GaussianExample_2D.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1-final"
    },
    "notify_time": "0",
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": false,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "colab": {
      "name": "GaussianExample_2D.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kmtsui/SummerProject2021/blob/master/GaussianExample_2D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dk7hq8vegZxZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e53e2324-a3ad-4851-f87b-82e7bd56c56f"
      },
      "source": [
        "# install latex\n",
        "! sudo apt-get update\n",
        "! sudo apt-get install texlive-latex-recommended \n",
        "! sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended  \n",
        "! wget http://mirrors.ctan.org/macros/latex/contrib/type1cm.zip \n",
        "! unzip type1cm.zip -d /tmp/type1cm \n",
        "! cd /tmp/type1cm/type1cm/ && sudo latex type1cm.ins\n",
        "! sudo mkdir /usr/share/texmf/tex/latex/type1cm \n",
        "! sudo cp /tmp/type1cm/type1cm/type1cm.sty /usr/share/texmf/tex/latex/type1cm \n",
        "! sudo texhash \n",
        "! apt install cm-super\n",
        "! pip install latex"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0% [Working]\r            \rGet:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:4 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n",
            "Hit:5 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Ign:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [697 B]\n",
            "Hit:9 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Get:10 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n",
            "Get:11 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [26.7 kB]\n",
            "Get:12 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,263 kB]\n",
            "Hit:13 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Get:14 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,420 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Get:16 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n",
            "Get:17 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [510 kB]\n",
            "Get:18 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [62.9 kB]\n",
            "Get:19 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:20 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,786 kB]\n",
            "Ign:22 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n",
            "Get:22 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [695 kB]\n",
            "Get:23 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [914 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,698 kB]\n",
            "Get:25 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [39.4 kB]\n",
            "Get:26 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [544 kB]\n",
            "Get:27 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,195 kB]\n",
            "Get:28 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [40.9 kB]\n",
            "Get:29 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [44.1 kB]\n",
            "Fetched 13.5 MB in 3s (4,076 kB/s)\n",
            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  fonts-droid-fallback fonts-lmodern fonts-noto-mono libcupsfilters1\n",
            "  libcupsimage2 libgs9 libgs9-common libijs-0.35 libjbig2dec0 libkpathsea6\n",
            "  libpotrace0 libptexenc1 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13\n",
            "  lmodern poppler-data t1utils tex-common texlive-base texlive-binaries\n",
            "  texlive-latex-base\n",
            "Suggested packages:\n",
            "  fonts-noto poppler-utils ghostscript fonts-japanese-mincho\n",
            "  | fonts-ipafont-mincho fonts-japanese-gothic | fonts-ipafont-gothic\n",
            "  fonts-arphic-ukai fonts-arphic-uming fonts-nanum debhelper gv\n",
            "  | postscript-viewer perl-tk xpdf-reader | pdf-viewer texlive-latex-base-doc\n",
            "  texlive-latex-recommended-doc texlive-pstricks\n",
            "The following NEW packages will be installed:\n",
            "  fonts-droid-fallback fonts-lmodern fonts-noto-mono libcupsfilters1\n",
            "  libcupsimage2 libgs9 libgs9-common libijs-0.35 libjbig2dec0 libkpathsea6\n",
            "  libpotrace0 libptexenc1 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13\n",
            "  lmodern poppler-data t1utils tex-common texlive-base texlive-binaries\n",
            "  texlive-latex-base texlive-latex-recommended\n",
            "0 upgraded, 24 newly installed, 0 to remove and 98 not upgraded.\n",
            "Need to get 68.4 MB of archives.\n",
            "After this operation, 223 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1 [1,805 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 poppler-data all 0.4.8-2 [1,479 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 tex-common all 6.09 [33.0 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-lmodern all 2.004.5-3 [4,551 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-noto-mono all 20171026-2 [75.5 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcupsfilters1 amd64 1.20.2-0ubuntu3.1 [108 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcupsimage2 amd64 2.2.7-1ubuntu2.8 [18.6 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 libijs-0.35 amd64 0.35-13 [15.5 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjbig2dec0 amd64 0.13-6 [55.9 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgs9-common all 9.26~dfsg+0-0ubuntu0.18.04.14 [5,092 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgs9 amd64 9.26~dfsg+0-0ubuntu0.18.04.14 [2,265 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libkpathsea6 amd64 2017.20170613.44572-8ubuntu0.1 [54.9 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 libpotrace0 amd64 1.14-2 [17.4 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libptexenc1 amd64 2017.20170613.44572-8ubuntu0.1 [34.5 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libsynctex1 amd64 2017.20170613.44572-8ubuntu0.1 [41.4 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libtexlua52 amd64 2017.20170613.44572-8ubuntu0.1 [91.2 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libtexluajit2 amd64 2017.20170613.44572-8ubuntu0.1 [230 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libzzip-0-13 amd64 0.13.62-3.1ubuntu0.18.04.1 [26.0 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu bionic/main amd64 lmodern all 2.004.5-3 [9,631 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu bionic/main amd64 t1utils amd64 1.41-2 [56.0 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 texlive-binaries amd64 2017.20170613.44572-8ubuntu0.1 [8,179 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu bionic/main amd64 texlive-base all 2017.20180305-1 [18.7 MB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu bionic/main amd64 texlive-latex-base all 2017.20180305-1 [951 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu bionic/main amd64 texlive-latex-recommended all 2017.20180305-1 [14.9 MB]\n",
            "Fetched 68.4 MB in 3s (26.4 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 24.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-droid-fallback.\n",
            "(Reading database ... 160837 files and directories currently installed.)\n",
            "Preparing to unpack .../00-fonts-droid-fallback_1%3a6.0.1r16-1.1_all.deb ...\n",
            "Unpacking fonts-droid-fallback (1:6.0.1r16-1.1) ...\n",
            "Selecting previously unselected package poppler-data.\n",
            "Preparing to unpack .../01-poppler-data_0.4.8-2_all.deb ...\n",
            "Unpacking poppler-data (0.4.8-2) ...\n",
            "Selecting previously unselected package tex-common.\n",
            "Preparing to unpack .../02-tex-common_6.09_all.deb ...\n",
            "Unpacking tex-common (6.09) ...\n",
            "Selecting previously unselected package fonts-lmodern.\n",
            "Preparing to unpack .../03-fonts-lmodern_2.004.5-3_all.deb ...\n",
            "Unpacking fonts-lmodern (2.004.5-3) ...\n",
            "Selecting previously unselected package fonts-noto-mono.\n",
            "Preparing to unpack .../04-fonts-noto-mono_20171026-2_all.deb ...\n",
            "Unpacking fonts-noto-mono (20171026-2) ...\n",
            "Selecting previously unselected package libcupsfilters1:amd64.\n",
            "Preparing to unpack .../05-libcupsfilters1_1.20.2-0ubuntu3.1_amd64.deb ...\n",
            "Unpacking libcupsfilters1:amd64 (1.20.2-0ubuntu3.1) ...\n",
            "Selecting previously unselected package libcupsimage2:amd64.\n",
            "Preparing to unpack .../06-libcupsimage2_2.2.7-1ubuntu2.8_amd64.deb ...\n",
            "Unpacking libcupsimage2:amd64 (2.2.7-1ubuntu2.8) ...\n",
            "Selecting previously unselected package libijs-0.35:amd64.\n",
            "Preparing to unpack .../07-libijs-0.35_0.35-13_amd64.deb ...\n",
            "Unpacking libijs-0.35:amd64 (0.35-13) ...\n",
            "Selecting previously unselected package libjbig2dec0:amd64.\n",
            "Preparing to unpack .../08-libjbig2dec0_0.13-6_amd64.deb ...\n",
            "Unpacking libjbig2dec0:amd64 (0.13-6) ...\n",
            "Selecting previously unselected package libgs9-common.\n",
            "Preparing to unpack .../09-libgs9-common_9.26~dfsg+0-0ubuntu0.18.04.14_all.deb ...\n",
            "Unpacking libgs9-common (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n",
            "Selecting previously unselected package libgs9:amd64.\n",
            "Preparing to unpack .../10-libgs9_9.26~dfsg+0-0ubuntu0.18.04.14_amd64.deb ...\n",
            "Unpacking libgs9:amd64 (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n",
            "Selecting previously unselected package libkpathsea6:amd64.\n",
            "Preparing to unpack .../11-libkpathsea6_2017.20170613.44572-8ubuntu0.1_amd64.deb ...\n",
            "Unpacking libkpathsea6:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Selecting previously unselected package libpotrace0.\n",
            "Preparing to unpack .../12-libpotrace0_1.14-2_amd64.deb ...\n",
            "Unpacking libpotrace0 (1.14-2) ...\n",
            "Selecting previously unselected package libptexenc1:amd64.\n",
            "Preparing to unpack .../13-libptexenc1_2017.20170613.44572-8ubuntu0.1_amd64.deb ...\n",
            "Unpacking libptexenc1:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Selecting previously unselected package libsynctex1:amd64.\n",
            "Preparing to unpack .../14-libsynctex1_2017.20170613.44572-8ubuntu0.1_amd64.deb ...\n",
            "Unpacking libsynctex1:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Selecting previously unselected package libtexlua52:amd64.\n",
            "Preparing to unpack .../15-libtexlua52_2017.20170613.44572-8ubuntu0.1_amd64.deb ...\n",
            "Unpacking libtexlua52:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Selecting previously unselected package libtexluajit2:amd64.\n",
            "Preparing to unpack .../16-libtexluajit2_2017.20170613.44572-8ubuntu0.1_amd64.deb ...\n",
            "Unpacking libtexluajit2:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Selecting previously unselected package libzzip-0-13:amd64.\n",
            "Preparing to unpack .../17-libzzip-0-13_0.13.62-3.1ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libzzip-0-13:amd64 (0.13.62-3.1ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package lmodern.\n",
            "Preparing to unpack .../18-lmodern_2.004.5-3_all.deb ...\n",
            "Unpacking lmodern (2.004.5-3) ...\n",
            "Selecting previously unselected package t1utils.\n",
            "Preparing to unpack .../19-t1utils_1.41-2_amd64.deb ...\n",
            "Unpacking t1utils (1.41-2) ...\n",
            "Selecting previously unselected package texlive-binaries.\n",
            "Preparing to unpack .../20-texlive-binaries_2017.20170613.44572-8ubuntu0.1_amd64.deb ...\n",
            "Unpacking texlive-binaries (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Selecting previously unselected package texlive-base.\n",
            "Preparing to unpack .../21-texlive-base_2017.20180305-1_all.deb ...\n",
            "Unpacking texlive-base (2017.20180305-1) ...\n",
            "Selecting previously unselected package texlive-latex-base.\n",
            "Preparing to unpack .../22-texlive-latex-base_2017.20180305-1_all.deb ...\n",
            "Unpacking texlive-latex-base (2017.20180305-1) ...\n",
            "Selecting previously unselected package texlive-latex-recommended.\n",
            "Preparing to unpack .../23-texlive-latex-recommended_2017.20180305-1_all.deb ...\n",
            "Unpacking texlive-latex-recommended (2017.20180305-1) ...\n",
            "Setting up libgs9-common (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n",
            "Setting up libkpathsea6:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Setting up libtexlua52:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Setting up fonts-droid-fallback (1:6.0.1r16-1.1) ...\n",
            "Setting up libsynctex1:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Setting up libptexenc1:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Setting up tex-common (6.09) ...\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76.)\n",
            "debconf: falling back to frontend: Readline\n",
            "update-language: texlive-base not installed and configured, doing nothing!\n",
            "Setting up poppler-data (0.4.8-2) ...\n",
            "Setting up fonts-noto-mono (20171026-2) ...\n",
            "Setting up libcupsfilters1:amd64 (1.20.2-0ubuntu3.1) ...\n",
            "Setting up libcupsimage2:amd64 (2.2.7-1ubuntu2.8) ...\n",
            "Setting up libjbig2dec0:amd64 (0.13-6) ...\n",
            "Setting up t1utils (1.41-2) ...\n",
            "Setting up libijs-0.35:amd64 (0.35-13) ...\n",
            "Setting up libpotrace0 (1.14-2) ...\n",
            "Setting up libzzip-0-13:amd64 (0.13.62-3.1ubuntu0.18.04.1) ...\n",
            "Setting up libgs9:amd64 (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n",
            "Setting up libtexluajit2:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Setting up fonts-lmodern (2.004.5-3) ...\n",
            "Setting up texlive-binaries (2017.20170613.44572-8ubuntu0.1) ...\n",
            "update-alternatives: using /usr/bin/xdvi-xaw to provide /usr/bin/xdvi.bin (xdvi.bin) in auto mode\n",
            "update-alternatives: using /usr/bin/bibtex.original to provide /usr/bin/bibtex (bibtex) in auto mode\n",
            "Setting up texlive-base (2017.20180305-1) ...\n",
            "mktexlsr: Updating /var/lib/texmf/ls-R-TEXLIVEDIST... \n",
            "mktexlsr: Updating /var/lib/texmf/ls-R-TEXMFMAIN... \n",
            "mktexlsr: Updating /var/lib/texmf/ls-R... \n",
            "mktexlsr: Done.\n",
            "tl-paper: setting paper size for dvips to a4: /var/lib/texmf/dvips/config/config-paper.ps\n",
            "tl-paper: setting paper size for dvipdfmx to a4: /var/lib/texmf/dvipdfmx/dvipdfmx-paper.cfg\n",
            "tl-paper: setting paper size for xdvi to a4: /var/lib/texmf/xdvi/XDvi-paper\n",
            "tl-paper: setting paper size for pdftex to a4: /var/lib/texmf/tex/generic/config/pdftexconfig.tex\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76.)\n",
            "debconf: falling back to frontend: Readline\n",
            "Setting up texlive-latex-base (2017.20180305-1) ...\n",
            "Setting up lmodern (2.004.5-3) ...\n",
            "Setting up texlive-latex-recommended (2017.20180305-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Processing triggers for tex-common (6.09) ...\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76.)\n",
            "debconf: falling back to frontend: Readline\n",
            "Running updmap-sys. This may take some time... done.\n",
            "Running mktexlsr /var/lib/texmf ... done.\n",
            "Building format(s) --all.\n",
            "\tThis may take some time... done.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  fonts-lato fonts-texgyre ghostscript gsfonts javascript-common libjs-jquery\n",
            "  libruby2.5 preview-latex-style rake ruby ruby-did-you-mean ruby-minitest\n",
            "  ruby-net-telnet ruby-power-assert ruby-test-unit ruby2.5\n",
            "  rubygems-integration tex-gyre texlive-pictures texlive-plain-generic tipa\n",
            "Suggested packages:\n",
            "  ghostscript-x apache2 | lighttpd | httpd ri ruby-dev bundler\n",
            "  texlive-fonts-recommended-doc python-pygments icc-profiles\n",
            "  libfile-which-perl libspreadsheet-parseexcel-perl texlive-latex-extra-doc\n",
            "  dot2tex prerex ruby-tcltk | libtcltk-ruby texlive-pictures-doc vprerex\n",
            "The following NEW packages will be installed:\n",
            "  dvipng fonts-lato fonts-texgyre ghostscript gsfonts javascript-common\n",
            "  libjs-jquery libruby2.5 preview-latex-style rake ruby ruby-did-you-mean\n",
            "  ruby-minitest ruby-net-telnet ruby-power-assert ruby-test-unit ruby2.5\n",
            "  rubygems-integration tex-gyre texlive-fonts-recommended texlive-latex-extra\n",
            "  texlive-pictures texlive-plain-generic tipa\n",
            "0 upgraded, 24 newly installed, 0 to remove and 98 not upgraded.\n",
            "Need to get 69.8 MB of archives.\n",
            "After this operation, 221 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-lato all 2.0-2 [2,698 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 ghostscript amd64 9.26~dfsg+0-0ubuntu0.18.04.14 [51.3 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 dvipng amd64 1.15-1 [78.2 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-texgyre all 20160520-1 [8,761 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 gsfonts all 1:8.11+urwcyr1.0.7~pre44-4.4 [3,120 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 javascript-common all 11 [6,066 B]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjs-jquery all 3.2.1-1 [152 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 rubygems-integration all 1.11 [4,994 B]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 ruby2.5 amd64 2.5.1-1ubuntu1.10 [48.6 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic/main amd64 ruby amd64 1:2.5.1 [5,712 B]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 rake all 12.3.1-1ubuntu0.1 [44.9 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu bionic/main amd64 ruby-did-you-mean all 1.2.0-2 [9,700 B]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 ruby-minitest all 5.10.3-1 [38.6 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu bionic/main amd64 ruby-net-telnet all 0.1.1-2 [12.6 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu bionic/main amd64 ruby-power-assert all 0.3.0-1 [7,952 B]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu bionic/main amd64 ruby-test-unit all 3.2.5-1 [61.1 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libruby2.5 amd64 2.5.1-1ubuntu1.10 [3,071 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu bionic/main amd64 preview-latex-style all 11.91-1ubuntu1 [185 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tex-gyre all 20160520-1 [4,998 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu bionic/universe amd64 texlive-fonts-recommended all 2017.20180305-1 [5,262 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu bionic/universe amd64 texlive-pictures all 2017.20180305-1 [4,026 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu bionic/universe amd64 texlive-latex-extra all 2017.20180305-2 [10.6 MB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu bionic/universe amd64 texlive-plain-generic all 2017.20180305-2 [23.6 MB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tipa all 2:1.3-20 [2,978 kB]\n",
            "Fetched 69.8 MB in 3s (24.4 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 24.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-lato.\n",
            "(Reading database ... 168171 files and directories currently installed.)\n",
            "Preparing to unpack .../00-fonts-lato_2.0-2_all.deb ...\n",
            "Unpacking fonts-lato (2.0-2) ...\n",
            "Selecting previously unselected package ghostscript.\n",
            "Preparing to unpack .../01-ghostscript_9.26~dfsg+0-0ubuntu0.18.04.14_amd64.deb ...\n",
            "Unpacking ghostscript (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n",
            "Selecting previously unselected package dvipng.\n",
            "Preparing to unpack .../02-dvipng_1.15-1_amd64.deb ...\n",
            "Unpacking dvipng (1.15-1) ...\n",
            "Selecting previously unselected package fonts-texgyre.\n",
            "Preparing to unpack .../03-fonts-texgyre_20160520-1_all.deb ...\n",
            "Unpacking fonts-texgyre (20160520-1) ...\n",
            "Selecting previously unselected package gsfonts.\n",
            "Preparing to unpack .../04-gsfonts_1%3a8.11+urwcyr1.0.7~pre44-4.4_all.deb ...\n",
            "Unpacking gsfonts (1:8.11+urwcyr1.0.7~pre44-4.4) ...\n",
            "Selecting previously unselected package javascript-common.\n",
            "Preparing to unpack .../05-javascript-common_11_all.deb ...\n",
            "Unpacking javascript-common (11) ...\n",
            "Selecting previously unselected package libjs-jquery.\n",
            "Preparing to unpack .../06-libjs-jquery_3.2.1-1_all.deb ...\n",
            "Unpacking libjs-jquery (3.2.1-1) ...\n",
            "Selecting previously unselected package rubygems-integration.\n",
            "Preparing to unpack .../07-rubygems-integration_1.11_all.deb ...\n",
            "Unpacking rubygems-integration (1.11) ...\n",
            "Selecting previously unselected package ruby2.5.\n",
            "Preparing to unpack .../08-ruby2.5_2.5.1-1ubuntu1.10_amd64.deb ...\n",
            "Unpacking ruby2.5 (2.5.1-1ubuntu1.10) ...\n",
            "Selecting previously unselected package ruby.\n",
            "Preparing to unpack .../09-ruby_1%3a2.5.1_amd64.deb ...\n",
            "Unpacking ruby (1:2.5.1) ...\n",
            "Selecting previously unselected package rake.\n",
            "Preparing to unpack .../10-rake_12.3.1-1ubuntu0.1_all.deb ...\n",
            "Unpacking rake (12.3.1-1ubuntu0.1) ...\n",
            "Selecting previously unselected package ruby-did-you-mean.\n",
            "Preparing to unpack .../11-ruby-did-you-mean_1.2.0-2_all.deb ...\n",
            "Unpacking ruby-did-you-mean (1.2.0-2) ...\n",
            "Selecting previously unselected package ruby-minitest.\n",
            "Preparing to unpack .../12-ruby-minitest_5.10.3-1_all.deb ...\n",
            "Unpacking ruby-minitest (5.10.3-1) ...\n",
            "Selecting previously unselected package ruby-net-telnet.\n",
            "Preparing to unpack .../13-ruby-net-telnet_0.1.1-2_all.deb ...\n",
            "Unpacking ruby-net-telnet (0.1.1-2) ...\n",
            "Selecting previously unselected package ruby-power-assert.\n",
            "Preparing to unpack .../14-ruby-power-assert_0.3.0-1_all.deb ...\n",
            "Unpacking ruby-power-assert (0.3.0-1) ...\n",
            "Selecting previously unselected package ruby-test-unit.\n",
            "Preparing to unpack .../15-ruby-test-unit_3.2.5-1_all.deb ...\n",
            "Unpacking ruby-test-unit (3.2.5-1) ...\n",
            "Selecting previously unselected package libruby2.5:amd64.\n",
            "Preparing to unpack .../16-libruby2.5_2.5.1-1ubuntu1.10_amd64.deb ...\n",
            "Unpacking libruby2.5:amd64 (2.5.1-1ubuntu1.10) ...\n",
            "Selecting previously unselected package preview-latex-style.\n",
            "Preparing to unpack .../17-preview-latex-style_11.91-1ubuntu1_all.deb ...\n",
            "Unpacking preview-latex-style (11.91-1ubuntu1) ...\n",
            "Selecting previously unselected package tex-gyre.\n",
            "Preparing to unpack .../18-tex-gyre_20160520-1_all.deb ...\n",
            "Unpacking tex-gyre (20160520-1) ...\n",
            "Selecting previously unselected package texlive-fonts-recommended.\n",
            "Preparing to unpack .../19-texlive-fonts-recommended_2017.20180305-1_all.deb ...\n",
            "Unpacking texlive-fonts-recommended (2017.20180305-1) ...\n",
            "Selecting previously unselected package texlive-pictures.\n",
            "Preparing to unpack .../20-texlive-pictures_2017.20180305-1_all.deb ...\n",
            "Unpacking texlive-pictures (2017.20180305-1) ...\n",
            "Selecting previously unselected package texlive-latex-extra.\n",
            "Preparing to unpack .../21-texlive-latex-extra_2017.20180305-2_all.deb ...\n",
            "Unpacking texlive-latex-extra (2017.20180305-2) ...\n",
            "Selecting previously unselected package texlive-plain-generic.\n",
            "Preparing to unpack .../22-texlive-plain-generic_2017.20180305-2_all.deb ...\n",
            "Unpacking texlive-plain-generic (2017.20180305-2) ...\n",
            "Selecting previously unselected package tipa.\n",
            "Preparing to unpack .../23-tipa_2%3a1.3-20_all.deb ...\n",
            "Unpacking tipa (2:1.3-20) ...\n",
            "Setting up libjs-jquery (3.2.1-1) ...\n",
            "Setting up gsfonts (1:8.11+urwcyr1.0.7~pre44-4.4) ...\n",
            "Setting up texlive-pictures (2017.20180305-1) ...\n",
            "Setting up tex-gyre (20160520-1) ...\n",
            "Setting up tipa (2:1.3-20) ...\n",
            "Regenerating '/var/lib/texmf/fmtutil.cnf-DEBIAN'... done.\n",
            "Regenerating '/var/lib/texmf/fmtutil.cnf-TEXLIVEDIST'... done.\n",
            "update-fmtutil has updated the following file(s):\n",
            "\t/var/lib/texmf/fmtutil.cnf-DEBIAN\n",
            "\t/var/lib/texmf/fmtutil.cnf-TEXLIVEDIST\n",
            "If you want to activate the changes in the above file(s),\n",
            "you should run fmtutil-sys or fmtutil.\n",
            "Setting up preview-latex-style (11.91-1ubuntu1) ...\n",
            "Setting up ghostscript (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n",
            "Setting up fonts-texgyre (20160520-1) ...\n",
            "Setting up fonts-lato (2.0-2) ...\n",
            "Setting up ruby-did-you-mean (1.2.0-2) ...\n",
            "Setting up ruby-net-telnet (0.1.1-2) ...\n",
            "Setting up rubygems-integration (1.11) ...\n",
            "Setting up javascript-common (11) ...\n",
            "Setting up texlive-fonts-recommended (2017.20180305-1) ...\n",
            "Setting up texlive-plain-generic (2017.20180305-2) ...\n",
            "Setting up ruby-minitest (5.10.3-1) ...\n",
            "Setting up ruby-power-assert (0.3.0-1) ...\n",
            "Setting up texlive-latex-extra (2017.20180305-2) ...\n",
            "Setting up dvipng (1.15-1) ...\n",
            "Setting up rake (12.3.1-1ubuntu0.1) ...\n",
            "Setting up ruby2.5 (2.5.1-1ubuntu1.10) ...\n",
            "Setting up ruby (1:2.5.1) ...\n",
            "Setting up ruby-test-unit (3.2.5-1) ...\n",
            "Setting up libruby2.5:amd64 (2.5.1-1ubuntu1.10) ...\n",
            "Processing triggers for tex-common (6.09) ...\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76.)\n",
            "debconf: falling back to frontend: Readline\n",
            "Running mktexlsr. This may take some time... done.\n",
            "Running updmap-sys. This may take some time... done.\n",
            "Running mktexlsr /var/lib/texmf ... done.\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
            "--2021-08-09 09:51:08--  http://mirrors.ctan.org/macros/latex/contrib/type1cm.zip\n",
            "Resolving mirrors.ctan.org (mirrors.ctan.org)... 5.35.249.60\n",
            "Connecting to mirrors.ctan.org (mirrors.ctan.org)|5.35.249.60|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://mirrors.concertpass.com/tex-archive/macros/latex/contrib/type1cm.zip [following]\n",
            "--2021-08-09 09:51:10--  https://mirrors.concertpass.com/tex-archive/macros/latex/contrib/type1cm.zip\n",
            "Resolving mirrors.concertpass.com (mirrors.concertpass.com)... 162.219.248.112\n",
            "Connecting to mirrors.concertpass.com (mirrors.concertpass.com)|162.219.248.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 328566 (321K) [application/zip]\n",
            "Saving to: ‘type1cm.zip’\n",
            "\n",
            "type1cm.zip         100%[===================>] 320.87K  1.17MB/s    in 0.3s    \n",
            "\n",
            "2021-08-09 09:51:10 (1.17 MB/s) - ‘type1cm.zip’ saved [328566/328566]\n",
            "\n",
            "Archive:  type1cm.zip\n",
            "   creating: /tmp/type1cm/type1cm/\n",
            "  inflating: /tmp/type1cm/type1cm/type1cm.fdd  \n",
            "  inflating: /tmp/type1cm/type1cm/type1cm.ins  \n",
            "  inflating: /tmp/type1cm/type1cm/type1cm.txt  \n",
            "  inflating: /tmp/type1cm/type1cm/type1cm-doc.pdf  \n",
            "  inflating: /tmp/type1cm/type1cm/type1cm-doc.tex  \n",
            "This is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/Debian) (preloaded format=latex)\n",
            " restricted \\write18 enabled.\n",
            "entering extended mode\n",
            "(./type1cm.ins\n",
            "LaTeX2e <2017-04-15>\n",
            "Babel <3.18> and hyphenation patterns for 3 language(s) loaded.\n",
            "(/usr/share/texlive/texmf-dist/tex/latex/base/docstrip.tex\n",
            "Utility: `docstrip' 2.5e <2014/09/29>\n",
            "English documentation    <2017/03/13>\n",
            "\n",
            "**********************************************************\n",
            "* This program converts documented macro-files into fast *\n",
            "* loadable files by stripping off (nearly) all comments! *\n",
            "**********************************************************\n",
            "\n",
            "********************************************************\n",
            "* No Configuration file found, using default settings. *\n",
            "********************************************************\n",
            "\n",
            "(./type1cm.ins\n",
            "\n",
            "Generating file(s) ./type1cm.sty \n",
            "\n",
            "Processing file type1cm.fdd (package,ams) -> type1cm.sty\n",
            "Lines  processed: 410\n",
            "Comments removed: 25\n",
            "Comments  passed: 7\n",
            "Codelines passed: 263\n",
            "\n",
            ") ) )\n",
            "No pages of output.\n",
            "Transcript written on type1cm.log.\n",
            "texhash: Updating /usr/local/share/texmf/ls-R... \n",
            "texhash: Updating /var/lib/texmf/ls-R-TEXLIVEDIST... \n",
            "texhash: Updating /var/lib/texmf/ls-R-TEXMFMAIN... \n",
            "texhash: Updating /var/lib/texmf/ls-R... \n",
            "texhash: Done.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  cm-super-minimal pfb2t1c2pfb\n",
            "The following NEW packages will be installed:\n",
            "  cm-super cm-super-minimal pfb2t1c2pfb\n",
            "0 upgraded, 3 newly installed, 0 to remove and 98 not upgraded.\n",
            "Need to get 24.5 MB of archives.\n",
            "After this operation, 59.9 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 cm-super-minimal all 0.3.4-11 [5,810 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 pfb2t1c2pfb amd64 0.3-11 [9,342 B]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 cm-super all 0.3.4-11 [18.7 MB]\n",
            "Fetched 24.5 MB in 1s (17.0 MB/s)\n",
            "Selecting previously unselected package cm-super-minimal.\n",
            "(Reading database ... 186715 files and directories currently installed.)\n",
            "Preparing to unpack .../cm-super-minimal_0.3.4-11_all.deb ...\n",
            "Unpacking cm-super-minimal (0.3.4-11) ...\n",
            "Selecting previously unselected package pfb2t1c2pfb.\n",
            "Preparing to unpack .../pfb2t1c2pfb_0.3-11_amd64.deb ...\n",
            "Unpacking pfb2t1c2pfb (0.3-11) ...\n",
            "Selecting previously unselected package cm-super.\n",
            "Preparing to unpack .../cm-super_0.3.4-11_all.deb ...\n",
            "Unpacking cm-super (0.3.4-11) ...\n",
            "Setting up pfb2t1c2pfb (0.3-11) ...\n",
            "Setting up cm-super-minimal (0.3.4-11) ...\n",
            "Setting up cm-super (0.3.4-11) ...\n",
            "Creating fonts. This may take some time... done.\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
            "Processing triggers for tex-common (6.09) ...\n",
            "Running mktexlsr. This may take some time... done.\n",
            "Running updmap-sys. This may take some time... done.\n",
            "Running mktexlsr /var/lib/texmf ... done.\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Collecting latex\n",
            "  Downloading latex-0.7.0.tar.gz (6.5 kB)\n",
            "Collecting tempdir\n",
            "  Downloading tempdir-0.7.1.tar.gz (5.9 kB)\n",
            "Collecting data\n",
            "  Downloading data-0.4.tar.gz (7.0 kB)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from latex) (0.16.0)\n",
            "Collecting shutilwhich\n",
            "  Downloading shutilwhich-1.1.0.tar.gz (2.3 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from data->latex) (1.15.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from data->latex) (4.4.2)\n",
            "Collecting funcsigs\n",
            "  Downloading funcsigs-1.0.2-py2.py3-none-any.whl (17 kB)\n",
            "Building wheels for collected packages: latex, data, shutilwhich, tempdir\n",
            "  Building wheel for latex (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for latex: filename=latex-0.7.0-py3-none-any.whl size=7605 sha256=c5f48ab8e8a5689a2741b7f0d1936693aa97eac2f13d01f448d34773898fd1ee\n",
            "  Stored in directory: /root/.cache/pip/wheels/57/d6/11/5d5718227890d9e479b6137cb406410c37a0b84b2ab35b872b\n",
            "  Building wheel for data (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for data: filename=data-0.4-py3-none-any.whl size=7246 sha256=d346b302ca57800d848f630649114ca095f1af14dd9fbf3fbdfd690e80ad9c70\n",
            "  Stored in directory: /root/.cache/pip/wheels/28/76/4b/7db27e02c1de4d2181559d108fcdfed1680ff7d3534553d460\n",
            "  Building wheel for shutilwhich (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shutilwhich: filename=shutilwhich-1.1.0-py3-none-any.whl size=2782 sha256=6fe7015ebbe5aa88de7be4cd987acd8c85a1552a7854736898b1bec259e5605a\n",
            "  Stored in directory: /root/.cache/pip/wheels/4c/c0/ce/5a47f35186acbe73e38811df30daaf798c81a7599bb2d5995a\n",
            "  Building wheel for tempdir (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tempdir: filename=tempdir-0.7.1-py3-none-any.whl size=2213 sha256=4c44f43c02960b7980fc1f8f4b433f04820d9e86fcc7f06622ff71cefd4a4a66\n",
            "  Stored in directory: /root/.cache/pip/wheels/7a/f1/d7/124b54c21a9ba4973bc60fcda6cdc264271f3c4d607be0486d\n",
            "Successfully built latex data shutilwhich tempdir\n",
            "Installing collected packages: funcsigs, tempdir, shutilwhich, data, latex\n",
            "Successfully installed data-0.4 funcsigs-1.0.2 latex-0.7.0 shutilwhich-1.1.0 tempdir-0.7.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:48:59.924067Z",
          "start_time": "2020-11-17T03:48:57.190005Z"
        },
        "id": "tfa8V3BGf05e"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import math\n",
        "\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from keras.layers import Dense, Input\n",
        "from keras.models import Model\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vhRcUmif06s"
      },
      "source": [
        "# Define default plot styles  \n",
        "\n",
        "from matplotlib import rc\n",
        "import matplotlib.font_manager\n",
        "\n",
        "rc('font', family='serif')\n",
        "rc('text', usetex=True)\n",
        "rc('font', size=22)\n",
        "rc('xtick', labelsize=15)\n",
        "rc('ytick', labelsize=15)\n",
        "rc('legend', fontsize=15)\n",
        "\n",
        "plot_style_0 = {\n",
        "    'histtype': 'step',\n",
        "    'color': 'black',\n",
        "    'linewidth': 2,\n",
        "    'linestyle': '--',\n",
        "    'density': False\n",
        "}\n",
        "\n",
        "plot_style_1 = {\n",
        "    'histtype': 'step',\n",
        "    'color': 'black',\n",
        "    'linewidth': 2,\n",
        "    'density': False\n",
        "}\n",
        "\n",
        "plot_style_2 = {'alpha': 0.5, 'density': False}"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:48:59.971307Z",
          "start_time": "2020-11-17T03:48:59.954584Z"
        },
        "id": "P9ZC5FAAf06z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6d5767c-29bf-41e0-8cf1-86f55e4d211d"
      },
      "source": [
        "# Check Versions\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dUjZeV_f06-"
      },
      "source": [
        "# OmniFold Gaussian Toy Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G36B6qzjf07H"
      },
      "source": [
        "## Setup the Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:48:59.978107Z",
          "start_time": "2020-11-17T03:48:59.974831Z"
        },
        "id": "TzOWxfp8f07O"
      },
      "source": [
        "# Synthetic (MC) data\n",
        "mu0 = [0,0]\n",
        "sigma0 = [[9,0.5*9],[0.5*9,9]]\n",
        "\n",
        "# Natural (real) data\n",
        "mu_unknown = [1,2]\n",
        "sigma_unknown = [[2.25,-3*0.5],[-3*0.5,4]]\n",
        "\n",
        "# Background distribution, assumed to be the same in both MC and real data\n",
        "mu_background = [0,0]\n",
        "sigma_background = [[0.25,0],[0,0.25]]\n",
        "\n",
        "eff = 0.1 #fraction of true but not reco\n",
        "fake = 0.1 #fraction of reco but not true\n",
        "\n",
        "back = 0.1 #fraction of a background process that we would like to subtract"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:49:24.308730Z",
          "start_time": "2020-11-17T03:48:59.981078Z"
        },
        "id": "RarF1cJYf07V"
      },
      "source": [
        "N = 10**5\n",
        "# param = (mu, sigma)\n",
        "theta0_param = (mu0, sigma0)  # synthetic sample\n",
        "theta_unknown_param = (mu_unknown, sigma_unknown)  # this is the data (the target)\n",
        "background_param = (mu_background, sigma_background)  # background\n",
        "epsilon = 0.5  # Smearing width\n",
        "\n",
        "dummyval = -99 #a value for examples that don't pass one of the measured/ideal selections\n",
        "\n",
        "#Synthetic\n",
        "theta0_G = np.random.multivariate_normal(theta0_param[0], theta0_param[1],N)  # Generator-level synthetic sample\n",
        "theta0_S = np.array([(x + np.random.normal(0, epsilon)) for x in theta0_G])  # Detector smearing for synthetic sample\n",
        "pass_reco = np.random.binomial(1,1.-eff,len(theta0_G)) # truth events that do not pass reco\n",
        "pass_truth = np.random.binomial(1,1.-fake,len(theta0_G)) # reco events that are not true\n",
        "theta0_S[pass_reco==0] = dummyval\n",
        "theta0_G[pass_truth==0] = dummyval\n",
        "\n",
        "# Background generation\n",
        "theta0_background = np.random.multivariate_normal(background_param[0],background_param[1], int(N*back))\n",
        "# Combine with MC data\n",
        "theta0_S_withback = np.concatenate([theta0_S,theta0_background])\n",
        "theta0_G_withback = np.concatenate([theta0_G,-np.ones((int(N*back),2))*dummyval])\n",
        "\n",
        "theta0 = np.stack([theta0_G, theta0_S], axis=1)\n",
        "labels0 = np.zeros(len(theta0))\n",
        "\n",
        "#Natural\n",
        "theta_unknown_G = np.random.multivariate_normal(theta_unknown_param[0],theta_unknown_param[1], N)\n",
        "theta_unknown_S = np.array([(x + np.random.normal(0, epsilon)) for x in theta_unknown_G]) \n",
        "pass_reco = np.random.binomial(1,1.-eff,len(theta_unknown_G))\n",
        "pass_truth = np.random.binomial(1,1.-fake,len(theta_unknown_G))\n",
        "theta_unknown_S[pass_reco==0] = dummyval\n",
        "theta_unknown_G[pass_truth==0] = dummyval\n",
        "\n",
        "theta_background = np.random.multivariate_normal(background_param[0],background_param[1], int(N*back))\n",
        "theta_unknown_S_withback = np.concatenate([theta_unknown_S,theta_background])\n",
        "theta_unknown_G_withback = np.concatenate([theta_unknown_G,-np.ones((int(N*back),2))*dummyval])\n",
        "\n",
        "theta_unknown = np.stack([theta_unknown_G, theta_unknown_S], axis=1)\n",
        "labels_unknown = np.ones(len(theta_unknown))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:49:29.592393Z",
          "start_time": "2020-11-17T03:49:24.310793Z"
        },
        "id": "8gs4fo2xf07e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "143599b0-729d-4834-9d0a-ff9ed96d9cb9"
      },
      "source": [
        "bins = np.linspace(-11, 11, 41)\n",
        "\n",
        "fig, ax = plt.subplots(3,\n",
        "                       2,\n",
        "                       figsize=(12, 15),\n",
        "                       #constrained_layout=True,\n",
        "                       sharey='row')\n",
        "\n",
        "# plot the x1 and x2 variables\n",
        "for i in range(2):\n",
        "        ax[i,0].set_xlabel('$x$'+str(i+1))\n",
        "        ax[i,0].set_ylabel('Events per bin')\n",
        "        ax[i,0].hist(theta0_G[theta0_G[:,i]!=dummyval][:,i], bins=bins, **plot_style_2, label='Generation') # This is the MC truth distribution\n",
        "        ax[i,0].hist(theta0_S_withback[theta0_S_withback[:,i]!=dummyval][:,i], bins=bins, **plot_style_2, label='Simulation') # MC reco distribution with background\n",
        "        ax[i,0].hist(theta0_S_withback[theta0_G_withback[:,i]==-dummyval][:,i], bins=bins, color=\"darkorange\", label='Background') # MC background prediction\n",
        "        legend = ax[i,0].legend(\n",
        "            title='Synthetic\\n($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "            loc='upper left',\n",
        "            frameon=False)\n",
        "        plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "        ax[i,1].set_xlabel('$x2$')\n",
        "        ax[i,1].hist(theta_unknown_G[theta_unknown_G[:,i]!=dummyval][:,i], bins=bins, **plot_style_2, label='Truth',color=\"green\") # Real data truth distribution\n",
        "        ax[i,1].hist(theta_unknown_S_withback[theta_unknown_S_withback[:,i]!=dummyval][:,i], bins=bins, **plot_style_2, label='Data',color=\"black\") # Real data reco distribution with background\n",
        "        ax[i,1].hist(theta_unknown_S_withback[theta_unknown_G_withback[:,i]==-dummyval][:,i], bins=bins, label='Background',color=\"black\") # Real data background\n",
        "        legend = ax[i,1].legend(\n",
        "            title='Natural\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "            loc='upper left',\n",
        "            frameon=False)\n",
        "        plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "# plot the 2d distribution\n",
        "# you can clearly see the different correlation in the two datasets\n",
        "ax[2,0].set_xlabel('$x1$')\n",
        "ax[2,0].set_ylabel('$x2$')\n",
        "h1 = ax[2,0].hist2d(theta0_G[theta0_G[:,0]!=dummyval][:,0], theta0_G[theta0_G[:,1]!=dummyval][:,1] , bins=(bins,bins),label='Generation')\n",
        "fig.colorbar(h1[3],ax=ax[2,0])\n",
        "legend = ax[2,0].legend(\n",
        "    title='Synthetic\\n($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[2,1].set_xlabel('$x1$')\n",
        "ax[2,1].set_ylabel('$x2$')\n",
        "theta0_S_withback_plot1=theta_unknown_S_withback[:,1]\n",
        "h2 = ax[2,1].hist2d(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,0], theta_unknown_G[theta_unknown_G[:,1]!=dummyval][:,1] , bins=(bins,bins),label='Truth')\n",
        "fig.colorbar(h2[3],ax=ax[2,1])\n",
        "legend = ax[2,1].legend(\n",
        "    title='Natural\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1080 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9irkgGtf07k"
      },
      "source": [
        "## Unfold Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:49:29.597528Z",
          "start_time": "2020-11-17T03:49:29.594288Z"
        },
        "id": "w_Mfftycf07t"
      },
      "source": [
        "# Number of unfolding iterations\n",
        "iterations = 5"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:49:29.688675Z",
          "start_time": "2020-11-17T03:49:29.599367Z"
        },
        "id": "vtooHsx2f07x"
      },
      "source": [
        "# Processed MC reco data for training\n",
        "xvals_1 = np.concatenate((theta0_S, theta_unknown_S))\n",
        "# labels0 = {0}, labels_unknown = {1}\n",
        "yvals_1 = np.concatenate((labels0, labels_unknown))\n",
        "\n",
        "# Processed MC truth data data for training\n",
        "xvals_2 = np.concatenate((theta0_G, theta0_G))\n",
        "yvals_2 = np.concatenate((labels0, labels_unknown))\n",
        "\n",
        "# Push and pull weights in unfolding\n",
        "weights = np.empty(shape=(iterations, 2, len(theta0)))\n",
        "# shape = (#iteration, #step, #event)\n",
        "\n",
        "# tensorflow DNN model \n",
        "inputs = Input((2, )) # 2d vector as input\n",
        "# three hidden layers \n",
        "hidden_layer_1 = Dense(50, activation='relu')(inputs)\n",
        "hidden_layer_2 = Dense(50, activation='relu')(hidden_layer_1)\n",
        "hidden_layer_3 = Dense(50, activation='relu')(hidden_layer_2)\n",
        "outputs = Dense(1, activation='sigmoid')(hidden_layer_3)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "# Early stopping criteria to avoid overtraining\n",
        "earlystopping = EarlyStopping(patience=10,\n",
        "                              verbose=1,\n",
        "                              restore_best_weights=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T03:49:29.695163Z",
          "start_time": "2020-11-17T03:49:29.690659Z"
        },
        "id": "MBezAHNGf075"
      },
      "source": [
        "# from NN (DCTR)\n",
        "def reweight(events):\n",
        "    # f is the probability of the event is a real signal, 1-f is the probability of the event is a real background\n",
        "    f = model.predict(events, batch_size=10000)\n",
        "    # with such weight we can reweight a \"background\" distribution to a \"signal\" distribution\n",
        "    weights = f / (1. - f)\n",
        "    return np.squeeze(np.nan_to_num(weights))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RyqlyOYf079",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1bc68dc-6c27-4088-9577-c859c72cff14"
      },
      "source": [
        "#We can do the background subtraction as part of the iterative unfolding, but it is easier to do it first.\n",
        "\n",
        "# \"Signal\" here is background subtracted MC reco distribuion, \"background\" is the background-included MC reco distribuion  \n",
        "x_data_and_MCback = np.concatenate([theta0_S_withback[theta0_G_withback[:,0]==-dummyval],\n",
        "                                    theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval],\n",
        "                                    theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval]])\n",
        "    \n",
        "y_data_and_MCback = np.concatenate([np.ones(len(theta0_S_withback[theta0_G_withback[:,0]==-dummyval])),\n",
        "                                    np.ones(len(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval])),\n",
        "                                    np.zeros(len(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval]))])\n",
        "    \n",
        "# -1 means subtracting the background from the original reco distribution\n",
        "W_data_and_MCback = np.concatenate([-1.*np.ones(len(theta0_S_withback[theta0_G_withback[:,0]==-dummyval])),\n",
        "                                    np.ones(len(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval])),\n",
        "                                    np.ones(len(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval]))])\n",
        "\n",
        "# sklearn function to split the training data into training and validation set\n",
        "X_train_1, X_test_1, Y_train_1, Y_test_1, w_train_1, w_test_1 = train_test_split(\n",
        "    x_data_and_MCback, y_data_and_MCback, W_data_and_MCback)\n",
        "\n",
        "    \n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='Adam',\n",
        "              metrics=['accuracy'])\n",
        "model.fit(X_train_1,\n",
        "          Y_train_1,\n",
        "          sample_weight=w_train_1,\n",
        "          epochs=200,\n",
        "          batch_size=10000,\n",
        "          validation_data=(X_test_1, Y_test_1, w_test_1),\n",
        "          callbacks=[earlystopping],\n",
        "          verbose=1)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "16/16 [==============================] - 19s 391ms/step - loss: 0.6357 - accuracy: 0.4943 - val_loss: 0.6256 - val_accuracy: 0.4723\n",
            "Epoch 2/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6249 - accuracy: 0.4743 - val_loss: 0.6237 - val_accuracy: 0.4745\n",
            "Epoch 3/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6232 - accuracy: 0.4778 - val_loss: 0.6220 - val_accuracy: 0.4738\n",
            "Epoch 4/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6232 - accuracy: 0.4741 - val_loss: 0.6209 - val_accuracy: 0.4744\n",
            "Epoch 5/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6212 - accuracy: 0.4768 - val_loss: 0.6194 - val_accuracy: 0.4736\n",
            "Epoch 6/200\n",
            "16/16 [==============================] - 0s 20ms/step - loss: 0.6190 - accuracy: 0.4762 - val_loss: 0.6183 - val_accuracy: 0.4740\n",
            "Epoch 7/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6176 - accuracy: 0.4764 - val_loss: 0.6174 - val_accuracy: 0.4743\n",
            "Epoch 8/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6175 - accuracy: 0.4772 - val_loss: 0.6165 - val_accuracy: 0.4739\n",
            "Epoch 9/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6166 - accuracy: 0.4752 - val_loss: 0.6162 - val_accuracy: 0.4730\n",
            "Epoch 10/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6167 - accuracy: 0.4761 - val_loss: 0.6158 - val_accuracy: 0.4718\n",
            "Epoch 11/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6151 - accuracy: 0.4766 - val_loss: 0.6158 - val_accuracy: 0.4705\n",
            "Epoch 12/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6154 - accuracy: 0.4779 - val_loss: 0.6158 - val_accuracy: 0.4721\n",
            "Epoch 13/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6164 - accuracy: 0.4803 - val_loss: 0.6157 - val_accuracy: 0.4722\n",
            "Epoch 14/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6160 - accuracy: 0.4759 - val_loss: 0.6158 - val_accuracy: 0.4723\n",
            "Epoch 15/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6166 - accuracy: 0.4753 - val_loss: 0.6156 - val_accuracy: 0.4705\n",
            "Epoch 16/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6142 - accuracy: 0.4779 - val_loss: 0.6155 - val_accuracy: 0.4763\n",
            "Epoch 17/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6152 - accuracy: 0.4758 - val_loss: 0.6154 - val_accuracy: 0.4734\n",
            "Epoch 18/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6172 - accuracy: 0.4798 - val_loss: 0.6156 - val_accuracy: 0.4708\n",
            "Epoch 19/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6155 - accuracy: 0.4766 - val_loss: 0.6155 - val_accuracy: 0.4711\n",
            "Epoch 20/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6164 - accuracy: 0.4798 - val_loss: 0.6155 - val_accuracy: 0.4732\n",
            "Epoch 21/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6157 - accuracy: 0.4777 - val_loss: 0.6154 - val_accuracy: 0.4699\n",
            "Epoch 22/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6164 - accuracy: 0.4771 - val_loss: 0.6155 - val_accuracy: 0.4697\n",
            "Epoch 23/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6145 - accuracy: 0.4759 - val_loss: 0.6155 - val_accuracy: 0.4707\n",
            "Epoch 24/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6152 - accuracy: 0.4752 - val_loss: 0.6155 - val_accuracy: 0.4719\n",
            "Epoch 25/200\n",
            "16/16 [==============================] - 0s 22ms/step - loss: 0.6157 - accuracy: 0.4771 - val_loss: 0.6156 - val_accuracy: 0.4707\n",
            "Epoch 26/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6145 - accuracy: 0.4748 - val_loss: 0.6155 - val_accuracy: 0.4710\n",
            "Epoch 27/200\n",
            "16/16 [==============================] - 0s 21ms/step - loss: 0.6163 - accuracy: 0.4779 - val_loss: 0.6155 - val_accuracy: 0.4732\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00027: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0d519d1c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lNFMlA0f08C"
      },
      "source": [
        "# reweigh real data distribution to subtract background\n",
        "w_data = reweight(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ca87LlVPf08F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "056bd473-e20e-4bdd-e081-15c5a2a20080"
      },
      "source": [
        "bins = np.linspace(-11, 11, 41)\n",
        "\n",
        "fig, ax = plt.subplots(5,\n",
        "                       2,\n",
        "                       figsize=(12, 25),\n",
        "                       #constrained_layout=True,\n",
        "                       sharey='row')\n",
        "\n",
        "for i in range(2):\n",
        "        ax[i,0].set_xlabel('$x$')\n",
        "        ax[i,0].set_ylabel('Events per bin')\n",
        "        ax[i,0].hist(theta0_G[theta0_G[:,0]!=dummyval][:,i], bins=bins, **plot_style_2, label='Generation') # MC event at generation\n",
        "        ax[i,0].hist(theta0_S[theta0_S[:,0]!=dummyval][:,i], bins=bins, **plot_style_2, label='Simulation (no back.)') # MC event after reco\n",
        "        legend = ax[i,0].legend(\n",
        "            title='Synthetic\\n($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "            loc='upper left',\n",
        "            frameon=False)\n",
        "        plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "        ax[i,1].set_xlabel('$x$')\n",
        "        ax[i,1].hist(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,i], bins=bins, **plot_style_2, label='Truth',color=\"green\") # Real event at generation\n",
        "        ax[i,1].hist(theta_unknown_S[theta_unknown_S[:,0]!=dummyval][:,i], bins=bins, **plot_style_2, label='Data (no back.)',color=\"black\") # Real event after reco\n",
        "        ax[i,1].hist(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval][:,i], bins=bins, histtype=\"step\", label='Data (reweighted)',color=\"black\",weights=w_data) # Reweighing background-included real reco event to subtract background\n",
        "        legend = ax[i,1].legend(\n",
        "            title='Natural\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "            loc='upper left',\n",
        "            frameon=False)\n",
        "        plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[2,0].set_xlabel('$x1$')\n",
        "ax[2,0].set_ylabel('$x2$')\n",
        "h1 = ax[2,0].hist2d(theta0_G[theta0_G[:,0]!=dummyval][:,0], theta0_G[theta0_G[:,0]!=dummyval][:,1] , bins=(bins,bins),label='Generation') # MC event at generation\n",
        "fig.colorbar(h1[3],ax=ax[2,0])\n",
        "legend = ax[2,0].legend(\n",
        "    title='Generation\\n($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[2,1].set_xlabel('$x1$')\n",
        "ax[2,1].set_ylabel('$x2$')\n",
        "h2 = ax[2,1].hist2d(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,0], theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,1] , bins=(bins,bins),label='Simulation (no back.)') # MC event after reco\n",
        "fig.colorbar(h2[3],ax=ax[2,1])\n",
        "legend = ax[2,1].legend(\n",
        "    title='Simulation (no back.)\\n($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[3,0].set_xlabel('$x1$')\n",
        "ax[3,0].set_ylabel('$x2$')\n",
        "h3 = ax[3,0].hist2d(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,0], theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,1] , bins=(bins,bins),label='Truth') # Real event at generation\n",
        "fig.colorbar(h1[3],ax=ax[3,0])\n",
        "legend = ax[3,0].legend(\n",
        "    title='Truth\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[3,1].set_xlabel('$x1$')\n",
        "ax[3,1].set_ylabel('$x2$')\n",
        "h4 = ax[3,1].hist2d(theta_unknown_S[theta_unknown_S[:,0]!=dummyval][:,0], theta_unknown_S[theta_unknown_S[:,0]!=dummyval][:,1] , bins=(bins,bins),label='Data (no back.)') # Real event after reco\n",
        "fig.colorbar(h4[3],ax=ax[3,1])\n",
        "legend = ax[3,1].legend(\n",
        "    title='Data (no back.)\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[4,0].set_xlabel('$x1$')\n",
        "ax[4,0].set_ylabel('$x2$')\n",
        "h5 = ax[4,0].hist2d(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval][:,0], theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval][:,1] , bins=(bins,bins),label='Data (with back.)') # Real event after reco + background\n",
        "fig.colorbar(h5[3],ax=ax[4,0])\n",
        "legend = ax[4,0].legend(\n",
        "    title='Data (with back.)\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[4,1].set_xlabel('$x1$')\n",
        "ax[4,1].set_ylabel('$x2$')\n",
        "h6 = ax[4,1].hist2d(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval][:,0], theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval][:,1] , bins=(bins,bins),label='Data (reweighted)',weights=w_data) # Reweighing background-included real reco event to subtract background\n",
        "fig.colorbar(h6[3],ax=ax[4,1])\n",
        "legend = ax[4,1].legend(\n",
        "    title='Data (reweighted)\\n($\\mu$, $\\sigma$) = {}'.format(theta_unknown_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "fig.show()\n",
        "plt.savefig(\"plot_\"+str(0)+\"_datareweight.pdf\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1800 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RT-yq10f08I"
      },
      "source": [
        "# Push and pull weights in unfolding\n",
        "# initial iterative weights are ones\n",
        "weights_pull = np.ones(len(theta0_S))\n",
        "weights_push = np.ones(len(theta0_S))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHA39C89f08K"
      },
      "source": [
        "# Processed MC reco data for training\n",
        "xvals_1 = np.concatenate((theta0_S, theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval]))\n",
        "# labels0 = {0}, labels_unknown = {1}\n",
        "yvals_1 = np.concatenate((labels0, np.ones(len(theta_unknown_S_withback[theta_unknown_S_withback[:,0]!=dummyval]))))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T04:28:20.621248Z",
          "start_time": "2020-11-17T03:49:29.711813Z"
        },
        "tags": [
          "outputPrepend"
        ],
        "id": "xJmPvkd8f08O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eab807e-aeac-45b1-b804-dc277f3dba25"
      },
      "source": [
        "for i in range(iterations):\n",
        "    print(\"\\nITERATION: {}\\n\".format(i + 1))\n",
        "\n",
        "    # STEP 1: classify Sim. (which is reweighted by weights_push) to Data\n",
        "    # weights reweighted Sim. --> Data\n",
        "    print(\"STEP 1\\n\")\n",
        "\n",
        "    # weights_push = weights from previous interation\n",
        "    # w_data is the background subtracted real data\n",
        "    weights_1 = np.concatenate((weights_push, w_data))\n",
        "    # actual weights for Sim., ones for Data (not MC weights)\n",
        "\n",
        "    X_train_1, X_test_1, Y_train_1, Y_test_1, w_train_1, w_test_1 = train_test_split(\n",
        "        xvals_1, yvals_1, weights_1)\n",
        "\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='Adam',\n",
        "                  metrics=['accuracy'])\n",
        "    model.fit(X_train_1[X_train_1[:,0]!=dummyval], # Classification at reco level\n",
        "              Y_train_1[X_train_1[:,0]!=dummyval],\n",
        "              sample_weight=w_train_1[X_train_1[:,0]!=dummyval],\n",
        "              epochs=200,\n",
        "              batch_size=10000,\n",
        "              validation_data=(X_test_1[X_test_1[:,0]!=dummyval], Y_test_1[X_test_1[:,0]!=dummyval], w_test_1[X_test_1[:,0]!=dummyval]),\n",
        "              callbacks=[earlystopping],\n",
        "              verbose=1)\n",
        "\n",
        "    # weights after reco level reweight\n",
        "    weights_pull = weights_push * reweight(theta0_S)\n",
        "    \n",
        "    ###\n",
        "    #Need to do something with events that don't pass reco.\n",
        "    \n",
        "    #One option is to take the prior:\n",
        "    #weights_pull[theta0_S==dummyval] = 1. \n",
        "    \n",
        "    #Another option is to assign the average weight: <w|x_true>.  To do this, we need to estimate this quantity.\n",
        "    # Training for events that don't pass reco\n",
        "    # \"Signal\" is true MC events after reco level reweight, \"background\" is the un-weighted true MC events\n",
        "    xvals_1b = np.concatenate([theta0_G[theta0_S[:,0]!=dummyval],theta0_G[theta0_S[:,0]!=dummyval]])\n",
        "    yvals_1b = np.concatenate([np.ones(len(theta0_G[theta0_S[:,0]!=dummyval])),np.zeros(len(theta0_G[theta0_S[:,0]!=dummyval]))])\n",
        "    weights_1b = np.concatenate([weights_pull[theta0_S[:,0]!=dummyval],np.ones(len(theta0_G[theta0_S[:,0]!=dummyval]))])\n",
        "    \n",
        "    X_train_1b, X_test_1b, Y_train_1b, Y_test_1b, w_train_1b, w_test_1b = train_test_split(\n",
        "        xvals_1b, yvals_1b, weights_1b)    \n",
        "    \n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='Adam',\n",
        "                  metrics=['accuracy'])\n",
        "    model.fit(X_train_1b,\n",
        "              Y_train_1b,\n",
        "              sample_weight=w_train_1b,\n",
        "              epochs=200,\n",
        "              batch_size=10000,\n",
        "              validation_data=(X_test_1b, Y_test_1b, w_test_1b),\n",
        "              callbacks=[earlystopping],\n",
        "              verbose=1)\n",
        "    \n",
        "    average_vals = reweight(theta0_G[theta0_S[:,0]==dummyval])\n",
        "    weights_pull[theta0_S[:,0]==dummyval] = average_vals\n",
        "    ###\n",
        "    \n",
        "    weights[i, :1, :] = weights_pull\n",
        "\n",
        "    # STEP 2: classify Gen. to reweighted Gen. (which is reweighted by weights_pull)\n",
        "    # weights Gen. --> reweighted Gen.\n",
        "    print(\"\\nSTEP 2\\n\")\n",
        "\n",
        "    # \"Signal\" is MC truth events after reco level reweight, \"background\" is the un-weighted true MC events\n",
        "    weights_2 = np.concatenate((np.ones(len(theta0_G)), weights_pull))\n",
        "    # ones for Gen. (not MC weights), actual weights for (reweighted) Gen.\n",
        "\n",
        "    X_train_2, X_test_2, Y_train_2, Y_test_2, w_train_2, w_test_2 = train_test_split(\n",
        "        xvals_2, yvals_2, weights_2)\n",
        "\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='Adam',\n",
        "                  metrics=['accuracy'])\n",
        "    model.fit(X_train_2,\n",
        "              Y_train_2,\n",
        "              sample_weight=w_train_2,\n",
        "              epochs=200,\n",
        "              batch_size=2000,\n",
        "              validation_data=(X_test_2, Y_test_2, w_test_2),\n",
        "              callbacks=[earlystopping],\n",
        "              verbose=1)\n",
        "\n",
        "    # weights after truth level reweight\n",
        "    weights_push = reweight(theta0_G)\n",
        "    ###\n",
        "    #Need to do something with events that don't pass truth    \n",
        "    \n",
        "    #One option is to take the prior:\n",
        "    #weights_push[theta0_G==dummyval] = 1. \n",
        "    \n",
        "    #Another option is to assign the average weight: <w|x_reco>.  To do this, we need to estimate this quantity.\n",
        "    # Training for events that don't pass truth\n",
        "    # \"Signal\" is reco MC events after truth level reweight, \"background\" is the un-weighted reco MC events\n",
        "    xvals_1b = np.concatenate([theta0_S[theta0_G[:,0]!=dummyval],theta0_S[theta0_G[:,0]!=dummyval]])\n",
        "    yvals_1b = np.concatenate([np.ones(len(theta0_S[theta0_G[:,0]!=dummyval])),np.zeros(len(theta0_S[theta0_G[:,0]!=dummyval]))])\n",
        "    weights_1b = np.concatenate([weights_push[theta0_G[:,0]!=dummyval],np.ones(len(theta0_S[theta0_G[:,0]!=dummyval]))])\n",
        "    \n",
        "    X_train_1b, X_test_1b, Y_train_1b, Y_test_1b, w_train_1b, w_test_1b = train_test_split(\n",
        "        xvals_1b, yvals_1b, weights_1b)    \n",
        "    \n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='Adam',\n",
        "                  metrics=['accuracy'])\n",
        "    model.fit(X_train_1b,\n",
        "              Y_train_1b,\n",
        "              sample_weight=w_train_1b,\n",
        "              epochs=200,\n",
        "              batch_size=10000,\n",
        "              validation_data=(X_test_1b, Y_test_1b, w_test_1b),\n",
        "              callbacks=[earlystopping],\n",
        "              verbose=1)\n",
        "    \n",
        "    average_vals = reweight(theta0_S[theta0_G[:,0]==dummyval])\n",
        "    weights_push[theta0_G[:,0]==dummyval] = average_vals\n",
        "    ###    \n",
        "    \n",
        "    weights[i, 1:2, :] = weights_push"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "ITERATION: 1\n",
            "\n",
            "STEP 1\n",
            "\n",
            "Epoch 1/200\n",
            "15/15 [==============================] - 6s 380ms/step - loss: 0.6593 - accuracy: 0.5582 - val_loss: 0.5485 - val_accuracy: 0.6604\n",
            "Epoch 2/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.5366 - accuracy: 0.6716 - val_loss: 0.5108 - val_accuracy: 0.7023\n",
            "Epoch 3/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.5068 - accuracy: 0.7051 - val_loss: 0.4954 - val_accuracy: 0.7114\n",
            "Epoch 4/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4939 - accuracy: 0.7132 - val_loss: 0.4858 - val_accuracy: 0.7223\n",
            "Epoch 5/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4851 - accuracy: 0.7234 - val_loss: 0.4789 - val_accuracy: 0.7314\n",
            "Epoch 6/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4775 - accuracy: 0.7329 - val_loss: 0.4746 - val_accuracy: 0.7364\n",
            "Epoch 7/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4724 - accuracy: 0.7380 - val_loss: 0.4717 - val_accuracy: 0.7360\n",
            "Epoch 8/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4728 - accuracy: 0.7349 - val_loss: 0.4696 - val_accuracy: 0.7370\n",
            "Epoch 9/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.4698 - accuracy: 0.7354 - val_loss: 0.4680 - val_accuracy: 0.7365\n",
            "Epoch 10/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4683 - accuracy: 0.7361 - val_loss: 0.4669 - val_accuracy: 0.7360\n",
            "Epoch 11/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4660 - accuracy: 0.7367 - val_loss: 0.4663 - val_accuracy: 0.7353\n",
            "Epoch 12/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4673 - accuracy: 0.7345 - val_loss: 0.4657 - val_accuracy: 0.7361\n",
            "Epoch 13/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4676 - accuracy: 0.7343 - val_loss: 0.4655 - val_accuracy: 0.7365\n",
            "Epoch 14/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4670 - accuracy: 0.7338 - val_loss: 0.4651 - val_accuracy: 0.7364\n",
            "Epoch 15/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4662 - accuracy: 0.7351 - val_loss: 0.4649 - val_accuracy: 0.7371\n",
            "Epoch 16/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4656 - accuracy: 0.7352 - val_loss: 0.4647 - val_accuracy: 0.7361\n",
            "Epoch 17/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4652 - accuracy: 0.7369 - val_loss: 0.4647 - val_accuracy: 0.7347\n",
            "Epoch 18/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4657 - accuracy: 0.7353 - val_loss: 0.4647 - val_accuracy: 0.7361\n",
            "Epoch 19/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4643 - accuracy: 0.7370 - val_loss: 0.4649 - val_accuracy: 0.7351\n",
            "Epoch 20/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4656 - accuracy: 0.7355 - val_loss: 0.4647 - val_accuracy: 0.7366\n",
            "Epoch 21/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4648 - accuracy: 0.7362 - val_loss: 0.4644 - val_accuracy: 0.7369\n",
            "Epoch 22/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4637 - accuracy: 0.7374 - val_loss: 0.4643 - val_accuracy: 0.7374\n",
            "Epoch 23/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4668 - accuracy: 0.7348 - val_loss: 0.4644 - val_accuracy: 0.7382\n",
            "Epoch 24/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4658 - accuracy: 0.7364 - val_loss: 0.4643 - val_accuracy: 0.7381\n",
            "Epoch 25/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4650 - accuracy: 0.7379 - val_loss: 0.4644 - val_accuracy: 0.7389\n",
            "Epoch 26/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4637 - accuracy: 0.7382 - val_loss: 0.4642 - val_accuracy: 0.7386\n",
            "Epoch 27/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4646 - accuracy: 0.7387 - val_loss: 0.4641 - val_accuracy: 0.7385\n",
            "Epoch 28/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4648 - accuracy: 0.7381 - val_loss: 0.4641 - val_accuracy: 0.7386\n",
            "Epoch 29/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4633 - accuracy: 0.7393 - val_loss: 0.4641 - val_accuracy: 0.7368\n",
            "Epoch 30/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4637 - accuracy: 0.7393 - val_loss: 0.4641 - val_accuracy: 0.7391\n",
            "Epoch 31/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.4637 - accuracy: 0.7388 - val_loss: 0.4643 - val_accuracy: 0.7389\n",
            "Epoch 32/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4647 - accuracy: 0.7379 - val_loss: 0.4641 - val_accuracy: 0.7372\n",
            "Epoch 33/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4648 - accuracy: 0.7370 - val_loss: 0.4640 - val_accuracy: 0.7392\n",
            "Epoch 34/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4660 - accuracy: 0.7372 - val_loss: 0.4642 - val_accuracy: 0.7388\n",
            "Epoch 35/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4649 - accuracy: 0.7367 - val_loss: 0.4642 - val_accuracy: 0.7403\n",
            "Epoch 36/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4636 - accuracy: 0.7391 - val_loss: 0.4640 - val_accuracy: 0.7385\n",
            "Epoch 37/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4663 - accuracy: 0.7365 - val_loss: 0.4641 - val_accuracy: 0.7395\n",
            "Epoch 38/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4646 - accuracy: 0.7378 - val_loss: 0.4640 - val_accuracy: 0.7373\n",
            "Epoch 39/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4643 - accuracy: 0.7378 - val_loss: 0.4642 - val_accuracy: 0.7388\n",
            "Epoch 40/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4660 - accuracy: 0.7379 - val_loss: 0.4640 - val_accuracy: 0.7386\n",
            "Epoch 41/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4644 - accuracy: 0.7380 - val_loss: 0.4642 - val_accuracy: 0.7363\n",
            "Epoch 42/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4646 - accuracy: 0.7377 - val_loss: 0.4640 - val_accuracy: 0.7392\n",
            "Epoch 43/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4626 - accuracy: 0.7395 - val_loss: 0.4641 - val_accuracy: 0.7387\n",
            "Epoch 44/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4651 - accuracy: 0.7375 - val_loss: 0.4640 - val_accuracy: 0.7388\n",
            "Epoch 45/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4637 - accuracy: 0.7375 - val_loss: 0.4646 - val_accuracy: 0.7370\n",
            "Epoch 46/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4657 - accuracy: 0.7374 - val_loss: 0.4654 - val_accuracy: 0.7393\n",
            "Epoch 47/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.4650 - accuracy: 0.7392 - val_loss: 0.4647 - val_accuracy: 0.7361\n",
            "Epoch 48/200\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.4643 - accuracy: 0.7386 - val_loss: 0.4648 - val_accuracy: 0.7400\n",
            "Epoch 49/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.4649 - accuracy: 0.7377 - val_loss: 0.4641 - val_accuracy: 0.7386\n",
            "Epoch 50/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4649 - accuracy: 0.7402 - val_loss: 0.4642 - val_accuracy: 0.7399\n",
            "Epoch 51/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.4635 - accuracy: 0.7402 - val_loss: 0.4642 - val_accuracy: 0.7375\n",
            "Epoch 52/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.4625 - accuracy: 0.7412 - val_loss: 0.4643 - val_accuracy: 0.7380\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00052: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 382ms/step - loss: 10.2062 - accuracy: 0.5018 - val_loss: 4.8012 - val_accuracy: 0.4987\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 3.9212 - accuracy: 0.4996 - val_loss: 1.1892 - val_accuracy: 0.5001\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.9834 - accuracy: 0.5012 - val_loss: 0.9052 - val_accuracy: 0.4981\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.7687 - accuracy: 0.5019 - val_loss: 0.6284 - val_accuracy: 0.4984\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.6170 - accuracy: 0.5032 - val_loss: 0.5952 - val_accuracy: 0.4988\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5896 - accuracy: 0.5004 - val_loss: 0.5719 - val_accuracy: 0.4990\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5749 - accuracy: 0.5018 - val_loss: 0.5616 - val_accuracy: 0.4995\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5628 - accuracy: 0.5003 - val_loss: 0.5498 - val_accuracy: 0.4991\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5514 - accuracy: 0.5006 - val_loss: 0.5459 - val_accuracy: 0.4985\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5506 - accuracy: 0.5003 - val_loss: 0.5446 - val_accuracy: 0.4988\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5452 - accuracy: 0.5018 - val_loss: 0.5450 - val_accuracy: 0.4982\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5456 - accuracy: 0.5000 - val_loss: 0.5427 - val_accuracy: 0.4988\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5442 - accuracy: 0.5002 - val_loss: 0.5401 - val_accuracy: 0.4983\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5442 - accuracy: 0.4999 - val_loss: 0.5389 - val_accuracy: 0.4984\n",
            "Epoch 15/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5426 - accuracy: 0.4998 - val_loss: 0.5373 - val_accuracy: 0.4985\n",
            "Epoch 16/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5438 - accuracy: 0.5002 - val_loss: 0.5372 - val_accuracy: 0.4983\n",
            "Epoch 17/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5409 - accuracy: 0.5010 - val_loss: 0.5374 - val_accuracy: 0.4983\n",
            "Epoch 18/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5409 - accuracy: 0.5002 - val_loss: 0.5362 - val_accuracy: 0.4985\n",
            "Epoch 19/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5384 - accuracy: 0.4992 - val_loss: 0.5353 - val_accuracy: 0.4981\n",
            "Epoch 20/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5386 - accuracy: 0.5006 - val_loss: 0.5361 - val_accuracy: 0.4981\n",
            "Epoch 21/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5393 - accuracy: 0.5011 - val_loss: 0.5358 - val_accuracy: 0.4982\n",
            "Epoch 22/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5391 - accuracy: 0.5026 - val_loss: 0.5362 - val_accuracy: 0.4985\n",
            "Epoch 23/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5383 - accuracy: 0.5017 - val_loss: 0.5352 - val_accuracy: 0.4984\n",
            "Epoch 24/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5365 - accuracy: 0.4989 - val_loss: 0.5343 - val_accuracy: 0.4985\n",
            "Epoch 25/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5380 - accuracy: 0.4996 - val_loss: 0.5350 - val_accuracy: 0.4980\n",
            "Epoch 26/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5385 - accuracy: 0.5007 - val_loss: 0.5356 - val_accuracy: 0.4977\n",
            "Epoch 27/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5381 - accuracy: 0.4994 - val_loss: 0.5340 - val_accuracy: 0.4986\n",
            "Epoch 28/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5376 - accuracy: 0.5005 - val_loss: 0.5360 - val_accuracy: 0.4983\n",
            "Epoch 29/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5388 - accuracy: 0.4989 - val_loss: 0.5356 - val_accuracy: 0.4985\n",
            "Epoch 30/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5387 - accuracy: 0.5009 - val_loss: 0.5341 - val_accuracy: 0.4982\n",
            "Epoch 31/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5377 - accuracy: 0.5013 - val_loss: 0.5341 - val_accuracy: 0.4986\n",
            "Epoch 32/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5369 - accuracy: 0.4989 - val_loss: 0.5345 - val_accuracy: 0.4982\n",
            "Epoch 33/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5389 - accuracy: 0.4998 - val_loss: 0.5354 - val_accuracy: 0.4986\n",
            "Epoch 34/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5375 - accuracy: 0.5007 - val_loss: 0.5346 - val_accuracy: 0.4981\n",
            "Epoch 35/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5401 - accuracy: 0.4993 - val_loss: 0.5348 - val_accuracy: 0.4986\n",
            "Epoch 36/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5389 - accuracy: 0.5005 - val_loss: 0.5339 - val_accuracy: 0.4981\n",
            "Epoch 37/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5385 - accuracy: 0.4989 - val_loss: 0.5336 - val_accuracy: 0.4984\n",
            "Epoch 38/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5366 - accuracy: 0.4997 - val_loss: 0.5347 - val_accuracy: 0.4984\n",
            "Epoch 39/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5378 - accuracy: 0.4999 - val_loss: 0.5348 - val_accuracy: 0.4985\n",
            "Epoch 40/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5386 - accuracy: 0.5014 - val_loss: 0.5352 - val_accuracy: 0.4982\n",
            "Epoch 41/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5370 - accuracy: 0.5010 - val_loss: 0.5339 - val_accuracy: 0.4987\n",
            "Epoch 42/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5373 - accuracy: 0.4991 - val_loss: 0.5338 - val_accuracy: 0.4988\n",
            "Epoch 43/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5340 - accuracy: 0.5004 - val_loss: 0.5335 - val_accuracy: 0.4988\n",
            "Epoch 44/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5361 - accuracy: 0.5008 - val_loss: 0.5341 - val_accuracy: 0.4986\n",
            "Epoch 45/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5367 - accuracy: 0.4997 - val_loss: 0.5335 - val_accuracy: 0.4987\n",
            "Epoch 46/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5370 - accuracy: 0.4979 - val_loss: 0.5344 - val_accuracy: 0.4986\n",
            "Epoch 47/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5378 - accuracy: 0.5002 - val_loss: 0.5333 - val_accuracy: 0.4989\n",
            "Epoch 48/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5376 - accuracy: 0.4985 - val_loss: 0.5347 - val_accuracy: 0.4988\n",
            "Epoch 49/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5379 - accuracy: 0.5004 - val_loss: 0.5344 - val_accuracy: 0.4982\n",
            "Epoch 50/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5366 - accuracy: 0.4992 - val_loss: 0.5338 - val_accuracy: 0.4985\n",
            "Epoch 51/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5372 - accuracy: 0.4997 - val_loss: 0.5345 - val_accuracy: 0.4987\n",
            "Epoch 52/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5397 - accuracy: 0.5009 - val_loss: 0.5357 - val_accuracy: 0.4982\n",
            "Epoch 53/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5372 - accuracy: 0.5014 - val_loss: 0.5357 - val_accuracy: 0.4987\n",
            "Epoch 54/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5418 - accuracy: 0.4987 - val_loss: 0.5376 - val_accuracy: 0.4985\n",
            "Epoch 55/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5380 - accuracy: 0.5003 - val_loss: 0.5336 - val_accuracy: 0.4985\n",
            "Epoch 56/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5392 - accuracy: 0.5001 - val_loss: 0.5336 - val_accuracy: 0.4981\n",
            "Epoch 57/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5364 - accuracy: 0.5003 - val_loss: 0.5338 - val_accuracy: 0.4990\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00057: early stopping\n",
            "\n",
            "STEP 2\n",
            "\n",
            "Epoch 1/200\n",
            "75/75 [==============================] - 7s 79ms/step - loss: 0.5616 - accuracy: 0.5032 - val_loss: 0.5394 - val_accuracy: 0.4990\n",
            "Epoch 2/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5402 - accuracy: 0.4997 - val_loss: 0.5390 - val_accuracy: 0.4979\n",
            "Epoch 3/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5357 - accuracy: 0.5017 - val_loss: 0.5397 - val_accuracy: 0.4970\n",
            "Epoch 4/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5380 - accuracy: 0.5008 - val_loss: 0.5401 - val_accuracy: 0.4990\n",
            "Epoch 5/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5374 - accuracy: 0.5004 - val_loss: 0.5395 - val_accuracy: 0.4989\n",
            "Epoch 6/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.5378 - accuracy: 0.5002 - val_loss: 0.5394 - val_accuracy: 0.4990\n",
            "Epoch 7/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5384 - accuracy: 0.5002 - val_loss: 0.5390 - val_accuracy: 0.4989\n",
            "Epoch 8/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5384 - accuracy: 0.5030 - val_loss: 0.5403 - val_accuracy: 0.4998\n",
            "Epoch 9/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5348 - accuracy: 0.5015 - val_loss: 0.5392 - val_accuracy: 0.4990\n",
            "Epoch 10/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5370 - accuracy: 0.5004 - val_loss: 0.5396 - val_accuracy: 0.4997\n",
            "Epoch 11/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5378 - accuracy: 0.4998 - val_loss: 0.5387 - val_accuracy: 0.4992\n",
            "Epoch 12/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5368 - accuracy: 0.4981 - val_loss: 0.5407 - val_accuracy: 0.4971\n",
            "Epoch 13/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5364 - accuracy: 0.5026 - val_loss: 0.5439 - val_accuracy: 0.4974\n",
            "Epoch 14/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5384 - accuracy: 0.5009 - val_loss: 0.5400 - val_accuracy: 0.4994\n",
            "Epoch 15/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5381 - accuracy: 0.4994 - val_loss: 0.5397 - val_accuracy: 0.4972\n",
            "Epoch 16/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5366 - accuracy: 0.5006 - val_loss: 0.5392 - val_accuracy: 0.5001\n",
            "Epoch 17/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5353 - accuracy: 0.5009 - val_loss: 0.5390 - val_accuracy: 0.4997\n",
            "Epoch 18/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5362 - accuracy: 0.5016 - val_loss: 0.5420 - val_accuracy: 0.4987\n",
            "Epoch 19/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5409 - accuracy: 0.4998 - val_loss: 0.5391 - val_accuracy: 0.4976\n",
            "Epoch 20/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5347 - accuracy: 0.5015 - val_loss: 0.5411 - val_accuracy: 0.4990\n",
            "Epoch 21/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5378 - accuracy: 0.5007 - val_loss: 0.5399 - val_accuracy: 0.4989\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00021: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 388ms/step - loss: 0.5945 - accuracy: 0.4991 - val_loss: 0.5727 - val_accuracy: 0.5012\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5655 - accuracy: 0.5008 - val_loss: 0.5645 - val_accuracy: 0.5003\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5614 - accuracy: 0.5007 - val_loss: 0.5627 - val_accuracy: 0.5003\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5625 - accuracy: 0.4995 - val_loss: 0.5607 - val_accuracy: 0.5002\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5602 - accuracy: 0.5007 - val_loss: 0.5611 - val_accuracy: 0.5004\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5609 - accuracy: 0.5001 - val_loss: 0.5609 - val_accuracy: 0.5003\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5612 - accuracy: 0.5005 - val_loss: 0.5612 - val_accuracy: 0.5005\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5629 - accuracy: 0.4989 - val_loss: 0.5616 - val_accuracy: 0.5008\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5608 - accuracy: 0.4995 - val_loss: 0.5617 - val_accuracy: 0.5003\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5635 - accuracy: 0.4985 - val_loss: 0.5613 - val_accuracy: 0.5005\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5608 - accuracy: 0.4997 - val_loss: 0.5612 - val_accuracy: 0.5000\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5609 - accuracy: 0.4995 - val_loss: 0.5638 - val_accuracy: 0.4998\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5615 - accuracy: 0.5024 - val_loss: 0.5617 - val_accuracy: 0.5001\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5612 - accuracy: 0.4985 - val_loss: 0.5619 - val_accuracy: 0.5004\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00014: early stopping\n",
            "\n",
            "ITERATION: 2\n",
            "\n",
            "STEP 1\n",
            "\n",
            "Epoch 1/200\n",
            "15/15 [==============================] - 6s 377ms/step - loss: 0.7069 - accuracy: 0.6870 - val_loss: 0.6750 - val_accuracy: 0.6870\n",
            "Epoch 2/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6724 - accuracy: 0.6978 - val_loss: 0.6723 - val_accuracy: 0.6549\n",
            "Epoch 3/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6719 - accuracy: 0.6718 - val_loss: 0.6718 - val_accuracy: 0.6908\n",
            "Epoch 4/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6697 - accuracy: 0.6817 - val_loss: 0.6718 - val_accuracy: 0.7005\n",
            "Epoch 5/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6706 - accuracy: 0.6939 - val_loss: 0.6716 - val_accuracy: 0.6869\n",
            "Epoch 6/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6700 - accuracy: 0.6881 - val_loss: 0.6715 - val_accuracy: 0.6815\n",
            "Epoch 7/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6698 - accuracy: 0.6867 - val_loss: 0.6716 - val_accuracy: 0.6791\n",
            "Epoch 8/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6684 - accuracy: 0.6854 - val_loss: 0.6716 - val_accuracy: 0.6734\n",
            "Epoch 9/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6705 - accuracy: 0.6839 - val_loss: 0.6719 - val_accuracy: 0.6649\n",
            "Epoch 10/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6704 - accuracy: 0.6689 - val_loss: 0.6718 - val_accuracy: 0.7032\n",
            "Epoch 11/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6676 - accuracy: 0.6919 - val_loss: 0.6718 - val_accuracy: 0.6899\n",
            "Epoch 12/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6704 - accuracy: 0.6883 - val_loss: 0.6716 - val_accuracy: 0.6880\n",
            "Epoch 13/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6697 - accuracy: 0.6836 - val_loss: 0.6716 - val_accuracy: 0.6887\n",
            "Epoch 14/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6713 - accuracy: 0.6797 - val_loss: 0.6717 - val_accuracy: 0.6983\n",
            "Epoch 15/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6700 - accuracy: 0.6852 - val_loss: 0.6721 - val_accuracy: 0.7097\n",
            "Epoch 16/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6679 - accuracy: 0.6933 - val_loss: 0.6717 - val_accuracy: 0.6928\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00016: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 384ms/step - loss: 0.6189 - accuracy: 0.5003 - val_loss: 0.5256 - val_accuracy: 0.5036\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5189 - accuracy: 0.4991 - val_loss: 0.5001 - val_accuracy: 0.5014\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5047 - accuracy: 0.4988 - val_loss: 0.4983 - val_accuracy: 0.5028\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5018 - accuracy: 0.5000 - val_loss: 0.4977 - val_accuracy: 0.5022\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5020 - accuracy: 0.4984 - val_loss: 0.4975 - val_accuracy: 0.5024\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5014 - accuracy: 0.4981 - val_loss: 0.4980 - val_accuracy: 0.5003\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5029 - accuracy: 0.4992 - val_loss: 0.4974 - val_accuracy: 0.5023\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5039 - accuracy: 0.4969 - val_loss: 0.4975 - val_accuracy: 0.5007\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5030 - accuracy: 0.4983 - val_loss: 0.4973 - val_accuracy: 0.5023\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5014 - accuracy: 0.4979 - val_loss: 0.4974 - val_accuracy: 0.5024\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.5018 - accuracy: 0.5003 - val_loss: 0.4973 - val_accuracy: 0.5004\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5021 - accuracy: 0.4994 - val_loss: 0.4974 - val_accuracy: 0.5005\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5040 - accuracy: 0.4991 - val_loss: 0.4971 - val_accuracy: 0.5006\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5017 - accuracy: 0.4984 - val_loss: 0.4972 - val_accuracy: 0.5018\n",
            "Epoch 15/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5019 - accuracy: 0.5011 - val_loss: 0.4984 - val_accuracy: 0.5022\n",
            "Epoch 16/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5025 - accuracy: 0.4966 - val_loss: 0.4976 - val_accuracy: 0.5019\n",
            "Epoch 17/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5022 - accuracy: 0.4995 - val_loss: 0.4991 - val_accuracy: 0.5021\n",
            "Epoch 18/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5007 - accuracy: 0.5001 - val_loss: 0.4988 - val_accuracy: 0.5021\n",
            "Epoch 19/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5005 - accuracy: 0.5014 - val_loss: 0.4974 - val_accuracy: 0.5023\n",
            "Epoch 20/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5020 - accuracy: 0.4989 - val_loss: 0.4978 - val_accuracy: 0.5023\n",
            "Epoch 21/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5011 - accuracy: 0.5001 - val_loss: 0.4971 - val_accuracy: 0.5002\n",
            "Epoch 22/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5027 - accuracy: 0.4997 - val_loss: 0.4973 - val_accuracy: 0.5006\n",
            "Epoch 23/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5019 - accuracy: 0.4978 - val_loss: 0.4983 - val_accuracy: 0.5024\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00023: early stopping\n",
            "\n",
            "STEP 2\n",
            "\n",
            "Epoch 1/200\n",
            "75/75 [==============================] - 6s 78ms/step - loss: 0.5087 - accuracy: 0.5019 - val_loss: 0.5016 - val_accuracy: 0.4973\n",
            "Epoch 2/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5037 - accuracy: 0.5003 - val_loss: 0.5017 - val_accuracy: 0.4975\n",
            "Epoch 3/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5024 - accuracy: 0.4988 - val_loss: 0.5021 - val_accuracy: 0.4971\n",
            "Epoch 4/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5012 - accuracy: 0.5017 - val_loss: 0.5013 - val_accuracy: 0.4973\n",
            "Epoch 5/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5013 - accuracy: 0.4979 - val_loss: 0.5015 - val_accuracy: 0.4972\n",
            "Epoch 6/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4992 - accuracy: 0.5008 - val_loss: 0.5017 - val_accuracy: 0.4966\n",
            "Epoch 7/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5014 - accuracy: 0.5010 - val_loss: 0.5024 - val_accuracy: 0.4969\n",
            "Epoch 8/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5019 - accuracy: 0.5039 - val_loss: 0.5014 - val_accuracy: 0.4971\n",
            "Epoch 9/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5010 - accuracy: 0.4997 - val_loss: 0.5015 - val_accuracy: 0.4972\n",
            "Epoch 10/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.5024 - accuracy: 0.4984 - val_loss: 0.5014 - val_accuracy: 0.4972\n",
            "Epoch 11/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4995 - accuracy: 0.4996 - val_loss: 0.5023 - val_accuracy: 0.4966\n",
            "Epoch 12/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5014 - accuracy: 0.5019 - val_loss: 0.5021 - val_accuracy: 0.4972\n",
            "Epoch 13/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5001 - accuracy: 0.5026 - val_loss: 0.5019 - val_accuracy: 0.4968\n",
            "Epoch 14/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.5025 - accuracy: 0.5019 - val_loss: 0.5025 - val_accuracy: 0.4973\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00014: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 384ms/step - loss: 0.5200 - accuracy: 0.5023 - val_loss: 0.5144 - val_accuracy: 0.4986\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5085 - accuracy: 0.5016 - val_loss: 0.5038 - val_accuracy: 0.4994\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5054 - accuracy: 0.5006 - val_loss: 0.5042 - val_accuracy: 0.4985\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5032 - accuracy: 0.4998 - val_loss: 0.5041 - val_accuracy: 0.4991\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5032 - accuracy: 0.5015 - val_loss: 0.5042 - val_accuracy: 0.4991\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5060 - accuracy: 0.5005 - val_loss: 0.5054 - val_accuracy: 0.4982\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5071 - accuracy: 0.5015 - val_loss: 0.5045 - val_accuracy: 0.4981\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5049 - accuracy: 0.5011 - val_loss: 0.5046 - val_accuracy: 0.4993\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5047 - accuracy: 0.5002 - val_loss: 0.5058 - val_accuracy: 0.4989\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5072 - accuracy: 0.4997 - val_loss: 0.5050 - val_accuracy: 0.4989\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5069 - accuracy: 0.4999 - val_loss: 0.5042 - val_accuracy: 0.4989\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5065 - accuracy: 0.5016 - val_loss: 0.5043 - val_accuracy: 0.4978\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00012: early stopping\n",
            "\n",
            "ITERATION: 3\n",
            "\n",
            "STEP 1\n",
            "\n",
            "Epoch 1/200\n",
            "15/15 [==============================] - 6s 381ms/step - loss: 0.6826 - accuracy: 0.7098 - val_loss: 0.6517 - val_accuracy: 0.6766\n",
            "Epoch 2/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6394 - accuracy: 0.7024 - val_loss: 0.6464 - val_accuracy: 0.7174\n",
            "Epoch 3/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6423 - accuracy: 0.7317 - val_loss: 0.6455 - val_accuracy: 0.7348\n",
            "Epoch 4/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6379 - accuracy: 0.7343 - val_loss: 0.6455 - val_accuracy: 0.7374\n",
            "Epoch 5/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6379 - accuracy: 0.7392 - val_loss: 0.6454 - val_accuracy: 0.7392\n",
            "Epoch 6/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6370 - accuracy: 0.7414 - val_loss: 0.6454 - val_accuracy: 0.7289\n",
            "Epoch 7/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6413 - accuracy: 0.7215 - val_loss: 0.6455 - val_accuracy: 0.7364\n",
            "Epoch 8/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6379 - accuracy: 0.7236 - val_loss: 0.6454 - val_accuracy: 0.7366\n",
            "Epoch 9/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6389 - accuracy: 0.7407 - val_loss: 0.6453 - val_accuracy: 0.7255\n",
            "Epoch 10/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6395 - accuracy: 0.7274 - val_loss: 0.6455 - val_accuracy: 0.7146\n",
            "Epoch 11/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6378 - accuracy: 0.7230 - val_loss: 0.6454 - val_accuracy: 0.7384\n",
            "Epoch 12/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6399 - accuracy: 0.7383 - val_loss: 0.6456 - val_accuracy: 0.7390\n",
            "Epoch 13/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6379 - accuracy: 0.7358 - val_loss: 0.6453 - val_accuracy: 0.7340\n",
            "Epoch 14/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6381 - accuracy: 0.7330 - val_loss: 0.6453 - val_accuracy: 0.7376\n",
            "Epoch 15/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6389 - accuracy: 0.7354 - val_loss: 0.6453 - val_accuracy: 0.7199\n",
            "Epoch 16/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6404 - accuracy: 0.7275 - val_loss: 0.6454 - val_accuracy: 0.7264\n",
            "Epoch 17/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6417 - accuracy: 0.7352 - val_loss: 0.6453 - val_accuracy: 0.7301\n",
            "Epoch 18/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6399 - accuracy: 0.7326 - val_loss: 0.6456 - val_accuracy: 0.7454\n",
            "Epoch 19/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6407 - accuracy: 0.7416 - val_loss: 0.6453 - val_accuracy: 0.7216\n",
            "Epoch 20/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6374 - accuracy: 0.7321 - val_loss: 0.6458 - val_accuracy: 0.6880\n",
            "Epoch 21/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6381 - accuracy: 0.7218 - val_loss: 0.6457 - val_accuracy: 0.6920\n",
            "Epoch 22/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6423 - accuracy: 0.7211 - val_loss: 0.6454 - val_accuracy: 0.7382\n",
            "Epoch 23/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6401 - accuracy: 0.7368 - val_loss: 0.6457 - val_accuracy: 0.7457\n",
            "Epoch 24/200\n",
            "15/15 [==============================] - 0s 21ms/step - loss: 0.6410 - accuracy: 0.7300 - val_loss: 0.6454 - val_accuracy: 0.7464\n",
            "Epoch 25/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6378 - accuracy: 0.7465 - val_loss: 0.6454 - val_accuracy: 0.7241\n",
            "Epoch 26/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6392 - accuracy: 0.7318 - val_loss: 0.6454 - val_accuracy: 0.7327\n",
            "Epoch 27/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6404 - accuracy: 0.7272 - val_loss: 0.6458 - val_accuracy: 0.7481\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00027: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 399ms/step - loss: 1.5183 - accuracy: 0.4993 - val_loss: 0.5293 - val_accuracy: 0.5000\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5517 - accuracy: 0.5018 - val_loss: 0.4958 - val_accuracy: 0.5008\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4928 - accuracy: 0.5020 - val_loss: 0.4956 - val_accuracy: 0.4989\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4883 - accuracy: 0.4998 - val_loss: 0.4927 - val_accuracy: 0.5004\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4872 - accuracy: 0.4984 - val_loss: 0.4924 - val_accuracy: 0.5000\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4872 - accuracy: 0.5000 - val_loss: 0.4923 - val_accuracy: 0.5004\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4870 - accuracy: 0.4999 - val_loss: 0.4927 - val_accuracy: 0.5001\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4869 - accuracy: 0.5006 - val_loss: 0.4922 - val_accuracy: 0.4987\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4886 - accuracy: 0.4980 - val_loss: 0.4924 - val_accuracy: 0.4989\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4877 - accuracy: 0.5010 - val_loss: 0.4938 - val_accuracy: 0.5002\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4882 - accuracy: 0.5023 - val_loss: 0.4929 - val_accuracy: 0.4989\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4897 - accuracy: 0.5002 - val_loss: 0.4919 - val_accuracy: 0.5002\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4866 - accuracy: 0.4982 - val_loss: 0.4919 - val_accuracy: 0.5001\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.4860 - accuracy: 0.5007 - val_loss: 0.4921 - val_accuracy: 0.4991\n",
            "Epoch 15/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.4879 - accuracy: 0.5004 - val_loss: 0.4928 - val_accuracy: 0.5001\n",
            "Epoch 16/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4884 - accuracy: 0.5000 - val_loss: 0.4925 - val_accuracy: 0.4986\n",
            "Epoch 17/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4866 - accuracy: 0.5006 - val_loss: 0.4918 - val_accuracy: 0.4988\n",
            "Epoch 18/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4869 - accuracy: 0.5004 - val_loss: 0.4918 - val_accuracy: 0.5001\n",
            "Epoch 19/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4869 - accuracy: 0.4993 - val_loss: 0.4919 - val_accuracy: 0.4987\n",
            "Epoch 20/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4902 - accuracy: 0.5011 - val_loss: 0.4917 - val_accuracy: 0.4988\n",
            "Epoch 21/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4889 - accuracy: 0.5026 - val_loss: 0.4928 - val_accuracy: 0.5000\n",
            "Epoch 22/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4880 - accuracy: 0.5001 - val_loss: 0.4919 - val_accuracy: 0.4988\n",
            "Epoch 23/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.4868 - accuracy: 0.5004 - val_loss: 0.4920 - val_accuracy: 0.4987\n",
            "Epoch 24/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4886 - accuracy: 0.4997 - val_loss: 0.4926 - val_accuracy: 0.4991\n",
            "Epoch 25/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4875 - accuracy: 0.4992 - val_loss: 0.4917 - val_accuracy: 0.4984\n",
            "Epoch 26/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.4863 - accuracy: 0.5029 - val_loss: 0.4933 - val_accuracy: 0.4997\n",
            "Epoch 27/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4885 - accuracy: 0.5006 - val_loss: 0.4918 - val_accuracy: 0.5005\n",
            "Epoch 28/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4873 - accuracy: 0.5011 - val_loss: 0.4930 - val_accuracy: 0.5002\n",
            "Epoch 29/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4890 - accuracy: 0.5010 - val_loss: 0.4924 - val_accuracy: 0.5007\n",
            "Epoch 30/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4886 - accuracy: 0.5001 - val_loss: 0.4918 - val_accuracy: 0.4999\n",
            "Epoch 31/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4872 - accuracy: 0.5011 - val_loss: 0.4919 - val_accuracy: 0.4991\n",
            "Epoch 32/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4882 - accuracy: 0.5013 - val_loss: 0.4917 - val_accuracy: 0.4997\n",
            "Epoch 33/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.4878 - accuracy: 0.5013 - val_loss: 0.4945 - val_accuracy: 0.5004\n",
            "Epoch 34/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4870 - accuracy: 0.4988 - val_loss: 0.4930 - val_accuracy: 0.4990\n",
            "Epoch 35/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4873 - accuracy: 0.5002 - val_loss: 0.4941 - val_accuracy: 0.4988\n",
            "Epoch 36/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4888 - accuracy: 0.4992 - val_loss: 0.4940 - val_accuracy: 0.5006\n",
            "Epoch 37/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4870 - accuracy: 0.5016 - val_loss: 0.4928 - val_accuracy: 0.5003\n",
            "Epoch 38/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4888 - accuracy: 0.4997 - val_loss: 0.4919 - val_accuracy: 0.5002\n",
            "Epoch 39/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4887 - accuracy: 0.5002 - val_loss: 0.4919 - val_accuracy: 0.5009\n",
            "Epoch 40/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4856 - accuracy: 0.4992 - val_loss: 0.4926 - val_accuracy: 0.4993\n",
            "Epoch 41/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4861 - accuracy: 0.5021 - val_loss: 0.4920 - val_accuracy: 0.4988\n",
            "Epoch 42/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4877 - accuracy: 0.5014 - val_loss: 0.4917 - val_accuracy: 0.4998\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00042: early stopping\n",
            "\n",
            "STEP 2\n",
            "\n",
            "Epoch 1/200\n",
            "75/75 [==============================] - 6s 78ms/step - loss: 0.4939 - accuracy: 0.4983 - val_loss: 0.4878 - val_accuracy: 0.5012\n",
            "Epoch 2/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4887 - accuracy: 0.5008 - val_loss: 0.4891 - val_accuracy: 0.5007\n",
            "Epoch 3/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4905 - accuracy: 0.4989 - val_loss: 0.4885 - val_accuracy: 0.5006\n",
            "Epoch 4/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4899 - accuracy: 0.4993 - val_loss: 0.4887 - val_accuracy: 0.5010\n",
            "Epoch 5/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4908 - accuracy: 0.4989 - val_loss: 0.4880 - val_accuracy: 0.5002\n",
            "Epoch 6/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4878 - accuracy: 0.4986 - val_loss: 0.4889 - val_accuracy: 0.5009\n",
            "Epoch 7/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4921 - accuracy: 0.5002 - val_loss: 0.4881 - val_accuracy: 0.5014\n",
            "Epoch 8/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4882 - accuracy: 0.4998 - val_loss: 0.4881 - val_accuracy: 0.5009\n",
            "Epoch 9/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4909 - accuracy: 0.4998 - val_loss: 0.4897 - val_accuracy: 0.5010\n",
            "Epoch 10/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4890 - accuracy: 0.4994 - val_loss: 0.4897 - val_accuracy: 0.5002\n",
            "Epoch 11/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4906 - accuracy: 0.5009 - val_loss: 0.4889 - val_accuracy: 0.5005\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00011: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 391ms/step - loss: 0.5248 - accuracy: 0.4985 - val_loss: 0.5091 - val_accuracy: 0.4999\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5084 - accuracy: 0.5022 - val_loss: 0.5070 - val_accuracy: 0.5001\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5057 - accuracy: 0.5012 - val_loss: 0.5068 - val_accuracy: 0.4982\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5048 - accuracy: 0.5012 - val_loss: 0.5065 - val_accuracy: 0.5000\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5046 - accuracy: 0.5003 - val_loss: 0.5066 - val_accuracy: 0.4980\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5040 - accuracy: 0.5000 - val_loss: 0.5066 - val_accuracy: 0.4983\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5066 - accuracy: 0.4999 - val_loss: 0.5067 - val_accuracy: 0.5005\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5047 - accuracy: 0.5018 - val_loss: 0.5074 - val_accuracy: 0.4988\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5068 - accuracy: 0.5005 - val_loss: 0.5066 - val_accuracy: 0.5001\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5066 - accuracy: 0.5009 - val_loss: 0.5066 - val_accuracy: 0.5002\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 26ms/step - loss: 0.5038 - accuracy: 0.4999 - val_loss: 0.5067 - val_accuracy: 0.4981\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.5035 - accuracy: 0.5005 - val_loss: 0.5067 - val_accuracy: 0.5001\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5056 - accuracy: 0.5003 - val_loss: 0.5077 - val_accuracy: 0.4980\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5066 - accuracy: 0.4999 - val_loss: 0.5109 - val_accuracy: 0.4977\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00014: early stopping\n",
            "\n",
            "ITERATION: 4\n",
            "\n",
            "STEP 1\n",
            "\n",
            "Epoch 1/200\n",
            "15/15 [==============================] - 6s 385ms/step - loss: 0.6987 - accuracy: 0.7031 - val_loss: 0.6585 - val_accuracy: 0.6763\n",
            "Epoch 2/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6557 - accuracy: 0.6922 - val_loss: 0.6489 - val_accuracy: 0.7052\n",
            "Epoch 3/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6496 - accuracy: 0.7122 - val_loss: 0.6485 - val_accuracy: 0.7018\n",
            "Epoch 4/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6475 - accuracy: 0.7086 - val_loss: 0.6487 - val_accuracy: 0.6621\n",
            "Epoch 5/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6497 - accuracy: 0.6721 - val_loss: 0.6483 - val_accuracy: 0.6916\n",
            "Epoch 6/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6465 - accuracy: 0.6932 - val_loss: 0.6485 - val_accuracy: 0.6876\n",
            "Epoch 7/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6492 - accuracy: 0.6879 - val_loss: 0.6484 - val_accuracy: 0.6874\n",
            "Epoch 8/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6510 - accuracy: 0.6966 - val_loss: 0.6485 - val_accuracy: 0.6870\n",
            "Epoch 9/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6524 - accuracy: 0.6700 - val_loss: 0.6485 - val_accuracy: 0.6801\n",
            "Epoch 10/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6485 - accuracy: 0.6878 - val_loss: 0.6484 - val_accuracy: 0.6789\n",
            "Epoch 11/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6502 - accuracy: 0.6824 - val_loss: 0.6486 - val_accuracy: 0.6747\n",
            "Epoch 12/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6475 - accuracy: 0.6749 - val_loss: 0.6484 - val_accuracy: 0.6758\n",
            "Epoch 13/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6522 - accuracy: 0.6745 - val_loss: 0.6485 - val_accuracy: 0.6719\n",
            "Epoch 14/200\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.6511 - accuracy: 0.6779 - val_loss: 0.6486 - val_accuracy: 0.6749\n",
            "Epoch 15/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6504 - accuracy: 0.6668 - val_loss: 0.6492 - val_accuracy: 0.6713\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00015: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 388ms/step - loss: 0.8024 - accuracy: 0.4984 - val_loss: 0.5350 - val_accuracy: 0.5047\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5296 - accuracy: 0.4980 - val_loss: 0.4933 - val_accuracy: 0.5001\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4898 - accuracy: 0.4989 - val_loss: 0.4914 - val_accuracy: 0.5046\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 26ms/step - loss: 0.4907 - accuracy: 0.4992 - val_loss: 0.4906 - val_accuracy: 0.5006\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4865 - accuracy: 0.5004 - val_loss: 0.4908 - val_accuracy: 0.5007\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4873 - accuracy: 0.4989 - val_loss: 0.4900 - val_accuracy: 0.5008\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4877 - accuracy: 0.4972 - val_loss: 0.4901 - val_accuracy: 0.5041\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4892 - accuracy: 0.4989 - val_loss: 0.4900 - val_accuracy: 0.5005\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4875 - accuracy: 0.4989 - val_loss: 0.4901 - val_accuracy: 0.5008\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4876 - accuracy: 0.4992 - val_loss: 0.4902 - val_accuracy: 0.5043\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4886 - accuracy: 0.4995 - val_loss: 0.4904 - val_accuracy: 0.5008\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4884 - accuracy: 0.4984 - val_loss: 0.4902 - val_accuracy: 0.5006\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4845 - accuracy: 0.5012 - val_loss: 0.4899 - val_accuracy: 0.5043\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.4896 - accuracy: 0.4986 - val_loss: 0.4907 - val_accuracy: 0.5041\n",
            "Epoch 15/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4875 - accuracy: 0.4973 - val_loss: 0.4900 - val_accuracy: 0.5039\n",
            "Epoch 16/200\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.4879 - accuracy: 0.5007 - val_loss: 0.4899 - val_accuracy: 0.5040\n",
            "Epoch 17/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.4876 - accuracy: 0.4979 - val_loss: 0.4902 - val_accuracy: 0.5004\n",
            "Epoch 18/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4873 - accuracy: 0.5019 - val_loss: 0.4914 - val_accuracy: 0.5008\n",
            "Epoch 19/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4877 - accuracy: 0.5006 - val_loss: 0.4914 - val_accuracy: 0.5006\n",
            "Epoch 20/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4880 - accuracy: 0.4988 - val_loss: 0.4900 - val_accuracy: 0.5007\n",
            "Epoch 21/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4861 - accuracy: 0.4977 - val_loss: 0.4901 - val_accuracy: 0.5041\n",
            "Epoch 22/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4877 - accuracy: 0.4992 - val_loss: 0.4904 - val_accuracy: 0.5008\n",
            "Epoch 23/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4911 - accuracy: 0.4980 - val_loss: 0.4907 - val_accuracy: 0.5004\n",
            "Epoch 24/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4894 - accuracy: 0.5001 - val_loss: 0.4903 - val_accuracy: 0.5039\n",
            "Epoch 25/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4885 - accuracy: 0.4992 - val_loss: 0.4901 - val_accuracy: 0.5003\n",
            "Epoch 26/200\n",
            "14/14 [==============================] - 0s 26ms/step - loss: 0.4861 - accuracy: 0.5013 - val_loss: 0.4901 - val_accuracy: 0.5008\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00026: early stopping\n",
            "\n",
            "STEP 2\n",
            "\n",
            "Epoch 1/200\n",
            "75/75 [==============================] - 7s 79ms/step - loss: 0.4975 - accuracy: 0.4997 - val_loss: 0.4884 - val_accuracy: 0.5008\n",
            "Epoch 2/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4887 - accuracy: 0.4985 - val_loss: 0.4878 - val_accuracy: 0.5000\n",
            "Epoch 3/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4900 - accuracy: 0.4986 - val_loss: 0.4879 - val_accuracy: 0.5007\n",
            "Epoch 4/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4881 - accuracy: 0.4994 - val_loss: 0.4890 - val_accuracy: 0.5002\n",
            "Epoch 5/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4880 - accuracy: 0.4993 - val_loss: 0.4878 - val_accuracy: 0.5005\n",
            "Epoch 6/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4879 - accuracy: 0.4987 - val_loss: 0.4909 - val_accuracy: 0.5001\n",
            "Epoch 7/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4890 - accuracy: 0.4992 - val_loss: 0.4874 - val_accuracy: 0.5005\n",
            "Epoch 8/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4903 - accuracy: 0.5005 - val_loss: 0.4882 - val_accuracy: 0.4999\n",
            "Epoch 9/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4899 - accuracy: 0.4985 - val_loss: 0.4883 - val_accuracy: 0.5001\n",
            "Epoch 10/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4884 - accuracy: 0.5019 - val_loss: 0.4875 - val_accuracy: 0.5002\n",
            "Epoch 11/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4895 - accuracy: 0.5007 - val_loss: 0.4899 - val_accuracy: 0.4998\n",
            "Epoch 12/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4872 - accuracy: 0.5015 - val_loss: 0.4877 - val_accuracy: 0.5005\n",
            "Epoch 13/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4879 - accuracy: 0.5006 - val_loss: 0.4876 - val_accuracy: 0.4999\n",
            "Epoch 14/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4889 - accuracy: 0.4972 - val_loss: 0.4877 - val_accuracy: 0.4999\n",
            "Epoch 15/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4881 - accuracy: 0.4994 - val_loss: 0.4877 - val_accuracy: 0.4999\n",
            "Epoch 16/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4904 - accuracy: 0.5016 - val_loss: 0.4879 - val_accuracy: 0.5002\n",
            "Epoch 17/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4894 - accuracy: 0.4979 - val_loss: 0.4878 - val_accuracy: 0.5003\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00017: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 390ms/step - loss: 0.5262 - accuracy: 0.5021 - val_loss: 0.5173 - val_accuracy: 0.5018\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5234 - accuracy: 0.4985 - val_loss: 0.5140 - val_accuracy: 0.5015\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5174 - accuracy: 0.5001 - val_loss: 0.5143 - val_accuracy: 0.5012\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5180 - accuracy: 0.5011 - val_loss: 0.5141 - val_accuracy: 0.5014\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5192 - accuracy: 0.4997 - val_loss: 0.5141 - val_accuracy: 0.5013\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5185 - accuracy: 0.4997 - val_loss: 0.5151 - val_accuracy: 0.5012\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5209 - accuracy: 0.4998 - val_loss: 0.5151 - val_accuracy: 0.5015\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5201 - accuracy: 0.4982 - val_loss: 0.5143 - val_accuracy: 0.5012\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5176 - accuracy: 0.5001 - val_loss: 0.5142 - val_accuracy: 0.5012\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5184 - accuracy: 0.4999 - val_loss: 0.5146 - val_accuracy: 0.5017\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5193 - accuracy: 0.4980 - val_loss: 0.5146 - val_accuracy: 0.5011\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.5208 - accuracy: 0.5007 - val_loss: 0.5149 - val_accuracy: 0.5013\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00012: early stopping\n",
            "\n",
            "ITERATION: 5\n",
            "\n",
            "STEP 1\n",
            "\n",
            "Epoch 1/200\n",
            "15/15 [==============================] - 6s 401ms/step - loss: 0.7526 - accuracy: 0.7125 - val_loss: 0.6965 - val_accuracy: 0.6427\n",
            "Epoch 2/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6815 - accuracy: 0.6522 - val_loss: 0.6818 - val_accuracy: 0.6783\n",
            "Epoch 3/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6736 - accuracy: 0.6656 - val_loss: 0.6797 - val_accuracy: 0.6842\n",
            "Epoch 4/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6712 - accuracy: 0.6796 - val_loss: 0.6787 - val_accuracy: 0.6306\n",
            "Epoch 5/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6725 - accuracy: 0.6410 - val_loss: 0.6789 - val_accuracy: 0.6498\n",
            "Epoch 6/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6726 - accuracy: 0.6354 - val_loss: 0.6788 - val_accuracy: 0.6216\n",
            "Epoch 7/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6676 - accuracy: 0.6506 - val_loss: 0.6788 - val_accuracy: 0.5320\n",
            "Epoch 8/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6734 - accuracy: 0.5338 - val_loss: 0.6792 - val_accuracy: 0.6741\n",
            "Epoch 9/200\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.6681 - accuracy: 0.6349 - val_loss: 0.6788 - val_accuracy: 0.6135\n",
            "Epoch 10/200\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.6732 - accuracy: 0.6502 - val_loss: 0.6787 - val_accuracy: 0.5909\n",
            "Epoch 11/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6685 - accuracy: 0.6268 - val_loss: 0.6787 - val_accuracy: 0.5992\n",
            "Epoch 12/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6711 - accuracy: 0.5768 - val_loss: 0.6791 - val_accuracy: 0.6441\n",
            "Epoch 13/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6694 - accuracy: 0.6349 - val_loss: 0.6787 - val_accuracy: 0.5874\n",
            "Epoch 14/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6708 - accuracy: 0.5890 - val_loss: 0.6787 - val_accuracy: 0.6129\n",
            "Epoch 15/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6677 - accuracy: 0.6552 - val_loss: 0.6786 - val_accuracy: 0.5086\n",
            "Epoch 16/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6739 - accuracy: 0.5387 - val_loss: 0.6793 - val_accuracy: 0.6686\n",
            "Epoch 17/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6712 - accuracy: 0.6410 - val_loss: 0.6788 - val_accuracy: 0.6274\n",
            "Epoch 18/200\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.6693 - accuracy: 0.6485 - val_loss: 0.6787 - val_accuracy: 0.5698\n",
            "Epoch 19/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6673 - accuracy: 0.6144 - val_loss: 0.6787 - val_accuracy: 0.5934\n",
            "Epoch 20/200\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.6679 - accuracy: 0.6137 - val_loss: 0.6792 - val_accuracy: 0.6916\n",
            "Epoch 21/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6718 - accuracy: 0.6686 - val_loss: 0.6788 - val_accuracy: 0.6071\n",
            "Epoch 22/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6717 - accuracy: 0.6272 - val_loss: 0.6790 - val_accuracy: 0.6295\n",
            "Epoch 23/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6699 - accuracy: 0.6410 - val_loss: 0.6787 - val_accuracy: 0.6196\n",
            "Epoch 24/200\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.6698 - accuracy: 0.6393 - val_loss: 0.6790 - val_accuracy: 0.6741\n",
            "Epoch 25/200\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6694 - accuracy: 0.6639 - val_loss: 0.6788 - val_accuracy: 0.6037\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00025: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 396ms/step - loss: 1.9223 - accuracy: 0.4992 - val_loss: 0.6262 - val_accuracy: 0.5043\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5651 - accuracy: 0.4993 - val_loss: 0.5041 - val_accuracy: 0.5030\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4982 - accuracy: 0.4995 - val_loss: 0.4815 - val_accuracy: 0.5028\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4856 - accuracy: 0.4984 - val_loss: 0.4776 - val_accuracy: 0.5030\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4832 - accuracy: 0.4972 - val_loss: 0.4763 - val_accuracy: 0.5033\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4800 - accuracy: 0.4993 - val_loss: 0.4759 - val_accuracy: 0.5038\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.4804 - accuracy: 0.4992 - val_loss: 0.4760 - val_accuracy: 0.5043\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4808 - accuracy: 0.4983 - val_loss: 0.4759 - val_accuracy: 0.5039\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4824 - accuracy: 0.4996 - val_loss: 0.4759 - val_accuracy: 0.5042\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4800 - accuracy: 0.4994 - val_loss: 0.4760 - val_accuracy: 0.5036\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.4804 - accuracy: 0.4959 - val_loss: 0.4761 - val_accuracy: 0.5039\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4819 - accuracy: 0.4987 - val_loss: 0.4773 - val_accuracy: 0.5038\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4803 - accuracy: 0.4959 - val_loss: 0.4760 - val_accuracy: 0.5039\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.4822 - accuracy: 0.5000 - val_loss: 0.4761 - val_accuracy: 0.5037\n",
            "Epoch 15/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4831 - accuracy: 0.4992 - val_loss: 0.4760 - val_accuracy: 0.5040\n",
            "Epoch 16/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4818 - accuracy: 0.4989 - val_loss: 0.4761 - val_accuracy: 0.5035\n",
            "Epoch 17/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4802 - accuracy: 0.4982 - val_loss: 0.4762 - val_accuracy: 0.5035\n",
            "Epoch 18/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4821 - accuracy: 0.4975 - val_loss: 0.4759 - val_accuracy: 0.5037\n",
            "Epoch 19/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.4817 - accuracy: 0.4998 - val_loss: 0.4771 - val_accuracy: 0.5039\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00019: early stopping\n",
            "\n",
            "STEP 2\n",
            "\n",
            "Epoch 1/200\n",
            "75/75 [==============================] - 7s 79ms/step - loss: 0.4914 - accuracy: 0.5007 - val_loss: 0.4845 - val_accuracy: 0.4995\n",
            "Epoch 2/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4839 - accuracy: 0.5010 - val_loss: 0.4827 - val_accuracy: 0.5015\n",
            "Epoch 3/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4814 - accuracy: 0.4999 - val_loss: 0.4829 - val_accuracy: 0.5018\n",
            "Epoch 4/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4810 - accuracy: 0.5007 - val_loss: 0.4842 - val_accuracy: 0.4990\n",
            "Epoch 5/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4800 - accuracy: 0.5026 - val_loss: 0.4824 - val_accuracy: 0.5016\n",
            "Epoch 6/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4808 - accuracy: 0.5006 - val_loss: 0.4867 - val_accuracy: 0.5013\n",
            "Epoch 7/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4813 - accuracy: 0.5017 - val_loss: 0.4826 - val_accuracy: 0.5011\n",
            "Epoch 8/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4817 - accuracy: 0.4982 - val_loss: 0.4836 - val_accuracy: 0.4990\n",
            "Epoch 9/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4790 - accuracy: 0.4999 - val_loss: 0.4830 - val_accuracy: 0.5016\n",
            "Epoch 10/200\n",
            "75/75 [==============================] - 1s 7ms/step - loss: 0.4797 - accuracy: 0.4994 - val_loss: 0.4828 - val_accuracy: 0.5017\n",
            "Epoch 11/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4803 - accuracy: 0.4991 - val_loss: 0.4822 - val_accuracy: 0.5014\n",
            "Epoch 12/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4814 - accuracy: 0.5003 - val_loss: 0.4823 - val_accuracy: 0.4989\n",
            "Epoch 13/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4818 - accuracy: 0.5015 - val_loss: 0.4856 - val_accuracy: 0.4989\n",
            "Epoch 14/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4831 - accuracy: 0.4999 - val_loss: 0.4821 - val_accuracy: 0.4988\n",
            "Epoch 15/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4816 - accuracy: 0.5000 - val_loss: 0.4823 - val_accuracy: 0.5011\n",
            "Epoch 16/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4811 - accuracy: 0.5003 - val_loss: 0.4828 - val_accuracy: 0.4992\n",
            "Epoch 17/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4817 - accuracy: 0.5001 - val_loss: 0.4826 - val_accuracy: 0.5014\n",
            "Epoch 18/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4816 - accuracy: 0.5006 - val_loss: 0.4825 - val_accuracy: 0.5011\n",
            "Epoch 19/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4803 - accuracy: 0.5018 - val_loss: 0.4819 - val_accuracy: 0.5013\n",
            "Epoch 20/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4803 - accuracy: 0.4996 - val_loss: 0.4831 - val_accuracy: 0.4990\n",
            "Epoch 21/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4798 - accuracy: 0.5005 - val_loss: 0.4830 - val_accuracy: 0.4989\n",
            "Epoch 22/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4800 - accuracy: 0.5001 - val_loss: 0.4833 - val_accuracy: 0.5015\n",
            "Epoch 23/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4811 - accuracy: 0.4969 - val_loss: 0.4833 - val_accuracy: 0.4989\n",
            "Epoch 24/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4814 - accuracy: 0.5008 - val_loss: 0.4822 - val_accuracy: 0.5015\n",
            "Epoch 25/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4788 - accuracy: 0.5005 - val_loss: 0.4822 - val_accuracy: 0.4990\n",
            "Epoch 26/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4810 - accuracy: 0.5024 - val_loss: 0.4822 - val_accuracy: 0.4992\n",
            "Epoch 27/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4800 - accuracy: 0.4990 - val_loss: 0.4824 - val_accuracy: 0.4989\n",
            "Epoch 28/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4823 - accuracy: 0.4995 - val_loss: 0.4860 - val_accuracy: 0.5016\n",
            "Epoch 29/200\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.4817 - accuracy: 0.4983 - val_loss: 0.4841 - val_accuracy: 0.4989\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00029: early stopping\n",
            "Epoch 1/200\n",
            "14/14 [==============================] - 6s 391ms/step - loss: 0.5142 - accuracy: 0.5007 - val_loss: 0.5113 - val_accuracy: 0.4993\n",
            "Epoch 2/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5051 - accuracy: 0.5022 - val_loss: 0.5084 - val_accuracy: 0.4991\n",
            "Epoch 3/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5040 - accuracy: 0.4993 - val_loss: 0.5090 - val_accuracy: 0.4975\n",
            "Epoch 4/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5010 - accuracy: 0.5009 - val_loss: 0.5083 - val_accuracy: 0.4987\n",
            "Epoch 5/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5024 - accuracy: 0.4991 - val_loss: 0.5086 - val_accuracy: 0.4987\n",
            "Epoch 6/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5023 - accuracy: 0.5002 - val_loss: 0.5093 - val_accuracy: 0.4971\n",
            "Epoch 7/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5039 - accuracy: 0.5012 - val_loss: 0.5092 - val_accuracy: 0.4987\n",
            "Epoch 8/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5036 - accuracy: 0.5005 - val_loss: 0.5088 - val_accuracy: 0.4973\n",
            "Epoch 9/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5037 - accuracy: 0.5009 - val_loss: 0.5087 - val_accuracy: 0.4974\n",
            "Epoch 10/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5008 - accuracy: 0.5010 - val_loss: 0.5088 - val_accuracy: 0.4973\n",
            "Epoch 11/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5023 - accuracy: 0.4991 - val_loss: 0.5088 - val_accuracy: 0.4987\n",
            "Epoch 12/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5023 - accuracy: 0.5013 - val_loss: 0.5089 - val_accuracy: 0.4973\n",
            "Epoch 13/200\n",
            "14/14 [==============================] - 0s 24ms/step - loss: 0.5023 - accuracy: 0.5000 - val_loss: 0.5091 - val_accuracy: 0.4976\n",
            "Epoch 14/200\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.5027 - accuracy: 0.5030 - val_loss: 0.5085 - val_accuracy: 0.4987\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00014: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-11-17T04:28:25.400950Z",
          "start_time": "2020-11-17T04:28:20.626783Z"
        },
        "scrolled": false,
        "id": "yDxCfJVff08T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "875e5599-e72e-4ad4-b8a8-b563c49c1b68"
      },
      "source": [
        "# Plot the initial distribution\n",
        "\n",
        "bins = np.linspace(-11, 11, 41)\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "fig, ax = plt.subplots(3,\n",
        "                       2,\n",
        "                       figsize=(12, 18),\n",
        "                       #constrained_layout=True,\n",
        "                       sharey='row')\n",
        "\n",
        "for ix in range(2):\n",
        "        #Detector-level\n",
        "        ax[ix,0].set_title(\"Initialization, Detector-level\",style='italic',loc='right')\n",
        "        hist0 = ax[ix,0].hist(theta0_S[theta0_S[:,0]!=dummyval][:,ix], # initial MC reco \n",
        "                        bins=bins,\n",
        "                        color=\"orange\",\n",
        "                        label=r'($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "                        **plot_style_2)\n",
        "\n",
        "        _,_,_= ax[ix,0].hist(\n",
        "            theta0_S[theta0_S[:,0]!=dummyval][:,ix], # MC reco after reweight\n",
        "            bins=bins,\n",
        "            label='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param),\n",
        "            **plot_style_1)\n",
        "        _,_,_= ax[ix,0].hist(theta_unknown_S[theta_unknown_S[:,0]!=dummyval][:,ix], # data reco\n",
        "                        bins=bins,\n",
        "                        label=r'($\\mu$, $\\sigma$) = ?',\n",
        "                        #weights=weights[i, 0, :][theta_unknown_S[:,0]!=dummyval],\n",
        "                        **plot_style_2,\n",
        "                        color=\"black\")\n",
        "        ax[ix,0].legend(frameon=False)\n",
        "        ax[ix,0].set_xlabel(r\"$x_S$\"+str(ix+1))\n",
        "        ax[ix,0].set_ylabel(\"Events per bin\")\n",
        "\n",
        "        #Particle-level\n",
        "        ax[ix,1].set_title(\"Initialization, Particle-level\",style='italic',loc='right')\n",
        "        _,_,_=ax[ix,1].hist(theta0_G[theta0_G[:,0]!=dummyval][:,ix], # initial MC truth\n",
        "                        bins=bins,\n",
        "                        label=r'($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "                        **plot_style_2)\n",
        "\n",
        "        _,_,_= ax[ix,1].hist(\n",
        "            theta0_G[theta0_G[:,0]!=dummyval][:,ix],\n",
        "            bins=bins,\n",
        "            label='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param), # MC truth after reweight\n",
        "            **plot_style_1)\n",
        "        _,_,_= ax[ix,1].hist(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,ix], # real data truth\n",
        "                        bins=bins,\n",
        "                        label=r'($\\mu$, $\\sigma$) = ?',\n",
        "                        **plot_style_2,\n",
        "                        color=\"green\")\n",
        "        ax[ix,1].legend(frameon=False)\n",
        "        ax[ix,1].set_xlabel(r\"$x_G$\"+str(ix+1))\n",
        "        ax[ix,1].set_ylabel(\"Events per bin\")\n",
        "\n",
        "ax[2,0].set_title(\"Initialization, Particle-level\",style='italic',loc='right')\n",
        "ax[2,0].set_xlabel('$x1$')\n",
        "ax[2,0].set_ylabel('$x2$')\n",
        "h5 = ax[2,0].hist2d(theta0_G[theta0_G[:,0]!=dummyval][:,0], theta0_G[theta0_G[:,0]!=dummyval][:,1] , bins=(bins,bins),label='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param)) # MC truth after reweight\n",
        "fig.colorbar(h5[3],ax=ax[2,0])\n",
        "legend = ax[2,0].legend(\n",
        "    title='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param),\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "ax[2,1].set_title(\"Initialization, Particle-level\",style='italic',loc='right')\n",
        "ax[2,1].set_xlabel('$x1$')\n",
        "ax[2,1].set_ylabel('$x2$')\n",
        "h6 = ax[2,1].hist2d(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,0], theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,1] , bins=(bins,bins),label=r'($\\mu$, $\\sigma$) = ?') # real data truth\n",
        "fig.colorbar(h6[3],ax=ax[2,1])\n",
        "legend = ax[2,1].legend(\n",
        "    title=r'($\\mu$, $\\sigma$) = ?',\n",
        "    loc='upper left',\n",
        "    frameon=False)\n",
        "plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.savefig(\"plot_0.pdf\")\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1296 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYrQS4ECf08V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cfef146d-de10-4d58-b48a-d38eb73e89e0"
      },
      "source": [
        "#Iterations\n",
        "for i in range(iterations):\n",
        "    print(\"ITERATION {}:\".format(i + 1))\n",
        "    plt.figure(figsize=(12, 5))\n",
        "    \n",
        "    fig, ax = plt.subplots(3,2,\n",
        "                       figsize=(12, 18),\n",
        "                       #constrained_layout=True,\n",
        "                       sharey='row')\n",
        "    \n",
        "    for ix in range(2):\n",
        "            #Detector-level\n",
        "            ax[ix,0].set_title(\"Iteration \" +str(i+1)+ \", Step 1\",style='italic',loc='right')\n",
        "            hist0 = ax[ix,0].hist(theta0_S[theta0_S[:,0]!=dummyval][:,ix], # initial MC reco \n",
        "                            bins=bins,\n",
        "                            color=\"orange\",\n",
        "                            label=r'($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "                            **plot_style_2)\n",
        "\n",
        "            _,_,_= ax[ix,0].hist(\n",
        "                theta0_S[theta0_S[:,0]!=dummyval][:,ix], # MC reco after reweight \n",
        "                bins=bins,\n",
        "                label='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param),\n",
        "                weights=weights[i, 0, :][theta0_S[:,0]!=dummyval],\n",
        "                **plot_style_1)\n",
        "            _,_,_= ax[ix,0].hist(theta_unknown_S[theta_unknown_S[:,0]!=dummyval][:,ix], # real data reco\n",
        "                            bins=bins,\n",
        "                            label=r'($\\mu$, $\\sigma$) = ?',\n",
        "                            #weights=weights[i, 0, :][theta_unknown_S[:,0]!=dummyval],\n",
        "                            **plot_style_2,\n",
        "                            color=\"black\")\n",
        "            ax[ix,0].legend(frameon=False)\n",
        "            ax[ix,0].set_xlabel(r\"$x_S$\"+str(ix+1))\n",
        "            ax[ix,0].set_ylabel(\"Events per bin\")\n",
        "\n",
        "            #Particle-level\n",
        "            ax[ix,1].set_title(\"Iteration \" +str(i+1)+ \", Step 2\",style='italic',loc='right')\n",
        "            _,_,_=ax[ix,1].hist(theta0_G[theta0_G[:,0]!=dummyval][:,ix], # initial MC truth \n",
        "                            bins=bins,\n",
        "                            label=r'($\\mu$, $\\sigma$) = {}'.format(theta0_param),\n",
        "                            **plot_style_2)\n",
        "\n",
        "            _,_,_= ax[ix,1].hist(\n",
        "                theta0_G[theta0_G[:,0]!=dummyval][:,ix],\n",
        "                bins=bins,\n",
        "                label='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param), # MC truth after reweight\n",
        "                weights=weights[i, 1, :][theta0_G[:,0]!=dummyval],\n",
        "                **plot_style_1)\n",
        "            _,_,_= ax[ix,1].hist(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,ix], # real data truth\n",
        "                            bins=bins,\n",
        "                            label=r'($\\mu$, $\\sigma$) = ?',\n",
        "                            **plot_style_2,\n",
        "                            color=\"green\")\n",
        "            ax[ix,1].legend(frameon=False)\n",
        "            ax[ix,1].set_xlabel(r\"$x_G$\"+str(ix+1))\n",
        "            ax[ix,1].set_ylabel(\"Events per bin\")\n",
        "\n",
        "    ax[2,0].set_title(\"Iteration \" +str(i+1)+ \", Particle-level\",style='italic',loc='right')\n",
        "    ax[2,0].set_xlabel('$x1$')\n",
        "    ax[2,0].set_ylabel('$x2$')\n",
        "    h5 = ax[2,0].hist2d(theta0_G[theta0_G[:,0]!=dummyval][:,0], theta0_G[theta0_G[:,0]!=dummyval][:,1], # MC truth after reweight\n",
        "                        bins=(bins,bins),label='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param),weights=weights[i, 1, :][theta0_G[:,0]!=dummyval])\n",
        "    fig.colorbar(h5[3],ax=ax[2,0])\n",
        "    legend = ax[2,0].legend(\n",
        "        title='($\\mu$, $\\sigma$) = {}\\nDCTR wgt.'.format(theta0_param),\n",
        "        loc='upper left',\n",
        "        frameon=False)\n",
        "    plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "    ax[2,1].set_title(\"Iteration \" +str(i+1)+ \", Particle-level\",style='italic',loc='right')\n",
        "    ax[2,1].set_xlabel('$x1$')\n",
        "    ax[2,1].set_ylabel('$x2$')\n",
        "    h6 = ax[2,1].hist2d(theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,0], theta_unknown_G[theta_unknown_G[:,0]!=dummyval][:,1] , bins=(bins,bins),label=r'($\\mu$, $\\sigma$) = ?') # real data truth\n",
        "    fig.colorbar(h6[3],ax=ax[2,1])\n",
        "    legend = ax[2,1].legend(\n",
        "        title=r'($\\mu$, $\\sigma$) = ?',\n",
        "        loc='upper left',\n",
        "        frameon=False)\n",
        "    plt.setp(legend.get_title(), multialignment='center')\n",
        "\n",
        "    fig.tight_layout()\n",
        "    \n",
        "    plt.savefig(\"plot_\"+str(i+1)+\".pdf\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ITERATION 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ITERATION 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ITERATION 3:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ITERATION 4:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ITERATION 5:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1296 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzsAAATuCAYAAADqez09AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdXWwj6X3v+d/fyAI9OfaEUscbjC9mZ6jEAdqGk0jqAwRzMZ2YHOxF5mYhdSOA5y5DOoD3AM5xpDSwQKtvjkJ5ToyFDSTU5GqQIOiRsheZG8dkr9uwB75oiU4MZ5BMoJocA07b67REO3M8uji7z16wqCYpFl+KxXrj9wMILfKpl6eqq+vf/3qeeh5zzgkAAAAA8uZDSVcAAAAAAOaBZAcAAABALpHsAAAAAMglkh0AAAAAuUSyAwAAACCXSHaAFDCzmpkdm5nzf878z6Wk64b5M7NVMzvo+WmYWcUvK3R/B4BBxI/FRvwYzxh6GkgPMzuQtCFpzTnXSro+82JmDUlyzpWTrksQM9uQVJZUlLQsyZO0G/Xfix+INiVtOufaA9+vSWpLknNuO8r9JsHMGmn+OweyjPiRHsSPaER1HmnZAdLF8/9sBy1gZrWY6hLaBHVc939Sycy2JMk5V3XOlZ1za5IeSjrulkW0n6KkmgYClb/vfUkHkobuLwvXQZeZlczsRBJPmoH5IX6kAPEjGlGeR5IdIHuKSVdgAiPr6Jxbcs4txVWZaZjZqqS2c+6w93vn3J6kfUk1f5koVCW9ORioevbZlNQMWDf114HfvaYhaVXBxwEgPqm/b4j4Mancxo+ozyPJDpA9WXg6noU6Bqn6T8WG6T4Nux3RvlY14imsrx7wferPsXNu238ityfpJOn6AEj/fUPZqGMQ4kc0Ij2PJDtAhvj9VwtJ12OULNRxjJKZnZjZpWNwznW7iUT1ZG6SbV3qm5yDcwwgZlm4b2ShjmMQP6IR6Xkk2QEywB9RZUOdPriplIU6Tqit0U38bXVelIxCQ52beuBTNv/GXpdydY4BxCQL940s1HFCxI9oRHoef27m6gCIw211nmJ4kord0Wh8Db+b0AX/hnZdna5DBUlXe0dj8V9sPPDL5Jxb8UdvKfjr1f3+vt3lu2WStOLvs68v7aR1NLNjf1tF55wNO1i/L25JnRtaQdJVSfd6R2Dxn/jc98uXnXNL/k2/+7RnRdLJ4LkZxzm3ZmaFoH7Q/v4ief/EObdnZrclNcxsT53z7g1ZrvvdPK+D7jnsriN1gs3Dac8hgFQhfhA/pAzFj8jPo3OOH374ScmPOn1RnTo38mHllc4/27HbOBiy3snAd90b1Yk6w5Wu+t+f9a4vqTJkH8eD+5i0jv5Nqh60jL9+Y8j3B4N16d2WOsGtNFB+Mqz+M/z9bPj72ohwm6v+OXf+z4l/TKUR68zjOnB+eXGgvDHs7yPEcW6NqzM//PAT/of4QfzIa/yY9TxGtnN++OFn9p9Zg5V/w3aSCkPKTiRtDdle3w3dv3kW/d8L/o10MEis+vtZnbaO/jIbw5bxb5yjju9scJ89N76tIcvXJR1H+PdzHOX2erZb6AbpnqDlhh3vHK+DoX+ffvnQ8zvlMZLs8MPPHH+IH8SPvMaPWc8j7+wA+VKT1HTDm34P1RmqclBR0lH3g3Ou5YY0hfdyT7oDhB2+Mqhpuq5OPYO8Ken1gLJhTdonimiITX9c/6I6E7hFyjnXds7tu87IZabOJGr76gSxY7+7wDTCXAe9f6+DukN9pv2lVgDhET/6ET8uSzR+hD2PJDtAvqxqyOgrvsAbd9BNyr+JLrngISCjHqu/pM6kYUFOFDwCy8gAOwu/D/htSZ8eF8ij4JxrOueq6syALQUPHxok1HUwQrdvd2on8gMwM+LHHBA/ookfs5xHBigAcqJngq2i/0LooJlejPSfDm34H8eN7R9m+936j9p2u7vsYIANeAoVRb0K6jwN/PSIJ1dht33pOHo551r+i6cTzxY9p+ugd6hPJgcFcob4QfzobtP/NVXxY9bzSLIDZNyQEUsaI56kDRsVZeRN3r/5va7OTepitBczq41ab0wdZxXV0J2TOJC0HXWg8lUV0CWgR13SlpkVRz3NiuA6mMRc/kMAIBnEj7kjfjwxy9/hTOeRbmxA9t2U+roSRPZehR+ojtUJUtszNMHfHLfAhPXvlh2NWCYyZlaXVHM9w6j630d1jidp1j+V+oYPDTK360BPui3Ect4BxIb4MSfEjwszxY8oziPJDpAvLUm3ggpDvqjYDnjCc3Gj8cfWj0JLnZcrg5QlefPqctDLfxGyMXiD9Q1r3g+7n3HbWtf0/cmjvg7K0sgXUAFkH/EjIsSPPqHjR1TnkWQHyJZuF4DeJxq9v78qaXXEzWhck/egdQ15GtPTr/eq/2dvt4BxdRzlVXVmhA5avqQ5jGYzyA++nrs88V3X1cEvZnhaVx8TPLbV+U9Dr7lcBz1/r73fdYc1nfbaAZAuxA/ih5SR+BHmPAYh2QHSpXtzGXrj859utBXQrO8/OamqM6Ny3zb8J0D3hqw26ib7pqT1ITfikjpDUF6q77g69i4/uN2e+t8fXMHMgvrsLg/blm/im2HPflbVCQ5lM6sP+WkMWedA0pk/A/e0yuoMy3npKZXffH/pyeicrgNp+H8U7ks6HNF/e1JT/10AmArxg/jRu93Mxo8w53Hk9vwJegAkyH9Zs6Qnw2K21XkiNqyfalGdlw5bkh5L2h9slvdvFFV/OyfqBIfDnpdDu9tY98s8/2dzyLa21LmhNvztyTm372/jwF+v3lvPoDr6N8KDgf3WnXN9Lzz69b/lr3vVX7beG6gCttV0zlV76tY9n0P3M4yZTTK0ZrX35u2fo+qk++hZr9Ldjv8Uq9t1oBswDoKCRBTXQW89/Lqb/3t3/yvqdCEYNXfFyONT50lqUU/OaVtPrrdX4+hSAuQZ8YP4kbf4EeY8jtweyQ4ARKM3+GRJb7BKui4AsIiIH/NDNzYAAAAAuUSyAwAR8Jv8GZoZADAV4sd8kewAQDRuMTQzACAE4scckewAwIz8UXTqSddjWmZW9Ee1qfmfT2yKmc0BALMhfswfAxQAAAAAyKWfS7oCWfKLv/iL7rnnnku6GgCQO8fHx//mnPto0vWYF+IHAMzHuPhBsjOF5557TkdHvD8GAFEzs/+WdB3mifgBAPMxLn7wzg4AAACAXCLZAQAAAJBLJDsAAAAAcolkBwAAAEAukewAAAAAyCWSHQAAAAC5RLIDAAAAIJdIdgAAAADkEskOAAAAgFwi2QEAAACQSyQ7AAAAAHKJZAeQ5Hle7Ptst9ux71PqP9ZWq6Vqtar9/f257nN/f1/ValWtVmuq9QbrF1d9pxXV8aXF4PEMO76krl8gbYgfxI9ZED/mz5xzse4wy9bX193R0VHS1Ui/7+4ks99Phdtvs9nU8vKyVldXo63PGHt7e6pUKioUCrHtc/BYm82mJKlUKvXVq1gsyvM8lUqlqc9L0PqtVkunp6d9+5qkvr31G1bf7e1trays6OTkRNVqVcVicar6Du6v3W5rY2NjaHk3qNy8eVOnp6eq1+uq1WqSojm+qOoyjaDzN3g8g5+jvn7N7Ng5tx7JxlKI+DGZLzXeTWS/ny9/PNR6xA/iR+/+iB/pjB+07GChtdtttVqt2AOVJG1tbWl3dze2/U1yrJubmyqVStrY2NDW1pa2t7en2ses60+rXC7r1q1bqlQqqtVq2tzcnGl729vbOj09DSxvt9uqVqtaWlpSuVxWtVqdaX9J12WW8xf39QukzSLHj50HO3rj79/QG3//hnYe7GjnwY4+ceMTOimcED8CED+eiPv6JdnBQtvd3VWlUkls/ysrK1M3XYc1ybEOBu5isXjx9GgSs64/Dc/zdHR01Le/5eXl0PtrNptjn+oVCgU553R2dqaTk5OZngImXZcozl+c1y+QNsSPfo/++ZGe+fgzF5+JH/2IH/3ivH5JdrDQPM+LtRvAoJs3b+revXux7GvcsTabzUvlhUJBjUZjou3Puv60Wq2WlpeX+74rFouhb57tdvvS9oLM+5qJoy5RnL84r18gbYgfPeXHnq78hyt93xE/hiN+dMR5/ZLsYGG1Wq3ApxnNZrOvCX3w86TbL5fLWlpakplpZWVFKysrfS/mFQqFWJ5sjDrWrmEvDF69enXil29nXX9ahUJhaDP9ycnJ1Ns6PDwM7Ns8aH9/X4eHh9re3p7L311cdYni/MV1/QJpQ/zod/7++aXviB+XET/6txFX/Pi5WPYCpJDnebp+/frQsoODA5XL5b7Pa2trE2+72WyqVqtdPJUql8uq1WqBfbvb7fbIJyyTBMpyuRz4cuOoY+0a1b93ErOuP6319fVLATLMk9Zx575XqVS6CPobGxtaWVnR8fFxZE/q4qxLVOdPmq7eQB4QP/p98NMPxu5jFOLH7IgfwUh2sLA8zwsMHt1g03V0dDTVk7nt7W3dv3//4vPq6uql/q1d3Scko/6xhxklpdeoY+2atNl7XutPq1AoaGtrS81mU6VS6eJGO23f42azOfGTsMFtFwoFvfnmm5H124+zLlGdv0muXyBviB/9nnr6qZn2QfyYHfEjGN3YsLAeP3489Pvu04ref3ye5038j7jb5D+4/vr68FER47jJBx1rr0KhcOlJzePHjyc+7lnXD6NWq6ndbuvw8PBi3ysrKxOvP83fq+d5Wlpa6vuuWCyG6vaQlrrMev6k+P+TAqQB8aPflQ9f0fl/7+/KRvzoX5b4cVlc8YOWHSyswf7PXc1ms+8pVvfJhTTZDeXo6OhSk/+o4UlPT0/H/oOfZFjI7rCdwwQda69SqXSpK0G73Z54OMlZ1w+r90mW53m6efPmxOt2x/7vzn9ydHR0cQzDnnANPiFtt9tT39zTVpdZzp802fUL5A3xo19xrXipKxvxox/x47K44gfJDhbW8vLy0H7CjUaj7x9fvV7X9evX+17S7L5UNywAra+vq16vX3ze29sb2Y1gkv6qvdsLI+hYB5VKpb7A2p3YTRp9zJOsP2iS7Y2ztLSk9957T4VCQYeHh7p169bFuZxk+4NN/o1GQ+Vy+SI49G6jWCz2Bfx2uy3P8wKb/ac9vijrMum+R52/SfG+DhYR8eOy4lpRj959JN3ofCZ+ED/GiSt+kOwgep/aSboGEymVSkP7UXefzB0eHqpQKOj27du6d+9eX7/le/fuqdVqDR0Wc3V1VSsrKxczFK+urk41K/I8BB3roNdff127u7vyPE8PHz7sC7KjjnmS9QdNsr1xarWams3mRSDe2toKvf29vb2LWaeXl5e1sbGhe/fuqd1uX/xnoVKpaG9vT4VCQcfHxyO3PcvxzVqXSfc96vwBSfh8+eNJV2EixI/LXv7Cy/rmX35Th8uHxA/iR7o45/iZ8Gdtbc0hXzY2Nvo+n52duUKhMNG6BwcHM+//+PjY1ev1mbczicFjdc65RqPhGo3GxNuY5ZiPj48v7Wvc9gbrF2d9pxXF8UVp1mMfPJ5hxxfl9SvpyKXgPj+vH+JH/ixy/Ljz9Tvulddeca+89oq78/U7fT9BiB/BiB+zGRc/GKAAC61arfbN+NvbvzoO9Xp96j6uYQ0eKzCrOK9fIG2IH0B4cV6/JDtYaN0+wl2e5+nWrVtj1xv1wuikupPGxfW+w+CxTiuKY57n9uLefpr3H8e+475+gbQhfkyO+JGd/ecyfoxq9uGHbgiL4OzsLNbm6q64uh/0GjzW4+Njt7GxMfe61Ot1t7Gx4Y6Pj6dab7B+cdV3WlEdX1oMHs+w44u6zqIbGzJoUePHna/fcZV6xV178Zr7nT/4nYm6sYVF/BguK8eThvhhnWUwifX1ddcd1g8AEB0zO3bODZ9MJAeIH8iTnQc7wWU3gsuAeRgXP+jGBgAAACCXSHYAAAAA5BLz7AAAACASo7q4SXRzQ/xIdoAFZGYTLcc7fQAAIMvoxgYAAAAgl0h2AHXmR4hbu92OfZ9S/7EeHx+rUqmoXq/3DdMYtf39fVWr1annaWi1WqpWq9rf3x/6OS2iOr60GDyeYceX1PULpM2ixo9H7z7SW//1LR2/dTzXfRI/hsvK8aQhftCNDQuv2WxqeXk59v3u7++rUqnEOinj4LGenp5qc3Ozb9bvvb29i9/DTC62t7enYrEoz/NUKpW0urqqSqWiVqul09PTqbY1WL9h9d3e3tbKyopOTk5UrVZVLBan2kevZrOpdrutjY2NoeXdoHLz5k2dnp6qXq+rVqtFdnxR1GVaw87f4PEMO74krl8gbRY5fnzw7x/oEzc+oeLak3vu23/1tpY+tqSzfz1Tca2oZz7+zFT7IH5MjvgxOVp2sNDa7XZiMxVvbW1pd3c3tv1NcqzDAsE0uutvbGxoa2tr6vWnVS6XdevWLVUqFdVqNW1ubs60ve3t7ZEBp91uq1qtamlpSeVyWdVqdab9JV2XWc5f3NcvkDbEj34HOwcqrhV17cVreuF3X1BzvznVPogf0SF+9CPZwULb3d1VpVJJbP8rKytTN12HNexY33jjDb3xxhva2dnRzs6O7t+/r7/5m7+5KC8Wi2o2Jw9Yg8Fw2vWn4Xmejo6O+va3vLwcen/NZnPsU71CoSDnnM7OznRycjLTU8Ck6xLF+Yvz+gXSZtHjx6BH//yoryWn8ExB3vHkXfyIH9EgflyWmmTHzGpmVhr4rmBmW/7PgZmtDpRvmdmG/2ckZVgsnucl2g3n5s2bunfvXiz7GnesnufpypUrfd/t7++rXC7LzIb+9Go2m5e2XygU1Gg0ojuIHq1W61L3kWKxGPrm2W63J+6OMu9rJo66RHH+4rx+gbQhfvSUH3u68h/648eVD1/RydHJRNsnfkSH+HFZ4smOmZXMbEvSsI6FNefcnnNuT9K2pPtmVvDXO5DUdM4d+uW1nm2GKsNiabVagU8zms1mXxP64OdJt18ul7W0tCQz08rKilZWVvpezCsUCrE82Rh1rF3n5+cz7WPYC4dXr16d28u7hUJhaDP9yclkwbXX4eFhYN/mQfv7+zo8PNT29vZc/u7iqksU5y+u6xdIG+JHv/P3L8ePn3/659V+NNmL6MSPaBA/hkt8gALnXFNS08zKvd+bWVHSSc9ynpl5kiqS9iStOud6z5JnZiV/e2HLsEA8z9P169eHlh0cHKhcLvd9Xltbm3jbzWZTtVrt4qlUuVxWrVYL7O/cbrdHPmGZJFCWy+WhLypKo4+164MPPrj4/c6dO9rZ2dHe3p4ePnyog4ODvmWHzdMz7cuVs1pfX78UIMM8aR137nuVSqWLoL+xsaGVlRUdHx9H9qQuzrpEdf6k6eoN5AHxo98HP/1gZPk4xI/ZET+CJZ7sjFBQp9Vlb+D7q353t8HHAG1JZf8/YVOXSSLZWTCe5wUGj26w6To6Oprqydz29rbu379/8Xl1dfVS/9au7hOSUf/Yw4yS0mvUsXY99dRTM+0j7hGJCoWCtra21Gw2VSqVLm600/Y9bjabEz8JG9x2oVDQm2++GVm//TjrEtX5m+T6BfKG+NHvqaeJH+MQP4ZvJ474kXg3tiB+68vgo5BVSQ11EqFBjyUVZyjDgnn8+PHQ77tPK3r/8XmeN/E/4m6T/+D66+vrQ5eP4yYfdKy9rly5cqkr2+PHjyc+7kKhcOlJzzTrh1Gr1dRut3V4eHix75WVlYnXn+bv1fM8LS0t9X1XLBZDdXtIS11mPX9S/P9JAdKA+NHvyoev6Py/98ePn/30Zyo8M9l/YokfsyF+jJbmlh31djczs4o679o0/d+DjDpzRGVcGOz/3NVsNvueYnWfXEiT3VCOjo4uNfmPGrLz9PR07D/4SYaFDBpvXwo+1l7FYrGvK5vUCdyTDidZKpUudUWYZv2wep9keZ6nmzdvTrxud+z/o6MjSZ2/u965AQYNPiFtt9tT39zTVpdZzp802fUL5A3xo19xrXipK9v5++f6xI1PjN23RPyYFfFjtFQnO13+oASbzrluJ9hRnTvDlgXtu6LOe0J69tlnp1194Qx7lyNOzrmJl11eXh7aT7jRaPT946vX67p+/XrfS5rdl+qGBaD19XXV6/WLz3t7eyO7EUzSX7V3e2EEHeugYrGoR48e6ZlnOsOHdid2k0Yfc1epVOoLzL3rD5pke+MsLS3pvffeU6FQ0OHhoW7dunVxLifZ/mCTf6PRULlcvggOvdsoFot9Ab/dbsvzvMBm/2mPL8q6TLrvUedvUryvE4z4MR3iR8bjx1pRj959Mvx0+1H7YsLRR+8+kqSRk4wSP54gfkQrE8mOOu/u9Kb3bV3uknZVkjdD2VDOuX1J+5K0vr4++Z0QqVcqlYb2o+4+mTs8PFShUNDt27d17969vn7L9+7dU6vVGjos5urqqlZWVi5mKF5dXQ28Yccl6FgHvfzyy/rmN7+ps7MzvfDCC/rkJz+pnZ0dSZ2b5w9/+EO98sorgeu//vrr2t3dled5evjw4cggPeocTqpWq6nZbF4E4q2trdDb39vbu5h1enl5WRsbG7p3757a7fbFfxYqlYr29vZUKBR0fHw8ctuzHN+sdZl036POH2ZH/Mgv4sdlL3/hZX3zL7+ps0dn+sE//kClypN6f+/r39MP//mHeuU14gfxI342zZOMeTKzhjpDTTcHvt+SdOic8/zPq865lpmdOeeWeparSzrwu7mFKhtXx/X1dddtIsRw3SdzcV9XYfe7ubnZN9JYu93W888/r7Ozs7HrTjPEY5BWq6Wjo6NYJqbrPdbu+eomLpP29X3nnXd07do13b17V9J057vbzN4buMedw+4EZd11Bj+PE8Xf0aSiOL4ozXrsg8cz7PiivH7N7Ng5N/zFhBwgfoxH/JhOUvFDknYe7FxMGNptvRnnnW+8o2svXtPOjZ2p90/8uIz48cS4+JHaAQokycw2JLUknfoTjK5K6h5Mc2BC0GJPwhK2DAumWq32zfjb2786DvV6feo+rmENHiswqzivXyBtiB9AeHFev4knO2a2amY1SSVJ235LTneenQN1Rl8783+O9aTL2auSbpnZhr9+bxtr2DIsmG4f4S7P83Tr1q2x6416YXRS3Unj4nrfYfBYp9X7Lk8UojiHSW4/zfuPY99xX79A2hA/Jvfo3Ud65leIH1nYfx7jR+Lv7PgjrrU0kHT43dYC31Z0zrV71jmMogyLqVKpXDTZTtrnNIobQVzdD3p1j7Xrqaee0re+9S2dnZ2NnfRulkRnf39fjUZDt2/fvvhuknO4vLx80Ye7Uqlc+jxKnIEiquOLyqzHPng8w44viesXSJtFjB+bm5vSDUn/Lun7kpYkfax/2Tt37vR9HjUwwTjEj+GIH5NLzTs7WUCf6/Gy1ud6UXXP12BAmlSYd3aAUXhnB8SPbDCzTrIzwqjYEuadHWCUcfEj8ZYdAAAAZMuwhKb7IAxIE5IdLCzP81Sr1bS/v6+NjY2LidxOTk5UKBQCh71sNps6ODjQysrKRX/T7jCOW1tbarVa+vSnP62bN29qZWVFjUZDzWZTtVpNjx8/vhiV5Pj4eGQdRk3yBgBITt7jx86DneDCG6E3CySCZAcLq1gsql6va39/X7dv3+7rp3p4eKi1tTUdHx/3rbO3t6eHDx/2DcEpdYblbLfb2trakud5un///sX2isWijo6O+vpzd+csGFWHlZUVbW9vx9av9fj4eOx7OwAA4seg47eOtfYy8QPpRLKDuUh6JuxZbWxsqNFoXDxtkzpP5Or1uk5OTi4tX6vVVK1WLz6Pe8Gv+wRuXB1qtVoswer8/HyiGbIBYN6IHxmLH++f6/Rf++PHqO5sd3/rLu9HIVaJDz0NpNXm5mbfrNHb29t9AalXsVi8mJhzkgk6J53Ec9LlZnF+fq633npr7vsBgEWxMPHj/XO99RrxA+lGsoNIOecS/YnS+npnYA/P60ztNG7s+W4f7UmGbRy3TLvdVqvVUr1eD1xmf39fS0tLKpfL8jxP5XJZZnYxtPTa2prW1tb66r+9vd039PRbb70lz/N0fn6uH/7wh3r77bcvlgeAOBE/0hM/9iv72q/s6+xfzyR15slp1Bt65xvvSCeS/h/prf/6lrxjT+fvn+uH//xDvf1Xb+uV33lFd+7cCfwBkkA3NiBA9+XR3onUlpeXxy4fVrPZlOd5evjwoVqtlg4ODkZus1Kp6OTkRFevXlWxWFSj0dDS0pI2Njb6uoGsrKwEbuPll1+WJJ2dnelnP/uZXnjhhZmOAQCQ7fghSaVqSR/71Y/pyoevSJIO7h7oP/3lf+qs7OdQL/9nP37865l+9tOf6YXfJX4gnWjZAQK0221J/V0M5vleS6lUuuhnvbm5OdFgAdVq9eLpXavVUrFYVLPZnFsdAQDjZT1+nL9/fpHoSLpo4ZEkPaXOhKJARpDsAAG6ganbZWB1dbXvKd2g3u5hs6pUKvI8b+T+pCd9sj3PuxiGtHekn94uGqVSSScnJ3LOaWNjI/bZtwFgUWQ9fvQmOpJUXCs+SXj+XdL/HFl1gbkj2QEC1Ov1vuE+a7XayD7Q8zDJ+zMbGxsXgbJUKgW27FSrVTWbTR0eHqpareqZZ54Zutw777wTvsIAgEzHj2azqeJa/+AGay+vyTv2Ou/sPCPpI8O39843iB9IH5IdYIjuDf/27dsX35VKJVWrVW1ubl5avjupW5SKxaIePnwoaXTQqlar2t3d7XuCOMzDhw9VqVS0sbFxabK5paUlnZ+fX1rn0aNHevToUdhDAICFk/X4MTgPkCT94B9/oLWX13TtxWvSwKtHSx9b0vn7Q+LHu4/06F3iB5LHAAVYWN1me0na3d29mLvg8ePHknRpQjhJ2traUrPZVLVavTQD9rDtHx4e6t69e2q326pWq1pbW+tbtt1u680335TUeRK4vb190bXg4ODgYvS0QqEQOIxosVhUqVS6KK9Wq0O7RFy9elVLS0taXl6+qHe5XNaVK1d07do1/cM//IOOj4+1tLR0sc73vvc9nZ+fXwxkAADId/xot9v6nr7Xt9zPP/3zqr1c01NPPyX9VNJHnrzXc+3Fa/qHr/+Djt861tLHeuLH17+n8/fPLwYyAJJiTOw0ufX1de4Qu44AACAASURBVHd0dJR0NYCxuqOxdYf6fPTokTzP09ramq5cuaLz83N5nqfj42O98sorU2+/O2Ec9w9ExcyOnXPrSddjXogfyJKdBzsXvz9695G8Y09rL6/pyoev6O7/cVc6k4pPFfXKa9PFj7t370oPiB2I1rj4QTc2YAF4nqdnnnlGV650XjrttuYAADCKd+zpmY8/82TQgv9JDFCATKEbG7AAXnjhBb399tt69OiRlpaW9MEHH+j8/Hxo/3EAALpe+N0X9PZfva1H7z7qdFP7V0n/Q9r8P4kfyAaSHWBBMGEoACCMvglDH3T+GByeGkgrurEBAAAAyCWSHQAAAAC5RDc2AKF1R30bhtF2AACX3Ogf7W3Qzo3gMiAMWnYAAAAA5BLJDoCp3blzR865oT8AAABpQbIDqDMPTdza7Xbs+wQARIv4AaQb7+wgcjs7O5nab7PZ1PLycrSVmcD+/r4qlYoKhcLc9/Xo0aOLuXV+8IMf6JOf/KSeeeaZue8XAKYx6l2Oue435HsiCxE/3n2kD/79A52/f64f/OMPpH+X9JG57xaIDC07WGjtdlutVkurq6ux73tra0u7u7tz38/5+bneeOMNFYtFXbt2Tevr63rjjTfmvl8AyLOFiB/vn+uN//yGimtFXXvxmtZfXpf+fu67BSJFsoOFtru7q0qlktj+V1ZW1Gq15rqPK1euXDrG8/Pzue4TAPJuIeLHh6+oUh84xv8x110CkaMbGxaa53mxdAMIcvPmTe3u7s79yeDS0tLF79/61rdUKpXmuj8AyLu8xg8zk24EFP6TpGKkuwPmjpYdLKxWq6Vicfhdu9lsant7O/DzpNsvl8taWlqSmWllZUUrKyt9L5YWCoW5P5nrdXZ2puXlZb3wwgux7RMA8mYR44c+kPSUpGfj2yUQBVp2sLA8z9P169eHlh0cHKhcLvd9Xltbm3jbzWZTtVpNjUZDklQul1Wr1QKfwLXb7ZFPCCcJlOVyeWyLzTvvvEOiAwAzWoT4cefOnb7Pb//V23rhd4kfyB6SHSwsz/MCg0c32HQdHR1N9WRue3tb9+/fv/i8urqqo6OjofsrFAo6PT0dGax66zKLK1euRLIdKblR9wAgaQsZPz4cXfwA4kQ3Niysx48fD/2+202gN3h4nhfYZWFQt3vD4Prr6+tDl49z2NLed3cAAOEsZPz4GPED2UTLDhbWYP/nrmaz2fcErdlsXjTvTxK0jo6OLnVvGDU86enp6diAVa1WR5ZL0ubm5thubJMGXABAsIWMH2vED2QTyQ4W1vLysk5PTy9932g0+oJHvV7X9evX+15I7b4UOiwAra+vq16vX3ze29sb2Y1gXH/rbh1mdX5+Ls/zdO3atZm3BQCLbOHix/vn8o49XXuR+IHsoRsbFlapVNLx8fGl75vNpk5PT3V4eKhms6nbt2/r8ePHfcOM3rt3L7AP9urqqlZWVrS/v6/9/X2trq5qY2NjrscyCc/z9K1vfSvpagBA5i1c/Dj29K2/JH4gm2jZQeSy8uJ698XOXu12W6enpzo4OOj7fvAJXK1W0+HhYeC2t7a2JqpDq9XS5ubmhDWezbVr12jVAZBqOzd2kq7CRBYufrx4jVYdZBbJDhZatVrt61Pd+3sc6vV6ZCPlAADiQ/wI7+7du8Flv3VXzrkYa4O8S003NjOrmdmlu4SZbZnZhv/n6rzLsFhKpVLfpGye5+nWrVtj1xv1wuikupPGJTkDNwAgHOIHkA2Jt+z4Cc6qpA1JjYGyA0m7zrmW/7khqTyvMiymSqWiw8NDbWxsTNx9YNZAJXVG3alUKjNvBwCQDOLHdAYnKh00qsUHCCvxlh3nXNM5tyfJG1K82k1KfF5P6888yrCACoVCIi+AZjFQAQCeIH4A6Zd4y04QPwEZHMS+LalsZoq6TFJz9loDAAAASIvEW3ZGGNYR9bGk4pzKAAAAAORImpOdUVMCz6MMAAAAQI6kOdm5PDXxfMuGMrOKmR2Z2dGPf/zjaVcHACwo4gcAJC/NyU5bl7udXVVnIIN5lA3lnNt3zq0759Y/+tGPTnUAAIDFRfwAgOSlNtlxzjV1udtZQVJjHmXR1BoAAABAWqQ22fE1Byb9LPoJy7zKAAAAAORE4kNP+4nHLUml7md/3h1JelXSbTMrSrouabtn1XmUAQAAAMiJxJMdf4LPloYkHc65ds/3h/MuAwAAAJAfae/GBgAAAAChkOwAAAAAyCWSHQAAAAC5RLIDAAAAIJdIdgAAAADk0tySHTP77XltGwCANCDWAUC6zSXZMbOnxfw1AIAcI9YBQPqFmmfHv8EfSvp00CKSXNhKAQCQNGIdAGRf2JadP5d0IGld0vKQn1+W9J0oKggAQEKIdQCQcaFadiQ1nHOvjyj/iZnthtw2AABpQKwDgIwL27JzOm4B59xfh9w2AABpQKwDgIwL27LTNrPnnHP/ErSAmX3BOfdayO0DAJA0Yh1yaefBTnDhjbhqAcQjbLLjJG2Y2YqkY11++rUsqSqJAAAAyCpiHQBkXNhk59D/05N0fUh5QdLzIbcNYAwzS7oKwCIg1gFAxoVNdjzn3PqoBczszZDbBgAgDYh1AJBxYZOdzQmWYaI1YM6cC57iY2dnJ76KAPlErAOAjAs1Gptz7r0JFguahA0AgNQj1gFA9o1t2TGz5ySpdzQaM/vfJtj2tjoTsgEAkGrEOgDIp0m6sbUknaj/5czujd0LWIeXNoEY0FUNiAyxDgByaJJk59OS2gPf8dImACBPiHUAkENjkx3n3HeGfD1JH2Ve2gQAZAKxDgDyKdRobM65n3R/N7OnJXWffB05537qLzPJi50AAKQSsQ4Asi/UaGxS52VOM/uapDNJTf/nzMz+1sw+ElUFAQBICrEOALItVLJjZs+rc8M/UOdJ15Jz7kOSflnSfUkt/ykYAACZRKwDgOwLO6loxTn3y4Nf+s35e2a2L+m2/wMAQBYR65A7ZibdSLoWQHzCdmM7GlXonGsreKhOAACygFgHABkXtmXHRbQMAABpRaxDbt25cyfpKgCxCNuyYxMscxZy2wAApAGxDgAybmTLjpn9uqTlIUVnZnZPUkOXm/CLkgrOudeiqSIAAPNDrAOA/BrXje3PJa0quE/yWtCKZrbinPv9sBUDACAmxDoAyKlxyY4naZNJ0wAAOUasA4CcGvfOzi43fwBAzhHrACCnRiY7zrnvxFURAACSQKwDgPwKOxobAAAAAKQayQ4AAACAXAo7qSgAjGQ2eooS55iLEQAAzBctOwAAAAByKfUtO2ZWkFSR1JZUkNRyzjV7yrfUGTa0KKnpnGvNWgYgvDt37mhnZyewfFyLDwAAQFRSn+xIqjjn9rofzKxmZkfOubaZHagzZGjLL2tIKvu/hyoDAAAAkA+hkx0ze9o599MoKxOgLGmv5/OJOq0xLUmrAy0ynpmV/JafsGUAZjSqZQfIkhhjHQBgDkIlO2b2NUlrkq5GW52hls2s5pzb9j+XnXP7ZlZSp2tbr7akst9NZuoySSQ7AABJscc6AJJ0Q9p5sBNYvHMjuAwYJuwABQfqtK7E4VVJFTM79t+z6SY9hSHLPvbrFbYMAICuOGMdAGAOwiY7p5KWRi1gZn8actt9/O5m++okKTU9CTzLI1YLW3aJmVXM7MjMjn784x9PsyoAINtminXEDwBIXth3dk4kbZjZVf93b8gypdC16mFmdUk159y2/3vDzNbUCUJBwpZd4pzbVyfZ0vr6OhODAMDimCnWET8AIHlhk53/W52WlmE3fqnTevILIbd9wcxWJZ045zxJcs5VzexEUlWd7gWDXdKu+nVqhywDAKArllgHoN/du3eDy37rLpNSYyphkx3PObc+agEzezPktnsVdTnI7KvT0tM0s8EuaQVJB2HLIqgvACA/4op1AIA5CZvsvDrBMtvjFxmrKel1SYc935Uk1bvlZtY7jHSxZ/josGUAAEjxxToA6kxKPcqoFh8gSKhkxzn3Hakz/4CkdUmnzrm/87/7defc3znn3pu1cv7EobtmVlOnv7TUedLWTVJelXTbzIqSrqs/6IQtAwAgtlgHAJifWSYV/WNJW5LOJL0p6ff9op+Y2Recc69FUL/uaGytgLK2niQqh1GUAQDQFVesAwDMR6ihp83sD/1fl5xzV9UzGadz7j3n3Gtm9ntRVBAAgCQQ6wAg+8K27LSdc1/s+TxsWIyzkNsGACANiHUAkHFhJxV9PMEyz4fcNgAAaUCsA4CMC5vs/MeBz9b3wew5Sb8cctsAAKQBsQ4AMi5sN7a6mT2U9F8kfUd+075/49+UVJG0FkH9AABICrEOADIu7NDT75lZVZ0JPlclObOLB15NSS85534aTRUBAIgfsQ4Asi/00NP+kNDr/lw1v+F/3WLOAQBAXhDrACDbQic7Pf5NT0ajmeRlTgAAsoZYBwAZFHaAApnZc2b2NXVu/k3/58zMvmpmH4mqggAAJIVYBwDZFnZS0ecltfyfdUlL/s91Sf9NUsvMno6qkgAAxI1YBwDZF7Yb25aktSF9lluSqma2Kqkm6fdnqRwAAAki1gFAxoXtxuaNejnTf6HTC7ltAADSgFgHABkXOtmJaBkAANKKWAcAGTfLAAWB/ZT9Mjfw3RfC7gsAgCQQ6wAg28K+s9OW9LqZ3fN/71WUVJZ0z8x+2/+uIKkq6bWQ+wMAIG7EOgDIuLDJzoE6N/W1Ecv0li1L+oWQ+wIAIAnEOgDIuLDJjuecW59mBTN7M+S+AABIArEOADIu7Ds7r4ZYZzvkvgAASAKxDgAyLlSy45z7Toh1AofvBAAgbYh1AJB9oUdjAwAAAIA0I9kBAAAAkEskOwAAAAByiWQHAAAAQC6R7AAAAADIJZIdAAAAALkUabJjZk+b2XNRbhMAgDQh1gFAdoRKdszsj83sb83sC2b2tP/d30o6lvRHZnaPQAAAyDJiHQBk38+FXO+hpHp38jQz+0NJRefcr3QXMLMvSHpt9ioCAJAIYh1y69snj0Ov+5srVyOsCTBfYZOdpYFZoquS/nhgmZ+E3DaAKPzowejyX7oRRy2ALCPWAUDGhU12Lm7+Zva8pOclNQeWCf/IAMBkxiU0AGZBrAOAjAub7PxCz+8bkt5zzv3LwDK0cQIAsoxYh0z6UuPdpKsApEbYZOcnfj9lk1RTJwjIf4GzLOmPJG1GUkNgQZlZ0lUAFh2xDgAyLlSy45y7b2aepJKklZ4+zbckFSS9KWlV0r9EUUkAAOJGrAOGGzW4AYMXIG3CtuzIv+m/PvDd6wGLAwjpzp07wwt4XweYO2IdAGRbqGTHzH7dOfd3A9/9gqSb6vRxbjvn/jyC+gEgqQESQaxDVn31+18OLrwRWzWAVAjbslOV9Pu9XzjnfqKep19m9ntRBQEzK6rTV7rt72u/p2xLkiepKKnpnGvNWgYsBIamBsaJNdYBAKIXuhtbXPxEp+ac2/Q/H5vZkXOuZWYHkna7iYqZNdR5aVRhywAAAADkw0TJjpl9WpLr+apoZr8dsHhB0nX/zyiedtUk1Xs+f9o51/Z/Xx1okfHMrOSca85QBgBYQAnHOiASZkZXNaDHpC07njojztxSpzuZk7QyYvmGc+73R5RPxMwKkja6rTqS1E10zKwkv1tbj7aksj9k79RlujxZHABgcSQS6wAA8zNRsuOPRvOepL/233V5PqYbfFFS209sCv7nlt8CUxiy/GM9edIWpgwAsKASjHVA5F76zOeSrgKQCh8KsU5dnadfI41o+p9G0f/z1Dl36Jzbk1Tz3+NZHrFe2LJLzKxiZkdmdvTjH/94mlUBANk1c6wjfgBA8qZOdpxzP3HOfXHUMv7s0tuha/VEW1Jh8P0adUbIOR2xXtiyS5xz+865defc+kc/+tFpVgUAZFQUsY74AQDJCzvPztOSDiV9OmgR9b/kGZany+/XdIeLbuhyl7SrPeuEKQMAQFKssQ4AMCdhh57+c0kH6jzRGpYkXJX0ZthKdTnnPH+Qgl4FSZ5zrmlmg13SCpIOwpbNWl8AQK7EEuuAaX2p8W7SVQAyI2yy03DOvT6i/Cdmthty24P2BoaFXteTp2xNM+sdRrrYs1zYMgAApHhjHZAL3z55PLL8N1euxlQToCNssjP2vRfn3F+H3PbgdrbNrDsowYqkV3vm2XlV0m2/7Lr6+06HLQPi892dpGsAIFhssQ4AMB9hk522mT3nnPuXoAXM7AvOuddCbr+Pc25oMuInPd2ywyjKAPh+9GB0+S/diKMWQJJijXUAgOiFTXacpA0zW5F0rMtPv5bVGTGNAAAAyCpiHQBkXNhkp9sa4mn4ZJwFSc+H3DYAAGlArAOAjAub7HjOufVRC5gZI9QACGRmI8udY0RfJI5YBwAZN/Wkor7NCZbhpX8AQJYR6wAg40K17Djn3pMuJlxbl3TqnPs7/7tfd879XXcZAOh1586dkeV3796NqSbAaMQ6AMi+sC07MrM/ltRWZ8K1ak/RT8zsC7NWDACApBHrACDbQiU7ZvaH/q9Lzrmrki4m5HTOveece83Mfi+KCgIAkARiHQBkX+h5dpxzX+z5POxN4rOQ2wYAIA2IdQCQcWGTnccTLMNwnACALCPWARH79snof1a/uXI1pppgUYRNdv6jpP+r53PfGLJm9pykXw65bQAA0oBYh8R8qfFu0lUAciFsslM3s4eS/ouk78hv2vdv/JuSKpLWIqgfgLT60YPgsl+6EVctgHki1gFpc0PaebATWLxzI7gMiyn00NNmVpW0L2lVkuuZILAp6SXn3E+jqSIAAPEj1gFA9oVKdvz5BVqS1s3seXWCgCS1mHMA6PHdnaRrACAkYh0AZF/YbmyvS7ouXUy6xk0fAJA3xDoAyLiwk4qumdk9M/vtSGsDAEB6EOsAIOPCtuxsO+e+aGbPm9mr6ry02XTO/Ut0VQMAIFHEOgDIuLADFHzR//M9dZr5ZWa/YWZldeYlaPLSJgAgy4h1QDrdvXs3uOy37sq5YfP/YlGF7cZ2iXPuO86519WZh+A9M/vTqLYNAEAaEOsAIFvCdmPr4885UFVnzoFTdZ6A7UaxbQAA0oBYByTrzp07I8tHtfhgcYVq2fFf2PyImf2emR1JOpFUlHTTOfcrzrk/cs79JNKaAgAQI2IdAGRf2JadTUkbklqS6n6TPgAAeUKsA4CMC5vstCWtMakaACDHiHVAzL598nhk+W+uXI2pJsiLsMnOLjd/AIF+9GB0+S/diKMWwKyIdQCQcaHe2ekOx9nlv7QJAEBuEOsAIPvGtuz4N/ei/yMNn1BtxZ93YEXSb0jyJJ04516LrKZAGn13J+kaAIgAsQ4A8mmSbmyepIakatCs0c65+5LuS5KZ/YKkP1dnaE4CAAAgC4h1iNWXGu8mXQVgIUyS7LQlbU46S7Q/DOemmf1/M9UMAID4EOsAIIcmeWfnaNKb/4BWiHUAAEgCsQ4AcmiSZMfr/WBmf2hmXzOz/9fMHprZF8zst8etBwBAihHrACCHJunG5vo+dEan+aKZnTrnro9Y73SmmgEAEB9iHQDk0CQtOxbwfTPKigAAkCBiHQDk0CTJjgv4fvQUt8HrAQCQNsQ6AMihSbqxrZvZb+nyU69iwPddpZlqBgBAfIh1AJBDkyQ7a+o04w+70ZdHrMfTLgBAVhDrACCHJkl2WpI2p9yuSXpz+uoAAJAIYh0A5NAkyU7TOffetBs2s8hf6jSzkqSCc+6w57stdYb+LKpT19asZQCAhZOaWAcAiM7YZMc590dhNhx2vTFqkurdD2Z2IGm3m6iYWUN+d4OwZQCAxZOyWAcAiMgko7Glgt+qMzh52+pAi4znLzdLGQAAAIAcmKQbW1oU1DN5m5+ctAeWaUsqm5nClIn5FAAAAIDcyESyY2YbzrlDM+vtalYYsuhjSddnKANiY792N+kqAAAA5Frqkx0zK+hyS4wkLY9YLWwZgDj86EHSNQAAAAsg9cmOpFLv6Gs9Tod8N2vZJWZWkVSRpGeffXaaVZEH392Z+y7ufPbFue8DQPyIHwCQvFQnO2ZW1OVBCbrautwl7aq/fNiyS5xz+5L2JWl9fZ3J4wAAEyF+ANH79snjpKuAjEl1siNpVdKyma37n9f9z3LO7ZvZYJe0gqQD51wzTFnktQcAAACQmFQnO4Pd1/wBChr+0zJJappZ7zDSRedcc8YyAACAmXyp8W7SVQCglCc7vcxsS1JJUsHMTv1E6FVJt/3ubtclbfesErYMAAAAQA5kJtlxzu1J2hv4rq0nicphFGUAAAAA8iEzyQ4AAACkr37/y8GFN2KrBpAJH0q6AgAAAAAwD7TsAAAAZMQfvPSrtN4AU6BlBwAAAEAu0bIDAACQMS995nNJVwHIBFp2AAAAAOQSyQ4AAACAXCLZAQAAAJBLvLMDIJV2dnZClQEAAHTRsgMAAAAgl2jZAZBKd+/eHVnmnIuxNgAAIIto2QEAAACQS7TsAEiVO599UfqlG4Hlo1p8AAAAepHsYLF9dyfpGmCYHz1IugYAgIz6UuPdwLLPlz8eY02QBiQ7AAAAUxr1H2oA6cE7OwAAAAByiZYdAAAA5MMN6avf/3Jg8ecVXIZ8omUHAAAAQC6R7AAAAADIJZIdAAAAALlEsgMAAAAgl0h2AAAAAOQSo7EBAAAgN772F18JLLPf+4qcczHWBkmjZQcAAABALtGyAwAAgMx76TOfG1k+qsUH+UXLDgAAAIBcItkBAAAAkEt0YwMAAMDC+FLj3aHff7788ZhrgjjQsgMAAAAgl2jZAZBN390JLvvUiDIAALAwSHYAAAAGBHV1ApAtJDvIv1EtAAAAAMgt3tkBAAAAkEskOwAAAAByiWQHAAAAQC6R7AAAAADIJQYoAObEfu1u0lUAAABYaKlPdsysIKnif7wuadc51+op35LkSSpKakZRBgAAACD7Up/sSKo556qSZGZFScdm9rxzrm1mB+pJfsysIans/x6qDIjanc++mHQVAAAAFlKq39nxk5uT7mfnnKdOa0y3pWd1oEXGM7PSjGUAAAAAciDVyY6kgqTakO+v+slJe+D7tqRy2LII6gsAAAAgJVLdjc051zKztYGvVyVtq5MIDXqszns9YcsAAAAS9Qcv/WrSVQByI9XJjtRJeLq/m1lFncEEmv7vQZZDll3i76ciSc8+++w0qyIu391JugZIm1HXxKdGlAERIn4AQPJSn+x0+aOybTrnut3NTkcsHrbsEufcvqR9SVpfX3fTrAsAWFzEj/T7UuPdpKsw0p987Z+Gfv/V73855poshnHXw+fLH4+pJohSZpIddd7d2ez53NblLmlX1RnAIGwZAABA8m6Q1ABRSPsABZIu5sSpOefa/udV51xTl7ukFSQ1wpZFX3MAAAAASUl9y46ZbUhqSTr1u7IVJa373zX9xKf7Xk/RT2Y0QxmADLBfuzuy3P39nZhqAgAA0irVyY4/z87BkKLuezuvSrrtL3ddnVHaNGMZAAAAgBxIdbLjTyJqI8rbepKoHEZRBiDd7nz2xZHld//sGzHVBAAApF0m3tkBAAAAgGmR7AAAAADIJZIdAAAAALmU6nd2AAAAgMjcCJ6/6H999n+Pty6IBckO0u+7O0nXAAAAABlEsgNgsYxLnj81phwAkGlf+4uvDP/+wVf0J1/7p5hrg3njnR0AAAAAuUTLDgAAyKUvNd5NugpIkZc+87nAsqDWnl7jrqfPlz8+dZ0wf7TsAAAAAMglkh0AAAAAuUSyAwAAACCXSHYAAAAA5BLJDgAAAIBcYjQ2ICT7tbtJVwEAAAAj0LIDAAAAIJdo2UHyxs1on3J3Pvti0lVAlMZdj58aUw4AAFKDZAcAAGQSk4YiTZh0NJ1IdgAAAGL0By/9atJVABYG7+wAAAAAyCVadgDk0qjR8tzf34mxJgAw3J987Z8Cy776/S/HWBMgv0h2AAAA4naDhAaIA8kOgFwZNTre3T/7Row1AQAASeOdHQAAAAC5RMsOAEyDeXgAAMgMkh3MX8YnDQUAJIN5dBC3ccOCjxpUYhzm4UkGyQ4AAABwY0z5gxjqgMiR7AABRg1dDAAA8uOlz3xuZPnX/uIrMdUEUWOAAgAAAAC5RMsOMMaoCSh3/vRBfBUBAGTCuPc+AMSHZAfRyPEgBCQ0mAqjtQETYwACAPNGsgMAADAHo0bu+ur3vxxjTZAFo5J/RmoLj2QHwMIZN/jEqK6LAAAgO0h2AAAAonaD1hsgDUh2MJkcv5ODxXHnsy+OLL/7Z9+YfyV4pwcLhHdyACSNZAcAAACYwLiR9ka9pzWLcQ8OeKcnGMkOFhaThgIAwmBo6QV1Y0z5gxjqgKktdLJjZluSPElFSU3nXCvhKiWHbmrAhUQHMBj1b5EubkghuqphEbz0mc+NLP/aX3wlppoMR8tPsIVNdszsQNJuN8Exs4akcrK1QhLGvccBAMAwDC0NpN/CJjuSVgdacjwzKznnmonVaJ4WtOWGrmqYxqQDGCTW8sPgBkjAIrbcTNRN7QYJDS5L6p2ecRa55Wchkx0zK0lqD3zdVqdlJ5vJDskMkBokQ8iSRUxmJN67wRzcGFP+IL3JUJ4tZLIjqTDku8eSrs9zp2Y2z81jBLqqIQpRDV2dXJI+v/065+a2bRA/EnFj/HsaQNdE7/TcGLORB8kl4X+QyF475h0/bBEDlJlVJFWdc2s9321Juu6c2xyybMX/+KuSkki5f1HSvyWw3yzg3ATj3ATj3ARL6tz8L865jyaw37khfqQe5yYY5yYY5yZYKuPHorbsnE66oHNuX9L+HOsylpkdOefWk6xDWnFugnFugnFugnFuokP8SDfOTTDOTTDOTbC0npsPJV2BhLR1uSvbVXWGoQYAAACQAwuZ7Pgjri0PfF2Q1EigOgAAAADmYCGTHV/TzFZ7PhdTPOx0ot0gUo5zE4xzE4xzE4xzky/8fQbjOlE3bAAAIABJREFU3ATj3ATj3ARL5blZyAEKJMnMCpJuS3qozihs9wbm3QEAAACQYQub7ADAojOzmqTGYKu2PzqlJ6koqcmDIABAryzFj0UdjS1zsnRRIXlcF8P5QwFL0pvqvLdXdc5tJ1ilRPgTK69K2tDAu4pmdiBpt3vNmFlDnQmXkVHED0yKa2I4YscTWYwfJDspl8WLKincjDq4LkYqSKpJqqsT0BfyvPj/6W2a2bDjXx34D45nZqUUv9OIAMSPyRA7OrgmRiJ2+LIYPxZ5gIJMcM41nXN7Gj4s9tCLKqaqpVFBnRvRmTqBvZ5sdRLDdRGs7ZwzSUvOuRXnHMPN9/Cvk/bA120tcGDPMuLHxIgdHVwTwYgdY6Q5fpDsZFSaL6oELfzNiOtiMs65wXOEjsH5xyTpsTpdWpAT3CcuIXZwTUyE2DFSauMH3diyK+iiuh53RdJmwW9GXBdj+F1WTsUojMMMzj+GfOI+MQSx45KFvyZ6ETvGSm38INnJrtReVEniZsR1MUaz56ntoZmdmNnagv8np9dp0hVALLhPDCB2cE2MQewYL7Xxg2Qnu1J7USWImxHXxUhDuqe0Jd1USidCS0Bbl5/wXtXwdz6QXdwn+hE7uCZGInZMJLXxg2QnZv7To7Uxi9Um6DOc2osqKdyMJHFdBDKzoqRj59xSz9eepJWEqpQ6zrmmmQ0+4S1IOkiiPuhH/JgPYockrolAxI7JpDl+kOzEzDkXyc0zzRdVVKYJ7NyMOhbhupjR4HCyBUknSVQkxZpm1jsqUzHpYUPRQfyYDLFjenm/JiJA7JhMKuMHyU62pfKiikqIwM7NqCPX10VY/n9sLp5c+r8Xo/oPZJaY2aqkW5JK3c/+EMWS9Kqk2/5/Aq/r8r8r5ENu7xPEjtBye03MgtjRL4vxw5xzSdcBI/RcVFuSmurMgr3nlxUk3Zb0UIv7UuUFM9saODfHzrmFejoncV2M4p+bijpdNtY0WZcfIJOIH5MhdnRwTQQjdmQbyQ5yg5sRAGBaxA4g30h2AAAAAOTSh5KuAAAAAADMA8kOAAAAgFwi2QEAAACQSyQ7AAAAAHKJZAcAAABALpHsAAAAAMglkh0AMrOKmTWSrgcAIFuIH0i7n0u6AgCSY2Z1dSbSK0haTrg6AICMIH4gK0h2gAXmnKtKnSdzktYTrg4AICOIH8gKurEBAAAAyCWSHQAAAAC5RDc2IAPMbEudvtFlSbv+7xt+8Uq3OwEAAF3EDoBkB0g9M6s557b9348kHUhqOueqfl/pir+Ml2hFAQCpQewAOkh2gBQzsw1J9wa+LqoTtCSpKak6LFj56xb1ZLScQ0kl59z+/GoMAEjajLGjIOm2v7wn6bG//LIkOeeac6o2MBckO0C6tZ1zrZ7P69KTYOMHqkvJi991oeWc2+v5riGpPt/qAgBSIGzs2JBU1UAi5LcE1ZxzS3OtNTAHDFAApNiQJ2hldZ6wjVMesm5jwnUBABkWJnb4CU3VOVcebPHxewTQ3Q2ZRLIDZEtJnaQlkJmtBhS1nHPt6KsEAEi5kbHDzIqSapI2R2xjsFsckAkkO0BG+MGoIKk18H1fcuN3XVg3s4bfJaH7Pa06ALBgJowddXUGLwh8INbbLRrIEpIdIMXMrOK/LCo9GS70qKd84/JakqQ1dbocvG5mzn+HBwCwAELEjpJouUFOkewAKWVmJXWetq37X60MlBckFQdeQpXUefnUOVf1XybdlHS7J/ANM6oMAJAR08aOnhaewZafgpltmdmB/xP0cI34gVQz51zSdQAwhB+QapJOJF11zm37waYs6Vi6eGm0d53VYcmPPxLbpWFGzayuzvCi6+oErKY6LUL1YdsBAKTbtLHD7+Z2ImlpWDc2P360unP29HxP/EAmkOwAOdI7idzA98fOubUk6gQASDczO5O0OezdTjNzGj7CJ5AJdGMD8mVjcMAC/32d3YTqAwBIv1fVaQ3q4w9HLfW87wNkDS07QE74XRduSjpVZ6brbj/qFk/kAACj+O/6VCU9lNTtztaUtMFIbMgykh0AAAAAuUQ3NgAAAAC5RLIDAAAAIJdIdgAAAADkEskOAAAAgFwi2QEAAACQSyQ7AAAAAHKJZAcAAABALpHsAAAAAMglkh0AAAAAuUSyAwAAACCXSHYAAAAA5BLJDgAAAIBcItkBAAAAkEskOwAAAAByiWQHAAAAQC6R7AAAAADIJZIdAAAAALlEsgMAAAAgl0h2AAAAAOQSyQ4AAACAXCLZAQAAAJBLJDsAAAAAcolkB0gBM6uZ2bGZOf/nzP9cSrpumD8zWzWzg56fhplV/LJC93cAGET8WGzEj/HMOZd0HQD4zOxA0oakNedcK+n6zIuZNSTJOVdOui5BzGxDUllSUdKypP+fvbuJjSS97zz/+xteoK31tJMsNwT5oO3O9FiABVg2yRpAGAOi3Mm+9WEHZBV2Id/cTGmhi7Uy6TqRdVk6Ka17AC3gJdsnexcLNumDxxeNMntcA2MgLIpMyY1GY90DZnsE2GVBLlZK9qj7svvsISKzMpMRmZGRkfGW3w+QKDKeeHkiMhj/euJ560o6Svp78QPRjqQd51xvbPm6pJ4kOef2kzxuFsyslefvHCgy4kd+ED+SkdR1pGYHyJeu/28vbAUza6aUl9gi5HHD/+SSme1JknOu4Zzbcs6tS3os6aqfltBxqpKaGgtU/rFPJZ1LCjxeEe6DPjOrm9m1JN40A4tD/MgB4kcykryOFHaA4qlmnYEIJubRObfinFtJKzOzMLM1ST3n3MXwcufcsaRTSU1/nSQ0JL09HqiGjtmW1A7ZNvf3gd+8piVpTeHnASA9uX9uiPgRVWnjR9LXkcIOUDxFeDtehDyGafhvxYL034Y9SOhYa5rwFtZ3ErI899fYObfvv5E7lnSddX4A5P+5oWLkMQzxIxmJXkcKO0CB+O1XK1nnY5Ii5HGKupldm9mtc3DO9ZuJJPVmLsq+brVNLsE1BpCyIjw3ipDHKYgfyUj0OlLYAQrAH1FlW14b3FwqQh4j6mlyFX9PXkfJJLTkPdRD37L5D/YTqVTXGEBKivDcKEIeIyJ+JCPR6/izc2cHQBoeyHuL0ZVU7Y9G42v5zYQG/AfaXXlNhyqS7gyPxuJ3bDz30+Scq/mjt1T87U789r799ftpklTzjznSljZqHs3syt9X1TlnQSfrt8Wty3ugVSTdkXQ2PAKL/8bnHT991Tm34j/0+297apKux6/NNM65dTOrhLWD9o+XSP8T59yxmT2Q1DKzY3nXvRuwXn/ZIu+D/jXsbyN5webxrNcQQK4QP4gfUoHiR+LX0TnHhw+fnHzktUV18h7kQem73p/t1H2cB2x3Pbas/6C6ljdc6Zq//Nnw9pJ2A45xNX6MqHn0H1InYev427cClp+P52V4X/KCW30s/Too/3N8P9v+sbYT3Oeaf82d/7n2z6k+YZtF3AfOT6+OpbeCvo8Y57k3Lc98+PCJ/yF+ED/KGj/mvY6JHZwPHz7zf+YNVv4D20mqBKRdS9oL2N/IA91/eFb9nyv+g3Q8SKz5x1mbNY/+OttB6/gPzknn92z8mEMPvr2A9U8kXSX4/Vwlub+h/Vb6QXooaLmg813gfRD4ffrpgdd3xnOksMOHzwI/xA/iR1njx7zXkT47QLk0JbVdcNXvhbyhKsdVJV32f3HOdVxAVfgw97w5QNzhK8Oqpk/k5TPM25LeCkkLqtK+VkJDbPrj+lflTeCWKOdczzl36ryRy0zeJGqn8oLYld9cYBZx7oPh73Vcf6jPvHdqBRAf8WMU8eO2TONH3OtIYQcolzUFjL7iC31whz2k/IfoigsfAjLpsfrr8iYNC3Ot8BFYJgbYefhtwB9IenVaIE+Cc67tnGvImwFbCh8+NEys+2CCftvu3E7kB2BuxI8FIH4kEz/muY4MUACUxNAEW1W/Q+i4uTpG+m+Htv1fp43tH2f//fxP2nevv+54gA15C5VEviry3ga+OuHNVdx93zqPYc65jt/xNPJs0Qu6D4aH+mRyUKBkiB/Ej/4+/R9zFT/mvY4UdoCCCxixpDXhTVrQqCgTH/L+w+8teQ+pwWgvZtactN2UPM4rqaE7oziXtJ90oPI1FNIkYMiJpD0zq056m5XAfRDFQv5DACAbxI+FI348N893ONd1pBkbUHz3pJGmBIn1q/AD1ZW8ILU/RxX8vWkrRMx/P+1ywjqJMbMTSU03NIyqvzypaxylWv9GGhk+NMzC7gM9b7aQynUHkBrix4IQPwbmih9JXEcKO0C5dCTdD0uM2VGxF/KGZ/Cg8cfWT0JHXufKMFuSuotqcjDM7wjZGn/A+oKq9+MeZ9q+NjR7e/Kk74MtaWIHVADFR/xICPFjROz4kdR1pLADFEu/CcDwG43hn9+QtDbhYTStynvchgLexgy1673j/zvcLGBaHid5Q96M0GHr17WA0WzG+cG3625PfNd3Z3zBHG/rTqYEj315/2kYtpD7YOh7HV7WH9Z01nsHQL4QP4gfUkHiR5zrGIbCDpAv/YdL4IPPf7vRU0i1vv/mpCFvRuWRffhvgM4CNpv0kH1b0kbAg7gubwjKW/mdlsfh9cf3O5T/d8Y3MLOwNrurQfvyRX4YDh1nTV5w2DKzk4BPK2Cbc0nP/Bm4Z7Ulb1jOW2+p/Or7W29GF3QfSMH/UXhH0sWE9ttRzfxdAJgJ8YP4MbzfwsaPONdx4v78CXoAZMjvrFnX82Exe/LeiAW1U63K63TYkfRU0ul4tbz/oGj4+7mWFxwuhjqH9vex4ad1/c9OwL725D1QW/7+5Jw79fdx7m93MpzPsDz6D8LzseOeOOdGOjz6+b/vb3vHX/dkOFCF7KvtnGsM5a1/PQOPE8TMogyt2Rh+ePvXqBH1GEPb7fb347/F6jcd6AeM87AgkcR9MJwPP+/m/9w/fk1eE4JJc1dMPD95b1Kren5Ne3p+v72RRpMSoMyIH8SPssWPONdx4v4o7ABAMoaDT5EMB6us8wIAy4j4sTg0YwMAAABQShR2ACABfpU/QzMDAGZC/FgsCjsAkIz7DM0MAIiB+LFAFHYAYE7+KDonWedjVmZW9Ue1afq/X9sMM5sDAOZD/Fg8BigAAAAAUEo/m3UGiuQXf/EX3csvv5x1NgCgdK6urv7ROfdS1vlYFOIHACzGtPhBYWcGL7/8si4v6T8GAEkzs/+SdR4WifgBAIsxLX7QZwcAAABAKVHYAQAAAFBKFHYAAAAAlBKFHQAAAAClRGEHAAAAQClR2AEAAABQSrkZetqfdbXlnGuPLd+T1JVUldR2znUWmQYAAACgHDIv7JhZXdKapG1JrbG0c0lH/cKImbUkbS0qDQAAAEB5ZN6MzTnXds4dy6tpGbc2VuvS9QtHi0oDAAAAUBKZ1+yE8QsgvbHFPUlbZqak0yS1BQAAAKA0Mq/ZmaASsOypvH42i0gDAAAAUCJ5LuysppyGJdbtBrWiXKxeb7ySMR3D59rpdNRoNHR6errQY56enqrRaKjTmW0skPH8pZXfWSV1fnkxfj5B55fV/QvkDfGD+DEP4sfi5bYZm6SblNMCmdmupF1J+vSnPz3r5svp3cNsjvtr8Y7bbre1upp+Ofj09FS7u7uqVIIqHBdj/Fxvbm60s7Ojev15t7Xj42NVq1V1u13V63Wtra3NdIyg7Xd3d9XpdHRzM9uf4Hj+gvK7v7+vWq2m6+trNRoNVavxK2rb7bZ6vZ62t7cD0/tB5d69e7q5udHJyYmazWZi55dEXmYVdP3Gzyfo/LK4f4uG+DG7N1sfZHLc3936lVjbET+IH33Ej/zGjzzX7PR0u9nZHXkDGSwiLZBz7tQ5t+Gc23jppZdmOgHkX6/XU6fTmfmBnIS9vT0dHR2ldrwo59p/cG5vb2tvb0/7+/szHWPe7We1tbWl+/fva3d3V81mUzs7O3Ptb39/f2LA6fV6ajQaWllZ0dbWlhqNxlzHyzov81y/tO/fIiJ+lBvxY1TU5//ho8PAz2c3P6vryjXxIwHEj1G5Lez48+2Mvy6p6PlcPImmJZNrFM3R0ZF2d3czO36tVpu56jquKOc6Hsyq1ara7ehjd8y7/Sy63a4uLy9Hjre6uhr7eO12e+pbvUqlIuecnj17puvr67neAmadlySuX5r3L5A3xI9R8z7/n/znJ/rUr3wq9vazIH4sV/zIbWHH1zaz4dcI1aFJRxeRhiXT7XYzbYZz7949nZ2dpXKsaefabrdvpVcqFbVa0d4FzLv9rDqdzq3mI9VqNfbDs9frRW6Osuh7Jo28JHH90rx/gbwhfjw37/O/e9XVC//tC7G3nxXxY768FC1+ZF7YMbM1M2tKqkvaN7O9oeQ3JN03s21/nf0Fp2GJdDqd0LcZ7XZ7pAp9/Peo+9/a2tLKyorMTLVaTbVabaRjXqVSSeXNxqRz7QvqMHjnzp3InW/n3X5WlUolsJr++vp65n1dXFyEtm0ed3p6qouLC+3v7y/ku0srL0lcv7TuXyBviB+j5n3+f/zPH8+1/ayIH8sVPzIfoMCf4LOjgEKHc643tPxi0WlYLt1uV3fv3g1MOz8/19bW1sjv6+vrkffdbrfVbDYHb6W2trbUbDZD2zv3er2Jb1iiBMqtra3AjorS5HPtm7VzZNLbz2pjY+NWgI3zpnXatR9Wr9cHQX97e1u1Wk1XV1eJvalLMy9JXT9ptnwDZUD8GDXv8/+jn3w01/azIn4sV/zIvLADZKXb7YYGj36w6bu8vJzpzdz+/r7eeeedwe9ra2u32rf29d+QTPpjjzNKyrBJ59o374hCaY9IVKlUtLe3p3a7rXq9PnjQztr2uN1uR34TNr7vSqWit99+O7F2+2nmJanrF+X+BcqG+DFq3uf/z734c3NtPyvix3LFj8ybsQFZefr0aeDy/tuK4T++brcb+Y+4X+U/vv3Gxkbg+mkUEsLOdVilUrn1pubp06eRz3ve7eNoNpvq9Xq6uLgYHLtWq0XefpbvtdvtamVlZWRZtVqN1ewhL3mZ9/pJ6RdygTwgfoya9/n/ws+/oI//62hTNuJHdMSPyajZwdIab//c1263R95i9d9cSNEeKJeXl7eq/CcN2XlzczP1Dz7KsJBh4+1L4ec6rF6v32qK0Ov1Ig8nOe/2cQ2/yep2u7p3717kbftj/19eXkryvrvhuQHGjb8h7fV6Mz/c85aXea6fFO3+BcqG+DFq3ud/db16qykb8SM64sdkFHawtFZXVwPbGbdarZE/vpOTE929e3ekk2a/U11QANrY2NDJycng9+Pj44nNCKK0Vx3eXxxh5zquXq+PBNb+xG7S5HOOsv24KPubZmVlRR9++KEqlYouLi50//79wbWMsv/xKv9Wq6Wtra1BcBjeR7VaHQn4vV5P3W43tNp/1vNLMi9Rjz3p+kVFfx0sI+LHbfPGj+p6VU8+eCJt6tb244gfi8tLGeMHhR0k79cOs85BJPV6PbAddf/N3MXFhSqVih48eKCzs7ORdstnZ2fqdDqBw2Kura2pVqsNZiheW1sLfWCnJexcx7311ls6OjpSt9vV48ePR4LspHOOsv24KPubptlsqt1uDwLx3t7zwRxn3f/x8fFg1unV1VVtb2/r7OxMvV5v8J+F3d1dHR8fq1Kp6OrqauK+5zm/efMS9diTrh+Qhd/d+pWssxAJ8eO2eePH619/XX/1f/6VLlYviB/Ej2Q55/hE/KyvrzuUy/b29sjvz549c5VKJdK25+fncx//6urKnZyczL2fKMbP1TnnWq2Wa7VakfcxzzlfXV3dOta0/Y3nL838ziqJ80vSvOc+fj5B55fk/Svp0uXgOb+oD/GjfIgf8Z7HB395MPEThPhxG/HjuWnxg5odLLVGozHSpnr45zScnJzMPVJOVOPnOq/Dw8NYaSiPNO9fIG+IH0B8ad6/jMaGpdZvY9zX7XZ1//79qdtN6jAaVX/SuLT6O4yf66ySOOdF7i/t/ef5+GkcO+37F8gb4kd0xI/iHL+M8YPCDpbe7u6uLi68+WX39vYijVPf7+Q3j8vLy8hj4idl+Fwlr+PpycnJoH34JPOc8+npqY6OjkY67kbZ33j+0srvrJI6v6TMe+7j5xN0flncv0DeED+IH/MifiyeeU3dEMXGxobrD+sHLDMzm7oOzxbMwsyunHPBk4mUAPED8Bw+Opycvjk5HRg3LX5QswMAAACglBigAEBsBwcHt5Y9fPgwg5wAAADcRmEHAAAAuUAzNySNwg6AW6L0yQEAAMg7CjsAFmJagYkBDACgfKbVzABpY4ACQN78CGnr9XqpH1MaPddOp6NGoxE6dOWkGYmD+uskbTx/0/KbldPTUzUajZnnoSjK+QSdX1b3L5A3xI/FPr+u/uJKf/G//oWefPBkpu2IH9nIY/ygZgdLr91uj4z/npbT01Pt7u6mOinj+Lne3NxoZ2dnZFbs4+Pjwc9xJher1+tqNBrqdruq1+uD7Tudjm5ubrS1tRV5X+P5C8rv/v6+arWarq+v1Wg05pofoN1uq9frhY7/3w8q9+7d083NzWAG6N3d3cH5zSLofObNy6yCrt/4+QSdXxb3L5A3xI/b8eP9j97Xs79/pup6VZ/6lU/NdIz/9H/9J6380srI9uuvr+uXPvglffRPH820L+IH8aOPmh0stV6vl9lMxXt7ezo6OkrteFHONSgQzOL8/FzValXvvfeefvrTn2pnZ0eHh4c6PDzUycmJ/uRP/iR2/oNsbW3p/v372t3dVbPZ1M7Ozlz729/fnxhwer2eGo2GVlZWtLW1pUajMdfxss7LPNcv7fsXyBvix6h+/PjVL/yq/vX/8K/VPm3PdIzzw3NV16uxt58V8WN54gc1O1hqR0dHevDgQWbHr9VqqQXLKOc6npdqtap2ux345ijIkydP9KlPPX+TV6lU1O12FzITdbfb1eXl5Uh+V1dXZ8rvsHa7PTWflUpFzjn1er2FvpFKIy9JXL80718gb4gfo/p5+XeP/p0kqfKpirpXXVXXnz/LJk5N8H9L79v7krxpDYK2TwrxY7niBzU7WGrdbjfTZjj37t3T2dlZKseadq7tdvtWeqVSUavVirz/F154YWTZCy+8oOvr69kzG0Gn07nVfKRarc7c7rmv1+tFbo6y6Hsmjbwkcf3SvH+BvCF+PBcUP174+Rd0fRnx+X+jW6/fZ9p+RsSP+fJStPhBYQdLq9PphL79aLfbI024xn+Puv+trS2trKzIzFSr1VSr1UY65lUqldgP11nzMu1NT1CHwTt37kTufPvxxx/fWvaJT3xiYR0RK5VKYDV9nMLVxcVFaNvmcaenp7q4uND+/v5Cvru08pLE9Uvr/gXyhvgxKug5/4kXP6Hek+Dn/8HBwchn59/sjLQKmLb9vIgfyxU/aMaGpdXtdnX37t3AtPPz85GO9Ofn51pfX4+873a7rWazOagV2draUrPZDK2unVaVHCVQbm1thVYfTzrXvlk7R4776KPZOo/Oa2Nj41aAjfOmdZZq/Hq9Pgj629vbqtVqurq6SuxNXZp5Ser6SbPlGygD4seouePHT4gf8yJ+hKOwg6XV7XZDg0c/2PRdXl7O9GZuf39f77zzzuD3tbW1W+1b+/pvSCb9sccZJWXYpHPtm3dEoZ/7uZ+ba/tZVSoV7e3tDdoI9x+0s/YParfbkd+Eje+7Uqno7bff1u7u7kzHzENekrp+Ue5foGyIH6Pmjh8vEj/mRfwIRzM2LK2nT58GLu+/rRj+45ulk32/yn98+42NjcD10xi2NOxch1UqlVtvav78z/9cf//3fz8YUW34M+6FF1641ZTtpz/96UIfYs1mU71eTxcXF4O812q1yNvP8r12u12trKyMLKtWq4n1ScoiL/NePymd+xfIG+LHqKD48dOf/FSVT0V7/r/w8y/o4/86Fj9m2D4O4sd8eSlS/KBmB0trvP1zX7vdHnmLNTy6SJQHyuXl5a0q/0kjjtzc3Ez9g48yLGTYePtS+LkOq9frt5oifPzxx/rsZz879diS97Acb8o2y/ZxDb/J6na7unfvXuRt+2P/X15eSvK+u+G5AcaNvyHt9XozP9zzlpd5rp8U7f4Fyob4MSowfvzzx/rsZsT4sV691ZRtlu3jIn7Ml5eixA8KO0iUmWV6fOdc5HVXV1cD2xm3Wq2RP76TkxPdvXt3pJNmv1NdUADa2NjQycnJ4Pfj4+OJzQiitFcd3l8cYec6rl6v35rluH/OT554s1ePdyIdVq1WR4afHt5+3KRrGNXKyoo+/PBDVSoVXVxc6P79+4NrGWX/41X+rVZLW1tbg+AwvI9qtToS8Hu9nrrdbmi1/6znl2Reoh570vWLiv46SArxo2Tx40lvMGz0kw+eTN2+ul7V+//0vvQvbm8/jvixuLyUMX5Q2MHSqtfrge2o+2/mLi4uVKlU9ODBA52dnY20Wz47O1On0wkclnltbU21Wm0wQ/Ha2lqscfuTFHau4956662Rib6G8/3ee+/pH/7hH/Tbv/3bodu//vrr+qu/+is9e/ZMf/d3fzfxvCddw6iazaba7fYgEO/t7cXe//Hx8WDW6dXVVW1vb+vs7Ey9Xm/wn4Xd3V0dHx+rUqno6upq4r7nOb958xL12JOuH4BwxI/b+vHjw098qL/7f/5O9d2h+PGX7+kf/vM/DAoyQV7/+ut6/396X/pIap20RrYfR/xYXF7KGD9sljcZy25jY8P1qwgRrP9mLu37Ku5xd3Z2dH5+Pvi91+vplVde0bNnz6ZuO8sQj2E6nY4uLy8T66A4yfi5Sl5glnQrmPav58HBwcjy999/X7/6q78a6/hPnjzRRx99pD/90z+V5H1X067heP7C8hsmie8oqn4zguG8zXp+SZr33MfPJ+j8krx/zezKORfcMaEEiB/TET9mk9f4cfjoMHAf7//H93X+yNvHeGzp6086Op7+5IPRyx/qAAAgAElEQVQn+uifPlJ1varDTW//xA/iR9+0+MEABVhqjUZj8MCQRttXp+Hk5GTmNq5xjZ8rMK80718gb4gfQHxp3r8UdrDUxtsYd7td3b9/f+p2kzqMRtWfNC6t/g7j5zqr4b44SUjiGma5/zwfP41jp33/AnlD/IjuyQdP9Kl/SfwowvHLGD/os4Olt7u7O6iyjdrmNIkHQVrND4YNn6vkdTw9Ojqa2FGyb56CztXVlbrdrn7zN39zsCzKNRzP3yz5TTNQnJ6eqtVq6cGDBzMdf5bzmcW85z5+PkHnl8X9C+QN8WP0+WVm0mayx736iyt1r7r6zf+R+DGM+BEdfXZmQJvr6YrW5hrBwvrsJKHfJpvvCsPoswPiR/FFLezM2mdnWL/PDtA3LX5QswMAAIDELOJFGRAXhR0srW63q2azqdPTU21vbw8mcru+vlalUgmd26Ddbuv8/Fy1Wm3Q3rQ/jOPe3p46nY5effVV3bt3T7VaTa1WS+12W81mU0+fPh2MStJv2hWWh0mTvAEAskP8AIqDwg6WVrVa1cnJiU5PT/XgwYORdqoXFxdaX1/X1dXVyDbHx8d6/PjxrSE4d3Z21Ov1tLe3p263q3feeWewv2q1qsvLy5H23P05CybloVaraX9/P7V2raenp/TBAIAIiB+jiB/IMwo7WIisZ8Ke1/b2tlqt1uBtm+S9kTs5OdH19fWt9ZvNphqNxuD3aR38+m/gpuWh2WymEkB6vV7geQFA2ogfxI9+353AtC8+pH8VZsLQ00CInZ2dkVmj9/f3RwLSsGq1qmq1Ovh5mijrzLLePHq9nt54442FHwcAlgXxA8gPanaQqDK9bdnY8Ab26Ha7qlar6nQ6oe2wJQ3SogzbOG2dXq+nTqejk5OT0HVOT0+1v7+vjY0NnZycDCZ9Oz8/1/b2ttbX1yVJ5+fng/yfnZ3p7t27evz4se7evatWq6Wtra3B8Y6PjzOdWwDA8iJ+FD9+zGvawAaTanyAMBR2gBD9zqPDE6mtrq5OXT+udrutbrerx48fq9Pp6Pz8fOI+d3d3dX19rTt37qhararVamllZWUwB0Kz2dTGxsZgHzs7O4OmBq1WS5IGwbDb7erp06eR54kAAIRb1vgxXJu1MJvS4aPD0GSGpsY4mrEBIXq9nqTRJgY3NzcLO169Xh+0s97Z2Rm8WZuk0WgMAk6n01G1WlW73Zbk5X842HW73cHPtVpNjx8/TvgMAAAS8QPIEwo7QIh+YOo3GVhbWxt5Szfu4uIisWPv7u6q2+1OPJ70vE12t9sdDEPaH+ln/K1evV4fBKzHjx/r/v37ieUXAPAc8QPIDwo7QIiTk5ORZl3NZnNiG+hFGH6bFmZ7e3sQKOv1utrtttrt9q05Fvptsi8uLtRoNELbfScZdAFgGS1r/ADyiMIOEKD/wH/w4MFgWb1eV6PR0M7Ozq31+5O6JalarQ6aCkwKWo1GQ0dHRyNvEMfncZC8t3G7u7va3t6+Fciq1eqg2cWwJ0+e6MmTJ/OcBgAsFeKH58kHT/TkA+IHsscABVha/Wp7STo6OhrMXfD06VNJujUhnCTt7e2p3W6r0WjcmgE7aP8XFxc6OztTr9dTo9HQ+vr6yLq9Xk9vv/22JO9N4P7+/qBpwfn5ufb393VxcaFKpRI6jGi1WlW9Xh+kNxqNwMBz584draysaHV1VZVKRRsbG2o2m6pUKtre3tbZ2ZlOT09HjvPee+/p448/1uuvvz7lagLA8iB+BMePYe/95Xv6+J8/1uv/M/ED2bIyDfW4aBsbG+7y8jLrbAAz63Q6arfb2t3dVaVSUa/XG0xy1x9ZZ1h/Ur9pw4DG0R86lGcPhpnZlXNuI+t8LArxA0U1S/wwM2lzMbFDeh4/Ju2f0diWz7T4QTM2YAm0222tra0N3iT238YBADAJ8QNFRzM2YAns7e3p+Ph4MLzozc2Ner1eYNtsAAD6iB8oOgo7wJIYHhmo30wtlQngAACFNj7h9OGjQ/3b7//b2ytuppMfYBY0YwMAAABQStTsAEts0iABh4eHCz32pP0v+tgAAGA5UNgBkIn+qDphaYzWBgAA5kUzNgAAAACllPuaHTOrSNqV1JNUkdRxzrWH0vckdSVVJbWdc5150wAszrT5FybV+AAAAMwi94UdSbvOueP+L2bWNLNL51zPzM4lHfULKmbWkrTl/xwrDcup2+2GzjC9KL1ebzBvQVboGwMA81nW+AEURREKO1uSjod+v5ZXG9ORtDZWI9M1s7pf8xM3DXPK6j/QcY/bbre1urqabGYiOD09HcxIvWidTmcwN8Ljx48XfjwAiOPw0WE2x92Md9xljB/3799f+DGBJBWhsLNqZk3nXH9CkC3n3KmZ1eU1bRvWk7TlzyEyc5okCjtLptfrqdPp3JpDIA17e3va399Xs9lc6HF6vZ5effVVPXv2TJK0tram4+PjKVsBACZZ1vhRq9WYTweFUoQBCt6QtGtmV34/m36hJ+h1xlN5tT5x07Bkjo6OtLu7m9nxa7WaOp3FdherVCq6urpa6DEAYNkQP4BiyH3NjnOuY2ankrYlNeU1X+tKmlRvHDftFjPblTdAgj796U/PsikKoNvtZtru+d69ezo6OtLa2tpCjzPcnnzRbwIBeIgf5bbs8WPaYDNAXuS+ZsfMTiSdOOdqkk4ltcxsTdLNhM3ipt3inDt1zm045zZeeumlWTZFznU6ndBOpe12W/v7+6G/R93/1taWVlZWZGaq1Wqq1Wrq9Z63oqxUKgt/Mzes2+16TRAALBzxo7yIH0Bx5Lpmxy/UXDvnupLknGuY2bWkhqRz3W6SdkderU8vZhqWSLfb1d27dwPTzs/PtbW1NfL7+vp65H232201m021Wi1J0tbWlprNZugbuGkj60QJlFtbW6rX6xPXubi4GLT1BgDEQ/wAiiPXhR15/WjGCyGnkprOubaZjTdJq0g6j5uWWK5RCN1uNzR49INN3+Xl5UwP+P39fb3zzjuD39fW1nR5eRl4vEqlopubm4nBKqmmZwxVCgDzI34AxZH3ZmxtSeNjHNYlnfTT/dqfvurQ8NFx07Aknj59Gri830xg+ME+yzwK/eYN49tvbGwErp/msKVpzwUBAGVE/ACKI9c1O/7EoUdm1pQ3v44kdYfmyHlD0gMzq0q6q+cjtc2ThiUx3v65r91uj7xBa7fbg+r9KEHr8vLyVvOGTqcT+hbw5uZmasBqNBoT0yVpZ2dnajOEaekAgOmIH9l6+PBheNoXH8o5l2JukHe5LuxI3mhs8kZgC0rr6XlB5SKJNCyP1dVV3dzcHq+i1WqNBI+TkxPdvXt3pENqv1NoUADa2NjQycnJ4Pfj4+OJzQiizIQ9vL+4er2e2u22tre3594XACwz4gdQHLkv7ACLUq/XA9tR99/MXVxcqFKp6MGDBzo7Oxtpo312dqZOpzPoQDqsP+na6enp4Pc8vBFrt9s6OjoiWAHAnIgf2Zg23PWkGh8sLwo7SNzh4WHWWYik37FzWK/X083Njc7PR8erGH8D12w2dXERXikYdUbtTqejnZ2diDmez/b2duaBCgAmOdw8zDoLkRA/gOLI+wAFwEI1Gg2128/HphhuX52Gk5MT3bt3L7XjAQCSQfwAioHCDpZavV4fmZSt2+3q/v3xAQBvm9RhNKr+pHEM5wkAxUP8AIqBZmxYeru7u7q4uND29nbk5gPzBirJG3Vnd3d37v0AALJB/ADyj5odLL1KpZJJW2QCFQAUG/EDyD8KOwAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASonCDgAAAIBSorADAAAAoJQo7AAAAAAoJQo7AAAAAEqJwg4AAACAUvrZrDMAAEHMbGK6cy6lnAAAgKKiZgcAAABAKVHYAZArBwcHcs6FfgAAAKKisAMAAACglBZW2DGz31rUvgEAyANiHQDk20IKO2b2oqT9RewbAIA8INYBQP7FGo3Nf8BfSHo1bBVJNK4HABQWsQ4Aii/u0NN/LOlc3hutbkD6HUlvx80UAAA5QKwDimZTOnx0GJp8uBmehnKKW9hpOefempD+YzM7irlvAADygFgHAAUXt7BzM20F59yfxdw3gCV3eHiYdRYAiVgHAIUXd4CCnpm9PGkFM/t6zH0DAJAHxDoAKLi4NTtO0raZ1SRd6fbbr1VJDUnfnCNvAABkiVgHAAUXt7Bz4f/blXQ3IL0i6ZWY+wYAIA+IdQBQcHELO13n3MakFcyMEWqAjJhZ1lkAyoBYBwAFF7ewsxNhHSZaAwAUGbEOS2nS0M3aTCsXQDJiDVDgnPswwmphk7ABSIlzLvRzcHCQdfaAXCPWAUDxTa3Z6Y9E45z726Fl/ybCvvflTcgGICMM4QxEQ6wDgHKK0oytI+lao50z+w/2oBmlJTptAgCKhVgHACUUpbDzqqTe2DI6bQIAyoRYBwAlNLWw45z7XsDiKG2U6bQJACgEYh0AlFOs0diccz/u/2xmL0rqv/m6dM79xF8nSsdOAAByiVgHFNPDhw/D0774UM65FHODrMUderrfmfNU3puv/qQezszakradc/80d+4AAMgQsQ5l9Gbrg8kr/Dfp5ANIQ6zCjpm9IqklqSmvCr/rnPuxv3xHUsfM1vtvvgAAKBpiHVAs06ZUmFTjg/KKW7Oz65z75fGFfnX+sZmdSnrgfwAswruHWecAKDtiHZbSd6+fZp0FIDGxJhWVdDkp0TnXU/hQnQAAFAGxDgAKLm5hJ0rPLnp/AQCKjFgHAAUXt7Bj01fRs5j7BgAgD4h1AFBwE/vsmNmvS1oNSHpmZmfyOm6OV+FXJVWcc99MJosAACwOsQ4AymvaAAV/LGlN4W2S18M2NLOac+4rcTMGIAE/fBSe9snNtHIB5B2xDphR2CAGn6/dSTknwGTTCjtdSTtMmgYAKDFiHUpl6jw6wBKZ1mfniIc/AKDkiHUAUFITa3acc99LKyMAAGSBWAc897XXPiNtZp0LIDlxJxUFkAYmDgUAAIiNwg6wrCYNXiAxgAEALLHXvvTVrLMAJCLuPDsAAAAAkGsUdgAAAACUUiGasZlZVdK2pJ4kOedOh9L25A0bWpXUds515k0DAAAAUHy5L+z4BZ2mc27H//3KzC6dcx0zO5c3ZGjHT2tJ2vJ/jpUGAAAAoBxiN2MzsxeTzMgETUknQ7+/OlQLszZWI9M1s/qcaQAASEo11gEAFiBWYcfMviNp4ROwmVlF0rZzrt1f5pzr+Wl1+c3ahvQkbcVNSzLvAIBiSyvWAQAWJ27Nzrm8vi6LVpXUM7O6mW2b2d5QDUwlYP2n/jZx0wAA6Esr1gEAFiRuYedG0sqkFczsj2Lue1g/yNw45y6cc8eSmn4/ntUJ28VNu8XMds3s0swuf/SjH82yKQCg2OaKdcQPAMhe3AEKriVtm9kd/+duwDpJ9IHpSaqM96+R1JD0eMJ2NzHTbvFHfjuVpI2NDTfLtgCAQpsr1hE/ACB7cQs7/0Fec7CgB7/k1Z78Qsx9D+vqdv+a/nDRLd1uknZnaJs4aQD6fvhocvonN9PIBZCltGIdAGBB4hZ2us65jUkrmNnbMfc94Jzr+oMUDKv4x2+b2XiTtIqk87hp8+YXAFAqqcQ6YFZvtj7IOguhvnv9dGL652t3UsoJ4InbZ+eNCOvsx9z3uOOxYaE3JB35P7fNbG0orTo0clvcNAAApHRjHQBgAWLV7DjnvicN5h/YkDeAwPf9Zb/unPu+cy6R4Tqdc/tm1h+UoCbpjf7w0/IC0QM/7a5Gg07cNCA97x5mnYPCMrOJ6c7RRQLzSTPWAQAWI24zNpnZH0jak/RM0tuSvuIn/djMvu6c+2YC+ZPkFXhClvf0vKBykUQaAAB9acY6AEDy4k4q+nv+jyvOuTuShif9/NA5900z+50kMggAww4ODuScC/0ASSHWAUDxxa3Z6TnnvjH0e9D/MJ7F3DcAAHlArAOAgos7QMHkoTY8r8TcNwAAeUCsA4CCi1vY+Vdjv4/0FDazlyX9csx9AwCQB8Q6ACi4uM3YTszssaT/RdL35Fft+w/+HUm7ktYTyB8AAFkh1gFAwcUdevpDM2tIOpW0JskNDQPblvSac+4nyWQRAEYdHh5mnQUsAWIdABRf7KGnnXMdSRv+XDW/4S/uMOcAAKAsiHVAyWxKh48OA5MON4OXo9hiF3aG/KOej0YTpTMnAABFQ6wDgAKKO0CBzOxlM/uOvId/2/88M7Nvm9m/SCqDAABkhVgHAMUWd1LRVyR1/M+GpBX/c1fSf5HUMbMXk8okAABpI9YBQPHFbca2J2k9oM1yR1LDzNYkNSV9ZZ7MAQCQIWIdkLDvXk9uBfr52p2UcoJlEbcZW3dS50y/Q2c35r4BAMgDYh0AFFzswk5C6wAAkFfEOgAouNijsZnZi2HzC/htmN3Ysq87574Z93gAAKSNWAeUz8OHD4OXf/GhnHOBaSiuuIWdnqS3zOzM/3lYVdKWpDMz+y1/WUVSQxIBAMvl3cOscwAgPmIdABRc3MLOubyH+vqEdYbTViX9QsxjAQCQBWIdUCIHBwehaWG1PSi+uIWdrnNuY5YNzOztmMcCMMY+l4OH8g8fhad9cjOtXACLRKwDgIKLW9h5I8Y2+zGPBQBAFoh1yMybrQ+yzgJQCrEKO86578XYJnT4TgDxuL8Or5I//KNH6WUEKCFiHQAUX+zR2ABkjwINAGBW3/7Bt8ITN1PLBpCKuPPsAAAAAECuUdgBAAAAUEoUdgAAAACUEoUdAAAAAKVEYQcAAABAKSVa2DGzF83s5ST3CQBAnhDrAKA4YhV2zOwPzOzfm9nXzexFf9m/l3Ql6ffN7IxAAAAoMmIdABRf3Hl2Hks66U+eZma/J6nqnPuX/RXM7OuSvjl/FgEAyASxDgAKLm4ztpWxWaIbkppj6/w45r4BAMgDYh0AFFzcws7g4W9mr0h6RVJ7bJ2ncTMFAEAOEOsAoODiFnZ+YejnbUkfOuf+dmydOzH3DQBAHhDrAKDg4vbZ+bHfTtnkVelvS94INZK2JP2+pJ1EcggAQDaIdSidr732GWkz61wA6YlV2HHOvWNmXUl1SbWhNs33JVUkvS1pTdLfJpFJAAXzw0eT0z+5mUYugLkQ6wCg+OLW7Mh/6L81tuytkNUBACgcYh3K6rUvfTXrLACpiFXYMbNfd859f2zZL0i6J6+Nc88598cJ5A8AgEwQ6wCg+OLW7DQkfWV4gXPuxxp6+2Vmv0MQAAAUGLEOSNl3rycPcPj5GmOCYDaxm7EBkPTuYdY5AAAU0JutD7LOArAUIhV2zOxVSW5oUdXMfitk9Yqku/6/vO0CABQCsQ4AyidqzU5X3ogz9+UNvekk1Sas33LOfWVCOgAAeUOsA4CSiVTY8Uej+VDSn5nZnqRXeMADAMqEWAcA5fMzMbY5kff2a6IJVf8AAOQdsQ4ASmDmwo5z7sfOuW9MWsefXXo/dq4AAMgQsQ4AyiHuPDsvSrqQ9GrYKhrt5AkAQKEQ6wCg+OI0Y5O8kWfOJW1IWg34/LKk7yWRQQAAMkKsA4CCizvPTss599aE9B+b2VHMfQMAkAfEOgAouLg1OzfTVnDO/VnMfQMAkAfEOgAouLiFnZ6ZvTxpBTP7esx9AwCQB8Q6ACi4uM3YnKRtM6tJutLtt1+rkhqSvjlH3gAgNjMLTXOOPuWIhFgHAAUXt7Bz4f/blXQ3IL0i6ZWY+wYAIA+IdQBQcHELO13n3MakFczs7Zj7BoDYDg4OdHh4GJg2qbYHCECsA4CCi9tnZyfCOky0BgAoMmIdABRcrJod59yH0mDCtQ1JN8657/vLft059/3+Okkys7qkinPuYmjZnrwmBlVJbedcZ940AMUWVrMDzCKrWAcASE7cmh2Z2R9I6smbcK0xlPTjBY5O05TXIbSfh3N5BZUL59yxnz5XGgAAfRnFOgBAQmIVdszs9/wfV5xzdyS1+2nOuQ+dc980s99JIoNDx6zLq4kZtjZWI9P115snDQCATGIdACBZcQco6DnnvjH0e9A4rs9i7jtMRUPDfvqFk954viRt+Z2QZ07TUCADACy9LGIdACBBcQs7TyOsk9hwnGa27Zy7MLOtocWVgFWfyhseNG4aAAB9qcY6ABnblA4fHYYmH26GpyG/4vbZ+Vdjv4+M5+rPOP3LMfc9umOzim7XxEhDfXcSTAMAoC+1WAcAWIy4hZ0TM3tsZv+9/7B3kvfg99s4tyTtJZNF1Z1zQc3LxmeyTiLtFjPbNbNLM7v80Y9+NMumAIBimyvWET8AIHuxh542s4akU0lrktzQZH1tSa85534yb+bMrKrbgxL09XS7Sdodf/24abc4507lnac2NjaC2msDAEpo3lhH/Fhub7Y+yDoLpfTd68mtSz9fu5NSTlAUsQo7/vwCHUkbZvaKvCAgSZ2E5xxYk7RqZv0ZrDf83+WcOzWz8SZpFUnnzrl2nLQE8w0AKLgUYx2AnHj48GF42hcfyjneWxRN3AEK3pLfod9/4C/koT88eagk+QMUtPy3ZZLUNrPhYaSrQ03e4qYBACClFOsAAIsTt7CzbmZnkk6cc/8hyQyFMbM9SXVJFTO78QtCb0h64Dd3uytpf2iTuGnAc+8eZp2Dcvrho8npn9xMIxfANKnHOgDZODg4mJg+qcYH+Ra3sLPvnPuGmb1iZm/I67TZds79bXJZG+WcO5Z0PLasp+cFlYsk0gAA8KUe6wAAyYo7QME3/H8/lFfNLzP7Db+Z2VN5wWDuAQqAZWaf4y0SkCViHQAUX9yhp29xzn3POfeWvHkIPjSzP0pq3wAA5AGxDgCKJW4zthH+/AMNSbvy5rF5S9JREvsGlt3Bl7+QdRYAiFgHAEUUd+jpM0m/I+m+pC9L+g15fV/uOefeSS57AABkg1iHovr2D74VnriZWjaAXIhbs7MjaVtSR94oNW8llyUAAHKBWAcABRe3sNOTtM6kagCAEiPWAUDBxR2g4IiHPwCg5Ih1AFBwsQo7/eE4+/xOmwAAlAaxDgCKb2ozNv/hXvU/UvCEajV/3oGavA6cXUnXzrlvJpZTAAAWhFgHAOUUpc9OV1JLUiNs1mh/VJp3JMnMfkHSH8sbmpMAAAAoAmIdAJRQlMJOT9JO1FminXM/lrRjZv/fXDkDACA9xDoAKKEofXYuoz78x3RibAMAQBaIdQBQQlEKO93hX8zs98zsO2b2/5rZYzP7upn91rTtAADIMWIdAJRQlGZsbuQXb3Sab5jZjXPu7oTtbubKGQAA6SHWoRS+9tpnpM2scwHkR5SaHQtZ3k4yIwAAZIhYBwAlNHPNzpCnMbcDACBviHUolde+9NWsswDkQpTCzoaZfVG333pVQ5b31efKGQAA6SHWAUAJRSnsrMurxg960G9N2I63XQCAoiDWAUAJRSnsdCTtzLhfk/T27NkBUvbuYdY5AJAPxDoAKKEohZ22c+7DWXdsZnTqBBDPDx9NTv/kZhq5wHIh1gEl8N3ryd3sPl+7k1JOkBdTCzvOud+Ps+O42wEAkDZiHWb1ZuuDrLMAIIIoQ08DAAAAQOFQ2AEAAABQShR2AAAAAJQShR0AAAAApURhBwAAAEApUdgBAAAAUEoUdgAAAACUUpRJRQGgVMxsYrpzLqWcAACARaJmBwAAAEApUbMDYGkcHBxMTH/48GFKOQEAAGmgZgcAAABAKVHYAQAAAFBKNGMDAAAAptmUDh8dhiYfboanITvU7AAAAAAoJQo7AAAAAEqJwg4AAACAUqKwAwAAAKCUGKAAyIh9jjldYvvho8npn9xMIxcAACDnKOyg/N49zDoHAACgBCZNPv3wiw/lnEsxN4iCwg6QMffXB6Fph3/0KL2MAAAG3mx9kHUWACSAwg6QMQo0AACk47vXT0PTPl+7E5p2cBD+YlKaXOODbDFAAQAAAIBSorADAAAAoJQo7AAAAAAoJfrsAAAAFMi3f/Ct8MTN1LIBFAI1OwAAAABKicIOAAAAgFKiGRsAAEBBfO21z9BUDZgBNTsAAAAASomaHQAAgIJ57UtfzToLQCFQswMAAACglHJfs2NmFUm7/q93JR055zpD6XuSupKqktpJpAEAAAAovtwXdiQ1nXMNSTKzqqQrM3vFOdczs3MNFX7MrCVpy/85VhoAAACAcsh1Mza/cHPd/90515VXG9Ov6Vkbq5Hpmll9zjQAAAAAJZDrwo6kiqRmwPI7fuGkN7a8J2krbloC+QUAAACQE7ku7Pi1L+tji9ckteQVhMY9ldcHJ24aAAAAgJLIfZ+dsUEFduUNJtD2fw6zGjPtFv84u5L06U9/epZNkZZ3D7POAfLmh4/C0z65mVYusOSIH/n3ZuuDrLOAHPnu9dOJ6Z+v3UkpJ0hSrmt2hvmjsu045/rNzW4mrB437Rbn3KlzbsM5t/HSSy/NsikAYIkRPwAge4Up7Mjru7Mz9HtPt5uk3ZE3gEHcNAAAAAAlUYjCjj8nTtM51/N/X3POtXW7SVpFUituWvI5BwAAAJCV3Bd2zGxbUkfSjZlVzGxN0oaf3PZ/76v6hZl50gAAAACUQK4HKPDn2TkPSOr323lD0gN/vbuS9ofWiZsGYMmZ2cR051xKOQEAAPPIdWHHn0Q09H8dfrO2fkHlIok0AAAAAOWQ68IOAKTp4OBgYvrDhw9TygkAAEhC7vvsAAAAAEAc1OwAAAAA89qUDh8dhiYfboanYXEo7AAAAAAJmNTc+eEXHzLATQYo7AALYp+jfwcAAECWKOwAAAAAc2CAm/yisAMs2MGXv5B1FgAAAJYSo7EBAAAAKCUKOwAAAABKicIOAAAAgFKizw7y793DrHOAMvnho8npn9xMIxcAMNG3f/Ct4ITNVLMBFB6FHQAAgBz52mufoVCTQ9+9fjox/fO1OynlBLOgGRsAAACAUqJmBwAAlNKbrQ+yzmIh9D0AACAASURBVMJcXvvSV7POAlB41OwAAAAAKCUKOwAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASonCDgAAAIBSorADAAAAoJSYVBTZe/cw6xwAMzGzienOuZRyAgAAJqGwAwDDfvgo6xwAiOjN1gdZZwFAzlHYAYCIDr78BemTm6HpDx8+TC8zAABgKgo7AAAAwJy+e/006ywgAIUdAAAAYNE2JfvihD6fj+jzuQgUdoCY7HM0WQIAAMgzCjsAAAAp+tprn8k6C0jZa1/6qj5fuxOaTp/PxaGwA8zp4MtfyDoLAAAACEBhBwAAIAN/+J2/CU379g++lWJOgPKisAMAAJC2TQo0QBp+JusMAAAAAMAiUNgBAAAAUEo0Y8PivXuYdQ6A5PzwUdY5AAAUEJOOZoPCDgAkzGzCpHFi0jggqjdbH2SdBQAFRzM2AAAAAKVEYQcAEnLw5S/IORf6AQAA6aIZGxDCPsdsxpjd4eFh1lkAABTUpGbQvDSLh5odAAAAAKVEzQ4whfvrg9C0wz96lF5GAACF8LXXPpN1FlAwr33pq/r3fxk8yey0QW8wGYUdAEgSQ1MDAGI4fHQYnLAp6VF6+SgbCjtIRonn0qH2BgAWo+xDS//hd/4mNO3bPwh+i4/lxTw8i0FhBwAAIGmbFGiAPKCwAwApY9JRAADSwWhsAAAAAEqJmh1EU8I+Ocyjg7QdfPkLOvzKZmg69yQAAMmisAMAKWLACwBAHDSBjofCDpYe8+gAwGKUdbQ15tFB6janpD9KIQ8FRWEHS48CDfJm0ts73twBwHJ57UtfnZj+nf/jf0spJ8VEYQeeEvbJAQAsXllrb6banP6fUCBNk/4Wf3frV1LMSb5Q2EGp0eEbRTJpAAPuZSA9NFMDymOpCztmtiepK6kqqe2c62ScJQBLbmqzykm1sL82IQ0AUGqTCulf0/I2g17awo6ZnUs66hdwzKwlaSvbXC1QSZupRX3bffDlLyw4JwCAsvnD7/xNaNq3f/CtFHMCTLE5Jf3R8jZzW9rCjqS1sZqcrpnVnXPtzHIEAFNMLuA/XNo3d1icsvbJmdpUbZMCDYoh6gAGy1rzs5SFHTOrS+qNLe7Jq9kpZmGnoDU3SfVDoOYG8DAPA2a1tIUZafrbcKAsNqekPypv/FjKwo6kSsCyp5LuLvKg024iAAgzrUD/8H//j5H2s6jnUFGDYFEQP2LanL4KI6qh7CLV/GxO2cmj4sYPW8YAZWa7khrOufWhZXuS7jrndgLW3fV//Yyk8Aa8i/OLkv4xg+MWAdcmHNcmHNcmXFbX5r9zzr2UwXEXhviRe1ybcFybcFybcLmMH8tas3MTdUXn3Kmk0wXmZSozu3TObWSZh7zi2oTj2oTj2oTj2iSH+JFvXJtwXJtwXJtweb02P5N1BjLS0+2mbHfkDUMNAAAAoASWsrDjj7i2Ora4IqmVQXYAAAAALMBSFnZ8bTNbG/q9muNhpzNtBpFzXJtwXJtwXJtwXJty4fsMx7UJx7UJx7UJl8trs5QDFEiSmVUkPZD0WN4obGdj8+4AAAAAKLClLewAwLIzs6ak1nittj86ZVdSVVKbF0EAgGFFih/LOhpb4RTppkL2uC+C+UMBS9Lb8vrtNZxz+xlmKRP+xMprkrY11lfRzM4lHfXvGTNryZtwGQVF/EBU3BPBiB3PFTF+UNjJuSLeVFnhYeThvpioIqkp6UReQF/K6+L/p7dtZkHnvzb2H5yumdVz3KcRIYgf0RA7PNwTExE7fEWMH8s8QEEhOOfazrljBQ+LHXhTpZS1PKrIexA9kxfYT7LNTma4L8L1nHMmacU5V3POMdz8EP8+6Y0t7mmJA3uRET8iI3Z4uCfCETumyHP8oLBTUHm+qTK09A8j7otonHPj1wie8fnHJOmpvCYtKAmeE7cQO7gnIiF2TJTb+EEztuIKu6nupp2RvFnyhxH3xRR+k5UbMQpjkPH5x1BOPCcCEDtuWfp7YhixY6rcxg8KO8WV25sqSzyMuC+maA+9tb0ws2szW1/y/+QMu8k6A0gFz4kxxA7uiSmIHdPlNn5Q2Cmu3N5UGeJhxH0xUUDzlJ6ke8rpRGgZ6On2G947Cu7zgeLiOTGK2ME9MRGxI5Lcxg8KOynz3x6tT1mtGaHNcG5vqqzwMJLEfRHKzKqSrpxzK0OLu5JqGWUpd5xzbTMbf8NbkXSeRX4wivixGMQOSdwToYgd0eQ5flDYSZlzLpGHZ55vqqTMEth5GHmW4b6Y0/hwshVJ11lkJMfaZjY8KlM162FD4SF+REPsmF3Z74kEEDuiyWX8oLBTbLm8qZISI7DzMPKU+r6Iy/+PzeDNpf9zNan/QBaJma1Jui+p3v/dH6JYkt6Q9MD/T+Bd3f67QjmU9jlB7IittPfEPIgdo4oYP8w5l3UeMMHQTbUnqS1vFuxjP60i6YGkx1reTpUDZrY3dm2unHNL9XZO4r6YxL82u/KabKwrWpMfoJCIH9EQOzzcE+GIHcVGYQelwcMIADArYgdQbhR2AAAAAJTSz2SdAQAAAABYBAo7AAAAAEqJwg4AAACAUqKwAwAAAKCUKOwAAAAAKCUKOwAAAABK6WezzgCA7AxNOliRVJXUlbTvnOtlmjEAQK4RP1AUFHaAJeUHqg3n3P7QsqakD83sFQIWACAI8QNFQjM2YHnVnXOnwwv8wHUj6a1ssgQAKADiBwqDwg6wvB6Y2XbA8gtJ9bQzAwAoDOIHCoPCDrC8biStBix/Kq8NNgAAQYgfKAz67AAFYGZ7knqStiQd+T/336rVnHONWffpnKuFJN2V19EUAFBgi4gdEvEDxWLOuazzAGACM2v2O4H6nULPJbWdcw0z25V0Ii9oJRJgzOyZpCPn3HES+wMApC/t2OEfh/iB3KFmB8gxv0302djiqrygJUltSY2gYOVvW5X3Jq8ivy31eKfSsW32JHUJVABQXHPGjoqkB3o+nPRTf/1VSXLOtUOOSfxALlHYAfKt55zrDP2+IT0PNn6gulV48YNOZzjomFlL3pu8QP6bv4ak9WSyDgDISNzYsS0vDowUhPyaoKZzbiXoYMQP5BnN2IACMbNzSRXn3NaU9Vrj6/gFoNOw+Q/8wlDgmz4AQHFFiR1+gWYnbB0zu3LOBRZmiB/IM0ZjA4qlLqk1aQX/DVuQzoSCzrkIVABQVhNjh5lVJTUl7UzYx3izuP62xA/kGoUdoCD8YFSR1BlbPlK48ZsubJhZa3gehAntrJvyOpQON1kIKzABAAokYuw4kTd4QeALMUkK6otD/EARUNgBcszMdv3OotLz4UIvh9KDJnWTvHbTXUlvmZnzm7AF7l/S2VjbbolJ4QCgsGLEjrpCam4mHUPEDxQAAxQAOWVmdXlv27ryRsKpjaVXJFWdcxfj2/pv2RqSGn5Qe8vMRvrr+PuvSer6P0ve279VeXMlAAAKZtbYMVQTM17zU5G0q+fx4GxoG+IHCoMBCoCc8gNNU9K1pDvOuX2/4LIl6UqSxoeRNrO1gLdstzqP+vt+NuHwp3EnmwMAZGfW2OE3c7uWtBLUjM2PH52hOXuIHygUCjtAiQxPIje2PHQUHQDAcvMnA90J6ttpZk7SVli/TyDv6LMDlMv2eOdQv7/OUUb5AQDk3xvyaoNG+P1ypKH+PkDRULMDlITftOCepBt57ab7nVM7vJEDAEzi971pSHosqd+crS1pO2gkNqAoKOwAAAAAKCWasQEAAAAoJQo7AAAAAEqJwg4AAACAUqKwAwAAAKCUKOwAAAAAKCUKOwAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASonCDgAAAIBSorADAAAAoJQo7AAAAAAoJQo7AAAAAEqJwg4AAACAUqKwAwAAAKCUKOwAAAAAKCUKOwAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASonCDgAAAIBSorCTAjNrmtmVmTn/88z/vZ513rA4ZrYX8L23hj5X/ufEzCpZ5zdJZlbxz+3azFzRjxNHnvOG4iB+LCfiB/Ejr3krInOOa5gWMzuXtC1p3TnXyTo/i2JmLUlyzm1lnZcwZrYtaUtSVdKqpK6ko0V8L2Z2ImlXId+7me1JeiBp3zl3mvTx40jiO/QDcFPSrnPOkspbVseJI895Q7EQP/KD+DEZ8SMZec5b0VCzk66u/28vbAUza6aUl9gi5HHD/+SSHxzknGs457acc+uSHku66qclrDf27wjn3LGkI0knZra2gOPfksZ36JzrSWrNs488HSeOPOcNhUP8yAHiB/EjLXnOW9FQ2MmfatYZiGBiHp1zK865lbQyMws/GPSccxfDy/2AcSqpmVbAGNN/I/dWSsdL6zsM/Y9ZwtI6Thx5zhvKhfixQMSPAeJHevKct8KgsJM/RWiHXYQ8hmlMqOrvv616kFZm+vw3OF1Jaym1vy7ydwggWBH+rouQxzDED0+Rv0MsIQo7OeK3A851R8Mi5HGKut/h79Y5OOf6zUSyeDMnPX+Ds9C3syX4DgGMKcLfdRHyOAXxo/jfIZbQz2adAQw6odUlnWedlzBFyGNEPU0ORj15HU6zUJGkRXU+LtF3CMBXhL/rIuQxIuJH8b9DLCEKO/nwQN4DtCup2h/JxNfy2wMP+G9W7kq6lveAu+Oc2x9Kr8p7IPUffjUz2/V/vyvpxDnXHlq/nyZJNf+YI22So+bRzK78fVXDRg/x2zTX5QWGiqQ7ks6GH9L+g/UdP33VObfiD7XaDzQ1Sdfj12Ya59y6mVX8av8gFUntkLSF8c+3KilotJ2p30+U71ze6EFJfYdVSfvy7sG+9qyBdtq9nIQIfy9NeaMd9a/xqXOuMZRekfShn96T9IZz7iKNvAMRED+IH8QP4gcmcc7xSekjr02vk/cQCErf9b6Sqfs4D9juemxZ/+F1LW+40jV/+bPh7eUNaTh+jKvxY0TNo7w/2pOwdfztWwHLz8fzMrwvecGtPpZ+HZT/Ob6fbf9Y22l+75PWmeX7meU7n/M73PaPMZ7Xev+YY8vC9hPpXo54jQOPM8sxxq/TWNqapKuY+w29Bnz4RP0QP4gfs65D/Ih8jYkfJf9knoFl+swbrPo3vaRKQNq1pL2A/Y080P0/uqr/c8X/Ax0PEmv+cdZmzaO/znbIg6M65fyeBTzs+gFkL2D9k+EHSALfz1WS+4vyvfvfQdP/nsbPPe73E/qdJ/AdVhQS0BUQRCcEkZnu5QjX+NZxYvy9NCU9m7D/esz9Eqz4zP0hfhA/AtKIHxHu5QjXmPhR8g8DFBRLU15Vb1AV+oWkRsDyqqTL/i/OuY573pEykHtelRy3o2NYFf+JvHyGeVvhQ2cGNQ24VkKdMf35EaqSdpLYX4imebOhN/1Zr8/lByrnXM1FrMKP8P3M/J0HCPsOmwoYejWGOPfyoo9xIqniNy8Yt+WeN91JI+9A0ogfo4gfwYgf8Y5B/Mgx+uwUy5qksDbGoQ/usIeg/8c2aSz8pEd1qctrpxvmWt5boyCzPmwj89uAP5D0aoyH+ixmmmH7/2fv3p7kONP8vv+ePuGMKRwWmF1wRLLJ2eFIJqVpgKvZXe/sSgPY2nDYipDBof8AE1Q4HL6xRIj2vamGbnwlGT2O8I0vNAPYEbYjtFqjxyFNxM6udoDWiJQ0Q3HQJDWgdhtDNIo4NdCN7scXldXIzs73rVPWISu/n4gKoOrNysqq7s6nnnwPTy8/n05ep0NnlQqEmdc83cF+uvpd7lBHr+Huy2a2pEaw2Q7GyZjrO93uFxgRxI8+IH50hPhB/BgKkp2SSBUqm00mDmb1NDEymTDYvCJReBGr1PHH9l1vbps92QaughRxXDU1rgZ+u48n+J71++fTgVn1OAG337/LPb7Ge5KuZCYhf0dJ0b5BHDtQNOIH8UPEj0G8BvFjRJHsjLiclV+uebioWd7VguiJLfnj+64af2CXm1emkhVGuj3GXg1y6c4rki6OaqDq8ufT8c+iDz/DdrT8XTazu8qv6bDo7ueKeI00b6ySIzWuEDfbX8r5bDr9OwQGjvjRd8QPET+aiB+jizk7o+870o5u5cKKeSUnwhtqnAQv9tAF/51WG7R5/M223G7uopnZZUnzqbG0zccL+4x7UeDPpx0tf4aJZfXYzd7h7/JpNZZLzd6i45p7/HtZaO4/uSL644L2Cwwa8aNPiB87ED+eIX6MIJKdclmS9GaoMfnD6kRzsmDeFYbtP8bAhLtuLKmxVn/IOUnLg7hClEwovZYNVInQuO9BG/TPpx1Lks6EGpNaFu3up+Xvsrsvh25FvUaOy2oMM5hTY9Wg7GTaov8OgUEgfhSE+NE14gfxYyhIdkZLs4s5nfWn//+WpLnIH0Onq3icUc5VsNS40mPJv+lhAa2OMeYtSWcjV77Oqr+r2UjaPrkvR1aEOZZ9YEhX67r5+bSj159hLTDeWIp/Gcnup8jf5cJeI7n6thw5hkEcO9Ap4gfxI4340Rvixxgh2Rms5i937okhuUpUV6BLOPkjelvStezJMzl5fC/nabGT0Pclnck5EZ9VYzWRXcfb6hjT22f3mzr+H2SfkCyjmTf2+WjevhK7gkoryYl+XtK5ZPnO7O1aznOuSLrbwVWnrG4DXcc/n3Zer8efYV2NLxQXU0FTybYX1Liq1fJYuvxdjtn1Oj2+xmU1rtDu+kLTxX4ZsoAiED+IH50gfrSP+DHufASK/Yz7TY2T4w01Ckm5GoW+rilT0TnZdjZpm5f0jvILT82p8cc0r8Yf1DvaWfSruY+7yevdTO7n7eudpO2dZF8XUvu4ocYEzGzl6dxjVOOPMvu6ecXc5lLPnU/eS15BtOy+LmeOzWOvE/hZ3Ew9L3TLFmF7p5PXyDwv7+e+q5hbi320/Pl08jMv6GfYrLbdfH72dzD48+vkd7mNz6fl63TzGs39ttim1d9hW58BN26xm4gfecdP/Gh/H8SP8OdD/KjIzZIPFUALZnbBwyuoAACQi/gBDA/D2AAAAACMJZIdoA3J+OKBLGkKABgfxA9guEh2gPa86SNaOA4AMNKIH8AQMWcHaCFZRafdtfkBAJBE/ABGAclOB2Zsj+/VgWEfBoCSu6+7n7v7r/Syj//0rx3wO6ubPR3Hjfef/KG7/42edoK2ED8AFIH40bmpYR9AmezVAf1V+/awDwNAyS361U973ced1U396R/+hZ72MfmrHx3v9TjQHuIHgCIQPzpHsgMAJeSStrQ17MMAAJRM1eIHyQ4AlJJr06sTrAAARalW/CDZAYASalyZY84lAKAzg4ofZjYv6Zq7L2Yen5V0XlJdktIFd83sHUnLkmYlLaZXMoy1xZDsAEBJVWkYAgCgOP2MH8kqhHNqJDTXMm2zkubd/Y3k/g0zu+7uS2Z2RdJ7zSTGzK5JOpf8P9jWCnV2AAAAABTC3Rfd/ZIavTBZ85Iup+5/O9VDM5fprVlOEqdWbVH07ABACblcm5QOAAB0aFjxw8xqks43e3Ukyd3rSdtZJcPaUuqSzpmZQm2SFtUCyQ4AlBRzdgAA3RhS/JiVVE8Sm1pyfymZ01PL2f6OpNdbtLVUumQnMtmp7UlL3U5wAoBR4ZI2SXYAAB0aYvyYTf5dbX6PT+bsvCHpaOR5sbaWSpPstJjs1PakpV4mOAEAAADQcTO7nrq/kF5VLaAuqZadeyPpbUk/jjxvtctjlFSiZCfJABfNLC8xyZ20lO396WJbABhZDGMDAHSjgPjxubuf6fA5y9o996Y50uqadg9XO5Z6TqitpdKvxhab0NTLtgAwylzSpntPNwBA9Qwrfrj7snYnLTVJy0mnQ3a4Wk3Ppq7ktrXzuqXp2YnoZNJSTxOcivTIH2i/HRz0y3akjMf4U7+he7orSZrTtzRtM8M6NEnSR/6+VnVbT7Wh39C3ezqevPc2au+3V0V+XtLo/T70Ipmv2JzUefq116apsgMA6MoQ48elzIiqM5K+nfx/0czSI7BmU9vF2qLGIdnpZNJSTxOcivKJf6hTenHYh9HSmh5qzR/qmJ0c9qHkyvscN7Suv9resusD8VV7TZL0vv9xz/vKe2+j9n5bed//WCf1FR1STdOa2ZV8FPl5ScV/Pq2Ov5/c/aK0Pe8QFVXGi1AAqsXM5iS9qcYFOiVJyiWpEcvMbD4pLvqSpLeay09LekvSu0nb65IupnYba4sah2Snk0lLHU9wMrMLki5I0l7t7/Tpu9zy5cYXpRJcYT5mJ/WRv699fmDkAlcnn+OGr+szfax9OiCpkcS9YF8r5Dj6ue9efOTva1p7tKEnkp4lEf1wy5e1TwfaSopv6zPd1mfb96d8Wi/rVT1ns5Fn9dcoHX/Se3NHjbHI2wlOHpezGtuIKzp+cKEMQBH6HT+S3pclBRKSUGxLkp5m29V221oZh2Snk0lLHU9wSlaWWJCkw3a0p9+MDV/Xqlb0mv1mL7sZqBf0in6qG3pNo3PMnX6OH+hP9IrmthO2R/5AS/5Dzdm3ej6Wfu67W3/kf6BX9U0dtiOSpBW/1bdj2vB1/UxL+oZ+p63tX9arOqUXtaaHmtL00JPoUTp+M7sp6Y3USpHnzeyau+fPKXRpk1xnpBUZP7hQ1ps7vqKn2pDU6PG9r7t6Wa+W4vMEClex+FH6BQo6mbTU6wSnXn2in+kFvTKIlyrMtM1onw7ont8d9qFs6+RzbF61Twfc/XZQU5rWit/q6Tj6ue9u/dRv6JBq24mOJJ2057Sh9b78DFd0S1Oa7ug50zajw3ZkJL4Ejcrxm9llNQqrba8U6e5XJR1NhgPs4mqMue7lhnJoXuApU0/JC3pFP9cHwz4MSY3z4rRmdNKe00l7Ts/ZrE7oOf2pfjDsQwOGomrxo/TJTmIx84Vge9KSmc1l2oLb9tuqbu/4EloWJ/UVfaKfDfswtnXyOd7WLR3S7m0P66g+a2/FwqHsu1sruqWj2v2F6LCOFP4zvOMrOqoThe5zkEbs+L+j/Isu1yW9m/8U02aPt3YkY6uDE5/M7KyZnc889k7SM/VONlmLtSEfF8p6s6rb2wuVNB2zk1rTQ93xlSEdFTBMg4kfo6I0w9hik50Un7T0phq9N2+3sW3f3PO7OpzzxbgMDtsR3ffsit3D0ennuKrbel6759Ds04Fdwa9T/dx3NzZ8XU+1oWntHpZxSEf0qT4s9PWeaqPRu1HSrvBROX4zq6lxjsqbU3hDAzpHZcUKOWfMS7qcel6wcDNFnbuzqtt9nXfXL80LZcMeBv3b9vu7HnvkD4ZwJACGoTTJTmyyU4sJTRfb3bafVvSL3CvuWX/kf6Bv6Hd2DJHZ8HX9kf5Av63fL2R88Sf+oT7Tstb0MLf97M6LtJKkQ6o1Eo0h90y1+zmm5X35n9L09vjtXvRz30Xb0Hph+7rly11Pyk8P8bunVb2gVwY+br5kx5+7iqRL2upjotaikLOk7YQo240ZK9xMUecOcaGsPz5TYw5UmYYGAkXpd/wYNaVJdspuVbd1Ul+JbnPHV7Smh7u+QK/oVuOKfQFfqJb8h5IaE62nNK2f6wOt6aHm1Ji8Hpq/sE8HGsPHhhx02/kcmza89Zf7DV/v6nPt5767NW0zmvLp3KRmTQ8KS8B6+fK1qhW9qm9ufy5H/YT+VD/Qb2v3ldd+GbXjd/e6mdWVn9S8pPz6YJI0CkMJdvRIxQo3m5lCbZJIdgK4UFa8O76i+6qXatl+oGgjED8GhmRnQJ5qo+VE6FWtaJ8O7ApKq1rRofD3nbY1E530qlyH/Yj+uf7vxv8jwSi9jHEr7/sfB4NhyCnNtnWlvZ3PsamdnowNref2zgxz3714Qa9oVSt6Tjs/y+bPo4gE7J7udt0rkl0RbtpmdMhr+sjfH9gwnRE9/vfU+NK/kHl8VmoMdUvVIZCUVMAeYrAys/PufjXT8xMr3DwyRZ3LhAtlxftUHw51xUxg2IYdPwaNZGdA1vSw5epNq7qdO2F6Vbd7rq1wx1e0qtv6Xf0XOx5v9gbcU/xq9z4d0JraG+Pcz6W12/kcs2KJSa/JSD/33Y0X7Gta8h/uuJK64rd0WEd1W5/1/KWnl+FfIft0QLf1mb6q/ic7o3r87n7JzK6lq0Mnk/5/LOl8NtEZtmSeUd4xxQo3j0RR57LhQllcuxfKmlb8ll7Wqx29BoByI9kZIfdV37XiTnPSeafzVLJ+rg90Qqdyv+w+1cZ2UcyQKU13HISGrZlsxIZvdfvlv5/77tWcfUsrfmt7FaSTek6f6Gctf8atPPIHPe8jzz4dHMjv1qgfv7ufS1YpO5M89H01VmILLu235T1fmTtuZtdT9xeS2jCtnE2Wxs6KFW7uuKgzuFBWtA2t66Q91/fXAUZdAfGjNEh2RkRz+ctswFrVbUnqeRJlXiKVft12hhh0Wo9k2OJzWBpFIWVdrL7uW+3te4iywfypb/R8hXdVt3Vfd3U7U0PoqTb0qT7UbW8sex36IvFH/gd6Wa8O7YtGGY4/m0AkPShLuduqkGEIn7v7mdab7TimWYUTsFjh5o6LOqM9XCgD0AmGsaEvpjQdnS+xqhVNaTo6DGHD17Wh9Y6HcTWX2Mz7svuZlnVUJ1r2QuSNBw/p51CEVp9j1mEdye19aXwJ6K3GSj/3XbRV3dYr6q2kSejn85l/rOf1tbYS8rwvRmt6UMhQm1ZKevxn9WzZ/B1cps3hlEqbU6PYaTNJOpPcl7svmFle4eYr7r4Yauvz8Y41LpR1ZhjDi4FRM8T4MRQkOwMyrZnohPVV3c79IrWiWzqpxpXkz/SxXrDddV1aCdUSeeQPtKrb+g19u+U+Glfw2kuy+jkUodXnmHVCz2lVu4vGrWpFp9Tb3I1+7rtbP/Ub2tD6jp/BHW8k0sNeYvWUZnPH9t/WZ7n1ikZNP4/fzC5LOurub6QeOyupPmrLMuf0Pp2TdC01/G0xPfdIOws3YReDswAAIABJREFUx9qQgwtlcZ3O2WEIG1A9JDsDcki1xtjrQMJwP2eu7yf+oaY1s51kZCd5LvkPdUqzbZ28T+lF3dZneiH5Urbh6/pAf6JX9c22AuCaHvQ8HKIIrT7HrOdsVp/5sh75g+33uT2XJfO5LW39c52yWZ209pa27mTfeTr5+XUinWg98gf6VB/qVX2zr6+f18O15D/c0WNyVCd2LRDwiX+ofTrQ1peVfn1eUvHH3+WxpgtzzqpRD+yN8Ob9HXPdopBzc5t3kvaama0miVCscPNQijqXGRfKAPQDc3ZQuMM6Grxi1fyC/Lx+XT/1G9uB4ZRe1FGd0Cf6mT7xD3f1FqzpoVa1sh3QYr5up/WRv69P/ENJjcSp3USn+VqjcEUs9jmGzOlbjQn63niva3qQu+zomh5o1VfaTnY62XeeTn5+7XpZr+oT/Uyr/qzHKV0XpsjX/6nf0D01fnd/qhta8V/oZb26/Tu1poc7flaH7Yjk0kf+vqRnX4KG9Xn18/i7ONaLaiQB6WWc34itwtbvMdexQs6pbS5JupR5LFbkeShFncuMC2XFued39XN9EDwnAlXBnB30xQmd0s+0tKv+ifTsylxenY5pzeg15V/t+m37/R3V3Fvptg5IOwU0ByX2OYZM20xbywL/9sR/phX/RUfH0+6+c1+vw59f0cfT6+t/3U633H/WYTvSdb2Noj+vfh5/p8eaSQLaZNr06oy5rioulBVnQ+vbFziAaqtW/CDZGZD9drAxbjpn7HVRtRD6ZUW3dKLA3odexD5HoEpc0laFJphWFRfKinPMTur39DeHfRjA0FUtflTnnY6AF/SKVrQ7wDRqJHTezd+vuiFZq1opvBBjL0KfY68an2dnE3h7f73+//xG9fU7VabjLdOxYrTtt4Pa0Hpu4sCFMgBojWRngE7a7tW7msMQulmqeFW3o5Wri3DHV0ZmvHVT3udok5OyyUnJbPv/zx6bCN9SVrWy8/P0rdxbdv87Xism89qr9ksdnjjWXa2fAgzi96dIZTreQR3rpqynG8qBC2UFiMWBNuJDR/vrx/OAglUpfvDXNWBf1+ntCc5SYwzxIdU6XhJUCtcNKdJIBauU7OdYhOfspUL3N2qvt/v1R+/nGlOm4x3Esbo3xlz3ckM5cKEMQJGqFj+YszNg0zajUz6rO76iY3aycRvRgHDLl7seq91v2c9x+3HN6E+e/r+SpNOTvzf0eT0f+fta1e3cpY07Na0Z/YukJMmcvqVpm8l9rMyK/Lyk/M+srMxsXskyz83Htkp2dQ3da17gaZ6Ty3ChbFTjB4BqxQ+SnSHYbwfbrhMzTKN+JT3vc/yLk68P6Wjy7Qj2PQ5TyFs9rNWKYmVT9Jejcfp83H3Ham2//uq+YR0KhoALZQDQHZIdACihRp2Ecg0lQG+4UAagCFWLHyQ7AFBK1aqTAAAoSrXiB8kOAJRQ1eokAACKUbX4QbKDsfbR5r/SHV/Rfd3VIR3RYT1bgWhD63qqDZ2yWZ20r7Tc1yf+oVa1oqnNxsIAknTSvqJjE1/Whq9rxX+hU3pBkrToVzWlaR3WEU1pWtOa0arf1poe6oSe07RmtKF1remh7uuuTunF7fklzUn691XXIdV2HLMk7dNBvWBfK+gTQpltenUmmAIAilOl+EGyg7H21cm/rF/bvK8f+T/Ry/bajpXbpEaF75/6dX3my5qb+N3cfdzzu/pAf6ITOqU5+9aOejp3tv5cK1u/0D1f3V7p65E/0CHVdq3+9Yn+nX7u7+u1id/asf8V/4VW/N8/O2Z7TY/8gX6kf6qX9equY77jK/pn/n/pN/TtrlZi6qdH/kBrerjrmAEAAIaBZAcjJVaY0zc3u2qbmdgjbUpmkk3svJIxvTWj1+y39P7Wj/T+1o92JiI2oXt+V3+qH+gb9rvbX+Btanp7k+P6iu5t3dH76z/SVye/oYmpvdraeqgX/VXtmTy880Aee3KwWzsePqlTWtWf7zwuhZdIPmYnddKf05/qB/o9/c3gdsNwX3VNabr1huiZyyo1wRQYtJaFooNPjJwDM+f/nU1d/j1H9gnkqVr8qM47BSK+bmd0W7e2C/U1feA/0inNRnsqDk8c03OTL2/f3/B1HbajHb3+vg5XWDqkI3qqjV3HO2wr+sWwD6FStnyipxsAoJqqFD/KdbRAn0zbjE7oOf3Ur28/dstvak0P9by90vL5Jyee3/7/hta1f+JQR6+/Twc62v6+GknOqPSibPi6PvEPdVufDftQKqO5dGgvNwBA9VQtfjCMDUgctqO67be279/2W9qnA23Nizk2+ava2FyXJH158vkWW+920p5re9tbvqzP9LFO6FRbx/aRv6/P9HFjMYZkIYRbvqxP9WGyYMIpvWa/ub3tp/p32qcDelXf1GFrLI6w4rd0T6vap4Na0wOd1Fd0L0m41vRA09qjNT2QJH2qD7c/x1Oa3d4HAACoDjObl3TN3RcD7Wcl1dz9auqxdyQtS5qVtOjuS+20xZDsAIlm78ojf6D9dlD3tKrDan84WjdJTiurWtFT35Ak3dPqdtLyvH697QrlX7XXdNiP6qe6sb3i23M2q8N+REv64Xai09x2zR/ueGzFb+kT/Ux/1c5uP/ZH/gf6DX1b0zaje353O6FZ9dt6Xl9jgYIBcFmlVtMBABSj3/EjSWLmJJ2XdC2y6byky6nnXZH0XjOJMbNrks61amuFZAdITCWLAtzXXe3XQT3VxtCHiR3Vye3E4aSe0wv+SmOYXIersJ205/SB/8l2Iic1krvmvJ9078tR7UxUPtNyzvLXB/SZPtYL+ho9N0NUpToJAIDi9DN+JD05i2YWTEaShGg58/Bcprdm2czOJvuLtUWNVaQ0s8tmNtvmtheSW83MZpOuNlTYUzWGoTWTnilN66k2hnlIu0zbjFZ1W5/4hx0/94RO6bPUeWVVt/W8fn3HY3d8RUd1Ysfz8uYTbSSfFYbHXdr0iZ5uAIDqGZH4UZO02ryTJD/1zDZ1Sedibe280LhFu+9IumlmnrqFlquqqdF1dleNLrbLge1QEc0v8M0v94d1dHseSjsebd3vy3FlHdUJ/Vwf6JG3f2ySdFJf2bGAwIbWdUqzWtGzeUprerir1+h5fU2ruq0Nb3w+j/zB9vwfAACATpjZ+fQ8nUQtZ9M7aszPibW1NG7D2Ba0M2kJfUCSVHd3M7Oau2ezRQxJrF5OTLw+z9PGvy75lu9om5h+9ifwYOML7fODOjjT+JX5smb1b5/8sZ7s22y5utqGP9H9p1/owHR4SJc9nZKeShN79uYc4873be7SU8kmp6TMFZRp7ZVcWrVfan8HS1anh7JNa2Z78YVpn9k1lG3n683oVX2zkRS59FQb2/N1Wtnw9ba2QzdMW2LODtCrbP217cdnwueuaMzZitS92Qy3WawGTzQ2dlefDlVWSPw4bmbXU/cX3H2h5Sub1bS7l0ZSdJJ0Z/U8MsYm2Uk+vMvuvpx67GyrD55EB1LjS/mfb32qvzz9O9uPfWXma/p4/V/r4/UP9Jf2/lbk2dKdp3+mY1O/2u/DlNQYyiaX1vyBOj1XNYey7dNBPZeM+Dyl2cZQNp/dNYRNklZ0q7GggTqfm7OiW3quvQsv6JBLDEUDAHSsoPjxubuf6eJ5Z3N6daTUkLYO21oam0jp7vVMonOhVaKTzNk5b2bzZjbX/6PEqPro6U902I7p2MSXdzz+V/b9nm5t/Dvdefofgs/d8Cfa8Ceatj39Pswd1vQwdQztzaHJDmWTGgnQim7pnu7mLnzQafHS9DynUZvzNG6qVCcBAFCcYcSPZF59dlGCprp2j8Y6lmwfa2tpbHp20pJentDwtabFVHJ01cxumtnpbE+PmV2QdEGS9mp/8QeLvms1mf7fbvypvvA7OjP97V1thyeP6Tf3/+f6ydo/08np5/W1PTsvYjzauq+VjU/04p5XB3rMpzSrVa1s32+ujNZKcyjbtJ4Nz2gOZQslJodUS1ZkO91y/0d1Qve0qpNq1A3Krma35D9kaeqCuExbLD090ogfAEbREOPHnKSjZtb8MnUmuS93XzCz7HC1mqQr7r4YamvnRccy2ZH0rlosOJDuBUrU1VjgYCGz3ULzscN21IVS+WjzX+mON5KCn/v7uu3PhmJtaF1P15/qy5N/QX9x8jeC+zg8eUzfOvhf6uMnH+j6oz/UlM1o38QhzWiP9k0capnofPjkuu5v3tEXm3ckSX/85J9ovx3Uc5Nf1bHJ3UPfPtr8ie5s/bkk6aebP9ZRndDXJ3YmWV+fOKP3t36kj/x97dPB3OFnIc/r13cVMX1eXwvu45id1M/9Ay2mep2nNK3DOqKX9eqOuT5ft9P6qd/QreTPqzlUrmlND3f0SAHjjPgBAM9kh68lS1NfS43EWjSz9BLTs6mlpWNtUeOa7Fxw94uhxqQb7Ya7pychLEt6qe9HhoH66uRf1stb4e8Y6QUKWnlxz6t6UZ334DR7g3zjaVvbf3Xyr+irqfmmoQmmr038lhSZ0Brcf04x0mxS0rTh6/q5PtDXdXo7qdnwdW1oXfdV15J+qDn/1q6EJ+S37fc7Pl6EMRQNANCNfsaPZGrIm5LONu+7+6XMNu8k7TUzW00SobckvZt8T39dUvq7fKwtauySneRDaDWETdr9IdUk3Sz+iIDy+kwf65CO7Ehmpm1G05rRfh3Umj/Uqm53tXgBeuOStligAADQoX7Hj6T3ZUmRhCRJfi5lHqunnnO13bZWxjFSzipnSTszm2suQpAMYaul2mpqdIe1XDIPqJKjOqHbqTo8Wata0QmdGuAR4RnTZo83AEAVVSt+jF3PTuJ6zmNvqpHgvJ3cX0i60OqSTqvNKqwogIVz7FjtgmjbzHSwTRORnD5SD8H2BlZXmwr/2WzdiayOGDv+ie7+FD1WQqGLIW5Zh+2IXvE5feIf7lps4Kk29IrmcldwA4CBi8WWQC0dSbKp/PhhkXO9IkOgJyLHEat74+vhhWmsy33KAsO4C4gPQFmMXbKTTFbaNWEpO4cn6Q67lN0OwE777WBbK71hsBjGBgDoRtXix9glOwBQFWUbSgAAGA1Vih8kOwBQQu5WqStzAIBiVC1+kOwAAILMbF6NOgiLqcdqSoplqrEE6Hup2gfNJUWX1VgwZrHdNgAAikayAwAltdnHK3NmdlaNatfnJV3LNM+7+9vJdrOSbpjZi+5eN7MrSiU/ZnZNyQIwsTYAwOD0M36Mmuq8UwAYIy5pS9bTLbp/98WkDsJy+vEkubmZ2m452abZ0zOX6a1ZThKnVm0AgAHod/wYNfTsAEAp2bCuzNUkzWv3apbHksQlW+esLumcmSnUppwVNAEA/TK0+DEUJDsAUEKNpUMHf3XN3ZfM7HTm4Tk1KlvXcp5yR415PbE2AMCADCt+DAvJDvojUgAtVswsVlwzWORT0tbDR8G2yS8dDh9LrBjpnlBR0XBx0Il9e8P7++J+sClWTE5bgaJwLfS74CiqK7PgwAU1FhpYTP4fcrT/R4YqixYOjRV1Dp23Y/s7kpe7Nxsjz4sVDo2d69ceB5v8yZNg29bj/DbiA6qEZAcASmqz92mXx83seur+grsvtPvkZFW2N9y9ucjAamTzWBsAYIAKiB+lQbIDACXksiKGIXzu7md6eP68pDdS9+vaPVztmBoLGMTaAAADUlD8KA2SHQAoqa0hXplL6uXMu3s9uT+XDGXLDlerSboSaxvA4QIAUoYZPwatOu8UAFAIMzsvaUnSqpnVzGxOUrOHaDG53zSbKkgaawMAoHD07ABACblLm30chpAkJW9KOtu87+6Xkjo7eb0xzXk7b0l6N9nudTVWaVMbbQCAAeh3/Bg1JDsAUFL9HHOdrLi2pExCkhQRDb5wMqyt+Zyr7bYBAAaHOTsAgJHWmGDKSGQAQGeqFj9IdtAfsVo6kXoHHqszsLERbIrW0tkzE26bDv8JbB07lPu4T4avhmwcDtcC2rMSrsFjj8J1EnQ3W3Q+JVJ7IfYziNZYAIASisUPm4p8sQvU04nV0tk8cjB8HJHXMg8f48RaOMZp40B4n5+HV3WfCNT82YrV7aEGD8YMyQ4AlNRmeDQZAABBVYofJDsAUEKuao25BgAUo2rxg2QHAEqpWmOuAQBFqVb8INkBgJLaqtAwBABAcaoUP6qT1gEAAACoFHp2AKCEqlYUDgBQjKrFD5IdACipKo25BgAUp0rxg2QH4yFQJ0GStDdc++bpyS8F29Zr+fV5Ng6E6wRNbIZrKPhEuC7DnjvhP8WJqfDr2ed3g21bsZpFAb5JAZ6yaBSFq86VOUCSZJEaNrE4MBl53p5AjIjVYdsbPi8//pVwzFk/GD6OqbXwOXv6QfjcvC9W12flTu7jE5F6P1tra8E234p8QaYGT2lULX5UJ60DAAAAUClj1bNjZheS/35f0lFJb7v7xcj270haljQradHdl/p/lABQjCqtpgMAKM4g4oeZzUu65u6Lqcdqkprf11+X9F76+3fsu3m339vHKtmRVJM0L+myGh/GudCGZnZFqQ/YzK7FtgeAUVK1onAAgGL0O36Y2VlJc5LOS7qWaZ5397eT7WYl3TCzF929Hvtu3sv39nEbxlZ3d5N0xN1fcvflyLZzmYxwOfnhAEApbPlETzcAQDX1M364+6K7X1Kj42FbktzcTG23nGzT7OmJfTfv+nv7WEY7d6/H2pMPJ7tNXfTsAAAAAP3QHIGVdSz23bzX7+3jNoytOW9nVY1xgN8LjOer5Tx2J3kOAIw+r9ZqOgCAggwpfrj7kpmdzjw8J+mi4t/Ne/rePm7JzmJq6NpVM7tpZqdzenqOtrvDJHm6IEl7tb+gwwSA3rhYoGDUET8AjKJhxo/MggMX1PjuvphaZCxP29/b84zVMLacOTp1Sd/J2XS1g30uuPsZdz8zrfDa+QAwaFvJ1blub+gv4geAUVVA/DhuZtdTt1iyskuyKtsb7t4cihb7bt729/Y8Y9Oz01zRwd2PpB5elvRSzuZ17e4SO6bMRCoMnk1GCmhOT4efGGnb/JXDwbbHJ/YG2x6eyD+WjQPhw/Dw4Wv/L8PXFtZOhI//0KfhtuknG8E2ixR483uBAnUWKURKwbiRwmpsqKTYecgiMSJSRDP4lMnw39f6kfyi05L06Hik4Oix2CuGX2/f5+H4sbk3XBx7/578z2Tyz/KLjUqSbTwNtulpOOZQcLQ8Coofn7v7mR6ePy/pjdT92Hfznr63j1XPjhpj/tJqSq360JSs953tEqtp9/J4AAAAAAqS1MuZb04zMbO52HfzXr+3j02ykwxh2876ku6xWXdfSO7Pmdlc6imLmfuz6aJHADDqGMYGAOjGsOKHmZ2XtCRp1cxqyXfxZg9R7Lt519/bx2YYW2IhyRbrkk5r55J0b6qRDL2d3H9L0rvJ8LfXtbtXCABGlouEBQDQuX7HjyQpeVPS2eZ9d7+UfOe+kvOU5vf12Hfzrr+3j1Wyk3SHXQq0XczZtvnY1T4fGgAUjtXYAADd6Gf8SFZcW1ImIUlGYQVfOPbdvJfv7WMzjA0AAAAA0saqZwcAKsNZjQ0A0IWKxQ+SHQAoIZaeBgB0o2rxg2QHA+ebgRovLZiFa+Koy30+roVHcj4+nv/4w9lIbZunkf2dCLdNPQifdDzyvmtPwjWEJtfXg232+El+w5NIbZ7uPmL0UZWCFdBSt7VcQvEjUvdtcyZ8Pn8SqfX+8OVI/JgK1wJ6XA9/XXv8WaTAm/bnPnpwPVxLZyJSk2jr83B9Ht8MxxyMnirFD5IdACghVmMDAHSjavGDBQoAAAAAjCV6dgCgpLxCV+YAAMWpUvwg2QGAkqLODgCgG1WKHyQ7AFBCXrGlQwEAxaha/CDZAQAEmdm8pGvuvph5/B1Jy5JmJS0mFbN7agMAoGgkOwBQUv0cc21mZyXNSTov6Vqm7Yqk95qJipldk3SulzYAwOAwZwcYEovUNVBk7X/t3RPeZ6SewNST8D5DtRK+dOJB8Dl7psKv9eBx+BjXVg4E22wr/JlMrYWfd3gtUs/h8eP8hs1InZ2tyOffbX0L9KC/S4cmPTmLZpaXjMxlemSWzexs8pxu24DhiJz3Qp7uDf/trYfLn+nw8YfBtl/70hfBtienwl/XPt5/Mtg29ST/eVOPwrFj3xfhGGcHI7Hqi3Axtm7r66FfqrX0NMkOAJTUMK7MJT0+9czDdUnnzEzdtEki2QGAAapSzw51dgAAnajlPHZHjTk43bYBANAX9OwAQAm5hraaTmCAZ09tAIABGWL8GAqSHQAoI49PY2vTcTO7nrq/4O4LLZ6z2oc2AMCgFBM/SoNkBwBKqoCicJ+7+5kOn1PX7iFpx9RYTrrbNgDAAFFUFAAw0lzDmWDq7otmlh2SVpN0pdu2fh0rAGC3YcWPYWGBAgBApxbNbC51fza1fHS3bQAAFI6eHQAopf7WSUiSkjclnW3ed/dLSfNbkt41s1lJr0u6mHpqt20AgIGgzg4wNNHCY+vrwSaLFcOcCf+aW+TlJh/nnwhORQq/PX8gPAf7K3vvBtv+ycG/FGz7bOJ4sG3P3fB72/fL/cG2mS8CheHWI4VIIz8bf0pR0WHo5wTTpPjnknISEnevpx6/WkQb0KtY4WOLtHmoKHLknDcZKUjtE+EvkScO3Q+2/c0v/6tg269M3Qu2Xd3/erDtTyx/Zffp+9PB50zfCy+cOBWLETMzwbZY/Kbg6HCwQAEAYORVacw1AKA4VYofzNkBAAAAMJbo2QGAEnKv1pU5AEAxqhY/SHYAoKSqNMEUAFCcKsUPkh0AKKkqTTAFABSnSvFjrJIdM6tJupDcfV3Se8mKQnnbNrf7vqSjkt52d5ZBBQAAAMbEWCU7kubd/W1JSuo43DCzF5PlTrNqkuYlXZa0LOnc4A4TAHpXpTHXAIDiVCl+jE2ykyQ3N5v33X3ZzJbV6Om5lPOUurubmdUCyRCGIFonwSJ/mE+eBJsmVx+G9/lcuBaNAofy+cNAjRpJeyefBtv+4al/EWz7+t7Pgm3/297/ONh289/n11CQpPVapI7Cgfz3bQ/Xgs+xjfB7i9ZJCNWwQE9cVqlgBbQUOdfEzlEWqB1jD8Lnw+kH4f1NbIQXun24vifY9sqe/xBs+4vT4fo8j4//JNj2779Sy328/msng8/Zdyd8jAfvhOOfPQjH2ljNIursDN6g4oeZzUu65u6LmcffUaOjYVbSYnoUVrdtMWOT7OhZT002sTkWexKJDoCyqtCQawBAgfoZP8zsrKQ5SeclXcu0XVFqmomZXVMyuqrbtlbGps5O8uZPZx6eU+ZDTjOzC2Z23szmzWyurwcIAEVKlg7t5QYAqKA+xw93X3T3S2r0wmTNZXpklpPkqJe2qHHq2VGmq+uCGl1ci4HNF929+UO4amY3zex0tqcn2c8FSdqryJAnAABSiB8A8EySnGRHVNUlnUumKnTcJin0PX/b2PTspCWrsr3h7sHurVSi01SX9J2c7Rbc/Yy7n5lWeBwrAAyc93hDXxE/AIys4cSPvElkd9SYg9NtW0tj1bOTMi/pjVBjc6U2dz+SenhZ0kv9PjAAKApD0QAA3RhS/Djah7aWxi7ZSVZqmG8ORzOz7Bi/pmxNnZpSq7kBwKirUlE4AEBxCogfx83seur+grsvtHjOah/aWhqrZMfMzktakrSaDGWblXRG0lJzAQJ3X0qWpa6lnleTNNvGDwkAAACous/d/UyHz6lr95C0Y2qMruq2raWxSXaSoWlXcpqa83beVOODeju5v5D0AtXVWMWNoqIjzgO1ECTJp8NtmpkMNlmkrs/kev7jn68eCj7nv3v5/wu2/WAtfBxfnfllsG3/VPi9Pf3Gg2Dbg9WDwba9v8yfLD11P1In4dGjYJNNhLvDnRIKfeFiGBvQti5q8NjT8Mlrph4IEJL2roZrnH3+RbhOzS82wpUyfm9vOFb9zr5Pg23/5ss/z338H586HnzOvfvhr4Z7fxmOK1MPwzEiFr9lkenj1Gnri2HFD3dfNLPskLSapCvdtrXzumOT7CQLDgR/cu5+MXO/rvxiowAw+lwSyQ4AoFPDjR+LmSkms6mVk7ttixqbZAcAqoY5OwCAbvQzfiRTR96UdLZ5P6m7I0lvSXo3GZH1unbOoe+2LYpkBwAAAEAhkt6XJeUkJMnIqubjV4toa4VkBwDKip4dAEA3KhQ/SHYAoJSMBQoAAF2oVvwg2QGAsqrQlTkAQIEqFD9IdgCgjJylpwEAXahY/CDZwWiJrakf+cP0jfAa/hN37wfb9v3ZvmDbnl/Lr4ewdi9cQ+H//uU3gm3/7a/9INj2b5/8arDtm7Vwzayf18N1Gdb3heshbO7Jr/kzNRU5JVjkxBirkyAK7QAYrlAtHUnSxtP8xx8/CT5l8mG47Usf7w22Pfy1/BpnkvRP/8J/FGz73X3/T7Dti63weftXZ+q5j+89Eamb9q/DteR8MhIHJsJxwGbCcdOeRmrobVGDB70j2QGAsqrQMAQAQIEqFD9IdgCgtKozDAEAUKTqxA+SHQAoqwpdmQMAFKhC8SM20B4AAAAASoueHQAoqwpdmQMAFKhC8YNkBwDKyBVdoRAAgFwVix8kOwBQUl6hK3MAgOJUKX4wZwcAAADAWKJnB6XhW+HLEPY0UBROkj9+HGybuvMg2HboVn5huCe1/IKckvRvfvVksO1/9nPBtr914kaw7f5muEDdo8czwTYLP00bh/Lfw569kf1Nh4vC+Xq4KFy04ChF4XpToStzQN8EzkMeiSu2ei/YNnMsXDh03+3wifmPP3wp2Pbd/b8ZbDs4FY5xXzzNP5b1x+GvfxNfCjZp42A4DkxPhs/1sc8yihjRPxWKHyQ7AFBWfR5zbWY1SRck1SXVJC25+2Kq/R1Jy5JmJS26+1I7bQCAIWPODgBg1Fn/r8xdcPdL269nNm9m1929bmZXJL0HRlCKAAAgAElEQVTXTGLM7Jqkc8n/g20AgOEbQPwYGczZAYAy8gJurWUTlJtq9NRI0lymt2bZzM620QYAGKbBxI+RQbIDAAg5ambzqfvn3H0pSVzqmW3rks7F2vp4nAAA5GIYGwCUkg1izPVbkn6QJDDfk3QxebyWs+0dSa+3aAMADN1A4sfIoGcHAMqq92EIx83seup2YcfuG0PRFtRIYOb1bAjb0chRxdoAAKOAYWzPmNlhM/uemd0xs00z+8jM/vuc7b5hZn832fYf9udwAQDbeg9Wn7v7mdRtIb17M7ss6bK7v6RG0nPNzOYkrUaOKtY28oh5ACqhQslOdBibmX1J0sdqBK/v6tlQhP/RzP4rSX/d3e9Lkrv/S0n/Mrky+I8k/Tf9PHBUUGS9fd/cDD9vLVyDwB48DLbt+/MDuY8fmQnXUFiNFCj4yVf3BNv+7MHhYNv9x+HnrdX3BduO3A02aWKj2DOVTYS7wz3yo8HoSpKam+6+LEnu/raZ3ZT0tqQr2j1c7ZgaS03XI20jjZhXTaH4sfXwUfA5ExY+502vhOu3Hf4kXMvMJ8Nt//vTbwbbvvL858G2I3vWch/fXAt//ZsOh0U93R+5Rr4vXEPIIm2+EanBE1syjBo8aFOrOTt/X9KCu/+9bENSQ2HJzM66+6eppjtFHiAAIKC/V9dmtTtBWZA07+6LZpYdrlaTdCXW1qfjLBIxD0A1lKx3phethrG9lHfSl6Sk9sKbkr5rZs+nmrKr8AAAiuZqTDDt5Ra3qMY5Pu2spMvN9qT3p2k2VXA01jbKiHkAxl//48dIadWzEx12kExe/U/M7Ptm9j+5+0+KO7TudFK1mwrfAMqsn0XhksKh7yVLT99MHl5OnSffkvSumc2qMdTrYurpsbZRVrqYBwDdqFJR0VbJTltXrNz9O2b29y0yhnUQOqnaTYVvAKXX52CVnB9zLwK5e13Pkpir7baNuFLFPADoWoWSnVbD2L5nZv+1JJnZ32r+P0/S9f+6hpswdFK1mwrfAIC0ssU8AEAL0WQnWW3mhpn9XUn/q1oMRXD376oxznvgl7s6qdpNhW8AQFaZYh4AoD2thrFtL68p6R+0s8NkJZ4jvR5YFzqp2k2FbwClV6Ux14NSopgHAF2rUvxomezkMbMX3P2TULu7f9H1EXWvk6rdbW+b1FC4IEl7Fa6vguHyrchfbWQNf3+UX4NAkiZX8n+ND0RqymxNh39H7m2G6wysfhJuWz8RPv7pO5FaCY/Cn8nk4/z6BD49GXyOTYVfq0LnzNFSshVxyqrbmEf8KLlYbbcnT4JtE/fCdXYOfRyupWNb+bXdJMltOtj256tfDrbdOpD/HqYehwf2TIbfmvbe2Qi2ReOHhV/PJsPPi9bQQ2/6HD/MrKbG+a9Ze20pvSpnbJGwohcQazVnJ+/g/xdJN7NjmZNq0n+ll4PpUSdVu9ve1t0XmtXFpxUu7ggAGD+9xDziB4AKu+Dul5Lz4CVJ55IEqLlI2KK7X03a5ptPirV1q+NkR40lSP+2pB+kH0y6/l8ys7/T60F1qZOq3aWt8A0AkpI6CT3e0I5RjXkA0J3BxI/sPPibavTUSPFFwgpfQKybZEfu/l13/zjn8f9DjYJrwRVs+iXpGsur2n2tl20BYGSR7AzEKMY8AOhJ/+PH0aROW9M5d1+KLRLWrwXEukl2rprZPzKzv5bXOKT5Ok3Bqt1mNpdpK2uFbwCQ1Jhg2ssNbRnlmAcAXRlA/HhL0gUzu5HMwWmubhlaJGy2RVvXukl2ampkWD8ws00z+0Mz+ztm9tfN7LCZHZZ0upeD6sFbkt40s/NJNpleNvRNSW+3uS0AANJoxzwAGJbjZnY9dbuQbkyGoi2ocQ6d17OEJbZIWCeLjbWtm9XY3pX0RvL/1yWdlfQ/qPFmmrneGznP67sWFb0vtrstAJQCvTODMLIxDwC61nv8+Nzdz4QazeyypHl3v5j8/5qZnVZ8kbBOFhtrWzfJzrVkYqbUqEWwIElmNivpvBrDwf7Pgo4PABBCsjMIxDwA46eP8SOZJnLT3Zclyd3fNrObaoywuqLwImF9WUCs22Fsu7j7crLE3N82s/d6OSgAQFyv462Zs9M2Yh6AsTKA+DGr3QnKghRfJKxfC4h13LPj7v/AzP6+pH/s7j9JtyVjl8+oMZkIGJxY8bdITTJfCxcVleUX3JqaDv/Z7N8b+5MKF5OL1fZa/w/hfU6E641G26Ye5ReGsy/CxfA88hkrUhTOIkXhYj8btIGion1HzKuuWLHq6Hnti3vBttgV5oNb4XOsbR0Ktj0+Gt7r4yP55+bJcG1QzdyLHUfkW+7TSByOxQ8MR3/jx6Kk72rnNJGzki43280svcR0epGwWFtXul16+u+pUV/gr2ea3lTjDb7Uy0EBADAqiHkA0L5kXvx7ZjZvZheSxQvqqQQmtkhY4QuIdTNnR9J2fYHsY981s2VJ13s6KgBAawxFGxhiHoCx0uf4kSQ2S4G22IJihS8g1nWyE+LuP2i9FQCgV8y7GT5iHoAyqlL8KDzZAQAMSIWCFQCgQBWKH13N2QEAAACAUUfPDgCUEctHAwC6UbH4QbIDAGVVoWAFAChQheIHyQ7GX7QGT6TQy9rj3IdttR58yp6J8Lr1U4/2B9ueHpwOts3cj9SwiZQumNgIn8kmVx/mN2xGdhhp841wUZ9YrQr0iI8W6J9I7NiKnPNitWgmph6F226HD2V/pJbZzBfhGm4HVgLPixzj1Fo4Lk4+eBJss7VwWzQOUINnOCoUP5izAwAAAGAs0bMDACVVpTHXAIDiVCl+0LMDAAAAYCzRswMAZVWhK3MAgAJVKH6Q7ABAGVVs6VAAQEEqFj8YxgYAAABgLNGzAwBlVaErcwCAAlUofpDsoNKia/8/3ch9eGttLfgU+2W4XsDkw/DzJr50INg2PRP+M/XpcO2FiYfhmgcK1YjYitQ7iLVhOCoUrICREq3fFnnao3CdHUXqvk0+Ddf1mTwYjh++f0/+45HYYU8j5/qNyJt7Eok5kfcWi8MWqV0X+5zRhgrFD5IdACghU7XGXAMAilG1+MGcHQAAAABjiZ4dACirCl2ZAwAUqELxg2QHAMpoQEuHmtmspPOS6pLk7guptnckLUualbTo7kvttAEAhqhiS0+T7ABAWfU5WCWJzry7v5Hcv2Fm1919ycyuSHqvmcSY2TVJ55L/B9sAACOgQskOc3YAACHzki6n7n871UMzl+mtWTazs220AQAwMPTsAEBZ9fHKnJnVJJ1v9upIkrvXk7azSoa1pdQlnTMzhdokLfbtgAEA7atQzw7JDgCUVJ/HXM9KqieJTS25v+Tui8n9rDuSXm/RBgAYAczZKaHkKuSF5O7rSo0Xz9m2ud33JR2V9La7X+z/UWLkxArDbQVGeUYLp8WKsYWLwtn6erht397wPqenw20eOZMF3sPWF/e621/kc0Qf9R6sjpvZ9dT9hdQCBLPJv6tJgtOcs/OGGufNkFgbMP4i58OtWByIFNeciJ1/N/ILYEuSreXHD5sKFxWVhQt5aj38Wv7gYbgt8rwYjxQjRY9Idkpp3t3flrYn1d4wsxebwy4yano2Fn1ZTJwFUDauIoLV5+5+JtBWl1TLzr2R9LakH0f2udrzUQEA+qeY+FEaY7FAQZLc3Gzed/dlNYLyhcBT6u5uko64+0vJ9gCAZ5a1e+5NcynpunYPVzuWek6oDQCAgRqLZEfPemqyjsWeFOj1AYBSMO/tFpNcBMomLTVJy8mwtuxwtZqka7G2rt8oAKBQ/Ywfo2Yskp1kmMXpzMNzigRXM7tgZufNbN7M5lpsd93Mrm8oMlcDAAbNe7y1dimzZPQZSe8l/1/MnDtnm3N7WrRVBvEDwMjqf/wYGWMzZydTufuCGhW7Q8F1MTV07aqZ3TSz03k9Pclk3QVJOmxHS/bjBTDO+n11zd0vJheEZiW9JOmt1HnyLUnvJm2vS0ov8hJrqwziB4BRVbbemV6MTbLTlKzK9oa7BxcdyJmjU5f0HSVBCQDQEFqpMkl6mm1X220DAFRDcsHrvJL5n6nVPmVm7+jZPNDFTKdFsK0bI5vsJL0z2aFpWfM5icu8pDfyNk72OyvphrsfST28rMZVSwAojwpdmQMAFKjP8SP5vj3fLEydlC647u5LZnZFqRIxZnZNycrIsbZujWyyk87+2pVkgvOpKt9zgWwwe6WyptRqboCkYK2EYP0dSXoaqSUQq+kTqSUwEavdEzMZPk5/EqjrE3utbmvpUIOnP0o4bhqovGgciDwtUotNsVo0obo+05Gvf5F6P7HXisUxj8XGGIvEW2JL9wYTP5olXpq+nRoGnf1+vmxmZ5PpJ7G2rozFAgWSZGbnJS1JWjWzWjI59kzSNtecLJtdYSgZ9jbbTXIFAMNiBdwAANXT7/iRfLc+n05QUh0RZ7W7rEFd0rlYW2fvcKeR7dnpRNJVdiWnqfnhvKlGgvN2cn8h6QWqqzFUjqKiAAAAQO9mJdWT5KWW3F9Kkp9sSQNJuqPGYjaxtq6NRbKT9NYEE83sBNsku7zU7+MCgL5iGBsAoBu9x4/jZnY9dX8hNUpqNvl3tdm7k8zZeUO767Clxdq6NhbJDgBUUZWWDgUAFKeA+PG5u58JtNUl1bJzb9QYYfXjyD5Xez6qHGMzZwcAKqdCReEAAAXqb/xY1u65N82lpOvaPVztWOo5obaukewAQFmR7AAAutHH+JFdDCxRk7ScDGvLDlerSboWa2v/je1GsgMAAACgSJeSBQqazkh6L/n/YnOV5MRsauW2WFtXmLMDdCpWJyFWg0eRWgiRugZbkdo3FqmVEKt5EHy9LmsBRV8L/eHM2QGqYitUL0eSxc7Ngfo8NjkZfrFYbZuIbuOAx+r6UEunPwYQP9z9opnNJysmvyTprVSdnbckvZu0va6d9S9jbV0h2QGAsiLZAQB0YwDxI7sacurxup4lMVfbbesWyQ4AlBQ9OwCAblQpfjBnBwAAAMBYomcHAMqqQlfmAAAFqlD8INkBgJKq0jAEAEBxqhQ/SHYAoIyolQMA6EbF4gfJDgCUVYWCFQCgQBWKHyxQAAAAAGAs0bMDFClalDPyvNjg2VhRtWiB0wpdtqkgU7XGXANjr8sCmvFi1qHnRIqUTlhXxxF/PU5Wo6Rq8YNkBwDKqkLBCgBQoArFD5IdACgp8wpFKwBAYaoUP5izAwAAAGAs0bMDAGVUsaVDAQAFqVj8INkBgJKq0gRTAEBxqhQ/SHYAoKwqFKwAAAWqUPxgzg4AAACAsUTPDjAKovVywtckuq2V0FXNgy5rQKB/qjQMAUBAN+dmi8WVPpxYiB8jp0rxg2QHAMqqQsEKAFCgCsUPkh0AKCOv1pU5AEBBKhY/SHYAoKwqFKwAAAWqUPxggQIAAAAAY2lsenbM7ELy3+9LOirpbXe/GNn+HUnLkmYlLbr7Uv+PEgCKYRrsMAQzOyup5u5XU48Fz6OcYwFgNA06fgzb2CQ7kmqS5iVdViPAngttaGZXJL3XDL5mdi22PQCMJB9otGqeXyXFz6OcYwFgxA02fgzVOA1jq7u7STri7i+5+3Jk27nMVcbl5KolAJSGeW+3tl+ncX7MnlNj51HOsQAwwgYVP0bBOPXsSJLcvR5rTwJudpu6GlcdF3O2vyDpgiTt1f6CjhLoQJf1CXyz4ONAldUkrTbvxM6jZqZQm3LOseOM+IGRRd0bVMg49ezIzC6Y2XkzmzezucBmtZzH7qgxrnwXd19w9zPufmZaewo7VgDoiRdwa4OZnU/P00nEzqMdnWPHGfEDwEgaUPwYFePUs7OYGrp21cxumtnpnJ6eo4M+MADoB+vzxVkzq2l3L40UP49yjgWAEdfv+DFKxqZnJ2eOTl3Sd3I2Xc15DADKp/crc8fN7HrqdiHzCmfdPW/oWew8yjkWAEYdPTvDlwTd0y02m3f3ZTOblXTD3Y+k2pYlvZTznLp2D7M4pt2TbwFg3H3u7mfyGpLzaui8GDuPco4FAIyMkU123H2hw6dka+rUJN3M2e+imWWHWdQkXenw9QBgqPq8Is6cpKNm1kyGziT35e4LofMo51gAGH1VqtM2sslOJ5Lene0ricn/Z5sJU3OxgtSHtWhm6aVRZwNDNQBgNLn6WichuyiBmZ2TdC11ISp2HuUcCwCjqs/xI8dQ67SNRbKTWEgywboaw9/SH8ybalxZfDu5/5akd5NhGq9rd68QAIy8QV2ZS86tZyXVzGw1SYRi51HOsQAwwgYYP9qu05ZcFIu1dWVskp1k1bVLgbaLOds2H8suqQoA5TCgYOXul5Q5v8bOo5xjAWDEDa5jZ+h12sZmNTYAAAAAo2FU6rSNTc8OAFSJabATTAEA42EQ8WOU6rSR7ABAGbkPeoIpAGAcFBM/jpvZ9dT9hcxKymdzenWkIdRpI9kBgJKiZwcA0I0C4kdp6rSR7AAAAAAoykjVaSPZAYCyomcHANCNPsaPUavTRrIDACXFMDYAQDeqVKeNZAcAysglbZHtAAA6NMD4MQp12qizAwAAAGAs0bMDAGVFxw4AoBsVih8kOwBQUszZAQB0o0rxg2QHAMqKoqIAgG5UKH6Q7ABASVXpyhwAoDhVih8sUAAAAABgLNGzAwBl5KrUBFMAQEEqFj9IdgCghEySVWjMNQCgGFWLHyQ7AFBWW8M+AABAKVUofjBnBwAAAMBYomcHAEqqSsMQAADFqVL8INkBgDKq2ARTAEBBKhY/SHYAoJS8UkXhAABFqVb8YM4OAAAAgLFEzw4AlFSVKmADAIpTpfhBsgMAZVWhYQgAgAJVKH6Q7ABAGblkFaqTAAAoSMXix9jM2TGzy2Y22+a2F5JbzcxmzWy+38cHAIVz7+0GAKimCsWPsUl2JH1H0k0z89TtbmDbmqTLku5Kupb8HwAAAMAYGadhbAvambTMqpHU5Km7u5lZzd3r/T80AOiDcl1cAwCMigrFj7FIdsysJumyuy+nHjvr7gux55HoACizKlXABgAUp0rxYyySnSRp2U5czOxCq0THzC5IWpX0uqTvuftSZLsLkrRX+ws7ZgDoWYWCVRkRPwCMrArFj7FIdtKSXp7Q8LWmxVQv0FUzu2lmp/N6epKkaUGSDtvR6vxmAAB6QvwAgOEbpwUKmt6VdDW2QXq4W6KuxgIHAFAOLmmrxxsAoHoqFj9Gtmcn6f4/3WKz+ZzE5YK7X4zsd1bSDXc/knp4WdJL3R0pAAyeySs15hoAUIyqxY+RTXZazbnJkyQyrYawSVI2GapJutnp6wHAUPU5WCXDgi8kd1+X9F56fqOZvaPGxaJZNYYHt9UGABgykp3SmlVqoYImM5uTJHdfcvflJIA322qSZrtJrgBgzM27+9vSs15xM3vR3etmdkWp5MfMrkk6l/w/2AYAwCCN45yd6zmPvSnp7dT9BTN7JxkqNy+CMIAy6mMF7CS52e7xToYML+tZT89cprdm2czOttEGABi2PsaPUTNWPTvuvihpMefxi5n7dUmXBnVcAFC45gTT/qmpcTEoe648liQu2V70uqRzZqZQm3LOzwCAAet//BgpY5XsAECV9HOCqbsvmVl2kZg5NeY85s2NvKPGvJ5YGwBgBPR7gYJRmvNJsgMAZdV7sDpuZumhvwvp+YuZ4HNBjaCzmPw/5GivBwUA6LP+D0UbmTmf4zhnBwDQns/d/UzqlrtQS3KF7g13bwac1cg+Y20AgDE3anM+SXYAoJR6nFza2VW9eUlvpO7XtXu42jE1glmsDQAwdH2PH805n1mt5nwG27p5l00kOwBQRq6BJDvJ2On5ZGEXmdlcshhMdrhaTdK1WFtP7xcAUIw+x4+kZyZvzuc1hed1hmplNtu6RrIDAGW11eOtBTM7L2lJ0qqZ1ZKaZWeS5sVmDbPEbJLotGoDAAxb7/HjuJldT912zOUMzflUfF5nX+Z8skABAGCXZMz1lZym5nCCtyS9m2z3uhqrtKmNNgBA+X3u7mdabTQKcz5JdgCgpPq89PSyJIu01/UsibnabhsAYPj6vfR0ytDnfJLsAEBZlayKNQBgRAwgfoTmfJpZ3rzOK7G2Xo6DOTsAUEYuact7uwEAqmcA8WOU5nzSswMApdTx8tEAAKjf8WPU5nyS7AAAAAAoxKjN+STZAYCyomcHANCNCsUPkh0AKKsKBSsAQIEqFD9IdgCgjJoTTAEA6ETF4gersQEAAAAYS/TsAEApueRbwz4IAEDpVCt+kOwAQFlVaMw1AKBAFYofJDsAUEYVG3MNAChIxeIHc3YAAAAAjCV6dgCgrCo0DAEAUKAKxQ+SHQAoqwoFKwBAgSoUP0h2AKCUvFLBCgBQlGrFD5IdACgjl7RVnaVDAQAFqVj8YIECAAAAAGOplD07ZjYv6Zq7L2Yef0fSsqRZSYvuvhTZR9vbAsBIqtAwBABAgSoUP0qV7JjZWUlzks5LupZpuyLpvWbSYmbXJJ0L7KftbQFgZFUoWAEAClSh+FGqYWzuvujul9Tokcmay/TOLCfJUZ5OtgWAEeSNonC93AAAFVSt+FGqZCckSVTqmYfryumt6WRbAAAAAOVVqmFsEbWcx+5Ier3HbQFgNLnkXp3VdAAABalY/BiXZOdon7aVmV2QdEGS9mp/J08FgP4q2VCCqiF+ABhZFYof45LsrPZpW7n7gqQFSTpsR6vzmwFg9FVogmkZET8AjKwKxY+hJjvJVa/TLTabd/e8BQnS6to9PO2Y8hcy6GRbAAAAACU11GQnuepVxH4WzSw7PK0m6Uov2wLAyHKvVAVsAEBBKhY/xmI1tsSimc2l7s82i46a2VymLbgtAJSGe283AEA1VSh+lGrOTpKgvCnpbPN+UndHkt6S9K6ZzaqxstrF1FPfVKP35u02tgWAUvAKXZkDABSnSvGjVMlOUgh0STnJibvXU49fzbRdbHdbACiH8l1dAwCMgmrFj3EaxgYAAAAA20rVswMASLgqVScBAFCQisUPkh0AKKsKVcAGABSoQvGDZAcASsgl+QCuzJnZO2rUIZuVtJjMnQQAlFTV4gfJDgAgl5ldkfReM0CZ2TVJ54Z7VACAUTdK8YMFCgCgjNwbwxB6ubU2l7kSt2xmZ/v0jgAAg1Cx+EGyAwAl5Vve0y0mCUr1zMN10bMDAKVXpfjBMDYAKKv+TjCt5Tx2R41CzACAMqtQ/CDZ6cB93f180a9+2uHTjkv6vB/HM+Kq+r6l6r73qr5vqfP3/nyvL3hfd/9w0a8e73E3e83seur+grsvJP8/2uO+kdJF/ODvqXqq+r6l6r73bt438aNDJDsdcPdf6fQ5Znbd3c/043hGWVXft1Td917V9y0N5727+9/o80us9nn/ldJp/ODvqXrvvarvW6ruex/W+65a/GDODgAgT127hyIcU2MZUQAAQkYqfpDsAAB2cfdF7R6KUJN0bQiHAwAoiVGLHyQ7/bfQepOxVNX3LVX3vVf1fUvj+94XzWwudX82CWLov3H9nWpHVd97Vd+3VN33Ps7ve2Tih7n3v4IqAKB8zKwm6V1JP1ZjFZ3vDasCNgCgPEYpfpDsAECbzGxe0rXs1Skze0eNscizkhZJCAAAacSP4WE1NqAgVT1hmdmF5L/fV2OM7tvufnGIh1S4pEDanKTzyow5NrMrkt5r/rzN7JoovAmgA1WMH1WIHRLxYxQwZ2cAzGw++WXPPv6OmZ1P/p3Ley7KITlhLbr7VXe/JGl+2Mc0QDVJlyXdVeNEfnm4h1M8d19Mfq55K8nMZb6YLOf9vQOdInZUQ4Xjx9jHDon4MQpIdvrIzM4mV2vO57RV5uRmZheSW83MZpOu3HFT5RNW3d1N0hF3f8ndK7M0cfIzrmcerosrc+gBseMZ4sdYq2zskIgfg0Sy00dk89vG+uoNJ6wGd89+BlWQrSMgSXfUGIoCdIXYsQPxY8xVNHZIxI+BYc7OELQ4uY3jsq51dzczq43pSS10wnp90AcyLMnY61VVb8WubB0BoG8qGDsk4sdYq3DskIgfA0OyMxyVPLmNaaCSOGEtpoYfXDWzm2Z2eox/3mmrwz4AVEolY4dE/BhTVY4dEvFjYBjGNhyVO7klY67PJxNux21CbaVPWDnjrOuSvjOMYxmCunZ/AT2m/OFHQK8qFzsk4se4qnjskIgfA0PPznBU7eQ27ldvKnvCMrNZSTfc/Ujq4WVJLw3pkAbK3RfNLPsFtCbpyjCOB2OvarFDIn6MparHDon4MUgkOx1IxpaebrHZfBsrilTq5Ba5erMwhMMpHCcsZesi1CTdHMaBDMmimaUnjc9mi8ah2ogd3SN+jLWqx47/v707vGkrCaMAer8OkNIB6WBFOoAOkLaChQ6IUsEqJZAOVrsdhA42oYOlhnQw+8PjxTJkZYPxs+ed8wf7GaRBhnd9x+N5ifzYC2VnC621nZxcj/3ktk1wz2j2ZpYnrP4c//fiq98+3dX/yqHoS2d+TXK+vN93y0qS35J86n/rH/I0wJk52fFIfjxrdvkxl+xI5MchUHamc7QntxecjOYwezPnE9aXfk2QH1m8iBluy9T+f3qfZ57XvpxmefyvfY6LWTra7Ejkx0/MNT+Gz45EfhyCaq1NPYZhrbT5myy2Bf26bPN9FuNTkr8z+JaLVXWz9nt/b62NNjMHsBOy45H8AF5L2eHN9YC6yuPszSZr0wGYOfkBvJayAwAADMl1dgAAgCEpOwAAwJCUHQAAYEjKDgAAMCRlB/agqq6q6uvU4wDgeMgOeD0XFYU3VFW3WWyZepJk/crnAPCE7IDdUXbgDbXWrpPF7FySs4mHA8ARkB2wO5axAQAAQ1J2AACAIVnGBluqqpss1lJfJPm9377sD79fLj8AgCXZAdNQdmALVfW5tfax3/6W5M8kd6216762+qp/z8OkAwXgYMgOmI5lbLChqrpM8sfa4dMsQitJ7pJcCysAluW6FaEAAAETSURBVGQHTMs7O7C5H621+5X7Z0nSWrvrXx+SfJliYAAcLNkBE/LODmxoGUwrLrKYkQOAZ8kOmJayAy93nsSVrQHYhuyAPVJ24AWq6jSLK1vfrx3/ZZoRAXDoZAfsn7IDG6qqq6o66XeX24V+W3n88ulPATBnsgOmpezABqrqPMlt+gdLk7xfe/wkyenah1BXnfzkOACDkh0wvWqtTT0GOHg9kD4n+SfJu9baxz4bd5Hke5K01p7splNVt1lsMXqWRWjdJXlIcvs/4QbAAGQHTE/ZAQAAhmQZGwAAMCRlBwAAGJKyAwAADEnZAQAAhqTsAAAAQ1J2AACAISk7AADAkJQdAABgSMoOAAAwJGUHAAAY0r/xzxNKbqPeDwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x1296 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1296 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1296 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzsAAATuCAYAAADqez09AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdXWwk533v+d/f8AKjxFaanBjBGFiv1J04wDhwErLnYBNdaGx3C3sR3SzIGQSw7iJ2DHgPYMchM8ACw7kJQ1onBk4MJKRyJSQbjMjsjfYicZdOxrCELDBkOzEcIVHAUk4AZ5x1hmw7OhYvzu6zF13N6bfqrq5+qZf+foDGsPupl6eaNfXnv56nnseccwIAAACAvPlQ0hUAAAAAgFkg2QEAAACQSyQ7AAAAAHKJZAcAAABALpHsAAAAAMglkh0gBcxs18xOzMwFr/PgfSXpui0iM1sxs5U57++w41U3s42grND+GQB6ET/ShfiRPsbQ00B6mNmhpDVJq865RtL1mRUzq0uSc66adF0GMbNdSZuSmpKOg3+LkgqS9p1ze1Pc14akdUnrzrlmz+erwb7lnNua1j6TYmb1tP7OgawjfqQD8WN6zGxNUlWt729Zki9pZ9zzm5YdIF384N9m2ALBhTTVItSxHLzS7Cj4txK8fLUCyjQDVVHSrnoClSQ55w4kHaoVNAetm/rzoM3MKmZ2qtb3CGA2iB/pQfyYkJltSpJzruacqzrnViU9lHTSLovqw7OoIICZKiZdgQiG1tE5tzSvisTlnFufw25qkl7vDVQddfDMzAtZN/XnQRBQVyTVJXmSFr47BZCw1F83RPyIKrfxI+gG2HTOHXV+7pzbM7OSpF0z86K28NCyA2RPFu6OZ6GOabCiIXdhA/shn6f+O3bObQV35PYknSZdHwDpv24oG3VMgzzHj1rQOjVIu1XqTtSNkewAGRL0Xy0kXY9hslDHlBn1IGvfnSu+YwDjysJ1Iwt1TJm8xo+KmZ2aWV89nXPt7pqRB4Eg2QEyIBhRZU2tPriplIU6plBdrYt66F224MK+L/EdAxhfFq4bWahjCuU5frQHdRhWvhx1YzyzA2TDHbXuYviSiu3RaAL13ocegwvaDbW6DhUkXe0cjSV4sPEwKJNzrhSM3lII1tt3znkdy7fLJKkU7LOrL23UOprZSbCtonPOBh1s0F+3otYFrSDpqqT7nf1zgzs+bwbly865peCi377bU5J0OskDoR3H3ew47rA+0GML+h/fkVQ3sz21vnd/wHLtz2Z5HrS/w/Y6UivYPJzmQ7UA5o74QfyQMhQ/nHOrZlYIex4p2Gf079I5x4sXr5S81OqL6tS6kA8q32j9tx25jcMB6532fNa+UJ2qNVzpSvD5eef6kjYG7OOkdx9R6xhcpPbDlgnWrw/4/LC3Lp3bUjDqTU/56aD6R/w9hB332pR/5yvBd+6C12lwTJUh68ziPHBBebGnvD7o9xHjODdH1ZkXL17xX8QP4kde40fPNteC/UX+Lqe2c168eE3+mjRYBRdsJ6kwoOxU0uaA7XVd0IOLZzH4uRBcSHuDxEqwn5Vx6xgsszZomeDCOez4znv32XHh2xyw/L6kkxi/h7Dvv72vvu93wt97oR2kO4KWG3S8MzwPBv4+g/KB3++Yx0iyw4vXDF/ED+JHXuNHz/ZOxv298MwOkC+7kjw3uOn3SK2hKnsV1Zr4TJLknGu4AU3hndyT7gBxh68Ma5re15P5CQZ5XdKrIWWDmrRPFaOOYcfvnnS9iDwKTMT9NZ1zB641cpmpNYnagVpB7CToLjCOOOdB5++114FaQ32m/aFWAPERP7oRP/olGj+C+XWKak2kGhnJDpAvKxow+kog9MIddpEKLqJLLnwIyGmP1V9Ra9KwMKcKH4FlaICdouaQOkyFc85zztXUmgFbCh8+NEys82CIdt/utE/kByA+4sfsET9iCp7FuiPpc6MS6l4MUADkRHAhkFoPHg6avHG8B/r6t19UqxleGj22f5ztt+s/bNvN9rK9ATbkLtQsnGlKf/QPOo5OzrlG8OBp5NmiZ3QedA71ObUHbAGkA/GD+NHeZvBjquJH0Cr0qlqJTqSJRDuR7AAZN2DEkvqQO2mDRkUZepEPLn6vqnWRuhztxcx2h603oo6Tijzk5LjM7FCtPterQxY7m9LuagrpEtBhX9KmmRWH3c2awnkQxbz+IAAwB8SP6SJ+DDXJ7/BQ0lacREeiGxuQB7ekrq4EU3uuIghUJ2oFqa1xm4473Bq1QMT6t8uOhywzqZURdVhWeBP/uKLc4TuTwvuBd5jZeaAn3RZm+b0DmD/ix3QRP/pNFD/MbF/SrusZtnucZ4BIdoB8aUi6HVYY80HFZsgdnssLTTC2/jQ01Hq4MkxVkj/jLgdHzrnSoIIgeBf0pA/yxEK6CnQqa/z+5NM+D6rS0AdQAWQf8WNyxI9+seNHMCBB2PxEo479EskOkC3tLgCddzQ6f35Z0sqQi9GoJu9eZQ24G9PRr/dq8G9nt4BRdRzmZbVmhA5bvqIxR2GJ4f6Q4FtTK1j2Be8JRprZHxE8ttT6o6HTTM6Djt9r52ftYU3HPXcApAvxg/ghZSR+BN+j7/onoG27GvJ5H5IdIF3aF5eBF77g7kZTIc36wZ2TmlozKndtI7gDdH/AasMusq9LKg+4EFfUGoKyr76j6ti5fO92O+r/Zu8KQV/oQX12lwdtKxD5YthThxu9ASv4/ioacOcwqNt5MAP3uKpqDcvZd5cqaL7vuzM6o/NAGvyHwptq3a0M678d1di/CwBjIX4QPzq3m9n4ESROu5KqZrY/4DVW65gFE/QASFDwsGZFT4akbKp1R2xQP9WiWg8dNiQ9lnTQ2ywfXChqwXZO1QoORx0Ph7a3UQ7K/OC1PmBbm2pdUOvB9uScOwi2cRist99Zz7A6BhfCw5797jvnuh54DOp/O1j3arDsfmegCtmW55yrddSt/X0O3M8wQbCqqhUMC5IazrmtkGU31fq+x93HRjsIBPtrdx1oB4zDsCAxjfOgsx5B3S34ub3/klpdCIbNXTH0+NS6k1rUkz9smnpyvr08x1GQgFwifhA/8hY/zCzKENe1qEkUyQ4ATEln8MmSzmCVdF0AYBERP2aHbmwAAAAAcolkBwCmIGjyZ2hmAMBYiB+zRbIDANNxm6GZAQAxED9miGQHACYUjKKzn3Q9xmVmxWBUm93g/amNMbM5AGAyxI/ZY4ACAAAAALn04aQrkCU//dM/7Z555pmkqwEAuXNycvJvzrmPJV2PWSF+AMBsjIofJDtjeOaZZ3R8zPNjADBtZvZfk67DLBE/AGA2RsUPntkBAAAAkEskOwAAAAByiWQHAAAAQC6R7AAAAADIJZIdAAAAALlEsgMAAAAgl0h2AAAAAOQSyQ4AAACAXCLZAQAAAJBLJDsAAAAAcolkBwAAAEAukewAknzfn/s+m83m3PcpdR9ro9FQrVbTwcHBTPd5cHCgWq2mRqMx1nq99ZtXfcc1reNLi97jGXR8SZ2/QNoQP4gfkyB+zJ455+a6wywrl8vu+Pg46Wqk33e2k9nvp+Pt1/M8LS8va2VlZbr1GWFvb08bGxsqFApz22fvsXqeJ0mqVCpd9SoWi/J9X5VKZezvJWz9RqOhs7Ozrn1FqW9n/QbVd2trS6VSSaenp6rVaioWi2PVt3d/zWZTa2trA8vbQeXWrVs6OzvT/v6+dnd3JU3n+KZVl3GEfX+9x9P7ftrnr5mdOOfKU9lYChE/ovla/d1E9vul6idjrUf8IH507o/4kc74QcsOFlqz2VSj0Zh7oJKkzc1N7ezszG1/UY51fX1dlUpFa2tr2tzc1NbW1lj7mHT9cVWrVd2+fVsbGxva3d3V+vr6RNvb2trS2dlZaHmz2VStVtPS0pKq1apqtdpE+0u6LpN8f/M+f4G0WeT4sf1gW6/97Wt67W9f0/aDbW0/2Nanbn5Kp4VT4kcI4scT8z5/SXaw0HZ2drSxsZHY/kul0thN13FFOdbewF0sFi/vHkUx6frj8H1fx8fHXftbXl6OvT/P80be1SsUCnLO6fz8XKenpxPdBUy6LtP4/uZ5/gJpQ/zo9ugfH+naJ69dvid+dCN+dJvn+Uuyg4Xm+/5cuwH0unXrlu7fvz+XfY06Vs/z+soLhYLq9Xqk7U+6/rgajYaWl5e7PisWi7Evns1ms297YWZ9zsyjLtP4/uZ5/gJpQ/zoKD/xdeUnr3R9RvwYjPjRMs/zl2QHC6vRaITezfA8r6sJvfd91O1Xq1UtLS3JzFQqlVQqlboezCsUCnO5szHsWNsGPTB49erVyA/fTrr+uAqFwsBm+tPT07G3dXR0FNq3udfBwYGOjo60tbU1k9/dvOoyje9vXucvkDbEj24X71/0fUb86Ef86N7GvOLHh+eyFyCFfN/XjRs3BpYdHh6qWq12vV9dXY28bc/ztLu7e3lXqlqtand3N7Rvd7PZHHqHJUqgrFaroQ83DjvWtmH9e6OYdP1xlcvlvgAZ507rqO++U6VSuQz6a2trKpVKOjk5mdqdunnWZVrfnzRevYE8IH50++BHH4zcxzDEj8kRP8KR7GBh+b4fGjzawabt+Ph4rDtzW1tbevPNNy/fr6ys9PVvbWvfIRn2nz3OKCmdhh1rW9Rm71mtP65CoaDNzU15nqdKpXJ5oR2377HneZHvhPVuu1Ao6PXXX59av/151mVa31+U8xfIG+JHt6eefmqifRA/Jkf8CEc3Niysx48fD/y8fbei8z+f7/uR/xO3m/x71y+XB4+KOI+LfNixdioUCn13ah4/fhz5uCddP47d3V01m00dHR1d7rtUKkVef5zfq+/7Wlpa6vqsWCzG6vaQlrpM+v1J8/8jBUgD4ke3Kx+5oov/1t2VjfjRvSzxo9+84gctO1hYvf2f2zzP67qL1b5zIUW7oBwfH/c1+Q8bnvTs7Gzkf/gow0K2h+0cJOxYO1Uqlb6uBM1mM/JwkpOuH1fnnSzf93Xr1q3I67bH/m/Pf3J8fHx5DIPucPXeIW02m2Nf3NNWl0m+Pyna+QvkDfGjW3G12NeVjfjRjfjRb17xg2QHC2t5eXlgP+F6vd71n29/f183btzoekiz/VDdoABULpe1v79/+X5vb29oN4Io/VU7txdH2LH2qlQqXYG1PbGbNPyYo6zfK8r2RllaWtJ7772nQqGgo6Mj3b59+/K7jLL93ib/er2uarV6GRw6t1EsFrsCfrPZlO/7oc3+4x7fNOsSdd/Dvr+oeF4Hi4j40a+4WtSjdx9JN1vviR/Ej1HmFT9IdjB9n95OugaRVCqVgf2o23fmjo6OVCgUdOfOHd2/f7+r3/L9+/fVaDQGDou5srKiUql0OUPxysrKWLMiz0LYsfZ69dVXtbOzI9/39fDhw64gO+yYo6zfK8r2Rtnd3ZXneZeBeHNzM/b29/b2LmedXl5e1tramu7fv69ms3n5x8LGxob29vZUKBR0cnIydNuTHN+kdYm672HfH5CEL1U/mXQVIiF+9HvxKy/qW3/6LR0tHxE/iB/p4pzjFfG1urrqkC9ra2td78/Pz12hUIi07uHh4cT7Pzk5cfv7+xNvJ4reY3XOuXq97ur1euRtTHLMJycnffsatb3e+s2zvuOaxvFN06TH3ns8g45vmuevpGOXguv8rF7Ej/xZ5Phx96/uupdeecm99MpL7u5f3e16hSF+hCN+TGZU/KBlBwutVqt19anu/Hke9vf3Jx4pJ6reYwUmNc/zF0ibRY0fZtbqqtbu2fZ/dS977zP31Pr7Ewg3z/OX0diw0Np9hNt839ft27dHrjfsgdGo2pPGzet5h95jHdc0jnmW25v39tO8/3nse97nL5A2xI/oiB/Z2X8u48ewZh9edENYBOfn53Ntrm6bV/eDTr3HenJy4tbW1mZel/39fbe2tuZOTk7GWq+3fvOq77imdXxp0Xs8g45v2nUW3diQQYsYPyQ53ZTb2N9w15+/7n7ty7922YVNN+Vaf1pOD/FjsKwcTxrih7WWQRTlctm1h/UDAEyPmZ045wZPJpIDxA/kRbsb2927d/vK7t27Jz1o3UgH5mVU/KAbGwAAAIBcItkBAAAAkEuMxgYAAIDpuCltP9gOLd6+GV4GzAItOwAAAAByiWQHAAAAQC6R7ABqzY8wb81mc+77lLqPtdFoqFar6eDgYKb7PDg4UK1WG3ueht76zau+45rW8aVF7/EMOr6kzl8gbRY1fjx695He+E9v6OSNk5nuk/gxWFaOJw3xg2d2sPA8z9Py8vLc93twcKCNjY25TsrYe6xnZ2daX1/vmvV7b29PxWJRvu+rUqmMPbnYoPU3NjbUaDR0dnY2egMdeus3qL5bW1sqlUo6PT1VrVZTsVgcax+dPM9Ts9nU2trawPJ2ULl165bOzs4uZ4Ce1vFNoy7jGvT99R7PoONL4vwF0maR48cH//6BPnXzUyqudlxz/1nSFentP3tbxdWirn3y2lj7IH5ER/yIjmQHC63ZbKrRaGhzc3Pu+97c3NTW1lasC0wcncdqZpHWqVQqqtfrkfexvr6uO3fuXCZI1Wp1rPXHVa1Wtbu7e7m/1dVVnZzEv8u4tbWlWq0WWt5sNi+XKRaLMz22edRlku9v3ucvkDaLGj/CHG4fSkuSPip573rSoaRf7F/u3mfuDZyHh/gxPcSPbnRjw0Lb2dnRxsZGYvsvlUpjN13HFedYi8WiPM+LvHyj0ehqCRp3/XH4vq/j4+Ou/S0vL8fen+d5I+/qFQoFOed0fn6u09PTie4CJl2XaXx/8zx/gbQhfnR79I+PpI92fHBF0hiNFcSP6SB+9EtNsmNmu2ZW6fmsYGabwevQzFZ6yjfNbC34dyplWCy+7yfaDefWrVu6f//+XPY16FhfeuklvfTSS7p7965eeuklXbt2rWtW7EKhEPmOj+d5fdsfZ/1xNRqNvu4jxWIx9sWz2WxG7o4y63NmHnWZxvc3z/MXSJtFjx9d5Se+rvzkFd29e/fy9avP/6p+9fqvdn0WhvgxPcSPfoknO2ZWMbNNSYM6Fu465/acc3uStiS9aWaFYL1DSZ5z7igo3+3YZqwyLJZGoxF6N8PzPG1tbYW+j7r9arWqpaUlmZlKpZJKpVLXg3mFQmEudzaGHWvbxcVF32dXr16N/PDtoAcOx1l/XIVCYWAf59PT07G3dXR0FNq3udfBwYGOjo60tbU1k9/dvOoyje9vXucvkDbEj24X7/fHj594+ifUfBTtQXTix3QQPwZL/Jkd55wnyTOzaufnZlaUdNqxnG9mvqQNSXuSVpxznd+Sb2aVYHtxy7BAfN/XjRs3BpYdHh6qWq12vV9dXY28bc/ztLu7e3lXqrdva69mszn0DkuUQFmtVgc+qCgNP9a2Dz74YOQ+hhn34cpJlcvlvgAZ507rqO++U6VSuQz6a2trKpVKOjk5mdqdunnWZVrfnzRevYE8IH50++BHxI9RiB+DzSN+JJ7sDFFQq9Vlr+fzq0F3t97bAE1J1eDB67HLJJHsLBjf90ODRzvYtB0fH491Z25ra0tvvvnm5fuVlZW+/q1t7Tskw/6zT/oQ37BjbXvqqacm2se8RyQqFAra3NyU53mqVCqXF9px+x57nhf5TljvtguFgl5//fWp9dufZ12m9f1FOX+BvCF+dHvqaeLHKMSPwduZR/xIvBtbmKD1pfdWyIqkulqJUK/HkooTlGHBPH78eODn7bsVnf/5fN+P/J+43eTfu365XB64/Dwu8mHH2unKlSt9XdkeP34c+bgLhULfnZ5x1o9jd3dXzWZTR0dHl/sulUqR1x/n9+r7vpaWlro+KxaLsbo9pKUuk35/0vz/SAHSgPjR7cpHrujiv3XHjx//6McqXIv2RyzxYzLEj+HS3LKjzu5mZrah1rM2XvBzmGHfHFEZl3r7P7d5ntd1F6t950KKdkE5Pj7ua/LvHWWm09nZ2cj/8MOGkGwLG29fCj/WTsVisa8rW7PZ1Pr6+sh9S61m8d6uCOOsH1fnnSzf93Xr1q3I67bH/j8+PpbU+t11zg3Qq/cOabPZHPvinra6TPL9SdHOXyBviB/diqvFvq5sF+9f6FM3PzVy3xLxY1LEj+FSney0BYMSrDvn2p1gh3XujFsWtu8NtZ4T0ic+8YlxV184UedvmZVBY/eHWV5eHthPuF6vd/3n29/f140bN7oe0mw/VDcoAJXLZe3v71++39vbG9qNIEp/1c7txRF2rL2KxaIePXp0+b49sZs0/JjbKpVKV2DuXL9XlO2NsrS0pPfee0+FQkFHR0e6ffv25XcZZfu9Tf71el3VavUyOHRuo1gsdgX8ZrMp3/dDm/3HPb5p1iXqvod9f1HxvE444sd4iB8Zjx+rRT1699HlRKLNR83LCUcfvfto2KqSiB+diB/TlYlkR61ndzrT+6b6u6RdleRPUDaQc+5A0oEklcvl6FdCpF6lUhnYj7p9Z+7o6EiFQkF37tzR/fv3u/ot379/X41GY+CwmCsrKyqVSpczFK+srIResOcl7Fh7vfjii/rWt751+b5YLGp7e1tS6+L5/e9/Xy+99JIkXX7e6dVXX9XOzo5839fDhw+HBulh32FUu7u78jzvMhB3Tng37vb39vYuZ51eXl7W2tqa7t+/r2azefnHwsbGhvb29lQoFHRycjJ025Mc36R1ibrvYd8fJkf8yC/iR78Xv/KivvWn39L5o3N97++/p8rGk3p/96++q+//4/e75+HpQfyYzvERP/rZOHcyZsnM6moNNe31fL4p6cg55wfvV5xzDTM7d84tdSy3L+kw6OYWq2xUHcvlsms3EWKw9p25eZ9Xcfe7vr6uw8PDy/fNZlPPPvuszs/PR647zhCPYRqNho6Pj+cyMV3nsba/r3bi0tu14t69e5LUNy/CO++8o+vXr0sanOwM025m7wzco77D9gRl7XV6348yjd9RVNM4vmma9Nh7j2fQ8U3z/DWzE+fc4AcTcoD4MRrxYzyJxo+b0ku/FsSP1WjPirzzzXd0+OBQejD+d0386Ef8eGJU/EjtAAWSZGZrkhqSzoIJRlcktQ/G65kQtNiRsMQtw4Kp1WpdM/529q+eh/39/bH7uMbVe6zApOZ5/gJpQ/wA4pvn+Zt4smNmK2a2K6kiaStoyWnPs3Oo1uhr58HrRE+6nL0s6baZrQXrd7axxi3Dgmn3EW7zfV+3b98eud6wB0ajak8aN6/nHXqPdVyPHj3StWvXplafaXyHSW4/zfufx77nff4CaUP8iO7Ru4907eeIH1nYfx7jR+LP7AQjrjXUk3QE3dZCn1Z0zjU71jmaRhkW08bGxmWTbdQ+p9O4EMyr+0Gn9rG2PfXUU3rrrbd0fn4+cNK7dne2Qe7duxe5K8LBwYHq9bru3Llz+VmU73B5efmyD/fGxkbf+2HmGSimdXzTMumx9x7PoONL4vwF0mah48dHn9Jb/8dbOv+Xc62+OHzS1PagBXEQPwYjfkSXmmd2soA+16Nlrc/1omp/X73P5LQNS3I68X1jWnhmB8SPbGg/sxMWP4a5d+9erGd2gGFGxY/EW3YApM+oIBY1GQIAAEgSyQ4Wlu/72t3d1cHBgdbW1i4ncjs9PVWhUAgd9tLzPB0eHqpUKl32N20P47i5ualGo6HPfe5zunXrlkqlkur1ujzP0+7urh4/fnw5KsnJycnQOgyb5A0AkJy8x4/tB9vhhTdjbxZIBMkOFlaxWNT+/r4ODg50586drn6qR0dHWl1d1cnJSdc6e3t7evjwYddwo1JrWM5ms6nNzU35vq8333zzcnvFYlHHx8dd/bnbcxYMq0OpVNLW1tbc+rWenJwMfG4HANCN+NHt5I2Tkc/tAElJfDQ25JOZzfU1bWtrayqXy9rb27v8zPM87e/v9wUqSX138UY94Ne+AzeqDsMmVZumi4uLSDNkA8CsET8yFj/ev9DZvxA/kF4kO0CI9fX1rlmjt7a2VKvVBi5bLBYvJ+bsnaAzbPkooi43iYuLC73xxhsz3w8ALIqFiR/vX+iNV4gfSDeSHUyVcy7R1zSVy62BPXy/NbXTqLHn23fRogzbOGqZZrOpRqOh/f390GUODg60tLSkarUq3/dVrVZlZpdDg66urmp1dbWr/ltbW11Dh77xxhvyfV8XFxf6/ve/r7fffvtyeQCYJ+JHeuLHwcaBDjYOdP4v55Ja8+TU9+t655vvSKeS/h/pjf/0hvwTXxfvX+j7//h9vf1nb8s/IX4gfXhmBwjRfni0cyK15eXlkcvH5XmefN/Xw4cP1Wg0dHh4OHSbGxsbOj091dWrV1UsFlWv17W0tKS1tTVJreBZLpcvt7G+vq7T09Oubbz44ouSpPPzc/34xz/Wc889N9ExAACyHT/MTPq0pI9K//nV/9xa4f+W9D9L+r6k/9766MXfCuLHv5zrxz/6sZ77deIH0omWHSBEs9mU1N3FYJbPtVQqlct+1uvr65EGC6jVapd37xqNhorFojzPk9Sqf2ewo8UGAOYj6/FD/13S/9Cx8EXHz09J+vdp1RyYPZIdIEQ7MLW7DKysrHTdpevV2T1sUhsbG/J9f+j+pCd9sn3fvxyGtP0AbO9dvUqlQsIDAHOQ9fihD7fmW2u/iqtF/ceX/6Pu3r2r6//jdW387/MZ5Q2YBpIdIMT+/n7XcJ+7u7tD+0DPQpTkZG1t7TJQVioVeZ4nM7vsg91+eZ6nUqk0cvShd955Zyp1B4BFleX4IUnq6XG3+uKq/BNf73zzHa2+uKprn7w2cHvvfJP4gfQh2QEG8DxPnufpzp07l59VKhXVajWtr6/3Ld+e1G2aisWiHj58KGl40KrVatrZ2em6gziupaUlXVxc9H3+6NEjPXr0aOztAcCiymP8+N7ff0+rL67q+vPXVVztHuVt6eNLunh/QPx495EevUv8QPIYoAALq91sL0k7OzuXcxc8fvxYkvomhJOkzc1NeZ6nWq3WNwP2oO0fHR3p/v37ajabqtVqWl1d7Vq22Wzq9ddfl9S6E7i1tXXZteDw8PBy9LRCoRA6jGixWFSlUrksr9Vql3fqOkcY2tvb087OjpaXly/rfXFxoStXruj69V3k67cAACAASURBVOv6u7/7O52cnGhpaelyne9+97u6uLi4HMgAALAY8aPTTzz9E9p9cVdPPf2UrvzkFV37+Wuq1qq68pEruv78df3dX/2dTt440dLHO+LHX31XF+9fXA5kACTFpj3cYp6Vy2V3fHycdDWAkdpd1e7evSup1ULj+75WV1d15coVXVxcyPd9nZyc6KWXXhp7+/fu3ZOkqQ/XisVlZifOuXLS9ZgV4geywsykmx3x491H8k98rb64qisfuaKL9y/kn/g6eeNEL70yXvy4d++e9IDYgekaFT/oxgYsAN/3de3aNV25ckWSLltzAAAYxj/xde2T13TlI0H8CFpzgKygGxuwAJ577jm9/fbbevTokZaWlvTBBx/o4uJiYP9xAADanvv15/T2n72tR+8+0tLHl/TBjz7QxfsXWt8mfiAbSHaABcGEoQCAOJgwFFlGNzYAAAAAuUSyAwAAACCX6MYGAACA+bgpbT/YDi3evhleBsRByw4AAACAXKJlBwAAAHPTnqttYNln7jEPD6aKlh1ArXlo5q3ZbM59nwCA6SJ+AOlGyw6mbnt7O1P79TxPy8vL061MBAcHB9rY2FChUJj5vh49enQ5t873vvc9/cIv/IKuXbs28/0CwDiGPcsx0/3GfE5kIeLHu4/0wb+35tb53t9/T7/wmV/QtU/Gix93794dWj6sxQeIi5YdLLRms6lGo6GVlZW573tzc1M7Ozsz38/FxYVee+01FYtFXb9+XeVyWa+99trM9wsAebYQ8eP9C732W6+puFrU9eevq/xiWa/9FvED2UKyg4W2s7OjjY2NxPZfKpXUaDRmuo8rV670HePFxcVM9wkAebcQ8eMjV7Sx3xM/3id+IFtIdrDQfN+fSzeAMLdu3dL9+/dnvp+lpaXLn9966y1VKpWZ7xMA8mxh4sfHO+LHn72lygbxA9lCsoOF1Wg0VCwWB5Z5nqetra3Q91G3X61WtbS0JDNTqVRSqVTqerC0UCjM/M5cp/Pzcy0vL+u5556b2z4BIG8WMn78y7mWP76s536d+IFsYYACLCzf93Xjxo2BZYeHh6pWq13vV1dXI2/b8zzt7u6qXq9LkqrVqnZ3d0P7djebzaF3CKMEymq1OrLF5p133plqomNmoWUMHQogrxYyfnzzHRIdZBLJDhaW7/uhwaMdbNqOj4/HujO3tbWlN9988/L9ysqKjo+PB+6vUCjo7OxsaLDqrMskrly5MpXtAMAiW8j48RHiB7KJZAcL6/HjxwM/b3cT6Awevu+Hdlno1e7e0Lv+7du3By4/z2FLO5/dmcSw4UMZOhRA3i1k/Pj4dOIHMG8kO1hYvf2f2zzP67qD5nneZfN+lKB1fHzc171h2PCkZ2dnIwNWrVYbWi5J6+vrI7shRA24AIBwCxk/VokfyCaSHSys5eVlnZ2d9X1er9e7gsf+/r5u3LjR9UBq+6HQQQGoXC5rf3//8v3e3t7QbgSj+lu36zCpi4sL+b6v69evT7wtAFhkCxc/3r+Qf+Lr+vPED2QPo7FhYVUqFZ2cnPR97nmezs7OdHR0JM/zdOfOHT1+/LhrmNH79++H9sFeWVlRqVTSwcGBDg4OtLKyorW1tZkeSxS+7+utt95KuhoAkHkLFz9OfL31p8QPZBMtO5i67e3tpKsQSfvBzk7NZlNnZ2c6PDzs+rz3Dtzu7q6Ojo5Ct725uRmpDo1GQ+vr6xFrPJnr16/TqgMg1bZvbiddhUgWLn48f51WHWQWLTtYaLVaTZ7nXb7v7F89D/v7+7p169bc9gcAmA7iB5ANqUl2zGzXzPquEma2aWZrwb8rsy7DYqlUKl2Tsg0b9abTsAdGo2pPGpfkDNwAgHiIH0A2JN6NLUhwViStSar3lB1K2nHONYL3dUnVWZVhMW1sbOjo6Ehra2uRuw9MGqik1qg7GxsbE28HAJAM4geQfom37DjnPOfcniR/QPFKOykJ+B2tP7MowwIqFAqJPABKoAKAbCN+AOmXeMtOmCAB6R3EvimpamaadpkkTwAAAAByI/GWnSEGdUR9LKk4ozIAAAAAOZLmZGfYlMCzKAMAAACQI2lOdvqnJp5t2UBmtmFmx2Z2/IMf/GDc1QEAC4r4AQDJS3Oy01R/t7Orag1kMIuygZxzB865snOu/LGPfWysAwAALC7iBwAkL7XJjnPOU3+3s4Kk+izKplNrAAAAAGmR2mQn4PVM+lkMEpZZlQEAAADIicSHng4Sj9uSKu33wbw7kvSypDtmVpR0Q9JWx6qzKAMAAACQE4knO8EEnw0NSDqcc82Oz49mXQYAAAAgP9LejQ0AAAAAYiHZAQAAAJBLJDsAAAAAcolkBwAAAEAukewAAAAAyKWZJTtm9tlZbRsAgDQg1gFAus0k2TGzp8X8NQCAHCPWAUD6xZpnJ7jAH0n6XNgiklzcSgEAkDRiHQBkX9yWnT+WdCipLGl5wOtnJX17GhUEACAhxDoAyLhYLTuS6s65V4eU/9DMdmJuGwCANCDWAUDGxW3ZORu1gHPuz2NuGwCANCDWAUDGxW3ZaZrZM865fwpbwMy+4px7Jeb2AQBIGrEOubT9YDu88Oa8agHMR9xkx0laM7OSpBP13/1allSTRAAAAGQVsQ4AMi5usnMU/OtLujGgvCDp2ZjbBgAgDYh1yB0zo/UGCyVusuM758rDFjCz12NuGwCANCDWAUDGxU121iMsw0RrAIAsI9Yht+7evZt0FYC5iDUam3PuvQiLhU3CBgBA6hHrACD7RrbsmNkzktQ5Go2Z/a8Rtr2l1oRsAKbMzJKuApArxDoAyKco3dgakk7V/XBm+8Luh6zDQ5sAgCwh1gFADkVJdj4nqdnzGQ9tAilAn2tgaoh1AJBDI5Md59y3B3wcpY8yD20CADKBWAcA+RRrNDbn3A/bP5vZ05Lad76OnXM/CpaJ8mAnAACpRKwDgOyLNRqb1HqY08y+Ielckhe8zs3sL83so9OqIAAASSHWAUC2xUp2zOxZtS74h2rd6Vpyzn1I0s9KelNSI7gLBgBAJhHrACD74k4quuGc+9neD4Pm/D0zO5B0J3gBAJBFxDoAyLi43diOhxU655oKH6oTAIAsINYBQMbFTXbclJYBACCtiHUAkHFxk50o07efx9w2AABpQKwDgIwb+syOmf2SpOUBRedmdl9SXf1N+EVJBefcK9OpIgAAs0OsA4D8GjVAwR9LWlF4n+TVsBXNrOSc+0LcigEAMCfEOgDIqVHJji9pnUnTAAA5RqwDgJwa9czODhd/AEDOEesAIKeGJjvOuW/PqyIAACSBWAcA+RV3NDYAAAAASDWSHQAAAAC5RLIDAAAAIJdIdgAAAADk0qihpxNnZgVJG5KakgqSGs45r6N8U61hQ4uSPOdcY9IyAAAAANmX+mRH0oZzbq/9xsx2zezYOdc0s0O1hgxtBGV1SdXg51hlAKbDzIaWO+fmVBMAALCoYndjM7Onp1mRIXqTkFO1WmMkaaWnRcY3s8qEZQAASJprrAMQMLOhL2AcsVp2zOwbklYlXZ1udQZaNrNd59xW8L7qnDsIkpNmz7JNSdXgP8LYZZI8AZjI3bt3h5bfu3dvTjUBJjPnWAdAkm6OKH8whzogV+J2YzuUtD7NigzxsqQ3g+TmvqR20lMYsOxjSTcmKAMAoG2esQ5YeNwswyzE7cZ2Jmlp2AJm9ocxt90l6G52oFaSsqsnXdiWh6wWt6yPmW2Y2bGZHf/gBz8YZ1UAQLZNFOuIHwCQvLgtO6eS1szsavCzP2CZqTwDY2b7knadc1vBz3UzW1UrCIWJW9bHOXegVrKlcrnME9UAsDgminXEDwBIXtxk57+o1dIy6MIvtVpPfirmti+Z2YqkU+ecL0nOuZqZnUqqqdW9oLdL2tWgTs2YZQAAtM0l1gEAZidusuM758rDFjCz12Nuu1NR/UHmQK2WHs/MerukFSQdxi2bQn0BAPkxr1gHAJiRuM/svBxhma3Ri4zkSbrd81lF0n67PGj9aSt2TDgatwwAAGl+sQ4AMCOxWnacc9+WLucfKEs6c879TfDZLznn/sY5996klQsmDt0xs121+ktLrTtt7TlyXpZ0x8yKao2m1hl04pYBADC3WAcAmJ243dhkZr8naVPSuaTXJX0hKPqhmX3FOffKFOrXHo2tEVLW1JNE5WgaZQAAtM0r1gEAZiNWNzYz++3gxyXn3FV1TMbpnHvPOfeKmf3GNCoIAEASiHUAkH1xW3aazrmvdrwfNKTmecxtAwCQBsQ6AMi4uAMUPI6wzLMxtw0AQBoQ6wAg4+ImO/+h5711vTF7RtLPxtw2AABpQKwDgIyL241t38weSvpdSd9W0LQfXPjXJW1IWp1C/QAASAqxDgAyLu7Q0++ZWU2tCT5XJDmzyxtenqQXnHM/mk4VAQCYP2IdAGRf7KGngyGhy8FcNb8cfNxgzgEAQF4Q6wAg22InOx3+TU9Go4nyMCcAAFlDrAOADIo7QIHM7Bkz+4ZaF38veJ2b2V+Y2UenVUEAAJJCrAOAbIs7qeizkhrBqyxpKXjdkPRfJTXM7OlpVRIAgHkj1gFA9sXtxrYpaXVAn+WGpJqZrUjalfSFSSoHAECCiHUAkHFxu7H5wx7ODB7o9GNuGwCANCDWAUDGxU52prQMAABpRawDgIybZICC0H7KQZnr+ewrcfcFAEASiHUAkG1xn9lpSnrVzO4HP3cqSqpKum9mnw0+K0iqSXol5v4AAJg3Yh0AZFzcZOdQrYv66pBlOsuWJf1UzH0BAJAEYh0AZFzcZMd3zpXHWcHMXo+5LwAAkkCsA4CMi/vMzssx1tmKuS8AAJJArAOAjIuV7Djnvh1jndDhOwEASBtiHQBkX+zR2AAAAAAgzUh2AAAAAOQSyQ4AAACAXCLZAQAAAJBLJDsAAAAAcolkBwAAAEAuTTXZMbOnzeyZaW4TAIA0IdYBQHbESnbM7PfM7C/N7Ctm9nTw2V9KOpH0O2Z2n0AAAMgyYh0AZN+HY673UNJ+e/I0M/ttSUXn3M+1FzCzr0h6ZfIqAgCQCGIdcuuvTx/HXvdXSlenWBNgtuImO0s9s0TXJP1ezzI/jLltANPwrw+Gl//MzXnUAsgyYh0AZFzcZOfy4m9mz0p6VpLXs0z8WwYAohmV0ACYBLEOADIubrLzUx0/r0l6zzn3Tz3L0MYJAMgyYh0y6Wv1d5OuApAacZOdHwb9lE3SrlpBQMEDnFVJvyNpfSo1BBbY9vZ20lUAFhmxDpljZklXAUiVWMmOc+5NM/MlVSSVOvo035ZUkPS6pBVJ/zSNSgIAMG/EOmCwYYMbMHgB0iZuy46Ci/6rPZ+9GrI4AACZQ6xDVv3+N/4htOwv/vkP5lgTIFmxkh0z+yXn3N/0fPZTkm6p1ce56Zz74ynUD1hsDEAAJIZYh8y6SUIDtMVt2alJ+kLnB865H6rj7peZ/ca0goCZFdXqK90M9nXQUbYpyZdUlOQ55xqTlgELgaGpgVHmGusAANMXuxvbvASJzq5zbj14f2Jmx865hpkdStppJypmVlfroVHFLQPSgodMAQAAJhMp2TGzz0lyHR8VzeyzIYsXJN0I/p3G3a5dSfsd7z/nnGsGP6/0tMj4ZlZxznkTlAEAFlDCsQ4AMANRW3Z8tUacua1WdzInqTRk+bpz7gtDyiMxs4KktXarjiS1Ex0zqyjo1tahKaka3BEfu0z9k8UBibv7m88nXQVgUSQS6wAAsxMp2QlGo3lP0p8Hz7o8O6cLfFFSM0hsCsH7RtACUxiw/GM9udMWpwwAsKASjHUAgBn5UIx19tW6+zXUkKb/cRSDf8+cc0fOuT1Ju8FzPMtD1otb1sfMNszs2MyOf/CDH4yzKgAguyaOdcQPAEje2MmOc+6HzrmvDlsmmF16K3atnmhKKvQ+X6PWCDlnQ9aLW9bHOXfgnCs758of+9jHxlkVAJBR04h1xA8ASF7ceXaelnQk6XNhi6j7Ic+4fPU/X9MeLrqu/i5pVzvWiVMGAICkucY6AMCMxB16+o8lHap1R2tQknBV0utxK9XmnPODQQo6FST5zjnPzHq7pBUkHcYtm7S+AIBcmUusA8b1tfq7SVcByIy4yU7dOffqkPIfmtlOzG332usZFrqsJ3fZPDPrHEa62LFc3DIAAKT5xjogF/769PHQ8l8pXZ1TTYCWuMnOyOdenHN/HnPbvdvZMrP2oAQlSS93zLPzsqQ7QdkNdfedjlsGzM93tpOuAYBwc4t1AIDZiJvsNM3sGefcP4UtYGZfcc69EnP7XZxzA5ORIOlplx1NowxA4F8fDC//mZvzqAWQpLnGOgDA9MVNdpykNTMrSTpR/92vZbVGTCMAAACyilgHABkXN9lpt4b4GjwZZ0HSszG3DQBAGhDrACDj4iY7vnOuPGwBM2OEGgBAlhHrACDj4iY76xGW4aF/AKHMbGi5c0xfgsQR6wAg4z4UZyXn3HtSa8I1M/usmf1Su6z9c3sZAACyiFgHANkXt2VHZvZ7kjYlnas1qdoXgqIfMjoNgDB3794dWn7v3r051QQYjVgHANkWq2XHzH47+HHJOXdV0uWEnM6595xzr5jZb0yjggAAJIFYBwDZF3ueHefcVzveD+pcfx5z2wAApAGxDgAyLm6y8zjCMgzHCQDIMmIdMGV/fTr8v9WvlK7OqSZYFHGTnf8g6f/seN81rJKZPSPpZ2NuGwCANCDWITFfq7+bdBWAXIib7Oyb2UNJvyvp2wqa9oML/7qkDUmrU6gfgLT61wfhZT9zc161AGaJWAekzU1p+8F2aPH2zfAyLKZYyY5z7j0zq0k6kLQiyXXMmeFJesE596PpVBEAgPkj1gFA9sVKdszsl5xzDUllM3tWrSAgSQ3mHAA6fGc76RoAiIlYBwDZF7cb26uSbkiXE6px0QcA5A2xDgAyLtY8O5JWzey+mX12qrUBACA9iHUAkHFxW3a2nHNfNbNnzexltR7a9Jxz/zS9qgEAkChiHQBkXNwBCr4a/PueWs38MrNfNrOqWvMSeDy0CQDIMmIdkE737t0LL/vMPTk3aP5fLKq43dj6OOe+7Zx7Va15CN4zsz+c1rYBAEgDYh0AZEvcbmxdgjkHamrNOXCm1h2wnWlsGwCANCDWAcm6e/fu0PJhLT5YXLFadoIHNj9qZr9hZseSTiUVJd1yzv2cc+53nHM/nGpNAQCYI2IdAGRf3JaddUlrkhqS9oMmfQAA8oRYBwAZFzfZaUpaZVI1AECOEeuAOfvr08dDy3+ldHVONUFexE12drj4Awj1rw+Gl//MzXnUApgUsQ4AMi7WMzvt4Tjbgoc2AQDIDWIdAGTfyJad4OJeDF7S4AnVSsG8AyVJvyzJl3TqnHtlajUF0ug720nXAMAUEOsAIJ+idGPzJdUl1cJmjXbOvSnpTUkys5+S9MdqDc1JAAAAZAGxDnP1tfq7SVcBWAhRkp2mpPWos0QHw3Cum9n/N1HNAACYH2IdAORQlGd2jqNe/Hs0YqwDAEASiHUAkENRkh2/842Z/baZfcPM/l8ze2hmXzGzz45aDwCAFCPWAUAORenG5rretEan+aqZnTnnbgxZ72yimgEAMD/EOgDIoSgtOxbyuTfNigAAkCBiHQDkUJRkx4V8PnyK2/D1AABIG2IdAORQlG5sZTP7jPrvehVDPm+rTFQzAADmh1gHADkUJdlZVasZf9CFvjpkPe52AQCyglgHADkUJdlpSFofc7sm6fXxqwMAQCKIdQCQQ1GSHc859964GzazqT/UaWYVSQXn3FHHZ5tqDf1ZVKuujUnLAAALJzWxDgAwPSOTHefc78TZcNz1RtiVtN9+Y2aHknbaiYqZ1RV0N4hbBgBYPCmLdQCAKYkyGlsqBK06vZO3rfS0yPjBcpOUAQAAAMiBKN3Y0qKgjsnbguSk2bNMU1LVzBSnTMynAAAAAORGJlp2zGyt8zmdQGHAoo/VegYnbhkAAACAnEh9y46ZFdTfEiNJy0NWi1sGYB7+9UHSNQAAAAsg9cmOpMqAVh2po0vbFMv6mNmGpA1J+sQnPjHOqsiD72zPdPPbf/hgptsHkBziBwAkL9XJjpkV1T8oQVtT/V3SrgbLxy3r45w7kHQgSeVymcnjAACRED+A6fvr08dJVwEZk+pkR9KKpGUzKwfvy8F7OecOzKy3S1pB0qFzzotTNvXaA0PYL95LugoAAAC5lupkp7f7mplVJdWDu2WS5JlZ5zDSReecN2EZAADARL5WfzfpKgBQypOdTma2KakiqWBmZ0Ei9LKkO0F3txuStjpWiVsGzNXd33w+6SoAAADkUmaSHefcnqS9ns+aepKoHE2jDAAAAEA+ZCbZAQAAWHRffuHnpZtDFhhWBiygTEwqCgAAAADjomUHAAAgY174/BeTrgKQCbTsAAAAAMglkh0AAAAAuUSyAwAAACCXSHYAAAAA5BLJDgAAAIBcYjQ2AKm0vb0dqwwAAKCNlh0AAAAAuUSyAwAAACCX6MYGIJXu3bs3tMw5N8faAACALKJlBwAAAEAu0bKDxfad7aRrgB53f/P5oeX3/uibc6oJACCLvlZ/N7TsS9VPzrEmSAOSHQAAgDEN+4MaQHrQjQ0AAABALtGyAwAAgHy4Kf3FP/9BaPGXFF6GfKJlBwAAAEAukewAAAAAyCWSHQAAAAC5RLIDAAAAIJdIdgAAAADkEqOxAQAAIDe+8SdfDy2z3/i6nHNzrA2SRssOAAAAgFyiZQcAAACZ98Lnvzi0fFiLD/KLlh0AAAAAuUSyAwAAACCX6MYGAACAhfG1+rsDP/9S9ZNzrgnmgZYdAAAAALlEyw6AbPrOdnjZp4eUAQCAhUGyAwAA0COsqxOAbCHZQf4NawEAAABAbvHMDgAAAIBcItkBAAAAkEskOwAAAAByiWQHAAAAQC4xQAEwQ9t/+CDpKgAAACys1Cc7ZlaQtBG8vSFpxznX6CjflORLKkryplEGAAAAIPtSn+xI2nXO1STJzIqSTszsWedc08wO1ZH8mFldUjX4OVYZMC32i/eSrgIAAMBCS/UzO0Fyc9p+75zz1WqNabf0rPS0yPhmVpmwDAAAAEAOpL1lpyBpV9Jez+dXg+Sk2fN5U1LVzBSnTJI3hToDXe7+5vNJVwEAAGAhpbplJ2h9We35eEVSXa1EqNdjtZ7BiVsGAAAAICfS3rKjnkEFNtQaTMALfg6zHLOsT7CfDUn6xCc+Mc6qmJfvbCddA6TNsHPi00PKgCkifgBA8lKf7LQFo7KtO+faAwmcDVk8blkf59yBpANJKpfLbpx1AQCLi/iRfl+rv5t0FQb68gs/n3QVFtKo8+FL1U/OqSaYpswkO2o9u7Pe8b6p/i5pV9UawCBuGQAAQPJuJl0BIB8ykewEc+LsOueawfuVoCtbb5e0gqTDuGUzqTwAAEAML3z+i0lXAci8VA9QIElmtiapIenMzApmtiKpHBR7wfu2onPOm7AMAAAAQA6kumUnmGdnUItL+7mdlyXdCZa7IWmrY5m4ZQAyYNSkre5v786pJgAAIK1SnewEk4jakPKmniQqR9MoAwAAAJAPqU52AKDXqEla7/3RN+dUEwAAkHapf2YHAAAAAOIg2QEAAACQS3RjAwAAwGK4Kf3FP//BwKL/5RP/23zrgrkg2UH6fWc76RoAAAAgg0h2ACyWUcnzp0eUAwCAzOCZHQAAAAC5RMsOAADIpa/V3026Ckihb/zJ1wd//uDr+v1v/EPoeqPOpy9VPzlRvTAbtOwAAAAAyCVadgAAAJB7L3z+i6FlYa09yD5adgAAAADkEskOAAAAgFwi2QEAAACQSyQ7AAAAAHKJAQqAmOwX7yVdBQAAAAxBsoPkjZrRHpinUefjp0eUAwCA1CDZASZ09zefT7oKALCQmDQUacKko+nEMzsAAAAAcolkBwAAAEAu0Y0NAABgjr78ws8nXQVgYZDsAMilYaPlub+9O8eaAMAAN5OuALAYSHYAAAAS8MLnv5h0FYDcI9kBkCvDRse790ffnGNNAABA0kh2AGAczMMDAEBmkOxg9pg0FAAQA/PoIE+YhycZJDsAAADATenLvztkpLwH0u9/4x/mVRtMCfPsAAAAAMglWnaAEMOGLgYAAPkxamS8b/zJ1+dUE0wbLTsAAAAAcomWHUxHjgchGDaUMdCH0dqAyBiAAMCskewAAABM0ZdfGPKQOxBiWPLPSG3x0Y0NAAAAQC7RsgNg4YwafML97d051QRAbt0c/dA7gNkj2UE0OX4mB5grnunBAuGZHABJI9kBsDBGDTZx74++OaeaAACyaNTzWLOadHTUjQOe6QlHsoOFxTw6AAAgspsjyh/MoQ4Y20InO2a2KcmXVJTkOecaCVcpOXRTAy4l+kzPsP+LdHFDCi1iVzVGW1s8aZ90lJafcAub7JjZoaSddoJjZnVJ1WRrhSQwjw4AAJiGpLq5IdzCJjuSVnpacnwzqzjnvMRqNEsL2nJDVzWMI+ozPYm1/DC4ARKwiC03kdxktDVkxyK3/CxksmNmFUnNno+barXsZDPZIZkBUoNkCFmyqMkMXdEwTS98/ovS58PLv/EnX5ce0PKThIVMdiQVBnz2WNKNWe7UzGa5eQxBVzVMw7RGc0suSZ/dfp1zM9s2iB+JuJl0BZA7N0eUP0guCf9yInttmXX8sEUMUGa2IanmnFvt+GxT0g3n3PqAZTeCtz8vKYmU+6cl/VsC+80CvptwfDfh+G7CJfXd/E/OuY8lsN+ZIX6kHt9NOL6bcHw34VIZPxa1Zecs6oLOuQNJBzOsy0hmduycKydZh7TiuwnHdxOO7yYc3830ED/Sje8mHN9NOL6bcGn9bj6UdAUS0lR/V7arag1DDQAAACAHFjLZCUZcW+75uCCpnkB1AAAAAMzAQiY7Ac/MVjreF1M87HSi3SBSju8mHN9NOL6bcHw3+cLvMxzfhzUDWAAAIABJREFUTTi+m3B8N+FS+d0s5AAFkmRmBUl3JD1UaxS2+z3z7gAAAADIsIVNdgBg0ZnZrqR6b6t2MDqlL6koyeNGEACgU5bix6KOxpY5WTqpkDzOi8GCoYAl6XW1nturOee2EqxSIoKJlVckrannWUUzO5S00z5nzKyu1oTLyCjiB6LinBiM2PFEFuMHyU7KZfGkSgoXoxbOi6EKknYl7asV0Bfyewn+6PXMbNDxr/T8geObWSXFzzQiBPEjGmJHC+fEUMSOQBbjxyIPUJAJzjnPObenwcNiDzyp5lS1NCqodSE6Vyuw7ydbncRwXoRrOudM0pJzruScY7j5DsF50uz5uKkFDuxZRvyIjNjRwjkRjtgxQprjB8lORqX5pErQwl+MOC+icc71fkdo6Z1/TJIeq9WlBTnBdaIPsYNzIhJix1CpjR90Y8uusJPqxrwrkjYLfjHivBgh6LJyJkZhHKR3/jHkE9eJAYgdfRb+nOhE7BgptfGDZCe7UntSJYmLEefFCF7HXdsjMzs1s9UF/yOn01nSFcBccJ3oQezgnBiB2DFaauMHyU52pfakShAXI86LoQZ0T2lKuqWUToSWgKb67/Be1eBnPpBdXCe6ETs4J4YidkSS2vhBsjNnwd2j1RGL7UboM5zakyopXIwkcV6EMrOipBPn3FLHx76kUkJVSh3nnGdmvXd4C5IOk6gPuhE/ZoPYIYlzIhSxI5o0xw+SnTlzzk3l4pnmk2paxgnsXIxaFuG8mFDvcLIFSadJVCTFPDPrHJWpmPSwoWghfkRD7Bhf3s+JKSB2RJPK+EGyk22pPKmmJUZg52LUkuvzIq7gD5vLO5fBz8Vp/QGZJWa2Ium2pEr7fTBEsSS9LOlO8EfgDfX/v0I+5PY6QeyILbfnxCSIHd2yGD/MOZd0HTBEx0m1KclTaxbsvaCsIOmOpIda3IcqL5nZZs93c+KcW6i7cxLnxTDBd7OhVpeNVUXr8gNkEvEjGmJHC+dEOGJHtpHsIDe4GAEAxkXsAPKNZAcAAABALn0o6QoAAAAAwCyQ7AAAAADIJZIdAAAAALlEsgMAAAAgl0h2AAAAAOQSyQ4AAACAXCLZASAz2zCzetL1AABkC/EDaffhpCsAIDlmtq/WRHoFScsJVwcAkBHED2QFyQ6wwJxzNal1Z05SOeHqAAAygviBrKAbGwAAAIBcItkBAAAAkEt0YwMywMw21eobXZW0E/y8FhSX2t0JAABoI3YAJDtA6pnZrnNuK/j5WNKhJM85Vwv6Sm8Ey/iJVhQAkBrEDqCFZAdIMTNbk3S/5+OiWkFLkjxJtUHBKli3qCej5RxJqjjnDmZXYwBA0iaMHQVJd4LlfUmPg+WXJck5582o2sBMkOwA6dZ0zjU63pelJ8EmCFR9yUvQdaHhnNvr+KwuaX+21QUApEDc2LEmqaaeRChoCdp1zi3NtNbADDBAAZBiA+6gVdW6wzZKdcC69YjrAgAyLE7sCBKamnOu2tviE/QIoLsbMolkB8iWilpJSygzWwkpajjnmtOvEgAg5YbGDjMrStqVtD5kG73d4oBMINkBMiIIRgVJjZ7Pu5KboOtC2czqQZeE9ue06gDAgokYO/bVGrwg9IZYZ7doIEtIdoAUM7ON4GFR6clwoccd5Wv9a0mSVtXqcvCqmbngGR4AwAKIETsqouUGOUWyA6SUmVXUuttWDj4q9ZQXJBV7HkKV1Hr41DlXCx4mXZd0pyPwDTKsDACQEePGjo4Wnt6Wn4KZbZrZYfAKu7lG/ECqmXMu6ToAGCAISLuSTiVddc5tBcGmKulEunxotHOdlUHJTzASW98wo2a2r9bwomW1ApanVovQ/qDtAADSbdzYEXRzO5W0NKgbWxA/Gu05ezo+J34gE0h2gBzpnESu5/MT59xqEnUCAKSbmZ1LWh/0bKeZOQ0e4RPIBLqxAfmy1jtgQfC8zk5C9QEApN/LarUGdQmGo5Y6nvcBsoaWHSAngq4LtySdqTXTdbsfdYM7cgCAYYJnfWqSHkpqd2fzJK0xEhuyjGQHAAAAQC7RjQ0AAABALpHsAAAAAMglkh0AAAAAuUSyAwAAACCXSHYAAAAA5BLJDgAAAIBcItkBAAAAkEskOwAAAAByiWQHAAAAQC6R7AAAAADIJZIdAAAAALlEsgMAAAAgl0h2AAAAAOQSyQ4AAACAXCLZAQAAAJBLJDsAAAAAcolkBwAAAEAukewAAAAAyCWSHQAAAAC5RLIDAAAAIJdIdgAAAADkEskOAAAAgFwi2QFSwMx2zezEzFzwOg/eV5Ku2yIysxUzW5nz/g47XnUz2wjKCu2fAaAX8SNdiB/pY865pOsAIGBmh5LWJK065xpJ12dWzKwuSc65atJ1GcTMdiVtSmpKOg7+LUoqSNp3/z97dxPjSHrfef73GDq0Z7wyM8taoX3QdpMeCZAAy2ZmDSDMAp1yk32aviwyqzCAfHOT0kIXa+Wk61SZl0kzJaAH8ALazPLJ2tlFdqYXMHYP9pBtl2ENdKhMStsQhN02ku01YJdlubJCskfdh8U+e4ggK8iMCAaDQcYLvx+AyCSfeHkiGBn/fOJ5s/Y4xX21JO1J2rPWOlOfb3n7lrW2k9Y+s2KM6eX1OweKjviRD8SP9BhjdiU15Z6/TUlDSUfzXt/U7AD5MvR+OmELeDfSXIuRx23vlWcX3s+G9xrKDShpBqqqpK6mApUkWWtPJZ3LDZpB6+b+OhgxxjSMMddyzyOA5SB+5AfxY0HGmH1Jsta2rbVNa+2WpCeSrkZpcX1sGRkEsFTVrDMQQ2QerbUbq8pIUtbavRXspi3pnelA5ctD3xjTD1k399eBF1DrknqS+pLWvjkFkLHc3zdE/IirtPHDawboWGsv/J9ba4+NMTVJXWNMP24NDzU7QPEU4el4EfKYB3VFPIX1nIR8nvtzbK3teE/kjiVdZ50fAPm/b6gYecyDMsePtlc7FWRUK/Ug7sYo7AAF4rVfrWSdjyhFyGPOzOrIeuvJFecYwLyKcN8oQh5zpqzxo2GMuTbG3MqntXbUXDP2IBAUdoAC8EZU2ZXbBjeXipDHHOrJvamHPmXzbuwnEucYwPyKcN8oQh5zqMzxYzSoQ1T6ZtyN0WcHKIYHcp9iDCVVR6PReHrTnR69G9pduU2HKpLu+Edj8To2nntpstbWvNFbKt56J9bavm/5UZok1bx9TrSljZtHY8yVt62qtdYEHazXXrch94ZWkXRH0pm/fa73xOddL33TWrvh3fRHT3tqkq4X6RDqO27Hd9xhbaDn5rU/fiCpZ4w5lnvehwHLjT5b5nUwOoejdSQ32DxJs1MtgJUjfhA/pALFD2vtljGmEtYfydtn/HNpreXFi1dOXnLbolq5N/Kg9Jb7ZztzG+cB611PfTa6UV3LHa607n3+3L++pFbAPq6m9xE3j95N6iRsGW/9XsDn59N58W9L3qg3U+nXQfmP+T2EHfduyt953Tvn1ntde8fUiFhnGdeB9dKrU+m9oO8jwXHuz8ozL168kr+IH8SPssaPqW3uevuLfS5T2zkvXrwWfy0arLwbtpVUCUi7lrQfsL2JG7p386x6v1e8G+l0kKh7+6nPm0dvmd2gZbwbZ9TxPZ/ep+/Gtx+w/ImkqwTfQ9j5H+3r1vld8HuvjIK0L2jZoONd4nUQ+H166YHnd85jpLDDi9cSX8QP4kdZ48fU9q7m/V7oswOUS1dS3wZX/V7IHapyWlXuxGeSJGvtwAZUhfvZF80Bkg5fGVY1faIX8xMEeUfSo5C0oCrtayXIY9jx2xdNL2KPAhNzf4619tS6I5cZuZOoncoNYldec4F5JLkO/N/rtFO5Q33mvVMrgOSIH5OIH7dlGj+8+XWqcidSjY3CDlAudQWMvuIJvXGH3aS8m+iGDR8CMu2x+htyJw0Lc63wEVgiA2yKnIg8pMJa27fWtuXOgC2FDx8aJtF1EGHUtjvvE/kBSI74sXzEj4S8vlgPJL0+q0A9jQEKgJLwbgSS2/EwaPLG+Tr03d5+VW41vDR7bP8k2x/lP2rbzmjZ6QAb8hRqGW6U0j/9QcfhZ60deB1PY88WvaTrwD/UZ2odbAHkA/GD+DHapvdrruKHVyv0SG5BJ9ZEon4UdoCCCxixpBfxJC1oVJTIm7x383sk9yY1Hu3FGNONWm9GHhcVe8jJeRljzuW2ud6KWOwmpd21FdIkwOdE0r4xphr1NCuF6yCOVf1DAGAFiB/pIn5EWuQ7PJfUSVLQkWjGBpTBPWmiKUFq/Sq8QHUlN0h15q069rk3a4GY+R+lXUYss6j6jDxsKryKf15xnvDdSOHtwH2Wdh3oRbOFZZ53AKtH/EgX8eO2heKHMeZEUtdODds9Tx8gCjtAuQwk3Q9LTNhR0Ql5wjO+0Xhj66dhILdzZZimpOGSmxxcWGtrQQle8K7oRRvkhYU0FfDb1vztydO+DppSZAdUAMVH/Fgc8eO2xPHDG5AgbH6iWcc+RmEHKJZREwD/Ew3/729JqkfcjGZVeU/bVsDTGF+73jveT3+zgFl5jPKW3Bmhw5ZvaM5RWBI4iwi+bbnB8lbwXmCkmZMZwaMj958Gv6VcB77v1f/ZaFjTea8dAPlC/CB+SAWJH955HNrbE9CO3An5/BYKO0C+jG4ugTc+7+mGo5Bqfe/JSVvujMoT2/CeAJ0FrBZ1k31H0nbAjbghdwjKW/mdlUf/8tPb9eX/3ekVvLbQQW12N4O25Yl9M5zKw93pgOWdv4YCnhx6eXvuzcA9r6bcYTlvPaXyqu9vPRld0nUgBf+j8K7cp5Vh7bfjmvu7ADAX4gfxw7/dwsYPr+DUldQ0xpwEvOaqHTPeBD0AMuR11mzoxZCUjtwnYkHtVKtyOx0OJD2TdDpdLe/dKNredq7lBocLX+fQ0Ta2vbSh99oL2Na+3Btqz9uerLWn3jbOvfVO/PkMy6N3Izyf2u+JtXaiw6OX//veune8ZU/8gSpkW31rbduXt9H5DNxPFC9YNeUGw4qkgbW2E7LsvtzzPe8+WqMg4O1v1HRgFDDOw4JEGteBPx9e3o33+2j/NblNCKLmrog8PrlPUqt68Y+NoxfX21srHAUJKCXiB/GjbPHDGBNniOt23EIUhR0ASIk/+BSJP1hlnRcAWEfEj+WhGRsAAACAUqKwAwAp8Kr8GZoZADAX4sdyUdgBgHTcZ2hmAEACxI8lorADAAvyRtE5yTof8zLGVL1Rbbre+2szx8zmAIDFED+WjwEKAAAAAJTSx7LOQJH80i/9kn3llVeyzgYAlM7V1dU/Wms/kXU+loX4AQDLMSt+UNiZwyuvvKLLS/qPAUDajDH/T9Z5WCbiBwAsx6z4QZ8dAAAAAKVEYQcAAABAKVHYAQAAAFBKFHYAAAAAlBKFHQAAAAClRGEHAAAAQCnlZuhpb9bVnrW2P/X5vqShpKqkvrV2sMw0AAAAAOWQeWHHGNOQVJe0K6k3lXYu6WhUGDHG9CQ1l5UGAAAAoDwyb8Zmre1ba4/l1rRMq0/Vugy9wtGy0gAAAACUROY1O2G8Aogz9bEjqWmMUdppkvoCAAAAUBqZ1+xEqAR89kxuP5tlpAEAAAAokTwXdjZXnIY1NhwGtaJcLseZrmRcDf+xDgYDtdttnZ6eLnWfp6enarfbGgzmGwtkOn+ryu+80jq+vJg+nqDjy+r6BfKG+EH8WATxY/ly24xN0s2K0wIZY1qSWpL0qU99at7V19N7B9ns91eT7bff72tzc/Xl4NPTU7VaLVUqQRWOyzF9rDc3N9rb21Oj8aLb2vHxsarVqobDoRqNhur1+lz7CFq/1WppMBjo5ma+P8Hp/AXlt9PpqFar6fr6Wu12W9Vq8orafr8vx3G0u7sbmD4KKvfu3dPNzY1OTk7U7XZTO7408jKvoPM3fTxBx5fF9Vs0xI/5vd17P5P9/nbz04nWI34QP0aIH/mNH3mu2XF0u9nZHbkDGSwjLZC19tRau22t3f7EJz4x1wEg/xzH0WAwmPuGnIb9/X0dHR2tbH9xjnV049zd3dX+/r46nc5c+1h0/Xk1m03dv39frVZL3W5Xe3t7C22v0+lEBhzHcdRut7WxsaFms6l2u73Q/rLOyyLnb9XXbxERP8qN+DEp7v3/4PFB4OtzO5/TdeWa+JEC4sek3BZ2vPl2ph+XVPRiLp5U09LJNYrm6OhIrVYrs/3XarW5q66TinOs08GsWq2q348/dsei689jOBzq8vJyYn+bm5uJ99fv92c+1atUKrLW6vnz57q+vl7oKWDWeUnj/K3y+gXyhvgxadH7/9O/eqqXP/1y4vXnQfxYr/iR28KOp2+M8T9GqPomHV1GGtbMcDjMtBnOvXv3dHZ2tpJ9zTrWfr9/K71SqajXi/csYNH15zUYDG41H6lWq4lvno7jxG6OsuxrZhV5SeP8rfL6BfKG+PHCovf/4dVQL/3LlxKvPy/ix2J5KVr8yLzPjlfwuC+pMXrvzbsjSW9JemCMqUq6K8lfp7mMNKyRwWAQ+jSj3++r1+uN27BOv4+7/U6no8vLSzmOM97X1dXV+AZTqVRW8mQj6lhHgjoM3rlzR0+ePIm1j0XXn1elUgmspr++vp57WxcXF9rd3Y0VWE9PT7W5uaknT57o/v37qTdhWVVe0jh/q7p+gbwhfkyKe/83xkg7ARv4B0lPpcPDQx1+8VDWWuJHAsSPYJkXdrwJPgcKKHRYax3f5xfLTsN6GQ6Hunv3bmDa+fm5ms3mxPutra3Y2+73++p2u+MbTrPZVLfbDb2ZOI4T+YQlTtvlZrMZ2FFRij7WkXk7R6a9/ry2t7dvBdgkT1pnnXu/RqMxDvq7u7uq1WoT/3wsapV5Sev8SfPlGygD4sekhe///+9iq8+L+LFe8SPzwg6QleFwGBo8RsFm5PLycq7Okp1OR+++++74fb1ev9W+dWT0hCTqjz3JKCl+Ucc6suiIQqsekahSqWh/f1/9fl+NRmN8o5237XG/3w8dsWba9LYrlYreeeed1NrtrzIvaZ2/ONcvUDbEj0nz3v8fPnw48f6Hf/FDfec/fkdP9XTu/CVB/Fiv+JH3PjvA0jx79izw89HTCv8f33A4jP1HPKryn15/e3s7cPlVFBLCjtWvUqncelLz7Nmz2Me96PpJdLtdOY6ji4uL8b5rtVrs9ef5XofDoTY2NiY+q1ariZo95CUvi54/afWFXCAPiB+TFr3/v/QLL+mj//JR4vWTIH4slpcixQ9qdrC2arVaYDvjfr8/8RRr9ORCindDuby8vFXlHzVk583Nzcw/+DjDQoaNty+FH6tfo9G41RTBcZzYw0kuun5S/idZw+FQ9+7di73uaOz/y8tLSe53558bYNr0E1LHcea+uectL4ucPyne9QuUDfFj0qL3/+pWVR/+9MPE6ydF/FgsL0WJHxR2sLY2NzcD2xn3er2JP76TkxPdvXt3opPmqFNdUADa3t7WycnJ+P3x8XFkM4I47VX920si7FinNRqNicA6mthNij7mOOtPi7O9WTY2NvTBBx+oUqno4uJC9+/fH5/LONufrvLv9XpqNpvj4ODfRrVanQj4juNoOByGVvvPe3xp5iXuvqPOX1z018E6In7cFid+RKluVfXDf/rh+D3xg/iRFgo7SN+vHmSdg1gajUZgO+rRk7mLiwtVKhU9ePBAZ2dnE+2Wz87ONBgMAkc8qdfrqtVq4xmK6/V66A17VcKOddqjR490dHSk4XCoJ0+eTATZqGOOs/60ONubpdvtqt/vjwPx/v5+4u0fHx+PZ53e3NzU7u6uzs7O5DjO+J+FVqul4+NjVSoVXV1dRW57keNbNC9x9x11/oAs/Hbz01lnIRbix21x4keUN7/+pn7437uFnU6nQ/wgfqTHWssr5mtra8uiXHZ3dyfeP3/+3FYqlVjrnp+fL7z/q6sre3JysvB24pg+Vmut7fV6ttfrxd7GIsd8dXV1a1+ztjedv1Xmd15pHF+aFj326eMJOr40r19JlzYH9/llvYgf5UP8mP9+LMlqR/bhnz8MfGlH1v3XdBLx4zbixwuz4gc1O1hr7XZ7ok21//dVODk5WXiknLimj3VRBwcHidJQHqu8foG8IX4Aya3y+mU0Nqy1URvjkeFwqPv3789cL6rDaFyDwUDNZnNl/R2mj3VeaRzzMre36u3nef+r2Peqr18gb4gf8RE/irP/MsYPCjtYe61WSxcX7vyy+/v7scapH3XyW8Tl5WXsMfHT4j9Wye14enJyMm4fHmWRYz49PdXR0dFEx90425vO36ryO6+0ji8tix779PEEHV8W1y+QN8QP4seiiB/LZ9ymbohje3vbjob1A9YdzdiQJmPMlbU2eDKREiB+AJIxRtq5PanoyOHhofTY7U8OxDUrflCzAwAAAKCUGKAAwNyMMZHph4eHPJkDAACZo7ADAACAfNiRDh4fhCYf7ISnAUEo7ABILKjd9eHhYQY5AQCURVQcOfwiLQcwHwo7AAAAWNhoAIJQUWnAkjBAASB3foRVcxxn5fuUJo91MBio3W6nPnTltNPTU7Xb7bnnaZjO36ryO6+0ji8vpo8n6Piyun6BvCF+pHf/evjw4a3Xv63/W9V/oa7Wv2vNtS3iRzbyGD+o2cHa6/f7E+O/r8rp6alardZKJ2WcPtabmxvt7e1NzIp9fHysTqez8L6Oj4/VaDRUr9fVarU0GAx0c3Mz1zam8xeU306no1qtpuvra7Xb7YXmB+j3+3IcJ3T8/1FQuXfvnm5ubsYzQKd1fGnkZV5B52/6eIKOL4vrF8gb4kfA/esfpMZ/21B1q6qXP/3yXPv4z//rf9bGL2/o+d89H6+/9eaWfvn9X9aH//ThXNsifhA/RqjZwVpzHCezmYr39/d1dHS0sv3FOdawG2dc5+fn499/9rOfaW9vTwcHBzo4ONDJyYn+8A//MNU5eJrNpu7fv69Wq6Vut6u9vb2FttfpdCIDjuM4arfb2tjYULPZVLvdXmh/WedlkfO36usXyBvix6Tx/eO/lv7Nv/s36p/259rH+cG5qltVffa1zyZaf17Ej/WJHxR2sNaOjo7Uas1XNZ6mWq02d9V1UnGOdTqYtVot9Xo9WWtvvYIGJ3j69On498PDQw2HQx0eHurw8FCnp6f69re/rcPDw5lDV8cxHA51eXk5kd/NzU31+8kCZL/fn/lUr1KpyFqr58+f6/r6emkzbK8iL2mcv1Vev0DeED8mTeel8nJFw6v4Tfye/tXTiZqgedefB/FjveIHhR2steFwmGkznHv37uns7Gwl+5p1rP1+/1Z6pVJRr9eLvf2XXnppoTzOYzAY3Go+Uq1WE988HceJ3Rxl2dfMKvKSxvlb5fUL5A3x44Wg+PHSL7yk68vreNu/GuqlfzkZP+ZZf17Ej8XyUrT4QWEHa2swGIQ+zej3+xP9Vqbfx91+s9nUxsaGjDGq1Wqq1WoTHfMqlcpKnmxEHetIUIfBO3fuxO58+9FHH0ma7GDaaDT02c9+Vg8fPlSr1dJv/uZvzp/5EJVKJbCa/vp6/uB4cXER2rZ52unpqS4uLtTpdJby3a0qL2mcv1Vdv0DeED8mBcWPf/HxfyHnabyO6B/980cLrT8v4sd6xQ8GKMDaGg6Hunv3bmDa+fm5ms3mxPutra3Y2+73++p2u+NakWazqW63G9re2XGcyCcscQJls9kM7W8Tdawj83aOnPbhh/N1Hl3U9vb2rQCb5EnrrHPv12g0xkF/d3dXtVpNV1dXqT2pW2Ve0jp/0nz5BsqA+DFp4fjxU+LHoogf4SjsYG0Nh8PQ4DEKNiOXl5dzPZnrdDp69913x+/r9fqt9q0joyckUX/sSUZJ8Ys61pFFRxT6+Z//+YXWn1elUtH+/r76/b4ajcb4Rjtv2+N+vx/7Sdj0tiuVit55553U2u2vMi9pnb841y9QNsSPSQvHj48TPxZF/AhHMzasrWfPngV+Pnpa4f/jGw6Hsf+IR1X+0+tvb28HLr+KYUvDjtWvUqncelLzx3/8x/q7v/u78Yhq/te0l156adyUbeRnP/vZUm9i3W5XjuPo4uJinPdarRZ7/Xm+1+FwqI2NjYnPqtVqomYPecnLoudPWs31C+QN8WNSUPz42U9/psrL8e7/L/3CS/rov0zFjznWT4L4sVheihQ/qNnB2ppu/zzS7/cnnmKNnlxI8W4ol5eXt6r8o4bsvLm5mfkHH2dYyKhho8OO1a/RaNxqivDRRx/pc5/73Mx9S+7Ncrop2zzrJ+V/kjUcDnXv3r3Y647G/r+8vJTkfnf+uQGmTT8hdRxn7pt73vKyyPmT4l2/QNkQPyYFxo9//kif24kZP7aqt5qyzbN+UsSPxfJSlPhBYQepSmNI4UVYa2Mvu7m5GdjOuNfrTfzxnZyc6O7duxOdNEed6oIC0Pb2tk5OTsbvj4+PI5sRxGmv6t9eEmHHOq3RaNya5Xh0zKNhpV9+OXySuGq1qqdPn46X8a8/LeocxrWxsaEPPvhAlUpFFxcXun///vhcxtn+dJV/r9dTs9kcBwf/NqrV6kTAdxxHw+EwtNp/3uNLMy9x9x11/uKivw7SQvwofvy4uLh4kbenjqpbXvx434sfEZOMVreqevr+i+Gn/etPI34sLy9ljB8UdrC2Go1GYDvq0ZO5i4sLVSoVPXjwQGdnZxPtls/OzjQYDAKHZa7X66rVauMZiuv1+kITdaYh7FinPXr0aGKiL3++f/CDH+jv//7vI0dUe/PNN/WXf/mXev78uf72b/828rijzmFc3W5X/X5/HIj39/cTb//4+Hg86/Tm5qZ2d3d1dnYmx3HG/yy0Wi0dHx+rUqno6uoqctuLHN+ieYm776jzByAc8eO2R48euYWdf5B6Jz01Wr748ec/0N//1d/rN78ZET++/qb+8j/+pZ4/fa6//b/+dmL9acSP5eWljPHDzPMkY91tb2+eSnjjAAAgAElEQVTbURUhgo2ezK36ukq63729PZ2fn4/fO46jV199Vc+fP5+57jxDPIYZDAa6vLxcycR008cqaTwB2HQwHZ3P6YlDf/jDH+qzn/1sov0/ffpUH374ob797W9Lcr+rWedwOn9h+Q2TxncU16gZgT9v8x5fmhY99unjCTq+NK9fY8yVtTa4Y0IJED9mI37MJ4/xwxgj7dyOHZL0w7/4oT77WsL48f5TffhPH+rb/8e3pcfED4n44TcrfjBAAdZau92emPHX3756FU5OTuZu45rU9LECi1rl9QvkDfEDSG6V1y+FHay16T4qw+FQ9+/fn7leVIfRuEaTxq2qv8P0sc7L3xcnDWmcwyy3n+f9r2Lfq75+gbwhfsT39P2nevlfET+KsP8yxg8KO1h7rVZr3Klyf38/VtXtqJPfIi4vL1dWRT7iP1bJ7Xh6cnIybh8e5eWXX741XGVcV1dX+s53vjMxF0+cczidv3nym8Z3FNfp6amOjo4mOiYnOb60LHrs08cTdHxZXL9A3hA/YsaPT7+sjV9OGD/+9yt953/5jn7+vyJ++BE/4qPPzhxocz1b0dpcI1hYn500HB4eSuK7wiT67ID4UXxRfXbScHh4OO6zA4zQZwcAAADAWmLoaayt4XCobrer09NT7e7ujidyu76+VqVSCZ3boN/v6/z8XLVabdzedDSM4/7+vgaDgV5//XXdu3dPtVpNvV5P/X5f3W5Xz549G49KcnV1FZmHqEneAADZIX4AxUEztjnQDGG2IjZDMMbo6upqokPexcWFjo6OdHV1NbHs8fGxnjx5cmsIzr29PTmOo16vp4uLC1Wr1fH2Li4u9NZbb00MR9rpdCaCYVAearWaOp3OSoYVldx2taN90YwNq0YzNhA/ih8/aMaGLNCMDZkwxqz0lbbd3V1tb2/r+Ph4/Fm/39fJycmtQCXp1lO8WSOZjJ7AzcpD1MzZaXIcR9fX1yvZFwBEIX4QP4A0UdgBQuzt7U3MGt3pdNRutwOXrVar49FL4oxiEnekk1WMBuM4jt56662l7wcA1gXxA8gPCjtIlbU201eatrfdGtHhcChp9tjzo6doccann7WM4zgaDAY6OTkJXeb09FQbGxtqNpsaDodqNpsyxoyHBt3a2tLW1tZE/judji4uLsY/RxPFjfZ3fHzMxHEAMkH8KH78APKIAQqAEKPOo/6J1PzjxIctn1S/39dwONSTJ080GAx0fn4euc1Wq6Xr62vduXNH1WpVvV5PGxsb47Hru92utre3x9vY29sbNzXo9XqSNA6Gw+FQz5490/7+/kLHAABY3/jhr81amh3p4PFBaPLBTnga1hM1O0AIx3EkTTYxuLm5Wdr+Go3GuJ313t6etra2Zq7TbrfHAWcwGKharY5rZhzHmQh2oyd0ktt59cmTJykfAQBAIn4AeUJhBwgxCkyjJgP1en3iKd00/8zSi2q1WhoOh5H7k160yR4Oh+NhSEcdYKef6jUajXHAevLkie7fv59afgEALxA/gPygsAOEODk5mWjW1e12I9tAL4P/aVqY3d3dcaBsNBrq9/vq9/u35lgYta8etbUOa/edZtAFgHVU5vhxcXGhra2t8Wh2nU5Hx8fHSxnZDkgDhR0gwOiG/+DBg/FnjUZD7XZbe3t7t5YfTeqWpmq1Om4qEBW02u22jo6OJp4gBg1v+uTJE7VaLe3u7t4KZNVqddzswu/p06d6+vTpIocBAGul7PEjrqfvP9XT94kfyB4DFGBtjartJeno6Gg8d8GzZ88k6daEcJK0v7+vfr+vdrt9awbsoO1fXFzo7OxMjuOo3W5ra2trYlnHcfTOO+9Icp8EdjqdcdOC8/Pz8ag3lUoldBjRarWqRqMxTm+324EFlzt37mhjY0Obm5uqVCra3t5Wt9tVpVLR7u6uzs7OdHp6OrGfH/zgB/roo4/05ptvzjibALA+1jl+jDx//nxiAINms6lqtarvfOw7kqQf/PkP9NE/f6Q3/wfiB7JlmIU2PmbARlENBgP1+321Wi1VKhU5jjOe5G40so7fqDnCMmbBPjw8lMQM2Jg0awbsoiN+oKim48coPjQajcD4ETVS2qJG8SMqNjEa2/qZFT+o2QHWQL/fV71eHz+FG9XmrLoNOQCgWKbjRx6MCj2BaV885GEaJlDYAdbA/v6+jo+Px8OL3tzcyHGcwLbZAACMTMePEeIHioLCDrAmmDAUAJDErfixI/2H7/+HledjVtPqqBofrC9GYwMAAABQStTsAMjEwcFBojQAAIC4qNkBAAAAUEoUdgAAAACUUu6bsRljKpJakhxJFUkDa23fl74vaSipKqlvrR0smgYAAACg+HJf2JHUstYej94YY7rGmEtrrWOMOZd0NCqoGGN6kpre74nSsJ6Gw2HoDNPL4jhOZvMWjCaFAwAsZt3iB1A0RSjsNCUd+95fy62NGUiqT9XIDI0xDa/mJ2kaFpRV5/Kk++33+9rc3Ew3MzGcnp6OZ6RetsFgMJ5b58mTJ0vfHwAkcfD4IJv97iTbL/EDyL8iFHY2jTFda23He9+01p4aYxpym7b5OZKa3lPrudMkUdhZM47jaDAYZDIHzf7+vjqdjrrd7lL34ziOXn/9dT1//lySVK/XdXzsPj+YNWcBACDYuscPoCiKMEDBW5Jaxpgrr5/NqNAT9Djjmdxan6RpWDNHR0dqtVqZ7b9Wq2kwWG53sUqloqurq6XuAwDWDfEDKIbcF3a85mancgspXb0olETVGydNu8UY0zLGXBpjLn/84x/PsyoKYDgcZtru+d69ezo7O1v6fvztyZf9JBCAi/hRbsQPoBhy34zNGHMiqWut7Xi/94wxW5JuIlZLmnaLtfZUbmFL29vbdp51kW+DwSC0U2m/31ev1xvf2Kffx91+p9PR5eWlHMcZ7+vq6mocICuVytKfzPkNh0PVarWV7S/K4eFhZJq1/Lmh2Igf5UX8AIoj14UdY0xd0rW1dihJ1tq2MeZaUlvSuW43SbsjdzhpJ2Ea1shwONTdu3cD087Pz9VsNifeb21txd52v99Xt9tVr9eTJDWbTXW7XdXr9cDlZ42s0+l0QtNGms2mGo1G5DIXFxfjtt4AgGSIH0Bx5LqwI7fJ2nQh5FRuTU/fGDPdJK0i6TxpWmq5RiEMh8PQ4DEKNiOXl5dz3eA7nY7efffd8ft6va7Ly8vA/VUqFd3c3EQGq7SaDuRhqNJZgyJE1fgAQB4QP4DiyHufnb6k+1OfNSSdjNK92p+Rqm/46KRpWBPPnj0L/Nxx3MH6/Df2eeZRGDVvmF5/e3s7cPlVDlu66rkgAKCMiB9AceS6ZsebOPTIGNOVO7+OJA19c+S8JemBMaYq6a5ejNS2SBrWRK1WGwcmv36/P/EErd/vj6v34wSty8vLW80bBoNB6FPAm5ubmQGr3W5HpkvS3t7ezGYIs9IBALMRP4DiyHVhRxqPxhbYA89a6+hFQeUijTSsj83NTd3c3B6votfrTQSPk5MT3b17d6JD6qhTaFAA2t7e1snJyfj98fFxZDOCODNh+7eXlOM46vf72t3dXXhbALDOiB9AceS9GRuwNI1GI3D+gH6/r5ubG11cXKjf7+vBgwd69uzZxDCjZ2dnoW2w6/W6arWaTk9PdXp6qnq9nosA0e/3dXR0lHU2AKDwiB9AceS+ZgfFc3BwkHUWYhl17PRzHEc3Nzc6P58cr2L6CVy329XFRXilYNwZtQeDgfb29mLmeDG7u7u5CJoAEOZg5yDrLMRC/ACKg5odrLV2u61+/8XYFP721atwcnKie/furWx/AIB0ED+AYqCwg7XWaDQmJmUbDoe6f396AMDbojqMxjUYDNRsNhnOEwAKiPgBFAPN2LD2Wq2WLi4utLu7G7v5wKKBSnJH3Wm1WgtvBwCQDeIHkH/U7GDtVSqVTNoiE6gAoNiIH0D+UdgBAAAAUEoUdgAAAACUEoUdAAAAAKVEYQcAAABAKVHYAQAAAFBKFHYAAAAAlBKFHQAAAAClRGEHAAAAQClR2AEAAABQShR2AAAAAJQShR0AAAAApURhBwAAAEApUdgBAAAAUEoUdgAAAACU0tIKO8aY31jWtgEAyANiHQDk21IKO8aYj0vqLGPbAADkAbEOAPLvY0lW8m7wF5JeD1tEkk2aKQAAskasA4DiS1TYkfQHks7lPtEaBqTfkfRO0kwBAJADxDqgaHakg8cHockHO+FpKKekhZ2etfZRRPpPjDFHCbcNAEAeEOsAoOCSFnZuZi1grf2jhNsGABljItOtpfUQlo5YBwAFl3SAAscY80rUAsaYryfcNgAAeUCsA4CCS1qzYyXtGmNqkq50++nXpqS2pG8ukDcAa+jhw4eR6YeHhyvKCUCsA4CiS1rYufB+DiXdDUivSHo14bYBAMgDYh1QQFEPxQ6/eEgz6DWTtLAztNZuRy1gjGGEGgBAkRHrAKDgkhZ29mIsw0RrAIAiI9ZhLUUN3aydVeVifjSDRpBEAxRYaz+IsVjYJGwAAOQesQ4Aim9mzc5oJBpr7V/7PvvvYmy7I3dCNgAZODg4yDoLQGEQ6wCgnOI0YxtIutZk58zRjT1oRmmJTptApmbNUQPgFmIdAJRQnMLO65Kcqc/otAkAKBNiHQCU0MzCjrX2ewEfx2mjTKdNIGOzOmsCcBHrAKCcEo3GZq39yeh3Y8zHJY2efF1aa3/qLROnYycAALlErAOA4ks69PSoM+ep3Cdfow4C1hjTl7Rrrf2nhXMHAECGiHUoo7d774emfe2Nz+R6eGlgXomGnjbGvCqpL+lc7pOuDWvtz0n6FUnvShp4T8EAACgkYh0AFF/Smp2WtfZXpj/0qvOPjTGnkh54LwDL8N5B1jkAyo5Yh7X1xpe+Gpr2hdqdFeYEWEyimh1Jl1GJ1lpH4UN1AgBQBMQ6ACi4pIUdm9IyAADkFbEOAAouaWEnzoyFzxNuGwCAPCDWAUDBRfbZMcb8mqTNgKTnxpgzST3drsKvSqpYa7+ZThYBAFgeYh0AlNesAQr+QFJd4W2St8JWNMbUrLVfSZoxACn40ePwtE/urCoXQN4R64A5fPf6WWgagxcgb2YVdoaS9pg0DQBQYsQ6lErUPDrAupnVZ+eImz8AoOSIdQBQUpGFHWvt91aVEQAAskCsA4DySjqpKIBVYOJQAACAxCjsAOsqavACiQEMAABA4SWdZwcAAAAAco3CDgAAAIBSKkQzNmNMVdKuJEeSrLWnvrR9ucOGViX1rbWDRdMAAAAAFF/uCzteQadrrd3z3l8ZYy6ttQNjzLncIUMHXlpPUtP7PVEaAAAAgHJI3IzNGPPxNDMSoSvpxPf+dV8tTH2qRmZojGksmAYAgKSVxjoAwBIkKuwYY/6TpKVPwGaMqUjatdb2R59Zax0vrSGvWZuPI6mZNC3NvAMAim1VsQ4AsDxJa3bO5fZ1WbaqJMcY0zDG7Bpj9n01MJWA5Z956yRNAwBgZFWxDgCwJEkLOzeSNqIWMMZ8K+G2/UZB5sZae2GtPZbU9frxbEaslzTtFmNMyxhzaYy5/PGPfzzPqgCAYlso1hE/ACB7SQcouJa0a4y54/0+DFgmjT4wjqTKdP8aSW1JTyLWu0mYdos38tupJG1vb9t51gUAFNpCsY74AQDZS1rY+TO5zcGCbvySW3vyiwm37TfU7f41o+Gie7rdJO2Ob50kaQBGfvQ4Ov2TO6vIBZClVcU6AMCSJC3sDK2121ELGGPeSbjtMWvt0BukwK/i7b9vjJluklaRdJ40bdH8AgBKZSWxDpjX2733s85CqO9eP4tM/0LtzopyAriS9tl5K8YynYTbnnY8NSz0tqQj7/e+MabuS6v6Rm5LmgYAgLTaWAcAWIJENTvW2u9J4/kHtuUOIPB977Nfs9Z+31qbynCd1tqOMWY0KEFN0luj4aflBqIHXtpdTQadpGnA6rx3kHUOAIRYZawDACxH0mZsMsb8nqR9Sc8lvSPpK17ST4wxX7fWfjOF/ElyCzwhnzt6UVC5SCMNAICRVcY6AED6kk4q+jverxvW2juS/JN+fmCt/aYx5rfSyCAAAFkg1gFA8SWt2XGstd/wvQ8aUvN5wm0DAJAHxDoAKLikAxRED7XhejXhtgEAyANiHQAUXNLCzr+eem8m3hjziqRfSbhtAADygFgHAAWXtBnbiTHmiaR/L+l78qr2vRv/nqSWpK0U8gcAQFaIdQBQcEmHnv7AGNOWdCqpLskaM37g1Zf0hrX2p+lkEQBuOzg4SJQGxEWsA4DiSzz0tLV2IGnbm6vm172PB8w5AAAoC2IdUDI70sHjg8Ckg53gz1FsiQs7Pv+oF6PRxOnMCQALOzw8jEyzNmjgLCAxYh0AFNAik4q+Irdq/3W96LRpjTE9SXvW2n9aOHcAAGSIWAeUT9jDssMv8qCsjBIVdowxr0q6khsAOpKGXlJNUlvSwBizRVtmAGl7+PBhZHpUjQ8wD2IdABRf0pqdfUlbAW2WB5Laxpi6pK6kryySOQAAMkSsA1L23evoVqBfqN1Z2r6jHpbxoKy8ks6zM4zqnOl16ByGpQMAUADEOgAouMSFnZSWAQAgr4h1AFBwSQs7MsZ8fEaanfrs60n3BQBAFoh1AFBsSfvsOJIeGWPOvN/9qpKaks6MMb/hfVaR25nzmwn3BxTTewdZ5wBAcsQ6ACi4pIWdc7k39a2IZfxpm5J+MeG+AADIArEOAAouaWFnaK3dnmcFY8w7CfcFII9+9Dg87ZM7q8oFsEzEOgAouKSFnbcSrNNJuC8AALJArENm3u69n3UWgFJINECBtfZ7CdYJHb4TAIC8IdYBQPElrdkBkLGDbz3OOgsAgAL6k7/5/fDEnZVlA1gJCjtAAZnPM9MzAADALInn2QEAAACAPKNmByiwh19+LessAAAA5BaFHQAAgDXxtTc+Q78crBWasQEAAAAopVRrdowxH5e0aa396zS3CwBAXhDrUAZvfOmrWWcBWIlENTvGmN8zxvypMebr3k1fxpg/lXQl6XeNMWfGmFfSyyYAAKtFrAOA4ktas/NE0slo8jRjzO9Iqlpr/9VoAWPM1yV9c/EsAgCQCWIdABRc0j47G1OzRLcldaeW+UnCbQMAkAfEOgAouKSFnfHN3xjzqqRXJfWnlnmWNFMAAOQAsQ4ACi5pYecXfb/vSvogoKPmnYTbBgAgD4h1AFBwSfvs/MRrp2zkVunvSuMRapqSflfSXio5BAAgG8Q6ACi4RIUda+27xpihpIakmq9N831JFUnvSKpL+us0MgmgYH70ODr9kzuryAWwEGIdABRf4nl2vJv+o6nPHoUsDgBA4RDrAKDYEhV2jDG/Zq39/tRnvyjpntw2zo619g9SyB8AAJkg1gFA8SWt2WlL+or/A2vtT+R7+mWM+S2CAACgwIh1wIp99zp6gMMv1BgTBPNJ3IwNgKT3DrLOAQCggN7uvZ91FoC1EKuwY4x5XZL1fVQ1xvxGyOIVSXe9nzztAgAUArEOAMonbs3OUO6IM/flDr1pJdUilu9Za78SkQ4AQN4Q6wCgZGIVdrzRaD6Q9EfGmH1Jr3KDBwCUCbEOAMrn5xKscyL36VekiKp/AADyjlgHACUwd2HHWvsTa+03opbxZpfuJM4VAAAZItYBQDkknWfn45IuJL0etogmO3kCAFAoxDoAKL4kzdgkd+SZc0nbkjYDXr8i6XtpZBAAgIwQ6wCg4JLOs9Oz1j6KSP+JMeYo4bYBAMgDYh0AFFzSmp2bWQtYa/8o4bYBAMgDYh0AFFzSwo5jjHklagFjzNcTbhsAgDwg1gFAwSVtxmYl7RpjapKudPvp16aktqRvLpA3AACyRKwDgIJLWti58H4OJd0NSK9IejXhtgEAyANiHQAUXNLCztBaux21gDHmnYTbBgAgD4h1AFBwSfvs7MVYhonWAABFRqwDgIJLVLNjrf1AGk+4ti3pxlr7fe+zX7PWfn+0TJqMMQ1JFWvthe+zfblNDKqS+tbawaJpAIrv4OBgrs+BaVnFOgBAepLW7MgY83uSHLkTrrV9ST9Z4ug0XbkdQkd5OJdbULmw1h576QulAQAwklGsAwCkJFFhxxjzO96vG9baO5L6ozRr7QfW2m8aY34rjQz69tmQWxPjV5+qkRl6yy2SBgBAJrEOAJCupAMUONbab/je24BlnifcdpiKfMN+eoUTZzpfkprGGCVJky+QASi2w8PD0M+tDbplAbdkEesAAClKWth5FmOZ1IbjNMbsWmsvjDFN38eVgEWfyR0eNGkaAAAjK411ADK2Ix08PghNPtgJT0N+JS3s/GtJ/5vvvfEnejNO/0rCbU8wxlR0uyZG8vXdSTENQME9fPgwNC2stgcIsbJYBwBYjqSFnRNjzBNJ/17S9+RV7Xs3/j1JLUlbKeRPkhr+0dd8pmeyTiPtFmNMS+7x6FOf+tQ8qwIAim2hWEf8AIon6qHY4RdpBl1EiYeeNsa0JZ1KqkuyXl8Yye338oa19qeLZs4YU9XtQQlGHN1uknbHWz5p2i3W2lO5x6nt7W2ucABYE4vGOuLHenu7937WWSil715Hty79Qu3OinKCokhU2PHmFxhI2jbGvCo3CEjSIOU5B+qSNo0xoxmst733staeGmOmm6RVJJ1ba/tJ0lLMNwCg4FYY6wBkLKoJtEQz6CJL2oztkbwO/d4Nfyk3/enma94ABT3vaZkk9Y0x/mGkq9ba/oJpAABIK4p1AIDlSVrY2TLGnEk6sdb+WZoZCmOM2ZfUkFQxxtx4BaG3JD3wmrvdldTxrZI0DXjhvYOsc1BOP3ocnf7JnVXkAphl5bEOAJCupIWdjrX2G8aYV40xb8nttNm31v51elmbZK09lnQ89ZmjFwWVizTSAADwrDzWAQDSlXSAgm94Pz+QW80vY8yve83MnskNBgsPUAAAQFaIdQBQfD+X1oastd+z1j6SOw/BB8aYb6W1bQAA8oBYBwDFkrQZ2wRvzoG23PkEbuQ+ATtKY9vAOjv41uOsswDAQ6wDgOJJOvT0maTfknRf0pcl/brcvi/3rLXvppc9AACyQaxDUf3J3/x+eOLOyrIB5ELSmp09SbuSBnJHqXmUXpYASJL5PGP6Axkj1gFAwSUt7DiStphUDQBQYsQ6ACi4pIWdI27+wGo8/PJrWWcBWFfEOgAouESjsY2G4xzxOm0CAFAaxDoAKL6ZNTvezb3qvaTgCdVq3rwDNbkdOIeSrq2130wtpwAALAmxDgDKKU4ztqGknqR22KzR3qg070qSMeYXJf2B3KE5CQAAgCIg1gFACcUp7DiS9uLOEm2t/YmkPWPM/7dQzgAAWB1iHUrha298huGlAZ84fXYu4978pwwSrAMAQBaIdQBQQnGbsY0ZY35HUlPS63Jv8meSBtbaP4taDwCAHCPWoVTe+NJXs84CkAtxCjt24o07Os03jDE31tq7EevdLJQzAABWh1gHACUUpxmbCfm8n2ZGAADIELEOAEooTmHHhnz+LOF6AADkDbEOAEooTjO2bWPMF3X7qVc15PORxkI5AwBgdYh1AFBCcQo7W3Kr8YNu9M2I9XjaBQAoCmIdAJRQnMLOQNLenNs1kt6ZPzvAir13kHUOAOQDsQ4ASihOYadvrf1g3g0bY+jUCSCZHz2OTv/kzipygfVCrANK4LvX0d3svlC7s6KcIC9mFnastb+bZMNJ1wMAYNWIdZjX2733s84CgBjijMYGAAAAAIVDYQcAAABAKVHYAQAAAFBKFHYAAAAAlBKFHQAAAAClRGEHAAAAQClR2AEAAABQShR2AAAAAJTSzElFAaBsDg4OEqUBAIBioWYHAAAAQClR2AEAAABQSjRjA7B2Dg8PI9OstSvMDQAAWBYKOwAAAMAsO9LB44PQ5IOd8DRkh8IOgLXx8OHDyPSoGh8AAFA89NkBAAAAUEoUdgAAAACUEoUdAAAAAKVEnx0AxfOjx9Hpn9xZRS4AAGsmcjTPLzKaZx5R2EH5vXeQdQ4AAACQAQo7QEbM5xn5CwDy6u3e+1lnATnCaJ7FRWEHAAAAa+G7189C075Qu7PCnGBVKOwAGXv45deyzgIAAEApMRobAAAAgFKisAMAAACglGjGBgAAUCB/8je/H564s7JsAIVAzQ4AAACAUqKwAwAAAKCUKOwAAAAAKCX67AAAABTE1974DP1ygDlQswMAAACglKjZAQAAKJg3vvTVrLMAFELuCzvGmIqklvf2rqQja+3Al74vaSipKqmfRhoAAACA4st9YUdS11rbliRjTFXSlTHmVWutY4w5l6/wY4zpSWp6vydKAwAAAFAOue6z4xVurkfvrbVDubUxo5qe+lSNzNAY01gwDQAAAEAJ5LqwI6kiqRvw+R2vcOJMfe5IaiZNSyG/AAAAAHIi14Udr/Zla+rjuqSe3ILQtGdy++AkTQMAAABQErnvszM1qEBL7mACfe/3MJsJ027x9tOSpE996lPzrIpVee8g6xwgb370ODztkzurygXWHPEj/97uvZ91FpAj371+Fpn+hdqdFeUEacp1zY6fNyrbnrV21NzsJmLxpGm3WGtPrbXb1trtT3ziE/OsCgBYY8QPAMheYQo7cvvu7PneO7rdJO2O3AEMkqYBAAAAKIlCFHa8OXG61lrHe1+31vZ1u0laRVIvaVr6OQcAAACQldwXdowxu5IGkm6MMRVjTF3Stpfc996PVL3CzCJpAAAAAEog1wMUePPsnAckjfrtvCXpgbfcXUkd3zJJ0wAAAACUQK4LO94koiYi3dGLgspFGmkAcHBwkCgNAADkS+6bsQEAAABAErmu2QGALBweHkamWWtXmBsAAJAUhR0AAABgUTuS+WJo7wvpsXhYlgEKOwDgefjwYWR6VI0PAADIHwo7AAAAwAJ4WJZfFHaAJTGf58YGAACQJUZjAwAAAFBK1OwAS/bwy69lnQUAAIC1RHPYst0AACAASURBVM0OAAAAgFKisAMAAACglGjGhvx77yDrHKBMfvQ4Ov2TO6vIBQBE+pO/+f3ghJ2VZgMoPAo7AAAAwAzfvX4Wmf6F2p0V5QTzoBkbAAAAgFKiZgcAAJTS2733s85CIl974zM0VwNSQs0OAAAAgFKiZgcAACCH3vjSV7POAlB41OwAAAAAKCUKOwAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASomhp5G99w6yzgEAAABKiMIOAPj96PHMRYwxkenW2pQyAyDK2733s84CgJyjGRsAAACAUqJmBwBievjl16RP7oSmHx4eri4zAIBc+e71s5nL0DJg9SjsAAAAAMu2MyP98QrysIYo7AALOPjW46yzAAAAcu6NL31VX6jdCU2nZcDyUNgBEjKf58YEAACQZwxQAAAAAKCUqNkBFvTwy69lnQUAAAAEoLADAACwQl974zPRndWj0gDMhWZsAAAAAEqJmh0AAIAMvPGlr2adBaD0KOxg+d47yDoHQHp+9DjrHAAACijOpKNIH4UdAEgZM2QD6Xi7937WWQBQcPTZAQAAAFBKFHYAICUPv/yarLWhLwAAohhjQl9IhmZsAJCig4ODrLMAAAA8FHaAEObzh1lnAQAArIE3vvRV/emf/35gGrU6i6EZGwAAAIBSomYHmMH+nw9D0w6+9Xh1GUExMDQ1ACCBg8cHwQk7kh6vLh9lQ2EH6SjxXDoUaABgORhaGniBeXiWg8IOAABAir72xmfcp/FhotIApIrCDgCsGJOOAgCwGhR2AAAAluCNL3016ywAa4/CDuIpcZ8cYFUefvk1HXxlJzSd4c4BAEgXhR2sLf6xRBYY8AIAkARNoJOhsAMAAJaC0daAlOzMSH+8gjwUFIUdrL2HX34t6ywAE6Ke3vHkDsje1974TNZZwBqZ1ffrP/3P/+OKclJMFHbgok8OACCBta292ck6A8CkqL/F325+eoU5yRcKOyg1+uWgSKIGMOBaBvKH0daA/Fvrwo4xZl/SUFJVUt9aO8g4SwDW3MwBDKJqYX81Ig1AbDRTQxFFXbdf0/o2g17bwo4x5lzS0aiAY4zpSWpmm6slWvNmavTLAQAApbUzI/3x+jZzW9vCjqT6VE3O0BjTsNb2M8sR5kbTHqyb6Gv+cG2f3GF5ytonZ2btzQ7N1FAMcQcwWNean7Us7BhjGpKcqY8duTU7xSzsFLTmhsIKkC7mYcC81rYwIzHIANbHzoz0x+WNH2tZ2JFUCfjsmaS7y9zprIsIydFMDWU36xo//J/+ItZ2lnUfKmoQLAriR0I7sxeh9gZlF6vmZ2fGRh4XN36YdQxQxpiWpLa1dsv32b6ku9bavYBlW97bz0j6v1eW0Rd+SdI/ZrDfIuDchOPchOPchMvq3Pw31tpPZLDfpSF+5B7nJhznJhznJlwu48e61uzcxF3QWnsq6XSJeZnJGHNprd3OMg95xbkJx7kJx7kJx7lJD/Ej3zg34Tg34Tg34fJ6bn4u6wxkxNHtpmx35A5DDQAAAKAE1rKw4424tjn1cUVSL4PsAAAAAFiCtSzsePrGmLrvfTXHw05n2gwi5zg34Tg34Tg34Tg35cL3GY5zE45zE45zEy6X52YtByiQJGNMRdIDSU/kjsJ2NjXvDgAAAIACW9vCDgCsO2NMV1JvulbbG51yKKkqqc+DIACAX5Hix7qOxlY4RbqokD2ui2DeUMCS9I7cfntta20nwyxlwptYuS5pV1N9FY0x55KORteMMaYnd8JlFBTxA3FxTQQjdrxQxPhBYSfninhRZYWbkYvrIlJFUlfSidyAvpbnxfunt2+MCTr++tQ/OENjTCPHfRoRgvgRD7HDxTURidjhKWL8WOcBCgrBWtu31h4reFjswItqRVnLo4rcG9FzuYH9JNvsZIbrIpxjrTWSNqy1NWstw837eNeJM/WxozUO7EVG/IiN2OHimghH7Jghz/GDwk5B5fmiytDa34y4LuKx1k6fI7im5x+TpGdym7SgJLhP3ELs4JqIhdgRKbfxg2ZsxRV2Ud1ddUbyZs1vRlwXM3hNVm7EKIxBpucfQzlxnwhA7Lhl7a8JP2LHTLmNHxR2iiu3F1WWuBlxXczQ9z21vTDGXBtjttb8nxy/m6wzgJXgPjGF2ME1MQOxY7bcxg8KO8WV24sqQ9yMuC4iBTRPcSTdU04nQsuAo9tPeO8ouM8Hiov7xCRiB9dEJGJHLLmNHxR2Vsx7erQ1Y7FujDbDub2ossLNSBLXRShjTFXSlbV2w/fxUFItoyzljrW2b4yZfsJbkXSeRX4wifixHMQOSVwToYgd8eQ5flDYWTFrbSo3zzxfVGmZJ7BzM3Ktw3WxoOnhZCuSrrPISI71jTH+UZmqWQ8bChfxIx5ix/zKfk2kgNgRTy7jB4WdYsvlRZWWBIGdm5Gr1NdFUt4/NuMnl97v1bT+gSwSY0xd0n1JjdF7b4hiSXpL0gPvn8C7uv13hXIo7X2C2JFYaa+JRRA7JhUxfhhrbdZ5QATfRbUvqS93FuxjL60i6YGkJ1rfTpVjxpj9qXNzZa1dq6dzEtdFFO/ctOQ22dhSvCY/QCERP+Ihdri4JsIRO4qNwg5Kg5sRAGBexA6g3CjsAAAAACiln8s6AwAAAACwDBR2AAAAAJQShR0AAAAApURhBwAAAEApUdgBAAAAUEoUdgAAAACU0seyzgCA7PgmHaxIqkoaSupYa51MMwYAyDXiB4qCwg6wprxAtW2t7fg+60r6wBjzKgELABCE+IEioRkbsL4a1tpT/wde4LqR9CibLAEACoD4gcKgsAOsrwfGmN2Azy8kNVadGQBAYRA/UBgUdoD1dSNpM+DzZ3LbYAMAEIT4gcKgzw5QAMaYfUmOpKakI+/30VO1mrW2Pe82rbW1kKS7cjuaAgAKbBmxQyJ+oFiMtTbrPACIYIzpjjqBep1CzyX1rbVtY0xL0oncoJVKgDHGPJd0ZK09TmN7AIDVW3Xs8PZD/EDuULMD5JjXJvps6uOq3KAlSX1J7aBg5a1blfskryKvLfV0p9KpdfYlDQlUAFBcC8aOiqQHejGc9DNv+U1Jstb2Q/ZJ/EAuUdgB8s2x1g5877elF8HGC1S3Ci9e0Bn4g44xpif3SV4g78lfW9JWOlkHAGQkaezYlRsHJgpCXk1Q11q7EbQz4gfyjGZsQIEYY84lVay1zRnL9aaX8QpAp2HzH3iFocAnfQCA4ooTO7wCzV7YMsaYK2ttYGGG+IE8YzQ2oFgaknpRC3hP2IIMIgo65yJQAUBZRcYOY0xVUlfSXsQ2ppvFjdYlfiDXKOwABeEFo4qkwdTnE4Ubr+nCtjGm558HIaKddVduh1J/k4WwAhMAoEBixo4TuYMXBD4Qk6SgvjjEDxQBhR0gx4wxLa+zqPRiuNBLX3rQpG6S2256KOmRMcZ6TdgCty/pbKptt8SkcABQWAliR0MhNTdR+xDxAwXAAAVAThljGnKftg3ljoRTm0qvSKpaay+m1/WesrUltb2g9sgYM9Ffx9t+TdLQ+11yn/5typ0rAQBQMPPGDl9NzHTNT0VSSy/iwZlvHeIHCoMBCoCc8gJNV9K1pDvW2o5XcGlKupKk6WGkjTH1gKdstzqPett+HrH706STzQEAsjNv7PCauV1L2ghqxubFj4Fvzh7iBwqFwg5QIv5J5KY+Dx1FBwCw3rzJQPeC+nYaY6ykZli/TyDv6LMDlMvudOdQr7/OUUb5AQDk31tya4MmeP1yJF9/H6BoqNkBSsJrWnBP0o3cdtOjzqkDnsgBAKJ4fW/akp5IGjVn60vaDRqJDSgKCjsAAAAASolmbAAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASonCDgAAAIBSorADAAAAoJQo7AAAAAAoJQo7AAAAAEqJwg4AAACAUqKwAwAAAKCUKOwAAAAAKCUKOwAAAABKicIOAAAAgFKisAMAAACglCjsAAAAACglCjsAAAAASonCDgAAAIBSorADAAAAoJQo7AAAAAAoJQo7AAAAAEqJwg4AAACAUqKwAwAAAKCUKOysgDGma4y5MsZY7/Xce9/IOm/ryBhTN8bUV7Cf/YDvved7XXmvE2NMZdn5WSVjTMU7tmtjjC36fpLIc95QHMSPfCF+LB/xI995KyIKOytgre1Ya7ckXXgfvW6t3bLW9rPM17KMbsZZ5yPCfUlXvuBx7rup7Ke1E2vtsfe9n3ofvW6tbfpeW176taQPjDGttPa9qEW/Q2utI+l1SUu9xle1nyTynDcUB/Ejd4gfMxA/FpfnvBURhZ3VGno/nbAFjDHdFeUlsRh53PZeeTb6x6HhvYaS9qy1x0vYlzP1c4K3zyNJJ6t4Yiit5jv0btZL/6dlVftJIs95Q+EQP/KD+BGN+JGCPOetaD6WdQZwSzXrDMQQmUdr7caqMpKUtXYv6zxMOZXUlfRI0tYK9req7zD0H7OUrWo/SeQ5bygX4scKED+IHyuU57wVBjU7+VOEdthFyGOheE9whpLqK2p/zXcIlE8R/q6LkMdCIX4A0Sjs5IgxZldSrjsaFiGPBTZ6grPUp7N8h0D5FOHvugh5LDDiBxCCZmw54D2JaUg6zzovYYqQxxKoSJK1drCMjfMdAuVThL/rIuSxBIgfQAgKO/nwQFJdbjV0dWoUk950p0fvycpduaOwVCTdsdZ2fOlVuTek0c2v5o3UUvHWO/GP5ONLk6Sat89RB8y58miMufK2VbXWmqCD9TpRNuQ+iapIuiPpzH+T9m6s73rpm9baDW+o1VEHzJqk60U6hPqO2/EddyYjn3jHW5V0K1DF+X7ifOeSmkrvO6xK6si9Bkf68wbaWddyGmL8vXQl+c/xqbW27UuvSPpAL66Vt6y1F6vIOxAD8YP4QfwgfiCKtZbXil5yOxBauTeBoPSW+5XM3MZ5wHrXU5+Nbl7XknYl1b3Pn/vXl9QK2MfV9D7i5lHuH+1J2DLe+r2Az8+n8+LflrxRb6bSr4PyH/N7CDvu3VV/71HLzPP9zPOdL/gd7nr7mM5rY7TPqc/CthPrWo55jgP3M88+ps/TVFpd0lXC7YaeA1684r6IH8SPeZchfsQ+x8SPkr8yz8A6vRYNVqOLXlIlIO1a0n7A9iZu6N4fXdX7veL9gU4Hibq3n/q8efSW2Q25cVRnHN/zgJvdrpeX/YDlT/w3kDm+h7DzP9rXrfO7rO/d+w663vc0fexJv5/Q7zyF77Di7ftWUFdAEI0IInNdyzHO8a39JPh76Up6HrH9RsLtEqx4LfwifhA/AtKIHzGu5RjnmPhR8hcDFBRLV25Vb9BQhBeS2gGfVyVdjt5YawfW2mHAcvIv41s3ibChEk/0Yn6CIO/IHTozSFDzgGslyGPY8dsXVfsP5t1mTF1vNvSuN+v1ubxAZa2t2ZhV+DG+n7m/8wBh32FXkmNvN1OZV5Jredn7OJFU8ZoXTGvaF01UVpF3IG3Ej0nEj2DEj2T7IH7kGH12iqUuKayNceiNO+wm6P2xRY2Hn/aoLg257XTDXMt9ahRk3pttUo5etOv+/9m7vye57jO/759nfuE31BhQgLSAluSQkqjNkhsNAK1s1WrXKyDxlquyVQ5I5A8IAZcvE5swk/twB75NxcEouUpSZQlIqpK4Im9mlPKqSitFAsa75MYUTWFIWuCuByIGTRDAADODeXJxTg8Oes739K9zuvv0eb+quoDu7+nub/fM9NPP+f548vZWuwFJ6u3n08nzdOi0EoGw6Tk7qe/Q1e9yhzp6DndfNrMlRcFmOxjHc65vd/u4wJAgfhSP+JGN+EH8GAiSnZJIVEaeiRcONqsp/exVu48/o2joWSqgiFWi/1mPXW8c2/xhGzgLUoRVDWH17qJ/Ph2YUQ+/Z1Lxv8s9Psdbkq6YWS3xO/eaoqJ9fek7kDfiB/FDxI9+PAfxY0iR7Ay5pj8aKVqcOR84PO1sQeYHW/zH911Ff2CXG8PV8Q4j3faxV9M5PtZT4qH/mRZnkVaLev5Odfnz6fhnUcDPsB0tf5fN7I7SazosuvuZPJ4jyaNdcqToDHGj/YWU96bTv0Og74gf+SJ+BJ+H+CHixzBjzc7we016alg5t2Je8QfhdUUfghe7mJfb8FqrA9rsf6MtdZg7J7Mt+jCtlO07ByHHn087Wv4MY8vqcZi9w9/lE4q2S22+ZM5r7vHvZb7x+PEZ0Z/n9LhAvxE/8kX8SEf8eIL4MYRIdsplSdK5UGP8h9WJxmLBtDMM23+MgQV33VhStFd/yBlJywWfIbrq7i+kNcTBoSZpIa19APr982nHkjKmacS1LNp9nJa/y+6+HLrk9RwpLiuaZjCraNeg5sW0ef8dAv1A/Ogd8aM3xA/ix0CQ7AyXxhBzMutP/v91SbMZfwyd7uJxUilnwRLzSg/H/yanBbTqY5bXJZ1uum/SaUmvtvlY3fpexof7BUXBckdwyOhzkbr5+bSj159hLTDfWMr+MtL8OHn+Luf2HPHZt+WMPvSj70CniB/EjyTiR2+IHyOEZKe/Gr/cqR8M8daEdQWGhOM/oguSFpo/POMPj++l3C3rQ+j7kk6mfBCfVrSbyI7+tupj8vjmx030/4fNd4jnQl9M2QVmOu2xYodTbssUP/6p5oAVv3+nlfJhG/ftTgdnnZp1G+g6/vm083w9/gzrir5QXEwETcXHnld0VqtlX7r8Xc6y43l6fI7LiuZd79gitYvHZcoC8kD8IH50gvjRPuLHiDOPihahQPFiwNN6siVlXdEZl7nE3uuNY2cU/aEsKdqucL55WD7+kLgQP84NRX8MVxOLDxuPcTJuW44vr6Y81huKPqAX4seTu8/Hj3Elvt/lZD9DfYz/cK80Pe9ld39qwV3c/3PxfQ/Hx15OBqrAYy26+4VE3xrvZ+rzZImD1RlFwbAmacndU7c1jd+jC108xxvx62z+uacF5azHaPnzSfzb8mceP26vP8MZRdMklvVkW83k72Dw59f0OJm/y228Py2fp5vnaDxu1iLWNv4O23oPgCzED+JHfBPxg/hB/OgSyQ7QJjM7n7GDCgAAqYgfwOAwjQ0AAADASCLZAdoQDzkXuaUpAGAEET+AwSLZAdpzrt150gAAJBA/gAFizQ7QQryLTrt78wMAIIn4AQwDkp0OTNku3619g+4GgJL7THc+cffP9/IY//Hf2ee3Vx/31I/rbz/6U3f/uz09CNpC/ACQB+JH5yYG3YEy2a19+l37zqC7AaDkFv3qR70+xu3Vx/rZn/5mT48x/sX3n+m1H2gP8QNAHogfnSPZAYAScklb2hp0NwAAJVO1+EGyAwCl5Hrs1QlWAIC8VCt+kOwAQAlFZ+ZYcwkA6Ey/4oeZzUlacPfFpttnJJ2VVJekZMFdM3tD0rKkGUmLyZ0Ms9qykOwAQElVaRoCACA/RcaPeBfCWUUJzUJT24ykOXd/Nb5+3cyuufuSmV2R9FYjiTGzBUln4v8H21qhzg4AAACAXLj7ortfUjQK02xO0uXE9e8kRmhmm0ZrluPEqVVbJkZ2AKCEXK7HlA4AAHRoUPHDzGqSzjZGdSTJ3etx22nF09oS6pLOmJlCbZIW1QLJDgCUFGt2AADdGFD8mJFUjxObWnx9KV7TU0s5/rakUy3aWipdspOx2KntRUvdLnACgGHhkh6T7AAAOjTA+DET/7va+B4fr9l5VdJ0xv2y2loqTbLTYrFT24uWelngBAAAAEDPmNm1xPX55K5qAXVJtea1N5IuSPp5xv1Wu+yjpBIlO3EGuGhmaYlJ6qKl5tGfLo4FgKHFNDYAQDdyiB+fuPvJDu+zrJ1rbxozrRa0c7ra4cR9Qm0tlX43tqwFTb0cCwDDzCU9du/pAgConkHFD3df1s6kpSZpOR50aJ6uVtOTpSupbe08b2lGdjJ0smippwVOeXrg97TX9vf7aTtSxj6+69d1V3ckSbP6tiZtalBdkyS9729rVbe0qQ19Q9/pqT9pr23YXm+v8ny/pOH7fehFvF6xsajzxCuvTFJlBwDQlQHGj0tNM6pOSvpO/P9FM0vOwJpJHJfVlmkUkp1OFi31tMApLx/6ezqm5wfdjZbWdF9rfl+H7eigu5Iq7X3c0Lp+t71t1/viy/aKJOlt/0nPj5X22obt9bbytv9ER/UlHVBNk5rakXzk+X5J+b8/rfpfJHe/KG2vO0RFlfEkFIBqMbNZSecUnaBTnKRckqJYZmZzcXHRFyS93th+WtLrkt6M205Juph42Ky2TKOQ7HSyaKnjBU5mdl7SeUnarb2d3n2Hm74cfVEqwRnmw3ZU7/vb2uP7hi5wdfI+bvi6PtYH2qN9kqIk7jn7ai79KPKxe/G+v61J7dKGHkl6kkQU4aYva4/2tZUU39LHuqWPt69P+KRe1Ms6bjMZ9yrWMPU/Hr25rWgu8naCk8bl7MY25PKOH5woA5CHouNHPPqypEBCEoptcdLTaLvablsro5DsdLJoqeMFTvHOEvOSdNCme/rN2PB1rWpFr9jf6uVh+uo5vaR3dV2vaHj63On7+I5+qpc0u52wPfB7WvIfada+3XNfinzsbv3Yf6CX9U0dtEOSpBW/WVifNnxdv9CSvq7fa+v4F/Wyjul5rem+JjQ58CR6mPpvZjckvZrYKfKsmS24e/qaQpcek+sMtTzjByfKenPbV7SpDUnRiO9nuqMX9XIp3k8gdxWLH6XfoKCTRUu9LnDq1Yf6hZ7TS/14qtxM2pT2aJ/u+p1Bd2VbJ+9j46x9MuDutf2a0KRW/GZP/Sjysbv1rl/XAdW2Ex1JOmrHtaH1Qn6GK7qpCU12dJ9Jm9JBOzQUX4KGpf9mdllRYbXtnSLd/aqk6Xg6wA6uaM51LxeUQ+MET5lGSp7TS/ql3hl0NyRFn4uTmtJRO66jdlzHbUZHdFw/0w8H3TVgIKoWP0qf7MQWm74QbC9aMrPZprbgsUVb1a2nvoSWxVF9SR/qF4PuxrZO3sdbuqkD2nnsQU3r4/Z2LBzIY3drRTc1rZ1fiA7qUO4/w9u+omkdyfUx+2nI+v+a0k+6XJP0ZvpdTI97vKAcOFHWm1Xd2t6opOGwHdWa7uu2rwyoV8AgVSt+lCbZiZOWxm5EF83sjUTz65LOxdM+5vT0HMFziooVtXNsYe76HR1M+WJcBgftkD7bsWP3YHT6Pq7q1vZ6mqQ92rcj+HWqyMfuxoava1MbmtTOaRkHlP/PcFMbQzE6061h6b+Z1RSNMKetKbyuqJgyKowTZb35lv3RjnV1D/zegHoDoN9Ks2Yna7FTiwVNF9s9tkgr+lXqGfdmP/Yf6Ov6vae+hG34un6sH+hb+qNc5hd/6O/pYy1rTfdT20/b2R23HVAtSjQGHHDbfR+T0r78T2hye/52L4p87LxtaD23x7rpy10vyk9O8burVT2nl/o+b75k/U/dRdIlbVVoznVVlf5EmQ/HibJmHytaA1WmqYFAXqoWP0qT7JTdqm7pqL6UecxtX9Ga7u/4Ar2im9EZ+xy+UC35jyRFC60nNKlf6h2t6b5mFS1eD61f2KN90dnFAQfddt7Hhg1v/eV+w9e7el+LfOxuTdqUJnwyNalZ073cErBevnytakUv65vb78u0H9HP9EN9S3+US9/aMWz9d/e6mdWVntS8oPT6YJLUl6kE8Qj4QvN033j7z7OKCzXHi/EbbW/oSVXsxeRapKw27MSJsvzd9hV9pnqptu0H8la2qWi9INnpk01ttFwIvaoV7dG+HUFpVSs6EP6+07ZGopPcleugH9Kf6f+I/p8RjJLbGLfytv8kGAxDjmmmrTPt7byPDe2MZGxoPXV0ZpCP3Yvn9JJWtaLjevq9bPw88kjA7upO16MizTvCTdqUDnhN7/vbhW6PnTSk/X9L0hnFO3clzEjRVLdEHQJJcQXsAoOVmZ1WNIXurJrWE8WJzpy7vxpfv25m19x9Ka4D9FZiV7kFRa9NWW1Ix4my/H2k9wa6YyYwaEXHj2FDstMna7rfcn3Aqm6lLphe1a2eayvc9hWt6pZ+X//JU7c3RgPuKvts9x7t05ram+Nc5Nba7byPzbISk16TkSIfuxvP2Ve15D966kzqit/UQU3rlj7u+UtPL9O/QvZon27pY31ZxSc7w9p/d79kZgvJ6tBmdlbSzyWdbU50+iEeyVk0s7RkZE7S5cT17yT6ONs0WrOcqJad1YYUnCjL1u6JsoYVv6kX9XJHzwGg3Eh2hshnqu/Ycaex6LzTdSrNfql3dETHUr/sbmojdaF90oQmOw5Cg9ZINrKmb3X75b/Ix+7VrH1bK35zexekozquD/WLlj/jVh74vZ4fI80e7e/L79aw99/dz8Qbp5yMb/q+op3Yglv7bXn/z8zFGyqcbYzqSNtrIRujQc2JWV3SGTNTqE0SyU4AJ8rytaF1HbXjhT8PMOwGET8GhWRnSDS2v2wOWKu6JUk9L6JMS6SSz9vOFINO65EMWvYalqgopCxjQ0IP7yTf1mMPUHMw3/SNns/wruqWPtMd3WqqIbSpDX2k93TLo22vQ18kfuw/0It6eWBfNMrQ/7i2zrY4sUhd0zLAaQgzkupxYlOLry/FozNpv2S3JZ1q0YYecKIMQCeYxoZCTGgyc73EqlY0ocnMaQgbvq4NrXc8jauxxWbal92PtaxpHWk5CpE2HzykyKkIrd7HZgd1KHX0JfoS0FuNlSIfO2+ruqWXetzBOPTz+dg/0LP6alsJedoXozXdy2WqTSsl7f9pPb11/jaX6fFgqgc03sjVRD2z62b2qgI7x8Wy2tAlTpR1ZhDTi4FhM8D4MRAkO30yqanMBeuhmi0ruqmjis4kf6wP9Jx9tePn3mv7ozS+yQO/p1Xd0jf0nZaPEZ3Bay/JKnIqQqv3sdkRHdeqdhaNW9WKjqm3tRtFPna33vXr2tD6Uz+D2x4l0oPeYvWYZlLnBG/RUgAAIABJREFU9t/Sx3pWnf9e91uR/Tezy5Kmk1PDGlPCCl7P8oyZXUtcn0/uqhZQl1RrXnujKCn7ecb90uoIoQVOlGXrdM0OU9iA6iHZ6ZMDqkVzrwMJQ1rBxw/9PU1qajvJaF7kueQ/0jHNtPXhfUzP65Y+1nPxl7INX9c7+qle1jfbCoBrutfzdIg8tHofmx23GX3sy3rg97Zf5/Zalqb3bWnrz3TMZnTU2tvaupPHTtPJz68TyUTrgd/TR3pPL+ubhT5/2gjXkv/oqRGTaR3ZsUHAh/6e9mhfW19Winq/pPz732Vftxf8x7udXZT0avjwXOZcf+LuJ1sf9pRl7Vx709hKekE7p6sdTtwn1IYATpQBKAJrdpC7g5oOnrFqfEF+Vl/Ru359OzAc0/Oa1hF9qF/oQ39vx2jBmu5rVSvbAS3L1+yE3ve39aG/JylKnNpNdBrPNQxnxLLex5BZfTtaoO/Ra13TvdRtR9d0T6u+0nay08ljp+nk59euF/WyPtQvtOpPRpySdWHyfP53/bruKvrdfVfXteK/0ot6eft3ak33n/pZHbRDkkvv+9uSnnwJGtT7VWT/u+jrRUlvNu189mrWLmyDmnPt7svxWqKkmqRld180s+bpajVJV7LaiurrKOBEWX7u+h39Uu8EPxOBqmDNDgpxRMf0Cy3tqH8iPTkzl1anY1JTekXpZ7u+ZX/0VDX3VrqtA9JOAc1+yXofQyZtqq1tgb819ve04r/qqD/tPnbq83X488u7P70+/9fsRMvHb3bQDnVdbyPv96vI/nfa1zipudj2HSRJpsc+sDnXl5q2jD4pbZ/mX0xuoS1pJnFcVhtScKIsPxta3z7BAVTbQONH35Hs9Mle2x/Nm06Ze51XLYSirOimjuQ4+tCLrPcRqBKXtFXgAlMzm5V0TtEmCYqTlEuS5O4XzWwunm73gqTXE6NQrysapZpRtNNaMonLakMKTpTl57Ad1R/ojwfdDWDgio4fw4Zkp4+e00ta0c0dQavbnbKKqhvSbFUrQzWXOvQ+9ip6PztbwNv78xX/8xvW5+9Umfpbpr6GxKMvSwokJO4euj05SnW13Tak40QZAPSmOmndEDhqO3fvakxD6Gar4lXdyqxcnYfbvjI0860b0t5HGx+XjY9LZtv/f3LbWPiSsKqV9t7PNh+vlX78/Ib5+TtVpv72q6+PZT1dUA6NEzzNomKinX8+9/NEWSc7pRUq63O7m8/zft8PyFmV4gd/XX32NZ3YXuAsRXOID6jW8ZagUrhuSJ6GKlglNL+PeThuL+T6eK2fb7Dv66Cfv1Nl6m8/+uoezbnu5YJy4EQZgDxVLX4wja3PJm1Kx3xGt31Fh+1odBnSgHDTl7ueq1205vdx+3ZN6aeb/7ck6cT4Hwx8Xc/7/rZWdSt1a+NOTWpK/2+8lntW39akTaXeVmZ5vl9S+ntWVmY2p2j9zPa8pa2SnV1D9xoneBqfyWU4UTas8QNAteIHyc4A7LX9bdeJGaRhP5Oe9j7+1vipAfUmXZ7BPm33sFY7ipVN3l+ORun9aV4j85WX9wyqKxgATpQBQHdIdgCghKI6CeWaSoDecKIMQB6qFj9IdgCglKpVJwEAkJdqxQ+SHQAooarVSQAA5KNq8YNkByPt/cd/qdu+os90Rwd0SAf1ZAeiDa1rUxs6ZjM6al9q+Vgf+nta1YomPNoYQJKO2Jd02I5qw9e1ol/puJ6XJC36VU1oUgd1SBOa1KSmtKpbWtN9HdExTWpKG1rXmu7rM9V1TM9vry9pLNL/THUdUO2pPkvSHu3Xc/bVvN4ilNhjr84CUwBAfqoUP0h2MNK+PP47+o3Hn+nP/f/Si/bKUzu3SVGF73f9mj72Zc2O/X7qY9z1O3pHP9URHdOsffupegi3fUUr/ivd9VVN2i5JUQ2LA6rt2P3rQ39Pv9Q7Owq0rvhNrehXT/psr+iB39Of61/qRb28o8+3fUX/yv93fUPf6WonpiI98Hta0/0dfQYAABgEkh0MFRsfD7b548ddtU2N7ZIeS2aSjT19JmNya0qv2N/W21t/rre3/lyvjP3tRGfGdNfv6Gf6ob5uv7/9BX5s6kkC83l9SXe3buud9Z/oy+Nf19j4lLa2pOf9t7VrvCkReZTev6N2XKv+dA2NxshRmsN2VEf9uH6mH+oP9MfB4wbhM9U1oclBd6MSXFapBaZAv2XFo+w7ZnwG+lZGU5d/zxmPCaSpWvyozisFMnzNTuqWbm4X6mt4x/9cxzSTOVJxcOywjo+/uH19Q+s6ODbd0fPv6XCHpQM6pE1t7OjvoCVHqFC8LR/r6QIAqKYqxY9y9RYoyKRN6YiO612/tn3bTb+hNd3Xs/ZSy/sfHXt2+/8bvt7x9LI92tfR8Z8pSnKGZRRlw9f1ob+nW/p40F2pjMbWob1cAADVU7X4wTQ2IHbQpnXLb25fv+U3tUf72kpcDo9/URuP1yVJXxj/zY6f+6gdb/vYm76sj/WBjuhYW31739/Wx/og2owh3gjhpi/rI723vWFCYx3R+/62PtK/1R7t08v6pg5atDnCit/UXa1qj/ZrTfd0VF/S3TjhWtM9TWqX1nRPkvSR3tt+H49pZvsxAABAdZjZnKQFd18MtJ+WVHP3q4nb3pC0LGlG0qK7L7XTloVkB4g1Rlce+D3ttf26q1UdVPvT0b4w/mzuc6dXtaJN35Ak3dXqdtLyrL7SdoXyL9srOujTelfXt3d8O24zOuiHtKQfPbVhwpftFa35/aduW/Gb+lC/0O/a6e3bfuw/0Df0HU3alO76ne2EZtVv6Vl9lQ0K+sBlldpNBwCQj6LjR5zEzEo6K2kh49A5SZcT97si6a1GEmNmC5LOtGprhWQHiE3EmwJ8pjvaq/3a1MbAp4lN6+h24nBUx/Wcv6QNdT5N7qgd1zv+0+1EToqSu8a6n+Toy7SeTlQ+1nLK9tf79LE+0HP6KiM3A1SlOgkAgPwUGT/ikZxFMwsmI3FCtNx082zTaM2ymZ2OHy+rLdNIRUozu2xmM20eez6+1MxsJh5qQ4VtKpqG1kh6JjSpTW0Msks7TFpUr+dDf6/j+x7RMX2c+FxZ1S09q688ddttX9G0jjx1v7T1RBvxe4XBcZce+1hPFwBA9QxJ/KhJWm1ciZOfetMxdUlnstraeaJRi3avSbphZp64hLarqikaOrujaIjtcuA4VETjC3zjy/1BTW+vQ2nHg63PCulXs2kd0S/1jh54+32TpKP60lMbCGxoXcc0oxU9Wae0pvs7Ro2e1Ve1qlva8Oj9eeD3ttf/AAAAdMLMzibX6cRqKYfeVrQ+J6utpVGbxjavp5OW0BskSXV3NzOruXtztogByaqXkyW7Ps9m9K9LvuVPtY1NPvkTuLfxqfb4fu2fin5lvuDP69+s/1QPd21o79iBJ881tvMcwYY/0mebn2rv+OfCfdwclx4/XafnSR+fft3m0bE2NhZtm5LQqMGzqlva28GW1cmpbJOa2t58YdKndkxla36+l/XNKClyaVMb2+t1Wtnw9baOQzdMW2LNDtCr5vpr27enfFZvt2XFnK2MtZuPw22WVYMnMzZ2V58OVZZL/HjGzK4lrs+7+3zLZzaraecojaTMRdKd1fNoMjLJTvzmXXb35cRtp1u98SQ6kKIv5f9+6yP9zuTvbd92fPIr+mDjr/Thxl/pt3b9rYx7S7c3/0aHJ764IzEpwqRNSa6ORp0aGlPZ9mi/jsczPo9pJprK5jM7prBJ0opuRhsaqPO1OSu6qePtnXhBh1xiKhoAoGM5xY9P3P1kF/c7nTKqIyWmtHXY1tLIREp3rzclOudbJTrxmp2zZjZnZrPF9xLD6v3Nv9BBO6zDY1946vbf2fX7urn5vm4//uvgfTf8kTb8kSZtV9HdfMqa7if60N4amuapbFKUAK3opu7qTurGB50WL02ucxq2NU+jpkp1EgAA+RlE/IjX1TdvStBQ187ZWIfj47PaWhqZkZ2keJQnNH2tYTGRHF01sxtmdqJ5pMfMzks6L0m7tTf/zqJwrRbT/5uNn+lTv62Tk9/Z0XZw/LC+ufvv6S8f/ZmOTjyrr0ydeKr9wdZnWtn4UM/vermvfT6m57WqW9vXGzujtdKYytaYCidpeypbKDE5oFq8I9uJ1PakaR3RXa3qqKK6Qc272S35j9iaOicu0xZbTw814geAYTTA+DEradrMGiNCJ+Prcvd5M2uerlaTdMXdF0Nt7TzpSCY7kt5Uiw0HkqNAsbqiDQ7mm46bb9x20Kb7MEkJeXr/8V/qtq9Ikn7pb+uWP5mKtaF1ba5v6gvjv6nfGv9G8DEOjh/W7+39+/pg/a90bW1Bk2NT2jN2QFPapT1jB1omOv92/bruPl7V3a1PJEk/Wf+B9tp+HR//8o6RpOY+v7t1TdM6sl0fp+FrdkJv+0/0vr+tPdqfOv0s5Fl9ZUcR02f11eBjHLaj+qW/o8XEqPOEJnVQh/SiXn5qrc/X7ITe9eu6Gf95NabKNazp/lMjUsAoI34AwBPN09firakXEjOxFs0sucX0TGJr6ay2TKOa7Jx394uhxngY7bq7JxchLEt6ofCeoa++PP47enEr/B0juUFBK89P/bae12+nblCQZXs0aHOzreO/PP47+nLiemiBabLwZyfSipE2JyUNG76uX+odfU0ntpOaDV/Xhtb1mepa0o8069/ekfCEfMv+qKs+Ix1T0QAA3SgyfsRLQ85JOt247u6Xmo55I26vmdlqnAi9LunN+Hv6KUnJ7/JZbZlGLtmJ34RWU9iknW9STdKN/HsElNfH+kAHdOipZGbSpjSpKe3Vfq35fa3qVlebF6A3LmmLDQoAAB0qOn7Eoy9LykhI4uTnUtNt9cR9rrbb1sooRsoZpWxpZ2azjU0I4ilstURbTdFwWMst84AqmdYR3UrU4Wm2qhUd0bE+9ghPmB73eAEAVFG14sfIjezErqXcdk5RgnMhvj4fD6HVJZ1Qm1VYkQML59hZtQsy23Zn7IRmGX+UGTUIgjUWxsP99/qnGf3Iet3huzXXBmpqDLfl4KAd0ks+qw/9vR2bDWxqQy9pNnUHNwDou6zP2EAtHUmyicnA7RlfkTKmQI9l9COr7o2vhzemsS4fUxaIHwXHDmCYjFyyEy9W2rFgqXkNTzwcdqn5OABP22v729rpDf3Vr2lsZjanaAFp6kJQMzstqZZceBqfSFpWNNK+mFhQmtkGAChe1aZBj1yyAwBVUeRUgjiJmZV0VtJCxqFzSux+aWZXJL3VSGLMbEHxyHlWGwCgf8o2Fa0XJDsAUELuVvQC00VFW30Gk5E4IWrexn+2abRm2cxOx4+X1QYA6IOi48ewqc4rBQDkrSZptXElTn6aN4ipSzqT1VZoDwEAlcbIDgCU1OMBnpkzs7PufrVp5Cdt2//bimoiZLUBAPpokPGj30h2AKCEXNLWgOZcx9v179jiX9J0xt2y2gAAfTLI+DEIJDsAUEqWx5m5Z8wsuVX/fJv1xk4nd19LWE25rZ02AEDf5BI/SoNkBwBKKNo6tOczc5+4+8lO7mBmM9q5KUFDXTunqx2Oj89qAwD0SU7xozRIdlCMjAJo2cXMMgqH7tkdbNu6dz/8iNNpSwViE3vCbaGicRmvbSyjj16/G27LKiaXUVTUM2rJUTQOBZmVNG1mjSTpZHxd7j5vZs3T1WqSrrj7Yqit4P6iIjILh2YVpQ59bmc93qGMuJJRyNq6/KzX2sNgkz96FGzbepjeRuxAlZDsAEBJPR7AhprN09fiDQoWEtPfFs0sucX0TGJr6aw2AECfDCJ+DArJDgCUkMsKnYZgZrOSzkk63bju7peajnkjbq+Z2WqcCL0u6c14utspSRcTd8lqAwD0QdHxY9iQ7ABASW0VeGYuHn1ZUkZCEic/l5puqyfuc7XdNgBA/xQZP4ZNdV4pAAAAgEphZAcASshdelyhaQgAgHxULX6Q7ABASVVpzjUAID9Vih8kOwBQQtECU2YiAwA6U7X4QbKDYmTt099lDZ6sWgLjnzsYfszJyXDbRLj2wtbhwGN6uBbC4/27wt3YHW6zz8J1grbqnwbbFC7ZQB0FAJXiGXVqbCIj7gTq6WTV0nl8aH+4HxnPZRnxY2xtI9imjX3hx/xkNfyYgZo/W1l1e4gdGDEkOwBQUo9VnWkIAID8VCl+kOwAQAm5qjXnGgCQj6rFD5IdACilas25BgDkpVrxg2QHAEpqq0LTEAAA+alS/KhOWgcAAACgUhjZAYASqlpROABAPqoWP0h2AKCkqjTnGgCQnyrFD5IdDJXMOgkZbZ6x97/tmgq2bX4hXEdh/XPp9Xm2psIfEGOb4T5KB4Itk4FaCFK4ToIk+ad3g21bD8N1iYJ1FKihUBpRUbjqnJkDJGXWabNAvRxJ0njG/XYFaqBNhr8ibe0O12h7+PlwTbX1/eF+TKyF48fkvXDxmz1ZdX1WbqfePpZR72drbS3Y5lvd1cnDcKla/KhOWgcAAACgUkZqZMfMzsf//b6kaUkX3P1ixvFvSFqWNCNp0d2Xiu8lAOSjSrvpAADy04/4YWZzkhbcfTFxW01S4/v6KUlvJb9/Z3037/Z7+0glO5JqkuYkXVb0ZpwJHWhmV5R4g81sIet4ABgmVSsKBwDIR9Hxw8xOS5qVdFbSQlPznLtfiI+bkXTdzJ5393rWd/NevreP2jS2urubpEPu/oK7L2ccO9uUES7HPxwAKIUtH+vpAgCopiLjh7svuvslRQMP2+Lk5kbiuOX4mMZIT9Z3866/t49ktHP3elZ7/OY0H1MXIzsAAABAERozsJodzvpu3uv39lGbxtZYt7OqaB7g9wLz+dK24Lod3wcAhp9XazcdAEBOBhQ/3H3JzE403Twr6aKyv5v39L191JKdxcTUtatmdsPMTqSM9Ey3+4Bx8nReknZrb07dBIDeuNigYNgRPwAMo0HGj6YNB84r+u6+mNhkLE3b39vTjNQ0tpQ1OnVJr6UcutrBY867+0l3Pzmp8N75ANBvW/HZuW4vKBbxA8CwyiF+PGNm1xKXrGRlh3hXtlfdvTEVLeu7edvf29OMzMhOY0cHdz+UuHlZ0gsph9e1c0jssJoWUqH/bDxcqC2zKNxERvG32r5g29qR8BeQtWfS+7KVXms0asvo/t5fh/s/GShgKkl7/134tdnmZrAt60xGqGhcsNgohg67saGSsgpXWsaHc0YRzeBdxsN/X+uHwsWqHwRihyQ9PJz1jOHn2/NJ+BP98e7PBdv27kp/T8b/Jr3YqCTZRjiuaHMj2ETB0fLIKX584u4ne7j/nKRXE9ezvpv39L19pEZ2FM35S6opsetDQ7zfd/OQWE07t8cDAAAAkJO4Xs5cY5mJmc1mfTfv9Xv7yCQ78RS27awvHh6bcff5+Pqsmc0m7rLYdH0mWfQIAIYd09gAAN0YVPwws7OSliStmlkt/i7eGCHK+m7e9ff2kZnGFpuPs8W6pBN6eku6c4qSoQvx9dclvRlPfzulnaNCADC0XCQsAIDOFR0/4qTknKTTjevufin+zn0l5S6N7+tZ3827/t4+UslOPBx2KdB2MeXYxm1XC+4aAOSO3dgAAN0oMn7EO64tqSkhiWdhBZ8467t5L9/bRyrZAQDky8zm9GTOdOO2mp5UvD4l6a2m7UTfULRwdEbRtqJttQEAkDeSHQAoIy92N7a4YvWspLPauQh0zt0vxMfNSLpuZs+7e93MriiR/JjZguIpClltAIA+KTh+DJuR2aAAAKqksXVoUQtM3X3R3S+paWvPOLm5kThuOT6mMdIz2zRasxwnTq3aAAB9UHT8GDaM7KD/Mvbbz6rzYgrXUPCMejNZ9RU29ofz/VA9hLUvZNRryGh6+Pnwc+1azTjvYAeCTXs3Mt6wjXA9BFtfT73dt7JeG3UShs2AAk5NUX2E5vWRh+PEpd50e13SGTNTqE0SO2Gid91+Rj0OfI5m1H17PBX+zH6UUev9/osZn8sT4c/fh/Xw17WHH2cUeNPe1Fv3r2fUaMuImVufhOvz+OP0uILhVLaEpRckOwBQQoPajc3dl8zsRNPNs4oWjjYXfZOk24rW9WS1AQD6pGq7eTKNDQDQkaYNB84r2mggrehbUlYbAACFYGQHAErKez8z94yZXUtcn28UYm5HvCvbq+7e2GRgNePwrDYAQB/lED9Kg2QHAEoqhzoJn7j7ydaHBc1JejVxva6d09UOK9rAIKsNANBHVarTRrIDACXkA946NK6XMxcXemtUyF40s+bpajVJV7La+tBdAEBs0PGj31izAwDoiJmdVVQde9XMamY2K6kxQrQYX2+YSRQkzWoDACB3jOwAQEkVOec6TkrOSTrduO7ul+I6O2mjMY11O69LejM+7pSiXdrURhsAoE9YswMMiI1l/PFl7P1vk+EaPPYgvPf/5L1wXYZHtfTaBfaFtfDjTYbr3qw/Cv+5bf717mCbLHy/8YcHg2271x6FH3M9vdaDZdTZ8U3q7AyXYrcOjXdcW1JTQhIXEQ0+cTytrXGfq+22AQPzuPPPts3d4b+99fDHsg4+cz/Y9huf+zTY9uhYOA58sPdosG0iEHcmHuwL3mfPp/eCbbY/fD/7NBz/PFTLCANSra2nSXYAoKSqdGYOAJCfKsUP1uwAAAAAGEmM7ABACbmqtZsOACAfVYsfJDsAUEaeuYwNAIB0FYsfJDsAUFJVKgoHAMhPleIHyQ4AlJCrWgtMAQD5qFr8YIMCAAAAACOJkR0AKKVq1UkAAOSlWvGDZAdDxTOKWlpWUbKtcFE4nwgPYI5vhJ9v4kH67c9+4dfB+/zG3nBRuM9PhQu1/Ys9/0Gw7d7kgWDb1KfhYqpTn4SLv42tpRdGzXqPM4vCOQVHB6FKC0yBVjLjR1bB5NDnV8Zn3vijjMfLKI595MBnwbY//sJfBts+P3E32HZ176lg209tJvX2yc/CsWPy7nSwbSJQkFqSbGoq2Kb1cHFvCo4ORpXiB8kOAJRUleZcAwDyU6X4wZodAAAAACOJkR0AKCH3ap2ZAwDko2rxg2QHAEqqSgtMAQD5qVL8INkBgJKq0gJTAEB+qhQ/RirZMbOapPPx1VOS3nL3pcCxjeO+L2la0gV3v1h8LwEAAAD0w0glO5Lm3P2CJJnZjKTrZva8u9dTjq1JmpN0WdKypDP96yYA9K5Kc64BAPmpUvwYmWQnTm5uNK67+7KZLSsa6bmUcpe6u5uZ1QLJEAahy3otnrX3/6f3g21j6weDbeMP02//9f39wftMjYXrBfyPX/pxsO2F3beCbf/T/t8Ntn360ReDbRvTe4Jtu+6kt9mD9Po7kmTjm8E236TOTr+5rFLBCmgpI35k1XKxQPywe+HPw8l74ccb2whvdHt/fVew7aVdfx1s+63JcH2eh8/8RbDt332plnp7/TeOBu+z53a4j/tvh+u32b1wrM2qWUSdnf7rV/wwszlJC+6+2HT7G4oGGmYkLSZnYXXblmVkkh09GalpTmwOZ92JRAdAWVVoyjUAIEdFxg8zOy1pVtJZSQtNbVeUWGZiZguKZ1d129bKyNTZiV/8iaabZ9X0JieZ2XkzO2tmc2Y2W2gHASBP8dahvVwAABVUcPxw90V3v6RoFKbZbNOIzHKcHPXSlmmURnbUNNR1XtEQ12Lg8EV3b/wQrprZDTM70TzSEz/OeUnarb1FdBsAMIKIHwDwRJycNM+oqks6Y2bqpk1S6Hv+tpEZ2UmKd2V71d2Dw1uJRKehLum1lOPm3f2ku5+cVHgeKwD0nfd4QaGIHwCG1mDiR9oistuK1uB029bSSI3sJMxJejXU2Nipzd0PJW5elvRC0R0DgLwwFQ0A0I0BxY/pAtpaGrlkJ96pYa4xHc3Mmuf4NTTX1KkpsZsbAAy7KhWFAwDkJ4f48YyZXUtcn3f3+Rb3WS2graWRSnbM7KykJUmr8VS2GUknJS01NiBw96V4W+pa4n41STNt/JAAAACAqvvE3U92eJ+6dk5JO6xodlW3bS2NTLITT027ktLUWLdzTtEbdSG+Ph+PAtUV7eJGUdEht7URrvMyNr4ebLOx8FCtbYVPbUwFyhqs3vxc8D7/8A/+LNj2rx6G+/HNPeG/16sT4Y0Cb74Sft17fz0VbJu6Faizcz+8rsAePQq2uWUs/+uydhKyuZjGBrStixo8thmu/zJVD3/27l6dDLZ98mm4Ts2vNsKVMv5gdzhW/d6ej4Jt/98Xfpl6+z8/9kzwPnc/C3813P3rcJ25ifsPgm1ZtfBE/Oi7QcUPd180s+YpaTVJV7pta+d5RybZiTccCP7k3P1i0/W60ouNAsDwc0kkOwCATg02fiw2LTGZSeyc3G1bppFJdgCgalizAwDoRpHxI146ck7S6cb1uO6OJL0u6c14RtYpPb2Gvtu2TCQ7AIAgM5uTtNB8Bi2eBrysaG3kYlOds67aAADlF3+uLyklIYlnVjVuv5pHWyskOwBQVsWemTstaVbSWUkLTW1XJL3VSFTMbEHxusdu2wAAfVShmQEjWVQUAEafyb23SxZ3X4ynHaTtntG8pf9ynBz10gYA6Iti48ewYWQHAMpqAGfm4uSk3nRzXdIZM1M3bZLaWmQKAMhJhUZ2SHYAoIx8YFtPN9c6kKTbihaMdtsGAOiXwcWPgSDZwWgI1EmQJL97L9g2dStcM2DX0fQ6Nbv/Jvxn87/c/Eaw7b94biHYtrx+JNj29UM3g20f3ArXZVjfH66z83hfej2diclwfYisWghZtYw8/KPB4HVTAbu51kEebUChQrV0JEmhGm4Pw7XFxu+H2z73we5g2/3f2Bts+5e/+dvBtt/f838G2z7dCsekL041D6ZGdh8J18SxvzoQbPPxjC/IYxkxYiocW2wzXIPHt6jBg96R7ABAWfU+DaGbCtirBbQBAPqJaWwAgOE3kGkIde2cknZY0UYG3bYBAPqqOtPpJIf0AAAgAElEQVTY2I0NAMrKe7x085RRvZ3mKWk1PanF03Fbdz0BAHRtAPFjUEh2AACdWowrZDfMJIqOdtsGAEDumMYGAGVVbFHRWUnnJJ1uXI/r7kjS65LeNLMZRbupJatkd9sGAOiXko3O9IJkBwDKyCUVuHVoXPxzSSkJibvXE7dfzaMNANAnBcePYUOyAwAl5RU6MwcAyE+V4gdrdgAAAACMJEZ2UB4ZBcQyC8atrwebxlbvBtv2BAqOPqyFi3V+cPPzwbb/dvwPg23ffuaXwbZHGQXjtjbD5ys29gWbtB54DRMZBVM1nnFuJFCTDwWr0Jk5oDCB2OKb4Q82y4gdU4fDhUP33AoXHP3Jey8E2767928F2/ZPPAy2fbqZ3pf1h+HP+rHPBZu0sT9cHHQyI0ZkvZeZKBxanArFD5IdACirCs25BgDkqELxg2QHAErKKnRmDgCQnyrFD5IdACijEhZ2AwAMgYrFDzYoAAAAADCSGNkBgFKySs25BgDkpVrxg2QHAMqqQtMQAAA5qlD8aDmNzcwOmtn3zOy2mT02s/fN7L9MOe7rZvaP42P/u2K6CwDY5j1esAMxD0AlVCh+ZI7smNnnJH0gaVXSdyXdlnRK0n9tZv+ZpD90988kyd3/taR/bWbnJf0zSf+wyI4DSb4V/suzjP39fW0t2LZr5V7q7Z/bdTB4n8dTu4Jt760dD7b99RfDhQ0ePgrXNdiqh2v+7L6T8Z6E3q/x8eB9MlnGeZOsLV+ooYAhQsyrplCdtq37D4L3GbPwFKDJQOyQpIMfhj+zfTzc9j9vfjPY9qVnPwm2HdqVHuMer4W//k3eDzZpc2/GZ/2ecA0hy2jzjYwaPMQP5KDVNLY/kTTv7v+kucHM3pC0ZGan3f2jRNPtPDsIAAgo2dm1EiDmAaiGCsWPVtPYXkj70Jckd78k6Zyk75rZs4mmel6dAwAEuKIFpr1c0IyYB2D0VSx+tBrZWc5qdPclSf+RmX3fzP4bd/+L/LrWnfjs27KkGUmLcR97PhYAhk2VisL1SeliHgB0o0rxo1Wy09YZK3d/zcz+xDLmsPaDmV2R9FYjaTGzBUlnej0WAIZShYJVn5Qq5gFA1yoUP1pNY/uemf3nkmRmf7/x/zTx0P8pDTZhmG0anVk2s9M5HAsAGH1li3kAgBYyk514t5nrZvaPJf0Pki62OP67khYl9f10V5yoNJ+VqyslEHVyLACgGsoU8wAA7WlZVLSxvaakf9rOA7r7opkd6rVjXail3NbYNrSXYwFgKFVpznW/lCjmAUDXqhQ/WiY7aczsOXf/MNTu7p923aPuTRdxbFxD4bwk7dbeTvuEfsnYbz9UQ0GStPYw2DS2ejf19t1T4T+b2sS+YNv4RriGzdqttPw7snko/Nr23AoPzk6she83/ijQllFnxybD9X6UVcso4+1Hj0q2I05ZdRvziB8llxVXHj0Kto3dDdfZOfBBuJaObYXjh1v48/ffr34h2HZzX/prmHgYjh3j4Zem3bc3gm0+mRE/MmqxWUbcyYzf6E3B8cPMaoo+/+qKBhmW3H0x0R7cJCzvDcRardlJ6/x/L+lG81zmuJr0f9hLZ3q0WsSx7j7v7ifd/eSkwgUjAQCjp5eYR/wAUGHn3f1S/Dl4SdKZOAFqbBK26O5X47a5xp2y2rrVcbIj6YakfyDph8kb46H/F8zsH/XaqS41Msekw0rfSrSTYwFg+HgOF7RjWGMeAHSnP/GjeR38DUUjNVL2JmG5byDWTbIjd/+uu3+Qcvv/qqjgWnAHm6LEQ2PN09NqkhZ6ORYAhhbJTl8MY8wDgJ4UHz+mzSw5KnPG3ZeyNgkragOxbpKdq2b2z8zs76Q1Dmi9TsOimc0mrs805gea2WxTW/BYACgD894uaMswxzwA6Eof4sfrks6b2fV4DU5jd8vQJmEzLdq61k2yU1OUYf3QzB6b2Z+a2T8ysz80s4NmdlDSiV461YPXJZ0zs7NxNpncNvScpAttHgsAgDTcMQ8ABuUZM7uWuJxPNsZT0eYVfYbO6UnCkrVJWCebjbWtm93Y3pT0avz/U5JOS/qvFL2YRq73asr9CufudT1JWq42tV1s91gAKAVGZ/phaGMeAHSt9/jxibufDDWa2WVJc+5+Mf7/gpmdUPYmYZ1sNta2bpKdhXhhphTVIpiXJDObkXRW0XSw/y2n/gEAQkh2+oGYB2D0FBg/4mUiN9x9WZLc/YKZ3VA0w+qKwpuEFbKBWLfT2HZw9+V4i7l/YGZv9dIpAEC2Xudbs2anbcQ8ACOlD/FjRjsTlHkpe5OwojYQ63hkx93/qZn9iaR/7u5/kWyL5y6fVLSYCBgKmUXJ1tfD9/s0vajo2K5wUbg9ExmF07Z2B9v2/U24uNfa4XDBtYmH4U+c8fBL08S9QOODtfCdsmQVjBsLvzYKjvaoQkXhBoWYV12+Ff58tYy4EoodUvYZ5v1b4SKmtnUg2PZwOvyoDw+lx4/xcG1QTd3N6kfGt9zNjCKsGQVaMSDFxo9FSd/V08tETku63Gg3s+QW08lNwrLautLNNDa5+z8xs//UzKbd/f9JNJ1T9EIuB+4KACiP83FRN0mSmc2Z2TV3r8eF395qBCQzW1C8PWhWWxkR8wCgfXGMeCveAOxGfPNyIoF5XdKb8XTgU3p6k7Cstq50lexI2/UFmm/7rpktS7rWU68AAK0VPxXtjKRLieuNonBLChR+i8/AZbWVEjEPwEgpOH7EMSB1RL/FhmK5byDWVVHRLO7+Q+oOAEDx+rBmZ2iKwg0rYh6AMqrSms+uR3YAAANWfMB5XVF9mdOSvqfWReFOtWgDAAyDkiUsvch9ZAcAUBqlKQoHAEA3GNkBgDLKZypBaYrCAQByUsKpaL0g2QGAsqpQUTgAQI5IdoBqyKrBs/XwUertY78Ol9QYz3i8vQ/2Bdse79sVbJu6OxlsU8Y2+bYZ/iQbq99Pb8io85DV5psZRRtQnGKDVago3Jy7L5pZWuG3K1ltBfUTKEZGbZitjc1gW1YtmrGJB+G2W+Gu7B0P11ub+jRc+23fSuB+GX2cWAvHsfF76XFRkmwt3JZVsyjrfUaBKpTssGYHAJBmUVEdmaQdReESbTuKwgXaAADoG0Z2AKCkipxzPWxF4QAA+WHNDgCg8oapKBwAAN0g2QGAsqrQmTkAQI4qFD9IdgCgjCq2dSgAICcVix9sUAAAAABgJDGyAwBlVaEzcwCAHFUofpDsAAGhGjz+KFxLQLfvBJvswVqwbeLA/nDbnfCfqe8K1+CxRxm1bzYCbVl1drJYeJA4q5YRelShYAUMlYzaMJ7xkecPwnV2lPFZOb4Zruszvj9cw833ptdw88lw3R7bzIgDGxkvLis2Zry2rBo8NhYuJpf1PqMNFYofJDsAUEKmas25BgDko2rxgzU7AAAAAEYSIzsAUFYVOjMHAMhRheIHyQ4AlFHFtg4FAOSkYvGDZAcAyqpCwQoAkKMKxQ/W7AAAAAAYSYzsAEBZVejMHAAgRxWKHyQ7AFBSVZpzDQDIT5Xix8gkO2ZWk3Q+vnpK0lvuvhQ4tnHc9yVNS7rg7heL7yVKJVA0bmsjXNzNsoqjZd1vPVwA1HanF4WTJJsMFxVVRjE2PUwv/rZ17374Pl7AJ2NGMdKson2IVShYAaWR8dnVbfwYy/r8DRWJlmRru9NvnwgXFZVlxI6MWOUZ8cMz7peFotQFqlD8GJlkR9Kcu1+QJDObkXTdzJ5393rKsTVJc5IuS1qWdKZ/3QSAHLgqFawAADmpWPwYiQ0K4uTmRuO6uy8rSmLOB+5Sd3eTdMjdX4iPBwAAADBCRiLZ0ZORmmaHs+4UGPUBgFIw7+0CAKimKsWPkZjG5u5LZnai6eZZScF1OPG6nVVF63u+12J9z3lJ2q29+XQYAPJQsoBTNcQPAEOrQvFjJJIdKUp4Gv+PA8yiuy8GDl9MTF27amY3zOxE2kiPu89LmpekgzZdoV8NAMOubGfXqob4AWBYVSl+jEyy0xDvyvaquwc3HUhZo1OX9JrioAQAAACge/Ga+rOKvmc3TgA12t5QtL5+RtEgxFI7bd0Y2mQnHp1pnprWbC4lcZmT9GrG485Iuu7uhxI3L0t6oauOAsCgVOjMHAAgRwXHj/j79py7vxpfv25m1+KlJ1eUKBFjZguKd0bOauvW0CY7yeyvXXEmONeYjmZms4FssHktT02J3dyATBk1FDyrJEBW3Zi1jLasOgNZ9RDGM+oobAZqPYRub6XbmjjU0ulexbYOBUZCl/HD19fDjVkxIlTXZzLj619GvZ+s58qqieOb3dXZoRZbQfoTPxolXhq+k1gu0vz9fNnMTsfLT7LaujIqu7HJzM5KWpK0amY1M5uVdDJum42vN6aw1RL3q0ma6Sa5AoBBsRwuAIDqKTp+xN+tzyYTlMRAxGnF09oS6pLOZLV19gqfNrQjO52Ih8qupDQ13pxzihKcC/H1+XgUqK5oqhxFRQEAAIDezUiqx8lLLb6+FCc/tZTjbyvaHTmrrWsjkezEozXBRNPdLzZdr0u6VHS/AKBQTGMDAHSj9/jxjJldS1yfT8ySmon/XW2M7sRrdl6VNJ3xmFltXRuJZAcAqqhKW4cCAPKTQ/z4xN1PBtrqkmrNa28UzbD6ecZjrvbcqxQkOwBQVn1IdoZl61AAQI6KjR/L2rn2phEPFrRzutrhxH1CbV0j2QGAsqrQ1qEAgBwVGD/cfTnepCCpJmnZ3RfNrHm6Wk3Slay2XvozMruxAQByl7Z1aGOEJnV70DbaAACj71LT5/5JSW/F/19s7JIcm0ns3JbV1hVGdoA8ZdVQ2AqfW/CtcH0by6h5YFm1dPxRuK0LmTUUsuoyUAuhGF7smp3E1qHbRZrb3DpUoTZJPQUsoKq2QvVyJFnWZ3OgPk9m7MiqbZMhK0Zk3o/40X8Fxw8p2hzMzObiGQIvSHo9UWfndUlvxm2n9HT9y6y2rpDsAEBZFRushmrrUABAjvqw5rN5N+TE7XU9SWKuttvWLZIdACipHM7MlWbrUABAfqq0myfJDgBUV2m2DgUAoBtsUAAAZeU9XrJlbR2atT1oIVuHAgByVGz8GCqM7ABASRU5DWHYtg4FAOSnStPYGNkBgDLq9axce4FuaLYOBQDkpD/xY2gwsgMAZVWhrUMBADkqWcLSC5IdAEDQsGwdCgBAN0h2gH7psjiaZ9VpG5aCa8PSjwoxVWvONTDyuo0RGQWrw/fJKFI6Zl31I/v5+LAaJlWLHyQ7AFBWFQpWAIAcVSh+kOwAQEmZVyhaAQByU6X4wW5sAAAAAEYSIzsAUEYl3P4TADAEKhY/SHYAoKSqtMAUAJCfKsUPkh0AKKsKBSsAQI4qFD9YswMAAABgJDGyAwy7jNoLmTV4rItzGdTLKZUqTUMAENDN53ZGfCikJg6xZehUKX6Q7ABAWVUoWAEAclSh+EGyAwBl5NU6MwcAyEnF4gfJDgCUVYWCFQAgRxWKH2xQAAAAAGAkjczIjpmdj//7fUnTki64+8WM49+QtCxpRtKiuy8V30sAyIepWtMQAAD5qFr8GJlkR1JN0pyky4qSmDOhA83siqS3GgmOmS1kHQ8AQ8krFK0AAPmpUPwYpWSn7u5mZjV3r7c4drZpJGfZzE67+2KRHQSAPFXpzBwAID9Vih+jlOxIklolOmZ2WlLzMXVFIzs7kp14etx5SdqtvTn1EugD6hoAA0X8wNAiPqBCRirZiQPLqqRTkr4XWIdTS7ntdnyfHdx9XtK8JB206QrlwQCGmqtSu+mUEfEDwFCqWPwYpWRn0d2X4/9fNbMbZnYiZaRnut8dA4AiGCdnAQBdqFL8GJmtpxOJTkNd0msph672oTsAUDzv8QIAqKYKxY+hHdmJp6SdaHHYnLsvm9mMpOvufijRtizphZT71LVzKtvh+HgAAAAAI2Jok514rnMnmmvq1CTdSHncRTNrnspWk3Slw+cDgIGq0m46AID89DN+xJuD1dz9auK2YL3LvGthDm2y04l4dGd7tCb+/0wjYTKz2fi4xpu1aGbJ7adn2HYaQKm4KlUnAQCQk/7Hj0YdTEnZ9S6LqIU5EslObD7OBOuKpr8l35hzikZvLsTXX5f0Zjz97ZR2jgoBwNBjZAcA0I1+xY94VKd5qUhWvcvca2GOTLIT77p2KdB2MeXYxm1Xd94DAEqAZAcA0I3+xY+aEpuDZdW7NDOF2pRSC7NdI5PsAACKM+g51wCAcjGzs+5+1cySs62y6l12VAuzXSQ7AFBCpr5PYxvonGsAQD76ET/i9fPNozRSdr3LQmphjkydHQCoFPfeL23qZM51G20AgEHKJ348Y2bXEpfzTc8SWmeTVe+ykFqYjOwAQEn1cWRn4HOuAQD5ySF+fOLuJ1MfO9oALFS/MqveZSG1MEl2AABBwzLnGgBQGrOSps2skQydjK/L3edD9S6LqoVJsgMAZVWhOdcAgBwVGD+SG9lIUnyybKFR/1LZ9S5zr4VJsgMAJZXDNIRnzOxa4vp8IhhJ0ZzrtO35+z7nGgCQnz7W2XlD0mlJNTNbjWNKVr3L3GthkuwAQBm5pK2eo1Vp5lwDAHKST/xo76ncL6mpDmZWvcsiamGS7AAA0gzVnGsAALpBsgMAZVWhOdcAgBz1t07bQJHsAEBJVWnONQAgP30uSj1QJDsAUFYdFAbt7WkGP+caAJCjPsWPYUCyAwAlVaUzcwCA/FQpfowNugMAAAAAUARGdgCgjFyVWmAKAMhJxeIHyQ4AlJBJsgrNuQYA5KNq8YNkBwDKamvQHQAAlFKF4gdrdgAAAACMJEZ2AKCkqjQNAQCQnyrFD5IdACijii0wBQDkpGLxg2QHAErJK1UUDgCQl2rFD9bsAAAAABhJjOwAQElVqQI2ACA/VYofJDsAUFYVmoYAAMhRheIHyQ4AlJFLVqE6CQCAnFQsfozMmh0zu2xmM20eez6+1Mxsxszmiu4fAOTOvbcLAKCaKhQ/RibZkfSapBtm5onLncCxNUmXJd2RtBD/HwAAAMAIGaVpbPN6OmmZUZTUpKm7u5lZzd3rxXcNAApQrpNrAIBhUaH4MRLJjpnVJF129+XEbafdfT7rfiQ6AMqsShWwAQD5qVL8GIlkJ05athMXMzvfKtExs/OSViWdkvQ9d1/KOO68JO3W3tz6DAA9q1CwKiPiB4ChVaH4MRLJTlI8yhOavtawmBgFumpmN8zsRNpIT5w0zUvSQZuuzm8GAKAnxA8AGLxR2qCg4U1JV7MOSE53i9UVbXAAAOXgkrZ6vAAAqqdi8WNoR3bi4f8TLQ6bS0lczrv7xYzHnZF03d0PJW5elvRCdz0FgP4zeaXmXAMA8lG1+DG0yU6rNTdp4kSm1RQ2SWpOhmqSbnT6fAAwUBUKVgCAHFUofozaNLYZJTYqaDCzWTOblbansNUSbTVJM90kVwAAAACG19CO7PTgWspt5xQlOBfi6/Nm9oaixOiEpDN96hsA5KdCZ+YAADmqUPwYqWTH3RclLabcfrHpel3SpX71CwBy11hgCgBAJyoWP0Yq2QGAKqnSAlMAQH6Kjh/xMpHz8dVTkt5K1rSMZ1gtK1qCsthuWzdIdgCgrEh2AADdKD5+zLn7BenJTshm9ry7183sihLJj5ktKF5SktXWrVHboAAAkBMzq5nZG/HlSmOjl0T7G2Z2Nv637TYAwOiKk5vtXY7jzcGW9WSkZ7ZptGbZzE630dYVRnYAoJS8UmfmAAB5KTx+1CTNaef6+MNx4tK8c3Jd0hkzU6hNKWvy28XIDgCUkSsKVr1cMgzbmTkAQE4Kjh/x5/+JpptnJS0ovR7mbUXrc7LaukayAwBltdXjJVvjzFyzVmfmgm3tvCQAQB/0Hj+eMbNricv55MM3bThwXtFGA4uSpjN6ldXWNaaxAQB2cPclM0s7M3dR4bNvp1q0AQBGwyfufrLVQfGubK+6e+OE12rG4VltXSPZAYCSymHr0GfMLFmIed7d5xtXQmfmms/gNSnkzBwAID99LF0wJ+nVxPW6dp4UO6xomnRWW9dIdgCgrHoPVqU5MwcAyFEfkp24Xs6cu9fj67PxCbPmk2I1SVey2nrpB2t2AKCMXNKW93Zp38DPzAEActKH+GFmZyUtSVqNyxjMSmqcXFtsKkkwE6/nadXWFUZ2AKCU+rL19NCcmQMA5KXY+BHv5pn2md+YHfC6pDfj404pWguqNtq6QrIDAEjVfGZO0fafJ+PbFuPEp7GuZ8eZuUAbAGCExaUKLKO9ridJzNV227pFsgMAZVWhM3MAgBz1b4OCgSPZAYCyKjBYDduZOQBAjkh2AABDrbHAFACATlQsfrAbGwAAAICRxMgOAJSSS7416E4AAEqnWvGDZAcAyqpCc64BADmqUPwg2QGAMqrYnGsAQE4qFj9YswMAAABgJDGyAwBlVaFpCACAHFUofpDsAEBZVShYAQByVKH4QbIDAKXklQpWAIC8VCt+kOwAQBm5pK3qbB0KAMhJxeIHGxQAAAAAGEmlHNkxszlJC+6+2HT7G5KWJc1IWnT3pYzHaPtYABhKFZqGAADIUYXiR6mSHTM7LWlW0llJC01tVyS91UhazGxB0pnA47R9LAAMrQoFKwBAjioUP0o1jc3dF939kqIRmWazTaMzy3FylKaTYwFgCHlUFK6XCwCggqoVP0qV7ITEiUq96ea6UkZrOjkWAAAAQHmVahpbhlrKbbclnerxWAAYTi65V2c3HQBATioWP0Yl2Zku6FiZ2XlJ5yVpt/Z2clcAKFbJphJUDfEDwNCqUPwYlWRntaBj5e7zkuYl6aBNV+c3A8Dwq9AC0zIifgAYWhWKHwNNduKzXidaHDbn7mkbEiTVtXN62mGlb2TQybEAAAAASmqgyU581iuPx1k0s+bpaTVJV3o5FgCGlnulKmADAHJSsfgxEruxxRbNbDZxfaZRdNTMZpvagscCQGm493YBAFRTheJHqdbsxAnKOUmnG9fjujuS9LqkN81sRtHOahcTdz2naPTmQhvHAkApeIXOzAEA8lOl+FGqZCcuBLqklOTE3euJ2682tV1s91gAKIfynV0DAAyDasWPUZrGBgAAAADbSjWyAwCIuSpVJwEAkJOKxQ+SHQAoqwpVwAYA5KhC8YNkBwBKyCV5H87MmdkbiuqQzUhajNdOAgBKqmrxg2QHAJDKzK5IeqsRoMxsQdKZwfYKADDshil+sEEBAJSRezQNoZdLa7NNZ+KWzex0Qa8IANAPFYsfJDsAUFK+5T1dssRBqd50c12M7ABA6VUpfjCNDQDKqtgFprWU224rKsQMACizCsUPkp0OfKY7nyz61Y86vNszkj4poj9DrqqvW6rua6/q65Y6f+3P9vqEn+nOny761Wd6fJjdZnYtcX3e3efj/0/3+NhI6CJ+8PdUPVV93VJ1X3s3r5v40SGSnQ64++c7vY+ZXXP3k0X0Z5hV9XVL1X3tVX3d0mBeu7v/3YKfYrXgx6+UTuMHf0/Ve+1Vfd1SdV/7oF531eIHa3YAAGnq2jkV4bCibUQBAAgZqvhBsgMA2MHdF7VzKkJN0sIAugMAKIlhix8kO8Wbb33ISKrq65aq+9qr+rql0X3ti2Y2m7g+EwcxFG9Uf6faUdXXXtXXLVX3tY/y6x6a+GHuxVdQBQCUj5nVJL0p6eeKdtH53qAqYAMAymOY4gfJDgC0yczmJC00n50yszcUzUWekbRIQgAASCJ+DA67sQE5qeoHlpmdj//7fUVzdC+4+8UBdil3cYG0WUln1TTn2MyuSHqr8fM2swVReBNAB6oYP6oQOyTixzBgzU4fmNlc/MvefPsbZnY2/nc27b4oh/gDa9Hdr7r7JUlzg+5TH9UkXZZ0R9EH+eXBdid/7r4Y/1zTdpKZbfpispz29w50ithRDRWOHyMfOyTixzAg2SmQmZ2Oz9acTWmrzIebmZ2PLzUzm4mHckdNlT+w6u5ukg65+wvuXpmtieOfcb3p5ro4M4ceEDueIH6MtMrGDon40U8kOwUim9820mdv+MCKuHvze1AFzXUEJOm2oqkoQFeIHU8hfoy4isYOifjRN6zZGYAWH26juK1r3d3NzGoj+qEW+sA61e+ODEo893pV1duxq7mOAFCYCsYOifgx0iocOyTiR9+Q7AxGJT/cRjRQSXxgLSamH1w1sxtmdmKEf95Jq4PuACqlkrFDIn6MqCrHDon40TdMYxuMyn24xXOuz8YLbkdtQW2lP7BS5lnXJb02iL4MQF07v4AeVvr0I6BXlYsdEvFjVFU8dkjEj75hZGcwqvbhNupnbyr7gWVmM5Kuu/uhxM3Lkl4YUJf66v9v7w6P2jjCMAC/XweaSQdyBzHpADpgJhUEOsDjCjKUAB1knA6sDmLTganBHWx+6BQ0AjsSCJ3Ye54/nO7EzDJC+963Wu221hZVtXkDOkvyaYz20L2pZUciP7o09exI5MchKXZ2MMwtff8/T7veYkWRSXVuPxm9uR2hOXunw8rmvgizJN/GaMhIFlW1/qXx+eamcUyb7Hg++dG1qWdHIj8OQrGzg9baXjrXt9657RLcExq9mWSHNbzG/918Dcfzfb1XjsUwdeb3JKerx8NqWUnyR5KPw//6b3kc4Eyc7HggP540ufyYSnYk8uMYKHbG82Y7t2d0RlMYvZlyh3U77AnyPcubmO6WTB3ep3d54nUdptOszv99yHYxSW82OxL58QNTzY/usyORH8egWmtjt6Fba9X8VZbLgn5eVfPDKMbHJP+k8yUXq+pq4+/+2lrrbWQOYC9kxwP5AbyUYodXNwTURR5Gb7aZmw7AxMkP4KUUOwAAQJfsswMAAHRJsQMAAHRJsQMAAHRJsQMAAHRJsQMHUFUXVfV57HYA8HbIDng5m4rCK6qqmyyXTJ0l2dz5HAAekR2wP4odeEWttctkOTqX5GTk5gDwBsgO2C3HjPwAAAF4SURBVB/T2AAAgC4pdgAAgC6ZxgY7qqqrLOdSnyX5czg+Hy6/W00/AIAV2QHjUOzADqrqurX2YTj+kuRTkkVr7XKYW30xPOd+1IYCcDRkB4zHNDbYUlWdJ/lr4/Q8y9BKkkWSS2EFwIrsgHH5ZAe29721drf2+CRJWmuL4ed9ktsxGgbA0ZIdMCKf7MCWVsG05izLETkAeJLsgHEpduD5TpPY2RqAXcgOOCDFDjxDVc2z3Nn6buP8r+O0CIBjJzvg8BQ7sKWquqiq2fBwtVzol7Xr549/C4Apkx0wLsUObKGqTpPcZPhiaZJ3G9dnSeYbX0JdN/vBeQA6JTtgfNVaG7sNcPSGQLpO8i3JL621D8No3FmSr0nSWnu0mk5V3WS5xOhJlqG1SHKf5OYn4QZAB2QHjE+xAwAAdMk0NgAAoEuKHQAAoEuKHQAAoEuKHQAAoEuKHQAAoEuKHQAAoEuKHQAAoEuKHQAAoEuKHQAAoEuKHQAAoEv/ArO5MSqo9dMNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x1296 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}