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git-svn-id: file:///afs/psi.ch/project/sls_det_software/svn/slsDetectorCalibration@13 113b152e-814d-439b-b186-022a431db7b5
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bergamaschi
2013-12-12 16:05:24 +00:00
parent 1ae2d78bf5
commit 8d09f061da
9 changed files with 599 additions and 258 deletions
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97
slsDetectorCalibration/MovingStat.h
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@ -6,43 +6,60 @@
class MovingStat
{
/** @short approximated moving average structure */
public:
/** constructor
\param nn number of samples parameter to be used
*/
MovingStat(int nn=1000) : n(nn), m_n(0) {}
/**
clears the moving average number of samples parameter, mean and standard deviation
*/
void Clear()
{
m_n = 0;
m_oldM=0;
m_newM=0;
m_oldM2=0;
m_newM2=0;
}
void SetN(int i) {n=i;};
/** sets number of samples parameter
\param i number of samples parameter to be set
*/
void SetN(int i) {if (i>=1) n=i;};
/**
gets number of samples parameter
\returns actual number of samples parameter
*/
int GetN() {return n;};
/** calculates the moving average i.e. adds if number of elements is lower than number of samples parameter, pushes otherwise
\param x value to calculate the moving average
*/
inline void Calc(double x) {
if (m_n<n) Add(x);
else Push(x);
}
/** adds the element to the accumulated average and standard deviation
\param x value to add
*/
inline void Add(double x) {
m_n++;
// See Knuth TAOCP vol 2, 3rd edition, page 232
if (m_n == 1)
{
m_oldM = m_newM = x;
m_oldM2 = x*x;
}
else
{
m_newM = m_oldM + x;
m_newM2 = m_oldM2 + x*x;
//m_newM2 = m_oldM2 + (x*x - m_oldM*m_oldM)/m_n;
// set up for next iteration
m_oldM = m_newM;
m_oldM2 = m_newM2;
m_newM = x;
m_newM2 = x*x;
} else {
m_newM = m_newM + x;
m_newM2 = m_newM2 + x*x;
}
}
@ -50,50 +67,64 @@ class MovingStat
inline void Push(double x)
{
if (m_n == 1)
/** adds the element to the accumulated average and squared mean, while subtracting the current value of the average and squared average
\param x value to push
*/
if (m_n == 0)
{
m_oldM = m_newM = x;
m_oldM2 = x*x;
}
else
{
m_newM = m_oldM + (x - m_oldM/m_n);
m_newM2 = m_oldM2 + (x*x - m_oldM2/m_n);
//m_newM2 = m_oldM2 + (x*x/m_n - m_oldM*m_oldM/(m_n*m_n));
// set up for next iteration
m_oldM = m_newM;
m_oldM2 = m_newM2;
m_newM = x;
m_newM2 = x*x;
m_n++;
} else {
m_newM = m_newM + x - m_newM/m_n;
m_newM2 = m_newM2 + x*x - m_newM2/m_n;
}
}
/** returns the current number of elements of the moving average
\returns returns the current number of elements of the moving average
*/
int NumDataValues() const
{
return m_n;
}
/** returns the mean, 0 if no elements are inside
\returns returns the mean
*/
inline double Mean() const
{
return (m_n > 0) ? m_newM/m_n : 0.0;
}
/** returns the squared mean, 0 if no elements are inside
\returns returns the squared average
*/
double M2() const
{
return ( (m_n > 1) ? m_newM2/m_n : 0.0 );
}
/** returns the variance, 0 if no elements are inside
\returns returns the variance
*/
inline double Variance() const
{
return ( (m_n > 1) ? (M2()-Mean()*Mean()) : 0.0 );
}
/** returns the standard deviation, 0 if no elements are inside
\returns returns the standard deviation
*/
inline double StandardDeviation() const
{
return ( (Variance() > 0) ? sqrt( Variance() ) : -1 );
}
private:
int n;
int m_n;
double m_oldM, m_newM, m_oldM2, m_newM2;
int n; /**< number of samples parameter */
int m_n; /**< current number of elements */
double m_newM; /**< accumulated average */
double m_newM2; /**< accumulated squared average */
};
#endif