protected void Aggregate <Tseries, Tval>(TimeSeriesResampler <Tseries, Tval> resampler, IEnumerable <ITimeSeries <Tval> > series, Aggregator defaultFunc, Func <Tval, decimal> selector, Func <decimal, Tval> resultCast) where Tval : struct where Tseries : TimeSeriesBase <Tval>
{
switch (AggregatorFunction == Aggregator.Default ? defaultFunc : AggregatorFunction)
{
case Aggregator.Minimum:
{
resampler.SampleAggregate(series, x => resultCast(x.Min(selector) * CorrectionFactor + CorrectionOffset));
break;
}
case Aggregator.Maximum:
{
resampler.SampleAggregate(series, x => resultCast(x.Max(selector) * CorrectionFactor + CorrectionOffset));
break;
}
case Aggregator.Sum:
{
resampler.SampleAggregate(series, x => resultCast(x.Sum(selector) * CorrectionFactor + CorrectionOffset));
break;
}
case Aggregator.Average: goto default;
default:
{
resampler.SampleAggregate(series, x => resultCast(x.Average(selector) * CorrectionFactor + CorrectionOffset));
break;
}
}
}
public static void Aggregate <Tseries, Tval>(this TimeSeriesResampler <Tseries, Tval> resampler, IEnumerable <ITimeSeries <Tval> > series, Aggregator aggregator, Func <Tval, double> selector, Func <double, Tval> resultCast, double correctionFactor = 1d, double correctionOffset = 0d) where Tval : struct where Tseries : TimeSeriesBase <Tval>
{
Func <IEnumerable <double>, double> aggregationFunc = GetAggregationFunction(aggregator);
resampler.SampleAggregate(series, x => resultCast(aggregationFunc(x.Select(selector)) * correctionFactor + correctionOffset));
if (aggregator == Aggregator.AverageZero)
{
var resamplingRate = Math.Max(1d, series.First().Span.Duration / resampler.Resampled.Span.Duration);
resampler.SampleAggregate(series, x => resultCast(x.Sum(selector) / resamplingRate * correctionFactor + correctionOffset));
}
}
public void TestIntTimeSeriesOddResampling()
{
var timeseries = new TimeSeriesStream <int>(new TimeSeriesSpan(begin, end, 5));
timeseries[0] = 7;
timeseries[1] = 10;
timeseries[2] = 23;
timeseries[3] = 50;
timeseries[4] = 12;
// Resample with slightly later start and slightly earlier end
var resampler1 = new TimeSeriesResampler <TimeSeriesStream <int>, int>(
new TimeSeriesSpan(begin.AddSeconds(5), end.AddSeconds(-5), 5));
resampler1.SampleAggregate(timeseries, x => (int)x.Average());
Assert.Equal(new List <int>()
{
10, 23, -1, 50, 12
}, resampler1.Resampled.Trimmed(-1));
// Resample with slightly earlier start and slightly later end
var resampler2 = new TimeSeriesResampler <TimeSeriesStream <int>, int>(
new TimeSeriesSpan(begin.AddSeconds(-5), end.AddSeconds(5), 5));
resampler2.SampleAggregate(timeseries, x => (int)x.Average());
Assert.Equal(new List <int>()
{
7, 10, 36, 12
}, resampler2.Resampled.Trimmed(-1));
}
public void TestDoubleTimeSeriesSampleAverage()
{
var timeseries = new TimeSeriesStream <double>(span);
timeseries[0] = 0d;
timeseries[1] = 10d;
timeseries[2] = 0.0;
timeseries[3] = 0.5;
timeseries[4] = 0.01;
timeseries[5] = 0.02;
timeseries[6] = 50.7;
timeseries[7] = 60.7;
timeseries[8] = 50000;
timeseries[9] = 100000;
var resampler = new TimeSeriesResampler <TimeSeriesStream <double>, double>(spanDownsampling);
resampler.SampleAggregate(timeseries, x => x.Average());
// results get cropped to 1 decimal place and to a max of short.MaxValue / 10
Assert.Equal(new List <double>()
{
5d, 0.2, 0.0, 55.7, 3276.7
}, resampler.Resampled.Trimmed(-1d));
}
void ReadAndSaveSensor(Sensor sensor, TimeSeries <double> seriesToWriteInto, int index)
{
if (!zone.Value.ContainsKey(sensor))
{
return;
}
var resampler = new TimeSeriesResampler <TimeSeries <double>, double>(seriesToWriteInto.Span)
{
Resampled = seriesToWriteInto
};
resampler.SampleAggregate(zone.Value[sensor], x => x.Average());
dbSensorSeries.SetSeries(index, resampler.Resampled);
}
public void TestSumMultipleSeriesIntoOneBucket()
{
var timeseries = new List <TimeSeries <int> >();
for (int i = 0; i < 5; i++)
{
var series = new TimeSeries <int>(new TimeSeriesSpan(begin.AddSeconds(i), begin.AddSeconds(i + 1), count));
for (int j = 0; j < 10; j++)
{
series[j] = 1;
}
timeseries.Add(series);
}
var resampler = new TimeSeriesResampler <TimeSeries <int>, int>(span);
resampler.SampleAggregate(timeseries, x => x.Sum());
Assert.Equal(new List <int>()
{
50
}, resampler.Resampled.Trimmed(-1));
}
public void TestIntTimeSeriesMerging()
{
var timeseries = new List <TimeSeries <int> >();
for (int i = 0; i < 5; i++)
{
var series = new TimeSeries <int>(new TimeSeriesSpan(begin.AddMinutes(count * i), end.AddMinutes(count * i), count));
for (int j = 0; j < 10; j++)
{
series[j] = j * 10 + i;
}
timeseries.Add(series);
}
var resampler = new TimeSeriesResampler <TimeSeries <int>, int>(new TimeSeriesSpan(begin.AddMinutes(15), begin.AddMinutes(25), count));
resampler.SampleAggregate(timeseries, x => x.FirstOrDefault());
Assert.Equal(new List <int>()
{
51, 61, 71, 81, 91, 2, 12, 22, 32, 42
}, resampler.Resampled.Trimmed(-1));
}
public void TestBoolTimeSeriesResampling()
{
var timeseries = new TimeSeries <bool>(span);
timeseries[0] = true;
timeseries[1] = true;
timeseries[2] = null;
timeseries[3] = null;
timeseries[4] = true;
timeseries[5] = false;
timeseries[6] = null;
timeseries[7] = true;
timeseries[8] = null;
timeseries[9] = false;
var resampler = new TimeSeriesResampler <TimeSeries <bool>, bool>(spanDownsampling);
resampler.SampleAggregate(timeseries, x => x.Any(b => b));
Assert.Equal(new List <bool>()
{
true, false, true, true, false
}, resampler.Resampled.Trimmed(false));
}
public void TestIntTimeSeriesSampleAverage()
{
var timeseries = new TimeSeriesStream <int>(span);
timeseries[0] = 0;
timeseries[1] = 10;
timeseries[2] = 0;
timeseries[3] = 50;
timeseries[4] = 10;
timeseries[5] = 20;
timeseries[6] = 50;
timeseries[7] = 60;
timeseries[8] = 5;
timeseries[9] = 10;
var resampler = new TimeSeriesResampler <TimeSeriesStream <int>, int>(spanDownsampling);
resampler.SampleAggregate(timeseries, x => (int)x.Average());
Assert.Equal(new List <int>()
{
5, 25, 15, 55, 7
}, resampler.Resampled.Trimmed(-1));
}