c = conv(a,b) : computes the convolution of iData objects @iData/conv function to compute the convolution of data sets (FFT based). A deconvolution mode is also possible. When used with a single scalar value, it is used as a width to build a gaussian function, with same width along all dimensions When used with a vector of same length as the object dimension, a nD gaussian function with width as vector elements along each diemsions The syntax: conv(a, 'tas') upgrades the data object with a neutron TAS configuration using ResLibCal. Any RESCAL-type parameters are sent to ResLibCal and if a Model exists, it is upgraded with a 4D convolution. input: a: object or array (iData or numeric) b: object or array (iData or numeric or scalar) shape: optional shape of the return value full Returns the full two-dimensional convolution. same Returns the central part of the convolution of the same size as a. valid Returns only those parts of the convolution that are computed without the zero-padded edges. Using this option, y has size [ma-mb+1,na-nb+1] when all(size(a) >= size(b)). deconv Performs an FFT deconvolution. deconv_iter Performs an iterative deconvolution. pad Pads the 'a' signal by replicating its starting/ending values in order to minimize the convolution side effects. center Centers the 'b' filter so that convolution does not shift the 'a' signal. normalize Normalizes the 'b' filter so that the convolution does not change the 'a' signal integral. background Remove the background from the filter 'b' (subtracts the minimal value) Default shape is 'same' output: c: object or array (iData) ex: c=conv(a,b); c=conv(a,b, 'same pad background center normalize'); Version: Mar. 22, 2017 See also iData, iData/times, iData/convn, iData/fft, iData/xcorr, fconv, fconvn, fxcorr, conv, deconv

- double d = double(s) : convert iData into doubles
- end b = end(s,index,n) : end value for iData objects
- getaxis [val, lab] = getaxis(s, AxisIndex) : get iData axis value and label
- iData d = iData(a, ...) : iData class object constructor
- isscalar b = isscalar(s) : True for scalar iData objects
- mean b = mean(s, dim) : mean value of iData object
- ndims d = ndims(s) : get the dimensionality of iData object
- reshape c = reshape(a) : reshape the object Signal
- size size(s) : get iData object size (number of elements)
- zeros s = zeros(s,N,M,P,...) : initialize an iData array

- xcorr c = xcorr(a,b,shape) : computes the correlation of iData objects

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