/// <summary>
/// Estimates the Hurst Exponent, considering the specified hypothetical next value of the already stored time series.
/// </summary>
/// <remarks>
/// Operation does not change the instance data.
/// </remarks>
/// <returns>The resulting linear fit object.</returns>
public LinearFit ComputeNext(double simValue)
{
//Affect the simulated next value into the existing averages
double[] avgValues = new double[_avgCollection.Count];
Parallel.For(0, _subIntervalLengthCollection.Count, _subIntervalIdx =>
{
int intervalLength = _subIntervalLengthCollection[_subIntervalIdx];
RescaledRange intervalRescaledRange = new RescaledRange(intervalLength);
for (int valueIdx = (_valueCollection.Count - intervalLength) + 1; valueIdx < _valueCollection.Count; valueIdx++)
{
intervalRescaledRange.AddValue(_valueCollection[valueIdx]);
}
intervalRescaledRange.AddValue(simValue);
avgValues[_subIntervalIdx] = _avgCollection[_subIntervalIdx].SimulateNext(intervalRescaledRange.Compute());
});
//Add updated existing points
LinearFit linFit = new LinearFit();
for (int i = 0; i < _avgCollection.Count; i++)
{
double x = Math.Log(_subIntervalLengthCollection[i]);
double avg = avgValues[i];
double y = 0;
if (avg != 0)
{
y = Math.Log(avg);
}
linFit.AddSamplePoint(x, y);
}
//Return
return(linFit);
}
public SensitivityAnalysis(GrowthCurve GD, MatrixArrayPlot.ArrayPlot AP)
{
this.GD = GD;
if (GD.ExpModelFitted && GD.ODValues.Max() > .08)
{
double current = GD.GrowthRate.GrowthRate;
double MaxAllowed = 1.2 * current;
double MinAllowed = .8 * current;
double BaseGrowthRate = GD.ExpFit.GrowthRate;
Measurements = (GD.TimeValues_As_Double.Zip(GD.ODValues, (time, od) => new Measurement()
{
ODValue = od, Time = time
})).ToList();
TrimCurve();
double[,] Array = new double[Measurements.Count, Measurements.Count];
double lastValue = Double.NaN;
for (int i = 0; i < Measurements.Count; i++)
{
Array[i, i] = Double.NaN;
for (int j = (i + 1); j < Measurements.Count; j++)
{
double value = lastValue;
var Fit = from b in Enumerable.Range(i, (j - i + 1)) select Measurements[b];
double[] times = (from b in Fit select b.Time).ToArray();
double[] ods = (from b in Fit select b.ODValue).ToArray();
int count = times.Length;
value = Double.NaN;
double[] LogValues = ods.Select(z => Math.Log(z)).ToArray();
if (count > 2)
{
ExponentialFit EF = new ExponentialFit(times, ods);
//LinearFit EF = new LinearFit(times, LogValues);
value = EF.GrowthRate;
AllFitValues.Add(value);
}
if (count == 2)
{
AllFitValues.Add(value);
LinearFit LF = new LinearFit(times, LogValues);
value = LF.Slope;
}
if (value <MinAllowed | value> MaxAllowed)
{
value = Double.NaN;
}
Array[i, j] = value;
Array[j, i] = value;
lastValue = value;
}
}
string[] rowNames = (from x in Measurements select x.ODValue.ToString("g2")).ToArray();
string[] colNames = Enumerable.Range(0, rowNames.Length).Select(x => x % 2 == 0?rowNames[x]:"").ToArray();
AP.SetMatrixForPlotting(Array, rowNames, colNames);
}
else
{
//throw new Exception("Your maximum OD is below 0.2 or your curve has not been fit. Obtain higher quality data before fitting");
}
}
protected internal override double CalculateGrowthRate(double[] times, double[] ods)
{
double value = Double.NaN;
int count = times.Length;
if (count >= 2)
{
double[] LogValues = ods.Select(z => Math.Log(z)).ToArray();
LinearFit LF = new LinearFit(times, LogValues);
value = LF.Slope;
}
return(value);
}
/// <summary>
/// Estimates the Hurst Exponent.
/// </summary>
/// <returns>The resulting linear fit object</returns>
public LinearFit Compute()
{
LinearFit linFit = new LinearFit();
for (int i = 0; i < _avgCollection.Count; i++)
{
double x = Math.Log(_subIntervalLengthCollection[i]);
double avg = _avgCollection[i].Result;
double y = 0;
if (avg != 0)
{
y = Math.Log(avg);
}
linFit.AddSamplePoint(x, y);
}
return(linFit);
}
/// <summary>
/// Computes Hurst exponent estimation for next hypothetical value in time series.
/// Function does not change the instance, it is a simulation only.
/// </summary>
/// <param name="simValue">Next time series value</param>
public double ComputeNext(double simValue)
{
//Affect new value to existing averages
double[] avgValues = new double[_avgCollection.Count];
Parallel.For(0, _subIntervalLengthCollection.Count, _subIntervalIdx =>
{
int intervalLength = _subIntervalLengthCollection[_subIntervalIdx];
RescalledRange intervalRescalledRange = new RescalledRange(intervalLength);
for (int valueIdx = (_valueCollection.Count - intervalLength) + 1; valueIdx < _valueCollection.Count; valueIdx++)
{
intervalRescalledRange.AddValue(_valueCollection[valueIdx]);
}
intervalRescalledRange.AddValue(simValue);
avgValues[_subIntervalIdx] = _avgCollection[_subIntervalIdx].SimulateNext(intervalRescalledRange.Compute());
});
//Add updated existing points
LinearFit linFit = new LinearFit();
for (int i = 0; i < _avgCollection.Count; i++)
{
double x = Math.Log(_subIntervalLengthCollection[i]);
double avg = avgValues[i];
double y = 0;
if (avg != 0)
{
y = Math.Log(avg);
}
linFit.AddSamplePoint(x, y);
}
//Add new point for sub-interval length = (_timeSeries.Length + 1)
RescalledRange fullRescalledRange = new RescalledRange(_valueCollection.Count + 1);
foreach (double value in _valueCollection)
{
fullRescalledRange.AddValue(value);
}
fullRescalledRange.AddValue(simValue);
double fullRangeAvg = fullRescalledRange.Compute();
linFit.AddSamplePoint(Math.Log(_valueCollection.Count + 1), fullRangeAvg == 0 ? 0 : Math.Log(fullRangeAvg));
//Return
return(linFit.A);
}
protected internal virtual double CalculateGrowthRate(double[] times, double[] ods)
{
double value = Double.NaN;
int count = times.Length;
if (count > 2)
{
ExponentialFit EF = new ExponentialFit(times, ods);
//LinearFit EF = new LinearFit(times, LogValues);
value = EF.GrowthRate;
}
if (count == 2)
{
double[] LogValues = ods.Select(z => Math.Log(z)).ToArray();
LinearFit LF = new LinearFit(times, LogValues);
value = LF.Slope;
}
return(value);
}
//获取某元素波长的方程，前置约束为调用者已经完成所有所有标样的读取
//函数返回方程的同时，将返回方程进行计算时用到的标样点
public static LinearFit.LfReValue get_equation(spec_metadata spec_obj, int element_index, ref double[] concentration, ref double[] standards_integration_average_strenth, int bs)
{
double[] wave_all = spec_obj.read_wave_all;
double[] env = spec_obj.env_spec;
standard[] standards = spec_obj.standards;
int standard_cnt = spec_obj.standard_cnt;
select_element element = spec_obj.elements[element_index];
double[,,] spec_standard = spec_obj.read_standard_spec;
//提取该元素列标样数据，得到方程
standards_integration_average_strenth = new double[standard_cnt];
//标样平均的积分平均强度
for (int i = 0; i < standard_cnt; i++) //每个标样
{
double standard_element_interval_average = 0.0; //平均的平均
//如果点击的为标样，在计算方程的循环中直接获取多次积分平均值
double[] strenght_oneshot = get_oneshot_all_strength(spec_obj, i, element_index);
for (int t = 0; t < standards[i].average_times; t++)
{
standard_element_interval_average += strenght_oneshot[t];
}
standards_integration_average_strenth[i] = standard_element_interval_average / standards[i].average_times;
}
concentration = new double[standard_cnt]; //浓度
for (int j = 0; j < standard_cnt; j++)
{
concentration[j] = standards[j].standard_ppm[element.sequece_index - 1];
}
LinearFit.LfReValue equation;
//过空白或不过空白读取
if (bs == 0)
{
equation = LinearFit.linearFitFunc(concentration, standards_integration_average_strenth, standard_cnt);
}
else
{
equation = LinearFit.linearFitFunc_blank(concentration, standards_integration_average_strenth, standard_cnt);
}
return(equation);
}
//根据实际测量数据spec_standard和spec_sample，选择的各元素，标样，样本，计算出将要显示的表格数据
//返回值standard_val和sample_val分别为计算后表格要显示的标样/样本的浓度或强度数据
private static void fill_datagrid_data(spec_metadata spec_obj, ref double[,] standard_val, ref double[,] sample_val, int bs)
{
select_element[] elements = spec_obj.elements;
standard[] standards = spec_obj.standards;
sample[] samples = spec_obj.samples;
double[] wave_all = spec_obj.read_wave_all;
double[] env = spec_obj.env_spec;
double[,,] spec_standard = spec_obj.read_standard_spec;
double[,,] spec_sample = spec_obj.read_sample_spec;
int standard_cnt = spec_obj.standard_cnt;
int sample_cnt = spec_obj.sample_cnt;
int element_cnt = spec_obj.element_cnt;
int pixel_cnt = spec_obj.read_wave_all.Length;
double[,] standards_integration_average_strenth = new double[standard_cnt, element_cnt];
double[,] samples_integration_average_strenth = new double[sample_cnt, element_cnt];
//标样平均的积分平均强度
for (int i = 0; i < standard_cnt; i++) //每个标样
{
if (!standards[i].is_readed)
{
continue;
}
for (int t = 0; t < standards[i].average_times; t++)
{
double[] spec_temp_all = new double[pixel_cnt];
for (int k = 0; k < pixel_cnt; k++)
{
spec_temp_all[k] = spec_standard[i, t, k];
}
for (int j = 0; j < element_cnt; j++) //每一个元素
{
standards_integration_average_strenth[i, j] += data_util.get_integration_average(wave_all, spec_temp_all, env, elements[j].interval_start, elements[j].interval_end);
}
}
for (int j = 0; j < element_cnt; j++)
{
standards_integration_average_strenth[i, j] /= standards[i].average_times;
}
}
//样本的平均积分平均强度
for (int i = 0; i < sample_cnt; i++) //每个标样
{
if (!samples[i].is_read)
{
continue;
}
for (int t = 0; t < samples[i].average_times; t++)
{
double[] spec_temp_all = new double[pixel_cnt];
for (int k = 0; k < pixel_cnt; k++)
{
spec_temp_all[k] = spec_sample[i, t, k];
}
for (int j = 0; j < element_cnt; j++) //每一个元素
{
samples_integration_average_strenth[i, j] += data_util.get_integration_average(wave_all, spec_temp_all, env, elements[j].interval_start, elements[j].interval_end);
}
}
for (int j = 0; j < element_cnt; j++)
{
samples_integration_average_strenth[i, j] /= samples[i].average_times;
}
}
//显示强度,直接提取spec数组数据,根据元素(波长)积分区间来得到计算值
if (datagrid_control.show_strenth)
{
standard_val = standards_integration_average_strenth;
sample_val = samples_integration_average_strenth;
}
// 当已经读取所有标样，则计算线性拟合方程，计算样本浓度
else
{
//所有标样都已经读取完，才需要计算样本浓度
bool is_need_calculate = true;
for (int i = 0; i < standard_cnt; i++)
{
if (!standards[i].is_readed)
{
is_need_calculate = false; //标样没有全部读取完，则不需要计算方程
}
for (int j = 0; j < element_cnt; j++)
{
standard_val[i, j] = standards[i].standard_ppm[j];
}
}
//样本需要根据线性方程进行计算
if (is_need_calculate)
{
// 根据标样浓度和对应强度计算
for (int i = 0; i < element_cnt; i++)
{
double[] concentration = new double[standard_cnt]; //浓度
double[] strenth = new double[standard_cnt];
for (int j = 0; j < standard_cnt; j++)
{
concentration[j] = standards[j].standard_ppm[i];
strenth[j] = standards_integration_average_strenth[j, i];
}
LinearFit.LfReValue equation;
if (bs == 0)
{
equation = LinearFit.linearFitFunc(concentration, strenth, standard_cnt);
}
else
{
equation = LinearFit.linearFitFunc_blank(concentration, strenth, standard_cnt);
}
//根据方程进行样本浓度推算
for (int j = 0; j < sample_cnt; j++)
{
if (!samples[j].is_read)
{
continue;
}
sample_val[j, i] = (samples_integration_average_strenth[j, i] - equation.getA()) / equation.getB();
}
}
}
else //不需要计算时设置为0
{
for (int i = 0; i < sample_cnt; i++)
{
for (int j = 0; j < element_cnt; j++)
{
sample_val[i, j] = 0;
}
}
}
}
}
