/// <summary>
/// Run Model which calculates the required computations
/// </summary>
/// <param name="startingSaltSplit">starting Salt Split</param>
/// <param name="currentSaltSplit">current Salt Split</param>
/// <param name="resinLifeExpectancy">resin Life Expectancy</param>
/// <param name="simulationConfidence">simulation Confidence</param>
/// <param name="numberSimulationIterations">number Simulation Iterations</param>
/// <param name="simulationMethod">simulation Method</param>
/// <param name="standardDeviationThreshold">standard Deviation Threshold</param>
/// <param name="resinAge">resin Age parameter</param>
/// <param name="replacementLevel">replacement Level</param>
/// <param name="rtiCleaningLevel">rti Cleaning Level</param>
/// <param name="donotReplaceResin">dont Replace Resin</param>
/// <returns>Returns the Price data</returns>
public PriceData RunModel(double startingSaltSplit, double currentSaltSplit, double resinLifeExpectancy, int simulationConfidence, int numberSimulationIterations, string simulationMethod, int standardDeviationThreshold, double resinAge, double replacementLevel, double rtiCleaningLevel, bool donotReplaceResin)
{
try
{
var trainList = dataToSend.TrainList;
var numberOfTrains = dataToSend.NumberTrains;
if (calculationRun)
{
dataToSend = new PriceData();
}
dataToSend.TrainList = trainList;
dataToSend.NumberTrains = numberOfTrains;
double numberOfWeeks = Convert.ToDouble(resinLifeExpectancy);
var standardDevData = ProcessData.WeeklyWeightedAverageCondstdDev;
RandomNumberSimulation conductivity = new RandomNumberSimulation();
double roundedAgeValues = Convert.ToDouble(string.Format("{0:0.00}", resinAge));
Dictionary<int, double> grainForeCast = conductivity.BeginCalculation(standardDevData, simulationConfidence, numberSimulationIterations, simulationMethod, standardDeviationThreshold, roundedAgeValues);
int grainCount = grainForeCast.Count;
int numberOfLoops = Convert.ToInt16(resinLifeExpectancy);
if (numberOfLoops >= grainCount)
{
int weeksToAdd = Convert.ToInt32(numberOfLoops - grainCount);
int lastGrainWeek = 0;
if (grainForeCast != null && grainForeCast.Count > 0)
{
lastGrainWeek = grainForeCast.Last().Key + 1;
}
int grainIndx = 0;
for (int loop = 0; loop <= weeksToAdd; loop++)
{
double grainToAdd = 0;
if (grainForeCast != null && grainForeCast.Count > 0)
{
grainToAdd = grainForeCast.ElementAt(grainIndx).Value;
grainForeCast.Add(lastGrainWeek, grainToAdd);
lastGrainWeek++;
grainIndx++;
}
}
}
dataToSend.GrainForcast = grainForeCast;
double rtiCleaningeffectivness = 28.0;
ThroughputBuilder tp = new ThroughputBuilder();
Dictionary<DateTime, Tuple<int, double, string>> throughputCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, trainResinAmount, rtiCleaningeffectivness, selectedTrainGPM, resinAge, startingSaltSplit, numberOfWeeks, true, donotReplaceResin);
var afterConditions = tp.AfterSystemConditions;
Dictionary<DateTime, Tuple<int, double, string>> throughputNoCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, trainResinAmount, rtiCleaningeffectivness, selectedTrainGPM, resinAge, startingSaltSplit, numberOfWeeks, false, donotReplaceResin);
var beforeConditions = tp.BeforeSystemConditions;
List<double> reginWeek = new List<double>();
List<double> hoursWeek = new List<double>();
List<double> reginTime = new List<double>();
List<double> tpWeek = new List<double>();
List<double> ssWeek = new List<double>();
List<double> beforeSaltSplit = new List<double>();
List<double> afterSaltSplit = new List<double>();
List<int> weeks = new List<int>();
List<Tuple<int, double>> regweekToSendNormal = new List<Tuple<int, double>>();
List<Tuple<int, double>> regweekToSendClean = new List<Tuple<int, double>>();
int weekNumber = 0;
foreach (var week in beforeConditions)
{
weekNumber++;
weeks.Add(weekNumber);
reginWeek.Add(week.Item2);
hoursWeek.Add(week.Item3);
reginTime.Add(week.Item4);
tpWeek.Add(week.Item5);
ssWeek.Add(week.Item6);
beforeSaltSplit.Add(week.Item6);
Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, week.Item2);
regweekToSendNormal.Add(reginsPerWeeK);
}
dataToSend.RegensPerWeekNormalOps = regweekToSendNormal;
dataToSend.CleanThroughput = throughputCleanPrediction;
dataToSend.NormalOpsThroughput = throughputNoCleanPrediction;
double averageTPBefore = 0;
if (tpWeek != null && tpWeek.Count > 0)
{
averageTPBefore = Math.Round(tpWeek.Average(), 0);
}
if (reginWeek != null && reginWeek.Count > 0)
{
dataToSend.RegensPerWeekAverageBefore = Math.Round(reginWeek.Average(), 2);
}
if (hoursWeek != null && hoursWeek.Count > 0)
{
dataToSend.HoursPerRunAverageBefore = Math.Round(hoursWeek.Average(), 2);
}
if (reginTime != null && reginTime.Count > 0)
{
dataToSend.RegenTimeAverageBefore = Math.Round(reginTime.Average(), 2);
}
dataToSend.ThroughputAverageBefore = averageTPBefore;
if (ssWeek != null && ssWeek.Count > 0)
{
dataToSend.NumberRegens = Math.Round(ssWeek.Average());
}
if (withCleaning || withAndWithoutCleaning)
{
reginWeek.Clear();
hoursWeek.Clear();
reginTime.Clear();
tpWeek.Clear();
ssWeek.Clear();
foreach (var week in afterConditions)
{
reginWeek.Add(week.Item2);
hoursWeek.Add(week.Item3);
reginTime.Add(week.Item4);
tpWeek.Add(week.Item5);
ssWeek.Add(week.Item6);
}
if (reginWeek != null && reginWeek.Count > 0)
{
dataToSend.RegensPerWeekAverageAfter = Math.Round(reginWeek.Average(), 2);
}
if (hoursWeek != null && hoursWeek.Count > 0)
{
dataToSend.HoursPerRunAverageAfter = Math.Round(hoursWeek.Average(), 2);
}
if (reginTime != null && reginTime.Count > 0)
{
dataToSend.RegenTimeAverageAfter = Math.Round(reginTime.Average(), 2);
}
if (tpWeek != null && tpWeek.Count > 0)
{
dataToSend.ThroughputAverageAfter = Math.Round(tpWeek.Average(), 0);
}
}
weekNumber = 0;
foreach (var week in afterConditions)
{
weekNumber++;
Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, week.Item2);
afterSaltSplit.Add(week.Item6);
regweekToSendClean.Add(reginsPerWeeK);
}
Dictionary<int, Tuple<double?, double?>> saltSplitData = new Dictionary<int, Tuple<double?, double?>>();
int length;
if (beforeSaltSplit.Count < afterSaltSplit.Count)
{
length = afterSaltSplit.Count;
}
else
{
length = beforeSaltSplit.Count;
}
for (int i = 0; i < length; i++)
{
int aftercount = afterSaltSplit.Count;
int beforecount = beforeSaltSplit.Count;
double? before = 0;
double? after = 0;
int week = 0;
bool shouldAdd = true;
if (beforecount > aftercount)
{
if (i < aftercount)
{
before = beforeSaltSplit.ElementAt(i);
after = afterSaltSplit.ElementAt(i);
week = i + 1;
}
else
{
shouldAdd = false;
}
}
else if (beforecount < aftercount)
{
if (i < beforecount)
{
before = beforeSaltSplit.ElementAt(i);
after = afterSaltSplit.ElementAt(i);
week = i + 1;
}
else
{
shouldAdd = false;
}
}
else if (beforecount == aftercount)
{
before = beforeSaltSplit.ElementAt(i);
after = afterSaltSplit.ElementAt(i);
week = i + 1;
}
Tuple<double?, double?> temp = new Tuple<double?, double?>(before, after);
if (shouldAdd)
{
saltSplitData.Add(week, temp);
}
}
dataToSend.RegensPerWeekClean = regweekToSendClean;
dataToSend.CleanThroughput = throughputCleanPrediction;
dataToSend.NormalOpsThroughput = throughputNoCleanPrediction;
dataToSend.SaltSplit = saltSplitData;
dataToSend.NumberWeeks = numberOfWeeks;
return dataToSend;
}
catch
{
throw;
}
}
/// <summary>
/// Run Model which calculates the required computations
/// </summary>
/// <param name="startingSaltSplit">starting Salt Split</param>
/// <param name="currentSaltSplit">current Salt Split</param>
/// <param name="resinLifeExpectancy">resin Life Expectancy</param>
/// <param name="simulationConfidence">simulation Confidence</param>
/// <param name="numberSimulationIterations">number Simulation Iterations</param>
/// <param name="simulationMethod">simulation Method</param>
/// <param name="standardDeviationThreshold">standard Deviation Threshold</param>
/// <param name="resinAge">resin Age parameter</param>
/// <param name="replacementLevel">replacement Level</param>
/// <param name="rtiCleaningLevel">rti Cleaning Level</param>
/// <param name="donotReplaceResin">dont Replace Resin</param>
/// <returns>Returns the Price data</returns>
public PriceData RunModel(double startingSaltSplit, double currentSaltSplit, double resinLifeExpectancy, int simulationConfidence, int numberSimulationIterations, string simulationMethod, int standardDeviationThreshold, double resinAge, double replacementLevel, double rtiCleaningLevel, bool donotReplaceResin, List<double> regenTimes, int selectedTrainId, List<train> trains, List<vessel> vessels)
{
try
{
var trainList = dataToSend.TrainList;
var numberOfTrains = dataToSend.NumberTrains;
if (calculationRun)
{
dataToSend = new PriceData();
}
dataToSend.TrainList = trainList;
dataToSend.NumberTrains = numberOfTrains;
double numberOfWeeks = Convert.ToDouble(resinLifeExpectancy);
var standardDevData = ProcessData.WeeklyWeightedAverageCondstdDev;
RandomNumberSimulation conductivity = new RandomNumberSimulation();
double roundedAgeValues = Convert.ToDouble(string.Format("{0:0.00}", resinAge));
Dictionary<int, double> grainForeCast = conductivity.BeginCalculation(standardDevData, simulationConfidence, numberSimulationIterations, simulationMethod, standardDeviationThreshold, roundedAgeValues);
int grainCount = grainForeCast.Count;
int numberOfLoops = Convert.ToInt16(resinLifeExpectancy);
if (numberOfLoops >= grainCount)
{
int weeksToAdd = Convert.ToInt32(numberOfLoops - grainCount);
int lastGrainWeek = 0;
if (grainForeCast != null && grainForeCast.Count > 0)
{
lastGrainWeek = grainForeCast.Last().Key + 1;
}
int grainIndx = 0;
for (int loop = 0; loop <= weeksToAdd; loop++)
{
double grainToAdd = 0;
if (grainForeCast != null && grainForeCast.Count > 0)
{
grainToAdd = grainForeCast.ElementAt(grainIndx).Value;
grainForeCast.Add(lastGrainWeek, grainToAdd);
lastGrainWeek++;
grainIndx++;
}
}
}
dataToSend.GrainForcast = grainForeCast;
double rtiCleaningeffectivness = 28.0;
ThroughputBuilder tp = new ThroughputBuilder();
Dictionary<DateTime, Tuple<int, double, string>> throughputCleanPrediction = new Dictionary<DateTime,Tuple<int,double,string>>();
Dictionary<DateTime, Tuple<int, double, string>> throughputNoCleanPrediction = new Dictionary<DateTime, Tuple<int, double, string>>();
List<Tuple<double, double, double, double, double, double>> beforeConditions = new List<Tuple<double, double, double, double, double, double>>();
List<Tuple<double, double, double, double, double, double>> afterConditions = new List<Tuple<double, double, double, double, double, double>>();
List<Dictionary<DateTime, Tuple<int, double, string>>> throughputCleanPredictionAllTrains = new List<Dictionary<DateTime, Tuple<int, double, string>>>();
List<Dictionary<DateTime, Tuple<int, double, string>>> throughputNoCleanPredictionAllTrains = new List<Dictionary<DateTime,Tuple<int,double,string>>>();
List<List<Tuple<double, double, double, double, double, double>>> beforeConditionsAllTrains = new List<List<Tuple<double,double,double,double,double,double>>>();
List<List<Tuple<double, double, double, double, double, double>>> afterConditionsAllTrains = new List<List<Tuple<double, double, double, double, double, double>>>();
List<double> reginWeek_before = new List<double>();
List<double> hoursWeek_before = new List<double>();
List<double> reginTime_before = new List<double>();
List<double> tpWeek_before = new List<double>();
List<double> ssWeek_before = new List<double>();
List<double> reginWeek_after = new List<double>();
List<double> hoursWeek_after = new List<double>();
List<double> reginTime_after = new List<double>();
List<double> tpWeek_after = new List<double>();
List<double> ssWeek_after = new List<double>();
List<double> beforeSaltSplit = new List<double>();
List<double> afterSaltSplit = new List<double>();
List<int> weeks = new List<int>();
List<Tuple<int, double>> regweekToSendNormal = new List<Tuple<int, double>>();
List<Tuple<int, double>> regweekToSendClean = new List<Tuple<int, double>>();
List<double> grainsWeek_before_AllTrains = new List<double>();
List<double> reginWeek_before_AllTrains = new List<double>();
List<double> hoursWeek_before_AllTrains = new List<double>();
List<double> reginTime_before_AllTrains = new List<double>();
List<double> tpWeek_before_AllTrains = new List<double>();
List<double> ssWeek_before_AllTrains = new List<double>();
List<double> grainsWeek_after_AllTrains = new List<double>();
List<double> reginWeek_after_AllTrains = new List<double>();
List<double> hoursWeek_after_AllTrains = new List<double>();
List<double> reginTime_after_AllTrains = new List<double>();
List<double> tpWeek_after_AllTrains = new List<double>();
List<double> ssWeek_after_AllTrains = new List<double>();
List<double> beforeSaltSplit_AllTrains = new List<double>();
List<double> afterSaltSplit_AllTrains = new List<double>();
List<int> weeks_AllTrains = new List<int>();
List<Tuple<int, double>> regweekToSendNormal_AllTrains = new List<Tuple<int, double>>();
List<Tuple<int, double>> regweekToSendClean_AllTrains = new List<Tuple<int, double>>();
List<DateTime> dateWeekTpPair_AllTrains = new List<DateTime>();
List<Tuple<double, double, string>> beforeWeekTP_Pair_AllTrains = new List<Tuple<double,double,string>>();
List<Tuple<double, double, string>> afterWeekTP_Pair_AllTrains = new List<Tuple<double,double,string>>();
int weekNumber = 0;
double numRegensBefore = 0;
double numRegensAfter = 0;
if (selectedTrainId != 0)
{
throughputCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, trainAnionResinAmount, rtiCleaningeffectivness, selectedTrainGPM, resinAge, startingSaltSplit, numberOfWeeks, true, donotReplaceResin, regenTimes.Average(), selectedTrainId, trains.Count);
afterConditions = tp.AfterSystemConditions;
throughputNoCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, trainAnionResinAmount, rtiCleaningeffectivness, selectedTrainGPM, resinAge, startingSaltSplit, numberOfWeeks, false, donotReplaceResin, regenTimes.Average(), selectedTrainId, trains.Count);
beforeConditions = tp.BeforeSystemConditions;
foreach (var week in beforeConditions)
{
weekNumber++;
weeks.Add(weekNumber);
reginWeek_before.Add(week.Item2);
hoursWeek_before.Add(week.Item3);
reginTime_before.Add(week.Item4);
tpWeek_before.Add(week.Item5);
ssWeek_before.Add(week.Item6);
beforeSaltSplit.Add(week.Item6);
Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, week.Item2);
regweekToSendNormal.Add(reginsPerWeeK);
}
weekNumber = 0;
foreach (var week in afterConditions)
{
weekNumber++;
Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, week.Item2);
afterSaltSplit.Add(week.Item6);
regweekToSendClean.Add(reginsPerWeeK);
}
}
else // If selected train == 0 (all trains)
{
// Perform the simulation for each train in the system
for (int train = 1; train <= trains.Count; train++) // for each train in the system
{
int trainID = Convert.ToInt32(trains.ElementAt(train-1).trainID);
selectedTrainGPM = Convert.ToDouble(trains.ElementAt(train - 1).gpm);
double regenTime = Convert.ToInt32(trains.ElementAt(train - 1).regen_duration);
double amtAnionResin = 0;
List<double> resinAges = new List<double>();
var train_vessels = vessels.Where(ves => ves.train_trainID == trainID);
int count = 0;
foreach (var vessel in train_vessels) // for each vessel in the current train
{
if (count % 2 != 0) // If it is an anion vessel
{
amtAnionResin += Convert.ToInt32(vessel.size);
DateTime purchaseDate = DateTime.Parse(vessel.date_replaced);
TimeSpan span = DateTime.Today - purchaseDate;
resinAges.Add(span.TotalDays);
}
count++;
}
double resAge = resinAges.Count() != 0 ? resinAges.Average() / 7 : 0;
throughputCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, amtAnionResin, rtiCleaningeffectivness, selectedTrainGPM, resAge, startingSaltSplit, numberOfWeeks, true, donotReplaceResin, regenTime, trainID, trains.Count);
throughputCleanPredictionAllTrains.Add(throughputCleanPrediction);
afterConditions = tp.AfterSystemConditions;
afterConditionsAllTrains.Add(afterConditions);
throughputNoCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, amtAnionResin, rtiCleaningeffectivness, selectedTrainGPM, resAge, startingSaltSplit, numberOfWeeks, false, donotReplaceResin, regenTime, trainID, trains.Count);
throughputNoCleanPredictionAllTrains.Add(throughputNoCleanPrediction);
beforeConditions = tp.BeforeSystemConditions;
beforeConditionsAllTrains.Add(beforeConditions);
numRegensBefore += beforeConditions.Average(regens => regens.Item2);
numRegensAfter += afterConditions.Average(regens => regens.Item2);
beforeConditions = new List<Tuple<double, double, double, double, double, double>>(); // Reset the before conditions
afterConditions = new List<Tuple<double, double, double, double, double, double>>(); // Reset the after conditions
throughputCleanPrediction = new Dictionary<DateTime, Tuple<int, double, string>>(); // Reset the before conditions
throughputNoCleanPrediction = new Dictionary<DateTime, Tuple<int, double, string>>(); // Reset the after conditions
tp = new ThroughputBuilder(); // Reset the TP builder class
}
int beforeMaxLength = beforeConditionsAllTrains.Select(len => len.Count()).ToList().Max();
int afterMaxLength = afterConditionsAllTrains.Select(len => len.Count()).ToList().Max();
List<Tuple<int, double, double, double, double, double>>[] before_conditions = new List<Tuple<int, double, double, double, double, double>>[beforeMaxLength];
List<Tuple<int, double, double, double, double, double>>[] after_conditions = new List<Tuple<int, double, double, double, double, double>>[afterMaxLength];
foreach (var trains_beforeConditions in beforeConditionsAllTrains) // Cycle through all of the before system conditions for each train
{
foreach (var week in trains_beforeConditions) // Go through each week's before conditions for each train in the system and add to an array of lists
{
Tuple<int, double, double, double, double, double> thisWeeksBeforeConditions = new Tuple<int,double,double,double,double,double>(weekNumber+1, week.Item2, week.Item3, week.Item4, week.Item5, week.Item6);
if (before_conditions[weekNumber] == null) // Initialize the array object if it is empty
before_conditions[weekNumber] = new List<Tuple<int, double, double, double, double, double>>();
before_conditions[weekNumber].Add(thisWeeksBeforeConditions);
weekNumber++;
}
weekNumber = 0;
}
weekNumber = 0;
foreach (var trains_afterConditions in afterConditionsAllTrains) // Cycle through all of the before system conditions for each train
{
foreach (var week in trains_afterConditions) // Go through each week's after conditions for each train in the system and add to an array of lists
{
Tuple<int, double, double, double, double, double> thisWeeksAfterConditions = new Tuple<int, double, double, double, double, double>(weekNumber+1, week.Item2, week.Item3, week.Item4, week.Item5, week.Item6);
if (after_conditions[weekNumber] == null) // Initialize the array object if it is empty
after_conditions[weekNumber] = new List<Tuple<int, double, double, double, double, double>>();
after_conditions[weekNumber].Add(thisWeeksAfterConditions);
weekNumber++;
}
weekNumber = 0;
}
foreach (var weekly_conditions in before_conditions) // Average together the data for each of the trains before conditions
{
weekNumber++;
weeks_AllTrains.Add(weekNumber + 1);
double _grainWeek = weekly_conditions.Select(grains => grains.Item1).ToList().Average();
double _reginWeek = weekly_conditions.Select(regenPerWeek => regenPerWeek.Item2).ToList().Average();
double _hoursWeek = weekly_conditions.Select(hrPerRun => hrPerRun.Item3).ToList().Average();
double _reginTime = weekly_conditions.Select(regenTime => regenTime.Item4).ToList().Average();
double _tpWeek = weekly_conditions.Select(throughput => throughput.Item5).ToList().Average();
double _ssWeek = weekly_conditions.Select(SaltSplit => SaltSplit.Item6).ToList().Average();
double _beforeSS = weekly_conditions.Select(SaltSplit => SaltSplit.Item6).ToList().Average();
grainsWeek_before_AllTrains.Add(_grainWeek);
reginWeek_before_AllTrains.Add(_reginWeek);
hoursWeek_before_AllTrains.Add(_hoursWeek);
reginTime_before_AllTrains.Add(_reginTime);
tpWeek_before_AllTrains.Add(_tpWeek);
ssWeek_before_AllTrains.Add(_ssWeek);
beforeSaltSplit_AllTrains.Add(_beforeSS);
Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, weekly_conditions.Select(regenPerWeek => regenPerWeek.Item2).ToList().Average());
Tuple<double, double, double, double, double, double> beforeCondition = new Tuple<double,double,double,double,double,double>(_grainWeek, _reginWeek, _hoursWeek, _reginTime, _tpWeek, _ssWeek);
regweekToSendNormal_AllTrains.Add(reginsPerWeeK);
beforeConditions.Add(beforeCondition);
}
// Use the new average value from each of the train simulations BEFORE
weeks = weeks_AllTrains;
reginWeek_before = reginWeek_before_AllTrains;
hoursWeek_before = hoursWeek_before_AllTrains;
reginTime_before = reginTime_before_AllTrains;
tpWeek_before = tpWeek_before_AllTrains;
ssWeek_before = ssWeek_before_AllTrains;
weekNumber = 0;
foreach (var weekly_conditions in after_conditions) // Average together the data for each of the trains after conditions
{
weekNumber++;
double _grainWeek = weekly_conditions.Select(grains => grains.Item1).ToList().Average();
double _reginWeek = weekly_conditions.Select(regenPerWeek => regenPerWeek.Item2).ToList().Average();
double _hoursWeek = weekly_conditions.Select(hrPerRun => hrPerRun.Item3).ToList().Average();
double _reginTime = weekly_conditions.Select(regenTime => regenTime.Item4).ToList().Average();
double _tpWeek = weekly_conditions.Select(throughput => throughput.Item5).ToList().Average();
double _ssWeek = weekly_conditions.Select(SaltSplit => SaltSplit.Item6).ToList().Average();
double _afterSS = weekly_conditions.Select(SaltSplit => SaltSplit.Item6).ToList().Average();
grainsWeek_after_AllTrains.Add(_grainWeek);
reginWeek_after_AllTrains.Add(_reginWeek);
hoursWeek_after_AllTrains.Add(_hoursWeek);
reginTime_after_AllTrains.Add(_reginTime);
tpWeek_after_AllTrains.Add(_tpWeek);
ssWeek_after_AllTrains.Add(_ssWeek);
afterSaltSplit_AllTrains.Add(_afterSS);
Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, weekly_conditions.Select(regenPerWeek => regenPerWeek.Item2).ToList().Average());
Tuple<double, double, double, double, double, double> afterCondition = new Tuple<double, double, double, double, double, double>(_grainWeek, _reginWeek, _hoursWeek, _reginTime, _tpWeek, _ssWeek);
regweekToSendClean_AllTrains.Add(reginsPerWeeK);
afterConditions.Add(afterCondition);
}
// Use the new average value from each of the train simulations AFTER
reginWeek_after = reginWeek_after_AllTrains;
hoursWeek_after = hoursWeek_after_AllTrains;
reginTime_after = reginTime_after_AllTrains;
tpWeek_after = tpWeek_after_AllTrains;
ssWeek_after = ssWeek_after_AllTrains;
regweekToSendNormal = regweekToSendNormal_AllTrains;
regweekToSendClean = regweekToSendClean_AllTrains;
beforeMaxLength = throughputNoCleanPredictionAllTrains.Select(len => len.Count()).ToList().Max();
afterMaxLength = throughputCleanPredictionAllTrains.Select(len => len.Count()).ToList().Max();
List<Dictionary<DateTime, Tuple<int, double, string>>>[] before = new List<Dictionary<DateTime, Tuple<int, double, string>>>[beforeMaxLength];
List<Dictionary<DateTime, Tuple<int, double, string>>>[] after = new List<Dictionary<DateTime, Tuple<int, double, string>>>[afterMaxLength];
weekNumber = 0;
foreach (var trains_beforeConditions in throughputNoCleanPredictionAllTrains) // Cycle through all of the before system conditions for each train
{
foreach (var week in trains_beforeConditions) // Go through each week's before conditions for each train in the system and add to an array of lists
{
Dictionary<DateTime, Tuple<int, double, string>> dictionary = new Dictionary<DateTime, Tuple<int, double, string>>();
Tuple<int, double, string> thisWeeksBeforeConditions = new Tuple<int, double, string>(week.Value.Item1, week.Value.Item2, week.Value.Item3);
dictionary.Add(week.Key, thisWeeksBeforeConditions);
if (before[weekNumber] == null) // Initialize the array object if it is empty
before[weekNumber] = new List<Dictionary<DateTime, Tuple<int, double, string>>>();
before[weekNumber].Add(dictionary);
weekNumber++;
}
weekNumber = 0;
}
weekNumber = 0;
foreach (var trains_afterConditions in throughputCleanPredictionAllTrains) // Cycle through all of the before system conditions for each train
{
foreach (var week in trains_afterConditions) // Go through each week's before conditions for each train in the system and add to an array of lists
{
Dictionary<DateTime, Tuple<int, double, string>> dictionary = new Dictionary<DateTime, Tuple<int, double, string>>();
Tuple<int, double, string> thisWeeksAfterConditions = new Tuple<int, double, string>(week.Value.Item1, week.Value.Item2, week.Value.Item3);
dictionary.Add(week.Key, thisWeeksAfterConditions);
if (after[weekNumber] == null) // Initialize the array object if it is empty
after[weekNumber] = new List<Dictionary<DateTime, Tuple<int, double, string>>>();
after[weekNumber].Add(dictionary);
weekNumber++;
}
weekNumber = 0;
}
weekNumber = 0;
foreach (var weekly_conditions in before) // Average together the data for each of the trains before conditions
{
List<int> grains = new List<int>();
List<double> throughputVal = new List<double>();
List<string> cleaOrReplace = new List<string>();
int len = weekly_conditions.Count()-1;
for (int trn = 0; trn <= len; trn++)
{
grains.Add(weekly_conditions.ElementAt(trn).Values.ElementAt(0).Item1);
throughputVal.Add(weekly_conditions.ElementAt(trn).Values.ElementAt(0).Item2);
cleaOrReplace.Add(weekly_conditions.ElementAt(trn).Values.ElementAt(0).Item3);
}
//dateWeekTpPair_AllTrains = weekly_conditions
//weekNumber++;
//weeks_AllTrains.Add(weekNumber + 1);
//grainsWeek_AllTrains.Add(weekly_conditions.Select(grains => grains.Item1).ToList().Average());
//reginWeek_AllTrains.Add(weekly_conditions.Select(regenPerWeek => regenPerWeek.Item2).ToList().Average());
//hoursWeek_AllTrains.Add(weekly_conditions.Select(hrPerRun => hrPerRun.Item3).ToList().Average());
//reginTime_AllTrains.Add(weekly_conditions.Select(regenTime => regenTime.Item4).ToList().Average());
//tpWeek_AllTrains.Add(weekly_conditions.Select(throughput => throughput.Item5).ToList().Average());
//Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, weekly_conditions.Select(regenPerWeek => regenPerWeek.Item2).ToList().Average());
//regweekToSendNormal_AllTrains.Add(reginsPerWeeK);
//if (grains.First() == 120 || weekNumber == 80)
//{
// // stop here
//}
Tuple<int, double, string> thisWeeksBeforeConditions = new Tuple<int, double, string>(grains.First(), throughputVal.Average(), cleaOrReplace.First());
//dictionary.Add(weekly_conditions.ElementAt(0).Keys.ElementAt(0), thisWeeksAfterConditions);
throughputNoCleanPrediction.Add(weekly_conditions.ElementAt(0).Keys.ElementAt(0), thisWeeksBeforeConditions);
//.Select(x => tranform(x))
// .Where(y => y != null) //Check for nulls
// .ToList();
weekNumber++;
}
weekNumber = 0;
foreach (var weekly_conditions in after) // Average together the data for each of the trains before conditions
{
List<int> grains = new List<int>();
List<double> throughputVal = new List<double>();
List<string> cleaOrReplace = new List<string>();
int len = weekly_conditions.Count() - 1;
for (int trn = 0; trn <= len; trn++)
{
grains.Add(weekly_conditions.ElementAt(trn).Values.ElementAt(0).Item1);
throughputVal.Add(weekly_conditions.ElementAt(trn).Values.ElementAt(0).Item2);
cleaOrReplace.Add(weekly_conditions.ElementAt(trn).Values.ElementAt(0).Item3);
}
Tuple<int, double, string> thisWeeksAfterConditions = new Tuple<int, double, string>(grains.First(), throughputVal.Average(), cleaOrReplace.First());
throughputCleanPrediction.Add(weekly_conditions.ElementAt(0).Keys.ElementAt(0), thisWeeksAfterConditions);
weekNumber++;
}
}
dataToSend.RegensPerWeekNormalOps = regweekToSendNormal;
dataToSend.CleanThroughput = throughputCleanPrediction;
dataToSend.NormalOpsThroughput = throughputNoCleanPrediction;
double averageTPBefore = 0;
if (tpWeek_before != null && tpWeek_before.Count > 0)
{
averageTPBefore = Math.Round(tpWeek_before.Average(), 0);
}
if (reginWeek_before != null && reginWeek_before.Count > 0 && selectedTrainId != 0)
{
dataToSend.RegensPerWeekAverageBefore = Math.Round(reginWeek_before.Average(), 2);
}
else
{
dataToSend.RegensPerWeekAverageBefore = Math.Round(numRegensBefore, 2);
}
if (hoursWeek_before != null && hoursWeek_before.Count > 0)
{
dataToSend.HoursPerRunAverageBefore = Math.Round(hoursWeek_before.Average(), 2);
}
if (reginTime_before != null && reginTime_before.Count > 0)
{
dataToSend.RegenTimeAverageBefore = Math.Round(reginTime_before.Average(), 2);
}
dataToSend.ThroughputAverageBefore = averageTPBefore;
if (ssWeek_before != null && ssWeek_before.Count > 0)
{
dataToSend.NumberRegens = Math.Round(ssWeek_before.Average());
}
if (withCleaning || withAndWithoutCleaning)
{
//reginWeek_after.Clear();
//hoursWeek_after.Clear();
//reginTime_after.Clear();
//tpWeek_after.Clear();
//ssWeek_after.Clear();
foreach (var week in afterConditions)
{
reginWeek_after.Add(week.Item2);
hoursWeek_after.Add(week.Item3);
reginTime_after.Add(week.Item4);
tpWeek_after.Add(week.Item5);
ssWeek_after.Add(week.Item6);
}
if (reginWeek_after != null && reginWeek_after.Count > 0 && selectedTrainId != 0)
{
dataToSend.RegensPerWeekAverageAfter = Math.Round(reginWeek_after.Average(), 2);
}
else
{
dataToSend.RegensPerWeekAverageAfter = Math.Round(numRegensAfter, 2);
}
if (hoursWeek_after != null && hoursWeek_after.Count > 0)
{
dataToSend.HoursPerRunAverageAfter = Math.Round(hoursWeek_after.Average(), 2);
}
if (reginTime_after != null && reginTime_after.Count > 0)
{
dataToSend.RegenTimeAverageAfter = Math.Round(reginTime_after.Average(), 2);
}
if (tpWeek_after != null && tpWeek_after.Count > 0)
{
dataToSend.ThroughputAverageAfter = Math.Round(tpWeek_after.Average(), 0);
}
}
//// Clear previous data used in ALL TRAINS calculation
//if (selectedTrainId != 0)
//{
// tp = new ThroughputBuilder();
// throughputCleanPrediction = new Dictionary<DateTime, Tuple<int, double, string>>();
// throughputNoCleanPrediction = new Dictionary<DateTime, Tuple<int, double, string>>();
// beforeConditions = new List<Tuple<double, double, double, double, double, double>>();
// afterConditions = new List<Tuple<double, double, double, double, double, double>>();
// regweekToSendClean = new List<Tuple<int, double>>();
// regweekToSendNormal = new List<Tuple<int, double>>();
// throughputCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, trainAnionResinAmount, rtiCleaningeffectivness, selectedTrainGPM, resinAge, startingSaltSplit, numberOfWeeks, true, donotReplaceResin, regenTimes.Average(), selectedTrainId, trains.Count);
// afterConditions = tp.AfterSystemConditions;
// throughputNoCleanPrediction = tp.ThroughputBuild(replacementLevel, rtiCleaningLevel, currentSaltSplit, grainForeCast, trainAnionResinAmount, rtiCleaningeffectivness, selectedTrainGPM, resinAge, startingSaltSplit, numberOfWeeks, false, donotReplaceResin, regenTimes.Average(), selectedTrainId, trains.Count);
// beforeConditions = tp.BeforeSystemConditions;
// weeks.Clear();
// reginWeek.Clear();
// hoursWeek.Clear();
// reginTime.Clear();
// tpWeek.Clear();
// ssWeek.Clear();
// beforeSaltSplit.Clear();
// afterSaltSplit.Clear();
// foreach (var week in beforeConditions)
// {
// weekNumber++;
// weeks.Add(weekNumber);
// reginWeek.Add(week.Item2);
// hoursWeek.Add(week.Item3);
// reginTime.Add(week.Item4);
// tpWeek.Add(week.Item5);
// ssWeek.Add(week.Item6);
// beforeSaltSplit.Add(week.Item6);
// Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, week.Item2);
// regweekToSendNormal.Add(reginsPerWeeK);
// }
// weekNumber = 0;
// foreach (var week in afterConditions)
// {
// weekNumber++;
// Tuple<int, double> reginsPerWeeK = new Tuple<int, double>(weekNumber, week.Item2);
// afterSaltSplit.Add(week.Item6);
// regweekToSendClean.Add(reginsPerWeeK);
// }
//}
Dictionary<int, Tuple<double?, double?>> saltSplitData = new Dictionary<int, Tuple<double?, double?>>();
int length;
if (beforeSaltSplit.Count < afterSaltSplit.Count)
{
length = afterSaltSplit.Count;
}
else
{
length = beforeSaltSplit.Count;
}
for (int i = 0; i < length; i++)
{
int aftercount = afterSaltSplit.Count;
int beforecount = beforeSaltSplit.Count;
double? before = 0;
double? after = 0;
int week = 0;
bool shouldAdd = true;
if (beforecount > aftercount)
{
if (i < aftercount)
{
before = beforeSaltSplit.ElementAt(i);
after = afterSaltSplit.ElementAt(i);
week = i + 1;
}
else
{
shouldAdd = false;
}
}
else if (beforecount < aftercount)
{
if (i < beforecount)
{
before = beforeSaltSplit.ElementAt(i);
after = afterSaltSplit.ElementAt(i);
week = i + 1;
}
else
{
shouldAdd = false;
}
}
else if (beforecount == aftercount)
{
before = beforeSaltSplit.ElementAt(i);
after = afterSaltSplit.ElementAt(i);
week = i + 1;
}
Tuple<double?, double?> temp = new Tuple<double?, double?>(before, after);
if (shouldAdd)
{
saltSplitData.Add(week + ((throughputCleanPrediction.Values.ElementAt(0).Item1) - 1), temp);
}
}
dataToSend.RegensPerWeekNormalOps = regweekToSendNormal;
dataToSend.RegensPerWeekClean = regweekToSendClean;
dataToSend.CleanThroughput = throughputCleanPrediction;
dataToSend.NormalOpsThroughput = throughputNoCleanPrediction;
dataToSend.SaltSplit = saltSplitData;
dataToSend.NumberWeeks = numberOfWeeks;
return dataToSend;
}
catch
{
throw;
}
}