1. Initializing

Initializing is easy You must be in “ipython –pylab” enviroment.:

from PyMUSE.musecube import MuseCube)
cube = MuseCube(filename_cube, filename_white)

If for any reason you do not have the white image, you can still initialize the cube just typing:

cube = MuseCube(filename_cube)

This method will collapse the spectral dimension of the cube and save a create a file named ‘new_white.fits’

2. Spectral analysis

2.1. Get a spectrum

You can get an spectrum of a geometrical region by using:

spectrum = cube.get_spec_from_ellipse_params(134, 219, 5, mode='wwm')

This spectrum is an XSpectrum1D object of the spaxels within a circle of radius 5 at position x,y=(134, 219)

You can also define an elliptical aperture by using instead:

spectrum = cube.get_spec_from_ellipse_params(134,219,[10,5,35], mode='wwm')

where [10,5,35] corresponds to the semimajor axis, semiminor axis and rotation angle respectively You also may want to get the spectrum of a region defined by a single string line in DS9 format (e.g. see http//ds9.si.edu/doc/ref/region.html) To do this, you can use the function:

spectrum = cube.get_spec_from_region_string(region_string, mode = 'wwm')

In both of the get_spec() functions you can set save = True to save the spectrum to the hard_disk

Another extra feature is given by the function:

spectrum = cube.get_spec_and_image(center,halfsize,mode='wwm')

This code will, in addition of extract the spectrum given by center = (x,y) and halfsize either the radius of a circula/ region or a set of [a,b,theta] parameters defining an ellipse, will plot the spectrum and will show the source that is being analysed in a subplot/

If you want to insert the input positions and semi-axes in degrees, you can set the coord_system parameter to wcs by adding:

coord_system = 'wcs'

Finally, you are able to get the spectrum of a single spaxel of the cube by using:

spectrum = cube.get_spec_spaxel(x,y,coord_system ='pix')

Again, you can set coord_system = 'wcs' if you want to insert an xy coordinate in degrees

2.1.1. Get a spectrum interactively

To use this feature, the class must have been initialized in a ipython --pylab qt enviroment It’s also needed the package roipoly. Installation instructions and LICENSE in https//github.com/jdoepfert/roipoly.py/

This feature allows the user to interactively define a region in the canvas as a polygon. To do this:


This will turn interactive the canvas. To select the spaxel that will be the vertices of the region, just press left click on them/ When you have finished, just press right click and then enter to continue. The last vertex that you selected will link the first one to define the contour of the region.

2.1.2. Get the spectrum of a region defined in a DS9 .reg file

You also can define a region in a ds9 .reg file The only thing needed is that the .reg file MUST be saved in physical coordinates. Once this is done, you can get the spectrum:

spectrum = cube.get_spec_from_ds9regfile(regfile,mode='wwm')

2.2. Modes of spectrum extraction

As you have noted, all the diferent get_spec_ functions have the keyword argument “mode”. The mode availables to combine the spectrum of the diferent spaxels in a region ar/

  • ivar - Inverse variance weighting, variance is taken only spatially, from a “white variance images.
  • sum - Sum of total flux.
  • gaussian - Weighted mean. Weights are obtained from a 2D gaussian fit of the bright profil/
  • wwm - ‘White Weighted Mean’. Weigted mean, weights are obtained from the white image, smoothed using a gaussian filter of sigma = npix. If npix=0, no smooth is done
  • ivarwv - Weighted mean, the weight of every pixel is given by the inverse of it’s variance.
  • mean - Mean of the total flux
  • median - Median of the total flux
  • wwm_ivarwv - Weights given by both, ivarwv and wwm
  • wwm_ivar - Weghts given by both, wwm and ivar
  • wfrac - It only takes the fraction frac of brightest spaxels (white) in the region.
    (e.g. frac=0.1 means 10% brightest) with equal weights.

Note The gaussian method is not available in get_spec_from_ds9regfile() nor get_spec_from_interactive_polygon_region()

2.3. Other keyword parameter

Also, all the get_spec_ function have the keyword arguments npix , empirical_std, n_figure and save, frac.

Some modes of extraction require a npix value (default = 0). This value correspond to the sigma of the gaussian function that will smooth the white image, where the bright profile will be obtained. If npix = 0, no smooth is done.

The parameter frac (default = 0.1) will be used in mode = wfrac, and it defines the fraction of brightest spaxels that will be considered in the sum of the flux.

If empirical_std = True (default = False) the uncertainties of the spectrum will be calculated empirically

n_figure is the number of the figure that will display the new_spectrum

if save = True (default = False) The new spectrum extracted will be saved to the hard drive.

2.3.1. Read a spectrum saved by get_spec_method

If you used the ::
save = True

Option, you saved the spectrum to the hard-disk as a fits file. To access the data you can use:

from linetools.spectra.io import readspec
spectrum = readspec('spectrum_fitsname')

This will create a XSpectrum1D object from the fits file. You can access to the spectrum wavelength, flux and sigma by typing spectrum.wavelength, spectrum.flux and spectrum.sig. Additional information on the XSpectrum1D Class can be found in https//github.com/linetools/linetools/blob/master/linetools/spectra/xspectrum1d.py

2.4. Use a SExtractor output file as an input

The software allows the extraction and save of a set of sources detected in a SExtractor output files To do this, you should have at least the next parameters in the SExtractor output file:


(Assuming that you ran SExtractor in the white image or any image with the same dimensions and astrometry of the cube/ First, to plot your regions, you can use:

cube.plot_sextractor_regions('sextractor_filename', flag_threshold=32, a_min=3.5)

Where sextractor_filename is the name of the SExtractor’s output. Every source with a SExtractor flag higher than flag_threshold will be marked in red.

The a_min value correspond to the minimum number of spaxels that will have the semimajor axis of a regions The original (a/b) ratio will be constant, but this set a minimum size for the elliptical apertures

Once you are satisfied with the regions that will be extracted, you can run:

cube.save_sextractor_spec('sextractor_filename', flag_threshold=32, redmonster_format=True, a_min=3.5, n_figure=2,
                          mode='wwm', mag_kwrd='mag_r', npix=0, frac = 0.1)

This will save in the hard disk the spectra of all the sources defined in the sextractor_filename which flags be lower or equal than flag_threshold using the specified modes. If redmonster_format = True, the spectra will be saved in a format redeable for redmonster software http//www.sdss.org/dr13/algorithms/redmonster-redshift-measurement-and-spectral-classification/ You can access to the data of a file writen in this format doing the next:

import PyMUSE.utils as mc
wv,fl,er = mcu.get_rm_spec(rm_spec_name)

where rm_spec_name is the name of the fits file. Also, you can set the parameter mag_kwrd which by default is 'mag_r' to the keyword in the new fits_image that wil/ contain the SExtractor’s MAG_AUTO value. It is possible the usage of a different image as an input for SExtractor. If this is the case, you should not use the X_IMAGE, Y_IMAGE, A_IMAGE, B_IMAGE given by SExtractor (although they still must be included in the parameters list), because the spaxel-wcs conversion in the image given to SExtractor will be probably different to the conversion in the MUSE cube. You may want to include the parameters:


You also may want to be sure that the astrometry between the 2 images in consistent (on the other hand, the regions defined by SExtractor in the image will be shifted in the cube. Once you included them in the parameters list, you should set the parameter wcs_coords = True in both functions:

cube.plot_sextractor_regions('sextractor_filename', flag_threshold=32, a_min=3.5, wcs_coords=True)

to plot the regions and:

cube.save_sextractor_spec('sextractor_filename', flag_threshold=32, redmonster_format=True, a_min=3.5, n_figure=2/
                          mode='wwm', mag_kwrd='mag_r', npix=0, frac = 0.1, wcs_coords = True)

to save them. Save a set of spectra defined by a multi regionfile DS9 .reg file ——————————————————————- You can save all the spectra of regions defined by a DS9 region file to the hard disk. Just use:

cube.save_ds9regfile_specs(regfile,mode='wwm',frac=0.1,npix=0,empirical_std=False,redmonster_format=True,id_start=1, coord_name = False)

Again, you can select between all available modes (except gaussian). The different spectra in the file will be identified by an id/ starting from id_start (default = 1). The coord_name variable will determine how the different spectra are named. If False, The spectra will be named as ID_regfile.fits. If True, The name will depend of the first (X,Y) pair of each region. This is particularly good for ellipses and circles, but not as exact in polygons.

2.5. Save a set of spectra defined by a MUSELET output fits table.

MUSELET (for MUSE Line Emission Tracker) is an emission line galaxy detection tool based on SExtractor from MPDAF (MUSE Python Data Analysis Framework) Python package <(http//mpdaf.readthedocs.io/en/latest/muselet.html)> PyMUSE allow the user te extraction of a set spectra given a MUSELET output fits table. The method:

cube.save_muselet_specs(self, filename, mode='wwm', params=4, frac=0.1, npix=0, empirical_std=False, redmonster_format=True, ids='all')

Will do it easily. Most of the keyword parameters are related to the extraction modes. The important parameters are params and ids`/ ``params by default is set to 4 and correspond to the elliptical parameter of the extraction for ALL the sources in the catalog. It can be either a int or a iterable [a,b, theta] (in spaxel units) ids by default is set to ‘all’. This means that save_muselet_specs() will extract all the sources in the MUSELET catalog. If you set ids = [1,5,23] for example, the function will extract only the sources with that IDs in the MUSELET catalog.

2.6. Saving a single spectrum to the hard drive

To do this you can use the XSpectrum1D functions:


You also may want to save the spectrum in a fits redeable for redmonster. In that case use the MuseCube function:

       mcu.spec_to_redmonster_format(spectrum, fitsname, n_id=None, mag=None)

If ``n_id`` is not  ``None``, the new fitsfile will contain a ID keyword with n_id in it

If mag is not None, must be a tuple with two elements. The first one must contain the keyword that will be in the header (example mag_r) and the second one must contain the value that will be in that keyword on the header of the new fitsfile.

3. Imaging

3.1. Estimate seeing

The method:


Will allow you to estimate the seeing using the white image. The user must insert as the input the xy coordinates in spaxel space of a nearly point source expanded by the seeing. The method will fit a 2D gaussian to the bright profile and will associate the FWHM of the profile with the seeing. The halfsize parameter indicates the radius size in spaxels of the source that will be fited.

3.2. Image creation

Create image collapsing the Cube

You can create a 2D image by collapsing some wavelength slices of the cube using the method:

cube.get_image(wv_input, fitsname='new_collapsed_cube.fits', type='sum', n_figure=2, save=False, stat=False)

IMPORTANT!! wv_input must be list. The list can contain either individual wavelength values (e.g [5000,5005,5010]) o/ a wavelength range (defined as [[5000,6000]] to collapse all wavelength between 5000 and 6000 angstroms)/ If save is True, the new image will be saved to the hard disk as fitsname. The type of collapse can be either ‘sum/ or ‘median’. n_figure is the figure’s number to display the image if save = True. Finally, if stat = True, the collapse wil/ be done in the stat extension of the MUSE cube/ If you want to directly create a new “white” just use:

cube.create_white(new_white_fitsname='white_from_colapse.fits', stat=False, save=True)

This will sum all wavelengths and the new image will be saved in a fits file named by new_white_fitsname. If stat=True, the ne/ image will be created from the stat extension, as the sum of the variances along the wavelength range.

Maybe you want to collapse more than just one wavelength range (for example, the range of several emission lines To do that, you may want to use the method.:

cube.get_image_wv_ranges(wv_ranges, substract_cont=True, fitsname='new_collapsed_cube.fits', save=False, n_figure=3)`

wv_ranges must be a list of ranges (for example [[4000,4100],[5000,5100],[5200,5300]]). You can use the method:


To define the ranges that correspond to the [OII, Hb, OIII 4959,OIII 5007, Ha]. This method will return the list of the rang/ of these transitions at redshift z, and the width given (in angstroms). The method will only return those ranges that remains inside the MUSE wavelength range. Finally, if substract_cont is True, the flux level around the ranges given by wv_ranges will be substracted from the image

3.3. Create a smoothed white image

The method:

cube.get_smoothed_white(npix=2, save=True, kwargs)

returns a smoothed version of the white image. npix defines the sigma of the gaussian filter. kwargs are passed to scipy.ndimage.gaussian_filter(). The method cube.spatial_smooth(npix, output="smoothed.fits", **kwargs) do the same for the whole cube, and save. the new MUSE Cube under the name given by output (The STAT extension is not touched)

3.4. Compose a filtered image

If you want to do a photometric analysis from the Muse Cube, you would need to convolve your data with a photometric filter and compose a new filtered image. To do this, you can use the method:

cube.get_filtered_image(_filter = 'r')

This method will write a new filtered image that will be useful to photometry analysis Available filters u,g,r,i,z,V,R (The Johnson filters V and R have been slightly reduced in order to fit the MUSE spectral range)

4. Extra features

4.1. Emission line kinematics

An useful thing to do with a MuseCube is a kinematic analysis of an extended source. The function::
cube.compute_kinematics(x_c,y_c,params,wv_line_vac, wv_range_size=35, type=’abs’, z=0)

estimates de kinematics of the elliptical region defined by (x_c,y_c,params) in spaxels. The method extract the 1-D spectrum of every spaxel within the region and fit a gaussian + linear model, in order to fit and emi/abs line and the continuum. The required paramters are:

* x_c
* y_c
* params
That define the elliptical region::
  • wv_line_vac wavelength of the transition in vacuum
  • wv_range_size Angstroms.
Space at each side of the line in the spectrum. Set this parameter in order to fit the complete transition but do not include near additional lines::
  • type ‘abs’ or ‘emi’. Type of transition to fit. ‘abs’ for absorption and ‘emi’ for emissions
  • z redshift of the galaxy

This function returns the kinematic image of the region, and saves the image in a .fits file IMPORTANT Select strong lines that be spatially extended.

4.2. Create Video

As an extra analysis to your data, the MuseCube Class allows the user to create 2 types of videos (need the cv2 package)::

Will create a video which frames will be, at each redshifts, the sum of all wavelengths that would fall at strong emission lines (Ha,Hb,OII,OIII):


Will create a movie that goes from wavelength = w_ini suming a number of wavelength values given by width, to wavelength = w_en