changes to class names, and tutorials

.gitignore

@@ -172,6 +172,8 @@ dev.py
 bug.py
 scratch.py
 TODO.md
+BUILD.md
+build.py
 
 /.data
 /pickle_files

examples/0_getting_started.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌀 It's all starting to unravel!\n",
+    "\n",
+    "First run `pip install unravelsports` if you haven't already!\n",
+    "\n",
+    "\n",
+    "-----\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install unravelsports --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Quick Start"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing frames: 100%|██████████| 500/500 [00:02<00:00, 221.19it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading 477 graphs into CustomSpektralDataset...\n",
+      "Loading 477 graphs into CustomSpektralDataset...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from unravel.soccer import GraphConverter, CustomSpektralDataset\n",
+    "\n",
+    "from kloppy import skillcorner\n",
+    "\n",
+    "from unravel.utils import dummy_labels, dummy_graph_ids\n",
+    "\n",
+    "# Load Kloppy dataset\n",
+    "kloppy_dataset = skillcorner.load_open_data(\n",
+    "    include_empty_frames=False,\n",
+    "    limit=500,  # limit to 500 frames in this example\n",
+    ")\n",
+    "\n",
+    "# Initialize the Graph Converter, with dataset, labels and settings\n",
+    "converter = GraphConverter(\n",
+    "    dataset=kloppy_dataset,\n",
+    "    labels=dummy_labels(kloppy_dataset)\n",
+    ")\n",
+    "\n",
+    "# Compute the graphs and add them to the CustomSpektralDataset\n",
+    "dataset = CustomSpektralDataset(\n",
+    "    data=converter.to_spektral_graphs()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spektral.data import DisjointLoader\n",
+    "\n",
+    "N, F, S, n_out, n = dataset.dimensions()\n",
+    "\n",
+    "train, test = dataset.split_test_train(\n",
+    "    split_train=4, split_test=1, random_seed=42\n",
+    ")\n",
+    "\n",
+    "loader_tr = DisjointLoader(train, batch_size=16, epochs=150)\n",
+    "loader_te = DisjointLoader(test, batch_size=16, epochs=1, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Build GNN Model\n",
+    "\n",
+    "For a functional implementation of a Graph Neural Network see the [Full Graph Converter Tutorial](1_tutorial_graph_converter.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}