classification_mc_dropout.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mc_dropout_for_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPnwtKdCp5BdoLHRPH21QZU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thunderhoser/cira_uq4ml/blob/main/mc_dropout_for_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries and define constants"
      ],
      "metadata": {
        "id": "TS6_AQGSxFdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "from matplotlib import pyplot\n",
        "import matplotlib.colors\n",
        "import shapely.geometry\n",
        "from descartes import PolygonPatch\n",
        "from sklearn.metrics import auc as area_under_curve\n",
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, Dense, Dropout, Flatten, \\\n",
        "    BatchNormalization\n",
        "from tensorflow import keras as tf_keras\n",
        "from keras.datasets import mnist\n",
        "\n",
        "NUM_GRID_ROWS = 28\n",
        "NUM_GRID_COLUMNS = 28\n",
        "NUM_MONTE_CARLO_ESTIMATES = 1000\n",
        "\n",
        "MEAN_PREDICTION_STDEVS_KEY = 'mean_prediction_stdevs'\n",
        "BIN_EDGE_PREDICTION_STDEVS_KEY = 'bin_edge_prediction_stdevs'\n",
        "RMSE_VALUES_KEY = 'rmse_values'\n",
        "EXAMPLE_COUNTS_KEY = 'example_counts'\n",
        "SPREAD_SKILL_RELIABILITY_KEY = 'spread_skill_reliability'\n",
        "MEAN_CENTRAL_PREDICTIONS_KEY = 'mean_central_predictions'\n",
        "MEAN_TARGET_VALUES_KEY = 'mean_target_values'\n",
        "\n",
        "DISCARD_FRACTIONS_KEY = 'discard_fractions'\n",
        "ERROR_VALUES_KEY = 'error_values'\n",
        "EXAMPLE_FRACTIONS_KEY = 'example_fractions'\n",
        "MONOTONICITY_FRACTION_KEY = 'monotonicity_fraction'\n",
        "\n",
        "HISTOGRAM_FACE_COLOUR = numpy.full(3, 152. / 255)\n",
        "HISTOGRAM_EDGE_COLOUR = numpy.full(3, 0.)\n",
        "HISTOGRAM_EDGE_WIDTH = 2.\n",
        "\n",
        "MEAN_PREDICTION_LINE_COLOUR = numpy.array([117, 112, 179], dtype=float) / 255\n",
        "MEAN_PREDICTION_COLOUR_STRING = 'purple'\n",
        "MEAN_TARGET_LINE_COLOUR = numpy.array([27, 158, 119], dtype=float) / 255\n",
        "MEAN_TARGET_COLOUR_STRING = 'green'\n",
        "\n",
        "REFERENCE_LINE_COLOUR = numpy.full(3, 152. / 255)\n",
        "REFERENCE_LINE_WIDTH = 2.\n",
        "\n",
        "SPREAD_SKILL_COLOUR = numpy.array([217, 95, 2], dtype=float) / 255\n",
        "SPREAD_SKILL_LINE_WIDTH = 3.\n",
        "SPREAD_SKILL_MARKER_TYPE = 'o'\n",
        "SPREAD_SKILL_MARKER_SIZE = 12\n",
        "\n",
        "DISCARD_TEST_COLOUR = numpy.array([217, 95, 2], dtype=float) / 255\n",
        "DISCARD_TEST_LINE_WIDTH = 3.\n",
        "DISCARD_TEST_MARKER_TYPE = 'o'\n",
        "DISCARD_TEST_MARKER_SIZE = 12\n",
        "\n",
        "LEVELS_FOR_PEIRCE_CONTOURS = numpy.linspace(0, 1, num=11, dtype=float)\n",
        "ROC_CURVE_COLOUR = numpy.array([228, 26, 28], dtype=float) / 255\n",
        "ROC_CURVE_WIDTH = 3.\n",
        "\n",
        "LEVELS_FOR_CSI_CONTOURS = numpy.linspace(0, 1, num=11, dtype=float)\n",
        "LEVELS_FOR_BIAS_CONTOURS = numpy.array([0.25, 0.5, 0.75, 1, 1.5, 2, 3, 5])\n",
        "PERF_DIAGRAM_COLOUR = numpy.array([228, 26, 28], dtype=float) / 255\n",
        "PERF_DIAGRAM_LINE_WIDTH = 3.\n",
        "\n",
        "FREQUENCY_BIAS_COLOUR = numpy.full(3, 152. / 255)\n",
        "FREQUENCY_BIAS_LINE_WIDTH = 2\n",
        "FREQUENCY_BIAS_STRING_FORMAT = '%.2f'\n",
        "FREQUENCY_BIAS_LABEL_PADDING_PX = 10\n",
        "\n",
        "RELIABILITY_COLOUR = numpy.array([228, 26, 28], dtype=float) / 255\n",
        "RELIABILITY_LINE_WIDTH = 3\n",
        "\n",
        "NO_SKILL_LINE_COLOUR = numpy.array([31, 120, 180], dtype=float) / 255\n",
        "NO_SKILL_LINE_WIDTH = 2\n",
        "SKILL_AREA_TRANSPARENCY = 0.2\n",
        "\n",
        "ATTRIBUTES_DIAG_HIST_FACE_COLOUR = numpy.array([228, 26, 28], dtype=float) / 255\n",
        "ATTRIBUTES_DIAG_HIST_EDGE_COLOUR = numpy.full(3, 0.)\n",
        "ATTRIBUTES_DIAG_HIST_EDGE_WIDTH = 2\n",
        "\n",
        "ATTRIBUTES_DIAG_HIST_Y_SPACING = 0.1\n",
        "ATTRIBUTES_DIAG_HIST_X_TICKS = numpy.linspace(0, 1, num=6, dtype=float)\n",
        "\n",
        "FONT_SIZE = 30\n",
        "INSET_FONT_SIZE = 16\n",
        "\n",
        "pyplot.rc('font', size=FONT_SIZE)\n",
        "pyplot.rc('axes', titlesize=FONT_SIZE)\n",
        "pyplot.rc('axes', labelsize=FONT_SIZE)\n",
        "pyplot.rc('xtick', labelsize=FONT_SIZE)\n",
        "pyplot.rc('ytick', labelsize=FONT_SIZE)\n",
        "pyplot.rc('legend', fontsize=FONT_SIZE)\n",
        "pyplot.rc('figure', titlesize=FONT_SIZE)"
      ],
      "metadata": {
        "id": "TSh_9pbHxLVT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define private methods\n",
        "\n",
        "If a method's name begins with an underscore, it is private.  Private methods should be called only by other methods, never directly by the user."
      ],
      "metadata": {
        "id": "YUfTisS0xXxb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def _plot_means_as_inset(\n",
        "        figure_object, bin_centers, bin_mean_predictions,\n",
        "        bin_mean_target_values, for_spread_skill_plot):\n",
        "    \"\"\"Plots means (mean prediction and target by bin) as inset in another fig.\n",
        "\n",
        "    B = number of bins\n",
        "\n",
        "    :param figure_object: Will plot as inset in this figure (instance of\n",
        "        `matplotlib.figure.Figure`).\n",
        "    :param bin_centers: length-B numpy array with value at center of each bin.\n",
        "        These values will be plotted on the x-axis.\n",
        "    :param bin_mean_predictions: length-B numpy array with mean prediction in\n",
        "        each bin.  These values will be plotted on the y-axis.\n",
        "    :param bin_mean_target_values: length-B numpy array with mean target value\n",
        "        (event frequency) in each bin.  These values will be plotted on the\n",
        "        y-axis.\n",
        "    :param for_spread_skill_plot: Boolean flag.\n",
        "    :return: inset_axes_object: Axes handle for histogram (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    \"\"\"\n",
        "\n",
        "    if for_spread_skill_plot:\n",
        "        inset_axes_object = figure_object.add_axes([0.625, 0.3, 0.25, 0.25])\n",
        "    else:\n",
        "        inset_axes_object = figure_object.add_axes([0.625, 0.55, 0.25, 0.25])\n",
        "\n",
        "    target_handle = inset_axes_object.plot(\n",
        "        bin_centers, bin_mean_target_values, color=MEAN_TARGET_LINE_COLOUR,\n",
        "        linestyle='solid', linewidth=2,\n",
        "        marker='o', markersize=8, markeredgewidth=0,\n",
        "        markerfacecolor=MEAN_TARGET_LINE_COLOUR,\n",
        "        markeredgecolor=MEAN_TARGET_LINE_COLOUR\n",
        "    )[0]\n",
        "\n",
        "    prediction_handle = inset_axes_object.plot(\n",
        "        bin_centers, bin_mean_predictions, color=MEAN_PREDICTION_LINE_COLOUR,\n",
        "        linestyle='dashed', linewidth=2,\n",
        "        marker='o', markersize=8, markeredgewidth=0,\n",
        "        markerfacecolor=MEAN_PREDICTION_LINE_COLOUR,\n",
        "        markeredgecolor=MEAN_PREDICTION_LINE_COLOUR\n",
        "    )[0]\n",
        "\n",
        "    y_max = max([\n",
        "        numpy.nanmax(bin_mean_predictions),\n",
        "        numpy.nanmax(bin_mean_target_values)\n",
        "    ])\n",
        "    inset_axes_object.set_ylim(0, y_max)\n",
        "    inset_axes_object.set_xlim(left=0.)\n",
        "\n",
        "    for this_tick_object in inset_axes_object.xaxis.get_major_ticks():\n",
        "        this_tick_object.label.set_fontsize(INSET_FONT_SIZE)\n",
        "        this_tick_object.label.set_rotation('vertical')\n",
        "\n",
        "    for this_tick_object in inset_axes_object.yaxis.get_major_ticks():\n",
        "        this_tick_object.label.set_fontsize(INSET_FONT_SIZE)\n",
        "\n",
        "    inset_axes_object.legend(\n",
        "        [target_handle, prediction_handle],\n",
        "        ['Mean target', 'Mean prediction'],\n",
        "        loc='upper center', bbox_to_anchor=(0.5, -0.2),\n",
        "        fancybox=True, shadow=True, ncol=1, fontsize=INSET_FONT_SIZE\n",
        "    )\n",
        "\n",
        "    return inset_axes_object\n",
        "\n",
        "\n",
        "def _plot_histogram(axes_object, bin_edges, bin_frequencies):\n",
        "    \"\"\"Plots histogram on existing axes.\n",
        "\n",
        "    B = number of bins\n",
        "\n",
        "    :param axes_object: Will plot histogram on these axes (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    :param bin_centers: length-(B + 1) numpy array with values at edges of each\n",
        "        bin. These values will be plotted on the x-axis.\n",
        "    :param bin_frequencies: length-B numpy array with fraction of examples in\n",
        "        each bin. These values will be plotted on the y-axis.\n",
        "    :return: histogram_axes_object: Axes handle for histogram only (also\n",
        "        instance of `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    \"\"\"\n",
        "\n",
        "    histogram_axes_object = axes_object.twinx()\n",
        "    axes_object.set_zorder(histogram_axes_object.get_zorder() + 1)\n",
        "    axes_object.patch.set_visible(False)\n",
        "\n",
        "    histogram_axes_object.bar(\n",
        "        x=bin_edges[:-1], height=bin_frequencies, width=numpy.diff(bin_edges),\n",
        "        color=HISTOGRAM_FACE_COLOUR, edgecolor=HISTOGRAM_EDGE_COLOUR,\n",
        "        linewidth=HISTOGRAM_EDGE_WIDTH, align='edge'\n",
        "    )\n",
        "\n",
        "    return histogram_axes_object\n",
        "\n",
        "\n",
        "def _log2(input_array):\n",
        "    \"\"\"Computes logarithm in base 2.\n",
        "\n",
        "    :param input_array: numpy array.\n",
        "    :return: logarithm_array: numpy array with the same shape as `input_array`.\n",
        "    \"\"\"\n",
        "\n",
        "    return numpy.log2(numpy.maximum(input_array, 1e-6))\n",
        "\n",
        "\n",
        "def _get_pofd_pod_grid(pofd_spacing=0.01, pod_spacing=0.01):\n",
        "    \"\"\"Creates grid in POFD-POD space.\n",
        "\n",
        "    POD = probability of detection\n",
        "    POFD = probability of false detection\n",
        "\n",
        "    M = number of rows (unique POD values) in grid\n",
        "    N = number of columns (unique POFD values) in grid\n",
        "\n",
        "    :param pofd_spacing: Spacing between grid cells in adjacent columns.\n",
        "    :param pod_spacing: Spacing between grid cells in adjacent rows.\n",
        "    :return: pofd_matrix: M-by-N numpy array of POFD values.\n",
        "    :return: pod_matrix: M-by-N numpy array of POD values.\n",
        "    \"\"\"\n",
        "\n",
        "    num_pofd_values = 1 + int(numpy.ceil(1. / pofd_spacing))\n",
        "    num_pod_values = 1 + int(numpy.ceil(1. / pod_spacing))\n",
        "\n",
        "    unique_pofd_values = numpy.linspace(0., 1., num=num_pofd_values)\n",
        "    unique_pod_values = numpy.linspace(0., 1., num=num_pod_values)[::-1]\n",
        "    return numpy.meshgrid(unique_pofd_values, unique_pod_values)\n",
        "\n",
        "\n",
        "def _get_peirce_colour_scheme():\n",
        "    \"\"\"Returns colour scheme for Peirce score.\n",
        "\n",
        "    :return: colour_map_object: Colour scheme (instance of\n",
        "        `matplotlib.colors.ListedColormap`).\n",
        "    :return: colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`,\n",
        "        defining the scale of the colour map.\n",
        "    \"\"\"\n",
        "\n",
        "    this_colour_map_object = pyplot.cm.Blues\n",
        "    this_colour_norm_object = matplotlib.colors.BoundaryNorm(\n",
        "        LEVELS_FOR_PEIRCE_CONTOURS, this_colour_map_object.N\n",
        "    )\n",
        "\n",
        "    rgba_matrix = this_colour_map_object(this_colour_norm_object(\n",
        "        LEVELS_FOR_PEIRCE_CONTOURS\n",
        "    ))\n",
        "    colour_list = [\n",
        "        rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\n",
        "    ]\n",
        "\n",
        "    colour_map_object = matplotlib.colors.ListedColormap(colour_list)\n",
        "    colour_map_object.set_under(\n",
        "        numpy.full(3, 1.)\n",
        "    )\n",
        "\n",
        "    colour_norm_object = matplotlib.colors.BoundaryNorm(\n",
        "        LEVELS_FOR_PEIRCE_CONTOURS, colour_map_object.N\n",
        "    )\n",
        "\n",
        "    return colour_map_object, colour_norm_object\n",
        "\n",
        "\n",
        "def _add_colour_bar(\n",
        "        axes_object, colour_map_object, values_to_colour, min_colour_value,\n",
        "        max_colour_value, colour_norm_object=None,\n",
        "        orientation_string='vertical', extend_min=True, extend_max=True,\n",
        "        fraction_of_axis_length=1., font_size=FONT_SIZE):\n",
        "    \"\"\"Adds colour bar to existing axes.\n",
        "\n",
        "    :param axes_object: Existing axes (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    :param colour_map_object: Colour scheme (instance of\n",
        "        `matplotlib.pyplot.cm`).\n",
        "    :param values_to_colour: numpy array of values to colour.\n",
        "    :param min_colour_value: Minimum value in colour map.\n",
        "    :param max_colour_value: Max value in colour map.\n",
        "    :param colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`,\n",
        "        defining the scale of the colour map.  If `colour_norm_object is None`,\n",
        "        will assume that scale is linear.\n",
        "    :param orientation_string: Orientation of colour bar (\"vertical\" or\n",
        "        \"horizontal\").\n",
        "    :param extend_min: Boolean flag.  If True, the bottom of the colour bar will\n",
        "        have an arrow.  If False, it will be a flat line, suggesting that lower\n",
        "        values are not possible.\n",
        "    :param extend_max: Same but for top of colour bar.\n",
        "    :param fraction_of_axis_length: Fraction of axis length (y-axis if\n",
        "        orientation is \"vertical\", x-axis if orientation is \"horizontal\")\n",
        "        occupied by colour bar.\n",
        "    :param font_size: Font size for labels on colour bar.\n",
        "    :return: colour_bar_object: Colour bar (instance of\n",
        "        `matplotlib.pyplot.colorbar`) created by this method.\n",
        "    \"\"\"\n",
        "\n",
        "    if colour_norm_object is None:\n",
        "        colour_norm_object = matplotlib.colors.Normalize(\n",
        "            vmin=min_colour_value, vmax=max_colour_value, clip=False\n",
        "        )\n",
        "\n",
        "    scalar_mappable_object = pyplot.cm.ScalarMappable(\n",
        "        cmap=colour_map_object, norm=colour_norm_object\n",
        "    )\n",
        "    scalar_mappable_object.set_array(values_to_colour)\n",
        "\n",
        "    if extend_min and extend_max:\n",
        "        extend_string = 'both'\n",
        "    elif extend_min:\n",
        "        extend_string = 'min'\n",
        "    elif extend_max:\n",
        "        extend_string = 'max'\n",
        "    else:\n",
        "        extend_string = 'neither'\n",
        "\n",
        "    if orientation_string == 'horizontal':\n",
        "        padding = 0.075\n",
        "    else:\n",
        "        padding = 0.05\n",
        "\n",
        "    colour_bar_object = pyplot.colorbar(\n",
        "        ax=axes_object, mappable=scalar_mappable_object,\n",
        "        orientation=orientation_string, pad=padding, extend=extend_string,\n",
        "        shrink=fraction_of_axis_length\n",
        "    )\n",
        "\n",
        "    colour_bar_object.ax.tick_params(labelsize=font_size)\n",
        "    return colour_bar_object\n",
        "\n",
        "\n",
        "def _get_sr_pod_grid(success_ratio_spacing=0.01, pod_spacing=0.01):\n",
        "    \"\"\"Creates grid in SR-POD (success ratio / probability of detection) space.\n",
        "\n",
        "    M = number of rows (unique POD values) in grid\n",
        "    N = number of columns (unique success ratios) in grid\n",
        "\n",
        "    :param success_ratio_spacing: Spacing between grid cells in adjacent\n",
        "        columns.\n",
        "    :param pod_spacing: Spacing between grid cells in adjacent rows.\n",
        "    :return: success_ratio_matrix: M-by-N numpy array of success ratios.\n",
        "        Success ratio increases with column index.\n",
        "    :return: pod_matrix: M-by-N numpy array of POD values.  POD decreases with\n",
        "        row index.\n",
        "    \"\"\"\n",
        "\n",
        "    num_success_ratios = 1 + int(numpy.ceil(1. / success_ratio_spacing))\n",
        "    num_pod_values = 1 + int(numpy.ceil(1. / pod_spacing))\n",
        "\n",
        "    unique_success_ratios = numpy.linspace(0., 1., num=num_success_ratios)\n",
        "    unique_pod_values = numpy.linspace(0., 1., num=num_pod_values)[::-1]\n",
        "    return numpy.meshgrid(unique_success_ratios, unique_pod_values)\n",
        "\n",
        "\n",
        "def _csi_from_sr_and_pod(success_ratio_array, pod_array):\n",
        "    \"\"\"Computes CSI (critical success index) from success ratio and POD.\n",
        "\n",
        "    POD = probability of detection\n",
        "\n",
        "    :param success_ratio_array: numpy array (any shape) of success ratios.\n",
        "    :param pod_array: numpy array (same shape) of POD values.\n",
        "    :return: csi_array: numpy array (same shape) of CSI values.\n",
        "    \"\"\"\n",
        "\n",
        "    return (success_ratio_array ** -1 + pod_array ** -1 - 1.) ** -1\n",
        "\n",
        "\n",
        "def _bias_from_sr_and_pod(success_ratio_array, pod_array):\n",
        "    \"\"\"Computes frequency bias from success ratio and POD.\n",
        "\n",
        "    POD = probability of detection\n",
        "\n",
        "    :param success_ratio_array: numpy array (any shape) of success ratios.\n",
        "    :param pod_array: numpy array (same shape) of POD values.\n",
        "    :return: frequency_bias_array: numpy array (same shape) of frequency biases.\n",
        "    \"\"\"\n",
        "\n",
        "    return pod_array / success_ratio_array\n",
        "\n",
        "\n",
        "def _get_csi_colour_scheme():\n",
        "    \"\"\"Returns colour scheme for CSI (critical success index).\n",
        "\n",
        "    :return: colour_map_object: Colour scheme (instance of\n",
        "        `matplotlib.colors.ListedColormap`).\n",
        "    :return: colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`,\n",
        "        defining the scale of the colour map.\n",
        "    \"\"\"\n",
        "\n",
        "    this_colour_map_object = pyplot.cm.Blues\n",
        "    this_colour_norm_object = matplotlib.colors.BoundaryNorm(\n",
        "        LEVELS_FOR_CSI_CONTOURS, this_colour_map_object.N\n",
        "    )\n",
        "\n",
        "    rgba_matrix = this_colour_map_object(this_colour_norm_object(\n",
        "        LEVELS_FOR_CSI_CONTOURS\n",
        "    ))\n",
        "    colour_list = [\n",
        "        rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\n",
        "    ]\n",
        "\n",
        "    colour_map_object = matplotlib.colors.ListedColormap(colour_list)\n",
        "    colour_map_object.set_under(numpy.array([1, 1, 1]))\n",
        "    \n",
        "    colour_norm_object = matplotlib.colors.BoundaryNorm(\n",
        "        LEVELS_FOR_CSI_CONTOURS, colour_map_object.N\n",
        "    )\n",
        "\n",
        "    return colour_map_object, colour_norm_object\n",
        "\n",
        "\n",
        "def _get_histogram(input_values, num_bins, min_value, max_value):\n",
        "    \"\"\"Creates histogram with uniform bin-spacing.\n",
        "\n",
        "    E = number of input values\n",
        "    B = number of bins\n",
        "\n",
        "    :param input_values: length-E numpy array of values to bin.\n",
        "    :param num_bins: Number of bins (B).\n",
        "    :param min_value: Minimum value.  Any input value < `min_value` will be\n",
        "        assigned to the first bin.\n",
        "    :param max_value: Max value.  Any input value > `max_value` will be\n",
        "        assigned to the last bin.\n",
        "    :return: inputs_to_bins: length-E numpy array of bin indices (integers).\n",
        "    \"\"\"\n",
        "\n",
        "    bin_cutoffs = numpy.linspace(min_value, max_value, num=num_bins + 1)\n",
        "\n",
        "    inputs_to_bins = numpy.digitize(\n",
        "        input_values, bin_cutoffs, right=False\n",
        "    ) - 1\n",
        "\n",
        "    inputs_to_bins[inputs_to_bins < 0] = 0\n",
        "    inputs_to_bins[inputs_to_bins > num_bins - 1] = num_bins - 1\n",
        "\n",
        "    return inputs_to_bins\n",
        "\n",
        "\n",
        "def _vertices_to_polygon_object(x_vertices, y_vertices):\n",
        "    \"\"\"Converts two arrays of vertices to `shapely.geometry.Polygon` object.\n",
        "\n",
        "    V = number of vertices\n",
        "\n",
        "    This method allows for simple polygons only (no disjoint polygons, no\n",
        "    holes).\n",
        "\n",
        "    :param x_vertices: length-V numpy array of x-coordinates.\n",
        "    :param y_vertices: length-V numpy array of y-coordinates.\n",
        "    :return: polygon_object: Instance of `shapely.geometry.Polygon`.\n",
        "    \"\"\"\n",
        "\n",
        "    list_of_vertices = []\n",
        "\n",
        "    for i in range(len(x_vertices)):\n",
        "        list_of_vertices.append(\n",
        "            (x_vertices[i], y_vertices[i])\n",
        "        )\n",
        "\n",
        "    return shapely.geometry.Polygon(shell=list_of_vertices)\n",
        "\n",
        "\n",
        "def _plot_background(axes_object, observed_labels):\n",
        "    \"\"\"Plots background of attributes diagram.\n",
        "\n",
        "    E = number of examples\n",
        "\n",
        "    :param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.\n",
        "        Will plot on these axes.\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    \"\"\"\n",
        "\n",
        "    # Plot positive-skill area.\n",
        "    climatology = numpy.mean(observed_labels.astype(float))\n",
        "    skill_area_colour = matplotlib.colors.to_rgba(\n",
        "        NO_SKILL_LINE_COLOUR, SKILL_AREA_TRANSPARENCY\n",
        "    )\n",
        "\n",
        "    x_vertices_left = numpy.array([0, climatology, climatology, 0, 0])\n",
        "    y_vertices_left = numpy.array([0, 0, climatology, climatology / 2, 0])\n",
        "\n",
        "    left_polygon_object = _vertices_to_polygon_object(\n",
        "        x_vertices=x_vertices_left, y_vertices=y_vertices_left\n",
        "    )\n",
        "    left_polygon_patch = PolygonPatch(\n",
        "        left_polygon_object, lw=0, ec=skill_area_colour, fc=skill_area_colour\n",
        "    )\n",
        "    axes_object.add_patch(left_polygon_patch)\n",
        "\n",
        "    x_vertices_right = numpy.array(\n",
        "        [climatology, 1, 1, climatology, climatology]\n",
        "    )\n",
        "    y_vertices_right = numpy.array(\n",
        "        [climatology, (1 + climatology) / 2, 1, 1, climatology]\n",
        "    )\n",
        "\n",
        "    right_polygon_object = _vertices_to_polygon_object(\n",
        "        x_vertices=x_vertices_right, y_vertices=y_vertices_right\n",
        "    )\n",
        "    right_polygon_patch = PolygonPatch(\n",
        "        right_polygon_object, lw=0, ec=skill_area_colour, fc=skill_area_colour\n",
        "    )\n",
        "    axes_object.add_patch(right_polygon_patch)\n",
        "\n",
        "    # Plot no-skill line (at edge of positive-skill area).\n",
        "    no_skill_x_coords = numpy.array([0, 1], dtype=float)\n",
        "    no_skill_y_coords = numpy.array([climatology, 1 + climatology]) / 2\n",
        "    axes_object.plot(\n",
        "        no_skill_x_coords, no_skill_y_coords, color=NO_SKILL_LINE_COLOUR,\n",
        "        linestyle='solid', linewidth=NO_SKILL_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "    # Plot climatology line (vertical).\n",
        "    climo_line_x_coords = numpy.full(2, climatology)\n",
        "    climo_line_y_coords = numpy.array([0, 1], dtype=float)\n",
        "    axes_object.plot(\n",
        "        climo_line_x_coords, climo_line_y_coords, color=REFERENCE_LINE_COLOUR,\n",
        "        linestyle='dashed', linewidth=REFERENCE_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "    # Plot no-resolution line (horizontal).\n",
        "    no_resolution_x_coords = climo_line_y_coords + 0.\n",
        "    no_resolution_y_coords = climo_line_x_coords + 0.\n",
        "    axes_object.plot(\n",
        "        no_resolution_x_coords, no_resolution_y_coords,\n",
        "        color=REFERENCE_LINE_COLOUR, linestyle='dashed',\n",
        "        linewidth=REFERENCE_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "\n",
        "def _floor_to_nearest(input_value_or_array, increment):\n",
        "    \"\"\"Rounds number(s) down to the nearest multiple of `increment`.\n",
        "\n",
        "    :param input_value_or_array: Input (either scalar or numpy array).\n",
        "    :param increment: Increment (or rounding base -- whatever you want to call\n",
        "        it).\n",
        "    :return: output_value_or_array: Rounded version of `input_value_or_array`.\n",
        "    \"\"\"\n",
        "\n",
        "    return increment * numpy.floor(input_value_or_array / increment)\n",
        "\n",
        "\n",
        "def _plot_forecast_histogram(figure_object, num_examples_by_bin):\n",
        "    \"\"\"Plots forecast histogram as inset in the attributes diagram.\n",
        "\n",
        "    B = number of bins\n",
        "\n",
        "    :param figure_object: Instance of `matplotlib.figure.Figure`.  Will plot in\n",
        "        this figure.\n",
        "    :param num_examples_by_bin: length-B numpy array, where\n",
        "        num_examples_by_bin[j] = number of examples in [j]th forecast bin.\n",
        "    \"\"\"\n",
        "\n",
        "    num_bins = len(num_examples_by_bin)\n",
        "    bin_frequencies = (\n",
        "        num_examples_by_bin.astype(float) / numpy.sum(num_examples_by_bin)\n",
        "    )\n",
        "\n",
        "    forecast_bin_edges = numpy.linspace(0, 1, num=num_bins + 1, dtype=float)\n",
        "    forecast_bin_width = forecast_bin_edges[1] - forecast_bin_edges[0]\n",
        "    forecast_bin_centers = forecast_bin_edges[:-1] + forecast_bin_width / 2\n",
        "\n",
        "    inset_axes_object = figure_object.add_axes([0.575, 0.175, 0.3, 0.3])\n",
        "    inset_axes_object.bar(\n",
        "        forecast_bin_centers, bin_frequencies, forecast_bin_width,\n",
        "        color=ATTRIBUTES_DIAG_HIST_FACE_COLOUR,\n",
        "        edgecolor=ATTRIBUTES_DIAG_HIST_EDGE_COLOUR,\n",
        "        linewidth=ATTRIBUTES_DIAG_HIST_EDGE_WIDTH\n",
        "    )\n",
        "\n",
        "    max_y_tick_value = _floor_to_nearest(\n",
        "        1.05 * numpy.max(bin_frequencies), ATTRIBUTES_DIAG_HIST_Y_SPACING\n",
        "    )\n",
        "    num_y_ticks = 1 + int(numpy.round(\n",
        "        max_y_tick_value / ATTRIBUTES_DIAG_HIST_Y_SPACING\n",
        "    ))\n",
        "\n",
        "    y_tick_values = numpy.linspace(0, max_y_tick_value, num=num_y_ticks)\n",
        "    pyplot.yticks(\n",
        "        y_tick_values, axes=inset_axes_object, fontsize=INSET_FONT_SIZE\n",
        "    )\n",
        "    pyplot.xticks(\n",
        "        ATTRIBUTES_DIAG_HIST_X_TICKS, axes=inset_axes_object,\n",
        "        fontsize=INSET_FONT_SIZE\n",
        "    )\n",
        "\n",
        "    inset_axes_object.set_xlim(0, 1)\n",
        "    inset_axes_object.set_ylim(0, 1.05 * numpy.max(bin_frequencies))"
      ],
      "metadata": {
        "id": "ikUFOR-sxYTn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define helper methods\n",
        "\n",
        "The first two methods compute the mean and standard deviation of the predictive distribution, which consists of many estimates created by Monte Carlo sampling.\n",
        "\n",
        "The next two methods create the spread-skill plot, which you can read about in [Delle Monache *et al.* (2013)](https://doi.org/10.1175/MWR-D-12-00281.1).\n",
        "\n",
        "The next two methods after that run and plot the discard test, which you can read about in [Barnes and Barnes (2021)](https://arxiv.org/abs/2104.08236) -- their Figures 2d, 4d, 8d, and 10d.\n",
        "\n",
        "The next two methods after that create and plot the receiver operating characteristic (ROC) curve, which you can read about in the [CIRA machine-learning short course](https://docs.google.com/document/u/1/d/1SPNxZrbHMaIEaS2dbntDow9x_tgSuFTUTOugfa2NuRo/edit).\n",
        "\n",
        "The next two methods after that create and plot the performance diagram, which you can also read about in the CIRA ML course.\n",
        "\n",
        "The next three methods after that create and plot the attributes diagram, which you can also read about in the CIRA ML course.\n",
        "\n",
        "The last method computes the Brier skill score, which you can also read about in the CIRA ML course."
      ],
      "metadata": {
        "id": "5-5I-4lCxcm3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_mean_predictions(prediction_matrix):\n",
        "    \"\"\"Estimates mean from Monte Carlo estimates.\n",
        "\n",
        "    E = number of examples\n",
        "    Q = number of Monte Carlo estimates\n",
        "\n",
        "    :param prediction_matrix: E-by-Q numpy array of predicted values.\n",
        "    :return: mean_predictions: length-E numpy array of mean predictions.\n",
        "    \"\"\"\n",
        "\n",
        "    return numpy.mean(prediction_matrix, axis=1)\n",
        "\n",
        "\n",
        "def get_predictive_stdevs(prediction_matrix):\n",
        "    \"\"\"Estimates standard deviation from Monte Carlo estimates.\n",
        "\n",
        "    E = number of examples\n",
        "    Q = number of Monte Carlo estimates\n",
        "\n",
        "    :param prediction_matrix: E-by-Q numpy array of predicted values.\n",
        "    :return: predictive_stdevs: length-E numpy array with standard deviation of\n",
        "        each predictive distribution.\n",
        "    \"\"\"\n",
        "\n",
        "    return numpy.std(prediction_matrix, axis=1, ddof=1)\n",
        "\n",
        "\n",
        "def get_spread_vs_skill(\n",
        "        prediction_matrix, target_values, bin_edge_prediction_stdevs):\n",
        "    \"\"\"Computes model spread vs. model skill.\n",
        "\n",
        "    B = number of bins\n",
        "    E = number of examples\n",
        "    Q = number of Monte Carlo estimates\n",
        "\n",
        "    :param prediction_matrix: E-by-Q numpy array of predicted values.\n",
        "    :param target_values: length-E numpy array of target values, all integers in\n",
        "        0...1.\n",
        "    :param bin_edge_prediction_stdevs: length-(B - 1) numpy array of bin\n",
        "        cutoffs.  Each is a standard deviation for the predictive distribution.\n",
        "        Ultimately, there will be B + 1 edges; this method will use 0 as the\n",
        "        lowest edge and 1 as the highest edge.\n",
        "    :return: result_dict: Dictionary with the following keys.\n",
        "    result_dict['mean_prediction_stdevs']: length-B numpy array, where the [i]th\n",
        "        entry is the mean standard deviation of predictive distributions in the\n",
        "        [i]th bin.\n",
        "    result_dict['bin_edge_prediction_stdevs']: length-(B + 1) numpy array,\n",
        "        where the [i]th and [i + 1]th entries are the edges for the [i]th bin.\n",
        "    result_dict['rmse_values']: length-B numpy array, where the [i]th\n",
        "        entry is the root mean squared error of central (mean or median)\n",
        "        predictions in the [i]th bin.\n",
        "    result_dict['spread_skill_reliability']: Spread-skill reliability (SSREL).\n",
        "    result_dict['example_counts']: length-B numpy array of corresponding example\n",
        "        counts.\n",
        "    result_dict['mean_central_predictions']: length-B numpy array, where the\n",
        "        [i]th entry is the mean central (mean or median) prediction for the\n",
        "        [i]th bin.\n",
        "    result_dict['mean_target_values']: length-B numpy array, where the [i]th\n",
        "        entry is the mean target value for the [i]th bin.\n",
        "    \"\"\"\n",
        "\n",
        "    # Check input args.\n",
        "    assert numpy.all(bin_edge_prediction_stdevs > 0.)\n",
        "    assert numpy.all(bin_edge_prediction_stdevs < 1.)\n",
        "    assert numpy.all(numpy.diff(bin_edge_prediction_stdevs) > 0.)\n",
        "\n",
        "    bin_edge_prediction_stdevs = numpy.concatenate((\n",
        "        numpy.array([0.]),\n",
        "        bin_edge_prediction_stdevs,\n",
        "        numpy.array([numpy.inf])\n",
        "    ))\n",
        "\n",
        "    num_bins = len(bin_edge_prediction_stdevs) - 1\n",
        "    assert num_bins >= 2\n",
        "\n",
        "    mean_predictions = get_mean_predictions(prediction_matrix)\n",
        "    predictive_stdevs = get_predictive_stdevs(prediction_matrix)\n",
        "    squared_errors = (mean_predictions - target_values) ** 2\n",
        "\n",
        "    binned_mean_prediction_stdevs = numpy.full(num_bins, numpy.nan)\n",
        "    binned_rmse_values = numpy.full(num_bins, numpy.nan)\n",
        "    binned_example_counts = numpy.full(num_bins, 0, dtype=int)\n",
        "    binned_mean_predictions = numpy.full(num_bins, numpy.nan)\n",
        "    binned_mean_target_values = numpy.full(num_bins, numpy.nan)\n",
        "\n",
        "    for k in range(num_bins):\n",
        "        these_indices = numpy.where(numpy.logical_and(\n",
        "            predictive_stdevs >= bin_edge_prediction_stdevs[k],\n",
        "            predictive_stdevs < bin_edge_prediction_stdevs[k + 1]\n",
        "        ))[0]\n",
        "\n",
        "        binned_mean_prediction_stdevs[k] = numpy.mean(\n",
        "            predictive_stdevs[these_indices]\n",
        "        )\n",
        "        binned_rmse_values[k] = numpy.sqrt(numpy.mean(\n",
        "            squared_errors[these_indices]\n",
        "        ))\n",
        "        binned_example_counts[k] = len(these_indices)\n",
        "        binned_mean_predictions[k] = numpy.mean(\n",
        "            mean_predictions[these_indices]\n",
        "        )\n",
        "        binned_mean_target_values[k] = numpy.mean(\n",
        "            target_values[these_indices]\n",
        "        )\n",
        "\n",
        "    these_diffs = numpy.absolute(\n",
        "        binned_mean_prediction_stdevs - binned_rmse_values\n",
        "    )\n",
        "    these_diffs[numpy.isnan(these_diffs)] = 0.\n",
        "    spread_skill_reliability = numpy.average(\n",
        "        these_diffs, weights=binned_example_counts\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        MEAN_PREDICTION_STDEVS_KEY: binned_mean_prediction_stdevs,\n",
        "        BIN_EDGE_PREDICTION_STDEVS_KEY: bin_edge_prediction_stdevs,\n",
        "        RMSE_VALUES_KEY: binned_rmse_values,\n",
        "        SPREAD_SKILL_RELIABILITY_KEY: spread_skill_reliability,\n",
        "        EXAMPLE_COUNTS_KEY: binned_example_counts,\n",
        "        MEAN_CENTRAL_PREDICTIONS_KEY: binned_mean_predictions,\n",
        "        MEAN_TARGET_VALUES_KEY: binned_mean_target_values\n",
        "    }\n",
        "\n",
        "\n",
        "def plot_spread_vs_skill(result_dict):\n",
        "    \"\"\"Plots spread vs. skill.\n",
        "\n",
        "    :param result_dict: Dictionary in format returned by `get_spread_vs_skill`.\n",
        "    :return: figure_object: Figure handle (instance of\n",
        "        `matplotlib.figure.Figure`).\n",
        "    :return: axes_object: Axes handle (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    \"\"\"\n",
        "\n",
        "    # Check input args.\n",
        "    mean_prediction_stdevs = result_dict[MEAN_PREDICTION_STDEVS_KEY]\n",
        "    rmse_values = result_dict[RMSE_VALUES_KEY]\n",
        "\n",
        "    nan_flags = numpy.logical_or(\n",
        "        numpy.isnan(mean_prediction_stdevs),\n",
        "        numpy.isnan(rmse_values)\n",
        "    )\n",
        "    assert not numpy.all(nan_flags)\n",
        "\n",
        "    # Do actual stuff.\n",
        "    figure_object, axes_object = pyplot.subplots(\n",
        "        1, 1, figsize=(12, 12)\n",
        "    )\n",
        "\n",
        "    max_value_to_plot = max([\n",
        "        numpy.nanmax(mean_prediction_stdevs),\n",
        "        numpy.nanmax(rmse_values)\n",
        "    ])\n",
        "    perfect_x_coords = numpy.array([0, max_value_to_plot])\n",
        "    perfect_y_coords = numpy.array([0, max_value_to_plot])\n",
        "    axes_object.plot(\n",
        "        perfect_x_coords, perfect_y_coords, color=REFERENCE_LINE_COLOUR,\n",
        "        linestyle='dashed', linewidth=REFERENCE_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "    real_indices = numpy.where(numpy.invert(nan_flags))[0]\n",
        "    axes_object.plot(\n",
        "        mean_prediction_stdevs[real_indices],\n",
        "        rmse_values[real_indices],\n",
        "        color=SPREAD_SKILL_COLOUR, linestyle='solid',\n",
        "        linewidth=SPREAD_SKILL_LINE_WIDTH,\n",
        "        marker=SPREAD_SKILL_MARKER_TYPE, markersize=SPREAD_SKILL_MARKER_SIZE,\n",
        "        markeredgewidth=0,\n",
        "        markerfacecolor=SPREAD_SKILL_COLOUR, markeredgecolor=SPREAD_SKILL_COLOUR\n",
        "    )\n",
        "\n",
        "    axes_object.set_xlabel('Spread (stdev of predictive distribution)')\n",
        "    axes_object.set_ylabel('Skill (RMSE of central prediction)')\n",
        "    axes_object.set_xlim(0, max_value_to_plot)\n",
        "    axes_object.set_ylim(0, max_value_to_plot)\n",
        "\n",
        "    bin_frequencies = (\n",
        "        result_dict[EXAMPLE_COUNTS_KEY].astype(float) /\n",
        "        numpy.sum(result_dict[EXAMPLE_COUNTS_KEY])\n",
        "    )\n",
        "\n",
        "    bin_edges = result_dict[BIN_EDGE_PREDICTION_STDEVS_KEY]\n",
        "\n",
        "    if numpy.isnan(mean_prediction_stdevs[-1]):\n",
        "        bin_edges[-1] = bin_edges[-2] + (bin_edges[-2] - bin_edges[-3])\n",
        "    else:\n",
        "        bin_edges[-1] = (\n",
        "            bin_edges[-2] + 2 * (mean_prediction_stdevs[-1] - bin_edges[-2])\n",
        "        )\n",
        "\n",
        "    histogram_axes_object = _plot_histogram(\n",
        "        axes_object=axes_object,\n",
        "        bin_edges=result_dict[BIN_EDGE_PREDICTION_STDEVS_KEY],\n",
        "        bin_frequencies=bin_frequencies * 100\n",
        "    )\n",
        "    histogram_axes_object.set_ylabel('% examples in each bin')\n",
        "\n",
        "    inset_axes_object = _plot_means_as_inset(\n",
        "        figure_object=figure_object, bin_centers=mean_prediction_stdevs,\n",
        "        bin_mean_predictions=result_dict[MEAN_CENTRAL_PREDICTIONS_KEY],\n",
        "        bin_mean_target_values=result_dict[MEAN_TARGET_VALUES_KEY],\n",
        "        for_spread_skill_plot=True\n",
        "    )\n",
        "\n",
        "    inset_axes_object.set_xticks(axes_object.get_xticks())\n",
        "    inset_axes_object.set_xlim(axes_object.get_xlim())\n",
        "\n",
        "    inset_axes_object.set_title(\n",
        "        'Mean target and prediction\\nin each bin', fontsize=INSET_FONT_SIZE\n",
        "    )\n",
        "    inset_axes_object.set_xlabel('Spread', fontsize=INSET_FONT_SIZE)\n",
        "\n",
        "    return figure_object, axes_object\n",
        "\n",
        "\n",
        "def run_discard_test(\n",
        "        prediction_matrix, target_values, discard_fractions):\n",
        "    \"\"\"Runs the discard test.\n",
        "\n",
        "    F = number of discard fractions\n",
        "    E = number of examples\n",
        "    Q = number of Monte Carlo estimates\n",
        "\n",
        "    :param prediction_matrix: E-by-Q numpy array of predicted values.\n",
        "    :param target_values: length-E numpy array of target values, all integers in\n",
        "        0...1.\n",
        "    :param discard_fractions: length-(F - 1) numpy array of discard fractions,\n",
        "        ranging from (0, 1).  This method will use 0 as the lowest discard\n",
        "        fraction.\n",
        "\n",
        "    :return: result_dict: Dictionary with the following keys.\n",
        "    result_dict['discard_fractions']: length-F numpy array of discard fractions,\n",
        "        sorted in increasing order.\n",
        "    result_dict['error_values']: length-F numpy array of corresponding error\n",
        "        values.\n",
        "    result_dict['example_fractions']: length-F numpy array with fraction of\n",
        "        examples left after each discard.\n",
        "    result_dict['mean_central_predictions']: length-F numpy array, where the\n",
        "        [i]th entry is the mean central (mean or median) prediction for the\n",
        "        [i]th discard fraction.\n",
        "    result_dict['mean_target_values']: length-F numpy array, where the [i]th\n",
        "        entry is the mean target value for the [i]th discard fraction.\n",
        "    result_dict['monotonicity_fraction']: Monotonicity fraction.  This is the\n",
        "        fraction of times that the error function improves when discard\n",
        "        fraction is increased.\n",
        "    \"\"\"\n",
        "\n",
        "    assert numpy.all(discard_fractions > 0.)\n",
        "    assert numpy.all(discard_fractions < 1.)\n",
        "\n",
        "    discard_fractions = numpy.concatenate((\n",
        "        numpy.array([0.]),\n",
        "        discard_fractions\n",
        "    ))\n",
        "\n",
        "    num_fractions = len(discard_fractions)\n",
        "    assert num_fractions >= 2\n",
        "\n",
        "    mean_predictions = get_mean_predictions(prediction_matrix)\n",
        "    predictive_stdevs = get_predictive_stdevs(prediction_matrix)\n",
        "    cross_entropy_by_example = (\n",
        "        target_values * _log2(mean_predictions) +\n",
        "        (1. - target_values) * _log2(1. - mean_predictions)\n",
        "    )\n",
        "\n",
        "    discard_fractions = numpy.sort(discard_fractions)\n",
        "    discard_error_values = numpy.full(num_fractions, numpy.nan)\n",
        "    discard_example_fractions = numpy.full(num_fractions, numpy.nan)\n",
        "    discard_mean_predictions = numpy.full(num_fractions, numpy.nan)\n",
        "    discard_mean_target_values = numpy.full(num_fractions, numpy.nan)\n",
        "    use_example_flags = numpy.full(mean_predictions.shape, 1, dtype=bool)\n",
        "\n",
        "    for k in range(num_fractions):\n",
        "        this_percentile_level = 100 * (1 - discard_fractions[k])\n",
        "        this_inverted_mask = (\n",
        "            predictive_stdevs >\n",
        "            numpy.percentile(predictive_stdevs, this_percentile_level)\n",
        "        )\n",
        "        use_example_flags[this_inverted_mask] = False\n",
        "\n",
        "        discard_example_fractions[k] = numpy.mean(use_example_flags)\n",
        "        discard_error_values[k] = -numpy.mean(\n",
        "            cross_entropy_by_example[use_example_flags == True]\n",
        "        )\n",
        "        discard_mean_predictions[k] = numpy.mean(\n",
        "            mean_predictions[use_example_flags == True]\n",
        "        )\n",
        "        discard_mean_target_values[k] = numpy.mean(\n",
        "            target_values[use_example_flags == True]\n",
        "        )\n",
        "\n",
        "    monotonicity_fraction = numpy.mean(numpy.diff(discard_error_values) < 0)\n",
        "\n",
        "    return {\n",
        "        DISCARD_FRACTIONS_KEY: discard_fractions,\n",
        "        ERROR_VALUES_KEY: discard_error_values,\n",
        "        EXAMPLE_FRACTIONS_KEY: discard_example_fractions,\n",
        "        MEAN_CENTRAL_PREDICTIONS_KEY: discard_mean_predictions,\n",
        "        MEAN_TARGET_VALUES_KEY: discard_mean_target_values,\n",
        "        MONOTONICITY_FRACTION_KEY: monotonicity_fraction\n",
        "    }\n",
        "\n",
        "\n",
        "def plot_discard_test(result_dict):\n",
        "    \"\"\"Plots results of discard test.\n",
        "\n",
        "    :param result_dict: Dictionary in format returned by `run_discard_test`.\n",
        "    :return: figure_object: Figure handle (instance of\n",
        "        `matplotlib.figure.Figure`).\n",
        "    :return: axes_object: Axes handle (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    \"\"\"\n",
        "\n",
        "    discard_fractions = result_dict[DISCARD_FRACTIONS_KEY]\n",
        "    error_values = result_dict[ERROR_VALUES_KEY]\n",
        "\n",
        "    figure_object, axes_object = pyplot.subplots(\n",
        "        1, 1, figsize=(12, 12)\n",
        "    )\n",
        "    axes_object.plot(\n",
        "        discard_fractions, error_values,\n",
        "        color=DISCARD_TEST_COLOUR, linestyle='solid',\n",
        "        linewidth=DISCARD_TEST_LINE_WIDTH,\n",
        "        marker=DISCARD_TEST_MARKER_TYPE, markersize=DISCARD_TEST_MARKER_SIZE,\n",
        "        markeredgewidth=0,\n",
        "        markerfacecolor=DISCARD_TEST_COLOUR, markeredgecolor=DISCARD_TEST_COLOUR\n",
        "    )\n",
        "\n",
        "    axes_object.set_xlabel('Discard fraction')\n",
        "    axes_object.set_ylabel('Model performance (cross-entropy)')\n",
        "    axes_object.set_xlim(left=0.)\n",
        "\n",
        "    inset_axes_object = _plot_means_as_inset(\n",
        "        figure_object=figure_object, bin_centers=discard_fractions,\n",
        "        bin_mean_predictions=result_dict[MEAN_CENTRAL_PREDICTIONS_KEY],\n",
        "        bin_mean_target_values=result_dict[MEAN_TARGET_VALUES_KEY],\n",
        "        for_spread_skill_plot=False\n",
        "    )\n",
        "\n",
        "    inset_axes_object.set_xticks(axes_object.get_xticks())\n",
        "    inset_axes_object.set_xlim(axes_object.get_xlim())\n",
        "\n",
        "    inset_axes_object.set_title(\n",
        "        'Mean target and prediction\\nafter discard', fontsize=INSET_FONT_SIZE\n",
        "    )\n",
        "    inset_axes_object.set_xlabel('Discard fraction', fontsize=INSET_FONT_SIZE)\n",
        "\n",
        "    return figure_object, axes_object\n",
        "\n",
        "\n",
        "def get_points_in_roc_curve(observed_labels, forecast_probabilities):\n",
        "    \"\"\"Creates points for ROC curve.\n",
        "\n",
        "    E = number of examples\n",
        "    T = number of binarization thresholds\n",
        "\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    :param forecast_probabilities: length-E numpy array with forecast\n",
        "        probabilities of label = 1.\n",
        "    :return: pofd_by_threshold: length-T numpy array of POFD (probability of\n",
        "        false detection) values.\n",
        "    :return: pod_by_threshold: length-T numpy array of POD (probability of\n",
        "        detection) values.\n",
        "    \"\"\"\n",
        "\n",
        "    assert numpy.all(numpy.logical_or(\n",
        "        observed_labels == 0, observed_labels == 1\n",
        "    ))\n",
        "\n",
        "    assert numpy.all(numpy.logical_and(\n",
        "        forecast_probabilities >= 0, forecast_probabilities <= 1\n",
        "    ))\n",
        "\n",
        "    observed_labels = observed_labels.astype(int)\n",
        "    binarization_thresholds = numpy.linspace(0, 1, num=1001, dtype=float)\n",
        "\n",
        "    num_thresholds = len(binarization_thresholds)\n",
        "    pofd_by_threshold = numpy.full(num_thresholds, numpy.nan)\n",
        "    pod_by_threshold = numpy.full(num_thresholds, numpy.nan)\n",
        "\n",
        "    for k in range(num_thresholds):\n",
        "        these_forecast_labels = (\n",
        "            forecast_probabilities >= binarization_thresholds[k]\n",
        "        ).astype(int)\n",
        "\n",
        "        this_num_hits = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 1, observed_labels == 1\n",
        "        ))\n",
        "\n",
        "        this_num_false_alarms = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 1, observed_labels == 0\n",
        "        ))\n",
        "\n",
        "        this_num_misses = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 0, observed_labels == 1\n",
        "        ))\n",
        "\n",
        "        this_num_correct_nulls = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 0, observed_labels == 0\n",
        "        ))\n",
        "\n",
        "        try:\n",
        "            pofd_by_threshold[k] = (\n",
        "                float(this_num_false_alarms) /\n",
        "                (this_num_false_alarms + this_num_correct_nulls)\n",
        "            )\n",
        "        except ZeroDivisionError:\n",
        "            pass\n",
        "\n",
        "        try:\n",
        "            pod_by_threshold[k] = (\n",
        "                float(this_num_hits) / (this_num_hits + this_num_misses)\n",
        "            )\n",
        "        except ZeroDivisionError:\n",
        "            pass\n",
        "\n",
        "    pod_by_threshold = numpy.array([1.] + pod_by_threshold.tolist() + [0.])\n",
        "    pofd_by_threshold = numpy.array([1.] + pofd_by_threshold.tolist() + [0.])\n",
        "\n",
        "    return pofd_by_threshold, pod_by_threshold\n",
        "\n",
        "\n",
        "def plot_roc_curve(observed_labels, forecast_probabilities, axes_object):\n",
        "    \"\"\"Plots ROC curve.\n",
        "\n",
        "    E = number of examples\n",
        "\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    :param forecast_probabilities: length-E numpy array with forecast\n",
        "        probabilities of label = 1.\n",
        "    :param axes_object: Will plot on these axes (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    :return: pofd_by_threshold: See doc for `get_points_in_roc_curve`.\n",
        "    :return: pod_by_threshold: Same.\n",
        "    \"\"\"\n",
        "\n",
        "    pofd_by_threshold, pod_by_threshold = get_points_in_roc_curve(\n",
        "        observed_labels=observed_labels,\n",
        "        forecast_probabilities=forecast_probabilities\n",
        "    )\n",
        "\n",
        "    pofd_matrix, pod_matrix = _get_pofd_pod_grid()\n",
        "    peirce_score_matrix = pod_matrix - pofd_matrix\n",
        "    colour_map_object, colour_norm_object = _get_peirce_colour_scheme()\n",
        "\n",
        "    pyplot.contourf(\n",
        "        pofd_matrix, pod_matrix, peirce_score_matrix,\n",
        "        LEVELS_FOR_PEIRCE_CONTOURS, cmap=colour_map_object,\n",
        "        norm=colour_norm_object, vmin=0., vmax=1., axes=axes_object\n",
        "    )\n",
        "\n",
        "    colour_bar_object = _add_colour_bar(\n",
        "        axes_object=axes_object, colour_map_object=colour_map_object,\n",
        "        colour_norm_object=colour_norm_object,\n",
        "        values_to_colour=peirce_score_matrix, min_colour_value=0.,\n",
        "        max_colour_value=1., orientation_string='vertical',\n",
        "        extend_min=False, extend_max=False\n",
        "    )\n",
        "    colour_bar_object.set_label('Peirce score')\n",
        "\n",
        "    random_x_coords = numpy.array([0., 1.])\n",
        "    random_y_coords = numpy.array([0., 1.])\n",
        "    axes_object.plot(\n",
        "        random_x_coords, random_y_coords, color=REFERENCE_LINE_COLOUR,\n",
        "        linestyle='dashed', linewidth=REFERENCE_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "    nan_flags = numpy.logical_or(\n",
        "        numpy.isnan(pofd_by_threshold), numpy.isnan(pod_by_threshold)\n",
        "    )\n",
        "\n",
        "    if not numpy.all(nan_flags):\n",
        "        real_indices = numpy.where(numpy.invert(nan_flags))[0]\n",
        "        axes_object.plot(\n",
        "            pofd_by_threshold[real_indices], pod_by_threshold[real_indices],\n",
        "            color=ROC_CURVE_COLOUR, linestyle='solid', linewidth=ROC_CURVE_WIDTH\n",
        "        )\n",
        "\n",
        "    axes_object.set_xlabel('POFD (probability of false detection)')\n",
        "    axes_object.set_ylabel('POD (probability of detection)')\n",
        "    axes_object.set_xlim(0., 1.)\n",
        "    axes_object.set_ylim(0., 1.)\n",
        "\n",
        "    return pofd_by_threshold, pod_by_threshold\n",
        "\n",
        "\n",
        "def get_points_in_perf_diagram(observed_labels, forecast_probabilities):\n",
        "    \"\"\"Creates points for performance diagram.\n",
        "\n",
        "    E = number of examples\n",
        "    T = number of binarization thresholds\n",
        "\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    :param forecast_probabilities: length-E numpy array with forecast\n",
        "        probabilities of label = 1.\n",
        "    :return: pod_by_threshold: length-T numpy array of POD (probability of\n",
        "        detection) values.\n",
        "    :return: success_ratio_by_threshold: length-T numpy array of success ratios.\n",
        "    \"\"\"\n",
        "\n",
        "    assert numpy.all(numpy.logical_or(\n",
        "        observed_labels == 0, observed_labels == 1\n",
        "    ))\n",
        "\n",
        "    assert numpy.all(numpy.logical_and(\n",
        "        forecast_probabilities >= 0, forecast_probabilities <= 1\n",
        "    ))\n",
        "\n",
        "    observed_labels = observed_labels.astype(int)\n",
        "    binarization_thresholds = numpy.linspace(0, 1, num=1001, dtype=float)\n",
        "\n",
        "    num_thresholds = len(binarization_thresholds)\n",
        "    pod_by_threshold = numpy.full(num_thresholds, numpy.nan)\n",
        "    success_ratio_by_threshold = numpy.full(num_thresholds, numpy.nan)\n",
        "\n",
        "    for k in range(num_thresholds):\n",
        "        these_forecast_labels = (\n",
        "            forecast_probabilities >= binarization_thresholds[k]\n",
        "        ).astype(int)\n",
        "\n",
        "        this_num_hits = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 1, observed_labels == 1\n",
        "        ))\n",
        "\n",
        "        this_num_false_alarms = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 1, observed_labels == 0\n",
        "        ))\n",
        "\n",
        "        this_num_misses = numpy.sum(numpy.logical_and(\n",
        "            these_forecast_labels == 0, observed_labels == 1\n",
        "        ))\n",
        "\n",
        "        try:\n",
        "            pod_by_threshold[k] = (\n",
        "                float(this_num_hits) / (this_num_hits + this_num_misses)\n",
        "            )\n",
        "        except ZeroDivisionError:\n",
        "            pass\n",
        "\n",
        "        try:\n",
        "            success_ratio_by_threshold[k] = (\n",
        "                float(this_num_hits) / (this_num_hits + this_num_false_alarms)\n",
        "            )\n",
        "        except ZeroDivisionError:\n",
        "            pass\n",
        "\n",
        "    pod_by_threshold = numpy.array([1.] + pod_by_threshold.tolist() + [0.])\n",
        "    success_ratio_by_threshold = numpy.array(\n",
        "        [0.] + success_ratio_by_threshold.tolist() + [1.]\n",
        "    )\n",
        "\n",
        "    return pod_by_threshold, success_ratio_by_threshold\n",
        "\n",
        "\n",
        "def plot_performance_diagram(\n",
        "        observed_labels, forecast_probabilities, axes_object):\n",
        "    \"\"\"Plots performance diagram.\n",
        "\n",
        "    E = number of examples\n",
        "\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    :param forecast_probabilities: length-E numpy array with forecast\n",
        "        probabilities of label = 1.\n",
        "    :param axes_object: Will plot on these axes (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).\n",
        "    :return: pod_by_threshold: See doc for `get_points_in_perf_diagram`.\n",
        "        detection) values.\n",
        "    :return: success_ratio_by_threshold: Same.\n",
        "    \"\"\"\n",
        "\n",
        "    pod_by_threshold, success_ratio_by_threshold = get_points_in_perf_diagram(\n",
        "        observed_labels=observed_labels,\n",
        "        forecast_probabilities=forecast_probabilities\n",
        "    )\n",
        "\n",
        "    success_ratio_matrix, pod_matrix = _get_sr_pod_grid()\n",
        "    csi_matrix = _csi_from_sr_and_pod(success_ratio_matrix, pod_matrix)\n",
        "    frequency_bias_matrix = _bias_from_sr_and_pod(\n",
        "        success_ratio_matrix, pod_matrix\n",
        "    )\n",
        "    this_colour_map_object, this_colour_norm_object = _get_csi_colour_scheme()\n",
        "\n",
        "    pyplot.contourf(\n",
        "        success_ratio_matrix, pod_matrix, csi_matrix, LEVELS_FOR_CSI_CONTOURS,\n",
        "        cmap=this_colour_map_object, norm=this_colour_norm_object, vmin=0.,\n",
        "        vmax=1., axes=axes_object\n",
        "    )\n",
        "\n",
        "    colour_bar_object = _add_colour_bar(\n",
        "        axes_object=axes_object, colour_map_object=this_colour_map_object,\n",
        "        colour_norm_object=this_colour_norm_object,\n",
        "        values_to_colour=csi_matrix, min_colour_value=0.,\n",
        "        max_colour_value=1., orientation_string='vertical',\n",
        "        extend_min=False, extend_max=False\n",
        "    )\n",
        "    colour_bar_object.set_label('CSI (critical success index)')\n",
        "\n",
        "    bias_colour_tuple = ()\n",
        "    for _ in range(len(LEVELS_FOR_BIAS_CONTOURS)):\n",
        "        bias_colour_tuple += (FREQUENCY_BIAS_COLOUR,)\n",
        "\n",
        "    bias_contour_object = pyplot.contour(\n",
        "        success_ratio_matrix, pod_matrix, frequency_bias_matrix,\n",
        "        LEVELS_FOR_BIAS_CONTOURS, colors=bias_colour_tuple,\n",
        "        linewidths=FREQUENCY_BIAS_LINE_WIDTH, linestyles='dashed',\n",
        "        axes=axes_object\n",
        "    )\n",
        "    pyplot.clabel(\n",
        "        bias_contour_object, inline=True,\n",
        "        inline_spacing=FREQUENCY_BIAS_LABEL_PADDING_PX,\n",
        "        fmt=FREQUENCY_BIAS_STRING_FORMAT, fontsize=FONT_SIZE\n",
        "    )\n",
        "\n",
        "    nan_flags = numpy.logical_or(\n",
        "        numpy.isnan(success_ratio_by_threshold), numpy.isnan(pod_by_threshold)\n",
        "    )\n",
        "\n",
        "    if not numpy.all(nan_flags):\n",
        "        real_indices = numpy.where(numpy.invert(nan_flags))[0]\n",
        "        axes_object.plot(\n",
        "            success_ratio_by_threshold[real_indices],\n",
        "            pod_by_threshold[real_indices], color=PERF_DIAGRAM_COLOUR,\n",
        "            linestyle='solid', linewidth=PERF_DIAGRAM_LINE_WIDTH\n",
        "        )\n",
        "\n",
        "    axes_object.set_xlabel('Success ratio (1 - FAR)')\n",
        "    axes_object.set_ylabel('POD (probability of detection)')\n",
        "    axes_object.set_xlim(0., 1.)\n",
        "    axes_object.set_ylim(0., 1.)\n",
        "\n",
        "    return pod_by_threshold, success_ratio_by_threshold\n",
        "\n",
        "\n",
        "def get_points_in_relia_curve(\n",
        "        observed_labels, forecast_probabilities, num_bins, climatology):\n",
        "    \"\"\"Creates points for reliability curve.\n",
        "\n",
        "    The reliability curve is the main component of the attributes diagram.\n",
        "\n",
        "    E = number of examples\n",
        "    B = number of bins\n",
        "\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    :param forecast_probabilities: length-E numpy array with forecast\n",
        "        probabilities of label = 1.\n",
        "    :param num_bins: Number of bins for forecast probability.\n",
        "    :param climatology: Climatological event frequency.\n",
        "    :return: mean_forecast_probs: length-B numpy array of mean forecast\n",
        "        probabilities.\n",
        "    :return: mean_event_frequencies: length-B numpy array of conditional mean\n",
        "        event frequencies.  mean_event_frequencies[j] = frequency of label 1\n",
        "        when forecast probability is in the [j]th bin.\n",
        "    :return: num_examples_by_bin: length-B numpy array with number of examples\n",
        "        in each forecast bin.\n",
        "    :return: brier_skill_score: Brier skill score.\n",
        "    \"\"\"\n",
        "\n",
        "    assert numpy.all(numpy.logical_or(\n",
        "        observed_labels == 0, observed_labels == 1\n",
        "    ))\n",
        "\n",
        "    assert numpy.all(numpy.logical_and(\n",
        "        forecast_probabilities >= 0, forecast_probabilities <= 1\n",
        "    ))\n",
        "\n",
        "    assert num_bins > 1\n",
        "\n",
        "    inputs_to_bins = _get_histogram(\n",
        "        input_values=forecast_probabilities, num_bins=num_bins, min_value=0.,\n",
        "        max_value=1.\n",
        "    )\n",
        "\n",
        "    mean_forecast_probs = numpy.full(num_bins, numpy.nan)\n",
        "    mean_event_frequencies = numpy.full(num_bins, numpy.nan)\n",
        "    num_examples_by_bin = numpy.full(num_bins, -1, dtype=int)\n",
        "\n",
        "    for k in range(num_bins):\n",
        "        these_example_indices = numpy.where(inputs_to_bins == k)[0]\n",
        "        num_examples_by_bin[k] = len(these_example_indices)\n",
        "\n",
        "        mean_forecast_probs[k] = numpy.mean(\n",
        "            forecast_probabilities[these_example_indices]\n",
        "        )\n",
        "        mean_event_frequencies[k] = numpy.mean(\n",
        "            observed_labels[these_example_indices].astype(float)\n",
        "        )\n",
        "    \n",
        "    uncertainty = climatology * (1. - climatology)\n",
        "\n",
        "    this_numerator = numpy.nansum(\n",
        "        num_examples_by_bin *\n",
        "        (mean_forecast_probs - mean_event_frequencies) ** 2\n",
        "    )\n",
        "    reliability = this_numerator / numpy.sum(num_examples_by_bin)\n",
        "\n",
        "    sample_climatology = numpy.average(\n",
        "        mean_event_frequencies[num_examples_by_bin > 0],\n",
        "        weights=num_examples_by_bin[num_examples_by_bin > 0]\n",
        "    )\n",
        "\n",
        "    this_numerator = numpy.nansum(\n",
        "        num_examples_by_bin *\n",
        "        (mean_event_frequencies - sample_climatology) ** 2\n",
        "    )\n",
        "    resolution = this_numerator / numpy.sum(num_examples_by_bin)\n",
        "    brier_score = uncertainty + reliability - resolution\n",
        "\n",
        "    try:\n",
        "        brier_skill_score = 1. - brier_score / uncertainty\n",
        "    except ZeroDivisionError:\n",
        "        brier_skill_score = numpy.nan\n",
        "\n",
        "    return (\n",
        "        mean_forecast_probs, mean_event_frequencies, num_examples_by_bin,\n",
        "        brier_skill_score\n",
        "    )\n",
        "\n",
        "\n",
        "def plot_reliability_curve(\n",
        "        observed_labels, forecast_probabilities, climatology,\n",
        "        num_bins=20, axes_object=None):\n",
        "    \"\"\"Plots reliability curve.\n",
        "\n",
        "    E = number of examples\n",
        "\n",
        "    :param observed_labels: length-E numpy array of class labels (integers in\n",
        "        0...1).\n",
        "    :param forecast_probabilities: length-E numpy array with forecast\n",
        "        probabilities of label = 1.\n",
        "    :paeam climatology: Same.\n",
        "    :param num_bins: Number of bins for forecast probability.\n",
        "    :param axes_object: Will plot on these axes (instance of\n",
        "        `matplotlib.axes._subplots.AxesSubplot`).  If `axes_object is None`,\n",
        "        will create new axes.\n",
        "    :return: mean_forecast_probs: See doc for `get_points_in_relia_curve`.\n",
        "    :return: mean_event_frequencies: Same.\n",
        "    :return: num_examples_by_bin: Same.\n",
        "    :return: brier_skill_score: Same.\n",
        "    \"\"\"\n",
        "\n",
        "    (\n",
        "        mean_forecast_probs,\n",
        "        mean_event_frequencies,\n",
        "        num_examples_by_bin,\n",
        "        brier_skill_score\n",
        "    ) = get_points_in_relia_curve(\n",
        "            observed_labels=observed_labels,\n",
        "            forecast_probabilities=forecast_probabilities,\n",
        "            climatology=climatology, num_bins=num_bins\n",
        "        )\n",
        "\n",
        "    if axes_object is None:\n",
        "        _, axes_object = pyplot.subplots(\n",
        "            1, 1, figsize=(12, 12)\n",
        "        )\n",
        "\n",
        "    perfect_x_coords = numpy.array([0, 1], dtype=float)\n",
        "    perfect_y_coords = perfect_x_coords + 0.\n",
        "    axes_object.plot(\n",
        "        perfect_x_coords, perfect_y_coords, color=REFERENCE_LINE_COLOUR,\n",
        "        linestyle='dashed', linewidth=REFERENCE_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "    real_indices = numpy.where(numpy.invert(numpy.logical_or(\n",
        "        numpy.isnan(mean_forecast_probs), numpy.isnan(mean_event_frequencies)\n",
        "    )))[0]\n",
        "\n",
        "    axes_object.plot(\n",
        "        mean_forecast_probs[real_indices], mean_event_frequencies[real_indices],\n",
        "        color=RELIABILITY_COLOUR, linestyle='solid',\n",
        "        linewidth=RELIABILITY_LINE_WIDTH\n",
        "    )\n",
        "\n",
        "    axes_object.set_xlabel('Forecast probability')\n",
        "    axes_object.set_ylabel('Conditional event frequency')\n",
        "    axes_object.set_xlim(0., 1.)\n",
        "    axes_object.set_ylim(0., 1.)\n",
        "\n",
        "    return (\n",
        "        mean_forecast_probs, mean_event_frequencies, num_examples_by_bin,\n",
        "        brier_skill_score\n",
        "    )\n",
        "\n",
        "\n",
        "def plot_attributes_diagram(\n",
        "        observed_labels, forecast_probabilities, climatology, num_bins=20,\n",
        "        figure_object=None, axes_object=None):\n",
        "    \"\"\"Plots attributes diagram.\n",
        "\n",
        "    :param observed_labels: See doc for `plot_reliability_curve`.\n",
        "    :param forecast_probabilities: Same.\n",
        "    :param climatology: Same.\n",
        "    :param num_bins: Same.\n",
        "    :param figure_object: Will plot on this figure (instance of\n",
        "        `matplotlib.figure.Figure`).  If `figure_object is None`, will create a\n",
        "        new one.\n",
        "    :param axes_object: See doc for `plot_reliability_curve`.\n",
        "    :return: mean_forecast_probs: See doc for `get_points_in_relia_curve`.\n",
        "    :return: mean_event_frequencies: Same.\n",
        "    :return: num_examples_by_bin: Same.\n",
        "    :return: brier_skill_score: Same.\n",
        "    \"\"\"\n",
        "\n",
        "    (\n",
        "        mean_forecast_probs,\n",
        "        mean_event_frequencies,\n",
        "        num_examples_by_bin,\n",
        "        brier_skill_score\n",
        "    ) = get_points_in_relia_curve(\n",
        "            observed_labels=observed_labels,\n",
        "            forecast_probabilities=forecast_probabilities,\n",
        "            climatology=climatology, num_bins=num_bins\n",
        "        )\n",
        "\n",
        "    if figure_object is None or axes_object is None:\n",
        "        figure_object, axes_object = pyplot.subplots(\n",
        "            1, 1, figsize=(12, 12)\n",
        "        )\n",
        "\n",
        "    _plot_background(axes_object=axes_object, observed_labels=observed_labels)\n",
        "    _plot_forecast_histogram(\n",
        "        figure_object=figure_object, num_examples_by_bin=num_examples_by_bin\n",
        "    )\n",
        "\n",
        "    plot_reliability_curve(\n",
        "        observed_labels=observed_labels,\n",
        "        forecast_probabilities=forecast_probabilities,\n",
        "        climatology=climatology, num_bins=num_bins, axes_object=axes_object\n",
        "    )\n",
        "\n",
        "    return (\n",
        "        mean_forecast_probs, mean_event_frequencies, num_examples_by_bin,\n",
        "        brier_skill_score\n",
        "    )"
      ],
      "metadata": {
        "id": "wOwV_5YGxqbh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NN without batch normalization\n",
        "\n",
        "The following cell creates a neural-net architecture with MC dropout and **without** batch normalization.  The problem domain is MNIST digit classification, except I have turned the problem into binary classification (\"is the digit a 9, or not?\") instead of multi-class classification (\"is the digit a 0, 1, 2, 3, 4, 5, 6, 7, 8, or 9?\")."
      ],
      "metadata": {
        "id": "5m9c5FacyTh9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNRn4AhSM2re",
        "outputId": "5e62772d-dcb4-41e3-fa54-eb53a4ec7a06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_7 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 24, 24, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 12, 12, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 12, 12, 64)        0         \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 128)               1179776   \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,198,721\n",
            "Trainable params: 1,198,721\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# 28 (rows) x 28 (columns) x 1 (channel) is the size of MNIST images.\n",
        "input_layer_object = Input(shape=(NUM_GRID_ROWS, NUM_GRID_COLUMNS, 1))\n",
        "\n",
        "current_layer_object = Conv2D(\n",
        "    32, kernel_size=(3, 3), activation='relu'\n",
        ")(input_layer_object)\n",
        "\n",
        "current_layer_object = Conv2D(\n",
        "    64, (3, 3), activation='relu'\n",
        ")(current_layer_object)\n",
        "\n",
        "current_layer_object = MaxPooling2D(\n",
        "    pool_size=(2, 2)\n",
        ")(current_layer_object)\n",
        "\n",
        "# You must set `training=True`.\n",
        "current_layer_object = Dropout(\n",
        "    0.25\n",
        ")(current_layer_object, training=True)\n",
        "\n",
        "current_layer_object = Flatten()(current_layer_object)\n",
        "\n",
        "current_layer_object = Dense(\n",
        "    128, activation='relu'\n",
        ")(current_layer_object)\n",
        "\n",
        "# You must set `training=True`.\n",
        "current_layer_object = Dropout(\n",
        "    0.5\n",
        ")(current_layer_object, training=True)\n",
        "\n",
        "current_layer_object = Dense(\n",
        "    1, activation='sigmoid'\n",
        ")(current_layer_object)\n",
        "\n",
        "model_object_without_bn = Model(\n",
        "    inputs=input_layer_object, outputs=current_layer_object\n",
        ")\n",
        "model_object_without_bn.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NN with batch normalization\n",
        "\n",
        "The following cell creates a neural-net architecture with MC dropout and **with** batch normalization.\n",
        "\n",
        "During training, there is nothing complicated about using dropout and batch norm at the same time.\n",
        "\n",
        "However, things get a bit complicated at inference time, as you will see later (go to section \"Train NN with batch norm\")."
      ],
      "metadata": {
        "id": "NPrO_8S8zRlv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 28 (rows) x 28 (columns) x 1 (channel) is the size of MNIST images.\n",
        "input_layer_object = Input(shape=(NUM_GRID_ROWS, NUM_GRID_COLUMNS, 1))\n",
        "\n",
        "current_layer_object = Conv2D(\n",
        "    32, kernel_size=(3, 3), activation='relu'\n",
        ")(input_layer_object)\n",
        "\n",
        "current_layer_object = Conv2D(\n",
        "    64, (3, 3), activation='relu'\n",
        ")(current_layer_object)\n",
        "\n",
        "current_layer_object = MaxPooling2D(\n",
        "    pool_size=(2, 2)\n",
        ")(current_layer_object)\n",
        "\n",
        "# You must set `training=True`.\n",
        "current_layer_object = Dropout(\n",
        "    0.25\n",
        ")(current_layer_object, training=True)\n",
        "\n",
        "current_layer_object = BatchNormalization()(current_layer_object)\n",
        "current_layer_object = Flatten()(current_layer_object)\n",
        "\n",
        "current_layer_object = Dense(\n",
        "    128, activation='relu'\n",
        ")(current_layer_object)\n",
        "\n",
        "# You must set `training=True`.\n",
        "current_layer_object = Dropout(\n",
        "    0.5\n",
        ")(current_layer_object, training=True)\n",
        "\n",
        "current_layer_object = Dense(\n",
        "    1, activation='sigmoid'\n",
        ")(current_layer_object)\n",
        "\n",
        "model_object_with_bn = Model(\n",
        "    inputs=input_layer_object, outputs=current_layer_object\n",
        ")\n",
        "model_object_with_bn.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRuk47Bpiw1o",
        "outputId": "9f1873bb-0074-496d-ca7c-a8eef25accac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_8 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 24, 24, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 12, 12, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 12, 12, 64)        0         \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 12, 12, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten_6 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 128)               1179776   \n",
            "                                                                 \n",
            " dropout_14 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,198,977\n",
            "Trainable params: 1,198,849\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load MNIST data and add noise\n",
        "\n",
        "The following cell loads the MNIST data and turns the problem into binary, instead of multi-class, classification.\n",
        "\n",
        "Also, I add some Gaussian noise to the predictors (greyscale images of handwritten digits).  Otherwise, this is a very easy problem to solve, causing the neural nets to produce very low uncertainty estimates, which is boring."
      ],
      "metadata": {
        "id": "bM-2vmwFz2f9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(\n",
        "    (training_predictor_matrix, training_target_values),\n",
        "    (testing_predictor_matrix, testing_target_values)\n",
        ") = mnist.load_data()\n",
        "\n",
        "training_target_values = (training_target_values == 9).astype(float)\n",
        "testing_target_values = (testing_target_values == 9).astype(float)\n",
        "\n",
        "training_predictor_matrix = training_predictor_matrix.reshape(\n",
        "    training_predictor_matrix.shape[0], NUM_GRID_ROWS, NUM_GRID_COLUMNS, 1\n",
        ")\n",
        "testing_predictor_matrix = testing_predictor_matrix.reshape(\n",
        "    testing_predictor_matrix.shape[0], NUM_GRID_ROWS, NUM_GRID_COLUMNS, 1\n",
        ")\n",
        "\n",
        "training_predictor_matrix = training_predictor_matrix.astype('float32') / 255\n",
        "testing_predictor_matrix = testing_predictor_matrix.astype('float32') / 255\n",
        "\n",
        "training_predictor_matrix = training_predictor_matrix + numpy.random.normal(\n",
        "    loc=0., scale=0.5, size=training_predictor_matrix.shape\n",
        ")\n",
        "testing_predictor_matrix = testing_predictor_matrix + numpy.random.normal(\n",
        "    loc=0., scale=0.5, size=testing_predictor_matrix.shape\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_tHqmG5z3tf",
        "outputId": "c0f98123-a051-4342-8d74-628fbb70ab4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train NN without batch norm\n",
        "\n",
        "The following cell trains the NN **without** batch norm and looks at some predictions."
      ],
      "metadata": {
        "id": "r4qdS91iz7an"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_object_without_bn.compile(\n",
        "    loss=tf_keras.losses.binary_crossentropy,\n",
        "    optimizer=tf_keras.optimizers.Adadelta(),\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# model_object_without_bn.fit(\n",
        "#     training_predictor_matrix, training_target_values,\n",
        "#     batch_size=128, epochs=30, verbose=1,\n",
        "#     validation_data=(testing_predictor_matrix, testing_target_values)\n",
        "# )\n",
        "\n",
        "model_object_without_bn.fit(\n",
        "    training_predictor_matrix, training_target_values,\n",
        "    batch_size=128, epochs=10, verbose=1,\n",
        "    validation_data=(testing_predictor_matrix, testing_target_values)\n",
        ")\n",
        "\n",
        "num_testing_examples = len(testing_target_values)\n",
        "testing_prediction_matrix_without_bn = numpy.full(\n",
        "    (num_testing_examples, NUM_MONTE_CARLO_ESTIMATES), numpy.nan\n",
        ")\n",
        "\n",
        "for j in range(NUM_MONTE_CARLO_ESTIMATES):\n",
        "    if numpy.mod(j, 10) == 0:\n",
        "        print((\n",
        "            'Have run model for {0:d} of {1:d} Monte Carlo estimates...'\n",
        "        ).format(\n",
        "            j, NUM_MONTE_CARLO_ESTIMATES\n",
        "        ))\n",
        "\n",
        "    testing_prediction_matrix_without_bn[:, j] = model_object_without_bn(\n",
        "        testing_predictor_matrix, training=True\n",
        "    )[:, 0]\n",
        "\n",
        "print('\\n\\n')\n",
        "\n",
        "predictive_stdevs = get_predictive_stdevs(testing_prediction_matrix_without_bn)\n",
        "print('Min, mean, and max predictive stdevs = {0:.4f}, {1:.4f}, {2:.4f}'.format(\n",
        "    numpy.min(predictive_stdevs),\n",
        "    numpy.mean(predictive_stdevs),\n",
        "    numpy.max(predictive_stdevs)\n",
        "))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDcPf00R0HQN",
        "outputId": "9b661330-8d48-4f92-d430-771ab522cccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 5s 10ms/step - loss: 0.3131 - accuracy: 0.9008 - val_loss: 0.3144 - val_accuracy: 0.8991\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 5s 10ms/step - loss: 0.3089 - accuracy: 0.9008 - val_loss: 0.3084 - val_accuracy: 0.8991\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 6s 13ms/step - loss: 0.3043 - accuracy: 0.9008 - val_loss: 0.3059 - val_accuracy: 0.8991\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.3000 - accuracy: 0.9008 - val_loss: 0.2993 - val_accuracy: 0.8991\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2959 - accuracy: 0.9008 - val_loss: 0.2946 - val_accuracy: 0.8991\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2909 - accuracy: 0.9008 - val_loss: 0.2894 - val_accuracy: 0.8991\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 4s 10ms/step - loss: 0.2855 - accuracy: 0.9008 - val_loss: 0.2831 - val_accuracy: 0.8991\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2804 - accuracy: 0.9008 - val_loss: 0.2767 - val_accuracy: 0.8991\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 4s 10ms/step - loss: 0.2748 - accuracy: 0.9008 - val_loss: 0.2707 - val_accuracy: 0.8991\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2697 - accuracy: 0.9008 - val_loss: 0.2661 - val_accuracy: 0.8991\n",
            "Have run model for 0 of 1000 Monte Carlo estimates...\n",
            "Have run model for 10 of 1000 Monte Carlo estimates...\n",
            "Have run model for 20 of 1000 Monte Carlo estimates...\n",
            "Have run model for 30 of 1000 Monte Carlo estimates...\n",
            "Have run model for 40 of 1000 Monte Carlo estimates...\n",
            "Have run model for 50 of 1000 Monte Carlo estimates...\n",
            "Have run model for 60 of 1000 Monte Carlo estimates...\n",
            "Have run model for 70 of 1000 Monte Carlo estimates...\n",
            "Have run model for 80 of 1000 Monte Carlo estimates...\n",
            "Have run model for 90 of 1000 Monte Carlo estimates...\n",
            "Have run model for 100 of 1000 Monte Carlo estimates...\n",
            "Have run model for 110 of 1000 Monte Carlo estimates...\n",
            "Have run model for 120 of 1000 Monte Carlo estimates...\n",
            "Have run model for 130 of 1000 Monte Carlo estimates...\n",
            "Have run model for 140 of 1000 Monte Carlo estimates...\n",
            "Have run model for 150 of 1000 Monte Carlo estimates...\n",
            "Have run model for 160 of 1000 Monte Carlo estimates...\n",
            "Have run model for 170 of 1000 Monte Carlo estimates...\n",
            "Have run model for 180 of 1000 Monte Carlo estimates...\n",
            "Have run model for 190 of 1000 Monte Carlo estimates...\n",
            "Have run model for 200 of 1000 Monte Carlo estimates...\n",
            "Have run model for 210 of 1000 Monte Carlo estimates...\n",
            "Have run model for 220 of 1000 Monte Carlo estimates...\n",
            "Have run model for 230 of 1000 Monte Carlo estimates...\n",
            "Have run model for 240 of 1000 Monte Carlo estimates...\n",
            "Have run model for 250 of 1000 Monte Carlo estimates...\n",
            "Have run model for 260 of 1000 Monte Carlo estimates...\n",
            "Have run model for 270 of 1000 Monte Carlo estimates...\n",
            "Have run model for 280 of 1000 Monte Carlo estimates...\n",
            "Have run model for 290 of 1000 Monte Carlo estimates...\n",
            "Have run model for 300 of 1000 Monte Carlo estimates...\n",
            "Have run model for 310 of 1000 Monte Carlo estimates...\n",
            "Have run model for 320 of 1000 Monte Carlo estimates...\n",
            "Have run model for 330 of 1000 Monte Carlo estimates...\n",
            "Have run model for 340 of 1000 Monte Carlo estimates...\n",
            "Have run model for 350 of 1000 Monte Carlo estimates...\n",
            "Have run model for 360 of 1000 Monte Carlo estimates...\n",
            "Have run model for 370 of 1000 Monte Carlo estimates...\n",
            "Have run model for 380 of 1000 Monte Carlo estimates...\n",
            "Have run model for 390 of 1000 Monte Carlo estimates...\n",
            "Have run model for 400 of 1000 Monte Carlo estimates...\n",
            "Have run model for 410 of 1000 Monte Carlo estimates...\n",
            "Have run model for 420 of 1000 Monte Carlo estimates...\n",
            "Have run model for 430 of 1000 Monte Carlo estimates...\n",
            "Have run model for 440 of 1000 Monte Carlo estimates...\n",
            "Have run model for 450 of 1000 Monte Carlo estimates...\n",
            "Have run model for 460 of 1000 Monte Carlo estimates...\n",
            "Have run model for 470 of 1000 Monte Carlo estimates...\n",
            "Have run model for 480 of 1000 Monte Carlo estimates...\n",
            "Have run model for 490 of 1000 Monte Carlo estimates...\n",
            "Have run model for 500 of 1000 Monte Carlo estimates...\n",
            "Have run model for 510 of 1000 Monte Carlo estimates...\n",
            "Have run model for 520 of 1000 Monte Carlo estimates...\n",
            "Have run model for 530 of 1000 Monte Carlo estimates...\n",
            "Have run model for 540 of 1000 Monte Carlo estimates...\n",
            "Have run model for 550 of 1000 Monte Carlo estimates...\n",
            "Have run model for 560 of 1000 Monte Carlo estimates...\n",
            "Have run model for 570 of 1000 Monte Carlo estimates...\n",
            "Have run model for 580 of 1000 Monte Carlo estimates...\n",
            "Have run model for 590 of 1000 Monte Carlo estimates...\n",
            "Have run model for 600 of 1000 Monte Carlo estimates...\n",
            "Have run model for 610 of 1000 Monte Carlo estimates...\n",
            "Have run model for 620 of 1000 Monte Carlo estimates...\n",
            "Have run model for 630 of 1000 Monte Carlo estimates...\n",
            "Have run model for 640 of 1000 Monte Carlo estimates...\n",
            "Have run model for 650 of 1000 Monte Carlo estimates...\n",
            "Have run model for 660 of 1000 Monte Carlo estimates...\n",
            "Have run model for 670 of 1000 Monte Carlo estimates...\n",
            "Have run model for 680 of 1000 Monte Carlo estimates...\n",
            "Have run model for 690 of 1000 Monte Carlo estimates...\n",
            "Have run model for 700 of 1000 Monte Carlo estimates...\n",
            "Have run model for 710 of 1000 Monte Carlo estimates...\n",
            "Have run model for 720 of 1000 Monte Carlo estimates...\n",
            "Have run model for 730 of 1000 Monte Carlo estimates...\n",
            "Have run model for 740 of 1000 Monte Carlo estimates...\n",
            "Have run model for 750 of 1000 Monte Carlo estimates...\n",
            "Have run model for 760 of 1000 Monte Carlo estimates...\n",
            "Have run model for 770 of 1000 Monte Carlo estimates...\n",
            "Have run model for 780 of 1000 Monte Carlo estimates...\n",
            "Have run model for 790 of 1000 Monte Carlo estimates...\n",
            "Have run model for 800 of 1000 Monte Carlo estimates...\n",
            "Have run model for 810 of 1000 Monte Carlo estimates...\n",
            "Have run model for 820 of 1000 Monte Carlo estimates...\n",
            "Have run model for 830 of 1000 Monte Carlo estimates...\n",
            "Have run model for 840 of 1000 Monte Carlo estimates...\n",
            "Have run model for 850 of 1000 Monte Carlo estimates...\n",
            "Have run model for 860 of 1000 Monte Carlo estimates...\n",
            "Have run model for 870 of 1000 Monte Carlo estimates...\n",
            "Have run model for 880 of 1000 Monte Carlo estimates...\n",
            "Have run model for 890 of 1000 Monte Carlo estimates...\n",
            "Have run model for 900 of 1000 Monte Carlo estimates...\n",
            "Have run model for 910 of 1000 Monte Carlo estimates...\n",
            "Have run model for 920 of 1000 Monte Carlo estimates...\n",
            "Have run model for 930 of 1000 Monte Carlo estimates...\n",
            "Have run model for 940 of 1000 Monte Carlo estimates...\n",
            "Have run model for 950 of 1000 Monte Carlo estimates...\n",
            "Have run model for 960 of 1000 Monte Carlo estimates...\n",
            "Have run model for 970 of 1000 Monte Carlo estimates...\n",
            "Have run model for 980 of 1000 Monte Carlo estimates...\n",
            "Have run model for 990 of 1000 Monte Carlo estimates...\n",
            "\n",
            "\n",
            "\n",
            "Min, mean, and max predictive stdevs = 0.0118, 0.0391, 0.0764\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train NN with batch norm\n",
        "\n",
        "The following cell trains the NN **with** batch norm and looks at some predictions.\n",
        "\n",
        "At inference time (bottom of cell), note that we have to explicitly turn training **off** for the batch-norm layers.  If we don't turn training off, the batch-norm layers will try to update their parameters based on every new batch of testing data, as if the batch contained training data."
      ],
      "metadata": {
        "id": "2HSYMQeM1-hi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_object_with_bn.compile(\n",
        "    loss=tf_keras.losses.binary_crossentropy,\n",
        "    optimizer=tf_keras.optimizers.Adadelta(),\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# model_object_with_bn.fit(\n",
        "#     training_predictor_matrix, training_target_values,\n",
        "#     batch_size=128, epochs=30, verbose=1,\n",
        "#     validation_data=(testing_predictor_matrix, testing_target_values)\n",
        "# )\n",
        "\n",
        "model_object_with_bn.fit(\n",
        "    training_predictor_matrix, training_target_values,\n",
        "    batch_size=128, epochs=10, verbose=1,\n",
        "    validation_data=(testing_predictor_matrix, testing_target_values)\n",
        ")\n",
        "\n",
        "SMALL_NUM_ESTIMATES = 100\n",
        "\n",
        "num_testing_examples = len(testing_target_values)\n",
        "testing_prediction_matrix_with_bn = numpy.full(\n",
        "    (num_testing_examples, SMALL_NUM_ESTIMATES), numpy.nan\n",
        ")\n",
        "\n",
        "for j in range(SMALL_NUM_ESTIMATES):\n",
        "    if numpy.mod(j, 10) == 0:\n",
        "        print((\n",
        "            'Have run model for {0:d} of {1:d} Monte Carlo estimates...'\n",
        "        ).format(\n",
        "            j, SMALL_NUM_ESTIMATES\n",
        "        ))\n",
        "\n",
        "    testing_prediction_matrix_with_bn[:, j] = model_object_with_bn(\n",
        "        testing_predictor_matrix, training=True\n",
        "    )[:, 0]\n",
        "\n",
        "predictive_stdevs = get_predictive_stdevs(testing_prediction_matrix_with_bn)\n",
        "print((\n",
        "    'Min, mean, and max predictive stdevs with training ON for batch-norm '\n",
        "    'layers = {0:.4f}, {1:.4f}, {2:.4f}'\n",
        ").format(\n",
        "    numpy.min(predictive_stdevs),\n",
        "    numpy.mean(predictive_stdevs),\n",
        "    numpy.max(predictive_stdevs)\n",
        "))\n",
        "\n",
        "print('\\n\\n')\n",
        "\n",
        "# Turn training OFF in batch-norm layers.\n",
        "for layer_object in model_object_with_bn.layers:\n",
        "    if 'batch' in layer_object.name.lower():\n",
        "        print('Layer \"{0:s}\" set to NON-TRAINABLE!'.format(\n",
        "            layer_object.name\n",
        "        ))\n",
        "        layer_object.trainable = False\n",
        "\n",
        "print('\\n\\n')\n",
        "\n",
        "testing_prediction_matrix_with_bn = numpy.full(\n",
        "    (num_testing_examples, NUM_MONTE_CARLO_ESTIMATES), numpy.nan\n",
        ")\n",
        "\n",
        "for j in range(NUM_MONTE_CARLO_ESTIMATES):\n",
        "    if numpy.mod(j, 10) == 0:\n",
        "        print((\n",
        "            'Have run model for {0:d} of {1:d} Monte Carlo estimates...'\n",
        "        ).format(\n",
        "            j, NUM_MONTE_CARLO_ESTIMATES\n",
        "        ))\n",
        "\n",
        "    testing_prediction_matrix_with_bn[:, j] = model_object_with_bn(\n",
        "        testing_predictor_matrix, training=True\n",
        "    )[:, 0]\n",
        "\n",
        "predictive_stdevs = get_predictive_stdevs(testing_prediction_matrix_with_bn)\n",
        "print((\n",
        "    'Min, mean, and max predictive stdevs with training OFF for batch-norm '\n",
        "    'layers = {0:.4f}, {1:.4f}, {2:.4f}'\n",
        ").format(\n",
        "    numpy.min(predictive_stdevs),\n",
        "    numpy.mean(predictive_stdevs),\n",
        "    numpy.max(predictive_stdevs)\n",
        "))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "at3WvwD22ke4",
        "outputId": "f835a8d2-096e-4f83-8930-5332702eba8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 5s 10ms/step - loss: 0.2381 - accuracy: 0.9033 - val_loss: 0.2340 - val_accuracy: 0.9048\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2288 - accuracy: 0.9063 - val_loss: 0.2281 - val_accuracy: 0.9046\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2218 - accuracy: 0.9093 - val_loss: 0.2172 - val_accuracy: 0.9105\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2190 - accuracy: 0.9109 - val_loss: 0.2143 - val_accuracy: 0.9093\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2132 - accuracy: 0.9123 - val_loss: 0.2111 - val_accuracy: 0.9147\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2088 - accuracy: 0.9152 - val_loss: 0.2068 - val_accuracy: 0.9151\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2046 - accuracy: 0.9162 - val_loss: 0.1969 - val_accuracy: 0.9187\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.2003 - accuracy: 0.9189 - val_loss: 0.1984 - val_accuracy: 0.9166\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.1969 - accuracy: 0.9202 - val_loss: 0.1953 - val_accuracy: 0.9217\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.1944 - accuracy: 0.9209 - val_loss: 0.1926 - val_accuracy: 0.9229\n",
            "Have run model for 0 of 100 Monte Carlo estimates...\n",
            "Have run model for 10 of 100 Monte Carlo estimates...\n",
            "Have run model for 20 of 100 Monte Carlo estimates...\n",
            "Have run model for 30 of 100 Monte Carlo estimates...\n",
            "Have run model for 40 of 100 Monte Carlo estimates...\n",
            "Have run model for 50 of 100 Monte Carlo estimates...\n",
            "Have run model for 60 of 100 Monte Carlo estimates...\n",
            "Have run model for 70 of 100 Monte Carlo estimates...\n",
            "Have run model for 80 of 100 Monte Carlo estimates...\n",
            "Have run model for 90 of 100 Monte Carlo estimates...\n",
            "Min, mean, and max predictive stdevs with training ON for batch-norm layers = 0.0000, 0.0682, 0.2749\n",
            "\n",
            "\n",
            "\n",
            "Layer \"batch_normalization_2\" set to NON-TRAINABLE!\n",
            "\n",
            "\n",
            "\n",
            "Have run model for 0 of 1000 Monte Carlo estimates...\n",
            "Have run model for 10 of 1000 Monte Carlo estimates...\n",
            "Have run model for 20 of 1000 Monte Carlo estimates...\n",
            "Have run model for 30 of 1000 Monte Carlo estimates...\n",
            "Have run model for 40 of 1000 Monte Carlo estimates...\n",
            "Have run model for 50 of 1000 Monte Carlo estimates...\n",
            "Have run model for 60 of 1000 Monte Carlo estimates...\n",
            "Have run model for 70 of 1000 Monte Carlo estimates...\n",
            "Have run model for 80 of 1000 Monte Carlo estimates...\n",
            "Have run model for 90 of 1000 Monte Carlo estimates...\n",
            "Have run model for 100 of 1000 Monte Carlo estimates...\n",
            "Have run model for 110 of 1000 Monte Carlo estimates...\n",
            "Have run model for 120 of 1000 Monte Carlo estimates...\n",
            "Have run model for 130 of 1000 Monte Carlo estimates...\n",
            "Have run model for 140 of 1000 Monte Carlo estimates...\n",
            "Have run model for 150 of 1000 Monte Carlo estimates...\n",
            "Have run model for 160 of 1000 Monte Carlo estimates...\n",
            "Have run model for 170 of 1000 Monte Carlo estimates...\n",
            "Have run model for 180 of 1000 Monte Carlo estimates...\n",
            "Have run model for 190 of 1000 Monte Carlo estimates...\n",
            "Have run model for 200 of 1000 Monte Carlo estimates...\n",
            "Have run model for 210 of 1000 Monte Carlo estimates...\n",
            "Have run model for 220 of 1000 Monte Carlo estimates...\n",
            "Have run model for 230 of 1000 Monte Carlo estimates...\n",
            "Have run model for 240 of 1000 Monte Carlo estimates...\n",
            "Have run model for 250 of 1000 Monte Carlo estimates...\n",
            "Have run model for 260 of 1000 Monte Carlo estimates...\n",
            "Have run model for 270 of 1000 Monte Carlo estimates...\n",
            "Have run model for 280 of 1000 Monte Carlo estimates...\n",
            "Have run model for 290 of 1000 Monte Carlo estimates...\n",
            "Have run model for 300 of 1000 Monte Carlo estimates...\n",
            "Have run model for 310 of 1000 Monte Carlo estimates...\n",
            "Have run model for 320 of 1000 Monte Carlo estimates...\n",
            "Have run model for 330 of 1000 Monte Carlo estimates...\n",
            "Have run model for 340 of 1000 Monte Carlo estimates...\n",
            "Have run model for 350 of 1000 Monte Carlo estimates...\n",
            "Have run model for 360 of 1000 Monte Carlo estimates...\n",
            "Have run model for 370 of 1000 Monte Carlo estimates...\n",
            "Have run model for 380 of 1000 Monte Carlo estimates...\n",
            "Have run model for 390 of 1000 Monte Carlo estimates...\n",
            "Have run model for 400 of 1000 Monte Carlo estimates...\n",
            "Have run model for 410 of 1000 Monte Carlo estimates...\n",
            "Have run model for 420 of 1000 Monte Carlo estimates...\n",
            "Have run model for 430 of 1000 Monte Carlo estimates...\n",
            "Have run model for 440 of 1000 Monte Carlo estimates...\n",
            "Have run model for 450 of 1000 Monte Carlo estimates...\n",
            "Have run model for 460 of 1000 Monte Carlo estimates...\n",
            "Have run model for 470 of 1000 Monte Carlo estimates...\n",
            "Have run model for 480 of 1000 Monte Carlo estimates...\n",
            "Have run model for 490 of 1000 Monte Carlo estimates...\n",
            "Have run model for 500 of 1000 Monte Carlo estimates...\n",
            "Have run model for 510 of 1000 Monte Carlo estimates...\n",
            "Have run model for 520 of 1000 Monte Carlo estimates...\n",
            "Have run model for 530 of 1000 Monte Carlo estimates...\n",
            "Have run model for 540 of 1000 Monte Carlo estimates...\n",
            "Have run model for 550 of 1000 Monte Carlo estimates...\n",
            "Have run model for 560 of 1000 Monte Carlo estimates...\n",
            "Have run model for 570 of 1000 Monte Carlo estimates...\n",
            "Have run model for 580 of 1000 Monte Carlo estimates...\n",
            "Have run model for 590 of 1000 Monte Carlo estimates...\n",
            "Have run model for 600 of 1000 Monte Carlo estimates...\n",
            "Have run model for 610 of 1000 Monte Carlo estimates...\n",
            "Have run model for 620 of 1000 Monte Carlo estimates...\n",
            "Have run model for 630 of 1000 Monte Carlo estimates...\n",
            "Have run model for 640 of 1000 Monte Carlo estimates...\n",
            "Have run model for 650 of 1000 Monte Carlo estimates...\n",
            "Have run model for 660 of 1000 Monte Carlo estimates...\n",
            "Have run model for 670 of 1000 Monte Carlo estimates...\n",
            "Have run model for 680 of 1000 Monte Carlo estimates...\n",
            "Have run model for 690 of 1000 Monte Carlo estimates...\n",
            "Have run model for 700 of 1000 Monte Carlo estimates...\n",
            "Have run model for 710 of 1000 Monte Carlo estimates...\n",
            "Have run model for 720 of 1000 Monte Carlo estimates...\n",
            "Have run model for 730 of 1000 Monte Carlo estimates...\n",
            "Have run model for 740 of 1000 Monte Carlo estimates...\n",
            "Have run model for 750 of 1000 Monte Carlo estimates...\n",
            "Have run model for 760 of 1000 Monte Carlo estimates...\n",
            "Have run model for 770 of 1000 Monte Carlo estimates...\n",
            "Have run model for 780 of 1000 Monte Carlo estimates...\n",
            "Have run model for 790 of 1000 Monte Carlo estimates...\n",
            "Have run model for 800 of 1000 Monte Carlo estimates...\n",
            "Have run model for 810 of 1000 Monte Carlo estimates...\n",
            "Have run model for 820 of 1000 Monte Carlo estimates...\n",
            "Have run model for 830 of 1000 Monte Carlo estimates...\n",
            "Have run model for 840 of 1000 Monte Carlo estimates...\n",
            "Have run model for 850 of 1000 Monte Carlo estimates...\n",
            "Have run model for 860 of 1000 Monte Carlo estimates...\n",
            "Have run model for 870 of 1000 Monte Carlo estimates...\n",
            "Have run model for 880 of 1000 Monte Carlo estimates...\n",
            "Have run model for 890 of 1000 Monte Carlo estimates...\n",
            "Have run model for 900 of 1000 Monte Carlo estimates...\n",
            "Have run model for 910 of 1000 Monte Carlo estimates...\n",
            "Have run model for 920 of 1000 Monte Carlo estimates...\n",
            "Have run model for 930 of 1000 Monte Carlo estimates...\n",
            "Have run model for 940 of 1000 Monte Carlo estimates...\n",
            "Have run model for 950 of 1000 Monte Carlo estimates...\n",
            "Have run model for 960 of 1000 Monte Carlo estimates...\n",
            "Have run model for 970 of 1000 Monte Carlo estimates...\n",
            "Have run model for 980 of 1000 Monte Carlo estimates...\n",
            "Have run model for 990 of 1000 Monte Carlo estimates...\n",
            "Min, mean, and max predictive stdevs with training OFF for batch-norm layers = 0.0000, 0.0690, 0.2607\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Case studies\n",
        "\n",
        "Now we look at 10 case studies for the NN with batch norm.  For each case study:\n",
        "\n",
        " - \"Mean prediction\" is the mean psuedo-probability, over all Monte Carlo estimates, that the digit is a nine\n",
        " - \"Predictive stdev\" is the standard deviation of the same predictive distribution\n",
        " - \"95% CI\" is the 95% confidence interval of the same predictive distribution\n",
        " - \"90% CI\" is the 90% confidence interval of the same predictive distribution\n",
        " - \"80% CI\" is the 80% confidence interval of the same predictive distribution\n",
        "<br><br>\n",
        "\n",
        "Note that the model has more uncertainty for weird-looking nines, manifesting in a larger predictive stdev and wider confidence intervals."
      ],
      "metadata": {
        "id": "cHdfYUB63e2v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TOLERANCE = 1e-6\n",
        "\n",
        "mean_predictions = get_mean_predictions(testing_prediction_matrix_with_bn)\n",
        "predictive_stdevs = get_predictive_stdevs(testing_prediction_matrix_with_bn)\n",
        "\n",
        "bottom_predictions_ci95 = numpy.percentile(\n",
        "    testing_prediction_matrix_with_bn, 2.5, axis=1\n",
        ")\n",
        "top_predictions_ci95 = numpy.percentile(\n",
        "    testing_prediction_matrix_with_bn, 97.5, axis=1\n",
        ")\n",
        "\n",
        "bottom_predictions_ci90 = numpy.percentile(\n",
        "    testing_prediction_matrix_with_bn, 5, axis=1\n",
        ")\n",
        "top_predictions_ci90 = numpy.percentile(\n",
        "    testing_prediction_matrix_with_bn, 95, axis=1\n",
        ")\n",
        "\n",
        "bottom_predictions_ci80 = numpy.percentile(\n",
        "    testing_prediction_matrix_with_bn, 10, axis=1\n",
        ")\n",
        "top_predictions_ci80 = numpy.percentile(\n",
        "    testing_prediction_matrix_with_bn, 90, axis=1\n",
        ")\n",
        "\n",
        "positive_target_indices = numpy.where(testing_target_values == 1)[0][:10]\n",
        "\n",
        "for i in positive_target_indices:\n",
        "    _, axes_object = pyplot.subplots(\n",
        "        1, 1, figsize=(6, 6)\n",
        "    )\n",
        "\n",
        "    axes_object.imshow(testing_predictor_matrix[i, ..., 0])\n",
        "    axes_object.set_xticks([])\n",
        "    axes_object.set_yticks([])\n",
        "\n",
        "    title_string = (\n",
        "        '\\n\\nMean prediction = {0:.2f}\\n'\n",
        "        'Predictive stdev = {1:.2f}\\n'\n",
        "        '95% CI = [{2:.2f}, {3:.2f}]\\n'\n",
        "        '90% CI = [{4:.2f}, {5:.2f}]\\n'\n",
        "        '80% CI = [{6:.2f}, {7:.2f}]'\n",
        "    ).format(\n",
        "        mean_predictions[i], predictive_stdevs[i],\n",
        "        bottom_predictions_ci95[i], top_predictions_ci95[i],\n",
        "        bottom_predictions_ci90[i], top_predictions_ci90[i],\n",
        "        bottom_predictions_ci80[i], top_predictions_ci80[i]\n",
        "    )\n",
        "\n",
        "    axes_object.set_title(title_string, fontsize=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IJOpsVv33m_y",
        "outputId": "245ac1f2-ffc4-4937-aece-ad3cfa36de0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAHxCAYAAAAof6RmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZgcVdU/8O+ZLZns+0JCGCABgiA7QiAhAQQUkEVQBIWAiCi+7gsuP42KqK8LirxuKAYFBAUFkUUhEBAhEUiQNZCE7Ps+M5nMfn5/nNtMpaa6596Z3MxM5vt5nn4mqTp1q7q6+nR1dd17RFVBRES7XlFnbwAR0Z6KCZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWOo0IjJTRFREKhLTKty0mRHXO1tENFb7RDk9JsG6N62KSLOI7F8g7vFE7PTduIm0i2Ql7p5ORIpF5DMi8qKI7BCRzSLyoIhM6mC7vUTkZbe/V2bM7ysil4jIHSKyQES2i0iViDwnIp8TkbKOrL+r6zEJ1mkEIAA+nDVTRCYAmOriqHOsAjARwJcjruNSt44eQUQEwJ0AfgygDMBNAP4KYAqAJ0XknA40fz2AfQrMnwzgNgCnA3gZwM8A3AFgDIAfAnhcRHp3YP1dWk9LsOsAPAfgchEpyZh/pft7/+7bJEpS1QZVXaCqayKuY7mqLojVfhd0EYALADwN4HBV/YKqfhjANABNAG4Wkf6hjYrIVACfAfCFAmFrAXwQwGhVvUBVr1XVjwI4AMA8AJMAXBO67u6ipyVYALgZwCgAZyUnikgpgOmwg/DVfAuLyBAR+a6IvOa+am0TkVkiclpG7EAR+YKIPCYiK0WkXkQ2iMjfROT4PO2ru0Y4TER+LSJrRKRORF4RkctDnqiILHWPgSJyk4isEpFaEXlVRD7pzmyS8W9d/xSRA0TkLhFZ7y6rTE3Ene6+Xm5027ZYRH4gIoPybMepIvIv9/Vws4jcKyIH5YnNew1WRPqIyJfc18sqEal2r8ONIjIyt/8AXOYWWZK43LM00U7mNVgRKRKRq0XkWdf2dvfvj4lIq/fKrnytIvuY+/s1Va3NTVTVZwHcBWA4LAF7E5EBAGYCmKWqv8wXp6ovqOrtqlqfml4F4Efuv1ND1t2dZJ3F7en+CPuqdCWAexPT3wNgBIAvARiftaCI7ANgNoAKAP8C8DCAvrBk/bCIfFRVb04sMhHAdwA8CeABAFsAjHPrepeInK2qD2esahCAfwOoB3A3gF4ALgRwi4g0q+qtAc+3DMCjrs073f/fC+CnAA5E9tnD/gDmAngDwO0AygFUun3wDQAzAGwG8HcA6wG8HcDnAbxbRI5X1cpcQyJyAexNXO/+rgFwIoBnALzo+yREZDCAxwEcBuB1ALe4NvcHcDmAv8C+oXwTwLku7qcAtromtqJtfwBwMYAVAH4DQAGcB+DnbpsvyVhmV75Wu5z7+j0JQA3smE17CMCHAJwM4HcBTd8IYDDyXG7z1OD+7rmX5FS1Rzxgb5aV7t+/gb2oYxPzHwawDUAfANe5+OmpNmYDaAZwUWr6IAAvANgBYGRi+kAAwzK2ZSyA1QBey7Od6raxODH9YLfNrwY856WuracA9EpMHwJgsZs3JTG9IrH+6zPam+bmPQ1gUGredDfvhsS0fgA2wd5IR6fib0isqyJjG2am4u9w038BoCg1rx+AgYn/z0y3m/E6amraB9wy8wD0S0zvC7uspAAujvhanQv74PJ9fNqz3be5bXwpz/yj3fy5Adt6nlvmw1nvr4B2HnLLfTRkue706PQN2G1PdOcE+w73/6+7/+8Duxb1c/f/VgkWdkakAP6cp/1z3PyPe27PjS5+XMZ2bgcwIGOZJ9z8fp7rWOriJ2fMm+7m/S4xrcJNW4tEQk7M/6ub/7Y865sPYH3i/5e4+FszYgfCzirbTLCwbxZNsA+lvh7Pe2a63dT82WidYB9xy5yWEX+Km/dYxNcqt82+j6We7U5y8U/lmT/BzX/ds72RADYAeDDf+8uznU+4ZeYDKPVdrrs9euIlAqjqXBF5CcAVInId7HJBEez6bD65a6YDRWRGxvzh7u9Ov06LyAkAPuWWHwH7ip40BsDy1LSFmvianbDC/R0MoLrAtiY1ws4402a7v0dkzPuvqtZlTD8edjZ6oYhcmDG/DMBwERmqqpsAHOmmP5EOVNVtIvICgJPa2H4AOAb2+jypqts94tvjSNi3k9kZ856AJfisfbVLXitVnQ770OvqboZdWryyrcB8ROR8AD+BfZC/V1Ub2lik2+qRCda5GXYW+S7YNbznVXV+gfih7u873SOffrl/iMh5sOtytbAzpMWwM55m2IX9k2DX7NLyXS/MXasqLrD+tI2q2pQxfa37O7DAvLShsGPmG22sM3dpINf2ujxx+daTlvvxbJVnfHsMBLBZUz/GAICqNorIRtgHZNqufK1i2Ob+Zr3OyeltXqMWkUsBnA3gMlVd3Z6NEZFzYb8FrAcwTVXfbE873UVPTrB/APB9AL+EnUV+q4343IH6KVW90XMd34b9+HG0qr6WnCEiv4Lf2VtHDROR4owkO8r93ZZeAPbVLcs22PXPIZ7rzrU9Ms/8UXmmp+Xe/GM849tjG4AhIlKaPqNyt/QNg/uhLwaXeA4PWGSrqv7EI24x7Ox7PxEpUdX0D0oT3N83PNrKfSO5VUSyfrwbk7g7Y7Cq7pS03beeO2AfrCer6kKPdXZrPTbBqupWEbkb9gvqdtjdBYXMcX8nw858fYwH8EpGci2C/Sq9O5TArsOlf0Ge6v4WOmtPmwPgTBF5m6q+4hE/z/09Cfar/1tEZCD8E8p/YGf9U0Skr8dlgtyHScjZ43zYtdYpAGal5k1xbc1LL7QLnYuW28t8LIN9zS5IVWtF5GnYcTsZdidG0rvc38c81vkMEt/QUj4Mu1Mh9z7a6RKTiFwC4FbYt5A9/sz1LZ19EXh3PZBxER52y9S5AE5ITc93F8GTsDfvFXnWcSiAEYn/L4Cd9eyVmCaws+XcjxVTM7Zzdp72Z6LAjzcZ8UuR/y6CRch/F8HMPO3lfux5OvmcEvP7Ajgu8f9+sNu5dsVdBLfD/y6C/3Wx0/I8j9lo/SPXxW6Z/wDok5jex01TAJfEeq0iH/u5OyT+DaB3YvoxsES4Hqkf6gCMBnBQcr+Gvr8S8y5z75s3AezT2ftjdz567BksYD160PoHpkIuhn3S/1ZEPgm7V3Qr7LartwM4BPZD0HoXfwPsEsR8EbkHlmhOgN3Gcz/selZsa2DXeV8Wkb8BKIXdVD4adtfEk74NqeosEbkWwHcBLBSRBwEsgSW4fWBnqk8BOMPFV4vIVbD7X/8lIsn7YA+BfWBN8Vz9J9wyVwOYKiL/gF1+2RfWDfM9aPmBahasd9HNbr9Xwb5S31Tgud0h1mX0fQBeEZF7YUnjXLeOu1T1ds9t7WruBHA+7HWfLyL3w66nvx92Zv4Rbf1D3XdhifFy2IdFu4jINNi3lyLY2fPlqf4tgP/lju6nszP87nog4DYS5DmDdfP6A/gKgOdhvw7vgCWZBwBchdRtRLBfhl+AXYbYCLvV6VDYvYy74wx2KeyHjP+DfT2rA/AagE8CkFR8BQqcwSbiTgTwJ9htU/Ww23ZegHXgODoj/p2wxFsD62xxH+zsqNXzKbQNsDPkr8I6KNTAEuersK/KI1Kxn3XPsw6p25qQcQbrphcB+Djsvtca93ge1hmjKCO+W5zBuu0pgXVrfckds1sAPAhgUhvb3+o9EPL+QsvtgB2+5aw7PsTtBNoD5bqHqmpF524JUc/UE8ciICLaLZhgiYgiYYIlIoqE12CJiCLhGSwRUSRMsEREkTDBdhG50fFT02a46VMjrTN6BdeuKPZ+JcrpUQk2UT4k92hyZU8eE5GLO3v7YshK3HsCYeXYICJysIj8SawEUK2IvC4i3xSR8sB2LhCRn7kSQJXuNbitjWV6icg1IvIf935LlvrZp2PPrGvrqV1lv+n+lsJ6FJ0DYJqIHK2qn+28zWrlJlg3x5DuvCFyFVyzRtSiPYSIvAPWxbsUNnzmCliJmK8DOEVETtHs8X+zfA02+Hw1gJWw90+hdZfAui6fABub44+w3nXHAPgfAJeKyCRVzVsHrzvrkQlWVWck/y8ip8DGa/20iNyoqks7Y7vSVHUjrHttrPYbYAc97aFEpBhWa6sPgHNU9W9uehGsu/N7YV1ov+fZ5GdgiXURbOyJ9OhcaefBkussWLWI5sS2fROW5D8P4ArP9XcrPeoSQT6qOguWaAT2ybrTdToRuVhE5rqvNktzy4lVOf2yiLwgVoG0WkSeEZEPZK1HRMpE5P+JVWGtE5ElInKdiGQNul3wWqGIHCQit4hVja1zX/3+JSIfc/OnJ8bmPCl1aWSGi2l1DVZEHnbTDsuzTe9383+Ymu5dbbcQEZksIveLVeGtE5G1IjJHrNhiLkbRRuVYF3eUez5V7uvso5Knmm9imYPc5YcVYlWA14nIHSJyYCquXfupE5wE+5byZC65AoBLdF90/71apPUILFlU9XFVXaj+93fu5/4+kEyuzn3u73DsoXrkGWweuQMsfeB8DjZYyf2wT+uBACBWovoxWBmReWgZMeh0AHeIjZn6tbcatwP4T7DLEYthX//LYJ/chwZtqMiZAP4MGyXrYdjXrkGwr25fhA3p9wLsUsg3YGOHzkw0MbtA87e653Cpe+5pucT2VnsSXm033/M6AzZoTiWAv8EuYQyBJYiPo+XSTpuVY0VkEqyabhms4uwi2Pizs5Fn7FO3/r/Avkrf75YZCxuJ6kwRmaaquTFhg/dTJznZ/W1VvVhV3xSRNwAcAEuEiyOsPzdu8LtE5KepJHuW+/tohPV2DZ092szufMCN3pMx/VTYgM7NcONVomW0q+0AjshYZqab/8XU9N6wg7kZwOGJ6bnxRp/BzmNyJiu8zk61lduGqYlpw2DXTOsBnJSxXWMznvPsdJybV4HWBQZ7wxLVWgAlqfhRsFIoz6emz0ZAtd0Cr889bnsOy5g3LPX/3P6vyIgV2DcShX0tTs77FFpGcUru18GwEaY2Ajg4tcwhsGuO8zqynwo870EIqyg7I3lstdH2n91zfW+e+X9389/VjvfTVLfsbQViJPG6vgL7QPwB7EOuHjZ4fUnourvLo9M3YLc+2ZY3Vu4g/Q7son+jm/7jROwMN+2GjHaGumWezbOeXAXa/01My1UtbTUINFqGdJudmp7bhqmJaZ9z034a8Jxn55lXgezBrX/tpp+Zmv55N/2TGc+1w9V2E2/EAzxiZyJ/gj3BzXsiY14xWgYbT+7XXOK9Js/6cgOEH5yY5r2f2nguudch5DHds+1/uvhT88zPDWT+gXa8n6aijQTr4sQdy42p5/AoEgO074mPnnqJIHc9T2FnIf8C8FtVzbrd5D8Z046BvVHfup6ZUur+JivM5qqWPpURP7vtTX7Lce7vQwHLhJoJ4COwr7kPJKZfBhs0/I7EtHZV283jdtjX8blig3M/DuDfqrrSe8tNoWq2TSLyFID9U7Nyz+OwPM/jAPd3ImwMWiBsP+Wl9qOq1zXQ7kZEegP4Paw0zTWw6641sA/BGwE8KSIXqup9+VvpvnpkglXVkIM5q/JprsLsMe6RT7J+Ua5qaVaJYt/qqsBuqLCqqk+7a3PvEZHBqrpFRI6EfVW+V+3uhpzgarsF1vsXETkLdpZ+BYCPAoCIPA/gy6r6iOdTaE8129zz+Egbbb/1PAL3U2fZZVVl2+laABfCioX+KjH9IRG5AHYJ6ado+cFrj9IjE2wgzZiWO2hvUP/7ZvNWLYV/dVVg5wqrLwUsF+r3sMoO74eVvcn9aJOuJtqeart5qeoDAB4Qkb4A3gH7IeRjAP4uIkeo3/2S7almm1vmMFV9MWCTffdTXu4H008HrBOwBP6CR9zr7u8BeeaHVJVtj9wPWY+nZ6jqf0VkC4B9RGSoqm6KtA2dhgm2fXJVTicHLDMP9mPaiWh9sE0NaGcOrLbSu5Dxy3CGZoRVV835Paw442Ui8ltY4byN2PmrcG57gLBqu21Sqxz7GIDH3JvwW7DnnEuwhSrHJqvZ7sTdF5pV0XcO7J7QybCSNL5891Mhg9By2crXUtjZX1seg5XZOQNWZ+stIrIfLPEugxUkjCF3C2KrW7Hc7Yn93X/rI62/U/E+2HZQ1fWw64VHu/taW73JRWR/Edk3Mel37u933HWpXNwQWO8YX7fCbmP6mIi0KhgoImNTkzYB2DugfQCAqq6AvTmPg/0ANBzAHemzb1V9DnYN+3wRybxZXEQOFZERba1TRKaI9fxJy52J1iSm5c52xmXEPw07c5siVsgw6RNoff0VsNdnK4BviMixGdtWlHU/su9+KkRVl6qqBD5mejb/BKw22RQReU/y+QD4vvvvL9X9GuXmlbr7gbP2U6hcufivZNzvPQN2kvesqlbtgnV1OTyDbb9PwL5efQvAh9wPJ+sA7AX7IeQY2NnMEhf/R9jXyPfAKrzeh5YKr88i+03fiqpuFBs34W4Aj4vIQ7AzrgGwyrZ7w6qg5swCcJFYJdF5sB9fnlS/arK3ws66r0/8P0totd18bgQwRkT+DTtDqwdwFOxezmWwbsPJ55VZOVZVVUQ+DLtz4x4RSd4HewrszP+M5IpVdZO7JvhXAHNEZBbstiKF7dPjYddpe6M13/2027kf9S6HvT53i8jdsK7XpwA4GlbK+4bUYmNgSXkZ7A6Ht4jIubB7kIGWSy3HJzqrbFTVzycW+Q6sevIpABaIyMOw2/ZOAHCs+/enOvYsu7DOvo1hdz6Q5z7YPLEzkFH1NRVTBku0T8Ou4dXBDt5ZsGtqQzPivw77OlYHSyLfgX2N8rpNKzHvbbCvp6tgiWgd7GzlqlTcCNiv2etgX6sVwAw3rwIFqsjCulduczEvtbG/gqrt5mnjfbAPooWujUoAL7t9NDwjPm/lWDf/KFgyrXKPR2GJstB+rYB1AlkIoNZtwwIAfwBwbkf3Uyce+wfD7ond6PbXG7AOG+V59kGr/Zk6JvM9spYZDuCH7rWqdcfrMti3hoM6e9/EfLCiARFRJLwGS0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwVKXJVYOJ1nq5ri2l6LOJlY6Kfm6VXT2NnUWJtgEMR+Rlvpb20XkORG52vXdTsdPldalwJOPVoXkRGSCiDwgIptd3aebRKR/Os7F3iZWt6vNof4KPKdjReS3YmWaq8TqXC0TkbtF5H3JcRSkpUbX0vauL4JtsB5H34QV29uJWC2wn0hLbbLVYrXK0mMyFOT203dF5CGxOmAqIgXHoZV2lLBuLxG5TKzsdbVYvbPZYkM7hrQxu43jVcUGrEkvt6+I/FJEFohIjVidsmdE5CoRKctY1fWw1+u/7Xy6ewyORbCz22D96tfDumzWwMY4/QWASbD6S1meQPag2TsNri02BN8sWL2q38P66l8DG8zkwlTsmQAuAfBOVa0OfSIiUgrr2381rIvsE7Buq3VuvSfDRo+6BzYeQle1VVNVgHNEZCism/IBsL72d8LKSF8Oq6F1vKr6jhJ1MaxPfANsxK58Qx0mBZWwbi+xwomfc+u4Gdbl+iIA94vI/6jqTZ5NzUT+wd3/B1a+aKeB3EXkGNjob+Wwbsf3wca9OBvArwC8V0TO0ESXUFW93i1bAds/PVdn99XtKg9YeWGFjRMwLDG9DFYATwGcn1pmKhJ9+z3W8X4XPyUx7Xdu2ojEtIGwN9NvOvB8cuVMXgRwYMb8YgAfQqLUCwr0Qe+k12RpoW2BvcEVwI9S0z/ppj8csK7DYQUsy9z/FcDKNpaZBhvwR+BZPqUd+2CSa3cRgMGp12oTrG9/RQfXcaBbx1oApal5D7h5l6Wm90XLYDhT8rQ7E3nK+vSUBy8RtDjP/f2RJkaiV9V6AP/P/fcTHVzHPu5vsgzNf1LzAOBH7m9WtdI2icgJsJH5NwM4XVVfT8eoapOq/gHAB9uzjs7mLpt8CFaUckZq9k2wwUROFxvztE2q+oKqznevtxcNL2HdHle7v99R1S2JdS8F8H+wgYIu7+A6rnJ/f6eth1nM7b+/JSeqjdc7y/13jy273VFMsC1yQ69lfaXMTZuc55rTeBH5hIh8RUSuEJEJGTGAjbQF2ChPOUe7v8sAQEROBfBhAFer6ja0T+4N82tVXVMoUFXr2rmOznYc7GvrvzU1lqhaaeh/uP9O290btovlLbuNlq/zJ2fM8yI2RuulsDPNrNLqubLbZ6aW6+PWWwOrlEwZeA22Re6sdd+MeblP8RL37wWp+Ze4x1vcGKUfSZ51wEokrwRwr/sxZCzs+udfVHW9Oyu7GcDtqvr3DjyX3Ij9swpG7QJufNDDAxbZqqo/2QWrPtD9zVfqZKH7m69USpfnrtmPAVCd54NyVzzH82Gl4B/R7OvVX4NdppgpIu+DXZ8eACsFUwLgAlVd3YH179GYYFs8ABsg+7Micqeqbgbe+rHom4m4wYl/b4AVdXsAdr2wN+yM9HrYD0ijRGSKO6OCqla7M9SfwEp118DqOH3Jtfc92FnZp0RkHOwr4Kmwcsd/BfAJVa30eC6j3d/QaqztcS5a6lD5WAZ7/h2VK9aX7yw/N31Qnvndwe54jm9928maqaoL3A9df4T9sHW2m9UAex3nZC1Hhgm2xZ2wa3qnA3hVrOJALSzBjYZ9vR8Hq3EFAFDVV9DyFQqwX5MfFpGnYfWSToAdkPcllnkdVltqJyIyGcDHYYNOb4aNxj8C9ut2PwA/gyXfC9PLdiZVnQ77sKBuxl3KmgobjD2zqquIHAHgXtidNZNhx/Ug2LX76wCcKyLHdOBy1h6N12AdVW2CJcNrYWeml7nHQthXpNx1vrbKnsCdZd7h/tuqblaaiJQD+C3sUsHdsKR+BIBrVfWv7seoHwG4QPzqJOW+To7xiO2uOrsc9e4Q+zkW+nELYvXR/gT7EetsVX1KVatVdaWqfg/2oT8BwGfauf49Hs9gE9xB9n20FIMDAIgVKZwAqze0JGvZDBvc374esd+G1Xu6xv1/ovs7LxHzvPt7MIDFbbT3FOxa8SmIfB22E6/BdnY56uhUdbuIrILVKRudcR223c/R/Vh7GfL/uAXYfb3jAcxT1bUZ8x+HlUY6KmMegQnW10Ww+2H/GLBMrltnwRvdxSqYfhrAdFVdl5qdrMKZVWwvn1/Dfhm+SkR+mtFucv29OngnQWddg50DVzxPRPon7yRwve5Oc/9Nl0jvbh6DXbo6Ay2ViXPelYgJdR7szDTfj1tAy/E3LM/83O1Ze2TJ7V2is2/E7UoPAAMyph0OOxvdDGCv1Lyj87TzQdi12joUuMkalrRfAfD31PRTYWcWX09My3VI2N/zueQ6GswHMCFjfhHsR70/JaZVYA/vaACr3nsQUjfUZ8S12dEgFT8VHh0N3HPyvvke7ehoAEuIByHRYSaj3Vmu3fcWiOkFYIuLuzI1bxCsiKEC+Hie5WeGPNc98cEz2J09IiI7YJVMq2Bf1c+EnSmdra1vR7lbRBoBPAf7xb43rFz3sbBf/j+qdkN4Pl+HXSc9LTV9FuySwNdFZB/Yj1zvg/W6auvyQM41sC6yVwN4TURmw/qG17l1ngy7Texuz/a6oq/AEttnReRwWKeNiQDOgV0rvyZjmVmwTh37wpIdAEBEDoJdf08aLC3lqAHg85rohCLhJayBlt89Ggs/NaOqT4vIj2EVdF8UK7tdBusVOATA/2QcY58A8A3Y3S8z0m2KyHjY/cHrkOpAkFp3nYh8GvbhfrOIXAT7wB4MKz8/HPZNotX4BeR0dobvSg8AX4Altq2wRPQm7FapsXnivwT7tX8FLAnXwq6P/g7AYW2s63DYrS5X5pk/FvbrbbXbnpnIOMP2eE7vgL0B3nBt1bnt/SssaRclYivQjc5gXcwQAD+FXXqoh/3Ad0uB12wpMs6q0HIGWuiRXmZGG/FLU/GDYR96T7VjX0wH8Cys51oVbGyJs/LE5rZrRp7533fzv+u57ikA/uL2bYM7jp6HfSD1LrDczKz91pMeLNtNXVZuVC9VrejcLdk1ROQ9sNuhzlTVBzt7e2JzZ/KXAdhXC3+T22PxEgF1dfuISO4s4HhV7c43tp8E4IU9PbmKSDX87p7Z4zHBUlf2E+zcS2l39EyLRlXbNXhPN3Q97DpxTne+F7lDeImAiCgS9uQiIoqECZaIKBImWCKiSJhgqcvKKNJ3UWdvE7VNWFX2LUywCWK8q8omljvLJYNtbrm5IpLZP19ERonIHSKy3lXnvE1ERuSJvU5EtopIu0fFEpGDxCqfvuy2r16s8uoDIvJhN6J9Ml4Tt0V1Fbmqsi+nZ4hIuYh8U6xqbq3br38SkYmtm8lPRCa6du4TkeWJ5OB9p42ITBGRJrfcdSHr92i7U6rKisi5InKXWEXZLSKyQ0QWisgfReToPKtiVVmHdxEkiMjtaKkq+ze0VJWdCOAPqtqqqqyIfAI2bNsmAHfBehNdAOuJ9SNNdJV0SXougLfBern0gY1b8ByASeoG5naxh8N67nxMVX/TzufzdViXySJYWY/nYL1wRsJ6Lu0H4HlVPTqxjHXEV5X2rHNXct17T8q3Le7DYRZs3N3nYIOe7A0bM7cewMmqOtdzXZ8GcAOsp9VCWK+23rAxC9rs1ipWev1F2DgA/WA1tL7ms26PtpNVZXNdZS9CS1dZr6qyIjId9ryy5KrKXqg2ZGZumd/B7t99FsBq2H4dD+tCXgbgqnzHJzsagN0jZFAAACAASURBVF1lcw+0r6psBax77CYkugPCukQucsscn5j+Djft0sS0b7hpxyamlcD6fD/SgefzFdfucgDvyBNzFoDHU9PUDosu8ZrMLrQtAL7stvfP2LnL7zlu+ivJ6W2s60D3+pS7/y91bZR4Ln8LbECg3H6/bhftg86uKpvZFRbAoW7dW+Eq8WbEzEQP7yrLSwQt2lNV9grYiEM3aeITWq0O1/Xuv1cn4vdxf9uqKnst7CzhI2FPwbhrXjNg/cbfrXnO4tTqfp3RnnV0NhERtOzbL2ri7F9V7wPwL9jYuSf5tKeqr6vqXFXd0Y5tOQdW2fWTsLO8XalTq8qqam3WAqr6Emw0rYFgVdm8mGBbtKeqbGjFT5+qsgfDEvqXtf1fqy4HUArgHlVtdd0ySbtvVdn9YSV83tDsQdA7XHHVh7t+fjOAe1X1tgir6OyqsvmWOwB25rsRLRU0KIVdZVu0p6ps3sqmqrpGRLYDGCsifVS1BnYdax6AX4nIJLRcg30WwHMiUgz7qjkXdnbSXruzquxU2PVcb6o6YxesuqtUlb0ZdqJydVuBoaRrVJXNbcupsOOqDPYeyRU/vDL57YF2xgTboj1VZX2qfvZ1cTWq2iQiZ8N+THkf7KzhbgCfUdVmEfk8gLcDOAzAIBH5Gex6YimAf8J+8Frl8Vx2Z1XZqbDryCFm7IL1dnpVWRG5AjYu6vu1QNWIDuj0qrIJp6Kl+jFg12unq+o/OrDuPR4vEbS4E8A/YF89XxWRX4nIT2FVNCej5et9hz6tVXW1qr5fVYer6ghV/YCqrhWr8PktWBWDhbAxZc+CDRp9EYAjAfzFXXvsMlR1hqpKyKOzt3lXcNe5fwIbBP1Pnbs17SMeVWVzVPVa99r1gx2LjwF4SES+Gns7uzMmWEfbV1XWt+pnwZLGLmn+FsBLAG5wB/45AH6oqr9X1Xthv5gfCxuJvi2sKhu/quwtsEHWPx6pfaCTq8pmUdXtqjpfVS+BnZB8W0SOaef693i8RJCg4VVlX4ddvzoAdp9pcpnRsMsDK93110Kugd0idIS7jFCoquzb0HaRu6dgP3ycgsjlPDrxGmxnV5U9EpbgNuT5UvFVd3Z3n6qemxXQFu38qrJteRh2F0ruPllKYYL1k6+q7GOwm9zPQCrBwrPip/uq+V0A31bVV1Oz21tV9newM973isjBGe0m19/RqrJT0TnXYBfDLtscICL7ZtxJ0JGKqz5+D/uRMm0CrMTKC7APxfkdXE9nVpVtS+4bkld9sR6ps2/E7UoPhFeV3RcBHQ3yrPMR2JuwJDFtvFv2lsS0XIeEkz2fS+6G9yXIX/32DACPpabt0R0NYLd2HQSgTxvrXoqAjgaJ5aajQEcDdL+qspm15WDFPSthyfWgPDEzQ57rnvjgGezOgqrKquoSEfkCgBtht1lldZVNn9m+RUSuhJ0BHquJ7piqukhE/grgchHpBzuQp8M6JTzu80RU9XrXj/4bAJ4Vkaexc1fZKbCzred82uuifgz7IfACAHNFZBYsgV4I6+Z8hba+hej3sK+002AJHAAgIsMA/DARN8z9/W1ibIbvqeoCdEy3qSoLoBzACyLyIuw9sRJ21j4RLffefmEX7JM9V2dn+K70QGBV2cRyZ8OqfFbBqn4+C+CyNpYZ49aT70xnEIBbXcx2WIXZMe14ThNhYyW8DEvUucqrDwH4MIBeqfhucwbrYvrA7r5Y6F6zDbAz2oMLtQlgamp6Re65F3hM9djm6chzBotuVlUWdnvg12DfslbCzpZ3wM6mf488XbATy89EDz+D5WAv1GW1NdhLdyOsKtvj8BIBdXmJr+gfUNU7O3VjOuYksKpsj8IES13ZTCSukyJjPNjuRFlVtsfhJQIiokjYk4uIKBImWCKiSJhgiYgiYYKlLktEZqYK8l3b2dtEbRORv6det+mdvU2dhQk2RUTOFJF/ishKV0HzTRH5s4gcX2CZSSLyoIhsdsu8KCKfdgNop2MHiMjPXfubROR+Edk/T7tXikiDiBzRgeezt4h8T0Sed1VBG8Qqrz4qIp8SkYGp+KXS9Uot/xTWK+mp9AwRKRaRz7h9vsO9Bg+6Ac29ichYEfmqe60XiUiz2w/j21juUBG53S2zQ0RWicjjIvJ+KVCJOFTIMebZ3gUi8g8R2ShWjXe5WEXd4/LE9xKRz4nIsyJSKVZx+Q0RuVVE0iVj7oC9XgWHQOwROrunQ1d6oKWHy0YAvwHwPdiA2PWwcWA/mLHMObBuj9Wwkat+AKt4oLCxQtPxf3HxMwH8HNalcylSfePRRk8vz+dzJaz3jcIGH/k5gO8A+CXslieFjRCWXGYpukjvG7TREwiAwHptqdvnP3CvQbXbx+cErOtc104zbCCZLe7/4wssczas7lmd247vueNmk1v25l20H4KOsTbaKgFwu1v2DVhPxethwy++BuCajGVGwSrmKuxD7kcA/hc2hvJaAIfkWdd0t8z0zj6WOu0Y7uwN6CoPdxA1uQNmRGreNHegvJmaPgA2PmwdEgOqwEa+etotc1Fi+kg37euJabkh496Xavt+2GAlmRU7PZ7PJa7dzbCeQ1kxJ8BufE9O604J9gNu/r+RqH4KG4ikzr02/T3XNRY2sPoA9//ZHgn2FRdzUsaxtM7NG9fBfRB0jHm09x23zHXIqLiL1lVliwA86dZ/dka8ACjOsy4m2M7egK7yQEtJ7fvyzK8EUJWadoVb5taM+JPdvCcS0451085ITJvopn0hMe2DsGRfsK93gefSHy1nUae1EZsei6A7Jdgn3fxpGfN+7+Zd3s51+yTYHQC25Zn3N7f8UR3cB0HHWBttjXKJ8pmA9Z/v1vG9dmx7j0+wvAbbYiHsUsCxbmSlt4jIFFjSejS1TKGKn0/Cvv5PEqvcCfhVlR0JK0Vyg+Ypt+3hAthIS3NU9Z+FArWbVpV1g6BPgu3jf2WE7I6qsq8AGCAiJyYnilWaPRY2qE7esXg9hR5jhVwA62F1p4iUu+uw14rINSJyWJ5lLnZ//ygiI0XkwyLyZRG5XET25IoZuwS7yjqqullEvgQbAu9VEbkXdha4P6yw3SMAPpparFBV2UYRWQKrQLAfgNfUam/dB+Ab7oetWtglguWwoouAXRPbDCvd3V67s6rs4bDrlyF+oqod7T65P4Bi2GWbrKH/dkdV2c8A+DuAR93r+iZsmMNzYdfPL1bVHR1cR9Ax1kZbudIufWDXcMclZ4rIPQAu1Z0rcOSWORb2wZ8cZLxBRL6lqtf5PJEeqbNPobvaA/bm2Iydh6lbCHuzpGPfQIGvkbBrg4rEoNuwMiO/ArDarecBABPcvAtgP7JMhg12/DMXUw/7ypo5BF/Geh906726Hc9/KcIGhJ6e2lc+D9+2Z+aLR8tA1JlD/8HGulUAr7fzOJhd6LVNxB2Mlh8Mc49K2IDnvduz7o4eYwXaesjFNsKGOzwCNijLO2BDISqAmalldiSW+T/YIPMDYZcOcteZp7dxbGTO7wkPXiJIEJEvwu4amAk7Q+oL+zr/JoDbReR/O7oOVd2mqh9V1b1UdYiqnqmqC0VkCICbAPxcVf8Fu6PhKtjYnu8BMBTAw+6rcZehqjM1sKqs7iFD14nIO2GXJ1bBjpO+sOPmN7Afk2aJDXreVeTe75thP1jNVytiOBd2jFUD+FDqq39umUdV9RpVXeKO4b/A7lIBrLIEZWCCdcSK930fwN9U9bOq+qaq1qjqPFj9olUAPici+yUW25VVP2+EnS1cKyJ9AXwMwB9U9UZVfRhWvXRvtFwTK4RVZSNXlXUfiHfBXrPzVHWeO17eVNXPwgZInwT7wbIjduXzzMXMUtXK5Ay1gopzYTnh6Ixl/prR3oOwb1cHpO+nJtOVPl0721nu7+PpGapaIyL/gSXaI2BntIBVNj0adp3v+eQy7sxlX9hXq4JF5UTkTNhtVe9U1WoReTvsx4h8VWXb8hTs1+dT0LFruW3qxGuwi2F3WuwnIiXa+jps7Kqyk2AVCh7X7KrBj8P2y1Gwb0TttUuOsURbQP5kvMX9LU8tMyJrGbUKyJWw687laKM8fU/EBNsi9ytsulcKUtPrE9MegyXGM9C64uwU2A8CT2qBX+rdJ/+vAPxWVdN3KbS3quzdsPpSx4vIqRntJtff0aqyhyO8quxMdPDMUlVrxeqMTXaP9Adj7Kqy7Tle2qPDx1jCo7AP3EPyzM99eC9JLTPZLXNXMtjd8TIMdmlho8f6e57OvgjcVR4A3ge7IL8WqdpXsDdrM+zr4NDE9AGwGlDtvgkcdr1uJYCBiWl9XZuzEtMuc+1d4fl8ch0NNgE4PU/McQDmpaYtRfe5D9ano8GA1DKjYRVXB7ax7tko/OPSXrBeXE1I3WsMu5Sz3i3/7jzPabrnPgg+xmBJ9yCkOjnAOgXMd8ucl5r3EbT8oFucmD4WVgdsM4D9EtOL0XKv8S15tn16yHPdEx+dvgFd5QG79vQIWn4FvhXumqxLrgrgUxnLnYuWboy/gXUhfKsbI9yg5nnWeaqLa1W8Dna7mMLuf7zJHeTLEfDLNHbuKjsf9ivwdQB+Aes6qwA2pJbpTgk22VX2NbfvC3aVLZTg3LzcY62Luycx7cRU/NddTBOs3/333XFT5ab/JWMduaTUqtt1gf0QdIzBKhUrgNkZbb0ddimg2W3zD9By10k1gEkZy1zm4nPvixvQkqhfR57y4GCCZYLdaWdYFc1PA5iDlprv62H3OubtEQXrcvqgO3B3AHgJdo9kZhdCt0w/2Fex2/LM7wW773CDS5KPwfM2rVQ7e7s3/jzY1/IG1+bj7rmmz/CWFkpqu/n1mNnWtsAuc33G7fMd7jV4MCtRpNqcnjFP23hkLXMO7PanDe54qYSdWX4s6/V3iakSwODAfeF9jBVKsG7+vm4/rHbHwxoAtwE4sMD6p8I+7LfALnssgiX6QQWW6fEJliVjqMuSPawqqYgMgl2y+ZGqfrGztyc2sWEKfwfrrjyzc7emc/A2LeoOlsieMR7sZNgZ4487e0NiEjceLCy59mi8i4C6snthlyxyWo0H252o6v0Iuxuku7oDwHOJ/7/QWRvS2XiJgIgoEl4iICKKJOgSQXHfvlo6eIh3fFHALdZNPoOtJZU2h4VvDfgsCT2rL5KgcGnyb7+5JKzt5tKg8KikKSy+tNp/gfoBYZVSNLCwSknAGFhFDWHHYl2+rgn52t8Rdh7UXOZ/fPXaGLbtoce6FvvHa2DbDf2CwoFi//1SUuJ/LNat34bGbTWZGx+UYEsHD8HYT37GO77fMv+2K8eHJTUZXRsUP/Ie/wxeFJAAAaCxd9gboKzK/8XbMTTsMvn20aFvAP9YCXwvlm0N248jn/Hv3LXq1MFBbTf0DwrHkFf8X6M+68M6ay25Kmxber1S3nZQQs24rNEbsx148/agtpv6lQXFN/TzP34b+oZ9Cq45ISgc2t9/vwwf6d/r97VP5v8tj5cIiIgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiCeompKWKxlH+vVZqq/17T417KKw3zNIPBnaVDPgo2TgxrPfUjoqwbe+9wr83TFll2zFJoV1CEdDZqnx9WM+sfa8Iqze4pO4A79jisI58qNrfvxcPAFRV+R8DpTVhx0vfZ8P6MzcHjnnXZ7n/Aos/F7YtsjxsMLB+S/1jqyqCmsbAhYFda/v6v++KHxnq3/Dm/PubZ7BERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRRLWCa9RULTJv2vdO856yTt2TtGhQZtSvDas22ZDH//YuiFh1f1GPxLYlzGgemB9/7DugDUjwzZl9Bz/4n61g8L64S65xb/rKwBsOsm/y3H/+WFliEsqA/sQBxxeKmGvUWNYDUPUDw471su2+G/PiHvDur42l4RtS+0Q/3O44v2qgtoe+ETYtoecThbv8H+PFjXk3yc8gyUiioQJlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJIgjrRl1Uq9n7Ev+/6q/89xL/xipAtARpHhpXKHv6L9d6x28aPDWp7zclhJaHLV/qP5yBhTaPPurD4zZdu944dcFf/oLbXvDNw42v9xwuoOiqsbvf+vw3rQ7/+CP8BA9YfFTYWRe3YhqD4ouqwcRT6r/B/roOeWxvU9hsfHR0UP+HmNd6xi0eNCmp71dSgcDQO9j8ee63xH+ui4bX8Yz/wDJaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiCepE3VwqqBnhv8iGk/3HCzjo80tDNgWv3zAuKH7xFXsHRIf1Wx/zSNjn1Oop/uM59N4Q1g+98oCw/v8HzfDflpWnhz3Pve8Pi6/v5x+/Y0TvoLZXTQkKDzL633VB8dv2LwuKb+iXv697lg1HN3vHDp0dNi7CgMVB4Xjt88O9Y/ssC3ueO0b5P08AkHr/40uLw3JAPjyDJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCiSoK6y0gyU1vh3Tytd5d8lcNlVB4ZsCvo9HxSOooAepP1X+ncfBYCihrBudQNf9y/bPeLZqqC2V00LK629etoQ79jt+4TtF2kOK2ddM9r/2GrqE9Yl+Ji3h/XxXPPj8d6xS84P685cUhkUjsELwo6v6kP9u7++9sWwEvUj54RtizT6d38NeY8CwNAXwrrWbjrcP7bfcv/YogIjAvAMlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJImGCJiCIJK9tdAtQM98/JvTf49xXutSWsj/O2d28Piu/Xp9Y7tqiXf7lxAFi2YlhQfFHZDu/YuiFhYws0HVIdFF+3rK93bN+lYX3uG8uDwlFU73+8lG0NG+dg4cthY11sO8f/eOk3wD8WAOoqBwbFH/axF4Pi5//67d6xxWGHOtadEFYqe/gc/3yx8YiwtsfdszYovqpiL+/Y7af6jwHS/Ej+7eYZLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUUS1KFbi4G6wf79xXeMbvJvfFFYP3cNG7oADbP9xwuoXRvWJ3rC0rC+6Frivw8XXxiwDwEM/Yf/2AIAUD3Of1sawoZFQPm6sPjakf77vf+hW4LaHtLHf/wHANi6fIR37Dn7vhTU9iEHrwyK/3/3XRQUX3y6/3gUI+4IGzBizKP+xwsANPXyf6P2XRV2vrf6DP+xBQCgYvIy79g1lQO8Y0XyP0eewRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUSVjZ7lKgdoR/d8ayLf75u6wqrO/rmG/XBcW/9vlS79jtY/xjAaCpLKy74aap/ts+4f9qgtpedFFYV9nBr/jHllWHvUb7fWpBUPzTC/fzjv3uxL8GtX3tq+cHxaPB/9hdV+ffrRIApvQLK60+9ojVQfEbq/2PgW0VYeXPt+8d1o28bLP/fpSwptHYJzD+WyO9Yxsm9/aO1Zr83fx5BktEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRiAbUv+4/aKweceInvePXnODfz1nDqnZjUFg3dwxc4l9ae+W0sLEF+i8N66O/8Sj/Tte914XtmJKwoQsw4LS13rGbqsLGOajd1iso/tAJ/uWs39w0NKjt7evDtv2S457xjn21clRQ2y/OHR8U/5vzfxUUf+XcS71jrzvqvqC2v/vaGUHx5X8e6B1b3z+sJHj9wMD4QQElxFf4t73wTz9GzfrsBXgGS0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEEFUVv6CdYfaL/IsOPXOcdW/O3sP7cW0/fHhRffod/v3hpDGoaNaPC+kQPftn/c23oy2GDCyw/LaxY/Ig+Vd6xRw5bEdT2oqrhQfFvrPOPL322f1DbvcOGIsB9d0z2jp10wfygtm957y+C4n+5ZlpQfFNlmXfslx+/MKht+A+jAQAoGuh/rG+fHPaeLvtv2ItaXOf/Pq0d4d9uc2n+eTyDJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCiSoK6yWqJoGO7fj3TNukHesTI+rA9e7xfDusmtmeRfsre0MqhpNPUOi68e6x/b0C+s6+vkM/4bFN+3pM479r4XDwtq++PHzA6KX7DiJO/YxiPCuhDvPXxLUPzmB8Z4x/575X5BbY/vsz4o/plXw8p8Sx//9+iA58IOXg08Jas+0f91GvB42LEu6v+eDjXwzXrv2NVV+beDZ7BERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUSdBYBNIoKN3ov0jjmCb/tpvCSl+Xrw/rh1x/6A7v2DoN3JYXyoPitci/fZkc1od+ZK+wgRTuW3Kof/CO4qC2b/nz6UHxWuE/LsK+YzYFtb103dCg+JFnrPWO/eC4uUFtP7V1QlB8+fICdaEz9Fnj/x4dtLg2qO2qvXsFxVev9x/roGZU2PtO/NMLAGDgieu8Y7ff5l9Cvrk0/3bzDJaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiCRqLoKge6LfcP357k3+/5fJ1Yf2QURQ2FsGQh/3HC1g/pSGo7bohYdtSWun/XEuKwzpc/2PlxKD4syte9o59onx8UNvrK0cGxY8atdU7dvmcsUFtVxy3Mih+zSN7e8e+++OvB7Ud6tneYa/p1on+x2PNSP+xAgCgrCooHBX313vHrj8qbJyD+v5h77sNL4/wjtVj/NtufCL/PJ7BEhFFwgRLRBQJEywRUSRMsEREkTDBEhFFwgRLRBQJEywRUSRMsEREkTDBEhFFwgRLRBRJUFdZAEBA77Syrf5dQhv7hG1G5YTmoPjyNf4lp0s3hpVJHj4/rMuein98xVkbgtp+cd1eQfHztvh3Ca3cEdat8vRT5gXFz1p6gHfsfn8MK2e+cpv/8wQAHLfNO/S8/14R1PS79n4tKL6kOqwb+ahn/LtXb7oirO/roH7bg+IrLt7oHbvsqUOC2h42PygcW95T4x1b/FI/71hpzD+PZ7BERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUSdBYBCU7mjH05R3e8WtO8B9goGxbWH9+afIfWwAAmsv8Y0u2h/X9rto7LH77GP9xFNYu2jeobWjYtry5YJx3bPmGsLZfezGsb/moEv/210zrH9T2iNPCynZPHLjOO7ZBw85Tbp9zfFD8XovCSrevP9r/bV271b+cPQDUrPHvow8A+Ptw79DSY8P245b3VAfFlz3vv+2NYcNu5MUzWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIookaCwCAID49xdv7Os/vkCZfxl6a9t/mAMAQPl6/23ZPjas7WEvhfUVb+jvv9tLV4V1ih7z6Jag+CVfrfeO3VEU1g99+6jSoPh1J/qP0SDldUFt12wYHBR/5JAV3rEPPnNsUNvFtWFjOmyeGHYe1NjH/1gv7t0Y1HbRurDXdPto/20X/5fftuWVsOOxOSDbFTX4x0qB3c0zWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIookaCyC+gFFWHGqfx31ulH+HXrL14f1ca4d59+HHgC0xL99Hb89qO0VI3sFxZ/69v96x/5r2X5BbW9dMzAofvC9/rFrTwwbc2Hgwh1B8TUX+veLP3nvhUFtr94Rtl/++YfjvWP7Bo7o8bZzFwTFz1u+d1B8Y43/sT7iYf/3MwDU9w8bR2HQYv9jYNOUsPO94nVlQfGlVf7b3tTbfzwHLbDZPIMlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKJKgTn5aDDQM9K+tW77cv8teca1/1zQA6LsorJtc/UD/9mVR36C2R78ctu1z3jjMO7bx8LDupv1WhJWzXnamf1nw8hE1QW0DYd0qi2b5l9Zuviys7UmDFwfFz9vrAO/YfivCtmXdt/cPih8yrDgofusE//OmjYeH1coe+Z+wY33RB/1TTPnisO7yIWW4AaB8vf+2Vwb0UNcCLz/PYImIImGCJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCiSoN68pVXAXk/69+ctqvcvw7zylLBcL01hfaLLtvi3P2hxWP/sAa9XBcXvGOZfQrpsQVhZZWkOGy+guNa/H/2OSv9xCwBg9ZSw8SKGnrbaO7ZfcdiYC3ctPyoovuIB//ZXnBJWtr3ygLBjfcQzQeEoCRi+on5o2Pto275h4yKgpME7dNAbYe+7/svCjvXNB/uPMaIhT5NjERAR7X5MsEREkTDBEhFFwgRLRBQJEywRUSRMsEREkTDBEhFFwgRLRBQJEywRUSRMsEREkTDBEhFFEjQWQXMpUD3av5OuNPv3cx6wOKy2fNWksH7IO/r594uvuGtDUNurzh4dFN93tX+f675rid0dNgAABqNJREFUw/qKr5zWJygeAd2/ZXtYP/QdR4a9RrMPudc79sQXzw9qu+TmYUHxVeP8zz16bwo7dsuqwvZjUVNYH/2+q/yPme3jm4LaLq0O2/b9bvOP3TIhbD/uGN4vLH6E/34praj2jpWy/K8Pz2CJiCJhgiUiioQJlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJImGCJiCJhgiUiiiSoq2xTb6ByvH+3vZK9/LtK1m8NK33ct7d/OWAAKH3KvwvpivPCur42hVWnxtbx/p9rpWG9TTHy2fqg+A2H+2986dawz+O++wTUjwZw3AsXeMdueml4UNt9xoV1w6w8yL/kvNSHtT1gUVh303XHBYVjwCL/7Rk6tzSo7R1hPY5RuY//8bX1kLAuwSP33xgUv3X1YO/Yojr/1KgFeuDyDJaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiYYIlIoqECZaIKBImWCKiSJhgiYgiCRqLQEqbUTzav3P8iDvLvWNXvjOsPPX2dX2D4ive9B+7YP0RYf2zS/0r/AIAakb7P9dBi8P2y6ppYdteUuUfO/DIsL7fl+07Jyj+h3NP847tsy2s/39z4HgRg17yf2tsPSps/IdeW8LOa5pWhsVvH+N/zDSODtv2smVhY4Y09g57nUIU/yZsYITRJf7bsiEkB9Tnf314BktEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRBI1FoA1FaFrbxzt+5Tv965yffez8kE3B6x8/KCh+5Sn9vWObw7pbo7ZP2HgBWuIfX983rC/3wEM2BcXX1Pn3ud6wzL+uPAC8MGxcUHxRif/xUr4+bJ8jsEt8Q7+ABWrDzlNqRoVtzI4j/Mf/AIBBs/zHABl2d21Q203lTUHxtcP930wN/YuD2gbCtqWx3H+/j57j3/a67fmPRZ7BEhFFwgRLRBQJEywRUSRMsEREkTDBEhFFwgRLRBQJEywRUSRMsEREkTDBEhFFwgRLRBRJWNnuJqBsq39OLipQzjZtwe1vC9kULLnAvzsgAJQGlKcurQxqGuUbwuK3TPTvsjd87pagtqu2hXVn3XqM/2v0jhNfD2p7afWQoPiQ7qz1A8O6m5ZWB3atDQgff0dY6euixrDuqauK+gXF1w/yj33jUv8u5AAw5omw/bjytIAS9S8FNY21k8KOgX0e9H+dKvfxr/OuBcqB8wyWiCgSJlgiokiYYImIImGCJSKKhAmWiCgSJlgiokiYYImIImGCJSKKhAmWiCgSJlgiokiYYImIIgkbi6ARKNvqH19d4V+G+c1zw8YWKGoMCkfflf59orfvFdbHWTSsf3bvjf6fa5uPDBxbYEJQON523JvesXMX7BfUdul6/5LgANBnc8B+DxxaYOvEsAWay/zLNhfXhh275ZvDtqWxT1A4em0OCC4K2xZpCosf8W//Utw15wckFwBNa8LGaKgd6n88lgWMXSEFDhWewRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkTLBERJEwwRIRRcIES0QUCRMsEVEkQWMRNJcBNXv599HVYv/YA7/5esimYPWHJgbF997q37e8ZpR//2kA2HhY2NgFg1/13y/b9g9ru7ksrK/40nv294494Mang9pe/YVJQfEDl/i/Rqve6T/OBQAMeT7oUEe/Nf6xq6YGDoywKOw1HffPmqD4kgXLvWNXX3xQUNu1g8Oe67aAsTHq14WNLdB3edhrumaK//E1cO9t3rHNz+dvl2ewRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUUS1NesuA4YsNg/vnqsf/7eeE5Yl71RT1cGxS9/1wDv2IGLwrphbisO+5yq998U1A0PrE8e+pG5yv8QWPK944OaHvJy2H5cd7T/xg96Kaw7c1FDWBfPvm/6d5UcNGpIUNubjgl7TYcX6IqZ5fWvHuAdWzQqrBtu07ywGuL9lvvv98b1YV1fh75SFxS/ckCZd2z9HP/XVKvzH4s8gyUiioQJlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJImGCJiCJhgiUiioQJlogoEiZYIqJIRNW/r7CIbACwLN7mEBF1O/uo6vCsGUEJloiI/PESARFRJEywRESRMMESEUXCBEtEFAkTLBFRJEywRESRMMESEUXCBEtEFAkTLBFRJP8fBJW69caDFUcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAHxCAYAAAAof6RmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd5wdZfU/8M/ZZNM7qZBKKCHSCV06CnyR3hQEAgqCYEOsX38aFQtfC4jYRQMK0ok0UVqA0EOIdAgljTTSN22T7J7fH+e57GQy9+5zNvtkN8nn/Xrd1yYz5z4zd+7cc+fOzPMcUVUQEVHzq2rpFSAi2lwxwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIESy1GRMaKiIrI0My0oWHa2ITLHS8imqp9opItJsGGD62KSL2IDK8Q92gmdvRGXEVqJkWJe0snIm1E5Csi8pKIrBSRhSJyv4gc4GznYBH5m4i8IiILRGSViLwnIneLyBEVnreLiNwoIm+H5b8fPmtniMhmm4c22xdWxloAAuAzRTNFZHsAh4Y4ahnvA9gJwLcSLuOcsIwtgogIgJsB/BJAOwDXArgLwMEAHheRExzNHR4ebwG4EcBVAJ4CcBiAh0TkhwXLPw7AJACnAngRwK8A/AvArmG9/tCkF7YpUNUt4gFAAcwE8DyA2QDaFsRcGeLuDH9Ht/R6b84PAGPDdh66KbS7qT4AfCpsjycBdMhM3xtALYB5ALpGttWhzPRtAMwFUAdgQG7eq2H5h+Sm9w/PUQCDW3o7pXhsaUewAPAn2Bv7iexEEakGMBr2bfxauSeLSC8R+YmIvB5+6iwRkYdF5OMFsd1F5Gsi8oiIzBSR1SLyQfg5tX+Z9jWcI+wtIn8UkdkiUisir4rIeZ4XKiJTw6O7iFwbfpatEpHXROSL4cgmG//h+U8R2UFEbhGReeG0yqGZuKPCz8v5Yd3eEZGfiUiPMutxpIg8ISLLw0/TcSIyokxs2XOwItJJRL4hIhNFpEZEloX34RoR6VfafgDODU95L3O6Z2qmncJzsCJSJSIXicjzoe3l4d8XF/2Mbc73KrGLw9/vqOqq0kRVfR7ALQD6wI4uG5V9fm76+7DPThWAbXOztwWwVFUfyz1nDoBnw3/7xCx/U9O2pVegBfwD9lPpswDGZaYfD6AvgG8A2K7oiSIyBMB4AEMBPAHgAQCdYcn6ARH5nKr+KfOUnQD8CMDjAO4DsAjA4LCsY0TkOFV9oGBRPWBHG6sB3A6gPYDTAPxFROpV9XrH620H4KHQ5s3h/6fAfqbtCOCSgucMh+34pZ+BHQEsDdvgewDGAFgI4F7Y0c+uAC4H8D8isr+qLi01JCKnwj7Eq8Pf2QA+CuBpAC/FvggR6QngUQC7AXgTwF9Cm8MBnAf71TEXwPcBnBjifgVgcWhiMRr3NwBnApgB4M+wI6uTAPw2rPNZBc9pzveq2YlIBwAHAFgB22fz/gXgbNjP/r9uwHL6AtgXdkT8Zm72qwD2EpGPquqE3HP2ge0TZQ9qNmktfQi9sR4IpwjCv/8MO886MDP/AQBLAHQCcAUKThHAkms9gE/mpvcAMBnASgD9MtO7A+hdsC4DAcwC8HqZ9dSwjm0y00eGdX7N8ZqnhrYmAGifmd4LwDth3sGZ6UMzy/9xQXuHhXlPAeiRmzc6zLsqM60LgAUA1gAYlYu/KrOsoQXrMDYXf1OY/jsAVbl5XQB0z/x/bL7dgvdRc9NKP6MnAeiSmd4ZwMQw78yE79WJsC+u2MeXI9v9SFjHl8vMHxXmP+v8PI0K63FF2N4Lwmu+uCD2INhnaxXsS/YnsF+SHwCYAmDvDf18t9ZHi6/ARnuh6ybYfcP/vxv+PwR27ui34f/rJVjYEZECuK1M+yeE+Z+PXJ9rUHDuKUxbDqBbwXMeC/O7RC5jaog/qGDe6DDvr5lpQ8O0Ocgk5Mz8u8L8j5RZ3osA5mX+f1aIv74gtjvsqLLRBAv7ZVEH+1LqHPG6x+bbzc0fj/UT7IPhOR8viD8izHsk4XtVWufYx9TIdg8I8RPKzN8+zH/T+Xm6KLc+SwGcXSF+JIBXCp7zbZQ5r7s5PLbEUwRQ1WdF5GUA54vIFbDTBVWwb9VySudMu4vImIL5pXNI61ydFpEDAXwpPL8v7Cd61jYApuemTdHMz+yMGeFvTwDLKqxr1lrYEWfe+PB3j4J5/1XV2oLp+8OORk8TkdMK5rcD0EdEtlLVBQD2DNMfyweq6hIRmQzgkEbWH7CLMVUAHlfV5RHxTbEn7NfJ+IJ5j8ESfNG2apb3SlVHw770Ngmq+nsAvw+nIIbBEu4NInKgql6UjRWRj8FOT02E3cHxBuw6yKWwU2jHisghqrrZ3b2zRSbY4E+wo8hjYOfwXlDVFyvEbxX+fiw8yulS+oeInAQ7L7cKdoT0DuyIpx52O9ghsHN2eeXOF5Z2wDYVlp83X1XrCqbPCX+7V5iXtxVsn/leI8ssnRootT23TFy55eSVLp69HxnfFN0BLFTV1fkZqrpWRObDviDzmvO9SmFJ+Fv0Pmenx5yjXo/aRa/XAXxJRNoD+JyIPKSqtwN2URh2WmAFgJNUdUV46rsALhORYbDTI5+GHcVvVrbkBPs32G1Zv4cdRf6gkfjSjvolVb0mchk/hF38GKWqr2dniMgfEHf0tqF6i0ibgiTbP/xdkn8C7OdbkSWw85+9Ipddartfmfn9y0zPK334t4mMb4olAHqJSLWqrsnOEJG2AHojXOhLQUROBLC74ymLVfXqiLh3YEff24pI24KjxO3D37ccyy7nXwA+Bzt4uD1MOwB2FP9oJrlmPQpLsHuBCXbzoaqLReR22BXU5bC7Cyp5Jvw9CHbkG2M7AK8WJNcq2FXpjaEtbCfPX0E+NPytdNSe9wzs59xHVPXViPhJ4e8hsKv+HxKR7ohPKM/BjvoPFpHOEacJSl8mnqPHF2HnWg8G8HBu3sGhrUn5JzWjE9Fwe1mMaQAaTbCqukpEnoLttwfBElrWMeHvI45ll1P6Aswm8dIvtHK3YZWmr/fLYXOwJd4Hm/Ud2G04R6lqTaVAVZ0IS1Ini8j5RTFi3QGzPyOnAtheRLbOxAjs6uvIDVt1l5+En2+ldegFe+2A79acq8LfP2VfU6bdziKyX2bSP2G3pp0pIqNy4WNQ/mfrOlT1A9g5vAEAfp6/J1VEuoSEXbIg/B0c035Q+gL4iYh0yrTdCcBPw3+vc7TnoqqjVVUcj6GO5n8X/l4RzpkCAERkbwBnwK7m35F9gogMEJERue0KEdmnaAFi3c+/Hf57X2bW07CEe6Dk7hUXkUGwI15g/S+1zcIWewQLAKo6HetfYKrkTNg3/XUi8kXYvaKLYbdd7QpgZ9iFoHkh/irYKYgXReQO2AWiA2HJ9R4AxzXDy2jMbNhRxCsicjeAathN5QNgd008HtuQqj4sIt+E3WYzRUTuB/Ae7JzrENiR6gQAR4f4ZSJyIewc3BMikr0PdmfY/cEHRy7+0vCciwAcKiL/hh31DANwFOze4vEh9mEAX4N9EdwBoAb2k/raCq/tJrEuo6cDeFVExsFOlZwYlnGLqt4Yua6tzc0ATkboqioi98DOp58BOzK/oOBC3U9gR9TnYd2f7v8RkXmwI/4ZsBwyHPaetwXwa1V9sBSsqrPEus9+H8C/ROReNFzkOhm279ylqvc36ytuLVr6NoaN9UDmNq2I2ML7YMO8rrBv6hdgV4dXwpLMfQAuRO42ItiV4cmw0xDzYbc67QI7glMAhxas5/gy6zUWji6gsCPoqbAjxd/ALhLVwi5KfBGA5OKHouAe1IJ2PwrgVthtU6thR0CTYR04RhXEfwyWeFfAjmj/CWBE0euptA6we1L/F9ZBYQUscb4G+6ncNxd7WXidtcjd1oSC27TC9CoAn4dd7V4RHi/AOmNUFcQ323u1Efb/tgC+AuDlsM8uAnA/gAMaWf/RuelfhHUwmRa2Ty3sIOU22C/Bcss/AXaO9gPYEe1S2N0tFyNzD/Hm9pDw4mkzVOoeqr6fk0TUTLb0c7BERMkwwRIRJcIES0SUCM/BEhElwiNYIqJEmGCJiBJhgm0lSqPj56aNCdMPTbTM5BVcW6PU25WoZItKsJnyIaVHnVjZk0dE5MyWXr8UihL35kBYOdZFREaKyK1iJYBWicibIvJ9EenobOdUEfm1WAmgpeE9+HuF+LEFn7v8Y7PsJgtsuV1lvx/+VsN6FJ0A4DARGaWql7Xcaq3nWlg3R093Xo9SBdeiEbVoMyEi+8K6eFfDRrmaASsR810AR4jIEVo8/m+R78AGn18GKyJaWFstYxysN2GRs2H1uv4VuexNzhaZYFV1TPb/YvXcHwTwZRG5RlWntsR65anqfFj32lTtr4H1C6fNlIi0gQ3o0wnACap6d5heBevufAqsC+1Pyzayrq/AEuvbsLEn8qNzrUNVx2Hd2nel9eoB4OuwrtZjI5e9ydmiThGUo6oPwxKNwEbPX+c8nYicKSLPilUanVp6nliV02+JyGSxCqTLRORpEflU0XJEpJ2I/D+xKqy1IvKeiFyRHekqF1/2XGEY6egvYlVja8NPvydE5OIwf7Q0VE49JPeTbEyIWe8crIg8EKbtVmadzgjzf56bHl1ttxIROUhE7hGrwlsrInNE5BmxYoulGEUjlWND3F7h9dSEn7MPSZlqvpnnjAg/a2eIVQGeKyI3iciOubgmbacWcAjsV8rjpeQKAKpaD0twAHCRyLoVhstR1UdVdYpu+P2dZ8OKad4ZDiQ2S1vkEWwZpR0sv+N8FTZYyT2wb+vuwIffwI/AyohMgg13VwUb2ekmsTFTS0MCloYpvBV2OuId2M//dgDOhw3+Er+iIsfCBtdoDyvW+A/YqP+7wT40v4MNvvJ9WPWBaVj3KGF8heavD6/hnPDa80qJ7cP2xF9tt9zrOho2aM5SAHfDTmH0giWIz6Ph1E6jlWNF5ABYNd12sIqzb8PGnx2PMmOfhuXfCfspfU94zkDYqE/HishhqloaE9a9nVrI4eHvetWLVfVdEXkLwA6wn+rvbMT1uiD8/eNGXObG19KjzWzMB0KxtYLpR8IGdK4HMCRMG4OGonZ7FDxnbJj/9dz0DrCduR7A7pnpZ4b4p5Ep8oZ1K7yOz7VVWodDM9N6w86ZrgZwSMF6DSx4zePzcWHeUKxfYLADLFHNAdA2F98fNhLSC7np4+Gotlvh/bkjrM9uBfN65/5f2v5DC2IF9otEYT+Ls/O+VNoPctu1J2yEqfkARuaeszPsnOOkDdlOFV53D/gqyo7J7luNtH1beK2nlJl/b5h/TBM+T4eG5/7d+bz90YRCi5viY4s8gpWGooXVAHaEHQ0JrOT0tFz4HzVXq0tEtoLVEJqoqv+Xnac2gvw3YEc3Z8ISDGDjagLAt9XqGJXiF4qNlxk78PW5ALoBuEZVi4oJzoxsp1BY/1thRxhHYd3Bkz8NGz/0+tKE8BP5EAC3q+rNubYWh5/242Dn+n4buRorC9bL8zPyANj7+riq/jM371oAX4CNYZp1DizRXaqqr+WW/YqI/Al2jn6kqr7m3U6N6IHG65zlTUXDvlVJacDschcyS9N7lJmfwoXhb6O/ajZ1W2SCRcPOrLCjkCcAXKeqRbebPFcwbW/YB0iluMJsdfibrTBbqlo6oSB+fOOr/KFSxYCUV17HwhLHuVg3cZwLGzT8psy0JlXbLeNG2M/xZ8UG534UwJNN+NKoVM22TkQmYP0EW3odu5V5HTuEvzvBxqAFfNupLLWLqlHnQDd1YhUSTsdmfnGrZItMsKrq2ZmLKp+WKszuHR7ldMn8u1S1dE1BXGx1VWAjVFhV1afCubnjRaSnqi4SkT1hP5XH5Y4m3dV2Kyz3ThH5BOyc5vkI5URE5AUA39LMSPmNaEo129LruKBgXtaHr8O5nVpK0qqyTfBp2B0NN7eS7ZPUFplgnYqulpZ22qs0/r7ZslVLEV9dFVi3wurLjud53QCr7HAGrOxN6aJN/mdvU6rtlqWq9wG4T0Q6A9gXdqHsYgD3isge+Z/vZTSlmm3pObup6kuOVY7dTmWFC6ZfdiwTsAQec4rgzfB3hzLzm7OqbIzSF9gfNtLyWlZLnwTemA+UuchVJnYMCkq6hHl9YZVLn3cs+8HQ3mEF80Yj/iLXV8O0X0Uutw7AE2XmDUX58iyDwnOfhp3ymAcr91GdixsV2rgt4fv2/8IyvpqZdl2YNrwg/sAw77GCeW1gdwfkt+vlYdolznWL2k6NtFF6HzyP0ZFtH15hW2wb5k1FrnxQZNuHwnGRC/aFuUVc3Co9eB9sE6jqPNj5wlHhvtb1ykOLyHARGZaZVLqI9SNZt7JntsJrjOthtzFdLCLrFQwUkYG5SQtgScBFVWfAbmfaD3blvQ+AmzR39K1Nq7ZbSEQOFpGiX1WlI9EVmWmVKsc+BTtyO1iskGHWpVj//Ctg789iAN+TgsqpIlJVdD9y7HaqRFWnqq+irKjq2MjmH4PVJjtYRI7Pvh4AV4b//l5DBgzzqsP9wEXbaUOULm5t3rdmZfAUQdNdCvt59QMAZ4cLJ3MBbA27ELI3gE/BCiICdq/qGbDqp6+IyD/RUOH1eRR/6NejqvPFxk24HcCjIvIvWBHAbrDKtoNgVVBLHgbwSbFKopNgF18e17hqstfDbmH7ceb/RbzVdsu5BsA2IvIk7KhqNYC9YEdh02DdhrOvq7ByrKqqiHwG9qvhDhHJ3gd7BOw2uqOzC1bVBSJyKqwo5TNi/eNfhR1xDQrrvxXs9qy82O200ald1DsP9v7cLiK3w7peHwH79fEkGsqxl2wDS8rTYEfXHxKRE2F33QANp1r2z3RWma+ql+fXQ0S6wfb/WrSi7ZNcSx9Cb8wHmukUQSamHSzRPgU7h1eqsPkw7JzaVgXx3wXwboidCuBHsA4DUacIMvM+Ajv/9z4sEc2FHa1cmIvrC7uaPRf2U1YBjAnzhqJCFVnYxYglIeblRraXq9pumTZOh30RTQltLAXwSthGfQriy1aODfP3giXTmvB4CJYoK23XobBbuaYAWBXW4Q0AfwNw4oZupxbc90fC7omdH7bXW7AOGx3LbIP1tmdunyz3WO854XkXh/n/aOltsTEfrGhARJQIz8ESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkyw1GqJlcPJlrrZr/FnUUsTK52Ufd+GtvQ6tRQm2AwxF0hD/a3lIjJRRC4Kfbfz8YdK5XLE6xWSE5HtReQ+EVkY6j5dKyJdy6zP38XqdjU61F+F17SPiFwnVqa5RqzO1TQRuV1ETs+OoyANNbqmNnV5CSyB9Tj6PqzY3jrEaoFdLQ21yWaJ1SrLj8nQKBHpKFbK+k2x0tbzxEpdlx3LVkROEZHxYjXIVorIq2J12tp5l9/Iup0rIs+F/XJJWOYnnG2Mb2R/VRG5LvecMY3EH12wqB/D3q//bsBL3ixwLIJ1/R3Wr34erMvmCtgYp7+DjZJ/TpnnPYbiQbPXGVxbbAi+h2H1qm6A9dW/BDaYyWm52GMBnAXgY6q6zPtCRKQa1rf/IlgX2cdg3VZrw3IPh1UZuAM2HkJrtVhzVYBLxCpLPAUbiu8R2FgFI2DVI44Vkf1V9d2YhYgVnnwQNhLXRFitr0Gw9+VYETlcVZ/NPefHAL4F69Z7B4CFAA6CJZgjROQYdQz6UmHdfg4bRW0mrApAOwCfBHCPiHxBVa+NbGosyg/u/gVY+aJyA7lfj+Ly22/nJ6jqj8N6D4XVTdtytXRf3dbyAHASrK/0u8jUf4LtzPeEeSfnnnMoMn37I5ZxRog/ODPtr2Fa38y07rAP05834PX8MbT7EoAdC+a3gVX2vC0zbSgq9CdvgfdkaqV1gY0pqgB+kZv+xTD9AceyvhWecxuAqsz0E8L0V3PT9wzTFwHYNjNdYF/ICuCyZtgGB4S23gbQM/deLYCNlzB0A5exY1jGHKw/HOUYNDImR4V2x6JM3bQt5cFTBA1OCn9/oZmR1lV1NWw8UsAGdtkQQ8LfbBma53LzAOAX4W9RtdJGiciBsIGNFwI4SlXfzMeoap2q/g02wvwmJ5w2ORtWlHJMbva1sJGgjhKRbSPaEtiRPmBFLOtL89Rqej0BGyzlkMzTSiNK/VkzR8lqmeXb4b+XxL6eCkrr9SNVXZRZzlQAv4ENFHRewfM8SsMI/lWb4YibGjDBNigNvVb0k7I07aAy59a2E5FLReTbInK+iGxfEAPYSFuAjfJUMir8nQYAInIkgM8AuEhVyxWqa8yH426q6uxKgapa28RltLT9AHSE1eyqyc4ICfLf4b+HRbQ1HDau7Fuq+l7B/NLP5sMz08ruLyERLgKwraw7JnBTlC27XWa9XMKpkXNgR5qVihB+VEQuF5FviMgZItK7qcvckvAcbIPSUWvRB6J0FNQ2/PuN3PyzwuNDYmOUXpA96oCVSJ4JYJyI/B12LvRUAHeq6rxwVPYnADeq6r0b8Fo+Gv4+vAFtRBEbH3R3x1MWq+rVzbDoHcPfcqVOpoS/5UqlbGhbZfcXsRIwPTNtFyXtRoVz9tsAWFbmi9LzGss5GVYK/kGtfL76h7n/14rIzwB8Nxy1U5GWPkfRWh6wBFk619UrM70awD/RMN7l/pl5HwHwDdiA0l1gO+rRsIGtFXaRqyq3nB1hRx6LYGO5/g5AtzDvWth5sK1gR1T3wMZVrYFdFOsW+VpWhOWPcG6DoXCeg0XDebbYh6ftqeXiYT/DFcAVZeZfEOb/IWI5Z6JC6RPYhU4F8O/MtFJZmkXInGOEnYP9Teb1fmoD9smtQxszy8yvDvNrN2AZj4Y2Ti0z/yTYKYhhsMHGBwP4bNhPFcCPI/aNoU1dv039wSPYBjfDzukdBeA1sYoDq2Aj1Q+A/bwfDCu9DQBQ1VdhFz9KlgF4QESegtWsPxDAcbAEXXrOmwCOyS9cRA4C8HnYoNMLYVe0+8I+/F0A/Br2k/i0/HNbkqqOhtUU26Ko6pPhlqbPAHgp/GIp3UWwK+xXzghk9pfWJpzKOhQ2GPs/i2JU9a7cpOkA/iwikwA8A+ByEfmlbgEVYpuC52ADVa2DJcNvworWnRseU2BXckvn+RorewJVXQqrIgAA69XNyhORjrAifneq6u2wpL4HgG+q6l1qF6N+AeBUiauTVPo5uU1E7KaqOctRN7WtC2Clxd+EfTF+DlYB4VAA74SYRveXBOsVq8kXt1R1EuwCbTWsSgQV4BFsRtjJrkRDMTgAgFiRwu1h9YZiz6d9EP52joj9Iey0QOmqc+nG9kmZmBfC35Fo+PCWMwF2rvgIJD4P24LnYJuzHHWT2lL7HfxHFBTxE5FdYEevk/LzYqnqchF5H1anbICufx62ySW3w8Xac9H4xa1KPPv4FokJNs4nYffD/sPxnFK3zoo3uotVMP0yrAzz3Nzs9pl/FxXbK+ePsCvDF4rIrwrazS6/vW7YnQQnwj6osaYBaI4E+wzs/PSBItJVM3cSiPW6+3j476MRbb0D++m7g4gMK/gSLZ3SeSRmxcSqzw4GcI82/U6Qkkdgp66ORkNl4iatV85JsAq4jV3cKhQ6suwZ/ut+/hajpU8Ct6YHCi4iwY7OPoCdX9s6N29UmXY+DTt6qUWFE/ywpP0qgHtz04+EHVl8NzOt1CFheORrKXU0eBHA9gXzq2BVb2/NTBuKzbyjAeyWrBFY/4Z6V0eDCvvLENiF0loAu5Z5TdEXftCEjgawi60jkOkwU9Duw6HdUyrEdEVxJ5V2aLiQ93p+u2Tixnpe6+b44BHsuh4UkZWwSqY1sJ/qx8KOlI5T1Vm5+NtFZC2sa+VM2FHm3gD2AbAWwOfUbggv57uw86Qfz01/GHZK4LsiMgR2ket0WK+rxk4PlFwC6yJ7EYDXRWQ8rG94bVjm4bDbxG6PbK81+jbsfOdlIrI77JzgTrCkOA/FN/o/DEuCw7Bu189fAvgE7La5Z8XKdg+GXVRcAeB8zXRACK4L788k2BfwMFhZ9moAZ6vqSwXLL133WBvzAlX1KRH5JayC7ktiZbfbwXoF9gLwhYJ97FIA34ONBzAm36aIbAe7P3gugLsrLH4r2L4zEZZIZ8OOeg+Dvdb5sLskWu2FvBbX0hm+NT0AfA2W2BbDEtG7sG/qgWXivwG72j8DloRXwX5u/hXAbo0sa3cAawB8tsz8gQDGwe5MWAw7Goi6TSvXzr6wC2hvhbZqw/reBUva2aO1odiEjmBDTC/YuAHTYOXLZwP4S4X3bCrKHFXBym//AHZhsxb2y+U2ACPLtHUugCdhR5Krw3a9HsBOZeJ7wr70JjRhW4wG8Dys51oNbGyJT5SJHYMKXbhh1xgUwE8aWWY32HgWz8Buy1od9qH/AvgpMt27yzx/bLltvaU8WLabWq3SqF6qOrRl16R5iMjxsNuhjlXV+1t6fVITkbGwL6FhWvmX3GaLpwiotRsiIqWjgP1V9ZkWXZsNcwiAyZt7chWRZeCdBQCYYKl1uxpAj8z/1xsPdlOiqk0avGcT9GPYeeKSpt6nu8njKQIiokTYk4uIKBEmWCKiRJhgiYgSYYKlVqugSN8nW3qdqHEicm/ufRvd0uvUUphgM8REV5XNPO8T0lBZdFl4fmH/fBHpLyI3iVUsnStWObZvmdgrRGSxiDR5VCwRGSEivxaRV8L6rRarvHqfiHwmjGifjdfMbVGtRamq7Cv5GdKESrBFRGSn0M4/RWR6JjkU3mkjIp1F5KzwXr4R9pWasL98VZq/qmz0PlahjbHSeFXZwsGBxOY9J+kAACAASURBVFfV9ibY+1U4BOIWpaV7OrSmB4AbYT1P5sJGGPoVgNfCtBvKPOfSMH8+rNfXVbAePQrg57nYKlhvnBUAfgvr6bIW1lMm38+9Yk+vyNfzXVjPIYVVX70GdgvNdbAeZwpgYu45ijBQVEs/YBVQy64LbDCcCWGdn4f1ULopbLflAPZ1LOvLoZ21sG6hK8P/25aJPzrMXwDrbvxT2NgIs8P0JwF0aKbtEL2PNdLOibBeXkWP0v5wecHzfh7mzQjL/k143Qrg0grLGx1iRrf0vtRi+3BLr0BreaBpVWWHwrrHLsC6o9r3hA3OoVi3AsK+Ydo5mWnfC9P2yUxrCxuk5cENeD2lEf+nl0s0sL73j+ambUoJ1j1AS4W2dgzvT8fw/6mNJNjdYVUw2uWmd4V1t1YAX22GbeDax5q4jB6wL/1a5AaIwQZUtWWCZYLN7gw3hJ3hkoJ5u4d5j+Sm/yBM/37Bc84P867PTDsduVIusCHnFMBpmWnfgfU3H9rE1zIU1m98NYCdG4ltn/v/JpFgYaVZpoX1HVYw//Ew77AmLrtigm3kuaUSNPc0wzZw7WNNXMYXQjv/KJhX+lyc51m3MH+LT7A8B9ugKVVlvRU/Y6rKjoSVCf+WNr3/9nmwEZ3uUNX1zltm6aZbVbYplWA3llJ1gKgRsxqRtKpscEH4u97A4Rtp+ZstdpVt0JSqsmWrkarqbBFZDmCgiHRS1RWw84STAPxBRA6Ajd706TB9ooi0gY0E9SzsPFdTbcyqsofChgyMpqpjmmHRzVlVtrmdH/4WJSUv7z7mIiL7A9gF9kX1aG7exqhqu1ljgm1wH2wA6stE5GZVXQh8OHL79zNxPTP/LtVEKjdq/RLYoBfdAaxQ1ToROQ52oaB0uuB2AF9R1XoRuRxWMG83AD1E5New84nVAP4D4GJVfT/itQwIfzdG3/1DYeeRPcY0w3Jjtj2w7lgGyYnIpbALYJNhX5YbyrWPNaH9Ul2uorIxrXIbb0qYYBu4q8o2hdqg3Wfkp4tV+PwBrIrBFBEZB0tel8AK6V0L4E4R2U/DCa7WIByNjmnh1WgVRORk2AA1c2CVAlyFBDc2EekO+6JfDbujhZoZz8EG2rSqsrFVPyvWZRIRgd069TKAq0KyPQF2C84NqjoOdsV8H9ho8o1hVdkNr7jqIlb88WbY/nGoNqHOVRnNso+V8WnYaao7tbjsdqvaxpsiHsFmqL+q7Juw+kc7AHg695wBsJ9uMyPOjV0Cu0Voj3AaoVJV2Y+g8SJ3E2AXHo6AJe5kWvAcbHNWld0gInIa7P7bOQAOV9UpjTzFo7n2sSKli1t/KJqpCavabimYYOOUqyr7CIADYefcns7Ni6r4KSJDAfwEwA9V9bXc7KZWlf0r7Ij3FBEZWdBudvkbWlX2ULTMOdhmrQTbVCJyFqxMzPuwW8Kau8LqBu9jRURkX9i5/rdUdXwjy09R1XbL0NL3ibWmB/xVZYdhA28Ch9X0ehGZ+y0BbBee+5fMtFKHhMMjX0upo8F7KF/99misf2/vJnEfbJjflEqwg2EVVzs1suypaOQ+WNgppDrYbXxDPK8JdhohJt69j8F+uo8AMKBCu9chojME2NFggx48gl2Xq6qsqr4nIl+DdUGdKCK3wC4YnAorWvgLVc0fdXxIRD4LOwLcR1U/vGdSVd8WkbsAnCciXWAXuUbDqqY+WtDUelT1x6Ef/fcAPC8iT8Gq3y4D0A/AwbCfeBNj2mulmlIJ9gZY6ZbDYMkOACAivWFdQkt6h7/XZcZm+KmqvhHiD4PdJVAFe0/Os1Pp61isqlfnpnmryjZlHzsJdrR5PWy/WYeIdINdaK0NMZWW35SqtlTS0hm+NT3grCqbed5xsCqfNbA+8M8DOLeR52wTlnNFmfk9YDv/4tDmOADbNOE17QTg17AvjaVoqLz6LwCfwSbakysT460EOx4FR5BoqKhb6XFoJn50RPzU3DIEdtT3Hpw9xDz7WGbdxpaZfzHK9NyqsPzRiKxqW7Aeo1t6X2qpB0vGUKslIuMBHKKq6x0abopEZFdYyetLVPW3Lb0+qYkNU/hXWDfbsS27Ni2Dt2lRqyebz3iwh8BGamuODgitloTxYLH+RbEtDo9gqdUKR0BDM5Nu10bGVqCWJyJnYt3b58ap6uSWWp+WxARLRJQITxEQESXCBEtElAgTLBFRIkyw1GoVFOn7ZkuvEzVOWFX2Q0ywOSJyrIj8R0RmishKEXlXRG4LAxOXe84BInK/iCwMz3lJRL4cBtDOx3YTkd+G9heIyD0iMrxMu58VkTUisscGvJ5BIvJTEXlBRBaF9uaJyEMi8qUwZF02fmr4UAxt6jIT+BVsTN4J+Rki0kZEvhK2+crwHtwfBjSPJiIDReR/w3v9tojUh+2wXZn4odJ4hVYVkYOa9pLXW170PhbZ3qki8m8RmS9WjXe6WEXd/XJxPUTkayJyo4i8JiJrw+s6skLzrCobsKtshohcCeDrsN4242BVDraD9W0/RUTOUdW/555zAoA7YH2yb4GNWVAaVPtAWLfNrLEAjgfwd1h3ztEAHhYblOXDEZHESnX/HMCVqvpiE1/PZ2HjyLaH3eD+DwCLAGwFq3pwNaw8Te9ybbQSV2tBd0yxvqk3w7qNvgl7rb1g3TgfF5FTVDX2Qz4KwBVoGL9hCSoPJL0Y6w7EnjUIVtVgAax78wZpwj5Wqa22sB6CZ8J6v90Ce639AewPK2f0TOYpQwH8X/j3TNhnol+lZajqTWFZo2GfnS1XS3clay0P2A5WBxtyrm9u3mEIFWdz07vBxv+sRWZAFdjIV0+F53wyM71fmPbdzLRzw7TTc23fAxuspF0TX89Zod2FAI4tE3MggMm5aVPD84a2gvdkbKV1gVWgUORKZAPYO7wn8wB0jVzWQAAHIQz4g4Yutds1Yb1/Ep77y2bYBq59LKK9H4XnXIGCirsAqnP/7wkb9rJX7j05MmJZo7GFd5XlKYIGQ2CnTJ5V1eyg2lCrVVQDoE/uOaeGaTer6sRM/CpYZVjA+n1nlwGse1TzXG4eROTTAP4HNljJau8LEZGusMFBAPvw3VcUp6pPwsah3VSVtu13wjYHAKjq87Ajsz6w96hRqjpTVZ9Q1aUbskJiJYZGh/8WFRH08u5jldatP4DLATyjqt/R9QfCgeaqMKjqIlV9WEMJJfJhgm0wBTYQyj5hZKUPicjBsHr3D+WeU6ni5uOwUwAHiEhpXNeYqrL9YD/dr1LVZ5vwOgD7UPaCfZD+UylQN9GqsmKDoB8A28ZPFIS0VMXT42G/hh7XMPLWBvLuY5WcChsJ62YR6RjOw35TRC4Rkd2aYV0ph+dgA1VdKCLfgA2B95pYTawFsPLQx8PGbf1c7mmVKn6uFZH3YBUItgXwuqrOEav19b1wYWsV7BTBdFjRRcBG71oIOzfaVBuzquzuAE50Pu1qVd3QMiPDAbSBnbYpGvqvpSqelooIFlYJaALXPtZIW3uHv51glZEHZ2eKyB0AztGmVUegAkywGap6tYhMhQ3GcUFm1tuwod/m5Z7SlKqb58IuGhwHO482HsCX1cpznArgZNigIPViVWXPAtAFdr7t81qhOkHGxqwquzv8FQ3GYsPrOLW6iqfhzouPwb6Y72imZpvzdfYNf38IO299Iixx7wy7QHgKbLzg0U1ZUVofTxFkiMjXYWW0x8KOkDrDfs6/C+BGEfm/8s+Oo6pLVPVzqrq1qvZS1WPVqsj2gu3kv1XVJ2B1wS6ElVc5Hnbl/4Hw07jVUNWxqirOx9SWXu9ELoCN+Xp9Kz31Uvq8L4QNIP+iqi4Pp6KOhyXXs8MdLNQMmGADseJ9VwK4W1UvU9V3VXWFqk6CjRD/PoCvisi2mac1Z9XNa2CVE74pIp1hFy7+pqrXqOoDAD4Pu/3nzIi2WFV241eVbQvgvPDf5ri4VdKcr7MU83D+Yp5aQcNnYTlhVP6J1DQ8RdDgE+HveiVZVHWFiDwHS7R7wI5oAbv3chTsPN8L2eeED9wwWGmQioXwRORY2KmAj6nqMrGBmduhfFXZxkyA3Yd5BDbsXG6jWvAc7Duw2+q2FZG2BedhN3bF0+Ngp2YeU9U3Gwt2aJZ9LNMWUD4ZLwp/O/pXk4owwTYoXYXN34qF3PTsbVOPwBLj0Vi/4uzBsIsJj1f6uRh6Uv0BwHWqmr9LoalVZW+HdVLYX0SOLGg3u/wNrSrbIudgVXWVWJ2xg8Ij/8W4sSueli5uNefRK9AM+1jGQ7Av3J3LzC99eb/XhPWkIi19I25reQA4HXZT9Bzkal/BPqz1sJ/wW2Wmd4PVgGryTeAA/gy7GNU9M61zaPPhzLRSh4TzI19PqaPBAgBHlYnZD8Ck3LSp2Lw6GnTLPWcArOJq90aWPR6RHQ1g9zDXwXo5tY98TaMjt4F7H4Ml3REABuemC6yCsQI4KTfvgjB9CoA2EevPjgYRDx7BNrgd9g1/JIDXxaq6zoEVDfwEbOf8pqouKD1BVZeKyAXhueNF5GbYBYTjYbfX3A674b1Q6M/9GdgFhw+vEqvdUfAbAF8RkQdgdzGcB2AGrJ93o1T1RhHpCLtw9oCITIZ9IEtdZfcHsBssKWyqbobddXEqgBdF5B7YazsDdgvXBbp+x4GfwL6szoMliw+JSPb/I8LfK0WkJvz7z6q63ngIAD4LO3cZc3HLW1W2KfvYPrAj+sdgVYtLbamInBum3xG211uwI9djYAUNz1XVumxjIvJzNHSnLt0C+LXQIQawigXjYl7PFqelM3xregCoBvBlWF/spbAPwTwA9wL4eIXnHQjgfljyWgngZQBfQeUjgS6wn2J/LzO/PazDwQew+2UfQZlKqY28pkGwi3eTYD/L14Q2Hw2vNX+ENxWbyBFsiGkbtvXLYdsvCu/FAY20ObpgnjbyKHpOG9gFUAWwY8RrejHsWz2d2yJ6H4MlVQUwvkxbw8J2mBX2h9mwsTEK1z+zT5R7jCnzvNHlttuW8mDJGGq1whHluQCG6WZwa5eI9ICdsvmFqn69pdcnNWFVWd6mRZuE92TzGA/2INgR4y9bekVSElaV/RDPwVJrNg7287Sk6PznJkNV74HvbpBN1U0AJmb+v0VWlAVYVZaIKBmeIiAiSsR1iqBNl87adque0fFVtRIdW9/BdyTdZnl82wBQl/CHWdWaxmPW4Vh173aRKmf88vjv2Pr2zrbrfO+Rp3VZbyTTRtqudq57dfwCdK3vOKVtu7rGgzLWOtuvXhQfv7azq2lo23S/ePt18Q3Fu2hNJ1d83cLq6Nj2W61qPChYMacGtYtXFe7srgTbdque6P+/X4qO7zolvvmaHXxZqtcLvtPHS3Z07Bj1vsTQaY4z2beLj125o6+TVdsOvu3Y8dku0bFLd4i6dfND7Rb6ykXVO97S6mW+bb5ygG/dO/aNH7Fv5QJfz9J+gxY1HpQxf1FXV/yAO+J3sLn7+JL3mj7OownHZ+myAyoOXbyeO2ft7oqvuXXr6Nhtz43vYf3w+eUHTuMpAiKiRJhgiYgSYYIlIkqECZaIKBEmWCKiRJhgiYgSYYIlIkqECZaIKBEmWCKiRFyDvXTqN0i3++Rl0fHL9lsZ33bn+K5pALBqpaM7FIA1i9o3HhR873Df4OxX3nyqK75uxLLo2LXzfL2Eek/yfWcuHRbf06aNs3JX3xdWNx6U8f558fEdn47vgQYAbVf6ungu3De+x5Ks9PVYa7PC9x7VObsoj9h1enTs6+/E924CgOp58d1NAWBtd0eXY/F2l/dt965THb3/HKFTbvklVsydUfgMHsESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElIircmBd53pX99eRA2dHx05bFF+tFgA6dPR1w1yzJL6r7M9u8HV97XrgB6741Q/0iY5dsofvdc7fyxUObR/flXGXHWe42p41b5grftu+8fvLm7v6ukpjje9YotM78e1/7ZzbXW1fc/UprvhVW/nWfcnvBkfHttvFVzzyqGMmuuIfv37v6Fg5cqGr7f7/6wrH4l16RMfO+Vh8kcy6e8p38eURLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXiKtvdYeAgHfjFr8S37qjCe86xj8YHA7jprVGu+FWzO0fH9nva1z973j6ucHSYF/+91mGBs9z0HnWu+G6vxw9HUdvL1TQ6zfGte/f34vt/1wxyDaOBpUcsd8XLlPj9pWqtb3/xfC4AYNWg+BLiACDV8eNLDL7Nd4w1e3/fdm+3NH7brO3gahptfMN0oNt78dtl7r7x7c76xdWoncGy3UREGxUTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJuDoWy1qgw7z4vsUDH5gfHTvu3cM8q4IOx/lqqK9yxKqza3l9Z1///+qa+O+1JTs4O647rf5oTXTsmlpfP/SVdR1d8YtHxm+XXi+5msZBQ991xU+asGt0bIcF8X3cAWDhR3w7WP9H27jiF29fHR0786wVrrY7dVrmiu96U/fo2EU7+o73+j1X64qf/tn4z2m3CZ2iY+dWWA0ewRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUiKvvY5vVQLdp8d3NXv9ij+jYqi6ezqyAzohvGwAO2OvN6Ninum3rarvbi756w6u7xcf2/8g8V9vL7+7vil82pEt0bH03X5fgQQ/6SmVPOza+e6Iev8DV9oQHd3HFt+kZH7u2k+84pW5bX/fUDzr69q/+T8W/T0tr4rc5ANSM9HWX7rkyvhtx2+W+7bjjla+64t99MX4fWLJPfDfcuvvKd2fnESwRUSJMsEREiTDBEhElwgRLRJQIEywRUSJMsEREiTDBEhElwgRLRJQIEywRUSJMsEREiTDBEhEl4upYvKYzMGc/R05uvzo6tPuTvv7Wi0atccW/8MDI6Nje03ylshfs7ivbPOjB+L7iK2b2c7W91Lku3Ycvio7t2Wmlq+3dfjPDFd92yYDo2IUrfX3o29X4SmWv2CX+tXZ/yrfv1r3qW/e+k337+vSj4j+jbZf79vVB9/qOyebvHJ9iZO8lrrYn3LynK7599/jXWru1Y5tXaJZHsEREiTDBEhElwgRLRJQIEywRUSJMsEREiTDBEhElwgRLRJQIEywRUSJMsEREiTDBEhElwgRLRJSIr8h5laKua3w/+l69a6JjF2/fy7UqHaa3c8XXV8f3Q14UP2wBAEB7+vqKzz43Pr7TE9Wutk8+5BlX/MQFg6NjV6zxrcudr+3uim8zM75P/5p+8eNcAMCnPjXBFT99Zfz+ePGBj7jafqhmZ1f8LXv4+txvc2Pn6NjZp/nGl6ia4NsHqpfFxy6d3cXVtu7o+9wNGfpBdOz0KY4xQLT8OBc8giUiSoQJlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEmGCJiBJhgiUiSoQJlogoEVdX2TYrBT0nt4mOX1Qb392w3cDlnlWBLOrqiq9eHl+2uXrXha62l9Z0dMV3nBDfJXDxzmtdbQ9u71v3/ywfER1bM627q+0O833f32s7x3dnrp7r6yp960MHuuIlvkc4Vn80/jMBAM+/NcwV3+UN32vd5vI3omMX3hv//gPArE/7Pqedn4zvWqtVvhLiAx7xbff2F8R/lnr+N37fnbei/DwewRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEirrEI6rrUY8lHV0XHx/f+B9o/6RtbYFVvX79l1MevTb+u8eXGAWDVJF/JcTkyfryAnuJ7nb997WBXfFVVfXRsl+m+7+OanXyltbu9HN/nfumOvjEaht/qi19yeXy96Ree297V9jEHTXbFPzh/D1f8s//dLjq2x1JX09BX40uCA0Dbo+dHxw76Uw9X2/PPdtQEBzD/uUHRsW37xeeL+grDLfAIlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEmGCJiBJxjUWANVWQ2R2iwztutyQ6dmW/+HYBoK6jr49+vaO0/IwHh7ja3uqtOlf8ir3i173qHt84B3/41u9c8aPv/1x0bFffy8SQwfH90AFgxtL+0bEDH/KMdAF867rrXfEX3XFhdGzPKa6m8a/Ou7jiOy/0vdb66vjjpiX7xI8tAgCoqdDxvsizvaNDV+3oa7rLvb7xS5ZtE78dt/v4u9Gxc26pLTuPR7BERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJeLrKltdD2wd37WuzaPxZXiHXfOUa1Xe+dn+rviho2ZGx743dytX2x906uiKXz2ze3Rsl+6+bpLn33GxK77r9oujY5evil9vAFgx31eGub5zfF9cqXN2lf1BfNdXAKg7onz3x7z2z/u6j1Z3j28bAFYMaOOK147x27HnM+1dba/p4tvuXafHl4Wfv7uvbcAXr45st3Blp+jYtfXlj1N5BEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJuMYiqFpZhfavxPe7b7Mqvjz19DEHeFYF7YYtdcXPu3tQdGxn59fOyj6+EuLd3orf7N2nrnW1/f8uvMUV/1TN9tGx//39Hq62lw72jdFQ2yu+b/nMk33lpvs85OvPX/VBfJ33VecsdLW9doGv3LS2j+/PDwBtF8bvX0t28O27PV9zhaNmUPyHqY2zgnhtT9+6d9wpftyNeS/2i45du6L8WBQ8giUiSoQJlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEXGMRAEBVfMl1LB4Z31dY+q70rcjULq7wVdvHr3jnqb5+651n+eqz9/5v/Gt955PxfeIB4PWV27jiO1Wtjo4945f3u9r++eSPueKrpjnGuZjT3tX2vIN9YzqgTfy+u+TNXq6mtYdzXap8fe47LIzfH/s/5fvcvX12+X73RbYdPjc6duqcrVxt1y/3pa9BXZZHx1bvGz/Wyby/lP8M8QiWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokRcfc3aLlf0nVgbHb+2U3yX0xmn+XJ9z7d83VOXDYmPXzYivvsoAHSY4evO+t7xHaJjO/b3lScf+9yBrvhrD/tbdOwXnjrT1Xb1NF931m2eWBMdO2cf3zYfvFd8l00A+MKQR6JjfzX1CFfb097s74rXjo7+6QDU8dGYs18nV9uH7/aSK/7xd7eLjm3/hq/Me6fZvi7E78+N70ZetSZ+I66uKb8v8giWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEvHVva0C6jrE5+Tpp8T3oW433ddvvf1SXz/kPncti46dfVA3V9tV8V3oAQAd91oYHVv7jK+U8VmnP+GKv35O/NgFX9wrvn8+ANzce5QrfvXk+Ne6ume9q+3PDX7MFf/Ikp2iY+uu7edqWw7xjaPRYY5v3IXaXvGfjboB8WOLAMDEf+zqise+K6JD13T1faZrfEMXoGp1/HYf8GT8dnl/Wfn15hEsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJSKq8f1/Ow4YpEPPvyw6Xh0jHcja+FgA2P7od1zxM/++bXRszRDfuqzt4utDLX3i+znXL/D1Q+/1su87c+nhjr7ii33jRbRd2sYVf/yRz0bH3jl5T1fbXifsPjk69vH3h7vaXjythyu+erHvPV0zaHV0bJt5vv2ryjd0AdqujO//v3a3+PFCAGDNPN9gBLI2fl22eSx+rIvJj/4KyxbNLGycR7BERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUiGO0AKBHr2U44fQJ0fF33v3R6Nhu+33gWRX8943Brvi+q+Jju051NY0VA3zfU1uPi+8TPXt/X3/+BfusccXLmvj2jxn1kqvtBx/ZwxV/18vx8Vtvs9DV9l69Z7ji758yMjq2R9eVrra1U50rHkt9+1eHKfFjRtTu5Fv3+vm+8SgG/zu+/dl1XVxtr94hfswFAOjwXvy4C21Wxg+OIhWGLeARLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJuLrKLpvZGc98Y5/o+KHfnBYdu/qnAzyrgmX7VrvilzoqKw+7db6r7de/1N0V/8458d9r7Wb5SoJ3e9W5XUbEd9t95F++rq+yfXxJcADABx2iQ2uf6Odq+shvPuCKn9xzYHTsns5uuHe/PcoVv7ajbx+orol/T9u/6St9PewG32udc1T8dlw+LL57KgC0m+Xb11dtF19zfFbH+G61a14uv715BEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJiGp8P+deO/XRI/9ycnT8nOXdomNnveAbi6D/nnNc8XMn9o+OVefXTl17X1/x+p7xpbU7d3fUGwdQ/6JvXIROjrEOFh/hK/Hc8yFfP/flA+L70Hvt/j+vu+KP7z05OvaHr/yPq+1V07u64jvM8+2Q7ZY6gp2bfPGuvrLwHWfEjxfQJn6oAIv37Y5Y2Td+V4dH1gAAByhJREFUX+83Mb60+uTxv8KyRTMLtySPYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCiRtp7glQs74PVbR0TH18eXFod09/XnnzMpfmwBAKgbHN+nX+a1d7Xd93lXOKrq4vtnz93HV/tdd/R10O40u0N0bNU039gCq3r5Orr3f351dOx7p7uaxouzBrril66J3y7b957vanvhDZ198efWuOJrHeNRVMVvcgCArPYdk8nuS6Jju93cxdX2wp3auOLrOsbnmKVD4lNjfbvy+zmPYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKxNVVVgCIo0frshGOfnhrfLl+t52mueL/+/ag6NhOC3zrMuJLL7vin/7PztGxO1z5tqvtd3/j60JctTY+tut7rqaxcF9fP8wVA+N3x449lrnarq/3ddt97ZXB0bHavt7VduedXB87rJzmK/PdY278h7TrTMcOAGBeO1/X7fr58d12l23jahpdp/m611fFV+LGvCPja4jX/bP8evAIlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEmGCJiBJhgiUiSoQJlogoESZYIqJEmGCJiBJxdYquawcsHxjf/3eX7WZGx778jq+s8mtPbuuKH/mnWdGxM0/wdYqesriPK77jbouiY+eetJ2r7bZtl7rilwx3tD3S1/ZFOzzniv/DY4dHx65929c/f013R0d0AH8+5s/RsRfddqGrba8e2y10xa9Y3Ds6duH+vnEUOnb17QO1s+JLlK8e7hu7olNv37p88Erf6FhZ5BhzoY5lu4mINjomWCKiRJhgiYgSYYIlIkqECZaIKBEmWCKiRJhgiYgSYYIlIkqECZaIKBEmWCKiRJhgiYgS8RVor1Ks7Rrfp/uNp4dFx3ab46tbf+BZk1zx/+60e/y6vOVqGose6++KX909fjyHbr7NgtVvdnPFr+m3NjpWXomvcQ8Af5p6hCt+wLPxsbOOWuNqu80i365+RMf4/by6xvcmDT36PVf8ot8MdsVXnTs/OrbNE/HjFgDA6u7tXPE9ZsTH9ntipavtxbs6xwA5bUl0bIe74/f1eSvKz+MRLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJOLvKAtIpvmvl2s7xscvbtHetykMP7eGKr3L0Zly8l698sLT1lT7uNrFDdOyiA1e52u7xtG879tlndnTs1LmDXG33GrHAFb98WnzXx17POsoqA1j00VpX/G8Wx7/WlYN93XZfe9XX9VWP9O1fQ34X381zzbauprFiJ99no6Yqvmvtol193bzbb7XcFb/NrztGx67oH9+dHRVCeQRLRJQIEywRUSJMsEREiTDBEhElwgRLRJQIEywRUSJMsEREiTDBEhElwgRLRJQIEywRUSJMsEREifjGIlBAV7eJDu/Uq0I92xwZ7usrvnxuZ1c8HGMRfGT4+66mX5/pK9td76h8vO1fXU1j9n6+EtKzlsb3/17Ty9cnfs3a+H0FAJYPjO//Xb3E9zoH91/oir9v7i7xwe1826V9L9/4EtVPd3XFz3MM0zHosOmutmufcJYQd3ysBzzjaho1A33bZfefxS/grif3jo5d+3j5eTyCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokSYYImIEmGCJSJKhAmWiCgRJlgiokRcYxHIWkH1B/FP6fFgfF/hxcN9ub7fQfNc8R7vL4mvKw8AXZ+Lr7cOAHWHLY6OXVDbw9X2wIdrXPEz9orf7n2e8b1HNYN7ueJ15MroWFns2+ZLxm3til/gGOriHxf/2tX2hdd+wRW/9SemueKnPzIkOvatab5xNHrOjR8vAgC6zKqLju32lRmutpeMG+aKv/PFPeOD2zlep5SP5REsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIkwwRIRJcIES0SUCBMsEVEiTLBERIn4ynbXA22Xx5dLXjo0PnZVH1/p48639HHF154U3z116QddXG1vtcLXfXDJvPh+mG2G+NrucGx8qXQA6Hxb3+jY1acucrW9akknV/zgm+J3x1kH+rbLqj6++D4vxMdedJWv6+vy4b59/c23fd18q7vEv9Zdhs90tT3v4aGueDiqq7/2qq8keOd2vlXp+1h1dOwH+3reo/IvkkewRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElAgTLBFRIkywRESJMMESESXCBEtElIioxvdbFpEPAPhqCBMRbd6GqGrh4CiuBEtERPF4ioCIKBEmWCKiRJhgiYgSYYIlIkqECZaIKBEmWCKiRJhgiYgSYYIlIkqECZaIKJH/D0Xu4HH3jebFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Objective evaluation"
      ],
      "metadata": {
        "id": "La082HsS3_9j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Spread-skill plot"
      ],
      "metadata": {
        "id": "hh5u_dtm4BBf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BIN_EDGE_PREDICTION_STDEVS = numpy.linspace(0.01, 0.61, num=21)\n",
        "\n",
        "result_dict = get_spread_vs_skill(\n",
        "    prediction_matrix=testing_prediction_matrix_with_bn,\n",
        "    target_values=testing_target_values,\n",
        "    bin_edge_prediction_stdevs=BIN_EDGE_PREDICTION_STDEVS\n",
        ")\n",
        "\n",
        "_, axes_object = plot_spread_vs_skill(result_dict)\n",
        "title_string = 'Spread-skill reliability = {0:.2g}'.format(\n",
        "    result_dict[SPREAD_SKILL_RELIABILITY_KEY]\n",
        ")\n",
        "axes_object.set_title(title_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "iG9rfKPe4DWC",
        "outputId": "bc3cbf20-e0bf-4800-cfe8-5de83f55c370"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n",
            "  out=out, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Spread-skill reliability = 0.095')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discard test"
      ],
      "metadata": {
        "id": "Av1o091M4LPM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DISCARD_FRACTIONS = numpy.linspace(0.05, 0.95, num=19)\n",
        "\n",
        "result_dict = run_discard_test(\n",
        "    prediction_matrix=testing_prediction_matrix_with_bn,\n",
        "    target_values=testing_target_values,\n",
        "    discard_fractions=DISCARD_FRACTIONS\n",
        ")\n",
        "\n",
        "_, axes_object = plot_discard_test(result_dict)\n",
        "title_string = 'Monotonicity fraction = {0:.2f}'.format(\n",
        "    result_dict[MONOTONICITY_FRACTION_KEY]\n",
        ")\n",
        "axes_object.set_title(title_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "YyT7tYub4MJ7",
        "outputId": "cb9e3152-6ad2-47ff-8fa8-d77877b6786e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Monotonicity fraction = 0.89')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ROC curve"
      ],
      "metadata": {
        "id": "86HFhXLvCAe-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_, axes_object = pyplot.subplots(\n",
        "    1, 1, figsize=(12, 12)\n",
        ")\n",
        "\n",
        "pofd_by_threshold, pod_by_threshold = plot_roc_curve(\n",
        "    observed_labels=testing_target_values,\n",
        "    forecast_probabilities=\n",
        "    get_mean_predictions(testing_prediction_matrix_with_bn),\n",
        "    axes_object=axes_object\n",
        ")\n",
        "\n",
        "real_indices = numpy.where(numpy.invert(numpy.logical_or(\n",
        "    numpy.isnan(pofd_by_threshold),\n",
        "    numpy.isnan(pod_by_threshold),\n",
        ")))[0]\n",
        "\n",
        "auc_value = area_under_curve(\n",
        "    x=pofd_by_threshold[real_indices], y=pod_by_threshold[real_indices]\n",
        ")\n",
        "\n",
        "title_string = 'Area under curve = {0:.3f}'.format(auc_value)\n",
        "axes_object.set_title(title_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "fjeZSAhFCBaf",
        "outputId": "bcbaa441-3829-4c08-ffea-fb51fda369f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:451: UserWarning: The following kwargs were not used by contour: 'axes'\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Area under curve = 0.950')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performance diagram"
      ],
      "metadata": {
        "id": "ObW_5NwPCkCd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_, axes_object = pyplot.subplots(\n",
        "    1, 1, figsize=(12, 12)\n",
        ")\n",
        "\n",
        "pod_by_threshold, success_ratio_by_threshold = plot_performance_diagram(\n",
        "    observed_labels=testing_target_values,\n",
        "    forecast_probabilities=\n",
        "    get_mean_predictions(testing_prediction_matrix_with_bn),\n",
        "    axes_object=axes_object\n",
        ")\n",
        "\n",
        "csi_by_threshold = _csi_from_sr_and_pod(\n",
        "    success_ratio_array=success_ratio_by_threshold,\n",
        "    pod_array=pod_by_threshold\n",
        ")\n",
        "\n",
        "title_string = 'Max CSI = {0:.3f}'.format(numpy.nanmax(csi_by_threshold))\n",
        "axes_object.set_title(title_string)"
      ],
      "metadata": {
        "id": "rNmGBNGSutki",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "outputId": "1189a66f-6f86-41a4-e150-c50c799bba9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:545: RuntimeWarning: invalid value encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:259: RuntimeWarning: divide by zero encountered in reciprocal\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:272: RuntimeWarning: divide by zero encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:272: RuntimeWarning: invalid value encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:590: UserWarning: The following kwargs were not used by contour: 'axes'\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:610: UserWarning: The following kwargs were not used by contour: 'axes'\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:259: RuntimeWarning: divide by zero encountered in reciprocal\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Max CSI = 0.538')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Attributes diagram"
      ],
      "metadata": {
        "id": "m5kQqkL4HYWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure_object, axes_object = pyplot.subplots(\n",
        "    1, 1, figsize=(12, 12)\n",
        ")\n",
        "\n",
        "brier_skill_score = plot_attributes_diagram(\n",
        "    observed_labels=testing_target_values,\n",
        "    forecast_probabilities=\n",
        "    get_mean_predictions(testing_prediction_matrix_with_bn),\n",
        "    climatology=numpy.mean(training_target_values),\n",
        "    num_bins=20, figure_object=figure_object, axes_object=axes_object\n",
        ")[-1]\n",
        "\n",
        "title_string = 'Brier skill score = {0:.3f}'.format(brier_skill_score)\n",
        "axes_object.set_title(title_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "v4NVQ-svHZtt",
        "outputId": "ea9b328a-3892-403c-d2fe-caa966f95872"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n",
            "  out=out, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "/usr/local/lib/python3.7/dist-packages/descartes/patch.py:65: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n",
            "  for t in polygon])\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n",
            "  out=out, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Brier skill score = 0.484')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-yiy1VIjD5fd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}