adss 1.33

Astronomical Data Smart System

ADSS

Astronomical Data Smart System

ADSS is a database/server project that provides access to ADSS compatible astronomical services.

This repository provides a set of tools for querying astronomical ADSS services using ADQL. You can perform cone searches, cross-match queries between tables, and even cross-match against user-supplied data. The library supports both synchronous and asynchronous query execution. Download of images, cutouts, colored images and spectra is also supported.

Github repository: https://github.com/schwarzam/adss

Installation

pip install adss

or

git clone https://github.com/schwarzam/adss.git
cd adss
pip install .

About ADSS compatible services

ADSS is a project that is still under development. Currently, some of the ADSS services are available at https://ai-scope.cbpf.br/ and https://splus.cloud/.

New Features

ADSS supports different queries, including cone searches, cross-matches between tables, and cross-matches against user-supplied data. The library supports both synchronous and asynchronous query execution.

Also some improvements in the ADQL parsing were made, allowing queries with wildcards in the SELECT statement, such as:

SELECT psf_* FROM my_table WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', 150.0, 2.0, 0.1))=1

This will select all columns that start with "psf_".

Starting a client

To start using ADSS, you need to initialize a client with the base URL of the ADSS service and your credentials. Here's an example:

import adss

cl = adss.ADSSClient(
    base_url="https://ai-scope.cbpf.br/", 
    username="your_username",
    password="your_password"
)

The client will handle authentication and session management for you.

Performing Queries

You can perform various types of queries using the client. ADSS inherited a lot of the concept of the Table Access Protocol (TAP). Specially the sync and async modes of queries.

• Synchronous Queries (Short Lived Queries): These queries are executed immediately, and the results are returned in the body of the first request if found! With a timeout of ~10 seconds usually. Good for small tables or queries that return a small number of rows <1000. Example:

cl.query(
"""
select * 
from my_table 
where CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', 150.0, 2.0, 0.1))=1
"""
)

• Asynchronous Queries: These queries are executed in the background, and you can check the status of the query and retrieve the results once they are ready. Good for large tables or queries that return a large number of rows or long queries.

We have two ways of doing async queries. This first send the query to the server and wait until it's done. Example:

tab = cl.query_and_wait(
    query_text="""
    select top 100 * 
    from splus.splus_idr6 where field = 'HYDRA-0091'
    """,
    mode="adql", # or sql
    file=None, # dataframe
    table_name=None,
)
# Print the dataframe
print(tab.data)

The second way is a more controlled way, where you create the query, check the status and fetch the when you want results:

# Create a asynchronous query
query = cl.async_query(
    query_text="""
    select top 100 id, ra, dec, mag_psf* 
    from splus.splus_idr6 where field = 'HYDRA-0091'
    """, 
    mode="adql", # or sql
    file=None, # dataframe
    table_name=None, 
)

# Check the status of the query and fetch results if complete
query = cl.queries.get_status(query.id)
if query.is_complete:
    print("Query is complete. Fetching results...")
    results = cl.queries.get_results(query.id)
else:
    print("Query is not complete yet.")

Uploading user tables

In the last example we left the file and table_name parameters as None. This means that we are not uploading any user table to the server. If you want to upload a user table, you can do it by passing a pandas DataFrame to the file parameter and a name for the table to the table_name parameter. The uploaded table should be referenced as upload.table_name in the query..

import pandas as pd
# Create a sample dataframe
data = {
    "id": [1, 2, 3],
    "ra": [150.1, 150.2, 150.3],
    "dec": [2.1, 2.2, 2.3]
}
df = pd.DataFrame(data)

# Create a asynchronous query with user table
query = cl.query_and_wait(
    query_text="""
        select a.*, b.mag_psf_r 
        from upload.my_table as a
        join splus.splus_idr6 as b on a.id = b.id
    """,
    mode="adql", # or sql
    file=df, # dataframe
    table_name="my_table",
)

### Images - File Collections

ADSS also supports downloading images, cutouts, colored images. These are handled as Collections. You can list the available file collections in the database metadata:

```python
cl.get_image_collections()

[
{
    'name': 'splus dr4',
    'path': '/dados/splus',
    'description': 'splus dr4 collection',
    'id': 1,
    'created_at': '2025-04-22T15:27:36.698058',
    'updated_at': '2025-07-31T23:27:51.497554',
    'last_scanned': '2025-05-08T20:28:54.420350',
    'patterns': {'': 'swp.', 'weight': 'weight'}
}
]

And then to list the files in a collection:

cl.list_files(1) ## pass the collection ID

[
{
    'filename': 'SPLUS-s17s23_F515_swpweight.fz',
    'full_path': '/dados/splus/SPLUS-s17s23 SPLUS-s17s23_F515_swpweight.fz',
    'file_type': 'fz',
    'ra_center': 316.45153076969416,
    'dec_center': -21.580560694390957,
    'width': 11000,
    'height': 11000,
    'pixel_scale': 0.55000000000008,
    'hdus': 2,
    'data_hdu': 1,
    'object_name': 'SPLUS-s17s23',
    'filter': 'F515',
    'instrument': 'T80Cam',
    'telescope': 'T80',
    'date_obs': None,
    'file_size': 51353280,
    'id': 28,
    'collection_id': 1,
    'created_at': '2025-04-22T15:35:05.487208',
    'updated_at': '2025-05-08T19:53:09.541437'},
},...]

You can then download a file by its filename:

file_bytes = cl.download_file(
    file_id=28,
    output_path=None
)

Then handle the bytes. Example:

# if a fits you may open like 
import io
from astropy.io import fits

hdul = fits.open(io.BytesIO(file_bytes))

# or a image 
from PIL import Image
import matplotlib.pyplot as plt

image = Image.open(io.BytesIO(file_bytes))
plt.imshow(image)

Image Tools

Now notice that (if) the image collection has some wcs parameters as ra_center, dec_center, pixel_scale. This allows us to do some image cutouts and colored images in real time. Example:

cutout_bytes = cl.create_stamp_by_coordinates(
    collection_id = 1, 
    ra = 0.1, 
    dec = 0.1, 
    size = 300, 
    filter = "R", 
    size_unit="pixels", 
    format = "fits", 
    pattern="swp."
)

hdul = fits.open(BytesIO(cutout_bytes))

or if the image collection has object_name info you may filter by it, forcing the cutout from that object:

cutout_bytes = cl.stamp_images.create_stamp_by_object(
    collection_id=1, 
    object_name="STRIPE82-0002", 
    size=300, 
    ra=0.1,
    dec=0.1,
    filter_name="R", 
    size_unit="pixels", 
    format="fits"
)
cutout = fits.open(BytesIO(cutout_bytes))

or just by file_id, this will force the cutout from that specific file:

cl.stamp_images.create_stamp(
    file_id=28,
    size=300,
    ra=0.1,
    dec=0.1,
    size_unit="pixels", 
    format="fits"
)

Colored images

Colored images API is very similar to the cutouts. You just need to provide a list of filters and the output format (png or jpg). Example with lupton et al. (2004) algorithm:

im_bytes = cl.create_rgb_image_by_coordinates(
    collection_id=1,
    ra=0.1,
    dec=0.1,
    size=300,
    size_unit="pixels",
    r_filter="I",
    g_filter="R",
    b_filter="G",
)

im = Image.open(BytesIO(im_bytes))
im.show()

Or trilogy algorithm:

im_bytes = cl.trilogy_images.create_trilogy_rgb_by_coordinates(
    collection_id=1,
    ra=0.1,
    dec=0.1,
    size=300,
    size_unit="pixels",
    r_filters=["I", "R", "Z", "F861", "G"],
    g_filters=["F660"],
    b_filters=["U", "F378", "F395", "F410", "F430", "F515"],
    satpercent=0.15,
)

im = Image.open(BytesIO(im_bytes))
im.show()