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:
- Attributes:
- files
list
ofstr
List of cubes FITS filenames.
- nfiles
int
Number of files.
- flux_scales
list
ofdouble
List of flux scales corrections.
- flux_offsets
list
ofdouble
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.
- files
Attributes Summary
Methods Summary
Checks if all cubes are compatible.
Checks if all cubes have same dimensions.
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
ortuple
offloat
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.
- madbool
Use MAD (median absolute deviation) statistics for sigma-clipping.
- nmax
- 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).
- cube
- 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
ortuple
offloat
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.
- madbool
Use MAD (median absolute deviation) statistics for sigma-clipping.
- nmax
- 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.
- cube