EIS pointing

Tools to correct the pointing of Hinode/EIS.

This Python package implements the method described in the paper Pelouze et al. 2019, Sol Phys 294:59.

Usage

From the command line

This tool can be run from the command line by calling compute_eis_pointing:

usage: compute_eis_pointing [-h] [-s STEPS_FILE] [--io IO] [-c CORES]
                            [--cache-aia-data]
                            filename [filename ...]

Determine the pointing of Hinode/EIS.

positional arguments:
  filename              The names of the level 0 EIS files, eg.
                        'eis_l0_20100815_192002'.

optional arguments:
  -h, --help            show this help message and exit
  -s STEPS_FILE, --steps-file STEPS_FILE
                        Path to a yaml file containing the registration steps.
  --io IO               Directory where output files are written,
                        default: ./io.
  -c CORES, --cores CORES
                        Maximum number of cores used for parallelisation,
                        default: 4.
  --cache-aia-data      Cache the AIA data to a file. This uses a lot of
                        storage, but speeds things up when the same raster is
                        aligned for the second time.

Examples (command line):

compute_eis_pointing -c16 eis_l0_20140810_042212
compute_eis_pointing --steps-file steps/shift_only.yml eis_l0_20140810_042212

As a Python module

The tool can also be used from within a Python script, using eis_pointing.compute().

compute(*filename, steps_file=None, io='io', cores=4, cache_aia_data=False)
    Perform all computation steps to determine the optimal EIS pointing.

    Parameters
    ==========
    filename : list
        The names of the level 0 EIS files, eg. 'eis_l0_20100815_192002'.
    steps_file : str or None (default: None)
        Path to a yaml file containing the registration steps.
    io : str (default: 'io')
        Directory where output files are written.
    cores : int (default: 4)
        Maximum number of cores used for parallelisation.
    cache_aia_data : bool (default: False)
        Cache the AIA data to a file. This uses a lot of storage, but speeds
        things up when the same raster is aligned for the second time.

Examples (Python):

import eis_pointing
eis_pointing.compute('eis_l0_20140810_042212', cores=16)
eis_pointing.compute('eis_l0_20140810_042212', steps_file='steps/shift_only.yml')

Installation

Install the latest release by running: pip install eis_pointing.

Alternatively, the latest version can be installed from GitHub by cloning this repository with git clone https://github.com/gpelouze/eis_pointing, then running cd eis_pointing, and pip install ..

Optional: install SolarSoft

Before computing the optimal pointing, this tool can download, prepare, and export the EIS data by calling external IDL routines from SolarSoft. For these features to be available, a functioning installation of SolarSoft containing the EIS instrument is required. Install SolarSoft, and set the environment variable $SSW to your installation path (by default, SolarSoft is assumed to be installed installed into /usr/local/ssw).

It is perfectly fine not to install or configure SolarSoft to run with this tool. In this case, you will need to manually download the EIS level0 FITS, prepare them into level1 FITS, and save a windata structure containing the Fe XII 195.119 Å line to a .sav file placed in <io directory>/windata/eis_windata_<date>.sav. See pipeline for details on how to do this.

Customisation

The registration steps used to find the optimal pointing can be customised in a YAML file, and passed to eis_pointing using the --steps-file parameter (see examples above). The file should have a top-level key named steps that contains a list of registration steps. Each step must specify at least a type, chosen between shift, rotshift, and slitshift.

By default, EIS data are coaligned with synthetic AIA raster. To coalign with a single AIA image, add the top-level key single_aia_frame: True. In this case, the reference AIA image chosen at the middle of the EIS raster.

See files in steps/ for examples.

When no file is specified, the default behaviour is the same as using steps/full_registration.yml.

Code structure

Pipeline

All the steps required to determine the optimal pointing data from EIS level 0 files are defined in driver.py. The appropriate functions are called by the executable compute_eis_pointing when using the tool from the CLI, or by eis_pointing.compute() when using it as a Python module.

1. Download data Download the required EIS level 0 FITS, and place them in the EIS data files and directory structure described in EIS Software Note #18 (eg. $HINODE_DATA/eis/level0/2014/08/10/eis_l0_20140810_042212.fits).

2. Prepare data Generate EIS level 1 FITS from level 0, and save it to the EIS data files and directory structure (eg. $HINODE_DATA/eis/level1/2014/08/10/eis_l1_20140810_042212.fits). Performed by eis_pointing/prep.pro, which calls the SolarSoft routine eis_prep.pro.

3. Export windata Save a windata structure containing the Fe XII 195.119 Å line, obtained using the SolarSoft function eis_getwindata (see EIS Software Note #21). The structure is saved to <io>/windata/eis_windata_<date>.sav (eg. ./io/windata/windata_20140810_042212.sav). Performed by eis_pointing/export_windata.pro.

---

Alternative to steps 1-3 without SolarSoft If SolarSoft is not installed or configured, you will need to separately generate a windata structure containing the Fe XII 195.119 Å line, and save it to <io>/windata/eis_windata_<date>.sav.

Example (SSW):

wd = eis_getwindata('eis_l1_20140810_042212.fits', 195.119, /refill)
save, wd, filename='./io/windata/windata_20140810_042212.sav'

Once this is done, run the tool normally, either from the command line, or as a Python module. It will detect the existing .sav file, and skip steps 1-3.

---

4. Compute the EIS emission Generate an intensity map of the Fe XII 195.119 Å line by summing the spectra between 194.969 and 195.269 Å. Data are saved to <io>/eis_aia_emission/eis_aia_emission_<date>.fits (eg. ./io/eis_aia_emission/eis_aia_emission_20140810_042212.fits). Performed by eis_pointing.eis_aia_emission.compute().

5. Determine the optimal pointing Determine the optimal pointing for EIS using the intensity map generated at the previous step, and AIA 193 data retrieved from Medoc as a reference. (The AIA FITS are downloaded to ./sdo/aia, or to $SDO_DATA/aia/ if the environment variable $SDO_DATA is set set.) Results from the alignment (ie. new EIS coordinates) are saved to <io>/pointing/eis_pointing_<date>.fits. Diagnostics plots, correlation cubes, as well as a YAML file containing the results from the coregistration are also saved to <io>/pointing_verification/<date>/. Performed by eis_pointing.eis_aia_registration.optimal_pointing().

Coregistration functions: eis_pointing.coregister

• images contains functions to register images in translation, relatively to another image.
• rasters contains functions to register images in translation and rotation, relatively to a synthetic raster.
• slits functions to register slit positions (ie. vertical columns in an image) separately, relatively to a synthetic raster.
• tools functions shared among the previous submodules.

Functions shared by different components eis_pointing.utils

• aia_raster: defines AIARasterGenerator that builds synthetic rasters from AIA data. Also contains SimpleCache and FileCache.
• cli: argument parsing and output display.
• eis, aia.py: functions to handle native EIS and AIA data, filenames, and data queries. This does not take care of transformed data such as AIARasterGenerator.
• files: manage local filenames (ie. those in io/); canonical EIS or AIA filenames are handled in eis.py or aia.py.
• idl: run IDL or SSW code from Python, load and format data returned by IDL. Contains IDLFunction, SSWFunction and IDLStructure.
• num: tools that extend numpy or scipy.
• plots: help generate plots at step 4.
• sun: generic solar computations.

Reference / License

If you use this package for a publication, please acknowledge the following paper: Pelouze et al. 2019, Sol Phys 294:59.

This package is released under a MIT open source licence. See LICENSE.txt.