Notebook Tutorials
These Jupyter Notebook tutorials demonstrate access methods and techniques for working with data served by IRSA. They cover topics like querying IRSA services, cloud access, Parquet catalogs, and general parallelization techniques.
Images
- Searching 2MASS Allsky Atlas Images (html) (ipynb)
- Searching AllWISE Atlas Images (html) (ipynb)
- Searching COSMOS Images (html) (ipynb)
- Searching Spitzer Enhanced Imaging Products (html) (ipynb)
These notebooks show how to query IRSA's image services, inspect the results, and download and visualize images. They use the tools: PyVO, Astropy.
Catalogs
- AllWISE Source Catalog (html) (ipynb)
- access examples for the Parquet version of the AllWISE Source Catalog, located in AWS S3 cloud storage
- tools: Pandas, PyArrow, Astropy
- CosmoDC2 Mock V1 catalogs (html)
- shows how to query IRSA's Table Access Protocol (TAP) service
- tools: PyVO
- NEOWISE Single-exposure Source Table
- Strategies to Efficiently Work with NEOWISE Single-exposure Source Table in Parquet (html) (ipynb)
- Demonstrates Parquet methods for large use cases to work efficiently with this very large dataset.
- tools: PyArrow, HPGeom, Pandas
- Make Light Curves from NEOWISE Single-exposure Source Table (html) (ipynb)
- Applies Parquet strategies to load light curves for a sample of target CVs.
- tools: PyArrow, Astropy, Matplotlib
Visualizations
- Vetting SEDs in Firefly (html) (ipynb)
- shows how to make and vet Spectral Energy Distributions (SEDs) using Firefly
- tools: Firefly Client Library, Astropy, PyVO
- Finding Light Curves of Solar System Objects (html) (ipynb)
- demonstrates how to plot light curves from NEOWISE data using the Firefly Python API
- tools: Firefly Client Library, Astropy
Cloud Access
- Cloud Access Introduction (html) (ipynb)
- demonstrates basic access to the IRSA-curated datasets available in AWS S3 cloud storage buckets
- tools: Astropy, Pandas, PyArrow, PyVO, S3Fs
- AllWISE Source Catalog (html) (ipynb)
- access examples for the Parquet version of the AllWISE Source Catalog, located in AWS S3 cloud storage
- tools: Pandas, PyArrow, Astropy
- OpenUniverse 2024 Data Preview: Roman Simulated Time Domain Survey (html) (ipynb) (auxiliary data file: csv)
- explore time-domain observations, find supernovae, make cutouts
- tools: Astropy, Pandas, Numpy, Matplotlib
- OpenUniverse 2024 Data Preview: Roman Simulated Wide Area Survey (html) (ipynb)
- explore simulated Roman coadds, browse the S3 bucket
- tools: Astropy, Numpy, S3Fs