Copyright (c) 1991-2010, Pete R. Jemian
Use this reference to cite this code in a publication.
PhD Thesis, 1990, Northwestern University, Evanston, IL, USA, Pete R. Jemian. http://jemian.org/pjthesis.pdf
These instructions are very limited. This code is a short-lived effort to move the code base to Java. Amongst the small-angle scattering scientific community, Python is far more popular than Java for this type of code.
See either the code projects for desmearing in C or Python at https://github.com/prjemian
- lake.c was derived from the FORTRAN program: Lake.FOR 25 May 1991
- Lake.FOR was created by Pete Jemian, (http://jemian.org/pjthesis.pdf)
- ref: J.A. Lake; ACTA CRYST 23 (1967) 191-194.
- by: Pete R. Jemian, Late-Nite(tm) Software
- Glatter; ACTA CRYST 7 (1974) 147-153
- W.E. Blass & G.W.Halsey (1981). "Deconvolution of
- Absorption Spectra." New York City: Academic Press
- P.A. Jansson. (1984) "Deconvolution with Applications
- in Spectroscopy." New York City: Academic Press.
- G.W.Halsey & W.E. Blass. "Deconvolution Examples"
- in "Deconvolution with Applications in Spectroscopy." Ed. P.A. Jansson. (see above)
This program applies the iterative desmearing technique of Lake to small-angle scattering data. The way that the program works is that the user selects a file of data (x,y,dy) to be desmeared. If a file was not chosen, the program will end. Otherwise the user is then asked to specify the slit-length (in the units of the x-axis); the X at which to begin fitting the last data points to a power-law of X, the output file name, and the number of iterations to be run. Then the data file is opened, the data is read, and the data file is closed. The program begins iterating and shows an indicator of progress on the screen in text format.
It is a mistake to run this program on data that has been desmeared at least once (by this program) as you will see. The problem is that the program expects that the input data has been smeared, NOT partially desmeared. Lake's technique should be made to iterate with the original, smeared data and subsequent trial solutions of desmeared data.
The integration technique used by this program to smear the data is the trapezoid-rule where the step-size is chosen by the spacing of the data points themselves. A linear interpolation of the data is performed. To avoid truncation effects, a power-law extrapolation of the intensity is made for all values beyond the range of available data. This region is also integrated by the trapezoid rule. The integration covers the region from l = 0 up to l = lo. (see routine SMEAR). This technique allows the slit-length weighting function to be changed without regard to the limits of integration coded into this program.