public WorkoutSamples ExtractData()
{
if (numSamples <= 0)
{
return new WorkoutSamples(0);
}
var woSamples = new WorkoutSamples(numSamples);
foreach (var sample in _file.workout[0].sample)
{
if (sample.pwrSpecified)
woSamples.AddPoint(sample.timeoffset, sample.pwr, WorkoutSampleDataType.Power);
else
woSamples.AddPoint(sample.timeoffset, -1, WorkoutSampleDataType.Power);
if (sample.cadSpecified)
woSamples.AddPoint(sample.timeoffset, sample.cad, WorkoutSampleDataType.Cadence);
else
woSamples.AddPoint(sample.timeoffset, -1, WorkoutSampleDataType.Cadence);
if(sample.hrSpecified)
woSamples.AddPoint(sample.timeoffset, sample.hr, WorkoutSampleDataType.HeartRate);
else
woSamples.AddPoint(sample.timeoffset, -1, WorkoutSampleDataType.HeartRate);
if(sample.spdSpecified)
woSamples.AddPoint(sample.timeoffset, sample.hr, WorkoutSampleDataType.Speed);
else
woSamples.AddPoint(sample.timeoffset, -1, WorkoutSampleDataType.Speed);
}
woSamples.SummaryTrainingStressScore = ExtractSummaryTSS();
woSamples.SummaryDuration = ExtractSummaryDuration();
woSamples.SummaryWork = ExtractSummaryWork();
return woSamples;
}
public void CalculateNormalizedPower_With_No_Samples_Returns_Zero()
{
var samples = new WorkoutSamples(0);
IAthlete athlete = new Athlete();
var workoutCalculator = new WorkoutSamplesCalculator(samples,athlete);
var average = workoutCalculator.GetNormalizedPower();
Assert.Equal(0, average);
}
public void CalculateNonZero_Average_With_No_Data_Returns_Zero()
{
var samples = new WorkoutSamples(0);
var vector = new WorkoutSampleVector(3, WorkoutSampleDataType.Power);
IAthlete athlete = new Athlete();
var workoutCalculator = new WorkoutSamplesCalculator(samples, athlete);
var average = workoutCalculator.CalculateNonZeroVectorAverage(vector);
Assert.Equal(0, 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);
}
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 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 CalculateVectorAverage_With_No_Samples_Returns_Zero()
{
var samples = new WorkoutSamples(0);
IAthlete athlete = new Athlete();
var workoutCalculator = new WorkoutSamplesCalculator(samples, athlete);
var average = workoutCalculator.GetAverageCadence();
Assert.Equal(0, average);
}
public WorkoutSamplesCalculator(WorkoutSamples woSamples, IAthlete athlete)
{
_workoutSamples = woSamples;
_athlete = athlete;
}