The EIS Python Analysis Code (EISPAC) is a software package designed for easy and accurate analysis of spectroscopic data from the EIS instrument on board the Hinode spacecraft.
What Can EISPAC Do?
Search and download EIS observations
Explore data contents
Fit spectral line using multigaussian fit templates
Compute measurements (line intensities, velocities, and widths)
Generate coordinate-aware, Sunpy Maps of the fit measurements
Subcomponents of EISPAC
There are three main subcomponents of EISPAC,
An archive of Level-1 data files
These files are saved in the HDF5 file format and come in pairs of header and data files. The archive is updated regularly and can be found at https://eis.nrl.navy.mil/
A set of GUI and command line tools
Can be used to quickly search and and download EIS data, view available fit templates, and batch process multiple files at once using parallel processing.
The Python package itself
Provides classes and functions that can read the Level-1 HDF5 files, perform all of the necessary calibration and pointing adjustments, and create user-friendly Python objects that can be manipulated as needed. Also included are functions for fitting the intensity profiles with multi-Gaussian functions using template files and a Python port of the venerable
MPFITlibrary 1. Installing the Python package also automatically installs and registers the GUI and command line tools with your Python environment.
Reading and fitting EIS data requires minimal time and effort. Below is a basic, but complete, example:
import eispac if __name__ == '__main__': # input data and template files data_filepath = './eis_20190404_131513.data.h5' template_filepath = './fe_12_195_119.2c.template.h5' # read fit template tmplt = eispac.read_template(template_filepath) # Read spectral window into an EISCube data_cube = eispac.read_cube(data_filepath, tmplt.central_wave) # Fit the data using parallel processing fit_res = eispac.fit_spectra(data_cube, tmplt, ncpu='max')
Markwardt, C. B. 2009, in Astronomical Society of the Pacific Conference Series, Vol. 411, Astronomical Data Analysis Software and Systems XVIII, ed. D. A. Bohlender, D. Durand, & P. Dowler, 251