FINDCLUMPS - Identify clumps of emission within a 1, 2 or 3 dimensional NDF

Description:

This application identifies clumps of emission within a 1, 2 or 3 dimensional NDF. It is assumed that any background has already been removed from the data array (for instance, using CUPID:FINDBACK). Information about the clumps is returned in several different ways:

• A pixel mask identifying pixels as background, clump or edge pixels is written to the Quality array of each output NDF (see parameters OUT and QOUT). Three quality bits will be used; one is set if and only if the pixel is contained within one or more clumps, another is set if and only if the pixel is not contained within any clump, and the other is set if and only if the pixel is in a clump but on the edge of the clump (i.e. has one or more neighbouring pixels that are not inside a clump). These three quality bits have names associated with them which can be used with the KAPPA applications SETQUAL, QUALTOBAD, REMQUAL, SHOWQUAL. The names used are "CLUMP", "BACKGROUND" and "EDGE". For instance, to overlay the outline of a set of 2D clumps held in NDF "fred" on a previously displayed 2D image, do "qualtobad fred fred2 background" followed by "contour noclear mode=good fred2".

• Information about each clump, including a minimal cut-out image of the clump and the clump parameters, is written to the CUPID extension of the output NDF (see the section "Use of CUPID Extension" below).

• An output catalogue containing clump parameters can be created (see parameter OUTCAT).

The algorithm used to identify the clumps (GaussCLumps, ClumpFind, etc) can be specified (see parameter METHOD).

Usage:

findclumps in out outcat method

Parameters:

BACKOFF = _LOGICAL (Read)

If TRUE, the background level in each clump is removed from the clump data values before calculating the reported clump sizes and centroid position (the background level used is the minimum data value in the clump). If FALSE, the full data values, including background, are used when calculating the clump sizes and centroid position.

If BACKOFF is FALSE, a clump that sits on a high background level will have a larger reported width than an identical clump sitting on a lower background level. The position of the centroid may also be affected by the background level. This is usually undesirable, and so the default value for BACKOFF is usually TRUE. The main reason you may want to set BACKOFF to FALSE is if you want to compare clump properties found by FINDCLUMPS with those found by the IDL version of CLUMPFIND (which includes the background in its calculations). For this reason, the dynamic default value got BACKOFF is TRUE, unless METHOD is "ClumpFind" and the ClumpFind.IDLAlg configuration parameter is non-zero, in which case the dynamic default for BACKOFF is FALSE.

Note, the other reported clump properties such as total data value, peak data value, etc, are always based on the full clump data values, including background. []

CONFIG = GROUP (Read)

Specifies values for the configuration parameters used by the clump finding algorithms. If the string "def" (case-insensitive) or a null (!) value is supplied, a set of default configuration parameter values will be used.

The supplied value should be either a comma-separated list of strings or the name of a text file preceded by an up-arrow character "$\wedge$", containing one or more comma-separated list of strings. Each string is either a "keyword=value" setting, or the name of a text file preceded by an up-arrow character "$\wedge$". Such text files should contain further comma-separated lists which will be read and interpreted in the same manner (any blank lines or lines beginning with "#" are ignored). Within a text file, newlines can be used as delimiters as well as commas. Settings are applied in the order in which they occur within the list, with later settings over-riding any earlier settings given for the same keyword.

Each individual setting should be of the form:

$<$keyword$>$=$<$value$>$

where $<$keyword$>$ has the form "algorithm.param"; that is, the name of the algorithm, followed by a dot, followed by the name of the parameter to be set. If the algorithm name is omitted, the current algorithm given by parameter METHOD is assumed. The parameters available for each algorithm are listed in the "Configuration Parameters" sections below. Default values will be used for any unspecified parameters. Unrecognised options are ignored (that is, no error is reported). [current value]

DECONV = _LOGICAL (Read)

Determines if the clump properties stored in the output catalogue and NDF extension should be corrected to remove the effect of the instrumental beam width specified by the FwhmBeam and VeloRes configuration parameters. If TRUE, the clump sizes will be reduced and the peak values increased to take account of the smoothing introduced by the beam width. If FALSE, the undeconvolved values are stored in the output catalogue and NDF. Note, the filter to remove clumps smaller than the beam width is still applied, even if DECONV is FALSE. [TRUE]

ILEVEL = _INTEGER (Read)

Controls the amount of diagnostic information reported. It should be in the range 0 to 6. A value of zero will suppress all screen output. Larger values give more information (the precise information displayed depends on the algorithm being used). Note, this screen output describes the progress of the specific clump finding algorithm selected using the METHOD parameter, and therefore clump parameters such as clump size, etc, will be displayed using the definition most natural to the chosen algorithm. These definitions may not be the same as those used to create the output catalogue, since the output catalogue contains standardised columns chosen to allow comparison between different algorithms. For instance, the clump sizes displayed on the screen by the GaussClumps algorithm will be FWHM in pixels, but the clump sizes stored in the output catalogue are the RMS deviation of each pixel centre from the clump centroid, weighted by the corresponding pixel data value. [1]

IN = NDF (Read)

The 1, 2 or 3 dimensional NDF to be analysed.

LOGFILE = LITERAL (Read)

The name of a text log file to create. If a null (!) value is supplied, no log file is created. [!]

METHOD = LITERAL (Read)

The algorithm to use. Each algorithm is described in more detail in the "Algorithms:" section below. Can be one of:

• GaussClumps

• ClumpFind

• Reinhold

• FellWalker

Each algorithm has a collection of extra tuning values which are set via the CONFIG parameter. [current value]

NCLUMPS = _INTEGER (Write)

The total number of clumps descrriptions stored within the output NDF (and catalogue).

OUT = NDF (Write)

The output NDF which has the same shape and size as the input NDF. Information about the identified clumps and the configuration parameters used will be stored in the CUPID extension of this NDF. See "Use of CUPID Extension" below for further details about the information stored in the CUPID extension. Other applications within the CUPID package can be used to display this information in various ways. The information written to the DATA array of this NDF depends on the value of the METHOD parameter. If METHOD is GaussClumps, the output NDF receives the sum of all the fitted Gaussian clump models including a global background level chosen to make the mean output value equal to the mean input value. If METHOD is ClumpFind, FellWalker or Reinhold, each pixel in the output is the integer index of the clump to which the pixel has been assigned. Bad values are stored for pixels which are not part of any clump. The output NDF will inherit the AXIS and WCS components (plus any extensions) from the input NDF.

OUTCAT = FILENAME (Write)

An optional output catalogue in which to store the clump parameters. No catalogue will be produced if a null (!) value is supplied. The following columns are included in the output catalogue:

• Peak1: The position of the clump peak value on axis 1.

• Peak2: The position of the clump peak value on axis 2.

• Peak3: The position of the clump peak value on axis 3.

• Cen1: The position of the clump centroid on axis 1.

• Cen2: The position of the clump centroid on axis 2.

• Cen3: The position of the clump centroid on axis 3.

• Size1: The size of the clump along pixel axis 1.

• Size2: The size of the clump along pixel axis 2.

• Size3: The size of the clump along pixel axis 3.

• Sum: The total data sum in the clump.

• Peak: The peak value in the clump.

• Volume: The total number of pixels falling within the clump.

The coordinate system used to describe the peak and centroid positions is determined by the value supplied for parameter WCSPAR. If WCSPAR is FALSE, then positions are specified in the pixel coordinate system of the input NDF. In addition, the clump sizes are specified in units of pixels, and the clump volume is specified in units of cubic pixels (square pixels for 2D data). If WCSPAR is TRUE, then positions are specified in the current coordinate system of the input NDF. In addition, the clump sizes and volumes are specified in WCS units. Note, the sizes are still measured parallel to the pixel axes, but are recorded in WCS units rather than pixel units. Celestial coordinate positions are units of degrees, sizes are in units are arc-seconds, and areas in square arc-seconds. Spectral coordinates are in the units displayed by the KAPPA command "ndftrace".

If the data has less than 3 pixel axes, then the columns describing the missing axes will not be present in the catalogue.

The catalogue inherits any WCS information from the input NDF.

The "size" of the clump on an axis is the RMS deviation of each pixel centre from the clump centroid, where each pixel is weighted by the corresponding pixel data value (optionally with the background removed - see parameter BACKOFF). If parameter DECONV is set TRUE, the values stored for "Size..." and "Peak" are corrected to take account of the smoothing introduced by the instrumental beam. These corrections reduced the "size..." values and increase the peak value. Beam sizes are specified by configuration parameters FWHMBeam and VeloRes.

For the GaussClump algorithm, the Sum and Volume values refer to the part of the Gaussian within the level defined by the GaussClump.ModelLim configuration parameter.

The values used for configuration parameters and ADAM parameters are written to the history information of the output catalogue.

The KAPPA command "listshow" can be used to draw markers at the central positions of the clumps described in a catalogue. For instance, the command "listshow fred plot=mark" will draw markers identifying the positions of the clumps described in file fred.FIT, overlaying the markers on top of the currently displayed image. [!]

PERSPECTRUM = _LOGICAL (Read)

This parameter is ignored unless the supplied input NDF is 3-dimensional and includes a spectral axis. If so, then a TRUE value for PERSPECTRUM will cause all spectra within the supplied cube to be processed independenly of the neighbouring spectra. That is, each identified clump will contain pixels from only a single input spectrum. If a clump extends across multiple spectra, then it will be split up into multiple clumps, one for each spectrum. Currently, this parameter can only be used with the FellWalker and ClumpFind methods. A value of FALSE is always used for other methods. [FALSE]

QOUT = NDF (Write)

An optional output NDF that is a copy of the input NDF, except that any Quality component in the input NDF is discarded and a new one created. The new Quality component defines 3 flags that indicate if each pixel is inside a clump, on the edge of a clump or outside all clumps. If a null (!) value is supplied, no NDF is created. [!]

REPCONF = _LOGICAL (Read)

If a TRUE value is supplied, then the configuration parameters supplied by the CONFIG parameter will be listed to standard output. [current value]

RMS = _DOUBLE (Read)

Specifies a value to use as the global RMS noise level in the supplied data array. The suggested default value is the square root of the mean of the values in the input NDF's Variance component. If the NDF has no Variance component, the suggested default is based on the differences between neighbouring pixel values. Any pixel-to-pixel correlation in the noise can result in this estimate being too low. The value supplied for this parameter will be ignored if the RMS noise level is also given in the configuration file specified by parameter CONFIG.

WCSPAR = _LOGICAL (Read)

If a TRUE value is supplied, then the clump parameters stored in the output catalogue and in the CUPID extension of the output NDF, are stored in WCS units, as defined by the current coordinate frame in the WCS component of the input NDF (this can be inspected using the KAPPA:WCSFRAME command). For instance, if the current coordinate system in the 3D input NDF is (RA,Dec,freq), then the catalogue columns that hold the clump peak and centroid positions will use this same coordinate system. The spatial clump sizes will be stored in arc-seconds, and the spectral clump size will be stored in the unit of frequency used by the NDF (Hz, GHz, etc). If a FALSE value is supplied for this parameter, the clump parameters are stored in units of pixels within the pixel coordinate system of the input NDF. The dynamic default for this parameter is TRUE if the current coordinate system in the input NDF represents celestial longitude and latitude in some system, plus a recogonised spectral axis (if the input NDF is 3D). Otherwise, the dynamic default is FALSE. []

Use of CUPID Extension

This application will create an NDF extension called "CUPID" in the output NDF and will add the following components to it:

• CLUMPS: This a an array of CLUMP structures, one for each clump identified by the selected algorithm. Each such structure contains the same clump parameters that are written to the catalogue via parameter OUTCAT. It also contains a component called MODEL which is an NDF containing a section of the main input NDF which is just large enough to encompass the clump. Any pixels within this section which are not contained within the clump are set bad. So for instance, if the input array "fred.sdf" is 2-dimensional, and an image of it has been displayed using KAPPA:DISPLAY, then the outline of clump number 9 (say) in the output image "fred2.sdf" can be overlayed on the image by doing:

contour noclear "fred2.more.cupid.clumps(9).model" mode=good labpos=$\backslash$!

• CONFIG: Lists the algorithm configuration parameters used to identify the clumps (see parameter CONFIG).

• QUALITY_NAMES: Defines the textual names used to identify background and clump pixels within the Quality mask.

Algorithms

• GaussClumps: Based on the algorithm described by Stutski & Gusten (1990, ApJ 356, 513). This algorithm proceeds by fitting a Gaussian profile to the brightest peak in the data. It then subtracts the fit from the data and iterates, fitting a new ellipse to the brightest peak in the residuals. This continues until the integrated data sum in the fitted Gaussians reaches the integrated data sum in the input array, or a series of consecutive fits are made which have peak values below a given multiple of the noise level. Each fitted ellipse is taken to be a single clump and is added to the output catalogue. In this algorithm, clumps may overlap. Any input variance component is used to scale the weight associated with each pixel value when performing the Gaussian fit. The most significant configuration parameters for this algorithm are: GaussClumps.FwhmBeam and GaussClumps.VeloRes which determine the minimum clump size.

• ClumpFind: Described by Williams et al (1994, ApJ 428, 693). This algorithm works by first contouring the data at a multiple of the noise, then searches for peaks of emission which locate the clumps, and then follows them down to lower intensities. No a priori clump profile is assumed. In this algorithm, clumps never overlap. Clumps which touch an edge of the data array are not included in the final list of clumps.

• Reinhold: Based on an algorithm developed by Kim Reinhold at JAC. See SUN/255 for more information on this algorithm. The edges of the clumps are first found by searching for peaks within a set of 1D profiles running through the data, and then following the wings of each peak down to the noise level or to a local minimum. A mask is thus produced in which the edges of the clumps are marked. These edges however tend to be quite noisy, and so need to be cleaned up before further use. This is done using a pair of cellular automata which first dilate the edge regions and then erode them. The volume between the edges are then filled with an index value associated with the peak position. Another cellular automata is used to removed noise from the filled clumps.

• FellWalker: Based on an algorithm which walks up hill along the line of greatest gradient until a significant peak is reached. It then assigns all pixels visited along the route to the clump associated with the peak. Such a walk is performed for every pixel in the data array which is above a specified background level. See SUN/255 for more information on this algorithm.

GaussClumps Configuration Parameters

The GaussClumps algorithm uses the following configuration parameters. Values for these parameters can be specified using the CONFIG parameter. Default values are shown in square brackets:

• GaussClumps.FwhmBeam: The FWHM of the instrument beam, in pixels. The fitted Gaussians are not allowed to be smaller than the instrument beam. This prevents noise spikes being fitted. [2.0]

• GaussClumps.FwhmStart: An initial guess at the ratio of the typical observed clump size to the instrument beam width. This is used to determine the starting point for the algorithm which finds the best fitting Gaussian for each clump. If no value is supplied (or if FwhmBeam is zero), the initial guess at the clump size is based on the local profile around the pixel with peak value. []

• GaussClumps.MaxBad: The maximum number of bad pixels which may be included in a clump. Clumps will not be excluded if they contain more bad pixels than this value [4]

• GaussClumps.MaxClumps: Specifies a termination criterion for the GaussClumps algorithm. The algorithm will terminate when "MaxClumps" clumps have been identified, or when one of the other termination criteria is met. [unlimited]

• GaussClumps.MaxNF: The maximum number of evaluations of the objective function allowed when fitting an individual clump. Here, the objective function evaluates the chi-squared between the current gaussian model and the data being fitted. [100]

• GaussClumps.MaxSkip: The maximum number of consecutive failures which are allowed when fitting Guassians. If more than "MaxSkip" consecutive clumps cannot be fitted, the iterative fitting process is terminated. [10]

• GaussClumps.ModelLim: Determines the value at which each Gaussian model is truncated to zero. Model values below ModelLim times the RMS noise are treated as zero. [0.5]

• GaussClumps.NPad: Specifies a termination criterion for the GaussClumps algorithm. The algorithm will terminate when "Npad" consecutive clumps have been fitted all of which have peak values less than the threshold value specified by the "Thresh" parameter, or when one of the other termination criteria is met. [10]

• GaussClumps.RMS: The global RMS noise level in the data. The default value is the value supplied for parameter RMS. []

• GaussClumps.S0: The Chi-square stiffness parameter "S0" which encourages the fitted gaussian value to be below the corresponding value in the observed data at every point (see the Stutski & Gusten paper). [1.0]

• GaussClumps.Sa: The Chi-square stiffness parameter "Sa" which encourages the peak amplitude of each fitted gaussian to be close to the corresponding maximum value in the observed data (see the Stutski & Gusten paper). [1.0]

• GaussClumps.Sb: An additional Chi-square stiffness parameter which encourages the background value to stay close to its initial value. This stiffness is not present in the Stutzki & Gusten paper but is added because the background value is usually determined by data points which have very low weight and is thus poorly constrained. It would thus be possibly to get erroneous background values without this extra stiffness. [0.1]

• GaussClumps.Sc: The Chi-square stiffness parameter "Sc" which encourages the peak position of each fitted gaussian to be close to the corresponding peak position in the observed data (see the Stutski & Gusten paper). [1.0]

• GaussClumps.Thresh: Gives the minimum peak amplitude of clumps to be fitted by the GaussClumps algorithm (see also GaussClumps.NPad). The supplied value is multipled by the RMS noise level before being used. [2.0]

• GaussClumps.VeloRes: The velocity resolution of the instrument, in channels. The velocity FWHM of each clump is not allowed to be smaller than this value. Only used for 3D data. [2.0]

• GaussClumps.VeloStart: An initial guess at the ratio of the typical observed clump velocity width to the velocity resolution. This is used to determine the starting point for the algorithm which finds the best fitting Gaussian for each clump. If no value is supplied (or if VeloRes is zero), the initial guess at the clump velocity width is based on the local profile around the pixel with peak value. Only used for 3D data. []

• GaussClumps.Wmin: This parameter, together with GaussClumps.Wwidth, determines which input data values are used when fitting a Gaussian to a given peak in the data array. It specifies the minimum weight which is to be used (normalised to a maximum weight value of 1.0). Pixels with weight smaller than this value are not included in the fitting process. [0.05]

• GaussClumps.Wwidth: This parameter, together with GaussClumps.Wmin, determines which input data values are used when fitting a Gaussian to a given peak in the data array. It is the ratio of the width of the Gaussian weighting function (used to weight the data around each clump during the fitting process), to the width of the initial guess Guassian used as the starting point for the Gaussian fitting process. The Gaussian weighting function has the same centre as the initial guess Gaussian. [2.0]

ClumpFind Configuration Parameters

The ClumpFind algorithm uses the following configuration parameters. Values for these parameters can be specified using the CONFIG parameter. Default values are shown in square brackets:

• ClumpFind.AllowEdge: If set to a zero value, then clumps are rejected if they touch any edge of the data array. If non-zero, then such clumps are retained. Note, other implementations of ClumpFind often include such clumps but flag them in some way. [0]

• ClumpFind.DeltaT: The gap between the contour levels. Only accessed if no value is supplied for "Level1", in which case the contour levels are linearly spaced, starting at a lowest level given by "Tlow" and spaced by "DeltaT". Note, small values of DeltaT can result in noise spikes being interpreted as real peaks, whilst large values can result in some real peaks being missed and merged in with neighbouring peaks. The default value of two times the RMS noise level is usually considered to be optimal, although this obviously depends on the RMS noise level being correct. The value can be supplied either as an absolute data value, or as a multiple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [2$*$RMS]

• ClumpFind.FwhmBeam: The FWHM of the instrument beam, in pixels. If application paremeter DECONV is set TRUE, the clump widths written to the output catalogue are reduced (in quadrature) by this amount. If a direct comparison with other implementations of the ClumpFind algorithm is required, DECONV should be set to FALSE. [2.0]

• ClumpFind.IDLAlg: If a non-zero value is supplied, then FINDCLUMPS emulates the ClumpFind algorithm as implemented by the IDL package available from Jonathan Williams WWW site on 28th April 2006. The default value of zero causes FINDCLUMPS to use the algorithm described in the Williams et al ApJ paper of 1994. These two algorithms differ in the way that pixels within merged clumps are allocated to individual clumps. Also the ApJ algorithm rejects clumps that do not not extend above the second contour level, whereas the IDL algorithm accepts such clumps. See also parameter BACKOFF. [0]

• ClumpFind.Level$<$n$>$: The n'th data value at which to contour the data array (where $<$n$>$ is an integer). Values should be given for "Level1", "Level2", "Level3", etc. Any number of contours can be supplied, but there must be no gaps in the progression of values for $<$n$>$. The values will be sorted into descending order before being used. If "Level1" is not supplied (the default), then contour levels are instead determined automatically using parameters "Tlow" and "DeltaT". Note clumps found at higher contour levels are traced down to the lowest supplied contour level, but any new clumps which are initially found at the lowest contour level are ignored. That is, clumps must have peaks which exceed the second lowest contour level to be included in the returned catalogue. The values can be supplied either as absolute data values, or as mutliples of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS").[]

• ClumpFind.MaxBad: The maximum number of pixels in a clump that are allowed to be adjacent to a bad pixel. If the number of clump pixels adjacent to a bad pixel exceeds this value, the clump is excluded. If a direct comparison with other implementations of the ClumpFind algorithm is required, a very large value should be used. [4]

• ClumpFind.MinPix: The lowest number of pixel which a clump can contain. If a candidate clump has fewer than this number of pixels, it will be ignored. This prevents noise spikes from being interpreted as real clumps. The default value is based on the supplied values for the other parameters that specify the minimum peak height, the background level and the instrumental beam widths, limited to be at least 16 pixels (for 3D data), 7 pixels (for 2D data) or 3 pixels (for 1D data, or if "PERSPECTRUM" is set TRUE). If a direct comparison with other implementations of the ClumpFind algorithm is required, a value of 5 should be used (for 3D data) or 20 (for 2D data). []

• ClumpFind.Naxis: Controls the way in which contiguous areas of pixels are located when contouring the data. When a pixel is found to be at or above a contour level, the adjacent pixels are also checked. "Naxis" defines what is meant by an "adjacent" pixel in this sense. The supplied value must be at least 1 and must not exceed the number of pixel axes in the data. The default value equals the number of pixel axes in the data. If the data is 3-dimensional, any given pixel can be considered to be at the centre of a cube of neighbouring pixels. If "Naxis" is 1 only those pixels which are at the centres of the cube faces are considered to be adjacent to the central pixel. If "Naxis" is 2, pixels which are at the centre of any edge of the cube are also considered to be adjacent to the central pixel. If "Naxis" is 3, pixels which are at the corners of the cube are also considered to be adjacent to the central pixel. If the data is 2-dimensional, any given pixel can be considered to be at the centre of a square of neighbouring pixels. If "Naxis" is 1 only those pixels which are at the centres of the square edges are considered to be adjacent to the central pixel. If "Naxis" is 2, pixels which are at square corners are also considered to be adjacent to the central pixel. For one dimensional data, a value of 1 is always used for "Naxis", and each pixel simply has 2 adjacent pixels, one on either side. Note, the supplied "naxis" value is ignored if the ADAM parameter "PERSPECTRUM" is set TRUE. []

• ClumpFind.RMS: The global RMS noise level in the data. The default value is the value supplied for parameter RMS. []

• ClumpFind.Tlow: The lowest level at which to contour the data array. Only accessed if no value is supplied for "Level1". See also "DeltaT". The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [2$*$RMS]

• ClumpFind.VeloRes: The velocity resolution of the instrument, in channels. The velocity width of each clump written to the output catalogue is reduced (in quadrature) by this amount. If a direct comparison with other implementations of the ClumpFind algorithm is required, a value of zero should be used. [2.0]

Reinhold Configuration Parameters

The Reinhold algorithm uses the following configuration parameters. Values for these parameters can be specified using the CONFIG parameter. Default values are shown in square brackets:

• Reinhold.FwhmBeam: The FWHM of the instrument beam, in pixels. If application paremeter DECONV is set TRUE, the clump widths written to the output catalogue are reduced (in quadrature) by this amount. [2.0]

• ReinholdClumps.MaxBad: The maximum number of pixels in a clump that are allowed to be adjacent to a bad pixel. If the number fo clump pixels adjacent to a bad pixel exceeds this value, the clump is excluded. [4]

• Reinhold.MinLen: The minimum number of pixels spanned by a peak along any one dimension in order for the peak to be considered significant. If a peak is spanned by fewer than this number of pixels on any axis, then it is ignored. [4]

• Reinhold.MinPix: The lowest number of pixel which a clump can contain. If a candidate clump has fewer than this number of pixels, it will be ignored. This prevents noise spikes from being interpreted as real clumps. The default value is based on the supplied values for the other parameters that specify the minimum peak height, the background level and the instrumental beam widths, limited to be at least 16 pixels(for 3D data), 7 pixels (for 2D data) or 3 pixels (for 1D data). []

• Reinhold.Noise: Defines the data value below which pixels are considered to be in the noise. A peak is considered to end when the peak value dips below the "noise" value. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [2$*$RMS]

• Reinhold.Thresh: The smallest significant peak height. Peaks which have a maximum data value less than this value are ignored. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [Noise$+$2$*$RMS]

• Reinhold.FlatSlope: A peak is considered to end when the slope of a profile through the peak drops below this value. The value should be given as a change in data value between adjacent pixels. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [1.0$*$RMS]

• Reinhold.CAThresh: Controls the operation of the cellular automata which is used to erode the (previously dilated) edges regions prior to filling them with clump indicies. If the number of edge pixels in the 3x3x3 pixel cube (or 2x2 pixel square for 2D data) surrounding any pixel is greater than CAThresh, then the central pixel is considered to be an edge pixel. Otherwise it is not considered to be an edge pixel. The default value is one less than the total number of pixels in the square or cube (i.e. 8 for 2D data and 26 for 3D data). []

• Reinhold.CAIterations: This gives the number of times to apply the cellular automata which is used to erode the edges regions prior to filling them with clump indicies. [1]

• Reinhold.FixClumpsIterations: This gives the number of times to apply the cellular automata which cleans up the filled clumps. This cellular automata replaces each output pixel by the most commonly occuring value within a 3x3x3 cube (or 2x2 square for 2D data) of input pixels centred on the output pixel. [1]

• Reinhold.RMS: The global RMS noise level in the data. The default value is the value supplied for parameter RMS. []

• Reinhold.VeloRes: The velocity resolution of the instrument, in channels. The velocity width of each clump written to the output catalogue is reduced (in quadrature) by this amount. [2.0]

FellWalker Configuration Parameters

The FellWalker algorithm uses the following configuration parameters. Values for these parameters can be specified using the CONFIG parameter. Default values are shown in square brackets:

• FellWalker.AllowEdge: If set to a zero value, then clumps are rejected if they touch any edge of the data array. If non-zero, then such clumps are retained. [1]

• FellWalker.CleanIter: This gives the number of times to apply the cellular automata which cleans up the filled clumps. This cellular automata replaces each clump index by the most commonly occuring value within a 3x3x3 cube (or 2x2 square for 2D data) of neighbours. The supplied value is ignored and a value of zero is assumed if "PERSPECTRUM" is set TRUE. [1]

• FellWalker.FlatSlope: Any initial section to a walk which has an average gradient (measured over 4 steps) less than this value will not be included in the clump. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [1.0$*$RMS]

• FellWalker.FwhmBeam: The FWHM of the instrument beam, in pixels. If application paremeter DECONV is set TRUE, the clump widths written to the output catalogue are reduced (in quadrature) by this amount. [2.0]

• FellWalker.MaxBad: The maximum number of pixels in a clump that are allowed to be adjacent to a bad pixel. If the number fo clump pixels adjacent to a bad pixel exceeds this value, the clump is excluded. [4]

• FellWalker.MinDip: If the dip between two adjacent peaks is less than this value, then the peaks are considered to be part of the same clump. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [3.0$*$RMS]

• FellWalker.MinHeight: If the peak value in a clump is less than this value then the clump is not included in the returned list of clumps. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [MinDip$+$Noise]

• FellWalker.MinPix: The lowest number of pixel which a clump can contain. If a candidate clump has fewer than this number of pixels, it will be ignored. This prevents noise spikes from being interpreted as real clumps. The default value is based on the supplied values for the other parameters that specify the minimum peak height, the background level and the instrumental beam widths, limited to be at least 16 pixels (for 3D data), 7 pixels (for 2D data) or 3 pixels (for 1D data). []

• FellWalker.MaxJump: Defines the extent of the neighbourhood about a local maximum which is checked for higher pixel values. The neighbourhood checked is square or cube with side equal to twice the supplied value, in pixels. [4]

• FellWalker.Noise: Defines the data value below which pixels are considered to be in the noise. No walk will start from a pixel with data value less than this value. The value can be supplied either as an absolute data value, or as a mutliple of the RMS noise using the syntax "[x]$*$RMS", where "[x]" is a numerical value (e.g. "3.2$*$RMS"). [2$*$RMS]

• FellWalker.RMS: The global RMS noise level in the data. The default value is the value supplied for parameter RMS. []

• FellWalker.VeloRes: The velocity resolution of the instrument, in channels. The velocity width of each clump written to the output catalogue is reduced (in quadrature) by this amount. [2.0]