Paradigm

Note

GooseEYE is a research code. The best reference is the code itself: this reader just gives an overview and points in the right directions.

Header-only

Just

#include <GooseEYE/GooseEYE.h>

Everything is contained in the namespace GooseEYE.

Ensemble or individual image

There are two modes of using the code:

• Individual image: use individual functions (e.g. GooseFEM::S2(...), GooseFEM::W2(...), etc.)

• Ensemble of images: use the GooseFEM::Ensemble class. See example

The individual functions are simply a wrapper around the GooseFEM::Ensemble class. The general structure for an ensemble of images is as follows:

1. Initialize the ensemble, defining some settings of which the shape of the region-of-interest is mandatory. For example:

   GooseEYE::Ensemble ensemble({51, 51});
   

2. Compute the statistics by evaluating a sequence of images in the ensemble. For example:

   ensemble.S2(Ia, Ia);
   ensemble.S2(Ib, Ib);
   ensemble.S2(Ic, Ic);
   ...
   

3. Evaluate the result:

   auto result = ensemble.result();
   

   Note

   The variance around the average can be obtained using

   ensemble.variance();
   

   Note

   To obtain the raw result and normalisation use:

   // first moment : x_1   + x_2   + ...
   ensemble.data_first();
   
   // second moment: x_1^2 + x_2^2 + ...
   ensemble.data_second();
   
   // normalisation (number of measurements)
   ensemble.norm();
   

Using the individual images wrapper, all these steps are combined in a single function call with almost the same arguments. The only limitation is the the raw data and normalization cannot be accessed.