diff --git a/contrib/translations/PDE-reduction and translations.ipynb b/contrib/translations/PDE-reduction and translations.ipynb
index 632c6a4516ae6ad5d7f60ab5ccac587deccf225e..62a626236719ecb7448c8e42b5991506c18d2202 100644
--- a/contrib/translations/PDE-reduction and translations.ipynb	
+++ b/contrib/translations/PDE-reduction and translations.ipynb	
@@ -234,20 +234,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 69,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "if 1:\n",
+    "if 0:\n",
     "    reduction_mat = nullsp.T\n",
-    "    expansion_mat = nullsp"
+    "    expansion_mat = nullsp\n",
+    "elif 1:\n",
+    "    chosen_indices_and_coeff_ids = [\n",
+    "        (i, cid) for i, cid in enumerate(mpole_expn.get_coefficient_identifiers())\n",
+    "        if cid[0] < 2\n",
+    "    ]\n",
+    "    chosen_indices = [idx for idx, _ in chosen_indices_and_coeff_ids]\n",
+    "    \n",
+    "    expansion_mat = np.zeros(\n",
+    "        (len(mpole_expn.get_coefficient_identifiers()), len(chosen_indices_and_coeff_ids))\n",
+    "        )\n",
+    "    for i, (idx, _) in enumerate(chosen_indices_and_coeff_ids):\n",
+    "        expansion_mat[idx, i] = 1\n",
+    "        \n",
+    "    reduction_mat = (nullsp @ la.inv(nullsp[chosen_indices])).T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,14 +275,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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um9m+GEkvSHonwXbBQa5+4GO/XbTXg/RBP0xsQv5HOjl+LrFFYo8ATgIejP83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\nlVQn3F/R5xik3Bt0U1pyElj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azgZijDHxpAhBjw31bjdaVd2tqu+HHx8DNgGD4h2YMcbEmjrc3KJDv05EZCih9QnfjfBy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\nNsYYl7IEbYwxLvX/AcRnoY4TI7qLAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7f883bf8d0>"
+       "<matplotlib.figure.Figure at 0x7f7f41cd8240>"
       ]
      },
      "metadata": {},
@@ -287,14 +299,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.79429987221e-15\n"
+      "2.80866677486e-15\n"
      ]
     }
    ],
@@ -304,14 +316,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.38107769179\n"
+      "0.0116062369243\n"
      ]
     }
    ],
@@ -324,16 +336,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-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"
+      "[-3.07295099 -0.08541702 -1.62768408 -2.17149444 -0.06559717 -2.66984178\n",
+      " -0.05931738 -1.14908652 -0.08143883 -1.25881949 -0.02604215 -0.04555359\n",
+      " -0.40284643 -0.05019419 -0.39528495]\n"
      ]
     }
    ],
@@ -343,16 +355,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.0217762427070678"
+       "0.1169116976203841"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,24 +375,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 76,
    "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,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -391,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -400,7 +404,7 @@
        "0.00029429326299543407"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }