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Sia v2 cleanup
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‎tutorials/wise/sia_allwise_atlas.md‎

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---
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authors:
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- name: IRSA Data Science Team
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- name: Troy Raen
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- name: "Brigitta Sipőcz"
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- name: Jessica Krick
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- name: Andreas Faisst
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- name: Jaladh Singhal
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- name: Vandana Desai
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- name: Dave Shupe
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jupytext:
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text_representation:
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extension: .md
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format_name: myst
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format_version: 0.13
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jupytext_version: 1.16.2
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jupytext_version: 1.19.1
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kernelspec:
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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authors:
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- name: David Shupe
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- name: IRSA Science Team
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display_name: python3
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language: python
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---
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# Searching for AllWISE Atlas Images
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This notebook tutorial demonstrates the process of querying IRSA's Simple Image Access (SIA) service for AllWISE Atlas images, making a cutout image (thumbnail), and displaying the cutout.
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# Searching for AllWISE Images with SIA v2
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## Learning Goals
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By the end of this tutorial, you will:
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* Learn how to search the NASA Astronomical Virtual Observatory Directory web portal for a service that provides access to IRSA's WISE AllWISE Atlas (L3a) coadded images.
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* Use the Python pyvo package to identify which of IRSA's AllWISE Atlas images cover a specified coordinate.
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* Download one of the identified images.
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* Learn how to access IRSA's WISE AllWISE Atlas (L3a) coadded images via the Simple Image Access (SIA) service.
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* Identify which of IRSA's AllWISE Atlas images cover a specified coordinate.
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* Visualize one of the identified images using Firefly.
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* Create and display a cutout of the downloaded image.
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https://irsa.ipac.caltech.edu/Missions/wise.html
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The [NASA/IPAC Infrared Science Archive (IRSA)](https://irsa.ipac.caltech.edu) at Caltech is the archive for AllWISE images and catalogs. The AllWISE Atlas images that are the subject of this tutorial are made accessible via the [International Virtual Observatory Alliance (IVOA)](https://ivoa.net) [Simple Image Access (SIA)](https://wiki.ivoa.net/internal/IVOA/SiaInterface/SIA-V2-Analysis.pdf) protocol. IRSA's AllWISE SIA service is registered in the NASA Astronomical Virtual Observatory (NAVO) [Directory](https://vao.stsci.edu). Based on the registered information, the Python package [pyvo](https://pyvo.readthedocs.io) can be used to query the SEIP SIA service for a list of images that meet specified criteria, and standard Python libraries can be used to download and manipulate the images.
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Other datasets at IRSA are available through other SIA services:
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The [NASA/IPAC Infrared Science Archive (IRSA)](https://irsa.ipac.caltech.edu) at Caltech is the archive for AllWISE images and catalogs. The AllWISE Atlas images that are the subject of this tutorial are made accessible via the [International Virtual Observatory Alliance (IVOA)](https://ivoa.net) [Simple Image Access (SIA)](https://www.ivoa.net/documents/SIA/) protocol.
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https://irsa.ipac.caltech.edu/docs/program_interface/api_images.html
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```{note}
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IRSA supports both SIA v1 and SIA v2 protocols. The version used depends on the specific dataset. This IRSA [website](https://irsa.ipac.caltech.edu/ibe/sia.html) provides information on which version each service uses and how to access them. Further information on how to access IRSA data with different techniques is available [here](https://irsa.ipac.caltech.edu/docs/program_interface/api_images.html). This tutorial uses SIA v2 for AllWISE Atlas images.
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```
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## Imports
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- `pyvo` for querying IRSA's AllWISE Atlas SIA service
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- `numpy` for working with tables
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- `astropy.coordinates` for defining coordinates
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- `astropy.nddata` for creating an image cutout
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- `astropy.wcs` for interpreting the World Coordinate System header keywords of a fits file
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- `astropy.units` for attaching units to numbers passed to the SIA service
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- `matplotlib.pyplot` for plotting
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- `astropy.utils.data` for downloading files
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- `astropy.io` to manipulate FITS files
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- `firefly_client` for visualizing images
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- `astroquery.ipac.irsa` for IRSA data access
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- `astropy.visualization` for color stretch display
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```{code-cell} ipython3
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# Uncomment the next line to install dependencies if needed.
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# !pip install matplotlib astropy pyvo
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# %pip install matplotlib astropy astroquery jupyter_firefly_extensions
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```
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```{code-cell} ipython3
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import pyvo as vo
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import numpy as np
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from astropy.coordinates import SkyCoord
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from astropy.nddata import Cutout2D
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from astropy.wcs import WCS
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import astropy.units as u
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import matplotlib.pyplot as plt
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from astropy.utils.data import download_file
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from astropy.io import fits
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from firefly_client import FireflyClient
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from astroquery.ipac.irsa import Irsa
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from astropy.visualization import simple_norm
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```
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## Section 1 - Setup
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## 1. Define the target
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Set images to display in the notebook
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```{code-cell} ipython3
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%matplotlib inline
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```
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Define coordinates of a bright star
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```{code-cell} ipython3
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pos = SkyCoord(ra=ra, dec=dec, unit='deg')
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```
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## Section 2 - Lookup and define a service for AllWISE Atlas images
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## 2. Discover AllWISE Atlas images
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Start at STScI VAO Registry at https://vao.stsci.edu/keyword-search/
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IRSA provides Simple Image Access (SIA) services for various datasets. A list of available datasets and their access URLs can be found [here](https://irsa.ipac.caltech.edu/ibe/sia.html).
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This tutorial uses SIA v2 for AllWISE Atlas images.
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To search for other datasets on SIA v2, try changing the filter string.
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Or remove the filter keyword altogether to get a full list of available SIA v2 datasets at IRSA.
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Limit by Publisher "NASA/IPAC Infrared Science Archive" and Capability Type "Simple Image Access Protocol" then search on "AllWISE Atlas"
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Locate the SIA2 URL https://irsa.ipac.caltech.edu/ibe/sia/wise/allwise/p3am_cdd?
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First we need to know the name of the dataset on the IRSA system.
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```{code-cell} ipython3
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allwise_service = vo.dal.SIAService("https://irsa.ipac.caltech.edu/ibe/sia/wise/allwise/p3am_cdd?")
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names = Irsa.list_collections(filter="allwise")
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names
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```
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## Section 3 - Search the service
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We see from the resulting table that the dataset collection we are interested in is called "wise_allwise".
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Use this collection name in query below.
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Search for images covering within 1 arcsecond of the star
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## 3. Search for images
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Which images in the IRSA allwise dataset include our target of interest?
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Get a table of all images within 1 arcsecond of our target position.
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```{code-cell} ipython3
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im_table = allwise_service.search(pos=pos, size=1.0*u.arcsec)
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im_table = Irsa.query_sia(pos=(pos, 1 * u.arcsec), collection='wise_allwise')
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```
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Inspect the table that is returned
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Inspect the table that is returned.
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```{code-cell} ipython3
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im_table
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```
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Look at a list of the column names included in this table.
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```{code-cell} ipython3
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im_table.to_table().colnames
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im_table.colnames
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```
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Look at the unique values in one of the columns.
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```{code-cell} ipython3
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im_table.to_table()['sia_bp_id']
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print(np.unique(im_table['energy_bandpassname']))
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```
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## Section 4 - Locate and download an image of interest
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## 4.Locate and visualize an image of interest
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We start by filtering the image results for the W3 band images.
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Then look at the header of one of the resulting W3 band images of our target star.
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Finally, we create an interactive FITS display of the W3 image(s) by [using Firefly](https://caltech-ipac.github.io/firefly_client/index.html), an open-source interactive visualization tool for astronomical data.
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To understand how to open the Firefly viewer in a new tab from your Python notebook, refer to [this documentation](https://caltech-ipac.github.io/firefly_client/usage/initializing-vanilla.html) on how to initialize FireflyClient.
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Let's search the image results for the W3 band image.
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You can put the URL from the column "access_url" into a browser to download the file.
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Or you can work with it in Python, as shown below.
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```{code-cell} ipython3
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for i in range(len(im_table)):
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if im_table[i]['sia_bp_id'] == 'W3':
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break
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print(im_table[i].getdataurl())
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w3_mask = im_table['energy_bandpassname'] == 'W3'
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w3_table = im_table[w3_mask]
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```
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Download the image and open it in Astropy
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Lets look at the access_url of the first one.
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Then use Astropy to examine the header of the URL from the previous step,
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and grab the data and wcs from the header.
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```{code-cell} ipython3
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fname = download_file(im_table[i].getdataurl(), cache=True)
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image1 = fits.open(fname)
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```
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## Section 5 - Extract a cutout and plot it
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image_url = w3_table['access_url'][0]
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image_url
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```{code-cell} ipython3
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wcs = WCS(image1[0].header)
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with fits.open(image_url, memmap=False) as hdul:
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hdul.info()
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data = hdul[0].data
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wcs = WCS(hdul[0].header)
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```
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```{code-cell} ipython3
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cutout = Cutout2D(image1[0].data, pos, (60, 60), wcs=wcs)
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wcs = cutout.wcs
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```
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Visualize an image by sending its URL to the Firefly viewer.
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Try using the interactive tools in the viewer to explore the data.
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```{code-cell} ipython3
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# Uncomment when opening a Firefly viewer in a tab within Jupyter Lab with jupyter_firefly_extensions installed
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# fc = FireflyClient.make_lab_client()
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ax = fig.add_subplot(1, 1, 1, projection=wcs)
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ax.imshow(cutout.data, cmap='gray_r', origin='lower',
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vmax = 1000)
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ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
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# Uncomment when opening Firefly viewer in contexts other than the above
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fc = FireflyClient.make_client(url="https://irsa.ipac.caltech.edu/irsaviewer")
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# Visualize an image by sending its URL to the viewer.
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fc.show_fits_image(file_input=image_url,
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plot_id="image",
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Title="Image"
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)
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```
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## 5. Extract a cutout and plot it
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If you want to see just a cutout of a certain region around the target, we do that below using astropy's Cutout2D.
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```{code-cell} ipython3
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# make 1' x 1' cutout
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cutout = Cutout2D(data, position=pos, size=1 * u.arcmin, wcs=wcs)
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#add quick normalization/stretch
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norm = simple_norm(cutout.data, stretch="sqrt", percent=99)
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# display
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plt.imshow(cutout.data, origin='lower', norm = norm)
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plt.colorbar(label="Image value")
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plt.title("ALLWISE W3 (quicklook)")
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plt.xlabel("Pixel X")
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plt.ylabel("Pixel Y")
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```
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***
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## About this notebook
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**Updated:** 2022-02-14
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**Contact:** [the IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or reporting problems.
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## Acknowledgements
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- [Caltech/IPAC-IRSA](https://irsa.ipac.caltech.edu/)
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## Citations
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## About this notebook
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**Updated:** 2 March 2026
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If you use `astropy` for published research, please cite the authors. Follow these links for more information about citing `astropy`:
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**Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or problems.
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* [Citing `astropy`](https://www.astropy.org/acknowledging.html)
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**Runtime:** As of the date above, this notebook takes about 20 seconds to run to completion on a machine with 8GB RAM and 4 CPU.
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This runtime is dependent on archive servers which means runtime will vary for users.
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Please include the following in any published material that makes use of the WISE data products:
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## Citations
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"This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration."
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**Astropy:**
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This work made use of [Astropy](http://www.astropy.org) a community-developed core Python package and an ecosystem of tools and resources for astronomy (Astropy Collaboration et al., 2013, Astropy Collaboration et al., 2018, Astropy Collaboration et al.,2022).
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Please also cite the dataset Digital Object Identifier (DOI): [10.26131/IRSA153](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA153)
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**Astroquery:**
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This work made use of [Astroquery](https://astroquery.readthedocs.io/en/latest/) a set of tools for querying astronomical web forms and databases (Ginsburg, Sipőcz, Brasseur et al 2019.).
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```{code-cell} ipython3
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```
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**WISE:**
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This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration."
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Digital Object Identifier (DOI): [10.26131/IRSA153](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA153)

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