xgrg/roivalues.ipynb

## roivalues.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating random maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "from nilearn import datasets\n",
    "from nilearn import image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = []\n",
    "\n",
    "# Load a simple brain mask (MNI space)\n",
    "atlas = datasets.load_mni152_brain_mask()\n",
    "\n",
    "# Generate 50 random maps\n",
    "for i in range(0, 50):\n",
    "    # Using random matrices w/ same shape as the mask\n",
    "    d = np.random.rand(*atlas.dataobj.shape) * atlas.dataobj\n",
    "    # Create an image object with the matrix\n",
    "    im = image.new_img_like(atlas, d)\n",
    "    # Smooth it a little (why not)\n",
    "    im = image.smooth_img(im, 5)\n",
    "    \n",
    "    # Save it into some .nii.gz file\n",
    "    _, f = tempfile.mkstemp(suffix='.nii.gz')\n",
    "    im.to_filename(f)\n",
    "    images.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AuO+hqzPXwxcaF/B6ve7Hg7wJWsZoqWDtgrVr1/rfOCe1PkOwUAQElGPMCxKjRaVSyRWl4cLNhZzmZP7e3d3tF2wNqSO4wH/66Fl+X0LDQbnI8UVsiycpssVsU7Tbbf8yUdKlttXpdHL+QXXNcAGmQNHT05Nb0LmAL13q0skWuT52taI0Oxu857VaLec648tUQ4jpouju7vb3Q+eUuscqlUrpnFQyJOffNUffj+t+fnZhv1vGJw0A1/38bFxz9P2Z9tTXTHzshK9hw8DfAXFeyDj3rBsBAE89/beZYywJk8K6Crd8Hvfee28ALlyQ7g6GplLYoItOBYudHW63s2ETTwHpmFoCbbVanFJd77N1qX7mM1miryWP221vb69vRxUSPaaTuNk6cccoVKRbJn99H+RC4xgdWQfLina5a87O6Wuucft8+9tMJuX6NHdu2l6r5b5bsICn1NU6X8js1792xMv9988mdePz9lan0A7hnwEBAQEBAQGjRrBIbCUssU3L4+6///4AUg2R0rp1A1ADzEnNEmo3MDCQc2WoNYCfM6GdND/TFIsYnXY7lbjZmEmlrSWg1WdnWdAqhZP8pgQ8JoWx5Drr7gBS7YWuD1oq1qxZ49nPY9UyocxtzqUJEybk0qnTEsYQTmrgVlNSF5qGoRFWo9fEU7zHygxvtVreysB+f/0pFzKc+prpu23nQpHZHj+ffso33b7tTo7pH4kP+/hjvgQAePDRvwKQJkKzljaNEtDQ1ClTpngrDlnwr7/uSjm/+uqrAFw4KZD6pAcGBnLtjwWo+5NroKbXd+moWd67kTn2mmvc/OX42aJbvCcE97H3lcfYcgBA/j7f+aPLM+0zLTYAXHj8rRiJIqzaMAOP/PIqvyZxbrCNTZs2+eOTXXKuRZsyXp9bfv7CF9y6rknSOp2Ob2/u3KwVoV53xy5cmDWTVKs1dHe77+jm5fhoUcW3ynIWLBIBAQEBAQEBo0awSLxJUMKkxD1p0iTvk6Ylgp/pw6akaTUgtSJorDDRbDYzYZcWKsmzzbNPuz4rlccxYiRSKX3M4muu1Wo5CVuZv7Y0dCot1zPtaN+slqlkuodXfQ4AcM6h3waQWi9eeuklAMAf//hHb50Ya5YJtfjQ+kCrw8yZM3MprMnFoVbI8VbLkoVq0dbHqvO3rAR9UVv87a+OcZFQX//BKW6f5Pfz330LOp1x/lz23J5FbzXMZA57flDSDvWsex50yYKazY3JNi1ixLnD9jVBnP3McdawbI4pt7RQrFixwvvmxxIhk2PGucd5ybBiWsPmzBnB3ntnCb6EJhPj+A0NDeXmGu8hNXnOmYGBgUyBNiAfJq/ztKurK123mg2gy0WJNBoNnHbwf2aO4T287zdX+GPUmkzY33VfXauLCt/RCqOcN64BV1/tnsfrrx9IjgXi2P1Gno+GNpPTYwn4OxLBIhEQEBAQEBAwagSLxBawuRC7d7zjHQBSjgT9bOqXovQ+YcKEXMlbZdda1q9aIjRFK9sgOu02PBM54UtEcZaJ3xatKYqiXGY4TWVLCbyvr6+Up0GtjxqE7ZtyPS48yJXqveulz2TaP+Xgr2fOC6QStvpId1dwrKjZzZzpSgm/853v9J85nzg/OO+4Vf9scXnmrDZn/ciaApv7atImIoqiwkJeAHD5EXcBSDWkarUrtw/Pd8GsW5JvaAkBgAgR8trl4nsuTfrm2tUQ4nq9XppJkddqrWvq76bvX59LWiT7+vp8ZAejjjSyY3eyTHAuca3g/OR6qPwnG1VhE4wVwVokdE3juqLfR1GUm4dl65ZNgDbr8BuTfd1KuUfvUpx5yPXgsqLry8DAgLdgaTnxoiJwZWXt+exwHbPPi1p3c6UQEpBb0m63fbvr17tx4f3gltYyyxXakQiCREBAQEDAZqEuDQoQdAnxZW0JviqkqsusSKjVzKJ8AWv7NtRdFS4NheSLeu6xd6ATu31ZXyWOsyRMH/+ZtH3O++fjnmcvB5AKEqpwUTgYGRnJ1dpQxZHboorNmhJABQD+3mw2vdDKc/O8dDFZofatSFIVBIkSlOWQp/XBllu28fpA3spQ5ENTJrxqgbVaLTehtmTN6MSxj92uVauIRlKJ3Me/czKZtilxl2mr1gqh2q6W1M3nz49yCwe38w5bAgC49bcuMf1DKz4LAPjogV/x48O+Mb5ffaW7i9anli/ybQ4++GAAqUWCmjKQ5QMA2XwiQHpP5ky/Hfevcxq8Rm9o4aE4jktTl5fda2vF0JwQ7Iu1evE3XfCjpPhctZbOsagZATFQScYnlj7pnLLcHLUilFkmKpVK6UuJ848at9XI+TJlRIfmRXmrY/kDAnYWgiAREBAQEFCKSqXihTEqVPvuuy+AlEipbrBWq5WzJpRlSLWKlq38ao+x5ETACYFs1yZTA1JtnMKtdRX4xFY+7N255apJWz5c2bgoeAwFX4KCqHVX8Dv2zbpt7DVbKwH/V5KoZpC116AEXyogJF/y94GBgdyY7ggEQUKglgimFiYfgjfqh4P/HdVVySRf64Zx3mGLAeRNeJZHoLHPqlnbB1J/s0WSAONbnn2r63yclmgu80f6hzn53Gq1cuY21e6stlrG2yA0sxvTHBf9RlBfY5W+x9f/NU57338AyJsT33jjDQCpD3BnVbvbnmBxKiA1GdMCceCBBwJILRRRFOUsD7OnLkxaonaOzO8jzRinjb8BQHoPliy/EEBqxbBpqzWniTLpNbujLcOsJmnCWuC0DPPcOQu4l2uvYA7TEsEsg7PPnO+u4955APJp3W3mWT4vOq+tZU8tgpx3XAfYFq2PEydOzEV28BrZb85R236wTgTsjgiCRALN/08Bggk/KIH/YNP/DABoNNKh4+LABZKLj/VpAW7hKnMJaBpioDzsTiXWzMJLs3anA8QuIVXbvswT8zG8KzDOEZmIooQrKv1vSZCI47g0jDBdXJkOl26ZGr673CUY+sQB38i0r0RT+jrfjrBkMJLUGE5HwYEaHzWOOI5x6jgnFLTbFPSo5WTHIUuy9GcF4Nwd7hg39veu+SQAR0jTBFFlwinvn3XZ6XywYcYAcN3PzsKs/W8o3Ne66AAgiuN0DnOuZK4inyDJCrBlgoRqaHEc50IH1bWmnxuNRq59gvswhHnVqlU+0ZW6o94OqFarfn5qHRclmNv1jVq4upKUXGjXGfXnK7GR984lvHLHcfw1zFxJ5NVKJXXtJmDRLG+1kLXkrmfmodHIhn2WufyGh4d9/9ieCtQ6v4oUPi2Ipu+GWq3m3zFqleE6wfOuXLnS1zbake7gEP4ZEBAQEBAQMGoEiwScBEhplhogXRlMNvXExr8BAF/l0IVUZv1dlMBVylQ3BlAexkhp0ZpqVUq3abqBVFKN41Sbi5tNxHHHJ6SqJNp+JSFjxgU+OkL9kVbqVRMytQzVBmyo4BYT9bCkboVJsypA1f3/4DJnmThh+pcBpOGgTFU8MjLyttTygPQ+9vX1+fTW3GpxrZMaLm10FEXodOgyo8WAvtQSH2iU3vcy/2tR4SHdl9CEV9VqtaBMcnF67U4cY+EfXMWi0/f+ZmZfJYkiTqvYqsYbiTarlg+betgPg2iq9ncloapfvLSPSCMZGKpLLZ0WiRdeeMEnsqJl4u3gkrNWWq6Lmgqb95XzlWPcbDZzVYX1HvGztVCURRjoHGw0GjnLppb31uRQ9/76UzjzkOvNudNIjVjOa/kbWmiR51HOhD1OOQxqibBrq7rc1HKgz2pXV1fO2q3nI09k0qRJnqiuLtHtiWCRCAgICAgICBg1gkUCTiokiYq+aXIinuz/G78PoNYGJ9md966bAKQ+ag1rs9yGIo3GHZu3OlCK1aQsKq0zTfA5p12fSTxFubPT6XiNtAiq1WkCFgtNtqM+am3LEs3KNBL1xbv2s9f48EqXVvvQPf5XAKnlaGhoKMc32dWhmvGkSZN8WDEtEhz7s/dwRNqYj2qc15qUzEqkfuaUR6OoJFYgnue7/Z/KkWxV21FugD2vstL1mi97/xIfJtlsuvavu8mVIL/6clf4C+Y+xiSQSjsQ33lR0iOdq+wvLQiq1dl91WIw/1mX8nveoZ/w51HtkltqxLYAlIaCMox5VyZh2uRonJfkjmnhM362z71qwEo+1zliCbKplTU7nyxfQH9TK5hyezpmTVSUzds4jku5QbynNoxY02hrhIo+szY8vsiay3aBrCWQ5+F3GsZNIvDUqVP988ZSBMrb2B4IFomAgICAgICAUWNMWySspEcNl+GdTw38HQCgXs9q2JRN4zjGxe91YXetVj3TribssVKp/qbaOWG5BfR30TJRpnnftvgi/1saUue00TvvvyxzPrbd3R3lNFkNz7QZ6jTFq/ZbfcyWf5LndLg2rv3wQ5nvv/7Ux9Fq5nklAPBM9L8DAN4z82/9mJTFau9qUD6CLQDHjHTU+C5/t9OA4zjR4tqppqQJo9QiodfvUvd6+xQAawXKzzvVbsoy7dnrUU1MM/tZfzjT+FJj5TjccfclAICLznUp1O1V3H7XJ/257JbtK1u+UqngguNdyu04uXYmtXrwl5/JXMfw8HBOU9SIgquPOjt37bqPbc+2YRN2EUyvvSsmWCsqHEeLBKM1yJXQyAgbcaAWVc10WRTBoGW8NdRY1xCLMt6PXX9pmetUKj4Fe6VSSS123Nc8W8pd4Jb3zhbZ2lK66yK+HMdQ57Zacu3cVKumrrs2kSKfN/LLdkQK92CRCAgICAgICBg1xqRFQuN0J06c6LkRv4j/NwBAg6mgeRDTOh9+JwCykrNaeS7Rkkh81v+mxygLulqt5rQtlexVI7X/L7zzYhx77DREUYSnn74UgJNCz/3AzQBceVx7PiDv+1PN1ObC16x0eu3Wb6m+0c1xIwDgig/cjW/86FR3DLWMxJffqDgJnFE1AwMD3vdnC0/titBcJUx3veeee+bSrWvMup1LeT4Nxy8bZWEtCmVammpMzWazkI1ucf4hi5I20uJa9z4/L9OeakjUnLq7u73/ViN+qGXe99DVANx9POqovZIIjBcz7SlvoyifBCOV4orP0AUAOP0D/5E5ZvFT83I8B82hor5um79ENUm1DNlMhJyb3IeMepu8yh67M6Bzpa+vz89Lbmk52BynoSwpHqG8ARuJwWdaS79b7b0scZ5aOmxOGz/v3ZX6v35NSvrWKeEd8dpsv621jfvrGq31OaxVQLlGGh1inx2C16o1NzSCpbe3N8cJ2hGW2zEpSAQEBAQEFENfYD09Pf4ldtNNTsBPlQB3zBe+8FMA6ctpeHjYv9xUgLYvdiCrYJQRhym4WEF4S6Gi+sJvt9u485eu5oyrApqkyEY+0RkLdTWbKTFRhXAlpRe96MtIx1aQV4WK167CuBVKKAwwnJgCKF0aN998GgDg9NNv8cIff6OQtj1TZo9JQYI3jvHee+21l2fNvzJU4uMS36/VtDUeuqy4kc22p5q8MsC7urpK/cEan20x7/i7AbgsbgMT3w0AuODoBUb6dH095/3zAQD3//bKnA9c/bmWnaxSrPouixaJMp+cZhy0MdHkn9z6uwsz18qH7PvrvwgAOGz6P2D16tUAUqmcUvqOzC2/NSjS8IA0S+A+++zj/ZheswMzlKY5FbQ9WiJoGdDU2HYeMmqnExfnJCE6nU7O13zh+xdn2k3LfDM9KnDOgTdnvvPl6hN+QtXnsajiziSPBM/N0s45Vn8co3/cTAAxLjh6Qe43APj2Y2cCSF80dnF/8BmXg+SMI7+ZOdZfR9LXC46/Fd/7/Rcyx6t1jrAWF7UylEULVKvVXDE2Lupcg1555RUAwJo1a/x5dwW+REDAm8GYFCQCAgICAoqh4Ybjx4/3gi6FVxXKvvzlowAAV175KAAnzFPQoqau7gn+bsMSVTlSl6kN1VUhmFBFzrqL+NuSX1yCo46ajiiK8F+/uKQgFffGzPUVoagYWZH1wO5blNxNLRBaC0YVot7e3tw+CxeelTlfNUno193dnXNH8XzbM5HfmBIklBtBX+3MmTPxw0EXpcEboLn8eftv+vV5AIBLDrndPxjq/1ItxmqIfIioiagWZvuoRbp0wqnmE0WRL3zFzG1xHCfZAZG5Dmp01h+uPt4ilr5mtuQDPW6vAAAgAElEQVREphWgiA+h40PoNdsscNTYLjnE1YRYIFoskj7uMXEPH/GgfuYdES+9NVArio3SAFLtdPr06d5K4RcNKs2ghp+3hJXljeDBdqGlRaJaKfb5P7DhsqSPad2Js5L8KFGk0SBZQ3CM2Of98JlVO8ULcLvTxpnvdBaITjc1+sSnrTH43oee54WwL5868d7MWNAkXavV/HNy+5MuGmTOR3g9+efmE+///5JxSixez30xcx7N99But3Px/kRRnhQ+w+TFcMvoB322165d+7bN2Bow9jAmBQl9qPfcc0/UNmYXsaIKhADwqSPuSr5vlAoQurAUJTvRMCGb1KSs30r80sX1ipPuR1TJXofvA89dkFxFE+mwD3QV8LoajYYXgAgKEpoevKigkxKy9BgKKT09Pbk0zGWFvyZMmOBDJhnepGmId5aLQyvJsgQzBQgKQOPHjy9MjgRYImX6osknNaMropI5JhU4KqhUsuRB4v51lyb7us9dXV34xL7XAQA6MdOpZwWIVB5MhQV+5wWeTlaYoYABoxWmqbCz+1aNiyCK3M9KzEPJ8zn7iJt8Wwt+fEHmPAsfd8LoxR9dmDmm0+nkijkdd+CXMtf46G//G4A8Adn2W4mSC557DABwyftOybnxKGxwbvBYCuQjIyN+Hr/VidbUVdvb22v6j0yfeC8vusgJdOvXpwKqrotla4RVRsoKoemciaIoR7ZURbGoiq11wVoip65jlrhZFsqs31t3F69Z12zCrvO2Cq1tV+e8vQ5e28KFDEvWZxN+LLhma6EvVSa2BSH8MyAgICAgIGDUGFMWCSW70aw4fvx4VDbRpSGpTaUN62eihMjEJJRmy8iXFqq9aPIRW3JcLSlKTqREfMsPzsXliak31diAqFJBVh6GZy83GikRjFv2RZNPRVGUS57Ca7flfe11VKvVHJFUfZdK/Ovu7i4NhVITui2pS8mbLgQtY/xWQUPWtBDcgQceCCAlW3Z3d+eJs1H2n5RYmaZiT0trJ2NRLS77XZSG9761ztyvWtDIyAg63o3iepASHMXtYkifDM8Fd/FWuHxZcW8VKUlmpgTnKCoPFdZr5TGdOMbcY27P/Hb3r5z75s4fXQ4gfX4uOO4WeNsOwz55vuS6Tnrf/5sZi3t+ckXOwpbXPtPVQ58LnfO0qjE1/4YNG3Lzd0eTL8tccbVaLRd6zufQW18WOB89LRNWk1dLBNtV66tNVsZnWrVyu9W1U0nNRXOmqNRBtVrNkWytFUi/K1vXi5KUKZeBx3IM2u22/03LrKsbOePCLimsVzRHOJZ0qXKd13u6LQgWiYCAgICAgIBRY0xYJJSFTA2RPur7X78GtZpwCvw/Cakr4UZUq32+LU2KU5Zmtyj5D4mByo2wKWG1HC6ldU0Jbdm9tz99EQB4bQxICJgisfLY3t7eXOimci+stYHfKWlULSw24ZZqbuwvLR4qETcajZxEP/dgF/5382/OBwBceNCtSVt5CxO5EjujMJIt5kTJn/Psne98JwDggAMOAJDeTxsi67WpSDSNpFR4JaoiVdizc7YseVIURd7CQU4ELQU876xkPN13nKfFPtS8xl3xFhOSOcmR8FYMT6RN+RokzHqCZtIaP6eFxvJp3HOgtmY+V+S5m3OUu0Y+I5aMeeHxt2avlRpfnLUgsG9nfvBbuCMhcWoaZ47P3Pec7L9X9r5a47Ro4IoVK7B+/fpMuzuafMkx5vOkGqxFxZN2ufa57y0xvIyEqvPWhsSTt8ZzKuegyGKg7bIPWobA8ipssb+hoSF/DM9rNXy9V2WcNZuQitfEiBRN5GcTnqlVgddWlFCLW/b3s599KjNOBM83MJDeT65Dy5YtA5C+g4JFIiAgICAgIGCnYkxaJDTda2VjxXMjqBXRApEyi7OM3Vqt5tujVq6So0qsVoqmpKrZ36z2r9wC5V5oRMPIyEjmnFGUXI+RxMlO5/ntOCiHQRNVRVGU0740xbeGXFrtoyyFtfqNbcEyPYaWCMvu5jXT0rFy5UoA2eQ+wM6zSFDL4jw5v+1STNc2pJYa3pd7ei717WRguq7pm+mvZtSGWpSiKMK9q1zRq0YjGzVw4WEu2RStEC4hFa9FLBGV1Eebu27hcnjrWyeWY9JjvcVO/bu6TXg+QBp9pFYLtT4U8YJ4LNPE83x3/vJSLPmh40+c+2EXmhqJlbEjcyeOY8w+9gYAwI0Pzcn8pmHUlifELeeqpnWmtXTmzJn+GeU+OyqKo6hkAACfpG/KlCl+Ln/mM08AAK6/3llbOMfmzftu0lbq11frJc+jnAm7LXtGi1KjK49KNXu12NrMmYODg75/di202Sl1fNQSkU9VX8mt+WxPn1lamy2XRLkSWkbBronKteBnXZ8bjQa+88anAQAfmfSvAPL5JLZH9MaYECQCAgICAoqhggRdbnQV3vfqVai+kbx0kpfblVfcA8AKRGkOGKIs0ZKSFq3gpSGjZdtWq5ULw9RwfPbNuhes24OETUtsL6rOyZe11hdRZcdm/lV3ttaTscqTusR0n6I6LxwHJZZ+8+kzAABXHeVIr/OfmY1q1fWTLg4qjkpK3xYBdUwIEnqDinx1yolQCZiWA6Kvr89PSi2GUhYH7KwExVqdPmwjIyM5raWMmWszxuXaj7MZ3dTKMDw87GPXtcwvtzZSQ6VjfXAI21ftf1m5XBsXrrknbHEx7ROvn+PFfA30Bb4VKbPtHNO8HJqfg/yENgMNvD4PnDPg8iDcGzkN+ZwpSUltk4uEURopj0LyigijPo5jPzdnH+zaS5+BrBWt3W4Z/3e2XX+touvT+sVf3XfJJ7LYpU27k190k6+ZFMrvGQOdAt84AETCndA8MPbaNLKDe1xw9AL/251PX5lpb9YxN6SdMBt3vDtm3sedVee2xxz3QteFIpa95ktgHzmXp02b5uctuT5amCkgYFfBmBAkAgICAgKKQeGGAi8174dXfR4A0N0deeGL7qH/+MnpAIBzD5gPoFhJU5K11iRR14RVVFRp0lDRoaEh356m4NYU0BTENm7cmEmyZUOeKaRpcqh6ve4tM+ybnscqgaoslcGSSDXMXouAqUWi1Wr5Y9T9fe2x303OkCT0iuBDsr+7/FoAwKFT/xcAwOuvv+7HxV7HaLBbCxJqibCZE+3nKLJ5I/K+UAv7wKg5TH1b3FrGrnIKCB7LNpvNptdI1CfHPnHy0GRVr9cLJ4NNkU3Yh5kTSTVPnpcLS19fnx+zLRX6sotEWUpn9WFaf3HZIqTx941Gw/ePsfhkJ5OTsG7dukz7O4IrYTVN9kfzWqRjJtkfqylHhwu2LlbWotRJzMiM5PAWCM9HQOZYC5/hlENg1X5kM+PlM7Ky/Ur22E46v9i3jhTrSl8wwJLfuwyTZx92Q+aa/bOVufcRgDjH28nlCCjgbSivQdeDzEsq+W1Wkhlz8c8uzuyjUU8wL8pqknX00lOcZeLOhG/h+2EKoalfWvlT3Npy65xHmv10VylKFxCwWwsSAQEBAQGbB4U8+s7pGnyDFVuNS9anqk4E+4XPOYFr1ju+DSBbBVjDGTXhmFodms2mb19JhEUuVHWREmnoo1PEWB149erVXhibNGkSWq0WoijC+vXrPY+CShU1/t7eXn9OTZZFWN6Ghnmy/+r6tRYJHRd1fxWFvBI6Lqrcdtpp+ndbVgDIE0G3Bbu1IKEkojIW/VXvuA83/HIWAE8yz00IJdzUarWcJYLgw6Sx0O12OxfjrBEHVvNSQg2hfAdrYvOZ9569HIcdNgNxHOOhX1ziY9JpBWCTrVYrx+1g/2npKOJg6KTWPloSkGacU94Jt7qf3Vez/NlS8HyweT/Y7+35oJRB59i4ceM8854WEvYnvbe0SCRRFZ1KThs/Y6PT1h/v+xwA4PiKKyrlCGJZTTStY+E2RWbmuYcvSY7nMck4dqLM9+56staJWPgBMbJWjU4c54p1pQubu08PvXqV72+16u7l7T+d668JyBe0ajabePe790ar1cIjT84GgJw5e95xLrqKHIqc5cB1IrNVM3mEvLXvvCTnxN0/uQJAOv/O+8uklgdSK4aft0l75/2liwphnglL3FOzPqF5Erq6urxFghY25kfZWRlbAwLKMCYECXUn2NSvgFtYvGlWmLjqPqBwUKlUMimd7bFqvrQFqFTCpmChSZra7XYu/EfDp5hQhEKCLZDV1dWVL45kUHQe9kWrmnIhHBwczJmF1VysaLfbOfO8CmkqXA0MDOTcEOoesaxo9SHq9eyIIjWEphOePHkypk+fDiB9AVAD+PTE/3L9EDeG1VXYMwoZ/qXRnb7oUmt7MfkW4siyqXvLLr1aS8z0cSXDKAcAtIrP51++iHHLz8/JtKfCdE9P6rZS3y/noj57IyMjnlmvvmAee1MiYHC+XHb83QBcJdGazJUi8jNRxrZnqOgtT7kQz1secYXALvv4klRoSdrwGmXymdVGb3nkfCjUVVckmFOg4nxaunQpgHwiodHOZ71W3isKMNVB89xI+DgF0dNm/HtyHcUVfi3KEpvx2jdt2uTHQ5NhqfJUr9f9PFILgZLFuT6uX78+Q9pm5Mfg4KB/zugGJa+i1Wp5y4byEtQKUKRcauir9s0mK1M3riqXVshUgbTMzT73vQt9Er/UNZl9L+p1jGY+hYRUAQEBAQEBAaPGbm2RIFRDVX9fp9PB5YffCSCVRJV9q5pQp9Px7dJaoeW3ubXEOdWaKXFr2dzh4eFcnDWPpYRMUyc1lEaj4bXf8ePHex9gf39/rpQvJe+hoSHv4qHmo6Qu+g+jKMpZKzSOWVPaxnGcK19LKZoaF6/LhrlRGtdCa9bNQvCcbIdjqr5AXsf2MAkXuTQA51+mS0PJlj6sMmnDWlki8Ru32k6z4Jjc3e/cAO12G7OmunThmoaan31J7GRcqtUK7v2jM7Of/W6nYaduCoZLJjpFxWgoMfvp2rv9NxcCSOcQ56EtVVzGurfalLLdudUCcPQ52/LMqr2p5erWH54HwFmG6Os/+Z1fz56PLiV3eRmLkH5Hl80FR7sxp2Xi3p9ehdnHznf7JK6qVjvOHMs+zf3oQtz+BBOCZa1vZW4Ka8lTlw+fAR67vSxs+eJg6T1M6bRue+FBLow4ipzlQPkKlUolF9qqhf3UHdput3MadVFKaR7L9nVt1qKAnFf9/f1+7Lq7uzNreBm5e2hoKEcK11BeuzZpGm1eI9vjGmSLeGlouyb1K4pgKUriZ6/d5qag4Y1TjOOmlvltQbBIBAQEBAQEBIwau7VFQn0/yoq1/sktsV+LksFoUhlNU6ptNpvNTHwykC+pa8Mm1YelRbSYAprxwLVazWthe+65p0+ZvWHDBi8B04qxYsUKAE4ynjZtmj/GQkmR1Wp1i8m9eB5u7bVpER0tFczrsmXENdZctY+hoaFM0TIgtUgw/tumowW2T5Ea5UZQW5w8ebL37+q9TTl/aRpntsXrsdo4AGzqOA3GhgAyuRMq5Cok4xsVl/9m9j73f/Jdkg0rqmb7xARmdt+7nnPadLud1cws8dEWawKKLVP8XdNEKz/IPpfkSZBNr1YtLbls7zE1yUf+/FkAwAn7fdX1haoZfcIFqZk13JS/X/KXznL53T9cgwd+4VIPn3r4N9wxnSSVPPLrztwTnUXjniThlfKf9LNdZzifSeKlBkxNe1stbOobT8NLk/lVifGpw+/K7FOpuGdLNXnCWlQ07N6mb7ZtdDodP6fUUsvv7b66brBdWluZKn/t2rUAUgsa97V91qgKa0HQhHZ6XqKrqytnydY1pyhzppJ/y7631oZcdEZBSXN+f9F73NxjhA3XEq6PHGtNTLg12K0FiYCAgICxjG9+85tb3EdJ43zRnBo7AS+KRrDXuKMAlIcbFrlXyrL4qnvXuiTUlVFWGTOO49I+8EV4xBFHAEhfkFa4qdfr2GuvvRBFEc477zx/rArJURTlIvbKCOeVSiV3bWUuCOsCebOChD2mbEwVJCsDwLuPcOMwpetjAIBjjz0WQOpuUQVY8elPf7rwe2A3FyTKWO1FfjFNvlSU3xxIJ+XIyIhvh/HXZb4n7jc0NJTLq17G2raTUv1eNo02kMY+N5tNr4XR+lGv17FmzRp/LKV0WiSAVDIty+1uM7qVha+pBqcWBdtfLVSkUQB9fX25B1AlbhsWqhYnO4ZFfd0WqEVGme6TJk3C5z/jMgIuunNRph+LIsf4n4vFSVtpu/TF39o5FwCwod/xXvr6sizvKIqwZLlrZ870bPuEph+LkC48TAY1570L3b7ZfEvJ/26s7054FZ1Otg9aVtn6tssWfstWV7+xvgC4sDF7YRzHngekoJZLi5DlQSlv595ffwpA+rI498hb3L5x7G9GmXbvX1rJ57MO/Tbu/62zLtz1Y9fu2Udf59pIxr9qojhsRBKQWlL0OSnSzrmecJ2xIegct23hSZRZR6Z3rcj0CSgXCvT89nNZ7Qhta2tekEURN7qOKG9ga8bItllWDiAXvWSEm7IIobLt5vYtGtuy8S4aP343vW9l0t/sulgmyGwN0qDxtynejMSt1Ri5VXPo5rA1DwqhN9UuUluqubG5PigJ0qZYteRQStwrV67MSdw29wUXKpsh0/bXTtIyNxFRJHHrA1GWYXBzJto386AQumjTBJya97d9yivZ0oYW77fvfgCAlatWFh4zDcXfA8CyjnNNqbBVNEf3bKzIfrGZy1rVnJY9ttcd68+cGUPX0KoBR74tE2it9limQW5uDuk+Kox2Oh1MnToVcRx7AVih42PnZdkcZft7jlte0GKZZpf9HEUR1gzslWlvyviluX203RXrphf8lj/GvizYPp9zzuff/OY3+NrXvoahoaFRzWmuf3vt5a7jkEMOAQAM/93vAADHfe9yv19Z6LZWmySazaa/J7RwaH4XdVv09/fnCI0awm3DHcsyfC5f7u7rr371KwDAiy++6M9DgXPatGm44oorUK/XsXjxYn89rM/DUNuRkRHMnDkTAHDYYYcBAPbee2//G5CuM7VaLScgKlmfsH1WBZHHcryIzbkeyp4/+xvH6/snuORh479yRGacXnvtNQApiXdrsFtbJLYGZUKASpb24dZFcms04LKXX5GwUSbd6iS1D7OdSPZ/7aPNbVFUzMx+tr587XeZ1cfuP24k4WAku/Z3rSq8PvsSKIs9L7q+Ld2z7QnV4jiG9Xo9FRimupe3ChSK1dUZZhxTk6pF0TivHHZ5KqZ2Je2TgyFFtWLkha01QzMAAHt0L0uOYbv5F1zZOBa99MoKzBUJEmWWJL3monZ08d1c6e4yrOp3L/VigcLHXsg2xR697mWzepMby1Ub3HbqxGW5fZW3UrZW2PtUVA7ebm3Z6oCAnYm3vSCxOb8NHzKanQ888EAAqeRNyXLcuHE5YpyaRZUk09/fn0s6okVvNKST4ZhAKpVzYaS2QakzjuNcmVzVtF966SUAwMsvvwzAhYHynJMnT8Y111yDer2OBQsW+POQbElCUnd3N/bff38AwMEHH5y5DtsXXleZZE3pvOlDDrMSOQAc+vKZmfae2ecnmWNselxqS/rC0DEhodT+Ro3t97//PQDg2WefBZBqG0qS2hpoyNc+++wDADjooIMAAPvttx+uvfrazL5PP/105vouiu8AkI7RExOuyIXl8np5L5TIC6Qa5YX7LEm+KfNJd/Doqosz5+Q+Z77rRtdX/5KqeNfGY4lrg1oix1XnRVdXVymhVa1AURT5OVEU8gyk4czr1q3DnDlz0Ol0sGDBgsz1ax0aapo0/0+YMMGH/9qEcEWYndTXsP30REzRxPl9rVr1VUof/dVlANJ5NefDbkxtSC/n6/e/78JHOX+0MqwNb+WaQLIgn/df//rXANLnfjSIoigXukwX58okOdlPTnUhnsc89MkcabZM6SgisGsSprTGUd59rGsdoRYK7m/b4z6cPzbUk21yvLu7u73C0t3dnSO52jnPvmgVVnUF2czChCZDtCnEee0qXFqlxF6XJWSri7xM+bPHf//EG9w5h7PC+PbA216Q2By2FLVhiTdaHlv9+Voa2PqFD3/9nMx5alXX1u8OeCDzfVdXV46wwxeIbu1NVjM3+zpjxgzfLuC4EpY41dXVhSiKMGHCBH9d3Jc5I7q7u307XFBUAyoaU42D51bH3Jo4OR7vffE0AMD7XzsbAHz+g5/PWOKvj+NcVPiM7fI8ZZnh+MCrT39bwGvhgqRpjK+9+lrf5zvuvqOwjSUN91L3wqSJMLAvZyBvErXaP8f47kRImD3V8R70hXnP6kt8SnTN4bDkd6709Zz3uWOrEfDdV1w661ot62NW64uN6rGZAIF0IWYfbU4NnSO64NvInFqthna7nWOW89x8afBe093HrIV2X1t8zvb1th+d76/xkx9O+CvJ2JVZWgCgnrQ/63AnOCz+uYtuIWeCAoU95rJPuDl+30+vzlw7YV8aPE7L0WtOlcHBwVBaPGCnYrcWJAICAgIC8qhUKl4wYVjpsmueAgB01RILThIS/JNP3IYP3HeePw7IJzFSBcOSxcu4MJroybarJN3NETVV6dA+WZ4FQ9z32GMP1Go1VCqVTFVj5Yex4JftL4VVtSYVkUXVmqDKDhOu2XaoaL18yPczbVkrho6pbu19ShUP104lKca26rPOIoy/ynIxRoMxIUiUWSbUhATkyYhldRqq1SqOXeMY8E1kw2Z+sZeLNe+Ksya8arWay/JYVKwLcFq0FgNTYh8XAG5HRkYyWjfjmqdMmZKbrDbGW0k91O7YNx5jXQL6oCsByGphyumgBYLgxD5mtdOOX5z+vZy5Ukld1hfO/rG/ZeXJtwV6DRqt8YXPfsH/bguKWZRxDFqtVi7ahdevGquFLub3rnEa8aw9F2S+L/Kjaw6Hu5NcERMmTEC9Xswa59xlX7mgrlixwtcz0DmrY2DLype9lCzZjPeXc5z7av0DtYjZBVRrMehnm0l1/veddZE1Ozri6iDZoRJFPiqDv7Eux8KfuAygnnVSqfjMf3zN6AtLyYyW48Hnk2NAayIjr/r7+4NFImCnYrcWJNQsycWCixC31kemUqyazblgve/F0/2qUK+5fShA3PGDVwCkKZGvPP0Q34b69cqSWdmXtlbjLONijIyM+GscGRlBrVZDp9NBX1+fb09fVPQVAnmeBhc1G1JXRqpU15AVaG5/8pXM2EYfc4v0kctcqCM1H7bV09OTM8G/mZCxMpb+9oAKn1owyPevEvkCWPTbqyamAo/lzuh4qj/Z/q/VVLm9Z3WWD2GJwfoMFLHL+T/njKY2tr5awL3I6GLQl5+SIG1Yo3KMtNIrUa1W/VhyDMtcHbzODRs2+N84hlpN1wppnNfc5/rHHJ/n8hPucX2g1mkE2EjmGZ8MVg5tt7MkbAt11akgYO+D5SgBaZlvckBWrlyZ4zBsCbVazd8jtjdQe8P1VxKmVaJyFwzPpy44e03c59BX3JhyXfzd/t8BkHVTanprQjXwnp6eXFFB5fLwe+43fvx4f62TJk3yQv+4ceNy4eYc23a7neP5UIAm1NVk29F13ioPbJP3+ZCXz3D7VNw+737O5Xt46ZDHM+ez80mj5NTtaYnrTDqnnLIyvsvWIKTIDggICAgICBg1xpRFQuN+aY6tVquZFMQAcPCfTwWQSmltFuZppiZ8SnZ/PNCVh54EFrBZmjmW2JwJUtNtx3GcYRgDKSNdzf7WBG2JhTacUosF2W0ZoayIKa0uHy3MoxJyp9PB2Ue7MMh7f+rC7G773gsAgHFnuXF77wunZcZi32eP89YeukHitMpUpv3f7v+dnLtG/bVlIapbAy38psRAIu7Evs8f/cuPAgAeeuwhAHmN+4xjz/D9WvTwosw1qOXDXpuOsWpM6hYZGhryc51p1Wl9oqZvi26VRcyoq4j7TZkyxR+v6ZDVmtZoNHL9Vyuc+sXteHDL9qk58nosAdRGAdljNUrBFhJT6xFdHZ868d5Mn+I4RptaZdLHjhQFu+mBWQCAK8+4JxeSe86HXCz/kqfm+fYA4JZbXBtXX92dcYna6+C1l3ED3gwYrQCklo5BPjdJ1E7FPC6amr5I87V9bbVafr4c/lpCRq9k3UMaAQLkXVUa9WBD1XlOruec25zr/J1zZMqUKf6e020WRZEv4GWvx665GoquIcd8hnp7e3NJsHRctEyAJZaXlfV+5+9PAgC8ctgTANwcVxd8WVhxp9PxfTnhcUcCfuwvv+XaR/bebQuCRSIgICAgICBg1NitLRJEWWwvJTWmmAaAv1jl0g9D+GmqGRf5rK/7zm/codVi9nCr1cql+lWeg+UwaO4ERVEhmCKOgs1JoRpXq9XKhbbauGt7flt0TEPSVNsvigf3mluczYfBktleu+lUSjkRep5DXjoDfzro4cLx0AJYo7VIRFGUs0iU5QrpdDr+vJrXQjXwxY8t9v3T9pUgacl4Wnpa7xNhc56wsNurr76a6RPDVpXgCOQtU3pPbMpmtVRpYTY7T2wmVgvl79ispXqNHH/lxWisv+2vhixTq+vp6clZ7HIZYL22TsuYSZ3MExkipu1ThMjzDdLpnN0n1fBTjbWsoJfmvNkaFOUXyGUabWcteB95ZB6iWnF/db5uLh21WhWVn9PpdHL+ey3KZq0wylvjOs4+aGn7yZMn++/q9XpmXNXKYPdje1q4US1E/Gz30TVI+U3WWkxuin/HJHNk6VE/cteO7FoDbDnpnrU4+++S9Xfmt08AAKzELwuP3RqMCUEiICAgICALdV15836VybjSF7W6NvjyUzLwu/5wsmsrNhl62R42nyHVZtlV921RTaKyrJ7qJqbQOX78+FwiwDiO0Wq1ckqbFZDKKngqubPdbudckdyXwo5mEbb5iKgQqduoOpAVrmx/VcBWoTmO49wYfuBeF8r7UuelzPdEEal+S9itBYmi1MtAKkFSo4rjOMOgB9KHKBK/YUEG5j4AACAASURBVNp4GqVx6z1/zrRPXDv7yEyb9Xrda+GUnjWvun241f+lnAXVsLS6HSeE1UKK4rHVF6vRAJZlz4dHNU4+IJoEyErc1arLqklJ+6ZH/ggAmHOsy6D3oVUX+mO8ZkLrDrkRyX147l0P+fNWo2y9haK8/NsCm/SKi5JmUrQWH513ukiphmn9vWo5+tjXXFIrzsd2u+XTLdPnfNflp2b6q7VYli1b5i0R3GqdFpusSeeDciY0kmnTpk05n7BdvG1bzWazNAJAI5hGRkZy906f4TKODsuP2/Z07lueT1niM96Hm55wfIdLkzLiSUOZfXXc+ExnIotKLGxauvub39yAz31uSmG/qeVuz/DmgIBtwW4tSAQEBAQEpLACkpLPvUAkxNBms+kFHa1srCGqab2Xikm7nlXG1LpgBWsKX5obxiatApywpgKtatFKUrdVhS1Budls5sLMLfGRQquW21ZlcHh4OFf4UF0bGiY/MDCQcwuqEKsCfbVazZFDy4rl1et13xfNQrylsuFbgzEpSKivsdPp+MH9wQRXWvgj61yCntKManH5jYiFA2CTN2nMv+Zgt6XBqS1qUS59EG0qaKup0bff1dWV01psnzWuv4h7wa3WBuF4aK0Fq515bdtrcOwEMtfz4ykuTfOxa+caM6jb57fvuD+zbx35uHVNX63Jv0ZrmSiqS8BEVJ/79OfctRlLAs+z+B7HgdBaD3ofG41Gzvx4zrcf4MkBpHPKTjX6VNk+75/mqbBJotSXzToOrNtQqVT88WS7q9+ai6VdDJUXoumcrfm0KOeA3afIgqbXWFbUjajVav761VKlqb7t+OvCrM99Zst+st+c57LwW4sE97n/Z1cnh/ZnriuO8+Z6XgfvFSuhWpP6jihMFxDwZjEmBYmAgICAsQybHI9CzCGLXe0bZtD0bspGqrFreLIK588f9AgA4F3PnYw/JO7HMr5A3MwSNvv6+ny7FJI0Y6rdamJB7su+qtu1Wq0W8jMqlUqpO6/RaOQUNp5Hk7BFUZS7Vg331KyqmzZtwkmDLvydlqAY+T4CQG0oJWiq5eHh+v2Z8bEKnhYz43UUlSMfLcakIFGUCdEybgHgmX1c9sUyf3scx+hGlj2tD5WScoaGhnJxxjSXsX1rbtQbbZnoto0ijoRl3ltCTxEDvyxbpV6HjUnmNVkTmh2vIo2R/l/VPFVjf3bmvbmFamQom7mvqP9qQdkRWpr68xfd6fI/XHz+xb4/ah4tY7LbbIq89gtuedS1U00Z/0Cq5QJpnoLF805xfZE8GewjTbrWvMmFjFEcrAb72muvAXBzjBVIyQPRmgyM9LDErjKtvyjHR45FLtEs9gVHC4/G92tabc4hm7pcIy/0ZWGtdBrLr2NZVMrbZ7YssWIQMUwkh1g22IeFC9m39PdvfMNZIObNc/eOFgnWf+DasT2K0QUEbAvGtCDBbb1ez0mKKq2rH6u7u9tIvPR7ZV+UemwRO1jJVpqUx+7DhVEXbS6e9juaO+M4xvDwcG4hVgKaPWeZIGHT1pYVjVHW8+akXk0YU1SOV8OolEDX3d3tz6FEVpW8RytY2FK/ZcnNbrztRgDAvLnzcmlwtWaJCknNZhMXL3zcnYsCgy/U4DZM0Rx3Ytxy/kcAALWkXWXQF5FyOUc41hQynnvuOQDIkDH5ouJcoTtn//33z4wL53+r1Sol6g5LH632py4erdrKpFJ2PpaFGWqCNF6nbY9aLmGfNU2gpdejx0ZRWmtD1xP2afax893nTgf6qi+rsWPXBXUjUnBgX7alqq3VnnmfVZiyW/XxaxI7FaJ++47voD2UvUcq6GuSMVs8i1Bh0KZtV5cPt2UJ+4aGhnKWFSqRKvzbBGvsL+cInw8K4URvb2/u2S6r3GzfPTlCsSea5BVebnUt+9jwGZnzsc2fVJ7IWVT0PaKlzUeDkJAqICAgICAgYNQYExYJlYg11LOnpycX4qaa5zmTXfjXfevv9cekiWHoRshqF1oJ0ZL2lIym1UBt2KS6V1T7IiyTeXBwMKMpqYnZuj62lO6W57OJk8qIjLxmK+1qO6nZOEves5qDFu0pK+JVqVRyTGhNs7ut4XHWn8w2eQ5aPzgXbll0i/9fLRJaBI3bixd+32sfXqvNpQR329svORmDiUbE40n81AQ81nViCxcBaQVJ+k2pZS1fvtx/xzGnds/rYprhadOm+X6Uhf8WJarSEFe1eNmwZ52bqgkXaZiAm3ezTrohs0+zlbSbJFy69b4k+RzyGrWSPHMhvIZETLBvdz3j0l6fdfR17nuYRFZMtV9iJbNWTf42MJC1SOi8Ho2lrSjJmmqkNppC3XRqXdDns9ls4qMD2bBkTepX7bhjfoEf+WM15F0tttZaRQsELQN8zjg/iyoUs7+9vb0+UZ8tkGaLGSpUc+ezz/NPnjzZlylXV7Wmx2cbJ/R/HO1Kch/5kIsLrpq8Xxju/WZI49aNpxYrawEC8qUXBgYGtnrNDBaJgICAgICAgFFjt7ZIqPZC3yIlWZaUXXzsPC/9XfV7Z3HQ7Gj3b7gPADB7yhwAwOOdx0rJc2UJsMaNG1daXlY1FBtTXVRKGsiHn/b09OSS7HQ6nUyaVyW02bTahGod9jo1W5z2rUhT4T5fuPAYAMDXl/yMI5Y5n9WAishtdl/LjlbpWfkMW0oj+2agFgnPPBcffa1Wy6WYVovJvMU/SK6JKXHTSELVNdjG3Vc4Znezvz/nY9awV2vZAbLl5cnp4D58BmbMmAHAab0kW5aFDCsj3SaxUjKxlgqPoqhUS+NnS9xlYSW9lxpCWmSZqFRIBk3mkE9L7baXzV7i26rVpPiaJKB74AefyZ1XeUKcXSzIZZsgyfLBX/5VMmbq/96UjE/atlrYNP33tlrayhKkaap8y5H4i5XHZdvYQMKp22Q4UzqZuavPMu7++eAyx/l5uvNkLtRd5xPn8YYNG7wlgOu5Er71Ou11NJtNP8Y2eZlyfAYHB73lgftoCQGef9myZd4aQqKycuE493k9T4x/GH+5LskGyg77NOzZ4bPrf1khQp7vifEuS2Z3nBYkK8pHASBTyAxwYxssEgEBAQEBAQFvGXZriwRBicwmewJS7cwmEfrWwWcCAK557jsAUKBdJv7+Ti3n+6YWYxm5QFZ7Vy1G/eiWia0+TIL7UJMjnBaWTdUcRVHGL01o4Sf7/+Z8cDy3JvVSP6uVgvnd15f8NNMW/cHUtNjH08edgUqv+/+pqtPe82OdSvhlxbm2V/hnUcy9ckFsIjAda17/ZUueAgBEnsOQtp9Wc8pqz/Qn22srS7ClfbSZ+NRKw37TInHAAQcAcPeNoaA8J8M9udV6BfYaCe2DtVCVRcAQllPD0M8y/o5qqkVjk/qLqR1uLlwzmwiNP6n1p9PpoJWc+9annJXykyctSo5JIm8Sy0fcjrE4KRfe05MNl061w+HM+Sz0GjVaY7TzXC2RfIZtMTbXpwjve/EDri+RWKmwZe05QtbcxmP8PEh4I+vWrctZRzREnPNp48aN3iLB7xiezH3JGaJ1oN1u59KMt9vtjAbOY/k8t9ttv1bqesxjaMGrVCq5lPA2og7IRzPFcZyrQeItf2hlxs2XYUf+nv9g4qOZ9qNkjg8ODuZ4LMrh0ki+0STuGxOCREBAQEBAFkqCVtcAfz/kpSPRipPqlSLglikf9kWnQmC6k9s8ULkLANAcaPrj9IWuLtT+/n6fGIqwia3s1mYNtgIoFYRNmzZ5pVKzt3Z3d+fI0RQs+Jkv71WrVvkMsez3PvvsAyAdWyWTRlGEh2rOnX5y63TfN93H9i1C5AUHL3jFWQXOuoZUeSCK0hOMFru1IKE3QB8Ybuf+bAEWfHBucow7lhKrVnOjI+u4+Hjct85NgE+e9C4AwC2PPp/ZV/kP1mKgkqnmcGg2m94XqtqeauDE8PBwhuVu/1cmvx0TZdFr/LVN9a0Fg/hZHzarLSkvoy1litnWWRPP9r//uOGY3GQsq2XIsrnLmPxEWZz/1kCtWmUMevvg6j1NxzUZ7zgdB6a7Vq3t3qtcfHjFLAaaN4JQHoTNvKf5FXQM7JylBsdrpabHSA9qWXaelHFZNF13V1dX7j6of7owtbQ8y3qvdQ6MjIwYjTs75wneByBCp9P2/9tj2ITykeI4xqKfuCJzc0+8OTmUOUCyY7vg8blotTZlxkM5WJUCRr5GrBCjKR8eELAjsVsLEgEBAQEBKaxgZpO6AflU1lRkfrTH4zhq2YcBlCeiq1UTV0QlFRJzJOqo2CVnqxirEkChUpNzbdy40feTLgySBrmlYGoVIZsUjoJqtVr1Y1CUKtsmN7N94Pe0PmzcuBErVqzI9IGuDvZRFblKpeKv+d72HX4cAGBOt8uWe9eIq0E0rjEu7ftwsXtOhfGhoaGce1ET+Onvo0lwtlsLEjq4NnrCbru6unDxz93NuuWI8wEA1x18FgDgM88/mGnzvg2JGap+ivfR2XLkQJ4NbdnzNsOf3WqMsp1g5HQQquESNoU1+0OLgJ7PlmxWbV81RutLU7NnWSY3a7ng9V90gvPD3/SwKx9++ScOBgCc2nta0uN0gdH0v8p21j4XjY/6tUdbtMv2g4sI/aLMv8A5YKM2CF/Ea97HAQDn3fxw7nfv/0wuZ8llbt+qWIlsiXWNXFCGu11klJGv94kL6bRp03LzmRYIZavbeViUldKeJ2fZK+inRgvQp20rVdpy5BZq+m42m1jy8GUAgNknz8/sy5wOFfBlmPKbdGHmGHAO3/ebKwAAZxz5Tcz9qFszIikgxxl51w8vBwCcdMHtPncFp/hDt34CQDpviuamWmFyUSKhUFfALoLdWpAICAgICMjDJqRSocmmoQacgPpw437/PwA0OwmpL6kNQnfYif1OQLKkWhWAGJrItqKh1AqgQqW6V8mLGBkZ8cKj1oBRCwLJ3AyFB1KLRLVaxfjx40uVjt7eXi9Ia8Vjfs/z21B6DWm2RbSAbJhpGYH9ntbtAFJBkmNgE4SpgqDbOI5zSpkqqHSBWgLo1mJMCBJKKioqGcybde4PXc2EJR92LOtvHuQIMFf89h4AJtJjfBsX7u14FfeudQW+lByjWSSttUD7UpRdkhNAWe1aJttOEGtFYOTG8PBwziKRjbfPLigaUWLLXVsSEpBqgtyHk1H99RYXHLcfgLxWSTy46Tu5vmiufbvg6Hhba4v9fluglhHGjtOUyeiHiRMn5rR+zUGh2mmECPdddWamr1WpLVLEwVAegvrx7WKitRII7Vur1fLzivdYozQ4BtYKxeO5aHPuagSTbUeJZ/oSseZztqvPizLRi3gbdzx0CQDg3I/dmDk2Njkb0iiN4uyB7NuZR/1nsmMFNdn3oV9dCwD4wMccZ+KEOW7NaI5UoNU28vyHfKQR/1Vrj2bmDZaJgJ2N3VqQKCNbErdfdC4A4MrvPJw75qzHXXrb+068CkB+Ybxz1RKcP92Rrc6f7lLtTjzFlc3VRY2Sq63OSeiCosLH5qAvfPtiplDR6XR88SM7FvZlQxePmq65YNkkRkoS0wQymnCryN/GfU8fd0bSf/f5zlWLfR81JEk1FNuuvoBU2Nkerg2CY80X28qVKwFkBQlN16xjs+iTJ2X6BQDjkn05V3jdHHvr2tEiPGVkRZumV4m5mqpdk7bZ/itBuCjcVpNMaUppG+bMfXhtqrHa8aJArG6QMkKrDVlkn8792PzkPLyyKPM5jtPQ0GqVrsXsPOPYPvybz/vvP3jqbf5cAHDk9FuS37JCgS2QRRKtzmMlnsYxPAGUa4+Wgd7WhFRlYfFs3yodW6rYy+v72YynfPtHLXW8CoZ30qqBkez57b1TrZxQYamnp8fPYVoENPRSj221Whl3N60ytliYumjtO0OfIa4zPL9dk9TyoCURbPRJWYpyhT1/mUWiaM3x43Gui46pMVPa//gLAOk6rxWdtwaB/hsQEBAQEBAwaowJi4SmBtWwuZtnnw5qKRcvdmRKSnrn/9hpGS2RvJ1fjcOXJXWqhmWlRzX9E5ZkCWStC2WFsdSMbMuFV6tVHxq5du3aDOHT9q2rq8tr0yw4Q0lfLRL2+pX0ppYOwpZo5zFsl+F3d61enGkriiIvYau7guB1DA4O4sZzTsv8dup/zs9cx/awRBDqwlmzZg0A+CRO3d3d/jfVqopcakA2yVRaCC4bksttvV7PxX8T1MaLStRrfL6mOOc9sunUVevkfdPQV1sanChLV2zJt9SwlfjJMR0eHsaRRx7pU70DebIo5ybH3Fpj1PSfui0gnytGs05DQt1vWQL1kR+/xZ+/2Uy0dM2t0M4mWoqQkmkjWSsUKV8htcKQ2Kvlw0fj0rCWDy1iSIuEWh36+vpyFkENu1XLSqPRwLP7uQR0/nnY5OY2LR4aFt5oNPw5OX80mRI1+gkTJvjj+B3vkRKI7dqhY0Yrq000ZrfWgqap6XneomRrWoxR102OY6PRyJH0NT0Bx4/jZtshlAxun/2h0xe49sF1AZn2y0oKbA2CRSIgICAgICBg1NitLRKESs1KqIqiVKNaeIErF37F/f8FALkETFYzerT5CADglC4XqqdapJLSOp2Ol/o1pa9aSdrtdi68j6BGSKLfG2+8ASDV8NjeiSeeiCiK8Pzzz+fIjzakk9nX2H9yJpTFa4ve8DrKwkCVrWz3pabwvRGXnY3Su7VylN0ztfpomnD7m2o825OUxjZ5L3gP7HccRyX78nubtU/TnqsVyrZRtq8eY8myZexujZ9vt9s537XyHRRFZaaVF2I/U2uyKbztZ87jjRs3YmBgANVq1c91TS2tmr0lYdLqwoJccZzVmgn3/Ou1Za0Yf3GaC/XstNN52ZHEamqRsJomUxZrcixrgbDHtNttnHTScgDA73630o8HsH1IltYioZYtJdna7I48p3J3iM1ZVItKc+u+em1qfeP9nzRpkj+GORuKQoyB7HrPeTjnvQsx0LsfokqEWQfdgkolS+K950+OoDs0NJQjMZcVyevp6clZocvWKWsBUaur5fnYa7dtq0VY76XnuZ2+IE27zrmWUGvKcoKMBsEiERAQEBAQEDBq7NYWCQ0dpBRNX2ORRH/1g05LhvAq1DdeqVS8BPlY69FMe8owt9KiSv+qTRZpfepDZnuMqaY2vGbNmoy/bnBw0FskNFSQn3t6evw1MgWy5oW3IXwqJZflabe8FD237kOLBCVxO0567eqLLypIphYh1Si2B1Rj4r1ot9v+f9WQ+Jnx7vvuuy8Ax7fReaascc06aKF5APTeZKIGJC15kVZSFg2kETk2WkajTDQig7AJtXhtmmlv+XKnia9cuRJDQ0OoVCp4/vnnM+1RC2UfirRd5dlQ4WKUUJoWuzgayJ6PfIe4k16Pt0Am1gS1RFguTJrxMXu+vAXH7XDqqWvw4ovuuV62bBmAdN3alpoIRKfTyXFevOY+x4Wzj3/iar+/WvV03ui6AqTjTusBLXFlc9qGEauWrNyD7u5uPw+ZN0LnJVH0/EdRlNBgosQi5X8BkM6dgYEBw+nKhjLr893V1VWq1WsqfbZRtN6rRbEovFvXXX2miK5HL/f/bzjFhT9X7j4PADA4+KdM+9uS6Gy3FiQCAgICAvJgsSoAPkMvXSfdiWC0+gQXAt/ptNH7XWfq19BifelZJUoVOXUFqPuL4er2uzLXqe0L2yXUDWpdHbMOusX3z2bS9e/O5Ctbl0lDQ3k+Tdttc5doOGYZbDh3WY0mDe/udDr+5a+BBEUkzIEzb3LtJoW9+hMFi4Ip+78tro3dWpBQDY2+V7LCm620FDTZ2qo1K2yOBdWOy1L+Wslbv1OGruVZqNSpfA2Nqti4cWMmYROPW716dc5aQlQqlVIfoMJq/5orgdAoDmvFKCsXTUuETbClESmaUMlqRCpB6zjtyMQ9RZYJ9lnvMePc+ZkWIDf/sknLyu5Fq9XKaaQ8t3ImigqncdFQBrrNtKf5BIiy9Ojd3d05n23RPoB7JjSZGc+n19Hf3+8X2tdffz3THuc85wwzK3KuDQ4O+jm48AGXOO68j9+UjIdmXIxRqdASVMJZaMlzU634gnKVWCKWaHVIrBC1es0XZdMXZMpjcQedeaYbizfeWOEjgZiGneMSElAF7GrYrQWJgICAgIA8bP0SLd60hwiz9fsuRKeWJeBawZPtWdh010UhiUDqDrRKgmrl6pKzbksVupVIXOQuJqnSS3uJFUTJrx+f+S0AwD2Dl+TcBkostlYUtlOUCrvoGNZDsvuoW1Db3HDqtxExO24irA5XsgpHKmzGafGXBFSwVGHYlgRnu7UgUXaDfJrrdp6tesNZLlf8VQ88kmlLmexDQ0O5G81JqFkZCVtlrqyPNhsh+0lN5NJnXL6Frx9wSubYojwJVlPXQl72PJZxz+8uf/ZOt3PS7OJj5/nzqBlOo01sPvii34G8711Na7VaLbdwlJkIrWmQW2ab3J4s9y3BjrVqnbo4aqTE8PBwLn5ezb+2cp/m1CB0kS/yVysXQDNErl+/3nMU+GLRrHw8P79vNBo5rgL94UUF1NQUrS8jNZPHceyfAb581DJGrd36rbUg3v/1/Icz5/3rK7/nxySmVYGpqjXjpERxRIhQqWbnJPdpIYlySawQlaiSK8rGlzitpCee6J6b11931/fyyy9j6dKlAPIF7AICdjXs1oJEQEBAQEAxytLzR3fNcVsmNqvGGUsAkApnmkCNsARcCoYUiKg4qEVi/PjxPt00hVaeV8PWbdp0JW0rh8Fepye9JjJZnLTlBcUkTPc7L1yeXFda3EyrIReFTepY6hiowthoNHIESSWc8vfBs25O+lhhLIAPW/aCKvth0r97SCiq1nLaFkVrTAgSSvrRyIk4jnMaR1mtCG/uq9dzFoctac+WgKQaoWqgw8PDuPDpW5N9NQ+/26p/2GYNjOPYT1ZqhxacnBMnTvS+e81AyYVk7k9dZrR7Trgyk80QKM5DYNsvskgQnMDM3Ed0dXXltHktwMXxu3XOWeg0XTvH/N9fBQD8KfEts93twXIfDThGXOCmTZsGAJgxYwaAdLxHRkb8WKi1Qa0pth6BziHlrfjqiqaMuFrAaLXhor506VJvkSDI5VBrmq3FwX6Toc9rphnV1gopK4OuFROt1YFRGtxyzvMFQyuUfeHoQqwWob/9F1fGftq0aT6769UX3gcAmH/nuZm+zL76SddWUosjRuwjOMoK4tH60G630R5x92jRNz4EAFi1ykWhkK+l92H16tX+/x0RdRQQsD0xJgQJNfFzcTv6//wyAOAX//C3qMbJyzOx5//7KScASAt6qZ/KpnFW87AWmrLSfFEqWSDvS7vop7ehTdNohcIHMu1zMd177719W9bfxZf4XnvtlTN787wTJ070Lwou/ncffwUAYNaT33Z9TcbkzO/9JxYefXFmPFTCLiKgqmClApGGaVYqlRxJTwUS63dtJy4qss/1pbKzTMIqbPElSIHCFknTMSGUDFmtVnMvYHWxFSWw4f22qcWBNHT4pZdeAuAECf7G/nJ+aGpvG3bKc6lroyjkuiyduva7t7cX1WoVjUYD7373uwG4uQykc599feGFFwAAL774IgD3IlayqJKg2celS5f6e/JPrx6R7O3a47N9w/9zGID03nV1deHUT7qkdd7FkVwO+3//fFecbe3atT6Ec8WKnwNIhVz1U9tU9jtSgLD3zLrNis5nK85qCKS6OK17l3NBn28lpVsBWNdOVQLsXFfXlaZ85phqxWDXUbdZNTAND/3+ooKiZC3ftiZMU26GJcWrhYZ900J7dgzUaqFh/vxcXeyKQ7bmLMqtt7kZQhecWR9JGKYbkHNveyTsCwmpAgICAgICAkaNMWWRUF8dNdcP/etX8JO/+2sAaflghoGpydlq3CqdK6tXE+7Y4kZlx575PccW7sD45hKF+sYjZgMAeqSAkS2uYwshNRoNRFGEfffdN5f2liGXvb29XjNT6dkn2PHjmDK92V+NZ1bJfmRkJFdMRwstaUGmrq4ub/nRsto85lunnez6OjKCD3/pfwAAnkskbY7BzjIJl5XFLkvANTIyUhpyq98PDw/nQmF1Duk9sO1omCa1k1dffRWAeyY4D5g4S0vG69aeU60j6iJkaXBeN5BqRpwX7OPEiRN9HD+Td7FPdMdp3L51l3EeqFarKbrXrVvn1wLeE4JjSpfN2rVrAbiy8Xf8x7GZa1R30cqVfwDgrD50F/E3Wh7KrAFvhRVNE9uR3MkET1M/94TbMU7XRdBfr+7b5BHrvf9SAG7OlxWjUheqJYDr3CizmgD5KA1NlqdWnkqlgsW/vRCAS5FtMWf6IiSNAAAeWH+ZP78NRy7qG1GtVnNRGfY3jguQTWyoFjpdN/Taexaei85cVxI8MiHMDomL3lgN6bZf8fXjAABLlz4LIM9R2RYEi0RAQEBAQEDAqDEmLBIEJTxqKtRCbLEVSnZRlJWEizKu8X8bBmdR5PPiMec+dWNyHiRbsokTCTOO07StJb5A1cZs6CFLU1erVcyYMSOXRtYmg6KGnAtX9QQ9pgAGLvnl7cm5s37yJR+elxmfbOx2dl8lv6r2apNkKQdDuQSn/Pv1eC7xP+8q3AhC/aLUaCwHAHDXpvwD1UYsD0eJrBr+qlpRUfs6jtZfTasVtX4tOa7J02q1WmnRNuU92DTIJBpSW+dzyfPPnDkTXV1daDQauQJotqw6kE2dbPez10joODWbTX+P1PfMz+wreTgzZszwpFlNkkUrD3kRa9as8b/peOxMAqWuh0z6Rf7JHpbLIM+7T8Ng8jEAwMbT57v9ADQf/FTmGLU88l5ZInDZGpEvBZ9a21SDJ4o4bLP2dMTxeHUETAD2rK/AudMWod3mc5BPXc4+XHrA/Znzpc8On60qFr3uSLrW4gDk013bXBHKUdAieboWDA0NoWehO0+b1z5nUebauXZXKhW0k+O4PnKraOQFOAAAHONJREFUnI9twZgSJAICAgICHLTuCgmgFJpWf+NEAImLg4InX6JeoGCeDQcrvPNFpYmh1CVrCYr6MlWXj7rogLxCoiTOogg8K7/FSAUYkjBtjZ/zZtwB+yPJtXT9UqmyFWeVoEkUJfSy7mjbbyW0WuFf3cKtm87MtFub54Se6uIL8Ebitly69BkA6X3eHmGfxJgSJHijNGSsXq/jA//HvwJIQ/PoE22Lz/SWc90EueTOPD+AN16jESxrOZWwKdUywU028Q2SbwHgjmM+6do3IYAWnIibNm3yWm+tVvOZ1lqtVq4vFrRWaKKg7535BQDAxx78GgCgE7XRKZl0cxILy+3HXFL4O5COD/uvVh4bmaHsZl1YZt/sJPA/LF/ufbt8QHaVcDmbTh1I/evcMirCWoK0AJcy3oeHh/1ioWx0Df8sSiOujHBq+ozMqFQq/v4wpp+ftX6ATbOtPBg9j91yjvJ+UYPn88Rx6evr8y8XlhHnS06tXOrn37Rp05u2SNlCZVqojlv1k/f39/v1g/dD4/MtH2JXsEAEBOwojClBIiAgICAgCxV4KQBRaOq74TSMv8qFuno5aJEzrXuBLvm6bQSxqJF1p6llQl0SnU4n59ooc2nYGj6zpzriJJWwha/OzpzHhkWX5fpJ93VtnjvNtdlqtXPHRKbYl8Xdq+bm3MMa6koXnBU+1bpAaCi9dR9q2u5c4a/7nat53cC6TDVdYMdkSh2TggS1Iz4oK1eu9DeY2jm1sUWO5Os5E1W4G7Xg/A4+dV92+NTXpRwGi8inz/UkCbdJd8ADJ38aAFCXDGTK/KXfdunSpV7Lq1arGB4eRrPZxJ///Odc9jROuBkzZnhLAPuZS61NX2C16lnCNHHSQnHXcc4fWhH/fKvVypk0CS1KplqmvWaNDaf/eenSpbmiRjubG0GoFYD3ibkOaNLdY489cqXGdfG1Jl4tRqbjxgXHmnS10h/vLS1vbGPcuHG+D8yZoItfEV+Ix7NvmvbczgdaBK0lzfaf84RFu5rNJp555plMvxVq9dm0adM2+X7LymVbfzutIGVpx8sY/AEBuxvGpCAREBAQEOCgAi8FcyYrazQaqN94hv8fADqd4kysb+Y8SmC37jsluVIY01DPrq4u3w4JjmltIqdMUbila64s43BkQu1TawkTcMFrdwtenQXApKwWgu64canAqMkI1aJiScM+4ZS4JlUwLSpt7pXAs+7JXBOPffmrh3qLxI50/Y5JQUIndH9/f46Aouxv9V1HUYSbZjnt6396fFJmH+Uh2OyV51zPm3e12xx4A4DUMhGZyU7Ogmp51C6p4b7yyisAXC4A7hNFke/Hyy+/7NvUFODDw8PeCkPNU7XLe064EgAw68kb0lTY3vTYyeyrJsl6vZ7TZDUSQRFF6YOtvBb6ynnNS5cu9eOxs1Jhbwkar8/7wXHZd999vQWsrKy85eHoQqAWCc3BEUWR1/o5jtyXkRmc7319ff5/svc1UqKwhkFB4Ti7jwX7z/um5c95PatXr/b8Hmau1DmjFgN9SW1v2GyGyqcI/IeAsYoxKUgEBAQEBGRBIYxKFcNBe3t7cegXfwcgFfao8Gy683QAeSFzytxHvbt2wx2uorKGcmtK94GBAd8+hWEqPBSKbYkCCqIULimQcmtD3AEnJNfrDDVNLjpKEgVK0au7Vs71bZURcKmA+TGJolxY8paEcEuELnOFT7vk+0lbqdUkEgsKFdGOKMkrVqzwCueODIsf04KENa2RYMSHiJMR6MscYzUv1RbLsrKd+u8mppihPMlv1Pa1rXa7jY5kUuOW2iV5AnzgV69enZn0zC5ni2KpH73dbnvNk1tqqWpSe+S0z/v/2Qduz7+O5fRiu0GlEuGBz2RNmFwAaMVQ/sb8swdT3zr5GsnYHvbP2Wteu3btdg1j2hHQMDs+2LZ+hppfNfKC+/b29pbm5FdTrs29oFEVnN9q+bDH0zJhi3/ZvvDY8ePH57ILqkXJPhMcB81oyTbInRgYGECz2USr1fLzTO/xrnDPd4U+BATsTIxpQYJot9t+UaNfkNUAR0ZcsSArdRJcQL5ynDvmmu+6F3E+6UzKtuXRTQoZ30j8fJ/MmndtSBrdFT6+O/Fh2pcp4BZeW1yH0m4qFOXdL+1226dHpiCxzz77AMiHs1rfHL87d37ywmDdIvoEEwm/WqnmUggTSii8ebbrk2VFd5Lr+ciXXUXPPyX+Po7F4ODgdkmosiOh5FW+OClQ9Pb2+tTPnHdlVVOtG0FJfvq99cdq7L6OmdWGlHmuKY5VoOjp6cmxxwklHo6MjHhBwQoMQDrPbfImJnELhMUdD43eoAKydu3aXPRBLVF4Jl/4cKYNTzCN06eY+2y6y/EsuPZQoOTWRlVwPlGYVQ2/1WrlioApNHTcPYfsVaa+Nu5bc2nmmHbbtd1oNHLrlwrsfLZs+nrfspTuLiqQZddXILW6TOLYxiwVnioXfp1N2uhI9eXff/VQAMBrrz3r18odGRYfUmQHBAQEBAQEjBrBIgEniVMbYkIbSqFH/ouT3p7+7wcAMMS2SsX7pTRBz+z5lM+K/ViugchuCrVMamzqwqAmS0mT1hSbghsoCOM0sAQxXjO1AJICaaGgL3B4eDhHQm13ujPnUtN2pRLlyGi3ncf+kMia1aht2lhaIl5JrCYci12dYFkEZanzGlasWOHLeHN8OVbUTmypYV5zmYWHv1OzB1KtT91u6kqxFgkl5qqrw/qm1ZWhxcEsWZZWP2q8StQtmgcB/3975x8cVZXl8e/rDklFJBhgFPAHMyhsraviltasirrWjGWFGgFh+emyAjKsUpSojCvqWgOKjoM18mMcfliggiLUUlGriCWrS8lA4Y4Ii0ZHLbUkCUnGhJAfkHQITXe//eP1uX3f6W4ILUmn4/dT9aqT7vfuu6/79utzzz3ne7oP7TlramrCh4v/DgBw6zPe908vPQa0hHYsZmbQpgjbFE+LorW0BEDiPqO9HUByAK720NmaEzIu9b1Hy7S3tbVhy8nxZt+fDx2EQCCA9+unIhj0j2k71kEv/2mvgn3PTVccT5e0t7Mu5PtkBzwDQFA8EPATc13jqY0pz0Q4fr5q6355LqWw00GPBCGEEEIyhh6JOLqAjaToyWxp2MPe2vygQYMAAPsW/TRtYZ+t07w1s2lbPAtT7NVoFMYFYYIrTeCaf7ba0tJiZqkiYCSpj2JhitWuYzI6g50CK9cs55MYDPFEiDDRxRdf7EspBIDVd3jXeP+7QwAAeXlxL4PMLhwnSSlPCuTolEHp07iNJ4xF/XW8TzKLlXgQ+VxyMdBNPie5hsbGxqTgSpnBiHS1BE4CyWu1uiyzLgxkezF0sG06JUF7H+0V0boDra2tvticVH2R2Wdtba35LGVcpFo3JtlDf75NTU1Gi0DGkclGkFluvPSF+QRTeF8DamxoD4KtVqm9XunSy4HEDF6PU+21PHHihPlblxnQ4niJrifHJAn6/us4TpL3Ip00uv09Ee+EXIf0LfLfk7x9xrwVPyZ+rNXuKRVwvfs/vftFVZX3m9HY2Ngtweg0JAghhCQhP+JtbW1mErPnyb8HAPximTfZCMiSpjzGjw2k0BaJxX8YAwHvB1inOfbp0ydJiMpe0gP8QeK6xpFe6hDstNBUAZqu6ybVhNHLhECyyqz0RZ7Pz89Pyo6y1V/t89rLklooS+vDSJGwgGXQiI5PJOK19z//4dWnqanx9Fbk87ID8LsSGhJxtEiVzKBsqxxI6JX/dGG9GcAykP5q5SsDQFvIn/EhZb2BxKD4r3+NV95r8lfeq6urM54IuxCR3Uedk5zpdcug1lH08h7IzPH48ePGSyE3AZkpr/yF92WV7APZLxKJ4MjfvEEtno4bnvfPTOyCVADw2bFjJh5EZkJtKhW2N0Tx2zEG8sW3Z09A4rpNETmrVLzMwKTIla48aGfryHut8/PlZiXnO3nyZNrxpKW57ZgPGTsyZrQqn1xfTU2N8Sr1lOJqhJAfBg0JQgghSdgqnrq66sHnPM/Ez5+s9HYO+FMUAWspwGoHSBiiWj8mLy8v6Tm9DGi76XX6uPZIyCRHjGbHcXwpxqKz09bWlrQUkaq4V7rlCjuoPV3wt1ZeFYLBoOm3Lj1uUl51Y5anR6S8ZZImgfjdHYxOQ0LhqkGvVdPkQ25oaEhSaNN68LMH/BVAYjY5cOBAI/Zkqr995S82JAOhoaHBDA7tiegqdFaBduG1tbUZb4t+1BoXcp3hcNisics+er1euxXb29uNF0QXmeqNs9doNGrGl3z+OrNDbop2vIM8N3ToUAAJ/Y9Uee8yVgWdZWO/rvP8dXaI3Kjt/HnxHImUtfzw6AySpqamHldcjRDyw6AhQQghJC2xWCxJRE0etfqtCUy0ZvJi+gdKSgEAJ7fdkfI8kUgkaaKQKjZCv6alt00p7bihK4ZvNBr1TQwlvTgUCpl9dL2XYDCYVP/GXJebHDSul/R0rIU8/mTGn732UxQS0++hG4sHRNtp0fF2PlzsTR5aWr4A4A+wtvvY1dCQOAPpZumhUCitkqBWArS13nXOs56x2cWVUn15uhPtVjx16lRS/IQuQS5r8PJ/OBw2s+p0+cw6HzsWi/3oSjDr91pLtduS5rKveH1kLElGkcSn2CqkOitER9/bSoLSBzm3eIXSyXc3NTWZjJ/vvvsOQLJGgK0ySG0IQnoXNCTOEr2Glgq5seuqnc3NzUmVGnV79g9oT3Hj22uAJu1ICQ0lC1Elfmx0cF5PrJfQU9Cpd3oZyP5b3mO9VKBnUPbnlg57RqmX7PS6tbwu47q+vj5p+Ur6dC4Cgkl2SRWQLbES+5/9BwDADb/1qvHq7z+QSBHVGQupMhlkpi7j5fuNNye1B3hjXdcnEuS+q6XcA4GAWQ4UWXfXdVFUVGT2nQ7Pa/Jm/t3mmHSp0vq+VlBQ4KufY1+bXM/ge/bE/094F9Kll5q6Rf5/EY1GcPD3XiB/be3nAJKTA7r7+0ZBKkIIIYRkDD0SXUCqUrEaLRudK5xOdpuce1KNnVTll4HErESWOGxPgo5C17M4OxVT9pGlEZmRSRt24CTgiYXJ370pPZck0AUEJbhWlmoDz4wAkMjiyM/PRyCoxOnibRRP2wkgUV5c7oUXzvizX8gKwJCZewEARzb/MwD/Ulk6j5levrPvtTKWi4qKkJeXh4HROswsKDNeE9dNXaAOSK7CrCW4I5FIUpEx4cK4pyUW90RIq9FYzKcP4UMJeYl2xKfLRuDbb78CkBDqk2XjbH3v6JEghBBCSMbQI5Elcs0TQXoOOk5FUodldiLrwLaqnszOUhU/AvxruXotW69P2wW4gO4rDESyjxbsO3zYi40wXoAlwwF4MRMFUuBPFS2UQl/9J7/nb9wJ+AsbAogF/LEFta+O9p5PUbRLPBOpNCDsvgPAr1pfxfHob7zTAglPiPImBwKBJE+BTve3BeB0sbGL7/1frz34vQvm/u+6poSCvE/KL2H63dHhxX588803poSDfA7ZFnejIUEIIaRTaJ0dyS7SWUdXhazg3/gPrvxAxtL82AUcB64KRhfkmIv+bTcA4G8bb07KFJPlBC18ZQcuyw+8iFEBQCQaQSwmBoTXZzEICgsLzXn0UopWenVd1xjZ8hhd/08AgJ/dt993XfIOONZzQbUUFIlfh/T/nQXedVZWVpjJQ7aCKzU0JAjJUeQGJpH0MjuUG5OIetkFikRALF0JZlscS0fX63ZlnbylpaXH3NAIId0PDYkz8PLLL+Pee+/FFVdcYXLkCUlHfn4+1q5di0mTJqG9vR3PP/88VqxYke1uEXJO0Z4JrRvyxj0/wWWXDQYA3P68F4ibqIwphqm/rRgSxb8SKY+p04cvufcvaHtrTLy95NRQIOEVsNOjxbsQcxJLcLFoItV+U3gcACAvz7uugoIC0166FHe74qcu2iVGfHTHvwBILlQWi8USFVN/9TaAxAThixVeau0XX3hiUxUV3u9PY2Njj6t+TEPiNIwePRqXX355trtBcoglS5ZgxIgRGDZsGAYPHoxdu3bhyy+/xHvvvXfmg88SucGlk9eWAnOFhYXmZidlycU9KwW/7PVluRnqKooSB6Hl3EOhEGMjCPkRk/OGxCOPPIIbbrgBkyZNMs+tWrUKruvioYceyrjdYDCIF198ETNnzsRnn312LrpKejjDhw/H/v37cfvtt+OTTz7BkCFDUF5ejsmTJ2P37t2damPmzJmYNWsWWlpa0NLSgvXr12PWrFldYkgQkm10qqUse9lpiLse/xkA4LLLLgMAXLXwSwAw6aFGqM46prW0xHceXXOnoKAAhYX5vj7Y6ql2u7YQnry2OTABo91BcBwHf8EkuBDPhz/uIRKJpE3V1/WYgsGgUZuVc4pHQp43Hor4Ma2traY+UcvqfwSQCJr+7rv/AwBUV1cDSBjwp6vQmy1y3pDYvHkzlixZgv79++PYsWMIBoOYNm0axowZg9WrV+Puu+9Oedzhw4cxatSotO0+/PDD2LNnDz7//POu6jrpYRw6dAiLFi3C5s2bcf311+PVV1/Fpk2bsHv37k6NpQsuuABDhw5FeXm5ea28vBx33XVXl/ZbbkriypUbnNzU+/TpYwLSxNsweLDndhY5bfvGJN4JccNqj0dNTQ2ARKDdqVOnetyNjRDSfeS8IVFXV4c9e/Zg8uTJ2LBhA0pKSnD06FEcPHgQBw8exPz588+6zUsuuQT33Xcfrrvuui7ocffx9ddfZ7sLOceGDRswduxY7Nu3D67rYtw4b810/vz5ZxxLknYp68Xyt8xGyNnDMZwbaM9EKBQyS2timMryWv6frgWQqIoshMNhE9hbWOgvNa5jC/Lz8017so8uoiXPixFtlx2wH21PhW4jGo0mZZBoD4UtPCj91yJuul6NGPv19fWoqKgAkPA8iEdC3j+pVZTtFM/TkfOGBABs2rQJ8+bNw4YNGzBjxgy8/vrrnT725ptvxo4dOwAAVVVVuOqqq7By5Uo8/fTTJho+V1m+fHm2u5CTrF+/HmVlZZg7d66JN+gM8oUvKioyN4GioiLjGehqdIE5O+tCF4fTJeltt7C+2cm16BueXFdXxkdwDBPS83GQCJDNWQoKCvD999/jlltuwUcffYQrr7wS1dXVWLt2LWbMmJHyGDEaUtHc3Oxbhxo8eDAaGhrw4IMPYuvWrV12HST79O3bF+Xl5di1axfGjBmDq6++Gs3NzZ0eS7W1tZg5cyZ27vRkgJ966imMHDkS06dP77Zr0DiOYwInL730UgDANddc4/tfXrcFqcQwkrTSr77yZHmlYJMuzEWIjczKJeVYpNsvvPBC3/+yvLZwwUIEHM+I3bRlE4CEJ0KE1OQxGAyavyXuQIxjmf2LITzhzgkA/OPUcRy0h9txpOEIPvzwQ0ydONXX91TFx+xjAeDtd7wsC/m+2AXvdGGvyXdN9rURiXrPL31uKb799lsAie+VTGC1TkVPpld4JE6ePInS0lJs2bIFH3/8sZkxzZs3D/PmzTvr9kaOHOkbQHV1dRg7dqxv7Zv0TlatWoUDBw5g7ty5eOmll7Bu3TpMnTq102Pptddew5NPPokDBw7goosuwty5czF79uxu6DkhhGSHXuGRALxUzb1792L27NnYuHHjOW3bdV3qSPwIGDduHNasWWO8EH379sWnn36KxYsXY8uWLZ1qw9aROHHiBJYtW9YjdCRkdjhw4EAAiQh6SQft378/AM8zITMsiRI/dOgQABhZXvFUMOWTdAaZpWslSomREM/EwIEDsfS3S33HaDVJeXz7nbcxcexEb5+gt0/ZjjIAiRn8uDFefJNd6MvWguiIdMB1XRQEC/DHtX8EAMz/dy8Oyi7apQth6b5Jm++89w7uLLkTgKdNYZ9b+nQq4nlJFi9dDMDz9tXW1gJILBXqrJNcoFd4JADvA2lvb8ebb755zttOqspGeiXbt2/H9u3bzf+hUAgjRow4qzbC4TDmzJmDOXPmnOvuEUJIj6RXeCQcx8Hy5ctRVFTEGzghKZDZk8wGZV1aMkrkUV4HEjOkuro6AEjS9yfkbNDKkBLjIGOvuLjYeMwGDBgAAFj8hDdzF2+GPDqOk5RhkS5Wx/YKyL6O4yAcC+Nw9WGUlpaaMS7t/+6p3wEA+uT3gaPKaGlPhL4+ILnE+NPPPQ0gISsvBe8aGxuNh09njuQSOe+ROO+881BfX4+qqiqUlJSc+QBCCCHdjhaO0kGRoVDIpE6LSNOC3ywAkAjQlMcF8xac9XljsRhi8aJcf1j5B0yZMgUdHR2orKw0ywtiJDz4yIMAgOXLlvsksKWdVO3bgcqLn/EMIMl4amryZMLl+sR4OHnypK/ybq6S84ZEe3s78/QJOQNy8xOZa3mUSHFCCMmUnDckCCGE5B668Fc0GjXLZjJjl+U0WQqQwMy2trakJYZ0Sxzy/NLfLzVqrMeOHUN7ezvC4TAqKyvN83KspDb/et6vsXrlagCJoE5JURWWPLsEgGecS+qmtCf/64q64oXIpYDK05GcJEsIIYQQ0kl6RbAlIYSQ3oN4G7TktIhPnX/++UboSl6TfWW2r0WlOjo6jKegra0NL7zwAqLRKB544AHjKRCkzcLCQhOALIGY+nwiHHXixAkT96HFpOzCYb0ReiQIIYSk5LbbbsMHH3yAlpYWI5Fuc+ONN2Lfvn04fvw4ysvLMXr06Cz0kmQbxkgQQghJSSgUwiuvvIKtW7fiiSee8L1WXFyMsrIy3H///Xjrrbcwffp0lJWVYfjw4Wbmnym6BLjM7O0YCl20S5BZv/ZIRCIR4ykIh8O+NrWnQM4TiURMYLJuT2Iv7D5qz0Nv9UBo6JEghJBeypQpU9Da2mq2jo4O7Nq1q9PH79+/H5s3bzbqpjY33XQT6urqUFpailgshjfeeAMNDQ2YOHHiubwEkgPQI0EIIb2Ubdu2Ydu2bQA84ad9+/Zh69atWLRoER577LG0xxUXF3eq/VSiTOmKIf4QdJlyO8PjTMrDdjaH7TGQEvWpvAa25oWWgk93vh+L9yEdLjdu3Lhx672b4zhuWVmZu2bNmoyO/+Uvf+lWVFT4nhswYIDb3NzsTps2zc3Ly3PvueceNxqNuuvWreuW6wkEAm4gEHCDweBpt7y8PDcvL88NBoOu4ziu4zg/+Nyptmx/xtncuLRBCCG9nGeffRb9+vXDggWdV4Q8E01NTRg/fjwWLlyI+vp6lJSUYOfOnaipqTln50iHeBdisZjxGqTbIpEIIpEIotEoXNf9wZ4DaUNvP3aybs1w48aNG7eu2aZOnepWVFS4gwYNMs89/vjjbmtra9pNt5HKI6G3YDDoVlVVuXfccUfWr5lbt29Z7wA3bty4ceuC7dprr3WPHDnijho1KqPjHcdxCwoK3JKSEreystItKChw+/Tp42s/Ly/P7devn7tixQp37969Wb9mbt2/cWmDEEJ6KePHj0dxcTH27t1rMjfefffdTh9/6623oqOjAzt27MCwYcPQ0dGB999/37z+6KOP4ujRo6iursaQIUMwYcKErrgM0sOhsiUhhBBCMoYeCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGQMDQlCCCGEZAwNCUIIIYRkDA0JQgghhGTM/wNEhCw+1Z73AwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us see the results\n",
    "from nilearn import plotting as npl\n",
    "\n",
    "# With some atlas ROIs\n",
    "name = 'cort-maxprob-thr50-2mm'\n",
    "atlas_fp = datasets.fetch_atlas_harvard_oxford(name)['maps']\n",
    "\n",
    "for each in images[:3]:\n",
    "    npl.plot_roi(atlas_fp, bg_img=each)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collecting mean values from the ROIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/tmp_9rh_8p3.nii.gz\n",
      "/tmp/tmpqht6np22.nii.gz\n",
      "/tmp/tmpk0117ahy.nii.gz\n",
      "/tmp/tmpq6pnpx0r.nii.gz\n",
      "/tmp/tmpm2un1_ri.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# Our list of images\n",
    "for i in images[:5]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/grg/nilearn_data/fsl/data/atlases/HarvardOxford/HarvardOxford-cort-maxprob-thr50-2mm.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# Our atlas (or any ROI volume)\n",
    "print(atlas_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'Background'),\n",
       " (1, 'Frontal Pole'),\n",
       " (2, 'Insular Cortex'),\n",
       " (3, 'Superior Frontal Gyrus'),\n",
       " (4, 'Middle Frontal Gyrus')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get label names from Harvard Oxford atlas\n",
    "from nilearn import datasets\n",
    "\n",
    "name = 'cort-maxprob-thr50-2mm'\n",
    "labels = datasets.fetch_atlas_harvard_oxford(name)['labels']\n",
    "\n",
    "# Store them in a dict\n",
    "labels = dict(enumerate(labels))\n",
    "list(labels.items())[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    1.3s\n",
      "[Parallel(n_jobs=8)]: Done  50 out of  50 | elapsed:    1.7s finished\n"
     ]
    },
    {
     "data": {
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       "      <td>0.487534</td>\n",
       "      <td>0.460258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpqht6np22.nii.gz</th>\n",
       "      <td>0.103357</td>\n",
       "      <td>0.471021</td>\n",
       "      <td>0.498914</td>\n",
       "      <td>0.488398</td>\n",
       "      <td>0.478122</td>\n",
       "      <td>0.480315</td>\n",
       "      <td>0.496140</td>\n",
       "      <td>0.486054</td>\n",
       "      <td>0.471220</td>\n",
       "      <td>0.479839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.497902</td>\n",
       "      <td>0.505975</td>\n",
       "      <td>0.505111</td>\n",
       "      <td>0.513902</td>\n",
       "      <td>0.507292</td>\n",
       "      <td>0.496123</td>\n",
       "      <td>0.514430</td>\n",
       "      <td>0.513238</td>\n",
       "      <td>0.538621</td>\n",
       "      <td>0.456594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpk0117ahy.nii.gz</th>\n",
       "      <td>0.103379</td>\n",
       "      <td>0.470353</td>\n",
       "      <td>0.497508</td>\n",
       "      <td>0.494957</td>\n",
       "      <td>0.490264</td>\n",
       "      <td>0.462238</td>\n",
       "      <td>0.503560</td>\n",
       "      <td>0.488197</td>\n",
       "      <td>0.483536</td>\n",
       "      <td>0.482748</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501623</td>\n",
       "      <td>0.505212</td>\n",
       "      <td>0.523972</td>\n",
       "      <td>0.501424</td>\n",
       "      <td>0.496880</td>\n",
       "      <td>0.487006</td>\n",
       "      <td>0.514189</td>\n",
       "      <td>0.499250</td>\n",
       "      <td>0.457970</td>\n",
       "      <td>0.461195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpq6pnpx0r.nii.gz</th>\n",
       "      <td>0.103438</td>\n",
       "      <td>0.469670</td>\n",
       "      <td>0.507529</td>\n",
       "      <td>0.492020</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.468317</td>\n",
       "      <td>0.483713</td>\n",
       "      <td>0.493656</td>\n",
       "      <td>0.478836</td>\n",
       "      <td>0.498611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487922</td>\n",
       "      <td>0.492715</td>\n",
       "      <td>0.497087</td>\n",
       "      <td>0.498138</td>\n",
       "      <td>0.503407</td>\n",
       "      <td>0.504541</td>\n",
       "      <td>0.500196</td>\n",
       "      <td>0.480250</td>\n",
       "      <td>0.476086</td>\n",
       "      <td>0.461005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpm2un1_ri.nii.gz</th>\n",
       "      <td>0.103383</td>\n",
       "      <td>0.471193</td>\n",
       "      <td>0.501927</td>\n",
       "      <td>0.491835</td>\n",
       "      <td>0.474059</td>\n",
       "      <td>0.472013</td>\n",
       "      <td>0.500466</td>\n",
       "      <td>0.473487</td>\n",
       "      <td>0.480920</td>\n",
       "      <td>0.493313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519001</td>\n",
       "      <td>0.509719</td>\n",
       "      <td>0.510084</td>\n",
       "      <td>0.502092</td>\n",
       "      <td>0.515010</td>\n",
       "      <td>0.498099</td>\n",
       "      <td>0.500320</td>\n",
       "      <td>0.479862</td>\n",
       "      <td>0.541620</td>\n",
       "      <td>0.462065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0         1         2         3         4   \\\n",
       "subject                                                                     \n",
       "/tmp/tmp_9rh_8p3.nii.gz  0.103160  0.465694  0.503227  0.495352  0.475365   \n",
       "/tmp/tmpqht6np22.nii.gz  0.103357  0.471021  0.498914  0.488398  0.478122   \n",
       "/tmp/tmpk0117ahy.nii.gz  0.103379  0.470353  0.497508  0.494957  0.490264   \n",
       "/tmp/tmpq6pnpx0r.nii.gz  0.103438  0.469670  0.507529  0.492020  0.477040   \n",
       "/tmp/tmpm2un1_ri.nii.gz  0.103383  0.471193  0.501927  0.491835  0.474059   \n",
       "\n",
       "                               5         6         7         8         9   \\\n",
       "subject                                                                     \n",
       "/tmp/tmp_9rh_8p3.nii.gz  0.458292  0.500774  0.484376  0.474922  0.509686   \n",
       "/tmp/tmpqht6np22.nii.gz  0.480315  0.496140  0.486054  0.471220  0.479839   \n",
       "/tmp/tmpk0117ahy.nii.gz  0.462238  0.503560  0.488197  0.483536  0.482748   \n",
       "/tmp/tmpq6pnpx0r.nii.gz  0.468317  0.483713  0.493656  0.478836  0.498611   \n",
       "/tmp/tmpm2un1_ri.nii.gz  0.472013  0.500466  0.473487  0.480920  0.493313   \n",
       "\n",
       "                         ...        39        40        41        42  \\\n",
       "subject                  ...                                           \n",
       "/tmp/tmp_9rh_8p3.nii.gz  ...  0.506539  0.501405  0.499656  0.496808   \n",
       "/tmp/tmpqht6np22.nii.gz  ...  0.497902  0.505975  0.505111  0.513902   \n",
       "/tmp/tmpk0117ahy.nii.gz  ...  0.501623  0.505212  0.523972  0.501424   \n",
       "/tmp/tmpq6pnpx0r.nii.gz  ...  0.487922  0.492715  0.497087  0.498138   \n",
       "/tmp/tmpm2un1_ri.nii.gz  ...  0.519001  0.509719  0.510084  0.502092   \n",
       "\n",
       "                               43        44        45        46        47  \\\n",
       "subject                                                                     \n",
       "/tmp/tmp_9rh_8p3.nii.gz  0.501897  0.502139  0.473712  0.485254  0.487534   \n",
       "/tmp/tmpqht6np22.nii.gz  0.507292  0.496123  0.514430  0.513238  0.538621   \n",
       "/tmp/tmpk0117ahy.nii.gz  0.496880  0.487006  0.514189  0.499250  0.457970   \n",
       "/tmp/tmpq6pnpx0r.nii.gz  0.503407  0.504541  0.500196  0.480250  0.476086   \n",
       "/tmp/tmpm2un1_ri.nii.gz  0.515010  0.498099  0.500320  0.479862  0.541620   \n",
       "\n",
       "                               48  \n",
       "subject                            \n",
       "/tmp/tmp_9rh_8p3.nii.gz  0.460258  \n",
       "/tmp/tmpqht6np22.nii.gz  0.456594  \n",
       "/tmp/tmpk0117ahy.nii.gz  0.461195  \n",
       "/tmp/tmpq6pnpx0r.nii.gz  0.461005  \n",
       "/tmp/tmpm2un1_ri.nii.gz  0.462065  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from roistats import collect\n",
    "import numpy as np\n",
    "\n",
    "# Now collect the mean value from each ROI on every image\n",
    "res = collect.roistats_from_maps(images, atlas_fp,\n",
    "                                 func=np.mean,\n",
    "                                 subjects=images,\n",
    "                                 n_jobs=8)\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Background</th>\n",
       "      <th>Frontal Pole</th>\n",
       "      <th>Insular Cortex</th>\n",
       "      <th>Superior Frontal Gyrus</th>\n",
       "      <th>Middle Frontal Gyrus</th>\n",
       "      <th>Inferior Frontal Gyrus, pars triangularis</th>\n",
       "      <th>Inferior Frontal Gyrus, pars opercularis</th>\n",
       "      <th>Precentral Gyrus</th>\n",
       "      <th>Temporal Pole</th>\n",
       "      <th>Superior Temporal Gyrus, anterior division</th>\n",
       "      <th>...</th>\n",
       "      <th>Temporal Occipital Fusiform Cortex</th>\n",
       "      <th>Occipital Fusiform Gyrus</th>\n",
       "      <th>Frontal Operculum Cortex</th>\n",
       "      <th>Central Opercular Cortex</th>\n",
       "      <th>Parietal Operculum Cortex</th>\n",
       "      <th>Planum Polare</th>\n",
       "      <th>Heschl's Gyrus (includes H1 and H2)</th>\n",
       "      <th>Planum Temporale</th>\n",
       "      <th>Supracalcarine Cortex</th>\n",
       "      <th>Occipital Pole</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/tmp/tmp_9rh_8p3.nii.gz</th>\n",
       "      <td>0.103160</td>\n",
       "      <td>0.465694</td>\n",
       "      <td>0.503227</td>\n",
       "      <td>0.495352</td>\n",
       "      <td>0.475365</td>\n",
       "      <td>0.458292</td>\n",
       "      <td>0.500774</td>\n",
       "      <td>0.484376</td>\n",
       "      <td>0.474922</td>\n",
       "      <td>0.509686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506539</td>\n",
       "      <td>0.501405</td>\n",
       "      <td>0.499656</td>\n",
       "      <td>0.496808</td>\n",
       "      <td>0.501897</td>\n",
       "      <td>0.502139</td>\n",
       "      <td>0.473712</td>\n",
       "      <td>0.485254</td>\n",
       "      <td>0.487534</td>\n",
       "      <td>0.460258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpqht6np22.nii.gz</th>\n",
       "      <td>0.103357</td>\n",
       "      <td>0.471021</td>\n",
       "      <td>0.498914</td>\n",
       "      <td>0.488398</td>\n",
       "      <td>0.478122</td>\n",
       "      <td>0.480315</td>\n",
       "      <td>0.496140</td>\n",
       "      <td>0.486054</td>\n",
       "      <td>0.471220</td>\n",
       "      <td>0.479839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.497902</td>\n",
       "      <td>0.505975</td>\n",
       "      <td>0.505111</td>\n",
       "      <td>0.513902</td>\n",
       "      <td>0.507292</td>\n",
       "      <td>0.496123</td>\n",
       "      <td>0.514430</td>\n",
       "      <td>0.513238</td>\n",
       "      <td>0.538621</td>\n",
       "      <td>0.456594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpk0117ahy.nii.gz</th>\n",
       "      <td>0.103379</td>\n",
       "      <td>0.470353</td>\n",
       "      <td>0.497508</td>\n",
       "      <td>0.494957</td>\n",
       "      <td>0.490264</td>\n",
       "      <td>0.462238</td>\n",
       "      <td>0.503560</td>\n",
       "      <td>0.488197</td>\n",
       "      <td>0.483536</td>\n",
       "      <td>0.482748</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501623</td>\n",
       "      <td>0.505212</td>\n",
       "      <td>0.523972</td>\n",
       "      <td>0.501424</td>\n",
       "      <td>0.496880</td>\n",
       "      <td>0.487006</td>\n",
       "      <td>0.514189</td>\n",
       "      <td>0.499250</td>\n",
       "      <td>0.457970</td>\n",
       "      <td>0.461195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpq6pnpx0r.nii.gz</th>\n",
       "      <td>0.103438</td>\n",
       "      <td>0.469670</td>\n",
       "      <td>0.507529</td>\n",
       "      <td>0.492020</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.468317</td>\n",
       "      <td>0.483713</td>\n",
       "      <td>0.493656</td>\n",
       "      <td>0.478836</td>\n",
       "      <td>0.498611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487922</td>\n",
       "      <td>0.492715</td>\n",
       "      <td>0.497087</td>\n",
       "      <td>0.498138</td>\n",
       "      <td>0.503407</td>\n",
       "      <td>0.504541</td>\n",
       "      <td>0.500196</td>\n",
       "      <td>0.480250</td>\n",
       "      <td>0.476086</td>\n",
       "      <td>0.461005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/tmp/tmpm2un1_ri.nii.gz</th>\n",
       "      <td>0.103383</td>\n",
       "      <td>0.471193</td>\n",
       "      <td>0.501927</td>\n",
       "      <td>0.491835</td>\n",
       "      <td>0.474059</td>\n",
       "      <td>0.472013</td>\n",
       "      <td>0.500466</td>\n",
       "      <td>0.473487</td>\n",
       "      <td>0.480920</td>\n",
       "      <td>0.493313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519001</td>\n",
       "      <td>0.509719</td>\n",
       "      <td>0.510084</td>\n",
       "      <td>0.502092</td>\n",
       "      <td>0.515010</td>\n",
       "      <td>0.498099</td>\n",
       "      <td>0.500320</td>\n",
       "      <td>0.479862</td>\n",
       "      <td>0.541620</td>\n",
       "      <td>0.462065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Background  Frontal Pole  Insular Cortex  \\\n",
       "subject                                                             \n",
       "/tmp/tmp_9rh_8p3.nii.gz    0.103160      0.465694        0.503227   \n",
       "/tmp/tmpqht6np22.nii.gz    0.103357      0.471021        0.498914   \n",
       "/tmp/tmpk0117ahy.nii.gz    0.103379      0.470353        0.497508   \n",
       "/tmp/tmpq6pnpx0r.nii.gz    0.103438      0.469670        0.507529   \n",
       "/tmp/tmpm2un1_ri.nii.gz    0.103383      0.471193        0.501927   \n",
       "\n",
       "                         Superior Frontal Gyrus  Middle Frontal Gyrus  \\\n",
       "subject                                                                 \n",
       "/tmp/tmp_9rh_8p3.nii.gz                0.495352              0.475365   \n",
       "/tmp/tmpqht6np22.nii.gz                0.488398              0.478122   \n",
       "/tmp/tmpk0117ahy.nii.gz                0.494957              0.490264   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                0.492020              0.477040   \n",
       "/tmp/tmpm2un1_ri.nii.gz                0.491835              0.474059   \n",
       "\n",
       "                         Inferior Frontal Gyrus, pars triangularis  \\\n",
       "subject                                                              \n",
       "/tmp/tmp_9rh_8p3.nii.gz                                   0.458292   \n",
       "/tmp/tmpqht6np22.nii.gz                                   0.480315   \n",
       "/tmp/tmpk0117ahy.nii.gz                                   0.462238   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                                   0.468317   \n",
       "/tmp/tmpm2un1_ri.nii.gz                                   0.472013   \n",
       "\n",
       "                         Inferior Frontal Gyrus, pars opercularis  \\\n",
       "subject                                                             \n",
       "/tmp/tmp_9rh_8p3.nii.gz                                  0.500774   \n",
       "/tmp/tmpqht6np22.nii.gz                                  0.496140   \n",
       "/tmp/tmpk0117ahy.nii.gz                                  0.503560   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                                  0.483713   \n",
       "/tmp/tmpm2un1_ri.nii.gz                                  0.500466   \n",
       "\n",
       "                         Precentral Gyrus  Temporal Pole  \\\n",
       "subject                                                    \n",
       "/tmp/tmp_9rh_8p3.nii.gz          0.484376       0.474922   \n",
       "/tmp/tmpqht6np22.nii.gz          0.486054       0.471220   \n",
       "/tmp/tmpk0117ahy.nii.gz          0.488197       0.483536   \n",
       "/tmp/tmpq6pnpx0r.nii.gz          0.493656       0.478836   \n",
       "/tmp/tmpm2un1_ri.nii.gz          0.473487       0.480920   \n",
       "\n",
       "                         Superior Temporal Gyrus, anterior division  ...  \\\n",
       "subject                                                              ...   \n",
       "/tmp/tmp_9rh_8p3.nii.gz                                    0.509686  ...   \n",
       "/tmp/tmpqht6np22.nii.gz                                    0.479839  ...   \n",
       "/tmp/tmpk0117ahy.nii.gz                                    0.482748  ...   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                                    0.498611  ...   \n",
       "/tmp/tmpm2un1_ri.nii.gz                                    0.493313  ...   \n",
       "\n",
       "                         Temporal Occipital Fusiform Cortex  \\\n",
       "subject                                                       \n",
       "/tmp/tmp_9rh_8p3.nii.gz                            0.506539   \n",
       "/tmp/tmpqht6np22.nii.gz                            0.497902   \n",
       "/tmp/tmpk0117ahy.nii.gz                            0.501623   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                            0.487922   \n",
       "/tmp/tmpm2un1_ri.nii.gz                            0.519001   \n",
       "\n",
       "                         Occipital Fusiform Gyrus  Frontal Operculum Cortex  \\\n",
       "subject                                                                       \n",
       "/tmp/tmp_9rh_8p3.nii.gz                  0.501405                  0.499656   \n",
       "/tmp/tmpqht6np22.nii.gz                  0.505975                  0.505111   \n",
       "/tmp/tmpk0117ahy.nii.gz                  0.505212                  0.523972   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                  0.492715                  0.497087   \n",
       "/tmp/tmpm2un1_ri.nii.gz                  0.509719                  0.510084   \n",
       "\n",
       "                         Central Opercular Cortex  Parietal Operculum Cortex  \\\n",
       "subject                                                                        \n",
       "/tmp/tmp_9rh_8p3.nii.gz                  0.496808                   0.501897   \n",
       "/tmp/tmpqht6np22.nii.gz                  0.513902                   0.507292   \n",
       "/tmp/tmpk0117ahy.nii.gz                  0.501424                   0.496880   \n",
       "/tmp/tmpq6pnpx0r.nii.gz                  0.498138                   0.503407   \n",
       "/tmp/tmpm2un1_ri.nii.gz                  0.502092                   0.515010   \n",
       "\n",
       "                         Planum Polare  Heschl's Gyrus (includes H1 and H2)  \\\n",
       "subject                                                                       \n",
       "/tmp/tmp_9rh_8p3.nii.gz       0.502139                             0.473712   \n",
       "/tmp/tmpqht6np22.nii.gz       0.496123                             0.514430   \n",
       "/tmp/tmpk0117ahy.nii.gz       0.487006                             0.514189   \n",
       "/tmp/tmpq6pnpx0r.nii.gz       0.504541                             0.500196   \n",
       "/tmp/tmpm2un1_ri.nii.gz       0.498099                             0.500320   \n",
       "\n",
       "                         Planum Temporale  Supracalcarine Cortex  \\\n",
       "subject                                                            \n",
       "/tmp/tmp_9rh_8p3.nii.gz          0.485254               0.487534   \n",
       "/tmp/tmpqht6np22.nii.gz          0.513238               0.538621   \n",
       "/tmp/tmpk0117ahy.nii.gz          0.499250               0.457970   \n",
       "/tmp/tmpq6pnpx0r.nii.gz          0.480250               0.476086   \n",
       "/tmp/tmpm2un1_ri.nii.gz          0.479862               0.541620   \n",
       "\n",
       "                         Occipital Pole  \n",
       "subject                                  \n",
       "/tmp/tmp_9rh_8p3.nii.gz        0.460258  \n",
       "/tmp/tmpqht6np22.nii.gz        0.456594  \n",
       "/tmp/tmpk0117ahy.nii.gz        0.461195  \n",
       "/tmp/tmpq6pnpx0r.nii.gz        0.461005  \n",
       "/tmp/tmpm2un1_ri.nii.gz        0.462065  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns with ROI labels\n",
    "res = res.rename(columns=labels)\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.to_excel('/home/grg/my_rois.xls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the ROI values (accounting for some covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>Background</th>\n",
       "      <th>Frontal Pole</th>\n",
       "      <th>Insular Cortex</th>\n",
       "      <th>Superior Frontal Gyrus</th>\n",
       "      <th>Middle Frontal Gyrus</th>\n",
       "      <th>Inferior Frontal Gyrus, pars triangularis</th>\n",
       "      <th>Inferior Frontal Gyrus, pars opercularis</th>\n",
       "      <th>Precentral Gyrus</th>\n",
       "      <th>Temporal Pole</th>\n",
       "      <th>...</th>\n",
       "      <th>Temporal Occipital Fusiform Cortex</th>\n",
       "      <th>Occipital Fusiform Gyrus</th>\n",
       "      <th>Frontal Operculum Cortex</th>\n",
       "      <th>Central Opercular Cortex</th>\n",
       "      <th>Parietal Operculum Cortex</th>\n",
       "      <th>Planum Polare</th>\n",
       "      <th>Heschl's Gyrus (includes H1 and H2)</th>\n",
       "      <th>Planum Temporale</th>\n",
       "      <th>Supracalcarine Cortex</th>\n",
       "      <th>Occipital Pole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/tmp/tmp_9rh_8p3.nii.gz</td>\n",
       "      <td>0.103160</td>\n",
       "      <td>0.465694</td>\n",
       "      <td>0.503227</td>\n",
       "      <td>0.495352</td>\n",
       "      <td>0.475365</td>\n",
       "      <td>0.458292</td>\n",
       "      <td>0.500774</td>\n",
       "      <td>0.484376</td>\n",
       "      <td>0.474922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506539</td>\n",
       "      <td>0.501405</td>\n",
       "      <td>0.499656</td>\n",
       "      <td>0.496808</td>\n",
       "      <td>0.501897</td>\n",
       "      <td>0.502139</td>\n",
       "      <td>0.473712</td>\n",
       "      <td>0.485254</td>\n",
       "      <td>0.487534</td>\n",
       "      <td>0.460258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/tmp/tmpqht6np22.nii.gz</td>\n",
       "      <td>0.103357</td>\n",
       "      <td>0.471021</td>\n",
       "      <td>0.498914</td>\n",
       "      <td>0.488398</td>\n",
       "      <td>0.478122</td>\n",
       "      <td>0.480315</td>\n",
       "      <td>0.496140</td>\n",
       "      <td>0.486054</td>\n",
       "      <td>0.471220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.497902</td>\n",
       "      <td>0.505975</td>\n",
       "      <td>0.505111</td>\n",
       "      <td>0.513902</td>\n",
       "      <td>0.507292</td>\n",
       "      <td>0.496123</td>\n",
       "      <td>0.514430</td>\n",
       "      <td>0.513238</td>\n",
       "      <td>0.538621</td>\n",
       "      <td>0.456594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/tmp/tmpk0117ahy.nii.gz</td>\n",
       "      <td>0.103379</td>\n",
       "      <td>0.470353</td>\n",
       "      <td>0.497508</td>\n",
       "      <td>0.494957</td>\n",
       "      <td>0.490264</td>\n",
       "      <td>0.462238</td>\n",
       "      <td>0.503560</td>\n",
       "      <td>0.488197</td>\n",
       "      <td>0.483536</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501623</td>\n",
       "      <td>0.505212</td>\n",
       "      <td>0.523972</td>\n",
       "      <td>0.501424</td>\n",
       "      <td>0.496880</td>\n",
       "      <td>0.487006</td>\n",
       "      <td>0.514189</td>\n",
       "      <td>0.499250</td>\n",
       "      <td>0.457970</td>\n",
       "      <td>0.461195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/tmp/tmpq6pnpx0r.nii.gz</td>\n",
       "      <td>0.103438</td>\n",
       "      <td>0.469670</td>\n",
       "      <td>0.507529</td>\n",
       "      <td>0.492020</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.468317</td>\n",
       "      <td>0.483713</td>\n",
       "      <td>0.493656</td>\n",
       "      <td>0.478836</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487922</td>\n",
       "      <td>0.492715</td>\n",
       "      <td>0.497087</td>\n",
       "      <td>0.498138</td>\n",
       "      <td>0.503407</td>\n",
       "      <td>0.504541</td>\n",
       "      <td>0.500196</td>\n",
       "      <td>0.480250</td>\n",
       "      <td>0.476086</td>\n",
       "      <td>0.461005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/tmp/tmpm2un1_ri.nii.gz</td>\n",
       "      <td>0.103383</td>\n",
       "      <td>0.471193</td>\n",
       "      <td>0.501927</td>\n",
       "      <td>0.491835</td>\n",
       "      <td>0.474059</td>\n",
       "      <td>0.472013</td>\n",
       "      <td>0.500466</td>\n",
       "      <td>0.473487</td>\n",
       "      <td>0.480920</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519001</td>\n",
       "      <td>0.509719</td>\n",
       "      <td>0.510084</td>\n",
       "      <td>0.502092</td>\n",
       "      <td>0.515010</td>\n",
       "      <td>0.498099</td>\n",
       "      <td>0.500320</td>\n",
       "      <td>0.479862</td>\n",
       "      <td>0.541620</td>\n",
       "      <td>0.462065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   subject  Background  Frontal Pole  Insular Cortex  \\\n",
       "0  /tmp/tmp_9rh_8p3.nii.gz    0.103160      0.465694        0.503227   \n",
       "1  /tmp/tmpqht6np22.nii.gz    0.103357      0.471021        0.498914   \n",
       "2  /tmp/tmpk0117ahy.nii.gz    0.103379      0.470353        0.497508   \n",
       "3  /tmp/tmpq6pnpx0r.nii.gz    0.103438      0.469670        0.507529   \n",
       "4  /tmp/tmpm2un1_ri.nii.gz    0.103383      0.471193        0.501927   \n",
       "\n",
       "   Superior Frontal Gyrus  Middle Frontal Gyrus  \\\n",
       "0                0.495352              0.475365   \n",
       "1                0.488398              0.478122   \n",
       "2                0.494957              0.490264   \n",
       "3                0.492020              0.477040   \n",
       "4                0.491835              0.474059   \n",
       "\n",
       "   Inferior Frontal Gyrus, pars triangularis  \\\n",
       "0                                   0.458292   \n",
       "1                                   0.480315   \n",
       "2                                   0.462238   \n",
       "3                                   0.468317   \n",
       "4                                   0.472013   \n",
       "\n",
       "   Inferior Frontal Gyrus, pars opercularis  Precentral Gyrus  Temporal Pole  \\\n",
       "0                                  0.500774          0.484376       0.474922   \n",
       "1                                  0.496140          0.486054       0.471220   \n",
       "2                                  0.503560          0.488197       0.483536   \n",
       "3                                  0.483713          0.493656       0.478836   \n",
       "4                                  0.500466          0.473487       0.480920   \n",
       "\n",
       "   ...  Temporal Occipital Fusiform Cortex  Occipital Fusiform Gyrus  \\\n",
       "0  ...                            0.506539                  0.501405   \n",
       "1  ...                            0.497902                  0.505975   \n",
       "2  ...                            0.501623                  0.505212   \n",
       "3  ...                            0.487922                  0.492715   \n",
       "4  ...                            0.519001                  0.509719   \n",
       "\n",
       "   Frontal Operculum Cortex  Central Opercular Cortex  \\\n",
       "0                  0.499656                  0.496808   \n",
       "1                  0.505111                  0.513902   \n",
       "2                  0.523972                  0.501424   \n",
       "3                  0.497087                  0.498138   \n",
       "4                  0.510084                  0.502092   \n",
       "\n",
       "   Parietal Operculum Cortex  Planum Polare  \\\n",
       "0                   0.501897       0.502139   \n",
       "1                   0.507292       0.496123   \n",
       "2                   0.496880       0.487006   \n",
       "3                   0.503407       0.504541   \n",
       "4                   0.515010       0.498099   \n",
       "\n",
       "   Heschl's Gyrus (includes H1 and H2)  Planum Temporale  \\\n",
       "0                             0.473712          0.485254   \n",
       "1                             0.514430          0.513238   \n",
       "2                             0.514189          0.499250   \n",
       "3                             0.500196          0.480250   \n",
       "4                             0.500320          0.479862   \n",
       "\n",
       "   Supracalcarine Cortex  Occipital Pole  \n",
       "0               0.487534        0.460258  \n",
       "1               0.538621        0.456594  \n",
       "2               0.457970        0.461195  \n",
       "3               0.476086        0.461005  \n",
       "4               0.541620        0.462065  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "df = pd.read_excel('/home/grg/my_rois.xls')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>apoee4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>53.620190</td>\n",
       "      <td>female</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53.531222</td>\n",
       "      <td>female</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>51.073742</td>\n",
       "      <td>female</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>54.184375</td>\n",
       "      <td>male</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>52.552064</td>\n",
       "      <td>female</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group        age     sex apoee4\n",
       "0      1  53.620190  female     NC\n",
       "1      1  53.531222  female     NC\n",
       "2      1  51.073742  female     NC\n",
       "3      1  54.184375    male     HT\n",
       "4      1  52.552064  female     HT"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us add some totally random covariates\n",
    "\n",
    "df['group'] = df.index\n",
    "df['group'] = df.apply (lambda row: int(row['group'] > len(df)/2) + 1, axis=1)\n",
    "df['age'] = df.apply (lambda row: random.random() * 5 + 50, axis=1)\n",
    "df['sex'] = df.apply (lambda row: random.choice(['male', 'female']), axis=1)\n",
    "df['apoee4'] = df.apply (lambda row: random.choice(['HO', 'HT', 'NC']), axis=1)\n",
    "df['index'] = df.index\n",
    "\n",
    "# How do they look\n",
    "covariates = ['group', 'age', 'sex', 'apoee4']\n",
    "df[covariates].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Used model for significance estimation: y ~ apoee4_NC + apoee4_HO + age + sex + group + 1\n",
      "WARNING:root:Used contrast: 1.0 * apoee4_NC - 1.0 * apoee4_HO - p-value: 0.378648527782241\n",
      "WARNING:root:Used model for significance estimation: y ~ apoee4_NC + apoee4_HT + age + sex + group + 1\n",
      "WARNING:root:Used contrast: 1.0 * apoee4_NC - 1.0 * apoee4_HT - p-value: 0.1722287451027075\n",
      "WARNING:root:Used model for significance estimation: y ~ apoee4_HO + apoee4_HT + age + sex + group + 1\n",
      "WARNING:root:Used contrast: 1.0 * apoee4_HO - 1.0 * apoee4_HT - p-value: 0.6960469250248639\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from roistats import plotting\n",
    "region = 'Temporal Pole'\n",
    "\n",
    "# Plot data from this region\n",
    "# Do it *accounting for covariates*\n",
    "_ = plotting.boxplot(region, data=df, by='apoee4',\n",
    "                     covariates=['age', 'sex', 'group'],\n",
    "                     palette='default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Used model for significance estimation: y ~ sex_female + sex_male + age + group + 1\n",
      "WARNING:root:Used contrast: 1.0 * sex_female - 1.0 * sex_male - p-value: 0.5782026733225305\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now by sex\n",
    "_ = plotting.boxplot(region, data=df, by='sex',\n",
    "                     covariates=['age', 'group'],\n",
    "                     palette='default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 726.225x496 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plotting.lmplot(y=region, x='age', data=df,\n",
    "                    order=1,\n",
    "                    covariates=['sex', 'group'],\n",
    "                    hue='apoee4',\n",
    "                    palette='default',\n",
    "                    s=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>value</th>\n",
       "      <th>age</th>\n",
       "      <th>apoee4</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inferior Frontal Gyrus, pars triangularis</td>\n",
       "      <td>0.458292</td>\n",
       "      <td>53.62019</td>\n",
       "      <td>NC</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Superior Parietal Lobule</td>\n",
       "      <td>0.508143</td>\n",
       "      <td>53.62019</td>\n",
       "      <td>NC</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frontal Orbital Cortex</td>\n",
       "      <td>0.480872</td>\n",
       "      <td>53.62019</td>\n",
       "      <td>NC</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Opercular Cortex</td>\n",
       "      <td>0.496808</td>\n",
       "      <td>53.62019</td>\n",
       "      <td>NC</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Supracalcarine Cortex</td>\n",
       "      <td>0.487534</td>\n",
       "      <td>53.62019</td>\n",
       "      <td>NC</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      region     value       age apoee4  \\\n",
       "0  Inferior Frontal Gyrus, pars triangularis  0.458292  53.62019     NC   \n",
       "0                   Superior Parietal Lobule  0.508143  53.62019     NC   \n",
       "0                     Frontal Orbital Cortex  0.480872  53.62019     NC   \n",
       "0                   Central Opercular Cortex  0.496808  53.62019     NC   \n",
       "0                      Supracalcarine Cortex  0.487534  53.62019     NC   \n",
       "\n",
       "      sex  \n",
       "0  female  \n",
       "0  female  \n",
       "0  female  \n",
       "0  female  \n",
       "0  female  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For this last one reshape the table a bit (\"unpivot\")\n",
    "cov = df[['age', 'apoee4', 'sex']]\n",
    "melt = pd.melt(df, id_vars='index',\n",
    "               value_vars=labels.values(),\n",
    "               var_name='region').set_index('index')\n",
    "melt = melt.join(cov)\n",
    "\n",
    "# Only keep 5 regions for this example\n",
    "regions = list(set(melt['region']))\n",
    "melt = melt[melt['region'].isin(regions[10:15])]\n",
    "\n",
    "# It looks like this\n",
    "melt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x960 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 856.625x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plotting.hist(melt, by='apoee4',\n",
    "                  region_colname='region',\n",
    "                  covariates=['age', 'sex'],\n",
    "                  ylim=[0.45, 0.55],\n",
    "                  aspect=2,\n",
    "                  palette='default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x960 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 873.625x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again by sex\n",
    "_ = plotting.hist(melt, by='sex',\n",
    "                  region_colname='region',\n",
    "                  covariates=['age', 'apoee4'],\n",
    "                  ylim=[0.45, 0.55],\n",
    "                  aspect=2,\n",
    "                  palette='default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}