diff --git a/examples/notebook_solar_system.ipynb b/examples/notebook_solar_system.ipynb
index 57c0842fc7beaf6cf29dac789888acf0f2b355ef..67556a6556f73188f9c888b0538a3a4aa40be24e 100644
--- a/examples/notebook_solar_system.ipynb
+++ b/examples/notebook_solar_system.ipynb
@@ -19,68 +19,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "ec48125c-6bf5-48f4-9357-8261800b5d8b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "s 0 0x2295520 (nil) (null)\n",
-      "s 1 0x22955b8 0x2293e10 Venus\n",
-      "s 2 0x2295650 0x22938f0 Earth\n",
-      "s 3 0x22956e8 0x239ad10 Mars\n",
-      "s 4 0x2295780 0x239ad30 Jupiter\n",
-      "s 5 0x2295818 0x239ad50 Saturn\n",
-      "s 6 0x22958b0 0x2292fa0 Uranus\n",
-      "s 7 0x2295948 0x239ad70 Neptune\n",
-      "s 8 0x22959e0 0x22948c0 Pluto\n",
-      "Shift 0x22955b8 (0x2293e10) to 0x2295520 ((nil))\n",
-      "s 0 0x2295520 name=0x2293e10=Venus\n",
-      "s 1 0x22955b8 name=0x22938f0=Earth\n",
-      "s 2 0x2295650 name=0x239ad10=Mars\n",
-      "s 3 0x22956e8 name=0x239ad30=Jupiter\n",
-      "s 4 0x2295780 name=0x239ad50=Saturn\n",
-      "s 5 0x2295818 name=0x2292fa0=Uranus\n",
-      "s 6 0x22958b0 name=0x239ad70=Neptune\n",
-      "s 7 0x2295948 name=0x22948c0=Pluto\n",
-      "realloc 8\n",
-      "s 0 0x2295520 (nil) (null)\n",
-      "s 1 0x22955b8 0x22938f0 Earth\n",
-      "s 2 0x2295650 0x239ad10 Mars\n",
-      "s 3 0x22956e8 0x239ad30 Jupiter\n",
-      "s 4 0x2295780 0x239ad50 Saturn\n",
-      "s 5 0x2295818 0x2292fa0 Uranus\n",
-      "s 6 0x22958b0 0x239ad70 Neptune\n",
-      "s 7 0x2295948 0x22948c0 Pluto\n",
-      "Shift 0x22955b8 (0x22938f0) to 0x2295520 ((nil))\n",
-      "s 0 0x2295520 name=0x22938f0=Earth\n",
-      "s 1 0x22955b8 name=0x239ad10=Mars\n",
-      "s 2 0x2295650 name=0x239ad30=Jupiter\n",
-      "s 3 0x22956e8 name=0x239ad50=Saturn\n",
-      "s 4 0x2295780 name=0x2292fa0=Uranus\n",
-      "s 5 0x2295818 name=0x239ad70=Neptune\n",
-      "s 6 0x22958b0 name=0x22948c0=Pluto\n",
-      "realloc 7\n",
-      "s 0 0x2295520 (nil) (null)\n",
-      "s 1 0x22955b8 0x239ad10 Mars\n",
-      "s 2 0x2295650 0x239ad30 Jupiter\n",
-      "s 3 0x22956e8 0x239ad50 Saturn\n",
-      "s 4 0x2295780 0x2292fa0 Uranus\n",
-      "s 5 0x2295818 0x239ad70 Neptune\n",
-      "s 6 0x22958b0 0x22948c0 Pluto\n",
-      "Shift 0x22955b8 (0x239ad10) to 0x2295520 ((nil))\n",
-      "s 0 0x2295520 name=0x239ad10=Mars\n",
-      "s 1 0x22955b8 name=0x239ad30=Jupiter\n",
-      "s 2 0x2295650 name=0x239ad50=Saturn\n",
-      "s 3 0x22956e8 name=0x2292fa0=Uranus\n",
-      "s 4 0x2295780 name=0x239ad70=Neptune\n",
-      "s 5 0x2295818 name=0x22948c0=Pluto\n",
-      "realloc 6\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from binarycpython.utils.functions import temp_dir\n",
@@ -125,15 +67,29 @@
    "id": "55c8ea24-0fc0-452c-8121-1e7667433479",
    "metadata": {},
    "source": [
-    "There is a lot of data here! Let's split it into a set of lists of lists, one for each planet, and let's select only objects that are still orbiting their central star."
+    "There is a lot of data here if you uncomment the print statement! \n",
+    "\n",
+    "Let's split it into a dict of lists of data, one list for each planet, and let's select only objects that are still orbiting their central star."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "323aad96-408d-404a-a56f-da98d51844dd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'pop' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_216542/1241251901.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'CS1'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# remove first element of the list \"Object\" - this is superfluous\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# remove second element of the list \"index\" - this is superfluous\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'pop' is not defined"
+     ]
+    }
+   ],
    "source": [
     "import re\n",
     "import pandas as pd\n",
@@ -153,10 +109,7 @@
     "            x.pop(0) # remove first element of the list \"Object\" - this is superfluous\n",
     "            x.pop(0) # remove second element of the list \"index\" - this is superfluous\n",
     "            x.pop(0) # remove third element of the list \"name\" - this is superfluous (it's the dict key)\n",
-    "            x.pop(1) # remove fourth element \"CS1\" - this is superfluous (we select this already) \n",
-    "            # x[0] = math.log10((max(float(x[0]),1e-6)))\n",
-    "            for i in range(0,10):\n",
-    "                x[i] = float(x[i])\n",
+    "            x.pop(1) # remove (originally) fourth element \"CS1\" - this is superfluous (we select this already) \n",
     "            data[name].append(x)            "
    ]
   },
@@ -170,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "5be09aa5-02cf-4db5-97eb-a4aefbddfac6",
    "metadata": {},
    "outputs": [],
@@ -178,6 +131,7 @@
     "dataframes = {}\n",
     "for planet in data:\n",
     "    dataframes[planet] = pd.DataFrame(data[planet], \n",
+    "                                      dtype=float, # required! argh!\n",
     "                                      columns=['time',\n",
     "                                               'mass',\n",
     "                                               'angular momentum',\n",
@@ -202,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "ff18274d-0ed7-42cc-8830-b2261a893c6f",
    "metadata": {},
    "outputs": [
@@ -212,13 +166,13 @@
        "[None]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -256,7 +210,7 @@
     "    p.text(xx,yy,planet)\n",
     "    \n",
     "p.set_xlabel(\"time/Myr\")\n",
-    "p.set_ylabel(\"separation/Rsun\")\n",
+    "p.set_ylabel(\"separation/au\")\n",
     "#p.set(xlim=(0,5)) # might be necessary?\n",
     "p.set(yscale=\"log\")"
    ]
@@ -266,14 +220,14 @@
    "id": "4ab65543-b864-41d9-98e6-10a5ef051a22",
    "metadata": {},
    "source": [
-    "The inner objects are swallowed by the Sun when it becomes a red giant. Earth survives, although tides mess with its orbit somewhat. Jupiter is pushed out beyond the orbits of Saturn and Uranus, and this simple model assumes they are ejected in the interaction because Jupiter is (far) more massive. There are options to detect when its orbit is too close to Neptune, and hence possibly eject Neptune, but I'll let you explore these.\n",
+    "The inner objects are swallowed by the Sun when it becomes a red giant. Earth survives, although mass loss from the red-giant Sun and tides mess with its orbit somewhat. Jupiter is pushed out beyond the orbits of Saturn and Uranus, and this simple model assumes they are ejected in the interaction because Jupiter is (far) more massive. There are options to detect when its orbit is too close to Neptune, and hence possibly eject Neptune, but I'll let you explore these.\n",
     "\n",
     "We now construct a plot of the temperature of each planet vs time. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "13214947-a209-4695-a6e2-692614af05dd",
    "metadata": {},
    "outputs": [
@@ -283,7 +237,7 @@
        "[None]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },