{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f2463db-2b85-4445-8363-299a4b474ae7",
   "metadata": {},
   "source": [
    "# Tutorial 1: 2D flow past a cylinder\n",
    "\n",
    "### Outline\n",
    "1. Loading the data\n",
    "2. Computing a metric and adding geometries\n",
    "3. Creating a new mesh\n",
    "4. Exporting the data\n",
    "5. Optional: performing an SVD\n",
    "6. Using the `DataLoader`\n",
    "7. More about geometry objects\n",
    "8. References and further material\n",
    "\n",
    "In this introductory tutorial, we will use $S^3$ for a flow past a cylinder at $Re = 100$. You can find the numerical setup in the [flow_data](https://github.com/AndreWeiner/flow_data) repository.\n",
    "This tutorial uses `OpenFOAM` as CFD tool to generate the data, however, $S^3$ works for other data formats as well.\n",
    "\n",
    "In general, $S^3$ expects three things:\n",
    "1. a point cloud representing the coordinates of the cell centers\n",
    "2. a value (metric) associated with each cell center, indicating the importance of that cell\n",
    "3. a stopping criterion, e.g. the max. number of cells or a min. threshold for approximating the metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054e5b68-3034-4e3f-9d6f-b4cbdf0790dc",
   "metadata": {},
   "source": [
    "## 1. Loading the data\n",
    "First we load and inspect our simulation data. Therefore, $S^3$ implicitly uses the [flowTorch](https://github.com/AndreWeiner/flowtorch/tree/aweiner) package, which provides a variety of APIs for loading CFD data. Depending on your data structure, you can use the best-suited dataloader within `flowTorch.data` directly."
   ]
  },
  {
   "cell_type": "code",
   "id": "bbb8626b-2eef-456e-9df4-cbdd5a64d20f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:08.031506139Z",
     "start_time": "2026-02-19T14:35:07.979116697Z"
    }
   },
   "source": [
    "import sys\n",
    "import torch as pt\n",
    "\n",
    "from os import environ\n",
    "from os.path import join\n",
    "\n",
    "environ[\"sparseSpatialSampling\"] = join(\"..\", \"..\", \"..\")\n",
    "sys.path.insert(0, environ[\"sparseSpatialSampling\"])\n",
    "\n",
    "from sparseSpatialSampling.export import ExportData\n",
    "from sparseSpatialSampling.geometry import CubeGeometry, SphereGeometry\n",
    "from sparseSpatialSampling.sparse_spatial_sampling import SparseSpatialSampling\n",
    "from sparseSpatialSampling.utils import load_foam_data, export_openfoam_fields"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "1a51c5c4-47b2-459f-88f4-dd9f36872408",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:08.087193035Z",
     "start_time": "2026-02-19T14:35:08.033548680Z"
    }
   },
   "source": [
    "# define load paths to the CFD data, assuming they are in the top-level of the repository\n",
    "load_path = join(\"..\", \"..\", \"..\", \"flowTorch_Workshop_2025\", \"cylinder_2D_Re100\")\n",
    "\n",
    "# here we use the approximated metric field as stopping criterion\n",
    "# how much of the metric within the original grid should be captured at least\n",
    "min_metric = 0.75\n",
    "\n",
    "# define the path to where we want to save the results and the name of the file\n",
    "save_path = join(\"..\",\"..\", \"..\", \"run\", \"tutorials\", \"tutorial_1\")\n",
    "save_name = \"cylinder2D_metric_{:.2f}\".format(min_metric)"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "172117d3-c177-4b8e-bcf1-5e12a8651308",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:09.485134142Z",
     "start_time": "2026-02-19T14:35:08.088550630Z"
    }
   },
   "source": [
    "# now we can load the data. Since we used OpenFOAM, we can use the load_foam_data function provided by S^3.\n",
    "# Otherwise, we have to use flowtorch dataloaders directly, refer to the flowtorch documentation\n",
    "\n",
    "# define boundaries of the masked domain for the cylinder, here we want to load the full domain\n",
    "bounds = [[0, 0], [2.2, 0.41]]  # [[xmin, ymin], [xmax, ymax]]\n",
    "\n",
    "# load the CFD data, we want to compute the metric based on the velocity in the quasi-steady state, so omit the first 4 seconds\n",
    "field, coord, _, write_times = load_foam_data(load_path, bounds, field_name=\"U\", t_start=4, scalar=False)\n",
    "\n",
    "# display some information about the grid\n",
    "print(f\"Grid size: {field.shape[0]} cells.\")\n",
    "print(f\"Found {field.shape[-1]} snapshots.\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:08] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:35:08] INFO     Loading precomputed cell centers and volumes from processor1/constant\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid size: 9800 cells.\n",
      "Found 301 snapshots.\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "c2dabcee-2a49-4d5e-b746-93cd413ac725",
   "metadata": {},
   "source": [
    "we can visualize the flow field and the grid in paraview:\n",
    "\n",
    "![U_field_t10s_cell_centered.png](attachment:4f717b1e-dd47-4e86-af79-1c1f19dd0d24.png)\n",
    "![grid.png](attachment:6f0e6363-e54f-4d47-b090-ef340ea555ae.png)\n",
    "\n",
    "\n",
    "Since the grid is already quite coarse, we don't expect a significant data reduction when using $S^3$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5803f1a6-4842-46b4-99ef-10f73dbb832f",
   "metadata": {},
   "source": [
    "## 2. Computing a metric and adding geometries"
   ]
  },
  {
   "cell_type": "code",
   "id": "f871587e-2d77-4f35-bc2e-6c83963ce296",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:10.041416735Z",
     "start_time": "2026-02-19T14:35:09.489081133Z"
    }
   },
   "source": [
    "# now we compute a metric. in this case, we just use the temporal mean of the abs. velocity vector\n",
    "metric = pt.mean(field.abs().sum(1), 1)\n",
    "\n",
    "# create geometry objects for the domain and the cylinder, we will learn in section 7 more about these geometry objects\n",
    "# we don't want to refine the domain boundaries, so keep all the optional arguments as default\n",
    "domain = CubeGeometry(\"domain\", True, bounds[0], bounds[1])\n",
    "\n",
    "# define the properties of the cylinder\n",
    "position = [0.2, 0.2]\n",
    "radius = 0.05\n",
    "\n",
    "# we want to refine the cylinder surface, so we set refine=True\n",
    "# by default this refines the cylinder with the max. level encountered at the geometry. However, we can also increase the\n",
    "# resolution of the cylinder by passing a min_refinement_level\n",
    "geometry = SphereGeometry(\"cylinder\", False, position, radius, refine=True, min_refinement_level=9)\n",
    "\n",
    "# create a S^3 instance, since this is a quite small case, we set the number of CPUs (n_jobs) to 4. \n",
    "# Further, we want to stop the refinement process based on the approximation of the metric, so we have to pass the threshold min_metric as well\n",
    "s_cube = SparseSpatialSampling(coord, metric, [domain, geometry], save_path, save_name, \"cylinder2D\", min_metric=min_metric, n_jobs=4)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:09] INFO     Selecting min. approximation of the metric as stopping criterion.\n",
      "[2026-02-19 15:35:09] INFO     \n",
      "\tSelected settings:\n",
      "\t\tpre_select           :\tFalse\n",
      "\t\tn_jobs               :\t4\n",
      "\t\tmax_delta_level      :\tFalse\n",
      "\t\tgeometry             :\t['domain', 'cylinder']\n",
      "\t\tmin_metric           :\t0.75\n",
      "\t\tmin_level            :\t5\n",
      "\t\tcells_per_iter_start :\t9\n",
      "\t\tcells_per_iter_end   :\t9\n",
      "\t\tcells_per_iter       :\t9\n",
      "\t\tcells_per_iter_last  :\t1000000000.0\n",
      "\t\treach_at_least       :\t0.75\n",
      "\t\tn_dimensions         :\t2\n",
      "\t\tn_cells_orig         :\t9800\n",
      "\t\trelTol               :\t0.001\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "id": "06bf8850-309a-46b5-85a2-df6d0fb1774c",
   "metadata": {},
   "source": [
    "## 3. Creating a new mesh\n",
    "Now we are ready to create a new grid using $S^3$. This can be achieved by simply calling the `execute_grid_generation()` method:"
   ]
  },
  {
   "cell_type": "code",
   "id": "b603edfe-3666-411a-bac9-4078ba0363cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:28.441779787Z",
     "start_time": "2026-02-19T14:35:10.043775231Z"
    }
   },
   "source": [
    "# execute the mesh generation with S^3\n",
    "s_cube.execute_grid_generation()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:10] INFO     Starting grid generation.\n",
      "[2026-02-19 15:35:10] INFO     Starting uniform refinement.\n",
      "\tStarting iteration no. 0, N_cells = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n",
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n",
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n",
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tStarting iteration no. 1, N_cells = 4\n",
      "\tStarting iteration no. 2, N_cells = 8\n",
      "\tStarting iteration no. 3, N_cells = 16\n",
      "\tStarting iteration no. 4, N_cells = 64\n",
      "[2026-02-19 15:35:17] INFO     Finished uniform refinement.\n",
      "[2026-02-19 15:35:17] INFO     Starting metric-based refinement.\n",
      "\tStarting iteration no. 0, captured metric: 13.55 %, N_cells = 192\n",
      "\tStarting iteration no. 1, captured metric: 14.41 %, N_cells = 217\n",
      "\tStarting iteration no. 2, captured metric: 14.97 %, N_cells = 244\n",
      "\tStarting iteration no. 3, captured metric: 15.32 %, N_cells = 271\n",
      "\tStarting iteration no. 4, captured metric: 16.18 %, N_cells = 298\n",
      "\tStarting iteration no. 5, captured metric: 16.33 %, N_cells = 325\n",
      "\tStarting iteration no. 6, captured metric: 16.48 %, N_cells = 352\n",
      "\tStarting iteration no. 7, captured metric: 16.68 %, N_cells = 379\n",
      "\tStarting iteration no. 8, captured metric: 16.82 %, N_cells = 406\n",
      "\tStarting iteration no. 9, captured metric: 17.43 %, N_cells = 433\n",
      "\tStarting iteration no. 10, captured metric: 18.54 %, N_cells = 459\n",
      "\tStarting iteration no. 11, captured metric: 19.3 %, N_cells = 486\n",
      "\tStarting iteration no. 12, captured metric: 19.89 %, N_cells = 513\n",
      "\tStarting iteration no. 13, captured metric: 20.48 %, N_cells = 540\n",
      "\tStarting iteration no. 14, captured metric: 21.05 %, N_cells = 567\n",
      "\tStarting iteration no. 15, captured metric: 21.75 %, N_cells = 594\n",
      "\tStarting iteration no. 16, captured metric: 22.61 %, N_cells = 621\n",
      "\tStarting iteration no. 17, captured metric: 23.45 %, N_cells = 647\n",
      "\tStarting iteration no. 18, captured metric: 24.08 %, N_cells = 674\n",
      "\tStarting iteration no. 19, captured metric: 24.72 %, N_cells = 701\n",
      "\tStarting iteration no. 20, captured metric: 25.02 %, N_cells = 728\n",
      "\tStarting iteration no. 21, captured metric: 25.69 %, N_cells = 755\n",
      "\tStarting iteration no. 22, captured metric: 26.15 %, N_cells = 782\n",
      "\tStarting iteration no. 23, captured metric: 26.83 %, N_cells = 809\n",
      "\tStarting iteration no. 24, captured metric: 27.34 %, N_cells = 836\n",
      "\tStarting iteration no. 25, captured metric: 27.91 %, N_cells = 863\n",
      "\tStarting iteration no. 26, captured metric: 28.29 %, N_cells = 890\n",
      "\tStarting iteration no. 27, captured metric: 28.83 %, N_cells = 917\n",
      "\tStarting iteration no. 28, captured metric: 29.22 %, N_cells = 944\n",
      "\tStarting iteration no. 29, captured metric: 29.59 %, N_cells = 971\n",
      "\tStarting iteration no. 30, captured metric: 30.03 %, N_cells = 998\n",
      "\tStarting iteration no. 31, captured metric: 30.51 %, N_cells = 1025\n",
      "\tStarting iteration no. 32, captured metric: 30.89 %, N_cells = 1052\n",
      "\tStarting iteration no. 33, captured metric: 31.09 %, N_cells = 1079\n",
      "\tStarting iteration no. 34, captured metric: 31.47 %, N_cells = 1106\n",
      "\tStarting iteration no. 35, captured metric: 31.61 %, N_cells = 1133\n",
      "\tStarting iteration no. 36, captured metric: 31.96 %, N_cells = 1160\n",
      "\tStarting iteration no. 37, captured metric: 32.29 %, N_cells = 1187\n",
      "\tStarting iteration no. 38, captured metric: 32.52 %, N_cells = 1214\n",
      "\tStarting iteration no. 39, captured metric: 32.75 %, N_cells = 1241\n",
      "\tStarting iteration no. 40, captured metric: 32.93 %, N_cells = 1268\n",
      "\tStarting iteration no. 41, captured metric: 32.97 %, N_cells = 1295\n",
      "\tStarting iteration no. 42, captured metric: 33.17 %, N_cells = 1322\n",
      "\tStarting iteration no. 43, captured metric: 33.43 %, N_cells = 1349\n",
      "\tStarting iteration no. 44, captured metric: 33.53 %, N_cells = 1376\n",
      "\tStarting iteration no. 45, captured metric: 33.89 %, N_cells = 1403\n",
      "\tStarting iteration no. 46, captured metric: 34.09 %, N_cells = 1430\n",
      "\tStarting iteration no. 47, captured metric: 34.18 %, N_cells = 1457\n",
      "\tStarting iteration no. 48, captured metric: 34.32 %, N_cells = 1484\n",
      "\tStarting iteration no. 49, captured metric: 34.42 %, N_cells = 1511\n",
      "\tStarting iteration no. 50, captured metric: 34.71 %, N_cells = 1538\n",
      "\tStarting iteration no. 51, captured metric: 34.92 %, N_cells = 1565\n",
      "\tStarting iteration no. 52, captured metric: 35.2 %, N_cells = 1592\n",
      "\tStarting iteration no. 53, captured metric: 35.44 %, N_cells = 1619\n",
      "\tStarting iteration no. 54, captured metric: 35.78 %, N_cells = 1646\n",
      "\tStarting iteration no. 55, captured metric: 36.04 %, N_cells = 1673\n",
      "\tStarting iteration no. 56, captured metric: 36.25 %, N_cells = 1700\n",
      "\tStarting iteration no. 57, captured metric: 36.57 %, N_cells = 1727\n",
      "\tStarting iteration no. 58, captured metric: 36.8 %, N_cells = 1754\n",
      "\tStarting iteration no. 59, captured metric: 37.12 %, N_cells = 1781\n",
      "\tStarting iteration no. 60, captured metric: 37.6 %, N_cells = 1808\n",
      "\tStarting iteration no. 61, captured metric: 37.94 %, N_cells = 1835\n",
      "\tStarting iteration no. 62, captured metric: 38.15 %, N_cells = 1862\n",
      "\tStarting iteration no. 63, captured metric: 38.39 %, N_cells = 1889\n",
      "\tStarting iteration no. 64, captured metric: 38.76 %, N_cells = 1916\n",
      "\tStarting iteration no. 65, captured metric: 39.09 %, N_cells = 1943\n",
      "\tStarting iteration no. 66, captured metric: 39.33 %, N_cells = 1970\n",
      "\tStarting iteration no. 67, captured metric: 39.57 %, N_cells = 1997\n",
      "\tStarting iteration no. 68, captured metric: 39.98 %, N_cells = 2024\n",
      "\tStarting iteration no. 69, captured metric: 40.17 %, N_cells = 2051\n",
      "\tStarting iteration no. 70, captured metric: 40.45 %, N_cells = 2078\n",
      "\tStarting iteration no. 71, captured metric: 40.78 %, N_cells = 2105\n",
      "\tStarting iteration no. 72, captured metric: 41.05 %, N_cells = 2132\n",
      "\tStarting iteration no. 73, captured metric: 41.33 %, N_cells = 2158\n",
      "\tStarting iteration no. 74, captured metric: 41.68 %, N_cells = 2185\n",
      "\tStarting iteration no. 75, captured metric: 42.06 %, N_cells = 2212\n",
      "\tStarting iteration no. 76, captured metric: 42.42 %, N_cells = 2239\n",
      "\tStarting iteration no. 77, captured metric: 42.75 %, N_cells = 2266\n",
      "\tStarting iteration no. 78, captured metric: 43.09 %, N_cells = 2293\n",
      "\tStarting iteration no. 79, captured metric: 43.43 %, N_cells = 2320\n",
      "\tStarting iteration no. 80, captured metric: 43.77 %, N_cells = 2347\n",
      "\tStarting iteration no. 81, captured metric: 44.15 %, N_cells = 2374\n",
      "\tStarting iteration no. 82, captured metric: 44.52 %, N_cells = 2401\n",
      "\tStarting iteration no. 83, captured metric: 44.9 %, N_cells = 2428\n",
      "\tStarting iteration no. 84, captured metric: 45.29 %, N_cells = 2455\n",
      "\tStarting iteration no. 85, captured metric: 45.6 %, N_cells = 2482\n",
      "\tStarting iteration no. 86, captured metric: 45.96 %, N_cells = 2509\n",
      "\tStarting iteration no. 87, captured metric: 46.34 %, N_cells = 2536\n",
      "\tStarting iteration no. 88, captured metric: 46.66 %, N_cells = 2563\n",
      "\tStarting iteration no. 89, captured metric: 47.02 %, N_cells = 2590\n",
      "\tStarting iteration no. 90, captured metric: 47.45 %, N_cells = 2617\n",
      "\tStarting iteration no. 91, captured metric: 47.8 %, N_cells = 2644\n",
      "\tStarting iteration no. 92, captured metric: 48.15 %, N_cells = 2671\n",
      "\tStarting iteration no. 93, captured metric: 48.42 %, N_cells = 2698\n",
      "\tStarting iteration no. 94, captured metric: 48.82 %, N_cells = 2725\n",
      "\tStarting iteration no. 95, captured metric: 49.09 %, N_cells = 2752\n",
      "\tStarting iteration no. 96, captured metric: 49.29 %, N_cells = 2779\n",
      "\tStarting iteration no. 97, captured metric: 49.49 %, N_cells = 2806\n",
      "\tStarting iteration no. 98, captured metric: 49.68 %, N_cells = 2831\n",
      "\tStarting iteration no. 99, captured metric: 49.95 %, N_cells = 2858\n",
      "\tStarting iteration no. 100, captured metric: 50.12 %, N_cells = 2885\n",
      "\tStarting iteration no. 101, captured metric: 50.28 %, N_cells = 2912\n",
      "\tStarting iteration no. 102, captured metric: 50.43 %, N_cells = 2939\n",
      "\tStarting iteration no. 103, captured metric: 50.69 %, N_cells = 2966\n",
      "\tStarting iteration no. 104, captured metric: 50.93 %, N_cells = 2993\n",
      "\tStarting iteration no. 105, captured metric: 51.12 %, N_cells = 3020\n",
      "\tStarting iteration no. 106, captured metric: 51.35 %, N_cells = 3047\n",
      "\tStarting iteration no. 107, captured metric: 51.55 %, N_cells = 3074\n",
      "\tStarting iteration no. 108, captured metric: 51.8 %, N_cells = 3101\n",
      "\tStarting iteration no. 109, captured metric: 51.98 %, N_cells = 3128\n",
      "\tStarting iteration no. 110, captured metric: 52.12 %, N_cells = 3155\n",
      "\tStarting iteration no. 111, captured metric: 52.28 %, N_cells = 3182\n",
      "\tStarting iteration no. 112, captured metric: 52.47 %, N_cells = 3209\n",
      "\tStarting iteration no. 113, captured metric: 52.86 %, N_cells = 3236\n",
      "\tStarting iteration no. 114, captured metric: 53.15 %, N_cells = 3263\n",
      "\tStarting iteration no. 115, captured metric: 53.39 %, N_cells = 3290\n",
      "\tStarting iteration no. 116, captured metric: 53.64 %, N_cells = 3317\n",
      "\tStarting iteration no. 117, captured metric: 53.77 %, N_cells = 3344\n",
      "\tStarting iteration no. 118, captured metric: 54.02 %, N_cells = 3371\n",
      "\tStarting iteration no. 119, captured metric: 54.34 %, N_cells = 3398\n",
      "\tStarting iteration no. 120, captured metric: 54.61 %, N_cells = 3425\n",
      "\tStarting iteration no. 121, captured metric: 54.82 %, N_cells = 3452\n",
      "\tStarting iteration no. 122, captured metric: 55.09 %, N_cells = 3479\n",
      "\tStarting iteration no. 123, captured metric: 55.32 %, N_cells = 3504\n",
      "\tStarting iteration no. 124, captured metric: 55.71 %, N_cells = 3531\n",
      "\tStarting iteration no. 125, captured metric: 55.92 %, N_cells = 3558\n",
      "\tStarting iteration no. 126, captured metric: 56.21 %, N_cells = 3585\n",
      "\tStarting iteration no. 127, captured metric: 56.46 %, N_cells = 3612\n",
      "[2026-02-19 15:35:24] INFO     Finished metric-based refinement.\n",
      "[2026-02-19 15:35:24] INFO     Starting geometry refinement.\n",
      "[2026-02-19 15:35:24] INFO     Starting refining geometry cylinder.\n",
      "[2026-02-19 15:35:24] INFO     Found a minimum cell level of 6. Target level is 9.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 7 / 9. \n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 9. \n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 9. \n",
      "[2026-02-19 15:35:24] INFO     Finished geometry refinement.\n",
      "[2026-02-19 15:35:24] INFO     Starting renumbering final mesh.\n",
      "[2026-02-19 15:35:28] INFO     Finished refinement in 18.3238 s \n",
      "\t\t\t\t\t\t\t\t(128 iterations).\n",
      "\t\t\t\t\t\t\t\tTime for uniform refinement: 7.9080 s\n",
      "\t\t\t\t\t\t\t\tTime for metric-based refinement: 6.6841 s\n",
      "\t\t\t\t\t\t\t\tTime for geometry refinement: 0.3106 s\n",
      "\t\t\t\t\t\t\t\tTime for renumbering the final mesh: 3.4060 s\n",
      "\t\t\t\t\t\t\t\t\n",
      "                                    Number of cells: 3734\n",
      "                                    Minimum ref. level: 6\n",
      "                                    Maximum ref. level: 9\n",
      "                                    Captured metric of original grid: 56.56 %\n",
      "                  \n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "94aecb54-1499-48ea-a8d3-e6058f55203f",
   "metadata": {},
   "source": [
    "As we notice from the output, we don't reach the specified metric of `min_metric = 0.75`, but the refinement process stops at around $56.56\\%$.\n",
    "The reason for that is another stopping criterion, which aborts the refinement process based on the relative improvement between two consecutive iterations. This parameter `relTol` is by default set to `relTol = 0.001`.  We can disable it either by setting it to zero, or by setting the parameter `reach_at_least` to one. `reach_at_least` sets the threshold when to activate the `relTol` stopping criterion and defaults to `reach_at_least=0.75`, meaning that we have to approximate the metric by $75\\%$ before the `relTol` stopping criterion is activated. However, this will increase the required runtime:"
   ]
  },
  {
   "cell_type": "code",
   "id": "95ef6e4f-da75-4ca5-8be1-cac02a7a73e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:28.493526230Z",
     "start_time": "2026-02-19T14:35:28.443908648Z"
    }
   },
   "source": [
    "# omit the relTol stopping criterion, change cell type if you want to test this\n",
    "# s_cube = SparseSpatialSampling(coord, metric, [domain, geometry], save_path, save_name, \"cylinder2D\", min_metric=min_metric, n_jobs=4,\n",
    "#                                relTol=0) \n",
    "# s_cube.execute_grid_generation()"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "id": "8fdcfb09-8c22-4b24-b692-ee98371558d2",
   "metadata": {},
   "source": [
    "### Output files\n",
    "$S^3$ will create two output files:\n",
    "\n",
    "1. `mesh_info_<save_name>.pt`\n",
    "2. `s_cube_<save_name>.pt`\n",
    "\n",
    "The first file contains statistics about the mesh generation and the final mesh while the latter contains the `s_cube` object itself. This is useful in case we want to carry out the grid generation process independently from the export of the flow fields. \n",
    "In this case, instead of executing the grid generation again, we can simply load the `s_cube` object as:\n",
    "\n",
    "`s_cube = pt.load(\"s_cube_<save_name>.pt\", weights_only=False)`\n",
    "\n",
    "and then continue with the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316186af-c26c-44f0-aba5-412f7c74dcbb",
   "metadata": {},
   "source": [
    "## 4. Exporting the data\n",
    "We can now interpolate the original flow fields from CFD onto the new grid generated by $S^3$ and export it to `HDF5`. Therefore, $S^3$ provides the `ExportData` class which takes care of the interpolation and export. The procedure is shown in the following."
   ]
  },
  {
   "cell_type": "code",
   "id": "02b38227-5de2-413c-9e72-457e71d3cdb1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:33.965196082Z",
     "start_time": "2026-02-19T14:35:28.533566634Z"
    }
   },
   "source": [
    "# create export instance, export all fields into the same HFD5 file and create single XDMF from it\n",
    "export = ExportData(s_cube)\n",
    "\n",
    "# by default, solutions are only exported at the cells center. We can set the parameter interpolate_at_vertices to True to \n",
    "# export the solution at the cell vertices as well\n",
    "export = ExportData(s_cube, interpolate_at_vertices=True)\n",
    "\n",
    "# export all available write times for fields available\n",
    "export_openfoam_fields(export, load_path, bounds)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:28] WARNING  Argument ``write_times`` is ``None``. Make sure to set the ``write_times`` before calling the ``export()`` method.\n",
      "[2026-02-19 15:35:28] WARNING  Argument ``write_times`` is ``None``. Make sure to set the ``write_times`` before calling the ``export()`` method.\n",
      "[2026-02-19 15:35:28] INFO     Exporting batch 1 / 1\n",
      "[2026-02-19 15:35:28] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:35:28] INFO     Loading precomputed cell centers and volumes from processor1/constant\n",
      "[2026-02-19 15:35:30] INFO     Initializing KNN and computing interpolation weights.\n",
      "[2026-02-19 15:35:30] INFO     Starting interpolation and export of field U.\n",
      "[2026-02-19 15:35:31] INFO     Writing HDF5 file for field U.\n",
      "[2026-02-19 15:35:31] INFO     Writing XDMF file for file cylinder2D_metric_0.75.h5\n",
      "[2026-02-19 15:35:32] INFO     Finished export of field U in 3.649s.\n",
      "[2026-02-19 15:35:32] INFO     Exporting batch 1 / 1\n",
      "[2026-02-19 15:35:32] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:35:32] INFO     Loading precomputed cell centers and volumes from processor1/constant\n",
      "[2026-02-19 15:35:32] INFO     Starting interpolation and export of field p.\n",
      "[2026-02-19 15:35:33] INFO     Writing XDMF file for file cylinder2D_metric_0.75.h5\n",
      "[2026-02-19 15:35:33] INFO     Finished export of field p in 1.766s.\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "id": "05dff3d7-d0d2-47b0-8bcb-25aaefe7f156",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:35.835759953Z",
     "start_time": "2026-02-19T14:35:33.968036778Z"
    }
   },
   "source": [
    "# Note: executing this cell will overwrite the files containing the exported fields in the previous cell\n",
    "# we have to instantiate another export object since otherwise this leads to issues with HDF5\n",
    "export = ExportData(s_cube)\n",
    "\n",
    "# alternatively, we can export data available at only certain time steps, but we need to assure that we don't get any round-off issues.\n",
    "# so we specify the precision first\n",
    "export.write_times = [\"{:.3f}\".format(i.item()) for i in pt.arange(4, 10, 0.001)]\n",
    "\n",
    "# now export the velocity field, since we used that to compute our metric, we don't need to re-load it\n",
    "export.export(coord, field, \"U\")\n",
    "\n",
    "# now we can load and export the pressure field\n",
    "field, _, _, _ = load_foam_data(load_path, bounds, t_start=4)\n",
    "\n",
    "# export() expects a field of [N_cells, N_dimensions, N_snapshots], so for a scalar field we have to add a dimension\n",
    "export.export(coord, field.unsqueeze(1), \"p\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:33] WARNING  Argument ``write_times`` is ``None``. Make sure to set the ``write_times`` before calling the ``export()`` method.\n",
      "[2026-02-19 15:35:33] INFO     Initializing KNN and computing interpolation weights.\n",
      "[2026-02-19 15:35:34] INFO     Starting interpolation and export of field U.\n",
      "[2026-02-19 15:35:34] INFO     Writing HDF5 file for field U.\n",
      "[2026-02-19 15:35:34] INFO     Writing XDMF file for file cylinder2D_metric_0.75.h5\n",
      "[2026-02-19 15:35:34] INFO     Finished export of field U in 0.972s.\n",
      "[2026-02-19 15:35:34] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:35:34] INFO     Loading precomputed cell centers and volumes from processor1/constant\n",
      "[2026-02-19 15:35:35] INFO     Starting interpolation and export of field p.\n",
      "[2026-02-19 15:35:35] INFO     Writing XDMF file for file cylinder2D_metric_0.75.h5\n",
      "[2026-02-19 15:35:35] INFO     Finished export of field p in 0.878s.\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "6afa0df1-f634-4c52-a955-4b53c8e446b5",
   "metadata": {},
   "source": [
    "This creates two new files:\n",
    "\n",
    "1. `<save_name>.h5` which contains all the exported flow fields along with the metric and cell levels\n",
    "2.  `<save_name>.xdmf` which is a markdown file for loading the results into paraview\n",
    "\n",
    "we can now take a look at the grid, metric and flow field by loading the `cylinder2D_metric_0.75.xdmf` into paraview. When opening the `XDMF` file in Paraview, **it is important to select the `Xdmf3ReaderS`**, all other readers will lead to an incorrect assignment of the values to the respective cells. Once we have loaded the file, we can visualize the different quantities:\n",
    "\n",
    "![metric_metric_0.75.png](attachment:7a5bbf9f-1855-4e5a-9567-5bc76125505d.png)\n",
    "![grid_metric_0.75.png](attachment:8f727595-7be5-45a0-b99b-bf839145de27.png)\n",
    "![U_field_10s_cell_centered.png](attachment:5f3a36a5-9686-4ed1-b86d-18209fce9cde.png)\n",
    "\n",
    "The metric and cells levels are saved in the first time step folder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a462cad-e6f0-4fad-acbf-1cfb05d8cd2b",
   "metadata": {},
   "source": [
    "## 5. Optional: performing an SVD\n",
    "\n",
    "We can further perform an SVD for analysing flow patterns. The results of the SVD are saved in separate `HDF5`and `XDMF`files. It is important to note that the SVD is computed separately for each specified field. \n",
    "\n",
    "Also, **the data matrix is weighted with the square-root of the cell volume** when computing the `SVD` using the utility `write_svd_s_cube_to_file()`. This is an important step when comparing the results of the SVD for different grid topologies, i.e. the results on the original grid with the results of $S^3$. The weighing has to be reversed after computing the SVD.\n",
    "\n",
    "The general procedure is:\n",
    "\n",
    "```\n",
    "data_matrix *= cell_volumes.sqrt()\n",
    "svd = SVD(data_matrix, rank=rank)\n",
    "\n",
    "# reverse the weighting for the modes\n",
    "svd.U /= cell_area.sqrt()\n",
    "```\n",
    "which has to be applied when computing the corresponding SVD on the original data from CFD."
   ]
  },
  {
   "cell_type": "code",
   "id": "cc8196f6-7432-4581-84a9-8d0f71184625",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:36.323358010Z",
     "start_time": "2026-02-19T14:35:35.839029008Z"
    }
   },
   "source": [
    "from sparseSpatialSampling.utils import write_svd_s_cube_to_file\n",
    "\n",
    "# compute the SVD on grid generated by S^3 separately for each field and export the results to HDF5 & XDMF\n",
    "# make sure to skip the initial transient phase\n",
    "write_svd_s_cube_to_file([\"p\", \"U\"], save_path, save_name, False, 50, rank=int(1e5), t_start=4)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:35] INFO     Performing SVD for field p.\n",
      "[2026-02-19 15:35:36] INFO     Writing XDMF file for file cylinder2D_metric_0.75_p_svd.h5\n",
      "[2026-02-19 15:35:36] INFO     Performing SVD for field U.\n",
      "[2026-02-19 15:35:36] INFO     Writing XDMF file for file cylinder2D_metric_0.75_U_svd.h5\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "id": "eb2a9788-0f1d-4936-8cb3-d3696dd67b8a",
   "metadata": {},
   "source": [
    "## 6. Using the `Dataloader` (`flowtorch.SCUBEDataloader`)\n",
    "\n",
    "We can load flow fields or other results from files generated by $S^3$ using the `Dataloader`class:"
   ]
  },
  {
   "cell_type": "code",
   "id": "cff253af-9c92-408c-89a3-92e29e0953f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:36.396275243Z",
     "start_time": "2026-02-19T14:35:36.325387925Z"
    }
   },
   "source": [
    "from flowtorch.data import SCUBEDataloader\n",
    "from sparseSpatialSampling.data import Dataloader\n",
    "\n",
    "# we can use the dataloder to load and post-process Scube data\n",
    "dataloader = Dataloader(save_path, f\"{save_name}.h5\")\n",
    "\n",
    "# alternatively, we can use the SCUBEDataloader within flowTorch\n",
    "dataloader = SCUBEDataloader(save_path, f\"{save_name}.h5\")\n",
    "\n",
    "# we can now use the dataloader for post-processing etc.\n",
    "print(f\"Number of cells: {dataloader.vertices.size(0)}\")\n",
    "print(f\"Field names at t = {dataloader.write_times[0]}s: {dataloader.field_names[dataloader.write_times[0]]}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells: 3734\n",
      "Field names at t = 4.000s: ['U', 'p']\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "id": "5b9d80b7-5a15-482f-bca0-9dd2a97fe792",
   "metadata": {},
   "source": [
    "Note that the results from SVD can't be loaded with the `Dataloader`. For now, you can find an example on how to do that in:\n",
    "\n",
    "[post_processing/compare_svd_results_cylinder3D_Re3900.py](https://github.com/JanisGeise/sparseSpatialSampling/blob/main/post_processing/compare_svd_cylinder3D_Re3900.py)\n",
    "\n",
    "We will present a more detailed explanation on how to use the $S^3$ `Dataloader` and `Datawriter` classes in [tutorial 5](tutorial5_data_loader_writer.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "684db0c6-2cf2-4860-8cc9-03a14a09764b",
   "metadata": {},
   "source": [
    "## 7. More about geometry objects\n",
    "So far, we have used geometry objects without an explanation about what they are doing. This section will give a brief overview about geometry objects, their purpose and limitations.\n",
    "\n",
    "Geometry objects can be used to mask out an area in the flow field. Since $S^3$ creates the grid solely based on the metric field, it also interpolates the metric into regions where (in CFD) a geometry is present. In case there is a gradient within the metric field across the geometry, e.g. between the lower and upper side of an airfoil (compare [tutorial 2](tutorial2_oat15_buffet.ipynb)), this will generate a large number of cells 'inside' the geometry. Geometry objects can be used to avoid this behavior by masking out these regions, so that there is no grid generated.\n",
    "\n",
    "By default, **$S^3$ need at least a single geometry object which represents the domain**. All of the available geometry objects can be used either as domain (`keep_inside=True`) or as geometry (`keep_inside=False`). When used as domain, this means all cells outside of this geometry object will be removed. This is required since $S^3$ starts the grid generation by creating a single cell based on the main dimension of the flow field. If the numerical domain is not quadratic (2D) or cubic (3D), this will lead to cells being generated outside the actual (numerical) domain. The domain geometry object avoids this behavior. When setting `keep_inside=False` this means this object is regarded as geometry and all cells inside this geometry are removed.\n",
    "\n",
    "Currently, we can only pass **exactly one** geometry object representing the domain to $S^3$. For geometry objects representing geometries or areas to mask out in the flow field, there is no limitation with respect to the number of objects passed to $S^3$.\n",
    "\n",
    "We can check out the available geometries for masking using the `list_geometry` function:"
   ]
  },
  {
   "cell_type": "code",
   "id": "d0fc9e9d-862a-4830-b760-c65b554008ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:36.473281347Z",
     "start_time": "2026-02-19T14:35:36.402109322Z"
    }
   },
   "source": [
    "# we can check out the available geometry object classes currently implemented in S^3\n",
    "from sparseSpatialSampling.sparse_spatial_sampling import list_geometries\n",
    "list_geometries()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:36] INFO     \n",
      "\tAvailable geometry objects:\n",
      "\t---------------------------\n",
      "\t\t- CubeGeometry          : rectangles (2D) or cubes (3D)\n",
      "\t\t- CylinderGeometry3D    : cylinders, conical objects and cones (3D)\n",
      "\t\t- GeometryCoordinates2D : 2D coordinates for geometries\n",
      "\t\t- GeometrySTL3D         : usage of STL files for geometries (3D)\n",
      "\t\t- PrismGeometry3D       : prisms (3D)\n",
      "\t\t- PyramidGeometry3D     : square pyramids (3D)\n",
      "\t\t- SphereGeometry        : circles (2D) or spheres (3D)\n",
      "\t\t- TetrahedronGeometry3D : tetrahedrons (3D)\n",
      "\t\t- TriangleGeometry      : triangles (2D)\n",
      "\n",
      "\tFor a more detailed description check out the documentation.\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "id": "79b837e8-9729-4506-805d-4faab0a0d12f",
   "metadata": {},
   "source": [
    "All of these geometry objects can be used either as domains or geometries, but so far we only used the `CubeGeometry` as domain since all of our CFD domains had a rectangular shape.\n",
    "\n",
    "To illustrate the usage of other geometry objects, we will now use the same simulation (flow past a cylinder), but replace the cylinder with a triangle. Further, we add some polygons to mask out specific areas within the flow field."
   ]
  },
  {
   "cell_type": "code",
   "id": "1aa04b8d-f902-41ca-aa70-2d2ebc149b9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:37.877197133Z",
     "start_time": "2026-02-19T14:35:36.480408952Z"
    }
   },
   "source": [
    "from sparseSpatialSampling.geometry import TriangleGeometry, GeometryCoordinates2D\n",
    "\n",
    "# initialize everything\n",
    "field, coord, _, write_times = load_foam_data(load_path, bounds, field_name=\"U\", t_start=4, scalar=False)\n",
    "save_name = \"cylinder2D_withMore_geometries\"\n",
    "\n",
    "# create some geometry objects\n",
    "domain = CubeGeometry(\"domain\", True, bounds[0], bounds[1])\n",
    "t1 = TriangleGeometry(\"front triangle\", False, [(0.1, 0.2), (0.25, 0.1), (0.25, 0.3)], refine=True, min_refinement_level=9)\n",
    "\n",
    "# make up some coordinates for the two polygons\n",
    "coord_s = [\n",
    "        (0.48, 0.29),\n",
    "        (0.4, 0.29),\n",
    "        (0.4, 0.19),\n",
    "        (0.46, 0.19),\n",
    "        (0.46, 0.13),\n",
    "        (0.42, 0.13),\n",
    "        (0.42, 0.15),\n",
    "        (0.4, 0.15),\n",
    "        (0.4, 0.11),\n",
    "        (0.48, 0.11),\n",
    "        (0.48, 0.21),\n",
    "        (0.42, 0.21),\n",
    "        (0.42, 0.27),\n",
    "        (0.46, 0.27),\n",
    "        (0.46, 0.24),\n",
    "        (0.48, 0.24),\n",
    "        (0.48, 0.29)\n",
    "    ]\n",
    "\n",
    "coord_3 = [\n",
    "        (0.5, 0.24),\n",
    "        (0.5, 0.27),\n",
    "        (0.52, 0.29),\n",
    "        (0.56, 0.29),\n",
    "        (0.58, 0.27),\n",
    "        (0.58, 0.22),\n",
    "        (0.56, 0.20),\n",
    "        (0.58, 0.18),\n",
    "        (0.58, 0.13),\n",
    "        (0.56, 0.11),\n",
    "        (0.52, 0.11),\n",
    "        (0.5, 0.13),\n",
    "        (0.5, 0.16),\n",
    "        (0.52, 0.16),\n",
    "        (0.52, 0.14),\n",
    "        (0.53, 0.13),\n",
    "        (0.55, 0.13),\n",
    "        (0.56, 0.14),\n",
    "        (0.56, 0.17),\n",
    "        (0.55, 0.18),\n",
    "        (0.53, 0.2),\n",
    "        (0.56, 0.23),\n",
    "        (0.56, 0.258),\n",
    "        (0.55, 0.268),\n",
    "        (0.53, 0.268),\n",
    "        (0.52, 0.258),\n",
    "        (0.52, 0.24),\n",
    "        (0.5, 0.24)\n",
    "    ]"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:36] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:35:36] INFO     Loading precomputed cell centers and volumes from processor1/constant\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "id": "96bf420b-8515-4269-8cbe-a0bba77f93e7",
   "metadata": {},
   "source": [
    "It is important to note that the coordinates for the `GeometryCoordinates2D` class have to be in 2D. Further they have to form a closed line. For 3D coordinates, the `GeometrySTL3D` can be used. Therefore, the coordinates have to be converted into an STL file, e.g., by using PyVista."
   ]
  },
  {
   "cell_type": "code",
   "id": "b93846d1-df99-414f-86a4-251e2034d44f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:35:58.977102085Z",
     "start_time": "2026-02-19T14:35:37.881802076Z"
    }
   },
   "source": [
    "# initialize all geometries\n",
    "g1 = GeometryCoordinates2D(\"S\", False, coord_s, refine=True, min_refinement_level=10)\n",
    "g2 = GeometryCoordinates2D(\"3\", False, coord_3, refine=True, min_refinement_level=10)\n",
    "t2 = TriangleGeometry(\"rear triangle\", False, [(0.88, 0.2), (0.73, 0.1), (0.73, 0.3)],\n",
    "                                refine=True, min_refinement_level=9)\n",
    "\n",
    "# create a S^3 instance and execute\n",
    "s_cube = SparseSpatialSampling(coord, pt.mean(field.abs().sum(1), 1), [domain, t1, g1, g2, t2], save_path, save_name,\n",
    "                               \"cylinder2D\", min_metric=min_metric, n_jobs=4)\n",
    "s_cube.execute_grid_generation()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:37] INFO     Selecting min. approximation of the metric as stopping criterion.\n",
      "[2026-02-19 15:35:37] INFO     \n",
      "\tSelected settings:\n",
      "\t\tpre_select           :\tFalse\n",
      "\t\tn_jobs               :\t4\n",
      "\t\tmax_delta_level      :\tFalse\n",
      "\t\tgeometry             :\t['domain', 'front triangle', 'S', '3', 'rear triangle']\n",
      "\t\tmin_metric           :\t0.75\n",
      "\t\tmin_level            :\t5\n",
      "\t\tcells_per_iter_start :\t9\n",
      "\t\tcells_per_iter_end   :\t9\n",
      "\t\tcells_per_iter       :\t9\n",
      "\t\tcells_per_iter_last  :\t1000000000.0\n",
      "\t\treach_at_least       :\t0.75\n",
      "\t\tn_dimensions         :\t2\n",
      "\t\tn_cells_orig         :\t9800\n",
      "\t\trelTol               :\t0.001\n",
      "[2026-02-19 15:35:37] INFO     Starting grid generation.\n",
      "[2026-02-19 15:35:37] INFO     Starting uniform refinement.\n",
      "\tStarting iteration no. 0, N_cells = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n",
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n",
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n",
      "Warning: TecplotDataloader can't be loaded. Most likely, the 'paraview' module is missing.\n",
      "Refer to the installation instructions at https://github.com/FlowModelingControl/flowtorch\n",
      "If you are not using the TecplotDataloader, ignore this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tStarting iteration no. 1, N_cells = 4\n",
      "\tStarting iteration no. 2, N_cells = 8\n",
      "\tStarting iteration no. 3, N_cells = 16\n",
      "\tStarting iteration no. 4, N_cells = 64\n",
      "[2026-02-19 15:35:43] INFO     Finished uniform refinement.\n",
      "[2026-02-19 15:35:43] INFO     Starting metric-based refinement.\n",
      "\tStarting iteration no. 0, captured metric: 13.55 %, N_cells = 192\n",
      "\tStarting iteration no. 1, captured metric: 14.41 %, N_cells = 217\n",
      "\tStarting iteration no. 2, captured metric: 14.97 %, N_cells = 244\n",
      "\tStarting iteration no. 3, captured metric: 15.32 %, N_cells = 271\n",
      "\tStarting iteration no. 4, captured metric: 16.18 %, N_cells = 297\n",
      "\tStarting iteration no. 5, captured metric: 16.33 %, N_cells = 323\n",
      "\tStarting iteration no. 6, captured metric: 16.48 %, N_cells = 350\n",
      "\tStarting iteration no. 7, captured metric: 16.68 %, N_cells = 377\n",
      "\tStarting iteration no. 8, captured metric: 16.82 %, N_cells = 404\n",
      "\tStarting iteration no. 9, captured metric: 17.43 %, N_cells = 431\n",
      "\tStarting iteration no. 10, captured metric: 18.52 %, N_cells = 453\n",
      "\tStarting iteration no. 11, captured metric: 19.27 %, N_cells = 480\n",
      "\tStarting iteration no. 12, captured metric: 19.87 %, N_cells = 507\n",
      "\tStarting iteration no. 13, captured metric: 20.45 %, N_cells = 534\n",
      "\tStarting iteration no. 14, captured metric: 21.02 %, N_cells = 559\n",
      "\tStarting iteration no. 15, captured metric: 21.72 %, N_cells = 586\n",
      "\tStarting iteration no. 16, captured metric: 22.58 %, N_cells = 613\n",
      "\tStarting iteration no. 17, captured metric: 23.42 %, N_cells = 637\n",
      "\tStarting iteration no. 18, captured metric: 24.05 %, N_cells = 664\n",
      "\tStarting iteration no. 19, captured metric: 24.69 %, N_cells = 691\n",
      "\tStarting iteration no. 20, captured metric: 24.99 %, N_cells = 716\n",
      "\tStarting iteration no. 21, captured metric: 25.66 %, N_cells = 743\n",
      "\tStarting iteration no. 22, captured metric: 26.12 %, N_cells = 770\n",
      "\tStarting iteration no. 23, captured metric: 26.81 %, N_cells = 797\n",
      "\tStarting iteration no. 24, captured metric: 27.31 %, N_cells = 824\n",
      "\tStarting iteration no. 25, captured metric: 27.88 %, N_cells = 850\n",
      "\tStarting iteration no. 26, captured metric: 28.36 %, N_cells = 876\n",
      "\tStarting iteration no. 27, captured metric: 28.71 %, N_cells = 901\n",
      "\tStarting iteration no. 28, captured metric: 29.13 %, N_cells = 925\n",
      "\tStarting iteration no. 29, captured metric: 29.45 %, N_cells = 951\n",
      "\tStarting iteration no. 30, captured metric: 29.89 %, N_cells = 977\n",
      "\tStarting iteration no. 31, captured metric: 30.38 %, N_cells = 1004\n",
      "\tStarting iteration no. 32, captured metric: 30.79 %, N_cells = 1031\n",
      "\tStarting iteration no. 33, captured metric: 30.93 %, N_cells = 1058\n",
      "\tStarting iteration no. 34, captured metric: 31.31 %, N_cells = 1085\n",
      "\tStarting iteration no. 35, captured metric: 31.5 %, N_cells = 1112\n",
      "\tStarting iteration no. 36, captured metric: 31.89 %, N_cells = 1139\n",
      "\tStarting iteration no. 37, captured metric: 32.14 %, N_cells = 1166\n",
      "\tStarting iteration no. 38, captured metric: 32.36 %, N_cells = 1192\n",
      "\tStarting iteration no. 39, captured metric: 32.59 %, N_cells = 1219\n",
      "\tStarting iteration no. 40, captured metric: 32.78 %, N_cells = 1246\n",
      "\tStarting iteration no. 41, captured metric: 32.83 %, N_cells = 1273\n",
      "\tStarting iteration no. 42, captured metric: 32.96 %, N_cells = 1297\n",
      "\tStarting iteration no. 43, captured metric: 33.22 %, N_cells = 1324\n",
      "\tStarting iteration no. 44, captured metric: 33.43 %, N_cells = 1351\n",
      "\tStarting iteration no. 45, captured metric: 33.69 %, N_cells = 1378\n",
      "\tStarting iteration no. 46, captured metric: 33.89 %, N_cells = 1405\n",
      "\tStarting iteration no. 47, captured metric: 33.99 %, N_cells = 1432\n",
      "\tStarting iteration no. 48, captured metric: 34.13 %, N_cells = 1459\n",
      "\tStarting iteration no. 49, captured metric: 34.29 %, N_cells = 1486\n",
      "\tStarting iteration no. 50, captured metric: 34.51 %, N_cells = 1513\n",
      "\tStarting iteration no. 51, captured metric: 34.83 %, N_cells = 1540\n",
      "\tStarting iteration no. 52, captured metric: 35.01 %, N_cells = 1567\n",
      "\tStarting iteration no. 53, captured metric: 35.34 %, N_cells = 1594\n",
      "\tStarting iteration no. 54, captured metric: 35.69 %, N_cells = 1621\n",
      "\tStarting iteration no. 55, captured metric: 35.81 %, N_cells = 1648\n",
      "\tStarting iteration no. 56, captured metric: 36.11 %, N_cells = 1675\n",
      "\tStarting iteration no. 57, captured metric: 36.39 %, N_cells = 1702\n",
      "\tStarting iteration no. 58, captured metric: 36.73 %, N_cells = 1729\n",
      "\tStarting iteration no. 59, captured metric: 37.01 %, N_cells = 1756\n",
      "\tStarting iteration no. 60, captured metric: 37.47 %, N_cells = 1783\n",
      "\tStarting iteration no. 61, captured metric: 37.81 %, N_cells = 1810\n",
      "\tStarting iteration no. 62, captured metric: 38.01 %, N_cells = 1837\n",
      "\tStarting iteration no. 63, captured metric: 38.25 %, N_cells = 1864\n",
      "\tStarting iteration no. 64, captured metric: 38.6 %, N_cells = 1888\n",
      "\tStarting iteration no. 65, captured metric: 38.93 %, N_cells = 1915\n",
      "\tStarting iteration no. 66, captured metric: 39.16 %, N_cells = 1942\n",
      "\tStarting iteration no. 67, captured metric: 39.53 %, N_cells = 1969\n",
      "\tStarting iteration no. 68, captured metric: 39.8 %, N_cells = 1996\n",
      "\tStarting iteration no. 69, captured metric: 40.0 %, N_cells = 2021\n",
      "\tStarting iteration no. 70, captured metric: 40.33 %, N_cells = 2048\n",
      "\tStarting iteration no. 71, captured metric: 40.6 %, N_cells = 2075\n",
      "\tStarting iteration no. 72, captured metric: 40.77 %, N_cells = 2097\n",
      "\tStarting iteration no. 73, captured metric: 41.17 %, N_cells = 2123\n",
      "\tStarting iteration no. 74, captured metric: 41.54 %, N_cells = 2150\n",
      "\tStarting iteration no. 75, captured metric: 41.89 %, N_cells = 2177\n",
      "\tStarting iteration no. 76, captured metric: 42.24 %, N_cells = 2204\n",
      "\tStarting iteration no. 77, captured metric: 42.58 %, N_cells = 2231\n",
      "\tStarting iteration no. 78, captured metric: 42.92 %, N_cells = 2258\n",
      "\tStarting iteration no. 79, captured metric: 43.27 %, N_cells = 2285\n",
      "\tStarting iteration no. 80, captured metric: 43.64 %, N_cells = 2312\n",
      "\tStarting iteration no. 81, captured metric: 43.99 %, N_cells = 2339\n",
      "\tStarting iteration no. 82, captured metric: 44.39 %, N_cells = 2366\n",
      "\tStarting iteration no. 83, captured metric: 44.79 %, N_cells = 2393\n",
      "\tStarting iteration no. 84, captured metric: 45.13 %, N_cells = 2420\n",
      "\tStarting iteration no. 85, captured metric: 45.45 %, N_cells = 2447\n",
      "\tStarting iteration no. 86, captured metric: 45.87 %, N_cells = 2474\n",
      "\tStarting iteration no. 87, captured metric: 46.2 %, N_cells = 2499\n",
      "\tStarting iteration no. 88, captured metric: 46.6 %, N_cells = 2526\n",
      "\tStarting iteration no. 89, captured metric: 46.99 %, N_cells = 2553\n",
      "\tStarting iteration no. 90, captured metric: 47.35 %, N_cells = 2580\n",
      "\tStarting iteration no. 91, captured metric: 47.7 %, N_cells = 2607\n",
      "\tStarting iteration no. 92, captured metric: 47.99 %, N_cells = 2634\n",
      "\tStarting iteration no. 93, captured metric: 48.38 %, N_cells = 2661\n",
      "\tStarting iteration no. 94, captured metric: 48.66 %, N_cells = 2688\n",
      "\tStarting iteration no. 95, captured metric: 48.86 %, N_cells = 2715\n",
      "\tStarting iteration no. 96, captured metric: 49.11 %, N_cells = 2742\n",
      "\tStarting iteration no. 97, captured metric: 49.29 %, N_cells = 2768\n",
      "\tStarting iteration no. 98, captured metric: 49.57 %, N_cells = 2794\n",
      "\tStarting iteration no. 99, captured metric: 49.75 %, N_cells = 2821\n",
      "\tStarting iteration no. 100, captured metric: 49.91 %, N_cells = 2848\n",
      "\tStarting iteration no. 101, captured metric: 50.07 %, N_cells = 2875\n",
      "\tStarting iteration no. 102, captured metric: 50.25 %, N_cells = 2900\n",
      "\tStarting iteration no. 103, captured metric: 50.5 %, N_cells = 2926\n",
      "\tStarting iteration no. 104, captured metric: 50.66 %, N_cells = 2949\n",
      "\tStarting iteration no. 105, captured metric: 50.85 %, N_cells = 2971\n",
      "\tStarting iteration no. 106, captured metric: 51.02 %, N_cells = 2995\n",
      "\tStarting iteration no. 107, captured metric: 51.27 %, N_cells = 3022\n",
      "\tStarting iteration no. 108, captured metric: 51.41 %, N_cells = 3046\n",
      "\tStarting iteration no. 109, captured metric: 51.55 %, N_cells = 3073\n",
      "\tStarting iteration no. 110, captured metric: 51.71 %, N_cells = 3099\n",
      "\tStarting iteration no. 111, captured metric: 51.9 %, N_cells = 3126\n",
      "\tStarting iteration no. 112, captured metric: 52.3 %, N_cells = 3153\n",
      "\tStarting iteration no. 113, captured metric: 52.53 %, N_cells = 3177\n",
      "\tStarting iteration no. 114, captured metric: 52.77 %, N_cells = 3201\n",
      "\tStarting iteration no. 115, captured metric: 53.06 %, N_cells = 3226\n",
      "\tStarting iteration no. 116, captured metric: 53.17 %, N_cells = 3253\n",
      "\tStarting iteration no. 117, captured metric: 53.43 %, N_cells = 3278\n",
      "\tStarting iteration no. 118, captured metric: 53.74 %, N_cells = 3302\n",
      "\tStarting iteration no. 119, captured metric: 54.05 %, N_cells = 3329\n",
      "\tStarting iteration no. 120, captured metric: 54.21 %, N_cells = 3354\n",
      "\tStarting iteration no. 121, captured metric: 54.43 %, N_cells = 3379\n",
      "\tStarting iteration no. 122, captured metric: 54.79 %, N_cells = 3406\n",
      "\tStarting iteration no. 123, captured metric: 55.05 %, N_cells = 3429\n",
      "\tStarting iteration no. 124, captured metric: 55.26 %, N_cells = 3456\n",
      "\tStarting iteration no. 125, captured metric: 55.58 %, N_cells = 3483\n",
      "\tStarting iteration no. 126, captured metric: 55.74 %, N_cells = 3508\n",
      "\tStarting iteration no. 127, captured metric: 55.87 %, N_cells = 3535\n",
      "\tStarting iteration no. 128, captured metric: 56.05 %, N_cells = 3560\n",
      "\tStarting iteration no. 129, captured metric: 56.27 %, N_cells = 3587\n",
      "\tStarting iteration no. 130, captured metric: 56.42 %, N_cells = 3614\n",
      "\tStarting iteration no. 131, captured metric: 56.76 %, N_cells = 3641\n",
      "\tStarting iteration no. 132, captured metric: 57.0 %, N_cells = 3665\n",
      "\tStarting iteration no. 133, captured metric: 57.16 %, N_cells = 3692\n",
      "\tStarting iteration no. 134, captured metric: 57.4 %, N_cells = 3716\n",
      "\tStarting iteration no. 135, captured metric: 57.65 %, N_cells = 3743\n",
      "\tStarting iteration no. 136, captured metric: 57.81 %, N_cells = 3770\n",
      "\tStarting iteration no. 137, captured metric: 57.93 %, N_cells = 3795\n",
      "\tStarting iteration no. 138, captured metric: 58.21 %, N_cells = 3820\n",
      "\tStarting iteration no. 139, captured metric: 58.45 %, N_cells = 3847\n",
      "\tStarting iteration no. 140, captured metric: 58.71 %, N_cells = 3874\n",
      "\tStarting iteration no. 141, captured metric: 58.95 %, N_cells = 3901\n",
      "\tStarting iteration no. 142, captured metric: 59.22 %, N_cells = 3928\n",
      "\tStarting iteration no. 143, captured metric: 59.38 %, N_cells = 3955\n",
      "\tStarting iteration no. 144, captured metric: 59.53 %, N_cells = 3982\n",
      "\tStarting iteration no. 145, captured metric: 59.65 %, N_cells = 4009\n",
      "\tStarting iteration no. 146, captured metric: 59.91 %, N_cells = 4036\n",
      "[2026-02-19 15:35:55] INFO     Finished metric-based refinement.\n",
      "[2026-02-19 15:35:55] INFO     Starting geometry refinement.\n",
      "[2026-02-19 15:35:55] INFO     Starting refining geometry front triangle.\n",
      "[2026-02-19 15:35:55] INFO     Found a minimum cell level of 7. Target level is 9.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 9. \n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 9. \n",
      "[2026-02-19 15:35:56] INFO     Starting refining geometry S.\n",
      "[2026-02-19 15:35:56] INFO     Found a minimum cell level of 7. Target level is 10.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 10.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 10.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 10 / 10.\n",
      "[2026-02-19 15:35:56] INFO     Starting refining geometry 3.\n",
      "[2026-02-19 15:35:57] INFO     Found a minimum cell level of 7. Target level is 10.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 10.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 10.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 10 / 10.\n",
      "[2026-02-19 15:35:57] INFO     Starting refining geometry rear triangle.\n",
      "[2026-02-19 15:35:58] INFO     Found a minimum cell level of 6. Target level is 9.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 7 / 9. \n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 9. \n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 9. \n",
      "[2026-02-19 15:35:58] INFO     Finished geometry refinement.\n",
      "[2026-02-19 15:35:58] INFO     Starting renumbering final mesh.\n",
      "[2026-02-19 15:35:58] INFO     Finished refinement in 20.9226 s \n",
      "\t\t\t\t\t\t\t\t(147 iterations).\n",
      "\t\t\t\t\t\t\t\tTime for uniform refinement: 5.4298 s\n",
      "\t\t\t\t\t\t\t\tTime for metric-based refinement: 12.1724 s\n",
      "\t\t\t\t\t\t\t\tTime for geometry refinement: 3.1586 s\n",
      "\t\t\t\t\t\t\t\tTime for renumbering the final mesh: 0.1460 s\n",
      "\t\t\t\t\t\t\t\t\n",
      "                                    Number of cells: 5666\n",
      "                                    Minimum ref. level: 6\n",
      "                                    Maximum ref. level: 10\n",
      "                                    Captured metric of original grid: 60.00 %\n",
      "                  \n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "id": "ab633515-149b-4e7b-896e-0b8672f68087",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:36:03.353178305Z",
     "start_time": "2026-02-19T14:35:58.994193638Z"
    }
   },
   "source": [
    "# export the data\n",
    "export = ExportData(s_cube)\n",
    "export_openfoam_fields(export, load_path, bounds)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:35:58] WARNING  Argument ``write_times`` is ``None``. Make sure to set the ``write_times`` before calling the ``export()`` method.\n",
      "[2026-02-19 15:35:59] INFO     Exporting batch 1 / 1\n",
      "[2026-02-19 15:35:59] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:35:59] INFO     Loading precomputed cell centers and volumes from processor1/constant\n",
      "[2026-02-19 15:36:01] INFO     Initializing KNN and computing interpolation weights.\n",
      "[2026-02-19 15:36:01] INFO     Starting interpolation and export of field U.\n",
      "[2026-02-19 15:36:01] INFO     Writing HDF5 file for field U.\n",
      "[2026-02-19 15:36:01] INFO     Writing XDMF file for file cylinder2D_withMore_geometries.h5\n",
      "[2026-02-19 15:36:02] INFO     Finished export of field U in 3.013s.\n",
      "[2026-02-19 15:36:02] INFO     Exporting batch 1 / 1\n",
      "[2026-02-19 15:36:02] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:36:02] INFO     Loading precomputed cell centers and volumes from processor1/constant\n",
      "[2026-02-19 15:36:02] INFO     Starting interpolation and export of field p.\n",
      "[2026-02-19 15:36:03] INFO     Writing XDMF file for file cylinder2D_withMore_geometries.h5\n",
      "[2026-02-19 15:36:03] INFO     Finished export of field p in 1.308s.\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "attachments": {
    "8f40085f-661f-4f15-b853-2b0799db665d.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "af2aa865-382c-4b98-a76a-0720e7152dfc",
   "metadata": {},
   "source": [
    "As for the previous cases, we can load the results into paraview and visualize the grid and flow field:\n",
    "\n",
    "![grid_sign_s3.png](attachment:da75e6a6-bec4-4c01-b682-aaa392bd8440.png)\n",
    "![sign_s3_U_field_t10s.png](attachment:8f40085f-661f-4f15-b853-2b0799db665d.png)\n",
    "\n",
    "## 8. References and further material\n",
    "This completes the first tutorial. In the next tutorial we will learn how to deal with other data formats, such as HDF5, and other geometry representations."
   ]
  },
  {
   "cell_type": "code",
   "id": "d4de4dcd-2028-451e-888f-98ab94d5580a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:36:03.450667004Z",
     "start_time": "2026-02-19T14:36:03.355474758Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 20
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}