{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e954576-1b51-4afc-a7c4-fb55259f84ee",
   "metadata": {},
   "source": [
    "# Tutorial 3: How to select the best settings and advanced options\n",
    "\n",
    "### Outline\n",
    "1. Avoid large size differences between neighboring cells\n",
    "2. Controlling the uniformity of the grid\n",
    "3. Accelerating the refinement process for large *3D* datasets\n",
    "\n",
    "In this tutorial, we will learn about how to avoid large size differences between neighboring cells as encountered in the previous tutorial. Then we will look at some important parameters, which can be customized to control the topology of the grid. We will lastly see how we can use these parameters for large grids in combination with STL files for geometry objects.\n",
    "\n",
    "In this tutorial we will again use the cylinder2D from the first tutorial since it is easy and fast to execute and publicly available for everybody.\n",
    "\n",
    "## 1. Avoid large size differences between neighboring cells\n",
    "For some applications, e.g., when computing gradients or in case a good resolution of the geometry has to be ensured, large size differences between two neighboring cells may cause problems. We need to ensure smooth transitions within cell sizes across the grid in these cases. $S^3$ provides an additional argument, which can be set to ensure a max. level difference between two neighboring cells of one. The cell level refers here to the number of refinement we have to employ to get to this specific cell size.\n",
    "\n",
    "To activate this criterion, we have to set `max_delta_level=True` when instantiating the `s_cube` object. This constraint will lead to smooth transitions, but also to longer execution times and mesh sizes, so it is recommended to only activate it when necessary."
   ]
  },
  {
   "cell_type": "code",
   "id": "bac87c9d-3c95-4509-92d8-0182e6ba1ed7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:38:32.692453601Z",
     "start_time": "2026-02-19T14:38:29.620269368Z"
    }
   },
   "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.sparse_spatial_sampling import SparseSpatialSampling\n",
    "from sparseSpatialSampling.geometry import CubeGeometry, SphereGeometry\n",
    "from sparseSpatialSampling.utils import load_foam_data"
   ],
   "outputs": [
    {
     "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"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "2eec0519-e390-4101-86f2-a1c7725fe75d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:38:33.215311217Z",
     "start_time": "2026-02-19T14:38:32.714152655Z"
    }
   },
   "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",
    "# define the path to where we want to save the results and the name of the file\n",
    "save_path = join(\"..\", \"..\", \"..\", \"run\", \"tutorials\", \"tutorial_3\")\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 8 seconds\n",
    "field, coord, _, write_times = load_foam_data(load_path, bounds, field_name=\"U\", t_start=8, scalar=False)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:38:32] INFO     Loading precomputed cell centers and volumes from processor0/constant\n",
      "[2026-02-19 15:38:32] INFO     Loading precomputed cell centers and volumes from processor1/constant\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "537aafe0-2b78-4877-b7e2-5fdff11379c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:38:33.289257165Z",
     "start_time": "2026-02-19T14:38:33.227331169Z"
    }
   },
   "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\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",
    "# we explicitly increase the resolution of the cylinder to cause large level differences when the constraint is not activated\n",
    "geometry = SphereGeometry(\"cylinder\", False, [0.2, 0.2], 0.05, refine=True, min_refinement_level=12)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "157f79d9-a8cf-4d30-88f5-5e07b560b93c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:38:47.954930825Z",
     "start_time": "2026-02-19T14:38:33.290744696Z"
    }
   },
   "source": [
    "# now we artificially create poor grid containing large level differences\n",
    "s_cube = SparseSpatialSampling(coord, metric, [domain, geometry], save_path, \"cylinder2D_nodeltaLevel_constraint\", \"cylinder2D\", \n",
    "                               min_metric=0.5, n_jobs=4)\n",
    "s_cube.execute_grid_generation()\n",
    "\n",
    "# export only the last time step for demonstration purposes\n",
    "export = ExportData(s_cube, write_times=\"10\")\n",
    "export.export(coord, field[:, :, -1].unsqueeze(-1), \"U\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:38:33] INFO     Selecting min. approximation of the metric as stopping criterion.\n",
      "[2026-02-19 15:38:33] 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           :\t0.5\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 15:38:33] INFO     Starting grid generation.\n",
      "[2026-02-19 15:38:33] 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 15:38:38] INFO     Finished uniform refinement.\n",
      "[2026-02-19 15:38:38] INFO     Starting metric-based refinement.\n",
      "\tStarting iteration no. 0, captured metric: 13.64 %, N_cells = 192\n",
      "\tStarting iteration no. 1, captured metric: 14.5 %, N_cells = 217\n",
      "\tStarting iteration no. 2, captured metric: 15.03 %, N_cells = 244\n",
      "\tStarting iteration no. 3, captured metric: 15.55 %, N_cells = 271\n",
      "\tStarting iteration no. 4, captured metric: 16.13 %, N_cells = 298\n",
      "\tStarting iteration no. 5, captured metric: 16.37 %, N_cells = 325\n",
      "\tStarting iteration no. 6, captured metric: 16.52 %, N_cells = 352\n",
      "\tStarting iteration no. 7, captured metric: 16.65 %, N_cells = 379\n",
      "\tStarting iteration no. 8, captured metric: 16.9 %, N_cells = 406\n",
      "\tStarting iteration no. 9, captured metric: 17.45 %, N_cells = 433\n",
      "\tStarting iteration no. 10, captured metric: 18.37 %, N_cells = 459\n",
      "\tStarting iteration no. 11, captured metric: 18.99 %, N_cells = 486\n",
      "\tStarting iteration no. 12, captured metric: 19.58 %, N_cells = 513\n",
      "\tStarting iteration no. 13, captured metric: 20.31 %, N_cells = 540\n",
      "\tStarting iteration no. 14, captured metric: 21.01 %, N_cells = 567\n",
      "\tStarting iteration no. 15, captured metric: 21.71 %, N_cells = 594\n",
      "\tStarting iteration no. 16, captured metric: 22.66 %, N_cells = 621\n",
      "\tStarting iteration no. 17, captured metric: 23.54 %, N_cells = 648\n",
      "\tStarting iteration no. 18, captured metric: 24.35 %, N_cells = 674\n",
      "\tStarting iteration no. 19, captured metric: 25.03 %, N_cells = 701\n",
      "\tStarting iteration no. 20, captured metric: 25.58 %, N_cells = 728\n",
      "\tStarting iteration no. 21, captured metric: 26.14 %, N_cells = 755\n",
      "\tStarting iteration no. 22, captured metric: 26.75 %, N_cells = 782\n",
      "\tStarting iteration no. 23, captured metric: 27.54 %, N_cells = 809\n",
      "\tStarting iteration no. 24, captured metric: 27.94 %, N_cells = 836\n",
      "\tStarting iteration no. 25, captured metric: 28.2 %, N_cells = 863\n",
      "\tStarting iteration no. 26, captured metric: 28.54 %, N_cells = 890\n",
      "\tStarting iteration no. 27, captured metric: 28.97 %, N_cells = 917\n",
      "\tStarting iteration no. 28, captured metric: 29.42 %, N_cells = 944\n",
      "\tStarting iteration no. 29, captured metric: 29.78 %, N_cells = 971\n",
      "\tStarting iteration no. 30, captured metric: 30.16 %, N_cells = 998\n",
      "\tStarting iteration no. 31, captured metric: 30.78 %, N_cells = 1025\n",
      "\tStarting iteration no. 32, captured metric: 31.05 %, N_cells = 1052\n",
      "\tStarting iteration no. 33, captured metric: 31.18 %, N_cells = 1079\n",
      "\tStarting iteration no. 34, captured metric: 31.5 %, N_cells = 1106\n",
      "\tStarting iteration no. 35, captured metric: 31.74 %, N_cells = 1133\n",
      "\tStarting iteration no. 36, captured metric: 32.06 %, N_cells = 1160\n",
      "\tStarting iteration no. 37, captured metric: 32.36 %, N_cells = 1187\n",
      "\tStarting iteration no. 38, captured metric: 32.66 %, N_cells = 1214\n",
      "\tStarting iteration no. 39, captured metric: 32.76 %, N_cells = 1241\n",
      "\tStarting iteration no. 40, captured metric: 32.79 %, N_cells = 1268\n",
      "\tStarting iteration no. 41, captured metric: 32.93 %, N_cells = 1295\n",
      "\tStarting iteration no. 42, captured metric: 33.13 %, N_cells = 1322\n",
      "\tStarting iteration no. 43, captured metric: 33.47 %, N_cells = 1349\n",
      "\tStarting iteration no. 44, captured metric: 33.61 %, N_cells = 1376\n",
      "\tStarting iteration no. 45, captured metric: 33.93 %, 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: 34.44 %, N_cells = 1484\n",
      "\tStarting iteration no. 49, captured metric: 34.57 %, N_cells = 1511\n",
      "\tStarting iteration no. 50, captured metric: 34.72 %, N_cells = 1538\n",
      "\tStarting iteration no. 51, captured metric: 35.12 %, N_cells = 1565\n",
      "\tStarting iteration no. 52, captured metric: 35.46 %, N_cells = 1592\n",
      "\tStarting iteration no. 53, captured metric: 35.7 %, N_cells = 1619\n",
      "\tStarting iteration no. 54, captured metric: 35.94 %, N_cells = 1646\n",
      "\tStarting iteration no. 55, captured metric: 36.14 %, N_cells = 1673\n",
      "\tStarting iteration no. 56, captured metric: 36.28 %, N_cells = 1700\n",
      "\tStarting iteration no. 57, captured metric: 36.52 %, N_cells = 1727\n",
      "\tStarting iteration no. 58, captured metric: 36.85 %, N_cells = 1754\n",
      "\tStarting iteration no. 59, captured metric: 37.1 %, N_cells = 1781\n",
      "\tStarting iteration no. 60, captured metric: 37.4 %, N_cells = 1808\n",
      "\tStarting iteration no. 61, captured metric: 37.75 %, N_cells = 1835\n",
      "\tStarting iteration no. 62, captured metric: 38.0 %, N_cells = 1862\n",
      "\tStarting iteration no. 63, captured metric: 38.21 %, N_cells = 1889\n",
      "\tStarting iteration no. 64, captured metric: 38.51 %, N_cells = 1916\n",
      "\tStarting iteration no. 65, captured metric: 38.85 %, N_cells = 1943\n",
      "\tStarting iteration no. 66, captured metric: 39.13 %, N_cells = 1970\n",
      "\tStarting iteration no. 67, captured metric: 39.34 %, N_cells = 1997\n",
      "\tStarting iteration no. 68, captured metric: 39.8 %, N_cells = 2024\n",
      "\tStarting iteration no. 69, captured metric: 40.03 %, N_cells = 2051\n",
      "\tStarting iteration no. 70, captured metric: 40.27 %, N_cells = 2078\n",
      "\tStarting iteration no. 71, captured metric: 40.59 %, N_cells = 2105\n",
      "\tStarting iteration no. 72, captured metric: 40.92 %, N_cells = 2132\n",
      "\tStarting iteration no. 73, captured metric: 41.27 %, N_cells = 2159\n",
      "\tStarting iteration no. 74, captured metric: 41.63 %, N_cells = 2186\n",
      "\tStarting iteration no. 75, captured metric: 41.94 %, N_cells = 2212\n",
      "\tStarting iteration no. 76, captured metric: 42.3 %, N_cells = 2239\n",
      "\tStarting iteration no. 77, captured metric: 42.7 %, N_cells = 2266\n",
      "\tStarting iteration no. 78, captured metric: 43.05 %, N_cells = 2293\n",
      "\tStarting iteration no. 79, captured metric: 43.43 %, N_cells = 2320\n",
      "\tStarting iteration no. 80, captured metric: 43.71 %, N_cells = 2347\n",
      "\tStarting iteration no. 81, captured metric: 44.12 %, N_cells = 2374\n",
      "\tStarting iteration no. 82, captured metric: 44.52 %, N_cells = 2401\n",
      "\tStarting iteration no. 83, captured metric: 44.81 %, N_cells = 2428\n",
      "\tStarting iteration no. 84, captured metric: 45.19 %, N_cells = 2455\n",
      "\tStarting iteration no. 85, captured metric: 45.59 %, N_cells = 2482\n",
      "\tStarting iteration no. 86, captured metric: 46.0 %, N_cells = 2509\n",
      "\tStarting iteration no. 87, captured metric: 46.38 %, N_cells = 2536\n",
      "\tStarting iteration no. 88, captured metric: 46.73 %, N_cells = 2563\n",
      "\tStarting iteration no. 89, captured metric: 47.13 %, N_cells = 2590\n",
      "\tStarting iteration no. 90, captured metric: 47.54 %, N_cells = 2617\n",
      "\tStarting iteration no. 91, captured metric: 47.91 %, N_cells = 2644\n",
      "\tStarting iteration no. 92, captured metric: 48.3 %, N_cells = 2671\n",
      "\tStarting iteration no. 93, captured metric: 48.57 %, N_cells = 2698\n",
      "\tStarting iteration no. 94, captured metric: 48.92 %, N_cells = 2725\n",
      "\tStarting iteration no. 95, captured metric: 49.24 %, N_cells = 2752\n",
      "\tStarting iteration no. 96, captured metric: 49.51 %, N_cells = 2779\n",
      "\tStarting iteration no. 97, captured metric: 49.73 %, N_cells = 2806\n",
      "\tStarting iteration no. 98, captured metric: 49.83 %, N_cells = 2831\n",
      "[2026-02-19 15:38:43] INFO     Finished metric-based refinement.\n",
      "[2026-02-19 15:38:43] INFO     Starting geometry refinement.\n",
      "[2026-02-19 15:38:43] INFO     Starting refining geometry cylinder.\n",
      "[2026-02-19 15:38:43] INFO     Found a minimum cell level of 6. Target level is 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 7 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 10 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 11 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 12 / 12.\n",
      "[2026-02-19 15:38:44] INFO     Finished geometry refinement.\n",
      "[2026-02-19 15:38:44] INFO     Starting renumbering final mesh.\n",
      "[2026-02-19 15:38:47] INFO     Finished refinement in 14.5280 s \n",
      "\t\t\t\t\t\t\t\t(99 iterations).\n",
      "\t\t\t\t\t\t\t\tTime for uniform refinement: 5.4432 s\n",
      "\t\t\t\t\t\t\t\tTime for metric-based refinement: 4.8844 s\n",
      "\t\t\t\t\t\t\t\tTime for geometry refinement: 1.0879 s\n",
      "\t\t\t\t\t\t\t\tTime for renumbering the final mesh: 3.0965 s\n",
      "\t\t\t\t\t\t\t\t\n",
      "                                    Number of cells: 4301\n",
      "                                    Minimum ref. level: 6\n",
      "                                    Maximum ref. level: 12\n",
      "                                    Captured metric of original grid: 50.06 %\n",
      "                  \n",
      "[2026-02-19 15:38:47] INFO     Initializing KNN and computing interpolation weights.\n",
      "[2026-02-19 15:38:47] INFO     Starting interpolation and export of field U.\n",
      "[2026-02-19 15:38:47] INFO     Writing HDF5 file for field U.\n",
      "[2026-02-19 15:38:47] INFO     Writing XDMF file for file cylinder2D_nodeltaLevel_constraint.h5\n",
      "[2026-02-19 15:38:47] INFO     Finished export of field U in 0.039s.\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "592c9195-2112-416c-97ca-152a342f0af4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:38:59.657713351Z",
     "start_time": "2026-02-19T14:38:47.957068749Z"
    }
   },
   "source": [
    "# now we activate the max_delta_level constraint to see how the grid quality improves\n",
    "s_cube = SparseSpatialSampling(coord, metric, [domain, geometry], save_path, \"cylinder2D_deltaLevel_constraint\", \"cylinder2D\", \n",
    "                               min_metric=0.5, n_jobs=4, max_delta_level=True)\n",
    "s_cube.execute_grid_generation()\n",
    "\n",
    "export = ExportData(s_cube, write_times=\"10\")\n",
    "export.export(coord, field[:, :, -1].unsqueeze(-1), \"U\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:38:47] INFO     Selecting min. approximation of the metric as stopping criterion.\n",
      "[2026-02-19 15:38:47] INFO     \n",
      "\tSelected settings:\n",
      "\t\tpre_select           :\tFalse\n",
      "\t\tn_jobs               :\t4\n",
      "\t\tmax_delta_level      :\tTrue\n",
      "\t\tgeometry             :\t['domain', 'cylinder']\n",
      "\t\tmin_metric           :\t0.5\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 15:38:47] INFO     Starting grid generation.\n",
      "[2026-02-19 15:38:47] 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.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",
      "\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 15:38:53] INFO     Finished uniform refinement.\n",
      "[2026-02-19 15:38:53] INFO     Starting metric-based refinement.\n",
      "\tStarting iteration no. 0, captured metric: 13.64 %, N_cells = 192\n",
      "\tStarting iteration no. 1, captured metric: 14.5 %, N_cells = 217\n",
      "\tStarting iteration no. 2, captured metric: 15.03 %, N_cells = 244\n",
      "\tStarting iteration no. 3, captured metric: 15.55 %, N_cells = 271\n",
      "\tStarting iteration no. 4, captured metric: 16.37 %, N_cells = 301\n",
      "\tStarting iteration no. 5, captured metric: 16.61 %, N_cells = 328\n",
      "\tStarting iteration no. 6, captured metric: 16.75 %, N_cells = 355\n",
      "\tStarting iteration no. 7, captured metric: 16.89 %, N_cells = 382\n",
      "\tStarting iteration no. 8, captured metric: 17.13 %, N_cells = 409\n",
      "\tStarting iteration no. 9, captured metric: 17.68 %, N_cells = 436\n",
      "\tStarting iteration no. 10, captured metric: 18.44 %, N_cells = 465\n",
      "\tStarting iteration no. 11, captured metric: 19.06 %, N_cells = 492\n",
      "\tStarting iteration no. 12, captured metric: 19.65 %, N_cells = 519\n",
      "\tStarting iteration no. 13, captured metric: 20.38 %, N_cells = 546\n",
      "\tStarting iteration no. 14, captured metric: 21.07 %, N_cells = 573\n",
      "\tStarting iteration no. 15, captured metric: 21.89 %, N_cells = 603\n",
      "\tStarting iteration no. 16, captured metric: 22.9 %, N_cells = 630\n",
      "\tStarting iteration no. 17, captured metric: 23.77 %, N_cells = 657\n",
      "\tStarting iteration no. 18, captured metric: 24.46 %, N_cells = 683\n",
      "\tStarting iteration no. 19, captured metric: 25.12 %, N_cells = 710\n",
      "\tStarting iteration no. 20, captured metric: 25.82 %, N_cells = 737\n",
      "\tStarting iteration no. 21, captured metric: 26.35 %, N_cells = 764\n",
      "\tStarting iteration no. 22, captured metric: 27.03 %, N_cells = 791\n",
      "\tStarting iteration no. 23, captured metric: 27.69 %, N_cells = 818\n",
      "\tStarting iteration no. 24, captured metric: 27.99 %, N_cells = 845\n",
      "\tStarting iteration no. 25, captured metric: 28.35 %, N_cells = 872\n",
      "\tStarting iteration no. 26, captured metric: 28.74 %, N_cells = 899\n",
      "\tStarting iteration no. 27, captured metric: 29.16 %, N_cells = 926\n",
      "\tStarting iteration no. 28, captured metric: 29.71 %, N_cells = 956\n",
      "\tStarting iteration no. 29, captured metric: 30.24 %, N_cells = 983\n",
      "\tStarting iteration no. 30, captured metric: 30.81 %, N_cells = 1013\n",
      "\tStarting iteration no. 31, captured metric: 31.25 %, N_cells = 1040\n",
      "\tStarting iteration no. 32, captured metric: 31.46 %, N_cells = 1067\n",
      "\tStarting iteration no. 33, captured metric: 31.52 %, N_cells = 1094\n",
      "\tStarting iteration no. 34, captured metric: 31.7 %, N_cells = 1121\n",
      "\tStarting iteration no. 35, captured metric: 32.11 %, N_cells = 1148\n",
      "\tStarting iteration no. 36, captured metric: 32.36 %, N_cells = 1175\n",
      "\tStarting iteration no. 37, captured metric: 32.56 %, N_cells = 1202\n",
      "\tStarting iteration no. 38, captured metric: 32.67 %, N_cells = 1229\n",
      "\tStarting iteration no. 39, captured metric: 32.77 %, N_cells = 1256\n",
      "\tStarting iteration no. 40, captured metric: 32.83 %, N_cells = 1283\n",
      "\tStarting iteration no. 41, captured metric: 33.26 %, N_cells = 1313\n",
      "\tStarting iteration no. 42, captured metric: 33.34 %, N_cells = 1340\n",
      "\tStarting iteration no. 43, captured metric: 33.65 %, N_cells = 1367\n",
      "\tStarting iteration no. 44, captured metric: 33.93 %, N_cells = 1394\n",
      "\tStarting iteration no. 45, captured metric: 34.12 %, N_cells = 1421\n",
      "\tStarting iteration no. 46, captured metric: 34.27 %, N_cells = 1448\n",
      "\tStarting iteration no. 47, captured metric: 34.38 %, N_cells = 1475\n",
      "\tStarting iteration no. 48, captured metric: 34.66 %, N_cells = 1502\n",
      "\tStarting iteration no. 49, captured metric: 34.82 %, N_cells = 1529\n",
      "\tStarting iteration no. 50, captured metric: 35.04 %, N_cells = 1556\n",
      "\tStarting iteration no. 51, captured metric: 35.39 %, N_cells = 1583\n",
      "\tStarting iteration no. 52, captured metric: 35.74 %, N_cells = 1610\n",
      "\tStarting iteration no. 53, captured metric: 35.92 %, N_cells = 1637\n",
      "\tStarting iteration no. 54, captured metric: 36.11 %, N_cells = 1664\n",
      "\tStarting iteration no. 55, captured metric: 36.27 %, N_cells = 1691\n",
      "\tStarting iteration no. 56, captured metric: 36.42 %, N_cells = 1718\n",
      "\tStarting iteration no. 57, captured metric: 36.68 %, N_cells = 1745\n",
      "\tStarting iteration no. 58, captured metric: 37.05 %, N_cells = 1772\n",
      "\tStarting iteration no. 59, captured metric: 37.33 %, N_cells = 1799\n",
      "\tStarting iteration no. 60, captured metric: 37.62 %, N_cells = 1826\n",
      "\tStarting iteration no. 61, captured metric: 37.91 %, N_cells = 1853\n",
      "\tStarting iteration no. 62, captured metric: 38.12 %, N_cells = 1880\n",
      "\tStarting iteration no. 63, captured metric: 38.46 %, N_cells = 1907\n",
      "\tStarting iteration no. 64, captured metric: 38.71 %, N_cells = 1934\n",
      "\tStarting iteration no. 65, captured metric: 39.07 %, N_cells = 1961\n",
      "\tStarting iteration no. 66, captured metric: 39.23 %, N_cells = 1988\n",
      "\tStarting iteration no. 67, captured metric: 39.72 %, N_cells = 2015\n",
      "\tStarting iteration no. 68, captured metric: 39.95 %, N_cells = 2042\n",
      "\tStarting iteration no. 69, captured metric: 40.17 %, N_cells = 2069\n",
      "\tStarting iteration no. 70, captured metric: 40.48 %, N_cells = 2096\n",
      "\tStarting iteration no. 71, captured metric: 40.84 %, N_cells = 2123\n",
      "\tStarting iteration no. 72, captured metric: 41.15 %, N_cells = 2150\n",
      "\tStarting iteration no. 73, captured metric: 41.5 %, N_cells = 2177\n",
      "\tStarting iteration no. 74, captured metric: 41.86 %, N_cells = 2204\n",
      "\tStarting iteration no. 75, captured metric: 42.17 %, N_cells = 2230\n",
      "\tStarting iteration no. 76, captured metric: 42.59 %, N_cells = 2257\n",
      "\tStarting iteration no. 77, captured metric: 42.94 %, N_cells = 2284\n",
      "\tStarting iteration no. 78, captured metric: 43.3 %, N_cells = 2311\n",
      "\tStarting iteration no. 79, captured metric: 43.63 %, N_cells = 2338\n",
      "\tStarting iteration no. 80, captured metric: 44.03 %, N_cells = 2365\n",
      "\tStarting iteration no. 81, captured metric: 44.38 %, N_cells = 2392\n",
      "\tStarting iteration no. 82, captured metric: 44.69 %, N_cells = 2419\n",
      "\tStarting iteration no. 83, captured metric: 45.06 %, N_cells = 2446\n",
      "\tStarting iteration no. 84, captured metric: 45.45 %, N_cells = 2473\n",
      "\tStarting iteration no. 85, captured metric: 45.86 %, N_cells = 2500\n",
      "\tStarting iteration no. 86, captured metric: 46.3 %, N_cells = 2527\n",
      "\tStarting iteration no. 87, captured metric: 46.61 %, N_cells = 2554\n",
      "\tStarting iteration no. 88, captured metric: 47.02 %, N_cells = 2581\n",
      "\tStarting iteration no. 89, captured metric: 47.41 %, N_cells = 2608\n",
      "\tStarting iteration no. 90, captured metric: 47.77 %, N_cells = 2635\n",
      "\tStarting iteration no. 91, captured metric: 48.18 %, N_cells = 2662\n",
      "\tStarting iteration no. 92, captured metric: 48.51 %, N_cells = 2689\n",
      "\tStarting iteration no. 93, captured metric: 48.83 %, N_cells = 2716\n",
      "\tStarting iteration no. 94, captured metric: 49.14 %, N_cells = 2743\n",
      "\tStarting iteration no. 95, captured metric: 49.39 %, N_cells = 2770\n",
      "\tStarting iteration no. 96, captured metric: 49.62 %, N_cells = 2797\n",
      "\tStarting iteration no. 97, captured metric: 49.79 %, N_cells = 2822\n",
      "\tStarting iteration no. 98, captured metric: 49.96 %, N_cells = 2849\n",
      "[2026-02-19 15:38:58] INFO     Finished metric-based refinement.\n",
      "[2026-02-19 15:38:58] INFO     Starting geometry refinement.\n",
      "[2026-02-19 15:38:58] INFO     Starting refining geometry cylinder.\n",
      "[2026-02-19 15:38:58] INFO     Found a minimum cell level of 6. Target level is 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 7 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 10 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 11 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 12 / 12.\n",
      "[2026-02-19 15:38:59] INFO     Finished geometry refinement.\n",
      "[2026-02-19 15:38:59] INFO     Starting renumbering final mesh.\n",
      "[2026-02-19 15:38:59] INFO     Finished refinement in 11.5554 s \n",
      "\t\t\t\t\t\t\t\t(99 iterations).\n",
      "\t\t\t\t\t\t\t\tTime for uniform refinement: 5.0861 s\n",
      "\t\t\t\t\t\t\t\tTime for metric-based refinement: 5.0659 s\n",
      "\t\t\t\t\t\t\t\tTime for geometry refinement: 1.3162 s\n",
      "\t\t\t\t\t\t\t\tTime for renumbering the final mesh: 0.0720 s\n",
      "\t\t\t\t\t\t\t\t\n",
      "                                    Number of cells: 5891\n",
      "                                    Minimum ref. level: 6\n",
      "                                    Maximum ref. level: 12\n",
      "                                    Captured metric of original grid: 50.18 %\n",
      "                  \n",
      "[2026-02-19 15:38:59] INFO     Initializing KNN and computing interpolation weights.\n",
      "[2026-02-19 15:38:59] INFO     Starting interpolation and export of field U.\n",
      "[2026-02-19 15:38:59] INFO     Writing HDF5 file for field U.\n",
      "[2026-02-19 15:38:59] INFO     Writing XDMF file for file cylinder2D_deltaLevel_constraint.h5\n",
      "[2026-02-19 15:38:59] INFO     Finished export of field U in 0.041s.\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "attachments": {
    "26780478-0ab1-4002-a5c3-64049d9f08d3.png": {
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"
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"
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msxkEQbPZtJlg84+yiGigPXjwwPIFbv8WwQQmMCGtE+x/AZibm0vBrzH2z8J+G+yzfxb2x4IJTDCf8PDhw4mJCaVUp9N5+/ZttCH2m8EEJmRwwldfffXhhx9KP1XiaeTi4kIyMRQEwW9+85t//vOfZ2dnb968OT8/Pz8/f/v27ddff/3111//v8v++9//3rp4GIa1Ws33fc/zbgVKqXfeeefdyx48ePDgwYOHDx+Ojo6Ojo6OjY0ppb7xjW/85Cc/6bU4Ed1jP/vZz/7zn/8opSK8Pyizt4j+MUHHBB0TdMMywcEvAOn4NUZ8Fufn50qp0dHRwW2DfeKzsD8W/WOCjgk6+wkqjiFM0DFBl5EJ/X9ujo+Pf/jhh9///vfz+fyti8ebo6sqREREREREREQpy9F9VYiIiIiIiIiIUhZXVYiIiIiIiIiIosRVFSIiIiIiIiKiKHFVhYiIiIiIiIgoSlxVISIiIiIiIiKKEldViIiIiIiIiIiixFUVIiIiIiIiIqIocVWFiIiIiIiIiChKXFUhIiIiIiIiIooSV1WIiIiIiIiIiKLEVRUiIiIiIiIioihxVYWIiIiIiIiIKEpcVSEiIiIiIiIiihJXVYiIiIiIiIiIosRVFSIiIiIiIiKiKHFVhYiIiIiIiIgoSlxVISIiIiIiIiKKEldViIiIiIiIiIiixFUVIiIiIiIiIqIocVWFiIiIiIiIiChKXFUhIiIiIiIiIooSV1WIiIiIiIiIiKLEVRUiIiIiIiIioihxVYWIiIiIiIiIKEpcVSEiIiIiIiIiihJXVYiIiIiIiIiIosRVFSIiIiIiIiKiKHFVhYiIiIiIiIgoSlxVISIiIiIiIiKKEldViIiIiIiIiIiixFUVIiIiIiIiIqIocVWFiIiIiIiIiChKXFUhIiIiIiIiIooSV1WIiIiIiIiIiKL0/wGVdQYAV9tXlAAAAABJRU5ErkJggg=="
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"
    }
   },
   "cell_type": "markdown",
   "id": "ff677366-cec1-49c7-8e4c-55c7393c3aee",
   "metadata": {},
   "source": [
    "As already discussed in the introduction, we can see that the number of cells increased from $4301$ to $5891$ when activating the constraint. Now let's load the data into ParaView and compare the grids.\n",
    "\n",
    "First we take a look at the grid without the constraint:\n",
    "\n",
    "![grid_noDeltaLevel_constraint.png](attachment:26780478-0ab1-4002-a5c3-64049d9f08d3.png)\n",
    "\n",
    "Here, we can already see large level differences at the domain boundaries and near the cylinder. We can zoom in the region near the cylinder to see it better: \n",
    "\n",
    "![grid_noDeltaLevel_constraint_zoomed.png](attachment:2a4d3c5f-a209-4f1a-80f3-56f3a6a298e4.png)\n",
    "\n",
    "As explained, these differences within the cell size can cause problems, e.g. when computing gradients on that grid. As a comparison we can now take a look athe the grid which was created with the delta level constraint:\n",
    "\n",
    "![grid_withDeltaLevel_constraint.png](attachment:717512d8-c10a-4bb6-b661-a3808bb30a86.png)\n",
    "\n",
    "When we zoom in the same region as before we can see that the grid quality improved significantly:\n",
    "\n",
    "![grid_deltaLevel_constraint_zoomed.png](attachment:f9a1c503-3716-4180-84cc-140f81da7eba.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c62e2c-e540-4d0e-a15e-22b798dd5a05",
   "metadata": {},
   "source": [
    "## 2. Controlling the uniformity of the grid\n",
    "The next parameter we want to look at is `uniform_levels`, which controls the number of uniform grid levels. $S^3$ starts by creating a uniform background grid before starting the adaptive refinement in order to accelerate the grid generation process.\n",
    "\n",
    "A rule of thumb is: when increasing the number uniform levels, the grid will become more uniform but the execution time decreases and vice versa. In general it is only sensible to adjust this parameter if the grid is expected to be very coarse (then `uniform_levels` has to be decreased), or if the number of grid levels is expected to be large. In the latter case, the number of uniform levels can be increased.\n",
    "\n",
    "Since this parameter is quite intuitive, we will briefly show what happens if it gets increased in the following."
   ]
  },
  {
   "cell_type": "code",
   "id": "a510dbb4-8de0-46bf-b9df-78f9a8918fa8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:39:06.927386445Z",
     "start_time": "2026-02-19T14:38:59.659196480Z"
    }
   },
   "source": [
    "# execute for higher n_uniform levels\n",
    "# The default is 5 uniform levels, but the min_level is already 6 so we don't expect the grid to be adaptive\n",
    "s_cube = SparseSpatialSampling(coord, metric, [domain, geometry], save_path, \"cylinder2D_6_uniform_levels\", \"cylinder2D\", \n",
    "                               min_metric=0.5, n_jobs=4, uniform_levels=7)\n",
    "s_cube.execute_grid_generation()\n",
    "\n",
    "# export\n",
    "export = ExportData(s_cube, write_times=\"10\")\n",
    "export.export(coord, field[:, :, -1].unsqueeze(-1), \"U\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2026-02-19 15:38:59] INFO     Selecting min. approximation of the metric as stopping criterion.\n",
      "[2026-02-19 15:38:59] 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           :\t0.5\n",
      "\t\tmin_level            :\t7\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 15:38:59] INFO     Starting grid generation.\n",
      "[2026-02-19 15:38:59] 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",
      "\tStarting iteration no. 5, N_cells = 192\n",
      "\tStarting iteration no. 6, N_cells = 766\n",
      "[2026-02-19 15:39:05] INFO     Finished uniform refinement.\n",
      "[2026-02-19 15:39:05] INFO     Starting metric-based refinement.\n",
      "\tStarting iteration no. 0, captured metric: 54.73 %, N_cells = 3056\n",
      "[2026-02-19 15:39:05] INFO     Finished metric-based refinement.\n",
      "[2026-02-19 15:39:05] INFO     Starting geometry refinement.\n",
      "[2026-02-19 15:39:05] INFO     Starting refining geometry cylinder.\n",
      "[2026-02-19 15:39:05] INFO     Found a minimum cell level of 7. Target level is 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 8 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 9 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 10 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 11 / 12.\n",
      "\t\t\t\t\t\t\t\t\tRefining level 12 / 12.\n",
      "[2026-02-19 15:39:06] INFO     Finished geometry refinement.\n",
      "[2026-02-19 15:39:06] INFO     Starting renumbering final mesh.\n",
      "[2026-02-19 15:39:06] INFO     Finished refinement in 7.1241 s \n",
      "\t\t\t\t\t\t\t\t(1 iterations).\n",
      "\t\t\t\t\t\t\t\tTime for uniform refinement: 6.0477 s\n",
      "\t\t\t\t\t\t\t\tTime for metric-based refinement: 0.0646 s\n",
      "\t\t\t\t\t\t\t\tTime for geometry refinement: 0.8870 s\n",
      "\t\t\t\t\t\t\t\tTime for renumbering the final mesh: 0.1054 s\n",
      "\t\t\t\t\t\t\t\t\n",
      "                                    Number of cells: 4538\n",
      "                                    Minimum ref. level: 7\n",
      "                                    Maximum ref. level: 12\n",
      "                                    Captured metric of original grid: 55.06 %\n",
      "                  \n",
      "[2026-02-19 15:39:06] INFO     Initializing KNN and computing interpolation weights.\n",
      "[2026-02-19 15:39:06] INFO     Starting interpolation and export of field U.\n",
      "[2026-02-19 15:39:06] INFO     Writing HDF5 file for field U.\n",
      "[2026-02-19 15:39:06] INFO     Writing XDMF file for file cylinder2D_6_uniform_levels.h5\n",
      "[2026-02-19 15:39:06] INFO     Finished export of field U in 0.041s.\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "attachments": {
    "2f7f6182-89dc-4507-b90f-e3ba801c69bb.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "fb275238-671a-417d-8aff-5b9567cb3454",
   "metadata": {},
   "source": [
    "As we expected, the resulting grid is much more uniform than the grid using the default value of `uniform_levels=5`:\n",
    "\n",
    "![grid_7uniformLevels.png](attachment:2f7f6182-89dc-4507-b90f-e3ba801c69bb.png)\n",
    "\n",
    "This happens because our grid using `uniform_levels=5` has a minimum refinement level of $6$. So if we increase `uniform_levels`to $7$ the adaptive refinement is never activated. \n",
    "\n",
    "## 3. Accelerating the refinement process for large *3D* datasets\n",
    "\n",
    "For large grids and in case STL file(s) are used as geometry objects, there are three more parameters which we can adjust in order to accelerate the refienment process. However, since our cylinder2D test case is too simple to see an actual effect, we will just explain the usage of these parameters briefly in the following. \n",
    "\n",
    "The first two parameters are `n_cells_iter_start` and `n_cells_iter_end`. These parameters control how many cells are generated each iteration. By default `n_cells_iter_start=0.001 n_cells_original_grid` with `n_cells_original_grid` denoting the number of cells in the mesh from the simulation  and `n_cells_iter_end = n_cells_iter_start`. Each iteration, the number of cells to refine is computed based on properties of the current grid.\n",
    "\n",
    "When increasing `n_cells_iter_start` and/or `n_cells_iter_end`, this leads to generating more cells per iteration and therefore a more uniform grid. Decreasing them analogously leads to a more adaptive grid, however, it also increases the runtime of $S^3$. In general, these parameters have a more complex influence on the runtime and resulting grid than, e.g., `uniform_levels`, so usually it should be avoided to modify them. But it may be helpful in some special cases. \n",
    "\n",
    "The last parameter is `pre_select`, which is useful when STL files are used as geometry objects. Especially if the expected number of grid points is large and the STL file has a high resolution (contains many points). To accelerate the grid generation, the class `GeometrySTL3D` provides an additional parameter `reduce_by`, which compresses the STL file by $x\\%$. The optimal compression ratio depends on the application, but values up to `reduce_by=0.9` generally shouldn't cause any problems.\n",
    "If that is still not able to decrease the required runtime significantly, the parameter `pre_select`can be set to `True` when instantiating the `s_cube` object. Since this parameter determines possible cells in the vicinity of the geometry much faster than the 'standard' way when dealing with STL files, this can significantly accelerate the refinement process.\n",
    "\n",
    "**Note:** The parameter `pre_select` works best if the edges of a box drawn around the STL file have nearly the same length, meaning that the shape of the STL file is nearly cubic. For STL files which have a dominant length, e.g. aircraft with high aspect ratio wings, setting `pre_select=True` may even increase the required runtime!\n",
    "\n",
    "This concludes the third tutorial. The next tutorial presents different options when exporting the data to HDF5. "
   ]
  },
  {
   "cell_type": "code",
   "id": "fb31f7b1-d7b8-45a8-b043-67bdde9a52d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-19T14:39:06.976953795Z",
     "start_time": "2026-02-19T14:39:06.928797370Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 6
  }
 ],
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