The healpix_alchemy
Python package extends SQLAlchemy will provide spatial
indexing for astronomical sky coordinates, regions, and raster images (e.g.
LIGO/Virgo and Fermi probability sky maps) in a relational database. It does
not rely on any database extensions.
This package is a work in progress. Initially, healpix_alchemy
focuses on
spatial indexing of point clouds while we work out the SQLAlchemy abstraction
design. Once this is mature, we will incorporate the raster indexing strategies
from https://github.com/growth-astro/healpix-intersection-example.
You can install healpix_alchemy
the Python Package Index:
$ pip install healpix-alchemy
from healpix_alchemy.point import Point
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
# Create two tables Catalog1 and Catalog2 that both have spherical coordinates.
class Catalog1(Point, Base):
__tablename__ = 'catalog1'
id = Column(Integer, primary_key=True)
class Catalog2(Point, Base):
__tablename__ = 'catalog2'
id = Column(Integer, primary_key=True)
...
# Populate Catalog1 and Catalog2 tables with some sample data...
session.add(Catalog1(id=0, ra=320.5, dec=-23.5))
...
session.add(Catalog2(id=0, ra=18.1, dec=18.3))
...
session.commit()
# Cross-match the two tables.
separation = 1 # separation in degrees
query = session.query(
Catalog1.id, Catalog2.id
).join(
Catalog2,
Catalog1.within(point, separation)
).order_by(
Catalog1.id, Catalog2.id
)
for row in query:
... # do something with the query results
# Do a cone search around literal ra, dec values.
separation = 1 # separation in degrees
point = Point(ra=212.5, dec=-33.2)
query = session.query(
Catalog1.id
).filter(
Catalog1.within(point, separation)
).order_by(
Catalog1.id
)
for row in query:
... # do something with the query results