mrcImageNormalizing

Usage

Usage: mrcImageNormalizing
Options:
    [-i[nput]            In                  (NULL      ).as(inFile              ) ] :Essential :InputDataFile
    [-o[utput]           Out                 (NULL      ).as(outFile             ) ] :Essential :OutputDataFile
    [-A                  A                   (1.0       ).as(Real                ) ] :Optional  :A
    [-B                  B                   (0.0       ).as(Real                ) ] :Optional  :B
    [-ContourMin         ContourMin          (0.0       ).as(Real                ) ] :Optional  :ContourMin
    [-ContourMax         ContourMax          (1.0       ).as(Real                ) ] :Optional  :ContourMax
    [-ContourSolvent     ContourSolvent      (0.0       ).as(Real                ) ] :Optional  :ContourSolvent
    [-c[onfig]           configFile          (NULL      ).as(inFile              ) ] :Optional  :ConfigurationFile
    [-m[ode]             mode                (0         ).as(Integer             ) ] :Optional  :Mode
----- mode -----
----- Mode for lmrcImageNormalizing -----
  0: Double Exponential: Solvent and Object
         Fitting histgram to double exponentials as Solvent and Object  
		   data = A*(data-MeanOfSolvent)/(MeanOfObject-MeanOfSolvent) + B 
  1: Min-Max: Background and Object
		   data = A*(data-Min)/(Max-Min) + B 
  2: Contour
		   data = A*(data-ContourMin)/(ContourMax-ContourMin) + B 
  3: Contour and Solvent
		   if data < ContourSolvent, data = ContourSolvent.  After this, calculate the below. 
		   data = A*(data-ContourMin)/(ContourMax-ContourMin) + B 
  4: No Estimation
		   data = A*data + B 
  5: Assume the density as gaussion.
		   data = A*Normalized(data) + B , where normalized means (average=0, SD=1)
  6: Min-75percent: Background and Object(75)
		   data = A*(data-Min)/(75percent-Min)x0.75 + B