void blas_gemm(const MatrixXcd& a, const MatrixXcd& b, MatrixXcd& c)
{
int M = c.rows(); int N = c.cols(); int K = a.cols();
int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
zgemm_(¬rans,¬rans,&M,&N,&K,(double*)&cdone,
const_cast<double*>((const double*)a.data()),&lda,
const_cast<double*>((const double*)b.data()),&ldb,(double*)&cdone,
(double*)c.data(),&ldc);
}
complex<double> compute_Determinant(MatrixXcd& K) {
FullPivLU<MatrixXcd> Kinverse;
Kinverse.compute(K);
complex<double> determinant;
if (K.rows()>0) { // Check needed when the matrix is predominantly diagonal.
MatrixXcd LU = Kinverse.matrixLU();
determinant = log(LU(0,0));
for (int k=1; k<K.rows(); ++k) {
determinant+=log(LU(k,k));
}
// Previous version which had some underflow.
// determinant = log(abs(K.determinant()));
}
return determinant;
};
PyObject* calc_Gavg(PyObject *pyw, const double &delta, const double &mu,
PyObject *pySE, PyObject *pyTB, const double &Hf, PyObject *py_bp,
PyObject *py_wf, const int nthreads) {
try {
if (nthreads > 0) omp_set_num_threads(nthreads);
MatrixXcd SE;
VectorXcd w;
VectorXd bp, wf;
VectorXd SlaterKosterCoeffs;
numpy::from_numpy(pySE, SE);
numpy::from_numpy(pyTB, SlaterKosterCoeffs);
numpy::from_numpy(py_bp, bp);
numpy::from_numpy(py_wf, wf);
numpy::from_numpy(pyw, w);
assert(w.size() == SE.rows());
double t = SlaterKosterCoeffs(0);
VectorXd xl(DIM), xh(DIM);
xl << -2*t;
xh << 2*t;
double BZ1 = 1.;
MatrixXcd result(SE.rows(), MSIZE*MSIZE);
VectorXcd tmp(MSIZE);
GreenIntegrand green_integrand(w, mu, SE, SlaterKosterCoeffs, Hf);
result.setZero();
for (int n = 0; n < w.size(); ++n) {
green_integrand.set_data(n);
tmp = md_int::Integrate(xl, xh, green_integrand, bp, wf);
for (int i = 0; i < MSIZE; ++i)
result(n, MSIZE*i + i) = tmp(i);
}
result /= BZ1;
return numpy::to_numpy(result);
} catch (const char *str) {
std::cerr << str << std::endl;
return Py_None;
}
}
