zap 2.1

ZAP (the Zurich Atmosphere Purge)

Tired of sky subtraction residuals? ZAP them!

ZAP is a high precision sky subtraction tool which can be used as complete sky subtraction solution, or as an enhancement to previously sky-subtracted MUSE data. The method uses PCA to isolate the residual sky subtraction features and remove them from the observed datacube. Future developments will include modification for use on a variety of instruments.

The last stable release of ZAP can be installed simply with pip:

pip install zap

Or into the user path with:

pip install --user zap

\bibitem[Soto et al.(2016)]{2016MNRAS.458.3210S} Soto, K.~T., Lilly, S.~J., Bacon, R., Richard, J., \& Conseil, S.\ 2016, \mnras, 458, 3210

2.1 (2019-07-03)

• Zap now requires Python 3.5 or later, and Astropy 2.0 or later.

• Compatibility with the NFM-AO mode.

• Ignore the notch filter region in the ‘median’ and ‘fit’ methods for the continuum filter (cftype).

• Change the default continuum filter method (cftype) to ‘median’ and remove the ‘weight’ method. The previous default method (‘weight’) has some problems with the treatment of spectra edges and with the notch filter edges for the AO modes, and is also introducing bumps in the red wavelengths.

• Add a command-line argument for nevals.

• Scipy 0.18.1 or later is required, because of SVD convergence issue with previous versions.

• Add a function to mask the cube edges (zap.mask_nan_edges): the spaxels at the edges of the cube may have a lot of NaNs in their spectra, so ZAP does not subtract the sky on these which leaves high residuals.

2.0 (2017-09-08)

• Compatibility with the WFM-AO mode.

• Use Scikit-learn’s implementation for the PCA instead of the custom one. This solved an issue with spatial variations introduced by zap. Also it is much faster than the previous implementation. A drawback however is that it is no more possible to save the SVD file.

• Use only one sky segment by default, which means that the cube is no more split on the wavelength axis. Originally zap used 11 segments, whose goals were to have coherent groups of sky emission lines, with a smaller number of eigenvalues per segment. And it also allowed to parallelize the computation. But, the segments were also responsible for continuum oscillations, and made the choice of the number of eigenvalues per segment very difficult and very sensitive. With only one segment the performance of the sky subtraction is much better, thanks to the higher correlation between sky lines on the whole wavelength range.

  So using only one segment allows to greatly reduce the risk of killing an emission line with the new PCA. It is still possible to modify the segments if needed:

  from zap.zap import SKYSEG
  SKYSEG[:] = [0, 5400, ..., 10000]

• New continuum filter type with a polynomial fit (cftype='fit'). Must be used with care though, as the fit can easily go out of control in the red part of the spectrum.

• Change the default median filter width to 300, for the median and weight continuum filters. The values used previously, 100 and 50, were too small which explains the background oscillations in the red part of the spectra.

• New parameter (ncpu) to set the number of used CPU.

• Remove the possibility to save the SVD result in a FITS file. This is because of the change of PCA implementation. It is still possible to pass the SVD computed by SVDoutput to process, passing the in-memory object directly without saving it to disk.

• Speed improvements, mostly thanks to the new PCA implementation.

• New parameter to save the explained variance curves.

1.0 (2016-04-02)

First public release. This is the version described in the 2016MNRAS.458.3210S paper.