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 coordinates
- unit : 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 N-1.
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 sigma-clipping.
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
, Table cube, 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)
- out :
-
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 N-1.
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 sigma-clipping.
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: `~mpdaf.obj.Cube`
Cube which contains the number of rejected values for each pixel.