add multiply add and constant score functions

basicmath.cpp

@@ -49,6 +49,12 @@ std::complex<fvalue> eis::absGrad(const std::vector<eis::DataPoint>& data, size_
 								std::abs((data[index+1].im.imag()-data[index-1].im.imag())/(data[index+1].omega-data[index-1].omega)));
 }
 
+void mulitplyAdd(std::vector<eis::DataPoint>& data, fvalue mult, fvalue add)
+{
+	for(eis::DataPoint& point : data)
+		point.im = point.im*mult + add;
+}
+
 fvalue eis::grad(const std::vector<fvalue>& data, const std::vector<fvalue>& omega, size_t index)
 {
 	assert(data.size() == omega.size());
@@ -197,6 +203,28 @@ fvalue eis::pearsonCorrelation(const std::vector<eis::DataPoint>& data)
 	return sumDeltaReDeltaIm/(sqrt(sumDeltaReSq)*sqrt(sumDeltaImSq));
 }
 
+
+fvalue eis::nonConstantScore(const std::vector<eis::DataPoint>& data)
+{
+	std::complex<fvalue> meanValue = mean(data);
+
+	fvalue reDeviationMax = std::numeric_limits<fvalue>::min();
+	fvalue imDeviationMax = std::numeric_limits<fvalue>::min();
+
+	for(const eis::DataPoint& point : data)
+	{
+		fvalue reDeviation = 1-(std::abs(point.im.real())/std::abs(meanValue.real()));
+		fvalue imDeviation = 1-(std::abs(point.im.imag())/std::abs(meanValue.imag()));
+
+		if(reDeviationMax < reDeviation)
+			reDeviationMax = reDeviation;
+		if(imDeviationMax < imDeviation)
+			imDeviationMax = imDeviation;
+	}
+
+	return std::min(reDeviationMax, imDeviationMax);
+}
+
 fvalue eis::nyquistAreaVariance(const std::vector<eis::DataPoint>& data, eis::DataPoint* centroid)
 {
 	assert(data.size() > 2);

eisgenerator/basicmath.h

@@ -30,6 +30,8 @@ namespace eis
 	* @{
 	*/
 
+	void mulitplyAdd(std::vector<eis::DataPoint>& data, fvalue mult, fvalue add);
+
 	/**
 	* @brief Calculates the element wise absolute gradient at the given point of the data given.
 	*
@@ -117,6 +119,8 @@ namespace eis
 	*/
 	fvalue pearsonCorrelation(const std::vector<eis::DataPoint>& data);
 
+	fvalue nonConstantScore(const std::vector<eis::DataPoint>& data);
+
 	/**
 	* @brief Calculates the variance of the distance of the data from a centroid in the nyquist plane.
 	*

model.cpp

@@ -576,6 +576,14 @@ std::vector<size_t> Model::getRecommendedParamIndices(eis::Range omegaRange, dou
 			continue;
 		}
 
+		fvalue nonConstantess = std::abs(pearsonCorrelation(data));
+		if(nonConstantess < 0.1)
+		{
+			eis::Log(eis::Log::DEBUG)<<"skipping output for step "<<i
+			<<" as data is too constant: "<<nonConstantess;
+			continue;
+		}
+
 		std::vector<std::vector<eis::DataPoint>>::iterator search;
 		if(threaded)
 		{