3 #pragma GCC system_header
10 #include "TObjArray.h"
11 #include "TObjString.h"
20 #define REGRESSION_FILTER_LENGTH 8
21 #define REGRESSION_MATRIX_FRACTION 0.95
22 #define REGRESSION_SOLVE_EIGEN_THR 0.
23 #define REGRESSION_SOLVE_EIGEN_NUM 10
24 #define REGRESSION_SOLVE_REGULATOR 'h'
25 #define REGRESSION_APPLY_THR 0.8
31 #define USE_REGRESSION
40 cout <<
"-----> CWB_Plugin_DataConditioning.C" << endl;
41 cout <<
"ifo " << ifo.Data() << endl;
42 cout <<
"type " << type << endl;
52 cout <<
"CWB_Plugin_DAtaConditioning.C -> implemented only for 2G" << endl;
62 #ifdef USE_LPR_FILTER // used in 1G
69 if(
id<0) {cout <<
"Plugin : Error - bad ifo id" << endl; gSystem->Exit(1);}
76 for(
int level=cfg->
l_high; level>=cfg->
l_low; level--) {
84 #ifdef USE_LPR_FILTER // used in 1G
91 #ifdef USE_REGRESSION // used in 2G
118 double layer =
j+0.01;
132 #ifdef USE_LPR_FILTER // used in 1G
monster wdmMRA
list of pixel pointers for MRA
std::vector< char * > ifoName
detector * getifo(size_t n)
param: detector index
void white(double dT=0, int wtype=1, double offset=0., double stride=0.)
what it does: see algorithm description in wseries.hh
void setMatrix(double edge=0., double f=1.)
std::vector< double > * getmdcTime()
cout<< "skymap size : "<< L<< endl;for(int l=0;l< L;l++) sm.set(l, l);sm > const_cast< char * >("skymap.dat")
void bandpass(wavearray< DataType_t > &ts, double flow, double fhigh, int n=-1)
#define REGRESSION_SOLVE_REGULATOR
cout<< endl;cout<< "ts size = "<< ts.size()<< " ts rate = "<< ts.rate()<< endl;tf.Forward(ts, wdm);int levels=tf.getLevel();cout<< "tf size = "<< tf.size()<< endl;double dF=tf.resolution();double dT=1./(2 *dF);cout<< "rate(hz) : "<< RATE<< "\t layers : "<< nLAYERS<< "\t dF(hz) : "<< dF<< "\t dT(ms) : "<< dT *1000.<< endl;int itime=TIME_PIXEL_INDEX;int ifreq=FREQ_PIXEL_INDEX;int index=(levels+1)*itime+ifreq;double time=itime *dT;double freq=(ifreq >0)?ifreq *dF:dF/4;cout<< endl;cout<< "PIXEL TIME = "<< time<< " sec "<< endl;cout<< "PIXEL FREQ = "<< freq<< " Hz "<< endl;cout<< endl;wavearray< double > x
#define REGRESSION_APPLY_THR
size_t add(WSeries< double > &target, char *name, double fL=0., double fH=0.)
network ** net
NOISE_MDC_SIMULATION.
#define REGRESSION_SOLVE_EIGEN_THR
wavearray< double > hot[2]
#define IMPORT(TYPE, VAR)
#define REGRESSION_FILTER_LENGTH
void apply(double threshold=0., char c='a')
WSeries< double > pTF[nRES]
int getLayer(wavearray< DataType_t > &w, double n)
param: n - layer number
void solve(double th, int nE=0, char c='s')
wavearray< double > getClean()
WSeries< double > * getTFmap()
param: no parameters
#define REGRESSION_SOLVE_EIGEN_NUM
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)
#define REGRESSION_MATRIX_FRACTION
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
size_t setsim(WSeries< double > &, std::vector< double > *, double=5., double=8., bool saveWF=false)
virtual WSeries< double > white(double, int, double=0., double=0.)
what it does: each wavelet layer is devided into k intervals.
void CWB_Plugin(TFile *jfile, CWB::config *cfg, network *net, WSeries< double > *x, TString ifo, int type)
COHERENCE.