diff --git a/contrib/translations/PDE-reduction and translations.ipynb b/contrib/translations/PDE-reduction and translations.ipynb
index b3771a1e735ac7a0881fd4f9412debf257caa947..632c6a4516ae6ad5d7f60ab5ccac587deccf225e 100644
--- a/contrib/translations/PDE-reduction and translations.ipynb	
+++ b/contrib/translations/PDE-reduction and translations.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,9 +20,17 @@
     "\n",
     "order = 4\n",
     "\n",
-    "knl = LaplaceKernel(2)\n",
+    "if 0:\n",
+    "    knl = LaplaceKernel(2)\n",
+    "    pde = [(1, (2,0)), (1, (0, 2))]\n",
+    "    extra_kernel_kwargs = {}\n",
+    "    \n",
+    "else:\n",
+    "    helm_k = 1.2\n",
+    "    knl = HelmholtzKernel(2)\n",
+    "    extra_kernel_kwargs={\"k\": helm_k}\n",
     "\n",
-    "pde = [(1, (2,0)), (1, (0, 2))]\n",
+    "    pde = [(1, (2,0)), (1, (0, 2)), (helm_k**2, (0, 0))]\n",
     "\n",
     "mpole_expn = VolumeTaylorMultipoleExpansion(knl, order)\n",
     "local_expn = VolumeTaylorLocalExpansion(knl, order)\n",
@@ -34,15 +42,13 @@
     "        knl,\n",
     "        mpole_expn_class=type(mpole_expn),\n",
     "        local_expn_class=type(local_expn),\n",
-    "    \n",
-    "        #YukawaKernel(2), extra_kernel_kwargs={\"lam\": 5},\n",
-    "        #HelmholtzKernel(2), extra_kernel_kwargs={\"k\": 0.3},\n",
+    "        extra_kernel_kwargs=extra_kernel_kwargs,\n",
     "        )\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,20 +62,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([  5.00000000e+01,  -6.69626794e-01,  -2.33234202e+00,\n",
-       "         2.68444605e+00,  -8.96436375e-01,   1.81021063e+00,\n",
-       "        -3.27003166e-02,  -2.03802375e-01,  -5.63064621e-03,\n",
-       "        -6.23905999e-02,   3.71388362e-02,  -1.37580839e-02,\n",
-       "         1.05569439e-01,  -1.39756658e-02,   1.91970436e-02])"
+       "array([  5.00000000e+01,   4.76258789e+00,   6.63902810e-01,\n",
+       "         2.17149444e+00,   6.22396090e-01,   2.36567252e+00,\n",
+       "         5.93173776e-02,   6.33392972e-02,   1.15590385e-01,\n",
+       "         2.35250166e-02,   2.60421537e-02,   1.58948983e-02,\n",
+       "         9.97399769e-02,   1.12510066e-02,   3.13387666e-02])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -80,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,16 +141,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "7.3852783309125538e-16"
+       "4.3183836498795062e-16"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,16 +184,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fb83bb70f28>"
+       "<matplotlib.image.AxesImage at 0x7f7f8841ceb8>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -195,7 +201,7 @@
      "data": {
       "image/png": 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y/FKhDL9UqP8H8zJzDMuV3ZMAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb83be18dd8>"
+       "<matplotlib.figure.Figure at 0x7f7f42944978>"
       ]
      },
      "metadata": {},
@@ -208,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +223,7 @@
        "(15, 9)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 44,
    "metadata": {
     "collapsed": true
    },
@@ -241,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,14 +263,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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jxo38+te/BiArKyui8XdHy3POyck5qnVYVVUVtDvqggsuYO3atbz33nuMHDmS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\n+dHT/5e09O7NweF2Bxor+Mc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um9m+GEkvSHonwXbBQa5+4GO/XbTXg/RBP0xsQv5HOjl+LrFFYo8ATgIejP83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\nogAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb83bb10b38>"
+       "<matplotlib.figure.Figure at 0x7f7f883bf8d0>"
       ]
      },
      "metadata": {},
@@ -281,14 +287,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.24344978758e-14\n"
+      "3.79429987221e-15\n"
      ]
     }
    ],
@@ -298,14 +304,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.06581410364e-14\n"
+      "0.38107769179\n"
      ]
     }
    ],
@@ -318,17 +324,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  0.00000000e+00   1.11022302e-16   0.00000000e+00  -2.24732834e+00\n",
-      "   2.22044605e-16  -2.24732834e+00   1.91654814e-02  -9.90567683e-01\n",
-      "   1.91654814e-02  -9.90567683e-01  -5.35500227e-02   2.34496156e-02\n",
-      "  -3.03526052e-01   2.34496156e-02  -2.49976029e-01]\n"
+      "[-32.9950621   -2.48614725 -16.86618617 -14.44539704  -1.56949022\n",
+      " -18.88759082  -1.72649114  -7.47870901  -2.20046784  -8.27892976\n",
+      "   5.88044482  -1.08992376   9.7345129   -1.18252057   3.7437777 ]\n"
      ]
     }
    ],
@@ -338,16 +343,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9.4262983793905305e-15"
+       "4.0217762427070678"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,6 +361,54 @@
     "la.norm(reduction_mat @ (trans_proj.coeffs - trans_unproj.coeffs))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/andreas/src/sumpy/sumpy/p2p.py:186: LoopyWarning: 'lang_version' was not passed to make_kernel(). To avoid this warning, pass lang_version=(2018, 1) in this invocation. (Or say 'from loopy.version import LOOPY_USE_LANGUAGE_VERSION_2018_1' in the global scope of the calling frame.)\n",
+      "  nresults=len(self.kernels)))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.011804658035654577"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.l_inf(trans_unproj - pt_src, 1.2, center=[3, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00029429326299543407"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.l_inf(mexp - pt_src, 1.2, center=[3, 0])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,