/**
* Initializes drought days for each food type to zero. Also checks to see if food deplete rates
* and times are valid for each food type. Valid random food deplete rates and times will be
* generated using the environments random number generator for each invalid entry.
*/
private void setupDraughtDeplete() {
draughtdays = new int[getTypeCount()];
for (int i = 0; i < getTypeCount(); ++i) {
draughtdays[i] = 0;
if (foodData[i].depleteRate < 0.0f || foodData[i].depleteRate > 1.0f)
foodData[i].depleteRate = simulation.getRandom().nextFloat();
if (foodData[i].depleteTime <= 0)
foodData[i].depleteTime = simulation.getRandom().nextInt(100) + 1;
}
}
private void depleteFood(ComplexFoodParams food, int type) {
// the algorithm for randomly selecting the food cells to delete
// is as follows:
// We iterate through all of the cells and the location of each
// one containing food type i is added to
// a random position in our vector. We then calculate exactly
// how many food items we need to destroy, say N,
// and we destroy the food at the positions occupying the last N
// spots in our vector
List<Location> locations = new ArrayList<Location>();
for (int x = 0; x < simulation.getTopology().width; ++x) {
for (int y = 0; y < simulation.getTopology().height; ++y) {
Location currentPos = new Location(x, y);
if (env.hasFood(currentPos) && env.getFoodType(currentPos) == type)
locations.add(currentPos);
}
}
Collections.shuffle(locations, simulation.getRandom());
int foodToDeplete = (int) (locations.size() * food.depleteRate);
for (int j = 0; j < foodToDeplete; ++j) {
Location loc = locations.get(j);
env.removeFood(loc);
}
draughtdays[type] = food.draughtPeriod;
}
private void dropFood(int type) {
float foodDrop = foodData[type].dropRate;
while (simulation.getRandom().nextFloat() < foodDrop) {
--foodDrop;
Location l;
int j = 0;
do {
++j;
l = simulation.getTopology().getRandomLocation();
} while (j < DROP_ATTEMPTS_MAX && env.hasAnythingAt(l));
if (j < DROP_ATTEMPTS_MAX) {
env.addFood(l, type);
}
}
}
private void growFood() {
// create a new ArrayEnvironment and a new food type array
// loop through all positions
AbioticMutator abiotic = env.getPlugin(AbioticMutator.class);
for (int y = 0; y < simulation.getTopology().height; ++y) {
for (int x = 0; x < simulation.getTopology().width; ++x) {
Location currentPos = new Location(x, y);
if (!env.hasAnythingAt(currentPos)) {
// we should grow food here
// the following code block tests all adjacent squares
// to this one and counts how many have food
// as well how many of each food type exist
double foodCount = 0;
int[] mostFood = new int[getTypeCount()];
for (Direction dir : simulation.getTopology().ALL_4_WAY) {
Location checkPos = simulation.getTopology().getAdjacent(currentPos, dir);
if (checkPos != null && env.hasFood(checkPos)) {
foodCount++;
mostFood[env.getFoodType(checkPos)]++;
}
}
// and if we have found any adjacent food, theres a
// chance we want to grow food here
if (foodCount > 0) {
int max = 0;
int growingType;
// find the food that exists in the largest quantity
for (int i = 1; i < mostFood.length; ++i) if (mostFood[i] > mostFood[max]) max = i;
// give the max food an extra chance to be chosen
if (sameFoodProb >= simulation.getRandom().nextFloat()) {
growingType = max;
} else {
growingType = simulation.getRandom().nextInt(getTypeCount());
}
// finally, we grow food according to a certain
// amount of random chance
ComplexFoodParams thisType = foodData[growingType];
float growRate = thisType.growRate;
for (int i = 0; i < thisType.abioticParams.factorParams.length; i++) {
AbioticPreferenceParam factorParams =
thisType.abioticParams.factorParams[i].preference;
float factorValue = abiotic.getValue(i, currentPos);
float discomfort = 1 + factorParams.score(factorValue);
growRate *= discomfort;
}
if (foodCount * growRate > 100 * simulation.getRandom().nextFloat()) {
env.addFood(currentPos, growingType);
}
}
}
}
}
}