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Commit af32999

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author
Martin D. Weinberg
committed
Remove unused loop variable
1 parent b049baf commit af32999

1 file changed

Lines changed: 87 additions & 61 deletions

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‎expui/BiorthBasis.cc‎

Lines changed: 87 additions & 61 deletions
Original file line numberDiff line numberDiff line change
@@ -612,7 +612,7 @@ namespace BasisClasses
612612
Eigen::VectorXd workE;
613613

614614
// M loop
615-
for (int m=0, moffset=0, moffE=0; m<=l; m++, L++) {
615+
for (int m=0, moffset=0; m<=l; m++, L++) {
616616

617617
fac = factorial(l, m) * legs[tid](l, m);
618618

@@ -4055,7 +4055,7 @@ namespace BasisClasses
40554055
expcoef(ix, iy, iz) += - mass * curr(0)*curr(1)*curr(2) * norm;
40564056

40574057
if (pcavar)
4058-
g[index1D(ix, iy, iz)] = - mass * curr(0)*curr(1)*curr(2) * norm;
4058+
g[index1D(ix, iy, iz)] = - curr(0)*curr(1)*curr(2) * norm;
40594059
}
40604060
}
40614061
}
@@ -4068,7 +4068,7 @@ namespace BasisClasses
40684068
sampleMasses(T) += mass;
40694069

40704070
meanV[T].noalias() += g * mass;
4071-
covrV[T].noalias() += g * g.adjoint() * mass;
4071+
if (covar) covrV[T].noalias() += g * g.adjoint() * mass;
40724072
}
40734073
}
40744074

@@ -4099,8 +4099,9 @@ namespace BasisClasses
40994099
MPI_Allreduce(MPI_IN_PLACE, meanV[T].data(), meanV[T].size(),
41004100
MPI_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);
41014101

4102-
MPI_Allreduce(MPI_IN_PLACE, covrV[T].data(), covrV[T].size(),
4103-
MPI_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);
4102+
if (covar)
4103+
MPI_Allreduce(MPI_IN_PLACE, covrV[T].data(), covrV[T].size(),
4104+
MPI_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);
41044105
}
41054106
// END: sample loop
41064107
}
@@ -4120,7 +4121,7 @@ namespace BasisClasses
41204121
for (int T=0; T<sampT; T++) {
41214122
ret[T].resize(1);
41224123
std::get<0>(ret[T][0]) = meanV[T];
4123-
std::get<1>(ret[T][0]) = covrV[T];
4124+
if (covar) std::get<1>(ret[T][0]) = covrV[T];
41244125
}
41254126
}
41264127

@@ -4243,10 +4244,12 @@ namespace BasisClasses
42434244
v.resize(Itot);
42444245
}
42454246

4246-
covrV.resize(sampT);
4247-
for (auto& v : covrV) {
4248-
v.resize(Itot, Itot);
4249-
}
4247+
if (covar) {
4248+
covrV.resize(sampT);
4249+
for (auto& v : covrV) {
4250+
v.resize(Itot, Itot);
4251+
}
4252+
} else covrV.clear();
42504253

42514254
sampleCounts.resize(sampT);
42524255
sampleMasses.resize(sampT);
@@ -4260,7 +4263,7 @@ namespace BasisClasses
42604263
{
42614264
for (int T=0; T<sampT; T++) {
42624265
meanV[T].setZero();
4263-
covrV[T].setZero();
4266+
if (covar) covrV[T].setZero();
42644267
}
42654268

42664269
sampleCounts.setZero();
@@ -5109,6 +5112,11 @@ namespace BasisClasses
51095112
}
51105113
}
51115114

5115+
// Check for existence of a covariance matrix (only Cube can
5116+
// toggle this so far)
5117+
//
5118+
int varsz = std::get<1>(covar[0][0]).size();
5119+
51125120
// Pack the coefficient data
51135121
//
51145122
if (floatType) {
@@ -5131,23 +5139,26 @@ namespace BasisClasses
51315139

51325140
// Pack the covariance data in an upper triangular format
51335141
//
5134-
real_part.resize(ltot*diagonalSize*sampleSize);
5135-
imag_part.resize(ltot*diagonalSize*sampleSize);
5142+
if (varsz) {
51365143

5137-
for (size_t T=0, c=0; T<sampleCounts.size(); T++) {
5138-
for (size_t l=0; l<ltot; l++) {
5139-
for (size_t n1=0; n1<nmax; n1++) {
5140-
for (size_t n2=n1; n2<nmax; n2++, c++) {
5141-
real_part(c) = std::real(std::get<1>(covar[T][l])(n1, n2));
5142-
imag_part(c) = std::imag(std::get<1>(covar[T][l])(n1, n2));
5144+
real_part.resize(ltot*diagonalSize*sampleSize);
5145+
imag_part.resize(ltot*diagonalSize*sampleSize);
5146+
5147+
for (size_t T=0, c=0; T<sampleCounts.size(); T++) {
5148+
for (size_t l=0; l<ltot; l++) {
5149+
for (size_t n1=0; n1<nmax; n1++) {
5150+
for (size_t n2=n1; n2<nmax; n2++, c++) {
5151+
real_part(c) = std::real(std::get<1>(covar[T][l])(n1, n2));
5152+
imag_part(c) = std::imag(std::get<1>(covar[T][l])(n1, n2));
5153+
}
51435154
}
51445155
}
51455156
}
5146-
}
51475157

5148-
// Create two separate, compressed datasets
5149-
stanza.createDataSet("covariance_real", real_part, dcpl3);
5150-
stanza.createDataSet("covariance_imag", imag_part, dcpl3);
5158+
// Create two separate, compressed datasets
5159+
stanza.createDataSet("covariance_real", real_part, dcpl3);
5160+
stanza.createDataSet("covariance_imag", imag_part, dcpl3);
5161+
}
51515162

51525163
} else {
51535164
Eigen::VectorXd real_part(ltot*nmax*sampleSize);
@@ -5169,24 +5180,26 @@ namespace BasisClasses
51695180

51705181
// Pack the covariance data in an upper triangular format
51715182
//
5172-
real_part.resize(ltot*diagonalSize*sampleSize);
5173-
imag_part.resize(ltot*diagonalSize*sampleSize);
5174-
5175-
for (size_t T=0, c=0; T<sampleCounts.size(); T++) {
5176-
for (size_t l=0; l<ltot; l++) {
5177-
for (size_t n1=0; n1<nmax; n1++) {
5178-
for (size_t n2=n1; n2<nmax; n2++, c++) {
5179-
real_part(c) = std::real(std::get<1>(covar[T][l])(n1, n2));
5180-
imag_part(c) = std::imag(std::get<1>(covar[T][l])(n1, n2));
5183+
if (varsz) {
5184+
real_part.resize(ltot*diagonalSize*sampleSize);
5185+
imag_part.resize(ltot*diagonalSize*sampleSize);
5186+
5187+
for (size_t T=0, c=0; T<sampleCounts.size(); T++) {
5188+
for (size_t l=0; l<ltot; l++) {
5189+
for (size_t n1=0; n1<nmax; n1++) {
5190+
for (size_t n2=n1; n2<nmax; n2++, c++) {
5191+
real_part(c) = std::real(std::get<1>(covar[T][l])(n1, n2));
5192+
imag_part(c) = std::imag(std::get<1>(covar[T][l])(n1, n2));
5193+
}
51815194
}
51825195
}
51835196
}
5184-
}
51855197

5186-
// Create two separate, compressed datasets
5187-
//
5188-
stanza.createDataSet("covariance_real", real_part, dcpl3);
5189-
stanza.createDataSet("covariance_imag", imag_part, dcpl3);
5198+
// Create two separate, compressed datasets
5199+
//
5200+
stanza.createDataSet("covariance_real", real_part, dcpl3);
5201+
stanza.createDataSet("covariance_imag", imag_part, dcpl3);
5202+
}
51905203
}
51915204
// END: sample loop
51925205

@@ -5436,16 +5449,21 @@ namespace BasisClasses
54365449
data0.real() = data_real.cast<double>();
54375450
data0.imag() = data_imag.cast<double>();
54385451

5439-
data_real =
5440-
stanza.getDataSet("covariance_real").read<Eigen::VectorXf>();
5452+
// Check for existence of covariance
5453+
//
5454+
if (stanza.exist("covariance_real")) {
5455+
5456+
data_real =
5457+
stanza.getDataSet("covariance_real").read<Eigen::VectorXf>();
54415458

5442-
data_imag =
5443-
stanza.getDataSet("covariance_imag").read<Eigen::VectorXf>();
5459+
data_imag =
5460+
stanza.getDataSet("covariance_imag").read<Eigen::VectorXf>();
54445461

5445-
// Resize the complex array and assign
5446-
data1.resize(data_real.size());
5447-
data1.real() = data_real.cast<double>();
5448-
data1.imag() = data_imag.cast<double>();
5462+
// Resize the complex array and assign
5463+
data1.resize(data_real.size());
5464+
data1.real() = data_real.cast<double>();
5465+
data1.imag() = data_imag.cast<double>();
5466+
}
54495467
} else {
54505468
// Get the real and imaginary parts
54515469
Eigen::VectorXd data_real =
@@ -5459,17 +5477,22 @@ namespace BasisClasses
54595477
data0.real() = data_real;
54605478
data0.imag() = data_imag;
54615479

5462-
// Get the real and imaginary parts
5463-
data_real =
5464-
stanza.getDataSet("covariance_real").read<Eigen::VectorXd>();
5480+
// Check for existence of covariance
5481+
//
5482+
if (stanza.exist("covariance_real")) {
54655483

5466-
data_imag =
5467-
stanza.getDataSet("covariance_imag").read<Eigen::VectorXd>();
5468-
5469-
// Resize the complex array and assign
5470-
data1.resize(data_real.size());
5471-
data1.real() = data_real;
5472-
data1.imag() = data_imag;
5484+
// Get the real and imaginary parts
5485+
data_real =
5486+
stanza.getDataSet("covariance_real").read<Eigen::VectorXd>();
5487+
5488+
data_imag =
5489+
stanza.getDataSet("covariance_imag").read<Eigen::VectorXd>();
5490+
5491+
// Resize the complex array and assign
5492+
data1.resize(data_real.size());
5493+
data1.real() = data_real;
5494+
data1.imag() = data_imag;
5495+
}
54735496
}
54745497

54755498
// Positions in data stanzas
@@ -5484,7 +5507,7 @@ namespace BasisClasses
54845507
// Coefficients
54855508
std::get<0>(e).resize(rank);
54865509
// Covariance matrix
5487-
std::get<1>(e).resize(rank, rank);
5510+
if (data1.size()) std::get<1>(e).resize(rank, rank);
54885511
}
54895512

54905513
// Pack the coefficient data
@@ -5499,11 +5522,14 @@ namespace BasisClasses
54995522
// Pack the covariance data
55005523
c = 0;
55015524
for (size_t l=0; l<lSize; l++) {
5502-
for (size_t n1=0; n1<rank; n1++) {
5503-
for (size_t n2=n1; n2<rank; n2++) {
5504-
std::get<1>(elem[l])(n1, n2) = data1(sCov + c++);
5505-
if (n1 != n2)
5506-
std::get<1>(elem[l])(n2, n1) = std::get<1>(elem[l])(n1, n2);
5525+
5526+
if (data1.size()) {
5527+
for (size_t n1=0; n1<rank; n1++) {
5528+
for (size_t n2=n1; n2<rank; n2++) {
5529+
std::get<1>(elem[l])(n1, n2) = data1(sCov + c++);
5530+
if (n1 != n2)
5531+
std::get<1>(elem[l])(n2, n1) = std::get<1>(elem[l])(n1, n2);
5532+
}
55075533
}
55085534
}
55095535
}

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