public List <XYZ> GetAccelerationXYZFromCSV()
{
List <XYZ> listToReturn = new List <XYZ>();
AllDataEntries.ToList().ForEach(x => listToReturn.Add(
new XYZ(x.INS_Accelerometer.X, x.INS_Accelerometer.Y, x.INS_Accelerometer.Z, x.INS_Accelerometer.TimeOfData)
));
return(listToReturn);
}
public ConcurrentQueue <Tuple <DataEntry, double, double> > CalculateVariance(int periodLength = 50)
{
List <DataEntry> data = AllDataEntries.ToList();
ConcurrentQueue <Tuple <DataEntry, double, double> > output = new ConcurrentQueue <Tuple <DataEntry, double, double> >();
List <DataEntry> batch = new List <DataEntry>();
double tendencySlope;
double tendencyOffset;
double residualSS;
int periodCount = data.Count() - periodLength;
for (int i = 0; i < periodCount; i++)
{
batch = data.GetRange(i, periodLength);
tendencySlope = CalculateTendencySlope(batch);
tendencySlope = Math.Pow(tendencySlope, 3);
tendencyOffset = CalculateTendensyOffset(batch, tendencySlope);
residualSS = CalculateResidualSumOfSquares(batch, tendencySlope, tendencyOffset);
output.Enqueue(new Tuple <DataEntry, double, double>(data[i + periodLength / 2], residualSS, tendencySlope));
}
return(output);
/*List<DataEntry> data = AllDataEntries.ToList();
* ConcurrentQueue<Tuple<DataEntry, double>> output = new ConcurrentQueue<Tuple<DataEntry, double>>();
* double maxEntry;
* double minEntry;
* double variance;
* double squareVariance;
*
* int periodCount = data.Count() / periodLength;
* for (int i = 0; i < periodCount; i++)
* {
* maxEntry = data.GetRange(i * periodLength, periodLength).Max(x => x.INS_Accelerometer.X);
* minEntry = data.GetRange(i * periodLength, periodLength).Min(x => x.INS_Accelerometer.X);
* variance = maxEntry - minEntry;
* squareVariance = Math.Pow(variance, 2);
*
* for (int j = i * periodLength; j < (i + 1) * periodLength; j++)
* {
* output.Enqueue(new Tuple<DataEntry, double>(data[j], variance));
* }
* }
* return output;*/
}
//Calculates variance, velocity, slope and slope difference
public ConcurrentQueue <Tuple <DataEntry, double, double, double, double> > CalculateAcceleration(int batchSize = 200)
{
List <DataEntry> data = AllDataEntries.ToList();
ConcurrentQueue <Tuple <DataEntry, double, double, double, double> > output = new ConcurrentQueue <Tuple <DataEntry, double, double, double, double> >();
List <DataEntry> accelerationList = new List <DataEntry>();
accelerationList.Add(new DataEntry(new XYZ(0.0, 0.0, 0.0), new XYZ(0.0, 0.0, 0.0), new XYZ(0.0, 0.0, 0.0), 0.0));
foreach (DataEntry entry in data)
{
double value = (entry.INS_Accelerometer.X / 1000.0) * _gravitationalConst;
double time = entry.INS_Accelerometer.TimeOfData / 1000.0;
DataEntry pos = new DataEntry(new XYZ(0.0, 0.0, 0.0), new XYZ(value, 0.0, 0.0, time), new XYZ(0.0, 0.0, 0.0), 0);
accelerationList.Add(pos);
}
List <DataEntry> velocityList = CalculateVelocity(accelerationList);
for (int i = batchSize; i < velocityList.Count - batchSize; i++)
{
List <DataEntry> firstBatchList = velocityList.GetRange(i - batchSize, batchSize).ToList();
List <DataEntry> secondBatchList = velocityList.GetRange(i - 1, batchSize).ToList();
double thresFirst = CalculateTendencySlope(firstBatchList);
double thresSecond = CalculateTendencySlope(secondBatchList);
var batch = data.GetRange(i - batchSize / 2, batchSize).ToList();
double tendencySlope = CalculateTendencySlope(batch);
double tendencyOffset = CalculateTendensyOffset(batch, tendencySlope);
double residualSS = CalculateResidualSumOfSquares(batch, tendencySlope, tendencyOffset);
output.Enqueue(new Tuple <DataEntry, double, double, double, double>(data[i], velocityList[i].INS_Accelerometer.X, tendencySlope, tendencySlope / (Math.Pow(residualSS, 2)), Math.Abs(thresFirst - thresSecond)));
}
return(output);
}
