examples/physlight_camera_model.ipynb

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      "source": [
        "Copyright (c) Contributors to the PhysLight Project\n",
        "\n",
        "SPDX-License-Identifier: Apache-2.0\n",
        "\n",
        "# Spectral Camera Model\n",
        "This notebook shows an example of using measured camera spectral sensitivity data to construct a virtual camera response model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vGmr8xdSn9A",
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      },
      "source": [
        "### Utility functions"
      ]
    },
    {
      "cell_type": "code",
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        "id": "JnsLxGHBSqDy",
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      "source": [
        "# Camera RGB to XYZ matrices are extracted from dcraw by Dave Coffin\n",
        "# https://www.dechifro.org/dcraw/\n",
        "\n",
        "# Camera spectral sensitivity and training data is taken from the rawtoaces repo:\n",
        "# https://github.com/ampas/rawtoaces\n",
        "\n",
        "!pip install -q colour-science\n",
        "!pip install -q matplotlib\n",
        "\n",
        "import colour\n",
        "import numpy as np\n",
        "import math\n",
        "\n",
        "from colour import SpectralDistribution, SpectralShape, MultiSpectralDistributions, sd_to_XYZ\n",
        "\n",
        "# Grab the given camera data json from the rawtoaces repo and return the \n",
        "# sensitivity curves\n",
        "def load_ampas_camera_data(camera_json):\n",
        "  import requests\n",
        "  import json\n",
        "\n",
        "  url = 'https://raw.githubusercontent.com/ampas/rawtoaces/master/data/camera/%s' % camera_json\n",
        "  json = json.loads(requests.get(url).text)\n",
        "\n",
        "\n",
        "  return json['spectral_data']['data']['main']\n",
        "\n",
        "\n",
        "# Grab the 190-swatch data set from rawtoaces repo and convert it to an array\n",
        "# of SpectralDistributions\n",
        "def load_ampas_training_data():\n",
        "  import requests\n",
        "  import json\n",
        "  \n",
        "  training_data_url = 'https://raw.githubusercontent.com/ampas/rawtoaces/master/data/training/training_spectral.json'\n",
        "  training_data_dict = json.loads(requests.get(training_data_url).text)['spectral_data']['data']['main']\n",
        "  \n",
        "  patches = []\n",
        "  first = True\n",
        "  for l, values in training_data_dict.items():\n",
        "    for i, v in enumerate(values):\n",
        "      if first:\n",
        "        patches.append({})\n",
        "      patches[i][int(l)] = v\n",
        "    first = False\n",
        "  \n",
        "  patches_sd = []\n",
        "  for p in patches:\n",
        "    patches_sd.append(colour.SpectralDistribution(p))\n",
        "\n",
        "  return patches_sd\n",
        "\n",
        "\n",
        "# Returns a tuple of (SpectralDistribution, xy) of the calculated colour\n",
        "# for the given temperature. Temperature must be <= 25000.\n",
        "# The returned spectral distribution is normalized such that Y=1\n",
        "def calculate_whitepoint(temperature=6500.0):\n",
        "  # CIE 015:2004 scale temperature so e.g. 6500 becomes 6504\n",
        "  temperature = float(temperature) * 1.4388 / 1.4380\n",
        "\n",
        "  # D is only defined over the domain [4000, 25000] so we use blackbody \n",
        "  # outside that domain\n",
        "  if temperature < 4000:\n",
        "    sd = colour.sd_blackbody(temperature)\n",
        "  else:\n",
        "    xy = colour.temperature.CCT_to_xy_CIE_D(temperature)\n",
        "    sd = colour.sd_CIE_illuminant_D_series(xy)\n",
        "\n",
        "  # Normalize such that Y=1\n",
        "  xyz = sd_to_XYZ(colour.sd_ones(), illuminant=sd)\n",
        "  # Calculate matching chromaticity coordinates\n",
        "  xy = colour.XYZ_to_xy(xyz)\n",
        "  return (sd / xyz[1], xy)\n",
        "\n",
        "\n",
        "# Calculate example whites we'll use below\n",
        "sd_d65, xy_d65 = calculate_whitepoint(6500.0)\n",
        "sd_3200, xy_3200 = calculate_whitepoint(3200.0)\n",
        "\n"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XtE_yOSLJeM_",
        "colab_type": "code",
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      "source": [
        "# Convert the BabelColor Average chart to XYZ via Camera RGB\n",
        "def create_chart(cmf, src_illuminant_sd, dst_illuminant_sd, rgb_to_xyz, rgb_white):\n",
        "  checkersd = colour.COLOURCHECKERS_SDS['BabelColor Average']\n",
        "  chart_data = []\n",
        "  for name, sd in checkersd.items():\n",
        "    xyz_cam = sd_to_CameraRGB_to_XYZ(sd, cmf, src_illuminant_sd, rgb_to_xyz, rgb_white)\n",
        "    chart_data.append(colour.XYZ_to_sRGB(xyz_cam))\n",
        "\n",
        "  return chart_data\n",
        "\n",
        "\n",
        "# Draw macbeth chart for comparison\n",
        "def draw_chart(swatches, labels=None, figsize=20):\n",
        "  import numpy as np\n",
        "  import matplotlib.pyplot as plt\n",
        "  from matplotlib.patches import Rectangle\n",
        "  from matplotlib.collections import PatchCollection\n",
        "\n",
        "  swatches = np.array(swatches)\n",
        "\n",
        "  if len(swatches.shape) != 3:\n",
        "    raise TypeError('swatches must be a list of lists of colors, i.e. shape is (N, 24, 3)')\n",
        "\n",
        "  if swatches.shape[0] > 5:\n",
        "    raise ValueError('only 5 sets supported')\n",
        "\n",
        "  num_sets = swatches.shape[0]\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(figsize, figsize))\n",
        "  ax.set_aspect(1)\n",
        "  ax.set_ylim((0.0, 0.8))\n",
        "  patches = [[] for s in range(num_sets)]\n",
        "  deltas = [[] for s in range(num_sets)]\n",
        "  num_cols = 6\n",
        "  num_rows = 4\n",
        "  gap = 0.03\n",
        "  offset = 0.04\n",
        "  base_size = 4\n",
        "  size = (1 - offset * 2 - (gap * (num_cols-1))) / num_cols\n",
        "  for row in range(0, num_rows):\n",
        "    for col in range(0, num_cols):\n",
        "      y = (num_rows - row - 1) * (size + gap) + offset * base_size\n",
        "      x = col * (size + gap) + offset\n",
        "\n",
        "      if num_sets == 1:\n",
        "        patches[0].append(Rectangle((x, y), size, size))\n",
        "      elif num_sets == 2:\n",
        "        # draw the second patch as the background, then the first as the inset\n",
        "        patches[0].append(Rectangle((x, y), size, size))\n",
        "        s = size/2\n",
        "        patches[1].append(Rectangle((x+s/2, y+s/2), s, s))\n",
        "        pass\n",
        "      elif num_sets == 3:\n",
        "        # draw the first two sets as left and right halves of the bg, then the\n",
        "        # last as the inset\n",
        "        s = size/2\n",
        "        patches[0].append(Rectangle((x, y), s, size))\n",
        "        patches[1].append(Rectangle((x+s, y), s, size))\n",
        "        patches[2].append(Rectangle((x+s/2, y+s/2), s, s))\n",
        "      elif num_sets == 4:\n",
        "        # Same but split the right half into quarters\n",
        "        s = size/2\n",
        "        patches[0].append(Rectangle((x, y), s, size))\n",
        "        patches[1].append(Rectangle((x+s, y), s, s))\n",
        "        patches[2].append(Rectangle((x+s, y+s), s, s))\n",
        "        patches[3].append(Rectangle((x+s/2, y+s/2), s, s))\n",
        "      elif num_sets == 5:\n",
        "        s = size/2\n",
        "        patches[0].append(Rectangle((x, y), s, s))\n",
        "        patches[1].append(Rectangle((x, y+s), s, s))\n",
        "        patches[2].append(Rectangle((x+s, y), s, s))\n",
        "        patches[3].append(Rectangle((x+s, y+s), s, s))\n",
        "        patches[4].append(Rectangle((x+s/2, y+s/2), s, s))\n",
        "\n",
        "  patches = [p for s in patches for p in s]\n",
        "\n",
        "  patches.insert(0, Rectangle((0, 0), 1, size*num_rows + offset * (base_size+1) + gap * (num_rows-1)))\n",
        "\n",
        "  coll = PatchCollection(patches)\n",
        "  colors = swatches.reshape(-1, swatches.shape[-1])\n",
        "  colors = np.insert(colors, 0, [0.1, 0.1, 0.1], 0)\n",
        "  colors = np.clip(colors, 0.0, 1.0)\n",
        "  coll.set_color(colors)\n",
        "  ax.add_collection(coll)\n",
        "\n",
        "  # Calculate deltas\n",
        "  def sRGB_to_Lab(rgb):\n",
        "    xyz = colour.sRGB_to_XYZ(rgb)\n",
        "    return colour.XYZ_to_Lab(xyz)\n",
        "\n",
        "  labs = [np.array(list(map(sRGB_to_Lab, s))) for s in swatches]\n",
        "\n",
        "  def print_delta(d, r):\n",
        "      plt.text(offset + 0.62, offset * float(r)/2 * 1.2, \n",
        "                u'ΔE min: %.2f max: %.2f mean: %.2f std: %.2f' \n",
        "                % (d.min(), d.max(), np.mean(d), np.std(d)), \n",
        "               color='grey', size=textsize*0.7)\n",
        "\n",
        "\n",
        "  if labels is not None:\n",
        "    textsize = figsize\n",
        "    c2 = 0.2\n",
        "    if num_sets == 1:\n",
        "      plt.text(offset, offset * 5.0/2 * 1.2, labels[0], color='white', size=textsize)\n",
        "    elif num_sets == 2:\n",
        "      deltas = [colour.delta_E(l, labs[-1]) for l in labs[:-1]]\n",
        "      plt.text(offset, offset * 5.0/2 * 1.2, 'Background:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 5.0/2 * 1.2, labels[0], color='white', size=textsize)\n",
        "      print_delta(deltas[0], 5)\n",
        "      plt.text(offset, offset * 4.0/2 * 1.2, 'Inset:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 4.0/2 * 1.2, labels[1], color='white', size=textsize)\n",
        "    elif num_sets == 3:\n",
        "      deltas = [colour.delta_E(l, labs[-1]) for l in labs[:-1]]\n",
        "      plt.text(offset, offset * 5.0/2 * 1.2, 'Left:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 5.0/2 * 1.2,  labels[0], color='white', size=textsize)\n",
        "      print_delta(deltas[0], 5)\n",
        "      plt.text(offset, offset * 4.0/2 * 1.2, 'Right:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 4.0/2 * 1.2, labels[1], color='white', size=textsize)\n",
        "      print_delta(deltas[1], 4)\n",
        "      plt.text(offset, offset * 3.0/2 * 1.2, 'Inset:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 3.0/2 * 1.2, labels[2], color='white', size=textsize)\n",
        "    elif num_sets == 4:\n",
        "      deltas = [colour.delta_E(l, labs[-1]) for l in labs[:-1]]\n",
        "      plt.text(offset, offset * 5.0/2 * 1.2, 'Left:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 5.0/2 * 1.2,  labels[0], color='white', size=textsize)\n",
        "      print_delta(deltas[0], 5)\n",
        "      \n",
        "      plt.text(offset, offset * 4.0/2 * 1.2, 'Bottom-right:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 4.0/2 * 1.2,  labels[1], color='white', size=textsize)\n",
        "      print_delta(deltas[1], 4)\n",
        "\n",
        "      plt.text(offset, offset * 3.0/2 * 1.2, 'Top-right:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 3.0/2 * 1.2,  labels[2], color='white', size=textsize)\n",
        "      print_delta(deltas[2], 3)\n",
        "\n",
        "      plt.text(offset, offset * 2.0/2 * 1.2, 'Inset:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 2.0/2 * 1.2,  labels[3], color='white', size=textsize)\n",
        "    elif num_sets == 5:\n",
        "      deltas = [colour.delta_E(l, labs[-1]) for l in labs[:-1]]\n",
        "      plt.text(offset, offset * 5.0/2 * 1.2, 'Bottom-left:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 5.0/2 * 1.2,  labels[0], color='white', size=textsize)\n",
        "      print_delta(deltas[0], 5)\n",
        "\n",
        "      plt.text(offset, offset * 4.0/2 * 1.2, 'Top-left:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 4.0/2 * 1.2,  labels[1], color='white', size=textsize)\n",
        "      print_delta(deltas[1], 4)\n",
        "\n",
        "      plt.text(offset, offset * 3.0/2 * 1.2, 'Bottom-right:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 3.0/2 * 1.2,  labels[2], color='white', size=textsize)\n",
        "      print_delta(deltas[2], 3)\n",
        "\n",
        "      plt.text(offset, offset * 2.0/2 * 1.2, 'Top-right:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 2.0/2 * 1.2,  labels[3], color='white', size=textsize)\n",
        "      print_delta(deltas[3], 2)\n",
        "      \n",
        "      plt.text(offset, offset * 1.0/2 * 1.2, 'Inset:', color='white', size=textsize)\n",
        "      plt.text(offset + 0.2, offset * 1.0/2 * 1.2,  labels[4], color='white', size=textsize)\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yki1eJ0UT8QR",
        "colab_type": "text"
      },
      "source": [
        "## Camera model code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUl_66OnTgxR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert a spectral distribution to camera RGB space using the given CMFs and\n",
        "# illuminant.\n",
        "# This is just a wrapper over sd_to_XYZ to make the name less confusing\n",
        "def sd_to_CameraRGB(sd, cmf, illuminant):\n",
        "  return sd_to_XYZ(sd, cmf, illuminant)\n",
        "\n",
        "\n",
        "# Convert a SpectralDistribution to CIE XYZ via Camera RGB space\n",
        "def sd_to_CameraRGB_to_XYZ(sd, cmf, src_illuminant_sd, rgb_to_xyz, rgb_white):\n",
        "  # Convert to Camera RGB under the source illuminant and divide by the white \n",
        "  # point in Camera RGB\n",
        "  rgb = sd_to_CameraRGB(sd, cmf, src_illuminant_sd) / (100 * rgb_white)\n",
        "  # Convert to XYZ using the supplied matrix\n",
        "  return colour.RGB_to_XYZ(rgb, xy_d65, xy_d65, rgb_to_xyz)\n",
        "\n",
        "\n",
        "# Solve the Camera RGB to XYZ matrix by linear least squares on the set of \n",
        "# swatches given under the given illuminant SpectralDistribution\n",
        "def solve_matrix(cmf, src_illuminant_sd, dst_illuminant_sd, rgb_white, swatches=None):\n",
        "  RGB = []\n",
        "  XYZ = []\n",
        "\n",
        "  if swatches is None:\n",
        "    swatches = colour.COLOURCHECKERS_SDS['BabelColor Average'].values()\n",
        "\n",
        "  for sd in swatches:\n",
        "    xyz = sd_to_XYZ(sd, illuminant=dst_illuminant_sd) / 100\n",
        "    rgb = sd_to_CameraRGB(sd, cmf, src_illuminant_sd) / (100 * rgb_white)\n",
        "\n",
        "    RGB.append(rgb)\n",
        "    XYZ.append(xyz)\n",
        "\n",
        "  return np.linalg.lstsq(RGB, XYZ, rcond=None)[0].T"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlru02YSn0Tx",
        "colab_type": "text"
      },
      "source": [
        "## Results\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9x1fVFt9o-H4",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Each of the following examples converts the BabelColor Average set of Macbeth chart measurements to Canon 5D Mark II Camera RGB space, then to CIE XYZ and finally to sRGB for display. \n",
        "\n",
        "The first set of results compares different Camera RGB to XYZ matrices. The second set investigates using different methods for handling white balance.\n",
        "\n",
        "In each set we compare against a straight spectral to XYZ conversion using the CIE 1931 2-degree standard observer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izMvgLqtUJ_7",
        "colab_type": "text"
      },
      "source": [
        "### CIE 1931 Standard Observer\n",
        "\n",
        "We'll first convert the chart to XYZ using CIE 1931 to use as a baseline for comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8ntIgi7sFRr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "66540779-7438-4c46-8cd9-82b3f0ea9d27"
      },
      "source": [
        "# Get a copy of BabelColor Average for plotting as CIE 1931\n",
        "bca = colour.COLOURCHECKERS['BabelColor Average']\n",
        "colour.COLOURCHECKERS['CIE 1931'] = colour.characterisation.ColourChecker(name='CIE 1931', data = bca.data, illuminant = bca.illuminant)\n",
        "\n",
        "chart_bca = []\n",
        "for _, sd in colour.COLOURCHECKERS_SDS['BabelColor Average'].items():\n",
        "  xyz = colour.sd_to_XYZ(sd, illuminant=sd_d65) / 100\n",
        "  chart_bca.append(colour.XYZ_to_sRGB(xyz))\n",
        "\n",
        "chart_bca = np.clip(chart_bca, 0.0, 1.0)\n",
        "draw_chart([chart_bca], labels=['CIE 1931'])\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iq69KzybTPy3",
        "colab_type": "text"
      },
      "source": [
        "### 5D dataset definition\n",
        "\n",
        "Next we'll load the spectral sensitivity curves for the Canon 5D Mark II from the rawtoaces repository."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_5f8oD9TJ5T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cmf_5d = MultiSpectralDistributions(load_ampas_camera_data('canon_eos_5d_mark_ii_380_780_5.json')).align(SpectralShape(360, 830, 1))\n",
        "\n",
        "# Our two whitepoints in Canon 5D RGB. We'll use these a lot below.\n",
        "rgb_5d_d65 = sd_to_CameraRGB(colour.sd_ones(), cmf_5d, illuminant = sd_d65) / 100\n",
        "rgb_5d_3200 = sd_to_CameraRGB(colour.sd_ones(), cmf_5d, illuminant = sd_3200) / 100\n"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezOU-MZoTjhq",
        "colab_type": "text"
      },
      "source": [
        "### dcraw matrix\n",
        "\n",
        "First of all, compare CIE 1931 against the 5D matrix extracted from dcraw."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kU4IsADHsbS-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "3ace9f2f-d7f1-4004-ebf7-f0ee4cb96a19"
      },
      "source": [
        "# This is the matrix extracted from dcraw\n",
        "rgb_to_xyz_5d_dcraw = [\n",
        "    [.84582686764400, -0.124656064433,  0.2292851967890], \n",
        "    [0.4151331895030,  0.668390503440, -0.0835236929433], \n",
        "    [0.0693495723842, -0.222697019629,  1.2421014472500]\n",
        "]\n",
        "\n",
        "chart_5d_dcraw = create_chart(cmf = cmf_5d, \n",
        "                              src_illuminant_sd = sd_d65, \n",
        "                              dst_illuminant_sd = sd_d65, \n",
        "                              rgb_to_xyz = rgb_to_xyz_5d_dcraw, \n",
        "                              rgb_white = rgb_5d_d65)\n",
        "\n",
        "draw_chart([chart_5d_dcraw, chart_bca], labels=['5D dcraw', 'CIE 1931'])"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nMmJJXFToUt",
        "colab_type": "text"
      },
      "source": [
        "### Solve matrix using Macbeth chart\n",
        "\n",
        "Next we'll try solving our own matrix using linear least squares with the BabelColor Average Macbeth chart as a dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuuQZOjksyKU",
        "colab_type": "text"
      },
      "source": [
        "Next compare our matrix solved using the 24-patch macbeth chart against CIE 1931 and the dcraw matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFD_AfuKtBGE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "ab707e23-7eff-4710-b413-c19a40ac851f"
      },
      "source": [
        "rgb_to_xyz_5d_macbeth = solve_matrix(cmf_5d, sd_d65, sd_d65, rgb_5d_d65)\n",
        "chart_5d_macbeth = create_chart(cmf = cmf_5d, \n",
        "                                src_illuminant_sd = sd_d65, \n",
        "                                dst_illuminant_sd = sd_d65, \n",
        "                                rgb_to_xyz = rgb_to_xyz_5d_macbeth, \n",
        "                                rgb_white = rgb_5d_d65)\n",
        "\n",
        "draw_chart([chart_5d_macbeth, chart_bca], labels=['5D matrix solve using Macbeth chart', 'CIE 1931'])\n"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eAswS70TvTJ",
        "colab_type": "text"
      },
      "source": [
        "### Solve matrix using rawtoaces training data\n",
        "\n",
        "Now we'll do the same but using the 190-patch dataset from rawtoaces."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kXQYQs4wPq6",
        "colab_type": "text"
      },
      "source": [
        " Next we'll compare using the 24-patch macbeth chart against the 190-patch training dataset from the AMPAS rawtoaces repo: https://github.com/ampas/rawtoaces"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34I3Aahkwxii",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "4c7edf44-426e-4f4b-e6e7-6ad8710aa97e"
      },
      "source": [
        "ampas_training_data = load_ampas_training_data()\n",
        "\n",
        "rgb_to_xyz_5d_6500 = solve_matrix(cmf_5d, sd_d65, sd_d65, rgb_5d_d65, swatches=ampas_training_data)\n",
        "chart_5d_ampas_6500 = create_chart(cmf = cmf_5d, \n",
        "                                  src_illuminant_sd = sd_d65, \n",
        "                                  dst_illuminant_sd = sd_d65, \n",
        "                                  rgb_to_xyz = rgb_to_xyz_5d_6500, \n",
        "                                  rgb_white = rgb_5d_d65)\n",
        "\n",
        "draw_chart([chart_5d_ampas_6500, chart_bca], labels=['5D matrix solve using AMPAS training data', 'CIE 1931'])\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AgIhEAAAAAEQkAgAAACAiEQAAAAARiQAAAACISAQAAABARCIAAAAAIhIBAAAAEJEIAAAAgIhEAAAAAEQkAgAAACAiEQAAAAARiQAAAACISAQAAABARCIAAAAAIhIBAAAAEJEIAAAAgIhEAAAAAEQkAgAAACAiEQAAAAARiQAAAACISAQAAABARCIAAAAAIhIBAAAAEJEIAAAAgIhEAAAAAEQkAgAAACAiEQAAAAARiQAAAACISAQAAABARCIAAAAAIhIBAAAAEJEIAAAAgIhEAAAAAEQkAgAAACAiEQAAAAARiQAAAACISAQAAABARCIAAAAAIhIBAAAAEJEIAAAAgEwZiarqvqp6rqrOVdXD27z/fVX1TFV9uap+r6q+bfajAgAAADAve0aiqlpO8liSdye5J8kDVXXPVcu+mORod39Xkk8l+cisBwUAAABgfqa5kujeJOe6+/nuXktyKsn9Wxd096e7+9XNm59Pcmi2YwIAAAAwT9NEoruSvLDl9vnN+3byY0l+93qGAgAAAODGWpnlJ6uq9yQ5muQHd3j/iSQnkmR5eXmWDw0AAADAdZgmEr2Y5O4ttw9t3vdnVNU7k3wgyQ9296XtPlF3n0xyMkkmk0lf87QAAAAAzMU0P252NsmRqjpcVZMkx5Oc3rqgqr47ya8nOdbdL81+TAAAAADmac9I1N3rSR5K8kSSZ5M83t1PV9WjVXVsc9kvJ7kjyW9X1Zeq6vQOnw4AAACAEZrqNYm6+0ySM1fd98iWt98547kAAAAAuIGm+XEzAAAAAF7nRCIAAAAARCIAAAAARKKF1t1DjzBajs34eY525/iMn+dod47P+HmGdubYLABP0u4cnwXgSdqd4zOUqV64mnHqOAlncdm/LDp7mNcDO5iFZgOz8GxixseVRAAAAACIRAAAAACIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAfD/2LvzuJzy93/gr7too6iENsmuskSRKJVUki0VshtjMAzC14zPbGbMGGasM8YMhll8jEmWQS1viPoAACAASURBVEVNEkmpkJGtlKUipEX7dv/+6HefT3f3Xd0lE+b1fDx6zDj3Oed+n3Of9TrX+zpERERERERgkIiIiIiIiIiIiMAgERERERERERERgUEiIiIiIiIiIiICg0RERERERERERAQGiYiIiIiIiIiICAwSERERERERERERGCQiIiIiIiIiIiIwSERERERERERERGCQiIiIiIiIiIiIwCARERERERERERGBQSIiIiIiIiIiIgKDREREREREREREBAaJiIiIiIiIiIgIDBIREREREREREREYJCIiIiIiIiIiIjBIREREREREREREYJCIiIiIiIiIiIjAIFGD5OfnN3cT6DX1Kmw7hYWFzd0Eeo29CttPcXFxczeBXlOvyrZTVlra3E2g19Srsu2Ul5c3dxPoNfUqbDuVlZXN3QR6Tf3bth2RWCxuli9WUVER6+npNct3ExERERERERG9iTIyMuLFYrFVY6ZlJhERERERERERETFIREREREREREREDBIREREREREREREYJCIiIiIiIiIiIjBIREREREREREREYJCIiIiIiIiIiIjAIBEREREREREREYFBIiIiIiIiIiIiAoNEREREREREREQEBomIiIiIiIiIiAgMEhERERERERERERgkIiIiIiIiIiIiMEhERERERERERERgkIiIiIiIiIiIiMAgERERERERERERgUEiIiIiIiIiIiICg0RERERERERERAQGiYiIiIiIiIiICAwSERERERERERERGCQiIiIiIiIiIiIwSERERERERERERGCQiIiIiIiIiIiIwCARERERERERERGBQSIiIiIiIiIiIgKDREREREREREREBAaJiIiIiIiIiIgIDBIREREREREREREYJCIiIiIiIiIiIjBI9MZIT0/HwYMHm7sZr5WDBw8iPT29uZtB9I/z8fFBeno6fHx8mrspDfZvOtbxGEUNMWTIEKSnp8PPz++F5/W67Wev8zGNiIjoVcMgUSOkp6fL/KWkpCA6OhpbtmxBt27dmruJRG+k6Ohouftfeno6Ll++LDO+5Kap+l9ycjIuXbqEQ4cO4cMPP4S5uXkzLMmL2bx5M9LT02FkZNTcTaF/ud9//x3p6emIjY2FklLtlxSSbTY9PR0rVqyodTxvb29hvJpBCnn7c2pqKuLi4vDjjz9i4MCBcueppqaG69evIz09Hd999129y+Th4YF9+/YhISEBd+/exbVr1xAeHo5t27bB29u73ukloqOjER0drfD41Pz8/PyQnp6OIUOGNHdTiIiImk2L5m7A62zjxo3C/2tpaaF///7w9vaGu7s7JkyYgMTExGZsHdGbKTc3F7t375YZXlBQUOs0Dx48gL+/PwBARUUFOjo66NOnDxYsWIAFCxbg8OHDWLVqFQoLC19au18lwcHBiI+Px+PHj5u7KVSHJUuWQF1dvbmbUatOnTph2LBhqKyshIGBAZycnPDXX3/VOU1ZWRkmTZqETZs2obKyUuZzX19flJWVoWXLlrXOo/r+rKGhgQEDBsDDwwOjRo3CvHnzcPLkSanxx44dizZt2qCyshKjRo2CtrY2srOz5c57/fr1mDZtGoqKihAWFob79+9DJBKhW7duGDlyJGxtbV/ZDJvLly/D3t4ez549e+F52dvbo6ioqAlaRURERK8bBolewKZNm2SGff7555gzZw7mzp2LZcuWNUOriN5seXl5cve9ujx48EDuNObm5tiyZQs8PT3Rtm1bTJ8+vama+Up7/vw5nj9/3tzNoHpkZGQ0dxPq5OvrCyUlJXz77bdYvHgxpk6dWm+Q6K+//sKoUaPg6OiIsLAwqc+6deuGQYMGISgoCO7u7rXOQ97+vHz5cvj5+eHjjz+WCRJNmzYNFRUV+OGHH/Duu+/C29sbO3fulJmvtbU1pk2bhoyMDIwdOxYPHz6U+rxFixavdIZJcXEx7ty50yTzaqr5EBER0euH3c2a2NmzZwEAurq6UsM1NTUxf/58+Pv7Iy4uDqmpqbh69Sr27t1ba4o8AHTt2hUbN25EdHQ0UlJSkJCQgMOHD2PGjBkKtWf+/Pl48OABjh49irZt20q1Z82aNYiLi8OdO3cQERGBefPmoVOnTkhPT8fmzZul5iPpKtCpUyfMnj0boaGhSE5Olnqiampqiq1btwrLFx8fj61bt8LU1FSmXXV1l6mtroKkPoeysjIWL16MyMhIpKSkIDY2FqtXr671yfPYsWMRHByM5ORkJCQkYNu2bejQoYNC64/ebImJiZg8eTKePn0KJycnuLq6KjSdZPs1NjbGrFmzEB4ejjt37iA6OhqLFy8WxvPw8MCJEyeQlJSEhIQErF27FmpqajLzc3V1xbZt23Du3DkkJSUhKSkJwcHBmDNnDkQikdS41etuxMTECN1uqndrkewrLVu2xNKlS3H27FmkpKQI+7W8+h1vv/020tPT5d4829nZ4f79+/jrr7/ktr+mdu3a4aOPPsLZs2eRlJSE69ev4+zZs9i8eTM6deokNa5IJML06dMRGBiI27dvIykpCYGBgZgxY4bMssvz1VdfIT09HS4uLnI/t7S0lLtcampqWLRoEUJCQpCUlITbt2/j2LFjGDduXL3fWV1dtVtqO86NHDkSf/zxBy5duoSUlBTEx8cjICAAM2fOlBpPXk2i6sdHc3Nz/Prrr7h+/TqSk5MREBAAKysruW1p3749Nm3ahISEBCQnJyMkJATe3t6NrmOjrKwMHx8f5OXlYcuWLUhISICTkxM6duxY53RHjhxBUVERfH19ZT6bOnUqgKoubA31888/AwBMTEygo6MjDO/ZsycGDhyIyMhIfP/99ygpKcGUKVPkzkOy7oKCgmQCRABQXl6Oc+fO1dsWyTo1NjaGsbGxVPe46udWybajp6eHr7/+GnFxcbh//76wX3bp0gUffPABgoKCcPXqVaSkpCAmJgbr16+Hvr5+rd/bFOdOedt19a5Yo0ePxokTJ5CcnIxr167h+++/r/W379evH/bv349bt27h5s2bOHDgAAYOHNiorl2dO3fGjz/+iMTERCQlJeHPP//EiBEjah3f1tYW69evR3h4OG7evInk5GSEhYVh2bJlUFVVlRo3Ojoay5cvBwAEBARI/W4SDf1NSHGtW7fGvHnz0K5du+ZuChHVw8vLq877V3r9MZOoiQ0bNgwAkJCQIDW8e/fuWLVqFWJiYhAWFoacnBwYGhrCxcUFjo6OmDVrFs6cOSM1zYgRI/Djjz9CRUUFZ86cwdGjR9GmTRuYmZlhwYIF+PXXX2tth0gkwpo1a/DWW28hMDAQixcvRklJCQBAVVUV/v7+6Nu3L/7++28cPnwYWlpaeO+99zBo0KA6l++zzz7DoEGDEBYWhtOnTwvdBfr164cDBw6gdevWwk1X165d4enpCRcXF0yePFlmnTTW9u3bMWjQIISHhyM/Px9OTk5499130a5dO5mL47fffhuffvopcnJycOjQIeTm5sLBwQF//vlnrZkUBw8ehK2tLby8vHDhwoUmaTM1HRUVFXh6esLQ0BCFhYW4ceMGoqOj5XZdUURWVhb27duHpUuXwtPTE6dOnVJ42o8++gi2trYIDQ3F2bNnMXLkSLz//vto2bIlcnJysHr1apw8eRIXL16EnZ0dZs+eDWVlZXzwwQdS81m9ejUqKytx+fJlPHz4EFpaWhg6dCg+//xz9O/fH++9954w7saNG+Hm5gZzc3Ps3r0bubm5AKoyrGratWsX+vXrh/DwcJw8eRJZWVm1LsuuXbswdOhQjB49GjNnzsQvv/wCANDT08O2bdtQUlKC+fPno7i4uM51oqamhqNHj8LU1BQREREIDQ2FSCSCkZERXFxcEBgYiPv37wvjb9u2DZ6enkhPT8fvv/8OsVgMNzc3rFu3DtbW1lJBN3kOHjyI6dOnw8vLCyEhITKfS2rISLonAVXdg/39/dGnTx9cvXoVBw4cgJKSEoYPH47vv/8ePXv2xIYNG+r83saaOnUqNmzYgMzMTISGhiI7Oxu6urro3bs3fHx8hPVen379+mHhwoWIj4/H77//DkNDQ7i7u+PAgQNwdXWVygTR1dXFsWPHYGxsjAsXLiAuLg7t27fHl19+iYiIiEYth4uLCzp06IB9+/ahuLgY/v7++OKLLzB58mRs2bKl1uny8vJw4sQJjB8/Hnp6enjy5AmAqv3ay8sLUVFRSElJaXB7qgcUxWKx8P+SwJO/vz9ycnLw119/YfTo0Rg0aBAuXrwoNQ9JFzR5DzYa4sGDB9i4cSPmzp0LAFLdY2t2Q2/bti2OHz+OgoICBAcHo7KyUlgno0aNwvTp0xEVFYW4uDiUlZWhR48e8PX1xciRI+Hu7o5Hjx4p3K6GnDvrMnPmTIwcORKhoaGIjo6GpaUlxo0bBzMzM7i4uKC0tFQYd/Dgwdi/fz+UlZURHByMe/fuoVevXvD398f58+cV/k6g6nc5duwYdHR0EBYWhuvXr6Nz58746aefEB4eLneahQsXolu3boiLi0NYWBhUVVVhbW2NFStWwNbWFpMmTRLOHbt374arqytsbW3h7++PBw8eyMyvqX+TV4Guri4mTpyIR48e4dixYzKfz5s3T+50586dw40bN5qsHQUFBfjtt9/qPcc0hoqKCoYOHQoTExMAwL1793D+/HmpbbWmzp07o3fv3mjXrh3U1dVx/PhxmeCxuro6bGxsYGhoCBUVFeTm5gqB+NeRSCSCtbU1jI2NoaWlhbKyMmRkZCAmJqbOLv36+voYM2aMzPA//vhDuEbp0aMHHBwcZMb56aefUFFR0WTL8Cpq1aoVhg0bBgMDA1RUVCA5Obne61ZFtq0pU6ZAU1NTarorV67InNsU1aNHDwwdOhR79+5t1PSKMDMzQ9++faGhoYHs7GxcuHCh3mOmkpISLC0t0b17d7Rq1QpFRUVISEgQzqe9evVC9+7dhQdEWVlZiI2NRWZm5ktbjn8DBoleQPWLKk1NTfTr1w/W1tYIDQ3FDz/8IDVuUlISBgwYIFMHQV9fHydOnMCnn34qdfDU1tbGd999JzytrVn8sq4nVqqqqvj2228xevRo7NmzBx9//LHURfOCBQvQt29fHD16FO+++64wfNu2bTJp+jVZWFjA1dVV5uJp69at0NLSwqJFi3DkyBFh+NixY7Fjxw5s27YNDg4OUu1oLBMTEzg5OSEnJwdAVSZBaGgovLy8sG7dOuEC28jICKtXr0Z2djbc3NyQlpYGAFi3bh1+/PFHjB49+oXbQv+8Dh064Ntvv5Uadu/ePfj5+TW6SOyFCxewdOlS9O/fv0HT9e3bF87OzsIJbuPGjTh//jwWLFiAoqIiuLm5CSd0FRUVnDp1CpMmTcI333wjFbCZMWMG7t27JzVvkUiEzZs3w9vbG3v37hUKc2/atAnGxsYwNzfHrl27hO1aHkNDQzg5OdVaf6WmZcuWISQkBB999BFiY2Nx48YNfPvtt2jfvj2WLVum0IWvnZ0dTE1NsWvXLnz66adSn7Vs2RIqKirCv8eNGwdPT0/8/fff8PT0FGpCrV+/HocOHYKnpyfCwsJw9OjRWr8vPj4ed+7cgbOzM9q2bSscF4CqdT527Fg8efJE6iZyzZo16NOnD9auXYsdO3YIw1VVVfHTTz9h8eLFCAwMfCl15aZNm4aSkhKMHDlSJminra2t8HycnZ2xbNkyqeDXtGnTsH79erz11ltYvXq1MPyDDz6AsbExtm/fji+//FIYvnv3bpw4caJRy1E9+AIAR48exccff4zJkydj69atdR7r//vf/8Lb2xuTJk0SCkm7ublBR0cH+/fvb1R7Zs2aBQC4e/eusL2rqqrC09MTubm5wrnN398fo0ePxrRp02QupM+cOYPc3FyMGDECe/fuxZ9//omEhASkpqY2qC1paWnYtGmTkBFUV/dYMzMzBAQEwM/PT+ZG6dChQ9i1a5fMjay9vT327duHJUuWyASc66LoubM+Dg4OGD16NG7evCkM++677zBhwgS4urri+PHjAKqOYRs3boSamhqmTZsmtQ9Onz4dX331lcJtB4AvvvgCOjo6+Pjjj/HTTz8Jw11cXGq9qVm9erVUUFpi5cqVWLp0KTw8PITAyO7du6GlpSUEieQ9JGrq3+RV0KtXL9y5cwcmJiYyx1CJiIgImfVYV4ClMcRi8Uurg+Xk5ITWrVsjKCgIQNXv5ejoWOdDoZYtWyIzMxPJyclwdHSUO46joyNUVVVx6tQpFBcXo3PnznB0dER+fv5rFywEqrrUtmvXDpcvX0ZWVhZUVFRgY2MDd3d3BAQE1HsN7+/vLzyQBiAT8CsrK8OBAwekhr3pASKRSIRRo0ahuLgYx44dg5qamnC/FxUVVet0im5b8fHxuH79uvDvsrKyl7YsL6pLly6wtbVFZGQkHj16BDMzM4waNQr+/v51BiFHjBiBVq1a4dy5c8jNzYW6ujpatPhfCENfXx8pKSmIiopCeXk5+vTpA3d3dxw6dEjuA1RSDLubvYDly5cLf/PmzcPgwYORlJSEo0ePymzsz58/l3uj9vDhQwQGBqJ79+4wMDAQhvv4+EBLSwu//vqr3BtfeanwQNVTyQMHDmDUqFFYu3YtPvroI5mDupeXFyoqKrBu3Tqp4RkZGXILAle3Y8cOmQCRlZUVunfvjri4OKkAEQAcO3YMMTExQq2JpvDll19KXcQUFRXhyJEjUFZWRr9+/YThnp6eUFFRwd69e6VupMViMdauXVvriWnJkiWwt7eX+7Ysal5//PEHfHx80K9fP3Tt2hVOTk747bffYGxsjH379sHMzKxR85XsTzW7idZny5YtUifrvLw8hISEQENDA7/++qtUUKW0tBTHjh2DqqoqunfvLjWfmgEioGo7leyPw4cPb1C7JL7++muFA0RAVSbFwoUL0bJlS+zYsQOrVq2CnZ0dDh06JBWMUIS8p8FlZWVSx8bJkycDqNqnqxcNLyoqEoIZ8rol1XTw4EGoqqrKdBUbOXIktLW1ceTIEWF/19bWhqenJ65cuSIVIAKAkpISfPnll1BSUsL48eMVXNKGq6ioQHl5uczwhvxWFy9elPlNDhw4gLKyMqlgZ8uWLTF+/Hjk5uZi69atUuNfv34dAQEBDWx9VfDR3t4eycnJiI+PBwAhS8fY2Fju0+LqYmNjkZSUJNXta+rUqcjOzhZu4upibGwMPz8/+Pn54cMPP8ShQ4ewbNkyVFRU4PPPPxfG8/DwgLa2No4fPy5sj+Hh4cjMzIS7uzvatGkjNd9Hjx5h7ty5SE1NhYuLC7Zv347IyEjcvHkT+/btg6enZ51vcGuMkpISfPbZZ3LPR48ePZJ7I3727FncunWrwccFRc+d9dmzZ49UgAiAENyrvu1ZWVnB1NQU58+fl8n02bdvX4PqHunr62P48OG4d++eTEAoJCSk1psteQEioCpzEmj4sbWpf5PmpqysjG7duiExMREPHjxAz5495Y5XWlqKoqIiqb+6bu6nTJmCAQMGYPjw4Zg1axZ8fX3RpUsXqKioYMSIEZg9ezYmTZoEQ0NDYZqa3c309fUxb948GBgYYPz48ZgzZw4mTJjQ4PN027Zt0alTJ5w7dw6PHz/G48ePce7cOZiYmMgcA6pLSkrCpUuXat2GgKqHVomJiXjy5AmeP3+Ov//+G/n5+Wjfvn2t0wwfPhyurq7o168fpk2bhlmzZgnXxwMHDsT06dMxbdo0mX2yT58+mDhxImbPno2pU6fC3t5e6qGLvb09vLy8oKysDKAqODFmzBiFu9EDVefooKAgpKSkIDc3F0+ePMG5c+egra0tVbKiNjW3EXlBpZrj1EWyDRgbG2PChAmYM2cOxowZg1atWkFfX19YH66urjLdR3v06AFvb2/MmTMHPj4+6NOnj9Tn9a3PHj16YPbs2TAwMICXlxdmz54NDw8Pmcyd+hgZGUFbWxvh4eHIyspCeno6YmJi0KtXrzpf0KDotlVWVia1PuVdW1TXsWNHjBs3DrNnz8asWbMwfvx4aGtrQ19fHw4ODmjZsiXmzZuHefPmCd3J1NTU4OLigjlz5mDKlCm1Hifq07dvX6HbcU5ODqKiolBYWFjntbuhoSEMDQ0RHByM9PR05Ofn48mTJ1L3weHh4UhMTERWVhZyc3MRGRmJsrIyGBsbN6qdVIWZRC+g+slNXV0dPXv2xOrVq7F9+3b07NkT69evlxrfysoKc+fOxcCBA6GrqytzQNPX1xcKlQ4YMAAAak2hlkdPTw9Hjx5Fp06dsHjxYrlP31u3bg1TU1Okp6fLzUCoL0XxypUrMsMkB97aUsfPnz+PwYMHw8LCAjExMYosSp3kdVuTrLfqJ3wLCwsAkBtku3//PjIyMuQeQF71YrH/ZjVrZd26dQvvv/8+CgoKMH/+fPj5+QldPBpC0lWloZluV69elRkmSW+V95kkoFQzE1BbWxvz58+Hk5MTTExM0KpVK6nPG1vrQt7+Wp/Y2Fhs3LgRq1atwuLFi5GSkoL3339f4ekvXLiAhw8f4t1334WFhQVOnz6N2NhYJCYmyqRW9+nTBxUVFXKf2F+4cAHl5eUwNzev9zsDAgKwcuVKeHt7S3XXktfVrF+/fmjRogXEYrHcLjaSi7aagbymcuTIEXzyyScIDw/HsWPHcOHCBcTGxjb4jVTytq/y8nI8ffpU6jjYtWtXqKurIyEhQe6TutjYWCErSFG+vr5QVlaWCVJJsnR8fX3rPXft378fn3zyCYYNG4a0tDTY2tpiz549Uk+ha2NsbCzUjikrK0NWVhYCAwOxc+dOxMXFCeNJluuPP/4QhlVUVODIkSOYP38+Jk6ciD179kjNOyoqCnZ2drC2tsaQIUNgbm4Oa2trODo6wtHREd7e3pg5c2aTZVGkpaXV2Q3U09MTPj4+MDMzQ5s2baSeniqyrqpT9NzZVPORnIPlXVeIxWLExcWha9euCn2nZF6xsbFyu2hcuHABtra2MsPV1dUxd+5cuLm5oUuXLmjdurVUoK++GlryNOVv0ty6dOmC0tJSPHr0CGpqarCzs8PFixebJOvbwsICcXFxuHz5MszMzODg4ICMjAzcuXMHsbGx6N+/P5ycnLB///46A06DBg1CTEwMCgsLYWtrCycnJ6l6WfPmzUN8fLwQsK6pQ4cOKC0tlep6kpmZibKyMnTo0EHoDtUYjx49QpcuXXDv3j2UlJTAxMQE6urqMrXkatLX10dBQQGOHz+Odu3awcnJCbq6unj69CmOHTsGAwMD2NnZIT09HU+fPgVQtc9cuHABeXl50NTUhK2tLYYOHSoca6OiouDp6QkbGxucP38elpaWaNOmjdSDAA8PDwBoUAapJHCiyDHP09MTysrKyM7OxqVLl2QeaCsrK2PKlCkQiUTIyspCXFxcncc/iYEDB+LChQsoLS2Fk5MTRowYgYqKCpw9exZisRjOzs4YOHCgECzu1asXrKyscP78eTx9+hTa2tqwt7dHZWWlkCFc3/qUtLd///6IiIhARUUFHBwcMGzYMAQHBwOouqfy9fXFmTNncPv2bbltb9++PbKzs6XOv2lpaULWVm0P/RXdtvr27Yv+/fsjPz8fqampSEhIqLUbm0gkgqurK27evInTp09DSUkJ7dq1g1gsRmZmJqKiomBtbS1ke0mykhwcHNC6dWsEBgaivLwcQ4YMkQmWDR8+HAYGBrXWFJR8V83zR1paWp11Yjt37ozHjx+jb9++6N69OyoqKvDgwQNcvHix1oCYkpISlJWVX7vj8auGQaImUlRUhCtXrmDu3LmIi4vDggUL8NtvvwkXTm5ubti5cydKSkpw9uxZ3Lt3D4WFhaisrMSQIUNga2srFcHW0tICgAalq+rp6UFTUxMPHz6sNdgj2alrSyuvL91c3iuzJW2tre+nZBrJeC9KXuqg5AJD8gSl+vfVtayMMr8ZfvvtN8yfPx82NjaNml5yo6DIxUp18rZFyUlLXs0ryXZa/emRlpYWAgMDYWJigkuXLiEgIAA5OTkoLy9HmzZtMHfuXKljQ0M09hX3QUFBWLFiBZSVlbF//36pLJ/65OfnY8yYMVi+fLlQcw2oWre//PILtm7dKqwjTU1N5OTkyE2PrqiowLNnzxQqYvrw4UNERkZi+PDh6NatG5KTk6GrqwsHBwdcu3ZNqm6GpEuXpaUlLC0ta51nzUBdU9m5cyeePXuGmTNnYs6cOXj77bdRWVmJ6OhofP7553KDP/LUdmNTXl4udRyUHPMlNxo1KdrFSEJJSQmTJk1CRUUFDh06JPWZJEtn5MiRUvWG5Dl48CBWrVqFKVOmIC0tDUpKSgp3NYuKihICgLXp1q2bkN176dIlqc/8/f0xf/58+Pr6ygSJgKqbh4sXL0qdR+3t7bFlyxbY29tjxowZ9WbdKqquffTTTz/F22+/jUePHuHMmTN49OiRkBHl4+PT4POXoufOxsxHsk839BysqMZcu7Ro0QL+/v4YMGAAbty4gWPHjuHZs2fC8Wb58uUNPrY29W/S3Hr27ClkvN6/fx9KSkro3LmzTBdLR0dHmQzBo0eP1pn9mJaWJnSDiYuLQ9++fZGXl4ekpCQAwKVLl9CrVy9oa2vXenySTCu5kb506RLGjRuHVq1aCTfdOTk5ddYxUldXl/t5UVERNDQ0ap1OEX/99RdGjBiBmTNnorKyEhUVFQgLC6v3WqK0tBTnz5+HWCxGbm6uUKMlNjYWQNXxvX///jAwMBDWzbVr14Tp8/PzERMTA1dXVyGoUV5ejvDwcIwdOxbFxcWwtLQUuipVn64hlJSUYGNjg3v37tXZHaiwsBDnzp3DkydPoKSkhO7du8PDwwPHjx8X7mNycnIQERGBZ8+eoWXLlrCwsMC4ceMQEBBQb5eguLg4YT43btzA0KFDcejQIWE9JyUlSdWSGzBgAGJiYoTt+Pnz57hy5QrMzMyEkyUMzAAAIABJREFUIFF961Oy/OfPnxfOt1evXpXKFqysrEROTk6dATQNDQ2ZjKni4mJUVlbWuf0psm1du3YNT58+RUlJCfT09DB48GBoamoKL1GqSUVFBaqqqrh//75wjVr9WkKyHNXb26ZNG3Tq1Al//vmncJ935swZIRNcorCwsM7fUU1NDUpKSjLroqioSCrpoiYtLS107NgRlZWVCA0NhaqqKmxtbaGhoVHr21Stra1RVlYmN0ufFMcgURPLy8vDnTt30LdvX1hYWAhBopUrV6K0tBSjRo2Sqeuxfv16mSdgkh2tY8eOMmndtbl+/Tp+//13bN68GYcPH4aPj49MmqzkoKCnpyd3HrUNl5D3dEnS1trSayXDq980S6Lc1Z/ASTR1MElPT09uhL++ZaXXh+Skqa6u3qjpJftfc3QxnDJlCkxMTLBx40aZ2iUDBw5sVGbUi1BVVcX3338PoKr7k6ROUUO6hjx8+BArVqwA8L9CiLNmzYKfnx+UlJTw9ddfA6g6JrRt2xYtWrSQeSKkrKwMHR2dWgvM13Tw4EEMHz4c3t7eWLduHTw9PdGyZUuZNzRJ5rdz506sWbNG4WWqTWVlpdzjGFD7sSwgIAABAQHQ0tKClZUV3NzcMHnyZOzfvx/29vYNziqqi+SmoLZgW0OPg87OzkJmW21P7oGqroQ1a4dVl52djZMnT8LNzQ35+fmIi4vDrVu3GtSWukybNg1AVUZYbU/1e/fuDSsrK6nso9qcPXsWGzZswMaNGzF06NAmCxLVlrGhq6uLOXPm4MaNGxg3bpzMDdrL7A7ZVF70euNF5+Xq6ooBAwbgjz/+kMkabN++vZCNpqg34TepTktLC/r6+kIWeGVlJVJSUtCzZ0+ZIFFMTIxMqYH6Ag7Vj2Pl5eUoKyuTGia5WazvvF39pljywEJNTU1Y/w3tCt2UrK2toaamhhMnTkjVjZEEJGuTnZ0tte8XFRXJZD0UFRVJrRsDAwP0798fbdu2hYqKipAtoaGhIayXJ0+e4PLly7CyshK6EFZX8wU5dRGJRHB0dBTqKdYlNzdXKtjw+PFjoVarJLgj6eonkZmZiYkTJ8LCwqLO2jyA/G2g+votLCwU1pWamhpat24NOzs74WVCkuWp/oIDRdZneXm51HIVFBRAWVkZqqqqKCkpQWFh4Uvb/hTZtv7++29hfEkA3NnZGTExMXKzaEpKSnDr1i2MGjUKGRkZSE9PR0pKSp0BwLZt26KyslLqt8vPz5d5eCgJcDY1yW8WFhYmBPjPnz+P0aNHQ11dXSboZGFhgd69eyMwMPCVrs/0OmCQ6CWQpFtXT2nu3Lkzbt++LRMgkrxJoKZLly7Bw8MDjo6ODTqoHz58GCUlJfjuu++EQFH1N8Xk5+fj7t27MDY2hpGRkUyXs8bUDZJE42t7ja3kBrz6wUxy0DUwMMDdu3elxm9IbYT62jV69Ggh9ba6Tp06SdWAotebpHtmXbUDaqOrqyvcUB4+fLhJ26UIydMvebVYasuMaszTf0V98sknMDc3x9atWxEdHY3//ve/2LFjBzw8PBrVxeb27du4ffs2Tp06hdjYWLi6ugpBomvXrsHOzg42NjaIjIyUms7GxgYtWrSQetpXl+DgYOTl5WHixIn46quv4O3tjbKyMpk6aZcvX0ZFRUWT1UjLzc2VeyxRUlKqt6tcXl4eTp8+LaR9T5kyBTY2NgrV5VFUcnIyioqK0Lt3b6mn7xLyzj91kXThCg0NlZu9oaysjEmTJtUbJAKqupyNHz8eampq+OKLLxrUjrqoqKhg4sSJqKiogL+/v9xAjL6+PhwdHeHr66tQkAiAsO6q32jUpaKiotFZgCYmJlBWVsbZs2dlfjN9fX106tSpUfP9J0n2XXn7mkgkgpWVVYPnZW1tDSUlJZnuFPKuPzp37gwAQteQ6mo7tkrmK+/Y+ib8JtX16tULWVlZUtlASUlJ8PDwkDlW1JclII+8Li/yhtW3P1WfRrIvK7oPAlXBFjU1NZnh6urqDcqSrUlTUxMWFhYICAgQbtqfPXuGjh07wsLCotZsDkB2PYjFYrnDJFq3bg03NzfcvHkTcXFxKCkpQbt27TBixAiZOmmSrIsXeeAqEokwYsQI6Ojo4Pjx443qtvP48eM6u5OKxWI8efJEoXbK225qHtcl24Tkv+fOnau1h4Oi67Mpul0WFhbKdKeSZNXUtv01dtuq3nOjtqzLiIgI/P333zA2NoaJiQmsra0REhJS50tQmoIke6pmULi+/bCwsBAFBQVSAR9Jbb3WrVtLBYksLCxgZWWF4ODgBmdJkywWrm5irq6uMDExQWlpqdSFZ1paGkxNTWUOFMuXL5dbAMzf3x95eXmYMWMGBg8eLPN5XTVKAgMDMW/ePGhrayMgIAA9evSQ+jwgIEDua7gNDAwalbUQGxuL5ORkDB48WOaNYZIgzZ07d6RS9yW1UmoWpe3VqxfeeuutBrdBnsOHD6O0tBSzZ8+GkZGRMFwkEuHDDz+s9QbbwMAAXbt2lXtRQc2nW7ducp84GhkZCTeYDQ3ymJmZ4cCBA9DV1UVYWBhCQ0ObpK0NIXnSV/Mmx9zcHIsWLZI7jeSivq4U3cZwd3fHzJkzcfHiRXzzzTc4e/Ysvv/+e5ibm8u8qaw2PXr0kJu1IhlW/YQu6ff+/vvvS+1vampqwvGptv7tNRUXF+P48eNCoUtzc3OcPn1aJu0/KysLR44cQf/+/bF06VK5hYhNTEwU7jZy+fJlGBkZwd7eXmr4kiVL5M5DXt0UQP76aQplZWU4duwY2rRpgyVLlkh9ZmZmBi8vL4XnZWBgAAcHB2RnZ+Odd97BypUrZf78/PwQExODzp07w87Ors75nT9/HrNmzcKcOXPkvnq7sdzd3aGjo4MzZ85gxYoVcts5f/58FBQUYMyYMUJXJgcHB4waNUpuZpiGhoZwflT0LYrZ2dnQ0dFp1LlEclyQBEWqt2PDhg11Fjx9VcTGxiI1NRVDhw6VeTvUtGnTFK5HBFRlJ0ZERMDExASzZ8+W+szFxUXuflXbsbVTp074z3/+I/d7JMdWeYHfN+E3kRCJROjRo4fMg8tHjx6hoKCg0YVpX0WZmZlQUVGRuv7u0KGD8PayxpIcJ2oGEsRicYOCWIrQ09ODkpISLly4gMePHyM3N1duV6U+ffqgXbt2OH78ONq3b69QTb+aRCIRnJ2dhQBRY89Jurq69QbhdHR0mvycV1RUhIKCAmhpaSEvL0/mD1B8fTaFx48fQ1tbW6oLu6GhoVBDUJ7GbluSou71rfdnz54hISEBJ06cQEZGhnCfWFFRITP/nJwcKCkpSfUWadWqVYPXV2VlJZ4+fSp1PwZUrYu69sNHjx6hVatWUudlSTJG9UzzPn36wMrKCidPnnyh/Zr+h5lEL6B6+rKGhgZ69OghXAh99dVXUjv/rl27sH79epw6dQpBQUEoKyuDtbU1evTogZCQELi4uEjNOzs7G4sWLcKPP/6IgwcPIjw8HDdu3EDr1q3Ru3dvGBgY1Jq5A1Q95Z0zZw5++uknBAQEYPLkyULf8B07dsDNzQ3jx49H165dERERAS0tLYwZMwbR0dEYNWpUrUXParN06VL8/vvv2LFjB06dOoU7d+6gS5cucHNzw/Pnz7FkyRKpg92pU6eQkpKCCRMmQF9fH5cvX4ahoSFcXFwQEhKCsWPHNuj75UlLS8O6devwySef4NSpUzh+/Djy8vLg4OAALS0tXL9+XW5F/a1bt8LW1hZeXl5yC+pS8xg7dizeeecdxMTEIC0tDfn5+ejcuTOcnJygrq6OsLAw/PDDD3KnlbwNCaiqB6Sjo4M+ffoIWWuHDh3CqlWr/rFlqS4gIAALFizAmjVrYGtri9TUVJiamsLZ2RnBwcEyb+wCgMjISCxcuBAbNmxAUFAQCgoKkJubi59//rnR7TAyMhLehvbuu+8Kx4ANGzbAxsYGM2fORGRkZL1ZLvb29vjwww8RHx+PlJQUZGVlQV9fHy4uLqioqJD6jY4ePQpXV1eMHTsW4eHhOHXqFMRisRBs//PPP2Uygepy8OBBTJ06VSi0XbOrmcR//vMfmJqaYuXKlZg4cSIuXryIJ0+eoGPHjujWrRssLS2xYMECmVR9eX788Uc4ODhgz549OH78OHJycmBlZQVjY2NERUXJ3Lzu3r0bhYWFuHTpEh48eACRSIRBgwbB0tISCQkJOHfunMLLq6gvv/wSQ4cOxbvvvgtLS0vEx8ejffv2GDNmDE6fPq3wMX/y5Mlo0aKFkLFam99//x2DBw/GtGnT6l2elxGYlWQ71RVgzM/Px4kTJzBp0iR4enril19+Qbdu3bBmzRpkZ2fj4sWLSE1NRXl5OfT19TFixAi0bdsWly5dUng/i4yMhKWlJfbt24eYmBiUlpbi+vXrCi3zkydPcPToUYwfPx4hISE4e/YsNDU1YW9vj5KSEly7dk0o5vyqEovFWLlyJfbt24e9e/ciKCgI9+7dQ+/evWFnZ4ewsDCMGDFC4euN//znPzh27Bg+++wzDB8+HNevX0fnzp3h5uYm9zoqNDQUqampeOedd9CrVy9cu3YNhoaGcHZ2RlhYmMzNClAVuKyoqMAHH3yAXr16CVnPW7dufSN+E4lOnTpBQ0MDz549E+q0STx8+BA9evSQquWloqIi85CmrKys3jcp/RN8fHyQmJgo1JqpKScnB/fv34ednZ2QgWFnZ4d79+4Jv6+GhgY8PDxw8eJFIbtdVVUVrVu3FrIBtbS0UFpaisLCQhQVFSEnJwe5ubkYNmwYoqOjhS5BRkZG9XbPaqjc3FwoKSnBwsICd+/eRfv27WXe1qWjowNra2ucPn0amZmZQp2+jIwMIfgpqStVWw8FkUgk1JQ7efIkgP91BywtLRWymGvOx8LCQniLs+SNeaampggJCRHmPWDAACEgo6KiAgsLC+jq6spkETeFuLg4DB06FKWlpUKtrXbt2qFVq1a4cuWKQutTEfK2m5rS0tKQnZ0NBwcHREdHQ01NDTY2Nrh586aQHaOnpwdHR0eEh4fjyZMnCm1b7du3R4cOHZCRkYHS0lLo6elhyJAhuHv3bq3dxzQ1NdG7d2+hxpSmpiZ0dXWF+8P8/Hy0aNEChoaGePr0qdDdrvr+U1FRARsbG5li89bW1mjfvj0CAwNrXV9Xr16Fo6MjHj9+jMzMTCHDuXrdyJrbVnJyMgYMGAAHBwfEx8dDRUUFtra2SElJEept9e3bF9bW1ggPD0dubq6wzUq6uVLjMEj0Aqr3Zy8vL0dWVhZCQ0Oxd+9emQvjffv2obS0FHPnzoW3tzeKi4sRExMDPz8/uLu7y1zcAFX9L93d3fHuu+9i6NChsLe3R25uLpKTk/Hdd9/V276IiAhMnz4dP//8M/z9/TF16lQkJCSguLgY3t7eWLFiBUaPHo23334bDx48wLfffouYmBiMGjWqwcXtLl++jNGjR2PJkiUYNmwYRo4ciWfPnuHo0aPYunWrTD2TkpISTJo0CR999BHs7OzQr18/3Lp1C4sWLUJOTk6TBImAqrojmZmZWLBgAby9vVFQUIAzZ87giy++UGgd0qsjKioKXbt2FdJJNTQ0kJeXh9jYWBw6dKjOV3lXfxtSUVER8vLykJqaih07duDIkSO1Xlz+EzIzMzFhwgSsXr0agwYNgoODA5KTk7F69WqcO3dObpAoIiICa9asga+vL+bOnQtVVVU8ePCg0UGiFi1aYMeOHWjbti3mzp0r9Ya/iooKLFy4ECEhIfj6669x9erVOtOSz5w5A0NDQwwePBiurq5o3bq18Mrhmm+fAoCFCxciOjoakyZNErr9JSUlYfXq1fj1118btBySzAVTU1NkZ2fXWtQwPz8fEydOxNSpUzFhwgS4u7tDVVUVT58+RWpqKj755BOFgzWRkZGYM2cOli1bhrFjx6KoqAhnz57F/PnzhbpM1a1btw7Dhw+HhYUFnJycUFxcjPT0dKxduxa//vrrS7npevr0KcaNG4f3338fTk5OsLS0REpKClavXo3CwkKFjvkikUgoVFlfdtfx48exZs0auLi4QFdXt8EF4V9Ely5dYGtri8ePH9cbjNm/fz8mTZqEqVOn4pdffsGhQ4fw/Plz2Nvbw8zMDIMHD0arVq2Ql5eH69ev48SJE9i/f7/CF51bt25FmzZtMHLkSFhbWwuFlBUNjC1fvhz379/HmDFjMHPmTDx79gwhISH45ptvsHPnToXm0dwuXLgALy8v/N///R9GjBgBoOp6wcfHBxMmTACgeDHd1NRUjBkzBqtXr8awYcMwZMgQ3LhxA2+99RZ0dXVlrqOKiorg4+ODDz74AEOGDMGgQYNw//59bNmyBTt37pR7bE1OTsbSpUsxf/58zJgxQ7jZ2Lp1K4A34zcBqrK2gaqsu9oYGhoK9byqF+uVuHTpksJdNV+mtm3b1putd/r0aQwdOlRY3nv37kmVIVBSUhJq00iYmJhIFeuWrAPJm9TEYjGCg4OFc13Lli2Rl5eHM2fONKrre12ePXuG8+fPo3///rC2tkZmZiaio6Ph7OwMoKp7pJOTE+7cuSMEK+7cuYNOnTrByckJR44cQWVlJVq3bl3n97Rq1Uropjlx4kSpz6q/wavmfJSVlWFjY4NWrVqhvLwc2dnZCA4OlnrQoqqqCjs7O2hoaKC0tFR4k9vL6BZ069YtlJeXo1+/frC2tkZFRQWys7OFa7361qei5G03NUm2k2HDhmHcuHEoLy9HcnKyVEZqixYthPqM1aepa9uqqKhAly5dMGDAACgrKyM/Px83b96s8622kheiODs7Q01NDUVFRUhOThamyczMxPXr1zFixAioqakJ23pERATs7e3h4eGB4uJixMfHywSNNTQ06u06mJKSAjU1NQwYMEAIUgcHB0udA2puW+Xl5QgMDMTQoUMxYcIElJSU4O7du1K9U8zNzaGsrCzz+926dQsRERF1tolqJ2qK/paNoaKiImbh4FePr68vvv76a6xatQr79u1r7uYQEdFLtGrVKrz33nvw9fXlxRT9o44ePQpLS0v06tWrybucEBER/dtlZGTEi8VixQsAVsOaRP9SNWsjAVV98JcuXYqysrJmqc1CREQvh7xjfq9evTBnzhxkZ2crXGeHqCHU1NTkPl328fGBtbU1IiIiGCAiIiJ6xbC72b/Url270KJFC/z999/Izc2FsbExnJ2doaGhgS+//JJFv4iI3iBBQUG4e/cubt26hcLCQpiamgpvclm1alWj3l5DVB9DQ0OcOnUK586dw927d6GsrAwLCwsMHjwYOTk5+Oyzz5q7iURERFQDu5v9S82cORMTJ06EqakpNDU1UVBQgMTEROzdu1fu62KJiOj1tWzZMri5ucHIyAitW7dGXl4eLl26hB9++IEF+umladOmDT788EMMGTIEenp6UFFRwZMnT3Du3Dls27YN9+7da+4mEhERvZFepLsZg0RERERERERERG8I1iQiIiIiIiIiIqIXwiARERERERERERExSERERERERERERAwSERERERERERERGCQiIiIiIiIiIiIwSERERERERERERGCQiIiIiIiIiIiIwCAREREREREREREBaNHcDXgdpaenAwAMDQ2buSW127x5M3x8fDB48GCkpaU1d3OImlTXrl0xa9Ys2NrawsDAAGpqanj27BkSExMRFBSEw4cPo7S0VBi/tn1WMrwuXl5euHDhQr3jjR49GjY2NjA3N4eZmRk0NTVx6NAhvPfee7VO06pVKyxatAju7u4wMjJCcXExrly5gh07diAyMlJmfFdXV4wfPx5mZmZo164dVFVV8ejRIyQkJODHH3/E1atXZaaxs7ODo6MjzM3NYW5uDm1tbVy8eBETJkyod5mIiIiIiOjfhUEiInqtLF26FH5+flBWVkZcXBwOHjyIwsJCtGvXDkOGDMHGjRsxY8YMuLu7KzzPjRs31vrZgwcPFJrHkiVLYG5ujvz8fDx8+BCampp1jt+mTRscOXIEPXv2xM2bN7Fv3z5oaGjA1dUVf/zxB5YvX44DBw5ITePq6op+/fohISEBmZmZKC0tRefOneHm5oaxY8fi//7v//D7779LTTNr1iy4ubmhqKgId+/ehba2tkLLQ0RERERE/z4MEhHRa2Px4sVYuXIl0tPT8c477+Dy5csy4zg7O+Odd95p0Hw3bdr0wm379NNP8fDhQ6SmpmLIkCEICAioc3w/Pz/07NkTgYGBWLBgASoqKgAAX331FYKDg7F27VpERETg4cOHwjQffPABSkpKZObVq1cvBAYG4qOPPkJAQADKysqEz77//nusX78eycnJMDAwQExMzAsvKxERERERvZlYk6iJGBkZIT09HZs3b4aRkRG+//57/P3337hz5w6CgoLg7OwsM03Lli0xZ84cnDx5EomJiUhOTkZ0dDT27NkDOzs7mfG7du2KzZs3IzY2Fqmpqbhy5Qq+++47dO3aVWq89PR0+Pj4AABiYmKQnp6O9PR0REdHv5yFJ/oHGBkZwc/PD6WlpZg+fbrcABEA/PXXX5g6deo/3DogKioKqampCo8/atQoAMA333wjBIgAICsrCzt37oS6ujomTZokNY28ABEA3Lx5E8nJyWjTpg10dXWlPouPj8ft27dRWVmpcNuIiIiIiOjfiZlETczIyAiBgYG4f/8+Dh06hLZt22LMmDHYs2cPJk+ejKioKGHczZs3Y8KECbhx4wYCAgJQXFyMjh07wtraGg4ODjh37pwwroODA3bv3o0WLVogNDQUd+/ehb6+PkaNGoURI0bA29sb165dA1DVdcbNzQ3m5ubYvXs3cnNzAQB5eXnC/CSZDlFRUfD29v6H1g5R402aNAkqKio4evQobt26Vee41esRvar09PQAAPfv35f57N69ewCAYcOGYcuWLfXOq0uXLujatSuysrKQmZnZtA0lIiIiIqJ/DYWCRCKRyA3AVgDKAHaLxeKvanxuD2ALgL4AJovF4rr7WbzBbG1t8c0332Dz5s3CsCNHjmD//v2YP3++ECTS1NTEuHHjkJCQAA8PD5mn/NXrhrRp0wbbt29HUVERPD09kZSUJHzWs2dPHD9+HN988w3c3NwAVHWdMTY2hrm5OXbt2sXC1fRGGDRoEADILej8ovz8/OQOLykpwfbt25v8+wDg2bNn6NixI4yNjaX2aQAwMTEBAJksQQk7OztYW1tDRUUFxsbGGDlyJABgxYoVEIvFL6W9RERERET05qs3SCQSiZQBbAcwEkAagFiRSHRMLBZfrzbafQCzAKx4GY18nTx48ABbt26VGhYREYG0tDRYWloKw8RiMZSUlFBaWiq3G0h2drbw/15eXmjbti1Wr14tczN569Yt7N+/H2+//Ta6d+8u83ltLl++DHt7exQVFTVk8YiaTfv27QFAqkZPU1m+fLnc4bm5uS8tSBQWFoapU6dixYoVWLBggXAc0NHRwdtvvw2gKkAsz7Bhw7Bo0SLh35mZmVi2bBkiIiJeSluJiIiIiOjfQZFMokEAksVicQoAiESiAwDGARCCRGKx+O7//+xfX/QiMTFRbtAnIyMDAwcOFP6dn5+PkJAQuLi4IDQ0FIGBgbh48SIuXbqE4uJiqWkl05mZmcnNeOjSpQsANChIVFxcjDt37ii8XERvMkNDw3/8O7/55hs4ODjAw8MDXbt2xfnz56Gurg5XV1c8evQIRkZGtdYRWrduHdatWwd1dXV06dIF8+fPx759+/D1119j27Zt//CSEBERERHRm0KRIJEhgOrvgE4DMPjlNOf1V73uT3Xl5eVQVlaWGrZgwQIsXLgQEyZMwMqVKwEARUVFCAoKwmeffYanT58C+F/Xs2nTptX53a1atXrR5hO9sh4/fowePXqgY8eOzd2UJvH48WO4u7tj6dKlcHZ2xowZM/Ds2TMcO3YMP/30E86fP4+srKw651FUVITExEQsXrwYbdu2xcqVKxEREYGEhIR/aCmIiIiIiOhN8o8WrhaJRPMAzAMgEzD5NyouLsamTZuwadMmGBgYYPDgwfDx8cHEiRNhZGQET09PAMDz588BVL3a+8aNG83ZZKJmc/HiRQwbNgzDhg3DgQMHmrs5TeLp06f48MMP8eGHH0oNHzp0KAA0KNhz5swZODk5wcbGhkEiIiIiIiJqFCUFxkkHYFzt30b/f1iDicXinWKx2EosFlspKSny1f8eGRkZOHLkCHx9fZGamorBgwcLGUTx8fEA/le4VxGSV2ozGEdvij/++AOlpaVwd3dH9+7d6xxXRUXlH2rVy+Hl5QWgqui9oiQZVpJ9n4iIiIiIqKEUidTEAuguEolMRSKRCoDJAI693Ga9+XR0dNCrVy+Z4RoaGtDQ0EBZWZnwGm9/f3/k5OTAz88P/fv3l5lGJBJhyJAhUsMkha9rq7WipqaGrl27wsDA4EUXhegfkZaWhk2bNkFVVRW//vor+vbtK3c8BwcH7Nu37x9uXcOJRCJoaGjIDJ84cSK8vLwQGxuLkydPCsNVVFRgZmYmd179+vXD9OnTUV5ejvDw8JfWZiIiIiIierPV291MLBaXi0SiRQBOAVAGsEcsFieKRKLPAMSJxeJjIpHIGsARANoAxohEojVisdj8pbb8Naevr4+QkBBcv34dN27cQEZGBjQ1NeHs7IwOHTpg9+7dKCgoAFAV8HnnnXewe/duHD9+HJGRkbh9+zbEYjEMDAwwYMAAaGtrS70uOzIyEgsXLsSGDRsQFBSEgoIC5Obm4ueffwYAWFpaIiAgAFFRUfD29m6OVUDUYN9++y2UlZXh5+eH4OBgxMbGIiEhAYWFhWjXrh1sbGzQpUsXXLlypUHzlVcQXuLUqVNITEysdx6urq5wc3MDAOjp6QGoKjq/efNmAFWvvP/888+F8dXV1ZGQkICzZ8/i3r17qKyshLW1NaysrHCs/5qCAAAKjElEQVT79m288847Uq+zV1NTQ2hoKK5fv46bN2/i4cOHUFdXR/fu3YXuaWvXrpUpSG9tbQ1fX18A/6tbZmpqKrQLAJYtW1bv8hERERER0ZtPoZpEYrE4CEBQjWEfV/v/WFR1QyMFPXjwAF9//TVsbW1ha2sLHR0d5OTk4M6dO/jyyy/x559/So0fGRkJZ2dnzJ8/Hw4ODhg0aBDKysqQmZmJqKgoBAYGSo0fERGBNWvWwNfXF3PnzoWqqioePHggBImIXldbtmzBiRMnMHPmTNja2mLSpElQVVVFdnY2EhMTsX37dhw+fLhB81y+fHmtn6WlpSkUJDI3N4ePj4/UsM6dO6Nz584Aqvb56kGi0tJS/Pnnnxg0aBDs7e0BAKmpqfjqq6+wa9cumbccFhYWYsOGDbCxscGQIUOgra0NsViMR48e4fDhw/j5559x+fJlmXaZmprKtEtPT09qGINEREREREQEAKLqT6r/SSoqKmLJ03YiIiIiIiIiInpxGRkZ8WKx2Kox07J6NBERERERERERMUhEREREREREREQMEhERERERERERERgkIiIiIiIiIiIiMEhERERERERERERgkIiIiIiIiIiIiMAgERERERERERERgUEiIiIiIiIiIiICg0RERERERERERAQGiYiIiIiIiIiICAwSERERERERERERGCQiIiIiIiIiIiIwSERERERERERERGCQiIiIiIiIiIiIwCARERERERERERGBQSIiIiIiIiIiIgKDREREREREREREBAaJiIiIiIiIiIgIDBIREREREREREREYJCIiIiIiIiIiIjBIREREREREREREYJCIiIiIiIiIiIjAIBEREREREREREYFBIiIiIiIiIiIiAoNEREREREREREQEBomIiIiIiIiIiAgMEhERERERERERERgkIiIiIiIiIiIiMEhERERERERERERgkIiIiIiIiIiIiMAgERERERERERERgUEiIiIiIiIiIiLC/2vv/kLurus4gL8/bWkXmYG7Cbea4IyWBcYQo4uCRWxebBdZOJA0hrsy+kdgFBV2ZVFCsP4sHJpQaruIBzJ2kYYQbTgQxAnGwwqdCyyz3Yja6tPFORdPD3PPb9tzzmHneb3ggd+f7/mdz82Hc573+f6+PyERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAACQgSFRVe2oquerarGq7j7L+cur6pHx+aNVtXm1CwUAAABgclYMiapqXZL9SXYm2ZpkT1VtXTZsb5JXu/vaJPcluXe1CwUAAABgcobMJLoxyWJ3n+juN5M8nGT3sjG7kzw43j6UZHtV1eqVCQAAAMAkDQmJrk7y4pL9k+NjZx3T3WeSnE5y1WoUCAAAAMDkrZ/mm1XVviT7xrtvnDp16tlpvj+QJNmQ5B+zLgLWIL0Hs6P/YDb0HszG+y/0hUNCopeSbFqyv3F87GxjTlbV+iRXJnll+YW6+0CSA0lSVce6e9uFFA1cOL0Hs6H3YHb0H8yG3oPZqKpjF/raIbebPZVkS1VdU1WXJbk1ycKyMQtJbh9v35Lk8e7uCy0KAAAAgOlacSZRd5+pqruSHE6yLsnB7j5eVfckOdbdC0nuT/JQVS0m+WdGQRIAAAAAl4hBaxJ192NJHlt27FtLtl9P8pnzfO8D5zkeWB16D2ZD78Hs6D+YDb0Hs3HBvVfuCgMAAABgyJpEAAAAAMy5iYdEVbWjqp6vqsWquvss5y+vqkfG549W1eZJ1wRrwYDe+0pVPVdVz1TV76vqfbOoE+bNSr23ZNynq6qrylNfYBUM6b2q+uz4s+94Vf1y2jXCvBrwvfO9VfVEVT09/u558yzqhHlSVQer6uWqevYtzldV/Wjcl89U1UeGXHeiIVFVrUuyP8nOJFuT7KmqrcuG7U3yandfm+S+JPdOsiZYCwb23tNJtnX3h5McSvK96VYJ82dg76WqrkjyxSRHp1shzKchvVdVW5J8PcnHuvuDSb409UJhDg387Ptmkke7+4aMHnL04+lWCXPpgSQ7znF+Z5It4799SX4y5KKTnkl0Y5LF7j7R3W8meTjJ7mVjdid5cLx9KMn2qqoJ1wXzbsXe6+4nuvu18e6RJBunXCPMoyGfe0ny3Yx+FHl9msXBHBvSe3cm2d/dryZJd7885RphXg3pv07yrvH2lUlOTbE+mEvd/WRGT5d/K7uT/KJHjiR5d1W9Z6XrTjokujrJi0v2T46PnXVMd59JcjrJVROuC+bdkN5bam+S3020IlgbVuy98VTfTd3922kWBnNuyOfedUmuq6o/VtWRqjrXr6/AcEP67ztJbquqkxk9NfsL0ykN1rTz/Z8wSbJ+YuUAl4Squi3JtiQfn3UtMO+q6m1JfpjkjhmXAmvR+oym3H8io9mzT1bVh7r7XzOtCtaGPUke6O4fVNVHkzxUVdd3939nXRjw/yY9k+ilJJuW7G8cHzvrmKpan9H0w1cmXBfMuyG9l6r6ZJJvJNnV3W9MqTaYZyv13hVJrk/yh6r6a5KbkixYvBou2pDPvZNJFrr73939lyR/zig0Ai7OkP7bm+TRJOnuPyV5R5INU6kO1q5B/xMuN+mQ6KkkW6rqmqq6LKNFyhaWjVlIcvt4+5Ykj3d3T7gumHcr9l5V3ZDkZxkFRNZlgNVxzt7r7tPdvaG7N3f35ozWA9vV3cdmUy7MjSHfOX+T0SyiVNWGjG4/OzHNImFODem/F5JsT5Kq+kBGIdHfp1olrD0LST43fsrZTUlOd/ffVnrRRG836+4zVXVXksNJ1iU52N3Hq+qeJMe6eyHJ/RlNN1zMaNGlWydZE6wFA3vv+0nemeTX47XiX+juXTMrGubAwN4DVtnA3juc5FNV9VyS/yT5WnebvQ4XaWD/fTXJz6vqyxktYn2HiQFwcarqVxn9+LFhvN7Xt5O8PUm6+6cZrf91c5LFJK8l+fyg6+pNAAAAACZ9uxkAAAAAlwAhEQAAAABCIgAAAACERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAACT5H79rb3jVzXvCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5J7rmnDT3WR",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Comparison\n",
        "\n",
        "Finally, let's compare all of these methods against one another. The chart below has the background of each patch divided into sections, each with the result of a different matrix. The inset foreground of each patch is the standard observer conversion."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcZEhh0qNIHy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "9773e6cc-ce10-4ceb-e9bb-c1a5f98c034d"
      },
      "source": [
        "draw_chart([chart_5d_dcraw, chart_5d_macbeth, chart_5d_ampas_6500, chart_bca], labels=['5D dcraw matrix', '5D Macbeth solve', '5D AMPAS solve', 'CIE 1931'])"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iNgI5UQn3Uy",
        "colab_type": "text"
      },
      "source": [
        "## White Balance\n",
        "\n",
        "Now we'll try rendering the Macbeth chart under 3200K illumination and setting the white point to match, using different methods to perform the balancing. For all of these we'll use the rawtoaces training data when solving the matrices."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evtH1xqlx7SN",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Divide by the RGB white point\n",
        "\n",
        " First of all using the divide method, which generates a matrix at 6500K, then just divides by the 3200K whitepoint in CameraRGB space.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhJxLOfgyME-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "d90fd397-9139-4e3d-d90e-888781f215e1"
      },
      "source": [
        "chart_5d_3200_divide = create_chart(cmf = cmf_5d, \n",
        "                                    src_illuminant_sd = sd_3200, \n",
        "                                    dst_illuminant_sd = sd_d65, \n",
        "                                    rgb_to_xyz = rgb_to_xyz_5d_6500, \n",
        "                                    rgb_white = rgb_5d_3200)\n",
        "draw_chart([chart_5d_3200_divide, chart_bca], labels=['5D white balance by division', 'CIE 1931'])"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bq9X9o3wyoTA",
        "colab_type": "text"
      },
      "source": [
        "### Solve a new matrix for the given whitepoint \n",
        "\n",
        "Next we'll do the same but solving a new matrix specifically for the 3200K whitepoint.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjKJPZAoyzfk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "b79a3373-c45c-43e0-8842-9f5817801ebb"
      },
      "source": [
        "unit_white = np.array([1,1,1])\n",
        "rgb_to_xyz_5d_3200 = solve_matrix(cmf_5d, sd_3200, sd_d65, unit_white, swatches=ampas_training_data)\n",
        "chart_5d_3200_solve = create_chart(cmf = cmf_5d, \n",
        "                                    src_illuminant_sd = sd_3200, \n",
        "                                    dst_illuminant_sd = sd_d65, \n",
        "                                    rgb_to_xyz = rgb_to_xyz_5d_3200, \n",
        "                                    rgb_white = unit_white)\n",
        "draw_chart([chart_5d_3200_solve, chart_bca], labels=['5D white balance by solving', 'CIE 1931'])"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjowF_3SmD48",
        "colab_type": "text"
      },
      "source": [
        "### Use the Bradford chromatic adaptation transform\n",
        "\n",
        "Here we just use the 6500 matrix we solved earlier and use the Bradford CAT to convert our 3200K colours to D65\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsEMY3pHgoFM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "67068048-fd1c-4f31-814a-f33a1cf6f252"
      },
      "source": [
        "checkersd = colour.COLOURCHECKERS_SDS['BabelColor Average']\n",
        "cam_checker_data = {}\n",
        "\n",
        "xyz_src = colour.sd_to_XYZ(colour.sd_ones(), illuminant=sd_3200)\n",
        "xyz_dst = colour.sd_to_XYZ(colour.sd_ones(), illuminant=sd_d65)\n",
        "\n",
        "# We need to account for the difference in luminance between the two white points\n",
        "# in camera RGB space\n",
        "cam_xyz_src = sd_to_CameraRGB_to_XYZ(colour.sd_ones(), cmf_5d, sd_3200, rgb_to_xyz_5d_6500, rgb_5d_d65)\n",
        "cam_xyz_dst = sd_to_CameraRGB_to_XYZ(colour.sd_ones(), cmf_5d, sd_d65, rgb_to_xyz_5d_6500, rgb_5d_d65)\n",
        "white_scale = cam_xyz_src[1] / cam_xyz_dst[1]\n",
        "\n",
        "chart_5d_3200_bradford = []\n",
        "for name, sd in checkersd.items():\n",
        "  xyz_cam = sd_to_CameraRGB_to_XYZ(sd, cmf_5d, sd_3200, rgb_to_xyz_5d_6500, rgb_5d_d65)\n",
        "  xyz_cam = colour.adaptation.chromatic_adaptation_VonKries(xyz_cam, xyz_src, xyz_dst, transform='Bradford') / white_scale\n",
        "  chart_5d_3200_bradford.append(colour.XYZ_to_sRGB(xyz_cam))\n",
        "\n",
        "draw_chart([chart_5d_3200_bradford, chart_bca], labels=['5D white balance by Bradford CAT', 'CIE 1931'])\n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TTSnNngnwhfYpZzcBZ6vqb+u9qNfbzarqXJK7gGPAFuBIVZ1Mci+wWFUT4AGa6YZLNIsu3dpnJmkWdKy9HwDvBn7TrhX/UlXtGyy0NAIda0/SJutYe8eAzyR5HvgP8PWqcva6dJE61t/XgF8k+QrNItZ3ODFAujhJfk3z48d8u97Xd4B3AlTVz2jW/7oZWAJeB77Y6brWpiRJkiRJkvq+3UySJEmSJEmXAJtEkiRJkiRJskkkSZIkSZIkm0SSJEmSJEnCJpEkSZIkSZKwSSRJkiRJkiRsEkmSJEmSJAmbRJIkSZIkSQL+ByxSH8BsAViUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62y1P3yMFPZ9",
        "colab_type": "text"
      },
      "source": [
        "### Interpolate a matrix from a sparse set of solves\n",
        "\n",
        "Finally, if we don't want to solve a matrix every time we could solve it at a few temperatures and interpolate between them. The interpolation method used here is described in the Adobe DNG specification.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHtuBfQaMkd-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "1244ccf2-661c-41b4-fc82-baa73a52f629"
      },
      "source": [
        "unit_white = np.array([1,1,1])\n",
        "sd_A = colour.ILLUMINANTS_SDS['A'] / 100\n",
        "sd_4000, _ = calculate_whitepoint(4000.0)\n",
        "rgb_to_xyz_5d_A = solve_matrix(cmf_5d, sd_A, sd_d65, unit_white, swatches=ampas_training_data)\n",
        "rgb_to_xyz_5d_4000 = solve_matrix(cmf_5d, sd_4000, sd_d65, unit_white, swatches=ampas_training_data)\n",
        "\n",
        "def interpolate_matrix(mtx_min, mtx_max, k_min, k_max, k):\n",
        "  m_min = 1.0 / k_min\n",
        "  m_max = 1.0 / k_max\n",
        "  m = 1.0 / k\n",
        "  t = (m - m_min) / (m_max - m_min)\n",
        "  return mtx_min * (1.0 - t) + mtx_max * t\n",
        "\n",
        "# First try interpolating between 2850K and 6500K\n",
        "# This is what the DNG spec suggests (D55 or D65), but it's a bit off\n",
        "rgb_to_xyz_5d_3200_i = interpolate_matrix(rgb_to_xyz_5d_A, rgb_to_xyz_5d_6500, 2850.0, 6500.0, 3200.0)\n",
        "chart_5d_3200_interp_6500 = create_chart(cmf = cmf_5d, \n",
        "                                        src_illuminant_sd = sd_3200, \n",
        "                                        dst_illuminant_sd = sd_d65, \n",
        "                                        rgb_to_xyz = rgb_to_xyz_5d_3200_i, \n",
        "                                        rgb_white = unit_white)\n",
        "\n",
        "# Now try interpolating between 2850K and 4000K\n",
        "# This looks better\n",
        "rgb_to_xyz_5d_3200_i = interpolate_matrix(rgb_to_xyz_5d_A, rgb_to_xyz_5d_4000, 2850.0, 4000.0, 3200.0)\n",
        "chart_5d_3200_interp_4000 = create_chart(cmf = cmf_5d, \n",
        "                                          src_illuminant_sd = sd_3200, \n",
        "                                          dst_illuminant_sd = sd_d65, \n",
        "                                          rgb_to_xyz = rgb_to_xyz_5d_3200_i, \n",
        "                                          rgb_white = unit_white)\n",
        "\n",
        "draw_chart([chart_5d_3200_interp_6500, chart_5d_3200_interp_4000, chart_bca], labels=['5D 3200 interpolated from 2850 and 6500', '5D 3200 interpolated from 2850 and 4000', 'CIE 1931'])\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLZgPnINQ2Ye",
        "colab_type": "text"
      },
      "source": [
        "### Comparison\n",
        "\n",
        "Again we compare all methods by dividing the chart patches into sections and showing one set of results per section. The inset foreground is standard observer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZhFQWsOQ6u8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "d5c87021-9190-4d4b-a5d7-b0b9d59d6342"
      },
      "source": [
        "draw_chart([chart_5d_3200_divide, chart_5d_3200_solve, chart_5d_3200_bradford, chart_5d_3200_interp_4000, chart_bca], labels=['Divide', 'Solve', 'Bradford', 'Interpolate', 'CIE 1931'])"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q3i3e/q6q+l9V9bmq+q9VdcvujwoAAADAXrlkJKqqxST3J3l7kluS3LVFBPpIa+37Wmvfn+QXk/zSrk8KAAAAwJ7ZziuJbk1yorX2ZGttNcmDSe7YfEBr7Zubbh5M0nZvRAAAAAD22tI2jrk+yVObbp9M8paLD6qqv5Lk3UlWkvzJXZkOAAAAgCti135wdWvt/tba65P8zSQ/t9UxVXW0qh6pqkcmk8lufWgAAAAAXqLtRKKnk9y46fYNG/dN82CSP7fVG1prD7TWjrTWjiws+IfVAAAAAEaxnVJzPMnNVXVTVa0kuTPJsc0HVNXNm27+mST/e/dGBAAAAGCvXfJnErXW1qrqniQPJ1lM8suttUer6r4kj7TWjiW5p6p+JMn5JF9P8hf2cmgAAAAAdle11ucfIltZWWmHDx/u8rEBAAAAXo5OnTr1mdbakZ28rx8MBAAAAIBIBAAAAIBIBAAAAEBEIgAAAAAiEgEAAAAQkQgAAACAiEQAAAAARCQCAAAAIMlS7wHmyfHPfjKHDh3qPcaQFrKYquo9xrCeffbZvOn739x1hg9/5Fdz4MCBrjOMrNqCHZ7h9OnT+amf+MmuM/zcB/529u3f33WGkVUrOzzF2bNn88EP/HzvMfLjd92Z5ZXl3mMMqWJ/Zzm/upoHP/LR3mPkyFvenMUl3z5syXPwTOtrazn+6U93neHwq1+dhQWvkZiqxQ5PMZlM8uUvf6n3GFeMz5LLIBBN5wllthF2RyCazQ7PNsL+CESz2eHp9g+yOwLRdPZ3tuWVld4jJIlANIMdnm2E3RGIZrPD011tu3N1/d8CAAAAsCWRCAAAAACRCAAAAACRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAAJIs9R6AnVvMQl+rROsAACAASURBVCrVe4zntdZ7ggu0JKlBzg1bGmp/B9Q2fjEuOzxbS7PDg3t+gwfZ4cGW5fnriN5TMEuNtL8DGuxTiinsMCPySqI55kllOmdmfPZ3NmdnfHZ4NudnfB6j6ZyZ8dnf2Zyd8dlhRiUSAQAAACASAQAAACASAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISAQAAABCRCAAAAICIRAAAAABEJAIAAAAgIhEAAAAAEYkAAAAAiEgEAAAAQEQiAAAAACISXZbWWu8RmFMj7M4IMzC/RtifEWZgPo2yO6PMwfwZZXdGmYP5M8LujDAD8+lq252l3gPMk+VyuuZJm2igmy1lMRnp+a16DzC+1uzwZnZ4Dg2yw6M8VIu12HsELkNro2xOMsoWL470PDzGKRnbQDtcAzxgC9V/BuZTXWW7M8bVGwAAAABdiUQAAAAAiEQAAAAAiEQAAAAARCQCAAAAICIRAAAAABGJAAAAAIhIBAAAAEBEIgAAAAAiEgEAAAAQkQgAAACAiEQAAAAARCQCAAAAICIRAAAAABGJAAAAAIhIBAAAAEBEIgAAAAAiEgEAAAAQkQgAAACAiEQAAAAARCQCAAAAINuMRFV1W1U9UVUnqureLd7+7qp6rKo+X1W/WVWv3f1RAQAAANgrl4xEVbWY5P4kb09yS5K7quqWiw77bJIjrbU3Jvl4kl/c7UEBAAAA2DvbeSXRrUlOtNaebK2tJnkwyR2bD2itfaK1dnrj5qeS3LC7YwIAAACwl7YTia5P8tSm2yc37pvmp5P825cyFAAAAABX1tJu/mZV9Y4kR5L88SlvP5rkaJIsLi7u5of+/+zdeVxU970//tcMMOw4sorsSmRTREEQcBDQ1F1cURN7bxavuWlimmpzk6bJN1t7b+yvxKRNHrFprGlsbERcwX0BNxbZ3HADVIRhU/Z9BpjfH3ROGWaAQTBg8no+HnlEzvo5h89h5rzP+/M+REREREREREQ0CPoEieQAXLr97PyvaRpEItFsAL8FMFOlUrXp2pBKpfoKwFcAIJFIVANuLRERERERERERPRb6DDfLBPCUSCTyEIlEEgCrARzsvoBIJJoC4C8AFqtUqsqhbyYRERERERERET1O/QaJVCpVO4BXARwDcANAvEqlyhOJRB+KRKLF/1rs/wNgAWC3SCS6JBKJDvayOSIiIiIiIiIiGoH0qkmkUqkOAzjcY9r/6/bv2UPcLiIiIiIiIiIi+gHpM9yMiIiIiIiIiIh+5BgkIiIiIiIiIiIiBomIiIiIiIiIiIhBIvqRUqmGuwXUHxV/SX3i6Rn52If7xtMz8vFX1DuemycAf0l94t/gJwF/STQy6VW4mqg/nSqD4W4CPYFG0k12J9iHaeBGUh9W8bkPPYKR0oNVEA13E+hJNFI6MNiH6VGNoE7MPkz/wm+URERERERERETEIBERERERERERETFIREREREREREREYJCIiIiIiIiIiIjAIBEREREREREREYFBIiIiIiIiIiIiAoNEREREREREREQEBomIiIiIiIiIiAgMEhERERERERERERgkIiIiIiIiIiIiMEhERERERERERERgkIiIiIiIiIiIiMAgERERERERERERgUEiIiIiIiIiIiICg0RERERERERERAQGiYiIiIiIiIiICAwSERERERERERERGCQiIiIiIiIiIiIwSERERERERERERGCQiIiIiIiIiIiIwCARERERERERERGBQSIiIiIiIiIiIgKDREREREREREREBAaJiIiIiIiIiIgIDBIREREREREREREYJCIiIiIiIiIiIjBIREREREREREREYJCIiIiIiIiIiIjAIBEREREREREREYFBIiIiIiIiIiIiAoNEREREREREREQEBomIiIiIiIiIiAgMEhERERERERERERgkIiIiIiIiIiIiMEg0II2NjcPdhBFLpVINdxNGtJHQd5qbm4e7CSMa+3DfRkL/aW1tHe4mjGjsw70bKX1HqVAMdxNGLPbfvo2UvtPe3j7cTRix2If71jEC+k5nZ+dwN2FEYx/u3U+t74iGqzNIJBKVnZ3dsOybiIiIiIiIiOjHqLS0NFulUgU9yrrMJCIiIiIiIiIiIgaJiIiIiIiIiIiIQSIiIiIiIiIiIgKDREREREREREREBAaJiIiIiIiIiIgIDBIREREREREREREYJCIiIiIiIiIiIjBIREREREREREREYJCIiIiIiIiIiIjAIBEREREREREREYFBIiIiIiIiIiIiAoNEREREREREREQEBomIiIiIiIiIiAgMEhERERERERERERgkIiIiIiIiIiIiMEhERERERERERERgkIiIiIiIiIiIiMAgERERERERERERgUEiIiIiIiIiIiICg0RERERERERERAQGiYiIiIiIiIiICAwSERERERERERERGCQiIiIiIiIiIiIwSERERERERERERGCQiIiIiIiIiIiIwCARERERERERERGBQSIiIiIiIiIiIgKDREREREREREREBAaJaBiEhoZCLpdj48aNg96WhYUFPvzwQ6Snp6OoqAhyuRx+fn5D0Er6sdm9ezfkcvmgthEbGwu5XI7Y2Fi919myZQvkcjmcnZ0HtW8iIiIiIqLHzXC4G/Ak0nWj2dbWhsrKSqSnp+Pzzz9HQUHBoPbh7OyMjIwMxMfH41e/+pXW/NDQUCQkJCAuLg6ffPLJoPb1JHvnnXfw85//HCdOnMCePXvQ0dGByspKbNmyBbGxsQgJCUFJSclwN5OGSM9rr62tDY2NjSgtLcXVq1dx+PBhnDlzBp2dncPUQiIiIiIioicXg0SDEBcXJ/zbysoKAQEBWLlyJebPn4+lS5ciLy9vGFv30zB79mwUFhbiueeeG+6m0A9Ife0ZGBjAysoKXl5eWL58OZ555hlcunQJGzZswJ07dzTW+eUvfwlTU9NB7ffIkSPIzs5GZWXloLZDREREREQ0EjFINAi6Mng++ugjvPDCC1i3bp3ODCAaWg4ODkhPTx/uZtAPTNe1Z2tri9/97ndYtGgRvv/+e8ybNw9VVVXC/NLS0kHvt6GhAQ0NDYPeDhERERER0UjEmkRD7OzZswAAGxsbrXkSiQSvvPIKTp48iYKCAty8eRN79+7FokWLNJbbuHEjMjIyAPy7Bor6v9jYWGzZsgUJCQkAgE2bNmnMDw0NHfD+gK7hbXK5HFu2bIGbmxu++uorXLt2Dbdu3cLOnTvh5eUFALC2tsbmzZuRk5ODwsJCHDp0CGFhYUNz8gBIpVK89dZbSElJQUFBAW7cuIFdu3YhIiJCYzl1fRmxWIywsDDh+NXT1TVjMjIyhHkMJv24PXz4EC+//DJSU1Ph5OSE1157TWN+z5pEixcvhlwux/vvv69zexKJBHl5ecjJyYGBgQGAvmsSyWQy7N27F/n5+bh27Rq2bduG8ePH99nmKVOm4KuvvkJubi7u3r2LzMxMbN68GQ4ODgM8eiIioieXhYUF1q9fD1tb2+FuChH1Izw8HAsXLhzuZtBjxEyiITZjxgwAwOXLlzWmGxkZ4bvvvkNYWBjy8/PxzTffwNTUFAsWLMDWrVvh5+eHjz/+GACQlpaGr7/+GuvWrUNeXh6OHj0qbCcvLw91dXUAum5YU1NTkZaWJswvLi4e8P66c3Z2RlJSEvLz8xEfHw9nZ2fMmzcPCQkJWLx4Mf7xj3+gsbERiYmJkEqlWLx4MXbs2AGZTDboTA0nJyckJCTA1dUV6enpSElJgampKWbPno3vvvsOb775Jnbu3Amg64Y/LS0NmzZtQnFxMeLj4wEAJSUlSEtLw9y5c+Hn54evv/5aOF/19fXCvtQ1nVJTU7Fy5cpBtZtGDpVKhc8++wxhYWGIiYnBe++91+uyx44dQ11dHZYsWYKPPvoIHR0dGvPnzJkDqVSKrVu3as3racGCBfjyyy+hUCiQmJiIiooKBAcH4+DBg7hx44bOdVatWoU//OEPUCgUOH78OEpLS+Hh4YE1a9Zg9uzZWLRo0ZBkPxEREfXGxsYGy5cvR3l5OQ4ePKg1f/369TrXO3fuXK+fb4+iqakJO3bsQGtr65BtU00ikSA8PBxubm4AgKKiIly4cAEKhaLXddzd3eHj4wNbW1uYmpoiMTERZWVlGsvIZDI4OTnBzMwMSqUSFRUVuHjxImpra4f8GH5Io0aNQnBwMMaOHQsDAwPU1tbi9OnTvR6Xo6OjzgfQu3btEr6DL1y4EGPHjtVaprq6Wnjw/WM1Z84c2NjYwMTEBAqFAnK5HBkZGWhubu51nTFjxmDy5MmwtbWFubk5UlJScPv2bY1lDA0NERwcDHd3d5iYmKCxsRE3btzA1atXH6mdFhYWeOaZZ7B37148fPjwkbbRF3d3d/j6+sLGxkboV7m5uSgqKupzvYFcv1ZWVli2bBlEIhG2b98+5MfwU8Ig0SB0fzuXpaUlJk+ejGnTpuHEiRPYunWrxrIvvfQSwsLCcOrUKTz//PPCTecnn3yCQ4cOYcOGDTh58iSysrKQlpaG4uJiIUjUc2hNXl4e6uvrERsbi7S0NJ1Dbwayv+7CwsKwefNm/OlPfxKmvf7663jjjTeQmJiIpKQk/OY3v4FKpQLQlTn1pz/9CevXr+81I0Nfn376KZydnfHyyy9rfFGxsrJCQkICPvzwQxw/fhwPHz4UgkLqIFHPc+Di4gI/Pz/89a9/ZeHqn5iLFy9CqVTCzs4OLi4uQuC0p7a2NiQmJmLt2rWIiorCyZMnNearg4e7d+/uc39mZmbYvHkzOjs7sWzZMly5ckWY9/777+O//uu/tNYZN24cPv74YxQXF2PFihUoLy8X5s2YMQM7d+7Ehx9+iHXr1ul93ERERAPl7e2NwsJCuLm5QSqV6gwEnDlzBvfv39eY1leA5VGoVCq0tLQM6TbVoqOjYWFhgcOHDwMAIiIiEBUVhWPHjvW6jpGRESoqKlBQUICoqCidyzx8+BD5+flobGyEsbExgoKCsGDBAuzcuVP4nvyksbS0xOLFi5Gfn4+kpCQoFApIpVIolcp+142Pj0dbW5vwc/eA34kTJyAW/3sAi4GBAVasWKFVP/LHSC6XIzc3F83NzTA3N0dISAh+9rOfYf/+/b2uY2RkhOrqaty+fbvX/hcaGgonJyckJyejoaEBjo6OiIiIQGtrK/Lz8x/X4TwyR0dHyOVyZGZmoq2tDZ6ennj66aeRlJSk8T24J32vX7FYjFmzZqG8vByOjo6P9Vh+ChgkGoRNmzZpTbt16xb279+PpqYmjemrV69GZ2cnPvjgA42shKqqKnz66aeIi4vDmjVrtII2j+pR93f//n18/vnnGtPi4+PxxhtvwNjYGB999JHGB9++ffsQFxc36NfO+/r6IiwsDElJSVpPsurr6/HHP/4R27dvx4IFC/D3v/99UPsCgNzcXERERDy2LyQ0fBQKBWpqamBvbw8bG5teg0RAVwBo7dq1WLlypUaQyM7ODjNnzsTVq1dx8+bNPvc3Z84cjB49Grt379YIEAFdBbZjY2MxatQojen/8R//AYlEgvfee0/rg/H8+fM4fvw4nn76aZibm2v9LSEiIhoKBgYG8PT0xLFjxyAWi+Hl5SWUO+hOoVAM6PvSmjVrcOvWLVhaWsLDwwMKhQLp6ekoKSmBTCaDq6srmpubcf78eWEYeM8sBnV2SlJSEoKDg2FtbY2amhqcPXtWo95gf6RSKVxdXXHgwAHhpRPnzp1DTEwMRo0aJWS69KS+yTY2Nu51290zqRobG5GZmYkVK1bAysqq1+3OnDkTJiYmKC8vx6RJk2BoaIjr16/j4sWLCAwMhK+vL1QqFa5evaoxKmHSpEmYMGECrKysoFAoUFxcjPT0dCFYFxERAXt7e+zbtw8dHR0QiURYuHAhFApFn8GwnqZNm4aSkhKNEg361mJsaWnRCBJ113O6p6cnDA0NcevWrV63p+4Tp06dgo+PD+zt7VFbW4uUlBSoVCrIZDLY2NigqqpKCJSoubq6IjAwEKNHj0ZzczMKCwuRnZ0tvP3W09MTEydOhFQqRUdHB8rKypCamipk9wxV/wOAa9euCf9ubGzE5cuXMWfOHBgYGPSaqV5cXCx8f42MjNS5jIODA/Lz84UMt/z8fHh5ecHe3r7XIJG5uTnCw8MxZswYGBoaorGxEdnZ2SgsLMQzzzwDAFi2bBmArlqeSUlJEIlECA4Ohre3NwDg9u3bEIlEAzoHADRGvgBATk4OXF1d4e7u3muQaCDXb0hICKqrq1FWVsYg0RBgkGgQnJychH+bmprCy8sLb7/9Nr744gt4eXlh8+bNALouSA8PD5SVlaGwsFBrOxcuXAAATJw4cUjaNZj95eXlab0+vKKiAgBw584drRvWzs5O4cNczc/PD3PmzNFYrr6+Hl9//XWvbQ4MDATQ9QSje4aWmrrGk6enZ6/bGIjW1lad54Z+HNQfXv09ycvKykJhYSFmz56t8WGzbNkyGBoaChlrfZk0aRIA7Q8/oOuLVV5enlbdLnV/nz59OiZPnqy1nq2tLQwNDTFu3LhHThsmIiLqy7hx46BQKFBeXg4TExPIZDJcvHhxSLJgJk6ciKysLOTm5sLX1xeRkZEoLS1FYWEhMjMzERAQgOjoaOzcubPPId3BwcHC0JywsDBER0drZPiuX78e2dnZyM7O1rm+g4MDFAqF8F0W6Ppeq1Qq4eDg0GswZ6AMDQ3h5eWl1wsuHB0d0dTUhMTERNja2iI6Oho2NjZ4+PAhDh48iLFjx0Imk0EulwvDflQqFdLS0lBfXw9LS0uEhYUhPDwcycnJAIDU1FQsW7YM06dPx4ULFzBlyhSMGjVKYyiXuoZMUlJSr21zdXXF5cuXMW/ePNja2qKhoQFXrlzRK+Nn2bJlMDAwQE1NDXJycrSG53Xn7e2N4uJivR6EBQYGCscuk8kQHR2N1tZWZGZmorW1FZGRkQgLCxOCYc7OzoiOjkZqairKyspgYWEBmUwGsVgsBEENDAyQnZ2N2tpamJiYICQkBLNmzUJiYqLGvgfb/3oyNjaGp6cnKioq+i1l0J/y8nK4ubnh5s2baGpqgoODA2xtbbUeWHY3Y8YMGBgYICkpCUqlUuMh5r59+7B06VIcPnwYVVVVwv2gv78/fHx8hACZn58fPD09NYakTZgwAZGRkdi5cycaGxv1PgYjI6NeA4uA/tevi4sLXF1dsWfPHowbN07v/VPvGCQaIi0tLbh06RLWrVuHrKwsvPzyy9ixYwdKS0thZWUFABodvDt1ZFS93GANZn+6PtjUf8S61/Tprr29HYaG/+5Kfn5+WllWxcXFfQaJRo8eDaDrCcvMmTN7Xc7c3LzXeURA1wewVCoFAL2e9uzevRtvvfUWYmJi8O233wIAVqxYAYVCgX379vW7vqWlJQD0On77wYMHWtPU/f0Xv/hFn9tmfyciosfFy8sLBQUFALoyycViMdzd3XH37l2N5aKiorSyGfbv34+amppet11SUoLr168D6Hog4+/vj/r6eiHDIScnB97e3hg9enSf9U+ysrKEYENOTg5iYmI0smxra2v7rGNkamqqc35LSwvMzMx6XU9fvr6+CAkJgZGREWpra3Ho0CGth609KRQKXLhwASqVCnV1dfD394eZmRkyMzMBAHV1dQgICMDYsWOFc9MzGyUjIwNz5swRgkTt7e1ITk7G4sWL0draiilTpuDYsWMax97fzbupqSkkEgkCAgKQlZWFixcvYuzYsYiOjoZSqew1M7u5uRnnzp3DgwcPIBaL8dRTT2HhwoVITEzUmSEyatQojB07Vu8Mp6tXrwr7vnLlCubOnYvjx48L/SIvLw/h4eHC8lOmTMHly5eFGj4NDQ3IyMhAdHS0ECTqnsHU0NCAc+fOYdWqVVoZ3IPtf2rBwcHw8/MThjF2rzf7qFJTUyGTyfDss88Kfe7ChQtaQ0O7s7CwwN27d1FdXQ1A875PnS3Y2tqqkTk4ceJEXL58WQgUpqamwtnZWWO7CoUCtbW1/fb97nx9fWFubt7n0Dh9rl8zMzNERETg+PHjaG9v13v/1DcGiYZYfX09CgsL4e/vj4kTJ6K0tFQIrtjb2+tcRz19qF6t/UPvr6f4+Hi9MjC6U7f53Xffxd/+9rfH0Sz6iQgODoaRkREqKyv1qke1Z88evPHGG1i5ciW+/fZb+Pn5wdfXF0ePHu3zC7Ca+jrq7Y0sdnZ2WtPU/d3Ly2tAT1yIiIiGgpWVFRwdHYXs8s7OTty5cwdeXl5aQaKMjAytAEF/n13qm1CgK4ChVCo1pqlvQk1NTfvcTveHPeqhQCYmJsJN+kC/bw61/Px8lJSUwMzMDJMnT8bs2bNx4MCBPrNEampqNLK1dA3Tamlp0Tg3Y8eORUBAAKRSKSQSCcRiMQwMDGBmZiaclwcPHiA3NxdBQUHIy8vT+p2lpKT0eSzqLOyioiIhi7mqqgp2dnbw8/PrNUhUV1enkZFVWVkp1GrVFSTy9vZGU1NTn8GM7rr3AXW/6dmXjIyMhOFbtra2sLOzQ0BAgMaxGRoawtTUFC0tLbCxsUFgYCBsbGxgbGwsHLuFhYVGkGio+t/ly5dx69YtWFhYIDAwENHR0Thy5Ihe6/bGz88PDg4OOHr0KBobG+Ho6Ijp06ejoaGh1++/165dg0wmg4uLC+RyOe7du9dnkNbIyAjm5uZaiQcPHjzQeJB579493Lt3T++2e3h4YPr06Th58uSgvwdHRUXh+vXrOh/K0qNjkOgxUKfuqQu0NTU14e7du3Bzc4OHh4fWh696KEr3YSXqSKz61ds9qT98dM1/lP0Nt5ycHABd40mHIkjU1/mhHy+RSITXXnsNAPosCNhdaWkpLly4gIiICIwfP154vX1/BavV1NdRaGgodu3apTHP0tJSZ72unJwcTJ48GSEhITh16pRe+yEiIhoq3t7eqKqq0ngYkp+fj4ULF2plUzQ3N/eaTd4bXRkFuqb1V9uk+zrqwMpA6qG0tLTAxMREa7qpqWmfb5fSl1KphFKpRH19PSorK/Gf//mfGDduXJ/ZET3Pg0ql0jlNzcLCAnPnzsXNmzeRlZWFtrY22NraYtasWRrFoIGut2J1dnY+0uiE1tZWdHZ2aj0gq6mpwfjx4we0rcrKSp3riMViTJgwATdv3tR7WKOuPtBXvxCJRMjJydE5RK61tRWGhoaYP38+5HI5kpOThT4SExOjdT4H2//U2tra0NbWhrq6OtTW1uLZZ5/FmDFj+izY3BcDAwMEBwfj5MmTQrCturoaNjY28Pf37zVIdOvWLZSUlMDFxQVOTk6IiYnBpUuX9B4uNxQ8PDwQFRWF5OTkfgOF+ly/Tk5OcHR0FEo5AF39bN26dTh//ny/tUVJN3H/i9BAzJkzB25ublAoFBpFoXft2gWxWIx33nlH4w/Q6NGj8frrrwMAvv/+e2G6OmVP1+siAQh/wLvXRepuoPsbbleuXEF6ejrmzZuHVatW6VzG29tbqE3Un/7Oj4mJCcaPH9/r+aUnj42NDb788kuEhYWhpKRE4w19/VE/CVq9ejViYmJQVVWl9baz3hw7dgw1NTVYsmQJ/P39NeZt2rRJq2g1AGzfvh0KhQLvv/++zrHTRkZGCA4O1rv9RERE+hKJRJgwYYIw1EytvLwcTU1N8PLyGqaWDb2KigpIJBI4ODgI0xwcHIRhP0NNJBJpBRoGy87ODmKxGGlpaaisrERdXZ3OoXKTJk2Cra0tEhMTYW9vP+CXynR2dqKyslIYsq8mlUoHnO1hY2OjMwinfl3747xxf/jwIaRSKerr67X+U6lUkEqlMDU1RWZmJsrLy1FXV9dvRtvjMJgH2epMsp6BNpVK1W8Qq6mpCTdv3sSpU6eQlZUlFKRWB8S6r69UKtHU1KQ1OkVXlrw+xo0bh6ioKKSkpGglMeiiz/W7e/du7NmzR/gvOzsb7e3t2LNnz0/i7XmPCzOJBqF7gWUzMzNMmDBBeE3hxx9/rJG+t3XrVkRFRWHu3Lk4ceIETp8+DVNTUyxcuBB2dnb44osvhLHIQNdTm9zcXISEhODPf/4z7ty5g87OThw/fhw3btxAYWEhysrKsHjxYiiVSsjlcqhUKiQkJEAulw94fyPBq6++ivj4eHzyySd48cUXkZubi7q6Ojg6OsLHxwc+Pj5YtGiRXnVmzp8/j1/84hf4wx/+gMOHD6OpqQl1dXX45ptvAHSNV05ISEBqaqrwqnN6cqivPbFYDCsrK3h5eWHatGkwNjZGTk4ONmzYoNdQMbUjR46gvr4e69atg0QiwbZt2/Qe19zc3Iw333wTX375Jfbu3YvExERUVFQgODgYXl5eSEtLQ2hoqMY6hYWF2LRpE+Li4nD69GmkpKTgzp07MDQ0hJOTE0JCQlBVVdVnfS4iIqJH4erqCjMzM1RXVws18tTKysowYcIEIcMbACQSidZNtFKpHBH1P2JjY5GXl4e8vDyd82tra3H//n3IZDKcPXsWACCTyVBUVCQMkTIzM8PChQtx8eJFYciMsbExLCwsIJFIAEB4q1hzczNaWlpgZWUFDw8PyOVytLS0wMLCAgEBAejo6NB7GJW+6urqIBaLMXHiRNy7dw/29vbCSzPUrK2tMW3aNJw+fRoVFRU4f/48Zs6cidLSUuH7kLquVF/Dzi5fvozZs2ejrKwMpaWlGDt2LMaPH69RP6jndiZOnIiGhgbU1NQIb8zz8PDA8ePHtbbv7e0NuVz+2EpeAF3Z2nPnzkVDQ4Nw/2RtbQ17e3tkZGSgsbERV8566AAAIABJREFU7e3t8PPzQ15eHqRSKYKCgh5pX/31P3t7e9ja2qK8vBwKhQJWVlYICgpCfX29RhZRz+0YGhoKDxlFIhEsLCxgY2OD1tZWNDU1QalUorS0FMHBwVAqlcJws6eeekrnGwrVQkNDUVxcjLq6OkgkEri4uAj9o6WlBe3t7XBxcRHOkVKpxLVr1xAQEIC6ujpUV1fD19dXY5gj0BX8Cw4ORlJSUq8ZeuPHj0dUVBTS09NRVlYm/E3p7OwUhlv23I4+12/P7/t2dnZQqVQDug8gbQwSDUL34szt7e2oqqrCiRMnsH37dpw7d05jWaVSiTVr1mD9+vVYsmQJnn/+eXR0dOD69et47733cODAAa3tv/baa3j//fcRFRWFJUuWQCwWo6ysDDdu3EBnZydefPFFvP3221i4cCEsLCwgFotx8eJFyOXyR9rfcCsrK8PcuXPxwgsvYP78+Vi6dCnEYjEePHiA/Px8bN++XeN1o305c+YMPvjgAzzzzDNYt24djI2NUVxcLASJ6Mmmvvba2trQ1NSEkpISJCQk4PDhwzhz5syA38zS2tqKpKQk4fWf+g41Uzt06BCeffZZbNy4EYsWLUJbWxsyMjKwePFivPrqq1pBIgDYu3cvrl+/jpdeeglhYWGIiIhAc3MzKioqcOjQIRw8eHBAbSAiItKHOnNg/vz5vS7j5OQkvJ5e1wOLnJwcjYz54SKVSnUOR+nu9OnTCA8PF463qKhIqMUEdD1wUtf6UXNzc9Mo1q0+B+o3WXV0dMDR0RH+/v6QSCRoaWlBWVkZ9u/fr1H0dyhUV1fjwoULCAgIwLRp01BRUYH09HTMnj0bQFdGSnR0NAoLC4UgV2FhIVxdXREdHY19+/ahs7MTFhYW/e6rqKgI586dQ0BAAMLCwlBXV4fk5GSNekQ9t2NgYIDp06fD3Nwc7e3tqKmpwZEjR7RqGFlaWsLJyemxD7MvKSnBkSNHMHXqVEyePBmdnZ2oq6sTClm3trYiJSUFwcHB8PX1RXV1NdLT0/u8HnrTX//r6OjAuHHjEBQUBENDQzQ3N6OkpASnTp3SqFvVczt2dnZYtGiR8HNQUBCCgoJw69YtnDlzBgBw6tQpBAcHIzo6GsbGxmhsbERWVlavASugK+AUHh4Oc3NzIdCkfjuvSqXChQsXEBgYiKlTp6K8vBxJSUm4cuWKUBwa6BqWWlBQoJFxJpFIIJVK+8yi8/HxgVgsRlhYmMZbf0tLS4U37unaTn/XLz0eoqF4zeWjkEgkqkdNVSMiIiIiIiIiIm2lpaXZKpXqkdLkWJOIiIiIiIiIiIgYJCIiIiIiIiIiIgaJiIiIiIiIiIgIDBIREREREREREREYJCIiIiIiIiIiIjBIREREREREREREYJCIiIiIiIiIiIjAIBEREREREREREYFBIiIiIiIiIiIiAoNEREREREREREQEBomIiIiIiIiIiAgMEv1opaenIz09fUi2FRMTg2PHjuHWrVuQy+X44IMPhmS7RCOJs7Mz5HI5tmzZMtxNISIiIiIiGhaGw92AJ4lcLh/Q8r/61a8QHx//mFrzwwgMDMTnn3+OoqIifPvtt2hpaUFOTg5CQ0ORkJCAuLg4fPLJJ8PdTPqJEIvFWL16NZYtWwZvb29YWFigrq4OlZWVuHTpEo4fP44TJ04MdzOJiIiIiIieSAwSDUBcXJzWtHXr1mHUqFH4+uuvUVdXpzEvLy/vh2raYzNr1iyIxWK8/vrryMrKEqaHhoYOY6vop0gsFuPvf/87oqOjUVtbi1OnTqGsrAwSiQQTJkzA0qVL4enpySARERERERHRI2KQaAB0ZczExsZi1KhR+Otf/4qSkpJhaNXj5eDgAAAoLy8f5pbQT92SJUsQHR2NvLw8LF++HA0NDRrzTUxMMHXq1GFqHRERERER0ZOPNYkeo0WLFmHPnj24ceMGCgoKcPLkSbz66quQSCRay6prCFlaWuJ3v/sdsrKyUFhYiOTkZLzwwgtD2q6YmBjs3r0b169fR2FhIVJSUvDLX/5So12xsbGQy+VYvXo1ACAjIwNyuVyo2ZKQkAAA2LRpkzBdLpczw4gem6CgIABAfHy8VoAIAFpbW5GamqoxTSKR4JVXXsHJkydRUFCAmzdvYu/evVi0aJFe+/zHP/4BuVwOX19fnfMXL14MuVyOd999V2O6VCrFW2+9hZSUFBQUFODGjRvYtWsXIiIi9NovERHRT4mFhQXWr18PW1vb4W4KEfUjPDwcCxcuHO5m0GPETKLH5K233sKGDRtQVVWF/fv3o6mpCVFRUfjNb36DmTNn4plnnoFSqdRYx8jICN9//z1GjRqFgwcPwsjICPPnz8dHH32E8ePH47e//e2g2xUXF4fVq1ejtLQUhw8fRn19PaZOnYr/+Z//wYwZM7B69Wp0dHQgLy8PcXFxmDt3Lvz8/DSG06mH0cXGxiI1NRVpaWnC9ouLi4V/p6enw8XFBSEhIT/KLCv6YdXU1AAAxo0bp9fyRkZG+O677xAWFob8/Hx88803MDU1xYIFC7B161b4+fnh448/7nMbu3fvRlRUFFasWIEPP/xQa/6KFSsAQKP2mJOTExISEuDq6or09HSkpKTA1NQUs2fPxnfffYc333wTO3fu1PewiYiIHhsbGxssX74c5eXlOHjwoNb89evX61zv3LlzuHHjxpC1o6mpCTt27EBra+uQbVNNIpEgPDwcbm5uAICioiJcuHABCoVC5/IikQjTpk2Di4sLrKysoFQqUVpaioyMDDQ1NQHoCmo988wzOtdPT0/HlStXhvw4fghr1qyBpaWl1vT79+/j6NGjva5nbm6OGTNmYOzYsejo6EBBQQHS09PR2dkpLOPr6ws/Pz9YWlqisbERubm5yM/PfyzHMZJMmTIFLi4usLGxgZGREb766qt+1zEyMkJQUBDc3d1hamqKhw8fIi0tDQ8ePACgXx8dKHWf3rt3Lx4+fPhI2+iLqakpQkNDYWtrCysrK+Tn5+PMmTP9rjdnzhzY2NjAxMQECoUCcrkcGRkZaG5uFpYZO3YsgoKCYG1tjfb2dty+fRuZmZlQqVRDfhw/FXoFiUQi0VwAnwEwAPC1SqX6uMf8CACfAvAHsFqlUiUMdUOfJIGBgdiwYQPkcjkWLFggXND/93//h23btuHpp5/Gf//3f+PPf/6zxnpjxozB/fv3ER0dLXxwxcXF4dChQ3juuedw8OBBZGRkPHK7YmNjsXr1ahw+fBgbNmzQ+CDeuHEjNm3ahOeeew7btm1DXl4e8vLy4OLiAj8/P63hdPX19YiNjUVaWhoLV9MP4vDhw/jFL36Bn//857CwsMCRI0dw5cqVXgvKv/TSSwgLC8OpU6fw/PPPo6OjA0DXsNFDhw5hw4YNOHnypEatrZ6OHj2Kuro6LF26FL///e+FbQCAnZ0dZs6ciStXruDWrVvC9E8//RTOzs54+eWXNb5wW1lZISEhAR9++CGOHz/+WD6AiYiIBsLb2xuFhYVwc3ODVCpFbW2t1jJnzpzB/fv3Nab1FmB5VCqVCi0tLUO6TbXo6GhYWFjg8OHDAICIiAhERUXh2LFjOpc3NDSEra0tcnNzUVVVBYlEgunTp2P+/PlISEiASqUSglrdubu7Y8aMGbh79+5jOY4fwr59+yASiYSfzczMsGzZMhQWFva6jkgkwrx589Da2oqDBw/CxMQEkZGRACBkePv4+CAkJARnz55FZWUl7O3tIZPJ0NbWptW3fmzEYjHu3r2LsrIyTJkyRa91IiIiYG1tjZSUFDQ1NeGpp57CggULEB8fj+bmZr366EhjYGCA1tZWXLp0Cd7e3nqvJ5fLkZubi+bmZpibmyMkJAQ/+9nPsH//fgCAtbU15s2bh0uXLiElJUUIWIpEokHdN//U9RskEolEBgC+APA0gBIAmSKR6KBKpbrebbH7AJ4D8OvH0cgnzapVqwAAn332mRAgAoCOjg58+OGHiI6Oxpo1a7SCREBXIKn7B29tbS0+++wzbNmyBatWrRpUZ3/xxRehVCqxadMmrSc1n376KZ5//nksXboU27Zte+R9dLdq1SoYGhqynhENiby8PLz22mv44IMPsHz5cixfvhxAV4ZReno6du3apVG0evXq1ejs7MQHH3ygEdypqqrCp59+iri4OKxZs6bPIFFbWxsSExOxdu1aREZG4tSpU8K8ZcuWwdDQELt37xam+fr6IiwsDElJSVpPZOvr6/HHP/4R27dvx4IFC/D3v/990OeEiIjoURkYGMDT0xPHjh2DWCyGl5eXzu+ZCoViQAGcNWvW4NatW7C0tISHhwcUCgXS09NRUlICmUwGV1dXNDc34/z588KDnp5ZDI6Ojli0aBGSkpIQHBwMa2tr1NTU4OzZs6iqqtK7LVKpFK6urjhw4AAqKysBdGVBxcTEYNSoUVovnQEApVIpBJTUzp07h9jYWEilUtTU1OgManl4eEAul+scEq8WGBgIDw8PXL58GUFBQTAxMcGdO3dw7tw5eHt7IyAgAIaGhrh9+zbS09OF9Tw9PTFx4kRIpVJ0dHSgrKwMqampQjbFlClT4Ofnh4SEBOE7fnR0NKRSKfbv36+R0dOXnvcH3t7eUCgUuHPnTq/rODs7Y/To0di5c6eQxZKRkYGIiAhkZmZCqVTiqaeews2bN4VgU0NDA+zs7BAQENBrkEjdJ06dOgUfHx/Y29ujtrYWKSkpUKlUkMlksLGxQVVVFZKTkzXOu6urKwIDAzF69Gg0NzejsLAQ2dnZwnno73wOVf8DgOzsbABd/UMfBgYG8PDwwIkTJ1BWViZsw9XVFb6+vsjKytKrj+pibm6O8PBwjBkzBoaGhmhsbER2djYKCwuFzLhly5YBAEpLS5GUlASRSITg4GAhsHP79m2NQKK+GhsbhaChvucCAK5du6axjcuXL2POnDkwMDBAR0cHxo8fj5qaGuE819fXIyMjA7Nnz0ZOTo7WyB3Sjz6ZRMEAClQq1R0AEIlE3wOIASAEiVQq1b1/zdPvL9CP3KRJkwAAFy5c0Jp3584dlJWVwc3NDZaWlhp/0JRKpc4bVvUFNXHiRGHaxo0btZaLj4/vdViXiYkJfH19UV1djXXr1ulcpq2tDU899VQfRzYwRUVFQ7YtIgBITEzEkSNHEBYWhuDgYEycOBHTpk3DvHnzMG/ePOzevRuvv/46zM3N4eHhgbKyMp1Pv9TXZvdrqje7d+/G2rVrsXLlSo0g0cqVK6FQKLBv3z5hWmBgIADA0tJS5zVqY2MDoOvLCRER0XAaN24cFAoFysvLYWJiAplMhosXLw5JFsLEiRORlZWF3Nxc+Pr6IjIyEqWlpSgsLERmZiYCAgIQHR2NnTt3ajzI6Sk4OFgYWhIWFobo6GiNhzPr169Hdna2cIPYk4ODAxQKBSoqKoRpFRUVUCqVcHBw0Bkk0kVdt7O3DCpLS0s4OTnh5MmT/W7L0tIS7u7uOHr0KMzNzfH000/DzMwMzc3NOHz4MKRSKWbPno2KigohK8nAwADZ2dmora2FiYkJQkJCMGvWLCQmJgIALl26BGdnZ8ycORPHjh3DU089BXd3d+zZs0cIjMycORNjx47FP//5T72OGQC8vLxQUFDQ5+/I3t4eNTU1GsOcSkpKhGyXsrIyGBgYoL29XWO99vZ22NnZQSQS9dnnAgMDkZaWhvr6eshkMkRHR6O1tRWZmZlobW1FZGQkwsLChMwwZ2dnREdHIzU1FWVlZbCwsIBMJoNYLBaCoP2dT7XB9r9HIRaLIRaLtc55R0cHxowZ0+t6/fVRAJgxYwYMDAyQlJQEpVKJUaNGCfP27duHpUuX4vDhw6iqqhL6jb+/P3x8fIQAmZ+fHzw9PTUy4idMmIDIyEjs3LkTjY2Nj3Tc+jA2NoanpycqKiqE86MOFnXX0dGh0f9o4PQJEjkBKO72cwmAkMfTnB8H9Vje7h9I3VVWVsLZ2RlWVlYaQaLq6mqdkX51NlL3McKbNm3SWi4tLa3XIJFUKoVYLIatra3OdYmeFO3t7Th79izOnj0LoOvDdMGCBYiLi8PKlSuFYWhA39cg0DUErD/qIvJPP/208NRx4sSJ8PHxwZEjRzSe1owePRpA1xexmTNn9rpNc3Nz/Q6WiIjoMVEHAICumjNisRju7u5aw6WioqKE4UNq+/fv7zVbAegKEly/3vU8OSsrC/7+/qivrxdq0OTk5MDb2xujR4/uc/h1VlaWcJOXk5ODmJgYmJubCwGJ2traPusYmZqa6pzf0tICMzOzXtfrTiwWY/r06SgqKuq13ou3tzdaWlpw7969frcnEomQkpICpVKJmpoaFBcXw9HREd999x06OztRW1uL8vJyjB07VvhddB/W3tDQgHPnzmHVqlXCuVCpVEhOTsby5csREhICHx8fpKWlaQTBmpubUV9fr9cxA101Fq2srPqtPWVmZqaVVdXa2orOzk7hHJeUlMDLywv37t3DgwcPYGtrC29vbxgYGMDExKTPTLWrV68KNU+vXLmCuXPn4vjx40K/yMvLQ3h4uLD8lClTcPnyZdy+fVs4XxkZGYiOjhaCRP2dT7XB9r9HoVQqUV5ejilTpqC6uhotLS0YP3487O3te/396dNHga7srLt376K6uhoANO5D1b+D1tZWjd/HxIkTcfnyZSGbLDU1Fc7OzhrbVSgUqK2t1TtjbaCCg4Ph5+cHIyMjVFRUaNTHKi4uxqRJk+Dp6YnCwkKYmpoKbzvW9xonbT9o4WqRSLQewHqgK+r3Y6W+4Ozt7XVm09jb22ssp2ZtbQ2xWKx1gdnZ2Wkt7+TkNKA2qf+oXL16FXPnzh3QukQjWWdnJxITE+Ht7Y3XX38d4eHhOH/+PIB/X2s99XYN9iYhIQFvvvkmFi9ejB07dmDlypUAoPE0Cfj3dfbuu+/ib3/72yMdDxER0eNmZWUFR0dHIbO2s7MTd+7cgZeXl1aQKCMjQ+PFJAD6zRZQ34QCXQ93lEqlxjT1TaipqWmf2+k+tEc9FMjExES4Ee7+4ojHQSQSISoqChKJpNcaRiKRCF5eXsjPz9crC6uxsVFjCExLSwvq6uo0vv+3tLTAxMRE+NnGxgaBgYGwsbGBsbGxMNzHwsJCOBfq4TxRUVEoKirSCu5kZmbqf+DoqiNUWVmp8Xt7VDk5OTAzM0NMTAyAruO7ffs2AgIC+l23ex9Q95uefcnIyEjIKLG1tRWGsqmJRCIYGhrC1NQULS0tep3Pnvv+IftfcnIyZs6cibVr16KzsxMPHz5EYWGhzrf/6dNH1a5duwaZTAYXFxfI5XLcu3evzyCtkZERzM3NtR66PnjwQOOB57179/QKkD6qy5cv49atW7CwsEBgYCCio6Nx5MgRAF01i9LT0zFjxgxERkaio6MDOTk5cHR0HJG1mZ4U+gSJ5ABcuv3s/K9pA6ZSqb4C8BUASCSSH+1v7dq1a/D390doaKhWkMjd3R2Ojo4oKirSigarK9lfvHhRY3pYWJiw3UfV3NyMmzdvYsKECb0WJhyI7il+RCOB+gurSCRCU1MT7t69Czc3N3h4eGh94VVfU1evXtVr2wkJCfj1r3+NlStX4p///CeWLFmCqqoqjeFnQNeXIAAICQlhkIiIiEYsb29vVFVVaWQD5efnY+HChVrZFAPNQAGgM6NA17T+apt0X0d9wzeQeig9gy1qpqamGm9H0kUkEmHWrFmwtrZGYmIi2tradC7n5uYGMzMz3Lx5U682DfTcGBoaYv78+ZDL5UhOThaOKSYmBmKxWGMdR0dHdHZ2wsLCQueDZ32ZmJjAzc1NZ+mMnpqbm+Hg4KC1vlgsFs5xR0cHzpw5g7NnzwpD69T1jvqrd6WrD/TVL0QiEXJycnTWUWptbR3Q+Rxs/3tUDQ0NSEpKgqGhIYyMjNDS0oJZs2ZpPdzUt4+q3bp1CyUlJXBxcYGTkxNiYmJw6dKlIR0u9zi0tbWhra0NdXV1qK2txbPPPosxY8YIdW+vXr2Kq1evwszMDG1tbbC0tERISIjeD4NJm7j/RZAJ4CmRSOQhEokkAFYD0H5HJgl27doFAPjlL38Ja2trYbpYLMa7774LAwMDfP/99zrX/c1vfiOMKQW6hom99tprGtt9VF999RWMjY0RFxenc5jNqFGj9KrRAvz7deR9ZTS5ublh/PjxMDT8QRPW6EcqJiYGMplM54eznZ0dnn32WQAQUol37doFsViMd955R+NDf/To0Xj99dcBoNfrsKfS0lJcuHABgYGBePHFF2Fra4t9+/Zpja+/cuUK0tPTMW/ePKGAfU/e3t5CbSIiIqIfmkgkwoQJE4ShZmrl5eVoamqCl5fXMLVs6FVUVEAikWgEMRwcHIRhK70RiUSYPXu2cPPdVyDD29sbpaWletc3GiipVApTU1NkZmaivLwcdXV1OjOw3N3d4enpiaSkJEgkEgQHBz/yPr28vIRX2fensrISo0eP1sgscXJyQnt7u1aWivrNcCqVCuPHj38sbzZ7+PAhpFIp6uvrtf5TqVR6n8+RoL29HS0tLZBIJHB2dtbI1hlIH+2uqakJN2/exKlTp5CVlSUUpFYHxLp/z1YqlWhqatLKzFePchlOuhIVmpubhWLWjY2NfJPwIPR7965SqdpFItGrAI4BMADwN5VKlScSiT4EkKVSqQ6KRKJpAPYBGA1gkUgk+kClUvk91paPYFlZWfjiiy/wyiuv4PTp0zh06BCam5sRFRUFHx8fZGRk4Msvv9Rar7y8HBKJBKdPn8bx48dhaGiIBQsWYMyYMfjmm28G/Rq/Xbt2wd/fH8899xxCQkKQkpICuVwuvPkhJCQE8fHxeOutt/rdVmFhIcrKyrB48WIolUrI5XKoVCokJCQIb6rYtWsXXFxcEBIS0mutJCJ9TZ06FevWrUNFRQUyMzOFLxaurq6YNWsWTE1NcfToUSQlJQEAtm7diqioKMydOxcnTpzA6dOnYWpqioULF8LOzg5ffPHFgFKvd+/ejYiICOH66DnUTO3VV19FfHw8PvnkE7z44ovIzc1FXV0dHB0d4ePjAx8fHyxatGjAb8cgIiIaCq6urjAzM0N1dbVQS0+trKwMEyZMEDJjga6CuD1vopVKpdaDkuEQGxuLvLw85OXl6ZxfW1uL+/fvQyaTCbUMZTIZioqKhKCOmZkZFi5ciIsXL+LevXsQiUR4+umnYWdnJ9Q+UR+/QqHQKJJrbm4OZ2dnpKSkPLZjbGxsRHt7O/z8/JCXlwepVIqgoCCNZczMzIS3iZWXlyM5ORkLFy5EcXGx8L182rRpsLe3x6FDh/rdp5eXFwoLC3X+jv38/ODn5ycMtSopKUFNTQ0iIyORnp4OExMTTJ8+HTdv3hSG1Y0aNQr29vaoqKiAsbEx/P39hVe8D7WcnBzMnTsXDQ0NuHPnDjo7O2FtbQ17e3tkZGTodT711V//A7r6iImJiVBbVv2gsK6uTji/Pbfj7OwMkUiE2tpaWFlZISQkBLW1tUItpYH00e5CQ0NRXFyMuro6SCQSuLi4CA/+W1pa0N7eDhcXF+EcKZVKXLt2DQEBAairq0N1dTV8fX2FbDA1d3d3BAcHIykpqc8MPfWxqxMibGxs0NHRIYxw6bkde3t72Nraory8HAqFAlZWVggKCkJ9fb3G27P9/f2FIbHu7u4ICAjAyZMnOdxsEPRK8VCpVIcBHO4x7f91+3cmuoah0b/87//+L65du4bnn38eK1asgKGhIYqKirB582b85S9/0fk6PqVSidWrV+Ott97C4sWLYW1tjfv37+OLL74YsqErv/3tb5GcnIyf//znkMlksLKyQm1tLeRyObZu3Yo9e/botZ3Ozk68+OKLePvtt7Fw4UIhrfXixYvChxHRUPrLX/6CO3fuQCaTwcfHBzNnzoSxsTFqamqQmpqK/fv3a7xpTKlUYs2aNVi/fj2WLFmC559/Hh0dHbh+/Tree+89HDhwYED7P3z4MH7/+98LRRx7G/5ZVlaGuXPn4oUXXsD8+fOxdOlSiMViPHjwAPn5+di+fXu/RSCJiIgeF3XmwPz583tdxsnJSfg+p+tFDDk5OTrfyPtDk0qlOoeTdXf69GmEh4cLx1tUVKQxjEosFkMqlQo3rubm5nB3dwcALF++XGNbKSkpQkFk4N+viO85rH0otba2IiUlBcHBwcKbitPT0zV+f5GRkXj48KEwjL68vByXLl1CZGQkEhIS0NbWBjMzM71e2OHo6AipVIrk5GSd801MTCCVSoWfVSoVjhw5ghkzZiAmJgbt7e0oKChAenq6sIxIJMKkSZMgk8nQ2dmJ0tJSHDhw4LG8CaukpARHjhzB1KlTMXnyZHR2dqKurk74velzPvWlT/8LCgrSyM5T96nExEShKHbP7agzwczNzdHW1oa7d+9qvHlwIH20O5FIhPDwcJibm0OpVKK0tBRpaWkAun6P6qz5qVOnory8HElJSbhy5YoQhAS6hqUWFBRo9AGJRCK8JKkvPdvq5uaGhoYG4Y17PbfT0dGBcePGISgoCIaGhmhubkZJSQlOnTqlEQhzcXHBlClTYGBggKqqKhw/flyrjhoNjGi4ImwSiUQ1ElLVRgr1H9Lp06cPc0uIiIiIiIiI6ElVWlqarVKpHilNTp+aRERERERERERE9CPHIBERERERERERETFIREREREREREREehaupsePtYiIiIiIiIiIaDgxk4iIiIiIiIiIiBgkIiIiIiIiIiIiBomIiIiIiIiIiAgMEhERERERERERERgkIiIiIiIiIiIiMEhERERERERERERgkIiIiIiIiIiIiMAgEY1Q6enpSE9PH/R2tmzZArlcDmdn5yFoFVH/YmNjIZfLERsbqzUvIiICBw4cwPXr1yGXy7Ft27YfvH2hoaGQy+XYuHHjD75vIiIiIiIa2QyHuwFPIrlcrjWtra0NlZWVSE9Px+eff45HA2chAAAgAElEQVSCgoJB7cPZ2RkZGRmIj4/Hr371K635oaGhSEhIQFxcHD755JNB7Ys0qYNT06dPH+aWUE8/xLX3uDg7O+Nvf/sb6uvrsWvXLjQ0NIzYthIRERER0U8Tg0SDEBcXJ/zbysoKAQEBWLlyJebPn4+lS5ciLy9vGFv3ZFu1atVwN4FGsCfx2pPJZDA1NcWvf/1r7N+/f7ibQ0REREREpIVBokHQlcHz0Ucf4YUXXsC6det0ZgCRfoqKioa7CTSCPYnXnoODAwCgoqJimFtCRERERESkG2sSDbGzZ88CAGxsbLTmSSQSvPLKKzh58iQKCgpw8+ZN7N27F4sWLdJYbuPGjcjIyADw7/om6v9iY2OxZcsWJCQkAAA2bdqkMT80NHTA+wO6hsLI5XJs2bIFbm5u+Oqrr3Dt2jXcunULO3fuhJeXFwDA2toamzdvRk5ODgoLC3Ho0CGEhYUN6Bx1r4kSEBCAb7/9FteuXdOoHdRbTSJLS0t88MEHyMrKQmFhIc6cOYP169fD1dVVaH9v1q5di5MnT6KwsBCXLl3C5s2bYWlpqdUuFxcXuLi4aJzXvrZLI0Nv1173GkGRkZHYvXs3bty4oTF0bc6cOfjTn/6Ec+fOIT8/H/n5+Thy5AheeOEFiEQinftzd3fHX/7yF+Tl5SE/Px8HDhzArFmztJZT96s33ngDAJCQkKDzevXw8MBnn32GrKws3L17F9nZ2fjss8/g4eGhtc2NGzcK6y9ZsgSJiYm4ffu2xjVja2uLP/7xj7h06RIKCgpw/PhxrFy5cgBnlIiI6IdhYWGB9evXw9bWdribQkT9mDNnDmbOnDnczaDHiJlEQ2zGjBkAgMuXL2tMNzIywnfffYewsDDk5+fjm2++gampKRYsWICtW7fCz88PH3/8MQAgLS0NX3/9NdatW4e8vDwcPXpU2E5eXh7q6uoAdN38pqamIi0tTZhfXFw84P115+zsjKSkJOTn5yM+Ph7Ozs6YN28eEhISsHjxYvzjH/9AY2MjEhMTIZVKsXjxYuzYsQMymQylpaUDOleBgYF49dVXkZmZiV27dsHa2hpKpbLX5Y2NjREfHw9/f39cvXoVe/fuhZWVFV577TUEBwf3ua933nkHM2fOxIkTJ3D27FmEhYVh7dq18PDwEAoMFxcXIy4uDuvWrQMAfP3118L63Ycvbdy4EZs2bWI9qBGmt2tPbcGCBYiMjERycjJ27NihUcz87bffRmdnJ3Jzc1FWVgYrKyuEh4fjo48+QkBAAF577TWNbXl4eODgwYOwtrbGqVOncP36dbi7u2Pbtm1ITk7WWFbdr0JDQxEWFob4+HjhOlX/f/Lkyfj+++9hYWGB48ePIz8/H+PHj8eyZcvws5/9DKtXr9Z5XC+99BJkMhlOnjyJ1NRUWFlZAfj/2bvvuKru+/HjL+5ly94gDkARQXEyFQXUiDtGY0bTrGaYNmmsSZqatt+maZvRJjEmtWkTv7Hfxp8xKqIizkRFAVmuKC6GItvBHlfW/f3B455yvZelGDPez8eDR+JZ93MO53DPeZ/35/0BR0dHtm3bxtChQ8nIyCAzMxN3d3feeustJZgmhBBCODs7s2jRIsrLy9m+fbvB/GeeecboeocPH+bs2bP91o6GhgY+//xzNBpNv21Tx9zcnEmTJjFkyBCgI1s9NTWV5ubmXq0fFRXFyJEjSU9P55tvvlGm29raEh4ejoeHB2q1mqKiItLS0mhqaur3ffi2WFlZERoaire3NxYWFpSVlZGamkptbW2X6wwdOpTAwECcnZ1Rq9VUV1dz/PjxLnsF+Pn5MW3aNAoLC9mzZ8+d2pXvBCcnJ8aOHYuHhweWlpbU19dz/vz5Lu9Vjenq/IuKimLgwIFYW1vT0tJCRUUFmZmZVFdX31JbPT09mTdvHv/3f//HjRs3bmkb3QkICGD48OE4OTkBcP36dbKysnrMsI+IiMDDwwNHR0eampr44osv9Oar1WomT56Mi4sLjo6OlJeXs2PHjn5v/4+NBIluQ+fRgWxtbRkzZgwhISHs27ePf/7zn3rLPvvss0RGRvL111/zxBNP0NbWBnR0m0lKSuKFF17gq6++Ijs7myNHjlBUVKQEiW4OROTk5FBbW8uSJUs4cuSI0UBFXz6vs8jISN555x0+/PBDZdqyZct45ZVXSExMZMeOHaxYsQKtVgt0ZG98+OGHPPPMM7z++ut9On7R0dG8+uqrrFu3rlfLP/fccwQHB7N161Z+8YtfKNM//PBDvUCaMePHj2fatGlKIEutVrNx40YmTZrE2LFjOXHiBMXFxbz//vtK0EgCQN9dfbn2dGJjY/npT3/KwYMHDeY9+uijBjczJiYmrFy5kvvvv5+1a9dy/PhxZd5f/vIXnJyc+J//+R+9Ecruuece1q5dq7cd3Xm1fPlyJUjUObALsGrVKuzs7Hj++edJSEhQps+fP5+PP/6YDz/8kOjoaOW605k0aRLz5883qMH0m9/8hqFDh/Lpp5/qXZdr1641+hAghBDixykgIID8/HyGDBmCg4OD0QfM5ORkLl++rDettwGW3tJqtXcsuBIbG4uNjQ07d+4EOkYajYmJ6VWAwsfHB1dXVxoaGvSmm5qaMmfOHCorK5UH0pCQEGbOnPm9rjt4zz33oNVq2bt3L83NzQQHBzNnzhw2bdpEa2ur0XU8PT0pKSkhKyuLGzduMGzYMGbMmMGOHTsoLy/XW9bW1pawsDDKysq+jd2561xdXdFoNBw4cID6+nrc3NyIiorCxMSEEydO9Lh+V+cfwLVr18jNzaW+vh4LCwsmTpzInDlzWL9+vcH94neBp6cnBQUFpKWl0drayujRo5k9ezbx8fHdBiFNTEy4cOECTk5ORkerNjExoa2tjZycHAYPHoy5ufmd3I0fDQkS3YaXXnrJYNr58+fZunWrwcX84IMP0t7ezh//+EclYAMdUdQPPviA9957j4ceesggaHOrbvXzLl++zN///ne9aRs3buSVV17BwsKCP/3pT3p/eBISEnjvvfcICgrqcxtPnz7d6wARwOLFi2lra+Ott97Sm15aWsqaNWt49dVXu1x35cqVeplObW1tfPnll4SHhytBot5au3Yt27Zto7KystfriP7Vl2tPZ8+ePUYDRGC8BpZWq2XNmjXcf//9TJ06VQkSeXp6MnXqVAoLCw0CQnv37iUtLa1PXTAnTpzI8OHDyc7O1gsQAWzfvp3HH3+csLAwQkNDlW6oOv/v//0/gwCRqakp9913H3V1dXoFvgG++eYbEhISlECoEEKIHy+1Ws2wYcPYs2cPKpWKESNGGHzPQEdAqC8BnIceeojz589ja2uLj48Pzc3NpKenU1xcTFRUFIMHD6axsZGUlBSl67eNjQ0PP/wwW7Zs4dq1a0pWw44dOwgNDcXJyYmqqioOHTrE9evXe90WBwcHBg8ezLZt27hy5QrQkQW1YMEC7O3tlex8Y2xsbIiMjCQpKYlZs2bpzXN3d8fW1pYtW7YoAbMDBw7w+OOPM3DgQKOjsQJMnToVS0tLysvLGT16NKamppw5c4bMzEwmTJhAYGAgWq2WU6dO6WWbjB49Gn9/f+zs7GhubqaoqIj09HTls6dMmYKbmxsJCQm0tbVhYmLC3LlzaW5u7nW2jr29Pe7u7mzevFm5xz18+DA//elP8fPz4/z580bXu/nF17Fjxxg8eDBDhw7VCxKZmJgwbdo0srKy8PLywtLSstv26M6BXbt2MXHiRBwdHbl69Sr79+/Hzs6OyMhI7OzsKC0t5eDBg3rZL/7+/owZMwZbW1vq6+s5e/Ysp06d6vXx9Pf3Z9KkSezZs4fIyEhsbW25evUqycnJ1NXV9ep4AgbHrK6uDhcXF3x8fHp89uju/AP0Mvnq6+vJyspi8eLF2NnZdXleOzo6EhkZiaurKyYmJtTW1pKWlkZdXZ1SjuSxxx5T2p6cnKxk6vj6+tLS0sLp06d7vf+d3Zxpn5KSwtChQxk0aFC3A86kpaUBEBwcbDRI1NraSkpKCtCRGSlBov4hNYluw8CBA5WfYcOGMWfOHK5du8bq1av1AhYDBgzAx8eHiooK8vPzDbaTmpoKwKhRo/qlXbfzeTk5ObS3t+tN06UBFhQUGDyAt7e3K1/mOkFBQSxfvlzvR9eFq7O+BGZsbGzw8fGhvLyc4uJig/mZmZndrt85PVNH9xbDwcGh1+0AqKqqIj8/n6qqqj6tJ/pPb6+9zro73xwdHVmxYgX79u3jwoULSs0g3Y1V5/Nbd91kZWUZXCtgeLPUk9GjRwP/vS5v1t312jm7SWfYsGFYW1uTk5Nj9Eamr+0TQgjxw+Tr60tzczPl5eXk5ubi7+/fZR2+vho1ahRXr15ly5YtFBQUEB0dTWxsLJcvXyY+Pp6ysjJiY2NRq9Xdbkf3giQ+Ph6NRkNsbKze/GeeeYYJEyZ0ub67uzvNzc16XVoqKipoaWlRBpQwxsTEhNjYWI4dO2Y0u0qtVqPVavVexLa1taHVavHw8Oh2nzw9PbG1tSUxMZHDhw8zZswYZs2ahUqlYvv27Rw9epSwsDC9+kxarZYjR46wadMm9u/fj6urK5MmTVLmp6WloVKpCA8PB2DcuHHY29uTnJysLDN37lzmzp3bZbtUKpWyH521tbX1uE83MzMzM+iyFBoaSl1dHbm5uX3a1oQJEzhy5Ahbt27FwsKCadOmMX78eA4dOkRiYiKOjo5650BAQAChoaFkZ2ezadMm0tPTGTNmjN4L7Z6OJ3T8jseOHUtycjLbtm3D3NxcKW0A/62j5e/v36f9MXZsbtbT+XczU1NTRowYQV1dXbdBrGnTptHY2EhCQgLx8fEcPXqUtrY2Ghoa2Lt3L9CRHPD5558rwZnw8HC8vb3Zt28fSUlJuLi46N0XQ8fvqKuuqV1RqVSo1eo70rVN3D7JJOonTU1NnDhxgqeeeors7Gyee+45Pv/8c0pLS5U6IV31udS92dAtd7tu5/OM/WHRfVl0lQrY2tqKqel/T6WgoCCDTI+ioiK9Gj+d29EbugLTV69eNTq/q+k6xiLqurRZ3Zei+H7q7trrrKvzzc7OjqSkJIYMGcKxY8fYvHkz1dXVtLa2Ym9vz1NPPaX3VuJ2z0Vjnw+3dr0a+yxd+65du9bt9oQQQvy4jRgxgry8PKAjk1ylUjF06FAuXryot1xMTAzR0dF607Zu3drty7Li4mLOnDkDQHZ2NsHBwdTW1ioBgmPHjhEQEICjo2OX31e6dXUv9Y4dO8aCBQsYMGCA8tKyurq62zpGVlZWRuc3NTVhbW3d5XoTJ07kxo0bXdZdunLlCq2trYSHhyvZV6GhoahUqm63Cx2ZWampqWi1WmpqaggODsba2pqsrCyg45517NixeHl5Kcemc/ZGfX09GRkZzJw5U8nOaG1t5cCBA8yfPx+NRsO4cePYs2eP3r7X19d3267q6mrq6uoIDQ3l0KFDtLS0MHr0aGxsbHrcp84CAwMZMGCAXjBo4MCB+Pr6Eh8f3+vt6GRnZysZSWfPnmXSpEnEx8crGWW5ubl6g3yMHz+ejIwM5Tyuq6vjxIkTBAYGKhkrPR1P6Hg+SE1NVZ4hvvnmG71ize3t7VRXV/ep66WzszP+/v4GWTU36+n80wkMDCQsLAwzMzOqq6tJSkoy+gJTx8bGhpMnTyr71PnZThesaWpqUv7f1NSUgIAAkpOTlZf0Bw8e5Cc/+YnedjUaTZ9rIYWEhNDS0iIjWn9HSZCon9XW1pKfn09wcDCjRo2itLRUuQDd3NyMrqOb3pf0xZ7a8G1+3s02btzIxo0be1yuL/1ldW11dXU1Or+r6eLHw9i11xsPPfQQQ4YMMVqIfMKECQZZcP19Lt7O9WrsGtIt19UIMV19jhBCiB8POzs7PD09lWzV9vZ2CgoKGDFihEGQKCMjQxloQaengEPnLvmtra20tLToTdN1X7Oysup2O527ljU2NgJgaWmpBIl6c7/ZV56envj7+3cb0NBoNOzbt4+oqCili1h+fj5Xr17t8f62qqpKb5nOD+Wdp3U+Nl5eXowdOxYHBwfMzc2VLAxra2vluFy9epXjx48zceJEcnJyDH5nXXW519Fqtezbt48pU6bw2GOP0d7eTklJiUE9qu74+PgQHh7OV199pZwjlpaWREdHs3///luqZWXsHOh8LjU2NirHytLSEhsbG6KiovSyfkxMTPSy5HpzPFtbW/VeMjc0NKBWq7GwsODGjRs0Njb26fyzt7dn1qxZnD592uAa66w3559Obm4uxcXFWFtbM2bMGKZPn862bdsMssF0Tp06xdSpU/H396e0tJSCgoJuu1za2dmhVqv1XmS2trYalNzIycnptsvYzUaNGsXIkSNJSkrqdtAicfdIkOgOsLe3B/6bodLQ0MDFixcZMmQIPj4+Bn8YdPVLOveV1UWBu0rD1V38xubfyud919XX13Pp0iUGDRqEt7e3QZeznkY364u2tjbpz/o9dfO11xu6t0+6gpad6dK2O9O9fQoJCUGlUhm8sek8rH1v6LbX1Xp9vV7z8vJobGwkKCgIW1tbg+BSX9snhBDihycgIIDr16/rZQPl5uYyd+5cvUwd6HgI766wrDHGshmMTeupe1vndXSBlb50iWtqajJa+8bKykoJBtzMy8sLa2trHnnkEWWaSqUiNDSUUaNGsX79egBKSkrYsGEDFhYWaLVampubeeSRR4yWeuhqn3T7ZWyajo2NDXFxcZw7d47s7Gxu3LiBi4sL06ZNM7jf8fDwoL29/ZZ7J1y7do0tW7ZgZmaGWq1Go9Fw77339ipL2sfHh5iYGA4cOKAXWHJ0dGTAgAHMmTNHmab7HT711FNs2rSp20CFsfPm5kCcbnu6/x4+fLjLDO3eHs/+LP5sb2/PvHnzyM/P77FERm/PP4CWlhZaWlqora3lypUrPPbYY/j6+nbZpe/o0aPk5uYyaNAgBg0axPjx40lJSemy3tSdMGrUKCZOnMiuXbv6nH0vvj3Sz6afzZw5kyFDhtDc3KxXFPrLL79EpVLxu9/9Tu8PkKOjI8uWLQNgw4YNyvTq6mra29vx8vIy+jm6L/WBAwcand/Xz/s+2Lx5M2q1mhUrVuhN9/LyMlrz6FZVVVXh5OTUZUE9R0dH/Pz8cHR07LfPFLevq2uvJ7o3bTcHT4KCgnj++ecNli8rKyM5OZkhQ4bwxBNP6M275557+lS0GjpqG+Xl5REWFqZ3AwUwZ84cwsPDe3VTodPa2sqWLVuwtbU16PYZHBzMwoUL+9Q+IYQQPywmJib4+/srXc10ysvLaWhoYMSIEXepZf2voqICc3NzvfpD7u7umJmZdRlEyMnJYfPmzcTHxys/DQ0NnDp1iqSkJIPlb9y4QXNzM15eXlhZWfV79xlXV1dUKhVHjhzhypUr1NTUGO3+NXr0aFxcXEhMTMTNze2WBpXRaWlpQaPRYGdnh4uLC5cuXep2eV9fX2JiYjh48KDBy+mrV6+yadMmveNZWFhIWVkZ8fHx/dqzoampiYaGBuzs7KitrTX4gd4fz/7i4ODAvHnzKCgo6FVdyL6ef52ZmJj0+KK0traWnJwcdu/ezfnz55XrXReM67x+bW0tbW1telnopqamyjD2fTV69GgmTpzI7t27u7z+xHeDZBLdhs7DcFtbW+Pv709MTAwAb7/9tl4f63/+85/ExMQQFxfHvn372L9/P1ZWVsydOxdXV1dWr16t9EWGjrc2x48fJywsjI8++oiCggLa29vZu3cvZ8+eJT8/n7KyMubPn09LSwslJSVotVo2b95MSUlJnz/v++Djjz8mLi6Oe++9Fz8/P5KTk7Gzs2PevHmkp6cza9asbvvh9lZKSgrjxo1j3bp1ZGRk0NzczJkzZ9i3bx8ATzzxBC+99JLR7kni29GXa68nmzdv5rnnnuOPf/wjkZGRXLx4ER8fH6ZPn86uXbtYsGCBwTq//e1v2b59O2+88QZTp07lzJkzDB06lLi4OPbu3cs999zTp/1ZtmwZX3zxBR9//DF79uwhPz8fX19f4uLiqKur48UXX+zTG623336byZMn8/TTTxMcHExmZibu7u7MmzeP/fv3M3PmzD61TwghxA/H4MGDsba2prKy0uCFV1lZGf7+/hw7dkyZZm5ubtAtrKWlpcsh0b9NS5Ys6barS3V1NZcvXyYqKopDhw4BEBUVRWFhoZK9Ym1tzdy5c8nMzOTSpUtoNBqDOkbt7e00NTXpZbz4+/tTXV1NU1MT7u7uREZGcurUqW6zYm5FTU0NKpWKUaNGcenSJdzc3JRBL3ScnJwICQlh//79VFRUkJKSwtSpUyktLVVeLOvqSnXX7czHxweNRkN9fT1OTk5ERkZSWFioN1rbzdvx8/MjJiaG9PR0ysrKlHOlvb2dGzdu0NraalC/6saNG5iYmNyRQWCys7OZNGkSzc3NSq0tFxcXBgwYwIkTJ3p1PHvj5vPGGEdHR+bMmUNZWRnHjx/Xu450XS5v5fyzs7PDx8eHkpISmpqasLGxYezYsbS1tXXZPVCtVhMeHk5BQQF1dXVYWVnh4eGh1Kqsq6tDq9UyePBgCgsLaW1tpbW1lfPnzxMWFoZGo6GxsZHx48cbZPMFBQURFBTUbfe74OBgQkJCOHDgADU1Ncqx0HVH7Wo7dnZ2mJmZYW1tjUqlwtnZGeh4qa977nNwcECtVmNpaYmZmZmyTF9GQhT6JEh0Gzq/pW9tbeX69evs27ePtWvXcvjwYb1lW1paeOihh3jmmWe49957eeKJJ2hra+PMmTP84Q9/YNu2bQbb/+Uvf8nrr79OTEwM9957LyqVirKyMs6ePUt7ezs/+9nPeO2115g7dy42NjaoVCoyMzMpKSm5pc/7rtNoNNx///28/PLLzJkzh6effpqioiI++ugjMjIymDVrVo995Htj1apV2NvbM2PGDEJCQjA1NWXjxo1KkEjcfX259npSUVHBwoULee211wgNDSU6Opq8vDxee+01ZZjcm128eJF58+bx2muvMXnyZCIiIjh79iw/+9nPcHZ27nOQ6Pjx48yZM4cXX3yRyZMnM2PGDCorK9m6dSurVq3qMXX9ZlVVVdx777385je/YcaMGQQHB1NQUMBrr71GUVGRBImEEOJHLCAgAIDZs2d3uUznYdw7F+vVOXbsWJ+ydu8UBweHHodS379/P5MmTVL2t7CwUG9EUZVKpdSm6etnh4aGYmFhQV1dHcePH78jpRwqKytJTU1l7NixhISEUFFRQXp6OtOnTwc6Hv5jY2PJz89XghX5+fkMHjyY2NhYEhISaG9vx8bGpsfPsra2JiIiQumOl5ubqxcwBAy2M3LkSFQqFZGRkXrZ1KWlpezYseM2977vzp8/T2trK2PGjCEkJIS2tjaqqqqUQGJPx7O3enPe+Pr6Ym1tjZ+fH35+fnrzPvnkk15v52ZtbW14enoSHByMubk5TU1NlJWVsXXrViX4dDOtVouFhQXR0dFYW1uj0Wi4fPky6enpQEeCQnZ2NiEhIUyZMoULFy6QnJxMeno6pqam3HPPPbS2tnL69Gm9AYugoxZUT6NFBwUFoVarDY7z+fPnlVH4jG1nypQpej1rFi1aBMD69euV575Zs2YpA7d0XkZ3jEXfmfRnf8u+MDc310qxYdFfHn74Yf72t7/x6quvsm7durvdHCGEEEIIIYQQ4q4oLS09qtVqJ97KulKTSHyvdO5TruPl5cWyZctoaWmRbB8hhBBCCCGEEOIWSXcz8b3y6aefYmpqqvT5HjRoENOnT8fa2po333xTiqAJIYQQQgghhBC3SIJE4nslPj6eRYsWMXv2bGxtbWloaOD48eOsXbuWXbt23e3mCSGEEEIIIYQQ31tSk0gIIYQQQgghhBDiB0JqEgkhhBBCCCGEEEKI2yJBIiGEEEIIIYQQQgghQSIhhBBCCCGEEEIIIUEiIYQQQgghhBBCCIEEiYQQQgghhBBCCCEEEiQSQgghhBBCCCGEEEiQ6Edr+fLllJSUEBERcVvbiYiIoKSkhOXLl/dTy4QQt2rTpk2UlJTc7WYIIYQQQgghvqckSNQHJSUlffpZsmTJ3W7yd1J/BajEj4/u2rpdS5YskWu0G+np6aSnp9/tZgghhBBCCCG+ZaZ3uwHfJ++9957BtKeeegp7e3vWrFlDTU2N3rycnJxvq2l9tnbtWrZt2yZZB0IIIYQQQgghhAAkSNQn77//vsG0JUuWYG9vz6effkpxcfFdaNWtqaqqoqqq6m43QwghhBBCCCGEEN8R0t3sDpo3bx7x8fGcPXuWvLw8vvrqK55//nnMzc0NltV177C1teXPf/4z2dnZ5Ofnc+DAAZ588sk+f7ZuezY2NvzhD38gPT2dS5cuKbWDuuvytXDhQnbv3k1eXh4nT57kww8/xN3dvcd6J0FBQfznP//hzJkz5OXlsXnzZiZOnGjQrpdeegmAzZs363XPE+JWeHt7U1JSwsqVK/H29uYf//gHp06dIj8/n507dzJ9+nS95Tdt2sTKlSsBWLlypd456O3trSynVqt57LHHSExM5Ny5c+Tl5bFnzx4ef/xxTExMumyDr68vH3/8MSdPnqSoqEi5xnTXj7m5Ob/+9a85cuQIBQUFpKam8qtf/QozMzOj+zd58mTWrVvH6dOnKSgo4PDhw6xYsQJbW9teHR8zMzMef/xx/vOf/5CRkUFBQQGnT59mw4YNxMTE6C2rqzE2aNAgBg0apHdsdMdMx8/Pj5UrV5KVlcXFixc5ceIEf//73/Hz8+tVu4QQQggdGxsbnnnmGVxcXO52U4QQPZg0aRJz5869280Qd5BkEt0hv/nNb3jhhRe4fv06W7dupaGhgZiYGFasWMHUqVN5+OGHaWlp0VvHzMyMDRs2YG9vz/bt2zEzM2P27Nn86U9/ws/Pj9/+9rd9aoOZmRkbN27EwWhWcZkAACAASURBVMGB5ORk6uvrKSoq6nad5557jt/97ndUVVWxefNmamtrmTJlClu3bqWurq7L9caMGcPPf/5zjh49yhdffMHAgQOZPXs2GzZsYObMmeTn5wOwZs0aZs6cSWRkJBs3bjTaHm9vbzIyMigqKiI8PLxP+yx+vLy9vUlKSuLy5cvEx8fj4ODAvHnz+Oyzz3jwwQdJS0sDOoI1tbW1xMXFsXv3br1uobW1tQCYmpry73//m5iYGPLy8ti6dSs3btwgMjKSv/zlL4wfP55f/vKXBm0YMmQIiYmJFBQUkJCQgKWlpcF1889//pMxY8aQlJREa2sr99xzDy+//DJjxozh8ccf11v2kUce4a233qKxsZEdO3Zw7do1IiMjef7555kxYwb33nuv0uauODg48MYbb5Cdnc2hQ4e4fv067u7uzJgxg3Xr1vHyyy/zxRdfAFBUVMR7773HU089BXRcrzqdj1N0dDRr1qzB1NSUffv2cenSJTw9PZk1axbTpk3j/vvv5/Tp0z39yoQQQtxlzs7OLFq0iPLycrZv324w/5lnnjG63uHDhzl79my/taOhoYHPP/8cjUbTb9vUMTc3Z9KkSQwZMgSAwsJCUlNTaW5u7nKdiRMn4uvry4ABA2hvb+fatWtkZ2dTUVFhdPm4uDgGDx7Mvn37uHjxYr/vw7elq993Tk4OqampRuep1WomT56Mi4sLjo6OlJeXs2PHDoPlAgMDCQoKwtbWlvr6eo4fP05ubm6/tv+7aNy4cQwaNAhnZ2fMzMz45JNPerWelZUVYWFhDBo0CDMzM+rq6khJSaGsrAzouFcNDQ1l6NChWFpaUl9fz9mzZzl16tQttdPGxoaHH36YLVu2cO3atVvaRnesrKyIiIjAxcUFOzs7cnNzSU5O7nG9hx56yODF6IkTJ8jMzFT+PWDAACZPnoyXlxdtbW3k5eWRnp5Oe3t7v+/Hj4UEie6ACRMm8MILL1BSUsKcOXO4evUqAG+99Rb/+7//y4wZM1i6dCkfffSR3noeHh5cvnyZ2NhY5YvrvffeIykpiccff5zt27eTkZHR63Z4eHiQm5vLokWLaGpq6nH5wYMH8+qrr3L9+nXi4uIoLS0F4M0332T16tXce++9Xa47ffp0fvWrX7Fx40Zl2iOPPMI777zDz372M1577TWg46HTzs5OCRIdOXKk1/sjRHciIyN599139TJeEhISWL9+PUuXLlWCRLpzNC4ujj179uidszq//OUviYmJ4bPPPuMPf/iD8iWjUqn461//ykMPPcSOHTvYu3ev3nphYWF89NFHvP322122c/jw4cTGxio1zN555x02bdrEjBkzWLRoEfHx8QAMHDiQN954g4aGBubMmaMEWqHjmnzsscf47W9/y6uvvtrtcampqSEsLEy5qdCxtbVl69at/Pa3vyUhIQGNRkNxcTHvv/++UtDbWBdbe3t7Vq9eTVNTE/fdd5/eDd6IESNITEzk3XffJS4urtt2CSGEuPsCAgLIz89nyJAhODg4UF1dbbBMcnIyly9f1pvWXYDlVmi12l7dq96K2NhYbGxs2LlzJwBTpkwhJiaGPXv2dLlOdXU1KSkp1NXVYWpqyujRo5k1axZffvmlQTuDg4PvSLvvhs8//1zv366ursTFxVFQUNDlOiYmJrS1tZGTk8PgwYON9pgYOXIkYWFhHDp0iCtXruDm5kZUVBQ3btwwOLd+aFQqFRcvXqSsrIxx48b1ah1zc3MWLFhAeXk5u3fvRqPRYGtrq3fuRUREMHDgQA4cOEBdXR2enp5MmTIFjUbznQy+qdVqNBoNJ06cICAgoE/rHj16lDNnzij/7pxoYWJiwqxZs9BoNGzfvh1LS0uio6MBlHt/0XcSJLoDHnjgAQBWrVqlBIgA2traeOONN4iNjeWhhx4yCBJBRyCp8xdvdXU1q1atYuXKlTzwwAN9ChIBvPHGG73+0l24cCFmZmZ89tlnSoBI580332Tu3LmYmho/ZTIzMw0etjds2MCf//xnxo4d26c2l5eXM2XKFFpbW/u0nvhxKyoqYtWqVXrTkpOTKS4u7vWXMnR82Tz55JNUVFTw+uuv672FaG9v54033uCBBx7gvvvuMwgSXblyxWhgpbMPPvhAr8j9jRs3eOutt9i8eTMPPvigEiRatGgRFhYWfPLJJ3oBIugILN13330sWrSI3//+993erDc3NxsEiADq6ur48ssv+cMf/sCYMWN6/bdl8eLFODg48NprrxnchJw/f57169fz9NNPM3z48O/kTYoQQogOarWaYcOGsWfPHlQqFSNGjDD6XdDc3NynAM5DDz3E+fPnsbW1xcfHh+bmZtLT0ykuLiYqKorBgwfT2NhISkqKUm7g5iwGT09P5s2bx44dOwgNDcXJyYmqqiolI7a3HBwcGDx4MNu2bePKlStARxbUggULsLe3Nxh0RicvL0/v30eOHCEgIABnZ2e9GqSurq6MGjWKLVu28Oijj/bYnqlTp2JpaUl5eTmjR4/G1NSUM2fOkJmZyYQJEwgMDESr1XLq1ClOnjyprDd69Gj8/f2xs7OjubmZoqIi0tPTle//KVOm4ObmRkJCAm1tbZiYmDB37lyam5u7DYbd7Obf85AhQ6iurjZ6H6HT2tpKSkoK0JGZZixINHz4cM6dO6fcz9TV1eHq6srYsWO7DBLpzomvv/6akSNH4ubmRnV1NQcPHkSr1RIVFYWzszPXr19XAiU6gwcPZsKECTg6OtLY2Eh+fj5Hjx5V7umGDRvGqFGjcHBwoK2tjbKyMtLS0mhsbATot/MPOgIcAD4+Pr1eZ8yYMTQ2NnLw4EFl2s2Z6e7u7uTm5iq/m9zcXEaMGIGbm1uX918DBgxg0qRJeHh4YGpqSn19PUePHiU/P5+HH34YgPvuuw+A0tJSduzYgYmJCaGhoUpg58KFCwZlF3qjvr5eCdr05VhAR1Coq79B3t7eODo6sn79ehoaGgDIyMhgypQpZGVlGfTcEb0jQaI7YPTo0QBG0zILCgooKytjyJAh2Nra6l3wLS0tZGdnG6yju6BGjRqlTNPVFups48aNel9cTU1NelHXngQFBQGQlZVlMK+kpITS0lIGDx5sdN1vvvnGYFprayvXrl3D3t6+123QrXfzQ7EQPcnJyTGaVlpaWsqECRN6vR1fX18cHR0pKCjgxRdfNLqMRqNh2LBhBtPPnDnT49tVY9lzmZmZtLa2Ktcg/Pd6N/Z3pKamhtOnTxMREcGwYcN6vM79/f157rnnCAsLw83NDSsrK735Hh4e3a7fme5YBgYGGv075OvrCyBBIiGE+I7z9fWlubmZ8vJyLC0tiYqKIjMzE61We9vbHjVqFNnZ2Rw/fpzAwECio6MpLS0lPz+frKwsxo4dS2xsLOvXr6etra3L7YSGhpKRkUFjYyORkZHExsayadMmZf4zzzzD0aNHlQfxm7m7u9Pc3KzXTayiooKWlhbc3d27DBJ1plKpGDlyJM3NzXoBAjMzM2JjYzl8+HCfusl5enrS0NBAYmIiLi4uxMbG4uzszLVr19i+fTteXl5ERUVRUlKidPvRarUcOXKE2tpabG1tiYyMZNKkSRw4cADoeFa47777CA8PJzU1lXHjxmFvb8/mzZuVz9XVkDHWFcwYU1NT/Pz8OHbsWK/3rStqtdrg5W9rayuurq6YmJh0e85NmDBB2feoqChiY2PRaDRkZWWh0WiIjo4mMjJSCYZ5e3sTGxtLWloaZWVl2NjYEBUVhUqlUoKgarWao0ePUl1djaWlJWFhYUybNo3ExES9z77d8+9WDR06lKKiIqZNm4aXlxcNDQ2cP39er+t/eXk5Q4YM4dy5czQ0NODu7o6Li4vRZzKdyZMno1ar2bFjBy0tLXrPaAkJCSxcuJCdO3dy/fp15Z46ODiYkSNHKgGyoKAghg0bptclzd/fn+joaNavX099fX2/HgtdG8aOHUt9fT0XL17k5MmTSvvc3NyoqqpSAkQAxcXFmJqa4uLi0m2AU3RNgkR3gK7fZFf9lq9cuYK3tzd2dnZ6QaLKykqjD7m6bKTO/TF1xZ87O3LkiF6QqK+Rbjs7O73Pu9m1a9e6DBJ19SXb2tqKWq3uUzuEuBVd1ebp6zno6OgIdNw8G7vOdAYMGGAwratrpzNj/bzb2tqorKzUK9ipux67+zvSebmujB8/no0bN6JWq0lJSWHfvn3U1dXR3t5OUFAQcXFxWFhY9NhuHd3xeeSRR7pdztjxEUII8d0xYsQIJWPm8uXLqFQqhg4dalBTJyYmRum+obN169ZuR8ktLi5WXmBkZ2cTHBxMbW2t8vLg2LFjBAQE4Ojo2G39k+zsbOUh79ixYyxYsIABAwYoD4TV1dXdBmisrKyMzm9qasLa2rrL9aAjG2XatGmYmprS2NhIUlKSXjbD5MmTKSoq6rHe582am5tJTU1Fq9VSU1NDcHAw1tbWykvampoaxo4di5eXl3JsOtf5q6+vJyMjg5kzZypBotbWVg4cOMD8+fPRaDSMGzeOPXv26O17Xx/ehw0bhlqt5sKFC31az5ji4mJGjBjBpUuXuHr1Ki4uLgQEBKBWq7G0tOw2U+3UqVPKMf7mm2+Ii4tj7969ynmRk5PDpEmTlOXHjRvHyZMnlXbX1dWRkZFBbGysEiQ6f/68snxdXR2HDx/mgQce0Du34PbPv1tla2tLYGAgp06d4sSJEzg7Oyv7qAsUpaWlERUVxU9+8hPl+TE1NbXb7ns2NjZcvHiRyspKZd91dL8DjUaj9/sYNWoUJ0+eVLocpqWl6Q30Ah3ndHV19R2pAXT69GmuXbvGjRs3cHV1JSwsDFtbWw4dOgSAtbW1wfmj0Whob2/v8RoXXZMg0R2gu+Dc3NwoLCw0mO/m5qa3nI6TkxMqlcrgAnN1dTVYfuDAgT22o69vgnTbd3V1NfqFICNOiB8D3XWwc+dOnn766T6t25trzsXFxaA7p1qtxsnJSe8a1wW93NzcjF6PXf0dudmLL76IlZUVixcvNshiev755/tcO0j3edOnT+/XoqVCCCG+PXZ2dnh6eirZqu3t7RQUFDBixAiDIJFuMJHOego46B5CoSOA0dLSojdN91B3c2brzTq/8NR1BbK0tFQe0o3VFewvpaWlxMfHY2lpSUBAANOnT2fr1q00NTUxfPhwnJ2dSUhI6PN2q6qq9O4XmpqauHHjht4yTU1NesfGy8uLsWPH4uDggLm5OSqVCrVajbW1tXJcrl69yvHjx5k4cSI5OTkGv7POXZd6IyAggEuXLvVLEOTYsWNYW1uzYMECoGP/Lly40KuSFJ3PAd15c/O5ZGZmhlqtpq2tDRcXF6Urm46JiQmmpqZYWVnR1NSEs7MzEyZMwNnZGQsLC6X7lI2NjV6Q6G6dfyYmJly9elUJHF6/fh17e3sCAwOVIFFQUBDu7u7s3r2b+vp6PD09CQ8Pp66uTi9poLPTp08TFRWljGB76dKlboO0ZmZmDBgwwOCF5dWrV/VeBl66dIlLly7d5l4b17kQd2VlJS0tLUyfPp2MjAyD60b0HwkS3QGnT58mODiYiIgIgyDR0KFD8fT0pLCw0CDzwczMjIkTJ+pVa4eOgry67d5JOTk5zJ49m5CQEIMuLgMHDsTLy6tfPkcXBJMMI3G36FLbVSqVwby8vDyqq6sZP348pqam/V4bKyIiQqk7pBMaGoqpqaleGnFOTg5z5swhIiJC6euvY2dnR1BQEE1NTT126Ro6dChVVVVGu7l1NXpgW1ub0ZoC0NG3fs6cOYSGhkqQSAghvqcCAgK4fv26XjZQbm4uc+fONcimaGxs7HEkzZsZyygwNq2n2iad19EFVvpSD6WpqQlLS0uD6VZWVspDf1daW1upra2ltraWK1eu8MADDxAQEMDx48fx8vLC0dGRJ554Qm+dadOmceXKFaMjxRnbJ+jYL2PTdGxsbIiLi+PcuXNkZ2dz48YNXFxcmDZtmsF9jIeHB+3t7T1mGffE2dkZNzc3oyUobkVbWxvJyckcOnRICWwFBAT0qt6VsXOgu/PCxMSEY8eOGS22rdFoMDU1Zfbs2ZSUlHDgwAHlHFmwYIHB8bzd8+9WNTY2GhSRr66uxsbGBuh4hgoNDeWrr75SMocqKytxdnYmODi4yyDR+fPnKS4uZtCgQQwcOJAFCxZw4sSJfu8udyd1zqS/evUqjY2NuLu76y1jaWmJSqXq8RoXXTN8QhK37csvvwQ63uA7OTkp01UqFb///e9Rq9Vs2LDB6LorVqzQezhzcHBQhtrWbfdOSUhIoKWlhSeffNIgILRixYoui1b3le6GpKugk64PtG6oUiH6m+4cNJaR19bWxtq1a/Hw8OBPf/qT0ZtLNzc3hg8ffkufvWzZMr0+4BYWFqxYsQLQv8bj4+Npbm7miSeeYOjQoXrbeOWVV7Czs2PLli091kAqKirC0dGRkSNH6k1/8MEHiYmJMbpOVVUVTk5ORvd948aNVFdXs3z5cqNvAE1MTIiIiOi2TUIIIe4eExMT/P39DYozl5eX09DQwIgRI+5Sy/pfRUUF5ubmeg+R7u7umJmZddmduysmJibKC86srCw2b95MfHy88gMdWVe6LmD9xdXVFZVKxZEjR7hy5Qo1NTVGu9GMHj0aFxcXEhMTcXNz06tz2FcBAQHU1tYqhcX7i1arpaGhAa1Wi5+f3x0Z2ezatWs4ODgoAb7OP1qtFgcHB6ysrMjKyqK8vJyampoeM9q+bRUVFQY1Xe3t7ZUMPl0m2c0Z7FqttscgVkNDA+fOnePrr78mOztbKUitC4h1Xr+lpYWGhgYle11H18vlbnB2dgb+m9l15coVHB0d9TKbBg4cqNTGFbdGMonugOzsbFavXs0vfvEL9u/fT1JSEo2NjcTExDBy5EgyMjL4+OOPDdYrLy/H3Nyc/fv3s3fvXkxNTZkzZw4eHh78+9//7vPIZn1VWFjIu+++y4oVK9i7dy+JiYnU1tYyZcoUHBwcyMnJMXjQvBWpqam0tbWxYsUKAgIClHpGupGpPDw8OHToEEVFRV1mOghxO44ePUpjYyNPPfUUjo6OSi2hzz77jLq6Oj744AMCAwN59NFHmTFjBqmpqZSVleHi4oKPjw8hISG88847t1SYOTc3V/m70Nrayj333IOPjw9fffWVXoHJ4uJiXn/9dd588012795NYmIi169fJyIigokTJ5Kbm8ubb77Z4+etWbOGmJgYEhISSExMpK6ujuDgYEJDQ9mxY4dSyLKzlJQUxo0bx7p168jIyKC5uZkzZ86wb98+qqqqePbZZ1mzZg2JiYmkpKRw4cIFtFotXl5ejB8/HkdHR/z8/Pp8bIQQQtx5gwcPxtramsrKSqXOnE5ZWRn+/v56xYrNzc0NHqJbWlq+E6PQLlmyhJycHL1M3M6qq6u5fPkyUVFRSg2TqKgoCgsLlftPa2tr5s6dS2ZmJpcuXcLMzIwxY8ZQWFhIY2MjVlZWBAUFMWDAACU7pbGx0WiWQn19fY/dwPuqpqYGlUrFqFGjuHTpEm5ubsogOTpOTk6EhISwf/9+KioqSElJYerUqZSWliovxnR1pXrqdqZWqxk+fLje6GqdhYSE4ObmRlJSkjLNwcFBqS9kZmamPMjrumvZ29vj5uZGRUUFFhYWBAcH4+Tk1OcucL1x7Ngx4uLiqKuro6CggPb2dpycnHBzcyMjI4P6+nplsJCcnBwcHByYOHHiLX1WT+cfdNRotLS0VGrL6o5NTU2Ncg3dvJ1Tp06xYMECxo0bR35+Ps7OzowaNUrpbdLS0kJpaSmhoaG0tLQo3c2GDx/e7fNiREQERUVF1NTUYG5uzqBBg5Tzo6mpidbWVgYNGqQco5aWFk6fPs3YsWOpqamhsrKSwMBAvW6O0JG1rruv7C57R7fvuoQIZ2dn2tralKypm7fj5uaGu7s7paWlNDc34+rqSkREBJcuXVKyHYuLi6mqqiI6Opr09HQsLS0JDw/n3LlzMrLZbZAg0R3y5ptvcvr0aZ544gkWL16MqakphYWFvPPOO/zrX/8yetK2tLTw4IMP8pvf/Ib58+fj5OTE5cuXWb16NZ999tm30u6///3vlJWV8cwzz7BkyRIaGho4ePAgf/nLX1i/fn2/fPHl5eWxbNkyli5dyqOPPqrceNw8fLkQd0pNTQ1PP/00y5cvZ8mSJcrbh/j4eOrq6mhtbeXJJ59k0aJFLFmyhGnTpjFgwACuX79OUVERf/vb326pDgHA0qVLWbZsGQsXLsTd3Z3y8nLeffddVq9ebbDs//3f/3Hx4kWWLl3K7NmzsbKyorS0lH/84x989NFHvUr/P3jwII899hgvvvgi8+fPp62tjRMnTnD//fczZMgQo0GiVatWYW9vz4wZMwgJCcHU1JSNGzeyb98+oCOINH36dJYuXUp0dLRyk1JRUUFaWprejaMQQojvFl3mwOzZs7tcZuDAgUoWydSpUw3mHzt2zOiIvN82BwcHo1mvne3fv59JkyYp+1tYWKhXVkGlUim1fqAjo8LR0ZERI0ZgaWmJRqPh6tWrbN++Xa8WzrelsrKS1NRUxo4dS0hICBUVFaSnpzN9+nSgI6gTGxtLfn6+UhcmPz+fwYMHExsbS0JCAu3t7UpXpZ74+flhamqqV9y5M2tra4PubLNmzdIbYGfRokUAfPLJJ0BHdsro0aOJioqivb2d0tJStm3bdkdGwiouLmbXrl2MHz+eMWPG0N7eTk1NjVLfUaPRcPDgQUJDQwkMDKSyspL09PRur4eu9Ob8mzhxol52nu7YJCYmKkWxb97O1atX2bNnD6GhoYwbN476+nqysrL0RrP9+uuvCQ0NJTY2FgsLC+rr68nOzu42YGViYsKkSZMYMGCAEmjSlSPQarWkpqYyYcIExo8fT3l5OTt27OCbb77B2tqaKVOmAB0vO/Py8nBwcFC2a25ujoODg9EyDp3p9l1nyJAh1NXV8cUXXxjdTltbG76+vowfPx61Wk19fT3nzp3jxIkTyja0Wi27du1i8uTJLFiwgNbWVvLy8khPT++2LaJ7Jv0xzOWtMDc3197NVLXvGt2J/F3NnLGxseHEiROcOXOG+fPn3+3mCPG9s2nTJiIjI3tVdF4IIYQQQgghblVpaelRrVZ7S2lyUpNI6HFycjKoPaRWq/mf//kfrKys2LVr111qmRBCCCGEEEIIIe4k6W4m9MyZM4eXX36Zw4cPU1paioODA+Hh4fj5+XH69GnWrl17t5sohBBCCCGEEEKIO0CCRELPsWPHyMzMJDw8XOlrWlRUxKpVq1i9ejUajeYut1AIIYQQQgghhBB3gtQkEkIIIYQQQgghhPiBkJpEQgghhBBCCCGEEOK2SJBICCGEEEIIIYQQQkiQSAghhBBCCCGEEEJIkEgIIYQQQgghhBBCIEEiIYQQQgghhBBCCIEEiYQQQgghhBBCCCEEEiQSQgghhBBCCCGEEEiQSAghhBBCCCGEEEIApne7Ad9HJSUlAAwcOPAut6RrK1euZMmSJYSFhVFcXHy3myNEv/Lz8+Pxxx8nMjISLy8vLC0tqaysJCcnh507d7Jlyxaam5uV5bu6ZnXTu7N48WKOHDnS43Jz5swhPDycoKAgAgMDsbW1JT4+nl/+8pddrjNgwACef/55Zs+ejbe3NxqNhhMnTvDxxx+TkpJisPzMmTO59957CQwMxMXFBQsLC8rLyzl58iT/+te/+OabbwzWiYqKIiYmhqCgIIKCgnB0dCQzM5OFCxf2uE9CCCGEEEKIHxcJEgkhvleWLVvG8uXLUavVZGdns2nTJhobG3FxcSEiIoL33nuPRx99lNmzZ/d6m++9916X84qKinq1jRdffJGgoCDq6+spKyvD1ta22+Xt7e1JSEhgxIgRnDt3jnXr1mFtbc3MmTP58ssveemll9iwYYPeOjNnzmTMmDGcPHmSiooKmpubGTp0KHFxccyfP59f//rXfPHFF3rrPP7448TFxdHU1MSlS5dwdHTs1f4IIYQQQgghfnwkSCSE+N544YUXeOWVVygpKeHZZ5/l+PHjBstMnz6dZ599tk/bff/992+7ba+//jplZWVcvHiRiIgINm/e3O3yy5cvZ8SIESQlJfHcc8/R1tYGwNtvv82uXbv485//THJyMmVlZco6K1as4MaNGwbbCggIICkpid///vds3ryZlpYWZd4//vEP3nnnHfLy8vDy8iIjI+O291UIIYQQQgjxwyQ1ifqJt7c3JSUlrFy5Em9vb/7xj39w6tQp8vPz2blzJ9OnTzdYx8zMjCeffJLdu3eTk5NDXl4e6enpfPbZZ0RFRRks7+fnx8qVK8nKyuLixYucOHGCv//97/j5+ektV1JSwpIlSwDIyMigpKSEkpIS0tPT78zOC/Et8Pb2Zvny5TQ3N/PTn/7UaIAI4KuvvuInP/nJt9w6SEtL4+LFi71eftasWQC8++67SoAI4Pr163zyySdYWVnxwAMP6K1jLEAEcO7cOfLy8rC3t8fZ2Vlv3tGjR7lw4QLt7e29bpsQQgghhBDix0kyifqZt7c3SUlJXL58mfj4eBwcHJg3bx6fffYZDz74IGlpacqyK1euZOHChZw9e5bNmzej0Wjw8PAgJCSE6OhoDh8+rCwbHR3NmjVrMDU1Zd++fVy6dAlPT09mzZrFtGnTuP/++zl9+jTQ0XUmLi6OoKAg1qxZQ01NDQC1tbXK9nSZDmlpadx///3f0tER4tY98MADmJubs3XrVs6fP9/tsp3rEX1Xubq6AnD58mWDeYWFhQBMnjyZDz74oMdt+fr64ufnx/Xr16moqOjfhgohhBBCCCF+NCRI1M8iIyN59913WblypTItISGB9evXs3TpUiVIZGtry4IFCzh58iRz5841eMvfuW6Ivb09q1evpqmpifvuu4/c3Fxl3ogRI0hMTOTdd98lLi4O4I0zCwAADPFJREFU6Og6M2jQIIKCgvj000+lcLX4QQgNDQUwWtD5di1fvtzo9Bs3brB69ep+/zyAyspKPDw8GDRokN41DTBkyBAAgyxBnaioKEJCQjA3N2fQoEHMmDEDgJdffhmtVntH2iuEEEIIIYT44ZMgUT8rKipi1apVetOSk5MpLi5m3LhxyjStVotKpaK5udloN5Cqqirl/xcvXoyDgwOvvfaawcPk+fPnWb9+PU8//TTDhw83mN+V48ePM2XKFJqamvqye0LcNW5ubgB6NXr6y0svvWR0ek1NzR0LEn399df85Cc/4eWXX+a5555T/g44OTnx9NNPAx0BYmMmT57M888/r/y7oqKCX/3qVyQnJ9+RtgohhBBCCCF+HCRI1M9ycnKMBn1KS0uZMGGC8u/6+nr27t3LPffcw759+0hKSiIzM5Njx46h0Wj01tWtFxgYaDTjwdfXF6BPQSKNRkN+fn6v90uIH7KBAwd+65/57rvvEh0dzdy5c/Hz8yM1NRUrKytmzpxJeXk53t7eXdYReuutt3jrrbewsrLC19eXpUuXsm7dOv72t7/x4Ycffst7IoQQQgghhPihkCBRP+tc96ez1tZW1Gq13rTnnnuOn//85yxcuJBXXnkFgKamJnbu3Mkbb7zBtWvXgP92PXvkkUe6/ewBAwbcbvOF+M66cuUK/v7+eHh43O2m9IsrV64we/Zsli1bxvTp03n00UeprKxk+/bt/O///i+pqalcv3692200NTWRk5PDCy+8gIODA6+88grJycmcPHnyW9oLIYQQQgghxA+JBInuIo1Gw/vvv8/777+Pl5cXYWFhLFmyhEWLFuHt7c19990HQF1dHdAxtPfZs2fvZpOFuGsyMzOZPHkykydPZsOGDXe7Of3i2rVr/O53v+N3v/ud3vRJkyYB9CnYc/DgQWJjYwkPD5cgkRBCCCGEEOKWqO52A0SH0tJSEhISePjhh7l48SJhYWFKBtHRo0eB/xbu7Q3dkNo3Zy8J8X315Zdf0tzczOzZsxk+fHi3y5qbm39LrbozFi9eDHQUve8tXYaV7toXQgghhBBCiL6SINFd4uTkREBAgMF0a2trrK2taWlpUYbx3rhxI9XV1SxfvpyxY8carGNiYkJERITeNF3h665qrVhaWuLn54eXl9ft7ooQ34ri4mLef/99LCws+M9//kNwcLDR5aKjo1m3bt233Lq+MzExwdra2mD6okWLWLx4MVlZWezevVuZbm5uTmBgoNFtjRkzhp/+9Ke0trZy4MCBO9ZmIYQQQgghxA+bdDe7Szw9Pdm7dy9nzpzh7NmzlJaWYmtry/Tp03F3d2fNmjU0NDQAHQGfZ599ljVr1pCYmEhKSgoXLlxAq9Xi5eXF+PHjcXR01BsuOyUlhZ///Of89a9/ZefOnTQ0NFBTU8O///1vAMaNG8fmzZtJS0vj/vvvvxuHQIg+++ijj1Cr1Sxfvpxdu3aRlZXFyZMnaWxsxMXFhfDwcHx9fTlx4kSftmusILzOnj17yMnJ6XEbM2fOJC4uDgBXV1ego+j8ypUrgY4h7//0pz8py1tZWXHy5EkOHTpEYWEh7e3thISEMHHiRC5cuMCzzz6rN5y9paUl+/bt48yZM5w7d46ysjKsrKwYPny40j3tz3/+s0FB+pCQEB5++GHgv3XLfHx8lHYB/OpXv+px/4QQQgghhBA/fBIkukuKior429/+RmRkJJGRkTg5OVFdXU1+fj5vvvkm27Zt01s+JSWF6dOns3TpUqKjowkNDaWlpYWKigrS0tJISkrSWz45OZk//vGPPPzwwzz11FNYWFhQVFSkBImE+L764IMP2LFjB4899hiRkZE88MADWFhYUFVVRU5ODqtXr2bLli192uZLL73U5bzi4uJeBYmCgoJYsmSJ3rShQ4cydOhQoOOa7xwkam5uZtu2bYSGhjJlyhQALl68yNtvv82nn35qMMphY2Mjf/3rXwkPDyciIgJHR0e0Wi3l5eVs2bKFf//73xw/ftygXT4+PgbtcnV11ZsmQSIhhBBCCCEEgEnnN9XfJnNzc63ubbsQQgghhBBCCCGEuH2lpaVHtVrtxFtZV2oSCSGEEEIIIYQQQggJEgkhhBBCCCGEEEIICRIJIYQQQgghhBBCCCRIJIQQQgghhBBCCCGQIJEQQgghhBBCCCGEQIJEQgghhBBCCCGEEAIJEgkhhBBCCCGEEEIIJEgkhBBCCCGEEEIIIZAgkRBCCCGEEEIIIYRAgkRCCCGEEEIIIYQQAgkSCSGEEEIIIYQQQggkSCSEEEIIIYQQQgghkCCREEIIIYQQQgghhECCREIIIYQQQgghhBACCRIJIYQQQgghhBBCCCRIJIQQQgghhBBCCCGQIJEQQgghhBBCCCGEQIJEQgghhBBCCCGEEAIJEgkhhBBCCCGEEEIIJEgkhBBCCCGEEEIIIZAgkRBCCCGEEEIIIYRAgkRCCPH/27ubELvuMo7j358ZWxfWCp2NNNEUmoqxCpVQKi4UIpJ0kVlYpYFiK6FZVeoLQkVRqasqWhDiS6ShtaBtzUIuWMnCVgpiQgcKpSlUhljaaYRqjdmUtkYfF+csxmGaOZnMuYec+/3AwHn533N/m4d757n/8z+SJEmSJDo2iZLsSfJCkqUk96xx/vIkj7bnTyTZvtlBJUmSJEmS1J91m0RJtgCHgL3ATmB/kp2rhh0AzlTVtcD9wH2bHVSSJEmSJEn96TKT6EZgqapOVdVbwCPAwqoxC8BD7fZRYHeSbF5MSZIkSZIk9alLk+hq4OUV+8vtsTXHVNU54Cxw1WYElCRJkiRJUv/mpvlmSQ4CB9vdN0+fPv3cNN9fEgDzwD+GDiHNIGtPGo71Jw3D2pOG8cGNvrBLk+gVYNuK/a3tsbXGLCeZA64EXlt9oao6DBwGSLJYVbs2ElrSxll70jCsPWk41p80DGtPGkaSxY2+tsvtZk8DO5Jck+Qy4FZgsmrMBLi93b4FeKKqaqOhJEmSJEmSNF3rziSqqnNJ7gKOAVuAI1V1Msm9wGJVTYAHgIeTLAH/pGkkSZIkSZIk6RLRaU2iqnoceHzVsW+v2H4D+NwFvvfhCxwvaXNYe9IwrD1pONafNAxrTxrGhmsv3hUmSZIkSZKkLmsSSZIkSZIkaeR6bxIl2ZPkhSRLSe5Z4/zlSR5tz59Isr3vTNIs6FB7X03yfJJnk/whyQeGyCmNzXq1t2LcZ5NUEp/6Im2CLrWX5PPtZ9/JJL+adkZprDp873x/kieTPNN+97x5iJzSmCQ5kuTVJM+9zfkk+XFbl88m+ViX6/baJEqyBTgE7AV2AvuT7Fw17ABwpqquBe4H7uszkzQLOtbeM8CuqvoocBT4/nRTSuPTsfZIcgVwN3BiugmlcepSe0l2AN8APlFVHwa+PPWg0gh1/Oz7FvBYVd1A85Cjn0w3pTRKDwJ7znN+L7Cj/TsI/LTLRfueSXQjsFRVp6rqLeARYGHVmAXgoXb7KLA7SXrOJY3durVXVU9W1evt7nFg65QzSmPU5XMP4Hs0P4q8Mc1w0oh1qb07gUNVdQagql6dckZprLrUXwHvabevBE5PMZ80SlX1FM3T5d/OAvDLahwH3pvkfetdt+8m0dXAyyv2l9tja46pqnPAWeCqnnNJY9el9lY6APy+10TSbFi39tqpvtuq6nfTDCaNXJfPveuA65L8KcnxJOf79VVSd13q77vAbUmWaZ6a/aXpRJNm2oX+TwjAXG9xJF0SktwG7AI+OXQWaeySvAP4EXDHwFGkWTRHM+X+UzSzZ59K8pGq+tegqaTZsB94sKp+mOTjwMNJrq+q/w4dTNL/63sm0SvAthX7W9tja45JMkcz/fC1nnNJY9el9kjyaeCbwL6qenNK2aQxW6/2rgCuB/6Y5EXgJmDi4tXSRevyubcMTKrq31X1V+AvNE0jSRenS/0dAB4DqKo/A+8C5qeSTppdnf4nXK3vJtHTwI4k1yS5jGaRssmqMRPg9nb7FuCJqqqec0ljt27tJbkB+DlNg8h1GaTNcd7aq6qzVTVfVdurajvNemD7qmpxmLjSaHT5zvlbmllEJJmnuf3s1DRDSiPVpf5eAnYDJPkQTZPo71NNKc2eCfCF9ilnNwFnq+pv672o19vNqupckruAY8AW4EhVnUxyL7BYVRPgAZrphks0iy7d2mcmaRZ0rL0fAO8GftOuFf9SVe0bLLQ0Ah1rT9Im61h7x4DPJHke+A/w9apy9rp0kTrW39eAXyT5Cs0i1nc4MUC6OEl+TfPjx3y73td3gHcCVNXPaNb/uhlYAl4HvtjputamJEmSJEmS+r7dTJIkSZIkSZcAm0SSJEmSJEmySSRJkiRJkiSbRJIkSZIkScImkSRJkiRJkrBJJEmSJEmSJGwSSZIkSZIkCZtEkiRJkiRJAv4HCCXsmAk1UZ0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}