{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### if the MR is in dicom "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for DICOM series in: /media/data/BrainIAC/src/BrainIAC/data/dicom\n",
      "Found 2 potential scan directories\n",
      "Converting scans: 100%|███████████████████████████| 2/2 [00:00<00:00, 15.50it/s]\n",
      "\n",
      "Conversion Summary:\n",
      "Successfully converted: 2 scans\n",
      "Failed conversions: 0 scans\n",
      "Output directory: /media/data/BrainIAC/src/BrainIAC/data\n"
     ]
    }
   ],
   "source": [
    "!python ./preprocessing/dicomtonifti_2.py -i ./data/dicom -o ./data/unprocessed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### if the MRs are in nifti "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting brain MRI preprocessing...\n",
      "\n",
      "Step 1: Image Registration\n",
      "Preloading step...\n",
      "100%|█████████████████████████████████████████████| 4/4 [00:00<00:00, 31.93it/s]\n",
      "Registering images...\n",
      "  0%|                                                     | 0/4 [00:00<?, ?it/s]Processing image 1: 00001_t1c\n",
      "Saved registered image to: ./data/sample/processed/temp_registered/00001_t1c_0000.nii.gz\n",
      " 25%|███████████▎                                 | 1/4 [00:26<01:18, 26.19s/it]Processing image 2: 00001_t1n\n",
      "Saved registered image to: ./data/sample/processed/temp_registered/00001_t1n_0000.nii.gz\n",
      " 50%|██████████████████████▌                      | 2/4 [01:16<01:20, 40.40s/it]Processing image 3: 00001_t2f\n",
      "Saved registered image to: ./data/sample/processed/temp_registered/00001_t2f_0000.nii.gz\n",
      " 75%|█████████████████████████████████▊           | 3/4 [02:08<00:45, 45.74s/it]Processing image 4: 00001_t2w\n",
      "Saved registered image to: ./data/sample/processed/temp_registered/00001_t2w_0000.nii.gz\n",
      "100%|█████████████████████████████████████████████| 4/4 [02:53<00:00, 43.36s/it]\n",
      "Successfully registered 4 images.\n",
      "Contents of output directory ./data/sample/processed/temp_registered:\n",
      "['00001_t1c_0000.nii.gz', '00001_t2w_0000.nii.gz', '00001_t1n_0000.nii.gz', '00001_t2f_0000.nii.gz']\n",
      "\n",
      "Checking temporary directory contents:\n",
      "['00001_t1c_0000.nii.gz', '00001_t2w_0000.nii.gz', '00001_t1n_0000.nii.gz', '00001_t2f_0000.nii.gz']\n",
      "\n",
      "Step 2: Brain Extraction\n",
      "Running brain extraction...\n",
      "Input directory: ./data/sample/processed/temp_registered\n",
      "Output directory: ./data/sample/processed\n",
      "File: ./data/sample/processed/temp_registered/00001_t1c_0000.nii.gz\n",
      "preprocessing...\n",
      "image shape after preprocessing:  (108, 137, 113)\n",
      "prediction (CNN id)...\n",
      "0\n",
      "running postprocessing... \n",
      "exporting segmentation...\n",
      "File: ./data/sample/processed/temp_registered/00001_t1n_0000.nii.gz\n",
      "preprocessing...\n",
      "image shape after preprocessing:  (108, 137, 113)\n",
      "prediction (CNN id)...\n",
      "0\n",
      "running postprocessing... \n",
      "exporting segmentation...\n",
      "File: ./data/sample/processed/temp_registered/00001_t2f_0000.nii.gz\n",
      "preprocessing...\n",
      "image shape after preprocessing:  (108, 137, 113)\n",
      "prediction (CNN id)...\n",
      "0\n",
      "running postprocessing... \n",
      "exporting segmentation...\n",
      "File: ./data/sample/processed/temp_registered/00001_t2w_0000.nii.gz\n",
      "preprocessing...\n",
      "image shape after preprocessing:  (108, 137, 113)\n",
      "prediction (CNN id)...\n",
      "0\n",
      "running postprocessing... \n",
      "exporting segmentation...\n",
      "Brain extraction complete!\n",
      "\n",
      "Contents of output directory after brain extraction:\n",
      "['00001_t1c_0000.nii.gz', '00001_t2w_0000.nii.gz', '00001_t2f.nii.gz', '00001_t1n.nii.gz', '00001_t2w.nii.gz', '00001_t1n_0000.nii.gz', '00001_t1c.nii.gz', '00001_t2f_0000.nii.gz', 'temp_registered']\n",
      "\n",
      "Preprocessing complete! Final results saved in: ./data/sample/processed\n",
      "Final preprocessed files:\n",
      "['00001_t1c_0000.nii.gz', '00001_t2w_0000.nii.gz', '00001_t2f.nii.gz', '00001_t1n.nii.gz', '00001_t2w.nii.gz', '00001_t1n_0000.nii.gz', '00001_t1c.nii.gz', '00001_t2f_0000.nii.gz']\n"
     ]
    }
   ],
   "source": [
    "!python ./preprocessing/mri_preprocess_3d_simple.py \\\n",
    "  --temp_img ./preprocessing/atlases/temp_head.nii.gz \\\n",
    "  --input_dir ./data/sample/unprocessed \\\n",
    "  --output_dir ./data/sample/processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BrainIAC - Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BrainIAC Loaded!!\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:00<00:00,  2.60batch/s]\n",
      "Features saved to ./data/output/features.csv\n"
     ]
    }
   ],
   "source": [
    "!python get_brainiac_features.py \\\n",
    "    --checkpoint ./checkpoints/BrainIAC.ckpt \\\n",
    "    --input_csv ./data/csvs/input_scans.csv \\\n",
    "    --output_csv ./data/output/features.csv \\\n",
    "    --root_dir ./data/sample/processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BrainIAC - Saliency map geneation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "approx. runtime \n",
    "-> 12 seconds on CPU\n",
    "-> 1.5 seconds on GPU (Nvidia A600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BrainIAC Loaded!!\n",
      "Generating saliency maps:   0%|                           | 0/1 [00:00<?, ?it/s]\n",
      "Computing GuidedBackpropSmoothGrad:   0%|                | 0/10 [00:00<?, ?it/s]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  10%|▊       | 1/10 [00:01<00:11,  1.29s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  20%|█▌      | 2/10 [00:02<00:09,  1.23s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  30%|██▍     | 3/10 [00:03<00:08,  1.17s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  40%|███▏    | 4/10 [00:04<00:06,  1.13s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  50%|████    | 5/10 [00:05<00:05,  1.12s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  60%|████▊   | 6/10 [00:06<00:04,  1.09s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  70%|█████▌  | 7/10 [00:07<00:03,  1.08s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  80%|██████▍ | 8/10 [00:08<00:02,  1.08s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad:  90%|███████▏| 9/10 [00:09<00:01,  1.07s/it]\u001b[A\n",
      "Computing GuidedBackpropSmoothGrad: 100%|███████| 10/10 [00:11<00:00,  1.11s/it]\u001b[A\n",
      "Generating saliency maps: 100%|███████████████████| 1/1 [00:11<00:00, 11.62s/it]\n",
      "Saliency maps generated and saved to ./data/output\n"
     ]
    }
   ],
   "source": [
    "!python get_brainiac_saliencymap.py \\\n",
    "    --checkpoint ./checkpoints/BrainIAC.ckpt \\\n",
    "    --input_csv ./data/csvs/input_scans.csv \\\n",
    "    --output_dir ./data/output \\\n",
    "    --root_dir ./data/sample/processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualize the saliency map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 128) (128, 128, 128) 1.0 0.0015814114594832063\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAHiCAYAAADf3nSgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMhklEQVR4nO2dbXfjSJKdEyAoqt6me+zdOetj+///r7XP2uvZ2a7urlKJJF78QTONiJtCAKBIJiU9zyemMpEAAZBJxNWNqIZhGBIAAABcnbr0AQAAALxXWIQBAAAKwSIMAABQCBZhAACAQrAIAwAAFIJFGAAAoBAswgAAAIVgEQYAACgEizAAAEAhmqUDq2rxUIAboSp9APAiSOYHr5thaGfH8CQMAABQCBZhAACAQrAIAwAAFAKhF24QtFxIKb8P0Ijh7cGTMAAAQCFYhAEAAApBOBoK8ZZCzm/1t2xf+gAEe88Qmoa3wVv99gAAALh5WIQBAAAKwSIMAABQCDRhuCCvWfc9z+/T6obPwTCrq17iN/q5dGbsS/A24EkYAACgECzCAAAAhSAcDS/kdsOtMct/f74opFzd7u/c7F0N5wkVx2HuNedjzfFE14hQNdwut/sNAQAA8MZhEQYAACgEizAAAEAh0IRhJbemAV/BSrRK112hNV9BLx7W6Lyrjmd63mrFPtfpx6dq1tiZ4HbhSRgAAKAQLMIAAACFYBEGAAAoBJow3CAFUkaGemh8PLG2W1YjruQUrNKIHbrdeKzZnNn7OE0/zvXi6Pycy1OcHwXAJeFJGAAAoBAswgAAAIUgHA0zXMqSdNrvv1sMMS8NI1dz7/kKliUNT4eEoWLbp+PWhK6nd/+yUPWlqjVdAkLe7xmehAEAAArBIgwAAFAIFmEAAIBCoAnDM1xCBztT6cBZ3XS6/1w6b6btrhm7+HiWz3MphmrUVXNr09iuRH8dtG22zTXpenJsnm3S7tN3Xs7OdA1O/byhJb8FeBIGAAAoBIswAABAIQhHwwU5MQR9NmvR8lDwS0LMS0PXc+HneJ5NuO1Son1E2bT64Tg51oatn8a2fp8m/BuFqp+Oz/bJQaywM62rzmS5tVB1xFwYm3D1a4AnYQAAgEKwCAMAABSCRRgAAKAQaMKQzmdJijTNmX2cqOVG1qLQ1jOjCUfzhNptNu+0lpuNXTFvxKm2KNVnrX5bD/6rou9H3Vf1Yt1/n1rX8mOn9eS8ApSf1U8k53JVyktLieeSS+nQ9gSiD98qPAkDAAAUgkUYAACgECzCAAAAhUATfpecMy3liTpwoO1Guu8af+8abVTH1nVj+mKPrtOPA513nbbs93mqzluv+J3dBx7eTPe176sXDVjG2mnVJ+z14pRq85WkY+1Eg3iRM5wcqjr08vs/1o/PxZpnoVP14ywH6InzwLnhSRgAAKAQLMIAAACFIBz9bihsQ1phLQptR5W/ZU+1Fs1ZiZaGmHU/2ldX2+m+7PjGY1iXGtMfe73QztRnlqRO+seQbzeoBSgI0WvE1GwapcZ8OgYbu5aQs7u99KtLQ+lT2+kO4+NZE7o+B/Ph73Ol3MS+dCvwJAwAAFAIFmEAAIBCsAgDAAAUAk34zXJ5DXj+EJbpvNpfi+4b2Y507FLdd07ntfPmY1WDHceqzhtpwhvTlx2fzhMde3CNatGLe6P7agnCTqxFfTW2MxuS3ae6xPRaGz3Zprt8Dm9ZkvvAasRhSsuZkohuYHg418Ho0nMa9LqUm0s1YuxLJeFJGAAAoBAswgAAAIVgEQYAACgEmvCb4vo6sNOwTkxFmZJou5E+O6MtRxpspBfn2vJmsi/SfWvRea3uu0m+L593MznWvu9s/zNpNadok9eAu+oo/ftx/7XfRzs8jn3iGW4DTTgjkC01paX7ugo9xF4H1pKIU+OuR1yGMWJdiUY776ke4qeZ4XLwJAwAAFAIFmEAAIBCEI6GdHL4OaUwlLY0FaXOk4WcbSi21rSV0+km87G2bzrcm1JKG2s7qjSMfFrIWfu03Zj2ZpgOgWdpKrPw9HT8tTehxU7C0cfqIPMss0X1EsbWc9kG4ejsPuieH5eSD08PWchbJx5fRiHnKFR9KdaFwIPQ9Ux1KB+ePtW+9DTzNISqXwpPwgAAAIVgEQYAACgEizAAAEAh0IRfNZcXtNaVcluu/YW6b5SKUvVQ0X2jdJNrUkg6LVf7VNsNNGHb3qad7xPd145VnXdjbEC5BnyaJtyqnh6Vc8w08/FYrZUppdzOZC1KeqzH/oc/QLtpoA8rameqFpYyvJ5FyaSmXGFJyo/PaLk6z0xZxsuAXvxSeBIGAAAoBIswAABAIQhHv0vOVRnJzxVlxcoq/azKZmXCtEH4+al/O9m3qab7NBTbVGPoWMPRUch5k6ZDzM0ws0+bMSuwKGm4eZNZlpaFo7PthukwdyPvy9qZsvORVYcy17aOs3u1wzjvoOHVFdHWwQ2WDYfp0PCwyrqz4ngWR2bFdqTHN0yPzT5T5n2eL5vWGsi8tQSehAEAAArBIgwAAFAIFmEAAIBCoAm/Kq6TY2+dLSmYJ0ppGViUoupHkQacUkpNdWfGTqebnNN57X626V7GBtWPVqSbbNQSFFUbsuNmfjtbTVivpd2j6oSqJVuNWO1M9nzl6S9FE7bXTys+RdkUZWibfjw/7hmcZUmkyCG4vbNzey7bz8KPVGhJSv5czo31u79USkt4KTwJAwAAFIJFGAAAoBAswgAAAIVAE343RPpsJJJNe39TissVRvNEum+UmjLSgFPyum9T+zSRNt2k9QGntC7dpPppdVu/zxUpCqtRi+tXWSr9+fJeYNF5XTsWKmvnf/b76IYxp2SkF6ekmn7sEw7tq2bTrvKpMqMUl+r9te3Mi6wE/9ewxlOclVqcmCf2BacU67XqxbcTrdF5L6URuwM605yvH56EAQAACsEiDAAAUIgV4ehL2WMIS8Rcx5bkd7nmt1lgLQrSVmZtG7IMUlNmFiWxw9gQdCPWIpeKUkKmUYUjHbvGWmRDzEof3fuVj6/akGU/Y2WyIec620f17Lh//MWPHCbH9mbsVlNaDr6ikT+eIGSqRFHRuVvUnL5BzmXfj8f3orSVK766QluUmUf3H4WnK5kzDK2vSmkJ14QnYQAAgEKwCAMAABSCRRgAAKAQN2BRikSh96hVnEsDPtGSNDPPmlSUKShPGJUrzMeatJBBKsqUvA6sNqQmWb1YNOHBW51cecJBSwVOl/zrRdOzVqNOtcnMOjPts7E9ldp85GNSB7pvpPPOtS1Wz1Ztu5Hz487fXHW7pRqxyp/B44TVgHWs6qh6D0c6q0shOaclB7qv+5xois0VlqRcI7atU1Na6n5IaXlueBIGAAAoBIswAABAIW4gHB3xXjKsFLAhud2fZklKSULHWYh5ubUorJQUzKMhZ9ufW4tsiNnPk2XMMiFUDT+rRcnjx2rI2aLhZzu2D6xNtYZIK+2ftobZELOGkbPQtcY3DTYTl56NIU/zZPr89eol/tqZ6kd9Fr437bmkTtEtbcbm2bT8Pn3VoiAUO/f1FHyVuWPIrETT88xm+wqPx8bkS4SY53SJ9wNPwgAAAIVgEQYAACgEizAAAEAhblwThnWc35YUWZKe5l2WmjKzJIWpKUWfNe3MohRU8NnI7R2losztOfWzry+J1YG7dJwcN8xYlDrTrxWfene9Yk046guNhZlXxu7f77Md/HXo04fxtabuDHTyNSku7X3ZS4rNQc6X011f8q8b9m0HcuiqtJkZ05alNdWYYsvSpSosvV94EgYAACgEizAAAEAhWIQBAAAK8Yo04bfkK7t8asp496f/9so04oWpKSNfcEop1bXVfQO9WLVcHZs2z75+7UR+Yz2X7TBqqRvZrjXtvDyh/0xtFt6ntWjAvQiQ1m9cS00/9Vx3pr3NPMUr9Nkg02JX2zSRoplnGrEp+bdCLw7TXUblHNVDLPNWgbZ8sm9YP9PFfcOv+bt9PTwJAwAAFIJFGAAAoBArwtGnrtf8C/v5WH4Nzlcpac0+p+fJqyadZlHSELO2s1DfQjQUW62oXhNh7U1qP+n1vdjQp9qQAqLUi13lw6uNScfZ6/VSy5INKwcpLDX8rMHEKI1lds+YkK/a0bbRNVlafSmlVA37P163M19PfWrjAf9gRTWoF6WbXIX9/Okx2FZ8PPa75DoVlt6S9DgPT8IAAACFYBEGAAAoBIswAABAIa5gUSLN2cssSWfSgV9gS/L7WJ6asjKaZ25fUm33xHmyUn3L3qeWrNPzbFMk9meSpNQOU1eqpZp0itn7SqZPj305VvvOLUnLtns6nkAjlrHRPlXPttcvKxlpLUtzHynTr/83cPA7dHSDatRGQ+99KtFQL16hEa9KW2n/z2JmH9fTni/N29aIeRIGAAAoBIswAABAIQpkzDpXePpthyiWcHr4ObIk+f4oQ9bz2z7fF4Wxn8aakHNgO5qzIPUuI5Sfx2Vcym4XrVpkbFFSvUfD00tD4Nm4KJwoffZ9zVW28ttpaLif7FP7ie2vtVJT8Hlr5TPdmbb2RWj4vnHnZyeDo4mmu1q9n3vJPmYbwVfXrJVpYcg5u7aaQesact5NZNCKeFvZtXgSBgAAKASLMAAAQCFYhAEAAArxiqoovRcu9btohdVpjZYbWYvOZIuyrNHE1MrjbD6z/1JgNWKt3DStEUf68Kx2bOeRPIOteS/1cLpFKSJP3Tmtt9mxqiXnmvB4vJFGreQaumln1+9EjTjKwvh0gH+Qqb5BxsZIIw7/r+FKGrD9bOZZRU/dZwk76uv/3yCehAEAAArBIgwAAFAIFmEAAIBCoAlfjJekqpyacWbOhRps5AuemzPSfdXfG+9zOZEulpfxsz7YaTL1Sk5tbfTHXLfU9JPTKS4b8xHTVI/qY7aJI7U8YGU0xm7FuYzSS85htV71BUepKDvR4lunCS/XCSMNfTPIV1ekEa/xEAca8dAH92Hl33Ok+9pUmHoMmmoy8g0PcuxRGsvz6b5wbngSBgAAKASLMAAAQCFecTj69f0r+nU5zZKUUpwmMk83eX6LUl7haLQIabiuD8KA+d7XpHu06S+1b3reqBKRsonC90HVolbSOWqI11aAyio3BedAw8oRvQtV+2twkBSgXbXcUuXC58OKe/hSKS7NKemrKBwdh3etZSkKMVcyz+uuhIRlaQk8CQMAABSCRRgAAKAQLMIAAACFeMWaMCy1JKWkOlSs86YVOq+1JUXa8hyDTcVY+dtycBaXeB/esiSlDMMUiZ4+SG0YKV2abrIfrLYcK8ZWD1VrUR8ImXpO+jPpiJFGbHXgVjRh1Uftec/1fmXU/yu5XrXT+2dKWppDbwL7UjaPjjVtTYPqplyhe+su+97oxXp/D9H9fX3UJqkpS2OCPJ/vGJ6EAQAACsEiDAAAUAgWYQAAgEKgCd8E1/gtNK3zqrZcG022Fn22rnxZv8gn7Mctd9Bm3sgVmRetbqf62hpFak3ZQz+vatbTGnW+z2VvVPdRz6TVPGUfL0G15CFIW6marPNnZ/r69HZRWzVy5yUPPLt/HzzOE+i+Qy19ckmslltFqSlnJFZfgvA166olPMS3CU/CAAAAhWARBgAAKESBcPRbDTtcJsw3WznJsTA0PJeKMphH23U93kJRSsvsGFbYq2w4cS6t30uqNbl9OjuKWnAkrDzYcyDhVhNSzSsjTVcmukbYeA57DBpi9ulBp1NsPm27MX3L0X1GwfzofEXVqjQFaJj1MLAvZdahzIZkbFoa1rZpK+VY31Yay4j3a1/iSRgAAKAQLMIAAACFYBEGAAAoxBU04XPF92+/JNUt47TSFTqvWpJsX0pz+vHm2XHPH9+aIoDnJ05pqTYaKZ9o3nc9dDJ27GtNObuneb3I2AZ9Vh9V/XFNCcII3WecRnN6n/0gXysu9WNchtGnptTjme7L7WjLjj3bTks/Vk74ddj0nJqqU9Nz9lU7Odb2rSFO2XpGnIVK7V7j+VmXwvJaBPlnbwSehAEAAArBIgwAAFCIFeHo9/Vv48u4so1kxn6ztFJSFoKrNMRsLCW1ZsySsQtDzlnIu/DvPw3dRcc+l+UpGmstS5usqpNae8axUU0e3S4KpSu1swvF928VhHSrwE6l7W4wliAN066QAZo0fa81QThabX72KmgItdVzYrr7avq8q0Sh91dXHcfjMa+fji+QdIKqSperqEQ2q2vCkzAAAEAhWIQBAAAKwSIMAABQiBuvonSb/1L+cs7122dFWsig2lFsUVpeRSk/utv9jTdnmbLkFXqm7VRz1X0suR66bKxup5YXSyWWG2sR0nehWm7jtErftw305E5sLK3ZU/cCe1VkO1LdNxobsYmsTsP0PJlFSdpdGnVgtST19djuB7ErrfkK1M91kHLTZlCtsr5AA86qTpmUsrKT27Qs3R63+y0JAADwxmERBgAAKASLMAAAQCFuTBO+dQ3h8r7gdaULT92HehG9Omh1YE0nqRrwRjTi10qk3aoGnHlUTZrGTfLnY2M8so183CKtMvICZ2kPg89NrWMDvS/z05prvc2ue3Sf+vPVGQGyHTS9o+fUFJy5RnwaunerA2fXKzjUXsoVWp9wJz7hNu3/eJ37hKdLG2b/16BpNfH3vgp4EgYAACgEizAAAEAhbiAcfesh6HNwrd8606kp42pH0221KGn4OZrXEtoehGieubE+LaMP5W3M7R7ZjFLy70sr62jI2aZT3A53MtaGo+P35UOxPpxpw+Vq88nnGfvn3qffh4ajx3ZT+75dba+7zCPtzhxur6HqXsPuplrUFb4acnuOb1tLV9P767c1UoNawZTehJE1HN3Vu3Hc4PuGWsL3/XhfZPvMouXmGum5tMWhpC8+J4S4zw1PwgAAAIVgEQYAACgEizAAAEAhVmjC70G7Va5TqjC0JUXlCU/UTuc1YaurxqXVNI3l9AEsG/YcdbVM607J67Ubub1tn+q82T5desdp21FKKd2Z/p2OtfPMXC+b7rGL3mem70mqwxN/W+vxWU3YasAppbTbjH1bEYFVE7aybycCZC+pIDvTHaU97Ge+jqK0jBHxsfvrfuxNmcpOPhfyvqxOr5pwXxv7kmrCaumqpq1qerrsPZNl3FzxPxoh9p6ROUljuQyehAEAAArBIgwAAFCIG7AovVWu//vGhZVfYPM5dWwdziPVfIJwmM5jswapRSoLFZtbejvsJsfm1Y4k45IJV+vYrexzZ/a5E0vXXW3D0XFctDWxz6OG9oIKPlF4Wm0s9r3oe15lUTLh6LuNnDs5HLtpp2FaCU/bZq/hzChD1YmRTg0/Z3Yruw/ZtjWWpYdW7pGjfBbMxraiUkoptdX+2dcp+QpLKfksWBqq7mVeF47OQsX2u8If6ho74WXQ75HSx3NZeBIGAAAoBIswAABAIViEAQAACoEmnFG4UlKmq0ZpIVdouS6l5fJUhlpFSfH2oeXzbmbmdWOd7cjrsdvkdV+rA2tfVNFIsfroRt7XTra9rxvzWuxLRi+d04StfeexE53VZrGc0T8j+5V9L5vMBuVpKnvsMo9pb6Wvkd2r7mpRi5IlyLQ4O3YpOqe+z+jYj0aq/ND46662rX5//8frrv/Jz2M14Vo0YbUsmRuhF2taZt8b6sk+d9ozDVgti2ZodqLftl57DXgSBgAAKASLMAAAQCFYhAEAAAqBJnw2DfgFv2dW+HTdZpmfdloDWjXvmX6bnTqPlt9rqlHbbQINOKWUdune9E2XFZzzyNr2Njse/76sDnzf+HnuN6YU3szp6Ppp/djqs7XoxXXv20dz3ntRS+370ves76uy+wz8tBt5X6qr2vcdeYi1X/t03miepb7hfB9+w+h4jua8//CVJ7MUoN0wftUe9x9c34/hT3+8PlQ/XF9bP8o8ow5cq4e4Ey+wSWuZacJD0IfOe1V4EgYAACgEizAAAEAh3mk4+jrVkca9rdlfVOEoriCUFlZKisLYc0Q2pKzC0iorVFC1yLQzS1IWnr4zfY2MXR6OtqHYrZyvO6koZG1I9xKb/dDYeVxXliLRVhDSEG+UElSPfW+sKV2QgnArNrEoZeMaojByE4Sqn/rHk7CVvq3pW2Ml0tC0bc6FxzU8bbEpOB/UUiYzH02Ky8fW39/f2o/jPJUPVe+re9fujJ0pq7QVyVPDdJ+Gn3We8mks3zY8CQMAABSCRRgAAKAQLMIAAACFeKea8Lk48TfMiakps74VFqVQE55pRyxNVTmnQ9cuneJ0aspmEJ130LGmlKEcm9VAMw1YjtfaddQutFVN2AiJd3LqbFv7VH+0mnCuWU+3FONMcTaalLwequ9L97mUOTuQnVU14Pvab3y/GSb77mqrF3udUrVm25trwtVkn2KvUSV2L6sJ55Yk3/5hNONvInZ/asf/Y/g2fPbbVb+59tGU8qxrf++rtmstS9aulJJqwnJRwjSWgT6s3xtoyYvgSRgAAKAQLMIAAACFIBx9IdbZkixBGHnGolRX4+Ws60b6xtBVHn72Yds6CGurVcb2qyXJHY/0xeFof+ybwYaYfRasRrNZmXnVgrMx1yTPDuWaPmNWreHoJG0zbzBWt8vsMCbamd89JoSa9cnEre3xM9lKTfqeNTwdFX3Kq+lMY9+nWn6i8PSnxqeh+rgZ3/lu48/CppoOfWpo2IboO6niFFV1yue120lYPQuzV8++fho73t+73lvubLa4lPzn2H6+UkqpT77iUvTdEZWdijJo5fYlfwSwHp6EAQAACsEiDAAAUAgWYQAAgEK8E034umkq891P245Sii1BTp9ZYS1S69AabdlqxLm1SPVjY/sRjSqyHek+G9O/ERvSJuxT3demv5yuEjSnf1otVcdm1p7q+dcprasKFFUJstJbK7plK7dPF0xUBTaoTR29r+V6cbZPuw/Zblupdjrqih9F9/3cjGL3vejFjViWrFapmvC+G9uH3t8/aumyGrHKqHaPtbyPrJKUTd2pqTrNid720/a8p3mNJjyXttJ+5ofp/8mgilJZeBIGAAAoBIswAABAIViEAQAACvFONGFVcwprxAFRasp5LXdZakpNdxeWQJvx97p5M33WaLlpWi9OyWu9qh9bL3DkC36ad7y2kY4ZacBP206P1btnjT66lEhbnisHuDG6ZqQzV5nWLfOY/rzknz3Put308Wk5x534aW37o9GAU0rp03b0we5EE95ufNtquW2n93f0taca8fhaPcVr8F5p/V8Fcy6zdKXTnynrGU4ppao6SHthadNMEw7KHr4gFaXNnTBERuV3Bk/CAAAAhWARBgAAKMQ7CUffOst+C81WP0qRtShIRRmEmDcS8opDzlvpC0LMYjWqgpDzdrgz4/R9acj5PLHhc83TDc+/fnafZ9mjnycPpdvQpycKK2805G0tN3Ph6MpWP5qujJRSSvcmrPxRQs4ftsaidOdTNDYbtSiNB3Fs1a6XVmC2lUistYppusvMzjRM91ny6l7LJacojWX43UFkuCg8CQMAABSCRRgAAKAQLMIAAACFQBO+MSIbUqQPaXtdn9qQjLY8t8/g+KwOrBpwph+btHqqUVudLLIkXQrVSiP6QF/Tvmhanca21SSiZQVjzdHsf6aUoW1H5Ry1ROPO306uXzXgD6LlfrSa8Nbrvp/uRwvO7t73bWRee06ao2rC02dI00/WTpb283RBPUe91lHZw+ieqYfgc5ylkF2u+0afcbgunH0AAIBCsAgDAAAU4p2Go21s5vrZs6KqSeeaN6qMlIWtNMS7tKpT8palRkLMNpQWhZ/1mNSiZCslzYWfTw1Ph/YczXCk1YaCeSKisHErMefI6qThTGeH0T7zWqLGGS5rmPT586N901mw7gNLUko+K9bHnc8A9eHD2L775LerNxKONm+0OfiTWZtj2Ej1pc1B7tN2HFslDSOPX59NHft8bG9+TZZ7hHzVsvh7JKqGFm6XZdA6D8uzZJ2zitPt+694EgYAACgEizAAAEAhWIQBAAAK8U414beBWhRcX2QlUktSkP5S01Rqe6lFKa8O48daHXiT7dNW89F5louwzuo0o/NaS86d9N3Jad+Zfq1oZO05c3pxlNqws9V8RARWq4zV3nI702DGrTh3QVWnuSpKtr0VDVY1YVsd6X7nbUi7z2Payu0X/54rKc80GOG8OYh+vDVpNB+lGpO09wer+96lKR47SbVa6+dkZI3iqZ+TCP1cvwI5FBJPwgAAAMVgEQYAACgEizAAAEAhVmjCp/pp34cwoVplkWOI/L2Rh/hEbTklr1lpij3bt5G0lZEXWFNT2nOb9Ym2a1vq73U6rwiXqvveb6KxrunSNKoeGmmnUWnDowiH/cTr5+aJ9OPIk5r7j22pPt+3NDXmU9vos3IOtLThvSlXuLv3+mzzyfh7v8h9IJqwPcBBfMLV3dje/BCf8J1v3xmNOEqN+aPz9/f2IGlazeG95MnH/w/G9P9nPNeG24SrBAAAUAgWYQAAgEJcwaI0F6Z9H+Hqc7Aq9Vw1nRZS59H0d9ZaFKWp1HZU/Uj71IbUuDBbNd2nlX6ShpzHsVt5Xzas/GHj+z5InPSj+WTcS7Rew9E2pLqmMtKx96Nt1FRD1zYc3GUpLactS9oXhaM3cjxHk35ym+1zOlQdWXC0gtGm0pSSY7uWndY7Y1XbSehVL5Lt202Ho2vp20i7+TF9vo7tuM97sUFpmN2lAJVr6z8nKq9Mf94uFW4ezpo2EubgSRgAAKAQLMIAAACFYBEGAAAoxA2krbQayK3rw7f1myVMTRmUMsx1pq2MNbajFTYIndfakqJUlE/99XSfOR61HWU2JDNWrUX3Rgf+JJaWL/4UpM/mk/FRyuTdid7XVNP3bWu00+Ogep9qudOK8jDxOqVck20XasJaUq+Wma1srsfmbFAzpRUjVPuuzbnUf4Gw7Uo0fG27iYO+LB1nnSX6HKfZi+77YOxLom3rlVxT4tJtd2PfOXB+uMIAAACFYBEGAAAoBIswAABAIW5AE7aocHLrGnFZMk3Yaaf+0tamvam8AKq6r/UJb5KOFS+wSVWpqSjtWE03udG2PXYtV2h030gDTsmnprwXTfij0QZVA/5pO0y2P2283reTtj0CVRT33dj7KD7cQdJ8tuYQIgmxz3zBSdrjH9rBH5EbmqXRlHnN8eo+7LHqp3TNpzbzGBvtWQ7dtYfWb1jpibentlb92HQ2kopS32g9/W7s6erkWmbnK9LQC3zPDakzr/EFl4QnYQAAgEKwCAMAABRiRTh6zXp9rvDGpexL5SseLcVVP5pJW+lsSJK20oagsxCzhK6t7Wgjt4hu61JcDhpytiktp1NRPo01lZI0jWYUjpZQ424ThaPH15/kzv/SaDi6M32t67vLbCwjauXZmTSadbsJxz6aaZvA05JVNJIw8hCEil3oUzo3YrVylZvUz3TiZ2juW2Sw6TCPfnS/H69DvZcwe+3tQ1luUYuJDQ+tzPMoIfkf4+vjD3/9HvZ342u5tg+d37893DZIO6qh6Z5Q8Upen4TJkzAAAEAhWIQBAAAKwSIMAABQiAtZlKK1/VSNA/uSsiptpS2BFpQuTCmlbdpN9jWDv2WsLelOxzptedqSlJLXgaPUlKoBa2rKSBP+YGS7T5KK8nPj78tPm1Fj/CSasFqUbPrJYy/vsxt32ooGfJCxW/M+9W6PLUvSvsJnw5Xmkz457Vnb0ss5aY2WetjL/zV8G8+7ppfMLEt35lzKPTOYE6YacPu7bx9+G+/3b993ru83owl/Pfp7//fW7/PB3EKPUovy2I/tdsX3o1oLIwb1e4WD5dyu2RZWw5MwAABAIViEAQAAClEgY5au+4Q6TiWvlGTsMNW0lSiyGWlbw8/bdOfbw9jWSkk2BK3h5yjkHNmQ1LpzF1iUdhKtu9/Y14P09dLunn2dUkp3jW/b6kPbYfp3rYaqH8QSZMO2p1bdUaLQ9Nwu7NHm1+v518+17XvRT3sr5+SxHe+37aNU9zLZq4beSwSNWJZqk/FMHHhpMJv2e5EIvvnB334fQ9BfHz64vr/tx75fDv5m+ybh6O8mXP4ooXOb1axL/t4apL0GMmG9DngSBgAAKASLMAAAQCFYhAEAAApxY1WU1rDGsvR60lRGVDOWBFu1qI4qLAWWpJS8DhxpwE9zjcekqSi9RUkqI0nbSo4bEUQ3ZtqdCI53ckp2TveVPqMp7qQ6zlb02a2xwGx0rNhjKrOtVvPZGT35sfMHpPNY3bwK7tk1BiQ9z64v0OVT8uddNeo60K+z//qwmTLFknQUDf3Hcbz3anmndtuu9dvdPXrtdLMb2/K2fPUjsUE9PPj7++vD/R+vf9n7z8kvh/FYv0qKzd8Ofp/fj+NOD72/7vs0itRt8lp3W/l2bzTi/iV6sdGhsSCVhSdhAACAQrAIAwAAFIJFGAAAoBCvWBOGNUTlCSMvcKQBp5TSnWlHqSnn9MfG+YTTZF8jPxt3IkjeB5qw9QZrOULVZ73mKSkR1d8blDbcmFKGjWwX6azXQPXiTKc3bdVVq4nXKeVeYNs+avnGTp8Dxv9X6EQvPvbjBT1I6cDdQcpNGo1402gaxvH14SC+4Mfp1JS/Hv3YX80xfPO7Tw+dv9Z7k6pyP3gt92h04K5Sn3AftuH1w5MwAABAIViEAQAACkE4ugBqCdCqRudA7Uy24opWX8nSVprwdBR+fupflpoySlOpba2UZNt5FSXXTDtzCJqa8s5Yjba1pozUtqnYk4WRp8PKQxC61nkuhQ0rh2krZ8LhLiQf7E8ir1n72I8T5eFnj600ZbfTtqa7bGXedjuGeDfH6XOgYe0HqYb03aTR/C5jf5jIsTikstSUe2NLOko4ujVWoy4dpc+3rS1pTagaG9LtwpMwAABAIViEAQAACsEiDAAAUAg04VfEXFmzqppOa+ksSmJJUmtKE6SijGxImprS6byiF6u22yzUfe/kZ6O2d0YHzlJRmrZqwGvsQb3YbKphmdarKRuvgVrDXF+WOnR6rCqKVveNNOCUUjoEb7sXG1JrLkQnXrXOnD+9Btm8yd5P03roQVKJarlJ297L+9p3Vr/287ZyT3SmfZTPcWdSU2qaStWIe1OHsRt83yBas7bhNuFJGAAAoBAswgAAAIV4xeHo69g9ljLI8URVcFbNe6K1oJLfV96iJBWWJCTos2tNh5+f+k02q8iiFISfU/LVkbIQcz3dp+2tmXYrfTa6Offr04Y7OwlRdoFlKRtr2lkmqSFun4qNKtcaOq+ef51Sfk6iO3gIw9G+vXcTzYWRzT6ye3hEs49pGLkxbZUewv3rNQn6hqBP2zYcrbYxa1E6pr3fTsLRtq3yVK+WpaBSUgnLkv1O1O/L07mtdeAUeBIGAAAoBIswAABAIViEAQAACvGKNeE1qG5wfatIjNFuRNdZc6SnWhJUI7a2pLlKO3WQmnJrKgipPrsT+4nVdu+1z7hIduLCinXfab2oE620FftJa/Tttp/WgFPy+mgv56cz82pVoEHG9k4zW05mrzIb57qv1fDFUibnMkpbae9S1YQlY2M6mHOQVaSS62CzifZi8/F68XKiKljqLlP92GrPqkNrta+l6Gfc67y+z1qSUvK2JO3TNhWXXgc8CQMAABSCRRgAAKAQLMIAAACFKKAJn6pT3IIfTI+97G+YNZqP9f7mZQ6n30eW2lD643KF4+s7TUUpu7Q6sJYntGN1O9XwgsyLTrvMdcvl11I9oOqd9vOOb0bTOaoOvTD75ez/Ceh1sFgdWL3aqhHb9KHq847OVu6Rff51SvF7Vq3bXus7SUWpqSnvm1Ef3W29Vmo14a0I4arb/+jGr0gtjbl1pTHjUp1L5ePIF5yS9wbPp620/2tCCstbhSdhAACAQrAIAwAAFOIK4ehb/Dd5G1a6NbvSNJdKNTdnQ1q6bR4+nK6MpKHQpZWSbAgwpZSa4FBbsb/YaKKGnzWNXmSH0XmjKj02naKmVtTQbJQiMSIKP2tlpK0L7fs+tYb5yla6T7OPhce5hCjkfG/aHxsfYv649aHZj7vDH693Oz+2acZ52lZStmo6zG7s/9Z6neSTCWXruVMLnk3pqmliI7LUlMaGNJeKMkxbGdgi4brwJAwAAFAIFmEAAIBCsAgDAAAUYoUmXEI3uAVb0hlQLTcT0aZ/C11Cr4ksSSn5kmOqKdZqvTDNyKKkKf5UY9wGNiTbp/NEliS9e/YuhaTv0zSWVpMdsj5JTTlMH4TtO85st9SipHp6KxvaVJCqxVvtUnXL+yAlqJ73LFWm3b+0I/1YNf570/648ff+F6P7/sloviml9KePj6798cvYv/3sddV6O77uvZSc7n6btvJYu1lKKX03GvFvoi1/9YeX7kwK10b+N2Du82ixNiT9bsjaZ/ofksro2avmVOtegfKJrwGehAEAAArBIgwAAFCId1JFKeIlFZZseGXa8lLduA2qShoeq559PdfW0HATZFyKLC9NkBVr7lejjcy2wXnXq95LpKw3NpKomk9KPqysFXusnUkzZs1VH7LY06ehYbUaWclAQ/uftmPfBwk/5+Foew977KFG4fiU/PXbaaY0CUd/bMZw6xexHf18vx9ff3lwfZ9+9vHfu/9i9v+T/5qr7BttJfPWr97OVG+///G6k+v37TjO++vRv7FPchPfGy/dVqxOmsFuKVGGrMsR1dOK+mAKnoQBAAAKwSIMAABQCBZhAACAQtyYJnwLlqTXmdJyDmuDWKPz5rYj1R+ff52SP3t6JtfYWCI7jOJUqOB2UttRK/vow3363669SaeolYiszqvaqaa/jO5+u0fVhDdBuslP8gn/Yuw5XxqxB0mVIKvlqn3q6LRu36dKoD28D5KK8lPjdc0vJh3lz7u967M68Je/+L7dv3hddfOXD2Pjpw+ur7o3J0E04fqX735sM7b/69Hr0N/2uz9efz36E/23g2jCJsXlXeWPdTuMx7Optuna6P+ErKrOdqp9Cf6AJ2EAAIBCsAgDAAAUgkUYAACgECs04VvQa98mXkvxukqUlvFcqCasqSp9n7aNT1i15UAvVlyayKAvNKxG2wl6PLWONQO0Lxtr0hB24hPuV/iE7VjdhZEU012m4Xs+mE/1z3d+pp+3Y/unrddj70WvtZ7nvaRafDQl/vbZ+9LjG+f5ILrzZ9GEf7ob/b4/ffKpKL/8kylP+N9EA/6fX1y7+pc/j40/+75hN2q51cF7kasvv7q2/YL88P131/fzrz/GY/1x7/o+Nf6rdWcOd1v7c9l049jcs+814sroyZVoy5WmiQyw+8k0YNJNXhWehAEAAArBIgwAAFCIG7Mo3RprUlpqyOa2ft/0ge2gD2K6GqqOiELOGhrWPdp2m51KY5laIYtotSOLhoI13aQ9XXolsyvrjk+PYXqfWRWl5w7079iRmvJTU1P+yUQwbfg5pZT+6c5YgO4k1WM9fY/86PxXxdaEQjedPwB9X9bqlFdG8mkif/owWo8+/+zD0Xd/Mef5v392fdX/+CfXHv7FtH/+2fc15io9eqtTJaHi6vcx5Nz85C1KHz+O5++TpLv8KGH3nZl3K+HejblrGgk/10lTXNbPvn62XU2PXWMnCkPXRXhb0uhtrRQAAADvCBZhAACAQrAIAwAAFAJN+BWjuo7VfVW7se02eVtIp+3BjhV9b5guu6a6b2Q70lSHLvNi4JDQ41mjQ0fU6r2yZfzUciPzViano+rQ9m1qmsrcomTmlOPbBHYvLUn4qZm2IVkd+OedaMIbP/ZobUl+aGqNLSkq7ZhSSlujCX9qvHb6WXTpTx9Hjfb+z37mzT+P6Serv/zk+pwGnFIa/vmfx8YXb1FKRp8dfve2o/Ttm2tWd8YSJGJ804zHt5H/KdDUorayoZb1bMwNvxn8V3JdaXvUjKtK9Owq0IQHtTONx75GL9Z9kKry5fAkDAAAUAgWYQAAgEIQjr4CgwRjKw3hLMx0Mxf6sf3d4DMBHatH89qHsQ6Dt0VszW1RZ6FYCU+bsGQnITgbct7oWw7CyhpGtqG8F1VYCsLTGj60YeVKrl+WYcy83kqseggsSno1o+Nz4Uw51p3YYawN6JOEmD+aDFU7CQ1v9SKZ7kbel21vpcRSI6HZezPvl62/Lz9LSPz+89jf/FlCqH/+ODYkC1b6059824Sghw8ffV83vrGqlwx1kkErPY7tfu/PZduOn5Nhxspn79OsCparcKYZs/w5qM13hVZc6it/Pddk0HLbvaCqEqyHJ2EAAIBCsAgDAAAUgkUYAACgEGjCr5jMomR04G7wum9r2ofqh+trxAbRGJtEpn+qJmzatVTa2Rjbj6airIOUoKotWy1VNeBII15jUdJ9WjeKWosa0UBtS+05a1hadarRY83a4xGpdUb1bctR0k9ai9JRrm2oX2ea8KilfpA0lR/uxSb12VhnPu1cX/Xhzm7o+oY7r48OzfRXW/U4/n9E9fDdd379zTX7v4397d/8TfzwMB7PQ+u1W7Xg2Xt4CM5dJRrwJktjaSxKSSxKQRrLzL5k+wY04JLwJAwAAFAIFmEAAIBCsAgDAAAUAk341jG671BJKspMEx71NvUJe03Yl2TL9SKjS2UpGqd9wqofWz0y1zg1veM4dpDB1sqpHstMIw5SZU7vPR9rbbGqZ4s86vRRPb5TyX3L4+umjsdaVKM+9OO1fThsdbjj0ZQv/Hb0XxUHPQmGrZREtO170YTvdt57WxnZt1LxOxD8q9bPk/amDOJBcm7+Nuq+1f/7D9c1/J9fXLv7v+P/T3z/qz9fXx/GNJq/yvl5kH8ysBbjSHFVn3Deb9JoBmkqtZ15htdUA7TbZrkK7Lwv0ZLfrw7NkzAAAEAhWIQBAAAKQTj65pgOy0SWpJRSqo21qOvFomRT482EsRqTDq8Ry8ReqrE0xt7QDGpxMZWIZqxFNo1lZl8K/DqR3SNCj0dZY2861zyRvSqsFiXtowlBP4jtqDZhU7XVaAWoRxNyfuzUojSO1fDzXXCwlVqmgvc1HER+eRjDytXvYi364K1FVWvC3vK5qX75dWz8mw9H9//7V9d++F/j6799/eT6/vpjtEl9lXD0t1bD0eP7PkqqzC74zKvtyPdJSksZawP0keRUicy1pqoSvByehAEAAArBIgwAAFAIFmEAAIBCoAnfBFZzmU4hl9lqVCPuTYk20YA6Y19qRS+ua2+9sKUOj+nO9W2l3Zrja+V4bErLQfRGLetXG3FXyyVWTvhVa5PMs9AiJJX50ppsk6rX9uaY+hUitf4Cjg7B9un+NUWi6sAWq+3quVI7097Yz3Qfdtt7mUjLOdqUl62mxjz69t33ca7N71Ka7z9HHbgSn1bViUXpg0l5qfalr7//8bL/1/90Xft/9f9n8ct/fP7j9b9/9yUR/7ofPzf/efDH85u4oh7a8ZwcRBO2n6Eu+f2/JIVkVVmrYVDm8EX//4Be/FJ4EgYAACgEizAAAEAhWIQBAAAKgSYccqYchGdiTh8ajDMw8hT3g7/svehQVpdqK68ltaItdaa0Wifikk1F2YlW6h2O3neqmqfdtJd91GI0jXy5kV4clURcQySv9TN6drStK4UnfYd+Wm/X9JJRkkHV6Y/99DVxpR4b37kV//F9Pd5vHw7yPwYPXq/dOD3Z32vbw5iKcvPot6t/9eU50725xyXvaP/LOM/hX/3/R3z9tw+u/X9/M5rwoy+t+NfD+D5/EQ3496M/Jz/MMTz2/n0dzeetl6vSJ9GzA5wGnHyJwtBv/JKUlvBieBIGAAAoBIswAABAIQhH3zg2rKwp/qLwtIajbTsLecnYrhrDY2qZ6CofHrO2pEFCzi6MrLYeDXktTMuoYexT0XOZVWNaMZcP405vqeFebdtzEo1tpS861ihcP3c8tq372LlCW2JRkvDmXT0O3s1UbupN+Pwg1Y/uv41h3Luv/r5sPvux1Z2xx/nob2pNhsvf/8OHmP/918+u/df92P8fB/91+fUw7uO3gz9538TT9dCNB7GXMPvRft5E/tHP+JrwdMT5LEoB+h108o7ednycJ2EAAIBCsAgDAAAUgkUYAACgEGjCN4ZquV67EQ1YNWLbH5WImymJ2A3TGlWmERuNqhX9yurFjRxQlMZSb8ohsOdE71N13jroW6MB6zHY44sUu1wDnj4HuV1oev861u9jemymAcvg6WSqKbX99BlrRIfeGk24qbxFqZPyl4/G3vRx78fuvo/34u5Xf1/u7nx705j/geikRKNJN/n1wVuS/v2Hb/9tP96Nvx39PN/MR+FBhPrHzn8WHgerCXv9+pj2z75OKU5jOZxJHz4fpLA8BZ6EAQAACsEiDAAAUAjC0RcjCuadBw0r25hqZlFyYazpPm1rOCzKoGWzZz21pzNmRRm0Wn1bJna8WRE31rO+MX/QebLQdWSZkjByX9kKUNF2vq1DF1uU5PyoZcme2zzk/Py458Za9HzZQ6gk/JyHo6fv/6Ocy3sTuv7t6O8nW53prvah2N1GKnhVJhwt+zj04z5+FcvUf4oN6dd2PPbvYnV6MCdsLydv3/vjO5rQsbUkpeQlnyz8nH2Oby0EfSqErv8BT8IAAACFYBEGAAAoBIswAABAIdCEQ1bkVrwBllqU8u28zuQtSl6jUgvFsRrT+h0Hr69tjP1kI5aWjeiGtn8juSntVVAlqRJdc3tq+aMVRFruuW6RyFqkGnArg48Ldd+jbDdEmnDwc131Yr0GtbH2qCVJK0DZlJdb2efGaO/29dPYYXKsYqtD/ej8zfZb63f60I5jD6rFm7aey1a03GOa1n1bV0XJfxaztksTq6lpl+vF2f+ThIOjfaLtvhSehAEAAArBIgwAAFAIFmEAAIBCoAkXQEt6VU7L1d9F0/7eyGunfsLB6TqiM1V+HpvGsh28BrypvO57rMb+g2rCxv3biBbY9r7dVNPe1o1NWznE+SatNHcueXiukNo1Cq1ZvTbSgJ/a0+eydX2SpjJ4I5GHuJa0kHrevf9ZUkh26ikeB+vla8wfVIdWDdj2R8ejPuUfIqsenO7r++y57OUuyNvjxuq1txpxP8SlDO3nOi9zuDw3gBt3IZ2X0oXL4EkYAACgECzCAAAAhSAcfXNoKGj57yQXfgpCTBryqgZv0+jNPrtB7RRqUTLh6MrfTs0wtht5HxsJTzuLktiXrIslS/14ootM59HNbH/Ul1KcStO9S42kr4i69ROvU4ptSBq6tmNb2U7PpQ2p1pqqc7CvpXqW2I6OjbX5aPjZ79Ne+yi1qPZpqsymtn3TJ1p79hIe79z7nJwmPHcp+dSUUZpYtSTp5y+SlSLLUvR9MBd+HsK7zw7ErnQKPAkDAAAUgkUYAACgECzCAAAAhUATvnG8XuM1lyoqSSiaWaQB9b2UVjOl5yqxKHViUbIa8VHtS+nwx+uN6M5badt0hrnGOb4ZvWFVhYpSXNo/aBpGlfusFhhZXPT4Kp3pRJtUVMqw6+fGTtuQ2qAv0jU7eV/HwObT1tOpKR9r1W6ntdy8FOU4dquasAy2WrOm0WyCa6LnMtKBI9SeY89lK2krrUUpsiRpf2RJSin+zEdWp8jOlHOpse8HnoQBAAAKwSIMAABQCMLRr4q5sFEQfqqsXaGWvumMWVr1ph0eXXuTxhB0Ho6+M+Ma6fPhaGtZaiS82VhbjbzlSuK9tQn7iRvG2V96mUfHalt2IoPN8Ujo04Yle3lfWQYml1nK93VBpqvIohRZujT8rJV/bAh60Oxa1r4k1+Ag2dDsed9W030pScg5sC9piPlOxt4ZD5P27cytp5c5kiWy8L15vSZjVhQ2VkuS2gltv/aprGT7V1mUXmBnOp33lSXLwpMwAABAIViEAQAACsEiDAAAUAg04TdKaFcI+rQ/052k3VajRlyr7lvt/ni9NfpwSikdJOWetSxphaXWpB3MKiOpnGVUPk1tOJh5qkwDFi3XNFWtUp3VHq5qubXuyKCa8L6b7rPtXC/27SGwIfm0ldMacEopHc016rKKPdMVhDTFpW1v5Hd/ls7UaMaqHzdOL/Z993KxnZYb9EUpR1Py/4OwygqW9Nwen32t7T5LU7m8Glr8mZfKacOyNJp/H50mcft4v7ruS+BJGAAAoBAswgAAAIVgEQYAACgEmvCtoT48p1mJv1fGVpEX0fZVog8F1RMr8Qmrj7E25Qr75PViW+bwKJpw5BvWMofexuz7RD4O9T6rKaoUuKm19JzpE41Tqt05f6/u0zYjD2pKXhM+iPB8DHzCevlsasrIvxppwE/948xHubat0Rhtmb459H5q5D6w6U3vNNVpb/pqSXvaaxrUyryWe9iWOcz+ycBjS0Ee5ILZ9jHT1+VcmnOk5QqjFJJd9j8ZkU84GCt97rsj8AXnkKby3PAkDAAAUAgWYQAAgEIQjl6FBhRPLJFz6t41/FxN/4YKw9FhyNuntdSQVyUhQtvfVdPWi1ZClp2EN61lKQ5He3oZa6sj6dmxYVpNd7mRfdgopYaYtQKT3VYtSVE4uldbi5nn2Ku1yG7n59GUkr5vep+6ndqQbAj6ILaaYzVtuemjC6YSwSDhaJsGdfBpULemr+39dnoueyNv6Pk6BqkxFReOlon2Rsc5DCrFHFy7TdNh5M6lidXUk9OpKKPws/aH3wczYePLpaqElHgSBgAAKAaLMAAAQCFYhAEAAAqBJvyGsNpOFfy+mtOWo9JleRo9Y1VRTaqyaQ99Xys2jaPp19J4VkztVCutfLs2NQizMnmmr5btKh1rmtlY8Sh5/fj01H32rV0qAaC1JWlqxbw9XqNjpZrwaD/L0zCKVmnmqaWE5VFTU1pNWL6etmlMgzoM965P9W17m6qla2dSXh6CtKIpeZPNQfKD/ujG97mXc7BPvuRnm/bmte/r+rFvLk2s1XkjDTilOP1sCssTBu0sxe2pdyopLv8BT8IAAACFYBEGAAAoBOHoV02Q6iqojBSFn58G2L54rM3oswkqtWhWpSyEWY1hyk5sUHvzuskyeE1X4dFqPjY8rSFvjUra/qgvJR+O1rC2Pbpsniy7VjXZF22nYfcoY5arsJRV+pnO8hRV/tHwc2ZZMvO27mrmsklrwtWNqcL1NM+0VUYtSk7C6CQEbtKsNYHN72mf1qLkz88P8z5/VD7EvK9+uHY7mHB0789B52xHcYg5yoKlIechDCPb9pqMWXBueBIGAAAoBIswAABAIViEAQAACoEm/A7J0tSJnGY148yiFKS/y9LxBakNVSNujaekkrFNshqn1/dqOR6rGWtqSten2u0QW1UsOrIOtGar16p2uxVxt7HVq2QfNr1ir8daT9u0lD5N68VtUClJU1FafTbSi1Py95BWEFKshamvtNqQOYa5y2XeWitfc/Y+0BSpmYZuNWE59kej+6oGfEgPrm1tSXkqSvM56Q+TfSmpRSnQgFPyNqRM9zUpLecsSos1YrTkU+BJGAAAoBAswgAAAIVgEQYAACgEmjBkOI1ItDf1JlqfZ1f528mVaNO0h+oXXViWsZfSd7X8juzMAW9EP7a+WNWAVT9eg51rI+9jY/q2tfTJ8VmaWrXl8XVWyjArwzidEtBqnuoLHirV+zvzOvhfAJ1HveRGIx5mNGHvbfV99h46ynXX+yC586XHM33elSh1p9WBH9M3v93g7+9j/8P0ed3X6sBzqSitDhzpvE/tpV7g5Vru6WkqYQqehAEAAArBIgwAAFAIwtFXIUgveSWWVlianSewLGUWJZuqb9i6Pg0/u2PSyPAwpi/sJdS6GdSyNM6jdhMb/s0qNb0AO1cn52dr9ykhcK38Uzs7k9+HtS9phsYus5gte296fvL2dMg5shpl4WlrdcruHz+2imxI5vD0/tFqTHas2qs0lG3R1JguPafY6vbGhrQffDj6OIhFyaSq7CRtpbMdrUhFGYefU0pu7Ar70KpKSdiSXgpPwgAAAIVgEQYAACgEizAAAEAh0ITfIVHpwqcBwcaBZUm1ZmtZagdf6m2TtjJ21N4iu0kjFiU9Hm9xmb69hxdowqqd1oEtyqIpElsRd9V6tJQ6K584PZHV91T/zNpVP9nntMmgLyWvA6sFJz++QGM0b7NOalWT/w0wpTH12GtzHVQvzlNwjvf3Mfl72NqQVAO2lqSUvA4clSecO5dr0k3GOvB0mcPLgb3pOXgSBgAAKASLMAAAQCFYhAEAAAqBJvwirMZxusZodbqXpE+0GlCUBjLa7u8HYTpnto18wkYHU5233YhveOnvwVn92sxbqfY27iPTnVeQaYyBN9l+xNRD3IlG3BmNuNO0msF1aEVM9vNMe4FznXe6dGCm85qxnei8Ok+oeQ7iE7ZabqAfZ/dL8LGJxurxRGUZNRWl/T+HSANOyX828lKG0+UJM8/uiekm15QnxBd8XXgSBgAAKASLMAAAQCEIR79RNMxmw5kS6czCdVEVJY1U9TZ8p1FtExLvJMzWSriuqscwpIbS1X7iN5w+vj6zrQxm2OlhNQ05+5C8Hvt4gJouseklRG/a+265fUmtTsfehqP9+2xtiLnSEPN0yDlPRTkdqs7D0/10nxyfTVsZSQa93CO9pC89VmOoWO+fPqgOpcdnQ9AaRrZ9Gn62lZG0Xy1K7hwEaWHnmLMswW3CkzAAAEAhWIQBAAAKwSIMAABQCDThV4XaFXyvzV6ofVY7VbuLasS+c3oe7e/F3lEZjbNLogFrKUOjpcb2k/vgYP3YOtPXrJUo0JkFtdwodq5Gzk9rLDia0rKV43vspjU8a1nSa3sUwXjfj/s8yrEfzTU6JtEtxdJlUzaqdcfqj5m1KbNiHSf7csuSnXf6GaGdqQ5aGY04+5+HNH3sqglbLVf/r8HpvEFfSlKuMCpPOJt68jzlCf3+59JJnqotk6ZyCTwJAwAAFIJFGAAAoBCEo28dZxda85spCF2rRSmIGmmoOreU2CxU/nZylo7AvvS0rQlH1yssSUF4eqjUmmL3KZmJXoCGvR3meDu10SQNydvBfppNP/apRerYS1jbhDv3EkY+mmpVrYSfjyIZ2BB0lFkqsiSl5O+ZuSpKfp5YBrDoOairZV9tWRg5yGYVh5iPk306z1UqI8GrgSdhAACAQrAIAwAAFIJFGAAAoBBowjeG2gVcVaWo2lF6xpZkhwb2pSzFpdFrI704Ja8ZZxVg7LTyc6/rV/z+i4bKOehNFSVNV7i4UlO2+3g7pz3L+bLbtqJxZsdjttV0kxtzTQZNUynzWh34h0nfmFJK+2qs9nNIUvknqCAUVUrKbEfDdIpLJbIs6fnpA426knShVbVMe87sQoNWgDKacHeQvuOz457mCXTfUOc9owZ89UpJWJJOgSdhAACAQrAIAwAAFIJFGAAAoBBowkWYyblnsNpNlYnAsUbshhq5porSVMrYSC9OSUokZqXwphoxlfh7I2053+eovWXH6jzNy9NWqjaZl8YzOqbs02qptZ74QJvXFJc+PahqwtNeYKsBp+R14GPyenHmmXUa7HR6x/wanKYBa39271kv+UzKRr0OS/cRlRnMvMDDdCrKqCThy1JRBv0vSk0JpeBJGAAAoBAswgAAAIUgHH025soNnWMPgX0ppZkUlzbkFe8nCldH9qYsBGjDdTpRlOlR57FN2a6vtq7dVdaipOHo6co6ShSu1pSIm7SdGJn8bTBzi/Tm+tUS/rUh3qHyJ0GrIR2rsb1PD66vNakp28GnYdSQc+/CttOVkjJL0oxlKRobzxPca9nGQdcwHf7VkLMPRwfVj2YsStENH1mU8sHLdZ3lIWjSXZaEJ2EAAIBCsAgDAAAUgkUYAACgEGjCN0HgwQmINJ/QwrHCopRtukIv9nqolHbTjc0fWkmn6DS82uuLtZTj2xh7jlqdnMVlRlO0+nGd2bL8vFajDVM0Vqo/avm9ZddeSxBquklbkrCV8oRWB24Hb1GK7EORrtsFWqnOG6dsTGfpy8aGevYKy1SoCa8pQbhCg72IBrzyGGb2Ci+DJ2EAAIBCsAgDAAAUgnD0zRGFiU4PVYfVmJQ1IWe7WVbVyYYhNeuUp7OhvX55GLKuJcxt7ENrwtFRpSSdRy1KrqJQupfjme5rpdJPdAzOoiT3iIajXchZsmK1/diXZYBaEyoOQtVRiDfKSPU09jQ7U0QcZo/D0SkMpZvKSBfKdJUNPTn8iw3pVuFJGAAAoBAswgAAAIVgEQYAACgEmvDFsNrNuVJYzuk6y6oxKaemv8z2sUIv1hSAVq+N7Etq88nSDhr7Tl372zvShFWJtDpwNYhFSbRcu89O+hqjA9sKT88dw1wqzT/myexemuJyPAarAafk9eK5lI2Wpfrwc2NjS9By+1BEZA3LxgY6r+qz/ly/wHa08L2ct9rRJXRgLEnnhidhAACAQrAIAwAAFIJFGAAAoBDVMMwVtvv7wAr5+Hycv8xhzum/rzKNOBy8dD+qq+p203qt13JF51W/r/MJB6UMtW+mPbWPp7Gbyb6NLa2o24XlEv3+NcWlJS9BaDRq9QLb8pK9lO07MaXkS7y+sz7dhccTsc77O51+ck15wkgDPq/ua7mUFxgd+FQ01elz8CQMAABQCBZhAACAQhBjLkJUbqg8Nlw2G5oOqzXZ33ga9tPB05WkbMpL/dWo6TBtyDAOa8+Eo4NQcV9NW4005NxVe9O3TRFzlZ2WYs+B2o5830wKSRe2jVNThvOEIV3Z9tSQ6ol2prNVP5p9X+cK6ZJ+8q3BkzAAAEAhWIQBAAAKwSIMAABQCDThm+ASKS7Pw5yWFWrGoV4cbCa7tCkvo3SXT4xt1YudJqzb6T4D/Tgba/qj1JRVdZjsm2NpSsunw5s+77aU4EtSSJ6s815KE47mDPe53KKUT3wJG9ItaL5Ykq4JT8IAAACFYBEGAAAoBOFoeBFLw25hpaaUFtuZtBqThq5tODMMVWcusaCiURB+1n7dZxRyXheOnrZMrSHKZmVD1U9jl4WjXxJiPjUL1jwLqxYVDz/P7BPePDwJAwAAFIJFGAAAoBAswgAAAIVAE4aroJpZqBFnWqnVJnVmr6dZnTXUizNdV3W56d+nan2KqIZzacLnmccyV8HIp618SSUiy4r0khfTiy0z+zibDozuC8/DkzAAAEAhWIQBAAAKwSIMAABQCDRhKEKkp1UrPMRZ2UNXyjDY/4wGvE4/jgi05UhSjPzFcyk3F2rEc5rwct13xie86nxdWTtdVQKRdI5wfngSBgAAKASLMAAAQCEIR8MZiUJ7a6oAiZ3pTNWYItaFrpXIzrQ83OnCyOE+43O52EK1wqIUXdv583NiiPkqFqWZQ3jV1ZDgNcCTMAAAQCFYhAEAAArBIgwAAFAINOGbI6uxV+QolrNU+3qJrjqek5eURIxYox8rkZ68hlhbNek4Z95XlOJy+f5SSkEpw1WaZ2Ft9zrWIjRgOA2ehAEAAArBIgwAAFAIwtFvluVVgdbNcynsfpaFplNaEJ6eIqjUtJaXhLKXE2TwEk6tPrQq5HymEPPrykJFyBnOD0/CAAAAhWARBgAAKASLMAAAQCHQhN8Nr0nPWq5nh9WYwpyWK87HbFWi657b82nQM8e98By9Ll1XeU2fC3iL8CQMAABQCBZhAACAQrAIAwAAFAJNGF4ByzzEyhqt8mz68aU4MR1nxqke4qvpvjdwrgGuCE/CAAAAhWARBgAAKATh6JvHhgFvvaLSNVgTrrxM6DoiDGu/hCuExKk2BHB9eBIGAAAoBIswAABAIViEAQAACoEmDG+YSH+8zO/PS+mqp2rNt5FSEh0YYAqehAEAAArBIgwAAFAIFmEAAIBCoAnDO+UyfuNLcRvaLrwP9P8PuPcuSflvFwAAgHcKizAAAEAhCEffPKSqLM/rCl3fHmeqAAXwBuEbAwAAoBAswgAAAIVgEQYAACgEmvDNgQb8ulHNk9+5Hj0faMTwvuEbAgAAoBAswgAAAIUgHH0TEIKG0zm1wtJLOF8Gr/cYnj7Xs897OFdvH56EAQAACsEiDAAAUAgWYQAAgEKgCQNcFKvbnec3bwkNODqG81Z4is7Ra9ZAL/G8cy093d5vVFQ6NzwJAwAAFIJFGAAAoBAswgAAAIVAEy7CNTQ9vIhviVvQgafQYzuvRmxZc0+Xvm9LPN+8R8/164cnYQAAgEKwCAMAABSCcPSbgt9UIFTBPTEQrgQoDd/aAAAAhWARBgAAKASLMAAAQCHQhF811/gNhe2hBC+yJEU6cDQOjfiNYa/vS64tqSovCU/CAAAAhWARBgAAKASLMAAAQCFYhAEAAArBIgwAAFAIFmEAAIBCVMMw8P/nAAAABeBJGAAAoBAswgAAAIVgEQYAACgEizAAAEAhWIQBAAAKwSIMAABQCBZhAACAQrAIAwAAFIJFGAAAoBD/H+23DINlyBWHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "# Load the NIfTI files\n",
    "mri_nifti_path = f'./data/output/00001_image.nii.gz' \n",
    "saliency_nifti_path = f'./data/output/00001_saliencymap.nii.gz'\n",
    "\n",
    "mri_img = nib.load(mri_nifti_path)\n",
    "saliency_img = nib.load(saliency_nifti_path)\n",
    "mri_data = mri_img.get_fdata()\n",
    "saliency_data = saliency_img.get_fdata()\n",
    "\n",
    "print(mri_data.shape, saliency_data.shape, np.max(mri_data), np.max(saliency_data))\n",
    "\n",
    "# Select a 2D slice based on a index number \n",
    "slice_idx = mri_data.shape[2] // 2 \n",
    "mri_slice = mri_data[:, :, slice_idx]\n",
    "saliency_slice = saliency_data[:, :, slice_idx]\n",
    "\n",
    "# Normalize the MRI slice using robust normalization (1st and 99th percentiles)\n",
    "def normalize_image(img):\n",
    "    p1, p99 = np.percentile(img, (1, 99))\n",
    "    img_clipped = np.clip(img, p1, p99)\n",
    "    img_normalized = (img_clipped - p1) / (p99 - p1)\n",
    "    return img_normalized\n",
    "\n",
    "mri_slice_norm = normalize_image(mri_slice)\n",
    "\n",
    "# Normalization function \n",
    "def normalize_saliency(saliency):\n",
    "    saliency[saliency < 0] = 0\n",
    "    if np.max(saliency) > 0:\n",
    "        saliency_normalized = saliency / np.max(saliency)\n",
    "    else:\n",
    "        saliency_normalized = saliency\n",
    "    return saliency_normalized\n",
    "\n",
    "# Apply Gaussian blur to smooth the saliency map and normalize\n",
    "saliency_slice_blurred = cv2.GaussianBlur(saliency_slice, (15, 15), 0)\n",
    "saliency_slice_norm = normalize_saliency(saliency_slice_blurred)\n",
    "\n",
    "# Apply a threshold to the saliency map\n",
    "threshold_value = 0.0 \n",
    "saliency_slice_thresholded = np.where(saliency_slice_norm > threshold_value, saliency_slice_norm, 0)\n",
    "\n",
    "#  thresholded saliency map\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(saliency_slice_thresholded.T, cmap='magma', interpolation='none', origin='lower')\n",
    "plt.axis('off')  \n",
    "\n",
    "#  MRI slice with contour overlay of the thresholded saliency map\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(mri_slice_norm.T, cmap='gray', interpolation='none', origin='lower')\n",
    "plt.contour(saliency_slice_thresholded.T, levels=10, cmap='magma', origin='lower', linewidths=3)  # Adjust the linewidth value as needed\n",
    "plt.axis('off')  # Remove axis for better visualization\n",
    "\n",
    "\n",
    "#  MRI slice alone (no overlay)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(mri_slice_norm.T, cmap='gray', interpolation='none', origin='lower')\n",
    "plt.axis('off') \n",
    "\n",
    "\n",
    "plt.show()  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BrainIAC - Feature extraction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BrainIAC Loaded!!\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:00<00:00,  2.58batch/s]\n",
      "Features saved to ./data/output/features.csv\n"
     ]
    }
   ],
   "source": [
    "## extract feature vector from the BrainIAC model  \n",
    "\n",
    "!python get_brainiac_features.py \\\n",
    "    --checkpoint ./checkpoints/BrainIAC.ckpt \\\n",
    "    --input_csv ./data/csvs/input_scans.csv \\\n",
    "    --output_csv ./data/output/features.csv \\\n",
    "    --root_dir ./data/sample/processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brain age prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "runtime :\n",
    "-> 1 second on GPU (Nvidia A6000) for a single scan inference \n",
    "-> 2 seconds on CPU for a single scan inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and checkpoint loaded!\n",
      "Inference:   0%|                                       | 0/1 [00:00<?, ?batch/s]/media/data/BrainIAC/src/BrainIAC/Brainage/infer_brainage.py:64: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with autocast():\n",
      "/home/divyanshu/miniforge3/envs/brainiac/lib/python3.9/site-packages/torch/amp/autocast_mode.py:266: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn(\n",
      "/media/data/BrainIAC/src/BrainIAC/Brainage/infer_brainage.py:75: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  results_df = pd.concat([results_df, result], ignore_index=True)\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:00<00:00,  1.72batch/s]\n",
      "Mean Absolute Error (MAE): 22.2586 months\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "\n",
    "# change the infer csv and model checkpoint in the config \n",
    "with open('./config.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "config['data'].update({\n",
    "    'test_csv': './data/csvs/brainage.csv', \n",
    "    'root_dir': './data/sample/processed'       \n",
    "})\n",
    "\n",
    "config['gpu'].update({\n",
    "    'visible_device': 'cpu'   # change to cuda:0 if you have a GPU\n",
    "})\n",
    "\n",
    "config['infer'].update({\n",
    "    'checkpoints': './checkpoints/brainage.24'\n",
    "})\n",
    "\n",
    "# Save changes \n",
    "with open('./config.yml', 'w') as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)\n",
    "\n",
    "# infer brainage\n",
    "!python -m Brainage.infer_brainage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Label map\n",
    "\n",
    "0 - T1w ,\n",
    "1 - T2w ,\n",
    "2 - T1CE ,\n",
    "3 - T2 FLAIR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and checkpoint loaded!\n",
      "Inference:   0%|                                       | 0/1 [00:00<?, ?batch/s]/media/data/BrainIAC/src/BrainIAC/SequenceClassification/infer_sequence.py:104: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with autocast():\n",
      "/home/divyanshu/miniforge3/envs/brainiac/lib/python3.9/site-packages/torch/amp/autocast_mode.py:266: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn(\n",
      "/media/data/BrainIAC/src/BrainIAC/SequenceClassification/infer_sequence.py:123: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  results_df = pd.concat([results_df, result], ignore_index=True)\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:00<00:00,  2.03batch/s]\n",
      "PredictedLabel [3]\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "\n",
    "# change the infer csv and model checkpoint in the config \n",
    "with open('./config.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "config['data'].update({\n",
    "    'test_csv': './data/csvs/sequenceclass.csv', \n",
    "    'root_dir': './data/sample/processed'       \n",
    "})\n",
    "\n",
    "config['infer'].update({\n",
    "    'checkpoints': './checkpoints/sequenceclass.00'\n",
    "})\n",
    "\n",
    "# Save changes \n",
    "with open('./config.yml', 'w') as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)\n",
    "\n",
    "# infer brainage\n",
    "!python -m SequenceClassification.infer_sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IDH Mutation Prediction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### label 1 : IDH mutated , label 0 : IDH wild type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and checkpoint loaded!\n",
      "Inference:   0%|                                       | 0/1 [00:00<?, ?batch/s]/media/data/BrainIAC/src/BrainIAC/IDHprediction/infer_idh.py:135: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with autocast():\n",
      "/media/data/BrainIAC/src/BrainIAC/IDHprediction/infer_idh.py:150: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  results_df = pd.concat([results_df, result], ignore_index=True)\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:01<00:00,  1.24s/batch]\n",
      "PredictedLabel 1\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "\n",
    "# change the infer csv and model checkpoint in the config \n",
    "with open('./config.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "config['data'].update({\n",
    "    'test_csv': './data/csvs/input_scans.csv', \n",
    "    'root_dir': './data/sample/processed',\n",
    "    'collate': 2   # collate 2 for loading T1ce and FLAIR images      \n",
    "})\n",
    "\n",
    "config['infer'].update({\n",
    "    'checkpoints': './checkpoints/idh_model.76'\n",
    "})\n",
    "\n",
    "# Save changes \n",
    "with open('./config.yml', 'w') as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)\n",
    "\n",
    "# infer brainage\n",
    "!python -m IDHprediction.infer_idh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Survival "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MCI Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and checkpoint loaded!\n",
      "Inference:   0%|                                       | 0/1 [00:00<?, ?batch/s]/media/data/BrainIAC/src/BrainIAC/MCIclassification/infer_mci.py:101: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with autocast():\n",
      "/media/data/BrainIAC/src/BrainIAC/MCIclassification/infer_mci.py:117: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  results_df = pd.concat([results_df, result], ignore_index=True)\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:00<00:00,  1.25batch/s]\n",
      "PredictedLabel 0    1\n",
      "Name: PredictedLabel, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "\n",
    "# change the infer csv and model checkpoint in the config \n",
    "with open('./config.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "config['data'].update({\n",
    "    'test_csv': './data/csvs/mci.csv', \n",
    "    'root_dir': './data/sample/processed' ,\n",
    "    'collate': 1      \n",
    "})\n",
    "\n",
    "config['infer'].update({\n",
    "    'checkpoints': './checkpoints/mci.77'\n",
    "})\n",
    "\n",
    "# Save changes \n",
    "with open('./config.yml', 'w') as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)\n",
    "\n",
    "# infer brainage\n",
    "!python -m MCIclassification.infer_mci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to stroke "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and checkpoint loaded!\n",
      "Inference:   0%|                                       | 0/1 [00:00<?, ?batch/s]/media/data/BrainIAC/src/BrainIAC/timetostroke/infer_stroke.py:94: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with autocast():\n",
      "/media/data/BrainIAC/src/BrainIAC/timetostroke/infer_stroke.py:105: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  results_df = pd.concat([results_df, result], ignore_index=True)\n",
      "Inference: 100%|███████████████████████████████| 1/1 [00:00<00:00,  1.82batch/s]\n",
      "/home/divyanshu/miniforge3/envs/brainiac/lib/python3.9/site-packages/sklearn/metrics/_regression.py:1266: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "\n",
      "Test Set Metrics:\n",
      "Mean Absolute Error (MAE): 49.2165\n",
      "Root Mean Squared Error (RMSE): 49.2165\n",
      "R² Score: nan\n",
      "Concordance Index: 0.0000\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "\n",
    "# change the infer csv and model checkpoint in the config \n",
    "with open('./config.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "config['data'].update({\n",
    "    'test_csv': './data/csvs/stroke.csv', \n",
    "    'root_dir': './data/sample/processed' ,\n",
    "    'collate': 1      \n",
    "})\n",
    "\n",
    "config['infer'].update({\n",
    "    'checkpoints': './checkpoints/stroke.03'\n",
    "})\n",
    "\n",
    "# Save changes \n",
    "with open('./config.yml', 'w') as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)\n",
    "\n",
    "# infer brainage\n",
    "!python -m timetostroke.infer_stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "brainiac",
   "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": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}