override public void Generate(SparseArray <float> production, SparseArray <float> attractions)
{
if (LoadData)
{
if (DailyRates == null)
{
this.LoadDailyRates.LoadData();
this.DailyRates = this.LoadDailyRates.GiveData();
}
if (TimeOfDayRates == null)
{
this.LoadTimeOfDayRates.LoadData();
this.TimeOfDayRates = this.LoadTimeOfDayRates.GiveData();
}
}
var flatProduction = production.GetFlatData();
var flatAttraction = attractions.GetFlatData();
var numberOfIndexes = flatAttraction.Length;
// Compute the Production and Attractions
ComputeProduction(flatProduction, flatAttraction, numberOfIndexes);
//We do not normalize the attraction
if (LoadData)
{
this.LoadDailyRates.UnloadData();
this.LoadTimeOfDayRates.UnloadData();
DailyRates = null;
TimeOfDayRates = null;
}
}
private SparseTriIndex <float> ConvertResults(float[] results, SparseArray <IZone> zoneSystem)
{
SparseTriIndex <float> ret = Data;
// first create the datastructure
if (ret == null)
{
ret = SparseTriIndex <float> .CreateSimilarArray(new SparseArray <int>(new SparseIndexing()
{
Indexes = new SparseSet[]
{ new SparseSet()
{
BaseLocation = 0,
Start = 0,
Stop = NumberOfWorkerCategories - 1
} }
}), zoneSystem, zoneSystem);
}
// now fill it
var r = ret.GetFlatData();
var numberOfZones = r[0].Length;
Parallel.For(0, numberOfZones, (int i) =>
{
for (int workerCategory = 0; workerCategory < r.Length; workerCategory++)
{
var workerCategoryMatrix = r[workerCategory];
var pos = sizeof(float) * ((workerCategoryMatrix.Length * workerCategoryMatrix.Length) * workerCategory + numberOfZones * i);
Buffer.BlockCopy(results, pos, workerCategoryMatrix[i], 0, numberOfZones * sizeof(float));
}
});
return(ret);
}
public override void Generate(SparseArray<float> production, SparseArray<float> attractions)
{
if(LoadData)
{
LoadExternalWorkerRates.LoadData();
LoadWorkAtHomeRates.LoadData();
LoadExternalJobsRates.LoadData();
ExternalRates = LoadExternalWorkerRates.GiveData();
WorkAtHomeRates = LoadWorkAtHomeRates.GiveData();
ExternalRates = LoadExternalJobsRates.GiveData();
}
var flatProduction = production.GetFlatData();
var flatWah = new float[flatProduction.Length];
var totalProduction = ComputeProduction(flatProduction, flatWah);
var totalAttraction = ComputeAttraction(attractions.GetFlatData());
Normalize(production.GetFlatData(), attractions.GetFlatData(), totalProduction, totalAttraction);
totalAttraction = RemoveWAHFromAttraction(attractions.GetFlatData(), flatWah);
StoreProductionData(production);
WriteGenerationFile(totalProduction, totalAttraction);
WriteAttractionFile(attractions);
if(LoadData)
{
LoadExternalWorkerRates.UnloadData();
LoadWorkAtHomeRates.UnloadData();
LoadExternalJobsRates.UnloadData();
WorkAtHomeRates = null;
ExternalRates = null;
ExternalJobs = null;
}
}
public override void Generate(SparseArray <float> production, SparseArray <float> attractions)
{
if (LoadData)
{
this.LoadExternalWorkerRates.LoadData();
this.LoadWorkAtHomeRates.LoadData();
this.LoadExternalJobsRates.LoadData();
this.ExternalRates = this.LoadExternalWorkerRates.GiveData();
this.WorkAtHomeRates = this.LoadWorkAtHomeRates.GiveData();
this.ExternalRates = this.LoadExternalJobsRates.GiveData();
}
var flatProduction = production.GetFlatData();
var flatWah = new float[flatProduction.Length];
var flatIntraZonal = new float[flatProduction.Length];
float elfgta = CalculateElfGTA();
var totalProduction = ComputeProduction(flatProduction, flatWah, flatIntraZonal);
var totalAttraction = ComputeAttraction(attractions.GetFlatData());
ApplyWahAndIntrazonal(production.GetFlatData(), attractions.GetFlatData(), flatWah, flatIntraZonal, totalProduction, totalAttraction, elfgta);
ApplyAgeCategoryFactor(production.GetFlatData(), attractions.GetFlatData());
Normalize(production.GetFlatData(), attractions.GetFlatData());
WriteGenerationFile(flatProduction.Sum(), attractions.GetFlatData().Sum());
WriteAttractionFile(production, attractions);
if (LoadData)
{
this.LoadExternalWorkerRates.UnloadData();
this.LoadWorkAtHomeRates.UnloadData();
this.LoadExternalJobsRates.UnloadData();
WorkAtHomeRates = null;
ExternalRates = null;
ExternalJobs = null;
}
}
private void ProcessEE(SparseTriIndex <float> distribution, SparseArray <float> population, SparseArray <float> employment)
{
var indexes = population.ValidIndexArray();
Parallel.For(0, indexes.Length, delegate(int i)
{
var zones = this.Root.ZoneSystem.ZoneArray;
var origin = indexes[i];
var originTotal = population[origin] + employment[origin];
var originZone = zones[origin];
// The factor to apply to the distribution to map it to the results
var factor = originTotal / (originZone.Population + originZone.Employment);
if (originTotal <= 0 | originZone.RegionNumber > 0 | float.IsInfinity(factor))
{
return;
}
var originIndex = zones.GetFlatIndex(origin);
var numberOfZones = zones.GetFlatData().Length;
foreach (var destination in distribution.ValidIndexes(origin))
{
var destinationIndex = zones.GetFlatIndex(destination);
// do not process EE trips
if (zones[destination].RegionNumber != 0)
{
continue;
}
foreach (var mode in distribution.ValidIndexes(origin, destination))
{
var ammount = distribution[origin, destination, mode] * factor;
AddData(mode, originIndex, destinationIndex, ammount, numberOfZones);
}
}
});
}
private void ProcessExternalToInternal(SparseTriIndex <float> distribution, SparseArray <float> population)
{
var indexes = population.ValidIndexArray();
Parallel.For(0, indexes.Length, delegate(int i)
{
var zones = Root.ZoneSystem.ZoneArray;
var zoneNumber = indexes[i];
var originPopulation = population[zoneNumber];
var originZone = zones[zoneNumber];
var factor = originPopulation / originZone.Population;
if (originPopulation <= 0 | originZone.RegionNumber > 0 | float.IsInfinity(factor))
{
return;
}
var originIndex = zones.GetFlatIndex(zoneNumber);
// The factor to apply to the distribution to map it to the results
var numberOfZones = zones.GetFlatData().Length;
foreach (var destination in distribution.ValidIndexes(zoneNumber))
{
var destinationIndex = zones.GetFlatIndex(destination);
// do not process EE trips
if (zones[destination].RegionNumber == 0)
{
continue;
}
foreach (var mode in distribution.ValidIndexes(zoneNumber, destination))
{
var ammount = distribution[zoneNumber, destination, mode] * factor;
AddData(mode, originIndex, destinationIndex, ammount, numberOfZones);
}
}
});
}
public override void Generate(SparseArray<float> production, SparseArray<float> attractions)
{
if ( LoadData )
{
if ( DailyRates == null )
{
this.LoadDailyRates.LoadData();
this.DailyRates = this.LoadDailyRates.GiveData();
}
if ( TimeOfDayRates == null )
{
this.LoadTimeOfDayRates.LoadData();
this.TimeOfDayRates = this.LoadTimeOfDayRates.GiveData();
}
}
var flatProduction = production.GetFlatData();
var flatAttraction = attractions.GetFlatData();
var numberOfIndexes = flatAttraction.Length;
// Compute the Production and Attractions
ComputeProduction( flatProduction, flatAttraction, numberOfIndexes );
//We do not normalize the attraction
if ( LoadData )
{
this.LoadDailyRates.UnloadData();
this.LoadTimeOfDayRates.UnloadData();
DailyRates = null;
TimeOfDayRates = null;
}
}
private void Save(int layerIndex, float[][] flatData, SparseTriIndex <float> data)
{
var origins = data.ValidIndexes(layerIndex).ToArray();
// if there is no data there is nothing to save!
if (origins.Length <= 0)
{
return;
}
var destinations = data.ValidIndexes(origins[0]).ToArray();
switch (FileFormat)
{
case FileFormats.MTX:
SaveMTX(layerIndex, flatData, origins, destinations);
return;
case FileFormats.CSVThirdNormalized:
SaveThirdNormalizedCSV(layerIndex, flatData, origins, destinations);
return;
case FileFormats.CSVSquare:
SaveSquareMatrixToCSV(layerIndex, flatData, origins, destinations);
return;
default:
throw new XTMFRuntimeException(this, "Unknown file format!");
}
}
private float UnemployedMobilityProbability(int mobility, SparseTriIndex <float> ncars, int age, SparseTwinIndex <float> dlicRate)
{
switch (mobility)
{
case 0:
return((1 - dlicRate[age, 0]) * ncars[0, age, 0]);
case 1:
return((1 - dlicRate[age, 0]) * ncars[0, age, 1]);
case 2:
return((1 - dlicRate[age, 0]) * ncars[0, age, 2]);
case 3:
return(dlicRate[age, 0] * ncars[1, age, 0]);
case 4:
return(dlicRate[age, 0] * ncars[1, age, 1]);
case 5:
return(dlicRate[age, 0] * ncars[1, age, 2]);
default:
throw new XTMFRuntimeException(this, "Unknown mobility type '" + mobility + "'!");
}
}
public override void Generate(SparseArray<float> production, SparseArray<float> attractions)
{
if ( LoadData )
{
if ( DailyRates == null )
{
this.LoadDailyRates.LoadData();
this.DailyRates = this.LoadDailyRates.GiveData();
}
if ( TimeOfDayRates == null )
{
this.LoadTimeOfDayRates.LoadData();
this.TimeOfDayRates = this.LoadTimeOfDayRates.GiveData();
}
}
var flatProduction = production.GetFlatData();
var numberOfIndexes = flatProduction.Length;
// Compute the Production
ComputeProduction( flatProduction, numberOfIndexes );
float totalProduction = flatProduction.Sum();
WriteGenerationCSV( totalProduction );
//The PoRPoS Model does NOT include having an attraction component. The distribution will handle this case.
if ( LoadData )
{
this.DailyRates = null;
this.TimeOfDayRates = null;
}
}
override public void Generate(SparseArray <float> production, SparseArray <float> attractions)
{
if (LoadData)
{
if (DailyRates == null)
{
this.LoadDailyRates.LoadData();
this.DailyRates = this.LoadDailyRates.GiveData();
}
if (TimeOfDayRates == null)
{
this.LoadTimeOfDayRates.LoadData();
this.TimeOfDayRates = this.LoadTimeOfDayRates.GiveData();
}
}
var flatProduction = production.GetFlatData();
var numberOfIndexes = flatProduction.Length;
// Compute the Production
ComputeProduction(flatProduction, numberOfIndexes);
float totalProduction = flatProduction.Sum();
WriteGenerationCSV(totalProduction);
//The PoRPoS Model does NOT include having an attraction component. The distribution will handle this case.
if (LoadData)
{
this.DailyRates = null;
this.TimeOfDayRates = null;
}
}
public override void Generate(SparseArray <float> production, SparseArray <float> attractions)
{
if (LoadData)
{
LoadExternalWorkerRates.LoadData();
LoadWorkAtHomeRates.LoadData();
LoadExternalJobsRates.LoadData();
ExternalRates = LoadExternalWorkerRates.GiveData();
WorkAtHomeRates = LoadWorkAtHomeRates.GiveData();
ExternalRates = LoadExternalJobsRates.GiveData();
}
var flatProduction = production.GetFlatData();
var flatWah = new float[flatProduction.Length];
var totalProduction = ComputeProduction(flatProduction, flatWah);
var totalAttraction = ComputeAttraction(attractions.GetFlatData());
Normalize(production.GetFlatData(), attractions.GetFlatData(), totalProduction, totalAttraction);
totalAttraction = RemoveWAHFromAttraction(attractions.GetFlatData(), flatWah);
StoreProductionData(production);
WriteGenerationFile(totalProduction, totalAttraction);
WriteAttractionFile(attractions);
if (LoadData)
{
LoadExternalWorkerRates.UnloadData();
LoadWorkAtHomeRates.UnloadData();
LoadExternalJobsRates.UnloadData();
WorkAtHomeRates = null;
ExternalRates = null;
ExternalJobs = null;
}
}
private void LoadJobOccupationDistribution()
{
JobOccupationRates = SparseTriIndex <float> .CreateSimilarArray(Root.ZoneSystem.ZoneArray, EmploymentStatus, OccupationCategories);
var occupationIndexes = OccupationCategories.ValidIndexies().ToArray();
using (CommentedCsvReader reader = new CommentedCsvReader(JobOccupationRateFile.GetFileName(Root.InputBaseDirectory)))
{
while (reader.NextLine())
{
if (reader.NumberOfCurrentCells >= 5)
{
reader.Get(out int pd, 0);
reader.Get(out int employmentStatus, 1);
reader.Get(out float professional, 2);
reader.Get(out float general, 3);
reader.Get(out float sales, 4);
reader.Get(out float manufacturing, 5);
foreach (var zone in PDZoneMap[pd])
{
JobOccupationRates[zone, employmentStatus, occupationIndexes[0]] = 0;
JobOccupationRates[zone, employmentStatus, occupationIndexes[1]] = professional;
JobOccupationRates[zone, employmentStatus, occupationIndexes[2]] = general;
JobOccupationRates[zone, employmentStatus, occupationIndexes[3]] = sales;
JobOccupationRates[zone, employmentStatus, occupationIndexes[4]] = manufacturing;
}
}
}
}
}
private void LoadWorkerCarDistribution()
{
this.WorkerVehicleRates = this.Root.ZoneSystem.ZoneArray.CreateSimilarArray <SparseTriIndex <float> >();
SparseArray <float> NumberOfVehicles =
new SparseArray <float>(new SparseIndexing()
{
Indexes = new SparseSet[] { new SparseSet()
{
Start = 0, Stop = 2
} }
});
SparseArray <float> DriversLicense =
new SparseArray <float>(new SparseIndexing()
{
Indexes = new SparseSet[] { new SparseSet()
{
Start = 0, Stop = 1
} }
});
if (!this.WorkerVehicleRateFile.ContainsFileName())
{
return;
}
using (CommentedCsvReader reader = new CommentedCsvReader(this.WorkerVehicleRateFile.GetFileName(Root.InputBaseDirectory)))
{
int pd;
int driversLic;
int occ;
float chanceZero;
float chanceOne;
float chanceTwo;
while (reader.NextLine())
{
if (reader.NumberOfCurrentCells >= 6) //Only read if the number of columns in the row matches.
{
reader.Get(out pd, 0);
reader.Get(out driversLic, 1);
reader.Get(out occ, 2);
reader.Get(out chanceZero, 3);
reader.Get(out chanceOne, 4);
reader.Get(out chanceTwo, 5);
foreach (var zone in this.PDZoneMap[pd])
{
var zoneData = this.WorkerVehicleRates[zone];
if (zoneData == null)
{
zoneData = SparseTriIndex <float> .CreateSimilarArray(DriversLicense, this.OccupationCategories, NumberOfVehicles);
this.WorkerVehicleRates[zone] = zoneData;
}
zoneData[driversLic, occ, 0] = chanceZero;
zoneData[driversLic, occ, 1] = chanceOne;
zoneData[driversLic, occ, 2] = chanceTwo;
}
}
}
}
}
public void UnloadData()
{
Loaded = false;
if (!KeepLocalData)
{
Data = null;
}
}
private void LoadData()
{
this.LoadDailyRates.LoadData();
this.LoadTimeOfDayRates.LoadData();
this.DailyRates = this.LoadDailyRates.GiveData();
this.TimeOfDayRates = this.LoadTimeOfDayRates.GiveData();
this.LoadDailyRates.UnloadData();
this.LoadTimeOfDayRates.UnloadData();
}
private void LoadData()
{
LoadDailyRates.LoadData();
LoadTimeOfDayRates.LoadData();
DailyRates = LoadDailyRates.GiveData();
TimeOfDayRates = LoadTimeOfDayRates.GiveData();
LoadDailyRates.UnloadData();
LoadTimeOfDayRates.UnloadData();
}
private void Save(SparseTriIndex <float> data)
{
var layerIndex = data.ValidIndexes().ToArray();
System.Threading.Tasks.Parallel.For(0, layerIndex.Length, (int i) =>
{
Save(layerIndex[i], data.GetFlatData()[i], data);
});
}
private float ApplyMobilityProbability(int mobility, int emp, int occ, SparseTriIndex <float> ncars, int age, SparseTwinIndex <float> dlicRate)
{
// make sure the age is greater than 0
age = (age == 0 ? 1 : age);
// now we have slightly different forms depending if the person is a worker or not
if (emp == 0)
{
return(UnemployedMobilityProbability(mobility, ncars, age, dlicRate));
}
return(EmployedMobilityProbability(mobility, emp, occ, ncars, age, dlicRate));
}
private void LoadTriIndexedData()
{
// first 2 columns are the first 2, the remainder are the last dimension enumerated
List <int> first = new List <int>();
List <int> second = new List <int>();
List <int> third = new List <int>();
List <float> data = new List <float>();
StoreData(first, second, third, data);
Data = SparseTriIndex <float> .CreateSparseTriIndex(first.ToArray(), second.ToArray(), third.ToArray(), data.ToArray());
}
private void LoadWorkerCarDistribution()
{
WorkerVehicleRates = Root.ZoneSystem.ZoneArray.CreateSimilarArray <SparseTriIndex <float> >();
SparseArray <float> numberOfVehicles =
new SparseArray <float>(new SparseIndexing {
Indexes = new[] { new SparseSet {
Start = 0, Stop = 2
} }
});
SparseArray <float> driversLicense =
new SparseArray <float>(new SparseIndexing {
Indexes = new[] { new SparseSet {
Start = 0, Stop = 1
} }
});
using (CsvReader reader = new CsvReader(GetFullPath(WorkerVehicleRateFile)))
{
if (WorkerVehicleRateFileHeader)
{
reader.LoadLine();
}
while (!reader.EndOfFile)
{
var length = reader.LoadLine();
if (length >= 6)
{
reader.Get(out int pd, 0);
reader.Get(out int driversLic, 1);
reader.Get(out int occ, 2);
reader.Get(out float chanceZero, 3);
reader.Get(out float chanceOne, 4);
reader.Get(out float chanceTwo, 5);
if (PdZoneMap.TryGetValue(pd, out List <int> zones))
{
foreach (var zone in zones)
{
var zoneData = WorkerVehicleRates[zone];
if (zoneData == null)
{
zoneData = SparseTriIndex <float> .CreateSimilarArray(driversLicense, OccupationCategories, numberOfVehicles);
WorkerVehicleRates[zone] = zoneData;
}
zoneData[driversLic, occ, 0] = chanceZero;
zoneData[driversLic, occ, 1] = chanceOne;
zoneData[driversLic, occ, 2] = chanceTwo;
}
}
}
}
}
}
private void LoadData()
{
LoadWorkAtHomeRates.LoadData();
LoadExternalWorkerRates.LoadData();
LoadExternalJobsRates.LoadData();
WorkExternal = LoadExternalWorkerRates.GiveData();
WorkAtHomeRates = LoadWorkAtHomeRates.GiveData();
ExternalJobs = LoadExternalJobsRates.GiveData();
LoadWorkAtHomeRates.UnloadData();
LoadExternalWorkerRates.UnloadData();
LoadExternalJobsRates.UnloadData();
}
private void LoadData()
{
this.LoadWorkAtHomeRates.LoadData();
this.LoadExternalWorkerRates.LoadData();
this.LoadExternalJobsRates.LoadData();
this.WorkExternal = this.LoadExternalWorkerRates.GiveData();
this.WorkAtHomeRates = this.LoadWorkAtHomeRates.GiveData();
this.ExternalJobs = this.LoadExternalJobsRates.GiveData();
this.LoadWorkAtHomeRates.UnloadData();
this.LoadExternalWorkerRates.UnloadData();
this.LoadExternalJobsRates.UnloadData();
}
private void LoadNonWorkerCarDistribution()
{
NonWorkerVehicleRates = Root.ZoneSystem.ZoneArray.CreateSimilarArray <SparseTriIndex <float> >();
SparseArray <float> numberOfVehicles =
new SparseArray <float>(new SparseIndexing {
Indexes = new[] { new SparseSet {
Start = 0, Stop = 2
} }
});
SparseArray <float> driversLicense =
new SparseArray <float>(new SparseIndexing {
Indexes = new[] { new SparseSet {
Start = 0, Stop = 1
} }
});
if (!NonWorkerVehicleRateFile.ContainsFileName())
{
return;
}
using (CommentedCsvReader reader = new CommentedCsvReader(NonWorkerVehicleRateFile.GetFileName(Root.InputBaseDirectory)))
{
while (reader.NextLine())
{
if (reader.NumberOfCurrentCells >= 6)
{
reader.Get(out int pd, 0);
reader.Get(out int driversLic, 1);
reader.Get(out int ageCat, 2);
reader.Get(out float chanceZero, 3);
reader.Get(out float chanceOne, 4);
reader.Get(out float chanceTwo, 5);
foreach (var zone in PDZoneMap[pd])
{
var zoneData = NonWorkerVehicleRates[zone];
if (zoneData == null)
{
zoneData = SparseTriIndex <float> .CreateSimilarArray(driversLicense, AgeCategories, numberOfVehicles);
NonWorkerVehicleRates[zone] = zoneData;
}
zoneData[driversLic, ageCat, 0] = chanceZero;
zoneData[driversLic, ageCat, 1] = chanceOne;
zoneData[driversLic, ageCat, 2] = chanceTwo;
}
}
}
}
}
public void LoadData()
{
float[][][] data = new float[TwinSources.Length][][];
SparseIndexing indicies = RootCreateIndices();
for (int i = 0; i < TwinSources.Length; i++)
{
TwinSources[i].LoadData();
var innerData = TwinSources[i].GiveData();
CreateIndices(indicies, i, innerData);
data[i] = innerData.GetFlatData();
TwinSources[i].UnloadData();
}
Data = new SparseTriIndex <float>(indicies, data);
Loaded = true;
}
public void Load()
{
ZoneSystem = Root.ZoneSystem.ZoneArray;
Zones = ZoneSystem.GetFlatData();
if (SaveWorkerCategory != null)
{
WorkerResults = new float[3][];
for (int i = 0; i < WorkerResults.Length; i++)
{
WorkerResults[i] = new float[Zones.Length];
}
}
Probabilities = Linkages.AquireResource <SparseTriIndex <float> >();
ConvertToProbabilities(Probabilities.GetFlatData());
Linkages.ReleaseResource();
}
private void LoadJobOccupationDistribution()
{
JobOccupationRates = SparseTriIndex <float> .CreateSimilarArray(Root.ZoneSystem.ZoneArray, EmploymentStatus, OccupationCategories);
var employmentIndexes = EmploymentStatus.ValidIndexies().ToArray();
var occupationIndexes = OccupationCategories.ValidIndexies().ToArray();
using (CsvReader reader = new CsvReader(GetFullPath(JobOccupationRateFile)))
{
int pd;
int employmentStatus;
float professional;
float general;
float sales;
float manufacturing;
if (JobOccupationRateFileHeader)
{
reader.LoadLine();
}
while (!reader.EndOfFile)
{
var length = reader.LoadLine();
if (length >= 5)
{
reader.Get(out pd, 0);
reader.Get(out employmentStatus, 1);
reader.Get(out professional, 2);
reader.Get(out general, 3);
reader.Get(out sales, 4);
reader.Get(out manufacturing, 5);
List <int> zones;
if (PDZoneMap.TryGetValue(pd, out zones))
{
foreach (var zone in zones)
{
this.JobOccupationRates[zone, employmentStatus, occupationIndexes[0]] = 0;
this.JobOccupationRates[zone, employmentStatus, occupationIndexes[1]] = professional;
this.JobOccupationRates[zone, employmentStatus, occupationIndexes[2]] = general;
this.JobOccupationRates[zone, employmentStatus, occupationIndexes[3]] = sales;
this.JobOccupationRates[zone, employmentStatus, occupationIndexes[4]] = manufacturing;
}
}
}
}
}
}
public void Load()
{
_zoneSystem = Root.ZoneSystem.ZoneArray;
_planningDistricts = ZoneSystemHelper.CreatePdArray <float>(_zoneSystem);
Zones = _zoneSystem.GetFlatData();
if (SaveWorkerCategory != null)
{
WorkerResults = new float[3][];
for (int i = 0; i < WorkerResults.Length; i++)
{
WorkerResults[i] = new float[Zones.Length];
}
}
_linkages = Linkages.AcquireResource <SparseTriIndex <float> >();
CopyLinkages(_linkages);
SaveLinkagesToFile(_linkages.GetFlatData());
Linkages.ReleaseResource();
}
public override void Generate(SparseArray <float> production, SparseArray <float> attractions)
{
if (LoadData && Rates == null)
{
LoadRates.LoadData();
Rates = LoadRates.GiveData();
}
InitializeDemographicCategory();
var flatProduction = production.GetFlatData();
var numberOfIndexes = flatProduction.Length;
// Compute the Production
SaveGenerationData(ComputeProduction(flatProduction, numberOfIndexes));
//The HBO Model does NOT include having an attraction component. The distribution will handle this case.
if (LoadData)
{
Rates = null;
}
}
public void LoadData()
{
// get a replica of the zone system
SparseArray <SparseTriIndex <float> > temp = this.Root.ZoneSystem.ZoneArray.CreateSimilarArray <SparseTriIndex <float> >();
var total = (this.EmploymentCategoryRange[0].Stop - this.EmploymentCategoryRange[0].Start + 1)
* (this.MobilityRange[0].Stop - this.MobilityRange[0].Start + 1)
* (this.AgeRange[0].Stop - this.AgeRange[0].Start + 1);
int[] first = new int[total];
int[] second = new int[total];
int[] third = new int[total];
float[] data = new float[total];
int pos = 0;
for (int i = this.EmploymentCategoryRange[0].Start; i <= this.EmploymentCategoryRange[0].Stop; i++)
{
for (int j = this.MobilityRange[0].Start; j <= this.MobilityRange[0].Stop; j++)
{
for (int k = this.AgeRange[0].Start; k <= this.AgeRange[0].Stop; k++)
{
first[pos] = i;
second[pos] = j;
third[pos] = k;
data[pos] = 0f;
pos++;
}
}
}
var flatTemp = temp.GetFlatData();
// initialize the first one, and use it for the first zone
flatTemp[0] = SparseTriIndex <float> .CreateSparseTriIndex(first, second, third, data);
//release memory resources before we allocate a bunch more
first = second = third = null;
// now create a clone of this for each zone [i starts at 1 since 0 has already been set]
for (int i = 1; i < flatTemp.Length; i++)
{
flatTemp[i] = flatTemp[0].CreateSimilarArray <float>();
}
this.Data = temp;
}
public void LoadData()
{
float[][][] data = new float[TwinSources.Length][][];
SparseIndexing indicies = RootCreateIndices();
for (int i = 0; i < TwinSources.Length; i++)
{
TwinSources[i].LoadData();
var innerData = TwinSources[i].GiveData();
CreateIndices(indicies, i, innerData);
var inner = innerData.GetFlatData();
if (CreateCopy)
{
inner = inner.Select(i2 => i2.Clone() as float[]).ToArray();
}
data[i] = inner;
TwinSources[i].UnloadData();
}
Data = new SparseTriIndex <float>(indicies, data);
Loaded = true;
}
public void SparseTriIndexTest()
{
var length = 1000000;
Random r = new Random();
int[] baseData = new int[length];
int[][] position = GeneratePositions(3, length);
for (int i = 0; i < length; i++)
{
baseData[i] = r.Next();
}
var sparseData = SparseTriIndex <int> .CreateSparseTriIndex(position[0], position[1], position[2], baseData);
for (int i = 0; i < length; i++)
{
if (baseData[i] != sparseData[position[0][i], position[1][i], position[2][i]])
{
Assert.Fail($"{baseData[i]} != {sparseData[position[0][i], position[1][i], position[2][i]]}");
}
}
}
public override void Generate(SparseArray<float> production, SparseArray<float> attractions)
{
if ( LoadData && Rates == null )
{
this.LoadRates.LoadData();
this.Rates = this.LoadRates.GiveData();
}
this.InitializeDemographicCategory();
var flatProduction = production.GetFlatData();
var numberOfIndexes = flatProduction.Length;
// Compute the Production
float totalProduction = 0;
totalProduction = ComputeProduction( flatProduction, numberOfIndexes );
SaveGenerationData( totalProduction );
//The HBO Model does NOT include having an attraction component. The distribution will handle this case.
if ( LoadData )
{
this.Rates = null;
}
}
public void SparseTriIndexTest()
{
var length = 1000000;
Random r = new Random();
int[] BaseData = new int[length];
int[][] Position = GeneratePositions(3, length);
for (int i = 0; i < length; i++)
{
BaseData[i] = r.Next();
}
var sparseData = SparseTriIndex <int> .CreateSparseTriIndex(Position[0], Position[1], Position[2], BaseData);
for (int i = 0; i < length; i++)
{
if (BaseData[i] != sparseData[Position[0][i], Position[1][i], Position[2][i]])
{
Assert.Fail(String.Format("{0} != {1}", BaseData[i], sparseData[Position[0][i], Position[1][i], Position[2][i]]));
}
}
}
private static float EmployedMobilityProbability(int mobility, int emp, int occ, SparseTriIndex<float> ncars, int age, SparseTwinIndex<float> dlicRate)
{
switch ( mobility )
{
case 0:
return ( 1 - dlicRate[age, emp] ) * ncars[0, occ, 0];
case 1:
return ( 1 - dlicRate[age, emp] ) * ncars[0, occ, 1];
case 2:
return ( 1 - dlicRate[age, emp] ) * ncars[0, occ, 2];
case 3:
return ( dlicRate[age, emp] * ncars[1, occ, 0] );
case 4:
return dlicRate[age, emp] * ncars[1, occ, 1];
case 5:
return dlicRate[age, emp] * ncars[1, occ, 2];
default:
throw new XTMFRuntimeException( "Unknown mobility type '" + mobility.ToString() + "'!" );
}
}
private void LoadData()
{
this.LoadWorkAtHomeRates.LoadData();
this.LoadExternalWorkerRates.LoadData();
this.LoadExternalJobsRates.LoadData();
this.WorkExternal = this.LoadExternalWorkerRates.GiveData();
this.WorkAtHomeRates = this.LoadWorkAtHomeRates.GiveData();
this.ExternalJobs = this.LoadExternalJobsRates.GiveData();
this.LoadWorkAtHomeRates.UnloadData();
this.LoadExternalWorkerRates.UnloadData();
this.LoadExternalJobsRates.UnloadData();
}
private float ApplyMobilityProbability(int emp, int occ, SparseTriIndex<float> ncars, int age, SparseTwinIndex<float> dlicRate)
{
switch(Mobility[0].Start)
{
case 0:
return (1 - dlicRate[age, emp]) * ncars[0, occ, 0];
case 1:
return (1 - dlicRate[age, emp]) * ncars[0, occ, 1];
case 2:
return (1 - dlicRate[age, emp]) * ncars[0, occ, 2];
case 3:
return dlicRate[age, emp] * ncars[1, occ, 0];
case 4:
return dlicRate[age, emp] * ncars[1, occ, 1];
case 5:
return dlicRate[age, emp] * ncars[1, occ, 2];
}
return 0;
}
private void ProcessEI(SparseTriIndex<float> distribution, SparseArray<float> population)
{
var indexes = population.ValidIndexArray();
Parallel.For( 0, indexes.Length, delegate(int i)
{
var zones = this.Root.ZoneSystem.ZoneArray;
var zoneNumber = indexes[i];
var originPopulation = population[zoneNumber];
var originZone = zones[zoneNumber];
var factor = originPopulation / originZone.Population;
if ( originPopulation <= 0 | originZone.RegionNumber > 0 | float.IsInfinity( factor ) )
{
return;
}
var originIndex = zones.GetFlatIndex( zoneNumber );
// The factor to apply to the distribution to map it to the results
var numberOfZones = zones.GetFlatData().Length;
foreach ( var destination in distribution.ValidIndexes( zoneNumber ) )
{
var destinationIndex = zones.GetFlatIndex( destination );
// do not process EE trips
if ( zones[destination].RegionNumber == 0 )
{
continue;
}
foreach ( var mode in distribution.ValidIndexes( zoneNumber, destination ) )
{
var ammount = distribution[zoneNumber, destination, mode] * factor;
AddData( mode, originIndex, destinationIndex, ammount, numberOfZones );
}
}
} );
}
private float GenerateForWorkingStudent(IZone[] zones, int zoneIndex, SparseTwinIndex<float> ageRates, SparseTwinIndex<float> empRates, SparseTriIndex<float> occRates, SparseTwinIndex<float> dlicRate, SparseTriIndex<float> ncars, int age, float ppGenerationRate, int emp, float studentFactor)
{
float temp = 0;
var ageEmpRate = ageRates[zones[zoneIndex].ZoneNumber, age]
* empRates[age, emp] * studentFactor;
foreach ( var occSet in this.OccupationCategory )
{
for ( int occ = occSet.Start; occ <= occSet.Stop; occ++ )
{
var nonMobilityRate = ageEmpRate * occRates[age, emp, occ];
foreach ( var mobilitySet in this.Mobility )
{
for ( int mob = mobilitySet.Start; mob <= mobilitySet.Stop; mob++ )
{
// calculate the probability of a person of this type for this zone
var catFactor = nonMobilityRate * ApplyMobilityProbability( mob, emp, occ, ncars, age, dlicRate );
// now apply that to
temp += zones[zoneIndex].Population * catFactor *
ppGenerationRate;
}
}
}
}
return temp;
}
private void ProcessExternalModel(SparseTriIndex<float> distribution, SparseArray<float> population, SparseArray<float> employment)
{
ProcessEI( distribution, population );
ProcessIE( distribution, employment );
ProcessEE( distribution, population, employment );
}
private void ProcessIE(SparseTriIndex<float> distribution, SparseArray<float> employment)
{
var indexes = employment.ValidIndexArray();
Parallel.For( 0, indexes.Length, delegate(int i)
{
var zones = this.Root.ZoneSystem.ZoneArray;
var destination = indexes[i];
var destinationEmployment = employment[destination];
var destinationZone = zones[destination];
var factor = destinationEmployment / destinationZone.Employment;
if ( destinationEmployment <= 0 | destinationZone.RegionNumber > 0 | float.IsInfinity( factor ) )
{
return;
}
var destinationIndex = zones.GetFlatIndex( destination );
// The factor to apply to the distribution to map it to the results
var numberOfZones = zones.GetFlatData().Length;
foreach ( var origin in distribution.ValidIndexes() )
{
var originIndex = zones.GetFlatIndex( origin );
// do not process EE trips
if ( zones[origin].RegionNumber == 0 )
{
continue;
}
foreach ( var mode in distribution.ValidIndexes( origin, destination ) )
{
var ammount = distribution[origin, destination, mode] * factor;
if ( ammount <= 0 )
{
continue;
}
AddData( mode, originIndex, destinationIndex, ammount, numberOfZones );
}
}
} );
}
private void LoadData()
{
this.LoadDailyRates.LoadData();
this.LoadTimeOfDayRates.LoadData();
this.DailyRates = this.LoadDailyRates.GiveData();
this.TimeOfDayRates = this.LoadTimeOfDayRates.GiveData();
this.LoadDailyRates.UnloadData();
this.LoadTimeOfDayRates.UnloadData();
}
private void LoadData()
{
this.LoadRates.LoadData();
this.Rates = this.LoadRates.GiveData();
this.LoadRates.UnloadData();
}
private float ApplyMobilityProbability(int mobility, int emp, int occ, SparseTriIndex<float> ncars, int age, SparseTwinIndex<float> dlicRate)
{
// make sure the age is greater than 0
age = ( age == 0 ? 1 : age );
// now we have slightly different forms depending if the person is a worker or not
if ( emp == 0 )
{
return UnemployedMobilityProbability( mobility, ncars, age, dlicRate );
}
else
{
return EmployedMobilityProbability( mobility, emp, occ, ncars, age, dlicRate );
}
}
