BSC.02 SLSP.01

Moving on from HERE, use the following to enable grain detection using PXO.

    [TimeSteps, All_Grains_time, All_GrainBoundaries_time, All_GrainAreas_time, GBL_time,...
        All_GrainEqGrainSize_time, GrainCentroid_xy_time, ogn, CFN, FileLocations, GenericFileName,...
        ImageFileName, DataFileNames, Number_of_grains, Grain_Area_PXL,...
        NGRAINS] = Characterize_Grain_Structure_2D(0, 'af2d', 'ALG_01');

Data in most cells is arranged as follows: All_GrainAreas_time is a 1 x Minst cell (Minst: number of temporal instances between 1 and M, with M being total number of MC steps defined in MC_Param.Num_MC_Steps). Number of Minst is the ratio (MC_Param.Num_MC_Steps) / (MC_Loop.DataOperation.txtwriteint). Each entry in All_GrainAreas_time{1, minst} is a 1 X Q cell (Q being total number of Pott's state defined previously in Lattice.q). Querry as following:

>> All_GrainAreas_time
All_GrainAreas_time =
  1×10 cell array
  Columns 1 through 5
    {1×10 cell}    {1×10 cell}    {1×10 cell}    {1×10 cell}    {1×10 cell}
  Columns 6 through 10
    {1×10 cell}    {1×10 cell}    {1×10 cell}    {1×10 cell}    {1×10 cell}

Each column contains data in minstth temporal instance. Now querry as:

>> All_GrainAreas_time{10}
ans =
  1×10 cell array
  Columns 1 through 5
    {1×13 cell}    {1×13 cell}    {1×9 cell}    {1×8 cell}    {1×10 cell}
  Columns 6 through 10
    {1×8 cell}    {1×15 cell}    {1×13 cell}    {1×13 cell}    {1×9 cell}

Here, in All_GrainAreas_time{10}, 10 is the 10th temporal instance. There are 10 columns. Each column points to a state q in q = 1:Q. Now querry grain area data for grains belonging to q = 1 Pott's state as below.

>> All_GrainAreas_time{10}{1}
ans =
  1×13 cell array
  Columns 1 through 9
    {[135]}    {[1]}    {[54]}    {[1]}    {[1]}    {[1]}    {[1]}    {[1]}    {[2]}
  Columns 10 through 13
    {[1]}    {[1]}    {[1]}    {[1]}

Each of the 13 columns correspond to a grain ng.(q=1) in ng.1 = 1:Ng.1. The value itseld, provides the area of the corresponding grain. Other similar variables have similar construct.

Bar graph below each temporal slice of GS shows the partitioning of number of grains with each state. Ng provides number of grains and q provides the corresponding state in 1:Q.

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Each grain can be visualized as patch as above using plot__Grain_Structure_in_patches('2d', TimeSteps, All_Grains_time). Alternatively, pixellated visualization can also be made using plot__Grain_Structure_in_pixels('2d', TimeSteps, All_Grains_time). Here, TimeSteps and All_Grains_time are outputs from [...] = Characterize_Grain_Structure_2D(...) used previosly.

NEXT STEPS HERE