├── .copier-answers.yml ├── .git_archival.txt ├── .gitattributes ├── .github ├── ISSUE_TEMPLATE │ ├── 0-general_issue.md │ ├── 1-bug_report.md │ ├── 2-feature_request.md │ └── 4-release_tracker.md ├── dependabot.yml └── workflows │ ├── add-issue-to-project-tracker.yml │ ├── asv-main.yml │ ├── asv-nightly.yml │ ├── asv-pr.yml │ ├── build-documentation.yml │ ├── pre-commit-ci.yml │ ├── publish-benchmarks-pr.yml │ ├── publish-to-pypi.yml │ ├── smoke-test.yml │ ├── testing-and-coverage.yml │ └── testing-windows.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yml ├── .setup_dev.sh ├── CITATION.bib ├── LICENSE ├── README.md ├── benchmarks ├── __init__.py ├── asv.conf.json ├── benchmarks.py ├── data │ ├── generate_data.ipynb │ ├── large_catalog │ │ ├── dataset │ │ │ ├── _common_metadata │ │ │ └── _metadata │ │ ├── partition_info.csv │ │ └── properties │ └── midsize_catalog │ │ ├── dataset │ │ ├── _common_metadata │ │ └── _metadata │ │ ├── partition_info.csv │ │ └── properties └── utils.py ├── docs ├── Makefile ├── _static │ ├── custom.css │ ├── gaia.png │ ├── lazy_diagram.svg │ ├── lincc_logo.png │ ├── ztf_catalog_lazy.png │ └── ztf_x_gaia.png ├── citation.rst ├── conf.py ├── contact.rst ├── developer │ ├── contributing.rst │ └── tutorial_template.ipynb ├── getting-started.rst ├── index.rst ├── lincc-logo.png ├── requirements.txt ├── tutorials.rst └── tutorials │ ├── _static │ ├── crossmatching-performance.png │ ├── dask-client-local-cluster.png │ ├── dask-dashboard-status.png │ ├── dask-dashboard-workers.png │ ├── margin-pix.png │ └── pixel-boundary-example.png │ ├── catalog_object.ipynb │ ├── column_filtering.ipynb │ ├── crossmatching.rst │ ├── dask-cluster-tips.rst │ ├── dask_client.ipynb │ ├── exporting_results.ipynb │ ├── getting_data.ipynb │ ├── import_catalogs.ipynb │ ├── margins.ipynb │ ├── nested_frame.rst │ ├── performance.rst │ ├── pre_executed │ ├── des-gaia.ipynb │ ├── index_table.ipynb │ ├── join_catalogs.ipynb │ ├── manual_verification.ipynb │ ├── map_partitions.ipynb │ ├── plotting.ipynb │ ├── ztf-alerts-sne.ipynb │ ├── ztf_bts-ngc.ipynb │ └── zubercal-ps1-snad.ipynb │ ├── region_selection.ipynb │ ├── remote_data.ipynb │ ├── row_filtering.ipynb │ ├── rubin_dp1.rst │ └── timeseries.rst ├── pyproject.toml ├── requirements.txt ├── src ├── .pylintrc └── lsdb │ ├── __init__.py │ ├── catalog │ ├── __init__.py │ ├── association_catalog.py │ ├── catalog.py │ ├── dataset │ │ ├── __init__.py │ │ ├── dataset.py │ │ └── healpix_dataset.py │ ├── map_catalog.py │ └── margin_catalog.py │ ├── core │ ├── __init__.py │ ├── crossmatch │ │ ├── __init__.py │ │ ├── abstract_crossmatch_algorithm.py │ │ ├── bounded_kdtree_match.py │ │ ├── crossmatch.py │ │ ├── crossmatch_algorithms.py │ │ ├── kdtree_match.py │ │ └── kdtree_utils.py │ ├── plotting │ │ ├── __init__.py │ │ ├── plot_points.py │ │ └── skymap.py │ └── search │ │ ├── __init__.py │ │ ├── abstract_search.py │ │ ├── box_search.py │ │ ├── cone_search.py │ │ ├── index_search.py │ │ ├── moc_search.py │ │ ├── order_search.py │ │ ├── pixel_search.py │ │ └── polygon_search.py │ ├── dask │ ├── __init__.py │ ├── crossmatch_catalog_data.py │ ├── divisions.py │ ├── join_catalog_data.py │ ├── merge_catalog_functions.py │ ├── merge_map_catalog_data.py │ └── partition_indexer.py │ ├── io │ ├── __init__.py │ ├── schema.py │ ├── to_association.py │ └── to_hats.py │ ├── loaders │ ├── __init__.py │ ├── dataframe │ │ ├── __init__.py │ │ ├── dataframe_catalog_loader.py │ │ ├── from_dataframe.py │ │ ├── from_dataframe_utils.py │ │ └── margin_catalog_generator.py │ └── hats │ │ ├── __init__.py │ │ ├── hats_loading_config.py │ │ └── read_hats.py │ ├── nested │ ├── __init__.py │ ├── accessor.py │ ├── backends.py │ ├── core.py │ ├── datasets │ │ ├── __init__.py │ │ └── generation.py │ ├── io.py │ ├── py.typed │ └── utils │ │ ├── __init__.py │ │ └── utils.py │ ├── py.typed │ └── types.py └── tests ├── .pylintrc ├── conftest.py ├── data ├── generate_data.ipynb ├── raw │ ├── cone_search_expected │ │ ├── catalog.csv │ │ └── margin.csv │ ├── reproducers │ │ ├── README.md │ │ └── issue_718.csv │ ├── small_sky │ │ └── small_sky.csv │ ├── small_sky_no_metadata │ │ ├── partition_info.csv │ │ └── properties │ ├── small_sky_source │ │ └── small_sky_source.csv │ ├── xmatch │ │ ├── small_sky_left_xmatch.csv │ │ └── small_sky_xmatch.csv │ └── xmatch_expected │ │ ├── xmatch_correct.csv │ │ ├── xmatch_correct_002_005.csv │ │ ├── xmatch_correct_05_2_3n_margin.csv │ │ ├── xmatch_correct_0_005.csv │ │ ├── xmatch_correct_3n_2t.csv │ │ ├── xmatch_correct_3n_2t_negative.csv │ │ ├── xmatch_correct_3n_2t_no_margin.csv │ │ └── xmatch_mock.csv ├── small_sky │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ └── Npix=11.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_left_xmatch │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── Norder=2 │ │ │ └── Dir=0 │ │ │ │ └── Npix=66.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_npix_alt_suffix │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ └── Npix=11.parq │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_npix_as_dir │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ └── Npix=11 │ │ │ │ └── part0.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_order1 │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_order1_collection │ ├── collection.properties │ ├── small_sky_order1 │ │ ├── dataset │ │ │ ├── Norder=1 │ │ │ │ └── Dir=0 │ │ │ │ │ ├── Npix=44.parquet │ │ │ │ │ ├── Npix=45.parquet │ │ │ │ │ ├── Npix=46.parquet │ │ │ │ │ └── Npix=47.parquet │ │ │ ├── _common_metadata │ │ │ └── _metadata │ │ ├── partition_info.csv │ │ ├── point_map.fits │ │ └── properties │ ├── small_sky_order1_id_index │ │ ├── dataset │ │ │ ├── _common_metadata │ │ │ ├── _metadata │ │ │ └── index │ │ │ │ └── part.0.parquet │ │ └── properties │ ├── small_sky_order1_margin_1deg │ │ ├── dataset │ │ │ ├── Norder=0 │ │ │ │ └── Dir=0 │ │ │ │ │ └── Npix=4.parquet │ │ │ ├── Norder=1 │ │ │ │ └── Dir=0 │ │ │ │ │ ├── Npix=44.parquet │ │ │ │ │ ├── Npix=45.parquet │ │ │ │ │ ├── Npix=46.parquet │ │ │ │ │ └── Npix=47.parquet │ │ │ ├── _common_metadata │ │ │ └── _metadata │ │ ├── partition_info.csv │ │ └── properties │ └── small_sky_order1_margin_2deg │ │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=4.parquet │ │ │ │ ├── Npix=7.parquet │ │ │ │ └── Npix=8.parquet │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ │ ├── partition_info.csv │ │ └── properties ├── small_sky_order1_default_columns │ ├── dataset │ ├── partition_info.csv │ └── properties ├── small_sky_order1_id_index │ ├── dataset │ │ ├── _common_metadata │ │ ├── _metadata │ │ └── index │ │ │ └── part.0.parquet │ └── properties ├── small_sky_order1_margin_1deg │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ └── Npix=4.parquet │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_order1_margin_2deg │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=4.parquet │ │ │ │ ├── Npix=7.parquet │ │ │ │ └── Npix=8.parquet │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_order1_nested_sources │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_order1_nested_sources_margin │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=4.parquet │ │ │ │ ├── Npix=7.parquet │ │ │ │ └── Npix=8.parquet │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_order1_no_pandas_meta │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_order1_source │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=16.parquet │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_order1_source_band_index │ ├── dataset │ │ ├── _common_metadata │ │ ├── _metadata │ │ └── index │ │ │ └── part.0.parquet │ └── properties ├── small_sky_order1_source_collection │ ├── collection.properties │ ├── small_sky_order1_source │ │ ├── dataset │ │ │ ├── Norder=1 │ │ │ │ └── Dir=0 │ │ │ │ │ ├── Npix=16.parquet │ │ │ │ │ ├── Npix=44.parquet │ │ │ │ │ ├── Npix=45.parquet │ │ │ │ │ ├── Npix=46.parquet │ │ │ │ │ └── Npix=47.parquet │ │ │ ├── _common_metadata │ │ │ └── _metadata │ │ ├── partition_info.csv │ │ ├── point_map.fits │ │ └── properties │ ├── small_sky_order1_source_band_index │ │ ├── dataset │ │ │ ├── _common_metadata │ │ │ ├── _metadata │ │ │ └── index │ │ │ │ └── part.0.parquet │ │ └── properties │ ├── small_sky_order1_source_margin │ │ ├── dataset │ │ │ ├── Norder=0 │ │ │ │ └── Dir=0 │ │ │ │ │ ├── Npix=7.parquet │ │ │ │ │ └── Npix=8.parquet │ │ │ ├── Norder=1 │ │ │ │ └── Dir=0 │ │ │ │ │ ├── Npix=16.parquet │ │ │ │ │ ├── Npix=44.parquet │ │ │ │ │ ├── Npix=45.parquet │ │ │ │ │ ├── Npix=46.parquet │ │ │ │ │ └── Npix=47.parquet │ │ │ ├── _common_metadata │ │ │ └── _metadata │ │ ├── partition_info.csv │ │ └── properties │ └── small_sky_order1_source_object_id_index │ │ ├── dataset │ │ ├── _common_metadata │ │ ├── _metadata │ │ └── index │ │ │ └── part.0.parquet │ │ └── properties ├── small_sky_order1_source_margin │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=7.parquet │ │ │ │ └── Npix=8.parquet │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=16.parquet │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_order1_source_object_id_index │ ├── dataset │ │ ├── _common_metadata │ │ ├── _metadata │ │ └── index │ │ │ └── part.0.parquet │ └── properties ├── small_sky_order3_source │ ├── dataset │ │ ├── Norder=3 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=264.parquet │ │ │ │ ├── Npix=707.parquet │ │ │ │ ├── Npix=708.parquet │ │ │ │ ├── Npix=709.parquet │ │ │ │ ├── Npix=710.parquet │ │ │ │ ├── Npix=711.parquet │ │ │ │ ├── Npix=712.parquet │ │ │ │ ├── Npix=713.parquet │ │ │ │ ├── Npix=714.parquet │ │ │ │ ├── Npix=715.parquet │ │ │ │ ├── Npix=716.parquet │ │ │ │ ├── Npix=717.parquet │ │ │ │ ├── Npix=718.parquet │ │ │ │ ├── Npix=719.parquet │ │ │ │ ├── Npix=720.parquet │ │ │ │ ├── Npix=723.parquet │ │ │ │ ├── Npix=724.parquet │ │ │ │ ├── Npix=726.parquet │ │ │ │ ├── Npix=727.parquet │ │ │ │ ├── Npix=728.parquet │ │ │ │ ├── Npix=729.parquet │ │ │ │ ├── Npix=730.parquet │ │ │ │ ├── Npix=732.parquet │ │ │ │ ├── Npix=733.parquet │ │ │ │ ├── Npix=734.parquet │ │ │ │ ├── Npix=735.parquet │ │ │ │ ├── Npix=736.parquet │ │ │ │ ├── Npix=737.parquet │ │ │ │ ├── Npix=738.parquet │ │ │ │ ├── Npix=739.parquet │ │ │ │ ├── Npix=740.parquet │ │ │ │ ├── Npix=741.parquet │ │ │ │ ├── Npix=742.parquet │ │ │ │ ├── Npix=743.parquet │ │ │ │ ├── Npix=745.parquet │ │ │ │ ├── Npix=747.parquet │ │ │ │ ├── Npix=748.parquet │ │ │ │ ├── Npix=750.parquet │ │ │ │ ├── Npix=752.parquet │ │ │ │ ├── Npix=753.parquet │ │ │ │ ├── Npix=754.parquet │ │ │ │ ├── Npix=755.parquet │ │ │ │ └── Npix=760.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_order3_source_margin │ ├── dataset │ │ ├── Norder=2 │ │ │ └── Dir=0 │ │ │ │ └── Npix=189.parquet │ │ ├── Norder=3 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=264.parquet │ │ │ │ ├── Npix=707.parquet │ │ │ │ ├── Npix=708.parquet │ │ │ │ ├── Npix=709.parquet │ │ │ │ ├── Npix=710.parquet │ │ │ │ ├── Npix=713.parquet │ │ │ │ ├── Npix=715.parquet │ │ │ │ ├── Npix=716.parquet │ │ │ │ ├── Npix=717.parquet │ │ │ │ ├── Npix=718.parquet │ │ │ │ ├── Npix=719.parquet │ │ │ │ ├── Npix=722.parquet │ │ │ │ ├── Npix=726.parquet │ │ │ │ ├── Npix=728.parquet │ │ │ │ ├── Npix=729.parquet │ │ │ │ ├── Npix=730.parquet │ │ │ │ ├── Npix=732.parquet │ │ │ │ ├── Npix=733.parquet │ │ │ │ ├── Npix=734.parquet │ │ │ │ ├── Npix=739.parquet │ │ │ │ ├── Npix=740.parquet │ │ │ │ ├── Npix=741.parquet │ │ │ │ ├── Npix=742.parquet │ │ │ │ ├── Npix=743.parquet │ │ │ │ ├── Npix=745.parquet │ │ │ │ ├── Npix=747.parquet │ │ │ │ ├── Npix=748.parquet │ │ │ │ ├── Npix=749.parquet │ │ │ │ ├── Npix=750.parquet │ │ │ │ ├── Npix=752.parquet │ │ │ │ ├── Npix=753.parquet │ │ │ │ ├── Npix=754.parquet │ │ │ │ ├── Npix=755.parquet │ │ │ │ └── Npix=760.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_source │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ └── Npix=47.parquet │ │ ├── Norder=2 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=176.parquet │ │ │ │ ├── Npix=177.parquet │ │ │ │ ├── Npix=178.parquet │ │ │ │ ├── Npix=179.parquet │ │ │ │ ├── Npix=180.parquet │ │ │ │ ├── Npix=181.parquet │ │ │ │ ├── Npix=182.parquet │ │ │ │ ├── Npix=183.parquet │ │ │ │ ├── Npix=184.parquet │ │ │ │ ├── Npix=185.parquet │ │ │ │ ├── Npix=186.parquet │ │ │ │ ├── Npix=187.parquet │ │ │ │ └── Npix=66.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_source_margin │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ └── Npix=47.parquet │ │ ├── Norder=2 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=176.parquet │ │ │ │ ├── Npix=177.parquet │ │ │ │ ├── Npix=178.parquet │ │ │ │ ├── Npix=179.parquet │ │ │ │ ├── Npix=180.parquet │ │ │ │ ├── Npix=181.parquet │ │ │ │ ├── Npix=182.parquet │ │ │ │ ├── Npix=183.parquet │ │ │ │ ├── Npix=184.parquet │ │ │ │ ├── Npix=185.parquet │ │ │ │ ├── Npix=186.parquet │ │ │ │ ├── Npix=187.parquet │ │ │ │ └── Npix=66.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties ├── small_sky_to_o1source │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ └── Npix=11.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── partition_join_info.csv │ └── properties ├── small_sky_to_o1source_soft │ ├── dataset │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── partition_join_info.csv │ └── properties ├── small_sky_to_xmatch │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ └── Npix=11.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── partition_join_info.csv │ └── properties ├── small_sky_to_xmatch_soft │ ├── dataset │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── partition_join_info.csv │ └── properties ├── small_sky_xmatch │ ├── dataset │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ └── Npix=46.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties ├── small_sky_xmatch_margin │ ├── dataset │ │ ├── Norder=0 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=4.parquet │ │ │ │ ├── Npix=7.parquet │ │ │ │ └── Npix=8.parquet │ │ ├── Norder=1 │ │ │ └── Dir=0 │ │ │ │ ├── Npix=44.parquet │ │ │ │ ├── Npix=45.parquet │ │ │ │ ├── Npix=46.parquet │ │ │ │ └── Npix=47.parquet │ │ ├── _common_metadata │ │ └── _metadata │ ├── partition_info.csv │ └── properties └── square_map │ ├── dataset │ ├── Norder=0 │ │ └── Dir=0 │ │ │ ├── Npix=0.parquet │ │ │ ├── Npix=1.parquet │ │ │ ├── Npix=10.parquet │ │ │ ├── Npix=11.parquet │ │ │ ├── Npix=2.parquet │ │ │ ├── Npix=3.parquet │ │ │ ├── Npix=4.parquet │ │ │ ├── Npix=5.parquet │ │ │ ├── Npix=6.parquet │ │ │ ├── Npix=7.parquet │ │ │ ├── Npix=8.parquet │ │ │ └── Npix=9.parquet │ ├── _common_metadata │ └── _metadata │ ├── partition_info.csv │ ├── point_map.fits │ └── properties └── lsdb ├── catalog ├── test_association_catalog.py ├── test_box_search.py ├── test_catalog.py ├── test_cone_search.py ├── test_crossmatch.py ├── test_index_search.py ├── test_join.py ├── test_margin_catalog.py ├── test_merge.py ├── test_moc_search.py ├── test_nested.py ├── test_order_search.py ├── test_pixel_search.py └── test_polygon_search.py ├── core ├── test_crossmatch_method.py └── test_kdtree_validation.py ├── dask └── test_divisions.py ├── io ├── test_to_association.py └── test_to_hats.py ├── loaders ├── dataframe │ └── test_from_dataframe.py └── hats │ └── test_read_hats.py ├── nested ├── test_accessor.py ├── test_datasets.py ├── test_io.py ├── test_nestedframe.py └── test_utils.py └── test_packaging.py /.copier-answers.yml: -------------------------------------------------------------------------------- 1 | # Changes here will be overwritten by Copier 2 | _commit: v2.0.7 3 | _src_path: gh:lincc-frameworks/python-project-template 4 | author_email: lincc-frameworks-team@lists.lsst.org 5 | author_name: LINCC Frameworks 6 | create_example_module: false 7 | custom_install: true 8 | enforce_style: 9 | - pylint 10 | - black 11 | - isort 12 | failure_notification: 13 | - slack 14 | include_benchmarks: true 15 | include_docs: true 16 | include_notebooks: true 17 | mypy_type_checking: basic 18 | package_name: lsdb 19 | project_license: BSD 20 | project_name: lsdb 21 | project_organization: astronomy-commons 22 | python_versions: 23 | - '3.10' 24 | - '3.11' 25 | - '3.12' 26 | - '3.13' 27 | test_lowest_version: direct 28 | -------------------------------------------------------------------------------- /.git_archival.txt: -------------------------------------------------------------------------------- 1 | node: c5f85abfbc9e16b29e2a6db38f355a1a3338816b 2 | node-date: 2025-06-10T10:35:39-04:00 3 | describe-name: v0.5.3-18-gc5f85abfb 4 | ref-names: HEAD -> main -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # For explanation of this file and uses see 2 | # https://git-scm.com/docs/gitattributes 3 | # https://developer.lsst.io/git/git-lfs.html#using-git-lfs-enabled-repositories 4 | # https://lincc-ppt.readthedocs.io/en/stable/practices/git-lfs.html 5 | # 6 | # Used by https://github.com/lsst/afwdata.git 7 | # *.boost filter=lfs diff=lfs merge=lfs -text 8 | # *.dat filter=lfs diff=lfs merge=lfs -text 9 | # *.fits filter=lfs diff=lfs merge=lfs -text 10 | # *.gz filter=lfs diff=lfs merge=lfs -text 11 | # 12 | # apache parquet files 13 | # *.parq filter=lfs diff=lfs merge=lfs -text 14 | # 15 | # sqlite files 16 | # *.sqlite3 filter=lfs diff=lfs merge=lfs -text 17 | # 18 | # gzip files 19 | # *.gz filter=lfs diff=lfs merge=lfs -text 20 | # 21 | # png image files 22 | # *.png filter=lfs diff=lfs merge=lfs -text 23 | 24 | .git_archival.txt export-subst -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/0-general_issue.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: General issue 3 | about: Quickly create a general issue 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/1-bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Tell us about a problem to fix 4 | title: 'Short description' 5 | labels: 'bug' 6 | assignees: '' 7 | 8 | --- 9 | **Bug report** 10 | 11 | 12 | **Before submitting** 13 | Please check the following: 14 | 15 | - [ ] I have described the situation in which the bug arose, including what code was executed, information about my environment, and any applicable data others will need to reproduce the problem. 16 | - [ ] I have included available evidence of the unexpected behavior (including error messages, screenshots, and/or plots) as well as a description of what I expected instead. 17 | - [ ] If I have a solution in mind, I have provided an explanation and/or pseudocode and/or task list. 18 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/2-feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: 'Short description' 5 | labels: 'enhancement' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Feature request** 11 | 12 | 13 | **Before submitting** 14 | Please check the following: 15 | 16 | - [ ] I have described the purpose of the suggested change, specifying what I need the enhancement to accomplish, i.e. what problem it solves. 17 | - [ ] I have included any relevant links, screenshots, environment information, and data relevant to implementing the requested feature, as well as pseudocode for how I want to access the new functionality. 18 | - [ ] If I have ideas for how the new feature could be implemented, I have provided explanations and/or pseudocode and/or task lists for the steps. 19 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/4-release_tracker.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Release Tracker 3 | about: Request release batch of LSDB, HATS, HATS-import packages. 4 | title: 'Release: ' 5 | labels: '' 6 | assignees: 'delucchi-cmu' 7 | 8 | --- 9 | 10 | 11 | ## Packages to release 12 | 13 | Please check which packages you would like to be released: 14 | 15 | - [ ] hats 16 | - [ ] lsdb 17 | - [ ] hats-import 18 | 19 | ## Additional context 20 | 21 | e.g. major/minor/patch, deadlines, blocking issues, breaking changes, folks to notify 22 | 23 | ## nested-* updates to include 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | ## github and pypi steps 32 | 33 | ### hats 34 | 35 | - [ ] tag in github 36 | - [ ] confirm on [pypi](https://pypi.org/manage/project/hats/releases/) 37 | 38 | ### lsdb 39 | 40 | - [ ] update pinned versions (e.g. hats and nested) (or confirm no updates to pins) 41 | - [ ] tag in github 42 | - [ ] confirm on [pypi](https://pypi.org/manage/project/lsdb/releases/) 43 | 44 | ### hats-import 45 | 46 | - [ ] update pinned versions (e.g. hats) (or confirm no updates to pins) 47 | - [ ] tag in github 48 | - [ ] confirm on [pypi](https://pypi.org/manage/project/hats-import/releases/) 49 | 50 | ## conda-forge steps 51 | 52 | ### hats 53 | 54 | - [ ] request new conda-forge version (open [bot command issue](https://github.com/conda-forge/hats-feedstock/issues/) with title `@conda-forge-admin, please update version`) 55 | - [ ] approve conda-forge PR 56 | - [ ] confirm on [conda-forge](https://anaconda.org/conda-forge/hats) 57 | 58 | ### lsdb 59 | 60 | - [ ] request new conda-forge version (open [bot command issue](https://github.com/conda-forge/lsdb-feedstock/issues/) with title `@conda-forge-admin, please update version`) 61 | - [ ] confirm tagged hats version and approve 62 | - [ ] confirm on [conda-forge](https://anaconda.org/conda-forge/lsdb) 63 | 64 | ### hats-import 65 | 66 | - [ ] request new conda-forge version (open [bot command issue](https://github.com/conda-forge/hats-import-feedstock/issues/) with title `@conda-forge-admin, please update version`) 67 | - [ ] confirm tagged hats version and approve 68 | - [ ] confirm on [conda-forge](https://anaconda.org/conda-forge/hats-import) 69 | 70 | ## Release announcement 71 | 72 | - [ ] send release summary to LSSTC slack #lincc-frameworks-lsdb 73 | -------------------------------------------------------------------------------- /.github/dependabot.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | updates: 3 | - package-ecosystem: "github-actions" 4 | directory: "/" 5 | schedule: 6 | interval: "monthly" 7 | - package-ecosystem: "pip" 8 | directory: "/" 9 | schedule: 10 | interval: "monthly" 11 | -------------------------------------------------------------------------------- /.github/workflows/add-issue-to-project-tracker.yml: -------------------------------------------------------------------------------- 1 | name: Add bugs to bugs project 2 | 3 | on: 4 | issues: 5 | types: 6 | - opened 7 | 8 | jobs: 9 | add-to-project: 10 | name: Add issue to project 11 | runs-on: ubuntu-latest 12 | steps: 13 | - uses: actions/add-to-project@v1.0.2 14 | with: 15 | project-url: https://github.com/orgs/astronomy-commons/projects/7 16 | github-token: ${{ secrets.ADD_TO_PROJECT_PAT }} 17 | -------------------------------------------------------------------------------- /.github/workflows/asv-main.yml: -------------------------------------------------------------------------------- 1 | # This workflow will run benchmarks with airspeed velocity (asv), 2 | # store the new results in the "benchmarks" branch and publish them 3 | # to a dashboard on GH Pages. 4 | name: Run ASV benchmarks for main 5 | 6 | on: 7 | push: 8 | branches: [ main ] 9 | 10 | env: 11 | PYTHON_VERSION: "3.11" 12 | ASV_VERSION: "0.6.4" 13 | WORKING_DIR: ${{github.workspace}}/benchmarks 14 | 15 | concurrency: 16 | group: ${{github.workflow}}-${{github.ref}} 17 | cancel-in-progress: true 18 | 19 | jobs: 20 | asv-main: 21 | runs-on: ubuntu-latest 22 | permissions: 23 | contents: write 24 | defaults: 25 | run: 26 | working-directory: ${{env.WORKING_DIR}} 27 | steps: 28 | - name: Set up Python ${{env.PYTHON_VERSION}} 29 | uses: actions/setup-python@v5 30 | with: 31 | python-version: ${{env.PYTHON_VERSION}} 32 | - name: Checkout main branch of the repository 33 | uses: actions/checkout@v4 34 | with: 35 | fetch-depth: 0 36 | - name: Install dependencies 37 | run: | 38 | pip install asv==${{env.ASV_VERSION}} 39 | pip install virtualenv==20.30 # Temporary fix to airspeed-velocity/asv#1484 40 | - name: Configure git 41 | run: | 42 | git config user.name "github-actions[bot]" 43 | git config user.email "41898282+github-actions[bot]@users.noreply.github.com" 44 | - name: Create ASV machine config file 45 | run: asv machine --machine gh-runner --yes 46 | - name: Fetch previous results from the "benchmarks" branch 47 | run: | 48 | if git ls-remote --exit-code origin benchmarks > /dev/null 2>&1; then 49 | git merge origin/benchmarks \ 50 | --allow-unrelated-histories \ 51 | --no-commit 52 | mv ../_results . 53 | fi 54 | - name: Run ASV for the main branch 55 | run: asv run ALL --skip-existing --verbose || true 56 | - name: Submit new results to the "benchmarks" branch 57 | uses: JamesIves/github-pages-deploy-action@v4 58 | with: 59 | branch: benchmarks 60 | folder: ${{env.WORKING_DIR}}/_results 61 | target-folder: _results 62 | - name: Generate dashboard HTML 63 | run: | 64 | asv show 65 | asv publish 66 | - name: Deploy to Github pages 67 | uses: JamesIves/github-pages-deploy-action@v4 68 | with: 69 | branch: gh-pages 70 | folder: ${{env.WORKING_DIR}}/_html -------------------------------------------------------------------------------- /.github/workflows/build-documentation.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, build the package and then build the documentation. 2 | 3 | name: Build documentation 4 | 5 | 6 | on: 7 | push: 8 | branches: [ main ] 9 | pull_request: 10 | branches: [ main ] 11 | 12 | concurrency: 13 | group: ${{ github.workflow }}-${{ github.ref }} 14 | cancel-in-progress: true 15 | 16 | jobs: 17 | build: 18 | 19 | runs-on: ubuntu-latest 20 | 21 | steps: 22 | - uses: actions/checkout@v4 23 | - name: Set up Python 3.11 24 | uses: actions/setup-python@v5 25 | with: 26 | python-version: '3.11' 27 | - name: Install dependencies 28 | run: | 29 | sudo apt-get update 30 | python -m pip install --upgrade pip 31 | if [ -f docs/requirements.txt ]; then pip install -r docs/requirements.txt; fi 32 | pip install . 33 | - name: Install notebook requirements 34 | run: | 35 | sudo apt-get install pandoc 36 | - name: Build docs 37 | run: | 38 | sphinx-build -T -E -b html -d docs/build/doctrees ./docs docs/build/html 39 | -------------------------------------------------------------------------------- /.github/workflows/pre-commit-ci.yml: -------------------------------------------------------------------------------- 1 | # This workflow runs pre-commit hooks on pushes and pull requests to main 2 | # to enforce coding style. To ensure correct configuration, please refer to: 3 | # https://lincc-ppt.readthedocs.io/en/stable/practices/ci_precommit.html 4 | name: Run pre-commit hooks 5 | 6 | on: 7 | push: 8 | branches: [ main ] 9 | pull_request: 10 | branches: [ main ] 11 | 12 | jobs: 13 | pre-commit-ci: 14 | runs-on: ubuntu-latest 15 | steps: 16 | - uses: actions/checkout@v4 17 | with: 18 | fetch-depth: 0 19 | - name: Set up Python 20 | uses: actions/setup-python@v5 21 | with: 22 | python-version: '3.11' 23 | - name: Install dependencies 24 | run: | 25 | sudo apt-get update 26 | python -m pip install --upgrade pip 27 | pip install .[dev] 28 | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi 29 | - uses: pre-commit/action@v3.0.1 30 | with: 31 | extra_args: --all-files --verbose 32 | env: 33 | SKIP: "check-lincc-frameworks-template-version,no-commit-to-branch,check-added-large-files,validate-pyproject,sphinx-build,pytest-check" 34 | - uses: pre-commit-ci/lite-action@v1.1.0 35 | if: failure() && github.event_name == 'pull_request' && github.event.pull_request.draft == false -------------------------------------------------------------------------------- /.github/workflows/publish-benchmarks-pr.yml: -------------------------------------------------------------------------------- 1 | # This workflow publishes a benchmarks comment on a pull request. It is triggered after the 2 | # benchmarks are computed in the asv-pr workflow. This separation of concerns allows us limit 3 | # access to the target repository private tokens and secrets, increasing the level of security. 4 | # Based on https://securitylab.github.com/research/github-actions-preventing-pwn-requests/. 5 | name: Publish benchmarks comment to PR 6 | 7 | on: 8 | workflow_run: 9 | workflows: ["Run benchmarks for PR"] 10 | types: [completed] 11 | 12 | jobs: 13 | upload-pr-comment: 14 | runs-on: ubuntu-latest 15 | if: > 16 | github.event.workflow_run.event == 'pull_request' && 17 | github.event.workflow_run.conclusion == 'success' 18 | permissions: 19 | issues: write 20 | pull-requests: write 21 | steps: 22 | - name: Display Workflow Run Information 23 | run: | 24 | echo "Workflow Run ID: ${{ github.event.workflow_run.id }}" 25 | echo "Head SHA: ${{ github.event.workflow_run.head_sha }}" 26 | echo "Head Branch: ${{ github.event.workflow_run.head_branch }}" 27 | echo "Conclusion: ${{ github.event.workflow_run.conclusion }}" 28 | echo "Event: ${{ github.event.workflow_run.event }}" 29 | - name: Download artifact 30 | uses: dawidd6/action-download-artifact@v10 31 | with: 32 | name: benchmark-artifacts 33 | run_id: ${{ github.event.workflow_run.id }} 34 | - name: Extract artifacts information 35 | id: pr-info 36 | run: | 37 | printf "PR number: $(cat pr)\n" 38 | printf "Output:\n$(cat output)" 39 | printf "pr=$(cat pr)" >> $GITHUB_OUTPUT 40 | - name: Find benchmarks comment 41 | uses: peter-evans/find-comment@v3 42 | id: find-comment 43 | with: 44 | issue-number: ${{ steps.pr-info.outputs.pr }} 45 | comment-author: 'github-actions[bot]' 46 | body-includes: view all benchmarks 47 | - name: Create or update benchmarks comment 48 | uses: peter-evans/create-or-update-comment@v4 49 | with: 50 | comment-id: ${{ steps.find-comment.outputs.comment-id }} 51 | issue-number: ${{ steps.pr-info.outputs.pr }} 52 | body-path: output 53 | edit-mode: replace -------------------------------------------------------------------------------- /.github/workflows/publish-to-pypi.yml: -------------------------------------------------------------------------------- 1 | # This workflow will upload a Python Package using Twine when a release is created 2 | # For more information see: https://github.com/pypa/gh-action-pypi-publish#trusted-publishing 3 | 4 | # This workflow uses actions that are not certified by GitHub. 5 | # They are provided by a third-party and are governed by 6 | # separate terms of service, privacy policy, and support 7 | # documentation. 8 | 9 | name: Upload Python Package 10 | 11 | on: 12 | release: 13 | types: [published] 14 | 15 | permissions: 16 | contents: read 17 | 18 | jobs: 19 | deploy: 20 | 21 | runs-on: ubuntu-latest 22 | permissions: 23 | id-token: write 24 | steps: 25 | - uses: actions/checkout@v4 26 | - name: Set up Python 27 | uses: actions/setup-python@v5 28 | with: 29 | python-version: '3.11' 30 | - name: Install dependencies 31 | run: | 32 | python -m pip install --upgrade pip 33 | pip install build 34 | - name: Build package 35 | run: python -m build 36 | - name: Publish package 37 | uses: pypa/gh-action-pypi-publish@release/v1 38 | -------------------------------------------------------------------------------- /.github/workflows/testing-and-coverage.yml: -------------------------------------------------------------------------------- 1 | 2 | # This workflow will install Python dependencies, run tests and report code coverage with a variety of Python versions 3 | # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions 4 | 5 | name: Unit test and code coverage 6 | 7 | on: 8 | push: 9 | branches: [ main ] 10 | pull_request: 11 | branches: [ main ] 12 | 13 | jobs: 14 | build: 15 | 16 | runs-on: ubuntu-latest 17 | strategy: 18 | matrix: 19 | python-version: ['3.10', '3.11', '3.12', '3.13'] 20 | 21 | steps: 22 | - uses: actions/checkout@v4 23 | - name: Set up Python ${{ matrix.python-version }} 24 | uses: actions/setup-python@v5 25 | with: 26 | python-version: ${{ matrix.python-version }} 27 | - name: Install dependencies 28 | run: | 29 | sudo apt-get update 30 | python -m pip install --upgrade pip 31 | pip install -e .[dev,full] 32 | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi 33 | - name: Run unit tests with pytest 34 | run: | 35 | python -m pytest --cov=lsdb --cov-report=xml 36 | - name: Upload coverage report to codecov 37 | uses: codecov/codecov-action@v5 38 | with: 39 | token: ${{ secrets.CODECOV_TOKEN }} 40 | test-lowest-versions: 41 | runs-on: ubuntu-latest 42 | steps: 43 | - uses: actions/checkout@v4 44 | - name: Set up Python 3.10 45 | uses: actions/setup-python@v5 46 | with: 47 | python-version: '3.10' 48 | - name: Install dependencies 49 | run: | 50 | sudo apt-get update 51 | python -m pip install --upgrade uv 52 | uv venv venv 53 | source venv/bin/activate 54 | uv pip compile --resolution=lowest-direct --constraint=requirements.txt -o requirements_lowest.txt pyproject.toml 55 | uv pip install --constraint=requirements_lowest.txt -e .[dev] 56 | - name: Run unit tests with pytest 57 | run: | 58 | source venv/bin/activate 59 | python -m pytest -------------------------------------------------------------------------------- /.github/workflows/testing-windows.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies and run tests in a Windows environment. 2 | # This is intended to catch any file-system specific issues, and so runs less 3 | # frequently than other test suites. 4 | 5 | name: Windows unit test 6 | 7 | on: 8 | push: 9 | branches: [ main ] 10 | pull_request: 11 | branches: [ main ] 12 | 13 | jobs: 14 | build: 15 | 16 | runs-on: windows-latest 17 | strategy: 18 | matrix: 19 | python-version: ['3.10'] 20 | 21 | steps: 22 | - uses: actions/checkout@v4 23 | - name: Set up Python ${{ matrix.python-version }} 24 | uses: actions/setup-python@v5 25 | with: 26 | python-version: ${{ matrix.python-version }} 27 | - name: Install dependencies 28 | run: | 29 | python -m pip install --upgrade pip 30 | pip install -e .[dev] 31 | pip install -r requirements.txt 32 | - name: Run unit tests with pytest 33 | run: | 34 | python -m pytest tests -------------------------------------------------------------------------------- /.readthedocs.yml: -------------------------------------------------------------------------------- 1 | # .readthedocs.yml 2 | # Read the Docs configuration file 3 | # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details 4 | 5 | # Required 6 | version: 2 7 | 8 | build: 9 | os: ubuntu-22.04 10 | tools: 11 | python: "3.11" 12 | apt_packages: 13 | - graphviz 14 | 15 | # Build documentation in the docs/ directory with Sphinx 16 | sphinx: 17 | configuration: docs/conf.py 18 | 19 | # Optionally declare the Python requirements required to build your docs 20 | python: 21 | install: 22 | - requirements: docs/requirements.txt 23 | - method: pip 24 | path: . 25 | -------------------------------------------------------------------------------- /.setup_dev.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # Bash Unofficial strict mode (http://redsymbol.net/articles/unofficial-bash-strict-mode/) 4 | # and (https://disconnected.systems/blog/another-bash-strict-mode/) 5 | set -o nounset # Any uninitialized variable is an error 6 | set -o errexit # Exit the script on the failure of any command to execute without error 7 | set -o pipefail # Fail command pipelines on the failure of any individual step 8 | IFS=$'\n\t' #set internal field separator to avoid iteration errors 9 | # Trap all exits and output something helpful 10 | trap 's=$?; echo "$0: Error on line "$LINENO": $BASH_COMMAND"; exit $s' ERR 11 | 12 | # This script should be run by new developers to install this package in 13 | # editable mode and configure their local environment 14 | 15 | echo "Checking virtual environment" 16 | if [ "${VIRTUAL_ENV:-missing}" = "missing" ] && [ "${CONDA_PREFIX:-missing}" = "missing" ]; then 17 | echo 'No virtual environment detected: none of $VIRTUAL_ENV or $CONDA_PREFIX is set.' 18 | echo 19 | echo "=== This script is going to install the project in the system python environment ===" 20 | echo "Proceed? [y/N]" 21 | read -r RESPONCE 22 | if [ "${RESPONCE}" != "y" ]; then 23 | echo "See https://lincc-ppt.readthedocs.io/ for details." 24 | echo "Exiting." 25 | exit 1 26 | fi 27 | 28 | fi 29 | 30 | echo "Checking pip version" 31 | MINIMUM_PIP_VERSION=22 32 | pipversion=( $(python -m pip --version | awk '{print $2}' | sed 's/\./\n\t/g') ) 33 | if let "${pipversion[0]}<${MINIMUM_PIP_VERSION}"; then 34 | echo "Insufficient version of pip found. Requires at least version ${MINIMUM_PIP_VERSION}." 35 | echo "See https://lincc-ppt.readthedocs.io/ for details." 36 | exit 1 37 | fi 38 | 39 | echo "Installing package and runtime dependencies in local environment" 40 | python -m pip install -e . > /dev/null 41 | 42 | echo "Installing developer dependencies in local environment" 43 | python -m pip install -e .'[dev]' > /dev/null 44 | if [ -f docs/requirements.txt ]; then python -m pip install -r docs/requirements.txt > /dev/null; fi 45 | 46 | echo "Installing pre-commit" 47 | pre-commit install > /dev/null 48 | 49 | ####################################################### 50 | # Include any additional configurations below this line 51 | ####################################################### 52 | -------------------------------------------------------------------------------- /CITATION.bib: -------------------------------------------------------------------------------- 1 | @ARTICLE{2025arXiv250102103C, 2 | author = {{Caplar}, Neven and {Beebe}, Wilson and {Branton}, Doug and {Campos}, Sandro and {Connolly}, Andrew and {DeLucchi}, Melissa and {Jones}, Derek and {Juric}, Mario and {Kubica}, Jeremy and {Malanchev}, Konstantin and {Mandelbaum}, Rachel and {McGuire}, Sean}, 3 | title = "{Using LSDB to enable large-scale catalog distribution, cross-matching, and analytics}", 4 | journal = {arXiv e-prints}, 5 | keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, 6 | year = 2025, 7 | month = jan, 8 | eid = {arXiv:2501.02103}, 9 | pages = {arXiv:2501.02103}, 10 | doi = {10.48550/arXiv.2501.02103}, 11 | archivePrefix = {arXiv}, 12 | eprint = {2501.02103}, 13 | primaryClass = {astro-ph.IM}, 14 | adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv250102103C}, 15 | adsnote = {Provided by the SAO/NASA Astrophysics Data System} 16 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2024, LINCC Frameworks 4 | 5 | Redistribution and use in source and binary forms, with or without 6 | modification, are permitted provided that the following conditions are met: 7 | 8 | 1. Redistributions of source code must retain the above copyright notice, this 9 | list of conditions and the following disclaimer. 10 | 11 | 2. Redistributions in binary form must reproduce the above copyright notice, 12 | this list of conditions and the following disclaimer in the documentation 13 | and/or other materials provided with the distribution. 14 | 15 | 3. Neither the name of the copyright holder nor the names of its 16 | contributors may be used to endorse or promote products derived from 17 | this software without specific prior written permission. 18 | 19 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 20 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 21 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 22 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 23 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 24 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 25 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 26 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 27 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 28 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | -------------------------------------------------------------------------------- /benchmarks/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/benchmarks/__init__.py -------------------------------------------------------------------------------- /benchmarks/data/large_catalog/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/benchmarks/data/large_catalog/dataset/_common_metadata -------------------------------------------------------------------------------- /benchmarks/data/large_catalog/dataset/_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/benchmarks/data/large_catalog/dataset/_metadata -------------------------------------------------------------------------------- /benchmarks/data/large_catalog/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=large_catalog 3 | dataproduct_type=object 4 | hats_nrows=196608 5 | hats_col_ra= 6 | hats_col_dec= 7 | -------------------------------------------------------------------------------- /benchmarks/data/midsize_catalog/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/benchmarks/data/midsize_catalog/dataset/_common_metadata -------------------------------------------------------------------------------- /benchmarks/data/midsize_catalog/dataset/_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/benchmarks/data/midsize_catalog/dataset/_metadata -------------------------------------------------------------------------------- /benchmarks/data/midsize_catalog/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=midsize_catalog 3 | dataproduct_type=object 4 | hats_nrows=30000 5 | hats_col_ra= 6 | hats_col_dec= 7 | -------------------------------------------------------------------------------- /benchmarks/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def upsample_array(input_array, num_points): 5 | if num_points <= len(input_array): 6 | return input_array 7 | original_points = np.arange(len(input_array)) 8 | upsampled_points = np.linspace(0, len(input_array) - 1, num_points) 9 | upsampled_array = np.interp(upsampled_points, original_points, input_array) 10 | return upsampled_array 11 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= -T -E -d _build/doctrees -D language=en 7 | EXCLUDENB ?= -D exclude_patterns="tutorials/*","_build","**.ipynb_checkpoints" 8 | SPHINXBUILD ?= sphinx-build 9 | SOURCEDIR = . 10 | BUILDDIR = ../_readthedocs/ 11 | 12 | .PHONY: help clean Makefile no-nb no-notebooks 13 | 14 | # Put it first so that "make" without argument is like "make help". 15 | help: 16 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 17 | 18 | # Build all Sphinx docs locally, except the notebooks 19 | no-nb no-notebooks: 20 | @$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(EXCLUDENB) $(O) 21 | 22 | # Cleans up files generated by the build process 23 | clean: 24 | rm -r "_build/doctrees" 25 | rm -r "$(BUILDDIR)" 26 | 27 | # Catch-all target: route all unknown targets to Sphinx using the new 28 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 29 | %: Makefile 30 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 31 | 32 | -------------------------------------------------------------------------------- /docs/_static/custom.css: -------------------------------------------------------------------------------- 1 | #lsdb h1 { 2 | text-align: center; 3 | } 4 | 5 | .skip-link { 6 | background-color: transparent !important; 7 | } -------------------------------------------------------------------------------- /docs/_static/gaia.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/_static/gaia.png -------------------------------------------------------------------------------- /docs/_static/lincc_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/_static/lincc_logo.png -------------------------------------------------------------------------------- /docs/_static/ztf_catalog_lazy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/_static/ztf_catalog_lazy.png -------------------------------------------------------------------------------- /docs/_static/ztf_x_gaia.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/_static/ztf_x_gaia.png -------------------------------------------------------------------------------- /docs/citation.rst: -------------------------------------------------------------------------------- 1 | About 2 | ========================== 3 | 4 | LSDB is an open source project and is free for all to use. It is released under the liberal terms of the 5 | `BSD 3-Clause License `_. 6 | 7 | Citation 8 | -------------------------- 9 | 10 | If LSDB has been significant in your research, and you would like to 11 | acknowledge the project in your academic publication, we suggest citing 12 | the conference proceedings: 13 | 14 | .. code-block:: bash 15 | 16 | @ARTICLE{2025arXiv250102103C, 17 | author = {{Caplar}, Neven and {Beebe}, Wilson and {Branton}, Doug and {Campos}, Sandro and {Connolly}, Andrew and {DeLucchi}, Melissa and {Jones}, Derek and {Juric}, Mario and {Kubica}, Jeremy and {Malanchev}, Konstantin and {Mandelbaum}, Rachel and {McGuire}, Sean}, 18 | title = "{Using LSDB to enable large-scale catalog distribution, cross-matching, and analytics}", 19 | journal = {arXiv e-prints}, 20 | keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, 21 | year = 2025, 22 | month = jan, 23 | eid = {arXiv:2501.02103}, 24 | pages = {arXiv:2501.02103}, 25 | doi = {10.48550/arXiv.2501.02103}, 26 | archivePrefix = {arXiv}, 27 | eprint = {2501.02103}, 28 | primaryClass = {astro-ph.IM}, 29 | adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv250102103C}, 30 | adsnote = {Provided by the SAO/NASA Astrophysics Data System} 31 | } 32 | 33 | 34 | Acknowledgements 35 | ------------------------------------------------------------------------------- 36 | 37 | This project is supported by Schmidt Sciences. 38 | 39 | This project is based upon work supported by the National Science Foundation 40 | under Grant No. AST-2003196. 41 | 42 | This project acknowledges support from the DIRAC Institute in the Department of 43 | Astronomy at the University of Washington. The DIRAC Institute is supported 44 | through generous gifts from the Charles and Lisa Simonyi Fund for Arts and 45 | Sciences, and the Washington Research Foundation. -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | .. lsdb documentation main file. 2 | You can adapt this file completely to your liking, but it should at least 3 | contain the root `toctree` directive. 4 | 5 | 6 | LSDB 7 | ======================================================================================== 8 | 9 | LSDB is a python tool for scalable analysis of large catalogs (e.g. querying 10 | and crossmatching ~10⁹ sources). It aims to address large-scale data processing 11 | challenges, in particular those brought up by `LSST `_. 12 | 13 | Built on top of `Dask `_ to efficiently scale and parallelize operations across multiple distributed workers, it 14 | uses the `HATS `_ data format to efficiently perform spatial 15 | operations. 16 | 17 | .. figure:: _static/gaia.png 18 | :class: no-scaled-link 19 | :scale: 80 % 20 | :align: center 21 | :alt: A possible HEALPix distribution for Gaia DR3 22 | 23 | A possible HEALPix distribution for Gaia DR3. 24 | 25 | 26 | Using this Guide 27 | ------------------------------------------------------------------------------- 28 | .. grid:: 1 1 2 2 29 | 30 | .. grid-item-card:: Getting Started 31 | :link: getting-started 32 | :link-type: doc 33 | 34 | Installation and QuickStart Guide 35 | 36 | .. grid-item-card:: Tutorials 37 | :link: tutorials 38 | :link-type: doc 39 | 40 | Learn the LSDB features by working through our guides 41 | 42 | .. grid:: 1 1 2 2 43 | 44 | .. grid-item-card:: API Reference 45 | :link: autoapi/index 46 | :link-type: doc 47 | 48 | The detailed API documentation 49 | 50 | .. grid-item-card:: Contact us 51 | :link: contact 52 | :link-type: doc 53 | 54 | Reach out for more help 55 | 56 | .. toctree:: 57 | :hidden: 58 | :caption: Using LSDB 59 | 60 | Home page 61 | Getting Started 62 | Tutorials 63 | API Reference 64 | 65 | .. toctree:: 66 | :hidden: 67 | :caption: Project 68 | 69 | About 70 | Contact us 71 | Contribution Guide 72 | -------------------------------------------------------------------------------- /docs/lincc-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/lincc-logo.png -------------------------------------------------------------------------------- /docs/requirements.txt: -------------------------------------------------------------------------------- 1 | 2 | ipykernel 3 | ipython 4 | ipywidgets 5 | jupytext 6 | lsst-sphgeom 7 | nbconvert 8 | nbsphinx 9 | sphinx 10 | sphinx-autoapi 11 | sphinx-copybutton 12 | sphinx-book-theme 13 | sphinx-design 14 | git+https://github.com/astronomy-commons/hats.git@main 15 | git+https://github.com/astronomy-commons/hats-import.git@main 16 | git+https://github.com/lincc-frameworks/nested-pandas.git@main 17 | -------------------------------------------------------------------------------- /docs/tutorials/_static/crossmatching-performance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/tutorials/_static/crossmatching-performance.png -------------------------------------------------------------------------------- /docs/tutorials/_static/dask-client-local-cluster.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/tutorials/_static/dask-client-local-cluster.png -------------------------------------------------------------------------------- /docs/tutorials/_static/dask-dashboard-status.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/tutorials/_static/dask-dashboard-status.png -------------------------------------------------------------------------------- /docs/tutorials/_static/dask-dashboard-workers.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/tutorials/_static/dask-dashboard-workers.png -------------------------------------------------------------------------------- /docs/tutorials/_static/margin-pix.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/tutorials/_static/margin-pix.png -------------------------------------------------------------------------------- /docs/tutorials/_static/pixel-boundary-example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/docs/tutorials/_static/pixel-boundary-example.png -------------------------------------------------------------------------------- /docs/tutorials/crossmatching.rst: -------------------------------------------------------------------------------- 1 | Crossmatching 2 | ============================================= 3 | 4 | TODO - Help wanted 5 | 6 | Learning Objectives 7 | ---------------------------------------- 8 | 9 | By the end of this tutorial, you will: 10 | 11 | * 12 | 13 | You should already have an understanding of: 14 | 15 | * 16 | 17 | Introduction 18 | ---------------------------------------- 19 | 20 | 21 | 1. First step 22 | ---------------------------------------- 23 | 24 | About 25 | ---------------------------------------- 26 | 27 | **Authors**: Your Name 28 | 29 | **Last updated on**: April 4, 2025 30 | 31 | If you use ``lsdb`` for published research, please cite following 32 | `instructions `__. -------------------------------------------------------------------------------- /docs/tutorials/nested_frame.rst: -------------------------------------------------------------------------------- 1 | NestedFrame 2 | ============================================= 3 | 4 | TODO - Help wanted 5 | 6 | Learning Objectives 7 | ---------------------------------------- 8 | 9 | By the end of this tutorial, you will: 10 | 11 | * 12 | 13 | You should already have an understanding of: 14 | 15 | * 16 | 17 | Introduction 18 | ---------------------------------------- 19 | 20 | 21 | 1. First step 22 | ---------------------------------------- 23 | 24 | About 25 | ---------------------------------------- 26 | 27 | **Authors**: Your Name 28 | 29 | **Last updated on**: April 4, 2025 30 | 31 | If you use ``lsdb`` for published research, please cite following 32 | `instructions `__. -------------------------------------------------------------------------------- /docs/tutorials/rubin_dp1.rst: -------------------------------------------------------------------------------- 1 | Working with rubin data preview 1 2 | ============================================= 3 | 4 | TODO - Help wanted 5 | 6 | Learning Objectives 7 | ---------------------------------------- 8 | 9 | By the end of this tutorial, you will: 10 | 11 | * 12 | 13 | You should already have an understanding of: 14 | 15 | * 16 | 17 | Introduction 18 | ---------------------------------------- 19 | 20 | 21 | 1. First step 22 | ---------------------------------------- 23 | 24 | About 25 | ---------------------------------------- 26 | 27 | **Authors**: Your Name 28 | 29 | **Last updated on**: April 4, 2025 30 | 31 | If you use ``lsdb`` for published research, please cite following 32 | `instructions `__. -------------------------------------------------------------------------------- /docs/tutorials/timeseries.rst: -------------------------------------------------------------------------------- 1 | TimeSeries 2 | ============================================= 3 | 4 | TODO - Help wanted 5 | 6 | Learning Objectives 7 | ---------------------------------------- 8 | 9 | By the end of this tutorial, you will: 10 | 11 | * 12 | 13 | You should already have an understanding of: 14 | 15 | * 16 | 17 | Introduction 18 | ---------------------------------------- 19 | 20 | 21 | 1. First step 22 | ---------------------------------------- 23 | 24 | About 25 | ---------------------------------------- 26 | 27 | **Authors**: Your Name 28 | 29 | **Last updated on**: April 4, 2025 30 | 31 | If you use ``lsdb`` for published research, please cite following 32 | `instructions `__. -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | hats @ git+https://github.com/astronomy-commons/hats.git@main 2 | nested-pandas @ git+https://github.com/lincc-frameworks/nested-pandas.git@main 3 | -------------------------------------------------------------------------------- /src/lsdb/__init__.py: -------------------------------------------------------------------------------- 1 | from hats.pixel_math import HealpixPixel 2 | 3 | from ._version import __version__ 4 | from .catalog import Catalog, MarginCatalog 5 | from .core.crossmatch.crossmatch import crossmatch 6 | from .core.search import BoxSearch, ConeSearch, PixelSearch, PolygonSearch 7 | from .loaders.dataframe.from_dataframe import from_dataframe 8 | from .loaders.hats.read_hats import open_catalog, read_hats 9 | from .nested.datasets import generate_catalog 10 | -------------------------------------------------------------------------------- /src/lsdb/catalog/__init__.py: -------------------------------------------------------------------------------- 1 | from .catalog import Catalog 2 | from .map_catalog import MapCatalog 3 | from .margin_catalog import MarginCatalog 4 | -------------------------------------------------------------------------------- /src/lsdb/catalog/association_catalog.py: -------------------------------------------------------------------------------- 1 | import hats as hc 2 | 3 | import lsdb.nested as nd 4 | from lsdb.catalog.dataset.healpix_dataset import HealpixDataset 5 | from lsdb.types import DaskDFPixelMap 6 | 7 | 8 | class AssociationCatalog(HealpixDataset): 9 | """LSDB Association Catalog DataFrame to perform join analysis of sky catalogs and efficient 10 | spatial operations. 11 | 12 | Attributes: 13 | hc_structure: `hats.AssociationCatalog` object representing the structure 14 | and metadata of the HATS catalog 15 | """ 16 | 17 | hc_structure: hc.catalog.AssociationCatalog 18 | 19 | def __init__( 20 | self, 21 | ddf: nd.NestedFrame, 22 | ddf_pixel_map: DaskDFPixelMap, 23 | hc_structure: hc.catalog.AssociationCatalog, 24 | ): 25 | super().__init__(ddf, ddf_pixel_map, hc_structure) 26 | 27 | def _create_modified_hc_structure( 28 | self, hc_structure=None, updated_schema=None, **kwargs 29 | ) -> hc.catalog.AssociationCatalog: 30 | """Copy the catalog structure and override the specified catalog info parameters. 31 | 32 | Returns: 33 | A copy of the catalog's structure with updated info parameters. 34 | """ 35 | if hc_structure is None: 36 | hc_structure = self.hc_structure 37 | return hc_structure.__class__( 38 | catalog_info=hc_structure.catalog_info.copy_and_update(**kwargs), 39 | pixels=hc_structure.pixel_tree, 40 | join_pixels=hc_structure.join_info, 41 | catalog_path=hc_structure.catalog_path, 42 | schema=hc_structure.schema if updated_schema is None else updated_schema, 43 | moc=hc_structure.moc, 44 | ) 45 | -------------------------------------------------------------------------------- /src/lsdb/catalog/dataset/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/catalog/dataset/__init__.py -------------------------------------------------------------------------------- /src/lsdb/catalog/map_catalog.py: -------------------------------------------------------------------------------- 1 | import hats as hc 2 | 3 | from lsdb.catalog.dataset.healpix_dataset import HealpixDataset 4 | 5 | 6 | class MapCatalog(HealpixDataset): 7 | """LSDB DataFrame to contain a continuous map. 8 | 9 | Attributes: 10 | hc_structure: `hats.MapCatalog` object representing the structure 11 | and metadata of the HATS catalog 12 | """ 13 | 14 | hc_structure: hc.catalog.MapCatalog 15 | -------------------------------------------------------------------------------- /src/lsdb/catalog/margin_catalog.py: -------------------------------------------------------------------------------- 1 | import hats as hc 2 | from hats.io import paths 3 | 4 | import lsdb.nested as nd 5 | from lsdb.catalog.dataset.healpix_dataset import HealpixDataset 6 | from lsdb.types import DaskDFPixelMap 7 | 8 | 9 | class MarginCatalog(HealpixDataset): 10 | """LSDB Catalog DataFrame to contain the "margin" of another HATS catalog. 11 | spatial operations. 12 | 13 | Attributes: 14 | hc_structure: `hats.MarginCatalog` object representing the structure 15 | and metadata of the HATS catalog 16 | """ 17 | 18 | hc_structure: hc.catalog.MarginCatalog 19 | 20 | def __init__( 21 | self, 22 | ddf: nd.NestedFrame, 23 | ddf_pixel_map: DaskDFPixelMap, 24 | hc_structure: hc.catalog.MarginCatalog, 25 | ): 26 | super().__init__(ddf, ddf_pixel_map, hc_structure) 27 | 28 | 29 | def _validate_margin_catalog(margin_hc_catalog, hc_catalog): 30 | """Validate that the margin and main catalogs have compatible schemas. The order of 31 | the pyarrow fields should not matter.""" 32 | margin_catalog_fields = set((f.name, f.type) for f in margin_hc_catalog.schema) 33 | main_catalog_fields = set((f.name, f.type) for f in hc_catalog.schema) 34 | 35 | dropped_fields = main_catalog_fields - margin_catalog_fields 36 | dropped_fields = [f for f in dropped_fields if f[0] not in paths.HIVE_COLUMNS] 37 | 38 | added_fields = margin_catalog_fields - main_catalog_fields 39 | added_fields = [f for f in added_fields if f[0] not in paths.HIVE_COLUMNS] 40 | 41 | if len(dropped_fields) or len(added_fields): 42 | raise ValueError("The margin catalog and the main catalog must have the same schema.") 43 | -------------------------------------------------------------------------------- /src/lsdb/core/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/core/__init__.py -------------------------------------------------------------------------------- /src/lsdb/core/crossmatch/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/core/crossmatch/__init__.py -------------------------------------------------------------------------------- /src/lsdb/core/crossmatch/crossmatch_algorithms.py: -------------------------------------------------------------------------------- 1 | from enum import Enum 2 | 3 | from lsdb.core.crossmatch.bounded_kdtree_match import BoundedKdTreeCrossmatch 4 | from lsdb.core.crossmatch.kdtree_match import KdTreeCrossmatch 5 | 6 | 7 | class BuiltInCrossmatchAlgorithm(str, Enum): 8 | """Cross-matching algorithms included in lsdb""" 9 | 10 | KD_TREE = "kd_tree" 11 | BOUNDED_KD_TREE = "bounded_kd_tree" 12 | 13 | 14 | def is_builtin_algorithm(algorithm_type) -> bool: 15 | """Check if a given algorithm is built-in.""" 16 | return algorithm_type in builtin_crossmatch_algorithms.values() 17 | 18 | 19 | builtin_crossmatch_algorithms = { 20 | BuiltInCrossmatchAlgorithm.KD_TREE: KdTreeCrossmatch, 21 | BuiltInCrossmatchAlgorithm.BOUNDED_KD_TREE: BoundedKdTreeCrossmatch, 22 | } 23 | -------------------------------------------------------------------------------- /src/lsdb/core/plotting/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/core/plotting/__init__.py -------------------------------------------------------------------------------- /src/lsdb/core/search/__init__.py: -------------------------------------------------------------------------------- 1 | from .box_search import BoxSearch 2 | from .cone_search import ConeSearch 3 | from .index_search import IndexSearch 4 | from .order_search import OrderSearch 5 | from .pixel_search import PixelSearch 6 | from .polygon_search import PolygonSearch 7 | -------------------------------------------------------------------------------- /src/lsdb/core/search/index_search.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from typing import Any 4 | 5 | import nested_pandas as npd 6 | import numpy as np 7 | from hats.catalog.index.index_catalog import IndexCatalog as HCIndexCatalog 8 | 9 | from lsdb.core.search.abstract_search import AbstractSearch 10 | from lsdb.types import HCCatalogTypeVar 11 | 12 | 13 | class IndexSearch(AbstractSearch): 14 | """Find rows by column values using HATS index catalogs.""" 15 | 16 | values: dict[str, Any] 17 | """Mapping of field name to the value we want to match it to""" 18 | 19 | index_catalogs: dict[str, HCIndexCatalog] 20 | """Mapping of field name to respective index catalog""" 21 | 22 | def __init__(self, values: dict[str, Any], index_catalogs: dict[str, HCIndexCatalog], fine: bool = True): 23 | super().__init__(fine) 24 | if not all(key in index_catalogs for key in values): 25 | raise ValueError( 26 | f"There is a mismatch between the queried fields: " 27 | f"{values.keys()} and the fields of the provided index" 28 | f" catalogs: {index_catalogs.keys()}" 29 | ) 30 | self.values = values 31 | self.index_catalogs = index_catalogs 32 | 33 | def filter_hc_catalog(self, hc_structure: HCCatalogTypeVar) -> HCCatalogTypeVar: 34 | """Determine the pixels for which there is a result in each field""" 35 | all_pixels = set(hc_structure.get_healpix_pixels()) 36 | for field_name, field_value in self.values.items(): 37 | field_value = field_value if isinstance(field_value, list) else [field_value] 38 | pixels_for_field = set(self.index_catalogs[field_name].loc_partitions(field_value)) 39 | all_pixels = all_pixels.intersection(pixels_for_field) 40 | return hc_structure.filter_from_pixel_list(list(all_pixels)) 41 | 42 | def search_points(self, frame: npd.NestedFrame, _) -> npd.NestedFrame: 43 | """Determine the search results within a data frame""" 44 | filter_mask = np.ones(len(frame), dtype=np.bool) 45 | for field_name, field_index_catalog in self.index_catalogs.items(): 46 | index_column = field_index_catalog.catalog_info.indexing_column 47 | mask = frame[index_column].isin([self.values[field_name]]) 48 | filter_mask = filter_mask & mask 49 | return frame[filter_mask] 50 | -------------------------------------------------------------------------------- /src/lsdb/core/search/moc_search.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from typing import TYPE_CHECKING 4 | 5 | import astropy.units as u 6 | import nested_pandas as npd 7 | from hats.catalog import TableProperties 8 | from mocpy import MOC 9 | 10 | from lsdb.core.search.abstract_search import AbstractSearch 11 | 12 | if TYPE_CHECKING: 13 | from lsdb.types import HCCatalogTypeVar 14 | 15 | 16 | class MOCSearch(AbstractSearch): 17 | """Filter the catalog by a MOC. 18 | 19 | Filters partitions in the catalog to those that are in a specified moc. 20 | """ 21 | 22 | def __init__(self, moc: MOC, fine: bool = True): 23 | super().__init__(fine) 24 | self.moc = moc 25 | 26 | def filter_hc_catalog(self, hc_structure: HCCatalogTypeVar) -> HCCatalogTypeVar: 27 | return hc_structure.filter_by_moc(self.moc) 28 | 29 | def search_points(self, frame: npd.NestedFrame, metadata: TableProperties) -> npd.NestedFrame: 30 | df_ras = frame[metadata.ra_column].to_numpy() 31 | df_decs = frame[metadata.dec_column].to_numpy() 32 | mask = self.moc.contains_lonlat(df_ras * u.deg, df_decs * u.deg) 33 | return frame.iloc[mask] 34 | -------------------------------------------------------------------------------- /src/lsdb/core/search/order_search.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from typing import TYPE_CHECKING 4 | 5 | import nested_pandas as npd 6 | 7 | from lsdb.core.search.abstract_search import AbstractSearch 8 | 9 | if TYPE_CHECKING: 10 | from lsdb.types import HCCatalogTypeVar 11 | 12 | 13 | class OrderSearch(AbstractSearch): 14 | """Filter the catalog by HEALPix order. 15 | 16 | Filters partitions in the catalog to those that are in the orders specified. 17 | Does not filter points inside those partitions. 18 | """ 19 | 20 | def __init__(self, min_order: int = 0, max_order: int | None = None): 21 | super().__init__(fine=False) 22 | if max_order and min_order > max_order: 23 | raise ValueError("The minimum order should be lower than or equal to the maximum order") 24 | self.min_order = min_order 25 | self.max_order = max_order 26 | 27 | def filter_hc_catalog(self, hc_structure: HCCatalogTypeVar) -> HCCatalogTypeVar: 28 | max_catalog_order = hc_structure.pixel_tree.get_max_depth() 29 | max_order = max_catalog_order if self.max_order is None else self.max_order 30 | if self.min_order > max_order: 31 | raise ValueError("The minimum order is higher than the catalog's maximum order") 32 | pixels = [p for p in hc_structure.get_healpix_pixels() if self.min_order <= p.order <= max_order] 33 | return hc_structure.filter_from_pixel_list(pixels) 34 | 35 | def search_points(self, frame: npd.NestedFrame, _) -> npd.NestedFrame: 36 | """Determine the search results within a data frame.""" 37 | return frame 38 | -------------------------------------------------------------------------------- /src/lsdb/core/search/pixel_search.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from typing import TYPE_CHECKING 4 | 5 | import nested_pandas as npd 6 | import pandas as pd 7 | from hats.pixel_math import HealpixPixel, get_healpix_pixel, spatial_index 8 | 9 | from lsdb.core.search.abstract_search import AbstractSearch 10 | 11 | if TYPE_CHECKING: 12 | from lsdb.types import HCCatalogTypeVar 13 | 14 | 15 | class PixelSearch(AbstractSearch): 16 | """Filter the catalog by HEALPix pixels. 17 | 18 | Filters partitions in the catalog to those that are in a specified pixel set. 19 | Does not filter points inside those partitions. 20 | """ 21 | 22 | def __init__(self, pixels: tuple[int, int] | HealpixPixel | list[tuple[int, int] | HealpixPixel]): 23 | super().__init__(fine=False) 24 | if isinstance(pixels, tuple): 25 | self.pixels = [get_healpix_pixel(pixels)] 26 | elif isinstance(pixels, HealpixPixel): 27 | self.pixels = [pixels] 28 | elif pd.api.types.is_list_like(pixels): 29 | if len(pixels) == 0: 30 | raise ValueError("Some pixels required for PixelSearch") 31 | self.pixels = [get_healpix_pixel(pix) for pix in pixels] 32 | else: 33 | raise ValueError("Unsupported input for PixelSearch") 34 | 35 | @classmethod 36 | def from_radec(cls, ra: float | list[float], dec: float | list[float]) -> PixelSearch: 37 | """Create a pixel search region, based on radec points. 38 | 39 | Args: 40 | ra (float|list[float]): celestial coordinates, right ascension in degrees 41 | dec (float|list[float]): celestial coordinates, declination in degrees 42 | """ 43 | pixels = list(spatial_index.compute_spatial_index(ra, dec)) 44 | pixels = [(spatial_index.SPATIAL_INDEX_ORDER, pix) for pix in pixels] 45 | return cls(pixels) 46 | 47 | def filter_hc_catalog(self, hc_structure: HCCatalogTypeVar) -> HCCatalogTypeVar: 48 | return hc_structure.filter_from_pixel_list(self.pixels) 49 | 50 | def search_points(self, frame: npd.NestedFrame, _) -> npd.NestedFrame: 51 | return frame 52 | -------------------------------------------------------------------------------- /src/lsdb/dask/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/dask/__init__.py -------------------------------------------------------------------------------- /src/lsdb/dask/divisions.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import numpy as np 4 | from hats.pixel_math import HealpixPixel 5 | from hats.pixel_math.healpix_pixel_function import get_pixel_argsort 6 | from hats.pixel_math.spatial_index import healpix_to_spatial_index 7 | 8 | 9 | def get_pixels_divisions(healpix_pixels: list[HealpixPixel]) -> tuple[int, ...] | None: 10 | """Calculates the Dask Dataframe divisions for a list of HEALPix pixels. 11 | 12 | Divisions include the minimum value of every HEALPix pixel spatial_index 13 | and the maximum value of the last HEALPix pixel spatial_index. In practice 14 | they represent the bounds of spatial_index values for the target pixels. 15 | 16 | Args: 17 | healpix_pixels (List[HealpixPixel]): The list of HEALPix pixels to 18 | calculate the spatial_index bounds for. 19 | 20 | Returns: 21 | The Dask Dataframe divisions, as a tuple of integer. 22 | """ 23 | if len(healpix_pixels) == 0: 24 | return None 25 | orders = [pix.order for pix in healpix_pixels] 26 | pixels = [pix.pixel for pix in healpix_pixels] 27 | divisions = healpix_to_spatial_index(orders, pixels) 28 | last_pixel = healpix_pixels[get_pixel_argsort(healpix_pixels)[-1]] 29 | divisions = np.append(divisions, healpix_to_spatial_index(last_pixel.order, last_pixel.pixel + 1)) 30 | return tuple(np.sort(divisions)) 31 | -------------------------------------------------------------------------------- /src/lsdb/dask/partition_indexer.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import numpy as np 4 | 5 | from lsdb.core.search.pixel_search import PixelSearch 6 | 7 | 8 | # pylint: disable=protected-access 9 | class PartitionIndexer: 10 | """Class that implements the square brackets accessor for catalog partitions.""" 11 | 12 | def __init__(self, catalog): 13 | self.catalog = catalog 14 | 15 | def __getitem__(self, item): 16 | if isinstance(item, int): 17 | item = [item] 18 | indices = np.arange(len(self.catalog._ddf_pixel_map), dtype=np.int64)[item].tolist() 19 | pixels = self._get_pixels_from_partition_indices(indices) 20 | return self.catalog.search(PixelSearch(pixels)) 21 | 22 | def _get_pixels_from_partition_indices(self, indices: list[int]) -> list[tuple[int, int]]: 23 | """Performs a reverse-lookup in the catalog pixel-to-partition map and returns the 24 | pixels for the specified partition `indices`.""" 25 | inverted_pixel_map = {i: pixel for pixel, i in self.catalog._ddf_pixel_map.items()} 26 | filtered_pixels = [inverted_pixel_map[key] for key in indices] 27 | return [(p.order, p.pixel) for p in filtered_pixels] 28 | -------------------------------------------------------------------------------- /src/lsdb/io/__init__.py: -------------------------------------------------------------------------------- 1 | from .to_association import to_association 2 | from .to_hats import to_hats 3 | -------------------------------------------------------------------------------- /src/lsdb/io/schema.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import dask.dataframe as dd 4 | import pyarrow as pa 5 | 6 | 7 | def get_arrow_schema(ddf: dd.DataFrame) -> pa.Schema: 8 | """Constructs the pyarrow schema from the meta of a Dask DataFrame. 9 | 10 | Args: 11 | ddf (dd.DataFrame): A Dask DataFrame. 12 | 13 | Returns: 14 | The arrow schema for the provided Dask DataFrame. 15 | """ 16 | # pylint: disable=protected-access 17 | return pa.Schema.from_pandas(ddf._meta).remove_metadata() 18 | -------------------------------------------------------------------------------- /src/lsdb/loaders/__init__.py: -------------------------------------------------------------------------------- 1 | from .dataframe import from_dataframe 2 | from .hats import open_catalog, read_hats 3 | -------------------------------------------------------------------------------- /src/lsdb/loaders/dataframe/__init__.py: -------------------------------------------------------------------------------- 1 | from .from_dataframe import from_dataframe 2 | -------------------------------------------------------------------------------- /src/lsdb/loaders/hats/__init__.py: -------------------------------------------------------------------------------- 1 | from .read_hats import open_catalog, read_hats 2 | -------------------------------------------------------------------------------- /src/lsdb/loaders/hats/hats_loading_config.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from dataclasses import dataclass, field 4 | from pathlib import Path 5 | 6 | from upath import UPath 7 | 8 | from lsdb.core.search.abstract_search import AbstractSearch 9 | 10 | 11 | @dataclass 12 | class HatsLoadingConfig: 13 | """Configuration for loading a HATS catalog in lsdb. 14 | 15 | Contains all parameters needed for a user to specify how to correctly read a hats-sharded catalog. 16 | """ 17 | 18 | search_filter: AbstractSearch | None = None 19 | """The spatial filter to apply to the catalog""" 20 | 21 | columns: list[str] | None = None 22 | """Columns to load from the catalog. If not specified, all columns are loaded""" 23 | 24 | margin_cache: str | Path | UPath | None = None 25 | """Path to the margin cache catalog. Defaults to None.""" 26 | 27 | kwargs: dict = field(default_factory=dict) 28 | """Extra kwargs for the pandas parquet file reader""" 29 | 30 | def __post_init__(self): 31 | # Check for commonly misspelled or mistaken keys 32 | for nonused_kwarg in ["margin", "maargin", "margins", "cache", "margincache"]: 33 | if nonused_kwarg in self.kwargs: 34 | raise ValueError( 35 | f"Invalid keyword argument '{nonused_kwarg}' found. Did you mean 'margin_cache'?" 36 | ) 37 | 38 | def make_query_url_params(self) -> dict: 39 | """ 40 | Generates a dictionary of URL parameters with `columns` and `filters` attributes. 41 | """ 42 | url_params = {} 43 | 44 | if self.columns and len(self.columns) > 0: 45 | url_params["columns"] = self.columns 46 | 47 | if "filters" in self.kwargs: 48 | url_params["filters"] = [] 49 | for filtr in self.kwargs["filters"]: 50 | # This is how HATS expects the filters to add to the url 51 | url_params["filters"].append(f"{filtr[0]}{filtr[1]}{filtr[2]}") 52 | 53 | return url_params 54 | 55 | def get_read_kwargs(self): 56 | """Clumps existing kwargs and `dtype_backend`, if specified.""" 57 | kwargs = dict(self.kwargs) 58 | return kwargs 59 | -------------------------------------------------------------------------------- /src/lsdb/nested/__init__.py: -------------------------------------------------------------------------------- 1 | from . import accessor, backends # noqa 2 | from .core import NestedFrame # noqa 3 | from .datasets import generate_catalog, generate_data # noqa 4 | from .io import read_parquet # noqa 5 | from .utils import count_nested # noqa 6 | -------------------------------------------------------------------------------- /src/lsdb/nested/backends.py: -------------------------------------------------------------------------------- 1 | import nested_pandas as npd 2 | import pandas as pd 3 | from dask._dispatch import get_collection_type 4 | from dask.dataframe.backends import meta_nonempty_dataframe 5 | from dask.dataframe.dispatch import make_meta_dispatch 6 | from dask.dataframe.extensions import make_array_nonempty 7 | from dask.dataframe.utils import meta_nonempty 8 | from nested_pandas.series.ext_array import NestedExtensionArray 9 | 10 | from .core import NestedFrame 11 | 12 | get_collection_type.register(npd.NestedFrame, lambda _: NestedFrame) 13 | 14 | # The following dispatch functions are defined as per the Dask extension guide: 15 | # https://docs.dask.org/en/latest/dataframe-extend.html 16 | 17 | 18 | @make_meta_dispatch.register(npd.NestedFrame) 19 | def make_meta_frame(x, index=None) -> npd.NestedFrame: 20 | """Create an empty NestedFrame to use as Dask's underlying object meta.""" 21 | 22 | dtypes = x.dtypes.to_dict() 23 | index = index if index is not None else x.index 24 | index = index[:0].copy() 25 | result = npd.NestedFrame({key: pd.Series(dtype=d) for key, d in dtypes.items()}, index=index) 26 | return result 27 | 28 | 29 | @meta_nonempty.register(npd.NestedFrame) 30 | def _nonempty_nestedframe(x, index=None) -> npd.NestedFrame: 31 | """Construct a new NestedFrame with the same underlying data.""" 32 | df = meta_nonempty_dataframe(x) 33 | if index is not None: 34 | df.index = index 35 | return npd.NestedFrame(df) 36 | 37 | 38 | @make_array_nonempty.register(npd.NestedDtype) 39 | def _(dtype) -> NestedExtensionArray: 40 | """Register a valid dtype for the NestedExtensionArray""" 41 | # must be two values to avoid a length error in meta inference 42 | # Dask seems to explicitly require meta dtypes to have length 2. 43 | vals = [pd.NA, pd.NA] 44 | return NestedExtensionArray._from_sequence(vals, dtype=dtype) # pylint: disable=protected-access 45 | -------------------------------------------------------------------------------- /src/lsdb/nested/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | from .generation import * # noqa 2 | -------------------------------------------------------------------------------- /src/lsdb/nested/py.typed: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/nested/py.typed -------------------------------------------------------------------------------- /src/lsdb/nested/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import * # noqa 2 | -------------------------------------------------------------------------------- /src/lsdb/nested/utils/utils.py: -------------------------------------------------------------------------------- 1 | import nested_pandas as npd 2 | import pandas as pd 3 | from nested_pandas import utils as npd_utils 4 | 5 | from ..core import NestedFrame # pylint: disable=cyclic-import 6 | 7 | 8 | def count_nested(df, nested, by=None, join=True) -> NestedFrame: 9 | """Counts the number of rows of a nested dataframe. 10 | 11 | Wraps Nested-Pandas count_nested. 12 | 13 | Parameters 14 | ---------- 15 | df: NestedFrame 16 | A NestedFrame that contains the desired `nested` series 17 | to count. 18 | nested: 'str' 19 | The label of the nested series to count. 20 | by: 'str', optional 21 | Specifies a column within nested to count by, returning 22 | a count for each unique value in `by`. 23 | join: bool, optional 24 | Join the output count columns to df and return df, otherwise 25 | just return a NestedFrame containing only the count columns. 26 | 27 | Returns 28 | ------- 29 | NestedFrame 30 | """ 31 | 32 | # The meta varies depending on the parameters 33 | 34 | # first depending on by 35 | if by is not None: 36 | # will have one column per unique value of the specified column 37 | # requires some computation to determine these values 38 | by_cols = sorted(df[nested].nest.to_flat()[by].unique()) 39 | out_cols = [f"n_{nested}_{col}" for col in by_cols] 40 | else: 41 | # otherwise just have a single column output 42 | out_cols = [f"n_{nested}"] 43 | 44 | # add dtypes 45 | meta = npd.NestedFrame({col: 0 for col in out_cols}, index=[]) 46 | 47 | # and second depending on join 48 | if join: 49 | # adds the meta onto the existing meta 50 | meta = pd.concat([df._meta, meta]) # pylint: disable=protected-access 51 | 52 | return df.map_partitions(lambda x: npd_utils.count_nested(x, nested, by=by, join=join), meta=meta) 53 | -------------------------------------------------------------------------------- /src/lsdb/py.typed: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/src/lsdb/py.typed -------------------------------------------------------------------------------- /src/lsdb/types.py: -------------------------------------------------------------------------------- 1 | from typing import TypeVar 2 | 3 | from hats.catalog.healpix_dataset.healpix_dataset import HealpixDataset as HCHealpixDataset 4 | from hats.pixel_math import HealpixPixel 5 | 6 | from lsdb.catalog.dataset.dataset import Dataset 7 | 8 | DaskDFPixelMap = dict[HealpixPixel, int] 9 | 10 | CatalogTypeVar = TypeVar("CatalogTypeVar", bound=Dataset) 11 | HCCatalogTypeVar = TypeVar("HCCatalogTypeVar", bound=HCHealpixDataset) 12 | -------------------------------------------------------------------------------- /tests/data/raw/reproducers/README.md: -------------------------------------------------------------------------------- 1 | # Reproducers 2 | 3 | Files in this directory are related to specific issues and enable tests that guard against specific behavior regressions. 4 | 5 | Files should be named with the LSDB issue that they relate to (or otherwise make clear what 6 | behavior they're guarding against by listing in this README). -------------------------------------------------------------------------------- /tests/data/raw/small_sky_no_metadata/partition_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Npix,Dir 2 | 0,11,0 3 | -------------------------------------------------------------------------------- /tests/data/raw/small_sky_no_metadata/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky 3 | dataproduct_type=object 4 | hats_nrows=131 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | -------------------------------------------------------------------------------- /tests/data/raw/xmatch_expected/xmatch_correct_002_005.csv: -------------------------------------------------------------------------------- 1 | ,ss_id,xmatch_id,dist 2 | 0,723,723.0,0.004230944133156015 3 | 1,811,723.0,0.004230944133156015 4 | 2,826,826.0,0.0033090395306666883 5 | 3,771,771.0,0.0033596080206150882 6 | 4,772,772.0,0.0030279559393869 7 | 5,733,733.0,0.004355207583297679 8 | 6,739,739.0,0.0032471204977342883 9 | 7,794,794.0,0.0045302797229310335 10 | 8,748,748.0,0.003062401989135037 11 | 9,768,768.0,0.004841109360845278 12 | 10,780,780.0,0.004330472842222864 13 | 11,787,787.0,0.003138165330728035 14 | 12,731,731.0,0.004538535485059356 15 | 13,823,823.0,0.0037591564718832612 16 | 14,710,710.0,0.003434579937558307 17 | 15,757,757.0,0.004122403365557745 18 | 16,821,821.0,0.004873156267964384 19 | 17,728,728.0,0.004209569964155282 20 | 18,732,732.0,0.00456330407561165 21 | 19,705,705.0,0.00240617318090223 22 | 20,779,779.0,0.0026783004493342623 23 | 21,828,828.0,0.004634109015995108 24 | 22,700,700.0,0.0038937371796661955 25 | 23,749,749.0,0.004273625020940555 26 | 24,774,774.0,0.0023656606962275902 27 | 25,701,701.0,0.004417089286820734 28 | 26,725,725.0,0.004078208611728575 29 | 27,785,785.0,0.004209440027670683 30 | 28,763,763.0,0.0031807665796119164 31 | 29,782,782.0,0.004930609902645136 32 | -------------------------------------------------------------------------------- /tests/data/raw/xmatch_expected/xmatch_correct_0_005.csv: -------------------------------------------------------------------------------- 1 | ,ss_id,xmatch_id,dist 2 | 0,700,700.0,0.0038937371796661955 3 | 1,701,701.0,0.004417089286822645 4 | 2,705,705.0,0.0024061731809319658 5 | 3,706,706.0,0.001408690476035035 6 | 4,710,710.0,0.0034345799375555225 7 | 5,718,718.0,0.0016663443024497987 8 | 6,723,723.0,0.004230944133156015 9 | 7,725,725.0,0.004078208611731745 10 | 8,728,728.0,0.004209569964155283 11 | 9,731,731.0,0.004538535485067151 12 | 10,732,732.0,0.00456330407561165 13 | 11,733,733.0,0.004355207583297679 14 | 12,739,739.0,0.003247120497737458 15 | 13,748,748.0,0.003062401989135037 16 | 14,749,749.0,0.004273625020931212 17 | 15,757,757.0,0.004122403365558441 18 | 16,763,763.0,0.003180766579609346 19 | 17,768,768.0,0.004841109360846844 20 | 18,771,771.0,0.0033596080206363273 21 | 19,772,772.0,0.0030279559393869 22 | 20,774,774.0,0.0023656606961959385 23 | 21,777,777.0,0.0008965826924609473 24 | 22,779,779.0,0.0026783004493342623 25 | 23,780,780.0,0.004330472842230649 26 | 24,782,782.0,0.004930609902645136 27 | 25,785,785.0,0.00420944002766141 28 | 26,787,787.0,0.003138165330728707 29 | 27,794,794.0,0.0045302797229310335 30 | 28,797,797.0,0.0018855556575143944 31 | 29,811,723.0,0.004230944133156015 32 | 30,813,813.0,0.0018518624989871174 33 | 31,821,821.0,0.004873156267967185 34 | 32,823,823.0,0.003759156471877518 35 | 33,826,826.0,0.003309039530666688 36 | 34,827,827.0,0.0009969016455611704 37 | 35,828,828.0,0.004634109015995284 38 | -------------------------------------------------------------------------------- /tests/data/small_sky/dataset/Norder=0/Dir=0/Npix=11.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky/dataset/Norder=0/Dir=0/Npix=11.parquet -------------------------------------------------------------------------------- /tests/data/small_sky/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky/dataset/_common_metadata -------------------------------------------------------------------------------- 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hats_creation_date=2025-03-18T13\:39UTC 13 | hats_estsize=110 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- /tests/data/small_sky_left_xmatch/dataset/Norder=1/Dir=0/Npix=44.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_left_xmatch/dataset/Norder=1/Dir=0/Npix=44.parquet -------------------------------------------------------------------------------- /tests/data/small_sky_left_xmatch/dataset/Norder=1/Dir=0/Npix=45.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_left_xmatch/dataset/Norder=1/Dir=0/Npix=45.parquet 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-------------------------------------------------------------------------------- /tests/data/small_sky_left_xmatch/partition_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Npix 2 | 1,44 3 | 1,45 4 | 1,46 5 | 1,47 6 | 2,66 7 | -------------------------------------------------------------------------------- /tests/data/small_sky_left_xmatch/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_left_xmatch 3 | dataproduct_type=object 4 | hats_nrows=132 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_npix_suffix=.parquet 8 | hats_max_rows=100 9 | hats_order=2 10 | moc_sky_fraction=0.08854 11 | hats_builder=hats-import v0.5.0 12 | hats_creation_date=2025-04-10T20\:12UTC 13 | hats_estsize=25 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- 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hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- /tests/data/small_sky_npix_as_dir/dataset/Norder=0/Dir=0/Npix=11/part0.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_npix_as_dir/dataset/Norder=0/Dir=0/Npix=11/part0.parquet -------------------------------------------------------------------------------- /tests/data/small_sky_npix_as_dir/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_npix_as_dir/dataset/_common_metadata -------------------------------------------------------------------------------- /tests/data/small_sky_npix_as_dir/dataset/_metadata: 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obs_collection=small_sky_order1 3 | dataproduct_type=object 4 | hats_nrows=131 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_npix_suffix=.parquet 8 | hats_max_rows=1000000 9 | hats_order=1 10 | moc_sky_fraction=0.08333 11 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 12 | hats_creation_date=2025-03-18T13\:39UTC 13 | hats_estsize=22 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_collection/collection.properties: -------------------------------------------------------------------------------- 1 | #HATS Collection 2 | name=small_sky_01 3 | hats_primary_table_url=small_sky_order1 4 | 5 | all_margins=small_sky_order1_margin_1deg small_sky_order1_margin_2deg 6 | default_margin=small_sky_order1_margin_1deg 7 | 8 | all_indexes=id small_sky_order1_id_index 9 | default_index=id 10 | 11 | obs_regime=Optical 12 | 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-------------------------------------------------------------------------------- /tests/data/small_sky_order1_collection/small_sky_order1/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_order1 3 | dataproduct_type=object 4 | hats_nrows=131 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_npix_suffix=.parquet 8 | hats_max_rows=1000000 9 | hats_order=1 10 | moc_sky_fraction=0.08333 11 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 12 | hats_creation_date=2025-03-18T13\:39UTC 13 | hats_estsize=22 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_collection/small_sky_order1_id_index/dataset/_common_metadata: -------------------------------------------------------------------------------- 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dataproduct_type=margin 4 | hats_nrows=23 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_primary_table_url=small_sky_order1 8 | hats_margin_threshold=3600.0 9 | hats_npix_suffix=.parquet 10 | hats_order=1 11 | moc_sky_fraction=0.16667 12 | hats_builder=hats-import v0.5.1.dev1+g60081bf 13 | hats_creation_date=2025-04-15T19\:36UTC 14 | hats_estsize=14 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_collection/small_sky_order1_margin_2deg/dataset/Norder=0/Dir=0/Npix=4.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_collection/small_sky_order1_margin_2deg/dataset/Norder=0/Dir=0/Npix=4.parquet -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | Norder,Npix 2 | 0,4 3 | 0,7 4 | 0,8 5 | 1,44 6 | 1,45 7 | 1,46 8 | 1,47 9 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_collection/small_sky_order1_margin_2deg/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_order1_margin_2deg 3 | dataproduct_type=margin 4 | hats_nrows=47 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_primary_table_url=small_sky_order1 8 | hats_margin_threshold=7200.0 9 | hats_npix_suffix=.parquet 10 | hats_order=1 11 | moc_sky_fraction=0.33333 12 | hats_builder=hats-import v0.5.1.dev1+g60081bf 13 | hats_creation_date=2025-04-15T19\:37UTC 14 | hats_estsize=20 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_default_columns/dataset: -------------------------------------------------------------------------------- 1 | ../small_sky_order1/dataset -------------------------------------------------------------------------------- /tests/data/small_sky_order1_default_columns/partition_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Npix 2 | 1,44 3 | 1,45 4 | 1,46 5 | 1,47 6 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_default_columns/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_order1_default_columns 3 | dataproduct_type=object 4 | hats_nrows=131 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_cols_default=ra dec id 8 | hats_npix_suffix=.parquet 9 | hats_max_rows=1000000 10 | hats_order=1 11 | moc_sky_fraction=0.08333 12 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 13 | hats_creation_date=2025-03-18T13\:39UTC 14 | hats_estsize=22 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_id_index/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_id_index/dataset/_common_metadata -------------------------------------------------------------------------------- /tests/data/small_sky_order1_id_index/dataset/_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_id_index/dataset/_metadata -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | #HATS Collection 2 | name=small_sky_01_source 3 | hats_primary_table_url=small_sky_order1_source 4 | 5 | all_margins=small_sky_order1_source_margin 6 | default_margin=small_sky_order1_source_margin 7 | 8 | all_indexes=object_id small_sky_order1_source_object_id_index band small_sky_order1_source_band_index 9 | default_index=object_id 10 | 11 | obs_regime=Optical 12 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_source_collection/small_sky_order1_source/dataset/Norder=1/Dir=0/Npix=16.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_source_collection/small_sky_order1_source/dataset/Norder=1/Dir=0/Npix=16.parquet 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dataproduct_type=source 4 | hats_nrows=17161 5 | hats_col_ra=source_ra 6 | hats_col_dec=source_dec 7 | hats_npix_suffix=.parquet 8 | hats_max_rows=1000000 9 | hats_order=1 10 | moc_sky_fraction=0.10417 11 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 12 | hats_creation_date=2025-03-18T13\:39UTC 13 | hats_estsize=938 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_source_collection/small_sky_order1_source_band_index/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_source_collection/small_sky_order1_source_band_index/dataset/_common_metadata -------------------------------------------------------------------------------- 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hats_primary_table_url=small_sky_order1_source 8 | hats_margin_threshold=7200.0 9 | hats_npix_suffix=.parquet 10 | hats_order=1 11 | moc_sky_fraction=0.27083 12 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 13 | hats_creation_date=2025-03-18T13\:39UTC 14 | hats_estsize=361 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_source_collection/small_sky_order1_source_object_id_index/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_source_collection/small_sky_order1_source_object_id_index/dataset/_common_metadata -------------------------------------------------------------------------------- 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hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_order1_source_object_id_index/dataset/_common_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_source_object_id_index/dataset/_common_metadata -------------------------------------------------------------------------------- /tests/data/small_sky_order1_source_object_id_index/dataset/_metadata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_order1_source_object_id_index/dataset/_metadata -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /tests/data/small_sky_order3_source_margin/partition_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Npix 2 | 3,264 3 | 3,707 4 | 3,708 5 | 3,709 6 | 3,710 7 | 3,713 8 | 3,715 9 | 3,716 10 | 3,717 11 | 3,718 12 | 3,719 13 | 3,722 14 | 3,726 15 | 3,728 16 | 3,729 17 | 3,730 18 | 3,732 19 | 3,733 20 | 3,734 21 | 3,739 22 | 3,740 23 | 3,741 24 | 3,742 25 | 3,743 26 | 3,745 27 | 3,747 28 | 3,748 29 | 3,749 30 | 3,750 31 | 3,752 32 | 3,753 33 | 3,754 34 | 3,755 35 | 2,189 36 | 3,760 37 | -------------------------------------------------------------------------------- /tests/data/small_sky_order3_source_margin/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_order3_source_margin 3 | dataproduct_type=margin 4 | hats_nrows=5102 5 | hats_col_ra=source_ra 6 | hats_col_dec=source_dec 7 | hats_primary_table_url=small_sky_order3_source 8 | hats_margin_threshold=300.0 9 | hats_npix_suffix=.parquet 10 | hats_order=3 11 | moc_sky_fraction=0.04948 12 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 13 | hats_creation_date=2025-03-18T13\:39UTC 14 | hats_estsize=405 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_source/dataset/Norder=1/Dir=0/Npix=47.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_source/dataset/Norder=1/Dir=0/Npix=47.parquet -------------------------------------------------------------------------------- /tests/data/small_sky_source/dataset/Norder=2/Dir=0/Npix=176.parquet: -------------------------------------------------------------------------------- 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dataproduct_type=source 4 | hats_nrows=17161 5 | hats_col_ra=source_ra 6 | hats_col_dec=source_dec 7 | hats_npix_suffix=.parquet 8 | hats_max_rows=3000 9 | hats_order=2 10 | moc_sky_fraction=0.08854 11 | hats_builder=hats-import v0.5.0 12 | hats_creation_date=2025-04-10T20\:11UTC 13 | hats_estsize=958 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- /tests/data/small_sky_source_margin/dataset/Norder=1/Dir=0/Npix=47.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_source_margin/dataset/Norder=1/Dir=0/Npix=47.parquet -------------------------------------------------------------------------------- /tests/data/small_sky_source_margin/dataset/Norder=2/Dir=0/Npix=176.parquet: 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hats_primary_table_url=small_sky_source 8 | hats_margin_threshold=180.0 9 | hats_npix_suffix=.parquet 10 | hats_order=2 11 | moc_sky_fraction=0.08854 12 | hats_builder=hats-import v0.5.0 13 | hats_creation_date=2025-04-10T20\:11UTC 14 | hats_estsize=147 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/small_sky_to_o1source/dataset/Norder=0/Dir=0/Npix=11.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/small_sky_to_o1source/dataset/Norder=0/Dir=0/Npix=11.parquet -------------------------------------------------------------------------------- /tests/data/small_sky_to_o1source/dataset/_common_metadata: -------------------------------------------------------------------------------- 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Norder,Npix,join_Norder,join_Npix 2 | 0,11,1,44 3 | 0,11,1,45 4 | 0,11,1,46 5 | 0,11,1,47 6 | -------------------------------------------------------------------------------- /tests/data/small_sky_to_o1source/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_to_o1source 3 | dataproduct_type=association 4 | hats_nrows=17161 5 | hats_primary_table_url=small_sky 6 | hats_col_assn_primary=id 7 | hats_col_assn_primary_assn=object_id 8 | hats_assn_join_table_url=small_sky_order1_source 9 | hats_col_assn_join=source_id 10 | hats_col_assn_join_assn=source_id 11 | hats_assn_leaf_files=True 12 | hats_order=0 13 | moc_sky_fraction=0.08333 14 | hats_builder=hats-import v0.4.6.dev1+gf00cd7a 15 | hats_creation_date=2025-03-05T19\:01UTC 16 | hats_estsize=117 17 | hats_release_date=2024-09-18 18 | hats_version=v0.1 19 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /tests/data/small_sky_to_o1source_soft/partition_join_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Dir,Npix,join_Norder,join_Dir,join_Npix,num_rows 2 | 0,0,11,1,0,44,5302 3 | 0,0,11,1,0,45,3947 4 | 0,0,11,1,0,46,5467 5 | 0,0,11,1,0,47,2395 6 | -------------------------------------------------------------------------------- /tests/data/small_sky_to_o1source_soft/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_to_o1source_soft 3 | dataproduct_type=association 4 | hats_nrows=17161 5 | hats_primary_table_url=small_sky 6 | hats_col_assn_primary=id 7 | hats_col_assn_primary_assn=object_id 8 | hats_assn_join_table_url=small_sky_order1_source 9 | hats_col_assn_join=source_id 10 | hats_col_assn_join_assn=source_id 11 | hats_assn_leaf_files=False 12 | hats_order=0 13 | 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/tests/data/small_sky_to_xmatch/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_to_xmatch 3 | dataproduct_type=association 4 | hats_nrows=111 5 | hats_primary_table_url=small_sky 6 | hats_col_assn_primary=id 7 | hats_col_assn_primary_assn=object_id 8 | hats_assn_join_table_url=small_sky_xmatch 9 | hats_col_assn_join=id 10 | hats_col_assn_join_assn=source_id 11 | hats_assn_leaf_files=True 12 | hats_order=0 13 | moc_sky_fraction=0.08333 14 | hats_builder=hats-import v0.4.6.dev1+gf00cd7a 15 | hats_creation_date=2025-03-05T19\:01UTC 16 | hats_estsize=18 17 | hats_release_date=2024-09-18 18 | hats_version=v0.1 19 | -------------------------------------------------------------------------------- /tests/data/small_sky_to_xmatch_soft/dataset/_common_metadata: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | Norder,Dir,Npix,join_Norder,join_Dir,join_Npix,num_rows 2 | 0,0,11,1,0,44,41 3 | 0,0,11,1,0,45,29 4 | 0,0,11,1,0,46,41 5 | -------------------------------------------------------------------------------- /tests/data/small_sky_to_xmatch_soft/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_to_xmatch_soft 3 | dataproduct_type=association 4 | hats_nrows=111 5 | hats_primary_table_url=small_sky 6 | hats_col_assn_primary=id 7 | hats_col_assn_primary_assn=object_id 8 | hats_assn_join_table_url=small_sky_xmatch 9 | hats_col_assn_join=id 10 | hats_col_assn_join_assn=source_id 11 | hats_assn_leaf_files=False 12 | hats_order=0 13 | moc_sky_fraction=0.08333 14 | hats_builder=hats-import v0.4.6.dev1+gf00cd7a 15 | hats_creation_date=2025-03-05T19\:01UTC 16 | hats_estsize=3 17 | hats_release_date=2024-09-18 18 | 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-------------------------------------------------------------------------------- /tests/data/small_sky_xmatch/partition_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Npix 2 | 1,44 3 | 1,45 4 | 1,46 5 | -------------------------------------------------------------------------------- /tests/data/small_sky_xmatch/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_xmatch 3 | dataproduct_type=object 4 | hats_nrows=111 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_npix_suffix=.parquet 8 | hats_max_rows=100 9 | hats_order=1 10 | moc_sky_fraction=0.06250 11 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 12 | hats_creation_date=2025-03-18T13\:40UTC 13 | hats_estsize=45 14 | hats_release_date=2024-09-18 15 | hats_version=v0.1 16 | -------------------------------------------------------------------------------- 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/tests/data/small_sky_xmatch_margin/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=small_sky_xmatch_margin 3 | dataproduct_type=margin 4 | hats_nrows=39 5 | hats_col_ra=ra 6 | hats_col_dec=dec 7 | hats_primary_table_url=small_sky_xmatch 8 | hats_margin_threshold=7200.0 9 | hats_npix_suffix=.parquet 10 | hats_order=1 11 | moc_sky_fraction=0.33333 12 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 13 | hats_creation_date=2025-03-18T13\:41UTC 14 | hats_estsize=24 15 | hats_release_date=2024-09-18 16 | hats_version=v0.1 17 | -------------------------------------------------------------------------------- /tests/data/square_map/dataset/Norder=0/Dir=0/Npix=0.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/square_map/dataset/Norder=0/Dir=0/Npix=0.parquet 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https://raw.githubusercontent.com/astronomy-commons/lsdb/c5f85abfbc9e16b29e2a6db38f355a1a3338816b/tests/data/square_map/dataset/_metadata -------------------------------------------------------------------------------- /tests/data/square_map/partition_info.csv: -------------------------------------------------------------------------------- 1 | Norder,Npix 2 | 0,0 3 | 0,1 4 | 0,2 5 | 0,3 6 | 0,4 7 | 0,5 8 | 0,6 9 | 0,7 10 | 0,8 11 | 0,9 12 | 0,10 13 | 0,11 14 | -------------------------------------------------------------------------------- /tests/data/square_map/properties: -------------------------------------------------------------------------------- 1 | #HATS catalog 2 | obs_collection=square_map 3 | dataproduct_type=map 4 | hats_nrows=12 5 | hats_npix_suffix=.parquet 6 | hats_max_rows=1000000 7 | hats_order=0 8 | moc_sky_fraction=1.00000 9 | hats_builder=hats-import v0.4.6.dev3+g8f5e4fd 10 | hats_creation_date=2025-03-18T13\:41UTC 11 | hats_estsize=21 12 | hats_release_date=2024-09-18 13 | hats_version=v0.1 14 | -------------------------------------------------------------------------------- /tests/lsdb/catalog/test_association_catalog.py: -------------------------------------------------------------------------------- 1 | import hats as hc 2 | import pandas as pd 3 | 4 | import lsdb 5 | import lsdb.nested as nd 6 | from lsdb.catalog.association_catalog import AssociationCatalog 7 | 8 | 9 | def test_load_association(small_sky_to_xmatch_dir): 10 | small_sky_to_xmatch = lsdb.open_catalog(small_sky_to_xmatch_dir) 11 | assert isinstance(small_sky_to_xmatch, AssociationCatalog) 12 | assert isinstance(small_sky_to_xmatch._ddf, nd.NestedFrame) 13 | assert small_sky_to_xmatch.get_healpix_pixels() == small_sky_to_xmatch.hc_structure.get_healpix_pixels() 14 | assert repr(small_sky_to_xmatch) == repr(small_sky_to_xmatch._ddf) 15 | for healpix_pixel in small_sky_to_xmatch.get_healpix_pixels(): 16 | hp_order = healpix_pixel.order 17 | hp_pixel = healpix_pixel.pixel 18 | path = hc.io.paths.pixel_catalog_file( 19 | catalog_base_dir=small_sky_to_xmatch_dir, 20 | pixel=healpix_pixel, 21 | ) 22 | partition = small_sky_to_xmatch.get_partition(hp_order, hp_pixel) 23 | data = pd.read_parquet(path, dtype_backend="pyarrow") 24 | pd.testing.assert_frame_equal(partition.compute(), data) 25 | 26 | 27 | def test_load_soft_association(small_sky_to_xmatch_soft_dir): 28 | small_sky_to_xmatch_soft = lsdb.open_catalog(small_sky_to_xmatch_soft_dir) 29 | assert isinstance(small_sky_to_xmatch_soft, AssociationCatalog) 30 | assert isinstance(small_sky_to_xmatch_soft._ddf, nd.NestedFrame) 31 | assert len(small_sky_to_xmatch_soft.compute()) == 0 32 | -------------------------------------------------------------------------------- /tests/lsdb/catalog/test_moc_search.py: -------------------------------------------------------------------------------- 1 | import astropy.units as u 2 | import numpy as np 3 | import pandas as pd 4 | from hats.pixel_math import HealpixPixel 5 | from mocpy import MOC 6 | 7 | 8 | def test_moc_search_filters_correct_points(small_sky_order1_catalog): 9 | search_moc = MOC.from_healpix_cells(ipix=np.array([176, 177]), depth=np.array([2, 2]), max_depth=2) 10 | filtered_cat = small_sky_order1_catalog.moc_search(search_moc) 11 | assert filtered_cat.get_healpix_pixels() == [HealpixPixel(1, 44)] 12 | filtered_cat_comp = filtered_cat.compute() 13 | cat_comp = small_sky_order1_catalog.compute() 14 | assert np.all( 15 | search_moc.contains_lonlat( 16 | filtered_cat_comp["ra"].to_numpy() * u.deg, filtered_cat_comp["dec"].to_numpy() * u.deg 17 | ) 18 | ) 19 | assert np.sum( 20 | search_moc.contains_lonlat(cat_comp["ra"].to_numpy() * u.deg, cat_comp["dec"].to_numpy() * u.deg) 21 | ) == len(filtered_cat_comp) 22 | 23 | 24 | def test_moc_search_non_fine(small_sky_order1_catalog): 25 | search_moc = MOC.from_healpix_cells(ipix=np.array([176, 180]), depth=np.array([2, 2]), max_depth=2) 26 | filtered_cat = small_sky_order1_catalog.moc_search(search_moc, fine=False) 27 | assert filtered_cat.get_healpix_pixels() == [HealpixPixel(1, 44), HealpixPixel(1, 45)] 28 | pd.testing.assert_frame_equal( 29 | filtered_cat.compute(), 30 | small_sky_order1_catalog.pixel_search([HealpixPixel(1, 44), HealpixPixel(1, 45)]).compute(), 31 | ) 32 | -------------------------------------------------------------------------------- /tests/lsdb/catalog/test_order_search.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import pytest 3 | 4 | import lsdb.nested as nd 5 | from lsdb.core.search import OrderSearch 6 | 7 | 8 | def test_order_search_filters_correct_pixels(small_sky_source_catalog, helpers): 9 | order_search_catalog = small_sky_source_catalog.order_search(min_order=1, max_order=1) 10 | assert isinstance(order_search_catalog._ddf, nd.NestedFrame) 11 | pixel_orders = [pixel.order for pixel in order_search_catalog.get_healpix_pixels()] 12 | assert all(order == 1 for order in pixel_orders) 13 | helpers.assert_divisions_are_correct(order_search_catalog) 14 | 15 | order_search_catalog = small_sky_source_catalog.order_search(min_order=1, max_order=2) 16 | pixel_orders = [pixel.order for pixel in order_search_catalog.get_healpix_pixels()] 17 | assert all(1 <= order <= 2 for order in pixel_orders) 18 | helpers.assert_divisions_are_correct(order_search_catalog) 19 | 20 | order_search_catalog = small_sky_source_catalog.order_search(min_order=1) 21 | pixel_orders = [pixel.order for pixel in order_search_catalog.get_healpix_pixels()] 22 | assert all(1 <= order <= 2 for order in pixel_orders) 23 | helpers.assert_divisions_are_correct(order_search_catalog) 24 | 25 | order_search_catalog = small_sky_source_catalog.order_search(max_order=1) 26 | pixel_orders = [pixel.order for pixel in order_search_catalog.get_healpix_pixels()] 27 | assert all(0 <= order <= 1 for order in pixel_orders) 28 | helpers.assert_divisions_are_correct(order_search_catalog) 29 | 30 | 31 | def test_order_search_keeps_all_points(small_sky_source_catalog): 32 | metadata = small_sky_source_catalog.hc_structure 33 | partition_df = small_sky_source_catalog._ddf.partitions[0].compute() 34 | search = OrderSearch(min_order=1, max_order=2) 35 | filtered_df = search.search_points(partition_df, metadata) 36 | pd.testing.assert_frame_equal(partition_df, filtered_df) 37 | 38 | 39 | def test_order_search_invalid_args(small_sky_source_catalog): 40 | with pytest.raises(ValueError, match="lower than or equal to the maximum order"): 41 | small_sky_source_catalog.order_search(min_order=2, max_order=1) 42 | with pytest.raises(ValueError, match="minimum order is higher than"): 43 | small_sky_source_catalog.order_search(min_order=3) 44 | -------------------------------------------------------------------------------- /tests/lsdb/dask/test_divisions.py: -------------------------------------------------------------------------------- 1 | import hats as hc 2 | import numpy.testing as npt 3 | 4 | from lsdb.dask.divisions import get_pixels_divisions 5 | 6 | 7 | def test_divisions_are_independent_of_pixel_order(small_sky_order1_catalog): 8 | hp_pixels = small_sky_order1_catalog.get_ordered_healpix_pixels() 9 | order = [p.order for p in hp_pixels] 10 | pixel = [p.pixel for p in hp_pixels] 11 | pixels = hc.pixel_math.spatial_index.healpix_to_spatial_index(order, pixel) 12 | npt.assert_array_equal(pixels, sorted(pixels)) 13 | # Calculate divisions for ordered pixels 14 | divisions = get_pixels_divisions(hp_pixels) 15 | # Swap the first two pixels and check that the computed divisions are the same 16 | hp_pixels[0], hp_pixels[1] = hp_pixels[1], hp_pixels[0] 17 | assert divisions == get_pixels_divisions(hp_pixels) 18 | -------------------------------------------------------------------------------- /tests/lsdb/nested/test_io.py: -------------------------------------------------------------------------------- 1 | """ 2 | Unit tests for the I/O functionality of the lsdb.nested module. 3 | 4 | This module tests the reproducibility of reading and writing Parquet files 5 | using the NestedFrame data structure. 6 | """ 7 | 8 | import pandas as pd 9 | import pyarrow as pa 10 | 11 | import lsdb.nested as nd 12 | 13 | 14 | def test_read_parquet(test_dataset, tmp_path): 15 | """test the reproducibility of read_parquet""" 16 | 17 | # Setup a temporary directory for files 18 | nested_save_path = tmp_path / "nested" 19 | test_save_path = tmp_path / "test_dataset" 20 | 21 | # Save Nested to Parquet 22 | flat_nested = test_dataset.nested.nest.to_flat() 23 | flat_nested.to_parquet(nested_save_path, write_index=True) 24 | 25 | # Save Base to Parquet 26 | test_dataset[["a", "b"]].to_parquet(test_save_path, write_index=True) 27 | 28 | # Now read 29 | base = nd.read_parquet(test_save_path, calculate_divisions=True) 30 | nested = nd.read_parquet(nested_save_path, calculate_divisions=True) 31 | 32 | # this is read as a large_string, just make it a string 33 | nested = nested.astype({"band": pd.ArrowDtype(pa.string())}) 34 | base = base.add_nested(nested, "nested") 35 | 36 | # Check the loaded dataset against the original 37 | assert base.divisions == test_dataset.divisions # equal divisions 38 | assert base.compute().equals(test_dataset.compute()) # equal data 39 | 40 | # Check the flat nested datasets 41 | base_nested_flat = base.nested.nest.to_flat().compute() 42 | test_nested_flat = base.nested.nest.to_flat().compute() 43 | assert base_nested_flat.equals(test_nested_flat) 44 | -------------------------------------------------------------------------------- /tests/lsdb/nested/test_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | Unit tests for utility functions in the lsdb.nested module. 3 | 4 | This module tests the behavior of utility functions, such as the `count_nested` 5 | wrapper, ensuring they behave as expected and are consistent with the 6 | nested-pandas library. 7 | """ 8 | 9 | import pytest 10 | from nested_pandas.utils import count_nested 11 | 12 | import lsdb.nested as nd 13 | 14 | 15 | @pytest.mark.parametrize("join", [True, False]) 16 | @pytest.mark.parametrize("by", [None, "band"]) 17 | def test_count_nested(test_dataset, join, by): 18 | """test the count_nested wrapper""" 19 | 20 | # count_nested functionality is tested on the nested-pandas side 21 | # let's just make sure the behavior here is identical. 22 | 23 | result_dsk = nd.utils.count_nested(test_dataset, "nested", join=join, by=by).compute() 24 | result_pd = count_nested(test_dataset.compute(), "nested", join=join, by=by) 25 | 26 | assert result_dsk.equals(result_pd) 27 | -------------------------------------------------------------------------------- /tests/lsdb/test_packaging.py: -------------------------------------------------------------------------------- 1 | import lsdb 2 | 3 | 4 | def test_lsdb_version(): 5 | """Check to see that we can get the lsdb version""" 6 | assert lsdb.__version__ is not None 7 | --------------------------------------------------------------------------------