test_nbs/generateapplication.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"applications/shearblade\"\n",
    "fname = \"shearblade.vtk\"\n",
    "\n",
    "image_size = 256\n",
    "\n",
    "magnitude_x = -100\n",
    "magnitude_y = -3000\n",
    "\n",
    "device = \"cpu\"\n",
    "use_ema = True\n",
    "\n",
    "model_checkpoint_path = \"results/model-230/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "namepath = Path(name)\n",
    "fnamepath = Path(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "def crop_image(image_path, bounds):\n",
    "    image = Image.open(image_path)\n",
    "    image = image.crop(bounds)\n",
    "    image.save(image_path)\n",
    "\n",
    "def find_image_bounds(image_path):\n",
    "    # common_config = \"-2 --color-map binary --no-scalar-bars --no-axes --window-size {},{} --off-screen\".format(image_size, image_size)\n",
    "    # os.system(\"sfepy-view box.mesh -f 1:vs {} --outline -o {}\".format(common_config, image_path))\n",
    "    image = Image.open(image_path)\n",
    "    # find the bounding box of the geometry by finding the first non-white pixel\n",
    "    image_data = image.load()\n",
    "    left = 0\n",
    "    right = image.size[0]\n",
    "    top = 0\n",
    "    bottom = image.size[1]\n",
    "    for x in range(image.size[0]):\n",
    "        for y in range(image.size[1]):\n",
    "            if image_data[x, y] != (255, 255, 255):\n",
    "                left = x\n",
    "                break\n",
    "        if left != 0:\n",
    "            break\n",
    "    for x in range(image.size[0] - 1, -1, -1):\n",
    "        for y in range(image.size[1]):\n",
    "            if image_data[x, y] != (255, 255, 255):\n",
    "                right = x\n",
    "                break\n",
    "        if right != image.size[0]:\n",
    "            break\n",
    "    for y in range(image.size[1]):\n",
    "        for x in range(image.size[0]):\n",
    "            if image_data[x, y] != (255, 255, 255):\n",
    "                top = y\n",
    "                break\n",
    "        if top != 0:\n",
    "            break\n",
    "    for y in range(image.size[1] - 1, -1, -1):\n",
    "        for x in range(image.size[0]):\n",
    "            if image_data[x, y] != (255, 255, 255):\n",
    "                bottom = y\n",
    "                break\n",
    "        if bottom != image.size[1]:\n",
    "            break\n",
    "    return left, top, right, bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datagen.custom_plotter import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sfepy: left over: ['__name__', '__doc__', '__package__', '__loader__', '__spec__', '__file__', '__cached__', '__builtins__', 'absolute_import', 'stiffness_from_youngpoisson', 'refine_mesh', 'data_dir', 'refinement_level', 'output_dir', 'young', 'poisson', 'save_regions', 'equations', 'verbose', '_filename']\n",
      "sfepy: reading mesh (/home/namanwsl/code/fea-diffusion/applications/shearblade/shearblade.mesh)...\n",
      "sfepy:   number of vertices: 5427\n",
      "sfepy:   number of cells:\n",
      "sfepy:     2_3: 10561\n",
      "sfepy: ...done in 0.02 s\n",
      "sfepy: warning: bad element orientation, trying to correct...\n",
      "sfepy: ...corrected\n",
      "sfepy: creating regions...\n",
      "sfepy:     Omega\n",
      "sfepy:     Constraint\n",
      "sfepy:     Force\n",
      "sfepy: ...done in 0.02 s\n",
      "sfepy: equation \"balance_of_forces\":\n",
      "sfepy: dw_lin_elastic.2.Omega(Asphalt.D, v, u)\n",
      "      = dw_point_load.0.Force(Load.val, v)\n",
      "sfepy: using solvers:\n",
      "                ts: no ts\n",
      "               nls: newton\n",
      "                ls: ls\n",
      "sfepy: updating variables...\n",
      "sfepy: ...done\n",
      "sfepy: setting up dof connectivities...\n",
      "sfepy: ...done in 0.00 s\n",
      "sfepy: matrix shape: (10466, 10466)\n",
      "sfepy: assembling matrix graph...\n",
      "sfepy: ...done in 0.01 s\n",
      "sfepy: matrix structural nonzeros: 144084 (1.32e-01% fill)\n",
      "sfepy: updating variables...\n",
      "sfepy: ...done\n",
      "sfepy: updating materials...\n",
      "sfepy:     Asphalt\n",
      "sfepy:     Load\n",
      "sfepy: ...done in 0.00 s\n",
      "sfepy: nls: iter: 0, residual: 3.001666e+03 (rel: 1.000000e+00)\n",
      "sfepy:   residual:    0.07 [s]\n",
      "sfepy:     matrix:    0.01 [s]\n",
      "sfepy:      solve:    0.47 [s]\n",
      "sfepy: nls: iter: 1, residual: 1.305884e-10 (rel: 4.350530e-14)\n",
      "sfepy: saving regions as groups...\n",
      "sfepy:   Omega\n",
      "sfepy:   Constraint\n",
      "sfepy:   Force\n",
      "sfepy: ...done\n",
      "sfepy: solved in 1 steps in 0.58 seconds\n"
     ]
    }
   ],
   "source": [
    "!cd {name} && sfepy-run {fname[:-4]}.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mesh from applications/shearblade/shearblade.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: node_groups, mat_id\n",
      "  vectors: u\n",
      "  steps:   1\n",
      "plot 0: None(step 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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BAgQIECCwC4ib3c4kAQIECBAgEBQQN8GjWIkAAQIECBDYBcTNbmeSAAECBAgQCAqIm+BRrESAAAECBAjsAuJmtzNJgAABAgQIBAXETfAoViJAgAABAgR2AXGz25kkQIAAAQIEggLiJngUKxEgQIAAAQK7gLjZ7UwSIECAAAECQQFxEzyKlQgQIECAAIFdQNzsdiYJECBAgACBoIC4CR7FSgQIECBAgMAuIG52O5MECBAgQIBAUEDcBI9iJQIECBAgQGAXEDe7nUkCBAgQIEAgKCBugkexEgECBAgQILALiJvdziQBAgQIECAQFBA3waNYiQABAgQIENgFxM1uZ5IAAQIECBAICoib4FGsRIAAAQIECOwC4ma3M0mAAAECBAgEBcRN8ChWIkCAAAECBHYBcbPbmSRAgAABAgSCAuImeBQrESBAgAABAruAuNntTBIgQIAAAQJBAXETPIqVCBAgQIAAgV1A3Ox2JgkQIECAAIGggLgJHsVKBAgQIECAwC4gbnY7kwQIECBAgEBQQNwEj2IlAgQIECBAYBcQN7udSQIECBAgQCAoIG6CR7ESAQIECBAgsAuIm93OJAECBAgQIBAUEDfBo1iJAAECBAgQ2AXEzW5nkgABAgQIEAgKiJvgUaxEgAABAgQI7ALiZrczSYAAAQIECAQFxE3wKFYiQIAAAQIEdgFxs9uZJECAAAECBIIC4iZ4FCsRIECAAAECu4C42e1MEiBAgAABAkEBcRM8ipUIECBAgACBXUDc7HYmCRAgQIAAgaCAuAkexUoECBAgQIDALiBudjuTBAgQIECAQFBA3ASPYiUCBAgQIEBgFxA3u51JAgQIECBAICggboJHsRIBAgQIECCwC4ib3c4kAQIECBAgEBQQN8GjWIkAAQIECBDYBcTNbmeSAAECBAgQCAqIm+BRrESAAAECBAjsAuJmtzNJgAABAgQIBAXETfAoViJAgAABAgR2AXGz25kkQIAAAQIEggLiJngUKxEgQIAAAQK7gLjZ7UwSIECAAAECQQFxEzyKlQgQIECAAIFdQNzsdiYJECBAgACBoIC4CR7FSgQIECBAgMAuIG52O5MECBAgQIBAUEDcBI9iJQIECBAgQGAXEDe7nUkCBAgQIEAgKCBugkexEgECBAgQILALiJvdziQBAgQIECAQFBA3waNYiQABAgQIENgFxM1uZ5IAAQIECBAICoib4FGsRIAAAQIECOwC4ma3M0mAAAECBAgEBcRN8ChWIkCAAAECBHYBcbPbmSRAgAABAgSCAuImeBQrESBAgAABAruAuNntTBIgQIAAAQJBAXETPIqVCBAgQIAAgV1A3Ox2JgkQIECAAIGggLgJHsVKBAgQIECAwC4gbnY7kwQIECBAgEBQQNwEj2IlAgQIECBAYBcQN7udSQIECBAgQCAoIG6CR7ESAQIECBAgsAuIm93OJAECBAgQIBAUEDfBo1iJAAECBAgQ2AXEzW5nkgABAgQIEAgKiJvgUaxEgAABAgQI7ALiZrczSYAAAQIECAQFxE3wKFYiQIAAAQIEdgFxs9uZJECAAAECBIIC4iZ4FCsRIECAAAECu4C42e1MEiBAgAABAkEBcRM8ipUIECBAgACBXUDc7HYmCRAgQIAAgaCAuAkexUoECBAgQIDALiBudjuTBAgQIECAQFBA3ASPYiUCBAgQIEBgFxA3u51JAgQIECBAICggboJHsRIBAgQIECCwC4ib3c4kAQIECBAgEBQQN8GjWIkAAQIECBDYBcTNbmeSAAECBAgQCAqIm+BRrESAAAECBAjsAuJmtzNJgAABAgQIBAXETfAoViJAgAABAgR2AXGz25kkQIAAAQIEggLiJngUKxEgQIAAAQK7gLjZ7UwSIECAAAECQQFxEzyKlQgQIECAAIFdQNzsdiYJECBAgACBoIC4CR7FSgQIECBAgMAuIG52O5MECBAgQIBAUEDcBI9iJQIECBAgQGAXEDe7nUkCBAgQIEAgKCBugkexEgECBAgQILALiJvdziQBAgQIECAQFBA3waNYiQABAgQIENgFxM1uZ5IAAQIECBAICoib4FGsRIAAAQIECOwC4ma3M0mAAAECBAgEBcRN8ChWIkCAAAECBHYBcbPbmSRAgAABAgSCAuImeBQrESBAgAABAruAuNntTBIgQIAAAQJBAXETPIqVCBAgQIAAgV1A3Ox2JgkQIECAAIGggLgJHsVKBAgQIECAwC4gbnY7kwQIECBAgEBQQNwEj2IlAgQIECBAYBcQN7udSQIECBAgQCAoIG6CR7ESAQIECBAgsAuIm93OJAECBAgQIBAUEDfBo1iJAAECBAgQ2AXEzW5nkgABAgQIEAgKiJvgUaxEgAABAgQI7ALiZrczSYAAAQIECAQFxE3wKFYiQIAAAQIEdgFxs9uZJECAAAECBIIC4iZ4FCsRIECAAAECu4C42e1MEiBAgAABAkEBcRM8ipUIECBAgACBXUDc7HYmCRAgQIAAgaCAuAkexUoECBAgQIDALiBudjuTBAgQIECAQFBA3ASPYiUCBAgQIEBgFxA3u51JAgQIECBAICggboJHsRIBAgQIECCwC4ib3c4kAQIECBAgEBQQN8GjWIkAAQIECBDYBcTNbmeSAAECBAgQCAqIm+BRrESAAAECBAjsAuJmtzNJgAABAgQIBAXETfAoViJAgAABAgR2AXGz25kkQIAAAQIEggLiJngUKxEgQIAAAQK7gLjZ7UwSIECAAAECQQFxEzyKlQgQIECAAIFdQNzsdiYJECBAgACBoIC4CR7FSgQIECBAgMAuIG52O5MECBAgQIBAUEDcBI9iJQIECBAgQGAXEDe7nUkCBAgQIEAgKCBugkexEgECBAgQILALiJvdziQBAgQIECAQFBA3waNYiQABAgQIENgFxM1uZ5IAAQIECBAICoib4FGsRIAAAQIECOwC4ma3M0mAAAECBAgEBcRN8ChWIkCAAAECBHYBcbPbmSRAgAABAgSCAuImeBQrESBAgAABAruAuNntTBIgQIAAAQJBAXETPIqVCBAgQIAAgV1A3Ox2JgkQIECAAIGggLgJHsVKBAgQIECAwC4gbnY7kwQIECBAgEBQQNwEj2IlAgQIECBAYBcQN7udSQIECBAgQCAoIG6CR7ESAQIECBAgsAuIm93OJAECBAgQIBAUEDfBo1iJAAECBAgQ2AXEzW5nkgABAgQIEAgKiJvgUaxEgAABAgQI7ALiZrczSYAAAQIECAQFxE3wKFYiQIAAAQIEdgFxs9uZJECAAAECBIIC4iZ4FCsRIECAAAECu4C42e1MEiBAgAABAkEBcRM8ipUIECBAgACBXUDc7HYmCRAgQIAAgaCAuAkexUoECBAgQIDALiBudjuTBAgQIECAQFBA3ASPYiUCBAgQIFAXeH5+zq4obrKnsRgBAgQIECCwCIibRc0MAQIECBAgkBUQN9nTWIwAAQIECBBYBMTNomaGAAECBAgQyAqIm+xpLEaAAAECBAgsAuJmUTNDgAABAgQIZAXETfY0FiNAgAABAgQWAXGzqJkhQIAAAQIEsgLiJnsaixEgQIAAAQKLgLhZ1MwQIECAAAECWQFxkz2NxQgQIECAAIFFQNwsamYIECBAgACBrIC4yZ7GYgQIECBAgMAiIG4WNTMECBAgQIBAVkDcZE9jMQIECBAgQGAREDeLmhkCBAgQIEAgKyBusqexGAECBAgQILAIiJtFzQwBAgQIECCQFRA32dNYjAABAgQIEFgExM2iZoYAAQIECBDICoib7GksRoAAAQIECCwC4mZRM0OAAAECBAhkBcRN9jQWI0CAAAECBBYBcbOomSFAgAABAgSyAuImexqLESBAgAABAouAuFnUzBAgQIAAAQJZAXGTPY3FCBAgQIAAgUVA3CxqZggQIECAAIGsgLjJnsZiBAgQIECAwCIgbhY1MwQIECBAgEBWQNxkT2MxAgQIECBAYBEQN4uaGQIECBAgQCArIG6yp7EYAQIECBAgsAiIm0XNDAECBAgQIJAVEDfZ01iMAAECBAgQWATEzaJmhgABAgQIEMgKiJvsaSxGgAABAgQILALiZlEzQ4AAAQIECGQFxE32NBYjQIAAAQIEFgFxs6iZIUCAAAECBLIC4iZ7GosRIECAAAECi4C4WdTMECBAgAABAlkBcZM9jcUIECBAgACBRUDcLGpmCBAgQIAAgayAuMmexmIECBAgQIDAIiBuFjUzBAgQIECAQFZA3GRPYzECBAgQIEBgERA3i5oZAgQIECBAICsgbrKnsRgBAgQIECCwCIibRc0MAQIECBAgkBUQN9nTWIwAAQIECBBYBMTNomaGAAECBAgQyAqIm+xpLEaAAAECBAgsAuJmUTNDgAABAgQIZAXETfY0FiNAgAABAgQWAXGzqJkhQIAAAQIEsgLiJnsaixEgQIAAAQKLgLhZ1MwQIECAAAECWQFxkz2NxQgQIECAAIFFQNwsamYIECBAgACBrIC4yZ7GYgQIECBAgMAiIG4WNTMECBAgQIBAVkDcZE9jMQIECBAgQGAR+LoM/dmZ9/f3zx/469evP/tj/TQCBAgQIEDgrxSIxs3Dw8PPnz9fXl7e3t7+SldLEyBAgACB0wLfv3///GXdfOKXj4+P5ma2IkCAAAECBAgMAv7PzYBmhAABAgQIEOgKiJvubWxGgAABAgQIDALiZkAzQoAAAQIECHQFxE33NjYjQIAAAQIEBgFxM6AZIUCAAAECBLoC4qZ7G5sRIECAAAECg4C4GdCMECBAgAABAl0BcdO9jc0IECBAgACBQUDcDGhGCBAgQIAAga6AuOnexmYECBAgQIDAICBuBjQjBAgQIECAQFdA3HRvYzMCBAgQIEBgEBA3A5oRAgQIECBAoCsgbrq3sRkBAgQIECAwCIibAc0IAQIECBAg0BUQN93b2IwAAQIECBAYBMTNgGaEAAECBAgQ6AqIm+5tbEaAAAECBAgMAuJmQDNCgAABAgQIdAXETfc2NiNAgAABAgQGAXEzoBkhQIAAAQIEugLipnsbmxEgQIAAAQKDgLgZ0IwQIECAAAECXQFx072NzQgQIECAAIFBQNwMaEYIECBAgACBroC46d7GZgQIECBAgMAgIG4GNCMECBAgQIBAV0DcdG9jMwIECBAgQGAQEDcDmhECBAgQIECgKyBuurexGQECBAgQIDAIiJsBzQgBAgQIECDQFRA33dvYjAABAgQIEBgExM2AZoQAAQIECBDoCoib7m1sRoAAAQIECAwC4mZAM0KAAAECBAh0BcRN9zY2I0CAAAECBAYBcTOgGSFAgAABAgS6AuKmexubESBAgAABAoOAuBnQjBAgQIAAAQJdAXHTvY3NCBAgQIAAgUFA3AxoRggQIECAAIGugLjp3sZmBAgQIECAwCAgbgY0IwQIECBAgEBXQNx0b2MzAgQIECBAYBAQNwOaEQIECBAgQKArIG66t7EZAQIECBAgMAiImwHNCAECBAgQINAVEDfd29iMAAECBAgQGATEzYBmhAABAgQIEOgKiJvubWxGgAABAgQIDALiZkAzQoAAAQIECHQFxE33NjYjQIAAAQIEBgFxM6AZIUCAAAECBLoC4qZ7G5sRIECAAAECg4C4GdCMECBAgAABAl0BcdO9jc0IECBAgACBQUDcDGhGCBAgQIAAga6AuOnexmYECBAgQIDAICBuBjQjBAgQIECAQFdA3HRvYzMCBAgQIEBgEBA3A5oRAgQIECBAoCsgbrq3sRkBAgQIECAwCIibAc0IAQIECBAg0BUQN93b2IwAAQIECBAYBMTNgGaEAAECBAgQ6AqIm+5tbEaAAAECBAgMAuJmQDNCgAABAgQIdAXETfc2NiNAgAABAgQGAXEzoBkhQIAAAQIEugLipnsbmxEgQIAAAQKDgLgZ0IwQIECAAAECXQFx072NzQgQIECAAIFBQNwMaEYIECBAgACBroC46d7GZgQIECBAgMAgIG4GNCMECBAgQIBAV0DcdG9jMwIECBAgQGAQEDcDmhECBAgQIECgKyBuurexGQECBAgQIDAIiJsBzQgBAgQIECDQFRA33dvYjAABAgQIEBgExM2AZoQAAQIECBDoCoib7m1sRoAAAQIECAwC4mZAM0KAAAECBAh0BcRN9zY2I0CAAAECBAYBcTOgGSFAgAABAgS6AuKmexubESBAgAABAoOAuBnQjBAgQIAAAQJdAXHTvY3NCBAgQIAAgUFA3AxoRggQIECAAIGugLjp3sZmBAgQIECAwCAgbgY0IwQIECBAgEBXQNx0b2MzAgQIECBAYBAQNwOaEQIECBAgQKArIG66t7EZAQIECBAgMAiImwHNCAECBAgQINAVEDfd29iMAAECBAgQGATEzYBmhAABAgQIEOgKiJvubWxGgAABAgQIDALiZkAzQoAAAQIECHQFxE33NjYjQIAAAQIEBgFxM6AZIUCAAAECBLoC4qZ7G5sRIECAAAECg4C4GdCMECBAgAABAl0BcdO9jc0IECBAgACBQUDcDGhGCBAgQIAAga6AuOnexmYECBAgQIDAICBuBjQjBAgQIECAQFdA3HRvYzMCBAgQIEBgEBA3A5oRAgQIECBAoCsgbrq3sRkBAgQIECAwCIibAc0IAQIECBAg0BUQN93b2IwAAQIECBAYBMTNgGaEAAECBAgQ6AqIm+5tbEaAAAECBAgMAuJmQDNCgAABAgQIdAXETfc2NiNAgAABAgQGAXEzoBkhQIAAAQIEugLipnsbmxEgQIAAAQKDgLgZ0IwQIECAAAECXQFx072NzQgQIECAAIFBQNwMaEYIECBAgACBroC46d7GZgQIECBAgMAgIG4GNCMECBAgQIBAV0DcdG9jMwIECBAgQGAQEDcDmhECBAgQIECgKyBuurexGQECBAgQIDAIiJsBzQgBAgQIECDQFRA33dvYjAABAgQIEBgExM2AZoQAAQIECBDoCoib7m1sRoAAAQIECAwC4mZAM0KAAAECBAh0BcRN9zY2I0CAAAECBAYBcTOgGSFAgAABAgS6AuKmexubESBAgAABAoOAuBnQjBAgQIAAAQJdAXHTvY3NCBAgQIAAgUFA3AxoRggQIECAAIGugLjp3sZmBAgQIECAwCAgbgY0IwQIECBAgEBXQNx0b2MzAgQIECBAYBAQNwOaEQIECBAgQKArIG66t7EZAQIECBAgMAiImwHNCAECBAgQINAVEDfd29iMAAECBAgQGATEzYBmhAABAgQIEOgKiJvubWxGgAABAgQIDALiZkAzQoAAAQIECHQFxE33NjYjQIAAAQIEBgFxM6AZIUCAAAECBLoC4qZ7G5sRIECAAAECg4C4GdCMECBAgAABAl0BcdO9jc0IECBAgACBQUDcDGhGCBAgQIAAga6AuOnexmYECBAgQIDAICBuBjQjBAgQIECAQFdA3HRvYzMCBAgQIEBgEBA3A5oRAgQIECBAoCsgbrq3sRkBAgQIECAwCIibAc0IAQIECBAg0BUQN93b2IwAAQIECBAYBMTNgGaEAAECBAgQ6AqIm+5tbEaAAAECBAgMAuJmQDNCgAABAgQIdAXETfc2NiNAgAABAgQGAXEzoBkhQIAAAQIEugLipnsbmxEgQIAAAQKDgLgZ0IwQIECAAAECXQFx072NzQgQIECAAIFBQNwMaEYIECBAgACBroC46d7GZgQIECBAgMAgIG4GNCMECBAgQIBAV0DcdG9jMwIECBAgQGAQEDcDmhECBAgQIECgKyBuurexGQECBAgQIDAIiJsBzQgBAgQIECDQFRA33dvYjAABAgQIEBgExM2AZoQAAQIECBDoCoib7m1sRoAAAQIECAwC4mZAM0KAAAECBAh0BcRN9zY2I0CAAAECBAYBcTOgGSFAgAABAgS6AuKmexubESBAgAABAoOAuBnQjBAgQIAAAQJdAXHTvY3NCBAgQIAAgUFA3AxoRggQIECAAIGugLjp3sZmBAgQIECAwCAgbgY0IwQIECBAgEBXQNx0b2MzAgQIECBAYBAQNwOaEQIECBAgQKArIG66t7EZAQIECBAgMAiImwHNCAECBAgQINAVEDfd29iMAAECBAgQGATEzYBmhAABAgQIEOgKiJvubWxGgAABAgQIDALiZkAzQoAAAQIECHQFxE33NjYjQIAAAQIEBgFxM6AZIUCAAAECBLoC4qZ7G5sRIECAAAECg4C4GdCMECBAgAABAl0BcdO9jc0IECBAgACBQUDcDGhGCBAgQIAAga6AuOnexmYECBAgQIDAICBuBjQjBAgQIECAQFdA3HRvYzMCBAgQIEBgEBA3A5oRAgQIECBAoCsgbrq3sRkBAgQIECAwCIibAc0IAQIECBAg0BUQN93b2IwAAQIECBAYBMTNgGaEAAECBAgQ6AqIm+5tbEaAAAECBAgMAuJmQDNCgAABAgQIdAXETfc2NiNAgAABAgQGAXEzoBkhQIAAAQIEugK/AeONKAA5yLfLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=756x756>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/outline.png\n"
     ]
    }
   ],
   "source": [
    "# !cd cantilever && sfepy-view regions.vtk -2 -f Constraint:vs --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen -o constraint.png\n",
    "# !cd cantilever && sfepy-view regions.vtk -2 -f Force:vs --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen -o force.png\n",
    "# !cd cantilever && sfepy-view cantilever.vtk -2 -f u:vs:c0 --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen -o displacement_x.png\n",
    "# !cd cantilever && sfepy-view cantilever.vtk -2 -f u:vs:c1 --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen -o displacement_y.png\n",
    "# !cd cantilever && sfepy-view cantilever.vtk -2 -f 0:vs --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen -o geometry.png\n",
    "# !cd {name} && sfepy-view {fname}.vtk -2 -f 0:vs --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen --outline -o outline.png\n",
    "plot(filenames=[str(namepath / fnamepath)], fields = [(\"0\", \"vs\")], window_size=(756, 756), outline=True, screenshot=str(namepath / \"outline.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mesh from applications/shearblade/shearblade.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: node_groups, mat_id\n",
      "  vectors: u\n",
      "  steps:   1\n",
      "plot 0: None(step 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=756x756>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/outline.png\n",
      "mesh from applications/shearblade/shearblade.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: node_groups, mat_id\n",
      "  vectors: u\n",
      "  steps:   1\n",
      "plot 0: None(step 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=591x591>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/outline.png\n"
     ]
    }
   ],
   "source": [
    "# !cd {name} && sfepy-view {fname}.vtk -2 -f 0:vs --color-map binary --no-scalar-bars --no-axes --window-size 756,756 --off-screen --outline -o outline.png\n",
    "plot(filenames=[str(namepath / fnamepath)], fields = [(\"0\", \"vs\")], window_size=(756, 756), outline=True, screenshot=str(namepath / \"outline.png\"))\n",
    "left, top, right, bottom = find_image_bounds(f\"{name}/outline.png\")\n",
    "image_size_rec = 512\n",
    "max_size = max(right - left, bottom - top)\n",
    "modified_image_size = round(\n",
    "    image_size_rec / (max_size / 756)\n",
    ")\n",
    "# !cd {name} && sfepy-view {fname}.vtk -2 -f 0:vs --color-map binary --no-scalar-bars --no-axes --window-size {modified_image_size},{modified_image_size} --off-screen --outline -o outline.png\n",
    "plot(filenames=[str(namepath / fnamepath)], fields = [(\"0\", \"vs\")], window_size=(modified_image_size, modified_image_size), outline=True, screenshot=str(namepath / \"outline.png\"))\n",
    "left, top, right, bottom = find_image_bounds(f\"{name}/outline.png\")\n",
    "lbound, ubound = (left, right) if right > bottom else (top, bottom)\n",
    "bounds = (lbound, lbound, ubound, ubound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mesh from applications/shearblade/regions.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: Omega, Constraint, Force, node_groups, mat_id\n",
      "  steps:   1\n",
      "plot 0: Constraint(step 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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Lq4GO7NGR0XVkpJ26dyrYuarWZ/uxagn3qz4SUe/0q7o6y4n6zK4rpIbrvOf6OZd3V0jXQKdoXedR2913uq56EVqjiahqlT3+nSXkg9mYMgmws/TovlxjCZRG0XWijmxT9fFO13NrjKjlVPw8artxVLxHfXZMz52oRbXMiDpdr3sn6vZYu6vJ9ugLTaUq0mHHPITS6/zLdm/WVc3UYa/7eKs7LZ2bdZ3Uec6BH/O67chkzuw69bmh9GYN1FUNnD57p992oo6scgs/0z+/7Z167lz1E0gXSP9WmfA5S51XtoLtvXOuftWLxM7Z2yENpZod0KNu2xsVqWyfauLthHPSjjzgOTTWFthzJz2fbeEdfB4dX289nxed6NZq73T2Bt2jubTzzz2hH2ujcbSLTpF+W/2WXEs9Ajz7+WyznjtjozzXRUcmUKmger/+G0eGZ+jt5P7A7ctK26zmezS4BNoqIfdoNJ2iHVu1sweq3zTP43vf+17Fazu68zhNtq4erejs83ZFv20hjeNcOP2q4v091Om6OfRoA5xJHYc+EmOnbslHu/d70fNdj7/55l3H9GP91Oft/TOFQE7xJtXjHNPzET6VO7JFBdJna6ad0Fq6mvp/Edr5dd63+Y5p3PXf+ykl3D2qT7XGrtmCPP8OSKNfdZn35vG59fMgXzy0tGv9H/3oR/t6FUfTzaL/wNaeaztmdO5lbev2bo82SoLt+7ZazwE1j3M9d2Qvwu1/hmwLtpW7pNtAXTMd1k7tu1vcnaJfnal06j7eR3p0uTb4BtlzjzPmns+PzfgMozG3C9u158de9KixRt7gq9/7Ff/Yxz7WcyftnWKjC+kc32f7185zC+v1ub2ee2KXa48W1S6pmZ7bEy0zhJbQ6irYFuz9um2v9E5rCa3fdj33OJdfF2fL75jzqc7Sejvy1On47kpV7o7f49xozmWcRi/a0wE2gq7/7oYBntvoudproGqV6i7ZVdpJm9cZRy+q1g6usR4trbXX0lnXWVT3yhNXVau9plxjAVahR/es/v+ds6gq9/FO15E9uiU1i9o+RPn0Y510oliqU5Fzouqc67ZTt8De785Vq52u7VEDvXnSpffPx8+iznlruyNDeFTnsce5vYbTNdyjF22Joi6EGjs3qT575t6/hlWtR0X6zt6u7vhOGs555NOjhmu7flpm82rV3fU6+OzYeq5aFfrtqdb+OdW6W52o67kLoVJnF7WQmqmxWn7M8tG/XBtHsztr7LnPdqKGG1T3qbZlS+hEHXamnOe5iyVTte6zGRYz9R9CxR+9Th77a6NP5d/ZA6nJOilCOqxt06LaOef9Duv9HucWeZ77YGc85+3FeVSq93v0fsuv1dulUZNtnjo566pIv0218/bcjwGegbYrGn0Lb4F1G9ox7LDA2/m9k2e7qFZbSI01hb6ydK7O26MRtFV6JJBwI+t/FKp4ZQPp0ep6nH6q02+D7fLvEq6r9CpS5Q7ox5bWWjr+aPT6tmM7Y902jnKiCZ4vTGcXte3bRb3Z8TVckw3i4LTY6h+lR9t93OPxv7q9k3AFe9TV+dx5cSp3ltxacm92ZI94a7uGO6DzRtFO6MU5/tzZ2q7toh6nVBQf//jHW+/Z8yH0qWYRTgvJNsA+2Cgj7VtXhz1uBQ/ojUe/lT+gU/2/p/nc5z7X4NthXSStv39MCKJH9Om0X9sZoefV1jwfDT36HDumbdEx/aoX57fnqgu6S/r2kds5u0/1Pw41gHZ/42lO59bWHr1FXZfK2Vi9aKv13COfNkG3j07U47ab20Dt8jKs397Ocl50PTT7Dr7r/X5sgV0q7YA6OQd0it7pI+cLUS9aYM8HoWu+i+rslQKjX7Vj2pGtokfHRNf75VxFem4tVe4jrbH659Yc462TztJtoh3Znq5sH28h/baFn5tvql2Et+PPi66Kf//3f+/gxnFSsPeTybxq5Wi3lR7nur19pFabVCOrw1bUHbnip/M67LDerO0enbp+zq86S41F1BJOqZ5T6kRV6HWHda5bG73ThdQp2k63m35vnpvX+WO0KXeWc3+s57ZWO6QZdRM8Vue67VM9OlfVOqxO6qpF3Ubcm22k9kBfcW5vnk+lEVHU3THv7K1qCdRbi6panz3H99zNtH+/ahd15729eV60/b7whS/UVf3fNnMzanDd/fttS6j/O9vugy0z8zppD/TbO8/VijpX96k7Vc+5kil0i9W2061gp+gjzb2aDbGF96K11EMgJ3Kq35tn77Xkvgl1LZ+U6oPtinN8H2lb9tzpTv2ez6N36vY898653h/fYQ20bztp8+qw03bFi/Caz7b2Ymn6naUt2kJ6RHS7ys5HznNLO/l055u3mgFWs+ceVW457bf2bQW7t7RR20UdEE7v9H7ddgU1l5pMo5XWYbOu20Zw9k8T7MVtw9R53w/atP0/Mufqu3WSRv+PWx0ewJbTOz3Oi9thT/Kig89ve1EnzahT9OjFobs9V7N7YI+aj7cDumPUZ9u+huu25QTbqvtt1ULrV62rJXffO3Xu7CSxDj6D6LkFdhV3Gba1gmrJ5/t0uf7JT37yzg8+sNcPLe0+//nPt+wgzk0/ze4jieR7tlHPDenpQrSTnuhT3VzuvO/fVbkGepzk6/nOy7VrrEl3PdfqbdPc9fF//Mc6b7uUZz26lrpW2yLdOrti21hdAP22eOi5PdcV2HXYi87bru0i7ArsuT47vjt4B7Qv//Gu/vEKDbQbRIuq+VZ07h0nF7vSule2op4LsNpuUT2HfA/nzeFkaldar7vSioo7N0P3o07ULaweouuWdA9n6SMVv9257q3CU/nUgznL6eTJz9Xl0J0rvR4tvBtij3ZXrwuYNl6qjeyk3eNvgk9lsf/IMW2w5n5usrXUfeNcNfdcs5xuRa2rrVK1O79wt2O7ytqxna7NfO5U3cfKg77T5NMBbbl8Coz22L3t5FvnzaWbUj8+dnN69On2q7/7os929kB6PNFQmuyt844/CV3bfeTUP9+uEu7U/fbM/Ym+K5yPtFXOl84+1QXYZXj7EtwduJt8qv0V+3f7vx8HPLS0a1s8/nbTHurbQdvrSWLpfijsqdme69ItHvpimMNdG+tch11IPe7quSutPxzbkV2B5wvEXQc83B9rr3tEvdXhXZ10bXRz6VcF/O0yu+uYp/Vj11sX5+P/RHhaRRz81AX6cta+bYJP/SPjR/b1tDba+X1beqKb+9M6aTHQvb7vjo//VF/dupV3H194oT2+24fyTtd7gH2tfPzZ/+ad//GH3Y93HlraPdFiuk+NbNYnqn/h9//n0v3P7fzC28nSCFxMYF3aXczXcggQIEBgg8Dd/4fFhp70QIAAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVkDazXqqRoAAAQIbBaTdxqnoiQABAgRmBaTdrKdqBAgQILBRQNptnIqeCBAgQGBWQNrNeqpGgAABAhsFpN3GqeiJAAECBGYFpN2sp2oECBAgsFFA2m2cip4IECBAYFZA2s16qkaAAAECGwWk3cap6IkAAQIEZgWk3aynagQIECCwUUDabZyKnggQIEBgVuD/AjxKfxMWfY0tAAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=591x591>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/constraint.png\n",
      "mesh from applications/shearblade/regions.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: Omega, Constraint, Force, node_groups, mat_id\n",
      "  steps:   1\n",
      "plot 0: Force(step 0)\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=591x591>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/force.png\n",
      "mesh from applications/shearblade/shearblade.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: node_groups, mat_id\n",
      "  vectors: u\n",
      "  steps:   1\n",
      "plot 0: u_0(step 0)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=591x591>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/displacement_x.png\n",
      "mesh from applications/shearblade/shearblade.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: node_groups, mat_id\n",
      "  vectors: u\n",
      "  steps:   1\n",
      "plot 0: u_1(step 0)\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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2rlFObNgiOBg2XtBfqVi2tbRHpsovg5U1YmyVPbW9qQ3P2pSSmn+/QipoQ6v5TktlQTwQgMCpBJ77hZ2sdulUdI+bv8M0hl0faSuqgU6TaWsjQ5e9rX+fZVMP3vmmlNS5VXgnPVWz4eEFyyUGPcfua3BRwiDwIAKP/o5MUzu74/q/afxe1nvQobwvslPQFlXNwTBL6UdqIy+NPpYy3VvdtPqOlLiupW9aLuZiQwACDyXw9G/9NLWL56373ZytXzaSH0/scI+Rl4VZV1q3s2jc9Ut70089X33T0jtS1LPlblpLiVXj2GrVJV46D9zOy7UIgMCzCKzx7biF2sWDj8pn/jfFzy90r985sK1hnVK2lqr1w+KuO/YFmrevz2Ml6thqHZ7VqX0EqqVwQmAxAst8O6apnd1u9ui1aAHdIX6tUiPLWT8Ka9UZjFFYp462/9LYqnmbFrUtb4pXt7sTVQEDAhC4LYF910J/O2fU7K+o2Wlqpw7ckMb4sEUkit/Iz75WnbjcITGtIrYdrdWJSTRaw3HN80XHV9warw4tcXwVZVWNA0tV67ecR/Xfqo8fAk8ksN734i5ql94Gv3zlrHKPymeRVfGLdapFLFExrYCRmPEinVW0376xSfM2LWe72BTvfe7L6u+RWQhAYBaBHZfAYKvnVR5p4KZql1qXlpi/xSuK3z7lG1lFMa02PKA1a/2/DEh7bw0HNW/rchbfab7VzL6sstpRdcrKLc+OzbZK4YfAAgTO+0acV3kQ+zPULm7Gr295qgTHla+absW1Sj+gP2t1+gGtWe3upWE7rUp7StykIpuCtdC+LKVPMd7nP6VtFoXAGQSW/zrMVLuXijJyoipiwdXTkvJVVeFlugKqxfuz1pIHVHNfzo5s32LO+JFnbbd67nS1L6tTkCkIQOAaAju+75saO7v+SDMz1U79STPcs5uL6lQrSPZslb7yVdO9eHXKCp43K0p94/Afef0dtZqxrBaiVkryv18hFWwN3+yzVRY/BJ5F4IIvwgVLjDC/hdqlRv2elXMHqVihmi7l2yp7qlwt67PVKdvOO7Oi0THGf+S1OiyLW8/jwWU6HghA4M4EPurbPU3t7Bq1J74HLe4xrBUT6yRb6dXcEdmrJnrZzpS10ZmtTllKp2zaV2s48iPPVmk10Co77j+1+Hgb/cjztt9fl1kI3ITAZV+ByxZ6CXaa2pWd+UUvf5VRjKkGKL00+rkd2VNiuWJnyhrw2TKrP+Wz1axyU1XPoOBZ7sgqtouRsNjJjpQD02MpbAhAoCSw9RtdVhj0XLbQSD8z1a5/J7pUaA8ltRhQziqxaii3THTZ6/wJZ5liS3jBa6aqO0rOzi5ipLVd7TnGmD0YlrJuOxzZ8m2bpzEIvEPgk1/+mWpnZybV0fm1DiNGljH9WRUvDU8sC778qVemaDutqarfs3ZMlXspPYM/8lqrx4IGaiRMKVvjlYgBAQicQWDT9/eQBq5fsd/2ZLUrm4u6ZbNVXorZMVuuaJ5OwdaPJE/pNFBO7UhRb2W16kZK50TBK5u5g2c3yTs0Tw8Q2Efg+tf++hVfkpmmdnb1++3vLbbQ9GMGZ1vFSzpesIxv/dRTA2VKq1TLb83YVFnHm+xMlbtInhHBSymHDN/p+ZAGKAIBCLSulA8kM03tEmvXADmrJxRjygDNbprSitHolOr81CvXtZpWaqvfslopranYfNV+KXitPlO1wbCUtWN40kJVsDvaIwUC9ycw8W2fuHTnXL7rzJ09ZTdaawmbik8Z1pnVVCernKp6vFQ5ZeJhT/K3grf6raylpOIadqYUUzXKhlPYYOXBMC++KTj1wxACENhNYKLeTFy6j2vyb7vyNqySimFlgGbTVMtvRHwqxbdItYJdP9Lf3mwFd/zVNlrx3nw1pdW//NZw6lZTbtii+yqnOvccLry1ewKnqykE5r7nc1fvA5/5267amV248SljNDs+1Upp+cvK5vHgcqr6m6kVbP6yQivYFy3jO/5qcHRWu40B1Q5jgNkjMUrZFKwsDAhAYCsBU5o7i83W7RweP+23XbriW4cU78oUMzLVSqn6k7PK2hdNkdUfeZZuwSnSnfZZ9ZfOVpGO36b6z8tfeP10n61ubSRxVkyV7axmrlx3fOPxC3Vlh6z1PoHxU35/rU6Fm7TR6nCa2qWG0jetSk0x5Wxrqu9PdTw4OVOfPqxGVjWvGmlFzF8utCm4VaTacHL2Ba/aW6rAcDqB8v15s6VNBf1dfXNF0t8nsOnU3l+uU+E+nbSanKZ29m3p3Lnpu5Q4xtnWVNW/21nF522kmi3NS2FWsJru/mpw6ewUqTY87rTeqsvFCiMxHj8eGetjRwIvjyMGX2B3+vEX+4IePnyJzhF8OJnW9qepnTfk2hCbq/41Cn1/ygNuTVX9W53lcrFVt71miiyFvBpmFcyfcjtOmyqDvY2tn2WHWys8KP4oaFdu+Yk9O59q5/rqXclw1bWqhOdu9oYtlUAmq13ZUNS/Uvnidybx1VTVv8/pNVNu2bN5LDKF+UbSFsqwam7L2fJXy1pw/+kL3kjNkZh+D8yKQHp/5F/DqO7O3p81dnfZLqoYL1u9tdA9uyq7vZ3axRYHlS+x1lco+jvOGGare2R0lp7YpOxqWKko1TBzxhW9ZtXpHY4Hq72qUbYXw1oNxJhB+8BSgysqrGSlqTsYN2/vVETl3u09OXXF5xYvWT13L7M6v7XaRSgd5dM3JL0Q7n/pfCcsdui2VUsrVhWlDGu1kaq1VjF/WbNs73DPlEUP38X1BavHen0bd1sxYfEvxd2avLKfBOTKpQfXun+H2sjM/73d7lfZ9MMfbcMNK+hP9Jcemy2dpcfDYqmqJwV4jFWL/la3McbtlOjVyrCWv0yv5kan9RaHyd5RMFWYO7zbV9H68Wculqes/o3W9//5lM7f7/MR74k1+f5OL6sw+bddeZluwqebuvzHY0YwltJCpfN9T/W0bMVY2WKs27LPFGNhZWLp8RWr/qqz2qGcZWOawjiKQHnQR1X+qDoJo73ti20/bXCx3c3dzmS1KzefXt/Bs6/KnkrFIu48w1PdS1zIAkpdKfuxMHOmxNJTLifPpmBltYyX1V4GtCp/gj+d4yds+co9Jrz2Kl65+oFrpY0cWPm8Uo/reabapVezyk4x1dnyIF32yp9QFhkreNkdnphiNa1O8qSWyoVaHaY6ZeXS02mgGpx6i8NShuPsIfbWlt5fNCF9v+CmCnNX39TqMsElc3vrbru7stvbtlo29sTmZ6pdIpjey0QzzqapVMeGrihmRNnzCjF30LM1pezHFopFvMPYm3nKmBFPNbFs4E1P2cmbBddOT2e99mZvvrvyLOxlnthz2c/EZj5t6WlqZ++cXrvqG6BZO5IUoKnkLw+v/CHluTGx6tkUYOtakZhSdlIGlD+nypgRT2v1MrfsKnrKfuJs3966Vr/aIbP94zhkibLIlEXLNvB0CLTOyN7hTta+qdZa+6rdJ+uh+5qmdvHk0ntWolRAmmr5Y3Gzd2ieV47LmScNrXLyxGHqwYapgjd26i+8csWyq+jpCN7WUrHsh9j90/8QCM/dJsc3eHbPBTXzf4HQgmsXq54U0/en4DS0q9ye6PRqyZOGFiPP1nglyojV3JlaMmcZM+LREhjXE7Dv/3OvgOtxsSIEphCYqXYuHvGzRNCalT+mVJ0xwOyvktfTPC8Ss8zTH/YDYq7ZKdg8I4KXilTrlJWrYWWp6Cmb0Wy1/uCsh/UrqNT7xmXa81XmnvQ/OXqfLRU+mcBl36wzIM9Uu3I/dhvGJwVoKvo7zhiW7KrmxRgvK09/aGEWoOByGKeqsx2N8dxUPxXUsBpWdSoFYzeBR3/zd++axI8l8PQXfpra2RUcn+oLpIA0W/W7M0aWnjhrdtK8Mt48MeWdYaxjdirlzcSYMmDEEyvstl9K7+7K1yRe8528ZpVriLEKBD6BwDS1S3DtKo9Pa7bqj04v0vfEWbP7mpcK2jCmbxrGRLNTrncSY8qAfZ7qWnGhcbtsYDx3mUjTOaRumdNkI4MEFnjnp6md3ZsuM9VfEjarJx7GoLMMc08slezUhsXHgDhMpcphKzH63Y5l3dNvw2LKlBFPufQZnrKTM1aZW3OB7/xcgKz+RAJrvPbT1C4euWTPjThltt2h/kT/N1/WJPOXYfKUdTRlRlo9BZeVY26c7SfGLLNjYpry4cuAalbp3FQniW5Z7baeU7+Wpxa/LVIag8AaBGaqXev+jeIXKVu8P6Vzk6csEtPTRW/Bmk2JccpiOsM0pYJupNnUQL9ytUKZ0gpz/6bP1O2m3EcHI3WPPj6a301gmTd/ptoZfbs641Oeh5QvTilFzpZHAWZYTGcYp3xReTqJnam0YopUcTfSbCl4/fi0VrVmqvBy+LKHlxVWCljmC7/SobCXCwgc++YfW23r9ierXWrXLn09aaojezHS0+XZNFSWG/G6L+souDNlMTYbI2WXRoy02bi6DdNsmT7oOapOa7mz67fWdf9J36WTyvb3wiwEIHAsgXupXdyb3Zt6or+UvTLMPcp6OVRkMjqSYzVjcBxG22LiMNox3e0021k9la2mV2M8cuJn2uPETgaXRuoGQRG2HoFjX/5jq+2gfV+1i5uxK9Kf6HTZi54U83Ko3BQpvxlpFYvUbLTNGYfR7k+p2g4jrbKjQj8lya2Cz15XC003pn8/pxOggY8lsN7LP1Pt7NLUM/hKlfFJjayOx6hgORyZUowb8d63gpqNtjnjMNqKd2N8Kq6b6qearcrlWqWnWmquc8c3bUdKf4+HF+wvxywEIHAqgZlqFzdmV7Ce6G/ZHqxZ17yoDSnAhgoen1KKGam4pvrVYphsM2I/0V9OxXXL2U6dVJbhJgJI3SZcBC9G4PD3//CCO4BPUzsXiSQVvoHOVNphme6ypzALiHYajkwpxowoPLGUTcXhiJ1S4ir9qRRZBsfVPXjEU5Z1T9xyK2Y9/x2+metRZUdPIbDq+z9N7eLB23WsJ/rNbvljWBkT72ifVXwcRtvXimGyo5EqxymrpuGIreCXRlzUgmPxl7knBXR66Eyd1IyVPfD7eWCp8/ZLZQhAYCuBmWpn12J5M7rTP+Nm5I/OZMcsU4goEnHKsmyo3BFbwWbEyjF3R9mUHldJU3EvaaGXwzKg6jEnD1LHO/DhBM74CpxRc8cxzVQ7b9eudT1pA1W/O1NkHFqAhlGZzBmnYp0RWzXdkPzE3BQTh2lpTUW/nG50plIkw0MI3OQ7echeKAKBHQTW/gpMU7uqSLiznJJf51d6NGVGqhA1L03ZUIkjtoLNkOBFZ6tIjEl2TElTcZiWS1n9Yayz1U7rbk2/IP6Qr+ghRS7YLEtAAAL7CExTO2/X7uj4xD3IXzr7Hs16BQ3jrW1T8m+1lWiGam4tEuNjwWQPhqWs6rAsVXqqiTghAIFPIHDSf+E7qeyOE5msdqlju3/1aGrco5RoeLp7TJyiPtmU+2OMnDbVsmN92a3grX4VdCOmq/lyyjwxshx6yoGfabkDK19f6j5fyOv3zooQMAKf8BWYqXZ2XcYnvXOakr/lSQEaRsNyNYyaEf2yfSGPl9OG0R6vlhJjkZat4qURmy9n+564XD9y7uymL96m4Oq+3q9QLYsTAhC4FYGZapdA2F2sJ065s+NJATaMwbJjWNSMGF+1q06VNWOkWozfascGUm5nyiL7syMBvlzcYGqAIQQg8HQC5/0XvvMq72A+U+3sLo5P7F5+OVueFOBDD9ZUNGzKh3aD6xKP8QqwMNkyojOWVakUoEQZrYDkj8WTHddKU3GVNMWwSuBW38ZqhzghcCqBz/kKzFS7dIR2U/sT/d983/9cSzHlUOlpKvplR+WwePfLsKFsGdGpOmZUS8WAVoWWv5Ub/WbH9DRVDjcFl+l387z5RX0z/W406AcCEOgQmKZ2du2aPOiJLdqUHvndMzIsI5UlI8ZYD9HvtgUMOhVmRiwlf7WUZvcZ1YW8VFzOPGlYLvcyoExZw/NcqbPO07PGibCL6wnYi3TeoqcW39H27+/IOSMlXt/fffe9BvtdLGq6mt1TnS2nUpj6N78H2+paVE4ZFi9bRnSqoBkq9TKyFRD9sXLLH2NuYj+o1ZsQS23ohU/+1vBlvJ1IKxf/xxJ4+dosRuZ7XbnPxkww/FFL9l31J3l8WE7FsKpdOuOKVlCVy0jN2lS0FSmjOhud0VbWDiPVScNUsD+bgvvDA0v1Fzp89m7fc+snPmfsN9Y3+/AlKAiBmxO4o9oJ2TfV+50/aYw3rNkaDtoqLkMVzGMrul9OGeaXLUNFoqEirZRqenRGO1aO/rhKjCntmFXODnrGlxss+H7Y06/sKD/v09haYe7qW7sl/nACZ399zq6/A8g0tbMrOD791u2qjbetJyrFhtHWUIbNyjZDdsySrYUUJiPV8ZQ4WxaRJ+a2nNVSMbhjz8rttLRv6oIvyQVLdPZuq/vTibl46ltHX/7z4qVZbgqBzzzoaWqXztgua3+SPw6/St73shfjo20pNvTE6JczBqh+jDxW8OK6cTnZVaOaZZHRrz7LCjGsnP1kz8Tv+Rcxub2ceJP37/OT32H2vo/ATLWzG1mPupfHDDmT4bLnTo9/aVcDUlkbalEJiTwyYpacMuKsbM3K0JQZ0RntGPOmncqm4ZvFp6Q/6Dp+qH48tO0pb+OzFr3gu3PBEjuYz1S72K7dv/6UzuiJtgTJnDHXbIXJrgZoVvFeyoeqr7DSqCbKqQrymNEvUo2Mzo6typ2YD5+6+EvogvF05r6Li9E9Hdpt+//kc5ypdnY769HLMeJRsMlJVBTL9SkvIlvxMaCcLcNUvJOoKaVHo6wQZ5Urw2ajHYNlxwDV16yMGCYnxjUE1tC5xGrJTaU9MlyYwEy1i1jtavZHzm+O3/mhZk4FyLAbX5d+LKLgltMrKEwFzZAzVk7xiimNWKq0FV9OJc94ZErcOrxsoYn/1fKapZeXBN/gNTC3vsbE9wl8+KlNUzu7XuOjQ5IzeVpD+b9KXuV/P2AFPWbEULW+salUqZexeFkqzrZsZVmA6psd/Sk3TaVhCq4O40LVgMuct/3S3raxM47GNvtR+z2D4ZU1LzusyxbaSm+a2qVG7fL1R/5vju+1yjxpVkMZupFjNSWOGCql4FhTs/sM1ZRRrdOfraas4Tzve3JeZSdv9c9e4p5H/LEbv+dx0FWHwEy1sztdj1pseTzAZ2OwbBkmTqU+WaIqvDRUSln7CqqO0uWJhlaRUZ2NzkG7WnAwl7BxAp+pc5EPmhdp3NDmFbVDmaZ26SK2oT96Ub45vlcp8/hsjIy2cs2QwMQspb80VErp+wqqTiddMdHQutEZ7Rig4hYQ/TF+GXvH93ZHyiAubvkIChqRxn3s897/co9XrlWu3vdMUztry+5oPerSLmt/kseHcaplK9GKK0uGZZntn33DU2JMWVAxbxplP7GgZqNzh92v05/dsdzyKXf+Yk+Ej+ZNhM/SHQIz1S5ery3ZU+sWrPiOrXg3vKzZKSV6YlnPkseHHiw7GorsGIovxVJZiolGfzZG7rMPqX9IkX39D2adpEknlR3c1P3D0LybnBEvqg5iptpZE3ZX+qOGkux9m3/xZ5gW5hVkqKAZHZnxeGWVRqwzUirF7xiqh5e5rcjoj/bLggQMEuAGAdQgAcLuQ2Cy2gmEXcp63FmVPZ/ySNkyzG92nPUp++wIntJbRvKXpTxAn96GDWVoqsxVjAwFR6M/a5GqHLPWs7fKzNb4EWJn1BxZ96Exhgtis87uYvIXL7eV6l3ULvZtN3u83O0e11UepxTTcsaaZsciPuUVYp3oN1tTya9Sya94GR6w+/OoOmrg8IKq/L5x86+KbdA6vH+T7x/EGRXgdgbVfk2YJz7T1M6uXX9SQxp+m//tH1F+Ubz23zqxLIv33NJQzWqFwdxUPA21qNZKAfK3erAAFZGhrJYxHtmqsLD/2G/7sdUWxt7amgGEYQsO/gsITFM77c3ua3/kSUac/aJ4XzUvOs32FDlLQzU93YYxqxwqXmHyuFHWcb/iZaTE3cOXBdVSWuJlYopnWBLgmi6Z7PNAch+3rVlwLonNVDu7heNF7MPoie36rHt0rcspwwJUoTTK9LSEDz2xlS6/2ohZZisg+X1onylR/mioiIw4+9IezBoMe7ncNQGzvsCz1r2G6vWrGE+Qnop9Ct4pi27COFPtvFG7cPUkT7kTi3SnCYY0Q84RQ+lay4wyMc7GgOSvDt1ZZskvo1xXHsVcacxd/did3v+7d+x+H1eNA3rckT294ZlqZ3erP4LYH3pYjImCZ34L0KwP3aNEN/wz5pZhZXrMla0i7mllya/EfUa1TtW5r/7LrLTfl/Ep4IkX3BN7TthvOzS24D38dEDaQjpN7eIdbbY/3uW3Uf6ncXEPirf7V1ewOVXBDMUkv+ooMWZpVlk7ZltF5NfSWmXEUPp6xrFf0aOqHVVnvfM6cEdABuaBBDqlpqmd9WT3ux5vsTqMU2knFu+el+KhSBkxUU43+sPUg5Z2f8pVsPzynGGkZqpLXNNJdel3nNffidev+A6fR+eC+tHHZ80/4gQnq53O2K5gf9zTsZWSIu2i97vecjUVjdLvs9UsBccKbutzJMaCU5jSfV0NDzfiutE+fKG7FTzki3dIkbuRuXM/AH//dGDYZzhT7awzu4X9UZdxWLWjM2a5XZUuX0if0ahmudMWKiPd6QGalW5VU1KwhjJUU4amZGhKhqZuaDz9W/f0/m/4Soy0ZNghPwKqGgO6KpbonKl2dnHr7nY7Db1Rn5ItQ8HyuCHB8wCFVYeeos8UEyurjoKjIcGLzn5KjEy2EmWkgDQcDEtZDCFwQwLc2jc8lDVamql2TtBuan/isGOn4HgMmpL2mMcC/FOGhjFXKe5MiS+dsZTZaYk0VHBaVP4rjVZvV/bwcq3xG3A8srXo+xValfEPEuAIBkEpbC6xuasLwktjmtrZJWt3vT/epXn8seE387daZUPFtAz3e659WuUYmSr4UE6P9JTkrEammJfpHmCf1cTobwWowktDG38ZSUBJ4Cnf27LzxTwcxGIHeoftTFO7uHnXPF3TduPr0pc9Yqimp8eCNqWaHubD5PSU6lTVGUtp9aozLZSCy6HiO0aZ9VAPV9tDD+7UtnkrBvECahDULdROvbrs+dBu+epF787qlOqY4QFVwUu5GsZ09aBSabaaFcVS8SkyDdWh4g800loHVr5nqTe/9m+m35PJo7viRF4eH4heIlLATLWzu7h6HVc1LwZ7ljwqIsO350PJiQ/jpyjExI5iVXPjWirYcaYYDWMPco4bL9NjQLTHl3gzcvfXcnfipoavWWVTSwQbAc7l5q/Bgw5optr5KdrN60861L7mWYrSzVAF+eNsR/BSvGdFwasGxOJup89qVtWZEm04GFYm4oHAkgQedJ9ezB8ym4BPUzu70/V4xz5M3SfNU6QZijcj+WMRn5XglZEeoCIxd1NwdaFYQZXTWqm3MizFW0DpUdabxnmV32zsZfo73/x3cl82RsD7BDigkiFMSiZ9zzS1s7Z0sZpRtdW6NE+RMd5LuSd+Kt2dLipK1Kyn69P9neAyMXqSXV0uxYwPB6u15HN8ofFIvnLjrIh8hwBv2jv0yDUCM9XOlrfr25+qnU5Il7ilKN4Nj5Q/OuNU1LAY7DHVzxhW2tWUuEoK8ArJqWF/VmGLGdNvsekNLHag522HkxJbUAjFuDFT7exy1/1etaPTt2RCIi3x3PhpMXHotlj4UOnV4JjikUpvGTEl2ik+TaVhPzjNftRw8Fs9GPZR6JbcLAd9q2N91nFMUztd92b4Y6coI9rR6SctHbIpj0zx0ekp5WfKraaMS2NZv1qwGmZO7SgFeJPJ2RpuCm4V+TT/s76un3Y61f1yZBCovhgvndPUzjqz29kf71K2DMW44WH+afLgCmHBcTYOo+1Z7vHEWO1A++USHtBasTXb8rfqyL87URUWNrg1Fj7cVbfGS7v7ZGeqnVTHbmRdyrJl2N58Nnp8w28KnsrGJWS3lmhleXzn0xM7ASNTKiJjJGvtGL7/a59vuTtOvGSC5yWBmWpnzZlc+WO2Xd/+yG4Z5tdj6WZboj5lRKfbnuV2NdEDDvyMPaSysaU0ZcP+bBl/Hw830X3OYuFOPvM1+8xdH/Uaz1Q7u9D9sc1I88yWs2rEAKdQ1S3L9cj06SnVzzLFw8r6ZaR7PN5tz+oslKY68SnyicN939J9WYN8Ti0+2ANh7xDgBN+h94G5M9VOuE0epBB26fu9L6dPWbAbaehFlKIwFa8aXqTMSktUc99xqvnxIjtSxotPj+TCmn4Ej27go96fu232bv28fJNvoXbepV3rutldh8zvnq8zv/NbTX7tsC9dZbx7YpZKqR95YpgnauqlUS7dT9lav18tzp5XOa5ysb3vK7cv6+KtsRwEIHAggWlqZzevP2kzcprASGPMaWH6lCGnF1G8/GWkezw+2p0UD06fI5W9n5QYh6mBOFXam4JfLl3WH/F89920F2akPWI+kMCH/BeXD9nmqS/wzMtLgmGG29qqPHZr+8XtAfJraClue24K1myM1yqtlBQQw6pTO5yx5x3ppLxDgIvjHXo3zOVAb3goN2xpptoZDrv0/ZEdGWlKGmYej6x+eq6CWzFexIPd9hT3pCw5Y3xsIwW8HMbVU3C1jRRjw06FMvi5Hq6w554dnR9LgO/CITxnqp3d2rq4ZcvQ9jzGlMDFwIetT88qI1M1T5fTjVhTU9VIzZoRs2Kw251OYpGj7NjAUTXvXGeNW8B20XnuzP8+vRnA+zRDJ/ckME3t7F72q9mNaBspdwqZhqV4KNGzlBIjNRWDFelOj09On5Iz1ZR/lpHam9XGs9a9ybUY5a0PUJH9MGZXJXCTNzbhvWdXqck0nKZ23ofd1/7YsGrEdi3AhlFy3FN+epYErBrgTo+UHSO1tGblMSNGVgNicMeu5iZnGnaqjU+dUXN8dY984hdm6x7LeFev0v/S44mfCe0lHAuAzAilT46ZqXZ24fpjB9Ay5PdDsqEZ44LnldNnOTSPPVJHH/pabuszxchvhsdXs2KY7PFIpWC8SWDuhehy9eYWLP2oOu93QoWzCcx9Y8/e3cX1J6ud79bufb/6q4bF+GwMNtWxR1mKiR5zWkyaKofmkVOGVkwFv8aOCpvnlj2ouFeLnx4cPQvYJ31jTyp7BvAz9OmMmmfs/cqaD3olrsTCWk5gmtpJRcyItrUVPT6MTu/bU7YKXqwW67htn0lsfBXNVo2RmGriDueVa+1o78CUla6tU/dyavEDD5RSOwhwuDugdVKmqZ31ZNLij9l2j/tVHg35k9P34/FWQYker08Ps0+LcTtGlmE+2/F7EVXzoX+mynHqHVstvVPkvNzy21h6zlv9EZUvAHLBEo9A7U1C40GHdXGrM9XOrnK/zauaZyAU4GHx0zEpPU657emRZoyJs4r3YIlZ8leHyekVqs7YSbK3xqd0hoMELr4HbbnLVrxsoUHUhL1PgDN9n2GqMFPtvBW76/26T5onv8+Wn0o3w3JTQBx6gH26s2qYs+pXigdUPzsxnalqKZxrELj+qrp+xdueFCjOPpqHEp6vdn4wpgouDFHzbMqdrU/lRsODU64Nyx9tKdKL9CM9RqWUYoaqRWeM7++izMLTIfDQ71tnR+9PweR9hjepwFGecRAz1c5uf3+0MQ1dTr7NV/7TUswbE5XiUz4bYzxYntJQtWS0IuWP8VVnDBi0+3X6s4NLzA1b+Ps8cWsTl577OrE6BF4SmK921qLd3fH69qGpVxIwj4zxMcv8MV7B0fCA6FGFjmHxMcWH+lSiPJ3gGIN9JYHLZOCyha6k97i1OIXHHdkFDU9TOxMJ14nS8G37bBQwxVtAzCrjPSD63U4eLxiDFVbqoqbcUIAPVSqF7RimyofX39HSxSnPvaru0Pkderj4hVlsOU7wpAOdpna+nyhaLhhRNnzWbn973I4x7rE67pThwT5UvGJsVku/Y6Tc6rDj9KkbfvJNu+Gh0BIEIPA+gZlq53KlT9uMbImTO+3TVcoD5ExD+fuG5LAaZk5/pIvfHN/LqjzjRlltPPfRkZ8jn/fZ6X06efSrS/OLEZipdiYArgFJ5Fzq/NNxux2Dza8wGQpOkQr2AH1qidJQTCu3E6BqMcbt1KoCOimKOdZ49IW4tfmt8ceiptoUAg899Ie2PeWIty46U+38iv+ieN/+bZbmKR/fkvnNiJE2dGc0PFiRPlWGWZ3SKU8sKGdpeDNaUQHuSUOFyfCAl2GKrxpvpldr4txB4G6X1N362YGUFAgcS2Ca2tk1HR9TDhcPc9oOy085FaaYZCgyGR5mzvjIKSPO+lrRU9rVxDIMDwQgAIEFCDz3v0hNUzs/dZMKVwv//KJ44S+kfJ38XvwsxT0WI7tquNPD3LZPf3whs32h5LShAmS0nJ6bPmNWmto3PLzgvjbIggAEzibwXCE5m8wh9Serne/BLnQ95pGYmS2/Gdqw2aWSKUCGl9LQDNkq5Uto+DJAkcmoJiqmP6uwmxvffXeLt+WGlLikbngotASBRGDm/WUaEGXAh/5pYmaPz+rTDW0gBlRjFG+GbEsfsbVKNJQow3qIAbIVIM8nGFz6n3DK7BECDyUwWe2MmglD1Aa3/dP17Mv0t3+Y54Zn2WcpeKmgxUdB+lIo/EZMU5brTzUmOr8FbvvPuNy2zOWiO7rYmVoOAxuCAASuIzBZ7b6IT3h838HxW61yj83KcNs+JXjVWXP6ExPNY8NvM79Ts5xSmIyY6055ZCgYYzoB5HP6EdDACAFe1BFK78TcQu1sA64TUS3k8Z9ENpTHDWW5EZ2yfSr+qLKpOBunYkGz4xNTor9vV7OqzmqdamTVWU3HCQEIQAACIjBN7fzWts/q4/3ZlBn22Rc8KZaX0t48PQ6jJ9q+SvREW/U9zAvGAC3xcipFPnrIfxV99PHRPAQ+jcA0tTPQJhiuGW7o06f8JBTwUvA8UmU1dE9SrDgbl4uL9m2ftU9VTjUVUDU2BVcr4IQABCAAgXECM9XOdMIeu/f96tfnV8dvP2wnGlqwhr5DT9FuPbI1TLkK87IappryY0AAAhCAwHMJzFQ7F6cvivdN89yjT8Mq7VGwO32oABtaET8Gn7JPH8qpAHnKmORJFTTshClmxDiqzshaxHwUAV4tCHzUCz+y2Zlq5/3ZS2nPF8ULmmdT7ncjRWrWYnzKPV5BHoVFT8cZw7S6O62yZuOipVOzMjwmDZVoRjkVl4uR2EaAf17IawABCOwgMFPt4i3vtt/yZsfHdmVD35v7oxi4J+485WrK/JZon/LIiAXNWY1RMAYEbk6AF/jmB0R7UwhMVjv7Wuqb6bYJjz3udI8+Bcg8HuNh5nfD/QpzvzntiU7Z1akyOHpatmruNmLl3UVIhAAEnkiAP7G44NSmqZ1d7unx3ZrTjCR45vFgNzxSgucp7rTPlr+fZbNaQqUGDVtxMJKwJQmkN3DJPT5rU5zIs87rmm6nqZ1tz9UlfcofRUvvrhue4oBiuue6P34q3tPjlOyqYqX4OIy2ipTGYFiZiKdFAKQtMu6HT58Psx9LYKbaJT3zb6m+q2ZIgcz2x87JDP80QwHx/Nzv8f4ZZ6OtmOh0u0ysrpUSLUvpacqHI0WqiTh3E9Ch7K4wknjNKi87uUkbL/skAALXE/j965fUivbN9Nu//L+S8T/F9gCbtaG+xvoDbvdYhZiuWa2SDNU0fyvYYlpTXu1lQFqUIQQuIODfiAsWuvkScDjvgPoX43nrHlJ5str5Hlyx0jvqWM2Z9MxTqtC9gk1tyoocVSE6zTZ/dcUUxvCTCfCSfPLps/f7E5ipdkbH1cWMzo88i4mCpxRXNc/1n3e7BcnvqbhK5+Q8uBPAFASuJ6DvxfVL32pFONzqOG7VzMx/bqf30gx/XPO+jX77z+eMl3lsyv3CZ0OfirOKKZ2dKdUsDcsqnVXPeGQ1HecCBGa9A7PWXeDI2MLnEJj2286+n/qK6leacXfB0z+K8yn/TL+9dv+SGzxdby+tYs7oScPByoQtTODiV8Lf0oV5btrac2lc/NpsorpM8DS1M4Lx1XQJkScKm9TFZqPfz8BmPStNKWvHUXnBdyrsWJSU8wjYgV55mpct5y/qedyoDIGVCEz+k0z7usbHyNrQP0290lTk7mHRU9oWk4p4wTJSHo/XMBk2mzzVoXqTUQ3DWSWwBrSXb1p17+POs+uPd3KfyDXenPvwXK+Tu/y2E1n/reafpi76I00LiE63PUt2jDenapaGfTFisOqUkfJYSqpZehTcN3Yn9ssyezcCJx20lb3bTqf3A5PpR3D/BmaqXak3zkvqZcMU41PJaWFJh8zjF419WvDv/d7veeXzPsuWzltrgcp+Ors38mb67nV3JFqrllW+n7tL7UgkBQIQMAIz1c6lyJqIP+D8VPx28JsiCYkEL2lYDE6Xi0/Fym4Pflp6KmiJVedgQcKuJzD3vPQGli/SCAqljwR/YAx8PvDQd2x5ptpZu/6aJj3zbfi9UAbovkivuPwqa0Z0elnPMr8ZIz/7LKws4qX4hMBWAvGl7bxXMWzrEp8WD6tPO/Hd+52pdhISM0x4bA/pR171OvAsT/F4hZnTiiQN88j0Q1C8PEUV5DejMxXDsCGwj4C/YPtyyXICMORNGCcwWe3sZTWlscff2vJHXmtKftuq3nhzxu+AhtWYyChWUA8xPQZ7tc5sCl516Gf3oN09ruEHsaVVCNyfwDS1s6vHngjI9MM8EhtNud+GmpLSlJ5Y020Fe8GqU2thvEPA2Cba+6qN1BmJ2bc6WU8h4N/lp3RLn9MJzPzf2/3v//6vva/2mGi5YTji0J326X6fku2GfbrhwT70gm77rHk0dE8cbrK9n1ZKf7aVhf8aApzONZwvWGW9o1xvRxe8BpuWmKl2drrSObP9se7NaPl9VpE2VGS5bQuLzjT0UkkFYzz2IQRK7IeUpcgnE+Cl+uTT3733yWpnb639wpNi2dAe24x9Jmfye4w+k+HB7pRdDs3jTjf4vJJAPJd96+6rsC9rX4dknUGAEzyD6ifUnKl2xtdeXH+StvlUdPpheHBMNNvD5PRIOX0YP61IHJqtsslfDq1s6fwQzyfv/UOO+P7bLL+89++ZDm9CYKbaSZBcbHzon/5O26eGHuPU5PShgt0wZ8eIKW7rU1nyYMwicOpZnFp8FrFPWJeD+4RTPm+PM9XO3t34mIb5n2qaYY9N2bbt02w3UrA7HY3HRI+nR49iPEUBPuw4FVBN0SzG9QQ4keuZz1qRs55Ffpl156udC5u/yvZpjzxmG+ivvt9+OHf5fVbO6Den5M39MdhTWp/eQGs2+VU8+RnemQCndufTKXv7kPP6kG2W53uNZ7LaRWHzk7ZPe6LfQMShzZonxvjQefms29Hf8qR4hXmu1uqExRTsKqiq0wl3iLWyOimbps6uv6kZgjsEOKkOHKbGCcxUu/JvY/prbZ/lY4Jnu3K/G+nTA5JTw6phTnuU6MP0aSsmzxrD1r5a/uquNwVXK7zvvEMP7++CCi0CnG+LDP6tBGaqnfVqr7KJjf5n5ja0R34fyuNDn02fNnSPgn341f1lFTckbPK4f+3Pl5t9GbAqn4/d+CMO1E6HA3rEST2lyZlqZ9rjL7R96u+nuMfw2awHmO0vvQ8VYEb0+DBxtwD3eIU0q2F/VmEYhxPok+/PqpnBMMVH453cWAf7WAKcy7E8qWYEZqqdvdD+WB9muHRJwKLTZuPQ7fRpQ388+Nvo+x92aTaFKR6jQ0D/7aETU526M+0791aFubyTE1n+iKdscNq/Fdp26++0fdq/Stj+v3vMsMvUjcgihnmWe/xfQGwp9v/m40V81vw+lDNWi3YMiHaMea7tMK/vfwrJNxd9M/16yAuvaGex8O5ebm2ZV/HnP/+5bfZnP/vZT3/605/85Cc/+tGPbGifbrzkcEbATLWzP710nbON2dUs9bKh/o/r7OxLCfQXwr8V0jbLco9mfcr9XtzscujOjl8BjzB8+5e1+v5y/Qr92bjN8ciYJfvNdNXB2E3AjmB3LonXE4h6Zqv/1V/9lX26nv3BH/zB9f28XHGm2vnLXf4Eicqn33wuV/Yp8XOPF7FPe3w2/tRL+7eY5GEIARHwV0hDjCsJ8N28kvbgWr/4xS8s0n+fmXF/Pevva6baWWf2ivuT/r/F7Wefe2zWN2CGiVkcusdmTf9Setyzzyo4VothH2InDtVdj8RUE/vO3WXHE8cjW62+X6FVGX+LgDFvTeE/m0BLz3749Tl79YvrT1Y736297v6nmi5armr+HfDbxz71gy/+gPMYxSfDhhfTPHU5R3HqEiPF/bd1P3Jrq1vj+6u/OXurZt7cy/3Tjfb9m3x0hx+lZ/2TuoXaWYv+0vtPOr9P/dP89iQZ83iJmQX4Jj0yzirXK9inle38EFQFL3jnz/dbjRWivXXX7+QOrjW+xHhkZ+lDinTqM2UEDDIcDiEQ9ewP//APf/zjH1tZ++dnS/4+e4fYXdTO9uBvvwue26ZMrk/+l1Z86CLntm6lNKU/BfXgPiC+dX0+B87qvMqanakyuO85pJS/FSPvT78ZZqsEHG91CmdJAD0rmezw3EjtrHv/DtinqZcrnDx275jtj99B30Zf/htieSvZrMKqXBRQnV3Mef1mD19xU8FNwZ2zPqpOZ4lPmzKkn7blkf1Kz/7oj/7I4vX3Qfh9NkJvMOZeamdN25dBP+9Mrkz27IeaOe2xWf2kS9vzAPuUwiX905QvkWZTNYYRV0lD/12knHrp6Vd+mX59wOMavh7R4IpGcjByyTD0bPqx3k7tjIh9K6Kq+XWjTzPcVkySLpudjvXKBpxGdcV3ZKlasOXs9NBKKf0vi7wMiDU3BcfE0rZS5kyvWRmGp0XAAbZml/GjZzc/yjuqnSGzr4c9flnbTz3z+P/wrnPjlOJnFTzepjp/M+XmJ+Q0Ohsv+9fGy6lOtX5WtVTfeXjBTvPVTo5twKrZKpsOotrVRzkd2jJbRs8efZT3Vbv055l+y9iXR6pm3G3oj9npGjJ/8tztnHZ3uDuxJHBgqbJ4y9NZtDPVqtb3379gv//nzhr5JzYf9Yy/3/jEE+z0fFO1s47t2xKFzT0mYOb3pyNmgz/mrE6nSIfavqkdy+1I2ddbmdVf2gj7XyNSYjW+6lRK1XiZ8jIgld0an9LLoRU055VvTtnDnT3O57Ydome3PZqzG7uv2tnO7WvjTxQ59zsXyWEMSNeQVXCPjD7TwbB+EW8ydfIy5dSAo/Z1SJNvNrM1fWv8yB6tpoXd6ohH2j41xpmcusRI8ahnFs/fbxyB9gkxd1c71zM7iRFhGzmwm3wh1WrrIi5/PClFRitXAS2jldjyV+uUHW5Kr9Z050idkZi4xNb4mNuxrazNfrjmOYQOpcOn0LPDkX5CwVurnR2AfZH8cduPxDwyZLsnfdps9SZq+VP6ZcPBfgbDYtulJsVZs3fUTBX6w1b9lr9f7Z3Z81a0yt5Y9WV7p+eb52rjh/eJnh2OlIIPUDv/VWd/qdIvbr+z7NMNO0K301m68w63j/pMHZbD8ciUWyaWnpQyMjykyMhCZczI0iMxqfKOlFShP7T6FnCHt67f55uzvs03i6BnbwIkfSuBu6ud7ce/Wn5PydZPFhkW6bpY/Sp6eotOf7aVVfqPqlNWPsQz0t5IjJqJ8N1ZTa86Lb7l75TS0uMx76ekCi+Hti+PWUz2tK+XBDwg6hl/v3EQGmHnEXiG2tnXLD5+iZjnPC6Dla2HrTdaqRAja6WF0nCkQoqJFaLdCUtThww7Sw/W31FhR8pgMynMFnLP1pck1Zk41BaqPaBnVSw470ngAWpn4PxHmxP0r58+7R4x257nXihnvxn79HWwq7J49SyqzpdLDGYNhsXlLMWGl70zvpw3cNmicb+b7Nht1DMrwt9v3ESS4FsReIba+dfPPv1uNcNFzlDGb2aL7EhMK/ckv29hpHg/UrMyVLP0aKpv7E7sl23N9pfrz6rmYJji3diXlYpsHdqiMWW6+Lme2b+MmH8fcTwX7PUIPEbt/I6wTzf8JMyOl0WcmntUqbF9zZQ/m6zOm5VjestO3cawNOXDMqD0vNN5tVq1kx3OU4uP9GMNxLD4Pkf/O7Z+n/30pz/ln5+9Q5LcRxN4htoZYr/6/S6wC8IfR2/2bc/AeuvcX/3ZAzdVFc6j6o/vohXZ8m/qcHcRS7SFOse0qY03g72ZVpFWk1HPLJc/b2wBxP/JBB6jdnYL+EXgn3ZmMuL5VZ0x4EDb1oq3z25F8Trxs9pkWi4Nqym7nal4Go6U3ZrSj+/Pqp/BMMVH453cWOckW3pmf97I77OTIFN2bQKPUTs/BruSqqJi/nfO6c308aXPuFJVU4b6KT02FZ0tWxUGjdahxP824KXiiql4Z8oi+7MqNRim+Gi8kxvr7LPRs33cyILAIIHHqJ3dRPakXZWeFHDe0JYur3JfrjPlAVVt6LSaCqZhJzFOvbloLFW1x+vv698WHUy0MAtunU61eTnfyVWRlhH1zGL488YWKPwQOIPAY9TONp/uU7+YBGXwKlT8BYZakrF10bTllL67bCux5bd1O1OpqzjcmvUy/mWAVh+PVIoMyzV7h16iZ2KIAYG7EXiS2jk7u4n80WVkw4jVhpqK/gPtQ5bwIptKtYLll6HNlh5NufEyIMV3hn1tjomdRTtTWxt+WSq2VNrV9Khn/POzEhoeCNyWwJPUzm4fe0qUVWcZtsljF3fr/+68vNOtAelrtMdX9Kz4OZ47Hll23s9Ne0nDfq5mW1ktvyV2przsy4CXqyugNFzP/viP/9j+vv5PfvKTH//4xxbzox/96IdfnzIeDwQg8AgCT1I7AbXLLtpSGjnPM3bcsyklDQdbTVlp2C9SDY7Olt0vW52tCmqsX83a4RyvWUaiZzuAkwKBpxN4ktrZtZVwl54U8HKoq1CGUry4pLQM8MiWX3WqRlUVRiLTchqWRrXaVqfKemIabq2W4jvVOlOpSHX4p3/6p+b/2c9+5n9f3/7I0Yb8PquywgmBDyHwJLWzI7FLUAcTbTkPN7SKZK+zREfDqtd31Wn1W/7O0oNTscO4Sssuy8bIctY8cQkFtLJafkvsTNms65n9eaP/+67++q//2pzomYBjQAACicDD1E7d969ChSVjX1YqEoetgvLLiFnJ9pj4mQJ8mEppKKOaVc3tRJZTI/VT1iGC53r2J3/yJ/bPz6y+65n9UzT++VmizRACEBgh8Dy1s8t3ZGOdGLuL7W+glJd46ekUsamt8aq2NTGJRytdfhlaMRqxWoxs2eVOY2SsvNWWntnvM/19EPRsK0biIQCBEQLPUzvblctVf3vxTu9HxtnxezxFxqHs0ohr7eswVlD96GzZm4JbRcb9vrs/+7M/sxT/80bTM//nZ+jZOEYiIQCBowg8TO1GdO5NNCOq0BGqzlS1sjtbn2kv1QoWI39ppAo+jE0qJdZJdmfoemZ/3uj//Oxv/uZvLNj0zP8Rmtk8EIAABO5A4GFq10dmF3cMsKE9+tsl0faw0tP3t2ardeSUEXvbakd9stx9Nbdmefyf//mf24qmZ/ZXHM1Az7aeHfEQgMAdCCylduNA071vwzLXBcbEMs5KOxWfSsmfjBSWhinYh9WY5NRQhkqVHk2ZEeXT9Mz2JT3727/9Wwvg91nEhQ0BCDydwCJqZzd7/yTscq/+u1EkCS8DqvWVbrNVOzpTjA1dcjym/EwrRn0qSyk4rej+v/zLvzTj5z//uf0jNDNcz+yfovHnjeKGAQEIrE1gEbXrH1JVAMoUCzNn+eutjDSPa4+C4xKSJTllVEu96fTi9i/Ut2Zcz/xfeWVl7feZPfZX9t9cgnQIQAACTyfwPLWzy92g+2dJX0pTTnmWSYIquFa5Wig+DeWPRopJwxgZ7RTmw1bDPhtT7B+YWcP2b73SX3G04v77zH6ixYWwIQABCEAgEXie2qUN2NAkwT+lXoqxKXvkdyPOJo9PWYoZNmVSpD//dKf7VcEM1Y/O6E8BaehZ7nQ9s/8Vmv8VR/9fVfvvM/Qs4WUIAQhAYBOBx6tdSzyMQmsqyphizJAdCZrThy1djH5V0C82edz4+7//e6tmfyvE9MyMf/zHf7RP+31mkoaeOWc+IQABCJxB4GFq59rjn4YjiorT0VSCpUSJk3ncjobslF4OLVKra2kr+Mtf/tKGf/EXf2H/CM2G0jP/I8eyDh4IQAACELiAwMPUboSI6ZA9UjLZ8Y8lvY6mZJjfbJ/1CuWK//RP/2RO+1sh9o/QJG/+L76yn2hlPB4IQAACEJhO4Hlq52pkn3ocog+jREV5s1npnxk2VFZMcee//Mu/mGH/FM3+EZr9lRD/X1X7v/gKPXNEfEIAAhB4FoGHqZ1UKlI2p/5QMdoeYx57ktT9+te/ttm/+7u/s3/xlf2Ro/RM/27iWB8bAhCAAASeTuBhame4Tbr886uKffnwM/ChVO1f//Vfzf8P//AP/o/QXM/i/7enZ/EJAQhAAAKfQOB5amenYsJmP+b+/d//3bTN/hqI/4tC/vmf/9mm/P87xv5KyCccHnuEAAQgAIFBArdWu//4j/+wbdjfcrTfZ2b86le/sk/0bPBoCYMABCAAARGYrHbomU4CAwIQgAAEziPw/d9OPG+NauX//u//5s8bq2RwQgACEIDA4QSmqd3hO6EgBCAAAQhAoEXgu9YEfghAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTAGrXRMMEBCAAAQgsQwC1W+Yo2QgEIAABCDQJoHZNNExAAAIQgMAyBFC7ZY6SjUAAAhCAQJMAatdEwwQEIAABCCxDALVb5ijZCAQgAAEINAmgdk00TEAAAhCAwDIEULtljpKNQAACEIBAkwBq10TDBAQgAAEILEMAtVvmKNkIBCAAAQg0CaB2TTRMQAACEIDAMgRQu2WOko1AAAIQgECTwP8HhQNJGrzA46sAAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=591x591>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namanwsl/anaconda3/envs/fea/lib/python3.8/site-packages/pyvista/jupyter/notebook.py:33: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "No module named 'trame'\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/displacement_y.png\n",
      "mesh from applications/shearblade/shearblade.vtk:\n",
      "  points:  5427\n",
      "  cells:   10561\n",
      "  bounds:  [(0.0, 1.0), (0.25, 0.75), (0.0, 0.0)]\n",
      "  scalars: node_groups, mat_id\n",
      "  vectors: u\n",
      "  steps:   1\n",
      "plot 0: None(step 0)\n"
     ]
    },
    {
     "data": {
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"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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=591x591>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: applications/shearblade/geometry.png\n"
     ]
    }
   ],
   "source": [
    "# !cd {name} && sfepy-view regions.vtk -2 -f Constraint:vs --color-map binary --no-scalar-bars --no-axes --window-size {modified_image_size},{modified_image_size} --off-screen -o constraint.png\n",
    "plot(filenames=[str(namepath / \"regions.vtk\")], fields = [(\"Constraint\", \"vs\")], window_size=(modified_image_size, modified_image_size), screenshot=str(namepath / \"constraint.png\"))\n",
    "plot(filenames=[str(namepath / \"regions.vtk\")], fields = [(\"Force\", \"vs\")], window_size=(modified_image_size, modified_image_size), screenshot=str(namepath / \"force.png\"))\n",
    "# !cd {name} && sfepy-view regions.vtk -2 -f Force:vs --color-map binary --no-scalar-bars --no-axes --window-size {modified_image_size},{modified_image_size} --off-screen -o force.png\n",
    "# !cd {name} && sfepy-view {fname}.vtk -2 -f u:vs:c0 --color-map binary --no-scalar-bars --no-axes --window-size {modified_image_size},{modified_image_size} --off-screen -o displacement_x.png\n",
    "plot(filenames=[str(namepath / fnamepath)], fields = [(\"u\", \"c0\")], window_size=(modified_image_size, modified_image_size), screenshot=str(namepath / \"displacement_x.png\"), scalar_bar_range=[-0.05, 0.05])\n",
    "plot(filenames=[str(namepath / fnamepath)], fields = [(\"u\", \"c1\")], window_size=(modified_image_size, modified_image_size), screenshot=str(namepath / \"displacement_y.png\"), scalar_bar_range=[-0.05, 0.05])\n",
    "# !cd {name} && sfepy-view {fname}.vtk -2 -f u:vs:c1 --color-map binary --no-scalar-bars --no-axes --window-size {modified_image_size},{modified_image_size} --off-screen -o displacement_y.png\n",
    "# !cd {name} && sfepy-view {fname}.vtk -2 -f 0:vs --color-map binary --no-scalar-bars --no-axes --window-size {modified_image_size},{modified_image_size} --off-screen -o geometry.png\n",
    "plot(filenames=[str(namepath / fnamepath)], fields = [(\"1\", \"vs\")], window_size=(modified_image_size, modified_image_size), screenshot=str(namepath / \"geometry.png\"))\n",
    "\n",
    "crop_image(f\"{name}/constraint.png\", bounds)\n",
    "crop_image(f\"{name}/force.png\", bounds)\n",
    "crop_image(f\"{name}/displacement_x.png\", bounds)\n",
    "crop_image(f\"{name}/displacement_y.png\", bounds)\n",
    "crop_image(f\"{name}/geometry.png\", bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.fdnunet import FDNUNet\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch import Tensor\n",
    "\n",
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import accelerate\n",
    "from ema_pytorch import EMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = FDNUNet(\n",
    "    input_dim=64,\n",
    "    num_channels=2, # geometry/displacement (2)\n",
    "    # num_condition_channels=1, # geometry (1)\n",
    "    num_auxiliary_condition_channels=3, # constraints (1) + force (2)\n",
    "    num_stages=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_by_division(tensor: Tensor, value: float) -> Tensor:\n",
    "        return tensor / value\n",
    "\n",
    "def normalize_to_negative_one_to_one(tensor: Tensor) -> Tensor:\n",
    "    return tensor * 2.0 - 1.0\n",
    "\n",
    "def unnormalize_from_negative_one_to_one(tensor: Tensor) -> Tensor:\n",
    "        return (tensor + 1.0) / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n",
    "import torchvision.transforms.functional as TF\n",
    "transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((image_size, image_size)),\n",
    "        transforms.Grayscale(),\n",
    "        # transforms.RandomHorizontalFlip() if self.augmentation else transforms.Lambda(lambda x: x),\n",
    "        # transforms.RandomVerticalFlip() if self.augmentation else transforms.Lambda(lambda x: x),\n",
    "        transforms.PILToTensor(),\n",
    "        transforms.Lambda(lambda x: normalize_by_division(x, 255.0)),\n",
    "        transforms.Lambda(lambda x: TF.invert(x)),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "path =  Path(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "geometry = normalize_to_negative_one_to_one(\n",
    "    transform(Image.open(path / \"geometry.png\")) * 0.5\n",
    ")\n",
    "geometry = torch.cat((geometry, geometry), dim=0).unsqueeze(0)\n",
    "constraints = [\n",
    "    transform(Image.open(path / \"constraint.png\")),\n",
    "]\n",
    "constraints = normalize_to_negative_one_to_one(\n",
    "    F.threshold(\n",
    "        torch.clamp(\n",
    "            255 * torch.sum(torch.stack(constraints, dim=0), dim=0),\n",
    "            min=0,\n",
    "            max=1.0,\n",
    "        ),\n",
    "        0.5,\n",
    "        0.0,\n",
    "    ).int()\n",
    ").float().unsqueeze(0)\n",
    "\n",
    "magnitudes = [(\"force.png\", (magnitude_x, magnitude_y))]\n",
    "forces = []\n",
    "\n",
    "for name, values in magnitudes:\n",
    "    force_tensor = transform(Image.open(path / str(name)))\n",
    "    force_tensor = torch.clamp(255 * force_tensor, min=0, max=1.0)\n",
    "    normalized_magnitude = tuple(\n",
    "        map(\n",
    "            lambda value: np.sign(value) * ((float(abs(value)) - 0) / (5000 - 0)),\n",
    "            values,\n",
    "        )\n",
    "    )\n",
    "    forces.append(\n",
    "        torch.cat(\n",
    "            (\n",
    "                force_tensor * normalized_magnitude[0],\n",
    "                force_tensor * normalized_magnitude[1],\n",
    "            ),\n",
    "            dim=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "forces = torch.clamp(torch.sum(torch.stack(forces, dim=0), dim=0), min=-1.0, max=1.0).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_checkpoint_path = Path(model_checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_ema:\n",
    "    ema_model = EMA(model)\n",
    "    accelerate.load_checkpoint_in_model(ema_model, str(model_checkpoint_path / \"custom_checkpoint_0.pkl\"))\n",
    "else:\n",
    "    accelerate.load_checkpoint_in_model(model, str(model_checkpoint_path / \"pytorch_model.bin\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = torch.cat((forces, constraints), dim=1)\n",
    "if use_ema:\n",
    "    ema_model.ema_model.to(device)\n",
    "    prediction = ema_model.ema_model(geometry.to(device), conditions.to(device))\n",
    "else:\n",
    "    model.to(device)\n",
    "    prediction = model(geometry.to(device), conditions.to(device))\n",
    "prediction = model(geometry.to(device), conditions.to(device))\n",
    "prediction = unnormalize_from_negative_one_to_one(prediction) * unnormalize_from_negative_one_to_one(geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imsave(\n",
    "    str(path / f\"predicted_displacement_x.png\"),\n",
    "    prediction[0, 0, :, :].detach().numpy(),\n",
    "    cmap=\"Greys\"\n",
    ")\n",
    "plt.imsave(\n",
    "    str(path / f\"predicted_displacement_y.png\"),\n",
    "    prediction[0, 1, :, :].detach().numpy(),\n",
    "    cmap=\"Greys\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9761520910>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure()\n",
    "figure.add_subplot(2, 3, 1)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Geometry\")\n",
    "plt.imshow(unnormalize_from_negative_one_to_one(geometry[0, 0, :, :].detach().numpy()), cmap=\"Greys\")\n",
    "figure.add_subplot(2, 3, 2)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Forces\")\n",
    "plt.imshow(unnormalize_from_negative_one_to_one(1 - forces[0, 1, :, :].detach().numpy()), cmap=\"Greys\")\n",
    "figure.add_subplot(2, 3, 3)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Constraints\")\n",
    "plt.imshow(unnormalize_from_negative_one_to_one(constraints[0, 0, :, :].detach().numpy()), cmap=\"Greys\")\n",
    "figure.add_subplot(2, 3, 4)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Displacement X\")\n",
    "plt.imshow(prediction[0, 0, :, :].detach().numpy(), cmap=\"Greys\")\n",
    "figure.add_subplot(2, 3, 5)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Displacement Y\")\n",
    "plt.imshow(prediction[0, 1, :, :].detach().numpy(), cmap=\"Greys\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fea",
   "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": "3.8.17"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}