diff --git a/Exercise sheet 5/exercise_sheet_05.ipynb b/Exercise sheet 5/exercise_sheet_05.ipynb
index 894cfe7..40542be 100644
--- a/Exercise sheet 5/exercise_sheet_05.ipynb	
+++ b/Exercise sheet 5/exercise_sheet_05.ipynb	
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "9df6a9cc",
    "metadata": {
     "deletable": false,
@@ -247,12 +247,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "testarray[325] = 0.6809296786477453\n",
-      "f[\"test\"][325] = 0.6809296786477453\n",
-      "test[325] = 0.6809296786477453\n",
-      "f[\"test-plus-one\"][325] = 1.6809296786477455\n",
-      "f[\"test\"][325] = 0.6809296786477453\n",
-      "f[\"test-plus-one\"][325] = 1.6809296786477455\n",
+      "testarray[325] = 0.13910269568464984\n",
+      "f[\"test\"][325] = 0.13910269568464984\n",
+      "test[325] = 0.13910269568464984\n",
+      "f[\"test-plus-one\"][325] = 1.1391026956846497\n",
+      "f[\"test\"][325] = 0.13910269568464984\n",
+      "f[\"test-plus-one\"][325] = 1.1391026956846497\n",
       "data sets in test.hdf5: ['test', 'test-plus-one']\n",
       "data sets in test.hdf5: ['test-plus-one']\n"
      ]
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 4,
    "id": "51e6fe2c",
    "metadata": {
     "deletable": false,
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 5,
    "id": "916e8389",
    "metadata": {
     "deletable": false,
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 6,
    "id": "23067631",
    "metadata": {
     "deletable": false,
@@ -413,37 +413,9 @@
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<HDF5 dataset \"random-walk\": shape (1001,), type \"<f8\">\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "2 indexing arguments for 1 dimensions",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [33]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(f[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom-walk\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m----> 8\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mN\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrandom-walk\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep $i$\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     10\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposition\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/jupyter-conda/lib/python3.9/site-packages/matplotlib/pyplot.py:2757\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2755\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   2756\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2757\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2758\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2759\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/jupyter-conda/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1392\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1634\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
-      "File \u001b[0;32m/opt/jupyter-conda/lib/python3.9/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    311\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/jupyter-conda/lib/python3.9/site-packages/matplotlib/axes/_base.py:488\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    486\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(xy) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    487\u001b[0m     x \u001b[38;5;241m=\u001b[39m _check_1d(xy[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m--> 488\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxy\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    490\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m index_of(xy[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n",
-      "File \u001b[0;32m/opt/jupyter-conda/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1327\u001b[0m, in \u001b[0;36m_check_1d\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1321\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings(record\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m w:\n\u001b[1;32m   1322\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m   1323\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malways\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1324\u001b[0m         category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mWarning\u001b[39;00m,\n\u001b[1;32m   1325\u001b[0m         message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSupport for multi-dimensional indexing\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1327\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mndim\n\u001b[1;32m   1328\u001b[0m     \u001b[38;5;66;03m# we have definitely hit a pandas index or series object\u001b[39;00m\n\u001b[1;32m   1329\u001b[0m     \u001b[38;5;66;03m# cast to a numpy array.\u001b[39;00m\n\u001b[1;32m   1330\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(w) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
-      "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m/opt/jupyter-conda/lib/python3.9/site-packages/h5py/_hl/dataset.py:710\u001b[0m, in \u001b[0;36mDataset.__getitem__\u001b[0;34m(self, args, new_dtype)\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fast_read_ok \u001b[38;5;129;01mand\u001b[39;00m (new_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    709\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 710\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fast_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    711\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m    712\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m  \u001b[38;5;66;03m# Fall back to Python read pathway below\u001b[39;00m\n",
-      "File \u001b[0;32mh5py/_selector.pyx:351\u001b[0m, in \u001b[0;36mh5py._selector.Reader.read\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mh5py/_selector.pyx:107\u001b[0m, in \u001b[0;36mh5py._selector.Selector.apply_args\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: 2 indexing arguments for 1 dimensions"
-     ]
-    },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -459,10 +431,9 @@
     "    assert \"random-walk\" in f\n",
     "    \n",
     "    N = len(f[\"random-walk\"])\n",
-    "    print(f[\"random-walk\"])\n",
     "    \n",
     "    plt.figure()\n",
-    "    plt.plot(np.arange(N), f[\"random-walk\"])\n",
+    "    plt.plot(np.arange(N), np.array(f[\"random-walk\"]))\n",
     "    plt.xlabel(\"step $i$\")\n",
     "    plt.ylabel(\"position\")\n",
     "    plt.title(\"Random walk from the origin with standard normal steps\")\n",
diff --git a/Exercise sheet 5/test.hdf5 b/Exercise sheet 5/test.hdf5
index d87e07d..d583a3f 100644
Binary files a/Exercise sheet 5/test.hdf5 and b/Exercise sheet 5/test.hdf5 differ