// Action_MultiDihedral::Setup();
Action::RetType Action_MultiDihedral::Setup(Topology* currentParm, Topology** parmAddress) {
Range actualRange;
// If range is empty (i.e. no resrange arg given) look through all
// solute residues.
if (resRange_.Empty())
actualRange.SetRange(0, currentParm->FinalSoluteRes());
else {
// If user range specified, create new range shifted by -1 since internal
// resnums start from 0.
actualRange = resRange_;
actualRange.ShiftBy(-1);
}
// Exit if no residues specified
if (actualRange.Empty()) {
mprinterr("Error: multidihedral: No residues specified for %s\n",currentParm->c_str());
return Action::ERR;
}
// Search for specified dihedrals in each residue in the range
if (dihSearch_.FindDihedrals(*currentParm, actualRange))
return Action::ERR;
mprintf("\tResRange=[%s]", resRange_.RangeArg());
dihSearch_.PrintTypes();
mprintf(", %i dihedrals.\n", dihSearch_.Ndihedrals());
// Print selected dihedrals, set up DataSets
data_.clear();
if (dsetname_.empty())
dsetname_ = masterDSL_->GenerateDefaultName("MDIH");
for (DihedralSearch::mask_it dih = dihSearch_.begin();
dih != dihSearch_.end(); ++dih)
{
int resNum = (*dih).ResNum() + 1;
// See if Dataset already present
DataSet* ds = masterDSL_->GetSet(dsetname_, resNum, (*dih).Name());
if (ds == 0) {
// Create new DataSet
ds = masterDSL_->AddSetIdxAspect( DataSet::DOUBLE, dsetname_, resNum, (*dih).Name());
// Add to outfile
if (outfile_ != 0)
outfile_->AddSet( ds );
}
// TODO: SetScalar
if (ds != 0)
data_.push_back( ds );
if (debug_ > 0) {
mprintf("\tDIH [%s]:", ds->Legend().c_str());
mprintf(" :%i@%i", (*currentParm)[(*dih).A0()].ResNum()+1, (*dih).A0() + 1);
mprintf(" :%i@%i", (*currentParm)[(*dih).A1()].ResNum()+1, (*dih).A1() + 1);
mprintf(" :%i@%i", (*currentParm)[(*dih).A2()].ResNum()+1, (*dih).A2() + 1);
mprintf(" :%i@%i\n", (*currentParm)[(*dih).A3()].ResNum()+1, (*dih).A3() + 1);
}
}
return Action::OK;
}
/** Calculate Pearson product-moment correlation between DataSets.
* \D1 DataSet to caclculate correlation for.
* \D2 DataSet to caclulate correlation to.
* \return Pearson product-moment correlation coefficient.
*/
double DS_Math::CorrCoeff( DataSet& D1, DataSet& D2 ) {
// Check if D1 and D2 are valid types
if ( !GoodCalcType(D1) ) return 0;
if ( !GoodCalcType(D2) ) return 0;
// Check that D1 and D2 have same # data points.
int Nelements = D1.Size();
if (Nelements != D2.Size()) {
mprinterr("Error: Corr: # elements in dataset %s (%i) not equal to\n",
D1.Legend().c_str(), Nelements);
mprinterr("Error: # elements in dataset %s (%i)\n",
D2.Legend().c_str(), D2.Size());
return 0;
}
// Calculate averages
double avg1 = Avg(D1);
double avg2 = Avg(D2);
// Calculate average deviations.
double sumdiff1_2 = 0.0;
double sumdiff2_2 = 0.0;
double corr_coeff = 0.0;
//mprinterr("DATASETS %s and %s\n", c_str(), D2.c_str());
for (int i = 0; i < Nelements; i++) {
double diff1 = D1.Dval(i) - avg1;
double diff2 = D2.Dval(i) - avg2;
sumdiff1_2 += (diff1 * diff1);
sumdiff2_2 += (diff2 * diff2);
corr_coeff += (diff1 * diff2);
}
if (sumdiff1_2 == 0.0 || sumdiff2_2 == 0.0) {
mprintf("Warning: Corr: %s to %s, Normalization is 0\n",
D1.Legend().c_str(), D2.Legend().c_str());
return 0;
}
// Correlation coefficient
corr_coeff /= ( sqrt( sumdiff1_2 ) * sqrt( sumdiff2_2 ) );
//mprintf(" CORRELATION COEFFICIENT %6s to %6s IS %10.4f\n",
// D1_->c_str(), D2_->c_str(), corr_coeff );
return corr_coeff;
}
/** Calculate time correlation between two DataSets.
* \D1 DataSet to calculate correlation for.
* \D2 DataSet to calculate correlation to.
* \Ct DataSet to store time correlation fn, must be DOUBLE.
* \lagmaxIn Max lag to calculate corr. -1 means use size of dataset.
* \calccovar If true calculate covariance (devation from avg).
* \return 0 on success, 1 on error.
*/
int DS_Math::CrossCorr( DataSet& D1, DataSet& D2, DataSet& Ct, int lagmaxIn,
bool calccovar, bool usefft )
{
int lagmax;
double ct;
// Check if D1 and D2 are valid types
if ( !GoodCalcType(D1) ) return 1;
if ( !GoodCalcType(D2) ) return 1;
// Check that D1 and D2 have same # data points.
int Nelements = D1.Size();
if (Nelements != D2.Size()) {
mprinterr("Error: CrossCorr: # elements in dataset %s (%i) not equal to\n",
D1.Legend().c_str(), Nelements);
mprinterr("Error: # elements in dataset %s (%i)\n",
D2.Legend().c_str(), D2.Size());
return 1;
}
if (Nelements < 2) {
mprinterr("Error: CrossCorr: # elements is less than 2 (%i)\n", Nelements);
return 1;
}
// Check return dataset type
if ( Ct.Type() != DataSet::DOUBLE ) {
mprinterr("Internal Error: CrossCorr: Ct must be of type DataSet::DOUBLE.\n");
return 1;
}
// Check if lagmaxIn makes sense. Set default lag to be Nelements
// if not specified.
if (lagmaxIn == -1)
lagmax = Nelements;
else if (lagmaxIn > Nelements) {
mprintf("Warning: CrossCorr [%s][%s]: max lag (%i) > Nelements (%i), setting to Nelements.\n",
D1.Legend().c_str(), D2.Legend().c_str(), lagmaxIn, Nelements);
lagmax = Nelements;
} else
lagmax = lagmaxIn;
// If calculating covariance calculate averages
double avg1 = 0;
double avg2 = 0;
if ( calccovar ) {
avg1 = Avg(D1);
avg2 = Avg(D2);
}
// Calculate correlation
double norm = 1.0;
if ( usefft ) {
// Calc using FFT
CorrF_FFT pubfft1(Nelements);
ComplexArray data1 = pubfft1.Array();
data1.PadWithZero(Nelements);
for (int i = 0; i < Nelements; ++i)
data1[i*2] = D1.Dval(i) - avg1;
if (&D2 == &D1)
pubfft1.AutoCorr(data1);
else {
// Populate second dataset if different
ComplexArray data2 = pubfft1.Array();
data2.PadWithZero(Nelements);
for (int i = 0; i < Nelements; ++i)
data2[i*2] = D2.Dval(i) - avg2;
pubfft1.CrossCorr(data1, data2);
}
// Put real components of data1 in output DataSet
norm = 1.0 / fabs( data1[0] );
for (int i = 0; i < lagmax; ++i) {
ct = data1[i*2] * norm;
Ct.Add(i, &ct);
}
} else {
// Direct calc
for (int lag = 0; lag < lagmax; ++lag) {
ct = 0;
int jmax = Nelements - lag;
for (int j = 0; j < jmax; ++j)
ct += ((D1.Dval(j) - avg1) * (D2.Dval(j+lag) - avg2));
if (lag == 0) {
if (ct != 0)
norm = fabs( ct );
}
ct /= norm;
Ct.Add(lag, &ct);
}
}
return 0;
}
