spectroflat 1.0.4

Flat field and smile correction spectro-polarimetric solar images.

spectroflat

Python based library to flat field spectro-polarimetric data.

When using this library to reduce your data, please cite:

Hoelken et al. "Spectroflat: A generic spectrum and flat-field calibration library for spectro-polarimetric data" (DOI: 10.1051/0004-6361/202348877)

Theoretical Background

Generally this is intended to be an extension of the "Precise reduction of solar spectra obtained with large CCD arrays" method by Wöhl et al. (2002) to spectro-polarimetric instruments covering a large spectral field of view with many lines.

The spectroflat algorithm is described and evaluated in Hölken et al. (2023) "Spectroflat: A generic calibration library for spectro-polarimetric data". [in preparation]

Input data

The input data must be calibrated for the camera zero-input response (dark current and bias level) and relevant non-linearity effects. If each modulation state is to be corrected with its own set of calibration data an average of all frames from the flat field recording belonging to each modulation state has to be provided.

Input data shall be provided as a numpy Array with dimensions modulation state, spatial location along the spectrograph slit and wavelength position.

Extracted Data

Dust-Flat

The dust-flat is a combination of the sensor and slit flat that contains most of the "hard" flat field features. Most prominently the following is corrected:

• Sensor features (e.g. column-to-column response patterns, dust on the sensor itself)
• Slit features (e.g. dust on the slit resulting in line features in the spectral direction)
• Fixed optical fringes and illumination impurities.

The dust flat might be split in Sensor Flat and Slit Flat by the mean profile method applied along the spectrograph slit dimension.

Smile offset map

spectroflat characterizes the smile distortion by tracking the change of every spectral absorption or emission line with respect to the reference profile generated from the central rows. The map list the offset each pixel has, compared to that reference, with sub-pixel precision.

Illumination pattern

Typically, the illumination patterns are in the 10−6 range and are not used.
This result is kept for compatibility with the approach of Wöhl et al. (2002).

PDF Report

A report summary with relevant plots to inspect the quality of the extracted products.

Technical Documentation

NOTE This library expects the spacial domain on the vertical-axis and the spectral domain on the horizontal axis. spectroflat does not include any file reading/writing routines and expects numpy arrays as input.

Please refer to the API Documentation for implementation details. Especially the entries on available Configuration values are of general interest.

The example.py script provides an usage example.

Contact

This code is developed and maintained at the Max Planck Institute for Solar System Research (MPS) Göttingen.

Maintainer

• Johannes Hoelken ([e-mail])

Contributions

• Alex Feller
• Francisco Iglesias

License

BSD clause-2, see LICENSE