def _create_J_with_numba(Ybus,
V,
pvpq,
pq,
createJ,
pvpq_lookup,
npv,
npq,
Ibus=None):
Ibus = zeros(len(V), dtype=complex128) if Ibus is None else -Ibus
# create Jacobian from fast calc of dS_dV
dVm_x, dVa_x = dSbus_dV_numba_sparse(Ybus.data, Ybus.indptr, Ybus.indices,
V, V / abs(V), Ibus)
# data in J, space preallocated is bigger than acutal Jx -> will be reduced later on
Jx = empty(len(dVm_x) * 4, dtype=float64)
# row pointer, dimension = pvpq.shape[0] + pq.shape[0] + 1
Jp = zeros(pvpq.shape[0] + pq.shape[0] + 1, dtype=int32)
# indices, same with the preallocated space (see Jx)
Jj = empty(len(dVm_x) * 4, dtype=int32)
# fill Jx, Jj and Jp
createJ(dVm_x, dVa_x, Ybus.indptr, Ybus.indices, pvpq_lookup, pvpq, pq, Jx,
Jj, Jp)
# resize before generating the scipy sparse matrix
Jx.resize(Jp[-1], refcheck=False)
Jj.resize(Jp[-1], refcheck=False)
# generate scipy sparse matrix
dimJ = npv + npq + npq
J = sparse((Jx, Jj, Jp), shape=(dimJ, dimJ))
return J
def _create_J_with_numba(Ybus, V, refpvpq, pvpq, pq, createJ, pvpq_lookup,
nref, npv, npq, slack_weights, dist_slack):
Ibus = zeros(len(V), dtype=complex128)
# create Jacobian from fast calc of dS_dV
dVm_x, dVa_x = dSbus_dV_numba_sparse(Ybus.data, Ybus.indptr, Ybus.indices,
V, V / abs(V), Ibus)
# data in J, space preallocated is bigger than acutal Jx -> will be reduced later on
Jx = empty(len(dVm_x) * 4, dtype=float64)
# row pointer, dimension = pvpq.shape[0] + pq.shape[0] + 1
if dist_slack:
Jp = zeros(refpvpq.shape[0] + pq.shape[0] + 1, dtype=int32)
else:
Jp = zeros(pvpq.shape[0] + pq.shape[0] + 1, dtype=int32)
# indices, same with the preallocated space (see Jx)
Jj = empty(len(dVm_x) * 4, dtype=int32)
# fill Jx, Jj and Jp
createJ(dVm_x, dVa_x, Ybus.indptr, Ybus.indices, pvpq_lookup, refpvpq,
pvpq, pq, Jx, Jj, Jp, slack_weights)
# resize before generating the scipy sparse matrix
Jx.resize(Jp[-1], refcheck=False)
Jj.resize(Jp[-1], refcheck=False)
# todo: why not replace npv by pv.shape[0] etc.?
# generate scipy sparse matrix
if dist_slack:
dimJ = nref + npv + npq + npq
else:
dimJ = npv + npq + npq
J = sparse((Jx, Jj, Jp), shape=(dimJ, dimJ))
return J
