MassParameterisedNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MassParameterisedNN.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JulianGlatzer/HplusML/blob/master/MassParameterisedNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "sYfP614dzeR9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Standard imports"
      ]
    },
    {
      "metadata": {
        "id": "dcNhGcP5zbUd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "region='ge6jge4b'\n",
        "invertTestTrain=False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QTQz7QFfzqum",
        "colab_type": "code",
        "outputId": "931cb459-3a1e-4006-dd3e-424ef77dbe16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "cell_type": "code",
      "source": [
        "! rm -rf HplusML pandas_INC_{region}.h5\n",
        "! wget https://jglatzer.web.cern.ch/jglatzer/hpml/pandas_INC_{region}.h5"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-03-21 15:58:06--  https://jglatzer.web.cern.ch/jglatzer/hpml/pandas_INC_ge6jge4b.h5\n",
            "Resolving jglatzer.web.cern.ch (jglatzer.web.cern.ch)... 188.184.64.89, 2001:1458:201:a4::100:53\n",
            "Connecting to jglatzer.web.cern.ch (jglatzer.web.cern.ch)|188.184.64.89|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 51165780 (49M) [text/plain]\n",
            "Saving to: ‘pandas_INC_ge6jge4b.h5’\n",
            "\n",
            "pandas_INC_ge6jge4b 100%[===================>]  48.79M  4.25MB/s    in 14s     \n",
            "\n",
            "2019-03-21 15:58:21 (3.49 MB/s) - ‘pandas_INC_ge6jge4b.h5’ saved [51165780/51165780]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ynBODauaztfj",
        "colab_type": "code",
        "outputId": "b3fb68cd-b519-4668-82a3-d7ca07e070da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/JulianGlatzer/HplusML\n",
        "import sys\n",
        "sys.path.append(\"HplusML\")\n",
        "!cd HplusML; git pull origin"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'HplusML'...\n",
            "remote: Enumerating objects: 86, done.\u001b[K\n",
            "remote: Counting objects:   1% (1/86)   \u001b[K\rremote: Counting objects:   2% (2/86)   \u001b[K\rremote: Counting objects:   3% (3/86)   \u001b[K\rremote: Counting objects:   4% (4/86)   \u001b[K\rremote: Counting objects:   5% (5/86)   \u001b[K\rremote: Counting objects:   6% (6/86)   \u001b[K\rremote: Counting objects:   8% (7/86)   \u001b[K\rremote: Counting objects:   9% (8/86)   \u001b[K\rremote: Counting objects:  10% (9/86)   \u001b[K\rremote: Counting objects:  11% (10/86)   \u001b[K\rremote: Counting objects:  12% (11/86)   \u001b[K\rremote: Counting objects:  13% (12/86)   \u001b[K\rremote: Counting objects:  15% (13/86)   \u001b[K\rremote: Counting objects:  16% (14/86)   \u001b[K\rremote: Counting objects:  17% (15/86)   \u001b[K\rremote: Counting objects:  18% (16/86)   \u001b[K\rremote: Counting objects:  19% (17/86)   \u001b[K\rremote: Counting objects:  20% (18/86)   \u001b[K\rremote: Counting objects:  22% (19/86)   \u001b[K\rremote: Counting objects:  23% (20/86)   \u001b[K\rremote: Counting objects:  24% (21/86)   \u001b[K\rremote: Counting objects:  25% (22/86)   \u001b[K\rremote: Counting objects:  26% (23/86)   \u001b[K\rremote: Counting objects:  27% (24/86)   \u001b[K\rremote: Counting objects:  29% (25/86)   \u001b[K\rremote: Counting objects:  30% (26/86)   \u001b[K\rremote: Counting objects:  31% (27/86)   \u001b[K\rremote: Counting objects:  32% (28/86)   \u001b[K\rremote: Counting objects:  33% (29/86)   \u001b[K\rremote: Counting objects:  34% (30/86)   \u001b[K\rremote: Counting objects:  36% (31/86)   \u001b[K\rremote: Counting objects:  37% (32/86)   \u001b[K\rremote: Counting objects:  38% (33/86)   \u001b[K\rremote: Counting objects:  39% (34/86)   \u001b[K\rremote: Counting objects:  40% (35/86)   \u001b[K\rremote: Counting objects:  41% (36/86)   \u001b[K\rremote: Counting objects:  43% (37/86)   \u001b[K\rremote: Counting objects:  44% (38/86)   \u001b[K\rremote: Counting objects:  45% (39/86)   \u001b[K\rremote: Counting objects:  46% (40/86)   \u001b[K\rremote: Counting objects:  47% (41/86)   \u001b[K\rremote: Counting objects:  48% (42/86)   \u001b[K\rremote: Counting objects:  50% (43/86)   \u001b[K\rremote: Counting objects:  51% (44/86)   \u001b[K\rremote: Counting objects:  52% (45/86)   \u001b[K\rremote: Counting objects:  53% (46/86)   \u001b[K\rremote: Counting objects:  54% (47/86)   \u001b[K\rremote: Counting objects:  55% (48/86)   \u001b[K\rremote: Counting objects:  56% (49/86)   \u001b[K\rremote: Counting objects:  58% (50/86)   \u001b[K\rremote: Counting objects:  59% (51/86)   \u001b[K\rremote: Counting objects:  60% (52/86)   \u001b[K\rremote: Counting objects:  61% (53/86)   \u001b[K\rremote: Counting objects:  62% (54/86)   \u001b[K\rremote: Counting objects:  63% (55/86)   \u001b[K\rremote: Counting objects:  65% (56/86)   \u001b[K\rremote: Counting objects:  66% (57/86)   \u001b[K\rremote: Counting objects:  67% (58/86)   \u001b[K\rremote: Counting objects:  68% (59/86)   \u001b[K\rremote: Counting objects:  69% (60/86)   \u001b[K\rremote: Counting objects:  70% (61/86)   \u001b[K\rremote: Counting objects:  72% (62/86)   \u001b[K\rremote: Counting objects:  73% (63/86)   \u001b[K\rremote: Counting objects:  74% (64/86)   \u001b[K\rremote: Counting objects:  75% (65/86)   \u001b[K\rremote: Counting objects:  76% (66/86)   \u001b[K\rremote: Counting objects:  77% (67/86)   \u001b[K\rremote: Counting objects:  79% (68/86)   \u001b[K\rremote: Counting objects:  80% (69/86)   \u001b[K\rremote: Counting objects:  81% (70/86)   \u001b[K\rremote: Counting objects:  82% (71/86)   \u001b[K\rremote: Counting objects:  83% (72/86)   \u001b[K\rremote: Counting objects:  84% (73/86)   \u001b[K\rremote: Counting objects:  86% (74/86)   \u001b[K\rremote: Counting objects:  87% (75/86)   \u001b[K\rremote: Counting objects:  88% (76/86)   \u001b[K\rremote: Counting objects:  89% (77/86)   \u001b[K\rremote: Counting objects:  90% (78/86)   \u001b[K\rremote: Counting objects:  91% (79/86)   \u001b[K\rremote: Counting objects:  93% (80/86)   \u001b[K\rremote: Counting objects:  94% (81/86)   \u001b[K\rremote: Counting objects:  95% (82/86)   \u001b[K\rremote: Counting objects:  96% (83/86)   \u001b[K\rremote: Counting objects:  97% (84/86)   \u001b[K\rremote: Counting objects:  98% (85/86)   \u001b[K\rremote: Counting objects: 100% (86/86)   \u001b[K\rremote: Counting objects: 100% (86/86), done.\u001b[K\n",
            "remote: Compressing objects: 100% (83/83), done.\u001b[K\n",
            "remote: Total 449 (delta 3), reused 82 (delta 3), pack-reused 363\u001b[K\n",
            "Receiving objects: 100% (449/449), 20.52 MiB | 21.93 MiB/s, done.\n",
            "Resolving deltas: 100% (187/187), done.\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3-vAZmHdzeSC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f8e7c27c-8744-4b58-f880-c6856d84b356"
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import HpAlgorithms\n",
        "import HpMLUtils\n",
        "import HpKerasUtils\n",
        "import HpMLPlots\n",
        "import HpHyperParameterOptimisation\n",
        "import HpTrainingFrame"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "h-aca1mRzeSG",
        "colab_type": "code",
        "outputId": "5970a614-20e0-43f3-fa94-d6e7a15f5fc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "df_mc=pd.read_hdf('pandas_INC_'+region+'.h5', 'INC_'+region)\n",
        "df_mc=df_mc[df_mc.process.apply(lambda x: True if x in [\"ttlight\",\"ttb\",\"ttc\"] or \"Hp\" in x else False)]\n",
        "#print df_mc.process.shape\n",
        "df_mc['process'].unique()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ttlight', 'ttc', 'ttb', 'Hp200', 'Hp225', 'Hp250', 'Hp275',\n",
              "       'Hp300', 'Hp350', 'Hp400', 'Hp500', 'Hp600', 'Hp700', 'Hp800',\n",
              "       'Hp900', 'Hp1000', 'Hp1200', 'Hp1400', 'Hp1600', 'Hp1800',\n",
              "       'Hp2000'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "U2pJdfQIzeSL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Prepare data for parameterised H+ classification"
      ]
    },
    {
      "metadata": {
        "id": "la_FKRryzeSN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "split into test and training data"
      ]
    },
    {
      "metadata": {
        "id": "KPK0-GRjadx6",
        "colab_type": "code",
        "outputId": "a7f165b1-1cd8-4a49-d65f-42aee8c7d8bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6409
        }
      },
      "cell_type": "code",
      "source": [
        "from HpMLUtils import WeightedStandardScaler, summarizeFitData,MultiSBWeightsScaler\n",
        "htf=HpTrainingFrame.HpTrainingFrame(df_mc)\n",
        "X_train, X_test, X_eval, y_train, y_test,y_eval, w_train, w_test, w_eval=htf.prepare(hpmass=\"multi\",addMass=True)\n",
        "#summarizeFitData(X_train, y_train, w=w_train,categories=X_train.hpmass)\n",
        "\n",
        "if invertTestTrain:\n",
        "  X_tmp=X_train\n",
        "  y_tmp=y_train\n",
        "  w_tmp=w_train\n",
        "  X_train=X_test\n",
        "  y_train=y_test\n",
        "  w_train=w_test\n",
        "  X_test=X_tmp\n",
        "  y_test=y_tmp\n",
        "  w_test=w_tmp\n",
        "\n",
        "print(X_train.hpmass.unique())\n",
        "\n",
        "#sb=HpMLUtils.MultiSBWeightsScaler(backgroundclass=-1)\n",
        "#sb.fit(X_train,X_train.hpmass, sample_weight=w_train)\n",
        "#X_train=sb.transform(X_train,X_train.hpmass, sample_weight=w_train)\n",
        "#w_train.groupby(X_train.hpmass).sum()\n",
        "\n",
        "\n",
        "sb=MultiSBWeightsScaler(backgroundclass=-1)\n",
        "sb.fit(X_train,X_train.hpmass, sample_weight=w_train)\n",
        "X_train=sb.transform(X_train,X_train.hpmass, sample_weight=w_train)\n",
        "X_test=sb.transform(X_test,X_test.hpmass, sample_weight=w_test)\n",
        "w_train.groupby(y_train).sum()\n",
        "summarizeFitData(X_train, y_train, w=w_train,categories=X_train.hpmass)\n",
        "\n",
        "hpmass_train=X_train.hpmass.copy()\n",
        "hpmass_test=X_test.hpmass.copy()\n",
        "\n",
        "#change the hpmass for background to randomly assigned hpmass labels\n",
        "from scipy.stats import rv_discrete\n",
        "\n",
        "#1. first get the PDF from signal\n",
        "signalsum=w_train[X_train.hpmass>0].sum()\n",
        "xk=[]\n",
        "pk=[]\n",
        "for name, group in w_train.groupby(X_train.hpmass):\n",
        "    if name>0:\n",
        "        xk.append((-1)*name)\n",
        "        pk.append(group.sum()/signalsum)\n",
        "\n",
        "print(xk,pk)\n",
        "\n",
        "#return\n",
        "custm=rv_discrete(values=(xk,pk))\n",
        "#print \"before resetting hpmass\", sorted(X_train.hpmass.unique())\n",
        "X_train[\"hpmass\"]=X_train.hpmass.apply(lambda x: x if x>0 else custm.rvs())\n",
        "X_test[\"hpmass\"]=X_test.hpmass.apply(lambda x: x if x>0 else custm.rvs())\n",
        "#print \"resetting hpmass\", sorted(X_train.hpmass.unique())\n",
        "\n",
        "print(\"the following is the difference between + and - mass\")\n",
        "print((w_train*((X_train.hpmass>0)-0.5)*2).groupby(X_train.hpmass.abs()).sum())\n",
        "print(\"the following is the sum of weights\")\n",
        "print(w_train.groupby(X_train.hpmass).sum())\n",
        "\n",
        "X_train.hpmass=X_train.hpmass.abs()\n",
        "X_test.hpmass=X_test.hpmass.abs()\n",
        "wss=WeightedStandardScaler()\n",
        "wss.fit(X_train,sample_weight=w_train)\n",
        "X_train=wss.transform(X_train)\n",
        "X_test=wss.transform(X_test)\n",
        "print(\"Summarizing train data after weighted standard scaler\")\n",
        "summarizeFitData(X_train, y_train, w=w_train,categories=hpmass_train)\n",
        "print(\"Summarizing test data after weighted standard scaler\")\n",
        "summarizeFitData(X_test, y_test, w=w_test,categories=hpmass_test)\n",
        "\n",
        "\n",
        "hpmasses=[200,225,250,275,300,350,400,500,600,700,800,900,1000,1200,1400,1600,1800,2000]\n",
        "hpmass_mean=wss.mean_[-1]\n",
        "hpmass_scale=wss.scale_[-1]\n",
        "print(wss.mean_, wss.scale_)\n",
        "for mass in hpmasses:\n",
        "  print(mass, (mass-hpmass_mean)/hpmass_scale)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1800 1000  600  700   -1  350 1200  275  200  300  800  500  250  225\n",
            " 2000  900 1400 1600  400]\n",
            "X.shape= (166812, 15) y.shape= (166812,)w.shape= (166812,)\n",
            "columns= Index(['nJets', 'nBTags_70', 'pT_jet1', 'Mbb_MindR_70', 'pT_jet5', 'H1_all',\n",
            "       'dRbb_avg_70', 'dRlepbb_MindR_70', 'Muu_MindR_70', 'HT_jets',\n",
            "       'Mbb_MaxPt_70', 'Mbb_MaxM_70', 'Mjjj_MaxPt', 'Centrality_all',\n",
            "       'hpmass'],\n",
            "      dtype='object')\n",
            "categories= [-1, 200, 225, 250, 275, 300, 350, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000]\n",
            "\n",
            "sum of weights per category\n",
            "       all        1.0\n",
            "        -1        0.5\n",
            "       200 0.006535947712418301\n",
            "       225 0.006535947712418302\n",
            "       250 0.0065359477124183\n",
            "       275 0.0065359477124183\n",
            "       300 0.00980392156862745\n",
            "       350 0.0130718954248366\n",
            "       400 0.0196078431372549\n",
            "       500 0.026143790849673203\n",
            "       600 0.026143790849673196\n",
            "       700 0.0261437908496732\n",
            "       800 0.026143790849673207\n",
            "       900 0.026143790849673207\n",
            "      1000 0.03921568627450982\n",
            "      1200 0.05228758169934641\n",
            "      1400 0.0522875816993464\n",
            "      1600 0.052287581699346414\n",
            "      1800 0.05228758169934641\n",
            "      2000 0.052287581699346414\n",
            "\n",
            "\n",
            "average\n",
            "  variable/class       all        -1       200       225       250       275       300       350       400       500       600       700       800       900      1000      1200      1400      1600      1800      2000\n",
            "           nJets      7.33      6.99      6.83      6.86      6.87       6.9      6.94      6.97      7.02      7.13      7.25      7.33      7.43      7.54      7.66      7.77      7.92      8.05      8.17      8.28\n",
            "       nBTags_70      4.09      4.06      4.07      4.07      4.07      4.08      4.08      4.07      4.07      4.08      4.09      4.09       4.1       4.1       4.1      4.11      4.11      4.13      4.13      4.13\n",
            "         pT_jet1  2.85e+05  1.77e+05  1.57e+05  1.46e+05  1.48e+05  1.52e+05  1.57e+05  1.67e+05   1.8e+05  2.06e+05  2.36e+05  2.64e+05  2.96e+05  3.23e+05  3.55e+05  4.11e+05  4.71e+05  5.23e+05  5.79e+05  6.33e+05\n",
            "    Mbb_MindR_70  1.12e+05  7.73e+04   6.4e+04  6.98e+04  7.78e+04   8.4e+04  8.81e+04  9.43e+04  1.02e+05  1.12e+05   1.2e+05  1.28e+05  1.33e+05  1.41e+05  1.46e+05  1.58e+05  1.65e+05  1.72e+05  1.81e+05  1.85e+05\n",
            "         pT_jet5  6.29e+04  5.05e+04  4.66e+04  4.77e+04  4.92e+04  5.01e+04  5.13e+04  5.25e+04  5.41e+04  5.79e+04  6.11e+04   6.4e+04  6.68e+04  7.09e+04  7.32e+04  7.86e+04  8.34e+04   8.8e+04  9.19e+04  9.52e+04\n",
            "          H1_all     0.195     0.227      0.22     0.216     0.218      0.22     0.214     0.208      0.21     0.202     0.192     0.185     0.182     0.176      0.17     0.157     0.145     0.134     0.125     0.119\n",
            "     dRbb_avg_70      2.34      2.28      2.27      2.29      2.32      2.33      2.34      2.36      2.37      2.39       2.4      2.41      2.42      2.41      2.42      2.43      2.42      2.42      2.42      2.43\n",
            "dRlepbb_MindR_70      2.24      2.31      2.13      2.16       2.2      2.22      2.21      2.24      2.26      2.24      2.23      2.21      2.17      2.17      2.16      2.17      2.14      2.12      2.12      2.09\n",
            "    Muu_MindR_70  1.12e+05  1.05e+05  1.02e+05  9.41e+04  9.53e+04  9.74e+04  9.49e+04  9.42e+04  9.57e+04  9.72e+04     1e+05  1.03e+05  1.06e+05  1.08e+05  1.12e+05  1.21e+05  1.28e+05  1.34e+05  1.44e+05   1.5e+05\n",
            "         HT_jets  8.23e+05  5.68e+05  5.11e+05     5e+05  5.11e+05  5.25e+05  5.42e+05  5.66e+05  5.97e+05  6.62e+05  7.29e+05  7.97e+05  8.62e+05  9.33e+05     1e+06  1.13e+06  1.25e+06  1.37e+06  1.49e+06  1.61e+06\n",
            "    Mbb_MaxPt_70  2.41e+05  1.66e+05  1.55e+05  1.53e+05  1.57e+05  1.64e+05  1.69e+05  1.82e+05  1.92e+05  2.19e+05  2.39e+05  2.59e+05  2.75e+05   2.9e+05  3.09e+05   3.4e+05  3.64e+05  3.86e+05  4.09e+05  4.31e+05\n",
            "     Mbb_MaxM_70  4.69e+05  3.17e+05     3e+05  2.94e+05  2.99e+05  3.07e+05  3.16e+05  3.33e+05   3.5e+05  3.86e+05  4.27e+05  4.67e+05  5.08e+05  5.42e+05  5.79e+05   6.6e+05   7.2e+05   7.9e+05  8.47e+05  9.04e+05\n",
            "      Mjjj_MaxPt  4.04e+05  3.02e+05  2.71e+05  2.67e+05  2.76e+05  2.79e+05  2.84e+05  3.02e+05  3.13e+05  3.46e+05   3.7e+05  4.04e+05   4.3e+05  4.57e+05   4.8e+05  5.32e+05  5.67e+05   6.2e+05  6.67e+05  7.01e+05\n",
            "  Centrality_all      0.59     0.566     0.579     0.577     0.574     0.575     0.577     0.582     0.585     0.592       0.6     0.605     0.608     0.614     0.616     0.621     0.627     0.628     0.628      0.63\n",
            "          hpmass   5.7e+02      -1.0     2e+02  2.25e+02   2.5e+02  2.75e+02     3e+02   3.5e+02     4e+02     5e+02     6e+02     7e+02     8e+02     9e+02     1e+03   1.2e+03   1.4e+03   1.6e+03   1.8e+03     2e+03\n",
            "\n",
            "\n",
            "variance\n",
            "  variable/class       all        -1       200       225       250       275       300       350       400       500       600       700       800       900      1000      1200      1400      1600      1800      2000\n",
            "           nJets      1.94      1.32      1.07      1.12       1.1       1.1      1.16      1.19      1.28      1.35      1.58      1.67      1.73      1.95      2.09      2.25      2.51      2.61      2.76      3.06\n",
            "       nBTags_70    0.0917    0.0667    0.0783    0.0749     0.074     0.083    0.0785     0.071    0.0714    0.0871    0.0941    0.0939     0.101     0.111     0.107     0.118     0.122     0.136     0.163     0.141\n",
            "         pT_jet1  4.19e+10  1.05e+10  7.69e+09  6.43e+09  6.24e+09  6.44e+09  6.62e+09  6.64e+09  6.54e+09  6.69e+09   7.9e+09  9.08e+09  1.12e+10  1.35e+10  1.74e+10  2.21e+10  3.02e+10  3.86e+10   4.8e+10  6.09e+10\n",
            "    Mbb_MindR_70  8.24e+09  2.47e+09  1.19e+09  1.21e+09  1.49e+09   1.9e+09  2.28e+09  2.68e+09  3.29e+09  4.73e+09  5.56e+09  6.76e+09  7.36e+09  8.75e+09  9.36e+09  1.17e+10  1.26e+10  1.49e+10  1.71e+10  1.83e+10\n",
            "         pT_jet5  1.01e+09  3.98e+08  3.08e+08  2.94e+08  3.18e+08  3.16e+08  3.44e+08  3.58e+08  4.15e+08  4.66e+08  5.74e+08  6.27e+08  7.05e+08   8.6e+08  9.15e+08  1.12e+09  1.35e+09  1.57e+09   1.8e+09     2e+09\n",
            "          H1_all    0.0385    0.0465    0.0436    0.0431    0.0437    0.0437    0.0432    0.0402      0.04     0.039    0.0356    0.0347    0.0334    0.0308    0.0288    0.0252    0.0224    0.0196    0.0173    0.0156\n",
            "     dRbb_avg_70     0.155     0.159     0.138     0.132     0.132     0.131     0.133     0.128     0.125      0.13     0.131     0.123     0.133     0.129     0.135     0.139     0.149     0.154     0.164     0.168\n",
            "dRlepbb_MindR_70     0.879     0.789     0.848     0.821     0.774     0.741     0.731     0.719     0.733     0.752      0.81     0.849      0.87      0.92     0.969       1.0      1.06      1.05       1.1      1.11\n",
            "    Muu_MindR_70  1.45e+10  1.02e+10  9.26e+09  7.65e+09   6.9e+09  8.25e+09  7.44e+09  6.81e+09  7.71e+09  7.28e+09  8.54e+09  1.01e+10  1.12e+10  1.18e+10  1.31e+10  1.79e+10  2.25e+10  2.41e+10  3.05e+10  3.49e+10\n",
            "         HT_jets  2.09e+11  5.87e+10  4.32e+10  3.92e+10  3.88e+10  3.83e+10  4.06e+10  4.01e+10  4.09e+10  4.14e+10  4.75e+10  5.23e+10  5.73e+10  7.12e+10   8.1e+10  9.58e+10  1.25e+11  1.53e+11  1.87e+11  2.27e+11\n",
            "    Mbb_MaxPt_70  4.38e+10  1.85e+10  1.76e+10  2.03e+10  1.67e+10  1.63e+10  1.57e+10  1.76e+10  1.74e+10   2.1e+10  2.58e+10  2.83e+10  3.25e+10  3.66e+10  4.22e+10   5.4e+10  5.85e+10  7.14e+10  8.09e+10  9.41e+10\n",
            "     Mbb_MaxM_70  1.02e+11  3.63e+10  2.96e+10  3.12e+10  2.89e+10  2.84e+10  2.73e+10  3.07e+10  2.88e+10  3.12e+10  3.51e+10  3.55e+10  4.26e+10  4.44e+10  5.31e+10  6.95e+10  9.05e+10  1.21e+11  1.52e+11  1.97e+11\n",
            "      Mjjj_MaxPt   9.1e+10  4.91e+10  4.06e+10  3.93e+10  4.05e+10  3.63e+10  3.67e+10  3.94e+10  3.89e+10  4.36e+10  5.01e+10  5.98e+10   6.8e+10   7.7e+10  7.92e+10  9.95e+10  1.06e+11   1.3e+11  1.48e+11  1.57e+11\n",
            "  Centrality_all    0.0191     0.019    0.0196    0.0193    0.0188    0.0192    0.0184    0.0178    0.0179    0.0175    0.0174    0.0168    0.0169    0.0164    0.0168    0.0176    0.0173    0.0177    0.0182    0.0192\n",
            "          hpmass  4.78e+05       0.0       0.0  8.08e-28       0.0  3.23e-27       0.0       0.0       0.0       0.0  1.29e-26       0.0  1.29e-26  1.29e-26  5.17e-26       0.0       0.0       0.0       0.0  5.17e-26\n",
            "\n",
            "\n",
            "min/max\n",
            "  variable/class   all/min   all/max    -1/min    -1/max   200/min   200/max   225/min   225/max   250/min   250/max   275/min   275/max   300/min   300/max   350/min   350/max   400/min   400/max   500/min   500/max   600/min   600/max   700/min   700/max   800/min   800/max   900/min   900/max  1000/min  1000/max  1200/min  1200/max  1400/min  1400/max  1600/min  1600/max  1800/min  1800/max  2000/min  2000/max\n",
            "           nJets       6.0      18.0       6.0      15.0       6.0      14.0       6.0      13.0       6.0      13.0       6.0      13.0       6.0      13.0       6.0      14.0       6.0      14.0       6.0      14.0       6.0      14.0       6.0      16.0       6.0      15.0       6.0      16.0       6.0      15.0       6.0      18.0       6.0      16.0       6.0      16.0       6.0      17.0       6.0      17.0\n",
            "       nBTags_70       4.0       9.0       4.0       6.0       4.0       7.0       4.0       6.0       4.0       7.0       4.0       6.0       4.0       6.0       4.0       6.0       4.0       6.0       4.0       6.0       4.0       7.0       4.0       7.0       4.0       7.0       4.0       7.0       4.0       7.0       4.0       7.0       4.0       7.0       4.0       7.0       4.0       9.0       4.0       7.0\n",
            "         pT_jet1   3.6e+04  1.91e+06   3.6e+04   1.5e+06  4.08e+04  1.24e+06  4.06e+04  8.96e+05  3.91e+04  9.36e+05  3.93e+04  7.93e+05   4.5e+04  1.08e+06  4.37e+04  1.17e+06  4.64e+04   1.2e+06  4.04e+04  9.37e+05  5.42e+04  1.01e+06  5.89e+04  1.05e+06  5.71e+04  1.21e+06  5.73e+04  1.24e+06   6.6e+04  1.49e+06  5.65e+04  1.75e+06  7.72e+04   1.5e+06  7.23e+04  1.55e+06  9.09e+04  1.65e+06   8.2e+04  1.91e+06\n",
            "    Mbb_MindR_70  1.33e+04  1.49e+06  1.36e+04  8.56e+05  1.52e+04  8.89e+05  1.45e+04   4.5e+05  1.47e+04  4.67e+05  1.43e+04  4.21e+05  1.56e+04   5.3e+05  1.33e+04   4.8e+05  1.44e+04  4.95e+05  1.47e+04  7.07e+05  1.46e+04   7.7e+05  1.79e+04  7.09e+05  1.46e+04   1.1e+06  1.58e+04  9.88e+05  1.45e+04  9.38e+05  1.46e+04  1.03e+06  1.51e+04  1.12e+06   1.5e+04  1.31e+06  1.36e+04  1.49e+06  1.59e+04  1.25e+06\n",
            "         pT_jet5  2.51e+04  4.52e+05  2.52e+04  3.54e+05  2.53e+04  2.44e+05  2.53e+04  1.73e+05  2.53e+04  1.87e+05  2.52e+04  1.71e+05  2.52e+04  2.02e+05  2.52e+04  1.86e+05  2.57e+04  2.38e+05  2.52e+04  2.18e+05  2.54e+04  2.16e+05  2.51e+04  2.63e+05  2.57e+04   3.4e+05  2.52e+04  2.89e+05  2.56e+04  2.84e+05  2.55e+04  2.72e+05  2.57e+04  3.14e+05  2.61e+04  4.52e+05  2.52e+04  4.12e+05  2.53e+04  4.01e+05\n",
            "          H1_all  4.93e-06     0.938  4.93e-06     0.933  7.81e-05     0.905  5.01e-05      0.92  8.78e-05     0.912  8.79e-05     0.938  7.14e-06     0.907  0.000177     0.919  6.52e-06     0.906  6.09e-05     0.918  2.16e-05     0.889  3.69e-05     0.931  2.72e-05     0.891  9.81e-06     0.873  2.48e-05     0.874  0.000116     0.851  2.52e-05     0.819  2.14e-05     0.825  3.31e-05     0.797  3.28e-05      0.81\n",
            "     dRbb_avg_70     0.656       4.0     0.756      3.94     0.732       4.0     0.831      3.48     0.844      3.61     0.815      3.96      0.88      3.63     0.774      3.86     0.888      3.73     0.786      3.73     0.976      3.86     0.971       3.8      1.04      3.69     0.861      3.74     0.938      3.71       1.0      3.91     0.744      3.85     0.799      3.95     0.656      3.87     0.722      3.93\n",
            "dRlepbb_MindR_70   0.00818      8.11    0.0511      8.11   0.00818      5.32    0.0819      5.37    0.0173      5.59    0.0159      5.28    0.0301      5.39    0.0567       5.0    0.0228      5.81    0.0356      5.83    0.0194      5.09    0.0328      5.14    0.0189       6.1    0.0159      5.78    0.0209      5.53     0.024       5.8    0.0133      5.38     0.015      5.29    0.0134      5.61    0.0323      5.43\n",
            "    Muu_MindR_70       0.0   3.2e+06       0.0  2.44e+06       0.0  1.46e+06       0.0     3e+06       0.0  1.36e+06       0.0  1.32e+06       0.0  1.04e+06       0.0  1.05e+06       0.0  1.66e+06       0.0  1.21e+06       0.0  1.36e+06       0.0  1.87e+06       0.0  1.58e+06       0.0  1.78e+06       0.0  1.53e+06       0.0  2.76e+06       0.0  2.11e+06       0.0  2.17e+06       0.0   3.2e+06       0.0  2.29e+06\n",
            "         HT_jets  1.82e+05  4.28e+06  1.89e+05   4.2e+06  1.99e+05   3.2e+06  1.82e+05  2.08e+06  1.99e+05  1.88e+06  2.12e+05     2e+06  2.03e+05  2.21e+06  2.21e+05   2.4e+06  2.08e+05  2.89e+06  2.39e+05  2.12e+06  2.19e+05  2.38e+06  2.39e+05  2.28e+06   2.5e+05  3.06e+06  2.51e+05  3.02e+06  2.78e+05  3.33e+06  2.91e+05  2.87e+06  3.67e+05  3.09e+06  3.18e+05  3.39e+06  3.27e+05  4.09e+06  3.53e+05  4.28e+06\n",
            "    Mbb_MaxPt_70  1.62e+04   3.1e+06  1.93e+04  2.45e+06   1.8e+04  1.37e+06  1.66e+04  2.35e+06  1.69e+04  1.33e+06  1.65e+04  1.38e+06  1.62e+04  1.54e+06  1.99e+04  1.75e+06   1.8e+04   1.5e+06  2.13e+04  1.59e+06  2.73e+04  2.04e+06  2.15e+04  1.99e+06  2.69e+04  2.11e+06  3.17e+04  2.73e+06     3e+04     2e+06  1.82e+04  2.41e+06  2.44e+04  2.16e+06  3.12e+04  2.63e+06  2.27e+04  2.89e+06   2.3e+04   3.1e+06\n",
            "     Mbb_MaxM_70  4.37e+04  3.92e+06  4.41e+04   2.9e+06  5.78e+04  2.25e+06  4.37e+04  2.35e+06   6.8e+04  1.81e+06  6.21e+04  1.84e+06  5.55e+04  1.92e+06  6.08e+04  1.75e+06  6.52e+04  1.66e+06     7e+04   1.6e+06  9.68e+04  2.74e+06  1.05e+05  2.03e+06  8.29e+04  2.74e+06  8.77e+04  2.73e+06  8.28e+04  2.55e+06  8.75e+04  2.41e+06  8.32e+04  2.62e+06  7.61e+04  3.17e+06  8.53e+04  3.92e+06  8.97e+04  3.38e+06\n",
            "      Mjjj_MaxPt   4.1e+04  4.07e+06   4.1e+04   3.2e+06  4.44e+04  2.34e+06  4.48e+04  3.52e+06   4.4e+04  2.22e+06  4.95e+04  2.37e+06  4.55e+04  2.08e+06  4.72e+04  2.52e+06  5.64e+04  2.81e+06  5.44e+04  2.42e+06  6.82e+04  2.52e+06  6.78e+04  2.86e+06   6.6e+04  2.93e+06  7.43e+04  3.08e+06  7.74e+04  2.77e+06     8e+04  2.84e+06  9.03e+04  3.42e+06  6.56e+04  3.79e+06  9.22e+04  3.63e+06  1.03e+05  4.07e+06\n",
            "  Centrality_all     0.221     0.985     0.231     0.971     0.221     0.959      0.24     0.975     0.246     0.981     0.237     0.959     0.245     0.954      0.25     0.961     0.232     0.947     0.244     0.968     0.251     0.973     0.229     0.972     0.256     0.974      0.23     0.965     0.258     0.973     0.228     0.959     0.251     0.976     0.262     0.977     0.239     0.985     0.243     0.967\n",
            "          hpmass      -1.0     2e+03      -1.0      -1.0     2e+02     2e+02  2.25e+02  2.25e+02   2.5e+02   2.5e+02  2.75e+02  2.75e+02     3e+02     3e+02   3.5e+02   3.5e+02     4e+02     4e+02     5e+02     5e+02     6e+02     6e+02     7e+02     7e+02     8e+02     8e+02     9e+02     9e+02     1e+03     1e+03   1.2e+03   1.2e+03   1.4e+03   1.4e+03   1.6e+03   1.6e+03   1.8e+03   1.8e+03     2e+03     2e+03\n",
            "\n",
            "\n",
            "[-200, -225, -250, -275, -300, -350, -400, -500, -600, -700, -800, -900, -1000, -1200, -1400, -1600, -1800, -2000] [0.013071895424836603, 0.013071895424836605, 0.013071895424836602, 0.013071895424836602, 0.019607843137254905, 0.026143790849673203, 0.03921568627450981, 0.052287581699346414, 0.0522875816993464, 0.05228758169934641, 0.05228758169934642, 0.05228758169934642, 0.07843137254901965, 0.10457516339869283, 0.10457516339869281, 0.10457516339869284, 0.10457516339869283, 0.10457516339869284]\n",
            "the following is the difference between + and - mass\n",
            "hpmass\n",
            "200     0.000417\n",
            "225    -0.000655\n",
            "250    -0.000215\n",
            "275    -0.000574\n",
            "300     0.001848\n",
            "350    -0.001268\n",
            "400     0.000549\n",
            "500    -0.001726\n",
            "600     0.001012\n",
            "700     0.000626\n",
            "800    -0.001403\n",
            "900     0.002454\n",
            "1000    0.000280\n",
            "1200    0.002352\n",
            "1400    0.000779\n",
            "1600   -0.001411\n",
            "1800    0.000197\n",
            "2000   -0.003263\n",
            "dtype: float64\n",
            "the following is the sum of weights\n",
            "hpmass\n",
            "-2000    0.055550\n",
            "-1800    0.052090\n",
            "-1600    0.053699\n",
            "-1400    0.051508\n",
            "-1200    0.049936\n",
            "-1000    0.038935\n",
            "-900     0.023690\n",
            "-800     0.027547\n",
            "-700     0.025518\n",
            "-600     0.025132\n",
            "-500     0.027869\n",
            "-400     0.019059\n",
            "-350     0.014339\n",
            "-300     0.007956\n",
            "-275     0.007109\n",
            "-250     0.006751\n",
            "-225     0.007191\n",
            "-200     0.006119\n",
            " 200     0.006536\n",
            " 225     0.006536\n",
            " 250     0.006536\n",
            " 275     0.006536\n",
            " 300     0.009804\n",
            " 350     0.013072\n",
            " 400     0.019608\n",
            " 500     0.026144\n",
            " 600     0.026144\n",
            " 700     0.026144\n",
            " 800     0.026144\n",
            " 900     0.026144\n",
            " 1000    0.039216\n",
            " 1200    0.052288\n",
            " 1400    0.052288\n",
            " 1600    0.052288\n",
            " 1800    0.052288\n",
            " 2000    0.052288\n",
            "Name: weight, dtype: float64\n",
            "Summarizing train data after weighted standard scaler\n",
            "X.shape= (166812, 15) y.shape= (166812,)w.shape= (166812,)\n",
            "columns= Index(['nJets', 'nBTags_70', 'pT_jet1', 'Mbb_MindR_70', 'pT_jet5', 'H1_all',\n",
            "       'dRbb_avg_70', 'dRlepbb_MindR_70', 'Muu_MindR_70', 'HT_jets',\n",
            "       'Mbb_MaxPt_70', 'Mbb_MaxM_70', 'Mjjj_MaxPt', 'Centrality_all',\n",
            "       'hpmass'],\n",
            "      dtype='object')\n",
            "categories= [-1, 200, 225, 250, 275, 300, 350, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000]\n",
            "\n",
            "sum of weights per category\n",
            "       all        1.0\n",
            "        -1        0.5\n",
            "       200 0.006535947712418301\n",
            "       225 0.006535947712418302\n",
            "       250 0.0065359477124183\n",
            "       275 0.0065359477124183\n",
            "       300 0.00980392156862745\n",
            "       350 0.0130718954248366\n",
            "       400 0.0196078431372549\n",
            "       500 0.026143790849673203\n",
            "       600 0.026143790849673196\n",
            "       700 0.0261437908496732\n",
            "       800 0.026143790849673207\n",
            "       900 0.026143790849673207\n",
            "      1000 0.03921568627450982\n",
            "      1200 0.05228758169934641\n",
            "      1400 0.0522875816993464\n",
            "      1600 0.052287581699346414\n",
            "      1800 0.05228758169934641\n",
            "      2000 0.052287581699346414\n",
            "\n",
            "\n",
            "average\n",
            "  variable/class       all        -1       200       225       250       275       300       350       400       500       600       700       800       900      1000      1200      1400      1600      1800      2000\n",
            "           nJets -5.63e-16    -0.245    -0.359    -0.336    -0.334     -0.31    -0.284    -0.258    -0.221    -0.142   -0.0613 -0.000748    0.0719     0.151     0.239     0.315     0.423     0.514     0.598     0.677\n",
            "       nBTags_70 -2.23e-15   -0.0729   -0.0378   -0.0455   -0.0389    -0.022   -0.0349   -0.0474   -0.0436  -0.00668    0.0185    0.0197    0.0421    0.0592    0.0596    0.0844    0.0917     0.149     0.158     0.156\n",
            "         pT_jet1 -4.02e-16    -0.528    -0.626    -0.677     -0.67     -0.65    -0.623    -0.575    -0.515    -0.389    -0.242    -0.101    0.0513     0.184     0.341     0.616     0.906      1.16      1.44       1.7\n",
            "    Mbb_MindR_70 -3.95e-10    -0.382    -0.529    -0.465    -0.376    -0.309    -0.264    -0.195    -0.112   0.00268    0.0845     0.174     0.226     0.317     0.378     0.504     0.587     0.666     0.757     0.805\n",
            "         pT_jet5 -5.59e-09    -0.389    -0.512    -0.476     -0.43    -0.401    -0.363    -0.325    -0.275    -0.157   -0.0563    0.0365     0.122     0.251     0.323     0.494     0.645      0.79     0.911      1.02\n",
            "          H1_all -6.19e-09     0.162     0.126     0.108     0.118     0.126    0.0981    0.0689    0.0765    0.0369   -0.0123   -0.0488   -0.0639   -0.0956    -0.125    -0.191    -0.253    -0.308    -0.354    -0.385\n",
            "     dRbb_avg_70 -4.16e-09    -0.161    -0.186    -0.123   -0.0508    -0.029   0.00164    0.0466    0.0744     0.119     0.151     0.161     0.192     0.176     0.192     0.211     0.197       0.2     0.191      0.21\n",
            "dRlepbb_MindR_70  8.08e-09    0.0798    -0.119   -0.0865   -0.0417   -0.0245   -0.0309  0.000691    0.0278   0.00545   -0.0126    -0.033   -0.0721   -0.0677   -0.0855   -0.0718   -0.0998    -0.125    -0.127    -0.156\n",
            "    Muu_MindR_70  6.61e-10    -0.062    -0.084    -0.151    -0.141    -0.123    -0.145     -0.15    -0.138    -0.125   -0.0991    -0.073   -0.0551   -0.0374  -0.00475    0.0764     0.135     0.178     0.266     0.315\n",
            "         HT_jets  8.69e-11    -0.558    -0.683    -0.706    -0.682    -0.652    -0.615    -0.563    -0.495    -0.353    -0.205    -0.057    0.0845      0.24     0.387     0.662     0.942       1.2      1.46      1.71\n",
            "    Mbb_MaxPt_70  7.42e-10    -0.361    -0.412    -0.423    -0.401    -0.371    -0.344    -0.281    -0.235    -0.107  -0.00969    0.0836      0.16     0.231     0.323     0.473     0.585     0.693     0.801     0.905\n",
            "     Mbb_MaxM_70  6.62e-09    -0.477     -0.53    -0.549    -0.532    -0.506    -0.479    -0.426    -0.372    -0.261    -0.132  -0.00686     0.122     0.227     0.344     0.597     0.785      1.01      1.18      1.36\n",
            "      Mjjj_MaxPt  2.03e-09    -0.337    -0.441    -0.455    -0.425    -0.414    -0.397    -0.337    -0.301    -0.192    -0.112  0.000916    0.0853     0.177     0.253     0.425     0.542     0.715     0.873     0.983\n",
            "  Centrality_all -7.26e-09    -0.173    -0.079   -0.0936    -0.117    -0.102   -0.0881    -0.056   -0.0307    0.0174    0.0784     0.111     0.132     0.174     0.192     0.226     0.268     0.275     0.281     0.294\n",
            "          hpmass -5.45e-16   0.00526      -1.7     -1.66     -1.61     -1.57     -1.52     -1.43     -1.34     -1.16    -0.982    -0.802    -0.621    -0.441     -0.26     0.101     0.462     0.823      1.18      1.54\n",
            "\n",
            "\n",
            "variance\n",
            "  variable/class       all        -1       200       225       250       275       300       350       400       500       600       700       800       900      1000      1200      1400      1600      1800      2000\n",
            "           nJets       1.0     0.678      0.55     0.576     0.564     0.568     0.596     0.612     0.661     0.696     0.813     0.859     0.892      1.01      1.08      1.16      1.29      1.35      1.42      1.58\n",
            "       nBTags_70       1.0     0.727     0.854     0.817     0.807     0.906     0.857     0.774     0.779      0.95      1.03      1.02      1.11      1.21      1.16      1.29      1.33      1.48      1.77      1.54\n",
            "         pT_jet1       1.0     0.251     0.184     0.153     0.149     0.154     0.158     0.158     0.156      0.16     0.188     0.217     0.268     0.322     0.414     0.526     0.721     0.921      1.15      1.45\n",
            "    Mbb_MindR_70       1.0     0.299     0.145     0.147      0.18      0.23     0.276     0.325     0.399     0.574     0.675     0.821     0.893      1.06      1.14      1.42      1.53      1.81      2.07      2.22\n",
            "         pT_jet5       1.0     0.393     0.304     0.291     0.314     0.312     0.339     0.353     0.409      0.46     0.566     0.619     0.696     0.849     0.903       1.1      1.33      1.55      1.77      1.97\n",
            "          H1_all       1.0      1.21      1.13      1.12      1.13      1.13      1.12      1.04      1.04      1.01     0.925     0.901     0.866     0.798     0.748     0.654     0.581     0.509     0.448     0.405\n",
            "     dRbb_avg_70       1.0      1.02     0.891      0.85     0.848     0.846     0.854     0.824     0.807     0.834     0.843     0.795     0.856      0.83      0.87     0.896     0.959     0.988      1.06      1.08\n",
            "dRlepbb_MindR_70       1.0     0.897     0.965     0.934     0.881     0.842     0.832     0.818     0.834     0.855     0.921     0.965      0.99      1.05       1.1      1.14      1.21      1.19      1.25      1.27\n",
            "    Muu_MindR_70       1.0     0.708     0.641     0.529     0.478     0.571     0.515     0.471     0.533     0.504     0.591     0.696     0.776     0.819     0.906      1.24      1.55      1.67      2.11      2.42\n",
            "         HT_jets       1.0      0.28     0.206     0.187     0.185     0.183     0.194     0.192     0.195     0.197     0.227      0.25     0.273      0.34     0.386     0.457     0.596      0.73     0.892      1.08\n",
            "    Mbb_MaxPt_70       1.0     0.421     0.401     0.463     0.382     0.372     0.358     0.401     0.397     0.479     0.588     0.645     0.742     0.836     0.964      1.23      1.33      1.63      1.85      2.15\n",
            "     Mbb_MaxM_70       1.0     0.356      0.29     0.306     0.284     0.278     0.268     0.301     0.282     0.306     0.345     0.349     0.418     0.435     0.521     0.682     0.887      1.19      1.49      1.93\n",
            "      Mjjj_MaxPt       1.0      0.54     0.446     0.432     0.445     0.399     0.403     0.433     0.428     0.479     0.551     0.657     0.747     0.847     0.871      1.09      1.17      1.43      1.62      1.72\n",
            "  Centrality_all       1.0     0.995      1.03      1.02     0.987      1.01     0.965     0.935     0.939     0.917     0.915     0.883     0.889      0.86      0.88     0.922      0.91     0.929     0.957      1.01\n",
            "          hpmass       1.0      1.01  4.93e-32       0.0  4.93e-32       0.0       0.0  4.93e-32       0.0       0.0       0.0  1.23e-32  1.23e-32  3.08e-33  3.08e-33       0.0  3.08e-33       0.0       0.0       0.0\n",
            "\n",
            "\n",
            "min/max\n",
            "  variable/class   all/min   all/max    -1/min    -1/max   200/min   200/max   225/min   225/max   250/min   250/max   275/min   275/max   300/min   300/max   350/min   350/max   400/min   400/max   500/min   500/max   600/min   600/max   700/min   700/max   800/min   800/max   900/min   900/max  1000/min  1000/max  1200/min  1200/max  1400/min  1400/max  1600/min  1600/max  1800/min  1800/max  2000/min  2000/max\n",
            "           nJets    -0.956      7.66    -0.956       5.5    -0.956      4.79    -0.956      4.07    -0.956      4.07    -0.956      4.07    -0.956      4.07    -0.956      4.79    -0.956      4.79    -0.956      4.79    -0.956      4.79    -0.956      6.22    -0.956       5.5    -0.956      6.22    -0.956       5.5    -0.956      7.66    -0.956      6.22    -0.956      6.22    -0.956      6.94    -0.956      6.94\n",
            "       nBTags_70    -0.285      16.2    -0.285      6.32    -0.285      9.62    -0.285      6.32    -0.285      9.62    -0.285      6.32    -0.285      6.32    -0.285      6.32    -0.285      6.32    -0.285      6.32    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      16.2    -0.285      9.62\n",
            "         pT_jet1     -1.22      7.93     -1.22      5.93     -1.19      4.65     -1.19      2.99      -1.2      3.18      -1.2      2.48     -1.17      3.87     -1.18      4.31     -1.17      4.49     -1.19      3.19     -1.13      3.56      -1.1      3.73     -1.11      4.53     -1.11      4.69     -1.07      5.87     -1.12      7.15     -1.02      5.94     -1.04      6.16    -0.948      6.64    -0.992      7.93\n",
            "    Mbb_MindR_70     -1.09      15.2     -1.08       8.2     -1.07      8.56     -1.07      3.72     -1.07      3.91     -1.08       3.4     -1.06       4.6     -1.09      4.06     -1.08      4.22     -1.07      6.55     -1.07      7.24     -1.04      6.57     -1.07      10.9     -1.06      9.65     -1.07       9.1     -1.07      10.1     -1.07      11.1     -1.07      13.2     -1.08      15.2     -1.06      12.6\n",
            "         pT_jet5     -1.19      12.2     -1.18      9.15     -1.18      5.68     -1.18      3.47     -1.18      3.91     -1.18      3.39     -1.18      4.37     -1.19      3.86     -1.17      5.52     -1.18      4.88     -1.18       4.8     -1.19      6.29     -1.17       8.7     -1.18       7.1     -1.17      6.95     -1.18      6.58     -1.17      7.88     -1.16      12.2     -1.18      11.0     -1.18      10.6\n",
            "          H1_all    -0.992      3.78    -0.992      3.76    -0.992      3.62    -0.992       3.7    -0.992      3.65    -0.992      3.78    -0.992      3.63    -0.992      3.69    -0.992      3.62    -0.992      3.69    -0.992      3.54    -0.992      3.75    -0.992      3.55    -0.992      3.46    -0.992      3.46    -0.992      3.35    -0.992      3.18    -0.992      3.21    -0.992      3.07    -0.992      3.13\n",
            "     dRbb_avg_70     -4.28      4.21     -4.03      4.05     -4.09      4.21     -3.84      2.87      -3.8      3.21     -3.88       4.1     -3.71      3.26     -3.98      3.86     -3.69      3.51     -3.95      3.51     -3.47      3.84     -3.48      3.69     -3.31      3.42     -3.76      3.54     -3.56      3.46      -3.4      3.98     -4.06      3.83     -3.92      4.09     -4.28      3.86     -4.11      4.02\n",
            "dRlepbb_MindR_70     -2.38      6.26     -2.33      6.26     -2.38      3.28      -2.3      3.34     -2.37      3.58     -2.37      3.24     -2.35      3.36     -2.33      2.94     -2.36       3.8     -2.35      3.83     -2.37      3.04     -2.35       3.1     -2.37      4.12     -2.37      3.77     -2.36      3.51     -2.36       3.8     -2.37      3.36     -2.37      3.26     -2.37      3.59     -2.35       3.4\n",
            "    Muu_MindR_70    -0.934      25.6    -0.934      19.4    -0.934      11.2    -0.934      24.0    -0.934      10.4    -0.934      10.0    -0.934      7.76    -0.934      7.76    -0.934      12.9    -0.934      9.12    -0.934      10.3    -0.934      14.6    -0.934      12.2    -0.934      13.9    -0.934      11.8    -0.934      22.0    -0.934      16.6    -0.934      17.2    -0.934      25.6    -0.934      18.1\n",
            "         HT_jets      -1.4      7.56     -1.39      7.39     -1.36       5.2      -1.4      2.74     -1.36      2.32     -1.33      2.58     -1.36      3.04     -1.32      3.45     -1.34      4.52     -1.28      2.83     -1.32      3.41     -1.28      3.19     -1.25      4.88     -1.25       4.8     -1.19      5.47     -1.16      4.48    -0.998      4.95      -1.1      5.61     -1.08      7.14     -1.03      7.56\n",
            "    Mbb_MaxPt_70     -1.08      13.7     -1.06      10.6     -1.07      5.39     -1.07      10.1     -1.07      5.22     -1.07      5.46     -1.08      6.19     -1.06      7.22     -1.07       6.0     -1.05      6.44     -1.02      8.57     -1.05      8.38     -1.02      8.91      -1.0      11.9     -1.01       8.4     -1.07      10.3     -1.04      9.18      -1.0      11.4     -1.04      12.6     -1.04      13.7\n",
            "     Mbb_MaxM_70     -1.33      10.8     -1.33      7.63     -1.29      5.59     -1.33      5.89     -1.26      4.19     -1.27       4.3      -1.3      4.54     -1.28      4.02     -1.26      3.71     -1.25      3.54     -1.17      7.13     -1.14      4.89     -1.21      7.12     -1.19      7.07     -1.21       6.5      -1.2      6.07     -1.21      6.72     -1.23      8.46      -1.2      10.8     -1.19      9.13\n",
            "      Mjjj_MaxPt      -1.2      12.1      -1.2      9.27     -1.19      6.43     -1.19      10.3     -1.19      6.01     -1.18      6.51     -1.19      5.55     -1.18      7.01     -1.15      7.98     -1.16       6.7     -1.11      7.01     -1.11      8.15     -1.12      8.39     -1.09      8.87     -1.08      7.85     -1.07      8.08     -1.04      9.99     -1.12      11.2     -1.03      10.7    -0.998      12.1\n",
            "  Centrality_all     -2.67      2.86      -2.6      2.76     -2.67      2.68     -2.53      2.79     -2.49      2.84     -2.55      2.68     -2.49      2.64     -2.46      2.69     -2.59      2.59     -2.51      2.74     -2.45      2.78     -2.61      2.77     -2.42      2.78      -2.6      2.72      -2.4      2.78     -2.62      2.68     -2.46       2.8     -2.37       2.8     -2.54      2.86     -2.51      2.74\n",
            "          hpmass      -1.7      1.54      -1.7      1.54      -1.7      -1.7     -1.66     -1.66     -1.61     -1.61     -1.57     -1.57     -1.52     -1.52     -1.43     -1.43     -1.34     -1.34     -1.16     -1.16    -0.982    -0.982    -0.802    -0.802    -0.621    -0.621    -0.441    -0.441     -0.26     -0.26     0.101     0.101     0.462     0.462     0.823     0.823      1.18      1.18      1.54      1.54\n",
            "\n",
            "\n",
            "Summarizing test data after weighted standard scaler\n",
            "X.shape= (166781, 15) y.shape= (166781,)w.shape= (166781,)\n",
            "columns= Index(['nJets', 'nBTags_70', 'pT_jet1', 'Mbb_MindR_70', 'pT_jet5', 'H1_all',\n",
            "       'dRbb_avg_70', 'dRlepbb_MindR_70', 'Muu_MindR_70', 'HT_jets',\n",
            "       'Mbb_MaxPt_70', 'Mbb_MaxM_70', 'Mjjj_MaxPt', 'Centrality_all',\n",
            "       'hpmass'],\n",
            "      dtype='object')\n",
            "categories= [-1, 200, 225, 250, 275, 300, 350, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000]\n",
            "\n",
            "sum of weights per category\n",
            "       all 1.0253195288267583\n",
            "        -1 0.5256223905970512\n",
            "       200 0.0066245315454426705\n",
            "       225 0.00663868631730497\n",
            "       250 0.006636220428502369\n",
            "       275 0.006665252523255507\n",
            "       300 0.01000609136116374\n",
            "       350 0.01309961141561722\n",
            "       400 0.019327476178802823\n",
            "       500 0.026374639887474502\n",
            "       600 0.025043921126216596\n",
            "       700 0.025622280459574584\n",
            "       800 0.026224367676920334\n",
            "       900 0.025519310594904303\n",
            "      1000 0.039679102457371176\n",
            "      1200 0.05208993701551309\n",
            "      1400 0.05216603862265192\n",
            "      1600 0.05356052017285363\n",
            "      1800 0.05245610184717983\n",
            "      2000 0.05196304859895776\n",
            "\n",
            "\n",
            "average\n",
            "  variable/class       all        -1       200       225       250       275       300       350       400       500       600       700       800       900      1000      1200      1400      1600      1800      2000\n",
            "           nJets -0.000901    -0.243    -0.372     -0.39    -0.331    -0.328    -0.306    -0.252     -0.21    -0.152   -0.0628  0.000508     0.105     0.177     0.233     0.325     0.447     0.524     0.609     0.686\n",
            "       nBTags_70  -0.00146   -0.0739   -0.0357   -0.0224   -0.0406   -0.0637     -0.06   -0.0523   0.00357   0.00909    0.0229  -0.00143    0.0258    0.0645     0.064     0.102     0.111     0.118     0.165     0.153\n",
            "         pT_jet1   -0.0195    -0.538    -0.621    -0.663    -0.666    -0.662    -0.627    -0.567    -0.519    -0.386    -0.248    -0.108    0.0494     0.203     0.328     0.616     0.887      1.16      1.41      1.71\n",
            "    Mbb_MindR_70   -0.0169    -0.391    -0.527     -0.45    -0.386    -0.326    -0.238    -0.184    -0.128   0.00935    0.0715     0.157     0.233     0.308     0.367     0.499     0.578     0.663     0.746     0.799\n",
            "         pT_jet5  -0.00803    -0.383    -0.512    -0.477    -0.423    -0.401     -0.37    -0.317     -0.26    -0.151   -0.0502    0.0423     0.142     0.261     0.306     0.486     0.637     0.765     0.898      1.02\n",
            "          H1_all   0.00823     0.174      0.16     0.171      0.14     0.123    0.0781    0.0615    0.0578     0.034   -0.0114   -0.0275   -0.0734   -0.0963    -0.136    -0.213    -0.256    -0.303    -0.356    -0.398\n",
            "     dRbb_avg_70  -0.00736    -0.166    -0.155    -0.109   -0.0675  -0.00609    0.0329    0.0704    0.0739     0.141     0.143     0.157     0.175     0.186     0.175     0.211     0.188     0.211     0.194     0.184\n",
            "dRlepbb_MindR_70    0.0229     0.115    -0.121   -0.0594     -0.04   -0.0159 -0.000879   0.00401    0.0347   -0.0091   -0.0191   -0.0186   -0.0675   -0.0657    -0.117   -0.0786   -0.0979     -0.11    -0.127   -0.0957\n",
            "    Muu_MindR_70   0.00362   -0.0574    -0.103    -0.129    -0.129    -0.145    -0.139    -0.137    -0.125    -0.106    -0.097   -0.0652   -0.0711   -0.0339   -0.0101     0.058     0.115     0.211     0.279     0.345\n",
            "         HT_jets   -0.0148     -0.56    -0.677    -0.703    -0.678    -0.659    -0.622    -0.552    -0.489    -0.351    -0.203   -0.0631    0.0946     0.261     0.377      0.66     0.925      1.19      1.44      1.73\n",
            "    Mbb_MaxPt_70   -0.0179    -0.378    -0.375     -0.42    -0.411    -0.375    -0.337    -0.285    -0.237    -0.108   0.00301    0.0728     0.182     0.257     0.336     0.465     0.566       0.7     0.785     0.894\n",
            "     Mbb_MaxM_70   -0.0214    -0.492    -0.504    -0.545    -0.533     -0.51    -0.471    -0.417    -0.367    -0.253    -0.128   -0.0122     0.111     0.246      0.34     0.589     0.782     0.993      1.17      1.34\n",
            "      Mjjj_MaxPt   -0.0142    -0.347    -0.439    -0.454    -0.429    -0.407    -0.382    -0.339    -0.291    -0.197   -0.0967   -0.0127    0.0859     0.172     0.239     0.421     0.555     0.712     0.851     0.992\n",
            "  Centrality_all   -0.0211    -0.201   -0.0893    -0.126     -0.13    -0.112   -0.0886    -0.073   -0.0477  6.78e-05    0.0598     0.102     0.142     0.165     0.202     0.232     0.248     0.264     0.273     0.311\n",
            "          hpmass  7.58e-05   0.00259      -1.7     -1.66     -1.61     -1.57     -1.52     -1.43     -1.34     -1.16    -0.982    -0.802    -0.621    -0.441     -0.26     0.101     0.462     0.823      1.18      1.54\n",
            "\n",
            "\n",
            "variance\n",
            "  variable/class       all        -1       200       225       250       275       300       350       400       500       600       700       800       900      1000      1200      1400      1600      1800      2000\n",
            "           nJets     0.995     0.669     0.557       0.5     0.559     0.559     0.569     0.626     0.659     0.713     0.794     0.866     0.983      1.02      1.08      1.15       1.3      1.36      1.43      1.56\n",
            "       nBTags_70     0.974     0.691     0.834     0.847     0.809     0.711     0.744     0.796      1.01      1.02      1.01     0.987      1.06      1.22      1.22      1.37      1.36      1.37      1.59      1.64\n",
            "         pT_jet1     0.988     0.246     0.193     0.166     0.159     0.159     0.159     0.174      0.15     0.177     0.202     0.216     0.266     0.318     0.369     0.514     0.691     0.931      1.18      1.46\n",
            "    Mbb_MindR_70     0.966      0.26     0.138      0.14     0.175     0.214     0.289     0.337     0.419     0.537     0.653     0.761     0.876      0.99      1.14      1.35      1.54      1.82      2.15      2.26\n",
            "         pT_jet5     0.999     0.431       0.3     0.297      0.34     0.502     0.334     0.388     0.415     0.472     0.545     0.627     0.748      0.85     0.906      1.11      1.29      1.49      1.73      1.96\n",
            "          H1_all       1.0       1.2      1.16      1.19      1.15      1.09      1.06      1.05      1.07     0.984     0.947     0.901     0.836     0.805     0.745     0.639     0.569       0.5     0.438     0.384\n",
            "     dRbb_avg_70      1.03      1.08     0.884     0.873     0.825     0.876     0.816      0.83     0.838      0.79     0.806     0.828     0.819     0.834     0.887     0.936     0.953     0.983      1.06      1.08\n",
            "dRlepbb_MindR_70      1.01     0.904     0.999      0.91     0.873      0.88     0.859     0.849     0.845     0.891     0.936     0.965      1.02      1.05       1.1      1.15      1.18      1.21      1.24      1.26\n",
            "    Muu_MindR_70       1.0     0.671     0.639     0.521     0.561     0.482     0.452     0.487     0.544     0.696     0.661     0.695     0.676     0.948     0.961      1.07      1.35      1.85      2.31      2.74\n",
            "         HT_jets       1.0     0.306     0.212     0.184     0.195      0.25     0.192     0.212     0.196     0.218     0.236     0.244     0.288     0.334     0.363     0.464     0.589     0.728     0.914      1.09\n",
            "    Mbb_MaxPt_70      0.98     0.407     0.513     0.382     0.371      0.41     0.389     0.435     0.405      0.49     0.579     0.656     0.777     0.861     0.954      1.17      1.38      1.63      1.81      2.01\n",
            "     Mbb_MaxM_70     0.982     0.336     0.336     0.268     0.262     0.295     0.286     0.312     0.312     0.299     0.344      0.36     0.403     0.443     0.511     0.682      0.89      1.18      1.52       1.9\n",
            "      Mjjj_MaxPt       1.0     0.553     0.454     0.397     0.413      0.44     0.425     0.468     0.457     0.509     0.565     0.637     0.741      0.86      0.87      1.09      1.22      1.39      1.57      1.74\n",
            "  Centrality_all     0.976     0.939      1.09     0.991     0.993     0.976     0.945      0.92     0.948     0.881      0.89     0.864     0.862     0.862     0.884     0.897     0.921     0.957     0.971      1.02\n",
            "          hpmass     0.999      1.01  4.93e-32  4.93e-32  4.93e-32  4.93e-32  4.93e-32  4.93e-32       0.0       0.0  1.23e-32       0.0  4.93e-32  3.08e-33  3.08e-33  1.93e-34  3.08e-33       0.0  1.97e-31       0.0\n",
            "\n",
            "\n",
            "min/max\n",
            "  variable/class   all/min   all/max    -1/min    -1/max   200/min   200/max   225/min   225/max   250/min   250/max   275/min   275/max   300/min   300/max   350/min   350/max   400/min   400/max   500/min   500/max   600/min   600/max   700/min   700/max   800/min   800/max   900/min   900/max  1000/min  1000/max  1200/min  1200/max  1400/min  1400/max  1600/min  1600/max  1800/min  1800/max  2000/min  2000/max\n",
            "           nJets    -0.956      6.94    -0.956       5.5    -0.956      6.22    -0.956      3.35    -0.956      4.79    -0.956       5.5    -0.956      4.79    -0.956      4.07    -0.956      4.79    -0.956      4.07    -0.956      4.79    -0.956      4.79    -0.956       5.5    -0.956      6.22    -0.956       5.5    -0.956      6.22    -0.956      6.94    -0.956      6.94    -0.956      6.22    -0.956      6.22\n",
            "       nBTags_70    -0.285      16.2    -0.285      9.62    -0.285      6.32    -0.285      6.32    -0.285      6.32    -0.285      6.32    -0.285      9.62    -0.285      6.32    -0.285      9.62    -0.285      6.32    -0.285      6.32    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      9.62    -0.285      16.2\n",
            "         pT_jet1     -1.22      8.92     -1.22      5.28     -1.21      4.14     -1.21      3.01     -1.18      2.99     -1.19       3.7     -1.18      3.66     -1.22      3.84     -1.18      4.05     -1.14      4.61     -1.17      4.68     -1.06      3.48     -1.07      4.65     -1.19      4.36      -1.1      4.84     -0.99      6.23    -0.991       5.5     -1.04      5.58    -0.953      6.49    -0.998      8.92\n",
            "    Mbb_MindR_70     -1.09      16.2     -1.08       7.7     -1.09      5.67     -1.08      2.37     -1.08      4.21     -1.06      2.96     -1.07      4.99     -1.07      5.11     -1.07      5.11     -1.04      5.93     -1.06      5.76     -1.05      6.35     -1.07      7.19     -1.05      7.68     -1.04      11.9     -1.06      12.5     -1.06      11.5     -1.07      16.2     -1.06      13.1     -1.07      15.9\n",
            "         pT_jet5     -1.19      12.8     -1.19      5.45     -1.18      5.41     -1.18       4.4     -1.18      5.03     -1.19      12.8     -1.18      4.57     -1.17      5.95     -1.19      5.12     -1.19       4.5     -1.19      4.85     -1.17      5.33     -1.18      5.52     -1.18      6.95     -1.18      7.03     -1.16      6.81     -1.17      9.51     -1.18      8.22     -1.18      10.7     -1.17      10.6\n",
            "          H1_all    -0.992      3.75    -0.992      3.74    -0.992      3.75    -0.992      3.59    -0.992      3.69    -0.991      3.64    -0.992      3.67    -0.991       3.7    -0.992      3.69    -0.992      3.71    -0.992      3.59    -0.992      3.55    -0.992      3.61    -0.992      3.59    -0.992      3.57    -0.992      3.53    -0.992      3.09    -0.992      3.38    -0.992      2.97    -0.992      2.98\n",
            "     dRbb_avg_70     -4.33      4.42     -4.07      3.87     -4.33      4.09     -3.26      2.92      -3.4       3.2      -3.4      3.02     -4.23      3.22     -3.18      3.47     -3.81      3.34      -3.3      3.55     -3.41      3.38     -3.59      3.49     -3.73      3.59     -3.78      3.45     -3.88      3.72     -3.78      3.95     -3.78      4.21     -3.67      4.23     -3.96      3.64     -3.96      4.42\n",
            "dRlepbb_MindR_70     -2.38      4.27     -2.31      3.62     -2.34      3.93     -2.32      4.05     -2.38      3.21     -2.35      3.06     -2.32      3.45     -2.36      3.29     -2.31      3.24     -2.31      3.25     -2.34      3.33     -2.33      3.52     -2.33      3.52     -2.34      3.55     -2.36      3.22     -2.34      3.84     -2.36      4.27     -2.36      3.42     -2.37      3.32     -2.37      3.86\n",
            "    Muu_MindR_70    -0.934      21.8    -0.934      20.5    -0.934      17.0    -0.934      11.7    -0.934      10.6    -0.934      7.86    -0.934      11.0    -0.934      8.93    -0.934      11.7    -0.934      21.8    -0.934      9.98    -0.934      11.6    -0.934      10.4    -0.934      14.1    -0.934      18.3    -0.934      15.3    -0.934      17.8    -0.934      16.9    -0.934      17.6    -0.934      18.1\n",
            "         HT_jets     -1.42      7.52      -1.4      4.47     -1.42      3.61     -1.39      3.12     -1.38      2.84     -1.36      7.17     -1.38      3.57     -1.38      3.59      -1.3      3.03     -1.31      3.27     -1.26      3.33     -1.23      4.21      -1.2      3.66     -1.33      3.92      -1.2      4.61     -1.13      5.14     -1.02      5.65     -1.11       5.4     -1.03      7.52     -1.06      7.15\n",
            "    Mbb_MaxPt_70     -1.08      12.7     -1.08      6.55     -1.07      7.65     -1.06      9.25     -1.07      7.63     -1.05      6.39     -1.06      6.26     -1.05      6.68     -1.05      8.52     -1.04      7.29     -1.05      10.2     -1.05      6.81     -1.04      9.61    -0.997      8.62      -1.0      8.22     -1.02      12.7     -1.04      12.2    -0.986      10.2     -1.02      11.4     -1.02      10.3\n",
            "     Mbb_MaxM_70     -1.33       9.9     -1.33       5.9     -1.31      4.46     -1.27      5.35     -1.28      4.29     -1.31      4.87     -1.29      3.72     -1.25      4.29     -1.32      5.99      -1.2      4.91     -1.16      5.95     -1.25      5.13     -1.21      5.59     -1.16       5.3     -1.21      5.67     -1.26       7.6     -1.16       9.9     -1.28      7.68      -1.2      7.67     -1.27       8.3\n",
            "      Mjjj_MaxPt     -1.19      11.6     -1.19      7.83     -1.18       6.1     -1.19      6.08     -1.17      5.87     -1.18      5.55     -1.18      8.18     -1.18      9.74     -1.13      5.14     -1.14      7.42     -1.18      7.37     -1.12      6.63     -1.11      7.63     -1.08      8.61     -1.11      8.05     -1.07      9.73     -1.07      9.01     -1.02      9.04      -1.0      11.6     -1.03      9.68\n",
            "  Centrality_all      -2.7      2.84      -2.7      2.66      -2.5      2.75     -2.53      2.65     -2.53      2.63     -2.54      2.67      -2.4      2.69     -2.57      2.67     -2.57      2.67     -2.52      2.75     -2.44      2.77     -2.29      2.84     -2.42      2.72     -2.39       2.8     -2.44      2.72     -2.53      2.69     -2.36      2.74      -2.5      2.79     -2.46      2.81      -2.6      2.75\n",
            "          hpmass      -1.7      1.54      -1.7      1.54      -1.7      -1.7     -1.66     -1.66     -1.61     -1.61     -1.57     -1.57     -1.52     -1.52     -1.43     -1.43     -1.34     -1.34     -1.16     -1.16    -0.982    -0.982    -0.802    -0.802    -0.621    -0.621    -0.441    -0.441     -0.26     -0.26     0.101     0.101     0.462     0.462     0.823     0.823      1.18      1.18      1.54      1.54\n",
            "\n",
            "\n",
            "[7.33208490e+00 4.08614210e+00 2.85043722e+05 1.12006759e+05\n",
            " 6.28844961e+04 1.94802454e-01 2.34337459e+00 2.23806049e+00\n",
            " 1.12237654e+05 8.23230534e+05 2.41337287e+05 4.69094694e+05\n",
            " 4.03983956e+05 5.89613569e-01 1.14408829e+03] [1.39313343e+00 3.02771118e-01 2.04727498e+05 9.07685011e+04\n",
            " 3.18304433e+04 1.96291392e-01 3.94176461e-01 9.37648445e-01\n",
            " 1.20225197e+05 4.57710020e+05 2.09368492e+05 3.19307675e+05\n",
            " 3.01675225e+05 1.38056936e-01 5.54009712e+02]\n",
            "200 -1.7041006134878616\n",
            "225 -1.6589750508197907\n",
            "250 -1.6138494881517196\n",
            "275 -1.5687239254836487\n",
            "300 -1.5235983628155776\n",
            "350 -1.4333472374794356\n",
            "400 -1.3430961121432936\n",
            "500 -1.1625938614710094\n",
            "600 -0.9820916107987254\n",
            "700 -0.8015893601264413\n",
            "800 -0.6210871094541572\n",
            "900 -0.4405848587818732\n",
            "1000 -0.2600826081095891\n",
            "1200 0.10092189323497901\n",
            "1400 0.46192639457954715\n",
            "1600 0.8229308959241153\n",
            "1800 1.1839353972686835\n",
            "2000 1.5449398986132516\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TnTPw-iwzeSb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "ok the columns include the H+ mass now\n",
        "\n",
        "Now on to scaling the weights to have a flat distributiong in H+ mass"
      ]
    },
    {
      "metadata": {
        "id": "DOPz2myNzeSo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training the classifier"
      ]
    },
    {
      "metadata": {
        "id": "aKL-x2o0zeSq",
        "colab_type": "code",
        "outputId": "6e556ab9-a6b1-45ea-c034-b4566bee7b42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5977
        }
      },
      "cell_type": "code",
      "source": [
        "model16=HpKerasUtils.HpFeedForwardModel(configuration=[16,16],dropout=0.1, verbose=True, input_dim=15)\n",
        "result16=model16.train((X_train.values, y_train.values, w_train.values),(X_test.values, y_test.values, w_test.values), patience=5)\n",
        "model32=HpKerasUtils.HpFeedForwardModel(configuration=[32,32],dropout=0.1, verbose=True, input_dim=15)\n",
        "result32=model32.train((X_train.values, y_train.values, w_train.values),(X_test.values, y_test.values, w_test.values), patience=5)\n",
        "model64=HpKerasUtils.HpFeedForwardModel(configuration=[64,64],dropout=0.1, verbose=True, input_dim=15)\n",
        "result64=model64.train((X_train.values, y_train.values, w_train.values),(X_test.values, y_test.values, w_test.values), patience=5)\n",
        "model128=HpKerasUtils.HpFeedForwardModel(configuration=[128,128],dropout=0.1, verbose=True, input_dim=15)\n",
        "result128=model128.train((X_train.values, y_train.values, w_train.values),(X_test.values, y_test.values, w_test.values), patience=5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 16)                256       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 545\n",
            "Trainable params: 545\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 166812 samples, validate on 166781 samples\n",
            "Epoch 1/100\n",
            "166812/166812 [==============================] - 6s 35us/step - loss: 2.6597e-06 - binary_accuracy: 0.7425 - val_loss: 2.3689e-06 - val_binary_accuracy: 0.7731\n",
            "roc-auc: 0.9089 - roc-auc_val: 0.9082\n",
            "Epoch 2/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.3467e-06 - binary_accuracy: 0.7997 - val_loss: 2.3243e-06 - val_binary_accuracy: 0.8181\n",
            "roc-auc: 0.9137 - roc-auc_val: 0.9123\n",
            "Epoch 3/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.3126e-06 - binary_accuracy: 0.8070 - val_loss: 2.3041e-06 - val_binary_accuracy: 0.8464\n",
            "roc-auc: 0.9157 - roc-auc_val: 0.9145\n",
            "Epoch 4/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2974e-06 - binary_accuracy: 0.8082 - val_loss: 2.2819e-06 - val_binary_accuracy: 0.8294\n",
            "roc-auc: 0.9173 - roc-auc_val: 0.9162\n",
            "Epoch 5/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2649e-06 - binary_accuracy: 0.8100 - val_loss: 2.2623e-06 - val_binary_accuracy: 0.8109\n",
            "roc-auc: 0.919 - roc-auc_val: 0.9179\n",
            "Epoch 6/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2490e-06 - binary_accuracy: 0.8063 - val_loss: 2.2464e-06 - val_binary_accuracy: 0.8010\n",
            "roc-auc: 0.92 - roc-auc_val: 0.9183\n",
            "Epoch 7/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2369e-06 - binary_accuracy: 0.8004 - val_loss: 2.2443e-06 - val_binary_accuracy: 0.8194\n",
            "roc-auc: 0.9213 - roc-auc_val: 0.9189\n",
            "Epoch 8/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2281e-06 - binary_accuracy: 0.8039 - val_loss: 2.2459e-06 - val_binary_accuracy: 0.8013\n",
            "roc-auc: 0.9216 - roc-auc_val: 0.9189\n",
            "Epoch 9/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2249e-06 - binary_accuracy: 0.8008 - val_loss: 2.2364e-06 - val_binary_accuracy: 0.7982\n",
            "roc-auc: 0.9223 - roc-auc_val: 0.9198\n",
            "Epoch 10/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2108e-06 - binary_accuracy: 0.8020 - val_loss: 2.2357e-06 - val_binary_accuracy: 0.7948\n",
            "roc-auc: 0.9226 - roc-auc_val: 0.9196\n",
            "Epoch 11/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2099e-06 - binary_accuracy: 0.7983 - val_loss: 2.2268e-06 - val_binary_accuracy: 0.7613\n",
            "roc-auc: 0.9231 - roc-auc_val: 0.9202\n",
            "Epoch 12/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2045e-06 - binary_accuracy: 0.7968 - val_loss: 2.2375e-06 - val_binary_accuracy: 0.8197\n",
            "roc-auc: 0.9235 - roc-auc_val: 0.92\n",
            "Epoch 13/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.2023e-06 - binary_accuracy: 0.7930 - val_loss: 2.2284e-06 - val_binary_accuracy: 0.8144\n",
            "roc-auc: 0.9238 - roc-auc_val: 0.9203\n",
            "Epoch 14/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1852e-06 - binary_accuracy: 0.7924 - val_loss: 2.2332e-06 - val_binary_accuracy: 0.8179\n",
            "roc-auc: 0.9244 - roc-auc_val: 0.9206\n",
            "Epoch 15/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1870e-06 - binary_accuracy: 0.7971 - val_loss: 2.2208e-06 - val_binary_accuracy: 0.7736\n",
            "roc-auc: 0.9238 - roc-auc_val: 0.9205\n",
            "Epoch 16/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1834e-06 - binary_accuracy: 0.7917 - val_loss: 2.2205e-06 - val_binary_accuracy: 0.7479\n",
            "roc-auc: 0.9247 - roc-auc_val: 0.9209\n",
            "Epoch 17/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1832e-06 - binary_accuracy: 0.7882 - val_loss: 2.2205e-06 - val_binary_accuracy: 0.7874\n",
            "roc-auc: 0.925 - roc-auc_val: 0.921\n",
            "Epoch 18/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1838e-06 - binary_accuracy: 0.7920 - val_loss: 2.2254e-06 - val_binary_accuracy: 0.7874\n",
            "roc-auc: 0.925 - roc-auc_val: 0.9208\n",
            "Epoch 19/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1758e-06 - binary_accuracy: 0.7877 - val_loss: 2.2162e-06 - val_binary_accuracy: 0.7645\n",
            "roc-auc: 0.9255 - roc-auc_val: 0.9211\n",
            "Epoch 20/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1835e-06 - binary_accuracy: 0.7897 - val_loss: 2.2128e-06 - val_binary_accuracy: 0.7730\n",
            "roc-auc: 0.9257 - roc-auc_val: 0.9215\n",
            "Epoch 21/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1728e-06 - binary_accuracy: 0.7910 - val_loss: 2.2163e-06 - val_binary_accuracy: 0.7749\n",
            "roc-auc: 0.9256 - roc-auc_val: 0.9212\n",
            "Epoch 22/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1675e-06 - binary_accuracy: 0.7862 - val_loss: 2.2163e-06 - val_binary_accuracy: 0.8160\n",
            "roc-auc: 0.926 - roc-auc_val: 0.9216\n",
            "Epoch 23/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1621e-06 - binary_accuracy: 0.7932 - val_loss: 2.2136e-06 - val_binary_accuracy: 0.8103\n",
            "roc-auc: 0.926 - roc-auc_val: 0.9216\n",
            "Epoch 24/100\n",
            "166812/166812 [==============================] - 6s 35us/step - loss: 2.1714e-06 - binary_accuracy: 0.7947 - val_loss: 2.2078e-06 - val_binary_accuracy: 0.7853\n",
            "roc-auc: 0.9262 - roc-auc_val: 0.9219\n",
            "Epoch 25/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1640e-06 - binary_accuracy: 0.7935 - val_loss: 2.2390e-06 - val_binary_accuracy: 0.8162\n",
            "roc-auc: 0.9255 - roc-auc_val: 0.9213\n",
            "Epoch 26/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1720e-06 - binary_accuracy: 0.7975 - val_loss: 2.2306e-06 - val_binary_accuracy: 0.8344\n",
            "roc-auc: 0.9264 - roc-auc_val: 0.9211\n",
            "Epoch 27/100\n",
            "166812/166812 [==============================] - 6s 34us/step - loss: 2.1591e-06 - binary_accuracy: 0.7992 - val_loss: 2.2157e-06 - val_binary_accuracy: 0.8068\n",
            "roc-auc: 0.9263 - roc-auc_val: 0.9213\n",
            "Epoch 28/100\n",
            "166812/166812 [==============================] - 6s 37us/step - loss: 2.1577e-06 - binary_accuracy: 0.8014 - val_loss: 2.2117e-06 - val_binary_accuracy: 0.7881\n",
            "roc-auc: 0.9268 - roc-auc_val: 0.9221\n",
            "Epoch 29/100\n",
            "166812/166812 [==============================] - 6s 35us/step - loss: 2.1627e-06 - binary_accuracy: 0.7948 - val_loss: 2.2122e-06 - val_binary_accuracy: 0.7814\n",
            "roc-auc: 0.9269 - roc-auc_val: 0.9214\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 32)                512       \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 1,601\n",
            "Trainable params: 1,601\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 166812 samples, validate on 166781 samples\n",
            "Epoch 1/100\n",
            "166812/166812 [==============================] - 6s 37us/step - loss: 2.6061e-06 - binary_accuracy: 0.7619 - val_loss: 2.3511e-06 - val_binary_accuracy: 0.8166\n",
            "roc-auc: 0.9102 - roc-auc_val: 0.9093\n",
            "Epoch 2/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.3451e-06 - binary_accuracy: 0.8173 - val_loss: 2.3034e-06 - val_binary_accuracy: 0.8176\n",
            "roc-auc: 0.9154 - roc-auc_val: 0.9138\n",
            "Epoch 3/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.2818e-06 - binary_accuracy: 0.8084 - val_loss: 2.2778e-06 - val_binary_accuracy: 0.8123\n",
            "roc-auc: 0.9181 - roc-auc_val: 0.9158\n",
            "Epoch 4/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.2579e-06 - binary_accuracy: 0.8038 - val_loss: 2.2663e-06 - val_binary_accuracy: 0.7999\n",
            "roc-auc: 0.9198 - roc-auc_val: 0.9168\n",
            "Epoch 5/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.2333e-06 - binary_accuracy: 0.8028 - val_loss: 2.2487e-06 - val_binary_accuracy: 0.8208\n",
            "roc-auc: 0.9213 - roc-auc_val: 0.9181\n",
            "Epoch 6/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.2160e-06 - binary_accuracy: 0.8050 - val_loss: 2.2377e-06 - val_binary_accuracy: 0.8002\n",
            "roc-auc: 0.9223 - roc-auc_val: 0.9189\n",
            "Epoch 7/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.2128e-06 - binary_accuracy: 0.8074 - val_loss: 2.2428e-06 - val_binary_accuracy: 0.8360\n",
            "roc-auc: 0.9241 - roc-auc_val: 0.9199\n",
            "Epoch 8/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1979e-06 - binary_accuracy: 0.8099 - val_loss: 2.2225e-06 - val_binary_accuracy: 0.7861\n",
            "roc-auc: 0.9242 - roc-auc_val: 0.9201\n",
            "Epoch 9/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1814e-06 - binary_accuracy: 0.8073 - val_loss: 2.2198e-06 - val_binary_accuracy: 0.8182\n",
            "roc-auc: 0.9248 - roc-auc_val: 0.9206\n",
            "Epoch 10/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1763e-06 - binary_accuracy: 0.8039 - val_loss: 2.2164e-06 - val_binary_accuracy: 0.7963\n",
            "roc-auc: 0.9257 - roc-auc_val: 0.9206\n",
            "Epoch 11/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1744e-06 - binary_accuracy: 0.8004 - val_loss: 2.2106e-06 - val_binary_accuracy: 0.7713\n",
            "roc-auc: 0.9265 - roc-auc_val: 0.9215\n",
            "Epoch 12/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1544e-06 - binary_accuracy: 0.8014 - val_loss: 2.2148e-06 - val_binary_accuracy: 0.7994\n",
            "roc-auc: 0.9263 - roc-auc_val: 0.9212\n",
            "Epoch 13/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1534e-06 - binary_accuracy: 0.8058 - val_loss: 2.2003e-06 - val_binary_accuracy: 0.8164\n",
            "roc-auc: 0.9275 - roc-auc_val: 0.9221\n",
            "Epoch 14/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1538e-06 - binary_accuracy: 0.8076 - val_loss: 2.1955e-06 - val_binary_accuracy: 0.8162\n",
            "roc-auc: 0.9275 - roc-auc_val: 0.9223\n",
            "Epoch 15/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1430e-06 - binary_accuracy: 0.8039 - val_loss: 2.2118e-06 - val_binary_accuracy: 0.7924\n",
            "roc-auc: 0.9277 - roc-auc_val: 0.9212\n",
            "Epoch 16/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1443e-06 - binary_accuracy: 0.8060 - val_loss: 2.2172e-06 - val_binary_accuracy: 0.8445\n",
            "roc-auc: 0.9276 - roc-auc_val: 0.9214\n",
            "Epoch 17/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1345e-06 - binary_accuracy: 0.8075 - val_loss: 2.1950e-06 - val_binary_accuracy: 0.7901\n",
            "roc-auc: 0.9284 - roc-auc_val: 0.9226\n",
            "Epoch 18/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1400e-06 - binary_accuracy: 0.7998 - val_loss: 2.2139e-06 - val_binary_accuracy: 0.7776\n",
            "roc-auc: 0.9277 - roc-auc_val: 0.9217\n",
            "Epoch 19/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1328e-06 - binary_accuracy: 0.8057 - val_loss: 2.2035e-06 - val_binary_accuracy: 0.8099\n",
            "roc-auc: 0.9292 - roc-auc_val: 0.9221\n",
            "Epoch 20/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1245e-06 - binary_accuracy: 0.8010 - val_loss: 2.2116e-06 - val_binary_accuracy: 0.7820\n",
            "roc-auc: 0.9292 - roc-auc_val: 0.9219\n",
            "Epoch 21/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1377e-06 - binary_accuracy: 0.7982 - val_loss: 2.2167e-06 - val_binary_accuracy: 0.8072\n",
            "roc-auc: 0.93 - roc-auc_val: 0.9223\n",
            "Epoch 22/100\n",
            "166812/166812 [==============================] - 6s 36us/step - loss: 2.1101e-06 - binary_accuracy: 0.8071 - val_loss: 2.1986e-06 - val_binary_accuracy: 0.8119\n",
            "roc-auc: 0.9294 - roc-auc_val: 0.9224\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_7 (Dense)              (None, 64)                1024      \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 5,249\n",
            "Trainable params: 5,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 166812 samples, validate on 166781 samples\n",
            "Epoch 1/100\n",
            "166812/166812 [==============================] - 7s 42us/step - loss: 2.5123e-06 - binary_accuracy: 0.7521 - val_loss: 2.3177e-06 - val_binary_accuracy: 0.7834\n",
            "roc-auc: 0.9144 - roc-auc_val: 0.9124\n",
            "Epoch 2/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.2751e-06 - binary_accuracy: 0.8045 - val_loss: 2.2724e-06 - val_binary_accuracy: 0.7691\n",
            "roc-auc: 0.9187 - roc-auc_val: 0.9163\n",
            "Epoch 3/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.2352e-06 - binary_accuracy: 0.7970 - val_loss: 2.2451e-06 - val_binary_accuracy: 0.7844\n",
            "roc-auc: 0.9212 - roc-auc_val: 0.9182\n",
            "Epoch 4/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.2113e-06 - binary_accuracy: 0.7992 - val_loss: 2.2515e-06 - val_binary_accuracy: 0.8276\n",
            "roc-auc: 0.9222 - roc-auc_val: 0.9188\n",
            "Epoch 5/100\n",
            "166812/166812 [==============================] - 7s 40us/step - loss: 2.1825e-06 - binary_accuracy: 0.8016 - val_loss: 2.2302e-06 - val_binary_accuracy: 0.7993\n",
            "roc-auc: 0.9237 - roc-auc_val: 0.9199\n",
            "Epoch 6/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.1721e-06 - binary_accuracy: 0.7995 - val_loss: 2.2186e-06 - val_binary_accuracy: 0.7934\n",
            "roc-auc: 0.9257 - roc-auc_val: 0.9206\n",
            "Epoch 7/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.1515e-06 - binary_accuracy: 0.8055 - val_loss: 2.2182e-06 - val_binary_accuracy: 0.8293\n",
            "roc-auc: 0.9265 - roc-auc_val: 0.9214\n",
            "Epoch 8/100\n",
            "166812/166812 [==============================] - 7s 43us/step - loss: 2.1441e-06 - binary_accuracy: 0.8025 - val_loss: 2.2079e-06 - val_binary_accuracy: 0.8034\n",
            "roc-auc: 0.9276 - roc-auc_val: 0.9216\n",
            "Epoch 9/100\n",
            "166812/166812 [==============================] - 7s 40us/step - loss: 2.1397e-06 - binary_accuracy: 0.8052 - val_loss: 2.2154e-06 - val_binary_accuracy: 0.7686\n",
            "roc-auc: 0.9276 - roc-auc_val: 0.9212\n",
            "Epoch 10/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.1294e-06 - binary_accuracy: 0.8060 - val_loss: 2.2022e-06 - val_binary_accuracy: 0.7739\n",
            "roc-auc: 0.9284 - roc-auc_val: 0.9219\n",
            "Epoch 11/100\n",
            "166812/166812 [==============================] - 7s 39us/step - loss: 2.1293e-06 - binary_accuracy: 0.8030 - val_loss: 2.2178e-06 - val_binary_accuracy: 0.8500\n",
            "roc-auc: 0.9292 - roc-auc_val: 0.9209\n",
            "Epoch 12/100\n",
            "166812/166812 [==============================] - 7s 40us/step - loss: 2.1249e-06 - binary_accuracy: 0.8079 - val_loss: 2.2034e-06 - val_binary_accuracy: 0.7787\n",
            "roc-auc: 0.93 - roc-auc_val: 0.9222\n",
            "Epoch 13/100\n",
            "166812/166812 [==============================] - 7s 40us/step - loss: 2.1115e-06 - binary_accuracy: 0.8074 - val_loss: 2.2059e-06 - val_binary_accuracy: 0.7856\n",
            "roc-auc: 0.9297 - roc-auc_val: 0.9217\n",
            "Epoch 14/100\n",
            "166812/166812 [==============================] - 7s 40us/step - loss: 2.1014e-06 - binary_accuracy: 0.8065 - val_loss: 2.2059e-06 - val_binary_accuracy: 0.8085\n",
            "roc-auc: 0.931 - roc-auc_val: 0.9221\n",
            "Epoch 15/100\n",
            "166812/166812 [==============================] - 7s 40us/step - loss: 2.1060e-06 - binary_accuracy: 0.8045 - val_loss: 2.2185e-06 - val_binary_accuracy: 0.8186\n",
            "roc-auc: 0.9301 - roc-auc_val: 0.9216\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_10 (Dense)             (None, 128)               2048      \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 18,689\n",
            "Trainable params: 18,689\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 166812 samples, validate on 166781 samples\n",
            "Epoch 1/100\n",
            "166812/166812 [==============================] - 10s 59us/step - loss: 2.4565e-06 - binary_accuracy: 0.7788 - val_loss: 2.3263e-06 - val_binary_accuracy: 0.7694\n",
            "roc-auc: 0.9149 - roc-auc_val: 0.9124\n",
            "Epoch 2/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.2557e-06 - binary_accuracy: 0.8059 - val_loss: 2.2601e-06 - val_binary_accuracy: 0.7944\n",
            "roc-auc: 0.9203 - roc-auc_val: 0.9177\n",
            "Epoch 3/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.2158e-06 - binary_accuracy: 0.8031 - val_loss: 2.2430e-06 - val_binary_accuracy: 0.7765\n",
            "roc-auc: 0.9221 - roc-auc_val: 0.9185\n",
            "Epoch 4/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.1790e-06 - binary_accuracy: 0.8034 - val_loss: 2.2285e-06 - val_binary_accuracy: 0.8006\n",
            "roc-auc: 0.9249 - roc-auc_val: 0.9197\n",
            "Epoch 5/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.1591e-06 - binary_accuracy: 0.8049 - val_loss: 2.2563e-06 - val_binary_accuracy: 0.8672\n",
            "roc-auc: 0.9269 - roc-auc_val: 0.9205\n",
            "Epoch 6/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.1401e-06 - binary_accuracy: 0.8090 - val_loss: 2.2142e-06 - val_binary_accuracy: 0.7639\n",
            "roc-auc: 0.9277 - roc-auc_val: 0.921\n",
            "Epoch 7/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.1247e-06 - binary_accuracy: 0.8056 - val_loss: 2.2283e-06 - val_binary_accuracy: 0.8431\n",
            "roc-auc: 0.9276 - roc-auc_val: 0.9214\n",
            "Epoch 8/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.1267e-06 - binary_accuracy: 0.8073 - val_loss: 2.2141e-06 - val_binary_accuracy: 0.8273\n",
            "roc-auc: 0.9295 - roc-auc_val: 0.9212\n",
            "Epoch 9/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.1052e-06 - binary_accuracy: 0.8120 - val_loss: 2.2086e-06 - val_binary_accuracy: 0.7861\n",
            "roc-auc: 0.9291 - roc-auc_val: 0.9217\n",
            "Epoch 10/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.1032e-06 - binary_accuracy: 0.8057 - val_loss: 2.2106e-06 - val_binary_accuracy: 0.8416\n",
            "roc-auc: 0.9312 - roc-auc_val: 0.9222\n",
            "Epoch 11/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.0915e-06 - binary_accuracy: 0.8107 - val_loss: 2.2040e-06 - val_binary_accuracy: 0.8182\n",
            "roc-auc: 0.9313 - roc-auc_val: 0.9218\n",
            "Epoch 12/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.0944e-06 - binary_accuracy: 0.8095 - val_loss: 2.2007e-06 - val_binary_accuracy: 0.8086\n",
            "roc-auc: 0.9317 - roc-auc_val: 0.9219\n",
            "Epoch 13/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.0847e-06 - binary_accuracy: 0.8117 - val_loss: 2.2334e-06 - val_binary_accuracy: 0.8612\n",
            "roc-auc: 0.9311 - roc-auc_val: 0.92\n",
            "Epoch 14/100\n",
            "166812/166812 [==============================] - 9s 55us/step - loss: 2.0852e-06 - binary_accuracy: 0.8125 - val_loss: 2.2097e-06 - val_binary_accuracy: 0.8510\n",
            "roc-auc: 0.9327 - roc-auc_val: 0.9218\n",
            "Epoch 15/100\n",
            "166812/166812 [==============================] - 9s 56us/step - loss: 2.0714e-06 - binary_accuracy: 0.8136 - val_loss: 2.2158e-06 - val_binary_accuracy: 0.8339\n",
            "roc-auc: 0.9337 - roc-auc_val: 0.9212\n",
            "Epoch 16/100\n",
            "166812/166812 [==============================] - 10s 57us/step - loss: 2.0584e-06 - binary_accuracy: 0.8173 - val_loss: 2.2035e-06 - val_binary_accuracy: 0.7996\n",
            "roc-auc: 0.9338 - roc-auc_val: 0.9221\n",
            "Epoch 17/100\n",
            "166812/166812 [==============================] - 10s 59us/step - loss: 2.0593e-06 - binary_accuracy: 0.8125 - val_loss: 2.2147e-06 - val_binary_accuracy: 0.8107\n",
            "roc-auc: 0.9335 - roc-auc_val: 0.9208\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_vHgr1VdOE2u",
        "colab_type": "code",
        "outputId": "b5587b68-6f6e-46ad-e1a9-5d19a41c9fc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2081
        }
      },
      "cell_type": "code",
      "source": [
        "model64.plotTrainingValidation()\n",
        "model128.plotTrainingValidation()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAFYCAYAAADtIa4rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFYCAYAAABKymUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqYuIiBhCpS4iImKI/wM3fZsDxKFyIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFYCAYAAAAlTUT9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XlclNX+wPHPMxtLDMEYuGAulyQV\nt3ypF/PmCliZC664lihgZUqZltxb2rXrmqaWCy22qCRJYnZvPy1Ku9001yJzSdOwMk1QVHZm+/0x\nOUgKbgzDzHzfr1evfJ6ZeeZ8mZnnfJ9zznOOYrVarQghhBDCo6icXQAhhBBC1DxJAIQQQggPJAmA\nEEII4YEkARBCCCE8kCQAQgghhAeSBEAIIYTwQBpnF6Am5eTkO7sItyww0Je8vCJnF8PhJE734glx\nTl2+HZVaYV5iZ2cXxeE84fME94gzKEhf6WPSAuBiNBq1s4tQIyRO9+IpcSrOLkAN8ZTP093jlARA\nCCGE8ECSAAghhBAeSBIAIYQQwgNJAiCEEEJ4IEkAhBBCCA8kCYAQQgjhgSQBEEIIITyQR00EVBu9\n8srL/PDDIc6dO0tJSQkNGoTg7387s2cvuOZrP/74I267zY9u3Xpc9fElSxYyZEgsDRqE3FTZ3nwz\nhU8/3cwddwQBUFpawqhRY+3vd/jwIZYvX0JxcTEmk5H77uvOww+PQ6223Tu7efN/WL9+HTqdFpPJ\nxIgRY+jRI/Kq7/XUUxPx8vJizpyF9n19+vTiP//5zL69b98eNmx4nxdfnA9AaupqMjM34+XlDViJ\nj3+M9u073FSsQgjhaSQBuEEZGRoWL9Zx5IiKsDALSUllxMSYbvp4TzzxJGCrzI8fP8bEiUnX/doH\nH+xb5eOTJ0+56XJdMmRILIMGDQPg4sULPPLICCIiOmM2m5k58+/MmjWXZs3CsFqtLFmykFWrXiM+\n/lG+++5bPvjgfRYvXo5erycv7xwTJsQRGnoXjRo1qfAeeXnnyM7+ibKyUgoKCvDz87tmuT75ZDNZ\nWftYufItdDodP/98gqSkx3j77ffw9/e/5biFEJ6r/DwPYWG+t3yer60kAbgBGRkaEhN97NuHDqn/\n2C6u9i/Hvn17WLduDUVFRUyc+CTffLOXbds+Q61W6NAhgri4BN58M4WAgACaNg1lw4b3URQVJ078\nRPfuvYiLS2DixASeemoaW7d+RmFhAT//fIKTJ39l0qQpdO7chTVr3iYz8xMaNAjBZDIRGzuyyito\nf//bqVPnDnJzc9m9+2u6du1Gs2ZhACiKQmLi4wwfPpDx4yfwwQdpxMXFo9fbpqEMDDTwxhur7duX\n++yzT+jSpSsFBfl88cXn9OnT75p/n/T0dUyf/jw6nQ6ARo0a88476656fCGEuF41eZ53NhkDcAMW\nL9Zddf+SJVfff6uOHfuRRYtepXnzFgAsX/4G77//Pv/3f/+msLCgwnMPHjzA3/8+k5Ur3+KDD9Ku\nONaZM7/z0ktLmTz5aTZt2sDFixfYsGE9KSmrePrpZ/n2233XLM/PP2eTl3eO4OC6nDhxgmbN7q7w\nuI+PDwaDgdzcnKs+Xlnl/OmnW4iMjCYysjefffbJNcsBcPr0KZo0aXpdxxdCiOtV0+d5Z5IWgBtw\n5MjV86XK9t+qu+5qZr/C9fb2ZuLEBHx8vDh//jwXL16s8Ny7726Ot7d3pcdq06YdAMHBwRQUFPDr\nr7/wl7+E4uXljZeXNy1ahF/1devXr2Pr1s8oKiqkrMzIjBkvotVqURSwWCxXPN9qtaJSqVAUMJvN\n14zxt99OkpNzhjZt2mE2m5k370Xy8vKqWMBCsb+P1WpFUTxl9nUhRE2o6fO8M7lfRA4UFnZlhVfV\n/lul1WoB29VuWtpaFi58hdWrV1OvXr0rnntp4F1lLn/cVnmCSlX+8VdWjw4ZEsurr77G/PlLsFot\nhIY2A6BRoyYcPnyownOLioq4ePEidercQaNGTTh06ECFx0+cyKaoqOLKWp9+upmysjLGjh3J+PFj\nMJtNbN2a+Uf8ugpJxvnz57njjjsAaNAghCNHfqhwrB9/PIrJ5F5NdEKImlXT53lnkgTgBiQllV11\n/+TJV99fXc6fP09gYCC+vr4cOHCA06dPYzQab+mY9evX5/jxY5hMJvLy8q6ozP/sjjvu4P77+/DW\nW68DEB39ANu3f8nhwwftz3ntteX07TsAgCFDhrNq1Wvk5Z0D4OzZXJ577hl+//10heNmZm5hyZIV\nvP12Km+/ncq//rWAzMwtALRt247MTFuXgMlkYvPmf/PXv94LwNChI1i2bDHFxcWArXvi+eefJT+/\nYsuIEELcCGed551BugBugG0ASDFLlpTfBTB5suNHhzZrFoaPjy+PPhrHX//aif79B7Jw4TzatGl7\n08c0GOoQFXU/8fFjaNy4KS1bhl+zFWHYsJE8/HAsDzzwEH/5SygLFixh4cK5FBUVYjKZ6NSpM6NG\nPQJAq1atSUh4nKeemoi3tw9qtZqkpKk0bfoX+/GOHj2CTudFaOhd9n1t297DuXPnOHXqFElJ01i4\ncC6bNm3AZDLRs2cUnTt3AaBXryiKigqZMGEsfn56dDodL7wwh8BAw03/TYQQouJ5Xk1YmLlGzvPO\noFitVquzC1FTcnLynV2EWxYUpK+2OD7++COiou5HrVYzZkwsixa9QnBw3Wo59q2qzjhrM4nTfUxd\nvh21WmFuYmdnF8XhPOHzBPeIs/LxVNIC4NHOnj1LQsLDaLU6oqPvrzWVvxBCCMeTBMCDjR79CKNH\nP+LsYgghhHACGQQohBBCeCBJAIQQQggPJAmAEEII4WQZGRq6dfOlfn0/unXzJSPD8T30MgZACCGE\ncCJnrT/g0BaA2bNnM2zYMGJjY/nuu+8qPJaZmcmgQYMYPnw4a9asAaC4uJjJkyczatQohgwZwtat\nWwE4deoUo0ePZsSIEUyePJmyMtuEDJs2bWLQoEEMGTKE9evXOzIUh0lMHHvFJDwrV77Ke++tuerz\nd+7cyT/+MQ2AZ5996orHP/ggjTffTKn0/X788Sg//3wCgBkzplNaWnKzRedf/5rJmDHDmDjRtvBQ\nYuJYsrK+tT++e/fXTJgQR2LiWMaOHUFGRnqF16emriYubiSPPjqORx+NY9++PZW+14gRg1iypHyp\n4FOnfmPcuNEVnvPxxx/x6quLAdvEQStWvMLYsSN49NFxTJo0gWPHfrzpWIUQ5VepGg01dpXqCZy1\n/oDDPr1du3Zx4sQJ0tLSOHbsGMnJyaSl2RapsVgszJo1i4yMDAICAoiPjycyMpJ9+/bRqlUr4uPj\nOXnyJHFxcfTo0YOlS5cyYsQIHnjgARYtWkR6ejoDBgxg2bJlpKeno9VqGTx4MFFRUQQEBDgqJIeI\niurN559/al/wB2Dbts955ZWV13zt3LmLbvj9vvjic5o3b0mjRo154YU5N/z6P0tMnEiXLvcBcPLk\nr0yZMol16zZw+vQpXn55AYsWvUq9evUxGo3MnJmMVqvhoYcG3NByvocPH8JqtbJt22c88cSTFaYw\nrkxq6rsUFOSzatVaFEVh//4skpOfZu3adDQaOWkJcaM8aZW8muas9QccdvQdO3YQGRkJQGhoKBcu\nXKCgwLaCXV5eHv7+/hgMBlQqFREREWzfvp0HH3yQ+Ph4wHbVX7eu7b70nTt30qtXLwB69OjBjh07\nyMrKonXr1uj1ery9vWnfvj379l17RbvaplevaP7736327cOHDxEUFERQUDC7d+8kMXEsEycmMH36\nlCum/+3Tx/Y32bNnF2PGDCMp6TEOHrTNv28ymXjhhX8wcWIC48aN5quvvuTYsR/58MMNpKS8ysGD\n3zN4cF+Kioo4c+Z3nnzycSZOTGDSpAn89ttJTp36jYkTE5g9+wXi4kYxd+6sa8YSEtKQoqJCzGYz\nGRnpDB4cS7169QHbugZPPDGF999/D7At5zthwhNXLOf758ofbOsF9O07gHr16l3XqoUAGzd+wIQJ\nT9gXC2rdui1vvLFaKn8hbpInrZJX05y1/oDDzoa5ubmEh5evMGcwGMjJycHPzw+DwUBhYSHZ2dmE\nhISwc+dOOnXqZH9ubGwsp0+fZuVK21VwcXGxvaKoU6cOOTk55ObmYjAYrjh+VQIDfdFoKp/udupU\nqO6ehCFDYMGCyh8PCtLTpEljTp36iTZt2vD2218wcOAAgoL0KIqRJUte5s4772TatGkcPvwtt912\nG15e2j8eVwgK0vPmmyt4+eVFNG/enPj4eG67zQudzkKvXt2JiYnhl19+YfLkyWzYsIFu3brSu3dv\nunXrjFqt4o47/Fi5cjEjRsTy4IMPsnnzZlJT3+KJJ57gyJHDvPrqUurUqUPXrl3x8rJWqKC9vbXc\nfruPfaap3bt3U7duMPXqBXD69K/07ftAhVmogoL05OdfpE6d2zhz5jQdO7apsJrfn2esCgrSY7FY\n+OKLz3jvvfeoU+d2vvpqK71796C09DY0GlWF1+j13vj66vD2Bl9fH/7ylwZXHK82qq3lqm7uHqda\nbfsuu2ucR45Utl/ttjFDzXyezz8Pw4dfuf+55xz7t62xy6HLZxxWFIW5c+eSnJyMXq+nYcOGFZ67\nbt06Dh06xNSpU9m0aVOlx7me/ZfLyyuq8vGiIi8slur9kxQVmcjJKa3yOd26RZKevpH69Zvy6aeZ\nrFixipycfFQqb555Zjpms5nffjtJeHg7mjcPpbTUSE5OPlarlZycfH755Vfq1AkhJyefli3bUFhY\nSlmZil279rJ2bSqKouLs2XPk5ORTUmLkwoVicnLyMZst5OYWkJX1HY88kkhOTj533RXO0qWvcO5c\nIQ0aNAS8OXu2EIPhDk6cOE29euUVdkmJkfnzF7By5WtcuHAeHx9f/v73f5KTk09ZmZmzZwuumEbT\nYrGSm1uA2WzhzJmLlTbnX5qCc9++PQQF1UWr1dOp030sW7acRx99knPnCjGZLBWOn59fQnGxkdzc\nAsrKjC4xhac7TDV6PTwhTrPZilqtuG2cYWG+HDp05QVUWJiZnJyqz623IiNDw+LF5euvJCXV3Lz8\nNfW97dULUlI0V6wz06uXiWtc116TU6YCDg4OJjc317595swZgoKC7NudOnUiNTUVgIULFxISEsL3\n339PnTp1qF+/Pi1atMBsNnPu3Dl8fX0pKSnB29ub33//neDg4Ksev127drdU5pkzS5k5s+rK2hG6\ndevBu++uIiqqN3fe2ch+lT1nziwWLFhMkyZNWbRoXqWvv7wSvZQIffrpZi5evMiyZW9w8eJFxo8f\nXdnLAcX+OqPRhKLYjvfnxYGulmRdGgNw9OgR5s17kUaNGgPQuHETfvjhIG3bln8mp0+fok6dOiiK\nYl/O9/KxDz/+eJQmTZpWaKb/9NPNnD59ikceGQFASUkJu3d/Tdu27SksLKhQlvPn86hTpw5+fn6Y\nTCbOnTuLwVDH/vgPPxwmLOzuCq0OQojrk5RUVmEMwCWOXCXPk8YdxMSYajwmh40B6NKlC1u22JZ1\nPXDgAMHBwfj5+dkfHz9+PGfPnqWoqIitW7fSuXNn9uzZw6pVqwBbF0JRURGBgYHce++99mN98skn\n3HfffbRt25b9+/dz8eJFCgsL2bdvHx06dHBUOA7l63sboaHNePfdt4iKut++v7CwgLp165Gfn8++\nfXsrXQL4jjuC+PnnbKxWK998sxewLSFcv34DVCoVX3zxuf21iqJgNpsrvL5Fi5b2Efjffru3QqV8\nvZo1CyMs7G77SP8BAwbzwQfvc/Lkr4BtTMIrryxi6FBbRX49y/kajUa++upL+1LBb7+dypNPTiUz\ncwu+vr4EBATa7zooLi5m69ZMOnaMAGDQoKEsXboIk8n2g/ruu2+ZPXum/Q4SIcSNiYkxkZJSTMuW\nZjQaaNnSTEqKYytiGXfgWA5rAWjfvj3h4eHExsaiKAozZsxgw4YN6PV6oqKiGDp0KHFxcSiKQkJC\nAgaDgdjYWP7+978zYsQISkpKeP7551GpVDzxxBM888wzpKWl0aBBAwYMGIBWq2XKlCmMGzcORVF4\n/PHH0etdtx8qKup+XnxxBjNmlA+2GzhwCI8+Oo4772zEyJFjWLXqNZ5+esoVr01IeIx//OMZ6tWr\nb1/Qp3v3njz77FMcPPg9ffr0Izg4mLfeep22be9h8eIF+Pr62l8/fvwE5syZxUcfbUSj0TJ9+nP2\nivNGxMc/Rnz8GHr2jKRevXo8//yL/POfz2G1WjEay4iOfpD77+8DXN9yvl9//RVt2rTl9tvL7+zo\n0SOS115bTmlpKc89909efnk+r79egtlsYtiwkfalhUeMGMO7764iLm4k/v634+fnx9y5i/Dy8rrh\nuIQQNpeuUm1N445r9r/EWaPjPYUsB+xiPKEvFSROd+MJccpywNWvW7erjzto2dLMtm2OT0Dc4Xtb\n1RgASaOEEELUSklJV++yc+S4A08iCYAQQohaqeK4A2uNjDvwJDIrihBCiFrLGaPjPYW0AAghhAty\nxupxwr3IN0YIIVyMJ90fLxxHWgCEEMLFyP3xojpIAiCEEC5G7o8X1UG+LUII4WKctXqccC+SAAgh\nhIuR++NFdZAEQAghXIzcHy+qg9wFIIQQLkjujxe3SloAhBBCCA8kCYAQQgjhgSQBEEIIITyQJABC\nCCGEB5IEQAghblFGhobTpxWyTyDz8guXIQmAEELcgkvz8huNgLV8Xn5JAkRtJwmAEELcApmXX7gq\nSQCEEOIWyLz8wlXJN1QIIW6BzMsvXJUkAEIIcQtkXn7hqmSUihBC3ALbdLzFbPgWTEZo2dLM5Mll\nMk2vqPUkARBCiFsUE2Ni+ykrarXCe7OLnF0cIa6LdAEIIYQQHkgSACGEEMIDSQIghBBCeCBJAIQQ\nDpORoaFbN180mpqbIvfSe9av7yfT8gpRBYf+MmbPnk1WVhaKopCcnEybNm3sj2VmZrJixQp0Oh19\n+vRh1KhRAMyfP5+9e/diMplITEwkOjqaSZMmkZeXB8D58+dp164diYmJ9O3bl1atWgEQGBjI0qVL\nHRmOEOIGXJoi95JLU+RCscNGyDvjPYVwVQ5LAHbt2sWJEydIS0vj2LFjJCcnk5aWBoDFYmHWrFlk\nZGQQEBBAfHw8kZGRZGdnc/ToUdLS0sjLyyMmJobo6OgKFfv06dMZMmQIAE2bNmX16tWOCkEIcQuq\nmiLXUZWxM95TCFflsC6AHTt2EBkZCUBoaCgXLlygoKAAgLy8PPz9/TEYDKhUKiIiIti+fTsdO3Zk\nyZIlAPj7+1NcXIzZbLYf8/jx4+Tn51doSRBC1E7OmCJXpuUV4vo57FeRm5tLYGCgfdtgMJCTk2P/\nd2FhIdnZ2RiNRnbu3Elubi5qtRpfX18A0tPT6dq1K2q12n6Md999195VcOk9Jk2aRGxsLJs2bXJU\nKEKIm+CMKXJlWl4hrl+NjY6xWq32fyuKwty5c0lOTkav19OwYcMKz83MzCQ9PZ1Vq1bZ95WVlbF3\n715mzpwJQEBAAJMnT6Zfv37k5+czZMgQIiIiCA4OrrQMgYG+aDTqSh93FUFBemcXoUZInK7t+edh\n+PAr9z/3nNphMTvjPS9RqxXAfT/PP5M4XZ/DEoDg4GByc3Pt22fOnCEoKMi+3alTJ1JTUwFYuHAh\nISEhAHz55ZesXLmSN954A72+/A+/e/fuCk3/fn5+DBo0CLC1KLRq1Yrjx49XmQDk5bn+DF1BQXpy\ncvKdXQyHkzhdX69ekJKiYckSHUeOqAkLs02R26uXiT8aAx38nirCwiwOf89LzGbbTIDu+nlezp2/\nt5dzhzirSmAc1gXQpUsXtmzZAsCBAwcIDg7Gz8/P/vj48eM5e/YsRUVFbN26lc6dO5Ofn8/8+fNJ\nSUkhICCgwvH2799P8+bN7dtff/01c+bMAaCoqIjDhw/TtGlTR4UjhLgJMTEmtm0rwmiEbduKamQg\n3qX3/O23ghp7TyFckcNaANq3b094eDixsbEoisKMGTPYsGEDer2eqKgohg4dSlxcHIqikJCQgMFg\nsI/+T0pKsh9n3rx5NGjQgJycHBo1amTf36FDBzZu3MiwYcMwm80kJCRQt25dR4UjhBBCuBXFennn\nvJtz9aYccI8mqeshcboXT4hz6vLtqNUKcxM7O7soDucJnye4R5xO6QIQQgghRO0lCYAQQgjhgSQB\nEEIIITyQJABCCCGEB5IEQAghhPBAkgAIIYQQHkgSACE8REaGhm7dfKlf349u3XzJyKixmcCFELWQ\nnAGE8AAZGRoSE33s24cOqf/YLpaZ8oTwUNICIIQHWLxYd9X9S5Zcfb8Qwv1JAiCEBzhy5Oo/9cr2\nCyHcn/z6hfAAYWGWG9ovhHB/kgAI4QGSksquun/y5KvvF0K4P0kAhPAAMTEmUlKKadnSjEZjpWVL\nMykpMgBQCE8mdwEI4SFiYkxS4Qsh7KQFQAghhPBAkgAIIYQQHkgSACGEEMIDSQIghBBCeCBJAIRw\ngkvz8ms0yLz8QginkLOOEDVM5uUXQtQG0gIgRA2TefmFELWBJABC1DCZl18IURvIGUeIGibz8gsh\nagNJAISoYTIvvxCiNpAEQIgaVnFefmRefiGEU8hdAEI4waV5+YOC9OTkFDm7OEIIDyQtAEIIIYQH\nkgRACCGE8EAO7QKYPXs2WVlZKIpCcnIybdq0sT+WmZnJihUr0Ol09OnTh1GjRgEwf/589u7di8lk\nIjExkejoaJ599lkOHDhAQEAAAOPGjaN79+5s2rSJd955B5VKxdChQxkyZIgjwxFCCCHchsMSgF27\ndnHixAnS0tI4duwYycnJpKWlAWCxWJg1axYZGRkEBAQQHx9PZGQk2dnZHD16lLS0NPLy8oiJiSE6\nOhqAp556ih49etiPX1RUxLJly0hPT0er1TJ48GCioqLsSYIQQgghKuewLoAdO3YQGRkJQGhoKBcu\nXKCgoACAvLw8/P39MRgMqFQqIiIi2L59Ox07dmTJkiUA+Pv7U1xcjNlsvurxs7KyaN26NXq9Hm9v\nb9q3b8++ffscFY4QQgjhVhyWAOTm5hIYGGjfNhgM5OTk2P9dWFhIdnY2RqORnTt3kpubi1qtxtfX\nF4D09HS6du2KWq0GYM2aNYwZM4Ynn3ySc+fOkZubi8FguOrxhRBCCFG1GrsN0Gq12v+tKApz584l\nOTkZvV5Pw4YNKzw3MzOT9PR0Vq1aBUD//v0JCAigRYsWvPbaa7z66qvcc889lR6/MoGBvmg06mqI\nxrmCgvTOLkKNkDjdi7vHqVYrgPvHeYnE6foclgAEBweTm5tr3z5z5gxBQUH27U6dOpGamgrAwoUL\nCQkJAeDLL79k5cqVvPHGG+j1tj98586d7a/r2bMnM2fOpHfv3lccv127dlWWKS/P9e+3tt03nu/s\nYjicxOlePCFOs9mKWq24fZzgGZ8nuEecVSUwDusC6NKlC1u2bAHgwIEDBAcH4+fnZ398/PjxnD17\nlqKiIrZu3Urnzp3Jz89n/vz5pKSkVBjM98QTT/DLL78AsHPnTpo1a0bbtm3Zv38/Fy9epLCwkH37\n9tGhQwdHhSOEEEK4FYe1ALRv357w8HBiY2NRFIUZM2awYcMG9Ho9UVFRDB06lLi4OBRFISEhAYPB\nYB/9n5SUZD/OvHnzGDlyJElJSfj4+ODr68ucOXPw9vZmypQpjBs3DkVRePzxx+0tBkIIIYSommK9\nns5zN+HqTTngHk1S10PidC+eEOfU5dtRqxXmJna+9pNdnCd8nuAecTqlC0AIIYQQtZckAEIIIYQH\nkgRACCGE8ECSAAghhBAeSBIAIYQQwgNJAiCEEEJ4IEkAhBBCCA8kCYAQQgjhgSQBEEIIITyQJABC\nCCGEB5IEQAghhPBAkgAIj5eRoaFbN1/q1/ejWzdfMjIctkaWEELUGnKmEx4tI0NDYqKPffvQIfUf\n28XExJicVzAhhHAwaQEQHm3xYt1V9y9ZcvX9QgjhLiQBEB7tyJGr/wQq2y+EEO5CznLCo4WFWW5o\nvxBCuAtJAIRHS0oqu+r+yZOvvl8IIdyFJADCo8XEmEhJKaZlSzMajZWWLc2kpMgAQCGE+5O7AITH\ni4kxSYUvhPA40gIghBBCeKBKE4C8vDweeeQRCgoK7PuysrKIi4ujuLi4RgonhBBCCMeoNAGYO3cu\n9957L35+fvZ9bdu2pUuXLrz00ks1UjghhBBCOEalCcCxY8dISEi4Yv+4ceM4ePCgQwslhBBCCMeq\nNAFQq9WVvshkkgFTQgghhCurNAFQqVRkZ2dfsf+HH35Aq9U6skxCCCGEcLBKbwN89NFHGTduHAkJ\nCbRu3Rqz2czevXt5++23eeWVV2qyjEIIIYSoZpUmAF27dmXZsmW8+eabrFu3DpVKRVhYGG+++Sah\noaE1WUYhhBBCVLMqJwJq3rw5CxYsqKmyCCGEEKKGVJoAjB49GkVR7NuKoqDX6+nbty+9e/e+roPP\nnj2brKwsFEUhOTmZNm3a2B/LzMxkxYoV6HQ6+vTpw6hRowCYP38+e/fuxWQykZiYSHR0NKdOnWL6\n9OmYTCY0Gg0LFiwgKCiI8PBw2rdvbz/m22+/XeXgRSGEEELYVJoAPPbYY1fsy83NZc2aNZw/f55h\nw4ZVeeBdu3Zx4sQJ0tLSOHbsGMnJyaSlpQFgsViYNWsWGRkZBAQEEB8fT2RkJNnZ2Rw9epS0tDTy\n8vKIiYkhOjqaxYsXM3ToUB588EHWrl3LW2+9xbRp0/Dz82P16tW3+CcQQgghPE+lCUDnzp2vuj8y\nMpK4uLhrJgA7duwgMjISgNDQUC5cuEBBQQF+fn7k5eXh7++PwWAAICIigu3bt9O/f397K4G/vz/F\nxcWYzWZmzJiBl5cXAIGBgRw4cODGIxVCCCGE3Q0vBuTj43NdtwHm5uYSHh5u3zYYDOTk5ODn54fB\nYKCwsJDs7GxCQkLYuXMnnTp1Qq1W4+vrC0B6ejpdu3atsM9sNpOamsrjjz8OQFlZGVOmTOHkyZP0\n7t2bsWPHVlmmwEBfNBrX7yIICtI7uwg1QuJ0L+4ep1pt6zJ19zgvkThd3w0nAGfPnsVsNt/wG1mt\nVvu/FUVh7ty5JCcno9fradjh5IHZAAAgAElEQVSwYYXnZmZmkp6ezqpVq+z7zGYz06ZNIyIiwt46\nMW3aNPr164eiKIwaNYoOHTrQunXrSsuQl1d0w+WubYKC9OTk5Du7GA4ncboXT4jTbLaiVituHyd4\nxucJ7hFnVQlMpQnAkiVLrth34cIF/ve///Hcc89d802Dg4PJzc21b585c4agoCD7dqdOnUhNTQVg\n4cKFhISEAPDll1+ycuVK3njjDfT68oJPnz6dxo0bM3HiRPu+4cOH2/8dERHBkSNHqkwARO2XkaFh\n8WIdR45AWJgvSUllslSvEEI4QJVTAf/5vzvvvJOUlBTuu+++ax64S5cubNmyBYADBw4QHBxcYWGh\n8ePHc/bsWYqKiti6dSudO3cmPz+f+fPnk5KSQkBAgP25mzZtQqvVMmnSJPu+48ePM2XKFKxWKyaT\niX379tGsWbOb+iOI2iEjQ0Niog+HDqkxm+HQITWJiT5kZNxwQ5UQwkH27lWxZAl8950Ki8XZpRG3\notIz6+VX2pcrKSlh48aNDBgwoMoDt2/fnvDwcGJjY1EUhRkzZrBhwwb0ej1RUVEMHTqUuLg4FEUh\nISEBg8FgH/2flJRkP868efNITU2ltLSU0aNHA7ZBhTNnzqRevXoMHjwYlUpFz549K9xmKFzP4sW6\nq+5fskQnrQBCOFlBAcye7cWbb2qx9ejeRlCQhZ49zURGmujWzcRl123CBSjWyzvnq7Bv3z42bNjA\nJ598QseOHVm2bJmjy1btXL0vB9yjT6oy9ev7YTYrV+zXaKz89luBE0rkeO78eV7OE+Kcunw7arXC\n3MSr30Hlyv77XzVPPeXNzz+ruOsuM1OmqNm61cjnn6vJzbU1JKtUVjp0MNOrl5levUy0amVBVWkb\nc+1ktcKxYwq7d6vZs0dNSYmO9u1L6N7dxF/+YkW58vRU693UGACA33//nY0bN5KRkUFZWRllZWVs\n2rSJevXqVXshhQgLs3Do0JV3aYSFSTujEM5w8SLMnOnFmjU61GorkyaV8vTTZdx5p55Bg0qwWGD/\nfhWZmRo++0zDnj1qdu3SMGeOF8HBttaBXr1qb+tAQQF8842tsr/0X15exVp+/XpvABo2tNC9u4lu\n3czcd5+JP+5id2mVJgDx8fF888039OjRg+eff56IiAgGDhwolb9wmKSkMhITfa7YP3lymRNKI8SN\nMZnAaASzGdxhQtJPP1Xz9NPenDqlomVLM0uWlNC2bcVkXKWCtm0ttG1bxpQpZZw7B198YUsGPv9c\nzbp1Wtat06JWX9k6UNNX01YrZGcr9op+9241Bw+qsFjKC9KokYUePUx07GimY0czTZrcxsaNJXzx\nhZovv9SwZo2ONWtAUay0aWOhWzdbQtCpk5k/pqpxKZUmAL/99huBgYE0btyYJk2aoFKpKkwNLER1\ns/XzF7NkiY4jR9SEhZmZPFnuAhC1V0kJ/PvfGlav1uLT0nZ+7N7dl6lTy3joIZPLNYEDnDsH//iH\nN+npWrRaK9OmlTJpUhm6qw/RqcBgsP2OY2JMWCy2gYKffWZLCHbtUrNzp4bZs2umdaC4GLKybBX9\n7t0q9uwp764A8PKyJSUdO1ro0MFMhw5m6tat2CMeFARjxhgZM8aI2WyL54svNHzxhZpdu9RkZXmx\ndCn4+FiJiDDbE4KWLWs+wbkZVY4ByMrKIj09nc2bNxMeHs7PP//Mli1brmsioNrIHfogPaEvFSRO\nd+NucR46pGLNGi3r12s5f952pn/w8U/QaOA/r0ZhNiuEh5uZNq2M++83uURlAPDRRxqeecaL3FwV\n99xjZvHiElq0uLIL7mY+z3PnYNs2WzKwdWt5ZVwdrQNWK5w8Wd53v2ePmv37VZhM5Qdq0MBCx47m\nPyp9M61aWa6Z1FQVZ0EBfP212p4QHD6svux1Frp2tSUE3bubqVfvuobaOURVYwCuaxBgcXEx//d/\n/0d6ejo//fQTAwcOZOrUqdVayJrgDicgdzuRVkbidC/uEGdhIWzapGH1ah179thO9kFBFoYPNzJi\nhJEVm79CrVZIiLqXl17y4oMPNFitCu3amXnmmVJ69jTX2kTg998Vpk/34t//1uLtbbvqnzDBiKaS\nNuJb/TwtFsjKKm8d2LdPhdVq++PUrVuxdeD22698fWmp7Wr88ub806fLr+61WlsT/aXKvkMHMw0a\n3HglfCNxnj6t8MUXtoTgv/9Vc+ZMeXmaNzfTrZstIejc2cxtt91wUW7aLScAlzt+/DgffPCBJABO\n4g4n0ushcboHqxVychR8fPzw88uvtRVgVb77TsXq1Vo2bNCSn6+gKFZ69DAzapSR3r1NXGoQ/fNd\nAEeOqFiwQMeHH9qe0LGjmWefLeW++258JlVHsVohPV3DP/7hTV6ewl//amLx4hJCQ6uuFqr7e3s9\nrQNNm1rYt89W2X/3nYqysvIvU3Dw5ZW9hbZtzXh733q5bjZOq9XWSnQpIdixQ01xsa28Wq2Vjh3L\nE4K2bS0OHTNSrQmAK3OHE627VxiXSJyuo7QUfvlFITtbxYkTqj/+r3DihG27qMh24gsOttCli5l7\n7zXTpYuJ0NDae1tVfj5s2KBlzRotWVm2s3P9+rar/ZEjjdx555WnzcpuA/z+exXz5+vYvNmWCHTp\nYuKZZ8qIiHBuIvDbbwpPP+1NZqYGX18rzz1Xytixxusat+DI721VrQNgSwrCw8ub8zt0MNOokWO+\nS9UVZ2kp7N6tticEWVnlMQUEWPnb30z2hKBJk+qtkiUB+IOrn2jBPSqM6yFx1h5WK5w9q3DiRHkl\nb6vobZX8qVNKhRP0JbfdZqVJEwtNmli47TYt27ZZKjSL1q1bMSFw9n3WVivs22e72t+4UUtRkYJa\nbSUqysSoUUZ69jRX2iQO154H4NtvVcyb58Vnn9kO0r27iWefLaV9+5q9zdVqhdWrtbzwghf5+Qr3\n3Wdi0aISGje+/qqgJr+3Z88qbNum5vRphXbtLLRrV3NN6I6K89w5+N//NPaE4Oefy38Xf/2riY8+\nKq6297rpeQCEEJ6hrAx+/dVWwZdX8uUVfmHhlTWzolhp0MDKvfeaadzYQuPGVho3tlX4jRtbMRjK\nK/SgIC1nzhRy7JjCV19p2L5dzVdfqdmwwda0DuUJge0/E02b1kxCcP48pKdrWb1aa5+HolEjCyNG\nlDF8uJH69avnGqldOwvvvVfMrl22RGDbNg3btmmIjjbxzDOltG7t+EQgO1thyhRvvvxSg15v5eWX\nSxgxwlhrW2IA6tSxMmiQe90JZDBAv34m+vUzYbWW8tNPin0woZeXLUmric+k0hYAi8XCypUrSUxM\nRP1HB8WxY8f45JNPePTRRx1fMgeo7Vda18MVrhirg8TpGDk5Cv/7n/qKCv7kyatfxfv6Wv+o0G2V\n+qUr+saNLTRsaL3ue5+vFqfVCj/+qOKrr9T2hCAnp/xKqF49yx+tA9WfEFitsHOnmtWrtXz0kYaS\nEgWNxsoDD9iu9rt1M9/wLXw3OhPgV1+pmTtXx86dtuuwPn2MTJtWdtVR97fKYoE339Tyr395UVSk\nEB1tYsGCkptObuT36TpuqgVg2bJl/PDDD5SVleHjY5ucpW7duhw+fJh3332XMWPGVH9JRa1SvjKf\nirAwi6zM56JMJvj8czVr12r59FNNhVujFMVK/fq2e5ibNLH+UdGXV/h33OG4q3BFgWbNLDRrZuGR\nR4z2hOB//1NftYWgfv2KCUGTJjdetrNnFdLSNKxdq+XoUduFzV/+YmHkyDKGDTMSHFxzPaJdupjZ\ntKmYbdvUzJvnxX/+o+XjjzUMGGBi6tRS7rqresry448KSUne7NqlITDQyksvFTNokOvcmigcp9IW\ngIEDB7Ju3Tp0f7pRsqSkhEceeYR169bVSAGrk6tnclBzGemllfn+LCWluEaSAHfIvK+HI+M8flwh\nNVVLWpqW33+3Xc62amVm4EAjzZvbruQbNrRWy2jpa7mZOK1WOHq0PCHYvr3iRC4NGpQnBPfeW3lC\nYLHA//6nZs0aLf/5jwajUUGns/LQQyZGjzZy773Vc3verawFYLXaZt6bN8+L/fvVqFRWBg82MWVK\nKU2b3lwiYDLB8uU6FizQUVqq0K+fkdmzS6slyZHfp+u4qRYAb2/vKyr/S/tVrji9lbghsjKfayos\ntE3mkpqq5euvbT/v22+3EhdXxogRRtq0cZ11FRTFtg5EWJiFuDhbC8GRI7Yug0vdBunpWtLTbS0E\nISEW+4DCe+814+MDaWm2kfzZ2bZz1t13227fGzLEWKvmclcUiI42ExVVxH/+o2HBAh3vv69lwwYN\nsbFGnnqqjIYNr7/iPnBARVKSN1lZaoKCLMydW0LfvvK7FRVVmgAUFRVRVFSEr69vhf0XLlygsLDQ\n4QUTznXkyNWTvMr2C+e5NHo9NVVLRoaWggLb5ex995kYOdLIAw+Y8LmyMcflKArcfbeFu+8uTwh+\n+KE8IdixQ8369bbZ+S7n42MlNtbIqFFldOxYu6doVRR46CETDz5o4sMPbYnAmjW2ZGDUKCNJSWVV\nzipXVmZL3hcv1mEyKQwdamTWrBICA2swCOEyKk0A+vfvz8SJE3n++edp0qQJAIcPH+aFF15g7Nix\nNVU+4SSyMl/tl5ursH69hvfe09qnIQ0JsZCYWEZsrPGGbutyRYoCzZtbaN7cwrhxRiwWW0JwafzA\n2bMK/fubGDTIeNXZ5Gozlco2p37fviY++EDDSy95sWqVjtRULQ8/bGTSpDKCgip+vt9+q2LyZG8O\nHVLToIGFl14qJjKy9kw6JGqfShOAsWPHotPpePjhh8nPz8dqtVKnTh0SExMZMGBATZZROIGszFc7\nmc2wdattQN+WLbYBfTqdlf79jQwfbhu97g4r0d0MlQpatLDQooUtIXAHGg0MG2Zi4EAT69ZpWbRI\nR0qKjtWrtYwbV8bjj5fh4wMLFuhYvlyHxaIwZkwZM2aUoq+861cI4BrzAIwcOZKRI0dSUFCAoijc\nVpMTGAunqrgyn+0uAFmZz3l++knhvfdsA/pOnbJ1w7RoYWbkSCODB9eu/mxR/bRaGD3ayNChRtas\n0bJ4sY5XXvHirbd0GAxWfv5ZRaNGFl5+ubhWTTUsarcqE4Djx4+zcuVKDh48iKIotGnThscee4yQ\nkJCaKp9wokvLegrnKCqyLTWbmqpl+3bbT9Xf38ojj9gG9LVtW7v7s0X18/KCceNsiw+9846WpUt1\n/PKLQkJCGdOnl9boIjPC9VWaAOzYsYPk5GTi4uIYPXo0AN999x1xcXHMmTOH9u3b11ghhfAUVqut\nL3ftWtuAvvx8Ww3/t7+ZGD7cSJ8+Jv40Lld4IB8fmDDBtk59bq5Co0buPd5DOEalCcDy5ctJSUkh\nLCzMvq9169ZERETwz3/+k3feeadGCiiEJzh7ViE93Xa1f2nwZf36FuLjbRPU3Oy94MK9+foilb+4\naZUmACUlJRUq/0tCQ0MpLq6+hQqE8FRmM2zeDMuXe7N5s22CGq3WSt++tibe7t09d0CfEMLxKk0A\njMbKR9FW9ZgQomqHD6t4/30N6elaTp8G0NK8uZkRI4wMHmzijjvkik4I4XiVJgB33nknmZmZREZG\nVtj/8ccf07RpU4cXTAh3kpurkJGh4f33y9eXv/12K4mJEBNTyD33yIA+IUTNqjQBmDp1KuPHj+fj\njz+mTZs2WCwWvvnmG3788UfWrl1bk2UUwiWVlsInn2hYv15DZqbtnn212kp0tIlhw4xERZm48049\nOTkyuZIQouZVmgA0atSIf//732zcuJGDBw/i4+NDr169WLRoEVqttrKXCeHRrFbYu1fF++9r2bhR\ny/nztsv61q3NDBtmJCbGdMUMbkII4QxVzgOg0+mIiopi6NChFfb/+uuvNGzY0KEFE8KV/PKLQnq6\nlvff13LsmG2inuBgC489Zpu8pWVLucoXQtQulSYAe/bs4cknn6SsrIzAwEBee+01GjVqxJo1a3jt\ntdf473//W5PlFKLWKSiwTdSTlqblq69sPyUfHysDB9oq/a5dzWiqTLGFEMJ5Kj09vfzyy7z99tuE\nhoby2Wef8dxzz2GxWLj99ttZv379dR189uzZZGVloSgKycnJtGnTxv5YZmYmK1asQKfT0adPH0aN\nGgXA/Pnz2bt3LyaTicTERKKjozl16hTTpk3DbDYTFBTEggUL0Ol0bNq0iXfeeQeVSsXQoUMZMmTI\nLf45hKia2QxffqkmLU3Lxx9rKC62NfF37mzr1+/b1yRzsAshXEKlCYBKpSI0NBSAXr16MWfOHJ55\n5hmioqKu68C7du3ixIkTpKWlcezYMZKTk0lLSwPAYrEwa9YsMjIyCAgIID4+nsjISLKzszl69Chp\naWnk5eURExNDdHQ0S5cuZcSIETzwwAMsWrSI9PR0BgwYwLJly0hPT0er1TJ48GCioqIICAiohj+L\nEBX98IOKtLRLt+7ZmvibNrUwdGgZgwe7/8p7Qgj3U2kCoPzpnqT69etfd+UPtqmEL91CGBoayoUL\nFygoKMDPz4+8vDz8/f0x/LGCSUREBNu3b6d///72VgJ/f3+Ki4sxm83s3LmTF154AYAePXqwatUq\nmjZtSuvWrdH/cbnVvn179u3bR8+ePW8gfCEqV9mte2PGlDF0qLHWry0vhBBVue4eyj8nBNeSm5tL\neHi4fdtgMJCTk4Ofnx8Gg4HCwkKys7MJCQlh586ddOrUCbVaje8fE52np6fTtWtX1Go1xcXF6HQ6\nAOrUqUNOTg65ubn2BOLy4wtxK6q6dW/oUCPR0Sa8vZ1dSiGEuHWVJgDffPMN3bt3t2+fPXuW7t27\nY7VaURSFbdu23dAbWa3lTaSKojB37lySk5PR6/VX3FGQmZlJeno6q1atqvI417P/coGBvmg0rj+3\nalCQZ3Qy12Schw/D0qWwbh3k5dn23XMPjBkDw4cr1K2r4Qby5Rsin6d7UKttF0nuHuclEqfrq/SM\ntnnz5ls6cHBwMLm5ufbtM2fOEBQUZN/u1KkTqampACxcuNC+xPCXX37JypUreeONN+zN+76+vpSU\nlODt7c3vv/9OcHDwVY/frl27KsuUl1d0SzHVBkFBenJy8p1dDIerqThPnlR46SUd772nxWJR/rh1\nz3TFrXuOalySz9N9mM1W1GrF7eMEz/g8wT3irCqBUVX2QEhISJX/XUuXLl3YsmULAAcOHCA4OBg/\nPz/74+PHj+fs2bMUFRWxdetWOnfuTH5+PvPnzyclJaXCYL57773XfqxPPvmE++67j7Zt27J//34u\nXrxIYWEh+/bto0OHDtf+awiBbfW955/3IiLiNtau1dGsmYVVq4r59ttCZs4slfv2hRBuz2F3Kbdv\n357w8HBiY2NRFIUZM2awYcMG9Hq9fXKhuLg4FEUhISEBg8FgH/2flJRkP868efN44okneOaZZ0hL\nS6NBgwYMGDAArVbLlClTGDduHIqi8Pjjj9tbDISoTEEBpKToWLZMR0GBwp13Wpg6tYQhQ0yy8p4Q\nwqMo1uvpPHcTrt6UA+7RJHU9qjvO0lJYvVrLokU6cnNV3HGHhSefLGPMGCNeXtX2NjdMPk/3MXX5\ndtRqhbmJnZ1dFIfzhM8T3CPOqroAZJ4y4dbMZvjgAw3z53vx888q/PysTJtWyoQJZVzWIyWEEB5H\nEgDhlqxW2LJFzezZXhw+rEans5KYWMbkyWXccYfHNHoJIUSlJAEQbmfHDjWzZnmxZ48alcrKiBFl\nPP10GQ0bSsUvhBCXSAIg3Mb+/Spmz/bis89sX+s+fYxMn15GWJiM6BdCiD+TBEC4vOPHFebN8yIj\nQwvA3/5m4h//KKV9e6n4hRCiMpIACJd1+rTCwoU61q7VYjIptG1r5u9/L6VbN7PM0S+EENcgCYBw\nOefPw6uv6nj9dR3FxQqhoRaSk0t46CGTVPxCCHGdJAEQLqOoCN54Q8crr+i4cEGhfn0L//pXKbGx\nRjTyTRZCiBsip01R6xmNsHatloULdfz+u4qAACszZpQQF2fEx8fZpRNCCNckCYCotSwW+PBDDXPm\neJGdrcLX18qTT5by2GNl3H67s0snhBCuTRIAUetYrbB5M0yd6sv336vRaq2MG1dGUlIZdevKvfxC\nCFEdKl0NUNQuGRkaunXzRaOBbt18ychwr9zNaoWDB1XMnaujSxdfHngADhxQMXiwka++KmTOnFKp\n/IUQohq5Vy3ipjIyNCQmlnd2Hzqk/mO7mJgYk/MKVg0OH1bx4YcaNm3ScPSobTk+Hx8rw4bBhAlF\nhIfLvfxCCOEIkgC4gMWLdVfdv2SJziUTgB9+ULFpk63S/+EHW6Xv7W3loYeM9O9vIjLSRJMmenJy\npPIXQghHkQTABRw5cvWemsr210ZHj5Zf6R8+XF7pP/igrdKPijLJ6nxCCFGDJAFwAWFhFg4dUl91\nf232448KmzZp+fBDjb38Xl5WHnjAVulHR0ulL4QQziIJgAtISiqrMAbgksmTy5xQmqodP67w4Ye2\nSv/gQVulr9NZuf9+I/36mejd24Re7+RCCiGEkATAFdj6+YtZskTHkSNqwsLMTJ5cVmv6/48fV/jo\nI1ul//335ZV+794m+vUz0ru3CX9/JxdSCCFEBZIAuIiYGBMxMSaCgvTk5BQ5uzj89JOt0t+0ScN3\n39kqfa3WSlSUrdK//36TTNYjhBC1mCQA4rqdOGHr09+0SUNWlq3S12isREaWV/oBAU4upBBCiOsi\nCYCo0sWLsHq1lk2btHzzTXml37Onif79bZV+YKCTCymEEOKGSQIgKnX6tMLQoT4cPqxGrbbSo4et\n0n/gAan0hRDC1UkCIK4qO1thyBBfTpxQMXZsGdOmlVGnjkzFK4QQ7kISAHGFQ4dUDB3qw++/q5g6\ntZSnny5DUZxdKiGEENVJEgBRwZ49KkaM8OX8eYUXXywhIcHo7CIJIYRwAEkAhN0XX6h5+GEfSkth\n6dJiYmNrxzwDQgghqp8kAAKAf/9bw4QJ3gC8+WYJDz4olb8QQrgz11lNRjjMe+9pGD/eG60W3nuv\nWCp/IYTwAA5tAZg9ezZZWVkoikJycjJt2rSxP5aZmcmKFSvQ6XT06dOHUaNGAXDkyBEee+wxHnnk\nEfu+SZMmkZeXB8D58+dp164diYmJ9O3bl1atWgEQGBjI0qVLHRmOW1q5Usvzz3sTGGjlvfeKaN++\ndi8wJIQQono4LAHYtWsXJ06cIC0tjWPHjpGcnExaWhoAFouFWbNmkZGRQUBAAPHx8URGRuLv78+s\nWbPo3LlzhWNdXrFPnz6dIUOGANC0aVNWr17tqBDcmtUK8+bpWLTIi3r1LLz/fjHNm0vlL4QQnsJh\nXQA7duwgMjISgNDQUC5cuEBBQQEAeXl5+Pv7YzAYUKlUREREsH37dnQ6Ha+//jrBwcFXPebx48fJ\nz8+v0JIgbpzFAsnJXixa5EWTJhY++qhIKn8hhPAwDmsByM3NJTw83L5tMBjIycnBz88Pg8FAYWEh\n2dnZhISEsHPnTjp16oRGo0GjqbxI7777rr1b4NJ7TJo0iTNnzjBixAj69etXZZkCA33RaNS3HpyT\nBQXd/Hq6RiOMHQtr10Lr1rBli4r69f2qsXTV51bidCUSp3tQq22TZbh7nJdInK6vxu4CsFrLZ5FT\nFIW5c+eSnJyMXq+nYcOG13x9WVkZe/fuZebMmQAEBAQwefJk+vXrR35+PkOGDCEiIqLS1gOAvDzn\nr6J3q2yrAebf1GuLiyE+3odPPtHQoYOZ1NQiNBrIyanmQlaDW4nTlUic7sNstqJWK24fJ3jG5wnu\nEWdVCYzDugCCg4PJzc21b585c4agoCD7dqdOnUhNTSUlJQW9Xk9ISEiVx9u9e3eFpn8/Pz8GDRqE\nVqvFYDDQqlUrjh8/Xv2BuIn8fBg+3Fb5d+9uYv36Ilm5TwghPJjDEoAuXbqwZcsWAA4cOEBwcDB+\nfuVNzePHj+fs2bMUFRWxdevWKwb+/dn+/ftp3ry5ffvrr79mzpw5ABQVFXH48GGaNm3qgEhcX26u\nwsCBvmzfrqFvXyOrVxdz223OLpUQQghnclgXQPv27QkPDyc2NhZFUZgxYwYbNmxAr9cTFRXF0KFD\niYuLQ1EUEhISMBgMfP/998ybN4+TJ0+i0WjYsmULr7zyCgEBAeTk5NCoUSP78Tt06MDGjRsZNmwY\nZrOZhIQE6tat66hwXNbJk7YV/Y4eVTNyZBkvvVSK2vWHQQghhLhFivXyznk35+p9OXBjfVLHjysM\nHuzLr7+qeOyxMmbMKHWZRX3coe/tekic7mPq8u2o1QpzE6tuzXQHnvB5gnvEWdUYAJkK2E3t369i\n2DAfcnNV/P3vpUyaJCv6CSGEKCcJgBv6+ms1o0b5kJ8P8+aVMHasrOgnhBCiIkkA3Mxnn6mJi/PB\naIQVK0oYOFDm9RdCCHElWQzIjWzcqGH0aB+sVnjnnWKp/IUQQlRKEgA38e67WhITvfHxgfffLyYq\nyuzsIgkhhKjFJAFwA0uX6nj6aW/q1LGycWMRERFS+QshhKiajAFwYVYrzJql49VXvQgJsbB+fRF3\n3eUxd3UKIYS4BZIAuCizGaZN82L1ah2hobbKv2FDqfyFEEJcH0kAXFBZGTz+uDcffqildWsz69YV\nExQklb8QQojrJwmAiykqgjFjfPj8cw2dO5tYvboYf39nl0oIIYSrkUGALuTCBYiOhs8/1xAVZWLd\nOqn8hRBC3BxJAFzIjBlefPUVDBxo5O23i/HxcXaJhBBCuCrpAnARP/2kkJampUULWLasRFb0E0II\ncUukBcBFvPyyF2azwowZSOUvhBDilkkC4AKOH1dYv17D3XebGTLE2aURQgjhDiQBcAGXrv6ffroM\nlXxiQgghqoFUJ7Xcpav/Fi3M9O0ri/sIIYSoHpIA1HKLFnlhscjVvxBCiOolVUotduyYQnq67eq/\nTx+5+hdCCFF9JAGoxSqkcgAAABHlSURBVBYulKt/IYQQjiHVSi31448KGzZoaNlSrv6FEEJUP0kA\naim5+hdCCOFIUrXUQkePqsjI0BAebubBB+XqXwghRPWTBKAWWrhQh8WiMHWqXP0LIYRwDKleapkj\nR2xX/61amXngAbn6F0II4RiSANQyCxfqsFptV/+K4uzSCCGEcFeSANQiP/ygYuNGDa1bm7n/frn6\nF0II4TgOTQBmz57NsGHDiI2N5bvvvqvwWGZmJoMGDWL48OGsWbPGvv/IkSNERkZW2Pfss8/St29f\nRo8ezejRo9m2bRsAmzZtYtCgQQwZMoT169c7MpQaUX71XypX/0IIIRxK46gD79q1ixMnTpCWlsax\nY8dITk4mLS0NAIvFwqxZs8jIyCAgIID4+HgiIyPx9/dn1qxZdO7c+YrjPfXUU/To0cO+XVRUxLJl\ny0hPT0er1TJ48GCioqIICAhwVEgOdfiwig8/1NCmjZnevc3OLo4QQgg357AWgB07dhAZGQlAaGgo\nFy5coKCgAIC8vDz8/f0xGAyoVCoiIiLYvn07Op2O119/neDg4GsePysri9atW6PX6/H29qZ9+/bs\n27fPUeE4nFz9CyGEqEkOSwByc3MJDAy0bxsMBnJycuz/LiwsJDs7G6PRyM6dO8nNzUWj0eDt7X3V\n461Zs4YxY8bw5JNPcu7cOXJzczEYDFc9vqs5dEjFpk0a2rUzEx0tV/9CCCEcz2FdAH9mtVrt/1YU\nhblz55KcnIxer6dhw4ZVvrZ///4EBATQokULXnvtNV599VXuueeeSo9fmcBAXzQa9c0F4ECPPw5W\nK7z4oprgYP01nx8UdO3nuAOJ0724e5xqta3pzt3jvETidH0OSwCCg4PJzc21b585c4agoCD7dqdO\nnUhNTQVg4cKFhISEVHqsy8cE9OzZk5kzZ9K7d+8rjt+uXbsqy5SXV3TDcTjawYMq1q+/jXvuMdOx\nYxHXasQICtKTk/P/7d1/UFT1/sfx57JHpBVMINCrlmNmNjqZohEqiSJcK0fLLETDxvxVmb8mkoTR\nsGFSUGtQ66qp6QyK0TiSzq0ujl65FwOpm11Ty6E0S9H8gT8o8Ee7cP/gK19N/NkeDuy+Hn/tHvTw\nfrN/7Ov9+Zzd82v9FGch9elZvKFPl6sau93m8X2Cd7ye4Bl9Xi/AmLYF0KdPH/Ly8gDYu3cvoaGh\n+Pv71/583LhxlJWVUVlZybZt2+q88O+SyZMnc+jQIQCKi4vp2LEjDz30ELt376a8vJyKigp27txJ\nz549zWrHNAsW+AJo719EROqVaSsAYWFhdOnShfj4eGw2G6mpqWzYsIGAgABiY2OJi4tjzJgx2Gw2\nJkyYQFBQEHv27CEjI4PS0lIMwyAvL4/Fixfz3HPPMW3aNO644w4cDgdz587Fz8+PxMRExo4di81m\n45VXXiEgoHEt1ezd68Pf/96EsDAXAwZo719EROqPrfpmNs89RENbynnhBT8++aQJ69ZV3nQA8IQl\nqZuhPj2LN/Q5/W+F2O020l+89mqmp/CG1xM8o09LtgDk+nbv9uGTT5rQo4eL6GhN/yIiUr8UAG5D\nbq5BVJSDv/zFn6goB7m5t76T8vbb2vsXERHr1NvHAD1Fbq7Biy/eUfv8u+/s//f8HEOH3tz39+/e\n7cOnn9ZM//37a/oXEZH6pxWAW5SZ6Vvn8YUL6z5el/nza/5tUpKmfxERsYYCwC0qKan7T3at43+0\ne7cP//hHE3r2dNGvn6Z/ERGxhgLALbr//qpbOv5Hmv5FRKQhUAC4RdOmXazz+NSpdR+/3Dff1Ez/\n4eFOoqI0/YuIiHUUAG7R0KFOli07R+fOLgyjms6dXSxbdnMXAM6f3xSA6dMvavoXERFL6VMAt2Ho\nUOdNX/F/yX//60NensEjjzjp21fTv4iIWEsrAPVkwYKa6T8pSdO/iIhYTwGgHnz9tQ+bNxtERDiJ\njNT0LyIi1lMAqAeX9v41/YuISEOhAGCynTt92LLFoHdvTf8iItJwKACY7PIr/0VERBoKBQAT/ec/\nPmzdatCnj5M+fTT9i4hIw6EAYKJLV/5r+hcRkYZGAcAkX37pwz//aRAZ6aR3b03/IiLSsCgAmETT\nv4iINGQKACb48ksftm0zePRRJ716afoXEZGGRwHABPPmafoXEZGGTQHAzYqL7fzrXwZ9+zqJiND0\nLyIiDZMCgJvNn+8LaPoXEZGGTQHAjXbssPPvfxtERTl55BFN/yIi0nApALjR/0//FyyuRERE5PoU\nANxkxw47BQUG/fo5CQ+vsrocERGR61IAcJN582qm/6QkTf8iItLwKQC4QWGhne3bDaKjnfTsqelf\nREQaPgUAN9Dev4iINDaGmSefM2cOu3btwmazkZKSQteuXWt/tmXLFpYsWYKvry+DBg0iISEBgJKS\nEiZOnMjo0aNrjx09epTk5GScTieGYTB//nxCQkLo0qULYWFhtedcvXo1drvdzJau8vnndj7/3GDA\nACc9emj6FxGRxsG0APDFF1/w008/kZOTw/79+0lJSSEnJweAqqoq0tLSyM3NpUWLFowfP56YmBia\nN29OWloavXr1uuJcmZmZxMXF8cQTT7B27VpWrVpFUlIS/v7+ZGVlmdXCTdH0LyIijZFpWwBFRUXE\nxMQA0KFDB86ePctvv/0GwOnTp2nevDlBQUH4+PgQERFBYWEhvr6+LF++nNDQ0CvOlZqaysCBAwEI\nDAzkzJkzZpV9S7Zvt1NYaBAT4yQsTNO/iIg0HqYFgJMnTxIYGFj7PCgoiBMnTtQ+rqio4ODBg/z+\n++8UFxdz8uRJDMPAz8/vqnM5HA7sdjsul4vs7GwGDx4MwMWLF0lMTCQ+Pp5Vq1aZ1Uqdqqs1/YuI\nSONl6jUAl6uurq59bLPZSE9PJyUlhYCAANq2bXvD/+9yuUhKSiIiIqJ2iyApKYkhQ4Zgs9lISEig\nZ8+ePPjgg9c8R2CgA8NwzzUC+flQVASDBsFf/9rMLee8WSEhAfX6+6yiPj2Lp/dpt9sAz+/zEvXZ\n+JkWAEJDQzl58mTt8+PHjxMSElL7PDw8nOzsbADefvtt2rRpc93zJScn065dOyZNmlR7bMSIEbWP\nIyIiKCkpuW4AOH268pb7uJaSEgOHw49XX63kxIn6W/4PCQngxIlf6+33WUV9ehZv6NPlqsZut3l8\nn+Adryd4Rp/XCzCmbQH06dOHvLw8APbu3UtoaCj+/v61Px83bhxlZWVUVlaybdu2qy78u9ymTZto\n0qQJU6ZMqT124MABEhMTqa6uxul0snPnTjp27GhWO1cZOtTJDz/8xoMPau9fREQaH9NWAMLCwujS\npQvx8fHYbDZSU1PZsGEDAQEBxMbGEhcXx5gxY7DZbEyYMIGgoCD27NlDRkYGpaWlGIZBXl4eixcv\nJjs7mwsXLjBq1Cig5qLC2bNn06pVK5555hl8fHyIjo6+4mOG9cGotw0UERER97JVX7457+Ea+1IO\neMaS1M1Qn57FG/qc/rdC7HYb6S9eezXTU3jD6wme0aclWwAiIiLScCkAiIiIeCEFABERES+kACAi\nIuKFFABERES8kAKAiIiIF1IAEBER8UIKACIiIl5IAUBERMQLKQCIiIh4Ia/6KmARERGpoRUAERER\nL6QAICIi4oUUAERERLyQAoCIiIgXUgAQERHxQgoAIiIiXkgBoBGZN28ew4cPZ9iwYWzevNnqckx1\n/vx5YmJi2LBhg9WlmGbTpk0MGTKEp59+mvz8fKvLMUVFRQWTJk1i1KhRxMfHU1BQYHVJblVSUkJM\nTAxr1qwB4OjRo4waNYqRI0cydepULl68aHGF7lFXn6NHjyYhIYHRo0dz4sQJiyt0jz/2eUlBQQGd\nOnWyqCrzKAA0Ejt27OD7778nJyeHFStWMGfOHKtLMtWSJUu48847rS7DNKdPn+a9994jOzubpUuX\nsnXrVqtLMkVubi7t27cnKyuLhQsX8tZbb1ldkttUVlaSlpZGr169ao8tWrSIkSNHkp2dTbt27Vi/\nfr2FFbpHXX1mZmYSFxfHmjVriI2NZdWqVRZW6B519Qlw4cIF3n//fUJCQiyqzDwKAI3Eww8/zMKF\nCwFo3rw5586dw+VyWVyVOfbv388PP/xAv379rC7FNEVFRfTq1Qt/f39CQ0NJS0uzuiRTBAYGcubM\nGQDKy8sJDAy0uCL38fX1Zfny5YSGhtYeKy4uZsCAAQD079+foqIiq8pzm7r6TE1NZeDAgcCVr3Fj\nVlefAEuXLmXkyJH4+vpaVJl5FAAaCbvdjsPhAGD9+vX07dsXu91ucVXmyMjIYMaMGVaXYarDhw9z\n/vx5XnrpJUaOHOkRbxR1GTRoEEeOHCE2NpaEhARef/11q0tyG8Mw8PPzu+LYuXPnat8ogoODPWJp\nvK4+HQ4Hdrsdl8tFdnY2gwcPtqg696mrzx9//JF9+/bx+OOPW1SVuQyrC5Bbs2XLFtavX88HH3xg\ndSmm+Pjjj+nWrRt333231aWY7syZM7z77rscOXKE559/nm3btmGz2awuy602btxI69atWblyJfv2\n7SMlJcWjr+u4nKd/y7rL5SIpKYmIiIirls09xdy5c5k5c6bVZZhGAaARKSgoYOnSpaxYsYKAgACr\nyzFFfn4+hw4dIj8/n19++QVfX19atWpF7969rS7NrYKDg+nevTuGYXDPPffQrFkzTp06RXBwsNWl\nudXOnTuJjIwE4IEHHuD48eO4XC6PXb1yOBycP38ePz8/jh07dtVysidJTk6mXbt2TJo0yepSTHHs\n2DEOHDjAa6+9BsDx48dJSEi46gLBxkwBoJH49ddfmTdvHqtXr6ZFixZWl2OazMzM2seLFy+mTZs2\nHvfmDxAZGcmMGTMYP348Z8+epbKy0qP2xy9p164du3btYuDAgZSWltKsWTOPffMH6N27N3l5eTz5\n5JNs3ryZRx991OqSTLFp0yaaNGnClClTrC7FNC1btmTLli21z6Ojoz3qzR8UABqNTz/9lNOnTzNt\n2rTaYxkZGbRu3drCquR2tWzZkoEDBxIXFwfAzJkz8fHxvEtyhg8fTkpKCgkJCTidTmbPnm11SW6z\nZ88eMjIyKC0txTAM8vLyWLBgATNmzCAnJ4fWrVvz1FNPWV3mn1ZXn2VlZTRt2pRRo0YB0KFDh0b/\n2tbV5+LFiz164NLtgEVERLyQ540cIiIickMKACIiIl5IAUBERMQLKQCIiIh4IQUAERERL6SPAYrI\ndR0+fJjHHnuM7t27X3E8KiqKcePG/enzFxcXk5mZybp16/70uUTk5ikAiMgNBQUFkZWVZXUZIuJG\nCgAicts6d+7MxIkTKS4upqKigvT0dO6//3527dpFeno6hmFgs9l44403uO+++zh48CCzZs2iqqqK\npk2bMnfuXACqqqpITU3lu+++w9fXl2XLlgGQmJhIeXk5TqeT/v378/LLL1vZrohH0TUAInLbXC4X\nHTt2JCsrixEjRrBo0SIAkpKSSE5OJisrixdeeIE333wTqLmN7NixY1m7di3Dhg3js88+A2puAT15\n8mQ++ugjDMNg+/btFBYW4nQ6yc7O5sMPP8ThcFBVVWVZryKeRisAInJDp06dqv3a10umT58OUHuz\nn7CwMFauXEl5eTllZWV07doVgPDwcF599VUAvvnmG8LDw4GaWwVDzTUA9957L3fddRcArVq1ory8\nnOjoaBYtWsTUqVOJiori2Wef9civSxaxigKAiNzQ9a4BuPzbxG0221W3NP7jt43XNcXXdYOg4OBg\nNm7cyNdff83WrVsZNmwYubm5V92zXURuj+K0iPwpO3bsAOCrr76iU6dOBAQEEBISwq5duwAoKiqi\nW7duQM0qQUFBAVBzg6t33nnnmufdvn07+fn59OjRg6SkJBwOB2VlZSZ3I+I9tAIgIjdU1xZA27Zt\nAfj2229Zt24dZ8+eJSMjA6i5U2V6ejp2ux0fH5/aO8XNmjWLWbNmkZ2djWEYzJkzh59//rnO39m+\nfXtmzJjBihUrsNvtREZG0qZNG/OaFPEyuhugiNy2Tp06sXfvXgxDs4RIY6MtABERES+kFQAREREv\npBUAERERL6QAICIi4oUUAERERLyQAoCIiIgXUgAQERHxQgoAIiIiXuh/9pliZY4pD10AAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAAFYCAYAAADgP95sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFYCAYAAABKymUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl4VNX5wPHvvTOTnSUhCULYN9kE\nRZYqLoCgVGotrUgAAZcWFf25K9CCIAoqLpVWraiIRQJCBavWFqoFxAVBQVRCApKwBBRIwpY9M3Pv\n74/rzUbWySx3Ju/neXwwy2TOzNx733vOec97FF3XdYQQQggR9NRAN0AIIYQQ3iFBXQghhAgREtSF\nEEKIECFBXQghhAgREtSFEEKIECFBXQghhAgR9kA3oLGys/MC3YQGi42N4tSpwkA3wyvktVhTqLyW\nUHkdD7/8BapN4enbLwl0U7wiVD6XYH0dCQnNavyZ9NQDwG63BboJXiOvxZpC5bWEyusAUALdAC8K\nlc8lVF5HRRLUhRBCiBAhQV0IIYQIERLUhRBCiBAhQV0IIYQIERLUhRBCiBAhQV0IIYQIERLUhRBC\niBAR9MVnhBBChJ6//vXP7N2bxsmTuRQXF9O2bRLNm7dg4cJn6nzsv//9AdHRMVx55fBqf7548XOM\nG5dMQkJPbzc74CSoCyGEaLR337Xzwgth7Nun0qOHxn33lTJ2rMvjv/d//3c/YATozMwM7r77vno/\n9tprr6v15/fe+6DH7bI6CepCCCEa5d137dx+e2TZ12lptp+/LmpUYK/Ozp1f8/bbKygsLOTuu+/n\nm292sHnz/9A0jUsuGcqtt05j6dIltGzZks6du7Ju3RoUReXQoQMMG3YVt946jbvvnsYDDzzC229/\nxokTuRw+fIijR49wzz0PcsklQ1mx4k0+/vi/tG2bhMvlIjl5EgMGDCxrw1dfbeP111/B4XDQrFkz\n5s9/CofDwQsvPMuePbux2Ww8/PAsunTpVu33fEmCurC0oiL497/t/OY3LmyhV9FRiJDwwgth1X5/\n8eIwrwd1gIyM/axatY6wsDC++WYHL7/8OqqqcuON1zN+/MRKv7tnTyorV65F0zTGjbuOW2+dVunn\nJ04c59ln/8KXX37Be++tpU+fvqxb9w9WrVpLQUEBycm/JTl5UqXH5OXlMXfuE7Rtm8Tjjz/Ktm1b\nCQ8P58SJ47z66pvs2rWT//3vI3Jzc8/5ngR10aS99ZaD2bMjiI4uZPRod6CbI4Soxr591edc1/T9\nxurWrTthYcaNREREBHffPQ2bzcbp06c5e/Zspd89//yeRERE1Pi3+vW7EIDExETy8/M5ciSLLl26\nEh4eQXh4BL169TnnMS1btuTpp5/A7Xbz449HufjiQZw6dZILLugPwIUXDuDCCweQkvL3c77naxLU\nhaXt3WtcFI4cUQEJ6kJYUY8eGmlp5w6l9eih+eT5HA4HAMeO/cTq1Sm88UYKUVFRTJ584zm/a6tj\niK/iz3VdR9dBVctvRpRqduJ58snHeeaZF+jUqTPPP/80AKpqQ9crv97qvudrsqRNWFpmpnGIZmeH\n0h5XQoSW++4rrfb7995b/fe95fTp08TGxhIVFcXevekcO3YMp9PZqL/Zpk0bMjMzcLlcnDp1ivT0\ntHN+p6Agn9atzyMvL4+dO3fgdDrp1as3O3d+DcC+fek899zT1X7P16SnLizNDOonTkhQF8KqjHnz\nIhYvLs9+v/fexmW/10f37j2IjIzizjtv5YILLuT663/Lc889Tb9+/T3+m3FxrRg1ajR/+MMUOnbs\nTO/efc7p7f/2t+O4887baN++A5MmTeGNN17lb397g44dOzN9+u8BePDBmXTt2o1PP/2k0vd8TdF1\nXff5s/hQdnZeoJvQYAkJzYKy3dXx5WspLIROnZoBMGqUi5SUIp88j0k+F+sJldfx8MtfYLMpPHX7\nJYFuileEyudS0+v4978/YNSo0dhsNqZMSeb55/9KYmLrALSwegkJzWr8mfTUhWUdOFA+OyQ9dSGE\nv+Tm5jJt2lQcjjCuvnq0pQJ6XSSoC8syh95BgroQwn8mT76ZyZNvDnQzPCKJcsKyKvbUs7MVNP8m\nkQohRNCRoC4sKzPT6J137+7G5VI4dUp660IIURsJ6sKyMjNVVFVn0CBjfboMwQshRO0kqAvLysxU\naddOp107Y4GGBHUhhKidBHVhSfn5cOKESufOGgkJRlCXAjRCNB23337LOYVfXnnlRVatWlHt7+/c\n+TWzZz8CwMyZD5zz87VrV7N06ZIan2///h84fPgQAHPnzqKkpNjTpgeUBHVhSWaSXJcuGomJ0lMX\noqkZNeoaNm78qNL3Nm/eyMiRV9f52Keeer7Bz/fJJxvJyjoMwGOPPUl4eM314q1MlrQJSzKXs3Xt\nqpGYaKS9nzgh96BCNBVXXXU1d955G9On3wNAenoaCQkJJCQkVrv1aUVjxlzFhx/+j6+/3s5f/vIc\ncXGtaNUqvmwr1QUL5pGdfQKns4QpU37Peee14b331vHJJxuJjY3l0UdnsXz5avLz83jyyfk4nU5U\nVWXmzDkoisKCBfNo2zaJ/ft/oEeP85k5c06l5//vf//DO++sxmZT6dSpKzNm/AmXy8UTT8zl+PGf\nCAsLZ/bsx4iNjTvnewkJiY163ySoC0syg7r01IUIvHnzwvngg/JwoaqgadGN+pvXXedi3rySGn8e\nGxtH27ZJ7Nmzm969+7Jx40eMGjUaqH7r06ioqHP+xpIlLzJnzuN0796Dhx66h7Ztk8jLO8vgwb/g\nl7/8FcXFp5k+/W7eeGMFQ4ZcwrBhV9G7d9+yx7/++iv86lfXc9VVV7Np08e88car3Hbb7ezdm8Zj\njy0kNjaOsWOvJS8vj2bNyqu8FRUV8dxzf6VZs2bcddcfyMjYz549u2nVqhXz5i3g44838NlnW7Db\n7ed8b+zYGxr1vkpQF5aUkVEe1M05dQnqQtQsK0vhvffs3Hqrk2riW1AaNWo0//vfR/Tu3ZfPP9/C\n3/72BlD91qfVBfWffvqJ7t17AMbWpyUlJTRr1py0tFTef38dYWEOzp49U+Pz792bxh133A3AgAED\nefPN1wFISmpPq1bxAMTHJ1BQkF8pqDdv3pxZsx4E4NChA5w5c5q9e9MZOHAQACNHXgPAs88+dc73\nGsunQX3hwoV8++23KIrCH//4R/r161f2s5SUFN5//31UVaVv37786U9/AmDp0qW8//772O125s6d\nW+kxounIzFSx2XTat9dxOKBFC10S5YSoxd/+Fsbrr4fx2Wd2/v73IsLDvfe3580rqdSrNmqmF3jv\nCWpw5ZXDWb78DUaNuob27TvQvHlzoPqtT6tTcQtVc5uTjz5az9mzZ3nppddxONyMHfvbWlqglD3O\n6XShKMbfq7rBS8UtVJxOJ88/v4g331xJq1bxPPLIfT8/RkXTKm+1Ut33Gstnk5Tbt2/n0KFDrF69\nmgULFrBgwYKyn+Xn57N06VJSUlJYtWoVGRkZ7Nq1ix9++IEPP/yQtWvXMn/+fDZv3uyr5gmLO3BA\noUMHI6ADJCZq0lMXoha7dxuX840b7dxxRwQu326Q5hdRUdF07dqd5cuXlQ29Q/Vbn1YnPj6Bw4cP\nous633yzAzC2a23Tpi2qqvLRRx+VPVZRFNxud6XHV9w6ddeuHfTs2avONhcWFmCz2WjVKp7jx4+R\nnp6Gy+WiZ8/e7Nz5FQCff/4py5e/Ue33GstnQX3r1q2MHDkSgK5du3LmzBny8/MBY4N7h8NBYWEh\nLpeLoqIiWrRowaZNm/jlL3+J3W6nT58+3HPPPb5qnrCwM2cgN1elS5fyurCJiTq5uSqN3CpZiJCk\n67Bnj42OHTUuu8zFhx86uPfeiJAorTxq1Gi++mobl112Rdn3zK1PFy1awKRJU1ix4k1yc3POeey0\nadOZPXsGM2bcX7Ypy7BhI/jii0+59947iYyMJDExkWXLXqN//4t44YVn+Prr7WWP//3v72D9+n9z\nzz138O9//4vbbru9zva2aNGSQYOG8PvfT2HZsteYOHEyf/nL81x11dUUFRVx993TWLNmFb/85a8Y\nOfKac77XWD7benXOnDlceeWVZYF94sSJLFiwgM6dOwPw/vvv88QTTxAeHs6YMWOYOXMmc+fOxWaz\ncejQIVwuF7NmzaJnz561Po/L5cZut9X6OyK4fPUVDB4M99wDixcb35swAd5+G44cgaSkwLZPiIa6\n7Yn/ArB0dt3LsTxx8CB07gw33ghLl8KoUfDll3DHHfDyy6DIIFeT4bdEuYr3Dvn5+SxZsoT169cT\nExPD1KlTSU9PR9d13G43r7/+Ojt27OBPf/oTa9eurfXvnjpV6Oume12o7EUMvnktO3bYgUjatCkm\nO9vomjdvHg6EkZ5eQFiYb7of8rlYT6i8Drdbx2ZTfPZatmwxzplu3UooKipl+XL47W+jeOUVG6pa\nyty5JV4N7KHyuQTr66htP3WfDb8nJiaSk1M+HHLixAkSEhIAyMjIoH379sTFxREWFsbAgQPZvXs3\n8fHxDBo0CEVRGDhwIEePHvVV84SFmcvZOneuPPwOkgEvRHVSU41zpk8fY064ZUtYs6aI7t3dvPxy\nGM89FxbI5gk/8llQHzp0KBs2bAAgNTWVxMREYmJiAEhKSiIjI4PiYqMM3+7du+nUqRNXXHEFn332\nGWAE/jZt2viqecLCKq5RN5UXoJGgLkRV5UG9/JyJj9d5550iOnTQWLQonL/9zRGo5gk/8tnw+4AB\nA+jTpw/JyckoisLcuXNZt24dzZo1Y9SoUdx2221MmTIFm83GRRddxMCBAwHYsmUL48ePB+DRRx/1\nVfOEhR04oOJwlG/kAhV76lJVToiqUlNtxMbqtGlTOUWqTRudtWsL+fWvo5g7N4LoaJgyRbJNQ5lP\n59QfeuihSl9XTHpLTk4mOTn5nMfcc889kvXexGVmqnTsqGGvcHRKARohqpefDwcPqlx2mavaefOO\nHY0e+/XXR/Lww+FERenccEMIrHcT1ZJuj7CUkyfh9GmFLl0q9zhkTl2I6u3Zc+7Qe1Xdu2usXl1E\n8+bwf/8Xwb//LcVEQ5UEdWEp1SXJgTE/qKq6BHUhqkhNNZb0mklyNbngAo1VqwoJD4dp0yLYtEmW\nAociCerCUqpLkgOw2aBVK13m1IWoorokuZoMHKixYkURqgo33xzJl19KYA81coUUllJTUAdjCF56\n6kJUlppqw27X6dGjfvUbLrvMzRtvFOFywcSJkXzzjYSBUCKfprCUAwdqDuoJCTr5+QqFwVdvSAif\n0DRIS1Pp3l1r0AYuI0e6eeWVYgoLITk5qmxeXgQ/+SSFpWRmqoSH6yQlnVu92EyWk93ahDAcPKhQ\nWKjQu3fDqyxed52LF14o5tQphXHjIsnIkPMqFEhQF5ah60ZQ79RJQ63myJQCNEJUVt8kuZokJ7t4\n8slisrNVbrghiqwsObeCnQR1YRk5OQp5eco5me8mKUAjRGUNSZKryW23OZk9u4SjR43Afvy4BPZg\nJldHYRnlSXLVbxwoa9WFqKy8p964TY7uuaeUBx4o4cABlXHjIsnNlXMsWElQF5Zx4IBxIakuSQ4k\nqAtRVWqqSkKCVnZuNMaMGaVMm1ZKerqN5ORIzp71QgOF30lQF5ZR23I2kKAuREWnT8ORI2qje+km\nRYHHHy9h0qRSvv3WxsSJkRQUeOVPCz+SoC4so+6gbnxfst+FgD17vDP0XpGiwLPPljB2rJPt2+3c\nfHMkP2+mKYKEBHVhGZmZKpGROuedV/1QYosWEBYmVeWEgHP3UPcWmw1efLGY0aOdfPKJnWnTInDK\nxm5BQ66OwhLqWs4GRi8iIUGqygkB3sl8r4nDAa++Wszll7tYv97B//1fBG7v3jsIH5GgLizhxAmj\niEZNQ++mxESd7GwFvfF5QUIEtdRUG2FhOt26eT+oA0REwPLlRQwe7GLdOgcPPxwu510QkKAuLMGc\nT+/ate6gXlKiSGauaNJcLkhPVzn/fA2Hw3fPEx0NK1cW0a+fmxUrwnj0UQnsVidBXVhCRkbtSXKm\n8qpycuiKpisjQ6WkRPHJ0HtVzZvD6tVFnH++myVLwnj66TCfP6fwnFwZhSVkZppr1GvvBiQkyLI2\nIXyVJFeTVq103nmniE6dNJ5/PpxXXvHL0woPSFAXlmAOv9dUItYka9WF8G2SXE1at9ZZu7aQli11\nZs5Eqs5ZlAR1YQkHDqhER+t1VsaSoC5E4zdy8VT79joPPljCmTPw3HMyDG9FEtRFwGmaEdS7dNFQ\n6ojVslObEEZPvW1bjdhY/z/3Lbc46dYNli1z8MMPEkKsRj4REXA//aRQXFz3cjaQndqEyMlROH7c\ne+VhGyosDJ55BtxuhcceCw9IG0TN5MooAq6u8rAVSaKcaOr8nSRXneuvh0svdfHf/9r55BNbwNoh\nziVBXQRcfZPkwFg3Gx2tS/130WQFIkmuKkWB+fNLUBSduXPDpdqchUhQFwHXkJ46GEPw0lMXTVWg\nkuSq6tdPY/x4F3v22Hj7bR9WwBENIkFdBFz5Pur1K1WVmKiRk6NI70A0SampxsZHnTsHvrTbrFkl\nREXpPPlkGPn5gW6NAAnqwgIyM1WaN9dp1aq+QV1H0xRZJyuanNJS+OEHlV69NGwWmMpu00bnrrtK\nOXFC5a9/lSVuViBBXQSU2w0HD9ZvOZtJ1qqLpmrfPhWnUwn40HtF06eXct55Gn/7WxhHjsg5GWgS\n1EVAHT2qUFpav+VsJgnqoqkyk+R69w5cklxV0dHwxz+WUFyssGCBLHELNAnqIqAakvlukqAumqry\nJDnrBHWAG2900a+fm7VrHezcKWElkOTdFwHV0Mx3kJ3aRNNlhTXq1VFVY4kbINuzBphcFUVAHTjQ\n8KBuFqCRteqiKdF12LNHpUMHjWbNAt2ac116qZtrr3Wyfbudf/3LHujmNFkS1EVAedZTl6Aump7j\nxxVyc1V697ZWL72iRx8tweHQeeyxcIqLA92apkmCugiozEyV2Fi9QRtTxMfLnLpoeqxQSa4uXbro\n3Hqrk8OHVV5/XQrSBIIEdREwLhccOtSwzHcwNpSIi9MkqIsmxapJclU9+GAJsbE6f/5zODk5co76\nmwR1ETBZWQoul9KgzHeTUSpWDl/RdFg1Sa6qli3h4YdLyMtTeOYZKUjjb3JVFAHjSZKcKSFB5/Rp\nhZISb7dKCGtKTVWJjtbp2NH6qeVTpzrp2lVj+XIHe/dKmPEnebdFwHiSJGcK1mQ5zdojp8Kiiopg\n/36V3r011CC4ajscMG9esey5HgBBcHiIUOWNoB5M8+qPPRbOoEHRMs8oGmzfPhVNs1Z52LpcfbWb\nyy938fHHdjZtskCh+iZCgroImMYFdbMATfAEyM8+s5GVpfL449JzEQ0TDJnvVSkKzJtXvue6yxXo\nFjUNEtRFwGRmqsTHazRv3vDHmgVogilZztzsYtUqB19+KT0XUX9W2UO9oS64QGPiRCfp6TZWrpQl\nbv4QPFdEEVJKS43sd0966RB8c+oFBZCbq5KUZLzeGTPCcToD3Kgg4A6uGOYzqakqiqLTq1fw9NRN\nM2eWEhWl89RTYeTlBbo1oU+CugiIw4cV3G6FLl08y+QNtjn1o0eNU234cBeTJ5eSlmbjtdek51Kb\nv/wljNat4aefguMz9hVdN3rqnTvrREcHujUN17q1zj33lJKTo/KXv8gSN1/zaVBfuHAh48ePJzk5\nme+++67Sz1JSUhg/fjwTJkxgwYIFlX6Wk5PDoEGD2LZtmy+bJwKoMfPpEHxB3Rx6b9dO509/KqFV\nK41Fi8I5ejQ42h8IGzfayM2F5cub9s3P0aMKZ84EV5JcVXfcUUrbthqvvBJGVpYc877ks6C+fft2\nDh06xOrVq1mwYEGlwJ2fn8/SpUtJSUlh1apVZGRksGvXrrKfL1q0iPbt2/uqacICGhvU4+J0bLbg\nKUCTlWW0s107jbg4o0Z2YaHCnDmSNFcdXYe0NGMe+a23HE16qiIYk+SqioqCP/2phJIShSeekGPe\nl3x2Rdy6dSsjR44EoGvXrpw5c4b8/HwAHA4HDoeDwsJCXC4XRUVFtGjRouxx0dHR9OjRw1dNExbg\nyT7qFamqkSwXLD11s3fSvr0xwjB+vIshQ1z8618O/vc/SZqr6sQJhVOnlJ//X+U//2m6u34Fa5Jc\nVb/7nYsLL3Tz7rsOvvoqOG7Gg5HP3tmcnBxiK+zSERcXR3Z2NgDh4eHcddddjBw5kuHDh9O/f386\nd+5MaWkpL730Evfff7+vmiUsorFBHYwh+OxsJSj2bj5yxHi97dsbr1dV4emnS7DZdGbOjKCoKJCt\ns549e4z3a9w44+s332y6Q/Ch0FOHqnuuRwTFeRuM/Hb7q1f4BPPz81myZAnr168nJiaGqVOnkp6e\nzscff8y4ceNo3oA1TrGxUdjtwdfTSUiw4IbIHvLktRw6BG3aQOfOnr8P7drBd99BZGQzr+0v7avP\n5dgxsNngggtisP981l15Jdx/Pzz7rMLrrzdj/nzvPmcwH2NHjhj/jhsHubmwcaOdnJxm9OoV2HZ5\nymYzRh08+UzS04166hdeGINioYEpT17LddfBDTfAO+/Y2LSpGePH+6BhDRTM50l1fBbUExMTycnJ\nKfv6xIkTJCQkAJCRkUH79u2Ji4sDYODAgezevZvPPvsMTdNISUnh8OHDfPfddyxevJju3bvX+Dyn\nThX66iX4TEJCM7KzQ2NthyevpbgYDh+O4Re/cJOd7XkXtWXLcCCMPXvyPc6ir8iXn8uBA9G0bQun\nThVU+v706bByZTRPP61w7bUFdO3qne5LsB9jX30VATjo2xcmTSpi48ZInn++lIULg7PYv9utY7Mp\nDf5MCgpg//4YLrnETU6OdYZzGnN8PfywwvvvR/PwwzqXXlpARISXG9cAwXqe1HYj4rPh96FDh7Jh\nwwYAUlNTSUxMJCYmBoCkpCQyMjIoLi4GYPfu3XTq1Im3336bNWvWsGbNGoYNG8bcuXNrDegiOB06\npKLrnq9RNwVLAZrSUjh2TKFdu3Nfb0wMPPFECaWlCjNmyJCkKT1dJSxMp3t3GD3aRevWGqtXOygo\nqPuxoSQtzThXgn3ovaLOnXV+/3snWVkqr74qS9y8zWdXwwEDBtCnTx+Sk5N54oknmDt3LuvWreOj\njz4iPj6e2267jSlTpjBhwgR69erFwIEDfdUUYTHl8+mNi2DBsqztxx8VdF2hXbvqX++YMS6uusrF\nli123nuv6SaEmTQN9u5V6d5dw243NgeZPNlJXp7CunVNa249VJLkqrr//hLi4jReeCHM8udvsPHp\nFeShhx6q9HXPnj3L/j85OZnk5OQaH/vUU0/5rF0isDIzjZO4sT31YKkqVzVJripFgYULi7nyymjm\nzAnnqqtcXssRCEYHDyoUFSk/V08zgtrkyU7+/Ocwli1zcNNNTkvNLftSqCTJVdWiBTz8cCmzZkWw\naFEYzz4bnNMqVmTtcUsRkhq7Rt0ULD31qsvZqtO5s86995Zy/LjK00837XW86elGIO/Zs/z4aNNG\nZ/RoF7t329ixo+lctlJTbdhsOuefH1pBHYw913v0cLNihYO0tKbzmfqavJPC7w4cMA67Tp0aG9SD\nY6e2ioVnanP33aV06aLx+usOvv++6Z6a5gW+d+/KQ8633GJUoFm2rGnMw2qasbSvWzctoMlkvmK3\nG7u4aZrCvHlN+0bWm5rulUMETGamStu2GlFRjfs75T11ax/GdQ2/m8LD4amnitE0hUceiUALvc5Z\nvaSnG+9XxZ46wOWXu+naVeO99+zk5lr7Rs4bDh1SKCgIrSS5qq66ys2VV7rYtMnOxo3BtzTZiqx9\nNRQhp7AQfvxRbfTQOxiZ45GR1q8qZ9Z9b9u27sTAYcPc/OY3TnbssLFiRdNKCjOlpak0a6aTlFT5\n/VIUuPnmUkpLFVatCv2EQjNJrnfv0A3qigKPPVaCqsqe694iQV341cGDja8kZ1KU4CgVm5WlkphY\n/yHU+fNLiInReeKJcHJyrP3avK2kBDIyVHr21KpNhhs/3klkpM6bb4aF/EiGmSTXt29oZb5X1bu3\nxqRJTvbutfHWW03zRtabJKgLv/JWkpzJLBVr1Qu8220saastSa6q887TmTmzhNOnFebPb1pzjT/8\noOJ2K/TsWX0ga9kSxo51cviwyqZNoT1cG6qZ79WZMaOU6GidRYvCOHs20K0JbhLUhV+VB3VvVU7T\ncLnKN/+wmuPHFZzO6gvP1ObWW5307evm7bcdfPllaAevisz59NqGnM2EuTffDO2EuT17bMTHa2W5\nI6EsMVHnvvtKyc1VeeGF0P5cfU2CuvCrAwe8s0bdZPVlbWbme11JclXZ7bBoUTGKojNjRniT2XrU\nzHyvmiRXUf/+GgMGuPnvf20cPmzNz72xzp6Fw4dVeveufhoiFE2bVkq7dhqvvhrGoUNN5EX7gAR1\n4VeZmSqKotOxo3eDulUL0JhJcjVVk6vNwIEaN93kJC3NxquvNo25xurWqFfn5ptL0XUlZOdg9+wx\nK8mF/tC7KTISZs82SibLnuuek6Au/CozU6VdO91r626t3lOv73K2msyeXUKrVhrPPBPO0aPWfI3e\nlJZmJBW2alX7TdD117to2VInJcVBSQgWIyufTw/tJLmqxo51cfHFbt57z8H27RKePCHvmvCb/Hw4\nflz1Sua7yepB3awm50lPHSA2FubOLaGwUGH27NDuveTlGTdBRnnY2kVGwoQJTnJyVD78MPSWtzWl\nJLmKjCVuxkZf8+aFYMUdP5CgLvzGrCTnrfl0qFhVzpqHcmN76gA33uhiyBAXH37o4OOPQzdprj7z\n6RVNnVoKwLJloTcEn5pqw+HQ6d69aQV1gMGDNUaMcPH116GbM+FL1rwSipDkm6Bu/Z56y5Y6P+86\n7BFVhUWLSrDZdGbOjKDIOttqe5U5n161PGxNunTRGTbMxbZtdvbsCZ1LmdttrALo0UMjrIkmgl99\ntVGFZtOm0BuF8bXQOROE5Xl7jTpU3FPdekFd142eemN66aZevTRuv91Yn714cWhe6RvaU4eKy9tC\np7eemalSVBTa5WHrMmKEEdTGDXiTAAAgAElEQVSldGzDSVAXfuOLoB4RAS1a6JbMfs/NNbYQbega\n9Zo89FAJbdtqvPhiGPv3W+/1NlZ6urEyokeP+r9fo0a5aNtW4x//cJCf78PG+VFTTZKrqFMnnS5d\nND791E5paaBbE1wkqAu/ycxUUFWdDh28W0wjIUGzZE/dXM7WkGpytYmJgSeeMJb8zJwZgR5CNUl0\n3eipd+yoEx1d/8fZ7TBlipOCAoV//CM0eutNNUmuqhEjXOTnK3z9tfTWG0KCuvCbzEyVDh10r88T\nJibq5OaqlivQUt8tVxtizBgXI0e62LLFzj//GTrzjSdOKJw8qdKrV8N7p5MmObHbdd580xESNzrm\nRi4S1GUI3hMS1IVfnD0LOTne2Z2tKjNZzmrbcTam8ExNFAUWLiwmIkJnzpzwkKmTbc6n12c5W1Wt\nW+uMGeMiLc3Gtm3BHwBSU1XOO6/utfqh7pJL3ISH62zcGDo3r/4gQV34hS/m001WzYD3xnK26nTq\nZNTJPnFC5emnQ2Ptulnz3ZOgDqGTMHfyJPz0k9rke+kA0dHwi1+42b3bxvHj1jq3rUyCuvCLphjU\nzcIz3g7qAHfdVUqXLhpLlzr4/vvgP43NoN6QzPeKLrnEzfnnu/ngA7vljoOGKB96b7pJchWZQ/Ch\nviOfNwX/1UAEBd8GdbMAjbUu5llZKlFROrGx3v/b4eHw9NPFaJrCI49EWHbr2fpKS7MRFqZ7fHwo\nCtx8sxOnU2HlyuDtrUuSXGUjRhg3N7Jevf4kqAu/MIO6N0vEmsp76tY6nM016r7aZevKK92MHetk\nxw4bK1YEbyDTNNi7V6VbNw1HI17GuHFOoqJ0li934A7Sjq4kyVXWo4dGUpLG5s32oP1M/c1aV0ER\nsg4cULHbda8t76rIigVo8vLgzBnFq0ly1XnssRJiYnSeeCLckmv16+PQIYXCQsXj+XRT8+Zwww1O\njhxRg7acbmqqSkSE5yMWoUZRjCH4U6cUdu2ScFUf8i4Jv8jMNNYg230wimbFOXVfLGerznnn6cya\nVcLp0wqPPx6cSXNmedjGBnUwhuABli0Lvqp7Tifs26fSs6fmk/MkWA0fbnTRJQu+fiSoC587dQpO\nnVJ81vto1UpHUXRLBXVvF56pzS23OOnb183bbzv48svg66GWL2dr/Phq374agwa52bTJxoED1jke\n6uOHH1RKSxVJkqviiitc2GyytK2+JKgLn/NlkhwYVcVatdItNadu9tR9kfleld0OzzxTjKLoPPJI\nuOWK8NSlsZnvVd1ySym6rrB8eXD11iVJrnrNm8OgQW6++Ubl5MlAt8b6rHMVFCHLl0lypsREa9V/\n99fwu+niizVuuslJerqNJUuCK2kuLU0lJkb3Wv7Bdde5aNVKY9UqO8XFXvmTfiFJcjUbMcKNpils\n2SK99bpIUBc+5+ueOhhBPS9PobDQZ0/RIP4cfjfNnl1Cq1Yazz4bTlaW3562UUpKICPDmEf21iqB\n8HCYONHJyZMq778fPEHA7KnXd+vZpqS8ZGzwfJ6BIkFd+Jwv9lGvykyWs0pv/cgRlbAwvaxd/hAb\nC7NmlVJYqLB8ud+etlH271dxuRSvzKdXNGWKE0XRgyphLjXVWALZokWgW2I9fftqxMdrbNxoC4n6\n/r4kQV34XGamEeCSknx3NlqtAE1WlkLbtjqqn8+w0aONHs3nn/v3eT3V2PKwNenYUeeqq9zs2GEL\niop7x48r5OSokiRXA1U1suBPnFDLRjRE9eTdET6l60ZQ79RJw+bDxGwrFaApLobsbNUvSXJVJSYa\na5y/+IKgKNZhZr57K0muoltuMTbiDoZ68OVD7zKfXpPhw2UIvj4CfwUUIS03V+HsWd8tZzNZaa36\n0aPe352tIYYMcXPmTHkv2MrMNeq+COojRrjp0EFj7VoHZ854/c97lSTJ1W3YMDeKoksd+DpY/6wX\nQS0z0whwnTv7NsBZKagfPuy/5WzVGTzY6KIHwzakaWkqCQka8fHePz5sNmNuvbBQYc0aa/fWy5ez\nBcHwSoDEx+v076+xbZuN/PxAt8a6JKgLn/JH5jtYq1SsueWqv5azVTVkiDFMuX27tYN6Xp6x9M/b\n8+kVTZjgJCxM5803HZZOsNqzRyU6WqdTJws30gJGjHDhcil8+qkMwddEgrrwKX9kvkN5opwVst8D\nsZytoq5ddeLjrR/UfZUkV1FCgs5117n44Qcbn39uzfejuNioJterl+b3xMpgU14y1pqfpRXIISR8\nyl899ZYtweHQyc4O/CHt78IzVSkKXHaZMWJg3mBYUXnNd98OOZfXg7fmEPy+fSput5SHrY+LL3bT\nvLnOpk12S4+8BFLgr4AipGVmGrtOtWnj2zNQUYx5dWsMvyuoqk7btoG76lx2mfGvlXvrvsx8r2jw\nYDe9e7v5z3/sHDsW+OOjKikPW392O1x5pYvDh1UyMqz3WVqBBHXhM+Zyts6d/TOsaAb1QN/BHzmi\nct55eqP2Bm8sM6hbOVnOHH4//3zfBjNFMTa9cbkUS+47X575Lj31+hgxQnZtq02dl9qMjAx/tEOE\noBMnFAoKFJ/WfK8oMVGnpETh7Fm/PF21nE748UclYJnvposugshI3bJBXdeNnnrHjhrR0b5/vt/9\nzklMjM5bbzlwuXz/fA2RmqqiKLpPcwtCiaxXr12dQf2ee+5hwoQJrF27lqKiIn+0SYQIfyXJmcqr\nygVuAOqnnxQ0TQnYGnVTWBhcdJGbtDTVkmu0s7MVcnNVn8+nm2Ji4MYbnfz0k8qGDdYJBrpu9NQ7\nddKJiQl0a4JD27Y6vXq5+eILGxKSzlXn1e/DDz/kscce48iRI0yePJk5c+bw3Xff+aNtIsiZa9S7\ndPFPgLPCsjZzOVuge+pgFKHRdYWvv7Zeb718D3X/vU9WTJj78UeF06clSa6hhg93U1yssHWr9Y7t\nQKtXl6ZHjx7ce++9zJw5k4yMDKZPn86kSZM4ePCgj5sngllGhr976oEP6llZga0mV9GQIUagsGKy\nnD+Ws1XVs6fGJZe42LLFbpkkK0mS84y5a9umTdYZdbGKOoP60aNHefHFFxk9ejRvvvkmd9xxB59+\n+ikzZszg4Ycf9kcbRZDy13I2k5V66oFazlbRwIFGWU0rzqubQd3Xme9V3XKL0Vt/801r7N62Z48k\nyXliyBA3UVG6rFevRp1BffLkyaiqyt///ndefPFFrrjiChRFoV+/fvTr188fbRRBKjNTJSpKp3Vr\n//RardBTD3ThmYqaNzc2CNm500ZpaaBbU1lamg2HQ6drV/8G9WuvdZGQoPH22w4KC/361NWSnrpn\nwsPhssvc/PCDjcOHrTHqYhV1BvX333+fTp060bp1awBWrVpFQUEBAHPmzKn1sQsXLmT8+PEkJyef\nMw+fkpLC+PHjmTBhAgsWLADA5XIxY8YMJkyYwI033sjXX3/t0YsSgadpcPCgSpcuGoqfzrnyqnKB\nS5Qz675boacORo+muFjhu++ss3pV04yeerdumt+X/YWFweTJTs6cUfjnPwM/dJuaqtKihW6J6Zpg\nY2bByxB8ZXWe6bNmzSInJ6fs6+LiYh555JE6//D27ds5dOgQq1evZsGCBWWBGyA/P5+lS5eSkpLC\nqlWryMjIYNeuXbz33ntERkayatUqFixYwFNPPeXhyxKBduyYQlGR73dnq8gqw+/x8RqRkQFrQiXm\nvLqVhuAPH1YoLFQCtoRr8mQnqqoHfAi+sNAYzerd2+23G99QYs6ryxB8ZXUG9dOnTzNlypSyr2+5\n5RbO1mMh8NatWxk5ciQAXbt25cyZM+T/vLWOw+HA4XBQWFiIy+WiqKiIFi1a8Otf/5pZs2YBEBcX\nx+nTpz16USLw/D2fDsaypejowFWV0zRj21UrDL2brLhjWyCS5CpKStK5+moXu3bZ+OabwI1gpKer\naJoiQ+8e6txZp3NnjU8/tVtueimQ6jyinU5npQI0u3fvxul01vmHc3JyiI2NLfs6Li6O7OxsAMLD\nw7nrrrsYOXIkw4cPp3///nTu3BmHw0F4eDgAf//73/nVr37V4BckrCEQQR0CWyo2O1uhtFSxzNA7\nGAGsXTuNr76yBbzSniktzT8132tjJswtWxa43rrsod54I0a4yM+35rLNQKlzMmLWrFlMnz6dvLw8\n3G43cXFxLFq0qMFPpFe4ouTn57NkyRLWr19PTEwMU6dOJT09nZ49ewLGfHtqaiqvvPJKnX83NjYK\nuz34PtCEhGaBboLXVPdajh0z/h0wIJKEBP+1JSkJvvgC4uKaYfPgsGjM52Le+/bo4SAhIfBroc3X\ncsUVsHIlnDrVjPPPD3CjgAMHjH+HDo2q17Hhi3Plhhuga1f45z8dvPSSg7g4rz9FJTabcaNZ8bVk\nZhr/XnZZBAkJEb5tgA9Y4Ro2diwsXQpffhnF9dd79jes8Dq8qc6g3r9/fzZs2MCpU6dQFIWWLVuy\nc+fOOv9wYmJipbn4EydOkPDzGZyRkUH79u2J+/lMGjhwILt376Znz5784x//YOPGjbz88ss46pFF\nc+qUBVJYGyghoRnZ2XmBboZX1PRadu+OABzExuaTne2/LmJsbASa5iA9Pb8sG76+Gvu5fP+9HYik\nVatisrPrHs3ypYqvpX9/BytXRvCf/xQTFxfYdgHs2hVFdLRKVFQ+Pw/e1ciX58rkyQ7mzYvgxReL\nufNO374vbreOzaZUei1ffx2JqtpITKz7fbAaq1zD+vSBsLAYPvxQ44EHGh4LrPI6Gqq2G5E6h9/z\n8/NJSUkhJSWFFStW8Oc//5l77rmnzicdOnQoGzZsACA1NZXExERifq6DmJSUREZGBsXFxYAxpN+p\nUyeysrJ4++23efHFF8uG4UVwOnBApVkznfh4/475BnJZm7nlqhWqyVVkpWS50lLYv1+lZ0//rYqo\nSXKyk4gII2FO8/NHpuvGGvVu3ayTVBmMoqPhF79w8/33No4fl2xDqEdP/b777qNt27Z89tlnXHPN\nNXz++efMmzevzj88YMAA+vTpQ3JyMoqiMHfuXNatW0ezZs0YNWoUt912G1OmTMFms3HRRRcxcOBA\nnn/+eU6fPs20adPK/s7SpUsJC7NGoQhRP+ZytkBcuAOZAW+lanIV9eyp0by5NYrQ7N+v4nIp9O4d\n+GIrcXFw/fUuVq928L//2Rg1yn9tOnxYIS9PYeRIa90ABqMRI4wqgZs32xg/3mK79QRAnUG9pKSE\n+fPnM3nyZGbMmMHp06d5/PHHyzLba/PQQw9V+tqcMwdITk4mOTm50s8feOABHnjggfq2XVjU0aMK\nJSX+Xc5mCmRP3Up13ytSVSML/uOP7Rw/rvitGFB1AlVJriZ/+EMpa9bYmT49kn/8o5ALL/RPuyRJ\nzntGjHAzb56xXr2+Qf3dd+288EIY+/ZBjx5R3HdfKWPHhsYNQb2y3wsLC9E0jVOnTtGyZUuysrL8\n0TYRpMzMd39tuVpRIHdqO3JEoXlznebN/f7UdbJKHXhzIxerBPV+/TRefLGYvDy44YYodu3yz3FT\nXkku8CMWwe788zXattXYvNmGux5v57vv2rn99kjS0ozfT0uzcfvtkbz7bmgUsanzCL7++utZs2YN\n48aN49prr2XMmDHEx8f7o20iSAVqORuU99Szs/3bU9d1Y07dSsvZKjLXqwc6qKenm8vZrPM+jRvn\n4sUXi8nPNwK7P9auS3lY71EUYwj+5EmVb7+t+7N74YXqp3MXLw6Nad46b03MOXGASy65hNzcXHr1\n6uXzhong1RSD+unTUFBgrcIzFV14oRuHQw94UE9LMyru+TuBsi433OACirn77gjGjYtizZpCBgzw\n3fGbmmqjVSstoFMhoWT4cDcrVsDGjXYGDKi9Es2+fdUH/pq+H2zqfBUVq8m1bt2a3r17lwV5Iapz\n4EDggroZLPw9p27V+XRTZCT076/x3XcqP2/d4Hf5+UZtfCv10iu64QYXL71k9NjHjYti507fXOTz\n8uDQIZXevQO/AiBUXHGFC5tNZ+PGuofQe/So/vir6fvBps6jtlevXixevJgtW7awdevWsv+EqElm\npkLLlrrPC3pUJywM4uI0vwd1q23kUp0hQ9y43Qo7dwamtx7o8rD18bvfGYG9oMAI7Dt2eD+wl2+3\nat33Idi0aGFsNbxzp8qpU7X/7n33Vd+Tv/fe0Kg1W+dtTVpaGkClHdMUReGSSy7xXatE0HK5jF7I\nBRcE7oKVmKhz7Jh/h9KstOVqTYYMcfHSS2Fs22bj8sv9n6Blxfn06vzudy4UpZjp0yO48cYoVq8u\nZOBA77VZkuR8Y8QIN9u22dmyxc7119ecyW5kuRexeHEY+/bZ6NHDzb33hk72e51B/a233vJHO0SI\nOHJEwelUApL5bkpI0ElPVygpMfZd9gdz+N3KPfVBg4y2BWq9ennmu/WD2W9/awT2O+80AvuaNd4L\n7JIk5xsjRrh48slwNm6sPaiDEdjHjnX9XFEu+KqS1qbOoD5x4sRq59BTUlJ80iAR3AKZJGcyC9Bk\nZyt+KwRj1cIzFbVqpdOjh5uvv7bhcoHdzyt4zOH3888PjmA2dmzlwL56dWHZjVFj7Nljw27XQ2YO\n1youuEAjPl5j40Zj86Kmmq9Qr4pyJqfTyZdffklUVJRPGyWCVyCT5EwVC9D4K8geOaISGen/srgN\nNXiwmxUrbOzZo9Kvn38/o7Q0lQ4dNH6uFh0UfvMbI7DfcUcE48c3PrAb66JVunfXkEKZ3qWqMGyY\nm3fecbBnj9pkR0LqnHgcPHhw2X9Dhw7lwQcfrNeGLqJpskJPvbwAjf9u1Y8cUUhKsn42c6D2V8/O\nVsjJUS1RHrahrr/exZIlxRQVwfjxUWzf7nm+xsGDCoWFsod6bd59186VV0bRpk0MV14Z1aCiMCNG\nGMPu9cmCD1V1Hp1ZWVmV/tu+fTsHzL0ThajCGkHdHH73T7Jcfj6cPKlaOknOFKjKclYrD9tQv/61\ndwJ7eXnY4Lu58YfK1d6UBld7GzbMjaLobNoU+H0OAqXOd2rq1Kll/68oCjExMdx9990+bZQIXpmZ\nKq1aabRoEbg2+Lv+ezAkyZk6ddJJTNTYts2/845mkpzVM99r8+tfG0Px06YZQ/Fvv11UdpNUX8Ge\nJOfrmum1VXurz/PEx+v0728c3/n5BHyqp/z9UunRQ/NLjfk6g/rGjRvRNA1VNQ5Gp9NZr33ORdPj\ndBq7T110UWAvWP4P6tZfzmZSFKO3/sEHDg4fVujY0T9tDvaeuum661y8+moxt98eQXJyJKtWFfGL\nX9Q/sAfzRi5mL9pk9qKhyGuByhvV3oYPd7FrVziffWZj9OjAjYj44/2qTp3v1IYNG5g+fXrZ15Mm\nTWL9+vU+a5AIXllZCm53YHZnq8jfQd3cRz0YeurQ+P3VPZnzTEuz4XDodO0aHO9RbczAXlICycmR\nfPll/d/H1FSVxEStbIWGtzRmHrq+/FEz3RvV3oYPN47vQM+rB6rGfJ1BfdmyZTzzzDNlX7/xxhss\nW7bMp40SwSkjI/Dz6QBxcTo2m+63ndrMnrqVl7NV1Jig7smcp6YZPfVu3UIn4/tXv3Lx2mvFlJbW\nP7BrGhw96v2s7MbOQ9eXP2qme6Pa28CBbpo3N0rG6gE8JQNVY77Ov67rOs2aNSv7OiYmRmq/i2pZ\nIUkOjKUtCQm63+fUrVr3vao+fTSiojzb3MWT3kdWlkJBgRLU8+nVGTOmcmDfurX297P057jk7SQ5\nf/UI/VEzfexYF0uWFNG7txu7Xad3bzdLljRsuNpuN2rBHz6skpkZuFgVqBrzdQb1vn37ct9997Fy\n5UpSUlK444476Nu3r08bJYKTVYI6GEE9O1vxy516VpaK3a5z3nnB0VO32+Hii93s3Wurs052VZ70\nPqw8n97YYesxY1y8/roR2CdMqD2wlwd1774P/uoR+qtm+tixLjZvLuTHH/PZvLnQo/nnESMCPwQf\nqBrzdX7qs2fPZvjw4WRkZHDgwAF+/etf88c//tGnjRLByUpBPTFRp7BQ8cuOZFlZCm3b6tiCaBWN\nOQT/1VcNa7QnvY+0NLPmu7WWcXlr2Praa10sXVqM01l7YPdVUPdXj7ByLxqPetH+Mnx44Nere2PU\nwRN1BvWioiIcDgdz5sxh9uzZnDlzhqKiIp82SgSnAweMJKBALyMB/yXLlZTA8eNq0Ay9mzydV/ek\n92HVnro3h61/+UsXS5cWlQX2L744930tdUJ4uE63bt59H/zZIzR70U4nHvei/SEpSadnTzdffGEj\nkOHq8svdxMbqLFxY4rf3q86gPmPGDHJycsq+Li4u5pFHHvFpo0TwKSkxEsas0EuHilXlfJuUcvRo\ncCXJmS6+2I3Npjc4qHvS+0hLU4mO1i235M/bw9ajR7t54w0jsE+cGMnnnxvv7bvv2jl2TKG0xFhS\n+MEH3u09BqpHaHXDh7spKlIatDrBm4qKYPLkSD7/3I7T6b/nrfPoPX36NFOmTCn7+pZbbuHs2bM+\nbZQIPocOqWialYJ6+aYuvhRMhWcqiomBvn01du2yUVzcsMc2ZM6ztBR++EGlZ08N1b+74dbJF8PW\n11xTHtgnTYrkqafCuP32yLKLenGx4pPMdG/MQ4eaQJaM1TS4664IduywMW6ck9//3n9Rvc7TzOl0\nkpGRUfb1999/j9Oftx0iKJhZpl26WKM31pDhdzNZym6nwclS5YVngiuogzEEX1qqsGuX73oyGRkq\nLpdiufl08N2w9TXXuFm2rAiXC/7858CsVRbG8R0VFZiSsfPnh/OvfzkYOtTFn/9c7Nc9Ieq8es2a\nNYvp06eTl5eHpmnExsayaNEif7RNBBEzSS6Q+6hXVN+g3tiqT2bhGasNLdfH4MFuXn3VqAPfkKpo\nDWHV+XTg58+3iMWLy8t43nuvd8p4Xn210WO/6abIan/u67XKAiIiYOhQNx99ZCcrS/HbOfrGGw5e\nfjmM7t2Nmzt/12ao88jq378/GzZsYO3atcycOZPExETuvPNOf7RNBBErZb5D/Xdqa2yyVLAOv4N/\ndmyzes13Xw5bX321u8bjQvZS9w9zCH7TJv8MwX/0kY0//jGc+HiNlSuLaNnSL09bSZ1BfdeuXTz6\n6KNcd911zJ8/nxtvvJFNmzb5o20iiJj7qFuvp177Id7YZKmsLAVF0UlKCr6e+nnn6XTsqPHVVzY0\nH31sVu6p+8OcOYFZqywM5UvbfD8E//33Kn/4QyTh4bBiRZHf9lWoqsYr12uvvca1117L/fffT1xc\nHGvXrqVDhw6MGTNGNnQR58jMVGnTRiMqKtAtMcTEQERE3VXlGpssdeSISuvWumXKnzY0P2DIEDen\nTys+Gw7es8dGfLz3a50HCzMz3e4AFGuv7Q5FXbrodOqksWWLbzPQjx5VmDgxkqIiePnlYgYMCNxN\nbI1n8gsvvIDD4eDJJ5/kvvvuo2PHjlIeVlSrqMioaW2VoXcwlg4lJtYd1BuTLOV2w48/KpZZzla5\nmAr1KqbS2M1dapOfD4cPq5YdeveXsWNdtDlPp1NHa6/tDlUjRrjIz1f4+mvf9NbPnjWWMB4/rjJ/\nfgljxgT2860xqG/evJkxY8Ywd+5cRo0axcsvvyxZ76JaBw9aaz7dZJaKrW1ouTFVso4dU3C5FMtk\nvnuSH+DLoL53b9MeehfWUL60zfvHuNMJt91m3Ej//velTJsW+BhZY1BPSEhg2rRpbNiwgYULF3L4\n8GGOHj3KHXfcwSeffOLPNgqLs1rmuykxUcPlUjh9uvbf87RKVnnmuzVetyf5Ad27a8TFaR5t7lKX\n9HSzPKw13h/RNF16qZuwMN3r69V1HR55JJxPPrFzzTUuHn+8xK9L12pSr4m0QYMG8dRTT/Hpp58y\nbNgwXnrpJV+3KyQ1Zj20lZVnvltjGNpU32Q5T1lty1VP8gMUBQYN0jh8WOXHH717RTIz33v2bPhy\nuVA9V4T/xcQYI1Lff2/j+HHvHeOLF4eRkhJG//5uXnmlyDJ7PzToahcTE0NycjJr1qzxVXtClifz\nncHiwAGz8Iy1emS+rv/ekJ56Y3cDqw9P8wPMpW3e7q2XB/WGHRehfK6IwDCH4Ddv9s4xvnatnYUL\nw2nXTmPFiiKio73yZ71CKiD4ib/2PA6EzEwVRTGyTL2psYHQ10G9vj11b+0GVhdP8wOGDDF+7oug\n3qFDwzf4CeVzRQSGuRWrN9arb91q4957I2jWTGflyiJat7bGSJ1Jbn39xF97HgdCZqZKUpJORIT3\n/mZjK72B/3rqdRWeqS1IeTsTeuxYF2PHukhIaEZ2dmG9HtO/v0Z4eMM3d6lNdrZCTo7KNdc0/PWF\n8rkiAqNnT402bTQ2bzZGfzwdKt+/X2Hq1Eg0DZYtK7JkEqicJX7irz2P/a2gAI4dU72eJOeN3pqv\nd2o7ckQhLk6rc+jN6kEqPBwuushNaqpKXp53/mZ55nvD59ND9VwRgaMoxhD8yZMq337r2XmXk6Mw\nYUIUp08rPP98MVdcYb39DECCut/4c89jf9q/3/jX2/Pp3giEZsETX/TUdd1Ym1+fJLlgCFJDhrjR\nNO+t5W1MedhQPVdEYJlD8J5kwZvbqB46pPLggyUkJ1u31oAEdT9pzHpoKzLnuwcMML4uKPBu4PRG\nIPRlUM/JUSgqqt8a9WAIUt5er96Y8rChdq4Ia7jiChc2W8OXtlXdRvWRR6xz3lZHgrofeboeuqF8\nnWldMfHLLOzyzjsOrz6PNwJhZCQ0b677ZE/1rKz6L2erHKR0SwapgQO9mwG/Z48Nu12nWzfPRiP8\nda6IpqNFC7j4Yjc7d6qcOlX/xwVyG1VPSFAPMf7ItPZHdrK3AmFiouaToG7uzlbfwjO+3A3MG1q2\nhF693OzYYWt0jWxdN3rq3bpplqmJLwQYQ/CaprBlS/2uh4HeRtUTEtRDjD8Crr8Sv7wRCBMTdXJz\nFa9v5tCQnnqwGDzYTeF02LUAACAASURBVFGRwvffN+5zzMpSKChQpJKcsJzyrVjrHpGywjaqnpCg\nHmL8EXCDIfHLlJioo+sKubne7a03tKceDMx59cYOwTf17VaFdfXrp9GqlcbGjXb0Wu7Hv/vOGtuo\nekKCeojxR8ANhsQvk6/WqptBva416sHEW8lyaWlmzXdrLvkRTZeqwrBhbo4dU8tWaFR15IjCpEnW\n2EbVExLUQ4w/Aq453925s3HRjo3VLJf4ZfJVUM/KUoiJ0YNmSK4+2rXTadtWY9s2W629mLp4Wh5W\nCH+obde2s2dh0iTrbKPqCQnqIcZfmdZjx7qYOdO4UXjwwVJLBnSoWIDG+z319u01y2fCNoSiGL31\nnBy1rJ6/J9LSVKKidDp0CJ4hS9F0DBtWfclYpxNuvdVa26h6QsrEhiCzVKivmbuzde1q3R5Z+Vp1\n792/njkDZ88qDBkSekFr8GA3777rYNs2G126NPwYcjph/36VCy7QUKXLICwoIUGnf383X35pIz/f\n+J6uw8MPh7Nli7W2UfWET0+7hQsXMn78eJKTk/nuu+8q/SwlJYXx48czYcIEFixYAIDT6eTBBx9k\nwoQJ3HTTTWRlZfmyeaKRrLqPekW+GH6vb833YGTu2ObpvHpGhorTqch8urC0ESNcOJ0KmzYZXy9e\nHMbKldbbRtUTPgvq27dv59ChQ6xevZoFCxaUBW6A/Px8li5dSkpKCqtWrSIjI4Ndu3bxr3/9i+bN\nm7Nq1SruuOMOnnvuOV81T3hBWpqK3Q7t21u3x+qLoG61fdS9qXdvjZgYnW3bPBvEk8x3EQyGDzdu\nOtevt/Y2qp7wWVDfunUrI0eOBKBr166cOXOG/J/HOhwOBw6Hg8LCQlwuF0VFRbRo0YKtW7cyatQo\nAC699FJ27tzpq+aJRtq9W2X3bhtXXw0OR6BbU7NWrXQURfdyUA+95Wwmmw0GDXKTkaGSk9Pw96wx\nNd+F8JeBA900b66zejWW3kbVEz6bU8/JyaFPnz5lX8fFxZGdnU1MTAzh4eHcddddjBw5kvDwcMaM\nGUPnzp3JyckhLi4OAFVVURSF0tJSwmop4xMbG4XdHnxjJQkJzQLdhEZ55x3j39tvt/5rSUiAkyft\n9WpnfX4nN9f4t1+/SBISGts63/H0cxkxAjZtgr17Y+jVq2GPzcw0/r3ssiivvTdWP77qw2YzbpBC\n4bWYgv21jBwJ69aB3a7w7rtw+eVB3kX/md8S5fQKa2Ty8/NZsmQJ69evJyYmhqlTp5Kenl7rY2py\n6lT99oy2EmOvay/tcRkABQXw1lsxtGmjc+21quVfS3x8FFlZKtnZ+bX+Xn0/l337IgAH0dH5ZGdb\n886+McdY3742IIr//reUoUNLGvTYXbuiiY8HVS0gO9ujp68k2M8Vk9utY7MpIfFaIDQ+lzFj7Lz3\nXiTPP19Ev34urxyv/lLbDZXPht8TExPJyckp+/rEiRMk/HzrnpGRQfv27YmLiyMsLIyBAweye/du\nEhMTyf75nXU6nei6XmsvXQTGe+/ZyctTmDjRiT0I1k8kJurk5SkUeun+LytLJTxcL8usDzUXXWQs\nh2xoZbmCAjh0SJX5dBEUrrvORV4elt5G1RM+C+pDhw5lw4YNAKSmppKYmEhMTAwASUlJZGRkUFxc\nDMDu3bvp1KkTQ4cOZf369QBs2rSJIUOG+Kp5ohGWLw9DVXUmTQqOdZxmspy3NnY5ckQhKUkP2SVb\nUVHQv7/Gt9+qDboR2rtXkuREcImMDHQLvM9n/awBAwbQp08fkpOTURSFuXPnsm7dOpo1a8aoUaO4\n7bbbmDJlCjabjYsuuoiBAwfidrv54osvmDBhAmFhYTz11FO+ap7w0Pffq+zcaWPUKFfQZH9XLEDT\n2BrOhYWQk6PSu3do3d1XNWiQsWPbN9/YGDq0fsvTzMx3SZITInB8Onj60EMPVfq6Z8+eZf+fnJxM\ncnJypZ/bbDaefPJJXzZJNNJbbxmp7lOmWK/Oe00qF6BpXMA5ejR0M98rGjLEzSuvGOvV6xvUzZrv\nPXvKGnUhAiVEBxCFL+TnwzvvOGjbVuOqq4Lnwu3NtermlqtWXpvvDWYRmobMq0vNdyECT4K6qLd/\n/tNBfn7wJMiZvBnUQ3F3tuokJOh07arx1Vc23PW8f0tPN+rhNwvulU5CBDUJ6qLe3nrLgarq3HRT\ncCTImbwb1JtGTx1gyBAXeXlKjVtUVpSbq3DihCrz6UIEmAR1US/ff6/yzTc2Ro1y07ZtcAU0M1HO\nG9nvhw83jZ46NGx/9fLysMEzLSNEKJKgLurl7383EuQmTw6eBDlTy5bgcOhkZzf+cD9yRMFm02nT\nJrhubDxhBvX6zKtLeVghrEGCuqhTfj6sXesgKSm4EuRMimIMwXtrTr1NGz2ocgo81bmzTny8xpdf\n2qiruKMkyQlhDRLURZ3efddBQYHCpEnOoN2S0Azq9ag8XCOnE44dU5rE0DsYN0ODB7v56Se1LJeg\nJmlpNux2ne7dm8Z7I4RVSVAXdVq+3EiQmzgxuBLkKkpM1CkpUTh71vO/8eOPCpqmNIkkOVN9huB1\n3ZhT79pVQ6o6CxFYEtT9bPNmGyNGwPHj3tsK1Je+/Vbl229tXH21K+gS5CpKSDCrynl+yIfylqs1\nqU+y3JEjCvn5isynC2EBEtT97Jlnwtm0CZ5+Oji6NMuXmwlywdtLB+8sazMLzwRLeVxvuOACjchI\nvdagXp75LkFdiECToO5H+/apfPWVcXFcudJRtgGGVeXnw7p1Dtq10xgxIvgS5CoqLxXbmKDedJaz\nmRwOuPhiN+npKqdPV/87ZnlY6akLEXjWjiohZuVKo9d7002gaQoLFli7t75uXfAnyJm80VNvisPv\nYCTL6brC119XfxCUZ74H942fEKFAgrqfOJ2wZo2d2Fid114zqnWtX++oV2GPQFm+3IHNFtwJciZv\nbL9qZoAnJTWd4XcorwNf07GalqYSFaU3egc8IUTjSVD3k48+spOTo3LDDU4iImDOnBIA5s8Pb9Qy\nK1/ZtUvlu++MLVZDodBK+farnh/yWVkqiYkaERHealVwGDTIjapWP6/udML+/Srnn6+F7P7yQgQT\nOQ39xBx6N3u9gwdr/PKXTr76ysb69darZGJusTp1avD30qHxc+qaZixpa0rL2UzNmkHv3hrffGOj\npKTyzzIzVUpLFXr1kqF3IaxAgrofHDum8PHHNi680E2fPuXzsbNnl6KqOgsWhOFyBbCBVeTlGRXk\n2rXTGDYsNC7WMTEQHe15VbkTJxRKS5tO4ZmqhgxxU1Ki8N13lS8ZkvkuhLVIUPeDNWscaJrChAmV\ne73du2tMmuRk3z4bb7/tCFDrzrV2rYPCQoWbbgr+BLmKGlMq9vDhprecraKa1qtLzXchrEWCuo/p\nujH0HhGh89vfnjuU/fDDpURG6ixaFEZhYQAaWIWuh1aCXEUJCRo5OUq99wevqKnso14TM1muamU5\nqfkuhLVIUPexbdtsZGaq/OpXLlq0OPfn552nc/vtpRw7pvLaa4Ff4rZrl8ru3UYFufPOC61eaWKi\njqYp5OY2vLfeVJezmdq21enQQWP7dhtahbcgLc1Gq1Za2eoCIURgSVD3sZSUygly1bn77lLi4jT+\n8pcwTp70V8uqZ1aQC5UEuYoas1bdrCbXFBPlTIMGuTl5UmX/fuOyUVAAhw4p9OypoQRH1WMhQp4E\ndR/Ky4MPPrDTsaPGpZfWPObbvDncf38peXkKL7wQ7scWVpaXZ+zI1r596CTIVdSYoN7Ue+pw7rz6\nvn0qui4134WwEgnqPvTuu0bC2cSJzjrX8N58s5P27TXeeMNRlpTlb++8U54gF4prjhtTgObIEYWW\nLXViYrzdquBRNahL5rsQ1hOCl27rWLXK2LJ0/Pi6h7LDw2HmzBJKSxWeftr/vXUzQc5uD70EOZOn\nBWh03Sg801ST5Eznn6/RooVeliy3Z4/xr5SHFcI6JKj7SHq6yo4dNoYPd9d7y9Lf/c5Fnz5u3nnH\nzu7d/v1ovvlGJTXVxjXXuGjdOjTnjT0dfj95UqGwsOmuUTepqpEFf/CgyvHjSllPXYbfhbAOCeo+\nUp8EuapU1Sgfq+sKTzzh3956qGyxWhtPh9/Nmu9NOUnOZA7Bb99uIz3dGL1o1izAjRJClJGg7gOl\npfDOO3ZatdK45pqGlYobPtzN5Ze72LjRzqef+qfyy9mz8M9/OujQITQT5Ezx8Z711M0tV5tykpzJ\nXK/+n//YOX5clV66EBYjQd0HNmywk5urcsMNLsIauPRcUco3e3n8cf9s9mImyE2eHJoJcqawMIiN\nbXhVObOn3lSryVV04YVuwsJ0PvjA2K9A5tOFsJYQvoQHjrl5y6RJng1lX3ihxm9+42TXLhvvv+/b\nzV4qJsglJ4fu0LspMVFrcKKcLGcrFxEB/ftrlJQYNzrSUxfCWiSoe9mPPyps2mTj4ovdjVrqM3Nm\nCXa7zoIF4Th9GGt37lTZs8fG6NGhmyBXUWKizunTyjm7jdWmqdd9r2rIkPIpJVnOJoS1SFD3stWr\nq9+8paG6dNGZOtXJwYNq2TaovrB8uTE/MGVK6PfSoXwL1v9v796jmrzPOIB/X94EbBBbblFxLQXq\nZbrjqpvOW+1EOV46unqryuTo1p52emx1Yq2glrUeFDpP622rWjrXpqLcxMus2tVJpy3qbB11aK2z\nnQJaDchFkVuSd39kiUEiAkl4ed98P/8lRHh+DeXJ7/Y8bTksV1LiA51OQlAQkzpw57CcKEro3ZtJ\nnagzYVJ3I4vFuvSu00mYPNn1JLl4cQP8/SWsXeuLW7fcEOBdqqqA3butFe9Gj/aOvdH2nIAvKbGe\n8mYpVKshQ6y/K1FRFvjJVwCRiJxgUnejzz8XcemSD2JjTW655hMaKmH+/AaUlfngnXfc3+wlJ0eL\n2lr1H5BzdKcATesy9K1bQGWlwKV3B0FBwB/+UIc33mjDHgYRdQgv+VPeMWwH5NxZkW3evAaEhFjw\npz/5trsXuDPedkDO5k4Bmtb96vM6m3Nz5jQiOto7VneIlIRJ3U2qqoC//lWDyEgLhg1z3x+7rl2B\nJUsaUFMj4K233DdbP3XKB+fOiZg40eRVbTPbWlWOhWeISEmY1N1k1y4t6uqszVvcvfcaH9+IiAgL\nPvhAi2+/dc83Nxi864CcTVuTum2m7u0lYolIGZjU3WTHDi1EsXXNW9pKqwWWL6+HySQgNdX1k0lV\nVcCePRo8+qgFTzzhXUuottPvTOpEpEZM6m5QVOSDf/1LxNixZo/d9Y6NNWHQIDN279bi9GnX3rbs\nbOsBObW2WG1JUJAEUZRavafO5XciUhIv+5PuGZ44IHc3d5WPlSTAYNBCq5VcvkuvRKJorQHf+j11\nH2i1klcU5iEi5WNSd1F9vfVqWEiIBTExbWve0lajRpkxdqwJx45pcORI+5q9/POf1gNykyaZ7EvR\n3kavl2A0Cq36YFRcLKBXL8nrVjSISJn4p8pFBw9qUFEh4NlnTdB6rvCb3YoV9RAECatW+cHSjm1e\nWwU5NbdYvR+9XsLt2wJqalp+XV2d9eobr7MRkVIwqbvI1je9vc1b2mrAAAumTzehqEhEbm7bmr1U\nVgJ792oQEWHBqFHedUDOUWtPwF+5wprvRKQsTOouKC4W8OmnIoYMMXdoDexXX62Hr6+E1FS/NjUm\nyc62XruLj2/w6uXkO1XlWv6PcPkyT74TkbJ48Z9212VmaiFJgkcPyDnz8MMSfvObRhQX+2Dbttat\n+dsqyGm1EmbM8Ozef2fX2vrvbLlKRErj0Wbdq1evRmFhIQRBQFJSEgYOHAgAuHbtGpYsWWJ/XXFx\nMRISEjB06FAkJSWhoaEBFosFiYmJ+NGPfuTJENvNYgF27rQ2b/nlLzt+f3rRonpkZGjx9tt+iItr\nRLduLb/+5EkR58+LeOaZRq89IGfT2uV323U2Lr8TkVJ4bKZ+8uRJXLp0CZmZmUhJSUFKSor9a927\nd4fBYIDBYMC2bdvQs2dPREdH4y9/+QtiYmJgMBiQkJCAt99+21PhuezoURGXL/vgmWca0bVrx//8\noCDg5ZcbUFEhYNOm+5eP/eAD64ze2yrIOdPaAjSs+05ESuOxpF5QUIBx48YBAKKiolBVVYVbTvqH\n5uXlYfz48fD390dgYCAqKysBANXV1QgMDPRUeC7bscPzd9Pv5/nnG9CzpwVbtvji6tV7J6iKCusB\nuchIC0aO9N4Dcjat7dRWUiLAx0dCWBhn6kSkDB5L6mVlZU2SclBQEIxGY7PXZWdnY9q0aQCAuXPn\n4qOPPsKECROwYsUKLFy40FPhuaSyEti/X4PHHjNjyBD5ZnE6HbB0aQNqawWsXXvv2Xp2thb19dYD\ncuwJ3vpObSUlPujRQ+qQq4pERO7g0T11R5KTSh+nT59GZGQkuv5//To9PR0TJ07EvHnzcOTIEaSl\npWHTpk0tft/AQB00mvYVYnG0cyewejVw9izQvz+QlATMnOn8tZmZ1qIzL7wgQq9vX+P00FA3NFwH\nsGABsHUrsH27L5KSfNGvX9OvSxKwfTvg6wssWNAFISFd3PJzHblrLB0lJATo0gW4cUPTLHbbY5MJ\nuHIFGDZMeeOzUWrcd1PDOETR+mlaDWOxUctY1DIOG48ldb1ej7KyMvvj69evIzQ0tMlr8vPzMXz4\ncPvjL7/8EosWLQIAjBw5Eq+//vp9f05FxW2XY83L0+DFFx+wPz5zBpg1C6iursXkyc1Pim/dqoMo\n+mDSpBoYjW1fmg0NDYDReNOlmB0tW6bBnDkPYPHiRrz/fl2Trx0/LuLcOR0mT26EJNXByWKJS9w9\nlo6i1/vjyhXAaLxTgcZxLMXFAszmrujevRFGY929vk2npdT35W5qGYfZLEEUBVWMBVDP+6LUcbT0\nQcRjy+8jR47EoUOHAABFRUXQ6/X2GbnNmTNn0M9hahkeHo7CwkIAwFdffYXw8HBPhdfEunXOl67X\nr2/+/JkzPjhzRkRMTOfpQz5hgglDh5pw4IAWJ082fUt5QM650FAJZWXCPavy8TobESmRx2bqgwcP\nxoABAzBz5kwIgoDk5GTs2rULAQEBiImJAQAYjUYEBwfb/82LL76I5cuX4+DBgwCA5cuXeyq8Jr75\nxvlnG2fPd3QFudawNntpQGysBm+84Yd9+2ohCMCNG8C+fRpERVkwYgQPyDnS6y1obBRRWWm9SXC3\n4mJ2ZyMi5fHonrrjXXQATWblALBv374mj/V6Pd59911PhuRUnz4WnDvXfF++T5+ms7TaWiA3Vwu9\n3oKxYztXkvzZz8yYMKERBw9qceiQiAkTzMjKsh2Qq+cBubs4HpYLCmo+G7fN1FlNjoiUhBXlACxa\n1OD0+YULmz5/4IAGVVUCZsxohKbDjhi23ooVDfDxkZCS4geTydpi1deXFeScuV8BGvZRJyIlYlIH\nMHmyCVu21KJ/fzM0Ggn9+5uxZUvzQ3K2pXc576a3pE8fC+LiGnH+vIjFi7vgwgURv/iFCcHBTEx3\nu18BGlvhmV69OFMnIuXohPNNeUyebHJ60t3m0iUBR49qMGyYCVFRnTdJvvJKA3Jztdi5kwfkWnK/\nmXpxsQ9CQizQ6ToyKiIi13Cm3kq2JNlZZ+k2PXtKeOEF67ZB795mDB/eufb+O4uWOrVZLEBpqcCa\n70SkOJypt4LZbE3qXbtKiI3t/PvTL73UgK+/FjFrViMPyN1DSzN1o1FAfb3A62xEpDhM6q3w6aci\nSkt9EB/fAH9/uaO5v27dAIOhVu4wOrWW9tTZnY2IlIrL763QGZq3kHs98ADQrZvktKc6C88QkVIx\nqd/HjRvWq2x9+5oxeDD/yKuJXm9xmtRthWd4R52IlIZJ/T5ycrRoaBAQF8f9abXR6yWUlwtovGsB\n5k7hGS6/E5GyMKm3wNrhTAuNRsL06Z3/gBy1jV4vQZIElJc3/bRmu6PO5XciUhom9RYUFvrg3DkR\n48ebEBLCWZva3OuwXEmJgIAACQ8+KEdURETtx6Tegs7YvIXcx9m1NkmyztQ5SyciJWJSv4fbt4Fd\nu7To2dOCMWNYwEWN7hSguZPUq6qAW7cE1nwnIkViUr+H/fs1uHlTwMyZjRCbN3AjFXDs1GZj20/n\nyXciUiIm9XvIyLAuvc+cyaV3tXK2/M6Wq0SkZEzqTnz3nYDPPtNg5EgTIiK4DKtWtqTueFfddked\ny+9EpERM6k4opXkLuSY4WIIgSE1m6lx+JyIlY1K/i615S0CAhKee4t10NdNorIndcU/dVvedM3Ui\nUiIm9bscOSLi6lUfTJnSyF7aXkCvl5rtqT/wgMS6BESkSEzqd+HddO8SGirh5k0Bt29bH5eUCOjV\ny8KSwESkSEzqDsrKBBw6pMEPf2jGj3/MPVVv4HhYrqYGKC/3Yc13IlIs9lN3kJ2tgckk4Fe/YvMW\nb+F4rc223cJqckSkVEzqDvbu1cLXV8K0aVx69xZ3qsr52D/IcaZORErFpO4gLq4RoighKEjuSKij\nOM7U6+qsz3GmTkRKxaTuID6eM3Rv47inXlFhfY4zdSJSKiZ18mqOM/XG/3+m40ydiJSKSZ28mmOn\ntupqQKOR0KMHZ+pEpExM6uTVHnoI0GolGI0++P57ICxMYlc+IlIs3lMnryYI1gI0JSUCrlxhzXci\nUjYmdfJ6er2Ea9d8IEms+U5EysakTl7PdlgO4EydiJSNSZ28nu2wHMCT70SkbEzq5PWaztS5/E5E\nysWkTl4vNJTL70SkDkzq5PUcZ+q9enGmTkTKxaROXs+W1Hv2BPz8ZA6GiMgFTOrk9WwH5R59VN44\niIhcxaROXi8sTMIjj1gQHS13JERErmGZWPJ6fn7AqVM1CA0NgNEodzRERO3HmToREZFKMKkTERGp\nBJM6ERGRSjCpExERqQSTOhERkUp49PT76tWrUVhYCEEQkJSUhIEDBwIArl27hiVLlthfV1xcjISE\nBMTGxuK9997D3r17odFokJycbP83RERE1DKPJfWTJ0/i0qVLyMzMxMWLF5GUlITMzEwAQPfu3WEw\nGAAAJpMJ8fHxiI6OxoULF7B//37k5ubi/PnzOHz4MJM6ERFRK3ksqRcUFGDcuHEAgKioKFRVVeHW\nrVvo2rVrk9fl5eVh/Pjx8Pf3x5EjRzBx4kRoNBoMGDAAAwYM8FR4REREquOxpF5WVtYkKQcFBcFo\nNDZL6tnZ2fjzn/8MACgtLYUoinjuuedgMpmQmJiIfv36tfhzAgN10GhE9w/Aw0JDA+QOwW04ls5J\nLWNRwzhEUQCgjrHYqGUsahmHTYdVlJOk5t2vTp8+jcjISHuilyQJZrMZ6enp+OKLL7B8+XLk5ua2\n+H0rKm57JF5PslYuuyl3GG7BsXROahmLWsZhNksQRUEVYwHU874odRwtfRDxWFLX6/UoKyuzP75+\n/TpCQ0ObvCY/Px/Dhw+3Pw4JCUFkZCQEQcBPf/pTlJaWeio8IiIi1fHYlbaRI0fi0KFDAICioiLo\n9fpmS+9nzpxpsrw+evRoHDt2DABw8eJF9OzZ01PhERERqY4gOVsXd5O1a9fi1KlTEAQBycnJOHv2\nLAICAhATEwMAiI2NxbZt2xASEmL/Nxs2bMBnn30GAFi2bBkGDRrkqfCIiIhUxaNJnYiIiDoOK8oR\nERGpBJM6ERGRSjCpExERqQSTOhERkUowqRMREakEk3oHe/PNNzFjxgxMnToVH3/8sdzhuKyurg7j\nxo3Drl275A6l3fbu3Yunn34aU6ZMQX5+vtzhtFtNTQ0WLFiA+Ph4zJw5E0ePHpU7pDb75ptvMG7c\nOHz44YcAgKtXryI+Ph5xcXFYuHAhGhoaZI6w9ZyNZe7cuZg9ezbmzp0Lo9Eoc4Std/dYbI4ePYq+\nffvKFFX73D2WxsZGJCQkYNq0aZgzZw6qqqpkjtA1TOod6Pjx47hw4QIyMzORnp6O1atXyx2Sy955\n5x08+OCDcofRbhUVFfjjH/+IjIwMbN68GYcPH5Y7pHbLy8tDREQEDAYD1q9fj5SUFLlDapPbt29j\n1apVTapMbtiwAXFxccjIyEB4eDhycnJkjLD1nI1l3bp1ePbZZ/Hhhx8iJiYG27ZtkzHC1nM2FgCo\nr6/H1q1bm1UK7cycjSUrKwuBgYHIycnBpEmTcOrUKRkjdB2TegcaMmQI1q9fDwDo1q0bamtrYTab\nZY6q/S5evIj//Oc/+PnPfy53KO1WUFCA4cOHo2vXrtDr9Vi1apXcIbVbYGAgKisrAQDV1dUIDAyU\nOaK28fX1xbvvvgu9Xm9/7sSJExg7diwAYMyYMSgoKJArvDZxNpbk5GSMHz8eQNP3qrNzNhYA2Lx5\nM+Li4uDr6ytTZG3nbCxHjhzB008/DQCYMWOG/fdNqZjUO5AoitDpdACAnJwcjB49GqKovA5zNmlp\naVi2bJncYbikpKQEdXV1+O1vf4u4uDjFJA1nnnrqKVy5cgUxMTGYPXs2Xn31VblDahONRoMuXbo0\nea62ttaeNIKDgxWzZO1sLDqdDqIowmw2IyMjA7GxsTJF1zbOxvLdd9/h66+/xsSJE2WKqn2cjaW0\ntBT/+Mc/EB8fj9/97neK+bB1L0zqMvjkk0+Qk5OD1157Te5Q2m337t14/PHH8fDDD8sdissqKyux\nadMmpKamIjEx0WlHQSXYs2cPwsLC8Le//Q3vv/8+3njjDblDciulvi+OzGYzli5dimHDhjVbzlaS\nNWvWIDExUe4w3EKSJPu2Ve/evbFlyxa5Q3JJh7VeJaujR49i8+bNSE9PR0CAcvv45ufno7i4GPn5\n+fj+++/h6+uLHj16YMSIEXKH1ibBwcEYNGgQNBoNHnnkEfj7++PGjRsIDg6WO7Q2+/LLLzFq1CgA\nQL9+/XD9+nWYzWZFrwbpdDrU1dWhS5cuuHbtWrMlYKVJTExEeHg4FixYIHco7Xbt2jV8++23WLJk\nCQBrB87Zs2c3k+5zdgAABDNJREFUO0SnFCEhIRgyZAgAYNSoUdi4caPMEbmGM/UOdPPmTbz55pvY\nsmULHnroIbnDccm6deuQm5uLrKwsTJ8+HfPnz1dcQges/xMfP34cFosFFRUVuH37tuL2om3Cw8NR\nWFgIwLqk6O/vr+iEDgAjRoywd3v8+OOP8cQTT8gcUfvt3bsXWq0WL7/8styhuKR79+745JNPkJWV\nhaysLOj1esUmdMDaHdR2U6SoqAgREREyR+QaNnTpQJmZmdi4cWOTX5q0tDSEhYXJGJXrNm7ciF69\nemHKlClyh9IuO3futJ+qnjdvnmIPytTU1CApKQnl5eUwmUxYuHChopZ4//3vfyMtLQ2lpaXQaDTo\n3r071q5di2XLlqG+vh5hYWFYs2YNtFqt3KHel7OxlJeXw8/Pz96COioqCr///e/lDbQVnI1l48aN\n9olJdHQ0/v73v8scZevc63csJSUFRqMROp0OaWlpTTqHKg2TOhERkUpw+Z2IiEglmNSJiIhUgkmd\niIhIJZjUiYiIVIJJnYiISCVYfIbIy5SUlGDChAkYNGhQk+effPJJPP/88y5//xMnTmDdunXYsWOH\ny9+LiNqGSZ3ICwUFBcFgMMgdBhG5GZM6Edn1798f8+fPx4kTJ1BTU4PU1FT06dMHhYWFSE1NhUaj\ngSAIeO211/DYY4/hv//9L1auXAmLxQI/Pz+sWbMGAGCxWJCcnIxz587B19fXXk87ISEB1dXVMJlM\nGDNmDObNmyfncIlUh3vqRGRnNpvRu3dvGAwGzJo1Cxs2bAAALF26FImJiTAYDPj1r3+N119/HYC1\nnehzzz2H7du3Y+rUqThw4AAAa1vel156CVlZWdBoNDh27Bg+//xzmEwmZGRkYOfOndDpdLBYLLKN\nlUiNOFMn8kI3btxAfHx8k+deeeUVALA3hRk8eDDee+89VFdXo7y8HAMHDgQADB06FIsXLwYAfPXV\nVxg6dCgAa+tXwLqnHhkZaS+12aNHD1RXVyM6OhobNmzAwoUL8eSTT2L69Onw8eG8gsidmNSJvFBL\ne+qOlaMFQYAgCPf8OgCns21njWSCg4OxZ88enD59GocPH8bUqVORl5fXrL81EbUfPyYTURPHjx8H\nAHzxxRfo27cvAgICEBoaau8AV1BQgMcffxyAdTZv63D10Ucf4a233rrn9z127Bjy8/Pxk5/8BEuX\nLoVOp0N5ebmHR0PkXThTJ/JCzpbff/CDHwAAzp49ix07dqCqqgppaWkArN0EU1NTIYoifHx87N3F\nVq5ciZUrVyIjIwMajQarV6/G5cuXnf7MiIgILFu2DOnp6RBFEaNGjUKvXr08N0giL8QubURk17dv\nXxQVFUGj4ed9IiXi8jsREZFKcKZORESkEpypExERqQSTOhERkUowqRMREakEkzoREZFKMKkTERGp\nBJM6ERGRSvwPZZAc00K/vRsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFYCAYAAABUA1WSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Q6BfkglqIAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7n5LfFJXhT4P",
        "colab_type": "code",
        "outputId": "84905d17-85bd-4f67-9f8b-c6c22201fab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1169
        }
      },
      "cell_type": "code",
      "source": [
        "models={\n",
        "    str([16,16]): model16,\n",
        "    str([32,32]): model32,\n",
        "    str([64,64]): model64,\n",
        "    str([128,128]): model128\n",
        "}\n",
        "def plotByConfiguration(configurations=[[16,16],[32,32],[64,64],[128,128]], variable=\"loss\", validationvariable=\"val_loss\"):\n",
        "  color=iter(plt.cm.rainbow(np.linspace(0,1,len(configurations))))\n",
        "  \n",
        "  if variable==\"rocauc\":\n",
        "    index=-1\n",
        "    for i,cb in enumerate(models[str(configurations[0])].callbacks):\n",
        "      if hasattr(cb, 'roc') and hasattr(cb, 'roc_val'):\n",
        "        index=i\n",
        "  \n",
        "  plt.figure()\n",
        "  for configuration in configurations:\n",
        "\n",
        "    if variable==\"rocauc\":\n",
        "      loss_values=models[str(configuration)].callbacks[i].roc\n",
        "      val_loss_values=models[str(configuration)].callbacks[i].roc_val\n",
        "    else:\n",
        "      loss_values=models[str(configuration)].history.history[variable]\n",
        "      val_loss_values=models[str(configuration)].history.history[validationvariable]\n",
        "    epochs=range(1,len(loss_values)+1)\n",
        "    c=next(color)\n",
        "    plt.plot(epochs, loss_values,\"o\", color=c,label=\"Training \"+str(configuration))\n",
        "    plt.plot(epochs, val_loss_values, color=c, label=\"Validation\"+str(configuration))\n",
        "    \n",
        "  plt.xlim(left=2)\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(variable)\n",
        "  plt.tight_layout()\n",
        "  plt.legend()\n",
        "  \n",
        " \n",
        "plotByConfiguration()\n",
        "plotByConfiguration(variable=\"binary_accuracy\", validationvariable=\"val_binary_accuracy\")\n",
        "plotByConfiguration(variable=\"rocauc\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAGACAYAAAC6OPj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERcbz/BawGQmPILO2pAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAGACAYAAAC6OPj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAGACAYAAAC6OPj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HMZdtECXzeSv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "let's save the [64,64] model"
      ]
    },
    {
      "metadata": {
        "id": "XEjzIujql0DH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from joblib import dump\n",
        "if invertTestTrain:\n",
        "  dump(wss, 'HplusML/models/massparameterisedNN_wss_'+region+'_train2mod1.joblib') \n",
        "  model64.model.save('HplusML/models/massparameterisedNN_'+region+'_train2mod1.h5')\n",
        "else:\n",
        "  dump(wss, 'HplusML/models/massparameterisedNN_wss_'+region+'_train2mod0.joblib') \n",
        "  model64.model.save('HplusML/models/massparameterisedNN_'+region+'_train2mod0.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wtKaUJjy2NtK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "e080e6bd-64e5-4200-afe8-20095f5a1e4d"
      },
      "cell_type": "code",
      "source": [
        "!cd HplusML/models/; tar cvfz massparameterisedNN_training.tar.gz massparameterisedNN_*.h5 massparameterisedNN_*.joblib"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "massparameterisedNN_ge6jge4b_train2mod0.h5\n",
            "massparameterisedNN_ge6jge4b_train2mod1.h5\n",
            "massparameterisedNN_wss_ge6jge4b_train2mod0.joblib\n",
            "massparameterisedNN_wss_ge6jge4b_train2mod1.joblib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_fXTVrYGl0Wy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Compare the results to the other methods (BDT/NN)"
      ]
    },
    {
      "metadata": {
        "id": "aumC06fm-6as",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "hpmasses=[200,225,250,275,300,350,400,500,600,700,800,900,1000,1200,1400,1600,1800,2000]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "reLXMNe5WvYP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "from keras.models import load_model\n",
        "from joblib import load\n",
        "from HpMLUtils import WeightedStandardScaler, summarizeFitData,MultiSBWeightsScaler\n",
        "\n",
        "df_mc=pd.read_hdf('pandas_INC_'+region+'.h5', 'INC_'+region)\n",
        "#df_mc=df_mc[df_mc.process.apply(lambda x: True if x in [\"ttlight\",\"ttb\",\"ttc\"] or \"Hp\" in x else False)]\n",
        "htf=HpTrainingFrame.HpTrainingFrame(df_mc)\n",
        "\n",
        "bdttrain=[]\n",
        "bdttest =[]\n",
        "bdtmassparamtrain=[]\n",
        "bdtmassparamtest =[]\n",
        "nntrain =[]\n",
        "nntest  =[]\n",
        "nnmassparamtrain=[]\n",
        "nnmassparamtest =[]\n",
        "\n",
        "bdttrainmassparammodel=load('HplusML/models/massparameterisedBDT_'+region+'_train2mod0.joblib')\n",
        "bdttestmassparammodel=load('HplusML/models/massparameterisedBDT_'+region+'_train2mod1.joblib')\n",
        "wsstrainmassparam=load('HplusML/models/massparameterisedNN_wss_'+region+'_train2mod0.joblib') \n",
        "wsstestmassparam =load('HplusML/models/massparameterisedNN_wss_'+region+'_train2mod1.joblib')  \n",
        "nntrainmassparammodel=load_model('HplusML/models/massparameterisedNN_'+region+'_train2mod0.h5')\n",
        "nntestmassparammodel =load_model('HplusML/models/massparameterisedNN_'+region+'_train2mod1.h5')\n",
        "  \n",
        "for hpmass in hpmasses:\n",
        "  print(hpmass)\n",
        "  X_train, X_test, X_eval, y_train, y_test,y_eval, w_train, w_test, w_eval=htf.prepare(hpmass=hpmass,addMass=True)\n",
        "  \n",
        "  sbtrain=MultiSBWeightsScaler(backgroundclass=0)\n",
        "  sbtrain.fit(X_train,y_train, sample_weight=w_train)\n",
        "  X_test =sbtrain.transform(X_test,y_test, sample_weight=w_test)\n",
        " \n",
        "  sbtest=MultiSBWeightsScaler(backgroundclass=0)\n",
        "  sbtest.fit(X_test,y_test, sample_weight=w_test)\n",
        "  X_train=sbtest.transform(X_train,y_train, sample_weight=w_train)\n",
        "  \n",
        "  X_test.hpmass=hpmass\n",
        "  X_testnohpmass=X_test.drop([\"hpmass\"],axis=1).copy() #copy is important as data is altered\n",
        "  X_train.hpmass=hpmass\n",
        "  X_trainnohpmass=X_train.drop([\"hpmass\"],axis=1).copy() #copy is important as data is altered\n",
        "  \n",
        "  # first calculate the standard BDT score\n",
        "  bdttrainmodel=load('HplusML/models/standardBDT_'+str(hpmass)+'_'+region+'_train2mod0.joblib') \n",
        "  bdttestmodel =load('HplusML/models/standardBDT_'+str(hpmass)+'_'+region+'_train2mod1.joblib') \n",
        "  roc_bdt_test =roc_auc_score(y_test, bdttrainmodel.predict_proba(X_testnohpmass)[:,1], sample_weight=w_test)\n",
        "  roc_bdt_train=roc_auc_score(y_train, bdttestmodel.predict_proba(X_trainnohpmass)[:,1], sample_weight=w_train)\n",
        "  bdttrain.append(1.-roc_bdt_train)\n",
        "  bdttest.append(1.-roc_bdt_test)\n",
        "  \n",
        "  # calculate the mass parameterised BDT score\n",
        "  roc_bdtmassparam_test =roc_auc_score(y_test, bdttrainmassparammodel.predict_proba(X_test)[:,1], sample_weight=w_test)\n",
        "  roc_bdtmassparam_train=roc_auc_score(y_train, bdttestmassparammodel.predict_proba(X_train)[:,1], sample_weight=w_train)\n",
        "  bdtmassparamtrain.append(1.-roc_bdtmassparam_train)\n",
        "  bdtmassparamtest.append(1.-roc_bdtmassparam_test)\n",
        "  \n",
        "  # calculate the standard NN activations\n",
        "  wsstrain=load('HplusML/models/standardNN_wss_'+str(hpmass)+'_'+region+'_train2mod0.joblib') \n",
        "  wsstest =load('HplusML/models/standardNN_wss_'+str(hpmass)+'_'+region+'_train2mod1.joblib') \n",
        "  X_testnohpmass=wsstrain.transform(X_testnohpmass)\n",
        "  X_trainnohpmass=wsstest.transform(X_trainnohpmass)\n",
        "  nntrainmodel=load_model('HplusML/models/standardNN_'+str(hpmass)+'_'+region+'_train2mod0.h5')\n",
        "  nntestmodel =load_model('HplusML/models/standardNN_'+str(hpmass)+'_'+region+'_train2mod1.h5')\n",
        "  roc_nn_test =roc_auc_score(y_test, nntrainmodel.predict(X_testnohpmass.values), sample_weight=w_test)\n",
        "  roc_nn_train=roc_auc_score(y_train, nntestmodel.predict(X_trainnohpmass.values), sample_weight=w_train)\n",
        "  nntrain.append(1.-roc_nn_train)\n",
        "  nntest.append(1.-roc_nn_test)\n",
        "  \n",
        "  # calculate the mass parameterised NN activations\n",
        "  X_test =wsstrainmassparam.transform(X_test)\n",
        "  X_train=wsstestmassparam.transform(X_train)\n",
        "  roc_nnmassparam_test =roc_auc_score(y_test, nntrainmassparammodel.predict(X_test.values), sample_weight=w_test)\n",
        "  roc_nnmassparam_train=roc_auc_score(y_train, nntestmassparammodel.predict(X_train.values), sample_weight=w_train)\n",
        "  nnmassparamtrain.append(1.-roc_nnmassparam_train)\n",
        "  nnmassparamtest.append(1.-roc_nnmassparam_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "68dQLc_eWvx4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "outputId": "3ab7bdad-5e1b-4469-c537-274f6b6fee24"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.ticker as ticker\n",
        "\n",
        "plt.style.use('seaborn-white')\n",
        "\n",
        "color=iter(plt.cm.rainbow(np.linspace(0,1,4)))\n",
        "\n",
        "bdt=[0.5*sum(x) for x in zip(bdttest, bdttrain)]\n",
        "bdtmassparam=[0.5*sum(x) for x in zip(bdtmassparamtest, bdtmassparamtrain)]\n",
        "nn=[0.5*sum(x) for x in zip(nntest, nntrain)]\n",
        "nnmassparam=[0.5*sum(x) for x in zip(nnmassparamtest, nnmassparamtrain)]\n",
        "\n",
        "plt.plot(hpmasses, bdt, color=next(color), label=\"BDT\")\n",
        "plt.plot(hpmasses, bdtmassparam, color=next(color), label=\"BDT mass parameterised\")\n",
        "plt.plot(hpmasses, nn, color=next(color), label=\"NN \")\n",
        "plt.plot(hpmasses, nnmassparam, color=next(color), label=\"NN mass parameterised\")\n",
        "plt.ylabel(\"1-ROC AUC\")\n",
        "plt.xlabel(\"H+ mass [GeV]\")\n",
        "plt.legend()\n",
        "\n",
        "color=iter(plt.cm.rainbow(np.linspace(0,1,4)))\n",
        "fig,ax=plt.subplots()\n",
        "plt.semilogy(hpmasses, bdt, color=next(color), label=\"BDT\")\n",
        "plt.semilogy(hpmasses, bdtmassparam, color=next(color), label=\"BDT mass parameterised\")\n",
        "plt.semilogy(hpmasses, nn, color=next(color), label=\"NN \")\n",
        "plt.semilogy(hpmasses, nnmassparam, color=next(color), label=\"NN mass parameterised\")\n",
        "plt.ylim([0.01,1])\n",
        "plt.ylabel(\"1-ROC AUC\")\n",
        "plt.xlabel(\"H+ mass [GeV]\")\n",
        "plt.legend()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3740c1de10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFYCAYAAAB6RnQAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XdcVfUfx/HXvZeLlyXzAsoQN4Ir\nU1NxJmqmpuZCTc0cUe5y5Yhyz8pdmpZ7k5pbSU0NR25cKRqKCIICArK5vz9IfpLgystlfJ6Phw+5\n55x77vv+fPx6c9b3q9DpdDqEEEIIUWAoDR1ACCGEEC9HylsIIYQoYKS8hRBCiAJGylsIIYQoYKS8\nhRBCiALGyNABXkRSUhJBQUFotVpUKpWh4wghhBB6l56eTmRkJJUrV0aj0WRbVyDKOygoiO7duxs6\nhhBCCJHnVq9eTc2aNbMtKxDlrdVqgcwv4OjoaOA0QgghhP6Fh4fTvXv3rA58UoEo78enyh0dHXF2\ndjZwGiGEECLv5HS5WG5YE0IIIQoYKW8hhBCigJHyFkIIIQoYKW8hhBCigJHyFkIIIQoYKW8hhBCi\ngCkQj4oJIYTILjQ0lDZt2lC5cmUAUlJSGDFiBLdu3WLOnDm4urqSkZGBtbU1o0aNwsXFhWnTpnHx\n4kUiIyNJTEzE1dUVS0tL5s+fb+BvI16WlLcQQhRQpUuXZuXKlQCcPHmSRYsW0apVK959911GjRoF\nwJEjR+jbty/btm1j9OjRAPj7+3Pt2rWsbUTBI6fNhRCiEIiKisLe3v6p5fXr16dWrVrs27fPAKmE\nvhTNI+/ERFi/Hrp2hWLFDJ1GCFGA7R0Blza+3n16dILmM5+/3c2bN+nRowfJyclERESwdOlSzp8/\n/9R2lStX5vr16683pDCoolnee/dC794QEwNDhxo6jRBCvJInT5sHBwczdOhQevbs+dR2CQkJMiNj\nIVM0y7tOHVAqYeNGKW8hxH/SfOaLHSXrW9myZSlWrBhK5dNXQ4OCgmjVqpUBUgl9KZrXvB0coGFD\n+OMPuHPH0GmEEOI/i4mJITIykrS0tGzLDx06xI0bN3j77bcNlEzoQ9E88gbo2BEOHgR/fxg0yNBp\nhBDipT2+5g2QnJzM+PHjefjwITt37iQoKIiEhARsbGyYN29ejkfkouAquuXdvn1maW/aJOUthChw\nnJ2dOXPmTI7r3n///We+93nrRf5XdH8VK1kSvLzg8GGIiDB0GiGEEOKFFd3yhsxT5zod/PKLoZMI\nIYQQL6xol/fjU0ebNhk2hxBCCPESinZ5u7hkPjZ28CBERho6jRBCCPFCinZ5A3ToAOnpsHWroZMI\nIYQQL0TKu0OHzL/l1LkQQogCoug+KvZY6dLw5psQEAAPHoCNjaETCSHEM+U2HWjNmjXx9/eXKUFf\n0p49e2jRosULbbt48WJq1arFG2+88UqftWrVKqKjoxn0Hx9RlvKGzLvOT52Cbdvgww8NnUYIIZ4r\np+lAly5dCiBTgr6E0NBQduzY8cLl3b9/fz0nejFS3pB56vyLLzJPnUt5CyEKmNymA4XsU4K2bt36\nufuaN28e0dHRhISEEBoaypAhQ9i8eTN37txhyZIllChRglGjRhEREcGjR48YNGgQTZo0YcuWLaxa\ntQq1Wo27uzt+fn45LntSgwYNaNGiBRcuXMDBwYFZs2bx4MEDRowYAUBaWhrTp0/H1dWV5s2b4+Hh\ngZeXF05OTsyZMwe1Wk3x4sX57rvvOHPmDCtWrEClUnHp0iV8fX05fPgwly9fZuTIkXh7e7N3716W\nLVuGkZERlStXZvTo0UyYMIHz588zf/58PvzwQ8aMGUNsbCzp6emMGzcOd3d3mjdvTsOGDbG1tSUk\nJIQWLVpQsWJFRowYgVKpJD09nZkzZ+Lo6Mj48eO5ffs2aWlpDB48mLp16xIYGMiUKVOws7NDq9Xi\n4uLyn//NpbwBypeHatVg3z6IjQVLS0MnEkIUELPu32dPQsJr3WcLMzOG29o+c5ucpgPNzctOCRob\nG8vSpUv59ttv2bJlC0uXLuW7774jICCANm3aUL9+fdq3b8/t27cZMmQITZo0YenSpSxevJgSJUqw\nefNmkpKSclym0WiyPufevXu0bt2acePGMWjQIH7//Xfs7e0ZMGAAderUYdOmTaxZs4bRo0dz+/Zt\nFixYQPny5dm1axezZs3CxcWFkSNHcuTIEczMzLh8+TK7d+/m5MmTDB8+nICAAM6dO8fKlSupW7cu\nixYtYv369RgbGzNkyBBOnTpFnz59WL16NQMHDmTBggU0aNCATp06cf36dSZPnsxPP/1EWloaDRs2\npGHDhllnL/bs2UO9evUYMGBA1qWIkydPotVqmTJlCg8ePKBXr178+uuvzJ49m5kzZ+Lu7k6/fv2k\nvF+rjh1h/HjYvh26dzd0GiGEeKacpgP9JZcBp152StAqVaoAoNVqs5bZ2dkRExND8eLFuXDhAuvX\nr0epVBITEwNA69atGTBgAO+99x6tW7dGo9HkuOxJpqamVK9eHYDq1atz8+ZNPD09mTRpEvPmzePh\nw4d4enoCYGJiQvny5QGwsbFh3LhxpKenc/v2berUqYOZmRnu7u4YGxuj1Wpxc3PD1NQUW1tb4uLi\nuH79OmFhYfTp0weAuLg4wsLCsp2xOHPmDA8ePGDbtm0AJCYmZq2rWrVqtuxeXl4MHDiQuLg4WrRo\nwRtvvMEvv/zCqVOnOH36NJA53nxKSgp37tzB3d0dgFq1apGcnPzC/xa5kfJ+7HF5b9ok5S2EeGHD\nbW2fe5Ssb4+nA717926O6192SlAjI6Mcf9bpdGzfvp3Y2FjWrFlDTEwMHTt2BODjjz+mTZs27Nmz\nh169erFq1aocl1lbW2ftLyMjI9u+FQoFc+fOpX79+nTt2pXdu3dz8OBBANRqdda2Y8aMYfHixZQt\nW5YJEyY8N/fj91euXPmpMxTHjx/Pts348eNzvBntyc8HqFChAlu3buXo0aN88803dOjQAbVaja+v\n71OXJ56cFEan0z2171chj4o95u4Onp6wcyfcuGHoNEII8cIeTwfq4ODw1LrXPSVodHQ0zs7OKJVK\n9u3bR0pKChkZGXz77bdotVp69+5N9erVCQsLy3HZk5KSkggKCgLg7NmzlCtXjujoaFxdXdHpdAQE\nBJCamvpUhvj4eEqUKMHDhw85fvx4jtv8W+nSpQkODub+/fsAzJ07l4iICJRKZdY0qtWqVWP//v0A\nXL9+nZ9++inX/e3YsYNr167h7e3NkCFDCAoKolq1agQEBABw//59vvnmGwAcHBy4ceMGOp2OEydO\nPDfri5Aj7yeNHQvdusHAgbBjBygUhk4khBA5ymk6UGNjYwC9TgnavHlzPvnkE86ePUuHDh1wdHRk\n4cKFmJmZ0aVLFywsLHBxcaFSpUocPXr0qWVPsrKyYtu2bUyZMgWtVkv9+vXR6XRMnDgRJycnevTo\nwfjx4zly5Ei293Xr1o2uXbvi5uZG3759mTdvHp999tkzc5uYmDBmzBj69euHsbExHh4e2Nvbo1ar\nuXTpElOmTGHw4MF88cUXdOvWjYyMDMaOHZvr/tzc3PDz88PU1BSVSsW4ceMoVaoUx44dw8fHh/T0\ndAYOHAjA0KFDGTJkCCVLlsTR0fEV/5fPTqF7XcfwehQaGkrTpk0JCAjA2dn5P+8vOj2dFbGx9La0\npPiT14F0OmjWLPOZ702b/j+AixBCiNfurbfeynbaWmT3rO4rkqfNzyYlsTgmhnUPH2ZfoVDAwoVg\nbAxDhkBcnGECCiGEEM9QJMv7DY0GJXDo0aOnV1aokPnM95078K9nEoUQQrw+ctT96opkeVupVLyh\n0XAuOZno9PSnNxg9GsqVgzlz4OzZvA8ohBBCPEORLG+ARqam6IDfczr61mhgwQLIyABf38y/hRBC\niHyiyJZ3Y1NTIJdT5wDNm4OPDxw/DkuW5GEyIYQQ4tmKbHmXUatxMTLi6KNHpOR2w/0330Dx4pmn\n0SMi8jagEEIIkYsiW94KhYJGpqbE63ScTkrKeaMSJWDyZIiJgSdG8RFCCEMKDQ2lUqVKXLlyJWuZ\nv78//v7+ALz99ttZQ6c+3v7xmNyicCiy5Q2Z170BDj5rUoFPPgFHR1i/Hv4ZhUcIIQytXLlyzJ49\nO8d1tra2bNiwgfj4+DxOJfJKkS7vmiYmmCoUHHz0KPfxZlWqzMFa7t+HAwfyNqAQQuTC09MTU1NT\nAgMDn1qn0Wjw8fF55kxjomAr0sOjGisUeJmasi8hgZupqZT5Z2jBp3TqlHn3+caNmSOwCSHEP8Lv\n7+VhwqXXus/iZh442jZ/7nbDhg1j1KhR1KlT56l1Xbp0oWPHjnTr1u21ZhP5g16PvKdMmUKXLl3w\n8fHh/PnzOW4ze/bsrPF5DaHR8+46B6hfP/PUub+/nDoXQuQbbm5ueHh4sHPnzqfWGRkZ8fHHHzNv\n3jwDJBP6prcj7xMnThASEsL69esJDg5mzJgxrF+/Pts2169f5+TJk09NtZaXGpqaoiCzvHtbWeW8\n0eNT5wsWZJ46l6NvIcQ/HG2bv9BRsr4MGDCAPn360L1796emwWzZsiXLly/n77//Nkw4oTd6O/IO\nDAzE29sbyJxrNjY29qmbJ6ZNm8awYcP0FeGF2KpUVC1WjNNJScTmNNraY507Z/69cWPeBBNCiBdg\nZ2eHt7c369aty3H9sGHDsqamFIWH3so7Kioq26TrNjY2REZGZr329/endu3aODk56SvCC2tkako6\ncCQxMfeNvLz+f+r8BeaOFUKIvPLRRx8RHh6e47q33noLOzu7PE4k9C3P7jZ/8m7umJgY/P396d27\nd159/DO90HXvJ+86P3gwb4IJIUQOnJ2dmTZtWtZrMzMz/vjjD95//32AbM94AyxevDjb9qLg01t5\n29vbExUVlfX63r17aLVaAI4dO8aDBw/o3r07AwcO5OLFi0yZMkVfUZ6rorExjioVhx89Iu1Z05s/\nPnW+YUPeBBNCCCFyoLfy9vLyYs+ePQBcvHgRe3t7zM3NAXjnnXfYuXMnGzZsYP78+Xh6ejJmzBh9\nRXkuhUJBIzMzHmZkcDa30dbg/6fOf/lFTp0LIYQwGL2Vd40aNfD09MTHx4dJkybh5+eHv78/+/bt\n09dH/iePJyo5+LxT5x07yqlzIYQQBqXXQVqGDx+e7bW7u/tT2zg7Oz91fcYQams0aP4Zbe0zGxuU\nCkXOG3bqBPPnZ546l0fGhBBCGECRHh71SRqlkrdNTbmZmsry2NjcN/TyypywRE6dCyGEMBAp7yeM\ntrPDTqXiuwcPOJfbtW8Z61wIIYSBSXk/wValYoa9PRnA5/fu5T5oiwzYIoQwIJkS9NUEBASQkpLy\nQttevnyZuXPnvvJnJSQk8Pbbb7/y+59Hyvtf3jIx4RNra+6mpTE+MjLn2cbk1LkQwsBkStCX9/PP\nP5P6gv/NrlSpEoMHD9ZzoldXpGcVy83HVlacSEwk4NEj1jx8SHdLy+wbKJWZd53Pmwc7d0LbtoYJ\nKoQosjw9PUlMTCQwMJC6detmW6fRaGjXrh1Lly5lyJAhOb4/NDSUkSNH4urqypkzZ+jatStXr17l\n3LlzdO/ene7du7Nt2zZWrVqFUqmkfPnyTJw4kbCwMEaMGIFSqSQ9PZ2ZM2eiUCieWvbk6JmjR4/G\n1NSUGzduEB0dzdSpU/Hw8GDq1KmcP3+e5ORkunbtSqdOnRg9ejRqtZqYmBimTp3K559/zqNHj0hK\nSmL8+PFUrVoVb29vOnfuzO7duylVqhSenp5ZP8+ePZuIiAjGjh1LamoqKpWKSZMmceLECc6ePUu/\nfv34+eef2bhxI7/++itKpRJvb28++ugj5s2bx+3btwkNDWXQoEGsXbuWuXPnMmnSJIKCgkhPT6dr\n1668//777N27l2XLlmFkZETlypUZPXo08fHxDBo0iOTkZN588029/vvLkXcOVAoFM+ztsVEqmXn/\nPheTk5/eyNc38+/Jk+FZA7sIIQq3ESPAze31/hkx4oU+etiwYXz33Xc5niHs0qULBw4cyDYs9b9d\nvnyZUaNG8cMPPzBr1iyGDh3K999/z4Z/BqJKTEzkxx9/ZN26ddy4cYOrV6+yZ88e6tWrx8qVKxk7\ndiyRkZE5Lvu3tLQ0fv75Z4YMGcKCBQtITk7GycmJtWvXsmbNGubMmZO1raWlJfPmzSMyMpJOnTqx\ncuVKPvvsM5YsWQJARkYGHh4ebN68mdOnT+Pk5MSmTZs4deoUDx8+ZM6cOXz00UcsX76cXr16sXDh\nQtq1a4dWq2XJkiVERESwe/du1q5dy+rVq9m7dy9hYWEApKamsmbNGpTKzHqMiYnh4MGDrFu3jjVr\n1pCWlkZCQgKLFi1ixYoVrFq1irt373Lq1Cm2bt1K+fLlWbNmDZUqVXqhf8NXJUfeubA3MmKqvT0f\nh4fzeUQEm5ydMVc+8buOh0fmjWubN8O+fdDccLMKCSGKphedErR///45vt/V1RVra2uMjY2xsbHB\nwcGBhIQE4uLigMwS/fTTTwEIDg4mJiYGLy8vBg4cSFxcHC1atOCNN97A1NT0qWX/Vq9ePQCqV6/O\nrFmzKFasGLGxsfj4+KBWq4mOjs7atmrVqkDmpCsLFy5k6dKlpKSkYPrPeByPt1EoFNja2uLh4QFk\nzqERFxfHmTNnuHnzJosWLSI9PR0bG5tsWS5cuEBISAg9e/YEMq9P37lzJ9tnP2ZlZYWbmxuffPIJ\n77zzDu3atePy5cuEhYXRp08fAOLi4ggLCyM4OJhatWoBULt27Rz/N39dpLyfob6pKf2srFgSE8O4\nyEi+tbdH8eTz3+PGZZb3xImZz3zn9my4EKLwmjkz84+B/JcpQVUqVdbP/35vSkoKEyZMYOvWrWi1\nWj7++GMAKlSowNatWzl69CjffPMNHTp0oF27djkue1JGRkbWzwqFghMnTnDs2DFWrlyJWq3OVviP\np4levnw5Dg4OzJw5kwsXLjBjxowcsz/5s06nQ61WM2fOHOzt7XP83mq1msaNGzNhwoRsy48dO5bj\nFNU//vgjFy9eZPv27WzdupXhw4dTuXJlli5dmm2706dPZx2xP/l99UFOmz/HQGtramo07EtIYOm/\nn/+uXh1at4YjR+DQIcMEFEIUafqaEjQhIQGVSoVWq+Xu3bsEBQWRmprKjh07uHbtGt7e3gwZMoSg\noKAcl/3bqVOnADhz5gxly5YlOjoaR0dH1Go1AQEBpKenP3UneHR0NK6urgDs37//hW82q1atGvv3\n7wcyp6f+9ddfgcxfGtLT0/H09OT48eMkJiai0+mYNGkSSbk8HhwaGsqKFSvw9PRk1KhRxMTEULp0\naYKDg7l//z4Ac+fOJSIigtKlS2d99+PHj79Q1lclR97PYaRQMNvens537jDnwQM8jI2p98SpG8aP\nh+3bYdIkaNzYYDmFEEXXRx99xNq1a3Nc96pTglpbW+Pl5UWHDh1wd3enb9++TJ06lSlTpjBhwgRM\nTU1RqVSMGzeOpKQk/Pz8si37t+TkZD7++GPu3r3LzJkzKVmyJEuWLOGDDz7A29ubxo0b89VXX2V7\nT9u2bRk1ahS7d++me/fubN++nc2bNz83+8CBAxkzZgw7duxAoVAwdepUIPNUdrdu3VixYgU9e/ak\ne/fuqFQqvL290Wg0Oe7L3t6eM2fOsHPnTtRqNR06dMDExIQxY8bQr18/jI2N8fDwwN7ennbt2jFg\nwAB69eql9xvWFLocn4XKX0JDQ2natCkBAQE4OzsbJMP5pCR6hoVhqlSywckJ5ydPrbRoAXv3wh9/\nwL/u+hRCiKJu9OjRtGjRgiZNmhg6SoHyrO6T0+YvqKpGwzg7O2IzMhgcEUHik9czHv+WOXGiYcIJ\nIYQoUqS8X0LH4sXpZGHB1ZQUvoqK+v/jGQ0aQKNGsGsX/HNdRwghRKZp06bJUfdrJuX9ksbY2VGt\nWDG2x8ez6uHD/694fPQ9aZJhggkhhCgypLxfkrFCwXcODtiqVMy8f58lMTGk6XTQtCnUqQNbtsD5\n84aOKYQQohCT8n4F9kZGzHNwwOqfGci63rnDlZQUOfoWQgiRJ6S8X1E1jYZtzs60NTfnUkoKXe7c\nYW6dOmTUqpU529g/zxUKIYQQr5uU939gpVIxxd6e7x0dsVOp+CE2lsFTppBRrBj07Qv37hk6ohBC\niEJIyvs1aGBqyjYXF3yKF+dAmTJ8N3x4ZnH36yeTlgghhHjtpLxfEzOlkvF2dnxjb8+yDz/kdL16\nsG0bLFtm6GhCCCEKGSnv50hNhAtrIPrGi23fwtycQba2jJg+nYTixdENGQLBwfoNKYQQokiR8s5F\nSgL8MRvmlgH/7vBjHQg/92Lv7W9lxRvly/P111+jSEhA16MHpKXpN7AQQogiQ8r7X5IfwuGpMMcN\n9g2HlHio7AOPomB5E7hz8vn7UCgUTNJq+btTJ3a2bo0iMBCmT9d7diGEEEWDlPcTgtbBd27w21gd\nxbQxNFt8jQ+v/kHdudtpvz2I5FgdK73h1tHn70ujVDLfwYFFkycT7uhIxldfwZ9/6vsrCCGEKAJk\nStB/pCXDuR3n8fr5OFYVI1GoM+eNfZAEJIHK/RTvnzrN3k7vsqqFHd22g1vjZ+/T3siIKRUq8NWM\nGXzfsycpH3yA8enT8OSUokIIIcRLkiPvf1z9NY1KQ3djVSkcjZkNxc0qY2/dBBeHzpQu+RHmpuVR\nFL9Ji13fU9H3N9a2S+X6nufvt4pGQ5v27Vn54YcYX71K8siR+v8yQgghCjUp73/8fe4SxawTMdPV\npZyzLy4OHdBaN6S4WSVMNS64OnTFxaEzRmoz3D89TNOtCzk05zI7R0QTHpSITpeR675bmZsTO2kS\n18uXp9iCBaTt3p2H30wIIURhI6fNgZgQMPPMnMrTqXSNHLdRKBQUN6uEmUlZIqMPgu4YdeZvAOA+\ncP8mkG6MkXExzE3LUtKuDQrF/383+rRkSWYtWMCwVq1I+vBDLC5eBFtbPX8zIYQQhZEceQMX/CPR\n1r5FRnQZjNU2z9xWpTTG0bY5ZV0+xsaiDoroakSfdudeoBvRl21IiMwgJu4s4fezH10rFQoGNG3K\nus8/xyIiglt9+sjoa0IIIV5JkT/yzkiHuNRT2AAl3d584fdpjB0ooW1BCS3wJkSch2Nz4MgvyTRc\nvQwqnqSYWouNZa2s95gplTT18+Pc/v1U27qV68uWUa5Pn9f/pYQQQhRqRf7IO3h/KiWanSMt3hwb\nm4qvvB+HqtB2KfQ7Voyz47qSdN+UsMhdxD/KPjRbSY0G1cqVJJiZ4ThsGKHXr//XryCEEKKIKfLl\nfePUJYwtkzBTVkehUP3n/dlWgM5rrDg7tgsZqUr+vr2R5JSobNtUdnfnysyZmMfFEd2jBwmpqf/5\nc4UQQhQdRbq8E+6BSYVT6DLApULON6q9CruK0HauK0HT26BQJ/HX1bWkpSdm2+ZNX1+utmpFlWPH\nOD5nzmv7bCGEEIVfkS7vC1vuYVvjNulRZSlmbP1a923nDs1GVSN4eX2UZg+4dGYDOl36/zdQKHBZ\nvJh4Cwtqff01YbduvdbPF0IIUXgV2fLW6SA2MXO4UqeXuFHtZWgrwVud3yb8oDsqm7+5cGQnuifu\nMDctWZIbEydiER9P+ODBeskghBCi8Cmy5X3rj1TsG50n7aE5tg4V9PY5Dp4KPGu1J/aqIyqn05zf\ndyLb+ioDB3KtRg1qbN3Kla1b9ZZDCCFE4VFkyzv4ZBDGxZPRZLzxWm5UexbHKsa4OfuQdN8MVek9\nnNv2/zvMFSoV/PADaSoVVoMHk5aQoNcsQgghCr4iWd5JsaAudRpdBrhVfn03qj1LySqW2Gt8yEhT\noiu1ifObI7PWla9Zk8BPPsHx1i3+Gj8+T/IIIYQouIpkef99PAKbaqGkhpejmLFVnn2uSxVniqe1\nRW2RTJLdGi5seJS1zn3SJMKcnKgwbx5x587lWSYhhBAFT5Es75K1U9GlG1Gmcr08/2y3KlXQJDbE\nzCWG+7r1BK3PvANda2lJ0DffYJSWRmy/fpCR+0QnQgghirYiWd7FrZypXH4sllalDfL5ZTwao072\nQFvrFiEh27lzKvMO9MadOvF7q1Y4nzzJve+/N0g2IYQQ+V+RLG9DUygUlKvYDkVSCdw6nuX05rPo\ndGCsUKD67jvizM0xGzMGXXi4oaMKIYTIh6S8DUSpVFOuQhfSE4vh2nUXF7fcB6Be2bJs++ILzGJj\nuTdkiIFTCiGEyI+kvA3I2MgSO5PWGJml8rCYPykJ6SgUCt4aNowLVavisGEDqXv3GjqmEEKIfEbK\n28BKlqlM4l/VsHQP49z+gwCUMzHhxHffkaZSkejrC4mJz96JEEKIIkXKOx/wrNuSR3esMfE8wr2Q\nvwHo2LAhG3r3pvjNmyRMnGjYgEIIIfIVKe98wMSyGMaR7dFlKAi7/wvp6YlYqlQYf/01d0uUoNis\nWXDpkqFjCiGEyCekvPOJqu1cCPVvhNrqIdev7ECn09GuRAmWTZ6MUWoqCf37y7PfQgghACnvfEOh\nBM9GDYj604U0k4vExJ3DSKGgqY8P+5o3x+zoUXQ//WTomEIIIfIBKe98xLWuktjf3yc1rhh3wneR\nnPqAOiYmHJ46lQQzM1JHjIDIyOfvSAghRKEm5Z3PNB5txblJrVAYpXD7rj86XTr9q1RhweefYxwd\nTdro0YaOKIQQwsCkvPOZ4s7gVrUKIVuqkJx2h8jo33FWq9EMGMBfFSqg+uknOHPG0DGFEEIYkJR3\nPlRvONz48V0SQq2IjD5MQmIIfezsWOznh0KnI2nwYNDpDB1TCCGEgei1vKdMmUKXLl3w8fHh/Pnz\n2dZt2LCBzp074+Pjw1dffYVOyiiL2gSafKXhxGft0engTuQvaHQpeL/3HgHe3miOHIFNmwwdUwgh\nhIHorbxPnDhBSEgI69evZ/LkyUyePDlrXWJiIjt27GD16tWsW7eOGzducEZOBWfj0QnMTF25PL8B\nqWmx3I3aQXNTU/b5+ZGqVpM0fLiMvCaEEEWU3so7MDAQb29vAMqWLUtsbCzx8fEAmJiYsHz5ctRq\nNYmJicTHx6PVavUVpUBSKOA707SkAAAgAElEQVSd7+DygkY8vOJMbEIQDxOC6Fu7Nqt79UJz6xZp\ns2cbOqYQQggD0Ft5R0VFYW1tnfXaxsaGyH895rR48WKaNWvGO++8g4uLi76iFFglakD1XkqO+r6P\nLs2Yu1E7cFUkEDtmDPdtbNBNmwZhYYaOKYQQIo/l2Q1rOV3T7t+/P/v37+fw4cOcOnUqr6IUKG9P\nhtQYa85NfJcMXQrh93fTp1Qplo0YgTohgUejRhk6ohBCiDymt/K2t7cnKioq6/W9e/eyTo3HxMRw\n8uRJADQaDQ0bNuT06dP6ilKgmTtAoy/h+qqqJIWWIu7RX5B0k0q+vlypVAnTVavgxAlDxxRCCJGH\n9FbeXl5e7NmzB4CLFy9ib2+Pubk5AGlpaYwePZqEhAQALly4QOnSpfUVpcB7azDYlFNwZMA7oIPw\n+3t4t7gFm/+ZbeyhPDomhBBFipG+dlyjRg08PT3x8fFBoVDg5+eHv78/FhYWNGvWjAEDBtCzZ0+M\njIyoWLEiTZs21VeUAk9lDM2/gXXvOXLvYA3sm5wmOu4UHVu3Zt8779Bs927S1qzBqHt3Q0cVQgiR\nBxS6AvCAdWhoKE2bNiUgIABnZ2dDxzGYjZ0g+EACrY/Ow6iYknIug1h89hJ96tUj1c4O87/+AjMz\nQ8cUQgjxGjyr+2SEtQKk5TxQZJhxcU5D0jMSiYw+yAfVq7Ohb1/Mw8KImz7d0BGFEELkASnvAsTc\nMfPZ76s/vkVShA0PHp7EOP0+VmPHEqnVUmzWLLh929AxhRBC6JmUdwFTtQeUbari1LjmgI7w+3t5\n19ER/y++wDgxkajPPzd0RCGEEHom5V3AKBTQ+ge4f6IC9wLLkpAYzKPE6zTw9SWoShXsNm4k9ehR\nQ8cUQgihR1LeBZClK3hPV3Dmqxbo0hWE39+Du8aYk1OnAhAzaBBkZBg4pRBCCH2R8i6gavqCTUkt\nwatrkpL2gAexx2n/zjvsa9MG7ZkzPPz5Z0NHFEIIoSdS3gWUQgltlsDV75uQEmvCvQe/Y04SyVOn\nkqjRwJgx8M9EMEIIIQqXXMs7IyODhQsXkp6enrUsODiYRYsW5Ukw8Xy2FcDrMxMuftsYHcnce/Ab\n73p4sMPXl+IREYRNmGDoiEIIIfQg1/JesGABly5dIiUlJWuZg4MDV65cYcWKFXkSTjxf3c/g0ZWa\nxP6lJfrhaZJTInAfN45wR0fs5s4l9cYNQ0cUQgjxmuVa3gcOHOCbb77BxMQka5m5uTnTp09n586d\neRJOPJ/SCNr+qOTCtBaggLCI3Xja2HDkyy8xTk4m7LPPDB1RCCHEa5ZreWs0GoyNjXNcrlTKpfL8\nxKEqVGhUlrD9FUhKCyEu4TJN+/XjwhtvUGrrVmJ++83QEYUQQrxGubbwo0ePePTo0VPLY2Njs2YD\nE/lHgzEQuqE5GSlKQsP2YamEsFmzAHg0ZAg8ce+CEEKIgi3X8m7bti0DBw7k77//zlp25coVfH19\n6d27d15kEy/BqBg0m2zL9RVvoTOK4V5UIN5NmnCoQwdKBgURsnixoSMKIYR4TXKdErR3794YGxvT\nq1cv4uPjycjIwNbWlo8//ph27drlZUbxgpzfgktbGpJ0/xyRqYextamO7fTpPNq5E8svvyS9WzdU\nlpaGjimEEOI/eubF6+7du3Po0CEOHTrEkSNH2L9/P506dcqrbOIVNBmv4ebyt1GqU7n5VwCVy5bl\n6ODBWEVF8deXXxo6nhBCiNcg1yPv+fPnZ3utUCiwsLCgadOmODk56T2YeDVqU6jR4Q3uXjqJlcc5\n4uNr8cbYsdxdtYpyixYR8+mnWFWsaOiYQggh/oNcj7zT0tKy/UlNTeXatWv06tWLP//8My8zipdU\npomSR2ffAeD6xd3YmptzbdIk1Kmp3B061MDphBBC/Fe5HnkPzeU/8nfu3GHMmDEsX75cb6HEf9dg\nkBtHN3ng2OQSt69coF7PnlxctAjP3bu5sWsXZVq2NHREIYQQr+ilH9iWU+YFg8YSnJ2bkZ6sIip+\nP6Snofj2WzIUCoyGDSM9NdXQEYUQQryily7v1NRUkpOT9ZFFvGYVW1gRc6wuxWzjOLf3KB716nHK\nxwfXq1c5O2+eoeMJIYR4RbmeNg8MDHxqWWxsLL/88gvNmzfXayjx+tRo3YCrN86iLv0HkddrUHrG\nDOJ//ZWykyYR27MnlnZ2ho4ohBDiJeVa3gsXLnxqmZmZGS1btpTnvAsQc60xxc56o9Bu4cqJfdTv\n3JFTn39Oza+/5ti4cdT5/ntDRxRCCPGSci3vlStX5vqmsLAwSpYsqZdA4vXzaFqVU4dOYlP7Imc2\n1qba6NHc/ekn3ly6lGuffkr5qlUNHVEIIcRLeOFr3snJyWzbto1evXrRuXNnfWYSr5lSqaB0+RYA\nJJrt5lFkMR5On446LY34YcPI0OkMnFAIIcTLeG55nz17lvHjx+Pl5cVXX31Fp06dOHDgQF5kE6+R\nnbML6eFVsPK4S+Dys1To3IVrXl688dtvBP7yi6HjCSGEeAm5lveSJUt49913GTZsGLa2tmzevBlX\nV1dat26NWq3Oy4ziNXGv5U16shptswAubEjBau5c0pVKnEaO5GFSkqHjCSGEeEG5lvd3332HWq1m\n6tSpDB06lFKlSqFQKPIym3jNjNXFsSrmhUabwM3LhzF1rsFfPXviFhzMsW++MXQ8IYQQLyjX8j54\n8CCtWrXCz8+PZs2asXDhQlJlYI8Cz9mtHhmJxSntc4z9Xz2g7PTpxBcvzlvTp3Pm6lVDxxNCCPEC\nci1vrVZL//792bNnD1OmTOHWrVvcuXMHX19fDh06lJcZxWukVKpxcWmGqlg6JlX3cSPQnuhp07B8\n+BDjrl25L6fPhRAi33uhu81r1arFtGnTOHz4MI0bN2bBggX6ziX0yNLCE3WGK07NrxD44w20Pr4E\nd+iA55kz/PnZZ3L3uRBC5HMvNTyqubk5Pj4+bNiwQV95RB5QKBS4uLRAp4OKn+5h70gdpZctI6JM\nGVosWsR++fcVQoh87aXHNheFg0mxkliZV8ey4j1iH53i7xPF0WzYQIqxMbV9fbl47ZqhIwohhMiF\nlHcR5mDbFHTGeH52gJ1DEzEu8yZ3Zs7EKiYGXdeuxMr1byGEyJeeWd5//vlnttcxMTGcOHFCr4FE\n3lEbmWNv24Bi1ok4tzvELx+A24BBXGvfnsqnTnFyxAh0cv1bCCHynVzLe+fOnYwdO5a4uLisZTEx\nMUyYMIGjR4/mSTihf7aWdVAbWVPuwxPEPLjJ75MVlPnpJ8Ld3PCeP5+DmzcbOqIQQoh/ybW8ly9f\nzvLly7GwsMha5ubmxo8//sgPP/yQJ+GE/ikVRjjbt0ehVFB3/iaO/xDLjT8sMf7n+nf1/v25Fhxs\n6JhCCCGekGt5GxkZ4ejo+NRyR0dHOZVayJhqXHC0bYGx9SPqLtjIlg/TUNjW4tb06VhHR5PctSsJ\nKSmGjimEEOIfuZb3k6fLn5SRkUF0dLTeAgnDsCleC0vzqthUvYP74F1s6ACl+g/h6nvvUfnkSY6P\nHCm/tAkhRD6Ra3m/+eabzJ49m4yMjKxlqampTJ48mcaNG+dFNpGHFAoFJe1aozF2oEzX02jKn2bH\npwpK/7yciFKleHvOHP7YssXQMYUQQvCM8h4+fDi3bt2iadOmfPLJJ/Tv358mTZoQFxfH0KFD8zKj\nyCNKpRoXhy4oFRpqTNjJrdNhnF9vhWL9elKMjfHo25ebN28aOqYQQhR5RrmtMDMzY86cOYSEhHD9\n+nVUKhXly5fHyckpL/OJPGastsbZoQO3wldTd9EGAtr3p/uvbxE8ZQqVhg/nTteuJP3+OxpjY0NH\nFUKIIuuZz3mnpqZy8uRJjh49ypEjR/jzzz+znUYXhZOFaTm01o0xLRFL7dmb2Ng5A9cPPuNyq1ZU\nPn6c4198YeiIQghRpOVa3rdv36Z169b8/vvvODk5UaJECQ4cOEC7du0IDw/Py4zCALRWDbEwrYC9\n101cu/zGpq4K3H5aQbirKw2+/ZYT27YZOqIQQhRZuZ42nzlzJkOHDqVly5bZlu/fv5/p06fz7bff\n6j2cMByFQoGTtj037izB3fcof3zixJFZlfBYt470Ro0o99FHhJ4+jbOrq6GjCiFEkfPMI+9/FzeA\nt7c3ISEheg0l8geVSoOLYxcUqKk9ewsXNkURG1aXKxMnYnP/PjFdu5KSlmbomEIIUeTkWt4qlSrX\nNz1rnShcNMb2ONm/h5FpCvV+WM/2T5Mp8d5ILrVsSeU//uDEmDGGjiiEEEVOruWt0WgIzmFYzL/+\n+guNRqPXUCJ/sTSvjK1lHSzKRFHty62s7wBOi1YS4eJCvVmzOLt9u6EjCiFEkZJreX/yySf069eP\ndevWcenSJYKCgli5ciW+vr6MkaOtIsfBxhtTTSmcW17GpsEf7B1uS+KaNaQbGeHSuzcRoaGGjiiE\nEEVGruXt5eXFDz/8wMmTJxk+fDh+fn5cuXKFH3/8kUqVKuVlRpEPKBQqXOw7YqSyoMqIAKLCbhB2\nvD4Xv/4a26goonx8SJPr30IIkSdyvdscoHz58syePfup5enp6XLduwgyMjLHxaEzN8N+os7czexv\n15+OK0YR1OIQlffsIXD8eOpOnWromEIIUejleuQdERHBsGHDaNOmDV9++SWJiYkAXL58mQ4dOuRZ\nQJG/mGqcKWH3TuYMZPM34N8jA9uZq4hwcqL2jBlc3LXL0BGFEKLQy7W8/fz8qF27NjNnzsTMzIxp\n06YxZ84cBg0axJAhQ/Iyo8hnrC1qYmVeDesqYVQYsJOd/e14uGI1GUoljr16cf/OHUNHFEKIQi3X\n8o6Pj6dr1664u7szatQodu/eTVxcHFu3bqVJkyZ5mVHkMwqFghJ2rdAYO1KmyxnUpU7x1+ZGnPXz\nwzYykvBu3chITzd0TCGEKLRyLW+lMvuqChUqMG7cOMzMzF5451OmTKFLly74+Phw/vz5bOuOHTtG\n586d8fHx4YsvvpAx0wuY/89AZsIbX+8i+Mgd1C5juNCsGZ6//84pPz9DRxRCiELrmROTPEmhULzU\njk+cOEFISAjr169n8uTJTJ48Odv6L7/8krlz57Ju3ToSEhI4fPjwS+1fGJ6x2goXhw4ojdKpt3AD\n+75IRDN2FRElS1Jj6lT+2rfP0BGFEKJQyvVu8+DgYEaOHJnr6xkzZjxzx4GBgXh7ewNQtmxZYmNj\niY+Px9zcHAB/f/+sn21sbIiOjn71byEMxty0LPY2b3NP8Rs1Z2xmd98PaLx4FbZtm2HTowexZ85g\nWaKEoWMKIUShkmt5Dx8+PNvrunXrvtSOo6Ki8PT0zHptY2NDZGRkVmE//vvevXscPXpUboIrwOys\n6pOYfAfqXcWpQwAXFjYjfvx46n71FZe7dqV4QAAKebRQCCFem1zLu3379k8tO3fuHNWqVXulD9Lp\ndE8tu3//Pr6+vvj5+WFtbf1K+xWGp1AocLJvx43QH6nY/w8CBziRxDjOvf071X77jdPjx1NjyhRD\nxxRCiELjha95AzkO2JIbe3t7oqKisl7fu3cPrVab9To+Pp5+/foxdOhQ6tev/zIxRD6kUmpwceyM\nAjW1Zm7lzOoHpPmuJtzJiRpTp3Jh7VpDRxRCiELjpco7p6Pn3Hh5ebFnzx4ALl68iL29fdapcoBp\n06bRq1cvGjZs+DIRRD6WOQNZ28wZyBat5+AwayIWb+GRqSll+/blxp9/GjqiEEIUCs8cHvXf+vbt\n+8Lb1qhRA09PT3x8fFAoFPj5+eHv74+FhQX169dny5YthISEsGnTJgBat25Nly5dXi69yHcszT0z\nr3+XDqTK2C0cG9uZ9EU/UqNXN0zbtSPq5Ens5AY2IYT4T16qvBs1agTAsmXL+Oijj567/b9venN3\nd8/6OSgo6GU+WhQgDjbeJCbfxanFFR6cO8rNXV3J+CKImlOncKl9e8wOHsREppUVQohX9lKnzR87\nePDga44hChOFQomLQ0eMVMWpPPw3IkODiVVO5Px77+Fx/Dhn+/WTQXmEEOI/yPXIu3v37jku1+l0\nXLt2TW+BROFgpDLDxaETN8N+ps78TQS070OT0Wv4+0Yd6q5axUFPTxqPHm3omEIIUSDlWt5qtZr6\n9etTtWrVbMt1Oh3Tpk3TezBR8JlqnHHStuEOW6i/dC17O/XlvYW/8mBgbeqPG8cfHh7Ue+89Q8cU\nQogCJ9fT5jNnzuT333/H09OT2rVrZ/156623st01LsSzWFlUw87KC/NSD3hrzka2D3Ahcs4mMlQq\nPHv2JOjCBUNHFEKIAifX8tZqtaxYsSLHiUieNzSqEE+yt26Khak79nVv4j5kF7vHNiB4xnwsY2Mx\nb9+e20+MByCEEOL5XumGtRLyqI94CZkjsLVHY+xA2W6nsG1wgj/W9+PKpwNxCw4monNnHqakGDqm\nEEIUGK9U3kK8LJXSGBfHrqhUZlQfv4dUVTBB977jxttNqXngAH8MGULqSwwCJIQQRZmUt8gzxkaW\nuDr4oFQqqbtoI7fOPCCk+kbCy5blne+/Z/uCBS81ip8QQhRVUt4iT5lqnCmpfQ8j02Qa/LyWY8uK\nEdZ3B/GWlrT6/HN2BQQYOqIQQuR7Ut4iz1lZVMXOqgGmJaPx+mEjOyeUI3TsOlTp6dTu2pXAv/4y\ndEQhhMjXpLyFQdhbN8HCtBK2Nf+m+pc72TKpBX+PnIFdVBRWHTpwNTra0BGFECLfkvIWBqFQKHC2\nb4fG2JHSnU/j8v5xfl09jBCfnlQKCiKsZ08iU1MNHVMIIfIlKW9hMEqlMa6OPhipzKk2di8mpa8T\ncH0Jd9+qQ5Pt2wkYN44kGQNdCCGeIuUtDEptZImLgw9KlYq6CzcTExPLcdstRDs54zNjBmtWrCBD\n7kAXQohspLyFwZlqnHDStkWlSabRyrVcDrTgYvNfSTI1xWfAANYeOWLoiEIIka9IeYt8wdK8Mlqr\nhmjso2n40wZ+W1WFax8ux/TRIxp37crumzcNHVEIIfINKW+Rb2itG1PczAOrKiHUnrGdX37owM1e\nX+J05w52Pj6cfvjQ0BGFECJfkPIW+YZCocBJ2w6NcQlc3jtLhT7HWLf5K+62eJ+aJ05w29eX2zIG\nuhBCSHmL/EWpVGfdgV555D5sal5jU9BK7leuStu1a9kxfToP09MNHVMIIQxKylvkO2qj4v+Mga7C\n6/vNpJnFs1uxnQQ7LX2//polmzfLJCZCiCJNylvkSyYaJ5y07VCoU2iydi237thwtNIv6FQq+vbv\nzw8nTsgkJkKIIkvKW+RbluaeaK0bobaKocmaDRw//hZBTRdhGRtLyw8+YP3t24aOKIQQBiHlLfI1\nrVUjipt5YlH+FnXn7mDbrt7cbDaUstevU7JnT1ZERMgRuBCiyJHyFvla5h3obdEUK0mJZmepPDSQ\nVftnEVW7BQ0PHcK9bVtm3rhBmhS4EKIIkfIW+Z5SqcbVwQcjlQXuA/dRssV1frr4C7FN21P7+HHa\nt2yJ36lTJMg46EKIIkLKWxQIaiMLXB19UCiNqDPXHyOnhyy7tJHoroMpf+0ag1u3xm/fPu6lpRk6\nqhBC6J2UtygwTIqVxEnbHlQpeG9eS3JKEgv85xDSZTbae/f4qmNHZq1dy18ykIsQopCT8hYFiqW5\nB/bWTVCaxvLeoXWYlnjEz+s/48+316NJTWXyRx+xds4cjj56ZOioQgihN1LeosCxs2qApXlVdCah\ntAz4gfI+t9kV0IkdbvvJMLfAb+RIzo0Zw6bYWENHFUIIvZDyFgXO4zHQ7a2bkEEc1Sb/TOMf/uDs\nVS9W6gJJKOHGp3PmoOjXj3kRETIfuBCi0JHyFgWSQqFAa90QtxI9MVKZYue9j/bH1nHf2JVFd4/x\nwOVNOmzcyBudOjH+xg2S5U50IUQhIuUtCjQzEzfKOn2MmUlpVNq/aHP0B2yap/L97UPcKvEu9Q8f\n5oP33mP42bPEyIQmQohCQspbFHhGRuaUcvwArXVjdKpY3lr0EzWmXuDnu1s4Z9WfSpcu8UWbNow+\ncICQ1FRDxxVCiP9MylsUCgqFEnvrRpQq0RMjlQkunffQKsCfHenf8pt6CiXDwpjx/vvM2ryZM0lJ\nho4rhBD/iZS3KFTMTUpTxskXM40bGrcrtD2xhKtv9sSfVZg+SmJ2r15s+OEH9sTHGzqqEEK8Milv\nUeiojcwpVaIHWquGYBxDw1XLUI0qx+r0XWToNEwdOpSLEyeyNDpaJjURQhRIUt6iUFIolNjbNKGU\n4weoVBrK9d9NxW33WWH0GzHGTnw2YwZmgwYxMTxcJjURQhQ4Ut6iUDM3LUtZZ19MNaWw8LxM3eNH\n8S+7jbtGVfBZvZoGPXow7MYNmdRECFGgSHmLQk9tZIFbiZ7YWTVAaRbNW7/uJPDDOVxXetMkIID+\n77/PwHPnCJdJTYQQBYSUtygSFAolDjZvU8qxOypVMSp+8Tvhy/twTv0BVc6fZ0Lbtow8fJgrycmG\njiqEEM8l5S2KFHPTcpR1/hhTjSvW9a5CYC2O2Q3D5fZt5nTowMxff+WwTGoihMjnpLxFkaM2Ko5b\niV7YWXlhZB2NRaANx+qNo3jMQxZ88AG//PwzP8fEyJjoQoh8S8pbFEmZp9G9cXXshsrIGIuVRlwY\nMhpS1cwaOJDYiRPpHRoqI7IJIfIlKW9RpFmYlqes88eYFHNGPUjNzZ8+JcHIgSHffMPQdu0YcuQI\nq2Nj5ShcCJGvSHmLIk9tZEnpkh9ia1mPjPqmhB75iJDS3rxx+jRrWrXm2rx59AkL444chQsh8gkp\nbyEAhUKFo20zXB18UNgXJ35/fa6P+hhFipqvxo/nww8+oM/p02x4+FBGZRNCGJyUtxBPsDCrSFnn\nT7EwdSe5fwlCDvQn3KUWjQ4eZO07LQlcuZJ+4eGEyTPhQggDkvIW4l/URha4OHTG2b4juGi5/1tL\nQoZ0w/xhIt8OHEjbTz7hg4sX2SxH4UIIA5HyFiIHCoUCS3NPyrkMwLJ4NeIHV+DvXf15UKISbbZu\nZU2LluzYupVPwsOJkKNwIUQek/IW4hmMVKY427+Pq4MPGRVLcfdgR8L6tUF7L5JlPXvi9cUXdLp2\nna1xcXIULoTIM1LeQrwAC7OKlHP+FCvrmkSPfpO//T8izs6VHsuX83Or1qwJCGBgRASRchQuhMgD\nUt5CvCCVSoOT9j1KOX5Aeo1K3D7Ug0ifxpS5GcyaDh2pNG0a7W/cZHt8vByFCyH0Sq/lPWXKFLp0\n6YKPjw/nz5/Pti45OZlRo0bx/vvv6zOCEK9d5jSjn2JtX497kxvz94qepBS3ZuCcOXzfoSOLjh1j\naEQE99PTDR1VCFFI6a28T5w4QUhICOvXr2fy5MlMnjw52/oZM2ZQqVIlfX28EHqlUhpTwu5d3Ep8\nSGqjmvx9oA/RrWtROeg8m1u1wf7772l7M4Q98fGGjiqEKIT0Vt6BgYF4e3sDULZsWWJjY4l/4j9k\nw4YNy1ovREFlZlKKss6+WLs0JWxOa24t6IxSrWbs118zs2dPpp87x2cREUTLUbgQ4jXSW3lHRUVh\nbW2d9drGxobIyMis1+bm5vr6aCHylFKpxtG2GaVL9iGlTSNuBvTnYUMP6gYeZWuLVhitXUubv2+z\nPyHB0FGFEIVEnt2wJjfwiMLOVONEGef+WJdvxe1lXQib3AbTtGRmDBvGuIED+fLKFUZERMhMZUKI\n/0xv5W1vb09UVFTW63v37qHVavX1cULkC0qFEfY2TSjr3J/Enq0I3tWfhBpuvLN7B9uatSJu+3Za\n37rNsPAILiYnGzquEKKA0lt5e3l5sWfPHgAuXryIvb29nCoXRYammCNlnPpiXb0jIet6EzHSG5u4\nKL7v25d173ZEuWE9XUNC6HkzjKOPHsmZKSHESzHS145r1KiBp6cnPj4+KBQK/Pz88Pf3x8LCgmbN\nmjF48GDCw8O5efMmPXr0oHPnzrRp00ZfcYTIcwqFCq11A4qbuXNnsBNxjcrh8M0hPH47y+whQ4iw\nKcnKfj343McHrbEdvs5WtLAww0ihMHR0IUQ+p9AVgF/5Q0NDadq0KQEBATg7Oxs6jhAvTafL4MHD\nE0RGH0YVfBub5cex2nQOVWIyicam/NKpAyt79+KRtjy9rS3xcbJAo5QxlIQoyp7VfXo78hZC/J9C\nocTWsg7WxWsRZ3eVGI/a3BsWhPX609isOEG31SvxWbOKQ43fZkWf3iyoXI/2aVZ8Wq04VkYqQ8cX\nQuQzUt5C5CGlQoWluQeW5h6k2MUQU+oMN/udwnTHcWx/CqTJgQCaHAjgrwruLO/TG2/le3hF2jHk\nDSvKaOX/rkKITHJeTggDMVZbYW/ThAplPsOq7xSids3mxqa+xL7rSfngv5g8ahT7vb0ov2sSH147\nTXf/exw+nkL+v9AlhNA3KW8hDEyhUGJhWh5Xxy64tP2WlNXf8/fh8UT1rYdFagIDv5vDb03q02X9\np8x6EEDr9eGsWpNIYoy0uBBFlZS3EPmI2sgcrVV93Or4YTJ3FeHnlnL3q1akl7DgvQ2b2fruu0yb\n34ajiWtouT+UaTMSCD0lJS5EUSPlLUQ+pFAoMDMp/b/27jwuynL///jrng2GnUE2UdRM0dy3csks\nTTNKTdTEk5jZ5unh0jma0slOdsrcTqeSPHVcKtPsWP7UbDmeo7/M44JYboiKhICAsgzrALLMcn//\nIFEUTU8iDH6ejwcPZu5luD5zz8yb+5qZ6yKkzRP4z/uC0kNfc27VVMruaU2XvT/xwTPP8OXcfuiq\nFhGVmcTM2SXErVSptDR0y4UQt4KEtxCNnE7rhp+pP8FT/o7yw25y//87FEX0wC8ji9mvvsm2J3sy\ntGwqbwTv5YnF+ax+0UraLuS9cSGaMAlvIZyEoii4ubYgYPCLeH4ZS3HCdxTOfAStFh79cD1fj7qP\nhXuHciYshmdPp/PilOuSy+kAABznSURBVFJ2LlKxZDZ0y4UQN5t890QIJ6TVuODb/iF49yEq5qdS\n+sm7uKzZTLtdh/nLrsNU+M3nh4iHWd72WdSYHjyQ5cXgkXrajwCdS0O3XgjxW8mZtxBOztWnDT4v\nvofLwdNYYr+k+OkH0dltDF/5BRsfH8o7OwdS1nURswpPM2NSKd/MUsk51tCtFkL8FhLeQjQRGo0e\nr75j8V61HfuZUxStfJnSAXcS+uNJ5r70Z776Y2ce1/+OL7pt5YVPC1j6qJUfP4CKooZuuRDiRkl4\nC9EEuXi1xOeZt3DbfRJL/HcUzRyJ4q7n/s++5tMnH+Pv/+qF692v8hcSmflkKV9MUkn9HlRHQ7dc\nCHE9JLyFaMI0ig6vLg/j8+5XOFKTKVz/JiXDOhGQeIbpry1myx+6MkUzmh/6fc7s7/J5814ru/4C\nxekN3XIhxLXIB9aEuE24GANwmfAKamQ0pal7sa3+O27rt3PPlh3cs2UHha2D2Pz4WJZ5P4Pp+bbc\nizu9Jit0GAU614ZuvRDiUnLmLcRtRlG0eN5xH74L/olyKpnCr9+hOKIX3ll5TFnyPl/O6sk0+zCO\nDV3F6wdymd/HynfTIetwQ7dcCHGBnHkLcRszGHwxPPoi6iMzKD13EOvaDzCu/RfdtsfSbXsspUFz\n+XbMY6wLfY7NL3emR747vZ9U6PI7MJoauvVC3L7kzFsIUT05SkgfTNEfoTt6ioKdKyicOABj6XnG\nL/+Y9XP6Mbt0IDmPvcfbyVm83tvKxkg4/R9w2Bu69ULcfuTMWwhRi17nhen+Z1EHPU1Z/nEq13+I\n67pvCdt7hNf3HqHCZx7bRz/K5jue499/u5uOU93pEaXQfTL4tmno1gtxe5AzbyFEnRRFg0ezLvjN\nWI5hXwIFBz6j4PnB6LQw4uMNfPTqEObl9MY2YQGrC8/w1n1WPh0Cx9aDtbyhWy9E0ybhLYT4VXqd\nB6Y+v8P3gx1UnD5I3urZlNzfntD4JOa89Sofrw5jeL8IDvT/mmWflLK0pcq3L8C5n2SCFCHqg4S3\nEOK6KYqCh3cYzaYsxbjjMIVHN5E36xFUX1eGfPkN7785kjfSOuE16U9s5GfefczKP7rD/vfgfF5D\nt16IpkPCWwjxP9Fp3TB1fgy/pV9jTTqC+Ys/UxzemYC0TKa9s4hVq+/ise7DSXnon3z0bQlLQ1W+\nHAc//0s+5CbEbyUfWBNC/CaKouDu1hr3ca9jj4im6Mxu7GtW4Pn5f+n/7ff0//Z7ClsEsOXJSLbb\nn+HHZzsQhJ7uk6FjBAR0Aa2+oasQwrlIeAshbhqt1ojpjmGo84dSHp2Jeefn6NZ8gdc38Tz14TKe\n1MRwdFBftnaZwhdxYwlc7I1eqxDYFZr3rv4J7gX+d0mgC3EtEt5CiJtOURTcjC1xC5+DffhMirP2\nY12/Gs/139NjZyw9dsZS2uwl9kQMJjGwD4kVvTlwthue273xTNFjMCgEdrss0DuCRl6xhAAkvIUQ\n9UyrccEUMgheGkT5jCxy9/8/tJ98jveWwwz/YhPD2QSAXaclu10rkh67i1MBvUmuuIefUnrg8a0v\nHmf06F0VgrrXDvRmHUCjbeAChWgAEt5CiFvG6BKMcdA0HAOfw/L2UaqO7EU9dBhd/CmMCWcJPplO\nyMkUHuAbABwaBXPbFpx+tCNJgb1ILr+Hw6d6Y/wqAPd0HQY3haAeF8O8eW/way+BLpo+CW8hxC2n\n0RjwMfWBwX1gMKiqnUprPpayTKwnf8Jx8CDaIycwJmTQ7EQWgT9n0J//1Oxf0CqI1OEdSA7qQdL5\n/sTH98GwsTluZ3W4eNQR6O1Ake/WiCZEwlsI0eAURYurIQBXQwD07wn9QVVVbHYL58vPUXXqEPaD\nB1AOJ2BMSMPreBa9tv1AL34A3gHAEtKM9Afbczq4O6fK+3P8YD+0n4dizNbi4qkQ3LN2oJvaSqAL\n5yXhLYRolBRFQa/zRu/pDb07Qu8nALA7KqioyKbk9BGsP+1HPRyPW3wybgnn6LxjH53ZB/wdgLIA\nH84OupPU5l05VdWPxH0DYM2dGPP0uHhdDPILv33vAEVpwKKFuE4S3kIIp6LVuFZ/r7xLa+jyGDwF\nDtVOVaWZsvRjVPy4D/XwYYzxP2NMyKT9rp9oz088xEcAVPi6k92/LWdCupBkv5vduwZhXd0J1wId\nrj5XBrpPawl00fhIeAshnJ5G0eLqGoRr+yC82w+FJ6q73a22YkrOnuD8T3uwHzyI8egp3I+n03pf\nPK2JZxCfAVDlacR8dxvSW9xFitKH/TsHUf5BDwwWA0ZTdZBfCPPArtWBLt9DFw1JwlsI0SQpioJB\n74OhdX88W/eHsdXL7fZyynKSKPnxv1gP/oQx/gQeCWk0//EEIQdO0I+N1dsZDeT1COVsaEdSdb04\nsmsQu97vi67UFY2uOsBN7cB058Xffu2ql8v30UV9k4eYEOK2otUacW/eDfdR3WBU9TKHaqMiP42i\nn3ZRdfAArkcS8DqWQkD8aQIPJ9OTr6u3M+iwdAiiIKQFOT6tyVLCOJ3UhdjtPbFnhGIo00qwi1tC\nHkZCiNueRtFhbHYnxuF3wvCngepu9ypLFnkHd1H5YyyuR+PxOpaMV3I2PomZ3MH+Wrdh83DBclcg\n+c1DyPFtw1ltGCdTumHe0xXH2RYYzTq0WuWKYPf75bIEu7gR8lARQog6KIqCi3dzQgZPgMETapbb\n7ZUUpyZQnHgYW9JJ9MnJGFPScUvLxiflHKYT6bQjttZtWT1cKL4riPyQELJMd5CpDSM+owt5B7vi\nyGqO+1kDeocEu7h+8nAQQogboNW64H1nL7zv7HXFOpu1jPK041hOHcV66jja5NMYUzIwpmVjSj5L\ns+NnCGNfrX2sHq4UtQvE3LIlWX53kK4P48ecLuSf7AzZwXik63E9r70Y7Jd0w0uw377kkAshxE2i\n07uja3c37u3uhkcvLldVFZvVQsWZE5QlHqXq1Ak0yadxSc3ELTUbv58z8T9+hrvYU+v2qjyNFLUK\nIie0BWeb3UGGIYzEgq4U7LgL1gXikWHAPU+Lb2vlivfXJdibNjmsQghRzxRFQW/wRt+uH27t+sGI\ni+tU1YG1qghr2nEqT8VTlXQS5efqYHdNy6FZUjoBCal0YXet26zyMlLQKpjsXi3IDGhLuiGMY2Vd\nKNzXETb7455hwDNLR7PmGnxag2cwuAeBR1D1ZY+giz8u3vJddmcj4S2EEA1IUTQYXEwYwgbiHjaw\n1jqHasdaWYA17Ti2U8eqgz05BUNKJi5pOQScOkPQsRS6899a+1V6uVHQOphzQ1qSEdiGTNe2/Ewr\niotbUJ4bRNW+QFzyXDCatbiatbiVavHyV2pC3f2SYL806N0DQedyC+8ccVUS3kII0UhpFC0urv64\ndLgfOtxfa53DYaWq0ow19QT2pATspxJRT18IdjOBiWkEx5/+Zfz32lSNQrnJk1J/X4rDTBQ08yPf\nJ5B8YxDnNCGY7S0pTQ+h/FRzHBYTrmYdrmYtrmYdnnoFz6C6g/7SsDeaZOz4+iThLYQQTkij0eNq\nbI7rXc3hrgdrrbM7KqisMGNNPY79VAJqWiqOnFyU3Dx0OQXo8orRmUvwy8gi4OSZa/4du0HH+Wbe\nlPj7UHyHiQKTP/megeS5BPMzLSiwhFJmbk5FXBDaQq+aoDcWa/EyKReD/bKu+kt/DO71eU81TRLe\nQgjRxGg1rhjdWmLs1BI6Db9ivaqq2B3nsdpLKS/OxZGVjiMrAzX7HI6sLNQcc3XQ5xaiNxfjYi7F\n/WQazatOX/PvWt1dKGvmg6WFL4U9/Cj08SffLYh8XTBpjpYUprek/OdgKkuD0RcYMeZWd9u7qxo8\ng5Rage4eWH32bjSBm9/Fy0YTGDzlPXoJbyGEuM0oioJO645O6w7+geDfBbrWva1DtWO3l1FpK8GW\nfw5HVjpqViaO7HM4srIhx4wmJx+9uRCd2YJ7Xhneh3IJdajXbEOFrwdlzbwp7mSiyM+PAu8A8lyD\nydU250RpKCUFzams8qWqqhmO857oS7UYLBr0Fg2GMg1eBg3u3kqtUDdeFvKX/7h6N52ufAlvIYQQ\nV6VRtGh0Xuh1XhASAiF9rrqtw1GFzV5GeVUx9pyMX4L+LPascziyc1ByzWhzC9DnFqHLK8E7Nx+/\nn8/+ahscOg2VXu6U+3hwvpknJW29sHh7UeLuQ5HRRLHBxDmtP8XWZpTn+FGRbaLC6ou1ygTlbtWB\nb9FgKNXioWjw0mnwdtHgcXn4X+UfAVcf0Ghv5r3620l4CyGEuCk0GgMGjQGD3hfuaA131L2dqqo4\nHJXY7KVUns/HnpWOmpWOI+ss9uxsHNk5aMz5aItK0BWVorWcR1tcjldRMb7pOSg2x3W3yepqoMLb\nnfPenpR6e2Lx9qbY25sibx8K3P3IMfhRrDVRZjVRkeNLebYfFTZfqmy+6MoMNcHv7tDgeSH4DRp8\nXLU14X+hW79ZBwi5++bcl79GwlsIIcQtpSgKWq0rWq0rLoZm4BMGHa++vUO1YbeXY3OUU2k7j92S\nhyM/B7XAjJpvRi0sgIJ81MIiKCpGU2hBeyH0i8pxsVTglp1LwM/pKNfuza+lwsNIubcnpT6elHh5\nUeztTaGbLwXePpzz8cNi9OW83oeySl8qsn0pSW3NilZheAfW/xvyEt5CCCEaNY2iQ6PzRI8nGAC3\n1hB07X1UVcWhWrHbz2N3lFNlP4/dWoqj6ELw56EW5ENBARQVohQUoxRZ0BSXoC0qR1tcgba4HPfi\ncrzSitCWVf1qO20GPWXPnwGCb0bZ1yThLYQQoslRFAWtYkCrMQA+F1d4A62uvp+qOrA7KrDby6s/\nke8orz7rLy/GUWiGAnN16BcWQEEhSmFRdegXleLwdMPXz7O+SwMkvIUQQogaiqJBp3VDp3UD/C6u\n8AQCrr6fQ7WBCppbNJi8hLcQQgjxG2kUHdzC7543kW+8CSGEELcPCW8hhBDCydRreL/11luMHz+e\nyMhI4uPja63bt28fY8eOZfz48Sxfvrw+myGEEEI0KfUW3gcOHODMmTNs2LCBBQsWsGDBglrr33zz\nTWJiYvj888/Zu3cvycnJ9dUUIYQQokmpt/COjY3lwQerZ7pp27YtxcXFlJaWApCRkYG3tzfBwcFo\nNBoGDRpEbGxsfTVFCCGEaFLqLbzz8vLw9fWtuW4ymTCbzQCYzWZMJlOd64QQQghxbbfsA2uqegNj\n0gkhhBDiquotvAMCAsjLy6u5npubi7+/f53rcnJyCAi4xrffhRBCCFGj3sJ7wIAB/Pvf/wbg+PHj\nBAQE4OHhAUCLFi0oLS0lMzMTm83Gzp07GTBgQH01RQghhGhS6m2EtZ49e9KpUyciIyNRFIXXXnuN\nTZs24enpydChQ5k/fz6zZs0CIDw8nDZt2tRXU4QQQogmpV6HR509e3at6x06dKi53KdPHzZs2HBd\nt2O32wHIzs6+eY0TQgghGrELmXchAy/lFGObX/gk+hNPPNHALRFCCCFuLbPZTKtWtadCU1Qn+Bh4\nRUUFCQkJ+Pv7o9VqG7o5QgghRL2z2+2YzWY6d+6Mq6trrXVOEd5CCCGEuEgmJhFCCCGcjIS3EEII\n4WQkvIUQQggnI+EthBBCOBmn+KrYrbJkyRIOHjyIzWbj+eef5/vvv+f48eP4+PgA8PTTT3P//fez\ndetW1qxZg0aj4fHHH2fcuHEN3PJfFxcXx8yZM2nXrh0A7du355lnnmHOnDnY7Xb8/f1ZunQpBoPB\nKesD+PLLL9m6dWvN9YSEBDp37sz58+dxc3MDYO7cuXTu3JlVq1axbds2FEVh2rRpDBo0qKGa/auS\nkpJ44YUXmDx5MhMnTiQrK+u6j5vVaiU6Oppz586h1WpZuHAhLVu2bOiSrlBXjS+//DI2mw2dTsfS\npUvx9/enU6dO9OzZs2a/Tz75BIfD0ehrvLy+6Ojo635tcYZjeHl9M2bMoLCwEICioiK6d+/O888/\nz4gRI+jcuTMAvr6+LFu2jJKSEmbNmkVJSQlubm68/fbbNfdLY3J5PnTp0qVhn4eqUFVVVWNjY9Vn\nnnlGVVVVLSgoUAcNGqTOnTtX/f7772ttV1ZWpg4bNky1WCxqeXm5+sgjj6iFhYUN0eQbsn//fnX6\n9Om1lkVHR6vfffedqqqq+vbbb6ufffaZ09Z3ubi4OHX+/PnqxIkT1VOnTtVal56ero4ePVqtrKxU\n8/Pz1Yceeki12WwN1NJrKysrUydOnKjOmzdPXbt2raqqN3bcNm3apM6fP19VVVXdvXu3OnPmzAar\n5WrqqnHOnDnqt99+q6qqqq5bt05dvHixqqqqevfdd1+xf2Ovsa76buS1xRnru1R0dLR69OhRNSMj\nQx09evQV62NiYtSVK1eqqqqq//znP9UlS5bUe5tvVF350NDPQ+k2/0WfPn147733APDy8qK8vLzO\nUW2OHj1Kly5d8PT0xNXVlZ49e3Lo0KFb3dybIi4ujiFDhgDwwAMPEBsb22TqW758OS+88EKd6+Li\n4hg4cCAGgwGTyURISAjJycm3uIXXx2AwsHLlyloT99zIcYuNjWXo0KEA9O/fv1Eey7pqfO2113jo\noYeA6jO0oqKiq+7f2Gusq766OOsxvFZ9KSkplJSU0LVr16vuf2l9Fx7PjU1d+dDQz0MJ719otdqa\nrtWNGzdy3333odVqWbduHZMmTeIPf/gDBQUF5OXlOe1c5MnJyUydOpUJEyawd+9eysvLMRgMAPj5\n+WE2m526vgvi4+MJDg6umcVu2bJlPPHEE/z5z3+moqLCqWrU6XRXDM5wI8ft0uUajQZFUaiqqrp1\nBVyHump0c3NDq9Vit9tZv349I0aMAKCqqopZs2YRGRnJxx9/DNDoa6yrPuC6X1uctT6ATz/9lIkT\nJ9Zcz8vLY8aMGURGRta8xXVpfX5+fuTm5tZ/o29QXfnQ0M9Dec/7Mjt27GDjxo189NFHJCQk4OPj\nQ8eOHVmxYgXvv/8+PXr0qLW96iRj3LRu3Zpp06bx8MMPk5GRwaRJk2r1LFytDmep71IbN25k9OjR\nAEyaNImwsDBCQ0N57bXX+Oyzz67Y3hlrvOBGj5sz1Wq325kzZw59+/alX79+AMyZM4eRI0eiKAoT\nJ06kd+/eV+znDDWOGjXqf35tcYb6oPofrYMHDzJ//nwAfHx8mDlzJiNHjqSkpIRx48bRt2/fWvs0\n9touzYdhw4bVLG+I56GceV9i9+7dfPjhh6xcuRJPT0/69etHx44dARg8eDBJSUl1zlPuDHORBwYG\nEh4ejqIohIaG0qxZM4qLi6moqAAuzqnurPVdKi4uruaFcOjQoYSGhgJXP4bONp+8m5vbdR+3gICA\nml4Fq9WKqqo1ZwuN3csvv0yrVq2YNm1azbIJEybg7u6Om5sbffv2rTmezlbjjby2OGN9AD/++GOt\n7nIPDw/GjBmDXq/HZDLRuXNnUlJSatXXmJ+Ll+dDQz8PJbx/UVJSwpIlS/jHP/5R80nH6dOnk5GR\nAVQHQrt27ejWrRvHjh3DYrFQVlbGoUOH6vzvv7HZunUrq1evBqoHuc/PzyciIqJmzvX//Oc/DBw4\n0GnruyAnJwd3d3cMBgOqqjJ58mQsFgtw8Rj27duXH374gaqqKnJycsjNzeXOO+9s4JZfv/79+1/3\ncRswYADbtm0DYOfOndxzzz0N2fTrtnXrVvR6PTNmzKhZlpKSwqxZs1BVFZvNxqFDh2jXrp1T1ngj\nry3OWB/AsWPHas0kuX//fhYuXAjA+fPnSUxMpE2bNrXqu/B4bmzqyoeGfh7K2Oa/2LBhAzExMbXm\nFY+IiGDdunUYjUbc3NxYuHAhfn5+bNu2jdWrV9d03Y0cObIBW359SktLmT17NhaLBavVyrRp0+jY\nsSNz586lsrKS5s2bs3DhQvR6vVPWd0FCQgLvvvsuq1atAuC7775j1apVGI1GAgMDWbBgAUajkbVr\n1/L111+jKAovvvhiTbdsY5OQkMDixYs5e/YsOp2OwMBA/vrXvxIdHX1dx81utzNv3jzS0tIwGAws\nWrSI4ODghi6rlrpqzM/Px8XFBQ8PDwDatm3L/PnzWbp0Kfv370ej0TB48GB+//vfN/oa66pv4sSJ\nrFix4rpeW5yxvpiYGGJiYujVqxfh4eEA2Gw25s2bR2pqKna7nQkTJjBmzBjKysp46aWXKCoqwsvL\ni6VLl+Lp6dnAVdVWVz4sWrSIefPmNdjzUMJbCCGEcDLSbS6EEEI4GQlvIYQQwslIeAshhBBORsJb\nCCGEcDIS3kIIIYSTkfAWohHJzMzkvvvuu2J5WFgYNputAVp0/WJiYhg+fDgrVqwAqoepffrpp4mI\niCAyMpLIyEj27NlzzduIjY3lscceu2L5Bx98wKuvvkpUVBS9e/cmMzOzXmoQwlnI8KhCiJvmueee\nIyIiArPZzIwZM3j33Xfp3r07UD3xxnPPPcdXX31FUFBQnfv37duXkpISEhMTaw3wsWXLFpYsWUK3\nbt2Iioq6JbUI0ZjJmbcQTiguLo6YmJirrh88eDArVqwgKiqK4cOHs3PnTqZOncqDDz7I5s2bATh9\n+jQTJ07kySefZMyYMezevRuoHglr3LhxREVFMX78eOLj47HZbERHRzN+/HgiIyN5/fXXr9m+devW\nMWrUqJrgBujWrRu7d++uCe7ExESeeuopoqKiiIyM5MSJEyiKwpgxY9iyZUvNfkeOHEGv19OtW7f/\n+f4SoqmRM28hmihfX1/Wrl1LdHQ0a9as4eOPP+bAgQO89dZbjB49mry8PGbOnEmfPn04fPgwb7zx\nBgMHDmTNmjU89dRThIeHk5KSQmpqKklJSRw9epR//etfAHzxxReUlJRcdSSs5OTkOru/Lx3P+aWX\nXmL58uWEhoaSmJjIn/70JzZt2kRERATjxo1j9uzZ6HQ6tmzZwtixY+vnThLCSUl4C9HIFBQUXLVr\neMOGDXzzzTdYLBZKSko4cOBAzdChl+vZsydQPSlNYGAgiqIQFBRESUkJAP7+/ixZsoR33nkHq9Va\nM2f2iBEj+Nvf/kZ8fDxDhgxhyJAhVFZW4uvry7PPPssDDzzAww8/fM0hLLVaba336OfPn8/p06ex\nWCyMGjWKUaNGkZqayiuvvFKzTWlpKQ6Hg6CgIDp06MCePXvo378/O3bsqJk+UghRTcJbiEbGZDKx\ndu3aWsvCwsIAGD9+POPHjycuLo4DBw4wffr0q96OTqer8/IFb7zxBo888ghjx44lKSmJqVOnAhAe\nHs69997Lnj17WL58OV27duWPf/wj69ev5/jx4+zcuZOxY8fy+eefX3UGqPbt23PkyBEefvhhgJp/\nLmJiYiguLsZgMKDX66+o84KxY8eyZcsWqqqq6NmzZ605koUQ8p63ELetvLw82rVrB1RP4FJVVQXA\nsmXLsNvthIeH88orr3D48GGOHTvG5s2b6dSpE9OmTaNTp06kpaVd9bajoqLYvn07sbGxNcuKioo4\ncuQIrq6ueHp60qJFC3bt2gVAamoq77//fs22gwcPJj4+ns2bN0uXuRB1kDNvIZzQPffc85unFZwy\nZQpz5syhRYsWTJ48me3bt7No0SI6duzIlClT8PLywuFwMH36dEJDQ1m+fDkbNmzAYDAQGhpa0y1f\nF29vb9atW8eCBQtYunQpRqORyspKwsPDmTRpEgCLFy/mzTffZMWKFTUfiLtAr9czbNgwtm3bxr33\n3vub6hSiKZJZxYQQN0VMTAwhISFERETU69+Jiopi4cKFtGjRol7/jhCNmXSbCyFumhUrVtQM0lIf\noqKiOHnyZL3dvhDOQs68hRBCCCcjZ95CCCGEk5HwFkIIIZyMhLcQQgjhZCS8hRBCCCcj4S2EEEI4\nGQlvIYQQwsn8H6LgMwb+Y3PRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAFeCAYAAAB3pD0/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XVY1ff///H7OYdDG4B0iGKC3Yot\n09k6C3t2izqdPZ3dXdPZrVOnUyd2Yw4DWyyQFAHpPL8/+H78zQkzJhzA5+26vC55nXq8ZePBu14v\nhUaj0SCEEEKIXEGp7QBCCCGE+HKk2IUQQohcRIpdCCGEyEWk2IUQQohcRIpdCCGEyEWk2IUQQohc\nRIpdCCGEyEWk2IUQQohcREfbAdJz69YtduzYgUajYfDgwdja2mo7khBCCJEjZOke+8OHD3Fzc2PL\nli1vx2bMmEGHDh1wd3fn1q1bAGzfvp3JkyczcOBAdu/enZURhRBCiBwty/bYY2NjmTp1KtWrV387\nduXKFZ4/f87OnTvx9fVl3Lhx7Ny5k+TkZHR1dTE3NycsLCyrIgohhBA5XpYVu66uLmvWrGHNmjVv\nx7y8vHBzcwPAycmJyMhIoqOjMTAwICEhgaCgIKytrf/1fePj4/Hx8cHc3ByVSpWp2yCEEEJoW0pK\nCqGhoZQqVQp9ff33Hs+yYtfR0UFH592Pe/XqFS4uLm+/NjU1JTQ0lA4dOjB58mRSUlIYMWLEv76v\nj48PnTt3zpTMQgghRHa1detWKlWq9N54trp47n8Lzbm4uDBz5syPeo25uTmQtoFWVlaZlk0IIYTI\nDoKCgujcufPb/vsnrRa7hYUFr169evt1SEhIhkEz8r/D71ZWVtjZ2X3RfEIIIUR2ldHpZ63ex+7q\n6oqnpycAd+7cwcLCAmNjY21GEkIIIXK0LNtj9/HxYfbs2bx8+RIdHR08PT1ZunQpLi4uuLu7o1Ao\nmDRpUlbFEUIIIXKlLCv2UqVKsXnz5vfGR44cmVURhBBCiFxPppQVQgghchEpdiGEECIXkWIXQggh\ncpFsdR+7EEKI/87f35/mzZtTqlQpABITExk1ahQvXrxg8eLFODg4kJqaiomJCaNHj8be3p5Zs2Zx\n584dQkNDiYuLw8HBgXz58rFs2TItb434VFLsQgiRCxUqVOjtBctXr15l5cqVNG3alCZNmjB69GgA\nzp8/T+/evTlw4ABjxowBYO/evTx69Ojtc0TOI4fihRAil3v16hUWFhbvjdesWZPKlStz7NgxLaQS\nmUX22IUQIpMcHQV3v/DK087toOHcDz/v6dOndO3alYSEBIKDg1m7du3bpbH/rlSpUjx+/PjLhhRa\nJcUuhBC50N8Pxfv6+jJs2DC6dev23vNiYmJkZcxcRopdCCEyScO5H7d3ndmcnJzQ09NDqXz/7KuP\njw9NmzbVQiqRWeQcuxBC5HIRERGEhoaSnJz8zviZM2d48uQJ9evX11IykRlkj10IIXKh/51jB0hI\nSGDixIm8efOGw4cP4+PjQ0xMDKampixdujTdPXmRc0mxCyFELmNnZ4e3t3e6j3333Xf/+toPPS6y\nP/k1TQghhMhFpNiFEEKIXESKXQghhMhFpNiFEEKIXESKXQghhMhFpNiFEEKIXERudxNCiFwkoyVb\nK1WqxN69e2XZ1k/k6elJo0aNPuq5q1evpnLlypQvX/6zPmvLli2Eh4czZMiQz3r9/0ixCyFELpPe\nkq1r164FkGVbP4G/vz+HDh366GLv27dvJif6OFLsQgiRi2W0ZCu8u2xrs2bNPvheS5cuJTw8nOfP\nn+Pv74+Hhwd79uzh5cuXrFmzBmtra0aPHk1wcDCxsbEMGTKEevXq8fvvv7NlyxbUajUlSpRg0qRJ\n6Y79Xa1atWjUqBG3b9/G0tKSefPm8fr1a0aNGgVAcnIys2fPxsHBgYYNG+Ls7Iyrqyu2trYsXrwY\ntVpN3rx5WbRoEd7e3mzatAmVSsXdu3fp378/586d4969e/z444+4ublx9OhR1q1bh46ODqVKlWLM\nmDFMmTKFW7dusWzZMr7//nvGjRtHZGQkKSkpTJgwgRIlStCwYUNq166NmZkZz58/p1GjRhQvXpxR\no0ahVCpJSUlh7ty5WFlZMXHiRPz8/EhOTmbo0KFUr14dLy8vZsyYQYECBTA3N8fe3v4/f8+l2IUQ\nIpPMCwvDMybmi75nIyMjRpqZ/etz0luyNSOfumxrZGQka9euZeHChfz++++sXbuWRYsWceLECZo3\nb07NmjVp3bo1fn5+eHh4UK9ePdauXcvq1auxtrZmz549xMfHpzumr6//9nNCQkJo1qwZEyZMYMiQ\nIZw9exYLCwsGDRpEtWrV+O2339i2bRtjxozBz8+P5cuXU7RoUf7880/mzZuHvb09P/74I+fPn8fI\nyIh79+5x5MgRrl69ysiRIzlx4gQ3b95k8+bNVK9enZUrV7Jz5050dXXx8PDg+vXr9OrVi61btzJ4\n8GCWL19OrVq1aNeuHY8fP2b69OmsX7+e5ORkateuTe3atd8e9fD09KRGjRoMGjTo7emNq1evYm5u\nzowZM3j9+jXdu3fnjz/+YP78+cydO5cSJUrQp08fKXYhhBDvS2/J1n379qX73E9dtrV06dIAmJub\nvx0rUKAAERER5M2bl9u3b7Nz506USiUREREANGvWjEGDBtGiRQuaNWuGvr5+umN/Z2hoSLly5QAo\nV64cT58+xcXFhWnTprF06VLevHmDi4sLAAYGBhQtWhQAU1NTJkyYQEpKCn5+flSrVg0jIyNKlCiB\nrq4u5ubmODo6YmhoiJmZGVFRUTx+/JiAgAB69eoFQFRUFAEBAe8c6fD29ub169ccOHAAgLi4uLeP\nlSlT5p3srq6uDB48mKioKBo1akT58uXZt28f169f56+//gLS5u9PTEzk5cuXlChRAoDKlSuTkJDw\n0d+LjEixCyFEJhlpZvbBvevM9r8lWwMDA9N9/FOXbdXR0Un37xqNhoMHDxIZGcm2bduIiIigbdu2\nAPTr14/mzZvj6elJ9+7d2bJlS7pjJiYmb98vNTX1nfdWKBQsWbKEmjVr0rFjR44cOcLp06cBUKvV\nb587btw4Vq9ejZOTE1OmTPlg7v+9vlSpUu8d2bh8+fI7z5k4cWK6F8b9/fMBihUrxv79+7lw4QIL\nFiygTZs2qNVq+vfv/94pj78vwKPRaN57788ht7sJIUQu9r8lWy0tLd977Esv2xoeHo6dnR1KpZJj\nx46RmJhIamoqCxcuxNzcnB49elCuXDkCAgLSHfu7+Ph4fHx8ALhx4wZFihQhPDwcBwcHNBoNJ06c\nICkp6b0M0dHRWFtb8+bNGy5fvpzuc/6pUKFC+Pr6EhYWBsCSJUsIDg5GqVS+Xeq2bNmyHD9+HIDH\njx+zfv36DN/v0KFDPHr0CDc3Nzw8PPDx8aFs2bKcOHECgLCwMBYsWACApaUlT548QaPRcOXKlQ9m\n/Riyxy6EELlMeku26urqAmTqsq0NGzZkwIAB3LhxgzZt2mBlZcWKFSswMjKiQ4cO5MmTB3t7e0qW\nLMmFCxfeG/u7/Pnzc+DAAWbMmIG5uTk1a9ZEo9EwdepUbG1t6dq1KxMnTuT8+fPvvK5Tp0507NgR\nR0dHevfuzdKlSxkxYsS/5jYwMGDcuHH06dMHXV1dnJ2dsbCwQK1Wc/fuXWbMmMHQoUMZO3YsnTp1\nIjU1lfHjx2f4fo6OjkyaNAlDQ0NUKhUTJkygYMGCXLp0CXd3d1JSUhg8eDAAw4YNw8PDAxsbG6ys\nrD7zX/5dCs2X2vfXEn9/fxo0aMCJEyews7PTdhwhhBBfQNWqVd85FC7+vw/1nhyKF0IIIXIRKXYh\nhBDZjuytfz4pdiGEECIXkWIXQgghchEpdiGEECIXkWIXQgghchEpdiGEyEX8/f0pWbIk9+/ffzu2\nd+9e9u7dC0D9+vXfTjf7v+f/b45zkTtIsQshRC5TpEgR5s+fn+5jZmZm7Nq1i+jo6CxOJbKKFLsQ\nQuQyLi4uGBoa4uXl9d5j+vr6uLu7/+uKbyJnkyllhRAikwSFHeVNzN0v+p55jZyxMmv4wecNHz6c\n0aNHU61atfce69ChA23btqVTp05fNJvIHmSPXQghciFHR0ecnZ05fPjwe4/p6OjQr18/li5dqoVk\nIrPJHrsQQmQSK7OGH7V3nVkGDRpEr1696Ny583tLlTZu3JiNGzfy7Nkz7YQTmUb22IUQIpcqUKAA\nbm5u7NixI93Hhw8f/nb5UJF7SLELIUQu1rNnT4KCgtJ9rGrVqhQoUCCLE4nMJsu2CiGEEDmILNsq\nhBBCfEWk2IUQQohcRIr9n27cgO++g+vXtZ1ECCGE+GRS7P8UEAD79kH16rBwIaSmajuREEII8dGk\n2P+pSRPw9ARTUxgxApo1g5AQbacSQgghPooUe3oaNoSbN6FRI/jzTyhbFo4f13YqIYQQ4oOk2NOh\n0WjA0hIOH4Z58yAsLK3sBw+GR4+0HU8IITIky7Z+nhMnTpCYmPhRz7137x5Lliz57M+KiYmhfv36\nn/36D5Fi/4fTMTHUefECz+hoUCrhhx/g4kUoXBiWL4dixeCbb2DvXkhK0nZcIYR4jyzb+uk2bNhA\n0kf+TC9ZsiRDhw7N5ESfT+aK/wdLHR3iU1MZERLCD8nJ9MiXD0WlSnDnTtpFdatWpR2WP34crK2h\nd28YNiztnLwQQmQDLi4uxMXF4eXlRfXq1d95TF9fn1atWrF27Vo8PDzSfb2/vz8//vgjDg4OeHt7\n07FjRx48eMDNmzfp3LkznTt35sCBA2zZsgWlUknRokWZOnUqAQEBjBo1CqVSSUpKCnPnzkWhULw3\nZmtr+/azxowZg6GhIU+ePCE8PJyZM2fi7OzMzJkzuXXrFgkJCXTs2JF27doxZswY1Go1ERERzJw5\nkx9++IHY2Fji4+OZOHEiZcqUwc3Njfbt23PkyBEKFiyIi4vL27/Pnz+f4OBgxo8fT1JSEiqVimnT\npnHlyhVu3LhBnz592LBhA7t37+aPP/5AqVTi5uZGz549Wbp0KX5+fvj7+zNkyBC2b9/OkiVLmDZt\nGj4+PqSkpNCxY0e+++47jh49yrp169DR0aFUqVKMGTOG6OhohgwZQkJCAhUrVszU77/ssf9DST09\nNtnYYKlSMf/1a6a+ekWyRgN6euDuDqdPp5X80KEQGwtTp0KFCuDtre3oQojsZtQocHT8sn9Gjfqo\njx4+fDiLFi0ivclFO3TowKlTpwgNDc3w9ffu3WP06NH88ssvzJs3j2HDhrFq1Sp27doFQFxcHL/+\n+is7duzgyZMnPHjwAE9PT2rUqMHmzZsZP348oaGh6Y79U3JyMhs2bMDDw4Ply5eTkJCAra0t27dv\nZ9u2bSxevPjtc/Ply8fSpUsJDQ2lXbt2bN68mREjRrBmzRoAUlNTcXZ2Zs+ePfz111/Y2try22+/\ncf36dd68ecPixYvp2bMnGzdupHv37qxYsYJWrVphbm7OmjVrCA4O5siRI2zfvp2tW7dy9OhRAgIC\nAEhKSmLbtm0olWnVGRERwenTp9mxYwfbtm0jOTmZmJgYVq5cyaZNm9iyZQuBgYFcv36d/fv3U7Ro\nUbZt20bJkiU/6nv4uWSPPR0l9PTYbmvLgKAgdkZFEZiczDxLS4z+75uJszMsXgwzZsCcOTBlCtSo\nAatXQ9eu2g0vhBB8/LKtffv2Tff1Dg4OmJiYoKuri6mpKZaWlsTExBAVFQWkFezAgQMB8PX1JSIi\nAldXVwYPHkxUVBSNGjWifPnyGBoavjf2TzVq1ACgXLlyzJs3Dz09PSIjI3F3d0etVhMeHv72uWXK\nlAHSFrhZsWIFa9euJTExEUNDw3eeo1AoMDMzw9nZGQBTU1OioqLw9vbm6dOnrFy5kpSUFEz/cbT1\n9u3bPH/+nG7dugFp58Nfvnz5zmf/T/78+XF0dGTAgAF8++23tGrVinv37hEQEECvXr0AiIqKIiAg\nAF9fXypXrgxAlSpV0v03/1Kk2DNgqaPDJhsbRgQHczYuju4BAaywssLi70sfGhnBzz9D5crQpQt0\n6wbXrqVdcKdWay+8ECJ7mDs37Y+W/JdlW1Uq1du///O1iYmJTJkyhf3792Nubk6/fv0AKFasGPv3\n7+fChQssWLCANm3a0KpVq3TH/i71b/OFKBQKrly5wqVLl9i8eTNqtfqdXwbU//ezdePGjVhaWjJ3\n7lxu377NnDlz0s3+979rNBrUajWLFy/GwsIi3e1Wq9XUrVuXKVOmvDN+6dKlt5/9d7/++it37tzh\n4MGD7N+/n5EjR1KqVCnWrl37zvP++uuvt3v6qZk8P4ociv8Xxkoly62saJsnD/cSE2ni58fYkBC8\nYmNJ+fvhrWbN4OpVcHGBJUvAzQ2Cg7UXXAghyLxlW2NiYlCpVJibmxMYGIiPjw9JSUkcOnSIR48e\n4ebmhoeHBz4+PumO/dP1/5vp09vbGycnJ8LDw7GyskKtVnPixAlSUlLeu2I9PDwcBwcHAI4fP/7R\nF76VLVuW4/93+7KXlxd//PEHkPYLRUpKCi4uLly+fJm4uDg0Gg3Tpk0jPj4+3ffy9/dn06ZNuLi4\nMHr0aCIiIihUqBC+vr6EhYUBsGTJEoKDgylUqNDbbb98+fJHZf1cssf+AWqFgskFClBcV5eNkZEc\niI7mQHQ0VioVzfLkoYWxMU66ulC0KFy6BD17wu7dUL48LFgAHTqAQqHtzRBCfKV69uzJ9u3b033s\nc5dtNTExwdXVlTZt2lCiRAl69+7NzJkzmTFjBlOmTMHQ0BCVSsWECROIj49n0qRJ74z9U0JCAv36\n9SMwMJC5c+diY2PDmjVr6NKlC25ubtStW5fJkye/85qWLVsyevRojhw5QufOnTl48CB79uz5YPbB\ngwczbtw4Dh06hEKhYObMmUDa4fFOnTqxadMmunXrRufOnVGpVLi5uaGvr5/ue1lYWODt7c3hw4dR\nq9W0adMGAwMDxo0bR58+fdDV1cXZ2RkLCwtatWrFoEGD6N69e6ZfPCfLtn4CjUbDXwkJHIiKwjMm\nhqj/O5zirKtLU2NjGhsbY6lSwfz5MGECJCSAq2va+fhM/kYKIURONGbMGBo1akS9evW0HSXHkGVb\nvyCFQkFFfX1+NjfntIMD8y0sqGNoyMPEROa+fk2DFy/oGRjInr59ibp9O20xmQsX0s7B9+wJQUHa\n3gQhhBC5nOyxfwHhKSl4xsRwMCoK74QEIO03Jke1mqZXrtBu0iTM7txBkycPih9+gP7902a2E0II\nIT6R7LFnotQUiAqAfKhwz5uXLba2HLW3x8PEhPL6+oQkJ7O0fHnq7tvH5KlTCVerYfJkUhwciO3S\nJe2COyGEEOILkovnPtKrRxqenUkg4kUibwITiA5OJDYiEaVOEsnR+bEqbkaRRiqcGqnpa25CXxMT\nUjUaXiYncz8xkXsDB/KzuzuW27bRYdMmnLZuha1bCatcGUMPDwzatQNdXW1vphBCiBxOiv0j3NwV\nRrThb+SvH4QBYJ3Oc1ISVLx5ZM7p9VZoYi3Jb2VGXmsdjK1VlLdR4mqmRGmfh/ixIznu4cGeP/+k\nypo11D51CmWXLrwZMYKwPn2wGzwYtZVVVm+iEEKIXEKK/V+kJMLZFfcwqfc7+fMkkhJWCD1DYwzy\nqlEb6KFS6qJQ6pCY+JrolCDylwzBpNS7F8jFAXGJQGDa16lJOpSMLolboypEf9eanT53MFi5kgY7\ndlBo+nSS5szhdqtW6A8ZQpGaNVHIrXJCCCE+gRR7BqICU7i89wTWLbxIiVeTP/U7bCuXzvgFFqDR\npJKQFEZUeBCvX4QTH5lKfGQK8ZGpJESnkhSbjFnlJ+QpdJuX4bdJfJKXUtFlcZgxE79Zs/Bbu5bi\nq1dTevdu2L2b+xUrEj9pEmWbNkWhlMshhBBCfJgUezqeX47iRcBvWDd+QUKIGUVLtCdP/vSnH/w7\nhUKJvq45+pbmmKdz0btGAy+vaXhywJ8E3RuYu/qgtD+Hf9Q5oh/bY17CjTynhnDrxnF0lizB+fhx\naNGC+9Wroz99Oo5yn6cQQogPkGL/h6cXQ3mtsxHTsjEk+rtQxrU5Oiq9L/LeCgXYVVZgV9kesCfk\n3rc89bpPsuEN8pV+gkLph/+bPwkPL4lB51+4OySE1MXjKXXyJNSvzwM3NyxmzMDk/xYSEEIIIf5J\niv0fUtWvUBslog5rhHOtqpl6jtuipBqLkqWB0oS9iOT5nZto8t/EvOZt4DbRfvlJ6DST68PC0Zsx\nmVL/tw68b4sW2M6Ygb6LS6ZlE0IIkTNJsf+DU+WSaDTFUSiy9py2mUM+zBxqo9HUIjzYD3/fG+ib\n38bI/k8SXhuR9PNszkYFYz51OiUPHCDl4EH8OnTAdto0lIULZ2lWIYQQ2ZdckZWOrC71dz9bgamV\nA2VcW1DMcRhx92qi1ElGt8gp8hX3hc1z+HPTrzwpUgT77dtJLVGCkL59ISBAa5mFEEJkH1Ls2Zi+\noRGVmjbAscAwQo82IDlGh1QjL+yqBJF8ZC57VizgpY0NFmvWkOjkxJsZMyA5WduxhRBCaJEUew6Q\n306fuv1rUiDJg8ermxAXZEyqxpvi30Tx5swsts/5mTfGxuQdP55XVauSks56x0IIIb4OUuw5SMGa\nappPr4zi3mBu/NSaN48LoEx5QJk2Gp6dnMqpVs0o8NdfpFasSOjPP0NSkrYjCyGEyGJS7DmMUgWV\n+6tovbgM4QcGcLF/B8K8bTEwCsZifnU8N8zgtYkJ5pMnE1y5Mok3bmg7shBCiCwkxZ5DGZpB0xUK\nms8pge+iXlwe3prENyrsaiUSdGwyJ9u2xvLmTRRVqvBywgRITNR2ZCGEEFlAij2HsyoH359VULFt\nGS50GUDwucLoGAVjOaMax7csIMzMDNvp0wmsUIGoa9e0HVcIIUQmk2LPBRQKKNsVep3LS9yVLvw1\nqQnJiUlYV39DiOcsTrq3x/rOHQyqV8d39Gg08fHajiyEECKTSLHnIgYm0HS5ggYelbk1tj9h3rao\njJ9hPrEqZ3auJNTCAqc5cwgsV46wS5e0HVcIIUQmkGLPhWwqQed9ZhiE9uT+8nqgiqFApWBCfl/A\n6a6dsXnwgHw1a3J3xAhS4+K0HVcIIcQXJMWeS6VdPa+k0Y+18V/Xm8iH5uiY3Se/R1Uu7FhPsI0N\nzgsXEli2LH7nzmk7rhBCiC9Eij2XM7aExrOtsTHsi9/e6ujke02+Ci8IXL2M8z17YPvoETZ16+I9\nZAiJsbHajiuEEOI/kmL/SjjW1uEbj4ZEn/ue2MB8GBTzRrdLFS5u2kGQvT3lly0jpHRpHp48qe2o\nQggh/gMp9q+ISg3VehSkqGN/Xl+qgFHBYIwrP+Lp5F/x6tMPm6dPKeLmxqX+/YmOjtZ2XCGEEJ9B\niv0rZOKgR61OzdHx60hytD4mtS+Q0rgqXov2E1CoENV++YXIUqX4y9NT21GFEEJ8Iin2r1jxOsVw\nLjmQ+GclMSn7AuN6t7ndbRNXew/Bys+PCt9+y7levQiNiNB2VCGEEB9Jiv0rp29kSIV67ciX2hoU\nShy6HudNjWqc/uFPXhYpSq1160gqXZrze/ei0Wi0HVcIIcQHSLELFAoFdkXKULL4QHhTGKs6jzDp\ncIPz1XdwrdsILIKCqNmmDZfatsUvIEDbcYUQQvwLKXbxlq46L85lu1DAuDFq4yRKTf6D0HKuHGxz\nlqdlylF9717yuLhwduVKklNTtR1XCCFEOqTYxTsUCgWWFlUoVqg/Oim2OLS8jZ3HRc4W3sTZHrPQ\nTUyk9sCB3K1bl8d372o7rhBCiH+QYhfp0tM1o1iRnpjnr49+gTgqzd9LQtWC7K97kbt13Chz7hy2\nFStybvJk4mVJWCGEyDak2EWGFAolFqa1KFpwAHpKR2y+eUDJmYfxdpyK5/friTc2ptbPP/OyQgXu\nXLig7bhCCCGQYhcfQU/XDKeC3bAp0AK1gZJyP3li2DyZ/eXOcK15J5zu3KFE7dpcHDCAN2/eaDuu\nEEJ81aTYxUdRKBSY5C1P8cKDMdYvhWm5l1RctofnxXuwz/0PQuztqbFqFTEuLvy1f7+24wohxFdL\nil18Eh2VEQVt2uBg1RkdVV5KDLiATd8n/Fn8EGfdh1MgKIgKrVpxrW1bXgUGajuuEEJ8daTYxWfJ\nY1iE4oUHYJq3OkYOEVRd+RtRFWuzq8kpHpcpR6U9e1A7O3N11So0cmucEEJkGSl28dmUSl2sCzTE\nya4PaoU1jt/dpOTEi1woupYjXWeiTkyk8oAB3K1bl5f372s7rhBCfBWk2MV/ZqBnTVHH3liaNkQv\nfzKV5vyBrpstO2ue42atBricO4dp+fJc/flnkpOStB1XCCFyNSl28UUoFEoK5K9O0YIDMVQXwbLm\nEyou/JO7JSaxt/Na4oyMqTx5Mn7ly/PUy0vbcYUQIteSYhdflK46P452nbCzaIOOrh5lxpzEsmMi\ne8qd4GJTdwrduYNDzZpcGziQ+KgobccVQohcR4pdfHEKhYJ8xqUoVmgQ+YzKk79kMNVX7SOkQje2\ntt1PkJ09lVauJNLZmYfHjmk7rhBC5CpS7CLT6KgMsLNsgaN1d9RKM4p+f4Viw3w5Uvp3jrUfhllg\nIE7ffsuVH3+Uc+9CCPGFSLGLTGdk4EhRx/4UyF8bQ8toqi3ZT2qtGmxp/Aeh1jZUmTuXR7VqEfDk\nibajCiFEjifFLrKEUqmDpWk9nBz6oa9jj12Tu5SdehPPqtu5Wqc5JS9fxrBiRS7t2IFGo9F2XCGE\nyLGk2EWW0te1oLB9D6wLNEXXWEOluScIrN+HfU0XoB8XR7WOHbnQuzeRMTHajiqEEDmSFLvIcgqF\nAtO8lSji0Be10gKnLn9h3teQrRU88XMqRs116witXJlbN29qO6oQQuQ4UuxCa/R0zSji0Jv8xuUx\nKRVExZVeHHXahlf97hS5d48i1avjuWwZiTIlrRBCfDQpdqFVSqUaW4sW2Jq3RG2cQrWVBwmu7s7u\nGptI1VHTaMgQLrdty7OwMG1HFUKIHEGKXWQL+fOUw8m+D2qVGUV7XsZyeCJbrc7wxLkCtfbtQ1Wx\nIsdPn5YL64QQ4gOk2EW2oa908hUAAAAgAElEQVRrgZN9X/IZl8asgj9VdhzlRIHNnKs1Avvnz6nT\nsCG///wz4XLPuxBCZEiKXWQrKqUutuat066az59IjfW7CanShG3FDxGTNz+tf/6Z+40aceX5c21H\nFUKIbEmKXWQ7/7tqvrBtL9Q6JpQYeB6bySFs0rnIo7L1qX7qFI5VqrBj3z4S5dC8EEK8Q4pdZFsG\netY42fUlj2EJzKs8p+bB3zlu+Asny0/H9PVr2rdpw34PDx7Hxmo7qhBCZBtS7CJbUyn1sbdsj5VZ\nI/RM46i1aSuvXF3ZlP804Vb2tFu6lKjatdnv4yMX1gkhBFLsIgdQKBSY5atGIZseqNV5cRl+mkIL\nn7Ap3ou7ZdtQ/vp16rq6smbdOsJSUrQdVwghtEqKXeQYhvp2ONn1w9igKBY1nlDHcwcnVfP5s8gv\n6Ccm0rd3b85+/z0X5J53IcRXTIpd5Cg6KkMcrDpiYdoAPbNo6u7cSEQ9F9ZprhJSsCStt2yhgKsr\nKy9eJFZmrBNCfIUyLPbU1FRWrFhByt8Obfr6+rJy5cosCSZERhQKBeb5a+Jo3Q212ogyY45TfNVt\nNoSf56ZzX4o/eECP+vXZOGUKV2QxGSHEVybDYl++fDl3794lMTHx7ZilpSX3799n06ZNWRJOiH9j\nZOCIk20/jAwKYVn7IW7HNnFG5yf2mv9GioExA37+GRo0YMn168TI3rsQ4iuRYbGfOnWKBQsWYGBg\n8HbM2NiY2bNnc/jw4SwJJ8SH6OgYU9CqC+b5a6NXIJL6e9YR19SOFRF38C3WgiqXL9O7dm3WzJ6N\nl+y9CyG+AhkWu76+Prq6uumOK5Vyal5kHwqFEgvTehS06oKOjj7lJh6h6rbT/Ba+mf15NqNQqhk2\nbhypjRsz78YNomTvXQiRi2XY0LGxscSmM/FHZGQkMbLnI7IhY0MnnOz6YajvgFnV+zQ9v5yErk6s\nirvFY6tGuJ47R9/atVmxcCHn5L9hIUQulWGxt2zZksGDB/Ps2bO3Y/fv36d///706NEjK7IJ8cnU\nOnlxtP4e6wLN0NFV4vLDUWqfPYSn7Sr266xBN0nD6JEjSW7Zkhk+PkTKfe9CiFxGJ6MHevToga6u\nLt27dyc6OprU1FTMzMzo168frVq1ysqMQnyStLnmK5LXqCTBr48TgTd1d20k8HhZ1o++REO9QdQ7\ncYLytWqxaOpUavfoQT0jI23HFkKIL0Kh+Yh5OKOjo1EoFBhlwx9+/v7+NGjQgBMnTmBnZ6ftOCIb\nio33I/DVYeITg0hN1OPWrLqYbLmCm3IMuklx/NmkCVfmzcOjWDHyq1TajiuEEP/qQ72X4R77smXL\n3vlaoVCQJ08eGjRogK2t7ZdPKkQmMdS3p7BtH16/uUZI+EnK/eRJ7PdW7BjoSe1no2l8+DCVL19m\nzsyZ1OvUiW+y4S+wQgjxsTI8x56cnPzOn6SkJB49ekT37t25du1aVmYU4j9TKJSY5atCUbvB5DMu\ng6FDEBUPnuTB9FEcN5xOvohoZvTtS2zXrox/+JDXcu5dCJFDZbjHPmzYsHTHX758ybhx49i4cWOm\nhRIis+joGGNn0RqTPBUIfHUYu5a3SGlkwOFRmyl/YjYt9+2jqpcX02bN4ps2bfjWyAiFQqHt2EII\n8dE++YZ0OQwvcgMjg4I42fXF0rQhaoMUiiy9S9De3ly0HEWBkFcs+P57ovr0YbSvL6HJydqOK4QQ\nH+2Tiz0pKYmEhITMyCJEllIoVBTIX50i9oPIa+SCUYlg8l4wwmvwAgL1S9F+xw6G1qvH5N9+42BU\nlKz3LoTIETI8FO/l5fXeWGRkJPv27aNhw4aZGkqIrKTWyYu9ZVui49IOz5t6hBHarRMhnX0p/XA9\nyzt2ZHP37gz/6SfGOThgoZPh/zZCCKF1Gf6EWrFixXtjRkZGNG7cWO5jF7mSsUFhnOwGEBbpRaji\nLJrD9vjsG4fNTzvounEjtc6cYdy8+TT5xo3WefLIuXchRLaUYbFv3rw5wxcFBARgY2OTKYGE0Cal\nQoV5/prkMy5N0KsjRLW+T3AjdyL63qWw1z5+ad+ODb1703/MGCbY2WGvVms7shBCvOOjz7EnJCRw\n4MABunfvTvv27TMzkxBap6uTDwerDjhYdUI3rzkJW8rg++sAog1t6LV6NaMaNWLcoUNsiIggWc69\nCyGykQ8W+40bN5g4cSKurq5MnjyZdu3acerUqazIJoTW5TEsipPdQMxN6pJU35qXXt142dCNIo8e\nsb5tWxLGj6fb06fclwtKhRDZRIbFvmbNGpo0acLw4cMxMzNjz549ODg40KxZM9Ry+FF8RZRKHSxM\n6lDEbhBGZiWJWFmTJ+t7EW9gTr8VK5jUpAk/HT3K4tevSZAlYYUQWpZhsS9atAi1Ws3MmTMZNmwY\nBQsWlIuFxFdNV22Cg1VnbM1bklC3KH4XehD0bV2KP3jAttbfoZoyhfbPnnE9Lk7bUYUQX7EML547\nffo0+/btY9KkSaSmptK6dWuSkpKyMpsQ2Y5CoSB/nnIYGTgR+OowYcv1iD9TEPOhfzJ48WLqHzvO\n+LlzKF+lCiNMTTFWfvJUEUII8Z9k+FPH3Nycvn374unpyYwZM3jx4gUvX76kf//+nDlzJiszCpHt\nqHXyYG/ZHjuLtsTXc+HF+Z6EfuuK89077GrZinwzZ9Ly6VNOxcRoO6oQ4ivzUbsTlStXZtasWZw7\nd466deuyfPnyzM4lRLanUCjIZ+xCEftB5LGuTMjyb3i+pgvJ+vnwWLCAxa1as/D8eUYGBxMmi8oI\nIbLIJx0nNDY2xt3dnV27dmVWHiFyHB2VIXYW3+Fg6U78N+V5drYXYY2rUsrnNr81a4H1ggW0fPqM\n/TItrRAiC2TLE4AhISF4eHiwe/dubUcR4qPlMSpOEbuB5LWrQdCyxrxY1ZFUfSN+mDOH5W3bsfry\nZfoGBfFSrlURQmSiTC32hw8f4ubmxpYtW96OzZgxgw4dOuDu7s6tW7fSD6VU0qFDh8yMJkSmUKn0\nsTVvQUGrrsQ3rsqz030I/7YiZW96s7dJc4osX06rZ8/ZFBlJiuy9CyEywb8W+7Vr1975OiIigitX\nrnzUG8fGxjJ16lSqV6/+duzKlSs8f/6cnTt3Mn36dKZPnw7Ahg0bGDp0KEOHDmXJkiUUKFAAlUr1\nqdsiRLZhbJg273y+gnUJWN6cF8vao9DXY/T06fzSviPbr1+ny8sAHiUmajuqECKXybDYDx8+zPjx\n44mKino7FhERwZQpU7hw4cIH31hXV5c1a9ZgYWHxdszLyws3NzcAnJyciIyMJDo6mu+//54lS5aw\nZMkShg4d+l+2R4hsQ6XUxbpAYxxtepDQohZPTvQlsmFZKty4xr5vm1FqzWraPX/BstevSZS9dyHE\nF5JhsW/cuJGNGzeSJ0+et2OOjo78+uuv/PLLLx98Yx0dHfT19d8Ze/XqFSYmJm+/NjU1JTQ09L3X\nenl5sXXrVg4fPsyxY8c+akOEyK6M9B1wsu2HSeGG+K9ojd/itqj0dBg/ZQq/dujKgdu3+e6FP97x\n8dqOKoTIBTKcoEZHRwcrK6v3xq2srL7Ylb0ZvU/16tXfOYQvRE6nVKqxNHMjr7EzL7/bz5OqBbEe\nf4RKJy6xv2ET5kwcS5eOHWlsbIyHqamsGieE+GwZ7rH//RD836WmphIeHv5ZH2ZhYcGrV6/efh0S\nEoK5uflnvZcQOZGBng2FbftiUqwZ/r+0x39+a9S6CiZNnMjG1t15evUqzZ77MfPVK8Ll3nchxGfI\nsNgrVqzI/PnzSf3bohZJSUlMnz6dunXrftaHubq64unpCcCdO3ewsLDA2Nj4s95LiJxKqVBhYVKX\nwvb9SOjQGN+j/YiqV5JKty+wp3lz5vQbztlbt/jG9wWrX0cQLwvLCCE+QYaH4keOHMm4ceNo0KAB\nJUqUICUlhbt371KjRg3Gjh37wTf28fFh9uzZvHz5Eh0dHTw9PVm6dCkuLi64u7ujUCiYNGnSF90Y\nIXISfV1LCtv0IszIC79fTTA8/xDLuWdodPIADU4d5vfW7Vk2cjCb89kw3NqUlvmNUclCTEKID1Bo\nPnDC/Pnz5zx+/BiVSkXRokWxtbXNqmwfxd/fnwYNGnDixAns7Oy0HUeIz5KQFEZo+Fkio26R98gd\nLOafQe9pCAk6Bmzt3pVfB/Yjj9KCsQVNqZPfQFZaFOIr9qHe+9f72JOSkrh69SoXLlzg/PnzXLt2\n7Z1D80KIL0NPbYadRWuKOgxF1b4bvkcG8nJmC5QFdOm5djXHataj8eZFjHz5hLaXgrjxOkHbkYUQ\n2VSGxe7n50ezZs04e/Ystra2WFtbc+rUKVq1akVQUFBWZhTiq6GrNsHGvBlFCw9H1Wcgj08OJ2hs\nQ/T0kxm2YD7Hataj/NHVfB/ylO7HQvANkelphRDvyvBQ/NChQ2ncuDGNGzd+Z/z48eMcOnSIhQsX\nZknAD5FD8SI3S06J5XXkZcL9z2Ky5iQF1l1CGZtIoJUdC38cwZFGzal114Rx1fNjayuzNQrxNfjs\nQ/F+fn7vlTqAm5sbz58//7IphRDp0lEZYmFajyKlxqCcOoMnZ8cS1r0qVmGBzBkxgv0tmqKJ2Eez\noBf8uDaCoCdyqkyIr12Gxf5vc7XLPO5CZC2VUo8C+V0pXH48LF7C01PjCP+uLI5PH7GiTx+29G5N\noLEnzV/6M2luFMF3ZYpaIb5WGRa7vr4+vr6+740/fPjwvalihRBZQ6lUY5avCoVqTIING3jhOZY3\n35TA5cZNNru7s2JiJ3xKnuG7xy+ZNSaWgOvaTiyEyGoZ3sc+YMAA+vTpQ9++fSlTpgypqal4e3uz\nfv16li9fnpUZhRD/oFCoMMlTjvwNyvCmWmf8T27EZMYuKp+5wJ4zzTnbvCEzh43m2O2SNFlhSscB\nethU0nZqIURW+Nf72B89esSqVau4d+8eBgYGlChRgl69elG4cOGszPiv5OI5IdLWXYiOfUTM/lXk\nm7EDgzuBpOooOdG+BdMHjiLPqaJ0CzGj2WQlBqbaTiuE+C8+1HsfnKAmPSkpKdnmPLsUuxDviol5\nSuzWBeSdtR29p2Gk6KvZ068bC1v9SMUltvRsakT5HqD411kshBDZ1WdfFR8cHMzw4cNp3rw5P/30\nE3FxcQDcu3ePNm3aZF5iIcR/YmRUCPO+S0m9/Rdh8/qSmk+f9ovXcqh3PeLc9zM2NpilTZIJ9NZ2\nUiFEZsiw2CdNmkSVKlWYO3cuRkZGzJo1i8WLFzNkyBA8PDyyMqMQ4jMYGDlg9sMvJN++SkT3Opg8\nCWBth86MutyP3WPvMm5GFIeGaIiP0HZSIcSXlGGxR0dH07FjR0qUKMHo0aM5cuQIUVFR7N+/n3r1\n6mVlRiHEf2BgXpy8608QfngZ8UXNcdu6nyMd6mDeYAPzKwUxs24SNzfBp5+UE0JkRxkWu1L57kPF\nihVjwoQJGBkZZXooIcSXpVSoMP12EFy/zusfWmAQHsWcAUOZt7095xdcY/bpCNbV0RB8W9tJhRD/\n1UdfPiOrSQmR8+nnscdk7j4iz24mtlJBKnie589WDShfeAEbh/ozu0MiniMg4Y22kwohPleG97H7\n+vry448/Zvj1nDlzMjeZECJTKBRKTKp2JPH8N7xaNBKTaTsYNXEqrSvvY8T8+bw4VombpU1oPEtB\nKXeQ3+mFyFkyLPaRI0e+83X16tUzPYwQIuvo6hXA7Mf1RLRyR+UxkiKePuxr1ZSNA/uyfuUI/H6y\npdIafZosA3NnbacVQnysDIu9devW743dvHmTsmXLZmogIUTWUSgUmBT/lqSDNQjZPBmTsb/Sc9EK\nmhw6yA/T57H3Tj18a5hSq5+SOhNB11jbiYUQH/JJU1TMnz8/s3IIIbRIrZMX8+/nE3v9COFdqmP1\n6AVbOnSg55NBnPvtLnuuxLK8JNz9Ta6eFyK7+6Ri/4xJ6oQQOYRCoSCfbQ3ybDhG8P6pJBY2o+Xm\nXfzetS66HTdzbGQIm/ulsPVbCHuo7bRCiIx8UrH37t07s3IIIbIJHZURli0mkHDlJK88GpInLJwl\n/fozzrMjf+26zkmDKFaU1nBiPCTFajutEOKfPqnY69SpA8C6desyJYwQIvvIa1oak/n7CT25mNgK\n9tT88yT7W9ejYNUlnF8XiOeGJJaXhPu/y+F5IbKTz1oG4vTp0184hhAiO1Kp9LGsOZjUsycJntYW\ntSaZSeMmMH9pMx6sPcv1BpFsb6NhezN47avttEII+Jer4jt37pzuuEaj4dGjR5kWSAiR/RgbFcFw\n7FZCW+zC4IfplDrmzZ6Wjfll8CAOHhhK1DBbnrjoUnMMuI4GtYG2Ewvx9cqw2NVqNTVr1qRMmTLv\njGs0GmbNmpXpwYQQ2YtSqYtl6S7EHqhD4KYpFJi4g0ELFtH84AHGzZxLqHc9Emfk59ZmBd8ugWJN\ntZ1YiK9Thofi586dy9mzZ3FxcaFKlSpv/1StWhVjY7mZVYivlaG+PZZ9VhJ+aSuvO1bC4eETNrdv\nSzP/Ifx19D6++eLZ3gx2tIKIZ9pOK8TXJ8NiNzc3Z9OmTeku+iLTyQrxdVMqdLAo1AKD9X/wcvcP\nJBYypcOmrWzsVJfkAVvwXfkKn2OpLHeGs9MhOUHbiYX4enzWxXPW1tZfOocQIgcy0LPCps0c3lzY\nSeiQupi8esXyvn3odrArPn9688otllMTYGVp8D2q7bRCfB0+q9iFEOJ/FAol5lYNyDtvNwFHJhNb\n3g63Q0fY2qIeRq5L8D0QTFBYClsawa62EOmn7cRC5G5S7EKIL0JPtwC29SYQd2I3QZObo5+SwLQx\nYxk2ozmPd54jZng0d/doWF4Czs+GlERtJxYid5JiF0J8MQqFAjOTapiO20TA6Tm8cStOhUtX2dK0\nITaqyTy76E+iYzInxsCqcvD0pLYTC5H7SLELIb44XXV+7CoMJWXPdvyXd4Z8egybN5+JPRvwbNpB\nkla9IfSBhk0NYE9HiArQdmIhcg8pdiFEplAoFJjkLY9l35UEnV3E6w4VcLr/iHVt21D80lCeXn2M\nQctEfHbAsuLgtQBSkrSdWoicT4pdCJGp1Dp5sCveE9Wvm3i+oz9JBU3oumEjc5rXxq/5FlI9w1EY\naDj6A6yuAM/PajuxEDmbFLsQItMpFAryGbtg23Y+oaeXEzqwFhYhISzt3YtqS7ty7/BNLCYmEHIH\nNtSBfV3l8LwQn0uKXQiRZXRUhtg5dEB/7lqeHRxBbDlbGh88xNJv6vDSeDGpN19RoFoqt7bA4kJw\noA+8eqDt1ELkLFLsQogsl8ewKA7fTCPiyAoCf2qMUVI800aPpkn/Flydc4HCv8WRzwG8f4XlJdOm\np/W7qO3UQuQMUuxCCK1QKfWwsWxB3jEreHF8PFH1i1H1oherv3Ej9MpkXpz0p9qhRGwrw4P9sM4V\n1tWEBwdAk6rt9EJkX1LsQgitMjJwpGCVscTsXILfknYo8qgZMWcOQ2q5cvHGPO7ueo7rxQSKNgW/\nC7CjJaxwgb/Wyhz0QqRHil0IoXVKpRqrAo0o0Gchfid/5nWXytgFvuTn8eMZV6Uy3gcmc3nhI2rd\njafc9/DaF/7onXYe/vxsiI/Q9hYIkX1IsQshsg0DfVscSw0jZekCnpwby6s+NTCJiWTUrFlMq1SR\nh8tHcmLEbVwfx1FtpIbEaDgxBhY6wNGR8MZf21sghPZJsQshshWlQoV5/poUrjQJ5bwlPPOaSvDw\nehjopNB/+XLmVqlMwMT+HGp3lcq+sTSYrUHXGLzmp+3B//49hPhoeyuE0B4pdiFEtqRU6mCatyKF\nS43CYOpK/K/MJ/CnxihM9emyaRMLXGsQ068Lv1U6hcvdGJqt1WBaBG5uTFsmdltTeHYGNBptb4kQ\nWUuKXQiRrSkUCvIaFcfRqT/5xq4m6NIK/Oe2IrmQKS337WNRgwbotm/FjgJ/4Hg5mnYHNDjUhEeH\nYWNdWFsN7u6B1BRtb4kQWUOKXQiRYxjq2+Fg3wlzj195fX4tL37pTGw5W+ofO87cli2xatSArW+2\nkf/QG7pd0FCiFby8CrvbwvIScG0VJMVpeyuEyFxS7EKIHEdPbYaNRXNsev1C9PHNvNjel+haTlS8\ndIXpXbrgUqMqm2+ugnXh9L6XSoU+EPkCDg2ARQXh7DSIe63trRAic0ixCyFyLB2VERZm9bBrv4zE\nP7bz4uBIIps4U/TufX4aOJBaFcqy7fe5REwNpu/TVGqOhZREODUx7Ur6Pz0g4pm2t0KIL0uKXQiR\n4ymVakzzVca+yWwUO3fjf2Yq4e0rYPXyJaPGjKFZ2VLsWj6RFwP86PM8lYYLwMAEriyBJUVgb2cI\nuqHtrRDiy5BiF0LkGgqFkrzGztjVHI/uhr0EXl7Cq17VyRsVyeAZM2hf2pkDEz240+wR3R+l0GoT\nmJeE29vgl/KwpRE8OS5X0oucTYpdCJHrKBQKjAwKYlt+EHlW7CfYez0hw+qjp0yhx9JldCtbmpMD\nenC57C3aeifT6TA41gPfo7D5G1hdEXx2QGqytrdEiE8nxS6EyNX0dM2xKdYZk7n7Cbu5i6CfmqHI\np0fbDZvpVbEC3u5tOJnfi0ZHk+l9BZzbQfBN2NMRlhaFy0shMUbbWyHEx5NiF0J8FdQ6xljaN8N8\n0h7e3DpI4NyOJBc0oeGeA/R1deVZ0284FH6UaluTGPwAKg2A6CA4MhQWOcCpSRATqu2tEOLDpNiF\nEF8VlVIXM/NaWP2whfi/jhO4uh9xpa2pdvQ0fRs14k3d6uz13k3JhQkMewG1f0p73dkpaQV/aFDa\nIjRCZFdS7EKIr5JCoSRf3rJY9V5JqtdZgnaNIdq1MC4Xr9O7fXtUlcuwZ/8abMbEMewFfLsEjK3g\n2gpYVgx2t0+b/EaI7EaKXQjxVVMoFBgbOmHVbiY6Jy8S7DmHyG+dcfB5SLc+fTEtVZS9v8zFsPsb\nhjyCNtvBsizc3Q2/VoGN9eHxEbmSXmQfUuxCCPF/9HUtsWw4CsM/vHh18VfC21XEzC8Q9x9+xLF4\nIQ5MG0dSg1D6Xoeux8CpITw7BVsbw6qycHNT2gQ4QmiTFLsQQvyDWicv5tV6kXfHecK9f+NVr9oY\nRUbR6ueZlCxWiGMjB/KquB+dj2jo5w2lO0HoXfi9OyxyhHMzZcpaoT1S7EIIkQGVUh8zl9aYrjlJ\n1N1jBA9vgg6pNFywkvIlinKhbyeeGDyg9RYNQ32h2nBIjIaT42ChPRweDK8fa3srxNdGil0IIT5A\nqVCRz7EOFvMPEvfAi6BJ7pBXl5q/7qBaaRduuDfldtg13OZrGO4H38wDAzO4uhyWFoOd38GL83Ie\nXmQNKXYhhPhICoWCPBZlsZq8neSHtwmY358ku/yU3/UnNStV4VGTWpy9eYoKw1MZ6pt2oZ1NRbi/\nD9bXSlsb3menzGgnMpcUuxBCfAaDPAWxGbESxd0HBKwdR5yLNSWOXKBhnfr41a/C/mN/YN0uhd5X\n4PuzULxl2u1xe9xhiRN4LYCEN9reCpEbSbELIcR/oKtvhk3P6eh6PyJo10yiqzhS9Mx1vmvagjeu\n5dj62w6SqyTg/jtpM9oNTJvB7ugPsMAOPH9IWyteiC9Fil0IIb4AHR1DrNqNweDiXUIPLuJNnWIU\nuuxDl/Yd0a1SmjXr1/DIJoYmy9LOw9efDrpGcGkBLC4Mv7nLhDfiy5BiF0KIL0ilMsC8qQdGJ24S\ndmIVkY1csL/1iD49++JYwZkVyxZzShlOtbEaPJ5Byw1g4QJ3dqZNeLO+Ftz/HVJTtL0lIqeSYhdC\niEygUuljVr8fxoev8friRiJalcfS149BQ4dTrZwzK2bPYEtkCIW7pNDvRtqEN0Uap109v7M1LC8B\nV5bLynLi00mxCyFEJlIp9TGt3o08e7x4fXU7rztVxSQoFI+xE2hd1pkNP//EfL8X6NVJovNhGOAD\n5XtBpB/8OTjtfvgT4yAqQNtbInIKKXYhhMgCKqUeZuU7kG/zGcK9d/+/9u48Lspy///4a1a2mWET\n1FRsccnUAKvjli1a7mKiBsoippmVS2mplR09v05ZespOaCeXckM7pqVhlpZmfdUUVFRQI3LflUVg\n2JmZ+/fHrSSFpSdhGPo8H48ej7iYuef+OM68va7rvq+L7BFdMOflM+61N3g6tC1rX5nMy4cPc/6O\nEsIWwnMn1J3ltDrYNkNd0W7tMDi/39mViNpOgl0IIWqQTuuG/10D8Jm/iUupn5H1bFfcy0p5dtZs\nXr0nhF0vTuCZAwdINhXy4HSF505C33nge7u6Fv28EFj2KPz8lSx4I6omwS6EEE6g0xrxb9YXv/e+\nIvfAOjJf7IlBrzBizn94t/29nB//LLG7d/NpeT53jXTw7CEY8gXc+jAc3QQresN/2kDKQrCVOLsa\nUZtIsAshhBNptUb8mz6K/5uJ5B1Yz4W/90djMTL0wyUsvr8TxqdHEf3DD8Tn5uDXw8awb2FUCtwd\nDdkZsO5JmB0E3/1DvT9eCAl2IYSoBbRaA/4NHyJg2iry96/n/IzBOBqYCV+xklUPPUizkcMZuWUL\nr2ZmUti6jAHLYPxx6DwZHOXw/XR4NwjWPQVZ6c6uRjiTBLsQQtQiWq0B/8AuBE5agTXlC86+O5Sy\n2/3pvSaRNT168PCwaCZv3Mjoc+c45FdMtxnqgjc93wPzLZAyH+a2ghV94di3Mg//VyTBLoQQtZBW\nq8ffvxMNxi2hKHkdZz+Io6R1A7p+9TWfhoUxbMjjzFm3jkFnzrBBsRI6RmFMBjz+KTTpBD+vh6Xd\nYH472L8M7GXOrkjUFAl2IYSoxbQaPX4+7WkwagHF2xI5vfQpCu8LouP320iIiGDqoAEkrllDzxMn\n+MiaS6P+dp7YDiN2wF2D4UIqrI2Ff98G296E4kvOrkhUNwl2IYRwAVqNHj/ve7klei5lm9ZwatVY\nrA80IzRpNwuHDeP9x80mzQoAAB5aSURBVMLYt3Il3Y4fZ0ZWFtxTzuBPYNwRaP+cupPc5pdgdmP4\ncizkHHF2RaK6SLALIYQL0Wp0+Fra0XjgbGzrPuHEFy+Q36MVd+5PY87o0azu04uchAT6HD/OhAsX\nONmghJ6z4flT8Ogs8PCDXXMgvjmsDIcT/yfz8HWNBLsQQrggjUaHryWUoN5v4li1ghPfvETuY3cT\ndPgIs55/nq+7P4LXokXEHj9O9JkzbDMU0n6iwrijEL4CGraD9DWw+EF4/y51f/iiLGdXJW4GjaK4\n9r/VTp8+Tbdu3di8eTONGzd29ukIIYRTKIqDvII0Lu1fg/fcL/D5bB/aMju5DRvw/sgn+TQigkCz\nmVhvbx4zm3HXaDm5FXZ/AD9+ql5cpzNCq3BoNwpufQg0GmdXJaryR7knwS6EEHWIojjIKzxIzo+J\nWD74Ar+P96AtLqewnj8Lhz/B8qgo9N7eRFgsDLVYCNDrKcpSr5xPmf/LPfB+zaDdkxA8DEz1nVuT\nqEyCXQgh/oIUxUF+4SGyD6/HvGA9fkuT0RWUUuptYUXsMBYMG0aRry99TCaivL25y80NRYFT2yFl\nARz8RF2qVquHlv3hnlFw+yOgkQlcp5NgF0KIvzBFUdSAP7EBr0Vf4r9oJ/qcImxennweFU18XByZ\n9esT4uZGlLc3j3h5YdRoKL4Eacthz3y4mKYey+dWCB0JocPVxXCEc0iwCyGEuBzwP5J19ms8l22g\n3oIfMFyw4jAY2N23L+9ER5MWEkKATsfjFguDzWYC9HoUBc4kqwF/8L9QXgQaHbToo87FN+upbi0r\nao4EuxBCiAqKomAtSifz/GY8Vn6N3+KduP+s7h5zNjSUeTExfN6zJ4qbG929vIjy9ibYzQ2NRkNp\nPqR9rM7Fn0tRj2dpDKEjIPQJ8A5yYmF/IRLsQgghfkMN+Ayyc39As+U7/JYkYd6cgUZRKA4MYO2Q\noXwQGUlWYCCtjUaGenvTy8sLN606yX52jzoXn7YCyqyABpr3Ui+4a94HdAbn1leXSbALIYT4XcWl\n58jJS6Lox634JuzE95MUdPklOPR6Uvr1Y3ZUFPtCQvDV6RhksRBhsdBQrwegrEC90G7PfDiTpB7P\n1ABCnoB2I8H3NicWVkdJsAshhLguNlsBOdY95J7fjunTHfgtTcb954sAnAsNZUF0NJ/16oXDzY2u\nXl5EWSzc6+6O5vIN7xdSYc8CSF0GpXnqMW9/VO3F39lfvU9e/HkS7EIIIW6IQ7GRX3CQ7Nyd6L7/\nodIwfUlAAOuGDmVuRASZ9evTwmhkiMVCX5MJz8vD9OVFcGi1OlR/cpt6TM8ACIlTe/H+LZxXW10g\nwS6EEOJ/oigKRaWnyMnbSXH6DvwSkvH9ZC+6/GIUvZ69/frxTlQUe0NCsOh0hJvNRFosNDH8MsGe\neQhSFsL+JVCco7bd+pDai28VDnp359TmyiTYhRBC/Gll5bnk5CeTeyEJy5pk/Jb8Mkx/ISSEBbGx\nrO7ZE5ubGw94ehJlsdDRwwPt5WF6Wwn8uEbtxR/foh7Tww/ujoV7noSAu5xVmeuRYBdCCHHT2B1l\n5Fn3k523E/3WPfgvScK8+Sc0DoXSgAC+GDqU+MvD9LcZDAyxWOhvNmPS/rJkXfbPai9+3yIoUu+0\no0lndXW7uwaBwdNJxbkICXYhhBA3naIoFBQfISdvJ6U/78YvYRe+K38Zpk/t25e3o6LYExqKl1ZL\nf7OZoRYLtxl/uYLOXgY/JapX1B/9Rm1z84a7Y9RefP27nVRcLSfBLoQQolqVlmWSnZdE3sXdeK/d\ng9/SXbhnXAAgMySEhTExrOzVi3I3Nzp5eDDUYuEBT090V20fd+kopHyo9uILzqltjf6mrm7XJgKM\nJmdUVjtJsAshhKgRNnsxudYUcnKTMGxPxX9pEuZNvwzTbxg6lNmXh+mb6PVEWiwMMJvx1v2yJq3D\nBhnr1dXtDm8AxQFGM7Qdql5wd8s9TiywlpBgF0IIUaPUneV+JCcvifIjqeow/Sf70OUVoej1pPXr\nx+yhQ0kODcVdq6Xv5R3mWhgr3+iedwr2fgR7F0L+abWtYTs14NsMAXdvJxRXC0iwCyGEcJri0rNk\n5yVhzUzB+/P96jD9T+cByAoJ4aPYWFb07Em5mxv3ubsz1GKhq5cX+quG6R12OLJRnYvP+AIUO+g9\noPXj6n3xTTrDVQ+v8yTYhRBCOF25zcql/N3k5O3Cbcch/JckY96UjsahUFavHhujo3ln8GAuNmhA\nA52OCIuFQRYLfrrKW8dZz8K+JWov/tJRtc2/pRrwwbHgFeiE4mqYBLsQQohaw+GwkVeYRk5eEvZj\nP/1yNf3lYfpDffvyTlQUO0NDMWq19PLyYqi3N23c3CodR3HA8e9g74dw6FOwl4JWDy37qyF/+6N1\ndztZCXYhhBC1jqIoFJWcIDsviYLsVLwT06i3dDdu6eol8dnBwSyOjSWhVy/K3NwIdnNjqLc33b28\nMP5q3L04B1KXq4vfXExT2yxN1K1kQ4aDT9Oarq56SbALIYSo1crKL5GTn8ylvBTckzLUYfpv0tE4\nHJTVq8fmqChmPf44Fxo0wF+nY5DZTH+zmaaGynvDKgqc3a0ufnNghbrzHBq4o7vai28ZVjc2opFg\nF0II4RLsjlJyrfvIyUtGOXEU3+W78Fu5D11uIYpeT3qfPsyOjmZ7aChoNIS4udHPbKanlxc+v5qL\nLyuAg6vUufhTP6htngHqPHzoCAho5YQCbxIJdiGEEC5FURQKin4mOz+Jopx0vNcdUIfpfzwLQF5w\nMGtjYojv2ZNiDw8MwIOenvQ3m7nf0/M3Q/WZh9TFb/YvgeJsta1JZ7UXf9dgMHrVcIF/kgS7EEII\nl1VSdpGcvCRyrfvxSDqC/5JdmDf9iMbuwOHry8GoKOY8/jjbGjUCwEerpZfJRJjJRFs3t4q94gFs\npeoStnsXwpFvAOWqxW9GQsN7XOO2OQl2IYQQLs9mL+JS/h5y8nfBqdP4fbwHv0/2o8vKQ9FoKOzR\ng6+GDWNO+/ZkXY61Ww0Gwkwm+plM3PKr+fjc47B3Eez76JfFb+oHqwHfNgo8fGu4wBsgwS6EEKLO\nUBQ7eQUHyMrdTlnBOSwbDxGwPA233T+rv7/9do6OHMniAQNY7+ZG6eWIu8/dnTCTie4mU6Wd5hx2\nOPK12ov/KVFd0lbvDq0GqiHf9MHa14uXYBdCCFHnKIqCtSiDrNxtFJeexv3gOQI/PoRp7S40xSXg\n7k7ZkCFsi4tjabNm7CopAcBNo6Hr5fn4jh4elVa4K7gA+5eqIZ+dobb5NVMvtgseBuaGzqj0tyTY\nhRBC1Fnq/fAnycrdRkHxYXS5RdRbcxjf5cnojl0eY+/YkZynnuLTHj1YW17O8fJyAPx1Ovpcno+/\n02ismI9XFDi5TQ34g5+ArQQ0OmjRV+3FN+upLobjLBLsQggh/hJKSs+TlfcDeQUHwGHHe/t5Aj8+\niOHrH9AoCgQGoowcSXpcHJ/6+vJlQQF5DgcAzQ0Gwsxm+phM1Nf/ktoluZD2sRry51LUNvMt6sI3\noU+A7+01X6cEuxBCiL+UsvJLZOX9QK51H4piw/10MQ1XncBj+UY0ly6BVgv9+1P+zDNs7dCBzwsK\n+K6oCBugATp6eBBmMtHNywvPq+bjz6Wot82lLYfSPLXttm5qL/7Ox9S5+ZogwS6EEOIvyWYrIDs/\niZz8XTgcpehKoOGmfCyLv0Wzd5/6oFat4JlnyI2KYqNOR6LVyr7SUgA8NBoe9fIizGTibx4e6C4P\n1ZcXqevT710IJ/5PPYyHH9wdo4Z8YJvqrUuCXQghxF+a3VHKpfzdZOftxGYvQKNoqZfhjv+yZHSf\nJkJZGZhMEBMDzz7LiRYtWGe1sq6ggNM2GwD1dTr6mkyEmc00u2rf+Kyf1D3j9y+GwotqW6P2asC3\njgA3882vR4JdCCGEAByKjTxrKll52ykrzwHAp+gWAj87gmHhCjh1Sn3gQw/Bs8+ihIWR4nCQaLWy\nsbAQ6+X5+LuMRsLMZnqbTPhfXsrWXq7uFb93IRzeoO4+Z/CCNpFqyDdqf/Num5NgF0IIIa6iKA7y\nC9PJyt1GSZm6m5yXIYj620twX/gpms2b1QfecguMHg1PPklpYCDfFRXxeUEB24qKsAM64H5PT/qZ\nTDzs6Yn75fn4vFOwb7G6pWzeCfVQQfdD3P/dnHCXYBdCCCGqoCgKhSXHyMrdTmHxUQDcjQ0JPN8Q\n0+KNaJYuBasVDAYYOBCefRY6dybb4eCrggISrVYOlpUBYNJo6HH51rl27u5oNRoUBxzdrPbiywph\nSCJotL93RtdHgl0IIYT4A8WlZ8nK3U5+4SEAjAY//LXB+Hyeinbuf+CQ2k5wsBrwQ4eClxeHy8pY\nV1DAF1Yr5+12ABrp9ep8vMnErcabv0/sH+XeTfi3gxBCCOHaPNxuoUn9wTRrPAYfcyjl5bmcK93C\nz31Kydo2D/vmjTBoEBw4AKNGQaNGMGECzU6e5Hk/P74OCuLDhg3pbzJxyW5nXm4ufU6fZuiZM3yc\nl0fu5dCvCRLsQgghxGVuRn8aBYTRPGg8/t4dcTjKuHBpMxm3p3Bh3mhshw/Cq6+CuzvMng3Nm0PP\nnujWr6eD0cgbgYF837QpbwUE0MnDg7TSUv6ZnU3Y6dM4amiA3ImL4gkhhBC1k0FvoYF/dwJ8upBz\n+Va5rNxtZGv0+IxrT73Jz2JctwXmzIGNG9X/br0Vnn4azxEj6OvvT1+zmYs2G+sLCihXFGpqLxnp\nsQshhBDXoNN5EODbhRZBz9HQvzd6nYlL+bv5+cI8Tj9soGTzati3D558Ei5ehMmT1WH64cNh924C\n9XqG+/gwyte30t7w1UmCXQghhPgDWq0BP+/7aN5kLI0Cw3EzBpBXeIAjZ+ZxIvAAhf9+BeXUKXjn\nHWjSBBYvhvvug/btYdkyuLy7XI2ca429khBCCOHiNBotPqa23NFoNEENhuLp3pSC4sMcP7eYY8Vr\nyB/VGyU9HTZsgH79YNcuiI2Ftm3h8gI31U3m2IUQQogbpNFoMHs2x+zZnKKSU2Tlbsda9BOnLvwX\nN0MA9Tp1wrv7GjTHT8K8eZCff/OWnvsDEuxCCCHEn+Dp3oSgBpGUlF0kK/cH8grSOJP5ORdztuDv\n1xHfN/4fWu3Nv5/9WmQoXgghhLgJ3I2BNA58jOZB4/CztMfmKOZ89kYyTr5LVu6OGjsPCXYhhBDi\nJjLqvWlYryctgp4jwOdBADIvbUFRZI5dCCGEcFl6nSeBfg9Rz6czDqUMzc1YKP56XrdGXkUIIYT4\ni9JqDWgx1Nzr1dgrCSGEEKLaSbALIYQQdYgEuxBCCFGHSLALIYQQdYgEuxBCCFGHSLALIYQQdYgE\nuxBCCFGHSLALIYQQdYgEuxBCCFGHSLALIYQQdYgEuxBCCFGHSLALIYQQdYgEuxBCCFGHSLALIYQQ\ndYgEuxBCCFGH1Mr92Pfu3cuqVauw2+3ExMTQpk0bZ5+SEEII4RKqtceekZHBI488QkJCQkXbG2+8\nQUREBJGRkaSmplb5PA8PD6ZNm0ZcXBy7d++uzlMUQggh6pRq67EXFRXx2muv0bFjx4q25ORkTpw4\nwcqVKzly5Agvv/wyK1euZPHixaSkpADQrFkzxo0bR0FBAStWrGDixInVdYpCCCFEnVNtwW40Glmw\nYAELFiyoaNuxYwePPPIIAHfccQd5eXkUFBQQFxdHXFxcxeOsViuzZs1iwoQJ+Pj4/O7r2O12AM6f\nP3/zixBCCCFqmSt5dyX/fq3agl2v16PXVz58VlYWrVu3rvjZz8+PzMxMTCZTpcctWLCAwsJC3n//\nfe6991569OhxzdfJzMwEICoq6iaevRBCCFG7ZWZm0rRp09+0O/XiOUVRqmyfMGHCdR+jTZs2LF++\nnICAAHQ63c06NSGEEKJWstvtZGZmXvPC8hoN9sDAQLKysip+vnjxIgEBAX/qmO7u7tx7771/9tSE\nEEIIl1FVT/2KGr2PvXPnzmzcuBGAgwcPEhgY+JtheCGEEEL876qtx37gwAHeeustzpw5g16vZ+PG\njcTHx9O6dWsiIyPRaDRMmzatul5eCCGE+EvSKNea6BZCCCGEy5ElZYUQQog6pFYuKVsbzZw5kz17\n9mCz2Xjqqaf49ttvOXjwYMV99iNGjOChhx4iMTGRJUuWoNVqefzxxxk8eLCTz/yPJSUlMX78eJo3\nbw5AixYtGDlyJJMmTcJutxMQEMCsWbMwGo0uWd+qVatITEys+PnAgQO0adOGoqIiPD09AZg8eTJt\n2rRh4cKFbNiwAY1Gw5gxY3jwwQedddrXJSMjg2eeeYa4uDiio6M5d+7cdb9v5eXlTJkyhbNnz6LT\n6ZgxYwZNmjRxdkm/UVWNL730EjabDb1ez6xZswgICKB169a0a9eu4nmLFy/G4XDU+hp/Xd+UKVOu\n+7vFFd7DX9c3btw4Ll26BEBubi4hISE89dRT9OvXr+Iqb19fX9577z2sVisTJ07EarXi6enJ22+/\n/YdrmzjDr/Ohbdu2zv0cKuIP7dixQxk5cqSiKIqSk5OjPPjgg8rkyZOVb7/9ttLjCgsLle7duyv5\n+flKcXGx0qdPH+XSpUvOOOUbsnPnTmXs2LGV2qZMmaJ8+eWXiqIoyttvv60sX77cZeu7WlJSkjJ9\n+nQlOjpa+emnnyr97uTJk8qAAQOU0tJSJTs7W+nRo4dis9mcdKZ/rLCwUImOjlamTp2qLFu2TFGU\nG3vfPvvsM2X69OmKoijK1q1blfHjxzutlmupqsZJkyYp69evVxRFURISEpS33npLURRF+dvf/vab\n59f2Gquq70a+W1yxvqtNmTJF2b9/v3Lq1CllwIABv/l9fHy8smDBAkVRFOW///2vMnPmzGo/5xtV\nVT44+3MoQ/HX4b777uPf//43ABaLheLi4ipX/Nm/fz9t27bFbDbj7u5Ou3btKpbKdTVJSUl069YN\ngIcffpgdO3bUifrmzp3LM888U+XvkpKS6NKlC0ajET8/Pxo1asThw4dr+Ayv35XVHQMDAyvabuR9\n27FjB48++igAnTp1qpXvZVU1Tps2rWLRKl9fX3Jzc6/5/NpeY1X1VcVV38Pfq+/o0aNYrVbuvvvu\naz7/6vqu/H2ubarKB2d/DiXYr4NOp6sYsl29ejUPPPAAOp2OhIQEYmNjef7558nJySErKws/P7+K\n511ZWc8VHD58mNGjRzNkyBC2b99OcXExRqMRAH9/fzIzM126PoDU1FQaNmxYsXbCe++9R1RUFH//\n+98pKSlxufr0ej3u7u6V2m7kfbu6XavVotFoKCsrq7kCrkNVNXp6eqLT6bDb7axYsYJ+/foBUFZW\nxsSJE4mMjGTRokUAtb7GquoDrvu7xVXrA1i6dCnR0dEVP2dlZTFu3DgiIyMrps6urs/f35+LFy9W\n/0nfoKrywdmfQ5ljvwGbNm1i9erVfPTRRxw4cAAfHx9atWrF/PnzmTNnDqGhoZUer7jIDQe33nor\nY8aMoVevXpw6dYrY2NhKIxLXqsNV6rti9erVDBgwAIDY2FhatmxJUFAQ06ZNY/ny5b95vKvV92s3\n+r65Ur12u51JkybRoUOHio2mJk2aRFhYGBqNhujo6CoXrnKFGvv37/8/f7e4Qn2g/iNsz549TJ8+\nHQAfHx/Gjx9PWFgYVquVwYMH06FDh0rPqe21XZ0P3bt3r2h3xudQeuzXaevWrXzwwQcsWLAAs9lM\nx44dadWqFQBdu3YlIyOjypX1/miIrTaoX78+vXv3RqPREBQURL169cjLy6OkpASACxcuEBgY6LL1\nXZGUlFTxBfnoo48SFBQEXPv9u1K3K/H09Lzu9y0wMLBiRKK8vBxFUSp6GbXdSy+9RNOmTRkzZkxF\n25AhQ/Dy8sLT05MOHTpUvKeuVuONfLe4Yn0Au3btqjQEbzKZGDhwIAaDAT8/P9q0acPRo0cr1Veb\nP4+/zgdnfw4l2K+D1Wpl5syZzJs3r+KKzLFjx3Lq1ClADYzmzZsTHBxMWloa+fn5FBYWkpKS4hLL\n3SYmJvLhhx8C6qYC2dnZhIeHV6wS+PXXX9OlSxeXrQ/UD5eXlxdGoxFFUYiLiyM/Px/45f3r0KED\n3333HWVlZVy4cIGLFy/SrFkzJ5/5jenUqdN1v2+dO3dmw4YNAGzZsoX27ds789SvW2JiIgaDgXHj\nxlW0HT16lIkTJ6IoCjabjZSUFJo3b+6SNd7Id4sr1geQlpbGnXfeWfHzzp07mTFjBqBu+Z2ens5t\nt91Wqb4rf59rm6rywdmfQ1mg5jqsXLmS+Ph4brvttoq28PBwEhIS8PDwwNPTkxkzZuDv78+GDRv4\n8MMPK4YDw8LCnHjm16egoIAXXniB/Px8ysvLGTNmDK1atWLy5MmUlpZyyy23MGPGDAwGg0vWB+ot\nbu+++y4LFy4E4Msvv2ThwoV4eHhQv359Xn/9dTw8PFi2bBnr1q1Do9Hw3HPPVQzz1ka/Xt2xfv36\n/Otf/2LKlCnX9b7Z7XamTp3K8ePHMRqNvPnmmzRs2NDZZVVSVY3Z2dm4ublVLEd9xx13MH36dGbN\nmsXOnTvRarV07dqVp59+utbXWFV90dHRzJ8//7q+W1yxvvj4eOLj47nnnnvo3bs3ADabjalTp3Ls\n2DHsdjtDhgxh4MCBFBYW8uKLL5Kbm4vFYmHWrFmYzWYnV1VZVfnw5ptvMnXqVKd9DiXYhRBCiDpE\nhuKFEEKIOkSCXQghhKhDJNiFEEKIOkSCXQghhKhDJNiFEEKIOkSCXQgXcfr0aR544IHftLds2RKb\nzeaEM7p+8fHx9OzZk/nz5wPq8r4jRowgPDycyMhIIiMj2bZt2+8eY8eOHTz22GO/af/Pf/7Dq6++\nSkxMDPfeey+nT5+ulhqEcBWypKwQokaMGjWK8PBwMjMzGTduHO+++y4hISGAusnJqFGj+Pzzz2nQ\noEGVz+/QoQNWq5X09PRKi5usXbuWmTNnEhwcTExMTI3UIkRtJj12IeqYpKQk4uPjr/n7rl27Mn/+\nfGJiYujZsydbtmxh9OjRPPLII6xZswaAI0eOEB0dzbBhwxg4cCBbt24F1BXCBg8eTExMDBEREaSm\npmKz2ZgyZQoRERFERkbyj3/843fPLyEhgf79+1eEOkBwcDBbt26tCPX09HSGDx9OTEwMkZGRHDp0\nCI1Gw8CBA1m7dm3F8/bt24fBYCA4OPh//vMSoq6RHrsQf0G+vr4sW7aMKVOmsGTJEhYtWkRycjJv\nvPEGAwYMICsri/Hjx3Pfffexd+9eXnvtNbp06cKSJUsYPnw4vXv35ujRoxw7doyMjAz279/PV199\nBcAnn3yC1Wq95gphhw8frnJI/er1sV988UXmzp1LUFAQ6enpvPzyy3z22WeEh4czePBgXnjhBfR6\nPWvXrmXQoEHV84ckhIuSYBfCheTk5FxzuHnlypV88cUX5OfnY7VaSU5Orlhu9dfatWsHqBsA1a9f\nH41GQ4MGDbBarQAEBAQwc+ZMZs+eTXl5ecWe5/369eOdd94hNTWVbt260a1bN0pLS/H19eXJJ5/k\n4YcfplevXr+77KdOp6t0TcD06dM5cuQI+fn59O/fn/79+3Ps2DFeeeWViscUFBTgcDho0KABd955\nJ9u2baNTp05s2rSpYotPIYRKgl0IF+Ln58eyZcsqtbVs2RKAiIgIIiIiSEpKIjk5mbFjx17zOHq9\nvsr/v+K1116jT58+DBo0iIyMDEaPHg1A7969uf/++9m2bRtz587l7rvvZsKECaxYsYKDBw+yZcsW\nBg0axMcff3zNnbhatGjBvn376NWrF0DFPzzi4+PJy8vDaDRiMBh+U+cVgwYNYu3atZSVldGuXbtK\ne1wLIWSOXQhRhaysLJo3bw6oG+aUlZUB8N5772G32+nduzevvPIKe/fuJS0tjTVr1tC6dWvGjBlD\n69atOX78+DWPHRMTwzfffMOOHTsq2nJzc9m3bx/u7u6YzWYaN27M999/D8CxY8eYM2dOxWO7du1K\namoqa9askWF4IaogPXYh6pj27dv/6a0fn3jiCSZNmkTjxo2Ji4vjm2++4c0336RVq1Y88cQTWCwW\nHA4HY8eOJSgoiLlz57Jy5UqMRiNBQUEVQ/1V8fb2JiEhgddff51Zs2bh4eFBaWkpvXv3JjY2FoC3\n3nqLf/7zn8yfP7/i4rwrDAYD3bt3Z8OGDdx///1/qk4h6iLZ3U0IUe3i4+Np1KgR4eHh1fo6MTEx\nzJgxg8aNG1fr6whRm8lQvBCiRsyfP79igZrqEBMTw48//lhtxxfCVUiPXQghhKhDpMcuhBBC1CES\n7EIIIUQdIsEuhBBC1CES7EIIIUQdIsEuhBBC1CES7EIIIUQd8v8B8QOAFXy6OwwAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0g4PExYkWv4_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "085430f8-a4d8-4b6f-f195-9bd044573453"
      },
      "cell_type": "code",
      "source": [
        "ratiobdt=[]\n",
        "ratiobdtmassparam=[]\n",
        "rationn=[]\n",
        "rationnmassparam=[]\n",
        "for i in range(len(hpmasses)):\n",
        "  mn=min(bdt[i],bdtmassparam[i],nn[i],nnmassparam[i])\n",
        "  ratiobdt.append((bdt[i]-mn)/mn*100)\n",
        "  ratiobdtmassparam.append((bdtmassparam[i]-mn)/mn*100)\n",
        "  rationn.append((nn[i]-mn)/mn*100)\n",
        "  rationnmassparam.append((nnmassparam[i]-mn)/mn*100)\n",
        "\n",
        "color=iter(plt.cm.rainbow(np.linspace(0,1,4)))\n",
        "plt.plot(hpmasses, ratiobdt, color=next(color), label=\"BDT\")\n",
        "plt.plot(hpmasses, ratiobdtmassparam, color=next(color), label=\"BDT mass parameterised\")\n",
        "plt.plot(hpmasses, rationn, color=next(color), label=\"NN\")\n",
        "plt.plot(hpmasses, rationnmassparam, color=next(color), label=\"NN mass parameterised\")\n",
        "plt.ylabel(\"Difference to lowest [%]\")\n",
        "plt.xlabel(\"H+ mass [GeV]\")\n",
        "plt.legend()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3740aeff98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFaCAYAAAAzewU2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLZFIJBKJn/D/AfiI12cF6NNuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R-I4IJmuF58i",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We really have to do something against the mass parameterised methods blowing up at low masses. What can be done?\n",
        "\n",
        "This is clearly the region where we have lower jet pt than background.\n",
        "*  Do we need a higher complexity for the NN?\n",
        "*  Do we need a higher weight for the low masses?\n",
        "* Can we maybe pre-train on low masses only?\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "duMbo7wbzeTu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training the classifier at N-1 masses and checking at the remaining mass"
      ]
    },
    {
      "metadata": {
        "id": "JTkeXaV8zeTy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from scipy.stats import rv_discrete\n",
        "from joblib import dump\n",
        "from HpMLUtils import WeightedStandardScaler, summarizeFitData,MultiSBWeightsScaler\n",
        "\n",
        "sb=HpMLUtils.MultiSBWeightsScaler(backgroundclass=-1)\n",
        "\n",
        "trainvalues_cross=[]\n",
        "testvalues_cross=[]\n",
        "for hpmass in hpmasses:\n",
        "    print(hpmass)\n",
        "    X_train, X_test, X_eval, y_train, y_test,y_eval, w_train, w_test, w_eval=htf.prepare(hpmass=hpmass, addMass=True, invertsignal=True)\n",
        "    \n",
        "    if invertTestTrain:\n",
        "      X_tmp=X_train\n",
        "      y_tmp=y_train\n",
        "      w_tmp=w_train\n",
        "      X_train=X_test\n",
        "      y_train=y_test\n",
        "      w_train=w_test\n",
        "      X_test=X_tmp\n",
        "      y_test=y_tmp\n",
        "      w_test=w_tmp\n",
        "    \n",
        "    sb.fit(X_train,X_train.hpmass, sample_weight=w_train)\n",
        "    X_train=sb.transform(X_train,X_train.hpmass, sample_weight=w_train)\n",
        "    X_test =sb.transform(X_test, X_test.hpmass, sample_weight=w_test)\n",
        "    \n",
        "    signalsum=w_train[X_train.hpmass>0].sum()\n",
        "    xk=[]\n",
        "    pk=[]\n",
        "    for name, group in w_train.groupby(X_train.hpmass):\n",
        "        if name>0:\n",
        "            xk.append((-1)*name)\n",
        "            pk.append(group.sum()/signalsum)\n",
        "    \n",
        "    custm=rv_discrete(values=(xk,pk))\n",
        "    X_train[\"hpmass\"]=X_train.hpmass.apply(lambda x: x if x>0 else custm.rvs())\n",
        "    X_test[\"hpmass\"] =X_test.hpmass.apply(lambda x: x if x>0 else custm.rvs())\n",
        "    X_train.hpmass=X_train.hpmass.abs()\n",
        "    X_test.hpmass=X_test.hpmass.abs()\n",
        "    \n",
        "    wss=WeightedStandardScaler()\n",
        "    wss.fit(X_train,sample_weight=w_train)\n",
        "    X_train=wss.transform(X_train)\n",
        "    X_test =wss.transform(X_test)\n",
        "    \n",
        "    model=HpKerasUtils.HpFeedForwardModel(configuration=[64,64],dropout=0.1, verbose=True, input_dim=15)\n",
        "    result=model.train((X_train.values, y_train.values, w_train.values),(X_test.values, y_test.values, w_test.values), patience=5)\n",
        "\n",
        "    if invertTestTrain:\n",
        "      dump(wss, 'HplusML/models/massparameterisedNN_wss_allbut'+str(hpmass)+'_'+region+'_train2mod1.joblib') \n",
        "      model.model.save('HplusML/models/massparameterisedNN_allbut'+str(hpmass)+'_'+region+'_train2mod1.h5')\n",
        "    else:\n",
        "      dump(wss, 'HplusML/models/massparameterisedNN_wss_allbut'+str(hpmass)+'_'+region+'_train2mod1.joblib') \n",
        "      model.model.save('HplusML/models/massparameterisedNN_allbut'+str(hpmass)+'_'+region+'_train2mod0.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YKcvaG-J86w2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!cd HplusML/models/; tar cvfz massparamerisedNN_training.tar.gz massparamerisedNN_*.h5 massparamerisedNN_*.joblib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "44fqsotu82FP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now evaluate all classifiers at masses where they weren't trained"
      ]
    },
    {
      "metadata": {
        "id": "_LEeOlNO81cf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#TODO"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mVeS87y9zeT_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Smoothness of score"
      ]
    },
    {
      "metadata": {
        "id": "-a0-stvazeUI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "one problem with the BDT is probably that the BDT score per event is not exactly smooth (probably quite ok as the distribution should be pretty smooth)"
      ]
    },
    {
      "metadata": {
        "id": "LmzMlBcGzeUK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def decisionfunction(eventnumber):\n",
        "  testmasses=np.linspace(200,2000, 1801)\n",
        "  line1=X_test.iloc[eventnumber].copy()\n",
        "  df = pd.DataFrame([line1]*len(testmasses))\n",
        "  df[\"hpmass\"]=testmasses\n",
        "  y_pred=bdt.decision_function(df)\n",
        "  plt.plot(testmasses, y_pred, '-')\n",
        "  plt.xlabel('m(H+) [GeV]')\n",
        "  plt.ylabel('BDT score')\n",
        "  plt.show()\n",
        "    \n",
        "def nnactivation(eventnumber):\n",
        "  testmasses=np.linspace(200,2000, 1801)\n",
        "  line1=X_test.iloc[eventnumber].copy()\n",
        "  df = pd.DataFrame([line1]*len(testmasses))\n",
        "  df[\"hpmass\"]=testmasses\n",
        "  y_pred=nn.predict(wss.transform(df.values))\n",
        "  plt.plot(testmasses, y_pred, '-')\n",
        "  plt.xlabel('m(H+) [GeV]')\n",
        "  plt.ylabel('NN activation')\n",
        "  #plt.legend(loc=\"lower right\")\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "id": "453zXx7-zeUP",
        "colab_type": "code",
        "outputId": "343b1776-ac43-446b-f897-df1b96a65dec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1393
        }
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, X_eval, y_train, y_test,y_eval, w_train, w_test, w_eval=htf.prepare(hpmass=300, addMass=True)\n",
        "\n",
        "bdt=load('HplusML/models/massparameterisedBDT_'+region+'_train2mod0.joblib')\n",
        "\n",
        "for i in range(0,2):\n",
        "  decisionfunction(i)\n",
        "\n",
        "wss=load('HplusML/models/massparameterisedNN_wss_'+region+'_train2mod0.joblib') \n",
        "nn=load_model('HplusML/models/massparameterisedNN_'+region+'_train2mod0.h5')\n",
        "for i in range(0,2):\n",
        "  nnactivation(i)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFYCAYAAABKymUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFYCAYAAABKymUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFYCAYAAAB6RnQAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wGO1DBR8UCWmAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFYCAYAAAB6RnQAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTEUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uNDr31BqJzRT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "That's nice, having a smooth function certainly worked very well..."
      ]
    }
  ]
}