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tutorials

Jupyter Notebook tutorials using astronomical databases and virtual observatory tools

https://github.com/cds-astro/tutorials

Science Score: 36.0%

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Keywords

aladin astronomy astrophysics astropy-examples database-access jupyter-book jupyter-notebook open-source python pyvo simbad tutorials virtual-observatory vizier
Last synced: 4 months ago · JSON representation

Repository

Jupyter Notebook tutorials using astronomical databases and virtual observatory tools

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Topics
aladin astronomy astrophysics astropy-examples database-access jupyter-book jupyter-notebook open-source python pyvo simbad tutorials virtual-observatory vizier
Created about 6 years ago · Last pushed 8 months ago
Metadata Files
Readme Codemeta Zenodo

README.md

Accessing and using astronomical data: a series of Jupyter notebooks tutorials

DOI Code style: black Ruff

Welcome to the repository of Strasbourg astronomical Data Center CDS jupyter notebooks.

CDS logo

All tutorials here illustrate how astronomers can search, find, and access data they need from archives across the world. We offer a focus on archives compliant with Virtual Observatory (VO) protocols and standards, as defined by the International Virtual Observatory Alliance (IVOA).

Some tutorials hosted here are based on EURO-VO tutorials usually with the same or a similar name.

Table of contents

How to use the tutorials

On your machine

python ubuntu windows mac-os

You can download the notebooks to your personal machine and use them there.

Note the currently supported python versions and the list of dependencies in the file requirements.txt.

This can be done with the following commands:

git clone https://github.com/cds-astro/tutorials.git cd tutorials pip install -r requirements.txt jupyter lab

Note: the file justfile contains the instructions to create an environnement with conda.

In github codespaces

  • In Github, you can open a JupyterLab instance. To do so, go in your Settings, then Codespaces and in Editor Preference choose Jupyter Lab. And voil, you can now launch the tutorials, execute and modify them without any installation!

codespace

On mybinder

  • You can click on the Binder button here Binder

As a book

  • You can also explore the notebooks in a website here Jupyter Book Badge

Flight plan

The tutorials come in a recommended order ranging from basic usage of the CDS tools to more advanced examples.

1. Intro to CDS services in notebooks

Simbad Aladin Vizier Xmatch astroquery units

We show how to call Simbad, Aladin Lite, Vizier and X-match in a Jupyter Notebook.

2. Arp peculiar galaxies catalog __ First steps with MOCs

Vizier Aladin MOCpy astroquery astropy

We explore Arp's Catalog of peculiar Galaxies and extract the galaxies explored both in the SDSS and GALEX surveys by using MOCs. This tutorial highlights two python modules for querying data : astroquery and pyvo.

3. Abel1656, The Coma Cluster of Galaxies __ The Simple Spectral Access Protocol

Aladin Vizier Xmatch SSA Simbad astroquery astropy specutils pyvo

This tutorial focuses on the Coma Cluster of Galaxies. In particular, we search for redshift information of galaxies in the spatial vicinity of the cluster centre. To get this information we get catalogues with measured redshifts and a spectrum, from which we measure a redshift. It uses the Simple Spectral Access (SSA) protocol of the virtual observatory.

4. High Energy sources __ advanced use of CDS services

Simbad Aladin Xmatch Vizier astroquery astropy

For this tutorial we focus on gamma-ray and high energy spectral bands and start by querying suitable catalogues using VO tools. After that we cross-correlate catalogues to find entries for objects in different photon energy bands. We then apply selection criteria to extract sources from a catalogue and use the resulting observational measures of the selected objects to explore possible correlations. We close by displaying spectral energy distributions obtained from different photometric data sets.

5. Brown Dwarf search __ cross-matching catalogs

Simbad Aladin Xmatch Vizier astroquery astropy pyvo

Brown dwarfs are objects occupying the gap between the least massive stars and the most massive planets. They are intrinsically faint objects. Hence, heir detection is not straightforward and, in fact, was almost impossible until the advent of global surveys at deep optical and near-infrared bands like SDSS, 2MASS or DENIS. We propose here to mine the 2MASS point source catalogue (2MASS-PSC) and SDSS-DR9 databases to identify T-type brown dwarfs through an appropriate combination of colors in the optical and the infrared, an approach that perfectly fits into the Virtual Observatory.

6. Advanced usage of MOC to explore complex regions of interest

Vizier MOCpy astroquery astropy pyvo

This tutorial is centred on using HiPS and MOC to efficiently reach your science goals. In particular it will help you answer questions such as: What is the footprint of my imaging survey? Where on the sky do we have low foreground extinction? Where do these two regions overlap? How can I get interesting catalogue entries within these regions? This questions are answered by building MOCs from a set of images. We also build a MOC from the pixel values of an all-sky survey. We then move on to query catalogues in an intersection of these MOCs.

7. Multi Messenger Astronomy __ Generation of an observation plan with MOCs

MOCpy Aladin pyvo astropy astroquery

We will first explore Multi-Order Coverage (MOC) data structure manipulation, then we will see how astroplan and Space-Time Multi Order Coverage (STMOC) can be combined to plan observation according to a MOC. The final step is a concrete example illustrating how STMOCs can be built in a few seconds to plan observations from three ground observatories, with the aim to cover of the sky localization produced after detection of a gravitational wave.

Additional tutorials used in past conferences and schools

Each folder represents a different summer school and the associated tutorials. These are not maintained but still accessible.

Need help?

Don't hesitate to ask a question in the Q&A of our Discussions section.

Glossary

IVOA Standards

  • HiPS: Hierarchical Progressive Surveys - efficient tiling systems to serve (all-sky) imaging surveys, cubes or catalogues, the user only downloads and sees what they are interested in and the more they zoom the more they see, more information here
  • MOC also SMOC: MultiOrder Coverage map (or Space MultiOrder Coverage) - efficient way of describing arbitrary patches of the sky, logical operations such as union, intersection of MOC can be calculated, tables can be filtered by MOCs, more information here
  • STMOC: Space-Time MultiOrder Coverage map - is a MOC that encompasses time information.
  • SSA: Simple Spectral Access protocol to access spectrums, more information here
  • SAMP: Simple Application Messaging Protocol - a messaging system to share information between different astronomical softwares, such as python, Aladin, Topcat, etc. more information here

CDS Services

  • Simbad: dynamic astronomical database of objects appearing in scientific publications, updated every working day,
  • Aladin: an interactive sky atlas,
  • Vizier: a collection of published astronomical catalogues,
  • Xmatch: an efficient cross-matching tool between very large catalogues,
  • MOCpy: a python library allowing MOC manipulation

Contributions

Guidelines for contributions can be found in the discussion section

| escape-logo | eu-flag | This work has been supported by the ESCAPE project (the European Science Cluster of Astronomy & Particle Physics), funded from the European Unions Horizon 2020 research and innovation program under grant agreement n824064, the EOSC (European Open Science Cloud) Future project co-funded by the European Union Horizon Program call INFRAEOSC-03-2020 - grant agreement n101017536, and the project ASTERICS supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n653477. | | --- | --- | --- |

Owner

  • Name: CDS (Centre de Données astronomiques de Strasbourg)
  • Login: cds-astro
  • Kind: organization

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Dependencies

.github/workflows/notebooks-check_ubuntu.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/notebooks-check_windows.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/notebooks-checks_mac-os.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/validate_codemeta.yml actions
  • actions/checkout v2 composite
pyproject.toml pypi
requirements-dev.txt pypi
  • nbmake * development
  • pre-commit >=3.1.0.a development
  • pytest >=8.1.1,<9.0.0 development
  • pytest-xdist * development
requirements-docs.txt pypi
  • ghp-import ==2.1.
  • jupyter-book ==0.15.
requirements.txt pypi
  • MOCpy >=0.13.
  • astroplan >=0.8,<=0.9.1
  • astropy >=5.2.0.a,<6.1
  • astroquery ==0.4.
  • ipyaladin >=0.2.2
  • pandas >=2.0.3,<2.3.0
  • plotly >=5.14.0.a,<=5.20.0
  • pyvo >=1.4.1,<=1.5.1
  • regions >=0.7,<=0.8
  • requests >=2.4.3
  • specutils >=1.9.0.a,<=1.13.0
Past_Conferences_and_Schools/2024-11-14_ADASS/requirements.txt pypi
  • astroquery >=0.4.8.dev0
  • ipyaladin >=0.5.1
  • jupyterlab >4,<5
  • mocpy *
  • regions *
  • sidecar >=0.2.1