healpix-alchemy 0.2.0

SQLAlchemy extensions for HEALPix spatially indexed astronomy data

HEALPix Alchemy

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.

Installation

You can install healpix_alchemy the Python Package Index:

$ pip install healpix-alchemy

Usage

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