{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "0",
      "metadata": {},
      "source": [
        "# Registry ingest and search\n",
        "\n",
        "This notebook demonstrates how to ingest metadata sources into the \n",
        "BDF registry and query them with `bdf.search()` and `bdf.sparql()`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "Install BDF first: `pip install batterydf`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2",
      "metadata": {},
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import bdf\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3",
      "metadata": {},
      "source": [
        "## Crawl a trusted source\n",
        "\n",
        "This builds `registry.ttl` and `registry.db` under the default \n",
        "registry directory (or `BDF_REGISTRY_DIR` if set). Here the source is a \n",
        "small metadata snapshot committed under `examples/in/registry`; swap in the \n",
        "upstream repo URL (see the commented line) to crawl the full dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Build from the metadata snapshot committed under examples/in/registry\n",
        "# (keeps this notebook offline / CI-friendly).\n",
        "source = \"in/registry\"\n",
        "# To crawl the full upstream dataset repo instead, run locally with refresh=True:\n",
        "# source = \"https://github.com/DigiBatt/battery-data/tree/main\"\n",
        "registry_dir = Path(\"out/registry\")\n",
        "summary = bdf.build_registry(source, registry_dir=registry_dir, refresh=False)\n",
        "summary\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5",
      "metadata": {},
      "source": [
        "By default, `bdf.search()` and `bdf.sparql()` will query the official BDF registry.\n",
        "Until that exists, pass `registry_dir` to query a local registry build.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6",
      "metadata": {},
      "source": [
        "## Fast search\n",
        "\n",
        "`bdf.search()` queries the local `registry.db` index.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7",
      "metadata": {},
      "outputs": [],
      "source": [
        "bdf.search(\"graphite\", registry_dir=registry_dir)[:5]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8",
      "metadata": {},
      "outputs": [],
      "source": [
        "bdf.search(\"hppc\", registry_dir=registry_dir)[:5]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9",
      "metadata": {},
      "outputs": [],
      "source": [
        "bdf.search(\">=3 Ah\", registry_dir=registry_dir)[:5]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10",
      "metadata": {},
      "source": [
        "## SPARQL queries\n",
        "\n",
        "Use `bdf.sparql()` to run linked-data queries against `registry.ttl`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "11",
      "metadata": {},
      "outputs": [],
      "source": [
        "query = \"\"\"\n",
        "SELECT ?dataset ?name\n",
        "WHERE {\n",
        "  ?dataset a <https://schema.org/Dataset> ;\n",
        "           <https://schema.org/name> ?name .\n",
        "}\n",
        "LIMIT 5\n",
        "\"\"\"\n",
        "bdf.sparql(query, registry_dir=registry_dir)\n"
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}