26 template<
class DataType_t>
83 virtual void Dump(
const char*,
int=0);
108 f_low=f>0. ? f : 0.;
return;
184 virtual void resize(
unsigned int);
185 virtual void resample(
double,
int=6);
259 virtual void median(
double t,
bool norm=
false);
267 virtual void lprFilter(
double,
int=0,
double=0.,
double=0.);
325 virtual double fraction(
double=0.,
double=0.,
int=0);
wavearray< double > t(hp.size())
void mul(WSeries< DataType_t > &)
virtual void resize(unsigned int)
wavearray< double > a(hp.size())
virtual double percentile(double=0., int=0, WSeries< DataType_t > *=NULL)
param: f - black pixel fraction param: m - mode options: f = 0 - returns black pixel occupancy m = 1 ...
virtual WSeries< float > variability(double=0., double=-1., double=-1.)
param: first - time window to calculate normalization constants second - low frequency boundary for c...
virtual WSeries< DataType_t > & operator+=(WSeries< DataType_t > &)
void bandpass(wavearray< DataType_t > &ts, double flow, double fhigh, int n=-1)
void putSample(DataType_t a, int n, double m)
std::vector< int > vector_int
virtual double rSignificance(double, double=1., double=0.)
param: T - sliding window duration in seconds param: f - black pixel fraction param: t - sliding step...
virtual std::slice getSlice() const
std::slice getSlice(double n)
virtual WSeries< DataType_t > & operator*=(WSeries< DataType_t > &)
virtual double significance(double, double=1.)
param: n - sub-interval duration in seconds param: f - black pixel fraction options: f = 0 - returns ...
void setWavelet(const Wavelet &w)
virtual double gSignificance(double, double=1., double=0.)
param: T - sliding window duration in seconds param: f - black pixel fraction param: t - sliding step...
virtual wavearray< double > filter(size_t)
param: n - number of decomposition steps algorithm: 1) do forward wavelet transform with n decomposit...
double maxEnergy(wavearray< DataType_t > &ts, Wavelet &w, double=0, int=1, int=0, TH1F *=NULL)
virtual WSeries< DataType_t > & operator-=(WSeries< DataType_t > &)
virtual double fraction(double=0., double=0., int=0)
param: t - sub interval duration. If can not divide on integer
int getLayer(wavearray< DataType_t > &w, double n)
param: n - layer number
virtual double coincidence(WSeries< DataType_t > &, int=0, int=0, double=0.)
param: WSeries object used for coincidence param: coincidence window in seconds return pixel occupanc...
virtual WSeries< DataType_t > & operator[](const std::slice &)
void wavescan(WSeries< DataType_t > **, int, TH1F *=NULL)
wavearray< double > ts(N)
virtual void Browse(TBrowser *b)
virtual void resample(double, int=6)
void print()
param: int n if n<0, zero pixels defined in mask (regression) if n>=0, zero all pixels except ones de...
double Gamma2Gauss(TH1F *=NULL)
virtual double Coincidence(WSeries< DataType_t > &, double=0., double=0.)
param: WSeries object used for coincidence param: coincidence window in seconds param: threshold on s...
virtual void lprFilter(double, int=0, double=0., double=0.)
void Forward(int n=-1)
param: wavelet - n is number of steps (-1 means full decomposition)
Meyer< double > S(1024, 2)
virtual void median(double t, bool norm=false)
WSeries< DataType_t > & operator=(const wavearray< DataType_t > &)
virtual double pixclean(double=0.)
param: S - threshold on pixel significance return pixel occupancy.
DataType_t getSample(int n, double m)
WaveDWT< DataType_t > * pWavelet
double wdmPacket(int pattern, char opt='L', TH1F *=NULL)
void Inverse(int n=-1)
param: n - number of steps (-1 means full reconstruction)
void putLayer(wavearray< DataType_t > &, double n)
param: n - layer number
virtual void Dump(const char *, int=0)
virtual double rsignificance(size_t=0, double=1.)
param: n - sub-interval duration in domain units param: f - black pixel fraction options: f = 0 - ret...
virtual WSeries< double > calibrate(size_t, double, d_complex *, d_complex *, wavearray< double > &, wavearray< double > &, size_t ch=0)
param: number of samples in calibration arrays R & C param: frequency resolution param: pointer to re...
virtual WSeries< double > white(double, int, double=0., double=0.)
what it does: each wavelet layer is devided into k intervals.