private void SetupPopulation(SparseArray<IPerson[]> pop, Dictionary<int, List<IPerson>> tempPop, IZone[] flatZones)
{
var flatPop = pop.GetFlatData();
Parallel.For( 0, flatZones.Length, delegate(int i)
{
List<IPerson> temp;
if ( tempPop.TryGetValue( flatZones[i].ZoneNumber, out temp ) )
{
flatPop[i] = temp.ToArray();
}
else
{
flatPop[i] = new Person[0];
}
} );
this.Population = pop;
}
private void GenerateZone(SparseArray<IPerson[]> population, int zoneIndex, int pop, Random rand)
{
Household[] Households = new Household[3];
for ( int i = 0; i < 3; i++ )
{
Households[i] = new Household() { Zone = this.ZoneSystem.ZoneArray[zoneIndex], Cars = i };
}
Person[] people = new Person[pop];
population[zoneIndex] = people;
for ( int k = 0; k < pop; k++ )
{
people[k] = new Person();
}
/*
* To generate the population follow these steps, and at each one clear to integers
* Step 1) Split into Age Cat's
* Step 2) Split into ( Unemployed, Full-time, Part-Time )
* Step 3) Split into ( Non-Student, Student ) , ( Occupation )
* Step 4) Split into Driver's License ( Not Have / Have )
*/
SparseArray<int> agePop = SplitAges( zoneIndex, pop, rand );
int ageOffset = 0;
foreach ( var validAgeIndex in this.ValidAges )
{
var numberOfPeople = agePop[validAgeIndex];
var employmentPop = SplitEmployment( zoneIndex, validAgeIndex, numberOfPeople, rand );
int employmentOffset = 0;
foreach ( var validEmploymentStatusIndex in employmentPop.ValidIndexies() )
{
var numberOfEmploymentClassifiedPeople = employmentPop[validEmploymentStatusIndex];
int StudentPop = SplitStudents( zoneIndex, validAgeIndex, validEmploymentStatusIndex, numberOfEmploymentClassifiedPeople, rand );
int numberOfDriversLicenses = this.SplitDrivers( zoneIndex, validAgeIndex, validEmploymentStatusIndex, numberOfEmploymentClassifiedPeople, rand );
var OccupationSplit = this.SplitOccupations( zoneIndex, validAgeIndex, validEmploymentStatusIndex,
numberOfEmploymentClassifiedPeople, rand );
AssignEmploymentStatus( people, ageOffset, employmentOffset, validEmploymentStatusIndex, numberOfEmploymentClassifiedPeople );
AssignSchools( people, ageOffset, employmentOffset, StudentPop );
AssignOccupationAndLicenese( people, ageOffset, employmentOffset, OccupationSplit, numberOfEmploymentClassifiedPeople,
numberOfDriversLicenses, rand );
employmentOffset += numberOfEmploymentClassifiedPeople;
}
AssignAge( rand, people, ageOffset, validAgeIndex, numberOfPeople );
// Assign people to households depending on the number of cars they will have
foreach ( var validOccupationIndex in this.Demographics.OccupationCategories.ValidIndexies() )
{
int[] haveLicenseIndexes, doNotHaveLicenseIndexes;
var haveLicense = this.SplitCars( people, zoneIndex, validAgeIndex, validOccupationIndex,
true, ageOffset, numberOfPeople, rand, out haveLicenseIndexes );
var doNotHaveLicense = this.SplitCars( people, zoneIndex, validAgeIndex, validOccupationIndex,
false, ageOffset, numberOfPeople, rand, out doNotHaveLicenseIndexes );
AssignCars( people, haveLicenseIndexes, haveLicense, Households, ageOffset, rand );
AssignCars( people, doNotHaveLicenseIndexes, doNotHaveLicense, Households, ageOffset, rand );
}
ageOffset += numberOfPeople;
}
}
private SparseArray<int> SplitCars(Person[] people, int zoneIndex, int validAgeIndex, int validOccupationIndex, bool license, int ageOffset, int agePop, Random rand, out int[] indexes)
{
SparseArray<float> ret = new SparseArray<float>( new SparseIndexing() { Indexes = new SparseSet[] { new SparseSet() { Start = 0, Stop = 2 } } } );
// Because everything is random at this point we actually need to scan to see how many people we have
List<int> indexesList = new List<int>( agePop );
if ( validOccupationIndex == this.UnemployedOccupation )
{
for ( int i = 0; i < agePop; i++ )
{
var person = people[i + ageOffset];
if ( person.DriversLicense == license )
{
var range = this.Demographics.AgeCategories[validAgeIndex];
var age = person.Age;
if ( age >= range.Start && age <= range.Stop )
{
indexesList.Add( i );
}
}
}
var data = this.Demographics.NonWorkerVehicleRates[zoneIndex];
foreach ( var validCarsIndex in ret.ValidIndexies() )
{
ret[validCarsIndex] = data[license ? 1 : 0, validAgeIndex, validCarsIndex];
}
}
else
{
for ( int i = 0; i < agePop; i++ )
{
var person = people[i + ageOffset];
if ( person.DriversLicense == license
&& person.Occupation == validOccupationIndex )
{
indexesList.Add( i );
}
}
var data = this.Demographics.WorkerVehicleRates[zoneIndex];
foreach ( var validCarsIndex in ret.ValidIndexies() )
{
ret[validCarsIndex] = data[license ? 1 : 0, validOccupationIndex, validCarsIndex];
}
}
indexes = indexesList.ToArray();
return this.SplitAndClear( indexes.Length, ret, rand );
}
private void AssignOccupationAndLicenese(Person[] people, int ageOffset, int employmentOffset, SparseArray<int> OccupationSplit, int numberOfEmploymentClassifiedPeople,
int numberOfLicenses, Random r)
{
int[] personIndex = new int[numberOfEmploymentClassifiedPeople];
for ( int i = 0; i < numberOfEmploymentClassifiedPeople; i++ )
{
personIndex[i] = i;
}
// Randomize the population for assignment (Card shuffle algorithm)
for ( int i = 0; i < numberOfEmploymentClassifiedPeople; i++ )
{
var selectedIndex = r.Next( i, numberOfEmploymentClassifiedPeople );
var temp = personIndex[selectedIndex];
personIndex[selectedIndex] = personIndex[i];
personIndex[i] = temp;
}
// assign the occupations
int occOffset = 0;
foreach ( var occIndex in OccupationSplit.ValidIndexies() )
{
var occPop = OccupationSplit[occIndex];
for ( int i = 0; i < occPop; i++ )
{
people[personIndex[occOffset + i] + ageOffset + employmentOffset].Occupation = occIndex;
}
occOffset += occPop;
}
// Randomize the population for assignment (Card shuffle algorithm)
for ( int i = 0; i < numberOfEmploymentClassifiedPeople; i++ )
{
var selectedIndex = r.Next( i, numberOfEmploymentClassifiedPeople );
var temp = personIndex[selectedIndex];
personIndex[selectedIndex] = personIndex[i];
personIndex[i] = temp;
}
// assign the occupations
for ( int i = 0; i < numberOfLicenses; i++ )
{
people[personIndex[i] + ageOffset + employmentOffset].DriversLicense = true;
}
}
private void AssignCars(Person[] people, int[] indexes, SparseArray<int> split, Household[] households, int ageOffset, Random rand)
{
var numberOfPeople = indexes.Length;
// randomly shuffle the indexes before we actually assign the households
for ( int i = 0; i < numberOfPeople; i++ )
{
var selectedIndex = rand.Next( i, numberOfPeople );
var temp = indexes[selectedIndex];
indexes[selectedIndex] = indexes[i];
indexes[i] = temp;
}
int typeOffset = 0;
foreach ( var carType in split.ValidIndexies() )
{
var numberInType = split[carType];
for ( int i = 0; i < numberInType; i++ )
{
people[indexes[i + typeOffset] + ageOffset].Household = households[carType];
}
typeOffset += numberInType;
}
}
private void AssignAge(Random rand, Person[] people, int ageOffset, int validAgeIndex, int numberOfPeople)
{
Range ageRange = this.Demographics.AgeCategories[validAgeIndex];
for ( int p = 0; p < numberOfPeople; p++ )
{
people[p + ageOffset].Age = rand.Next( ageRange.Start, int.MaxValue == ageRange.Stop ? int.MaxValue : ageRange.Stop + 1 );
people[p + ageOffset].ExpansionFactor = 1;
}
}
private static void AssignSchools(Person[] people, int ageOffset, int employmentOffset, int StudentPop)
{
for ( int p = 0; p < StudentPop; p++ )
{
people[p + ageOffset + employmentOffset].StudentStatus = 1;
}
}
private static void AssignEmploymentStatus(Person[] people, int ageOffset, int employmentOffset, int validEmploymentStatusIndex, int numberOfEmploymentClassifiedPeople)
{
for ( int p = 0; p < numberOfEmploymentClassifiedPeople; p++ )
{
people[p + ageOffset + employmentOffset].EmploymentStatus = validEmploymentStatusIndex;
}
}
