Mout = sw_resconv(M,x,dx,func)


Mout = sw_resconv(M,x,dx,func) convolutes a 2D matrix with a Gaussian along the first dimension of the matrix. The convolution keeps the integrated intensity \(\sum I\cdot dx\) constant. It assumes the x vector contains the center points of the bins and the distances between the generated bin edges is calculated by interpolating from the distances between the given x bin center positions.

Input Arguments

Arbitrary matrix with dimensions of \([m_1\times m_2]\).
Column vector of coordinates along the first dimension of the matrix.
FWHM value of the Gaussian as a function of \(dx\). Either a function handle with a header fwhm = dx(xVal) or a vector of polynomial coefficients that produces the right standard deviation. In this case in the function the following line will be executed fwhm = polyval(dx,xVal) or a constant FWHM value.

The standard deviation of the Gaussian is calculated from the given \(dx\) value using the formula \(\sigma_G = fwhm_G/2/\sqrt{2\cdot log(2)} \sim fwhm_G\cdot 0.424661\) If a general resolution function is provided in the func argument, it will be called as y = func(x,[1 x0 fwhm]). In this case the fwhm can be a row vector and the meaning of the different parameters will depend on func.

Resolution function shape with header y = func(x,p), where x is a column vector, p is a row vector of parameters. The meaning of the first 2 elements of the parameter vector are fixed.
  • p(1) integral of the function.
  • p(2) center of mass position of the function.

Optional, default value is @swfunc.gaussfwhm.

Output Arguments

Matrix with same dimensions as the input M.

See Also

sw_res | swfunc.gaussfwhm