From 7e912c5cdd371ebdb153ac591760138ed4051626 Mon Sep 17 00:00:00 2001
From: p-zach <porterdzach@gmail.com>
Date: Tue, 15 Apr 2025 14:01:36 -0400
Subject: [PATCH] Clean up BN, Factor

---
 gtsam/inference/doc/BayesNet.ipynb | 38 ++++++++++++++++++++----------
 gtsam/inference/doc/Factor.ipynb   | 13 ++++++----
 2 files changed, 34 insertions(+), 17 deletions(-)

diff --git a/gtsam/inference/doc/BayesNet.ipynb b/gtsam/inference/doc/BayesNet.ipynb
index 7a0964f73..db64eda3d 100644
--- a/gtsam/inference/doc/BayesNet.ipynb
+++ b/gtsam/inference/doc/BayesNet.ipynb
@@ -57,7 +57,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 13,
       "metadata": {
         "id": "bayesnet_import_code"
       },
@@ -88,7 +88,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -127,7 +127,30 @@
             "\t1\n",
             "]\n",
             "  b = [ 0 ]\n",
-            "  Noise model: unit (1) \n",
+            "  Noise model: unit (1) \n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create a simple Gaussian Factor Graph P(x0) P(x1|x0) P(x2|x1)\n",
+        "graph = GaussianFactorGraph()\n",
+        "model = gtsam.noiseModel.Isotropic.Sigma(1, 1.0)\n",
+        "graph.add(X(0), -np.eye(1), np.zeros(1), model)\n",
+        "graph.add(X(0), -np.eye(1), X(1), np.eye(1), np.zeros(1), model)\n",
+        "graph.add(X(1), -np.eye(1), X(2), np.eye(1), np.zeros(1), model)\n",
+        "print(\"Original Factor Graph:\")\n",
+        "graph.print()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
             "\n",
             "Resulting BayesNet:\n",
             "\n",
@@ -155,15 +178,6 @@
         }
       ],
       "source": [
-        "# Create a simple Gaussian Factor Graph P(x0) P(x1|x0) P(x2|x1)\n",
-        "graph = GaussianFactorGraph()\n",
-        "model = gtsam.noiseModel.Isotropic.Sigma(1, 1.0)\n",
-        "graph.add(X(0), -np.eye(1), np.zeros(1), model)\n",
-        "graph.add(X(0), -np.eye(1), X(1), np.eye(1), np.zeros(1), model)\n",
-        "graph.add(X(1), -np.eye(1), X(2), np.eye(1), np.zeros(1), model)\n",
-        "print(\"Original Factor Graph:\")\n",
-        "graph.print()\n",
-        "\n",
         "# Eliminate sequentially using a specific ordering\n",
         "ordering = Ordering([X(0), X(1), X(2)])\n",
         "bayes_net = graph.eliminateSequential(ordering)\n",
diff --git a/gtsam/inference/doc/Factor.ipynb b/gtsam/inference/doc/Factor.ipynb
index 40a53193d..6e6ce1515 100644
--- a/gtsam/inference/doc/Factor.ipynb
+++ b/gtsam/inference/doc/Factor.ipynb
@@ -56,7 +56,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 1,
       "metadata": {
         "id": "factor_import_code"
       },
@@ -85,7 +85,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -102,7 +102,10 @@
             "Prior factor size: 1\n",
             "Between factor keys: [8646911284551352320, 8646911284551352321] (x0, x1)\n",
             "Between factor size: 2\n",
-            "Is prior factor empty? False\n"
+            "Is prior factor empty? False\n",
+            "Prior Factor: PriorFactor on x0\n",
+            "  prior mean:  (0, 0, 0)\n",
+            "  noise model: diagonal sigmas [0.1; 0.1; 0.05];\n"
           ]
         }
       ],
@@ -125,7 +128,7 @@
         "print(f\"Is prior factor empty? {prior_factor.empty()}\")\n",
         "\n",
         "# Factors can be printed\n",
-        "# prior_factor.print(\"Prior Factor: \")"
+        "prior_factor.print(\"Prior Factor: \")"
       ]
     },
     {
@@ -141,7 +144,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"