MUSE specific tools (mpdaf.MUSE)

Python interface for MUSE slicer numbering scheme

The Slicer class contains a set of static methods to convert a slice number between the various numbering schemes. The definition of the various numbering schemes and the conversion table can be found in the “Global Positioning System” document (VLT-TRE-MUSE-14670-0657).

All the methods are static and thus there is no need to instantiate an object to use this class.

For example, we convert slice number 4 in CCD numbering to SKY numbering:

In [1]: from mpdaf.MUSE import Slicer

In [2]: Slicer.ccd2sky(4)
Out[2]: 10

Now we convert slice number 12 of stack 3 in OPTICAL numbering to CCD numbering:

In [3]: Slicer.optical2sky((2, 12))
Out[3]: 25

MUSE LSF models


LSF class is currently under development

Only one model of LSF (Line Spread Function) is currently available.

LSF qsim_v1

This is a simple model where the LSF is supposed to be constant over the filed of view. It uses a simple parametric model of variation with wavelength.

The model is a convolution of a step function with a Gaussian. The resulting function is then sample by the pixel size:

LSF = T(y2+dy/2) - T(y2-dy/2) - T(y1+dy/2) + T(y1-dy/2)

T(x) = exp(-x**2/2) + sqrt(2*pi)*x*erf(x/sqrt(2))/2

y1 = (y-h/2) / sigma

y2 = (y+h/2) / sigma

The slit width is assumed to be constant (h = 2.09 pixels). The Gaussian sigma parameter is a polynomial approximation of order 3 with wavelength:

c = [-0.09876662, 0.44410609, -0.03166038, 0.46285363]

sigma(x) = c[3] + c[2]*x + c[1]*x**2 + c[0]*x**3

To use it, create a LSF object with attribute ‘typ’ equal to ‘qsim_v1’:

In [4]: from mpdaf.MUSE import LSF

In [5]: lsf = LSF(typ='qsim_v1')

Then get the LSF array by using get_LSF:

In [6]: lsf_6000 = lsf.get_LSF(lbda=6000, step=1.25, size=11)

In [7]: import matplotlib.pyplot as plt

In [8]: import numpy as np

In [9]: plt.plot(np.arange(-5,6), lsf_6000, drawstyle='steps-mid')
Out[9]: [<matplotlib.lines.Line2D at 0x7f1b51585908>]

MUSE FSF models


FSF class is currently under development

Two models of FSF (Field Spread Function) are currently available:

  • OldMoffatModel (model='MOFFAT1'): the old model with a fixed beta.
  • MoffatModel2 (model=2): a circular MOFFAT with polynomials for beta and FWHM.

Example with MOFFAT1

The MUSE FSF is supposed to be a Moffat function with a FWHM which varies linearly with the wavelength:

\(FWHM = a + b * lbda\)


  • beta (float) Power index of the Moffat.
  • a (float) constant in arcsec which defined the FWHM.
  • b (float) constant which defined the FWHM.

We create the FSFModel object like this:

In [10]: from mpdaf.MUSE import FSFModel, OldMoffatModel

In [11]: fsf = OldMoffatModel(a=0.885, b=-2.94E-05, beta=2.8, pixstep=0.2)

In [12]: isinstance(fsf, FSFModel)
Out[12]: True

Various methods allow to get the FSF array (2D or 3D, as mpdaf Image or Cube) for given wavelengths, or the FWHM in pixel and in arcseconds.

In [13]: lbda = np.array([5000, 9000])

In [14]: fsf.get_fwhm(lbda)
Out[14]: array([0.738 , 0.6204])

In [15]: fsf.get_fwhm(lbda, unit='pix')
Out[15]: array([3.69 , 3.102])

In [16]: im5000 = fsf.get_2darray(lbda[0], shape=(25, 25))

In [17]: fsfcube = fsf.get_3darray(lbda, shape=(25, 25))

In [18]: plt.figure()
Out[18]: <Figure size 640x480 with 0 Axes>

In [19]: plt.imshow(im5000)
Out[19]: <matplotlib.image.AxesImage at 0x7f1b771595c0>

In [20]: plt.figure()
Out[20]: <Figure size 640x480 with 0 Axes>

In [21]: plt.imshow(fsfcube[1])
Out[21]: <matplotlib.image.AxesImage at 0x7f1b5b64fc50>
_images/FSF1.png _images/FSF2.png

The FSF model can be saved to a FITS header with mpdaf.MUSE.FSFModel.to_header, and read with

MUSE mosaic field map


FieldsMap class is currently under development

FieldsMap reads the possible FIELDMAP extension of the MUSE data cube.

In [22]: from mpdaf.MUSE import FieldsMap

In [23]: fmap = FieldsMap('sdetect/subcub_mosaic.fits', extname='FIELDMAP')

get_pixel_fields returns a list of fields that cover a given pixel (y, x):

In [24]: fmap.get_pixel_fields(0,0)
Out[24]: ['UDF-06', 'UDF-09']

In [25]: fmap.get_pixel_fields(20,20)
Out[25]: ['UDF-06']

get_field_mask returns an array with non-zeros values for pixels matching a field:

In [26]: plt.figure()
Out[26]: <Figure size 640x480 with 0 Axes>

In [27]: plt.imshow(fmap.get_field_mask('UDF-06'), vmin=0, vmax=1)
Out[27]: <matplotlib.image.AxesImage at 0x7f1b5ba43fd0>

In [28]: plt.figure()
Out[28]: <Figure size 640x480 with 0 Axes>

In [29]: plt.imshow(fmap.get_field_mask('UDF-09'), vmin=0, vmax=1)
Out[29]: <matplotlib.image.AxesImage at 0x7f1b5b47eba8>
_images/fmap1.png _images/fmap2.png


mpdaf.MUSE Package


FSF([typ]) This class offers Field Spread Function (FSF) models for MUSE.
Moffat2D(fwhm, beta, shape[, center, normalize]) Compute Moffat for a value or array of values of FWHM and beta.
create_psf_cube(shape, fwhm[, beta, wcs, …]) Create a PSF cube with FWHM varying along each wavelength plane.
get_FSF_from_cube_keywords(cube, size) Return a cube of FSFs corresponding to the keywords presents in the MUSE data cube primary header (‘FSF***’)


FSFModel() Base class for FSF models.
FieldsMap([filename, nfields]) Class to work with the mosaic field map.
LSF([typ]) This class offers Line Spread Function models for MUSE.
MoffatModel2(fwhm_pol, beta_pol, lbrange, …)
OldMoffatModel(a, b, beta, pixstep[, field]) Moffat FSF with fixed beta and FWHM varying with wavelength.
Slicer Convert slice number between the various numbering schemes.

Class Inheritance Diagram

Inheritance diagram of mpdaf.MUSE.fsf.FSFModel, mpdaf.MUSE.FieldsMap.FieldsMap, mpdaf.MUSE.PSF.LSF, mpdaf.MUSE.fsf.MoffatModel2, mpdaf.MUSE.fsf.OldMoffatModel, mpdaf.MUSE.slicer.Slicer