Coadd

Purpose

Reduce resolution through combination of adjacent variables or samples.

Synopsis

databin = coadd(data,binsize,options)

databin = coadd(data,binsize,dim)

databin = coadd(data,options)

Description

COADD is used to combine, or "bin", adjacent variables, samples, or slabs of a matrix. Unpaired values at the end of the matrix are padded with the least biased value to complete the bin. Unlike DERESOLV, COADD reduces the size of the data matrix by a factor of 1/binsize for the dimension defined by input (dim).

Inputs

• data = a data array to be "binned".
• binsize = the number of elements to combine together {default: 2}.

Optional Input

• dim = mode (dimension) to coadd (scalar integer {default = 2}).
• options = discussed below.

Outputs

• databin = the coadded / binned data.
• options = options structure that can be used to bin new data with the same settings.

Options

Options = structure array with the following fields:

dim : Dimension / mode in which to perform binning {default = 2},

mode : [ 'sum' | {'mean'} | 'prod' ] method of binning. See algorithm notes for details of these modes.

labels: [ {'all'} | 'first' | 'middle' | 'last' ] method of combining labels (if any exist). Each method labels a new object using one of the following rules:

'all' = concatenate all labels for the combined items (the default.)

'first' = use only the label from the first object in each set of combined objects.

'middle' = use only the label from the "middle" object in each set of combined objects.

'last' = use only the label from the last object in each set of combined objects.

binsize: [2] default binsize to use if no binsize is provided in the input.

Algorithm

The input (options.mode) defines the type of binning to apply. The different types are described for (options.dim = 2) [i.e., for variables] as follows.

'sum' : groups of variables are added together and stored. The resulting values will be larger in magnitude than the original values by a factor on the order of the number of variables binned.

'mean' : groups of variables are added together and that sum is divided by the number of variables binned. The resulting values will be similar in magnitude to the original values.

'prod' : groups of variables are multiplied together.

Example

Given a matrix, (data), size 300 by 1000, the following coadds variables in groups of three:

databin = coadd(data,3);

and the following coadds samples in groups of two:

options.dim = 1;

databin = coadd(data,2,options);

The following is equivalent to the previous two lines using the "shortcut" input of (dim).

databin = coadd(data,2,1);

See Also

deresolv