diff --git a/pyro/multigrid/MG.py b/pyro/multigrid/MG.py
index 6d1a3416d..e08b014d1 100644
--- a/pyro/multigrid/MG.py
+++ b/pyro/multigrid/MG.py
@@ -714,8 +714,7 @@ def v_cycle(self, level):
 
             if self.verbose:
                 self._compute_residual(level)
-                self.grid_info(level, indent=2)
-                print("  before G-S, residual L2: {}".format(fp.get_var("r").norm()))
+                orig_resid = fp.get_var("r").norm()
 
             # smooth on the current level
             self.smooth(level, self.nsmooth)
@@ -724,7 +723,9 @@ def v_cycle(self, level):
             self._compute_residual(level)
 
             if self.verbose:
-                print("  after G-S, residual L2: {}\n".format(fp.get_var("r").norm()))
+                new_resid = fp.get_var("r").norm()
+                nx = self.grids[level].grid.nx
+                print(f"  level = {level:2}, nx = {nx:4}, residual change: {orig_resid:11.6g} → {new_resid:11.6g}")
 
             # restrict the residual down to the RHS of the coarser level
             f_coarse = cp.get_var("f")
@@ -751,15 +752,15 @@ def v_cycle(self, level):
 
             if self.verbose:
                 self._compute_residual(level)
-                self.grid_info(level, indent=2)
-                print("  before G-S, residual L2: {}".format(fp.get_var("r").norm()))
+                orig_resid = fp.get_var("r").norm()
 
             # smooth
             self.smooth(level, self.nsmooth)
 
             if self.verbose:
-                self._compute_residual(level)
-                print("  after G-S, residual L2: {}\n".format(fp.get_var("r").norm()))
+                new_resid = fp.get_var("r").norm()
+                nx = self.grids[level].grid.nx
+                print(f"  level = {level:2}, nx = {nx:4}, residual change: {orig_resid:11.6g} → {new_resid:11.6g}")
 
         else:
             # bottom solve: solve the discrete coarse problem.  We
@@ -767,15 +768,11 @@ def v_cycle(self, level):
             # (like CG), but since we are 2x2 by design at this point,
             # we will just smooth
             if self.verbose:
-                print("  bottom solve:")
+                print("  bottom solve")
 
             self.current_level = level
             bp = self.grids[level]
 
-            if self.verbose:
-                self.grid_info(level, indent=2)
-                print("")
-
             self.smooth(level, self.nsmooth_bottom)
 
             bp.fill_BC("v")
diff --git a/pyro/multigrid/multigrid-constant-coefficients.ipynb b/pyro/multigrid/multigrid-constant-coefficients.ipynb
index 6efa1f158..05912c397 100644
--- a/pyro/multigrid/multigrid-constant-coefficients.ipynb
+++ b/pyro/multigrid/multigrid-constant-coefficients.ipynb
@@ -245,443 +245,135 @@
       "source norm =  1.097515813669473\n",
       "<<< beginning V-cycle (cycle 1) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 1.097515813669473\n",
-      "  after G-S, residual L2: 1.502308451578657\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.0616243965458263\n",
-      "  after G-S, residual L2: 1.4321452257629033\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.011366277976364\n",
-      "  after G-S, residual L2: 1.281872470375375\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.903531158162907\n",
-      "  after G-S, residual L2: 0.9607576999783505\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.6736112182020367\n",
-      "  after G-S, residual L2: 0.4439774050299674\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.30721142286171554\n",
-      "  after G-S, residual L2: 0.0727215591269748\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.04841813458618458\n",
-      "  after G-S, residual L2: 3.9610700301811246e-05\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.925006722484123e-05\n",
-      "  after G-S, residual L2: 1.0370099820862674e-09\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.07010129273961899\n",
-      "  after G-S, residual L2: 0.0008815704830693547\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.4307377377402105\n",
-      "  after G-S, residual L2: 0.007174899576794818\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.911086486792154\n",
-      "  after G-S, residual L2: 0.01618756602227813\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.1945438349788615\n",
-      "  after G-S, residual L2: 0.022021327892004925\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.313456560108626\n",
-      "  after G-S, residual L2: 0.02518650395173617\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 1.3618314516335004\n",
-      "  after G-S, residual L2: 0.026870007568672097\n",
-      "\n",
+      "  level =  7, nx =  256, residual change:     1.09752 →     1.50231\n",
+      "  level =  6, nx =  128, residual change:     1.06162 →     1.43215\n",
+      "  level =  5, nx =   64, residual change:     1.01137 →     1.28187\n",
+      "  level =  4, nx =   32, residual change:    0.903531 →    0.960758\n",
+      "  level =  3, nx =   16, residual change:    0.673611 →    0.443977\n",
+      "  level =  2, nx =    8, residual change:    0.307211 →   0.0727216\n",
+      "  level =  1, nx =    4, residual change:   0.0484181 → 3.96107e-05\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 3.92501e-05 → 3.92501e-05\n",
+      "  level =  2, nx =    8, residual change:   0.0701013 →   0.0701013\n",
+      "  level =  3, nx =   16, residual change:    0.430738 →    0.430738\n",
+      "  level =  4, nx =   32, residual change:    0.911086 →    0.911086\n",
+      "  level =  5, nx =   64, residual change:     1.19454 →     1.19454\n",
+      "  level =  6, nx =  128, residual change:     1.31346 →     1.31346\n",
+      "  level =  7, nx =  256, residual change:     1.36183 →     1.36183\n",
       "cycle 1: relative err = 0.999999999999964, residual err = 0.02448256984911586\n",
       "\n",
       "<<< beginning V-cycle (cycle 2) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 0.026870007568672097\n",
-      "  after G-S, residual L2: 0.025790216249923552\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.018218080204017304\n",
-      "  after G-S, residual L2: 0.023654310121915274\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.01669077991582338\n",
-      "  after G-S, residual L2: 0.01977335201785163\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.013922595404814862\n",
-      "  after G-S, residual L2: 0.013577568890182053\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.009518306167970536\n",
-      "  after G-S, residual L2: 0.006115159484497302\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.004244630812032651\n",
-      "  after G-S, residual L2: 0.0010674120586864006\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.0007108144252738053\n",
-      "  after G-S, residual L2: 5.818246254772703e-07\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 5.765281065294632e-07\n",
-      "  after G-S, residual L2: 1.5231212503339452e-11\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.0010291471590693868\n",
-      "  after G-S, residual L2: 1.2950948742201083e-05\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.006239446983842889\n",
-      "  after G-S, residual L2: 0.00010483463130232172\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.014573363314854\n",
-      "  after G-S, residual L2: 0.00026233988398787004\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.021564270263952755\n",
-      "  after G-S, residual L2: 0.0003944827058086955\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.02579092712136628\n",
-      "  after G-S, residual L2: 0.00048636495715121916\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 0.028051324215592862\n",
-      "  after G-S, residual L2: 0.0005440874957950154\n",
-      "\n",
+      "  level =  7, nx =  256, residual change:     0.02687 →   0.0257902\n",
+      "  level =  6, nx =  128, residual change:   0.0182181 →   0.0236543\n",
+      "  level =  5, nx =   64, residual change:   0.0166908 →   0.0197734\n",
+      "  level =  4, nx =   32, residual change:   0.0139226 →   0.0135776\n",
+      "  level =  3, nx =   16, residual change:  0.00951831 →  0.00611516\n",
+      "  level =  2, nx =    8, residual change:  0.00424463 →  0.00106741\n",
+      "  level =  1, nx =    4, residual change: 0.000710814 → 5.81825e-07\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 5.76528e-07 → 5.76528e-07\n",
+      "  level =  2, nx =    8, residual change:  0.00102915 →  0.00102915\n",
+      "  level =  3, nx =   16, residual change:  0.00623945 →  0.00623945\n",
+      "  level =  4, nx =   32, residual change:   0.0145734 →   0.0145734\n",
+      "  level =  5, nx =   64, residual change:   0.0215643 →   0.0215643\n",
+      "  level =  6, nx =  128, residual change:   0.0257909 →   0.0257909\n",
+      "  level =  7, nx =  256, residual change:   0.0280513 →   0.0280513\n",
       "cycle 2: relative err = 13.739483825281054, residual err = 0.0004957445615074047\n",
       "\n",
       "<<< beginning V-cycle (cycle 3) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 0.0005440874957950154\n",
-      "  after G-S, residual L2: 0.0005095844930046698\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0003597879816772893\n",
-      "  after G-S, residual L2: 0.00044648485218937167\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.0003147892995472901\n",
-      "  after G-S, residual L2: 0.0003492541721056348\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.0002457276904804801\n",
-      "  after G-S, residual L2: 0.00022232862524233384\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.0001558932199490972\n",
-      "  after G-S, residual L2: 9.511093023364078e-05\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 6.616899520585456e-05\n",
-      "  after G-S, residual L2: 1.711006102346096e-05\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.139522901981679e-05\n",
-      "  after G-S, residual L2: 9.33004809910226e-09\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 9.245125097272049e-09\n",
-      "  after G-S, residual L2: 2.442311694447821e-13\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.64991725637487e-05\n",
-      "  after G-S, residual L2: 2.0771258971860784e-07\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.00010097720436460624\n",
-      "  after G-S, residual L2: 1.7241727900979902e-06\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.0002575410544503153\n",
-      "  after G-S, residual L2: 4.766282851613449e-06\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.00041133882050328275\n",
-      "  after G-S, residual L2: 7.600616845344458e-06\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0005232809692242086\n",
-      "  after G-S, residual L2: 9.860758095018993e-06\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 0.0005945070122423073\n",
-      "  after G-S, residual L2: 1.1466134915427874e-05\n",
-      "\n",
+      "  level =  7, nx =  256, residual change: 0.000544087 → 0.000509584\n",
+      "  level =  6, nx =  128, residual change: 0.000359788 → 0.000446485\n",
+      "  level =  5, nx =   64, residual change: 0.000314789 → 0.000349254\n",
+      "  level =  4, nx =   32, residual change: 0.000245728 → 0.000222329\n",
+      "  level =  3, nx =   16, residual change: 0.000155893 → 9.51109e-05\n",
+      "  level =  2, nx =    8, residual change:  6.6169e-05 → 1.71101e-05\n",
+      "  level =  1, nx =    4, residual change: 1.13952e-05 → 9.33005e-09\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 9.24513e-09 → 9.24513e-09\n",
+      "  level =  2, nx =    8, residual change: 1.64992e-05 → 1.64992e-05\n",
+      "  level =  3, nx =   16, residual change: 0.000100977 → 0.000100977\n",
+      "  level =  4, nx =   32, residual change: 0.000257541 → 0.000257541\n",
+      "  level =  5, nx =   64, residual change: 0.000411339 → 0.000411339\n",
+      "  level =  6, nx =  128, residual change: 0.000523281 → 0.000523281\n",
+      "  level =  7, nx =  256, residual change: 0.000594507 → 0.000594507\n",
       "cycle 3: relative err = 34.347638624909216, residual err = 1.0447352805871284e-05\n",
       "\n",
       "<<< beginning V-cycle (cycle 4) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 1.1466134915427874e-05\n",
-      "  after G-S, residual L2: 1.054466722279011e-05\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 7.442814693866286e-06\n",
-      "  after G-S, residual L2: 8.955050475722099e-06\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 6.311313968968047e-06\n",
-      "  after G-S, residual L2: 6.734553609148436e-06\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 4.737984987500691e-06\n",
-      "  after G-S, residual L2: 4.091799997658277e-06\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 2.871028473858937e-06\n",
-      "  after G-S, residual L2: 1.6319551993366253e-06\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.1372178077508109e-06\n",
-      "  after G-S, residual L2: 2.961040430099916e-07\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.9721864323458624e-07\n",
-      "  after G-S, residual L2: 1.61503943872384e-10\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.6003411195777404e-10\n",
-      "  after G-S, residual L2: 4.2274326344473505e-15\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 2.855691101825338e-07\n",
-      "  after G-S, residual L2: 3.5961118754371857e-09\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.7893831203170535e-06\n",
-      "  after G-S, residual L2: 3.1136282101831173e-08\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 4.97129807196115e-06\n",
-      "  after G-S, residual L2: 9.544819739422644e-08\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 8.281644276572538e-06\n",
-      "  after G-S, residual L2: 1.56637783149839e-07\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.0888850082357996e-05\n",
-      "  after G-S, residual L2: 2.0777271327080248e-07\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 1.2717522622400765e-05\n",
-      "  after G-S, residual L2: 2.464531349025277e-07\n",
-      "\n",
+      "  level =  7, nx =  256, residual change: 1.14661e-05 → 1.05447e-05\n",
+      "  level =  6, nx =  128, residual change: 7.44281e-06 → 8.95505e-06\n",
+      "  level =  5, nx =   64, residual change: 6.31131e-06 → 6.73455e-06\n",
+      "  level =  4, nx =   32, residual change: 4.73798e-06 →  4.0918e-06\n",
+      "  level =  3, nx =   16, residual change: 2.87103e-06 → 1.63196e-06\n",
+      "  level =  2, nx =    8, residual change: 1.13722e-06 → 2.96104e-07\n",
+      "  level =  1, nx =    4, residual change: 1.97219e-07 → 1.61504e-10\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 1.60034e-10 → 1.60034e-10\n",
+      "  level =  2, nx =    8, residual change: 2.85569e-07 → 2.85569e-07\n",
+      "  level =  3, nx =   16, residual change: 1.78938e-06 → 1.78938e-06\n",
+      "  level =  4, nx =   32, residual change:  4.9713e-06 →  4.9713e-06\n",
+      "  level =  5, nx =   64, residual change: 8.28164e-06 → 8.28164e-06\n",
+      "  level =  6, nx =  128, residual change: 1.08889e-05 → 1.08889e-05\n",
+      "  level =  7, nx =  256, residual change: 1.27175e-05 → 1.27175e-05\n",
       "cycle 4: relative err = 0.17409776671446628, residual err = 2.24555429482631e-07\n",
       "\n",
       "<<< beginning V-cycle (cycle 5) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 2.464531349025277e-07\n",
-      "  after G-S, residual L2: 2.2491138140311698e-07\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.5874562191875262e-07\n",
-      "  after G-S, residual L2: 1.886249099391391e-07\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.3294481979637655e-07\n",
-      "  after G-S, residual L2: 1.397710191717015e-07\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 9.836928907527788e-08\n",
-      "  after G-S, residual L2: 8.269030961692836e-08\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 5.8062531341283565e-08\n",
-      "  after G-S, residual L2: 3.034725896415429e-08\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 2.116912379336852e-08\n",
-      "  after G-S, residual L2: 5.467519592468213e-09\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.6418116003284676e-09\n",
-      "  after G-S, residual L2: 2.982625229812215e-12\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 2.955484162036181e-12\n",
-      "  after G-S, residual L2: 7.806739482450516e-17\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 5.273610709946236e-09\n",
-      "  after G-S, residual L2: 6.642323465658688e-11\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 3.4146989205844565e-08\n",
-      "  after G-S, residual L2: 6.052228076583688e-10\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.031248597196911e-07\n",
-      "  after G-S, residual L2: 2.0541497445308587e-09\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.7585349306604133e-07\n",
-      "  after G-S, residual L2: 3.421022608879089e-09\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.3383756442516674e-07\n",
-      "  after G-S, residual L2: 4.552170797983864e-09\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 2.7592842790687426e-07\n",
-      "  after G-S, residual L2: 5.41488950707315e-09\n",
-      "\n",
+      "  level =  7, nx =  256, residual change: 2.46453e-07 → 2.24911e-07\n",
+      "  level =  6, nx =  128, residual change: 1.58746e-07 → 1.88625e-07\n",
+      "  level =  5, nx =   64, residual change: 1.32945e-07 → 1.39771e-07\n",
+      "  level =  4, nx =   32, residual change: 9.83693e-08 → 8.26903e-08\n",
+      "  level =  3, nx =   16, residual change: 5.80625e-08 → 3.03473e-08\n",
+      "  level =  2, nx =    8, residual change: 2.11691e-08 → 5.46752e-09\n",
+      "  level =  1, nx =    4, residual change: 3.64181e-09 → 2.98263e-12\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 2.95548e-12 → 2.95548e-12\n",
+      "  level =  2, nx =    8, residual change: 5.27361e-09 → 5.27361e-09\n",
+      "  level =  3, nx =   16, residual change:  3.4147e-08 →  3.4147e-08\n",
+      "  level =  4, nx =   32, residual change: 1.03125e-07 → 1.03125e-07\n",
+      "  level =  5, nx =   64, residual change: 1.75853e-07 → 1.75853e-07\n",
+      "  level =  6, nx =  128, residual change: 2.33838e-07 → 2.33838e-07\n",
+      "  level =  7, nx =  256, residual change: 2.75928e-07 → 2.75928e-07\n",
       "cycle 5: relative err = 0.005391244339065405, residual err = 4.933769007818501e-09\n",
       "\n",
       "<<< beginning V-cycle (cycle 6) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 5.41488950707315e-09\n",
-      "  after G-S, residual L2: 4.948141362729419e-09\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 3.4929583962703016e-09\n",
-      "  after G-S, residual L2: 4.154445183511443e-09\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 2.9288841397931397e-09\n",
-      "  after G-S, residual L2: 3.074779198797186e-09\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 2.164991235492634e-09\n",
-      "  after G-S, residual L2: 1.788028730183651e-09\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.2562223343389894e-09\n",
-      "  after G-S, residual L2: 6.021983813990021e-10\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 4.2028073688787063e-10\n",
-      "  after G-S, residual L2: 1.0655724637281067e-10\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 7.097871736854444e-11\n",
-      "  after G-S, residual L2: 5.813506543301849e-14\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 5.760611936011378e-14\n",
-      "  after G-S, residual L2: 1.521555112430923e-18\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.027891920456506e-10\n",
-      "  after G-S, residual L2: 1.294879454701896e-12\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 6.914011940773812e-10\n",
-      "  after G-S, residual L2: 1.2453691230551983e-11\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 2.2570491487662195e-09\n",
-      "  after G-S, residual L2: 4.639035392364569e-11\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 3.908967396962745e-09\n",
-      "  after G-S, residual L2: 7.803740782474827e-11\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 5.196394306272565e-09\n",
-      "  after G-S, residual L2: 1.033274523100204e-10\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 6.117636729623554e-09\n",
-      "  after G-S, residual L2: 1.2199402602477584e-10\n",
-      "\n",
+      "  level =  7, nx =  256, residual change: 5.41489e-09 → 4.94814e-09\n",
+      "  level =  6, nx =  128, residual change: 3.49296e-09 → 4.15445e-09\n",
+      "  level =  5, nx =   64, residual change: 2.92888e-09 → 3.07478e-09\n",
+      "  level =  4, nx =   32, residual change: 2.16499e-09 → 1.78803e-09\n",
+      "  level =  3, nx =   16, residual change: 1.25622e-09 → 6.02198e-10\n",
+      "  level =  2, nx =    8, residual change: 4.20281e-10 → 1.06557e-10\n",
+      "  level =  1, nx =    4, residual change: 7.09787e-11 → 5.81351e-14\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 5.76061e-14 → 5.76061e-14\n",
+      "  level =  2, nx =    8, residual change: 1.02789e-10 → 1.02789e-10\n",
+      "  level =  3, nx =   16, residual change: 6.91401e-10 → 6.91401e-10\n",
+      "  level =  4, nx =   32, residual change: 2.25705e-09 → 2.25705e-09\n",
+      "  level =  5, nx =   64, residual change: 3.90897e-09 → 3.90897e-09\n",
+      "  level =  6, nx =  128, residual change: 5.19639e-09 → 5.19639e-09\n",
+      "  level =  7, nx =  256, residual change: 6.11764e-09 → 6.11764e-09\n",
       "cycle 6: relative err = 7.51413991329132e-05, residual err = 1.111546863428753e-10\n",
       "\n",
       "<<< beginning V-cycle (cycle 7) >>>\n",
       "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 1.2199402602477584e-10\n",
-      "  after G-S, residual L2: 1.121992266879251e-10\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 7.921861122082639e-11\n",
-      "  after G-S, residual L2: 9.493449600138316e-11\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 6.694993398453784e-11\n",
-      "  after G-S, residual L2: 7.050995614737483e-11\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 4.9666563586565975e-11\n",
-      "  after G-S, residual L2: 4.045094776680348e-11\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 2.843147343834713e-11\n",
-      "  after G-S, residual L2: 1.2576313722677801e-11\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 8.777954081387978e-12\n",
-      "  after G-S, residual L2: 2.170559196862902e-12\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.445876195415056e-12\n",
-      "  after G-S, residual L2: 1.1842925278593641e-15\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.1735184729034125e-15\n",
-      "  after G-S, residual L2: 3.0994757710835167e-20\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 2.094012660676073e-12\n",
-      "  after G-S, residual L2: 2.6382579574150587e-14\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.466147487151147e-11\n",
-      "  after G-S, residual L2: 2.6760553592700965e-13\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 5.130705216489902e-11\n",
-      "  after G-S, residual L2: 1.0810419626613159e-12\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 9.001551103280705e-11\n",
-      "  after G-S, residual L2: 1.8342879121275396e-12\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.1914921193827463e-10\n",
-      "  after G-S, residual L2: 2.4124327865487605e-12\n",
-      "\n",
-      "  level: 7, grid: 256 x 256\n",
-      "  before G-S, residual L2: 1.3907209384461257e-10\n",
-      "  after G-S, residual L2: 2.8429898342353533e-12\n",
-      "\n",
+      "  level =  7, nx =  256, residual change: 1.21994e-10 → 1.12199e-10\n",
+      "  level =  6, nx =  128, residual change: 7.92186e-11 → 9.49345e-11\n",
+      "  level =  5, nx =   64, residual change: 6.69499e-11 →   7.051e-11\n",
+      "  level =  4, nx =   32, residual change: 4.96666e-11 → 4.04509e-11\n",
+      "  level =  3, nx =   16, residual change: 2.84315e-11 → 1.25763e-11\n",
+      "  level =  2, nx =    8, residual change: 8.77795e-12 → 2.17056e-12\n",
+      "  level =  1, nx =    4, residual change: 1.44588e-12 → 1.18429e-15\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 1.17352e-15 → 1.17352e-15\n",
+      "  level =  2, nx =    8, residual change: 2.09401e-12 → 2.09401e-12\n",
+      "  level =  3, nx =   16, residual change: 1.46615e-11 → 1.46615e-11\n",
+      "  level =  4, nx =   32, residual change: 5.13071e-11 → 5.13071e-11\n",
+      "  level =  5, nx =   64, residual change: 9.00155e-11 → 9.00155e-11\n",
+      "  level =  6, nx =  128, residual change: 1.19149e-10 → 1.19149e-10\n",
+      "  level =  7, nx =  256, residual change: 1.39072e-10 → 1.39072e-10\n",
       "cycle 7: relative err = 7.062255558417692e-07, residual err = 2.590386214782638e-12\n",
       "\n"
      ]
@@ -728,7 +420,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa429e8fe10>"
+       "<matplotlib.image.AxesImage at 0x7f1d0461ee90>"
       ]
      },
      "execution_count": 9,
@@ -737,14 +429,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -783,7 +473,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa429ed0bd0>"
+       "<matplotlib.image.AxesImage at 0x7f1d01f7b790>"
       ]
      },
      "execution_count": 11,
@@ -792,14 +482,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
diff --git a/pyro/multigrid/multigrid-general-linear.ipynb b/pyro/multigrid/multigrid-general-linear.ipynb
index 683a88d9f..4a9f01575 100644
--- a/pyro/multigrid/multigrid-general-linear.ipynb
+++ b/pyro/multigrid/multigrid-general-linear.ipynb
@@ -430,443 +430,139 @@
       "source norm =  1.775181492337501\n",
       "<<< beginning V-cycle (cycle 1) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.775181492337501\n",
-      "  after G-S, residual L2: 188.9332667507471\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 129.93801550392877\n",
-      "  after G-S, residual L2: 56.28708770794367\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 38.88692621665777\n",
-      "  after G-S, residual L2: 18.722754099081875\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 12.926068140514912\n",
-      "  after G-S, residual L2: 6.7418584016115615\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 4.646478379380239\n",
-      "  after G-S, residual L2: 2.0651261541465873\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.3745334259197388\n",
-      "  after G-S, residual L2: 0.022445197218592287\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.03125252087247734\n",
-      "  after G-S, residual L2: 8.232822131646219e-05\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 2.805976863110291\n",
-      "  after G-S, residual L2: 0.07481536016730257\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 8.772402436595389\n",
-      "  after G-S, residual L2: 0.24361942694526753\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 19.59101132435104\n",
-      "  after G-S, residual L2: 0.5448263647958932\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 50.46410889948471\n",
-      "  after G-S, residual L2: 1.3597629173942607\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 160.21311638468657\n",
-      "  after G-S, residual L2: 4.125142056231161\n",
-      "\n",
-      "cycle 1: relative err = 0.9999999999999981, residual err = 2.323786088373031\n",
+      "  level =  6, nx =  128, residual change:     1.77518 →     188.933\n",
+      "  level =  5, nx =   64, residual change:     129.938 →     56.2871\n",
+      "  level =  4, nx =   32, residual change:     38.8869 →     18.7228\n",
+      "  level =  3, nx =   16, residual change:     12.9261 →     6.74186\n",
+      "  level =  2, nx =    8, residual change:     4.64648 →     2.06513\n",
+      "  level =  1, nx =    4, residual change:     1.37453 →   0.0224452\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change:   0.0312525 →   0.0312525\n",
+      "  level =  2, nx =    8, residual change:     2.80598 →     2.80598\n",
+      "  level =  3, nx =   16, residual change:      8.7724 →      8.7724\n",
+      "  level =  4, nx =   32, residual change:      19.591 →      19.591\n",
+      "  level =  5, nx =   64, residual change:     50.4641 →     50.4641\n",
+      "  level =  6, nx =  128, residual change:     160.213 →     160.213\n",
+      "cycle 1: relative err = 0.9999999999999981, residual err = 2.323786088373021\n",
       "\n",
       "<<< beginning V-cycle (cycle 2) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 4.125142056231161\n",
-      "  after G-S, residual L2: 2.424731184614396\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.6915411385849446\n",
-      "  after G-S, residual L2: 1.048624109440289\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.7283416353571882\n",
-      "  after G-S, residual L2: 0.4554818109365319\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.3165327512850212\n",
-      "  after G-S, residual L2: 0.22128563126748188\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.15332496186655636\n",
-      "  after G-S, residual L2: 0.07471968817844332\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.049749391872944894\n",
-      "  after G-S, residual L2: 0.000813357286041042\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.0011325179143730421\n",
-      "  after G-S, residual L2: 2.983377839180104e-06\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.10152627387884115\n",
-      "  after G-S, residual L2: 0.0027007047002410205\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.2981467241559551\n",
-      "  after G-S, residual L2: 0.008199107952269191\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.521884811462466\n",
-      "  after G-S, residual L2: 0.014956130961989571\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.9910630869232032\n",
-      "  after G-S, residual L2: 0.028422939317571935\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.044187745817753\n",
-      "  after G-S, residual L2: 0.05829382601881336\n",
-      "\n",
-      "cycle 2: relative err = 0.036315310129801166, residual err = 0.032838234439935464\n",
+      "  level =  6, nx =  128, residual change:     4.12514 →     2.42473\n",
+      "  level =  5, nx =   64, residual change:     1.69154 →     1.04862\n",
+      "  level =  4, nx =   32, residual change:    0.728342 →    0.455482\n",
+      "  level =  3, nx =   16, residual change:    0.316533 →    0.221286\n",
+      "  level =  2, nx =    8, residual change:    0.153325 →   0.0747197\n",
+      "  level =  1, nx =    4, residual change:   0.0497494 → 0.000813357\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change:  0.00113252 →  0.00113252\n",
+      "  level =  2, nx =    8, residual change:    0.101526 →    0.101526\n",
+      "  level =  3, nx =   16, residual change:    0.298147 →    0.298147\n",
+      "  level =  4, nx =   32, residual change:    0.521885 →    0.521885\n",
+      "  level =  5, nx =   64, residual change:    0.991063 →    0.991063\n",
+      "  level =  6, nx =  128, residual change:     2.04419 →     2.04419\n",
+      "cycle 2: relative err = 0.036315310129800826, residual err = 0.03283823443993396\n",
       "\n",
       "<<< beginning V-cycle (cycle 3) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.05829382601881336\n",
-      "  after G-S, residual L2: 0.04172011870730236\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.029246699093089436\n",
-      "  after G-S, residual L2: 0.023356326397591245\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.016306296792817917\n",
-      "  after G-S, residual L2: 0.012906629461195626\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.009011110787954003\n",
-      "  after G-S, residual L2: 0.0073152629389098165\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.005081499522860255\n",
-      "  after G-S, residual L2: 0.00256252651715607\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.0017064130732668981\n",
-      "  after G-S, residual L2: 2.79123870467358e-05\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.886526925433716e-05\n",
-      "  after G-S, residual L2: 1.0238217009498558e-07\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.0034819145217796638\n",
-      "  after G-S, residual L2: 9.252096659806975e-05\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.010064990348705135\n",
-      "  after G-S, residual L2: 0.00027440544182565064\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.016032310448840032\n",
-      "  after G-S, residual L2: 0.00045582265432728727\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.024303743880187103\n",
-      "  after G-S, residual L2: 0.0007098551729201522\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.037775318915905735\n",
-      "  after G-S, residual L2: 0.0011035122820145216\n",
-      "\n",
-      "cycle 3: relative err = 0.0012532978372416998, residual err = 0.000621633498759303\n",
+      "  level =  6, nx =  128, residual change:   0.0582938 →   0.0417201\n",
+      "  level =  5, nx =   64, residual change:   0.0292467 →   0.0233563\n",
+      "  level =  4, nx =   32, residual change:   0.0163063 →   0.0129066\n",
+      "  level =  3, nx =   16, residual change:  0.00901111 →  0.00731526\n",
+      "  level =  2, nx =    8, residual change:   0.0050815 →  0.00256253\n",
+      "  level =  1, nx =    4, residual change:  0.00170641 → 2.79124e-05\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 3.88653e-05 → 3.88653e-05\n",
+      "  level =  2, nx =    8, residual change:  0.00348191 →  0.00348191\n",
+      "  level =  3, nx =   16, residual change:    0.010065 →    0.010065\n",
+      "  level =  4, nx =   32, residual change:   0.0160323 →   0.0160323\n",
+      "  level =  5, nx =   64, residual change:   0.0243037 →   0.0243037\n",
+      "  level =  6, nx =  128, residual change:   0.0377753 →   0.0377753\n",
+      "cycle 3: relative err = 0.0012532978372415558, residual err = 0.0006216334987521017\n",
       "\n",
       "<<< beginning V-cycle (cycle 4) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0011035122820145216\n",
-      "  after G-S, residual L2: 0.0008898317346927453\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.000625739872070729\n",
-      "  after G-S, residual L2: 0.000607740119080587\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.00042604165447622126\n",
-      "  after G-S, residual L2: 0.000397674018255364\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.0002784624522902657\n",
-      "  after G-S, residual L2: 0.00024268300992371118\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.00016881840301228077\n",
-      "  after G-S, residual L2: 8.634352400013625e-05\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 5.7501328044023475e-05\n",
-      "  after G-S, residual L2: 9.407985171363897e-07\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.3099714803249488e-06\n",
-      "  after G-S, residual L2: 3.4508339509542035e-09\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.00011732421042711928\n",
-      "  after G-S, residual L2: 3.1157531467700274e-06\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.00033850867119458294\n",
-      "  after G-S, residual L2: 9.177601887988707e-06\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.0005249527904411394\n",
-      "  after G-S, residual L2: 1.4651643230949486e-05\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.0007080871923349976\n",
-      "  after G-S, residual L2: 2.0290645679874954e-05\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0009185166831419814\n",
-      "  after G-S, residual L2: 2.657030047801386e-05\n",
-      "\n",
-      "cycle 4: relative err = 4.257466296345195e-05, residual err = 1.4967652937293164e-05\n",
+      "  level =  6, nx =  128, residual change:  0.00110351 → 0.000889832\n",
+      "  level =  5, nx =   64, residual change:  0.00062574 →  0.00060774\n",
+      "  level =  4, nx =   32, residual change: 0.000426042 → 0.000397674\n",
+      "  level =  3, nx =   16, residual change: 0.000278462 → 0.000242683\n",
+      "  level =  2, nx =    8, residual change: 0.000168818 → 8.63435e-05\n",
+      "  level =  1, nx =    4, residual change: 5.75013e-05 → 9.40799e-07\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 1.30997e-06 → 1.30997e-06\n",
+      "  level =  2, nx =    8, residual change: 0.000117324 → 0.000117324\n",
+      "  level =  3, nx =   16, residual change: 0.000338509 → 0.000338509\n",
+      "  level =  4, nx =   32, residual change: 0.000524953 → 0.000524953\n",
+      "  level =  5, nx =   64, residual change: 0.000708087 → 0.000708087\n",
+      "  level =  6, nx =  128, residual change: 0.000918517 → 0.000918517\n",
+      "cycle 4: relative err = 4.257466296364851e-05, residual err = 1.4967652930826935e-05\n",
       "\n",
       "<<< beginning V-cycle (cycle 5) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.657030047801386e-05\n",
-      "  after G-S, residual L2: 2.3098223934902124e-05\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.627485739129596e-05\n",
-      "  after G-S, residual L2: 1.7906142640602113e-05\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.2585882397882331e-05\n",
-      "  after G-S, residual L2: 1.2880701433409095e-05\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 9.035061892447504e-06\n",
-      "  after G-S, residual L2: 8.103003187752153e-06\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 5.641504287281656e-06\n",
-      "  after G-S, residual L2: 2.90121290636155e-06\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.9321695175514817e-06\n",
-      "  after G-S, residual L2: 3.1616756017989616e-08\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 4.402332099236765e-08\n",
-      "  after G-S, residual L2: 1.159697431375499e-10\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 3.942265874668158e-06\n",
-      "  after G-S, residual L2: 1.0466257645320907e-07\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.1405869020197195e-05\n",
-      "  after G-S, residual L2: 3.081954658477386e-07\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.7696025211153306e-05\n",
-      "  after G-S, residual L2: 4.853326074732553e-07\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 2.281722184539092e-05\n",
-      "  after G-S, residual L2: 6.339093026169473e-07\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.720450664755927e-05\n",
-      "  after G-S, residual L2: 7.617366442287773e-07\n",
-      "\n",
-      "cycle 5: relative err = 1.4372233556002303e-06, residual err = 4.291035297048683e-07\n",
+      "  level =  6, nx =  128, residual change: 2.65703e-05 → 2.30982e-05\n",
+      "  level =  5, nx =   64, residual change: 1.62749e-05 → 1.79061e-05\n",
+      "  level =  4, nx =   32, residual change: 1.25859e-05 → 1.28807e-05\n",
+      "  level =  3, nx =   16, residual change: 9.03506e-06 →   8.103e-06\n",
+      "  level =  2, nx =    8, residual change:  5.6415e-06 → 2.90121e-06\n",
+      "  level =  1, nx =    4, residual change: 1.93217e-06 → 3.16168e-08\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 4.40233e-08 → 4.40233e-08\n",
+      "  level =  2, nx =    8, residual change: 3.94227e-06 → 3.94227e-06\n",
+      "  level =  3, nx =   16, residual change: 1.14059e-05 → 1.14059e-05\n",
+      "  level =  4, nx =   32, residual change:  1.7696e-05 →  1.7696e-05\n",
+      "  level =  5, nx =   64, residual change: 2.28172e-05 → 2.28172e-05\n",
+      "  level =  6, nx =  128, residual change: 2.72045e-05 → 2.72045e-05\n",
+      "cycle 5: relative err = 1.437223355768636e-06, residual err = 4.2910353907176844e-07\n",
       "\n",
       "<<< beginning V-cycle (cycle 6) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 7.617366442287773e-07\n",
-      "  after G-S, residual L2: 6.887955204981268e-07\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 4.858303555357937e-07\n",
-      "  after G-S, residual L2: 5.69884466371041e-07\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 4.0114485792406196e-07\n",
-      "  after G-S, residual L2: 4.288730508746916e-07\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 3.011320281769793e-07\n",
-      "  after G-S, residual L2: 2.722913591694268e-07\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.8967555845982626e-07\n",
-      "  after G-S, residual L2: 9.770491535790604e-08\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 6.507167346101259e-08\n",
-      "  after G-S, residual L2: 1.064857909707231e-09\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.4827137267543486e-09\n",
-      "  after G-S, residual L2: 3.905880545305021e-12\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.327670545122848e-07\n",
-      "  after G-S, residual L2: 3.5242457874456e-09\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 3.856314482142684e-07\n",
-      "  after G-S, residual L2: 1.0398885055660428e-08\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 6.038836839330361e-07\n",
-      "  after G-S, residual L2: 1.6338312450885264e-08\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 7.682416327633833e-07\n",
-      "  after G-S, residual L2: 2.077211614487108e-08\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 8.865086249123311e-07\n",
-      "  after G-S, residual L2: 2.401917734271176e-08\n",
-      "\n",
-      "cycle 6: relative err = 4.849259893743e-08, residual err = 1.3530547409597026e-08\n",
+      "  level =  6, nx =  128, residual change: 7.61737e-07 → 6.88796e-07\n",
+      "  level =  5, nx =   64, residual change:  4.8583e-07 → 5.69884e-07\n",
+      "  level =  4, nx =   32, residual change: 4.01145e-07 → 4.28873e-07\n",
+      "  level =  3, nx =   16, residual change: 3.01132e-07 → 2.72291e-07\n",
+      "  level =  2, nx =    8, residual change: 1.89676e-07 → 9.77049e-08\n",
+      "  level =  1, nx =    4, residual change: 6.50717e-08 → 1.06486e-09\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 1.48271e-09 → 1.48271e-09\n",
+      "  level =  2, nx =    8, residual change: 1.32767e-07 → 1.32767e-07\n",
+      "  level =  3, nx =   16, residual change: 3.85631e-07 → 3.85631e-07\n",
+      "  level =  4, nx =   32, residual change: 6.03884e-07 → 6.03884e-07\n",
+      "  level =  5, nx =   64, residual change: 7.68242e-07 → 7.68242e-07\n",
+      "  level =  6, nx =  128, residual change: 8.86509e-07 → 8.86509e-07\n",
+      "cycle 6: relative err = 4.849259894834445e-08, residual err = 1.3530556515124825e-08\n",
       "\n",
       "<<< beginning V-cycle (cycle 7) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.401917734271176e-08\n",
-      "  after G-S, residual L2: 2.212526516368908e-08\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.561380930193641e-08\n",
-      "  after G-S, residual L2: 1.8869605253841384e-08\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.329268715576614e-08\n",
-      "  after G-S, residual L2: 1.448574192361666e-08\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.0177211702855934e-08\n",
-      "  after G-S, residual L2: 9.198083215964472e-09\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 6.409466931052197e-09\n",
-      "  after G-S, residual L2: 3.301837694167828e-09\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 2.1990605773774708e-09\n",
-      "  after G-S, residual L2: 3.5987499029820695e-11\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 5.010919220080045e-11\n",
-      "  after G-S, residual L2: 1.3200150076369783e-13\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 4.486791915539427e-09\n",
-      "  after G-S, residual L2: 1.1908944659498306e-10\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.3081161953516478e-08\n",
-      "  after G-S, residual L2: 3.5229822904429924e-10\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 2.0705036864241283e-08\n",
-      "  after G-S, residual L2: 5.54664336501412e-10\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 2.6280822545639532e-08\n",
-      "  after G-S, residual L2: 6.964954084454355e-10\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.994436334203582e-08\n",
-      "  after G-S, residual L2: 7.914114964975534e-10\n",
-      "\n",
-      "cycle 7: relative err = 1.6392149521630824e-09, residual err = 4.4582004708456516e-10\n",
+      "  level =  6, nx =  128, residual change: 2.40192e-08 → 2.21253e-08\n",
+      "  level =  5, nx =   64, residual change: 1.56138e-08 → 1.88696e-08\n",
+      "  level =  4, nx =   32, residual change: 1.32927e-08 → 1.44857e-08\n",
+      "  level =  3, nx =   16, residual change: 1.01772e-08 → 9.19808e-09\n",
+      "  level =  2, nx =    8, residual change: 6.40947e-09 → 3.30184e-09\n",
+      "  level =  1, nx =    4, residual change: 2.19906e-09 → 3.59875e-11\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 5.01092e-11 → 5.01092e-11\n",
+      "  level =  2, nx =    8, residual change: 4.48679e-09 → 4.48679e-09\n",
+      "  level =  3, nx =   16, residual change: 1.30812e-08 → 1.30812e-08\n",
+      "  level =  4, nx =   32, residual change:  2.0705e-08 →  2.0705e-08\n",
+      "  level =  5, nx =   64, residual change: 2.62808e-08 → 2.62808e-08\n",
+      "  level =  6, nx =  128, residual change: 2.99444e-08 → 2.99444e-08\n",
+      "cycle 7: relative err = 1.6392149576904378e-09, residual err = 4.458207725000789e-10\n",
       "\n",
       "<<< beginning V-cycle (cycle 8) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 7.914114964975534e-10\n",
-      "  after G-S, residual L2: 7.355563798981241e-10\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 5.192187012540891e-10\n",
-      "  after G-S, residual L2: 6.364663794896949e-10\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 4.4855051982056546e-10\n",
-      "  after G-S, residual L2: 4.92823302521467e-10\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 3.463709288611066e-10\n",
-      "  after G-S, residual L2: 3.1194153963395727e-10\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 2.1741809566511453e-10\n",
-      "  after G-S, residual L2: 1.1194508137188934e-10\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 7.455730160141668e-11\n",
-      "  after G-S, residual L2: 1.2201492319919709e-12\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.6989427313305885e-12\n",
-      "  after G-S, residual L2: 4.47548465864942e-15\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.5212136435309373e-10\n",
-      "  after G-S, residual L2: 4.037432498150632e-12\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 4.4491471941548957e-10\n",
-      "  after G-S, residual L2: 1.1972476764962434e-11\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 7.109786940662245e-10\n",
-      "  after G-S, residual L2: 1.891232351798468e-11\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 9.034023723354117e-10\n",
-      "  after G-S, residual L2: 2.3606468222333935e-11\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.023747974109598e-09\n",
-      "  after G-S, residual L2: 2.6771270744711738e-11\n",
-      "\n",
-      "cycle 8: relative err = 5.555103471809045e-11, residual err = 1.5080864047010877e-11\n",
+      "  level =  6, nx =  128, residual change: 7.91413e-10 → 7.35586e-10\n",
+      "  level =  5, nx =   64, residual change:  5.1922e-10 → 6.36466e-10\n",
+      "  level =  4, nx =   32, residual change:  4.4855e-10 → 4.92822e-10\n",
+      "  level =  3, nx =   16, residual change:  3.4637e-10 → 3.11941e-10\n",
+      "  level =  2, nx =    8, residual change: 2.17418e-10 → 1.11945e-10\n",
+      "  level =  1, nx =    4, residual change: 7.45572e-11 → 1.22015e-12\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 1.69894e-12 → 1.69894e-12\n",
+      "  level =  2, nx =    8, residual change: 1.52121e-10 → 1.52121e-10\n",
+      "  level =  3, nx =   16, residual change: 4.44914e-10 → 4.44914e-10\n",
+      "  level =  4, nx =   32, residual change: 7.10977e-10 → 7.10977e-10\n",
+      "  level =  5, nx =   64, residual change:   9.034e-10 →   9.034e-10\n",
+      "  level =  6, nx =  128, residual change:  1.0238e-09 →  1.0238e-09\n",
+      "cycle 8: relative err = 5.555097426033948e-11, residual err = 1.5072807373286882e-11\n",
       "\n"
      ]
     }
@@ -940,7 +636,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
diff --git a/pyro/multigrid/multigrid-variable-coeff.ipynb b/pyro/multigrid/multigrid-variable-coeff.ipynb
index 96d810fa3..a0678f8e3 100644
--- a/pyro/multigrid/multigrid-variable-coeff.ipynb
+++ b/pyro/multigrid/multigrid-variable-coeff.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "We'll do this with periodic boundary conditions.  Consider the coefficient $\\alpha$ of the form:\n",
     "\n",
-    "$$\\alpha = 2 + \\cos(2 \\pi x) * cos(2*\\pi x)$$\n",
+    "$$\\alpha = 2 + \\cos(2 \\pi x) \\cos(2\\pi x)$$\n",
     "\n",
     "and the source, $f$:\n",
     "\n",
@@ -365,387 +365,121 @@
       "source norm =  81.3868428575047\n",
       "<<< beginning V-cycle (cycle 1) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 81.3868428575047\n",
-      "  after G-S, residual L2: 112.09083313155548\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 79.20476910530611\n",
-      "  after G-S, residual L2: 101.02040748025243\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 71.24113182887879\n",
-      "  after G-S, residual L2: 68.07049203593525\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 47.65766921623646\n",
-      "  after G-S, residual L2: 14.457805865685266\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 9.844801065610545\n",
-      "  after G-S, residual L2: 0.017140908875684892\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.010614107357161864\n",
-      "  after G-S, residual L2: 1.6932877790644725e-16\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.076290261682047e-16\n",
-      "  after G-S, residual L2: 1.6899342091498585e-16\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.0168243001880073\n",
-      "  after G-S, residual L2: 5.923427095335638e-06\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 14.490074298606165\n",
-      "  after G-S, residual L2: 0.29986555275210147\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 69.37103937480296\n",
-      "  after G-S, residual L2: 1.4310505086782157\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 103.8840936379008\n",
-      "  after G-S, residual L2: 1.9330552480770324\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 115.51087187959779\n",
-      "  after G-S, residual L2: 2.0813236343995993\n",
-      "\n",
+      "  level =  6, nx =  128, residual change:     81.3868 →     112.091\n",
+      "  level =  5, nx =   64, residual change:     79.2048 →      101.02\n",
+      "  level =  4, nx =   32, residual change:     71.2411 →     68.0705\n",
+      "  level =  3, nx =   16, residual change:     47.6577 →     14.4578\n",
+      "  level =  2, nx =    8, residual change:      9.8448 →   0.0171409\n",
+      "  level =  1, nx =    4, residual change:   0.0106141 → 1.69329e-16\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 3.07629e-16 → 3.07629e-16\n",
+      "  level =  2, nx =    8, residual change:   0.0168243 →   0.0168243\n",
+      "  level =  3, nx =   16, residual change:     14.4901 →     14.4901\n",
+      "  level =  4, nx =   32, residual change:      69.371 →      69.371\n",
+      "  level =  5, nx =   64, residual change:     103.884 →     103.884\n",
+      "  level =  6, nx =  128, residual change:     115.511 →     115.511\n",
       "cycle 1: relative err = 1.0000000000000007, residual err = 0.025573219961900512\n",
       "\n",
       "<<< beginning V-cycle (cycle 2) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.0813236343995993\n",
-      "  after G-S, residual L2: 2.028614400082243\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.4344055030175733\n",
-      "  after G-S, residual L2: 1.836839797580815\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.2987058278437038\n",
-      "  after G-S, residual L2: 1.241442108554593\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.8774520735275128\n",
-      "  after G-S, residual L2: 0.2560408781548494\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.1805883954189716\n",
-      "  after G-S, residual L2: 0.00027228502856063117\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 0.0001874470340917452\n",
-      "  after G-S, residual L2: 6.558667547503905e-17\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.1921712762164279e-16\n",
-      "  after G-S, residual L2: 6.559818395441108e-17\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.00029855358236808725\n",
-      "  after G-S, residual L2: 1.1883088358215799e-07\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.33718020660845766\n",
-      "  after G-S, residual L2: 0.0073856927799486275\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.7280933155235387\n",
-      "  after G-S, residual L2: 0.03657676806410766\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 2.6097059179467794\n",
-      "  after G-S, residual L2: 0.049180332649561384\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.897205633139506\n",
-      "  after G-S, residual L2: 0.0527907326658854\n",
-      "\n",
+      "  level =  6, nx =  128, residual change:     2.08132 →     2.02861\n",
+      "  level =  5, nx =   64, residual change:     1.43441 →     1.83684\n",
+      "  level =  4, nx =   32, residual change:     1.29871 →     1.24144\n",
+      "  level =  3, nx =   16, residual change:    0.877452 →    0.256041\n",
+      "  level =  2, nx =    8, residual change:    0.180588 → 0.000272285\n",
+      "  level =  1, nx =    4, residual change: 0.000187447 → 6.55867e-17\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 1.19217e-16 → 1.19217e-16\n",
+      "  level =  2, nx =    8, residual change: 0.000298554 → 0.000298554\n",
+      "  level =  3, nx =   16, residual change:     0.33718 →     0.33718\n",
+      "  level =  4, nx =   32, residual change:     1.72809 →     1.72809\n",
+      "  level =  5, nx =   64, residual change:     2.60971 →     2.60971\n",
+      "  level =  6, nx =  128, residual change:     2.89721 →     2.89721\n",
       "cycle 2: relative err = 2.1803634390217064, residual err = 0.0006486396426301177\n",
       "\n",
       "<<< beginning V-cycle (cycle 3) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0527907326658854\n",
-      "  after G-S, residual L2: 0.05151285072452151\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.03642414235588237\n",
-      "  after G-S, residual L2: 0.04681129156855175\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.03309698413399808\n",
-      "  after G-S, residual L2: 0.03183229031793178\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.022497516183082926\n",
-      "  after G-S, residual L2: 0.006566311349661639\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.004631305942476971\n",
-      "  after G-S, residual L2: 6.9547898567487165e-06\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 4.792096888293702e-06\n",
-      "  after G-S, residual L2: 3.471550948460998e-16\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 6.308212385276108e-16\n",
-      "  after G-S, residual L2: 3.4715505620256274e-16\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 7.633092127499261e-06\n",
-      "  after G-S, residual L2: 3.040164299190507e-09\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.008648757461907228\n",
-      "  after G-S, residual L2: 0.00018948785759489225\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.04427890540920838\n",
-      "  after G-S, residual L2: 0.0009389342673250444\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.06654722333556268\n",
-      "  after G-S, residual L2: 0.001258977557235848\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0736818681268214\n",
-      "  after G-S, residual L2: 0.0013504077840749175\n",
-      "\n",
+      "  level =  6, nx =  128, residual change:   0.0527907 →   0.0515129\n",
+      "  level =  5, nx =   64, residual change:   0.0364241 →   0.0468113\n",
+      "  level =  4, nx =   32, residual change:    0.033097 →   0.0318323\n",
+      "  level =  3, nx =   16, residual change:   0.0224975 →  0.00656631\n",
+      "  level =  2, nx =    8, residual change:  0.00463131 → 6.95479e-06\n",
+      "  level =  1, nx =    4, residual change:  4.7921e-06 → 3.47155e-16\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 6.30821e-16 → 6.30821e-16\n",
+      "  level =  2, nx =    8, residual change: 7.63309e-06 → 7.63309e-06\n",
+      "  level =  3, nx =   16, residual change:  0.00864876 →  0.00864876\n",
+      "  level =  4, nx =   32, residual change:   0.0442789 →   0.0442789\n",
+      "  level =  5, nx =   64, residual change:   0.0665472 →   0.0665472\n",
+      "  level =  6, nx =  128, residual change:   0.0736819 →   0.0736819\n",
       "cycle 3: relative err = 0.04844393523115633, residual err = 1.659245815001406e-05\n",
       "\n",
       "<<< beginning V-cycle (cycle 4) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0013504077840749175\n",
-      "  after G-S, residual L2: 0.0013176203296963518\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.0009316676173494106\n",
-      "  after G-S, residual L2: 0.0011976473134532755\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.0008467506681862436\n",
-      "  after G-S, residual L2: 0.0008162390069498438\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.0005768371115526599\n",
-      "  after G-S, residual L2: 0.0001685017884673286\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 0.0001188401863833612\n",
-      "  after G-S, residual L2: 1.7839871941545406e-07\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.229249264581879e-07\n",
-      "  after G-S, residual L2: 2.1827021820630676e-16\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.966220650519861e-16\n",
-      "  after G-S, residual L2: 2.1827020969848608e-16\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.9580123197833577e-07\n",
-      "  after G-S, residual L2: 7.798609418629656e-11\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 0.0002219015543189487\n",
-      "  after G-S, residual L2: 4.86172669068517e-06\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 0.0011347029972948912\n",
-      "  after G-S, residual L2: 2.4095419570460435e-05\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 0.001702784680617488\n",
-      "  after G-S, residual L2: 3.235875436167669e-05\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 0.0018859723605450115\n",
-      "  after G-S, residual L2: 3.477574043062499e-05\n",
-      "\n",
+      "  level =  6, nx =  128, residual change:  0.00135041 →  0.00131762\n",
+      "  level =  5, nx =   64, residual change: 0.000931668 →  0.00119765\n",
+      "  level =  4, nx =   32, residual change: 0.000846751 → 0.000816239\n",
+      "  level =  3, nx =   16, residual change: 0.000576837 → 0.000168502\n",
+      "  level =  2, nx =    8, residual change:  0.00011884 → 1.78399e-07\n",
+      "  level =  1, nx =    4, residual change: 1.22925e-07 →  2.1827e-16\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 3.96622e-16 → 3.96622e-16\n",
+      "  level =  2, nx =    8, residual change: 1.95801e-07 → 1.95801e-07\n",
+      "  level =  3, nx =   16, residual change: 0.000221902 → 0.000221902\n",
+      "  level =  4, nx =   32, residual change:   0.0011347 →   0.0011347\n",
+      "  level =  5, nx =   64, residual change:  0.00170278 →  0.00170278\n",
+      "  level =  6, nx =  128, residual change:  0.00188597 →  0.00188597\n",
       "cycle 4: relative err = 0.0012759605329324085, residual err = 4.2728946362388976e-07\n",
       "\n",
       "<<< beginning V-cycle (cycle 5) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 3.477574043062499e-05\n",
-      "  after G-S, residual L2: 3.3894283999981624e-05\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 2.396594933012801e-05\n",
-      "  after G-S, residual L2: 3.0729958860342183e-05\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 2.172596347709224e-05\n",
-      "  after G-S, residual L2: 2.092170118561758e-05\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.4784500192962139e-05\n",
-      "  after G-S, residual L2: 4.320982022442191e-06\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 3.047368468741227e-06\n",
-      "  after G-S, residual L2: 4.571730794163797e-09\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.150429317910465e-09\n",
-      "  after G-S, residual L2: 4.9047091912236845e-17\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 8.91242010041316e-17\n",
-      "  after G-S, residual L2: 4.904709185073841e-17\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 5.0182069075539776e-09\n",
-      "  after G-S, residual L2: 1.9988571748929256e-12\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 5.68972253580501e-06\n",
-      "  after G-S, residual L2: 1.2466131770422332e-07\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 2.9070695207955434e-05\n",
-      "  after G-S, residual L2: 6.180575451262474e-07\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 4.3699213469710675e-05\n",
-      "  after G-S, residual L2: 8.346224078626535e-07\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 4.855568250485679e-05\n",
-      "  after G-S, residual L2: 9.008602975150272e-07\n",
-      "\n",
+      "  level =  6, nx =  128, residual change: 3.47757e-05 → 3.38943e-05\n",
+      "  level =  5, nx =   64, residual change: 2.39659e-05 →   3.073e-05\n",
+      "  level =  4, nx =   32, residual change:  2.1726e-05 → 2.09217e-05\n",
+      "  level =  3, nx =   16, residual change: 1.47845e-05 → 4.32098e-06\n",
+      "  level =  2, nx =    8, residual change: 3.04737e-06 → 4.57173e-09\n",
+      "  level =  1, nx =    4, residual change: 3.15043e-09 → 4.90471e-17\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 8.91242e-17 → 8.91242e-17\n",
+      "  level =  2, nx =    8, residual change: 5.01821e-09 → 5.01821e-09\n",
+      "  level =  3, nx =   16, residual change: 5.68972e-06 → 5.68972e-06\n",
+      "  level =  4, nx =   32, residual change: 2.90707e-05 → 2.90707e-05\n",
+      "  level =  5, nx =   64, residual change: 4.36992e-05 → 4.36992e-05\n",
+      "  level =  6, nx =  128, residual change: 4.85557e-05 → 4.85557e-05\n",
       "cycle 5: relative err = 3.301203447716335e-05, residual err = 1.1068868945958364e-08\n",
       "\n",
       "<<< beginning V-cycle (cycle 6) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 9.008602975150272e-07\n",
-      "  after G-S, residual L2: 8.762741263726478e-07\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 6.195925724042019e-07\n",
-      "  after G-S, residual L2: 7.904743215272961e-07\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 5.588523701408656e-07\n",
-      "  after G-S, residual L2: 5.360094105863821e-07\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 3.7875634480610824e-07\n",
-      "  after G-S, residual L2: 1.1073190109213942e-07\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 7.809114933602306e-08\n",
-      "  after G-S, residual L2: 1.1709544454314423e-10\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 8.069774491999004e-11\n",
-      "  after G-S, residual L2: 5.69861151519836e-16\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 1.0355031885859903e-15\n",
-      "  after G-S, residual L2: 5.698611515176775e-16\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.2854137396352388e-10\n",
-      "  after G-S, residual L2: 5.114049334415458e-14\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 1.4579494037250006e-07\n",
-      "  after G-S, residual L2: 3.194414288762389e-09\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 7.445198033520522e-07\n",
-      "  after G-S, residual L2: 1.5845029145659617e-08\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.124388644639508e-06\n",
-      "  after G-S, residual L2: 2.1592778513030064e-08\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 1.2565779969510642e-06\n",
-      "  after G-S, residual L2: 2.345575097735639e-08\n",
-      "\n",
+      "  level =  6, nx =  128, residual change:  9.0086e-07 → 8.76274e-07\n",
+      "  level =  5, nx =   64, residual change: 6.19593e-07 → 7.90474e-07\n",
+      "  level =  4, nx =   32, residual change: 5.58852e-07 → 5.36009e-07\n",
+      "  level =  3, nx =   16, residual change: 3.78756e-07 → 1.10732e-07\n",
+      "  level =  2, nx =    8, residual change: 7.80911e-08 → 1.17095e-10\n",
+      "  level =  1, nx =    4, residual change: 8.06977e-11 → 5.69861e-16\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change:  1.0355e-15 →  1.0355e-15\n",
+      "  level =  2, nx =    8, residual change: 1.28541e-10 → 1.28541e-10\n",
+      "  level =  3, nx =   16, residual change: 1.45795e-07 → 1.45795e-07\n",
+      "  level =  4, nx =   32, residual change:  7.4452e-07 →  7.4452e-07\n",
+      "  level =  5, nx =   64, residual change: 1.12439e-06 → 1.12439e-06\n",
+      "  level =  6, nx =  128, residual change: 1.25658e-06 → 1.25658e-06\n",
       "cycle 6: relative err = 8.544249588823554e-07, residual err = 2.88200772432267e-10\n",
       "\n",
       "<<< beginning V-cycle (cycle 7) >>>\n",
       "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 2.345575097735639e-08\n",
-      "  after G-S, residual L2: 2.2753069343643243e-08\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 1.608800052969807e-08\n",
-      "  after G-S, residual L2: 2.0378135946295804e-08\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.4406807384763748e-08\n",
-      "  after G-S, residual L2: 1.3725209913382864e-08\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 9.698124532772939e-09\n",
-      "  after G-S, residual L2: 2.8356346509872416e-09\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 1.9997148871595184e-09\n",
-      "  after G-S, residual L2: 2.9973192388519374e-12\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 2.065757572022064e-12\n",
-      "  after G-S, residual L2: 1.7490702049122712e-17\n",
-      "\n",
-      "  bottom solve:\n",
-      "  level: 0, grid: 2 x 2\n",
-      "\n",
-      "  level: 1, grid: 4 x 4\n",
-      "  before G-S, residual L2: 3.178261528034216e-17\n",
-      "  after G-S, residual L2: 1.749070204845573e-17\n",
-      "\n",
-      "  level: 2, grid: 8 x 8\n",
-      "  before G-S, residual L2: 3.290507210457819e-12\n",
-      "  after G-S, residual L2: 1.3129499254343699e-15\n",
-      "\n",
-      "  level: 3, grid: 16 x 16\n",
-      "  before G-S, residual L2: 3.733252631632496e-09\n",
-      "  after G-S, residual L2: 8.179799392138975e-11\n",
-      "\n",
-      "  level: 4, grid: 32 x 32\n",
-      "  before G-S, residual L2: 1.9059423053448905e-08\n",
-      "  after G-S, residual L2: 4.0598549777170336e-10\n",
-      "\n",
-      "  level: 5, grid: 64 x 64\n",
-      "  before G-S, residual L2: 2.8995855293378498e-08\n",
-      "  after G-S, residual L2: 5.600770588949833e-10\n",
-      "\n",
-      "  level: 6, grid: 128 x 128\n",
-      "  before G-S, residual L2: 3.266381812884192e-08\n",
-      "  after G-S, residual L2: 6.132405136584784e-10\n",
-      "\n",
+      "  level =  6, nx =  128, residual change: 2.34558e-08 → 2.27531e-08\n",
+      "  level =  5, nx =   64, residual change:  1.6088e-08 → 2.03781e-08\n",
+      "  level =  4, nx =   32, residual change: 1.44068e-08 → 1.37252e-08\n",
+      "  level =  3, nx =   16, residual change: 9.69812e-09 → 2.83563e-09\n",
+      "  level =  2, nx =    8, residual change: 1.99971e-09 → 2.99732e-12\n",
+      "  level =  1, nx =    4, residual change: 2.06576e-12 → 1.74907e-17\n",
+      "  bottom solve\n",
+      "  level =  1, nx =    4, residual change: 3.17826e-17 → 3.17826e-17\n",
+      "  level =  2, nx =    8, residual change: 3.29051e-12 → 3.29051e-12\n",
+      "  level =  3, nx =   16, residual change: 3.73325e-09 → 3.73325e-09\n",
+      "  level =  4, nx =   32, residual change: 1.90594e-08 → 1.90594e-08\n",
+      "  level =  5, nx =   64, residual change: 2.89959e-08 → 2.89959e-08\n",
+      "  level =  6, nx =  128, residual change: 3.26638e-08 → 3.26638e-08\n",
       "cycle 7: relative err = 2.210681933627904e-08, residual err = 7.534885150074738e-12\n",
       "\n"
      ]
@@ -785,7 +519,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f11444a88e0>"
+       "<matplotlib.colorbar.Colorbar at 0x7ff0ce8ee150>"
       ]
      },
      "execution_count": 15,
@@ -794,7 +528,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -894,7 +628,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f1144646920>"
+       "<matplotlib.colorbar.Colorbar at 0x7ff0cc6a0090>"
       ]
      },
      "execution_count": 19,
@@ -903,7 +637,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -920,13 +654,6 @@
     "               extent=[mg.xmin, mg.xmax, mg.ymin, mg.ymax])\n",
     "fig.colorbar(im, ax=ax)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -945,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,