science-catalogs 0.1.0b2

Reusable toolkit for science-ready catalog preparation with Dask, LSDB, and HATS.

science_catalogs

science_catalogs is a reusable Python library for building science-ready catalogs with LSDB-oriented workflows. The package focuses on the reusable core of the processing stack:

• column selection
• column transformations
• row filtering
• output materialization to memory, partitioned files, or HATS catalogs

The package is published on PyPI as science-catalogs and imported in Python as science_catalogs.

Installation

pip install science-catalogs

For local development:

pip install -e '.[dev]'

Or, if you prefer a requirements file for a full developer environment including build and PyPI publication tools:

pip install -r requirements-dev.txt

Main API

from science_catalogs import (
    build_catalog,
    materialize_catalog,
    materialize_lsdb_catalog,
    open_lsdb_catalog,
    prepare_catalog,
    write_catalog,
)

Beta API

The beta public API is:

• prepare_catalog
• materialize_catalog
• write_catalog
• materialize_lsdb_catalog
• open_lsdb_catalog
• build_catalog

Legacy names based on pipeline are not part of the beta API.

Usage

Prepare a catalog from a catalog-processing YAML configuration:

from science_catalogs import prepare_catalog

prepared = prepare_catalog("configs/catalog.yml")

Materialize the processed data in memory and keep track of the written output paths:

from science_catalogs import materialize_catalog

result = materialize_catalog(prepared, "./output")
frame = result["data"]
paths = result["path"]

Write the result to disk. The write mode follows the output configuration, including HATS when output.save_as: hats is selected:

from science_catalogs import write_catalog

written_paths = write_catalog(prepared, "./output")

Open the final result as an LSDB catalog after writing HATS output:

from science_catalogs import materialize_lsdb_catalog

result = materialize_lsdb_catalog(prepared, "./output")
catalog = result["data"]
hats_path = result["path"]

Execute the full flow from configuration and persist parquet output in one call:

from science_catalogs import build_catalog

paths = build_catalog("configs/catalog.yml", output_dir="./output")

Or force a HATS artifact from the same flow:

hats_path = build_catalog(
    "configs/catalog.yml",
    output_dir="./output",
    output_format="hats",
)

If you already have a HATS catalog on disk, you can open it directly:

from science_catalogs import open_lsdb_catalog

catalog = open_lsdb_catalog("./output/my_catalog")