/eispac

Primary LanguageJupyter NotebookMIT LicenseMIT

EISPAC - EIS Python Analysis Code

This software provides a set of tools for analyzing Hinode/EIS data within a python environment. The general approach is as follows:

  1. Sets of level 1 HDF5 files are processed from the latest EIS level 0 fits files and made available online by the NRL EIS team at https://eis.nrl.navy.mil/. The HDF5 files come in pairs of "data" and "header" files which contain corrected count rates, the calibration curve needed to convert counts into intensity, and all of the associated metadata and pointing information.

  2. This package provides python classes and functions that can read these 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 using the same template files and underlying methodology that is used in the IDL SolarSoft environment.

Please note that this package is under active development. If you have any questions or suggestions for future improvements, please email the development team

Installation and Requirements

Installing using pip

  1. Download or clone "eispac" to a convenient location on your computer (it does not matter where).
	> git clone https://github.com/USNavalResearchLaboratory/eispac.git
  1. Open a terminal and navigate to the directory
  2. To install:
	> python -m pip install .
  1. To upgrade:
	> python -m pip install --upgrade .

The package should then be installed to the correct location for your current Python environment. You can now import the package using import eispac. Since the package is not publically available or registered on PyPi,org, the only way to "update" the package is by repeating the process above (you do not need to uninstall the old version first, pip will automatically take care of that). A conda install script will be added in a future update

Required Packages

pip should automatically install the package dependencies. If it does not, here is a list of the required packages (older package versions might still work). Note: if you are using conda to manage your Python packages, you may wish to install or update the dependencies manually first, before installing eispac using pip.

  • python >= 3.7
  • numpy >= 1.18.1
  • scipy >= 1.4.1
  • matplotlib >= 3.1
  • h5py >= 2.9
  • astropy >= 3.1
  • sunpy >= 1.0.3
  • ndcube >= 1.2.1
  • wget
  • cURL

Getting Started

  • QUICK_GUIDE-cli.md: A very brief description of some command line tools for downloading and fitting the data

  • QUICK_GUIDE.md: A very brief description of the EISPAC objects.

  • users_guide/EISPAC_Users_Guide.pdf: A more detailed guide to the software.

  • notebooks: Tutorials using Juypter notebooks.

Code Organization

There are currently three core directories:

  1. eispac: main python code directory containing all of the programs required to read level 1 HDF5 files and fit templates and fit spectra using mpfit.

    Notable subdirectories:

    • ../eispac/core/: Main code directory. All functions here are loading into the top-level namespace (i.e. eispac.{function name})
    • ../eispac/data/: Example data containing a full EIS raster from 2019-04-04 at 13:15:13.
    • ../eispac/examples/: Example scripts showing how to load and fit the example data
    • ../eispac/templates/: fit templates for specific spectral lines. These HDF5 files are direct conversions of the ".genx" files used by some IDL users.

  2. scripts: GUI and command line tools

  3. users_guide: PDF with instructions and examples for how to use the code. Also includes the LaTeX source code.

The QUICK_GUIDE.md text document also give a very brief overview of some key functions. It should also be noted that mpfit.py was written by Mark Rivers and Sergey Kopsov and is direct Python port of the mpfit.pro IDL procedure written by Craig Markwardt. As such, much of the documentation online for the IDL version of the code is still applicable to the Python version (please see the Python doc for more information).

TODO list

Here, in no particular order, is a list of some things that may be added in future releases.

  • Expanded documentation
  • More unit and integration tests
  • More detailed logging (with option to send all log information to a file)
  • Scripts for quickly viewing data and spectra fits
  • Scripts and routines for creating new fit templates
  • Consider adding a subclass of NDCubeSequence which can hold multiple spectral windows
  • Consider storing the output fit parameters in another NDCube
  • Restructure project to use the Sunpy affiliated package template?