Skip to main content

For radiometric calibration and point spread function deconvolution of IRIS spsetcrograph data

Project description

irispreppy

For radiometrically calibrating and PSF deconvolving IRIS data.

To install simply run pip install irispreppy.

I dislike how I need to own proprietary software (IDL) just to simply prepare my data. I use Python for my analysis, why can't I radiometrically calibrate and deconvolve with it? This has been a passion project of mine during my PhD (and beyond). The radiometric calibration keeps itself up to date with the response files by checking https://hesperia.gsfc.nasa.gov/ssw/iris/response/ every time it is run. If it finds new files, it downloads them before continuing.

These scripts should be general purpose and "just work". No janky hacks are present.


tl;dr usage

irispreppy takes a single HDU object. To calibrate and deconvolve,

from astropy.io import fits
import irispreppy as ip

raw=fits.open("iris_raster.fits") #Raw data
rc=ip.radiometric_calibrate(raw)  #Radiometrically calibrated
rc_d=ip.deconvolve(rc)            #Radiometrically calibrated and deconvolved

To calibrate and deconvolve, and save,

from astropy.io import fits
import irispreppy as ip

raw=fits.open("iris_raster.fits")   #Raw data
ip.calibrate_and_save(raw)          #Radiometrically calibrated
rc=fits.open("iris_raster_rc.fits") #Radiometrically calibrated data
ip.deconvolve_and_save(rc)	        #Radiometrically calibrated and deconvolved

Documentation

Documentation for irispreppy can now be found here.


Acknowledgements

Thank you to Dr Graham S. Kerr for IRIS_SG_deconvolve.py and IRIS_SG_PSFs.pkl.

Special thanks to Dr C.M.J. Osborne for putting up with my incessant and innane questions.

Makes use of the excellent WENO4 algorithm (Janett et al. 2019) implemented in Python3 by Dr C.M.J. Osborne here.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

irispreppy-3.1.0.tar.gz (2.5 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

irispreppy-3.1.0-py3-none-any.whl (2.5 MB view details)

Uploaded Python 3

File details

Details for the file irispreppy-3.1.0.tar.gz.

File metadata

  • Download URL: irispreppy-3.1.0.tar.gz
  • Upload date:
  • Size: 2.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for irispreppy-3.1.0.tar.gz
Algorithm Hash digest
SHA256 f7d9cd34ba62916993d3cac1a4bdcddf51ffc3094befdbace40816173deec05b
MD5 964ea8797ccec6e3ec49c5eb38f5394f
BLAKE2b-256 00f923a02523df9ae018b077700ab62e4c91123bfb5fab7f95b3d4d5cdf72174

See more details on using hashes here.

File details

Details for the file irispreppy-3.1.0-py3-none-any.whl.

File metadata

  • Download URL: irispreppy-3.1.0-py3-none-any.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for irispreppy-3.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3f4fe07301b6115d5bd5b7518c03ae12c295738fcd8f49dbbf0b768aa438a0b3
MD5 b3f114ace6c9648f1c225e763fe75b23
BLAKE2b-256 0fe56fc33b29ec744905d1d9890e1748c1525e2a97d8ab7cdbb678cbe41fd4d3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page