diff --git a/.gitignore b/.gitignore
index 0e34eed34..e3f7613fe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,3 +18,4 @@
 CMakeLists.txt.user*
 xcode/
 /Dockerfile
+/python/gtsam/notebooks/.ipynb_checkpoints/ellipses-checkpoint.ipynb
diff --git a/python/gtsam/notebooks/ellipses.ipynb b/python/gtsam/notebooks/ellipses.ipynb
new file mode 100644
index 000000000..d1ce9a015
--- /dev/null
+++ b/python/gtsam/notebooks/ellipses.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ellipse Scaling\n",
+    "\n",
+    "The code to calculate the percentages included in ellipses with various values of \"k\" in `plot.py`.\n",
+    "\n",
+    "Thanks to @senselessDev, January 26, for providing the code in [PR #1067](https://github.com/borglab/gtsam/pull/1067)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pct_to_sigma(pct, dof):\n",
+    "    return np.sqrt(scipy.stats.chi2.ppf(pct / 100., df=dof))\n",
+    "\n",
+    "def sigma_to_pct(sigma, dof):\n",
+    "    return scipy.stats.chi2.cdf(sigma**2, df=dof) * 100."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0D\t    1    \t    2    \t    3    \t    4    \t    5    \n",
+      "1D\t68.26895%\t95.44997%\t99.73002%\t99.99367%\t99.99994%\n",
+      "2D\t39.34693%\t86.46647%\t98.88910%\t99.96645%\t99.99963%\n",
+      "3D\t19.87480%\t73.85359%\t97.07091%\t99.88660%\t99.99846%\n"
+     ]
+    }
+   ],
+   "source": [
+    "for dim in range(0, 4):\n",
+    "    print(\"{}D\".format(dim), end=\"\")\n",
+    "    for sigma in range(1, 6):\n",
+    "        if dim == 0: print(\"\\t    {}    \".format(sigma), end=\"\")\n",
+    "        else: print(\"\\t{:.5f}%\".format(sigma_to_pct(sigma, dim)), end=\"\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1D\n",
+      "\n",
+      "sigma=1.0 -> 68.26895%\n",
+      "sigma=2.0 -> 95.44997%\n",
+      "sigma=2.5 -> 98.75807%\n",
+      "sigma=5.0 -> 99.99994%\n",
+      "\n",
+      "2D\n",
+      "\n",
+      "sigma=1.0 -> 39.34693%\n",
+      "sigma=2.0 -> 86.46647%\n",
+      "sigma=2.5 -> 95.60631%\n",
+      "sigma=5.0 -> 99.99963%\n",
+      "\n",
+      "3D\n",
+      "\n",
+      "sigma=1.0 -> 19.87480%\n",
+      "sigma=2.0 -> 73.85359%\n",
+      "sigma=2.5 -> 89.99392%\n",
+      "sigma=5.0 -> 99.99846%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for dim in range(1, 4):\n",
+    "    print(\"{}D\\n\".format(dim))\n",
+    "    for sigma in [1, 2, 2.5, 5]:\n",
+    "        print(\"sigma={:.1f} -> {:.5f}%\".format(sigma, sigma_to_pct(sigma, dim)), end=\"\")\n",
+    "        print()\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "341996cd3f3db7b5e0d1eaea072c5502d80452314e72e6b77c40445f6e9ba101"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}