MUSE-PSFR - PSF reconstruction for MUSE WFM-AO ¶

The PSF reconstruction algorithm needs 3 values provided by SPARTA: the seeing, the Ground Layer fraction (GL), and the outer-scale (L0). These values can be provided directly as command-line arguments (see below), but the typical use is to provide a raw MUSE file.

Since the GLAO commissioning, the MUSE raw files contain a FITS table (SPARTA_ATM_DATA) containing the atmospheric turbulence profile estimated by SPARTA. This table contains the values for each laser, with one row every two minutes.

Number of reconstructed wavelengths ¶

To reduce computation time, the muse-psfr command reconstructs the PSF at three wavelengths: 500, 700, and 900 nm. But it is possible to reconstruct the PSF at any wavelength, with the compute_psf_from_sparta function. This function reconstructs by default for 35 wavelengths between 490nm and 930nm (which can specified with the lmin, lmax, and nl parameters).

Number of reconstructed direction ¶

Since the spatial variation is negligible over the MUSE field of view, the reconstruction is done by default only at the center of field. This can be changed in compute_psf_from_sparta with the npsflin parameter.

Usage ¶

Command Line Interface ¶

MUSE-PSFR can be used from the command line, either with a set of seeing, GL, and L0 values:

$ muse-psfr --no-color --values 1,0.7,25
[INFO] MUSE-PSFR version 1.1.dev18+g0c56a3d
[INFO] Computing PSF Reconstruction from Sparta data
[INFO] Processing SPARTA table with 1 values, njobs=1 ...
[INFO] Compute PSF with seeing=1.00 GL=0.70 L0=25.00
[INFO] --------------------------------------------------------------------
[INFO] Sparta Seeing: 1.00 arcsec GL: 0.70 L0:25.00 m
[INFO] LBDA 5000 7000 9000
[INFO] FWHM 0.85 0.73 0.62
[INFO] BETA 2.73 2.55 2.23
[INFO] --------------------------------------------------------------------
[INFO] Results saved to muse_psfr.log

Or with a MUSE raw FITS file which contains a SPARTA_ATM_DATA extension:

$ muse-psfr raw/MUSE.2018-08-13T07:14:11.128.fits.fz
MUSE-PSFR version 0.31
OB MXDF-01-00-A 2018-08-13T07:39:21.835 Airmass 1.49-1.35
Computing PSF Reconstruction from Sparta data
Processing SPARTA table with 13 values, njobs=-1 ...
4/13 : Using only 3 values out of 4 after outliers rejection
4/13 : seeing=0.57 GL=0.75 L0=18.32
Using three lasers mode
1/13 : Using only 3 values out of 4 after outliers rejection
1/13 : seeing=0.71 GL=0.68 L0=13.60
Using three lasers mode
6/13 : Using only 3 values out of 4 after outliers rejection
6/13 : seeing=0.60 GL=0.75 L0=16.47
Using three lasers mode
....

OB MXDF-01-00-A 2018-08-13T07:39:21.835 Airmass 1.49-1.35
--------------------------------------------------------------------
LBDA  5000 7000 9000
FWHM  0.57 0.46 0.35
BETA  2.36 1.91 1.64
--------------------------------------------------------------------

Results saved to muse-psfr.log

More information use of the command line interface can be found with the command:

$ muse-psfr --help
usage: muse-psfr [-h] [--values VALUES] [--logfile LOGFILE] [-o OUTFILE]
                 [--njobs NJOBS] [--verbose] [--no-color] [--plot] [--version]
                 [raw]

MUSE-PSFR version 1.1.dev18+g0c56a3d

positional arguments:
  raw                   observation raw file name

optional arguments:
  -h, --help            show this help message and exit
  --values VALUES       values of seeing, GL, L0, to use instead of the raw
                        file, comma-separated
  --logfile LOGFILE     name of log file
  -o OUTFILE, --outfile OUTFILE
                        name of a FITS file in which the results are saved:
                        table with individual and mean Moffat fits, and mean
                        reconstructed PSF
  --njobs NJOBS         number of parallel jobs (by default use all CPUs)
  --verbose, -v         verbose flag
  --no-color            no color in output
  --plot                plot reconstructed psf
  --version             show program's version number and exit

By default it saves the computed values in a log file (muse-psfr.log). It is also possible to save a FITS file with the fit results for all wavelengths and all SPARTA rows with the --outfile option.

Python interface ¶

The main entry point for the Python interface is the compute_psf_from_sparta function. This function takes a file with a SPARTA table,

>>> from muse_psfr import compute_psf_from_sparta, create_sparta_table
>>> tbl = create_sparta_table(seeing=1, L0=25, GL=0.7)
>>> tbl.data
FITS_rec([(25, 1, 0.7, 25, 1, 0.7, 25, 1, 0.7, 25, 1, 0.7)],
         dtype=(numpy.record, [('LGS1_L0', '<i8'), ('LGS1_SEEING', '<i8'), ('LGS1_TUR_GND', '<f8'), ('LGS2_L0', '<i8'), ('LGS2_SEEING', '<i8'), ('LGS2_TUR_GND', '<f8'), ('LGS3_L0', '<i8'), ('LGS3_SEEING', '<i8'), ('LGS3_TUR_GND', '<f8'), ('LGS4_L0', '<i8'), ('LGS4_SEEING', '<i8'), ('LGS4_TUR_GND', '<f8')]))
>>> from astropy.io import fits
>>> hdul = fits.HDUList([tbl])
>>> out = compute_psf_from_sparta(hdul, lmin=500, lmax=900, nl=3, plot=True)
Processing SPARTA table with 1 values, njobs=1 ...
1/1 : seeing=1.00 GL=0.70 L0=25.00

(Source code, png, hires.png, pdf)

Changelog ¶

1.0 (05/02/2020)¶

Documentation and other minor updates.
Add verbose parameter to compute_psf and compute_psf_from_sparta.
Remove deprecated function reconstruct_psf.

1.0.rc2 (09/07/2019)¶

Add --version for the script.
Use logging for all terminal outputs.

1.0.rc1 (31/01/2019)¶

Skip SPARTA row when L0<10 or L0>30.
Merge individual Moffat fits in one table.
Add command-line options (--plot, --outfile).
Add documentation.
Rename package to muse-psfr.

0.3 ¶

Improve MUSE PSF model.
Better limits to reject aberrant values from SPARTA.

0.2 ¶

Replace empirical seeing correction with PSF models.
Better handling of files with invalid values

0.11 ¶

Add 3 lasers mode.

0.1 ¶

First release.

API ¶

muse_psfr.compute_psf_from_sparta(filename, extname='SPARTA_ATM_DATA', npsflin=1, lmin=490, lmax=930, nl=35, lbda=None, h=(100, 10000), n_jobs=- 1, plot=False, mean_of_lgs=True, verbose=True)[source]¶

Reconstruct a PSF from SPARTA data.

Parameters

filename (str or astropy.io.fits.HDUList) – FITS file containing a SPARTA table.
extname (str) – Name of the SPARTA extension (defaults to SPARTA_ATM_DATA).
npsflin (int) – Number of points where the PSF is reconstructed (on each axis).
lmin (float) – Wavelength range (nm).
lmax (float) – Wavelength range (nm).
nl (int) – Number of wavelength planes to reconstruct.
lbda (array) – Array of wavelength values (nm). If not given it is computed from lmin, lmax and nl.
h (tuple of float) – Altitude of the ground and high layers (m).
n_jobs (int) – Number of parallel processes to process the rows of the SPARTA table.
plot (bool) – If True, plots the configuration if the AO system (positions of the LGS and the directions of reconstruction).
mean_of_lgs (bool) – If True (default), compute the mean seeing, GL and L0 over the 4 lasers. Otherwise a PSF is reconstructed for each laser.
verbose (bool) – If True (default), print more information.

muse_psfr.compute_psf(lbda, seeing, GL, L0, npsflin=1, h=(100, 10000), three_lgs_mode=False, verbose=True)[source]¶

Reconstruct a PSF from a set of seeing, GL, and L0 values.

Parameters

lbda (array) – Array of wavelength for which the PSF is computed (nm).
npsflin (int) – Number of points where the PSF is reconstructed (on each axis).
h (tuple of float) – Altitude of the ground and high layers (m).
three_lgs_mode (bool) – If True, use only 3 LGS.
verbose (bool) – If True (default) log informations

muse_psfr.create_sparta_table(nlines=1, seeing=1, L0=25, GL=0.7, bad_l0=False, outfile=None)[source]¶: Helper function to create a SPARTA table with the given seeing, L0, and GL values.

muse_psfr.fit_psf_with_polynom(lbda, fwhm, beta, deg=(5, 5), output=0)[source]¶

Fit MUSE PSF fwhm and beta with polynoms.

Parameters

lbda (array of float) – Wavelength in nm.
fwhm (array of float) – Moffat FWHM in arcsec.
beta (array of float) – Moffat beta parameter.
deg (tuple) – (fwhm_deg, beta_deg), polynomial degre in fwhm and beta.
output (int) – If set to 1, the fitted values are returned.

Returns

dictionary with fwhm_poly (array), beta_poly (array) if output=0 and lbda_fit, fwhm_fit, beta_fit if output=1

Return type

dict