Saravanan666/pypi-org huggingface dataset:
Package: astrojs-aws.construct
Astro Construct Library---The APIs of higher level constructs in this module are experimental and under active development.
They are subject to non-backward compatible changes or removal in any future version. These are
not subject to theSemantic Versioningmodel and breaking changes will be
announced in the release notes. This means that while you may use them, you may need to update
your source code when upgrading to a newer version of this package.IntroductionAstrois the all-in-one web framework designed for speed. This library is supported modes,static,lambdaandedge, to deploy astro website to AWS.UsageStatic HostingFor developing, it's not recommended to deploy with CloudFront, because setup CloudFront is really too slow, at least takes 5min.fromastrojs_aws.constructimportStaticAstroSitesite=StaticAstroSite(self,"Site",site_dir="/path/to/dist")CfnOutput(self,"Domains",value=site.domains.join(", "))It's recommended to deploy with CloudFront in production environment.fromastrojs_aws.constructimportStaticAstroSitesite=StaticAstroSite(self,"Site",site_dir="/path/to/dist",cf_options=CfOptions(domain="example.com",certificate_arn="arn:aws:acm:us-east-1:xxx-xxx-xxx"))CfnOutput(self,"Domains",value=site.domains.join(", "))Lambda Hostingfromastrojs_aws.constructimportLambdaAstroSitesite=LambdaAstroSite(self,"Site",server_entry="/path/to/server/entry.mjs",static_dir="/path/to/client")CfnOutput(self,"Domains",value=site.domains.join(", "))Deploy with CloudFront.fromastrojs_aws.constructimportLambdaAstroSitesite=LambdaAstroSite(self,"Site",server_entry="/path/to/server/entry.mjs",static_dir="/path/to/client",cf_options=CfOptions(domain="example.com",certificate_arn="arn:aws:acm:us-east-1:xxx-xxx-xxx"))CfnOutput(self,"Domains",value=site.domains.join(", "))Edge HostingAs we known that, edge function working in Edge node. But for developing, setup CloudFront is really too slow. We can only deploy the Lambda function, and useAWS SAMfor testing and debuging.fromastrojs_aws.constructimportEdgeAstroSiteEdgeAstroSite(self,"Site",server_entry="/path/to/server/entry.mjs",static_dir="/path/to/client",only_lambda=True)Deploy to production environment.fromastrojs_aws.constructimportEdgeAstroSitesite=EdgeAstroSite(self,"Site",server_entry="/path/to/server/entry.mjs",static_dir="/path/to/client",cf_options=CfOptions(domain="example.com",certificate_arn="arn:aws:acm:us-east-1:xxx-xxx-xxx"))CfnOutput(self,"Domains",value=site.domains.join(", "))
Package: astrolabe-py
Version:1.1.0Author:Tom Hicks <hickst@email.arizona.edu>This is a Python 3 library for curating image and data files for theAstrolabe projectAstrolabe_py contains scripts and modules to:Check FITS file metadata for validity,Show FITS file HDU information,Upload FITS files to iRods, optionally extracting FITS metadata and attaching
it to the uploaded files in iRods.InstallationInstall viaPyPipip install astrolabe-pyPython Build using CondaBuilding this software requiresPython 3.6+. Assuming you haveCondainstalled,
you can use it to build this project within a virtual environment:git clone https://github.com/AstrolabeProject/astrolabe_py.git
source activate
conda create -n alpy python=3.6
conda activate alpy
cd astrolabe_py
pip install -r requirements.txtRunning TestsThe tests can be run manually from thetestsubdirectory, as follows:cd test
python fits_ops_test.py
python fits_meta_test.py
python irods_help_test.py
python uploader_test.pyRunning the Uploader ScriptUploader Script Options:usage: uploader [-h] [-v] [-u] [--version] [--keyfile [metadata-keyfile]] images_path

FITS file metadata extraction and upload of a file or directory of files.

positional arguments:
   images_path           path to a FITS file or a directory of FITS files to be processed

optional arguments:
   -h, --help            show this help message and exit
   -v, --verbose         provide more information during execution
   -u, --upload-only     upload files to iRods only: do not process file metadata
   --version             show program's version number and exit
   --keyfile [metadata-keyfile]
                         a file specifying which metadata keys which should be processedExamples:uploader -v myDataDirectory
uploader --upload-only myImages/someImage.fits
uploader --keyfile just-these-keys.txt astrofilesRunning the Checker ScriptChecker Script Options:usage: checker [-h] [-a {check,info}] [-v] [--version] images_path

Perform verification actions on a FITS file or a directory of FITS files OR
Show HDU info for the specified FITS file or directory of FITS files

positional arguments:
  images_path           path to a FITS file or a directory of FITS files to be processed

optional arguments:
  -h, --help            show this help message and exit
  -a {check,info}, --action {check,info}
                        action to perform on FITS file(s): validate or show HDU info
  --version             show program's version number and exitExamples:checker myDataDirectory
checker -a check myDataDirectory

checker myImages/someImage.fits
checker -a check myImages/someImage.fits

checker -a info myDataDirectory
checker -a info myImages/someImage.fitsLicenseLicensed under Apache License Version 2.0.Copyright 2018 by Astrolabe Project: American Astronomical Society and the University of Arizona.
Package: astrolib.coords
This package aims to provide much of the IDL “astron” functionality thatpertains to coordinate manipulations in an OO framework. Our target user is
a typical astronomer who needs to analyze data, work with catalogs, prepare
observing proposals, and prepare for observing runs.It depends on numpy, which must already be installed on your
system.It incorporates the TPM library, graciously contributed by Jeff
Percival, to perform coordinate system transformations. This will be
installed as part of the package installation.Install this package in the usual way:python setup.py installAfter installation, you can test the package as follows:>>> import coords as C
>>> C._test()A successful test run will produce no output from this command.If you have questions about this module, send mail tohelp@stsci.eduand it will get to the appropriate
person.
Package: astro-limepy
Lowered Isothermal Model Explorer in PYthonPython environment for solving distribution function (DF) based
lowered isothermal models as described in Gieles & Zocchi 2015, MNRAS,
454, 576 (http://arxiv.org/abs/1508.02120)Notebooks with example can be found in <https://github.com/mgieles/limepy/tree/master/notebooks>Also check out LIMEPY within the browser with mybinderFree software: BSD licenseDocumentation:https://limepy.readthedocs.org.target:https://limepy.readthedocs.io/en/latest/?badge=latestalt:Documentation StatusFeaturesSolves Poisson’s equation used on input parametersFast solutions for isotropic/anisotropic, single/multi-mass modelsNormalised DF valuesDensity and velocity momentsProjected propertiesGenerate discrete samplesHistory0.1.0 (2014-01-11)First release on github.
Package: astromech
Astromech - AWS Service Utilities for Lambda FunctionsAstromech is a collection of utilities that I found myself copying all over my lambda functions and serverless
projects. For the sake of DRYing up my code, I collected them in a single library.Service ClientsAstromech implements lazy-initialization for Boto3 service clients:For each service, there is a global client variable that gets reused between invocations of the labmda function
container. Code that want access to the client achieves this by using aclient()function that initializes it
if necessary.Code example:fromastromechimports3,dynamodbs3.client().get_object(Bucket=...,Key=...)dynamodb.table().get_item(Key=...)LocalStack Support Made EasyThe service client initialization functions, look for the environment variableLOCALSTACK_[SERVICE]_URL(for example,LOCALSTACK_S3_URL).
If it finds one, then it uses its value as theendpoint_urlfor the Boto3 client function.All you have to do to run tests withLocalStackis to set these variables in your local
environment. No changes to the code are necessary.
Naturally, this works with any other downloadable version of AWS services, likeDynamoDB local.Utility FunctionsThere are a few utility functions in each service. These usually save 2-3 lines of boilerplate code. Not a lot,
but for very common functionality that is repeated all across the code base, it becomes worthwhile.For example, to check if an object exists on S3, you have to send ahead_objectrequest and catch the exception
if it doesn't exist. Withastromech.s3you just callexists(bucket, key), much like you would do with a local
file.Why "Astromech"?In the Star Wars universe, astromech is a type of utility droid, the most famous of which (whom?) is R2-D2.Read more onWookiepedia.
Package: astro-meshless-surfaces
A small, inefficient python module for computing effective surfaces for
meshless methods of particle hydrodynamics the Hopkins 2015 way
(https://arxiv.org/abs/1409.7395) or the Ivanova 2013
(https://arxiv.org/abs/1209.4302) way.It’s only written for 2d computations.It’s meant to test things out, generate some plots etc, not to be used for
serious computations. (Although I have recently extended it to work with numba,
so bigger computations are feasible now.)Also includes simple reading-in functions for SWIFT (https://ascl.net/1805.020)
hdf5 output.I haven’t written any sort of documentation apart from docstrings. If you
want to know something, feel free to contact me (mladen.ivkovic[at]hotmail[dot]com)
anytime, or leave a bug/issue onhttps://github.com/mladenivkovic/astro-meshless-surfaces.
Otherwise, there are some usecase examples in/examples.Requirementsnumpy > 1.15matplotlib > 3.0h5py > 2.0scipy > 1.1Optional:numba
Package: astro-metadata-translator
astro_metadata_translatorObservation metadata handling infrastructureThis package can be used to translate headers from astronomical
instrumentation into a standard form.  This allows the details
of particular headers to be hidden from the user of the data
files.This package was developed by the Large Synoptic Survey Telescope
Data Management team.It does not depend on any LSST software.Documentation:https://astro-metadata-translator.lsst.ioSource:https://github.com/lsst/astro_metadata_translatorPyPI:https://pypi.org/project/astro-metadata-translator/
Package: astromodels
astromodelsPyPiCondaAstromodels is a very flexible framework to define models for likelihood or Bayesian analysis of astrophysical data.Even though it has been designed having in mind analysis in the spectral domain, it can be used also as a toolbox containing functions of any variable.Astromodels is not a modeling package, it only gives you the tools to build a model as complex as you need. You then need a separate package (such as3ML) to fit that model to the data.Some of the features which distinguish astromodels from other similar packages are: * a model can contain an arbitrary number of sources at different positions in the sky * parameters can be linked through any function (not only identity) * parameters can vary with auxiliary variables such as time. For example, you can build a model where some parameters vary with time, and you can fit the parameters of the function which describe this variability. Similary you can build models where parameters vary with the phase of a pulsar, and so on. * models can be saved in and loaded from YAML file (a human-readable format) * physical units are fully supported in input, but they are handled so that they don’t slow down the actualy computation of the models.Astromodels has been designed with performance as priority, and is considerably faster than other python-based solution for the same problem, such as astropy.modeling and the modeling part of sherpa.
Documentation:http://astromodels.readthedocs.org/en/latest/
Package: astromorphlib
OverviewPython scripts to analyze the morphology of isolated and interacting galaxies.
The package is designed to download S-PLUS (https://splus.cloud/) and
Legacy (https://www.legacysurvey.org) images automatically. There are functions
to calculate a 2D sky background of the images and deblended segmentation maps of
interacting systems with merger isophotes. The non-parametric analysis is
performed by usingstatmorphpackage (https://github.com/vrodgom/statmorph).The user can study the environment of the object/system by downloading a list of
the galaxies within Field-of-View of S-PLUS/Legacy images from SIMBAD server
(http://simbad.u-strasbg.fr/simbad/). In addition, there is a function to
display DSS2 (http://alasky.u-strasbg.fr/hips-image-services/hips2fits) images
of any size.Website:https://gitlab.com/joseaher/astromorphlibDocumentationThe documentation is available athttps://astromorphlib.readthedocs.ioInstallationRequirementsastromorphlib requires to run the following packages:* statmorph
* splusdata
* astroplotlib
* numpy
* scipy
* matplotlib
* astropy
* astroquery
* wgetThis version can be easily installed via PyPI (https://pypi.org/project/astromorphlib/):% pip install astromorphlibIf you prefer to install astromorphlib manually, you can clone the developing
version athttps://gitlab.com/joseaher/astromorphlib. In the directory this
README is in, simply type:% pip install .or,% python setup.py installUninstallationTo uninstall astromorphlib, just type:% pip uninstall astromorphlibAuthorsJose Hernandez-Jimenez (joseaher@gmail.com)Angela KrabbeAcknowledgementsThis software was funded partially by Brazilian agency FAPESP,
process number 2021/08920-8.CitingIf you use this code for a scientific publication, please
citeHernandez-Jimenez & Krabbe et al. (2022).
The BibTeX entry for this package is:@software{hernandez_jimenez_2022_6940848,
  author       = {Hernandez-Jimenez, J. A. and
                  Krabbe, A. C.},
  title        = {{Astromorphlib: Python scripts to analyze the
                   morphology of isolated and interacting galaxies}},
  month        = jul,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {0.2},
}
Package: astromulti
astromultiThis is a collection of modules for complementing AstroPy in astronomy analysisfits_actions: various analysis/modification routines for FITS imagescutout_fits: make cutouts of 2D/3D FITS filesremove_axes_34: removes degenerate 3rd and 4th axesbg_filter_I: filter out background emission using 'un-sharp masking' method developed by Sofue & Reich (1979)bg_filter_QU: modified version of bg_filter_I to work for polarization data as well (-ve values)convert_to_JyPerBeam: convert a FITS file to Jy/beam units (original units in Kelvin)regioncut: get a cutout of a regionTTdata: get pixel values of two FITS files with same WCS (for temperature-temperature plots)measure_FD: measure flux density of a region in a fits filesmooth_fits: convolve a FITS image to a larger beam sizesmooth_fits_to_commonbeam: convolve two FITS images to a common beam sizegauss2Dfit_fits: perform 2D Gaussian fitting on a point sourcelinear_fitting: do least squares fitting to get a line or a planenew_colormap: make a new colormap that can be used in plotting dataregion_meta: meta details of a regionsimple_funcs: a collection of simple functionsAs the license states, no liability will be accepted.  Whatever tests I ran are on Linux, and for my own use case.source code available online:https://gitlab.com/drohi/astromulti
Package: astromy-ds9
Reproducing SAOImageDS9 Colormap in PythonSAOImageDS9is an image display and visualization tool for astronomical data, which can be used for fast image processing and inspection. In DS9, we can drag the mouse on the image viewer to manipulate the colormap and then visualize data effectively and quickly. Unlike the DS9, color normalization and stretch in Python should be set manually.Here we take notes about how to reproduce the colormap of DS9 in Python, supposing that you have already tuned the colormap parameters in DS9.Installationpipinstallastromy_ds9Usagefromastromy_ds9importds9_normimportastropyimportmatplotlib.pyplotaspltdata=astropy.io.fits.getdata('https://github.com/lmytime/Reproduce_DS9_colormap_in_Python/raw/main/doc/data/m51.fits')norm=ds9_norm(vmin=3053.38,vmax=13513.9,bias=0.581921,contrast=0.890152,stretch='sqrt')plt.imshow(data,norm=norm,cmap='gray',origin='lower',interpolation='None')There are 5 input parameters fords9_normfunction.vminandvmax: you can find the values inScale->Scale Parameters-> Below the histogram, Low isvminand High isvmax.biasandcontrast: you can find the values inColor->Color Parametersstretch: you can find it inScale-> see which one is marked with a check mark. Allowed values are 'linear' | 'log' | 'sqrt' | 'power' | 'squared' | 'asinh' | 'sinh'.Check example code intestandexample.MethodologyPlease check the methodology indoc. Also seeds9normfor reference.CitationIf you find this useful, please acknowledge.
Package: astronaut
A framework for AI to explore spacesFree software: GNU General Public License v3.0Documentation:https://astronaut.readthedocs.org.FeaturesTODOCreditsTools used in rendering this package:Cookiecuttercookiecutter-pypackageHistory0.1.3 (2018-06-25)Fixed the pyenv interference with travis-ci builds0.1.2 (2018-06-25)Added support for Python 3.60.1.1 (2018-06-25)Setup CI/CD0.1.0 (2018-06-19)First release on PyPI.
Package: astronautcount
Astronaut Twitter BotThis is the repository for@astronautcount, the astronaut count twitter bot.StructureProject structure consists ofterraform/- houses the IaC to build a backend (spot) instance that a Flask-based inbound webhook handler resides on.src/- which contains two small Python packages, includinghandler, the Flask app already mentioned, andbot, a Python-based bot that CircleCI executes every day for a daily morning tweet.Where does the data come from?At-current, this bot pulls data fromOpen Notify's API, which is maintained by Nathan Bergey (@natronics). This is, as far as I'm aware, both the only publicly-accessible and regularly updated API for these data.
Package: astronbs
astronbsJupyterLab extension to place template notebooks in t current directoryThis extension is composed of a Python package namedastronbsfor the server extension and a NPM package namedastronbsfor the frontend extension.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallastronbsUninstallTo remove the extension, execute:pipuninstallastronbsTroubleshootIf you are seeing the frontend extension, but it is not working, check
that the server extension is enabled:jupyterserverextensionlistIf the server extension is installed and enabled, but you are not seeing
the frontend extension, check the frontend extension is installed:jupyterlabextensionlistContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the astronbs directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Server extension must be manually installed in develop modejupyterserverextensionenableastronbs# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstall# Server extension must be manually disabled in develop modejupyterserverextensiondisableastronbs
pipuninstallastronbsIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedastronbswithin that folder.Testing the extensionServer testsThis extension is usingPytestfor Python code testing.Install test dependencies (needed only once):pipinstall-e".[test]"To execute them, run:pytest-vv-rap--covastronbsFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.Packaging the extensionSeeRELEASE
Package: astro-ndslice
astro-ndsliceA collection of simple tools to slicing astronomical images (many are applicable to general ndarray).Modulelists: Contains some universally usable convenience functions.is_list_like: Checks if anything is list-like (list, tuple, ndarray).listify: Convert scalar or iterable into list (scalar2list=True, none2list=False)ndfy: Convert scalar or iterable into ndarray (see docstring for details)Moduleslices: Contains tools to convert FITS convention (e.g., "[10:20, 1:2]") or bezel (e.g.,bezel=[10, 10]) into pythonslice.Most users will only needslicefy(e.g.,data[slicefy("[10:110, 50:150]")]).Moduleoffset: Contains tools to find the offsets between two images/FITS files (essential before image combination)To usecalc_offset_wcsorcalc_offset_physical,astropyis required.
BSD 3-Clause LicenseCopyright (c) 2023, Yoonsoo P. BachRedistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
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