internal static SortedDictionary<UtcDateTime, double?> CalculateMarketResolutionAverage(UtcDateTime firstTimePoint, UtcDateTime first, UtcDateTime last, TimeSpan marketResolution, TimeSpan dataResolution, SortedDictionary<UtcDateTime, double?> prod, bool isForecast=false)
{
int steps;
TimeResolutionType timeResolutionType = dataResolution.ToTimeResolution(out steps);
// Create TimePoints that we are interested to aggregate data on:
var time = last;//.Subtract(marketResolution);
var timePoints = new List<UtcDateTime> { last };
do
{
time = time.Subtract(marketResolution);
if (time >= first) timePoints.Add(time);
} while (time >= first);
var resolutionSamples = new SortedDictionary<UtcDateTime, List<double?>>();
for (int index = 0; index < timePoints.Count; index++)
{
if (index + 1 < timePoints.Count)
{
UtcDateTime point = timePoints[index]; // this should be end time e.g 12:00
UtcDateTime prevPoint = timePoints[index + 1].Add(dataResolution); // this should be time resolution before, e.g. an hour, thus 11:00, but maybe adding data resolution e.g. 10.
var enumer = new UtcDateTimeEnumerator(prevPoint, point, timeResolutionType, steps);
foreach (UtcDateTime utcDateTime in enumer)
{
if (prod.ContainsKey(utcDateTime))
{
double? production = prod[utcDateTime];
if (!resolutionSamples.ContainsKey(point)) resolutionSamples.Add(point, new List<double?>());
resolutionSamples[point].Add(production);
}
}
}
else // This is for the last point...
{
UtcDateTime point = timePoints[index]; // this should be end time e.g 12:00
UtcDateTime prevPoint = timePoints[index].Subtract(marketResolution).Add(dataResolution); // this should be time resolution before, e.g. an hour, thus 11:00, but maybe adding data resolution e.g. 10.
var enumer = new UtcDateTimeEnumerator(prevPoint, point, timeResolutionType, steps);
foreach (UtcDateTime utcDateTime in enumer)
{
if (prod.ContainsKey(utcDateTime))
{
double? production = prod[utcDateTime];
if (!resolutionSamples.ContainsKey(point)) resolutionSamples.Add(point, new List<double?>());
resolutionSamples[point].Add(production);
}
}
}
}
int productionStepsInResolution = CalculateProductionStepsInResolution(marketResolution, dataResolution);
var avgProds = new SortedDictionary<UtcDateTime, double?>();
foreach (UtcDateTime dateTime in resolutionSamples.Keys)
{
var prodInResolution = resolutionSamples[dateTime];
// When calculating observed production, we always calculate the average as sum devided by time steps in period (resolution)
double? avgPower;
if (!isForecast) avgPower = prodInResolution.Sum()/productionStepsInResolution;
else
{ // When calculating predicted production, we always calculate the pure average
double sum = 0;
int nrOfObsInPeriod = 0;
foreach (double? p in prodInResolution)
{
if (p.HasValue)
{
nrOfObsInPeriod++;
sum += p.Value;
}
}
if (nrOfObsInPeriod > 0) avgPower = sum/nrOfObsInPeriod;
else avgPower = null;
}
avgProds.Add(dateTime, avgPower);
}
return avgProds;
}
