",hdu=1)# manipulate data ...apd.to_fits(data,"mynewfile.fits")Matching on angular coordinates:importastropandasasapddata1=apd.read_fits("myfile1.fits")data2=apd.read_fits("myfile2.fits")# Match within one arcsecond on right ascension (ra) and declination (dec),## labeled "RA" and "DEC" in the data.# Here we assume that the column names are the same in both catalogues,# otherwise specify with left_ra=, right_ra=, etc.apd.match(left=data1,right=data2,ra="RA",dec="DEC",threshold=1/3600)# If no threshold is provided, it is computed automatically by finding the# distance at which the number of matches is almost stationary.
Package: astro-parsnip
ParSNIPDeep generative modeling of astronomical transient light curvesAboutParSNIP learns a generative model of transients from a large dataset
of transient light curves. This code has many applications including
classification of transients, cosmological distance estimation, and
identifying novel transients. A full description of the algorithms
in this code can be found in Boone 2021 (submitted to ApJ).Installation and UsageInstructions on how to install and use ParSNIP can be found on theParSNIP
readthedocs page.
Package: astropayments
Astro Python SDKPackage for connecting to Astro's rest API using python 3.6 or later.PrerequisitesPython version 3.6+Download fromhttps://www.python.org/downloads/Pip3 (optional)Should be automatically installed with python but if not:curl "https://bootstrap.pypa.io/get-pip.py" -o "get-pip.py"
python3 get-pip.py --user
pip3 --versionRequestsInstalled automatically if Pip is used.  For source code visithttps://github.com/psf/requestsInstallationDeploymentThis package requires a Astro account.  You will also need to create an API key with a PIN in your account.  It is recommend to run astropayments.api.set_authentication('key','pin') before any other calls.Version0.0.1AuthorsAstro-AstroLicenseThis project is licensed under the MIT License - see theLICENSE.txtfile for details
Package: astro-ph
astro-phTranslate and post arXiv articles to various appsInstallationUse pip or other package manager to install the Python package.$pipinstallastro-phUsageAfter installation, command line interface,astro-ph, is available, with which you can translate and post arXiv articles to various apps.
Note that onlyslackapp is currently available.
In this case, you need tocreate a custom Slack app to get an URL of incoming webhook.$astro-phslack--keywordsgalaxy,galaxies\--categoriesastro-ph.GA,astro-ph.IM\--language_toja\--webhook_urlhttps://hooks.slack.com/services/***/***The posted article looks like this.For detailed information, see the built-in help by the following command.$astro-phslack--helpExampleIt would be nice to regularly run the command by GitHub Actions.
Here is a live example in which daily (2 days ago) arXiv articles inastro-ph.GAandastro-ph.IMare posted to different channels of a Slack workspace.a-lab-nagoya/astro-ph-slack: Translate and post arXiv articles to SlackReferencesfkubota/Carrier-Owl: arxiv--> DeepL --> SlackThe astro-ph package is highly inspired by their worka-lab-nagoya/astro-ph-slack: Translate and post arXiv articles to SlackA live example using the astro-ph packagepyppeteer/pyppeteer: Headless chrome/chromium automation library (unofficial port of puppeteer)Used for async Chromium operationaio-libs/aiohttp: Asynchronous HTTP client/server framework for asyncio and PythonUsed for async article posts to Slackgoogle/python-fire: Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.Used for creating command line interface
Package: astroph-new
astropy_newis a python module to search fornew astrophysics submissionsinarXivbased on the user interests and to create a
summarized document that can be easily opened in any browser.Docs:https://astroph-new.readthedocs.ioInstallation:pip install-Uastroph-newFeaturesManaging user interests (subjects, author names, and keywords)Searching for new submissions inastro-phbased on the user interestsCreating a summarized HTML document of the interested submissionsDaily automatic searching and summary on a set time.LicenseAll files are under MIT License, seeLISENCE.
Package: astrophot
AstroPhot is a fast, flexible, and automated astronomical image modelling tool for precise parallel multi-wavelength photometry. It is a python based package that uses PyTorch to quickly and efficiently perform analysis tasks. Written byConnor Stonefor tasks such as LSB imaging, handling crowded fields, multi-band photometry, and analyzing massive data from future telescopes. AstroPhot is flexible and fast for any astronomical image modelling task. While it uses PyTorch (originally developed for Machine Learning) it is NOT a machine learning based tool.InstallationAstroPhot can be installed with pip:pip install astrophotIf PyTorch gives you any trouble on your system, just follow the instructions on thepytorch websiteto install a version for your system.Also note that AstroPhot is only available for python3.Seethe documentationfor more details.DocumentationYou can find the documentation at theGitHub Pages site connected with the AstroPhot projectwhich covers many of the main use cases for AstroPhot. It is still in development, but lots of useful information is there. Feel free to contact the author,Connor Stone, for any questions not answered by the documentation or tutorials.Credit / CitationIf you use AstroPhot in your research, please follow thecitation instructions here.
Package: astropic
astropicCode to modify the images using different redshifts and distances.
May be buggy, submit bugs in Github Issues.
To run your code, install the repository:
WARNING: poetry and poetry-core are needed to build the packagepip install astropicThen you are ready to run the main code.
To use the module in your code, dofrom astropic.main import convertand then run theconvert('info')There is also a bonus script fits2png.py, which modifies .fits files into .png files
Package: astro-pink
Parallelized rotation and flipping INvariant Kohonen maps (PINK)RequirementsC++ with ISO 17 standardCMake>= 3.18CUDA >= 9.1 (highly recommended)conan.io(optional for C++ dependencies) orPyBind11(optional for Python interface)google-test1.8.1 (optional for unit tests)doxygen 1.8.13 (optional for developer documentation)Conan.io will install automatically the C++ dependencies (PyBind11 and google-test). Otherwise you can also install these libraries yourself.InstallationWe provide deb- and rpm-packages athttps://github.com/HITS-AIN/PINK/releasesor you can install PINK from the sources:cmake-DCMAKE_INSTALL_PREFIX=<INSTALL_PATH>.
makeinstallPyPI installationPINK is also available asPyPi packagewhich can be installed bypipinstallastro-pinkHPC deployment with EasyBuildTheEasyBuildrecipe is available athttps://github.com/BerndDoser/easybuild-easyconfigs/tree/hits/easybuild/easyconfigs/p/PINK.UsageTo train a theself-organizing map(SOM) please executePink --train <image-file> <result-file>whereimage-fileis the input file of images for the training andresult-fileis the output file for the trained SOM. All files are in binary mode describedhere.To map an image to the trained SOM please executePink --map <image-file> <result-file> <SOM-file>whereimage-fileis the input file of images for the mapping,SOM-fileis the input file for the trained SOM, andresult-fileis the output file for the resulting heatmap.Please use also the commandPink -hto get more informations about the usage and the options.Python scriptsFor conversion and visualization of images and SOM some python scripts are available.convert_data_binary_file.py     Convert binary data file from PINK version 1 to 2show_heatmap.py:                Visualize the mapping resultshow_images.py:                 Visualize binary images file formatshow_som.py:                    Visualize binary SOM file formattrain.py:                       SOM training using the PINK Python interfacePublicationKai Lars Polsterer, Fabian Gieseke, Christian Igel,Bernd Doser, and Nikos Gianniotis. Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs.
24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 405-410, 2016.pdfLicenseDistributed under the GNU GPLv3 License. See accompanying file LICENSE or copy athttp://www.gnu.org/licenses/gpl-3.0.html.
Package: astroPIPS
Period-determination and Identification Pipeline Suite (PIPS)PIPS is a Python pipeline designed to analyze the lightcurves of astronomical objects whose brightness changes periodically. Our pipeline can be imported quickly and is designed to be user friendly. PIPS was originally developed to determine the periods of RR Lyrae variable stars and offers many features designed for variable star analysis. We have expanded PIPS into a suite that can obtain period values for almost any type of lightcurve with both speed and accuracy. PIPS can determine periods through several different methods, analyze the morphology of lightcurves via fourier analysis, estimate the statistical significance of the detected signal, and determine stellar properties based on preexisting stellar models. Currently our team is also exploring the possibility of using this pipeline to detect periods of exoplanets as well.A detailed description of PIPS and its algorithms is provided inour paper.NOTE: We are currently updating the documentation to match the content with the paper(last edit: February 24th, 2022)DevelopersHead developer:Yukei S. Murakami(sterling.astro@berkeley.edu)Developers:Arjun Savel(asavel@umd.edu),Andrew Hoffman(andrew@hoffman.aero),James Sunseri(jamessunseri@berkeley.edu)PublicationsPlease cite the following if PIPS is utilized for a scientific project:Hoffman et al. 2021(arxiv:2008.09778)Murakami et al. (Submitted to MNRAS. arxiv:2107.14223)Dependenciespython (>=3.7)numpyscipymatplotlibtimeastropyseabornUsage (ver 0.3.0)$ git clone https://github.com/SterlingYM/PIPS$ cd PIPS$ jupyter notebookStart a new Python notebook. In a jupyter cell, run the following:importPIPS# data preparation -- create [time,mag,mag_err] listdata=PIPS.data_readin_LPP('sample_data/005.dat',filter='V')phot_obj=PIPS.photdata(data)# period detectionstar.get_period(multiprocessing=False)# generate best-fit light curvex_th,y_th=star.get_bestfit_curve()# plot light curvestar.plot_lc()plt.plot(x_th/star.period,y_th,c='yellowgreen',lw=3,alpha=0.7)# x-axis normalized to unitless phaseplt.plot(x_th/star.period+1,y_th,c='yellowgreen',lw=3,alpha=0.7)Sample data credit: UCB SNe Search Team (Filippenko Group)
Package: astro-pi-replay
Astro Pi ReplayA CLI to replay historic data from previous ISS missions.All function calls from thepicamera,picamera2,sense_hat,skyfield, andorbitlibraries
will be mocked to return data from an historic run from the ISS, rather than from attached hardware.
This allows teams to test their code with representative data and provide a confidence boost that
their code will workQuickstartChange to your project directory (cd my-project), installAstro-Pi-Replayusingpip, and then run your program withAstro-Pi-Replay run main.py.For more detailed installation instructions, checkout the docs site.This will prepare a sequence of near-infrared images (NIR) images, together with the corresponding
data collected from the Sense Hat, to be returned by all calls topicamera,sense_hat, etc.
The CLI allows for some configuration of this behaviour - see theDocumentationfor more details.DocumentationSee thedocspage.
Package: astroplan
Observation planning package for astronomersCode:https://github.com/astropy/astroplanDocs:https://astroplan.readthedocs.io/License: BSD-3AttributionIf you use astroplan in your work, please citeMorris et al. 2018:@ARTICLE{2018AJ....155..128M,author={{Morris}, Brett M. and {Tollerud}, Erik and {Sip{\H{o}}cz}, Brigitta and {Deil}, Christoph and {Douglas}, Stephanie T. and {Berlanga Medina}, Jazmin and {Vyhmeister}, Karl and {Smith}, Toby R. and {Littlefair}, Stuart and {Price-Whelan}, Adrian M. and {Gee}, Wilfred T. and {Jeschke}, Eric},title="{astroplan: An Open Source Observation Planning Package in Python}",journal={\aj},keywords={methods: numerical, methods: observational, Astrophysics - Instrumentation and Methods for Astrophysics},year=2018,month=mar,volume={155},number={3},eid={128},pages={128},doi={10.3847/1538-3881/aaa47e},archivePrefix={arXiv},eprint={1712.09631},primaryClass={astro-ph.IM},adsurl={https://ui.adsabs.harvard.edu/abs/2018AJ....155..128M},adsnote={Provided by the SAO/NASA Astrophysics Data System}}
Package: astroplotlib
astroplotlibPython scripts to handle astronomical images. It allows the user building images
with any scale, overlay contours, and adding physical bars and orientation
arrows (N and E axes) automatically (e.g.,Hernandez-Jimenez 13,15). It is
possible to overlay pseudo-slits and obtain statistics from apertures
(e.g.,Dametto, N. Z. et al. 2014). The user can also estimate the background
sky of the images (e.g.,Buzzo 2021). There is a module to work with the output
table from theEllipsetask ofIRAF.  The user can overlay the fitted isophotes
and their respective contours on the image (e.g.,Mora et al. 2019,Buzzo et al. 2021,Brito-Silva et al. 2021). The package also has a GUI  to mask
areas in the images by using different polygons.  It is possible to obtain
statistical information (e.g, total flux, mean, std, etc.) from the masked areas
too. There is also a GUI  to overlay star catalogues on the image and an option
to download them directly from the Vizier server.version: 0.2.3(c) 2014-2022 J. A. Hernandez-JimenezE-mail:joseaher@gmail.comWebsite:https://gitlab.com/joseaher/astroplotlibInstallationastroplotlib requires:* numpy
* scipy
* matplotlib
* astropy
* astroquery
* tkinterThis version can be easily installed within Anaconda Enviroment via PyPI:% pip install astroplotlibIf you prefer to install astroplotlib manually, you can clone the developing
version athttps://gitlab.com/joseaher/astroplotlib. In the directory this
README is in, simply:% pip install .or,% python setup.py installUninstallationTo uninstall astroplotlib, simply% pip uninstall astroplotlibCitingIf you use this package for a scientific publication, please cite it (Hernandez-Jimenez 2022). The BibTeX entry for this package is:@MISC{2022ascl.soft04002H,
        author = {{Hernandez-Jimenez}, Jose A.},
        title = "{Astroplotlib: Python scripts to handle astronomical images}",
        keywords = {Software},
        howpublished = {Astrophysics Source Code Library, record ascl:2204.002},
        year = 2022,
        month = apr,
        eid = {ascl:2204.002},
        pages = {ascl:2204.002},
        archivePrefix = {ascl},
        eprint = {2204.002},
        adsurl = {https://ui.adsabs.harvard.edu/abs/2022ascl.soft04002H},
        adsnote = {Provided by the SAO/NASA Astrophysics Data System}
       }AcknowledgementsThis software was funded partially by Brazilian agency FAPESP, process number 2021/08920-8.
Package: astro-polaris
polarispolaris --- PythOnLibrary for AstRodynamIcSpolaris is a Python library for preliminary spacecraft trajectory design.DependenciesnumbanumpypandasscipyAlthough not necessary to run polaris, the following packages are also used within the example scripts and Jupyter notebooks:tqdmplotlyInstallationClone this repository with$gitclonehttps://github.com/Yuricst/polaris.gitUsageimportpolaris.SolarSystemConstantsassscimportpolaris.PropagatoraspropFor examples, go to./examples/to see Jupyter notebook tutorials.
The full documentation is available athttps://github.com/Yuricst/polaris/wiki.
Package: astro-prospector
PurposeConduct principled inference of stellar population properties from photometric
and/or spectroscopic data.  Prospector allows you to:Infer high-dimensional stellar population properties using parametric or
highly flexible SFHs (with nested or ensemble Monte Carlo sampling)Combine photometric and spectroscopic data from the UV to Far-IR rigorously
using a flexible spectroscopic calibration model and forward modeling many
aspects of spectroscopic data analysis.Read thedocumentationand the
codepaper.InstallationSeeinstallationfor requirements and dependencies.
Thedocumentationincludes a tutorial and demos.To install to a conda environment with dependencies, seeconda_install.sh.
To install just Prospector (stable release):python -m pip install astro-prospectorTo install the latest development version:cd <install_dir>
git clone https://github.com/bd-j/prospector
cd prospector
python -m pip install .Then, in PythonimportprospectCitationIf you use this code, please referencethis paper:@ARTICLE{2021ApJS..254...22J,
       author = {{Johnson}, Benjamin D. and {Leja}, Joel and {Conroy}, Charlie and {Speagle}, Joshua S.},
        title = "{Stellar Population Inference with Prospector}",
      journal = {\apjs},
     keywords = {Galaxy evolution, Spectral energy distribution, Astronomy data modeling, 594, 2129, 1859, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2021,
        month = jun,
       volume = {254},
       number = {2},
          eid = {22},
        pages = {22},
          doi = {10.3847/1538-4365/abef67},
archivePrefix = {arXiv},
       eprint = {2012.01426},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021ApJS..254...22J},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}and make sure to cite the dependencies as listed ininstallationExampleInference with mock broadband data, showing the change in posteriors as the
number of photometric bands is increased.