hipscatalog-gen 0.3.1

Python package for building HiPS-compliant catalog hierarchies from large astronomical tables using Dask and LSDB.

hipscatalog-gen

This project was created following the LINCC Frameworks Python Project Template (https://lincc-ppt.readthedocs.io/en/latest/).

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Overview

hipscatalog-gen is a Python package for building HiPS-compliant catalog hierarchies from large astronomical tables using Dask and LSDB. It is inspired by and extends the logic of the CDS Hipsgen-cat.jar tool, providing a scalable and parallelized Python implementation suitable for large-scale workflows. Documentation: https://linea-it.github.io/hipscatalog_gen/

The pipeline supports three selection modes, configured in the YAML file under algorithm.selection_mode:

• mag_global — global magnitude-complete selection.
• score_global — global selection driven by an arbitrary score/expression.
• score_density_hybrid — density-driven depths 1..density_up_to_depth (default 4) with score-based distribution afterwards.

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Quick Start (PyPI)

Install from PyPI into a fresh environment and run with a config file:

conda create -n hipscatalog-gen "python>=3.11"
conda activate hipscatalog-gen
pip install hipscatalog-gen

If you do not have Conda yet, install it first using the official docs:

• Conda install guide: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html
• Miniconda install guide: https://www.anaconda.com/docs/getting-started/miniconda/install

Fetch the example template and adapt it to your catalog:

curl -O https://raw.githubusercontent.com/linea-it/hipscatalog_gen/main/examples/configs/config.template.yaml
cp config.template.yaml config.yaml

Run the pipeline:

hipscatalog-gen --config config.yaml
# or: python -m hipscatalog_gen.cli --config config.yaml

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Developer Install

For local development (editable install + tooling):

git clone https://github.com/linea-it/hipscatalog_gen.git
cd hipscatalog_gen
conda create -n hipscatalog-gen-dev "python>=3.11"
conda activate hipscatalog-gen-dev
pip install -e .[dev]

Optionally expose the env as a Jupyter kernel:

python -m ipykernel install --user --name hipscatalog-gen --display-name "hipscatalog-gen"

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Configuration

The pipeline is fully configured through a YAML file.

A complete annotated template is provided in ./examples/configs folder as:

• config.template.yaml

When installed from PyPI, download the template directly:

curl -O https://raw.githubusercontent.com/linea-it/hipscatalog_gen/main/examples/configs/config.template.yaml

To create your own configuration:

cp config.template.yaml config.yaml

Then edit config.yaml to match your input catalog and selection preferences. Additional examples are available under ./examples/configs/.

Selection modes live under algorithm.selection_mode:

• mag_global, score_global, score_density_hybrid. Mode-specific parameters live inside blocks algorithm.mag_global, algorithm.score_global, and algorithm.score_density_hybrid (with optional shared defaults in algorithm.selection_defaults).

Cluster memory policy (current behavior):

• The pipeline now uses fixed defaults optimized for large catalogs:
  • no persistence of large intermediate DataFrames
  • avoid early large compute materializations whenever possible
• cluster.low_memory_mode is deprecated (accepted only with warning, no effect).
• cluster.persist_ddfs and cluster.avoid_computes_wherever_possible are deprecated and ignored.
• For streamed stage-2 writes (deeper depths), an active dask.distributed client is required.

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Running

The pipeline can be executed either as a Python library or from the command line.

Run as a library

from hipscatalog_gen.config import load_config, load_config_from_dict, display_available_configs
from hipscatalog_gen.pipeline.main import run_pipeline

cfg = load_config("config.yaml")
run_pipeline(cfg)

Run from the command line

List available selection modes:

hipscatalog-gen --list-modes

Run with a config file:

hipscatalog-gen --config config.yaml
# or: python -m hipscatalog_gen.cli --config config.yaml

No dedicated sbatch wrapper script is required. For HPC usage, set cluster.mode: slurm in the YAML and run the same command above.

Validate a config without running:

hipscatalog-gen --check-config config.yaml

Enable JSON logs (process.jsonl) via CLI flag (when running the pipeline):

hipscatalog-gen --config config.yaml --json-logs

Summarize an existing telemetry.json:

hipscatalog-gen --telemetry /path/to/telemetry.json

Output Structure

Each run generates a HiPS-compliant directory structure under output.out_dir:

• Norder*/Dir*/Npix*.tsv → Per-depth tiles.
• Norder*/Allsky.tsv → Optional all-sky tables.
• densmap_o.fits → Density maps for all depths up to level_limit.
• Moc.fits / Moc.json → Multi-Order Coverage maps.
• properties / metadata.xml → HiPS metadata descriptors.
• process.log / arguments → Run logs and configuration snapshot (optional process.jsonl when --json-logs).
• telemetry.json → Run summary with per-stage durations and input/output counts.
• Existing output.out_dir causes an error; set output.overwrite: true to clear it before writing.

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Mode Summary

• mag_global: magnitude-complete slices across all depths.
• mag_global hist_peak default bounds: when adaptive_range=hist_peak and mag_min/mag_max are not provided, the histogram range clips the global min/max to [-2, 40] (mag_min clipped to >= -2; mag_max from the peak within [-2, min(global_max, 40)]).
• score_global: score-based slices across all depths.
• score_density_hybrid: density-driven tiles for depths 1..density_up_to_depth (default 4), then score slices for deeper levels.
• For deeper streamed depths, bucket processing runs on Dask workers (Client.submit) and keeps the driver lightweight (orchestration only).
• Stream merge uses bounded fan-in (auto-tuned from worker concurrency + RLIMIT_NOFILE) to reduce EMFILE (Too many open files) risk.
• Ordering and ties: order_desc controls ascending/descending (default ascending); optional tie_column breaks ties before falling back to RA/DEC.
• Invalids: keep_invalid_values (per mode or in selection_defaults) can map NaN/Inf to a sentinel when adaptive_range=complete, sending them to the last slice; rejected for hist_peak.

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Development and Contributing

This project follows the LINCC Frameworks Python Project Template.

To set up a development environment:

pip install -e .[dev]
pre-commit install
pytest

Contributions, bug reports, and pull requests are welcome via GitHub Issues: https://github.com/linea-it/hipscatalog_gen/issues

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Acknowledgments

This project acknowledges the foundational work of the CDS HiPS Catalog Tool (Hipsgen-cat.jar) developed by the Strasbourg Astronomical Data Center (Unistra/CNRS, 2016), which inspired aspects of the software design. More information: https://aladin.cds.unistra.fr/hips/HipsCat.gml.

The mag-global mode builds on an idea originally suggested by Julia Gschwend.

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Citation

If you use this package in your research, please cite:

Silva, L. L. C., et al. (2026). hipscatalog-gen: A Python HiPS Catalog Pipeline. LIneA – Laboratório Interinstitucional de e-Astronomia. Available at: https://github.com/linea-it/hipscatalog_gen

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License

This project is licensed under the MIT License. See the LICENSE file for details.