test_ATAS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "import copy\n",
    "import edt\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import cv2\n",
    "import pandas as pd\n",
    "from sklearn.metrics.cluster import adjusted_rand_score\n",
    "from skimage.metrics import adapted_rand_error\n",
    "import h5py\n",
    "\n",
    "import torch\n",
    "from torch import from_numpy as from_numpy\n",
    "from torchsummary import summary\n",
    "\n",
    "from func.run_pipeline_super_vox import segment_super_vox_3_channel, semantic_segment_crop_and_cat_3_channel_output, img_3d_erosion_or_expansion, \\\n",
    "generate_super_vox_by_watershed, get_outlayer_of_a_3d_shape, get_crop_by_pixel_val, Cluster_Super_Vox, assign_boudary_voxels_to_cells_with_watershed, \\\n",
    "delete_too_small_cluster, reassign\n",
    "from func.run_pipeline import segment, assign_boudary_voxels_to_cells, dbscan_of_seg, semantic_segment_crop_and_cat\n",
    "from func.cal_accuracy import IOU_and_Dice_Accuracy, VOI\n",
    "from func.network import VoxResNet, CellSegNet_basic_lite\n",
    "from func.unet_3d_basic import UNet3D_basic\n",
    "from func.ultis import save_obj, load_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### init model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CellSegNet_basic_lite(\n",
       "  (conv1): Conv3d(1, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "  (conv2): Conv3d(16, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  (bnorm1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (resmodule1): ResModule(\n",
       "    (batchnorm_module): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_module): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  )\n",
       "  (conv4): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (resmodule2): ResModule(\n",
       "    (batchnorm_module): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_module): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  )\n",
       "  (conv5): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (resmodule3): ResModule(\n",
       "    (batchnorm_module): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_module): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  )\n",
       "  (deconv1): ConvTranspose3d(64, 64, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (bnorm2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (deconv2): ConvTranspose3d(64, 64, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (bnorm3): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (deconv3): ConvTranspose3d(64, 32, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (bnorm4): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv6): Conv3d(32, 3, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=CellSegNet_basic_lite(input_channel=1, n_classes=3, output_func = \"softmax\")\n",
    "load_path='output/model_ATAS.pkl'\n",
    "checkpoint = torch.load(load_path)\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "model.eval()\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dataset info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATAS_data_dict = load_obj(\"dataset_info/ATAS_dataset_info\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seg one img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "parameter setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we do not input the whole raw image to the model one time but input raw image crops\n",
    "crop_cube_size=64\n",
    "stride=64\n",
    "\n",
    "# hyperparameter for TASCAN, min touching area of two super pixels if they belong to the same cell\n",
    "min_touching_area=20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "choose a test image and load it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are test imgs: dict_keys(['plant15_72hrs.h5', 'plant15_0hrs.h5', 'plant15_68hrs.h5', 'plant15_28hrs.h5', 'plant15_64hrs.h5', 'plant15_44hrs.h5', 'plant15_80hrs.h5', 'plant15_56hrs.h5', 'plant15_84hrs.h5', 'plant15_12hrs.h5', 'plant15_76hrs.h5', 'plant15_32hrs.h5', 'plant15_20hrs.h5', 'plant15_48hrs.h5', 'plant15_24hrs.h5', 'plant15_4hrs.h5', 'plant15_16hrs.h5', 'plant15_36hrs.h5', 'plant15_60hrs.h5', 'plant15_8hrs.h5', 'plant15_52hrs.h5', 'plant15_40hrs.h5'])\n",
      "for test case plant15_0hrs.h5 : /data/CellSeg_dataset/ATAS_processed/plant15_0hrs.h5\n"
     ]
    }
   ],
   "source": [
    "print(\"there are test imgs: \"+str(ATAS_data_dict['test'].keys()))\n",
    "case = 'plant15_0hrs.h5'\n",
    "print(\"for test case \"+str(case)+\" : \"+str(ATAS_data_dict['test'][case]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_44465/1069643791.py:3: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  raw_img = np.array(hf[\"raw\"], dtype=np.float)\n",
      "/tmp/ipykernel_44465/1069643791.py:4: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  hand_seg = np.array(hf[\"ins\"], dtype=np.float)\n",
      "/tmp/ipykernel_44465/1069643791.py:5: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  boundary_gt = np.array(hf[\"boundary\"], dtype=np.float)\n",
      "/tmp/ipykernel_44465/1069643791.py:6: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  background_gt = np.array(hf[\"background\"], dtype=np.float)\n",
      "/tmp/ipykernel_44465/1069643791.py:7: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  foreground_gt = np.array(hf[\"foreground\"], dtype=np.float)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw_img shape: (139, 512, 512)\n",
      "hand_seg shape: (139, 512, 512)\n"
     ]
    }
   ],
   "source": [
    "hf = h5py.File(ATAS_data_dict['test'][case], 'r')\n",
    "#print(hf.keys())\n",
    "raw_img = np.array(hf[\"raw\"], dtype=np.float)\n",
    "hand_seg = np.array(hf[\"ins\"], dtype=np.float)\n",
    "boundary_gt = np.array(hf[\"boundary\"], dtype=np.float)\n",
    "background_gt = np.array(hf[\"background\"], dtype=np.float)\n",
    "foreground_gt = np.array(hf[\"foreground\"], dtype=np.float)\n",
    "\n",
    "print(\"raw_img shape: \"+str(raw_img.shape))\n",
    "print(\"hand_seg shape: \"+str(hand_seg.shape))\n",
    "raw_img_shape = raw_img.shape\n",
    "hand_seg_shape = hand_seg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "feed raw image crops to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: Transpose the image to be: [0, 1, 2]\n",
      "Get model semantic seg by combination\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_44465/580365679.py:45: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  seg_background_comp = np.array(seg_background_comp>0, dtype=np.int)\n",
      "/tmp/ipykernel_44465/580365679.py:46: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  seg_boundary_comp = np.array(seg_boundary_comp>0, dtype=np.int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed:  10.99596881866455\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_44465/580365679.py:47: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  seg_foreground_comp = np.array(1 - seg_background_comp - seg_boundary_comp>0, dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "# feed the raw img to the model\n",
    "raw_img_size=raw_img.shape\n",
    "    \n",
    "seg_background_comp = np.zeros(raw_img_size)\n",
    "seg_boundary_comp = np.zeros(raw_img_size)\n",
    "\n",
    "transposes = [[0,1,2]]#,[2,0,1],[0,2,1]]\n",
    "reverse_transposes = [[0,1,2]]#,[1,2,0],[0,2,1]]\n",
    "\n",
    "for idx, transpose in enumerate(transposes):\n",
    "    print(str(idx+1)+\": Transpose the image to be: \"+str(transpose))\n",
    "    with torch.no_grad():\n",
    "        seg_img=\\\n",
    "        semantic_segment_crop_and_cat_3_channel_output(raw_img.transpose(transpose), model, device, crop_cube_size=crop_cube_size, stride=stride)\n",
    "    seg_img_background=seg_img['background']\n",
    "    seg_img_boundary=seg_img['boundary']\n",
    "    seg_img_foreground=seg_img['foreground']\n",
    "    torch.cuda.empty_cache()\n",
    "    \n",
    "    # argmax\n",
    "    print('argmax', end='\\r')\n",
    "    seg=[]\n",
    "    seg.append(seg_img_background)\n",
    "    seg.append(seg_img_boundary)\n",
    "    seg.append(seg_img_foreground)\n",
    "    seg=np.array(seg)\n",
    "    seg_argmax=np.argmax(seg, axis=0)\n",
    "    # probability map to 0 1 segment\n",
    "    seg_background=np.zeros(seg_img_background.shape)\n",
    "    seg_background[np.where(seg_argmax==0)]=1\n",
    "    seg_foreground=np.zeros(seg_img_foreground.shape)\n",
    "    seg_foreground[np.where(seg_argmax==2)]=1\n",
    "    seg_boundary=np.zeros(seg_img_boundary.shape)\n",
    "    seg_boundary[np.where(seg_argmax==1)]=1\n",
    "        \n",
    "    seg_background=seg_background.transpose(reverse_transposes[idx])\n",
    "    seg_foreground=seg_foreground.transpose(reverse_transposes[idx])\n",
    "    seg_boundary=seg_boundary.transpose(reverse_transposes[idx])\n",
    "        \n",
    "    seg_background_comp+=seg_background\n",
    "    seg_boundary_comp+=seg_boundary\n",
    "print(\"Get model semantic seg by combination\")\n",
    "seg_background_comp = np.array(seg_background_comp>0, dtype=np.int)\n",
    "seg_boundary_comp = np.array(seg_boundary_comp>0, dtype=np.int)\n",
    "seg_foreground_comp = np.array(1 - seg_background_comp - seg_boundary_comp>0, dtype=np.int)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "print(\"Time elapsed: \", end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reassign unique numbers progress: 0.9954337899543378\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f19080e4940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show current result\n",
    "\n",
    "N=50\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"raw_img\")\n",
    "plt.imshow(raw_img[N,:,:])\n",
    "plt.figure()\n",
    "plt.title(\"hand_seg\")\n",
    "plt.imshow(reassign(hand_seg[N,:,:]))\n",
    "plt.figure()\n",
    "plt.title(\"model_seg_foreground\")\n",
    "plt.imshow(seg_foreground_comp[N,:,:])\n",
    "plt.figure()\n",
    "plt.title(\"model_seg_boundary\")\n",
    "plt.imshow(seg_boundary_comp[N,:,:])\n",
    "plt.figure()\n",
    "plt.title(\"model_seg_background\")\n",
    "plt.imshow(seg_background_comp[N,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TASCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "generate super vox by watershed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate super vox by watershed\n",
    "how_close_are_the_super_vox_to_boundary=1\n",
    "min_touching_percentage=0.51\n",
    "\n",
    "seg_foreground_erosion=1-img_3d_erosion_or_expansion(1-seg_foreground_comp, kernel_size=how_close_are_the_super_vox_to_boundary+1, device=device)\n",
    "seg_foreground_super_voxel_by_ws = generate_super_vox_by_watershed(seg_foreground_erosion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1326 super voxels\n"
     ]
    }
   ],
   "source": [
    "print(\"There are \"+str(len(np.unique(seg_foreground_super_voxel_by_ws)))+\" super voxels\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "super voxel clustearing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get val_outlayer area of all vals: 0.0\r",
      "get val_outlayer area of all vals: 0.0007547169811320754\r",
      "get val_outlayer area of all vals: 0.0015094339622641509\r",
      "get val_outlayer area of all vals: 0.0022641509433962265\r",
      "get val_outlayer area of all vals: 0.0030188679245283017\r",
      "get val_outlayer area of all vals: 0.0037735849056603774\r",
      "get val_outlayer area of all vals: 0.004528301886792453\r",
      "get val_outlayer area of all vals: 0.005283018867924529\r",
      "get val_outlayer area of all vals: 0.0060377358490566035\r",
      "get val_outlayer area of all vals: 0.006792452830188679\r",
      "get val_outlayer area of all vals: 0.007547169811320755\r",
      "get val_outlayer area of all vals: 0.00830188679245283\r",
      "get val_outlayer area of all vals: 0.009056603773584906\r",
      "get val_outlayer area of all vals: 0.009811320754716982\r",
      "get val_outlayer area of all vals: 0.010566037735849057\r",
      "get val_outlayer area of all vals: 0.011320754716981131\r",
      "get val_outlayer area of all vals: 0.012075471698113207\r",
      "get val_outlayer area of all vals: 0.012830188679245283\r",
      "get val_outlayer area of all vals: 0.013584905660377358\r",
      "get val_outlayer area of all vals: 0.014339622641509434\r",
      "get val_outlayer area of all vals: 0.01509433962264151\r",
      "get val_outlayer area of all vals: 0.015849056603773583\r",
      "get val_outlayer area of all vals: 0.01660377358490566\r",
      "get val_outlayer area of all vals: 0.017358490566037735\r",
      "get val_outlayer area of all vals: 0.018113207547169812\r",
      "get val_outlayer area of all vals: 0.018867924528301886\r",
      "get val_outlayer area of all vals: 0.019622641509433963\r",
      "get val_outlayer area of all vals: 0.020377358490566037\r",
      "get val_outlayer area of all vals: 0.021132075471698115\r",
      "get val_outlayer area of all vals: 0.02188679245283019\r",
      "get val_outlayer area of all vals: 0.022641509433962263\r",
      "get val_outlayer area of all vals: 0.02339622641509434\r",
      "get val_outlayer area of all vals: 0.024150943396226414\r",
      "get val_outlayer area of all vals: 0.02490566037735849\r",
      "get val_outlayer area of all vals: 0.025660377358490565\r",
      "get val_outlayer area of all vals: 0.026415094339622643\r",
      "get val_outlayer area of all vals: 0.027169811320754716\r",
      "get val_outlayer area of all vals: 0.027924528301886794\r",
      "get val_outlayer area of all vals: 0.028679245283018868\r",
      "get val_outlayer area of all vals: 0.02943396226415094\r",
      "get val_outlayer area of all vals: 0.03018867924528302\r",
      "get val_outlayer area of all vals: 0.030943396226415093\r",
      "get val_outlayer area of all vals: 0.03169811320754717\r",
      "get val_outlayer area of all vals: 0.03245283018867925\r",
      "get val_outlayer area of all vals: 0.03320754716981132\r",
      "get val_outlayer area of all vals: 0.033962264150943396\r",
      "get val_outlayer area of all vals: 0.03471698113207547\r",
      "get val_outlayer area of all vals: 0.03547169811320755\r",
      "get val_outlayer area of all vals: 0.036226415094339624\r",
      "get val_outlayer area of all vals: 0.0369811320754717\r",
      "get val_outlayer area of all vals: 0.03773584905660377\r",
      "get val_outlayer area of all vals: 0.038490566037735846\r",
      "get val_outlayer area of all vals: 0.03924528301886793\r",
      "get val_outlayer area of all vals: 0.04\r",
      "get val_outlayer area of all vals: 0.040754716981132075\r",
      "get val_outlayer area of all vals: 0.04150943396226415\r",
      "get val_outlayer area of all vals: 0.04226415094339623\r",
      "get val_outlayer area of all vals: 0.0430188679245283\r",
      "get val_outlayer area of all vals: 0.04377358490566038\r",
      "get val_outlayer area of all vals: 0.04452830188679245\r",
      "get val_outlayer area of all vals: 0.045283018867924525\r",
      "get val_outlayer area of all vals: 0.046037735849056606\r",
      "get val_outlayer area of all vals: 0.04679245283018868\r",
      "get val_outlayer area of all vals: 0.047547169811320754\r",
      "get val_outlayer area of all vals: 0.04830188679245283\r",
      "get val_outlayer area of all vals: 0.04905660377358491\r",
      "get val_outlayer area of all vals: 0.04981132075471698\r",
      "get val_outlayer area of all vals: 0.050566037735849056\r",
      "get val_outlayer area of all vals: 0.05132075471698113\r",
      "get val_outlayer area of all vals: 0.052075471698113204\r",
      "get val_outlayer area of all vals: 0.052830188679245285\r",
      "get val_outlayer area of all vals: 0.05358490566037736\r",
      "get val_outlayer area of all vals: 0.05433962264150943\r",
      "get val_outlayer area of all vals: 0.05509433962264151\r",
      "get val_outlayer area of all vals: 0.05584905660377359\r",
      "get val_outlayer area of all vals: 0.05660377358490566\r",
      "get val_outlayer area of all vals: 0.057358490566037736\r",
      "get val_outlayer area of all vals: 0.05811320754716981\r",
      "get val_outlayer area of all vals: 0.05886792452830188\r",
      "get val_outlayer area of all vals: 0.059622641509433964\r",
      "get val_outlayer area of all vals: 0.06037735849056604\r",
      "get val_outlayer area of all vals: 0.06113207547169811\r",
      "get val_outlayer area of all vals: 0.061886792452830186\r",
      "get val_outlayer area of all vals: 0.06264150943396227\r",
      "get val_outlayer area of all vals: 0.06339622641509433\r",
      "get val_outlayer area of all vals: 0.06415094339622641\r",
      "get val_outlayer area of all vals: 0.0649056603773585\r",
      "get val_outlayer area of all vals: 0.06566037735849056\r",
      "get val_outlayer area of all vals: 0.06641509433962264\r",
      "get val_outlayer area of all vals: 0.06716981132075471\r",
      "get val_outlayer area of all vals: 0.06792452830188679\r",
      "get val_outlayer area of all vals: 0.06867924528301887\r",
      "get val_outlayer area of all vals: 0.06943396226415094\r",
      "get val_outlayer area of all vals: 0.07018867924528302\r",
      "get val_outlayer area of all vals: 0.0709433962264151\r",
      "get val_outlayer area of all vals: 0.07169811320754717\r",
      "get val_outlayer area of all vals: 0.07245283018867925\r",
      "get val_outlayer area of all vals: 0.07320754716981132\r",
      "get val_outlayer area of all vals: 0.0739622641509434\r",
      "get val_outlayer area of all vals: 0.07471698113207548\r",
      "get val_outlayer area of all vals: 0.07547169811320754\r",
      "get val_outlayer area of all vals: 0.07622641509433963\r",
      "get val_outlayer area of all vals: 0.07698113207547169\r",
      "get val_outlayer area of all vals: 0.07773584905660377\r",
      "get val_outlayer area of all vals: 0.07849056603773585\r",
      "get val_outlayer area of all vals: 0.07924528301886792\r",
      "get val_outlayer area of all vals: 0.08\r",
      "get val_outlayer area of all vals: 0.08075471698113207\r",
      "get val_outlayer area of all vals: 0.08150943396226415\r",
      "get val_outlayer area of all vals: 0.08226415094339623\r",
      "get val_outlayer area of all vals: 0.0830188679245283\r",
      "get val_outlayer area of all vals: 0.08377358490566038\r",
      "get val_outlayer area of all vals: 0.08452830188679246\r",
      "get val_outlayer area of all vals: 0.08528301886792453\r",
      "get val_outlayer area of all vals: 0.0860377358490566\r",
      "get val_outlayer area of all vals: 0.08679245283018867\r",
      "get val_outlayer area of all vals: 0.08754716981132075\r",
      "get val_outlayer area of all vals: 0.08830188679245284\r",
      "get val_outlayer area of all vals: 0.0890566037735849\r",
      "get val_outlayer area of all vals: 0.08981132075471698\r",
      "get val_outlayer area of all vals: 0.09056603773584905\r",
      "get val_outlayer area of all vals: 0.09132075471698113\r",
      "get val_outlayer area of all vals: 0.09207547169811321\r",
      "get val_outlayer area of all vals: 0.09283018867924528\r",
      "get val_outlayer area of all vals: 0.09358490566037736\r",
      "get val_outlayer area of all vals: 0.09433962264150944\r",
      "get val_outlayer area of all vals: 0.09509433962264151\r",
      "get val_outlayer area of all vals: 0.09584905660377359\r",
      "get val_outlayer area of all vals: 0.09660377358490566\r",
      "get val_outlayer area of all vals: 0.09735849056603774\r",
      "get val_outlayer area of all vals: 0.09811320754716982\r",
      "get val_outlayer area of all vals: 0.09886792452830188\r",
      "get val_outlayer area of all vals: 0.09962264150943397\r",
      "get val_outlayer area of all vals: 0.10037735849056603\r",
      "get val_outlayer area of all vals: 0.10113207547169811\r",
      "get val_outlayer area of all vals: 0.1018867924528302\r",
      "get val_outlayer area of all vals: 0.10264150943396226\r",
      "get val_outlayer area of all vals: 0.10339622641509434\r",
      "get val_outlayer area of all vals: 0.10415094339622641\r",
      "get val_outlayer area of all vals: 0.10490566037735849\r",
      "get val_outlayer area of all vals: 0.10566037735849057\r",
      "get val_outlayer area of all vals: 0.10641509433962264\r",
      "get val_outlayer area of all vals: 0.10716981132075472\r",
      "get val_outlayer area of all vals: 0.1079245283018868\r",
      "get val_outlayer area of all vals: 0.10867924528301887\r",
      "get val_outlayer area of all vals: 0.10943396226415095\r",
      "get val_outlayer area of all vals: 0.11018867924528301\r",
      "get val_outlayer area of all vals: 0.1109433962264151\r",
      "get val_outlayer area of all vals: 0.11169811320754718\r",
      "get val_outlayer area of all vals: 0.11245283018867924\r",
      "get val_outlayer area of all vals: 0.11320754716981132\r",
      "get val_outlayer area of all vals: 0.11396226415094339\r",
      "get val_outlayer area of all vals: 0.11471698113207547\r",
      "get val_outlayer area of all vals: 0.11547169811320755\r",
      "get val_outlayer area of all vals: 0.11622641509433962\r",
      "get val_outlayer area of all vals: 0.1169811320754717\r",
      "get val_outlayer area of all vals: 0.11773584905660377\r",
      "get val_outlayer area of all vals: 0.11849056603773585\r",
      "get val_outlayer area of all vals: 0.11924528301886793\r",
      "get val_outlayer area of all vals: 0.12\r",
      "get val_outlayer area of all vals: 0.12075471698113208\r",
      "get val_outlayer area of all vals: 0.12150943396226416\r",
      "get val_outlayer area of all vals: 0.12226415094339622\r",
      "get val_outlayer area of all vals: 0.1230188679245283\r",
      "get val_outlayer area of all vals: 0.12377358490566037\r",
      "get val_outlayer area of all vals: 0.12452830188679245\r",
      "get val_outlayer area of all vals: 0.12528301886792453\r",
      "get val_outlayer area of all vals: 0.12603773584905661\r",
      "get val_outlayer area of all vals: 0.12679245283018867\r",
      "get val_outlayer area of all vals: 0.12754716981132075\r",
      "get val_outlayer area of all vals: 0.12830188679245283\r",
      "get val_outlayer area of all vals: 0.1290566037735849\r",
      "get val_outlayer area of all vals: 0.129811320754717\r",
      "get val_outlayer area of all vals: 0.13056603773584904\r",
      "get val_outlayer area of all vals: 0.13132075471698113\r",
      "get val_outlayer area of all vals: 0.1320754716981132\r",
      "get val_outlayer area of all vals: 0.1328301886792453\r",
      "get val_outlayer area of all vals: 0.13358490566037737\r",
      "get val_outlayer area of all vals: 0.13433962264150942\r",
      "get val_outlayer area of all vals: 0.1350943396226415\r",
      "get val_outlayer area of all vals: 0.13584905660377358\r",
      "get val_outlayer area of all vals: 0.13660377358490566\r",
      "get val_outlayer area of all vals: 0.13735849056603774\r",
      "get val_outlayer area of all vals: 0.13811320754716983\r",
      "get val_outlayer area of all vals: 0.13886792452830188\r",
      "get val_outlayer area of all vals: 0.13962264150943396\r",
      "get val_outlayer area of all vals: 0.14037735849056604\r",
      "get val_outlayer area of all vals: 0.14113207547169812\r",
      "get val_outlayer area of all vals: 0.1418867924528302\r",
      "get val_outlayer area of all vals: 0.14264150943396225\r",
      "get val_outlayer area of all vals: 0.14339622641509434\r",
      "get val_outlayer area of all vals: 0.14415094339622642\r",
      "get val_outlayer area of all vals: 0.1449056603773585\r",
      "get val_outlayer area of all vals: 0.14566037735849058\r",
      "get val_outlayer area of all vals: 0.14641509433962263\r",
      "get val_outlayer area of all vals: 0.1471698113207547\r",
      "get val_outlayer area of all vals: 0.1479245283018868\r",
      "get val_outlayer area of all vals: 0.14867924528301887\r",
      "get val_outlayer area of all vals: 0.14943396226415095\r",
      "get val_outlayer area of all vals: 0.150188679245283\r",
      "get val_outlayer area of all vals: 0.1509433962264151\r",
      "get val_outlayer area of all vals: 0.15169811320754717\r",
      "get val_outlayer area of all vals: 0.15245283018867925\r",
      "get val_outlayer area of all vals: 0.15320754716981133\r",
      "get val_outlayer area of all vals: 0.15396226415094338\r",
      "get val_outlayer area of all vals: 0.15471698113207547\r",
      "get val_outlayer area of all vals: 0.15547169811320755\r",
      "get val_outlayer area of all vals: 0.15622641509433963\r",
      "get val_outlayer area of all vals: 0.1569811320754717\r",
      "get val_outlayer area of all vals: 0.15773584905660376\r",
      "get val_outlayer area of all vals: 0.15849056603773584\r",
      "get val_outlayer area of all vals: 0.15924528301886792\r",
      "get val_outlayer area of all vals: 0.16\r",
      "get val_outlayer area of all vals: 0.16075471698113208\r",
      "get val_outlayer area of all vals: 0.16150943396226414\r",
      "get val_outlayer area of all vals: 0.16226415094339622\r",
      "get val_outlayer area of all vals: 0.1630188679245283\r",
      "get val_outlayer area of all vals: 0.16377358490566038\r",
      "get val_outlayer area of all vals: 0.16452830188679246\r",
      "get val_outlayer area of all vals: 0.16528301886792454\r",
      "get val_outlayer area of all vals: 0.1660377358490566\r",
      "get val_outlayer area of all vals: 0.16679245283018868\r",
      "get val_outlayer area of all vals: 0.16754716981132076\r",
      "get val_outlayer area of all vals: 0.16830188679245284\r",
      "get val_outlayer area of all vals: 0.16905660377358492\r",
      "get val_outlayer area of all vals: 0.16981132075471697\r",
      "get val_outlayer area of all vals: 0.17056603773584905\r",
      "get val_outlayer area of all vals: 0.17132075471698113\r",
      "get val_outlayer area of all vals: 0.1720754716981132\r",
      "get val_outlayer area of all vals: 0.1728301886792453\r",
      "get val_outlayer area of all vals: 0.17358490566037735\r",
      "get val_outlayer area of all vals: 0.17433962264150943\r",
      "get val_outlayer area of all vals: 0.1750943396226415\r",
      "get val_outlayer area of all vals: 0.1758490566037736\r",
      "get val_outlayer area of all vals: 0.17660377358490567\r",
      "get val_outlayer area of all vals: 0.17735849056603772\r",
      "get val_outlayer area of all vals: 0.1781132075471698\r",
      "get val_outlayer area of all vals: 0.17886792452830189\r",
      "get val_outlayer area of all vals: 0.17962264150943397\r",
      "get val_outlayer area of all vals: 0.18037735849056605\r",
      "get val_outlayer area of all vals: 0.1811320754716981\r",
      "get val_outlayer area of all vals: 0.18188679245283018\r",
      "get val_outlayer area of all vals: 0.18264150943396226\r",
      "get val_outlayer area of all vals: 0.18339622641509434\r",
      "get val_outlayer area of all vals: 0.18415094339622642\r",
      "get val_outlayer area of all vals: 0.18490566037735848\r",
      "get val_outlayer area of all vals: 0.18566037735849056\r",
      "get val_outlayer area of all vals: 0.18641509433962264\r",
      "get val_outlayer area of all vals: 0.18716981132075472\r",
      "get val_outlayer area of all vals: 0.1879245283018868\r",
      "get val_outlayer area of all vals: 0.18867924528301888\r",
      "get val_outlayer area of all vals: 0.18943396226415093\r",
      "get val_outlayer area of all vals: 0.19018867924528302\r",
      "get val_outlayer area of all vals: 0.1909433962264151\r",
      "get val_outlayer area of all vals: 0.19169811320754718\r",
      "get val_outlayer area of all vals: 0.19245283018867926\r",
      "get val_outlayer area of all vals: 0.1932075471698113\r",
      "get val_outlayer area of all vals: 0.1939622641509434\r",
      "get val_outlayer area of all vals: 0.19471698113207547\r",
      "get val_outlayer area of all vals: 0.19547169811320755\r",
      "get val_outlayer area of all vals: 0.19622641509433963\r",
      "get val_outlayer area of all vals: 0.1969811320754717\r",
      "get val_outlayer area of all vals: 0.19773584905660377\r",
      "get val_outlayer area of all vals: 0.19849056603773585\r",
      "get val_outlayer area of all vals: 0.19924528301886793\r",
      "get val_outlayer area of all vals: 0.2\r",
      "get val_outlayer area of all vals: 0.20075471698113206\r",
      "get val_outlayer area of all vals: 0.20150943396226415\r",
      "get val_outlayer area of all vals: 0.20226415094339623\r",
      "get val_outlayer area of all vals: 0.2030188679245283\r",
      "get val_outlayer area of all vals: 0.2037735849056604\r",
      "get val_outlayer area of all vals: 0.20452830188679244\r",
      "get val_outlayer area of all vals: 0.20528301886792452\r",
      "get val_outlayer area of all vals: 0.2060377358490566\r",
      "get val_outlayer area of all vals: 0.20679245283018868\r",
      "get val_outlayer area of all vals: 0.20754716981132076\r",
      "get val_outlayer area of all vals: 0.20830188679245282\r",
      "get val_outlayer area of all vals: 0.2090566037735849\r",
      "get val_outlayer area of all vals: 0.20981132075471698\r",
      "get val_outlayer area of all vals: 0.21056603773584906\r",
      "get val_outlayer area of all vals: 0.21132075471698114\r",
      "get val_outlayer area of all vals: 0.2120754716981132\r",
      "get val_outlayer area of all vals: 0.21283018867924527\r",
      "get val_outlayer area of all vals: 0.21358490566037736\r",
      "get val_outlayer area of all vals: 0.21433962264150944\r",
      "get val_outlayer area of all vals: 0.21509433962264152\r",
      "get val_outlayer area of all vals: 0.2158490566037736\r",
      "get val_outlayer area of all vals: 0.21660377358490565\r",
      "get val_outlayer area of all vals: 0.21735849056603773\r",
      "get val_outlayer area of all vals: 0.2181132075471698\r",
      "get val_outlayer area of all vals: 0.2188679245283019\r",
      "get val_outlayer area of all vals: 0.21962264150943397\r",
      "get val_outlayer area of all vals: 0.22037735849056603\r",
      "get val_outlayer area of all vals: 0.2211320754716981\r",
      "get val_outlayer area of all vals: 0.2218867924528302\r",
      "get val_outlayer area of all vals: 0.22264150943396227\r",
      "get val_outlayer area of all vals: 0.22339622641509435\r",
      "get val_outlayer area of all vals: 0.2241509433962264\r",
      "get val_outlayer area of all vals: 0.22490566037735849\r",
      "get val_outlayer area of all vals: 0.22566037735849057\r",
      "get val_outlayer area of all vals: 0.22641509433962265\r",
      "get val_outlayer area of all vals: 0.22716981132075473\r",
      "get val_outlayer area of all vals: 0.22792452830188678\r",
      "get val_outlayer area of all vals: 0.22867924528301886\r",
      "get val_outlayer area of all vals: 0.22943396226415094\r",
      "get val_outlayer area of all vals: 0.23018867924528302\r",
      "get val_outlayer area of all vals: 0.2309433962264151\r",
      "get val_outlayer area of all vals: 0.23169811320754716\r",
      "get val_outlayer area of all vals: 0.23245283018867924\r",
      "get val_outlayer area of all vals: 0.23320754716981132\r",
      "get val_outlayer area of all vals: 0.2339622641509434\r",
      "get val_outlayer area of all vals: 0.23471698113207548\r",
      "get val_outlayer area of all vals: 0.23547169811320753\r",
      "get val_outlayer area of all vals: 0.23622641509433961\r",
      "get val_outlayer area of all vals: 0.2369811320754717\r",
      "get val_outlayer area of all vals: 0.23773584905660378\r",
      "get val_outlayer area of all vals: 0.23849056603773586\r",
      "get val_outlayer area of all vals: 0.23924528301886794\r",
      "get val_outlayer area of all vals: 0.24\r",
      "get val_outlayer area of all vals: 0.24075471698113207\r",
      "get val_outlayer area of all vals: 0.24150943396226415\r",
      "get val_outlayer area of all vals: 0.24226415094339623\r",
      "get val_outlayer area of all vals: 0.24301886792452831\r",
      "get val_outlayer area of all vals: 0.24377358490566037\r",
      "get val_outlayer area of all vals: 0.24452830188679245\r",
      "get val_outlayer area of all vals: 0.24528301886792453\r",
      "get val_outlayer area of all vals: 0.2460377358490566\r",
      "get val_outlayer area of all vals: 0.2467924528301887\r",
      "get val_outlayer area of all vals: 0.24754716981132074\r",
      "get val_outlayer area of all vals: 0.24830188679245283\r",
      "get val_outlayer area of all vals: 0.2490566037735849\r",
      "get val_outlayer area of all vals: 0.249811320754717\r",
      "get val_outlayer area of all vals: 0.25056603773584907\r",
      "get val_outlayer area of all vals: 0.2513207547169811\r",
      "get val_outlayer area of all vals: 0.25207547169811323\r",
      "get val_outlayer area of all vals: 0.2528301886792453\r",
      "get val_outlayer area of all vals: 0.25358490566037734\r",
      "get val_outlayer area of all vals: 0.25433962264150944\r",
      "get val_outlayer area of all vals: 0.2550943396226415\r",
      "get val_outlayer area of all vals: 0.2558490566037736\r",
      "get val_outlayer area of all vals: 0.25660377358490566\r",
      "get val_outlayer area of all vals: 0.2573584905660377\r",
      "get val_outlayer area of all vals: 0.2581132075471698\r",
      "get val_outlayer area of all vals: 0.2588679245283019\r",
      "get val_outlayer area of all vals: 0.259622641509434\r",
      "get val_outlayer area of all vals: 0.26037735849056604\r",
      "get val_outlayer area of all vals: 0.2611320754716981\r",
      "get val_outlayer area of all vals: 0.2618867924528302\r",
      "get val_outlayer area of all vals: 0.26264150943396225\r",
      "get val_outlayer area of all vals: 0.26339622641509436\r",
      "get val_outlayer area of all vals: 0.2641509433962264\r",
      "get val_outlayer area of all vals: 0.26490566037735847\r",
      "get val_outlayer area of all vals: 0.2656603773584906\r",
      "get val_outlayer area of all vals: 0.2664150943396226\r",
      "get val_outlayer area of all vals: 0.26716981132075474\r",
      "get val_outlayer area of all vals: 0.2679245283018868\r",
      "get val_outlayer area of all vals: 0.26867924528301884\r",
      "get val_outlayer area of all vals: 0.26943396226415095\r",
      "get val_outlayer area of all vals: 0.270188679245283\r",
      "get val_outlayer area of all vals: 0.2709433962264151\r",
      "get val_outlayer area of all vals: 0.27169811320754716\r",
      "get val_outlayer area of all vals: 0.2724528301886792\r",
      "get val_outlayer area of all vals: 0.2732075471698113\r",
      "get val_outlayer area of all vals: 0.2739622641509434\r",
      "get val_outlayer area of all vals: 0.2747169811320755\r",
      "get val_outlayer area of all vals: 0.27547169811320754\r",
      "get val_outlayer area of all vals: 0.27622641509433965\r",
      "get val_outlayer area of all vals: 0.2769811320754717\r",
      "get val_outlayer area of all vals: 0.27773584905660376\r",
      "get val_outlayer area of all vals: 0.27849056603773586\r",
      "get val_outlayer area of all vals: 0.2792452830188679\r",
      "get val_outlayer area of all vals: 0.28\r",
      "get val_outlayer area of all vals: 0.2807547169811321\r",
      "get val_outlayer area of all vals: 0.28150943396226413\r",
      "get val_outlayer area of all vals: 0.28226415094339624\r",
      "get val_outlayer area of all vals: 0.2830188679245283\r",
      "get val_outlayer area of all vals: 0.2837735849056604\r",
      "get val_outlayer area of all vals: 0.28452830188679246\r",
      "get val_outlayer area of all vals: 0.2852830188679245\r",
      "get val_outlayer area of all vals: 0.2860377358490566\r",
      "get val_outlayer area of all vals: 0.28679245283018867\r",
      "get val_outlayer area of all vals: 0.2875471698113208\r",
      "get val_outlayer area of all vals: 0.28830188679245283\r",
      "get val_outlayer area of all vals: 0.2890566037735849\r",
      "get val_outlayer area of all vals: 0.289811320754717\r",
      "get val_outlayer area of all vals: 0.29056603773584905\r",
      "get val_outlayer area of all vals: 0.29132075471698116\r",
      "get val_outlayer area of all vals: 0.2920754716981132\r",
      "get val_outlayer area of all vals: 0.29283018867924526\r",
      "get val_outlayer area of all vals: 0.29358490566037737\r",
      "get val_outlayer area of all vals: 0.2943396226415094\r",
      "get val_outlayer area of all vals: 0.29509433962264153\r",
      "get val_outlayer area of all vals: 0.2958490566037736\r",
      "get val_outlayer area of all vals: 0.29660377358490564\r",
      "get val_outlayer area of all vals: 0.29735849056603775\r",
      "get val_outlayer area of all vals: 0.2981132075471698\r",
      "get val_outlayer area of all vals: 0.2988679245283019\r",
      "get val_outlayer area of all vals: 0.29962264150943396\r",
      "get val_outlayer area of all vals: 0.300377358490566\r",
      "get val_outlayer area of all vals: 0.3011320754716981\r",
      "get val_outlayer area of all vals: 0.3018867924528302\r",
      "get val_outlayer area of all vals: 0.3026415094339623\r",
      "get val_outlayer area of all vals: 0.30339622641509434\r",
      "get val_outlayer area of all vals: 0.3041509433962264\r",
      "get val_outlayer area of all vals: 0.3049056603773585\r",
      "get val_outlayer area of all vals: 0.30566037735849055\r",
      "get val_outlayer area of all vals: 0.30641509433962266\r",
      "get val_outlayer area of all vals: 0.3071698113207547\r",
      "get val_outlayer area of all vals: 0.30792452830188677\r",
      "get val_outlayer area of all vals: 0.3086792452830189\r",
      "get val_outlayer area of all vals: 0.30943396226415093\r",
      "get val_outlayer area of all vals: 0.31018867924528304\r",
      "get val_outlayer area of all vals: 0.3109433962264151\r",
      "get val_outlayer area of all vals: 0.31169811320754715\r",
      "get val_outlayer area of all vals: 0.31245283018867925\r",
      "get val_outlayer area of all vals: 0.3132075471698113\r",
      "get val_outlayer area of all vals: 0.3139622641509434\r",
      "get val_outlayer area of all vals: 0.31471698113207547\r",
      "get val_outlayer area of all vals: 0.3154716981132075\r",
      "get val_outlayer area of all vals: 0.31622641509433963\r",
      "get val_outlayer area of all vals: 0.3169811320754717\r",
      "get val_outlayer area of all vals: 0.3177358490566038\r",
      "get val_outlayer area of all vals: 0.31849056603773584\r",
      "get val_outlayer area of all vals: 0.3192452830188679\r",
      "get val_outlayer area of all vals: 0.32\r",
      "get val_outlayer area of all vals: 0.32075471698113206\r",
      "get val_outlayer area of all vals: 0.32150943396226417\r",
      "get val_outlayer area of all vals: 0.3222641509433962\r",
      "get val_outlayer area of all vals: 0.3230188679245283\r",
      "get val_outlayer area of all vals: 0.3237735849056604\r",
      "get val_outlayer area of all vals: 0.32452830188679244\r",
      "get val_outlayer area of all vals: 0.32528301886792454\r",
      "get val_outlayer area of all vals: 0.3260377358490566\r",
      "get val_outlayer area of all vals: 0.3267924528301887\r",
      "get val_outlayer area of all vals: 0.32754716981132076\r",
      "get val_outlayer area of all vals: 0.3283018867924528\r",
      "get val_outlayer area of all vals: 0.3290566037735849\r",
      "get val_outlayer area of all vals: 0.329811320754717\r",
      "get val_outlayer area of all vals: 0.3305660377358491\r",
      "get val_outlayer area of all vals: 0.33132075471698114\r",
      "get val_outlayer area of all vals: 0.3320754716981132\r",
      "get val_outlayer area of all vals: 0.3328301886792453\r",
      "get val_outlayer area of all vals: 0.33358490566037735\r",
      "get val_outlayer area of all vals: 0.33433962264150946\r",
      "get val_outlayer area of all vals: 0.3350943396226415\r",
      "get val_outlayer area of all vals: 0.33584905660377357\r",
      "get val_outlayer area of all vals: 0.3366037735849057\r",
      "get val_outlayer area of all vals: 0.3373584905660377\r",
      "get val_outlayer area of all vals: 0.33811320754716984\r",
      "get val_outlayer area of all vals: 0.3388679245283019\r",
      "get val_outlayer area of all vals: 0.33962264150943394\r",
      "get val_outlayer area of all vals: 0.34037735849056605\r",
      "get val_outlayer area of all vals: 0.3411320754716981\r",
      "get val_outlayer area of all vals: 0.3418867924528302\r",
      "get val_outlayer area of all vals: 0.34264150943396227\r",
      "get val_outlayer area of all vals: 0.3433962264150943\r",
      "get val_outlayer area of all vals: 0.3441509433962264\r",
      "get val_outlayer area of all vals: 0.3449056603773585\r",
      "get val_outlayer area of all vals: 0.3456603773584906\r",
      "get val_outlayer area of all vals: 0.34641509433962264\r",
      "get val_outlayer area of all vals: 0.3471698113207547\r",
      "get val_outlayer area of all vals: 0.3479245283018868\r",
      "get val_outlayer area of all vals: 0.34867924528301886\r",
      "get val_outlayer area of all vals: 0.34943396226415097\r",
      "get val_outlayer area of all vals: 0.350188679245283\r",
      "get val_outlayer area of all vals: 0.35094339622641507\r",
      "get val_outlayer area of all vals: 0.3516981132075472\r",
      "get val_outlayer area of all vals: 0.35245283018867923\r",
      "get val_outlayer area of all vals: 0.35320754716981134\r",
      "get val_outlayer area of all vals: 0.3539622641509434\r",
      "get val_outlayer area of all vals: 0.35471698113207545\r",
      "get val_outlayer area of all vals: 0.35547169811320756\r",
      "get val_outlayer area of all vals: 0.3562264150943396\r",
      "get val_outlayer area of all vals: 0.3569811320754717\r",
      "get val_outlayer area of all vals: 0.35773584905660377\r",
      "get val_outlayer area of all vals: 0.3584905660377358\r",
      "get val_outlayer area of all vals: 0.35924528301886793\r",
      "get val_outlayer area of all vals: 0.36\r",
      "get val_outlayer area of all vals: 0.3607547169811321\r",
      "get val_outlayer area of all vals: 0.36150943396226415\r",
      "get val_outlayer area of all vals: 0.3622641509433962\r",
      "get val_outlayer area of all vals: 0.3630188679245283\r",
      "get val_outlayer area of all vals: 0.36377358490566036\r",
      "get val_outlayer area of all vals: 0.36452830188679247\r",
      "get val_outlayer area of all vals: 0.3652830188679245\r",
      "get val_outlayer area of all vals: 0.3660377358490566\r",
      "get val_outlayer area of all vals: 0.3667924528301887\r",
      "get val_outlayer area of all vals: 0.36754716981132074\r",
      "get val_outlayer area of all vals: 0.36830188679245285\r",
      "get val_outlayer area of all vals: 0.3690566037735849\r",
      "get val_outlayer area of all vals: 0.36981132075471695\r",
      "get val_outlayer area of all vals: 0.37056603773584906\r",
      "get val_outlayer area of all vals: 0.3713207547169811\r",
      "get val_outlayer area of all vals: 0.3720754716981132\r",
      "get val_outlayer area of all vals: 0.3728301886792453\r",
      "get val_outlayer area of all vals: 0.37358490566037733\r",
      "get val_outlayer area of all vals: 0.37433962264150944\r",
      "get val_outlayer area of all vals: 0.3750943396226415\r",
      "get val_outlayer area of all vals: 0.3758490566037736\r",
      "get val_outlayer area of all vals: 0.37660377358490565\r",
      "get val_outlayer area of all vals: 0.37735849056603776\r",
      "get val_outlayer area of all vals: 0.3781132075471698\r",
      "get val_outlayer area of all vals: 0.37886792452830187\r",
      "get val_outlayer area of all vals: 0.379622641509434\r",
      "get val_outlayer area of all vals: 0.38037735849056603\r",
      "get val_outlayer area of all vals: 0.38113207547169814\r",
      "get val_outlayer area of all vals: 0.3818867924528302\r",
      "get val_outlayer area of all vals: 0.38264150943396225\r",
      "get val_outlayer area of all vals: 0.38339622641509435\r",
      "get val_outlayer area of all vals: 0.3841509433962264\r",
      "get val_outlayer area of all vals: 0.3849056603773585\r",
      "get val_outlayer area of all vals: 0.38566037735849057\r",
      "get val_outlayer area of all vals: 0.3864150943396226\r",
      "get val_outlayer area of all vals: 0.38716981132075473\r",
      "get val_outlayer area of all vals: 0.3879245283018868\r",
      "get val_outlayer area of all vals: 0.3886792452830189\r",
      "get val_outlayer area of all vals: 0.38943396226415095\r",
      "get val_outlayer area of all vals: 0.390188679245283\r",
      "get val_outlayer area of all vals: 0.3909433962264151\r",
      "get val_outlayer area of all vals: 0.39169811320754716\r",
      "get val_outlayer area of all vals: 0.39245283018867927\r",
      "get val_outlayer area of all vals: 0.3932075471698113\r",
      "get val_outlayer area of all vals: 0.3939622641509434\r",
      "get val_outlayer area of all vals: 0.3947169811320755\r",
      "get val_outlayer area of all vals: 0.39547169811320754\r",
      "get val_outlayer area of all vals: 0.39622641509433965\r",
      "get val_outlayer area of all vals: 0.3969811320754717\r",
      "get val_outlayer area of all vals: 0.39773584905660375\r",
      "get val_outlayer area of all vals: 0.39849056603773586\r",
      "get val_outlayer area of all vals: 0.3992452830188679\r",
      "get val_outlayer area of all vals: 0.4\r",
      "get val_outlayer area of all vals: 0.4007547169811321\r",
      "get val_outlayer area of all vals: 0.40150943396226413\r",
      "get val_outlayer area of all vals: 0.40226415094339624\r",
      "get val_outlayer area of all vals: 0.4030188679245283\r",
      "get val_outlayer area of all vals: 0.4037735849056604\r",
      "get val_outlayer area of all vals: 0.40452830188679245\r",
      "get val_outlayer area of all vals: 0.4052830188679245\r",
      "get val_outlayer area of all vals: 0.4060377358490566\r",
      "get val_outlayer area of all vals: 0.40679245283018867\r",
      "get val_outlayer area of all vals: 0.4075471698113208\r",
      "get val_outlayer area of all vals: 0.40830188679245283\r",
      "get val_outlayer area of all vals: 0.4090566037735849\r",
      "get val_outlayer area of all vals: 0.409811320754717\r",
      "get val_outlayer area of all vals: 0.41056603773584904\r",
      "get val_outlayer area of all vals: 0.41132075471698115\r",
      "get val_outlayer area of all vals: 0.4120754716981132\r",
      "get val_outlayer area of all vals: 0.41283018867924526\r",
      "get val_outlayer area of all vals: 0.41358490566037737\r",
      "get val_outlayer area of all vals: 0.4143396226415094\r",
      "get val_outlayer area of all vals: 0.41509433962264153\r",
      "get val_outlayer area of all vals: 0.4158490566037736\r",
      "get val_outlayer area of all vals: 0.41660377358490563\r",
      "get val_outlayer area of all vals: 0.41735849056603774\r",
      "get val_outlayer area of all vals: 0.4181132075471698\r",
      "get val_outlayer area of all vals: 0.4188679245283019\r",
      "get val_outlayer area of all vals: 0.41962264150943396\r",
      "get val_outlayer area of all vals: 0.420377358490566\r",
      "get val_outlayer area of all vals: 0.4211320754716981\r",
      "get val_outlayer area of all vals: 0.4218867924528302\r",
      "get val_outlayer area of all vals: 0.4226415094339623\r",
      "get val_outlayer area of all vals: 0.42339622641509433\r",
      "get val_outlayer area of all vals: 0.4241509433962264\r",
      "get val_outlayer area of all vals: 0.4249056603773585\r",
      "get val_outlayer area of all vals: 0.42566037735849055\r",
      "get val_outlayer area of all vals: 0.42641509433962266\r",
      "get val_outlayer area of all vals: 0.4271698113207547\r",
      "get val_outlayer area of all vals: 0.4279245283018868\r",
      "get val_outlayer area of all vals: 0.4286792452830189\r",
      "get val_outlayer area of all vals: 0.4294339622641509\r",
      "get val_outlayer area of all vals: 0.43018867924528303\r",
      "get val_outlayer area of all vals: 0.4309433962264151\r",
      "get val_outlayer area of all vals: 0.4316981132075472\r",
      "get val_outlayer area of all vals: 0.43245283018867925\r",
      "get val_outlayer area of all vals: 0.4332075471698113\r",
      "get val_outlayer area of all vals: 0.4339622641509434\r",
      "get val_outlayer area of all vals: 0.43471698113207546\r",
      "get val_outlayer area of all vals: 0.4354716981132076\r",
      "get val_outlayer area of all vals: 0.4362264150943396\r",
      "get val_outlayer area of all vals: 0.4369811320754717\r",
      "get val_outlayer area of all vals: 0.4377358490566038\r",
      "get val_outlayer area of all vals: 0.43849056603773584\r",
      "get val_outlayer area of all vals: 0.43924528301886795\r",
      "get val_outlayer area of all vals: 0.44\r",
      "get val_outlayer area of all vals: 0.44075471698113206\r",
      "get val_outlayer area of all vals: 0.44150943396226416\r",
      "get val_outlayer area of all vals: 0.4422641509433962\r",
      "get val_outlayer area of all vals: 0.4430188679245283\r",
      "get val_outlayer area of all vals: 0.4437735849056604\r",
      "get val_outlayer area of all vals: 0.44452830188679243\r",
      "get val_outlayer area of all vals: 0.44528301886792454\r",
      "get val_outlayer area of all vals: 0.4460377358490566\r",
      "get val_outlayer area of all vals: 0.4467924528301887\r",
      "get val_outlayer area of all vals: 0.44754716981132076\r",
      "get val_outlayer area of all vals: 0.4483018867924528\r",
      "get val_outlayer area of all vals: 0.4490566037735849\r",
      "get val_outlayer area of all vals: 0.44981132075471697\r",
      "get val_outlayer area of all vals: 0.4505660377358491\r",
      "get val_outlayer area of all vals: 0.45132075471698113\r",
      "get val_outlayer area of all vals: 0.4520754716981132\r",
      "get val_outlayer area of all vals: 0.4528301886792453\r",
      "get val_outlayer area of all vals: 0.45358490566037735\r",
      "get val_outlayer area of all vals: 0.45433962264150946\r",
      "get val_outlayer area of all vals: 0.4550943396226415\r",
      "get val_outlayer area of all vals: 0.45584905660377356\r",
      "get val_outlayer area of all vals: 0.45660377358490567\r",
      "get val_outlayer area of all vals: 0.4573584905660377\r",
      "get val_outlayer area of all vals: 0.45811320754716983\r",
      "get val_outlayer area of all vals: 0.4588679245283019\r",
      "get val_outlayer area of all vals: 0.45962264150943394\r",
      "get val_outlayer area of all vals: 0.46037735849056605\r",
      "get val_outlayer area of all vals: 0.4611320754716981\r",
      "get val_outlayer area of all vals: 0.4618867924528302\r",
      "get val_outlayer area of all vals: 0.46264150943396226\r",
      "get val_outlayer area of all vals: 0.4633962264150943\r",
      "get val_outlayer area of all vals: 0.4641509433962264\r",
      "get val_outlayer area of all vals: 0.4649056603773585\r",
      "get val_outlayer area of all vals: 0.4656603773584906\r",
      "get val_outlayer area of all vals: 0.46641509433962264\r",
      "get val_outlayer area of all vals: 0.4671698113207547\r",
      "get val_outlayer area of all vals: 0.4679245283018868\r",
      "get val_outlayer area of all vals: 0.46867924528301885\r",
      "get val_outlayer area of all vals: 0.46943396226415096\r",
      "get val_outlayer area of all vals: 0.470188679245283\r",
      "get val_outlayer area of all vals: 0.47094339622641507\r",
      "get val_outlayer area of all vals: 0.4716981132075472\r",
      "get val_outlayer area of all vals: 0.47245283018867923\r",
      "get val_outlayer area of all vals: 0.47320754716981134\r",
      "get val_outlayer area of all vals: 0.4739622641509434\r",
      "get val_outlayer area of all vals: 0.47471698113207544\r",
      "get val_outlayer area of all vals: 0.47547169811320755\r",
      "get val_outlayer area of all vals: 0.4762264150943396\r",
      "get val_outlayer area of all vals: 0.4769811320754717\r",
      "get val_outlayer area of all vals: 0.47773584905660377\r",
      "get val_outlayer area of all vals: 0.4784905660377359\r",
      "get val_outlayer area of all vals: 0.47924528301886793\r",
      "get val_outlayer area of all vals: 0.48\r",
      "get val_outlayer area of all vals: 0.4807547169811321\r",
      "get val_outlayer area of all vals: 0.48150943396226414\r",
      "get val_outlayer area of all vals: 0.48226415094339625\r",
      "get val_outlayer area of all vals: 0.4830188679245283\r",
      "get val_outlayer area of all vals: 0.48377358490566036\r",
      "get val_outlayer area of all vals: 0.48452830188679247\r",
      "get val_outlayer area of all vals: 0.4852830188679245\r",
      "get val_outlayer area of all vals: 0.48603773584905663\r",
      "get val_outlayer area of all vals: 0.4867924528301887\r",
      "get val_outlayer area of all vals: 0.48754716981132074\r",
      "get val_outlayer area of all vals: 0.48830188679245284\r",
      "get val_outlayer area of all vals: 0.4890566037735849\r",
      "get val_outlayer area of all vals: 0.489811320754717\r",
      "get val_outlayer area of all vals: 0.49056603773584906\r",
      "get val_outlayer area of all vals: 0.4913207547169811\r",
      "get val_outlayer area of all vals: 0.4920754716981132\r",
      "get val_outlayer area of all vals: 0.4928301886792453\r",
      "get val_outlayer area of all vals: 0.4935849056603774\r",
      "get val_outlayer area of all vals: 0.49433962264150944\r",
      "get val_outlayer area of all vals: 0.4950943396226415\r",
      "get val_outlayer area of all vals: 0.4958490566037736\r",
      "get val_outlayer area of all vals: 0.49660377358490565\r",
      "get val_outlayer area of all vals: 0.49735849056603776\r",
      "get val_outlayer area of all vals: 0.4981132075471698\r",
      "get val_outlayer area of all vals: 0.49886792452830186\r",
      "get val_outlayer area of all vals: 0.499622641509434\r",
      "get val_outlayer area of all vals: 0.5003773584905661\r",
      "get val_outlayer area of all vals: 0.5011320754716981\r",
      "get val_outlayer area of all vals: 0.5018867924528302\r",
      "get val_outlayer area of all vals: 0.5026415094339622\r",
      "get val_outlayer area of all vals: 0.5033962264150943\r",
      "get val_outlayer area of all vals: 0.5041509433962265\r",
      "get val_outlayer area of all vals: 0.5049056603773585\r",
      "get val_outlayer area of all vals: 0.5056603773584906\r",
      "get val_outlayer area of all vals: 0.5064150943396226\r",
      "get val_outlayer area of all vals: 0.5071698113207547\r",
      "get val_outlayer area of all vals: 0.5079245283018868\r",
      "get val_outlayer area of all vals: 0.5086792452830189\r",
      "get val_outlayer area of all vals: 0.5094339622641509\r",
      "get val_outlayer area of all vals: 0.510188679245283\r",
      "get val_outlayer area of all vals: 0.510943396226415\r",
      "get val_outlayer area of all vals: 0.5116981132075472\r",
      "get val_outlayer area of all vals: 0.5124528301886793\r",
      "get val_outlayer area of all vals: 0.5132075471698113\r",
      "get val_outlayer area of all vals: 0.5139622641509434\r",
      "get val_outlayer area of all vals: 0.5147169811320754\r",
      "get val_outlayer area of all vals: 0.5154716981132076\r",
      "get val_outlayer area of all vals: 0.5162264150943396\r",
      "get val_outlayer area of all vals: 0.5169811320754717\r",
      "get val_outlayer area of all vals: 0.5177358490566037\r",
      "get val_outlayer area of all vals: 0.5184905660377358\r",
      "get val_outlayer area of all vals: 0.519245283018868\r",
      "get val_outlayer area of all vals: 0.52\r",
      "get val_outlayer area of all vals: 0.5207547169811321\r",
      "get val_outlayer area of all vals: 0.5215094339622641\r",
      "get val_outlayer area of all vals: 0.5222641509433962\r",
      "get val_outlayer area of all vals: 0.5230188679245283\r",
      "get val_outlayer area of all vals: 0.5237735849056604\r",
      "get val_outlayer area of all vals: 0.5245283018867924\r",
      "get val_outlayer area of all vals: 0.5252830188679245\r",
      "get val_outlayer area of all vals: 0.5260377358490566\r",
      "get val_outlayer area of all vals: 0.5267924528301887\r",
      "get val_outlayer area of all vals: 0.5275471698113208\r",
      "get val_outlayer area of all vals: 0.5283018867924528\r",
      "get val_outlayer area of all vals: 0.5290566037735849\r",
      "get val_outlayer area of all vals: 0.5298113207547169\r",
      "get val_outlayer area of all vals: 0.5305660377358491\r",
      "get val_outlayer area of all vals: 0.5313207547169811\r",
      "get val_outlayer area of all vals: 0.5320754716981132\r",
      "get val_outlayer area of all vals: 0.5328301886792453\r",
      "get val_outlayer area of all vals: 0.5335849056603773\r",
      "get val_outlayer area of all vals: 0.5343396226415095\r",
      "get val_outlayer area of all vals: 0.5350943396226415\r",
      "get val_outlayer area of all vals: 0.5358490566037736\r",
      "get val_outlayer area of all vals: 0.5366037735849056\r",
      "get val_outlayer area of all vals: 0.5373584905660377\r",
      "get val_outlayer area of all vals: 0.5381132075471698\r",
      "get val_outlayer area of all vals: 0.5388679245283019\r",
      "get val_outlayer area of all vals: 0.539622641509434\r",
      "get val_outlayer area of all vals: 0.540377358490566\r",
      "get val_outlayer area of all vals: 0.5411320754716981\r",
      "get val_outlayer area of all vals: 0.5418867924528302\r",
      "get val_outlayer area of all vals: 0.5426415094339623\r",
      "get val_outlayer area of all vals: 0.5433962264150943\r",
      "get val_outlayer area of all vals: 0.5441509433962264\r",
      "get val_outlayer area of all vals: 0.5449056603773584\r",
      "get val_outlayer area of all vals: 0.5456603773584906\r",
      "get val_outlayer area of all vals: 0.5464150943396227\r",
      "get val_outlayer area of all vals: 0.5471698113207547\r",
      "get val_outlayer area of all vals: 0.5479245283018868\r",
      "get val_outlayer area of all vals: 0.5486792452830188\r",
      "get val_outlayer area of all vals: 0.549433962264151\r",
      "get val_outlayer area of all vals: 0.550188679245283\r",
      "get val_outlayer area of all vals: 0.5509433962264151\r",
      "get val_outlayer area of all vals: 0.5516981132075471\r",
      "get val_outlayer area of all vals: 0.5524528301886793\r",
      "get val_outlayer area of all vals: 0.5532075471698114\r",
      "get val_outlayer area of all vals: 0.5539622641509434\r",
      "get val_outlayer area of all vals: 0.5547169811320755\r",
      "get val_outlayer area of all vals: 0.5554716981132075\r",
      "get val_outlayer area of all vals: 0.5562264150943397\r",
      "get val_outlayer area of all vals: 0.5569811320754717\r",
      "get val_outlayer area of all vals: 0.5577358490566038\r",
      "get val_outlayer area of all vals: 0.5584905660377358\r",
      "get val_outlayer area of all vals: 0.5592452830188679\r",
      "get val_outlayer area of all vals: 0.56\r",
      "get val_outlayer area of all vals: 0.5607547169811321\r",
      "get val_outlayer area of all vals: 0.5615094339622642\r",
      "get val_outlayer area of all vals: 0.5622641509433962\r",
      "get val_outlayer area of all vals: 0.5630188679245283\r",
      "get val_outlayer area of all vals: 0.5637735849056604\r",
      "get val_outlayer area of all vals: 0.5645283018867925\r",
      "get val_outlayer area of all vals: 0.5652830188679245\r",
      "get val_outlayer area of all vals: 0.5660377358490566\r",
      "get val_outlayer area of all vals: 0.5667924528301886\r",
      "get val_outlayer area of all vals: 0.5675471698113208\r",
      "get val_outlayer area of all vals: 0.5683018867924529\r",
      "get val_outlayer area of all vals: 0.5690566037735849\r",
      "get val_outlayer area of all vals: 0.569811320754717\r",
      "get val_outlayer area of all vals: 0.570566037735849\r",
      "get val_outlayer area of all vals: 0.5713207547169812\r",
      "get val_outlayer area of all vals: 0.5720754716981132\r",
      "get val_outlayer area of all vals: 0.5728301886792453\r",
      "get val_outlayer area of all vals: 0.5735849056603773\r",
      "get val_outlayer area of all vals: 0.5743396226415094\r",
      "get val_outlayer area of all vals: 0.5750943396226416\r",
      "get val_outlayer area of all vals: 0.5758490566037736\r",
      "get val_outlayer area of all vals: 0.5766037735849057\r",
      "get val_outlayer area of all vals: 0.5773584905660377\r",
      "get val_outlayer area of all vals: 0.5781132075471698\r",
      "get val_outlayer area of all vals: 0.5788679245283019\r",
      "get val_outlayer area of all vals: 0.579622641509434\r",
      "get val_outlayer area of all vals: 0.580377358490566\r",
      "get val_outlayer area of all vals: 0.5811320754716981\r",
      "get val_outlayer area of all vals: 0.5818867924528301\r",
      "get val_outlayer area of all vals: 0.5826415094339623\r",
      "get val_outlayer area of all vals: 0.5833962264150944\r",
      "get val_outlayer area of all vals: 0.5841509433962264\r",
      "get val_outlayer area of all vals: 0.5849056603773585\r",
      "get val_outlayer area of all vals: 0.5856603773584905\r",
      "get val_outlayer area of all vals: 0.5864150943396227\r",
      "get val_outlayer area of all vals: 0.5871698113207547\r",
      "get val_outlayer area of all vals: 0.5879245283018868\r",
      "get val_outlayer area of all vals: 0.5886792452830188\r",
      "get val_outlayer area of all vals: 0.5894339622641509\r",
      "get val_outlayer area of all vals: 0.5901886792452831\r",
      "get val_outlayer area of all vals: 0.5909433962264151\r",
      "get val_outlayer area of all vals: 0.5916981132075472\r",
      "get val_outlayer area of all vals: 0.5924528301886792\r",
      "get val_outlayer area of all vals: 0.5932075471698113\r",
      "get val_outlayer area of all vals: 0.5939622641509434\r",
      "get val_outlayer area of all vals: 0.5947169811320755\r",
      "get val_outlayer area of all vals: 0.5954716981132075\r",
      "get val_outlayer area of all vals: 0.5962264150943396\r",
      "get val_outlayer area of all vals: 0.5969811320754717\r",
      "get val_outlayer area of all vals: 0.5977358490566038\r",
      "get val_outlayer area of all vals: 0.5984905660377359\r",
      "get val_outlayer area of all vals: 0.5992452830188679\r",
      "get val_outlayer area of all vals: 0.6\r",
      "get val_outlayer area of all vals: 0.600754716981132\r",
      "get val_outlayer area of all vals: 0.6015094339622642\r",
      "get val_outlayer area of all vals: 0.6022641509433962\r",
      "get val_outlayer area of all vals: 0.6030188679245283\r",
      "get val_outlayer area of all vals: 0.6037735849056604\r",
      "get val_outlayer area of all vals: 0.6045283018867924\r",
      "get val_outlayer area of all vals: 0.6052830188679246\r",
      "get val_outlayer area of all vals: 0.6060377358490566\r",
      "get val_outlayer area of all vals: 0.6067924528301887\r",
      "get val_outlayer area of all vals: 0.6075471698113207\r",
      "get val_outlayer area of all vals: 0.6083018867924528\r",
      "get val_outlayer area of all vals: 0.609056603773585\r",
      "get val_outlayer area of all vals: 0.609811320754717\r",
      "get val_outlayer area of all vals: 0.610566037735849\r",
      "get val_outlayer area of all vals: 0.6113207547169811\r",
      "get val_outlayer area of all vals: 0.6120754716981132\r",
      "get val_outlayer area of all vals: 0.6128301886792453\r",
      "get val_outlayer area of all vals: 0.6135849056603774\r",
      "get val_outlayer area of all vals: 0.6143396226415094\r",
      "get val_outlayer area of all vals: 0.6150943396226415\r",
      "get val_outlayer area of all vals: 0.6158490566037735\r",
      "get val_outlayer area of all vals: 0.6166037735849057\r",
      "get val_outlayer area of all vals: 0.6173584905660378\r",
      "get val_outlayer area of all vals: 0.6181132075471698\r",
      "get val_outlayer area of all vals: 0.6188679245283019\r",
      "get val_outlayer area of all vals: 0.6196226415094339\r",
      "get val_outlayer area of all vals: 0.6203773584905661\r",
      "get val_outlayer area of all vals: 0.6211320754716981\r",
      "get val_outlayer area of all vals: 0.6218867924528302\r",
      "get val_outlayer area of all vals: 0.6226415094339622\r",
      "get val_outlayer area of all vals: 0.6233962264150943\r",
      "get val_outlayer area of all vals: 0.6241509433962265\r",
      "get val_outlayer area of all vals: 0.6249056603773585\r",
      "get val_outlayer area of all vals: 0.6256603773584906\r",
      "get val_outlayer area of all vals: 0.6264150943396226\r",
      "get val_outlayer area of all vals: 0.6271698113207547\r",
      "get val_outlayer area of all vals: 0.6279245283018868\r",
      "get val_outlayer area of all vals: 0.6286792452830189\r",
      "get val_outlayer area of all vals: 0.6294339622641509\r",
      "get val_outlayer area of all vals: 0.630188679245283\r",
      "get val_outlayer area of all vals: 0.630943396226415\r",
      "get val_outlayer area of all vals: 0.6316981132075472\r",
      "get val_outlayer area of all vals: 0.6324528301886793\r",
      "get val_outlayer area of all vals: 0.6332075471698113\r",
      "get val_outlayer area of all vals: 0.6339622641509434\r",
      "get val_outlayer area of all vals: 0.6347169811320754\r",
      "get val_outlayer area of all vals: 0.6354716981132076\r",
      "get val_outlayer area of all vals: 0.6362264150943396\r",
      "get val_outlayer area of all vals: 0.6369811320754717\r",
      "get val_outlayer area of all vals: 0.6377358490566037\r",
      "get val_outlayer area of all vals: 0.6384905660377358\r",
      "get val_outlayer area of all vals: 0.639245283018868\r",
      "get val_outlayer area of all vals: 0.64\r",
      "get val_outlayer area of all vals: 0.6407547169811321\r",
      "get val_outlayer area of all vals: 0.6415094339622641\r",
      "get val_outlayer area of all vals: 0.6422641509433962\r",
      "get val_outlayer area of all vals: 0.6430188679245283\r",
      "get val_outlayer area of all vals: 0.6437735849056604\r",
      "get val_outlayer area of all vals: 0.6445283018867924\r",
      "get val_outlayer area of all vals: 0.6452830188679245\r",
      "get val_outlayer area of all vals: 0.6460377358490565\r",
      "get val_outlayer area of all vals: 0.6467924528301887\r",
      "get val_outlayer area of all vals: 0.6475471698113208\r",
      "get val_outlayer area of all vals: 0.6483018867924528\r",
      "get val_outlayer area of all vals: 0.6490566037735849\r",
      "get val_outlayer area of all vals: 0.6498113207547169\r",
      "get val_outlayer area of all vals: 0.6505660377358491\r",
      "get val_outlayer area of all vals: 0.6513207547169811\r",
      "get val_outlayer area of all vals: 0.6520754716981132\r",
      "get val_outlayer area of all vals: 0.6528301886792452\r",
      "get val_outlayer area of all vals: 0.6535849056603774\r",
      "get val_outlayer area of all vals: 0.6543396226415095\r",
      "get val_outlayer area of all vals: 0.6550943396226415\r",
      "get val_outlayer area of all vals: 0.6558490566037736\r",
      "get val_outlayer area of all vals: 0.6566037735849056\r",
      "get val_outlayer area of all vals: 0.6573584905660378\r",
      "get val_outlayer area of all vals: 0.6581132075471698\r",
      "get val_outlayer area of all vals: 0.6588679245283019\r",
      "get val_outlayer area of all vals: 0.659622641509434\r",
      "get val_outlayer area of all vals: 0.660377358490566\r",
      "get val_outlayer area of all vals: 0.6611320754716982\r",
      "get val_outlayer area of all vals: 0.6618867924528302\r",
      "get val_outlayer area of all vals: 0.6626415094339623\r",
      "get val_outlayer area of all vals: 0.6633962264150943\r",
      "get val_outlayer area of all vals: 0.6641509433962264\r",
      "get val_outlayer area of all vals: 0.6649056603773585\r",
      "get val_outlayer area of all vals: 0.6656603773584906\r",
      "get val_outlayer area of all vals: 0.6664150943396226\r",
      "get val_outlayer area of all vals: 0.6671698113207547\r",
      "get val_outlayer area of all vals: 0.6679245283018868\r",
      "get val_outlayer area of all vals: 0.6686792452830189\r",
      "get val_outlayer area of all vals: 0.669433962264151\r",
      "get val_outlayer area of all vals: 0.670188679245283\r",
      "get val_outlayer area of all vals: 0.6709433962264151\r",
      "get val_outlayer area of all vals: 0.6716981132075471\r",
      "get val_outlayer area of all vals: 0.6724528301886793\r",
      "get val_outlayer area of all vals: 0.6732075471698113\r",
      "get val_outlayer area of all vals: 0.6739622641509434\r",
      "get val_outlayer area of all vals: 0.6747169811320755\r",
      "get val_outlayer area of all vals: 0.6754716981132075\r",
      "get val_outlayer area of all vals: 0.6762264150943397\r",
      "get val_outlayer area of all vals: 0.6769811320754717\r",
      "get val_outlayer area of all vals: 0.6777358490566038\r",
      "get val_outlayer area of all vals: 0.6784905660377358\r",
      "get val_outlayer area of all vals: 0.6792452830188679\r",
      "get val_outlayer area of all vals: 0.68\r",
      "get val_outlayer area of all vals: 0.6807547169811321\r",
      "get val_outlayer area of all vals: 0.6815094339622642\r",
      "get val_outlayer area of all vals: 0.6822641509433962\r",
      "get val_outlayer area of all vals: 0.6830188679245283\r",
      "get val_outlayer area of all vals: 0.6837735849056604\r",
      "get val_outlayer area of all vals: 0.6845283018867925\r",
      "get val_outlayer area of all vals: 0.6852830188679245\r",
      "get val_outlayer area of all vals: 0.6860377358490566\r",
      "get val_outlayer area of all vals: 0.6867924528301886\r",
      "get val_outlayer area of all vals: 0.6875471698113208\r",
      "get val_outlayer area of all vals: 0.6883018867924529\r",
      "get val_outlayer area of all vals: 0.6890566037735849\r",
      "get val_outlayer area of all vals: 0.689811320754717\r",
      "get val_outlayer area of all vals: 0.690566037735849\r",
      "get val_outlayer area of all vals: 0.6913207547169812\r",
      "get val_outlayer area of all vals: 0.6920754716981132\r",
      "get val_outlayer area of all vals: 0.6928301886792453\r",
      "get val_outlayer area of all vals: 0.6935849056603773\r",
      "get val_outlayer area of all vals: 0.6943396226415094\r",
      "get val_outlayer area of all vals: 0.6950943396226416\r",
      "get val_outlayer area of all vals: 0.6958490566037736\r",
      "get val_outlayer area of all vals: 0.6966037735849057\r",
      "get val_outlayer area of all vals: 0.6973584905660377\r",
      "get val_outlayer area of all vals: 0.6981132075471698\r",
      "get val_outlayer area of all vals: 0.6988679245283019\r",
      "get val_outlayer area of all vals: 0.699622641509434\r",
      "get val_outlayer area of all vals: 0.700377358490566\r",
      "get val_outlayer area of all vals: 0.7011320754716981\r",
      "get val_outlayer area of all vals: 0.7018867924528301\r",
      "get val_outlayer area of all vals: 0.7026415094339623\r",
      "get val_outlayer area of all vals: 0.7033962264150944\r",
      "get val_outlayer area of all vals: 0.7041509433962264\r",
      "get val_outlayer area of all vals: 0.7049056603773585\r",
      "get val_outlayer area of all vals: 0.7056603773584905\r",
      "get val_outlayer area of all vals: 0.7064150943396227\r",
      "get val_outlayer area of all vals: 0.7071698113207547\r",
      "get val_outlayer area of all vals: 0.7079245283018868\r",
      "get val_outlayer area of all vals: 0.7086792452830188\r",
      "get val_outlayer area of all vals: 0.7094339622641509\r",
      "get val_outlayer area of all vals: 0.7101886792452831\r",
      "get val_outlayer area of all vals: 0.7109433962264151\r",
      "get val_outlayer area of all vals: 0.7116981132075472\r",
      "get val_outlayer area of all vals: 0.7124528301886792\r",
      "get val_outlayer area of all vals: 0.7132075471698113\r",
      "get val_outlayer area of all vals: 0.7139622641509434\r",
      "get val_outlayer area of all vals: 0.7147169811320755\r",
      "get val_outlayer area of all vals: 0.7154716981132075\r",
      "get val_outlayer area of all vals: 0.7162264150943396\r",
      "get val_outlayer area of all vals: 0.7169811320754716\r",
      "get val_outlayer area of all vals: 0.7177358490566038\r",
      "get val_outlayer area of all vals: 0.7184905660377359\r",
      "get val_outlayer area of all vals: 0.7192452830188679\r",
      "get val_outlayer area of all vals: 0.72\r",
      "get val_outlayer area of all vals: 0.720754716981132\r",
      "get val_outlayer area of all vals: 0.7215094339622642\r",
      "get val_outlayer area of all vals: 0.7222641509433962\r",
      "get val_outlayer area of all vals: 0.7230188679245283\r",
      "get val_outlayer area of all vals: 0.7237735849056603\r",
      "get val_outlayer area of all vals: 0.7245283018867924\r",
      "get val_outlayer area of all vals: 0.7252830188679246\r",
      "get val_outlayer area of all vals: 0.7260377358490566\r",
      "get val_outlayer area of all vals: 0.7267924528301887\r",
      "get val_outlayer area of all vals: 0.7275471698113207\r",
      "get val_outlayer area of all vals: 0.7283018867924528\r",
      "get val_outlayer area of all vals: 0.7290566037735849\r",
      "get val_outlayer area of all vals: 0.729811320754717\r",
      "get val_outlayer area of all vals: 0.730566037735849\r",
      "get val_outlayer area of all vals: 0.7313207547169811\r",
      "get val_outlayer area of all vals: 0.7320754716981132\r",
      "get val_outlayer area of all vals: 0.7328301886792453\r",
      "get val_outlayer area of all vals: 0.7335849056603774\r",
      "get val_outlayer area of all vals: 0.7343396226415094\r",
      "get val_outlayer area of all vals: 0.7350943396226415\r",
      "get val_outlayer area of all vals: 0.7358490566037735\r",
      "get val_outlayer area of all vals: 0.7366037735849057\r",
      "get val_outlayer area of all vals: 0.7373584905660377\r",
      "get val_outlayer area of all vals: 0.7381132075471698\r",
      "get val_outlayer area of all vals: 0.7388679245283019\r",
      "get val_outlayer area of all vals: 0.7396226415094339\r",
      "get val_outlayer area of all vals: 0.7403773584905661\r",
      "get val_outlayer area of all vals: 0.7411320754716981\r",
      "get val_outlayer area of all vals: 0.7418867924528302\r",
      "get val_outlayer area of all vals: 0.7426415094339622\r",
      "get val_outlayer area of all vals: 0.7433962264150943\r",
      "get val_outlayer area of all vals: 0.7441509433962264\r",
      "get val_outlayer area of all vals: 0.7449056603773585\r",
      "get val_outlayer area of all vals: 0.7456603773584906\r",
      "get val_outlayer area of all vals: 0.7464150943396226\r",
      "get val_outlayer area of all vals: 0.7471698113207547\r",
      "get val_outlayer area of all vals: 0.7479245283018868\r",
      "get val_outlayer area of all vals: 0.7486792452830189\r",
      "get val_outlayer area of all vals: 0.7494339622641509\r",
      "get val_outlayer area of all vals: 0.750188679245283\r",
      "get val_outlayer area of all vals: 0.7509433962264151\r",
      "get val_outlayer area of all vals: 0.7516981132075472\r",
      "get val_outlayer area of all vals: 0.7524528301886793\r",
      "get val_outlayer area of all vals: 0.7532075471698113\r",
      "get val_outlayer area of all vals: 0.7539622641509434\r",
      "get val_outlayer area of all vals: 0.7547169811320755\r",
      "get val_outlayer area of all vals: 0.7554716981132076\r",
      "get val_outlayer area of all vals: 0.7562264150943396\r",
      "get val_outlayer area of all vals: 0.7569811320754717\r",
      "get val_outlayer area of all vals: 0.7577358490566037\r",
      "get val_outlayer area of all vals: 0.7584905660377359\r",
      "get val_outlayer area of all vals: 0.759245283018868\r",
      "get val_outlayer area of all vals: 0.76\r",
      "get val_outlayer area of all vals: 0.7607547169811321\r",
      "get val_outlayer area of all vals: 0.7615094339622641\r",
      "get val_outlayer area of all vals: 0.7622641509433963\r",
      "get val_outlayer area of all vals: 0.7630188679245283\r",
      "get val_outlayer area of all vals: 0.7637735849056604\r",
      "get val_outlayer area of all vals: 0.7645283018867924\r",
      "get val_outlayer area of all vals: 0.7652830188679245\r",
      "get val_outlayer area of all vals: 0.7660377358490567\r",
      "get val_outlayer area of all vals: 0.7667924528301887\r",
      "get val_outlayer area of all vals: 0.7675471698113208\r",
      "get val_outlayer area of all vals: 0.7683018867924528\r",
      "get val_outlayer area of all vals: 0.7690566037735849\r",
      "get val_outlayer area of all vals: 0.769811320754717\r",
      "get val_outlayer area of all vals: 0.7705660377358491\r",
      "get val_outlayer area of all vals: 0.7713207547169811\r",
      "get val_outlayer area of all vals: 0.7720754716981132\r",
      "get val_outlayer area of all vals: 0.7728301886792452\r",
      "get val_outlayer area of all vals: 0.7735849056603774\r",
      "get val_outlayer area of all vals: 0.7743396226415095\r",
      "get val_outlayer area of all vals: 0.7750943396226415\r",
      "get val_outlayer area of all vals: 0.7758490566037736\r",
      "get val_outlayer area of all vals: 0.7766037735849056\r",
      "get val_outlayer area of all vals: 0.7773584905660378\r",
      "get val_outlayer area of all vals: 0.7781132075471698\r",
      "get val_outlayer area of all vals: 0.7788679245283019\r",
      "get val_outlayer area of all vals: 0.779622641509434\r",
      "get val_outlayer area of all vals: 0.780377358490566\r",
      "get val_outlayer area of all vals: 0.7811320754716982\r",
      "get val_outlayer area of all vals: 0.7818867924528302\r",
      "get val_outlayer area of all vals: 0.7826415094339623\r",
      "get val_outlayer area of all vals: 0.7833962264150943\r",
      "get val_outlayer area of all vals: 0.7841509433962264\r",
      "get val_outlayer area of all vals: 0.7849056603773585\r",
      "get val_outlayer area of all vals: 0.7856603773584906\r",
      "get val_outlayer area of all vals: 0.7864150943396226\r",
      "get val_outlayer area of all vals: 0.7871698113207547\r",
      "get val_outlayer area of all vals: 0.7879245283018868\r",
      "get val_outlayer area of all vals: 0.7886792452830189\r",
      "get val_outlayer area of all vals: 0.789433962264151\r",
      "get val_outlayer area of all vals: 0.790188679245283\r",
      "get val_outlayer area of all vals: 0.7909433962264151\r",
      "get val_outlayer area of all vals: 0.7916981132075471\r",
      "get val_outlayer area of all vals: 0.7924528301886793\r",
      "get val_outlayer area of all vals: 0.7932075471698113\r",
      "get val_outlayer area of all vals: 0.7939622641509434\r",
      "get val_outlayer area of all vals: 0.7947169811320755\r",
      "get val_outlayer area of all vals: 0.7954716981132075\r",
      "get val_outlayer area of all vals: 0.7962264150943397\r",
      "get val_outlayer area of all vals: 0.7969811320754717\r",
      "get val_outlayer area of all vals: 0.7977358490566038\r",
      "get val_outlayer area of all vals: 0.7984905660377358\r",
      "get val_outlayer area of all vals: 0.7992452830188679\r",
      "get val_outlayer area of all vals: 0.8\r",
      "get val_outlayer area of all vals: 0.8007547169811321\r",
      "get val_outlayer area of all vals: 0.8015094339622642\r",
      "get val_outlayer area of all vals: 0.8022641509433962\r",
      "get val_outlayer area of all vals: 0.8030188679245283\r",
      "get val_outlayer area of all vals: 0.8037735849056604\r",
      "get val_outlayer area of all vals: 0.8045283018867925\r",
      "get val_outlayer area of all vals: 0.8052830188679245\r",
      "get val_outlayer area of all vals: 0.8060377358490566\r",
      "get val_outlayer area of all vals: 0.8067924528301886\r",
      "get val_outlayer area of all vals: 0.8075471698113208\r",
      "get val_outlayer area of all vals: 0.8083018867924529\r",
      "get val_outlayer area of all vals: 0.8090566037735849\r",
      "get val_outlayer area of all vals: 0.809811320754717\r",
      "get val_outlayer area of all vals: 0.810566037735849\r",
      "get val_outlayer area of all vals: 0.8113207547169812\r",
      "get val_outlayer area of all vals: 0.8120754716981132\r",
      "get val_outlayer area of all vals: 0.8128301886792453\r",
      "get val_outlayer area of all vals: 0.8135849056603773\r",
      "get val_outlayer area of all vals: 0.8143396226415094\r",
      "get val_outlayer area of all vals: 0.8150943396226416\r",
      "get val_outlayer area of all vals: 0.8158490566037736\r",
      "get val_outlayer area of all vals: 0.8166037735849057\r",
      "get val_outlayer area of all vals: 0.8173584905660377\r",
      "get val_outlayer area of all vals: 0.8181132075471698\r",
      "get val_outlayer area of all vals: 0.8188679245283019\r",
      "get val_outlayer area of all vals: 0.819622641509434\r",
      "get val_outlayer area of all vals: 0.820377358490566\r",
      "get val_outlayer area of all vals: 0.8211320754716981\r",
      "get val_outlayer area of all vals: 0.8218867924528301\r",
      "get val_outlayer area of all vals: 0.8226415094339623\r",
      "get val_outlayer area of all vals: 0.8233962264150944\r",
      "get val_outlayer area of all vals: 0.8241509433962264\r",
      "get val_outlayer area of all vals: 0.8249056603773585\r",
      "get val_outlayer area of all vals: 0.8256603773584905\r",
      "get val_outlayer area of all vals: 0.8264150943396227\r",
      "get val_outlayer area of all vals: 0.8271698113207547\r",
      "get val_outlayer area of all vals: 0.8279245283018868\r",
      "get val_outlayer area of all vals: 0.8286792452830188\r",
      "get val_outlayer area of all vals: 0.8294339622641509\r",
      "get val_outlayer area of all vals: 0.8301886792452831\r",
      "get val_outlayer area of all vals: 0.8309433962264151\r",
      "get val_outlayer area of all vals: 0.8316981132075472\r",
      "get val_outlayer area of all vals: 0.8324528301886792\r",
      "get val_outlayer area of all vals: 0.8332075471698113\r",
      "get val_outlayer area of all vals: 0.8339622641509434\r",
      "get val_outlayer area of all vals: 0.8347169811320755\r",
      "get val_outlayer area of all vals: 0.8354716981132075\r",
      "get val_outlayer area of all vals: 0.8362264150943396\r",
      "get val_outlayer area of all vals: 0.8369811320754716\r",
      "get val_outlayer area of all vals: 0.8377358490566038\r",
      "get val_outlayer area of all vals: 0.8384905660377359\r",
      "get val_outlayer area of all vals: 0.8392452830188679\r",
      "get val_outlayer area of all vals: 0.84\r",
      "get val_outlayer area of all vals: 0.840754716981132\r",
      "get val_outlayer area of all vals: 0.8415094339622642\r",
      "get val_outlayer area of all vals: 0.8422641509433962\r",
      "get val_outlayer area of all vals: 0.8430188679245283\r",
      "get val_outlayer area of all vals: 0.8437735849056603\r",
      "get val_outlayer area of all vals: 0.8445283018867924\r",
      "get val_outlayer area of all vals: 0.8452830188679246\r",
      "get val_outlayer area of all vals: 0.8460377358490566\r",
      "get val_outlayer area of all vals: 0.8467924528301887\r",
      "get val_outlayer area of all vals: 0.8475471698113207\r",
      "get val_outlayer area of all vals: 0.8483018867924528\r",
      "get val_outlayer area of all vals: 0.8490566037735849\r",
      "get val_outlayer area of all vals: 0.849811320754717\r",
      "get val_outlayer area of all vals: 0.850566037735849\r",
      "get val_outlayer area of all vals: 0.8513207547169811\r",
      "get val_outlayer area of all vals: 0.8520754716981133\r",
      "get val_outlayer area of all vals: 0.8528301886792453\r",
      "get val_outlayer area of all vals: 0.8535849056603774\r",
      "get val_outlayer area of all vals: 0.8543396226415094\r",
      "get val_outlayer area of all vals: 0.8550943396226415\r",
      "get val_outlayer area of all vals: 0.8558490566037736\r",
      "get val_outlayer area of all vals: 0.8566037735849057\r",
      "get val_outlayer area of all vals: 0.8573584905660377\r",
      "get val_outlayer area of all vals: 0.8581132075471698\r",
      "get val_outlayer area of all vals: 0.8588679245283019\r",
      "get val_outlayer area of all vals: 0.859622641509434\r",
      "get val_outlayer area of all vals: 0.8603773584905661\r",
      "get val_outlayer area of all vals: 0.8611320754716981\r",
      "get val_outlayer area of all vals: 0.8618867924528302\r",
      "get val_outlayer area of all vals: 0.8626415094339622\r",
      "get val_outlayer area of all vals: 0.8633962264150944\r",
      "get val_outlayer area of all vals: 0.8641509433962264\r",
      "get val_outlayer area of all vals: 0.8649056603773585\r",
      "get val_outlayer area of all vals: 0.8656603773584906\r",
      "get val_outlayer area of all vals: 0.8664150943396226\r",
      "get val_outlayer area of all vals: 0.8671698113207548\r",
      "get val_outlayer area of all vals: 0.8679245283018868\r",
      "get val_outlayer area of all vals: 0.8686792452830189\r",
      "get val_outlayer area of all vals: 0.8694339622641509\r",
      "get val_outlayer area of all vals: 0.870188679245283\r",
      "get val_outlayer area of all vals: 0.8709433962264151\r",
      "get val_outlayer area of all vals: 0.8716981132075472\r",
      "get val_outlayer area of all vals: 0.8724528301886793\r",
      "get val_outlayer area of all vals: 0.8732075471698113\r",
      "get val_outlayer area of all vals: 0.8739622641509434\r",
      "get val_outlayer area of all vals: 0.8747169811320755\r",
      "get val_outlayer area of all vals: 0.8754716981132076\r",
      "get val_outlayer area of all vals: 0.8762264150943396\r",
      "get val_outlayer area of all vals: 0.8769811320754717\r",
      "get val_outlayer area of all vals: 0.8777358490566037\r",
      "get val_outlayer area of all vals: 0.8784905660377359\r",
      "get val_outlayer area of all vals: 0.879245283018868\r",
      "get val_outlayer area of all vals: 0.88\r",
      "get val_outlayer area of all vals: 0.8807547169811321\r",
      "get val_outlayer area of all vals: 0.8815094339622641\r",
      "get val_outlayer area of all vals: 0.8822641509433963\r",
      "get val_outlayer area of all vals: 0.8830188679245283\r",
      "get val_outlayer area of all vals: 0.8837735849056604\r",
      "get val_outlayer area of all vals: 0.8845283018867924\r",
      "get val_outlayer area of all vals: 0.8852830188679245\r",
      "get val_outlayer area of all vals: 0.8860377358490567\r",
      "get val_outlayer area of all vals: 0.8867924528301887\r",
      "get val_outlayer area of all vals: 0.8875471698113208\r",
      "get val_outlayer area of all vals: 0.8883018867924528\r",
      "get val_outlayer area of all vals: 0.8890566037735849\r",
      "get val_outlayer area of all vals: 0.889811320754717\r",
      "get val_outlayer area of all vals: 0.8905660377358491\r",
      "get val_outlayer area of all vals: 0.8913207547169811\r",
      "get val_outlayer area of all vals: 0.8920754716981132\r",
      "get val_outlayer area of all vals: 0.8928301886792452\r",
      "get val_outlayer area of all vals: 0.8935849056603774\r",
      "get val_outlayer area of all vals: 0.8943396226415095\r",
      "get val_outlayer area of all vals: 0.8950943396226415\r",
      "get val_outlayer area of all vals: 0.8958490566037736\r",
      "get val_outlayer area of all vals: 0.8966037735849056\r",
      "get val_outlayer area of all vals: 0.8973584905660378\r",
      "get val_outlayer area of all vals: 0.8981132075471698\r",
      "get val_outlayer area of all vals: 0.8988679245283019\r",
      "get val_outlayer area of all vals: 0.8996226415094339\r",
      "get val_outlayer area of all vals: 0.900377358490566\r",
      "get val_outlayer area of all vals: 0.9011320754716982\r",
      "get val_outlayer area of all vals: 0.9018867924528302\r",
      "get val_outlayer area of all vals: 0.9026415094339623\r",
      "get val_outlayer area of all vals: 0.9033962264150943\r",
      "get val_outlayer area of all vals: 0.9041509433962264\r",
      "get val_outlayer area of all vals: 0.9049056603773585\r",
      "get val_outlayer area of all vals: 0.9056603773584906\r",
      "get val_outlayer area of all vals: 0.9064150943396226\r",
      "get val_outlayer area of all vals: 0.9071698113207547\r",
      "get val_outlayer area of all vals: 0.9079245283018867\r",
      "get val_outlayer area of all vals: 0.9086792452830189\r",
      "get val_outlayer area of all vals: 0.909433962264151\r",
      "get val_outlayer area of all vals: 0.910188679245283\r",
      "get val_outlayer area of all vals: 0.9109433962264151\r",
      "get val_outlayer area of all vals: 0.9116981132075471\r",
      "get val_outlayer area of all vals: 0.9124528301886793\r",
      "get val_outlayer area of all vals: 0.9132075471698113\r",
      "get val_outlayer area of all vals: 0.9139622641509434\r",
      "get val_outlayer area of all vals: 0.9147169811320754\r",
      "get val_outlayer area of all vals: 0.9154716981132075\r",
      "get val_outlayer area of all vals: 0.9162264150943397\r",
      "get val_outlayer area of all vals: 0.9169811320754717\r",
      "get val_outlayer area of all vals: 0.9177358490566038\r",
      "get val_outlayer area of all vals: 0.9184905660377358\r",
      "get val_outlayer area of all vals: 0.9192452830188679\r",
      "get val_outlayer area of all vals: 0.92\r",
      "get val_outlayer area of all vals: 0.9207547169811321\r",
      "get val_outlayer area of all vals: 0.9215094339622641\r",
      "get val_outlayer area of all vals: 0.9222641509433962\r",
      "get val_outlayer area of all vals: 0.9230188679245283\r",
      "get val_outlayer area of all vals: 0.9237735849056604\r",
      "get val_outlayer area of all vals: 0.9245283018867925\r",
      "get val_outlayer area of all vals: 0.9252830188679245\r",
      "get val_outlayer area of all vals: 0.9260377358490566\r",
      "get val_outlayer area of all vals: 0.9267924528301886\r",
      "get val_outlayer area of all vals: 0.9275471698113208\r",
      "get val_outlayer area of all vals: 0.9283018867924528\r",
      "get val_outlayer area of all vals: 0.9290566037735849\r",
      "get val_outlayer area of all vals: 0.929811320754717\r",
      "get val_outlayer area of all vals: 0.930566037735849\r",
      "get val_outlayer area of all vals: 0.9313207547169812\r",
      "get val_outlayer area of all vals: 0.9320754716981132\r",
      "get val_outlayer area of all vals: 0.9328301886792453\r",
      "get val_outlayer area of all vals: 0.9335849056603773\r",
      "get val_outlayer area of all vals: 0.9343396226415094\r",
      "get val_outlayer area of all vals: 0.9350943396226415\r",
      "get val_outlayer area of all vals: 0.9358490566037736\r",
      "get val_outlayer area of all vals: 0.9366037735849057\r",
      "get val_outlayer area of all vals: 0.9373584905660377\r",
      "get val_outlayer area of all vals: 0.9381132075471698\r",
      "get val_outlayer area of all vals: 0.9388679245283019\r",
      "get val_outlayer area of all vals: 0.939622641509434\r",
      "get val_outlayer area of all vals: 0.940377358490566\r",
      "get val_outlayer area of all vals: 0.9411320754716981\r",
      "get val_outlayer area of all vals: 0.9418867924528301\r",
      "get val_outlayer area of all vals: 0.9426415094339623\r",
      "get val_outlayer area of all vals: 0.9433962264150944\r",
      "get val_outlayer area of all vals: 0.9441509433962264\r",
      "get val_outlayer area of all vals: 0.9449056603773585\r",
      "get val_outlayer area of all vals: 0.9456603773584905\r",
      "get val_outlayer area of all vals: 0.9464150943396227\r",
      "get val_outlayer area of all vals: 0.9471698113207547\r",
      "get val_outlayer area of all vals: 0.9479245283018868\r",
      "get val_outlayer area of all vals: 0.9486792452830188\r",
      "get val_outlayer area of all vals: 0.9494339622641509\r",
      "get val_outlayer area of all vals: 0.950188679245283\r",
      "get val_outlayer area of all vals: 0.9509433962264151\r",
      "get val_outlayer area of all vals: 0.9516981132075472\r",
      "get val_outlayer area of all vals: 0.9524528301886792\r",
      "get val_outlayer area of all vals: 0.9532075471698114\r",
      "get val_outlayer area of all vals: 0.9539622641509434\r",
      "get val_outlayer area of all vals: 0.9547169811320755\r",
      "get val_outlayer area of all vals: 0.9554716981132075\r",
      "get val_outlayer area of all vals: 0.9562264150943396\r",
      "get val_outlayer area of all vals: 0.9569811320754718\r",
      "get val_outlayer area of all vals: 0.9577358490566038\r",
      "get val_outlayer area of all vals: 0.9584905660377359\r",
      "get val_outlayer area of all vals: 0.9592452830188679\r",
      "get val_outlayer area of all vals: 0.96\r",
      "get val_outlayer area of all vals: 0.9607547169811321\r",
      "get val_outlayer area of all vals: 0.9615094339622642\r",
      "get val_outlayer area of all vals: 0.9622641509433962\r",
      "get val_outlayer area of all vals: 0.9630188679245283\r",
      "get val_outlayer area of all vals: 0.9637735849056603\r",
      "get val_outlayer area of all vals: 0.9645283018867925\r",
      "get val_outlayer area of all vals: 0.9652830188679246\r",
      "get val_outlayer area of all vals: 0.9660377358490566\r",
      "get val_outlayer area of all vals: 0.9667924528301887\r",
      "get val_outlayer area of all vals: 0.9675471698113207\r",
      "get val_outlayer area of all vals: 0.9683018867924529\r",
      "get val_outlayer area of all vals: 0.9690566037735849\r",
      "get val_outlayer area of all vals: 0.969811320754717\r",
      "get val_outlayer area of all vals: 0.970566037735849\r",
      "get val_outlayer area of all vals: 0.9713207547169811\r",
      "get val_outlayer area of all vals: 0.9720754716981133\r",
      "get val_outlayer area of all vals: 0.9728301886792453\r",
      "get val_outlayer area of all vals: 0.9735849056603774\r",
      "get val_outlayer area of all vals: 0.9743396226415094\r",
      "get val_outlayer area of all vals: 0.9750943396226415\r",
      "get val_outlayer area of all vals: 0.9758490566037736\r",
      "get val_outlayer area of all vals: 0.9766037735849057\r",
      "get val_outlayer area of all vals: 0.9773584905660377\r",
      "get val_outlayer area of all vals: 0.9781132075471698\r",
      "get val_outlayer area of all vals: 0.9788679245283018\r",
      "get val_outlayer area of all vals: 0.979622641509434\r",
      "get val_outlayer area of all vals: 0.9803773584905661\r",
      "get val_outlayer area of all vals: 0.9811320754716981\r",
      "get val_outlayer area of all vals: 0.9818867924528302\r",
      "get val_outlayer area of all vals: 0.9826415094339622\r",
      "get val_outlayer area of all vals: 0.9833962264150944\r",
      "get val_outlayer area of all vals: 0.9841509433962264\r",
      "get val_outlayer area of all vals: 0.9849056603773585\r",
      "get val_outlayer area of all vals: 0.9856603773584905\r",
      "get val_outlayer area of all vals: 0.9864150943396226\r",
      "get val_outlayer area of all vals: 0.9871698113207548\r",
      "get val_outlayer area of all vals: 0.9879245283018868\r",
      "get val_outlayer area of all vals: 0.9886792452830189\r",
      "get val_outlayer area of all vals: 0.9894339622641509\r",
      "get val_outlayer area of all vals: 0.990188679245283\r",
      "get val_outlayer area of all vals: 0.9909433962264151\r",
      "get val_outlayer area of all vals: 0.9916981132075472\r",
      "get val_outlayer area of all vals: 0.9924528301886792\r",
      "get val_outlayer area of all vals: 0.9932075471698113\r",
      "get val_outlayer area of all vals: 0.9939622641509434\r",
      "get val_outlayer area of all vals: 0.9947169811320755\r",
      "get val_outlayer area of all vals: 0.9954716981132076\r",
      "get val_outlayer area of all vals: 0.9962264150943396\r",
      "get val_outlayer area of all vals: 0.9969811320754717\r",
      "get val_outlayer area of all vals: 0.9977358490566037\r",
      "get val_outlayer area of all vals: 0.9984905660377359\r",
      "get val_outlayer area of all vals: 0.999245283018868\r",
      "processing: 0.0 pixel val: 434\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing: 0.999245283018868 pixel val: 1062]3]8]790 638]300]\r"
     ]
    }
   ],
   "source": [
    "# Super voxel clustearing\n",
    "cluster_super_vox=Cluster_Super_Vox(min_touching_area=min_touching_area, min_touching_percentage=min_touching_percentage)\n",
    "cluster_super_vox.fit(seg_foreground_super_voxel_by_ws)\n",
    "seg_foreground_single_cell_with_boundary = cluster_super_vox.output_3d_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "delete too small cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete too small cells\n",
    "min_cell_size_threshold=10\n",
    "\n",
    "seg_foreground_single_cell_with_boundary_delete_too_small = delete_too_small_cluster(seg_foreground_single_cell_with_boundary, threshold=min_cell_size_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "assign boudary voxels to their nearest cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign boudary voxels to their nearest cells\n",
    "seg_final=assign_boudary_voxels_to_cells_with_watershed(seg_foreground_single_cell_with_boundary_delete_too_small, seg_boundary_comp, seg_background_comp, compactness=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reassign unique numbers\n",
    "# seg_final=reassign(seg_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "see the final result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorful_seg_3d(seg):\n",
    "    unique_vals, val_counts = np.unique(seg, return_counts=True)\n",
    "    \n",
    "    background_val = unique_vals[np.argsort(val_counts)[::-1][0]]\n",
    "    \n",
    "    seg_RGB = []\n",
    "    for i in range(seg.shape[0]):\n",
    "        mask_gray = cv2.normalize(src=seg[i,:,:], dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)\n",
    "        seg_slice_RGB = cv2.cvtColor(mask_gray, cv2.COLOR_GRAY2RGB)\n",
    "        seg_RGB.append(seg_slice_RGB)\n",
    "    seg_RGB = np.array(seg_RGB)\n",
    "    \n",
    "    for idx, unique_val in enumerate(unique_vals):\n",
    "        print(str(idx/len(unique_vals)), end=\"\\r\")\n",
    "        if unique_val == background_val:\n",
    "            COLOR = np.array([0,0,0], dtype=int)\n",
    "        else:\n",
    "            COLOR = np.array(np.random.choice(np.arange(256), size=3, replace=False), dtype=int)\n",
    "        \n",
    "        locs = np.where(seg==unique_val)\n",
    "        \n",
    "        for i in range(3):\n",
    "            seg_RGB[locs[0], locs[1], locs[2], i] = COLOR[i]\n",
    "        \n",
    "    return seg_RGB\n",
    "\n",
    "def colorful_seg_2d(seg):\n",
    "    unique_vals, val_counts = np.unique(seg, return_counts=True)\n",
    "    \n",
    "    background_val = unique_vals[np.argsort(val_counts)[::-1][0]]\n",
    "    \n",
    "    mask_gray = cv2.normalize(src=seg, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)\n",
    "    seg_RGB = cv2.cvtColor(mask_gray, cv2.COLOR_GRAY2RGB)\n",
    "    \n",
    "    for idx, unique_val in enumerate(unique_vals):\n",
    "        print(str(idx/len(unique_vals)), end=\"\\r\")\n",
    "        if unique_val == background_val:\n",
    "            COLOR = np.array([0,0,0], dtype=int)\n",
    "        else:\n",
    "            COLOR = np.array(np.random.choice(np.arange(256), size=3, replace=False), dtype=int)\n",
    "        \n",
    "        locs = np.where(seg==unique_val)\n",
    "        \n",
    "        for i in range(3):\n",
    "            seg_RGB[locs[0], locs[1], i] = COLOR[i]\n",
    "        \n",
    "    return seg_RGB\n",
    "\n",
    "# seg_RGB = colorful_seg_3d(seg_final)\n",
    "# hand_seg_RGB = colorful_seg_3d(hand_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995454545454545536\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1900731250>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N=50\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "plt.title(\"hand seg\")\n",
    "plt.axis('off')\n",
    "plt.imshow(colorful_seg_2d(hand_seg[N,:,:]))\n",
    "#plt.savefig('ATAS_hand_seg_'+str(N)+'.png',bbox_inches='tight',dpi=fig.dpi,pad_inches=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995515695067264645\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f19006e6220>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "plt.title(\"model seg\")\n",
    "plt.axis('off')\n",
    "plt.imshow(colorful_seg_2d(seg_final[N,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1900645fd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "plt.title(\"raw img\")\n",
    "plt.axis('off')\n",
    "plt.imshow(raw_img[N,:,:],cmap=\"gray\")\n",
    "#plt.savefig('ATAS_raw_img_'+str(N)+'.png',bbox_inches='tight',dpi=fig.dpi,pad_inches=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/v-andongwang/anaconda3/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:389: RuntimeWarning: overflow encountered in long_scalars\n",
      "  return 2. * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) +\n",
      "/home/v-andongwang/anaconda3/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:390: RuntimeWarning: overflow encountered in long_scalars\n",
      "  (tp + fp) * (fp + tn))\n",
      "/tmp/ipykernel_44465/2334420070.py:3: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  VOI_val = VOI(seg_final.astype(np.int),hand_seg.astype(np.int))\n"
     ]
    }
   ],
   "source": [
    "ARE = adapted_rand_error(hand_seg.astype(int).flatten(), seg_final.astype(int).flatten())\n",
    "ARI = adjusted_rand_score(hand_seg.flatten(), seg_final.flatten())\n",
    "VOI_val = VOI(seg_final.astype(np.int),hand_seg.astype(np.int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI: 0.748866083089113\n",
      "ARE: (0.16084732144362268, 0.893167599297504, 0.7912983691038779)\n",
      "VOI: (0.3923305738251081, 0.5077998166832652)\n"
     ]
    }
   ],
   "source": [
    "print(\"ARI: \"+str(ARI))\n",
    "print(\"ARE: \"+str(ARE))\n",
    "print(\"VOI: \"+str(VOI_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(139, 512, 512) --> (41, 153, 153)\n"
     ]
    }
   ],
   "source": [
    "def img_3d_interpolate(img_3d, output_size, device = torch.device('cpu'), mode='nearest'):\n",
    "    img_3d = img_3d.reshape(1,1,img_3d.shape[0],img_3d.shape[1],img_3d.shape[2])\n",
    "    img_3d=torch.from_numpy(img_3d).float().to(device)\n",
    "    img_3d=torch.nn.functional.interpolate(img_3d, size=output_size, mode='nearest')\n",
    "    img_3d=img_3d.detach().cpu().numpy()\n",
    "    img_3d=img_3d.reshape(img_3d.shape[2],img_3d.shape[3],img_3d.shape[4])\n",
    "    \n",
    "    return img_3d\n",
    "\n",
    "scale_factor = 0.3\n",
    "org_shape = seg_final.shape\n",
    "output_size = (int(org_shape[0]*scale_factor), int(org_shape[1]*scale_factor), int(org_shape[2]*scale_factor))\n",
    "print(str(org_shape)+\" --> \"+str(output_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell count accuracy iou >0.7: 0.8822115384615384\n",
      "cell count accuracy dice >0.7: 0.9122596153846154\n",
      "cell count accuracy iou >0.5: 0.9206730769230769\n",
      "cell count accuracy dice >0.5: 0.9711538461538461\n",
      "avg iou: 0.843742070459904\n",
      "avg dice: 0.904141411686746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_44465/148196946.py:4: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  iou=np.array(accuracy_record[:,1]>0.7, dtype=np.float)\n",
      "/tmp/ipykernel_44465/148196946.py:7: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dice=np.array(accuracy_record[:,2]>0.7, dtype=np.float)\n",
      "/tmp/ipykernel_44465/148196946.py:10: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  iou=np.array(accuracy_record[:,1]>0.5, dtype=np.float)\n",
      "/tmp/ipykernel_44465/148196946.py:13: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dice=np.array(accuracy_record[:,2]>0.5, dtype=np.float)\n"
     ]
    }
   ],
   "source": [
    "accuracy=IOU_and_Dice_Accuracy(img_3d_interpolate(hand_seg, output_size = output_size), img_3d_interpolate(seg_final, output_size = output_size))\n",
    "accuracy_record=accuracy.cal_accuracy_II()\n",
    "\n",
    "iou=np.array(accuracy_record[:,1]>0.7, dtype=np.float)\n",
    "print('cell count accuracy iou >0.7: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "dice=np.array(accuracy_record[:,2]>0.7, dtype=np.float)\n",
    "print('cell count accuracy dice >0.7: '+str(sum(dice)/len(dice)))\n",
    "    \n",
    "iou=np.array(accuracy_record[:,1]>0.5, dtype=np.float)\n",
    "print('cell count accuracy iou >0.5: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "dice=np.array(accuracy_record[:,2]>0.5, dtype=np.float)\n",
    "print('cell count accuracy dice >0.5: '+str(sum(dice)/len(dice)))\n",
    "\n",
    "print('avg iou: '+str(np.mean(accuracy_record[:,1])))\n",
    "print('avg dice: '+str(np.mean(accuracy_record[:,2])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}