SH010-0009
Kamodo Open Source – A Community Resource for Data and Model Access and an Analysis Library for Space Physics
Kamodo Open Source – A Community Resource for Data and Model Access and an Analysis Library for Space Physics
Tuesday, 8 December 2020
Poster
Abstract:
Kamodo is a Python-based data and model output access and analysis library developed at the Community Coordinated Modeling Center (CCMC) that was open-sourced in 2019 and is available on GitHub (github.com/nasa/Kamodo/).
Kamodo utilizes the functional and object-oriented programming paradigms available in Python 3 with interactive visualizations based on Plotly and workflow development using Jupyter notebooks.
The library includes readers and access software for SWMF geospace modeling (GM/BATSRUS, IE/Ridely_Serial, UA/GITM), ENLIL heliosphere, MAS solar corona, CTIPe, TIEGCM and IRI ionosphere thermosphere and Tsyganenko magnetic field models.
Coordinate transformations in heliospace and geospace are available through Geopack and Cxform. Access to satellite data is provided through PySat and satellite trajectories are available from SSCWeb through the site’s REST interface.
We are working on natively reading other models via CDF, NetCDF or HDF Python packages and demonstrate research workflows using Jupyter notebooks, including timeseries extractions along satellite tracks and multi-satellite reconstruction tools for effects in the ionosphere and calculation of satellite distances to boundaries or features seen in magnetosphere or heliospheric simulations. Time series interpolations via Kamodo facilitate data-model comparison studies.
The space science community is invited to jointly develop the library by contributing readers for their own models, new visualizations and analysis workflows.
Kamodo utilizes the functional and object-oriented programming paradigms available in Python 3 with interactive visualizations based on Plotly and workflow development using Jupyter notebooks.
The library includes readers and access software for SWMF geospace modeling (GM/BATSRUS, IE/Ridely_Serial, UA/GITM), ENLIL heliosphere, MAS solar corona, CTIPe, TIEGCM and IRI ionosphere thermosphere and Tsyganenko magnetic field models.
Coordinate transformations in heliospace and geospace are available through Geopack and Cxform. Access to satellite data is provided through PySat and satellite trajectories are available from SSCWeb through the site’s REST interface.
We are working on natively reading other models via CDF, NetCDF or HDF Python packages and demonstrate research workflows using Jupyter notebooks, including timeseries extractions along satellite tracks and multi-satellite reconstruction tools for effects in the ionosphere and calculation of satellite distances to boundaries or features seen in magnetosphere or heliospheric simulations. Time series interpolations via Kamodo facilitate data-model comparison studies.
The space science community is invited to jointly develop the library by contributing readers for their own models, new visualizations and analysis workflows.