Models:
Samuel Callahan
SCallahan/
Hippocampus_Segmentation
0
Downloads: 1
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a b/Section 1 EDA/Final Project EDA.ipynb 

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{





  
  

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 "cells": [





  
  

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  {





  
  

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   "cell_type": "markdown",





  
  

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   "metadata": {},





  
  

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   "source": [





  
  

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    "# <a name=\"table-of-contents\"></a>Table of Contents\n",





  
  

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    "\n",





  
  

  9
  
    "- [Curating the Dataset](#introduction)  \n",





  
  

  10
  
    "    - [Loading NIFTI images using NiBabel](#load-nifti)  \n",





  
  

  11
  
    "\t- [Plot MRI Images](#plot-mri)  \n",





  
  

  12
  
    "\t- [Explore a single image data](#explore-one-data)  \n",





  
  

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    "- [Exploratory Data Analysis](#eda)  \n",





  
  

  14
  
    "\t- [Finding Outliers Among NIFTI Files](#find-outliers)  \n",





  
  

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    "\t\t- [Outlier #1 - Compare Segmentation Labels with UK Biobank Hippocampus Volume Distribution](#compare-volumes)  \n",





  
  

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    "\t\t- [Outlier #2 - Check That All Labels and Images Pairs Have Matching Dimensions](#compare-dimensions)  \n",





  
  

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    "- [Results](#results) \n",





  
  

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    "- [Curate a Collection of Data Files](#curate-dataset)  \n",





  
  

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    "- [Final Remarks](#final-remarks)  \n",





  
  

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    " "





  
  

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   ]





  
  

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  },





  
  

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  {





  
  

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   "cell_type": "markdown",





  
  

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   "metadata": {},





  
  

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   "source": [





  
  

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    "# <a name=\"introduction\"></a>Curating the dataset for hippocampus segmentation\n",





  
  

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    "\n",





  
  

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    "In this notebook you will use the skills and methods that we have talked about during our EDA Lesson to prepare the hippocampus dataset using Python. Follow the Notebook, writing snippets of code where directed so using Task comments, similar to the one below, which expects you to put the proper imports in place. Write your code directly in the cell with TASK comment. Feel free to add cells as you see fit, but please make sure that code that performs that tasked activity sits in the same cell as the Task comment.\n"





  
  

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   ]





  
  

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  },





  
  

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  {





  
  

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   "cell_type": "code",





  
  

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   "execution_count": 1,





  
  

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   "metadata": {},





  
  

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   "outputs": [],





  
  

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   "source": [





  
  

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    "# TASK: Import the following libraries that we will use: nibabel, matplotlib, numpy\n",





  
  

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    "\n",





  
  

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    "import matplotlib.pyplot as plt\n",





  
  

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    "import nibabel as nib\n",





  
  

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    "import numpy as np\n",





  
  

  43
  
    "import os\n",





  
  

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    "from PIL import Image\n",





  
  

  45
  
    "import glob\n",





  
  

  46
  
    "import shutil\n"





  
  

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   ]





  
  

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  },





  
  

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  {





  
  

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   "cell_type": "markdown",





  
  

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   "metadata": {},





  
  

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   "source": [





  
  

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    "It will help your understanding of the data a lot if you were able to use a tool that allows you to view NIFTI volumes, like [3D Slicer](https://www.slicer.org/). I will refer to Slicer throughout this Notebook and will be pasting some images showing what your output might look like."





  
  

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   ]





  
  

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  },





  
  

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  {





  
  

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   "cell_type": "markdown",





  
  

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   "metadata": {},





  
  

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   "source": [





  
  

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    "## <a name=\"load-nifti\"></a>Loading NIFTI images using NiBabel\n",





  
  

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    "\n",





  
  

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    "NiBabel is a python library for working with neuro-imaging formats (including NIFTI) that we have used in some of the exercises throughout the course. Our volumes and labels are in NIFTI format, so we will use nibabel to load and inspect them.\n",





  
  

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    "\n",





  
  

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    "NiBabel documentation could be found here: https://nipy.org/nibabel/\n",





  
  

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    "\n",





  
  

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    "Our dataset sits in two directories - *images* and *labels*. Each image is represented by a single file (we are fortunate to have our data converted to NIFTI) and has a corresponding label file which is named the same as the image file.\n",





  
  

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    "\n",





  
  

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    "Note that our dataset is \"dirty\". There are a few images and labels that are not quite right. They should be quite obvious to notice, though. The dataset contains an equal amount of \"correct\" volumes and corresponding labels, and you don't need to alter values of any samples in order to get the clean dataset."





  
  

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   ]





  
  

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  },





  
  

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   "cell_type": "code",





  
  

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   "execution_count": 2,





  
  

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   "metadata": {},





  
  

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   "outputs": [





  
  

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    {





  
  

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     "data": {





  
  

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       " 'hippocampus_311.nii.gz',\n",





  
  

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       " 'hippocampus_350.nii.gz',\n",





  
  

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       " 'hippocampus_373.nii.gz',\n",





  
  

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       " 'hippocampus_374.nii.gz',\n",





  
  

  328
  
       " 'hippocampus_375.nii.gz',\n",





  
  

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       " 'hippocampus_376.nii.gz',\n",





  
  

  330
  
       " 'hippocampus_378.nii.gz',\n",





  
  

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       " 'hippocampus_380.nii.gz',\n",





  
  

  332
  
       " 'hippocampus_381.nii.gz',\n",





  
  

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       " 'hippocampus_383.nii.gz',\n",





  
  

  334
  
       " 'hippocampus_385.nii.gz',\n",





  
  

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       " 'hippocampus_386.nii.gz',\n",





  
  

  336
  
       " 'hippocampus_387.nii.gz',\n",





  
  

  337
  
       " 'hippocampus_389.nii.gz',\n",





  
  

  338
  
       " 'hippocampus_390.nii.gz',\n",





  
  

  339
  
       " 'hippocampus_393.nii.gz',\n",





  
  

  340
  
       " 'hippocampus_394.nii.gz']"





  
  

  341
  
      ]





  
  

  342
  
     },





  
  

  343
  
     "execution_count": 2,





  
  

  344
  
     "metadata": {},





  
  

  345
  
     "output_type": "execute_result"





  
  

  346
  
    }





  
  

  347
  
   ],





  
  

  348
  
   "source": [





  
  

  349
  
    "path = os.path.join('..', 'data', 'TrainingSet')\n",





  
  

  350
  
    "os.listdir(os.path.join(path, 'labels'))"





  
  

  351
  
   ]





  
  

  352
  
  },





  
  

  353
  
  {





  
  

  354
  
   "cell_type": "code",





  
  

  355
  
   "execution_count": 3,





  
  

  356
  
   "metadata": {},





  
  

  357
  
   "outputs": [





  
  

  358
  
    {





  
  

  359
  
     "data": {





  
  

  360
  
      "text/plain": [





  
  

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       " 'hippocampus_304.nii.gz',\n",





  
  

  560
  
       " 'hippocampus_305.nii.gz',\n",





  
  

  561
  
       " 'hippocampus_308.nii.gz',\n",





  
  

  562
  
       " 'hippocampus_309.nii.gz',\n",





  
  

  563
  
       " 'hippocampus_310.nii.gz',\n",





  
  

  564
  
       " 'hippocampus_311.nii.gz',\n",





  
  

  565
  
       " 'hippocampus_314.nii.gz',\n",





  
  

  566
  
       " 'hippocampus_316.nii.gz',\n",





  
  

  567
  
       " 'hippocampus_317.nii.gz',\n",





  
  

  568
  
       " 'hippocampus_318.nii.gz',\n",





  
  

  569
  
       " 'hippocampus_319.nii.gz',\n",





  
  

  570
  
       " 'hippocampus_320.nii.gz',\n",





  
  

  571
  
       " 'hippocampus_321.nii.gz',\n",





  
  

  572
  
       " 'hippocampus_322.nii.gz',\n",





  
  

  573
  
       " 'hippocampus_325.nii.gz',\n",





  
  

  574
  
       " 'hippocampus_326.nii.gz',\n",





  
  

  575
  
       " 'hippocampus_327.nii.gz',\n",





  
  

  576
  
       " 'hippocampus_328.nii.gz',\n",





  
  

  577
  
       " 'hippocampus_329.nii.gz',\n",





  
  

  578
  
       " 'hippocampus_330.nii.gz',\n",





  
  

  579
  
       " 'hippocampus_331.nii.gz',\n",





  
  

  580
  
       " 'hippocampus_332.nii.gz',\n",





  
  

  581
  
       " 'hippocampus_333.nii.gz',\n",





  
  

  582
  
       " 'hippocampus_334.nii.gz',\n",





  
  

  583
  
       " 'hippocampus_335.nii.gz',\n",





  
  

  584
  
       " 'hippocampus_336.nii.gz',\n",





  
  

  585
  
       " 'hippocampus_337.nii.gz',\n",





  
  

  586
  
       " 'hippocampus_338.nii.gz',\n",





  
  

  587
  
       " 'hippocampus_340.nii.gz',\n",





  
  

  588
  
       " 'hippocampus_341.nii.gz',\n",





  
  

  589
  
       " 'hippocampus_343.nii.gz',\n",





  
  

  590
  
       " 'hippocampus_345.nii.gz',\n",





  
  

  591
  
       " 'hippocampus_349.nii.gz',\n",





  
  

  592
  
       " 'hippocampus_350.nii.gz',\n",





  
  

  593
  
       " 'hippocampus_351.nii.gz',\n",





  
  

  594
  
       " 'hippocampus_352.nii.gz',\n",





  
  

  595
  
       " 'hippocampus_353.nii.gz',\n",





  
  

  596
  
       " 'hippocampus_354.nii.gz',\n",





  
  

  597
  
       " 'hippocampus_355.nii.gz',\n",





  
  

  598
  
       " 'hippocampus_356.nii.gz',\n",





  
  

  599
  
       " 'hippocampus_358.nii.gz',\n",





  
  

  600
  
       " 'hippocampus_359.nii.gz',\n",





  
  

  601
  
       " 'hippocampus_360.nii.gz',\n",





  
  

  602
  
       " 'hippocampus_361.nii.gz',\n",





  
  

  603
  
       " 'hippocampus_363.nii.gz',\n",





  
  

  604
  
       " 'hippocampus_366.nii.gz',\n",





  
  

  605
  
       " 'hippocampus_367.nii.gz',\n",





  
  

  606
  
       " 'hippocampus_368.nii.gz',\n",





  
  

  607
  
       " 'hippocampus_370.nii.gz',\n",





  
  

  608
  
       " 'hippocampus_372.nii.gz',\n",





  
  

  609
  
       " 'hippocampus_373.nii.gz',\n",





  
  

  610
  
       " 'hippocampus_374.nii.gz',\n",





  
  

  611
  
       " 'hippocampus_375.nii.gz',\n",





  
  

  612
  
       " 'hippocampus_376.nii.gz',\n",





  
  

  613
  
       " 'hippocampus_378.nii.gz',\n",





  
  

  614
  
       " 'hippocampus_380.nii.gz',\n",





  
  

  615
  
       " 'hippocampus_381.nii.gz',\n",





  
  

  616
  
       " 'hippocampus_383.nii.gz',\n",





  
  

  617
  
       " 'hippocampus_385.nii.gz',\n",





  
  

  618
  
       " 'hippocampus_386.nii.gz',\n",





  
  

  619
  
       " 'hippocampus_387.nii.gz',\n",





  
  

  620
  
       " 'hippocampus_389.nii.gz',\n",





  
  

  621
  
       " 'hippocampus_390.nii.gz',\n",





  
  

  622
  
       " 'hippocampus_393.nii.gz',\n",





  
  

  623
  
       " 'hippocampus_394.nii.gz']"





  
  

  624
  
      ]





  
  

  625
  
     },





  
  

  626
  
     "execution_count": 3,





  
  

  627
  
     "metadata": {},





  
  

  628
  
     "output_type": "execute_result"





  
  

  629
  
    }





  
  

  630
  
   ],





  
  

  631
  
   "source": [





  
  

  632
  
    "os.listdir(os.path.join(path, 'images'))"





  
  

  633
  
   ]





  
  

  634
  
  },





  
  

  635
  
  {





  
  

  636
  
   "cell_type": "code",





  
  

  637
  
   "execution_count": 4,





  
  

  638
  
   "metadata": {},





  
  

  639
  
   "outputs": [],





  
  

  640
  
   "source": [





  
  

  641
  
    "# TASK: Your data sits in directory /data/TrainingSet.\n",





  
  

  642
  
    "# Load an image and a segmentation mask into variables called image and label\n",





  
  

  643
  
    "\n",





  
  

  644
  
    "img = nib.load(os.path.join(path, 'images', 'hippocampus_001.nii.gz'))\n",





  
  

  645
  
    "label = nib.load(os.path.join(path, 'labels', 'hippocampus_001.nii.gz'))"





  
  

  646
  
   ]





  
  

  647
  
  },





  
  

  648
  
  {





  
  

  649
  
   "cell_type": "markdown",





  
  

  650
  
   "metadata": {},





  
  

  651
  
   "source": [





  
  

  652
  
    "## <a name=\"plot-mri\"></a>Plot MRI Images"





  
  

  653
  
   ]





  
  

  654
  
  },





  
  

  655
  
  {





  
  

  656
  
   "cell_type": "code",





  
  

  657
  
   "execution_count": 5,





  
  

  658
  
   "metadata": {},





  
  

  659
  
   "outputs": [





  
  

  660
  
    {





  
  

  661
  
     "name": "stdout",





  
  

  662
  
     "output_type": "stream",





  
  

  663
  
     "text": [





  
  

  664
  
      "img shape is (35, 51, 35)\n",





  
  

  665
  
      "label shape is (35, 51, 35)\n"





  
  

  666
  
     ]





  
  

  667
  
    },





  
  

  668
  
    {





  
  

  669
  
     "data": {





  
  

  670
  
      "image/png": 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\n",





  
  

  671
  
      "text/plain": [





  
  

  672
  
       "<Figure size 1080x1080 with 5 Axes>"





  
  

  673
  
      ]





  
  

  674
  
     },





  
  

  675
  
     "metadata": {





  
  

  676
  
      "needs_background": "light"





  
  

  677
  
     },





  
  

  678
  
     "output_type": "display_data"





  
  

  679
  
    },





  
  

  680
  
    {





  
  

  681
  
     "data": {





  
  

  682
  
      "image/png": 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VvE8I4u43AjcCNFizDrKWoqfMStiUU2aDVcvY8d5a+mckOGX5SyyKtfPaOT/FKojDkSVxBuqXU9GZpPa5/SS2Dz/pSLkohszGprWy9/2LOLwqSc3ULv5y6pOFaIaESDHkVkrfeIuz/WY2M/0Nw0zgQC4bJZIHyqyEkXJ7jCNLa/nQhQ/yVy2PUGNRqq34v70O4k7XPKd7jlFxOE5VW2Nq2oHSFJrMelMDXW/t4YGzvkqtRaiLVAIa1SpDocmslIfxHsxxB7AmfX8NsDY3zRHJG2VWwki5PYZHoTnWzbRoLXWRKqIWgjkQLdXuIO4EcRhorCA6fRrRpiYsPv6JJopUcWc2EiU2YzrRFUvpXtzE1KZOpkeraYrWELf8FGYHkt2s643yw64ZDByqwgZL9pDWsCruzErZyWYq/f8kdaJkq5ntBv4J+Bxwu5ldDewELs5nI0XGQpmVMFJuy0Oyxnn1pEoq5yym+tUkdU+9QmL3K4Vu1riEMbPRhjr2XbSIjrf20tr0Kn++4H4ijO1SDGP1tUO/w3d/8VZqXzHmvzCIH9Is7YUSxsxK+clmtsbLR3jqHTlui0hOKLMSRspteUhWON1zne65ULMnRu22Othd6FaNTxgza9XVHDo1yX1nf4WZ0WoiWN5HX+9vW8LCtb1EH3kWTyZJ6hpnBRPGzEr5yfuEICIikyHa0IAvmEWyvorYkV58+26C7u5CN0tkKANPD9R4fgdsZDiRVCFWZZa3wxgB9ia6uK93LrsGm9m2ayondPUSlO4MnSKSQyrORKQkBMvmsfXKWhav2MPWp2az7NvApucL3SwRKUM/7lrBl+64gKbnYOGeAWzXvkI3SURCQsWZiJSE/tZqfvfNz3PrgvW8iwsY/EnruGc8EhGZiE3ds5nxcJLqtY8BkCxwe0QkPFSciUhoRerrCVYsoG96NQdPivG22tQMyMsb97N+9TyamlZTvacb2/wyQU9PgVsrIiIydhavwJYvond+PZFBp3rrgbK/VmIpU3EmIqFlc2bwwh/WcP4ZG1lUfZAPNGwAavnT1vuZdmUnO3pbWL/+ZJZ9rZVAf8hERCSEIo317LiwmVPet5mXO5rp/e4sGna+AoHGZEuRijMRCa2gtpLpSw/yjTkPp5fUArCyopqVrVvo90FOXbwQr6kqXCNFpLiYZkuUcLHKSnrmD3L9vDt5oHcmn5n2hzQUulGSNyrORKRkRYiwauYrPHPBCqpOb3l9eXTAadrUQbDxeX3zKFLqIlEiJy/n8MoG+loizF+4h8o8TJ//8mAXN7x6Do8fnM/OF6azdH9vzvchpS+6fAlHVrUyWHN0OtfBestbbqX4qDgTkZIVtyj/MPtnPHrVJo4ka15fvrVnBr+57c3M2hIn6FNxJlLKIlWVvPL2KZx9+ZOsqtvJ6qqXqbPKnO/n0b653Ln2TOb+opvlne3Yzj2aCETGJhLl4BlTmXP1i5zb8sLri2siA3nLrRQfFWciEj6RKBYxPBYhMsohSisqalhRcWDIsg01z7Ou5VSI5u86R5IfFkBfEKffB4kQyeu1qiaDm4ENc8EzXag4d6JR+luca1rvY1VlJZCfD7htiQbqdjn28NMEedmDlIOBRuOyGY9xSV37Mc+oMCsXKs5EJFQi9fX0nnMCh06I0zPTuWzGU4Vukkyihh193LTu9/jO7NW8dcGL/MOMe5kTqyt0s8YlUQMdJ06huvXUIcsrDnTh23YS9PUVqGUiIlIoKs5EJFQizVPY+Z4I//rO/2RGrJ0TKzp5bSIQKX2xJ1/khJ3NBLXV/PrDq7j80keZEwvnOMVgvXPwZCOSyJiwxqFpSwVT9h8EFWciImVHxZmIhEs0ik0Z4ILavdRFqhhvYRbEIdJQj2Uc2ujJJEFvnyYJKWJBZydBZycWi1F9YDUvDUzjxIqXqLFoOg+5lfSALu+nJ0iyLzmDRJC7E/I95iRiAEcPYbQAEtUGkXAfrikiIuOj4kxEys706CBLT9/B859cSCSjDqveb8y69xDBs1sK1zjJigdOy7P9fH7tB/hsS4K3vWkLX5x9D03RmtE3HoPtiR4+/vLFbNoyF0saJIxhzhATERHJCRVnIlJ2pkVruHnJf9G2MEIy46P2/93zbrbvXE7tswVsnGQnSFL50GaWPFMHjfXc99ETabvobppyPOD00mAT29Yt5MSbd5JsbWTvuY10zQ9QhSYiIvmg4kxEyk7UIkyL1jLtmA/yi2vb2BY/oTCNkjELenqgp4dobx+Vh6bzeN88eoI9rz8ft4BZUZ/QaFqfx4n1QHLvPqJAvKuBWK/hUQhijuvoQxHJs6QHHEj2sD8Z59neOUT7NJtrKVNxJiIioRb09zPj4QH+JXkpyaqjH1oGmpNc+bsP8vetzxLNwcVbvb2D1o1d1O2toq85SvuSCAON4ZyMRETC40Cyhz9+8VJeemwelYeNWU93g+t3T6lScSYiIqHm/f1U/moj8x+MQ+RoEZY8aRF3zj2Jv2/NzXGqyY4O7KnNVFmE6hMX0z2jiYHGnLy0iMiI2oIYLz02j2XXbyPo6MQHBnBdC7FkqTgTEZHQ88EBfHBgyLLYkR4OHmjkR91NTIt2clJFJ63RWg4ku3luoJ4jQQ1zY4dYEYeaSMWwr1sb6aevxbGVS4l09+H7DxJ0dhIZTDLK9c/H0HiIJIxoH1jCiPcEmjE0l5JJ4h3GnZ2n8EpyOyfED7IgVpOT0VSRyRC4EUlC0N2TOpxbSpqKMxERKU0HXmXu2ib+5ZmP0DU/4GPn380nmrZza/ub+Oq976R2d4S+07q59Yz/YHXl8C+xPN7Oeec/xX3LF9O7ezqLfzyFyH25v/B5xWGj5bkElYcGiR/sIujqzvk+ypUPDDDrgW5+1Pl2vtcMS87fxvcW/4Q6y/2lF0REJmrUr43MbK6Z/drMNpvZJjP7eHp5s5nda2YvpP9tyn9zRUanzErYKLP5kXz1ENU/fYKZX36M+XclePDQEgAePLSY+T8bZNZXnqD6kVq2D7aO+BpzYnV8edZDPHnGzVz1e+vpmJefD/QVHVC/cT+RBzeQ3PwC3t+fl/3kSpgy64kE9vBGpn39URbespNnnp9Lj2tkshyFKbdSvrIZ008A17r7CuAM4C/N7ETgOmCduy8F1qUfixQDZbaUDQwS2VvFF159Cze1z2DrYEmMMCiz+RIk8USCeMcAG3bN4QuHFrNx1xziHQP4YAILIPDj/ymMWoRKi9MU66a/yYgtmMfg1FqSwx8JOb5mVkBieiOxeXOItraE4SLU4cqseyoLff1U7ovx7wfP4sb2WWwe0CFiZSZcuZWyNOphje6+F9ibvt9pZpuB2cBFwNvSq90CrAc+lZdWioyBMlvagkOHWbh2KvdsPIfOuRGe+OCT3DD7kUI3a0KU2fyLvrSXud9ZwA+nvZO5+weJvrybsY6dLKhoo33lIJaYQ7IaBhtyd0J+X4uz78w6on21NG4bpPrhrSQ7OnL2+rkW1swGHR3Mv6ub+54/g7tmRlj/gc18d8E6nX9WJsKaWykvYzrnzMwWAKcCjwLT0yHH3fea2bTcN09kYpTZ0hP09GC/2UDjb2DKaSfx1LmzU39aS4Qymx/JtjYq7m7jtcGuJIx5dKol0k3TzA7ae5oglxOlGSRqnK4axxxivXGqK0c4Ca4IhSmz3t8Pj2yk8RFoXrmcp8+cRbDAKfpxSsm5MOVWykvWxZmZ1QE/Aj7h7h1mlu121wDXAFQx/guBioyVMlv6It39HHh+Klc3nc3CmoNc0vgEy+K1hW7WuCmzhZP0gHW9ldx5ZBUA75ryDO+s7iZuI3xsz+5Hk73XXi9ks2OHObPW08fAC7O4etbvEbej14xaVrufSxqeYmG8riDtkvwLc26l9GVVnJlZnFSIb3P3H6cX7zezmelvGGYCB4bb1t1vBG4EaLDmkP3ZkbBSZsuD797H0lsreeneFTz6pjjJyyP809TnCt2scVFmC6vL+/m7LZcR/X4zloT1ly7hd99yE01RfQAbSdgzG+xvY/EP6th5//IhxfZDbzmZmksH+FjTjkI0S/Is7LmV0pfNbI0G3ARsdvcvZjx1B7AmfX8NsDb3zRMZO2W2fASdnfhvn6XyZ48zdcMgL/WMPOtetjzXIyJZUGYLb9ADDu5tpGX9Lpp//TKde+rp82DIOsmcD5eFVylkNujpwZ/aROVdj1P5s6O3lk1Jtve1FLp5kgehzq1KwbKRzcjZWcAVwDNmtiG97NPA54DbzexqYCdwcV5aKDJ2yqyMy4qqPdx6OiSqzqR2f4KaJ3aQ3D/sF6i5pswWggc07Erw909cxPVTuqjfHMf7+iCRoHFLjAvnXUVtxdELW3f2V3D4QH1W0xyXgZLNbM2+fn78+Gk8sWjemLbbuaeF+XsSeWqV5Ehx59YD6ncl+fRvP8ANrUdeX/xqdw0NLwGDgwVplkyubGZrfJCRj65/R26bIzJxyqyM13k1u6l6361sP7+VG545lwVHZmKTUJwpswXiTt1vXmbJjml4vJqWA7tJHmnHA2f22l0kHpmCR6tfX72+qYL+N8XpnR7k/pyzkCnlzMY2vcwJX5lJsr5xTNst7+3Fdu0b8yygMnmKPrfu1D+4jdrt0wiqjuZv9mCS6N4dJIr8+oeSG2OarVFEpJgNBDH6feg3ixEiI0/qcIzWaC3vr+2C2i4enL2EQzXzieejoVI0km1t0NYGpC6A9JrEjl2wY9eQT3G18+fSMW8OfVMBSx8CW+ZFWilKHmmHI+1j/tEGo68iMqrXfidl5s8Z+vtJSpuKMxEpCVV7Onnq18s5eemc15eZOWfNe5l/mnU382KaeU0mxru6mbJtkHhPnIF6o2emk6jViSAiIpI7Ks5EpDRs3c6Sb3bjNVVHl0UiPHjZm9hy+aPMi+lYfZmY5OF2qh7ZSlVlJYkls9hzbq2KMxERySkVZyJSEoK+PoIdu4YujESpPnA62wamsbfi+dcXR82oszg1kQpEshYkSXZ0ABBrnUK0r5bIwAgHvxl4xPEIOvRRRESypuJMREqXB7Rs6ueLay/k840Zp+lXJ/nQKU/y2emPUWk6q0zG4VA7rc820P/K8H9Gg5jRNStCz0zHYxpdExGR7Kg4E5HS5U7FQ5tYsqkBixydAD2Y3syPPnkqn576GyqjKs5k7JJtr1LxcBcV0eEnm7GqKuxti+idFsH1l1ZERLKkPxkiUtKCvj7o6xuyLAbYoQU83t/ItGjXsNvt7W6gZlDzr8kIgiRBT8+IT0cGB4n3BMR6oyQdPApBzLM6xDGIgdXVEOmrxwcGcE2fLSJSNlSciUjZCbq6mXWf81ft/2PEQ87qdsCUnXs0fbGMiycS1Lx4mBmJKSRqoxxZFKV3pqem3z/edga9U42DZ88i3jODum1d2LMv4IMDx99QRERKgoozESk7QWcntXdtoO4XMcg43HGIwUFd8FPGzRMJki+8TOW2KNXNUxioXUzvjOy27W8NGGgyLBEB6qjfGldxJiJSJlSciUhZ8v5+HS4m+RUk8SAJA4NYkOWkIOmLW3vEiWCp2R5H+gJBRERKjn7ji4iIiIiIFAEVZyIiIiIiIkVAhzWKiIjkUzJJZUdAVVssdZhiWhCHRK0TVGQc8ugQ7TdiXUZ0ECo6A0gm3/iaIiJSklSciYiI5FHQ00Pdxr1U724YcrxK3/Qa2k6J0zftaHFmDtX7jKlP9xFr7yfadoREn86NFBEpFyrORERE8sgTCRI7dsGOoctrli0mtmz6G9avbHcqNu0i2damSzmIiJQZFWciIiIFYL391OwPwDKG0xyqDyZAU+eLiJQlFWciIiIFELQdpPkho6mqcshy6+gi2dVdoFaJiEghqTgTEREpgKCvj2DHrkI3Q0REioim0hcRERERESkCKs5ERERERESKwKjFmZlVmdljZva0mW0ys/+dXt5sZvea2Qvpf5vy31yR0SmzEjbKrISNMithpNxKGGQzctYPvN3dTwFWAe82szOA64B17r4UWJd+LFIMlFkJG2VWwkaZlTBSbqXojVqceUpX+mE8fXPgIuCW9PJbgPfno4EiY6XMStgosxI2yqyEkXIrYZDVOWdmFjWzDcAB4F53fxSY7u57AdL/Thth22vM7Ldm9ttB+nPUbJHjU2YlbJRZCRtlVsJIuZVil1Vx5u5Jd18FzAFWm9lJ2e7A3W9099Pc/bQ4laNvIJIDyqyEjTIrYaPMShgpt1LsxjRbo7sfAdYD7wb2m9lMgPS/B3LdOJGJUmYlbJRZCRtlVsJIuZVilc1sjVPNbEr6fjVwHrAFuANYk15tDbA2T20UGRNlVsJGmZWwUWYljJRbCYNYFuvMBG4xsyipYu52d7/TzB4Gbjezq4GdwMV5bKfIWCizEjbKrISNMithpNxK0Ru1OHP3jcCpwyx/FXhHPholMhHKrISNMitho8xKGCm3EgZjOudMRERERERE8kPFmYiIiIiISBFQcSYiIiIiIlIEVJyJiIiIiIgUARVnIiIiIiIiRUDFmYiIiIiISBFQcSYiIiIiIlIEzN0nb2dmbUA3cHDSdloYraiP+TDf3adO5g7Tmd1B6f9MS71/oMyWmlLvHxSuj5OaW2W2pJRFZkGfaUtI0WV2UoszADP7rbufNqk7nWTqY+kp9f6Wev+gPPqYqdT7W+r9g/LoY6ZS72+p9w/Ko4+ZyqG/pd7HYuyfDmsUEREREREpAirOREREREREikAhirMbC7DPyaY+lp5S72+p9w/Ko4+ZSr2/pd4/KI8+Zir1/pZ6/6A8+pipHPpb6n0suv5N+jlnIiIiIiIi8kY6rFFERERERKQITGpxZmbvNrPnzexFM7tuMvedD2Y218x+bWabzWyTmX08vbzZzO41sxfS/zYVuq0TZWZRM3vKzO5MPy65Pg6n1DIL5ZNbZVaZDRtlVpkNG2VWmQ2bMGR20oozM4sCXwXeA5wIXG5mJ07W/vMkAVzr7iuAM4C/TPfpOmCduy8F1qUfh93Hgc0Zj0uxj0OUaGahfHKrzCqzYaPMKrNho8wqs2FT9JmdzJGz1cCL7r7N3QeA7wMXTeL+c87d97r7k+n7naR+2LNJ9euW9Gq3AO8vSANzxMzmAO8D/iNjcUn1cQQll1koj9wqs8ps2CizymzYKLPKbNiEJbOTWZzNBnZlPN6dXlYSzGwBcCrwKDDd3fdCKuzAtAI2LReuB/4XEGQsK7U+DqekMwslndvrUWZBmQ2T61FmQZkNk+tRZkGZDZPrCUFmJ7M4s2GWlcRUkWZWB/wI+IS7dxS6PblkZhcAB9z9iUK3pQBKNrNQurlVZt9AmS1yyuwbKLNFTpl9A2W2yIUps7FJ3NduYG7G4znAnkncf16YWZxUiG9z9x+nF+83s5nuvtfMZgIHCtfCCTsLuNDM3gtUAQ1mdiul1ceRlGRmoeRzq8wepcyGgzJ7lDIbDsrsUcpsOIQms5M5cvY4sNTMFppZBXAZcMck7j/nzMyAm4DN7v7FjKfuANak768B1k5223LF3f/W3ee4+wJSP7NfufsfUkJ9PI6SyyyUfm6VWWU2bJRZZTZslFllNmzClNlJGzlz94SZfRS4B4gC33L3TZO1/zw5C7gCeMbMNqSXfRr4HHC7mV0N7AQuLkzz8qrk+1iimYXyzW2p90+ZLb2faan3T5ktvZ9pqfdPmS29n2nR9c/cS+YwWRERERERkdCa1ItQi4iIiIiIyPBUnImIiIiIiBQBFWciIiIiIiJFQMWZiIiIiIhIEVBxJiIiIiIiUgRUnImIiIiIiBQBFWciIiIiIiJFQMWZiIiIiIhIEfj/Ac4EBDFGujsAAAAASUVORK5CYII=\n",





  
  

  683
  
      "text/plain": [





  
  

  684
  
       "<Figure size 1080x1080 with 5 Axes>"





  
  

  685
  
      ]





  
  

  686
  
     },





  
  

  687
  
     "metadata": {





  
  

  688
  
      "needs_background": "light"





  
  

  689
  
     },





  
  

  690
  
     "output_type": "display_data"





  
  

  691
  
    },





  
  

  692
  
    {





  
  

  693
  
     "data": {





  
  

  694
  
      "image/png": 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\n",





 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  696
  
       "<Figure size 1080x1080 with 5 Axes>"





  
  

  697
  
      ]





  
  

  698
  
     },





  
  

  699
  
     "metadata": {





  
  

  700
  
      "needs_background": "light"





  
  

  701
  
     },





  
  

  702
  
     "output_type": "display_data"





  
  

  703
  
    },





  
  

  704
  
    {





  
  

  705
  
     "data": {





  
  

  706
  
      "image/png": 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 
  

  707
  
      "text/plain": [





  
  

  708
  
       "<Figure size 1080x1080 with 5 Axes>"





  
  

  709
  
      ]





  
  

  710
  
     },





  
  

  711
  
     "metadata": {





  
  

  712
  
      "needs_background": "light"





  
  

  713
  
     },





  
  

  714
  
     "output_type": "display_data"





  
  

  715
  
    },





  
  

  716
  
    {





  
  

  717
  
     "data": {





  
  

  718
  
      "image/png": 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\n",





  
  

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      "text/plain": [





  
  

  720
  
       "<Figure size 1080x1080 with 5 Axes>"





  
  

  721
  
      ]





  
  

  722
  
     },





  
  

  723
  
     "metadata": {





  
  

  724
  
      "needs_background": "light"





  
  

  725
  
     },





  
  

  726
  
     "output_type": "display_data"





  
  

  727
  
    },





  
  

  728
  
    {





  
  

  729
  
     "data": {





  
  

  730
  
      "image/png": "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\n",





  
  

  731
  
      "text/plain": [





  
  

  732
  
       "<Figure size 1080x1080 with 5 Axes>"





  
  

  733
  
      ]





  
  

  734
  
     },





  
  

  735
  
     "metadata": {





  
  

  736
  
      "needs_background": "light"





  
  

  737
  
     },





  
  

  738
  
     "output_type": "display_data"





  
  

  739
  
    }





  
  

  740
  
   ],





  
  

  741
  
   "source": [





  
  

  742
  
    "# Nibabel can present your image data as a Numpy array by calling the method get_fdata()\n",





  
  

  743
  
    "# The array will contain a multi-dimensional Numpy array with numerical values representing voxel intensities. \n",





  
  

  744
  
    "# In our case, images and labels are 3-dimensional, so get_fdata will return a 3-dimensional array. You can verify this\n",





  
  

  745
  
    "# by accessing the .shape attribute. What are the dimensions of the input arrays?\n",





  
  

  746
  
    "\n",





  
  

  747
  
    "img_np = img.get_fdata()\n",





  
  

  748
  
    "label_np = label.get_fdata()\n",





  
  

  749
  
    "print(f'img shape is {img_np.shape}')\n",





  
  

  750
  
    "print(f'label shape is {label_np.shape}')\n",





  
  

  751
  
    "\n",





  
  

  752
  
    "\n",





  
  

  753
  
    "# TASK: using matplotlib, visualize a few slices from the dataset, along with their labels. \n",





  
  

  754
  
    "# You can adjust plot sizes like so if you find them too small:\n",





  
  

  755
  
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",





  
  

  756
  
    "\n",





  
  

  757
  
    "fig1, n_ax1 = plt.subplots(1,5,figsize=(15,15))\n",





  
  

  758
  
    "n_ax1 = n_ax1.flatten()\n",





  
  

  759
  
    "\n",





  
  

  760
  
    "for i in range(5):\n",





  
  

  761
  
    "    n_ax1[i].imshow(img_np[:,:,(i*7)])\n",





  
  

  762
  
    "    n_ax1[i].set_title(f'img Axial slice {i*7}')\n",





  
  

  763
  
    "    \n",





  
  

  764
  
    "fig2, n_ax2 = plt.subplots(1,5,figsize=(15,15))\n",





  
  

  765
  
    "n_ax2 = n_ax2.flatten()\n",





  
  

  766
  
    "\n",





  
  

  767
  
    "for i in range(5):\n",





  
  

  768
  
    "    n_ax2[i].imshow(label_np[:,:,(i*7)])\n",





  
  

  769
  
    "    n_ax2[i].set_title(f'label Axial slice {i*7}')\n",





  
  

  770
  
    "    \n",





  
  

  771
  
    "fig3, n_ax3 = plt.subplots(1,5,figsize=(15,15))\n",





  
  

  772
  
    "n_ax3 = n_ax3.flatten()    \n",





  
  

  773
  
    "for i in range(5):\n",





  
  

  774
  
    "    n_ax3[i].imshow(img_np[:,i*10,:])\n",





  
  

  775
  
    "    n_ax3[i].set_title(f'img Coronal slice {i*10}')\n",





  
  

  776
  
    "    \n",





  
  

  777
  
    "fig4, n_ax4 = plt.subplots(1,5,figsize=(15,15))\n",





  
  

  778
  
    "n_ax4 = n_ax4.flatten()\n",





  
  

  779
  
    "\n",





  
  

  780
  
    "for i in range(5):\n",





  
  

  781
  
    "    n_ax4[i].imshow(label_np[:,(i*10),:])\n",





  
  

  782
  
    "    n_ax4[i].set_title(f'label Coronal slice {i*10}')\n",





  
  

  783
  
    "    \n",





  
  

  784
  
    "fig5, n_ax5 = plt.subplots(1,5,figsize=(15,15))\n",





  
  

  785
  
    "n_ax5 = n_ax5.flatten()    \n",





  
  

  786
  
    "for i in range(5):\n",





  
  

  787
  
    "    n_ax5[i].imshow(img_np[(i*7),:,:])\n",





  
  

  788
  
    "    n_ax5[i].set_title(f'img Sagital slice {i*7}')\n",





  
  

  789
  
    "    \n",





  
  

  790
  
    "fig6, n_ax6 = plt.subplots(1,5,figsize=(15,15))\n",





  
  

  791
  
    "n_ax6 = n_ax6.flatten()\n",





  
  

  792
  
    "\n",





  
  

  793
  
    "for i in range(5):\n",





  
  

  794
  
    "    n_ax6[i].imshow(label_np[(i*7),:,:])\n",





  
  

  795
  
    "    n_ax6[i].set_title(f'label Sagital slice {i*7}')"





  
  

  796
  
   ]





  
  

  797
  
  },





  
  

  798
  
  {





  
  

  799
  
   "cell_type": "code",





  
  

  800
  
   "execution_count": 6,





  
  

  801
  
   "metadata": {},





  
  

  802
  
   "outputs": [





  
  

  803
  
    {





  
  

  804
  
     "data": {





  
  

  805
  
      "image/png": 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9hIVnzQWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7h38BbmAzHtKFfcuaCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHYO/wIHCId0Wbvdua5OOXbLXhvHIrPmAkC7VeNSVc+rqquq6gNLTjuqqs6rqovm70fu3WECsEjWsubygiSn7nTamUnOH2OclOT8+XcASLKGuIwx3pzk6p1OfkCSs+efz07ywN5hAbDI9nSfy63HGFckyfz96JXOWFVnVNUFVXXBV3PdHv47ABbJXt+hP8Y4a4yxdYyx9ZAcurf/HQD7gT2Ny5VVdUySzN+v6hsSAItuT+PymiSnzz+fnuTVPcMBYDNYy0uRX5LkbUm+q6our6pHJnlqkvtW1UVJ7jv/DgBJ1vAO/THGQ1aYdO/msQB7wDvv+3nX/fp5hz4A7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB2qx7+Bdj3HNKln0O67FvWXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO0O3ugBwIHi3O3bNnoIm9Ipx27Z6CGwDGsuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2jn8C7DfcUiXxWfNBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDuHf4F1Onf7to0ewkJwSJcDizUXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaeYc+LMO77tfOO+9ZjjUXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7Rz+hQOGQ7qsnUO6sF7WXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALRz+Bc4QDikC/uSNRcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANp5hz77pXO3b9voIQDrYM0FgHbiAkC7VeNSVcdX1Rur6sKq+mBVPXY+/aiqOq+qLpq/H7n3hwvAIljLmsv1Sf7LGOMOSe6R5Jer6o5Jzkxy/hjjpCTnz78DwOpxGWNcMcZ49/zzF5JcmOQ2SR6Q5Oz5bGcneeBeGiMAC2a39rlU1QlJvjfJO5LceoxxRTIFKMnRK1zmjKq6oKou+GquW+dwAVgEa45LVR2R5OVJfmWM8fm1Xm6McdYYY+sYY+shOXRPxgjAgllTXKrqkExhedEY4xXzyVdW1THz9GOSXLV3hgjAolnLq8UqyXOTXDjGeMaSSa9Jcvr88+lJXt0/PAAW0VreoX/PJA9P8v6q2jaf9oQkT01yTlU9Msknkjxor4wQgIWzalzGGG9NUitMvnfvcNjMHNIFDhzeoQ9AO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaDdWo4tBuzHTjl2y0YPAW7AmgsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO28Qx/2Q951z6Kz5gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCdw7+wbudu37bRQ9h0duc6dagY9kfWXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALRz+BfYDzmkC4vOmgsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaDdwRs9APZP527fttFDABaYNRcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtHP4F9pFTjt2y0UOAfcaaCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoN3BGz0A9p1zt2/b6CFsSqccu2WjhwD7HWsuALRbNS5VdVhVvbOq3ltVH6yqp8ynH1VV51XVRfP3I/f+cAFYBGtZc7kuyX8YY9wlyZYkp1bVPZKcmeT8McZJSc6ffweA1eMyJtfOvx4yf40kD0hy9nz62UkeuDcGCMDiWdM+l6o6qKq2JbkqyXljjHckufUY44okmb8fvcJlz6iqC6rqgq/muqZhA7A/W1NcxhhfG2NsSXJckpOr6s5r/QdjjLPGGFvHGFsPyaF7OEwAFsluvVpsjPHZJG9KcmqSK6vqmCSZv1/VPTgAFtNaXi12q6q6+fzztyS5T5IPJ3lNktPns52e5NV7aYwALJi1vInymCRnV9VBmWJ0zhjjr6rqbUnOqapHJvlEkgftxXECsEBWjcsY431JvneZ0z+T5N57Y1AALDaHfzmAfMfLfmGjh7ApnZi3b/QQYL/j8C8AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB2B2/0AFifi595j40ewqZ04q++faOHAAvNmgsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0O3ijBwD7yom/+vaNHgIcMKy5ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaOfwL/upi595j40eAsAes+YCQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA779Bn4Z34q2/f6CEAO7HmAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoJ3Dv7BfckgXWGzWXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALRz+Jf91FoPf3LxM++xl0fSxyFd4MBhzQWAdmuOS1UdVFXvqaq/mn8/qqrOq6qL5u9H7r1hArBIdmfN5bFJLlzy+5lJzh9jnJTk/Pl3AFhbXKrquCT/McmfLzn5AUnOnn8+O8kDW0cGwMJa65rLHyZ5XJKvLznt1mOMK5Jk/n70chesqjOq6oKquuCruW49YwVgQawal6r6kSRXjTH+cU/+wRjjrDHG1jHG1kNy6J78CQAWzFpeinzPJD9WVfdPcliSm1XVC5NcWVXHjDGuqKpjkly1NwcKwOJYdc1ljPH4McZxY4wTkjw4yd+OMR6W5DVJTp/PdnqSV++1UQKwUNbzPpenJrlvVV2U5L7z7wCwe+/QH2O8Kcmb5p8/k+Te/UNi0XjnPbAz79AHoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQbrcO/8L+x6FXgP2RNRcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQTlwAaCcuALQTFwDaiQsA7cQFgHbiAkA7cQGgnbgA0E5cAGgnLgC0ExcA2okLAO3EBYB24gJAO3EBoJ24ANBOXABoJy4AtBMXANqJCwDtxAWAduICQDtxAaCduADQ7uC1nKmqLk3yhSRfS3L9GGNrVR2V5GVJTkhyaZLTxhjX7J1hArBIdmfN5d+PMbaMMbbOv5+Z5PwxxklJzp9/B4B1bRZ7QJKz55/PTvLAdY8GgE1hrXEZSd5QVf9YVWfMp916jHFFkszfj17uglV1RlVdUFUXfDXXrX/EAOz31rTPJck9xxjbq+roJOdV1YfX+g/GGGclOStJblZHjT0YIwALZk1rLmOM7fP3q5K8MsnJSa6sqmOSZP5+1d4aJACLZdW4VNXhVXXTHT8n+eEkH0jymiSnz2c7Pcmr99YgAVgsa9ksduskr6yqHed/8Rjj/1XVu5KcU1WPTPKJJA/ae8MEYJGsGpcxxiVJ7rLM6Z9Jcu+9MSgAFluNse/2sVfVp5J8fKeTb5nk0/tsEPvOZpwv87QYNuM8JZtzvhZ9nm43xrjVchP2aVyWHUDVBUvemLlpbMb5Mk+LYTPOU7I552szztMOji0GQDtxAaDd/hCXszZ6AHvJZpwv87QYNuM8JZtzvjbjPCXZD/a5ALD57A9rLgBsMuICQLsNjUtVnVpVH6mqi6tqU3weTFVdWlXvr6ptVXXBRo9nT1XV86rqqqr6wJLTjqqq86rqovn7kRs5xt21wjw9uar+eV5e26rq/hs5xt1VVcdX1Rur6sKq+mBVPXY+fWGX1S7maWGXVVUdVlXvrKr3zvP0lPn0hV1Oq9mwfS5VdVCSf0py3ySXJ3lXkoeMMT60IQNqMn9q59YxxiK/MSpV9e+SXJvkL8YYd55P+/0kV48xnjo/GThyjPEbGznO3bHCPD05ybVjjD/YyLHtqfmgsceMMd49HwPwHzN9ttIjsqDLahfzdFoWdFnVdPysw8cY11bVIUnemuSxSX4iC7qcVrORay4nJ7l4jHHJGOMrSV6a6QPI2A+MMd6c5OqdTl7oD4hbYZ4W2hjjijHGu+efv5DkwiS3yQIvq13M08Iak2vnXw+Zv0YWeDmtZiPjcpskly35/fIs+A1ottwHq20Wa/qAuAX0mKp637zZbGE3S1TVCUm+N8k7skmW1U7zlCzwsqqqg6pqW6aPJzlvjLFpltNyNjIutcxpm+F10fccY9w1yf2S/PK8KYb913OSfEeSLUmuSPL0DR3NHqqqI5K8PMmvjDE+v9Hj6bDMPC30shpjfG2MsSXJcUlOrqo7b/CQ9qqNjMvlSY5f8vtxSbZv0FjarPDBapvFpvuAuDHGlfOd/utJ/iwLuLzmbfgvT/KiMcYr5pMXelktN0+bYVklyRjjs0nelOTULPhy2pWNjMu7kpxUVd9eVTdO8uBMH0C2sHbxwWqbxab7gLgdd+zZj2fBlte8o/i5SS4cYzxjyaSFXVYrzdMiL6uqulVV3Xz++VuS3CfJh7PAy2k1G/oO/fmlhH+Y5KAkzxtj/O6GDaZBVd0+09pK8q8frLaQ81RVL0lyr0yHBL8yyZOSvCrJOUlum/kD4sYYC7ODfIV5ulemzSwjyaVJHr1jG/giqKofSPKWJO9P8vX55Cdk2kexkMtqF/P0kCzosqqq78m0w/6gTE/qzxlj/FZV3SILupxW4/AvALTzDn0A2okLAO3EBYB24gJAO3EBoJ24ANBOXABo9/8BPi3zgb7JR9YAAAAASUVORK5CYII=\n",





  
  

  806
  
      "text/plain": [





  
  

  807
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  808
  
      ]





  
  

  809
  
     },





  
  

  810
  
     "metadata": {





  
  

  811
  
      "needs_background": "light"





  
  

  812
  
     },





  
  

  813
  
     "output_type": "display_data"





  
  

  814
  
    }





  
  

  815
  
   ],





  
  

  816
  
   "source": [





  
  

  817
  
    "plt.imshow(label_np[14,:,:])\n",





  
  

  818
  
    "plt.title('hippocampus_001.nii.gz, Label, Sagital Slice 14')\n",





  
  

  819
  
    "plt.show()"





  
  

  820
  
   ]





  
  

  821
  
  },





  
  

  822
  
  {





  
  

  823
  
   "cell_type": "code",





  
  

  824
  
   "execution_count": 7,





  
  

  825
  
   "metadata": {},





  
  

  826
  
   "outputs": [





  
  

  827
  
    {





  
  

  828
  
     "data": {





  
  

  829
  
      "image/png": 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\n",





  
  

  830
  
      "text/plain": [





  
  

  831
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  832
  
      ]





  
  

  833
  
     },





  
  

  834
  
     "metadata": {





  
  

  835
  
      "needs_background": "light"





  
  

  836
  
     },





  
  

  837
  
     "output_type": "display_data"





  
  

  838
  
    }





  
  

  839
  
   ],





  
  

  840
  
   "source": [





  
  

  841
  
    "plt.imshow((img_np[:,:,14]+(label_np[:,:,14])*80))\n",





  
  

  842
  
    "plt.title('hippocampus_001.nii.gz, Overlay Label on MRI, Axial Slice 14')\n",





  
  

  843
  
    "plt.show()"





  
  

  844
  
   ]





  
  

  845
  
  },





  
  

  846
  
  {





  
  

  847
  
   "cell_type": "markdown",





  
  

  848
  
   "metadata": {},





  
  

  849
  
   "source": [





  
  

  850
  
    "Load volume into 3D Slicer to validate that your visualization is correct and get a feel for the shape of structures.Try to get a visualization like the one below (hint: while Slicer documentation is not particularly great, there are plenty of YouTube videos available! Just look it up on YouTube if you are not sure how to do something)\n",





  
  

  851
  
    "\n",





  
  

  852
  
    "![3D slicer](img/Slicer.png)"





  
  

  853
  
   ]





  
  

  854
  
  },





  
  

  855
  
  {





  
  

  856
  
   "cell_type": "code",





  
  

  857
  
   "execution_count": null,





  
  

  858
  
   "metadata": {},





  
  

  859
  
   "outputs": [],





  
  

  860
  
   "source": [





  
  

  861
  
    "# Stand out suggestion: use one of the simple Volume Rendering algorithms that we've\n",





  
  

  862
  
    "# implemented in one of our earlier lessons to visualize some of these volumes"





  
  

  863
  
   ]





  
  

  864
  
  },





  
  

  865
  
  {





  
  

  866
  
   "cell_type": "code",





  
  

  867
  
   "execution_count": 12,





  
  

  868
  
   "metadata": {},





  
  

  869
  
   "outputs": [





  
  

  870
  
    {





  
  

  871
  
     "name": "stdout",





  
  

  872
  
     "output_type": "stream",





  
  

  873
  
     "text": [





  
  

  874
  
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",





  
  

  875
  
      "sizeof_hdr      : 348\n",





  
  

  876
  
      "data_type       : b''\n",





  
  

  877
  
      "db_name         : b''\n",





  
  

  878
  
      "extents         : 0\n",





  
  

  879
  
      "session_error   : 0\n",





  
  

  880
  
      "regular         : b'r'\n",





  
  

  881
  
      "dim_info        : 0\n",





  
  

  882
  
      "dim             : [ 3 35 51 35  1  1  1  1]\n",





  
  

  883
  
      "intent_p1       : 0.0\n",





  
  

  884
  
      "intent_p2       : 0.0\n",





  
  

  885
  
      "intent_p3       : 0.0\n",





  
  

  886
  
      "intent_code     : none\n",





  
  

  887
  
      "datatype        : uint8\n",





  
  

  888
  
      "bitpix          : 8\n",





  
  

  889
  
      "slice_start     : 0\n",





  
  

  890
  
      "pixdim          : [1. 1. 1. 1. 1. 0. 0. 0.]\n",





  
  

  891
  
      "vox_offset      : 0.0\n",





  
  

  892
  
      "scl_slope       : nan\n",





  
  

  893
  
      "scl_inter       : nan\n",





  
  

  894
  
      "slice_end       : 0\n",





  
  

  895
  
      "slice_code      : unknown\n",





  
  

  896
  
      "xyzt_units      : 10\n",





  
  

  897
  
      "cal_max         : 0.0\n",





  
  

  898
  
      "cal_min         : 0.0\n",





  
  

  899
  
      "slice_duration  : 0.0\n",





  
  

  900
  
      "toffset         : 0.0\n",





  
  

  901
  
      "glmax           : 0\n",





  
  

  902
  
      "glmin           : 0\n",





  
  

  903
  
      "descrip         : b'5.0.10'\n",





  
  

  904
  
      "aux_file        : b'none'\n",





  
  

  905
  
      "qform_code      : scanner\n",





  
  

  906
  
      "sform_code      : scanner\n",





  
  

  907
  
      "quatern_b       : 0.0\n",





  
  

  908
  
      "quatern_c       : 0.0\n",





  
  

  909
  
      "quatern_d       : 0.0\n",





  
  

  910
  
      "qoffset_x       : 1.0\n",





  
  

  911
  
      "qoffset_y       : 1.0\n",





  
  

  912
  
      "qoffset_z       : 1.0\n",





  
  

  913
  
      "srow_x          : [1. 0. 0. 1.]\n",





  
  

  914
  
      "srow_y          : [0. 1. 0. 1.]\n",





  
  

  915
  
      "srow_z          : [0. 0. 1. 1.]\n",





  
  

  916
  
      "intent_name     : b''\n",





  
  

  917
  
      "magic           : b'n+1'\n"





  
  

  918
  
     ]





  
  

  919
  
    }





  
  

  920
  
   ],





  
  

  921
  
   "source": [





  
  

  922
  
    "print(nib.load(os.path.join(path, 'labels', 'hippocampus_001.nii.gz')).header)"





  
  

  923
  
   ]





  
  

  924
  
  },





  
  

  925
  
  {





  
  

  926
  
   "cell_type": "markdown",





  
  

  927
  
   "metadata": {},





  
  

  928
  
   "source": [





  
  

  929
  
    "## <a name=\"explore-one-data\"></a>Explore a single image data\n",





  
  

  930
  
    "In this section we will look closer at the NIFTI representation of our volumes. In order to measure the physical volume of hippocampi, we need to understand the relationship between the sizes of our voxels and the physical world."





  
  

  931
  
   ]





  
  

  932
  
  },





  
  

  933
  
  {





  
  

  934
  
   "cell_type": "code",





  
  

  935
  
   "execution_count": 13,





  
  

  936
  
   "metadata": {





  
  

  937
  
    "scrolled": true





  
  

  938
  
   },





  
  

  939
  
   "outputs": [





  
  

  940
  
    {





  
  

  941
  
     "name": "stdout",





  
  

  942
  
     "output_type": "stream",





  
  

  943
  
     "text": [





  
  

  944
  
      "Img format is <class 'nibabel.nifti1.Nifti1Header'>\n",





  
  

  945
  
      "Label format is <class 'nibabel.nifti1.Nifti1Header'>\n"





  
  

  946
  
     ]





  
  

  947
  
    }





  
  

  948
  
   ],





  
  

  949
  
   "source": [





  
  

  950
  
    "# Nibabel supports many imaging formats, NIFTI being just one of them. I told you that our images \n",





  
  

  951
  
    "# are in NIFTI, but you should confirm if this is indeed the format that we are dealing with\n",





  
  

  952
  
    "# TASK: using .header_class attribute - what is the format of our images?\n",





  
  

  953
  
    "\n",





  
  

  954
  
    "print(f'Img format is {img.header_class}')\n",





  
  

  955
  
    "print(f'Label format is {label.header_class}')"





  
  

  956
  
   ]





  
  

  957
  
  },





  
  

  958
  
  {





  
  

  959
  
   "cell_type": "markdown",





  
  

  960
  
   "metadata": {},





  
  

  961
  
   "source": [





  
  

  962
  
    "Further down we will be inspecting .header attribute that provides access to NIFTI metadata. You can use this resource as a reference for various fields: https://brainder.org/2012/09/23/the-nifti-file-format/"





  
  

  963
  
   ]





  
  

  964
  
  },





  
  

  965
  
  {





  
  

  966
  
   "cell_type": "code",





  
  

  967
  
   "execution_count": 14,





  
  

  968
  
   "metadata": {},





  
  

  969
  
   "outputs": [





  
  

  970
  
    {





  
  

  971
  
     "name": "stdout",





  
  

  972
  
     "output_type": "stream",





  
  

  973
  
     "text": [





  
  

  974
  
      "Img: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",





  
  

  975
  
      "sizeof_hdr      : 348\n",





  
  

  976
  
      "data_type       : b''\n",





  
  

  977
  
      "db_name         : b''\n",





  
  

  978
  
      "extents         : 0\n",





  
  

  979
  
      "session_error   : 0\n",





  
  

  980
  
      "regular         : b'r'\n",





  
  

  981
  
      "dim_info        : 0\n",





  
  

  982
  
      "dim             : [ 3 35 51 35  1  1  1  1]\n",





  
  

  983
  
      "intent_p1       : 0.0\n",





  
  

  984
  
      "intent_p2       : 0.0\n",





  
  

  985
  
      "intent_p3       : 0.0\n",





  
  

  986
  
      "intent_code     : none\n",





  
  

  987
  
      "datatype        : uint8\n",





  
  

  988
  
      "bitpix          : 8\n",





  
  

  989
  
      "slice_start     : 0\n",





  
  

  990
  
      "pixdim          : [1. 1. 1. 1. 1. 0. 0. 0.]\n",





  
  

  991
  
      "vox_offset      : 0.0\n",





  
  

  992
  
      "scl_slope       : nan\n",





  
  

  993
  
      "scl_inter       : nan\n",





  
  

  994
  
      "slice_end       : 0\n",





  
  

  995
  
      "slice_code      : unknown\n",





  
  

  996
  
      "xyzt_units      : 10\n",





  
  

  997
  
      "cal_max         : 0.0\n",





  
  

  998
  
      "cal_min         : 0.0\n",





  
  

  999
  
      "slice_duration  : 0.0\n",





  
  

  1000
  
      "toffset         : 0.0\n",





  
  

  1001
  
      "glmax           : 0\n",





  
  

  1002
  
      "glmin           : 0\n",





  
  

  1003
  
      "descrip         : b'5.0.10'\n",





  
  

  1004
  
      "aux_file        : b'none'\n",





  
  

  1005
  
      "qform_code      : scanner\n",





  
  

  1006
  
      "sform_code      : scanner\n",





  
  

  1007
  
      "quatern_b       : 0.0\n",





  
  

  1008
  
      "quatern_c       : 0.0\n",





  
  

  1009
  
      "quatern_d       : 0.0\n",





  
  

  1010
  
      "qoffset_x       : 1.0\n",





  
  

  1011
  
      "qoffset_y       : 1.0\n",





  
  

  1012
  
      "qoffset_z       : 1.0\n",





  
  

  1013
  
      "srow_x          : [1. 0. 0. 1.]\n",





  
  

  1014
  
      "srow_y          : [0. 1. 0. 1.]\n",





  
  

  1015
  
      "srow_z          : [0. 0. 1. 1.]\n",





  
  

  1016
  
      "intent_name     : b''\n",





  
  

  1017
  
      "magic           : b'n+1' \n",





  
  

  1018
  
      "\n",





  
  

  1019
  
      "Label: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",





  
  

  1020
  
      "sizeof_hdr      : 348\n",





  
  

  1021
  
      "data_type       : b''\n",





  
  

  1022
  
      "db_name         : b''\n",





  
  

  1023
  
      "extents         : 0\n",





  
  

  1024
  
      "session_error   : 0\n",





  
  

  1025
  
      "regular         : b'r'\n",





  
  

  1026
  
      "dim_info        : 0\n",





  
  

  1027
  
      "dim             : [ 3 35 51 35  1  1  1  1]\n",





  
  

  1028
  
      "intent_p1       : 0.0\n",





  
  

  1029
  
      "intent_p2       : 0.0\n",





  
  

  1030
  
      "intent_p3       : 0.0\n",





  
  

  1031
  
      "intent_code     : none\n",





  
  

  1032
  
      "datatype        : uint8\n",





  
  

  1033
  
      "bitpix          : 8\n",





  
  

  1034
  
      "slice_start     : 0\n",





  
  

  1035
  
      "pixdim          : [1. 1. 1. 1. 1. 0. 0. 0.]\n",





  
  

  1036
  
      "vox_offset      : 0.0\n",





  
  

  1037
  
      "scl_slope       : nan\n",





  
  

  1038
  
      "scl_inter       : nan\n",





  
  

  1039
  
      "slice_end       : 0\n",





  
  

  1040
  
      "slice_code      : unknown\n",





  
  

  1041
  
      "xyzt_units      : 10\n",





  
  

  1042
  
      "cal_max         : 0.0\n",





  
  

  1043
  
      "cal_min         : 0.0\n",





  
  

  1044
  
      "slice_duration  : 0.0\n",





  
  

  1045
  
      "toffset         : 0.0\n",





  
  

  1046
  
      "glmax           : 0\n",





  
  

  1047
  
      "glmin           : 0\n",





  
  

  1048
  
      "descrip         : b'5.0.10'\n",





  
  

  1049
  
      "aux_file        : b'none'\n",





  
  

  1050
  
      "qform_code      : scanner\n",





  
  

  1051
  
      "sform_code      : scanner\n",





  
  

  1052
  
      "quatern_b       : 0.0\n",





  
  

  1053
  
      "quatern_c       : 0.0\n",





  
  

  1054
  
      "quatern_d       : 0.0\n",





  
  

  1055
  
      "qoffset_x       : 1.0\n",





  
  

  1056
  
      "qoffset_y       : 1.0\n",





  
  

  1057
  
      "qoffset_z       : 1.0\n",





  
  

  1058
  
      "srow_x          : [1. 0. 0. 1.]\n",





  
  

  1059
  
      "srow_y          : [0. 1. 0. 1.]\n",





  
  

  1060
  
      "srow_z          : [0. 0. 1. 1.]\n",





  
  

  1061
  
      "intent_name     : b''\n",





  
  

  1062
  
      "magic           : b'n+1'\n"





  
  

  1063
  
     ]





  
  

  1064
  
    }





  
  

  1065
  
   ],





  
  

  1066
  
   "source": [





  
  

  1067
  
    "# TASK: How many bits per pixel are used?\n",





  
  

  1068
  
    "print(f'Img: {img.header} \\n')\n",





  
  

  1069
  
    "print(f'Label: {label.header}')"





  
  

  1070
  
   ]





  
  

  1071
  
  },





  
  

  1072
  
  {





  
  

  1073
  
   "cell_type": "markdown",





  
  

  1074
  
   "metadata": {},





  
  

  1075
  
   "source": [





  
  

  1076
  
    "#### Bits per voxel (pixel) is 8."





  
  

  1077
  
   ]





  
  

  1078
  
  },





  
  

  1079
  
  {





  
  

  1080
  
   "cell_type": "code",





  
  

  1081
  
   "execution_count": 10,





  
  

  1082
  
   "metadata": {},





  
  

  1083
  
   "outputs": [





  
  

  1084
  
    {





  
  

  1085
  
     "data": {





  
  

  1086
  
      "text/plain": [





  
  

  1087
  
       "'\\nxyzt_units indicate the unit of measurements for dim.  \\nFrom the Header, xyzt_units in binary is 10, translating to 2.  \\n2 translates to NIFTI_UNITS_MM - millimeter.\\n'"





  
  

  1088
  
      ]





  
  

  1089
  
     },





  
  

  1090
  
     "execution_count": 10,





  
  

  1091
  
     "metadata": {},





  
  

  1092
  
     "output_type": "execute_result"





  
  

  1093
  
    }





  
  

  1094
  
   ],





  
  

  1095
  
   "source": [





  
  

  1096
  
    "# TASK: What are the units of measurement?\n",





  
  

  1097
  
    "\n",





  
  

  1098
  
    "'''\n",





  
  

  1099
  
    "xyzt_units indicate the unit of measurements for dim.  \n",





  
  

  1100
  
    "From the Header, xyzt_units in binary is 10, translating to 2.  \n",





  
  

  1101
  
    "2 translates to NIFTI_UNITS_MM - millimeter.\n",





  
  

  1102
  
    "'''"





  
  

  1103
  
   ]





  
  

  1104
  
  },





  
  

  1105
  
  {





  
  

  1106
  
   "cell_type": "code",





  
  

  1107
  
   "execution_count": null,





  
  

  1108
  
   "metadata": {},





  
  

  1109
  
   "outputs": [],





  
  

  1110
  
   "source": [





  
  

  1111
  
    "# TASK: Do we have a regular grid? What are grid spacings?\n",





  
  

  1112
  
    "\n",





  
  

  1113
  
    "'''\n",





  
  

  1114
  
    "pixdim is grid spacings.\n",





  
  

  1115
  
    "pixdim = [1. 1. 1. 1. 1. 0. 0. 0.]\n",





  
  

  1116
  
    "pixdim[1], pixdim[2], pixdim[3] = 1.,1.,1.\n",





  
  

  1117
  
    "\n",





  
  

  1118
  
    "'''"





  
  

  1119
  
   ]





  
  

  1120
  
  },





  
  

  1121
  
  {





  
  

  1122
  
   "cell_type": "code",





  
  

  1123
  
   "execution_count": null,





  
  

  1124
  
   "metadata": {},





  
  

  1125
  
   "outputs": [],





  
  

  1126
  
   "source": [





  
  

  1127
  
    "# TASK: What dimensions represent axial, sagittal, and coronal slices? How do you know?\n",





  
  

  1128
  
    "'''\n",





  
  

  1129
  
    "sform_code = scanner\n",





  
  

  1130
  
    "srow_x, srow_y, srow_z are given.\n",





  
  

  1131
  
    "srow_x = [1. 0  0  1.]\n",





  
  

  1132
  
    "srow_y = [0  1. 0  1.]\n",





  
  

  1133
  
    "srow_z = [0  0  .  1.]\n",





  
  

  1134
  
    "\n",





  
  

  1135
  
    "From NIFITI documentation 3D IMAGE (VOLUME) ORIENTATION AND LOCATION IN SPACE section:\n",





  
  

  1136
  
    "In sform_code method, the (x,y,z) axes refer to a subject-based coordinate system,\n",





  
  

  1137
  
    "with +x = Right  +y = Anterior  +z = Superior.\n",





  
  

  1138
  
    "The srow_x, _y, _z vectors show that they translate to orthoganal i, j, k vectors.\n",





  
  

  1139
  
    "\n",





  
  

  1140
  
    "Hence, x dimension is sagital (medial and lateral/ left and right since this is the right side of the brain)\n",





  
  

  1141
  
    "y dimension is coronal (anterior and posterior)\n",





  
  

  1142
  
    "z dimension is axial (superior and inferior)\n",





  
  

  1143
  
    "'''"





  
  

  1144
  
   ]





  
  

  1145
  
  },





  
  

  1146
  
  {





  
  

  1147
  
   "cell_type": "code",





  
  

  1148
  
   "execution_count": 10,





  
  

  1149
  
   "metadata": {},





  
  

  1150
  
   "outputs": [





  
  

  1151
  
    {





  
  

  1152
  
     "data": {





  
  

  1153
  
      "text/plain": [





  
  

  1154
  
       "array([2., 2., 2., ..., 1., 1., 1.])"





  
  

  1155
  
      ]





  
  

  1156
  
     },





  
  

  1157
  
     "execution_count": 10,





  
  

  1158
  
     "metadata": {},





  
  

  1159
  
     "output_type": "execute_result"





  
  

  1160
  
    }





  
  

  1161
  
   ],





  
  

  1162
  
   "source": [





  
  

  1163
  
    "label_np[label_np > 0]"





  
  

  1164
  
   ]





  
  

  1165
  
  },





  
  

  1166
  
  {





  
  

  1167
  
   "cell_type": "code",





  
  

  1168
  
   "execution_count": 32,





  
  

  1169
  
   "metadata": {},





  
  

  1170
  
   "outputs": [





  
  

  1171
  
    {





  
  

  1172
  
     "name": "stdout",





  
  

  1173
  
     "output_type": "stream",





  
  

  1174
  
     "text": [





  
  

  1175
  
      "Volume of hippocampus label is 2948 mm^3\n"





  
  

  1176
  
     ]





  
  

  1177
  
    }





  
  

  1178
  
   ],





  
  

  1179
  
   "source": [





  
  

  1180
  
    "# By now you should have enough information to decide what are dimensions of a single voxel\n",





  
  

  1181
  
    "# TASK: Compute the volume (in mm³) of a hippocampus using one of the labels you've loaded. \n",





  
  

  1182
  
    "# You should get a number between ~2200 and ~4500\n",





  
  

  1183
  
    "\n",





  
  

  1184
  
    "'''\n",





  
  

  1185
  
    "One voxel = pixdim[1] * pixdim[2] * pixdim[3] = 1.0 mm^3\n",





  
  

  1186
  
    "'''\n",





  
  

  1187
  
    "\n",





  
  

  1188
  
    "print(f'Volume of hippocampus label is {np.count_nonzero(label_np > 0)} mm^3')"





  
  

  1189
  
   ]





  
  

  1190
  
  },





  
  

  1191
  
  {





  
  

  1192
  
   "cell_type": "code",





  
  

  1193
  
   "execution_count": 8,





  
  

  1194
  
   "metadata": {},





  
  

  1195
  
   "outputs": [





  
  

  1196
  
    {





  
  

  1197
  
     "name": "stdout",





  
  

  1198
  
     "output_type": "stream",





  
  

  1199
  
     "text": [





  
  

  1200
  
      "img values range: (0.0, 2776.8801)\n",





  
  

  1201
  
      "label values range: (0.0, 2.0)\n",





  
  

  1202
  
      "[[29 49  5]]\n"





  
  

  1203
  
     ]





  
  

  1204
  
    },





  
  

  1205
  
    {





  
  

  1206
  
     "data": {





  
  

  1207
  
      "image/png": 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\n",





  
  

  1208
  
      "text/plain": [





  
  

  1209
  
       "<Figure size 1080x2160 with 2 Axes>"





  
  

  1210
  
      ]





  
  

  1211
  
     },





  
  

  1212
  
     "metadata": {





  
  

  1213
  
      "needs_background": "light"





  
  

  1214
  
     },





  
  

  1215
  
     "output_type": "display_data"





  
  

  1216
  
    }





  
  

  1217
  
   ],





  
  

  1218
  
   "source": [





  
  

  1219
  
    "'''\n",





  
  

  1220
  
    "Understand min and max value in image and label numpy array\n",





  
  

  1221
  
    "'''\n",





  
  

  1222
  
    "img = nib.load(os.path.join(path, 'images', 'hippocampus_003.nii.gz'))\n",





  
  

  1223
  
    "label = nib.load(os.path.join(path, 'labels', 'hippocampus_003.nii.gz'))\n",





  
  

  1224
  
    "img_np=img.get_fdata().astype(np.single)\n",





  
  

  1225
  
    "label_np=label.get_fdata().astype(np.single)\n",





  
  

  1226
  
    "print(f'img values range: {np.amin(img_np), np.amax(img_np)}')\n",





  
  

  1227
  
    "print(f'label values range: {np.amin(label_np), np.amax(label_np)}')\n",





  
  

  1228
  
    "print(np.argwhere(img_np >= np.amax(img_np)))\n",





  
  

  1229
  
    "\n",





  
  

  1230
  
    "plt.subplots(1,2,figsize= (15,30))\n",





  
  

  1231
  
    "plt.subplot(1,2,1)\n",





  
  

  1232
  
    "plt.imshow(img_np[np.argwhere(img_np >= np.amax(img_np))[0][0]-10,:,:])\n",





  
  

  1233
  
    "plt.title('hippocampus_003.nii.gz, MRI, Sagital Slice')\n",





  
  

  1234
  
    "plt.subplot(1,2,2)\n",





  
  

  1235
  
    "plt.imshow(label_np[np.argwhere(img_np >= np.amax(img_np))[0][0]-10,:,:])\n",





  
  

  1236
  
    "plt.title('hippocampus_003.nii.gz, Label, Sagital Slice')\n",





  
  

  1237
  
    "plt.show()"





  
  

  1238
  
   ]





  
  

  1239
  
  },





  
  

  1240
  
  {





  
  

  1241
  
   "cell_type": "code",





  
  

  1242
  
   "execution_count": 9,





  
  

  1243
  
   "metadata": {},





  
  

  1244
  
   "outputs": [





  
  

  1245
  
    {





  
  

  1246
  
     "data": {





  
  

  1247
  
      "text/plain": [





  
  

  1248
  
       "679.38855"





  
  

  1249
  
      ]





  
  

  1250
  
     },





  
  

  1251
  
     "execution_count": 9,





  
  

  1252
  
     "metadata": {},





  
  

  1253
  
     "output_type": "execute_result"





  
  

  1254
  
    }





  
  

  1255
  
   ],





  
  

  1256
  
   "source": [





  
  

  1257
  
    "img_np.mean()+img_np.std()"





  
  

  1258
  
   ]





  
  

  1259
  
  },





  
  

  1260
  
  {





  
  

  1261
  
   "cell_type": "code",





  
  

  1262
  
   "execution_count": 10,





  
  

  1263
  
   "metadata": {},





  
  

  1264
  
   "outputs": [],





  
  

  1265
  
   "source": [





  
  

  1266
  
    "img_np= img_np/0xff"





  
  

  1267
  
   ]





  
  

  1268
  
  },





  
  

  1269
  
  {





  
  

  1270
  
   "cell_type": "code",





  
  

  1271
  
   "execution_count": 11,





  
  

  1272
  
   "metadata": {},





  
  

  1273
  
   "outputs": [],





  
  

  1274
  
   "source": [





  
  

  1275
  
    "img_np= img_np/np.amax(img_np)"





  
  

  1276
  
   ]





  
  

  1277
  
  },





  
  

  1278
  
  {





  
  

  1279
  
   "cell_type": "code",





  
  

  1280
  
   "execution_count": 12,





  
  

  1281
  
   "metadata": {},





  
  

  1282
  
   "outputs": [





  
  

  1283
  
    {





  
  

  1284
  
     "name": "stdout",





  
  

  1285
  
     "output_type": "stream",





  
  

  1286
  
     "text": [





  
  

  1287
  
      "After Normalizing, the maximum img_np value is 1.0\n"





  
  

  1288
  
     ]





  
  

  1289
  
    },





  
  

  1290
  
    {





  
  

  1291
  
     "data": {





  
  

  1292
  
      "image/png": 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\n",





  
  

  1293
  
      "text/plain": [





  
  

  1294
  
       "<Figure size 720x720 with 2 Axes>"





  
  

  1295
  
      ]





  
  

  1296
  
     },





  
  

  1297
  
     "metadata": {





  
  

  1298
  
      "needs_background": "light"





  
  

  1299
  
     },





  
  

  1300
  
     "output_type": "display_data"





  
  

  1301
  
    }





  
  

  1302
  
   ],





  
  

  1303
  
   "source": [





  
  

  1304
  
    "plt.subplot(1,2,1)\n",





  
  

  1305
  
    "plt.imshow(img_np[29-10,:,:])\n",





  
  

  1306
  
    "plt.title('hippocampus_003.nii.gz, MRI, Sagital Slice')\n",





  
  

  1307
  
    "plt.subplot(1,2,2)\n",





  
  

  1308
  
    "plt.imshow(label_np[29-10,:,:])\n",





  
  

  1309
  
    "plt.title('hippocampus_003.nii.gz, Label, Sagital Slice')\n",





  
  

  1310
  
    "print(f'After Normalizing, the maximum img_np value is {np.amax(img_np)}')"





  
  

  1311
  
   ]





  
  

  1312
  
  },





  
  

  1313
  
  {





  
  

  1314
  
   "cell_type": "code",





  
  

  1315
  
   "execution_count": 13,





  
  

  1316
  
   "metadata": {},





  
  

  1317
  
   "outputs": [





  
  

  1318
  
    {





  
  

  1319
  
     "data": {





  
  

  1320
  
      "image/png": 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\n",





  
  

  1321
  
      "text/plain": [





  
  

  1322
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  1323
  
      ]





  
  

  1324
  
     },





  
  

  1325
  
     "metadata": {





  
  

  1326
  
      "needs_background": "light"





  
  

  1327
  
     },





  
  

  1328
  
     "output_type": "display_data"





  
  

  1329
  
    }





  
  

  1330
  
   ],





  
  

  1331
  
   "source": [





  
  

  1332
  
    "plt.hist(img_np.ravel(), bins=100)\n",





  
  

  1333
  
    "plt.xlabel('Intensity')\n",





  
  

  1334
  
    "plt.ylabel('Count')\n",





  
  

  1335
  
    "plt.title('Normalized Intensity Profile for hippocampus_003.nii.gz MRI image')\n",





  
  

  1336
  
    "plt.show()"





  
  

  1337
  
   ]





  
  

  1338
  
  },





  
  

  1339
  
  {





  
  

  1340
  
   "cell_type": "markdown",





  
  

  1341
  
   "metadata": {},





  
  

  1342
  
   "source": [





  
  

  1343
  
    "# <a name=\"eda\"></a>Exploratory Data Analysis"





  
  

  1344
  
   ]





  
  

  1345
  
  },





  
  

  1346
  
  {





  
  

  1347
  
   "cell_type": "code",





  
  

  1348
  
   "execution_count": 43,





  
  

  1349
  
   "metadata": {},





  
  

  1350
  
   "outputs": [





  
  

  1351
  
    {





  
  

  1352
  
     "data": {





  
  

  1353
  
      "text/plain": [





  
  

  1354
  
       "['TestVolumes', 'TrainingSet']"





  
  

  1355
  
      ]





  
  

  1356
  
     },





  
  

  1357
  
     "execution_count": 43,





  
  

  1358
  
     "metadata": {},





  
  

  1359
  
     "output_type": "execute_result"





  
  

  1360
  
    }





  
  

  1361
  
   ],





  
  

  1362
  
   "source": [





  
  

  1363
  
    "os.listdir(\"../data\")"





  
  

  1364
  
   ]





  
  

  1365
  
  },





  
  

  1366
  
  {





  
  

  1367
  
   "cell_type": "code",





  
  

  1368
  
   "execution_count": 21,





  
  

  1369
  
   "metadata": {},





  
  

  1370
  
   "outputs": [





  
  

  1371
  
    {





  
  

  1372
  
     "data": {





  
  

  1373
  
      "image/png": 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\n",





  
  

  1374
  
      "text/plain": [





  
  

  1375
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  1376
  
      ]





  
  

  1377
  
     },





  
  

  1378
  
     "metadata": {





  
  

  1379
  
      "needs_background": "light"





  
  

  1380
  
     },





  
  

  1381
  
     "output_type": "display_data"





  
  

  1382
  
    },





  
  

  1383
  
    {





  
  

  1384
  
     "name": "stdout",





  
  

  1385
  
     "output_type": "stream",





  
  

  1386
  
     "text": [





  
  

  1387
  
      "262\n"





  
  

  1388
  
     ]





  
  

  1389
  
    }





  
  

  1390
  
   ],





  
  

  1391
  
   "source": [





  
  

  1392
  
    "# TASK: Plot a histogram of all volumes that we have in our dataset and see how \n",





  
  

  1393
  
    "# our dataset measures against a slice of a normal population represented by the chart below.\n",





  
  

  1394
  
    "\n",





  
  

  1395
  
    "train_set_files = glob.glob(os.path.join(path, 'labels', '*'))\n",





  
  

  1396
  
    "train_set_volumes = []\n",





  
  

  1397
  
    "train_set = []\n",





  
  

  1398
  
    "\n",





  
  

  1399
  
    "count = 0\n",





  
  

  1400
  
    "\n",





  
  

  1401
  
    "for i in train_set_files:\n",





  
  

  1402
  
    "    img = nib.load(i)\n",





  
  

  1403
  
    "    img_np = img.get_fdata()\n",





  
  

  1404
  
    "    i_vol = np.count_nonzero(img_np>0)\n",





  
  

  1405
  
    "    i_dim = img.header['dim']\n",





  
  

  1406
  
    "    train_set_volumes.append(i_vol)\n",





  
  

  1407
  
    "    train_set.append([i_vol, i, i_dim])\n",





  
  

  1408
  
    "    count+=1\n",





  
  

  1409
  
    "\n",





  
  

  1410
  
    "\n",





  
  

  1411
  
    "plt.hist(train_set_volumes, bins = 1000)\n",





  
  

  1412
  
    "plt.xlim(1000,25000)\n",





  
  

  1413
  
    "plt.title('Hippocampus Volumes in Training Dataset')\n",





  
  

  1414
  
    "plt.xlabel('Hippocampus Volume (mm^3)')\n",





  
  

  1415
  
    "plt.ylabel('Count')\n",





  
  

  1416
  
    "plt.show()\n",





  
  

  1417
  
    "print(count)   "





  
  

  1418
  
   ]





  
  

  1419
  
  },





  
  

  1420
  
  {





  
  

  1421
  
   "cell_type": "code",





  
  

  1422
  
   "execution_count": 22,





  
  

  1423
  
   "metadata": {},





  
  

  1424
  
   "outputs": [





  
  

  1425
  
    {





  
  

  1426
  
     "data": {





  
  

  1427
  
      "text/plain": [





  
  

  1428
  
       "array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)"





  
  

  1429
  
      ]





  
  

  1430
  
     },





  
  

  1431
  
     "execution_count": 22,





  
  

  1432
  
     "metadata": {},





  
  

  1433
  
     "output_type": "execute_result"





  
  

  1434
  
    }





  
  

  1435
  
   ],





  
  

  1436
  
   "source": [





  
  

  1437
  
    "nib.load(os.path.join(path, 'images', 'hippocampus_083.nii.gz')).header['dim']-nib.load(os.path.join(path, 'labels', 'hippocampus_083.nii.gz')).header['dim']"





  
  

  1438
  
   ]





  
  

  1439
  
  },





  
  

  1440
  
  {





  
  

  1441
  
   "cell_type": "code",





  
  

  1442
  
   "execution_count": 244,





  
  

  1443
  
   "metadata": {},





  
  

  1444
  
   "outputs": [





  
  

  1445
  
    {





  
  

  1446
  
     "data": {





  
  

  1447
  
      "text/plain": [





  
  

  1448
  
       "[[2948,\n",





  
  

  1449
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_001.nii.gz',\n",





  
  

  1450
  
       "  array([ 3, 35, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  1451
  
       " [3353,\n",





  
  

  1452
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_003.nii.gz',\n",





  
  

  1453
  
       "  array([ 3, 34, 52, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  1454
  
       " [3698,\n",





  
  

  1455
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_004.nii.gz',\n",





  
  

  1456
  
       "  array([ 3, 36, 52, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  1457
  
       " [4263,\n",





  
  

  1458
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_006.nii.gz',\n",





  
  

  1459
  
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       "  array([ 3, 38, 49, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 58, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 57, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 56, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 38, 52, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 34, 53, 24,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 38, 53, 30,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 36, 50, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 32, 52, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 51, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 36, 58, 28,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 55, 26,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 34, 51, 31,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [2863,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_257.nii.gz',\n",





  
  

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       "  array([ 3, 34, 51, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_259.nii.gz',\n",





  
  

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       "  array([ 3, 33, 51, 28,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3097,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_260.nii.gz',\n",





  
  

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       "  array([ 3, 35, 53, 29,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [4071,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_261.nii.gz',\n",





  
  

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       "  array([ 3, 36, 58, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3534,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_263.nii.gz',\n",





  
  

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       "  array([ 3, 36, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3842,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_264.nii.gz',\n",





  
  

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       "  array([ 3, 38, 51, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3109,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_265.nii.gz',\n",





  
  

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       "  array([ 3, 31, 54, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3054,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_268.nii.gz',\n",





  
  

  1966
  
       "  array([ 3, 34, 51, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3439,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_269.nii.gz',\n",





  
  

  1969
  
       "  array([ 3, 35, 49, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [2665,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_274.nii.gz',\n",





  
  

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       "  array([ 3, 35, 40, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3643,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_276.nii.gz',\n",





  
  

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       "  array([ 3, 35, 50, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3304,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_277.nii.gz',\n",





  
  

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       "  array([ 3, 33, 59, 29,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [2382,\n",





  
  

  1980
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_279.nii.gz',\n",





  
  

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       "  array([ 3, 34, 50, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [2613,\n",





  
  

  1983
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_280.nii.gz',\n",





  
  

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       "  array([ 3, 37, 47, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [20702,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_281.nii.gz',\n",





  
  

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       "  array([  3, 512, 512,  94,   1,   1,   1,   1], dtype=int16)],\n",





  
  

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       " [2416,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_282.nii.gz',\n",





  
  

  1990
  
       "  array([ 3, 37, 52, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3172,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_286.nii.gz',\n",





  
  

  1993
  
       "  array([ 3, 37, 45, 46,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3700,\n",





  
  

  1995
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_287.nii.gz',\n",





  
  

  1996
  
       "  array([ 3, 37, 50, 39,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3845,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_288.nii.gz',\n",





  
  

  1999
  
       "  array([ 3, 38, 50, 42,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [2738,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_289.nii.gz',\n",





  
  

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       "  array([ 3, 35, 49, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3167,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_290.nii.gz',\n",





  
  

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       "  array([ 3, 35, 49, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3479,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_292.nii.gz',\n",





  
  

  2008
  
       "  array([ 3, 38, 52, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3248,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_294.nii.gz',\n",





  
  

  2011
  
       "  array([ 3, 35, 44, 44,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3628,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_295.nii.gz',\n",





  
  

  2014
  
       "  array([ 3, 35, 53, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2015
  
       " [3918,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_296.nii.gz',\n",





  
  

  2017
  
       "  array([ 3, 35, 54, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2018
  
       " [2786,\n",





  
  

  2019
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_297.nii.gz',\n",





  
  

  2020
  
       "  array([ 3, 34, 51, 30,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [2857,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_298.nii.gz',\n",





  
  

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       "  array([ 3, 37, 50, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3202,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_299.nii.gz',\n",





  
  

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       "  array([ 3, 32, 54, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       " [3404,\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_300.nii.gz',\n",





  
  

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       "  array([ 3, 34, 53, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_301.nii.gz',\n",





  
  

  2032
  
       "  array([ 3, 31, 50, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 46, 39,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 48, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 36, 48, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 34, 49, 30,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 38, 48, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 34, 52, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 52, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 49, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 53, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 55, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 52, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 52, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 33, 46, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 34, 47, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 33, 44, 41,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 37, 43, 43,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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       "  array([ 3, 35, 46, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

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  2127
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_341.nii.gz',\n",





  
  

  2128
  
       "  array([ 3, 34, 48, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2129
  
       " [2618,\n",





  
  

  2130
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_343.nii.gz',\n",





  
  

  2131
  
       "  array([ 3, 32, 45, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2132
  
       " [2980,\n",





  
  

  2133
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_345.nii.gz',\n",





  
  

  2134
  
       "  array([ 3, 32, 49, 30,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2135
  
       " [2590,\n",





  
  

  2136
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_349.nii.gz',\n",





  
  

  2137
  
       "  array([ 3, 34, 50, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2138
  
       " [2942,\n",





  
  

  2139
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_350.nii.gz',\n",





  
  

  2140
  
       "  array([ 3, 35, 49, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2141
  
       " [3724,\n",





  
  

  2142
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_351.nii.gz',\n",





  
  

  2143
  
       "  array([ 3, 35, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2144
  
       " [3679,\n",





  
  

  2145
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_352.nii.gz',\n",





  
  

  2146
  
       "  array([ 3, 38, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2147
  
       " [2755,\n",





  
  

  2148
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_353.nii.gz',\n",





  
  

  2149
  
       "  array([ 3, 32, 51, 31,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2150
  
       " [2912,\n",





  
  

  2151
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_354.nii.gz',\n",





  
  

  2152
  
       "  array([ 3, 36, 50, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2153
  
       " [3335,\n",





  
  

  2154
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_355.nii.gz',\n",





  
  

  2155
  
       "  array([ 3, 33, 47, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2156
  
       " [3496,\n",





  
  

  2157
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_356.nii.gz',\n",





  
  

  2158
  
       "  array([ 3, 36, 51, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2159
  
       " [2887,\n",





  
  

  2160
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_358.nii.gz',\n",





  
  

  2161
  
       "  array([ 3, 35, 50, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2162
  
       " [2634,\n",





  
  

  2163
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_359.nii.gz',\n",





  
  

  2164
  
       "  array([ 3, 35, 49, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2165
  
       " [2931,\n",





  
  

  2166
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_360.nii.gz',\n",





  
  

  2167
  
       "  array([ 3, 34, 49, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2168
  
       " [3105,\n",





  
  

  2169
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_361.nii.gz',\n",





  
  

  2170
  
       "  array([ 3, 36, 50, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2171
  
       " [3506,\n",





  
  

  2172
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_363.nii.gz',\n",





  
  

  2173
  
       "  array([ 3, 38, 52, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2174
  
       " [3979,\n",





  
  

  2175
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_366.nii.gz',\n",





  
  

  2176
  
       "  array([ 3, 37, 47, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2177
  
       " [4151,\n",





  
  

  2178
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_367.nii.gz',\n",





  
  

  2179
  
       "  array([ 3, 36, 57, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2180
  
       " [4401,\n",





  
  

  2181
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_368.nii.gz',\n",





  
  

  2182
  
       "  array([ 3, 38, 55, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2183
  
       " [3398,\n",





  
  

  2184
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_370.nii.gz',\n",





  
  

  2185
  
       "  array([ 3, 35, 50, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2186
  
       " [3483,\n",





  
  

  2187
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_372.nii.gz',\n",





  
  

  2188
  
       "  array([ 3, 36, 50, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2189
  
       " [3689,\n",





  
  

  2190
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_373.nii.gz',\n",





  
  

  2191
  
       "  array([ 3, 34, 49, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2192
  
       " [3877,\n",





  
  

  2193
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_374.nii.gz',\n",





  
  

  2194
  
       "  array([ 3, 38, 48, 39,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2195
  
       " [3222,\n",





  
  

  2196
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_375.nii.gz',\n",





  
  

  2197
  
       "  array([ 3, 32, 54, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2198
  
       " [3581,\n",





  
  

  2199
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_376.nii.gz',\n",





  
  

  2200
  
       "  array([ 3, 35, 55, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2201
  
       " [2757,\n",





  
  

  2202
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_378.nii.gz',\n",





  
  

  2203
  
       "  array([ 3, 35, 52, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2204
  
       " [3435,\n",





  
  

  2205
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_380.nii.gz',\n",





  
  

  2206
  
       "  array([ 3, 35, 46, 42,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2207
  
       " [3152,\n",





  
  

  2208
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_381.nii.gz',\n",





  
  

  2209
  
       "  array([ 3, 33, 49, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2210
  
       " [3420,\n",





  
  

  2211
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_383.nii.gz',\n",





  
  

  2212
  
       "  array([ 3, 33, 55, 29,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2213
  
       " [3397,\n",





  
  

  2214
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_385.nii.gz',\n",





  
  

  2215
  
       "  array([ 3, 35, 48, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2216
  
       " [3208,\n",





  
  

  2217
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_386.nii.gz',\n",





  
  

  2218
  
       "  array([ 3, 37, 45, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2219
  
       " [3080,\n",





  
  

  2220
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_387.nii.gz',\n",





  
  

  2221
  
       "  array([ 3, 33, 51, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2222
  
       " [2947,\n",





  
  

  2223
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_389.nii.gz',\n",





  
  

  2224
  
       "  array([ 3, 34, 49, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2225
  
       " [3252,\n",





  
  

  2226
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_390.nii.gz',\n",





  
  

  2227
  
       "  array([ 3, 38, 51, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2228
  
       " [3682,\n",





  
  

  2229
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_393.nii.gz',\n",





  
  

  2230
  
       "  array([ 3, 36, 51, 31,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  2231
  
       " [3814,\n",





  
  

  2232
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_394.nii.gz',\n",





  
  

  2233
  
       "  array([ 3, 36, 52, 32,  1,  1,  1,  1], dtype=int16)]]"





  
  

  2234
  
      ]





  
  

  2235
  
     },





  
  

  2236
  
     "execution_count": 244,





  
  

  2237
  
     "metadata": {},





  
  

  2238
  
     "output_type": "execute_result"





  
  

  2239
  
    }





  
  

  2240
  
   ],





  
  

  2241
  
   "source": [





  
  

  2242
  
    "train_set"





  
  

  2243
  
   ]





  
  

  2244
  
  },





  
  

  2245
  
  {





  
  

  2246
  
   "cell_type": "markdown",





  
  

  2247
  
   "metadata": {},





  
  

  2248
  
   "source": [





  
  

  2249
  
    "<img src=\"../data/Hippocampus_Volume.jpg\" width=400 align=left>"





  
  

  2250
  
   ]





  
  

  2251
  
  },





  
  

  2252
  
  {





  
  

  2253
  
   "cell_type": "markdown",





  
  

  2254
  
   "metadata": {},





  
  

  2255
  
   "source": [





  
  

  2256
  
    "**Figure 1.** Right Hippocampus Volumes for Age Percentiles for Women"





  
  

  2257
  
   ]





  
  

  2258
  
  },





  
  

  2259
  
  {





  
  

  2260
  
   "cell_type": "markdown",





  
  

  2261
  
   "metadata": {},





  
  

  2262
  
   "source": [





  
  

  2263
  
    "## <a name=\"find-outliers\"></a>Find Outliers Among NIFTI files"





  
  

  2264
  
   ]





  
  

  2265
  
  },





  
  

  2266
  
  {





  
  

  2267
  
   "cell_type": "markdown",





  
  

  2268
  
   "metadata": {},





  
  

  2269
  
   "source": [





  
  

  2270
  
    "Do you see any outliers? Why do you think it's so (might be not immediately obvious, but it's always a good idea to inspect) outliers closer. If you haven't found the images that do not belong, the histogram may help you."





  
  

  2271
  
   ]





  
  

  2272
  
  },





  
  

  2273
  
  {





  
  

  2274
  
   "cell_type": "markdown",





  
  

  2275
  
   "metadata": {},





  
  

  2276
  
   "source": [





  
  

  2277
  
    "### <a name=\"compare-volumes\"></a>Compare Segmentation Labels with UK Biobank Hippocampus Volume Distribution"





  
  

  2278
  
   ]





  
  

  2279
  
  },





  
  

  2280
  
  {





  
  

  2281
  
   "cell_type": "code",





  
  

  2282
  
   "execution_count": 24,





  
  

  2283
  
   "metadata": {},





  
  

  2284
  
   "outputs": [





  
  

  2285
  
    {





  
  

  2286
  
     "name": "stdout",





  
  

  2287
  
     "output_type": "stream",





  
  

  2288
  
     "text": [





  
  

  2289
  
      "Number of hippocampus label volume greater than 4500: 1\n",





  
  

  2290
  
      "Number of hippocampus label volume less than 2800: 40\n",





  
  

  2291
  
      "Number of hippocampus label volume between 2900 and 4500 : 221\n"





  
  

  2292
  
     ]





  
  

  2293
  
    }





  
  

  2294
  
   ],





  
  

  2295
  
   "source": [





  
  

  2296
  
    "train_set=np.array(train_set)\n",





  
  

  2297
  
    "hi_outlier = []\n",





  
  

  2298
  
    "lo_outlier = []\n",





  
  

  2299
  
    "no_outlier = []\n",





  
  

  2300
  
    "\n",





  
  

  2301
  
    "for s in train_set:\n",





  
  

  2302
  
    "    if (int(s[0]) > 4600): \n",





  
  

  2303
  
    "        hi_outlier.append(s)\n",





  
  

  2304
  
    "    elif (int(s[0]) < 2800):\n",





  
  

  2305
  
    "        lo_outlier.append(s)\n",





  
  

  2306
  
    "    else:\n",





  
  

  2307
  
    "        no_outlier.append(s)\n",





  
  

  2308
  
    "#outlier=np.array(outlier)\n",





  
  

  2309
  
    "\n",





  
  

  2310
  
    "print(f'Number of hippocampus label volume greater than 4500: {len(hi_outlier)}')\n",





  
  

  2311
  
    "print(f'Number of hippocampus label volume less than 2800: {len(lo_outlier)}')\n",





  
  

  2312
  
    "print(f'Number of hippocampus label volume between 2900 and 4500 : {len(no_outlier)}')\n"





  
  

  2313
  
   ]





  
  

  2314
  
  },





  
  

  2315
  
  {





  
  

  2316
  
   "cell_type": "code",





  
  

  2317
  
   "execution_count": 25,





  
  

  2318
  
   "metadata": {},





  
  

  2319
  
   "outputs": [





  
  

  2320
  
    {





  
  

  2321
  
     "data": {





  
  

  2322
  
      "text/plain": [





  
  

  2323
  
       "[array([20702, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_281.nii.gz',\n",





  
  

  2324
  
       "        array([  3, 512, 512,  94,   1,   1,   1,   1], dtype=int16)],\n",





  
  

  2325
  
       "       dtype=object)]"





  
  

  2326
  
      ]





  
  

  2327
  
     },





  
  

  2328
  
     "execution_count": 25,





  
  

  2329
  
     "metadata": {},





  
  

  2330
  
     "output_type": "execute_result"





  
  

  2331
  
    }





  
  

  2332
  
   ],





  
  

  2333
  
   "source": [





  
  

  2334
  
    "hi_outlier"





  
  

  2335
  
   ]





  
  

  2336
  
  },





  
  

  2337
  
  {





  
  

  2338
  
   "cell_type": "code",





  
  

  2339
  
   "execution_count": 26,





  
  

  2340
  
   "metadata": {},





  
  

  2341
  
   "outputs": [],





  
  

  2342
  
   "source": [





  
  

  2343
  
    "hi_outlier_label = nib.load(hi_outlier[0][1])"





  
  

  2344
  
   ]





  
  

  2345
  
  },





  
  

  2346
  
  {





  
  

  2347
  
   "cell_type": "code",





  
  

  2348
  
   "execution_count": 36,





  
  

  2349
  
   "metadata": {},





  
  

  2350
  
   "outputs": [





  
  

  2351
  
    {





  
  

  2352
  
     "name": "stdout",





  
  

  2353
  
     "output_type": "stream",





  
  

  2354
  
     "text": [





  
  

  2355
  
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",





  
  

  2356
  
      "sizeof_hdr      : 348\n",





  
  

  2357
  
      "data_type       : b''\n",





  
  

  2358
  
      "db_name         : b''\n",





  
  

  2359
  
      "extents         : 0\n",





  
  

  2360
  
      "session_error   : 0\n",





  
  

  2361
  
      "regular         : b'r'\n",





  
  

  2362
  
      "dim_info        : 0\n",





  
  

  2363
  
      "dim             : [  3 512 512  94   1   1   1   1]\n",





  
  

  2364
  
      "intent_p1       : 0.0\n",





  
  

  2365
  
      "intent_p2       : 0.0\n",





  
  

  2366
  
      "intent_p3       : 0.0\n",





  
  

  2367
  
      "intent_code     : none\n",





  
  

  2368
  
      "datatype        : uint8\n",





  
  

  2369
  
      "bitpix          : 8\n",





  
  

  2370
  
      "slice_start     : 0\n",





  
  

  2371
  
      "pixdim          : [1.       0.734375 0.734375 5.       0.       0.       0.       0.      ]\n",





  
  

  2372
  
      "vox_offset      : 0.0\n",





  
  

  2373
  
      "scl_slope       : nan\n",





  
  

  2374
  
      "scl_inter       : nan\n",





  
  

  2375
  
      "slice_end       : 0\n",





  
  

  2376
  
      "slice_code      : unknown\n",





  
  

  2377
  
      "xyzt_units      : 10\n",





  
  

  2378
  
      "cal_max         : 0.0\n",





  
  

  2379
  
      "cal_min         : 0.0\n",





  
  

  2380
  
      "slice_duration  : 0.0\n",





  
  

  2381
  
      "toffset         : 0.0\n",





  
  

  2382
  
      "glmax           : 0\n",





  
  

  2383
  
      "glmin           : 0\n",





  
  

  2384
  
      "descrip         : b'5.0.10'\n",





  
  

  2385
  
      "aux_file        : b''\n",





  
  

  2386
  
      "qform_code      : scanner\n",





  
  

  2387
  
      "sform_code      : scanner\n",





  
  

  2388
  
      "quatern_b       : 0.0\n",





  
  

  2389
  
      "quatern_c       : 0.0\n",





  
  

  2390
  
      "quatern_d       : 0.0\n",





  
  

  2391
  
      "qoffset_x       : -375.26562\n",





  
  

  2392
  
      "qoffset_y       : -375.26562\n",





  
  

  2393
  
      "qoffset_z       : 0.0\n",





  
  

  2394
  
      "srow_x          : [   0.734375    0.          0.       -375.26562 ]\n",





  
  

  2395
  
      "srow_y          : [   0.          0.734375    0.       -375.26562 ]\n",





  
  

  2396
  
      "srow_z          : [0. 0. 5. 0.]\n",





  
  

  2397
  
      "intent_name     : b''\n",





  
  

  2398
  
      "magic           : b'n+1'\n"





  
  

  2399
  
     ]





  
  

  2400
  
    }





  
  

  2401
  
   ],





  
  

  2402
  
   "source": [





  
  

  2403
  
    "print(hi_outlier_label.header)"





  
  

  2404
  
   ]





  
  

  2405
  
  },





  
  

  2406
  
  {





  
  

  2407
  
   "cell_type": "code",





  
  

  2408
  
   "execution_count": 27,





  
  

  2409
  
   "metadata": {},





  
  

  2410
  
   "outputs": [





  
  

  2411
  
    {





  
  

  2412
  
     "name": "stdout",





  
  

  2413
  
     "output_type": "stream",





  
  

  2414
  
     "text": [





  
  

  2415
  
      "(512, 512, 94)\n",





  
  

  2416
  
      "[1.       0.734375 0.734375 5.       0.       0.       0.       0.      ]\n",





  
  

  2417
  
      "[  3 512 512  94   1   1   1   1]\n",





  
  

  2418
  
      "[[   0.734375    0.          0.       -375.265625]\n",





  
  

  2419
  
      " [   0.          0.734375    0.       -375.265625]\n",





  
  

  2420
  
      " [   0.          0.          5.          0.      ]\n",





  
  

  2421
  
      " [   0.          0.          0.          1.      ]]\n"





  
  

  2422
  
     ]





  
  

  2423
  
    },





  
  

  2424
  
    {





  
  

  2425
  
     "data": {





  
  

  2426
  
      "image/png": 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\n",





  
  

  2427
  
      "text/plain": [





  
  

  2428
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  2429
  
      ]





  
  

  2430
  
     },





  
  

  2431
  
     "metadata": {





  
  

  2432
  
      "needs_background": "light"





  
  

  2433
  
     },





  
  

  2434
  
     "output_type": "display_data"





  
  

  2435
  
    }





  
  

  2436
  
   ],





  
  

  2437
  
   "source": [





  
  

  2438
  
    "print(hi_outlier_label.header.get_data_shape())\n",





  
  

  2439
  
    "print(hi_outlier_label.header['pixdim'])\n",





  
  

  2440
  
    "print(hi_outlier_label.header['dim'])\n",





  
  

  2441
  
    "print(hi_outlier_label.header.get_sform())\n",





  
  

  2442
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_281.nii.gz')).get_fdata()[:,260,:],aspect = 94/(512*2))\n",





  
  

  2443
  
    "plt.title('hippocampus_281.nii.gz, MRI, Coronal Slice')\n",





  
  

  2444
  
    "plt.show()"





  
  

  2445
  
   ]





  
  

  2446
  
  },





  
  

  2447
  
  {





  
  

  2448
  
   "cell_type": "markdown",





  
  

  2449
  
   "metadata": {},





  
  

  2450
  
   "source": [





  
  

  2451
  
    "#### High Label Voxel volume Outlier has different dim, pixdim, and sform vectors.  \n",





  
  

  2452
  
    "#### hippocampus_281.nii.gz is not a hippocampus region"





  
  

  2453
  
   ]





  
  

  2454
  
  },





  
  

  2455
  
  {





  
  

  2456
  
   "cell_type": "markdown",





  
  

  2457
  
   "metadata": {},





  
  

  2458
  
   "source": [





  
  

  2459
  
    "### Labels with volume between 2900mm^3 and 4500mm^3:"





  
  

  2460
  
   ]





  
  

  2461
  
  },





  
  

  2462
  
  {





  
  

  2463
  
   "cell_type": "code",





  
  

  2464
  
   "execution_count": 28,





  
  

  2465
  
   "metadata": {},





  
  

  2466
  
   "outputs": [





  
  

  2467
  
    {





  
  

  2468
  
     "name": "stdout",





  
  

  2469
  
     "output_type": "stream",





  
  

  2470
  
     "text": [





  
  

  2471
  
      "221\n"





  
  

  2472
  
     ]





  
  

  2473
  
    }





  
  

  2474
  
   ],





  
  

  2475
  
   "source": [





  
  

  2476
  
    "no_outlier_shape = {}\n",





  
  

  2477
  
    "no_outlier_pixdim = {}\n",





  
  

  2478
  
    "no_outlier_dim = {}\n",





  
  

  2479
  
    "no_outlier_sform = {}\n",





  
  

  2480
  
    "no_outlier_bitpix = {}\n",





  
  

  2481
  
    "count = 0\n",





  
  

  2482
  
    "for label in no_outlier:\n",





  
  

  2483
  
    "    count+=1\n",





  
  

  2484
  
    "    fp = label[1]\n",





  
  

  2485
  
    "    keyshape = nib.load(fp).header.get_data_shape()\n",





  
  

  2486
  
    "    no_outlier_shape.setdefault(keyshape,[])\n",





  
  

  2487
  
    "    no_outlier_shape[keyshape].append(fp)\n",





  
  

  2488
  
    "    \n",





  
  

  2489
  
    "    keypixdim = str(nib.load(fp).header['pixdim'])\n",





  
  

  2490
  
    "    no_outlier_pixdim.setdefault(keypixdim,[])\n",





  
  

  2491
  
    "    no_outlier_pixdim[keypixdim].append(fp)\n",





  
  

  2492
  
    "    \n",





  
  

  2493
  
    "    keydim = tuple(nib.load(fp).header['dim'])\n",





  
  

  2494
  
    "    no_outlier_dim.setdefault(keydim,[])\n",





  
  

  2495
  
    "    no_outlier_dim[keydim].append(fp)\n",





  
  

  2496
  
    "    \n",





  
  

  2497
  
    "    keysf = str(nib.load(fp).header.get_sform())\n",





  
  

  2498
  
    "    no_outlier_sform.setdefault(keysf,[])\n",





  
  

  2499
  
    "    no_outlier_sform[keysf].append(fp)\n",





  
  

  2500
  
    "    \n",





  
  

  2501
  
    "    keybp = str(nib.load(fp).header['bitpix'])\n",





  
  

  2502
  
    "    no_outlier_bitpix.setdefault(keybp,[])\n",





  
  

  2503
  
    "    no_outlier_bitpix[keybp].append(fp)\n",





  
  

  2504
  
    "\n",





  
  

  2505
  
    "print(count)\n"





  
  

  2506
  
   ]





  
  

  2507
  
  },





  
  

  2508
  
  {





  
  

  2509
  
   "cell_type": "code",





  
  

  2510
  
   "execution_count": 29,





  
  

  2511
  
   "metadata": {},





  
  

  2512
  
   "outputs": [





  
  

  2513
  
    {





  
  

  2514
  
     "data": {





  
  

  2515
  
      "text/plain": [





  
  

  2516
  
       "dict_keys(['[1. 1. 1. 1. 1. 0. 0. 0.]'])"





  
  

  2517
  
      ]





  
  

  2518
  
     },





  
  

  2519
  
     "execution_count": 29,





  
  

  2520
  
     "metadata": {},





  
  

  2521
  
     "output_type": "execute_result"





  
  

  2522
  
    }





  
  

  2523
  
   ],





  
  

  2524
  
   "source": [





  
  

  2525
  
    "no_outlier_pixdim.keys()"





  
  

  2526
  
   ]





  
  

  2527
  
  },





  
  

  2528
  
  {





  
  

  2529
  
   "cell_type": "code",





  
  

  2530
  
   "execution_count": 30,





  
  

  2531
  
   "metadata": {},





  
  

  2532
  
   "outputs": [





  
  

  2533
  
    {





  
  

  2534
  
     "data": {





  
  

  2535
  
      "text/plain": [





  
  

  2536
  
       "array([35, 34, 36, 35, 34, 36, 36, 39, 42, 35, 36, 36, 38, 35, 36, 33, 36,\n",





  
  

  2537
  
       "       35, 36, 34, 37, 34, 36, 36, 37, 38, 36, 36, 38, 35, 38, 33, 34, 41,\n",





  
  

  2538
  
       "       34, 39, 35, 39, 36, 36, 37, 37, 32, 35, 33, 34, 35, 40, 34, 37, 36,\n",





  
  

  2539
  
       "       38, 34, 38, 34, 34, 37, 36, 36, 35, 33, 34, 35, 36, 36, 38, 32, 35,\n",





  
  

  2540
  
       "       39, 38, 35, 36, 39, 34, 36, 36, 34, 33, 37, 35, 36, 36, 38, 34, 38,\n",





  
  

  2541
  
       "       36, 41, 34, 36, 36, 34, 35, 34, 35, 37, 35, 33, 37, 35, 37, 35, 37,\n",





  
  

  2542
  
       "       35, 33, 38, 34, 36, 35, 35, 32, 38, 37, 39, 35, 37, 36, 37, 33, 34,\n",





  
  

  2543
  
       "       36, 33, 38, 37, 37, 37, 38, 34, 38, 35, 36, 32, 36, 37, 34, 34, 33,\n",





  
  

  2544
  
       "       35, 36, 31, 34, 35, 35, 33, 37, 37, 38, 35, 35, 35, 37, 32, 34, 31,\n",





  
  

  2545
  
       "       35, 35, 36, 34, 38, 34, 35, 37, 37, 33, 34, 38, 35, 36, 36, 38, 34,\n",





  
  

  2546
  
       "       35, 35, 33, 37, 35, 32, 35, 38, 36, 33, 36, 35, 36, 38, 37, 36, 38,\n",





  
  

  2547
  
       "       36, 34, 38, 35, 33, 33, 35, 37, 33, 34, 38, 36], dtype=int16)"





  
  

  2548
  
      ]





  
  

  2549
  
     },





  
  

  2550
  
     "execution_count": 30,





  
  

  2551
  
     "metadata": {},





  
  

  2552
  
     "output_type": "execute_result"





  
  

  2553
  
    }





  
  

  2554
  
   ],





  
  

  2555
  
   "source": [





  
  

  2556
  
    "dim_keys=[i for i in no_outlier_dim.keys()]\n",





  
  

  2557
  
    "dim_keys = np.array(dim_keys)\n",





  
  

  2558
  
    "dim_keys[:, 1]"





  
  

  2559
  
   ]





  
  

  2560
  
  },





  
  

  2561
  
  {





  
  

  2562
  
   "cell_type": "code",





  
  

  2563
  
   "execution_count": 31,





  
  

  2564
  
   "metadata": {},





  
  

  2565
  
   "outputs": [





  
  

  2566
  
    {





  
  

  2567
  
     "data": {





  
  

  2568
  
      "text/plain": [





  
  

  2569
  
       "Text(0, 0.5, 'count')"





  
  

  2570
  
      ]





  
  

  2571
  
     },





  
  

  2572
  
     "execution_count": 31,





  
  

  2573
  
     "metadata": {},





  
  

  2574
  
     "output_type": "execute_result"





  
  

  2575
  
    },





  
  

  2576
  
    {





  
  

  2577
  
     "data": {





  
  

  2578
  
      "image/png": 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\n",





  
  

  2579
  
      "text/plain": [





  
  

  2580
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  2581
  
      ]





  
  

  2582
  
     },





  
  

  2583
  
     "metadata": {





  
  

  2584
  
      "needs_background": "light"





  
  

  2585
  
     },





  
  

  2586
  
     "output_type": "display_data"





  
  

  2587
  
    }





  
  

  2588
  
   ],





  
  

  2589
  
   "source": [





  
  

  2590
  
    "shape_keys = [(i) for i in no_outlier_shape.keys()]\n",





  
  

  2591
  
    "shape_keys = np.array(shape_keys)\n",





  
  

  2592
  
    "plt.hist(shape_keys[:,0])\n",





  
  

  2593
  
    "plt.title('Image Shape, Dimension 0')\n",





  
  

  2594
  
    "plt.xlabel('Dimension 0 Value')\n",





  
  

  2595
  
    "plt.ylabel('count')"





  
  

  2596
  
   ]





  
  

  2597
  
  },





  
  

  2598
  
  {





  
  

  2599
  
   "cell_type": "code",





  
  

  2600
  
   "execution_count": 32,





  
  

  2601
  
   "metadata": {},





  
  

  2602
  
   "outputs": [





  
  

  2603
  
    {





  
  

  2604
  
     "data": {





  
  

  2605
  
      "image/png": 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\n",





  
  

  2606
  
      "text/plain": [





  
  

  2607
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  2608
  
      ]





  
  

  2609
  
     },





  
  

  2610
  
     "metadata": {





  
  

  2611
  
      "needs_background": "light"





  
  

  2612
  
     },





  
  

  2613
  
     "output_type": "display_data"





  
  

  2614
  
    }





  
  

  2615
  
   ],





  
  

  2616
  
   "source": [





  
  

  2617
  
    "plt.hist(shape_keys[:,1])\n",





  
  

  2618
  
    "plt.title('Image Shape, Dimension 1')\n",





  
  

  2619
  
    "plt.xlabel('Dimension 1 Value')\n",





  
  

  2620
  
    "plt.ylabel('count')\n",





  
  

  2621
  
    "plt.show()"





  
  

  2622
  
   ]





  
  

  2623
  
  },





  
  

  2624
  
  {





  
  

  2625
  
   "cell_type": "code",





  
  

  2626
  
   "execution_count": 33,





  
  

  2627
  
   "metadata": {},





  
  

  2628
  
   "outputs": [





  
  

  2629
  
    {





  
  

  2630
  
     "data": {





  
  

  2631
  
      "text/plain": [





  
  

  2632
  
       "array([[36, 42, 41],\n",





  
  

  2633
  
       "       [36, 40, 43],\n",





  
  

  2634
  
       "       [39, 41, 42]])"





  
  

  2635
  
      ]





  
  

  2636
  
     },





  
  

  2637
  
     "execution_count": 33,





  
  

  2638
  
     "metadata": {},





  
  

  2639
  
     "output_type": "execute_result"





  
  

  2640
  
    }





  
  

  2641
  
   ],





  
  

  2642
  
   "source": [





  
  

  2643
  
    "shape_keys[shape_keys[:,1]<42.5]"





  
  

  2644
  
   ]





  
  

  2645
  
  },





  
  

  2646
  
  {





  
  

  2647
  
   "cell_type": "code",





  
  

  2648
  
   "execution_count": 34,





  
  

  2649
  
   "metadata": {},





  
  

  2650
  
   "outputs": [





  
  

  2651
  
    {





  
  

  2652
  
     "data": {





  
  

  2653
  
      "image/png": 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\n",





  
  

  2654
  
      "text/plain": [





  
  

  2655
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  2656
  
      ]





  
  

  2657
  
     },





  
  

  2658
  
     "metadata": {





  
  

  2659
  
      "needs_background": "light"





  
  

  2660
  
     },





  
  

  2661
  
     "output_type": "display_data"





  
  

  2662
  
    }





  
  

  2663
  
   ],





  
  

  2664
  
   "source": [





  
  

  2665
  
    "plt.hist(shape_keys[:,2])\n",





  
  

  2666
  
    "plt.title('Image Shape, Dimension 2')\n",





  
  

  2667
  
    "plt.xlabel('Dimension 2 Value')\n",





  
  

  2668
  
    "plt.ylabel('count')\n",





  
  

  2669
  
    "plt.show()"





  
  

  2670
  
   ]





  
  

  2671
  
  },





  
  

  2672
  
  {





  
  

  2673
  
   "cell_type": "code",





  
  

  2674
  
   "execution_count": 35,





  
  

  2675
  
   "metadata": {},





  
  

  2676
  
   "outputs": [





  
  

  2677
  
    {





  
  

  2678
  
     "data": {





  
  

  2679
  
      "text/plain": [





  
  

  2680
  
       "array([[34, 53, 24]])"





  
  

  2681
  
      ]





  
  

  2682
  
     },





  
  

  2683
  
     "execution_count": 35,





  
  

  2684
  
     "metadata": {},





  
  

  2685
  
     "output_type": "execute_result"





  
  

  2686
  
    }





  
  

  2687
  
   ],





  
  

  2688
  
   "source": [





  
  

  2689
  
    "shape_keys[shape_keys[:,2]<25]"





  
  

  2690
  
   ]





  
  

  2691
  
  },





  
  

  2692
  
  {





  
  

  2693
  
   "cell_type": "code",





  
  

  2694
  
   "execution_count": 36,





  
  

  2695
  
   "metadata": {},





  
  

  2696
  
   "outputs": [





  
  

  2697
  
    {





  
  

  2698
  
     "data": {





  
  

  2699
  
      "text/plain": [





  
  

  2700
  
       "['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_243.nii.gz']"





  
  

  2701
  
      ]





  
  

  2702
  
     },





  
  

  2703
  
     "execution_count": 36,





  
  

  2704
  
     "metadata": {},





  
  

  2705
  
     "output_type": "execute_result"





  
  

  2706
  
    }





  
  

  2707
  
   ],





  
  

  2708
  
   "source": [





  
  

  2709
  
    "no_outlier_shape[(34,53,24)]"





  
  

  2710
  
   ]





  
  

  2711
  
  },





  
  

  2712
  
  {





  
  

  2713
  
   "cell_type": "code",





  
  

  2714
  
   "execution_count": 37,





  
  

  2715
  
   "metadata": {},





  
  

  2716
  
   "outputs": [





  
  

  2717
  
    {





  
  

  2718
  
     "data": {





  
  

  2719
  
      "text/plain": [





  
  

  2720
  
       "Text(0.5, 1.0, 'hippocampus_243.nii.gz, MRI, Sagital Slice 15')"





  
  

  2721
  
      ]





  
  

  2722
  
     },





  
  

  2723
  
     "execution_count": 37,





  
  

  2724
  
     "metadata": {},





  
  

  2725
  
     "output_type": "execute_result"





  
  

  2726
  
    },





  
  

  2727
  
    {





  
  

  2728
  
     "data": {





  
  

  2729
  
      "image/png": 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\n",





  
  

  2730
  
      "text/plain": [





  
  

  2731
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  2732
  
      ]





  
  

  2733
  
     },





  
  

  2734
  
     "metadata": {





  
  

  2735
  
      "needs_background": "light"





  
  

  2736
  
     },





  
  

  2737
  
     "output_type": "display_data"





  
  

  2738
  
    }





  
  

  2739
  
   ],





  
  

  2740
  
   "source": [





  
  

  2741
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_243.nii.gz')).get_fdata()[15,:,:])\n",





  
  

  2742
  
    "plt.title('hippocampus_243.nii.gz, MRI, Sagital Slice 15')"





  
  

  2743
  
   ]





  
  

  2744
  
  },





  
  

  2745
  
  {





  
  

  2746
  
   "cell_type": "code",





  
  

  2747
  
   "execution_count": 38,





  
  

  2748
  
   "metadata": {},





  
  

  2749
  
   "outputs": [





  
  

  2750
  
    {





  
  

  2751
  
     "data": {





  
  

  2752
  
      "text/plain": [





  
  

  2753
  
       "Text(0.5, 1.0, 'hippocampus_243.nii.gz, Label, Sagital Slice 15')"





  
  

  2754
  
      ]





  
  

  2755
  
     },





  
  

  2756
  
     "execution_count": 38,





  
  

  2757
  
     "metadata": {},





  
  

  2758
  
     "output_type": "execute_result"





  
  

  2759
  
    },





  
  

  2760
  
    {





  
  

  2761
  
     "data": {





  
  

  2762
  
      "image/png": "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\n",





  
  

  2763
  
      "text/plain": [





  
  

  2764
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  2765
  
      ]





  
  

  2766
  
     },





  
  

  2767
  
     "metadata": {





  
  

  2768
  
      "needs_background": "light"





  
  

  2769
  
     },





  
  

  2770
  
     "output_type": "display_data"





  
  

  2771
  
    }





  
  

  2772
  
   ],





  
  

  2773
  
   "source": [





  
  

  2774
  
    "plt.imshow(nib.load(os.path.join(path, 'labels', 'hippocampus_243.nii.gz')).get_fdata()[15,:,:])\n",





  
  

  2775
  
    "plt.title('hippocampus_243.nii.gz, Label, Sagital Slice 15')"





  
  

  2776
  
   ]





  
  

  2777
  
  },





  
  

  2778
  
  {





  
  

  2779
  
   "cell_type": "code",





  
  

  2780
  
   "execution_count": 178,





  
  

  2781
  
   "metadata": {},





  
  

  2782
  
   "outputs": [





  
  

  2783
  
    {





  
  

  2784
  
     "data": {





  
  

  2785
  
      "text/plain": [





  
  

  2786
  
       "{'[[1. 0. 0. 1.]\\n [0. 1. 0. 1.]\\n [0. 0. 1. 1.]\\n [0. 0. 0. 1.]]': ['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_001.nii.gz',\n",





  
  

  2787
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_003.nii.gz',\n",





  
  

  2788
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_004.nii.gz',\n",





  
  

  2789
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_006.nii.gz',\n",





  
  

  2790
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_007.nii.gz',\n",





  
  

  2791
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_008.nii.gz',\n",





  
  

  2792
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_010.nii.gz',\n",





  
  

  2793
  
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       " '[[1. 0. 0. 0.]\\n [0. 1. 0. 0.]\\n [0. 0. 1. 0.]\\n [0. 0. 0. 1.]]': ['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_039.nii.gz',\n",





  
  

  2990
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_040.nii.gz',\n",





  
  

  2991
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_074.nii.gz',\n",





  
  

  2992
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_093.nii.gz',\n",





  
  

  2993
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_094.nii.gz',\n",





  
  

  2994
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_245.nii.gz',\n",





  
  

  2995
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_253.nii.gz',\n",





  
  

  2996
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_268.nii.gz',\n",





  
  

  2997
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_301.nii.gz',\n",





  
  

  2998
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_302.nii.gz',\n",





  
  

  2999
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_305.nii.gz',\n",





  
  

  3000
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_321.nii.gz',\n",





  
  

  3001
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_322.nii.gz',\n",





  
  

  3002
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_331.nii.gz',\n",





  
  

  3003
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_332.nii.gz',\n",





  
  

  3004
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_361.nii.gz',\n",





  
  

  3005
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_375.nii.gz',\n",





  
  

  3006
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_376.nii.gz']}"





  
  

  3007
  
      ]





  
  

  3008
  
     },





  
  

  3009
  
     "execution_count": 178,





  
  

  3010
  
     "metadata": {},





  
  

  3011
  
     "output_type": "execute_result"





  
  

  3012
  
    }





  
  

  3013
  
   ],





  
  

  3014
  
   "source": [





  
  

  3015
  
    "no_outlier_sform"





  
  

  3016
  
   ]





  
  

  3017
  
  },





  
  

  3018
  
  {





  
  

  3019
  
   "cell_type": "code",





  
  

  3020
  
   "execution_count": 179,





  
  

  3021
  
   "metadata": {},





  
  

  3022
  
   "outputs": [





  
  

  3023
  
    {





  
  

  3024
  
     "data": {





  
  

  3025
  
      "text/plain": [





  
  

  3026
  
       "dict_keys(['8', '32'])"





  
  

  3027
  
      ]





  
  

  3028
  
     },





  
  

  3029
  
     "execution_count": 179,





  
  

  3030
  
     "metadata": {},





  
  

  3031
  
     "output_type": "execute_result"





  
  

  3032
  
    }





  
  

  3033
  
   ],





  
  

  3034
  
   "source": [





  
  

  3035
  
    "no_outlier_bitpix.keys()"





  
  

  3036
  
   ]





  
  

  3037
  
  },





  
  

  3038
  
  {





  
  

  3039
  
   "cell_type": "code",





  
  

  3040
  
   "execution_count": 180,





  
  

  3041
  
   "metadata": {},





  
  

  3042
  
   "outputs": [





  
  

  3043
  
    {





  
  

  3044
  
     "data": {





  
  

  3045
  
      "text/plain": [





  
  

  3046
  
       "['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_003.nii.gz',\n",





  
  

  3047
  
       " '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_243.nii.gz']"





  
  

  3048
  
      ]





  
  

  3049
  
     },





  
  

  3050
  
     "execution_count": 180,





  
  

  3051
  
     "metadata": {},





  
  

  3052
  
     "output_type": "execute_result"





  
  

  3053
  
    }





  
  

  3054
  
   ],





  
  

  3055
  
   "source": [





  
  

  3056
  
    "no_outlier_bitpix['32']"





  
  

  3057
  
   ]





  
  

  3058
  
  },





  
  

  3059
  
  {





  
  

  3060
  
   "cell_type": "code",





  
  

  3061
  
   "execution_count": 181,





  
  

  3062
  
   "metadata": {},





  
  

  3063
  
   "outputs": [





  
  

  3064
  
    {





  
  

  3065
  
     "name": "stdout",





  
  

  3066
  
     "output_type": "stream",





  
  

  3067
  
     "text": [





  
  

  3068
  
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",





  
  

  3069
  
      "sizeof_hdr      : 348\n",





  
  

  3070
  
      "data_type       : b''\n",





  
  

  3071
  
      "db_name         : b''\n",





  
  

  3072
  
      "extents         : 0\n",





  
  

  3073
  
      "session_error   : 0\n",





  
  

  3074
  
      "regular         : b'r'\n",





  
  

  3075
  
      "dim_info        : 0\n",





  
  

  3076
  
      "dim             : [ 3 34 52 35  1  1  1  1]\n",





  
  

  3077
  
      "intent_p1       : 0.0\n",





  
  

  3078
  
      "intent_p2       : 0.0\n",





  
  

  3079
  
      "intent_p3       : 0.0\n",





  
  

  3080
  
      "intent_code     : none\n",





  
  

  3081
  
      "datatype        : float32\n",





  
  

  3082
  
      "bitpix          : 32\n",





  
  

  3083
  
      "slice_start     : 0\n",





  
  

  3084
  
      "pixdim          : [1. 1. 1. 1. 1. 0. 0. 0.]\n",





  
  

  3085
  
      "vox_offset      : 0.0\n",





  
  

  3086
  
      "scl_slope       : nan\n",





  
  

  3087
  
      "scl_inter       : nan\n",





  
  

  3088
  
      "slice_end       : 0\n",





  
  

  3089
  
      "slice_code      : unknown\n",





  
  

  3090
  
      "xyzt_units      : 10\n",





  
  

  3091
  
      "cal_max         : 0.0\n",





  
  

  3092
  
      "cal_min         : 0.0\n",





  
  

  3093
  
      "slice_duration  : 0.0\n",





  
  

  3094
  
      "toffset         : 0.0\n",





  
  

  3095
  
      "glmax           : 0\n",





  
  

  3096
  
      "glmin           : 0\n",





  
  

  3097
  
      "descrip         : b'5.0.10'\n",





  
  

  3098
  
      "aux_file        : b'none'\n",





  
  

  3099
  
      "qform_code      : scanner\n",





  
  

  3100
  
      "sform_code      : scanner\n",





  
  

  3101
  
      "quatern_b       : 0.0\n",





  
  

  3102
  
      "quatern_c       : 0.0\n",





  
  

  3103
  
      "quatern_d       : 0.0\n",





  
  

  3104
  
      "qoffset_x       : 1.0\n",





  
  

  3105
  
      "qoffset_y       : 1.0\n",





  
  

  3106
  
      "qoffset_z       : 1.0\n",





  
  

  3107
  
      "srow_x          : [1. 0. 0. 1.]\n",





  
  

  3108
  
      "srow_y          : [0. 1. 0. 1.]\n",





  
  

  3109
  
      "srow_z          : [0. 0. 1. 1.]\n",





  
  

  3110
  
      "intent_name     : b''\n",





  
  

  3111
  
      "magic           : b'n+1'\n"





  
  

  3112
  
     ]





  
  

  3113
  
    }





  
  

  3114
  
   ],





  
  

  3115
  
   "source": [





  
  

  3116
  
    "print(nib.load(no_outlier_bitpix['32'][0]).header)"





  
  

  3117
  
   ]





  
  

  3118
  
  },





  
  

  3119
  
  {





  
  

  3120
  
   "cell_type": "code",





  
  

  3121
  
   "execution_count": 50,





  
  

  3122
  
   "metadata": {},





  
  

  3123
  
   "outputs": [





  
  

  3124
  
    {





  
  

  3125
  
     "data": {





  
  

  3126
  
      "text/plain": [





  
  

  3127
  
       "Text(0.5, 1.0, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_003.nii.gz, Sagital Slice 14')"





  
  

  3128
  
      ]





  
  

  3129
  
     },





  
  

  3130
  
     "execution_count": 50,





  
  

  3131
  
     "metadata": {},





  
  

  3132
  
     "output_type": "execute_result"





  
  

  3133
  
    },





  
  

  3134
  
    {





  
  

  3135
  
     "data": {





  
  

  3136
  
      "image/png": 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\n",





  
  

  3137
  
      "text/plain": [





  
  

  3138
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  3139
  
      ]





  
  

  3140
  
     },





  
  

  3141
  
     "metadata": {





  
  

  3142
  
      "needs_background": "light"





  
  

  3143
  
     },





  
  

  3144
  
     "output_type": "display_data"





  
  

  3145
  
    }





  
  

  3146
  
   ],





  
  

  3147
  
   "source": [





  
  

  3148
  
    "plt.imshow(nib.load(no_outlier_bitpix['32'][0]).get_fdata()[14,:,:])\n",





  
  

  3149
  
    "plt.title(f'{no_outlier_bitpix[\"32\"][0]}, Sagital Slice 14')"





  
  

  3150
  
   ]





  
  

  3151
  
  },





  
  

  3152
  
  {





  
  

  3153
  
   "cell_type": "code",





  
  

  3154
  
   "execution_count": 51,





  
  

  3155
  
   "metadata": {},





  
  

  3156
  
   "outputs": [





  
  

  3157
  
    {





  
  

  3158
  
     "data": {





  
  

  3159
  
      "text/plain": [





  
  

  3160
  
       "Text(0.5, 1.0, 'hippocampus_003.nii.gz, MRI, Sagital Slice 14')"





  
  

  3161
  
      ]





  
  

  3162
  
     },





  
  

  3163
  
     "execution_count": 51,





  
  

  3164
  
     "metadata": {},





  
  

  3165
  
     "output_type": "execute_result"





  
  

  3166
  
    },





  
  

  3167
  
    {





  
  

  3168
  
     "data": {





  
  

  3169
  
      "image/png": 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\n",





  
  

  3170
  
      "text/plain": [





  
  

  3171
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  3172
  
      ]





  
  

  3173
  
     },





  
  

  3174
  
     "metadata": {





  
  

  3175
  
      "needs_background": "light"





  
  

  3176
  
     },





  
  

  3177
  
     "output_type": "display_data"





  
  

  3178
  
    }





  
  

  3179
  
   ],





  
  

  3180
  
   "source": [





  
  

  3181
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_003.nii.gz')).get_fdata()[14,:,:])\n",





  
  

  3182
  
    "plt.title('hippocampus_003.nii.gz, MRI, Sagital Slice 14')"





  
  

  3183
  
   ]





  
  

  3184
  
  },





  
  

  3185
  
  {





  
  

  3186
  
   "cell_type": "code",





  
  

  3187
  
   "execution_count": 52,





  
  

  3188
  
   "metadata": {},





  
  

  3189
  
   "outputs": [





  
  

  3190
  
    {





  
  

  3191
  
     "data": {





  
  

  3192
  
      "text/plain": [





  
  

  3193
  
       "Text(0.5, 1.0, 'hippocampus_003.nii.gz, MRI, Axial Slice 14')"





  
  

  3194
  
      ]





  
  

  3195
  
     },





  
  

  3196
  
     "execution_count": 52,





  
  

  3197
  
     "metadata": {},





  
  

  3198
  
     "output_type": "execute_result"





  
  

  3199
  
    },





  
  

  3200
  
    {





  
  

  3201
  
     "data": {





  
  

  3202
  
      "image/png": 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\n",





  
  

  3203
  
      "text/plain": [





  
  

  3204
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  3205
  
      ]





  
  

  3206
  
     },





  
  

  3207
  
     "metadata": {





  
  

  3208
  
      "needs_background": "light"





  
  

  3209
  
     },





  
  

  3210
  
     "output_type": "display_data"





  
  

  3211
  
    }





  
  

  3212
  
   ],





  
  

  3213
  
   "source": [





  
  

  3214
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_003.nii.gz')).get_fdata()[:,:,14])\n",





  
  

  3215
  
    "plt.title('hippocampus_003.nii.gz, MRI, Axial Slice 14')"





  
  

  3216
  
   ]





  
  

  3217
  
  },





  
  

  3218
  
  {





  
  

  3219
  
   "cell_type": "code",





  
  

  3220
  
   "execution_count": 185,





  
  

  3221
  
   "metadata": {},





  
  

  3222
  
   "outputs": [





  
  

  3223
  
    {





  
  

  3224
  
     "name": "stdout",





  
  

  3225
  
     "output_type": "stream",





  
  

  3226
  
     "text": [





  
  

  3227
  
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",





  
  

  3228
  
      "sizeof_hdr      : 348\n",





  
  

  3229
  
      "data_type       : b''\n",





  
  

  3230
  
      "db_name         : b''\n",





  
  

  3231
  
      "extents         : 0\n",





  
  

  3232
  
      "session_error   : 0\n",





  
  

  3233
  
      "regular         : b'r'\n",





  
  

  3234
  
      "dim_info        : 0\n",





  
  

  3235
  
      "dim             : [ 3 34 53 24  1  1  1  1]\n",





  
  

  3236
  
      "intent_p1       : 0.0\n",





  
  

  3237
  
      "intent_p2       : 0.0\n",





  
  

  3238
  
      "intent_p3       : 0.0\n",





  
  

  3239
  
      "intent_code     : none\n",





  
  

  3240
  
      "datatype        : float32\n",





  
  

  3241
  
      "bitpix          : 32\n",





  
  

  3242
  
      "slice_start     : 0\n",





  
  

  3243
  
      "pixdim          : [1. 1. 1. 1. 1. 0. 0. 0.]\n",





  
  

  3244
  
      "vox_offset      : 0.0\n",





  
  

  3245
  
      "scl_slope       : nan\n",





  
  

  3246
  
      "scl_inter       : nan\n",





  
  

  3247
  
      "slice_end       : 0\n",





  
  

  3248
  
      "slice_code      : unknown\n",





  
  

  3249
  
      "xyzt_units      : 10\n",





  
  

  3250
  
      "cal_max         : 0.0\n",





  
  

  3251
  
      "cal_min         : 0.0\n",





  
  

  3252
  
      "slice_duration  : 0.0\n",





  
  

  3253
  
      "toffset         : 0.0\n",





  
  

  3254
  
      "glmax           : 0\n",





  
  

  3255
  
      "glmin           : 0\n",





  
  

  3256
  
      "descrip         : b'5.0.10'\n",





  
  

  3257
  
      "aux_file        : b'none'\n",





  
  

  3258
  
      "qform_code      : scanner\n",





  
  

  3259
  
      "sform_code      : scanner\n",





  
  

  3260
  
      "quatern_b       : 0.0\n",





  
  

  3261
  
      "quatern_c       : 0.0\n",





  
  

  3262
  
      "quatern_d       : 0.0\n",





  
  

  3263
  
      "qoffset_x       : 1.0\n",





  
  

  3264
  
      "qoffset_y       : 1.0\n",





  
  

  3265
  
      "qoffset_z       : 1.0\n",





  
  

  3266
  
      "srow_x          : [1. 0. 0. 1.]\n",





  
  

  3267
  
      "srow_y          : [0. 1. 0. 1.]\n",





  
  

  3268
  
      "srow_z          : [0. 0. 1. 1.]\n",





  
  

  3269
  
      "intent_name     : b''\n",





  
  

  3270
  
      "magic           : b'n+1'\n"





  
  

  3271
  
     ]





  
  

  3272
  
    }





  
  

  3273
  
   ],





  
  

  3274
  
   "source": [





  
  

  3275
  
    "print(nib.load(no_outlier_bitpix['32'][1]).header)"





  
  

  3276
  
   ]





  
  

  3277
  
  },





  
  

  3278
  
  {





  
  

  3279
  
   "cell_type": "code",





  
  

  3280
  
   "execution_count": 53,





  
  

  3281
  
   "metadata": {},





  
  

  3282
  
   "outputs": [





  
  

  3283
  
    {





  
  

  3284
  
     "data": {





  
  

  3285
  
      "text/plain": [





  
  

  3286
  
       "Text(0.5, 1.0, 'hippocampus_243.nii.gz, MRI, Sagital Slice 16')"





  
  

  3287
  
      ]





  
  

  3288
  
     },





  
  

  3289
  
     "execution_count": 53,





  
  

  3290
  
     "metadata": {},





  
  

  3291
  
     "output_type": "execute_result"





  
  

  3292
  
    },





  
  

  3293
  
    {





  
  

  3294
  
     "data": {





  
  

  3295
  
      "image/png": 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\n",





  
  

  3296
  
      "text/plain": [





  
  

  3297
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  3298
  
      ]





  
  

  3299
  
     },





  
  

  3300
  
     "metadata": {





  
  

  3301
  
      "needs_background": "light"





  
  

  3302
  
     },





  
  

  3303
  
     "output_type": "display_data"





  
  

  3304
  
    }





  
  

  3305
  
   ],





  
  

  3306
  
   "source": [





  
  

  3307
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_243.nii.gz')).get_fdata()[16,:,:])\n",





  
  

  3308
  
    "plt.title('hippocampus_243.nii.gz, MRI, Sagital Slice 16')"





  
  

  3309
  
   ]





  
  

  3310
  
  },





  
  

  3311
  
  {





  
  

  3312
  
   "cell_type": "markdown",





  
  

  3313
  
   "metadata": {},





  
  

  3314
  
   "source": [





  
  

  3315
  
    "#### Two NIFTI files have bitpix of 32, while the rest of label data set within acceptable hippocampus volume range have bitpix of 8.  May need to rescale these two files or remove from dataset for training."





  
  

  3316
  
   ]





  
  

  3317
  
  },





  
  

  3318
  
  {





  
  

  3319
  
   "cell_type": "markdown",





  
  

  3320
  
   "metadata": {},





  
  

  3321
  
   "source": [





  
  

  3322
  
    "## Labels with hippocampus volume less than 2850mm^3, which is below the 2.5th Percentile for any age in the range 52-71"





  
  

  3323
  
   ]





  
  

  3324
  
  },





  
  

  3325
  
  {





  
  

  3326
  
   "cell_type": "code",





  
  

  3327
  
   "execution_count": 53,





  
  

  3328
  
   "metadata": {},





  
  

  3329
  
   "outputs": [





  
  

  3330
  
    {





  
  

  3331
  
     "data": {





  
  

  3332
  
      "text/plain": [





  
  

  3333
  
       "[array([2773, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_057.nii.gz',\n",





  
  

  3334
  
       "        array([ 3, 35, 51, 34,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3335
  
       " array([2753, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_097.nii.gz',\n",





  
  

  3336
  
       "        array([ 3, 37, 48, 34,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3337
  
       " array([2535, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_099.nii.gz',\n",





  
  

  3338
  
       "        array([ 3, 33, 52, 27,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3339
  
       " array([2726, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_125.nii.gz',\n",





  
  

  3340
  
       "        array([ 3, 43, 42, 39,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3341
  
       " array([2629, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_135.nii.gz',\n",





  
  

  3342
  
       "        array([ 3, 32, 49, 38,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3343
  
       " array([2534, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_138.nii.gz',\n",





  
  

  3344
  
       "        array([ 3, 32, 46, 42,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3345
  
       " array([2714, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_141.nii.gz',\n",





  
  

  3346
  
       "        array([ 3, 33, 44, 42,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3347
  
       " array([2697, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_142.nii.gz',\n",





  
  

  3348
  
       "        array([ 3, 38, 43, 41,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3349
  
       " array([2397, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_143.nii.gz',\n",





  
  

  3350
  
       "        array([ 3, 32, 45, 41,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3351
  
       " array([2471, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_144.nii.gz',\n",





  
  

  3352
  
       "        array([ 3, 34, 45, 43,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3353
  
       " array([2739, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_175.nii.gz',\n",





  
  

  3354
  
       "        array([ 3, 33, 47, 35,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3355
  
       " array([2593, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_177.nii.gz',\n",





  
  

  3356
  
       "        array([ 3, 33, 44, 40,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3357
  
       " array([2714, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_178.nii.gz',\n",





  
  

  3358
  
       "        array([ 3, 35, 44, 41,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3359
  
       " array([2678, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_180.nii.gz',\n",





  
  

  3360
  
       "        array([ 3, 37, 45, 36,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3361
  
       " array([2708, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_193.nii.gz',\n",





  
  

  3362
  
       "        array([ 3, 33, 50, 29,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3363
  
       " array([2570, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_199.nii.gz',\n",





  
  

  3364
  
       "        array([ 3, 37, 52, 26,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3365
  
       " array([2704, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_205.nii.gz',\n",





  
  

  3366
  
       "        array([ 3, 32, 47, 32,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3367
  
       " array([2448, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_221.nii.gz',\n",





  
  

  3368
  
       "        array([ 3, 32, 48, 34,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3369
  
       " array([2684, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_222.nii.gz',\n",





  
  

  3370
  
       "        array([ 3, 34, 49, 36,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3371
  
       " array([2475, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_225.nii.gz',\n",





  
  

  3372
  
       "        array([ 3, 33, 53, 26,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3373
  
       " array([2546, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_226.nii.gz',\n",





  
  

  3374
  
       "        array([ 3, 32, 51, 28,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3375
  
       " array([2647, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_231.nii.gz',\n",





  
  

  3376
  
       "        array([ 3, 33, 47, 42,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3377
  
       " array([2665, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_274.nii.gz',\n",





  
  

  3378
  
       "        array([ 3, 35, 40, 40,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3379
  
       " array([2382, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_279.nii.gz',\n",





  
  

  3380
  
       "        array([ 3, 34, 50, 32,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3381
  
       " array([2613, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_280.nii.gz',\n",





  
  

  3382
  
       "        array([ 3, 37, 47, 32,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3383
  
       " array([2416, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_282.nii.gz',\n",





  
  

  3384
  
       "        array([ 3, 37, 52, 32,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3385
  
       " array([2738, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_289.nii.gz',\n",





  
  

  3386
  
       "        array([ 3, 35, 49, 36,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3387
  
       " array([2786, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_297.nii.gz',\n",





  
  

  3388
  
       "        array([ 3, 34, 51, 30,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3389
  
       " array([2422, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_319.nii.gz',\n",





  
  

  3390
  
       "        array([ 3, 33, 48, 34,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3391
  
       " array([2451, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_320.nii.gz',\n",





  
  

  3392
  
       "        array([ 3, 33, 47, 34,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3393
  
       " array([2635, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_333.nii.gz',\n",





  
  

  3394
  
       "        array([ 3, 33, 46, 38,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3395
  
       " array([2678, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_334.nii.gz',\n",





  
  

  3396
  
       "        array([ 3, 34, 47, 36,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3397
  
       " array([2532, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_335.nii.gz',\n",





  
  

  3398
  
       "        array([ 3, 32, 47, 41,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3399
  
       " array([2593, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_336.nii.gz',\n",





  
  

  3400
  
       "        array([ 3, 34, 47, 43,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3401
  
       " array([2588, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_341.nii.gz',\n",





  
  

  3402
  
       "        array([ 3, 34, 48, 35,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3403
  
       " array([2618, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_343.nii.gz',\n",





  
  

  3404
  
       "        array([ 3, 32, 45, 38,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3405
  
       " array([2590, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_349.nii.gz',\n",





  
  

  3406
  
       "        array([ 3, 34, 50, 34,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3407
  
       " array([2755, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_353.nii.gz',\n",





  
  

  3408
  
       "        array([ 3, 32, 51, 31,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3409
  
       " array([2634, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_359.nii.gz',\n",





  
  

  3410
  
       "        array([ 3, 35, 49, 35,  1,  1,  1,  1], dtype=int16)], dtype=object),\n",





  
  

  3411
  
       " array([2757, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_378.nii.gz',\n",





  
  

  3412
  
       "        array([ 3, 35, 52, 34,  1,  1,  1,  1], dtype=int16)], dtype=object)]"





  
  

  3413
  
      ]





  
  

  3414
  
     },





  
  

  3415
  
     "execution_count": 53,





  
  

  3416
  
     "metadata": {},





  
  

  3417
  
     "output_type": "execute_result"





  
  

  3418
  
    }





  
  

  3419
  
   ],





  
  

  3420
  
   "source": [





  
  

  3421
  
    "lo_outlier"





  
  

  3422
  
   ]





  
  

  3423
  
  },





  
  

  3424
  
  {





  
  

  3425
  
   "cell_type": "code",





  
  

  3426
  
   "execution_count": 54,





  
  

  3427
  
   "metadata": {},





  
  

  3428
  
   "outputs": [],





  
  

  3429
  
   "source": [





  
  

  3430
  
    "lo_outlier_shape = {}\n",





  
  

  3431
  
    "lo_outlier_pixdim = {}\n",





  
  

  3432
  
    "lo_outlier_sform = {}\n",





  
  

  3433
  
    "lo_outlier_bitpix = {}\n",





  
  

  3434
  
    "\n",





  
  

  3435
  
    "for label in lo_outlier:\n",





  
  

  3436
  
    "    fp = label[1]\n",





  
  

  3437
  
    "    keyshape = nib.load(fp).header.get_data_shape()\n",





  
  

  3438
  
    "    lo_outlier_shape.setdefault(keyshape,[])\n",





  
  

  3439
  
    "    lo_outlier_shape[keyshape].append(fp)\n",





  
  

  3440
  
    "    \n",





  
  

  3441
  
    "    keypixdim = str(nib.load(fp).header['pixdim'])\n",





  
  

  3442
  
    "    lo_outlier_pixdim.setdefault(keypixdim,[])\n",





  
  

  3443
  
    "    lo_outlier_pixdim[keypixdim].append(fp)\n",





  
  

  3444
  
    "    \n",





  
  

  3445
  
    "    keysf = str(nib.load(fp).header.get_sform())\n",





  
  

  3446
  
    "    lo_outlier_sform.setdefault(keysf,[])\n",





  
  

  3447
  
    "    lo_outlier_sform[keysf].append(fp)\n",





  
  

  3448
  
    "    \n",





  
  

  3449
  
    "    keybp = str(nib.load(fp).header['bitpix'])\n",





  
  

  3450
  
    "    lo_outlier_bitpix.setdefault(keybp,[])\n",





  
  

  3451
  
    "    lo_outlier_bitpix[keybp].append(fp)"





  
  

  3452
  
   ]





  
  

  3453
  
  },





  
  

  3454
  
  {





  
  

  3455
  
   "cell_type": "code",





  
  

  3456
  
   "execution_count": 61,





  
  

  3457
  
   "metadata": {},





  
  

  3458
  
   "outputs": [





  
  

  3459
  
    {





  
  

  3460
  
     "data": {





  
  

  3461
  
      "text/plain": [





  
  

  3462
  
       "Text(0, 0.5, 'count')"





  
  

  3463
  
      ]





  
  

  3464
  
     },





  
  

  3465
  
     "execution_count": 61,





  
  

  3466
  
     "metadata": {},





  
  

  3467
  
     "output_type": "execute_result"





  
  

  3468
  
    },





  
  

  3469
  
    {





  
  

  3470
  
     "data": {





  
  

  3471
  
      "image/png": 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\n",





  
  

  3472
  
      "text/plain": [





  
  

  3473
  
       "<Figure size 720x720 with 1 Axes>"





  
  

  3474
  
      ]





  
  

  3475
  
     },





  
  

  3476
  
     "metadata": {





  
  

  3477
  
      "needs_background": "light"





  
  

  3478
  
     },





  
  

  3479
  
     "output_type": "display_data"





  
  

  3480
  
    }





  
  

  3481
  
   ],





  
  

  3482
  
   "source": [





  
  

  3483
  
    "lo_shape_keys = [(i) for i in lo_outlier_shape.keys()]\n",





  
  

  3484
  
    "lo_shape_keys = np.array(lo_shape_keys)\n",





  
  

  3485
  
    "plt.hist(lo_shape_keys[:,1])\n",





  
  

  3486
  
    "plt.title('Image Shape, Dimension 1')\n",





  
  

  3487
  
    "plt.xlabel('Dimension 1 Value')\n",





  
  

  3488
  
    "plt.ylabel('count')"





  
  

  3489
  
   ]





  
  

  3490
  
  },





  
  

  3491
  
  {





  
  

  3492
  
   "cell_type": "code",





  
  

  3493
  
   "execution_count": 190,





  
  

  3494
  
   "metadata": {},





  
  

  3495
  
   "outputs": [





  
  

  3496
  
    {





  
  

  3497
  
     "data": {





  
  

  3498
  
      "text/plain": [





  
  

  3499
  
       "40"





  
  

  3500
  
      ]





  
  

  3501
  
     },





  
  

  3502
  
     "execution_count": 190,





  
  

  3503
  
     "metadata": {},





  
  

  3504
  
     "output_type": "execute_result"





  
  

  3505
  
    }





  
  

  3506
  
   ],





  
  

  3507
  
   "source": [





  
  

  3508
  
    "len(lo_outlier_shape.keys())"





  
  

  3509
  
   ]





  
  

  3510
  
  },





  
  

  3511
  
  {





  
  

  3512
  
   "cell_type": "code",





  
  

  3513
  
   "execution_count": 191,





  
  

  3514
  
   "metadata": {},





  
  

  3515
  
   "outputs": [





  
  

  3516
  
    {





  
  

  3517
  
     "data": {





  
  

  3518
  
      "text/plain": [





  
  

  3519
  
       "{'[[1. 0. 0. 1.]\\n [0. 1. 0. 1.]\\n [0. 0. 1. 1.]\\n [0. 0. 0. 1.]]': ['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_057.nii.gz',\n",





  
  

  3520
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_097.nii.gz',\n",





  
  

  3521
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_099.nii.gz',\n",





  
  

  3522
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_125.nii.gz',\n",





  
  

  3523
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_135.nii.gz',\n",





  
  

  3524
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_138.nii.gz',\n",





  
  

  3525
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_141.nii.gz',\n",





  
  

  3526
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_142.nii.gz',\n",





  
  

  3527
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_143.nii.gz',\n",





  
  

  3528
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_144.nii.gz',\n",





  
  

  3529
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_175.nii.gz',\n",





  
  

  3530
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_177.nii.gz',\n",





  
  

  3531
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_178.nii.gz',\n",





  
  

  3532
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_180.nii.gz',\n",





  
  

  3533
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_193.nii.gz',\n",





  
  

  3534
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_205.nii.gz',\n",





  
  

  3535
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_221.nii.gz',\n",





  
  

  3536
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_222.nii.gz',\n",





  
  

  3537
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_225.nii.gz',\n",





  
  

  3538
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_226.nii.gz',\n",





  
  

  3539
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_231.nii.gz',\n",





  
  

  3540
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_274.nii.gz',\n",





  
  

  3541
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_279.nii.gz',\n",





  
  

  3542
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_280.nii.gz',\n",





  
  

  3543
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_282.nii.gz',\n",





  
  

  3544
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_289.nii.gz',\n",





  
  

  3545
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_297.nii.gz',\n",





  
  

  3546
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_319.nii.gz',\n",





  
  

  3547
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_320.nii.gz',\n",





  
  

  3548
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_333.nii.gz',\n",





  
  

  3549
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_334.nii.gz',\n",





  
  

  3550
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_335.nii.gz',\n",





  
  

  3551
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_336.nii.gz',\n",





  
  

  3552
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_341.nii.gz',\n",





  
  

  3553
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_343.nii.gz',\n",





  
  

  3554
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_349.nii.gz',\n",





  
  

  3555
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_353.nii.gz',\n",





  
  

  3556
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_359.nii.gz',\n",





  
  

  3557
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_378.nii.gz'],\n",





  
  

  3558
  
       " '[[1. 0. 0. 0.]\\n [0. 1. 0. 0.]\\n [0. 0. 1. 0.]\\n [0. 0. 0. 1.]]': ['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_199.nii.gz']}"





  
  

  3559
  
      ]





  
  

  3560
  
     },





  
  

  3561
  
     "execution_count": 191,





  
  

  3562
  
     "metadata": {},





  
  

  3563
  
     "output_type": "execute_result"





  
  

  3564
  
    }





  
  

  3565
  
   ],





  
  

  3566
  
   "source": [





  
  

  3567
  
    "lo_outlier_sform"





  
  

  3568
  
   ]





  
  

  3569
  
  },





  
  

  3570
  
  {





  
  

  3571
  
   "cell_type": "code",





  
  

  3572
  
   "execution_count": 55,





  
  

  3573
  
   "metadata": {},





  
  

  3574
  
   "outputs": [





  
  

  3575
  
    {





  
  

  3576
  
     "data": {





  
  

  3577
  
      "image/png": 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/qOoPmNm7zOxBM3uwNnUh/zYwgItM5hy2cP6q/Gh4NgAsynKarXN9COdHPhgTQrgjhHBDCOGGfPf5/gF2AIgmcw5bOH8VlPHBRABIsZxm64ie/RfRtyoJPwOACwFzGIBVsZxm69uSdpnZ5Y2/iP0WJX+cFAAuBMxhAFbFkqMfQghVM/tVSXcr+bXpjzX+Ungqq0ltU+n9Xc7/DXOVN/m/vt9+2o8X6BjyNzC3fcCtZ2VFzfek36EzI/ohP7+8JP9DJ9LzyySpN2PsWTFVhdms8WUEcWVEL5ye6nTrN2/c69Yvbz/l1v9k/8vd+sCTGTlaWfEJoxOptawcrVDOOPFPDrvlfH+/W69PTrr1zGiHCf+zlvUpf9+1qqXMYQCwFMvK2QohfEnSl1ZoLACwqpjDAKwG/lwPAABARDRbAAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABEtK/rhfFlNKo6m5zHVOvzlax1+XlFp2O8ds7KkJrb7WUq5qr98m5OVNTeQd5etdvk5VbVp/1DZnP/Yg795lTO2P/i4n6U0tqvLrc8P+usvzxf89Vf8HK4j5ueMZWk7MebWQ3+fX+9OH1/99Ki7bC7rz1iVK27Z1vqPPYyN+fVjJ/3tW0aGWq3m1wHgEseVLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIaHVztupSwYlrqmbkbOUqft5P57Cf91MrZWRRZe2NjJytanv6+Op+hJfKPX49K0erbcqvF6bSM8AkPyNMknJz/oPvOTzv1st9Jbdem8zK2fJPjq42f/tD+9a49cHTj7t16/UPUCimjz/M+2PLDw746x70M740PeuWrZhx8mXkZNXJ0QKAZeHKFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABEtKo5WyEnlXu9O/jLdx/0e8POI5Nuferybrde6fJzvOp5f4C1jvTlew/6WUVtM/62szLCctXljb1nf0ZW05GTbr228Qq3npWhZhnjX9s+5a8gQ+++vL/9wX63Hmbn/OXH08fXtuMyd1nNl/16yHhiVP1zK79+nb98wZ8GQj4jn+7oCX/9AHCJ48oWAABARDRbAAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABHRbAEAAES0qjlbWfIZcUO5sp83dPo6L8RLmtnoZznlqv72ayV/+eC0ruUuv68dfHTcrfc/5edETW/tdOvtoxW33vbEIbdufT1ufeIy/1Sa2uVv/6orj7v17vy8Wz8ws8atj1/rH9xy31a3PvBk3a33f+Nwai10ltxlNTXt14/4OVZh60a3bqMT/vKloluv93e59XxlrVvXfr8MYHnuPrZnWcvfunn3iowD6biyBQAAEBHNFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAARrWrOltWlwlR63cupkqRKz9JzriSpb7+flTTX76+/3Lf07WdlfBVm/Ryr3nuecus9E37O1PyWPrc++vrnuPW5QX/nzg+4ZanNz0jb1XvKrR+f88f/jYf88fc+7eeUVTOisMauzHj8fZel1tbucU56SflCwa2Xr0lftyRVevynsZ/AJtW72t363Dp/53SOZeSEAXAtNycr5vbJ4FoZXNkCAACIiGYLAAAgIpotAACAiGi2AAAAIqLZAgAAiIhmCwAAICKaLQAAgIhWNWernpfKTlxStcPPYipM+VlVncN+jlZhqubWq+1Zu8MfX7UrfXyVbn/NcwN+39u7aZ2//GY/p2vkeX6WUm7eLWtmk//Yw5V+1lJ3e9WtPzPl54Q9/uRWt97/hJ+j1XXCP/b1Nv/cmu/165NOFNbkDv/gX/kpf+cXRvx9W+vodeuh6J/XtZJfz8/5z6t6f5dbBy51zc7RQvNxZQsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIlrVnK1QCJrdkp63lJvze7+snK2yk3MlSW0zfn1ujV/Pld2ycpX0Ws8BP6dq7dcOufXKtrVuffRqP0crP+tvv9LjP/au551266/d9qRbf3Rss1s/8Hc73PqaIX/8U34Ml8o9fg6X/IefqdqdnkUV1mScOFmGRtxybr2f45UbHnfrbXLC7yTVM3K4Rq/OCJF7wC8DF7qLOUcr67Hdunn3qozjQseVLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIaLYAAAAiWtXoB+Ul60yPfrDport4yPjt/WpG9ENt0q9nrb/S49c9m//mqFuvj0+49bFbLnPrpdPp0QOSNLHd76s7Xzbs1q9be9ytD837O2fvA9vd+sbHa259cqt/cNpm3LJKw350ROa51ZkRC7JhLn3dx0vushPXDrj19o3+vi09c8qtq+bv29nNXW791O6CWy/3+fsWwIWLaIeVwZUtAACAiGi2AAAAIqLZAgAAiIhmCwAAICKaLQAAgIhotgAAACKi2QIAAIhodXO2aiZNpGf2lIb83q/a6a8+P+/n/Uxs98OUQkbr2Tbr1zuHnKyr+bK77Mwrr3Hr01v8nKeOk25ZM9vS880kaaDNr++fWOPWD50YdOt9h/3xl7uW1/dnHZusDLYsE9f7x++5m9MPwMEOP0frWMHP0dr4DT/navj5W9365JV+zpZ6K265p3fKrXe2ZawfuMDdfWxPs4cQFVla8WX+D2dmHzOzITP73oLbBs3sq2a2t/Gv/78JADQJcxiAZlvM5YSPS3rdWbe9V9I9IYRdku5p/AwArejjYg4D0ESZzVYI4R8lnT7r5tsk3dn4/k5Jb1rZYQHAymAOA9BsS/2gzIYQwnFJavy7fuWGBADRMYcBWDXRfxvRzN5lZg+a2YO1Kf+DtgDQShbOXxXNN3s4AC5QS222TprZJklq/DuUdscQwh0hhBtCCDfku7uXuDkAWFGLmsMWzl8Fta/qAAFcPJbabH1R0u2N72+X9IWVGQ4ArArmMACrJjNny8w+LelmSWvN7IikfyPpQ5LuMrN3Sjok6c2L2VhuXup5Oj3rKj/r52TNrffrqvtZSvmsdwGcmCxJKk762+866mwg72d8TW32620zblkTu/z6xstH3PpzB4+79d62Obd+6IkN/gAyWN3ftzk/5kq1Dv/YZ2Wozb3Yf4v71TuecetPT6xNra3p8g9e7jL/sW9+wSm3Xsr7GWntOb/elvNzsk7O9i5r+YfcanwrOYfh4nSx52ih+TKbrRDCz6WUblnhsQDAimMOA9Bs/LkeAACAiGi2AAAAIqLZAgAAiIhmCwAAICKaLQAAgIhotgAAACLKjH5YUSZVS+nlqYy8Iav6WUr1Qsbmq/76S2N+fb7X376XFVU7Newu2zZ7mVuvF92yzI860tUDqSH/kqRy3T8VPvfIC916xwk/Jywrw6xW9PdtVo5WVkbb9BZ/+9duOunW6/K3PzGXni6+uXfCXba76AfAXd7lZ6RlOTw74Nbb6v7BOTnj/+WH3ozxA2iuWzfvbvYQLnlc2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiFY1ZyvkpUpfeh5SGKi4y+dP+GFTbbP+9ufW+llJpWf87Vc6MoK8nJytXI+fVVSY9bOOZtf7OVblDf7Y17dPuvWvHn6OW+95OD1HSpJCRts+70c9KZ8R1ZTLqNdK/rGtbPNXUM14AF35slu/ZetTqbXT5S532QdPbHPrbeafGzevfdKtZ+VsHZ/pdeudBf/cGp93wvOAFnD3sT3NHkJU5Gi1Pq5sAQAARESzBQAAEBHNFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBEq5qzpbrUNp2eh5Tf72c5FSf81ecrfh5R336/PrPO3x1ZWVJtE3Pp637JFe6y8z3+yqud/rafv+uwWz862+/Wp2b8fV/KeuzT6RljkpSf93Owql1+PT/nr3/8av/Y9vTPuPV1pSm3fl3XEbe+f35dam1k3s/Zmi/7592uniG3npe/b7L0FPwMstmqny9ntrztA/CRo3Xh48oWAABARDRbAAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABHRbAEAAES0ujlbGepFP6+n67hfNz9qSR3H03OwJGnqBX4eUt2PopKNp2c1zfcPuMtWO/ycqdktVbc+0O7nSD10Yqtbr1Xybj1LaPPHXzrtH5y2Wf/YDl/vj+/a6w/56884OZ4YXe/Wd3SMuPUnJzak1vaPDrrL5vP+2PZPr3HrV5b8HK6OfMWt39T/jFt/fHqTW394eItbB2K7+9ieZg9hWcjRuvhxZQsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIlrdnC2Tak6WVtuUn9U03+uvvveQn0WVn/XzhvLzftZTz9GaW68P9qTW5nv9x1bud8t60XP9LKRK3c+hmjjtZ4i1Hy669fysW1bIOJOycrQK0/6+rVxVduuvXPuUW394Yptb7+6bd+unyunHVpJOznSn1vI5P0drW/+YW+8p+GPbP7/OrV/bfcyt72o/sax6Vo7Xt90qAFz8uLIFAAAQEc0WAABARDRbAAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABGtas5WyEuVvvTMoY5TflbU9BY/q6ow7S/f8fScW+8cTs9KkqSeR4fc+sxV6XlH9WJGzlafn0N1dc9Jt36q7I89P+of6vZRt6x6u1+v+TFdqrX7j3+uv+DWt2/w931Pzj+2XW1+VtXOTn/9WTldIaQ/vsv6xtxlyxkZacWcnx83X/ePbda+OVxe49a3FUfc+tUdx906cKm7dfPuZg8BTcaVLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIaFVzthSk3Hx6f5cr+1lT7WN+VlOmop/l1PPQMbdeGzrl1qdeuSm1Zn5Ukmpd6fljknR8rs+tZ2UtZcmIctLUNv/YdB73j03ptL+BkVf4x+bmwcNufV3bpFt/Vd8Tbv1E1d+/u7r8HK5qSD+v56r+Yyvmam69I19x6wXzl//WxBVu/YU9h9x6JfjnVlYdWK67j+1p9hCAZeHKFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABEtKoBOYUpadM30vOkuvf7WUmzm7vceq7sZ1XZ6XG3Xjs96q9/xza37kQtZQolPytpulZ061OVdn/9GWMr9/r1XNmvt4/6OVylIxNu3Z7jj39Lu39svjZ+jVt/+9r73Hop52dZdbbPu3XPaLXTrY9XOtz6bM3P6drRNezWJ6olt96Xn3brI9Vut7654B8b4GJ36+bdzR4CWhxXtgAAACKi2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgolXN2crPVdXz/RHnDn7vV+7Nu/X+Lz3pD6DLzzuqveRatz66w88rylXTa5NX+DlUHf1zbv3UrJ91dGho0K23D/v7dmarM3hJHcf9U6X7uB/ENXn1gFvfMnjcrQ9Xetz6iTm//neT17n1zryfo5W1/a3F00uqSdJdx25w6+s6ptz6rvYTbn2m5meYfX1il1t/Sc9+t/7dme1uXXooow4AF7fMK1tmts3MvmZmj5vZY2b2nsbtg2b2VTPb2/jX/98UAFYZ8xeAVrCYtxGrkv6vEMI1km6S9Ctmdq2k90q6J4SwS9I9jZ8BoJUwfwFousxmK4RwPITwUOP7SUmPS9oi6TZJdzbudqekN0UaIwAsCfMXgFZwXh+QN7Mdkl4g6VuSNoQQjkvJhCZpfcoy7zKzB83swXJ1ZpnDBYClWe78VdHS/z4mgEvbopstM+uW9DlJvxZC8P+q8AIhhDtCCDeEEG4otvkfUAeAGFZi/irI/0UDAEizqGbLzApKJqpPhRD+qnHzSTPb1KhvkjQUZ4gAsHTMXwCabTG/jWiSPirp8RDChxeUvijp9sb3t0v6wsoPDwCWjvkLQCtYTM7WyyT9M0mPmtmexm3vl/QhSXeZ2TslHZL05sVtMT0ry2b9z0TUCubWQ/CzrDTQ65aPvbzDrZdG/PXXi+njyz/Hf+fixq0H3fr3hje5dTvkj72Q8XG52Yy2uy1j+fxsza0futU/1Xa3+xs4Otfv1vsKfk7ZA6M73Hp3wT/3utr8+g1d6VlU35za6S47XSm69c1d/r49XF7j1jcUxt36wTk/o+0b4/746/Kfl022svMXACxBZrMVQrhPSp1Nb1nZ4QDAymH+AtAK+HM9AAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABHRbAEAAES0mJytlVOuKBw6llquXn+lu3g9Y7Th6h1ufWaDn0VV7fRztKqzfp7Q7E1TqbWf2L7XXfbAtJ911N5Wdev5jLEpI4KscDo9/0ySyn5EWWZGWX2dn4N1cNx//M8Z9AO+pyr+n1LZ2jnm1rNytJ6c2ODW/7q+O7X22oHH3GWH+nvc+slZvz7XU3Drk/WSW1+uXNbJBWS4+9ieZg8BiIorWwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABERLMFAAAQ0ermbOVM1l5MLY88189q6j3kZ03ZfMWtT1zm5xVV+mpuvbzGzxN6xzUPptY2FMbdZb91crtbHx7yg646y25ZwY/RUseQn9NV82OsNL0rYwBVv68fOd3t1jdt9nPK2jv9c6Mz54/v4Jyf87WmfdqtH5hKX77c7+/8XZ1+hlhWjlXJ/PO+knXwM7Tl/OdFwerLWj/Q6m7dvLvZQ8AFjitbAAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABHRbAEAAEREswUAABDR6uZsZZhb62c9DT7uZylN7exz6xO7/Dyg0O3nCXX2zbp1L8/oSNnPcWrL+9u2qeVlJc33+1lNXUf95Yt+TJjmrvfHX5ssuPVQ9vv+7vy8Wx9s83Ow5oJ/qj8ystmtb+8ddes7e0+l1r4+sctd9qcHv+vWj833u/XTVT+jbLzm59cdmFzj1nuKc269lPefl8Ddx/Y0ewhAU3FlCwAAICKaLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIVjX6od7VrpmXXJFan1vvRzPMrSu69cmtfjyCVf34g/ZuP15g+6D/6/8HZtJ/hb6rzV933vyxdRz3H1u+7JZVL/qxGu0T/r4fv9Lvy6/cmB59IElPzW50622dGbEetXa3/viUv/7tnafdentG9MapWT9eYWdX+uM/PudHknx59Plu/R+OXOnW37Dj+249yzMn17r1G3cccOtTFf/YAFd+9t3NHsKy7NT9zR4CLnBc2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiFY1Z6taMp2+ppBaDzk/66iWkRU1s9nPqlLOr1+xbsStv3DgsFv/3vhmf/uOk6d73Xr/CX/sE+nxZZKkthl/3wW/rNnL/SCvI2N+llTxZPpxl6Rql38qPr7Zz9Ha0jnm1v/xhJ9V1VmouPU3bnrErX/5xHWptX3H1rnLrl874dZzGRlsT05scOtZdm0acutPj6fnx0nZGWW4+O37yE3NHgLQ0riyBQAAEBHNFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAARrWrOVr2zrunds6n1MO1nMc2sz7v1ylo/C6q9d96tb+rw846uKh136z/WvTe19mcnX+oum3u6w61Pb84IwpKfxbT20apbn9zq71ub8uuX/ZG//dnN/vYr3f76H9d2t77hpkm3Pl/xT/Ur+/yMtZlau1t/+kR6lla94j+2l294xq0/MrrFrZfa/IywU7Pdbr3N6m49a99NzJTcOtDqdv76/c0eAi5yXNkCAACIiGYLAAAgIpotAACAiGi2AAAAIqLZAgAAiIhmCwAAICKaLQAAgIhWNWers1jRC3YcTq3veWCnu/zMZj/LqdDt52zdsDV925K0tn3KrZ+s9rn1F5cOpdZOTPe6yw4+7j+2kevcsorjfg5XYcrPuap1+KdC25Tfl9dL/vKd9z3p1m2w361Pb9zq1u/f5udw3bTloFu/svOUW//00y9y62bpx2/jplF32dla0a13ZORoTVX8DLBr+k669f999Aq33tnuP68AAD6ubAEAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABERLMFAAAQEc0WAABARKuaszVTLmjPoW1LXr772tNu/ao1flbSy/v3uvWC1dz6A5OXu/W/0fNSa4ee2OAuu8mPwVLI+/V8RhTSyHUlt17t8JevrPUHePiWbre+1fx9V+3yT8WyH3Gm3o55t96R97OqthZH3Hq97ueYWa6eWtve6+dsVYL/muc5vX5O1tHZfrf++Lh/7vWU/H3X5jw2SRqfzjh5cMHb95Gbmj0E4ILGlS0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIqLZAgAAiGhVc7ZUzak+UkwtF7dNu4u3F/yspzbz84CycrRu7tzn1ifrflbV54/uTq11HfaDskae65ZlIbj14phfn97q50QVpvztF4b9U6U04m9/Zou/70av8vfP7DY/J+v6Pj+DravNz5L6yunr3PrzNh536wcnBlJr2eelX19bmHTr09V2t36gOujW29v859XwVJdbn5tJf04DABZxZcvMSmb2gJk9bGaPmdkHG7cPmtlXzWxv49/0/20AoAmYvwC0gsW8jTgv6dUhhOdL2i3pdWZ2k6T3SronhLBL0j2NnwGglTB/AWi6zGYrJM68yVRofAVJt0m6s3H7nZLeFGOAALBUzF8AWsGiPiBvZnkz2yNpSNJXQwjfkrQhhHBckhr/rk9Z9l1m9qCZPVibyvhgEACssJWavyryP/cHAGkW1WyFEGohhN2Stkp6iZn5nyZ+9rJ3hBBuCCHckO/2/1gxAKy0lZq/CvJ/EQEA0pxX9EMIYUzSvZJeJ+mkmW2SpMa/Qys9OABYKcxfAJplMb+NuM7M+hvfd0h6jaQnJH1R0u2Nu90u6QuRxggAS8L8BaAVLCZna5OkO80sr6Q5uyuE8Ddm9k1Jd5nZOyUdkvTmrBXlijV1bUvPDKrV/N6vt+h/ZuLabj8LKStH60St060fL/f7y4/1pNbaMtra8ho/A6xnn59Dlav6OVfyo5ySjww7MiLK1HHaX0HPPj8ranJrv7+Bup8TdmDcz5LycrAkaXvvqFu/qf8Zt76tI339w2X/7fMjM/1ufWfnSbc+XPZzsHavOerWZ2sFtz406Y+/p3fWrTfZis1fALBUmc1WCOERSS84x+0jkm6JMSgAWAnMXwBaAX+uBwAAICKaLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIFpOztXIm88r9Q39qefqFc/7yflSSvnH6Crf+/rVPuvUnKiW33pf384Ty+fQwq9lNftBVftDPECuNdLj1XM3Pueo45ZY1s8nPsSqv84O2LCMHyyr+8hu+PePW59b5j3/rc8fc+nee3OHWR8b9rKrre/ysqvXFidRazvxjs39ijVsfrqTnt0lSR77iL5+RwzVVWd6foekp8TcDLwb7PnJTs4cAXLS4sgUAABARzRYAAEBENFsAAAAR0WwBAABERLMFAAAQEc0WAABARDRbAAAAEa1qzlauJrWPpmcOVfb6OVfVzX5v2GF+3tBQbdqtb8yX3fpfzqx36zMn0/OMsrracNx/7MWpjJyueb8+fmXBrc9f7mclFU4U3XrI+VlS8xu63Xqt5O+h4Md4aXjWX39Hv5/hNnfCz6J6ZHKLW19bTD+3DkwPustOzPk5V3cfucatb+kZd+vDs/5j6y/5+XG1mn9sToz0uXUAuNRxZQsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIlrVnK1gUs2JFKpnjGZn7ym3/oLuQ259LvhZUJ8ZvdGtf/PQDrfeeTj9Acxt8HOwSkN+3zvX74+9NOaWVTrlLz+3y6/37vXX331oxq3PbOpw61Nb8m7dMo7dwX1+Blrv5km3nt/i179z8DK3/qarH06tdRf8DLNKzX/svSV/305W/JyuWkZI2VTZX75S9p+YwT+1gabb+ev3N3sIuMRxZQsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIlrVnK16UZre4mT+XONnHR2YXOPWr+k67tan635veXh2wK2Xhzrd+roj6VlQM9v9MKLZjW5ZIe+Pvdbu13M1f/2lvSW33rffz4qqlfxTKVfxc7Lapv16poyXDfWMrKlKxc+6an/YP/bDV3Sn1t6/5Uvusv9i+i1uvbvo7/usxzY81eXWT475jy1X8M/dXH6Zxw6rYt9Hbmr2EIBLFle2AAAAIqLZAgAAiIhmCwAAICKaLQAAgIhotgAAACKi2QIAAIiIZgsAACCiVc3ZCm1B8xuqqfWOnJ/X89RBP4wqn/PzgI72+TlaXfmyW8/N+r1ptcNZds5ftvOYX69nHKn5AT9rKZe+2yVJax7zg7jKff4A5vr98c9s9MdXGvGPfX7OX75t3M/Jmi45B0fZWVLt/qmhY9N9qbW95fXusv3ts259/+igW+8p+TlcWfWpvJ+xVpv3963N+HUgtp2/fn+zhwC4uLIFAAAQEc0WAABARDRbAAAAEdFsAQAARESzBQAAEBHNFgAAQEQ0WwAAABGtas5W25Rp4z+m93cnX9rlLl8c9XvD2na/PtA249b/18Fr/PV3+VlU9WL67uzd5+dE5Wf9nKnJHW5Z9aJfLw372z99dUaWkv/QNbc2KyfLr88U/fEVpvztZ+VwVWfa3XrvMxn7/zJ/+ycme1JrXxv3z6vZasGt14P/2Oar/tN4dNx/XoWa/7zJt2cc/BF//ABwqePKFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABEtLo5WxPzGrjnmdT66ede6S8/4+cNHRnrc+vP2XbcrXe1l9169aS/u7pOpOcRTW32c6xGXpSRZZTRFreN++uf3rbM9U/5d6iv8/dd1x4/56qSHlMlSZof9HOwgv/wlZ/NyOHqyNj+2rpbnxvrTK19r3OTv/IMtYwcrLL5D77wlP/gcv6hU/l5fj5drd3fN0CWnb9+f7OHAETFlS0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIqLZAgAAiGhVc7ZCqaDqzs2p9S1f8wN/Tl/rZzVNzRXd+kePvtytm/lZTvOXz7v1k53p27fgr7s46mcl5TMyxuY2+TlaxfV+VlJ5KD0nSpIK4/72c/P+sSlM+4+/3Oevf+AFp9z6yFi3W8+1V9z69a8/5NafGF3v1kcn0/ffoWNr3GXzRf/Y1YdKbn22lJFztanqlrv2+9NAdcx/XqnoH1tc/MjJAnxc2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiBads2VmeUkPSjoaQvgpMxuU9FlJOyQdkPSzIYRRdyV1KVdOzxTK5/yspeU6Mdnj1ieeHHTrBT8GTB0n08dvftSRyn1+vW3Wr89lRC3NT2XkYE34fXfNj3pSu3/kVenyj+38Oj9rqqvo7/y+DcNu/fqBo279pu6n3fqNvf6580fff1VqzUYL7rLVLn/fZ70iKoz5GW31bXNufXajv4X8rF+vt7d+ztaKzF8XOLKwgOY5nytb75H0+IKf3yvpnhDCLkn3NH4GgFbE/AWgaRbVbJnZVkk/KelPF9x8m6Q7G9/fKelNKzoyAFgBzF8Amm2xV7b+UNJvSFr4ZtWGEMJxSWr86/89EwBojj8U8xeAJspstszspyQNhRC+s5QNmNm7zOxBM3uwUp1eyioAYElWdP6S/7dRASDNYj4g/zJJP21mb5BUktRrZp+UdNLMNoUQjpvZJklD51o4hHCHpDskqbdrS+t/khbAxWTl5i8bZP4CsCSZV7ZCCO8LIWwNIeyQ9BZJfx9CeJukL0q6vXG32yV9IdooAWAJmL8AtILl5Gx9SNJrzWyvpNc2fgaACwHzF4BVs+icLUkKIdwr6d7G9yOSbjmv5fOmSk8xtV7t9POCshSe6nDrE1f5y1tGVlX3Qb/ee6iSWpve4O/qqe3+ukNGBllu3u+biyf9fVvp9d8hCTW/Pj+QMb6MnLG2jJyv0Rn/2F7WN+bW2zMGUMr5OV6dOf/zOtVq+vjrXX6GmKr+viuOZuRcFTOO3ZCfsZaf97cfMuLv2ipx8/FWynLnLwBYKhLkAQAAIqLZAgAAiIhmCwAAICKaLQAAgIhotgAAACKi2QIAAIjovKIfYut6Zsytz65Z49bn1izvV9Btu//nhGanut16fj59d9b8375X79N+ffQ6Pz6glBHtUJz0119//pRbrx70H3txwl//7BY/eqH9pH8qzsylR4ZI0oaN/gC2Fk+79ROVfrf+4e/7KQGVmfTxtR8v+Mv2+pkjWZEkISMxxTKiJXr3+svPbPSXL/cTrA4AHq5sAQAARESzBQAAEBHNFgAAQEQ0WwAAABHRbAEAAEREswUAABARzRYAAEBEq5qzZSEoP5+eF1XrLbnLT13m5/2ENj/vp6Nz3q2v7/Gzpo6Uutz63Nr08XUM+WPL1TKyivorbrky6/fNxcmMrKQ5PwuquN3fN3q0xy3nuvzxVzszwqIycr4e693k1v/3oSv97T/lj3/dHv/4bH94OLU29oK17rLHX+1nqFXn/WOXn83Il2v3x173D70s49SsDfjHFgAudVzZAgAAiIhmCwAAICKaLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIVjVnS2aqF9L7u1Mv7nQXr3ZkZFVV/LyhrBytoUk/y6nalZFX5OzNUnoMkyRpvisjQ6zq98X1jCylmY3+9rPWPz/mZ6BpU9WvT/thToWMqKasY3vi0Q1uvfdpf/2Fab/eNlP371BPr09sz3hN4ywrSebHcKmW8bxom/b33ay/6zS3wT+2+dGMoC4AuMRxZQsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIlrVnK163jQ/kJ7J0z7q5wVJfl5QPSPu5+jpPre+ZXDcrT/T2eXWO4+mDyBX9bOUyn1+31s6WHTrtYycrcrajByssr/94qm8v/iajDCoXMb4Bv3lrewf++KoP77xq/39Xxz1H/9Y0V9/xxXpQWaV3ozzOiPCq9rpL5+f9/eNZRz6Wre//tJJf5oo92c8AAC4xHFlCwAAICKaLQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIaLYAAAAiWtWcLZlUL6RnAnWM+FlLbfN+bxgyWsfRF/v1E+M9br3QO+/WK93pOVvzGTla9Ywj0b/XzzKa2OGvv7rZX77thJ/jVRnwl7e6n/VUOuw/wNltFbfev8PPQBvt9I/dhs1jbr2vfc6tn5jMODf29afWrOrvm2qnf+wKExnHtts/NhV/6LKseLsMIb/MFQDARY4rWwAAABHRbAEAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABERLMFAAAQ0armbFVL0ulr0vu7thm/9yuO+3k+OT+qSfWMLKjyXLtbf9VVT/kb2JFe+vuHrnUXLUzk3Xq13R97Ycota2bOX39x1F9/pTdj32dkoHUd8Zcv9/vjG633ufX8pL/8SGe3W9+6bcyt795+xK3f9bIbUmv9D/kZZqGQsW+rbln1dn/5tm3Tfv07fhDX3NrlHXsAuNQxSwIAAEREswUAABARzRYAAEBENFsAAAAR0WwBAABERLMFAAAQEc0WAABARKuasxUK0vy6WmrdjvtZSfMDGVlQ3X4eULXsP9wN68bd+tXdx/3t19PX/7XujLCkjJytLANP+SFj+Tn/sVf8qCUVR/zx1Ur+8tWOjJywiay6/7ogV/a3X5vpdOuPPXmVW//5f3afW9/0Y2OptTsOvsFdNjeb8Zqn7pezli/PZOR8ZWWoZeTXVTZm3AEALnFc2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiFY1Z8tqUpuTl1T344DUPurXK91+Pcxk5Gx1Trn1kvlZWUPV3tTamkF/3cMZY5P8nKvJbf7y7RMZGWSdfs5V2clHW4z5AX98IaPtDzl//CHnjz+0+cuXe/wwq08P3eTW52rpj292i3/elI77+6Ztzi0rTPmPfa7HP3eqXRn7NmPftZUyMuQA4BLHlS0AAICIaLYAAAAiotkCAACIiGYLAAAgIpotAACAiGi2AAAAIqLZAgAAiMhC8DN0VnRjZqckHVxw01pJw6s2gPPXyuNr5bFJrT2+Vh6bdPGNb3sIYV2swawW5q8V18rja+WxSa09vlYem9Sk+WtVm60f2bjZgyGEG5o2gAytPL5WHpvU2uNr5bFJjO9C0er7gfEtXSuPTWrt8bXy2KTmjY+3EQEAACKi2QIAAIio2c3WHU3efpZWHl8rj01q7fG18tgkxnehaPX9wPiWrpXHJrX2+Fp5bFKTxtfUz2wBAABc7Jp9ZQsAAOCiRrMFAAAQUVOaLTN7nZk9aWb7zOy9zRiDx8wOmNmjZrbHzB5sgfF8zMyGzOx7C24bNLOvmtnexr8DLTa+D5jZ0cY+3GNmb2jS2LaZ2dfM7HEze8zM3tO4ven7zxlbq+y7kpk9YGYPN8b3wcbtTd93zcYcdl5jYf5a+thadv7KGF/T91+rzV+r/pktM8tLekrSayUdkfRtST8XQvj+qg7EYWYHJN0QQmiJYDYz+3FJU5I+EUK4rnHb70s6HUL4UGOyHwgh/GYLje8DkqZCCH/QjDEtGNsmSZtCCA+ZWY+k70h6k6R3qMn7zxnbz6o19p1J6gohTJlZQdJ9kt4j6Z+oRc69ZmAOO++xMH8tfWwtO39ljK/pc1irzV/NuLL1Ekn7QgjPhBDKkj4j6bYmjOOCEUL4R0mnz7r5Nkl3Nr6/U8kJ3hQp42sJIYTjIYSHGt9PSnpc0ha1wP5zxtYSQmKq8WOh8RXUAvuuyZjDzgPz19K18vyVMb6ma7X5qxnN1hZJhxf8fEQtcnAWCJK+YmbfMbN3NXswKTaEEI5LyQkvaX2Tx3Muv2pmjzQu0zf9rSYz2yHpBZK+pRbbf2eNTWqRfWdmeTPbI2lI0ldDCC2375qAOWz5LoRzqCWeg2e08vwlteYc1krzVzOaLTvHba2WP/GyEMILJb1e0q80LjPj/PyxpCsl7ZZ0XNJ/auZgzKxb0uck/VoIYaKZYznbOcbWMvsuhFALIeyWtFXSS8zsumaNpYUwh138WuY5KLX2/CW17hzWSvNXM5qtI5K2Lfh5q6RjTRhHqhDCsca/Q5L+p5K3DVrNycb75WfeNx9q8nieJYRwsnGi1yX9iZq4Dxvv139O0qdCCH/VuLkl9t+5xtZK++6MEMKYpHslvU4tsu+aiDls+Vr6HGql52Arz19p42ul/dcYz5iaPH81o9n6tqRdZna5mRUlvUXSF5swjnMys67GB/1kZl2SfkLS9/ylmuKLkm5vfH+7pC80cSw/4szJ3PAzatI+bHxI8qOSHg8hfHhBqen7L21sLbTv1plZf+P7DkmvkfSEWmDfNRlz2PK19DnUQs/Blp2/pNaew1pu/gohrPqXpDco+W2epyX9380YgzO2KyQ93Ph6rBXGJ+nTSi7FVpS8qn6npDWS7pG0t/HvYIuN788kPSrpESUn96Ymje3lSt7ieUTSnsbXG1ph/zlja5V9d72k7zbG8T1Jv924ven7rtlfzGHnNR7mr6WPrWXnr4zxNX3/tdr8xZ/rAQAAiIgEeQAAgIhotgAAACKi2QIAAIiIZgsAACAimi0AAICIaLYAAAAiotkCAACI6P8HN9cb5F4yvMYAAAAASUVORK5CYII=\n",





 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      "text/plain": [





  
  

  3579
  
       "<Figure size 720x720 with 2 Axes>"





  
  

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      ]





  
  

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     },





  
  

  3582
  
     "metadata": {





  
  

  3583
  
      "needs_background": "light"





  
  

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     },





  
  

  3585
  
     "output_type": "display_data"





  
  

  3586
  
    }





  
  

  3587
  
   ],





  
  

  3588
  
   "source": [





  
  

  3589
  
    "plt.subplots(1,2)\n",





  
  

  3590
  
    "plt.subplot(1,2,1)\n",





  
  

  3591
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_279.nii.gz')).get_fdata()[15,:,:])\n",





  
  

  3592
  
    "plt.title('hippocampus_279.nii.gz, MRI, Sagital Slice 15')\n",





  
  

  3593
  
    "plt.subplot(1,2,2)\n",





  
  

  3594
  
    "plt.imshow(nib.load(os.path.join(path, 'labels', 'hippocampus_279.nii.gz')).get_fdata()[15,:,:])\n",





  
  

  3595
  
    "plt.title('hippocampus_279.nii.gz, MRI, Sagital Slice 15')\n",





  
  

  3596
  
    "plt.show()"





  
  

  3597
  
   ]





  
  

  3598
  
  },





  
  

  3599
  
  {





  
  

  3600
  
   "cell_type": "code",





  
  

  3601
  
   "execution_count": 193,





  
  

  3602
  
   "metadata": {},





  
  

  3603
  
   "outputs": [





  
  

  3604
  
    {





  
  

  3605
  
     "data": {





  
  

  3606
  
      "text/plain": [





  
  

  3607
  
       "{'8': ['..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_057.nii.gz',\n",





  
  

  3608
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_097.nii.gz',\n",





  
  

  3609
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_099.nii.gz',\n",





  
  

  3610
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_125.nii.gz',\n",





  
  

  3611
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_135.nii.gz',\n",





  
  

  3612
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_138.nii.gz',\n",





  
  

  3613
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_141.nii.gz',\n",





  
  

  3614
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_142.nii.gz',\n",





  
  

  3615
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_143.nii.gz',\n",





  
  

  3616
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_144.nii.gz',\n",





  
  

  3617
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_175.nii.gz',\n",





  
  

  3618
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_177.nii.gz',\n",





  
  

  3619
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_178.nii.gz',\n",





  
  

  3620
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_180.nii.gz',\n",





  
  

  3621
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_193.nii.gz',\n",





  
  

  3622
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_199.nii.gz',\n",





  
  

  3623
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_205.nii.gz',\n",





  
  

  3624
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_221.nii.gz',\n",





  
  

  3625
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_222.nii.gz',\n",





  
  

  3626
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_225.nii.gz',\n",





  
  

  3627
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_226.nii.gz',\n",





  
  

  3628
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_231.nii.gz',\n",





  
  

  3629
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_274.nii.gz',\n",





  
  

  3630
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_279.nii.gz',\n",





  
  

  3631
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_280.nii.gz',\n",





  
  

  3632
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_282.nii.gz',\n",





  
  

  3633
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_289.nii.gz',\n",





  
  

  3634
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_297.nii.gz',\n",





  
  

  3635
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_319.nii.gz',\n",





  
  

  3636
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_320.nii.gz',\n",





  
  

  3637
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_333.nii.gz',\n",





  
  

  3638
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_334.nii.gz',\n",





  
  

  3639
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_335.nii.gz',\n",





  
  

  3640
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_336.nii.gz',\n",





  
  

  3641
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_341.nii.gz',\n",





  
  

  3642
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_343.nii.gz',\n",





  
  

  3643
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_349.nii.gz',\n",





  
  

  3644
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_353.nii.gz',\n",





  
  

  3645
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_359.nii.gz',\n",





  
  

  3646
  
       "  '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_378.nii.gz']}"





  
  

  3647
  
      ]





  
  

  3648
  
     },





  
  

  3649
  
     "execution_count": 193,





  
  

  3650
  
     "metadata": {},





  
  

  3651
  
     "output_type": "execute_result"





  
  

  3652
  
    }





  
  

  3653
  
   ],





  
  

  3654
  
   "source": [





  
  

  3655
  
    "lo_outlier_bitpix"





  
  

  3656
  
   ]





  
  

  3657
  
  },





  
  

  3658
  
  {





  
  

  3659
  
   "cell_type": "markdown",





  
  

  3660
  
   "metadata": {},





  
  

  3661
  
   "source": [





  
  

  3662
  
    "All data has bitpix of 8."





  
  

  3663
  
   ]





  
  

  3664
  
  },





  
  

  3665
  
  {





  
  

  3666
  
   "cell_type": "code",





  
  

  3667
  
   "execution_count": 68,





  
  

  3668
  
   "metadata": {},





  
  

  3669
  
   "outputs": [





  
  

  3670
  
    {





  
  

  3671
  
     "data": {





  
  

  3672
  
      "image/png": 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\n",





  
  

  3673
  
      "text/plain": [





  
  

  3674
  
       "<Figure size 1080x1080 with 1 Axes>"





  
  

  3675
  
      ]





  
  

  3676
  
     },





  
  

  3677
  
     "metadata": {





  
  

  3678
  
      "needs_background": "light"





  
  

  3679
  
     },





  
  

  3680
  
     "output_type": "display_data"





  
  

  3681
  
    }





  
  

  3682
  
   ],





  
  

  3683
  
   "source": [





  
  

  3684
  
    "plt.figure(figsize=(15,15))\n",





  
  

  3685
  
    "plt.xlim(0,5000)\n",





  
  

  3686
  
    "plt.hist(train_set_volumes, bins = 1000)\n",





  
  

  3687
  
    "plt.axvline(2200,color='r')\n",





  
  

  3688
  
    "plt.title('Hippocampus Volumes in Training Volume, Under 5,000mm^3')\n",





  
  

  3689
  
    "plt.xlabel('Hippocampus Volume, mm^3')\n",





  
  

  3690
  
    "plt.ylabel('count')\n",





  
  

  3691
  
    "plt.show()"





  
  

  3692
  
   ]





  
  

  3693
  
  },





  
  

  3694
  
  {





  
  

  3695
  
   "cell_type": "markdown",





  
  

  3696
  
   "metadata": {},





  
  

  3697
  
   "source": [





  
  

  3698
  
    "### Did not identify one discernable outlier in the low hippocampus volume set."





  
  

  3699
  
   ]





  
  

  3700
  
  },





  
  

  3701
  
  {





  
  

  3702
  
   "cell_type": "markdown",





  
  

  3703
  
   "metadata": {},





  
  

  3704
  
   "source": [





  
  

  3705
  
    "### <a name=\"compare-dimensions\"></a>Check That All Remaining Labels and Images Pairs Have Matching Dimensions"





  
  

  3706
  
   ]





  
  

  3707
  
  },





  
  

  3708
  
  {





  
  

  3709
  
   "cell_type": "code",





  
  

  3710
  
   "execution_count": 195,





  
  

  3711
  
   "metadata": {},





  
  

  3712
  
   "outputs": [],





  
  

  3713
  
   "source": [





  
  

  3714
  
    "no_outlier2 = np.concatenate((no_outlier,lo_outlier))"





  
  

  3715
  
   ]





  
  

  3716
  
  },





  
  

  3717
  
  {





  
  

  3718
  
   "cell_type": "code",





  
  

  3719
  
   "execution_count": 263,





  
  

  3720
  
   "metadata": {},





  
  

  3721
  
   "outputs": [





  
  

  3722
  
    {





  
  

  3723
  
     "data": {





  
  

  3724
  
      "text/plain": [





  
  

  3725
  
       "array([[2948, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_001.nii.gz',\n",





  
  

  3726
  
       "        array([ 3, 35, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3727
  
       "       [3353, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_003.nii.gz',\n",





  
  

  3728
  
       "        array([ 3, 34, 52, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3729
  
       "       [3698, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_004.nii.gz',\n",





  
  

  3730
  
       "        array([ 3, 36, 52, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3731
  
       "       [4263, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_006.nii.gz',\n",





  
  

  3732
  
       "        array([ 3, 35, 52, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3733
  
       "       [3372, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_007.nii.gz',\n",





  
  

  3734
  
       "        array([ 3, 34, 47, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3735
  
       "       [3248, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_008.nii.gz',\n",





  
  

  3736
  
       "        array([ 3, 36, 48, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3737
  
       "       [3456, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_011.nii.gz',\n",





  
  

  3738
  
       "        array([ 3, 36, 50, 31,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3739
  
       "       [3622, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_014.nii.gz',\n",





  
  

  3740
  
       "        array([ 3, 39, 50, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3741
  
       "       [2819, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_015.nii.gz',\n",





  
  

  3742
  
       "        array([ 3, 42, 51, 28,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3743
  
       "       [3478, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_017.nii.gz',\n",





  
  

  3744
  
       "        array([ 3, 35, 48, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3745
  
       "       [3356, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_019.nii.gz',\n",





  
  

  3746
  
       "        array([ 3, 36, 47, 41,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3747
  
       "       [3611, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_020.nii.gz',\n",





  
  

  3748
  
       "        array([ 3, 36, 46, 43,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3749
  
       "       [3568, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_023.nii.gz',\n",





  
  

  3750
  
       "        array([ 3, 35, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3751
  
       "       [4030, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_024.nii.gz',\n",





  
  

  3752
  
       "        array([ 3, 38, 52, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3753
  
       "       [3326, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_025.nii.gz',\n",





  
  

  3754
  
       "        array([ 3, 35, 48, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  3755
  
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       "        array([ 3, 35, 49, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4109
  
       "       [3724, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_351.nii.gz',\n",





  
  

  4110
  
       "        array([ 3, 35, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4111
  
       "       [3679, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_352.nii.gz',\n",





  
  

  4112
  
       "        array([ 3, 38, 51, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4113
  
       "       [2912, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_354.nii.gz',\n",





  
  

  4114
  
       "        array([ 3, 36, 50, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4115
  
       "       [3335, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_355.nii.gz',\n",





  
  

  4116
  
       "        array([ 3, 33, 47, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4117
  
       "       [3496, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_356.nii.gz',\n",





  
  

  4118
  
       "        array([ 3, 36, 51, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4119
  
       "       [2887, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_358.nii.gz',\n",





  
  

  4120
  
       "        array([ 3, 35, 50, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4121
  
       "       [2931, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_360.nii.gz',\n",





  
  

  4122
  
       "        array([ 3, 34, 49, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4123
  
       "       [3105, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_361.nii.gz',\n",





  
  

  4124
  
       "        array([ 3, 36, 50, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4125
  
       "       [3506, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_363.nii.gz',\n",





  
  

  4126
  
       "        array([ 3, 38, 52, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4127
  
       "       [3979, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_366.nii.gz',\n",





  
  

  4128
  
       "        array([ 3, 37, 47, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4129
  
       "       [4151, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_367.nii.gz',\n",





  
  

  4130
  
       "        array([ 3, 36, 57, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4131
  
       "       [4401, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_368.nii.gz',\n",





  
  

  4132
  
       "        array([ 3, 38, 55, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4133
  
       "       [3398, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_370.nii.gz',\n",





  
  

  4134
  
       "        array([ 3, 35, 50, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4135
  
       "       [3483, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_372.nii.gz',\n",





  
  

  4136
  
       "        array([ 3, 36, 50, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4137
  
       "       [3689, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_373.nii.gz',\n",





  
  

  4138
  
       "        array([ 3, 34, 49, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4139
  
       "       [3877, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_374.nii.gz',\n",





  
  

  4140
  
       "        array([ 3, 38, 48, 39,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4141
  
       "       [3222, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_375.nii.gz',\n",





  
  

  4142
  
       "        array([ 3, 32, 54, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4143
  
       "       [3581, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_376.nii.gz',\n",





  
  

  4144
  
       "        array([ 3, 35, 55, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4145
  
       "       [3435, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_380.nii.gz',\n",





  
  

  4146
  
       "        array([ 3, 35, 46, 42,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4147
  
       "       [3152, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_381.nii.gz',\n",





  
  

  4148
  
       "        array([ 3, 33, 49, 37,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4149
  
       "       [3420, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_383.nii.gz',\n",





  
  

  4150
  
       "        array([ 3, 33, 55, 29,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4151
  
       "       [3397, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_385.nii.gz',\n",





  
  

  4152
  
       "        array([ 3, 35, 48, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4153
  
       "       [3208, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_386.nii.gz',\n",





  
  

  4154
  
       "        array([ 3, 37, 45, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4155
  
       "       [3080, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_387.nii.gz',\n",





  
  

  4156
  
       "        array([ 3, 33, 51, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4157
  
       "       [2947, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_389.nii.gz',\n",





  
  

  4158
  
       "        array([ 3, 34, 49, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4159
  
       "       [3252, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_390.nii.gz',\n",





  
  

  4160
  
       "        array([ 3, 38, 51, 33,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4161
  
       "       [3682, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_393.nii.gz',\n",





  
  

  4162
  
       "        array([ 3, 36, 51, 31,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4163
  
       "       [3814, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_394.nii.gz',\n",





  
  

  4164
  
       "        array([ 3, 36, 52, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4165
  
       "       [2773, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_057.nii.gz',\n",





  
  

  4166
  
       "        array([ 3, 35, 51, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4167
  
       "       [2753, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_097.nii.gz',\n",





  
  

  4168
  
       "        array([ 3, 37, 48, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4169
  
       "       [2535, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_099.nii.gz',\n",





  
  

  4170
  
       "        array([ 3, 33, 52, 27,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4171
  
       "       [2726, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_125.nii.gz',\n",





  
  

  4172
  
       "        array([ 3, 43, 42, 39,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4173
  
       "       [2629, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_135.nii.gz',\n",





  
  

  4174
  
       "        array([ 3, 32, 49, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4175
  
       "       [2534, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_138.nii.gz',\n",





  
  

  4176
  
       "        array([ 3, 32, 46, 42,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4177
  
       "       [2714, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_141.nii.gz',\n",





  
  

  4178
  
       "        array([ 3, 33, 44, 42,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4179
  
       "       [2697, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_142.nii.gz',\n",





  
  

  4180
  
       "        array([ 3, 38, 43, 41,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4181
  
       "       [2397, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_143.nii.gz',\n",





  
  

  4182
  
       "        array([ 3, 32, 45, 41,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4183
  
       "       [2471, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_144.nii.gz',\n",





  
  

  4184
  
       "        array([ 3, 34, 45, 43,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4185
  
       "       [2739, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_175.nii.gz',\n",





  
  

  4186
  
       "        array([ 3, 33, 47, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4187
  
       "       [2593, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_177.nii.gz',\n",





  
  

  4188
  
       "        array([ 3, 33, 44, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4189
  
       "       [2714, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_178.nii.gz',\n",





  
  

  4190
  
       "        array([ 3, 35, 44, 41,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4191
  
       "       [2678, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_180.nii.gz',\n",





  
  

  4192
  
       "        array([ 3, 37, 45, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4193
  
       "       [2708, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_193.nii.gz',\n",





  
  

  4194
  
       "        array([ 3, 33, 50, 29,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4195
  
       "       [2570, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_199.nii.gz',\n",





  
  

  4196
  
       "        array([ 3, 37, 52, 26,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4197
  
       "       [2704, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_205.nii.gz',\n",





  
  

  4198
  
       "        array([ 3, 32, 47, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4199
  
       "       [2448, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_221.nii.gz',\n",





  
  

  4200
  
       "        array([ 3, 32, 48, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4201
  
       "       [2684, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_222.nii.gz',\n",





  
  

  4202
  
       "        array([ 3, 34, 49, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4203
  
       "       [2475, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_225.nii.gz',\n",





  
  

  4204
  
       "        array([ 3, 33, 53, 26,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4205
  
       "       [2546, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_226.nii.gz',\n",





  
  

  4206
  
       "        array([ 3, 32, 51, 28,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4207
  
       "       [2647, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_231.nii.gz',\n",





  
  

  4208
  
       "        array([ 3, 33, 47, 42,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4209
  
       "       [2665, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_274.nii.gz',\n",





  
  

  4210
  
       "        array([ 3, 35, 40, 40,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4211
  
       "       [2382, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_279.nii.gz',\n",





  
  

  4212
  
       "        array([ 3, 34, 50, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4213
  
       "       [2613, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_280.nii.gz',\n",





  
  

  4214
  
       "        array([ 3, 37, 47, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4215
  
       "       [2416, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_282.nii.gz',\n",





  
  

  4216
  
       "        array([ 3, 37, 52, 32,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4217
  
       "       [2738, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_289.nii.gz',\n",





  
  

  4218
  
       "        array([ 3, 35, 49, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4219
  
       "       [2786, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_297.nii.gz',\n",





  
  

  4220
  
       "        array([ 3, 34, 51, 30,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4221
  
       "       [2422, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_319.nii.gz',\n",





  
  

  4222
  
       "        array([ 3, 33, 48, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4223
  
       "       [2451, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_320.nii.gz',\n",





  
  

  4224
  
       "        array([ 3, 33, 47, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4225
  
       "       [2635, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_333.nii.gz',\n",





  
  

  4226
  
       "        array([ 3, 33, 46, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4227
  
       "       [2678, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_334.nii.gz',\n",





  
  

  4228
  
       "        array([ 3, 34, 47, 36,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4229
  
       "       [2532, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_335.nii.gz',\n",





  
  

  4230
  
       "        array([ 3, 32, 47, 41,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4231
  
       "       [2593, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_336.nii.gz',\n",





  
  

  4232
  
       "        array([ 3, 34, 47, 43,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4233
  
       "       [2588, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_341.nii.gz',\n",





  
  

  4234
  
       "        array([ 3, 34, 48, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4235
  
       "       [2618, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_343.nii.gz',\n",





  
  

  4236
  
       "        array([ 3, 32, 45, 38,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4237
  
       "       [2590, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_349.nii.gz',\n",





  
  

  4238
  
       "        array([ 3, 34, 50, 34,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4239
  
       "       [2755, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_353.nii.gz',\n",





  
  

  4240
  
       "        array([ 3, 32, 51, 31,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4241
  
       "       [2634, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_359.nii.gz',\n",





  
  

  4242
  
       "        array([ 3, 35, 49, 35,  1,  1,  1,  1], dtype=int16)],\n",





  
  

  4243
  
       "       [2757, '..\\\\data\\\\TrainingSet\\\\labels\\\\hippocampus_378.nii.gz',\n",





  
  

  4244
  
       "        array([ 3, 35, 52, 34,  1,  1,  1,  1], dtype=int16)]],\n",





  
  

  4245
  
       "      dtype=object)"





  
  

  4246
  
      ]





  
  

  4247
  
     },





  
  

  4248
  
     "execution_count": 263,





  
  

  4249
  
     "metadata": {},





  
  

  4250
  
     "output_type": "execute_result"





  
  

  4251
  
    }





  
  

  4252
  
   ],





  
  

  4253
  
   "source": [





  
  

  4254
  
    "no_outlier2"





  
  

  4255
  
   ]





  
  

  4256
  
  },





  
  

  4257
  
  {





  
  

  4258
  
   "cell_type": "code",





  
  

  4259
  
   "execution_count": 223,





  
  

  4260
  
   "metadata": {},





  
  

  4261
  
   "outputs": [],





  
  

  4262
  
   "source": [





  
  

  4263
  
    "difference = []\n",





  
  

  4264
  
    "no_outlier2[0][1]\n",





  
  

  4265
  
    "for i in no_outlier2:\n",





  
  

  4266
  
    "    fN = os.path.split(i[1])[1]\n",





  
  

  4267
  
    "    delta = nib.load(os.path.join(path, 'labels', fN)).header['dim'] - nib.load(os.path.join(path, 'images', fN)).header['dim']\n",





  
  

  4268
  
    "    difference.append([fN, delta])"





  
  

  4269
  
   ]





  
  

  4270
  
  },





  
  

  4271
  
  {





  
  

  4272
  
   "cell_type": "code",





  
  

  4273
  
   "execution_count": 224,





  
  

  4274
  
   "metadata": {},





  
  

  4275
  
   "outputs": [





  
  

  4276
  
    {





  
  

  4277
  
     "name": "stdout",





  
  

  4278
  
     "output_type": "stream",





  
  

  4279
  
     "text": [





  
  

  4280
  
      "['hippocampus_010.nii.gz'\n",





  
  

  4281
  
      " array([   0, -476, -462, -210,    0,    0,    0,    0], dtype=int16)]\n"





  
  

  4282
  
     ]





  
  

  4283
  
    }





  
  

  4284
  
   ],





  
  

  4285
  
   "source": [





  
  

  4286
  
    "for i in np.array(difference):\n",





  
  

  4287
  
    "    if np.sum(np.abs(i[1])) != 0:\n",





  
  

  4288
  
    "        print(i)"





  
  

  4289
  
   ]





  
  

  4290
  
  },





  
  

  4291
  
  {





  
  

  4292
  
   "cell_type": "markdown",





  
  

  4293
  
   "metadata": {},





  
  

  4294
  
   "source": [





  
  

  4295
  
    "Found a second outlier!\n",





  
  

  4296
  
    "NIFTI file 'hippocampus_010.nii.gz' has a mismatch in the dimensions of its mask and its image."





  
  

  4297
  
   ]





  
  

  4298
  
  },





  
  

  4299
  
  {





  
  

  4300
  
   "cell_type": "code",





  
  

  4301
  
   "execution_count": 69,





  
  

  4302
  
   "metadata": {},





  
  

  4303
  
   "outputs": [





  
  

  4304
  
    {





  
  

  4305
  
     "data": {





  
  

  4306
  
      "text/plain": [





  
  

  4307
  
       "<matplotlib.image.AxesImage at 0x1c633d882e0>"





  
  

  4308
  
      ]





  
  

  4309
  
     },





  
  

  4310
  
     "execution_count": 69,





  
  

  4311
  
     "metadata": {},





  
  

  4312
  
     "output_type": "execute_result"





  
  

  4313
  
    },





  
  

  4314
  
    {





  
  

  4315
  
     "data": {





  
  

  4316
  
      "image/png": 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587wWnwe8X1Wvrj32f+THXxMROQWeytu/0dd+EfAz2Of6o8CfERF5vfd66H2/DPh9wK/Gjt+vHJ5T1Y+o6mL4B/yP2LH+RPkbWKTuMeB/46PPxyueLxGZA38bO+ePAb8Z+FOvINDeNrjAchzHeefxJ/IN9D7w14DPf41t/6SqPpO3/Rbsxnad/1hV96r6Q8APAF8hIhH4t4FvUNUrVf0g8F8BvzW/5muAP6qqP6rG+1T1QwCq+pfy2pKqfi/ws8AXXnu/D6jqd6pqD3wv8G7gP8tr+FtAg4mFgR9Q1X+gqnssbfcrROTdb+AYtVi67ilV3anqq0aBMgvg4qHHLoDlG3ivQRBef/3rvfZDqvrt+Th8N/Ak8PgbeK/rfAXwnar6E6q6wQTtRyEivx/4hcDv+Bj3/1Go6nfka2KPiexfIiLH1zZ5tfP1rwMfzOe+U9X/DfgrwG/8RNf0VuECy3Ec553H89e+3nC4wb8Sz1z7+kNYpGXgTFXXr/D8HaDK319/7l3563djkaqPQkR+m4j8WE4BngO/KO9v4IVrX2/B0m8PPXb984zrV9UVcP+hz/BqfD0gwD8VkZ8QkdcTFyssGnedI+DqFbZ9pdcO27/R147nMIsjeO3z+Eo8xYPn95mHN8ipx98L/AZV3X6M+394X1FEvjWngC+BD+anrp/fVztf7wG+aLgu8rXxlVgU9W2JCyzHcZxPba5Hez4N+Mi1709z6ubh51/mEAG6/tyH89fPYCnHB8j1Nt8O/B7gtqqeAD+OCZ1PeP05dXgrr3EQhrNr2443a1V9XlW/VlWfAn4Xlo66Hhl7mJ8APkNErkedfkl+/DVR1TPgubz9x/TaT5DnsPTnwAORvZzu/W7gK1T1o8TXx8G/A/x6LCV5DLx3eKtXWsND5+sZ4IdU9eTav4Wq/ns3sK63BBdYjuM4n9r8bhF5WkRuAX8QS8td5w+JSCUiX4ylcf5STlt9H/AtIrLMwun3AX8uv+ZPA/+BiPwyMT4rbzMHFHgJQER+OxbB+kT4dSLyr4hIhdX2/EhOeb6ECb7fkiMrv4Nrok9EvlxEBvFxltfVv9qbqOo/x2qGvklEJrk4/xdjaaxhnxOgzt/W+fuBPwt8o4icisgvBL4W+K5P5IO/Ab4P+O0i8rkiMgP+k2trPQL+KvCND6dHrzUIvPc19l3k4zD8K7GU5x6rt5sB//krvO4VzxfwPwOfIyK/VUTK/O+Xi8jnfvwf/63FBZbjOM6nNt+DFXu/P/+7bub4PCY+PoIVI/+7qvrT+bl/H4sSvR/44byf7wCrs8Lqub4HS4P9T8AtVf1JrFbrH2OpwH8R+Ec3sP5vwlJNvwxLKw18LfAfYjf8zwP+l2vP/XLgR0RkBXw/8HtV9QOv816/CfgC7Jh8K/Abs5Ab2HJIB/50/n7gm7C06YeAHwL+C1X9m8OTuZPvi1/3074OOd35lQCq+jeAPwH8PeB92HEHE0G/FPgFwB+73k2Yn393XueHeXX+2/z5hn/fiYnI4XU/CfyTV3jdK56v3Dzwa7Bj/BHs2vsjHATr2w5R/YS7Mj9hjuSWfpH8qrd6GY7jfBL5O/qX/1dV/YK3eh2fyojIB4GvUdW/8wrPfQnw51T16Yefe1QQke8CnlXVb3y9bR3I0aAfB2pV7V5ju28EXlLV//6Ttrh3IMXrb+I4juM4ztuRnMr8ASw9+0eAv/Za4gpAVd9WI2keVTxF6DiO4ziMsxpXr/TvrV7bJ8Dvwmrefg6rMXvbFo2/3fAIluM4zqcoqvre13ju7/NgB9ojh6p+9Q3v7x/ysVshPNKoqo/jeYvwCJbjOI7jOM4N4wLLcRzHcRznhvEUoeM4zkPkGW5/HIjAn1bVb32t7SupdcL8tTZxHOcdxI41je5f0yDXBZbjOM418py9/y/wrwHPAj8qIt+fPZxekQlz3GrGcT51+BH9u6+7jacIHcdxHuQLgfep6vtVtQH+Ijb+w3Ec5w3jAstxHOdB3sWDQ3Gf5TDE2HEc5w3hKULHcZwHeaW6io8aeSEiXwd8HcDkgXnCjuM4HsFyHMd5mGexWWwDT2Oz0R5AVb9NVb9AVb+gfPuOS3Mc503CBZbjOM6D/Cjw2SLy6SJSYcNnv/8tXpPjOG8zPEXoOI5zDVXtROT3AD+I2TR8h6r+xFu8LMdx3ma4wHIcx3kIVf3rwF9/q9fhOM7bF08ROo7jOI7j3DAusBzHcRzHcW4YF1iO4ziO4zg3jAssx3Ecx3GcG8YFluM4juM4zg3jAstxHMdxHOeGcYHlOI7jOI5zw7jAchzHcRzHuWFcYDmO4ziO49wwLrAcx3Ecx3FuGBdYjuM4juM4N4wLLMdxHMdxnBvGBZbjOI7jOM4N4wLLcRzHcRznhnGB5TiO4ziOc8O4wHIcx3Ecx7lhXGA5juM4juPcMC6wHMdxHMdxbhgXWI7jOI7jODeMCyzHcRzHcZwbxgWW4ziO4zjODeMCy3Ecx3Ec54ZxgeU4juM4jnPDuMByHMdxHMe5YVxgOY7jOI7j3DAusBzHcRzHcW4YF1iO4ziO4zg3jAssx3Ecx3GcG+Z1BZaIfIeIvCgiP37tsVsi8rdF5Gfz/6fXnvsGEXmfiPyMiHzpm7Vwx3Ecx3GcR5U3EsH6LuDLHnrsDwB/V1U/G/i7+XtE5F8AfhPwefk1f0pE4o2t1nEcx3Ec523A6wosVf0HwP2HHv71wHfnr78b+A3XHv+LqrpX1Q8A7wO+8GaW6jiO4ziO8/bg463BelxVnwPI/z+WH38X8My17Z7NjzmO4ziO43zKUNzw/uQVHtNX3FDk64CvA5gwu+FlOI7jOI7jvHV8vBGsF0TkSYD8/4v58WeBd1/b7mngI6+0A1X9NlX9AlX9gpL641yG4ziO4zjOo8fHK7C+H/iq/PVXAX/12uO/SURqEfl04LOBf/qJLdFxHMdxHOftxeumCEXkLwBfAtwRkWeBbwK+Ffg+EfmdwM8DXw6gqj8hIt8H/CTQAb9bVfs3ae2O4ziO4ziPJK8rsFT1N7/KU7/qVbb/FuBbPpFFOY7jOI7jvJ1xJ3fHcRzHcZwbxgWW4ziO4zjODXPTNg2O4ziO47yJ/OBHfuytXsLr8qVPff5bvYS3HI9gOY7jOI7j3DAusBzHcRzHcW4YF1iO4ziO4zg3jAssx3Ecx3GcG8YFluM4juM4zg3jAstxHMdxHOeGcYHlOI7jOI5zw7gPluM4juM8IrwdPK6cN4ZHsBzHcRzHcW4YF1iO4ziO4zg3jAssx3Ecx3GcG8YFluM4juM4zg3jAstxHMdxHOeGcYHlOI7jOI5zw7jAchzHcRzHuWFcYDmO4ziO49wwbjTqOI7jODeEG4U6Ax7BchzHcRzHuWFcYDmO4ziO49wwLrAcx3Ecx3FuGBdYjuM4juM4N4wLLMdxHMdxnBvGBZbjOI7jOM4N4wLLcRzHcRznhnGB5TiO4ziOc8O4wHIcx3Ecx7lhXGA5juM4juPcMC6wHMdxHMdxbhgXWI7jOI7jODeMCyzHcRzHcZwbxgWW4ziO4zjODeMCy3Ecx3Ec54Yp3uoFOI7jOI7z9uFLn/r8t3oJbws8guU4zqckIvIdIvKiiPz4tcduicjfFpGfzf+fvpVrdBzn7YsLLMdxPlX5LuDLHnrsDwB/V1U/G/i7+XvHcZyPGRdYjuN8SqKq/wC4/9DDvx747vz1dwO/4ZO5Jsdx3jm4wHIcxznwuKo+B5D/f+wtXo/jOG9TvMjdcRzn40BEvg74OoAJs7d4NY7jPGp4BMtxHOfACyLyJED+/8VX21BVv01Vv0BVv6Ck/qQt0HGctwcusBzHcQ58P/BV+euvAv7qW7gWx3HexniK0HGcT0lE5C8AXwLcEZFngW8CvhX4PhH5ncDPA1/+1q3QeRT5wY/82Fu9hE8Y97H65OACy3GcT0lU9Te/ylO/6pO6EMdx3pF4itBxHMdxHOeGcYHlOI7jOI5zw7jAchzHcRzHuWFcYDmO4ziO49wwLrAcx3Ecx3FuGBdYjuM4juM4N4zbNDiO4zjOpxCv5+XlPlk3g0ewHMdxHMdxbhgXWI7jOI7jODeMCyzHcRzHcZwbxgWW4ziO4zjODeMCy3Ecx3Ec54ZxgeU4juM4jnPDuMByHMdxHMe5YVxgOY7jOI7j3DBuNOo4juM4mdcz4Xw74EahjwYewXIcx3Ecx7lhHq0Ilsjha9VX3ibEw+ZB0L4HyTpR04Pbqto+JRyeG/YrgsSIJoXUj/uWGNGuPbz2odd81HupQoiEqgQgNe1hf6/0uYa1XFublAUiAiE8uC3YY+mhtadk76Ppwf1df7+Hvx/WPaztoTVJyGuJIb+VQt/b8X14X9c+t5QFEuPhPVOyNed1Dmifvw7X3jdp3n968BiPL7r2vsN7Dc+Ha38b5GMnIhDz9ZHPj7YdqCJF8eB75+e06yApaEKTIjEiVWn7z59/2K82rX2f9zNce8OxQ5Pt79rxHp97mCDQX7tOrl+jEh58n6TXNhP7Pp/7h/eveZ8PvDafcymrcZvr+7HzFw6vH7avaztHbfNRryXGfGyvrSPGwzlN/fjzNBxbO+d530UB7UcfFsdxnHcKj47AuiZStE/2S3m4AQ03HwmESW2/nMF+yW+3EO0XedrvbfOisJtj1yFFgVSVPZd/yWvXIlVFmE4gKf1qjQRBplNkMkGvrki7HWE6NTGz3yNFebjxpnwjBeh7wmIOd25BCMSzS9Ll5QPiJNT1uN5xnX2P1LXd0I+W6LSGqkTLaMKiV4iCFgHpFRVBUoJekZSIL7yMbnejINH9fhQIBDnc6IBQlcjE1pC2O7ux55uhVCVSVVCVSFmi8ynEgLQdcrUmXV6RdvtRBElRImWBth0yqQknx+hsgk5KW1vfozHaWtssNmIgbHZ2/GNAi4j0CZoW3WzR7XY8niaEQl7r1h6rKsJshsxndpyKiMYIUUDsGGkQtIz0dQQB6ZXQJuK6QfYt/XJCPy/R4XgVQnW2J1xukd0e9o1dL4s5/ekSohCudoTdHq0rdFYT712iq/V4yQ7XF0WBxID2CV2vD2JOgh3LosjHOwvJLAI1X69gYka7zq6LokB3O6hrRATdN3l/WcR0nR3bGJG6gqIYxa02jZ3Xuhp/jtL5hf3snBwjRUR3e3t+t0ObFplNkekUWjsfabNBioJw5zbatvQv3SM+dsd+FlZru2amU3S9sWspCJSVXWOq6HqDbrfIpEYWC/ss+fxq06JNQzg+gpc/0V8ajuM4jy6PhMCSIhJv3YL9nrTZHG62ZWU386ZBU77pbjbXXvgKkRrVB/7qTvs97HYW+cp/rUtRom1H31xeW0UkXV3B1dX4SNpsQIQwnRKeeAw9u0C32weiVFJWJjbunSOLOXrrmHC8QLqedO8M3e1NrE0mAAcxV1V2M1JFz86RbY0s5jCbQJ+Q7d5uyCImvKa1CYkyIvsEt07g+ZdI640JqKpChmMAJoZKu7GbMLTPH+oabZqDGC1NjMrsCJ1UaF2SqoikiiCCqBLycbWIj0XOpKqg70nnF4SuAxZQRAiHNUqf8g04mDCQQ+ROJxUiwhBT0s0Gktq6csQpLBYWPSoLdLtD93vCrVP0aG7iqle0DEjbEzrbd5E/O22HrLdo20LXUVwt4OnbqAipjvRFYHdnQlVFirNgwnRS0c9r6BLxhXP06gqVgMxMaJMFkDYNqmrCuijQi0u0NAEeTk/sHHSdCZzUm8CI0YQQwG6HbncmpDSLofU6C9CISjLBE4KJuKMFmv/4kBwJ1CBI25nI2e3ssT4R5jOYTiCLKID45NTO+f1zJAZkuTTRVlaE+fyw3t0O7TriE4+DCOncrvd4tDCRFoJd/6sVUq2JTz1hgvelM9LZGQBhMUdOT5DTY/TyCl2t7LPHiJQl3LlF2O7pX3R15TjOO5tHQmBp19O/9NLhgZwO0qZB2+aQagDIacEhqpFWaxMJWPQjbXejuArTCdqE8S95qaYW1RkiO4uFRaw2GxMB263tuyhtH3VtQiIlug98yKJRkwnx9BjdmRDQpiGt18SqQmcTiDm1tFojdYVUJf39M1K+CVr6qBxTaSICsxkym1ikpCrQIhCKiJxdok2D9D1MKtrTKd00Enpl8uylCaeVRdhCjoCMUbt886ZpLJowfJaqBFVijupp3yPTCbrZwmpDWJrIkyyEZD6DriNdriCY2NS+t+iSBEJVki6vkLZFJhOoSkJnUQ2NAcoC2ewsapKjaLJrTMxNKigiIkIoChOtbWciOyl6dQUixOUSqWvS1RX9Sy8T2tbObVkQ1v21dKvYcd3t0d2OlFOAYTpBJxXF8+d2XqZTijvHtLcm7O5WpCdrQqeERom7RPXy2qIyMZoQmk2hrtCX79v+nnrCojnPv4jMZ4Q7t0GVdO8+tJ2JpjiIyryf3R7t1hY1LStkPochgtW0UBYQscjQboduG+TkGKlK0gsvHVKV1SFVpyGndHN6L8wndg28+DJSVXbep1PSak28fUp4z7uQtiO9fH/8edLrqe66JkwmpPtnJogmE8JiTrq8MmG73do51mTRzvNL0jNXaN8TptPxM/XPfHhMCUoQi841rf0sD38AvFLa1HEc5x3EIyGwJEbCdE7a7qw2pOvQfU6hFYV93zQ5KlMRj+cmmvqeMJ/SX66upbAKpK4tLReE1LQW4Vke203zakXK+3ogYiVCPD6CuibdPyeenkLf019alCvevmWiCiw6Mpuhu51FjoqCdHWFnp8Tl0t4/A7cPkV2e/TsYozEaNch83mOVuRIRN8j+z2aeiSpRYFUoU8wqS26UVf0RxNC0zNZNYRtC52JxLhcmlBSteOVb2ZDWhMJaNeimw3SdYScdho+F0DfdsTjI4ug9T1hvUWHaFQRkemUkJS0WpP2OX00naJtR9rtDpHG3Q72jd3068pupm1nKcTTBexapOtt3yIWpcufQ6JFa9J2Z9Gb3d5EWtcd1hoidHv6F160lOFygZ4cWRpu11iab7+31GPXWWoz9aSkxGWL1qVFaq6uiMFSrpKmllYEE9DNoUZNSosM6rS2cz6dQoz0H37OjtPtWwCki0vSamXXjwRoTEjozqKtMp8RjpZjRIrt7oHatpQFeZhODkI0RktHdp2l5Ap7bxGxcw1j7dhYP7XZWrrv9MSE9N1bSNMS5zO6D3+EcDmF5QJ5lwlErmz/hIgUOcXZtch0Qn92gTQN8c5tZDE38fgZn2bp1IsV/f0zpCiIn/a0nePV2n4eJhPCE3ftnFyu7TM3DUHEoolFcUizrm7qN4jjOM6jxyMhsLTvSWv7pTvW+ZaVRbCu1akAaNvQnzcPPGYRm9rSbfs9dB39tXqstNuNaRRCNMGTi7kpCtLFVd7vxbjP/szeQ+qasFzQv3zvVdcvdU2YzayOaWIpvjSt6E9nhNMl4WKNXl5ZbVZOO1otU0lYzNGU4HhJqkooAloESCCbndUs1QWpjPTTiPQFYVpS3rPjNUTsABOhueB4KC4mRqslG9KnuV4mLJekq6uxPki7Dj2/yPU4k5zWC3bzbNvxPAyFz0MES8oqn7+tFaFPJ1Yjtd6Y2FvMLEq17+xxQPaNCatgES4NAlSwmFl0cL2x6Fzf58iHiQzd76Gsx7o63WzRe/cJx0ekJx+jf+KY0Carqzq/RJoGJRKOlyas7p9ZfVFVEScTZL2jKCPtYkY7D8RGCZ3SHU0gC9qwaaEItKcTitWMsNqhn/003aykvNwjXUKajmJj4hLVMVJHXdvnEBk/C0PUcFLna6Ufr0Pd7cb0JnVtonNoPCiKMZVH1x2idqrodmPvO5vavrqOdP/M3vv4yHb/xOPodkv/4kuEq5Wdp5Svh9SbIJpNoLPaxeLJx9H1mu7DHxnPu/74T1O86ymoK+LdO+huR/fBZ8ZGB20a0moFL72UU7xzZD63COlQ+5eUcOcWWlfwM6/1W8FxHOftzSMhsBAZb/jD90NqMC7nD6T1tE+joAAOEa78l3i8cxtyBAcOhcTx6AiZzyzakFNPA2E6JUwn9JeXJhiGLsL8+n6/txvUZmspu8rEH2U5RhS0aWy/OZIUjo6IxwtLLeXC7KK0m612PbSNRYBWa8J8hl5cEYrC0ozTCi0j6dYSaXukV4rLHXGVU0GrLbLaQFkSb5+iu52JQ1WkKsZomSaF3uqFxg7JpiEsA+H2KeFoSbq0Y57WG0g9AXIqqqQ7nkIQ4rQilCW6WlkULwoS67G2SlIirdeWVm07q/PJDQKy2kARSScLO57zmlQt7Ubb9siusxSoCBqC1Q6VpZ3zGJFZgTStpSpZWs0R2LGO0Yqlk8IHnqGoKuTkiHQ0Q5+6S7h1jJznz3f3BDlaUDS5dU0VnU/oJwXVecv0+Q6Ndk1IZ80EFIHuZEI/ifSTyO5WSSrmSFJCB81xQdwnik1P2E+tDiwlwsXGopExmJDc7ix6I2LCCcaap6ErNp29bKLkaAllaRG4qw0ym5kAvryyyNVsamJnt0P3jaWhF5aa09XaGgzKwsSWBPrnX0TbbqzTK556Er1a2XEcOlRLq9GTpkXblnS1sjrGriXeuW3vUxTw7qfQi5WJ3WmN9D3xMz7NPiugZTE2DNjPTpOjvEskBLqLHInM9VqO4zjvZB4NgZXTdWG5PHQIYjfR66kswGqgbj9uz19e2V/pdY1eXJJ2uzHSFCYTiyRMJ/SXK9vP5aXdxGaz8cYis6nd7LZWiJ52O1tHDJauWczp75/TPf/CYRG5aH0QJYNYswJ6E3zs93AlJjDqinQ8Jy2mxJfO0csr23Y6sRRerl/Sxcy68YCwb602JwR0UqJFIOX6rP6oojivkS5Zl+F6R8ypl9S0FiWKESmzFUXfW0t9EMLiCEJE802UlKx4PKelaFv6e2cEVcq2oz9ZWF3YtM41ammspRnb+4NYZHA6sW5GFdJZTiE98ThpOUe2DZQF/SKSqoC0CXa567CMaFVYF6JW1iF4NIemRdoOytaiWrOp1SoVBfH2KWx3pKuV1eMtFybSVhvCdoceL+3Smlv0TnYmrHQ2sU7NLhGutpTr3aHDDyyiVkaLIqoiSdEi0C4CXS2IQugglYDC/rgALaivEqFVinUPJxNCl4ibDtm2luJr7HxqLuIn12hJTp3F0xN0s6V77nkA4p3bhOWCdLVC14pMJxa5E7FzB3Y8GhNEIoJMakudlxW0DcRIfPJxi0oNhfBNixwfIW1rhe7Dz1vq0V1nXblJCSfHcLKEswv7A0CV9MFnCJ/5XrhcWQPBcm6fKx/3dH4xRoZT0xLmM6Su6D/yPBIjxdPvsohsWSBdD+//BH9vOI7jPMI8EgJLQiAuj0jbHam9uvaEjP+HXP+i2+2DYic3FcajI6uBOV4ilyvbZkgLDrsrChMbQyfibvdA1+DwXun6Y3kfg/gauF60LjFa6kwtdSYx0q/WcHkJIhSf9jThbGX1VUUk3L196H5sWpjM0CKidTGmfvqjCSpT4npPuNxA1xPLLHRETGxNI2G9R9Zb+quVdQOWxRh9ACzFFMRq23I07vrxHNKBQ7pQ6hoJYRRgYbM1e4QiIou5dRTmDjkTmIdoYlqvxzRWPDlGsmiQ51+yeqmyIOw7pM834UkBAbNDWO3GTklpWotYDud/UsPRAlYbi25NazQls4mYTdGr9SggrGMtjJEzrSuIgbSYkIocAWw6QqtoXVrtVtcf6t5ywblEO1fS9pYqbGraZbRGyJxdlgSsoJsG2pmACusnInFvNhHFviJ0inRQrjriLnfH5rSitD3sW6QtobUoXXHrxNJpua4uPH7Xom2rjUUWa7ND0PXWjm0M9gfD0Lhx+/RQ45QUXW/hWqH5IIJkPrMat5wyNVuUhCyXSGf+Vv37PkCYzSwFvNsRZjP6n3kfUtfEdz0J987ozi4s8rlcEm6dWKdgVSKFRcTYN8TTE/qzc4v6bnf0l5fEu3dxHMd5J/NICCxNaYxUjd5QVZktBqpc27G2QuOisPQTjC3zYTazzrPLS3jxpTFNN1gJEKP9Fd62By+s/d7Sffkv+MG4Mwz7TmkUUWE+t07Bxx/LUSU5vP9gRZD9jcJiYd1WQQi37yIxkF54CfnM99CeTom7ztJiTWtRk8XMam4A2bXotEKLQLzcWQSnTzmKFZGuRy5WEAJhYsXvaVLBE7cJKdG/+NLoN2QLz51ifX8QgYPho4ill+oaJFgEbLslrdfWgXlyDMcLurtHaBRClwibkhBzfdV2R6hKtI/j/sFq5MJkYinLi0srnp9M7LOIEJrWivaXNf20oJ8V6OkEglCsWoqzjUWpRCxKUhYWVYqR7j13ievG0ncBaK3rkDsnFpUCq/XKtVBalaTlhDQpkT6Z5cSmQXolVUUWeHMTOoAMjh8pIfvevLwwS4Ri0xK3HVoF6K0YXpLSTwrKq0AqhG4emdxX+okJrt1pQCULMSKSamKjlBulXPVU93eEEEjHc4tY3ju3iN1ybqL24pL+559Fk5r9Ql2NNiT2s1JZVKzPIreH/kPPWANAWVj6+XhJuncxFtUXTz5hth+XqzHNqoNpbf4DRpMSj4+In/vZtv8XXqZ46gk7h089jqw29B9+jjCdED/zPbRPHBNfvDJRO9SHXezoL1fWNQnEx+5C28JsSgxmAeE4jvNO5pEQWBIjEs3SYOgYHEwmR3IdEUEsVZUds4cUGDESqoq02RDm82zzYB1R6XJFt94cKuiH/WFO16EqCdOJGZJmk8W029l+phP6l+8hZWUFwrl7Dh50vX4l+pfN6yfUNf1PvY/y+AievEuaVaRZSVztrZOu79GyQCc1stlbjU5VopPabvbrLZyt0MpSmjoIEFX6aWERkenEInjZQHQ0Ou17q93JKSTEvIzGVFVnBdXEgExq4qQmrbe29nv3KV6cI7dOSMdzqwubTQgpWfH5UPsG6DU3/LTfW4p2Mc+O3yauNIgJyMouu7i/duySWg1TCHZMVC29N1wjqhRnG7QqzEKiU9tPVSBNR7jaWrpziPIBsm8JVzurqVIdPboISrxYEzbFoU7s+nVRV3aMAWk7YtORJhVBFbmX1zatTLgpSNMjfaBctTQnZvlQXyjz53r62tKLqRCLfAl0tdCXBdvbCyRBddVTXrbI8ZTi3grOLtFshRCffMIMQPfNocD92jkmCBKr0R+uePwxq8m7uLSO1q6zgvSjBbJv0PXWbBgwS4jRI61tLCW+XJrv1tkF6Wc/aH90zGb0z3xkNK6Nt06I73qSNJsgL92neOYjZh8xn1n92G5v9ZCPWSE8InQffm70YaMszbj02Vf/neA4jvN255EQWNr3aGqsRbwoH3D1HhymzYm9R4ebssjBIT3XGGlj3lWaDUtRhe0WKUri0cJMP7Ntw9A5R99b9IrWOvxywTpko9GtpUa0acyFfbsdxZl14FXj+BCpaxN2fRrb9oevpTA3dW1aglp6Ks1rqEuLpAiENpEWlYmNprcIV2PddnK0GD+3dD1pNqFfWh1WXO1HZ29tWvNcemjUisLBdwmsAzDlFv/cIGCWDubQztae06aB9RaZ1qR6QloWaG2u+7Gu6M/Ox/SUVFV2qw8mAJoWYjID1mmNTs3DSbpE7BIazSvLarACfRFIRSBMSxNFw1Kb7FxempgaIzidHSPp0+i5NRIsLSi9Il0iVYU1DKgdk/54bunJ1R49PTp0WeZjrFnAolaDBdh6j2dI26MihH1HCiYc46ohTQuKVUvoEt2spK8Dxa6nXHX000gqBRKkSiwq2CjtPLC5W6BPFBQ7JT41oVzfoli1lM9fwEX2MggCrebRPofzqL1NPBg6+dLVinByTLxzC84voWnpX3oZXr5vu7l1MtZ7gdVkQTt2k/bn50h2aw/5mtP1xtJ/85m9aduRnn/RUodtR7x7e5yegCqyNLsPcr0XKVEMP6uDM/21MUqO4zjvRB4JgSVFQTw6zZGr3cEoc6jzGb0bZIxkhfksmzdarZHESFgcmcBICppTZdkHqr+0NFZcWFei5nZ54NDynt8jTKdjBC1U5UFUiVj6JUbzGeqTRcxiOf7lPnh5hbq2tKYEE1ptZyaZ2zk8dsdu3FHo5zWhTXbTjjKKh1RFRA/F30PRu7SdRbaigECqc11TCOh682CqM6ftpG3z49fnO+YRQKrE0xPbtq4QVev8Gm6YZWEmqCFYtKZLSJ/9uia13azblv7swmrAUiLtzNg1HB8hx0ejGCTk85eXoUVAo5iYSpDKQOyTFe+Xh5mT3bIm7DvCvrPHRZB9m0VYtC7JtjNbiyHCM6QW5xO6RWX1T8neS6MQdo3VZR1NTbTlrsHRrBSrXxuvu2hrlzaLiCy+wq4jTQq6k5qwt3otjYFi01Ke9WhpthvFKoEyOrGnaUE3LUZriL4SumkWxDHSTQNanFIsZ8Szq1xLZd5w5FE85nKfx0ut1vZzUhRWZ7Uuxu3C6WkuYt9bR+MwNzAG61zNqfNwtCTcOjWbk/tn6EXuxO175N59yGk9EZtEEE5PxvE/Q2OHRa+CmY4GQZ9/0awaqpJ07352m5/Snx1GDjmO47wTeSQElva9iRFN4+iW1LRIWRDK+iCs8gxA7ToTXpgbu+ZZeWm1PgywzSJtHAmTfaEGl/Awqc09HCxFlruu0uXKDCpzcbj2h7qsUZgBbA9DnxUQGOtNtGlGm4RQhdzBZ8XI2nbI2SVhP0F2NTElK8bOHYKHz8ph9uBub1GashhHpqBK3LQ5+iXobEJ48nHCbk+6vLLIxOCLVRZWw9O2Vsg8dLDluYhpvbF6niGaRa5RazvYYvVWfSLo3NKbVXZfz597HNXSNHZshvRr08LVmpAFS3cyOdRKqY5zAQHiriNuLVqUJgWy7yEKqYyEtieuG4sada1FkqoCrcW6AZs887AsDtYIrfluyWZP2XQmEFOyQnYp0Lo0O4WuN0f5PiHr3BE37Kc32wWKCCmvu88RrV7ROqIxIF2ifGljdWEw1nRpjISms8+ZxSTB6rLCtqPolbgPpDLQV4FiZ2lEDdBNhPRYhdwpKTYzQpMoVy3xbIOsNrmGsEWbi3F2o2SXfikLO5cx2h8Bl5c2nzAGmM1ynZRFR9N28IcL0NjoIu16K5wvitGPjOnEooT3zsfrm5z+k+nUzmfXWe3Ybk+6vDTh/tTjcLGya6EskdKibtax+vH+xnAcx3n0eSQElgwDeLsu3wgiEtKhjiij1wq0ARNAOR12vRYqnhybOLhefD10WU0n1gXXNGMtlTTNKDwkC68hmoa21yJowaIBrQmXofNq+Cv++kBpKQozfBzSj3AY98MG2gbpF+jR3MSC5HRZDMgQKWnNxoC6MjPSYXiyKrLvrOswqQmSIiBNYXVCZfYGaxW0PaQLs6WCqlq0QcJo4SC7vdkJLObj+Ric0AdbhiCCFIE0t24/ioCEQOgTxWRi+006Hl87DnEsarf0ph1LUUyQZFGSivy5e0WaQ6QqbluLdBU2c5CuR1orZtdpLvpWi6hpEe35PlntT9NaWnZaIwW5U7C3Qvsyd2y2HbLZMg5Sxuqu7EIKJmhDFr6SBZAIIiaCQ9cdBnOHvMbeRJkM+1dFqUAi0uXvq8I+a9+DQrFqx2MzRLe6aaCbCN1ppNibQKuSEmOApkb2WUQPTRZFgYQwOsbraj3+DMjCjEV1tzu4wdcVoTSfreHYDOeNGMbZoIRgx6iuTUB1XR71ZM0omrtuh4YPmU0Jk4mlAi9WFgEtC8J8ag0Iw3DqZz7W3xSO4zhvHx4JgQVkcbO0X/BJxyjT6IKtajc1zWmhvI1EE1oytcL2/nKVRVfKnVPJRsZgXw8ibhwv0jZoH7OQyCmcMvtGpR4pK0uzLBc5ojEUcwtIQZjYzD/dbO1mBwfz02vjUIZxMqRkacSuGE09JQZ0WpNClZ2180DkOkKXPZz63mqOumy6WhTABKmKIeOGxiwyJBzGqKgCKQ/L7qHn2uM8MOdRuw7Z7kxUxEA8PbbHm3bstpTeDEI1CCqgsxLi0mwHtnszsRzcyvPcPIJ1IfZRRtE0pOqGiA6Y6DKBooQ2dwLGCNknS3MdFV0aRagV3lfZDR6rXRNLn2nTmP3BEFFrWxMgZBE1pIZz5JE8Q3EQibRqrvOVWQ8gecByFo2SvbK0joeIY8yRtCy6h/eQlGxIN9gw7Lan2DSj5YYV7icz71wH4rQkLio4Lcfi+H4a2McJcsvWK92DUa1xEHWekymzKfQpO/wLoKO4om1NtPdpHAqtbTteWzKZmKBKiXRxCbM8I3G3G2dbjmRrD8mdqHSdmdL2aTS9HWaIOo7jfKrwSAgszak/CeHg4STh2ry+YSBtOkSxrgsdsJtLCGO0KcynlkIZCrBVgGzsSE4jVtlTqGmgFxMRuaYlHi3txtB1oy2EajbYXOSC82jRgqGdfxyLolbgbfPiciQudz0SIxI6c3FnZy7lVYnsJsSNWS+Mxdop5YhN92D9VAgmHDYQBr+oIqK1dR6G2dTmG/Y9cDh2SMgGpLnLbhCCraWMtFVS3xOY5c5CsUL14fg2DbKJVqRf5EhJEawOrFcTgO2hu3O4icuuMTHZlvTzytJ+w3EaRE4QFFCxuXiybS36A8g60R9NSGUkYkJmrI9SK2InF8ynKhKo7RjuS1vPcP0kRZv9IdKTj7Pm88ikGt3NxzmJfW/zFcfrLpldRrabYJjZGM0H64FC+SKnYvt+nLlIStk8tbi2jUCXrScai1SGfUO1bQntlFQFupkVyXfTQCqFFCG2ZtpaFoFiUhLOV3ZdZF8r+mQ/H31Puriy8TyD+ByaPPL4JCltdBOTyUEcFhHdtCbUYhwtT0aGGZrDa5Kl5jVGu256M70d/dlyIT3DHzmO4zjvYB4JgTXyUGfRdWPP0dAz12gNWbtBuGjfm0Ei5JROOBSu51SjRLsZ0udIVhYcY0QrBEuP5SJggTxmJthNYnJtRl8IaLBia6t7KW2eXC72HdOeOWVjabN2rH8KRX5+TEfZ+1kErbBU4ZB+vH4zGmqDsqGnqJo7eVVYbVS0MTdS14S8/0Gw2ozCa8clH/OhUcC8vHLU6aFaNwDZ7wlNi3RzyD5cAlYcXgR0NrFutkGUpWTpzcKOsexbZFLQTwsSkbDrbVxOnyyyRi6iz9EtqUwgSdsRioBUByPWodMvNHnEjUTL2kYZj4MNMM4F6SnBtLa15ZRfmk0OdVmFubdbMb9alKlLhzqsfA0M54o+C9+mhdZqlYZRO8N1LH0WkMPYHBHoZUzpDW7x9DklvG/M+yuLcqlryvUWndYUs4p+VtFPI30d6GvrXuymAY0V/SRSVQVhNjHz1K1Fm+h5IAU4FsiTf5ZitGt3mGs4/AzlaKzmphNiQpYLE2/zmaX/soO/dt1BiFdZqA1/vOTrV2KwKHQI1hDSegGW4zjvbB4NgSWH6JHQQqhyKiOnpUQOdTDDDXYYMzLUjiQdC7e17Q6RKxFzMy/ySJeU8l/slYmNth1Th2PkTBNkk0RZzE0YzafmQzVENVKycS9DxELVCnnbxhyzd3urexrqeopiLEYOebwPnUVo7C98E2gUefukJrJEGNUH2I1M1SJtMaB1RXd7bqIizywcPuMg2oYohqa8xvTRwpVcjwUmWnW/t7FCQ3F8jjqMQ58HX6nCBFmaFOi0tPTftskjUeKhcD8LnbGwfQj0ZFdzuv5wbIfoUhzsEQLhcmPGqrOaVOfrQiEVFanMYjflDsUczZKYEDWRLF1CQiDNJ2PkTPqcip1Woy3DaC5aZjHepcMA7lwzpZVFn8J6fxAWbYekYNEbyD5VuVZJshAJAcoIWiD7Dtm1h3ovVdg3Nlg7O7HrvrFrdF0RZxNCVVLMJ/TTkjSJtPOCbir0tZCKgn4aiduK6nxPuCphMbM/ALZbZIhU1rVd30O35SCQhueHdHz+HOH4iLRa5zT8ZLQDMd+0IbXaoUNBPIzjdmQ2NcuPtjuM6hnS5sPndhzHeYfyaAis/Jez5ptVqMwzSh5KI2jTWHfhMNC2toGzab9Hoj0f6tpSPWRhNqQkrn+/30PEbhgA291YkzWYnUpVmbgoy9xFlpCrNbqYwdRuGmM0JKcECWLpp6KwgvBsF6BrG0cic+vMYiiwtkXZTW2zg3Zrr8/iSXpGManB/K/G2XEDMRC2HSgWDWq6PAJnat5WXQdNY/VqIYsHrKNSotWyjfMf8zgdbZvR00vmc4ty7fajuNK9maMO4k2lQkjjjL80q0dbgzQpTZjNA9LlerT2IQ+k62nQkAv9941FGqf5Osj2CZISim0z1IGJ5mjRuJ68X8kzD4fnUoIk1zo0c6QpJXRS0U/N8T3sO8KmGc+PdoyF6aO1xD6npGc1w6Bq6XuLeoVg6T5VkEMxfcjRziEipllMk4XNsGbK0iKH2S4jzHQ8/5aerNFNJK4L2qOKbhHpJlYQH6aB9qhA+hnlVW/dmZuWcO/SRFZKkCzCqw80YAQbLA32vjGMqflwtLTaq4urMd0t87mJpsEAuOtMM6c+dzc26MUlYTohnJ6Mo5IkD79mNgM3c3cc5x3MoyGwsA7BMJ1YWmq/P5hiXhMiEs3gkhBs5l8WDmgi3r5Furiyrr3cPn6wWuhHATUWeIvAbj+KtaFmSyY1YXpySKNpjiSlBJPavJau1iZ0+h5db+yGM/gTdZ05Yle5YL3tzGzxeEnKLuaybSFYQbdsdmO3F2D73O+tcLiqLP3W90iTcmrS6qK0zanJ3Z44mxJ25VgInmaVdai1PbLdI5vtg2OCmmb8rNL34/xEHYRMNltNSW3EzGxqEamhy0wCEhqL+Gy3xOkUnVSEPCh5SNuN4qoKFrkKOWKVzTlD1yPbJtc7dWjXW11P05BUCcuFibhswSCbXU4nZruHIoxiJeUIZSoDSBZxmrsTo8C+h9oiLJKPhbQdOq3pl7XVYYHVUeUapcHrSnLUUJrOHs/vKymBClra9TGIPBU5pEmvkxLs9mN6TAYPq7we3e8t8hkjYVKT8sSCfrVGNptR9HIZCdMpMp8ibSLuSsKypDmO9KW9f5oJu5NI6EtCN6W6mDN5fkN87mW0zXVoxSSbzEq+XvPxfPwOVCXhakO6d98iU4X5oVmqvLZj/tI9q8u7nnIeIrYxEup6HKZOEeHiygR8Nvx1HMd5J/NICCwJgXi0MB+rwU4hdykd/KTsZmTRqmiGn3lOoTYt/b37SFHajWm4SU0nFv3JA44JYbRnkLIYnx+c4G3wcTwUPxeRtDCPn25WEi/3SBHHAmW6HhYzQi7uHm6UMp+NN0/qym7Y2dOKLtEfmXGmibiJ1aUMEZycoqHvrVurLGFSW3pyqAeKEfIYGc1DibvjySFNRhYyIlaLFCR7JzW5oN+O8egwH2NOJ/botWyktjlFtc2pWBiFmPY9ZDNWqSqkT6RZJE1K+nnJYA6WohD6nLpTHT2rUhlN/EwrZLM7eHcFsXMRxDozJRy8mAorng77Bp1PSfPJ6IJv9VIWOEpVoJsXiELcdCae6kjY96OPVVjvSYuJCaa2vxYRC+jc7B/CviNsszdWVaJ1/nx1tM9WCMVmqCNTtMgRsbYnHU3HAvzBfqFfTKE4tnmIeUQS+8aunaZBqop4OjHR3nbW/ZpnBIbF3KKIbWe+bEU0cd12SDshdIm4L0mVjebpq0C5SZTrRKqEfhpob03Q+nHCqiFcrfOgcUtVp8HSoa5hvRn948K7n0J2Denle1aPVRaw2ZrgrcxcV7W3cTzzKbLeWtdhjBaxrSvoetK9M+tWrGtoWuLRAs5u9veI4zjOo8QjIbAQSNsd2nWEXEg+uKeHycRu6jnNF2azUSilzSYPhraC2nR1ZeN2qsq69HI0ZExjJLuBxKOF+UdtzRMonBwjy3JczjB2RWsTC6IQr3bopKA/qkEhbpoxraVVaWlJEYus5OJmGwUT810ftMwRKCXXlClpUqHLCWHTEtZb2OXW+aKw1vhg1gDSpEMNWtfBVXtIeVYlRduhk3J8P41hNLfUGM1uarczb69rnmHaDoOCe8JiMUa07LyIzRUMJnJkOhkjgdp1Yw2crjfWpr8riEMUpwh2nPr0QM0UCcJmR8wpQNqWNEQBy2Ic+wKM6VxN/VjUT1khixnStMS2I0KeP2hF4Km0VGR9tTv4aSVL8XXLmv4ojjMQpeksPRgOHYkhO9VrFPpFRb+oTJh1aYxqht6c54u12VW0RxWhNbPRsNmhkwrpWxNPZWEjkUTs/VbZhmRajylV2c1MDHc9emndfpqvTe3MZqE/66wmcDqxZopspQEgfU9YNZT7njQrKbbCrEm0xxXdLBD3CVDaRbTavmDXadi342eSxcxEH1izQlUS7p3Tvf/nLUJ869RMeLc7wnRiggzyMG/rxE0v37e08mwKTUv3/AtZNJ4Q7tyyz1aWyNFyHNXjOM7Hzpc+9flv9RKcN8AjIbC0TyjdOGR5JA8OhhwlybP2hr/qR7fqHNWRsjJfqyyqdN8TJpNxfIzNQdtbuqosLQWV5+T1ywntUUWx6ZAu0Z5YBC00Cdn3dMdTG0nTWQF5mpbW1QZjkXVockdcZR1T1ukGYdeiVWEeTarma5Xb/VG1m+lgENm2ueAY9Gp16FrM3V46FCcPx263MxuAaonGSD8vSXUc65BCpyYQ2tqiE3o5utyP3WXZJiNdXVnx9mIxWjiIiKUra4vqpMsrc34PQro2cFtUkfUa6privECPF3QnUzuOSYk7c2M376pirGsjTQh1RXrhJdJ2a5HLGM1xP5/7eHoKfW5caNrRLFPmZpI5zCQscnRMy0iqC3RWEVaWykxVQXG5o7xvcwm1DPRHkzHiN/ho2QlNhCYRVs0YtZLeuiBj24/WFCpCaBPV2R5pOtKiQicF8eVLqx+rS+h6wqahX9T0JxOkUzsO690hjViV1vkHcHpsA6YXC3S1gh2E5dIGZ7ftmC4F7DWq2doDtI55FI9F8OK+p355i7Q9m3cv6aaB9eM1qbI5iPWFMrnXUp3tCNleQ5sGnr+yqObJEcV7nkZXG3ST5xGK1RmGox6qknT/HHaMw8JtfmYLMRLv3AFNdC/moefzGelyNf4x5DiO807mkRBYgBXHJh68+YeA5jqUcUTNwDCgOClMKisKTjbiI5yeWFHukOpQJR4dmaFkToPo0dy63ETo51YUX7+8pZ+WtCc1cdNRnm3pTqb0s0FIVUiy7rOw70hVRAshbjpk35JmFX1dEVd2s+wXpc3XKwLF+Xa8+WtZEC5WFs3q+lx/k4fvDlGdLAqlqpBZTjnGaMInR8vYNch0Yial05LuyERh3Nk+JVsESG/1QjKpiY/dRS8uRzd3JIxO7UNnpe72UFrUqN/u4PJyFF7haGk3yvWGMHT55YibTOrrZwhJSnnZWH2UCPvHZvnY2cy+eLUzW4Ls3SRd94DAlrJCqpL+7FouKWR7gaqyGrqjhaVP2w72rR2b0oSG7LPxZ5sIV2Y3IX0ipIoUKuL64Jw+dBWq5DE8CyuQj5d7im0+t/Octo5CqiNx2xHW+9ETLKwaiEL3xAnxcoecXVoqc2rR0bC3iFc/r9DjLOC3HfFqR386t+9XeRpACMitU2TfoOs1enWFLJfoYmppzZTMY6ztkPUWuehMXN09Jc0q6ovdWKMWVjsWP76mffKU4vGavgo0C6GdCZIKusUcmBP2SrGz6F5ct3asLjbmxh8C/YsvWRo+N4eklzbIdEp/cWlp6CKb7u73Vi+WaxLHYdCA1hW62Zpovv+x/pJwHMd5+/BICCyJkXh0bJGs3f7QNp5ThiFbJdhMPzkUhGMpLm0bE1tA2mxsvMf1/RcF/eUlXF5ayrG0Qc3EiJaB0PR0y4rd3Zryqmfy/nt0jx2xe9fSohO9efloDFZvk2tq2LRoHemWNem4Iu56E14zq5cqrvbmb9S0VrNy/4L+uedtTSfmks5QzzQ4jBcFMp/ZnMTrNVm51ko3O2u7ryqrzSqLPB6msEhVMlGlhRWW2/fDgRBLufXpEAWEg8s8HOYoDqlDDkJnbMvP9g0pu7YDhzTk3VtjV2U8S2aBMK8swnSVDuNw9q1FYPreUmFVaQan2Uldc6E9qhRPv8vE6W5POjuz6FVRIHVlDQVdj84mMC/tM3cJ2Ziwpq7M0wqs1qmIttYrc6zXSXEYIp10fP94Zp+rP6rpJwWhN1uOVEe6WcwF9OUYuQy7FqJYmrKOpGJGnNdWb9Ur7XFFXweKbW9dfZtkqdzsRB/vXdHfXtLeXdhgakB2XZ4faGnzwZNNywgaDp8pe5XJ6THa9cR7VyY0U7ImivMLZLGgfOEC0aMcqc1eWtFMS0Ov9JNAPwmETmmOC4pVD6eTbPuwoGhv2/GrSsLLZxYZXK2Iy6XNE83pZykri4i+9NJH/6wXBeH2LfoXXnyjvx4cx3HeljwSAkv7nv78wswxpzaiQ3c7dLWyiMZuZ5GLIGYJdc2zafhLOa3XhPn8MKalKG1Ey9ERmgXXOCj68gppGhNuixlpVhF2PbOrht3dKZe/5DGmLzbUL25yGkjpFhVhKFaelaTKXLTj3m6YRVJ7rC6Iq4aw2Y92BSICL95D+0Tx6e+xlNxmZwKhbQmD0WMQ2DcmXLIp6bBmRCw6t1ygJ0vYNehmi4SAzqejMabVXlmdV+jSodut7ayDbRghBNDtD+fgmqAakGuWENq0aHdpn6Us7f/BNyspobD0UnrfB8dohiyXhMUM2TZoXdDPa1IZkWi1TlrkMT1iHku62pDOzh4YMaQwCmttGqvBI1tFBEEmFsEb/MMI0M9qmuMliFBeNsSNpWhFTdixb9C5uY8Pxe79tLBUrwzpwgmhTcR9IubIk5ZWV1bdb0a7htD0FC9fmeVCWVBcbO257KPVnVgdXdx2FBsT6WlakBbBLBTWDfRK99gx0iaqj5ybOBoMbsvC/hhQRWf14bgUAZUSKSMymyCXa/TiEm6fmthUJdUlzCq4e2xpS1WKD98nbJcWtRWhOSrRwq7x0Pb0k0CzjFRXPc1xQbnq2d+eMBlmSDYt3DuzjtNct6eqyGJOXMytHisbmBKDdd0OjvKdzdVk31C8+2n4+U/8d4fjOM6jyiMhsIAx3ddfXo4PSVEcbrbp4N5+Hd2brUE8OhoL5REZBUN/7a9oKbNL+TiHb4gGqBXal5HZz9v7axS0LpFtS9jsqF6+sG6rx29RXG4P9VP5xqpVQWx7ZJ1TXDES719C11tBbxDkeImenY8mjIMb9jCYeOi8kqceN1f3zQ5dr/PiLV3an50j5xcwn40jSmRr3WQ6m9i4nGyMqWIdmv2sQJc14faCeLkj3jsnnV+Yq/jQJFBXh7l1qqNppI2XaSxlNZ1Y5Gyo3drtYLtD4BA1DJFwfGSNBtst6cWN7a+uKQZzylywrtstKdtACBVSRMLtW2ZbkWvDUvblGkjrtUU1n3qC7s6Sfl7SVwej0dDljkUR2nmgWU4I3QRRpdhYbVXcdVab1ff0ywnNrQndLNCXQirs3EuvFHuLKqbsui8J+tK+D30uFg819eWM6qKzCGbTH2YrJiU0PX0d6ZZDtCsR+mTbBaFb1pYuXTd0RxP6+QnlvTVaRvqFFceHTbb12Pc56mXNEqFpzNQV0KM5HM3h/oVFBecz4tklupih8wn9rQXhakc6WWSPMvNNm71/ZXYVk4r2lonXxXnL7k5FsU3sTwtUYP3EMamE2cs9Yf8Y9dmeeGbzD2U/1A22pJfumTDPaURtWvrzc7s0ZjPScD07juO8w3l0BFbqrVsMrF4qFzqLWPHs4Kej6w2ymCPTqaWz8s24v7y0m/tsNno1adsQj46yM7UZJ2rTWHRlOrUIxkvn5vkTA+0Tx2zffUR50VgqcJ87+8AML7sC/YmfJXUd8fHH4NaxuXrnQb0UgXQyt4hM21txetfbja/rYbVBP+1J+7z5xkgRxwiF1RE1cP/cnpvNkJNj9PyCtFoTFvPD0OUqG6CS04Z7S5VJXZEWE6Qh2wsU2dVdSDHCoiYUtwgnRxaNaFp0s0U327FzE4DrWdbB52hrtglhUhOOlnD7lNB26PklIcaxbd/WmtOgq3X+fMlMN3ORvqZk52O7tXquqXWLpu3uIKiG9w3ROj/BvMYev0N7MqVdliaiFoF2IXQzSAWkGtplQiNID8VaKFdC3JpIio3auB8gtko3FbZ3hXappEpJZcJU40FYAUgnSA9amogrr4TqErpJYHdcUa2VYmvvG1pLzcYmEfY9cdOZw3zII37ysOp+Xtrlc2wRM5pEfzy1bbKVh9aRsG3RiRmdSq/mpZZAp/mPhmTpxvbz3k11bwNNh54ukc0eWe8IXU+aT214c13mPygC/WNL4q5Dti3l2Xb8w2LxvgvaOzNQ6GaWNqwue/anBaFQtJgQjivKy5bi/tpsQFbrbBjcoEPd3nRK8a6noO/pX75vtVea6C9XDwwocBzHeafxaAgsESs8z/U8QxRLysrMLdcbuDYTj5fvHdrLixJtG8JyOVo3XKe/ujqIBnJH1u3TbB6qpOM5aVaN35cXDc2tinYWqC575LE51dnO6lrWW+KTTxzMPu9fIEOX43SKzqdWFzUp6OeVzc9rexNbVUn/9F1zWu/VUlT5JktZWNpQFeksokQ2VKXrkfmckNTESC7aH49bVVnarCgsUrZeEzYTW0dtN98AJLUoT6rNDDSGxgTabAKzCXJ+ZQOYh/RhCKTd3iwdVB9oPpCyMGuFe2eEo0XuMEz0989G/7J0uSIkSx2REunefesgK4tDmrEq7bUS8v9CPD22c9t2pLPz8Tyn1Tq768/ppyXbx2u2twPbO0JzqvR1VkFR0VlPOWuYTxt2TYmI0vSB5qpC9hHZCwjEndDNFY4bqmnLYtIwrVqSCk0XaftI11nqsSh6JmXHvGqoQo+IsmpqztZTVtsKPasoLwOhLSgvoVwpoYPQQrE34RX3uTuxyanVIlBc7kdbiFQVpLokbqyeK+Uu1dBaw0HYtmMHI1Vh106XrIs0n+j6wxdobRFVWe/QaYVsGxNXTYtOc2flvoHzK2gb+2Njaj5qISm67+huz63QvU2UV0K3KNneKYiN0k4DfaUUO6GbRcKdmskLU4oQ0NU6+13lRgwRug/+vNVZPv6YXaN1TbxVwEeXaDmO47xjeDQEVi5oDrPZmF7Q1drqc6rS/jJWNeHR96Np6BAxCUcL+nvWkmQGpOWhWD5HsqSuiXduW83VpKQ7mdAcmSFmsbHUDkWgLwPVecvk+RbpD3VV3F6S3nXLzCevzFZBut4G305runlFP7M0kKgSmmRdhlOrcbGiaEE0u6x3NidP2v7wf7DZgoPpqGYjzbBtCXUFFxYpGh2389iS/vzcurtunVgtU1nk9GeBhsELK9DXgVRZobWGmn4ax66+eDwjPn9G/8JLNpPxmogLdZ1rt/J4nd0eQiDeuWWRqN2OtLboVmpaK3QuCtCEXlyad9J8asXQ2X5BmxaG7GdZHEYTFQV6eWUeZVWZmxtyGiyZq3q3qNjeCqyfFPa3E5w2zJc7jqaWrmz6SAyJSdFxenqPIMqmq7g8ntD0kT4J660JwVKF48WWx+YrqmDF5UVIzIuGQhKdWpqwTZFdX3DVTIghcWey4qjaMS8btouSq2XNaj2hWZfsnhCLdjVC3AnFJhB3gepKqc+VYpeIu0Sx6WlOJ8TWCujjpqW42ObUIPSTwwijoC1alIeoHpi4artc82RiJi1qSNAfVcTSbDs4nubrzWYuxk1jzQeLx+z6avJooKSoJKRpKX7uOTg5Qk9nZj+SFEklKlBd9jRHkXYmFDuFDva3J6TqDtWHI+nl+3B+YTYfRUHx+GN2/Wy29vPb8FEdp47zyeAHP/Jjb/USXhf3uHrn8GgILLCi3JyCYrUmVKXV36xshI70PWm1euXXbjZjNGdwaifYqBztWuLJsdV3vXyPsNshT9wlbjqmq8Y8kSqzEZBOqdZ7cxmvirEWR2OgX1RIp9lnqiDsWrtp7VqrgQJSXZCqQNimXBMEcdua8ejgZZkdzEPTW/1VDNAn0qwESutIg2zfYN2LqS6QtkLSYqxtGsfHlEtom8MYnCKaSAvY2Jy6QAshxUCqA9000BzlwdA7RTqLnogCT94iViX68n1Ls5blwbZBrTh+GIgtQeheeIlQlYS7d4gnx5aubVoTwyJonwgnx4Qn7lo92XY7ditK39tNt7O5dYD5k1UlcnpMPD4ycdbvDs0PSZHlnOa4YHtX2D3REY9bZvMdy8meKMq0aPmM40tuVRuSCuftlGlsOa223Ko3NClytp9xa7YloCwrq5kLonQpUIREFXraZIatAaXVQFKhCGnc/ryZsSj2fPbRSwRJ3G/mXJ5M2HcFL63ntH0kJUFV6PvAdlURzwqq80B1EaguldgUlNuEboWQrwlpheLFC7PyOJ7RT6wGql9U0Ctx3x9Sh5MCra1JQIswusanSUG8bEaTVDDj2SRC3Pd2/Ta9CaurjYnxWW372HXoTNBbC+IL5xSbHbqcEbZCcbahP57SnNRmxFoJKQrtY0KqobwsmJ9UTF48onjpEr13ZungriOdXxyaK2A0rHUcx3mn8kgILAmBUGcLgLK04vbs0YS2pM2GUNeExcJEB1jBdVmSLi7tF/cgMLLj+yC44vGRRVc0EZZWN5Smpd24ChNVw02om5W0y5LysjEB1EKqhu48a9+XJrt8z3Kn17I2caLmN1WsrJNPupQ75QJaB+LKUnth11lB+rSyG1p2Zw+NDWrWYAXqAraPXTv6Zw1mpBQF6WIwatweBl8HE2uaxRViXW5Js1lpgmKTckQL+koIEZpJRE6sY6ycFZR1Sbhck+7dp1+3NrbmocHbUhSESW1FzM+/OBbFp+3uge10tzergen0QXsN1XFG5JA2NNd466QkRmQ2pZjPLEq2WiOLOf3pnP1xpJsrMus5Wm54bLHipN4yje0YhVp3NfsU6VLkKkXmRcNRuaOQnicml5TSs08l5+2UpEJCqGJPlwJNikxiy74v2PUFQZQgSlJh01UktW0v2wmbrqIIPV2yVOLjs0tuT9Y0KZJUWLc1266kWUauTmo2F1P2lya0irVYZOtSmd7rCU1Je1QjOqc434/XhcaANNeO3dCY0av5m7U2eFpSQnYN0lRmjJtHAA3XJjHQ19EK72clGmvQ6Xj9olgHaGv7bN99BwSrDcsRzX5SUF40FCtLG+6PI8Vex6HboVOkTdZwcfw0st6RPvispeaPlxaZXdvYJg79LI7jOO84HgmBpblbTIoS+j3K3iIlMP5iT40ZH0qeA5gurg5z6wZn82S1QmG5BE2k9dZGekiw9NVmQ7gnhCLST7NnEpprk6KNOzkzgSApmVt7l0YH9/bWzCJUXUIa83TSPLhYMNd2LYLdkID2yGqg4rajO5mYAWlQM8LcWhebdDnCUBaWDi3Nm4vaHOBls8+iygxKpe+RVBKOl1YwDua0Pox7STmq1uVi5iI/LlgkqxBSmUfuiNAHiHslFdDOA5IK5NacouuR/Zywb8y5vcw1WCHkES42v26YEynTiY34GYRxJuSxRARBjqy+SoZuxa4zH6eus6gjJtBCUovODTP32m70DdMykiJoAVIk6rJjEjuq0LEsdkxjyz4VvLxfcNlO2HYlZei5t7OidhG7noqQWJT7LMgCTW9irNNAlwJnOiNka9tOA3XsKEJi25WEvI99X1CFnmXVMysaimBito4d09gSRDmttqx7E2XdInB2NOPl1Zz1rQm7TUF1L9LNhP1JQbmOVCulPu/huKY825rxKJBmNVpH+jKOfxhIl+xnoiqsyUKtK3awkKBnvHa1CJDUrs3CTFTZ5uunCJbODiAqpGmB5o7buGrM900V2TZUFxv74yC74U9ftmu9m0V2p5GrdxXsTpfU5z2T5zeEXYP8gs9AA7BtrLFCgolux3GcdzCvK7BE5N3AnwWewCwrv01V/7iI3AK+F3gv8EHgK1T1LL/mG4DfifUJ/T9U9Qdf731C7gqE7L8UqjHNNJhiisiY/pPstRPq2m72ecCzJrVC9+GmHnPaMCmCWUHIxYqyaa19fZ4L3Ds1ATKxkSp0/TgWxURPJLQ9cdfR15E0iTb2ZNeNM+/6qdVg9ZNI6NIDw4Xp1VI2287MP+sC6SNaA21nc+A2O2IR8yDjejTIlLYjVSXkomcJwYY95zSh5pQmKdlw4q5HNjvirrGZd8v6YB2gap1w0WwNUhQ0qnXKCfQTIRUVGo4pVZGzC0JvI1A0j3ZRYPDMkGDzCCXbLYQYLE042GWsN2aQmRTJtVsym45iTVs7d2gaB09r1yGApmsNC/kaUBH6GvpamUwbjusdp/WGaWxZdzUv7xc0KdKkgm1Xsu8KWjmMFpoUJnwKSazami7Z0OxNW9GrEEXpVUgq9CnQJ6GMiSutURViSCQVgigxJNo+su3K8bV3piuKYNGrYbvxvWPHu+YX3J6sOVvOuNrXXJxOWV9VFPdLirWw2wvlZWB6PzITKC5z12DfE646a0QoAmlSkMo4nkeJYsakXbJGCpuYk6+NbCjbpdzxcHCwH5o7SGqjLrPdhWTxpdGu/7Bt0VlNP6usxnDbUqxaUhVpF3Zdxmb4gwP6aWD79Jzi1mScf1helsSXLqBtxlmGjuM471TeSASrA/7fqvq/icgS+F9F5G8DXw38XVX9VhH5A8AfAH6/iPwLwG8CPg94Cvg7IvI5qvqaTdnadocaDQlW83PdUJQezZERGQp9+35s95cgh5u+yChGtOtMXA3da7kwHPKokjbRR/uL3wqNs8jLFgZSV+NoGi2CRbWSmlBqe4swqKJhgnSJ2OXI1rBG4VoEIRtEKkgRRl8jKc2lfay76nPaJ3dh0fXW9ZXMEkJjGGcUagiHwuehFqfPHXXDjXQwxWwV2dsNX3pLCaXKHL1TKfT5nyhoLAntkmLfWqF505iBa4w20zAEaK3eCrLxZ04TDm7wwOHYT+oxDUhKMKkPHltYNygwvod2nXksTWpkMBetS7p5QTcT0qSniIki2GcspKcqO+rYcb+ZcdGUrJqKPgViSGgWPJu2pIo9IQspAboU2DQlUZSy6On6QAz2uYJAnwJdH2j7OAT+CMHeOwalLjpmZcu+K1i3dT4VOq7NBF1Pp4FKeo7LHctyz1Vds5puuDefczGfsrusCNtINw2kMpCKiskkUmx7Qk7bSWvGsWHbmpgGuwaiubKDWXcMg75NnGWDVRhtRaS39GMq7RpFzLdruFZTXYzXUWh660pse4rzbNqbvdCk7akuEv2ksA7YFNkvhW4mxB2UtVBsbD96XDHZTEnP2RBo2tf6jeA4jvP25nUFlqo+BzyXv74SkZ8C3gX8euBL8mbfDfx94Pfnx/+iqu6BD4jI+4AvBP7xa79RGlNL2vdIWRCmE4twXDMQlaoaIx9jt5vIKMrMwd3qTrTvLYUYzE09nJ5YGq6uSMsJaVKOkR2wG4lsG+RyZUOX2w52BbKtxq5FmNlNqkuEYdRLnwirLWFfjNEugomdAemtAFly0XqSvKYuoaJoWSNNZ8aR2WST0grWZdcgV/kv/qKweqoQ7AZVFtAKaE4v5torUjrcIDuzCBgGGoemJ26tS7KfloRZQTeLpJiNNgVSKbSLknC8QDYbm/U4dBZm89ex3i2fM/KMQ3NZN5d2iRyEreQxONlqwsbv1MSqGvcx+igltdeX2TtrUpNO5uxPC/oJEJWq6KmCpQeHGql9X9ClQEAtGgU02WqhjHY89t2Dl30MiTLmVK1aYXrXCyELKDBBVeVo1JBmzFN1uNrV7LuCIvakthqFVZcsyZhUKENiUe05qnaUCL0KVehZlHuYwbxquDeZ0baR/VFNe1TQzQPtXChXkfoyUV51FCtzfh/HHwFCQovCIlXZwV8lG5MO56cux58Vc6UfxFf+o2SosRuiZVj0Kh8UUhXplxVxX1k9YZ+PRZsITYfse8K+ILQFcR9zx6ztt68D5bon7hPtnRnV5gnrNHQcx3kH8zHVYInIe4H/C/AjwONZfKGqz4nIY3mzdwH/5NrLns2PvbH3yAXVMoimEKzGKsbDnLp0iHZd92caoh6jf1Peh43OKaCuzPeqttSIFkJfhtzt11ldSq4vIkQosRTXdpdfXxO2O7ORqOyGJcnm7rHamACb1hb1KiPscxSnKsb6LFVLw4T2WmF+Fj6SU3502TsrBLSOFkEbapaGsSP5sxPDWLuVKusooy4IIRCuNpYqVEX6eizsH2wuFBNhJsByaqeyaFYqhH4a6Y8nlJsjYtuRNhs06ZgqHM5X/sJSe9Opmb3u92NUcjxXw7kNeQ353Kkq5LQgfU/K7f3a95ZK2u4IizlRhPLuFOkjJKEqrP6qDh1dipy3U+7vZ6yamj6n+FSFts9zKlOgiIl+6O7LaT04iCU7D2rpwT7S9lavpWqCq4wJEUVVzF+ri7RdZN8UVFU3CrUYbJs+2XYahU1bjd2K9p7KJHYsyz0XzYQ6diSE9iTy0vGczWROu4hUl4HunjCphAlQniekbUefKe1Bmo7Q5TE8VbROwyjjNZEqS4NLn8axPyoHSxGGgvg6H4h8WlWw5oiLHciUvo5IW+SOxzyXMyXCviXsGuKmoLws6Ocl+5OSFMVsKfY9cZO7dqf1GLl0HMd5p/KGBZaILIC/Avw/VfVyTNO9wqav8Jh+1EYiXwd8HcCE2bUbb0DKXESdR2xIUVokIyVU+zG6gdqol6HzEHJKKoLmiJgJADPv1LIgTUtSjixJmyj3PamMturCism5fYrsGxNWyyW6zj5cXYdSwNUKWcxNrIVA2FfI1cbsDHY7G4Y8qUcvKmk6tC4PM5fzqBS6ZHVWRSAVgW5hQiAO6Z+UoCzR+YTQJ5uhN0SN+t4mwuwDGqPdYCGnd6LZSqQEm30uPm+JqvZZg2RrhtwxViQoza1bEqTOzmIqrJ4sLiaE7QxRNaFn52+0YxjsHIYRQNQVXOZIJDHXwplIDtevm6FWqyhM0FaCBCEMBft9/8A/XW8oVx2hLSGoma0jXHXWgTpYKSQVtk15uPhUSEnoVNi3Zho6RKoIibYpx0BoDGn8Z58zd89lwdXk6NdQW6XApGqJQen6QNdb/dW0aqmKjphrtYYU5bYrSSpMchE8wDw2hEpZlPux0B7gZYF1NSXVBd000C4i3USYFUJ1JuZd1bT5jxFBCmuQ0Ghp477OtWfRRvQcor3my/aAiMrF85aWNpf30CYTZTmiGi92xOy3ZT8/OZJZ2TUumx2yj2iYm9P92v6IiG2uR9xbXSPZvoPnX+E3heM4zjuENySwRKTExNWfV9X/IT/8gog8maNXTwIv5sefBd597eVPAx95eJ+q+m3AtwEchdv6gMP3EEXKqSI0HQqhYyRM8oDn7c5G34iQtlu06wjzuaXPtslem2ftMZ3Q317QTwtCc62epe0JRaC5NaWfQhHFZqzts7fUyZJQRBMBXXeoM5pZgXw3K4j7ihKQtrX0V9IxCiZzK6KWzY7QJ3TfWIRrNjExp8HqyIqDbUSalGPxsQqkaQndhND3doxiMLG129lQ6La1Ibu1iUjZR9KkoJ9VFsna7AmX5ncUyhz1Guq08hidmNNBGoW4tZtiKnLHZNtbynSokUuKYudFyUX3+fVpvUF22Vuqrq95YuX6smwWm/Z721+M5nM2nUJlQkm6Hl2vkZSHBvfmZp/unrC7U5FKkCqN0aAt5djxV0jPvGwoc53Vrivod4GQU4pD9Glatqz2Ffu2sGBaNGFmjgVCEdMokJIKXUy2bUi5+D2M4ssiUokQEqmPY4qw7SMppLFoPl6ze9j1JSEpO1E6Dcxjw6JoqELHuqsJc+Wo3vHBcMoqzmjqAkmRYi20RwUap5RXBfFyb07uIZCqHFnabtG6oNi2aB0PjvCdnVNJNkhampQNRK3InWS1XKFLxLXSz6zesJ9X6HFtBfCdDYUOuxydLSP9rKJo7VzStMTnXiY+Z1YcMpvSPnFiP29NS/vYElEo+t4FlvMpiRuJfurwRroIBfgzwE+p6h+79tT3A18FfGv+/69ee/x7ROSPYUXunw3809d9nxjH1n3lUOxM/hoJNpQ4ZmPFXKclVWmzCbc7+qsrS00N0TC9NnuwKGxESJegV8K+RctIdzIxa4Xe3LSlS2aZcHpkUYHzK5hOSMupDXS+Z6N35OyScr2lmE8tQrTZjcOaLa3YWPH91ZXdzOraRIgE9PIK2hx5uH1CP89O2z25jkZtPV0ycSK5cL4qzdah7UxcZWsDco2Z9AkmFaKH6I3VxzSw21tHZs6FSe5W1NoGChcXW2I05+9+EpEmUV421nXWJzsv1wTvdXS3N7NXu2AI06mlbLdb2z5d628YtlO12ZF5kLJ25mTPYDZaHB2K/qcTS5N2icn9hnI9RXdxjFgN3le7vqRJBZ0Gdl1B00ViUKuNSoH9WIvVs25Kmq5gUrWUWVwN6b2kwr4tuNxmwSc6PlcVPUWw6FObgv2fU5Bdb4akbRfHDsJOgtWKxZ4y9uy7gl1XUMWeadkyLVqaPnLZHPHu+Rkv7xfUoeOx+oqrOGG7tHO5jhOarqbYBsq1UJ1bQXpaVHadtD1hs0frgrSoD/VXktPR2DDz8XrpDkHlVMdRPElS+mmBdEqxbkf/t+L+zsxiV2vkaEl39widlYRtR/mR+xatndYWFCtyqrqI9Cdz4tWetKjoT2YU5zu7pu6dv96vBMdxnLc1bySC9S8DvxX4P0Xkx/JjfxATVt8nIr8T+HngywFU9SdE5PuAn8Q6EH/363UQDp1pUtfjzVt3e/PGinmgcVITFH0/ml6G6QSZ1PQv2lCzUNc2E7Dr0O3WbvRYh5vudhCO8i9+oa/NGTxuW/vrvwgmRtqesNqg5xf2fk8/SfPkkXUI7nuad53amoeCnWzQGBY14XJrBel3btnct93OBNV6TX9+zjAoWWZTG/8DsN5QvDiBkyXpaEporUA5VQVUZquQCqutCdvsg7VrYVrboOXtziJCq7WJLI7QIuZh02ZQqkczZFqbHUTXm3Dpezi7QM4gVBV6sjQBuW0pLnf2+mx1QTKLBIai9RittirPYUSwuZG56UDbjni0RGdTGyS93R7qsKrqUOwEJvp2e0RyejbPXzQjzd4MKZsGriJhOSeWkXI1QTrJYqWEwkTRqq3ZtNUorvZtQZEL22NIlCGx3Vf0faAuO6qio+tjtl9Qdq39OLQ5CjWvG/oUaDrbZlJZ7dl6XzGrmwfE1axsoYR9H9ns8wzIkOj6yKYJNCFSxDTWbgVRymCCa1j/C9sjnp6dMy/2PLc7ImlgWrScTHdEUS4Ftn2NSqBZVlRXJZOznrhLVmOVlGLdETYt/fFkrPtDIOx6s+jY20ioVAZLJ4Zgo5JyxBKguGrGGZqpLunnJe27jtDixLziLvbWrJGjn/2dIyty3+zQSa4/HAx0z1ZQFhQvXtpEhNsL1k9PKZ9awt94vV89juM4b1/eSBfhD/PKdVUAv+pVXvMtwLe80UUM8/SuD2WWsrJBzvmmPRSy0/djYXVar2GzsRl3TUPa7azgfLBrSInUtIT5zFJpH/wIYbmw9NxQC5Rrh2LTWnqt7Ugvvmypq8fvkiYl5dmW9nRKW0eqF9YWERKxQncgbHaw3dn6iwK9WqO7nY2ZyQXhYbEgXV3ZOKDr0Z66tojNrkGmFd1RaSm7TYfsW7QqbGiyYvPmkqAzswLQlJAiwr1zEzFgAqxp7DNPzb9KtvucUtzT51mAhwOdHe9Vidua/vaSfjkhrhtkvQMVZNfQr9bj6zRYd1+YTOz4VqUJJybm5B7EPv9yaTPnuiV6tRpTuqMRaU75qgp6dYXs99YoUFY2/mcYAh3jOHsxXqyprhaErdkv3J2sOCk3vLBfcrY3O4dp2VIEMyHdNeZRNQih4/mWOvY2Rmc7oczRraYLFNFqsyZlR9cHrvK8whgtLbjeV0zKjlndcLGZkpKJrraPnF+ZmC/LnhgTUZSujxa9KrrxB6i/5qG17ez66dpAGcys9P2r2yzKPctiz0m1otPAtig51wlF2dOcdHTrkurCauTWj9sA5mqdCC1sH6uB2gZLd3kUUoC0LClWLTorTTwlJWw6wmqHVgX9InulRaGro/m7FRZRLK72xF0uli8CzUk+Ljsbu1O8cGEzEedTqynMhqJaWmOJirB/7xFaCNXZnqOfOqefV2/014PjOM7bkkfCyV2qknjrLnq1stTf4JeERaUIwdJTDwuDojBxdnlpYmU2IxwfoV1Pf+++CS4R0tUVxROPj91z0rSWwqrLg4noLsLzL9HdP7OxPLdObVhyEWhPJqB2Q6EIsAcZ0nPZzZ3FzG4y250Jh7oiXVyNg4rBRGOYTiyakz2eAEv5bTaEIBTDWJzGbn6oko5m43iUVGez0aTIzkajcHJEXMzRi0ur/eo62O5sjWPnZbIuv9y9N9SmSQyk7Y7+7JwwmxHbjjCfotNsTbHeopdX2eA1EuY2kFubxgY+5yaAfrW2Ic91bZGnpoWLSwhiju/ZZgOsm3BsShgK5avsNdbmTsk0DJbeWX2WWremzgvrSmtM8Ly0W3DWTOlSJEqiJbJtS/at2TVMq5YiF73XZcu+Lbh3vgBRlvMdKZlzu+RolMBYl1UVh5ThUPS+awvWu4qT+ZamK+iTpSmnyy19kuyVZXNjFNjsSzb7kqroCbnYXVVYZaFVFv3ooQWM8xKvupogynG5pQodp/WGi/2U5+ql1WSdFpTngck9odhCs4zEPVSrRLFT+kkgJaWdBmKrhFZJZUW56khlSdj3pElkd/eEuEuUl2a9MNRKyXZv11EI6HxKuL9HlzOYlkyfO7Prvq6gLGifPCFuGsKuM1EeI/3pEi3NTyu0PZOfec4iuo/dpnliOXbOOo7jvFN5JASWNi39Cy+aaIoRJQsr7AY8dq6V1TiyZWjzDsdH6HZH2mzGfwNSVoRbJ6TzC7oXXhwjZNetHaSqEKA/PydMp8Q7d6Bt6J79MPHuXZg9BgrFxnyjwMSVrrfoZjN6VungJ5TrwtJqbWnPLKIGgdi3DWE2Q46PYLsbhQlAWG+Q3Z4wt0iMFdonZLO17rvpIfJmNUk9eryADz+P7vajYzpiPlS0rd0kr6XkpKqQSU3a7khXV6Nf1fVzwW6HnB6j84kNHS4LYl2jV1f2mnwMkTB+PwreprHIY7bGkMWCcPc23D+nP7uAlL2uBsElwSJeD9dq5X2G2QzK3qKB2y1SFMRtolhZqm1StByXWy7aKduuRESpYj9aLWwbG21TFR0pC6nlYksMStvFMe1Xx94K4pOlD80LyyJt06ql7QMpBeqiJ8Wee5dzK4Yv+jH1V0bzzZpXDeumom8LZnWLiKUf27akKjrmdcO8bGhSZLWvudzVVEXP/e2Me5s5t2drimBF/Mtiz1Gx51a1YVnsba5hU9AEpZkGmjuBuIrUZ2bs2c0ioVXqi+yevk3EbSKVedD3sqZcJfS4QIMQ95ZebI9rJCnd1Nzhu4kQeph/aIXse7QqCS+fwbsfY/25j1NuOjQIxfme8qd+Hh6/Q5oU6Lw6WDj0PcWLF9bR+MQp7cmE4qohbjvu/aL5x/GbwnEc5+3DIyGwJATiySn9xSUyjr55cGgwIQL9AxEhwGwRwETLfA6tpcIgpx5feNEiM+95N3p+YWmqXCQOwPowsiNtNmP6LsxmkHrCB55l8vKS5j13SPNIedmg86nNx6uO8wiZJndmCbrd0V+uiIu51ZANgmi/t0L7k2P6e/cttXn3LsWdWybShoJuEbSIpPmE0NTw8pmJpKmNxtFpTZpVJrzaQLjakp5+0tZzfmm1XV0H6/VotMo1y4p0bRh2mM8tKpY/c3/W2FDe2XTcRjQPr77uGq+aHdqFsFyauOvag3t7PkcJkAsl9HOoKuLtW3Z+2m6s1bLX6XiOQ1WO+5GisDTwtXMk2x31ckZ1WXNxOeVsOaNLVlzeaeByV7NvD7YLIds2VEHZd4HURxt3k5S6tPfZt8Voo7DP9VbDgOdBqFVFT59MKAmwnO1pujhGuUSUbVPS9YHL9YRp3VLGPke1CkSUaWVia9OUnG+mlNHE2fDaquipi44XrpYcT3d0KbBq69EJPojy1OKCJ+aXrNqal9YLLlYT0nFg96Sg5xXlRaC8FNqlEFrYSCB0UGyVcmP1grvTaNYLQDeJxFbpJpFmYaKq2CrVKoHC+ecuqS8S5aqj+cwjJi/tmX3wnP2TR4Q20dyesP+sz2L+4V0exWPjpoZJBvvHTEhNnrlgcu+K/nRJmhTc+T9Wr/+LwXEc523MIyGwNCX6s7PRYiEcHdmNuE/IZAJdN84XDIu5FYl3HbrZInVtkatrnWxD+kmqinB6aunCD/683cAntdUL9WZEKtOJFaLnmilihNNji1Lt9sjRkvapU4rzndV01SVy75z+5XsHT6qhnutaDVl/eWkP5e/DZELa7ehfvkc8OrL3UUuFaZFH5RQ2K1HLgjQp6I5rillFfPHc1qLm/RQ3ljrUIqJFJNw7NxuFa++vbWcdllU11i8BJmBznVpar01oTSZWXJ4d19nvkUlNNytBKoqXe4uODRHG7EM2pm1DtIaCoRg+2waQUk7j9lYrliNUut8/4AJ/7UIwYR3MukG7zo7bfk9YLOy89uajVK4U3dkcwMdnlyQNNKkgTYS27Eabp8Gu4Wpfs5zsmRRdLoK3a2RWtiyqhi7ZwOd51VCExL4ruNrVNF1h0S+NY/F720euNjV1ZZ+hT8JqM0EETpcbqtizbUtiMGuHcoyo2UyaGKwrcd8WNJ1yNNtxa7YloMyzs/uuLylC4qTaUErKgi/Rpcg0tnzu8nkWd3e0qeDnNnd5dn3C6k7FC/eOaS5Lwj4Q9kKxFaSD7eNmwRH2QrmG0AjdDJoTi3QVG0F6aE4S/a0OiQlUiM9HZs8V1BeRcp24/PQp8p4pxU7pJkJ1lZjc69g9VlOdd1QfPof756MnWhkCOqnoby8I25Kw2UPA0u6O4zjvYB4JgSX5Jp+GSMXVFWEyQft0SEmVuV5os0GzeAFguyUsFuh+b8KlLOjPLkwAtB3ppZcIkwnh9uOQEuns3MRayGmxQWTUNZpyuuxqjZ4cmWFm2xEvdvTHE5rjkvKyJdw+IRaF1SYN9gXXaoxCXRPu3rFZgRdXoxCRuiYcHZEuL00oTiYmkiY1aWFFv9LlVGOfCHugS6O/ULq4NLFR12bU2CczQbWDaLVPfULbJqfr2uw/9WDqbUjvhdnMatGaxtKd4zEVOL+guLiEJx8z4ZQO6VXye4yknrTZ5BTuMI+wtW12O3uf+RyZzdDdnnBSPFDoDljdVW9CTnf7MeKnXZejiYn+4pJ4tABVip0SryLrpmTXl0yipfqGYnbBuv2aLo6RrLOLuaUVpw37va3zqrAU7qRqmZQd55vpaLFQxMSkalnvK7abmrLqSCFQly3H0924/+Vkz+35hvubKeerKUVhRqVFhFndUGWLh6aP7NpiHN0zrRvuzDYkhOcujgghISztucrqxT6kp6MdxbxuuDNdU8WOZzcnJMywdFns+dyT53l5v+D2dMP97YzzzZQwzGnM3l3bbUWoOnpAgzKrWkJbsN+XqChl1VHCGLlTFbazhuYzYbUrzIB0VVJcRkAo1kJ9PzA5C1RXieakoJvdRtJtJCnVva111rYd8QPPI2WJTmviC+fED7+CwHYcx3kH8UgILFUdU4JDZ9rDKcLrI1fGiAsWqUlXV0hRjFEjAL0mKtJuR/rwQ16nw/O5BmkoiA99T3jiMXuvGEnLKSRzrZ5+eG9FvOeX9OcXaN9bYf1yiW63h6L63Y70zLMf/UG77iCuytJMS0XsBnR2KEi3bsQ4zjwcI1MhjOad3fMv2HoXi1wkXpqrd1Wi+8a6Ch9Opw7H5qFUHmQBm8f/DMdbFgtUhLSYEqqSsJpYJG2ol2qzuMz+Wtp26FCTNRAiabMh2EmxaOFma92U1yJuDxMmE6uRw+ratO0I85mJLAnU56eEfUnbRzZdNaYJi5DGWqrBokHETEZvzbYAtClwe76hH1KLuVZrk+uvRHQcoaPArdmWNN3l/QpdH7noChaTPX0K3F/N6LrA3ZMVy7oZrRqarmCzq0l5XE5RpJwW7Nm3lk78+d0pVdmxmOy53EwoC+twvFhPqcqO4+mOk8mWfVewaite3s65Nd1wUm1oktlUnKXARTthEjtmRcNs2fArHvsA5+2Mi3Zisxmzwemmq9j3BU0fKUPi6eU5VezN7gI4qrYkDTy3OWJZ7nlqdkGbIj919jj3VzO07gh3UzZi7Xn30Rm7vuT9L92m/cic+l7g+OcSyw9szWrkaIrse7qnbwEQ1y3pzpLmVgX/86uefsdxnLc9j4TAMnf2GWkQKUC8e9ccz/eNFVfnAcGDgHkY7TqrH6or0tXqwY5DIB4dWZprdCPPflvDcOKB0kRPf2dpBo67DoqAFgFZJ/ORqmvC6Snp/GK0ihi8vEhqqcayRGZT0vnFA/vX/Z5+vyfeuQ0xkI5nVlsV8/DdprNButEeI0YSU6SIyPkVul5DFnZD6q+/Hn0aD6pYlC4PwJbJ0N3XjMdr3DTP/WOfDkaiqnTPfpjiXU8hp0doFPR0Cd3cbClyCpUu11M1zUcLpnBIC6bNhpCSdQRGE7UPpAmHDsOqOnSRDvvLwhVVpKwgCMW6I+4qmqZgVjQ8Vq94frfk/nZG2weCHIY8N01J2/dcrKe0TQGiFEVPCFaHFUIa66O6Plr0JkdxAK72FW0fmVUtk6ojBhNW59sJk7LjXacXbNuSs/XU5h0WPdOqZduUlGXHrDLbiCYLr74pmU32xKDjXMSmi5wuNjSd1WtRtay2NS9fzbnYTphW7WiGer6bcm87G6N1Vew5qndMYje6xP/c6g5PTK+YxJaNVhyXO4Iot+sNSYU79Yp9Klh3NfNiTyk9L+yXvLhd0mtgWe45rTf81PnjTIuWf/nx9/OLP/0ZXmiPuein/OzqMZ5dnfCBs9s8tljxi558juePjvjwM7dpjgrOP2fO7Dllcj9RbtI4mqddlkyfuWL2/uc++pp1HMd5B/FICCztc4qpKGxQcG82C2P6KLt+S1Vk2wahP78Ya6qGSFJ/dgYru1HHxx/LqTAhnZ0/0K0HHOqm8tcSI/GJx0m3j9g8uaB+YUN45nkTH0cLyrOrMXWVVlZAPgoA1YM9ggjahzwA2gp5RwEjgbiYI8sFerVCdg2hKkmTAumyh9Vg8JmCtc2nZJGpXWPzFVOySNF1QZOFFBy6L1PTmh1CNbXj+0C3XzlGiK4XuWuyfYXZzPadbRLCuqS/tbCIhEC/rG2Ezr5FNgvk3jk61L2VNsJomD0odXUwDe3TaGEhMZiZbF7ncB3ofm9DiIucauzagxjLDv5ptaa8t2b64pSziwkvnlhaLakwrxoudxP2XRzd12fZMLQvOibLNU1XUOTaLMFSd+t9lQ1H0zgjsFexmreQDjMO25L1rqLrAvNpw7YpuVxPUBWqqgMslWdWD0pd9LR9YLWr6bpAjIkq+3PVZWciS23W4eV2QttGisLO5WK6p4w9u9YicnEY0wNMio7j2kTTtiu52E/YdiV9Cjwxv6QKPauuopTEcbnj3n7+/2fvT2JtW9f0TOj5q1HMcq21q1Pf+kbhCOd1YDvSBXKCAdMhaUCChIRIyVI2aIFokKJHAylbIIREwyipUggygUSksLAzHbiKDDltR2FHHXGLuOece87ZxapmMcq/oPH9Y8y1b4SrcETcfU/OT9raexVzzDHHXGvOd3/f+z0vQzSsXE8XHB8fL3hnec/WtezHSlAXyVAZT+sdR1/QBUttRzZFx3/6/Mv8P17+CYwLKAV/4r2P+eknv8PK9FyPS3759h2awfH47XuaK0dzvWBcWpqDobgzXP7WSHnTEyrL4Wtbqkf1OSrnXOc61+e63giBBWK+BggPR0xKS2eoKiUyZhDgpTIGvV7DOM5ROEJKV7PoCC+v8yHUHLuTkpq7YKoo5m4JOWQ6tS36RlEPXqjny4WwpY5N7gjJ9+mLrZjFm1bu3xgZ0aUkBvLJVD6BUatS2FPDCIUTcbVcyBgws7liqdGDQjU97I8iqrQ6eZpSkrialPJj4sScyuJt2sCbxF+KD2KGcudnynskZFzCKPmNyXt0XRGPrYjdopAsyGHAFAV6Z2BTy0bhGEEjHLHCwnqBPrSotife70TY5q5WPDYn8GtRnIj9pVwTnVEUaC0Cs3DZFybjTuDUCctbkSozs7ToGWJSrG1PafzsNzLaSrZgUhy6EmcCRieavpi3B++ONebBGBGk0WJNpHYSh9MHw6ErCUGjtWwCbhYd/SjHX5YDqpLjDqOlcJ7BC7V9+uWqneei7hiD4dAXhKipijHH92gq59lUPWPUuGWkzyytmM99wkZMFZPitqm5TgusjrgcXh2iprKe3VAJmkIHumDRE7LCO14eVyyLAaMj/+jFOwBs647ajhyGkn1XyohUJ0rrCUnxO9dXfPnxNX/uJ77Nb+6eUdmRX3v5jH/YfEBdD6yqnreXO766eYVWkZg0988qvnP3iJvbJd3BcfzAUL1csfg0sf54kJ+hc53rXOf6HNcbI7CADMF0KKMzGZw59ibmfEHl5JSnN+6p06GsQ6+Ws6F82kBLefNu6nKpqpTOSfsgyw8RYnqxIJWO8fEKNBQf3ohpvOuJfpyN2SoLLeXs3FXhIEb317L6JjTCTsSSXi1FfFgrTCetUVVBcGUGh3oJeJ5OKhvxU0rQ9zKSi69T7VEaXVo5zxBzeHJ9up7TNZyyHZWao2uUMXJ+GY4aRi+E9oVwuBLC7wovX6HbJWZfSS7jZkGoChlhKoXuM5CybWEcZ0gsnECxPGBtTWZ2hlFYXXnjkBiJB1l0SDHBMUNJi0K6gEm2FwkB1fYsXgV2t5bjO44xafqx4r6v6fOYTwND0FgtnqHRGwZv6AaHtYFHq2b2Jh1yvI3RmbI+OmIS2OjloiXksOap2zXR2Ccxtap6Qh4z6uzhmsKofdTsDkuGwc5jyZQUzgZ0FN7WPhb4jJAYvJ0RDpPZPSRFSpYQhOU1GfeVSvSjnc+nGR3HocCawKbo5evBzuPE0vq5S/fOZodVkbu+5rP9ehaZ0/UxOrJrKpbVwEd3F/z6R29RVJ4QFHU1crU9MnojodrB8c3dY8Zg5s3NTdVhHwc+a65QXrAR5U4Cps+g0XOd61yf93ojBNa88g8iDtTpTV+AlQ5lcjcmDLNwST5loSBiJ+73s8BRriBmHMEUEp2GUTojRjIBp84JxogYMQYVBLxoOoErqrI4dciioB0mblOaRNeD8GMRfKeRXQphHtvhCtLlRqjsEeh66ablgN1YGqxR6GEUeGoWG3OYc1XCw7Favu/k47wAABnRoNUs1KbsxinHURmDslrO/8F5q6qUrlPbzc/LlDsYdgcJoE4J7QOqLYnLiuQ0KkGqHOrqAo7NSXzGNIvOOd4oJlQ+vnTa4mvC9fuBoympzDX7Pk+dVrjdY8oby82LDd92nqeL/Swe2sHN0FCApi9ICdZ1z1XdzOO0zlvawc0G+QmsoVRiGB37Y4W1kbocpIGpEo+W4mMao5aRXVTsmyX2ARMLwKhESGoGnNal+KjGYCjdOJPfJ0O9VmnGQkxYB58ZWk5H6oyfmBAT08hQqYAPhnZwJMj8LU2fbxuingVkSoqQFAs3susrjoPDmciqHBijZvBy/l0rI9NV3eeu38hbT+4JUTMGTe08x76Yz631jkfVkfcWdzx1e4yKvBjW3IxL3l3d81vXTzhUW0xvCKWivPunxpOe61znOtcPfb0RAivF0xtbzJBQYsh5hPr3ugEP46NjzvubBNAkCqY3+anTo4wWU/v3MZhUWaIeX5GqkmQ0xcujiKC7HXGffVTGyBgxZKZTirNZmwedoum+5rFdLR4ovV6h1kuiMSRjwAC6lPP1Ed2HTDZXwsMyRjbzpuw+rUjHmNlZQfTbZAzP5zYDRafbjv4kViZhqBTJZ1HzAJWQQhDBN4nFGEgpijjMj1NNnCytSJUjLhzJKvGKWY12Bs07mOevxPOWjz2NK2dqfIrSJdMq4xwedhLjQ70qI938ZE/YhokAb5sReyjBy3XwSbhYTTalAxy7Il9W6focupJ2kK6nzTmEOm8ZxnQSR4AQ18uBmBQhKsm+RsZzCebOGIBzkjdY5hxDOXXxcE2CSUbU4vPywWBNQHEaSYakKExgDIbeW4agSciPyuDtHOWjVZLuVZJz8uHUHTT5fvrRMObumvC3OHXNVKLLxytsmAUe+X4KG3i6OWB15Pq4QOuIM3H2gSmVeLVborUsAtxEzZPVkf1Q8QvN+zgTeFIfeFrueVru0STeXu/59pMS/70FSYOvf4/f63Od61zn+hzVGyGwJkP1JIxmIOXUlXn4jpu7IxPnauqC6NVKTOhZ2BAHiObBcQLKZC/XJIBijnOpK9KiIi4KVEiy0TcGVF1JN2kYiMOALq34sLLvSZG7Q9O4Y/ZDZdQCYkjXdYVarwhXwnBSfZCcQKPl4zGgBnnD0YeOdL+fwapTjI10gWTLbzKozwyuiVFVFBnzEGESoFrGgL9LDPanazULNEQkygjyFMY7jRrRSrxni2rORgzOEFca5RPaWwofBXCahdQk6uY4ntxRpHDiLzMGzCjPSza566rKz1s8XduY0HV1EmXeow8DxSGhOhEkmsS66OdtvZh9Sz7obKFLWHsSc1pJIzHmrEJ5rCIaRPjkn8MkcNAp8gaEsxVyB2sywU+ROdNtYjp1EeW+TkLGZk+YVoJtCEnR9AWHfJvKvf6fgNKNGJ3wIXfNgsEH6WJNOYlTd8ogHK0QtZDns4CaRnft4OZjdcm+9lgr5+d/T/mIKSnuDvUMVtUq8XR7YOEGmrHg0BfcHBfSBTSB2inu+prOO6wOvFPf85XNK3zSfPi4ZvkJM0n+XOf6vNVfeucbP+hTONcbUm+GwJoq5S0za0+jIm1e2yiTDk4CFefvB2Tclz9OXkaEp9vk7ljfz+NFYM4q5PGliKsxyGac1qhDkyNg/Ix2mPlRE6tqEnvZH8Q4ijjR+hRRM3WHmha1XRILg+7FEE4msaMUKojYIkYxf+fIoOk4ytmZgTUFIU9G+vn+J0GaO15KPfCDTdfIGDGUlyUqhHxN3Nzd05UQ1E8jTnleUkyoENCIuNAxQZRRHVqRFESrCAuHW69QbSejxilvEOZxr3yQ32AnQGrGZmDM7LVS03Wc7sN7Of/8OFTXS7hxq/FBY7UwpBSngGajxdMWgkaZRIx69lMZG3A2zB2emNQsrkA6Q5NIg0DpwmyiL/LtRGBN4zzxW03kdpWN59MYTysRRD7q3DmL8wjPZ3GUMooBsgcuKQIQgmEMUNrAougJSc2B1tOmo1IJg3TETuPDk5CJUdPkkaQP8rjK7LUag567aoMXn9nkA5u6dDEpVlWPUUmwFE2NUolxtISg2K46NlUnkUPZXK9V3m4l8aze8/Lrd7zSWzbfeuDJ+wGVUup94P8MvIVo7b+SUvpfK6WugH8f+CLwO8B/J6V0+4M6z3Od61w/nPVmCKwHY70p527+fH5znQOLk/iuyOZnZiGTxKSeuzpTp2nyX81gS3XyJCmjJe9u4lC1EbU7CghzGIjH4wzgVNYJ8HIY8jlZ4V3l7UIVOfUn8jhtQhVIvEtADR5lFCpEUj9AF+fAaeD0OKfHDCdxM3nQtBb2VXtigSmthUs1Zfg52Y5MGgiTdy2czi9vHOIcKqMT4pDHcIV7zaiv8vugclaOOXrZcBxGlLOYXs+mZZWABKnKG5rDKAItypbbNKYlxtmwn41NJ19Zvl5x8pE9IOQzoR+UkufOB9wh4HaGY1tIvEx+Q5+6OiGK4FKKWXjJx2JCL4yIsmDDPGqbxInPIzoRMGkeJULI3Zo034ePWkaCKs1erZSgsGMGmuaHoKWzOX3OKGaBVzn/GkXdqCTdUJiPH6IItJA/lq4Up9voQGECQzCEqKicJ0R1+hXKvrQhZy6Sr0nKnbM+R/yk7BVzZpRrWcDuWNFpwUsc2pK+dRTViDERa+U8j0MhpnsdqVcjCzvyol9jVWRpB77+6CW/3DuGV6t/jheGP/TywP8kpfQLSqk18PNKqf8E+DeBn0kp/TtKqX8b+LeB/+kP8DzPda5z/RDWGyGwlMrbgSn7bx56ieB15lMWXcra09en9X5XzFiGCZ+gjBGBlEdp87gsoxpA+FMAqcy4ha4T3II2IiyceH9U4eax5PzmP3WOHmzKKaWgrmbEAsaQ6pJkRZClukRN0TfHRkaIeTsyDaP40GI4dX+SBEq/1qFK8eQLm8aEsxdsFBbXVA/HMQ/9YnNHaFJi8STSHvq6cpcMrfN1F0Gnm05uaktUAj2EDEqNeQSYn9NZXJmZhYX3MxpCFQV6eg5C9oel8eSzm9Aa0/ak1pJT6QOm8RR3Bc2+pH9iWZQNSzfMZvBJJOk8SiusdKGsjoxRM2ahMm0DggiOmJi7VxJ7I96oEJWIrKQYvZljdSYju3SH9DwedCaLM9QsgiayvAirNCMWtAriu0oKayJaR/l/QhZKpT2Z3AcvG4eV8xiVGKN0oPrMzJImq5r/kNEL0qiRLlc/WpreoXXKo0FPN9rZQ2aNbFiOo0UrqMuRY1PSO8vFqkGv5eulCXTe0gx5wQMgag5DSaHDHOlTak9lPGU50q9/8CPClNKnwKf533ul1K8D7wL/TeBfy9/2fwL+FmeBda5znetfsN4IgZVSJHZ9xjD87m6OerCpN3WfpFM1znEvYfTCgQoPNw+1dK7aVoSWy2HBsn8PdUVaLfBXS8aVxTYB5RdopVBtJ1DLrod8bvHYzuIKMtepcCIIJk8RiLcqR/kQIylH4qA10clIUA8lqnXo0YmYewDilIOrByJTTOsqj8fiMIr53LnZu6RSOpnFH/jE5GPzmmhV+fzEBJ+7eXkhQFUyOpyPNXndMgF+gr7qukKtloKfMIawdITSyBhsDHnMm+/T6teijqbrR0wilCYGVUpzxw44dTXztZat0P60Fdp1mOOI7RJ46bgMwTJEQ4hy/f0D4ROiZt8K6sDaSGG9GMfn+z+JIDj5pEC2ECekQ9sXaJ3mCJxlNcy5f1oxizoRbharRSzxwJ81PDDCTyNL+dGRNqD4pGIe3xlSihgtyIllMVA7xZA3B6fHYDM2YvBiXg9JEfw0iosYLR0qZ3pSUpTOs6rCPBINeZOy7x3GRK6WDXdNTemEL1ZYEWYT8b52QqvvBofWUVhfUTN4+TkYo2aIsniwSxVNUfDu4o5//Yu/wt+qvsY3/+kvC3+kpZT6IvAngP8MeJbFFymlT5VST3+Q53auc53rh7PeCIE1za6mzbLYdrNZO40p+5ymqJdR/EOrJfQ9YZdN7nWFXq9I/SCgzCluZfJJKSXengypJCXS/Q7V97hhhLcu5Bxe3BLvd+JzmsaQWdioqhR8QtfPmIewky1DXVcwZQxqTVpUhGWBXxXYZsTsOvFfTQT0EAX4ud1A0woXKwu2FB50rHIHCfKGZYozyoIQiBN8c+q0ZQH5GmmeEwphGgcqY9CrpXx1vyeNSQRq15+O9YAYrzKpPUX5vlmQ+YBuegGPGo0eAnjp6Km6hr7POAwRzMrZuRNIyrFCKgvS0Z/Gw3Ay5ocwYzTAnDqXbYe5O1DfLHG3hpumZlX0s0+q92YWN1N0jsqdmKnTY3UkJMUwWBm5OYm5KbMZvh3ta1gEoxKmEDE1jdrawVEXYza2n06/sJ4qbxcOweCD+LWMOY0ZU1JEb7hYtDyqjnx23NDnnMOpO1YXI5dVy3EsuG8rusERo8LkMOpV1c+i5mLZohBhWbuR2o6M0eSunJrHnxNE9NCVDKMleI3Sie2q5YPHtzSjY9eVaC2drttO/uNjrZDcF+VASIpN1c9RQnetobSBdTngTKAZHR/fb3EmUGfT/vN2w8tuxeP6+C/5ovEHV0qpFfD/BP5HKaXdvNTxz77dvwX8WwAViz+8EzzXuc71Q1lvhsACSFEo7rnLordrCAKeTJkoPo38pvy/2bCupAvkX7w6jQhVhpXmcWGaQoyVwqzXQmmPMqpi9Nj7Fn9RE956hI6RtD+gL7akw/E0sjse525QbNtZtOjlQsRKVZ5GgVr4UMWrI6r3YI0AOVNC9Z5ktIQ/3+9nEZVCIB39yTgPJ+/ZZPpXao62AfFbEQOpf+Cxmr94MpjPm5kp+93GgXA7MFHtzaLM+X/xhMqYxpBwGku6bOIfRhkF9r3ATeMlqS5yMHYeP1oLwzBjNCYP23TtVB49qkWBWtSkrpvjiFLUJxr+ZMwnjxAnk/tqSTIatwsUOzvnElZmzF0dJ0IqG7tL5ylMYNeVM65gorFvqp7e29nwvetKvDeYCSIatWwmTsHNJs6doingeddUJ7uYjrR9QdOVFM5T5kzCacQnqAbpri3KgXZ0fKt5LPytLOik0yYjzdvDQsTUdK7BzKIKYF0ONKNj8JZDW5IS7HQlgoi88ZjvLyWFL4QmvygH+ZPF2F1X88ndhq4tWK46NlVPAtZVTzs6VqX8bHx2twGYsx21SiidMp3e0PcOPxhsESiXgfu24nq/5L2rO57Ve3Zj9ft/rfgDLKWUQ8TV/yWl9B/mTz9XSr2du1dvAy9+r9umlP4K8FcANurqBz/zPNe5zvVG1ZshsJRCr1bSqagr6RLd3X+f2drkzcJhziwk5xIyre2XBaqqiK+uJYpm9JKLV5iTQCtLiasZx5MHqXDERYFuPbobRJgtatLheGJmKTUzoebtvZy7l9qWVFVQl8RlSXQaPUbMZ7cZQaFFjNSZe7U7EG7vZvCnYBVOHadp7Pl7Xafv91P9E7834xlm35kr5/ubH9OEcAiBsNv97vuZBFoeT06ZirNPSymoyhyrM5LKgrAuSbXDvLxHpR6qEp3Hl6956VIgxUAYB1Tfo8vyBJed8BTZe6asIxFOoNQpluh+hxpG3HqBPZZ0mSq+tj37oaLPnCeQcV+Imna0XNQdISlcHsEdh4JdX1DaMI8WnQlzvA7A6M3c2Zo2+KZR4uTjWtf9a12iRTkIc2q09PnPtNk4jQCnKJ8qh07LOBC8N5TlOJvYLxZ7xmDovBxnMt+LYDM0SEctRMV60VFZTzs6CutxmfA+5nHhw83J0gSOQ8EnB+Fdlc6zrnsxsh8rDrsaV3qeXeyp3cihL3m0OPLH3vqUV60Y1ftg2BQ9RkcOQ0nnLZuqR6vEbVPTjZZN3WFUkq5WuOC/8OjDf8oLwh9NKWlV/bvAr6eU/pcPvvQfAf8D4N/Jf/+/fwCnd65zneuHvN4MgQXSuRgHyKHP5sljAMLN7Rx9I+HOYoqObUd6KAoAHsQY0p227KbIGYxBb9akqy1hWRBLK3EvQNKKZBX2YLE+QNvK96/X8oYeI/F+J+cRc+ZgyzxqU1VGKPiIMoItCE+3mJsD6W4ngm93IN7cCXKhrlA5yoeYYBxeCz+eR3IpzRyqh1yoieyeRk/K0TSqLE+ohyl+ZvJm+X7e1lRFNaMakveCl8i0+ZTp93O0TUyvUfZV4VCLRfaeZTDqOJIekN7johAjfz+Qjs0cnaPyxmQahhODK48OY9/nsWM8daqMgDXTmDtt1qFrGcGqRS1ke0CNgfpVZP+84uOrCz5Y31LbkeNY0OcNv8n0bXTi+rB4DbqpVGJT9ZnkzjzyqnLHy0eN1pF2cLSD42rZsF0daMaCIcfbtN6wO9QCG61EGO2HCq0jy2qgdnIN/ERCL8Z5RFjYYb7vCfzpo+Z2v8Abw3bZcn18fQQVMturLnJ3MEflTNuT9201x+60UXFs5VptVy3t4JCIHcche7fqcqB0ntIEro8LhsFysWn40avnGJV42a3wmQj/m998h/qqpSpGBi8bg4M3M+bh8aLB6MhtV7PKQmvhRvZ9idGRfV/wcy++9M98TfgjqD8H/PeBX1ZK/VL+3P8MEVb/gVLqLwMfAv/GD+b0zvUvWn/9k1/6QZ/Cuc4115shsFIi+XEOJE6jx3/62WvfoqsKnHiEYhZWylpU7nzo6c12uSRc38wkeL1aSuSMzuOp2zvUagHLAnMY0McOjJbQYqXAR+KmRmuNenVDvLufQ4dj2wJgnj4hPbsCrdEv74i7vVDQ1wvCpiBphR4iupPgZ1UWxNs7QUmsliIYu55wd//gAZqTET8mGatNm4Q5i296jKnrZ/O5MgbyNUjH46n7NG1ATj6mB7FAaRiku/dwWzF3lnRVEbtBunuIaNOrFXMothUPlXimxhw+rUVATpuQCuKyRGfIqo6RdHcvQdLTfULuYmUe2Xol57Y/nLYifXzQ8UqkcTh17B6Eguv1muXjBW4vYcWf6C03Tc0wyPlYG4lZSO2bEq0TNnerokp4b9gfalJSaBMoijALqiYV8xbhohzwQUvQchTBU2WgZ+k8X3/7BWM07PuSMWi2y5Z+tHMHy5pAYeXPvhXxNQm7XVeKuNInDMNm2fFstecwlHgb8khQ4ncW5TjT3yfelc8+L5+9ZUKzDzxdHXj8+DmHseSTw2buzBW2pzCBpRvwUfPJbsPLpmS97KhXLUZHfva3vop2kaLwbBYdX7685p13vs3PfPfrjMEQo+J4v6BYjDzZih/x47st7bHEusCTyz3t4Ni1FVZHjm3BdtWxcKeYph9UpZR+FmaP//fXX/yjPJdznetcn796MwRWrodQUOUKEV3W5W2+TrpS2sh4MEZi153I4/ljsj9JuUIy9G4f8AGVQj9+RKzFsOsvSsKzBW4/YvYd+v4IOdw57ffox49Q7z1D+QjDiHEWv61J+x59s5cxY12h3nk2U9ndy+OMY0DL3/HYiCF9GFHDgF6vScOAeXRFarvTht60/Tdt0VkrGIa+l9sff29jsK4qMagnezrW9/uyHo4Xp9FgnCJoTqDR+KDzp1whHbLMpFJlKaNO71FVBYuauF2SSoPZ98IkG0ZU7Qi1Y9xcUVw3qO+9EPO80bkDZ2dGmDLZIxYEMKseXcnmZdPmzwsMVdeVjHvzdVXLZR6tRll4ANxBMYyWd5b3PF3s+ey4YQhidB8y42qz7HLmn8GTGVdGOjhPlkeJfmkXdKNs/zkb8EFzaCqCNxTlyGbRMWacgmwBQtNXfPbZBcokimqcqeeLHLXjg6Gw0hELUbNdtBy6Eh81vRfaejM4QTQ8CJH+zU+eoU2kKsc5hsePlr537BCPVZW7WM5EYoJlNt8vy4FjX/DhzSW/E6+ywV88XGMUlMRxKPjkZgvA1eZI5Tz3h5qi8HRtwdvP7mbTfDM4fvWzt/mV9DY//tZnPCob+mi46Zd8+9UjXtytcC6wrAaerI903rJrK9q24Gp75LJqqS9G7vp6HsWe61znOtfntd4IgaXy+GemnmeYqNluiMcWXZWo5VJEQD+I5yklzMVWTPDeg9KYZ7JNnfaH2Qhu336LFKNsEXpPGkdUSISFIymF2w3oMZKcITzeYF7ey6be40ekZY3eNeJ1KnN+3bc/hfWS4UtPBe3QBtyrBtUP4CypsITagQJ735HKAr1cEPcHzKYk7A4n0Td1nCCP9tIMTUUpUtQopzCXl8T9Xsajq2U23ovYJIr4VK44+dXKMsfs6DlSZ4KuTpVGj16vM7YizqPK6TgpplO3SJt5ZKmqCrWo5LxjxNwdSIUT1MVGrlFxP6B7j+nAr0v40fexuw716TXs98RejNPKGGI276uyxJSFPPf7gyw8pIReLgUgm8eM02ONr16dum5ti7tao3sRHE+rPUO0NL6g9U46SplZNWQI5pP1kdKICIoI4uGT3WaGd04jOxnhBVYXe0rrOQzF7JnqMqxzu2h5ujpQPA7ctAt2XUk/WILVHNuSGDTaRJwT0RZz1+li0c7U9kNfsiwHics5VmzWDety4N3tPc1Y0Ixu9l65LKomw7pWOe8QYXc1g5tN+1olilKexxg1/Wj55FixXAgNHuCDx7c4HXh+WOFM5I+9/SlWRxpf8Ol+zbIY5w3KxVI6W9+8ecyvtAV+MLjK83h7wOnIbVNz7AraweG9wTnPV5694lWz4Dsvrwje8OjywHvruz+Il45znetc53pj640QWFOXZQJJ6s2G1DQirpY1se2IL1/O36OsJfU9IXdWdCWeonS/m9+Apzy7uMu5fnlsZq4uYPS425bkDEmLUNCHHq5v5Y3f2tmMTlkQl7VgCJxh+Mn30SFhWs/iW4JzSKuasFqhmwF9vUO1reAa6op0e09s27kzpFwhQiyHOOu6Et/TXA/yGLP2mgRZ8n72lk0iigm3MJnPh2EeHcqYMXektJEO03StnSXu94K8sJb0YDNRLxZoJ92zaQtTvqDzpl8n16hwsF2TKofZ99jrA3FdEVYyJlUxEY3GdF4E6tuP0EbD/iAjzYeh2zGRmla8VcsFpiwzhmMnvrwJ4uoK8bBdbGEcBC5bONLgWbyMfPbZin+0epdCBw5jQdMXlM7Po7xt3c1Cqc9drRA17eCw2eQ9+ZjGYOhGyziWMoLzws9aVsOMgCid52a/5NP2Am0j62UnoqkQcfZoc5w7QFZHXO5gdd5y31ayNThYnAuQjfOX26NAQEfHvi/oBsdm0fHWeo9Pmn0vgdVaJVZVzxgMu7aakQ6LYuT2WOcAa2FxxagknkdHtuuWQ1uyqHqeLI/ctAv2TUXhPCkpfv35Wwy9JY4aWwZ2iK/syVpGgFZHVm4grhW1HXm7vufTdsuL4wqdeV5KJd67ugPgpl2gFXz12SseVwe+ff+Yj/cX/wKvEOc617nO9cNXb4TAOvlr5MPw8mUWTYpwvxNzcx4LJu9l1FaW0tXQVrhRD0ZbD48FIkamTL9wfYNuO/TVJfFqDYVFjYFUOcLX3sM0A+pmD6McIB2O6BBFICxKbOOJhQizVDiJjDm02DvxMqX1AtYLaHvSbp8J8AWmrsFa2Y7s+zx+G2RzDk5eKMjsruK0RZe/92RSz0bxLFDC7e/eJEw5yBpXnmCiRgjok5dpEqqp718bu8bjcQ6U1hdbWFQSe3NsYBKDIUDQqH4gLivGRxXJKqJRuP2IPQziUTOKaDXJKHRIsnXYddAyG9fn0egwzFudKXe69GIxM78enjsPBCGAaS6pbi6we+FblUXPTSsBxH0OPFYqse9K7lKFQjYDtU44E4RXlY3iYzDzxp3VkWohokkhME4fDFeLlleHJce2wLnAo6sDRkfuj/WMVqjKgTH7opRKM/4hZb7VqpKu3Lrq6fJ2I0DnLZX1HLLZvrCBfrTcJ0VhAuuy5+nyQB8sd23NshhYFgOHvmTfllgjHbpmdPhB8AxXq0a2AAcBpj7ZjFwfFnyneURReOpyyL4sz+WipRkd767uuSxaNrbleb/h48MFV9WR/VgxRMPSDfzE5hN+Y/+Mu07Gfsty4OlqEMDo6HAm8Ghx5DsvH/Hq+QZdBJbrjmX5T9h+Pde5znWuz0m9EQJLGY0uqtnYrUphMikjfqs0jK+xnwBSL0HQD7sguqpQ242MCDNraQKCJmDKKwQIn72Az15gnj4mvH0FIeE+uxMBESNxfxBm1aIGo4nrBeg89rNa/pQGfbcnvHw1d6RmCOYDP9n3l7nYzhuBqe9PMTAwd68SJ7r6PKpLglh4rfMz5Tjmz01bllP4NEDSZC/VcNr8y8ea7ne6vnq5lMeglfislJJgaqXE6/RwazGT63XTYxaOmDR6gFAZwsISjSJUGhToIaGuSsqbEqs1xlrJfOz6vHlp8hZmxm289RR1bIm3t68/XhAfXuFki7As5TkzGrcfcXvHoS+o7ZiN33pGNfi88bcpxdQ9YRK0jhy7Aj8arAvoLDSmDpAP+jXzuDUSs/P2doePmvu2Yt+UFIVnVffUbmThBpqxmDfoYlK0o3TJjJbzknicRES2B/d9ybIYMCpx6GWcOwmRaRw4BBEur/xSwpR15LO7NTEqiuLkx3q5X7Kue56uDzPewecR4fQ4LpZtNuC7fD2ku/bysGRd9Xz3/orbqqMZHSFq/uxb38nAUs3vHK/QKvHt5jHvLe54q94TkqINjhftmvu2ysT7SIyaJ9sDX3z/hqXtue6X/Pb1k3/i68G5znWuc30e6o0QWCnEeYSmqyoDL7OYCCFv1528QZDJ6ROCIAuV2HUCvsz+rBSiZP0V7vXvB0ErLKU7Yp7fEa/W+Gdbklb4lcMeRuwrGYnQdOiPPoOrC/yTtYy/fMQce9Kyxqgn4otqmpNgKiTwOO73mM1mxivEY0u4z3iJhxtyGUPx0Is2MaDmjMPsqZozCQEm8Gqu+Ro8PL42IjSzD+1Een+QPZiXAmKGqeq6AjOiykKifrQmOYtKCdUW0GexNoyofsAdW8KTC/y2RIeE6iWapbiPMoZVcs2ISRAOTUucRqEwnxcIDFYphSoL6aApLSPjB92+2IX5caoibyHGhOlktHdZCSrAmUA3WpqupMq09bumBgR1UDhPCIZV3eNW0qXSKnHfVtzsZOOwKMTf5ExgWYy0o+PQycYiyCR3vehnSOn1YcG9qSjzyO2Qu2GFDZSZaF5aTztatDlt/8WkOLQyjjQ5F3HszRwyHaJ4xabRZDs4hq7gct1wVTfcdjX7jGeYvn67X8iPQVL43uLqke2qJSXFrq0IQWNMpLRh7rKtc2fta5cvuSoaNrbl0h35T6+/Shcsm6LjGxcfMybD33/1BTrvKLKfbQiWkDSPlg1mdeCuq6WL6C2/+Om7tPuK1UXDT7/9Ib/yz/n6cK5znetcP4z1RggsZQy6WhCb5uRVsvYE4ZwCj72f4Zep778vDubUwQovXkpuYO4ixe40fpu6YoBE4uQuD9c3kkFYV1il4WpLuFigu5FUF4xXz7CHEd2NhEUBSvhL3NyLTyjDR/V6LaO4Tjb/zLOnhJfX831P/KdpJDjBT6evff/jSuMwb/3Ffnz9GPna6OVy5ldNOYxzSPNDmv33Xa/pmk25iSmEeTQoJzzlQWriupLtyJiEvD541LFDxUTaH1EWdD9i9+JVA1A+ym3ggdhTJJMFVEZMACeI6PT4hkE6ZCkR727nbmTs+hnnMW0UpmEg3NzhPl1Sv1zwal/hH2tWTsaEE6up6QoRE3kcGLSe6e77ruSQWVFaJ0o38vRCjODHvmD0hn6w7A81xkaKnM83ZvbTxJbSmRg/EdWnYGdAfFOZyTYMFmMio4kzsFSrhMsibOJvHboSMl7COTnO4CXUWetEWY40g+PYX2ThNWJ04tgVhKDZrsRI3w4Ou4kUJtCOFmeidM/yOU8bjqXxXJUNMRsA+2i58wt+bfc2X1m95P3qhm+3T/iV+3f42voFf/rxd3nVr4goCu1pg0OryP1Qc98t6EdL2zmslVifP//jv8Y/vn6Hv/mtr/2+XivOda5zneuHpd4IgZVCODGmNpssBiIgQuJ3jdtiIGHQdS1vxnlDLo2edH2DWa8J+z26LFHLhXCwYB7j4SxquxGyOgirqhtIiywiUkLfHrD9QHi8IZYW97IBDUTQLoivqHRi2p7o6N6fQpKnjb5hOHVpHvis9GIx5wbOnZkpkNnm0Ojc9ZLomOlbklyLNHWxouALjBERUloZtQVmxMJrx32wqTl1tCbBZlZLUkoz9Z4pliZ/f3SGZMW8TmlQiwLdeXTpoOlQuyPaV8RNTbIaNXjMTScj2roklbK5SWQWca+FSE8fGwNtElN+XWEePyK8up6zGJPPF8M6FOKxUwA+UOwj7B23/YLajrOx3LhEsZSfsRA1x74gJUXTFxyT8Kwewj9DVOxyh8qoBPYUNTTV1JHqR0thPSHqeWFjXXc4Hefw45QUdd78MzpK9Mzg5licwvq54zV1fIyOPF4dGaN0lYZsurdG/FtVDqsO+Tbd4NAK+tFSFeNrwm4MEoAdoqIfHf0IV8uGpwvZuHxxXHHdlayrnpXr2bqO2oxEFH2wPCqPQmb3S94qdvz44hN+8fABtRnRKtEHeRxXRUNtRm57QV2kpHhycZjP9RdevM/7m1v+/LNv8xCdfq5znetcn7d6IwSWshZzcQkg5O8puy7nCs7gTH0ygacH220phMxZEpExbQ2mEOHYZB/XIFE3hcs8pwD9QFpWxLKAdYUegnRlJlO7UphXO0ySLg4+kFY1uhkw7YA6NKSU0FcX4ifK48iJsA4yvkp9L2Inj8RUWc5iQR5AflueOlqZSUWOhVFWInkkd1GfxMk4zGIr5WMlz+wFU1MjyrhZOD285iCCTZelCM5hlM6RtbOvK93ewc5g+i3q0Qa/LkVkBTnnWFtibVFbuS4A0RlUSoK+2C5RY0CFfG0nI384Mbjmc8ph1bPIAjHXzx278fXbZLGV5qzDA+Wdx+4KjkOB00FM6VHTjzp3kKQ3EzLHSmdPVT86Do1BqURdjrg8Mhu8ZQgaa+JsgE9JkXKOoYbcuYLCjrOYavoCm2NuBMipBf9gJEbHZyE3jfOmztJEQ7+oO/rsm5rE04RVmAjtY96CnLle+XutCfN9VoV0tPpRtgmVko3FVdXTecu3bx9hdKJ2I48vjlRmZGEHSjN1o3JXTntuhiW/3T/lSXXg2i7Z2I6dryi150v1K279gu+1Fxx8SaEDi0K6ayHK5mNlPbUb2Y8Vf/vTr/4LvEKc61znOtcPX70RAosYZUMNZv8VgCoqYTVpJebq3CFKIczgy8m7MwkTtEYVjrA7kPxIGhMqZPGhPWoYBAewXMLlhqQz/DIm6V4tK9T9EdUOpOORpLTQy42GSkCbKkQJNNYacjgxMaJn4ns3C4iZQVXX0Ir3JY3+RGUf/RwRE4dRzjloobAXTkZnKcn3GyM8rxBIw3jypXl/umYPzfK5c5Umz9Zkwp+EndLoQkKxU9OK52mzJvkgwi4+IKkbLWb0EInOkkoz8zX0EGQc6AzKR+x9K5mMVTbBDxp1N8g1m0z2ZYFar6BpRVBNY8yHY8LRixh2do4lkgOqkw9NaRGSSYSWu+8p7kuawXFVNzgt/qJowowpcCayKAdSUoxBY3QiREXpxrlDNWX1WR1RJs0bdjZIR2xiWRkdxTieFDoHazsbWDjpVnUZIiqdKTnliZ7eByuxPN4QoqJ2nrvRUTpPzOex70piUhTWs8if98HMnTAQ8/4EKF2WA22GpIaoZ3/XshoYvck+K4m1ebbas3HdjFaojQjVNsjz9lOb79JHx/Nxw9GXrF3HhWu4GxcENFZ5Su25G2u02rCxHU+rPeXouVcVEcWyGLAqUtuRwngaL5uRb60e5lqd61znOtfnr94IgZUegC5llJU7NCGKkXnaesuiYerkTOJFF066Xklo4HNHI0fMKGMIhyP4KIKtLFFlAT6gDy10hriqiIVBWU0yK8z1Hlwh+Ie2la2+Qrw/GCPaIr7eFZqAp8oJRyv2PUppGet14q2aAqNfM5vnEpEob24pyShQFYV08oZRRAicRGamoWtrpWs2dXseiI/ZwyYXGpKaRZcuTt6rqfuHMbKdpxTJGumq5c1K1fUYEL5VaWTcpyA5TbAaFUTMKh9JRmdsgyYWGl1s0N2IPvSoYyvA2K4Tr9qD7EOl0us+sul5rEoYR+Iwoq1CLRYnYexzkHSMqDGgRzGeb4qOw1ASk0A2gVlcFDbMHSbgtc2+qSbhkZLCB4UPhfiighjmSydjr5jUieWWNxfHTKh/OFKcqO9DMCQKutHOI0KVVEYy+Pk8Ezw4TwmNnqCiAIO3hKjET5YUCujz8SbxZ3ScA6rF+5ZYFCOroqfQchyfNFZFnA5oEk7Lz8+rcc3OVxxCSUya2gw4FVjaHh8NhohPhpUdeNGv+azbAGBVpDIerVq0SlwWDbfDgsNY0nrHtuio7A8+Kudc5zrXuf4w640QWMCJYj7Va/9Or3mJpjfWCbQ5d3imLblhkO/NfqWHIiaFgJpQB0dkZLioxeCuH8R3KCVdK6VguZBIm5QgZLGU0oMxl/iepPvCiYz+ez2OkMOMp00/kMcwnaN+YACfxmnGoEo9jxohj9NGj0qKlP1Sk6dLQpQnYWUenIc5Gdh9PAVJhyB5h2UhDCpnwRqSMyI6xxLdDahOxqIqBNSyFB6Y0wQnnCuVwPQQS4Pycq2MD0SnCaUhKSCCGb2EaY/jSTRnYZoIqJRzD4OIVmUWqEVNuh9lBFsUD0TqAxFWOFJIuH3i/ljRXbqcu2fljsk2uqizmJqCs+XuJpN6zJwq+bHMnaw8dpu27kC8ThNqwZqIz2NHpRJNX8zjOq3EmB7z13zUdGPOSdRRBFL2b6Wk6EY7nxswH2sa14UohvoYNSEpjEqYbJA3OtINjoSIrJgEUWJUwlg/P64ii7aIYutaNrYjJsWYDDZfg+fDJo9YDUvbU2rPmAxtKNAqUgJLI2HOVgVi0oxJ0wZHFxw+GnyS36lCB95dSPbm/VjN1/Vc5zrXuT6v9WYILKWELP7AxKyMmcnuxDgjGpSOEMijIX0SOtbKeFCpmXmVQvjd+YbDKG/aSjpRylrpFN0fxCg9jbBizKOs8nSO2dAeQ3yt6zJ332C+T12WMhZUSoKT40lwxSHMx5wEo0oqx9P4mYE1j8pCQFXl6XoAjOMphDoLL6XVPGKbR4UPzm2uKJ0xYiJp+RgvKIKklXC/ChkDRqtRzhAri2ksqunBB1Tn0SmR4tRtzGJlzHwvpyEpVB9mvxZKEZYO9BJttARYN5PQiq+NMmdBPQykXsKnU4jynE8LBd/X/SMm9LGlut1ye1tw/1QChsXrlGbPUxsEfeCjnqGfczdLR8jCBZi5VfO/nQiZlPlY1gSciVgd8UG4T0UOkn4okrRKJE6CSj53emom4eOjJj4YT47BMI4G5yQkeuqy2eznUnHyZYn4ki6ZmkGqwAw61QpWZS/mfxXYuo6l7cXMnhQ7X9NHI6Z+YOtaVqanj5mVls30pfZYLTBVpwJjkvsp9UgJXLh2fixjMnx4vJo7ZBdW8guH+Ga89JzrXOc61x9WvRGvcgpQhUPDCSeQO1Pp2MxbeOQVd2UzvX0CZVp74ihNImMKSg4AcRZkGDNjCQDoe9nui0mYT5N4AgFtjqNAR2MSRtQkilJEF8XcZUoZkjqzrJzLHbA8KvL+hBcgnGxQxpy6UtPW4IRrmPhVE/tKK5Sx82hRImLGOawZ8sZhYN5AnL1gOfOPiStmy9nfNX1fOjYSxXN1AaUjTcfUCqzCmxJdWvFcDR518GANUBIzjkHFhO49MVpS7mxNfwBUglhY1LJCh8zFApQOczcq+uEBpsKT9ntw7mRqj9Kx1HUlX+978e4phSkcxS5gjrI5Jx0fBajc0bF4byhcFpSIuDE6ZbK7CCCVxZjNGIWYRMQoFWkH6YwtymGmuw/BzP4uoxLGBLzSc1fMaBFGk7gScOlJDE2Cz+qItqeup1aJ9aKntD6b2KGyIRvb83gxP0YfxBRfWNlGxDKLriF7wWo38u7inqWVDuoQLa96+U/EhGbQJMakGaKlJc6dq0Mo6aPlrXJHSHoWVlsjgmrvKw6+4Kpo+KC8walAl+Qc78aaj5sLPlNrvrS8po2nXMxznetc5/o81hshsFJKhLt78UcpDeqBt0npk7DS6jQeGiSfTlUlqevnN9t4PM5cqdi2pw3EnM2ntH8NU5BPQLopZgtAuLl7zbuklwuUUsTvG1FOaInJv6TKUjpoKUkg9WQwj0HwEykRD4dMSp86P+HEwHronZpObczdtrKQMeAwygixcCcvlrOvHyvmrp7SsxhUWoGWDtic65fHakyiUovXTTWdmNyDdK5CKf4qrRPBapLRMnjMuATVB3nchSYqg21H7OEIVm4/Zz5mrpb2UURX6eCopIOV0klsqszDSGk28eu6Jk3nPgnwLLJTTCfh670Q3XcFx77gndW9bOF52dDrs7iqczbhZEA3OmKKmAWZRtqkzN9jFLljJXl/ozfsu3LuSPmMQfAhsqp6TI7mcdkHFVRiQMaKDz1blfNzEPWyHOhHO3fM+vEkoIZMoa+c4CDGLLZ81LNAczawtr0Y8LMZfuqiXS0bYlJcVc08tlvogYUecEVgoQfGZNiHSj6nAp/0F/TRsbI9CzXwalxhVOLe12xtK140OzImw4/Vn6CJfDw84rebp/ydg3CuIopH5ZEP6ls+qG/5XnfBxna8b2/+ZV4yznWuc53rja83QmABYgrfP9gsynEoGPOaByt2E6U8kgLEw/G1EGS0kS6Y0qcNuGnsaN3r2Adn55Dk5L3kFNY1erkQ5MJEf29beXPPrK3JLzabzKdNt9FLJwXhXKEVqeuJXRAYaRZVerGYoamzMR3m400Azbk75j007YxxmLYpdV3NhnRCzKNI6UPM8TkxSh4hvOYZY4rDcY50uQBrUN0AXjpluumE2j4G7DF7qFzuaBUab0v0GISD1Q2gwVcloTYkU1N8MqDaHuUM6CQCC/FnhdqixyiibVHL9Wwa6a45O48B0wO+2RTlE5tGnruimMfBIlB54LsDNaGy8j8Gn7EFWdC0gxOPVN7EO7QlWp86RzFqUgLnwmujOmsDSsE4GoI31IueuhgxWnhYCTGoO5c38kY3U9onr1ZhT1uCgmUQ/9a+FXL82MvPSZ1jb5yR523yYi3KAacjPggHrLCBInOmJp/Wvqk5HiuKcmRV97SjI0RFsfJsXYuPBm0SV/ZIFx3f6y84+hKrA8ZFRmV4Uuzpo2WhB14Ma26GBbUZcSpwPazoo+VFt2Y3Vvxc+DKPquPMwRqdCL3H5YFX/Yp/ePMBT+s9F67lW81jbvol8Df+RV4hznWuc53rh6reHIEFr3Wq5tFaJpvrxUJM0MM4+7JkW08Tbm9fG90p61CFkzfjh3mEU6adNkxBytLlOVHeU+ZA6en2KYl3q+/FLPz4kYiSYZDxVtvKhuL3beuF4TTmAhE1qpRNv3A4nvxVgC7cyYMUhhmgickiUOt5tIdWqEUt4ieHPpO3F4nxlIWYw6BTkEiZ6ZpMglKusRX0hFLC+CrciVMFgklwhuGiILoMYB0TapTlgWg1cV2iCyMG9+OI3Q9EZ/BP1uhuRIUkBvi8WajHSLKKUBtiUeOsFs7YtAkI4qWzVrqVU9erFsJ8jElEWFmgyhLtLHF3eA1JYe9byjsBXfqkqe3IURd0gxDQH68F3nnoSgZvMEa6UJPXqcwdLjhBOrWOLCrhag2DJSVYr1q0joSoWBYjV3VDMxYcB8erw1IacJl3NfO2vMBClUq5G6VeyzicRpaSgyhNxSZn+lW563bMOYXOhDkwuukL6bSVkZgUl4tWYKaj5W5fUxSB7aLlRbNmCJYvrq65GZccQslTt+dr9QuaWPDpsOUQSo5exoEf1Dd8r79g7yveq+7QKvFhe4lTkbere1rr+PLyFT9Wf8KNX/GbzVsAfHEhx/+t3VPeXux4d3HP7VATk2Zpe67KU7zTuc51rnN9HuuNEFhKa8xWVryxFoZxfrPVK4mBiW2H6h90dDK6YTKl67rO5PJAGoe5+0FMxK4Xz856DYUTYdQJYVzVFUzHN/qUi6gUerUi7vdilEdM8uH6Zjpp8WEtFphtfeqEDYP4m4w5xd7k74/Ho0BVNyt5HKOf8QrKRBQum/CFhQUjNPGEUAARl0Mrt80md5WZWvO1yZ0wUhKfWIzZCD/MXTBlLWq1JFWFRNtoTVjI8UwzCtKi6VC7hvLYkcpC2Ff5us/GdYCUCKuSUBpioUkGbBPQvSAe7K4hOUsqLamw+KKQ7tWQ+WSFkw3GzLJ6GOytrJ39dFgrgvPh9UByKVNwr/HA9ADtviQ+U1RmZF32c/zMbVMTgkbrNIM/p4iaMQiwsx3kWpTO82jZcN9VHNqSlBRlIR2opitY1tLBOg6OF/erGeLpTKTJx5BNRvFJpaTmjtWhFdyDAroctTMBTZveYbKfCwTJcGzL/OMunwtBM3hLjAqlhB5vVcTZwId3F3gvW491NfJ4dZxjcDau41W/4oP6hpXpOYSSW79gZXreLW8Zo+WFWhPHmt86POULixsK7fkHN1+gtiM/sfkEpwO/ePc+msSPbJ7zt+5+lOt+QWU8a9fxW4envGjWAHzabNgUHX/+6lt8uXzB3919nfux/hd5iTjXuc51rh+6eiME1uTBmit3mEgJug7lilmUxLZj7rHk8Vxsu9ObsjYQA3q5QK+WxNs7ks9v2g/euOeO14MuV+x6uW1VgXPEg4Q9m4sLGROGiLm4EEGjtYih/Z5wd/e612saV2bhN2/05Q7S/Fi1OZ3Tg7GlrutTx80UMoLMvq9wOJ5yDJ19nfu1WsqIrO+hrtDWygZeCGjnZu+SKoUPxuhRKUHhiJs6s68Uw6Ma03pS7VCDiNlQO0JtSRpMFzDHEd2Pc+6gue8wRqHaAZxlvKwZtyVqXZCUwt13mNsjyWiKY0+sCuLCEUuLWpTzaBNr0dPzk5IAYcdBxJW1xEl4b9akizXqYIg3d69thpLBsq7yrF3P0RfcNjVt7vKsahFbtZPtueNQzF6rSbwsComAOfQFTV+wqnrevbyf6elGR0rjOQwlY9Q4EykL2QBt+oK6GLlatDkqR7ALRieMDvN9bRbdjG9YVgPWBIxK3BwWKJW4qDtCUuy7ksp5LhcttR1pvZtHjz4KNsLqKBmFxvLisJLRp4XtsuXQlXzvZsvFqqUyI4/LA19cXM+dqLXr8hZhRUya+7Gi0IGf3n6HJ3bHf3L7xyiN5y+/97P8zfsf5W988iN8cXvDl1eviElhiLxT3fH15WccQsVvHp4B8Bee/TYAXXT00fJzt1/mZr1kZXv+1Ytv83//fb1anOtc5zrXD0e9EQKLlNCr5bzdF48NZruRbtKxIY0D4W4yvZ98SHhPzHMUvVzmkV5EuYJ4OBD3+xnDMNXsP5rGjpv1HPpsthtS2wq0tOuy0AsygswVbge+v/RyKZ2oY0PyHrORblwKQTYPf9cNzDzu04vFSRxmc3qaxmAPuk8TF8usluL3yuPJNAyo5UIESd/LMQon16YfZLvQWREfGVGRmhbqSjxMZU1alLNQIiTM4EEr4VxVFt17TDuiOw9WC9dqJQJJD9nTVihCZRneX6N9wvR5/DlEdDeKx25RoQ8N6W6P1gpTV6TywTaZMdB183gTeE0Um0dXEqZdFrK9GKOMEydemc5j0mPL4jpw86ri5dsr3l3c8cWLG161Kzov+Xg+aprRYfJm3eRfKitPO+ZOno5s6y5vHyoOWYhN8NCYoHZ+NpNrlaiKkUU+3nEoJGPQenpv6Uc74yBSUrSDY1t3OBO4bWp6XxCj5nLV0A6O4+BYlwOPls0MDr3tapo8IizdyDJnIU7n1IxyzAmp0HvLu9t73l7saIOjNiNtcLTB8aPL50QUn/QX8/bfu+UtlfI8c3f8/PFL/MyrH+XHN5/yvN/wf/j4z/GX3/+7/C/e/hvcRPg/3vwZrsflzMX6XvMBK9fzVrXjs27Dz3z6I1xULT+yfk6pPT+5+YS3izv66PiV4zv/zJeFc53rXOf6Ya43Q2DBSYjkrk+4383+J1Vk7MEEElXqFJacM/ZSCJirS8kkdJY02Fm4mAvZDsRaAYb2PaqupXP28ffka9pA36NXK0xRnGJ5UsQ8fYJyjni/k67WtHW43YixvWmITSPG67IUQZTREcoVMpoD6YwBsetIuVMW7u6YswOn8dc4zl4wtV6L2T9GUoworUnjCCkbwCeUhXPozVpExjiSMuEdMkKiFziqKpx0/G5uMVeXqKNG7Y/osiBul8TSMl6U4p1SYEbxToVaS0aj1cRiiqyBYC2m8ehDh7kecZ8qwtWKcVMQC824MiRdYoaIux+xOjO+BhFdAjXVqH6UcOe8TCDdKqHzyxKBZENSWOL+gAoL4uMNaVtjRz/HE+nFgpRDvHWvaL3jul9y2y845jDkCTLqo5vBoGX2PN211Wxw1/rU0bI64jPlfTKdT90skFFiNzjuDgvuVaIuB7RCfF6jJXi5rXWBuhxYlQMhKV7uVhgTxeNVjBgduc8jzCnWZhZ0UVFYGUGqnD/YjxZnA4UJcxj07f2SGBWu8Cyqge/db/nu9RUfXN2iqyO1Gbkban5peI/H5ZF3yju2i5b7UPNRd4VTgb97+1XWtufPPfomL4YNf2rzHb6xtvyt+x/jP3j+p3jVrvhgfUNMmsY7KuP5kfVzajPyabel0J7/6tu/QRMLfvnuHTZFx9IM/Oyrr3DX1vzE40//IF8+znWuc53rjas3RmDNpTSxbcWUncN/Y9+ftgR/r5tkMROub07erFzJ+9fHj9Pnp41FpeY3dLR+fZMxV3j+Yv63udjKZuAwnvxY0zH7fia0PyS166oieS+jw2mTsKqygMhhzTHMQky5Yg6kDq9eyQizlmOkvpcOmTGEw+7ECOt76di5Ar1ZSfdr4lxpAaaqHCVkLi9EsB2OpP0BtVmLD8pHlEsUtz2hks5fLA1YLZ0oH9GHHqMUYVFIdmNK+KVDFwZz6FGDx9weMYeesBKhozJGIRlFXBQoY9C9RR1b9PWd+O5sHu1pM3ejxLAv1yseDuhQCz6iKuFiPWcdpkUlgux4JPQ9JkVsc4WKgk5Y2AG7iOy6kmNX5KBn2RAUkL2mawuUTiyqAaXFbK4QgvvEyJq4WYO30q1ynmMnXadjQjxTOefQB80QNGUe7fmo5+BjHwzHQbb/rtZHrndLgnd4b4hRsax7DmNFVXrKbIRvuhJrJQNxwjecgqrFyO4Hy2LV8+7jO/Z9QeV8/llUKJf4bL8WAjyKH1t9xpU90sSCbzZP+V6zZVN0vFvd0caCLy2veez2LPTA1rb84+P7vFfe8o3Vh3yvuORDd0UbHFvX8l+5+jV+tXmXna+4dA1fXz7n2+1jPum3/InVh1w9PvIbx7d4XB54u5LfxX9w/YV/4u/zuc51rnN9HuqNEFjKGnS5OI2GlMoE9njCKrx2AzXDNWPXncZy0xZgXZHabjaV6+1GqOopEe/uZSNvtRQxt9u9JoxUeQJYAqA1ersh3u9IXf97e8UAvVrlfMOcMZiSnF/bzZ4svRTDvqrK1xhdZrVELbIH6tgQDkdSM6LrGvPoSu7Le+lQgXSwkJEZ+sFoEBGUqe1mXtZMfjfC6cIaQTGkhNpuss+th9sdyhpMXREXFSZGwqLA7QbU4KVzVTn8VjYK9WxSz3mEVpEKKyJt6ODQoPUFYVsxLi1Jge0CaojEVUGsLaawAixte9L+cILBFgWxaeZQb1wpo1Fj5DrFCMMonrSFxdrsXVuvUVWFWi9RIVFeKz67W/N0sRdgZzlQ2IDLOIYpSHlVjVgd2XclXTamK5WwmdjeDjaLMul2aZ2IwL6pWGRD+7IYWLqBT/ebeesvRs39oaYtHJXzMzXe2UCIim5wtL1jvegprMeoxBg1d4cF21UrrK2mYugtq1VH6Tw+5E5YBqBOHq5H2yObouez/ZoXuSvWjw7vNXUpPy/rquf95R0b23LrF+x8xcr2fGP9IX/x4sAxlrzya+59zcfdBR81l3xnd8WzxYE/efldfrt5yt8+fg2jI9+4+Jjn/YbbYcH/9Xt/mkfVkaUd+K3jU3w0rF2Hj4Z/99t/jtqNvLe64xdu3gfgWb3nK5tXZ0jDuc51rs91vRECK/lADA16uURfbCUIeJA/yhj0aikCCUhdLxuGSUCfuqpmXMEkZFTfo4oCc7El3O9e6zTppXi9wvWNiIyyxL79lmTw3e9m7INeLESwdT0xZA9W3hpUCzGup36YNwfj4QBHPWcEzp2lfJ/K2QebihJmnLzQ38N+D7td/maTI2+0jDi7fuaBpWEn951HjcLRGuTaWEva7+fNQkE8uJnUPhvtM0SUws1bmOkyb3A2HYxefFKpJjlD97SCBKaP2ONI+bInaU2scxdlCCStSFbjVw6bknSnlELf7tB3e5w1soVYOZKeiO8RQkK1PbRd3o4cCTsZ604duAk/wTCS0iCPaVGBs8RK8g1j7TCrFen2jnh7i44BFa8INbx1sedxeeTgJah5EjHHvqDrHHUl4iMkRek8i3KYY25i1LM3q6qku2hNmAOhXS0LF7uu5NCVM3qhdCM+GJwJ1MVIN1q60c4dpdGb2VBvTORut6Cqh1nA1eXAsSvmZux2Ix6sY1dgTZRcxByDM3W4ru9WvExrNquWq23D0g30wdJ7y5h9Y7uu5Ndvn/Fjl+KJejWu+Kg1/GJ4n+MojK1t0XFZNnxl8QqnAv/ms5/l/3P3DX7u+st8df2Sb1zt+VL5ku/0T3ivvuVbxye03qFVojYDT4qet4t7Ph22/E7ziL/03q9zNy7oo+VfefobbE3Dp+MF18PqX+IV41z/ea2//skv/aBP4Vzn+ueuN0JgKa3R9UJ8WLnrNNU84ru7f71zNYxiED8e5/HhZHSf6d5NI/4rVxCzgIltBzGcOEsx4j/9TMCmOZbGPLoS43v2RT3EBgj0M4sAa2dDulmvZQw4fc0VIhaPx9eM7nMvTmf6u1GzoXsWQVPwtVIQA7EXtpde1hAi8dW1PP7CyX3EiBpH1HKZu3GDdLFyPqEqZJNPOStirHAka+RvI5iLpDVpVaKHgL47oNoe4wOL+5a4LElWk5RivKyJVqEiqJRklDg9NqvoH1W40mKf35+2PbVG9Xk5oHKkwpLQqHYkVRIwrboT7yx5T+r6EzVfaxkTh0AqC9l8BNRlTaydGPRzJqUqClRVoftA9RI+en7JO8t71q7jvfUdt92C265mXfWsq36OuklJMXrD0YuBfEIkVM5jTZgp8JNQigmOXc2yGrioO4nV0ZFXhyWLcqDK5vjOW7Z1R+/tHLFTFSODN4zBUNjA5bKlGRxGi68qJUWVOVzrOtANbuZyHbqSBPO5T124spKtR6USn9xsGRoHo8Ztepb1QFWMLMuBd1b3aBXZ2JZ3yjtMBrGuTMdS9+xDzcfDJR+2V9yPFX/t0x/nreWOL6+uqc2Ij5q/cf1jPK0OGBv5yvIlX1u+4BBK7sYFn3UbnvcbnpU73q7uGZPhj68+4sP+EX/1k5/grq344OJuzl4817nOda7Pa70RAitFIbSrshSB8EBwzFt/meae/Ejy5CicTgSUEhCndIgyAyomVFUS7ndzFwyAMmU/VE9qGhkhrtczFiE1zQkRADn3rpqjXKbRnl4sgAwJzUZ3Ze3s0cIYEXUxiWDbHVBGoy+2YC3h+csMRRXRo3I3KoUogdbqlC8IGWWRgZq6LDGbVY4GkvtNRucA6EJEVI7PoR/mnMQ0jNC2c/YjbQ/rBXFZEursZ3KGUF+iB4/qA9gHeYQJ3F03oxvSRJqvLNEZGRXmcw5XK8yhh5e3eZPRoXYH1A4RdquFCKMYgSQ+KiDd3ss12RZiZs9jQb1Zz0Z/yT8E0474pSWWhrRaoPYH6QbGiF1UlPc1cTQ0vsAnzWfHDV0WOsdY4L2hLEecCcTMwtrWuQuaI3OOg2PfVhJ7kwOWJ0aWzgHSx/xxNzgxu+eO1Ryf4/XMxxqD5v5Yn26fMwjrzMMqreeuqRk7QUr0Vgzwu6YiJcWi6qmcpx0cPmqckTHm1GkDqMqRd67uWbph3jrctxVaRz5RW6rtyPO4oXEdhsjK9jSx4N7Xc5jzN9YfAbB43NPEkt/pHvF3PvsKf/zRJ/zU9iMiipWRa+VU4NYv2NiWq+LI0Zc4HfjG8rvc+BX/v5sfZQiG/977f5933C1/e/ej/OTyY/7qH8Brx7nOda5zvan1RggspbUIhk5o6XMo8kwlHzO2wM08qCnmZhJQk1fLXF5K+O8o+YRmuyEejrM4ARk/masLVFUJif32/sTd4hQzMwm95E9keXNxIcfvehnlTeHUSktW4OEoRPG+n7cDw+29CJwR4otRbpeDqmPfi4dqqslvNtHmowg7VTgRm5Ppf/KMWQdFlE7WspatvJSEcZU39ZQ1pCGcOmSDhBSnizVxVREWlpgp6wIRle5ZKg2qD6TilCXotyXKF5jjgGp6MJqUDCol+osSlKAZbErE2qEv1uL56vpT/I9SMhr0llQL6FQNHkKBuroQ833TZoBo9ltNI0PvSbsRrCUWV0KQPwyo3YGQrznOyQjRKug0Pmk+qHZcFg23w4L7vua+rxi8mU3nMSm6xnHUxUxUjw+2Bq2JxCTjvcr5OZrGh1OA87IahMJuAp23OCNbgUYlQt4ErN3Ipuo59CVdNqsP3rCtRzpvafqC7aKl6QucDSxy5E5pPMexICQJrS5soEBM76PXAk5VCfLX7tuKl/vVHMszjgY/Fqyrnpg01ggD7N3qnkp57kPNU7en0iOfDlt+s3mLjW2FBRZKfDT8z7/2H/Fr/bv8vbsvs3UtX65f8c3mKVfFka/Vz6nUSBNLftO/xd9+/jX+X80f52rR8qcef5e7ccG/9+FP86Q+8qQ68L/91l8Afu4P9oXkXOc617neoHojBFaKUQQQ2WSewZqpy90spU9CBhFAROn+ENM8WgIy3iHOvqxJKAnuoZIO2eiJ93u43+f8uuyXyht+KC1CrW1Rq6V01o4i0sL9Dr1czMINQC8eZP0Z2X4kVoT9nhSY+VopRBFKzhKPLcmPpwigUTYE5/FgikDmZaU4x/4ofYKXSrRODnjue/E1lUVGHxgBm/qQu0QLVBY6KWYhdbdHhwipRhVGcgJLKwb2LqBSIhnBM5guyZjQaJICvyowzqCbYQaILu5b4qKACLG2xMJmZpUciylH0mbBlhKqHVC9IhkjXTcfxIw/4SdARFcej6qmkw3QLH7HpSGpmqq/QI8jqWlRZUHSGtsmdK9pvePoS46h4DCW9EF+7MVrdeoSVsWINVmE5xBoH7SIowd5gj5ourEiRoXWMkas3UjIbKupw2V0JCUJdzY6YnSi6UUkOSP5gYUNaJW4byuMTlwuJEB8W3c0o+P6uECrRII5TsdoCaUeg2QqShRPBqRmD5k1cqzP7tcoBc8u9ry13FGZEadOhPjvtE9Y246FHrj1FYdQYlSiD5adLyl14Fm5w9jI//75f5Gn5Z4vL17xcljz8/cf8KXlNT5qfubVj7IfK7ZFy1dWr/g33vt5nAr8dvuMX71/m5Xr+QvPvsmlO3IIFV/94AU//wfx4nGuc53rXG9ovRECC5gxBJNgSu2YNwnTyXtVmDwm9KeuETKmi23GOMRwCnyGGVgqpnkZoTEKSFOvVxCidMHySE0plSnuRzGj7w+ow1G8WU8eibDpOsnHu9iSmmYWRsrIKn+culfWkcZhPta0Fal6NZ//tMF4ElZT/I8+eZACIvpWS8FWtB3KZVEYE6nthH7ussE9d7AAGZfmx5acFb/TMIrwApQP0j1yBjVG7CBeqlgZlJfMwWSdGNk1kiuYwDQefJSxnbOo0UOIaK2JlcUcBwgJNBDVKedwGEltl7tSDrWo89dybI41qEGJ2X+1lO/fHVDDKII7j4zVYkGsjJjvmzDfv+RLKlQIJAMqQOctF67hcXngztV8dLykGcXXNAkpee4lKDkmNcNDlUqUOatwJqdnr9OU2xii4tAXmDwynIznPnOyYlLEYICAswEd1Yx6GDL41JlAO9j5PpwJs2le5a6YVklYVzkfcWp2CtVCzO+HrsToyKbqeb5bE4KmLDyf3a759HrL5fbI1y9fAlA4z5WV/yQcgvwn4UlxoI+WZ8WOiGKhB77dPqYNji8sbmiDY2EG6vznl+/eodABrSLbouWyaHnRr/nN3TO0ijwqG35s8xl9dLwc1nzab9GkswfrXOc61+e+3gyBpXhdZCD5etrUMiIKQURXFg3KFeLryeTuCUg5G827XiJ0ui6b2tO88p9Gn7tWWkZRSe5zPnbO9ZOQZSUhw1M+4v4gvqMcOUPeclSPruQ+cjj0hIuYKvlxhmXGwwFMgXqQpaeqUgRBCIJbyKNB6VBNB4nyWPL1SaOf4aQpBLk/kzteNvvQrBETu9azX0pFuT3WiNBqO3RKKB/Fi1XmwO0xCr8qgUpRwp6Vml36oTIkp1G+QI8Bs5MNRNV0mCaLuXw/jF66Z0rJ/cIpozGrhNN24SjXF+TxLB1kdIQaRtTuQOw6lDQsiU7ifeYMwsUCtV4RnaHYR8oby8u7Fd9dXnFZtNyPFVZHruqGzjua0TF6CXOG01hw2vLLE2lSjrexOuYgZuFRSZdKzZ2vkBQqaIyWwOYQFWQS/BhMFlRiopeHr0hJxoRKye2nbUMfNDEfc/Tm1L3SEZPUvDCRkkKBiDHnaUfL9XHBOhvtR29Y1vLzOHrDbqz4YHHLQg9c2SNOBV75FS+HNYdQ4lTMm3+GD+pbVqan1J5Kj7xX3PLrzdsA1Hrgzz76Nk0o+Li7kM8ZGZM+Lg5c2oY+WZpQsLEtfbSsbM+9rxnim/HSc65znetcf1j1ZrzKTf8Tn96UYxIaedAn0aGV4AoQw/c0OgREOAVmkzgxkJI9bSNqBT6SAqhs2iZFcKV0v3aJ5Md5rJgPevrbGGCU0dV0W2NOXaLM7np4e2XzufpROlXen7bqchzOVGoc5byzeDpdlziPBVPgNU/WNEacBGOMCeU9OgSUr6Es5Jy0JpUOCpuzBjVxW6MSqDGg+lH+tD06RFQlW3mxMCSj0WNAH0dMSqfPOy0jvuk0tQBE9TFBl/EaWs/ds5T/KB+ky/TguWP0+Xvzx1pDWaBSL7yvnKGo+kG6VEqJd64qiEadfn6UkrHyEOB4RBcO5deQRDTFpKjNQKk9n3Vr9jlD0ActgkVPz4fwq6Z4nImornWax4ODt+gsrIgam7tNIcr5+KhRKuSf1VN3bPrb5FBnpRL9aOW+crcMpCNWZLJ8YQP9aPFRoxMEL2HUIeh8btK9QsN9U1Nmz1blPIMXQTeOhotVS8zbkkPurGmVuPHL/CunGKOhDY7H5YFSj5Taz3mFF67hF+/e566r+eNXn7CxLYdQEpLmzi/w0WB1YO8r1rZjZXvGZNAkFmbAqUAfLd9pHhGTZuP+yeDgc53rXOf6PNSbIbCmmkRHiiQf58xA+ZomTU2f3O3RtazUp9HP/5buSO4gKY0yD44/CbJMbVdTN+qhwb0opCPUy1ZjbBpUL3TxSWCpvMVGJrQzjBBk7Kicnc35AGnQIp4gm7z1PB5UD0TKfH5ZRJ3OKZKiPp3fdJvpPmLKtxtJPhGnceE4opwToeU1SmtiYUlOhFO0iqQdunOYY4/qRlTXo/pBomZCQVg6opXrpPsR5YVdlbx4sSZcQjIiuJIxKCP3lWKUc/AWReZ2pTTjFOR5zNpIa3netBIRNvr5mqUub4ZWpYwPKVAmiL9MK1QAFZL4zKYsx5RQo8e2AXdwNAfH/VDzqGyozTB3k8Zg5i7VxL3SWoSPRjxUhfVzdqFcdhFWU5yOMokYNT6omf6u89jR6IQ2cRZeEvYsXaghZxP6IGHNzqTZSD94EVR2EnH5uHIJlQg050lWzV2tIQuv/kGO4uhNjuFJ7JoKrROrqmdTdGgV6aOMKRd6ICrFhWvYWEVtRoyKOBV4u9qx9xUvhxUXRcOzas/S9vS5A/Vxd0mpPV9cXLMyPS/GNUO0XI9L9r5ijIZn5Y6nhcBeSx3QauRJ8bsTE851rnOd6/NUb4zASjEJnsAY0pj9TCqbn+G1IGRARm5ZgEn3yf2uY6oHtxXmVR4V5m5J8qfsu5TN4qquZFw3daTyth+FYBMkekbnsOEkIuYBt2tGCcQkvC1595bHkpJ8nBEMs/CCeTsSmEGj0rbKj+U1RtY44yiU0fPfKaXT/Uw0eR/k34AyYtZOKkFUKJKMZ0thU6nOoXLHUIWA9lmMTVDRbkAfWhk9OiOjx9wRVOLCJtUlSinhWnlP6vPGYn6syhp5LOqBuMzCS4UkcTnTGDMKmkMEbJjxG6hIrCyh0PPtlQ/ECXPhnIwifcQ2CX003BwXLN3Ao/JIZTwXVctxLDgOBT5T2kP2Sk1jOFmcTFnbxtfETMrdoIem+NPDkcdms29qMtIbHWdxZ3TEGuaA6BBlCzAGPYu6KW8QmMeNWkcq53FZCE6sLf/gPkw2w0/srOkYi0KM+F1wxKRxKnDpjlTK00T5D0pImj5ajqEkJMUjd+TKHWmD4+3yHq0Sd+OCUuftRu15q7zHqSCCrLhnH6rXgqU/7bbo7G/7Qn1NqUea7Pk617nOda7Pa70ZAktlJMOEZpiM7lNUCpxM34QTvqFtpRM0CaGMV3goYqZOj5pidLKwScOIrkrZcgsRNfpsYM/4gyKjIoosoNTkQUrS3aIUoZVy98SKZ4tBxJXctyZpg3KI+T4Ty6ctyGkkGPtRRooPDO3KKdKIbETqB5E3QSj28+g0G+vJXSCyMJVzGQUXYYyASIdRKOiriqgQ87nRBK3AKKidjATbUYRTTKgYZDRYWXQ3oDq5f+UsqSpQo/i7UmWJhUEZhY4JmlZEUUwz7oIQSXkJQZXyhi6GpNO1xVmJ9TEhf6+WEez+KKNEraFwhEVBLBSmj3PWIeSRbBtJbY2KSUahUXxMmsTWtRgXKY2n0JUIlPxrYPPWIOTLlzEORkdK5/OI0MydLhE9J0zD94dA+9whk+OdzPMxgbGR2vmTj2oaF84bhmG+/8mMH5PJ3TYZU04bjlOMT0oK52QzcVkMAjcd7Wy8l6dA87JZsrADG9ty7xfcZkE4JsOYDD4aQlIM0fJxd8FV0bC2HS+HNd/cPebL62seFQduxwVaRV6Nr1PZYz7ehW24sA3X45JjKLEqcDMscTrMm5znOte5zvV5rTfmVW7yKk3xKLqQmJfZlM4DRIPOXRprxaMTo/i3XDF3smLbEftsdh+EPaXKMguRnHM4DHO3RBlN2O1OdPbJQD76kyE74xxUWaL6Ab1cyAguKDG4hyDn8KCbpGyGpKaIWi5RE18rZn+Vs2I8d7Jll7zPUTsn1leKaRZWgpLI56yV+K966SYI5d5AXc0bbpO3K4UoPqaYBM2wrAgryRUEZgZWtJq0KDDtCCrNgc5ohb9YCJph1whOoct+NSuB0Bg1j+uUcyIehlEEkisktznIc5n6QQSs1hCUbDUqJWJrEkzWimi83JIWJWrfzluLaLBNHiNqRSqddB9nMRcwu57yvqK4Mxx2Ncd1wRAtS9NjVcQnibMZMgcrPPBBgSAPnI6MUTN4QzMWs9gCESu9N4yjxY8GYwNF4edQ6Gk7UKnEGE/xOJCJ7LlLNUFMY1JYHekGR59DqKf7MVnQTV0tn+Nvpg7YNJLsRsvdoeY2yRaAc4HSjby6XYNKbNcNRkPnHR+2VyytGNi1ShgihsjCilft+bDhwjb00dLGklJ7/rvv/kN+5fge3zo+oTYjOiMfCu1Z6IFP+y3X/ZKN61jVvfivdGCMhiYW0tFqN3x59epf7gXjXOc617ne8HpDBJaM8CZEwmtbg99v/EZQCmq5gExQn3EH8YBeLmYg59zZyl2w9ADoKYT2Er1Zk0IU6ro2c/SNdF9ykPNygVJKYnimMZ5SEhxtjFDGL7fSSRmzeDoeiRP3qihk9Jm5WcpZsDVpv5c8xbKEcSTmiJ+pY6WMgaJAZco8IQilfRznkGs5QfleZfQsrLACHFXAbERLaYaM6hhRMRKWxdw9iqWBBNr70+jPR2LtiE7njpBwrvSxhbYTI7o2ItoQP5byQbpbedMzTeHMIALKmFM3KjO5yFuEycn4UHXZKO8caX8U0WINqs3wV61yZM+DZQFrZ4aaqmvCIvuROuDO8dl6zZPqQFHJMawSIOgY9MyjetiRGryhi1mwJxnPJaAd3My+ijJlZbtsGYN+jaiu9OlndzLF+2BmptW0uTgGw7Et89MmQsplc/tkTJcOGBn3IFE5RiUOfUE3uFmMlYVnWfesy4FXhyXDYKiLkXce3zEEI14sNRBRXLiWL9QidG7HJQHNQg+zv+pL9Uu+2TylNiMXrqXSI/9g9yUJrNaB2gysTE9A82m3ITrFVxYv+aC+4bNeInPeq2+5tEc+6q7YjxUb1/FBfcPLYf27fq/Pda5znevzVG+GwEp5AzClOVdw2sqbuzqTmCBvEe4OgnK42J6M0zFJVApgri5JbYfabE7+pmncGJMQwfuecH07jyZ1Xc3jSYriFNWTt/l09nzFpiHs93J+IQhw9HCUTMBpTLlcoistIczjKOJKZ2DqxMkqS/BecgWXC3RVzrgItHTzUtefRqc5fBo4gUZhhpIm76VDN3WAnAUeeNNGLx6rLDpTSugx4lcioEwbUCGejO05Esfu5DmJtZje07okOYNeVKjdkXh9I0HT6/VpASBGqErxrvUDyVrp7GUYKEZDWWbxqcXY3rRwQJ6nqjptQrYd9KOMXJc1cS0B1LbJHcVpNyJ32tLoSTe3WK2olo79e4bkpEu0GyusDly4lm2h2Y/VLFQmcTR1igobMErGeGOQHEKV77DpHYMX8ZKSohkcVkcWeZw3jQIr5+dA6L53c0dp+lG0JmC04B9K5/HBMHjprI1eOmuLcqCynpAU+65kGCzHpkSbSFF4SucxpXTCDm1J05Tc3qxYrHucC7x6ucaWgQRUlZzfp/sNmsTOC5bB6sAQLWM0lNqzdh07X7GxHd9rL7A68FObD1ksB5pY8CcW3+V3hsc0oaRPlifFgY/aSz5uLnhSHfhy/YrHds99WPBi2DAmw9p1fNZteNGt+Mq5g3Wuc53rc15vhsACzHp96kaFk88KsrF7go1WpXRGshCKd/eAjO3SMGK2m8yAsqiFZL4ppcBq4m5P8h5dV5L/t1mJEPHhJGYKDX1CFY5wczvjFqbcwJRzCFMIwsxSSoKVhzFn7kmkT7y7R2/XItKqUjpuwzj7jNL9njQMc0ZiOjbE1v8u4KgqxKsUH2QkPqxppDmxtlRZCqYid8uwVkTPxMfKtPTUdGgfSKsFbhSfVSgFwyAEdyUROCERy7x9aHMnUClilUebiwq1eEfCnHtBNKAl2ifd72aOGUDMxH21qOVzE78LTtucrkAt69PjnEaHLucthoC+b9CFIdRWOm6DjCsn/IOqq9kzZw8j2+9atLfc+QvslyMXhUTAFNpTGs9hKFgUI1aLePVRz9E2D5EGChkbPqobWi+wz+PgUCpxtWjxUdNkinthA8EbdjnHUAGLSh5rPzq811gbcUa/ZpqfOFi7tqJynnXVMwTDvitnxMPFqmVZCG+qGd2cfbipO96+kFDzaSxXGk+7cbSjY1kMHIeCl9dr3n5yz6ZoebvacWWP7INsen6pfMlNWPLL+3f5oL7lsdvzdnHPLx/e5a89/2NolbgqG/6/H/84CyfxPtuy41F1xEfNtuh4v7pFq8jPH77IJ82WletZ2p7rfsmfuvgul/bIi3Hz+3ylONe5znWuH456IwTWZECXDoyRTs4wkIYwm4DljXkh4uHYEPseVRToy0tUVUqsTVXKCK1w0A8yJrOCWCBE1NtPZ0N4ilGQADALAHW5Ff/QagkhYnIeoFothTbe9qT73Ty+i1O+YRaDse+lAzdtIB6OIrQWCxESefxJzLfRmjSM2XxvJCZo8piNnth2udsl/rHJHC+bclYgq32Psg692cqxputQZUBqHsklZ0mlla1MjYz5+hG1O4rQuTwZlaPVqJAETqqyUbwbcxC0JZYaFWSD0BQWc70n3d3LeSkFWgzqqqpkTJlFIs6KCOv7zA7TsvFXODHke0+8vYW7e/SyRi2X8hh3e9jthbyvFGlZi6jyUc6tHWdMw0PAqyocsTCMtWbYQHKJ0RvuhpqN69jYjljvuChaGu84jiWHsaAbLf3oGEfBHEwROqXzdIPjw/5i9k8tipHBGz5+dUFZjhgTxZMVpXNldWRZihiaRNsYDN7KONHZQOn87AEjao5dwVceX3PX1dw2NXUx8vZmx2Eo2XclzSDds2mD0ZmI0SN3x5pxNLIJmf1gAMMgv+Zt6VhXPT/y3nM+vt/y88f3ebRqeGu543F5ZGl6/v7+SwD85Pp7fLd9jFGRUnneLu/5M9tvcQgVr8YV/6XL3+Bdd8v79o7v+Q0fjY8Yk+Hj4Ypf27+NVok/f/Hb/OXHf4df7t/j+bjly/UrXgxr7n3N1rZ/sC8i5zrXuc71htUbIbBSCMQ8GpwClPVqecruKwrSsSG8lIgPtMGs16jNWkZObSudnKst7BvChx+LH+fZE9JqIcHCcDJlZ6J4slnkbBYy8mpHUmFJRmFe7VCLSr7etKSX16jLC9IH76Bvd6Aluibe3AorK+MeQDYQhdguoil5L+PERS2epRz3M40f1XrFa/E2iE/LmNzd8l6EQ+6gTQb9OYx6HAjXN/O1YRghh2CrukZVlYiu0RLXNWFRYg49at+IuFEK8+IOM4yk1QKswV8sQIEaI/rYzddMd55YSTdJ9x7djsRVhbKP0btDRlHk0ejhmOGfEs7M/S6f4wNIadsKEd85Oc9JWHZ5jLpaop8+Ji5rGYmOMgadNh3DokDXjtTlhYB8XdMwEq9vsN6zbZ4Aa8ByP2wZ3zNYFdkULTFpvnfccnNcCCpBR5wJLMoBU58y/7rREjKzqnKCUehGy74tqYqRZ1c7utEyBkNdDlgjx1m5gSEaDn1Jn3EJE2i0LrrZ8zWOFqUS66rncX3g0+MGoxLvX9wRouYwlDgTeHuzI0TNbVfTjXbOFOxHy7IaWG0OXB8XhCDbh48XR1au56P9Bfu24nq/5MXdirL0vHdxz1c2r3ivvGVhepogRvbvdRf8hx99gy9sbrkZFkQU391dYtRXebrY869sv8ff232Foy/49VfP8FHzI49f8FPbj/h69Sk/Vn/Cd/on/LWXP8G/1/40X9rc8JXlS7ro0CrxxerVjIU417ke1l//5Jd+0KdwrnP9gZVK3zdy+kHU1j5Jf+bqvzV3MVII6PUKZS1xfyA2Dbqu0dvNqdvR9dINevfZqUN1bGWF/3JJLCzai58IQPso2Xka6QiNQYKGpy4WkKoChhF1bIlPLk5RPJWMfXQ3om8PpFWNOoiPKNWljKf2h9kLFttOshWzWIATIHQeM66WucskvqqZDeX9iUbf9SfMwoOtwDmjMXf7Ytue0BXTBuRkrp8yAJWCWgjoSWviwjFcVdjWY69b9L2Ebae2hYsNqSoJ61KyB0PC7joJWraGVBZyHXMpH0XExki6vc/eKicdq64/ia4s5uSOZASaXDbjH3NHI1/z5AOMWRC7QjpehTvxsK62xErOQ43SzXpNoB4aEbNPH9G+v6F7ZGkfa47vJtJ7HW89uufHLp8D8NHxgruuprIeo6NsBwZDP9qZ2j7F1zR9QYwyzltUYibf9wVtX/B0c8CZwKvDUrpUXjMOlqL0rOqeZTGgcrDzFJkTM8X9ct3MeAOtBEh6d6xRSsaSb632DMFw19W/y5RfmIDRkevjghg1q6qnHy3HtmC8L0HD47fvWRYDY9Qs3EgzOnatBFaXzvPWes+7i3tiEuDo1+rnNLHkPtS8HNZ8Y/Uhb9k7vtm/xafDFoBjKPn64jN+p3uMITImw95XgppQkZjxqE/LPVvT8o/37+KjptDyO/fv/9n/3c+nlP7k7/Nl442qjbpKP63+4g/6NH7o6/MgsP7SO9/4QZ/Cuf4I6j9LP8Mu3ah/2ve8MR2s8Opauk6rJXp9dcInvPMMXReoboRWxEZ8siQVFkLC3B2I2yV+VcBT2Uyy9y22GYiLArMfhTjeD6/l86XSyQZdrlgY9CiZe/3lU9wx4O56dBcx+07ezCOM7z2Sc368wu579PVu5nGB8LD0xVbGg02DXixkQ69tZYsvd53Cg41Gc7GdR4WEzH7KbDB1eSniYqq2RZns6VJKcAcPjotSpDGQxgFyNqKyVkaMw4AaF6TtCrTCHnPm3WVNeGclH6eE2fXCxVKQrEZ7TywsplWwO8g2YVmIIH0QJK1GD289Qe2Pp1Goy1BVa1AhiuDqevGGFU78XlVJutqiji3p7l6WBpRGb1ao7UZiccpJ5A45SsmjnCFUJVpr1NihQhQafD8Sb+9IIaJToh49ttmSVEl0mv3WMVwY9r5kbXveX94B8OKwks29eFKPWotM6EaLM2JQH7x0m2LUvNwviVE6Wx+/ukAB61UrOId6pN4cSfn2L/dLvDc4F9A6zrerS8/NbiGUChOxNkj36vKOzjtum5pvvnzMZtFxUbX0wbLrSsZgsBl82nQFm2VHSor7Y01KcLFuee+dT1nYEZ80v3N/hVKJfV9SWc+ffvtD3qtvCUnzvN9wO9RolWiDEzp79YouOr5QXfMfv/pxvn37iG3d8Wyx56poeFbsWOuO//r2l/md4TEA92HBR90VL/oVXZDn7OPjBWvX8ScvPqSJBVvb8Go8bxGe61zn+nzXGyGwlNHYL35R3qSHvGUWE2olIkI1vdDIq0K25MYAEVJp8M+26M7jnu/kDTwEwtMLwrLENAOpNBAS45NL7FE6UHr04APae+E11aWYqK1GVY7Nb38mnasnF8RFQdIK3XowCnvX5M6Lwa9L2D5B9wHdeTnPphOcwuUF+skjQUnc3mEuLsSvlfEJjH7enAz3O8jm+VlcVaVgI7puBoemYTzR2ttOvFvWYjZiGE4T9BROcTRTaZ0ZWqC6HpMSrEuiE0+W2w3odkSNgVg7oaDfd8TKMm4rdGnAKHRViBBqOtlItBk4msUTdzvYrE4IhmGENFH5xYCuykLM65C7j0HI750IaP3oCvqecHuPut+hV0tMXcu4N8gx07KGCGbfgyaPdrNBXynUdiPbh9qg2h6776luLf2lprg2XC9XXNUN79e33AxLQtKyjacFhzDhFB6WD6fFixg11nqMiSzKkdJ6fNVzzN2siU1118iihdHyfZQjbd44NDZw7GR7cb3sZnI8SBfr+X7Nohx4uj7go+aybKiMpwsWqyM3Tc2hqTAm8ngjJvPCeh7lseBvvHzGL374Jex2oKoHLheSR0hmb/3jV+/wm/YpV3XDyvY8rQ5c2IYmFvzS3Xv8in6HH1k/5yvVC/7bT3+eJ+/s+Gh8xK8273IMJc+HDT/36ssM0fBjF8/RJI6h4Gm557+8/HjGVRTK8/Fwxcf9JWM0/MLt+3x98+IP7gXkXOc617newHojBBZKEZ+/lDfax4/gYgPWEPM4ScVILKx4f5RChSh/RhE2flPBskCNgWQ05thjb3tUL5DL1LbYw1E6WHUlm3qZNRWbFu5BLWq0c6iuE0RA4eCbH2KvLsFZ4mZBKiyxtKgxokLAXR9ztl+GiRY5oqXpRER5yTTUTx+Tjq34pFJEWTcjFpRWsvmYx2AT7ypmv5Iqy5xxJwZ3Xcu2V7jfzd2x9EBMxdy1mun4U77hMKLaVgTMYgFPrkhaTOi6j4Ta4heO6DId3WcGmI/YZiRaTVg6MbanhBpG4v1OtimtRV1soa7gagtNJ+KqH2DaGFSZRh9GEWLGzFuRKqY5GzIplQV2xKyW0iGTRGMZ5+buFyFAaYmVm7EUsTAkrXA3DfHuXoj7U1fUWYrasf0WqGA5mIpv6qdYHfmR9XNYwrVdctfXHIeCwso1neCjzsSZc1UX47xBp0Ki94ZjV1A6T2E9zoQ8uous6w6X42vGIIDQdd3hc1zO082BmJSY3DPvCmBbdlSrkY92l9weFtTlwBgMm7JjU3Q8s3suy4bDqmTXV9wcFjgbOKaCe1XR9Y5FNfCnfvJbfO+wZV30dN7xzuoeTeLgS37q6iOeFntuvOQGtsHxqn/C2vb8+Uff4rET5Mm3uqf00WKIBDRHL8wurSL/+tv/CIDfbN5i7ys00gH7eLjCqMgn3QUfHS8AKEzgi8tr/mtPf43/+MWP/2G9mpzrXOc61xtRb4bAMpb09S+SnIbeZy9SnMOE46KQUVU7yqjQyAabCgnV9ri7/SnGputkM62uSJulbMlZi768hDFn1VXVTFvXzs3U8NkL5T2pHdFPHuVQYY3etwK8rEpS6cQX1Q+oTokBu7IolWGSb1+iBy9dLWfFnxUi5nIr5waCjMhG9XB3D0qd8hUfBEtPI8NpO27udi2XIiAeYhymQOhS8gAnv5YyeTMzhLkbppoO+yISrlaMmwIdEnoIoESkTIPl6HLXJk9AQbpFrBboshDfUwiyCHB9I0Lo8mKOD0oTuuLYCsqhcKTtes4PPHW5ElQlKji5RsYQb++E2VU4Ma+rlSwyZJhrXBSMK0eoDNMJq5BQocK9/Yy0PxB3B8LugClL7LUlVluqW0N/oWkeG141S6x6whCF1N6MjqYXcKdzQXhUE9yzlOfgtqnZNxUpQVF4LuoOoyM3xwUqqRxzwxy70/QF/WBRinkjEQRk2gyO0gYObUldDmzrjm3RcdfX3LQLrAk8Xh8Zo6Z2IxdFy9p1uBzW7KOm0QVXq2b+dWoGx2bZ0Q6Of/itL/C1917wuDpSm5HboeYwlmyKju91FwC8W97xzO0Yk8EpEZYf9lf8nVdf5d3FPV9avMIpR0yKPlr2vsSqiNOBv/r8J9kUHVZFLlzLhZPzuBmXjNHwTnXHN9Yf8r67plCBnz18nd9q3uIvv/uz/PV/uVeNc53rXOd6o+uNEFjJCNQyjYq4klBmCvHsKB8xL+/lTbgq5Q37bidYAMBcXoiY2otJW9U16djI13OYs14sgCjm65ig74n5a5NxOradiJWp4xIT4bMXQo1fLWU0V9ekVnxCqixIVxfSsQoBOiQAWckYMTlDrB0sCrhaofog9HMfCJ9+Jl2VzZrUdZlYP9MyTxuCmf01CyelMOu1dNR2sqUYux69XAhYNZvi0+glx2++fTxxtDI+Qo8eZbaYfYcaI1hNqKQjk2RWJde+HUhGERYFoTaEUmONxigFvfjbUtdJZ24p8SzEKCO8B1mNaSMbkfiAGkYZs1bZA5c/xhrZ9BxH0jhmXEY4edC8RzUd4WqF35SMK4uvlJxvLhUTbqdId/ciRgG9Ws3RPcWLI3qIRFPhl46b1YLHiyMXRUtlRoyuKUyQLD4vuYMK6AY3e7MSIpSmum1qvBekg89dLgmJPnGt1lU/d6dCUhy6U9hxOziebA60o+P6uOD6uODt9Z6FG2jGAh81lZVrEFHUZiQmRRscPgmMtB0tRqdseM+jOet5+z25Bj5pnndrKjPy1fUrSj1SmxGnAs/HjYRQJ8MnrRjY36nv+W+89Y+59wv+5suvs3YdT6oDP7b8lI3tJNxZe350qfmou5pBpS+HFUYlrtyRK3vk02HLrw7v8kvxAwCu+wXbouNn7n8c+IXfx6vFuc51rnP9cNQbIbAAwrIQplEfUCGgulE6HCFIQHFVyqZZMwrB/csfoPYN8eYW9oeZoD6BR/VqSWpT3kTcn+CleSMvhSgdqy77kpwltS3x2EgIdF0JJX4QCnvse1D3mMst+upCPn+/F6+Yla29iTcVF6WAOEPEHAfpuExG767HvPVMWF5396jlQs697Yhdn4OqNcpl5MOUa5jPPbYd6RAwq+WMfwBkXJc3CPWyPBHwQ0CZQnANUwi0kyDrdHsPTYe+WJPKAuXkHAFioTE+ykjWR0w7oscgbCxkKcCMQbIFQyDsDvN1NO+sRbimRFwUJ+ZW2582B3PAc3KGVDt53mMkbpewrsVnB3J8o2Xc23YkZxke1XRXln6rSNmPbltwx4htp05cDgePgXg4yCKCMejNBtuvWDlNKEvuzZLvuMDXHr9CK+kstZmDFaM8VmsD67rPYc0ag2ARjInUxcjaCgw0RC0sK8RLNQmedrQcfYE1EaUSIao503DCLEwgUKPlV/LlccnT1YHSeHwsGIJhDIadrqjNyNIMLG3PYSw5Dg6tYF329N4yeEPbOapq5Pq44IPtHV9eCBrh24fH/Pr9M768vmZjhdZ+My6xKrK0PV9dvmRhBl4Ma37m1Y8yRMsHy1ueldLhmszpC9PzUXfFW8WOH1t+wotxw8GX1GZkoQfuQ82vHt6hNiNPCtki/LC/wifN0RfU5iRQz3Wuc53r81hvBKZhs3wn/elv/A8x7YjqR+EzhUDyQTpF/SBdjMw5mitGVFUKCiEmyeJbLUn9QDw2p4ibuhJIaZJxVGw7QR0AU7D0nBfY97Lxp/ScCZhGLz4praRzNCEXjBHT9mopBv2Y5m5TyiHPlAWxsGA1upFwadV0pGUt7K1dI5t1GZQ5ecYmKjkpnujuORxaHns6jTnrCspSOkH5GqVhmAUXWom4mvINtZJunrXid7JWrvN6QVyWxMIQS4PyCdN5ETcRMFkQaSXxNDFmRMMwj/ymbcHkLHEl4dpJMd8OEKM6QCJT4zWhFFK8HiO6D5hmlPOoZVtUBQn9C5Vl94WC/lKRDNgGin3CNRHbRkwbKW5a1Kev5Hrkbtwk1CbwabracvzSlld/3NJ8eeD996+5qoTQfhiLLKYMvTcomDMBY1QYEymsRyuJupn8VDGpOd5GZyN5iHr+d0oKayLWhNmXNYQT7d17Q+k8y3JgU3YUOuCTZuV6NImbfiHhzmYkJo1PGptF4at2yc1hQek8SiXajJOQLcWBynm+sL4V+rt3xKQpjOf9+pZL19CEgp0XQ75WkZg0Vge2tuU7zSMOY8nzZs266Pmpy4/4jf0zGl9wGEqu6oaLouFJceDSNYzJ0IQijxMrtk4QHEO0aBKlHglo/jc/9X87Yxr+c1bf/F/9qz/oU/hDr6/+j//eD/oUzvVHUD80mAYVE+75vXQoYoQsdpQxIq5i7mYYIyDSDJtMIcAwCuHbe1LXE+/3IkbybVRhZWzmvQiYHHmjTEkcRulw9CK8lDFyzJTQpRP4aQ5fjjl6Jo2yuQcQR4/OW3DERJyQCMtFzqxTJJBMvz4Rl5krtapESI5BNhitEcSBD0KeN1oM4tkcrvKYLU4oB8jm9nw9hpGUReYM8TRGMp5DmIn4c3h0XZ2gqItaxpRdJ92nbkAXDn+1JFaG6DQ6WXQ7whAgJHCyUQiQtIZa2FpqDKAhlpJtmMwJdyDj3hzBo08hzcloUg5t1n0kOI2/LFCbYv7ZyItvJAX9hSVUoKKEOBf7RHGM2ZgvIiwZJSb8/T7jM14vXVcoY6g/tWzWa/zScfe4YlN2OBOokmffl9KtUg/+A6IShZXOk1aJdnAMvpi9VlqnWdT4zLfSOr2Wb+iDEZZWUtgsygrrT+iHpBiC+MGCES7Xdbecx45LN7CwIxvXzVt7H2cT+dWqIWQhWLqGlKRT9pXtK2ozMkRLoT1DNCxtR6H//+z9Waxka57dh/2+YY8xnSnnO9bQVV3V1V3skc1BbDYH0SMl2jJk2LAMk5AfDAvmg23pSYL8IhswaMDwC2EDJCAIFCnDpswGKXE0KbIndvVUPdR06055czxDjHv8vs8P/2/viLxV3V1Nd7PyXsYfSGTmOSciduyIjL1yrfVfq+emK3FoTu2OW+manU953kxpvGViG6am4Y+cfJ2vVXf47OyJFEKblk/PnvGwOuFBKYzxLKnZuIx3dmcskpqJbbjpChLled5M8UGx6TNS3XNZT7hTrv9FPiqOc5zjHOcjMy8FwMJ52FUSM9A0+M1WJLso4wEvFjXHehqR+pwULYcwFiUPXYN+uyN0vTBNShEIoIT1GbrvlLVSrwNjtEFA0tNVlgk4CwpdpOJjQnK7UDoCwGYfJppY8Q/F7bWh6JgYzqm3jeQ5RXDii0SYGasJRSp/7nqR0mKxNH0vAMr5WOrMt/Q0hrrZM1uIV0siG8IIGF+YGK2ANfJ4WSpm88Hr1XbYy41EVyRm9FGppkOFVtg5uz8GlELhBGzFeAbdBnwaQ0iHLb9Eo4LIjwI8ZRtUt9GnlUikhh5regRgKR/wRtMtLD5WQ9oqkC0DxdOOZC2g01uN7r0whTH7a3hdgvPCDMY6nbBcYYBpkVBdlCzvTKimWwor24GHTFTvtZjerWwH1m2C1j6yUR6wBMBFoOWcRuuAiWnuQxm0QuIahiBRowM2fj+1DbkV4Fb3krm1yKoRiKW6p/dS8/O8ntAHzVm6Y5FU6EngrfU5N7sCazzOqxGwWe15e3VOFj1cp9kOrYKY1JWnSMTP9aSdk+qei2TDnan4tt6uz3nWTln1OXfTFRuX8bA64ZOTZ5S65Quzh6xdThcMpW45tfBqfj0yWFPb0nnDvXxJpnu6YHjazHilvBlDVY9znOMc5+M6LwfA8k4kPe/HqhO/rSI48BEgWenyA+g6YZ+ihAcCKoZts0FGfEEegxf/bO2Yqn7YXweg00Q6/gAzn+OjcRxAJamEeOIhKAFoL9w4eot66aILUZJSbSceoDyFLCUkUqRMjENAR5O81dJ72PXCarUddB2qqvEx7FwZvfdXRRP7IBkCI7gaIiEGmVEYv1Y8a86JtDkp8EaBRx6zEc+Y8l76GknxZSpgsEnRu0aeS9vts668FFhTZNJjWMvt9Y7Ro4UxMM3keQ/qbOfQTY+KXi+Jlojgy3kBW73HrBuC1fh0Sjcx6E6Yq/JpS/pkI8eSpZBKAbWqGmEfnYvVQkNZeKzQicevjCZ5mjN7mLF9JWF1N2cya7lVbNkmHesmk84/7Ue2aTCqSxK7Iokp6s5LuroLSjKytCexsoXog0INhvkoFxotICsERec0WkEbGSznNRWQmRSrfAR58j6fJg19NNtLYrrjItvQBT2WP4NESSgl3Yud19zNdqTaoZWn9ZZll3Oa7tBBthGNChg8y77gsVtw1ZbMkpqpaVj1Bd/Y3WLdZ/ig+MrmDnfzFetOoh1u52sql7DtM6x2TE3DebLlQXbNJgKwzhvO7BaD57Kb0PqX46PnOMc5znF+v+bl+ZTzXi7YEGtgFApDOMzLHPKUtPimALlYJjYauPW46aesHQ3QKs+kD6/rCeFAboylzEopMbEPIEQrKZc25gWwMtS8qCQdPVEf9rAdgq3QtgJohtsPYKx3UGTy9QEUGUOwAs7kXMT7CwHlHCFN0LBn34xBKS2BpM7J5uDQTRjlN6XVyHoR9gwV3kdPm3wtJFYk18Sgo/mc4Ti9VAwFa/GThJBozNZA044AMHQdqk8EVA11NV7YKUIQ+RIwXSm9hTH6QTVO7iNEcNhrSMIoHyofRJoMAVzAVj3JVpNsA+mqJ7mqYrCrgDw9hKyGMJZdh82WgBtN//vzEfDbCr1cUzyZUX5Qcnl3yjyvOc9FDh6BzNAX2JsRHOkIjvbeKtneS4Audg42nR1N78PPDn/unfi1+njfwBhwmiWSpdU4C6bHu4Q+erkglkWHnpltxugEHzSZ6dl1SXxbiTRZZgIYZ7Zh3WdcNzOM8twu1vTesO2lf/BWvmRmatYup3IpJ2nFxMjm4zwWMxemQytPpnsab8lMz0W24cxued5N2ZKx7iSnLdM9TbBj5lame3Y+ZeMypqYBs28yOM5xjnOcj+O8HAArIBfF6FkKbay1iRfDEM3fKL1noQb2JHgBFzBKgYB838UtROeFHXNOvFlKoZQWLxGM9xm0BHoOCeloRdjWhK5F53LhGLcRo7cntO2+jsbakYUbjzvEUFSjgWYvw/UupsfH3CvYf89F1sXuM6mUMcLgDRVCB56y4H2UwaLvTMWvD/LgEDoKcl6HXyEIk5NGMGo1LskkY6z3wi51Dl23KOeEyUoNweboyspmoDEoLYwa2wq2FcoaCQMdZN0hakFrtPfjc35BuvRSSWOA0A7G9IBqYy6aBt04spsO3cp25rAggJEQVrpeQF3vZKszTdDDYoQfFh7S+F6Q8xM2W5L3Lzk9SahvZzydT5klDanpWaQ1WgW2bUrT2RHk9J4D9irQ9fJe6J0aoxlCUDinRylsAFiHIGswzYcgvi2MI4udhy529vVeU9oWNNiYUdU5Q9NbrtuCyiVo5dn1Ce4gzsEM0qYXM/t1W9AHeQ+UtiWLfYBn6ZbTZEep23g7T2FaEuXoYgSE/BMRc/qqL7idrunifXXesOwLMt1zL1/SeMuqz3m3OkMrz1m6o/VWMra846YTOf6V/OY7+GA4znGOc5yP7rwUACt4PxYWj7KXcy94nZTRoxxI9Fsdfh9EOiMmi/u6GYGQX4uhViXpWH+ihpiAtpULbd9LeXKaELqA327HMuXx+8PxDiXEB2npozzn/Lh5KAXPhtC1hF7iFzQ7YdTqGjozbvcBAkwieAyZpKaHNEVlifif6lZkxyG2IOZF0Xbx9rGGR/nI/Ln9MRojgMwHYfMiE6aYynPJM0Jux/yrYDSuTNFdNLaDyJBBHt9NU3yZoOsEvUtkM3JYBGidlDsHtQ9NtVaOoWlhKyyhBIrqeFwemk5Ku42OrFT0b3mP0hH01TZuYjYCruLrMIK2+HdJek8lI+3qJr7+0Zd1kK0V+h7//Irs2QnFkxmrV3L6U82tpGar0rGWptMevB63CZ0TE7uLgNdH8NX1Rjx7et8pKKdOpL8QDmMcfPx/gB6Ln0E6CnunMdozSVqslk7F3PScpVt80Fy1JbWz+KAojCNJK0rb0XrD013s5NSebZuy6TLOsh0nyQqPovPSw3gRwVWmYpG1T7nuSiqXso6G9LN0J0yXk2iFbZ/ypZtXeXNyycYJY2W1Y24bCtOS6Z5M91QqIdMumttTem+Y2JaTpOLd7Rm7dB+MepzjHOc4H8d5KQAWKmY/WTt6hXSa7BmnQaqLBckjM5Sm0YQeWZ2kkAuoVqiwFTDRd2PgpsozVPRv+XhfyiZiZo+RBoeSn0qsGNt1BH5pKgzEEDdg976w0LbCfA1+Ma0ObrNn3oITpoa2I4Q25kFpMcQfnhLvBYAZQ8ikZ488HcFIyFMBDK19oddwz2qFMWpCGMBmHzw6eNdM7DoE+bsWidOXqWzjaYWPuUyEILEKzgM6bgkqCFZyrnonXYdenvML9Tg2Brr2B3qvi5uNfZTunJdzNEjFgxSqJQl+8IWpdZT4jN6HmQ7HRwSfKiFkJb5M0UuD2lVyfro+Lj1okZYBU0xQZQFNT37pvd4YQwAAndNJREFU2T7JeHdxMsYhWO1HgzgIe5QYR1F2GBWooiQnG3thjG0A9mA+rkAOxdGDwX2IdghWjZuITZeQ2p4y7USeS2qsdviod990BZl2zJKakzQwMQ2laXnWzvAoNl3Gqs6iET9wUoq81wfN83YySnYn6Y7GW765OydRnllSU+qWqWm4m65IdM/D5nQMHn21uGZq5d9M5w3v7M64m6/53OwROyfSX6IcC7sTydBMuekFrGXa8Voh5vd76Q0/ufgNfm7zie/gg+E4xznOcT6683IArBDE6N6Kb0hl2bixhzYCkkIYy4qHCIbQCGjQWSZMxG4HQ2L5h6SxIUgUxMQ+bB2Grt3LfjECQlkr/qqu3xckpyl6PgfvhAECiU7Y7vayo937wpSO/q4Y6TAa7/MMt9qM3yd+f+wh7PpojI/ZXHkmYCIx+Bg+OtbWaKQ+aNhW9J7QO4ixFCFKpMN5JPgx/0tk0AS9mEkURtcJWzctUY2DRCRUlxmUD9hth2p78YkRySKj8JkhJJqQaHSWiqdquZb7NFqYLK2ll7Fu5Bji46npBCalAOuqkgLrqt4zegNrue4FfOUSODu8Z5QP+DLBTTJCqsFJbpeuxG8nz8OOW5KqlKT5UNdSkeQcfrdDA2pbM/2goFnk3MxmPMtaXp9fcyvfsGxznjth+hLj8F5zuZrgvSLLevKkxxg3bgpKVY7BRNO6UXvgpVUYq3KE1VJ0Toz0zmu03oedAry7PuXuZMV5JszVus/k/oBll/HUT1kk9VhR49KKT531LJuC928WBOAi33DTlljluJVvuMg2VC6lD5qTpKIPhnWX460i0z3f2N2icgn38iUT29I4yy9dvzLKhX/qzm/y/dOaf3L9Kd7enpFqkVOnpuG6m7BxGX0wFLrl9XxNpjuedzNeSa/Z+ZT/4tkP8+vP7/xuPiGOc5zjHOcjNy8HwIKxlDe0nWzgGTOGf+L9PssqbpsBo6QY+h49nYw9fQOzpdJ0ZLFCVY2AJ/R9rKfxqCQVo3gnUQh6Uo7gJlQVbrVBFzl+t8M9e7YHbwPYG8zvMRRUMra0bCEaM0qNBL9n4UIAbQlti4+gQ2fZyMYE5whti87EO6SqGuJzDJMCXyT40spzi7U1KoRYkuxGFk7KkzUQQdYgp0YGLjQN7uoGvMOcLGRJYLNDZ2ksrnaozuDKhH6aoHobjeviE1NtBLpW4wspwrbrBrVSwljFx6DvYTaBWQldj247aIRxC3Uj4a+b7chKAvKae49SOibWJyKHRpZOGU2Ylvg8GUGfTw3dLAFVoFxA94Hkqkb3vbwfEgtJir44EwZxVwl7dXZCKFIIkC09xfuWx+kpifZSjqwCZdKyacSLlSU984n0D/bOULWS+m6tG+MdQHxL07yhc1IG3XUGY+S1kcgHYb0S07GpM3lb6EDbW5EP0455JgsCIrGJDFe5lG0vWVqlbZklNV0ENJOs4aYreW99wsmkou0tX352j/vzFSj4ys0dbpdrbmcb7iTCYmX03M9uAHjcLMhMzxdm73PdT/iR6Td51s+5mk540s5pnCXRPVp5vjh/H6M879VnVC6h8SKnahVoekvnBVC+szvjWTVlkdbMU4me+POf+mf8e7/3HyPHOc5xjvPSzEsBsIYNrwEgAcJcRfBxuGY/gq4uMjHRqzX0zqHN6MMa4hdGYNU0+KZB5zl6Md8b5OtGfFVKQeXxm80LBmy/3Y4FykPAqZ5Oxi01ZYwkqTcNfrcbpcGBYTPxsfxmK1KkNXv5c3iMuh6Pf2Dd3GaLblupCSoKqBtU79BNis5S+nku6fJlCkUCJ1N006HrlrDdQTP40JQAiggsR6kyVubgjRROL1cjq6WLgpCnaPJ9KKgdwkPDXv/SMTi0D+imp5+m6PQW9skN/upmzDFTu0q2NielFD7HNHdV5oT5FLOYEZZrqTU6TOuP555ORUN+IqyfMrDeiin+zoJ2nuAThe4CpvJSl2OU+MTu3cZcryBJCEVGSCxhUggReHklYD45xdQ95TONSxNCkvKeOqe9a7g92ZAaxzxv2BnZHnRe0XTpyDoNyxXzssbGhPauNzxfCvOVZR152tE5Q9sb6jbBGE/bymtd5B3WRMAaFHWbxAqcgM4Cz5opz5opqe6ZJQ1n0cOklafULV0wNN7yuJqjVeCP3HmLX7l+QKUCnzl5Sh80Lig+OX3ONzYXPK5ndEEzjZuCzzsJA820/Lv6je09nlRz/tN3f5S7t5Zjd+Knz5/xtJ3zjc0Fn5w+54cmb/NKesXPb97knd0ZPihem1zz+ekHPOnmXLUTbmcbvjh/n1x3vNuc0XrL3332OeAf/m4/Ko5znOMc5yMzLwXAQil0OQEdDemR8SEEYZgSu08yP5AOzXSCKQqRu4p8rEMZJa/TU5Q10tPX7A3RvmlGP9cA3nQu6eah79GTCe7ySh5mUhLqJjJMSoCc0uP3xTjfEVarvck6ZmvpiRQcu+vrF55u8E5udxidcMCChc6NQCsECR8N9Y3kaJ0sUJsd9I5ksyNkKSFPJGKhjRKjNbCYofpSiq8HcJNl8Tw5keKcA68JfYeeTOT5ty1+uRZANJsKWJtP6Be5gKsQUChCovDajN2EKOgnFlM5lPO0r55jFxPMZQS+zkWju0iGqswJu5qwq1FNS9hVuPV6fD0OAW6IQFGliUi3m60kxxey2anrDlMbfGLoc2GzTOOwz3f7aqImboYqhZtl+MSg5hl2PiEkhn6e080TXKbJVh79lkZ1KU/CCdyD+9Mlt8s1uz7l6XZK06bjRqCNsl7vNbs2oUw7nBfP1a3FBq1CLI8WhmeeNxRJR9Nb6syOxvcmxjsohMnaVhltb8ltx+tTeb9VLsEFxarPqF1C6yzztBqlvs/PHrHqc76yvkNme66qkp999BqfvXjKB5sFv1w/4CIGqlYuYW5F2puZmjO7oQ2WX9m8ig+an7j4Cv/bV/82v1S/zqfTx3QY/h8f/FF++umb/MTdr/HVzW2+sr6DVQ6twpgU/0G14HE9p+4TPtjMyW3P/Xs3vF2f8/7uhB8/e4sfmr7N/+df4KPiOMc5znE+KvNSAKzgvUhpMBrdzWwmJuyqEhnNu7jVl48sklut9ncSbw+MYGkofhbvUfRAZRnm7FRYlOUKvxVflq9rGFik3X7Dya/XKGvHi7+ezfCbjYCVoW4mi4XL683IWimb7J/TkJsVtwuHIFP5ZgQTIaCMApXsj9k56Nw+Gd553JOnwr7VtfQJxmBTVbWESU4oU0k07ySiIVzMJCZiXQvQiM9R5FNPaDvxsLWdMHXWomcz8TxZK1ESRglpZUSeDQFU77GbVsBJmaAbh3IhgpSc9LKWKqD5BFVFebeuRabMM0mSj96ooBTMJtjFXIq14/kfEun1bIpfb/A3+9dlYChVVWPcLancUfI6JKsWs6yE3ctlC1MPNUQ2blOGQNCKYAyqarF+eC0sKoCtPcobTJfypDtDvR64VcrreVbsSIxj16SUWUsd/VJ50o8xCSA5WFebEu9V9Pt7tPbc7AquvPjB7BBG6jWZddEA7+mcgQxS49h1KW+tLzjNdljlsVq2E0/T3VjY3HjLSVLxdnUOwGdmT/jG5gJrRIqsXcIfvvMWlUt4a3PBSVphVKALhstuws9evcGz7ZTXF1f86Ytf59d39/kHzz/LT/VfINGOrz76E5zOd3z/xQf8a+df4+89+17mSc15tuV2tuYiWWMkcYyNy3nczFkkFX/i4jeZmYp3mgvOky1/9sGX+Ifrz/HV7V3gv/mOPh+Oc5zjHOejOC8FwALGnCnfNCibvAieEGCkjJEiZkAXBfpkMWZcofW4xSfepnq/1WatMDTG4Kua/tHjF+/bWuzdO4TZBLWrcU+eCpOV56hJKdU9gD5ZCHMVgnjCoo+L9Z6FUjGOQKUpelJEkFiPDJzSajy2sd5mKGg+lMaiQT+EwbAv7JaeTqDrZbOu7wlrkTPVbApVi2o1KjGE1KI6h7neys9rJbfJ5yL5rbeE9Vr8Z4CiAy3VQAPjNvjVlLUkd+8QFlN8meJKi8sNLjeYzmPqnpBovNVkVw0utzR3SggF2VUjMRODET+Gj6p6MOWLZEmSyOu2242bkD6eD9e26CwTkzuM8uIwYbPDAuZKy3NrO8JqDYnFzKbgvLB4ufQ+aqvH8mk3y9C5pVtkVBcJKLCVx24d0/cd2dJiKssTd4H5ZODzZ494Us1ZhpwuFjX7GCZqjGdbpxjjMTHzajGpxgqcXZewqTO09hSpI7OS39b2lkkuoaE3u4KuM0zLmj56t7KkxwfFxFpOixVGBVIt5+a96pRNl1HalmVX0DpDaTsWtuLfuP2L/Mz6U4AwXz/99E3KpOUHTh+y7TM8itZbCt3yP7n/s3whe8iX6tf4Jzffw5vlc7J5j8HzILvmP3rjLf6fNz/CTz99k8Zb/lu3v8zD5pREO667EhJwKN6rz/BBc55s2biMn1+9Tu8NfdAskpq/uvvD7PqUV8qb3/2HxHGOc5zjfITmpQBYSuu9BwnxVenJZC8XOSfy4MDkGCMX4upDYZVDB59S6NlM/D+JldsfMlwRbIUh8ds7+qfP4fGT8X4kaFTjrpcje+aeX8ptoxwZmubFBPoIGELX7yVJGPsOzWwm0qRW4vuK/YIjazRsFHYtvqrHEFM5KWIAH71mg5E9y8T8vq0kI6vIpBOw9/jM0p6fC2jYdphlTD73ATWboKYl4fpmX0sEI/AbEu6JfX5hs0FZg7IaQ/TJa4VPDD4zqD6gdKCbp+jekz/eQgj0s4z+LEO5AtN4zKZFN2JWD8ngDxNpUwHm4lzA8mY7Ak+VJlKmXdX7DLLhvdK1sNuhdxP0fCas2KHXq8zxZYo6n0Pn8EVCP0sJQwhqoglWY9ctk97TzhKCBZcb9LYnf1aj+gyfJDwsT5kkLUZ7ctszXyxpnGXbprRR3iuzjjSyWFWbsNrlXDuNYMieIu1IjCPRns7rsTOwjtU707whn/asoucpTzvypGeaNjEwtGRmG1LdkyjHZ6ZP0NEQ51FkqmfpCr68vs/PdG/yoLzhpz94gzLtOCuEAXxUz5nadpQb07TnHy+/h7/df4FPT57yxdl7POnm/NDkm/xa9Qr/fPUGP/XkC8zTmotiwy8+esC761N+7NbbNL3lBybv8eu7+3xtc5vStvigeNpM+fzsEZ8onvF2fUGpW276Eq08X5g95JvVre/os+E4xznOcT6q81IArOD9XrbbbPHb7QuACBi3B0PfjyGRhCAs02IubEvsnxvCRcetw7j1pyelgK2q3hvM12sBZEWBShMxe4cgeVxRqjKnp/jNFn12Qths9/4rYyRj6sCsfni8ejodowDCei2P9eEfyzKRCw8A5qGXC20ksDSyd4MsqaeTkaEbqmjQCtU7zKYiTAuCt6StG6U9n6eoRCQx1fWSS5VlmKKI4a2dgJq6kdgEGL1qKk1huUY3LWFSECa5BJE2PWrjx+gI2zm6i5LtG1OCUiTrHtN6yc2yiu4sR7epAK1tHeMlpDIo5FFyrVuJcOh7kUCzlLBaYyZlZCo7wna7zxlLE3lv1A1sd2N3pFnMJdZBKXxqcCe5GJyC/JJIhz5GPSS4zOAySfS3TiREbzXZZc3tLzlQOe9OT/mB+w9xXksMQpDtwRBLma1xtL2ldxprPKeTamSpnJe8qxAUOyfm+DzpqTsrEmHSoyLTNQSRDpEQz8OUi8WGB3GrsXIJrbJsXcbzZkLrLKfZjsZZLrINr5dXbHsBaf/9N77Mwu74xeVrzJJ67DWcmJabruDd7Sl3izVfmD3kqp/w9vqcR7s5P/XO5/m3P/EL/I8vfoYv16/yc8s3uFus+eFPv8vjdk7jLZ8unvJTz77AK+UNf+H+P+bGlbzfnrN0BR80C4zyvJE/Z+NypqbhfnrNO80Fb63Pv5OPhuMc5zjH+ciO+nCX3ndjFvZW+FH/E+PfzenpnuXpe7noxyBQlcRKmjSRxPbVesyzAl4wSA+bf2o6GZPMw24nW4ZpKuwWEKp67/sZkr6jJIcxL7BRg7l+ZFMGMETMyipyMWK37YusWcxgGlPGk0Q6EKtaIgeG12F4rjqa3g9HKwEjsM8GUwd5W1rCWtWkjCGfOsYtmIPtTI0vBJDgPXpdi0eqqkRyHDK+Do8V9qXVMTxU5ZkAnzLHTzK6WSrAZNcLeNm1BK3pFxn9xGB3DuUDqpNYi6BA9176CL1H9VJxMwaU3qwPFho0+ta5RDsMXrp4HKHvoarl+IwWX1pZEIoMN83opyk+ibEUev9a+Uxhdx67ic83AAq6WYIrNH2mCRqSypPe9OjG4UrL8+/PaH58zRcfPKT3mneWZxFUGapG5MF5UUtfoNN0ztB1IvUmiXisrHGSnaUCl5uS1DqSCNKcV7FIWk5DYhyTrKVIOjIjz720La8W12S657KbcCvdAPCwPmFma3zQPK5npNpxklb8g29+mq613L1YsmtSbk833ClXvJLf8Fp2Sa473mkueNbOmMR+w1O748xu+DvPvo9n1YQ/ee8rvJk946vVXW6nK/758g2+fnOB85r/9qu/RqZ6/tnVJ9AqcCuWT6/agmfVBKMCr0xvsNrxtJphtedHTt/hP/7+//IXQgg//G0/FD5iM1dn4cfUn/huH8ZLP1//S3/wu30Iv+/zqb/4M9/tQzjOv4T52fD3WYUr9dv9zEsBsIYPJ3OyAB9GQ7lK0r10BaNUp5IYwaC05FZlKSrKZGG9Rs1m+Jvlt8hJY2J8jEH4MEjRZYm7vt77veIF3t69I5lNgwH7IEpiLA9W+luZrAH8DN6xw9t598KfByP8h31YH75fneejl2vsGhx+PB73GCMRj08liYAha8Zk9aBUDAq16NahOicFzr2DuiE0kkw/ZpJZK+cgRl/oIocY86BKiXTweUp/kgmQamMMg5O6m36aokLAm+iTUhCUwjQOs22lc9B5YdZ8BGHrjcRT6BgzcTqXJxqCVAV9qMuQppX4iaGw2lrCrKQ7n4wMWlCgArhMo1tP/nCzrwhqe9qLkubEYpqAqYX9030gfbZFryua18/54I/k5D96yRsnV6y7nKtdgVZgjaPpLG1v8V6RWPdCJtbh773XkfHyrLY5adrjvWJWNCyymm2XcrMrYtaWxjmN1oF5WfPa/BqrPB7FzDbR31QxNQ2VS9i4bDwtlUv4Q4tvoFXg51dvkmgnmVXOxs0/Q+sst/INd7IVq16YptK0ZLrjT01+nUtf8jevf5Btn3EnW/Hl1X1WTU5iHG9Mr3hWT/nh03f4785+mV9qXuWf3HwPjbfMk5r72c2Y9P64ntM6yw+cvM/71Sl/5cf+yhFgfczmXwUA9TvNEWD9qzHfCcB6KSRCZQ06KyXh3BjMYi4y0BAGOoSORolwMLOjtCSpbzZwdbPPzIoho+b0VL4WPVChbWVzMIZ9jgXNiHHar9cCppyXtO/JBL/d0j95KseZpuiiGAuXw1BQHVPhR4DTtgIMBnlQKWGwYgyF0gqVx1TxtoVYR6OKAj34yCCWWQsAlPPRjl61QAw6jWZ4laaErse3lZyXjWwEqkSM6gzHlFgJ8LSGgMGsaylH3tXiXxpG71PwQ9tJt2Ni5XmksSsysoFUNaos0PMpadPtS6G1IljZQlS9JyQal2mp5OklCNRlhmAzcOLd0rsWVUVgfDInXN6MKe7qaimgKU/x8xJXChOnnB9DVwcJUDnpUwxW41ONS+V7ug/0paaZGdKNmNzNtkE1PYSAXVuCihuTgO4CQUNzd0qaGNJnW85/LeHhnROuI1OVJz3Oa+o2wRovW4Fa4Q8KoYdxXjoJh+iGvrMsphUulj5v6ozn1zNQgSRxKBVIrQMrRdCTtGXTZdwp1iySit4b5rpj4zK2fcbcVnyieM6jdsEvXz3gNNvx197/EXZdwidOLrHK0/p9GKrVntx2rPuMTJd8qnzCzmW835zSecPffP8HOCt2fN/8Az5dPOXvPv9efCyurrqExht+4uIr/NzNm/yzy0/wI2fv8IPzd3jezch1R6Y71i7naTPjzcklPz79Ov90/WnO0g9ZAI5znOMc52M2LwXACr3DewEGEr+w2YMl2Ne+wMhAjUyPNhKOGdPWB1YnNA1u2PJTGqWXkiOVZZg0HSW+oTBa5xl4L2Z3GIGdOT0dzef++ho/hJcOnX6Hz6NphBkKgbDZjF9XNiG0nQCU2UzASddL/lSWCavknERSxNqeFxLNhw3DWAvkm+YFVms8PwM7F6XF0PdRgowZUnH7UOW5yIaTAj/NBWxOcui9sEwXc1TvUU2H3tXCHnk/hpVyyLLFkNXgPFxeo+YzNBB0Rkhjv2GQkmi97bCbDm81IZWtQylzRkCkVvTzHF2mUgFUd3Bxiqpjz2MqkmRILOGglNonBp/Kc9a9By+hqKr3ElcRlVaXCdDykZXSfcBebVF1Q0gTVN2ii5Rgc7qpxjSBZNOjXCQqtUZd3jB9S7N4cMrT+1M+ffGcJ7spVZtgYh6WNV5ysGIxtFL7rUdrZLswBEVqHc5rdk1KlnSSEJ835Cc3XFUldWfH4ujEeLZNys2mYD6pRybsdrYmUY5M99z0JV0w/Or6AT4o/vCttwDYTDI+qBa8vz4hsz2Z6Wmc5bXpNV+YPmRhdixdycPmhJ+7eZNNL1uJF+mWH7/9TUmMNy0/9eT7yE3PK5MbfmLxG/z85hM8b6f848tP89rkmjfKS667kverU9Z9hg+K18srPlE845X0mrfrc/7B8nu5aif84Pzd38UnxHGOc5zjfPTmpQBYSut98jniV5KuPCulvP5FmXBQ5Ya6mtB3+J0X/1TTROmwED+R9/i6lrgDH3BdLz6uPBPQAVLqHFPWdZrEzbVCHu7AmD6AvIHZwQe8618Ag7CPNxiOURktDFlVjd4uMWensXS420uNckIEgDm3Z/BiPpiPz08ZYfNUku6lzuBGNmzsIAye0O87CJVNpHtvMYfeoTe1GMeNAg0Bhd61Uj/jPCGxqLYTn1MslFZpKtlf0dembDwfOhFZr+2kvsdZfGoFtFlNyCw+0agQ0I3DRAbLpxp8QDsl/YJo/CxDFwl618ay51Q2DXc1zEp8kkg9jx3KnuW3PjfCmHnJ6tJ99OO5gK0cqg+4wtBNpGMx5IkEt9aNSKsrg51lBJ2gW49uxBPWzRKas4ziukRfrjj5+pT3X5uzPVnx5vyKTZ+xbjNhdWIu1mBaHxiroXtQK9i1ls02J8u7sXKn7S07FXBeAmqHXsKmS9hWmjJvuT3bkGhHaVsK07HucyqXoAnRP6X59OQpG5fxsD4BRCbMTcenT57RecNZuqUwHc+bKT+3fAOrPBfZhgfZNZ8onnHLrkiV48vVK5S6pdQNV/2UP3Pn11hGEPe3Ln+Aq2YyFmK/uz3lC4sP+L78IY/ak2hwl5ytL61e5062ojAdd+2STxTPeNic/g6fCsc5znGO89GelwJghVhZM/iLxsJlHZkl2GdORZYLH8GHTQgusjgRIPmmwW+ijOfDmGeFtYT1Bl9V8hgHnXyhPyh2tlbCTUE20RApL2y3e19YiObogTUaTPQDCMsyuZ/tFt/IsegYTjqEjIauf8GLpbSS7cV+MF5HeXBgtYyBoGI9EOP3UCJ/qQ8xagy3idVBAysW2k6S2vNMfFlKoUBYLa0JWhOMIcytAJyuB5/LzzgnBvO2g5jF5Hf9aLDHaMnm2m4xizlqNiHkFtPJ8zQeMLFex0u9jk9MrOFRBKUImRaZL0A/z4X9ah2q6lCRSTMDQZjK8cumZAwI7QNBSTdhXxhQYGpPsAqlA6oP2J1Dd0HCULdbCZwNHg3odo5PROaUx/aYxqFrB+st7mZJ+RXLyf37fOPuLfQD2fhzXlMkAjis9jTO0DsjsV5KYY3DaHBeM8lbXNrhfWTetB+lxMQ46s7SOyNSoXHMCpEhn28mZImc91Q7ztIt97IdOoKZRDm6YKhcyrNaqnXOsx3rPmPXp9zUBV93FxjteTBdcpFt0chW4i8sX2fXp5ykOzLtOEl2PPUz3t6e0/QWj9TgnKVb3iwv+eLsfXY+RRPIdcdb1S1+dXmf0nZcZBt6b1j3ObezNVPTkMT3y85ldMH87j4kjnOc4xznIzYvBcACJV4h58fwzRfM3iBgQcVNucg84dwIVnRZ4qsK33aY2WzMo9KplAWz3cnmXioMzgBagBFM4R1+WEZMrGz5DZtsXiITPtwhOLJGcbtvNJ/7MH5tYLd828WyZB8BpCJwAKw+ZFofTfkxVT04/60s2sGm4cB4oWTj8hDw7XO7JMTTRYZQTSdigk+s5GdpRNbzXoBWjEkYwkBVCDEBXoCXbO+ZeL678bUiaFTbobcVwWf4STYCKBRRGhSAY3qPKxKCNfjB++QFJAUrmV6utJBbzK5FWU3IEnxmo3FdohWCUagAygYx2vtAsu7AKPCgmhB7Ez124yViomrwgzEeqVkym4ZkneLyKClaAYRGK+xsAjdLwuU1p79xQnVrwjfMLV6/c8k0bVg1Oa0ztAdbhEoFjAq0sQhaq4COQaQ+KAKQ6P3X2n7/z9J7Tesl2iGJPiytAp037PoEH6bc6IJEeTLTk+qeyiVMTcNrk2s2fcq6zyhMR+sM07Sh84bCdlxkW6yS98dUd2S6Z2syLpsJWnnmtmLd53xi+pxStyTasekzVn1OohyP3IJvbs95d3XKJG25VWw4z3ZMbEOpW3akNK1l3UtA7LoRtm0w5B/nOMc5zsd5XhKAFb1WzoFj3IQbvUjGiOE7epeAUXYTBku+LvUvcdMt1unAHqwpIPgIkoY4hMRipiLJDNEQhywTXtb/gwZFPK4sk8eJ0pxESBxIiNGfJYzTi5uBhwAtRLATvI6xDEn8exhB15CBFdxe5pMH0eM52PvMFCEyInuwNfixugOmTQtgco6wqyBzKJfIBmFi96nvWGGb8kQ8Sm0nP+OcnEulUAcl0qMnre+lfqd3sKtgV2GWllBItIPPrRjQtYLUigfMeVSn0EGjXNhHbfRROvVACPjUym1U3FB0Er8QEs0osg4YNQI5XYuBXfWynaiaDlXJogNtzP4azmPfo5cbkiJBuxQfjfPBiMw4hNCGtiX54IrzX8t4WhQ8TBbcPVmTxMLmprc4r0e/VR+BVWp7+bvXBK9JjMMaOXLnNT5AYjwTK2GjJm4aDvlZSgWqNqHtDVoFSrtmalusctx0BZk2JNrxpJlz0xacZWImb5xlmjTcLdasupxM91y1JVZ5JrZBm0BhOgrT8cnyGR7FN3cXrLsMTeDB9BpD4LKdkijPZTfhJKnITc8irzHKc5FtuZOu6ILhWTtl2RXMbMPM1mz7jE2f0nrLvWxJ41+Sj57jHOc4x/l9mpfnU845YY2MEUZpYGaUkk29mOYuUpkW71KUCJUxL2RE+YMspzGr6oDpUUoRBuARARvGoBjAjBNJkGhQHy72Ru87+rRGke23CNsWBjBAlOb8i5UuA1iCuL0YgcnA2sl9yUV0AEjBsWfH4rEBL/4ePLBn5A4e8Fv/7MOLPzOY15WWYuRYjYPzwnrFImlA2K1OQx/N6b0bz8cIiIc0+qEGKL5Ooe2g7VBGY4ocPysIWRK39eS1062A3iHLLGg15owNcQ8D4BJJUROGrmkvG53KhxGgjdJi3aO6fqzqCW0bOyOHcu0IwOPrGupaKoY86CxuQ2oluV11O74f/NUNk6+mnJW3eJZOefJpuH+6ZJ7VbJRI270T4KdVQGsvb1MCNkqCgzHeByV/9prOaXxIMDrQOzHBw96TFYLCRvar9QYXFIV2FKbDqDAmu5+kUiuV6p7n9ZR5WtN5McR7FIXpuIgZWqvo5SpMx06lLOyOe/mSWVLjg+Kr27tULuFOthIGrc9jfU/DnQK08rigeNbO5F+ACkxMi1aem65gbmsmtuF5M+Wrm9u0R4B1nOMc52M+L8mn3IF3KPqGxGckKeUDmyNAQnxCox+pi1KXspAIqBG5cZDSslGyCyHIIxkzPuIAjoYtvSGd/XAbT+TBGJ/gw9jPNwK7LkqCw8+HeBGPDNkAfAS4OLkfHVkkPQBITwgRYMT7kBsN7NYAOPdSpDw/Q3AH/qvDXK7h55QAQ+I5HUcrVIjnJbJsAVCZyIAqSMI6IJ4sI+eeJL4GWkErz00X+Sg/EoKY7GNCu4RPqbigIOyXVkrwZ25j6fLwOHqMSFAujAnxwcQuxs7J9qDVhDQWUcfXRym/Z9FCQHce1QqwUm035mQNW5BD1dEoFw+xFHUDdoc2GupYDj1khx3IoKFu4NFTFonFpac8T6a812vuny8pbBejECTVHaB3hgBjT6GUAGgER8rXQGRBiQKLr3VkwRREH5f4spzX1C6h94ZGJQKcgh6BUmFaHtdzrPLMUwFKj6o5uekobUum3ejbKkxH7w3bPuNpPeMs3fF9k/fZ+YylK2i8pTDy3CuXkig//n6SVDyuZ7TecjdfkShH4y1p0tN6y6rL8UFzlm45SXZcNhPuFUuOc5zjHOfjPC8JwJIJTSMXZmPAMW7G7TOfPsQ4xIiDASANQaUDAApthyoSQuVefIwDIISK5my1l9ZGRsmYPeiKmVbDcbxgUIcoHSUvACYQc/kYZmqMmPj77lvBDrwIrgZgdfi8I2DbP6SKwZomeoj2suMoDw7gCiJ4ZWTdxucLhK4bS5S1c5KUD6jeSWZWme+7A7XGlxkqTySgNBZuq64nVDVhV41boCoCr1Dt5PGHiIWqRntP8NIVGCxj0rpyQYBTCOBlu9EbLd65xMh24Lral3xHoz5DwOh4QoVlU10vTBrIudAKvB6lZ/GQdXtQPsi7Qw3S8H5Lk3EzlURea7/ZoN/9gJPcQpix3E744LOBT9y+HMucnU9wUbodjs55jfMao32Ma1C4WGGTWEdiHFWbkCU9M+Po/F4i1HEz0ahAbroxU+qmk2y1zIgP63E9R6vASVoxMQ3v7M7GgFF6uPEWH+bMk5pXy2sWtuL96hSr5Tz8ZnWPbZ/RB829bMnUNLxTn7HtM06SirvZki4YFqZi1Wf03jA1kgT/pJuRKM9ZuqXzhsumZGIbXsuumNqGB+k1/3e+u6OUyoF/DGTIZ+F/EUL4D5VSZ8B/DrwBvA38j0II19+t4zzOcY7z0ZzfEWD9i3wIKaX+A+DPIzDp3wsh/Fe/w2NE8JDKZltVCWsVgZVK7J6liIBlDBvVsuIfunYMKj30G/mqFlClzSjhKavlQh8rdoKT+psQgpi741ZhAOkwzDOJWADxeY31MQcA6OC5yM8lhG37rZ2K8T6H2IXxPonG8sj+BB8OWLsDY3v0hMHBIoDS4+3knNiRTRp8bQPg0gcLAiOgi5uIg8fLV6B6AUtoLSGfzkORiRk+SoYhMcIiKTDbVsCMtXtTvFYCvmAf9Nr1Eo/Q91A3qKZFd5LGHxIjkQ6pRCi4PIaUuoD2XkJKgxWZsDOSFu8igAJ5vOgRG5iq0PeSY2a0PJ/BpH/oXUvsfvEAkZj9bjf2MI7vvejd820nzF8n3i6/XmPe+oDT/i66X/BkXnIz33FW7MRXlfSxBkePQaMBMNpTpB1NJ4XP1niM9tRtwq5JmRU1zmsuNyVaC8NVpp2EjxpHZntaZ3lUL0Z2SStPoVsK3TIxLR6FVY6ty2idpbAd86Rm02WkuifVjtYbnjYz7mVLLrINV614Et/enDNNGj45ecZ1V/KsnXEnW1G5hC5o1k5kxWftjN4bWm/4+vYWmkBmeia2YdXnPKunvDG95LXsirXLue5KvrK5g3ysfFenAX4yhLBRSiXAf6OU+tvAnwP+fgjhP1FK/fvAvw/877+bB3qc4xznozffCYP1u/oQUkp9Dvi3gc8D94G/p5T6nhDCt2lElgneoycx1qCqUVlGaBrJpBqAQ4wZGAztGIPbCHhRWo1lzspo/BbJucpz0Ar37FJ+ziYjuzNsDg5Ax61WKGvRi/k+TFPv5UqdZWNyO1km2VmxZHkAVQL2fPSHNaM3KR7knlUKfm+iDwG/28WIAzMyW+It0weyZDcCqtCBns3QefZid+CwHKAUgb1v6xCQ+aaRr2WZMFdNE4GW24enxkWBoctRIjECqnci1fnoJYteKRWzpkIu/Y5K633IaZq+CAh9AOPkXBkDNZIyrw06Swl5ip4WY0SC6vzoszIx7iIkRrK32k76KnfVyHwOPZWDTItz+LoR8DguAhzKsG783uG51GUJRQ6xOFqlUgsU2k7k1NH7BiiFW64w7ytO2h6fnPM0O0d9IjBNW9ZNSufMKA0eBpKmscewSEVS7J0hTzusk2yszPYkk4pVlceHCvReU7UJASjTjtsTxUlS4VF03vCV9R1u5Ruum1JM6iowS2py2/FkN+OyKjnNK26qgs5r7k0ko+qymzA1DXNb8c2tgKsvzB6y7AvOky3PgubLy/t8bv6Im77kUT3ni7P3Wbucma1pvKX1Fhslwne3Z2jl+eHTd6h9wq9uHuCC4la6IdUf2hL+LkwQGncI/EjirwD8WeAn4tf/KvCPOAKs4xznOL/L+R0B1r/Ah9CfBf5aCKEBvqmU+jrwo8BP/5YPMpjYq0qSyqPvajSyR4ZnlOiMQSklYCiWKuvZTP4cE7/9difRDCDenelUwjH7XjKPcOgiFwmr7yU9HvDLVcyWUiOrpU8W+GfP90nt+5Mjvw1Pw1pUlqFTRmCkM+nwGxLYdZELKHB+74sa4hTGTCthk0L34ciGfTfhYQCqzvPRS6aMwX/IFH/oMRqN6FqPG5N6qAuKKfVDeGrYbsfH1BH4kiboWKYcnB5DSn2RgIt9e4lFaYWZTglNK0DE+T1INRFcDV2OIQjbFD1hqm7RRhPyTGS4SezWc4GQGekhjOyR0lp6KH1krKoav1yNr/th/+PhZqbKYr7awAJGEE+SEOpG+jAPALyP50IXxfgfgLGROUi3ot9s0cDpr1ra6QmPi1Mubq8okp7EeNrejDKfBJAqllVOAOrOksd8q7Y3JBF81TGyYV7UtL1luS1QKpAlPdZ42SrsE7YupTAdr+TX/Mj8m3yjvs0PzN6j8QlTU/OV3V1+5eoBZSKv8WVVcqfccJ5tmSX1mJ/VeLmv1ybXXCQb/t7Tz/LF0/f50fIt7s6W/FfpF2i85fX8kvfqM37m5k2+Z/qUM7vlaTvjfnZDqVseNidsk4bWGx43Cz5TPiZRjnvpDdf9BMee9f1ujlLKAL8AfAr4v4UQflYpdSeE8AgghPBIKXX7t7jtvwv8uwA55b+sQz7OcY7zEZnvyIP1u/wQegActl2+H7/2W9+/Nej5HLqWUDfCzDiHwoGJ0QuE0YQ8AAS3XI0+qAFw6NkMug59upA71waaZmSMVJaipxP8bifMUdOgigJ7+0IYC+8J18uxfsbvdvjNZn+hRpiwsXB6YJ2iHDVGQsT+Q7pOanvi47u2lfvSRszxw8/afUHvaLZumj0DNjz+IREYQYNvGkloL0vCwFAlNvqL5ELm204k0ugf22drCcNzGFJqFnNha7Y7kQizTMCfc9DIRqBqC8LpnJBK359yAd12e8/TcMxaSfRGnkVP3YEseVDGHZwnDIAmkfJlPZtCkmCabp9Mr8VnFbR4wUBM6aGq5FiLHDObQttJOv9h4bcxaCsJ+H673TOMg8et7yF2PerZTGTLqhbAH9P59z48JZJ2fB9IR6bHXTbYJOHk6yW7exnVSUJm+9FzpQ+M7G1v4mEJW1UjpysERdNZms6SWEcIiqpNKNKObNrjotwYgqJuJTneKM9nF0/4yuYOP928yRdOPuBnbj7Brk+xyvHa5JofOn+Xh/UJr06u8UHTR0P8uheWK9U98xirULmE50z5P7z5/+apm/GLuzf4T1f3eaW84dTu+PruNifJjv/lvX+EC5q32tvUNqELhrfrcx7Xc2qXcJLu8Cj+1uMv0HrDG7Mrfnj+NrDgZZjIrH9RKXUC/L+UUt/3u7jtXwb+MkjZ8+/PER7nOMf5qM53BLB+lx9C365d+ls+fD78v7+w24k5XSl8VY3bfLh273EaVvSbbx9SqPN8z+zEC+ULB9E0sBZAo8sSdXoCgF9v6B8/+fABQghinLdWZCjktn63A+8FqFX1fgMxTceYieAcYTgWbUYgaE5OpHOwrlFJTFY/kP9UkkqUQGSdzKwUFmdbfUu2ldx2kA3bF8BEcG4fejqY7G0iQMd7Oe66Hlmk4NizfG1HaHoxcsdQUZWmoySmMpEC9WZHaCUjKyRGvFlW4xYFynl01YkHy2jYVuKPapqYAq/EGzXEJvS9bCIeRE341Xo0lCujpbR7OhET/a4az/Mgg4a23QOgw5cygqPQOIjMFdrst0GzbM9GRWbPbzawFl/g0A4wSMZoNfr3xuWDKDXrMsffLCl+TXF28QaPbk0oXuvIrNTmdL0ZPVhDlY6PeVm7OsM5xe0TYVPXdUbvNFnSk9lA64wAr9bivcJaT5b0TLOWeVpz1Zbcztd8YvKcf/j409wqtvyBk/e4bKe8uz0lNz2vl1d8ZXWHbZ+ySCvuF0vupisS3bNxObVPKHXLs3bGsiv4C7/0P6PMWs6KHf/zB/+MX9m9yrvVGW+Wz9EE/oPf/HNc3Uy5f3HD/ekSqzyZ7nm1uMaheXtzTu8N33fyAVPb8LXNbf7KWz/OJ0+ff9t/w9+tCSHcKKX+EfBngCdKqXvxP473gKff3aM7znGO81Gc39UW4Xf4IfQ+8OrBzV4BPvg297X/358+D4MZPBAlL+fRRSIepfqg3FgbdJbsJbjFDELAXS/xdY3O5SI9Ao8PgTFdllIovdnCev0CM4VSwt4cJKC75UpW/nPxwAxerdD3hK1HnyzkItu0MZrAY87PUHmGv1ni12thi4oJoe1w1/tlpAEQjf6rg/BSgOAdbgBNgxQFexZLKcx8jlutxnMznqcPdTYqrfYgTKk92xUT4tFKwOleeTw4aQZ2u4NztRwBmy5yVJqglJYFhSzB3vSELJUMrSxBtz2hzEXWm5Yi7UW2K3SSjaVCILSdAO2mkWOcTgVAdR3BafzNEi6vRKazFjUpYmm3FGcrm4zvHWX0KPcRvDCb3n/L0sEh63j4fHVZCjsV5Vw9nYrsHMGVZJqJv0zF+1HG7JmxumH+jS2bBzOuT0runK3iSxZIo5kdxOieGkfrDKl1GO252RV0nWFa1hSJLFs0vcQ9WOOYzRu0CmzbhLa3XG1LrrYlZ5MdmyKj9ZbPnT7hlfyav/vos0ySls8s5D8QD6sTtPIk2rFuc95yCV+qX6XqLCdFzWm24zzb8qOzb/KkW/BHvvdrfDF/lzpY/s/v/+vkRsqiv3TzKtsu4w/d/SY/8T2/ycPulIfNKaVpeS19jlGBX9q+xrZPeW+14Ff7eyTG8efe+GX+N/f/a565GX/j27zV/mWOUuoW0MXPtQL4k8D/EfgvgX8H+E/i73/zu3eUxznOcT6qo0L47Zntb/Mh9F8jH0J/DLg8MLmfhRD+d0qpzwP/GeK7ug/8feDTv53Jfa7Owo9P/nvfln0Y62ISO24Q+rrZh5IeSDbm5GQEMAM7pE8We7N40+C3ldy2LEY/Ez68IDd+eMzJAncjuT0vFDl/+xP2AmgbL/h5FqXLE/ELtd3ehD0UVKeJsEYwhpSK9BRB1sCyDMf9oXOEd9/y+C/4nH67Yz64vS6KvWFcK6nEcW6ULHVZooqc0LTRx5Wg5zPIUlAKPyv2aexDYXR3cM5CIKQJGIWqWliu9/JgLJIOIeBvljHNP5FzqKK8V1Xj81HJnlkDvrVu6EOjy1IAYZ7jtzs5/uH9wbCp6UeJeDzvw6lK0heDbg/A/MBiAoSuxyzmVD/6ST74ownms2sWk4qms2OSu/cKYzwKSK3DGkfdJtyebmi9YVnl9F7jIoslEQ2wqTKaXYJJPGUpYCtLeiZpy2vTa25na3xQPG+li7D1lmWbc5pWZEYS3M/SHd8/eQ+tAktXsHMZue4odcs36lv88tUDFmnNrXzDl54+oOkS/sJn/imvJldMdMPTfsa53fAPlp/jyzf3uVVsyLRsDibK8aydsmoLtPJsu4w75Yovzt6n9gn//OY1UuP4G3/oL/9CCOGHf+s35u/vKKW+H/GPGiRx7a+HEP5jpdQ58NeB14B3gX8rhHD1293XXJ2FH1N/4vf7kL+r8/W/9Ae/24fwsZhP/cWf+Z1/6Dgv/fxs+PuswtW3U+zG+U4YrHvAX40+rOFD6G8ppX4a+OtKqT9P/BACCCH8mlLqrwO/DvTA/+q3A1fDvACuBiP54NkJjtC4F9io0LgxRsHMpwTnR3BlTk/HP7tnz158oFjoHJbrMbMKwJydCHgaQjIBlLAm7mY5XjxD1wv700VJq8j3fp++E9N4WUKaiIwVZcKBrfKPHn/Lc9cTkRp9Xb8obWrxUpnFXNidIbwzePQ0+qScl8cIHwIDA9DyTu4ntWNhtjJm9IehNb6WjU1MLo/T9dC2+N8CSPrtFgYDfJz+8RN57rOp5FXlArakQBrcaTkmr+veozctahdfz5M5ajETVqvrx61Ac3oyglPVdbjrm2+Vh7VCGQE14rGyIiVGg3zY7vbsojEi/e12QATMSSqxC1rtozmGUWos6CaCeRVlxHFjtarRAwsYzy3ey/KEcxRv3zB58xbL1xOy+QYduwat8aS2x4xMlKHpDYlxvH+zoGst00nNLG9oe0vTG0l/N46z2RYzD7igqCNgW24LVrucurcsy5xFKu+j3hus8nxy+pyty3haT+m94bGb8bXVD+KD4l654iLb0HqLC4rzZMufufvrfHV7h+u24H/xyZ/maTfn7zz5PD94+h6/dPMKAFZ7fvLiN/mBB++y9gUA1/2ELhjuZkt2LuMbuwtab9l0GX/jvT9ACIqLckvtkm/73vqXOSGEXwH+wLf5+iXw8UZLxznOcX7f53dksP5lzCK5Hf7QK/9TwmojTBJ8KxsTJaPBeEwIY1BkiOGd5uwUnJM8rHjBhL0EJ+GXImt9mAl5YQ6kNl2W6LNT8Sv1vfhzmka2AtsOnSao2UzCONuWsN2N7IYuCmHK4sXXV7XUueTZGOo5dCwOEQ/fVrIansPw5zSVn41Fy/r0RIz0l1ffwl7pwXMVWbKxbueAARvN3odMTZYJQ/ihc6SSVMq2o7EfojwWGcNBmlPWwvmJfM15gjWELAUNwRh8Ls9Hdw7VOVTbi0crptyPwaG9k95A7wWsOS+hqOvNvqT7gMH6bZk6GOVRVeSEzXaUR0PTyHMe0vaHYu3fgtX8dq9PONjeHDZE9ekp9R94nYf/WkL+fTeUWcuuScfqnN6ZsW/QaI/zmjLpmGc1z3YTdOweDEExyxuM9lRdwmqX0/eaLOtH4/yQ8r7a5jw4W3Ka7fjs7AnvVqc8rWZc7iZMs4ZUO87zLWfpjpuu4Hk9ofOG83zLabrjui2Z2YYfnL/DLbvim81tap+Q645fXT/gJKnQyvMgu+GXV69wlkrB87bP0DHh/Uk9Y2obPjd7xLIv+Mr6Dt+8OuPBYsn/+rW/jwuaf/NTv/JdZbB+L+fIYB3nO50jg/XxmO+EwXopANbw4TTIN6Ht9r6rQzZrmOj/ObwAHkp3uiz35mgVi4utFaZpvd6b0qPpeyyOHuIittsxA8p/G7P8IEuNXqwBEEXGSSmFmpT4zXYfDBo33AbDvMpzwna7BwnI9pyOPzOavWN21OBLGiXDQdI7AIMqywQ8RvnxW+Syw3N56Nc6OKcfLqce/FtDhMGQK/XCbSMrKLlcYohXaSIsmbWELEHVkUUq8pHdCkqB1fuuw+FQ2v7FzkGtQYPetaj1TrYbrRWzvVJyPobzHENF8UE2OPueoRh7kARf2CyMEurATr4APJN0TMHXWTZulao0Hc/zIcg/LN8OMUcNwL7+Cs/+tfs8+3HH3dcv0SqwqnLa1mKMl25BK49rtadzhr7XlHkbC6BVfCsYkqQni14t5zVNb3BO03WGLOtJbc9FuZMSaeXRyjNNGmqX8Hp5xfN2ykW64WF1wlvLc6z2fObkKV+cvQfA++0piXKs+pxtn1G5BK0Cb5SXzEzNZ/JHfCZ5ynv9grfa2zg0v7R+jcum5JXyBq0CC1vxyewJ77YX/H+ffRqNVPu8Mbni6+sLHi4XLIqan/7X/09HgPURmiPA+r2ZI8D6eMzvlUT4+z9ljr39Ku7xU/zNbm/CHgIdlRbvT54J87DdjSySPrkgdB3uKnqvhi0/pVB9/+1lrkEm/NDm3eGEphG5SCnMbDZeaEPb7eXCaBLXJwtUkoi3aiicvr6WY+nbfZ7VgV+M9frb+KlszOhi7wsaKnUG/xWDj0gYI3dz88IxD3EQaOlSVNaMsQf756/HwNSB/fE3S0Lfif9qSDePDKKvG2HwIthU1opEmUgd0SCDjucdYrq+JJ6rroM8IyymqKpB7WrxZSVWliIrv/doWdlG7E8KglXgAqaW7/WnJbpMUc0Mvd7uma5pIexXJccZmnbvW4uLAwNzSS4hsVKzpIURi6BV0v3jS5REJquV95+LxydAy4+evBfeM0NOqTYvMHphvWHxVs32fsHV6YQ7J2sWRY0uw5hzNbBUTWeZFg2VSqjbZEx4T63jfLFEq8CTzZTtLiexjjLtMNrjg2IXA02/+fScxaziznTNp6aXXCQbrrtyTHz/0tWrLNKKf/PVXwbgnfqcn1+9zp1szb10ydrlTE1D5VIy03MvW3LVTTDK83/55p+k95ofu/U2henYOKnSuZVvWPc5VnmmpuHd9gKA/8G9L/Fuc87Op7y9Oae0LX/uE7/Mz1+9/m3+1R3nOMc5zsdnXgqA5QrD7nvvktw/Q3dOmIqqgboRxqFtUS6u4R9IVr5p8E+fxziDmVwoDzKYDgucxyqYA1/SwOIoayX3qCxGUKcmJWG5Amtxl1foofdwSEpvW9RkgppNpAuvaaFuxiR0vZgT6gazmEui/BB5UNf41WbMpxpBQpQIQUCcb7sRCKihnzGGifrtFjV0GoaAnkwE8EUJc4wOiKDwWxinAWwNMRJKC3gd/EMDE2WM5EoVsgEY2nYEfe4g6HSQIgfwO0yoajlfeQZ1TXj8FCYlajpB9Q7qFjX0CeapJLB7j9r12E0lzKPaB43aZS3VOSHgF1Lngke8W1oTFlPCyUwOKS4SqK4XibbvRw+XStKx+mg8JTHFfmgPGPxq41OMX/cxYHRYXvgWgK5UXCqQUNPQd2ggebJi+n7Os/sF1aQmsz1Vl4ilLigmmbBVbW9wXpEYR5F29E6TWuklfPv5GWkqDFaedrS95WZTiNXNOnSMfJhNK9re8Hw3ofeaebqgdZaruqR1hruTNVZ7fuoDSVuZZzWlbXlYnVC5lE+VT/hE+hSP5mF3yiYCri+v7/Njt95mYSqM8mS6owuGKvqpHuQ3zExN7RPWLqfxlg+aE1xQPKunvLdaMEk7rsuSP3L+DX7b/qzjHOc4x/mIz0sBsIKG5ZsWu7Nka4+pC5QH1XtM7aS7TiuC1Xgr4MBWDl3H/rVVhWo7WZff7gi7agQ47ubmhS2vcbwTSW26gK7HLVeo9Xr00Zi2Q52dQO8wn/3UyOb0t6d4ozF1j24EwPnU4NMYGln3qM6h614u7iEQnl3in0tdT/Bh7FAMXazkiQnrQ9zAGNPQuD0T8+HR4kkDRilslEOHAuO2I3SxdHky28uobu9vGzb06Lo96IisWlBKQOP4Qu0Bx1jlE0GGryrJ5k6SKNkdSI2tJLerohCJ8epGQGyRC/jse1jJcWIlcT4kFrWLoZ9dL8xWaiUtvneoRgqpQ2II5T6kNZiYeN/FwFMdWaoQMLcuBBw+e77f/ovnYQBKYZB5k/1SgFQr1SNYV9aOwbV6Mtmfe9gvScRi7bF8/PEzTr5S0pzOeD6d8ZnXH3N/uuTpbsa6Sdm1CZOsJbM9uzYZg0R9UDSdktLnXFjVthdje5Z0nJQ9rTPsmpS6tVjrWG8lUiS1jrefn3E+3/Lq7IY/fverLEzFk26OD4o3J5c8b6as+4zaJWikUudhc8o/vvw059mOW+maR80CFxQ/MH8frTxP2zmNl9LoTxdP+OOz3+BrzV2e91Nc0Fx2E7Z9xiyRbK7ruiSJ/jCAtzYXPE1m3/59fZzjHOc4H5N5KQAWQF8qfALdzEAwmDageggGUGAa0J1UkgQFwaTYOifZOpJpim4cPjPgEeBTd6iqxdw+H3OXwnI9SkEoDUajkgRmU/Qb93GTDJ8Z+olBNx7lI2DygaAUPjd4o/CJpl3keAvKC0D0VqE86D6Jxx5QPqD7gHrlBNUH7KpGP3wq24UDmBlkwOFEfHgL8LfwyA3FyYB4pLTGxUT2waiNVnsfUZQaQ9vuu/hgD6AOjeIIcBhCNw9ZGkmdj9U6w+bc0FmoFH67EwCZmLE70teSLh9CNQJMlSbCcG3F00Qq3XqD54y+JwwxEXUjrGAEX1gjrGHXSwm1VgQjIacgwJwQ5D4jKyZ1TF7Kpd98TRi0KsqJg1k/xmaMAOmwZDyVAusQxNulPpS2P57RuEiAD3L7dB/7YN99yq3C4tKCr6q73LlzQ5l0GB3QShjG3gtj1fYCpJzTlHlD7zRNZ8mSHqOlIicERdXZ0SivM49RgZNJRdUmbKqMV89vOM12vF5esTAVz7spD6sTbtqC3ms+MbukMB1Pm+kYEnqRbPjsrUf8ZnWPVV/w44tv8HZ9wa+uH3DTFmzajFemNxSm46dvPsm7xTk+KCqXSsaWcmSm54NqQaodP3L+Dl+6flWKroPiXrFi26ff6UfDcY5znON8JOelAFi2Dpx+pac6N/Sx0stb8HlknBR0JWinJF8JIEA71ahzhWksygnYMV3Amwwd/24rj+4C6U2DnpYo7wlW089zunlCMAqXKnyi8EYYENMGwlzKhr1J8FZhuoCtA7oL6NaTX7UErXCpHo/JJ2LGDlrhCokkiAtemMqjXIp+/S7m+QpVNwJ6mnZkoAT4RKBzCLQ+nHs1fF8xBoqOPi2TvihxKY3SwmoFtzdf6xgjIHcVXtwYDG7sg1SpVAsNeV2h66Ohe0g0l7T9oDTQjbLkAOR0no2hpkMljzJ6D2LybP98BnAVvJjVvYOml+NLE0IPKv7cEJcgJz7GI6QJIYkmd8Qgj1LglMis1uCTUpgt5aMfK4EBWHVd9N3tk/IHL1yoqhFEjflY8WcHsKVSkR7DUEkUf04ZMdK7y2uS3wzcSl6lLzMu8wn6ZMMir9l1wlopFUYgpVQgWEUYqnFgTHZPtad1Bo0itW4EXG1v6JzBGs+0aHi0nNPPNLVL+KY553a2QSvPPKm5yCQ2IlGOz88eYZS892qf8MvbV1n3OYXp+Fp1h0z3fG76iFx3I4v1bnXGxDY8rE4A+MH5u9xPrnnWz+mC4YvTmqUr8EHzPfOnIkNuFvzco9f45NnLleR+nOMc5zi/1/NSACzVe4qHW9JlSl8aXK4jU6XwVgBLMMISBSUgStgsRVeoyCCBbQJ9pvApKAcE6DOD8oFmUZJuMpQTBsynCm/jrwRcqnAZ6E4eV7uA6iBbeUzlhIlycsFTPmA2Lbru976p1OKLBJ8YfCpSpgrQTQx9oehKQ3WrxFae7DQn2XToqkPV0rOneyfJ5k0rQCP4KBnG+qDGj1U8Q0QDPshmIoz+s9B3EVQpVFYKCBkA3Bic6fd5UkrHPC23j3CIJvfQxiDRmL811O9ABBnfJsFAbh9lUNx+mzHW/ATHPs4gCaN5X/kwhn3ivfx9YJYi86aMEeAFwmwd/r134oOL31NWZD60lt7CRH5OhUBILCGz+zDUNm45BmEdCX7MAwvIdmKInZPAfoM1yodjytvQMykHva/WgbGgnKomf/uKW9NbPGPK4+8x3L1Yjl2FdWfpY51O28mWYZZ06FgQDS+WQQNUnaXrLVkMKx0AWu90/H7CrULA1FVb4lFc1yU3bUFpW0rbsjI5SQRYHsWddMUPTt/hxpV8fXdHpEpv2bqMO+mKTPd8dioFzoMs+LA5Ye1ySiPvrZ1PyVXPo27BVStbhj4orm055nQd5zjHOc7HdV4KgAWgOoe9rrBrjc/tGEo5SmVm758KSozOQSvSVAugSRQEUMG/AMyG310KzcIIA9WD8kEYrzZgmwABdB/QjUf3AbtpUb0fowToPeZmIxfyxBK0QjWdgJcQ0NaiD7b1QpHhy5RkpXGFpV1Y6oWhKzXdJMXWUUp0AhaV2wMN5aK82DjMrkM1HSZmRCkfRsmLIRG+qgSIaQWYEcAM9S0hBDGyZwcv91BS7fy4qYkxAu4G4jCNRdsH8QUhREZtkBQHBmf4mkpG1kcRU+QPgluVVnt2x7m9XGn2bBo+EEKz32YcIhiUFFvj3D4X7IDFGp6XSqLXrEZAjjWyxWgj05RYQdl9PK6B+SoyAVpdB8NyQWTVFIydiQOwGtPkR8n0xWyxw17FobA8NA36+RXTL4Nyt3jeFTz+PNw6W0selvYCPIEkEYDZ9tI9mCU9NsY6OK/pnKZ3hhBfl7Y3+GiY37WJxDpYx7ZJ+fLjezw4XXKvXJJpBznCgKnAqi3YqIzzbMfcVkxtg1Ge36juY/AUpiXTPY/qBRPbsOpzHJrXsisy3fG8m9EFw/NmypWZcC9byt9bkR23fYpHse5ypknDsi14tJt/Jx8LxznOcY7zkZ2XA2B5j97VUgJsYqddYiU3PgIW1QuYIIRxuyxojc3EMC5epgBW460WQ3wqF9/BGC/RDeKtGkZiBDymcaiqQ28r6cxzDrJUjsOaPaDpOmFYEqntCVGyCn0PrdmDi65HX96Ac9g8JzmZkd4qqS4SuommKxR9lEAHH1fQMOpQSlg404aRsdNtQLsIQIwwZHbbY28qzEYynmiaMdNrSHofAc4APty+hmcsj44de0OExeghUpGlYQ8Wxh6+IZfLB0AYHWX1KDmiDtidCMhku9HvwziH3se4+Tj+rNK8QJFpPfqjDgNkh61OiFKncxFQRoM78p6ibSU7K0mgauRrxgi7BVFajK9zHiMWeje+F4L3Y+dg8GEEn8A+eNV9iNKL/q3xe0qjMLjVBt12TJTC21s8WeRU05pp3pBYh/ZyTImR+AXnRSYE6PoIEqMkCJKdBeLfAtg2KVZ78qLhZl1AUKRZT+cMuz4lSWpmtuHaF6zanF2XxsPVTKYNU1PT+ISbTtLZp6bhzG7ZJBm3E9kefdrNqH1C7ROetHNS3XM7l+8l2pHR01jLus+Z2Jb7+Q3XXcmv3dyj7i13Zt+u9PI4xznOcT4+89IArLCKH7hRSlGD+TqapyXx3MuFsduzCsPWHDCu+2sQZifP9kGWvRfWS6k9cAthtDypyJJJR56W+x1Yo6oRWWlSQJa+kNmkIPqF/F7uSmTlnyYChr5H36xJmxZTTWlOM1yh6TMBSQTwyV4CRYm53yfQFwoVlOCF6CsLKgIyC6ZJSNcZyXaG2fXCvLXx+HqH3tWE1VpKkSP7c5ivNZq1hwLjgYmKwAOtRW7jYEMO9oZvpWVbDuKBefBawFV4ETAp/SLIwusXgNdhofdwu9C5fdL8wLQpiUAghBe9WBF8jZuaw30Mniq97wpURgtLlSQCoEOQ190a8W4B9F7AlpfnrZIEbcwLoa8xZ2EMNCW48fjUAYEFEZhqeT193WCePGdapNQnpzxfzCheFymwcbKhV0V2SiGl0CGoEVgNnqyB1zU6YLTDeek5TKyjTDrq3Ep0Q1Hjg2LdiqRc2pbc9Gy6jDIRmVCrwLIrKLUwVq/nUr/XBMvOp5xaieAwyrOwsqSx8ykuKDG265gVhvi6TpMd58mWi2SND5raJyTacT7dYvV3lpB/nOMc5zgf1Xk5AJZSe8Nz9Bh9Sx8hyAVwrFGJ39tVxCAgQprIan80YuumI5Q5INUyeFBWQ+f3wMp7SQqPEqSf5ZIk3jnJVzJG7iMmjyuLsBmDDJen4wVedb2wbF0nuVcnM2FBYgCp6nrM8zVF3ePKBJcf+IK0bCd6K34z+QZ7n5gRqVO7ELcoFc6CN9BNFC61hAuL7jJMK2xXsusx2wI9n2C2lURYVLU8HozbbsC4fRhcZI8ONuNGENP1e5ZpADLRCyWAS43AxlfVHuQQAQ2xRmfYaPxQ1tTIVhojm4tKvVDmPHZFRqlRPFJxDiXLaKQPzo9gbmCQxiDYvovbjKmEjqaJ5HglFqVk+SEkB8wllpBnqEmBjudSzpEEsQp7lxC820umkaEbQGhwTgCRMeB73GaL/sZ7XGhNNzvh5rzg9mIDKTSdFV+Wk1LoJJH09izmXbW9iXENwz+NId3dcFZWbNuU613BPEY7PLuekaQ9909W+KAoTMfc1iTa0XnDxDb4IO/jLhhu2pKzZMvC7vBe8bSdMbc1G5ehVcDgWXoBY7fSDV0wbFzG1Ii8eN2XVC6lMC1J3JD0QfH5xSMcmq+vb/3OnwvHOc5xjvMRnpcDYAHMp1C3sddeR4lQLuxBKcm56p1IOsPW1lBVoiWENFxdy0VsNkOVUsLM00upcJlPBZDV8WLt/MiKqRD2MmAn2Uhhu5Nj6nphHQa5J0tFOkS8YAy5SyEIwMtT8WY1LWq9HcEfiSUUsjGnNhW26caTH1KRPFUnkpQaOvmUIiQWX2aEzBCG7bi4nRi0wqeaYGQLsisFoPW5FmP/qUH5DN0Fku2C9KrGLCtJUt/txMgdw1GVsmO34ng+I1MXPuw3GgzbAxM2AJ3gBZAexEAMzNSQOaVjRAMtkUX71rfCCK7SdIyc8E0jTNQIpBJUokbAxOD3OhiVJiin9/IiksTud7sRBPm6QcXqIT0pUJOJvAaROQ3WjBlcIRU2VBUpnMwEaO3k1xDFoJJ0zNAaWMEQjfMjW3iQbearGvP2Q84uCt57MKX9QkWRdDSdvDuSRAqhE+voesOmyrDWkRhHmbWkxuGi/wqvscbzbD3BGk/bWZ7WCeeLLWeLLVWbcFMVaAJ9MDxiQe81jbOsm4zEOB5MlxSm5cTumJmar27vMrcVc1uz6nMe13PuF0sy3fO0nnE7X/NadoVWnp3LyHXH1NQsTMX77Snv7U75hrvgh0/e5SLZ8F59xtalfPHkff727+Lj4TjHOc5xPmrzcgAspcRcPPia0kTA03BRj4GRIUvGxHNVSaJ2KHOUE3+MmmsBQCAXxsSKn2tXwc1KGLIhMb3f9xyGvhdDs1ICOJyEWPqrG/R8NkqCykG4vBEm7WQO04JglCTPb6oRFGG0sF6DV8xq1K5BrbYHoFEeg96JvywyXUM46cCcqKZF36z2fp7hlOkIHLJUgEpi8bMSN03pJxaXxfgJq+gLRV8YuklJUViSx0DbymOlyVgLpBLpGxwM5WoIPB3kuTiHgErZBJ3bF35uKNMW+Sz2x8TOv9B28nhjHpYZze7KmH2fH4AP+O12fI9AZN1icXc8EXtp82Ck/qYb/67ThND3uNVqPB5lpD9x7BOsG+mPHMDWdCrnvBdArg7Ov89T/GKCsrFQO75+oXcxxFZS90PTxOclaeejP22o8Ik9mcXXn3F27wFP7865uFhTt0l8+2u80ygd0FoKomGQBD1VJxLg8PWuN8yKhk2dUeYNifHUbUIS+wvb3vBsO2GatZzkFfO0pjAd6bxn2RVc1hNS7bg7XeGCRivPebJlZmTr7+58RaJ7DIE7ixVv1+f80voV3iwvKXUrMqByJKqn1C238zWZ7vna7jb3siWfKJ7xS+tX+Onnb/72nwnHOc5xjvMRn5cGYPnFFFU3Is0tpZB53MQatr+Grb2ug6IQT1Rcux9YqJAmhCIVwEMEWvOpgJrosxkSwfWuFkZsPhHjc9fD7XMBYFqj49p/2GwleynPUSdzAYBdj3q+FABV5nL8bTcCQ2GJKrAWXeaEPCNMCwEu2wpV9bCr9zEPJzOwCUFrVNPuN9xAwE30+ISqkhqdgwJmFQM59c0Kk+ckeUooM9w0oz1NaacGlyrqE0WzyMlupZQfTEg+uMY/eiIeq+lE2Ji23W/rwbglKKZuf7CtCMFL/dAQRKonE9Rsil9v5KapeNFC20Lw6KJAz2fyOlS1+MK6PgIQBIAFFY34bl97BAy5X8EHFG4M/hwl44HxG8qwh7eWMSIBhrDfkpSDB21HeU+ON5Wi8LgcEJYr1Ebv4zKGEuv5DO28vC+1Fq+fVqibNTSNgNaYyK+KYnw/4b0sIQwVTqmAY/f8EnWz5PyfZ1S3brH+8Y5P3nrOqsm53hUE60UmjPIgCJBqgsUaxyST59t7LaCrTbgzX5Nox6bNqIJiU2V0rUVpz93TNbfLNQ83C5yfcXuy4U6+5vXyis9Mn/BudcbSFbyWXfJ9k5q363M8invpDY/aE16xG95vz3glveInF7/BZT+lC2YEVjuf8W5zzrN2Rh80E9PyWnHF9xfv8Yu71zEq8GBy83vwwXGc4xznOC/vqNGI/F2cuToLP6b/5GhA1kUOSQpdi99W+3JeLXKbsnZkJ1RM6g7TUhihtkOtt8KCLKb4U6mTCUahN61sKzYCIsb+u8hmycagxhUJuu1RvUevdvjZAIxqVCxjJs9wJ1OwWrYPNzsBaImV+wExxzctYbvd+5T6Pl7wD2IF8hj6mWf4aUnIIqDsvQDOqhHws9kK42GMsC0DuwMveNZ0WaLKIlbPWMJ8QndrQn2eUp9oXAbpKjD9oCV9vIanl7jr5XjuR5AysEJR3lJZtt/+i3JdaLsXc6riDMGnKomgJLG41eZbA1MPRxt0muzB3cF7c/BifXiDcFwsGKTJ4VjTNMp07f77MWRVJVZCQbc7YZuG+z7cAjz8dxGPa/QJ6ihzDgxl3DoMxshW6rAR670UgEfW7rBHc3jNxiiMvsPMZqz+9Pfy8E95yosdedoRgsLogKRz7Fk6rT15jHHonPQXGj0Y4MUEX9UJWgfKvOV7zp8B8I3rC3qn8UFxMd1ynm/JTUftEmqXcCvb8NnpIzYu58JuyHTHg+SKf7D8HO/sznhzcsmjesEfP/1NnvcSz3Bqt2xczlUv3YfnyZZP5Y+Z6IYbN2Htcp73M562M97fnfDa5Jqvr2/xt//Y//UXQgg//Fu/IT46M1dn4cfUn/huH8b/X/P1v/QHv9uH8LGYT/3Fn/luH8Jx/iXMz4a/zypcqd/uZ14OBgvQRUFwDp1lAh6WK5GfJgVkGTQNfrndr7vHcEw9nUrS+Goj4Go+JSxmwmK1Pea9p5LEvatE+iqKvSdnuYGulQtc2wobpBQmxLLm6JnS1xsBENYS5lNCYtHrLfq9x4SmReU5YTGVDcO2E7nTaAFvRYaellJcXVWo+3f2HquhEqbtUFlK2Fbw7FLkSkBNStRiLhfwKEWF5WqUMfVs3+emDuTO0PUib+aZAIHtjuTyhnRSUl7MaM5zdrctN5/MmEwsxTTHbM7luKMvLXQRwGotIKHvCVWN3+1GIDMCsCEjKwKY4e86F7B2KPMNtTph2ACMJdbD+LpBFwWqLPDrzQi6Q9wq1Xke3zASvRCcGwNYdWQ8D0uv9WQyZmmFYcuwacagVWXtnoErSwlu9ZJXFboYzuodvo7ga7sdA0RVnomEHAJ+KhUEwWr6uydSWr6qUE2sJup7eW8PkR4fApO6lNvPf/2am09dUDzoRjZqWyUY47E2msW9BmfwMfk9BBV9WIpp3rCpSrrOcLbYSs7VLudnf/2TmElHUbQSZNpr3noy5S0TSCYdadZTZi2tM3RB80p+w88u3+TTk6d8ZXeXTPe8Obnkqp3wA/P3+JnVJ7mf35Aox6P2hMZbLpINU1PztJvzz9af5l66ZGF2fK26w3vVKbezDZ+bP6ZyCaX9LTo2j3Oc4xznYzIvB8BS4HeyAu6a/YU09D3uRpgVZS36ZIGaTiTxvJK8Krda4ddruThmGf7qBr/ZCHuSZZizU1nFn09jH+EKv9lKjUuajD14KrG46yVKK/RiTv/wgz1rEy/yfrPFf/BIgMWkROU5ejEfzfEBRD4cZD8djdJGw+kcTucCYlwELAO70baE9Tr6n6LHyDkx2nc9ajYlzCciH+oTVCXFw2iNX61FNo3xAYO0qlMpXfa7nUiAaUpYrjBX15QXZ2SXU7avltRnBp+WJJsMuxX/l+68bFL2HjY7WAnAxBgBszFlffiaMpmApzyLG5wtvq6lIPlwggAX4jkdc7QOmCOdJgIQh3LspkEl6ZiVNRYtH4CywUOGNi+YylFa2MMPjS5LVJqM74Phdr6qIL4PObw/zNifOPrjQiDUDf16jTlZiJwc4zq0UoRJQcgTQp4IEGsKQlXDcjUm4ksOWdxqjMdpnmWcfvWU9z+54BNvPpHDUIEya1HsM6/q3o5GeKUCk7Qjtz1X25Iia3ntbMuzrTBKn7r1nFdev+Hd7SlWeWpnWbcZszsN87QeC6fPih33yhWawJNmzo8tvsmvbF7BB8UrxTWlavk3Tn6Bh/0pn8sfYuIOZx0Sfr16QBcMy76k1C0X+YZ3mzN+6oPv43tPH/Pv3P2nXPZT1r7g3GwozN4fd5zjHOc4H8d5OQBWTPMZioSHi7PKMnRczQ9dj3t+CfHCq6zIhvbeXWFbQhBAApjzM2g7fNPQP3ocH0JhFnPUYo6+OJONxKoWf1XlUEWOOTshVDXu6hodvTOh7eTip4VBsg/uEzYbfFWPQE6XpTAZbUfwnjCbCEt1eYMqC8JiiqpbydKKW2mEWHZro3SYRCbHhdHsrqpmL4Verwizidx+NhGTv1Kou7dQdYt/9lzM2XkuG3dxo03nufiJul4AAsDNCts7JkB1v2B3bthdaFRIIIiZ39aBbOlI1iV2PUMvt4SblQCeGGkAAoxHqHPAVOnJZG90hxdDRX0Y5bkPdy2G4fZlKf4o7whdO97fCNr0Xo4cNx8j46TzTI6v62MyvHighufvN1v8skJHyVNyrbREeAAqz4TZOpD1QjyO4fmJBNkKizh4vEIYq4nUcg3zKX5e4PMEswS2lfi8Bpat20uWwxKBe/aM+S9NmL9+n3cnZ3zm/hOqPmHdSDxC01t6p8nTjtvTDY2zbNuUphPAdTbZ4bzm/ZsFedqRJz3v3Zzwa+/dwyaOdpdgModvDY+Nx6bSY6h14BngguZeKf+p+dL6NWa2pg+Gx82cRHn+o2d/llu5eOweFDfja3AvXUptDoqZrjm3G36w+Cb/1unP8Uv166xdwdJN+PL2PgBze6zKOc5xjvPxnpcCYCmlMBe3CNstfrcbL6ahbUVKshZd5KP/habBrVbjppiKYaPjxbeuMaen6PlcvDVNS6gqYcMiIwaMoAnvcZdXIuE4Lxf3AVSlCXp6KmCh6+jffyjHnKQia3Y9freT447MiHvyVADdbIa/WeIfPUFPSvR0ImBwMFobM/p5lJZw02CNGPHzDD8rRS7cNWOX3mB+D4upbCCuIsi7OMcvV/jtDp1lksMVOwgHBmusbul7/LNLzHrDdHlGfjGlOU3pS42PP+JSxfWnE3SfkF9lTB7lpEWGXm0FYC5XYAzmzm2REbtuNN2H3Q53ebX3Ph14pQbvls4zVJYJiBlYoyi9ha4dv6asRRUFoW4IXSv+tcFrZe2LoEveTBK9ELcZlTL77Usf8JvtmLY/3G6s3QEIfs+AWitAG/ZhtrDfZM1zkX1LCaDFGNhV+6T8dx9C32NmM9TpgnBbgL/e7PavhXOErhP5taox8zk0LWe/0bF9JecbyQVJ0kuFjgpxCVU2A282JSHA6WzHJHXsuoSbXcEsbzif7ti2AijPJjvuzNZs2gx9EmidoUg6Omd4cjWnryzoQLXMuWLOk/Mpr53ccJZtabxlbmtK0+KC5vMnj/BB8ZOL3+DGlSTKcdfekKuOX6pfZ+dyctXhgubGl7zXngPwlfoeue7GzKz3dqff8efDcY5znON8FOelAFjBe9yzZ6NR239I1hl9K4deniRFGY2v672HZjZDKYVbr3HX1y/ch8qyMQncnJ0IU7Zay4VcG/R0KmndxuC3W8zJArWY4x4/xR/clzk/E9kvgrbBdD1c2KkqAVrWjpEAejYb5aThZ+WJfWjBQClhVfJMAJyRGIYwKcZ8rBBZGFWJlOpvnYgZvm6FRRukr+1uvxzgHG69/tbH224xzpHsasx6ii9TvNX4zOCtIlsbqnPN7ramPs9Jlxnls5intWlQ6x3+6loku6HbcMi7ms2EvdtVKOfkz02L32wk7Nx7iURAtg9xDt92Y1GzSI9GGLL1WrxXwe5LqokbhYlFp+k+xiHPUPPZ6J8bNwxjZZA5mUvCvZfCa3+z3L+Gw8uQpGL2D9GbF03o4/kb7hMwd27DYjYuNpCn4gV0HjUtR/Zs6EEMZUbIUvR6O1Yb4QOqLLGnJwLgVytK53iQvM77Pznh1ueecFFsJA3dWTZthguKWS7nYtOko1zog+Lpcop3mrxo6R1cXk+ZTmtS6/BBIh5udgV1m5AXLfOzFZ0zVG3CNG8wKvBoPUOrQJJt8EHx/cV7THTD437BT68+yV97+qP80OIdPpM94u8sv59Vn/P90/dZmB1X/ZRH3QmdNyxsRRcMpWlofMKjesGb5XP+Oxe/wn/OcY5znON8fOelAFjiJxJz9F7SakcPzOj90Qa6luA8frsj9B16NkNPSvFrXUq1hy4K1GwqTEcEX36zFTbCGPxyPbIhZj7HNw0+gh+d59g3XyesN/TvvAeAOT0dO+Xc1fULoZt6MhFmDQRgtN2ekYkXYn8ArIZtN13kEkfQdmNCeagbfNOgQkArTdDJPvohMm2qbgQsRLZLr3aSgTXNUVmCQpqAQiyB3sct7M+xPFG54LubJaqqUTdL7HTC0K+oygI/KShmGd0ipT4zdIVic9+ib02xzQTTnGCru9hNh13WAvri1qS7WcJ6PQZvusur0RcHSNTEkPp+mHXlHLoshWHa7kYpOLSdGMHTZAS3Q+K8j7EMuijie6SXap+hR3LI1YoBsXS9xGg0srigZzO5nyoyWkYLsBu8YWMFUjTcOwfBjab44D1qWxGyNG5YKgF48bULbS2spREJN8xK+nuneKtRAZIPrgnXN/TPnonEW9f41ZrymysuvnTK4+YuT97ccu90RZm0ZLan7i3bNqF3htQ6MtujVUCrQJ9Ljc71piSxjvPTDattLgDLa7ZVQpI40qTHB8Xz5RTXG0KA7bJAWc+rt6/xQdHFdPe/8eyHuZ2t+R+e/Tw/efctrlzCb7Z3+Y36AZ/Mn6JV4Gk3J9Md99NrZlrO5QfdKXWwfCJ9RhcMj5sFX17f5+eu3wB+9l/gw+I4xznOcT4a8/LENJg/LX/58Bp/lI0GSUjlUSIyBqzFPX++ZxZimOW4ZRjlsVGiOpxBrkLCJpW1uM127wsKYZQQQ9xW01mGmsi21xCt4Le7F5ms6KkZ853SBFWW0LXfNqZA57kwN8GPuUgvMCrxGFRZiFm/yGWrUCkpfO49ar2TC3iayEU+BPF8VZWAkSivoZSAz2gUV8aMGVpD9INkN+UjmCVKdO72gt2rE+oTQxjSGwxjhY/pAnYXSDee7KbDbFrM9Zbw/Gov9cZ8LWWTWCStIlsXQzjrGr+t9l6nw5cryomH52V4rfWkfGHrU76/zxMLi+lYVzSE1IZZSUgtatfIBmpVv2jctzH09qCvcThH4/Fpg31wT3KztjtpBcjiazMtCVkqsQ1DxlofC6jT5IV4B5DtQ1W1+HcfijfLO9AG85lPsPzCOdffo6k/1XD/7jUXxZY+aLZdSucMWgXpGawzfJDKIq099+crbuoCHxS3JxumScOT3WwPxLzGao9Rnl2XcrUtqesEt5P/dxUnNfOyJrc9P3brbXqveVifcJbumNmaH5p8k1Q5XNBsfYpRQWp2XCmslW6Z6IZcddQhwQXNg+SarzV3WbqC//ALf+sY0/ASzTGm4fdmjjEN/2rMRyemQTECj3GNf5AFvZOqFmIMwGBIjt4elabROyXsEdqiswy3EZlv7J4bthOzbJ8ErrVIipstIIAjAHoSvVSDjygCH99ESQdGL9GQuaSnk31sgXNQ5AKoWvDVlWwnpgkY2Y5USsIs/WiwloiIYAwq9thJnYsSVuzqRo6r61A78R7pXMBWyBJULz2Iqnfi10osyk5RWSfhq7EjcZREY4J5cI6wa8b4C50m+NVGIi2MQcWNQdN1TFcV+cWM9iTF5ZpgwCWKPoduquhzRTdVNAtDtkzIU4s+naDrHl01UNWoth1jJkLf79m+4a1gjDCGw/kexh0wRsPmZQSxw/dV7wjWyDmottHon0iCfmKlCcCDv1hIkbNSAoLOFqhdRri6iZEKzYtS5Ld7z2qDWcyFIasb/DKylLsd+IDe7tAzie4IRqNaYSgPc7xUngkz2Ts5312PeXBPOiN3MW/t0VMWzmN35yxXGQ8/d0F4A86LHYWVnKxtm+K8GuW9zPY0veWqKkfpcNXkPN1OuV5OUCrQbRNM4ciLFsno9RRph1IBYpTDg9MlnTNcbkv+bv0ZrPH80bvf4DTZkemOf7b+NK9lV3TBxNJnPcY1vJ4+p9QNdUj4he2bGDwPsmu+tHuDLpgxGf44xznOcT6u83IArIB4acoSt17HGpaDy9ph4GRkF4LzwhQ1DS5KRFJBIvIO3uGWqxGMgTyGr2oBZtEz44canEQ2GHVS4LfVeEHfy0ZyQQ1tN263gchFoapgKDceAkVjtcooMxkj0mfTjOb40HcC+BDJLPSdAK3osxqzmmKCuISL7gSsnSyEbYqggiIXlmY4V8OfE4tKpXQ61A06SSIL08uWXYwMUDYhDKAmAsUwyHhaiTS53WGvbkiKgjAr8aWwNa5I6KaWdm6oTxXNQtGcWLZ3DNkqoPuAqQOmdqTLFr3ciezppJ4mRLP5UC3jlithpwZjPMjxte0+eT3KjRLPkUKeiT/NOckci0BmMN8HrUapUN9sIoMZIzRi5IWeToRlq+r9axEXKEY2dMgvG4JivRe/WzTOiwQcgWNVo+dTSXNXSuJAAIweWTvGyI1kz8DNJrDe4KsKM5vB0+dMq4Zkexvlch65W/DJZ9wpxbje9gajPasqp+/NmJcFkCc9PsDjqznnJxt+4NX3uW5K6lMrcnJks1Z1xnJdoHTAWsebF5c83024WRdMipZFUbNIax7WJ3xze44Pmh8/e4vP5B8w0zXvdee4oMh1R+0T3mkveNrOcGhO7Y6LZD16sR61J3zQLL7jj4fjHOc4x/kozksBsJQ1qKLAV/XokwJiGbB7wWQchq9FCU9l2chYBR/Qk2w0Jus8FvvGvCsJeFQoIuCydgRDAwvmN9sR0LjVZryQhs7hVhGMDJlIvOglCl0r+VYHJuhhQuvHzbQx0fuwkmb4+eDHlX0Vzdu+quVx03Sf5L7ZEqpaJLbpRL5W5vv78lLErHwQEGGNnOcshalsruH8GMo55Ezp6VR+JhY5j3U2Q2J+HU3ZfY+5EfBjQiAtcso0IWSW9taE+sxSn2mqC41pAngAQ7ApppmS7AJ250nXHcmjFeHR033FzQCghuLkmBQ/nD+VFqOMN4CroYIoNC1BK5Fwh1gKEKN5mohsGMvEVdejnCcYLSxTmhBWbv9e6yPoHaVUYTxxDpIoFzetAMOhysgPSw9677FLkrHSKAC0HW59PfZL6ulEpEQv3Zp4j75zC13VUqOTptC2ZG8941Z7DqHkA3OBf11xq9xS2I5Vm0niu5XH753BGofzCms8b96+5Nl2wq+8/4Bbp2tuFVu+9vyC3bIAFTCpR2uPd5rGaX7z115FdQp7f4cLimfrKZe6JDGO1xfX/MDJ+yz7gi9Xr46nuNQtqVox0S2vp8/5TP6IW2bF1me83d1i51M6b/nG5oJFcmSwjnOc43y856UAWMH50bR82CM3FAATGZbRXxULgUMvUpGP22Shjb6jCMB83ewN5NG0PNxPaA+67oLH7/z4eL5uUG07+rFUmkqS++itEmAFiISJZDGpXJLVw2i6llRwX9Xi0+n6aHAvRuA4SoHeC4s1AEgYvWTDuQi1yJjE+w9Ng+p7MalnmaTYl7kcc9eJ7wdQiCF+iIBQPkiWVtPCrhbwdFjmHEGDimyceLL2NS2jV2kAjE2DulEjMM2fXpOdn5A9mNHODV2p6XPwiQIFLoP6XNgju7HMThPKSY59vpT7jf18oe9e9EARDeZdj55N5biGjcxY1q2mpUiiRu9Bq4uALZaG+ywFG0HWEL/gvCT0X5yhVqm8H4f3ZteOEurwflEhiEne6P1x4UYJWr4or5mrGwFR04lEOpQF+vxEXpuqIaw3wsxpjdpsBTSC9FieLAibLf5miTIGu9lyZ3cPWPCkWJDdc9S9pXOaIunHnCxr3AiyAFZtxiuLJXpxwwebOV+/vKDMOvRpwDlNlvS4oKirlDzv4EGL1Z6zyY6HVwu0DizKhqpNWLYFv7G+y2m6493qjE9OnvFaesmj7oS1y7mfXnPLrpjpmof9Kb+ye42Ny3iQXbOwO/7Y2de46iffwSfDcY5znON8dOelAFjCFhzUrByYzINz0PvoR4oXsxD2oZlhv3EI+0yj4cIfHGP8wuClAvZZUXFUlo4xAoSASnMBfJ14fYhhp6MnLPqVSMpRQqJuxmNC6b28GIEZMIKpkZ2Lm3DD91Saj2GVwfnI5vj9eRr+PPx9MJB3PTp4kd6KXBiZEB+r6+XrIF6kPBUWZzge5yRRPoIKlaWx77EVcDmEdcbHlB9So4cL5wgD8HMuRjZ4im1FPinwZUo3S/Gppi819ULTTRXtAtr7gXZh2Z3PKZ9PyK46kqudcIx9j1uuCH2U9uKGqWxgJgKQejf2/hGN6appXzxHQxyC0lI/FGXBkKX7onCjCVpDnqFipMPALipr5b3Qdy+cd/oerLyPlJMuQynijvLkYaJ808h7LJNQ1DGYNEuhOEcP9UltJ/2RsR6JMkcnEr2Bk3Bc8+g5Z1/JqS9ynk6m3FmsSY3GxR5COURFmbVkxrFuUjZVwabOmGQtZdLRO03TG5yL4aoqkGiPz+TYtQqstzk3l1NQAZs5rjclWSIbjE93M66bEk1g1yc8K2bMbI3B89XqLu/oCxZ2R6lbPpU/GTOzHjanXHYTrtvyd/xYOM5xjnOcj/K8HABrmMMNu8F7Y4yAJNingXf9mAquRqkoi6DFxwtbiIxKug/cVAqGzKR4kRw7/ACG/r0sE9AS5anRgK8VaDsyTMF5FB3DJuYYeNp0QARP3smFnz3QGvw9qBABm9rLjIMBPj4+sGes/P5xpIx5SBSXi6LfVmgfRG4qcokpiJKWiueMEITZGh5PKzASdKpiFMTYRdg0ewnzsAj5oLh6OC5l2FcKNdHQvt2Oafx5ngmIKTKK05LmLGN321BfKPoCtg8UzZklXRpOvqHJPni694ANxxoCelJI8GgRgdCQnm7NGABK7/bbmCGIwX8EiAcANUnRkfFDqX2Ya2Jla/MgugHie8AeyI4RhB5KrJL6HrO0Ui1sX+y69OuNvDaJbBESs7GUC/uIhzRGc1TNWBweZhPZZt3Itmjoe7J3rrg1vcXjYsbV9zoupltMUHTGxIJoz7ZJ6a28v6x1OKdZ7XIoa4q0Y1NnY4q781IA7b2i7yzOaXxtUDZgsx7nNPU2pbUJ211GkvZkSc8riyU+aN5an6NVINWO++WSL07f5bZd8bA75Rv1bc7slrNkw+fLHbnq+MXd69/67/84xznOcT5G89IALJWkcoEaNvyiOV0ldgRWQMyjYvTGvMjuhJHNknLmZLywqqIQABWDH4e1/SFygCFqQQ0grt0DoyxWpxzEKQzereD8QVK4F7A1xET4MLIu8v0IkA5M92Bjenns5Ovc2Ks3skGRuVN6Dw4Gc/xgrCb48UIe+h61iwnjRS7yYGIFkPRulMqG4w5pIr/yBFW1coE3WrxWEWSFDx07WjPupx7Ki4d/Hkz01hLqWsCsVtjnKXYxJbucs7ufs71naE6hnQd8omiuLHmW7jf5opQaT8LIVI1G/uH94fqYRdaONTmDH28woYf43ILzKNOM8R8UuRR0D8Z3o1HOow8Yu6G2aCy6jp4+gt8XSh+wqSEEOW8RRNM0ccO0kPiLKC9ijEiOA8CLXZXj8WQx0mJSCHjse8Jyw/SrltPFBU/PppRZi1FyrFVMcNfaj/9sZnmDAnZtQtUm+KDIk54ilefUOU3TRV+h1wSnMIXDJj1dY/GdQVkvEQ6TjizpUSpQ9QmawLZL2bUJRdpR2pZH7QkGT666cWPQo5momrXPuZfecJzjHOc4H+d5OQCWUnt/k1KEQ+/RIXNy6FkKkmM1MgdtO4IAYPRuDVKPTpIXAkx1LHgGBMR0B34fLaGS+zymCILoRrYGkJ8bGLC+x0ePl0rEPC8XyaFj0I2baEPEAz6CHO/3z2PI/Rqe+2CaHp5X9HKpNBkBxCFgC0ORtHPClnSdMEtZlAUzkbNGpgfknFuDqgS0hGwvT6mmQ293YqofWMCYsj74yoYZ5EKRc8VThpPXTSV2BKxht4O6xi43zG9OSLYnrF+11GcKNeDlaYnaVXEhIMpWeaxLigb1EXgpBQR53nW9N+bH2h05tg/5uIKHYGSLzxjUcB6yRHohE2HptNHiSRvAlA/7bULnIM1Hhk0ldr8U4VwkMSWXavha8AGttbBjw7nKMuh7VCfBqMOygoqyoeqcvC7/v/buPVaS67rv/XdV9ftxHvPkiKQjQmISy1c24xCyAgWBFce27AShgsCADCTgHwaUADLgGLkIpPyTXOAG8D+J808cQEl8TdzEFhQnggjDcCQxUYzcG1umZNkSTSmkJUocznCGnDmPfj+qVv7Yu6r7DGdIDlni6XP4+wDC6a7Tp3tXdWt6ce211243Q6+t+QJGE7h5wPafdhne22H/TIez/RGNNGOW5CyzhHoas6PmLOL519KcZZ6QZQkzSzFSsjxhsUjJs2IKHlrdOc36kvkyZbZMQorXgWU4l0WWstWeMl7UqSc5DrQbC3r1OYeLFr9/85081z7Lw1vP8XDn2xzmLS7Pz3Il32WcN5hka5lAEZFTaDMCLAhFvqx9+Vk91LwUwZYlq0zF+pdYUfheC8vO8zlhWi5trFZ/zRdhc2HP4/Rgvlr1VUzXFZmwtSaTxWv4fE42m8XteVZf1MVUXrgTNgu2NCk3HobYpdxCkb41m7GeaxGzUqG+zGO9WXmuxRd5zIKFLEsszo91W6HWx8opq7KdAF5eDyC2QZiF1YadDvQ75LUEcsIqutEk1GhNZyEbFhu5eh77RNVS2OqRJAn54SBct6JHGaymauO1A8oMZNEss3yv0jRsZg2hJ9d0ho3HdCYzaqOzjN7RJGsazZtho+tsvQ8WYQrSe51wPd2BfG3FaV7uTVlk1DxbvX7xGSqyVyTFooiwQtOHo9Cw9MwOdNt4LQk9spYZTKehjgvKFalFY1er11erXNc+G7AK8o216d3YusNqtdX052wWMo3TaVj92Kutaudm87KNhMepULf4nMMR9cs3OPt0gytnu2Q/MOZse0y3Pme4aHDjMBSSNxtLxtMaSZJTS3PyPKHbmjOZ15nPwnSgL2OWNM1J6xnt5px6mjNb1Gh0FmHKcJ5imZHfbDKsNWjdtww9t7KU4bRJliXMWzV22hO2GxNqlvPM5CLjvMF7W5d5R32Pw7zNn228yO+P3/Xq/yBI5dRIVOSttSEB1urLh/XpMmLgUARSeXIkyFiflvEsxdLiWBq/SLNVM8q1bEsZuBR1UnFq0oqtV2Ixe9JqlVmwMpOWWFgdWG+ETudZXnZD98m0DBTX+zgVva2sXiNnupoqLGq2YrDkeTGltJa1KpqZpklZ7F1cp6Jg3osC/LVr45Ps6DgmU5LRGJv2Sbpt8l4nTBs26mE6rehFNQqF1L5chmLydjsENe1WCDZGo1XrjOI9qtXD9jJF+4kkjfVHVmbzinYQ5fRaYnH6N8H3D2gsltRv9sn6TdK9MT4KRd1l8JumYcNk95DBKdouZKEPlQ+GIWCKgWmZwSwu42KJJXnoct5u3dLJPi40yLLVSs3EyhYX1u+HrYviFF8+DNcgjcXoyXaffDAsP19JqxkyqlmGeRHYhc9OsdKS2CKEWi104I+vB3FlYZqULRvIHZvOsNkcbzfjfoY1EjN8PKH77CG7Z3Z5sXuW1rvDljmLLKXRWNKsZSyzhFZjQZLkzJc1eq0ZeVEEX1+QJo67MVuGALFZyxhNG+xP6uSjOkl3QZI6VsvJGzk2SaG7ZO+gS7M1ZzJo4dMUWxiTTovluYTm9pJZPqee1xhmLRJy/nL7ef5kvss3Zpf4/tYLFf3bISKymTYiwLI4ZZfPF7CclTVPxfSe1eqr7FWaksTC7vXpsdCzCLCEdHsLazXxWK9CloXGj8WKrjhlVRRlF9NeZWZsviCfTMpVfEXRfNLrkg9HZAeHZSd5LCm7opOmIfiKz+GLeVx9OF9NNxYBV6yvsUYj7B2Y5SSNZNUOoSjEj8FMvtZBPnS491WNUrHq0lYBRZn1Y+28lkv88JBkuSSZzUOX8XoNyyzUqC2XoTap04HxuKw5MgjZmloK3S7msRA+rhosNmkux0JsYxHbbjihZszabfA8FI7njmer9gfJdAaHA9LYMsJjywosvFfphfMhozSbx8+Bhe2AinFa3CS6qHfzHF94OdWcxF5Z5V6R66tSY92am5HkMWDudchbNUjjZ2M6wweDVZ2eJfhkQpJvQ79HMl+E/mfEYK5WC13ki5YiRf+wWm3VtywWtXstwetpnAoEb6bhfubYfInNlyHYKurnJvNyBSRAMhjR2t+mdbXGtfN9vu/MHvf19+nUFrw87dKphZYLN6Zd2rUFjWTJleE2s0WNbjP8bryo04wNSmfLlDQNwai1l7S7c+bzlPywBfUc7y1hkZBlxowGu2cHLLKU1JwkBrHP7+9w2G7xwXv+F9/fusIgb/Od5YychJvLHt+enQf++I3+kyEisvE2IsAqOognnQ75aFSu7isKxsuNn4vaprWWAeV0S61YAVYPncD3VwXhZaaibAi6xGehm3nSauJAfngYHxy/kHu9sL/dbB72Edw/CM+bZ2FlXL8XshazWcjcwKq4fb1ujLDMv+ixZTFrlB0cHr0Ia93Tb7uasiisXy5XKxnXsmO+1gm+7KVlfrRJZqwdygcDGI5IdrZD36hWM3xxm2Gd2OqglpIPR6Hx6nSGbW+FgDTP8eH0SMF3EcQUgXBSrMK0cC18uQwBYsySlXVocVoVD1sGWREcZaHmKOl0ymk0b4XpOh+OQvYnsRC0LJahfm6tzi28gataNhILQVWxF2Pc19DJys9YsbdhPl+E1Y9FG5AFIQg9v0uSGPn+QTgetxAiz0Mm7cwOxH5V4TMNLCfksci9+MxYtxPOpxmfP8tD/dd0ETJUnRY2yWEcdyeYhP0hvdMKtXGAjadhs+peh/z+C+T1lHSS03s+4aDT58qfz7hv+4A/vXmOyaRBtzOj0wzPd3PUYTqvc7Y/whoLDiYtJuMmmNNshq13sixhMavRaC3otOYsspQ8S7H2krSek2dGernFYjcjm8HNF3agloe6yMywVsY77tnjh889z3DZ5Ldvvpf7Wvv8xe5zpLHITlvliMhptxEBFhDqikYjAJJ2+0i380Jo1JmVGYSyNUOsCUr6fXwyCVNEzQ7Fdiq21SO/9tKqOWazCXmKLxdlcFR86Red0gGWV15cNRut1Ui2t8gPw1RU2KcwCYXXjcZqL0TPQzf6PCdfCygsTVfZk3FcNZms9jks2jcc6TNV9EoyC9mqoq5quSTpdMKUVBJ/l6YkzWYIEOqrDvUhaFi1mljfhzA/PCTJs5BZqqVxT8MsZKd6HazbJpnOj6wStG4Htvskszn5zf2wMrNej3sDhi/xI7VTRWAca8VCpnFtCtTCdF7S64Uxz+erKdPZDGs0SN9xT9i0ef8w9JLa6sFsHs49y1Z1cOv7OK7vZwllx3+fz8n29lbXmBCApzGrRBKn7YoFF7U0ZM3cQ+uG8SRsV1QEfsMRnNkJ59Fuk6RpaBwaP79Jt1tmYn04Wq1YvHkQ/jZJ4orXWuhIv3cQ2lFs9UNQ1W3DbI6Np6HPWmz7gOcwGJJcM9Izuyz75+i8lNEcGNeSHV788znvu/RdDhYtbky77I3b7HYmTOZ1eu0Zo1mDZn3Jud4I744ZzRvMlimt+pJ+c0bNcl4adRlPGySJs9UfM1/WGF3v0rxWI2s5tf2U5bmc9rkx02ETXxg0M0icaze3+O+zd7PTmeBufOXq/WQPJPz9M/+D9zSu8VuD977BfyhERE6GjQiwLEliZqeDT+LKMUtY3wS67EUVs1W+XK7VvIQNlPPBYK1ZacgI5eMx7O1hzSbpmd0Q5IzGq6xCrwuLkGGxRgNLk7gXXtEWIiU9e4Z8/6Dc/Dnd2QlZkcMh+XxBAqHZZMwk3bqBcRFoleNvNMrH+C2F3EeuS8x8Fa0Eyi2CivNavx5ZDqmFjNx8Tn5rEFNk8PLY4b6o6xqFzA6722UBtfdip3MzrNfGRtMyw+VF5/JmA7t0IQQek1lYaTcYhE7p01kswI/1TcW04WS6uq7xfSu65BeLECwNAaAvlmBOsrUV6qCm89WKwb1D8sEgZMXKHmSNsmWCz5ZHNmi2eniNLAbYobYuWS2sWC7JDo9mFJNWi2R3B3b6YWVhaiSTBUk3NJbNJ9PQUiMGiH4m7K1nk9DeIi32jhyHYCzd2cG2Q6d/JlMsSaDXDVPYRePZ0TgEaa1muXl13m1hM0ID0q0e3m6QDKf4zT2s22HxzovMzjaZbSdYDr3LMy79/3Wu2Bn++6jFztaYVm3JbmfC5Rs7pGlOrRYWJewP2wzSJknidJtz3rl7yGxZ48VBP/TFysMKyOm4zuR6B1o5NHKaP7TH4KCNj2skwxrLl/vUcsAgrzvZVkbj7IydzoTd5pjEnK3mlNGyyW8c/EW+PTnH3z775B0/9yIip8FGBFhefMFMJmHaqtUiu/bSWpuEyGIheBGwmJX9oMrsBYRaqWLD3mYzbOA7m5HdOBrM+GxtpVrMElmtFjJQZlivG7IRe3shY9Sok0+mZQbEmk2M9Mj0F0lK0u1i7VZZ+Jz2uiHbMp2RT6dlHY+laWh9UPS0Kvp/FQXva1vmAKttWGLWI2S9VlkaXxy9VkDcfDhOZ61tOxPqwsJ4bf8Av7lfbiKd1OvhdWppeJ48D1/wAL0O3gkNSW26KJth2jgEbPn+QaxbS1bvEyFgSXvdkG2KU3r5PDQztVqdpN0K03ZZho/HJJ0O6bkzocAewgq7eh3vtkOWLc9IixWPy2UIlhbZ6tyLVYzxXMvPS6t1JOAtj7fb4Xbsyu/LJfnhgCQ2bcUdH01C9/tel7TTCS0hBgOSeh3LMmyrHzJOnRZ2OAr1bLFlRrGVj6ehS38RrK/XAbp7qPNa+3ylkx28341Zr/3w3rSaWK+HHw5I/+gZuvfeQ+37dkmnGfUreySzLc7/YZeDwy7+IxN2WhO+cfUC3facTnNOlifUkpxGLWMyr1NPM7I84ds3z9CoLbnQH5LgYX9Dc/bHbTpnB7z8ch8O6hwueqv/D6XOshNX3NYdWhlpM2MybPKdKz2enxl506mdn7D1fVN+avePuFg/4HcHfw54ChGR02ojAqyyTgjIXr4RDiZpqMEppsXm8/CF1aivAoz5/JVflkCWZWUR+/q0Yrq1FYqKZ/Myi1XWXLXbWKcdXr/YFiVOWQJlhiPp98NecfM41dVISawVAoPZLHwxj0YwGpVjD/U/4TmTVlyZNwvZjaTTKVe/FbVL5arJcrowFqivZbCKmqGip5evZYaObI69nsmC1ZRZzPiEWqS0LNrPDwarQn13km4H3+qFbuKx9sem8xB8xa7wnsYVfe4k91wgmc7IR+O4Ii+J7RmycoujsOHxnKTbKRczeJaV06XF5yE/HGCTSZiyS1PotLHZgvzmXjmdHE4mBtppa1WYX26l5Ks+VWlCtn8Qsn7tNvlwuMqgjceruq04zejLJT6ehK74nRZ0WuF8X3ixzCAmnZB1Jcvw8YRkqx+CoTjd6csl6dYWttUP9WKDKWQZ6bmz4f2JK089trQoMn/WbJJPpuT7ByH4HE/Cc06nIetY7E7QbGLTOc0Xh2TdRujr9aXvsPPHLRo//oNc7ZyF98IP33+Z4aLJ1UGfPE+4MWrR6czY7kwYTJscHLTI5ykkzp71SRoZ/e6UWpozn9UY3WyTDGt43WnuTpmNGuCG14xkmJK3cmrbc5ajOr5fx9s5W/cf8t4LV2mnC7760r386cFZ/p/sL/PBs9/kXa3rr/nvgojISbYRAZZnWQiUbpnK8vl8VYtVPPbWrNbt5Bk+C8XvaZHNWsQv0Vn4Ek26MbCJGaxQGzU+8hrp1lbIKiUJyfmzZFeuhWnIAUen7Iov5nRVWF1uEJ2GTJnVG6TbW+FLM05ZJq3W6ou61YJ6PexvuL4noyXlljWhFUNWdr33opFl0UB1XZJi6Vqz1qKea/06mpHubIe2A7HnmGcZ2eFhGYQUX+iWJrC9FTJKxSKD2BHflhksM/J+l2Q4DoFUsxGu8XQaVwTmoeh7MCgDCJ/OyBeDVYBUr4fNpes1rN9f7RF4GPaHJGa3SEL9XdnyYLEM7/F81d3/SAC2Ll7TfDAI1yG+N8lWL6yeXCzK4JAkLrQYDEOBfTssBrDzZ7FpD2Yzsv0D0rNnQmuLLCO7sUey3YcLZ0lr9+B7+2SHQ5L5HGuFoOl208JlrdZ0WgZXRa2cjych4BzGGsXdHbzTIhmMyF56mezF66TLM6TLHn5mm1pcMdn90nNcaDzA1dYZ9i522O6PedfuDe5pHTLLa3xz/yJX97ZYTGvUW0suXdyjmS55edxhb6/H/s0una0pf/bSdUaLBu3agudunCHLEvq7Y4bf3cLrTt7NsEZOfq1FfRIzp8uUQ+vx/11/kGSY0vy+Ibu9MQ90b3BP7YD9THsRysn07l/8veMegpwQGxFgWZJQe8c7WF69FjM5scZqnoXi5zQJX8ax5ibpdLB+rywSLgqarR0yGFbUv4xGZbCQdDqhNog4VVN0Ha83yqwYWUY+mYTpojQlOzwsg6D8OyEQSs+dJd8/WE3tFUX26yv/eGVtlS/mZPtrQc1oguNlYX4+nR6ZZiwbnxbb5NRrq67gnmPtdlh91qiT7R2QdDprTTezsi8WrFokWLMR9hiMLS4g7F9YZLWSZmiVUTyXz+dhdWerhXW3w4q32Rxv1FcZLI/TXnHT5Hyrg5/tk4zn+DKu8Fwu8cUiNPUsVnvGthek/ZDFmUzD1jRbvZDtcYfZPGRrsgzb2Q2Zssk01DUVAdoo1rsV+wACSaMenrvYpHkwCIFLvRH7iYX3rrgGRRNW77WBNhazdfnhIBS0TybYbE6S7obXWmbhMzALLUWyvQOKZrBWr4WA6Er4LOfjMWmvi21vhc9PK2YHx5NQxxWziGVAmKTlVHXYOilmHHtdknNn8IND8r19bNpatbIgC8e6bbxVx7e7JDdCxnX7D69h+UVeeCRlmaW8ONri+cEOs0Ut7FmY5tR64bP6/LVd8kmNpL1ke3vMvdsHXBv2eeob99M5PyJJnAtbQ154eYfhd7dga0njcoNkYdSHMD3nLHYzbGHUhgkMUrrvPuDhH3yeq5Mtsjzh6cN7mGQNfvbs/7yLfyFERE6ejQiwPM9ZvnAl/Fe8e1kYDJSZhkJY7Wdk1186WjDd7eKTaSxAnpD0+6H2ab4I7QbG4zBFWDQcLVZjkeGjo9mfYuou3doiG45Iz50N9T+TSaidWV/Zl6SkO9tYr1c2qiyyXiwWoY5rPAktIbZ6ZDf3yIql/rBqQREzdx6bnK4eEOuIZqtVgJ6HWp0j575chuzO2nUqMle+XK6Ct/KP0rg90erci+nW9ekv6/fx+ZzltetgCelWD9vewq0ZisrTZFXoPpmGWqQsD/sg1tKwgXKjjk3WuvCnaQh+srBi0eo10novZMy67RDAuYe2B+NxuRCBYQyI6o3V3pSEgIq4StMXc/JpduR8rVY7spoPD9vqQAiYObNTFtJ7HLNv90hin6l8MMAWi7hyMQTiSb9HNpuVQVbReLWom0ubTbi4S5rlMJmGz8RkSjYYrFarek7S7MDOdviPhbhZucWFCkWQbo16mNo+OCQ7HIaALS70CAszYjD8re+SbvWg3sA9D13m5wt6zxxw4Qs73PjBHQYX5nS3J+x2JtTTjJrlDBcNDictdnZGtM8vub7fY//lVQbr/gdeYjBtcnjYZjRo4aMatXMzltMa83OhHUi+MyNbpNReaNHYN5ZdB4P5V3b5H/UdsndNOLcz5EJ3yD3NAz43eC/wLURETquNCLAsSUh6ffLhkKTZJO33yWcz0k5ntWweYBmmgoqaJoyyq3mxJU0RdJVL8aHMYBV1XOUWPLFOK9neguWSbG9/tcXNWrAS6rJiO4FYJ+XzeVnHlB0OoehrtV4LBaGIOxazZzduluMpV0IuQ0DlOeWXftnfKu7PeKRJaZwCs1bMWOWr6bDyeeN0WrkVyy2d48MwvczgFIrVdUWhd3kNinq42Dk/f+lGmA5t1MOYWqEInDzUFFk91k912iFwbDZCofZOPwRUg9HRru4WNlf2xQIbOtTrocYISHd3wj6KAHkWitmLADS+3/l0FXwWixrW951cbwESgtm03NIId9g7DGNZ764ei/eT82exThs/OAxjvnQ+NDN1J83y8j8A1oNVq8fx7g/IB4NyQ/CQVQxb/BQtQ7LDQ4hB161F+Um3G5veTrHYvqH4LBWflVBDt/qce5aTtBLyi+fC45c5yWjC2S/s0bl+P5c/2GCc5tTTjN3OhJfHHQbDNu7Gsp4ymjRptxbce/aARZZy9eVtbgDTSYNWZ858Vse3Qr8sMiPpLULt1jNdunvGfNuZns+pjY36YcLsYoa3M2qX2+x9q83svTV2Ghf4Py99jv8bEZHTayMCLM9XX1T5dBr2ZoOwrP5WlhzZO3D9y6aYPsTC9jZHgoVbarnwUKe13iKg3Nqm0SDZOhOyRLE5KPUG2Y2bR1bG+WIeVnpt9Si33In7GBZbrniWkY9GIYOSJKG+Z23lX9LvHy3kn83KFYS3dn8PPaRCEbfHomzgSLBWbihNrL1aK3K/bf1aUe8Ft10wEH6RrQJYS2KLh/jazWbIrjTD/8hz7HCMv3SD/MbNMDUZtxKyeh06bXx3K2aRYp1ZmpQbN/tiGXpBFcXqcbrWR2Oym3uU+yyubXFUBr2TSbh+RTDcClvi+DIP04P1Gvl4HKZzvUbS74f2FGmKTWdhSnKZwXwegq12K/TBKnpUZVnoR1V02Y+F8CxXbSGs3ij3yLRWWB1qUE7P+sHhauFB3BvRWs3Q0LUV69tiC4kiKEx3tkOjVwhThMNRuM6NRmhRUiz+gBCYTqfwrcurXlrtJnRaNK8MOf+VbV5sNxnWQsf1bnPOpUvXmWU1boxCXdRsUeO5y+eot5a0O3PcYas/4eCgQ62ehS2H8oSt80MGB22YJSy2c7IH5pg5vNgimRsk0HwpZXrRWfYySB2fNHju8Cz/b/MvAf/x9p83EZFTYCMCLIzyy7BoRRB6JoWgpyhCJvdQkL2+7L6zKpbNp2td1dfdmlWCsvFkscVL2b/KwxY0YSowX/uDkGFI+v0yaLNGA5/OQt+s4gs/1kwd+VvWpt2aTXwZ2jAkrWaov1rLMBXbvRQbFRdd0m8bHBUZj5xVzywot58prlGZrSu29ynGUWT0ilqt2CC1fPrZjGIrIGvUy7YQYT++YfhdY4x1O1irFbI4tZR8uwvbXZLRFAajkBVqNsL0X31tw+dmPWxPM1tAtsQWcQVlvY61QmDgZnBzP9SZtdtxzBnWSDGrkc8X5MXelLGvWdGpP58vyg74+XwVnBbvvzUbMFvg0yHZ4WGZvQxvt5H0utjOduiuDmF/xLjFENMZVq+XbR+Kovt8NC7fM/IcZrPYmiJu8ZMkqw3FkyRcx/li1fsLygarUOxcEKeki1q1RgPrhtYfllhZ5F/uIjBfkG5vrTJ0sYdZMpuz/VRO52qHl3+oy+GDbQYXpuw12ySJU0tycjf67RnvOneDq4NQN5blhplz4dwhe4MOi1kNM2ew3wl7EHaW1FtL5gdNmlfrNKbgKWR1Z9l12ldq2BImlzKWL7d5fr/FYNp85edZROQU2YwAyylrY4ov9fU6JF8uyOMU3JG2A+5HA6rbBFJ3fMn5nGyx/lxHAyJuKVovaqHWi6rLQnL38vflz1e8YGwQutYc8xUrJGehceWRY7cM647nc0s7hiPj8Ayf50eab2brY49eEZzG88onk9BFvBhQ8Teehem5+QJLDsv2BqHVwWqbHODICst15XY6xXMWdVoxuwNxX8M8O3K9ij0OcS+vUTH1G/4oBp/Fed/y2cjH41DjFFdO3vp+e07Yc3I0Cf29YtBrw9EqmIYyYC36khXTvdnhEBtNymzWrcpgbv0ar7/vMXsbWlossLjRdLFnohW/L67X2nXAszD24ejIhtdFlrD2LePSM1tc6nchTvO6Wdh30R2SBvP6ec4tsqNjSursLuZYPl2NPY8rPWsJNhuHTGB8HZIkZADni/AfDe1m8abjjbp2IhSRU20zAixY+9JeC1bWfveKAOLWv3sjr3enYOjV3KYA/a5f93a3X+1YFW593tuN/U6v/VrXqqghKx5+u8csl7c//iqONE69dXx3una3+ezc/slf5TO1/piiIWtx6E4B762vnWevWFl69PGvMc7bTGcfGddrjf1VXt8hLAK49upPcbsr9z36dIqInDrJcQ9ARERE5LRRgCUiIiJSMQVYIiIiIhVTgCUiIiJSMQVYIiIiIhVTgCUiIiJSMQVYIiIiIhVTgCUiIiJSMQVYIiIiIhXbnE7uIiLyPfPuX/y9V/39s7/8/rdoJJvtta6TyOulDJaIiIhIxRRgiYiIiFRMAZaIiIhIxRRgiYiIiFRMAZaIiIhIxRRgiYiIiFRMAZaIiIhIxdQHS0RETgX1sJJNogyWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYInI25aZpWb2h2b2W/H+GTP7vJk9E3/uHvcYReRkUoAlIm9nvwA8vXb/48AT7v4g8ES8LyJy1xRgicjbkpndB/x14N+uHX4EeCzefgz48Fs8LBE5JRRgicjb1b8E/hGQrx276O5XAeLPC8cwLhE5BRRgicjbjpn9DeC6u3/5TTzHR83sSTN7csGswtGJyGmgzZ5F5O3oA8DfNLOfBlrAlpn9e+CamV1y96tmdgm4fqcncPdPAp8E2LIz/lYMWkRODmWwRORtx90/4e73ufs7gY8A/9Xd/w7wOPBofNijwGePaYgicsIpwBIRWfkl4MfN7Bngx+N9EZG7pilCEXlbc/cvAl+Mt28AP3ac4zku7/7F33vV3z/7y+9/i0ZyZ681RpFNogyWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMXUB0tERF6TelCJ3B1lsEREREQqpgBLREREpGIKsEREREQqpgBLREREpGIKsEREREQqpgBLREREpGIKsEREREQqpgBLREREpGIKsEREREQq9roDLDNLzewPzey34v0zZvZ5M3sm/txde+wnzOxZM/ummf3k92LgIiIiIpvqbjJYvwA8vXb/48AT7v4g8ES8j5m9B/gI8APAh4BfMbO0muGKiIiIbL7XFWCZ2X3AXwf+7drhR4DH4u3HgA+vHf+Uu8/c/dvAs8D7KhmtiIiIyAnwejNY/xL4R0C+duyiu18FiD8vxOP3As+vPe5yPHaEmX3UzJ40sycXzO523CIiIiIb6zUDLDP7G8B1d//y63xOu80xf8UB90+6+8Pu/nCd5ut8ahEREZHNV3sdj/kA8DfN7KeBFrBlZv8euGZml9z9qpldAq7Hx18G7l/7+/uAK1UOWkRERGSTvWYGy90/4e73ufs7CcXr/9Xd/w7wOPBofNijwGfj7ceBj5hZ08weAB4EvlT5yEVEREQ21OvJYN3JLwGfNrOfA74L/AyAuz9lZp8G/gRYAh9z9+xNj1RERETkhLirAMvdvwh8Md6+AfzYHR73z4B/9ibHJiIiInIiqZO7iIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMVqxz0AEZGTbsDey1/w3/zO2qFzwMvHNZ7XSWOsxqaPcdPHBydzjH/mtf5AAZaIyJvk7ufX75vZk+7+8HGN5/XQGKux6WPc9PHB6R2jpghFREREKqYAS0RERKRiCrBERKr3yeMewOugMVZj08e46eODUzpGc/fvxUDuypad8R+xHzvuYYjIW+gL/ptf3vS6CxGRN0oZLBEREZGKKcASEamImX3IzL5pZs+a2cePezy3Y2bPmdnXzOyrZvbkcY8HwMx+1cyum9nX146dMbPPm9kz8efuBo7xn5rZC/FaftXMfvqYx3i/mf03M3vazJ4ys1+IxzfmWr7KGDfmWppZy8y+ZGZ/FMf4f8Xjd3UdFWCJiFTAzFLgXwE/BbwH+Fkze8/xjuqOPujuD23QFO2vAR+65djHgSfc/UHgiXj/OP0arxwjwC/Ha/mQu//2WzymWy2Bf+ju3w+8H/hY/Axu0rW80xhhc67lDPir7v5DwEPAh8zs/dzldVSAJSJSjfcBz7r7t9x9DnwKeOSYx3QiuPvvAjdvOfwI8Fi8/Rjw4bdyTLe6wxg3irtfdfevxNsD4GngXjboWr7KGDeGB8N4tx7/59zldVSAJSJSjXuB59fuX2bDvjgiBz5nZl82s48e92BexUV3vwrhSxm4cMzjuZOfN7M/jlOIxzqNuc7M3gn8BeD32dBrecsYYYOupZmlZvZV4DrweXe/6+uoAEtEpBp2m2PHv0z7lT7g7j9MmMr8mJn9leMe0An2r4F3EaaRrgL//FhHE5lZD/hPwD9w98PjHs/t3GaMG3Ut3T1z94eA+4D3mdn/cbfPoQBLRKQal4H71+7fB1w5prHckbtfiT+vA58hTG1uomtmdgkg/rx+zON5BXe/Fr+Ic+DfsAHX0szqhMDlP7j7f46HN+pa3m6Mm3gtAdx9H/giof7urq6jAiwRkWr8AfCgmT1gZg3gI8DjxzymI8ysa2b94jbwE8DXX/2vjs3jwKPx9qPAZ49xLLdVfNlGf4tjvpZmZsC/A55293+x9quNuZZ3GuMmXUszO29mO/F2G/hrwDe4y+uozZ5FRCrg7ksz+3ngvwAp8Kvu/tQxD+tWF4HPhO84asCvu/vvHO+QwMx+A/hR4JyZXQb+CfBLwKfN7OeA7wI/c3wjvOMYf9TMHiJMBT8H/L3jGl/0AeDvAl+L9UMA/5jNupZ3GuPPbtC1vAQ8FlcGJ8Cn3f23zOx/chfXUZ3cReRYqJO7iJxmmiIUERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqZgCLBEREZGKKcASERERqdjrCrDM7Dkz+5qZfdXMnozHzpjZ583smfhzd+3xnzCzZ83sm2b2k9+rwYuIiIhsorvJYH3Q3R9y94fj/Y8DT7j7g8AT8T5m9h7gI8APAB8CfsXM0grHLCIiIrLR3swU4SPAY/H2Y8CH145/yt1n7v5t4FngfW/idUREREROlNcbYDnwOTP7spl9NB676O5XAeLPC/H4vcDza397OR4TEREReVuovc7HfcDdr5jZBeDzZvaNV3ms3eaYv+JBIVD7KECLzuschoiIiMjme10ZLHe/En9eBz5DmPK7ZmaXAOLP6/Hhl4H71/78PuDKbZ7zk+7+sLs/XKf5xs9AREREZMO8ZoBlZl0z6xe3gZ8Avg48DjwaH/Yo8Nl4+3HgI2bWNLMHgAeBL1U9cBEREZFN9XqmCC8CnzGz4vG/7u6/Y2Z/AHzazH4O+C7wMwDu/pSZfRr4E2AJfMzds+/J6EVEREQ2kLm/ojzqLbdlZ/xH7MeOexgi8hb6gv/ml9favoiInCrq5C4iIiJSMQVYIiIiIhVTgCUiIiJSMQVYIiIiIhVTgCUiIiJSMQVYIiIiIhVTgCUiIiJSsY3og2VmLwEj4OXjHkuFznG6zgd0TifBSTqfP+Pu5497ECIi3wsbEWABmNmTp6np4Gk7H9A5nQSn7XxERE4qTRGKiIiIVEwBloiIiEjFNinA+uRxD6Bip+18QOd0Epy28xEROZE2pgZLRERE5LTYpAyWiIiIyKlw7AGWmX3IzL5pZs+a2cePezxvlJk9Z2ZfM7OvmtmT8dgZM/u8mT0Tf+4e9zhfjZn9qpldN7Ovrx274zmY2Sfi+/ZNM/vJ4xn1nd3hfP6pmb0Q36evmtlPr/1uo88HwMzuN7P/ZmZPm9lTZvYL8fiJfZ9ERE6jYw2wzCwF/hXwU8B7gJ81s/cc55jepA+6+0Nry+Q/Djzh7g8CT8T7m+zXgA/dcuy25xDfp48APxD/5lfi+7lJfo1Xng/AL8f36SF3/204MecDsAT+obt/P/B+4GNx7Cf5fRIROXWOO4P1PuBZd/+Wu8+BTwGPHPOYqvQI8Fi8/Rjw4eMbymtz998Fbt5y+E7n8AjwKXefufu3gWcJ7+fGuMP53MnGnw+Au19196/E2wPgaeBeTvD7JCJyGh13gHUv8Pza/cvx2EnkwOfM7Mtm9tF47KK7X4XwxQhcOLbRvXF3OoeT/N79vJn9cZxCLKbSTtz5mNk7gb8A/D6n830SETmxjjvAstscO6nLGj/g7j9MmO78mJn9leMe0PfYSX3v/jXwLuAh4Crwz+PxE3U+ZtYD/hPwD9z98NUeeptjG3teIiKnxXEHWJeB+9fu3wdcOaaxvCnufiX+vA58hjANc83MLgHEn9ePb4Rv2J3O4US+d+5+zd0zd8+Bf8NquuzEnI+Z1QnB1X9w9/8cD5+q90lE5KQ77gDrD4AHzewBM2sQinEfP+Yx3TUz65pZv7gN/ATwdcK5PBof9ijw2eMZ4Ztyp3N4HPiImTXN7AHgQeBLxzC+u1IEIdHfIrxPcELOx8wM+HfA0+7+L9Z+dareJxGRk652nC/u7ksz+3ngvwAp8Kvu/tRxjukNugh8Jnz3UQN+3d1/x8z+APi0mf0c8F3gZ45xjK/JzH4D+FHgnJldBv4J8Evc5hzc/Skz+zTwJ4SVbR9z9+xYBn4HdzifHzWzhwjTZM8Bfw9OxvlEHwD+LvA1M/tqPPaPOcHvk4jIaaRO7iIiIiIVO+4pQhEREZFTRwGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMUUYImIiIhUTAGWiIiISMX+NwnM/8BMSSxsAAAAAElFTkSuQmCC\n",





  
  

  4317
  
      "text/plain": [





  
  

  4318
  
       "<Figure size 720x720 with 2 Axes>"





  
  

  4319
  
      ]





  
  

  4320
  
     },





  
  

  4321
  
     "metadata": {





  
  

  4322
  
      "needs_background": "light"





  
  

  4323
  
     },





  
  

  4324
  
     "output_type": "display_data"





  
  

  4325
  
    }





  
  

  4326
  
   ],





  
  

  4327
  
   "source": [





  
  

  4328
  
    "plt.subplots(1,2)\n",





  
  

  4329
  
    "plt.subplot(1,2,1)\n",





  
  

  4330
  
    "plt.imshow(nib.load(os.path.join(path, 'images', 'hippocampus_010.nii.gz')).get_fdata()[16,:,:])\n",





  
  

  4331
  
    "plt.title('hippocampus_010.nii.gz, Image')\n",





  
  

  4332
  
    "plt.subplot(1,2,2)\n",





  
  

  4333
  
    "plt.title('hippocampus_010.nii.gz, Label')\n",





  
  

  4334
  
    "plt.imshow(nib.load(os.path.join(path, 'labels', 'hippocampus_010.nii.gz')).get_fdata()[16,:,:])"





  
  

  4335
  
   ]





  
  

  4336
  
  },





  
  

  4337
  
  {





  
  

  4338
  
   "cell_type": "markdown",





  
  

  4339
  
   "metadata": {},





  
  

  4340
  
   "source": [





  
  

  4341
  
    "The Image appears different from other images in the dataset, and doesn't match the label.  The label may represent a Hippocampus, but it has no corresponding image to train to.  Hence this file must be dropped 'hippocampus_010.nii.gz'"





  
  

  4342
  
   ]





  
  

  4343
  
  },





  
  

  4344
  
  {





  
  

  4345
  
   "cell_type": "code",





  
  

  4346
  
   "execution_count": 258,





  
  

  4347
  
   "metadata": {},





  
  

  4348
  
   "outputs": [],





  
  

  4349
  
   "source": [





  
  

  4350
  
    "no_outlier2 = no_outlier2[no_outlier2[:,1]!=os.path.join(path, 'labels', 'hippocampus_010.nii.gz')]"





  
  

  4351
  
   ]





  
  

  4352
  
  },





  
  

  4353
  
  {





  
  

  4354
  
   "cell_type": "code",





  
  

  4355
  
   "execution_count": 259,





  
  

  4356
  
   "metadata": {},





  
  

  4357
  
   "outputs": [





  
  

  4358
  
    {





  
  

  4359
  
     "data": {





  
  

  4360
  
      "text/plain": [





  
  

  4361
  
       "(260, 3)"





  
  

  4362
  
      ]





  
  

  4363
  
     },





  
  

  4364
  
     "execution_count": 259,





  
  

  4365
  
     "metadata": {},





  
  

  4366
  
     "output_type": "execute_result"





  
  

  4367
  
    }





  
  

  4368
  
   ],





  
  

  4369
  
   "source": [





  
  

  4370
  
    "no_outlier2.shape"





  
  

  4371
  
   ]





  
  

  4372
  
  },





  
  

  4373
  
  {





  
  

  4374
  
   "cell_type": "code",





  
  

  4375
  
   "execution_count": 260,





  
  

  4376
  
   "metadata": {},





  
  

  4377
  
   "outputs": [





  
  

  4378
  
    {





  
  

  4379
  
     "data": {





  
  

  4380
  
      "text/plain": [





  
  

  4381
  
       "array([False, False, False, False, False, False, False, False, False,\n",





  
  

  4382
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4383
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4384
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4385
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4386
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4387
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4388
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4389
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4390
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4391
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4392
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4393
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4394
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4395
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4396
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4397
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4398
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4399
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4400
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4401
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4402
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4403
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4404
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4405
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4406
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4407
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4408
  
       "       False, False, False, False, False, False, False, False, False,\n",





  
  

  4409
  
       "       False, False, False, False, False, False, False, False])"





  
  

  4410
  
      ]





  
  

  4411
  
     },





  
  

  4412
  
     "execution_count": 260,





  
  

  4413
  
     "metadata": {},





  
  

  4414
  
     "output_type": "execute_result"





  
  

  4415
  
    }





  
  

  4416
  
   ],





  
  

  4417
  
   "source": [





  
  

  4418
  
    "no_outlier2[:,1] == os.path.join(path, 'labels', 'hippocampus_010.nii.gz')"





  
  

  4419
  
   ]





  
  

  4420
  
  },





  
  

  4421
  
  {





  
  

  4422
  
   "cell_type": "markdown",





  
  

  4423
  
   "metadata": {},





  
  

  4424
  
   "source": [





  
  

  4425
  
    "## <a name=\"results\"></a>Results\n",





  
  

  4426
  
    "\n",





  
  

  4427
  
    "**Identified outlier files: hippocampus_010.nii.gz and hippocampus_281.nii.gz**"





  
  

  4428
  
   ]





  
  

  4429
  
  },





  
  

  4430
  
  {





  
  

  4431
  
   "cell_type": "markdown",





  
  

  4432
  
   "metadata": {},





  
  

  4433
  
   "source": [





  
  

  4434
  
    "In the real world we would have precise information about the ages and conditions of our patients, and understanding how our dataset measures against population norm would be the integral part of clinical validation that we talked about in last lesson. Unfortunately, we do not have this information about this dataset, so we can only guess why it measures the way it is. If you would like to explore further, you can use the [calculator from HippoFit project](http://www.smanohar.com/biobank/calculator.html) to see how our dataset compares against different population slices"





  
  

  4435
  
   ]





  
  

  4436
  
  },





  
  

  4437
  
  {





  
  

  4438
  
   "cell_type": "markdown",





  
  

  4439
  
   "metadata": {},





  
  

  4440
  
   "source": [





  
  

  4441
  
    "Did you notice anything odd about the label files? We hope you did! The mask seems to have two classes, labeled with values `1` and `2` respectively. If you visualized sagittal or axial views, you might have gotten a good guess of what those are. Class 1 is the anterior segment of the hippocampus and class 2 is the posterior one. \n",





  
  

  4442
  
    "\n",





  
  

  4443
  
    "For the purpose of volume calculation we do not care about the distinction, however we will still train our network to differentiate between these two classes and the background"





  
  

  4444
  
   ]





  
  

  4445
  
  },





  
  

  4446
  
  {





  
  

  4447
  
   "cell_type": "markdown",





  
  

  4448
  
   "metadata": {},





  
  

  4449
  
   "source": [





  
  

  4450
  
    "# <a name=\"curate-dataset\"></a>Curate a Collection of Data Files"





  
  

  4451
  
   ]





  
  

  4452
  
  },





  
  

  4453
  
  {





  
  

  4454
  
   "cell_type": "code",





  
  

  4455
  
   "execution_count": 268,





  
  

  4456
  
   "metadata": {},





  
  

  4457
  
   "outputs": [





  
  

  4458
  
    {





  
  

  4459
  
     "name": "stdout",





  
  

  4460
  
     "output_type": "stream",





  
  

  4461
  
     "text": [





  
  

  4462
  
      "Number of NIFTI files copied: 260\n"





  
  

  4463
  
     ]





  
  

  4464
  
    }





  
  

  4465
  
   ],





  
  

  4466
  
   "source": [





  
  

  4467
  
    "# TASK: Copy the clean dataset to the output folder inside section1/out. You will use it in the next Section\n",





  
  

  4468
  
    "count=0\n",





  
  

  4469
  
    "for f in no_outlier2:\n",





  
  

  4470
  
    "    count += 1\n",





  
  

  4471
  
    "    fn = os.path.split(f[1])[1]\n",





  
  

  4472
  
    "    shutil.copy(f[1], os.path.join('out', 'labels', fn))\n",





  
  

  4473
  
    "    shutil.copy(os.path.join(path, 'images', fn), os.path.join('out', 'images', fn))\n",





  
  

  4474
  
    "print(f'Number of NIFTI files copied: {count}')"





  
  

  4475
  
   ]





  
  

  4476
  
  },





  
  

  4477
  
  {





  
  

  4478
  
   "cell_type": "markdown",





  
  

  4479
  
   "metadata": {},





  
  

  4480
  
   "source": [





  
  

  4481
  
    "# <a name=\"final-remarks\"></a> Final remarks\n",





  
  

  4482
  
    "\n",





  
  

  4483
  
    "Congratulations! You have finished Section 1. \n",





  
  

  4484
  
    "\n",





  
  

  4485
  
    "In this section you have inspected a dataset of MRI scans and related segmentations, represented as NIFTI files. We have visualized some slices, and understood the layout of the data. We have inspected file headers to understand what how the image dimensions relate to the physical world and we have understood how to measure our volume. We have then inspected dataset for outliers, and have created a clean set that is ready for consumption by our ML algorithm. \n",





  
  

  4486
  
    "\n",





  
  

  4487
  
    "In the next section you will create training and testing pipelines for a UNet-based machine learning model, run and monitor the execution, and will produce test metrics. This will arm you with all you need to use the model in the clinical context and reason about its performance!"





  
  

  4488
  
   ]





  
  

  4489
  
  },





  
  

  4490
  
  {





  
  

  4491
  
   "cell_type": "markdown",





  
  

  4492
  
   "metadata": {},





  
  

  4493
  
   "source": [





  
  

  4494
  
    "[Back to Top](#table-of-contents)"





  
  

  4495
  
   ]





  
  

  4496
  
  }





  
  

  4497
  
 ],





  
  

  4498
  
 "metadata": {





  
  

  4499
  
  "kernelspec": {





  
  

  4500
  
   "display_name": "Python 3 (ipykernel)",





  
  

  4501
  
   "language": "python",





  
  

  4502
  
   "name": "python3"





  
  

  4503
  
  },





  
  

  4504
  
  "language_info": {





  
  

  4505
  
   "codemirror_mode": {





  
  

  4506
  
    "name": "ipython",





  
  

  4507
  
    "version": 3





  
  

  4508
  
   },





  
  

  4509
  
   "file_extension": ".py",





  
  

  4510
  
   "mimetype": "text/x-python",





  
  

  4511
  
   "name": "python",





  
  

  4512
  
   "nbconvert_exporter": "python",





  
  

  4513
  
   "pygments_lexer": "ipython3",





  
  

  4514
  
   "version": "3.8.12"





  
  

  4515
  
  }





  
  

  4516
  
 },





  
  

  4517
  
 "nbformat": 4,





  
  

  4518
  
 "nbformat_minor": 2





  
  

  4519
  
}