Abstract DGP2026-61

PlanetaryPy: A software package to democratize and ease access to planetary data, metadata, and algorithms

K-Michael Aye (1), Christian Tai Udovicic (2) and the PlanetaryPy Team (3)

(1) Department of Earth Sciences, Institute of Geological Sciences, Planetary Sciences and Remote Sensing, Freie Universität Berlin, Germany, (2) Department of Geography, University of Winnipeg, Winnipeg, Manitoba, Canada, (3) https://github.com/planetarypy

Access to planetary science data remains a significant barrier for newcomers to the field and researchers exploring unfamiliar datasets. Often the data is on various data archives, all with different user interfaces and idiosyncracies, and data downloading remains a work of pick and choose, with structured programmatic access available but only manageable by the technically inclined researcher.

We present PlanetaryPy, an open-source Python package that synthesizes 15 years of experience working with planetary data to greatly reduce these entrance hurdles.
The package addresses a common research scenario: when a reader encounters an interesting dataset in a publication with a given product_id, PlanetaryPy will enable them to simply provide the mission name, instrument name, and product_id to immediately access both metadata and data, vastly reducing the time and effort required for data retrieval and exploration.

Developed as a community-driven effort with peer-reviewed code changes and public technical committee meetings, PlanetaryPy prioritizes reliability over feature quantity.
The package currently supports three core capabilities: (1) automatic download, local storage, and retrieval of PDS data indexes via simple identifiers (e.g., "mro.ctx.edr"), (2) automatic download of archived SPICE kernel datasets from the NAIF archive (https://naif.jpl.nasa.gov/naif/data_pds_archived.html) with time-interval filtering and correctly formatted metakernels for local storage, and (3) a curated "general" planetary SPICE kernel set enabling basic SPICE illumination calculations for major solar system bodies.

The development roadmap includes planetary constants, common geospatial calculations (e.g., solar position determination), planetary coordinate reference system handling, programmatic access to planetary nomenclature databases (including USGS feature databases), and minimal data downloaders for all major planetary datasets. Many of these features have existing working implementations from mission operations and data analysis workflows.

PlanetaryPy is installable via conda and pip package managers and is currently in beta testing. The codebase has been extensively validated through ongoing mission operations planning and scientific data analyses. Upon reaching critical mass in both user adoption and feature completeness, the API will be stabilized for a 1.0 release accompanied by a peer-reviewed publication.

**Keywords:** planetary data, open source, Python, data access, SPICE kernels, PDS