semvec/pypi-packages huggingface dataset:
conesearch-alchemy 1.1.0
SQLAlchemy extension for indexed cone searches in astronomical catalogs
License: BSD-3-Clause
Requires Python: <4.0,>=3.10

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# Cone Search Alchemy

The `conesearch_alchemy` Python package enhances [SQLAlchemy] to provide fast,
indexed cone searches on astronomical catalogs using a PostgreSQL database. It
does not rely on any database extensions.

## Installation

You can install `conesearch_alchemy` the Python Package Index:

    $ pip install conesearch-alchemy

## Usage

```python
from conesearch_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
```

[SQLAlchemy]: https://www.sqlalchemy.org