| Presentation Title: | stixpy: STIX data analysis in python |
| Author(s): | Shane Maloney and the STIX Team |
Abstract:
Stixpy is a Python-based tool tailored for analysing STIX data, integrating with the scientific Python ecosystem. Leveraging popular libraries like NumPy, Matplotlib, Astropy, and SunPy, stixpy offers a user-friendly interface for accessing and manipulating STIX data, utilising familiar data containers and types such as sunpy Map, & TimeSeries and astropy Quantity and Time. The current version of stixpy supports various essential features including data search and download facilitated by the SunPy Fido interface, data visualisation using Matplotlib, and basic data manipulation operations such as detector selection and summation of counts over time/energy indices.
stixpy provides imaging capabilities through count-based techniques like Expectation Maximization and visibility-based methods such as Clean, Multi-scale Clean, and MEM via xrayvision. In its upcoming iterations, stixpy aims to introduce spectroscopy functionalities with sunkit-spex integration, enabling advanced data manipulation operations based on timesa & quantities. Additionally, enhancements in imaging will include visibility-based techniques like visforward_fit and visforward_fit_pso via xrayvision, along with the incorporation of regularised photon and electron visibilities.
Currently supported features
* Data search and download - via sunpy Fido interface
* Data visualisation - via matplotlib
* Basic data manipulation - sum, slice, detector/pixel selection
* Imaging
* Count based - Expectation Maximisation
* Visibility based - Clean, Multi-scale Clean and MEM - via xrayvision
Upcoming features
* Spectroscopy - via sunkit-spex
* Advanced data manipulation - sum, slice, subselect by quantities etc
* Imaging
* Visibility based - visfoward_fit, visforward_fit_pso - via xrayvision
* Regularised photon and electron visibilities
This abstract encapsulates the core functionalities and future roadmap of stixpy, showcasing its utility as a comprehensive tool for STIX data analysis within the Python environment.