{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bit of a review\n",
    "\n",
    "Importing out usual things!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = pd.read_csv(\"https://raw.githubusercontent.com/UIUC-iSchool-DataViz/spring2020/master/week01/data/GDP.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>DATE</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>243.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1947-04-01</td>\n",
       "      <td>245.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1947-07-01</td>\n",
       "      <td>249.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1947-10-01</td>\n",
       "      <td>259.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>265.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>20749.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>20897.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>21098.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>21340.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>21542.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE        GDP\n",
       "0    1947-01-01    243.164\n",
       "1    1947-04-01    245.968\n",
       "2    1947-07-01    249.585\n",
       "3    1947-10-01    259.745\n",
       "4    1948-01-01    265.742\n",
       "..          ...        ...\n",
       "286  2018-07-01  20749.752\n",
       "287  2018-10-01  20897.804\n",
       "288  2019-01-01  21098.827\n",
       "289  2019-04-01  21340.267\n",
       "290  2019-07-01  21542.540\n",
       "\n",
       "[291 rows x 2 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a bit of data manipulation\n",
    "gdp['DATE'] = pd.to_datetime(gdp['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(gdp['DATE'], gdp['GDP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# we also did this in a slightly different way\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(gdp['DATE'], gdp['GDP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Image data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Image submodule from the pillow library\n",
    "#. pillow = PIL\n",
    "import PIL.Image as Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'PIL.Image' from '/opt/anaconda3/envs/myNewEnv2/lib/python3.7/site-packages/PIL/Image.py'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = Image.open(\"stitch_reworked.png\",'r') # 'r' means read in (instead of write)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=430x483 at 0x7F83B8D05550>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im # quick way to look at our image\n",
    "#type(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn this image INTO data using NumPy\n",
    "data = np.array(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]]], dtype=uint8)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 430, 4)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the shape of this data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, last element of `.shape` is the RGBA channel list.\n",
    "\n",
    "483, 430 width/height in pixels\n",
    "\n",
    "Plot this data using matplotlib (use fig,ax again):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "ax.imshow(data) # imshow is specific to plotting images\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can say that the 483=height and the 430=width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we want to use a gray background to show what parts of our image are see through\n",
    "#. where the \"A\" channel is set to zero\n",
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "# set the facecolor of our ax object to be gray\n",
    "ax.set_facecolor('gray')\n",
    "\n",
    "ax.imshow(data) # imshow is specific to plotting images\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many colors are in this image?  What are their RGBA codes?\n",
    "\n",
    "We are going to use NumPy's `.unique` function to see how many unique colors are in this image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7, 55, 88])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "np.unique([5,6,7,88, 55, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0,   0],\n",
       "       [  0,   0,   0, 255],\n",
       "       [126,  22,  33, 255],\n",
       "       [255, 255, 255, 255]], dtype=uint8)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(data.reshape(-1, data.shape[2]), axis=0)\n",
    "# this will list out the unique colors\n",
    "# its a little handwavy (more info in the prep notebook)\n",
    "# at like ~30% of getting what is going on that is ok!\n",
    "\n",
    "# order of columns here is:\n",
    "#. R=red, G=green, B=blue, A=opacity\n",
    "#.   0,     1,      2,      3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This weird thing that I just copied in shows the unique RGBA combinations in this image data.\n",
    "\n",
    "We see this RGBA encoding is going from 0-255 (sometimes it goes from 0-1 in decimal numbers)\n",
    "\n",
    "Now that we know a bit about the colors in this image, we can mess with them!\n",
    "\n",
    "We'll do this with a \"boolean mask\" (subsetting a dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a quick boolean reminder:\n",
    "126 == 126 # testing for true-ness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can create an array, a MASK, that will hold for each pixel whether or not\n",
    "#. it has a R channel component equal to 126 or not (126, 22, 33, 355) is the maroon color\n",
    "#. 126 in the R channel uniquely defines these pixels\n",
    "\n",
    "# pick the R channel to look at\n",
    "image_boolean_mask = data[:,:,0] == 126"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_boolean_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 430)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_boolean_mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[200,250,0] # this will be y=200, x = 250, Red channel = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot JUST the red channel\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "ax.imshow(data[:,:,0], cmap='gray') # plotting just the red channel\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "ax.imshow(data[:,:,1], cmap='gray') # plotting just the GREEN channel\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xeBAIBIzrrSiK8UAxuhMUxSiNyr8Ttpw/f96UG1lV1RmdTygUQiKRMD4ryqg/tdy+yVAUBaFQaExt49JLL0UgEEBXVxfeeustRCIRHD16FENDQ7U4kiade8YVQg0Gg3jzzTexbNmyCd/19vZi+/bteOWVV/Dyyy8jlUoZT70a7DRuuvKAcU3TwMwoFouGaISwYrFYzTdkKBRCe3u7IQRBoVBANpsFM8Pn8yGVSiGTySCbzSIUChlV33KEDcyMfD6PfD6PQqEAABWrdPl83hCbpmnweDxGcMBs23fZbNYUoba1taGtrQ3JZNKoxUyFeLABo7UAURMTD7R8Pm/YlMvlwMzIZrMIBoPG9bWYSYXqiqpvOp3G7t27sXnzZgBALBbDoUOH8OSTT+Jvf/vbmGqIKBF8Pt+YEsrr9Y7Zpyg1i8UigsGgUaIIoY7ffjK8Xi9GRkaMG6FaRAKy8SLKZDLo7++ftP0zMjKCTCaDzs7OMc0BIS7xALE6ImcqfD4f/H7/rLsuRMkdCASgKMoEJ9R45s6di0AgUPE7TdOmvSaZTGbMMXK53JhzICK0tLQgFosZD0GzcIVQmRl//vOfsXbtWqTTaezZswc//OEPAYxevEAggJaWFmiaBk3TZuQUmC1i7sx8Po+enp5pbyZBKBSaMMxraGgIiURi2n3kcjlkMhnX9rOOR1VVaJqGXC5n6XHGN62CwWDFsEZFUUyfmtMVQgWAP/3pT1i1ahXy+TxGRkYAjD6529raEAwGbbZu1OnQ0tJSlfND2F2OrutViVSQTCYbMhAEGD0XM6qRoqqq6/q0HlZFUer2gC5vA5uFa4QKwGinKIqC9vZ2hEKhhspjFAgEphWqpmmYO3fuhJuqvP1UDdlsFvl8viFLVUVREAwGTb2hp/ufq3EWmUEmkzG6rMzEFSGE5SiKgs7OTrS2tjaUSIFRoU6VtcDn82H+/PkVxVUoFGZ0owlHSKNiRhtZONlUVa1qf/W4H2bjPJwK1wlV13XEYjG7zaiI6Oif7LtwODzpzSS8k9WiKEpDhxpWW4WfivIoplAoNOv9zZZYLGZJaQq4UKjAv6p9jUhbW9sEj7GiKOju7p5yTK3H45lRNdbn89Ul51CtVHKWzRRN04yH13Tn6/F4qvbU10omk7Gseu1KoTKzaaMWzEZV1TElHRHB7/dPexMJ73W12Nn9Ug3VVlenorwGoijKlM2delwPM0MGx+NKoQKjWd+sdtfXiuh0JyLMnTt3TBjfVFRbvVNVtWE9vuXMxkbRf1rOZB5+VVXR3t5e87GqQYRPWoVrhcrMRqBBoyG80V6v1wimqAZN09DR0THl9uFwGJ2dnXXtK64Vr9dbU/XX4/Ggq6ur4jlOVuuw2pGUzWalUGslkUiYMhbUbBRFgaZpyGazM4pgISK0trais7NzQlXZ7/ejvb0dkUikoZ1I5dTi8BJdb5NVZUWUUjlTOenMwuppVRrX22AS2WwWqVSqIYIeBLquGwLNZrMz7usMBoPw+/1IpVLI5/OGY6bRuqOqQYxJHRoamtITLB5S4XB4ylJYVdUJfbRW1y50XZ/wcDAb1wsVGB2+1dnZOSNnjJWUd77XKi4xksPpEBFCoRD8fj9GRkYQj8fHeE5F80CEgFZzvcLhsCFU0c9qJYlEwpJopHKaQqi6rmNwcBBdXV0NEalT/qda3WXgFDRNQyQSgcfjMUooEcE004fZ+O2t7KZKJpMYHh62bP+CphAqMOqVGx4exrx58+w2pS6hbE5ltt5qZh4T8CIitKx6QFvZd1qOq51J40mn06YPP5oN5eMjJebAzBOig6wSUjKZnFGGidkw7V1CRBcS0ctE9A4RHSWie0vrI0T0IhGdKL22l/3mASI6SUTHiegLVp7ATGBmy9sSM2H8gHDJ7BGjaQShUMiS0jSfz1edq8kMqnmcFwD8b2a+FMCnAdxFRMsB3A9gLzMvBbC39Bml724D8HEANwDYQkQN06kXi8VsF6us+lpHMpk0hOr1ei3rrhocHKxrmOq0bVRm7gHQU3ofJ6J3AMwHcDOAVaXNtgF4BcD/Ka1/ipmzAD4gopMArgTwN7ONrwVmRjweh9frtc2RIzrGRX4jsxHpVgQigZvX6zXSq4z3OossiqqqolgsjrHLSdXzVCplvLfq+qbTacuC7ydjRs4kIloI4JMADgDoKokYzNxDRMJLMx/A62U/O1Na1zBks1mjy6beXmCrx0UyM/r7+8fcsALRJi5PBiaEK8SpadoYoYqcSX6/35RAeispFApjwkatiO8d76yqF1VfdSIKAfgjgG8z88gUbatKX0y4M4loI4CN1R7fbHK5nKXewMkoL+3Mbp+KFCyTPe1Fys5yyttzIoWq2FZQKBSQyWQQj8ehKIqRClTTNHi93gnnIQRPRBXHxHo8HmM/ZjIwMDDmfKz4b+0oTYEqhUpEHoyK9HfM/ExpdS8RdZdK024AIlbvDIALy36+AMC58ftk5icAPFHavy2NtpGREQQCgbrGxSYSCcPzbEbAgq7rSKfTRkItM8Z5ToYQejVtM1EiV7JHZHP0+/3QNA3hcBiqqhrJymqhUChMsCudTpsakVYsFqvKdmgF0wqVRh+XvwHwDjM/VvbVbgB3AHik9LqrbP12InoMwEcALAXwdzONNotcLofh4WHMmTOnblU6UcJUk/WuEoVCAclkEplMBoqiVJwCohGYyiZR/RfV80QiAU3ToOu6ET0msjBWW+uIx+MTagtmlqi6rmNgYMC2QR7V3J1XA/g6gH8S0Vuldd/DqEB3ENE3AJwGcCsAMPNRItoB4BhGPcZ3MXPjDWEpkUgkkEql6jIZcnl6FK/XO+OSPBqNWpbqw07Kq9yiX1KML63mAVooFCp68s2qKeXzeSSTSVvHOLsiAbdJNiAYDBrTUlhBoVBAT08PisUi/H4/5s2bV9WxCoUCBgcHG3YwvFVEIhGEw+Fpt4vH4xWrpD6fDx0dHTV795kZyWRy2gEDJuLuBNxmIP6UXC6Hrq4uS6rCg4ODRtUpk8kgkUhMeyNOl3DbzSSTyaqEOlmCADGNiUh/I/I6TwUzo1AoIJ1OGxNwNQJSqOMQ86LMnTvX1I7y8V0HwKizY6obMZFI1PNp3nBU87CcLoClWCxiaGgIRARN09DS0mJktC+f+aBYLCKZTBpDBxvtmkuhVkAE8AeDwQmJsGulkrNjOsZPodBMqKo67bUXtZJqmm+iW0yE/UWj0TGz0Im5fBoVKdRJEJkMs9ksIpHIrKvClapQuVwOxWLREWlT6omY/nCqtmUul0Mymaw5jK+RE+BVQgp1CnRdRyqVQrFYxLx582oWVDQardhJXiwWjekTxyPaSs2ESFgWDoen7LoqFovo6+trqusjhVoF2WwWw8PD0yYWq4QISJisWjXVrNzNVNISUVVZOHK5HHp7e5vOueacaGubSSQSiMfjM36Kx2KxKaeWKPcEj6caj6fTETmOIpHIlCIVfdDNKFJAlqgzYmhoCMPDw5g7d25VwRFDQ0PTDizOZrOIx+MVp+/zeDwIBoPGw8GOrgJRg5iJo0X8RsT7ipnU8vm8EeghQgZDodC0NQcRCO/GYI9qkUKdIWJ0ipiJWnThCDElk0koioJkMlm1JzGVSqGtra1icHt56phoNIpCoWBkzRfBEslkEoVCwUgCLWJpxbZCKCKQXnwWI2lEW9nn80HXdXg8HhQKhTFZ/cu9z+JYIt1peVxvKBQySsaZzpdTiVwuh1QqVddB2o2IjEyaJSKeVNzItVbLgsHgrPM5iXGn5WNMARiJwmYT9D7VMctLXTP3n06n0d/f30xdVDIyySrMGuWfyWRmPZ+pEKRgfJXSirQv5ccza/+6riMejyMWi1UtUhG44FakUBsEXdcxMjKCSCTS1HmUMpkMBgYGqnbaaZqGYDCIYDBoRBSJqnwwGDS6ucTwQqeKWQq1gYjH4/D5fEaYW7OQy+WQTqerHpTt8XiM6+T3+41rNVXIZ1tbG7LZLDKZzJh2tTh2oyOF2mCIMY9mhS42OvF4HENDQ1WXdG1tbWhra6upLezz+SYEUoghdsPDww09Q7t0JjUgYvIkTdPg8XgQCAQaOlfRTGBmoxQT3upq7kExZM0MT3IlRJW5WCwaD0sbtDGpM0kK1QGoqmqM9lAUZczs2k7JEChic1Op1IzaiqqqIhKJ1DS1Ra2IUjYWi9W7Wiy9vk6mWCxiZGTE+CyGbOm6jkgkYnlmiloRSdHS6TRSqdSMS6iWlpYx/bL1orxGc/bs2YZwQEmhOpDyTIYDAwPIZDIIh8MNMQEWMBptFY1Gkc1ma+oD9fl8CIfDxjA0iRSq4xEJxZPJpFEdFgOiVVU1km6LkSlWU2tGCq/Xa+QOtqodOlNE2tN6ZsSfDClUl1CeIKwSRASfz4dgMGjk4h2f0lPk2y2v6pW/F5FPIhppvJdUhPtVW4qKWOaWlpaGEWc5IsHa0NCQ3aZIoTYLYpazTCYzqSBEDLHIpD9+agyxn0rvq0XEDwcCAQSDwYZ3hjWKfVKoTchUY2OTyeSUv1VVdUwJXB7fWy7w8aVqIBAwHEONcvNXQ6NMNC2F2kT4fD6jNBtPJpOZUJUV3UDFYtEIFBDeZgDG7ODC4SP6HkWyMIGodktqRwrVpYgunHA4DK/XC13Xx4TbjafWjIvlXtny943igZ4tYkig3YPVpVAbADGhsRgzWiwWDXEBoyWVKL2IyHhfXi0T7Um/3w9FUYw8tk6qZjYijTJliBSqzXg8HnR1dY2pPpaLthKN5h11K5lMZkZxyFYihWojwWAQc+fOHVPqlcf0SkHaR6FQQF9fX0OIFJBCtY1gMIjOzk4pxgZEiLQRqrwCKdQ6Q0QIh8MVcyRJ7EV4rAcHBxtmzhmBFGodISK0tbVVzDgomR3l88cIR5vH4xnT9h/fRyw8ueMDPOz28FZCCrWOBINBKVILiMViRtRVeZtyfI2lUdqbtSCFWkcadTiaUykWi4jFYmOGAJbjZGGORwq1Tvh8vrqPq3Qz+XwefX19DTGypR5IodYJn88nnUcmIaa2aCSvrNXIsJU60WheRCcj0oE2E1KoEsfRbCIFqhAqEfmJ6O9EdJiIjhLRv5XWR4joRSI6UXptL/vNA0R0koiOE9EXrDwBSfMRDAZdE/RfLdWUqFkAn2PmTwBYAeAGIvo0gPsB7GXmpQD2lj6DiJYDuA3AxwHcAGALETV94ptmrK5ZhcjE2ExMK1QeJVH66CktDOBmANtK67cBWF96fzOAp5g5y8wfADgJ4EpTrXYg+Xx+2kHZs0FkKuzt7UV/fz9yuZyrHwzNNptAVV7fUon4BoAlAH7JzAeIqIuZewCAmXuISExFNh/A62U/P1Na1/SMjIyYlkxbTHuoqipGRkYmzGqeSqXg8XiMpNVuG+4WCAQQiUQwODhotyl1oao7hpmLAFYQ0RwA/5eILpti80qPuQk9z0S0EcDGqqx0Cfl8Hr29vejq6pqxWHO53JgJj6ZLxSky0vf09GDOnDmujIhqaWnB8PCwq2sOghndLcwcJaJXMNr27CWi7lJp2g2gr7TZGQAXlv1sAYBzFfb1BIAngObKlC866tva2qbNW6vrOtLpNBKJxKwytsdiMfj9/pqzODQqiqIgGAwikUhMv7HDqcbr21kqSUFEAQDXA3gXwG4Ad5Q2uwPArtL73QBuIyIfES0CsBTA38023Mnkcjn09/fj3LlziEajhqMpn88jn88jkUigv78fZ8+eRX9//6ynVRAZCN2ISDXqdqopUbsBbCu1UxUAO5j5OSL6G4AdRPQNAKcB3AoAzHyUiHYAOAagAOCuUtVZMo5isYhoNDohVtWKqlwqlXJl9TcQCMDr9bo+lFBOEtVEzJs3D8Fg0G4zTKe/v99Sj3odmXSSKHe5AiVT4ta2XDP0qUqhNhFitm230Qi1QquRQm0idF1v6Fm1ayUUCrl+1jcp1CajzhPz1gWRSsXNSKE2Gclk0nUe0mYIJZRCbTKYGalUym4zTEVMBelmpFCbkGQy6fqqotuQQm1CxIzkEucghdqE5HK5hsxdWytuOpfJkEJtQkSyabfgpnOZDCnUJsVNzhczxvc2OlKoEsfjtu6mSkihNiFuq/q6LXtFJdx/hs+acC0AAAiISURBVJIJiEwRbsFN1fjJkEJtQrxer6uyPfh8PteXqu4+O8kEvF4vurq6XBXErmma64e6SaE2GUTkytKntbXVbhMsxX3/mGRKAoGAK6OS3J49Xwq1yXDzHK1uLlWlUJsIv9/v6uCAcDjsKidZOVKoTYLP50NnZ6crq73ldHZ2utKxJIXaBCiKgo6ODld5eidDVVXMnTvXdQ8kKVSXoygKwuEwvF6v3abUDY/Hg0AgYLcZpuLeBosEmqZh3rx5dRcpMyOZTCKXyxl5hL1eb127hVpbW5HJZFwTtSSF6kJUVUVHRwe8Xm9dnUfpdBqpVAqZTMYIlBezAGiahtbWVoTD4bpUSwOBANrb210z25sUqssIBoN1b4+KqTni8fik2xQKBWPmtba2trqUriK00A2lqmyjuggxH2o9RarrOoaGhqYUaTmxWAz9/f11EY/X63VNEIQUqksIBoPo7u6uu2e3lpnU0+l03eaK6ejocEXfsRSqCwgGg+js7Kx7DK+u6xgYGKjpt/VKWer1ehEKhepyLCuRQnU47e3ttgUyJJPJmse15nK5uo2JDQaDju9XlUJ1MMFgEK2trbbchKlUCoODgzVniigWi3WbB0fTNMdXf6VQHQoRIRKJ2DJkTfSTzpZqHVBm4HTPrxSqQ7EzwD6ZTJoi1FwuV5fEZIqiOD5SSQrVobS1tdlyXF3XjSAGM/ZVj1KVmR0f5yyF6lDsmj5xeHgYuVzOtP1ls1nLMyIWCgXTHi52IYXqUOpxg4+nlj7T6SAiS8+jWCyir6/P8elRqxYqEalEdIiInit9jhDRi0R0ovTaXrbtA0R0koiOE9EXrDC82VEUpe7e3pGREdOdMplMBtFo1NR9lpNIJFyRoHsmJeq9AN4p+3w/gL3MvBTA3tJnENFyALcB+DiAGwBsISJnNxAakEwmU9fJkawoTQWpVMoSMRWLxbp6lq2kKqES0QIANwH4j7LVNwPYVnq/DcD6svVPMXOWmT8AcBLAleaYKxHouo7+/v66VemsKE0FhUIB58+fN7Xdres6+vr6XJNovNoS9ecAvgug/J/qYuYeACi9ziutnw/gw7LtzpTWSUwmk8nUJWY2mUxaXjIVi0VTq6nxeLxuARX1YNqOOCL6IoA+Zn6DiFZVsc9KDacJj30i2ghgYxX7k0zB8PAwfD6fJaNEEomEMca0HojB5hdccEFN3SnMjGw2i0QiUbeg/3pRTY/51QC+REQ3AvADCBPRfwLoJaJuZu4hom4AfaXtzwC4sOz3CwCcG79TZn4CwBMAQETOdsnZiBgLamaeoFwuh1gsZsvNns/nMTAwAFVVxwx8F6+qqhqONF3XQUTIZDJGaczMrnAejYdm0sYplaj3MfMXiehnAAaZ+REiuh9AhJm/S0QfB7Ado+3Sj2DU0bSUmSf1fEihzp6Wlha0trbOKl1mPp/H4OCgLV0/1SKEqqoqisUiiMjUfl2beYOZP1Xpi9nEoD0CYAcRfQPAaQC3AgAzHyWiHQCOASgAuGsqkUrMQYT1tba2wufzwe/3G9XHyUpaMf1iOp1GOp1GJpNpeOdLsVhEsVh0Zak5FTMqUS0zQpaopiOqh0RkjMnUNM1I+KXrulG1rWc3j2RKLClRJQ1MeVdKoVBAKpWyPApIYh0yhLCJkCJ1LlKoEokDkEKVSByAFKpE4gCkUCUSByCFKpE4AClUicQBSKFKJA5AClUicQBSqBKJA5BClUgcgBSqROIApFAlEgcghSqROAApVInEAUihSiQOQApVInEAUqgSiQOQQpVIHIAUqkTiAKRQJRIHIIUqkTgAKVSJxAFIoUokDkAKVSJxAFKoEokDkEKVSByAFKpE4gCkUCUSByCFKpE4AClUicQBNMr8qAMAkqVXJzEX0uZ60Cw2f3SyLxpixnEAIKKDk8223KhIm+uDtFlWfSUSRyCFKpE4gEYS6hN2G1AD0ub60PQ2N0wbVSKRTE4jlagSiWQSbBcqEd1ARMeJ6CQR3W+3PQIi2kpEfUT0dtm6CBG9SEQnSq/tZd89UDqH40T0BZtsvpCIXiaid4joKBHd2+h2E5GfiP5ORIdLNv9bo9tcskElokNE9Fxd7GVm2xYAKoD3ACwG4AVwGMByO20qs+0zAFYCeLts3b8DuL/0/n4APy29X16y3QdgUemcVBts7gawsvS+FcB/l2xrWLsBEIBQ6b0HwAEAn25km0t2/C8A2wE8V497w+4S9UoAJ5n5fWbOAXgKwM022wQAYOZ9AIbGrb4ZwLbS+20A1petf4qZs8z8AYCTGD23usLMPcz8Zul9HMA7AOajge3mURKlj57Swmhgm4loAYCbAPxH2WpL7bVbqPMBfFj2+UxpXaPSxcw9wKgoAMwrrW+48yCihQA+idESqqHtLlUj3wLQB+BFZm50m38O4LsA9LJ1ltprt1CpwjonuqEb6jyIKATgjwC+zcwjU21aYV3d7WbmIjOvALAAwJVEdNkUm9tqMxF9EUAfM79R7U8qrJuxvXYL9QyAC8s+LwBwziZbqqGXiLoBoPTaV1rfMOdBRB6MivR3zPxMaXXD2w0AzBwF8AqAG9C4Nl8N4EtEdAqjTbXPEdF/wmJ77RbqPwAsJaJFROQFcBuA3TbbNBW7AdxRen8HgF1l628jIh8RLQKwFMDf620cERGA3wB4h5kfK/uqYe0mok4imlN6HwBwPYB3G9VmZn6AmRcw80KM3q8vMfPXLLe33t6yCt6zGzHqnXwPwIN221Nm1+8B9ADIY/Sp+A0AHQD2AjhReo2Ubf9g6RyOA1hrk83XYLRadQTAW6Xlxka2G8DlAA6VbH4bwPdL6xvW5jI7VuFfXl9L7ZWRSRKJA7C76iuRSKpAClUicQBSqBKJA5BClUgcgBSqROIApFAlEgcghSqROAApVInEAfx/6ggRZGHmx3sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "ax.imshow(data[:,:,2], cmap='gray') # plotting just the BLUE channel\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "ax.imshow(data[:,:,3], cmap='gray') # plotting just the A (Alpha, Opacity) channel\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_boolean_mask = data[:,:,0] == 126 # grabbing a MASK of all pixels that are maroon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have this mask, we can start messing with our colors.\n",
    "\n",
    "I'm going to replace Stitch's maroon parts with blue\n",
    "\n",
    "RGBA = [0,0,255,255]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[image_boolean_mask] = [0,0,255,255] # turning maroon into blue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "# set the facecolor of our ax object to be gray\n",
    "ax.set_facecolor('gray')\n",
    "\n",
    "ax.imshow(data) # imshow is specific to plotting images\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We downloaded `image_manipulations.py` file and stored it in the *SAME* directory as this notebook file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from image_manipulations import my_boolean_mask\n",
    "# from the image_manipulations library its importing a SPECFIC function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_boolean_mask?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the black lines and make them another color -- magenta.\n",
    "\n",
    "This time instead of doing it \"by hand\" we'll do this ith the `my_boolean_mask` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 430, 4)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_boolean_mask2 = my_boolean_mask(data, [0,0,0,255]) # all of pixels where we have black lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_boolean_mask2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 430)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_boolean_mask2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do something a little different - let's make a copy of our `data` variable and then do any changes to that copied variable (and save `data` to compare to)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataCopy = data.copy() # .copy is a NumPy function, a thing you can do to NumPy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 430, 4)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataCopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "# set the facecolor of our ax object to be gray\n",
    "ax.set_facecolor('gray')\n",
    "\n",
    "ax.imshow(data) # imshow is specific to plotting images\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now update my copied data - turn black lines into magenta lines\n",
    "dataCopy[image_boolean_mask2] = [255, 0, 255, 255]\n",
    "#dataCopy[image_boolean_mask2] = 'magenta'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 430, 4)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataCopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qpdpbLU8+QLJiTzc5wEVi2fvs3s+S8VFGSgqhBZuDNG9q5vtvfJ+ptVNxmk4MhyFBH/chaVZ9x5XbPRQUjAT3K7SWzXGYDn704Y+4+OmLCTQECDb3LDYcJPBjSGW7KNdZJ33GJ0Yj7rCER6baU02wrnvfod8KaigYYsGuBfKkHvH/nW4U8BUIucGirdzw0Q1SCK2X0KaUBonUR/jBaz9gcMtgMYgMBxYjvszTH/Bz8ijEv/ufyODjEWGdUzWHGx+7kUhFzwLbzbiJPqS5eNfFssHCsatR9ibvIZZuDVEVZWvJVrIKujeN90tB1VoTj8WpcybSZIdyyg0Cx2Qa7Pip/Lc4WEzugdxeKx0ZiURwv+3mx0/+mMEtiVCsHKSawDx6P/TtdJMP/AwJoLCB03QyrmEcloClR9k6LcEWsquyKQ4mQgbnIJktqaQeSRRJ+Po3Z20mNDDU7d30W0GNHooyqTYRk5pHzxz1qcAKE24ClaiBddnmy4hGuxeF0hlm3KSlqoWLP7mYglDC6TcCmUkvof+quifCjhibHqV1LfnTV39K8yfN3RZWt8fN9D3T2/a7gK5FXvUUjZz3q/L045yPeWLBE3gKu3+z9ktBNQwDS9Ai4V8g64y+1MNpIPBTwA3Dm4YT3NfzNRXIuir8cZhfPPELJtYndLUMJFxvKv3UydYN7MDVSFKkCzKjmdz9z7sJNHTdWBeJRIh9EGNadaInyRAk3zSVxID/BTSYmLwx+g0swyw9CuLol4IKYMQNVHIamXx6z+UokkHY50NmLJM5K+b0yFppmiaNtY3krsnlpvduwhvzypr0XGTdM5UzdyY9EhuSBnIJGMrAG/Ny0XMXEfg4gBk//rXVWhNtiZLZlEleOGEKH5XYZyqv30pgu/x3Q/YGGuY09LjJWL8VVEfQgcNM6C2nwg/WXaxIAyoFk6snE/B331XTWN7I6LdHc/vK2xnVNEqMH1chRpbJnHlrUkBrOGbBjxwkmilH0vEWVizkq+99lcbK41fXiEaieD/y8oNVP5CBLhsJwk/l9dPIujRh5Nyct5lYS88Ni/1WUJuKmvDbEk7wI0uv9RUuAwZAaVMpaq8iFuv6D9VwqIGy1WV85ZOvtNUn/jaiSk1J0fmeTqLAbuA3sPfzSL2i5N//IdFLdUgWylJarbUT/BO45NVL8O8/dkCEaZrM3Tm3LWLrO8D5qfsqAISRwmhA2AizZdgWLPaeR6qdGaubPcgI1tfUwGxEXXsKfvbqz/iZ92dklZ7YLB9sDqLrNdd8ck1bO457kMgnJx2DO1qQMJ5KpKdOMkVN03mPHdXu8z25XsnPmkikTThx7KfavWcqkr9al3j/UtpS6goQIRuIRAU5gWeRqKS3QbXAWBNoXw3akvibm3jfWODvSBzzBlhQuYC1m9fSMrRzH12oPsTUmkQRrXlIwbFUT1FbkOUJsD5nPeExYby+nnew77eC6hjgIGANSAmN15CLfzoicXS7vyOxIMHwi8AT81C2oYyq0hPn5MVaYvzoxR+1daCLAetpLT3Jx+2OF0AEdT9iFU2mnIFYNT9GBKcG8bfOQHJFm5Gk8faVGvci1nMnHYU8DhwEQsCHwLLE519NbEtehyTP0HFAOBKFRCHlI5k2m2VzVEUxlYkNG1FDLOU20wZxsUmwHPgK6N8DpaD+D5gPVr+VqQem8nbo7aNSxyLhCJe/dHlb3ulQJEHATJzfYaSqlx/5vQoT1yQZRK/bPVro+uC2h9ZKGAczDhIPxkV17yH9VlA7XDAn3Xf0x5Eb3KStxMghYBcy+o/o5DMNwFpETUuq24eRm3UlMkMYQG3itVzgI0BLtNLCPQt5uOHh4zaXaqxuJHt9dmuF9iDiQbD8/dhfJZI4rOE/YqJYm3hc2smHnMgNO6DdtprEwWx0TMHSie8bpTUBIkqbjC5Hij8MRWRvOLBNiwxWAk3I/Z+NbMvW4AmA2i3W0CpnFS+MeoEdZTsgF0a/P5qtM7dirbXirffia/Zx04ab8Ea88AIcXAvWb8Cg8sTOkfjqZauWEZgSaA3g16YmWBNkaNNQWZsORtqS/AEZlPYiA5kfGfAsyCA3IPH9hyMz41QkPe8bif+PTrwX2gxSlnY/RBTYIdctZIRYMWEFrryTqxPUbwXVarfy8cCPKdpXJKPziQwDIWRx/xoyWyxHZqkG5IcCUeMCyM3amSzFEu81aQ0H6w5FwSJiB47dsjESiWA2mtyw5gacphON1Gy7KXH6qxGN0UicXiVSVGEDIiALkYrnQSQxpRlJDjGRe8to91lPCIzkNekCzcikWgtsRVrHvpt4rQ7RwF3I/e1OvN+SOO9YYrs9cfxRyGTsBUKWEL8997dYJlvw2eXC7Lt8H1mOLCKDImi7piZaw33593HHW3eQF8lj8D7gbvlehxFN32nauHHljfxhzB9atQFlKAhCViShYhxIXNBEVGcEkXNN4iePgVHV7pp8lHhMBNPzPcRw2V7bGJP44vlI5k8OoiEkBnKLtmCJWk666ke/FVS73c7aYWu5ZP8lWFdZJeB5dCdvrAKeRO6qd5G7ODnqHYsW2oQ3QTTx0Rhy0Q62e20/8lsNQGQ/N7F9JXAbbXLpNJ0M3T2U+rH1nXb2Cu4J8s1Xv8mQoMSiPp445ecTxw0jN38xMBu58T9EjMvnAGclzuWXyA1o0vYD29s9liGXahhysyZrvbZnHHKv2ZFLuw25b5OKSGehBi2Jv/pOXmsf9FdK24S0JWsLoQEhsmxta/ekn9Ful0erzYo5xeT/Wf4f8zbM4/L9lxNDjN+/QeIhHsKgOFCMd7NXVPrkvurt5EVkTbQfaE4I6XvAEmSijCJK1GhkgrQkvn8GMB0psXk2MMsEeyOydEjS/kbo7HsbEaLOKA7TIQNHD+m3ggrQPKWZvR/vZWRgpBQqTQqqH1H7HkPu5B1i9gcZVNfEZSJdjSxrTUTDaS+72cB4ROheQGaG+sSjkzatEjrascx2/zeQ2e7biBCPB7649Ys8MPsBMgd0jNBoqmvC0eBgdIN8iSokvr6cjpyDjDtJjdVEbqyk2H8PEeKvIzdh0s7cPmitBlEokufZ2VKy/S3V24lJHyENiy4HKj2VeIo8JyxdY7fbsU2z8bb3bS4ovwC76cCGDDJLkd8mF4PhlcPZEduB1WrFNE0KDxW27uO7iEIFHVc8IAbnD9o9N9o9xpEBaz4dC11OQrSYJDFkef5DZDBKfiNvvZdYPIbV6Lm49WtBzcjK4N3idxm+eTjGW4lmPkFEr7pX3tOA5HM/jYyoB+nYcyOVxBGD5rOIKvouUNBSQMGqApouaupQNtKsNLn6w6sxMNDAE8jNk8SBzJRfRbSr5E0QM8Ls9exnp28n8yvn44q7mI7BHERQj8fxBDCVWYPtix2MrRvLsuCy1tnzuJ9TCpWdrCncZpY4hPy2uUDQHmwV+ua6Zs4/2OaHidE2cJ0I84jHMPDmEe95EXHrHkmA1vBkAtYA/jI/mZaTC53r14IKsHnSZlkTLEZUnijQKKrWauC/kBnxdKerbkKqcX4Tg++s+Q53jbyLrDJR9/w7/dz26m2UBErQiFr233Qc8bOQai85iAGm2lHDBwUfsHTWUmyFNmxOG4urFnPbM7KfLwD/Q+cq6unGR1vnsOxINvFIN86yRtZ9ILOWC1mjJg2qgxsGUx4vx2Kx4MnyYIuLONfToShiynAC1yBCGlMxnhz/JK4c10k1MYZ+HPCQxDbIxvv5iXJ3tUCjqI0/RPo+Ps/pF1KQc3gVGUfccTdFO4vQpqZ+dz1fe+VrlARKCCHGoO9w9NKnjLb48RUDV/Crb/6K1V9eTdaYLDw+j3S09hi8WPYiIIkhZafkm3WfaqQMMMDSwqXoeNd/Ie3TxJUIdtLDEkVmPIAGRwNBv4Q2KUOxaaBYghycmk4n85AITwC/zc/+6fu7pC2ciH4vqJ5MD89/9nlW5UnnjGrga0j63+mueXYkS2hTu2fvm43/gJ8L376Qcf5xgKzdJiPr5SMpQEZpE5PF4xaTmZd51A3gzHBSdljE04m4TPsiJmI6iAPnlZ/XdX0UsNRYWmdUf2Ifh2gz0OYF89B1UhBNa83oGlnzR+i4Tk8FNqR2mxv5nf5n6v9g99pPyoiUpN8LKoAjy8GieYsIWALkIbEPJ1+wo/cJIusaDcw+PJubnr+Ji8svxqKtNCKDS5DONYDttM0anuDRviPTNAl/EmZyjWQo1NBWVrcvklxnZ8QyKNjUtVqdsViMeEa8dUa9gLbYjieQ67bw4EJmr56N1pqm8ibG+scCYnBL9Trvh0iMCUCNo4aqSVW4M3pnHj8jBNXldmEvs7MvYx8KMamPPd0n1QkmsvZciwRATPRLwGo1Enr61LE/yi5aEzEYWjuU+r0dnSCGYeBodjAoNAgQP/6u3jz5XuZ/EbdP1IhSnV/dpc/EY3GmrJrS2rhqL22z5IfI9TEwmHJ4Co2HGrEftre2PdmNXJNUoRAXTlIsHzrrIRyZvZfsekYIKoDFauHZSc9S7ajGi5jN++KX20dboACI33EZYqE93kqtEVGdFQZf2PUF8nbm4d/vJx6PE4/H8e/3c83qayCxn9UpOfveowJYAdhNB3M2zKEl0HLcpAWtNZGWCEMahmBgEEOqiyZrPTYgxrMGJAkiY3cGl6++HBD1eDmptVXY6dixuzmrGZu99+ri9MV7uUdYLBai86L8vezvGMAvEJdIX6QBmV2bkECZ6+na+ulJZK1l0zbuXH4nFy6+kIzFGQx+fjB3/9/drTN0OfCX1Jx6r/Jo4vHyfZeTsyyH5sPNx6yG4d/n55a/3sI5h6TRkB8Z4JJo4Le0FlNg3ifz0EpEsxK4m9Quhwppa4+7Lnsd8aI4VlvvKdtnjKAqpbDarOwcvZOoiuIFfoREnPQ1nkbWmxsQl01X67LtQdbfjUg+5kXlF/HjlT/m5k03kxPJIQo8gIQcnipf8cmwDlFXrdrK7atv55zF5xBdG6WpXubJYHOQSDhC48ZGLlt8GaVNpRgYbEaK2+8/Yn9RxGceA86tOJfZh2cDbdFUqWQmbV0x1g5ai83du1Xm+r0f9Shy4ePcj5lZM5PrMBiGGB1SbfHrDrWIG2Ym0tFwQxc/14zEk1cilV4mICNtDFEjX0fC6k6+QtOpoQYJsxyJVBm8Yu8VXLbvMh4pf4Sdo3Yy6MAgspuzuXHLjVi1lTgySP2MjlFE7XkZCQzJ1XJrR2kz0qWSL9MWxLF30N5esfS254wTVE+uh2cufIaxz4wlM5bJLCR/+3n6jvM/C7k5DcTw1VVBTfIcMnNcmdjP3xDDUV+omNpdvod8l/9EQgCt2uA7G77T4aJoZJB6BHiQ4xvJapDY3Btpi4CqI7VqbzZtXVUClgBNA5rwuXu3/9AZo/omsVqtWAdYeeDsB6iz12FHGpmlOqG/O3hoG317OtLXI2FqP0GinvqjkILMkP+LxCf/Nx2/RwxRj+9BEg7u4cSW7BgizEmtIsrR8dK9iYE0qrsB+U2D1iBGtPfF6oybUQHcmW6aJzbzYOxBfvr+T8kGfoeUGzpR/Oup4MtIpEyMtgyqTzMaWQrcgaRxZiMuqyYkgqm7XXyaabPwxjhhgstJ8UXgV4ifNqZiPHjWg7jH9X4M1BkpqIZh4M3xUjexjo+2fsTU2qmUYvALpEtC+EQ7SDGpLn7XXzGBP3FybhQP0n4nacpxAtPo/vKiK1iQcMFkJNJrQ17DP9xPlrX3m9mccapve3wDfTy58Ek2Z21GIeWLUt2vtjscpmNqY5qT93U6kHS05I2tSN1sdBUSrgqw37OfpVOXklWcmo5TJxRUpdQQpdQ7SqmtSqnNSqnbE9tzlFJvKqV2JB6z233mX5VSO5VS25RSqW4acFxcw1w8/pnH2Zq5FQ+SJpbK4ujdoZa+F4/c38mgLVc3jhja9qbgOOORNXMy2vq1Ea9JLZoU0ZUZNQb8SGs9BlnT36qUGgvcCbyltS5F8oDvBEi89iUkSf4i4EGl1Knp6NsJDqcD72Qvz0x9BgP4l8TJnS4M2soQ94WsnjONL9DWNGETUm30yDzSk8WBREUl6xSszl3Nznk7j1sL62Q5oVagta4kkcqntW5SSm1FoqU+S1vRi/asPAEAACAASURBVGTVkH9JbH9aax0G9iilki7DFb198t2hoaSBxvcbyYxl8m3kR0xl7OexsNKWO1lB52VLThYXkuKWXAfnIDmgaxHDTLK2kYn4ly2J18NIimB24n3JGktdLKt02nEgtQOSs88uUlPyeSES1wsQtAR5esHTR1Xs6G26pb4rpYYh5Z9XAvkJIUZrXamUSkZQFSEx0knK6RgGeVrIHJzJ+tz1zK2ayywM/gcxq3eWUpZKLLSN+HF6f1bNAv6M+FhNTBQQV3Gs2kI1BvXIjO5JHL8JExtSHSGMwV4kmquOthIv+5Af/PHE/5P+6GNVST1dDKEtbDRV8c5uJOLNglzfhyc/TMaY1Ldq6LKgKqUyEF/797XWjcepcdPZC0f9nkqpm4GbAXy+3nUOd4ZSihdnvYhvmY/x/vFMwmA2p15QPUjZ2GTCc2/hRGbR+Yk/rWL851m/xjRMmjKbGHpwKKNqRjGxdiJWbWVR2SLiRpzDOYexR+y0uFooqC5gUOMgtlmi5AXzmHl4JpmxTIqNMLNUnK/H3VQjaz4LifpTHG1Fb0DC+4J03id4D2JI240IfW8EIxiI/zS/3bZUZA9diNQBV8DygR9QPrv8pMusdIUuCapSyoYI6ZNa638kNlcppQoSs2kBbcXsypHBLclgRMvrgNb6ESTYhMLCwpQPzMpQeKZ4eLTgUX7y2E8YEB7A95Cwu1T62Y7kq0i5WJDaaz3Fgdzk2cjNcxswTMUIug7ijGSj4y7qBtSRMTUDS9jCvrH72NmyE+N5g8JAIXsv3ovL7UpkosSwYaOGGnaW70TZFZiwf+l+vrb1azxT+gxrp6xl8M7BXL3larwqhifm4dLwwA7nFFMxgpYgzlgmAcS4kTTs1Nvq8UV9GBg0I4ENHyD+0z8hN4+VnquqJYhRpD2fQQrq9xaDEX98MoF/v28/ZuTUZD6fUFCVTJ1/AbZqrR9o99KLSEDGLxOP/2y3/W9KqQeQpIJSpHPIaUcphSfXwx/m/IGfLP0JE00H9wL/QVtXjFRiRaxxFmRWWtPNz9uQEp9XIZE8zYjLybD5ea3kJf5etpHYkBjXPnMt02qnkVudS5gwdoddSnB6YcmEJfzizV/IIn3m0cfIGpxFLBaj+B/FXLP9Gkxlsn7cetwlbupK6vjNjN/gyHQQ2RHhttdvozhYzJ8m/IkDgw4Qt8Zxtjhxt7hxRpxUFFRgC9uwR+wUHy7mum3X0WRt4oG5DzCmfAwl/hI+01DGDbFM9iJr6deQ36EJ+He6Hrl1C3KztWd3Z2/sITnIjF2ICOmmrE2smrOq253De0pXZtS5yESwUSm1LrHtJ4iALlJKfR3RdL4IoLXerJRahGiVMeBWrXVfCbPFZrfRNLGJilUVlARK+BpiKfwekkaWSmG10dYhcj3dm8ltSKrWHchsagBbM7fyZOnr7Bu/D99QHx6LB/9+P6MbRlPhqqAut04EpV0/ztCIENXLqlm4diGvl71Ohq/j+kqbGvWB4vM7P48n7uHZ4c9iL7W31drNl0fHeAd/8f+Fe96+hzpvHZFxETKyMjBNk2DinxMnDTsaGLthLNduv5a4ivPfs/+b0FkhVodXsyljE83bnmLuR3PJCmUx6dB8vq0tVLgq0FEfj8W8rcnyx2M4coOCCBFI3m5vhQ6OQQqWXYaovNWOWh696lGsHusJy5z2Fl2x+r7PsQNpzjvGZ+5DytL2SawOKw8teIjb3rqNwS2DyUIyLC5EuvHVHv/jPaaINkOSDzHg1Bz77YBc+JFI1szZKsqywrcZWz+WIcEhmMpkwe4FlFeXoxNDTKm/FFfcxT+H/xNGcVTTXFe2iw25G5hRPYOVK1dSM7EGHdBoi0ZZFGWryphxcAZZkSyW5S9j9ejVuF1tIXHa1ChDoQxF86hmapbX8OMVP+bOAXdiThQh0Ui9orxX8vj+pu+TEcvgoOsgv7vod6gihdflbS2Q6xjuYO2otSgULx96WRasNrhp0U1c4p/QJUG9AMkFNTF5ctSTXLz3YnIjedwOfIIMij3Bi2gvv6atdExURXl4xsPYMm097nXaE5TWp99uV1hYqG+55ZZTftyMlzO4ccONZEYzsWorGmnXcCWtrUN6DYWodcnoD434+H57nM8YSIjao5g47XU8W/Ys+y7Zh1qrOGvbWZxffj7OuFP6qiCZG28OeZP1xetpntJMRlbn1kj9ruZn7/4MgIPug9Tb6xkaGEpMxciIZVDtrGbRuEWUl5XjG+5DGYpIOEKgMsDQTUM5NOgQlIHL4yK2IsY9S+7Bb/ezM3Nnh+OMqx+HRVt4buRzbLhgA97crnUzazjUwA8f/yHlLYM5h+PnkjqQavnfAlosQe65+B4WrlnIwgopjV2JZOasQYxfJzIwuZH17vlIBk4yldDE5ID7AL+/4Pc4xjh61DX8RNxzzz0fa62nd/baGRnr21Uqzqrg36f/O9Pfms6cijkMDQxlNAZvIKFhnfVW6ikldKw4oZCb4XiCehNwPxByVXDfV/8Lh8+By+VCn6VZ5l7GuzPfxVPnwTBFUBsHNuIe4cZut5NxnO7OwWlBfsEvOOeTcxjjH4PdtHPIdYi3St5iT8keLCMsuDwusows4rE4jfsa+dIrX2JU4yhyIjmYmLy08SXe//z7xEfG+fvev7Nw/8LWfUWMCA22Bh6e+jAVJRW4R7jx2o8W0pag5MpYrJbWiootgRZ0s8aiLezgxKmJd9CWuVLrqCWaGeXV+a+S8VYGs6tnU4DUdm5G7BDPI77jRYiFswURzsHI2u1SxHqe9HUfdB1kY+5Glpctp2VcCxnZGadM3W3Pp3pGTdJY3YgZMbnh2RuYWD8RA4MVSBLyf/TSMX4J/JiOa4iXkbYOx+IxoHTAB7ww6wWs460dGg3F43Eslp4HfIVaQrQcbkFFFO6hotpardbW6v2RSATH+w6GVQ3jc7s+R2Yskw1ZG9iRvYMFBxeQFcnizaI3+WDUBwQnBdH7NBn1GbR4WlBaER0RxZvtPSqBWpuahkMNFK0tYv6e+cRVnOfmPodnggfTNHG+6+R7K79HfSyTz3N8g9v5iBBORFTSuy67i1hRDN8gH/5tfs5//3zmV87HG+s4SJiIkeoDxLgyHLGrJdMPYyrGupx1LCtZRvmMctw5bmy23q3Y0BnpGfUEJKNKnrrpKcxHTabWTWU2YkQ4CzE07TvJY0xAboKkscPAYBrS8Ol4+y4IFmAON4/qBnYyQgrgdDlxFjs7fc00TYIHgnxx4xeZWD+RfZ59/GHmH6iZVIPVbWX1ltVcvfJqFh5ciC/s45W5r2CZKOfjVm601rjU0W0GGw414Nzj5O537pZ+pYChDca/PJ7/8v8XYW+Yu1bcRb3p5H6OL6RTkeZQE0msTcuexDbehtsug46r2MUSzxLeqX2Hi1ZehKlMzq2Q0thWbSUDWdu2fmdMPsr9mNVFq9kxYQeuIS7sDjs+lXoff1dIC2o7XBkuXpj8AoOXDyYvnEcWBpcihorPIYWee8LdSOV6kDXhO0Pe4bpt15GH0WlTcBB1rAjID+WfMl8diJDGY3FmrZjFxPqJtFhaeOTsR3DOduJL9qWbBU/kPEH92/XMqJ7B0/ufJnt4a07GUaqh1ppQMMTwD4fznXXfIWJE+M2M31AxvILRm0Zzw5Yb+Nm7P6MGeB6D3yKRUJ2hEDX1Gdpa2O737GdT2Sa8jrar6XA6cAx2YBaaLBm5BKvdypL1S/A0eZizYw6zDksohqlMXhn2CptGbEJP0KLyc2pcLt0hLajtUEoRmhLiD/yBiXsnMv/gfPLCeczA4H5kZu1utkseYkBK/vT/HPVPdk3fxZW7rsQZ89L5nCYGlCpgWcEyrB4rkUiEYG2QjLyMTqvbxaKxHlW9i8fiGIbRqqI2b2qmeE8xV+6+kqgR5Z7L7kEP04QqQ1gcFrw5IgzuYjevzHuFmf+YyYgNIziQd6CDFdSMy+ASiUSIhqLkrczjW+u/hYHBI5MeoWJCBZiwa9guFjct5jP7ruBrtHVb6wwDmUmfRRJV4irG9szt/PnyP5M1snPhMgwDl0dmd9t0G+FQmLfPeps3Wt5ofY87043Teqxfom+QFtQj8Hg9xGbHWHvWWpbvXs7Pn/s5WdEscSQjxogKupZ8biDGjuRs+nbB2+wbug97lp2VA1dyXsX5/Aa4lqPLhcSAPwJvVM5nzENj2JKzhTpXHavnrMYxwEE8Ese73ostaqN2UC1GzIDh4npyOB0dZrV4PI5pmq3CY8ZNwg1hdETjOuQiPCyM4ZC16YCKAdy29jbq7HX8afqfsLXYGL5kOFFLlGZnM1FrtPV4Ol/TYmnh1nW3ck/OPQQmBzAsBvFtcUr2lgAw98BcssJZ5ERysCYKjl2/6XoatjXIuYXy2Wg6uQOpW3wsvEhR9WcRTUMB2zO389cb/kpWTtdnwOQSwunq24J5JGlj0nForGnEutvKXYvvIjOWSQSJz70Ziac8nrBaELfA15GbLKZi3Df/PsKzwzicDlyvubhj5R0oDH5O5+37spEYyxJkRB2tYoQtQfZm7MUb9VIcKEZhEDZCRI0oAWuAzdmb2ZW7C1O1qcvuiBtPxIM34iWu4mSFsyhrKMMRd6BQxFRb4WuLtuCOe9CYNFubicdd2LWtQ4OlsBFCoQgbYTJiGRgYbPdux1Qmfruf0XVTUKaDCsRo40f8xSORwa4S8VU7EYPZYY4f9+xDXFk/TnxGY/LG4Dd4a/pbOMc6Wwtdh1pCxGNxXJ6T7552Okgbk3pIZl4m8ew4r297nQv2X0BWNAsbkkUyGxndVyBWxCGIcF6N3JRfos2SaGLyqzm/IjA1gMcp6mHVjCr2b9rPsMAwrkAKex1ZG6g+sT+QBOU7tZUhsUxi/om8RVuwxOdNJ6Wmk2ExL5MrBzGm8jxaEENVOLGfKiTGYB9i6VyJVJfwIWvwLKTRUhbiSy7G4FAsk2XI7NU+7LzYdDICmGY6GEJiOdA0igzgJaRRUhhxh3RW76g7U8MUpKP63bT5M58Y/QTrz12Pb6BP1r8tIULlIQp2FlCbV0vAFsAasRLODuOqdhHyhdB2DS7wFnhpaWrp4GPWWp8Wl0t3SM+oXSDYHMTcbjJh2wSu2nkV7rjMLxFgG3Lj5SE3UvuC3yEjxCHXIZ6e+DR1s+uwu+wdzPxN65u4/4X7Qdt4DQll7EZjsw4oJADem9hHJHFOMUSQGhBhP1EkVHfJpa1mspPei+pSiE/zscQxAA45D/H45MepHFVJxpAMAjUBSlaVMHffXKbUTWkN/DgWdfY61uesZ0/2HraVbMO0mcRyY1hrrDgmOE6JC+Z4pGfUk8Sd4SYyPsL6IeuxmlbOPXAug0KDsNN524xKZyXr89bz5qw3sRRbcHvdeNTR4WaO0Q7W5q5lZs1MzkJUw096eI4aUSEPt9t2Khr3thfMQC/tMx8JYrgDEVITk13eXfz5M38mNiSGd5uXc5acw7i6cRSECtCIin3kFQ7TsYtbTiSHBYcWsODQAtgq+6111LImbw3LQstoLG1EWRRun/uk3V+9TVpQu4jdbsc+wM7qz6xmzeE1fHXZV8mIZlDvqMdUJlvytlDjraEup47Q8BDubDeZ9uPnKdrsNpYOX8rMmpkMRNr23U7/rdF7siQLkz2MWHWTydm/nfZbqvOqmb9+PrMWzyI3nIuBQRwJ9XwN6So/BnFrDUk8voIkyRchRQJKoEO8loHBgPAALjx4IedVnMfOD3eyqmAV+wbuo3ZkLRmFGcRj8T5heEqrvj0g0CRzR7Q8SsbIDAlSVwqF6nYrg2Z/M6VLSrl5083EEUPV3+hbLThSiUIyihYm/uYjKrqZMGY9Me4JBjUO4rJ9l+EwHWhE61gB/AMJ8wzSMfk8qQC332ZDbAZzkbV7feLY0xPHTZ4LiOFvffZ6Xpr4Es3ZzWRMPDVhg2nVt5fxeBNK1piT31dGVgbbS7cT2BrAHffwCFLS9JfIjXb6h9HUYUcC3x9ADF0KWddvzNzBCxNewBa18aWNX2JoYCgKgwhiSf9Pjl9rqrPwkCjSenH5EduzgUlIqOi0xDlYtZVpddOY8u4U1uas5a2KtwheFDymsCYzipKPqSAtqH0Az3gPf9n7F7679rtYMPgRMsscoE2128eZIbReJOxvAeI/LkGENKZivDjsRd6b/R6uWhdfWP0FJtZPxKZtRJHKA7cjFSEaevF86pGqfJcjxrcipOJEEeDGYFrdNIauG8r90+7H5XOR1EDDTWHRpPYrCssLqRpYhdIKNVkdFe7ZG6QFtQ9gWAz2ztrLxr0bGdk4kuy4h6sSr2nEtbIICYrwI/mVu5Af7zB9X4CtiNHtKiQUcwi0hk76bX6eH/YqG0dsJJQTQkUVty29rbVz+gHgLqR8SCqLldcm/rYh8d2TkCJmFwPZkWxGLB/BWQfOIisiwRXumJu4ipMfysembZiY1DhqeKL2CcpHleMb6etVdTm9Ru0jaK2JRqIMeXkI8/fPxxfxUdTSsXijmfiLIj5KDxIEsOiUn23XGImsCS9BbvgM2tTbj/M+5u2yt6kfVI/O0BRuLmTO3jlMqJtAZkyMcH8DnkKyjE7X+a/jaGvyiViVt4oXb3mx2+6e9Bq1H6CUwu6wU/G5Cv68888on8I4ZDBn/RzmVcxjQHhAa51dKxJOBxK5dB5SdOvIioqnawiehxR4no0EUBiIarvfdZBnJjxDRVkFhtugYE0Bl75/KWdXnY3NtKEwMJH0s98hNWdPNmvpZDgyWspst/qNqzhLC5aSEcmgMqOSqYenUhwsxsQkP5hPYE+ArFG9F9yfFtQ+hsViIass8QMPgvcGvMf7De8zausoAHIDueQH8plcOxm7acdnOvkmolbuSeyjCrEaH0KaIzUhat07pLbztgUR0idpKzTmt/l5YcQLbCrbRDwzjqXRwmeWfYYph6cwJDhEQiCRlLb3gf9DBpy+4KIKIFFcY4DNvs08M+0ZnCEnMWuM+qJ63CVu4rE44YYwKw6uYPi+4ewv2E9oZAi7q3crQKQFtY+TVZQFRVA1VurVbyvfhjPHyYvhFxn44UB+vPLHKKQiQc5x9hNCBOF1pGJ+DFnvxpFZTyGGq/aFtxWtpY0AUV1zE49hRK1tz3ikTUJ2u21bs7bS6GpkxqYZTK+azohmSU6LIaGKLyMpa7vpXSNRb1CDGJZ+A3hiHkKTQ1gyJRCiNRXODi63i3B2mKrpVTgS/3qbtKD2M7IGyw3idDupHF/JT70/JbshmwW7FgDgiDtwxB04406yI9l44rLCciIVEc5H/I6atkr9yRicMNKnxYe4ThxIWZKkXzKpdhuJz3WlPvzs6tnMrp4Nic/sR1TbxUgmkr9HV+HUkRw8BrUMIr4vTrg03KlVNxWW3vakBbUPEWwKoiyKyN4IOlOjYxoVU9j8NqJFUWweG/FoHDOUWCsZ0FzYTGBAgP8d+784DzsJ54Zx1DpwBV1cvvFyptZN7XAMTcdZsj0ZdGyg1dkaNxncbyAqqoO2MMKByA3lQFwdScJIF7EnEPW7jp7HNJ9qNiPXwWk6GbN1DOW15fhH+rFmWIlVxbAV2HBnuFPmP02SFtTTiDY1gcYA8YNxZq6dSWldKUFbkKxwFoddhxlbPxZTmRS0FBCwBtjt3U1WOOsoa3ASQxsd0tsMbbRaihWSMfMOned9LkCidNqzBln35iCZNSAuIg8yC+9ChDbZRKoQiQByckRyAtIHps8Ud+4G7QeUm7bchLnVhKWw1beVzXmb8Ya9rBm2hrqiOiz5ltasnN6OZEoLai/Q3UJjjbWNmHUmM1fOZFrVNEY1jTr6TUfohN6Yl0n1k068cy0xsFEkOOCPSMhdJvA2x648/26Xz74j7deV7QuK9zS5oK/hRwaiQiQ22NCyEJjgn8AEv6RkXHrgUjb7NrPHt4etg7ZycNxBfIN9rYXieoO0oJ4EpmnSVN2EUWFgG20j3Bg+YcW6hkMNFH1cxA3rbyAvkoeJ3AhxxErrRmawjYnHOHKTbEeMPiGkG0U2opYlsSDryQ8R1XIzYhxKRVvHTwsWJNUuaRyrs9fRaGtkaGDoUSl14xrGMa5hHBceuJC9W/fy8PkP453UtTrGXSEtqD0kEomQsziHWzfeilVbWfnxSvZn7eeTSZ8cMyqlJdiCpcLCt9Z8C0/cg0YE6vNIhFEcCV0LIOs+C7I+siJuleTzZPrWka4WG73bIe7TjIFYtf8DUe9DRog/Tf8T1eOqyd2ay+iK0Uw5PAVTmfgiPgYmGmbZtI3SplLO/ehcVk1c1WsqcFpQe4jFYiEjlNH6A11cfjFmuUnVziqWDF3CgbwDVI+pxlvgxWK1EGoJ4V3u5ZbVt7QK6QtIqZb2s15XWjhA5+u9tJD2HqVIJzgvEujw/PDnqZ5QjW+Qj1hhjA3mBj6o/QCjUmbWkt0lDKsbxrzKeWTGMplSPYUlVUvwDeqdcqNpQe0hFouFHSN3sKF8A+P942X9gkFBqICvbv8q5naT6IdRfj3n19SW1TJ92XSu3XEtBkarkN5I3/MdppFEgb8hFTMUsDtjD6sWrCJjQFu6m8ViwTfQJ6ZuoGZSDbtrdrN59WYG+QexcfRGnFm9l8eaFtSTwCwwefTyRxm3ZhwtthYu2nURJc0lWLUVAwOH6eCOD+6geVUzeRFxWDQgHcH/g7SQnm4siCAmHxVisX4IKUuaJDuSjXeXl0h2BIvFckx1NjMvE/8CPwF7gAyjd7uQpwX1JPDl+zDzTPYV7yNQF+DBCQ+SWZ7J7B2zmXtoLt6YF6fpxBmRkTWAxLD+nL6f8dLfcCLr+2sQf/B6xLWULJqeh9Skai9iA5B1fTZtAR6jaavRlCQnksOdS+/kgcgDNM9ubq0T3BmpCnxIC+pJYlhE5dUhjSXbQqQwwpKJS1i6bSk3fHADoxtHt773ZaQsaFpIew870irzXOBs2oIxoogPNOlVNhBh7opp55DzEOvy1vH+6Pf5+oqvUxIowR1385W1X+HPnj/jH+qX0M5TSFpQe4jWmqa6JjgIM9fPpKShhIgR4bDnMAd9B9FKs3ToUkZvEkGNI+loaYNP71EMfBcRVCti9DGVCdrAhkHSSZbMeokrs7WPrEqI7BbfllYbA8DvJ/+e3bN248v3YTNsPNv4LF/e+GWKWoooDhRz7+J7+Zer/gVdeGpLjKYFtRtorYlFYwT2Bsjfnc8NG2+gOFB8lE/N7KQYyIdIQHyak8dAfMaLgHFIQe5GazN/nPFH/Dl+CisLscVsGNpgqH8oWwdtxRlxsqtsFzpbBNVaacW0mxhDDCa9Monrtl8HwLVbruX+kfejCkQIa8+p5aHMh/j28m9T1FKEoQ1uWHIDz1c/T8uMFhwuxykp9p0W1G7QtKmJ69++npGNI1uD3Tujs/qyKzl2VFCa7jEDeBExuEZVlApXBY/NeIz4vDhuqxt/u7CuA+ED2B12akO1+JxtrpJITgSb3YZSirXj13LpnkvJjmaTF8njc+99jufcz5E9PBuXx4Weq3nQeJDbl97OwPBAJtVPouiDIv4U+xO1w2rxjejdag6dkRbUbpBbmcuE+gkYGERVlOX5y1k8cTHa0KBh2MFhZIQzOJx5GG+Ll2H+Ycw7NA+H6aALwX9pukgBYggC+HDgh7x03UvY7Dbs1qNzQJOdwY808rTvGK4Haf798n/n5y/+nMxYJvOq5tH0ThPLi5Zjd9iledioEH+v+jtf2PYFBoUGkRfJ487ld7J7/W4evOpBsouzSSVpQe0GNRNr+HDHh6wtXMvOMTtxl7hxOOQGME2THf4deHwewqEwVVSxk50Me2gYI5tHnuYzP7NI5t2GjBAvzXyprSpkD8nMyySWHeOljS+1+rpnHJ7BkoYl2AeKQHtzvBy+4jAPvf8Qd797N1ZtxaqtjGoaxaz3ZrFhwAac7tTV/z2hcq2UciqlViml1iulNiul7k1sz1FKvamU2pF4zG73mX9VSu1USm1TSl2YsrM/xbhz3Lx040vUXFlD1uisDqOyYRh4c7wYFmnz5/K4sNgs7PLtOo1nfGbyDyTpHGDsjrHEoieXNKcMhc1mY+1Fa3lo0kOYmOSGc/Fs83R4j2ExiM+Jc89597DHs6fVFnHNzmvQG1Nry+/KKjgMnKu1noRUsbxIKXUWcCfwlta6FEk3vBNAKTUWSWscB1wEPKiU6lv9AXqIzW7r1qhpt9tZUboCE5NCOuZopgoHZ76a5EeSz52mk6t3XE3z7u52re0cb66X3fN3s8+zDwODBVsWEI12tNPbbDZiU2P88aY/sjx/OTEVw8Dgio+uINiUOivECQVVC8krYUv8aaTqxuOJ7Y8jlSBJbH9aax3WWu9Bsq1m9upZ9yOaJjRx0H2QMjomZfcmdqQFxA+QqgmPIrmlfa9vdu/xIhLZ5Y67KdxVSCTcO9WgvDlePsqX7Nux9WMJHD66o47L7cKb6+WFq1/gsTGP8f7A9/nnzH8SaUpdRaou2ZWVUhal1DokyeNNrfVKIF9rXQmQeExEPVKElGNNUp7Y9qnEm+PlpZEvYQC3Ap1knvaIEUhLxvORkpobkQryFyN9Vt5Fyp3MomMNozOF1xAVLgZcvelqct88Mp6oZyil+GDWBwQsAVosLZ33jUxgWAx2X7Gb177xGu4ZbrIKUzc0dklL0lrHgclKqSzgeaXU+OO8vTM79VEKvFLqZqTVCj5f72QY9EWUUuwYs4Poxihl2sYi4ItIrmhXsSDtFpyIxfM6JAUrk7aaRxqTbZnbAChrLMODwXSkoNn9nHlhizGkP+29QHGwmLGHxvJe5D3s9pOv/pdZmsld192FxWvBl3vse7N9j9VU063ljNbar5R6F1l7VimlCrTWlUqpAto6/pUjxdCTDAYqOtnXI0hZWgoLC8+ke6gD4VCY0q2lWLQFhbRz+DvwK6TBUSWdC5AFiTk9B2lBJk9p7wAAF/FJREFUeB601rZLjoQmJiFLC0sGL2HdsHU0TmjErDfJPpDNV9Z8hRHNI7Ai7QvfSfydSfiRsMybgAUVC3ij5g3shScvqIZhkF3St/SQEwqqUmoAEE0IqQvRtn6FLBNuQPoZ3YB0HSCx/W9KqQeQ4gSlSOuQTxXa1ATWBbhyxZXMrJnZGgShkHYJTyLrgz8jzXoDyNpBIS0VLgTmICNeexWlxl7Dc6Oeo8HTQNgepn5CPe4sNza7jUwyIQ+ChUH+OPCPXP/W9QxrHkZWNIsFnHmCGkPSBecAo0wbRWuLaB7UnPJCY6eDrsyoBcDjCcutASzSWr+slFoBLFJKfR2pAvlFAK31ZqXUIqRIXQy4NaE6f2poCbQQPhTmR2/+iMEtg4G2ZrxRI0ppYyk2bWMoorrdjgSPJ2NTvbT9MLsydvFR/kdUZVZRm1VL48hGvIO8rTWafBytmrk8LlzjXTwz4hncH7i5+727uRhJrTvT2jm+hKzVR2PwpU1f4ndn/w6P7+T8qn2REwqq1noDMKWT7bWIRtbZZ+4D7jvps+uHhFpCOFc7uev9u/DGpGaO3+bnxeEvsunSTaBg9nOzmVk1U2JHOdptEzbCvD74TZZOWYr+/9s78+Coq2yPf86vu9PdSToLIUASlsSwyBYWEVRcEBjHcVRkRsTxuTyd8g3qVM0ba56jMmXNWNYb3yyONe9JqThM6XNQYdBRUWceshSLrLIN+5ZAWEKikH3p5XffH7chAYJESbr719xPVVf/+nZ3+vzSv2/fe88995xCRWpAp6O0LKs18XMH8Pl9nOh/gpNrTjIqlM1N6OFOspIdzMYutbFL7JjE38aSZF9yiynBYJDuS7pTcqSEtLD+Va/2VPPrm36N/wo/aZZuW3fXOlYcXIG7wc0VO684428cyjnEkSFHCPQOkO6+eGdFoCDAe/3f48GdD3E/ySnUz4F70PVgbI+dXF6zKEaonUhKSgpVuVVM2DABC4tmq5k/jfgT4aLwGfMmX6oP32AdOLFrzLmJNbM7cUHF5Xaxp3AP4V0hrlceCoGyTvvriUEqeh6fEc4guyIbe2h7pYydjRFqJxMuDrMvsI9FxYs4WHQQ70AvGb6M+NrUP0yNp4bsYHeuIfmE+hfgEfTyQrKSXAP5BCA9O503HnmDL2/7koySjC6vSdJRmz4p/AQ32pucFPGcbfDTWsO08HghTQ2JUAuuczFC7WTEEnx+X0I5M8QStgzego3N7bTWVk0WQrTmOG7wNhAJJd8iQ+JcTYYuJZKjL95M4Pb4mtLpZNHqOS/+spiU1M6tTZoIGKFeKlgQskIIWqjnz6PnbI5mHE2o0UxnkXxnZGgfgeW9lgM6M3/y+UU1h3IP4XYnn4/UCPUSwRPwEHTpmdxwzs1d62TanotlW7Q0t8TNlq7CCPUSwY609qFukmvom05rPLQoueiMD4mIEeolQiA7QFm3MkDHFMd3ZbdzOUhrMFLEFbnoHEqJiBHqJYQ/qPtRRXJF2Q2mYxnwnYwR6iVCOBxmX999gO5RO6/Ebvw5Vbay2WqmOrf6K1/rVIxQLxHq99Uz89OZgI7iKYyrNZ3Lqa0LLuXC03T+au9Oxgj1EqCpoYmpS6fij/ixgQ3o7BLJwufoCu1A0o6BjVCTnHAojGuLiwE1A7Cw2IDeaH0o3oZ1IvuA9YBHeRi2bxi1VbXxNqnTMUJNcmzbJq0+jW5BnV++hTa9TxLxCjrm994991K8opjqw8k1VzVCTWKUUkTCETIbMvHYHmz0kLdz0lUnFv8ATu3sffifD+OpSK65qhFqEmNHbLot78b92+/Xha3QOYCTkSAwBz1i8CgPjy59lJMHTl7gXc7BCDWJqdtXxyOfP0IgHMAGlgOl8Taqi1DAy8Cq6HHfhr6MWndOqi/HYoSapNi2zfVrrj+dYG0t8CCQfFuqW2kG/hVdl0awmHxwMtV7q5MipNAINckItgQJrgriXupmZNVIAKrQKUmPxNWy2FCOTstSC+Q15XHjyhuJRJy/kTz59gNdorQ0t9Bc00xKWQr3rbuPoTVDATiOHhJ+HlfrYss2dO2dKVhMOjyJZfuX4R0a/5Q4F4MRapLQVNrE4x8+Tl5THm7lxkavld6FDnCIZWxvAJiKrgiwMNq2E6iI0ecr4DXgeiA7kkZBWQG1g2sdvaHcuZYbAL0EU7e5jmfefYaCxgIs5eYYcCcwAR0IEAuRuoFbgf8BVqPLdPwUXTh3MdrJ8x/QTl7/rmEh8HT0eMbGGdTt+oqybA7ACNXBNDU0IcuEpz9+muxQNmCxALgaXSf1YIzsyANeBBagS0sOBWo81ShsBB3Vdxm6qtzPgdwY2bUaHdyRFklj0upJjnYqmaGvAwmFQgSbgoz4cATT900/PdTdhnYaHYuhLbnAb9A1WQW9g2XuoLmU5pfS64teHO1xlDF7x3DbwdtwKzdPoofEDwBfdLFtO9DlLXOAyUcm8+mhT8kqdmZ5Z9OjOpCmmia6r+rOD/b+ALdyo4B3gRuJrUgBLkfPgwVdM+eFq15g243bkGuEyimVuMa5WDVqFbOHz6bKW4Wgiy1PjYFtIWAGWqxe20vBLufW0zZCdRgNtQ3kbMzhZ2t/hoVFBC3SHwInYmxLNjrG1oOuVjev/zyqhlWR2SMTl8uFZVm43C4CIwIcn3qcV658hRU9VyDoOXQshnNbgHfQ8/Qx5WMcm0/JCNVBNDc2EzoaYsaGGVhY2OhK4g+i1w1jiQ9dZ7MvujfdnrWdTaM2kZXf/tBSRIhMjvD+1e8TljDDou+NBQvQgR7XHr+WpipnhnwYoTqI5upmRm0aRVYwCxvdk74ExMOfeVv0s9OARlcjb417C/9lF06Z5urv4kD6AfLRTq9YcAi9Fc5SFn03x+rnoXMxQnUQ7go3U/dPxcKiHr3cEY/NXOnA92kduq7NXUtoSIgU74Uz1Hv9XpYULgH0SCAWeNDLQhYW4w6Nc2RtGiNUB5F7PJe0cBoKHWAfqwCCs7mTaHl5dP3Xj676iLSMjmX+c7lcHCg8QIvVwgi0M6qrOQ4sRc9Tw1aYUGPojPSpTsAI1SEoW3F00FGaXc1EgD+ig9BjTTf0EpAF1LnrmDNiDlIoiHQ8B4priIuNORvJAR7tGjPPwI/+UVOAP+yn95reNDU0UVNZQ3NjPP6LXx+zjuoAaipqKFpTxPfKvkeKnYILmAgsirEdFvAceh0UYGWvlRwee5jMrK8Xb6SUwh/2I8CV6BzDXeUME6AI+DHa/iu/vJKSkyWsO7yOg5kH2dlvJ9U9q7GyLHwZPlJSErPAlBFqAtPU2ITapnhg9QOMODnijOfGoCttN8bQnkHA3eiLv9JbyadjPyUt++snu/b5fKzot4KRJ0fSAHRluHwvtNc3tU2b1/Zy3fHruO74dbBHn8uCQQso7VuKGqYSoqbt2XR46CsiLhHZJCILo4+7icgiEdkbvc9u89qnRGSfiOwWkW93heHJjG3bVJdXc8vrt/DcJ8+dI1KAGiCWAXEudK90avHlbwP+hvQW3J6v/1svllA6sJQWq4Ub0GGFXYGgHVYD0Rf6mu5r2Jq19ZzX9WjpwSNbH+GxJY9R8EliBkV8nTnqT9CbIE7xJLBYKTUAHXf9JICIDEH/8A4FbgZmiUiyFbnuElqaW6j9spba3bU8/tbjXHv8WjzKg43NZ7mf8dO7f8qHfT8EYBKxi5mFM3vTkITYOWwngW7fPI23K9/FobRDp4P5h3aOmWeQBzyMvshtbN4b9x5//v6f+fl3f86qHqt4t+hdDvsPY0dr2/Vr7EdJRQknS08SCoa6wKJvToeEKiK9ge+idw+dYgrwevT4deCONu1vK6ValFKl6CWssZ1jbvKilKKprInb5t/G79/5PX0a+2BhsTewl5dGvsRfb/wr6f3T2Vi0ERubAPqfHouM9x50sH236ONNOZtIKbi4uVx6VjpzrpnDwdSDDEInJ7v14sw8g1zgbVoTja/NXYs1zCKrTxZqkGL+zfPZev9Wfjf9d7xc8jInPTq/0vjj43n+f5+ncU8jdV/WYduJ4R3uaI/6IvAEZ5bV7KmUOgYQve8RbS9Ab7Q/xeFom6EdQsEQDTUNyDJh5rszmVAxASv6tezK2MVr173GiTtOkDkkE7fbTXX/amo8NbiAG9BLJV2JJ/oZD0cf29jMu24eXv/Fz+N8V/tYVbCKFquFAuBedB2Zi8WFTslybfRxeWo58ybNw+f3AZCWkUb2ZdmICOkF6ZTfXM57/d+jzl2HhYXP9vGLD37ByH+MpK4yMbbHXVCoInIrUKmU6miSgPb89OdsiRSRfxORDSKyobExli6RxKGhpoGmPU1Me2MaM5fPpHtQF7iv8FUwq2QWL9/x8jlXrivdxayxswDtCXyWrhk2uoCH0PtKT8XzAqzusZqIN4LL3Tmzma13bGVb1jYApqMdP9/0Vz0TmAy8CvwKfSFW+CqYfdVs3L3an0t7PB58qT723raXF8a/QEj0kLdbsBvT907HfTgx/K0dsWI8cLuI3IIO8cwQkTeB4yKSp5Q6JiJ5QGX09YeBPm3e3xs4evYfVUq9iv6fkp+fn0zFxS5IOBym/ng9+VvymbFxBmnhNCwsqrxVrO+xnkUTFpFRlEG2ddo/hx2xqTlYw7jPxjGldMrp9t7AU8CPgIZOsE2AUcDj6OgjH7oX3Z9eyjsl73BizAkysjqvaGOKL4WFwxfCP2HUiVFcjsUcdH6nzeiLSUXvQQcv1ERvOei15BuA+4FiIAUoib72iP8Is8fNpn5g/QWXkHypPlqubuE56znuW38f/ev741EevrXxW6wavQqx4lsr44JCVUo9hb4WEJEJwM+UUveKyG/R2wqfj96/H33LB8BcEXkByAcGAOs633RnEolEqN9fT/HOYmZsmYFb6a/gs9zPWDpwKaHJIbKkNbBdKUX1wWq67+vOf67+T3wRHxYWYQkTkQge28M90YHRG+gE299kf4gFDAH+G730kwYobJqtIK+MfIXy8eUEcgIEOnlW7HK54EZ4s/+bFL9RTFYoi5s4dwh26vFR9C6hE0BPdLB9CXoEcLaUglaQ6n7VZPfKpiN4fV5axrXwUv5L3LnoTsZXjmfUF6P4+NjHZBXEdx/rxfTrzwPzROSH6LjnaQBKqe0iMg+9bzcMPKaUcn4auE5CROi7py+Pbn709Fx0ZY+VLB68mLoRdQRECyESjhCJRIhsifDs/z1LRri1F9ueuZ254+bS0KuBZ95+hm7BbvwLeuj4Z/Sv4lL0EMdG78sMnmWHhV5bdKNd87cA16AzMQg6NPDV0a9SMbqC1G6pBDxd67YK5Af47YTfcteGuxhQO4DUSOoZz58SYe/orS217lrW9ljLkJNDWNRvEdP2TiMtkkZRQxGPvfsYs+6cRUbvDCzXhV0yXp8X7wAvH1R/wNiPtQ9UtcR/wCdKxd+I/Px89aMf/SjeZsSMmsoanpjzBM2uZuYPnc8XuV/gHeU9Y95XvbOah/7xEIX1hadz81Z6K5l/+XwOFB9A9VL4MnzkfJrDnTvuPO0lBt37fIHuWRvRv5hvAmXoeUwWetg4FS3YnmjB2tgcST3C+p7r2VK4hbrBdWT2iFWWI6g5XgMK/Pv95JzMoS5NO3JECX2q+uCOuMltzOWmwzdhYVGeWs4bo96gakAVqb1SCQfDuFPcDP/rcKbvm376/7GgaAErb1pJVl7He8Xqw9WkHkolOCSIL92H2931c9Vf/vKXnyulxrT3XGLMlC8xvAEvf5j0ByKXRUjPTifVau096k7UEamLcMPaGxhaPfT0xbYjcwezvz+bjIIMMlytvevRcUf549V/pGhFEd/Z9x2K64sRzlxjHYjOqhBGzznPdgO1WC0szlvGkpFLiFwWwZ/hx+1xkxmzVGSazJ7688I5YY7UHiEjR5+nQrE/uJ+mmiaUUoyYM4K85jx2Z+2m4boGsn16aHsqomjL1C3sX72fp1c+jYXFlLIprK5crRdWO0ggL4BVYJEiiRFSaIQaB3x+H2qMajeQ3Q7b/Hj+jymqL4puDrdZVLCIxaMXk56Xfs7wLSNXX8xl15cxa/ws0nek88TyJ0iLpJ1eyAfttT3lua1113My5STl6eWs77OeQ4MPkdYvjVTPmcPNeOH2uE+L9BSeFA+eXH0Gi/su5t4991LyZQkf13x8TsifP81PwzUNLDi6gGkHpuFWbobtHEbZ8LIOO4VcrsSK0TFCjRPn220SyAmwNm8txXuLCUuYV4e/ytFbj5KW8tUxtYFuAYItQZrSmwhZIYhAraeWZ6c8S1ZlFq6IiyZ/E5ZtUTegDivFwpvmxevzkoWzEn5tGr6Je/bcQ/eW7qTtSdNj97Pwpfr4bOJn5DTlMPHYRK6ouIId9Ts6vB0v0TBCTTAsl8XW/lvxh/xsLtpMbWEtWSkdE5JlWbiCLjy27nlcyoXyKVrGt5DiTcET7VOdJsyzkXyhNL2U4vpiJm6fyJKrlrQbc5zVO4u/j/k71y+8nuHVw/Ft9ums3A7E7EdNQIKFQdbetRbrBousfh0XldvjpqW4hYhoJ3vICmFlWR3KvOAkAjkB5g2fB8Dg6sFENkQ4n1NUBgrlqeWUp5Yz+sBoR2Z3ACPUhCSzZyb+1AvnH2oPf6afZQXLAO3FtdzJ+RVXXlZJg6uBXs29eHT5o9RWtb+j1Z3i5vUrX+fFB19kwz0bTocROo3k/BYvYcQSSnNKdeB+OIC1Nzm/4vTCdHZm6c1c+U35uA637/xJ8aYgE4SM3Aw8KZ64Rxh9U5LzW7yEscTCFu3t9dpeig4VObqUw/lwuV18NOwjPWrA4oodVzguD9LXwQg1yYhEIlT0qjgtVltsGquTb9ODiNA0tom5A+eyOnc1GwdvpO6LxNjp0hUYr2+S4fF4CLpbAwYnlk2kbFJZ/AzqQvxpfnZN28VOeydpXmcuu3QU06MmGWIJ7svdrMldg41Nn4Y+WLut83pFnY7H40k6r3Z7mB41CfH5fSy5fAlLBy3lxKATuAPma3Y65htMUtREhUI5PrjBoDFDX4PBARihGgwOwAjVYHAARqgGgwMwQjUYHIARqsHgAIxQDQYHYIRqMDgAI1SDwQEYoRoMDsAI1WBwAEaoBoMDMEI1GByAEarB4ACMUA0GB2CEajA4ACNUg8EBGKEaDA7ACNVgcAAJUchYRKqABnT9XSfRHWNzLLhUbO6nlMpt74mEECqAiGw4X7XlRMXYHBuMzWboazA4AiNUg8EBJJJQX423Ad8AY3NsuORtTpg5qsFgOD+J1KMaDIbzEHehisjNIrJbRPaJyJPxtucUIjJHRCpFZFubtm4iskhE9kbvs9s891T0HHaLyLfjZHMfEVkqIjtFZLuI/CTR7RYRn4isE5EtUZt/leg2R21wicgmEVkYE3uVUnG7AS5gP3AZkAJsAYbE06Y2tl0PjAa2tWn7DfBk9PhJ4L+ix0OitnuBoug5ueJgcx4wOnocAPZEbUtYuwEB0qPHHmAtcFUi2xy143FgLrAwFtdGvHvUscA+pdQBpVQQeBuYEmebAFBKLQdOnNU8BXg9evw6cEeb9reVUi1KqVJgH/rcYopS6phSamP0uA7YCRSQwHYrTX30oSd6UySwzSLSG/gu8Fqb5i61N95CLQDK2zw+HG1LVHoqpY6BFgXQI9qecOchIoXAKHQPldB2R4eRm4FKYJFSKtFtfhF4ArDbtHWpvfEWqrTT5kQ3dEKdh4ikAwuAf1dK1X7VS9tpi7ndSqmIUmok0BsYKyLDvuLlcbVZRG4FKpVSn3f0Le20fW174y3Uw0CfNo97A0fjZEtHOC4ieQDR+8poe8Kch4h40CL9i1Lq3WhzwtsNoJSqBpYBN5O4No8HbheRMvRUbaKIvEkX2xtvoa4HBohIkYikAHcDH8TZpq/iA+CB6PEDwPtt2u8WEa+IFAEDgHWxNk5EBPgTsFMp9UKbpxLWbhHJFZGs6LEfmAzsSlSblVJPKaV6K6UK0dfrEqXUvV1ub6y9Ze14z25Beyf3AzPjbU8bu94CjgEh9K/iD4EcYDGwN3rfrc3rZ0bPYTfwnTjZfC16WLUV2By93ZLIdgMlwKaozduAZ6LtCWtzGzsm0Or17VJ7TWSSweAA4j30NRgMHcAI1WBwAEaoBoMDMEI1GByAEarB4ACMUA0GB2CEajA4ACNUg8EB/D+K5sYCWS4b/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,4))\n",
    "\n",
    "# set the facecolor of our ax object to be gray\n",
    "ax.set_facecolor('gray')\n",
    "\n",
    "ax.imshow(dataCopy) # imshow is specific to plotting images\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# side-by-side figures to compare data & dataCopy\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
    "# ax object is now an ARRAY of axis\n",
    "\n",
    "# plot the original data object on first plot (index = 0)\n",
    "ax[0].set_facecolor('gray')\n",
    "ax[0].imshow(data)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# side-by-side figures to compare data & dataCopy\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
    "# ax object is now an ARRAY of axis\n",
    "\n",
    "# plot the original data object on first plot (index = 0)\n",
    "ax[0].set_facecolor('gray')\n",
    "ax[0].imshow(data)\n",
    "\n",
    "# second set of axis -- updated image object\n",
    "ax[1].set_facecolor('gray')\n",
    "ax[1].imshow(dataCopy)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at color components\n",
    "Recall: the whole purpose of Lilo's visualization was to measure & depict Stitch's goodness & badness levels.\n",
    "\n",
    "Now that we can make masks we can count up the number of pixels of a color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-read in our image data\n",
    "im = Image.open(\"stitch_reworked.png\", 'r') # reading in image\n",
    "data = np.array(im) # into data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# redo our plot from before\n",
    "fig, ax = plt.subplots(1,1, figsize=(4,4))\n",
    "\n",
    "ax.set_facecolor('gray') # one plot, so don't have to worry about indexing\n",
    "\n",
    "ax.imshow(data) # imshow NOT show!\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know how to mask we can grab # of pixels associate with a color.\n",
    "\n",
    "Goodness and badness mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "goodness_mask = my_boolean_mask(data,[255,255,255,255]) # white parts of ears\n",
    "badness_mask = my_boolean_mask(data, [126,22,33,255]) # maroon body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count pixels in each of these \"good\" or \"bad\" areas of the image\n",
    "npix_good = len(data[goodness_mask]) # number of pixels in the \"good\" subset\n",
    "npix_bad = len(data[badness_mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11929"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npix_good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40949"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npix_bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11929, 40949)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npix_good, npix_bad # on the same output line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22559476530882408"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the goodness as a percentage of pixels: good pixels/(good pixels & bad pixels)\n",
    "npix_good/(npix_good + npix_bad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stitch is ~23% good!\n",
    "\n",
    "# Meta-visualization of stitch's goodnes/badness as a histogram\n",
    "\n",
    "# .bar this time (instead of .hist) because we have the numbers pre-calculated\n",
    "#.   have to give .bar a set of labels (x) and values (y)\n",
    "labels = np.array(['badness', 'goodness'])\n",
    "values = np.array([npix_bad, npix_good])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['badness', 'goodness'], dtype='<U8'), array([40949, 11929]))"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels, values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(2,2))\n",
    "\n",
    "ax.bar(labels, values)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# badness color = maroon, XXXease of being able to see it, goodness = light grayXXX\n",
    "# goodness = 'white', change background to gray\n",
    "colors = np.array(['maroon', 'white'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(2,2))\n",
    "\n",
    "ax.set_facecolor('gray')\n",
    "\n",
    "myBarChart = ax.bar(labels, values) # SAVING what gets plotted on an axis as a variable\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x7f8398c6c890>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myBarChart[0] # this is indexing to the object that is drawn as the first bar on this chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so I can use myBarChart as a variable and update the colors\n",
    "for i in range(len(myBarChart)):\n",
    "    myBarChart[i].set_color(colors[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's reorganize\n",
    "\n",
    "# 1. Define all of the data elements\n",
    "labels = np.array(['badness', 'goodness'])\n",
    "values = np.array([npix_bad, npix_good])\n",
    "colors = np.array(['maroon', 'white'])\n",
    "\n",
    "# 2. Make the bar chart\n",
    "fig, ax = plt.subplots(1,1, figsize=(2,2))\n",
    "ax.set_facecolor('gray')\n",
    "myBarChart = ax.bar(labels, values) # SAVING what gets plotted on an axis as a variable\n",
    "\n",
    "# 3. Update the colors of said bar chart\n",
    "# so I can use myBarChart as a variable and update the colors\n",
    "for i in range(len(myBarChart)):\n",
    "    myBarChart[i].set_color(colors[i])\n",
    "    \n",
    "# 4. consistent\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Define all of the data elements\n",
    "labels = np.array(['badness', 'goodness'])\n",
    "values = np.array([npix_bad, npix_good])\n",
    "colors = np.array(['maroon', 'white'])\n",
    "\n",
    "# 2. Make the bar chart\n",
    "fig, ax = plt.subplots(1,1, figsize=(2,2))\n",
    "#ax.set_facecolor('gray')\n",
    "myBarChart = ax.bar(labels, values) # SAVING what gets plotted on an axis as a variable\n",
    "\n",
    "# 3. Update the colors of said bar chart\n",
    "# so I can use myBarChart as a variable and update the colors\n",
    "for i in range(len(myBarChart)):\n",
    "    myBarChart[i].set_color(colors[i])\n",
    "    myBarChart[i].set_edgecolor('black')\n",
    "    \n",
    "# 4. consistent\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we actually have a function for this\n",
    "from image_manipulations import color_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors, color_labels, color_rgb_labels, npix_of_a_color = color_components(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0,0,0,0', '0,0,0,255', '126,22,33,255', '255,255,255,255']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_rgb_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[135103, 19709, 40949, 11929]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npix_of_a_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#labels = np.array(['badness', 'goodness'])\n",
    "#values = np.array([npix_bad, npix_good])\n",
    "#colors = np.array(['maroon', 'white'])\n",
    "# based on our function we can replace:\n",
    "labels = color_rgb_labels\n",
    "values = npix_of_a_color\n",
    "colors = colors\n",
    "\n",
    "# 2. Make the bar chart\n",
    "fig, ax = plt.subplots(1,1, figsize=(6,1))\n",
    "#ax.set_facecolor('gray')\n",
    "myBarChart = ax.bar(labels, values) # SAVING what gets plotted on an axis as a variable\n",
    "\n",
    "# 3. Update the colors of said bar chart\n",
    "# so I can use myBarChart as a variable and update the colors\n",
    "for i in range(len(myBarChart)):\n",
    "    myBarChart[i].set_color(colors[i])\n",
    "    myBarChart[i].set_edgecolor('black')\n",
    "    \n",
    "# 4. consistent\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}