Welcome to the repository of tutorials to guide you through the process of analyzing DESI data. We recommend starting with the tutorials under 01_getting_started/ and then explore additional topics under 02_digging_deeper/.
Other directories in this repository cover detailed topics and tutorials presented at DESI collaboration meetings; these are not actively maintained and may not work with the latest data and software releases. Some tutorials require substantial amounts of data or are specific to the NERSC computing center used by the DESI collaboration, but may still be useful to read even without NERSC access.
See 01_getting_started/00_Setup.md for instructions on installing the necessary Python libraries and downloading example data.
Tutorials hosted in other packages include (but haven't been recently vetted):
- How to run survey simulations.
- How to convert an SED into a simulated DESI spectrum
- How to run fiber assignment
- How to run quicksurvey catalog-level simulations
- How to make all-sky plots
- Working with DESI target bits (Main, CMX, SV)
The NOIRLab Astro Data Lab serves a copy of the DESI DR1 and EDR databases as desi_dr1 and desi_edr.
These are accessible to users without a NERSC account.
Various modes of data access are described here.
For public access, there is a Table Access Protocol (TAP) handle that provides a convenient access layer for the
DESI catalog database tables. TAP-aware clients (such as TOPCAT) can point to https://datalab.noirlab.edu/tap,
select the desi_dr1 database, and see the database tables and descriptions.
Descriptions of the associated tables can also be found in the Data Lab table browser and on the DESI Data Documentation Database page.
The SPectra Analysis & Retrievable Catalog Lab (SPARCL) contains DESI DR1 and EDR spectra that were coadded per healpix and coadded across cameras. It is a searchable database that can be used via the Astro Data Lab JupyterLab Notebook. Alternatively, the Python client can be installed locally using:
pip install sparclclient
The client can be loaded within a Python session or program via:
>>> from sparcl.client import SparclClient
>>> client = SparclClient()
There are instructions and useful examples in the How-to-use-SPARCL tutorial notebook.
Any use of DESI data whether via NERSC or external databases requires DESI Data Acknowledgments.
Include at the beginning of the tutorial what is needed as a prerequisite for running the tutorial, e.g. specific codes, environment variables, datasets. It's OK to link elsewhere for detailed instructions (e.g. for how to install DESI code in general).
If you find a bug in the tutorials, please file a ticket at https://github.com/desihub/tutorials or ask for help on the DESI user forum at https://help.desi.lbl.gov .