Merge pull request #2376 from borglab/fix/examples

Small issues in examples

Author: dellaert

python/gtsam/examples/DiscreteMotionModel.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Getting Started with a simple conditional distribution model -- Python"
+        "# A Discrete Motion Model"
       ]
     },
     {

python/gtsam/examples/FisheyeExample.ipynb

@@ -5,7 +5,7 @@
       "id": "7762616d",
       "metadata": {},
       "source": [
-        "# Fisheye Canera Structure-From-Motion Example\n",
+        "# Fisheye Camera SfM Example\n",
         "\n",
         "A visualSLAM example for the structure-from-motion problem on a\n",
         "simulated dataset. This version uses a fisheye camera model and a GaussNewton\n",
@@ -29,7 +29,13 @@
         ]
       },
       "source": [
-        "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information"
+        "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n",
+        "Atlanta, Georgia 30332-0415\n",
+        "All Rights Reserved\n",
+        "\n",
+        "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n",
+        "\n",
+        "See LICENSE for the license information"
       ]
     },
     {
@@ -43,7 +49,11 @@
       },
       "outputs": [],
       "source": [
-        "try:\n    import google.colab\n    %pip install --quiet gtsam-develop\nexcept ImportError:\n    pass"
+        "try:\n",
+        "    import google.colab\n",
+        "    %pip install --quiet gtsam-develop\n",
+        "except ImportError:\n",
+        "    pass"
       ]
     },
     {
@@ -280,4 +290,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

python/gtsam/examples/FitBasisExample.ipynb

@@ -5,7 +5,7 @@
       "metadata": {},
       "source": [
         "\n",
-        "# FitBasis Example (Fourier + Chebyshev)\n",
+        "# FitBasis (Fourier + Chebyshev)\n",
         "\n",
         "<a href=\"https://colab.research.google.com/github/borglab/gtsam/blob/develop/python/gtsam/examples/FitBasisExample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
         "\n",
@@ -42,7 +42,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 1,
       "id": "50e0e944",
       "metadata": {
         "tags": [
@@ -61,7 +61,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 2,
       "id": "b8cf1aff",
       "metadata": {},
       "outputs": [],
@@ -76,7 +76,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 3,
       "id": "a040dcaf",
       "metadata": {},
       "outputs": [],
@@ -97,7 +97,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 4,
       "id": "dab48fee",
       "metadata": {},
       "outputs": [],
@@ -139,7 +139,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 5,
       "id": "c05c36fa",
       "metadata": {},
       "outputs": [
@@ -985,12 +985,12 @@
         "sequence : dict = {float(xi): float(yi) for xi, yi in zip(x_fourier, y)}\n",
         "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n",
         "fit = gtsam.FitBasisFourierBasis(sequence, model, N)\n",
-        "params = np.asarray(fit.parameters()).ravel()\n",
+        "params = fit.parameters()\n",
         "\n",
         "# Extend the time domain to show periodic behavior\n",
         "t_dense = np.linspace(-0.5, 1.5, 600)\n",
         "x_dense = 2.0 * np.pi * t_dense\n",
-        "W = np.asarray(gtsam.FourierBasis.WeightMatrix(len(params), x_dense))\n",
+        "W = gtsam.FourierBasis.WeightMatrix(len(params), x_dense)\n",
         "y_fit = W @ params\n",
         "\n",
         "plot_fit(t, y, t_dense, y_fit, \"FourierBasis Fit (periodic beyond [0, 1])\")\n"
@@ -1014,7 +1014,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 6,
       "id": "ddacb31c",
       "metadata": {},
       "outputs": [
@@ -1860,11 +1860,11 @@
         "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n",
         "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n",
         "fit = gtsam.FitBasisChebyshev1Basis(sequence, model, N)\n",
-        "params = np.asarray(fit.parameters()).ravel()\n",
+        "params = fit.parameters()\n",
         "\n",
         "t_dense = np.linspace(-0.1, 1.1, 600)\n",
         "x_dense = 2.0 * t_dense - 1.0\n",
-        "W = np.asarray(gtsam.Chebyshev1Basis.WeightMatrix(len(params), x_dense))\n",
+        "W = gtsam.Chebyshev1Basis.WeightMatrix(len(params), x_dense)\n",
         "y_fit = W @ params\n",
         "\n",
         "plot_fit(t, y, t_dense, y_fit, \"Chebyshev1Basis Fit (extrapolation outside [0, 1])\")\n"
@@ -1889,7 +1889,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 7,
       "id": "fc3d1235",
       "metadata": {},
       "outputs": [
@@ -2734,11 +2734,11 @@
         "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n",
         "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n",
         "fit = gtsam.FitBasisChebyshev2Basis(sequence, model, N)\n",
-        "params = np.asarray(fit.parameters()).ravel()\n",
+        "params = fit.parameters()\n",
         "\n",
         "t_dense = np.linspace(-0.1, 1.1, 600)\n",
         "x_dense = 2.0 * t_dense - 1.0\n",
-        "W = np.asarray(gtsam.Chebyshev2Basis.WeightMatrix(len(params), x_dense))\n",
+        "W = gtsam.Chebyshev2Basis.WeightMatrix(len(params), x_dense)\n",
         "y_fit = W @ params\n",
         "\n",
         "plot_fit(t, y, t_dense, y_fit, \"Chebyshev2Basis Fit (extrapolation outside [0, 1])\")\n"
@@ -2764,7 +2764,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 8,
       "id": "37db92b8",
       "metadata": {},
       "outputs": [
@@ -3625,14 +3625,14 @@
         "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n",
         "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n",
         "fit = gtsam.FitBasisChebyshev2(sequence, model, N)\n",
-        "params = np.asarray(fit.parameters()).ravel()\n",
+        "params = fit.parameters()\n",
         "\n",
         "t_dense = np.linspace(-0.1, 1.1, 600)\n",
         "x_dense = 2.0 * t_dense - 1.0\n",
-        "W = np.asarray(gtsam.Chebyshev2.WeightMatrix(len(params), x_dense))\n",
+        "W = gtsam.Chebyshev2.WeightMatrix(len(params), x_dense)\n",
         "y_fit = W @ params\n",
         "\n",
-        "cheb_points = np.asarray(gtsam.Chebyshev2.Points(N)).ravel()\n",
+        "cheb_points = gtsam.Chebyshev2.Points(N)\n",
         "t_cheb = 0.5 * (cheb_points + 1.0)\n",
         "y_cheb = params\n",
         "markers = go.Scatter(x=t_cheb, y=y_cheb, mode=\"markers\", name=\"Chebyshev2 points\", marker=dict(color=\"red\"))\n",
@@ -3649,12 +3649,6 @@
         "\n",
         "Chebyshev points are fixed nodes that cluster near the interval ends; this improves interpolation stability and reduces endpoint oscillations compared to equally spaced nodes (mitigating the Runge phenomenon)."
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7d4c0b1d",
-      "metadata": {},
-      "source": []
     }
   ],
   "metadata": {