diff --git a/examples/sem_reagan.py b/examples/sem_reagan.py
index 88dde57a6c5927ecb6f13bd0a6ce213008438fc9..42c3dcfec32907dd66cb125a0a51776636fcc720 100644
--- a/examples/sem_reagan.py
+++ b/examples/sem_reagan.py
@@ -35,129 +35,6 @@ from pyopencl.tools import pytest_generate_tests_for_pyopencl \
 
 
 
-def test_tim2d(ctx_factory):
-    dtype = np.float32
-    ctx = ctx_factory()
-    order = "C"
-
-    n = 8
-
-    from pymbolic import var
-    K_sym = var("K")
-
-    field_shape = (K_sym, n, n)
-
-    # K - run-time symbolic
-    knl = lp.make_kernel(ctx.devices[0],
-            "[K] -> {[i,j,e,m,o,gi]: 0<=i,j,m,o<%d and 0<=e<K and 0<=gi<3}" % n,
-           [
-            "ur(a,b) := sum(@o, D[a,o]*u[e,o,b])",
-            "us(a,b) := sum(@o, D[b,o]*u[e,a,o])",
-
-            #"Gu(mat_entry,a,b) := G[mat_entry,e,m,j]*ur(m,j)",
-
-            "Gux(a,b) := G$x[0,e,a,b]*ur(a,b)+G$x[1,e,a,b]*us(a,b)",
-            "Guy(a,b) := G$y[1,e,a,b]*ur(a,b)+G$y[2,e,a,b]*us(a,b)",
-            "lap[e,i,j]  = "
-            "  sum(m, D[m,i]*Gux(m,j))"
-            "+ sum(m, D[m,j]*Guy(i,m))"
-
-            ],
-            [
-            lp.GlobalArg("u", dtype, shape=field_shape, order=order),
-            lp.GlobalArg("lap", dtype, shape=field_shape, order=order),
-            lp.GlobalArg("G", dtype, shape=(3,)+field_shape, order=order),
-            # lp.ConstantArrayArg("D", dtype, shape=(n, n), order=order),
-            lp.GlobalArg("D", dtype, shape=(n, n), order=order),
-            # lp.ImageArg("D", dtype, shape=(n, n)),
-            lp.ValueArg("K", np.int32, approximately=1000),
-            ],
-            name="semlap2D", assumptions="K>=1")
-
-    seq_knl = knl
-
-    def variant_orig(knl):
-        knl = lp.tag_inames(knl, dict(i="l.0", j="l.1", e="g.0"))
-
-        knl = lp.add_prefetch(knl, "D[:,:]")
-        knl = lp.add_prefetch(knl, "u[e, :, :]")
-
-        knl = lp.precompute(knl, "ur(m,j)", np.float32, ["m", "j"])
-        knl = lp.precompute(knl, "us(i,m)", np.float32, ["i", "m"])
-
-        knl = lp.precompute(knl, "Gux(m,j)", np.float32, ["m", "j"])
-        knl = lp.precompute(knl, "Guy(i,m)", np.float32, ["i", "m"])
-
-        knl = lp.add_prefetch(knl, "G$x[:,e,:,:]")
-        knl = lp.add_prefetch(knl, "G$y[:,e,:,:]")
-
-        knl = lp.tag_inames(knl, dict(o="unr"))
-        knl = lp.tag_inames(knl, dict(m="unr"))
-
-        knl = lp.set_instruction_priority(knl, "D_fetch", 5)
-        print knl
-
-        return knl
-
-    for variant in [variant_orig]:
-        kernel_gen = lp.generate_loop_schedules(variant(knl))
-        kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
-
-        K = 1000
-        lp.auto_test_vs_ref(seq_knl, ctx, kernel_gen,
-                op_count=[K*(n*n*n*2*2 + n*n*2*3 + n**3 * 2*2)/1e9],
-                op_label=["GFlops"],
-                parameters={"K": K})
-
-
-
-
-def make_me_a_test_test_tim3d_slab(ctx_factory):
-    dtype = np.float32
-    ctx = ctx_factory()
-    order = "C"
-
-    n = 8
-
-    from pymbolic import var
-
-    # K - run-time symbolic
-    knl = lp.make_kernel(ctx.devices[0],
-            "[E] -> {[i,j,k, o, e]: 0<=i,j,k,o < n and 0<=e<E }",
-            """
-            <> ur[i,j,k] = sum(@o, D[i,o]*u[e,o,j,k])
-            <> us[i,j,k] = sum(@o, D[j,o]*u[e,i,o,k])
-            <> ut[i,j,k] = sum(@o, D[k,o]*u[e,i,j,o])
-            """,
-            [
-            lp.GlobalArg("u", dtype, shape="E,n,n,n", order=order),
-            # lp.GlobalArg("G", dtype, shape=(3,)+field_shape, order=order),
-            # lp.ConstantArrayArg("D", dtype, shape=(n, n), order=order),
-            lp.GlobalArg("D", dtype, shape=(n, n), order=order),
-            # lp.ImageArg("D", dtype, shape=(n, n)),
-            lp.ValueArg("E", np.int32, approximately=1000),
-            ],
-            name="semdiff3D", assumptions="E>=1",
-	    defines={"n": n})
-
-    seq_knl = knl
-
-    def variant_orig(knl):
-	return knl
-
-    for variant in [variant_orig]:
-        kernel_gen = lp.generate_loop_schedules(variant(knl))
-        kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
-
-        E = 1000
-        lp.auto_test_vs_ref(seq_knl, ctx, kernel_gen,
-                op_count=[E*(n*n*n*2*2 + n*n*2*3 + n**3 * 2*2)/1e9],
-                op_label=["GFlops"],
-                parameters={"E": E})
-
-
-
-
 def test_tim3d_slab(ctx_factory):
     dtype = np.float32
     ctx = ctx_factory()
@@ -165,9 +42,6 @@ def test_tim3d_slab(ctx_factory):
 
     Nq = 8
 
-    from pymbolic import var
-
-    # K - run-time symbolic
     knl = lp.make_kernel(ctx.devices[0],
             "[E] -> {[i,j, k, o,m, e]: 0<=i,j,k,o,m < Nq and 0<=e<E }",
             """
@@ -179,9 +53,9 @@ def test_tim3d_slab(ctx_factory):
             Gus(a,b,c) := G[1,e,a,b,c]*ur(a,b,c)+G[3,e,a,b,c]*us(a,b,c)+G[4,e,a,b,c]*ut(a,b,c)
             Gut(a,b,c) := G[2,e,a,b,c]*ur(a,b,c)+G[4,e,a,b,c]*us(a,b,c)+G[5,e,a,b,c]*ut(a,b,c)
 
-            lapr(a,b,c):= sum(m, D[m,a]*Gur(m,b,c)) 
-            laps(a,b,c):= sum(m, D[m,b]*Gus(a,m,c)) 
-            lapt(a,b,c):= sum(m, D[m,c]*Gut(a,b,m)) 
+            lapr(a,b,c):= sum(m, D[m,a]*Gur(m,b,c))
+            laps(a,b,c):= sum(m, D[m,b]*Gus(a,m,c))
+            lapt(a,b,c):= sum(m, D[m,c]*Gut(a,b,m))
 
             lap[e,i,j,k] = lapr(i,j,k) + laps(i,j,k) + lapt(i,j,k)
             """,
@@ -194,7 +68,7 @@ def test_tim3d_slab(ctx_factory):
             lp.ValueArg("E", np.int32, approximately=1000),
             ],
             name="semdiff3D", assumptions="E>=1",
-	    defines={"Nq": Nq})
+            defines={"Nq": Nq})
 
     for derivative in "rst":
         knl = lp.duplicate_inames(knl, "o", within="... < lap"+derivative, suffix="_"+derivative)
@@ -203,7 +77,7 @@ def test_tim3d_slab(ctx_factory):
 
     def variant_orig(knl):
         return knl
-	knl = lp.tag_inames(knl, dict(e="g.0", i="l.0", j="l.1"), )
+        knl = lp.tag_inames(knl, dict(e="g.0", i="l.0", j="l.1"), )
 
         for derivative, par_names in [
             ("r", ["j", "k"]),
@@ -214,7 +88,7 @@ def test_tim3d_slab(ctx_factory):
 
         print knl
 
-	return knl
+        return knl
     #print lp.preprocess_kernel(knl)
     #1/0