visualize_sensors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e214d477",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import argparse\n",
    "\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from scipy.spatial import ConvexHull\n",
    "\n",
    "from postprocessing.sampling_pts import get_samplingptshandler, PyfrmSamplingPts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "344f0f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "meshing_cfg_path = 'configs/mesh_gen/Omesh.json'\n",
    "sampling_cfg_path = 'configs/postprocessing/annulus_64_plane.json'\n",
    "pyfrm = '/storage/dataset500/cylinder/cylinder.pyfrm'\n",
    "\n",
    "meshing_cfg = json.load(open(meshing_cfg_path, 'r'))\n",
    "sampling_cfg = json.load(open(sampling_cfg_path, 'r'))\n",
    "\n",
    "L_m = meshing_cfg['generate']['magnify']\n",
    "\n",
    "sampling_pts = {pts:get_samplingptshandler(pyfrm = pyfrm, **sampling_cfg[pts]) for pts in sampling_cfg}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3b120dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices_xy, zs = PyfrmSamplingPts._extract_physical_grp_vertices(pyfrm, 'obstacle')\n",
    "\n",
    "vertices = []\n",
    "for z in zs:\n",
    "    for v in vertices_xy:\n",
    "        vertices.append([v[0], v[1], z])\n",
    "vertices = np.array(vertices)\n",
    "\n",
    "ch = ConvexHull(vertices)\n",
    "xc = vertices[ch.vertices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "03689950",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for sparse sensor visualization\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Mesh3d(x=xc[:, 0]/L_m, \n",
    "                        y=xc[:, 1]/L_m, \n",
    "                        z=xc[:, 2]/L_m, \n",
    "                        color=\"blue\", \n",
    "                        opacity=.5,\n",
    "                        alphahull=0))\n",
    "\n",
    "\n",
    "\n",
    "for pts in filter(lambda x: x != 'full_field', sampling_pts):\n",
    "    coords = sampling_pts[pts]._pts.reshape(-1, 3)\n",
    "    fig.add_trace(go.Scatter3d(\n",
    "        name=pts,\n",
    "        x=coords[:,0]/L_m,\n",
    "        y=coords[:,1]/L_m,\n",
    "        z=coords[:,2]/L_m,\n",
    "        mode='markers',\n",
    "        marker={'size':2}\n",
    "    ))\n",
    "\n",
    "fig.update_layout(scene_camera={\n",
    "                      'up':{'x': 0, 'y': 1, 'z': 0}\n",
    "                  },\n",
    "                  scene={\n",
    "                      'xaxis': {'range': [-2/L_m, 8/L_m]},\n",
    "                      'yaxis': {'range': [-5/L_m, 5/L_m]},\n",
    "                      'zaxis': {'range': [0/L_m, 10/L_m]}\n",
    "                  })\n",
    "fig.update_scenes(xaxis_title_text=\"x̄\", yaxis_title_text=\"ȳ\", zaxis_title_text=\"z̄\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8d55601",
   "metadata": {},
   "outputs": [
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('67078bd6-f41e-41de-b5f0-b9daca62dd08');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for plane measurement visualization\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Mesh3d(x=xc[:, 0], \n",
    "                        y=xc[:, 1], \n",
    "                        z=xc[:, 2], \n",
    "                        color=\"blue\", \n",
    "                        opacity=.5,\n",
    "                        alphahull=0))\n",
    "\n",
    "\n",
    "coords = sampling_pts[pts]._pts\n",
    "yshape, zshape = coords.shape[1:-1]\n",
    "coords_xy = coords[:,:,zshape//2].reshape(-1,3)\n",
    "coords_xz = coords[:,yshape//2,:].reshape(-1,3)\n",
    "\n",
    "fig.add_trace(go.Scatter3d(\n",
    "        name='xy-sensors',\n",
    "        x=coords_xy[:,0],\n",
    "        y=coords_xy[:,1],\n",
    "        z=coords_xy[:,2],\n",
    "        mode='markers',\n",
    "        marker={'size':2}\n",
    "    ))\n",
    "\n",
    "fig.add_trace(go.Scatter3d(\n",
    "        name='xz-sensors',\n",
    "        x=coords_xz[:,0],\n",
    "        y=coords_xz[:,1],\n",
    "        z=coords_xz[:,2],\n",
    "        mode='markers',\n",
    "        marker={'size':2}\n",
    "    ))\n",
    "fig.update_layout(scene_camera={\n",
    "                      'up':{'x': 0, 'y': 1, 'z': 0}\n",
    "                  },\n",
    "                  scene={\n",
    "                      'xaxis': {'range': [-2, 8]},\n",
    "                      'yaxis': {'range': [-5, 5]},\n",
    "                      'zaxis': {'range': [0, 10]}\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ca7cf47",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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