/// <summary>
/// 使用光谱校正系数，校正光谱，包含插值（包含X轴数据）
/// </summary>
/// <param name="coefData">光谱校正系数(BYTE)</param>
/// <param name="slaveData">光谱数据</param>
/// <returns>校正后的光谱Y轴数据</returns>
public static List <double[]> ApplyCoefficientWithXData(byte[] coefData, IList <double[]> slaveData)
{
try
{
if (coefData == null || slaveData == null || slaveData.Count == 1)
{
throw new Exception("Invalid parameters");
}
//判断是否需要插值
var para = GetStandardizingCoefParameter(coefData);
if (para.datasize == 0)
{
throw new Exception("Invalid parameters");
}
//插值
if (!CommonMethod.IsSameXDatas(slaveData[0], para.firstX, para.lastX, para.specCols))
{
slaveData = PreProcessor.SplineCubicInterpolation(slaveData, para.firstX, para.lastX, para.specCols);
if (slaveData == null)
{
throw new Exception(PreProcessor.ErrorString);
}
}
//保留xData
var xData = slaveData[0];
//标准化时不需要xData
slaveData.RemoveAt(0);
var retSpecs = ApplyCoefficient(coefData, slaveData);
if (retSpecs != null && retSpecs.Count != slaveData.Count)
{
throw new Exception(ErrorString);
}
//恢复xData
retSpecs.Insert(0, xData);
return(retSpecs);
}
catch (Exception ex)
{
ErrorString = ex.Message;
return(null);
}
}
/// <summary>
/// AdvancedIdentify定性分析(光谱的数据点数量必须相同, X轴也必须相同)
/// </summary>
/// <param name="apiList">API原始光谱插值后，一列X轴，一列Y轴</param>
/// <param name="inferList">干扰组分原光谱插值后，一列X轴，多列Y轴</param>
/// <param name="sampleList">待分析物原始光谱插值后，一列X轴，一列Y轴</param>
/// <param name="coefMethod">相关方法，0为标准，1为增强</param>
/// <param name="baseline">基线校正方法</param>
/// <param name="derivatives">导数平滑方法(有基线校正，忽略导数平滑)</param>
/// <param name="smoothPoints">平滑窗口大小(5-99)</param>
/// <param name="polyDegree">导数的阶数,2,3,4</param>
/// <param name="firstX">起始计算X轴，-1表示使用API光谱的firstX</param>
/// <param name="lastX">结束计算X轴，-1表示使用API光谱的lastX</param>
/// <returns></returns>
public static double Identify(List <double[]> apiList, List <double[]> inferList, List <double[]> sampleList, CoefficientMethod coefMethod,
BaseLineMethod baseline = BaseLineMethod.None, DerivativeMethod derivatives = DerivativeMethod.None,
int smoothPoints = 7, int polyDegree = 2, double firstX = double.MinValue, double lastX = double.MaxValue)
{
if (apiList.Count != 2 || inferList.Count < 2 || sampleList.Count != 2)
{
ErrorString = "Invalid parameters";
return(0);
}
if (smoothPoints > 99 || smoothPoints < 2 || polyDegree < 2 || polyDegree > 4)
{
ErrorString = "Invalid parameters";
return(0);
}
//需要截取一部分光谱区间
if (firstX != double.MinValue && lastX != double.MaxValue)
{
apiList = CommonMethod.GetRangeData(apiList, firstX, lastX);
inferList = CommonMethod.GetRangeData(inferList, firstX, lastX);
sampleList = CommonMethod.GetRangeData(sampleList, firstX, lastX);
}
firstX = apiList[0][0];
lastX = apiList[0][apiList[0].Length - 1];
int dataCount = apiList[0].Length;
//格式不相同，全部兼容到api光谱
if (!CommonMethod.IsSameXDatas(inferList[0], firstX, lastX, dataCount))
{
inferList = Ai.Hong.Algorithm.PreProcessor.SplineCubicInterpolation(inferList, firstX, lastX, dataCount);
}
if (!CommonMethod.IsSameXDatas(sampleList[0], firstX, lastX, dataCount))
{
sampleList = Ai.Hong.Algorithm.PreProcessor.SplineCubicInterpolation(sampleList, firstX, lastX, dataCount);
}
//限定2种计算系数的方法，1=无权重, 2=强度
coefMethod = coefMethod != CoefficientMethod.Normal ? CoefficientMethod.Weight : coefMethod;
//拷贝为数组，好在Ai.Hong.Algorithm中处理
var srcApiDatas = Ai.Hong.Algorithm.CommonMethod.CombineSpectrumDatas(apiList);
var apiDatas = Ai.Hong.Algorithm.CommonMethod.CombineSpectrumDatas(apiList);
var inferDatas = Ai.Hong.Algorithm.CommonMethod.CombineSpectrumDatas(inferList);
var sampleDatas = Ai.Hong.Algorithm.CommonMethod.CombineSpectrumDatas(sampleList);
if (apiDatas == null || inferDatas == null || sampleDatas == null)
{
ErrorString = CommonMethod.ErrorString;
return(0);
}
//需要基线校正
if (baseline != BaseLineMethod.None)
{
BaselineCorrect(apiDatas, apiList.Count, dataCount, baseline);
BaselineCorrect(inferDatas, inferList.Count, dataCount, baseline);
BaselineCorrect(sampleDatas, sampleList.Count, dataCount, baseline);
}
else if (derivatives != DerivativeMethod.None) //导数平滑
{
coefMethod = coefMethod == CoefficientMethod.Normal ? coefMethod : CoefficientMethod.SquareWeight; //纪南的程序里面，导数后，增强方法计算系数的方式不一样（使用强度平方）
PreProcessor.SGSmoothAndDerivative(apiDatas, apiList.Count, dataCount, smoothPoints, polyDegree, (int)derivatives);
PreProcessor.SGSmoothAndDerivative(inferDatas, inferList.Count, dataCount, smoothPoints, polyDegree, (int)derivatives);
PreProcessor.SGSmoothAndDerivative(sampleDatas, sampleList.Count, dataCount, smoothPoints, polyDegree, (int)derivatives);
//纪南的导数没有除以X轴步长
double stepx = apiDatas[1] - apiDatas[0];
for (int i = dataCount; i < apiDatas.Length; i++)
{
apiDatas[i] *= stepx;
}
stepx = inferDatas[1] - inferDatas[0];
for (int i = dataCount; i < inferDatas.Length; i++)
{
inferDatas[i] *= stepx;
}
stepx = sampleDatas[1] - sampleDatas[0];
for (int i = dataCount; i < sampleDatas.Length; i++)
{
sampleDatas[i] *= stepx;
}
}
return(Identify(srcApiDatas, apiDatas, inferDatas, inferList.Count, sampleDatas, coefMethod));
}