Ejemplo n.º 1
0
        /// <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;
            }
        }
Ejemplo n.º 2
0
        /// <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;
            }
        }