linmos (Linear Mosaic Applicator)

This page provides instruction for using the linmos program. The purpose of this software is to perform a linear mosaic of a set of images.

Running the program

It can be run with the following command, where “config.in” is a file containing the configuration parameters described in the next section.

$ linmos -c config.in

The linmos program is not parallel/distributed.

Parallel linmos (linmos-mpi)

There is a parallel version of linmos which will divide the mosaic by channel over the number of ranks. This improves the run time markedly as the I/O is distributed. Furthermore this also reduces the memory load. For continuum mosaics you can use multiple threads to speed up the run time. You may need to set OMP_NUM_THREADS>0 to enable this and your installation needs to be compiled with OMP enabled. Note that you could combine these options, but generally best performance for spectral line cubes is obtained by parallelising over channels only. There is an option to do the I/O using either collective operations or independently. For large files and with Lustre striping factors of, e.g., 16 or 32 a factor of two in speed can be gained with collective I/O. The use of collective I/O is only possible with fits images. There is also an option to trim the empty edges of the images (containing NaNs) that can potentially reduce the computation time and the size of the output files. Please refer to the configuration below on how to enable this feature.

Configuration Parameters

The following table contains the configuration parameters to be specified in the config.in file shown on above command line. Note that each parameter must be prefixed with “linmos.”. For example, the weighttype parameter becomes linmos.weighttype.

Note

There is no default option for weighttype. This option must be set.

Parameter	Type	Default	Description
names	vector<string>	none	Names of the input images. If these images start with “image” and have associated sensitivity images, the latter are integrated into a sensitivity image for the mosaic.
weights	vector<string>	null	Optional parameter (required if using weight images). Names of input images containing pixel weights. There must be one weight image for each image, and the size must match. Alternatively, a weightslog file can be specified for each input image. Use the useweightslog parameter and/or .txt extension to indicate weightslog inputs. If weights are required, but not specified, linmos will try to read them from the image headers. Ignored if weighttype=FromPrimaryBeamModel or if findmosaics=true.
useweightslog	bool	none	Optional parameter to specify whether or not to use weightslog files. If true, the “.txt” extension in the weights parameter can be omitted.
stokesinames	vector<string>	null	Optionally specify the Stokes I images to use when using removeleakage. There must be one Stokes I image for each input image, and the size must match. If not specified, removeleakage tries to find the Stokes I to use by replacing e.g., “.q.” by “.i.” in the name.
beams	vector<int>	null	Optionally specify the beam numbers to use for mosaicking and the removeleakage option. If specified the number of beams must match the number of images. By default the beam number is deduced from the image name by looking for the beam tag (e.g., “.beam01”) in the name
beamangles	vector<double>	null	Optionally specify the beam angles to use for mosaicking. If specified the number of beam angles must match the number of images. By default the beam angle is taken from the alpha value in the primary beam specification (see below). You only need to specify this here if the angle is not the same for all input images (e.g., if you are combining interleaves or different epochs). Angle units are radians.
outname	string	none	Name of the output image. Ignored if findmosaics=true.
outweight	string	none	Name of output image containing pixel weights. Ignored if findmosaics=true.
weighttype	string	none	How to determine the pixel weights. Options: FromWeightImages: from weight images. Parameter weights must be present and there must be a one-to-one correspondence with the input images. FromPrimaryBeamModel: using a primary-beam model. If beam centres are not specified (see below), the reference pixel of each input image is used. Combined: uses both the weight images and the PB model to form the pixel weight
weightstate	string	Corrected	The weighting state of the input images. Options: Corrected: Direction-dependent beams/weights have been divided out of input images. Inherent: Input images retain the natural primary-beam weighting of the visibilities. Weighted: Full primary-beam-squared weighting.
cutoff	float	0.01	Desired cutoff of the gain function used to form weights, relative to the maximum gain.
psfref	uint	0	Which of the input images to extract restoring-beam information from. The default behaviour is to use the first image specified (indices start at 0).
outputref	int	-1	Specify which of the input images to use for the output coordinates (i.e., projection centre). The default is to pick a central field or use outputcentre if specified
outputcentre	vector<string>	[,,J2000,SIN]	Optional parameter specifying output projection centre, coordinates (default J2000) and projection (default SIN). E.g., [12:34:56.7, -23.45.12.34] for J2000, RA/Dec in SIN projection (Note “.” separator in dec); [264.2deg,19.7deg,GALACTIC,MOL] for Galactic coordinates in Mollweide projection. See Projections for the list of supported projections. See Directions for the list of supported Direction types. For all-sky projections, like MOL or HPX, with invalid pixel areas best results are obtained with regrid.decimate=1
nterms	uint	-1	Process multiple taylor-term images. The string “taylor.0” must be present in both input and output image names (including weights images), and it will be incremented from 0 to nterms-1. Ignored if findmosaics=true.
findmosaics	bool	false	Instead of specifying specific input and output files to mosaic, search the current directory for suitable mosaics. Parameter names is used to specify a vector of tags, and all groups of images that have names that are equal apart from these tags are mosaicked together. Groups must have one image per tag. Currently only groups with prefixes of “image” and “residual” are allowed, with prefixes “weights” and “sensitivity” special cases that are searched for once groups are identified. Parameters weights, outname, outweight and nterms are ignored if findmosaic=true.
imagetype	string	“casa”	Type of the image handler (determines the format of the images, both which are written to or read from the disk). The default is to create casa images but “fits” can also be chosen.
imagealloc	string	“fast”	Set to “fast” to use the fast file allocation scheme. This can save a lot of time when creating large cubes. This is now the default, set to “” to turn off
imageaccess	string	“individual”	The imageaccess parameters are for fits images only: Choose between “individual” and “collective” I/O operations. For collective I/O the number of ranks must divide evenly into the number of channels.
imageaccess.axis	uint	0	Specify the channel axis of the cubes - usually 3 This is only used for collective I/O on fits cubes
imageaccess.order	string	“distributed”	How do the ranks read/write channels: “distributed” over the spectrum, one section per rank “contiguous” in rank order, a section at a time For collective I/O “contiguous” is automatically selected
imageaccess.write	string	“serial”	How does the output cube get written: “serial” one rank at a time “parallel” all ranks at the same time For casa images “serial” is used regardless.
imageHistory	vector<string>	none	Specify lines to add to the image history
calcstats	bool	false	If true, calculate the image statistics and write it to the image table. Note: only available for linmos-mpi task.
keywordsToCopy	vector<string>	<several>	Specify the header keywords to copy from the reference image to the output. By default TELESCOP,PROJECT,SBID,DATE-OBS and DURATION are copied. This will override that list.
trimming	bool	false	If true, trim the parts of the image below the beam cutoff. This can speed up computation and reduce file size. This feature is only supported for weighttype = Combined or FromPrimaryBeamModel. It is only available to linmos-mpi.
trimming.type	string	“conservative”	There are two types of trimming supported, namely conservative and aggressive. The conservative trimming type only applies the cutoff to (or clips) the input beams whereas the aggressive trimming type applies the cutoff to both the input and output beams. Note that the aggressive trimming type can result in the output image being slightly over trimmed at the edges. Also, this option is only valid if the trimming option is true.

If input images need to be regridded, the following ImageRegrid options are available:

Parameter	Type	Default	Description
regrid.method	string	linear	ImageRegrid interpolation method: nearest, linear, cubic or lanczos.
regrid.decimate	uint	3	ImageRegrid decimation factor. In the range 3-10 is likely to provide the best performance/accuracy tradeoff
regrid.replicate	bool	false	ImageRegrid replicate option.
regrid.force	bool	false	ImageRegrid force option.

Definition of beam centres

If weights are generated from primary-beam models (weighttype=FromPrimaryBeamModel), it is possible to set the beam centres from within the parset. Since this is most likely useful when each input image comes from a different multi-beam feed, feeds offset parameters from other applications are used for this. If the origin of the beams offset system is not specified, using either feeds.centre or feeds.centreref, any offsets are ignored and the reference pixel of each input image is used as the primary-beam centre.

The feeds parameters can be given either in the main linmos parset or a separate offsets parset file set by the feeds.offsetsfile parameter.

Parameter	Type	Default	Description
feeds.centre	vector<string>	none	Optional parameter (it or feeds.centreref required when specifying beam offsets). Two-element vector containing the right ascension and declination that all of the offsets are relative to.
feeds.centreref	int	none	Optional parameter (it or feeds.centre required when specifying beam offsets). Which of the input images to use to automatically set feeds.centre. Indices start at 0. If neither of these parameters are set, the reference pixel of each input image is used as the primary-beam centre.
feeds.spacing	string	none	Optional parameter (required when specifying beam offsets in the main linmos parset). Beam/feed spacing when giving offsets in the main linmos parset. If feeds.offsetsfile is given, this parameter will be ignored.
feeds.names[i] (one per input image)	vector<string>	none	Optional parameter (required when specifying beam offsets in the main linmos parset). Two-element vector containing the beam offset relative to the feeds.centre parameter. Offsets correspond to hour angle and declination. names[i] should match the names of the input images, given in linmos.names (see above). If feeds.offsetsfile is given, these parameters will be ignored.
feeds.offsetsfile	string	none	Optional parameter. Name of the optional beam/feed offsets parset. If present, any offsets specified in the main linmos parset will be ignored.
feeds.names	vector<string>	none	Optional parameter (required either here or below when specifying a beam offsets parset). The beam offsets parset should have one line per input image, with parameter keys (minus the feeds. prefix) specified by this parameter. If the offsets parset also contains a names parameter, the main linmos entry will hold, to allow a subset of beams from a general to be chosen.

If feed offsets are provided via an additional parset (i.e. not that one passed directly to the linmos program), the file shall have the following format:

Note

These parameters, specified in an external file, do not require the “limos.” prefix.

Parameter	Type	Default	Description
feeds.names	vector<string>	null	Optional parameter (required either here or above when specifying a beam offsets parset). The beam offsets parset should have one line per input image, with parameter keys (minus the feeds. prefix) specified by this parameter. If the offsets parset also contains a names parameter, the main linmos entry will hold, to allow a subset of beams from a general to be chosen.
feeds.spacing	string	none	Beam/feed spacing. When using this extra offsets parset, the spacing needs to be specified in this parset.
feeds.beamnames[i] (one per input image)	vector<string>	none	Two-element vector containing the beam offset relative to the feeds.centre parameter. Offsets correspond to hour angle and declination. beamnames[i] should match the names given in feeds.names* (see above).

Alternate Primary Beam Models

It is possible to select the model that is used for the weighting. This is selected in the linmos parset by the key “primarybeam”

Parameter	Type	Default	Description
primarybeam	string	“GaussianPB”	Optional parameter that allows the user to select which primary beam will be used in weighting. The parameters of which can also be altered if required. Also supported are ASKAP_PB, MWA_PB and SKA_PB.

Gaussian Primary Beam Options

You can choose the aperture size and scaling parameters both of the FWHM of the beam and a scaling of the exponent. In the parfile these are sub parameters of the Primary beam type. (e.g linmos.primarybeam.GaussianPB.aperture)

The default Gaussian Primary beam is now 2 dimensional. But unless the user specifies x and w widths they just get the symmetric beam as defined by the aperture.

Parameter	Type	Default	Description
aperture	double	12	Aperture size in metres.
fwhmscaling	double	1.09	Scaling of the full width half max of the Gaussian
expscaling	double	4 log(2)	Scaling of the primary beam exponent

The 2 dimensional beam is governed by the following parameters.

2D-Parameters	Type	Default	Description
(x/y)width	double	0.0	Angular width in rad. of the x (N-S) and y (E-W) Gaussian
(x/y)off	double	0.0	Angular offset from nominal beamcentre in rad., E, N are +ve
alpha	double	0.0	PA in rad. measured from North in an +ve RA direction

ASKAP Primary Beam Options

The ASKAP primary beam is specified using a 4D or 5D image file determined from antenna beam measurements (holography). There are two options to specify the frequency dependence of the beam:

• Taylor term planes (nterms > 0) or,

• a frequency cube (nterms = 0)

Use the image parameter to specify the beam file (FITS format, axes RA, Dec, Taylor term or Frequency, optionally polarisation, and beam number). The image file can contain more Taylor planes, but only the first nterms planes (up to 3) are used. In the parfile these are sub parameters of the Primary beam type. (e.g linmos.primarybeam.ASKAP_PB.nterms). A beam file with 4 axes can only be used to correct the primary beam response. A 5D file with a polarisation axis can be used to correct both primary beam response and leakage.

Parameter	Type	Default	Description
image	string	none	FITS image file with 4/5 axes (see above), note that you need to specify the full filename (including .fits) here
nterms	integer	0	Number of Beam Taylor terms to use, or 0 for a frequency cube
alpha	double	0	Position angle of the beam, optional rotation of measured beam pattern.
xscale,yscale	double	from FITS	Override x and y axis increments found in FITS header
freqoffset	double	from FITS	Override reference frequency found in FITS header
freqscale	double	from FITS	Override frequency increment found in FITS header
interpolation	string	linear	Interpolation method: nearest, linear, cubic or lanczos.

MWA Primary Beam Options

Parameter	Type	Default	Description
latitude	double	-26.703319 deg	Array latitude in radians
longitude	double	116.67081 deg	Array longitude in radians
dipole.separation	double	1.10 metres	Dipole separation
dipole.height	double	0.30 metres	dipole hheight

Primary Beam Corrections to the Taylor terms

The primary beam is a function of frequency. Therefore the apparent spectral index of a point source away from beam centre will contain a contribution from the frequency dependence of the primary beam. It is possible to estimate this contribution and remove it by scaling the Taylor term images appropriately.

This is an analytic correction for Gaussian beams. For ASKAP_PB it is calculated either by numerical differentiation (for spectral beam cubes) or by evaluation of the beam Taylor terms.

Parameter	Type	Default	Description
removebeam	bool	false	Remove beam from the Taylor term images

Leakage Corrections

The ASKAP holography beam measurements also measure the off-axis leakage across the field. We can use these measurements to correct the Stokes Q, U and V terms for leakage from Stokes I. You will need to specify a 5D FITS image file with axes Ra, Dec, Freq, polarisation and beam containing the holography measurements. Example 5 below shows how to specify this. Note that the beam images will be interpolated in the spatial directions, but for frequency the nearest plane will be used and scaled to the correct frequency. The stokesinames and beams parameters can be used to specify which Stokes I image and beam number to use for each input image if the naming scheme is non standard (i.e., does not contain polarisation and beam tags like “.q.” and “.beam04”)

Parameter	Type	Default	Description
removeleakage	bool	false	Remove off-axis leakage from Stokes Q, U and V images using a model of the Stokes-I dependent leakage

Examples

Example 1:

Example linmos parset to combine individual feed images from a 36-feed simulation. Weights images are used to weight the pixels.

linmos.weighttype = FromWeightImages

linmos.names      = [image_feed00..35_offset.i.dirty.restored]
linmos.weights    = [weights_feed00..35_offset.i.dirty]

linmos.outname    = image_mosaic.i.dirty.restored
linmos.outweight  = weights_mosaic.i.dirty

Example 2:

Example linmos parset to combine the four inner-most feed images from a 36-feed observation. Gaussian primary-beam models are used to weight the pixels. The primary-beam offsets are provided in an external file.

linmos.weighttype       = FromPrimaryBeamModel

linmos.names            = [image_feed14..15.i.dirty.restored, image_feed20..21.i.dirty.restored]

linmos.outname          = image_mosaic.i.dirty.restored
linmos.outweight        = weights_mosaic.i.dirty

linmos.feeds.centre     = [12h30m00.00, -45.00.00.00]

# specify a beam offsets file
linmos.feeds.offsetsfile = linmos_beam_offsets.in

# Specify which feeds from the "offsetsfile" (specified above) are to be used
linmos.feeds.names       = [PAF36.feed14..15, PAF36.feed20..21]

Below is the linmos_beam_offsets.in file refered to in the above parameter set:

feeds.spacing            = 1deg
<snip>
feeds.PAF36.feed14       = [-0.5, -0.5]
feeds.PAF36.feed15       = [-0.5,  0.5]
<snip>
feeds.PAF36.feed20       = [0.5, -0.5]
feeds.PAF36.feed21       = [0.5,  0.5]
<snip>

Example 3:

Example linmos parset to combine the four inner-most feed images from a 36-feed simulation. The primary-beam offsets directly in the parameter set.

linmos.weighttype       = FromPrimaryBeamModel

linmos.names            = [image_feed14..15.i.dirty.restored, image_feed20..21.i.dirty.restored]

linmos.outname          = image_mosaic.i.dirty.restored
linmos.outweight        = weights_mosaic.i.dirty

linmos.feeds.centre     = [12h30m00.00, -45.00.00.00]

linmos.feeds.spacing    = 1deg
linmos.feeds.image_feed14.i.dirty.restored = [-0.5, -0.5]
linmos.feeds.image_feed15.i.dirty.restored = [-0.5,  0.5]
linmos.feeds.image_feed20.i.dirty.restored = [0.5, -0.5]
linmos.feeds.image_feed21.i.dirty.restored = [0.5,  0.5]

Example 4:

Example linmos-mpi parset to combine individual feed images from a 36-feed simulation for each of three separate taylor terms 0, 1 and 2. The location of taylor.* in all inputs and outputs is given explicitly. Uses combined weighting, which includes primary beam response and weight images, and removebeam to remove the spectral index of the beam from the results. Uses the ASKAP_PB with 3 terms from a Taylor term beam FITS image derived from holography measurements.

linmos.weighttype  = Combined
linmos.weightstate = Inherent

linmos.names       = [image_feed00..35_offset.i.dirty.taylor.0.restored]
linmos.weights     = [weights_feed00..35_offset.i.dirty.taylor.0]

linmos.outname     = image_mosaic.i.dirty.taylor.0.restored
linmos.outweight   = weights_mosaic.i.dirty.taylor.0

linmos.nterms      = 3
linmos.removebeam  = true

linmos.primarybeam = ASKAP_PB
linmos.primarybeam.ASKAP_PB.image  = askap-beam-taylor.fits
linmos.primarybeam.ASKAP_PB.nterms = 3

Example 5:

Example linmos-mpi parset to combine polarisation Q image cubes for 36 beams while removing off axis leakage. Uses combined weighting, which includes primary beam response and weight images. Uses the ASKAP_PB with a 5D FITS image derived from holography measurements containing the primary beam response for Stokes I and the Stokes I leakage for Q, U and V for each channel and position. This example also uses a decimate value of 10 for regridding and collective I/O distributed over the channel axis to try and speed up processing.

linmos.weighttype       = Combined
linmos.weightstate      = Inherent
linmos.imagetype        = fits

linmos.names            = [image.restored.q.SB10007.contcube.POSSUM_2126-54.beam00..35.conv]
linmos.weights          = [weights.q.SB10007.contcube.POSSUM_2126-54.beam00..35]

linmos.outname          = image.restored.q.SB10007.contcube.POSSUM_2126-54.linmos.conv
linmos.outweight        = weights.q.SB10007.contcube.POSSUM_2126-54.linmos.convrestored

linmos.primarybeam      = ASKAP_PB
linmos.primarybeam.ASKAP_PB.image = askap-beam-cube_16881.fits
linmos.removeleakage    = true
linmos.imageaccess      = collective
linmos.imageaccess.axis = 3
linmos.regrid.decimate  = 10

Example 6:

Example linmos parset to combine individual feed images from a 36-feed simulation. A mosaics is made for each set of 36 images that has one image for each tag (param “names”) but filenames that are otherwise the same. Only the “image” and “residual” prefixes are currently supported. For example, if the outputs produced for Data Challenge 1A were produced for each feed and stored in a single directory, the following mosaics would be made: image_linmos.i.clean.taylor.0, image_linmos.i.clean.taylor.0.restored, image_linmos.i.clean.taylor.1, image_linmos.i.clean.taylor.1.restored, image_linmos.i.dirty.restored, residual_linmos.i.clean.taylor.0 and residual_linmos.i.clean.taylor.1. Associated weights and sensitivity images would also be made, however in situations where multiple mosaics have the same weights or sensitivites (e.g. image_linmos.i.clean.taylor.0, image_linmos.i.clean.taylor.0.restored and residual_linmos.i.clean.taylor.0), only one would be made.

Furthermore, since the DC1A does not seem to produce weights.*.taylor.2 and we have specified weighttype FromWeightImages, mosaic image_linmos.clean.taylor.2 would not be made. It would be produced if weighttype were FromPrimaryBeamModel.

linmos.weighttype  = FromWeightImages
linmos.findmosaics = true
linmos.names       = [feed00..35_offset]