This release is a pre-release and may not be stable for production use.
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_catalogmaterialize_catalogwrite_catalogmaterialize_lsdb_catalogopen_lsdb_catalogbuild_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")
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