Changes between Version 1 and Version 2 of DevNotes/Projects/McSASIntegration


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Timestamp:
Sep 4, 2018 8:28:54 AM (18 months ago)
Author:
butler
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  • DevNotes/Projects/McSASIntegration

    v1 v2  
    33McSAS fits data using an MC algorithm to extract a parameter distribution.  For example for a system of spheres, it will return a distribution of sphere sizes with minimal a priori information.  As such it most resembles an optimizer in !SasView which optimized the center and polydispersity of a parameter. The integration thus probably should involve changing the optimization framework in !SasView to allow a different optimization engine per !FitPage and provide McSAS as an optimizer.  Care needs to be taken with how the parameters are presented in this case as McSAS always optimizes scale and background and otherwise always fits a polydispersity -- so there may need to be a switching of GUI for parameter set ups.  Finally the output needs to be fed back so that a fit can continue using a different optimizer but maintaining the McSAS distribution output as a fixed array polydispersity on that parameter.  Of course the primary output of McSAS would be the plots of the distribution of the parameters which actually should use the underlying infrastructure created for ticket #17. 
    44 
    5 The approach  
     5We can take a stepwise approach where each part could be worked on independently.  For example number 2 and 3 would be useful independently of integrating or not McSAS. 
    661. Get McSAS running as an optimizer within !SasView 
    7 2.  
     72. Change UI to allow separate optimizer choice in each fit page 
     83. Build infrastructure to plot distribution of a parameter 
     94. Output McSAS "population" to work as an array distribution 
     105. Refine UI for  ease of use of McSAS fitting and switching between regular fitting vs McSAS fitting.