public void CalculateNonZero_Average_Returns_Average()
{
var samples = new WorkoutSamples(0);
var vector = new WorkoutSampleVector(3, WorkoutSampleDataType.Power);
vector.AddPoint(1, 2);
vector.AddPoint(2, 4);
vector.AddPoint(3, 0);
IAthlete athlete = new Athlete();
var workoutCalculator = new WorkoutSamplesCalculator(samples,athlete);
var average = workoutCalculator.CalculateNonZeroVectorAverage(vector);
Assert.Equal(3, average);
}
public void CalculateNormalizedPower_With_Under_30_Samples_Returns_Average()
{
//Initialise();
var samples = new WorkoutSamples(0);
var vector = new WorkoutSampleVector(2, WorkoutSampleDataType.Power);
vector.AddPoint(1, 2);
vector.AddPoint(2, 4);
IAthlete athlete = new Athlete();
var workoutCalculator = new WorkoutSamplesCalculator(samples, athlete);
var average = workoutCalculator.CalculateVectorNormalizedAverage(vector);
Assert.Equal(3, average);
}
public void CalculatCadenceClassificatoion_Under_2_Samples_returns_()
{
var samples = new WorkoutSamples(0);
var vector = new WorkoutSampleVector(1, WorkoutSampleDataType.Cadence);
vector.AddPoint(1, 2);
samples.CadenceVector = vector;
IAthlete athlete = new Athlete();
var workoutCalculator = new WorkoutSamplesCalculator(samples, athlete);
var classification = workoutCalculator.ClassifyWorkoutCadenceRanges();
Assert.IsType<List<ICadenceRange>>(classification);
}