{"id":41505190,"url":"https://github.com/eso/astroquery_examples","last_synced_at":"2026-01-23T19:16:34.983Z","repository":{"id":295981704,"uuid":"984633151","full_name":"eso/astroquery_examples","owner":"eso","description":"Jupyter notebooks for querying, accessing, and analyzing data from the ESO Science Archive using astroquery.eso. 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While we aim for accuracy, we **cannot guarantee** that everything in this repository currently works as intended.  \n\u003e Some functionality depends on features of `astroquery` that are **only available in the [ESO astroquery fork](https://github.com/eso/astroquery.git)**.  \n\u003e Until these changes are merged into a stable release, please install `astroquery` directly from that branch to ensure compatibility.  \n\u003e Features, functions, and outputs may change without notice. Please report any issues via GitHub.\n\n---\n\n# **Introduction**\n\nThis repository contains a suite of Jupyter notebooks designed to demonstrate how to explore, query, and retrieve data from the [ESO Science Archive](https://archive.eso.org/cms.html) using Python. The primary interface used is the `astroquery.eso` module, which is part of the broader [astroquery](https://astroquery.readthedocs.io/en/latest/) package developed under the Astropy ecosystem.\n\nThese notebooks walk through both introductory and advanced examples that are relevant for a wide range of scientific use cases — from downloading raw and reduced data products, to analyzing spectra and imaging datasets retrieved from ESO facilities at La Silla and Paranal (including the Very Large Telescope - VLT - and the Very Large Telescope Interferometer - VLTI), as well as from submillimeter and radio observatories -- APEX and ALMA.\n\nWhether you're new to archival astronomy or already familiar with ESO's observing systems, these examples provide practical guidance for integrating archive queries into reproducible, scriptable workflows.\n\n# 📚 Examples\n\nAll notebooks live under `examples/` and are grouped by purpose:\n\n```text\nexamples/\n├── simple/         # 🌱 Minimal, single‑feature recipes (copy/paste friendly)\n├── advanced/       # 🚀 Scenario‑driven workflows combining multiple functions\n└── case_studies/   # 🌌 Curated case studies for specific Phase 3/data collections\n```\n\nSee detailed indexes inside each folder:\n\n- [`examples/simple/README.md`](examples/simple/README.md)\n- [`examples/advanced/README.md`](examples/advanced/README.md)\n- [`examples/case_studies/README.md`](examples/case_studies/README.md)\n\n## ▶️ Quick Start: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgithub.com%2Feso%2Fastroquery_examples/main?urlpath=%2Fdoc%2Ftree%2FESO_Introduction.ipynb)\n\nTo get started, you can load and initialize the ESO archive interface directly in your notebook:\n\n```python\nfrom astroquery.eso import Eso  # Import the ESO module from astroquery\neso = Eso()                     # Create an instance of the ESO class\neso                             # Display the class instance (e.g. check login status, default settings)\n```\n\nA simple overview script can be found in [`examples/simple/00_introduction.ipynb`](examples/simple/00_introduction.ipynb).\n\n\u003e **Tip:** You can try all the examples directly in [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgithub.com%2Feso%2Fastroquery_examples/main?urlpath=%2Fdoc%2Ftree%2FESO_Introduction.ipynb), without installing anything.\n\nOnce initialized, you can search by program ID, target name, position, or instrument, and download data products directly into your working directory — all from within the notebook environment.\n\n\n![ipyaladin example](assets/overview.gif)\n\n# 🔭 **About the ESO Science Archive**\n\nThe ESO Science Archive Facility (SAF) is one of the largest ground-based astronomical data repositories in the world. It contains data from ESO telescopes at La Silla, Paranal, and APEX, as well as the submillimeter APEX telescope on Llano de Chajnantor and the ALMA array. The archive includes all raw data from La Silla Paranal Observatory, the corresponding calibrations, and a wide range of processed data products, either generated by ESO or contributed by the community. It also hosts selected external datasets, such as raw WFCAM/UKIDSS data from the UKIRT facility in Hawaii.\n\nAs of **May 2025**, the archive hosts over **4.6 million** science products, including:\n\n- [3.9 million products from La Silla, Paranal, and APEX ](https://archive.eso.org/scienceportal/home?observatory=%22La%20Silla%20Paranal%20APEX%22) \n- [700 000 ALMA products](https://archive.eso.org/scienceportal/home?observatory=ALMA)  \n\nSplit over:\n- [2.3 million spectra](https://archive.eso.org/scienceportal/home?dp_type=SPECTRUM) (1D extracted, fully calibrated)  \n- [950 000 imaging products](https://archive.eso.org/scienceportal/home?dp_type=IMAGE) in optical, near-IR, and sub-mm bands  \n- [650 000 spectral cubes](https://archive.eso.org/scienceportal/home?dp_type=CUBE) from IFU and radio/mm instruments  \n- [700 000 catalogue files](https://archive.eso.org/scienceportal/home?dp_type=CATALOG)\n\nProcessed data downloaded from the ESO Archive are assigned a Digital Object Identifier (DOI). You can browse the list of available DOIs [here](https://archive.eso.org/wdb/wdb/doi/collections/query).\n\nAccess to data follows ESO’s standard data policy: the Principal Investigator (PI) of an observing program has exclusive access to their data during a proprietary period (typically one year), after which the data become publicly available. See the [ESO Data Access Policy](https://archive.eso.org/cms/eso-data-access-policy.html) and [FAQ](https://archive.eso.org/cms/faq.html?q=proprietary) for more information. Browsing the archive does not require authentication, but login is necessary to access proprietary datasets. Use of ESO archival data should be acknowledged in all publications.\n\n## **Data Access**\n\nThere are four main ways to access the vast information in the ESO Archive:\n\n- [Raw Data query form](http://archive.eso.org/eso/eso_archive_main.html) - Full access to raw frames and their calibrations.\n- [Science Portal](http://archive.eso.org/scienceportal/home) - Browse and retrieve processed (Phase 3) data.\n- [Catalogue Query Interface](https://www.eso.org/qi/) - Access PI-contributed catalogue data.\n- [Programmatic access page](http://archive.eso.org/programmatic/) - Direct, scriptable access to raw and processed data, metadata, ambient conditions, and catalogues via Virtual Observatory (VO) protocols.\n\nThese notebooks focus on the last method: accessing the archive programmatically using the `astroquery.eso` module. Note that at present (May 2025), catalogue searches are not supported using the `astroquery.eso` module. Functionality for querying catalogues will be added in a future release.\n\n## **Contributing your own data to the ESO archive**\n\nThe scientific community plays a vital role in expanding and enriching the ESO Science Archive. ESO provides the [ESO Phase 3 process](https://www.eso.org/sci/observing/phase3.html), a structured way to submit processed data products, ensuring long-term accessibility and usability across a broad range of astronomical fields. By sharing your own data, you contribute to the collective scientific effort, allowing others to query your datasets (as you are here), and build upon your work. To learn more about the submission process, visit the [ESO Phase 3 guidelines](https://www.eso.org/sci/observing/phase3.html).\n\n# ⚙️ **Additional Information**\n\n## Prerequisites \u0026 Installation\n\nThese notebooks may use newer `astroquery.eso` features. As such it is recommended to install the latest pre-release version of `astroquery` from the [ESO GitHub repository](https://github.com/eso/astroquery), available on the [ESO GitHub](https://github.com/eso). This can be done using `pip`:\n\n```bash\n# optional: create an isolated env\nconda create -n astroquery_eso python=3.13 -y\nconda activate astroquery_eso\n\n# install the latest astroquery from GitHub\npython -m pip install --upgrade pip\npip install \"git+https://github.com/eso/astroquery.git\"\n``` \n\n## **Usage**  \nYou can access this file as an interactive jupyter notebook, which you can download and run on your machine [(instructions)](https://jupyter.org/install). To interact with the jupyter notebook (if you have download it): move up and down the various cells using the arrow keys, execute the code by pressing CTRL+ENTER; you can also modify the code and execute it at will.\n\n## **Version \u0026 Environment**\n\nThese notebooks were tested with:\n\n- Python **3.13.5**\n- `astroquery` from the [ESO astroquery fork](https://github.com/eso/astroquery.git)\n- Common scientific libraries: `numpy`, `matplotlib`, `astropy`, `requests`, `tqdm`\n\nOther recent Python 3 versions may work, but compatibility is only guaranteed with the tested environment above.\n\n# 🤝 Contributing examples\n**Contributions are welcome!** \n\nIf you'd like to contribute, please fork this repository and submit a pull request with your improvements.\n\n# 📜 License\nThis project is licensed under the ``MIT License`` - see the ``LICENSE`` file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feso%2Fastroquery_examples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feso%2Fastroquery_examples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feso%2Fastroquery_examples/lists"}