templates/3d_compute_median_pulse_height.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dbec154c",
   "metadata": {},
   "source": [
    "# MPH Notebook\n",
    "\n",
    "This notebook can be run to generate plots showing the median pulse heights for each FOV along with the estimated run time. This provides a way to assess the sensitivity of the detector, which we will then be adjusting for in notebook `4b_normalize_image_data.ipynb`. For more information see the [MPH section](https://github.com/angelolab/toffy#median-pulse-height) of the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29613b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append('../') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87437522-8ec9-4c03-a03a-0e63584ac24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import natsort as ns\n",
    "\n",
    "from toffy import mph_comp\n",
    "from ark.utils import io_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a66008-ce34-499c-9c73-8ce3bc150852",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define file parameters\n",
    "You will need to define the `run_name` argument below, which will automatically assign the correct bin file and saving directories. The rest of the cells in this notebook can be run just as they are.\n",
    " * `run_name`: should contain the exact name of the MIBI run to locate the mph data from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc2b28d1-1a05-45ac-ab7b-80d9f04acd96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up directories for current run\n",
    "run_name = 'YYYY-MM-DD_run_name'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5b85f6",
   "metadata": {},
   "source": [
    "Everything necessary for and subsequently outputted from this notebook is stored in the automatic directories established in `1_set_up_toffy.ipynb`. More information on the uses and locations of the directories in toffy can be found in the [README](https://github.com/angelolab/toffy#directory-structure)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3277f8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up directories\n",
    "bin_file_dir = os.path.join('D:\\\\Data', run_name)\n",
    "mph_out_dir = os.path.join('C:\\\\Users\\\\Customer.ION\\\\Documents\\\\run_metrics', run_name, 'fov_data')\n",
    "extracted_imgs_path = os.path.join('D:\\\\Extracted_Images', run_name)\n",
    "\n",
    "# path where plots will get saved\n",
    "plot_dir =  os.path.join('C:\\\\Users\\\\Customer.ION\\\\Documents\\\\run_metrics', run_name)\n",
    "\n",
    "# create mph_out_dir if it doesn't exist\n",
    "if not os.path.exists(mph_out_dir):\n",
    "    os.makedirs(mph_out_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e569bf40-d83c-41c1-8828-1b3d18357b95",
   "metadata": {},
   "source": [
    "### Compute median pulse height data\n",
    "The code below will compute the MPH for each FOV in the run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fefbfe63-8c92-45ba-9bad-0aac3b6a7c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve all the fov names from the extracted images directory\n",
    "fovs = io_utils.list_folders(extracted_imgs_path, substrs='fov-')\n",
    "\n",
    "# retrieve the total counts and compute pulse heights for each FOV run file\n",
    "# saves individual .csv  files to mph_out_dir\n",
    "for fov in ns.natsorted(fovs):\n",
    "    if not os.path.exists(os.path.join(mph_out_dir, '%s-mph_pulse.csv' % fov)):\n",
    "        print(\"Computing MPH for fov %s\" % fov)\n",
    "        mph_comp.compute_mph_metrics(bin_file_dir, mph_out_dir, fov)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4c4bf8-015c-4610-b9f5-ae5ccd497259",
   "metadata": {},
   "source": [
    "### Visualize MPH Plots\n",
    "Now, we can combine the metrics into a single `mph_pulse_combined.csv` file and then visualize the plots. The second visualization code cell will additionally produce a linear regession line. \n",
    "\n",
    "Note that the plot produced last will get saved as a png file in `C:\\\\Users\\\\Customer.ION\\\\Documents\\\\run_metrics\\\\run_name` for later reference. **If you would like to ensure which version of your plot you are saving, run the corresponding cell again before closing the notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "162f8549-6d7d-44f4-9436-09e70fcbef40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prior to generating the graphs, need to combine the data for each FOV into one combined .csv\n",
    "# saves directly to mph_out_dir\n",
    "mph_comp.combine_mph_metrics(mph_out_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a6da8b5-a83e-4be0-a4af-cf80838955e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the median pulse heights and save plot to plot_dir\n",
    "df_mph = pd.read_csv(os.path.join(mph_out_dir, 'mph_pulse_combined.csv'))\n",
    "\n",
    "mph_comp.visualize_mph(df_mph, plot_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2978b381-e574-4b0c-9f66-b5176eaade51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oAAAAAIBqRikC3EOrTh0UGhOp/uPHqoG7u77elKH0+ESd2J1ldjQAAAAAQDWhFAEeoJFPMw2ZMUVDI6eqkU8znf36kOxxicr+4itVlJWbHQ8AAAAAUAWUIsAjcHV3V//xY2SLiVKrTh1UlF+gjMXJ2vHZKpXcvGV2PAAAAADAE6AUAR6DxWJRz9ChssVGqWtwkG7fuKkdy1cpY0myrhQUmh0PAAAAAPAYKEWAJ+Tf6ynZYqMUEDZCkpSd9pXscQnKyzlicjIAAAAAwKOgFAGqqKlva4XMjNCgqRPl0aihjmXulT0uUYc2b5Vh1MtvIQAAAACoFyhFgGri0aihBk6ZoJCZEWrWxlcXTp6WfVGSdq9ep7KSErPjAQAAAAD+DqUIUM2sri4KGDVCttgotevdUzcuF2nb0hRtTfpMNy4XmR0PAAAAAPB/KEUAB+rcP1DDY6PVe/gw3Skp0e4167Q5PkkXTp5+pPcHhocpfPbraubbWkUFhUqdN19ZqWkOTg0AAAAAzoFSBKgBrTp1UOisSPUfP1YNPNyVY9+q9LgEHc/ce9/3BIaHKWLuHLl5elaulRYXK3nuhxQjAAAAAFANKEWAGtSwWVMNnTFFQyKnqnFzH+XlHJE9PkH7NmxURVn591773oYU+bRtc9cxLp/P1wejp9RUZAAAAACotyhFABO4urur3/OjZYuJUuvOHXWloFAZS5Zpx/KVun3jpiTpd9lbZbFa73qvUVGhdwKG1nRkAAAAAKh3KEUAE1ksFj01bLBssVHqNrC/bt+4qZ0pq5WxOFn/uPDPXCkCAAAAAA5EKQLUEn49u2t4bLQCRo+UJJ39+pD8enRTAw+PytfwTBEAAAAAqD6UIsADmLH7S9PWrTTshQgNmjZRno0b6U5JiVwbNGD3GQAAAACoZg/qB+5+mAHgRL7b/cWnbRtZrFb5tG2jiLlzFBge5tDzXim8oM//87/0y1ETteq383T9m8uyWK0qL70jj0YN1cDD3aHnBwAAAABwpQicXG3Z/cXq4qI+zw2XLTZa7Z/ppZtFV7R1aYq2Ji3XjUtFNZYDAAAAAOobbp8B7qM27v7SKShAw2Oj1Gt4iMrv3NHezzfIHp+owhOnTMkDAAAAAHXZg/oB1xrOAtQqRQWF97xSpKig0IQ03zq5N1sn92arRYd2ss2KVPDEcRo4dYIOZWyTPS5RuTt3m5YNAAAAAOoTrhSBU/vumSJunp6Va7Vt95eGTZto8IwpGhY1TY2b++jcoaOyxydq3/ovVV5WZnY8AAAAAKjVuH0GeAAzdp95Eq5ubgoaN1q2mEj5du2sK4UXtGVJsrYvX6Xb12+YHQ8AAAAAaiVKEaAesVgs6jF0oGyx0eo+KFi3b97UrpTPtXlxkorOF5gdDwAAAABqFUoRoJ5q26ObbLFRChwzSharRfu/TJc9LkFnDuSYHQ0AAAAAagVKEaCea9K6pYZFT9fgaZPk6d1YJ/dmKz0uUV+nZ8ioqDA7HgAAAACYhlIEcBLuXl4aMPl5hcycoeb+bXXx9FllLF6qzFVrVVp82+x4AAAAAFDjKEUAJ2N1cdHTI20aHhOlDgFP6+aVq9qWnKKtCct1/dJls+MBAAAAQI2hFAGcWMe+fWSLjdLTI0JVUVamvWvTZI9PVMGxE2ZHAwAAAACHoxQBoBbt/RU6K1LBE8fJzdNDh7fskD0+QUe3Z5odDQAAAAAchlIEQCWvJt4aHDFZw6Kny7tFc50/kqv0uETtW/eFysvKzI4HAAAAANWKUgTAXVwaNFDQuDDZYqLUplsXXS28qC2Jy7R92UoVX7tudjwAAAAAqBaUIgAeqMeQgbLFRqnHkIEquXVLu1Z8rs2Ll+py3nmzowEAAABAlVCKAHgkbbp3kS0mSoHhYbJarTqw0S57fKJOZx80OxoAAAAAPBFKEQCPxbtlCw2NmqYhMybLy9tbp/Yd0OkDX+uZkTY1822tooJCpc6br6zUNLOjAgAAAMADUYoAeCJunp4aMHmcnnvtJTVu7vO9z5UWFyt57ocUIwAAAABqtQf1A9YazgKgDiktLtaWhOW6U1Jy1+fcPD017u1/NCEVAAAAAFQPShEAD9XMt/U915u2bqXIX/1Mbbp3qeFEAAAAAFB1lCIAHqqooPCe6yU3b6nPqGf1zmeL9dpf/qgeQwbWcDIAAAAAeHKUIgAeKnXefJUWF39vrbS4WMt/+Vv9ctQkrf3jn+XbtbNe+8sf9U7KYgVPGieXBg1MSgsAAAAAj4YHrQJ4JIHhYQqf/fp9d59xcXVVYHiYbLFRatu9q65d/EZbEpdre/IK3bp6zcTkAAAAAJwZu88A9dTDigqzdB8cLFtMtJ4aNkglt4qVuWqtNi9aqktn88yOBgAAAMDJUIoA9VBgeJgi5s6Rm6dn5Vpt2ybXt2tn2WKiFDQuTFZXVx38arPscYk6tW+/2dEAAAAAOAnTtuQ9efKk9u/fr6ysLGVmZkqS3n//feXl5SkrK0tZWVkaO3Zs5evnzJmj3NxcHT58WGFhYZXrQUFB2r9/v3JzczVv3jxHRgbqjPDZr3+vEJG+3SY3fPbrJiW6W8GxE1r6rx/oV6OnaONf49Slf6DeXPQX/dPiBeoTNkJWFxezIwIAAABwYg69UuTkyZPq37+/Ll26VLn2/vvv68aNG/r973//vdf27NlTiYmJGjBggNq2basvv/xS3bt3V0VFhXbu3KnZs2drx44dSk1N1UcffaT169c/8NxcKYL67nfZW2Wx3t1rGhUVeidgqAmJHs7N00PBE8cpdFakWrT316W888pYvFS7Vnyuklu3zI4HAAAAoB4y7UqRxzFx4kQlJSWptLRUp06d0rFjxzRgwAD5+vrK29tbO3bskCTFx8dr0qRJ5oYFaoH7bZN7v/XaoLT4trYmfaYPx8/Qp7Pn6NqFi5o05239/IuVGvfWD+XdqqXZEQEAAAA4EYeWIoZhKC0tTbt379arr75auf6jH/1I2dnZ+uSTT9S0aVNJkp+fn86ePVv5mry8PPn5+cnPz095eXl3rQPO7n7b5KbOm29SokdnVFTo4Fd2/Vfs65oX/YqObN+l4S++oJ+tT1HUB/+qNt27mh0RAAAAgBNwaCkydOhQ9evXT2PHjtUbb7yhkJAQffzxx+rSpYv69u2r/Pz8yttoLBbLXe83DOO+6/fy6quvKjMzU5mZmXJ1da3eLwaoZbJS05Q890NdPp8vo6JCl8/n16qHrD6qMwdytOidn+nfx03X1qTP9MxzNr3z2SL9YMFHemrYILPjAQAAAKjHHNoc5OfnS5IuXryoFStWaMCAAcrIyKj8/IIFC/T5559L+vYKkHbt2lV+zt/fX+fPn1deXp78/f3vWr+XBQsWaMGCBZK+vWcIqO+yUtPqXAlyP5fP5WvVb/+otPmfaNC0iQqJjtCrH/9BBcdOyB6fpL1rN6istNTsmAAAAADqEYddKeLl5VX5IBMvLy+FhYXp4MGD8vX1rXzN5MmTdfDgQUnS6tWrFRkZKTc3N3Xs2FHdunXTrl27VFBQoOvXr2vgwIGSpJiYGK1atcpRsQGYrPjadW3638X6YMwUJfz031ReVqYZv3hXP0tboed+8JIaNm1idkQAAAAA9YTDdp/p1KmTVqxYIUlydXVVQkKCfv3rXys+Pl59+/aVYRg6deqUfvCDH6igoECS9O677+rll19WWVmZ3nrrrcodZvr166eFCxfK09NT69at05tvvvnQ87P7DFB/dBvYX7bYKPUMGaLS4tvKXLVWmxcl6ZszeQ9/MwAAAACn9qB+wKFb8pqJUgSof1p36STbrEj1Gz9GVldX5aRnKD0uUSf3ZpsdDQAAAEAtRSkCoF5p3NxHQ6OmaciMKWrYtInOHMhRelyCDnyZrorycrPjAQAAAKhFKEUA1EsNPNzVf0K4bLMi1bJje10+l6/Ni5dqV8oaldy6ZXY8AAAAALUApQiAes1isajX8GGyxUapS79AFV+7rh3LVykjIVlXCy+aHQ8AAACAiShFADiNdk/30vDYKPUZ9ayMCkP7Nnwpe1yizh0+anY0AAAAACagFAHgdJq19VXIzBkaOGW8PBo2VO6O3UqPT9CRLTtkGPXyrz0AAAAA90ApAsBpeTRupEFTJypkZoSatm6lguMntTk+UXs+36Cy0lKz4wEAAABwMEoRAE7P6uqivqNHyhYTLf9ePXT90mVtTfpM25am6GbRFbPjAQAAAHAQShEA+BtdB/STLTZKvUKH6s7tEmWuTtXmRUm6eOqM2dEAAAAAVDNKEQC4h1adOsgWE6V+48eogbu7vt6UofT4RJ3YnWV2NElSYHiYwme/rma+rVVUUKjUefOVlZpmdiwAAACgTqEUAYAHaNS8mYbOmKohM6aokU8znf36kOxxicr+4itVlJWbkikwPEwRc+fIzdOzcq20uFjJcz+kGAEAAAAeA6UIADwCV3d39Z8wVrZZkWrVqYMun8/XliXLtOOzVSq5eatGs7y3IUU+bdvctX75fL4+GD2lRrMAAAAAdRmlCAA8BovFop6hQ2WLjVLX4CDdvnFTOz5bpYzFybpSUFgjGX6XvVUWq/WudaOiQu8EDK2RDAAAAEB98KB+wLWGswBArWcYhnLsW5Rj3yL/Xk9peGyUQl6IUMgLEdqf9pXS4xKVl3PYoRmKCgrveaVIUQ2VMgAAAIAz4EoRAHgETX1bK2RmhAZNnSiPRg11LHOv7HGJOrR5qwyj+v8a5ZkiAAAAQPXgShEATqu6dnC5UlCovJwjKg67LncvT3UKDFDX4CBdOHla9kVJ2r16ncpKSqot93cZ2X0GAAAAcByuFAFQb1Xn1Rb3OlZZaamuXrio5v5+unG5SNuWpmhr0me6cbmo2r4GAAAAAFXzoH7g7qf4AUA9ET779e+VGJLk5ump8NmvV8uxXN3cZLFa9d8v/aNO7/9aYT98RT9LW6Hp789Rq04dqpQdAAAAgONx+wyAWq0qt7808239WOtPeqwTu7N0YneWWnXqoNBZkeo/fqwGTZuoHPtWpccl6Hjm3sc+HwAAAADH40oRALXWd7es+LRtI4vVKp+2bRQxd44Cw8Me6f3326nlSXZweZRjXTh5Wst/8Rv9MmySNvz3ArV7uqf+8X//W28vXaigcWGyuro89nkBAAAAOA6lCIBaq6q3v6TOm6/S4uLvrZUWFyt13vzHzvI4x7pZdEVp8/9Xvxo9Rclz/10NPNz1wof/pvfWfabhL74gj0YNH/v8AAAAAKofD1oFUGv9LnurLNa7u1ujokLvBAx9pGNU1+4zVTmWxWLRU8MGa/iL0eo6oJ9u37ypnSlrlLF4qYrOFzxRFgAAAACP5kH9AKUIgFrrvQ0p8mnb5q71y+fz9cHoKSYkqjq/nt01PDZaAaNHymKxaP8Xm5Qel6izB3PMjgYAAADUS5QiAOqk6txSt7Zp2rqVhr0QoUHTJsqzcSMd35Mle1yictK3yDDq5V/LAAAAgCmqVIpMmzZNy5cvf+habUMpAtQP1Xn7S23k3tBLA6dMUMjMCPm0baOLp87IvihJu1en6s7tErPjAQAAAHVelUqRPXv2qF+/fg9dq20oRQDUJVYXF/UZ9axssVFq/3Qv3Sy6om3JK7QlcZluXCoyOx4AAABQZz1RKTJmzBiFh4crIiJCS5curVz39vZWr169NHDgQIeErS6UIgDqqs79+soWG6VetmGqKCvTns83yB6fqMLjJ82OBgAAANQ5D+oHXO/3pvPnz2v37t2aMGGC9uzZU7l+/fp1vf3229WfEgAgSTqxZ59O7NmnFh3ayTYrUsETx2nglPE6tGW77AsTlLtzt9kRAQAAgHrhobfPuLq6qqysrIbiVB+uFAFQXzRs2kSDZ0zRsKhpatzcR+cOH5U9LlH71n+p8jr49zMAAABQk6r0TJEhQ4Zo7ty56tChg1xdXWWxWGQYhrp06eKIrNWGUgRAfePq5qagcaNli42Sb5dOulp4URkJydqxfJWKr103Ox4AAABQK1WpFDl06JDefvtt7dmzR+Xl5ZXrly9frtaQ1Y1SBEB9ZbFY1GPYIA2PiVa3Qf1VcuuWdqasUcbipbp8Lt/seAAAAECtUqVSZMeOHRo0aJAjcjkUpQgAZ+D3VHeFxkQqcMwoWawWHdhoV3pcgs7s/9rsaAAAAECt8ESlSGBgoCQpIiJCLi4uSklJUUlJSeXns7Kyqj9pNaIUAeBMmrRuqWHR0zV42iR5ejfWyaz9Sl+YoK/TM2RUVJgdDwAAADDNE5UiX3311X0PaBiGRo4cWS3hHIVSBIAzcvfy0oDJzytk5gw192+rb87kafOiJGWuWqvS4ttmxwMAAABqXJVun6mrKEUAODOri4ueHmnT8NhodejTWzevXNX25BXakrhc17+5ZHY8AAAAoMZUqRR5++2371q7evWq9uzZo+zs7GoJ6AiUIgDwrU6BfWSLjVbvZ0NUUVamvalpssclquDYCbOjAQAAAA5XpVJkyZIl6t+/v9asWSNJGjdunDIzM/XUU09p2bJl+o//+I9qD1wdKEUA4PtatPdX6KxIBU8cJzdPDx3eskP2+AQd3Z5pdjQAAADAYapUiqxfv15Tp07VzZs3JUkNGzbU8uXLNXnyZO3Zs0e9e/eu9sDVgVIEAO7Nq4m3BkdM1rDo6fJu0Vznj+TKHp+krNQ0lZeVmR0PAAAAqFYP6gesD3tz+/btVVpaWvnxnTt31KFDB92+fft7u9EAAOqGW1evaeOCOP0qbLKSfv4rWaxWRX3wc723IUUjXomRp7e32REBAACAGuH6sBckJCRox44dWrVqlSRp/PjxSkxMlJeXl3JychweEADgGOV37ihz5VplrlyrHkMGaviL0Rr31g/13GsvKnPl59q8aKku5Z0zOyYAAADgMI+0+0xQUJCGDRsmi8WiLVu2aM+ePTUQrWq4fQYAHl+b7l1li4lSYPgoWV1cdHCjXfa4RJ3KPmB2NAAAAOCJPNEzRRo3bqzr16+rWbNm93xjUVFRtQV0BEoRAHhy3q1aaljUNA2OmCQvb2+d2ndA6XEJOvjVZhkVFWbHAwAAAB7ZE5Uia9as0fjx43XixAkZhiGLxfK9/+3SpYsjM1cZpQgAVJ2bp6cGTB6n0FmRau7vp2/O5ilj8VLtWrFWpcXFZscDAAAAHqpKu8/UVZQiAPB4AsPDFD77dTXzba2igkKlzpuvrNQ0SZLFatUzI22yxUapY8AzunX1mrYvW6ktCct07eI3JicHAAAA7q/KpcgLL7ygTp066Ve/+pXatWsnX19fZWZmVnfOakUpAgCPLjA8TBFz58jN07NyrbS4WMlzP6wsRr7TMeAZ2WKj9PRImyrKy5WV+oXs8QnKP3q8pmMDAAAAD1WlUuTPf/6zKioqNGLECPXq1UtNmzZVWlqaBgwY4Iis1YZSBAAe3XsbUuTTts1d65fP5+uD0VPu+Z7m/n4KnTVDwZOel7uXp45s2yl7XKKObNvp6LgAAADAI3tQP2B92JsHDhyoH/3oR7p9+7Yk6cqVK3Jzc6vehAAAUzXzbf1Y65J0Ke+cVvz7f+qXoyZp7R8/lm/XznrtL3/UOymLFTxpnFwaNHBUXAAAAKBaPLQUuXPnjqxWqwzj2wtKWrRooQp2HgCAeqWooPCx1v9W8bVr+uqTeH0wZqoS3/ulDMNQ5C9/pp9tSNHIV2Pl1cS7uuMCAAAA1eKhpchHH32kFStWqFWrVvrVr36lLVu26Ne//nVNZAMA1JDUefPv2k2mtLhYqfPmP/Ixyu/c0e7Vqfr91Fn6y2uzlX/0mML/6XX9LG2lJr/7z2rezr+6YwMAAABV8kgPWu3Ro4dGjhwpi8WijRs36vDhwzUQrWp4pggAPJ4H7T7zpHy7dZEtJlJB40bL6uKig19tlj0uUaf27a+m1AAAAMCDVXn3GavVqtatW8vV1bVy7ezZs9UW0BEoRQCg9mjcormGRU3TkBlT5NXEW6ezDyo9PlEHN9pVUV5udjwAAADUY1UqRX70ox/p/fffV2FhocrLy2WxWGQYhgICAhyRtdpQigBA7ePm6aHgieMUOitSLdr761LeeWUsXqpdKz5Xya1bZscDAABAPVSlUiQ3N1cDBw7U5cuXHZHNYShFAKD2slit6j08RMNfjFanwD4qvnZd25etUEbCcl27cNHseAAAAKhHqlSKfPXVVxo1apTK69jlzZQiAFA3tO/TW8Njo/XMSJuMCkNZ675QelyC8o8eMzsaAAAA6oEnKkXefvttSVLv3r3Vo0cPrV27ViUlJZWf/8Mf/lD9SasRpQgA1C0+fm0UMnOGBk4ZL3cvLx3dkSl7XIIOb9lhdjQAAADUYU9Uivzrv/7rAw/6i1/8osrBHIlSBADqJk/vxho0baJCoiPUpHVLFRw7IXt8kvau3aCy0lKz4wEAAKCOqfLuM3URpQgA1G0urq7qO+Y52WKj5PdUd1375pK2Ji7XtqUpunX1mtnxAAAAUEdQigAA6rRuA/vL9mK0eg4brNLi28pctVabFyXpmzN5ZkcDAABALUcpAgCoF1p36SRbTJT6PT9aVldX5aRnKD0uUSf3ZpsdDQAAALUUpQgAoF5p3NxHQ6OmaciMKWrYtInOHMiRPS5B+79MV0Ud2y0NAAAAjlWlUqRbt276+OOP1bp1az3zzDN65plnNGHCBH3wwQeOyFptKEUAoP5r4OGu/hPCZZsVqZYd2+vyuXxtXrxUu1LWqOTWLbPjAQAAoBaoUimSnp6uH//4x/rLX/6ioKAgSdKBAwf0zDPPVHvQ6kQpAgDOw2KxqNfwYbLFRqlLv0AVX7uuHctXKSMhWVcLLz7wvYHhYQqf/bqa+bZWUUGhUufNV1ZqWg0lBwAAgKM9qB9wfdibvby8lJmZ+b21srKy6kkGAEA1MAxDX2/K0NebMtTu6V4aHhslW2yUQmdFat+GL2WPS9S5w0fvel9geJgi5s6Rm6enJMmnbRtFzJ0jSRQjAAAATsD6sBd888036ty5swzj2wtKpk6dqvz8fIcHAwDgSZw9mKNFP/65fh0+TVuSlqv3syH6f8vi9Ppf/6SeIUNksVgqXxs++/XKQuQ7bp6eCp/9ek3HBgAAgAkeevtMp06d9D//8z8aMmSIioqKdPLkSc2cOVOnT5+uoYhPhttnAACS5NG4kQZNnaiQmRFq2rqVCo6f1OZFSdqzZr0+zNwki/Xu/z5gVFTonYChJqQFAABAdauW3We8vLxktVp148aN6szmMJQiAIC/ZXV1Ud/RI2WLiZZ/rx66fumyXBo0kJd347tee/l8vj4YPcWElAAAAKhuD+oHHnr7zD/90z+pcePGunXrlv7whz9oz549GjVqVLWHBADAkSrKyrV3bZr+MONF/fnlN3T24CF5eTeuvD30O6XFxUqdN9+klAAAAKhJDy1FXn75ZV2/fl1hYWFq1aqVXnrpJX344Yc1kQ0AAIc4nrlXn/zoHf1mQqSO7dpTWYyU3r6t9LhEHrIKAADgJB66+8x3D6QLDw/Xp59+qv3793/vIXUAADhCTWyVe+Hkac3/hzfVyKeZhkZO1ZAZUxT2+svqGTJY9rhEZX/xlSrKyqv1nAAAAKg9HvpMkf/93/+Vn5+fOnXqpICAALm4uCg9PV39+/evoYhPhmeKAEDd9fdb5Urf3taSPPdDh17F4erurv4Txso2K1KtOnXQ5fP52rJkmXZ8tkolN2857LwAAABwnCo9aNVisahv3746ceKErl69Kh8fH/n5+enAgQOOyFptKEUAoO56b0OKfNq2uWu9ph6AarFY1DN0qGyxUeoaHKTbN25qx/JVyliSrCsFhQ4/PwAAAKrPE5UigYGBDzxoVlZWlYM5EqUIANRdv8veWmu2yvXv9ZRssVEKCBshScpO+0r2uATl5Ryp0RwAAAB4Mk9Uinz11Vf3PaBhGBo5cmS1hHMUShEAqLvMvlLkXpr6tlbIzAgNmjpRHo0a6ljmXtnjEnVo89a7drABAABA7VGl22fqKkoRAKi7zHqmyKPwaNRQA6dMUMjMCDVr46sLJ0/LvihJu1evU1lJianZAAAAcLcqlSKzZs265/qiRYuqHMyRKEUAoG6rid1nqsLq6qKAUSNki41Su949deNykbYmfaZtS1N043KR2fEAAADwf6pUinz00UeVv/bw8NDIkSO1d+9eTZ8+vVpDVjdKEQBATencP1DDY6LU+9kQ3Skp0e4167Q5PkkXTp42OxoAAIDTq9bbZ7y9vbVo0SJNnDixOrI5DKUIAKCmtezYXqGzIhU8IVwNPNyVY9+q9LgEHc/ca3Y0AAAAp1WtpYirq6v279+vXr16VUc2h6EUAQCYpWGzphoyY4qGRk5V4+Y+OptzWJvjE7Vvw0ZVlJWbHQ8AAMCpVKkUWb16deVT9V1cXNSzZ08lJyfrpz/9abUHrU6UIgAAs7m6u6vf86Nli4lS684ddaWgUBlLlmnH8pW6feOm2fEAAACcQpVKkdDQ0Mpfl5WV6fTp0zp37ly1BnQEShEAQG1hsVj01LDBssVGqdvA/rp946Z2pqxWxuJkFeUXmB0PAACgXntQP2B92Js3b96sI0eOqEmTJvLx8VFZWdkjn/jkyZPav3+/srKylJmZKUlq1qyZ0tLSdPToUaWlpalp06aVr58zZ45yc3N1+PBhhYWFVa4HBQVp//79ys3N1bx58x75/AAA1AaGYehQxjbN/4c39Z8Rsfo6PUPDoqbrp6nLNPO3v1C73j3NjggAAOC0jAfNK6+8Ypw+fdr49NNPjYULFxonT540XnrppQe+57s5efKk0bx58++t/eY3vzF+8pOfGJKMn/zkJ8aHH35oSDJ69uxp7Nu3z3BzczM6duxoHDt2zLBarYYkY+fOncagQYMMSUZqaqoxZsyYh577xo0bj5SRYRiGYcyYpq1bGc+//Ybxq21fGL8/sN34x4V/Nno/G2JYLBbTszEMwzAMw9SneUg/8OA3Hz582PDx8an82MfHxzh8+PAjnfhepcjhw4cNX19fQ5Lh6+tbeaw5c+YYc+bMqXzd+vXrjUGDBhm+vr7GoUOHKtcjIyON+fPnV/WLZhiGYZhaMe5eXkbIzBnGe+tTjN8f2G7MWbPUGBwx2Wjg4W56NoZhGIZhmPowD+oHHnr7TF5enq5fv1758fXr13X27NmHvU2SZBiG0tLStHv3br366quSpNatW6ug4Nv7pwsKCtSqVStJkp+f3/eOm5eXJz8/P/n5+SkvL++u9Xt59dVXlZmZqczMTLm6uj5SRgAAzFRy65YyFi/Vv4+brkXv/EzF129o2s//RT9PW6nRb7yqRs2bmR0RAACg3npoc3Du3Dnt3LlTq1atkmEYmjhxonbt2qW3335bkvSHP/zhvu8dOnSo8vPz1bJlS33xxRc6fPjwfV9rsVjuWjMM477r97JgwQItWLBA0rcPUgEAoK6oKC/Xvg0btW/DRnUKCtDw2Cg999qLevalF7T38w2yL0pS4fGTZscEAACoVx5aihw/flzHjx+v/HjVqlWSpMaNGz/04Pn5+ZKkixcvasWKFRowYIAKCwvl6+urgoIC+fr66sKFC5K+vQKkXbt2le/19/fX+fPnlZeXJ39//7vWAQCor07uzdbJvdlq0aGdQmfOUPDEcRo4dYIOZWyTPS5RuTt3mx0RAACg3nDIPTteXl5Go0aNKn+9detWY/To0cZvf/vb7z1o9Te/+Y0hyejVq9f3HrR6/Pjxyget7tq1yxg4cKAhffug1bFjx1bpniGGYRiGqUvTsGkT47nXXjTe3/S58fsD243/lxxn9Ht+jOHi6mp6NoZhGIZhmNo+D+oHLP/3i2rXqVMnrVixQpLk6uqqhIQE/frXv5aPj4+Sk5PVvn17nTlzRtOnT1dRUZEk6d1339XLL7+ssrIyvfXWW1q/fr0kqV+/flq4cKE8PT21bt06vfnmmw89/4P2IQYAoC5ydXNTUHiYbLFR8u3aWVcKL2hLwjJtX7ZSt69z2ygAAMC9PKgfcFgpYjZKEQBAffbUsEGyxUar+6Bgldy6pZ2frVHGkqW6fC7f7GgAAAC1CqUIAAD1VNse3WSLiVLg2FGyWC3a/2W67HEJOnMgx+xoAAAAtUKVSpEWLVro1VdfVceOHb+3ze0rr7xSrSGrG6UIAMCZeLdqqZDoaRo8fbI8vRvr5N5spccl6uv0DBkVFWbHAwAAME2VSpGtW7cqIyNDe/bsUXl5eeV6SkpKtYasbpQiAABn5O7lpQGTn1fIzBlq7t9WF0+fVcbipcpctValxbfNjgcAAFDjqlSKZGVlKTAw0BG5HIpSBADgzKwuLnp6pE3DY6LUIeBp3bxyVduTV2hL4nJd/+aS2fEAAABqTJVKkV/+8pfatm2b1q1b54hsDkMpAgDAtzr27SNbbJSeHhGqirIy7V2bJnt8ogqOnTA7GgAAgMNVqRS5du2aGjZsqJKSEt25c0cWi0WGYahJkyaOyFptKEUAAPi+5u38FTprhgZMel5unh46vGWH7PEJOro90+xoAAAADsPuMwAA1JDA8DCFz35dzXxbq6igUKnz5isrNc3sWN/j1cRbgyMma1j0dHm3aK7zR3Jlj09SVmqaysvKzI4HAABQrapcijRt2lTdunWTh4dH5VpGRka1BXQEShEAQE0LDA9TxNw5cvP0rFwrLS5W8twPa10xIkkuDRooaFyYbDFRatOti65euKgtCcu1fdlKFV+7ZnY8AACAalGlUuSVV17R7Nmz5e/vr3379mnQoEHavn27Ro4c6Yis1YZSBABQ097bkCKftm3uWr98Pl8fjJ5iQqJH12PIQNlio9RjyECV3CpW5srPtXnRUl3KO2d2NAAAgCp5UD9gfdibZ8+ereDgYJ0+fVojRoxQYGCgLl68WO0hAQCo65r5tn6s9drkyLad+p8fvKXfTZ2l/V9s0qDpkzTn86WK/c9fq2PAM2bHAwAAcIiHliK3b99WSUmJJMnNzU1HjhxRjx49HB4MAIC6pqig8LHWa6P8o8eU9LNf6oPRU7Tp0yXqOrCf3lz8P3pz8f/omeeGy2J96I8OAAAAdcZDf7LJy8tTkyZNtHLlSn3xxRdauXKlzp8/XxPZAACoU1LnzVdpcfH31kqLi5U6b75JiZ7ctYvfKHXex/rlc5O04t9/r8bNffTiH/5dcz5fqmHR07733BQAAIC66rF2nwkNDVWTJk20fv163blzx4Gxqo5nigAAzFAXdp95EharVU+PCNXw2Gh17PuMbl27pu3JK7UlYZmuXfzG7HgAAAD39UQPWm3cuLGuX7+uZs2a3fONRUVF1RbQEShFAABwjA4BT8sWE6VnRtpUUVGhrNQvZI9PUP7R42ZHAwAAuMsTlSJr1qzR+PHjdeLECRmGIYvFUvk5wzDUpUsXh4StLpQiAAA4VnN/P4XOmqHgSc/L3ctTR7btlD0uUUe27TQ7GgAAQKUqbclbV1GKAABQMzy9vTV4+iQNi56mJq1aKj/3uOzxidq7Nk3ltfx2WwAAUP89USkSGBj4wINmZWVVOZgjUYoAAFCzXBo0UODYUbLFRqlt9666dvEbbUlcru3JK3Tr6jWz4wEAACf1RKXIV199JUny8PBQ//79lZ2dLYvFoj59+mjnzp0KCQlxWODqQCkCAIB5ug8Oli0mWk8NG6SSW8XavTpV9vgkXTqbZ3Y0AADgZKp0+0xiYqI++OADHTx4UJLUu3dvvfPOO3rppZeqPWh1ohQBAMB8vt26yBYTqaBxo2V1cdHXmzKUvjBBp/btNzsaAABwElUqRbKysu66leZea7UNpQgAALVH4xbNNSxqmobMmCKvJt46nX1Q6fGJOrjRrorycrPjAQCAeqxKpUhCQoJu3rypxYsXyzAMzZw5U40aNVJ0dLQjslYbShEAAGofN08PBU8cp9BZkWrR3l+X8s4rY/FS7VrxuUpu3TI7HgAAqIeqVIq4u7vrhz/8oUJDQyVJmzdv1scff6ySkpJqD1qdKEUAAKi9LFareg8P0fDYKHUKClDxtevavnylMpYs07ULF82OBwAA6pEqb8nr4eGh9u3b6+jRo9WdzWEoRQAAqBvaP9NLttho9XluuIwKQ1nrv5A9LlHnj+SaHQ0AANQDVSpFxo8fr//4j/+Qm5ubOnfurICAAP3iF7/QxIkTHZG12lCKAABQt/j4tVHIzBkaOGW83L28lLtjt9Ljlujwlh1mRwMAAHVYlUqR3bt3a8SIEUpPT1dQUJAkKTs7WwEBAdUetDpRigAAUDd5ejfWoGkTFRIdoSatW6rg2AnZ45O0d+0GlZWWmh0PAADUMQ/qB6wPe3NZWZmuXbtW7aEAAADupfjadW3638X6YMwUJfz031ReVqYZv3hXP0tboed+8JIaNm1idkQAAFBPuD7sBQcPHlRUVJRcXFzUtWtX/dM//ZO2bdtWE9kAAIATKy8r057P12vP5+vVbWB/2WKjNPZHr2nkKzHavTpV9kVJ+ub0WbNjAgCAOuyht894enrqvffeU1hYmCwWizZs2KBf/vKX7D4DAABqXOsunWSLiVK/50fL6uqqnPQMpccl6uTebLOjAQCAWqrKu8/URZQiAADUX42b+2ho1DQNiZishs2a6syBHNnjErT/y3RVlJebHQ8AANQiVSpF+vXrp3fffVcdO3aUq+v/f7cND1oFAABma+Dhrv4TwmWbFamWHdvr8rl8ZSxJ1s7PVqvk1i2z4wEAgFqgSqXI4cOH9eMf/1gHDhxQRUVF5fqZM2eqNWR1oxQBAMB5WCwW9Ro+TLbYKHXpF6ji6ze0Y/kqbVmSrCuFF8yOBwAATFSlUiQjI0MhISGOyOVQlCIAADindr17yhYbpT6jnpUMKTtto9LjEnTu0FGzowEAABNUqRQZMWKEoqKitHHjxu89XHXFihXVGrK6UYoAAODcmrX1VcgLERo4dYI8GjZU7s7dsscl6vCW7TKMevlINQAAcA9VKkUWLVqkp556Sl9//XXl7TOGYeiVV16p9qDViVIEAABIkkejhho0daJCZkaoqW9rFZ44JXt8ovasWa+y0lKz4wEAAAerUimyf/9+9enTxxG5HIpSBAAA/C2rq4v6jh4pW0y0/Hv10PVLl7Ut6TNtXZqim0VXzI4HAAAcpEqlyP/8z//oD3/4gw4dOuSIbA5DKQIAAO6nS3CQhsdGq5dtqO7cLtHuNetkj0/UxVO1+0HyAADg8VWpFMnJyVGXLl108uRJlZSUyGKxyDAMtuQFAMDBAsPDFD77dTXzba2igkKlzpuvrNQ0s2PVK606dVBoTKT6jx+rBu7u+npThtLjE3Vid5bZ0QAAQDWpUinSvn37e66zJS8AAI4TGB6miLlz5ObpWblWWlys5LkfUow4QCOfZhoyY4qGRk5VI59mOvv1IdnjEpX9xVeqKCs3Ox4AAKiCKpUidRWlCACgLntvQ4p82ra5a/3y+Xx9MHqKCYmcg6u7u/qPHyNbTJRadeqgovwCZSxO1o7PVqnk5i2z4wEAgCdAKQIAQB3zu+ytslitd60bFRV6J2DoYx2L23Aen8Vi0VMhQzT8xWh1DQ7S7Rs3tWP5KmUsSdaVgkKz4wEAgMdAKQIAQB1TXVeKcBtO1fn36iFbbLQCwkZIkrLTvpI9LkF5OUdMTgYAAB4FpQgAAHVMdZUZ3IZTfZr6tlbICxEaNG2iPBo11LHMvbLHJerQ5q0yjHr54xQAAPUCpQgAAHVQddz2Up234eBb7g29NHDqBIW8ECGftm104eRp2RclaffqdSorKTE7HgAA+DuUIgAAOCmuFHEcq6uLAkaNkC02Su1699SNy0XatjRFW5M+043LRWbHAwAA/4dSBAAAJ8UzRWpG5/6BGh4Tpd7PhuhOSYl2r1mnzfFJunDytNnRAABwepQiAAA4MXafqTktO7ZX6KxIBU8IVwMPd+XYtyo9LkHHM/eaHQ0AAKdFKQIAAFCDGjZrqiEzpmho5FQ1bu6jszmHtTk+Ufs2bFRFWbnZ8QAAcCqUIgAA4IlwlUnVuLq7q9/zo2WLiVLrzh11paBQGUuWacfylbp946bZ8QAAcAqUIgAA4LHxPJLqY7FY9NSwwbLFRqnbwP66feOmdqasVsbiZBXlF5gdDwCAeo1SBAAAPDZ2rnEMv57dZYuJUt/Rz0kWaf8Xm2SPS9TZrw+ZHQ0AgHqJUgQAADy232VvlcVqvWvdqKjQOwFDazxPfbuVp2nrVhoWPV2Dpk+SZ+NGOr4nS/a4ROWkb5Fh1MsfzwAAMAWlCAAAeGy16UqR+nwrj7uXlwZMGa/QmTPk49dGF0+dkX1RknavTtWd2yVmxwMAoM6jFAEAAI+tNhURtamgcRSri4v6PDdctthotX+ml24WXdHWpSnamrRcNy4VmR0PqBPq2xVlAKoHpQgAAHgiteUfGLXtVh5H6xQUoOGxUeo1PETld+5o7+cbZI9PVOGJU2ZHA2qt2lTkAqhdKEUAAECd5gxXitxLiw7tFDpzhoInjpObp4cOZWyTPS5RuTt3mx0NqHWc9e8JAA/3oH7g7v/kAgAAUMukzpuv0uLi762VFhcrdd58kxLVjG9On1XKB7/Tr8Imad2f/iK/nj30+l//pP+XHKd+z4+Ri6ur2RGBWqOZb+vHWgcAiStFAABAHVFbbuUxk6ubm4LCw2SLjZJv1866WnhRGQnJ2r5spW5fv2F2PMBUXCkC4H64fQYAAKCe6TF0kIa/GK3ug4JVcuuWdqasUcbipbp8Lt/saIApeKYIgPuhFAEAAKin2vboJltMlALHjpLFatGBjXalL1yiMwdyzI4G1DiuKANwL5QiAAAA9Zx3q5YKiZ6mQdMnycvbWyf3Zis9LlFfp2fIqKgwOx4AAKahFAEAAHASbp6eGjD5eYXOmqHm/n765kyeNi9KUuaqtSotvm12PAAAahylCAAAgJOxurjo6RGhGh4brQ4BT+vmlavanrxCWxKX6/o3l8yOBwBAjaEUAQAAcGId+/aRLTZKT48IVUVZmfampskel6iCYyfMjgYAgMNRigAAANyHMz2YsXk7f4XOmqHgiePk7uWpI1t3KD0uUUe37zI7GgAADkMpAgAAcA/OuoWnp7e3hkRM1rDoafJu2ULnjx6TPS5RWalpKi8rMzseAADVilIEAADgHt7bkCKftm3uWr98Pl8fjJ5iQqKa5dKggYLCR8kWG6023bro6oWL2pKwXNuXrVTxtWtmxwMAoFpQigAAANzD77K3ymK13rVuVFTonYChJiQyT48hA2WLjVKPIQNVcqtYmSs/1+ZFS3Up75zZ0QAAqJIH9QOuNZwFAACg1igqKLznlSJFBYUmpDHXkW07dWTbTrXp3kW2mCgNmj5JQyKn6uBGu+xxiTqVfcDsiAAAVDuuFAEAAE7LWZ8p8ii8W7bQ0KhpGhIxWV5NvHVq3wGlxyXo4FebZVRUmB0PAIBHxu0zAAAA9+FMu888CTdPTwVPGqfQWTPUop2/vjmbp4zFS7VrxVqVFhebHQ8AgIeiFAEAAKgl6moJY7Fa9fSIUA2PjVbHvs/o1rVr2p68UlsSlunaxW/MjgcAwH1RigAAANQC9eV2nQ4BT8sWE6VnRtpUUVGhrNQvZI9PUP7R42ZHAwDgLpQiAAAAtUB92wLYx7+tQmfO0IDJz8vdy0tHt+9SelyijmzdYXY0AAAqUYoAAADUAvV1C2BP78YaPH2ShkVPV5NWLZWfe1z2+ETtXZum8jt3zI4HAHBylCIAAAC1QH27UuTvubi6qu/YURr+YrTadu+qaxe/0ZbE5dqevEK3rl4zOx4AwElRigAAANQC9eWZIo+i++Bg2WKi9dSwQSotvq3MVWtlj0/SpbN5ZkcDADgZShEAAIBaoq7uPvOkfLt2li0mSkHjwmR1ddXXmzKUvjBBp/btNzsaAMBJUIoAAADAVI2b+2ho9DQNiZiihk2b6HT2QaXHJ+rgRrsqysvNjgcAqMce1A/c/aSvama1WrV3716tWbNGkvT+++8rLy9PWVlZysrK0tixYytfO2fOHOXm5urw4cMKCwurXA8KCtL+/fuVm5urefPmOToyAAAAqtn1S5e1/k//o1+FTdJnv/oPeTVtotjff6A5nycr5IUIuXt5mR0RAOCEHH6lyNtvv63+/fvL29tb48eP1/vvv68bN27o97///fde17NnTyUmJmrAgAFq27atvvzyS3Xv3l0VFRXauXOnZs+erR07dig1NVUfffSR1q9f/8DzcqUIAABA7WWxWtV7+DANj41Wp6AAFV+7ru3LVypjyTJdu3DR7HgAgHrEtCtF/Pz8NG7cOP31r3996GsnTpyopKQklZaW6tSpUzp27JgGDBggX19feXt7a8eOb/e7j4+P16RJkxwZGwAAAA5mVFTo4Feb9V+xr2te9Cs6sn2XhsdG62frUxT1639V2x7dzI4IAHACDi1F/vjHP+pf/uVfVFFR8b31H/3oR8rOztYnn3yipk2bSvq2QDl79mzla/Ly8uTn5yc/Pz/l5eXdtQ4AAID64cyBHC1652f693HTtTXpMz0z0qZ/Xh6v1xf8SU8NG2R2PABAPeawUmTcuHG6cOGC9u7d+731jz/+WF26dFHfvn2Vn59feRuNxWK56xiGYdx3/V5effVVZWZmKjMzU66urtXwVQAAAKCmXD6Xr1W//aN+8dxEff6f/6WWndrr1Y//oB+vWKIBk8fL1c3N7IgAgHrGYaXI0KFDNWHCBJ08eVJJSUkaMWKEFi1apAsXLqiiokKGYWjBggUaMGCApG+vAGnXrl3l+/39/XX+/Hnl5eXJ39//rvV7WbBggYKDgxUcHKyysjJHfWkAAABwoNvXb2jTp0v06zFTlfDTf1N5WZlm/OJd/SxthZ77wUtq2LSJ2REBAPWI4eix2WzGmjVrDEmGr69v5fpbb71lJCYmGpKMXr16Gfv27TPc3NyMjh07GsePHzesVqshydi1a5cxcOBAQ5KRmppqjB079qHnvHHjhsO/LoZhGIZhGKZmpuuAfsY//Pn3xu8PbDf+fdcmY+rPfmy06NDO9FwMwzBM7Z8H9QM1fo/Jb3/7W/Xt21eGYejUqVP6wQ9+IEnKyclRcnKycnJyVFZWpjfeeKPyWSQ//OEPtXDhQnl6emrdunVat25dTccGAACAiY7t2qNju/aodeeOssVEacDk5zVo+iTl2LfIHpeoE3v2mR0RAFAHOXxLXrOwJS8AAED91ah5Mw2NnKahM6aoYbOmOnMgR/a4BO3/Ml0V5eVmxwMA1CIP6gcoRQAAAFBnNfBwV//x4bLFRKplx/a6fC5fGUuStfOz1Sq5dcvseACAWoBSBAAAAPWaxWJRr+HDZIuNUpd+gSq+fkM7lq/SliXJulJ4wex4AAATUYoAAADAabTr3VO22Cj1GfWsZEjZaRuVHpegc4eOmh0NAGACShEAAAA4nWZtfBUyM0IDp06QR8OGyt25W/a4RB3esl2GUS9/BAYA3AOlCAAAAJyWR6OGGjR1okJmRqipb2sVnjgle3yi9qxZr7LSUrPjAQAcjFIEAAAATs/q6qKAsJEaHhst/149dP3SZW1L+kxbl6boZtEVs+MBAByEUgQAAAD4G12CgzQ8Nlq9bEN153aJdq9ZJ3t8oi6eOmN2NABANaMUAQAAAO6hVacOCo2JVP/xY9XA3V1fb8pQenyiTuzOMjsaAKCaUIoAAADUQ4HhYQqf/bqa+bZWUUGhUufNV1Zqmtmx6qRGPs00ZMYUDY2cqkY+zXT260OyxyUq+4uvVFFWbnY8AEAVUIoAAADUM4HhYYqYO0dunp6Va6XFxUqe+yHFSBW4urur//gxssVEqVWnDirKL1DG4mTtTFmt2zdumh0PAPAEKEUAAADqmfc2pMinbZu71i+fz9cHo6eYkKh+sVgseipkiIa/GK2uwUG6feOmdny2ShmLk3WloNDseACAx0ApAgAAUM/8LnurLFbrXetGRYXeCRhqQqL6y79XD9lioxUQNkKSlJ32lexxCcrLOWJyMgDAo6AUAQAAqGe4UqTmNfVtrZAXIjRo2kR5NGqoY5l7ZY9L1KHNW2UY9fJHagCoFyhFAAAA6hmeKWIe94ZeGjh1gkJeiJBP2za6cPK07IuStHv1OpWVlJgdDwDwdyhFAAAA6iF2nzGX1dVFAaNGyBYbpXa9e+rG5SJtW5qirUmf6cblIrPjAQD+D6UIAAAA4ECd+wdqeEyUej8bojslJdqzZr3s8Ym6cPK02dEAwOlRigAAAAA1oGXH9gqdFangCeFq4OGuHPtWpccl6HjmXrOjAYDTohQBAAAAalDDZk01ZMYUDY2cqsbNfZSXc0T2+ATt27BRFWXlZscDAKdCKQIAAACYwNXdXf2eHy1bTJRad+6oKwWFyliyTDuWr9TtGzfNjgcAToFSBAAAADCRxWLRU8MGyxYbpW4D++v2zZvambJGGYuXquh8gdnxAKBeoxQBAADAPbGDTc3z69ldtpgo9R39nGSRDnyZrvS4RJ09mGN2NAColyhFAAAAcJfA8DBFzJ0jN0/PyrXS4mIlz/2QYqQGNGndUiHRERo0fZI8GzfS8T1ZssclKid9iwyjXv6IDgCmoBQBAADAXd7bkCKftm3uWr98Pl8fjJ5iQiLn5O7lpQFTxit05gz5+LXRxVNnZF+UpN2rU3XndonZ8QCgzqMUAQAAwF1+l71VFqv1rnWjokLvBAw1IZFzs7q4qM9zw2WLjVb7Z3rpZtEVbUteoS2Jy3TjUpHZ8QCgzqIUAQAAwF24UqT26hQUoOGxUeo1PEQVZWXa8/kG2eMTVXj8pNnRAKDOoRQBAADAXXimSO3XokM7hc6coeCJ4+Tm6aFDGdtkj0tU7s7dZkcDgDqDUgQAAAD3xO4zdUPDpk00OGKyhkZNk3eL5jp36Kjs8Ynat/5LlZeVmR0PAGo1ShEAAACgHnB1c1NQeJhssVHy7dpZVwsvKiMhWduXrdTt6zfMjgcAtRKlCAAAAFDPPDVskGyx0eo+KFglt25p52drlLFkqS6fyzc7GgDUKpQiAAAAQD3Vtkc32WKiFDh2lCxWi/Z/mS57XILOHMgxOxoA1AqUIgAAAEA9592qpUKip2nw9Mny9G6sk3uzlR6XqK/TM2RUVJgdDwBMQykCAAAAOAk3T08NmPy8QmfNUHN/P108fVYZi5cqc9ValRbfNjseANQ4ShEAAADAyVhdXPT0iFANj41Wh4CndfPKVW1PXqEtict1/ZtLZscDgBpDKQIAAAA4sY59+8gWG6WnR4SqoqxMe9emyR6fqIJjJ8yOBgAORykCAAAAQM3b+St01gwFTxwndy9PHd6yQ/b4BB3dnml2NABwGEoRAAAAAJU8vb01JGKyhkVPk3fLFjp/JFf2+CRlpaapvKzM7HgAUK0oRQAAAADcxaVBAwWNC5MtJkptunXR1QsXtSVhubYvW6Hia9fNjgcA1YJSBAAAAMAD9RgyULbYKPUYMlAlt4qVufJzbV60VJfyzpkdDQCqhFIEAAAAwCNp072LbDFRCgwPk9Vq1YGNdtnjE3U6+6DZ0QDgiVCKAAAAAHgs3i1baGjUNA2JmCyvJt46te+A0uMSdPCrzTIqKsyOBzySwPAwhc9+Xc18W6uooFCp8+YrKzXN7FioYZQiAAAAAJ6Im6engieNU+isGWrRzl/fnM1TxuKl2rVirUqLi82OB9xXYHiYIubOkZunZ+VaaXGxkud+SDHiZChFAAAAAFSJxWrV0yNCNTw2Wh37PqNbV69p+7KV2pKwTNcufmN2POAu721IkU/bNnetXz6frw9GTzEhEcxCKQIAAACg2nQIeFq2mCg9M9KmiooKZaWmyR6fqPyjx82OBlT6XfZWWazWu9aNigq9EzDUhEQwy4P6AdcazgIAAACgjjudfVDx//yefPzbKnTmDA2Y/LyCJ47TkW07ZY9L1JFtO82OCKiooPCeV4oUFRSakAa1FVeKAAAAAKgST+/GGjx9koZFT1eTVi2Vn3tc9vhE7V2bpvI7d8yOByfFM0XwHW6fAQAAAOBwLq6u6jt2lIa/GK223bvq2sVvtCVxubYnr9Ctq9fMjgcnxO4zkChFAAAAANSw7oODZYuJ1lPDBqnkVrEyV63V5kVLdelsntnRADgZShEAAAAApvDt2lm2mCgFjQuT1dVVB7/aLHtcok7t2292NABOglIEAAAAgKkaN/fR0OhpGhIxRQ2bNtHp7INKj0/UwY12VZSXmx0PQD1GKQIAAACgVnDz9FDwxHEKnRWpFu39dSnvvDIWL9WuFZ+r5NYts+MBqIcoRQAAAADUKharVb2Hh2h4bJQ6BQWo+Np1bV+2QhkJy3XtwkWz4wGoRyhFAAAAANRa7Z/pJVtstPo8N1xGhaGsdV8oPS5B+UePmR0NQD1AKQIAAACg1vPxa6OQF2Zo4NTxcvfy0tEdmbLHJejwlh1mRwNQh1GKAAAAAKgzPBo30uBpEzXshQg1bd1KBcdOyB6fpL1rN6istNTseADqGEoRAAAAAHWOi6ur+o55TrbYKPk91V3XvrmkrYnLtT15hW5euWp2PAB1BKUIAAAAgDqt28D+ssVGqWfIEJUW31bmqrXavChJ35zJMzsagFqOUgQAAABAvdC6c0fZYqLUb/wYWV1dlZOeofS4RJ3cm212NAC1FKUIAAAAgHqlUfNmGho5TUNnTFHDZk115kCO7HEJ2v9luirKy82OB6AWoRQBAAAAUC818HBX/wnhss2KVMuO7XX5XL4yliRr52erVXLrltnxANQClCIAAAAA6jWLxaJew4fJFhulLv0CVXztunYsX6WMhGRdLbxodjwAJqIUAQAAAOA02vXuKVtslPqMelYypH0bvpQ9LlHnDh81OxoAE1CKAAAAAHA6zdr4KmRmhAZOnSCPhg2Vu3O37HGJOrxluwyjXv4zCMA9UIoAAAAAcFoejRpq0NSJCpkZoaa+rVVw/KQ2L0rSnjXrVVZaanY8AA5GKQIAAADA6VldXdR39EjZYqLl36uHrl+6rK1Jn2nb0hTdLLpidjwADkIpAgAAAAB/o0twkIbHRquXbaju3C5R5upUbV6UpIunzpgdDUA1oxQBAAAAgHto1amDQmMi1X/8WDVwd9fXmzKUHp+oE7uzzI4GoJpQigAAAADAAzTyaaYhM6ZoaORUNfJpprNfH5I9LlHZX3ylirJys+MBqAJKEQAAAAB4BK7u7uo/foxsMVFq1amDLp/P15Yly7Tjs1UquXnL7HgAngClCAAAAAA8BovFop6hQ2WLjVLX4CDdvnFTO5avUsaSZF0pKDQ7HoDHQCkCAAAAAE/Iv1cP2WKjFRA2QpKUnfaV7HEJyss5YnIyAI+CUgQAAAAAqqipb2uFvBChQdMmyqNRQx3L3Ct7XKIObd4qw6iX/6wC6gVKEQAAAACoJu4NvTRw6gSFvBAhn7ZtdOHkadkXJWn36nUqKykxOx6Av0MpAgAAAADVzOrqooBRI2SLjVK73j1143KRti1N0dakz3TjcpHZ8QD8H0oRAAAAAHCgzv0DNTwmSr2fDdGdkhLtXrNOm+OTdOHkabOjAU6PUgQAAAAAakDLju0VOitSwRPC1cDDXTn2rUqPS9DxzL1mRwOcFqUIAAAAANSghs2aasiMKRoaOVWNm/soL+eI7PEJ2rdhoyrKys2OBzgVShEAAAAAMIGrm5v6PT9aoTFR8u3SSVcKCpWxZJl2LF+p2zdumh0PcAqUIgAAAABgIovFoqeGDZYtNkrdBvbX7Rs3tTNltTIWJ6sov8DseEC9RikCAAAAALWEX8/ussVEqe/o5ySLtP+LTbLHJers14fMjgbUSw/qB6yOPrnVatXevXu1Zs0aSVKzZs2Ulpamo0ePKi0tTU2bNq187Zw5c5Sbm6vDhw8rLCyscj0oKEj79+9Xbm6u5s2b5+jIAAAAAOAw5w4dVcJP/00fjJ2izfFJemrYYL2V9L/6x4V/Vu9nQ2SxWMyOCDgNh5cis2fP1qFD/3/jOWfOHG3cuFHdu3fXxo0bNWfOHElSz549FRkZqd69e2vMmDH685//LKv123gff/yxXnvtNXXr1k3dunXTmDFjHB0bAAAAABzqauFFff6H/9Yvn5uolb/5o5r5+urlj36rn6xO0uCIyWrg4W52RKDec2gp4ufnp3Hjxumvf/1r5drEiRMVFxcnSYqLi9OkSZMq15OSklRaWqpTp07p2LFjGjBggHx9feXt7a0dO3ZIkuLj4yvfAwAAAAB1XcmtW8pYvFT/Pm664t/5mYqv39C0n/+Lfp62UqPfeFWNmjczOyJQb7k68uB//OMf9S//8i9q3Lhx5Vrr1q1VUPDtg4QKCgrUqlUrSd8WKN8VH5KUl5cnPz8/3blzR3l5eXetAwAAAEB9UlFeruwNG5W9YaM6BQVoeGyUnnvtRT370gva+/kG2eMTVXjilNkxgXrFYaXIuHHjdOHCBe3du1c2m+2hr7/XfXOGYdx3/V5effVVvfbaa5IkV1eH9j0AAAAA4DAn92br5N5stejQTqEzZyh44jgNnDpBhzK2yR6XqNydu82OCNQLDmsOhg4dqgkTJig8PFweHh7y9vbWokWLVFhYKF9fXxUUFMjX11cXLlyQ9O0VIO3atat8v7+/v86fP6+8vDz5+/vftX4vCxYs0IIFCyR9+3RZAAAAAKjLvjl9Vikf/E4b/nuBBkdM1tCoaXr9r3/SuUNHZY9P1L71X6q8rMzsmECdZjh6bDabsWbNGkOS8dvf/tb4yU9+YkgyfvKTnxi/+c1vDElGr169jH379hlubm5Gx44djePHjxtWq9WQZOzatcsYOHCgIclITU01xo4d+9Bz3rhxw+FfF8MwDMMwDMMwTE2Oq5ubMWDS88aPVywxfn9gu/GvX642nn15puHRuJHp2Rimts6D+gHL//3CoWw2m9555x2NHz9ePj4+Sk5OVvv27XXmzBlNnz5dRUVFkqR3331XL7/8ssrKyvTWW29p/fr1kqR+/fpp4cKF8vT01Lp16/Tmm28+9JwP2ocYAAAAAOq6HkMHafiL0eo+KFglt25p52drlLFkqS6fyzc7GlCrPKgfqJFSxAyUIgAAAACcQdse3WSLiVLg2FGyWC3a/2W67HEJOnMgx+xoQK1AKQIAAAAA9Zx3q5YKiZ6mQdMnycvbWyf3Zis9LlFfp2fIqKgwOx5gGkoRAAAAAHASbp6eGjD5eYXOmqHm/n66ePqsMhYvVeaqtSotvm12PKDGUYoAAAAAgJOxurjo6RGhssVGqWPAM7p55aq2J6/QlsTluv7NJbPjATWGUgQAAAAAnFjHgGdkezFaT48IVUVZmfauTZM9PlEFx06YHQ1wOEoRAAAAAICat/NX6KwZCp44Tu5enjq8ZYfs8Qk6uj3T7GiAw1CKAAAAAAAqeXp7a0jEZA2Lnibvli10/kiu7PFJykpNU3lZmdnxgGpFKQIAAAAAuItLgwYKCh8lW2y02nTroqsXLmpLwnJtX7ZSxdeumR0PqBaUIgAAAACAB+oxZKBsMZHqMXSQSm4VK3Pl59q8aKku5Z0zOxpQJZQiAAAAAIBH0qZ7F9liohQYHiar1aoDG+2yxyfqdPZBs6MBT4RSBAAAAADwWLxbttDQqGkaEjFZXk28dWrfAaXHJejgV5tlVFSYHQ94ZJQiAAAAAIAn4ubpqeBJ4xQ6a4ZatPPXN2fzlLF4qXatWKvS4mKz4wEPRSkCAAAAAKgSi9Wqp58NkS02Wp0C++jW1WvavmyltiQs07WL35gdD7gvShEAAAAAQLXpEPC0bDFRemakTRUVFcpK/UL2+ATlHz1udjTgLpQiAAAAAIBq5+PfVqEzZ2jA5Ofl7uWlI9t2yh6XqCPbdpodDahEKQIAAAAAcBhP78YaPH2ShkVPV5NWLZWfe1z2+ETtXZum8jt3zI4HJ0cpAgAAAABwOBdXV/UdO0rDY6PUtkc3Xbv4jbYkLtf25BW6dfWa2fHgpChFAAAAAAA1qtugYNlio9Rz2GCV3CrW7tWpsscn6dLZPLOjwclQigAAAAAATOHbtbNsMVEKGhcmq6urvt6UofSFCTq1b7/Z0WqFwPAwhc9+Xc18W6uooFCp8+YrKzXN7Fj1CqUIAAAAAMBUjZv7aGj0NA2JmKKGTZvodPZBpccn6uBGuyrKy82OZ4rA8DBFzJ0jN0/PyrXS4mIlz/2QYqQaUYoAAAAAAGoFN08P9Z8QrtBZkWrZoZ0u5Z1XxuKl2rXic5XcumV2vBr13oYU+bRtc9f65fP5+mD0FBMS1U+UIgAAAACAWsVitar38GGyxUSpc7++Kr52XduXr1TGkmW6duGi2fFqxO+yt8pitd61blRU6J2AoSYkqp8oRQAAAAAAtVb7Z3rJFhutPs8Nl1FhKGv9F7LHJer8kVyzozkUV4rUDEoRAAAAAECt5+PXRiEvzNCAKc/Lo2FD5e7YrfS4JTq8ZYfZ0RyCZ4rUDEoRAAAAAECd4dG4kQZPm6hhL0SoaetWKjh2Qvb4JO1du0FlpaVmx6tW7D7jeJQiAAAAAIA6x8XVVQFjRmp4TLT8enbX9UuXtSVxubYvTdHNK1fNjoc6glIEAAAAAFCndR3QT7bYKPUKHarS4tvavTpV9kVJ+ub0WbOjoZajFAEAAAAA1AutO3eULSZK/caPkdXVVTn2LbLHJerEnn1mR0MtRSkCAAAAAKhXGjVvpqGR0zR0xhQ1bNZUZw7kyB6XoP1fpquivNzseKhFKEUAAAAAAPVSAw939R8fLltMpFp2bK/L5/KVsSRZO1NWq+TmLbPjoRagFAEAAAAA1GsWi0W9bENli41Wl/6BKr5+QzuWr9KWJcm6UnjB7HgwEaUIAAAAAMBptOvdU7bYKPUZ9axkSNlpG5Uel6Bzh46aHQ0moBQBAAAAADidZm18FTIzQgOnTJBHo4Y6tmuP0uMSdThjmwyjXv5TGPdAKQIAAAAAcFoejRpq0NSJCpkZoaa+rVV44pTs8Ynas2a9ykpLzY4HB6MUAQAAAAA4PauriwLCRsoWG6V2vZ7S9UuXtS3pM21dmqKbRVfMjgcHoRQBAAAAAOBvdOkfKFtstHoPH6Y7t0u0e8062eMTdfHUGbOjoZpRigAAAAAAcA+tOnVQaEyk+o8fqwbu7vo6fYvS4xJ0YneW2dFQTShFAAAAAAB4gEY+zTRkxhQNjZyqRj7NdPbrQ7LHJSr7i69UUVZudjxUAaUIAAAAAACPwNXdXf3Hj5EtJkqtOnVQUX6BMhYna2fKat2+cdPseHgClCIAAAAAADwGi8Wip0KGaPiL0eoaHKTbN25qx2erlLE4WVcKCs2Oh8dAKQIAAAAAwBPy79VDtthoBYSNkCTtT/tK6XGJyss5bHIyPApKEQAAAAAAqqipb2uFvBChQdMmyqNRQx3fnaX0hQk6tHmrDKNe/tO6XqAUAQAAAACgmrg39NLAqRMU8kKEfNq20YWTp2VflKTdq9eprKTE7Hj4O5QiAAAAAABUM6uri/o896xssVFq/3Qv3bhcpG1LU7Q16TPduFxkdjz8H0oRAAAAAAAcqHO/vhoeG63ez4boTkmJ9qxZL3t8oi6cPG12NKdHKQIAAAAAQA1o2bG9QmdFKnhCuBp4uCtn81bZ4xJ1bNces6M5LUoRAAAAAABqUMNmTTVkxhQNjZyqxs19lJdzRPb4BO3bsFEVZeVmx3MqlCIAAAAAAJjA1c1N/Z4frdCYKPl26aQrBYXKWLJMO5av1O0bN82O5xQoRQAAAAAAMJHFYtFTwwbLFhulbgP76/bNm9qZskYZi5eq6HyB2fHqNUoRAAAAAABqCb+e3WWLiVLf0c/JYrVo/xeblL4wQWe/PmR2tHqJUgQAAAAAgFqmSeuWComO0KDpk+TZuJGO78mSPS5ROelbZBj18p/qpqAUAQAAAACglnL38tKAKeMVOnOGfPza6OKpM7IvStLu1am6c7vE7Hh1HqUIAAAAAAC1nNXFRc88N1zDY6PV/pleull0RduSV2hL4jLduFRkdrw6i1IEAAAAAIA6pFNQgIbHRqnX8BBVlJVpz+cbZI9PVOHxk2ZHq3MoRQAAAAAAqINatPdX6KxIBU8cJzdPDx3asl32hQnK3bnb7Gh1BqUIAAAAAAB1WMOmTTQ4YrKGRk2Td4vmOnf4qOxxidq3/kuVl5WZHa9WoxQBAAAAAKAecHVzU1B4mGyxUfLt2llXCy8qIyFZO5avUvG162bHq5UoRQAAAAAAqGd6DB2k4bFR6j54gEpu3dLOlDXKWLxUl8/lmx2tVqEUAQAAAACgnmrTvatsMVEKCg+TxWrRgY12pS9cojMHcsyOVitQigAAAAAAUM95t2qpkOhpGjR9kry8vXUya7/SFybo6/QMGRUVZsczDaUIAAAAAABOws3TUwMmP6/QWTPU3N9P35zJ0+ZFScpctValxbfNjlfjKEUAAAAAAHAyFqtVz4y0yRYbpY4Bz+jmlavanrxCWxKX6/o3l8yOV2MoRQAAAAAAcGIdA56R7cVoPT0iVBVlZdqbmiZ7XKIKjp0wO5rDUYoAAAAAAAA1b+ev0FkzFDxxnNy9PHV4yw7Z4xN0dHum2dEchlIEAAAAAABU8vT21pCIyRoWPU3eLVvo/JFc2eOTlJWapvKyMrPjVStKEQAAAAAAnEBgeJjCZ7+uZr6tVVRQqNR585WVmnbf17s0aKCg8FGyxUarTbcuunrhorYkLNf2ZStVfO1aDSZ3HEoRAAAAAADqucDwMEXMnSM3T8/KtdLiYiXP/fCBxch3egwZKFtslHoMGaiSW8XKXPm5Ni9aqkt55xwZ2+EoRQAAAAAAqOfe25Ain7Zt7lq/fD5fH4ye8sjHadO9i2wxUQoMD5PVatWBjXbZ4xN1OvtgdcatMZQiAAAAAADUc7/L3iqL1XrXulFRoXcChj728bxbttDUn/1YvYYPk8ViUVF+wUNvx6mNHtQP3P27BQAAAAAA6pyigsLHWn+YLsFB6j44WFarVRaLRT5t2yhi7hwFhodVJWatQikCAAAAAEA9kDpvvkqLi7+3VlpcrNR585/oeOGzX//e80kkyc3TU+GzX3/ijLWNq9kBAAAAAABA1X13W8vj7D7zIM18Wz/Wel1EKQIAAAAAQD2RlZpWbc/8KCoovOeDW5/0dpzaiNtnAAAAAADAXar7dpzaiCtFAAAAAADAXar7dpzaiC15AQAAAABAvcWWvAAAAAAAAH+HUgQAAAAAADglShEAAAAAAOCUKEUAAAAAAIBTclgp4u7urp07d2rfvn06ePCg5s6dK0l6//33lZeXp6ysLGVlZWns2LGV75kzZ45yc3N1+PBhhYWFVa4HBQVp//79ys3N1bx58xwVGQAAAAAAOBnDUdOwYUNDkuHq6mrs2LHDGDhwoPH+++8b//zP/3zXa3v27Gns27fPcHNzMzp27GgcO3bMsFqthiRj586dxqBBgwxJRmpqqjFmzJiHnvvGjRsO+7oYhmEYhmH+v/buPSjq6/zj+BtYUCqIhESBRQENzhCrcgmGSjLWxEusbY0jUTomkkkmcRzb1FwmUjttbKadxraZpHW8VGpBW5WqaKKx4KXS0WlRUG6LgMKIyhYRbSNFizHI6R/83B+IaKzA4u7nNfPMuN895+xzlsd/nvme7yoUCoVCoXgw4k79gV49PnP16lUAvL298fb2xhjT7dhZs2aRlZXF9evXOXPmDDU1NUyYMIHg4GAGDx7MkSNHANi4cSPPPfdcb6YtIiIiIiIiIm6gV5sinp6eFBcX09jYyP79+ykoKADgu9/9LqWlpaxfv54hQ4YAYLVaqaurc8y12+1YrVasVit2u73LdRERERERERGR+9GrTZG2tjZiY2MJCwtjwoQJjBkzhjVr1jBq1ChiYmI4f/48H3zwAQAeHh5d5htjur1+O6+++iqFhYUUFhZisVh6djMiIiIiIiIi4lL65Ndnmpqa+Otf/8qzzz5LY2MjbW1tGGNIT09nwoQJQPsdIMOHD3fMCQsLo76+HrvdTlhYWJfrt5Oenk5CQgIJCQm0trb27qZERERERERE5IHWa02Rhx9+mICAAAAGDhzIlClTqKqqIjg42DFm9uzZlJeXA7Br1y5SUlLw8fEhIiKCqKgoCgoKaGhooLm5mSeeeAKABQsW8Mknn/RW2iIiIiIiIiLiJnrtjElISAgbNmzAy8sLT09Ptm7dyp49e9i4cSMxMTEYYzhz5gwLFy4EoKKigq1bt1JRUUFrayuLFy+mra0NgEWLFpGZmYmvry85OTnk5OT0VtoiIiIiIiIi4iY8aP8ZGpdz5coV/Pz8nJ2GiIiIiIiIiDjRnfoDffJMERERERERERGR/kZNERERERERERFxS2qKiIiIiIiIiIhbUlNERERERERERNySmiIiIiIiIiIi4pbUFBERERERERERt6SmiIiIiIiIiIi4JTVFRERERERERMQteQDG2Un0hhs3btDS0vI/z7dYLLS2tvZgRiL9k2pd3IHqXNyFal3cgepc3IHqvGf5+vri5eV12/dctilyvwoLC0lISHB2GiK9TrUu7kB1Lu5CtS7uQHUu7kB13nd0fEZERERERERE3JKaIiIiIiIiIiLiltQU6ca6deucnYJIn1CtiztQnYu7UK2LO1CdiztQnfcdPVNERERERERERNyS7hQREREREREREbekpsgtpk+fTlVVFdXV1SxdutTZ6Yjcs7CwMA4ePEhFRQXl5eW8/vrrAAQGBrJv3z5OnTrFvn37GDJkiGNOWloa1dXVVFVVMW3aNMf1uLg4ysrKqK6u5te//nVfb0Xkrjw9PSkqKmL37t2A6lxcU0BAANu2baOyspKKigoSExNV6+JylixZQnl5OTabjc2bNzNgwADVubiE9evXc+HCBWw2m+NaT9a2j48PWVlZVFdXc+TIEcLDw/tkX67GKNrD09PT1NTUmMjISOPt7W1KSkpMdHS00/NSKO4lgoODTWxsrAGMn5+fOXnypImOjjYrVqwwS5cuNYBZunSpef/99w1goqOjTUlJifHx8TERERGmpqbGeHp6GsAcPXrUJCYmGsD8+c9/Ns8++6zT96dQdIw33njDbNq0yezevdsAqnOFS0ZmZqZ55ZVXDGC8vb1NQECAal3hUhEaGmpOnz5tBg4caADzpz/9yaSmpqrOFS4RTz31lImNjTU2m81xrSdre9GiRWbNmjUGMPPmzTNZWVlO3/MDGE5PoN9EYmKiyc3NdbxOS0szaWlpTs9Lobif+Pjjj82UKVNMVVWVCQ4ONtDeOKmqqjLQtc5zc3NNYmKiCQ4ONpWVlY7rKSkpZu3atU7fj0JxM6xWqzlw4ICZPHmyoymiOle4Wvj7+5vTp093ua5aV7hShIaGmnPnzpnAwEDj5eVldu/ebaZOnao6V7hMhIeHd2qK9GRt3xwDGC8vL3Px4kWn7/dBCx2f6cBqtVJXV+d4bbfbsVqtTsxI5P6Eh4cTGxvL0aNHGTZsGA0NDQA0NDQwdOhQoPu6t1qt2O32LtdF+ouPPvqId955h7a2Nsc11bm4mpEjR3Lx4kUyMjIoKioiPT2dr3zlK6p1cSn19fX86le/4ty5c5w/f56mpib279+vOheX1ZO13XHOjRs3aGpqIigoqK+24hLUFOnAw8OjyzVjjBMyEbl/gwYNIjs7myVLltDc3NztuO7qXv8fpD+bOXMmjY2NFBUVfanxqnN5UFksFuLi4lizZg1xcXFcvXqVtLS0bser1uVBNGTIEGbNmkVkZCShoaEMGjSI+fPndztedS6u6n+pbdX9/VNTpAO73c7w4cMdr8PCwqivr3diRiL/G4vFQnZ2Nps2bWLnzp0AXLhwgeDgYACCg4NpbGwEuq97u91OWFhYl+si/UFSUhLf/va3qa2tJSsri6effpo//OEPqnNxOXa7HbvdTkFBAQDbt28nLi5OtS4uZcqUKdTW1nLp0iVaW1vZsWMHEydOVJ2Ly+rJ2u44x8vLi4CAAP71r3/11VZcgpoiHRQWFhIVFUVERATe3t6kpKSwa9cuZ6clcs/Wr19PZWUlH374oeParl27SE1NBSA1NZVPPvnEcT0lJQUfHx8iIiKIioqioKCAhoYGmpubeeKJJwBYsGCBY46Isy1btozhw4cTGRlJSkoKBw8e5MUXX1Sdi8u5cOECdXV1jB49GoBnnnmGiooK1bq4lHPnzpGYmIivry/QXueVlZWqc3FZPVnbHddKTk7m4MGDTtjRg8/pDzbpTzFjxgxz8uRJU1NTY5YtW+b0fBSKe42kpCRjjDGlpaWmuLjYFBcXmxkzZpiHHnrIHDhwwJw6dcocOHDABAYGOuYsW7bM1NTUmKqqqk5PaY+Pjzc2m83U1NSYlStXOn1vCsXtYtKkSY4HrarOFa4Y48ePN4WFhaa0tNTs3LnTDBkyRLWucLlYvny5qaysNDabzWzcuNH4+PiozhUuEZs3bzb19fXm+vXrpq6uzrz88ss9WtsDBgwwW7duNdXV1ebo0aMmMjLS6Xt+0MLj//4hIiIiIiIiIuJWdHxGRERERERERNySmiIiIiIiIiIi4pbUFBERERERERERt6SmiIiIiIiIiIi4JTVFRERERERERMQtqSkiIiLST7S2tlJcXOyI8PBwAJKSkjh69CiVlZVUVlby6quvApCamsrmzZs7rREUFERjYyM+Pj59nn9HeXl5xMfH33HM97//fXx9fR2v9+zZQ0BAQG+ndt9SU1MJCQnp9v0PP/yQp556CoD09HSio6N7PIeO69bW1hIUFARAc3MzACEhIWzbtq1HP/OXv/wlkydP7tE1RURE+gOn/y6wQqFQKBQKTHNzc5drw4YNM2fPnjWxsbEGMEFBQebYsWPmG9/4hvH39zcXL140vr6+jvELFy40v/vd75y+l7y8PBMfH3/HMbW1tSYoKMjpufbk3gIDA01+fn6f5tPxe7xdDfVUjBgxwuzdu9fp379CoVAoFD0ZulNERESkH1u8eDGZmZkUFxcD8M9//pN33nmHtLQ0mpubOXToEN/61rcc41NSUtiyZUuXdUaNGsX+/fspKSnh+PHjjBw5kkmTJrF7927HmJUrV5Kamgq0333ws5/9jL///e8UFhYSGxtLbm4uNTU1LFy4EOCO8ztavXo1hYWFlJeXs3z5cgC+973vERoaSl5eHgcPHnR8ZlBQEO+//z6LFi1yzH/33Xd58803AXj77bcpKCigtLTUsdatpk+fzvHjxykpKeHAgQMABAYGsnPnTkpLS8nPz2fs2LGOtd966y3HXJvNRnh4OOHh4VRUVLBu3TrKy8vZu3cvAwcOZM6cOTz++ONs2rSJ4uJiBg4c2Omzk5OTyc3NdbzueMdMc3MzP/3pTykpKSE/P5+hQ4d2yf3dd98lMzOTvXv3Ultby+zZs1mxYgVlZWXk5ORgsVi6rHs74eHh2Gw2AAYMGMDvf/97ysrKKCoq4utf/zrQfsdLdnY2OTk5nDp1ihUrVgDg6elJRkYGNpuNsrIylixZAsC5c+cICgpi2LBh3X6uiIjIg0ZNERERkX7C19fXcXRmx44dAIwZM4bjx493Gnfs2DHGjBkDwJYtW0hJSQHaj0yMHj2avLy8Lmtv2rSJVatWERMTw8SJEzl//vxd86mrq2PixIkcPnyYzMxMkpOTSUxM5L333runff3whz8kISGBcePGMWnSJMaOHcvKlSupr69n8uTJPP30053GZ2VlMW/ePMfruXPnsm3bNqZOnUpUVBQTJkwgJiaG+Ph4xzGVmx5++GHS09OZM2cOMTExPP/88wD85Cc/obi4mPHjx7Ns2TI2btx417yjoqJYtWoVX/3qV7l8+TJz5swhOzubY8eOMX/+fGJjY7l27VqnOUlJSV3+Xjf5+flx5MgRYmJiOHTokOMY1K1GjRrFzJkzmTVrFn/84x/Jy8tj3LhxtLS0MHPmzLvmfavFixcDMG7cOL7zne+wYcMGBgwYAEBMTAzz5s1j7NixzJs3j7CwMGJiYrBarYwdO5Zx48aRkZHhWKuoqIikpKR7zkFERKS/sjg7AREREWnX0tJCbGxsp2seHh4YY7qMvXnt008/ZfXq1fj7+zN37ly2b99OW1tbp7F+fn5YrVY+/vhjAD7//PMvlc+uXbuA9rsn/Pz8uHLlCleuXOHatWv39OyPuXPn8tprr2GxWAgJCeGxxx5z3MVwOyUlJQwdOpSQkBAeeeQRPvvsM+rq6nj99deZNm2a464ZPz8/oqKiOHz4sGNuYmIihw4d4syZMwB89tlnADz55JPMmTMHaL/LIigoiMGDB98x79raWkpLSwE4fvw4ERERd91rSEgIFy9evO17n3/+OZ9++qljvalTp952XE5ODq2trdhsNry8vBx3nthsti+Vw62efPJJVq5cCcDJkyc5e/Yso0ePBuAvf/kL//73vwGoqKggPDycEydOMHLkSH7zm9+wZ88e9u3b51irsbGR0NDQe85BRESkv1JTREREpB87ceIEjz/+eKdjKvHx8VRUVABw7do1cnNzmT17NikpKbzxxhtd1vDw8Ljt2q2trXh6/v9No7ceBbnZPGlra+vUSGlra8Nisdx1PkBERARvv/02CQkJXL58mYyMjNuOu9X27dtJTk4mODiYrKwsxz5+/vOfs27dum7ndddEut13YIy54x467vnGjRudHgrbnZaWlm7398UXX3Ra7+ZRmFvd/FxjTKc5N7/3e9Xd37/jZ3XM6fLly4wfP57p06ezePFi5s6dyyuvvAK0fz8tLS33nIOIiEh/peMzIiIi/diqVat46aWXGD9+PAAPPfQQK1as4Be/+IVjzJYtW3jzzTcZNmwYR44c6bJGc3MzdrudWbNmAeDj44Ovry9nz57lsccew8fHh8GDB/PMM8/cU25fZv7gwYO5evUqTU1NDB06lBkzZnTKy9/f/7ZrZ2VlkZKSQnJyMtu3bwdg7969vPzyywwaNAiA0NBQHnnkkU7z8vPzmTRpkuOOisDAQAAOHTrE/PnzgfZnoVy6dInm5mbOnDlDXFwcALGxsURGRt5133fKu7KykkcfffSua/SljnuPiopixIgRnDx5stvxQUFBeHp6smPHDn70ox85vh+A0aNHU15e3us5i4iI9BXdKSIiItKPNTQ08MILL5Ceno6/vz8eHh589NFHjmMYAPv27WPDhg2sX7++23VefPFFfvvb3/Lee+/xxRdf8Pzzz1NbW8vWrVspKyujurracSzly7Lb7XedX1ZWRnFxMSdOnOD06dP87W9/c7y3bt06cnJyOH/+fJfnilRUVODv788//vEPGhoaANi/fz/R0dHk5+cDcOXKFV544YVOx1UuXbrEa6+9xo4dO/D09KSxsZFp06axfPlyMjIyKC0t5T//+Y/jgbDZ2dksWLCA4uJiCgsLOXXq1F33nZmZydq1a2lpaeFrX/tap+eK7Nmzh4ULF97xb9HXVq9ezdq1aykrK6O1tZWXXnqJ69evdzvearWSkZHhuIPmBz/4AQAWi4VHH32UY8eO9UneIiIifcGD9p+hEREREZEecPjwYb75zW/S1NTk7FR61HPPPUdcXBw//vGPnZ2KiIhIj9HxGREREZEe9NZbbzFixAhnp9HjLBYLH3zwgbPTEBER6VG6U0RERERERERE3JLuFBERERERERERt6SmiIiIiIiIiIi4JTVFRERERERERMQtqSkiIiIiIiIiIm5JTRERERERERERcUtqioiIiIiIiIiIW/ovpq9lau4l7/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot with regression line\n",
    "regression = True\n",
    "\n",
    "mph_comp.visualize_mph(df_mph, mph_out_dir, regression)"
   ]
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}