Add sine notebook.

notebooks/sine.ipynb

@@ -4,7 +4,146 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sine Example"
+    "# Sine Example\n",
+    "\n",
+    "This example shows how to use a neural operator to learn the sine function in\n",
+    "a self-supervised manner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Import modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from continuity.data.sine import SineWaves\n",
+    "from continuity.plotting.plotting import *\n",
+    "from continuity.model.neuraloperator import NeuralOperator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data set\n",
+    "\n",
+    "Create a data set of sine waves with 32 sensors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = 8\n",
+    "dataset = SineWaves(\n",
+    "    num_sensors=32,\n",
+    "    size=size,\n",
+    "    batch_size=size,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural operator\n",
+    "\n",
+    "Create a neural operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = NeuralOperator(\n",
+    "    coordinate_dim=dataset.coordinate_dim,\n",
+    "    num_channels=dataset.num_channels,\n",
+    "    depth=2,\n",
+    "    kernel_width=32,\n",
+    "    kernel_depth=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "\n",
+    "Train the neural operator in a self-supervised manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+    "criterion = torch.nn.MSELoss()\n",
+    "\n",
+    "model.compile(optimizer, criterion)\n",
+    "model.fit(dataset, epochs=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "Plot training data and predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(size // 4, 4, figsize=(16, 3 * size // 4))\n",
+    "if size // 4 == 1:\n",
+    "    axs = [axs]\n",
+    "\n",
+    "for i in range(size):\n",
+    "    ax = axs[i // 4][i % 4]\n",
+    "    obs = dataset.get_observation(i)\n",
+    "    plot_evaluation(model, dataset, obs, ax=ax)\n",
+    "    plot_observation(obs, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot test observations and predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(4, 3))\n",
+    "obs = dataset.generate_observation((size-1) / 2)\n",
+    "plot_evaluation(model, dataset, obs, ax=ax)\n",
+    "plot_observation(obs, ax=ax)"
    ]
   },
   {
@@ -16,8 +155,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,