Coadd

Purpose

Reduce resolution through combination of adjacent variables or samples.

Synopsis

databin = coadd(data,binsize,options)

databin = coadd(data,binsize,dim)

Description

COADD is used to combine, or "bin", adjacent variables, samples, or slabs of a matrix. Inputs include the original array (data), the number of elements to combine together (binsize) {default: 2}, and an optional options structure (options). Alternatively, the input (options) can be replaced with a scalar value of (dim) which will be used for options.dim (see below) and all other options will be the default values.

The mode of co-adding (defined by the options value mode) defines how items within each bin are combined mathematically. See options below for details.

Unpaired values at the end of the matrix are padded with the least biased value to complete the bin. Output is the co-added data. Unlike DERESOLV, COADD reduces the size of the data matrix by a factor of 1/binsize for the dimension.

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