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MAINT: switch pyvo obscore query with astroquery #125

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MAINT: switch pyvo obscore query with astroquery #125

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bfa2673
MAINT: fix older of metadata keywords to make it easier to sed
bsipocz Jul 30, 2025
88bd7da
MAINT: swapping out pyvo obscore query with more convenient astroquer…
bsipocz Jul 30, 2025
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40 changes: 20 additions & 20 deletions tutorials/spherex/spherex_intro.md
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,9 @@ jupytext:
format_version: 0.13
jupytext_version: 1.17.2
kernelspec:
name: python3
display_name: Python 3 (ipykernel)
language: python
name: python3
---

# Introduction to SPHEREx Spectral Images
Expand Down Expand Up @@ -58,7 +58,7 @@ from astropy.table import Table
from astropy import units as u
from astropy.coordinates import SkyCoord

import pyvo
from astroquery.ipac.irsa import Irsa
from pyvo.dal.adhoc import DatalinkResults

from firefly_client import FireflyClient
Expand All @@ -73,31 +73,31 @@ Define some coordinates of interest.
```{code-cell} ipython3
ra_deg = 304.693508808
dec_deg = 42.4436872991

coord = SkyCoord(ra_deg, dec_deg, unit='deg')
search_radius = 1 * u.arcsec
```

Query IRSA for a list of Spectral Image MEFs that overlap this position.
Query IRSA for a list of Spectral Image MEFs that overlap this position. We use the [IRSA module in astroquery](https://astroquery.readthedocs.io/en/latest/ipac/irsa/irsa.html) and the Simple Image Access API.

```{code-cell} ipython3
# Define the TAP service URL for IRSA
tap_url = "https://irsa.ipac.caltech.edu/TAP"
+++

# Connect to the TAP service
service = pyvo.dal.TAPService(tap_url)
```{tip}
The IRSA SIA collections can be listed using using the ``list_collections`` method, we can filter on the ones containing "spherex" in the collection name:

# Define your ADQL query
query = "SELECT * FROM spherex.obscore WHERE CONTAINS(POINT('ICRS',"+str(ra_deg)+","+str(dec_deg)+"), s_region)=1"
Irsa.list_collections(filter='spherex')
```

# Submit the asynchronous query
job = service.submit_job(query)
+++

# Run the job (starts the query execution on the server)
job.run()
There are currently three collections available:

# Wait for the job to complete (polling)
job.wait(phases=["COMPLETED", "ERROR", "ABORTED"], timeout=300)
* `'spherex_qr'` -- the Quick Release Observations, for more information see [SPHEREx archive documentation at IRSA]().
* `'spherex_qr_cal'` -- the Quick Release Calibration products.
* `'spherex_qr_deep'` --
Comment on lines +95 to +97
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I'm guessworking here, @vandesai1 please provide guidence here.


# Capture the results
results = job.fetch_result()
```{code-cell} ipython3
results = Irsa.query_sia(pos=(coord, search_radius), collection='spherex_qr')
```

Each row of the results of your query represents a different spectral image.
Expand All @@ -112,10 +112,10 @@ len(results)
The query results provide a lot of metadata about each spectral image. These columns have standard names as defined by the IVOA. Let's list them:

```{code-cell} ipython3
list(results.fieldnames)
results.colnames
```

The 'access_url' column is particularly important because it tells you how to access the data. Let's look at the 'access_url' value for the first row:
The `'access_url'` column is particularly important because it tells you how to access the data. Let's look at the `'access_url'` value for the first row:

```{code-cell} ipython3
results['access_url'][0]
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The PR is draft as this line currently returns an incomplete HTTPS url rather than a datalink.

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This appears to have been fixed, at least in the sense that it is now returning a full url instead of a relative path.

>>> results = Irsa.query_sia(pos=(coord, search_radius), collection='spherex_qr')
>>> results['access_url'][0]
'https://irsa.ipac.caltech.edu/ibe/data/spherex/qr/level2/2025W20_2D/l2b-v12-2025-176/6/level2_2025W20_2D_0444_1D6_spx_l2b-v12-2025-176.fits'

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Thanks, that's great news!

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