def testDenseAddIdentity(): """ Simple test of denseAddIdentity to check it works like the scipy equivalent. """ N = 3 I = np.identity(N) A_sp = np.random.random(size=(N, N)) A = np.asfortranarray(A_sp) denseAddIdentity(A) assert_allclose(A, A_sp + I)
def testDenseAddIdentity(): """ Simple test of denseAddIdentity to check it works like the scipy equivalent. """ N = 3 I = np.identity(N) A_sp = np.random.random(size=(N,N)) A = np.asfortranarray(A_sp) denseAddIdentity(A) assert_allclose(A, A_sp+I)
def SpilsPrecSetup(self, t, c, fc, jok, jcurPtr, gamma, vtemp1, vtemp2, vtemp3): """ This routine generates the block-diagonal part of the Jacobian corresponding to the interaction rates, multiplies by -gamma, adds the identity matrix, and calls denseGETRF to do the LU decomposition of each diagonal block. The computation of the diagonal blocks uses the preset block and grouping information. One block per group is computed. The Jacobian elements are generated by difference quotients using calls to the routine fblock. This routine can be regarded as a prototype for the general case of a block-diagonal preconditioner. The blocks are of size mp, and there are ngrp=ngx*ngy blocks computed in the block-grouping scheme. """ cdata = c.data rewt = self.rewt self.errWeights(rewt) rewtdata = rewt.data uround = UNIT_ROUNDOFF P = self.P pivot = self.pivot jxr = self.jxr jyr = self.jyr mp = self.mp srur = self.srur #ngrp = self.ngrp ngx = self.ngx ngy = self.ngy #mxmp = self.mxmp fsave = self.fsave """ Make mp calls to fblock to approximate each diagonal block of Jacobian. Here, fsave contains the base value of the rate vector and r0 is a minimum increment factor for the difference quotient. """ f1 = vtemp1.data fac = fc.WrmsNorm(rewt) r0 = 1000.0 * np.abs(gamma) * uround * NEQ * fac if (r0 == ZERO): r0 = ONE for igy in range(ngy): jy = jyr[igy] #if00 = jy*mxmp for igx in range(ngx): jx = jxr[igx] #if0 = if00 + jx*mp #ig = igx + igy*ngx # Generate ig-th diagonal block for j in range(mp): # Generate the jth column as a difference quotient #jj = if0 + j save = cdata[:, igx, igy] r = np.max(srur * np.abs(save), r0 / rewtdata[:, igx, igy]) cdata[:, igx, igy] += r fac = -gamma / r self.fblock(t, cdata, jx, jy, f1) for i in range(mp): P[igx, igy, j, i] = (f1[i] - fsave[i, jx, jy]) * fac cdata[j, jx, jy] = save # Add identity matrix and do LU decompositions on blocks. for igy in range(ngy): for igx in range(ngx): denseAddIdentity(P[igx, igy, ...]) ier = denseGETRF(P[igx, igy, ...], pivot[igx, igy, ...]) if (ier != 0): return 1 jcurPtr = True return 0