[docs]
def mc_iterations_3d_alg224(self):
"""
DESIGNED TO ACHIEVE: Bi-modal grain size distribution
Each of the initial set of iterations is to contain the following
STEP 1: Do the regular iteration using any of the 200 series of
algorithms
STEP 2: Identify grains and their neighbours
STEP 3: Calculate state partitioned grain area distribution
STEP 4: Identify small grains with areas less than P % of mean area
for each state
STEP 5: Select the state with the largest mean area: S_large
STEP 6: Select the state with the smallest mean area: S_small
STEP 7: Prepare a merger list comprising of two columns. First column
is to have the global grain IDs of certain grains belonging to S_small.
The second column is to have a list of global grain IDs of neighbouring
grains belonging to S_large. If for a grain of S_small, no neighbouring
grains of S_large exit, then cancel the merger operation for the
current S_small grain. Iterate through all the remaining grains.
STEP 8: Calculate the grain area distribution. Calculate the modality
Calculate the shift in peaks.
STEP 9: If the peak shift is in the direction of target peak, then
accept the present iteration using a iteration transition probability.
"""
raise NotImplementedError("mc_iterations_3d_alg224 is not yet implemented.")