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   "source": [
    "# Building-to-Service Travel Time Matrix with Intermodal Graph\n",
    "This notebook demonstrates how to compute a time-based adjacency matrix between two GeoDataFrames\n",
    "(e.g., buildings and services) using a multimodal transport graph.\n",
    "\n",
    "The method utilizes the `IduEdu` library to:\n",
    "- Construct a multimodal graph (e.g., walk + public transport)\n",
    "- Calculate travel time-based adjacency matrix from one GeoDataFrame to another\n",
    "\n",
    "This matrix can be used in `ObjectNat` for further service provision analysis."
   ],
   "id": "9ca78071b77f245e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:29.962538Z",
     "start_time": "2026-07-10T10:05:29.959041Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# %%\n",
    "# Install required packages (uncomment if needed)\n",
    "# !pip install iduedu pyarrow"
   ],
   "id": "a3fd404cc3b83edd",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:38.142935Z",
     "start_time": "2026-07-10T10:05:30.282064Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Import necessary libraries\n",
    "from iduedu import get_intermodal_graph, od_matrix\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from shapely.ops import unary_union\n"
   ],
   "id": "9a3e15f423bedc31",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 1. Load Input Geospatial Data\n",
    "Load the GeoDataFrames of buildings (origins) and services (destinations).\n"
   ],
   "id": "68af599b21a7895d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:38.217274Z",
     "start_time": "2026-07-10T10:05:38.158957Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Read building and service datasets\n",
    "buildings = gpd.read_parquet('examples_data/buildings.parquet')\n",
    "services = gpd.read_parquet('examples_data/services.parquet')"
   ],
   "id": "ecaca9093632eb44",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 2. Create Coverage Polygon for Graph Download\n",
    "Compute a polygon that encompasses both datasets to define the spatial extent for graph download.\n",
    "This is done by computing a convex hull over all geometries and buffering it slightly.\n"
   ],
   "id": "5146507282cd8082"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:39.037844Z",
     "start_time": "2026-07-10T10:05:38.219279Z"
    }
   },
   "cell_type": "code",
   "source": [
    "polygon = unary_union(\n",
    "    buildings.to_crs(4326).geometry.to_list() + services.to_crs(4326).geometry.to_list()\n",
    ").convex_hull.buffer(0.001)"
   ],
   "id": "74e684470ea483a1",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 3. Download and Clip Intermodal Graph\n",
    "Download the intermodal (multi-modal) network graph using the defined polygon.\n",
    "This includes walking paths and public transport networks."
   ],
   "id": "b6b58fffdd714d38"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:42.788008Z",
     "start_time": "2026-07-10T10:05:39.050329Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Load multimodal graph clipped to polygon\n",
    "G_intermodal = get_intermodal_graph(territory=polygon, clip_by_territory=True)"
   ],
   "id": "1e643d3fdc052876",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parsing ground PT routes:   0%|          | 0/57 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
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     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-10 13:05:42.759 | WARNING  | Removing 393 nodes outside the largest strongly connected component. Retaining 21167 of 21560 nodes.\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 4. Compute Adjacency Matrix (Travel Time)\n",
    "Calculate a travel-time-based adjacency matrix from buildings to services.\n",
    "\n",
    "Parameters:\n",
    "- `weight`: edge attribute used for cost (e.g., \"time_min\")\n",
    "- `threshold`: maximum allowed travel time (in minutes)"
   ],
   "id": "812757b2e10fe745"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:43.782672Z",
     "start_time": "2026-07-10T10:05:42.843924Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Compute travel time matrix (in minutes)\n",
    "matrix: pd.DataFrame = od_matrix(\n",
    "    G_intermodal,\n",
    "    gdf_origins=buildings,\n",
    "    gdf_destinations=services,\n",
    "    weight=\"time_min\",\n",
    "    threshold=45,\n",
    ")\n"
   ],
   "id": "f763ed1656707714",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 5. Save Adjacency Matrix\n",
    "Export the result for further processing, e.g., with `ObjectNat`'s service provision tools."
   ],
   "id": "8c56b37f6c2f508f"
  },
  {
   "cell_type": "code",
   "source": [
    "# Save matrix to Parquet format\n",
    "# IduEdu returns a sparse DataFrame to keep large OD matrices compact in memory.\n",
    "# Parquet writers do not support pandas SparseDtype columns, so convert to a dense\n",
    "# DataFrame before saving. Unreachable pairs stay encoded as np.inf.\n",
    "matrix_dense = matrix.sparse.to_dense()\n",
    "matrix_dense.to_parquet('examples_data/matrix_time.parquet')\n"
   ],
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-07-10T10:05:43.818050Z",
     "start_time": "2026-07-10T10:05:43.798691Z"
    }
   },
   "id": "371f4607ed8ec9c9",
   "outputs": [],
   "execution_count": 7
  }
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