Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram_parallel/sampling_multivariate_normal_distribution_via_paradram_parallel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[MIT License](https://github.com/cdslaborg/paramonte#license)  \n",
    "[ParaMonte: plain powerful parallel Monte Carlo library](https://github.com/cdslaborg/paramonte).  \n",
    "Copyright (C) 2012-present, [The Computational Data Science Lab](https://www.cdslab.org/#about)  \n",
    "https://github.com/cdslaborg/paramonte  \n",
    "[References](https://www.cdslab.org/paramonte/notes/overview/preface/#how-to-acknowledge-the-use-of-the-paramonte-library-in-your-work)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "paramonte 2.3.0\n",
      "numpy 1.19.2\n",
      "scipy 1.5.2\n",
      "pandas 1.1.3\n",
      "seaborn 0.11.0\n",
      "matplotlib 3.3.2\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import paramonte as pm\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "print('\\n'.join(f'{m.__name__} {m.__version__}' for m in globals().values() if getattr(m, '__version__', None)))\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running ParaDRAM simulations in parallel on multiple processors  \n",
    "\n",
    "There are **two parallelism paradigms** currently implemented **in the ParaDRAM sampler**:\n",
    "\n",
    "1. The **single-chain parallelism**, in which only a single Markov chain is generated, but all processors contribute to the construction of this chain.  \n",
    "1. The **multi-chain parallelism**, in which each processor creates its Markov chain separately from the rest of the processors. However, at the end of the simulation, all processors communicate with each other to compute the probability that convergence to the target density has occurred and that all processors have sampled the same region of high probability in the domain of the objective function.  \n",
    "\n",
    "### Which parallelism paradigm should be used when?\n",
    "\n",
    "- The **single-chain parallelism** becomes very useful for large-scale problems that are highly computationally demanding.  \n",
    "- The **multi-chain parallelism** is useful when you suspect that the target objective function that has to be sampled is multi-modal. In such cases, the multi-chain parallelism could provide further evidence on whether convergence to single-mode or multi-modal target density function has occurred or not.  \n",
    "  \n",
    "  \n",
    "In either parallelism case, the ParaMonte library currently uses the MPI library for inter-process communications. As such, if you want to run a ParaDRAM simulation in parallel, you will have to first save your Python scripts in external Python files and then call them from the command line via the MPI launcher application.  \n",
    "  \n",
    "\n",
    "To see how this can be done, consider the simple toy problem of [sampling a 4-dimensional Multivariate Normal (MVN) distribution as described in this jupyter notebook](https://nbviewer.jupyter.org/github/cdslaborg/paramontex/blob/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram/sampling_multivariate_normal_distribution_via_paradram.ipynb).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::\n",
      "::::                                                                                       ::::\n",
      "\n",
      "       _/_/_/_/                                   _/_/    _/_/\n",
      "        _/    _/                                  _/_/_/_/_/                     _/\n",
      "       _/    _/ _/_/_/_/   _/ /_/_/ _/_/_/_/     _/  _/  _/   _/_/   _/_/_/   _/_/_/  _/_/_/\n",
      "      _/_/_/   _/    _/   _/_/     _/    _/     _/      _/  _/   _/ _/    _/   _/   _/_/_/_/                        \n",
      "     _/       _/    _/   _/       _/    _/     _/      _/  _/   _/ _/    _/   _/   _/    \n",
      "  _/_/_/       _/_/_/_/ _/         _/_/_/_/ _/_/_/  _/_/_/  _/_/  _/    _/   _/_/   _/_/_/\n",
      "\n",
      "\n",
      "                                          ParaMonte\n",
      "                                   plain powerful parallel\n",
      "                                     Monte Carlo library\n",
      "                                        Version 2.3.0\n",
      "\n",
      "::::                                                                                       ::::\n",
      ":::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::\n",
      "\n",
      "ParaMonte - NOTE: The ParaMonte::Kernel samplers have no Python package dependencies \n",
      "ParaMonte - NOTE: beyond numpy. However, the ParaMonte::Python post-processing and \n",
      "ParaMonte - NOTE: visualization tools require the following Python packages, \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     numpy : 1.19.2\n",
      "ParaMonte - NOTE:     scipy : 1.5.2\n",
      "ParaMonte - NOTE:     pandas : 1.1.2\n",
      "ParaMonte - NOTE:     seaborn : 0.11.0\n",
      "ParaMonte - NOTE:     matplotlib : 3.3.2\n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE: If you do not intend to use the postprocessing and visualization tools, \n",
      "ParaMonte - NOTE: you can ignore this message. Otherwise, UPDATE THE ABOVE PACKAGES TO \n",
      "ParaMonte - NOTE: THE REQUESTED VERSIONS OR NEWER, SO THAT THE VISUALIZATION TOOLS \n",
      "ParaMonte - NOTE: OF THE ParaMonte::Python LIBRARY FUNCTION PROPERLY.\n",
      "\n",
      "\n",
      "ParaMonte - NOTE: Intel MPI library for 64-bit architecture detected at: \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2020.1.216\\windows\\mpi\\intel64\\bin\n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE: To perform ParaMonte simulations in parallel on a single node, \n",
      "ParaMonte - NOTE: run the following two commands, in the form and order specified, \n",
      "ParaMonte - NOTE: on a Python-aware mpiexec-aware command-line interface such as \n",
      "ParaMonte - NOTE: Anaconda3 Windows command prompt: \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     \"C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2020.1.216\\windows\\mpi\\intel64\\bin\\mpivars.bat\"\n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     mpiexec -localonly -n NUM_PROCESSES python main.py\n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE: where, \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     0.   the first command defines the essential environment variables, \n",
      "ParaMonte - NOTE:          and the second command runs in the simulation in parallel, where, \n",
      "ParaMonte - NOTE:     1.   you should replace NUM_PROCESSES with the number of processors \n",
      "ParaMonte - NOTE:          you wish to assign to your simulation task and, \n",
      "ParaMonte - NOTE:     2.   the flag '-localonly' indicates a parallel simulation on only \n",
      "ParaMonte - NOTE:          a single node (this flag will obviate the need for the MPI \n",
      "ParaMonte - NOTE:          library credentials registration). For more information, visit: \n",
      "ParaMonte - NOTE:          https://software.intel.com/en-us/get-started-with-mpi-for-windows \n",
      "ParaMonte - NOTE:     3.   main.py is the Python file which serves as the entry point to \n",
      "ParaMonte - NOTE:          your simulation, where you call the ParaMonte sampler routines. \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE: Note that the above two commands must be executed on a command-line that \n",
      "ParaMonte - NOTE: recognizes both Python and mpiexec applications, such as the Anaconda \n",
      "ParaMonte - NOTE: command-line interface. For more information, in particular, on how \n",
      "ParaMonte - NOTE: to register to run Hydra services for multi-node simulations \n",
      "ParaMonte - NOTE: on Windows servers, visit: \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     https://www.cdslab.org/paramonte\n",
      "\n",
      "\n",
      "ParaMonte - NOTE: To check for the MPI library installation status or display the above \n",
      "ParaMonte - NOTE: messages in the future, type the following on the Python command-line: \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     import paramonte as pm\n",
      "ParaMonte - NOTE:     pm.verify()\n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE: To get started, type the following on the Python command-line,\n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     import paramonte as pm\n",
      "ParaMonte - NOTE:     pm.helpme()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import paramonte as pm\n",
    "pm.verify()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ParaMonte - NOTE: You have the latest version of the ParaMonte library. \n",
      "ParaMonte - NOTE: To see the most recent changes to the library, visit, \n",
      "ParaMonte - NOTE: \n",
      "ParaMonte - NOTE:     https://www.cdslab.org/paramonte/notes/overview/paramonte-python-release-notes\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pm.checkForUpdate() # check for new versions of the library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a single-chain ParaDRAM simulation in parallel on multiple processors\n",
    "\n",
    "We will save our parallel script in a file with the same name as this Jupyter Notebook's name,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./sampling_multivariate_normal_distribution_via_paradram_parallel_singleChain.py\",\"w\") as file:\n",
    "    contents = \"\"\"\n",
    "import numpy as np\n",
    "\n",
    "NDIM = 4                                # number of dimensions of the domain of the MVN PDF\n",
    "MEAN =  np.double([-10, 15., 20., 0.0]) # This is the mean of the MVN PDF.\n",
    "COVMAT = np.double( [ [1.0,.45,-.3,0.0] # This is the covariance matrix of the MVN PDF.\n",
    "                    , [.45,1.0,0.3,-.2]\n",
    "                    , [-.3,0.3,1.0,0.6]\n",
    "                    , [0.0,-.2,0.6,1.0]\n",
    "                    ] )\n",
    "\n",
    "INVCOV = np.linalg.inv(COVMAT) # This is the inverse of the covariance matrix of the MVN distribution.\n",
    "\n",
    "# The following is the log of the coefficient used in the definition of the MVN.\n",
    "\n",
    "MVN_COEF = NDIM * np.log( 1. / np.sqrt(2.*np.pi) ) + np.log( np.sqrt(np.linalg.det(INVCOV)) )\n",
    "\n",
    "# the logarithm of objective function: log(MVN)\n",
    "\n",
    "def getLogFunc(point):\n",
    "    '''\n",
    "    Return the logarithm of the MVN PDF.\n",
    "    '''\n",
    "    normedPoint = MEAN - point\n",
    "    return MVN_COEF - 0.5 * ( np.dot(normedPoint,np.matmul(INVCOV,normedPoint)) )\n",
    "\n",
    "import paramonte as pm\n",
    "\n",
    "pmpd = pm.ParaDRAM() # define a ParaMonte sampler instance\n",
    "\n",
    "pmpd.mpiEnabled = True # This is essential as it enables the invocation of the MPI-parallelized ParaDRAM routines.\n",
    "\n",
    "pmpd.spec.overwriteRequested = True # overwrite existing output files if needed\n",
    "pmpd.spec.randomSeed = 3751 # initialize the random seed to generate reproducible results.\n",
    "pmpd.spec.outputFileName = \"./out/mvn_parallel_singleChain\"\n",
    "pmpd.spec.progressReportPeriod = 20000\n",
    "pmpd.spec.chainSize = 30000 # the default 100,000 unique points is too large for this simple example.\n",
    "\n",
    "# call the ParaDRAM sampler\n",
    "\n",
    "pmpd.runSampler ( ndim = 4\n",
    "                , getLogFunc = getLogFunc\n",
    "                )\n",
    "\"\"\"\n",
    "    file.write(contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the saved [output MPI-parallelized Python script](https://github.com/cdslaborg/paramontex/blob/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram_parallel/sampling_multivariate_normal_distribution_via_paradram_parallel_singleChain.py).\n",
    "> **Note** the only difference in the above parallel script with the serial version, which is the extra Python statement `pmpd.mpiEnabled = True`. This flag tells the ParaDRAM sampler initiate the simulation in parallel and silence all output messages that would otherwise be printed by all processes.  ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**: At this point, we have assumed that you already have an MPI runtime library installed on your system. We highly recommend the use of the Intel MPI library on your system if it is Windows or Linux, and Open-MPI if it is macOS. You can run `pm.verify()` on your python command line, just as described in the Jupyter notebook for the serial sampling of the MVN distribution, to verify the existence of the MPI library on your system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now run this code in parallel on 3 processors. We will invoke the `mpiexe` launcher to run the code in parallel, however, depending on your system, your platform, or the supercomputer on which you are running this code, you may need a different MPI launcher (e.g., `ibrun`, `mpirun`, ...). In the following, we will assume that you will be using the Intel MPI library if your operating system is Windows (as implied by the flag `-localonly`).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we run the MPI-enabled Python script in parallel on three cores, **on the terminal** (not in the Python session). On Linux or macOS, we can try the following command,  \n",
    "```bash  \n",
    "mpiexec -n 3 python main_mpi_singleChain.py\n",
    "```  \n",
    "\n",
    "On windows, if you are using the Intel MPI library, we can try the following,  \n",
    "```batch  \n",
    "mpiexec -localonly -n 3 python main_mpi_singleChain.py\n",
    "```  \n",
    "Otherwise, the same syntax and flags as used in the cases of Linux and macOS should work fine. To understand the meaning of the extra `-localonly` flag, see the [ParaMonte library documentation page](https://www.cdslab.org/paramonte/notes/run/python/#running-python-simulations-on-the-command-prompt-on-multiple-processors).  \n",
    "\n",
    "The following command combines the above two commands in a single line so that it works, whether you are using a Windows machine or Linux/macOS,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "************************************************************************************************************************************\n",
      "************************************************************************************************************************************\n",
      "****                                                                                                                            ****\n",
      "****                                                                                                                            ****\n",
      "****                                                         ParaMonte                                                          ****\n",
      "****                                                  Plain Powerful Parallel                                                   ****\n",
      "****                                                    Monte Carlo Library                                                     ****\n",
      "****                                                                                                                            ****\n",
      "****                                                       Version 1.4.0                                                        ****\n",
      "****                                                                                                                            ****\n",
      "****                                              Build: Thu Oct 29 01:05:57 2020                                               ****\n",
      "****                                                                                                                            ****\n",
      "****                                                   Department of Physics                                                    ****\n",
      "****                                              Computational & Data Science Lab                                              ****\n",
      "****                                          Data Science Program, College of Science                                          ****\n",
      "****                                            The University of Texas at Arlington                                            ****\n",
      "****                                                                                                                            ****\n",
      "****                                                  originally developed at                                                   ****\n",
      "****                                                                                                                            ****\n",
      "****                                                 Multiscale Modeling Group                                                  ****\n",
      "****                                          Center for Computational Oncology (CCO)                                           ****\n",
      "****                                 Oden Institute for Computational Engineering and Sciences                                  ****\n",
      "****                               Department of Aerospace Engineering and Engineering Mechanics                                ****\n",
      "****                                     Department of Neurology, Dell-Seton Medical School                                     ****\n",
      "****                                            Department of Biomedical Engineering                                            ****\n",
      "****                                             The University of Texas at Austin                                              ****\n",
      "****                                                                                                                            ****\n",
      "****                                   For questions and further information, please contact:                                   ****\n",
      "****                                                                                                                            ****\n",
      "****                                                      Amir Shahmoradi                                                       ****\n",
      "****                                                                                                                            ****\n",
      "****                                                   shahmoradi@utexas.edu                                                    ****\n",
      "****                                                  amir.shahmoradi@uta.edu                                                   ****\n",
      "****                                                   ashahmoradi@gmail.com                                                    ****\n",
      "****                                                                                                                            ****\n",
      "****                                                       cdslab.org/pm                                                        ****\n",
      "****                                                                                                                            ****\n",
      "****                                             https://www.cdslab.org/paramonte/                                              ****\n",
      "****                                                                                                                            ****\n",
      "****                                                                                                                            ****\n",
      "************************************************************************************************************************************\n",
      "************************************************************************************************************************************\n",
      " \n",
      " \n",
      " \n",
      "************************************************************************************************************************************\n",
      "****                                                                                                                            ****\n",
      "****                                       Setting up the ParaDRAM simulation environment                                       ****\n",
      "****                                                                                                                            ****\n",
      "************************************************************************************************************************************\n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Variable outputFileName detected among the input variables to ParaDRAM:\n",
      "        ParaDRAM - NOTE: ./out/mvn_parallel_singleChain\n",
      "        ParaDRAM - NOTE: \n",
      "        ParaDRAM - NOTE: Absolute path to the current working directory:\n",
      "        ParaDRAM - NOTE: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\n",
      "        ParaDRAM - NOTE: \n",
      "        ParaDRAM - NOTE: Generating the requested directory for the ParaDRAM output files:\n",
      "        ParaDRAM - NOTE: .\\out\\\n",
      "        ParaDRAM - NOTE: \n",
      "        ParaDRAM - NOTE: ParaDRAM output files will be prefixed with:\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_singleChain\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Searching for previous runs of ParaDRAM...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: No pre-existing ParaDRAM run detected.\n",
      "        ParaDRAM - NOTE: Starting a fresh ParaDRAM run...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Generating the output report file:\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_singleChain_process_1_report.txt\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Running the simulation in parallel on 3 processes.\n",
      "        ParaDRAM - NOTE: Please see the output report and progress files for further realtime simulation details.\n",
      " \n",
      " \n",
      " \n",
      "                         Accepted/Total Func. Call   Dynamic/Overall Acc. Rate   Elapsed/Remained Time [s] \n",
      "                         =========================   =========================   ========================= \n",
      "\n",
      "                                      4490 / 20000             0.2223 / 0.2223             0.2480 / 1.4090\n",
      "                                     10297 / 40000             0.2901 / 0.2562             0.5330 / 1.0199\n",
      "                                     16265 / 60000             0.3000 / 0.2708             0.8100 / 0.6840\n",
      "                                     22469 / 80000             0.3083 / 0.2801             1.0840 / 0.3633\n",
      "                                    28630 / 100000             0.3101 / 0.2861             1.3600 / 0.0651\n",
      "                                    30000 / 104525             0.3081 / 0.2871             1.4200 / 0.0000 \n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the statistical properties of the Markov chain...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the final decorrelated sample size...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Generating the output sample file:\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_singleChain_process_1_sample.txt\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the statistical properties of the final refined sample...\n",
      " \n",
      " \n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Mission Accomplished.\n",
      " \n",
      " \n",
      " \n",
      " \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'ls' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!ls && \\\n",
    "mpiexec -n 3 python sampling_multivariate_normal_distribution_via_paradram_parallel_singleChain.py || \\\n",
    "mpiexec -localonly -n 3 python sampling_multivariate_normal_distribution_via_paradram_parallel_singleChain.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampler has now generated 5 output files that are [accessible here](https://github.com/cdslaborg/paramontex/tree/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram_parallel/out), all prefixed with `mvn_parallel_singleChain_*`. In particular, the [simulation report file](https://github.com/cdslaborg/paramontex/tree/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram_parallel/out/mvn_parallel_singleChain_process_1_report.txt) contains a lot of interesting information about the performance of the parallel simulation. We can process these files in the same way we did for [the serial version of sampling the MVN PDF via the ParaDRAM sampler](https://nbviewer.jupyter.org/github/cdslaborg/paramontex/blob/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram/sampling_multivariate_normal_distribution_via_paradram.ipynb). For example, to parse the contents of the report file, we can try,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaMonte Python Interface Version 2.3.0\n",
      "ParaMonte Python Kernel Version 1.4.0\n",
      "\n",
      "ParaDRAM - NOTE: 1 files detected matching the pattern: \"./out/mvn_parallel_singleChain*_report.txt\"\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_singleChain_process_1_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.008975 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: The processed report files are now stored in the newly-created\n",
      "ParaDRAM - NOTE: component `reportList` of the ParaDRAM object as a Python list.\n",
      "ParaDRAM - NOTE: For example, to access the entire contents of the first (or the only) report file, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].contents.print()\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: where you will have to replace `pmpd` with your ParaDRAM instance name.\n",
      "ParaDRAM - NOTE: To access the simulation statistics and information, examine the contents of the\n",
      "ParaDRAM - NOTE: components of the following structures:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].contents.print()  # to print the contents of the report file.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].setup             # to get information about the simulation setup.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.time        # to get the timing information of the simulation.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.chain       # to get the statistics of the simulation output sample.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.numFuncCall # to get information about the number of function calls.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.parallelism # to get information about the simulation parallelism.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].spec              # to get the simulation specification in the report file.\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For more information and examples on the usage, visit:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     https://www.cdslab.org/paramonte\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import paramonte as pm\n",
    "print(pm.version.interface.get())\n",
    "print(pm.version.kernel.get())\n",
    "\n",
    "pmpd = pm.ParaDRAM()\n",
    "pmpd.readReport(\"./out/mvn_parallel_singleChain\")\n",
    "report = pmpd.reportList[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of detailed information about different aspects of the parallel simulation in this file. Here is a glance through some of the information extracted from the file,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.313000702847873e-05\n",
      "This is the average pure time cost of each function call, in seconds.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.time.perFuncCall.value)\n",
    "print(report.stats.time.perFuncCall.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.315229732036249e-06\n",
      "This is the average time cost of inter-process communications per used (accepted or rejected or delayed-rejection) function call, in seconds.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.time.perInterProcessCommunication.value)\n",
    "print(report.stats.time.perInterProcessCommunication.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "This is the number of processes (images) used in this simulation.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.current.numProcess.value)\n",
    "print(report.stats.parallelism.current.numProcess.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.573338694004443\n",
      "This is the estimated maximum speedup gained via singleChain parallelization model compared to serial mode.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.current.speedup.value)\n",
    "print(report.stats.parallelism.current.speedup.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.573338694004443\n",
      "This is the predicted optimal maximum speedup gained via singleChain parallelization model, given the current MCMC sampling efficiency.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.optimal.current.speedup.value)\n",
    "print(report.stats.parallelism.optimal.current.speedup.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.962074970857193\n",
      "This is the predicted absolute optimal maximum speedup gained via singleChain parallelization model, under any MCMC sampling efficiency. This simulation will likely NOT benefit from any additional computing processors beyond the predicted absolute optimal number, 3, in the above. This is true for any value of MCMC sampling efficiency. Keep in mind that the predicted absolute optimal number of processors is just an estimate whose accuracy depends on many runtime factors, including the topology of the communication network being used, the number of processors per node, and the number of tasks to each processor or node.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.optimal.absolute.speedup.value)\n",
    "print(report.stats.parallelism.optimal.absolute.speedup.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13792, 9551, 6657]\n",
      "These are contributions of individual processes to the construction of the MCMC chain. Essentially, they represent the total number of accepted states by the corresponding processor, starting from the first processor to the last. This information is mostly informative in parallel Fork-Join (singleChain) simulations.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.processContribution.value)\n",
    "print(report.stats.parallelism.processContribution.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ParaDRAM sampler also automatically computes the strong scaling behavior of the parallel simulation under the current and absolutely optimal simulation conditions. For example, we can plot these scaling results like the following,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.4744375, 1.5733387, 1.5095088, 1.394962, 1.2742799]\n",
      "This is the predicted strong-scaling speedup behavior of the singleChain parallelization model, given the current MCMC sampling efficiency, for increasing numbers of processes, starting from a single process.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.optimal.current.scaling.strong.speedup.value)\n",
    "print(report.stats.parallelism.optimal.current.scaling.strong.speedup.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.7002015, 1.962075, 1.9436468, 1.809423, 1.6461051]\n",
      "This is the predicted absolute strong-scaling speedup behavior of the singleChain parallelization model, under any MCMC sampling efficiency, for increasing numbers of processes, starting from a single process.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.parallelism.optimal.absolute.scaling.strong.speedup.value)\n",
    "print(report.stats.parallelism.optimal.absolute.scaling.strong.speedup.description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "\n",
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       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x190cb224488>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "ax.plot(report.stats.parallelism.optimal.current.scaling.strong.speedup.value)\n",
    "ax.plot(report.stats.parallelism.optimal.absolute.scaling.strong.speedup.value)\n",
    "ax.set_ylabel(\"Speedup\")\n",
    "ax.set_xlabel(\"Number of Processors\")\n",
    "ax.legend(labels = [\"current\",\"absolutely-optimal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The efficiency of the parallel simulation  \n",
    "\n",
    "As we see in the above plot and the information extracted from the report file, the **Estimated maximum speedup gained via singleChain parallelization model compared to serial mode**, was only moderate (less than twice). This is partly because the example objective function here is too easy to compute and partly because this simulation was performed on a decent fast quad-core processor.  \n",
    "\n",
    "But, more importantly, note the **Predicted absolute optimal maximum speedup** gained via singleChain parallelization model, **under any MCMC sampling efficiency**, which tells us that no matter how you configure this simulation, the speedup gained by running this simulation in parallel can be **at most** a factor of ~2 better than the performance of the serial run of the same problem with the same simulation specifications, **regardless of how many CPU cores you may use for the parallel simulation**.  \n",
    "\n",
    "**The ability of the sampler to give us such detailed efficiency reports is remarkable as it can help us set up our parallel simulations more reasonably and optimally, without wasting any extra computational resources with no efficiency gain**.\n",
    "\n",
    "> When you are working with expensive large-scale simulations, it is, therefore, a good idea to run some tests of your simulation and check the output of the report file to find the predicted optimal number of physical cores for the parallel simulation and then, request the same number of cores as predicted when invoking the MPI launcher.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the report file, the rest of the simulation data can be also parsed and analyzed. However, such tasks are identical to the case of the serial simulation and we, therefore, suffice to direct the reader of this notebook to [the Jupyter notebook for the serial version of this simulation problem](https://nbviewer.jupyter.org/github/cdslaborg/paramontex/blob/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram/sampling_multivariate_normal_distribution_via_paradram.ipynb).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a multi-chain ParaDRAM simulation in parallel on multiple processors  \n",
    "\n",
    "There is another mode of parallelization, the **multiChain** or **multi-chain** mode, by which the ParaDRAM sampler can sample points from the objective function. In this mode, the sampler will generate multiple chains, each corresponding to one physical core on the computer. Each of these chains will independently explore the domain of the objective function.  \n",
    "\n",
    "To make the exploration even more interesting and robust (i.e., to ensure convergence to the target objective function by all independent chains), we can also let the initial starting point of the MCMC sampler to be chosen at random. To do so, we will have to also specify a domain from which the initial random start points will be sampled, otherwise, the default domain extends from negative infinity to positive infinity, which is problematic for computer simulations,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./sampling_multivariate_normal_distribution_via_paradram_parallel_multiChain.py\",\"w\") as file:\n",
    "    contents = \"\"\"\n",
    "import numpy as np\n",
    "\n",
    "NDIM = 4                                # number of dimensions of the domain of the MVN PDF\n",
    "MEAN =  np.double([-10, 15., 20., 0.0]) # This is the mean of the MVN PDF.\n",
    "COVMAT = np.double( [ [1.0,.45,-.3,0.0] # This is the covariance matrix of the MVN PDF.\n",
    "                    , [.45,1.0,0.3,-.2]\n",
    "                    , [-.3,0.3,1.0,0.6]\n",
    "                    , [0.0,-.2,0.6,1.0]\n",
    "                    ] )\n",
    "\n",
    "INVCOV = np.linalg.inv(COVMAT) # This is the inverse of the covariance matrix of the MVN distribution.\n",
    "\n",
    "# The following is the log of the coefficient used in the definition of the MVN.\n",
    "\n",
    "MVN_COEF = NDIM * np.log( 1. / np.sqrt(2.*np.pi) ) + np.log( np.sqrt(np.linalg.det(INVCOV)) )\n",
    "\n",
    "# the logarithm of objective function: log(MVN)\n",
    "\n",
    "def getLogFunc(point):\n",
    "    '''\n",
    "    Return the logarithm of the MVN PDF.\n",
    "    '''\n",
    "    normedPoint = MEAN - point\n",
    "    return MVN_COEF - 0.5 * ( np.dot(normedPoint,np.matmul(INVCOV,normedPoint)) )\n",
    "\n",
    "import paramonte as pm\n",
    "\n",
    "pmpd = pm.ParaDRAM() # define a ParaMonte sampler instance\n",
    "\n",
    "pmpd.mpiEnabled = True # This is essential as it enables the invocation of the MPI-parallelized ParaDRAM routines.\n",
    "\n",
    "pmpd.spec.overwriteRequested = True # overwrite existing output files if needed\n",
    "pmpd.spec.randomSeed = 3751 # initialize the random seed to generate reproducible results.\n",
    "pmpd.spec.outputFileName = \"./out/mvn_parallel_multiChain\"\n",
    "pmpd.spec.progressReportPeriod = 20000\n",
    "pmpd.spec.chainSize = 30000 # the default 100,000 unique points is too large for this simple example, so set it to 30000.\n",
    "\n",
    "# set up a random initial starting point for each of the independent MCMC chains,\n",
    "# by defining the domain of the random start points. The following defines the \n",
    "# boundaries of the NDIM(=four)-dimensional hypercube from within which the \n",
    "# random initial start points will be drawn by the sampler,\n",
    "\n",
    "pmpd.spec.randomStartPointRequested = True # This is essential, otherwise, random initialization won't happen\n",
    "pmpd.spec.randomStartPointDomainLowerLimitVec = NDIM * [-25]\n",
    "pmpd.spec.randomStartPointDomainUpperLimitVec = NDIM * [+25]\n",
    "\n",
    "# set the parallelization model to multichain\n",
    "\n",
    "pmpd.spec.parallelizationModel = \"multi chain\" # the value is case and white-space insensitive\n",
    "\n",
    "# call the ParaDRAM sampler\n",
    "\n",
    "pmpd.runSampler ( ndim = 4\n",
    "                , getLogFunc = getLogFunc\n",
    "                )\n",
    "\"\"\"\n",
    "    file.write(contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we run the MPI-enabled Python script in parallel on three cores, **on the terminal** (not in the Python session),  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "************************************************************************************************************************************\n",
      "************************************************************************************************************************************\n",
      "****                                                                                                                            ****\n",
      "****                                                                                                                            ****\n",
      "****                                                         ParaMonte                                                          ****\n",
      "****                                                  Plain Powerful Parallel                                                   ****\n",
      "****                                                    Monte Carlo Library                                                     ****\n",
      "****                                                                                                                            ****\n",
      "****                                                       Version 1.4.0                                                        ****\n",
      "****                                                                                                                            ****\n",
      "****                                              Build: Thu Oct 29 01:05:57 2020                                               ****\n",
      "****                                                                                                                            ****\n",
      "****                                                   Department of Physics                                                    ****\n",
      "****                                              Computational & Data Science Lab                                              ****\n",
      "****                                          Data Science Program, College of Science                                          ****\n",
      "****                                            The University of Texas at Arlington                                            ****\n",
      "****                                                                                                                            ****\n",
      "****                                                  originally developed at                                                   ****\n",
      "****                                                                                                                            ****\n",
      "****                                                 Multiscale Modeling Group                                                  ****\n",
      "****                                          Center for Computational Oncology (CCO)                                           ****\n",
      "****                                 Oden Institute for Computational Engineering and Sciences                                  ****\n",
      "****                               Department of Aerospace Engineering and Engineering Mechanics                                ****\n",
      "****                                     Department of Neurology, Dell-Seton Medical School                                     ****\n",
      "****                                            Department of Biomedical Engineering                                            ****\n",
      "****                                             The University of Texas at Austin                                              ****\n",
      "****                                                                                                                            ****\n",
      "****                                   For questions and further information, please contact:                                   ****\n",
      "****                                                                                                                            ****\n",
      "****                                                      Amir Shahmoradi                                                       ****\n",
      "****                                                                                                                            ****\n",
      "****                                                   shahmoradi@utexas.edu                                                    ****\n",
      "****                                                  amir.shahmoradi@uta.edu                                                   ****\n",
      "****                                                   ashahmoradi@gmail.com                                                    ****\n",
      "****                                                                                                                            ****\n",
      "****                                                       cdslab.org/pm                                                        ****\n",
      "****                                                                                                                            ****\n",
      "****                                             https://www.cdslab.org/paramonte/                                              ****\n",
      "****                                                                                                                            ****\n",
      "****                                                                                                                            ****\n",
      "************************************************************************************************************************************\n",
      "************************************************************************************************************************************\n",
      " \n",
      " \n",
      " \n",
      "************************************************************************************************************************************\n",
      "****                                                                                                                            ****\n",
      "****                                       Setting up the ParaDRAM simulation environment                                       ****\n",
      "****                                                                                                                            ****\n",
      "************************************************************************************************************************************\n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Variable outputFileName detected among the input variables to ParaDRAM:\n",
      "        ParaDRAM - NOTE: ./out/mvn_parallel_multiChain\n",
      "        ParaDRAM - NOTE: \n",
      "        ParaDRAM - NOTE: Absolute path to the current working directory:\n",
      "        ParaDRAM - NOTE: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\n",
      "        ParaDRAM - NOTE: \n",
      "        ParaDRAM - NOTE: Generating the requested directory for the ParaDRAM output files:\n",
      "        ParaDRAM - NOTE: .\\out\\\n",
      "        ParaDRAM - NOTE: \n",
      "        ParaDRAM - NOTE: ParaDRAM output files will be prefixed with:\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Searching for previous runs of ParaDRAM...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: No pre-existing ParaDRAM run detected.\n",
      "        ParaDRAM - NOTE: Starting a fresh ParaDRAM run...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Generating the output report file:\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain_process_1_report.txt\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain_process_2_report.txt\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain_process_3_report.txt\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Running the simulation in parallel on 3 processes.\n",
      "        ParaDRAM - NOTE: Please see the output report and progress files for further realtime simulation details.\n",
      " \n",
      " \n",
      " \n",
      "                         Accepted/Total Func. Call   Dynamic/Overall Acc. Rate   Elapsed/Remained Time [s] \n",
      "                         =========================   =========================   ========================= \n",
      "\n",
      "                                      2312 / 20000             0.1154 / 0.1154             0.2880 / 3.4490\n",
      "                                      6582 / 40000             0.2128 / 0.1641             0.6120 / 2.1774\n",
      "                                     11289 / 60000             0.2376 / 0.1886             0.9050 / 1.5000\n",
      "                                     16478 / 80000             0.2613 / 0.2068             1.1970 / 0.9823\n",
      "                                    21912 / 100000             0.2705 / 0.2195             1.4980 / 0.5529\n",
      "                                    27517 / 120000             0.2803 / 0.2296             1.8000 / 0.1624\n",
      "                                    30000 / 128508             0.2916 / 0.2337             1.9300 / 0.0000 \n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the statistical properties of the Markov chain...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the final decorrelated sample size...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Generating the output sample file:\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain_process_1_sample.txt\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain_process_2_sample.txt\n",
      "        ParaDRAM - NOTE: .\\out\\mvn_parallel_multiChain_process_3_sample.txt\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the statistical properties of the final refined sample...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Computing the inter-chain convergence probabilities...\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: The smallest KS probabilities for the inter-chain sampling convergence:\n",
      "        ParaDRAM - NOTE: 1.756220230600487E-002 for SampleVariable3 on the chains generated by processes 1 and 2.\n",
      "        ParaDRAM - NOTE: 1.756220230600487E-002 for SampleVariable3 on the chains generated by processes 2 and 1.\n",
      "        ParaDRAM - NOTE: 3.510737888357553E-002 for SampleVariable1 on the chains generated by processes 3 and 2.\n",
      " \n",
      " \n",
      " \n",
      "        ParaDRAM - NOTE: Mission Accomplished.\n",
      " \n",
      " \n",
      " \n",
      " \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'ls' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!ls && \\\n",
    "mpiexec -n 3 python sampling_multivariate_normal_distribution_via_paradram_parallel_multiChain.py || \\\n",
    "mpiexec -localonly -n 3 python sampling_multivariate_normal_distribution_via_paradram_parallel_multiChain.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike the the other modes of simulation, wether serial or `singleChain`-parallel, the `multiChain`-parallel ParadRAM simulation generates `5 * number_of_cores` [output files (prefixed by `mvn_parallel_multiChain*`)](https://github.com/cdslaborg/paramontex/tree/main/Python/Jupyter/sampling_multivariate_normal_distribution_via_paradram_parallel/out) on the system, separated from each other by their processor IDs (starting from 1).  \n",
    "\n",
    "#### The Kolmogorov-Smirnov test of similarity of the independent samples from the independent MCMC chains\n",
    "\n",
    "By looking at the end of any of the output `_report.txt` files, we will notice that the Kolmogorov-Smirnov (KS) probabilities of the similarities of pairs of these independent samples from indepedent MCMC chains is generally quite high, indicating the high level of similarities between the independent samples obtained from the independent MCMC chains. **This means that there is no evidence of the lack of convergence of the MCMC samples to the target objective function**. We know this for sure in this particular example, because the structure of the objective function is known to us. In other problems, however, this may never be known, in other words, *we can just hope that the convergence has occurred*.\n",
    "\n",
    "Here is a KS-probability-table excerpt from the first processor's output `*_report.txt` file,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaMonte Python Interface Version 2.3.0\n",
      "ParaMonte Python Kernel Version 1.4.0\n",
      "\n",
      "ParaDRAM - NOTE: 3 files detected matching the pattern: \"./out/mvn_parallel_multiChain*_report.txt\"\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_1_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.008976 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_2_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.007977 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_3_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.006981 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: The processed report files are now stored in the output variable as a\n",
      "ParaDRAM - NOTE: Python list. For example, to access the contents of the first (or the only) \n",
      "ParaDRAM - NOTE: report file stored in an output variable named reportList, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     reportList[0].contents.print()\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: where you will have to replace `pmpd` with your ParaDRAM instance name.\n",
      "ParaDRAM - NOTE: To access the simulation statistics and information, examine the contents of the\n",
      "ParaDRAM - NOTE: components of the following structures:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     reportList[0].contents.print()  # to print the contents of the report file.\n",
      "ParaDRAM - NOTE:     reportList[0].setup             # to get information about the simulation setup.\n",
      "ParaDRAM - NOTE:     reportList[0].stats.time        # to get the timing information of the simulation.\n",
      "ParaDRAM - NOTE:     reportList[0].stats.chain       # to get the statistics of the simulation output sample.\n",
      "ParaDRAM - NOTE:     reportList[0].stats.numFuncCall # to get information about the number of function calls.\n",
      "ParaDRAM - NOTE:     reportList[0].stats.parallelism # to get information about the simulation parallelism.\n",
      "ParaDRAM - NOTE:     reportList[0].spec              # to get the simulation specification in the report file.\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For more information and examples on the usage, visit:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     https://www.cdslab.org/paramonte\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import paramonte as pm\n",
    "print(pm.version.interface.get())\n",
    "print(pm.version.kernel.get())\n",
    "\n",
    "pmpd = pm.ParaDRAM()\n",
    "report = pmpd.readReport( \"./out/mvn_parallel_multiChain\"\n",
    "                        , renabled = True\n",
    "                        )[0] # keep only the first report file's contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       ProcessID   probKS(SampleLogFunc) probKS(SampleVariable1) probKS(SampleVariable2) probKS(SampleVariable3) probKS(SampleVariable4)                        2         0.13943076E+000         0.30169409E-001         0.27181411E+000         0.17562202E-001         0.69328775E+000                        3         0.58678698E+000         0.41178962E+000         0.73120508E+000         0.17759241E+000         0.16880407E+000 \n",
      "This is the table pairwise inter-chain Kolmogorov-Smirnov (KS) convergence (similarity) probabilities. Higher KS probabilities are better, indicating less evidence for a lack of convergence.\n"
     ]
    }
   ],
   "source": [
    "print(report.stats.chain.refined.kstest.prob.value)\n",
    "print(report.stats.chain.refined.kstest.prob.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also read and visualize the results of these parallel runs just as before,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ParaDRAM - WARNING: The ``delimiter`` is neither given as input to ``readMarkovchain()``\n",
      "ParaDRAM - WARNING: nor set as a simulation specification of the ParaDRAM object. \n",
      "ParaDRAM - WARNING: This information is essential, otherwise how could the output files be parsed?\n",
      "ParaDRAM - WARNING: For now, the ParaDRAM sampler will assume a comma-separated \n",
      "ParaDRAM - WARNING: file format for the contents of the chain file(s) to be parsed.\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: 3 files detected matching the pattern: \"./out/mvn_parallel_multiChain_*_chain.txt\"\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: processing chain file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_1_chain.txt\n",
      "ParaDRAM - NOTE: reading the file contents... done in 4.151893 seconds.\n",
      "ParaDRAM - NOTE: ndim = 4, count = 128508\n",
      "ParaDRAM - NOTE: parsing file contents... \n",
      "ParaDRAM - NOTE: computing the sample correlation matrix... \n",
      "ParaDRAM - NOTE: adding the correlation graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.014997 seconds.\n",
      "ParaDRAM - NOTE: computing the sample covariance matrix... \n",
      "ParaDRAM - NOTE: adding the covariance graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.013961 seconds.\n",
      "ParaDRAM - NOTE: computing the sample autocorrelations... \n",
      "ParaDRAM - NOTE: adding the autocrrelation graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: adding the graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.001003 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a line3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a jointplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a histplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot1 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot2 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contourf plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a grid plot object from scratch... done in 0.001012 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing chain file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_2_chain.txt\n",
      "ParaDRAM - NOTE: reading the file contents... done in 3.987285 seconds.\n",
      "ParaDRAM - NOTE: ndim = 4, count = 313770\n",
      "ParaDRAM - NOTE: parsing file contents... \n",
      "ParaDRAM - NOTE: computing the sample correlation matrix... \n",
      "ParaDRAM - NOTE: adding the correlation graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.015956 seconds.\n",
      "ParaDRAM - NOTE: computing the sample covariance matrix... \n",
      "ParaDRAM - NOTE: adding the covariance graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.014958 seconds.\n",
      "ParaDRAM - NOTE: computing the sample autocorrelations... \n",
      "ParaDRAM - NOTE: adding the autocrrelation graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: adding the graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a line3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a jointplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a histplot plot object from scratch... done in 0.000966 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot1 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot2 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contourf plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a grid plot object from scratch... done in 0.000997 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing chain file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_3_chain.txt\n",
      "ParaDRAM - NOTE: reading the file contents... done in 4.123999 seconds.\n",
      "ParaDRAM - NOTE: ndim = 4, count = 101395\n",
      "ParaDRAM - NOTE: parsing file contents... \n",
      "ParaDRAM - NOTE: computing the sample correlation matrix... \n",
      "ParaDRAM - NOTE: adding the correlation graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.01594 seconds.\n",
      "ParaDRAM - NOTE: computing the sample covariance matrix... \n",
      "ParaDRAM - NOTE: adding the covariance graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.014992 seconds.\n",
      "ParaDRAM - NOTE: computing the sample autocorrelations... \n",
      "ParaDRAM - NOTE: adding the autocrrelation graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.000997 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: adding the graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a line3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a jointplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a histplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot1 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot2 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contourf plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a grid plot object from scratch... done in 0.000997 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: The processed markovChain files are now stored in the output variable as a \n",
      "ParaDRAM - NOTE: Python list. For example, to access the contents of the first (or the only) \n",
      "ParaDRAM - NOTE: markovChain file stored in an output variable named markovChainList, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     markovChainList[0].df\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: where you will have to replace `pmpd` with your ParaDRAM instance name.\n",
      "ParaDRAM - NOTE: To access the plotting tools, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.<PRESS TAB TO SEE THE LIST OF PLOTS>\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For example,\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.line()         # to make 2D line plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.scatter()      # to make 2D scatter plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.lineScatter()  # to make 2D line-scatter plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.line3()        # to make 3D line plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.scatter3()     # to make 3D scatter plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.lineScatter3() # to make 3D line-scatter plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.contour()      # to make fast 2D kernel density plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.contourf()     # to make fast 2D kernel density filled contour plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.contour3()     # to make fast 3D kernel density contour plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.histplot()     # to make seaborn 1D distribution plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.kdeplot1()     # to make seaborn 1D kernel density plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.kdeplot2()     # to make seaborn 2D kernel density plots.\n",
      "ParaDRAM - NOTE:     markovChainList[0].plot.grid()         # to make GridPlot\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: To plot or inspect the variable autocorrelations or the correlation/covariance matrices, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     markovChainList[0].stats.<PRESS TAB TO SEE THE LIST OF COMPONENTS>\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For more information and examples on the usage, visit:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     https://www.cdslab.org/paramonte\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import paramonte as pm\n",
    "pmpd = pm.ParaDRAM()\n",
    "markovList = pmpd.readMarkovChain( \"./out/mvn_parallel_multiChain_*\"\n",
    "                                 , renabled = True\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaDRAM - NOTE: making the line plot... "
     ]
    },
    {
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       "        0,\n",
       "        0,\n",
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       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
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       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"960\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 1.252649 seconds.\n"
     ]
    }
   ],
   "source": [
    "markovList[0].plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take the log of the x-axis for better visualization,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaDRAM - NOTE: making the line plot... "
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"960\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 1.247663 seconds.\n"
     ]
    }
   ],
   "source": [
    "markovList[0].plot.line()\n",
    "markovList[0].plot.line.currentFig.axes.set_xscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare this plot with the resulting Markov chain from, say, processor #3,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaDRAM - NOTE: making the line plot... "
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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QGheillBZYtZQWWLWclfAUAAIKdyALQ/1cpfp1r566Sqz4d+XQCgDKU4R1Rg6bUIAEKyFQAQEikAIDQvxaygssWsoLLFrOSvACAAkFMZAOrUtyfSGJHGqFL8cejXBQDKUABACOIJAAiJFAAQmpdiVlDZYlZQ2WJW8lcAULYAqF7+CunVY0ivvhsANMGsJNWZAqDCHJHK0msBAIRkKwAgJFIAQGheillBZYtZQWWLWclSn6ei+gcqqn8YuR0AKFsA1K4vIWoxshsMADTBrCRVABAACJndAICQSAEAoXkpZgWVLWYFlS1mJTtt1/foQUKLjdwOAAQAcgoAGl8AEAAImd0AgJBIAQCheSlmBZUtZgWVLWYlOwUAyTdLAGQ1tkkJgIrUrm/bO2eDkd1anzrNRaSqCQFQi1G3uRlp1VMSmZWkOlMApM4R/Z2lVxUAhGQrACAkUgBAaF6KWUFli1lBZYtZyU7DAlC7/Naprm8WAairbTgxANlNlhIAqe75qNUDHlvrzU5SAEQtRmZ9v0RmJakCgABAyOwGAIRECgAIzUsxK6hsMSuobDEr2SkASL7d1kY98GhN9pu74rgDKBCAauv1P5q1cMT+f6Fi8T4qFu+jUuHO+AGoxqjb2hQANCsBACGIJwAgJFIAQGheillBZYtZQWWLWclOAUDytbV1POAR/Tjr9mCjyajb3CDaMcbdAVQNvgPIaBxFpDMinVGlcGNsAKRXD+sBUP9xANCM5O9zRGtZev07AAjJVgBASKQAgNC8FLOCyhazgsoWs5Kd5guASmQ0DiWjcSjVKl8L3CdPAGQ3o94BtCBzAGTUDpwYgJrVz5HV2sH9GcoD0BrqatsRadtRRz9/qjPqVwAQAAiZ3QCAkEgBAKF5KWYFlS1mBZUtZiU7zRcArXXhwmwcFLhPcgC0IEMAFHAHUG0jojqjdnXXwH2zDEBG/V3uexMOgFa721raUVOdUb8CgABAyOwGAIRECgAIzUsxK6hsMSuobDEr2SkASL65ACCJL4HW60cPcKbwg6kAULHwCJUKv6NiYdXI4wOAMpi1c0R/Y+l1LQAIyVYAQEikAIDQvBSzgsoWs4LKFrOSnQKA5DsrAGQ03s4B0H9LAVC5dAs1qx+nZvWfQwNQufhj6rY27/1K99YrRr4uAFAGAwBCEE8AQEikAIDQvBSzgsoWs4LKFrOSnQKAgtusnk/d1mbUbW1GpeJ9OQWgtdSsnU3N2tlUKd1Mqlonq/lqDoCukgIgo350D2WaTA6AdA6ASv8jDUBdbQ4AlLUAgBDEEwAQEikAIDQvxaygssWsoLLFrGSnAKDgarWTubtY7pAGoGbtPLK015GlvY5U9cXh9zIiAFmtHcnStiSjcZQ0ABXVp4kMRmQwMpr/0DtOJgDor2RpO5Kl7UiN2ucGPzttAy8A9X/eXX29Me8PACix/G2O6GWWXv+WPwC688476cwzz6S3ve1ttHjxYtpzzz3p2GOPpSuuuIIajcbIfRuNBq1YsYKOOuooWrJkCe2555503HHH0XXXXUedTmdKrwAZFQAQEikAIDQvxaygssWsoLLFrGSn7fpbAEABjQpARnOZu05VfZZa1TNJq36QaqXLeu9lRACy9XWJdEZW640BAHR4sgDkwEwMAFQo/Ml93mgeOfjZBQCQrY+76woAlFgAQNLpdDr0iU98ghRFCewhhxxCq1ev9t2/WCzS4YcfHrjv8uXLxwISknwAQEikAIDQvBSzgsoWs4LKFrOSnYYHoL2muj7vrPwtlwBkNxcQNRl16rv03ss4Aag5QBKjIXsH0Gs4APoWACiBzhQAvTxH9BJLry/nB4AuvvhiF2s+8YlP0OOPP07lcpmeeuopWrFiBS1evNhFoFar5dm32+3ScccdR4qi0F577UU33ngjqapKa9asoRUrVtCiRYtIURQ6++yzU3p1iBMAEBIpACA0L8WsoLLFrKCyxaxkp/kCoLUcdgCAogPQ1gCghAsAmn8AtHbtWhdpLrjgAt9t7rrrLheIrr76as9zt912m/vcQw89NLTv9ddf7z7/6KOPJvIaELkAgJBIAQCheSlmBZUtZgWVLWYlOwUABXd+A1CNaqVvU7u1mGx9Q+pq63sBiPtNXwCg4QKA5h8AXXfddaQoCi1cuJAKhULgdsuXLydFUejkk0/2PH7CCSeQoih0+umn++5n2zYdeeSRpCgKffazn4117Ui4AICQSAEAoXkpZgWVLWYFlS1mJZ129TmyDUaWvo37GAAouDMHQI13kF49hrqtDaQAyIUco/eaPQDE/Zp3OQDarA9AG5GqxgtARfWPVCzc4h6vq22b+t+1mQKgl+aIXmTp9aV8ANCll15KS5YsoYMPPnjkdp/61KdIURQ6/PDD3ccqlQotXLiQFEWh6667LnDfSy65hBRFoX333Te2dSPhAwBCIgUAhOalmBVUtpgVVLaYlXTaNTYhMhh19QGMzG8AWk216hVUq15BxcKTQ8/PGgCZjbcMfrX6lAFIfO/iBCCruU1vexeANkj97xoAaP4BkJNxX9Ls3AF0/PHHu4/df//97se7Vq1aFbjvLbfc4m730ksvxbZmJFwAQEikAIDQvBSzgsoWs4LKFrOSTgFA3tYql7s40qydP/R8LgGojzvd5sakqjIA9M3MAFC5+DPqautFByDnV8Zrk935FUcBQPMXgEbl6aefdu/0ueSSS9zHb7rpJhd21q5dG7j/Qw895G73wAMPTGPJiE8AQEikAIDQvBSzgsoWs4LKFrOSTvMPQC9Tt7UZdVubkV47mtvuRdLqHyCtfhpVSj+c3wDkoExrPVLVAADi8KZS+Ho0ADLiByAy+vtFAaAqACixvDhH9AJLry96AahWq4WuYRgpvXmDmKbpfs/PokWL6Nlnn3Wfu+qqq1zY0XU98BhPP/20u90dd9wxjWUjPgEAIZECAELzUswKKlvMCipbzEo6jQeA3prirPzN/aJfo36wu01RfcRFDr3+3tkHIG0uIgAtHboDqFz479EAZIwAIIMRmYxsI20A2qK3JheANk797xoAKDkA2nPPPV0Eke3KlSulXurKlSsTOXa326VPfvKT7j5f+cpXPM9fdtll7nO2bQce5/nnn3e3u/XWW6VeExJ/AEBIpACA0LwUs4LKFrOCyhazkk79AWiXPkQwUtUXAvfNNAAVVg0AqHHMzAOQ7eCKAybSALTbAFhCAhAFAVB/Pba+LnWarw4PQHVGneobqFk4bwIAegUAKMkAgCY6tmVZdP7557vbn3LKKdTpdDzbXH755QCgHAUAhEQKAAjNSzErqGwxK6hsMSvp1A+ArMaWvYvtJqNy8eeB+/IAVFBXk1Y/nbT66VQu3TLFWZk2AJ3onq+rbzjAlYgAZLW2JNvYyHM3TRYBqFT8LemN5dIANICXBb4A1NU28QCQ3jhm8BoqvbmyywuGAYi7u4kHoEb136hZ+yTVKt/uzTAAKNm8MEe0hqXXF6YHQKVSiVavXh2qpVIp8HiaptFHP/pRdx3HH3+875dEX3PNNe42oz6uho+AZSMAICRSAEBoXopZQWWLWUFli1lJp11jk/7F9Dqk1d9PqhoNgEqlO1wAcI4znVlJD4DIHIBGVADqanMDOJkmAPW37zbXlQKgRvWiwRrDAJDOPN/FQ5oDMiIAvWfwfAQAso11iQxGlvbG3gy3NgUAJZmMAVBevgOoUCjQscce64LNySefHPgbwm6++WZ3u0KhEHhM/kugH3744aSWjowJAAiJFAAQmpdiVlDZYlZQ2WJW0qkDQKQz6rQWkaqGAKB6Dwms6mtyBUC18repVrmMyqVbh46dDQAafSzPz0BfEB2AHFSZFIBaAQBk9NdV866JdEa2tmEiAGQ71Zjn42Jdbb3U/64BgJIDoDxk9erVdMghh7hYc9ZZZ5FpmoHbP/LII+62jz/+eOB2/K+Bf/nll5NYOiIRABASKQAgNC/FrKCyxaygssWsRG+l+GMyaoeRUTuMiuoDofadCID6XzrcbWwUEoAqZDVeTVbj1dSqfHj0ayv/gLT6B0mrf5BU9W8+syIJQK05ohajTvO17l0zndZOQ+fLKwDx6BIMQOv33puQAKTX30eW9qpgANICAMjs/7POogEQ/3qc8/DH8ADQgsH5nbUBgJLLmjmiv7L0uiZfAPTUU0/R3nvv7ULNF7/4Rep2uyP3aTQa7q+Hv+GGGwK3u+SSS0hRFNp7773jXjYSIgAgJFIAQGheillBZYtZQWWLWYneZuUcFyWqpe+F2jctAHLWa9bePnJbo3lkDxJMRoXCMz6zIglAxqb97zraIhUA0huHucdV1dUxAhALAUAh7gDq//ytxmup09rZu8awANRgYwGoq6/Xe64ZDoBsbUGvPP4AgJIPAEg6zz33HO27776kKAotXLiQrrnmGul9TzrpJFIUhc444wzf523bpiOPPJIURaHzzjsvriUjEQIAQiIFAITmpZgVVLaYFVS2mJXoTQSA+hffXX2rwH1lAKhR+yJ1zU2oa25ClfLN/cdf4ABo9G8QM5pHusetVS4lVS1PBkDaxi6aTBOALP2V7vtcUP83+wDUf412cz3qtLabDICaw+sSAch9rhEOgNz3HwA03fx1juh5ll7/mg8AMk2T3vve97p3/tx4442h9r/hhhtcOLrvvvuGnr/++uvdYz/xxBNxLRuJEAAQEikAIDQvxaygssWsoLLFrETvJABkjwIgk5FtrB+8rwQANeufIGozojajauW7pKp1Khd/5q7Xarxm5PpcAHI/PrVGHoBM1v9emPVdALK1dbnXOz0A6urru8ctFX+ZHwDSFpCtrxMvAGmMbG0dz29QiwRAzho05sUfAFDyAQBJ5dprr3WB5tOf/jQ1m82R1TTNs79lWXT00UeToii0dOlSuvbaa2nt2rX04osv0sqVK2nRokXu9wkh6QYAhEQKAAjNSzErqGwxK6hsMSvRmz0Aet9gbWMAqKtvSHrzGCqXfup7jngAiOUDgIze+2S3GRWLct/llAwAfX/wa9v544YBIP4LqVtsCIB6uDQCgPof+/Mcw++1AoDSCQBIKocddlioX01/8MEHDx1jzZo1dPDBBwfus3z58iE4QqYfABASKQAgNC/FrKCyxaygssWsRG84APoLNev/Qs36v1C5+OsYAehm9+K73dx1sLZRAKQNLvSb9Y/7niNNALK015Kqvux5fhoARG1GxeK9Uj/7KABUKvxyGID0wc+iXPh/kwGQwbx37IwCIOejXGEAiEcfAFA6+esc0XMsveYAgEqlUij8CQIgot4XQq9YsYLe+c530u6770677bYbHXPMMXT11VeP/E1iyPQCAEIiBQCE5qWYFVS2mBVUtpiV6A0DQOXSre7FtdY4jWx9ffci2dK2JVWNBkDl0n+7x+30j6OqUQHoBbKNBWQbC8jSt54MgHTnV4OvHxqAyOj9xi7++cQAyMGOaQBQ8afZASBx2yAAMtkwAPmtDwA0nQCAEMQTABASKQAgNC/FrKCyxaygssWsRO9kALQuB0DbkKrWyWpskTIArRnchaNvGRqAiuozVC1/0wsI/eOFBaCusQXZ7QVkGb33JvMAJNQFIJ2RbTLq6ItIax6WHQDShW2d3wIGAMp2AEAI4gkACIkUABCal2JWUNliVlDZYlaidzIAGlwkW1rvC5mtxms4AAq+cI4OQLclCkCt+ke8qDIJAJmbErUZdc3NSVVlAegRqlc/R7XKf7iPpQdAh/agpT2ozX+sKgwA8ZgzDoCaLFsApK+T+t/TmQKg5+eInmXp9XkAEJKtsLQXgOQzACA0L8WsoLLFrKCyxaxE7ywDUFffIPsA1P+eoVbtzN770n9NtjlAoTgAqKg+ReXib6hc/A2parV3rlEA5KCPAEAkApCDPf11V0v/5QUggyUDQDqTByBx/yAA4vdpDI7D/yzSKgAoxgKAkIyFpb0AJJ8BAKF5KWYFlS1mBZUtZiV64wOg15KqZg2A1h8DQC8PAKi2jFS1TnpjWcGHvKMAACAASURBVGgAMpv7UlfbhCx9LhIAkcmo3dqt97646LDAPX4cAGTW3+Ge1zYZ2ebGwwDEw4gDPvy/SwHQl8IDUIN5Acj52coCkPN8nADUYgCgpAIAQhBPWNoLQPIZABCal2JWUNliVlDZYlaid5oAZJsLyDYXkNE6lAOgjbMBQP07gNrNPQagMRKAdiBVLfZes7Zd76Nc+jpjAOiEEAA0+NhRKAAyGdWrn6NK6Sbv3xEOgHrH3zBZABLeu0AAcu62SRqATOYFIL4AoOnmuTmiv7D0+hwACMlWWNoLQPIZABCal2JWUNliVlDZYlai1wtA3x+57aQA5CCCqe3fu+hvMOrWN8gXABmD89arF/ZecyIAxMgytqJG7bPhAMh97Zt7/46MAiDndfoBEHdnUaoA5KwtTgBqMe+a+H24+QIAxRwAEIJ4wtJeAJLPAIDQvBSzgsoWszLfu7bfwthtMSvRmxoA1RlRnZFdXyANQLa+EdnORXsGAKhRPbf3mhMAIKd64wTqGjwA3T4agPooYrcY6bXlg78jowCIxxE/ADKZNAB1mq/N1h1APO7wsDMKgPg1AIDiz7NzRKtZen0WAIRkKyztBSD5DAAIzUsxK6hsMSvzu3b//6XvNJeM3RazEr2xAVBrM7JbrPfRLgeA9I25/WsxANB6g4v2DABQR9uJVLUQCEB2e0HvN2e1exAWBYDM1j6937zlAtAd4+8A6p9Drx1GPURNCICccgA0hDIi7PgBUFMAGP57epxjtZjn7p2RACRiTlQAajvvFQAo1gCAEMQTlvYCkHwGAITmpZgVVLaYlfldANB0Gt93AG3s/dXgJiNbX5/bfzIAajX+kWxj3WgA1K/WOClWAOqt6/QRAMT6ANT7QmdpAOLOYbb29hxTCoC439zVqH2+93ckbgDi7saRBiD+z+MAqMWdwxD+zH9vkQhA3G/v8oCOHwAZQvnHAUDJBQCEIJ6wtBeA5DMAIDQvxaygssWszO8CgKbT6AD0vqkCkG2u37sTJgoA9QHD0l8rB0Di98H4AZCLO6wHQHrv17nz+BAJgDrMc46uuYEHKczWHr27rJqMutqmYwGo1TiNyqXbyGzs5QWgNpscgGTvAOJhRxaAeGQyvHPlnscPgLjXOBaA+O9AAgBNL3+ZI/ozS69/AQAh2QpLewHI+Nx777300Y9+lPbff39avHgx7bPPPnT66afT7bffntqaAEBoXopZQWWLWZnfBQBNp1EByNJe67kTwwUgDgS8AFSdCICo3QcW/teHZwmA2gI8yAJQH1tscwHVKlcMARD/xc5kMOoaG/Sgo8moq208FoDsDiPqMOpqGw2OyQOKeLdTEncA+QGQzoIBiN9GBCD+t5OJ53I+IhYGgPjztbnHAEDJBQCEIJ6wtBeAjM4ll1xCiqIE9pOf/CR1u92prwsAhOalmBVUtpiV+V0HgNrN3cZuO2uzUireTc362dSsn03FwpOJnisTAFT8PgdA2wzWFgmAnuIAaL3QAGTpWw2OPS0A6iNNo3rxaABy3oMQdwB5AEi80yUsAInbTQJAPEI1mRdiZACI/61g/LqjAJD4/jnbAYCSCQAIQTxhaS8ACc5NN93kQs+JJ55Iv//976lQKNBjjz1GZ5xxhvvcN77xjamvDQCE5qWYFVS2mJXstlT6JdUqV1K18n1S1Voi5xgA0Py7A6hVP8tFgVrl6kTPNREAcRfflrZJZACqVz7hHrerbTpYWwQAKhZ+ywHQgggAtOUAaKYBQFzrlS+OBSAyGQdAG4UHIOe4sgCkc/uId+7EDUB+3xkkA0DiR8ZEABI/5gYASjer54j+xNLragAQkq2wtBeABOfwww8nRVHoXe96F5mm6XnOtm069dRTSVEUWrp06dDzSQcAhOalmBVUtpiV7LZj7uwChaqWEjkHAIh5AMg2F/TukNHfHOu5ZAGoUrqZGrWPJwRAn+IA6BWDtU0MQCybAOSAhABAjcpF4QBI38ALF0kAkLMPfzcND0AOrHS4f28xL6zEDUA84AQBkPgxviAA4l+X+BE3AFAyAQAhiCcs7QUg/qnX6+4dPldeeaXvNrfeequ7zR//+Meprg8AhOalmBVUtpiV7LZjvgkAlGBHA9AusZ5LFoC6+hYe3BgGoHEfAVM5AHqbB4Aqpe9wHwHbfrA2aQA6x92nWLhnADaZASBGrcZHBgDkbBMEQKb3HDMDQOIXLgcBkCk8HwaA/L4E2g+AxNcOAJpeAEAI4glLewGIfxqNhos7l19+ue82v/jFL9xtnnrqqamuDwCE5qWYFVS2mJXsFgCUbL0AdFXv/UgIgFrVeACoGwqA3j4GgIrU0Xcmy9hcCoD05j9Qs3YWNWvneQGIhwABgLrtfamt7TIVAKIOo47xxskByHkubgDi77iJCkD8awoCIL5BAMR/hIu/A0j8qFkUADK447cYACit/HmO6BmWXv8MAEKyFZb2ApDgHHHEEYEfASMi93uA9tlnHzIMY6prAwCheSlmBZUtZiW79QJQMZFzzG8AOjsdACp+P3C76QLQ3z3wMw6ALG0roiYju8mkAcjmv88mSQDqH6Orb+IFIOe1ZAWAeJCRBSAeUPjXFDcA8WAj7hsXADnvCwAo+QCAEMQTlvYCkODcfvvt9OY3v5kURaGTTz6ZHnzwQSoWi/SHP/yBzjnnHFIUhRYuXEg333zz1NcGAELzUswKKlvMSnY7XQCaf78FLLcAJHwp8PQA6NU9AGr1Aah/MR8LAHHfWyMFQBxy2A60aIxsjXkByBIAIwiA+GOGASAHoTq9x7vO6xgFQD74kRgAtdloABJfi9+XTU8KQPw2AKDpBQCEIJ6wtBeAjM4dd9xBy5Yt8/0V8EcccQTdeeedqawLAITmpZgVVLaYlex2Pt0BVCg8S1rjZGprS0hvvCf24zernyHb2IBsYx2y9K3I0rYlrf4+DoCu7L0fAKBhAOo/3jXmiPTed/64ACRWAoAq5ZvI0jf1AhCHF9IAxD2XCgBxUOIAkAdlZAFo1B1Azv4dFi8AOe+PCEDOdwABgPKfP80RPc3S658AQEi2wtJewCxn5cqVvnAzqitXrvQc48c//jG95z3v8d126dKl9KUvfYk0TZv6azPNTqrtdm0iIup27dTXgma7mBVUtpiV7LbbGfwWMNNsJXIOB4C6+u7j15PgrLTNVYOLaXMu9uNbxplDHx/qmCe472/bvKb3fvQBqGsuivf82nkugrT1HwT/PMwtPWvs6tt4AUjvAxB3oW4bG3DHaAzApn2QB4Da+nXccXcg06zLAVD/PbLN9V0AMo0HAwHINrdxZ0UEIEs7jOz2Fl4cGAKg9XwBqKtvLwVAVvOYkQDU0f8rFADZQQDEve6RAOS8B1EAyIG+Dnduk/v5iLgyCoD4XxsfJwCJIDYBAKX5v7dOZyYAIATxhKW9gFnOpAD0hS98wf2Y10UXXUTPPfccmaZJL730El1xxRW06667kqIo9P73vz8VBEIQBEFyGNv2Ng+xFPfim+yEvvPOubAzlk52nO6LRJ23E1kHE3WvC7+//Th3Qb3FZGvxS+eswYW9086Jg/e3e21vuz4AUXtxvOc3PzW4oO/8cMR2XgAi43XCXSQbez8eZDIiY8PB/rY+QIXOdt5929dzx309ka35ApD4kSz3PeoDUA8kHgkEIDJfN1iPAEBkHEnU3nKAAzx+OOfzAaDedjv4ApALLQ5YNZWRAETtS72zMAqAWoxIX3dwPv598cwSmx4A8UA3CoCcYwcBkHi8NhsAkHO8IADiz+sHQM6/jwMg57zONkh8eWaO6CmWXp8BACHZCv4XJsGUSiVavXp1qJZKJSIiuvfee10U+u53v+t7/Pvuu8/9jqArrrhiiq8MdwCh+SlmBZXtvJkV/VH34sXST0h/PTI/G88dQM1EzhHfHUD3umu1zJNDr8N7B9C2sb9Oy/jYEADl9g4g58LavQNoQ+4YgzuA7M467sW8rTNq698PdweQgxq+dwA9IH8HEHfh3zUP8t4B5ANAke8A6j9mO3ATdAeQ9uXgO4AcbPEAEHc+cc1+dwDxKCQDQCbzByCdTXYHkB8AmWwyAHJmUfw58OijsWAAagvb4w6g5AIAQhBPWNoLQPzjfMnzIYccQt1uN3C7s846ixRFoYMOOmiKq8N3AKH5KWYFle18mZVS8X73gkOvH5v6emTaMXfivgOokMg5nIu6Sb8DqFL5vrvWZv2sCD+fe90Lxba2OxULf4r1dbbqZwwBkFY/dvAdQNVvkarG+R1Aa6hYfJKKxSd754/zO4BEANI3JFV9kQz9KDL0IwIBqFr69gC/tO2pXPqfkADUgxnbYNSofWz4Ar+PKUPfAcRhjtl6B3WNzccAUMTvAAoCIOf1ON8BVL7YC0DOHU5BAOTcFeQAD/99PA56TQJAOgsGIH5NowBIF/YdBUDO3UVZAqD+zzHt/82dqe8AAgAhiCcs7QUg/nG+9+fcc88dud21117rfkxsmh8DAwCheSlmBZXtfJmVUuE+AJBPBwA0yW8BW0ut5kkcAJ0X/udTvNeDDeXSnbG+Tn8A+j+JAVDb2IfIYmRbvYtaLwB9z92uVrmCGvULqFa5jFQ1JAD1H+/qm1Gx8GQPOyzugtoPgPo40Gm9nirl68IDUH+btrbzAEnSAiBnXaMAyIETBy+ajOzmAi8AWcwfgAzmhSQegPhfo86jjM491uEe498HHj/4804KQDzGhAEg53iyAMTvFyMAEQAo3jw9R/S/LL0+DQBCshWW9gIQ/xx11FGkKAqdddZZI7e77rrr3I+K1Wq1Ka0OAITmp5gVVLbzZVa8AHRc6uuRqReA1ETOMeoOoKL6DNUqV1GtchUV1Wd8Z0VvnESWth3ZnQX5BqCYfwuYC0BtRlZrK+q2NvYFoK65ef+jUa/o/Vn3fgRsJAD1EcQ2N5QEoO+42NEx5qjVODkYgBx0mASArHuDAcjv41RxA5CzdgGAqCn86vq4AMi5K6vNnZv/zqCwAMRDkOwdQCIA8TgkApApbCMDQDqTAyBnf3GNAKDpBQCEIJ6wtBeA+OdjH/sYKYpC++23H3U6wZ/D/fjHP06KotABBxwwxdUBgND8FLOCyna+zEqpcG8OAeiNqQJQo/ofHJacSJZ5AlHnDM+saPXTqd16y+BjNFEBqPS7FACI+whYJaE7gPiL9UgAtPl4AOo3DAC5uCMDQM7j5oJwANS52gsfowDIAY5RANTaJjkA4lGCByBD2MYPgJyGASDn38MAEP/RqyAA4vcXAaghrHVSADKEf5cFIPHYAKBkAgBCEE9Y2gtA/HPbbbe5d/Z87Wtf893mwQcfpF122YUURaGLL754qusDAKF5KWYFle18mZVS4XcAIJ+O+ggYD0B2e4P+WjbyzIrZfIfnYjw+APp1rK/TH4COyxwAWcbrBhflJqOuubEUALn4MwKAtMah4QGI287mQGYIgDiwsfStRgDQ26hrvCoSANnGepMBkAMcUQGow4YBiPtZDQEQvy6D224cAPEfGZMFIH49fjDDA5DMHUD8WgBA+cxTc0R/ZOn1KQAQkq2wtBeA+Me2bTr55JNdBDr33HNp1apVVC6X6fnnn6dvfetbtNtuu5GiKHTggQdSuVye6voAQGheillBZTtfZgUA5N/wALQJAEiiowFo8CXQQwBkviYxANKb+yYPQP319H6D3dVD2GHz53WQhoeQJAGow6YLQDyG8NtlCYBEnAkDQKbPeQBA2QkACEE8YWkvAAlOtVqlU045xUUgvy5btoxWr1499bUBgNC8FLOCyna+zEqp8FsOgI5PfT3j2qhfQHZnXQ6A/p7AeWoRAGhzAYDe7gNA54ZeS7l0z4wB0L4zDkBvCgQgshjZndcMA5AlnHfcHUAGd+6wAMR/DMsBIKu/Xi0EADmvWQQg/jt3wgCQCDRBAMQjUBAA8fv4ARB/bEP4WcUBQLgDKNsBACGIJyztBSCj0+126dZbb6UPfehDtN9++9GiRYtor732ohNPPJG++93vTvU3f/EBAKF5KWYFle18mZVy8e5cAZChvctzsZ4OAP0nB0DrhwCg8HcApQ1AevNoUtW/pANAZg9duvpmLghlH4B2HoBBUgCkceceBUD8d+04AOScRwQgET4c3Glz23WGzxEJgNps+I6ZcQDE7yMCEH8cHlniBCDnNSQJQPydUACg5PK/c0RPsvT6vwAgJFthaS8AyWcAQGheillBZTtfZqUEAPJpmDuApg1Av4r8uoqFP5FlbkuWuSVpzVNIVYMA6PjBxb3FqFL+8fQBqL2JZ01d81UCAG0YHoCcn5kDFTwA8XfGJA1A7QgA1GJkO4AwbQByoEMGgPj9/UCjwx0z6wDEr9sPhqJ8BMz5dwBQegEAIYgnLO0FIPkMAAjNSzErqGzny6x4AeiE1NczrtkFoFcJAPS27AFQ8TH3OG19T1JVOQBq1D8TOwC5F8SBALRBMAA5F8ZxApBz3MQBaGsvAPGIEgRA/d/SNREAmT4A5PwM/ADIYHIA5BxrFADxyDFtABLvzNGFYwcBEL+9DADxa48CQOJ7z79uAFC8AQAhiCcs7QUg+QwACM1LMSuobOfLrJSKd+UcgNYmcJ5qDB8BiwuAfjt1AGrVT/AAkKnvlQMA2tALQGYIAGrtLQdAzvskbJMXAHKRZlIAEu9ayhMAyd4BlDQANYVjAYCmlz/OEf2Bpdc/AoCQbIWlvQAknwEAoXkpZgWV7XyZFR6ANABQv1kGoDsiv66RAMRdsHf0bRMDIEM/rIcTYwFow5AAtP7g4llAl4kByNnPYp6fhR8AdfRtZw+AeGzh31cZABIhiEelMADEgw3/Tz8AEr+rRxaAdDYMQM6+SdwB1OaOBQCaXgBACOIJS3sBSD4DAELzUswKKtv5Miul4m84AFqe+nrGNWkAqtUup1rtm/MAgN5CquoPQF1jYw+edIwd3d8ONTkALfPigwBA5dKd1G1vPrjbRhaAWutNB4Cc4wYAUNfYOBkAMmIAIJ150WZSAGpzxxLvEHL+nQegNvdaxS+L9gMgEUecdQQBkDMTUe8AmhSA+NfDYxAAKFt5co7oCZZenwQAIdkKS3sBSD4DAELzUswKKtv5Miul4p05+wjY0YkAULXyfTK0A6nb2ZQ65k4jAaguBUAH+ADQJ0Ovq1y6O10A6n/0aVoAVCndPPj5hgKgSe4A2s+LGYkC0CZE7avCAZCDCX4AZPW/mygMAPFAw/8aeB5qeADy21cEIP55Zxvh/ZMGIH5//guSRQBy0EkEIJ15X7PBHSssAMl+BEy880nEIH4fAFC6AQAhiCcs7QUg+QwACM1LMSuobOfLrJSKv+YAKI93AP0tluM26hf2jtvtNzQAiV8CPQxAbWMxtRofpVLpQel1pQZA/MW6NU0A+pH3zpOwAMTvJwJQ/zUNA9Db/AFIeF/kAGijYADqMLI7GxN1vhkOgJzj+wAQdYV1hrkDyHneuQPIYt67ZSyf44oA5Px5FADxPzcRgPjXx3+/TxQA4u+yMbh9ZADI+TMAyLcAoBgLAEIyFpb2ApB8BgCE5qWYFVS282VWysVfAYDUOjXrF0wIQGPuAOoOaujvkv/5pAJAm6YCQJXitVQp3zQ4LwdAdnuB98I6CID4/QIAyAUIo38HDX/MJAHIYmR31pcHIBFIHMzRuW1lAMjZftxHwCxuW1kAch7X2TAKtX3+6QdAbe5YIiDpwvMyAMTfScT/ux8AORgz7g4gEWn4NUQFIP5Y/H4AoGTzhzmix1l6/QMACMlWWNoLQPIZABCal2JWUNnOl1kpF+8AAKl1atb/NSIArddfyzpkmYOP0JnN/XMLQLa5boIAdFgwAJV+EAxAPhfFcQAQdYVjTgxA68gBEA8/lnCutACIx4g4AKgjnC8pAOIBJWkAEo8FAMpfAEAI4glLewFIPgMAQvNSzAoq2/kyK3kDIH3oO4DiAqDPhgCgi3wAiJHd3nYwP7EB0F3TBSC/j+tMFYB+mCwAOe+lc9HtB0AiWjhr4AHEec7gAIjHjCgA5OyvcecNC0Dc+mIBIB4lkgAg/ucyCQA5+2UBgAzu3/l9xC+BBgClEwAQgnjC0l4Aks8AgNC8FLOCyna+zEq59MucA9DLsRy3Wf/MFAHonSF+Pnf5AlCl/CNq1j9NjdoFpKoFqWMVi49OFYBKxV+S1dqarNbWVK98lVQ1BAAJIDAEQPwFdRgAcn4ecQEQvyYea5wLfBkAErFEFoCcO5jiAqA2ywYA+X0HUBgAMlm6ABTlDiD+nACgZPPEHNFjLL0+AQBCshWW9gKQfAYAhOalmBVUtvNlVsqlX7gXJlptNgGoVT2TOs1F1G7sGbhNs3F+tI+AdRb4AlC7uV9CAPTL3nxq+7gXh4XCX6SONW0AqpUvc9/HVu1jpKrjAahcvsELCVm+A4jHh6gA5PcxMBGAnGMnDUAdFj8A8e+rLADx6wwCIGftsgDkHCsqABnCcQFA+Q0ACEE8YWkvAMlnAEBoXopZQWU7X2alXLqdA6ATU1/PuOrau0MDUKexiEhnZGvBF1LNxr9EugOIxwC7vZ27TXwA9JsAANp7QgDag1Q1WQCyuQtkWQCqVq4ZrIf/SNBYAFqQPAD53d0SFwCNugNoFADx25jCWuICIB56kgQg8VzjAEgXjjXJHUDOubJ0B5AxqOxdfkkVABRjAUBIxsLSXgCSzwCA0LwUs4LKdr7MCn8HkF6fzwD0qRgAaJI7gMpULv2KyuVfULH4OPfz+U0iAGSZW5Kq1kmrHx8OgLQEAKiPFLXKeaQ1jhoGDSkA4n4e8wmAeCzh3y/neM7dL0EA5MANjylBAMR/Cbb4Mb1pARA/ByIAOe+dzB1APOLkAoD+nur/7s4cAD3K0isACMlYWNoLQPIZABCal2JWUNnOl1mplH6eMwDyfgSsoL40dh85APpkdADqX+B22zu627Sb+44AoKOGjlks/MkFgbaxu/v4MADd3pvPEQBUq36D2tpbqK29hYqFPw7OwQGQ3em9F0broNEA1P9neACqka0zOQDqv++W+QovUoQAIJuHlmkBEA8n/PYiALUH29ud9YnMnbMHQPydPGHvAEoKgJzj+gEQ/3r4L10O+xGwqADkHDcOADKEcwKAkg0ACEE8YWkvAMlnAEBoXopZQWU7X2alUvpZzgDo3YkAUKtxXgAA7T60bRAA2e03uNskD0B7BQKQ3jzeXV+5dOfgHAkCULN5HrXbu5NhvKP/2N88F8ihAcjZJgwA8Rf3fgDE44csAPHH448bGYDWITI3kAOgNhtec1oAxP8zTQDiH9OZd07CApADajwAiXfjyAAQ/xG0qADkh1wAoGQCAEIQT1jaC0DyGQAQmpdiVlDZzpdZqZRuBQCpdWo1zp34DiC7/Xp3m9AAVHwmAIDu9AWgdmwAdGAsANS1tiKye1+KbTSOIKNxaHYASAQWEYD4C/CkAKj/s7V54JABIPGuJRkAEtc+CoAc6MkSADn789gj/hySAiCNO1aSAMTfNQQAmm4enyN6hKXXxwFASLbC0l4Aks8AgNC8FLOCyna+zEqldEskADK0w8kydyBT32+q600OgD4eAwDt4G7Tbu4z0wBkaEeQ3V5AdnsBqWphAEDtBYMvdk4KgHgo6T/mASDxo0r8P7MMQDxs8T8HWQDi90kTgPifQxAAOftNAkD8nUIiAPFzE9cdQM6xAED5DgAIQTxhaS8AyWcAQGheillBZTtfZqVS+h/3wsRs7U1a81S3terlgft121sSWYy6nU2mut7h7wB6cew+cgB0TgwfAdve3WY0AP3D0DFlAahS/kXv+NpbRwDQcZMDEH9x7gNApvYO91iqWuQAaN34AYi/GPcDIAcwwgKQzrwA5Hen0LQAiL/bxw+AnHPxKMFDTZYAiP8ZBgGQOGNhAIhfYxIAxL+3k9wBZAr7jAIgcb0AoOTy2BzRKpZeHwMAIdkKS3sBSD4DAELzUswKKtv5MiuV0o8HH6Mx16GOsYjaxmKyO+uR3dmIyqW7ffebJQAqFJ6jVuMsstpbRAOg/oWf3X4TVco/JVWNAkBPBwDQr6MDkMWobe5GpdKv+ufIIAA5cDAKgBygmBUA4tc2DoB4OOEByHnc6L8GGQDi7xbi9xcByOAeDwIgfv9ZByAebJIAIJ3bDwCUfABACOIJS3sBSD4DAELzUswKKtv5MiuV0s2Diw2TkaEdRIZ2iHtxWi792ne/KABULV9PWv0DZDTeTmbzANLqp5HMr3HnO/wRsMkBqF5b2buIjnoHkHMhazFq1C8kVa1Tu7l3ugDUHpyvXvsaqWqdSsVHsgFAzsV2mgDk7JskALW5YwcBkAglsncARQUgi3nXKgNA/HseFYD41yELQPwdODIAJMKKKRxjEgBK8g6gIADifv6q+kyq/50AAMVYABCSsbC0F4DkMwAgNC/FrKCynS+zUi3f5Lnoq5ZvoGrlpkQAqKMtJDIYdfVN3fMVC38Itd5oALR4DAB9PToAWdw/pwpAe3IAtNr7Hg0B0KWkqjUqFe+eHgA5F9dBAMRfPIcFIB5KeADi76iJA4D476vhv/9lHACZzAtA3N0+Q79KfhQAiXCSBADx2CILQJZw/iwBEI8psgDEP58kAPHbygAQ93e4WHww1f9OzBQAPTpH9DBLr48CgJBshaW9ACSfAQCheSlmBZXtfJmVavlGAYB+KADQr3z3cwGoHQaAdiYyGFnalhMAkPgRsBfGn3fsHUArPEAT6kugBQCyzK3JMt5AlrbVCAA6cuiYxeJTkgD0c1JVfwCqlH9Ktdo3ydT2GQKgQmF178+yAORcrEsD0JZeAOIxYxYASLwDiEeZcXcAhQUgXfizCECGsI8fADk/62kAkLOfLADxx00agHRhez8A4j8iFgWADO58cd0B5KzTBaCHUv3vBAAoxgKAkIyFpb0AJJ8BAKF5KWYFle18mZVq+QYBhJDqtgAAIABJREFUgG6iSuVHEgC0Re+CNtQdQDsRmYzazbdyAPREqPUO3wEUBwB9LTYAstsLiNqMbH2jWACoWv0e2daGUgBkdbbv/yp2liIArZMdAOL/7Px7UgDErzlpABKxaVIAEiFnFgCozf1ZxJtpAZA4N2E/AgYASiYAIATxhKW9ACSfAQCheSlmBZXtfJmVavkHnou+SulmLwCV7/DdbwBAm0qfy9J2JDIZmc7HowxGxcLjodabDABdGh2ABGyIG4Bqte964MYBILMVMwDxawwNQIXwAMRfBPcBqGtumg4A8ciQNQDifgbuGsS1hgEg8Zw8Wjjv8TQASHwv/TDKD4D4x4IAiH9NYQCI/3f+z6MAqMU9FnTnD+4AylYemSN6iKXXRwBASLbC0l4Aks8AgNC8FLOCyna+zEq1/N8CAP2EKpWbkwEg/fV9ANqHA6DHQq03EgA1xwHQV0cAkN93AF0cOwDV6xe5r6ttLCZVfY5UtRQIQG1tiQ8AbRcAQF8dAUAHDYAibQBqbzgdALJYMAA571EWAcg5XhgAMrntxX35u1X4O2B4AHLe83EAxN9dxL+fUQBo1B1AMgDU5h4PAiBn/nhwCcKYMAAU9Q4gkw2vyQNA96T63wkAUIwFACEZC0t7AUg+AwBC81LMCirb+TIrzfonyPYA0C2hAKgbCoC27wPQfvEBUGHN2H2yB0BHcO//BdQxdiKrPed5XdRhVK9e5gNAPyNVTQmA2owsY2tS1Vo2AMh5bJoA1BaenyYA+SGOuGZTeEx8v+IAIH7/IAAScWdSAGqzYaThzy0DQOLfWUMo/zPm74YKAiCT+QOQiESxANDdqf53AgAUYwFASMbC0l4Aks8AgNC8FLOCyna+zEqrfqZwB9BPqVL+cUIAtG0fgA7gAOjRUOtNBoC+EgqAGiM/AsZCAVCr8VGiDqNue30fAPpGSADadgQA/Xlwp4jZAx1V/RsZzQPlAKjDiOxeVbUaGoAsYxsytQOobSz1B6BOH5miApAIPkkBUEd4XvxYlbPmaQIQf0dOFADizyt+0XRWAEi8k0bczhS2BwDF2pkCoFVzRA+y9LoKAIRkKyztBSD5DAAIzUsxK6hs58ustBpnCAB0mwBAv/Tdr9vePAIAbdMHoLe55ysVHwm13kkAiPQAAKp/OQMAtNFEANRonE92dwNpAKIpA5Czbcd8gz8AOY/FCUA8jvgBkMbyC0A85MjeAWQK+/kBkPgdNLIAxP8ckwQg8f3xuwOI/3spC0C6cA4RgIL+OQkAibgEAJpOAEAI4glLewFIPgMAQvNSzAoq2/kyK1rjwwIA/Zwq5Z9wAHS7735RAKirb90HoHdwALQq1HqTAaD/CglAAR8B476LJRIACShQr66kWu077sW83V6fKuVbSVXr1NZ29QBQx3xTbztfAPrKdAFIG7wPmQUg5/zzAYBM4fizBkA87ojnThKA2mwAQNzfK8++AKDs5eE5ogdYen0YAIRkKyztBSD5DAAIzUsxK6hs58usaI1/9FyslEu3U6X8PwkB0FZ9ADooRgD669h9xgFQo/6lKQPQ4e6xBgC0sQ8Afb0HQDZzfx7VyvdIVf0AaCc5AOIumKMCUKV0C1nm1ukAkMUdm39MBBX+NcgAkAgnPJSEBSAHBvICQDyWiK81CQDiX4cMAPkhz7QAiMenpAGozQBA0wgACEE8YWkvAMlnAEBoXopZQWU7X2ZFa5wmANAdHgCqlH/hu98AgF4hfa6uvkUfgA7OGABdknMA+vMEAPSOofWNA6BW8wzPOlVVHQCQc3E7CQBZ3nXGAkAdNn0A4vfhAWqaAMTDivi6JgWgDredCEAi7sQFQDzMxA1Azkz5ARB//lEAJN49BADKXgBACOIJS3sBSD4DAELzUswKKttZnZVa6XJq1/cgs743VUvXktY41QeAbpEAoFdFuAPoVUQmI6N5KAdAD4daf34AaMMYAGjFCADivwPoD9Qx3zgCgL5MxcKfggFIfO1jAMi21vEHIOf1JwlA4t0nIgDxP5NRdwAZbDQAicjhB0Ad4bGwACRiDI8W4wCIP+ekAMTDU5oAZAjnyAIA8cd3Huf/rDHvuoMAyHlsHADpwrkBQPHnoTmi37P0+hAACMlWWNoLQPIZABCal2JWUNnO6qy067v1Lk41Rkb9XaQ33i8A0K8FAPq573EGABTiDiDjlX0AOowDoIfG7lerXk5a81TSmqeSqS9NAIC+6F5oNRufIVN/B9naur33qHHY8PaxAtA/jQCgr40GIO7i2dT3jQRAjco5oQFIvONm6gDkbD8JADlrSxKA2tzzfgDkd+dS0gAknou/6yaLAMQfPy4AsrhjxQVAPKTxANQWzhEEQG3heACg5AIAQhBPWNoLQPIZABCal2JWUNnO6qx06ks4ADqS9OaJXgAq/oYq5Z8mBECv6APQEZIA9Hdq6/uSZW5HHXMH6rZfSd3ORgIAPT/+NXsAqDb0fKN+sXuhVSisoVrtcuq2XkGkMdJrJwxvPzEALXOPFR2AdvVcPI/+EuhRAHRBdgHIuRgOAiCdzQYAiXflpAFAYe4A4t/vMADEv+ZJAEj8GFybe57/ezkpAInnThqAeIQDACUXABCCeMLSXgCSzwCA0LwUs4LKdlZnhQcgs34o6c3jBQC6WwCgn/keJwoA2cYmfQA6kgOgB0fs87K7jo6hDC6iQwJQuxEGgF4gVa2T1dwxAwB0KdVq35YEoDcDgKICEP+z9AMgHh8mASDnPQkDQM5jYQHID3nGAZDzGmUBiH99QQDEA09eAMgUzu3M6TTuANKFcwOA4s+Dc0T3s/T6IAAIyVZY2gtA8hkAEJqXYlZQ2c7qrHTqu3IAdBDpzWMFALqHKuVb5QGo7QBQkQqF56le+zJprRNJay2nWu0rnn1sY8M+AB3FAdADgWstqC+662g1P5QgAF3EAdCLpKp5BKCFwQBU/8+EAehP1LVekW8A4u+4iBuAnHPyH7cSAYj/dxkA4l/zqLt9wgCQAw9JApBTPwAS35+kAYg/5igA4oEnDgAS9/P7CBgAKLkAgBDEE5b2ApB8BgCE5qWYFVS2szornfriAQA13kZ6870eACoVfycA0G2+x+m25zwAVK1+r4cDXUZW5zVE3d6XBTvbFwrPk6kdQKa2P2n1D3Dn+33gWguFFzgA+nBMAFQder5R/88BAKkvkapmBIBqXx0CoFr1mt5rGgIgJRCAGo0LAgDo5VgAqFL+wWBfETA05vny5lAAxIPPfAMg/hxJARAPGTxU8LAj8xEwi3l/jpMCEI8xaQOQiD7TBqD+3BaLt6b63w0AUIwFACEZC0t7AUg+AwBC81LMCirbWZ2VTn2RC0Dtxr5kNN8tANB9VCnfxgGQ/4WHCEC16jUuAHXMnYcAqFh8wkWCVuPD3PnuD1xrobBmAECNIAB6buxrdgGozahW++bQ8zwAqerLpKrJApCpD75YejQAfSUEAO08GoCKzyQIQDe4+9riXSVhAcjgju/gTlgA4tcQBEDO+ZMEIP7ukVEAJN5VIwNA/Dn9PgJmCY/lDYDEu3CyAkDieyYDQDr3Z517LAQAlUs/SPW/GzMFQA/MEd3H0usDACAkW2FpLwDJZwBAaF6KWUFlO6uz0qnvwgHQW8lovVMAoN9PBkA2I611Wh+A1nW3LxYfjwBAz3MAdMYEALSwd6FlM7K7m1Cx+ITn+Ub9P0YA0PFDx5MBoK6xKZmtt04RgIK/BHoIgPTez9/WGDUq/xoMQM7rkb0DyGbeC/m4Aci54J8GAHWFY4oAxEPGOAAadweQCEA8KAQB0Kg7gGQBSPxZBQGQIWzvB0DOmnjk4HFHPL/4+kQAMpgXQkYBkIgzMwlAP0z1vxsAoBgLAEIyFpb2ApB8BgCE5qWYFVS2szornfqbOQDag4zWPwgA9CBVyj/jAOinvscZBqDvcgD0QR8AemwAQPWPeO44ClprofAcB0D/NAEAKe6Fn9XZisoCajXqX+AA6G+996n5xokAyPa7S8MXgM7oAZC5iQ8AfXkEAC0WAGgnLwBx5xwCIG3QeADohmQBqMu80DIOgHhsGQVAJssOAPGIkxcAcraLCkAdYRsegMQvQ84qAPGQ0xaOFxaAnL8T4t1uFqNy6eZU/7sBAIqxACAkY2FpLwDJZwBAaF6KWUFlO6uz0qkvdAGo01hCRutwAYAepkr55xMC0OlDHwErFR/hAOifuPPdG7hWLwB9dDIAMgb7Nur/7nm+Uf93DoDW9t6nSQBIvNAOACCjdRjZ7XVGANB/Ua12tSQAvWkEAH0+EICatX+aGIDK5RuncwcQD0DO87MIQDwyhAUg8fk4AIgHFfHYkwCQeNykAIj/LV6mzz5+2MM/5nf8uACozSQB6Mep/ndjpgDo93NE97L0+vv8A5CqqrTPPvuQoij0k5/8ZOS2jUaDVqxYQUcddRQtWbKE9txzTzruuOPouuuuo06nM6UVI6PC0l4Aks8AgNC8FLOCynZWZ8ULQLuQ2TpUAKBVAgDd4nucbvuVAgB9ZwwAreIA6KPc+X4XuNZC4VkOgD4WIwD9m+f5Rv3fOAD6e+99GglAXxwNQAKQDAPQob116Uvdi9jwACR8BKz9xpEAVC7/dOgjYO4F7yQAZDHqdl4xeM4PgDgk6XY2Hb7YzQMA8SjCz2ASACSijQhA/L5JAZCzxrAAxN/R0hH2myYAtYVjTQpAIgSFASD+zwCg9AMAmjgf+chHSFGUsQBULBbp8MMPd7cVu3z5cmo0GlNcOeIXlvYCkHwGAITmpZgVVLazOiudujIAoObOZLYOFgDoEaqUfxEBgL7NAdA/9gFogbu9F4A+JglAq911NBv/PIQ/8gC08xgAuoC6nS2o29mKVLXQe59SBiC7vR41al/yAlCbkd44lhrVf6W2tksoAKrXLvYCEH8BPyEAuXf/+AGQACR2Z7183gEk4ktXeCwtAOJ/TiJSTQuA+DUawj7jAIh/vX53zKQJQPw+AKDUrjFiz/1zRL9j6fX+fAPQDTfc4EGcIADqdrt03HHHkaIotNdee9GNN95IqqrSmjVraMWKFbRo0SJSFIXOPvvsKb8CRAxLewFIPgMAQvNSzAoq21mdlU59ZxeArOaOZLYO9FxsFQuPCQD0P77HGQ1AH/IBoId9AahcvCdwrYXCnzkAOnuCO4BGA5BlvYGoy6jTXjR4nyb5CJg0AO3uD0BtRrbByGge7gUgByp0Rh1tp+kBUHcCABLeh6kAEL+GIAByLtqnCUAirsR5B5AMAIk/j1EAFOYjYCK8+e0TBEAinEwKQOJ7FxWA/I7nB0AGt08YAOLPCQCabgBAkbNmzRpaunSpFADddttt7jYPPfTQ0PPXX3+9+/yjjz6a9NKREWFpLwDJZwBAaF6KWUFlO6uz0qm/iQOgHcjU3iYA0BNULt8eAoA2I1WtD6AiCIBKD3EAdBYHQL8NXGux8CcOgM6JEYAu9DxvWW8gshm1zb0H75MLQMcNHa8u3gHkrCksAGkBdwD13xtLe/UIAHqj5wLbau84gJghALpgMgByjhkFgIT3IjQAOecIC0B+548TgKJ8BCxtABJn0g+ATOE4zmNJAVDcdwD5AVCU7wCKG4AM7vFRAMRDEQAomQCAIqXb7dJJJ51EiqLQpz/96bEAdMIJJ5CiKHT66af7Pm/bNh155JGkKAp99rOfTXLpyJiwtBeA5DMAIDQvxaygsp3VWenUd+IA6HXU1vYTAOgPAgD9xPc43fZm3juAeABqfoj7Tp3e9qXSgxwAnc0B0N2Bay0WnuEA6BMBAPTs2NcsBUDESNeXD96n5k5yAOQASCgAOqS3Li3gDiAXgLaNEYC+mF0AEjFCFoCcC2UZABIxJSoA8diXBwDij5k0ADn/lAEg8dxxAJAfePndASRiDn9uPwAyfPaZFIDE14s7gKaX++aI7mHp9b58AtCVV15JiqLQe9/7XnruuedGAlClUqGFCxeSoih03XXXBR7zkksuIUVRaN99901y6ciYsLQXgOQzACA0L8WsoLKd1Vnp1N9I1Ozhj9k8gNraErK1dTkA+iOVy78MAUCbUa16ObWaH+QA6MM+APQAB0DnSALQUxwAnev9OM8EANSsf97zvGW9nnoAdOLgfcoAAHVau1KtdlU4ACLu/OPuAOLxIY8A5LwfQR93agvbZhGAnD+LAMQDhwwA8SAjIKnnnEEIw78nWQIgnTtuGABy/hwGgPh/imtrC/uJaBYGgER0AgBNNwCg0Hn66adp1113pV133ZWeeeYZeuGFF0YC0P333+8+v2rVqsDj3nLLLe52L730UpIvARkRlvYCkHwGAITmpZgVVLazOisOAOnV46lZP59M7QDqahtzAPSUAED+Fx7d9ma9C/r2Auqam1LH2GkMAP3efaxZ/zgHQHcFrrVY/F8OgM5LEIB2oB4AnTR4n0YAULX8vZgAiP8I2KaD7+6RASB9BAB1vefsAdBFyQKQg0BxAZBzYRwWgHgcCAKgLpMDIHHfuACIRxw/AOLfv7AA5MykeFdUkgDEH58HIIt5fxZ+5+b/XeYOIB5WRJQRX5MIQCLUhAEgcb8gAHIeiwpA/HbcHY/V8v9N9b8bAKAYmzMAMk2T3v3ud5OiKPStb32LiGgsAN10003u82vXrg089kMPPeRu98ADDyT2GpDRYWkvAMlnAEBoXopZQWU7q7Ni1Xd0AahWvZy0xqnU1TblAOgZKpfvGABQ5Wbf43Q7rxhccHUYWe1XcwD0ER8Aup8DoHM5APpN4FqLxT9yaPPJGAHoAu97Ym1PPQB6n/vYaAC6wXuxHcsdQJtTx3j9BAD0hkAAMvR3UKPxifEAZLPsABC/zrjvALLZeACyfPaVASDnvKMAiH9dcQKQs1YRgHjEkQUg02ffIADi18r/VixL2C8OABLvAIoTgPiPc2UNgCorUv3vxkwB0L1zRL9l6fVeLwDVarXQNQxjam/XV77yFfdXtluWRUTjAeiqq65yn9d1PfDYTz/9tLvdHXfckdhrQEaHpb0AJJ8BAKF5KWYFle2szopVf0MfgE5wH+u0diBbZ2Tr61GhsFoOgNpeAOqYO7pA0GqewQFQjVS1TqXSvRwAnccB0J2Bay0Wn3TX0aj/SwAA/WXsax4PQNtRD4DeP3hPEgWgg3vr8twBFBaAdpQDIPeLud/pD0BtNjkA8a89zwDE3/WTNgBxf7d8X0cYAPK7A0j8WaUNQPyxRADi4SUqAIl4w78v07oDiL87SgQg8fUJAFSv/Eeq/90AAMVYAYD23HNPz2/VkunKlSulXurKlSsnOvaqVatol112od12242effZZ9/FxAHTZZZe5z9u2Hbi+559/3t3u1ltvlXpNSPxhaS8AyWcAQGheillBZTurszIAoOWex5u186lZO59UtS4A0I98jzMagP7JB4B+xwHQpyQB6AkOgM6PEYD+1fueWNsSESNNO9l9bABAxw4dL5cApI0AIIvFA0BN5r2oHgdA4nfFyACQiCEO3CQBQB1hX1kA6rB0AMiZkSQBiD93EgDEzXhiAOR3F9A4APLDoDgBaMwdQACgGAMAkjp2q9Wiww47jBRFoe9973ue444DoMsvvxwAlKOwtBeA5DMAIDQvxaygsp3VWbHqr+8D0Imex2uVy6lWuZxUtU7l0q8iANAbAgCo2j/mPQMAqnEAVPp14FqLxcenBECvox4AneI+1mm+SR6AHBQJDUDjPgK22AtA3MVmR3+DLwC1zSWkNT8wHQDiLlAnBiC/fWTvABr18alxAGQLx5kUgJz90gIgZ9u8AxD/samkAYj/d114vO2zDQAo3/ndHNHdLL3+bnoAVCqVaPXq1aFaKpWIiOiCCy4gRVHolFNOGUKccQB0zTXXuM+P+rgaPgKWjbC0F4DkMwAgNC/FrKCyndVZseo79AHopMBteACqVm7y3abb3nQEAH3UB4B+ywHQpzkA+lXgOorFxzgA+kyMAPQ573viAtCp7mPZAaArvXcA9Y/X0V4vANDribqMdO1YqpR/5ANA7woGIH5bHiIcUHAec563uD87r1sEIPEOnH67owBI3If/Xp6kAIiE44wDIOe5sADE/3tWAIgHDQEYUwEgHmFGAZB4l1cQADnv9SgACjp+UgCkCecSAYj/7WcAoGSSMQDK4ncA3X333aQoCu2xxx704osvDj0/DoBuvvlm9/lCoRB4Hv5LoB9++OFYXwMiH5b2ApB8BgCE5qWYFVS2szorVn37PgC9L3CbcunXEQDo9WMA6G4OgM7nAOiOwHUUi49yAPS5AABaPfY1dxpvGgNA29C0AahWu5w6xvYJANBxvgCkj7oDqM28AMS/Dv5uinEA1PK5GBagwubRg/9tUWkBUJg7gHh8iQuAnOM6507jDiARgPj3z+8xGQBqC9tFBSARX7IAQPy5k7gDyFmzB4D+PdX/bgCAYuzvsv9bwD796U+HvitJURR3/0ceecR97PHHHw88D/9r4F9++eVpvDTEJyztBSD5DAAIzUsxK6hsZ3VWBgD0/sBtvAD0Q99thgFoBw6AzuQAqNI/5l0cAH1GCoBKxVUcAF2QIAC9lnoA9IHBPiMB6AcTAZDV2Zaszqup295QAKA3TA+AxItzGQBynosCQM73E8kCEA8lWQEgZw1dFh2AeNjyAyBxrTzqyAKQ+DyPLVEByPQ5Ng9AznFlAUh8nXHfAcTjSloAZHJ/nhiAcAdQbLlnjugull7vmX0AajQatHDhQlIUhW644YbA81xyySWkKArtvffe03hZSEBY2gtA8hkAEJqXYlZQ2c7qrFj17SQA6E4JANpEAKDtOQD6mA8A/YYDoM9GAKDPxwhAn/W+J9ZrqAdApw32cQHo/wwdb1IAcr9Lh3v/uuYWyQGQxcjmoSAqAE1yB5AIQM6Fb5IA5AcneQAg8c6fSQGI328UAIk/u7AA5PzM4gIgHkn4P48DIH5dYQCIh5owACTOncEGH/UaB0DOYwCg6QUANDamaVKz2Qzsn//8Zxd9brzxRvdxPieddBIpikJnnHGG7zls26YjjzySFEWh8847bxovCwkIS3sBSD4DAELzUswKKttZnRWrvi1Rk5FWPTlwGy8A3ei7jTwAlXvHLN/JAdC/cgD0y8B1NGoXcgB0YWIA1O1uTT0A+uBgn+bOOQOgHUYCkOdjSxobvjifRQAyhW2nBUAWSx6AxHPy244CIP7Y0wYgEYHCAFCYO4BEABKPETcATXIHEABo+rlnjug3LL3mAIDGZdx3ABER3XDDDaQoCi1cuJDuu+++oeevv/569xhPPPFE0ktGRoSlvQAknwEAoXkpZgWV7azOilV/XR+ATgncplz6DQdAN/huMwxA23EA9M8+APQrDoAu4ADodt/jd8zFZFvrULe9FTUb51Gp9NsAAPrz2NfcaewkANBnvK+l+2rqAdDpg30SAqCu9cqEAej4fAKQeNcOAEgegMRt4wAg/jWLAMSfcxQAdYRzhQEgUzj+OAASX5cIQPy+fgAkQk0cAOS3BhkAcvYHACUTANDEkQEgy7Lo6KOPJkVRaOnSpXTttdfS2rVr6cUXX6SVK1fSokWLSFEUOuuss6a8ekQMS3sBSD4DAELzUswKKttZnRWrvk0fgE4N3KZcuis0AFnmthwAncUBUKl3zPId7mON2ufdC51K6RfuMWu1y6lW6/0q+k77jURW77eL1aqXU6l03wQAJN4BJALQVtQDoH8c7JPUHUA2CwlA38oeAPGQEhaA+HNmEYDEcwQBkMU9Zgn7tVk8AMQfb1IA6gjHFlHD+VnzIOIHHNMEoDB3AGUNgLSANQCA0g8AaOLIABAR0Zo1a+jggw8O/M6g5cuXk6ZpU1w54heW9gKQfAYAhOalmBVUtrM6K1b9tX0A+kDgNtEA6HUcAJ09BoD+zQNAjfrFZBpvJ6uzNbXbu5Kq9gGoDxqqWo8XgBrne19Ld0vKJgAtighANw8DkPPnWQAgHjH47zPyu3APA0Ai5MgCUEfYz2QDAOLfPx6AxLUFARCPO5MCEI8wYQFI/HnmEYD4Jg1AuAMou/ntHNGdLL3+dv4AEFHvC6FXrFhB73znO2n33Xen3XbbjY455hi6+uqryTTNKa0YGRWW9gKQfAYAhOalmBVUtrM6KwMAOi1wm3LpbgkA2thzURcMQMXeMcu/5ADo3zkA+jkZxjIPjKhqnaz2jgIA3R/fR8ACAehDg31GAtD1GQSg7d33q1b5v17USQuA+LtjrP/P3p3HuU1diwO/CWFJWIZ9KW2htNhOJhulCSQBUpbAAwohFAi0IW1pKaVtXimvPAKFAimh8IC0JNAUKC2FtJSw79uP5bXAA1qWANlD9kwSjT1jy5sWS+f3hy356FqyZVuyLM85n8/5zIx8pXt0fTPz0TdXMrcvDyFuAYhHjGYBSLM5jhsAqnYLWFAAhL8WHI5jB0DG+2QHQPh2rKAByA6k8H6tBCCZ64cAKDxBAERBYQkWdAEDNS699FKIRCLwr3/9q2ZbQRDgxhtvhClTpkB3dzeMHz8eZsyYAU899VQLKrUPAiDKsCTNFUq32alzpZA+oE4A+rttmwoAUg5CAPQzGwB6CQHQHARAz4MsHW8DQIeWQOM8EAQREol3bAEo3ruq5jlXAtCV1nPR9obOAaDzmgMg/jy8BCAeOLwCIA39XC8AFdBx/ASgArMCED5PvwDIro0dALlZAYTPo1kAkrmv7QpAPPY4ARCtAApXEABRUFiCBV3AQIxFixaZy+hqAdDatWvh6KOPdryX8ic/+QmoqtqiystBAEQZlqS5Quk2O3WuaOn9iwDU/z3HNn3GA5erAZDKA9CBVQGov+9FBEC/RgD0HMjSsRwApRAATQdBECGReNdHANoLigB0cXmfTKQ2ABkXeJ4A0D7tAUBGOz8AyK8VQI0CED5HJwDC426cjxsAyqN+7QAIr9JxAiA8Ll4CEH5feQCx/ZdjAAAgAElEQVTKM+t74QRAPKC4ASAe1NoRgPIO7QIHoBsD/bvRUQD0v10Ar7Lg8n8JgCjaK1jQBQy0WLx4MUSjUVcAlE6n4YQTToBIJAKTJ0+GF198ERKJBKxZswauv/568xi33HJLC8+gGARAlGFJmiuUbrNT54qW3q8EQBc5tsEAJCZvhVTy95DLzIRc5tulrzNBU/bgAOgABECXIQDqBUEQob/vhTIAJedaAEiRj+YAKOkzAP23dUy0PaEIQD8s7+MWgGRGAGQHQBhM/AIgo40fAKRyr9cLQBLqt9UA5DQHvQYgDC1eARCuqRq44L5rARCPOk4AZIdFXgFQHn3PAxA+DwIg/4MAiILCEizoAgZKyLIMN9xwQ8UKnmoAdM8990AkEoGRI0fC+vXrK16/7bbbIBKJQHd3N2zZssXP8iuCAIgyLElzhdJtdupc0dL7lgDo+45t+hL/NC88cpmZ5oWbkj8K5PwkkPOToKDsywHQ/iZsZDI/twGg5xEA3YQA6FlQ5COrAND5IAgiJOLvNQ9ApX2dACibvaS8T9sA0B9cAtDn/QcgDB/tAEAaaw0AKegYxkV5LQDCyMFDUp7rQ2bWsakHgHg04qHGCwCSbI7bCADhGu1qdgtAEte3VyuAvAAg45h2K4DcAhCejwRA3gcBEAWFJVjQBQyEePnll2HKlCkm+kybNq0mAOm6DscccwxEIhG49tprbdvkcjkYN24cRCIRuOuuu/w8hYogAKIMS9JcoXSbnTpXtPQ+dQLQheaFXC473VwBpMpfcglAAgiCCP19z5rbxOTNCICeAUUZywFQPxSUQzgA+pcDAK10Po/4S5BJXgaFTFcNAOqCIgD9yNxWFYD6/9YyAMrlvlMnAE2HVP9dwQBQjlkvxu0ACB/PuBg2+uOxo9angHkFQMaFeT0AZLR1AiAndMHnhevkccwtAPFjgvGoHgAyju3VCiC7MeBrrAUtnQRAMvc9AVBw8UYXwP9jweUbBEAU7RUs6AI6PVKplIk9Y8eOhUWLFsE777xTE4CWLl1qtnn99dcdjz9r1iyIRCJw9tln+3QG9kEARBmWpLlC6TY7da5o6b1LAPQDxzaJxJsIgGaYF3K9vRvNNvnsuRwA7VcGoPR/2QDQM+a2VP9dIGeOBzUfAyl3OqhKFArqvgiA+sofa569oFhT/N91A1AuNaN4IZVl5Qt1jYGixCCXm2mmpu0LhcKBkMn8yty3YQByuvgunbuOEagGAGmFnUHXBzsA0BfbE4CMC1k/AIhf7cIDkMIdH68uqgZAxnHsAIgHiwI6JgYgfJ48VvFj2UoAUlj1WvwCILt++bQDFgIgAiC/gwCIgsISLOgCOj1SqRQMHz4crrjiCvM2LTcA9Oijj5pttm3b5nj8hQsXmreBtfJh0ARAlGFJmiuUbrNT50oZgC52bJNIvIUA6NtlABI2mW3y2fM4ANrXAYC2gyCI0N/3tLmtv+8FEAQRMpnZIMuTiokeBG0FoG8Va2oIgL5tBaBC8fi6NrTcbykzGetHw5cBaFrFcVsFQLo2GABYMABUYFYoqAZAxrnnmGVOVAUgDCGtACB821U9AOS0AggjV70AxGMFD0AYcewACONKOwGQsb8bAMLn0ioAskOcIAEoz6xjTgDUmiAAoqCwBAu6gE4PRVFg06ZNlm1uAOiOO+4wn/+j67rj8Z988knzWJs3b/a09mpBAEQZlqS5Quk2O3WuaOm96gSgbyEA2my24QFIU/ZBAPSLSgDqfwoB0IsgCCKkUgshl50JuexMyOfPQACUKD/U2ASg970BII1BobCnZQVQLjcTUqmFln3VTNQnABpUBYC+1AQAHdwcABWYtwBkQEgtADIeLO0WgDDIYADiV5UYffEAZJxnswBkHC9oAOLhpxoA4XOqBUD8KhU7AMKA4haAVK59pwAQv4qnHgCiFUCtjde7AF5hweXrBEAU7RUs6AIGYrgBIONTviZMmFD1WK+++qp5rGXLlvlRrm0QAFGGJWmuULrNTp0rWnrPEgD90LFNIvE2AqALEABtMdtUAtDeDgC0DQRBhL6+Z0GWJoAsTYRE4pWKPjPpaxEAxREAfbtYkyMArXA8DycAUuQRNccpbACkqfuhh0C3EQAZ52+0MS6OqwGQsQ+6ba8mAGGUwACkob68BKAcV2OjAGR81bh96gEgO1xpBQBhaKkGQDxsKdy2oAAIH6uVAISRhwCo9UEAREFhCRZ0Ae0e8+fPr/jkrlo5f/78qsd0A0BXXXUVRCIROP7446se66233jKP9eGHHzZ8nvWGLKuBpqYVV0Vpmh54LZTtnTRXKN1mp84VPVMEoELuJ45tFPl988KjIJdvAZPlfrNNQfmW5aJOV8srgFTlahMLZDlVPKbynLlNkf9R0aeq/trcX5azoBeKAFRQZ5baLLMFIFle73gehfx3bAFIU0fWHqfccIAcg0L+HJvxedRzANKVfUFT3QOQJh9qvZguHb+gfgMU6d72AyAMNhkWHACprP0ACK8AcgNAdrDiFwDhFSv1AJBTnW4BqBq41ANABsa4XQFUDZ8aBSBcc4MAJMu3Bv63o2PitS6Al1lw+RoBEEV7BQu6gHaPoADommuuaWsAoqCgoKAISWS6ihfg0n86t9E/Rhcf5YdAg54pt9FmWC/qCuUVQKBdU8YCPVs65vNo2zs2fc5Fn5AlAWifL7X/Xun1VbYABHqV253l79kCEGijao9TfnjpU63OrXxNe9wZgDRWCQkYT3R7AAJ1XwD1MAsAgRMAKQxA+aJ1f+P42hkAhT+Vt4UNgHD7VgCQUatbADLGxk8AwsdsBoBw7Y0AkFELhg++X68BSGWVtfHgwvddC4D4FUD8M3eMepwASGH29eDx8RmAQJ1X+3cWhbsgAKKgsAQLuoB2j0QiAWvWrKkrE4lE1WO6AaC5c+dCJBKBiRMnVj0WvgVs+fLlDZ9nvRH0/0p06v/UU3qfNFco3WanzhU9s0dpBdAsxzaK/AFaAVS+BcxYzSPLdiuA9nJYAZQsHlMpPwNIkd+u6FNVb0QrgNKgFz5fWtHy3VKbjaCpZ4CuDmrpCiAt902b8XmkYQDStcHNrwCyu5g2VwCdXlwBZIITswcgO6DwG4AMaPETgIx0C0C4Lh4YZWZFmGYACONN0ACkMWstQQGQyh2jGgBhTLHbr1kAMuaIE/7UC0D8SqV6AAjPV/P36Myav7P8zo4JAiAKCkuwoAsYiOEGgBYsWACRSARGjx5d9VhPPPGEq08L8zroGUCUYUmaK5Rus1PnipbeHSDDINv/Y8c2icQ75gVkPnOuebFmPNBZEETIZ8+3XNRp6p7oGUBXomcAbQVBECHZ/3dzW1/ifyv6TKevR88AEqCgfA5AY5DLXmitXx1m+Z9xKX8qSPmTQZYmQTZjPSenZwCpcnfNcVLTxWcAKbkjIJX6PQhCynwt2f9X60UmD0D4Yt4lAFV8CpjMQNd2gKoAZFw0KuXjS7nTis8AqgZAGEOCBiBjX3xRbWS1h0Ar6HjVAKjArBfUjQIQhppaAJRH++Ba/QQg/pheAJAxx+0AiEeYZgCIHxevAYi/BcwOXJwACNejcMdxA0D4Z7cAhFcAmb9Tpwb6d6OjngH0ahfASyy4fJUAiKK9ggVdwEAMNwD0zDPPmG2qrSiij4HvrAs1Su+T5gql2+zUuVI/AJ2DAEgw21QCUJcDAPWAIJQenFzalki8WdGnFYC2IwCaaa1f2b1YWwlGVOUw0ArF1UeFwgGWts0AUCF7gAVWkv0Pma81B0A72AOQspsVgAy8aRSACqwuANILg6zteADC52f07RaAeGhxAiB+zLwAoHpXAOE6GgWgareAtSsAGfVhjHADQDyUNHILmMSs5+Y1AOW4Y4YBgIzbNAmAvA8CIAoKS7CgCxiI4QaAVqxYYbZ58803HY81a9YsiEQiMG3aNL/KtQ0CIMqwJM0VSrfZqXNFS+9WAqCfOLZJJN51AKBes011ALqqEoCSixAAvV3RZzp9AwKgbVBQD7IFoIKynwWAZOkoKKifLwKQ+gVL21xqhgMAjaw5TpUA9DfzNc8ACF1Q68pO9QMQvjg1AejU+gDI6D+sAITxpBEAwpCEb90zjt1KAFJsjukFAPErboyxwXjiBoDs+g4CgHjk8ROA8L+BQAHolED/bhAAeZgEQBRtFizoAgZiuAEgXddh8uTJEIlEYM6cObZtcrkcjBs3DiKRCMyb19qHxREAUYYlaa5Qus1OnSt6etcSAP3UsY0FgLJnIwCKm23y2Qs4ANrdFoCMj45PJh9AAPRORZ/p9BwOgA4sAdB3LO0sAAQMxNTvQMqfCqAzUJTRlrbOK4BcAFBufysAJReZryX7F7kHIONi12kFkAlAO1QCUOkcKwDIbnVC6fj53NmQ6r+zAQCytjN/xhfCGGvaBYDwedULQCo6TpAAZNSLAUhh9ufKwwqGGFwrvwKowPXlBEDVbgGz65ufixgx/AIgflvYAcjumURZRgDkV/y/LoAXWXD5/wiAKNorWNAFDMRwA0AAAPPmzTOfA7Rq1aqK12+77Tbz9q9WPv8HgACIMjxJc4XSbXbqXNHTw0oANMuxTSL+HgKgaQiAEmab6gB0dQUApZL3IwB6r6JPKwBtRQD0XUs7ewA6rTYA5Uuo4RqA9uMA6AHztboAyLhQrAlAO7oHIHz7EgYgnUE+N605ADL67SQA4s+5lQCE++DxwC0A4eNXAyB+rN0CEK7PDjL4up0AiF/F0gkAhOtp1QogAiB/gwCIgsISLOgCBmK4BaB0Og2TJk2CSCQCEyZMgCeffBLi8TisXbsWbrjhBvMYN998cwurLwYBEGVYkuYKpdvs1Lmip4eWAOg/Hdsk4v9yAKA+s00lAO1WA4DuKwNQ/N8VfabTv0YA1AMF9QCXADTfBCBVGWVpawGgbBmAFGlUzXGS0qeBJu1u1pxK3m++1hwADfEOgPCFai0AwmDSCgDCOILHIGgAMi6qgwYgfFy/AMjuZ2O86gUgNyuAWgFAeZttuB3GnDACkDFPKwBoWqB/NwiAPEwCIIo2CxZ0AQMx3AIQAMCSJUvM27zs8qc//SlomtaiystBAEQZlqS5Quk2O3Wu6OldSgD0M8c2VgA6ywGArB8D7whAvZtBEERIJe9FAPRBRZ/pdPlj4Ht7tyAA+p6lHQ9AqdR8kPKnlwDIurKnGQASBBGy6YsRAP3J3N7eAORwC1irAQhDSL0AxNfpFwDh4+S54+HxrgVABbRvtVvAMJK0EoD4/psBIB7qmgUgOyhsFIAw1OTR9zmujRMA8audWgFABlDVBKBzAv270VEA9EoXwAssuHyFAIiivYIFXcBAjHoACACgt7cX5s6dC1OmTIGRI0fC2LFjYfr06bB48eJA8KdYU7AXNJ16oUbpfdJcoXSbnTpXygB0mWObRPzfDgDUb7apBKBh5sVKOv1LGwC629wWj39U0Wc6PdcCQJq6v0sAWgBS/hu+AFA6dT0CoD+a20MFQAZYdDIA8djR7AogPGatACDjuDwA4frqBSAexewACOOGWwDi+w8SgPh2XgFQK1YA4e9dAdA3Av27QQDkYRIAUbRZsKALoAhnEABRhiVprlC6zU6dK3p65zoB6EwEQEmzTXUAugYB0CYQBBFSqYUIgD6u6NMKQJtBU/crAdBFlnaVAHQnAiDrx7s3C0Dx+IcIgO4xtw8EAPL0IdBOACRxx+RBBwOQjPriUYOHkDADEJ5X9QAQX6uXAMQjS1AAhG+b6kQAkpgzAKXOD/TvRkcB0MtdAM+z4PJlAiCK9goWdAEU4QwCIMqwJM0VSrfZqXNFT+9UAqCfO7ZJxN83LzolCwClzDb57Lc5ABpaBiDxWhsA+j0CoE8r+kyLNyEA2oQA6PuWdhUAlLwLpPwZJQAaYWnrDECjXY1VPL7ErBl/HH1/319BzR8CunGRWRcA7egMQPKXPAKgu6z1NApA+FahsAAQjxw8ABl1+w1AeN9aAGTU2ywA2d0y5gaAjHrsQKTaCiAMIvhYXgMQhpYwABC/eqoWAOXRvo4ANCPQvxsEQB4mARBFmwULugCKcAYBEGVYkuYKpdvs1Lmip3csAdDljm2sAHQGAqByGwxAusKtALIA0EYQBBHE1AIEQMsq+qwEoH0dAGh/DoB+D1L+zBIADbe0bRaAens3QTZzMRTUL5RWF20FQRAh2f9A9RVAGCY4ANIKu/sMQN+0ByA7WGkWgArcdgOA+JUw9QCQHVSFEYDqWQHEAxC/qsZuPjUDQHgu2a0Awqtu+AcjuwUgpxqrARCeL3YrgJyQphoAqcw9ALl9CDQGLonZ1+L2GUDGVwKg1gUBEAWFJVjQBVCEMwiAKMOSNFco3WanzhU9PaQEQP/l2MYKQN+oDkCli01N3QUB0K9sAOiOMgD1rqzo0wpAG0FT93EJQH8AKT/VAkDZzCWgSIdDIb9bUwBUrOsWVNd6EASXAIQvHhEAqerBtQHIgAYvVwD5AUAYFGoBkLF/owBknKMXAGQ8U8gLAOJX6LQagBSuj3oACPflBEA8uPDn1igA4Z/reQaQ1wDE7+MFAPFjVwuAcD0YgND4ZVJnBfp3o6MA6KUugOdYcPkSARBFewULugCKcAYBEGVYkuYKpdvs1Lmip3cAyDDI9P/CsY0VgE4vAcUgS5tcdgYHQDs7ANAGEAQRxNRv0bbVFX2mxd8gaNmAAOgHlnaVzwAqA1BB+QKoyuGgqXtBQTmgvILBIwASU7dCLjMTpOxxTQDQ530GoBauAGpnADLGyw6AMDo4AZDdSpV2ACCFVZ5zNQBSuZ/tAEhG27wCICeostvPDQDhmpoBIPzvlt/H7S1grQSg0tzKimcH+neDAMjDJACiaLNgQRdAEc4gAKIMS9JcoXSbnTpX9PRgFwD0AQKg00pAMdjSpgKAChiArjf3z2VmQC47E6T8FFCVL4Eifc18LhDOtHgzB0B7uwSgu0HKn1UCoM+VoUUa4R0AlS76dXUoqNLhoCs7NAFAX2xvACpd6OrqoHADULUVQPzPQQKQkQMVgBRWHYB4hPEKgFSbfYICIKdbwCwARJ8C5lkQAFFQWIIFXQBFOIMAiDIsSXOF0m120lxJ9S2EXGompPoWIgC6wrF9PP4hAqBTS0Cxg6VNJQDtZAtAuroTyNIkMzPp2bZ9WgFoPQKgiy3tKgHoHsjnp1UAUCb9n1UAaIzrsRPF/ylf9BcYyPmjyhexbQlA59h/CpifAGT0Uy8A8R8DzwGQ7iUA5bg2ea4/NwBkPJup1QBkd44q138jAGSkirY1C0AYRbwGIB5c/FgBxG+vBkD8934CUPq0QP+GdBQAvdgF8CwLLl8kAKJor2BBF0ARziAAogxL0lyhdJudMFd6hbWQTf4IFHE0FDL7g5IeB3p6UJ0A9B8loBhiaZPLXmi52NQdAEhTd4FcdqaZqdRC2z6tz9pZB5q6lwMAcc8ASt2LAOggBECX+QJAYnJ+kwB0aGcBkIT6wRfdbgBI5o7Jg4pXAGSADX5fmgWgPKqrGQDC9Q50AOLBpdUAlLfZ3moAMmCRA6Bc+tRg/5YQABEAUXRssKALoAhnEABRhiVprlC6zU6YK4ned038gFwpM6wEQP/tuF88/pENAO1oaVMJQDsiAJpTBiCly1Wtovg/HADtWQSgzA8t7TRuBVAy+UfI58+2AaCfWwBIy+zRIADdygHQguYASDkMdJ2B7hKAVPWw4qoh1wB0bnAAhC+aawEQ3m63AogHIIw5zQCQMYatACA8TvUCkBP42MFKJwOQ7HDssAAQP45uAEhh5U8BMwHoPwL9W0IA5GESAFG0WbCgC6AIZxAAUYYlaa5Qus1OmCt98TeqANCVjvtZAeiUElDsZGmTy8zkAGiIAwDt6apWJwDKZi6xtNO4FUDJ5H0OAHR5GYCk4rkX8p+DfPZbkEoucD2GPAClkgutACQz0I3XXQBQf98LkMnMLu7jAoAk6QwQjfFs5QqgwuByn3YAhC90awEQjzpOK4CaASB+FVGYAQijj58AhLEpSACSmfVcmwUg4/31EoBw3dUAiEcfAqD2ixe6AJ5hweULBEAU7RUs6AIowhkEQJRhSZorlG6zE+ZKf/wFKwBlGQIg+2fxCIII8fgSBEAnl4BiZ0ubagAkir9GALSXq1pF8VYEQGvrAKA/IQA6EAHQf1UAUD51bt1jWAlAf7ACkMRAl4YAFPYEXRvsCoBSqYV1ANCZZZDzYgWQcaFbC4C0IdVXAOELXuM1o65GAAijiBMAcTVWBSBjvKrdAobbBgVAGNEULjsZgCS0jQcgvD1sAOTFCiCZFf+NEAD5EwRAFBSWYEEXQBHOIACiDEvSXKF0m50wV/oTjzsDUN9VjvtZAWhKCSh2sbSpBKAdHABob1e1iuJtDgD0I0u7SgD6M+Tz3ywCkHoAAqBfWAFIYiD231r3GIribRYASvbfaw9AAKAVptoDkFx8iHYu+z0QhO0gCCLoPMD4CUAYATAAISRoGoBwXY0CkF3bsAOQHcA4AZAdGnUaAOFz9hKA+FraAYDsxpEAKPggAKKgsAQLugCKcAYBEGVYkuYKpdvshLmSTPytCgBd7bhfPP4xAqCTSkAx1NIml/lOFQCaiwBoH1e1WgHoM9DUrqoApGsMCuoXoK/vZcjnzrEBoCtsAOi2useQB6B470egSKPrBiBN3tnyAGydAwJd2REK8gE2ADS1fgDq8xmA+ItpYz+nTwELEoCM5zMFCUC4VrcAxIOOFwCEj0sAZA9A/LH5+gIDoFMC/VvSUQD0fBfA0yy4fJ4AiKK9ggVdAEU4gwCIMixJc4XSbXbCXEkl/tg8AOUMABpmaVMJQIObBKDbEQCtAU3dowRAl1raacr+ABoDXS2vSCoD0P5lABKvtAGg2+seQx6AenvXgCp9uX4AknazHNcOgGTpqPJFbqH4YG1RvNE9AGkMspmLPQGgXHoGaOqu9QFQFtXYDACpqK3Kve41AMlcX/UAEIaXdgEgfsWPHcKEAYBwn24BCK9AaxaAcK1uAEhiledHK4DaMwiAKCgswYIugCKcQQBEGZakuULpNsM8V5KJByCXnAFyahyo6cNBEUfYANAvHfePxz9BAHRiCSh2tbSpCkCpmxAA7euqZlGc5wBAP7a0K9gC0Lk2ADTbBoB+W/dYiuLtrgFIUf4Ocv54kPPjawOQcaGIACifO6N8kZsvpiBsqROALrEHIAwFrgBoJmjKsOYAiF9lY1yYtwsA4ZVFNs9FqsAXHoB43OpUAOLBwwsAyqPvnQBIqXJsNwBk7EMA1HR2FAA91wXwFAsunyMAomivYEEXQBHOIACiDEvSXKF0m2GeK6oYA8gw0NL7gCxOglzyuzYAdI3j/vH4pwiATgBQGWiKFTBymW8BKKwIGQUGemGQAwDt56pmKwCtBk3dvQ4AOs8GgK62AaDf1T2WlQD0GajSV2wByJgr/X1P2ADQ7pbj2q0Asgeg3toAJJfrE1O/qQ1AOfSzCwAy3uPQrQDCYIMByDj2QAMgjHntDEC4BgIgAiCvgwCIgsISLOgCKMIZBECUYUmaK5RuM8xzRRUPB8gwUMVDIJecCen+a2wA6FrH/TEAybnjSwBkBQw5e1z5oqdQAgQTgH5j7l9Q9ndVsyj+1tw/3rsKAdBPLO2qAZCm7meiR1r8pQ0A3VH3WNoDEL8CaEfLXHEFQNzFfXUAWlIbgErHEYREEYAM4LADoCz62RGAvmO+B5q6sz0AGRe+XgEQvuDHyILBoh4AMsbA7patdgAg42ejlkYByG482g2A+Fr9ACB821dQAMT3QQDUfkEAREFhCRZ0ARThDAIgyrAkzRVKtxnmuaKKhwFkGOST54AgiJDofc8GgH7luH88vtQGgPawjg8HQGABoJsbAKDfWQGosFsdADTdBoCusQGgBXWPpSjOc7ECiAegJz0EoHgDALSgdQCEISSLtjcKQBgwvAQgfqWMVwCEVl85wle9AMSfa6sACEOFHQAZ27xYAVTtGUD1ABCe/04AxD/c2Q0A4e34/WwFACmMAMjPeLYL4EkWXD5LAETRXsGCLoAinEEARBmWpLlC6TbDPFcK4qElAJoOgiBCovf9CgBK913nuL8VgL5eBCC5yzo+rgHoAFc1WwAovhL0wq4lAPqp9dxsAeh8GwC61gaA7qx7LCsBaG1nAJCxcqcJANKNi1d+BZDRVzMAZFy41wtA+DUnANJYawEIg0w7AxC/AsYPAFLRcXgAUrn9nQAI/buyBSCMOTwAuV0BhLe3egWQDQBlCYC8CwIgCgpLsKALoAhnEABRhiVprlC6zTDPlYL4xRIAfQsEQYR47xIbALrecf94fBkCoMklANrTOj5VAeh/6geg1B0IgFYgAJplPbeqALQvAqBfmYhSBqC76h5LUfxtZwIQRoh6AAilpuzk/S1gbgCIxxUnAMqx6gBkHLcWAOGVKfUCkIK2ewlA+LhergDiV74Yc5EHmGoAxM3JpgFIsemfX9FTDYByNu1bBUDGa04AZIwvrQBqXRAAUVBYggVdAEU4gwCIMixJc4XSbYZ5rhTEgwEyDHLJGSAIIsR7lzYBQMc5ANCx5QuZqgB0oKuaxdR8DoCGOQDQATYAdEEJgPZBAHR9BQCl+hfWPZaNAdBTHgJQojUAxGFCaAEIH6dZAOJX2DQCQDzIyGgfo9ZmVgDxAMSDUyMAhJHGKwDC7YIGIAwuPAA5PWOIACiYCww/4tkugCdYcEkARNFmwYIugCKcQQBEGZakuULpNsM8VwrigSUA+g4Iggjx3pU2AHSD4/7x3uUIgI4tAdBe1vGpCkC3IgA6yFXNVgBajgDoP63nZgtA36oEoNQNPgHQOlClwxsAIOszlFoKQDyA2AEQfr0dAYg/jlsA4tsbX9sBgDA8NHMLmB2IVQMg3AeuPygAwquD/AAgfmVPNQCSuSQAAkEgACIAopacVsAAACAASURBVOjkYEEXQBHOIACiDEvSXKF0m2GeK1p6vyIA9V8EgiBCb+8aGwCa47i/FYCOKQHQ3tbxcQtA8udc1SymFiAAWgZ6YWgDALQ3AqA5NgD0h7rHUhR/1wAAPd0gAJ1ZHYDwBTK+MLYAUJ87AMJoYQAQAg8p8w1/ACjH/AUgCR3HaQVQqwDIbqVOpwMQDyu1AMiurR0ASejrAASgtDgt0L8pBEAeJgEQRZsFC7oAinAGARBlWJLmCqXbDPNc0dL7lADoByAIIvQK62wA6NeO+8d7V9gA0D7W8akKQLchADrYVc1i6k4OgHYpAdDPLO3sAejbNgD0axsAuqfusWwtANmtAOqD3t4lxWcfGRf1NQAo3vsxZFM/dgYgvDLGYQWQlD0ZAdBO3gCQWp5/jgCEIcANABmvaaXb2PBzqbwAIFxXswDUzAogfpWNEwAVuP46DYDwuLsFIB51+GO0AoDwA58bAKBM+pxA/6Z0FAA90wXwOAsunyEAomivYEEXQBHOIACiDEvSXKF0m2GeK1p6zxIA/RAEQYReYQuomS9DIbM/6OmhUBAPBDHh/EBkKwBNKgHQvtbxqQpA8xAAfd5VzVYAWooA6DJLOzsAyuVmVABQf+JZUHMHWgGo7966x7IxAHrGHQCpDHR1UI0VQH0Qj39svbivAUDF93CVMwDxaKGWPtHLAkBTvAMgDBJeA5ABJ7oNAGUdzr8VK4D4lTt+ARCuE7cPCoB4PHECILtVOJ0EQHmufTUAwqubCID8DwIgCgpLsKALoAhnEABRhiVprlC6zTDPFS29B0CGQbb/Usv2TP9skFOTINM/u+r+8d6VCIAmlgBoP+v4eA5AdyEA+hT0ws4NANBeJnok4u+Dlt+VA6A/1j2WYuoOfwBIG1RCiyE1VwC1BIAw5CgMpOxJzQMQvqhvBIBkbt9GAYgHknYBIIwFXq0A4vttNQDxEGLXd9gACI+xEwDh8awXgIy2VQHo7ED/phAAeZgEQBRtFizoAijCGQRAlGFJmivhzPTaOZBfdTZIK08EZeWRkF8zDRI97/jaZ5jnip7etQRAP7VsTyUWQq5/JqQS1R+GHO9dhQBoQgmA9reOT1UA+i0CoC+4qjmV+gPo2s6ga0Mgnzsb9MKOoBeGQCb9C0s7ewC6sARAeyIA+gC0/F4AOQZ6jkEmdTkYOFJPugOgnSxzxQ0ApcVfQSb936Ape9dYAdRfBiC1vL0hAFIZFOR9QJcH1wlAO4NeQB8VXw2AjItxHiMM4HACIIwttQAIf68zZwDKsdoAJKGfGwUg4zx4AMLn0wgA8cBTDwBVWwGEvzfOv14AwphYDwDVWgGE+yUAMjMtnh3o35SOAqCnuwAeY8Hl0wRAFO0VLOgCKMIZBECUYUmaKyHM7SmAJQzgEwaFZQcBLGMAyxikNtb/iU71ZJjnip4eWgKgnzW0vwWA8keX4OAA6/hkj3EJQF+sq+9MejbI0iSQpUmQSVeuVLIHoJn2AJTbxwSgRj4BTBDsAGi9CwB61gaAumyPX5APNgFIsl0BZANAjawAQoClS+ihzm4AqFD8CHtNHVYJQFm0XzMAhGGnGgBh+GgWgHANrQQgowbj/PwEICeEwQBktMdzyW8AyqF2dgDU7AogHrPsAIjvpxEA4tvKNseqBkAYiwwAkhmtAPIzCIAoKCzBgi6AIpxBAEQZlqS5EsLc3l8EoI8ZKMuHmwAkbnR+ho0XGea5oqd3KgHQzxvav7d3tQ0AHWgdn6oAdAcCoEPq6juVWgi57EzIZWdCKlWJNqo0AvTCjlBQyiBVBqAuEz3i8Q+hkNvfBKBGx1JMzbcBoEjIAWiXilUiOn5gs8JAyp6AAGg3EAQRpNzk4AEIJw9AGAraAYDw+dQCIHyOjQIQP35O44nr9gOAeNQpsMpzqgVAza4AcgNAtVYA4RrsjlMNgHLoGJ4A0DcD/ZvSUQD0VBfAoyy4fIoAiKK9ggVdAEU4gwCIMixJcyWEuT1eBKAlDLKf/QwB0AJf+w3zXNHTOwBkGGT6f9HQ/lYAOqoEQAdZxyc7yRcAaiRzue/YAlCy7wGQ08eDJJ7a8LHtAehwALmEJrYA9JyHAJSEePyT1gAQWgEkZyfbANDprQcghXutEQDCx8EAhOevDQDpdgCU5dobF/k8APGAVAuAcPtaAKRw7YMCILtVRW4BqNYtYPx73cwtYO0AQDnUdwMAJKYubfh3mBdJAORhEgBRtFmwoAugCGcQAFGGJWmuhDC3bzMBKPPZLxAAzfe13zDPFeMiO9P/3w3t39u7BgHQ+BIAfc46PjYrgLTCXqAV9imiiQlAh/p+vrncd20A6CMQBBFSfQsh1df47YJiaoH9CiB0cecdANk9A6heAOovvYdrQSvsAbqfACS7ACCjtkYAiMcGLwDISLcAxINIMwCEgcAtAOH+7QBIZpVjw4+ZGwDC21oJQHbgxQMQxpiwAlAeZUMANCvQvykEQB4mARBFmwULugCKcAYBEGVYkuZK+LJ322YEQFeWAWjD73ztN7xzJVUGoL6rGhtzBEBKflwJgA62jg8PQKWLSFUaXkQTE4C+1IJzTkBBPQR0bTACoCWeHLsSgDZUAJAmHWSZK/2J510DUH/iWUj13w3J/r86rABKNQRARmoF6yd4FQFoqCcAVJB38QeA8KoPJwAy9uUBCANLOwMQPjbfnm/D/8y/hvsxtrkFoDzXr8ys52WgRb0AxGNUNQDikaURAOKPmWfWOnBtjQKQE/zgcSAAav94sgvgERZcPkkARNFewYIugCKcQQBEGZakuRK+7N22oQxAa3+JAGier/2Gd670IQD6ZWNj3vuZDQBZP8694hYwA4DyIy0ApMqHteS8C+rB5Vue/AagPL8CaDfLXKkHgHDarwAKEoD2KH0KWFepPo8ByICRWgDEYwqHR3qrACjP6gMg/D55CUCyzfduAajA7csDkMSs59XoCiA7AMLnaLcCCL/v/HuNsaRRAMLP4QkKgIyf3QCQMXYFBpn0uYH+XSEA8jAJgCjaLFjQBVCEMwiAKMOSNFfCl71b15oAlF57HQKg23ztN7xzpRcB0K8aG3MLAH2tCECS9ePcnQBIyY8BMXVnAAD0BQ6APvbkuGLqTo8AaM+afSXib4Gu7mBZLeAHAKm5r7gAoONAU3ct3uqlFGv3FIAMCKgXgDBW1AtA6IK67QAIn78bACow63tvh2P1ApCBE60GID4l7qsdAOG53ywAqdw+7QJACC0z9DHw3gUBEAWFJVjQBVCEMwiAKMOSNFfCl/GtqxAA/RoB0K2+9hveubLNvMhO993Q0DF6e9dBQfkcaMqeUJAOAl0ZBqpkfZaPMwB9lQOgL7fkvAvqF1sEQBttAGh3y1zpT7zQEAAJggi6uiMHQCLE4596CkCF3OdaD0BGX/UAEEYD3B5jRr0rgIyvfgKQ8ZrTM2QwyLQrAOGf7QAIH7MRAMoxa808AGGAaRaAcDt+lRP//tod1zgP/HyjRgGonlvALAB0XqB/VzoKgJ7oAljMgssnCIAo2itY0AVQhDMIgCjDkjRXwpfxrctNABLX3WwCUHrDLb72G9650oMA6MamjpURZ4OcmwRybhJkxNnW8XEEoHEgpu5CAPSVlpx3QT2UA6BPPDluIwAUjy8BJTcelNzYwAFIlo7yH4CMC2ivAQjjhQMA6YU6AQgjjTGWPACV+tbxhX09AJRm5ZVACndcJwDikch43hAPQBhy8GvNABCGFzcAxB+zFgDhc7UDIP4ceNDxG4Bkbh8eoHgAUmz25X9uFoCM8SEA8j4IgCgoLMGCLoAinEEARBmWpLkSvoz3fGICUGrdbQiAfuNrv2GdK73CJgRANzV1rFRyIeQyMyGXmQmppPWTtJwB6GgOgA5vyXn7B0B31Q1AlvciYAASBBG0wlAOgA7uDACSGegyA10d5A0AFVA9mksAUpg9AOWYPQDhFTT86pFGAcjumTnovTT3w3U2A0Ayd0y/AMhpBRDeT3J4rRoAVXsGkMIIgOz+rhAAEQBRdGywoAugCGcQAFGGJWmuhC/jPR8hAPodAqDmcKNWhnWu9ArrEQDd7N/4OACQnJsEqdTvWw5AqnoYB0CfenLcSgDaRAAUFACVVvpUAJAyqAg2fgKQMfZuAEhk9QEQPtdmAAhjhN0KoEYAyGjjJQBh4PADgIz3SubSCYBkbv82BKA0AZB38XgXwMMsuHycAIiivYIFXQBFOIMAiDIsSXMlfJnoeb8MQOvvLAPQ+rm+9hvWudIrrDUvssU+/56TJGcnOgDQZA6AIi05b1X9cgsB6PC6AUiXhrnqrxUAlEldBro6NHgAMuaOWwAqtdfRxbsuM9DzgwDyxe/bAoBSrAxA/AomPwEIg8lABiC8Agin00Og/VoBZBzTLQAZ44JfIwDyPgiAKCgswYIugCKcQQBEGZakuRK+TGx5FwHQ3QiAfu1rv2GdK73CGgRA8/wbH8cVQMdDKrUQAVC0JeetKodzALTUk+OmUr93sQJoD4e50g/9fU9APnMW5NIzXPXXPAAlK47JA5AgiKBIIy3vmybvVAWA9gJBEEHKfcNbADJAwnjfePyxAyAZrfSxAyCMHgRA9QGQcSy3AFRg7gAIz4OBBkD1rACqAkB0C5iH8VgXwN9ZcPkYARBFewULugCKcAYBEGVYkuZK+DKx5W0TgJLr70MA1NgnXLnNsM6VuLASdGEn0IVdQBTu8G98HFcAnWQFICnWkvNWlQgHQMs8OW4FAAmbbVYAOQFQ6Rj9CyHVv9BVfxiA9FwJgHqXmmOsGxeUzQKQPMYKQMrQmgCUy14IemGQdwBkoIvGyrd2OQGQwkCXd3QAoCHNAZDEOhuAMGB4DUD4tiuvAAjjiRsA4ts3AkB8H40CkMSsdXq0AigjXhDo3xUCIA+TAIiizYIFXQBFOIMAiDIsSXMlfNm35R9lANrwFwRA1/nab1jnSlxYDiAwAIGBKMz3b3wcAEjKnhIQAMV8AqCFNgDkdgVQ/VkVgDQGujTIIwAaW76dSmWgyV01AUgQRBDFufUBkIJ+5gEox6wAxEMAtwJIk4bZAlAhe7AVgFR0fAxARp08AImsAoB0A4DwCpdaAGT00wgA8d/7BUDcaioL4Li5BQx/bwdA+OHLbgGITzcAhM+9XgAyzrEWANmtTKoFQHbP9bEDILuVS1UB6MJA/64QAHmYBEAUbRYs6AIowhkEQJRhSZor4cu+za8jAPqbCUCZ9b/ytd+wzpW4sBQB0F3+jY8jAJ3OAdDwlpy3qgznAGi5J8ftCABShxXBRjkQBGEtCEIJgLQyABXk/UBXhyAAOrYxAMqWjuknAKGLd18AqIA+Xt4NABn71gtAGFtyzIolbgAIAwMPINUAiMcd/Lpi0wYDjh8A1MgKILziiG+P6/QDgPjkAQgfqxYA4bYEQK2JR7sAHmLB5aMEQBTtFSzoAijCGQRAlGFJmivhy77Nr5gA1L/xEQRA1/jab1jnSlz4xASglODutqOGxic7wQGAzuQAaERLzltVRrQIgLaEEICGmq/39q4HQRBBkXgA2h/i8Q8dAKj8CWY1ASjN/F8BhC7eNWkXfwBIHWR/C1ieVQKQxPXjBoD4770AIIwRPAAVbPquBkASaw6AVGYd81oAhG/tyqAanQAIjxMGIL5OnPjZPvUAkMwq66kXgHLMPQDh1UEEQN4HARAFhSVY0AVQhDMIgCjDkjRXwpf9m19EAPQ4AqCrfe03rHMlLixBAPQH/8bHACCJQUb8Jaj54SDnjoNk/0OQSv4BAVB3S85bVUZyALTCk+O6A6Auz+aK/wC0AQTBCYA+Bl0eBKAwkNoSgPYGXR5crLn0ekE6xBsAMlb2mAA0pBKAMHjymILrDxqA7FYA8TXz+MCvoLEDoAJrPQDhepsFoEZXADUDQMY8MABIYgRAQQcBEAWFJVjQBVCEMwiAKMOSNFfCl/2bnkMA9CwCoKt87TescyUufIgA6B7/xse4BUxi0CtsglRyIaSSxRVHwQDQqI4AIDH1ayjk9i6CRn7H4nvau6z5W8AMABI2gSCIoEhHVACQICTN4yq5se5uATPAxw6AMFzwAGRc7OJn7tQAoL7EcyAIIujqzt4DUIZxALRT6wGIhw0MQBhU6gEgGf3cTgCU5c7XzQogvp0TAPGII3P78s8RMjJns2/bANBFgf5d6SgAeqQL4G8suHyEAIiivYIFXQBFOIMAiDIsSXMlfNm/6akyAG1+sQxA6670td+wzpWE8D4CoD/6Nz5oBVCvsNnyWip5t3nhokgjW3LeijLGCkC9Kz05Lg9AgtDjKwAJgghq9ivF4+YHgyB4AUBoBVDpvbIHoFT9AGSsnjEu6hsEIE3du3kAkqxj0TwADbIHION77hYws347AMLIUg2ADCAJMwDhFTbVAMhu9Y2XAMRjTS0AyjLr2LgBIPxe2qGORwCUJgDyLgiAKCgswYIugCKcQQBEGZakuRK+7N/0uAlAfZtfAX3Z4BIAXeFrv2GdKwnh3wiA/uTf+FgAaIvltVTyj6AXBoNeYFCQv9iS81aUsT4B0B9CBkCpiuNZAaj4XmXEa0CWJhSRwwQgEQHQGFsAymXPKwOQtIcVgDLMHQAZ7dsegHZwtwLIePi1EwDh8+EByNiOAch4zQmAjHOoBkAYZYz2+Ha9ZgCIH8daAMTN0ZYAUA61cQIgiXvdGJ9GAciuTmM8ml4B9P1A/64QAHmYBEChiS1btsDixYttX1uwYAHccccdsHHjxhZX5X2woAv4wQ9+4HlefPHFQZ9WxwcBEGVYkuZK+DK5cTECoNdBX7ZDCYB+4Wu/YZ0rCeE9BED3+zc+CIAEoce2TSY9GzLp2S0573zuAigo+4FW2AUKyqHmx503m3YApOSPLD6LxjcA+rLHALRzCXk+B4LQZ3lNV3YqvbYfCEJtAMpnzwk/ABkX+UEBkPE9xgwnAMLnhMeDAKg9AEjhjoWfM+QEQBh53K4AStEzgDyLxV0Af2XB5WICoHYPRVFgzpw50N3dDSNGjABJkiranHXWWRCLxaC7uxvmz58fQJXeBQu6gGg0CrFYzPOk8DcIgCjDkjRXwpfJjQ+VAWjLP0BftiPAMgbZdf/la79hnSsJ4R0TgJLCA/6NjwWAttq2wc8FakVm0rNBliZ5ik6VAFQ+Vz0/tARAe3o6V9TsYSUA2gEEoXkA0gs7FSFH3a3yNcXAoeIKIAMxlNxoWwCKxz8C3QSgvRsDoBq3gOkyg2T/g+0PQMZrdgCUZFYICQqAjNfsAIivwS8A4t8XLwHImEd2ACRzGWIAyqRoBZBnQQBEUSNmzZoFsVjMdInly5dXtDnxxBMhGo2abW666aYAKvUmWNAFPPXUU3DUUUeZg+5FEgD5HwRAlGFJmivhy+SGRSYAJba8DfqynUsA9HNf+w3rXEkIbyMAWuTf+GSPRgC0LfDzFoTi83py2ZmQSnmHTqnU3S0HoJw4E9RsBJTMkSAIIsR7l3sCQLq6S+VrdQJQ8XjMdwAyH0gdRgASGUAfq38FEH9LUaMAxD9zqBEAyjN3AIT7bAUA8Q/LrgeA8EOevQQgHnxqARD+SgDU+iAAoqgSr7zyiukH3/jGN+DVV1+FQqFQ0U7Xdfjwww9h2rRpZvt///vfAVTcfLCgCwAA2LhxI5xwwgkQjUahu7sb3njjjaBLoqgRBECUYUmaK+HL1Ib7EQC9C/qyoSUA+pmv/YZ1riSENxEA/c2/8bGsANoe+Hn7lcnkX0ArDHMAoF18ASA+rQDE6gag/r4nIJ+dCrns+RWv8QAkCOtAENaDIGx1D0AiAy2/izMAGbDiEwApmREWANKNccmy4FYAhRGA+O/dABCuK6wAZIxhjjUOQMa44Gf8GH3lS8fGuMMDEK7RFoDoIdCexcNdAItYcPlw+ABIFEVYsGABTJ06FY444ggYNWoUnHbaaTBv3jxIJpNV9xUEAW688UaYMmUKdHd3w/jx42HGjBnw1FNPtaj6+uKSSy6BaDQKp556KmSz2ZrtZVmGk08+GWKxGFx++eUtqND7YEEXYMS2bdvguOOOg2g0CuPGjYPt27cHXRJFlSAAogxL0lwJX6Y2/LEMQD3vg75sWAmAZvnab1jnSp/wDwRAf/dvfCwrgDoXgARBBFU+DAFQebVTSwGodFGtS0PqBiBBcL4drxKAyukagFIM1PSBgQGQmo2UAUgqQYwBKJ0MQBgvmgUgHmHyNsd2C0AyCx8AGUjTCAAZ+zcLQPztYejfTjpJAORZEADVFZ9++ilMnDgRIpGIbU6ePBnWr19vu+/atWvh6KOPdtz3Jz/5Caiq2uIzqh6TJk2CWCwGjz/+uOt9Hn74YYhGozB58mT/CvMxWNAF4Pjggw+gu7sbYrEY/OhHPwq6HIoqQQBEGZakuRK+TK2/2wSgeM9HoC3frQhAa3/qa79hnSt9whsIgB72b3wsACQEft5+pjMA+XMLGJ/x3uXmhaGe36khAHJKzwBIbFMA4m9TagSAePTBqIEByHhPMAApXDYKQBhQWglAjawAcgNAGe7YQQCQsb2dACjLvVZKsf/qQH8HdhQA/b0L4EEWXP49PAC0bds2GDduHEQiERg/fjwsXrwYenp6YM2aNXDnnXdCd3c3RCIROOOMMypuk0qn03DCCSeYSPTiiy9CIpGANWvWwPXXX28i0C233BLQ2dmHYQ9Llixxvc/7778P0WgURo4c6WNl/gULugA+FixYYN5X9/bbbwddDoVDEABRhiVproQvU+t/jwDoE9CW71ECoB/72m9Y50qf8BoCoEf8Gx8LAPUGft5+pip9pQYA7eXrXDEBKMdAyw+rBCAEJe0AQLonAJTyBoBEdMGPUcIrADJWs2TRrWduAQh/HxQA8cjSagCSbfrOMOv5BAFA/Lm7ASA85jwAGcfHAGT0wQOQUSvqJ9n/m0B/BxIAeZghAqCf/exnEIlE4Mgjj7R9EPLixYtNyHnttdcsr91zzz0QiURg5MiRtiuEbrvtNohEItDd3Q1btmzx7RzqjWOPPRZisRi8/vrrrvd58803IRqNwvjx4/0rzMdgQRfAhyRJMHnyZIhGo/Ctb30r6HIoHIIAiDIsSXMlfCmuX1AGoK3LQVveVQKgH/nab1jnSp/wiglA/cJj/o2PBYDigZ+3n6lKhwcMQCsQAO1aCQsIBuo9tm8AZFz41wIglYFeYKDKEe8ASBpsD0B4vFwAkKbsXB8AGT+7ASAePaoBEMaIdgOgAurTLQAZyNMoAOHtxvjXAiDj9ioFHcsJgGTuWBKrHCuvAQi/57YAdGugvwMJgDzMkACQIAgQi8UgEonAfffdZ9tGVVUYN24cjBw5EhYsWGBu13UdjjnmGIhEInDttdfa7pvL5czVRXfddZcv59BIzJgxA2KxGMyePdv1PjfccANEo1E477zzfKzMv2BBF2AXGzZsgHfffRfee++9oEuhcAgCIMqwJM2V8KW47rcIgFaBtnzPEgBd4mu/YZ0rfcLLCICe8G98skchAEoEft5+pipFEACVn3dUfgYQAVDDAJRnoOeKH3fvHQDt0NgKIJlZVwDJda4AMrbxAGRAQT0AlGeVtwM1A0AqqwQgldsXfxS91wBk4Eo9AGQ3Vngs6lkBxAOQ0wogvK1RAMIPwvYIgPr7/hzo78COAqCHugAeYMHlQ+EAoAcffBAikQiMHTsW8vm8Yzu715YuXWquDKq2kmbWrFkQiUTg7LPP9qJkT+Khhx6CaDQKI0aMgJdffrlm+7feesu8bezuu+9uQYXeBwu6AIpwBgEQZViS5kr4Ulx3qwlAvVvXgrZ8b4BlDHJrL/a137DOlX7hRQRAT/s3PhYA6gv8vP1MVYrVAKBdfZ0rrQAgTdm94jVPAEhingOQkj/KfN5OXQBkoEm7ABBezdIsAPFtmgEgA3q8BiBcGw9AeE67ASCcfgIQro3fj9+fXwHEAxB+9g/ehwCotUEA5CquuOIKiEQi8N3vfrfitVoPbn700UdNANq2bZtju4ULF5q3gbXLw6AzmQxMnjwZYrEYDB8+HGbPng3/93//B6JY/jcgiiK89957cO2110J3dzdEo1GYMGECpNPpACtvPFjQBVCEMwiAKMOSNFfCl+l1N5cBaNsG0JbvWwKg7/vab1jnSr/wPAKgZ/0bHwsA9Qd+3r6Oad9jkE7dBGLyt5Zz7RWWQzb1IyhIl/k6V/wEoHj8fcimL4JsuvKWSt8BqHRRrOeLdVcAkMxsAUgQRNCVIeFfAdQKAMLb6gUgmXvdDQDhMWo1AOFVOY0AEN5eDwDlWGMAZGAPHusct40AyPsgAHIVZ511FkQiEbjuuusAAOCll16CCy+8EMaMGQPRaBS+/vWvw8033wz9/f0V+95xxx3m8390XXfs48knnzShaPPmzX6dSt3x0UcfwejRo83nEBs5YsQIGDFihGVbNBqF0aNHw7/+9a+gy244WNAFUIQzCIAow5I0V8KX6bVzEQBtBm35fiUA8vdjccM6V/qFZxEAPe/f+AwgAKqWqf6FoEp/9HWu+AlAguD8EfGaultjAJQbBAV5P3sASpcu8NE5SJmpxeOGBYBkhD12AFQaE98AyBhPvwGIr9cLAMLIYwdABhI2swJIZY0DEF8P37eXAMRjDwFQa+JvXQB/YcHl36wAlEql6k5JknwfpkmTJkEkEoF58+bBVVddVfVj4NesWWPZ1/iUrwkTJlTt49VXXzWPs2zZMj9Pp+5Ys2YNnH/++RCNRqvmN7/5TdsHZIcpWNAF1BOFQgG2bdsGq1atAlmWAQBA07SAqxqYQQBEGZakuRK+TK+dYwKQsH0bFJYfUAKg7/nab1jnSr/wNAKgF/0bHwsAJQM/7yDT77kS713pKwA5ZX0rgD4PenpwEYDEocXnJuEL4aABCF9Q8xfmrQAg/HDnagBkvKd+AZCMjl8LgPjXje15Zh0r3Fc7+nX9ywAAIABJREFUApDxmh0A4fFzC0A8AuWYdfwJgNo72gyAjjzySEdcccr58+e7OtX58+c3fOyxY8dCJBIxIei73/0ufPTRRyDLMmzfvh3uuece82Pgp0yZAtls1uzXAKPjjz++an1vvfWW2e+HH35Y5xvZmlizZg3cc889cPXVV8Mll1wC3//+9+Hyyy+H3//+9/DRRx8FXZ4nwYIuwE3885//hIsvvhjGjh1rLr8y5O2NN96Ak046CR544AEoFAoBVzpwggCIMixJcyV8mVn7KwRAcSisOKgIQJ99x9d+wzpX+oUnTQDqE172rR8lOx4BUH0fPd5pGQgAYQRqCwD6IujpISUAGmYFIAMVDADKMfcAhM6vKQAy4MYJgNIMNHlw6wHI+Jm/RcoJgPDKGzcAhGHFDoAwhvAPMuYByKizFgDxq614AJKZtwCUtXnNTwCy67/aCqAcGrd6Aah0zHjcv9/lbpIAyMMMCQAZnwAWiUTg4osvtr2ufv755802f/rTn8zt11xzTccA0EAIFnQB1UJVVbjqqqss99wZ9+YZALRo0SJz2/Tp0yGZTAZctbu49NJLIRKJuLp/cPny5fDLX/4SpkyZAqNGjYIxY8bAKaecAnPmzIGNGze2oNrKIACiDEvSXAlfZj67GgFQPxRWHFwCoAt97Tesc6VfeBwB0Cu+9WMFoODPO8gMHICU4kW4kv+qp/3WBKD8XmUASh3iHoDyrEEAKj/TqlEA0vJDyhfYGIBEBmr+kGBXANUCIP7Wq0YACGOGEwDJrLUAhNEE12GHMPytXW4BCG/zGoBq3QLmBoAUm23G+KkM4vF3Av0d11EA9NcugPtZcPnX1gFQIpGANWvW1JWJRAIAyiuAIpFIxS1eOKZNmwaRSASmT59ubps7dy5EIhGYOHFi1frwLWBhv40qzMGCLqBaXHvttSb6fO1rX4Mf/vCHFQD00EMPwfDhw83tM2bMCLjq2rFo0SJz8tcCoD//+c8wfPhwx18IY8aMgZdeeqlFlZeDAIgyLElzJXyZ+exKBEApKKz4PMAyBvnPvu1rv2GdK0nhUQRAr/rWDwFQ6+ZKTQAqXbDq+aGe9lsbgPb2F4AwJrgBoDwDMXkL5MTzagOQcUHuIwDpGAzaEYDs0McOgDCk2AGQyqyg4icANbsCiD8+D0BGDdUAiK/BawDi33MCIG+jzQCo3Z8BVAtxDOz52te+Zm5bsGABRCIRGD16dNV9n3jiCVefFhZkKIoCyWQSEokExOPxmhnGYEEX4BQffvihiTqXX365+TFrPAABAKxfvx7OPfdc87XnnnsuqLJrxuLFiyEajboCoFdeecVsN23aNHjjjTcgkUjAunXrYPHixTBx4kTzietLly5t4VkQAFGGJ2muhC8za34BsISBvqT4UdGFFV8sAdC3fO03rHMlKSxGAPS6b/0YAKQTAA1gAPJ5BRB3EYwBSJN3BV0ZDEq+GwHQYBAEEcT+W70FIIm1BoByDHR5kDMAyaXVNtUACKNHMwBkAIYHAKTbAZAd0DQKQDmb1/wCILux9gKAZLQNv0YA5H20GQC1a0yfPt3VbVzGJ36NGjXK3PbMM8+Y16zGiiK7aMePgQcoos+9994Lp512muUTv2rl8OHDgy69oWBBF+AUV155JUSjUZg2bZrlQc92AAQAkM/n4eSTT4ZYLAaXXHJJq8utGbIsww033FCxgqcaAJ166qkQiUTgzDPPhHw+X/H61q1bYfz48RCJROCHP/yhn+VXBAEQZViS5kr4MrvmshIA7QyCIEJhxaElADrf137DOleSwsMIgP7Xt34IgFo3V9wD0M6e9msCkDrMst0egA5tHoBygwHyDAq5YVALgHD6AUCaumv9K4CMfpoCoMGuAEivF4AU5j0Ald4XTdnR2p/xmgF6xnuB29h9ShcGoGoIwwOQ3WtuAIgfvzzXt93x+dVWeC5JzApAxj45lARAwcdfuwD+zILLkADQddddZ+JMtRVHv/rVryASicCJJ55obluxYoV5bfvmm2867jtr1ixzYUO7hK7r8L3vfa/ikTNuMhaLBV1+Q8GCLsApTjzxRIjFYvDII49YtjsBEEDxdrBoNAqTJk1qVZmu4uWXX4YpU6ZYVvPUAqDVq1ebbZ5//nnHY99+++3mP1ZFUfw6hYogAKIMS9JcCV9m18wqAtDHu4IgYAA6z9d+wzpXksJDCID+6Vs/BECtmyvx3lUuAWgHb+dS/19Ayp0M+ezZlu0GAKn5z5vYoSTHegBAQwCyDAqZ/SBoAIr3fmwFIA596gIg/F55AUBqcdWN7hcAYcThV8TkKvtSckfUB0AGilQDoHpWANUDQBh5jO15LqsBEL8/D0B4rBoBIP41AiDvgwDIVbz22mvmtafT3TS6rpvXtJdffrll++TJkyESicCcOXNs983lcjBu3DiIRIofNd8u8dhjj5m+EIvFYOrUqTBr1iy48sorYfbs2TUzjMGCLsApRo8eDbFYrOLj1qoB0Pvvvw/RaBS6u7tbVWbNSKVS5j+msWPHwqJFi+Cdd96pCUCvv/46fPWrX4VIJAKbN292PP4jjzxiHmv79u1+nUZFEABRhiVproQvs6svBVjCQPt4DxAEEdQVh5UA6Bxf+w3rXEkKfzUBKCG85Vs/SnYcAZDQmrkSFAAJggip5EJIJRdatpkAJB9mYoecPAoB0E6VAKQyUKWDbQEo7zcApcsX8xUAlGMIgA4HXR0MujoY4r2fWACoIO0Pam5/bwCIR4pGAYi/hckNABnJAxC+TQuPO78qh8cOmYHsNwApzB55MJoo3PZaAIRX29QDQPxHvlcDIP4WMB6A8Hsgoz7w+BAAeR+LugD+xILLReEAIFVV4etf/zpEIhE44YQToK+vr6LNgw8+aF53/vOf/7S8Nm/ePPM5QKtWrarY97bbbjMXLbTT838uvPBC83nDnfIx77WCBV2AUxx55JEQi8Xg/ffft2yvBkBvv/02RKNRGDduXKvKrBmpVAqGDx8OV1xxBWzZsgUAwBUAGZHJZCy3wPFh/GOLRCK2t4n5FQRAlGFJmivhy9zqH5YAqPgcEnXlV4oAtOabvvbb6rmS7LkfpI0ng7TxVOjdtqrx4wgPIgD6P9/qJQBq3VyxAtBuLQUgu3QEoMwggDSDgrhfEYC4i2FVOiQYABLLF/PVACjbf7H1PC0AdCBkUj/2HoAk9L2XAGS3GsUJgPDtR24ByLICaGx9AJRj5Y+kx8hSC4Dkyr4tACShfnG7VgOQ0V5FfbgBION7AiB/gwDIdbzxxhvms2qnTJkCzz33HAiCABs2bIDbb7/d/Kj4H//4xxX7ptNp80HSEyZMgCeffBLi8TisXbvW8hiUm2++OYAzc47x48dDLBaDe++9N+hSWhYs6AKc4swzz4RYLAb333+/ZXs1ALrlllsgGo3C1KlTW1VmzVAUBTZt2mTZVg8AVQtJkuC4444L5F5KAiDKsCTNlfBlbvX3iwD0yb4gCCKoKyMlADrb1369mCu92zdDfNtySG+cA7n13wRx81wQtgv257nxXIB1DGAdg8TWxm/dSgp/QQD0rm/jQwDk7VypltZnALUxAGUHAWQYqOLnqgNQjoUSgPoTj3sPQFzWBKDSe1/Id/kDQHibio5XLwAp5a8WADKO4QaA8KeZuQUgvt6gVwDlUBv+E77s3gMCIP+DAKiuePLJJ2HUqFGOn0B94YUXQjKZtN13yZIl5m1edvnTn/606sKGIMK46+iDDz4IupSWBQu6AKcwMOekk06yrGxxAqCVK1fCmDFjIBaLtZ0s8uEVAP3mN78xj/Poo496WGHtIACiDEvSXAlf5lZ9B2AJg8InB4IgiKCujAIsYyCtOcvXfr2YK+kN1wGsYACrGMBnxezvsb+IzW882wSg+NbGV+6khD8jAPqXb+OTT58LWn530PJdgc+RoLOlAJTbrYQ9Q9oTgLIMFLEbBGErZFKXVgKQ8UyeAQJAum8AtJ89AEk2XxtdAcQDTaMAZPTZCgDCK6rw+fMAZLz31QCIr8dLALJbCYS3EQD5Fw92AdzHgssHwwVAAAAbN26EOXPmwJQpU2DUqFEwfvx4uOCCC+CRRx6p+eldvb29MHfuXJgyZQqMHDkSxo4dC9OnT4fFixe3Hf4AAEyZMgVisRi88847QZfSsmBBF+AUW7duNUXuoosugt7eXgCoBCBN0+Dpp5+Go48+2nz+D7/ippmYP3++o2I65fz586se0wsA+vvf/24e4/zzz4dCodDQcRoNWVYDTU3TAQBA0/TAa6Fs76S5Er4srP9O8SHQS79QfA9XdwMsY6BtONvXfr2YK+r2XxcBaCUzAUhJvWjf39azTACSMx833mf6AROA5Nyngb9/AyF9/70ibSgDgbECyLgAxQAkDWnJ+ermJ2V92cQOLTPRBCAtOwpkWQU1f4XlYlhXDgXoYxUAVJCL/5b1fBGA9Nz+IMtKBQAp8iv245+PmgAkyyqo0h3OACQd5ghAau5H1vOUh5ThRT4IZOnZ+gAotWN1AMoXcUTLxsrvr7KDKwDSpf3tHwJdDYByrD4AwreoVQEgWZ4HqvyjqgCk2wEQv8rGDlAkVh2AjOPxAITb1wIgfnu9AGSMZyMAxPfpAECy/GHgv+c6JgiAKKrE9ddfD7FYDG6//fagS2lZsKALqBaLFy82wae7uxumTp1q/nzxxRfDRRddBEcddZT5kW2xWAzuu+8+T2toRwB6+OGHzXswJ02aBFu3bm309CgoKCjaLzZcCLCEASw7tPjzZ6MAljGATe3zsaGOEa8EIMi+aN922xkmAIG8tPE+8+VnAEFhdePHoWif0LagC8hjAeSzAeRvVAAQSENaVM8ZANqJANosEzsgOwmgBECQH11sp8y2XgyrXyoCEHcLGKjfLLYvARDkDgDQ9QoAAu01+3qkWOlCenDxZ3VBJQAZx1DGA0gH2wIQSNxzLBAAgXwQQOEF68oTDoDMi3djTFI7Vr8FzEhNKL+/SvUVQGWY2N/6c55Z0cIOgIwxcXsLmEsAAvVpAO1R64OYEQCZq4+qARBfOw9AfG38Chy/AMgYW/xAZy8AyGnVkQMAgfaJP/+WB2IQAFFUiXXr1sGYMWNg7NixsGLFiqDLaUmwoAuoFY899hiMHTsWotGo5SPajDS2jxgxwpeHNyUSCVizZk1dmUgkqh6zGQBauHChue/RRx8NK1eubOb0Go6g/1eCVnVQuk2aK+HLwrrziyuAln2l+B6uGV1cAbT+TF/79WQF0LbrK1cAJZ+2P8+eU0wAUjIf1NWPvnVf0HuGgLa9Gwr9F5VXAOVXBv7+DYT0fwXQevMCspA/HVT5j6DKC0BXdg9kBVA5l5agg4EuHuBiBdBhtgDk/Qqg3zkCkKYcU9wnt3eTK4AG268AQgBUcwWQXPwod1nuQSuA3N0CZrsCqBkAMsYCY4vrFUBPgiL/3YqReAWQ4mIFEB4bvwAowyoBCH81UuLSOEaWNQZABujg97NOAKIVQB7GA10Af2TB5QMEQO0eL730EowaNQqOOOIIuO222+Dtt9+GjRs3Qjwer5lhDBZ0AW5i+/btMG/ePJg6dSoMHz7cRJ9oNAonn3wyzJkzB9avXx90ma6jEQBSVRWuvvpqc79jjz0W1qxZ43OlzkHPAKIMS9JcCV/mV54NsISB+mkEBEEEZeXo0jOATvW1Xy/mSmbD1RUAlOx52LattGGyCUB9dT4EWu/ZCaCHgbZ1KMjbjwBV+DJIwinQK2wO/P0bCOn375Xe3hUmEEjiqWj7au4ZQENaet6J+PsmAGniUO4ZQCJkUpdZLoYL0qG2ANSSZwAZF9L5iSAIIhTSe1UAUD3PANLy3gFQr7CmbgCyfQaQFwBkJH9bVhUA6u97GJL9D1YHoMyg2gBUeo/0WgCEbjPT6wEg47xrARC/Csjo2w0A8XiDAchuBVCOuQIgegaQh0EARFElzjrrLDjrrLNg/PjxtotNquXw4cODLr+hYEEXUG8UCgVIJBIgCALIshx0OQ1FvQCUTqfhoosuMvc57bTToKenpwWVOgcBEGVYkuZK+FJaeWYRgJYOB0EQQVk5tghAq//D1349AaD1s4sAtIKVAWjL3+z72zARAdAbdfWj9wwB2MoAtjEobNsPcsJMSAkLA3/vBkr6D0BroZDdDwrZ/SCb/BnavqbNAejnzgCEECGXvgAEobUApKYrVwC1AwBpuWGQTc8s1pZhNQBoiBUUpMrjewpA+HYpJwCSUJ+lr0UAGlwcI3mQewDCY4drwuMhc/0Zx8TtgwAguxVAPADlmPW9IABqTRAAUVQJvLCk3ozFYkGX31CwoAsYiFEPAKVSKZg2bZrlo/dSqVSLKnUOAiDKsCTNlfCltOL0EgCNAkEQQVl1RAmATva1X28A6EobAFpk21bZMB4B0Kt19aP37GACkLrty4G/ZwMtg/q90v4A9F/VAaiUOfE7IAgdtAIoXUxNHFreVgWABKEH9BIQqJkvgpi8tQxAGAgqAGhQ6wAIw4obAJLLx9JzGIAGuwMgY4VPtRVATgAksfoBiF+ZUw2AVFSzVwDktJKLAMj7+EsXwL0suPwLAVA7x5VXXgmzZ89uOMMYLOgCBmK4BSBJkmD69Olm28suu6xtVj0RAFGGJWmuhC+lFacALGGgLB0LgiCCsupIgGUM5NUn+dqvNwB0BcAKBroFgP5i21bZMAYB0Mt19aP3DEIA9JXA37OBlsEB0GcEQEJjACSJ4/wDoEwx9fRO5U8GqwpAoglASvpIEIQ4aLmdywCUQ+39BCCjL4fbrTwDIKOGTgYgu1vAMPIY+GO3rx0A9f5foL/jCIA8TAIgijYLFnQB1113ned5/fXXB31aVcMtAN1www1muyuuuAI0TWthldWDAIgyLElzJXwprzipCEDLjgRBEEFZ9bUSAJ3gb7+eANAvSgA0yASg1JY/Wdr0bX0Fkj1/A2XDcBOA+re+4L6f7UmAHoYA6PDA37OBlsEB0NoOAaDvgiDwACSaF8YFuQuk3IkQ7/3Etg43AKSrg0FXB4GUK8KxJE5qKwDSsruDlh0GebH4PCQtP6w+AMK44AUA4ePaARA6j77EYgIg3HeWWQEI9+EWgNA2XSquEgvydxwBkIdJAETRZsGCLqDehy25zXYONwC0YsUKiEaj5jN/EokEZDKZqqnresvOgQCIMixJcyV8KS8/vgRAR4EgiKCsGl8CoOP97ddTANoBAdC9ljbShlMB1jIorNsTAZD9Sgfb3L6VA6BI4O/ZQMuBCkDx+IegGwCUcgNAh4DeN6g+AMozkLMTqtbhBoB6hdWWfVoKQBIDTRlSFYD4bAiAMGBIHI7UC0B4VZEdAKFbxvoSj0I8/gFo8p6NA5DE/AMgvI2/5a0aAMncsbj3wqyDByC8AohfnYVvB3MCIG6fIgAF+zuuowDo/i6Ae1hweT8BEEV7BQu6gGYevBTWBzK5ASD8iV9uc9OmTS07BwIgyrAkzZXwpbz8WIAlDOTlxVs3lFVHlwDo6/7268Fcya6/vARAQxAA/cHSRtpwMsBaBuq6/REAPWl7vNS2hZDbOpPL8zgAigb+ng20DA6A1gULQL1L0AqgnZ0BSGag5g4AQVhb/wqgDgEgJRsBOTvRYwAaUrlqJF+uJ9n3AGjSLiYAFfK7+QZAgiCCkh/THACZ5zjYHwDCuIMBiP94dr4u41jG2BhjIaL3vxYA4VoIgIINAiCKKvHee+81lWEMFnQBmzdv9iXbOdwA0Omnn04AVCXpop7SbdJcCV/KyyeWAOg4EAQRlFUTSgB0nL/9egJAPy8B0E4IgH5v7WfDCSUAipUBqOcxS5v+rc+BvOkYKGw5BJStR4K8dRLKCaD1DEUAFAv8PRtoOVABKNH7DxOAdHFwDQA6GAQh5QBA3wNBaDUATfQPgGweAq1kjoT+vke8BaC8zQqgLAM9y6Av8RIIQg9I2ZNMAEol7mxvADLBY+f6AAjf5mW8780CkNEHBh3j+2oAlGcWANIJgNov/twFcDcLLv9MANTO0czdSPQx8BQDKgiAKMOSNFfCl8qy8SUAKt7yJa+aVAKgY33t1xsA+lkJgHY2AUjcvMDaz4avlwBopAlAyZ6HLW1SW/8MsJEBbGKQ33pmxSogrWdfBEDDA3/PBloSANkDkCCsMW/nUbMxqAlA2V0Asgy0zN4gCG0OQLlhNQFIF3dqPQDlGOi5QeXzbEcAMs6LGwujTi0/zBsAwmPjBoByzFsAkhsAIIxRBED+BAEQRZXo5LuOnIIFXQBFOIMAiDIsSXMlfKks+2oRgFYUH94qrz62CECrJvnarzcA9J8lANoFAdDvrP2sPwZgLQNl3RgEQA9Z2qS23lsEoI0MEtverxyjnnEIgEYE/p4NtAwOgNa3OQCJoJcuchXxq1ALgAqZ/UorgA4AQWg/AErE/xcK+f2hkN8f1EzMBoAGBQtAJVxoPQA9BYLQPAAV8nuBIPSAlt/LHoDw83p4ADKABgMQDzr1AJCMjmkHQMbcMs7TLQChcXEEIIURAPkZBEAUVeK+++6rmvfeey/MmzcPrrjiCjjmmGMgGo3CySefDO+//z6sWrUq6PIbChZ0AW7j008/hbvvvhsuv/xy+P73vw+XXnopXHXVVbBo0SJYv3590OUNuCAAogxL0lwJXypLxwIsYSCt+I/ie7h6MsAyBsqq6heFzaY3APTTEgANQwB0u/X81k8oAtD6IxEALbK0SW39gwlA8W1LKseoZzwCoO6G66UMbq40lttByp5ehIq2BaBBoGcZyOJ4EASxAoD0PIO++BultkM9WQEkCEkQhF6QUlMcASgvTihebGeYawCy7J86qxKAcoOgkNsP9PTgzgKgUmryUPcAhF7HAKTldwXdEYD2KZ17FQDi9vMdgAywkbk2xrxxAiADb5wAKMvsASjDCID8jj91AfyBBZd/IgDqlJBlGebOnQvRaBQuuOCCln4Ak5fBgi6gVixZsgTOOeecmvffXXrppbBt27agyx0wQQBEGZakuRK+VJeOLAHQ6cX3cPXXSwB0lK/9egJA634CsIKBtmJXBED/Y2mjrB9XAqDxJgCleu63tBF77ioD0PZPK/qxAtDIwN+zgZZB/17RpN1LF5s7trRfNwDEpwlA5sXtYPM17wComFLqlCorgCaYK3UaASApdXolAGV3KtaTOrwEQMPaFoB0OwDKseoAJO3WNACpYhS0egEIrzxqdAUQBp9WAJDxIPBGAIh7TwmAPA4CIAqPY+bMmRCLxeChhx4KupSGggVdQLV44YUXoLu7G2KxmOV+u+7ubuju7q64B2/ChAmwevXqoMseEEEARBmWpLkSvlSXDi8C0Mqpxfdw9fElABrva7/eANCPiwC0cncTgNKbf2Npo6z/KsBaBvL6iQiA7rO0EXvuQAC0oqIfpecoAqAAM+jfK5q0BwFQGwFQIXVwCYB2bCsASoYUgAr5g5oHIANd+NuwDMwhAKqaBEAeJgFQx8Vzzz0H0WgUpk+fHnQpDQULugCn6OnpgTFjxpi4M3v2bHj33XchnU6bbZLJJPzjH/+AWbNmmRB0/PHHQy6XC7DygREEQJRhSZor4Uv108MBljDIrzy7+B6uPrEEQF/ztV9vAOhHJQDaAwHQXEsbZf2YEgAdiwDoHksbsWeeCUC929dU9KP0HI0AaFTg79lAy6B/r7QfADkjJAEQCwyAUon5DQOQLrHiSjMXAKRLpePXAqA8qw1AMoN85pugewBAOu7TuP3PDoBkRgDEZUcB0H1dAAtZcHkfAVCnxUcffQTRaBTGjRsXdCkNBQu6AKe46aabzNU+r732Ws32jz/+uHlL2N13392CCgd2EABRhiVproQvC59+qQRA54IgiCCtnlICoK/62q83AHRJCYC6ygC0aY6ljbp+JMBaBtL6E8oAtGWhpY3Y8z8IgNZV9IMBSNk2OvD3bKBl0L9XCIA6EIByQ62Q0WoA4jBEEBKg5r9cFYDk3Lgi2ig7FGurBkAZ5hqABEEENX1Y0w+B1vE51QIgiVnRR2LuAMjouxYApbkaCIBaFwRAFB7HM888A9FoFMaOHRt0KQ0FC7oApzjllFMgFovBjTfe6Hqf2bNnQzQahbPOOsvHyigACIAow5M0V8KXhU8PKQLQqgtAEESQVp9cAqAjfO3Xi7mSW3dxCYD2RAB0naWNuj5WAqApJgCJW+60tEn3/AYB0MaKfpSeCQiAxgT+ng20DPr3Cj0DyF8A0uXBIOcmcMf+hr8AJO0KIDPQ8kP9ASBpsOcApOTGmA/2rglAadYcAOVZsABkzBsMQPnyXKgKQMb+BEDBBAEQhYeRTqfhzDPPhGg0ClOnTg26nIaCBV2AU4wePRpisRi8++67rvd58803IRqNwhFHHOFjZRQABECU4UmaK+HLwicHAyxhkFs1AwRBBGn1fxQBaOVYX/v1BoB+UAKgvU0Aymy6xtJGXXc4wFoG+Q2nIgC6w9Im3XMjAqAtlbX2TEQA5O+4UPozV5pJAiB/AUjNf6miz7oAKM9AzoyrE4B2KwHQMEcASvXfAVquqwwP+f/P3pnHOVHf/3/U1l7fdq09fr2P77fNzLIcKzeCKH5VhEIrhyIKq6CCCKgc4qKiolbxpqDFe/kqVqXKoaAg1opVxAUWlmvv+ySbzebaZJNN8vr9kexsQo6dhEw+88m8X4/H80E7O5O8J3k5uE/nEPgSQDZBPQHUI1BCuxZNAPWcrRNNAAXlCwmgDBNAr2QBfxfY8QoJIC1n586dinj33Xfxt7/9DZdeeql8i5rnnnuO9fhJRWA9QKyMGDECkiTh4MGDircpKiri+no8nkICiOAF6gp/eI/9LCiAboTRaENX5cSgAFL3UqfUCKCbggLoRyECaGX4/tX8PiiAJocIoPBHxdubH5QFkPHUqchZm0eHCCB1z4wi1OnKmaB3AWQ27YK14wVYzAWbJTmRAAAgAElEQVRhy9UUQE5rXkA+9CWAgiLAY0v0DKBYAuhsWQAZjTZ4nb/sFQqycNGZAHIG/1QqgLqC9wOKJoBCb1Adei+jMxVAITOTAGIcEkCUOOmROYkgiiIuvvjisHsT8xSB9QCxMn/+fEiShLVr1yre5rXXXoMoisjLy1NxMgpAAojgB+oKf/iO/TgggCpugtFoQ1fln4CTArrL1H3aVWoE0NygAPpJiABaEbaOt+a3QQE0NUQAhT8q3tG8KkQAtUfOSgKIKayPK+wE0F5NCKBYqCmAAp/7j5ITQCFPwGImgFzfALqEwL2GFAugP6gqgDzOQG/6FEAhZ9X0SDi/+2zlAsgVRQC5BHUEUIh0IgGkkbycBTwvsONlEkBaTuhTxZUye/Zs1NbWsh496QisB4iVwsJCSJKE3NxcHD9+vM/1m5ubMWrUKEiShH/9619pmFDfIQFE8AJ1hT98R88LCqB5MBpt6KqcHBRA/VR939QIoDlAqQBv2f+Dv+rsoABaHraOt+aXQLUAZ901sgCyN4U/Kt7RvDJEAFkiZ20eEyKABjP/zvQG6+MKTwLIz5EA8rgGwuv6DVyOq2N87mkUQF0C/F3nwN91TkICqN30OawdzwVofzlEAJ0bFEDfiymA/KcLIGfyAshnPzf4OQyKKYC6OicE9j2eAOq5v08yAsglxBdAPTenTqEActv6R5y1JG/vEkgAsQgJIEqcbNmypU+2bt2K999/H5999hmam5tZj3zG0awAAoCCggL5nj4FBQWw2SIPRh6PB7t27cKYMWMgSRKeeeYZBpPqLySACF6grvCH7+gPgGIBnZW3wWi0oavyL0EBlK3q+6ZGAN0QFEA/g7/qm0CVgM6GpWHreGt+FhRA14cIoEfC1nE0rQDqBfjrz4o+a8tFIQJoCPPvTG+wPq6wewpYZgugvj/3MxBAnQKc1lmwmN+K+to2y1Nw2JbBaZshfw52y72Bm0InIIBCsba/lqAAOid5AeT8ZvgZQPazAUfPGUBn9S2AgoLMF+xHmADqETxBAWQxvyFL0LQLIGfI+mcigHrmJQGkfkgAUShhYS6ABg4cGJee6+wkSUK/fv0wceJEzJo1C7NmzcLkyZMxaNAgeZ3c3Fxcd911uP7661nvVsaHBBDBC9QVjXGqHcZTFlhrN8BZnhcVf/E3ggJoMYxGG1yVU4ICSNR8V5w1eUEB9Av4q74dEED1d4St46v5cVAA3dArgBpXh63T2bQsKIC+EX3WlrEhAmgo++9VZ7A+rmjvMfAKBJArICrsll7ZmekCyGQsg7drnLy+vePRPt/Dan6OTwF0+iVgyQigrsAZUEZjfAFkNNrgs/cKIH86BZBL6EMAiRGXrcUVQKGvRQJInbyUBTwnsOMlEkAUbYW5AErmurtYhMoiirohAUTwAnVFW9grHwCKBPgPnwP3yVFwnxwdyYkL4ahcAeMpM4xGG1yVU4MC6I+a74qzelZAAJX/Ev6q7wUF0KKwdXzVPwwIoPqb4K/5Jvw134SnbjCcjXmwNq2FqfUoOpvuCAqgb0efNUwADWP+veoN1scV7Qmg2Pfn8pt7f0nutoefxefLcAFkNNrQ3fUMxwLoj4kJIJcAr/N8WMxvhAugznMC26otgFzBe/CcqQAKvUQrUQFk/0XfAshCAiitIQFEAbB161Zs3boVVquV9SjMw1wA9ZzNk2oo6oYEEMEL1BVt4ai4KyiAzoKzbHbMs4CstRvkbVyV04IC6A+a74qr+vqgAPo1fFU/CAqgBWHr+Kp/AFQL6GwILHc05cNdPxreup8CdQK66/+IzqZFQL0AX/1/RZ+15eIQATSc+feqN1gfV0gAxaIVRmN1kPB7Z7ESQO6uDsB3Eu6uYzAam/t8D20JoASfAuYS4HEEvuNQAdRlu/DMBVDPfZGCnY8tgM5JTgD1rBeyTVQBdLq0iTgD6BdhrxchgOwkgNIeEkAU9D7tq6SkhPUozMNcAFH4DAkggheoK9qis3whUCTAdzi62IiGq+rqgAAq/W9VZ0uNAJoZFEC/ga/qPKBKgLN+Xtg6/urvBQVQ4BI3a/MGOBvz4KnPAeoE+GvORlf9xUEBlBV91pZLQgTQCObfq95gfVzJPAH0PRiNqRBAsWEmgBLsyukCqKP9XVg7XoW5fUfgvVQUQL7Oc4L7JcBotMLc/hE8neIZCyCnZWaEAPK7z4al483A5xpLADl7P2tYA+Kk2xq4FFi7Aui38QWQjQRQ2vNiFrBeYMeLJIC0ECUCqLq6GldddRWmTJmSxsnSHxJAlKRCAojgBeqKtnCWzQkIoCM/VbyNq2oGcFKAt/R3qs6WGgF0bVAA/Ra+6h8FBdBNYev4q78VFEB3hi03t/4Hrrpr4K3+Jby15wcF0PnRZ20ZFyKARjL/XvUG6+NKpgggi/k1dFkvh8syFUYjCSCjMVIAnf7zlAmgoJjwdv6w93WCl2oFBFCw6/axMQWQxbwRDtvtcNiWJSGAvtX7uaopgOR9CxFAPesnI4B6ZFKPtOl5PVkA9Y8UQE4SQExDAogCZQKopKREF7eTIQFESSokgAheoK5oC1fpDKBIgLdYucxxVV0bFEC/VXW21AigGUEB9Dv4qn8aFEBzwtbxV38DqI58PLzRaIOrbgZQJcBf+62gAPpJ9FnDBFDqf1km1O/KmeB1/Sz4C+65aX3fVAsgo9EGq3kDrObAJZ9qCiBHx51nLIDM7R+jy/G/qgogi7kAftc34Hd9A7aOxyO/+1AB1NkjN86O+lrRBdD34ev6jixiTG2FSQugsO9ZkwLorNQLIJsAr/38cGkTTwB1nfaeJIDSHxJAFJAACg0XAsjn86G9vR0tLS1oamqKoL6+HpWVlTh8+DA2b96MuXPnsh4540MCiOAF6oq26Do5GSgS0H20n+JtXFXXBQXQb1SdLTUC6OqgAPo9vNU/DwqgvLB1UC0EBdCKiO2d9bcEBdC5QL0Ab/3Po88aIoDcrRcy/171Buvjis+ZpbEzgOI9BeysuAIolG6nhG6nBIctUo6eKTbzY2csgIxGG5y2OaoKoL6ILoASOAPIlQV350UkgEIEkNfx/agCyJ+UAMpJTgCFQAIoxXkhC1gnsOMFEkBaCAmg3mhaAJ06dQpLly6VH/WeCBR1QwKI4AXqirZwn7gUKBLgOa780eXOqllBAfQrdWdLiQCaDpQK6C7/H3irfwVUCXDVXd+7zilLrwBqvCdie0fD8qAAEoIC6NfRZ225NEQAjWb+veoN1scVj2Mw/K7vwOtUfillKkj6MfAKBZCaqCmAujt+D1gF+Czf50IA2S0Pw+McDo9zOExtXzMXQEZjK7zOH0UXQD3yRA0BFPxsvI5fhgsgZ/C+Rl1n9V5KdroAChU8jl5c1uHhj3p3BZ9MRgKIXUgAUUACKDSaFUBOpxNXXHGF/Gj3RMjNzWU9fsaHBBDBC9QVbeE5PgooEuA+cbHibZxVeUEB9AtVZ0uNAJoaFEB/gLf6t0EBdG3vOqdMIQJoVcT29saHgSoB6BFADdEvlXO3/G+IABrD/HvVG1o4rjis+XBY89P6niSAYpwB1PGLwGdiOZcLARS6jqmtKOQm0N9XRQB1mmf1fu5RBZANXueP4wsgSwCPZRCMxjQIIKcAn+v8wKxKBFDwteyWeeECyBldAPntwTOMSACpHxJAFJAACo1mBdDGjRvlL2Do0KG47bbbcP/990MURfTr1w+rV69Gfn4+8vLy0L9/f3ndV155BQ6Hg/X4GR8SQAQvUFe0hefYIKBIQNfJKxVv46y6ISiAfqbqbKkVQAZ4q38fFEBX965z6pQsgOyNqyO2tzX+LVwA1Uc/w4MEEFu0cFyxdmyAtWNDWt+zvW0vCSAtCSC7gE7LbJiMRdE7kqgAcpwfIYCs5g29l0UlKYCs7c+FCSC/W4DP/YPT9kuZAOq2GGA0hgggd2ICyK+GAAp5f7tlSa8AcgqxBZBFgN91NgmgdGRDFvA3gR0bSABpISSAeqNZAXTjjTdCFEWMGjUKra2t8vLJkydDkiQUFxfLyxoaGjB16lSIoogJEybA7XazGFlXIQFE8AJ1RVt0H/0DUCTAVTJV8TbO6jkBAVTy/1SdLRVd6aq6KiiARHRX/yEogHr3te1UU4gAeiRi+7bWSphb/g1Xw3Xw1A9CZ+NN0WdtuSxEAF3E/HvVG3o9rsQWQMnfBDpdpEoAmUwHAmfKdArwdwbOYkmrAOr83WkC6PaY66ZCABmNNvhd30xKAPkdArptUrgAsgnoNN8WZd5qmIwlMBorYDSqJIA6NSKArAL8VgG+zu/0fj+hc5MASm1IAFFAAig0mhVAo0ePhiRJePHFF8OWr1q1CpIk4eWXXw5bbjQaMXLkSEiShH/+85/pHFWXIQFE8AJ1RVt4i38JFAlwls5WvI2z+ibgpABfSfQnYqWK1AigvwQEUIWE7hoRqBLQXf0bOOvzAtRdGyKAHov5OtbmDXA25sHaHP0Mj66Wy0ME0Fjm36ve0OtxhQRQAK/j/wUF0LcD/z+tAuj0M4CUCSCv85fwOn8OtyP8nmFKBJDPcW6vjFEogMLnUCKAwnFarlUugHpu1KxUAPUImhQJIJ/jrHABZBN6Lzc7XQAF++F3CPA5zuudwf4LEkBq5e9ZwFqBHX8nAaSFkADqjWYFUP/+/SFJEvbu3Ru2fNOmTRBFEXfeeWfENk8++SREUaSngKUhJIAIXqCuaAvf4fMCAqhsnuJtegXQj1SdLTUC6M9BAdQP5pZP4KqbCU/NBXDXjg5QMwqemv5w1l0LU+uJ5N+n5YoQAaT8fkqEdrrCIySAAvAogBwdS6Ouo+gMIPvZ8v2G0iWArO1/Vy6Awm64fA7gEuDr+gbc9sFndAaQ0VgPo7EqcAZULAHkEtDt+AUJIC2HBBAFJIBCo1kBlJubG3GpFwB88cUXEEURkydPjthm7969EEURY8eOTdeYug0JIIIXqCvawl90LlAkoLNiieJtnNW3BAXQ+arOlhoBNCkogAL3RLE2beg9+ycEa9OZ3bslXABdwvx71Rt6Pa6QAAqQqQLI6/gp/J1nw995dvh3qEAAddnHwW0fCYdleZQ5EhdAlvYNfQug4Bk44ZeAfRN+51nwdv4Edsv9ZyiAgvvvOjdcANl+GHajZxJAGg8JIAp6BdC0adMwe/bsqEybNk1eL9Y6oeTl5bHeraSiWQE0btw4SJKEf/3rX2HL6+rqIIoiBgwYAK/XG/az4uJi+WcUdUMCiOAF6oqGOGUGigSgSICj8l7F23VWzw8KoPP6eP1WWOtegqvkz3CW5cHY2pz2rnRV/SkogAao+ll2tYwPEUDj2H+3OkOvx5XYTwHjQQA9GvZLfEBE3JjUa2WqAHLZ/hL9O1QggOKR1BlAZgVnAMk34+6VMW5H7xmR6gmg80kA8ZS/ZwHPCuwgAaSJ9IidVMHzmUKaFUALFy6EJElYtWpV2PLu7m7k5ORAkiQcOXIk7GcffvghCaA0hQQQwQvUFe3Q1tooCyB71V8Vb9dZvSAogLLirmdqPgwUC8CRAO1NB9Lela6qCUCpAE/FQFU/y66WK0kAMUSvx5VME0BOEkDaFkDBfeyyjoffcpa2BJAtMJut42F4bKJyAdQpwOf4PrptEgmgdIUEEAUBAZRqSAClOJs3b4YoisjJycHrr78edrbPlClTIEkSli9fLi/z+Xy4/vrrIYoiLrvsMhYj6yokgAheoK5oB1NLuSyAbNXPKt6us/q2oAD6ftz12psPAUdDBdC+tHelq+rKoAAapOpnGS6ALmX+3eoNvR5XMk8A3ZDUa5EASq8AcnTkw285W3sCyCagw/QuPLZ+igWQ13m+/PlFCKCem0p3qfvESyWQACIBlGlpbGxUBR6jWQHkdrsxduxY+TSriy66SP7ZK6+8Ilu3G264AU888QSmTJkiLzv9rCFK6kMCiOAF6op2MDUflgWQteZFxdt1Vi8KCqD/irtee/PXYQLI3Lg37V3pqroiKIAuUPWz7GqZECKA/pf5d6s39HpcIQEUILME0GH4g5LCZZse/TvMRAEUwpkKILe1VwDZOpbGFkCdAjz23r8bHJZ8+DvPDhdALgE+F/snO2aUAHo+C3hGYMfzJIAo2opmBRAAHD9+HMOHD4+4sbPL5cLll18ecS2fKIrIzc1FTU0Nu6F1EhJABC9QV7RDe+M+WQBZat9QvF1nze3ASQH+k9+N//pNX54mgD5Je1e6qi5PkwCaGCKALmP+3eoNvR5XThdAZtMemE27YDIejrkNCSBtCyAlZJYAOisgZ2IKoMCj4/2u0wSQI7YA8tiy5cu37BblAshotMHvOJcEkNohAUShhEXTAggAOjo68MwzzyA/Pz9seUNDA66++uqw6/DGjBmDffv2MZpUXyEBRPACdUU7mBv2yAKoo/49xdt11twZFEDfjv/6TXvDBFBHw0dp74q76rKgABqi6mfZ1fInWQB1tV7O/LvVG3o9rpwugJRso20BlJfUa5EA4lcAeR3fD5M//s6z4XFkhzxF7FzAKcDXeX7v/qsogHz2bwF2Ad02EkCqhQQQhRIWzQugvnLs2DHs3LkT+/btg9vtZj2ObkICiOAF6op26Kjf3iuAGnYp3q6zZklQAJ0bdz1z06enCaD3094Vd+WlQQE0VNXPMlwAXcH8u9Ubej2uhAugbyjahgQQCSAtC6Au65jAenEEkM/5vbgCyNVxhfzPRXvbW1EFkD+GAPLbA5eXeW0/l9f3Otnf1y2jBNBzWcDTAjueIwGk5WzdujUpdu7cic8++wwnT57kzkFwL4AobEICiOAF6op2sNS9LQugRO7P01mzLCiAvhl3PXPjnnABlMBZRqnqirtyXEAAVQ5X9bPsapkUIoDGM/9u9YZejyvtbXvlX3R9tvj35OrBbxaCj70+B13WScxmDxVAfts34Ld9Gw7z8qReK54A8trPh8+eBadluipdSUQAGY1GmIxFMBmLYDQ2J/2eTARQe7gA6mjbhg7TuzC1BW7ur0QAGY21MJt2wWzaBav5aVjNz8Njzw1sFxR1TstUtJs+h9M6B07rHPic34kQQN0OA/yOc+IIoKlAuwC0CzC1fRJFAH1LmQAKzuTtvDihz1cNSACRANJLUvF4+P79+2PRokXc3BSaBBAlqZAAIniBuqIdrLWvygKovemg4u0cNcuDAuicuOt1NH4UJoAs9e+kvSvuykuCAmiEqp9lV8vkEAF0JfPvVm/o9biStAAyC/CYhzGdXRZAdgG29jVn9FrxBJDaXUlMAKUGNgJoQ68AMudH/DyaAOp2/gwd7Zvjvq7bNjawXXAWu/m+8Nd1fi9CABmNNnhsQ0gA8RoSQJQ4SeUj4YcMGYLi4mLWu9RnmAugl156SSbW8mSgqBsSQAQvUFe0g61mvSyATM0nFW/nqLkrKIDOirteR+MH4QKoblPau+KuvDgogEaq+ll2tfw5RABNYP7d6g29HldIAPVgCYEEUF+kRQB1CnA7+hbvqRdA78QWQA4BHls2TMYSEkAssz4LeEpgx3oSQFpOW1sbdu/ejf79+0MURUyYMAGvvPIKDhw4gKqqKlRXV+PQoUN4/fXXMXXqVFn2PP3009iyZQsKCgowf/58+YFU48aNg91uZ71bccNcAIWedhVreTJQ1A0JIIIXqCvawV71uCyA2lprFW/nqLkbOCkAJ4W463U0bAsTQNa6grR3xV05FigV4K68UNXPMlwATWT+3eoNvR5XSABFJ50CqNvxm4CIIQHETgBZQwXQ5j4E0KDAZ0gCiF1IAFHixGg0YtiwYZAkCQ8//DC8Xm/c9Z977jn54VMWi0Ve/sknnyAnJweSJOGtt95Se+wziiYEUI/sibY82VOwKOqGBBDBC9QV7WCvfEAWQMZTRsXbOWpX9gqgU9aY61ka3g0XQLUvpb0r7soxQQE0WtXPsqvlLyEC6E/Mv1u9odfjCgmg6KT3DKBfBESBfH+cO1X/7LQpgH4UuKlz53dJAKlARgmgdVnAkwI71pEA0nIefvhhiKKIG2+8UfE2CxYsgCRJeOKJJ8KW5+fnQxRF5OXlpXrMlIa5APr6669lYi1PBoq6IQFE8AJ1RTs4KpYDRQL8RWfHFTkR29XeGyKAOmKuZ2l4J0wA2WqfT3tX3JWjgwLoIlU/y66Wq0IEELsb6+oVvR5XSABFh6UA6jQvVP2z89u+qT0BZPs+YBfgs52XkAByWG6H135ewgLIabkRfkUC6IvAuiSAtBMSQJQ4ueyyyyBJEnbv3q14m08++QSiKOKKK64IW75nzx75MjAth7kAihXeHqemt5AAIniBuqIdOssXAkUCfIe/n9B2jtr7QgRQe8z1LPX/CBdANWvT3hVP5aigABqr6mfpaplCAoghej2u8C2AHukVQGYSQIngtwXOAPI7z4bX+WOYTZ8ntL0aAqiz41Z0WSbCYV6UkAAKvPb6mALIaKyH0Vgd/PP0z+EsBQKoMLAuCSDthAQQJU4GDBgASZISunlzcXExRFHEgAEDFC3XWjQrgG699Vbk5eXhs88+Yz0KJUpIABG8QF1JP50Vt8FRdR/slffBWZon03XiUniP/ApdJxN7apWj9v4QAWSKuZ6l/vXTBNCTae+Kp3JkUABdoupnHC6AJjP/zvWGXo8rPAsgk7EIjo58ODruhsl4KKWvnU4B5LJei277L9MvgOwCuq1/SGp7NQRQ2HwpFUDxPgcSQFzmb1nAEwI7/kYCSMsZPnw4JEnC+++/r3ibDz74AKIoYuTIkWHLv/zyS4iiiAsuuCDVY6Y0mhVAo0ePhiRJKCgoYD0KJUpIABG8QF1JL22tTfAf+SZwRIDn+GC4j48OA0UCHBXx/2X+dOy1D4YIoNj3DrLWvxYmgOzVj6a9K57K4UEBNE7Vz9nVMjVEAP2Z+feuN/R6XGlv+5xbAaQm6RRARqMNHaa30iqAuq394LX+Gq6OGUltTwKIBBDTkACixMkNN9wAURQxbdq0Pm8ADQA+nw/XXHMNJEnCDTfcEPazF198EaIoYtKkSSpNm5poVgD1798fkiShqKiI9SiUKCEBRPACdSW9WOrekgVMZ/ltYWcAOUvz4D4+BtaaDQm9pr32oRAB1BpzPWvdS+ECqOqh5LtyyoK21hpY69fCWT0Lzuq8SKquh7NqJqz1z8iv4akYxkAA/YX596439HpcIQEUnUwXQGeK1gSQpf35XgHUvkrxfvhtZ8NvE+C1/ihlAshobJUhAaRSSABR4mTbtm3yQ6Ruu+22sCd7nR673Y4lS5bI62/ZskX+WUtLCy666CJIkoTVq1enY/Sko1kBNH78eEiShO3bt7MehRIlJIAIXqCupBdn+VzgiAD/kW/B2Bpb1iSCvfbhEAHUHHM9a92GMAHkqFL+L/and6WttRY4EXhPb9nv4a4YHYG37CdAqQBf6Q/k1/BUDA0IoKr/VfVzdrVMCxFAVzH/3vWGXo8rJICiQwIoPloTQGFnALXfm/D+dJpvTaEA6sFCAkitrM0CHhfYsZYEkJbj9/sxb948WeoMHjwYy5cvx8aNG7F9+3Zs3boVr732GpYtW4YhQ4ZAkiSIoohZs2bJr7Flyxbk5uZCkiT069cPZWVlDPeo72hWAG3ZskW+i3Z1dTXrcSinhQQQwQvUFfWx1m6A++QYOMvy4D36E+CIAHfJpSl7fXvtX2UB1NbaFHM9W91z4QKoMrFLzUK7YmopkwWQu2JE1DOAusv/EBBAZVnya3gqBgcF0GWqfuahAsjVOoV5B/SGXo8rJICiQwIoPskIIKOxFSbjfpiM+2E0NsZdVxZA9tEK5yEB1BckgEgA6SldXV2YP38+RFGURVA0en5+8803w+FwyNuvXLlS/tnzzz/PcE+URbMCyG6348UXX0R2djZycnIwZ84crF27Fu+88w527tzZJxR1QwKI4AXqinq0tVTCV5wFX/EPAtLnxGi4j1+IzrKbYG78LGXvY697NEQANcRcz1a7FjjWK4A6K5cl3RVTy5GAADohwFr/QtT1nTVzgwLox/IyT8UFQKmArqorVP3sXS3TQwTQVOZd0Bt6Pa7wfBNoNSEBFJ/kBJBy/I6gALLRGUCpggQQCSA9Zs+ePcjLy0NOTo4sdEKZMWNGVM+Qn5+PW2+9FZ9++imDqROPZgXQ6bYtlomLRnZ2NuvxMz4kgAheoK6oR3vzIVm2dB8zwFmWB2dZHqy1id3jpy/sdWtCBFBdzPVstU+FC6CK25PuSnvz/hABtDHq+s6aeUEBdJ68zFORGxRA41X97F0tV5MAYohejyskgKKTfgH0WogAupH5/vdF2gSQdZTCeUgA9UVGCaBns4A1AjueJQHEW2w2Gw4dOoSPP/4YO3fuxL59+2A2m1mPlbJoVgBFs25KkSSJ9fgZHxJABC9QV9TD3PiZLFss9e+o9j72usdDBFBN7PVqHz9NAC1Iuivmps9kAWRpiL5vnTULg/cA6v1F2FMxKCiAJqj62YcLoGnMu6A39HpcIQEUnXQLIEf7UlkAua2DmO9/31TBYb5NM2cAWUzrgQ4B6BBgM92f8PupJYC8neMA16Xodj2T8EyphgQQCSBK5kazAmjLli1nBEXdkAAieIG6oh4dDR/IsqWj4UPV3sdW96QyAVTzaJgAcpbfnHRXOhp3yQKoo3Fb1PU7a+8ESgX4S78lL+uuGBAUQBNV/exdLdeECKDpzLugN/R6XGlv2yv/4uux5ijahgQQCSCjMXjZmkYEkMlYDIdpORym5TAZv074/U4XQM6OawCLAFgEmNq+7p0pRAC5bePgto2BveOBmK+rpeMKCSASQHrOyZMnsXXrVmzcuBGvv/46tm/fjmPHjsHv97MeLSXRrACiaDskgAheoK6oh6XuTVm2mJv+o9r72OqeDhFAVTHXs9c8dJoASuzSiHABtE0WQOam3VHXd9QuDwqgs+Vl3RX9gwLoT3qR/b0AACAASURBVKp+9q6WGSEC6GrmXdAbej2utLftlcWDyzpD0TZ6EEAdbVtgNf0NFlNBWrpCAiicRAXQmRJ5BtAUINjzWGcAKUFLx5WMEkDPZAGPCex4hgQQD/H7/SgoKMCYMWNi3mZm5MiReOGFF7gXQSSAKEmFBBDBC9QV9bDWbpBli6m5WLX3sdU9EyKAKmKu56i5P0wAucquT7orloa3QwTQ3ujvV7cSKBWAUgHGUxYYjTZ0V+QEBdBkVT97V8u1IQLoGuZd0Bt6Pa6QANJGVyymTUDwkiKHeTHzfVSCmgLI3nEfHB33osO0JS37QgKIs5AAovQRp9OJG2+8MeJpX7FuNTNr1iw4nU7WYyedjBRAzc3NrEfI+JAAIniBuqIetuonZNnS1hL70qwzfp+6tbIAMrWWx1zPUX3vaQIoMTES2hVrfYEsgNqbo18i4Ki7P0QAtcFotKG7IjsogP6s6mcfLoCU/SJOpA69HleSEUA+83fhN38bXe2TmM/PAhJAAdQUQOlGmQA6KyiAYt3zJz1dSZaMEkBPZwGPCux4mgSQ1nP77bfLgmf48OFYvXo1tm3bhi+++AJ79+7Fli1bcP/992PYsGGyBFqxYgXrsZOO5gWQxWLB+++/j5dffhnPP/881q9fH8HatWvxxBNPYNWqVZg5cyZycnJYj53xIQFE8AJ1RT0cVffJssV4yqTa+9jq1/UKoJbS2PNU3x0ugEqnJN0Va/0GWQCZWqKf3WSvezhEADXDaLShu0IKCqCrVP3sXS0zQwTQtcy7oDf0elxJRgDpHRJAAXQngOzBM4ASuERPS8cVEkAkgPSSffv2yVJn3rx5sFqtMde1Wq245ZZb5PWLiorSOGnqomkB9MEHH+CCCy5I6BHw9BSw9IQEEMEL1BX16Ky4AzgiwH/k26q+j63+uRABdDLmeo7q5cAxAf6gAOoqTeyMg9Cu2OqeDRFAZVHXt9c/JgugnsfTd5cbgFIBrurE5FOiuFquCxFAM5l3QW/o9bhCAihxSAAFIAHEpivJQgKIBJBesmzZMoiiiAkTJsDtdve5vtvtxoQJEyBJElauXJmGCVMfzQqgqqoq5OTk9Hkd3umMHTsWd911F+vxMz4kgAheoK6oh7N8DnBEgO/oT1R9H1v98yEC6HjM9Tqr7gwKoLMCAqhkfNJdsdc+JgugHrkTOdeTIQIocHPq7vI/BgXQVFU/E1fL9SEC6DrmXdAbej2ukABKHBJAAUgAselKsmSUAHoqC/irwI6nSABpOePGjYMkSXj77bcVb/POO+/I0ojHaFYAPfzww/LZPLfffjv27NmDw4cPIzc3F/369cP+/fvx9ddfY+vWrZg7d668bkFBAevRdRESQAQvUFfUw1U2DTgioPvY/6j6Ptb6DSEC6FjM9TqrFgcF0NnAEQHukkuT7oq99gFZABlPGaOub6tfKwsgU2vgLKHu8v8JCqBp6n72YQIosZtdE2eOXo8r7W2fkwBKEHUE0JvwOwT4HQIc5tuZ76MSTMZ98FgGwmMZCIvpNebznAkkgDgLCSBKnAwcOBCSJOHIkSOKtzly5AhEUURubq6Kk6kXzQqgyZMnQ5IkzJw5M2z5nDlzIEkS9uzZE7b8b3/7G0RRxMCBA9HQ0JDOUXUZEkAEL1BX1KOr5ArgiADPCeU3uUwGa/0LIQIo9tPGOqsWBAXQOQEBdHJs0l1x1NwdIoCsUde3NTzfK4CCZyZ1l/93UABNV/UzcbbMkgWQs3UW8y7oDb0eV0gAJY5eu5LJkADiLCSAKHEyePBgSJKE/fv3K95m//79EEURQ4YMUXEy9aJZATR8+HBIkoStW7eGLX/66achiiIef/zxiG2mTZsGSZLw5JNPpmtM3YYEEMEL1BX18JwcmZRoSRRr/UshAuhwzPWclbcEBFDxNwJi6uSopLvSWX0HcEKA/0Ts+xtZG14MEUCBubzlvw8KIHUfze5smU0CiCF6Pa7QJWCJo9euZDIkgDjLk1nAIwI7niQBpOX85S9/gSRJeOaZZxRv88wzz0AURUyePFnFydSLZgVQz/1/Dh48GLZ827ZtEEURc+fOjdjmH//4B0RRxLRp09I1pm5DAojgBeqKenQfz0nqZsuJYq1/WRZA7S2HYq7nrJwbFEDfDJ6ZNDTprjir5gEnBPhOnhd7robXZAHU3nIARqMN3vLfBQWQur8cO1vyQgTQbOZd0Bt6Pa6QAEocvXYlkyEBxFlIAFHi5PHHH4coihg8eDCqq6v7XL+mpkY+a2jNmjVpmDD10awA6jkD6MCBA2HLDx8+DFEUMW7cuIhtDhw4AFEUMXz48HSNqduQACJ4gbqiHt5jvwk8br1M3adQWRte7RVAzfEE0A1BAfStoACK/Bdva/0GOKvyIrDWbwh0xdsBt70YXeXj4Dv5A3hLfhvz/SyNb/QKoOZ9gc+k/LdBAaTuZxIugPKYd0Fv6PW4QgIocfTalUyGBBBnIQFEiZPGxkYMGDAAkiRh9OjR+Pjjj+H3+6Ou+/HHH2PMmDEQRRH9+/dHY2NjmqdNTTQrgCZNmgRJkvDee++FLTeZTPINn61Wa9jPCgsLIYoicnJy0jmqLkMCiOAF6op6+IrPA44IcFbMU/V9rA0FIQLoQMz1XBXXBwXQdwI3pz7eT/5ZZ81ieMqHwlM+BO6K0WH4Sn8Eb9mv0H3qCaDjDfneP57SIXDU5Md8P0vj27IAMjfvhdFog7f810EBpO6TuZwtN4QIoBuYd0Fv6PW4QgIocfTalUyGBBBneSILeFhgxxP8CaAvv/wSCxYswIUXXoicnByMGDECc+fOxe7du/vc1m63Y+3atZg4cSIGDBiAIUOGYPr06di0aRO6u7vTMH3i2bhxo+wXJEnChRdeiPnz52PlypVYuXIl5s+fj9GjR8tPJ5ckCRs3bmQ9dtLRrAB64IEHIIoirr76ang8nrCfDR06FJIk4cMPPwxb/tprr8mncGk9CxYsgMFgiDjDSUl8Ph+uv/56GAwG3HPPPSpM13dIABG8QF1RiVNW+Wlbjsrlqr6XtWFjiAD6OuZ6rooZwDEBvqPfDQqgP8o/85SPAk4K6C79fcTZP/6S7wAlAryNcwHzq7IAclVMhbV+Q8z3szS+GyKAPoHRaIO3/FdAqQBntbr35XG23BgigG5k3wedodfjCgmgxNFrVzIZJQLIbR0Nj3UYHObY/xFBy10hAaRfAbRmzRoYDIaYLF++HD6fL+q2JpMJV1xxRcxtZ8yYAbvdnuY9UpaNGzeif//+EEUxTAb10LM8JyeH+6eOa1YAHTx4UP7wZ8yYgX//+9/yzxYsWABRFDF+/Hg0NTUBAI4fP44LL7wQkiRh+vTpjKZWlk2bNsn/ICQjgF5++WV5exJA7P+SJLQNdUUlWluAIwJwRIC96iFV38vS8HqIAPoq5nqu8mlBAfRfwBEB3mO/C/zslBW+kvOAkwKcVZGXS3WX/TEggOqnA+YXZAFkaimPO1dH03ZZAHU07YLRaIO37BdBAaTufXmcLXNCBNAc9n3QGXo9rpAAShy9diWTUSKAeO9KRgmgx7OAhwR2PM6PAHr33Xfl3zGvvfZa7N+/H21tbThy5Ajmz58v/2z9+vUR2/p8PkyfPh0GgwHDhg3D5s2bYTQaUV9fj7Vr16Jfv34wGAy4/fbbGeyZsjQ2NuKRRx7BhAkTwqSPKIqYMGECHnnkEdTX17Me84yjWQEEAIsXL5Yl0KBBg+Tln3/+ubw8JycHo0aNCjsl69VXX2U4dfxs3rwZoigmLYDKysrQv39/EkAa+kuS0DbUFXUwtZTJAshW86yq72Vp2BQigPbFXM9VPiUogH4AHBHgO/7DwFk+lVfL29vrHo3YzlMxBCgR4Ku9HGhfJwugttaauHN1NO0MEUAfwGi0wVv284AAqlH3vjzhAmgu8z7oDb0eV0gAJY5eu5LJRAqgqb0CyBj7LFWeukICSJ8CqOfsnUmTJsHtdof9zO/3Iy8vDwaDAbm5uRE/37lzZ9zfb9966y3554cPH1Z1P1IRr9eL9vZ2mEymiKuReI+mBVBXVxdWrFgBSZJw5ZVXhv3s3nvvDTtFq+d/T58+PaKQWojb7cbq1asjToVLRAB5PB785S9/CdueBBD7vyQJbUNdUYf2poOyALLWvqrqe1ka/hEigL6IuV5X+WTgmADv8d/AXv0IusonwF0+Gu6yEegu/SPc5SNhbv48siOV4wICqHo4YHo6RAA1xp3L3PRxiADaCqPRBm/Zz4ICSN3Lspwtc0ME0E3M+6A39HpcIQGUOHrtSiZjMhbBatoAq2kDjMZauMxTAKMAGAWYjHQGkOZCAkhRbDab/PvlSy+9FHWdHTt2yOucOHEi7GfXXHMNDAZD1Cd1AwGBdOWVVzL9/ZUSCHMBdMstt2D37t1xbwpVW1uLPXv2RCx/8803MWnSJAwYMABjx47FY489psnrCj/++GNcfvnl8j8wU6ZMSUoAPf300zAYDJg1axYuueQSEkDQxl+ShLahrqiDufFTWQBZ6jer+l6WhrdkARRN4PTQVTYBOCbAUzIQRmPkE79i3c+nq+oqoESAv1IC2tbIAsh46lT8z6D504AAKhFgaXgdxlMt8JX9KCiA1L0sy9lyEwkghuj1uEICKHH02hU9QQJI41mTBawW2LGGDwFkt9vl3083bNgQdZ1du3bJ65SUlMjLOzo65CtcNm3aFPM9eu4vNHLkyJTPn84UFBTgqquuwpQpU1iPklSYC6CeM3hGjhyJRx99FKWlpaxHSmmsVqv8D0pubi42bdqE/fv3JyyAioqKkJ2djQsuuAANDQ0YN24cCSBo4y9JQttQV9Sho36bLIA6Gnap+l6WhndCBNDemOt1lV0RFEAXJPT6zuq8gAAq/zlgfChEAHXE3c7c/B9ZAPlLBPhLzoK/9OygAFJXyoQLoJuZ90Fv6PW4QgIocfTaFT1BAkjjIQGkOOPHj495CRgA+T5AI0aMQFdXl7z8q6++kn+3PXToUMzXf//99+X1eu7jy2PWrFkjOwweowkBdPrdtqdOnYo333wTNhv/Bx+r1Yrs7GzcddddctETFUCdnZ3yGUTvvPMOAJAA0tBfkoS2oa6og6XuDVkAtTfFvi9PSt6rcXOvAGr6d+zvuuzSgAAqHZbQ63fWLApInNLvAqdWAScE+E+c1ed27c37gRIhkrQLoFuY90Fv6PW4QgIocfTaFT1BAkjjIQGkOLt374YkSfIVJ4WFhTCZTDh27BjuuOMOGAwGiKKILVu2hG0XevPo1tbWmK9/4MABeb2vv/5a7d1RLSSAzjClpaVYs2YNLrrooggRNHDgQCxduhRffvkl6zGTjsfjQUNDQ9iyRAXQ/fffD4PBgJtvvlleRgJIO39JEtqGuqIO1tq/ywLI1HxM1feyNL4bIoD+Ffu7Lrs4KIBGJfT6jtqVvfKm9a6gADq3z+3aWqvQWbMYnrIceMqy0Vl9G7oqL4Oragram79U9TNxttwcIoDmMe+D3tDrcYUEUOLotSt6wmm6JkQA0U2gNZfHsoAHBXY8Fi6ArFZrwoSebaN29uzZE3brklDGjx+PTz/9NGKb0CdUu1yumK9dWloqrxft9i68hARQiuL3+/Hll19ixYoVyM3NjZBB48aNw7p16yJkCo9JRAB9/vnnMBgMGDp0aJhRJQGknb8kCW1DXYmNtXYD3CfHwFmWF/Fnn8tODJUFUFtrrapzdjS+FyKA9sT+rktHA8cEuEvHJPT69rrHegVQ823ACQG+k/+leHtHbT4ctfmBz7RhA6wN0e81lEqcLbcALQL8LefA0bqCeZf0hn6PKyaY2r6Gqe1rGI3q/nOfKei3K/rB1TaVBJCWozEBNGTIkKhyJR7r1q1TtKvr1q0749feunVrxEOHQm9n8vjjj8PpdIZt89xzz8nr+P3+mPPV1tbK6+3YsUPhF6i9kABSIU6nE9u3b8dNN92Efv36hcmg7Oxs5OXl4f3330/L075S8Q/S6VEqgCwWC8aMGQODwYBt27aF/Yy1AHK7u5ni8wUOLj6fn/kshLahrkTHW3sHfKWjA49LLxsb8Wdfy3wlFwT/90Vwd7lUndXT8aEsgDzWvbG/64oLA4+Br7o0odfvNhXAX/Zj+Et/DJT/PnAGUMkPE9j+FXSbXknv99e+AGgUgEYB3vbbmfdJb9BxhVAKdSXz8VqulgWQ21mc9OtorSsZExJAil/7oYceki/z+utf/4qamhq43W40NTXhhRdeQP/+/WEwGHD99deHSaANGzaQAOIomhRAoWlra8Nrr72Gq666KuKsoKFDh+KBBx5AcXGxau/PUgDdeeedMBgMWLhwYcTPWAsgCoXCcfw+4NA5QJEAHP8NUH8TUHFx+J99Lev53+0F6s/r2CMLIHT+J/Z6lSOAYwJQMz6596m5SL4BNEp/mtxrpCuWRbIAgmUZ62koFApFv7HNkAUQuo+znoZyeh7LAh4Q2JFGAdTe3o7KysqEaG9vBwB8+eWX8vsVFBREff19+/bJ9wh64YUX5OUbN26Ut413uRpdAqaNaF4Ahaa8vBxPPPEELr744ggZNGnSJBQUFMglTlXO5B+kWFEigHbs2AGDIfCYvGivx1oAsf6vElr7rySEdqGuRKGzFTgkAIcEdDc9xX6ePvB0fNx7BpDl09jfdfngwBlA1ROT60r9VFkA+Ut/yXy/4+E1Lww5A2g583n0Bh1XCKVQVzIfOgNI49GYANLqPYB6bvJ86aWXwufzxVxv8eLFMBgMuOSSS+RlW7ZskX+3bWtri7lt6E2gDx48mNL50xkSQAzi9/vx1VdfIT8/H0OHDg2TQTk5OVi0aBHrEeOmLwHU2tqK4cOHw2AwYPfu3VFfg7UAonsAEbxAXYmkvemALICsNa8wn6cvOhp3yAKoo3FnzPW6SwYAxwR0lU9M6n26Tz0pCyBvya+Z73c8OltulQWQo3k583n0Bh1XCKVQVzIfugeQxqMxAaTV9Nz3Z+nSpXHXe+ONN+TLxHouAysqKpJ/t413ZU7oY+Cbm5tTOn86QwKIcTweD/bu3Yt7770XI0aM4OLL6EsAvffeewmfGmgwGLB///607QMJIIIXqCuRdNR/JAugjvptzOfpc96mD0ME0I6Y63WX9AsKoD8n9T7dxudCBNDvme93PMIFEN0EOt3QcYVQCnUl8yEBpPE8mgXcL7DjUT4E0MSJE2EwGLB48eK4623atEn+3dNqtQIA7HY7RFGEwWDAO++8E3PbNWvWwGAwYPjw4SmdPd0hAcQ4bW1t2LRpE+bOnYsBAwZw8WWQADpztPSXJKFtqCuRWGrfkAVQe+MXzOfpi46mXSEC6P2Y63WfNADHBLjKpyb1Pt1tr8oCqLv0D8z3Ox6dzSECqOUe5vPoDTquEEqhrmQ+JIA0HhJAirJw4UIYDAaMGjUK3d2xLwHsuUft6NGjw5bPnDkTBoMB8+fPj7qd3+/HlVdeCYPBgGXL0n/vwsLCwpRx5513cuEcYoVLAWS1WrF582bMnj0b/fr1k+8DJIoiBg8ejHvvvZf1iHHTlwDq7u6Gw+GIyyWXXAKDwYC7775bXub1etO2DySACF6grkRiq35WFkCmljLm8/SFuWl3iACKfcZS98n/DgigiquTep/u9n+ECCCR+X7Ho7N5fogAWsV8Hr1BxxVCKdSVzIcEkMbz1yxglcCOv/IhgHbu3Cn/fvrss89GXaewsBDZ2dkwGAx49NFHw372zjvvyJeG7du3L2Lbt956S379o0ePqrIP8RJ67+BUQAIoDXG5XNixYwduvfVW9O/fP+LDnz17NrZu3QqXy8V61D6j9Clg8UL3ANLOX5KEtqGuROKouEcWQMZTJubz9IW5aU+IANoacz3vid8GBdC1Sb2Px7w1RABlM9/veIQKIHvLg8zn0Rt0XCGUQl3JfEgAaTwkgBTF7/dj1qxZ8u+oS5cuxaFDh2A2m1FbW4sXX3wRAwcOhMFgwMUXXwyz2Ry2vdfrxeTJk2EwGJCbm4s33ngDra2taGxsxLp169CvXz9Fl5iplZ57BqcSEkAqxOv14t///jeWLVuGCy64IEz6iKKISy+9FOvXr0dDQwPrURMKCaAzR0t/SRLahroSibNsHnBIgK8oi/ksSjA3fRIigN6LuZ73xK+AYwKcFbOSeh9Px64QAZTDfL/j0dl8S4gAeoT5PHqDjiuEUqgrmQ8JII2HBJDiWCwWzJ49O+4tRy6//HJUVlZG3b6+vl7+HTUaM2bMkG8cne7k5+erAo/RpAAqLCzEqlWrMGLEiAjpk5ubixUrVuCrr75iPWbSIQF05mjpL0lC21BXInGdnBp4BHzx/zCfRQnm5r3wlZwP34n/QlfZZXBW5EXFd/yH8B3/HjqrFiT1Ph7LZ7IA8pT2Z77f8QgXQI8yn0dv0HGFUAp1JfMhAaTxPJIF3Cew4xF+BBAA+Hw+7NixAzfffDNGjRqFfv36YdiwYbj22mtRUFDQp8Cx2+1Yu3Yt/vSnP2HQoEEYOHAgrrrqKrzyyitwu91p2gtKvGhGAJ04cQJr1qzBxRdfHCF9RFHEzJkzsXnzZtjtdtajnnFIAJ05WvpLktA21JUon8nxscAhAZ5jI5jPkgiO6ny4S0fHxVGdn/Tre2xfhwiggcz3Nx6dzTeHCKDHmc+jN+i4QiiFupL5kADSeEgAUShhYS6A1q9fjyuvvDLipkqiKGLs2LF45plnUFNTw3pMymkhAUTwAq9dsdZsgPv4GLiPj4GzNC/sz1jLrNXPwXjK0udrdx/tBxwS0HXyT8z3M6HPpG5DzLN/erDWbUi+K/ajvQKobBDz/Y1HqACytTzNfB69wetxhUg/1JXMhwSQxkMCiEIJC3MB1HMDpR7pM2DAACxZsgT/+c9/4Pf7WY9HiRESQAQv8NYVS+078Bb9Dr6iHwNFAlAkwH18dNifsZb5i85Be1Nhn+/hO/xT4JAAZ+mNzPdXS7gdFdwIIGfz3BABtJb5PHqDt+MKwQ7qSuZDAkjjeTgLuFdgx8MkgHiJ1+vFhx9+iFWrVmHOnDmYOnWqfCJKSUkJnn/+ebS1tbEdMgXRhAASRRHTp0/Hm2++CavVynokioKQACJ4gbeuWKs2AIUCcECA59hQRWcAeY7myhLIWvNC/Pc4ZYH/0DnAIQGO8uXM91dLuDsbQwRQLvN54hEugJ5jPo/e4O24QrCDupL5kADSeEgAURTkq6++wrhx4yKeNF5SUgIA2Lp1q3w/4k2bNjGe9szCXACtWbMGFRUVrMegJBgSQAQv8NYVW+XTAQFUKKCtpVLZdqfa4S/6FlAkoLP81rjrtrXUyI+At1c9xnx/tYTb1R5yDyCtC6A5sgCytiR/2RuRHLwdVwh2UFcyHxJAGg8JIEof+fTTT5GTkxNxD+JQAbR+/fqw5S+++CLjqZMPcwFE4TMkgAhe4K0r9opHegVQa5Pi7TzHhgBFAjzH49/Yub3poCyArDUvM99fLeHu6uToErBQAfQi83n0Bm/HFYId1JXMhwSQxvNQFnCPwI6HSABpOWazGUOGDIEoihg9ejS2bt0Kk8kUIYDa29uxdu1aDBw4EKIoIicnB+Xl5YynTy5cCCC9XI/HU0gAEbzAW1cc5ffIAsh4yqx4O2fZTYH7AB3+LoynOmKu19GwSxZAHfXbmO+vlnB3eTgSQDeECKBXmc+jN3g7rhDsoK5kPiSANB4SQJQ4WbduHURRxLBhw1BfXy8vP10A9eTgwYMYNGgQJEnCgw8+mO5xUxLNCyA9XY/HU0gAEbzAW1c6S5cAhQL8hecmtJ2t5jn5PkDtTbH/BdRS+4YsgNobv2C+v1rC7e4GTn4b/hPfgLv8EubzxMPZnBcigDYyn0dv8HZcIdhBXcl8SABpPCSAKHEybdo0SJKEZ599Nmx5LAEEBG5hI4oirrzyynSNmdJoWgDp7Xo8nkICiOAF3rrSWTIfKBTgO3heQtu1N34ZciPo2PeEsVU/KwsgU3Mp8/3VEjx1JVQAWVrfYD6P3uCpKwRbqCuZDwkgjWd1FrBSYMdqEkBazrBhwyBJEr766quw5fEE0Oeffw5RFDFo0KB0jZnSaFYA6fF6PJ5CAojgBd664jyZBxQK8B76eWLbht0Ien7M9RwV98oCyHjKxHx/tQRPXXE2zw4RQG8xn0dv8NQVgi3UlcyHBJDGQwKIEicDBgyAJEk4evRo2PJ4AujIkSMkgNSIHq/H4ykkgAhe4K0rrhPTgUIB3UX/nfC2nuNDgzeCHh5znc6y+cAhAb6iLOb7qjV46oqz+foQAfQO83n0Bk9dIdhCXcl8SABpPA9mAfkCOx4kAaTl9Nxq5qOPPgpbHk8Avf322xBFEePGjUvXmCmNZgWQHq/H4ykkgAhe4K0rXccnBQTQ4ZyEt3WW3Ry8EfR3Yt5A2nVyKnBIQHdx4oIp0+GpK/aWv8Lb9At4G38Oc+te5vPoDZ66QrCFupL5kADSeEgAUeJkyZIlEEURCxYsCFseyzm43W5MmDABkiRhyZIl6Rw1ZdGsANLj9Xg8hQQQwQu8dcV99FKgUIDnyNCEt7XW/D1wH6BDAjxHL4CzNA/u42PC/vQVZQV+fiz+4+L1CG9dIdhBXSGUQl3JfFymKYBJAEwCTMYDSb+OlrpCAogEkF7yn//8R/YLmzdvlpdHcw52ux0LFiyQf/bvf/+bwcRnHs0KID1ej8dTSAARvMBbVzzFo4BCAe6jYxPetr3pEJyls9F14grgkAD3sdEx/7RVr2W+r1qDt64Q7KCuEEqhrmQ+JIA0ngeygLsFdjxAAkjrWbhwoewY5s+fj40bN8r//6233sKOHTvwyCOPYOTIkfKTyW+88UbWYycdzQogPV6Px1NIABG8wFtXPEdygUIBXcfGJ/0a1poNEWf+hP4Z7ylheoa3rhDsoK4QSqGuZD4kgDQeEkCUPuJ0OpGXlyd7NvLLbAAAIABJREFUhlj0PHl8ypQpsFgsrMdOOpoVQHq8Ho+nkAAieIG3rnQfNgCFAlzHpzCfRW/w1hWCHdQVQinUlcyHBJDGQwKIoiB+vx8FBQW45JJLZNFzOsOHD8e6devgdrtZj3tG0awA0uP1eDyFBBDBC7x1xXvo1wEBdOI65rPoDd66QrCDukIohbqS+ZAA0njuzwJWCOy4nwQQT/H7/SgpKcF7772HV155BS+++CLefvttHDp0CB6Ph/V4KYlmBRCgv+vxeAoJIIIXeOuK7+CPgEIBzpKbmM+iN3jrCsEO6gqhFOpK5mNtex5u0xi4TWNgNFYl/Tpa6goJIBJAlMyNpgWQ3q7H4ykkgAhe4K0r/gPfBQoFdJYuZj6L3uCtKwQ7qCuEUqgrhFK01BUSQCSAKJkbTQsgQF/X4/EUEkAEL3DVlVNWoFAACgU4ylawn0dncNUVginUFUIp1BVCKVrqSkYJoFVZwF0CO1aRANJCiouLVYHHaF4A9UQP1+PxFBJABC9w1ZVWoyyA7OUPsp9HZ3DVFYIp1BVCKdQVQila6goJIBJAmZa+rihKhuzsbNa7lVS4EUAUbYUEEMELPHWlraW2VwBVrmE+j97gqSsEW6grhFKoK4RStNSVjBJA92UBywV23EcCSAuJdSXRmSBJEuvdSiokgChJhQQQwQs8dcXUXCILIFvVOubz6A2eukKwhbpCKIW6QihFS10hAUQCKNOyfv16VeAxzAUQXY/HZ0gAEbzAU1famw7JAsha/TLzefQGT10h2EJdIZRCXSGUoqWukAAiAUTJ3DAXQHQ9Hp8hAUTwAk9daW/4QhZAlpo3mc+jN3jqCsEW6gqhFOoKoRQtdSWjBNC9WcAygR33kgCiaCuaEEB0PR5/IQFE8AJPXTHX75EFUEfdFubz6A2eukKwhbpCKIW6QihFS10hAUQCSM8xmUw4cOAAPvroI3zyyScoLi6Gw+FgPVbKwlwA0fV4fIYEEMELPHWlo257rwCq38V8Hr3BU1cItlBXCKVQVwilaKkrJIBIAOkxW7ZswTXXXIPs7OyIK4wGDBiA+fPnY9++fazHPOMwF0AUPkMCiOAFnrpiqX1bFkDmhr3M59EbPHWFYAt1hVAKdYVQipa6klEC6J4sYKnAjntIAGk9VqsVM2bMkGVPvKuMJElCfn4+vF4v67GTDgkgSlIhAUTwAk9dsdYUyAKovfEA83n0Bk9dIdhCXSGUQl0hlKKlrpAAIgGkl3R3d+Paa6+VJc/gwYNx99134//+7//wwQcfYNu2bSgoKMDSpUuRm5sri6BVq1axHj3pcCWAMv16PJ5CAojgBZ66Yq36uyyATM3Hmc+jN3jqCsEW6gqhFOoKoRQtdYUEEAkgveTNN9+Upc6iRYtgtVpjrms2m3HLLbfI6x84cCCNk6YuXAggvVyPx1NIABG8wFNXbJVPyQKoraWa+Tx6g6euEGyhrhBKoa4QStFSVzJKAK3MApYI7FhJAkjLmTFjBkRRxIwZM+D3+/tc3+PxYMqUKZAkCYsXL07DhKmPpgWQ3q7H4ykkgAhe4Kkr9oqHZQFkbG1mPo/e4KkrBFuoK4RSqCuEUrTUFRJAJID0ksGDB0OSJHz00UeKt9m+fTtEUcS4ceNUnEy9aFYA6fF6PJ5CAojgBZ664ihf2SuATnUwn0dv8NQVgi3UFUIp1BVCKVrqCgkgEkB6SW5uLiRJQnFxseJtiouLIYoiBg0apOJk6kWzAkiP1+PxFBJABC/w1JXO0juBQgH+wm8xn0WP8NQVgi3UFUIp1BVCKVrqSkYJoPws4E6BHfkkgLScSZMmQZIkvPvuu4q3+eijjyCKIq688koVJ1MvmhVAerwej6eQACJ4gaeuOEvmAYUCfAfPYz6LHuGpKwRbqCuEUqgrhFK01BUSQCSA9JKXXnoJoijiiiuugN1u73N9v9+PmTNnQpIkrFu3Lg0Tpj6aFUB6vB6Pp5AAIniBp644T84GCgV4D/2C+Sx6hKeuEGyhrhBKoa4QStFSVzJOAN0hsIMEkKYTetuZ6dOno76+Pua6nZ2dWLZsGURRxMSJE+FyudI4aeqiWQGkx+vxeAoJIIIXeOqK68Q0oFBAd9H/MJ9Fj/DUFYIt1BVCKdQVQila6goJIBJAesnOnTvx7rvvYvTo0RBFETk5OZg3bx5eeOEFbNmyBdu3b8frr7+OlStXYvjw4fLDpxYtWoQHHnggKg8++CDr3YobzQogPV6Px1NIABG8wFNXuo79KSCADg9gPose4akrBFuoK4RSqCuEUrTUFRJAJID0ktAnivc8dTz0/yv92eloOZoVQHq8Ho+nkAAieIGnrriPjgMKBXiKhzOfRY/w1BWCLdQVQinUFUIpWupKRgmgu7OA2wV23E0CSMvpeeJ4KiEBlGT0eD0eTyEBRPACT13xFI8ECgW4j17CfBY9wlNXCLZQVwilUFcIpWipKySASADpJY2Njaqg5WhWAOnxejyeQgKI4AWeuuI5MggoFNB1bALzWfQIT10h2EJdIZRCXSGUoqWukAAiAUTJ3GhWAOnxejyeQgKI4AWeutJd9EegUIDr+FTms+gRnrpCsIW6QiiFukIoRUtdySgBtCILWCywYwUJIIq2omkBpLfr8XgKCSCCF3jqivfQrwIC6OT1zGfRIzx1hWALdYVQCnWFUIqWukICiAQQJXOjWQGkx+vxeAoJIIIXeOqK7+D5QKEAZ8nNzGfRIzx1hWALdYVQCnWFUIqWukICiASQnuJ0OrF+/XpMmTIFgwcPVnxlUXZ2NuvRk4pmBRBF2yEBRPACT13xH/gOUCigs/R25rPoEZ66QrCFukIohbpCKEVLXckoAXRXFrBIYMddJIC0HLvdjkmTJoXdWibTry4iAURJKiSACF7gpiunrEChABQKcJTdzX4eHcJNVwjmUFcIpVBXCKVoqSskgEgA6SVPPfWULHRyc3Nxww03YOnSpcjPz1cEjyEBREkqJIAIXuCmK62nZAFkr1jNfh4dwk1XCOZQVwilUFcIpWipKySASADpJVdccQVEUcSECRNgMplYj5OWaFoA6e16PJ5CAojgBV660tZSIwsgW+UTzOfRI7x0hWAPdYVQCnWFUIqWupJRAmh5FrBQYMdyEkBazoABAyBJEj744APWo6QtmhVAerwej6eQACJ4gZeumJpP9gqgqueYz6NHeOkKwR7qCqEU6gqhFC11hQQQCSC9ZOTIkZAkCcXFxaxHSVs0K4D0eD0eTyEBRPACL11pbzokCyBr9avM59EjvHSFYA91hVAKdYVQipa6klECaFkWcJvAjmUkgLScOXPmQJIk7Ny5k/UoaYtmBZAer8fjKSSACF7gpSvmhv/IAshS+xbzefQIL10h2ENdIZRCXSGUoqWukAAiAaSX7NmzB6IoYsaMGfD5fKzHSUs0K4D0eD0eTyEBRPACL10x138sC6COuq3M59EjvHSFYA91hVAKdYVQipa6QgKIBJCesnLlSoiiiIULF6KxsZH1OKpHswJIj9fj8RQSQAQv8NKVjrptsgAy1+9mPo8e4aUrBHuoK4RSqCuEUrTUlYwSQEuzgAUCO5aSAOIh8+bNk+8lfPnll+O6667D7Nmz45KXl8d67KSiWQGkx+vxeAoJIIIXeOmKpfatXgHU8DnzefQIL10h2ENdIZRCXSGUoqWukAAiAaSn3HvvvWEPnlLy1HGeHzylWQGkx+vxeAoJIIIXeOmKtfpVWQC1Nx5kPo8e4aUrBHuoK4RSqCuEUrTUFRJAJID0krfffluWOT0PoMrJyUFubq4ieIxmBRCgv+vxeAoJIIIXeOmKrep5WQCZmk8wn0eP8NIVgj3UFUIp1BVCKVrqSkYJoCVZwK0CO5aQANJypk2bJj91/M0330R7ezvrkVSPpgUQoK/r8XgKCSCCF3jpiq3ySVkAtbXUMJ9Hj/DSFYI91BVCKdQVQila6goJIBJAesnQoUMhSRJee+011qOkLZoWQHq7Ho+nkAAieIGXrtgrHpIFkLG1lfk8eoSXrhDsoa4QSqGuEErRUldIAJEA0kuGDBkCSZJw6NAh1qOkLZoVQHq8Ho+nkAAieIGXrjjK8nsF0CkL83n0CC9dIdhDXSGUQl0hlKKlrmSUALozC5gvsONOEkBazvTp0yFJEvbs2cN6lLRFswJIj9fj8RQSQAQv8NKVztI7gEIB/gPfYT6LXuGlKwR7qCuEUqgrhFK01BUSQCSA9JI33ngDoihi7ty5rEdJWzQrgPR4PR5PIQFE8AIvXXGW3AIUCvAdPJ/5LHqFl64Q7KGuEEqhrhBK0VJXSACRANJLvF4vrr76akiShHvuuQcWi4X1SKpHswJIj9fj8RQSQAQv8NIV58lZQKEA76FfMp9Fr/DSFYI91BVCKdQVQila6kpGCaA7soB5AjvuIAGk5Xz22WfYvn07Ro4cCUmSMGDAAFxzzTVYsmQJ7rvvPjzwwAMxefDBB1mPn1Q0K4D0eD0eTyEBRPACL11xHZ8KFAroLvoD81n0Ci9dIdhDXSGUQl0hlKKlrpAAIgGkl5z+oCmlD57qgcdoVgDp8Xo8nkICiOAFXrrSdWwiUCjAc2Qg81n0Ci9dIdhDXSGUQl0hlKKlrmSUALo9C7hFYMftJIC0nJ6HTSUDCaAUJ9Ovx1uwYAEMBgMOHDjQ57perxebN2/GrFmzMHz4cOTk5OCSSy5Bfn4+Kisr0zBtZEgAEbzAS1fcR8cFBFDxCOaz6BVeukKwh7pCKIW6QihFS10hAUQCSC9pbGw8I3iMZgVQJl+Pt2nTJhgMBkUCqKOjA1dffbW8/ukMGDCAyWVyJIAIXuClK57iEUChAPfRccxn0Su8dIVgD3WFUAp1hVCKlrpCAogEECVzo1kBlKnX423evBmiKCoSQD6fDzNmzIDBYIAkSXj66adRXV2N5uZmbNu2DaNGjYLBYMCgQYPSbiBJABG8wEtXPEcGAoUCuo5NZD6LXuGlKwR7qCuEUqgrhFK01JWMEkCLs4CbBXYsJgFE0VY0LYAy6Xo8t9uN1atXR5zBE08Avfnmm/J6W7Zsifh5ZWUlcnJyYDAYsGbNGjXHjwgJIIIXeOlKd9EfgEIBrhPTmM+iV3jpCsEe6gqhFOoKoRQtdYUEEAkgSvx0dXXhgw8+YD1GUtGsAMqk6/E+/vhjXH755bLMmTJliiIBNHHiRBgMBsyfPz/mOvPnz0d2djZuvPFGNUaPGRJABC/w0hXvoV8ChQKcJ2czn0Wv8NIVgj3UFUIp1BVCKVrqCgkgEkB6yxdffIH77rsPt9xyC2644QbMnj07guuuuw7Tp0/H+PHj0b9/f2RnZ7MeO6loVgBlSqxWqyx7cnNzsWnTJuzfv79PAVRWVqZIErndbvj9frXGjxkSQAQv8NIV38EfBgRQyTzms+gVXrpCsIe6QiiFukIoRUtdySgBtCgLuElgxyISQFrP6tWrFd9mRutXHSkJCSCVY7VakZ2djbvuugtNTU0AoEgA/fOf/5Rv8tzd3R32M4/Ho/rcfYUEEMELvHTFf+DbQKGAztI7mM+iV3jpCsEe6gqhFOoKoRQtdYUEEAkgveTLL7+UZU6P2Bk0aJC8LDc3F9nZ2WHSR5IkLFy4EP/85z9Zj59UMk4Aae16PI/Hg4aGhrBlSgTQo48+CoPBgAkTJgAATpw4gSVLlmD48OEwGAwYNmwYlixZgoqKCtX3IVpIABG8wEVXTlmAQgEoFOAoy2c/j07hoiuEJqCuEEqhrhBK0VJXSACRANJLli1bBlEU0a9fP2zatAk2mw1+vx8XXnghsrOz0draCr/fj+bmZmzYsAEDBgyAJElYuXIl69GTjuYFUCZej6dEAC1ZsgQGgwEzZ87E5s2b5Zs9R3sM/K5du9K8BySACH7goiutrbIAslc8xH4encJFVwhNQF0hlEJdIZSipa5klABamAXMFdixkH8BtGDBgj5vS9ITo9GIRx55BJdffjlycnIwfPhwzJo1C9u3b+9zW7fbjVdffRVTpkzBoEGDkJubi8mTJ2PDhg1wOp2p2JWIXHbZZZAkCStWrAhbvnjxYkiShK1bt4Yt/+yzz+SzgIqKilSZSe1oWgBp4Xq8devWRRUv8Vi3bl3c11QigObNmweDwYCRI0ciOzsbl112GT7++GM4HA7Y7Xbs3LkTF110EQwGA/r374+SkpKU7bOSuN3dTPH5Avc98vn8zGchtA0XXelskQVQd9Na9vPoFC66QmgC6gqhFOoKoRStdSVjQgLojLJp0yZF96UFgOrqaowcOTLm78gLFy6MuLVJT5xOJ2bMmBFz2/Hjx6O1tTXl+zd48GBIkoSPPvoobPnf//53iKKIBx54IGKbRYsWQRRF5Ofnp3yedESzAkgr1+OxEkB5eXnyOmPHjkV7e3vEOnV1dbjgggv6fFIYhULReLrqZAEE46usp6FQKBQKhULJjNyWBcwR2HEbvwJo8+bNEEVRkQCy2+249NJLYTAYcPHFF2PXrl1ob29HZWUlHnzwQfk1Hn/88ajbL168WL665dVXX0VzczNaWlpQUFCAQYMGwWAwYPr06Sl/+FFOTg4kSUJxcXHY8t27d0MURVx33XUR2+zYsQOiKGLixIkpnSVd0awA0sr1eD3FTYRosiY0SgTQ/Pnz5XX+8Y9/xHytxx57DAaDATk5Oejs7DyjfU0krP+rhNb+KwmhXbjoivVY7xlArZvYz6NTuOgKoQmoK4RSqCuEUrTWlYwJCaCE43a7sXr16oiTHOIJoJdeekm+MqW2tjbi50899ZT8O2vPg5F6UlxcLL/Htm3bIrb9/PPP5Z/v2LHjzHcwJKNHj4YkSdi3b1/Y8pKSEoiiiBEjRkRsc/jwYYiiiMGDB6d0lnRFswIok6/HUyKAli5dKq9TWVkZ87X27Nkjr5fOy8DoHkAEL/DQFXPD57IAstS+zXwevfL/2bvz8Djrev3jjwsez/H8TlBP9XiOih7xSbq3FEqhaSGlrexQoBSOLLIIIkVFqWwWKWvRUkqhFKRsUoRWdggICC07WAoUhBSMrXRNszZJm33m/v0R85SQpZ88mZnnO8+87+t6Xdfvl9m+M96Gw21mJhu6AjfQFVjRFVi51JVYfQYQA1Cf8vTTT2vSpEnBv19OmTJlp//emkwmVVhYKN/3NXPmzG6v09DQoL322ku+72vBggWdLuv47NsDDzywx7/wOfXUU+X7vk4++eR+Pb9PZtq0aSooKNBtt3X+C/z6+vrgXUafHKxefPFF5efna/DgwSk9S6bi7AAU5/fjWQaguXPnBtf55LeIfTyvvvpqcL233347XUfuEgYgZIts6Er1R38KBqCajx6J/Dy5Khu6AjfQFVjRFVi51JVYDUBn5Uk/8KJzVvYMQLW1tcG/V44YMUKLFy82/Xvre++9F1xn2bJlPd5/x9u8jjrqqOBnyWRSe+65p3zf1+zZs3u8bcdnEQ0cOFB1danr59y5c5Wfn6/999+/y9Azfvx4FRQU6M477+z089mzZys/P19jxoxJ2TkyGWcHoDi/H8/yX6Ti4uLgOp/8k7Serrdp06Z0HblLGICQLbKhK3V/v1Ztb+6mpr8erMqNb0V+nlyVDV2BG+gKrOgKrFzqCgNQ7g5AAwcO1IwZM4IxxPLvrffff39wnd4+qHnhwoXB28A6Pgx63bp1wW0fffTRHm+7cuXK4Hp/+ctf+vEsO2f9+vXB7jB69OhOI9TFF1+s/Px8jRo1So888ohKS0t1++23B9c/9dRTU3aOTMbZASjO78ez/BeppqYm+Or37v7aqSMXXnhh8EHRiUQiTSfuGgYgZIts6Erj+0dLb3hKrMxT+ZbqyM+Tq7KhK3ADXYEVXYGVS11hAMrNAailpaXLO08s/956/fXXB5//09uHND/88MPBfW3YsEFS53ezrFy5ssfbbt68Objegw8+GOLZ9Zybb745+GKpvfbaK/h5aWlpMPZ88tvHu3unUrbE2QEozu/Hs/wXSdrxOUCDBg3q8pdQkvTuu+8GI9G1116bziN3CQMQsoXzXdlSo8SbX5Te8NT4/pToz5PDnO8KnEFXYEVXYOVSV2I3AJ3sRecTA1BtbW2fNTU1RfTi2f69teNbvvbZZ59e7+vZZ58N7uv999+XJD3xxBOmz72tr68PrnfHHXeEfj495f7779d+++2nI444osvPBw8eHAxEHS655JKUnyFTcXYAivP78awD0JYtWzR69OjgfZi333671q1bp/Lyci1dujT4IK39999f9fX1GXwGDEDIHq53pXr9M9IbnvSGp9o1N0V+nlzmelfgDroCK7oCK5e6wgCUvgFo1KhRXb5da2fmz59veqrz589P+X1b/r214x0pRUVFvd7Xyy+/HNzXW2+9JUl66KGHTJ9729raGlzvlltu2ckrET4df5n08axevVqXXXaZTjvtNJ133nl69tln0/b4mYizA1Cc349nHYCk9re8jRs3rsf/0u6///4Z/favjjAAIVu43pVtH/4yGIAqN38Y+XlymetdgTvoCqzoCqxc6goDEANQRyz/3vqrX/0q9AD06KOPOjUA5UKcHYCk+L4fry8DkNT+J2833XSTpkyZopEjR2rEiBE67LDDtGDBAtXU1GTgxF3DAIRs4XpXWt4ZKb3hqWXVsMjPkutc7wrcQVdgRVdg5VJXYjUA/ShPOsmLzo8yNwBVVVWptLS0T6qqqnq9T8u/t1555ZXyfV/77rtvr/f18beAdfwBwzPPPNPnt4B98l1A6UpZWZkee+wxLVq0SI899lhGv3ApnXF6AJJy6/142RQGIGQLl7tSsbk0+Ouf7R/+IvLz5DqXuwK30BVY0RVYudQVBqD0DUBx/AygG264Qb7va9iwYb3e18ff7tXxbWGvv/568LO33367x9tu2rQpuN4jjzwS/gmpfdi59tprdcYZZ3T7TppkMqkrrrhCQ4YM6fTHJoMHD9YFF1ygbdu29evxo47zA1BHcuH9eNkUBiBkC5e7Urvm5mAAqln3p8jPk+tc7grcQldgRVdg5VJXGIDSNwBlWywD0GOPPRZcp7e/KOrua+C3bNkS3PaJJ57o8bYf/xp4yztoeso999zTadj585//3OU65557bvDuok8qKCjQkUceGdm7cFKRrBmAiFthAEK2cLkrfP27W1zuCtxCV2BFV2DlUldiNQCdmSed6EXnzPgPQKtXrw6u89JLL/V4X+ecc45839eUKVM6/bzjS4/mzp3b423vvvtu+b6vgoIC1dWF6+f9998fjDgd3xxeXFzc6TpPPvlkp7Hn5JNP1p///Ge98soruuqqq4KPoZkxY0aoM7iQrByA4vp+vGwKAxCyhbNd+djXvze9d2T054G7XYFz6Aqs6AqsXOoKAxADUEcsA1AymdR+++0n3/d12WWXdXudhoaG4BusPzn0nHfeefJ9X4ceemiP5zj11FPl+76OO+64UM+jrq5OY8aMCT5b+L777lNDQ0OX602aNKnT+NPW1tbp8oceeii4/K9//Wuos0QdpwagXH8/XjaFAQjZwtWuVG14Vck3Pi294aluzYLIzwN3uwL30BVY0RVYudSVWA1AZ+RJJ3jROSP+A5AkzZ07N/gcoA8//LDL5XPmzAne/tXx+T8deemll4LH+OMf/9jlti+88EJwedgve1qyZIny8/M1ZMgQvfvuu91eZ9WqVZ3++ue9997r9nrTpk1TQUGBfvOb34Q6S9RxZgDi/XjZFQYgZAtXu1Jbcrtan91NrS/upsp1r0d+HrjbFbiHrsCKrsDKpa4wADEAdcQ6ANXX12vs2LHyfV/77LOPHn74YVVWVmrNmjWaNWtWcB+zZ8/u9vY//OEPg4Hohhtu0IYNG1RWVqa77rpLI0aMkO/7OuaYY5RIJEI9j7PPPlsFBQW68MILe7zO9ddfH+wKvf010h133KH8/Pwub2XLljgxAPF+vOwLAxCyhatdqX/naqnYk4o9VWz6KPLzwN2uwD10BVZ0BVYudYUBiAGoI9YBSGr/C5qOt3l1Z/r06T0OONXV1TriiCN6vO3kyZNVWVkZ+nlMnjxZBQUFvX7Q9HHHHRdsDT0NVZL08ssvKz8/X2PHjg19nigT+QDE+/GyMwxAyBaudmXbWxcFAxAfAO0GV7sC99AVWNEVWLnUlVgNQD/Mk77vReeHuTMASVJFRYWuvPJKTZo0SUOGDNGIESM0bdo0LV26dKd/vdPc3KxFixZpypQpGjFihIYMGaKDDjpI1113nerr6/v1PEaNGqWCgoIev2q+ublZQ4YMCfaE5cuX93hf77zzTvB2smxM5AMQ78fLzjAAIVu42pXtb0yXij0ln/xC5GdBO1e7AvfQFVjRFVi51BUGIAaguGXYsGEqKCjocW947bXXgr1h8ODBvQ5OL730kvLz8zVmzJh0HTetiXwA4v142RkGIGQLV7vS8PrJUrGntqf/K/KzoJ2rXYF76Aqs6AqsXOoKAxADUNxSVFSkgoICPf/8891ePm/evGBvmDp1aq/3dffddys/P18HHXRQOo6a9kQ+APF+vOwMAxCyhatdaXzlKKnYU+uz3438LGjnalfgHroCK7oCK5e6EqsB6PQ86f+86JzOAORCfvCDH6igoEA33nhjt5cfeuihwd4wf/58032dffbZ6Thq2hP5AMT78bIzDEDIFq52pfmliVKxp5ble0R+FvzzPxNHuwL30BVY0RVYudQVBiAGoLjl9ttvV35+voqKitTY2NjpsjfeeKPTx82sXr26x/tZsWJFcL1777033cdOSyIfgHg/XnaGAQjZwtWutDy/t1TsqfnF/SM/C9q52hW4h67Aiq7AyqWuMAAxAMUt5eXlGjlypAoKCnTiiSdq7dq1SiQSWrFihQ444IBg1Dn++ON7vI+PPvpIRUVFys/P14gRI1RRUZHBZ5C6RD4A8X687AwDELKFq11pfW5WCCI2AAAgAElEQVSQVOyp6eXDIj8L2rnaFbiHrsCKrsDKpa7EagA6LU863ovOaQxArqTj84ILCgpUUFCggQMHBv/v/Px8DRs2TKWlpZ1u09jYqBdffFFXXnmlRo4cGdy+p7eSZUMiH4B4P152hgEI2cLVrrQ98w2p2FPja/8X+VnQztWuwD10BVZ0BVYudYUBiAEorvnd736nwYMHB+8w6jBy5Mhu/yDlnXfe6TQS5efna/r06WptbY3g9KlJ5AMQ78fLzjAAIVu42pXEn3aVij1tX3Fm5GdBO1e7AvfQFVjRFVi51BUGIAagOGfDhg2aN2+epk+frunTp2vBggU9vp1r48aNnT6O5je/+Y0SiUSGT5zaRD4A8X687AwDELKFk13ZUqtk8aelYk/b3jwv+vNA5eWOdgVOoiuwoiuwcqkrsRqATs2TjvOicyoDUDanublZc+fO1QMPPKCqqqqoj5OSRD4ASbwfLxvDAIRs4WRXNm+Sij2p2FP9qsuiPw9UXu5oV+AkugIrugIrl7rCAMQAROIbJwYgiffjZVsYgJAtXOxK5YYPggGo7q/zIj8P2rnYFbiJrsCKrsDKpa7EagA6JU+a5kXnFAYg4lacGYAk3o+XTWEAQrZwsStVH70RDEC1JbdFfh60c7ErcBNdgRVdgZVLXWEAYgAi8Y1TA1BfEsf342VTGICQLVzsSvWa54IBaOuHSyM/D9q52BW4ia7Aiq7AyqWuMAAxAJH4JmsHIBJtGICQLVzsSs3fHgkGoJq//yny86Cdi12Bm+gKrOgKrFzqSqwGoB/kScd60fkBAxBxKwxAJFQYgJAtXOzK1tWLgwGo6h8vR34etHOxK3ATXYEVXYGVS11hAGIAIvENAxAJFQYgZAsXu1L7/sJgAKpc/07k50E7F7sCN9EVWNEVWLnUFQYgBiAS3zAAkVBhAEK2cLEr9e9eEwxAFZvWRn4etHOxK3ATXYEVXYGVS12J1QB0cp401YvOyQxAxK0wAJFQYQBCtnCxK9ve/lUwAJWXVUZ+HrRzsStwE12BFV2BlUtdYQBiACLxDQMQCRUGIGQLF7uyfeVPpWJPySc+H/lZsIOLXYGb6Aqs6AqsXOoKAxADEIlvGIBIqDAAIVukuyuVa1arvKxGtX9ZqOalhe2WjFXtK9f2eJuGv5wiFXtKPPWVyF8fZK4riA+6Aiu6AiuXuhKrAeikPOkYLzonMQARt8IAREKFAQjZIt1dablppBJX76qWu8dIC7xA4nd5Pd6m8dWjpWJPrX/+TuSvDzLXFcQHXYEVXYGVS11hAGIAIvENAxAJFQYgZIt0dqXiozVKzvqUNMtT073fU/PSQrXeuVswAlWs7/4DnptemiwVe2pZNjLy1weZ6Qriha7Aiq7AyqWuMAAxAJH4hgGIhAoDELJFOrtS+9IiaZYnzfJU81axysvrVPPuw8EAVPPOg93eruX5faRiT80vjI/89UFmuoJ4oSuwoiuwcqkrsRqATsyTjvaicyIDEHErDEAkVBiAkC3S2ZXGe4+XZnlKXPXvKt/c/m1eFRv+EQxA25b/qtvbtT43RCr21PTyIZG/PshMVxAvdAVWdAVWLnWFAYgBiMQ3DEAkVBiAkC3S1pUtW5X4zVfa3/5118GdLmu781vSAk9ND3U/8LQ9s5tU7KnxteMif32Qga4gdugKrOgKrFzqSqwGoBPypKO86JzAAETcCgMQCRUGIGSLdHWl6v2Xgrd/1T3X+Ru/Gh85Slrgqe32/+72tok/fVEq9tSw4oeRvz5If1cQP3QFVnQFVi51hQGIAYjENwxAJFQYgJAt0tWV+qdmBQNQZemqzpe9eHnwNrDKjz7sfNsttUo+8Vmp2NP2N38R+euD9HcF8UNXYEVXYOVSVxiAGIBIfMMAREKFAQjZIl1dab51nDTLU+u8/+1yWc1fHw8GoK1vL+l8edkWqdiTij3Vr7o08tcH6e8K4oeuwIquwMqlrsRqAPp+njTFi873GYCIW2EAIqHCAIRskY6uVGzYqOTlu0izPDU8cEbXyzeu3/FB0Msu+MRlfwsGoLq/zo389UF6u4J4oiuwoiuwcqkrDEAMQCS+YQAiocIAhGyRjq5sff2+4O1fW/+ypNvrtN61e/sHQT/4vU4/r1q3MhiAaktujfz1QXq7gniiK7CiK7ByqSsMQAxAJL5hACKhwgCEbJGSrqzf1On/33D/6dIsT8nLd1H5xk3d3qbxsanSAk+JRV9R+Zba4OfVa5cHA9DWD7sfj5DFXUFOoCuwoiuwcqkrsRqA/i9POtKLzv8xABG3wgBEQoUBCNmiP12pKF2r6mXFaj1/iBrnHq2Kj9arvLxObdd9W5rlqXnRfj3etv6lq3Z8EPQ/SoKf15Q+FgxANaVPRP76IDVdQW6hK7CiK7ByqSsMQAxAJL5hACKhwgCEbNGfrmx9+PfSoZ50cru2n+2mrcvuDN7+Vf/UZT3etua9P+34IOi3/rDjPj/4QzAAVa99MfLXB6npCnILXYEVXYGVS11hAGIAIvENAxAJFQYgZIv+dKXurmul/Txp6o4RKPmTTwUDUFXJKz3fftMmJRd8qv2DoJ87L/h57fs3BwNQ5bq3I399kJquILfQFVjRFVi51JXYDUBHeNFhACKOhQGIhAoDELJFf7qybf4FUqGn5DhP9X+8SslTPyf9zFPyUk9tv/1qp8/26U7r3QXSAk/N9x8Q/Kzu3d8GA1DFxjWRvz5ITVeQW+gKrOgKrFzqCgMQAxCJbxiASKgwACFb9KcrDZefJhV6Shz8ZZWX16nq7ZfVcvFIVb21XNXv7fztW42PH9/+QdC3fikYi7a9fUkwAJWXVUT++iA1XUFuoSuwoiuwcqkrsRqAjs+TDveiczwDEHErDEAkVBiAkC3605Wm8w6XCj21Hl+w4+c7+aufj6t7+Tc7Pgh67bsqL6/T9pXnSsWekk98LvLXBqnrCnILXYEVXYGVS11hAGIAIvENAxAJFQYgZIv+dKXlh/tIhZ6afzQu1GNXlzwjLfCUXOBp65uLVV5ep4a/nCYVe0o89eXIXxukrivILXQFVnQFVi51hQGIAYjENwxAJFQYgJAt+tOV1mN3lwo9Nf7yqHCPv6lMrUuHKHHnF7T9+V+ovLxOja9OlYo9tf3525G/NkhdV5Bb6Aqs6AqsXOpKrAag4/Kkw7zoHMcARNwKAxAJFQYgZIv+dCXxvV2lQk8NV54R+vFbl+RLt3lqemySysvr1PTSgVKxp5ZlwyN/bZC6riC30BVY0RVYudQVBiAGIBLfMACRUGEAQrYI3ZWNlVKhJxV62nbjxaEfv/FPU6XbPLUt/mr7eV4YKxV7an6hMPLXBinqCnIOXYEVXYGVS11hAGIAIvENAxAJFQYgZIuwXaksWR0MQHV3zwv9+PWvXC7d5km3eapY9ze1LBsmFXtqevmgyF8bpKYryD10BVZ0BVYudSVWA9C0POlQLzrTGICIW2EAIqHCAIRsEbYr1a+9GAxAWx+9J/Tj17z3SDAA1fz1QbX9+VtSsafGV4+N/LVBarqC3ENXYEVXYOVSVxiAGIBIfMMAREKFAQjZImxXap56MBiAqp9/OvTjV6xfEwxA9S9fqsRTX5aKPTX85bTIXxukpivIPXQFVnQFVi51hQGIAYjENwxAJFQYgJAtwnalduktwQBU9fab/TpD2z3/Ld3mqfHJo5R84nNSsaftK8+N/LVBarqC3ENXYEVXYOVSV2I1AB2bJx3iRedYBiDiVhiASKgwACFbhO1K/a1XBgNQxdr1/TpD0+MHSrd5ar3vO1KxJxV72vb2JZG/NkhNV5B76Aqs6AqsXOoKAxADEIlvGIBIqDAAIVuE7cr2OT+VCj0l9/+cyrfU9usM216YId3mKXm7FwxAde/+NvLXBqnpCnIPXYEVXYGVS11hAGIAIvENAxAJFQYgZIuwXWmc+X9Soae2I7/e7zNsXbW4/XOA7twxANW+f0vkrw1S0xXkHroCK7oCK5e6EqsBaGqedLAXnakMQMStMACRUGEAQrYI25Wmn0ySCj21nDyy32eoXLuqfQC6a8cAtPWDeyN/bZCariD30BVY0RVYudQVBiAGIBLfMACRUGEAQrYI25WWk0dKhZ6afjKp/+fYslWJu/5DunvHAFRT+njkrw1S0xXkHroCK7oCK5e6EqsB6Jg86SAvOscwABG3wgBEQoUBCNmiu65s++0vlJj8ZbUd/nVVvfZSt7drO/LrUqGnxpn/l5pzPDRWWrxjAKpe+3zkrw123hWgO3QFVnQFVi51hQGIAYjENwxAJFQYgJAtuutK80l7S2M9aaynrQ/d1fV2W2qVLPoXqdDT9jk/Tck5tj/3I+meHQNQ1br+fbU8MtMVoDt0BVZ0BVYudYUBiAGIxDcMQCRUGICQLbp0ZUOFkvv8azAA1S+8tMttKv6xIfgK+PrfXZGSc9S+cZP0hx0DUMXG0shfG+ykK0AP6Aqs6AqsXOpKrAago/OkA73oHM0ARNwKAxAJFQYgZItPdqXmmWJpD0/at30Aavj1SV1uU7nqrWAAql1yc0rOUfW3l6T7dgxA5WVbIn9t0HtXgJ7QFVjRFVi51BUGIAYgEt8wAEWUs846S77va8WKFTu97t///nddfPHFmjBhggYPHqyRI0dq6tSpuuOOO9TU1JSB03YNAxCyxSe7sv2aX0h7eEru0z4ANZ9R2OU21S88EwxANX96IDVnKatUcumnpWJPyeJPq3xLbeSvDXrvCtATugIrugIrl7rCAMQAROIbBqAIsnjxYvm+bxqAnnzySQ0dOjS4/icdfvjhqqyszNDJd4QBCNnik11pmTZS2sNTYtIXpbGe2g7/ny632frYH4IBqPrVF1J2lraHv9w+AD3+2chfF+y8K0BP6Aqs6AqsXOpKrAago/Kk73nROYoBiLgVBqAMZ+nSpcrPzzcNQKWlpRo8eLB839d+++2n4uJilZWV6W9/+5uuueYaDRw4UL7v68QTT8zgM2gPAxCyxce7UlG6VslRn5L28NR86j7B5wCVr+/8dqy6u+cFA1Dl+yUpO0v96zO1/cVTVP9m188dQvT4vQIrugIrugIrl7rCAMQAROIbBqAMpbm5WbNmzeryFzy9DUAzZ86U7/saPny41q5d2+XyG264IbiflStXpvH0XcMAhGzx8a7U3n9n++f/7OFp27W/CAagqjde73SbbTdeHAxA5RsrIn8OyHxXoj4L3EZXYEVXYOVSVxiAGIBIfMMAlIE8/fTTmjRpUjDWTJkyxTQAHXHEEfJ9X6effnq3l5eXlwf3c8cdd6Tp9N2HAQjZ4uNdabjo5H9+/s+/qnrZn3Z8Ffxj93a6TcOVZ0iFnhKT8yI/P6LpStRngdvoCqzoCqxc6kqsBqApedJkLzpTGICIW2EASnNqa2uDkWbEiBFavHixXnvtNdMA1DEUnXLKKd1eXl1dHdzPXXfdla6n0G0YgJAtgq40tajtwG+2v/3rjImqKF2746vgb72y020af3mUVOip9djdIz8/IugKv1ewE3QFVnQFVi51hQGIAYjENwxAaU5tba0GDhyoGTNmaOPGjZJkHoA+/hawjz76qMvlt912W3A/7777btqeQ3dhAEK2CLpS+n7w9q/6hVepfEutEpN3bf8q+MtO63ybH42TCj21/HCfyM+PCLrC7xXsBF2BFV2BlUtdYQBiACLxDQNQmtPS0qL169d3+pl1AFq3bp1Gjx4t3/c1YcIEPfHEEyorK9Pf//53zZ8/P/iA6AsvvDDdT6NLGICQLTq60nrP9cEAVPX6qyovr1PLySPbvwr+x0WdbtN6fIFU6KnpvMMjPz8y3xV+r2Bn6Aqs6AqsXOpKrAagI/OkSV50jmQAIm6FASiCWAcgSfrwww91wgkndPsV8HvttZduv/12JRKJDJ18RxiAkC06upL42eHSHp7aDviqyrfUqry8To3nH93+VfBTdut0m8TBX5YKPTVcflrGz4vou8LvFewMXYEVXYGVS11hAGIAIvENA9BOMn/+/G7Hl97Mnz+/1/vsywD0xhtv6Mwzz+z01fEdCgoK9NOf/lTr1q1L5VM2pbm5NVKJRFKSlEgkIz8L3JZIJKWWFiXH/Uf7AHTR94PL2hZeJI31lBz3aTXXb2v/eUOjkuM+JRV6arvlV5GfHxnuCr9XYEBXYEVXYOVaV2KTI/KkiV50jmAAIm6FAWgniXIAKi4u1qBBg+T7vk499VS99dZbam5uVnV1tR555BGNHz9evu+rsLCw26+JJ4T8M2+9FLz9S4//fsfPn7gz+CBofbS6/WdVZcFXwOuBG6M5LyGEEEII6X8YgAjpFAagnaSqqkqlpaV9UlVV1et9Wgagmpoa7bHHHvJ9X2effbaSyWSX65SVlWns2LHBQJTJRP2/Srj2v5LAXYlEUrr518EA1LxxXXBZy8rlwQDU8vwj7T8reTMYgFqevjfy8yPDXeH3CgzoCqzoCqxc60pswgBESKcwAEUQywB0zz33BNfp7S1eixcvNl0v1eEzgJAtmptbpVP2lfbw1HrUoE6XVXxYuuOr4G+/RuXldap55pFgAKpZ9kTk50eGu8LvFRjQFVjRFVi51JVYfQbQ4XnSAV50DmcAIm6FASiCWAagK6+8Ur7va+zYsb3e1wcffBDc1/Lly9Nx3G7DAIRs0VxVKe31GWkPT9uvmN758i21Sh7wBWmsp+1Xnany8jrV3r8oGICq3lwR+fmRwa7wewVGdAVWdAVWLnWFAYgBiMQ3DEARxDIAXXrppfJ9X6NHj+71vj788MPgvp555pl0HLfbMAAhW7Q8fX/w9q+aJx7ocnnr94dKYz01nTNJ5eV1qr/t6mAAqvj72sjPj8zh9wqs6Aqs6AqsXOoKAxADEIlvGIAiiGUAuuuuu4LrlJSU9Hhff/jDH4LrlZaWpuvIXcIAhGzRdsWPpD08Jff+nMrXlXW5vOm8I6SxnlqP+Y7Ky+u0fe4vpEJPyfGfUXnZ1sjPj8zh9wqs6Aqs6AqsXOpKrAagw/KkCV50DmMAIm6FASiCWAagTZs2afDgwfJ9XyeddJJaWlq6XKeioiL4JrAjjzwy3cf+xGPzD0lkh+QR35X28JT44f7dXr59zrntXwU//rMq31ythl+f1P4V8Id+NfKzI7P4vQIrugIrugIrl7rCAMQAROIbBqAIYv0a+Ouvvz643jHHHKNnn31W5eXlKisr08MPP6yioiL5vq/BgwfrjTfeyOAzYABC6tX+bqHq51yt7Zf/TM0nF/ao4cKT7JedNFaJI3aXvj9KrYvndfu4dffcEHwQdOU7q9T0s4OkQk+tJwyN/DVBZvF7BVZ0BVZ0BVYudYUBiAGIxDcMQBHEOgAlk8ngw6B7MmLEiIx+9k9HGICQai0H7CkN8NQ27hvBZ/Z0p/nksX2/7OZf99iVmueKgwGo5umH1HLKnlKhp+aziyJ/TZBZ/F6BFV2BFV2BlUtdidUAdGieVORF59DsH4DOOuusnf57a0dKSkp08cUXa9KkSRo6dKiGDx+u733ve7rssst2+o3ViURCS5cu1bRp0zRy5EgNGzZMBx54oObMmaPq6upUPZ2cDwNQBLEOQB158803dd5556moqEhDhgzRiBEjdOihh+qaa65RWVlZBk7cNQxASKkttUp8Z1dpgKfmY8am7i+ATi5U4rTx0iN39NiVyvdLggGo7q5r1Xb0t6RCT40XHRv964KM4vcKrOgKrOgKrFzqCgMQA1BHFi9ebP731jvuuEMDBw7s8Q8Xhg8frqeeeqrb2yYSCU2fPr3H244dO1arV69Ox1PMuTAAkVBhAEIqVaxeIw3wpAGe6n9zZUrve6ddKduqZNHn278KfvbZSk78glToafs1Z0f+uiCz+L0CK7oCK7oCK5e6EqsB6JA8aX8vOodk7wC0dOlS5efnmwagZ555JrjelClTtHz5clVVVWnt2rVaunSp9t13X/m+ryFDhui9997rcvvZs2fL930VFBRo7ty5+uijj1RRUaEHHnhAo0ePlu/7KioqUkNDQzqfck6EAYiECgMQUqn6yaeCAWjr0iUpvW9LV1qPKwi+Cr7jK+Drb7408tcFmcXvFVjRFVjRFVi51BUGoNwegJqbmzVr1qwuf4XT2wB00EEHyfd9HX744WpsbOxy+ebNm4Mh54wzzuhyWceXHy1YsKDLbUtKSjRkyBD5vq9bbrml/08wx8MAREKFAQipVLfwxmAAqlrxZkrv29KVpp8d3P5V8Ed/OxiA6u5dEPnrgszi9wqs6Aqs6AqsXOoKA1DuDkBPP/20Jk2a1OmveXY2AP3tb38LrvPEE0/0eN/XXntt8AVGH/+G6zlz5sj3fe25557djkeSNHPmTPm+r4kTJ/bvCRIGIBIuDEBIpe2//Kk0wFPya59V+caqlN63pSvbr5ne/lXw++0SDEBbn0jtXyLBffxegRVdgRVdgZVLXYndALSfF50sGoBqa2s7fdHQ4sWLTZ9du2zZMu2xxx7yfV8bNmzo8f7/+Mc/Bve1ZcuW4OeHHXaYfN/XOeec0+Ntly1bFtz2ww8/DP8kCQMQCRcGIKRS07EHSwM8tY7+bsrv29KVut/PDT4IumMAqn75uchfF2QWv1dgRVdgRVdg5VJXGIBydwAaOHCgZsyYoY0bN0rq25cXbdu2TYlEosfL586dG9xXx1/6tLS0aNCgQfJ9XwsXLuzxtmVlZcFtH3rooRDPjnSEAYiECgMQUql19HelAZ6ajj045fdt6UrN0w93GYAq//pu5K8LMovfK7CiK7CiK7ByqSsMQLk5ALW0tGj9+vWdftbXb6/uKU1NTRo/fnzwtrKOrFu3Lrj/Rx99tMfbJxKJ4HOCbrjhhtDnIAxAJGQYgJAym6qV/NpnpQGetv/ypym/f0tXKt99p8sAVL5uc/SvDTKK3yuwoiuwoiuwcqkrsRqADs6TxnvRObjzAFRbW9tnTU1NEb14qRuArr766uB+7r///uDnq1atCn6+fPnyXu+j40Okr7jiitDnIAxAJGQYgJAqVSveDD4Aum7hjSm/f1NXNle3f/7PPweg5MQvRP66IPP4vQIrugIrugIrl7rCAJS+AWjUqFFdvl1rZ+bPn296qvPnz0/5fadiALrvvvuC+zjuuOPU1tYWXLZixYrgstdee63X+xk3bpx839fMmTNDnYO0hwGIhAoDEFJl69IlwQBU/eRTKb9/a1dap+4eDEBtR38r8tcFmcfvFVjRFVjRFVi51BUGIAagjvR3AFqyZIkKCgrk+77Gjh2rzZs3d7p85cqVDEAZDgMQCRUGIKRK/W+uDAagitVrUn7/1q40/WRyMAC1nLpX5K8LMo/fK7CiK7CiK7ByqSuxGoAOypPGedE5KHMDUFVVlUpLS/ukqqqq1/vszwC0cOHC4LZjxozRBx980OU6JSUlwXWef/75Xu+v4y1gV155ZZ/OQTqHAYiECgMQUqXhrB9IAzwlvrOryrfUpvz+rV3ZftWZwQDUdG7qP4wa7uP3CqzoCqzoCqxc6goDUPoGoFz4DKDW1lZddNFFwe3GjRun0tLSbq+7YcOG4HrFxcU93ufHPwT6pptuCvVcSHsYgEioMAAhZf9ZHjRWGuCp5YA903P/xq7U335NMAA1Xjg18tcFmcfvFVjRFVjRFVi51BUGoPQNQNmWvg5A9fX1OvXUU4PbHHzwwdq0aVOP108kEho2bJh839ett97a4/X4GvjUhQGIhAoDEFKlbeBXpQGeGk87Pi33b+3K1ieW7BiALj4u8tcFmcfvFVjRFVjRFVi51JVYDUAH5gXf8BqJA3NnAKqtrdWUKVOC65944omqra3d6WN03ObnP/95j9d57rnngvvt7q1kxB4GIBIqDEBIhYo1G4LP/9l2xSVpeQxrV6pef1nJos8rMfEL2jb355G/Nsg8fq/Aiq7Aiq7AyqWuMAAxAHXEOgA1NTVp2rRpwXV/9rOfqbm52fQY1113XfA5QT293W3mzJnyfV/jx49XMpkM9VxIexiASKgwACEVqp9dFgxAWxffnZbHoCuwoiuwoiuwoiuwcqkrsRqAvpfX/hfeUflebgxAs2bNCq43Y8YMJRIJ82OUlpYG3xQ2b968LpeXlJRoyJAh8n1fixYtCvU8yI4wAJFQYQBCKtTe9rtgAKp6+bW0PAZdgRVdgRVdgRVdgZVLXWEAYgDqiGUAWr16tfLz84PP/KmqqtK2bdt69cm/4rn00kuDx7n88su1Zs0aVVZW6qGHHtLee+8t3/c1YcIE1dfXZ+JpxzoMQCRUGICQCtt+NUMa4Cn5lU+pfN2WtDwGXYEVXYEVXYEVXYGVS11hAGIA6ohlAPr4N35ZrV+/vtN9NDY26pRTTunx+mPGjNGaNWsy8ZRjHwYgEioMQEiFxhOmSAM8tY3YLW2PQVdgRVdgRVdgRVdg5VJXYjUATc6T9vWiMzn+A9AhhxzS7wFIav9GsKVLl+q4447TqFGjNHjwYE2cOFFXXHGFysvL0/1UcyYMQCRUGICQCq2FQ6UBnpqPPCBtj0FXYEVXYEVXYEVXYOVSVxiAGIBIfMMAREKFAQj9VrZVyW/8qzTA0/Zzf5S2x6ErsKIrsKIrsKIrsHKpKwxADEAkvmEAIqHCAIT+qnz7veADoOuuvzZtj0NXYEVXYEVXYEVXYOVSV2I1AE3Kk/bxojOJAYi4FQYgEioMQOivmocfDgagmkcfTdvj0BVY0RVY0RVY0RVYudQVBiAGIBLfMACRUGEAQn/VXffbYACqXPV+2h6HrsCKrsCKrsCKrsDKpa4wADEAkfiGAYiECgMQ+qvhp2e0fwX8N/9N5WVb0/Y4dAVWdAVWdAVWdAVWLnUlVgPQxDxpjBediQxAxK0wAJFQYQBCv/8zPGKCNMBTy7hh6X0cugIjugIrugIrugIrl7rCAMQAROIbBiASKgxA6K+24d+UBo/bMNsAACAASURBVHhqPOmotD4OXYEVXYEVXYEVXYGVS11hAGIAIvENAxAJFQYg9Mu6LUp+5VPSAE/bLjk/rY9FV2BFV2BFV2BFV2DlUldiNQAdkCft7UXnAAYg4lYYgEioMAChP6pefCX4AOja2xel9bHoCqzoCqzoCqzoCqxc6goDEAMQiW8YgEioMAChP7befVcwAFU/tzytj0VXYEVXYEVXYEVXYOVSV2I1AE3Ik0Z70ZnAAETcCgMQCRUGIPTHtst+FQxAFWs2pPWx6Aqs6Aqs6Aqs6AqsXOoKAxADEIlvGIBIqDAAoT8aT50mDfDUNui/0v5YdAVWdAVWdAVWdAVWLnWFAYgBiMQ3DEAkVBiA0B8tE0ZJAzw1Hzwu7Y9FV2BFV2BFV2BFV2DlUldiNQAV5Ul7edEpYgAiboUBiIQKAxBC21KrxP/mSQM8NZx9atofj67Aiq7Aiq7Aiq7AyqWuMAAxAJH4hgGIhAoDEMKqeL80+Pyf+t9elfbHoyuwoiuwoiuwoiuwcqkrDEAMQCS+YQAiocIAhLBqnngyGIC2/nFp2h+PrsCKrsCKrsCKrsDKpa7EagDaP0/a04vO/gxAxK0wAJFQYQBCWHUL5gcDUOWKt9L+eHQFVnQFVnQFVnQFVi51hQGIAYjENwxAJFQYgBDW9hnnSAM8Jb+2i8o3Vaf98egKrOgKrOgKrOgKrFzqCgMQAxCJbxiASKgwACGspqkHSQM8te6dn5HHoyuwoiuwoiuwoiuwcqkrsRqA9suTRnnR2Y8BiLgVBiASKgxACKt1r92lAZ6ajjs0I49HV2BFV2BFV2BFV2DlUlcYgBiASHzDAERChQEIoWysUvJrn5UGeNp+wbkZeUy6Aiu6Aiu6Aiu6AiuXusIAxABE4hsGIBIqDEAIo+r1lcEHQNfdvCAjj0lXYEVXYEVXYEVXYOVSV2I1AI3Pk/bwojOeAYi4FQYgEioMQAhj6333BQNQ9Z+ezshj0hVY0RVY0RVY0RVYudQVBiAGIBLfMACRUGEAQhj1sy8PBqCKD9Zm5DHpCqzoCqzoCqzoCqxc6kqsBqBxedJILzrjGICIW2EAIqHCAIQwGn50sjTAU2L3L2bsMekKrOgKrOgKrOgKrFzqCgMQAxCJbxiASKgwACHUf24H7isN8NQyaXTmHpOuwIiuwIquwIquwMqlrjAAMQCR+IYBiIQKAxDCSBQMkAZ4avzh9zP2mHQFVnQFVnQFVnQFVi51JVYDUGGeNMKLTiEDEHErDEAkVBiA0FcVpeuCz/+pv/LSjD0uXYEVXYEVXYEVXYGVS11hAGIAIvENAxAJFQYg9FX1n58NBqCt9yzO2OPSFVjRFVjRFVjRFVi51BUGIAYgEt8wAJFQYQBCX9XeenMwAFW98nrGHpeuwIquwIquwIquwMqlrsRuABruRYcBiDgWBiASKgxA6KttF58nDfCU/OqnVb6hImOPS1dgRVdgRVdgRVdg5VJXGIAYgEh8wwBEQoUBCH3V9P0jpQGe2vb4VkYfl67Aiq7Aiq7Aiq7AyqWuMAAxAJH4hgGIhAoDEPqqdexgaYCnpqMmZfRx6Qqs6Aqs6Aqs6AqsXOpKrAagsXnSMC86YxmAiFthACKhwgCEPinbquTXPy8N8LT95z/O6GPTFVjRFVjRFVjRFVi51BUGIAYgEt8wAJFQYQBCX1S+9W7wAdB18+dm9LHpCqzoCqzoCqzoCqxc6kqsBqB986ShXnT2ZQAiboUBiIQKAxD6oubBB4MBqObRxzL62HQFVnQFVnQFVnQFVi51hQGIAYjENwxAJFQYgNAXddf9JhiAKt9dndHHpiuwoiuwoiuwoiuwcqkrDEAMQCS+YQAiocIAhL5o+MkPpQGeErv9u8q31Gb0sekKrOgKrOgKrOgKrFzqSqwGoH3ypCFedPZhACJuhQGIhAoDEPr0n9fhRdIATy37jcj8Y9MVGNEVWNEVWNEVWLnUFQYgBiAS3zAAkVBhAHJDzUPFqptzjZoPLuy3hjNPStttEt/9kpJf/7waTz6GrsBZdAVWdAVWdAVWLnWFAYgBiMQ3DEAkVBiAHLChUokv/Lv0b17w+Tr90XzQ2LTeZtsl56v8ozK6AmfRFVjRFVjRFVi51JVYDUBj8qTBXnTGMAARt8IAREKFASh6NY88KXme9C+eWvfynf4LoOaDC1V7y0K6AqfRFVjRFVjRFVi51BUGIAYgEt8wAJFQYQCK3rafz2gfgDxPFe/9PfLzuIquwIquwIquwIquwMqlrjAAMQCR+IYBKEN57rnn9OMf/1iFhYUaPHiwRo0apaOPPlo333yz6uvre71tfX295s2bp4MPPlhDhw7VqFGjdMwxx2jx4sVqbW3N0DPoHAag6LXsMUryPLUMGRb5WVxGV2BFV2BFV2BFV2DlUldiNQDtnScN8qKzNwMQcSsMQGlOa2urzj33XPm+36MJEyaotLS029tXVlZq8uTJPd522rRpOx2Q0hEGoIj/wfzBP5T81Kckz9P26T+L/Dwuy/WuwI6uwIquwIquwMqlrjAAMQCR+IYBKM256qqrgrHm3HPP1apVq1RdXa2SkhLNmzdPgwcPDkag7du3d7ptIpHQMcccI9/3tddee2np0qUqLy/XunXrNG/ePA0aNEi+7+snP/lJxp8XA1C0ti66K3j7V839j0Z+HpfleldgR1dgRVdgRVdg5VJXGIAYgEh8wwCUxpSVlQUjzcyZM7u9zvLly4OBaNGiRZ0uKy4uDi5bsWJFl9vee++9weVvvfVWWp5DT2EAilbDiT+QPE/Jz39e5evKIz+Py3K9K7CjK7CiK7CiK7ByqSuxGoBG50kDveiMZgAiboUBKI1ZvHixfN9Xfn6+KioqerzetGnT5Pu+TjjhhE4/P/bYY+X7vk499dRub5dMJnXggQfK931ddNFFKT37zsIAFKEttWr7xjclz1Nz0QHRn8dxOd0V9AldgRVdgRVdgZVLXWEAYgAi8Q0DUBozd+5cDR06VEVFRb1eb8aMGfJ9X5MnTw5+VlNTo/z8fPm+r8WLF/d429mzZ8v3fY0ZMyZl57aEASg6Va+uDN7+VT/rqsjP47pc7gr6hq7Aiq7Aiq7AyqWuxGoA2itPKvCisxcDEHErDEAZyM4+pLnjL4CmTp0a/OzVV18N3t61cuXKHm/76KOPBtfbuHFjys68szAARafu6jnBAFS1/NXIz+O6XO4K+oauwIquwIquwMqlrjAAMQCR+IYBKOKsXr06+Euf2bNnBz+///77g2GnrKysx9uvWLEiuN7rr7+eiSNLYgCKUtOBB0uep7avfFXlW2ojP4/rcrkr6Bu6Aiu6Aiu6AiuXusIAxABE4hsGoAjT3NwcfM7PoEGDtGbNmuCyW2+9NRh2Ghsbe7yP1atXB9d75plnMnFsSQxAkdlYpcS//z/J89R47PHRnycL5GxX0Gd0BVZ0BVZ0BVYudSVWA9CeeVK+F509GYCIW2EA2knmz58fDCxW8+fP3+n9JhIJnXfeecFt5syZ0+nyG2+8MbgsmUz2eD//+Mc/gus9/vjj/X6+1jQ3t0YqkWh/TRKJZORnyaSW55YFb/9qvePOyM+TDXK1K+g7ugIrugIrugIr17oSmzAAEdIpDEA7SToGoLa2Nl1wwQXB9U888US1tnb+Rbtw4UKnByASUWbODAYgbd4c9WkIIYQQQghxNwxAhHQKA9BOUlVVpdLS0j6pqqrq8f4aGhp01llnBaPN1KlTu/2Q6DvvvDO4TlNTU4/3F9VbwKL+XyVc+19JMva8R4+WPE+JYcMiP0u2yNWuoO/oCqzoCqzoCqxc60psMipP8r3ojGIAIm6FASiDqaio0NFHHx0MNieccEKP3xD24IMPBterqKjo8T4//iHQb7zxRrqO3iV8BlAE78f+8CMlP/1pyfO0/eyfRn6ebJGLXUE4dAVWdAVWdAVWLnUlVp8BxABESKcwAGUopaWlmjBhQjDWnHPOOWpubu7x+m+++WZw3VWrVvV4vY9/DfymTZvScfRuwwCUeVtvuzt4+1fN0ocjP0+2yMWuIBy6Aiu6Aiu6AiuXusIAxABE4hsGoAykpKREo0ePDoaaq6++WolEotfb1NfXB18Pv2TJkh6vN3v2bPm+r9GjR6f62L2GASjzGk48RfI8JT//eZV/tCXy82SLXOwKwqErsKIrsKIrsHKpK7EagPbIk77rRWcPBiDiVhiA0py1a9dqzJgx8n1f+fn5uvPOO823Pf744+X7vs4888xuL08mkzrwwAPl+75+8YtfpOrIpjAAZdiWWrV9czfJ89S8/4Toz5NFcq4rCI2uwIquwIquwMqlrjAAMQCR+IYBKI1pbm7WlClTgr/8Wbp0aZ9uv2TJkmA4euWVV7pcfu+99wb3/c4776Tq2KYwAGVW5WtvBW//qv/1FZGfJ5vkWlcQHl2BFV2BFV2BlUtdYQBiACLxDQNQGnP33XcHA83555+vbdu29aqhoaHT7dva2nTYYYfJ932NGDFCd999t8rKyrRhwwbNnz9fgwYNCj5PKNNhAMqsutnXBgNQ1bJXIj9PNsm1riA8ugIrugIrugIrl7oSqwFoZJ60uxedkQxAxK0wAKUxEydODAYgi6Kioi73sW7dOhUVFfV4m2nTpnUZjjIRBqDMajrwkPavfx/wFZVvqY38PNkk17qC8OgKrOgKrOgKrFzqCgMQAxCJbxiA0pSqqqo+jT89DUBS+wdCz5s3T4cccoiGDx+uYcOG6cgjj9SiRYt6/SaxdIYBKIM2Vinx//5D8jw1HjMt+vNkmZzqCvqFrsCKrsCKrsDKpa7EagAakSd9x4vOCAYg4lYYgEioMABlTvVjTwdv/6q98ZbIz5Ntcqkr6B+6Aiu6Aiu6AiuXusIAxABE4hsGIBIqDECZs+0X5wcDUOW7H0Z+nmyTS11B/9AVWNEVWNEVWLnUFQYgBiAS3zAAkVBhAMqclj1HS56n1kFDIj9LNsqlrqB/6Aqs6Aqs6AqsXOpKrAag4XnS/3rRGc4ARNwKAxAJFQagDP0D+G/rlPz0pyXP0/azzon8PNkoV7qC/qMrsKIrsKIrsHKpKwxADEAkvmEAIqHCAJQZW29fHLz9q2bJQ5GfJxvlSlfQf3QFVnQFVnQFVi51hQGIAYjENwxAJFQYgDKj4eTTJM9T8l/+ReUfbYn8PNkoV7qC/qMrsKIrsKIrsHKpK7EagIblSd/2ojOMAYi4FQYgEioMQJnRttu3JM9T8/iiyM+SrXKlK+g/ugIrugIrugIrl7rCAMQAROIbBiASKgxA6Vf5+tvB27/qL7k88vNkq1zoClKDrsCKrsCKrsDKpa4wADEAkfiGAYiECgNQ+tX95rpgAKp67uXIz5OtcqErSA26Aiu6Aiu6AiuXuhKrAWhonvQtLzpDs38AOuuss+T7vlasWNHn2yYSCX3/+9+X7/u66KKLdnrdpUuXatq0aRo5cqSGDRumAw88UHPmzFF1dXXY45NPhAGIhAoDUPo1HXyY5HlKDPiKysu2Rn6ebJULXUFq0BVY0RVY0RVYudQVBiAGoI4sXrxYvu+HHoBuvfXW4Pa9DUCJRELTp08PrvtJY8eO1erVq/vzVMg/wwBEQoUBKM02VSvx//5D8jw1Hn1s9OfJYrHvClKGrsCKrsCKrsDKpa4wADEASdLSpUuVn58fegD64IMPNGTIENMANHv2bPm+r4KCAs2dO1cfffSRKioq9MADD2j06NHyfV9FRUVqaGjo79PK+TAAkVBhAEqv6sefCd7+VXvDzZGfJ5vFvStIHboCK7oCK7oCK5e6ErsBaDcvOlk4ADU3N2vWrFld/gqnLwNQS0uLjjjiiE6372kA2rx5swYPHizf97VgwYIul5eUlARD0i233BL6eZH2MACRUGEASq9tMy4MBqDKdz+M/DzZLO5dQerQFVjRFVjRFVi51BUGoNwdgJ5++mlNmjQpGG2mTJkSagC69tpr5fu+TjjhBO2///69DkBz5syR7/vac8891djY2O11Zs6cKd/3NXHixFDPi+wIAxAJFQag9GrZa2/J89Q6cFDkZ8l2ce8KUoeuwIquwIquwMqlrsRqABqSJ33Ti86Q7BmAamtrg7FnxIgRWrx4sV577bU+D0BvvvmmBg4cqJEjR2r9+vUqKirqdQA67LDD5Pu+zjnnnB7vc9myZcE5Pvzww1DPj7SHAYiECgNQGv+hW7peyc98RvI8bf/R9MjPk+3i3BWkFl2BFV2BFV2BlUtdYQDK3QFo4MCBmjFjhjZu3ChJfR6Atm/fHvwF0ZIlSySp1wGopaVFgwYNku/7WrhwYY/3W1ZWFpzjoYceCvkMicQAREKGASh9tt75h+DtXzX3PRj5ebJdnLuC1KIrsKIrsKIrsHKpKwxAuTkAtbS0aP369Z1+1tcB6JJLLpHv+zr99NODn/U2AK1bty64/0cffbTH+00kEsHnBN1www19eFbkk2EAIqHCAJQ+DT84TfI8JT/3OZX/oyzy82S7OHcFqUVXYEVXYEVXYOVSV2I1AA3Ok77hRWdw5wGotra2z5qamiJ68fo2AL3wwgvBZ/mUlZUFP+9tAFq1alVw/8uXL+/1/ju+DeyKK64I92SIJAYgEjIMQOnT9q1vS56n5nH7R36WOIhzV5BadAVWdAVWdAVWLnWFASh9A9CoUaO6fLvWzsyfP9/0VOfPn5/y+7YOQFu3blVhYaF839fDDz/c6bLeBqAVK1YE9//aa6/1epZx48bJ933NnDmz1+uR3sMAREKFASg9Kv+yKnj7V/3MyyI/TxzEtStIPboCK7oCK7oCK5e6wgDEANQR6wD0s5/9TL7v6+yzz+5yWW8D0MqVKxmAMhwGIBIqDEDpUffbecEAVPXsS5GfJw7i2hWkHl2BFV2BFV2BlUtdidUANChP+roXnUGZG4CqqqpUWlraJ1VVVb3ep2UAevzxx+X7vsaMGdPt/fU2AJWUlAT3//zzz/d6lo63gF155ZW9Xo/0HgYgEioMQOnRdMjhkucp8Z//qfKyrZGfJw7i2hWkHl2BFV2BFV2BlUtdYQBK3wAUt88AKisrC4aZp556qtv76G0A2rBhQ3D/xcXFPZ7j4x8CfdNNN4V/QoQBiIQLA1AabKpWIm9XyfPUeNTU6M8TE7HsCtKCrsCKrsCKrsDKpa4wAKVvAMq27GwAeuCBB/r8F00ff7tXIpHQsGHD5Pu+br311h7PwdfApy4MQCRUGIBSr/qJPwdv/6qdvzDy88RFHLuC9KArsKIrsKIrsHKpK7EagAbmSf/jRWcgA1BvA5AkTZkyRb7v6+c//3mP53juueeC237wwQdpea65EgYgEioMQKm37ZcXBQNQ5arVkZ8nLuLYFaQHXYEVXYEVXYGVS11hAGIA6sjOBqDW1lZt27atV/vvv79839f5558f/KytrS24j+uuuy74DKGe3u42c+ZM+b6v8ePHK5lMpu355kIYgEioMAClXsvoMZLnqbVgYORniZM4dgXpQVdgRVdgRVdg5VJXYjUAFeRJ/+1FpyDeA5AlvX0GkCSVlpaqoKBAvu9r3rx5XS4vKSnRkCFD5Pu+Fi1aFOoMZEcYgEioMACl+B+0f9+g5Gc+I3metp95duTniZO4dQXpQ1dgRVdgRVdg5VJXGIAYgDqSiQFIki699NLgcS6//HKtWbNGlZWVeuihh7T33nvL931NmDBB9fX1YZ8K+WcYgEioMACl1ta77g3e/rX13vsjP0+cxK0rSB+6Aiu6Aiu6AiuXusIAxADUkUwNQI2NjTrllFN6/MygMWPGaM2aNWGfBvlYGIBIqDAApVbDKadLnqfk5z6n8rWbIz9PnMStK0gfugIrugIrugIrl7oSqwEoP0/6mhedfAYgywAktX8j2NKlS3Xcccdp1KhRGjx4sCZOnKgrrrhC5eXloR6bdA0DEAkVBqDUav3f70iep+Zx+0V+lriJW1eQPnQFVnQFVnQFVi51hQGIAYjENwxAJFQYgFKncsU7wdu/6n81K/LzxE2cuoL0oiuwoiuwoiuwcqkrDEAMQCS+YQAiocIAlDp1c64PBqDqP78Q+XniJk5dQXrRFVjRFVjRFVi51JVYDUB+nvRfXnR8BiDiVhiASKgwAKVO02FHSp6nxJe/rPKyrZGfJ27i1BWkF12BFV2BFV2BlUtdYQBiACLxDQMQCRUGoBTZXKNE3q6S56nxqGOiP08MxaYrSDu6Aiu6Aiu6AiuXusIAxABE4hsGIBIqDECpUf3ks8Hbv2qvvyny88RRXLqC9KMrsKIrsKIrsHKpK7EagL6bJ33Vi853GYCIW2EAIqHCAJQa2y74VTAAVb5dEvl54iguXUH60RVY0RVY0RVYudQVBiAGIBLfMACRUGEAStHzGLOv5HlqzS+I/CxxFZeuIP3oCqzoCqzoCqxc6goDEAMQiW8YgEioMACl4B+uf9+g5Gc/K3metp9xVuTnias4dAWZQVdgRVdgRVdg5VJXYjUA7Z4nfcWLzu4MQMStMACRUGEA6r+tv78vePvX1j/8MfLzxFUcuoLMoCuwoiuwoiuwcqkrDEAMQCS+YQAiocIA1H8Np50heZ6Su+yi8rWbIz9PXMWhK8gMugIrugIrugIrl7oSqwHoO3nSAC8632EAIm6FAYiECgNQ/7V+Z3fJ89RcOD7ys8RZHLqCzKArsKIrsKIrsHKpKwxADEAkvmEAIqHCANQ/lSv/Grz9q/7iX0d+njjL9q4gc+gKrOgKrOgKrFzqCgMQAxCJbxiASKgwAPVP3dwbggGo+pnnIz9PnGV7V5A5dAVWdAVWdAVWLnUlVgPQ/+ZJ/+lF538ZgIhbYQAiocIA1D+Nh0+RPE+JL31J5WVbIz9PnGV7V5A5dAVWdAVWdAVWLnWFAYgBiMQ3DEAkVBiA+mFzjRK77ip5nhqPPCr688RcVncFGUVXYEVXYEVXYOVSVxiAGIBIfMMAREKFASi86qeWBW//qpu3IPLzxF02dwWZRVdgRVdgRVdg5VJXYjUAfTtP+rIXnW8zABG3wgBEQoUBKLxtF84MBqDKt96P/Dxxl81dQWbRFVjRFVjRFVi51BUGIAYgEt8wAJFQYQDqx9n3GSt5nlq/60d+llyQzV1BZtEVWNEVWNEVWLnUFQYgBiAS3zAAkVBhAAr5D9Q1G5XcZRfJ87T9hz+K/Dy5IFu7gsyjK7CiK7CiK7ByqSuxGoC+lSd9yYvOtxiAiFthACKhwgAUztbFS4K3f229Z2nk58kF2doVZB5dgRVdgRVdgZVLXWEAYgAi8Q0DEAkVBqBwtp9+puR5Su6yi8rXbIr8PLkgW7uCzKMrsKIrsKIrsHKpKwxADEAkvmEAIqHCABRO6+7flTxPzfsWRn6WXJGtXUHm0RVY0RVY0RVYudSVWA1Au+VJX/SisxsDEHErDEAkVBiA+q7yzfeCt39tu+iSyM+TK7KxK4gGXYEVXYEVXYGVS11hAGIAIvENAxAJFQagvqu77sZgAKp+ennk58kV2dgVRIOuwIquwIquwMqlrsRqAPpmnrSrF51vMgARt8IAREKFAajvGo84SvI8Jb74RZVvron8PLkiG7uCaNAVWNEVWNEVWLnUFQYgBiAS3zAAkVBhAOqjzTVKfPGLkuep8Yijoj9PDsm6riAydAVWdAVWdAVWLnWFAYgBiMQ3DEAkVBiA+qb66eXB27/qrrsx8vPkkmzrCqJDV2BFV2BFV2DlUldiNwDledFhACKOhQGIhAoDUN9su+iSYACqfPO9yM+TS7KtK4gOXYEVXYEVXYGVS11hAGIAIvENAxAJFQagPp537DjJ89S6+3cjP0uuybauIDp0BVZ0BVZ0BVYudYUBiAGIxDcMQCRUGID6YM0mJXfZRfI8bT/9zOjPk2OyqiuIFF2BFV2BFV2BlUtdidUA9I086T+86HyDAYi4FQYgEioMQHZb71kavP1r6+IlkZ8n12RTVxAtugIrugIrugIrl7rCAMQAROIbBiASKgxAdtvPOEvyPCU/+1lVrNkY+XlyTTZ1BdGiK7CiK7CiK7ByqSsMQAxAJL5hACKhwgBk1+rnS56n5n3GRn6WXJRNXUG06Aqs6Aqs6AqsXOpKrAagr+dJ/8+LztcZgIhbYQDKQJ577jn9+Mc/VmFhoQYPHqxRo0bp6KOP1s0336z6+vpeb7tu3TpdccUVOvjggzVixAgNHTpUBxxwgC644AKVlJRk6Bl0DQOQTeXbJcHbv7ZdODPy8+SibOkKokdXYEVXYEVXYOVSVxiAGIBIfMMAlMa0trbq3HPPle/7PZowYYJKS0u7vX1xcbGGDx/e420HDRqkxYsXZ/hZtYcByKb2+puCAaj6T89Ffp5clC1dQfToCqzoCqzoCqxc6kqsBqD/yZP+3YvO/zAAEbfCAJTGXHXVVcFYc+6552rVqlWqrq5WSUmJ5s2bp8GDBwcj0Pbt2zvd9t133w0unzRpkoqLi7VlyxZt2LBBxcXFOuCAA+T7vvLz87Vs2bKMPzcGIJvGKUdLnqfErruqfHNN5OfJRdnSFUSPrsCKrsCKrsDKpa4wADEAkfiGAShNKSsr06BBg+T7vmbOnNntdZYvXx4MRIsWLep02emnny7f9zVu3DhVVVV1uW19fb0mT54s3/d1yCGHpOU59BYGIIOyrUp86UuS56nx8CnRnydHZUVX4AS6Aiu6Aiu6AiuXusIAxABE4hsGoDRl8eLFwV/oVFRU9Hi9adOmyfd9nXDCCcHP6uvrg/Ho1ltv7fG29913XzAgbdiwIaXn31mcGIDuuEOJ8ePVvG+hm0aPUduX/1OJf/uC6ubeEPk/zHOVS/8HFdxGV2BFV2BFV2DlUldiNQD9d570BS86/80ARNwKA1CaMnfuXA0dOlRFRUW9Xm/GjBnyfV+TJ08OflZSUqK99tpLvu/r9ddf7/G2r7zySjAAvfnmmyk74jm/dwAAGjpJREFUuyVODEC//nXw+Tou2/bzX6p8fUXk/zDPVS79H1RwG12BFV2BFV2BlUtdYQBiACLxDQNQmrOzb/nq+AugqVOndrmssbFRLS0tPd52yZIlwQDU0wdJpytODECu/wXQP9XOXxj5P8hzmUv/BxXcRldgRVdgRVdg5VJXGIAYgEh8wwAUYVavXq38/Hz5vq/Zs2f36bbJZFJHHXWUfN/XPvvso0QikaZTdh8nBiC58Q9JuI2uwIquwIquwIquwMqlrsRqAPpanvRvXnS+xgBE3AoDUERpbm7WscceG3yd+5o1a/p0+9///vfBX/9cf/31aTplz2EAQragK7CiK7CiK7CiK7ByqSsMQAxAJL5hAOol8+fPD0YWq/nz5+/0fhOJhM4777zgNnPmzOnTuZYvXx58SPTkyZO1bdu2sE8xdJqbWyOVSCQlSYlEMvKzwG10BVZ0BVZ0BVZ0BVaudSU2YQAipFMYgHpJOgagtrY2XXDBBcH1TzzxRLW22n/JLlu2TEOHDpXv+xoxYoRKSkr6+zQJIYQQQgghJH75rzzpX73o/BcDEHErDEC9pKqqSqWlpX1SVVXV4/01NDTorLPOCsafqVOn7vRDoj+eBx98MPjLn+HDh+vVV19NxdMMlaj/VwnX/lcSuIuuwIquwIquwIquwMq1rsQmDECEdAoDUIZSUVGho48+Ohh/TjjhhD6NP9dff31w21GjRmnFihVpPO3Ow2cAIVvQFVjRFVjRFVjRFVi51JVYfQYQAxAhncIAlIGUlpZqwoQJwYBzzjnnqLm52XTblpYWnX/++cFtx40b58TbvhiAkC3oCqzoCqzoCqzoCqxc6kqsBqCv5kmf96LzVQYg4lYYgNKckpISjR49Ohhwrr76avNXtjc3N+v0008PbnvooYdq06ZNaT6xLQxAyBZ0BVZ0BVZ0BVZ0BVYudYUBiAGIxDcMQGnM2rVrNWbMGPm+r/z8fN15553m2yaTSU2fPj30W8bSHQYgZAu6Aiu6Aiu6Aiu6AiuXuhKrAegredK/eNH5CgMQcSsMQGlKc3OzpkyZEgw4S5cu7dPtFy1aFNz25JNPVlNTU5pOGi4MQMgWdAVWdAVWdAVWdAVWLnWFAYgBiMQ3DEBpyt133x0MOOeff762bdvWq4aGhuC2lZWVGj58uHzf17777qv169fv9PZtbW0ZfX4MQMgWdAVWdAVWdAVWdAVWLnWFAYgBiMQ3DEBpysSJE4MByKKoqCi47YIFC/p0W9/39dprr2X0+TEAIVvQFVjRFVjRFVjRFVi51JVYDUAD8qTPedEZwABE3AoDUBpSVVXV5wHn4wPQGWecwQCURf+QhNvoCqzoCqzoCqzoCqxc6goDEAMQiW8YgEioMAAhW9AVWNEVWNEVWNEVWLnUFQYgBiAS3zAAkVBhAEK2oCuwoiuwoiuwoiuwcqkrsRqA/jNP2sWLzn8yABG3wgBEQoUBCNmCrsCKrsCKrsCKrsDKpa4wADEAkfiGAYiECgMQsgVdgRVdgRVdgRVdgZVLXWEAYgAi8Q0DEAkVBiBkC7oCK7oCK7oCK7oCK5e6EqsB6Mt50me96HyZAYi4FQYgEioMQMgWdAVWdAVWdAVWdAVWLnWFAYgBiMQ3DEAkVBiAkC3oCqzoCqzoCqzoCqxc6goDEAMQiW8YgEioMAAhW9AVWNEVWNEVWNEVWLnUlVgNQF/Kkz7jRedLDEDErTAAkVBhAEK2oCuwoiuwoiuwoiuwcqkrDEAMQCS+YQAiocIAhGxBV2BFV2BFV2BFV2DlUldiNQB9MU/6tBedLzIAEbfCAERChQEI2YKuwIquwIquwIquwMqlrjAAMQCR+IYBiIQKAxCyBV2BFV2BFV2BFV2BlUtdYQBiACLxDQMQCRUGIGQLugIrugIrugIrugIrl7oSqwFo1zzpU150dmUAIm6FAYiECgMQsgVdgRVdgRVdgRVdgZVLXWEAYgAi8Q0DEAkVBiBkC7oCK7oCK7oCK7oCK5e6wgDEAETiGwYgEiqVle3/cIjKx/8hGeU54D66Aiu6Aiu6Aiu6AiuXulJZGaMB6JvflPLyovPNb0b9ChDSKQxAhBBCCCGEEEIIITEPAxAhhBBCCCGEEEJIzMMARAghhBBCCCGEEBLzMAARQgghhBBCCCGExDwMQIQQQgghhBBCCCExDwMQIYQQQsj/b+/en6uo7zCO/zefc0s0QBqFpJqAiSEmYEQaW4LIZYy0RCQqCVrwQq0US6cSL0VAhKQ1MiOlUAcV0DqFKOZiuAg/EC3IRQoTFRJsLuQ8/aGTHeM5SZaSZDeb92vmzDi73918knkmi0/27AEAAAg4CiAAAAAAAICAowACAAAAAAAIOAogAAAAAACAgKMAAgAAAAAACDgKIAAAAAAAgICjAAIAAAAAAAg4CiAAAAAAAICAowACAAAAAAAIOAogAAAAAACAgKMAAgAAAAAACDgKIAAAAAAAgICjAAIAAAAAAAg4CiAAAAAAAICAowACAAAAAAAIOAogAAAAAACAgKMAwrjS1tamlStX6rbbbtNNN92ke+65R++9957XY2EMaGlpUSQSUUNDg9ejwGf+9a9/qaKiQpMnT1YsFlN2drZWr16tK1eueD0afObkyZP61a9+pSlTpigtLU2lpaX65z//6fVY8Llr166ppKREkUjE61HgQ+Xl5TKzpC/+zQLgxyiAMG50dHSoqKhIqampWrt2rf7yl79o9uzZMjP99a9/9Xo8+Njp06eVlZXFP6aQ4MKFC8rIyNCECRP0wgsv6M0331RVVZXC4bAKCwv1/fffez0ifKK1tVUTJkzQlClT9Morr2jr1q0qKiqSmemdd97xejz42Pr162VmFEBIKi8vTzNmzNDOnTsTXm1tbV6PB8BnKIAwbmzYsEFmpn379jnburq6dPfddysjI0NXr171cDr41QcffKCMjAz+moakKioqFA6Hdfjw4X7bt27dKjPT66+/7tFk8JuysjLFYjG1trY629rb25WVlaXs7GwPJ4OfNTc3KxKJKCUlhQIICa5evapwOKzVq1d7PQqAMYICCOPG9OnTNXXq1ITtO3bskJlpz549HkwFP6uoqJCZKT8/X8uWLaMAQoLJkyeruLg4Yfvly5dlZlq8eLEHU8Fv4vG4ysvL9eijjybs63v7xrfffuvBZPCz9vZ2TZs2TWVlZSotLaUAQoKWlhaZmerq6rweBcAYQQGEceHKlSsKhUJatmxZwr7W1laZmX73u995MBn8bMaMGaqurlZnZ6dzCz4FEH7o7Nmz+vLLLxO2f/HFFzIzLVmyxIOpMFb09PQoLy9P6enp6u3t9Xoc+ExVVZVuvfVWXbp0iQIISdXV1cnM1NjYKOl/dwTxuwTAYCiAMC6cPHlSZqY1a9Yk7Gtvb5eZqby83IPJ4GddXV3Of1MA4Xo8+eSTMjPV1NR4PQp86Ntvv1VjY6MefPBBcoKk9uzZIzPT3r17JYkCCEk9/fTTMjM9++yzyszMlJnp5ptv1mOPPcbzfwAkRQGEcaG5uVlmpurq6oR9PT09MjMtWrTIg8kwVlAAwa1du3bJzJSTk8NDoJFUSUmJ81yxX/7yl+ro6PB6JPhI38Pln3jiCWcbBRCS+fnPfy4z0+zZs7Vjxw69++67WrVqlcLhsPLy8vg0SgAJKIAwLjQ1NQ1YAHV3d8vM9OCDD3owGcYKCiC4sXv3bkWjUaWlpeno0aNejwOf2rt3r/bu3avnnntO0WhUBQUF+u6777weCz4Qj8c1b948TZs2rV8xSAGEZLZv366XX35ZPT09/ba/8cYbMjP98Y9/9GgyAH5FAYRx4cSJEzIzrV27NmHflStXZGaqqKjwYDKMFRRAGMqmTZsUCoU0ceJEcgLX/vznPw/4BwqMP5s3b1YoFNK+ffvU1tbmvEpKShSJRNTW1sZdHRhSV1eXotGoZs+e7fUoAHyGAgjjQt9zfpKVPH3PB3rhhRc8mAxjBQUQBtLb26tnnnlGZqbMzEx9/vnnXo+EMaTv+lRWVub1KPCB0tJS5+2BA71KS0u9HhNjwC233KKCggKvxwDgMxRAGDfuvPNO5eXlJWx/++23ZWZ6//33PZgKYwUFEJKJx+OqqqqSmSk/P19nz571eiT40JkzZ5Sbm6tVq1Yl7Ltw4YLMTA8//LAHk8Fvjh49qoMHDya8ioqKFA6HdfDgQd5eCknS+fPnVVhYqMrKyoR9bW1tMjM99NBDHkwGwM8ogDBuVFdX9/tEDel/t8jefffdmjx5sjo7Oz2cDn5HAYRkNm3aJDNTUVGRvvnmG6/HgU9du3ZNt99+uyZOnKgzZ8702/fUU0/JzLR7926PpsNYwDOA8GO9vb3Kzs5WamqqWltb++379a9/zR83ASRFAYRxo6OjQ7m5ubrpppu0bt06vfnmm/rZz36mUCikXbt2eT0efI4CCD/23XffKS0tTWam9evXa+fOnQmv+vp6r8eET3z44YeKRCLKysrSq6++qpqaGi1YsMC5+ycej3s9InyMAgjJ9P1eueWWW1RdXa2amhrNnz9fZqbly5d7PR4AH6IAwrhy8eJFVVZWKiMjQ2lpaZo1a1a/O4KAgVAA4cf+8Y9/DPmsjnnz5nk9JnyksbFRCxYs0IQJE5SSkqKioiJt27ZNvb29Xo8Gn6MAwkD6fq9MnDjR+b1SW1vL7xUASVEAwTcWL17s+n+wjx07pmXLlikzM1OxWExZWVl6+OGH1dTUNAqTwg/IC9wgJ3CLrMAtsgK3yAoAv6EAgi/U1tY6fzEf6iK5Z88eRaPRpH9tD4fD2rhx4yhNDa+QF7hBTuAWWYFbZAVukRUAfkQBBM9t375doVDI1UXy6NGjSklJkZnpF7/4hRobG9XW1qampibNnTtXZqZQKKQPP/xw9L4BjCryAjfICdwiK3CLrMAtsgLAryiA4Jmuri4988wzCX/pGOwiuWjRIpmZ7rrrroRP7eru7tacOXNkZpo+fTrvfQ4Y8gI3yAncIitwi6zALbICwO8ogOCJ999/X7m5uc6Fsbi4eMiLZGtrq7Pm73//e9I1n332mevbbTF2kBe4QU7gFlmBW2QFbpEVAGMBBRBG3eXLl50LWVpammpra/XJJ58MeXHbsmWLzEyRSERXrlxJuiYej2vy5MkyM61Zs2Ykvw2MEvICN8gJ3CIrcIuswC2yAmCsoADCqLt8+bIikYgef/xxnTt3TpJcXSSXL18uM1NeXt6g558/f77MTHPnzh322TH6yAvcICdwi6zALbICt8gKgLGCAgijrru7W2fOnOm3zc1Fsu890PPnzx/0/E888YTMTDk5OcM2M7xDXuAGOYFbZAVukRW4RVYAjBUUQPAFNxfJgoICmZmWLl066Lmef/55mZkmTZo0EqPCB8gL3CAncIuswC2yArfICgA/ogCCL7i5SE6bNk1mpsrKykHP9Yc//EFmppSUlJEYFT5AXuAGOYFbZAVukRW4RVYA+BEFEHzBzUUyLy+PiyQkkRe4Q07gFlmBW2QFbpEVAH5EAQRfcHORnDlzpsxMjzzyyKDn6rtNNj09fSRGhQ+QF7hBTuAWWYFbZAVukRUAfkQBBF9wc5EsLS2VmWnhwoWDnmvFihUyM+Xm5o7EqPAB8gI3yAncIitwi6zALbICwI8ogOALbi6SK1eulJmpoKBg0HP1fVTm/fffPxKjwgfIC9wgJ3CLrMAtsgK3yAoAP6IAgi+4uUhu27ZNZqZYLKarV68mXROPx3XrrbfKzLRmzZqRHBkeIi9wg5zALbICt8gK3CIrAPyIAgi+4OYiefr0aWfNu+++m3RNc3Ozs+bgwYMjOTI8RF7gBjmBW2QFbpEVuEVWAPgRBRB8wc1FUpJKSkqcW2U7Ojr67evu7tacOXNkZiosLFQ8Hh/pseER8gI3yAncIitwi6zALbICwI8ogOALbi+SLS0tCoVCMjMVFxfr448/1jfffKPPPvtM999/v8xMoVBI+/fvH8XpMdrIC9wgJ3CLrMAtsgK3yAoAP6IAgi+4vUhKUl1dncLhsLP+x68tW7aMztDwDHmBG+QEbpEVuEVW4BZZAeBHFEDwheu5SErSsWPHVFFRoaysLMViMWVkZKisrEz19fWjMC28Rl7gBjmBW2QFbpEVuEVWAPgRBRAAAAAAAEDAUQABAAAAAAAEHAUQAAAAAABAwFEAAQAAAAAABBwFEAAAAAAAQMBRAAEAAAAAAAQcBRAAAAAAAEDAUQABAAAAAAAEHAUQAAAAAABAwFEAAQAAAAAABBwFEAAAAAAAQMBRAAEAAAAAAAQcBRAAAAAAAEDAUQABAAAAAAAEHAUQAAAAAABAwFEAAQAAAAAABBwFEAAAAAAAQMBRAAEA4GOHDx/W888/r3vvvVc/+clPFI1GNWnSJBUWFmrlypVqaGjwesRRdeLECa9HAAAAGJMogAAA8KEvvvhC8+bNk5kN+Vq0aJH+/e9/ez3yiDp16pQWLlyo0tJSr0cBAAAYkyiAAADwmQ8++EA333yzU/DMmjVLr7/+uurr63XkyBEdOHBAL7/8srKzs5012dnZOnPmjNejj5hp06bJzCiAAAAA/k8UQAAA+EhTU5NisZjMTLFYTDt37hxw7ffff69ly5Y5JdDMmTPV09MzitOOnpycHAogAACAG0ABBACAT3R2djp3ukSjUe3bt2/IY3p6enTvvfc6JVBtbe0oTDr6KIAAAABuDAUQAAA+UVNT4xQ5VVVVro9raGhwjsvPzx/BCb1DAQQAAHBjKIAAAPCJwsJCp8g5ffr0dR27atUqbdq0SS0tLYrH4wn7m5ubVVVVpTvuuEOpqalKS0tTQUGBVq9erS+//HLA85aWlsrMlJKSMujXv+uuu2RmysnJSdjX9z1t2bJFkvTee+9p0aJFmjJlimKxmG6//XYtX75cn3/++YBfP9lr/fr1Q/xUAAAA0IcCCAAAHzh//rxTbBQUFAzbebu6ulRZWTnop4hFo1G9+uqrSY8fzgJo8+bNqqioGHCOcDist956K+nXpwACAAC4MRRAAAD4wL59+5xiY+XKlcNyzt7eXi1ZssQ5b05OjrZu3arGxkYdOnRI1dXVSk9PH7RQGc4CKDMzU2am6dOn66233lJLS4s++ugjPfLII86alJQUnTt3zjn21KlTOn78uHNscXGxjh8/ruPHj+vixYs3/kMCAAAYJyiAAADwga1btzolyEB341yvv/3tb84577vvPrW3tyesOXv2rPN8nVAopCNHjvTbP5wFkJlp3rx56uzsTFizatUqZ82GDRsS9vMMIAAAgBtDAQQAgA+88sorTgFSV1c3LOecMWOGU958/fXXA647dOiQ87WXLl3ab99wF0AnT55Mevzx48edNRUVFQn7KYAAAABuDAUQAAA+sGHDBqcA2bZt2w2f7+LFi875lixZMuT6goICmZnS09PV29vrbB/OAuinP/3pgMe3t7c768rKyhL2UwABAADcGAogAAB8oK6ublgfblxfX++cb+PGjUOuX7FihbP+7NmzzvbhLIDuu+++AY+Px+POuoULFybspwACAAC4MRRAAAD4wA8LmyeffPKGz/fOO+8459uxY8eQ69etW+es/+HHsQ9nAbRgwYJBzzHYOgogAACAG0MBBACAD7S3tysSicjMlJube93H79q1Sy+++KI++eQTdXV1XXcBtHbtWmf9iRMnnO1uC6Dp06dTAAEAAPgYBRAAAD7RV7aYmb766qvrOrakpMQ5tr6+XgcOHLiut4BVVlY668+fP58wUywWG/T4rKwsCiAAAAAfowACAMAn3n77bacEee6551wfd/ToUee4zMxMXbt2TZcuXXK2lZeXD3mOO++8U2amtLS0fg+BfuCBB5zzXLt2Lemx//nPfxSLxSiAAAAAfIwCCAAAn+ju7tbUqVNlZopGo2poaBjymM7OTt1zzz1OebJlyxZnX1FRkfP2rQsXLgx4jo8//tg5fvHixf32lZeXO/tOnz6d9Pj9+/c7a0aqAOr7uVAAAQAA/H8ogAAA8JH6+nqFw2GZmSZOnKg9e/YMuLatrU3z5893ipNZs2apq6vL2b9jxw5n35w5c9TR0ZFwjnPnzjl314RCIX366af99r/00kvOOX7zm98kHH/p0iXn7qGRLIDy8/NlZiouLh70HAAAAEiOAggAAJ+pq6tTKBRyCpHZs2frjTfe0KFDh3T48GHt379fv/3tb5Wenu6sycvL6/fsHul/H63+0EMPOWumTp2qbdu2qampSZ9++qleeuklZWRkOPvXrVuXMMupU6cUjUadNStWrNCBAwfU0NCgjRs3Os/+ueOOO0a0AJo7d65zZ9T27dt15MiRAe9IAgAAQCIKIAAAfOijjz5y7swZ6lVeXq62trak5+ns7NSjjz466PHRaFSvvfaa4vF40nPU1tY6dyX9+BUOh/WnP/1Ja9asGdECqKamJuFrL1++3MVPEgAAABIFEAAAvtXV1aXdu3eroqJC+fn5mjBhgiKRiCZNmqQZM2bo2Wef1bFjx1ydq6GhQY8//rimTp2q1NRUpaena+bMmfr973/v6k6aY8eO6bHHHtNtt92mWCymzMxMLV26VM3NzZI04gVQPB7X5s2blZ+fr9TUVE2aNElLlixx9b0DAACAAggAAAAAACDwKIAAAAAAAAACjgIIAAAAAAAg4CiAAAAAAAAAAo4CCAAAAAAAIOAogAAAAAAAAAKOAggAAAAAACDgKIAAAAAAAAACjgIIAAAAAAAg4CiAAAAAAAAAAo4CCAAAAAAAIOAogAAAAAAAAAKOAggAAAAAACDgKIAAAAAAAAACjgIIAAAAAAAg4CiAAAAAAAAAAo4CCAAAAAAAIOAogAAAAAAAAAKOAggAAAAAACDgKIAAAAAAAAACjgIIAAAAAAAg4CiAAAAAAAAAAo4CCAAAAAAAIOAogAAAAAAAAAKOAggAAAAAACDg/gus1UbCgOfVFwAAAABJRU5ErkJggg==\" width=\"960\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 1.001355 seconds.\n"
     ]
    }
   ],
   "source": [
    "markovList[2].plot.line()\n",
    "markovList[2].plot.line.currentFig.axes.set_xscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or perhaps, compare all of the independent (compact) chains (of uniquely sampled points) on the same plot,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ParaDRAM - NOTE: 3 files detected matching the pattern: \"./out/mvn_parallel_multiChain*_report.txt\"\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_1_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.012984 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_2_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.007978 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing report file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_3_report.txt\n",
      "ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: parsing the report file contents...\n",
      "done in 0.008975 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: The processed report files are now stored in the newly-created\n",
      "ParaDRAM - NOTE: component `reportList` of the ParaDRAM object as a Python list.\n",
      "ParaDRAM - NOTE: For example, to access the entire contents of the first (or the only) report file, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].contents.print()\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: where you will have to replace `pmpd` with your ParaDRAM instance name.\n",
      "ParaDRAM - NOTE: To access the simulation statistics and information, examine the contents of the\n",
      "ParaDRAM - NOTE: components of the following structures:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].contents.print()  # to print the contents of the report file.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].setup             # to get information about the simulation setup.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.time        # to get the timing information of the simulation.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.chain       # to get the statistics of the simulation output sample.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.numFuncCall # to get information about the number of function calls.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].stats.parallelism # to get information about the simulation parallelism.\n",
      "ParaDRAM - NOTE:     pmpd.reportList[0].spec              # to get the simulation specification in the report file.\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For more information and examples on the usage, visit:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     https://www.cdslab.org/paramonte\n",
      "\n",
      "\n",
      "ParaDRAM - WARNING: The ``delimiter`` is neither given as input to ``readChain()``\n",
      "ParaDRAM - WARNING: nor set as a simulation specification of the ParaDRAM object. \n",
      "ParaDRAM - WARNING: This information is essential, otherwise how could the output files be parsed?\n",
      "ParaDRAM - WARNING: For now, the ParaDRAM sampler will assume a comma-separated \n",
      "ParaDRAM - WARNING: file format for the contents of the chain file(s) to be parsed.\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: 3 files detected matching the pattern: \"./out/mvn_parallel_multiChain*_chain.txt\"\n",
      "\n",
      "\n",
      "ParaDRAM - NOTE: processing chain file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_1_chain.txt\n",
      "ParaDRAM - NOTE: reading the file contents... done in 0.043915 seconds.\n",
      "ParaDRAM - NOTE: ndim = 4, count = 30000\n",
      "ParaDRAM - NOTE: parsing file contents... \n",
      "ParaDRAM - NOTE: computing the sample correlation matrix... \n",
      "ParaDRAM - NOTE: adding the correlation graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.014946 seconds.\n",
      "ParaDRAM - NOTE: computing the sample covariance matrix... \n",
      "ParaDRAM - NOTE: adding the covariance graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.014961 seconds.\n",
      "ParaDRAM - NOTE: computing the sample autocorrelations... \n",
      "ParaDRAM - NOTE: adding the autocrrelation graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: adding the graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.001 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a line3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a jointplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a histplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot1 plot object from scratch... done in 0.000998 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot2 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contourf plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a grid plot object from scratch... done in 0.000998 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing chain file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_2_chain.txt\n",
      "ParaDRAM - NOTE: reading the file contents... done in 0.04488 seconds.\n",
      "ParaDRAM - NOTE: ndim = 4, count = 30000\n",
      "ParaDRAM - NOTE: parsing file contents... \n",
      "ParaDRAM - NOTE: computing the sample correlation matrix... \n",
      "ParaDRAM - NOTE: adding the correlation graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.01496 seconds.\n",
      "ParaDRAM - NOTE: computing the sample covariance matrix... \n",
      "ParaDRAM - NOTE: adding the covariance graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.015957 seconds.\n",
      "ParaDRAM - NOTE: computing the sample autocorrelations... \n",
      "ParaDRAM - NOTE: adding the autocrrelation graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.001034 seconds.\n",
      "ParaDRAM - NOTE: adding the graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a line3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter3 plot object from scratch... done in 0.001057 seconds.\n",
      "ParaDRAM - NOTE: creating a jointplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a histplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot1 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot2 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contourf plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a grid plot object from scratch... done in 0.001995 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: processing chain file: D:\\Dropbox\\Projects\\20180101_ParaMonte\\paramontex\\Python\\Jupyter\\sampling_multivariate_normal_distribution_via_paradram_parallel\\out\\mvn_parallel_multiChain_process_3_chain.txt\n",
      "ParaDRAM - NOTE: reading the file contents... done in 0.042885 seconds.\n",
      "ParaDRAM - NOTE: ndim = 4, count = 30000\n",
      "ParaDRAM - NOTE: parsing file contents... \n",
      "ParaDRAM - NOTE: computing the sample correlation matrix... \n",
      "ParaDRAM - NOTE: adding the correlation graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.013932 seconds.\n",
      "ParaDRAM - NOTE: computing the sample covariance matrix... \n",
      "ParaDRAM - NOTE: adding the covariance graphics tools... \n",
      "ParaDRAM - NOTE: creating a heatmap plot object from scratch... done in 0.013963 seconds.\n",
      "ParaDRAM - NOTE: computing the sample autocorrelations... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaDRAM - NOTE: adding the autocrrelation graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: adding the graphics tools... \n",
      "ParaDRAM - NOTE: creating a line plot object from scratch... done in 0.001029 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a line3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a scatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a lineScatter3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a jointplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a histplot plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot1 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a kdeplot2 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour3 plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contourf plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a contour plot object from scratch... done in 0.0 seconds.\n",
      "ParaDRAM - NOTE: creating a grid plot object from scratch... done in 0.000997 seconds.\n",
      "\n",
      "ParaDRAM - NOTE: The processed chain files are now stored in the newly-created\n",
      "ParaDRAM - NOTE: component `chainList` of the ParaDRAM object as a Python list.\n",
      "ParaDRAM - NOTE: For example, to access the contents of the first (or the only) chain file, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].df\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: where you will have to replace `pmpd` with your ParaDRAM instance name.\n",
      "ParaDRAM - NOTE: To access the plotting tools, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.<PRESS TAB TO SEE THE LIST OF PLOTS>\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For example,\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.line()         # to make 2D line plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.scatter()      # to make 2D scatter plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.lineScatter()  # to make 2D line-scatter plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.line3()        # to make 3D line plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.scatter3()     # to make 3D scatter plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.lineScatter3() # to make 3D line-scatter plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.contour()      # to make fast 2D kernel density plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.contourf()     # to make fast 2D kernel density filled contour plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.contour3()     # to make fast 3D kernel density contour plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.histplot()     # to make seaborn 1D distribution plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.kdeplot1()     # to make seaborn 1D kernel density plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.kdeplot2()     # to make seaborn 2D kernel density plots.\n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].plot.grid()         # to make GridPlot\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: To plot or inspect the variable autocorrelations or the correlation/covariance matrices, try:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     pmpd.chainList[0].stats.<PRESS TAB TO SEE THE LIST OF COMPONENTS>\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE: For more information and examples on the usage, visit:\n",
      "ParaDRAM - NOTE: \n",
      "ParaDRAM - NOTE:     https://www.cdslab.org/paramonte\n",
      "\n"
     ]
    },
    {
     "data": {
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       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
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       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParaDRAM - NOTE: making the line plot... done in 0.096741 seconds.\n",
      "ParaDRAM - NOTE: making the line plot... done in 0.042886 seconds.\n",
      "ParaDRAM - NOTE: making the line plot... done in 0.041889 seconds.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import paramonte as pm\n",
    "pmpd = pm.ParaDRAM()\n",
    "pmpd.readReport(\"./out/mvn_parallel_multiChain\")\n",
    "pmpd.readChain(\"./out/mvn_parallel_multiChain\")\n",
    "\n",
    "plt.figure() # one figure for all plots\n",
    "\n",
    "for process in range(pmpd.reportList[0].stats.parallelism.current.numProcess.value):\n",
    "\n",
    "    pmpd.chainList[process].plot.line.figure.enabled = False # all plots appear in the same plot\n",
    "    pmpd.chainList[process].plot.line.ccolumns = None # turn off color-mapping\n",
    "    pmpd.chainList[process].plot.line()\n",
    "\n",
    "pmpd.chainList[process].plot.line.currentFig.axes.set_xscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impressively, all independent chains, even though all start at random locations in the domain of the objective function, end up at the same sole peak of the objective function, as expected and illustrated in the above figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**There are many more functionalities and features of the ParaMonte library that were neither explored nor mentioned in this example Jupyter notebook. You can explore them by checking the existing components of each attribute of the ParaDRAM sampler class and by visiting the [ParaMonte library's documentation website](http://cdslab.org/paramonte/)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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