Saravanan666/pypi-org huggingface dataset:
Package: astronify
Sonification of astronomical data.Tools for sonifying astronomical data.This package is currently in the very beginning stages and is under active development to
include sonification of time series data, specifically light curves.Project StatusDeveloper DocumentationInstallation$gitclonehttps://github.com/spacetelescope/astronify.git$cdastronify$pipinstall.For active development, install in develop mode$pipinstall-e.TestingTesting is run withtox(pip install tox).
Tests can be found intests/sub-directories.$tox-etestTests can also be run directly with pytest:$pipinstall-e.[test]$pytestDocumentationDocumentation files are found indocs/.We build the documentation withtox(pip install tox):$tox-ebuild_docsYou can also build the documentation with Sphinx directly using:$pipinstall-e.[docs]$cddocs$makehtmlThe built docs will be indocs/_build/html/, to view them go tofile:///path/to/astronify/repo/docs/_build/html/index.htmlin the browser of your choice.Release ProtocolFollow theAstropy template release instructions.ContributingWe love contributions! Astronify is open source,
built on open source, and we’d love to have you hang out in our community.Imposter syndrome disclaimer: We want your help. No, really.There may be a little voice inside your head that is telling you that you’re not
ready to be an open source contributor; that your skills aren’t nearly good
enough to contribute. What could you possibly offer a project like this one?We assure you - the little voice in your head is wrong. If you can write code at
all, you can contribute code to open source. Contributing to open source
projects is a fantastic way to advance one’s coding skills. Writing perfect code
isn’t the measure of a good developer (that would disqualify all of us!); it’s
trying to create something, making mistakes, and learning from those
mistakes. That’s how we all improve, and we are happy to help others learn.Being an open source contributor doesn’t just mean writing code either. You can
help out by writing documentation, tests, or even giving feedback about the
project (and yes - that includes giving feedback about the contribution
process). Some of these contributions may be the most valuable to the project as
a whole, because you’re coming to the project with fresh eyes, so you can see
the errors and assumptions that seasoned contributors have glossed over.Note: This disclaimer was originally written byAdrienne Lowefor aPyCon talk, and was adapted by
Astronify based on its use in the README file for theMetPy project.LicenseThis project is Copyright (c) Clara Brasseur, Scott Fleming, Jennifer Kotler, Kate Meredith and licensed under
the terms of the BSD 3-Clause license. This package is based upon
theAstropy package templatewhich is licensed under the BSD 3-clause licence. See the licenses folder for
more information.
Package: astroNN
Getting StartedastroNN is a python package to do various kinds of neural networks with targeted application in astronomy by using Keras API
as model and training prototyping, but at the same time take advantage of Tensorflow’s flexibility.For non-astronomy applications, astroNN contains custom loss functions and layers which are compatible with Tensorflow. The custom loss functions mostly designed to deal with incomplete labels.
astroNN contains demo for implementing Bayesian Neural Net with Dropout Variational Inference in which you can get
reasonable uncertainty estimation and other neural nets.For astronomy applications, astroNN contains some tools to deal with APOGEE, Gaia and LAMOST data. astroNN is mainly designed
to apply neural nets on APOGEE spectra analysis and predicting luminosity from spectra using data from Gaia
parallax with reasonable uncertainty from Bayesian Neural Net. Generally, astroNN can handle 2D and 2D colored images too.
Currently astroNN is a python package being developed by the main author to facilitate his research
project on deep learning application in stellar and galactic astronomy using SDSS APOGEE, Gaia and LAMOST data.For learning purpose, astroNN includes a deep learning toy dataset for astronomer -Galaxy10 Dataset.astroNN DocumentationQuick Start guideUncertainty Analysis of Neural Nets with Variational MethodsAcknowledging astroNNPlease cite the following paper that describes astroNN if astroNN is used in your research as well as linking it tohttps://github.com/henrysky/astroNNDeep learning of multi-element abundances from high-resolution spectroscopic data[arXiv:1808.04428][ADS]AuthorsHenry Leung-Initial work and developer-henryskyAstronomy Student, University of TorontoContact Henry: henrysky.leung [at] utoronto.caJo Bovy-Project Supervisor-jobovyAstronomy Professor, University of TorontoLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: astronomer-aeroscope
Astronomer Aeroscope ProviderThe Astronomer Aeroscope Provider contains a Plugin and Operator from Astronomer.
These provide a method to run Telescope in environments where other methods are unavailableInstallationAdd the following line to yourrequirements.txtin your source environment:astronomer-aeroscopeUsage Option 1) Plugin UsageNavigate to the top navigation bar in your Source Airflow environment.Click theAstronomermenu, thenRun ReportUsage Option 2) Operator UsageAdd the following DAG to your source Airflow environmentfromdatetimeimportdatetimefromairflow.modelsimportDAGfromastronomer.aeroscope.operatorsimportAeroscopeOperatorwithDAG(dag_id="astronomer_aeroscope",schedule_interval=None,start_date=datetime(2021,1,1),)asdag:AeroscopeOperator(task_id="run_report",presigned_url='{{ dag_run.conf["presigned_url"] }}',organization='{{ dag_run.conf["organization"] }}',)Trigger theastronomer_aeroscopeDAG with the configuration given by your Astronomer Representative{
	"presigned_url": "presigned-url-here",
	"organization": "organization-name-here"
}
Package: astronomer-analytics
The analytics-python client is created bySegmentand adapted byAstronomer.It is the simplest way to integrate analytics into your application.
One API allows you to turn on any other analytics service. No more learning
new APIs, repeated code, and wasted development time.This is the official Python client that wraps theAstronomer API.Documentation and more details athttps://github.com/astronomerio/analytics-python
Package: astronomer-cosmos
Run your dbt Core projects asApache AirflowDAGs and Task Groups with a few lines of code. Benefits include:Run dbt projects against Airflow connections instead of dbt profilesNative support for installing and running dbt in a virtual environment to avoid dependency conflicts with AirflowRun tests immediately after a model is done to catch issues earlyUtilize Airflow’s data-aware scheduling to run models immediately after upstream ingestionTurn each dbt model into a task/task group complete with retries, alerting, etc.QuickstartCheck out the Getting Started guide on ourdocs. See more examples at/dev/dagsand at thecosmos-demo repo.Example UsageYou can render a Cosmos Airflow DAG using theDbtDagclass. Here’s an example with thejaffle_shop project:https://github.com/astronomer/astronomer-cosmos/blob/24aa38e528e299ef51ca6baf32f5a6185887d432/dev/dags/basic_cosmos_dag.py#L1-L42This will generate an Airflow DAG that looks like this:CommunityJoin us on the AirflowSlackat #airflow-dbtChangelogWe followSemantic Versioningfor releases.
CheckCHANGELOG.rstfor the latest changes.Contributing GuideAll contributions, bug reports, bug fixes, documentation improvements, enhancements are welcome.A detailed overview an how to contribute can be found in theContributing Guide.As contributors and maintainers to this project, you are expected to abide by theContributor Code of Conduct.LicenseApache License 2.0
Package: astronomer-e2e-test
Astronomer end to end testingThis repository contains a set of test files
for validating Astronomer Platform functionality.
It uses thePython Behaveframework to create literate tests and output.CircleCI:https://circleci.com/gh/astronomer/workflows/astronomer_e2e_testDocker Hub:https://hub.docker.com/r/astronomerinc/ap-e2e-testTestsSee the platform/features directory for details.Current:AuthLogin with CLILogin with APIWorkspacesListCreateDeleteChange ActiveDeploymentListCreate [local, celery, k8s]DeleteTODODAGs of various types (will require Docker engine be available)Known regressions that should be fixedUsageThese tests are used viahelm testin the astronomer/astronomer repository.You will need a user on the platform you intend to test.With DockerRundockerrun--rm-it\-eASTRO_USER="name@domain.com"\-eASTRO_PASS="..."\-eHOUSTON_URL="https://houston.my-base-domain.com/v1"\astronomerinc/ap-e2e-test:latestBuilding the packageDocker imagedockerbuild-tastronomerinc/ap-e2e-test:latest.Python wheelpoetrybuild-fwheelRuncdplatform# Run all testsbehave# Run specific testbehavefeatures/000_auth.featureReleasingFirst, bump the version and show th your changes:poetryversion<major|minor|patch>
gitcommit-m"version 9.8.7"# whatever version shows up in `git diff`poetrypublish--username="$PYPI_USERNAME"--password="$PYPI_PASSWORD"
Package: astronomer-nova
Astronomer Nova(Formerly Airflow V2 Upgrader)This project aims to automate common tasks when upgrading DAGs from Airflow 1 to Airflow 2,
making use ofRedBaronto parse and manipulate the Python syntax tree.FeaturesAutomatically upgrade DAG source code from Airflow 1 to Airflow 2Scan Dags and report changes (Coming Soon)InstallationYou can install the Astronomer Nova CLI through Pippipinstallastronomer-novaThe simplest way to get started with Astronomer Nova is by using the astro cli.
Simply addastronomer-novato your list of dependencies inrequirements.txt.
Then you can run it with the following commands:astrodevstart
astrodevbash
novaInstallation from sourcesTo install astronomer-nova indevelopment mode:pipinstall--editable.[lint,test]To build from sources:python -m buildORpython -m pip wheel .Running tests:python-mpytestHow to Usenovadags/dags/upgraded/For more information, you can access the help menu with:nova--helpHow it worksAstronomer Nova performs the upgrade by:Building an import map for classes inairflow.hooks,airflow.operators,airflow.sensors, andairflow.providers, which maps the classes to the current Airflow version import path.Modifying Python files in a DAG directory to updateImportsDAG access controlsXCOM pushXCOM pullWriting updated DAG to either the same DAG directory or a parameter output directory with_upgradedadded to the filename.The script is configured to run against./dagsand to write output to./dags/upgradedby default.CaveatsIn order to load the Airflow import map, Airflow provider packages must be installed. There are often cross-dependencies within Airflow providers (egairflow.operators.s3_to_hive_operatorrequired the Hive provider), so make sure therequirements.txtfile includes all necessary .The script currently tries to correct imports that do not need to be changed going from Airflow 1 to 2, and logging does not include which files are currently being acted against.The code uses Astronomer-centric opinions, like removing DAG-level RBAC. In the future, I'd rather have these decisions configurable at runtime, as well as generating per-DAG reports on the DAGs that can be used for further remediation, as well as understanding of DAG requirements, like the use of Connections and Variables.ContributeIssue Tracker:https://github.com/astronomer/astronomer-nova/issuesSource Code:https://github.com/astronomer/astronomer-novaContactcabella@astronomer.io
Package: astronomer-starship
Astronomer Starshipcan send your Airflow workloads to new places!What is it?Starship is a utility to migrate Airflow metadata such as Airflow Variables,
Connections, Environment Variables, Pools, and DAG History between two Airflow instances.Read more at the official documentation!ArtworkOrbiter logoby Lorenzoused with permission
fromThe Noun ProjectunderCreative Commons.
Package: astronomer-starship-provider
An Apache Airflow Provider package containing Operators from Astronomer. The purpose of these operators is to better assist customers migrating Variables, Connections, and Environment Variables to Astronomer hosted Airflow environments from MWAA, GCC, and OSS environments. This provider is meant for MWAA 2.0.2 and Google Cloud Composer 1 environments in particular, as webserver plugins are unavailable in these environments.InstallationInstall and update usingpip:pipinstallastronomer-starship-providerUsageAdd the following line to yourrequirements.txtin your source environment:astronomer-starship-providerAdd the following DAG to your source environment:fromairflowimportDAGfromastronomer.starship.operatorsimportAstroMigrationOperatorfromdatetimeimportdatetimewithDAG(dag_id="astronomer_migration_dag",start_date=datetime(2020,8,15),schedule_interval=None,)asdag:AstroMigrationOperator(task_id='export_meta',deployment_url='{{ dag_run.conf["deployment_url"] }}',token='{{ dag_run.conf["astro_token"] }}',)Deploy this DAG to your source Airflow environment, configured as described in theConfigurationsection belowOnce the DAG is available in the Airflow UI, click the “Trigger DAG” button, then click “Trigger DAG w/ config”, and input the following in the configuration dictionary:astro_token:  To retrieve an Astronomer token, navigate tocloud.astronomer.io/tokenand login using your Astronomer credentialsdeployment_url: To retrieve a deployment URL - navigate to the Astronomer Airlow deployment that you’d like to migrate to in the Astronomer UI, clickOpen Airflowand copy the page URL (excluding/homeon the end of the URL)For example, if your deployment URL ishttps://astronomer.astronomer.run/abcdt4ry/home, you’ll usehttps://astronomer.astronomer.run/abcdt4ryThe config dictionary used when triggering the DAG should be formatted as:{"deployment_url":"your-deployment-url","astro_token":"your-astro-token"}Once the DAG successfully runs, your connections, variables, and environment variables should all be migrated to AstronomerConfigurationTheAstroMigrationOperatorcan be configured as follows:variables_exclude_list: List the individual Airflow Variables which youdo notwant to be migrated. Any Variables not listed will be migrated to the desination Airflow deployment.connection_exclude_list:  List the individual Airflow Connections which youdo notwant to be migrated. Any Variables not listed will be migrated to the desination Airflow deployment.env_include_list:  List the individual Environment Variables which youdowant to be migrated. Only the Environment Variables listed will be migrated to the desination Airflow deployment. None are migrated by default.AstroMigrationOperator(task_id='export_meta',deployment_url='{{ dag_run.conf["deployment_url"] }}',token='{{ dag_run.conf["astro_token"] }}',variables_exclude_list=["some_var_1"],connection_exclude_list=["some_conn_1"],env_include_list=["FOO","BAR"])
Package: astronomer-telescope
Astronomer Telescopeis a tool to observe distant (or local!) Airflow installations,
  and help you better understand your deployments.What is it?It is a CLI that runs on your workstation and accesses remote Airflows to collect a common set of data.Optionally, it can be installed and run as an Airflow plugin viaStarship.Telescope has been purpose-built to help administrators understand their Airflow installations
and provide metadata to assist with migrations.Main featuresAnalyze your Airflow deployment and execution environment to provide a snapshot of all configurations.Summarizes Airflow-specific settings (variables, connections, pools, etc.)Generates a report of runtime configurations (airflow.cfg)Generates a DAGs report including code quality metrics, execution statistics and more.Can be run on most deployment environments (Docker, Kubernetes, SSH Remote) and Airflow versions.Security and anonymity are built-in.Kubernetes and airflow.cfg sensitive values are redacted.Individual user information and secrets are never accessed.Reports can be parameterized to obfuscate DAG IDs and filenames.Read more at the official documentation!ArtworkOrbiter logoby b fariasused with permission
fromThe Noun ProjectunderCreative Commons.
Package: astronomia
Astronomia - Quick GuideAstronomia is a collection of subroutines and applications for calculating the
positions of the sun, moon, planets and other celestial objects. The emphasis
is on high accuracy over a several thousand year time span. Note that the
techniques used are overkill for most calendar applications.The subroutine library attempts to implement some the techniques described inAstronomical Algorithms, second edition 1998, by Jean Meeus,Willmann-Bell,
Inc.Currently there are no graphical applications apart from some demo CGI
interfaces.Astronomia will work with Python 2.6+ and 3.0+.DocumentationReference documentation is athttp://timcera.bitbucket.io/.InstallationAt the command line:$ pip install astronomia
# OR
$ conda install -c conda-forge astronomiaUsageTo use Astronomia in a project:import astronomiaDevelopmentDevelopment is managed on bitbucket athttps://bitbucket.org/timcera/astronomia/overview.HistoryAstronomia is a fork of the Astrolabe library created by Bill McClain.  The
Astrolabe library is no longer available.I (Tim Cera) used the Astrolabe library within my tidal analysis packageTAPPy. In 2013 I pulled Astrolabe out of TAPPy and
forked Astronomia.  I have since fixed many bugs and added features.  The most
important added feature is the ability for most functions to work with array
inputs.  Bill McClain had dual Python and ‘C’ code, but I focused only on the
Python code, updating with newer data and equations as I found them.
Package: astronomical
<pre><h1>astronomical</h1><h2>description</h2>This is a module that can reliablyascertain the health status of software.(It's like a testing kit)<h2>install</h2>[SH] pip install astronomical<h2>documentation</h2>[SH] astronomical shares<h2>astronomical of astronomical (the internal astronomical of the astronomical module)</h2><p>These checks are run with pypi "body_scan"</p><p>"astronomical" is a fork of "body_scan"</p><p>("body_scan" checks are written with "unittest")</p>[SH] astronomical astronomical<h2>health checks</h2><p>Checks are started simultaneously, unless "--simultaneous no"</p><h3>create a status file</h3># status_1.pydef check_1 ():print ("check 1")def check_2 ():print ("check 2")def check_3 ():raise Exception ("not 110%")checks = {"check 1": check_1,"check 2": check_2,"check 3": check_3}<h3>start from a directory deeper than the status file "status_1.py"</h3>[SH] astronomical status<h3>The report then should appear like this:</h3><code>paths: [{"path": "../status_1.py","empty": false,"parsed": true,"stats": {"passes": 2,"alarms": 1},"checks": [{"check": "check 1","passed": true,"elapsed": [4.054199962411076e-05,"seconds"]},{"check": "check 2","passed": true,"elapsed": [1.72930003827787e-05,"seconds"]},{"check": "check 3","passed": false,"exception": "Exception('not 110%')","exception trace": ["Traceback (most recent call last):","  File \"/home/veganecology/.local/lib/python3.11/site-packages/astronomical/processes/scan/process/keg/check.py\", line 68, in start","    checks [ check ] ()","  File \"<string>\", line 13, in check_3","Exception: not 110%"]}]}]alarms: [{"path": "../status_1.py","checks": [{"check": "check 3","passed": false,"exception": "Exception('not 110%')","exception trace": ["Traceback (most recent call last):","  File \"/home/veganecology/.local/lib/python3.11/site-packages/astronomical/processes/scan/process/keg/check.py\", line 68, in start","    checks [ check ] ()","  File \"<string>\", line 13, in check_3","Exception: not 110%"]}]}]stats: {"alarms": 0,"empty": 0,"checks": {"passes": 2,"alarms": 1}}</code><h2>advanced configurations</h2><p>It's recommended to run astronomical programmatilly.An example of this can be found in the "advanced configurations"section of the documentation.</p><h2>Contacts</h2><p>status600@proton.me</p></pre>
Package: astronomical-matching
Astronomical MatchingCollection of algorithms to create matchings between astronomical catalogs.BackgroundAstronomical photos inherintly have error in them. To best predict where objects are, multiple photos of the same part of the sky are combined. This is equivalent to matching objects in one image to objects in another image. Existing methods rely on nearest-neighbor heuristics which do not take into account the fact that two objects from one image cannot be assigned to the same object.
Package: astroobs
Name:astroobsWebsite:https://github.com/ceyzeriat/astroobsAuthor:Guillaume SchworerVersion:1.4.4Check theastroobs.euwebsite for a friendly web-interface of that library! (soon)Astroobs provides astronomy ephemeris (airmass, azimuth, altitude, moon separation, etc) of a night sky target as a function of the date-time and the longitude/latitude of the observer.
A list of international observatories is provided as well as a SIMBAD-querier to easily import targets.
This package is based on pyephem ephemeris calculations. The main difference with this latter package is that astroobs provides a very straight-forward library for the observer to get the critical information in order to plan an observation.
It also provides convenient turn-key tools to convert epochs and plot diagrams.
It is released under the GNU General Public License v3 or later (GPLv3+).importastroobsasobso=obs.Observation('vlt',local_date=(2015,1,1),moonAvoidRadius=15,horizon_obs=40)o.add_target('aldebaran')o.add_target('canopus')o.plot()aldebaran=o.targets[0]aldebaran.whenobs(o,(2015,1,1),(2015,2,1))DocumentationRefer to this page,http://pythonhosted.org/astroobs/astroobs.htmlRequirementsastroobs requires the following Python packages:NumPy: for basic numerical routinesAstropy: for angle unitsAstroquery: for querying Simbadpyephem: for the calculations of ephemerismatplotlib: for plotting (optional)pytz: for timezones managementre, os, sys, datetime, time: for basic stuffastroobs is tested on Linux and Python 2.7 only, but should cross-plateform and python3 friendly without too many issues.InstallationThe easiest and fastest way for you to get the package and run is to install astroobs through pip:$ pip install astroobsYou can also download astroobs source from GitHub and type:$ python setup.py installDependencies will not be installed automatically. Refer to the requirements section. If you have an anaconda distribution, you will only need to install astroquery and pyephem.ContributingCode writingCode contributions are welcome! Just send a pull request on GitHub and we will discuss it. In theissue trackeryou may find pending tasks.Bug reportingIf you think you’ve found one please refer to theissue trackeron GitHub.Additional optionsYou can either send me an e-mail or add it to the issues/wishes list on GitHub.CitingIf you use astroobs on your project, pleasedrop me a line <mailto:{my first name}.{my family name}@obspm.fr>, you will get fixes and additional options earlier.Licenseastroobs is released under the GNU General Public License v3 or later (GPLv3+). Please refer to the LICENSE file.Changelog1.4.4 (2016-08-06)Changed license to GNUUpdated documentationAdded show_all_obs() to show all available observatoriesAutomated the version managementAdded disclaimer at importSet default legend parameter to True in plot() and polar()Fixed deprecation warning at display of Target1.0.0 (2016-05-03)Initial release.