tudat-team/tudatpy
Aug 2025 – Jun 2026
5 Months active
Languages Used
C++PythonASCIIMarkdown
Technical Skills
C++PythonSoftware DevelopmentSoftware EngineeringData ProcessingDocumentation
Michael Plumaris
PROFILE
Michael Plumaris
Contributed to tudatpy by developing advanced features for ionospheric modeling, relativistic time propagation, and observation simulation, focusing on scientific accuracy and extensibility. Integrated the NeQuick-2 model with regional subsetting and CCIR data, enabling precise TEC corrections for GNSS and space missions. Enhanced the API to expose custom frequency calculators and improved SINEX data processing for station configuration. Strengthened the testing framework with comprehensive unit and integration tests, addressing both C++ and Python bindings. Fixed critical bugs in time propagation and station rotation, achieving high-precision validation. Emphasized robust documentation, maintainability, and cross-language support throughout the software engineering process.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
20Total
Bugs
2
Commits
20
Features
8
Lines of code
19,077
Activity Months5
Your Network
17 peopleShared Repositories
17
Work History
June 2026
4 Commits • 2 Features
Jun 1, 2026June 2026 TudatPy monthly summary focusing on delivering high-precision relativistic time propagation, Python-bindings usability, and strengthened validation. Key features delivered include second-order tightening of the direct-vs-chain ladder and exposure of RelativisticTime base to Python, plus the ability to save dependent variables via settings with accuracy tests against benchmarks. A robust integrated-sum test for proper-time-rate decomposition was added to validate kinematic and potential term consistency across propagators. Major bugs fixed include the ground-station proper-time rotation fix in direct-from-metric propagation, eliminating a microsecond diurnal error and aligning results with analytic references. Expanded test coverage and validations across unit/integration suites confirmed accuracy, stability, and cross-checks between PN and direct-from-metric paths. Technologies demonstrated span C++ with pybind11 bindings, PN/GR time propagation, topocentric and BCRS/TCG/TCB time transformations, and comprehensive testing. Business value includes improved timing accuracy down to tens of picoseconds for key bodies, increased reliability for mission simulations, and a smoother Python experience enabling faster development and validation.
4 Commits • 2 Features
Jun 1, 2026June 2026 TudatPy monthly summary focusing on delivering high-precision relativistic time propagation, Python-bindings usability, and strengthened validation. Key features delivered include second-order tightening of the direct-vs-chain ladder and exposure of RelativisticTime base to Python, plus the ability to save dependent variables via settings with accuracy tests against benchmarks. A robust integrated-sum test for proper-time-rate decomposition was added to validate kinematic and potential term consistency across propagators. Major bugs fixed include the ground-station proper-time rotation fix in direct-from-metric propagation, eliminating a microsecond diurnal error and aligning results with analytic references. Expanded test coverage and validations across unit/integration suites confirmed accuracy, stability, and cross-checks between PN and direct-from-metric paths. Technologies demonstrated span C++ with pybind11 bindings, PN/GR time propagation, topocentric and BCRS/TCG/TCB time transformations, and comprehensive testing. Business value includes improved timing accuracy down to tens of picoseconds for key bodies, increased reliability for mission simulations, and a smoother Python experience enabling faster development and validation.
June 2026
April 2026
12 Commits • 3 Features
Apr 1, 2026April 2026 Tudatpy monthly summary highlighting key features, major fixes, and overall impact for business value and technical excellence. - Key features delivered include a comprehensive upgrade to ionospheric modeling with NeQuick-2 (path-integrated corrections), upgraded IONEX reader, ancillary data downloader, Python bindings, and enhanced data access including regional subsetting and CCIR coefficient data. These changes enable more accurate TEC modeling for receivers in challenging ionospheric conditions and improve end-to-end workflows for ionospheric analyses. - IONEX spatial subsetting by ground-station location was added, enabling cropping of IONEX grids to a bounding box around target stations to reduce memory usage and interpolation time for regional analyses; CCIR coefficient data and MODIP files were bundled and accessible. - Light-Time Corrections Management was introduced, with caching and retrieval of per-correction contributions, exposed per-leg correction components, and related tests, improving transparency and accuracy of time-delay budgets across transmitter–receiver legs. - A frame alignment bug in the first-order TCB to proper-time correction was fixed by rotating the station position to the inertial frame before evaluation, eliminating a frame mismatch that caused large residuals in time propagation tests (ns-level accuracy achieved in unit testing). - Relativistic Time Propagation Enhancements added new dependent variables for kinematic and potential terms of the proper-time-rate integrand, wired to both the PN chain and direct-from-metric pipelines, with Python bindings and unit tests demonstrating numerical precision at the 1e-24 s/s scale (noise floor). - INPOP19a TCB-TCG validation was extended to the direct-from-metric pipeline, with PN-chain and direct-from-metric arms both passing: max_abs_diff of 3.07e-12 s (PN chain) and 1.78e-12 s (direct-from-metric). - Overall impact and accomplishments: The month delivered a significantly more capable and robust toolkit for ionospheric modeling, time-scale propagation, and observational analysis. The work improves business value by enabling region-specific, data-driven ionospheric corrections, more reliable light-time computations for space missions and GNSS applications, and stronger validation coverage, all with improved cross-language support and maintainability. - Technologies/skills demonstrated: C++ core modeling with ITU-R P.531 and CCIR/MODIP data integrations; high-quality Python bindings; extensive unit testing (Boost.Test) and regression validation; modern data I/O improvements; design patterns for factory-based data readers; multi-language documentation and maintainability.
April 2026
12 Commits • 3 Features
Apr 1, 2026April 2026 Tudatpy monthly summary highlighting key features, major fixes, and overall impact for business value and technical excellence. - Key features delivered include a comprehensive upgrade to ionospheric modeling with NeQuick-2 (path-integrated corrections), upgraded IONEX reader, ancillary data downloader, Python bindings, and enhanced data access including regional subsetting and CCIR coefficient data. These changes enable more accurate TEC modeling for receivers in challenging ionospheric conditions and improve end-to-end workflows for ionospheric analyses. - IONEX spatial subsetting by ground-station location was added, enabling cropping of IONEX grids to a bounding box around target stations to reduce memory usage and interpolation time for regional analyses; CCIR coefficient data and MODIP files were bundled and accessible. - Light-Time Corrections Management was introduced, with caching and retrieval of per-correction contributions, exposed per-leg correction components, and related tests, improving transparency and accuracy of time-delay budgets across transmitter–receiver legs. - A frame alignment bug in the first-order TCB to proper-time correction was fixed by rotating the station position to the inertial frame before evaluation, eliminating a frame mismatch that caused large residuals in time propagation tests (ns-level accuracy achieved in unit testing). - Relativistic Time Propagation Enhancements added new dependent variables for kinematic and potential terms of the proper-time-rate integrand, wired to both the PN chain and direct-from-metric pipelines, with Python bindings and unit tests demonstrating numerical precision at the 1e-24 s/s scale (noise floor). - INPOP19a TCB-TCG validation was extended to the direct-from-metric pipeline, with PN-chain and direct-from-metric arms both passing: max_abs_diff of 3.07e-12 s (PN chain) and 1.78e-12 s (direct-from-metric). - Overall impact and accomplishments: The month delivered a significantly more capable and robust toolkit for ionospheric modeling, time-scale propagation, and observational analysis. The work improves business value by enabling region-specific, data-driven ionospheric corrections, more reliable light-time computations for space missions and GNSS applications, and stronger validation coverage, all with improved cross-language support and maintainability. - Technologies/skills demonstrated: C++ core modeling with ITU-R P.531 and CCIR/MODIP data integrations; high-quality Python bindings; extensive unit testing (Boost.Test) and regression validation; modern data I/O improvements; design patterns for factory-based data readers; multi-language documentation and maintainability.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) TudatPy monthly summary focused on strengthening test coverage for ILRS/SLR observation models. Key feature delivered: Testing Framework Enhancement for ILRS/SLR Observation Models, adding unit tests for ILRS/SLR functionality, including tests for various correction types and handling of dependent variables to ensure robustness in simulation of light time corrections and observation-dependent variables.
1 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) TudatPy monthly summary focused on strengthening test coverage for ILRS/SLR observation models. Key feature delivered: Testing Framework Enhancement for ILRS/SLR Observation Models, adding unit tests for ILRS/SLR functionality, including tests for various correction types and handling of dependent variables to ensure robustness in simulation of light time corrections and observation-dependent variables.
March 2026
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered ILRS SINEX integration for TudatPy station configuration and SINEX data processing, enabling ILRS bindings to handle SINEX data end-to-end for station configurations. Added new reading/processing functions for SINEX files and improved management of station states and eccentricities to support more accurate satellite tracking and positioning. Documentation updates accompany the workflow changes to ensure clear usage and reproducibility.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered ILRS SINEX integration for TudatPy station configuration and SINEX data processing, enabling ILRS bindings to handle SINEX data end-to-end for station configurations. Added new reading/processing functions for SINEX files and improved management of station states and eccentricities to support more accurate satellite tracking and positioning. Documentation updates accompany the workflow changes to ensure clear usage and reproducibility.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 (tudatpy): Delivered an API enhancement to improve frequency modeling and simulation fidelity. Exposed a new configurability point in VehicleSystems to support a custom transmitted frequency calculator, enabling precise onboard frequency modeling and more realistic environmental simulations.
2 Commits • 1 Features
Aug 1, 2025August 2025 (tudatpy): Delivered an API enhancement to improve frequency modeling and simulation fidelity. Exposed a new configurability point in VehicleSystems to support a custom transmitted frequency calculator, enabling precise onboard frequency modeling and more realistic environmental simulations.
August 2025
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Quality Metrics
Correctness96.0%
Maintainability85.0%
Architecture92.0%
Performance86.0%
AI Usage39.0%
Skills & Technologies
Programming Languages
ASCIIC++MarkdownPython
Technical Skills
API DevelopmentBoost.TestC++C++ developmentC++ programmingData ProcessingData processingDocumentationFile I/OIonospheric modelingNumerical methodsPythonPython developmentPython programmingSoftware Development
Repositories Contributed To
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