diff --git a/docs/examples/amor.ipynb b/docs/examples/amor.ipynb
index d67ab9f..96d8d0a 100644
--- a/docs/examples/amor.ipynb
+++ b/docs/examples/amor.ipynb
@@ -37,8 +37,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "normalized = pipeline.compute(NormalizedIOverQ)\n",
-    "normalized.plot(norm='log') + normalized.mean('detector_number').plot(norm='log')\n"
+    "# Compute I over Q and the standard deviation of Q\n",
+    "ioq, qstd = pipeline.compute((NormalizedIOverQ, QStd)).values()"
    ]
   },
   {
@@ -47,14 +47,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Diagnostics plot\n",
-    "(pipeline.compute(ThetaData[Sample])\n",
-    "    .bins.concat('detector_number')\n",
-    "    .hist(\n",
-    "        theta=sc.linspace(dim='theta', start=0.0, stop=1.2, num=165, unit='deg').to(unit='rad'),\n",
-    "        wavelength=sc.linspace(dim='wavelength', start=0, stop=15.0, num=165, unit='angstrom'),\n",
-    "    )\n",
-    "    .plot())\n"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig = plt.figure(figsize=(5, 7))\n",
+    "ax1 = fig.add_axes([0, 0.55, 1.0, 0.45])\n",
+    "ax2 = fig.add_axes([0, 0.0, 1.0, 0.45])\n",
+    "cax = fig.add_axes([1.05, 0.55, 0.03, 0.45])\n",
+    "fig1 = ioq.plot(norm='log', ax=ax1, cax=cax, grid=True)\n",
+    "fig2 = ioq.mean('detector_number').plot(norm='log', ax=ax2, grid=True)\n",
+    "fig1.canvas.xrange = fig2.canvas.xrange\n",
+    "fig1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make a $(\\lambda, \\theta)$ map\n",
+    "A good sanity check is to create a two-dimensional map of the counts in $\\lambda$ and $\\theta$ bins. To achieve this, we request the `ThetaData` from the pipeline. In the graph above we can see that `WavelengthData` is required to compute `ThetaData`, therefore it is also present in `ThetaData` so we don't need to require it separately."
    ]
   },
   {
@@ -63,8 +73,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "res = pipeline.compute((NormalizedIOverQ, QStd))\n",
-    "res[QStd].plot()"
+    "from essreflectometry.reflectometry.types import ThetaData\n",
+    "pipeline.compute(ThetaData[Sample])\\\n",
+    "    .bins.concat('detector_number')\\\n",
+    "    .hist(\n",
+    "        theta=sc.linspace(dim='theta', start=0.0, stop=1.2, num=165, unit='deg').to(unit='rad'),\n",
+    "        wavelength=sc.linspace(dim='wavelength', start=0, stop=15.0, num=165, unit='angstrom'),\n",
+    "    )\\\n",
+    "    .plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot can be used to check if the value of the sample rotation angle $\\omega$ is correct. The bright triangles should be pointing back to the origin $\\lambda = \\theta = 0$."
    ]
   }
  ],