@@ -234,36 +234,3 @@ def test_maysweepupdate(self):
234234 update = 1.0 + (problem .lambda_s [0 ] + problem .lambda_f [0 ])* level .sweep .coll .weights .dot (Mat_sweep .dot (np .ones (nnodes )))
235235 uend_matrix = update * self .pparams ['u0' ]
236236 assert abs (uend_matrix - uend_sweep )< 1e-14 , "Node-to-node sweep plus update yields different result than update function computed through K-sweep matrix"
237- #
238- #
239- #
240- def test_canrunmatrixsweep (self ):
241- step , level , problem , nnodes = self .setupLevelStepProblem ()
242-
243- QE = level .sweep .QE [1 :,1 :]
244- QI = level .sweep .QI [1 :,1 :]
245- Q = level .sweep .coll .Qmat [1 :,1 :]
246-
247- P = np .eye (nnodes ) - step .status .dt * problem .lambda_s [0 ]* QE - step .status .dt * problem .lambda_f [0 ]* QI
248-
249- Pinv = np .linalg .inv (P )
250- M = np .eye (nnodes ) - step .status .dt * ( problem .lambda_s [0 ] + problem .lambda_f [0 ] )* Q
251- #M = step.status.dt*( (problem.lambda_s[0]+problem.lambda_f[0])*Q - problem.lambda_f[0]*QI - problem.lambda_s[0]*QE )
252- #print QI
253- #print P
254- #print Pinv
255- #print M
256- #level.sweep.predict()
257- #u0full = np.array([ level.u[l].values.flatten() for l in range(1,nnodes+1) ])
258- #ufull = u0full + Pinv.dot( u0full ) - Pinv.dot( M.dot(u0full) )
259- #print u0.values
260- #print Pinv.dot(u0full)
261- #print Pinv.dot(M)
262- #print Pinv.dot(M.dot(u0full))
263- #ufull = Pinv.dot(M.dot(u0full)) + Pinv.dot(u0full)
264- #ufull = np.linalg.inv(M).dot(u0full)
265- #print ufull
266- #uend = u0.values + step.status.dt*level.sweep.coll.weights.dot( (problem.lambda_f[0]+problem.lambda_s[0])*ufull )
267- #print "Matrix: %s" % uend
268-
269-
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