CubeList¶

class
mpdaf.obj.
CubeList
(files, scalelist=None, offsetlist=None)[source]¶ Bases:
object
Manages a list of cubes and handles the combination.
To run the combination, all the cubes must have the same dimensions and be on the same WCS grid. A global flux offset and scale can be given for each cube:
(data + offset) * scale
.Parameters: files : list of str
List of cubes FITS filenames.
scalelist: list of float, optional
List of scales to be applied to each cube.
offsetlist: list of float, optional
List of offsets to be applied to each cube.
Attributes
files (list of str) List of cubes FITS filenames. nfiles (int) Number of files. flux_scales (list of double) List of flux scales corrections. flux_offsets (list of double) List of flux offsets corrections. shape (tuple) Lengths of data in Z and Y and X (python notation (nz,ny,nx)). wcs ( mpdaf.obj.WCS
) World coordinates.wave ( mpdaf.obj.WaveCoord
) Wavelength coordinatesunit (str) Possible data unit type. None by default. Attributes Summary
checkers
Methods Summary
check_compatibility
()Checks if all cubes are compatible. check_dim
()Checks if all cubes have same dimensions. check_wcs
()Checks if all cubes have same world coordinates. combine
([nmax, nclip, nstop, var, mad, header])Combines cubes in a single data cube using sigma clipped mean. info
([verbose])Print information. median
([header])Combines cubes in a single data cube using median. pycombine
([nmax, nclip, var, nstop, nl, …])Combines cubes in a single data cube using sigma clipped mean. pymedian
([header])save_combined_cube
(data[, var, method, …])Attributes Documentation

checkers
= ('check_dim', 'check_wcs')¶
Methods Documentation

combine
(nmax=2, nclip=5.0, nstop=2, var='propagate', mad=False, header=None)[source]¶ Combines cubes in a single data cube using sigma clipped mean.
Parameters: nmax : int
Maximum number of clipping iterations.
nclip : float or tuple of float
Number of sigma at which to clip. Single clipping parameter or lower / upper clipping parameters.
nstop : int
If the number of not rejected pixels is less than this number, the clipping iterations stop.
var : {‘propagate’, ‘stat_mean’, ‘stat_one’}
propagate
: the variance is the sum of the variances of the N individual exposures divided by N**2.
stat_mean
: the variance of each combined pixel is computed as the variance derived from the comparison of the N individual exposures divided N1.
stat_one
: the variance of each combined pixel is computed as the variance derived from the comparison of the N individual exposures.
mad : bool
Use MAD (median absolute deviation) statistics for sigmaclipping.
Returns: cube :
Cube
The merged cube.
expmap:
mpdaf.obj.Cube
Exposure map data cube which counts the number of exposures used for the combination of each pixel.
statpix:
astropy.table.Table
Table that gives the number of NaN pixels and rejected pixels per exposures (columns are FILENAME, NPIX_NAN and NPIX_REJECTED).

median
(header=None)[source]¶ Combines cubes in a single data cube using median.
Returns: out :
Cube
,mpdaf.obj.Cube
, Tablecube, expmap, statpix
cube
will contain the merged cubeexpmap
will contain an exposure map data cube which counts the number of exposures used for the combination of each pixel.statpix
is a table that will give the number of Nan pixels pixels per exposures (columns are FILENAME and NPIX_NAN)

pycombine
(nmax=2, nclip=5.0, var='propagate', nstop=2, nl=None, header=None, mad=False)[source]¶ Combines cubes in a single data cube using sigma clipped mean.
This is less optimized but more flexible version, compared to
CubeList.combine
. It is useful mostly forCubeMosaic
, where we need to shift the individual cubes into the output one.Parameters: nmax : int
Maximum number of clipping iterations.
nclip : float or tuple of float
Number of sigma at which to clip. Single clipping parameter or lower / upper clipping parameters.
nstop : int
If the number of not rejected pixels is less than this number, the clipping iterations stop.
var : {‘propagate’, ‘stat_mean’, ‘stat_one’}
propagate
: the variance is the sum of the variances of the N individual exposures divided by N**2.
stat_mean
: the variance of each combined pixel is computed as the variance derived from the comparison of the N individual exposures divided N1.
stat_one
: the variance of each combined pixel is computed as the variance derived from the comparison of the N individual exposures.
mad : bool
Use MAD (median absolute deviation) statistics for sigmaclipping.
Returns: cube :
Cube
The merged cube.
expmap:
mpdaf.obj.Cube
Exposure map data cube which counts the number of exposures used for the combination of each pixel.
statpix:
astropy.table.Table
Table that gives the number of NaN pixels and rejected pixels per exposures (columns are FILENAME, NPIX_NAN and NPIX_REJECTED).
rejmap:
Cube
Cube which contains the number of rejected values for each pixel.
