{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis with a configuration file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps the simplest approach to Hazel is to use configuration files. In this notebook we show how to use a configuration file to run Hazel in different situations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single pixel synthesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/miniconda3/envs/py36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018.06.07\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "import hazel\n",
    "import h5py\n",
    "print(hazel.__version__)\n",
    "label = ['I', 'Q', 'U', 'V']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configuration files can be used very simply by just instantiating the `Model` with a configuration file. First, let's print the configuration file. The architecture of the file is explained in the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Hazel configuration File\r\n",
      "\r\n",
      "[Working mode]\r\n",
      "Output file = output.h5\r\n",
      "Number of cycles = 1\r\n",
      "\r\n",
      "# Topology\r\n",
      "# Always photosphere and then chromosphere\r\n",
      "# Photospheres are only allowed to be added with a filling factor\r\n",
      "# Atmospheres share a filling factor if they are in parenthesis\r\n",
      "# Atmospheres are one after the other with the -> operator\r\n",
      "# Atmosphere 1 = ph2 -> ch1 -> ch2\r\n",
      "\r\n",
      "[Spectral regions]\r\n",
      "    [[Region 1]]\r\n",
      "    Name = spec1\r\n",
      "    Wavelength = 10826, 10833, 150\r\n",
      "    Topology = ph1 -> ch1 -> te1\r\n",
      "    Stokes weights = 1.0, 1.0, 1.0, 1.0\r\n",
      "    LOS = 0.0, 0.0, 90.0\r\n",
      "    Boundary condition = 1.0, 0.0, 0.0, 0.0       # I/Ic(mu=1), Q/Ic(mu=1), U/Ic(mu=1), V/Ic(mu=1)\r\n",
      "    Wavelength file = 'observations/10830.wavelength'\r\n",
      "    Wavelength weight file = 'observations/10830.weights'\r\n",
      "    Observations file = 'observations/10830_stokes.1d'    \r\n",
      "    Weights Stokes I = 1.0, 0.0, 0.0, 0.0\r\n",
      "    Weights Stokes Q = 0.0, 0.0, 0.0, 0.0\r\n",
      "    Weights Stokes U = 0.0, 0.0, 0.0, 0.0\r\n",
      "    Weights Stokes V = 0.0, 0.0, 0.0, 0.0\r\n",
      "    Mask file = None\r\n",
      "\r\n",
      "[Atmospheres]\r\n",
      "\r\n",
      "    [[Photosphere 1]]\r\n",
      "    Name = ph1\r\n",
      "    Reference atmospheric model = 'photospheres/model_photosphere_200.1d'\r\n",
      "    Spectral region = spec1\r\n",
      "    Wavelength = 10826, 10833\r\n",
      "    Spectral lines = 300,\r\n",
      "\r\n",
      "        [[[Ranges]]]\r\n",
      "        T      = 2500.0, 9000.0\r\n",
      "        vmic   = 0.0, 3.0\r\n",
      "        v      = -10.0, 10.0\r\n",
      "        Bx     = -1000.0, 1000.0\r\n",
      "        By     = -1000.0, 1000.0\r\n",
      "        Bz     = -1000.0, 1000.0\r\n",
      "        ff     = 0.0, 1.001\r\n",
      "\r\n",
      "        [[[Nodes]]]\r\n",
      "        T      = 1, 1, 5, 5\r\n",
      "        vmic   = 1, 0, 1, 1\r\n",
      "        v      = 0, 0, 1, 1\r\n",
      "        Bx     = 0, 0, 1, 1\r\n",
      "        By     = 0, 0, 1, 1\r\n",
      "        Bz     = 0, 0, 1, 1\r\n",
      "        ff     = 0, 0, 0, 0\r\n",
      "\r\n",
      "\r\n",
      "    [[Chromosphere 1]]\r\n",
      "    Name = ch1                                              # Name of the atmosphere component\r\n",
      "    Spectral region = spec1                                 # Spectral region to be used for synthesis\r\n",
      "    Height = 3.0                                            # Height of the slab\r\n",
      "    Line = 10830                                            # 10830, 5876\r\n",
      "    Wavelength = 10828, 10833                         # Wavelength range used for synthesis\r\n",
      "    Reference atmospheric model = 'chromospheres/model_chromosphere.1d'    # File with model parameters\r\n",
      "\r\n",
      "        [[[Ranges]]]\r\n",
      "        Bx     = -500, 500\r\n",
      "        By     = -500, 500\r\n",
      "        Bz     = -500, 500\r\n",
      "        tau    = 0.1, 2.0\r\n",
      "        v      = -10.0, 10.0\r\n",
      "        deltav = 3.0, 12.0\r\n",
      "        beta   = 0.9, 2.0\r\n",
      "        a      = 0.0, 1.0\r\n",
      "        ff     = 0.0, 1.001\r\n",
      "        \r\n",
      "\r\n",
      "        [[[Nodes]]]\r\n",
      "        Bx     = 0, 0, 1, 1\r\n",
      "        By     = 0, 0, 1, 1\r\n",
      "        Bz     = 0, 0, 1, 1\r\n",
      "        tau    = 0, 0, 0, 0\r\n",
      "        v      = 0, 0, 0, 0\r\n",
      "        deltav = 0, 0, 0, 0\r\n",
      "        beta   = 0, 0, 0, 0\r\n",
      "        a      = 0, 0, 0, 0\r\n",
      "        ff     = 0, 0, 0, 0\r\n",
      "    \r\n",
      "    [[Parametric 1]]\r\n",
      "    Name = te1\r\n",
      "    Spectral region = spec1\r\n",
      "    Wavelength = 10828, 10833\r\n",
      "    Reference atmospheric model = 'telluric/model_telluric.1d'    # File with model parameters\r\n",
      "    Type = Voigt           # Voigt, MoGaussian, MoVoigt \r\n",
      "\r\n",
      "        [[[Ranges]]]\r\n",
      "        Lambda0 = 10832.0, 10834.0\r\n",
      "        Sigma = 0.1, 0.5\r\n",
      "        Depth = 0.2, 0.8\r\n",
      "        a = 0.0, 1.2\r\n",
      "        ff = 0.0, 1.001\r\n",
      "    \r\n",
      "        [[[Nodes]]]\r\n",
      "        Lambda0 = 0, 0, 0, 0\r\n",
      "        Sigma = 0, 0, 0, 0\r\n",
      "        Depth = 0, 0, 0, 0\r\n",
      "        a = 0, 0, 0, 0\r\n",
      "        ff = 0, 0, 0, 0\r\n"
     ]
    }
   ],
   "source": [
    "%cat conf_single.ini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use this file and do the synthesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-09-07 08:50:49,092 - Adding spectral region spec1\n",
      "2018-09-07 08:50:49,094 -   - Reading wavelength axis from observations/10830.wavelength\n",
      "2018-09-07 08:50:49,097 -   - Reading wavelength weights from observations/10830.weights\n",
      "2018-09-07 08:50:49,101 -   - Using observations from observations/10830_stokes.1d\n",
      "2018-09-07 08:50:49,102 -   - No mask for pixels\n",
      "2018-09-07 08:50:49,103 -   - Using LOS ['0.0', '0.0', '90.0']\n",
      "2018-09-07 08:50:49,104 -   - Using boundary condition ['1.0', '0.0', '0.0', '0.0']\n",
      "2018-09-07 08:50:49,105 - Using 1 cycles\n",
      "2018-09-07 08:50:49,106 - Not using randomizations\n",
      "2018-09-07 08:50:49,106 - Adding atmospheres\n",
      "2018-09-07 08:50:49,107 -   - New available photosphere : ph1\n",
      "2018-09-07 08:50:49,108 -     * Adding line : [300]\n",
      "2018-09-07 08:50:49,109 -     * Magnetic field reference frame : vertical\n",
      "2018-09-07 08:50:49,110 -     * Reading 1D model photospheres/model_photosphere_200.1d as reference\n",
      "2018-09-07 08:50:49,112 -   - New available chromosphere : ch1\n",
      "2018-09-07 08:50:49,113 -     * Adding line : 10830\n",
      "2018-09-07 08:50:49,114 -     * Magnetic field reference frame : vertical\n",
      "2018-09-07 08:50:49,115 -     * Reading 1D model chromospheres/model_chromosphere.1d as reference\n",
      "2018-09-07 08:50:49,117 -   - New available parametric : te1\n",
      "2018-09-07 08:50:49,118 -     * Reading 1D model telluric/model_telluric.1d as reference\n",
      "2018-09-07 08:50:49,119 - Adding topologies\n",
      "2018-09-07 08:50:49,120 -   - ph1 -> ch1 -> te1\n",
      "2018-09-07 08:50:49,120 - Removing unused atmospheres\n",
      "2018-09-07 08:50:49,121 - Number of pixels to read : 1\n"
     ]
    }
   ],
   "source": [
    "mod = hazel.Model('conf_single.ini', working_mode='synthesis', verbose=3)\n",
    "mod.synthesize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now do some plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/miniconda3/envs/py36/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAALICAYAAABiqwZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XmYm3d5L/zvLWkkzYw0+77ZY3u8\nJ7Zjx3H2EAK1DSRspQkNtJQ0QBsa6DkvJ5SX0xdo37YUeqAl0AZICwQIAUIJwcQsIRvZPHYc2+Ml\ntsfL7Pu+SCPpPn9IMmNnFs3oefQ80nw/1+UrluaRdNuZkb/zm/t3/0RVQUREREREUQ6rCyAiIiIi\nshMGZCIiIiKiaRiQiYiIiIimYUAmIiIiIpqGAZmIiIiIaBoGZCIiIiKiaRiQiYiIiIimYUAmIiIi\nIpqGAZmIiIiIaBqX1QWkWklJiS5fvtzqMohoidm/f3+vqpZaXYdd8L2YiKyQ6HvxkgvIy5cvR2Nj\no9VlENESIyLnrK7BTvheTERWSPS9mC0WRERERETT2DYgi8hOETkhIqdE5L5ZrrlXRI6ISJOIfCzV\nNRIRERFR5rFlQBYRJ4D7AewCsB7AHSKy/pJrNgL4cwDbAWwC8FYRaUh1rURERESUWWwZkBENvadU\ntVlVgwAeBnDbJdesA/Ciqo6ragjA0wDeMdOTicjdItIoIo09PT2mFk5ERERE6c2uAbkaQMu0262x\n+6Y7AuAGESkWkRwAuwHUzvRkqvqAqm5T1W2lpdxETkRERESzs+sUC5nhPr3ohuoxEfknAL8CMArg\nVQChFNRGRERERBnMrivIrbh4NbgGQPulF6nqN1X1ClW9AUA/gJMpqo+IiIiIMpRdA/I+AA0iUi8i\nbgC3A3js0otEpCz23zoA7wTw/ZRWSUREREQZx5YtFqoaEpF7AOwF4ATwoKo2AYCI7AFwl6q2A/ix\niBQDmALwl6o6YFnRRERERJQRbBmQAUBV9wDYM8P9u6f9/vqUFkVEREREGc+2AZmI7EFVcaZ3DCe7\nR9E6MIGxQCj6KxhCMBSB0yEQETgEcIjAIYIsp8Cb5YTH5bjwX8+0236vC3neLORnZyHPmwWf1wWn\nY6a9uURERKnHgExEM+oZCeAbzzXj54c60DowcdHHPC4Hcj0uuJ0ORFQR0WiQDqsiHFGEworJUBiq\nszz5JUQAn+f3obk8z4PyPO+0Xx5UFWRjeXEust1OE/60REREv8eATEQXUVU8eqANn338KMYCIVzX\nUIK/uGkVNlTlYVlxDnweF1zO+ff3qiqmYkE5MBXB5FQYgVAYk1MRjEyGMDw5heGJKQxPhmL/ncLw\nRAgD40F0DU/icNsw+sYCrwvZlfleLC/OxepyHzZW52NjdT4aynwJ1URERJQIBmQiusjXnj6Nzz9x\nAtuWFeIf33U5VpX5FvU8IgK3S+B2OQDv4mqZCkfQOxpA59AkWgcmcLZ3DGd6x9DcO4Yf7m/Ft144\nByC6or2lrgDXN5Ti+oYSbKzKh4MtG0REtEgMyER0wZ7DHfj8Eydw66YqfOmPNlseMrOcDlTmZ6My\nPxtb6gov+lg4Eu2NbmofwqHWITx/ug//vPcE/nnvCRTmZOFN68tx66ZqXLOy2PI/BxERpRcGZKIF\nmpwK43svncfTr/XgcNsQKvK82FCVh796YwNqi3KsLm/RzvSO4eM/OIitywrx+XdfbvtQ6XQIVpX5\nsKrMh9s2R0+i7xkJ4HenevHUiW7sOdyJRxpbUVuUjTuvWob3XlUHvzfL4qqJiCgdMCATLcCLzX34\n5KOHcaZ3DCtLc/HGtWXoHgng54c78ERTJz7/rsux67JKq8tclH/9zUmIAF/74yvgzUrPjXClfg/e\nvqUab99SjcmpMH51tAvfefEc/uEXx/HVp07jz6+vx13Xr0jbPx8REaUGAzJRgn56sA0f/8FB1BTm\n4KEPXoXrGkoufOx83zg++v0D+Mh3D+Dbf7YdN6wutbDShTvdM4qfHmzDXdevQFneIhuGbcab5cTb\nNlXhbZuqcLh1CF/+zWv4wi9fww8aW/CZWzfg5rXlVpdIREQ2xW3fRAl4/FA7Pv6Dg7hyeRF+ce/1\nF4VjAKgrzsEPPnQ1Vpbm4pOPHsZoIGRRpYvzlSdPweNy4u4bVlhdiikuq8nHN/7kSnz/z3fA43Li\nz/6rEf/7p0cwORW2ujQiIrIhBmSieRxqHcTHf3AQ25YV4T8/cCVyPTP/4MWb5cTn370J7UMT+Ic9\nx1Jc5eK1D07gpwfb8L6rl6HE57G6HFNdvbIYe/7qevz59fX49gvn8If//gK6RyatLouIiGyGAZlo\nDmOBEP7q+6+gxOfBA+/fihz33F1JW5cV4gPX1OO7L53Hmd6xFFWZnL1NnYgocPuVtVaXkhJulwOf\nest6PPC+rTjVPYr3/PsLaOkft7osIiKyEQZkojl85mdNONc/jv/zR5tRkONO6DEfvnEFnA7BDxtb\nTK7OGL9s6kJDmQ8rShc37zhdvXlDBR666yr0jwXxnv94AZ1DXEkmIqIoBmSiWew7249HGlvx4RtX\nYseK4oQfV5bnxU2rS/HjA60IhSMmVpi8gbEgXj7bjz/YUGF1KZbYuqwQ3/vzHRiemMIHv7UPY2nW\nO05EROZgQCaaQSSi+MzPmlCZ78VHb1614Mf/4bZadA0H8MzJHhOqM86vj3UhHNElG5ABYGN1Pr7y\n3itwrGMYH//BQeilZ1sTEdGSwzFvZAvBUATPvNaDZ0724HjHCHpGA8hxO1FbmINdl1XglnXls26O\nM8OP9rfiSNswvnz75nn7jmdy89oyFOe68ci+VluPE9vb1IXqgmxsrM6zuhRLvWFtGT65ax3+fs8x\n/LCxFe9ZIv3YREQ0MwZkslTfaADffO4MvvfyeQyOTyHX7cS6yjxsqMrDRDCMV1oG8ERTJ8rzPPjH\nd12ON6wpM72myakwvvirE7iirgC3bqpa1HO4XQ68fUs1vvX8WYwGQvClMNwnanIqjGdP9uCO7XUQ\nsfepeanwwevq8ZvjXfjs40dxzapi1BSm76mIRESUHPv9q01LwkQwjH9/+jQeeKYZk6Ewdm6owB9u\nq8H1DaXIcv6+8ycSUbx4pg//32NN+MB/7sNd19XjU29ZZ2qg+9H+VnQNB/Av79mc1OvcuLoU33zu\nDPafG8CNNjw4pKl9CIFQBNesTLy/OpM5HIJ/fvcm7PzSM/ibnxzBt/9su9UlERGRRRiQKeWeOtGN\nT/3kCNoGJ/DWyyvxsVtWY1XZzBMUHA7BNStL8LOPXoe/e/wYvvHcGTgcgk/uWmtKSJ4KR/C1p05j\nS11B0sFx67JCuByCl5r7bBmQXzk/CADYXFdgcSX2UVuUg79+8xp87vGjePZkD65vsN//NyIiMh8D\nMqXMeDCEzz1+FN9/uQWrynx4+O4dCU+H8Lic+OxtGwAADzzTjMIcNz5y00rDa/zvV9rQNjiBz719\nQ9IBPNfjwmU1+Xixuc+g6ox1sGUQ1QXZKPNnxtHSRrlzRx0efO4M/umJ47h2ZQkcDrafEBEtNZxi\nQSlxsmsEt33ld3h4Xws+dOMKPP7R6xY0Og0ARASfuXUD3nJ5Jb7wyxNoPNtvaI3hiOJrT53G+so8\nw3qdd6woxqHWIYwH7Tc+7GDLIDbXcvX4Uh6XE3/9ptU40jaMnx/usLocIiKyAAMyme6JI5249Su/\nw8B4EA998Cp8ctc6eLOci3ouh0Pwj++8DFUFXtz78EEMTUwZVueewx1o7h3DPTevMqx946r6IoQi\niv3nBgx5PqP0jgbQOjDBgDyLt2+pxppyP/71Nyc59o2IaAliQCbTqEZXZD/80H6sqfDj5391Pa5d\nVZL08/q9Wfi3O65A1/AkPvOzJgMqjW4GvP+3p7CqzIedBs4E3ra8CE6H4KVmY1e7k/VqS7T/eBMD\n8oycDsEHr6/Hye5RvGDTFhkiIjIPAzKZIhJRfPbxo/inJ47jbZuq8PDdO1CeZ1yv6+baAnzoxhV4\n9EAbXjidfID5zfFuHO8cwV++YaWhPac+jwsbq+3Xh3ywZRBOh+Cy6nyrS7GtWzdVoTAnC996/qzV\npRARUYoxIJPhwhHFJ358CP/5u7P4s2vr8eU/2rzoloq53POGBtQUZuPTPz2CYGjxRzqrKr7y5EnU\nFeXgbZcvbu7xXLYvL8Sh1iFbHTt9sGUQa8r9yHYb//8lU3iznLh9ex1+dbQLrQPjVpdDREQptOQC\ncvdIwOoSMlokorjvx4fwo/2t+NgtDfj0W9eZNgUg2+3E527biFPdo/j6s82Lfp5nT/bi1dYhfOSm\nlXA5jf+SWFuRh2A4grN9Y4Y/92KoKl5tGeR4twTcuWMZAOC7L523uBIiIkqlJReQu4Yn8cAzp60u\nIyOpKv72sSb8cH8r7n1jAz52y2rTT2h7w9oy7NxQgX978iRa+he3yveVJ0+hMt+Ld15RbXB1UWsq\n/ACA450jpjz/QnWPBDA8GcK6WF00u+qCbNy0pgw/faUNkUjmbtYTkZ0ickJETonIfQu9RkScIvKK\niDyemoqJiMy15AJyfnYW/v89x/HogVarS8k4X33qNL7z4jl86IYV+NgtDSl73f/9tvVwiOBvH2ta\n8MSBl5r78PLZftx9wwp4XOa0G6wq88HpEJywSUA+3T0KAFhZOvPhLHSxt22qRPvQJA6ct9ckEqOI\niBPA/QB2AVgP4A4RWb/Aa+4FcCw1FRMRmW/JBeTawhxcu6oY/+vHh7DP4Dm6S9lPD7bhn/eewG2b\nq3CfSafczaaqIBt//abVePJ4N/Y2dSX8OFXFF3/1Gkp8btx+ZZ1p9XmznKgvybXNCvLpnlhAnuX0\nQrrYLevK4XY58PihjJ2JvB3AKVVtVtUggIcB3JboNSJSA+AtAL6RwpqJiEy15AKyCPDV925FTWEO\nPvLQfrQNTlhdUto73DqE/+dHh7C9vgiff/flKQ3HcX96zXKsrfDjMz9rwlggsUM5fnm0Cy+f6ce9\nt6w2fbPamgq/bVaQT3WPwudxoczvsbqUtOD3ZuHmNWX4+eEOhDOzzaIaQMu0262x+xK95ksAPgFg\n3l2oInK3iDSKSGNPT8/iKyYiMtmSC8gAkJ+Tha+/fxsCUxF8+Dv7k5qAsNQNjAXx4Yf2oyTXja/9\n8RWmtSnMx+V04O/fsREdQ5P40q9fm/f6YCiCf9hzDKvKfLjjylrT61tb7sf5/vGEw7uZTveMYWVp\nriXfyKSrt26qRM9IAC+dsde4PoPM9Ilw6XcCM14jIm8F0K2q+xN5IVV9QFW3qeq20tLShdZJRJQy\nSzIgA9G+0C+8ZxMOtw3h808ct7qctKSq+PgjB9EzEsDX7tyKYp+1K5JblxXhju21ePB3Z3G4dWjO\nax/83Rmc7RvHp3avM2VyxaXiG/VOdFm/iny6Z5TtFQv0xrXlyM5yYk9mHj3dCmD6d4k1ANoTvOZa\nALeKyFlE2y5uFpGHzCuViCg1lmxABoA/2FCB91+9DN947gyePJ547ypFffel83jqRA/+37eus82J\nbP9r51qU+z340Hca0TPLSL/Gs/34wt4T+IMN5bhpTWpWsdZW5AGA5W0Wo4EQOoYmuUFvgbLdTly7\nqgRPnejJxKOn9wFoEJF6EXEDuB3AY4lco6qfVNUaVV0eu+9JVb0zlcUTEZlhSQdkAPib3euwtsKP\nT/zoMAbHg1aXkzbO9o7h739+DNc3lOB9sVmxdlCQ48YD79+G/vEgPvSdRowHL25p6B6ZxD3fewXV\nhdn4/Ls3pazNoKYwGzlup+UB+UxPdBYzA/LC3bimFK0DEzjTa4951kZR1RCAewDsRXQSxSOq2gQA\nIrJHRKrmuoaIKBMt+YDszXLii+/ZhMHxID77+FGry0kL4Yjif/7wVbicYtmmvLlsrM7HF/9wM15p\nGcSuLz+L50/3on8siCeOdGL3l5/DwHgQX/3jK5CfnZWymhwOwepyP453DqfsNWdyqica0FeV5Vpa\nRzq6sSH604anX8u8zWWqukdVV6vqSlX9+2n371bV9rmumXbtU6r61lTWTURkliUfkAFgQ1U+PnLT\nSjx6oA2/Pd5tdTm29/Vnm9F4bgCfvW0DKvOzrS5nRm+5vBLfu2sHwhHFe7/+Eq743K/w4Yf2o8zv\nwU/+4lpsqMpPeU2ry3041W3t6uPp7jE4HYK6IgbkhaorzsGKktyMDMhERHQxl9UF2MU9N6/CE0c6\n8emfHsGvV94Ib5Y10xjs7njnMP7ll69h18YKvH2zOSfPGeXqlcV44mM34BeHOzAeDMPnceHWzVXI\nSsGmvJksK85F72grxoMh5Lit+dI73TOKZcU5cLv4vfFi3LC6FA/vO4/JqTDfI4iIMhj/lYzxuJz4\nzK0b0Dowga8/02x1ObakqvibRw8jL9uFv3v7Rtu1VszE53HhD7fV4k+uWY53ba2xLBwDQF1RDgDg\n/CKPxDbC6Z5R9h8n4cY1pZiciuDlMzxkiIgok9k2IIvIThE5ISKnROS+Wa75uIg0icgREfm+iHiT\nec1rVpVg92UVuP+pU2jnASKv8/ihDhw4P4hP/MFay0e6paNlxdGAfK7PmoCsqjjXN47lsTpo4XbU\nF8PtdODZk2yzICLKZLYMyCLiBHA/gF0A1gO4Q0TWX3JNNYC/ArBNVTcCcCI6Zigpf7N7HQBwNvIl\nJqfC+MdfHMe6yjy8a2uN1eWkpWWxvt/zFgXk3tEgAqEIaosYkBcr2+3Eptp8vHx2wOpSiIjIRLYM\nyAC2Azilqs2qGkR0AP1tM1znApAtIi4AOXj9cHsACzvetKYwBx+4th4/fbXd8okDdvKt58+ibXAC\nn37LOjgd9m+tsKP8nCzkZ2fhXL81G/VaB6LBvKbQnhsr08X2+iI0tQ29boQgERFlDrsG5GoALdNu\nt8buu0BV2wB8AcB5AB0AhlT1lzM92UKPN/3QDSvg87jwhb3zH1m8FIwGQvj3p0/jxtWluGZVidXl\npLW6ohyc77emfadlIPq6NYVcQU7GlcuLEIooXjk/aHUpRERkErsG5JmWKC86vkpEChFdVa4HUAUg\nV0QMOcGpIMeND92wAr8+1oX95/ij1G89fxYD41P4+JtWW11K2qsrzsH5PmtXkKsLuIKcjK3LCuEQ\n4CVu1CMiylh2DcitAGqn3a7B69snbgFwRlV7VHUKwKMArjGqgA9cW4/iXDe+8uRJo54yLY0GQvj6\ns814w5pSbLbJcdLpbFlRDloHJhAKR1L+2q0DEyjKdSPXw+mOyfB7s7C+Kg/7GJCJiDKWXQPyPgAN\nIlIvIm5EN989dsk15wHsEJEcic4beyOiR6AaItfjwp9esxy/PdGDYx1Ltxf5Oy+cw+D4FO69havH\nRlhWnINQRNExNJny124dmGD/sUGuXF6EV1oGEAyl/hsdIiIyny0DsqqGANwDYC+iofcRVW0CABHZ\nIyJVqvoSgB8BOADgMKJ/lgeMrOP9Vy9HrtuJ/3j6tJFPmzaCoQj+6/kzuG5VCVePDRKfIGHFqLfW\ngXG2VxjkqvoiTE5FcKR9yOpSiIjIBLYMyACgqntUdbWqrlTVv592/25VbY/9/m9Vda2qblTV96lq\nwMga8nOycMf2OvzsUAdaLDzcwSqPH2pH13AAd11fb3UpGWNZcWzUW4o/n1QVbVxBNsy25UUAgMaz\nbLMgIspEtg3IdvHB6+shAP7r+bNWl5JSqopvPHsGDWU+3Lh6/skflJiKPC/cTkfKR731jAYQCEU4\nwcIgJT4P6opycLCFkyyIiDIRA/I8KvOzsXNjBX7Y2IKJYNjqclLmheY+HO0Yxl3X16fFkdLpwukQ\n1BRlp/ywkNYLI964gmyUTbUFOMhRb0REGYkBOQHv27EMw5Mh/OzVGc8hyUjfffE8CnOycNvm6vkv\npgWJzkK2KiBzBdkom2sL0D40ie7h1G+4JCIiczEgJ2B7fRHWlPvx7RfPQlXnf0Ca6xkJYG9TJ951\nRQ28WU6ry8k4VQXZKZ9icWEGMleQDbO5Nh8A2GZBRJSBGJATICK48+plONI2vCT+MfzxgVaEIorb\nt9dZXUpGqsr3on8siMmp1LXstA5MoDAnCz7OQDbMhqp8uByCV1sz/z2BiGipYUBO0Du2VCM7y4lH\nGlutLsVUkYji4ZfPY3t9EVaV+awuJyNVxUattQ+m7sjp6AxktlcYyZvlxNpK/5L4ppmIaKlhQE6Q\nz+PCro0VePxQe0pX/lLtxTN9ONs3jvdy9dg0lfnRgJzKNos2zkA2xebaAhxqGUIkkvmtV0RESwkD\n8gK8a2sNRiZD+PWxLqtLMc1PDrTB53Fh58YKq0vJWFUFXgBAWwpXkDuHJlEZe10yzqaaAowEQmju\nHbW6FCIiMhAD8gLsWFGMynwvfrw/M9ssJqfCeOJIJ3ZurODmPBNV5EeDasdgalaQRyanMBYMoyKP\nAdloW+qiJ0webOGJekREmYQBeQGcDsE7tlTjmZO96B7JvNFOvz3ejZFACG/naDdTeVxOlPg86BhK\nzQpyZ6yVIx7MyTgrSnzwe1w42DJgdSlERGQgBuQFeucV1QhHFHsOdVhdiuH++2AbSv0eXL2y2OpS\nMl51gTdlLRadsTm9XEE2nsMhuLw2H69yBZmIKKMwIC/QqjI/1pT7sedIp9WlGGpofAq/Pd6Dt11e\nBaeDJ+eZrTI/dbOQ468T3xxIxtpUU4BjHcMZvXmXiGipYUBehF2XVWDf2f6MarP41bEuBMMR3Lq5\nyupSloTKAi/aBydScvBMVywgl+V5TH+tpWhzbQFCEUVT+7DVpRARkUEYkBdh92WVUAX2NmXONIu9\nTZ2ozPdiU02+1aUsCdUF2RgPhjE8ETL9tTqGJ1GU6+bGS5Nsro1v1OM8ZCKiTMGAvAgNZT6sLM3F\nLw5nRh/yRDCMZ0/24M3ryyHC9opUiLc7pKIPuWtoEuXsPzZNWZ4XVflevMqATESUMRiQF0FEsPuy\nSrzY3Ie+0YDV5STt6dd6MDkVwR9s4OzjVInPQk7FJIuOoUlUcoKFqTbVFnAFmYgogzAgL9LOjRWI\nKPDk8W6rS0naL5s6kZ+dhSvri6wuZcm4cNx0CjbqdQ1zBdlsm2sLcL5/HP1jQatLISIiAzAgL9L6\nyjyU53nw2xPpHZCnwhH85ng33riuDFlOfjqkSonPA5dD0G5yi0UgFEbfWJAryCbbFOtDZpsFEVFm\nYCJaJBHBG9aU4dnXejEVjlhdzqI1nh3A0MQU3rye7RWp5HQIKvK96DA5IHcPR1uAOAPZXJdV58Mh\n3KhHRJQpGJCT8Ia1ZRgJhLDvbL/VpSza06/1wOUQXLuKh4OkWlV+tuktFh08RS8lcj0urC73MyAT\nEWUIBuQkXLeqBG6nA79N4z7kZ17rwdZlhfB7s6wuZckpz/eia9jcgHzhFD0GZNNtri3Aq62DKZlt\nTURE5mJATkKux4WrVhSl7Ua97uFJHO0Yxo1rSq0uZUmqzPeiY2jS1EDVGZuSwYBsvs21BRgcn8K5\nvnGrSyEioiQxICfpDWvKcLpnDC396feP4jMnewEAN65mQLZCeZ4XwVAEg+NTpr1G51AAOW4n/B6X\naa9BUZt4YAgRUcZgQE7SDatLAAC/O9VrcSUL9/RrPSjxebCuIs/qUpak+Ma5DhP7kDuHJ1CR7+UB\nMCmwutyPHLeTAZmIKAMwICdpZakPZX4Pfne6z+pSFiQcUTx7sgc3rC6Bw8HwZIV424OZfcidQ5Oc\nYJEiTodgY3U+AzIRUQZgQE6SiOCalcV44XRvWm3OOdo+jMHxKdzQwPYKq8QDcqfZAZn9xymzpbYA\nR9uHEQyl7+hHIiJiQDbENatK0DsaxImuEatLSdiLzdEV76tXcrybVcr8HoiY12IRjii6RwI8JCSF\nNtUWIBiOoKl9yOpSiIgoCQzIBrh2VbwPOX3aLF5s7sOKklweQWyhLKcDJT4PukwKyH2jAYQiyhaL\nFNoeO679heb0eS8gIqLXY0A2QHVBNpYX5+CF0+mxUS8cUbx8ph9XreDqsdUq873oMKnF4vczkLNN\neX56vRKfB2sr/Hg+jb5ZJiKi12NANsg1q0rwYnM/Qmlw7PTR9mGMBELYsaLI6lKWvPI8r2kryBdO\n0eMKckpds7IE+872Y3IqbHUpRES0SAzIBrl6RTFGAyEc7Ri2upR5xfuPd3AF2XIVeV50xA7zMFoX\nT9GzxDUrixEIRXDg/IDVpRAR0SIxIBtk2/JCAEDjWfv/o8j+Y/uoyPdieDKEiaDxq40dQ5PIcgqK\nc92GPzfN7qoVRXA6hG0WRERpjAHZIJX52aguyEbjuX6rS5kT+4/tJd7+YMaot66hSZT5vZxznWJ+\nbxYur8nH79JkT0KciOwUkRMickpE7kv0GhGpFZHfisgxEWkSkXtTWzkRkfEYkA105fJCNJ4dsPU8\n5BOdIxgJhLC9vtDqUgi4MILNjDaLDs5Atsy1K0twqHUIw5PmHSNuJBFxArgfwC4A6wHcISLrE7wm\nBOB/qOo6ADsA/OWljyUiSjcMyAbaurwI3SMBtPSb01NqhHhf5NY6btCzg3ITT9PrGmZAtsrN68oQ\njij2HOqwupREbQdwSlWbVTUI4GEAtyVyjap2qOoBAFDVEQDHAFSnsHYiIsMxIBvoylgf8r6z9m2z\nOHB+ACU+N2qLOPrLDuItFkYfFqKq0RVk9plbYkttARrKfHh4X4vVpSSqGsD0Ylvx+pA77zUishzA\nFgAvzfQiInK3iDSKSGNPT0+SJRMRmYcB2UCry/zwe11oPGffjXqvnB/ElrpCiLAv1Q5yPS74vS7D\nR70NT4YwMRXmKXoWERHcvr0OB1sGcbzT/pNtAMz0hnBpr9ic14iID8CPAXxMVWf8Q6vqA6q6TVW3\nlZbymHsisi/bBuT5NoyIyBoROTjt17CIfMyKWuMcDsHWZYVotOkKct9oAGd6x7B1GfuP7aQy32v4\nCnJn7Pk4qcQ679hSDbfTgYdfTotV5FYAtdNu1wBoT/QaEclCNBx/V1UfNbFOIqKUsGVATmTDiKqe\nUNXNqroZwFYA4wB+kvJiL7FtWSFOdo9iaMJ+m3NeOT8IALiijgHZTsrzvIb3IMenYnAF2TpFuW68\neUM5fvJKG1r6x60uZz77ADQgf5xqAAAgAElEQVSISL2IuAHcDuCxRK6R6I+jvgngmKr+S0qrJiIy\niS0DMhLbMDLdGwGcVtVzKaluDptqCwAAh1uHLK7k9Q6cH4DLIbi8Jt/qUmia6GEhRq8gRzeKcgXZ\nWn9x0yqoKt71teetLmVOqhoCcA+AvYhusntEVZsAQET2iEjVHNdcC+B9AG6e9hO93Zb8QYiIDOKy\nuoBZzLQZ5Ko5rr8dwPdn+6CI3A3gbgCoq6szor5ZXV4dDcivtg7iuoYSU19roQ6cH8CGqjx4s5xW\nl0LTVOZ70TsaQCgcgctpzPesHWyxsIX1VXn40UeuwZ88+LLVpcxLVfcA2DPD/bvnukZVn8PM/clE\nRGnLrivIiWwYiV4Y/VHfrQB+ONuTpXJjSH5OFupLcvFqy6Cpr7NQ4Yji1ZYhbGF7he2U53sRUaBn\nNGDYc3YNT6LE54bbZdcv8aVjdbkfj/7FNVaXQUREC2DXfz0T2TAStwvAAVXtMr2qBF1ek49DNmux\nON0ziompMDbVsr3Cbn5/WIhxbRY8JMReKvM5VpGIKJ3YNSAnsmEk7g7M0V5hhctrCtA5PGnK4Q+L\nFQ/sl1UzINtNvA3CyFFvnZyBTEREtGi2DMiJbBiJ/T4HwJsA2Gqs0ObYKq2d2iyOtA0hx+1EfYnP\n6lLoEvHVRSNXkDt5ih4REdGi2XWTXqIbRsYBFKeyrkSsr8yH0yE41DqEN2+osLocAMDhtiFsrIrW\nRfZSmJMFt8th2E8cJqfCGByf4goyERHRItlyBTndZbudWFPux6ut9lhBDoUjaGofwka2V9iSiKA8\nz2PYCnL8kJAK9r0SEREtCgOySTbVRjfqqc44fCOlTveMYXIqgstq8qwuhWZRmZd94XCPZPGQECIi\nouQwIJtkfVU+hiam0G7wARCLcSi2kn1ZbEYz2U95vvfCym+yeMw0ERFRchiQTbK+Mrpae7R92OJK\nohv0ct1OrCjJtboUmkVlvhedw5OG/MQhvoLMTXpERESLw4BskrUVfojYIyAfbhvChqp8OLhBz7bK\n87wIhiIYHJ9K+rk6hybh97jg89h2Dy4REZGtMSCbJNfjQn1xLo52WHtgSDiiONYxgg3V7D+2MyMP\nC+nkISFERERJYUA20bqqPBztsHYF+Xz/OCamwlhXyYBsZxcOCzFgo14HZyATERElhQHZROsr89DS\nP4GhieR/bL5Yx2MBfV0FA7KdVRi4gtzFU/SIiIiSwoBsovVV0VB63MJV5GOdI3AI0FDOE/TsrMzv\ngQjQOTSR1POEwhF0j3AFmYiIKBkMyCbaEJ9kYWFAPt4xjPqSXHiznJbVQPPLcjpQ6vMkPRawdzSI\niHKCBRERUTIYkE1U6vegxOe2dJLFsc5hrGX/cVqoLsxG20ByK8gdsRVotlgQEREtHgOyiUQE6yrz\n0GRRQB6ZnEJL/wTWVfgteX1amOqCbLQNJheQuzgDmYiIKGkMyCZbW+HHqZ5RhCOpP3L6ta6RWA1c\nQU4H1YXZ6BiaQCSJz5X4Jj+uIBMRES0eA7LJGsr9CIYiONc3lvLXPtYRC8iVXEFOBzUF2ZgKK7pH\nAot+js7hSbidDhTlug2sjIiIaGlhQDbZ6vJoOH2tazTlr328cxh+jwvVBdkpf21auJrCHABA2+D4\nop+jfTA6wUKEpyYSEREtFgOyyRrKouPVTsbaHVLpeMcI1lT4GZbSRHVh9BuZ1iQ26rUOjKO2iN8Q\nERERJYMB2WS5sRXc17pTu4KsqnitKxqQKT3EV/qT2ajX0j+B2thKNBERES0OA3IKrC73pXwFuWc0\ngOHJEFaV8YCQdJHrcaEgJ2vRo94mgmH0jgZQU8gVZCIiomQwIKfA6nI/mnvGEApHUvaap2Ir1gzI\n6SWZUW/x3uXaIq4gExERJYMBOQUayv0IhiM427f4zVcLFQ/IDWVssUgn1QWLPyykpT/6OK4gExER\nJYcBOQVWl6d+o96p7lH4PC6U53lS9pqUvOrC6Aqy6sJnIbcOxFaQ2YNMRESUFAbkFIi3OaRy1NvJ\nrlGsKvNxgkWaqS7IxngwjMHxqQU/tmVgAm6XAyU+flNERESUDAbkFMhxu1BblI3XulO4gtwzyv7j\nNBRvj1hMH3LrwDhqCrPhcPCbIiIiomQwIKdIQ5kfp1K0gjw0PoWekQADchqqLoi2RyxmFnJL/8SF\nw0aIiIho8RiQU2RFSS7O9o0hEll4b+lCneqJrlQ3MCCnneokVpBbBsZRyw16RERESWNATpEVpT4E\nQhG0Dy3+EIhEccRb+irMyYLf48K5vrEFPW5kcgqD41Mc8UZERGQABuQUqS/JBQCc6V1Y8FmMU92j\ncLsc/HF7GhIR1JfmLvjzJN6SwRFvREREyWNATpGVpdGA3NyTmoC8oiQXTm7WSksrSnIX/HkSD8gc\n8UZERJQ8BuQUKfV7kOt2pmQF+UzvGFaWsr0iXdWX+NA2OIHJqXDCj2npj85A5goyERFR8hiQUyT+\no/NmkwPyVDiCloEJLC/hSmK6WlG68Hac8/3jyHU7UZTrNqssIiKiJYMBOYVWlPjQ3GPuqLe2gQmE\nI4rlxbmmvg6ZZzEB+WT3CFaV+3kwDBERkQEYkFOoviR3wT86X6gzsekHy0sYkNNVfEPnQr6ZOtk1\nyrF+REREBmFATqEVpblQjf443CxnY6uOXEFOXzluFyrzvQlv1Bsan0L3SIABmYiIyCAMyCm0oiQa\nYMxsszjXNw6fx4USH3tR09mKBfSrn4wdYd5QzoBMRERkBAbkFKqPj3ozcaPemd4xLC/JYS9qmqsv\nyUVzzyhU5z958WTsYJiGMr/ZZRERES0JDMgp5PO4UOb3mDoL+WzfGJaxvSLtrSjxYXgyhP6x4LzX\nnuwaRXaWE9UFHPGWyUTkWyJSMO12oYg8aGVNRESZigE5xZaX5C74GOFETYUjaB2YQD0DctpbsYCf\nNpzsHsGqMh8cPBgm012uqoPxG6o6AGCLhfUQEWUsBuQUqyvKQUv/hCnP3Rof8cYJFmkv3q9+unv+\nfvVT3ZxgsUQ4RKQwfkNEigC4LKyHiChjmR6QReRXM/xYcG8Cj9spIidE5JSI3DfLNQUi8iMROS4i\nx0TkaiNrN0NtYQ46hydNGfUWn2BRz0NC0l5NYTb8XhcOtQ3Ned3I5BQ6hiaxihv0loIvAnheRD4n\nIp8D8DyAz1tcExFRRkrFCnLJDD8WLJvrASLiBHA/gF0A1gO4Q0TWz3DplwE8oaprAWwCcMywqk1S\nVxztE20bNH4V+WysdYM9yOnP4RBsri3AwfODc14X36C3mhv0Mp6qfhvAuwB0AegG8E5V/Y4Rz53g\ngsSM1yTyWCKidJOKH89FRKROVc8DgIgsAzDf1vztAE6panPsMQ8DuA3A0fgFIpIH4AYAfwoAqhoE\nMOOOJhG5G8DdAFBXV5fMnyVptYXR1d3z/eNYWWrsqt/Z3jH4PS4U87jhjLC5tgBffeo0JoJhZLud\nM15zqis2wYIryBkr1koR1wnge9M/pqr9ST5/fEHiTQBaAewTkcdU9eh81wA4Md9jZ9PcM4Y/+o8X\nkimdiMg0qQjInwLwnIg8Hbt9A2JhdQ7VAFqm3W4FcNUl16wA0APgP0VkE4D9AO5V1dftalLVBwA8\nAADbtm2bf26WieqKogG51YTDQs71j6OumCPeMsXm2gKEI4rDbUPYXl804zWvtAzA73WhppBtNRls\n/yW34+9hEvv9iiSff94FiTmueSqBx14wfbHCV7kyybKJiMxjekBW1SdE5AoAOxB9Q/+4qvbO87CZ\nEt6lwdYF4AoAH1XVl0TkywDuA/DpZGs2U6nfA4/LYcppem0DExemH1D621wbbd0/2DIwa0B+sbkf\nV9UXw8kJFplstapOmfj8iSxIzHZNIo+94NLFih98yPbbRogowzzy4cSuM60HWUSuiP8CUAegHUAb\ngLrYfXNpBVA77XZN7PGXXtOqqi/Fbv8I0cBsayKCWhMmWagq2gYnUF3AlcRMUezzoLYoG6/M0ofc\nOTSJM71j2LFi5vBMGeMFEflvEfmwiCw34fkTWZCY7ZpEHktElHbMXEH+4hwfUwA3z/HxfQAaRKQe\n0VB9O4D3XvQEqp0i0iIia1T1BIA3YpYf69lNXVGO4SvIA+NTGA+GUVPIwyIyyebaQjSenbnF9MXm\nPgDAjhXFqSyJUkxVt8X2buwC8CURqQbwHIBfAHhaVQNJvkSiCxIzXZPIY4mI0o6ZAfmPVXVRb5Sq\nGhKRewDsBeAE8KCqNgGAiOwBcFfsuT8K4Lsi4gbQDOADxpRurtrCbOw70w9VNaxfuG0guiJdzYCc\nUTbXFuBnr7aja3gS5Xneiz72YnMf8rwurKvMs6g6ShVVPSci3wbwAgA3gBIAtwD4OxHpUdW3JPH0\n8y5IzHHNiQQeS0SUdswMyN+MDbV/CsATAJ5T1VCiD1bVPQD2zHD/7mm/PwhgW/KlplZtUQ5GAiEM\nTUyhIMeYiRNtg9EVaR43nFm21EX7kF86049bN1Vd9LEXm/uwnf3HGS+2APB5AO8DcBbR1rgyAF9R\n1e0ickMyz5/ogsQc18x4PxFROjMtIKvqLhHxArgJwDsAfEFEziMalp+Ij31bimpjkyxa+icMC8it\nsRVktlhklsur81GZ78WP9rdeFJA7hiZwtm8cd+5YZmF1lCJfAJADYLmqjgAXxlx+QUS+BmAngPpk\nXiDBBYnZrpnxfiKidGbqFAtVnUQsEANA7MdwuwB8RUQqVHW7ma9vV/FRb+f7x3FZTb4hz9k6MAGf\nx4X87CxDno/sweV04D3bavGvT55ES//4hW+ufnOsGwD7j5eI3QAaVPXC5jdVHRaRjwDoRfQ9lYiI\nDJSKk/QuUNUzqvpVVb0VwHWpfG07ubCCPGDcRr3oBItszkDOQO+5shYC4JHG6DStiWAY//bkSWyu\nLcCGKvYfLwGR6eE4TlXDAHpU9UULaiIiymhmjnkbEZHhGX6NiMhw7OS7JcnncaEo123oJIu2gQlu\n0MtQ1QXZuHF1KX6wrwVT4Qge/N0ZdA0H8De71/EboqXhqIi8/9I7ReROAMcsqIeIKOOZ2YPsN+u5\nM0FlvhedQ5OGPV/rwDi2Lis07PnIXu7csQwf/FYjrv6HJzEeDOFN68tnPTyEMs5fAnhURP4M0VP1\nFMCVALIR3d9BREQGS8VR0zSD8jwvuoaNCcgjk1MYngxxg14Ge+O6cjzwvq149EAbjrQP4b5da60u\niVJEVdsAXCUiNwPYgOjhHL9Q1d9YWxkRUeZiQLZIeZ4Hh9uGDHmutkHOQF4K3ryhAm/eUGF1GWQR\nVX0SwJNW10FEtBSkdJMe/V6p34ve0QBC4UjSz9UaO7aaM5CJiIiIkseAbJHyPA9Ugd7R5PcqcgWZ\niIiIyDgMyBYp90ePDTaiD7ltcAIelwOlPk/Sz0VERES01DEgW6QsLxpmu0cCST9X1/AkyvI8HPlF\nREREZAAGZIuU5xm3gtw/FkRxLlePiYiIiIzAgGyR4lw3HAJ0GxCQ+0aDKM51G1AVERERETEgW8Tl\ndKDY5zGkxaJvLIBiHwMyERERkREYkC1UnudJusVCVdE/FkQRWyyIiIiIDMGAbKFyvxddw8mtII8E\nQpgKK0q4gkxERERkCAZkC5XledE9ktwKcl9sjnIRe5CJiIiIDMGAbKEyvwd9Y0FMJXGaXv9YdAWa\nAZmIiIjIGAzIFirP88ZO01t8m0X8JL4SHhJCREREZAgGZAuVxw4LSaYPuX+MLRZERERERmJAtlCZ\nAcdNMyATERERGYsB2ULlBhw33TsagM/jgjfLaVRZREREREsaA7KFin2epE/Ti85A5uoxERERkVEY\nkC3kdAgKc9zoi7VJLEb/WJCn6BEREREZiAHZYj6vC2OB0KIf3zsaRDFXkImIiIgMw4BsMZ/HhdHJ\nxQfk/rEAinnMNBEREZFhGJAt5vO4MLLIFWRVjfYgs8WCiIiIyDAMyBbzexe/gjw8GcJUWNliQURE\nRGQgBmSL+TwujC5yBTk+A5mb9IiIiIiMw4BssWQ26fXFjqguYg8yERERkWEYkC3m82Qtugc5Ph6O\nLRZERERExmFAtpjf60IwFEEgFF7wY/tG2WJBREREZDQGZIv5PC4AwFhg4QG5fyzeYsGATERERGQU\nBmSL5cYC8mImWQxPhuDNcsDjchpdFhEREdGSxYBssfgK8khgasGPHQuEkOt2GV0SERER0ZLGgGwx\nv3fxK8gTwTCy3Vw9JiIiIjISA7LF4ivIi5mFPB4McwWZiIiIyGC2DcgislNETojIKRG5b5ZrzorI\nYRE5KCKNqa7RCD7v4gPyWDDEFWQiIiIig9ly+VFEnADuB/AmAK0A9onIY6p6dIbL36CqvSkt0ED+\nJFaQJ4Jh5DAgExERERnKrivI2wGcUtVmVQ0CeBjAbRbXZApfEj3IY8EwcthiQURERGQouwbkagAt\n0263xu67lAL4pYjsF5G7Z3syEblbRBpFpLGnp8fgUpOTneWEQxa7ghziCjIRERGRwewakGWG+3SG\n+65V1SsA7ALwlyJyw0xPpqoPqOo2Vd1WWlpqZJ1JExH4PC6MLGIFeTwYRq6HAZmIiIjISHYNyK0A\naqfdrgHQfulFqtoe+283gJ8g2pqRdvzerEVPscjOYosFERERkZHsGpD3AWgQkXoRcQO4HcBj0y8Q\nkVwR8cd/D+DNAI6kvFID5HqcC+5BVlWMs8WCiIiIyHC2XH5U1ZCI3ANgLwAngAdVtQkARGQPgLsA\neAH8RESA6J/je6r6hEUlJ8XncS14BTkQiiCiQA5bLIiIiIgMZcuADACqugfAnhnu3z3t5qbUVWQe\nnzcLQxMLO2p6PBgGAORkMSATUXJEZCeALyO6IPENVf3HRK8RkVoA3wZQASAC4AFV/XKqaiciMoNd\nWyyWFL/HhdHJhQbk6Iozx7wRUTKmzZ3fBWA9gDtEZP0CrgkB+B+qug7ADkQ3TF/0eCKidMOAbAM+\njwtjgfCCHjMRX0FmiwURJSeRufOzXqOqHap6IPb7EQDHMPNYTluP3CQimo4B2QZ83oX3II/FAzI3\n6RFRchKZO5/QbHoRWQ5gC4CXZnohO4/cJCKajj+ft4H4Jr1IROFwzDQC+vXYYkFEiRKRXyPaI3yp\nTyGxufPzXiMiPgA/BvAxVR1eTJ1ERHbBdGUD/thx02PBEPzerIQeM8EVZCJKkKreMtvHRORqzD93\nfs7Z9CKShWg4/q6qPpp0wUREFmOLhQ34PNGAvJA2C7ZYEJFB5p07P9c1Ep21+U0Ax1T1X1JYNxGR\naRiQbcAXW0FeyGEhE2yxICIDqGoIQHzu/DEAj0yfOy8iVXNdA+BaAO8DcLOIHIz92v26FyIiSiNM\nVzYQX0EeWcgKcoAryERkjETmzs9xzXOYuUeZiChtcQXZBi60WCxkBXkqGpCzGZCJiIiIDMWAbAMX\nWiwWsII8HgzB5RC4nfxfSERERGQkpisbWNQmvUAY2W4novtjiIiIiMgoDMg24PdER7stbJNemP3H\nRERERCZgQLaB3Nhx0SMLCMjjU2HkcoIFERERkeEYkG3A5XTA7XJc2HiXiPFAiBv0iIiIiEzAgGwT\n2VlOTC4kIAe5gkxERERkBgZkm8hxOzEeXFiLBVeQiYiIiIzHgGwT2VlOTExFEr5+PBDiJj0iIiIi\nEzAg20S223nh+OhEjAfDPGaaiIiIyAQMyDYRXUFeSA8yV5CJiIiIzMCAbBPZbifGgwvbpMeATERE\nRGQ8BmSbyM5yYiLBgByOKAKhCFssiIiIiEzAgGwTOe7EWyzi0y64gkxERERkPAZkm4hu0kssIMev\n45g3IiIiIuMxINtEdpYr4YAc71WOH1FNRERERMZhQLaJbHfiR02PxVossrPYg0xERERkNAZkm8hx\nuxCKKIKh+Q8LmeAKMhEREZFpGJBtwpsVDbuJrCLHWyy4SY+IiIjIeAzINhEPu4n0IY+zxYKIiIjI\nNAzINpG9iBVktlgQERERGY8B2SbiI9viq8NzGeeYNyIiIiLTMCDbRHwFeTKhFeT4QSFssSAiIiIy\nGgOyTeRcWEGePyCPBWKb9LK4gkxERERkNAZkm7gwxSLBTXo5biccDjG7LCIiIqIlhwHZJuL9xIls\n0hsLhtleQURERGQSBmSbWMiYt7FAiBMsiIiIiEzCgGwT8U16ifYg53IFmYiIiMgUDMg2sZAWi/Eg\nV5CJiIiIzMKAbBNupwMOSbDFgj3IRERERKaxbUAWkZ0ickJETonIfXNc5xSRV0Tk8VTWZzQRQY7b\nldgmPfYgExEREZnGlgFZRJwA7gewC8B6AHeIyPpZLr8XwLFU1WYmb5YzoR7k8UCIPchEREREJrFl\nQAawHcApVW1W1SCAhwHcdulFIlID4C0AvjHXk4nI3SLSKCKNPT09phRshBy3M6GT9MaCYeR6GJCJ\niIiIzGDXgFwNoGXa7dbYfZf6EoBPAIjM9WSq+oCqblPVbaWlpcZVabDsLOeFY6TnEj8ohIiIiIiM\nZ9eAPNMRcXrRBSJvBdCtqvtTU5L5st1OTEzNmfURCIUxFVauIBMRERGZxK4BuRVA7bTbNQDaL7nm\nWgC3ishZRFswbhaRh1JTnjmys5yYmGcFeTwQbcHI5QoyERERkSnsGpD3AWgQkXoRcQO4HcBj0y9Q\n1U+qao2qLo99/ElVvTP1pRonx+2cd4rFWCxA53AFmYiIiMgUtgzIqhoCcA+AvYhOqHhEVZsAQET2\niEiVlfWZxeuef4pF/OOcYkFERERkDtumLFXdA2DPDPfvnuG+pwA8ZX5V5srJcmJynoA8GoivILPF\ngoiIiMgMtlxBXqqy3U6Mz9Ni8fseZNt+b0NERESU1hiQbSTb7Zz3qOl4DzJP0iMiIiIyBwOyjWRn\nOREIRRCO6KzXxOckcwWZiIiIyBwMyDYSP/xjrtP0RmMtFuxBJiIiIjIHA7KNZGdFQ+9ckyzGA1xB\nJiIiIjITA7KNZMdC71wryGPBMER+H6aJiJIlIjtF5ISInBKR+xZzjYg4ReQVEXnc/IqJiMzFgGwj\nia4g52Q54XDMdBo3EdHCiIgTwP0AdgFYD+AOEVm/0GsA3Ivo3HoiorTHgGwj8R7kuU7TGwuGeYoe\nERlpO4BTqtqsqkEADwO4bSHXiEgNgLcA+MZcLyQid4tIo4g09vT0GPqHICIyEgOyjXgvrCCHZr1m\nLBBCrpvtFURkmGoALdNut8buW8g1XwLwCQCRuV5IVR9Q1W2quq20tHTxFRMRmYxLkTaSyBSL8WAI\nOdygR0QLICK/BlAxw4c+BWCmfq1LZ03Oeo2IvBVAt6ruF5GbkqmTiMgumLRsJNs9fw/yWCAMH1ss\niGgBVPWW2T4mIlcDqJ12Vw2A9ksua53jmmsB3CoiuwF4AeSJyEOqemfShRMRWYQtFjYS36Q312l6\n48EQZyATkZH2AWgQkXoRcQO4HcBjiV6jqp9U1RpVXR67/0mGYyJKdwzINhJvsRgLzN6DPBoIcQYy\nERlGVUMA7gGwF9EpFI+oahMAiMgeEama6xoiokzEpGUjBTluOAToGwvOes14MHwhSBMRGUFV9wDY\nM8P9u+e75pLrnwLwlMHlERGlHFeQbcTpEBT7POgZCcx6zVgghFz2IBMRERGZhgHZZsr8swdkVcV4\nMIxc9iATERERmYYB2WZK/R70jM4ckAOhCEIR5Zg3IiIiIhMxINtMqc+D7uGZA3J8/BsPCiEiIiIy\nDwOyzZT6PegdDSASuXRO/++nW/CoaSIiIiLzMCDbTKnfg1BEMTgx9bqPxVeQeVAIERERkXkYkG2m\nzO8FgBk36o3GV5DZYkFERERkGgZkmyn1ewDMHJDHg9GAzDFvREREROZhQLaZeEDuHpl83cfGAtEW\nC64gExEREZmHAdlm5lpBjrdY8KhpIiIiIvMwINtMrtuJ7CznjAH5VPcospyCqoJsCyojIiIiWhoY\nkG1GRGY9LKSpfQgNZX64XfzfRkRERGQWJi0bmum4aVXF0fZhbKjKs6gqIiIioqWBAdmGSv0edF8S\nkLuGA+gbCzIgExEREZmMAdmGSmdYQW5qHwIAbKjOt6IkIiIioiWDAdmGSn0eDE1MIRAKX7jvSNsw\nRIB1lVxBJiIiIjITA7INxUe99Y4GL9zX1D6E5cW5PGaaiIiIyGQMyDZUlvf6WchN7cNYz/5jIiIi\nItNxOdKGSn1eAMCn//sIVpX5cM3KYrQNTuDOHcssroyIiIgo8zEg29DqCh/evrkK5/rH8ezJHvzk\nlTYA4AQLIiIiohRgQLYhj8uJL92+BQAQjiheON2HQ22DuHplscWVEREREWU+BmSbczoE1zWU4LqG\nEqtLISIiIloSuEmPiIiIiGga2wZkEdkpIidE5JSI3DfDx70i8rKIvCoiTSLyGSvqJCIiIqLMYsuA\nLCJOAPcD2AVgPYA7RGT9JZcFANysqpsAbAawU0R2pLZSIiIiIso0tgzIALYDOKWqzaoaBPAwgNum\nX6BRo7GbWbFfmtoyiYiIiCjT2DUgVwNomXa7NXbfRUTEKSIHAXQD+JWqvjTTk4nI3SLSKCKNPT09\nphRMRERERJnBrgFZZrjvdavDqhpW1c0AagBsF5GNMz2Zqj6gqttUdVtpaanBpRIRERFRJrFrQG4F\nUDvtdg2A9tkuVtVBAE8B2GluWURERESU6ewakPcBaBCRehFxA7gdwGPTLxCRUhEpiP0+G8AtAI6n\nvFIiIiIiyii2PChEVUMicg+AvQCcAB5U1SYAEJE9AO4CUALgW7GJFw4Aj6jq41bVTERERESZwZYB\nGQBUdQ+APTPcvzv223YAW1JaFBERERFlPLu2WBARERERWUJUl9boYBEZAXDC6jrmUQKg1+oi5sEa\njcEajZEONa5RVb/VRdgF34sNwxqNwRqNkQ41JvRebNsWCxOdUNVtVhcxFxFpZI3JY43GYI3GEJFG\nq2uwGb4XG4A1GoM1GlxodL0AACAASURBVCNdakzkOrZYEBERERFNw4BMRERERDTNUgzID1hdQAJY\nozFYozFYozHSocZUSoe/D9ZoDNZoDNZojIRqXHKb9IiIiIiI5rIUV5CJiIiIiGbFgExERERENA0D\nMhERERHRNAzIRERERETTMCATEREREU3DgExERERENA0DMhERERHRNAzIRERERETTLOmALCJOEXlF\nRB63upZLiYhXRF4WkVdFpElEPmN1TZcSkVoR+a2IHIvVeK/VNc1ERB4UkW4ROWJ1LXEislNETojI\nKRG5z+p6ZmPHv7vp0uFzMB2+lq3G9+LkpMPXAWDP95N0eC+249/bdOnw+beYr+MlfZKeiPw1gG0A\n8lT1rVbXM52ICIBcVR0VkSwAzwG4V1VftLi0C0SkEkClqh4QET+A/QDerqpHLS7tIiJyA4BRAN9W\n1Y02qMcJ4DUAbwLQCmAfgDvs9vcG2O/v7lLp8DmYDl/LVuN7cXLS4esAsN/7Sbq8F9vt7+1S6fD5\nt5iv4yW7giwiNQDeAuAbVtcyE40ajd3Miv2y1Xczqtqhqgdivx8BcAxAtbVVvZ6qPgOg3+o6ptkO\n4JSqNqtqEMDDAG6zuKYZ2fDv7iLp8DmYDl/LVuJ7cfLS4esAsOX7SVq8F9vw7+0i6fD5t5iv4yUb\nkAF8CcAnAESsLmQ2sR87HgTQDeBXqvqS1TXNRkSWA9gCwLY12kg1gJZpt1thszeTdGTnz8F0+lq2\nAN+LDWTnrwMb4nuxwez8+bfQr2NXaspKPRH5NYCKGT70KQBhAN2qul9EbkppYdPMVaOq/lRVwwA2\ni0gBgJ+IyEZVTWkP0nw1xq7xAfgxgI+p6nAq64u9/rw12ozMcJ+tVqTSjdWfg/Oxw9eyVfhenJoa\nY9fwvXhh+F5sIKs//+az0K/jjA3IqnrLbB8TkX8AcKuI7AbgBZAnIg+p6p0pKxBz13jJdYMi8hTw\nf9m79zDJ6ure/59Vl7733Ge4zAwOIiCICjoiF5NwkCQjmni8RCHq7/f84glJlBNNMDn4M3lMzkme\nxKNRcyE5EvWYHA1IvMcfiqASokHCcFEZB3RAlGG4DHPt7unuuq3fH3vv6l3Vu7uqp6p71+5+v55n\nHrqqdlV9q3bVl9Wr13d9tUPSkk7KrcYY1vJ8VtKn3P1zSzOqRu2+jz1kr6StsctbJO1LaSyZ1wuf\nwXal+V1OC3NxdzAXLwrm4i7phc9fu9r9Hq/IEgt3f7e7b3H3bZKukPSNpZ6QWzGzjeFvOTKzQUmX\nSXow3VE1CovePyZpt7t/MO3xZMjdkk43s1PNrE/BZ/BLKY8pk7LwGczCdzktzMXdkYXvQY9iLu6C\nLHz+jud7vCID5Iw4SdI3zex7Cr7Et7p7r7VAuljSWyRdamb3h/8uT3tQzczsBkl3SjrTzPaa2VvT\nHI+7VyRdLekWBYsZbnL3XWmOaS699t4lyMJnMAvfZcwtC+cvC9+DnptPsjIX99r7liALn78Ff49X\ndJs3AAAAoBkZZAAAACCGABkAAACIIUAGAAAAYgiQAQAAgBgCZAAAACCGABkAAACIIUAGAAAAYgiQ\ngRXIzP7azO41s5ekPRYAWKmYi3sXATKwwpjZsKRNkn5D0qtSHg4ArEjMxb2NABldYWYfMrN3xi7f\nYmYfjV3+CzP73S4/53iXH2+Nmb0tdnmbmT3Q4WN+3MyeTnocM9thZg+Z2R4zuzZ2/e+Y2S4ze8DM\nbjCzgfD6ATP7DzP7bnj7H8/3WOH4J83s/vjzuvuEgm03b5f0V+Gxg+H2oCUz29DJawaQHubiOR+T\nuRgLQoCMbvl3SRdJkpnlJG2Q9LzY7RdJ+nYK41qINZLe1vKohfmEpB3NV5pZXtJ1kl4h6WxJV5rZ\n2Wa2WdJvS9ru7udIyku6IrzbtKRL3f2Fks6VtMPMLpjrscL7POzu5zY993pJQ5LGJFUlyd0nw+P2\nde2VA0gDc3GyT4i5GAtAgIxu+bbCSVnBZPyApDEzW2tm/ZLOknSfmX3BzO4Jf+u+Krqzmb2vKWPw\nR2Z2jZm9OfxN/X4z+0g4ATWY65jwt/bdZvb34fN9zcwGw9v+0MweNLNbw8zAuyT9uaTTwsd5f/jw\n+aT7t8vd75B0MOGm8yXtcfdH3L0k6UZJrw5vK0gaNLOCgslzX/hY7u5RpqYY/vMWj5XkDyR9QNIu\nBZM4gOWDuTgBczEWigAZXeHu+yRVzOwUBZPznZLuknShpO2SvhdOGL/m7i8Or/vt8DdoKZhI3hh7\nyDdI2hled3H4G3VV0pviz2tmZ7U45nRJ17n78yQdlvQ6M9su6XWSzpP02nAsknStwt/y3f335rr/\n8b5HTTZLeix2ea+kze7+uIIJ86eSnpB0xN2/Fnu9+fDPdE9LutXd75rrsZKe1My2KTg/n5a0W42Z\nJQAZx1y8YMzFSFRIewBYVqLMxUWSPqhgYrhI0hEFf/aTgon4NeHPWxVMegfc/T4z22RmJ0vaKOmQ\npOdLerGku81MkgYVTEZxL29xzI/dPar7ukfSNgV/cvyiu09Kkpn9yzyvKen+3WAJ17mZrVWQcThV\nwf8E/tnM3uzun5Qkd69KOtfM1kj6vJmdM9djzfG8fyLpv7u7mxmTMrA8MRe3j7kYiQiQ0U1R7dvz\nFfxZ7zFJ10g6KunjZnaJpMskXejux8zsdkkDsft/RtLrJZ2oIIthkv7B3d89z3O2OmY69nNVwaSd\nNInNJen+jQMwe7ukXw8vXh5mcFrZq+B/SpEtCv58d5mC/xHsDx/7cwre00/G7+zuh8P3b4eC/xkm\nPVbzOM9VkKV5mZldp+C9/34bYwWQLczFzMXoECUW6KZvK2hVc9Ddq+5+UMFiiwsV/JlvtaRD4YT8\nXEkXNN3/RgWLIF6vYIL+uqTXm9kmSTKzdWb2rKb7tHNMs29J+iULViKPSHpleP2YpNGFvmh3vy78\nU+C5bU7IknS3pNPN7FQz61Pwur+k4M95F5jZkAVpmJcr+PObzGxjmK1QWH93maQH53msZu+T9Evu\nvs3dt0l6ochaAMsRczFzMTpEgIxu+r6CP5l9p+m6I+7+jKSvSiqY2fck/Y+m4+TuuxRMio+7+xPu\n/gMFixi+Ft7nVgUtceL3aXlMM3e/W8Gk9V1Jn1NQX3fE3Q9I+rYFLX3eP99jtMvMblDwP6QzzWyv\nmb01HENF0tWSblEw6d7k7rvCOrbPSLpXwXuXk3R9+HAnSfpm+DrvVlD39uW5HqtpHJdKGnb3r8fe\nh6ckDZvZum68VgA9g7m4CXMxFsrc5yqPAZYvMxtx93EzG5J0h6Sr3P3etMfVTeEikC970KKo3fs8\nqqCt0TOLNCwAqGMunvM+j4q5OFVkkLFSXR+uQL5X0meX24QcqkpabU3N6ZNY2JxeQbui2qKPDAAC\nzMUxzMW9gwwyAAAAEEMGGQAAAIghQAYAAABiCJABAACAGAJkAAAAIIYAGQAAAIghQAYAAABiCJAB\nAACAGAJkAAAAIIYAGQAAAIghQAYAAABiCJABAACAGAJkAAAAIIYAGQAAAIghQAYAAABiCmkPAACA\nxWZmz5b0Hkmr3f31ZvYzkt6k4P+DZ7v7RakOEEBPIYMMAOhZZvZxM3vazB5oun6HmT1kZnvM7NpW\nj+Puj7j7W2OX/83df1PSlyX9Q/dHDiDLCJABAKkxs01mNtp03XNiFz8haUfT7XlJ10l6haSzJV1p\nZmeHtz3fzL7c9G/TPEP4VUk3dOGlAFhGKLEAAKTp5yT9lpld7u5TZvbrkl4j6XJJcvc7zGxb033O\nl7TH3R+RJDO7UdKrJf3A3b8v6VXtPLGZnSLpiLsf7corAbBskEEGAKTG3f9Z0lcl3Whmb5L0a5Le\n0OJumyU9Fru8N7xuTma23sz+l6TzzOzd4dVvlfS/j2vgAJY1MsgAgFS5+/8Ms8B/J+k0dx9vcRdL\nepgWz3FA0m82XffeBQ0UwIpBBhkAkKqwo8Q5kj4vqZ2gda+krbHLWyTtW4ShAVihCJABAKkxs/Mk\n/b2CGuL/R9I6M/uTFne7W9LpZnaqmfVJukLSlxZ3pABWEgJkAECahiT9irs/7O41Sf+3pJ9EN5rZ\nDZLulHSmme01s7e6e0XS1ZJukbRb0k3uviuFsQNYpsx93rItAAAAYEUhgwwAAADEECADAAAAMSuu\nzduGDRt827ZtaQ8DwApzzz33POPuG9MeR69gLgaQhnbn4hUXIG/btk07d+5MexgAVhgz+0nro1YO\n5mIAaWh3LqbEAgAAAIghQAYAAABiCJABAACAGAJkAAAAIIYAGQAAAIghQAYAAABiCJABAACAGAJk\nAAAAIIYAGQAAAIghQAYAAABiCJABAMfNzLaa2TfNbLeZ7TKzd6Q9JqBbxqcreuDxI2kPAykgQAYA\ndKIi6Rp3P0vSBZLebmZnpzwmoCtuuOunet3f/bsq1VraQ8ESI0AGABw3d3/C3e8Nfx6TtFvS5qRj\nzewqM9tpZjv379+/lMMEjsvYdEXTlZoqNU97KFhiBMgAgK4ws22SzpN0V9Lt7n69u2939+0bN25c\nyqEBx6UWBsY1J0BeaQiQAQAdM7MRSZ+V9E53P5r2eIBuqIaBMRnklYcAGQDQETMrKgiOP+Xun0t7\nPEC3VKMMMgHyikOADAA4bmZmkj4mabe7fzDt8QDdFAXIZJBXHgJkAEAnLpb0FkmXmtn94b/L0x4U\n0A1RgFwlQF5xCmkPAACQXe7+LUmW9jiAxUCAvHKRQQYAAEgQLdIjQF55CJABAAASVKsEyCsVATIA\nAEAC2rytXATIAAAACdgoZOUiQAYAAEgQZY4rVQLklYYAGQAAIEFUYkEGeeUhQAYAAEhQY6OQFYsA\nGQAAIEGl3ge5lvJIsNQIkAEAABLU6gFyygPBkiNABgAASDDT5o0IeaUhQAYAAEgQbRBCfLzyECAD\nAAAkqNbIIK9UBMgAAAAJKmwUsmIRIAMAACSosVHIikWADAAAkICNQlYuAmQAAIAEVTYKWbEIkAEA\nABJU632QCZBXmtQDZDPbYWYPmdkeM7t2oceYWd7M7jOzLy/NiAEAwEpAgLxypRogm1le0nWSXiHp\nbElXmtnZCzzmHZJ2L82IAQDASkGJxcqVdgb5fEl73P0Rdy9JulHSq9s9xsy2SHqlpI/O9yRmdpWZ\n7TSznfv37+/6iwAAAMtPfZEeAfKKk3aAvFnSY7HLe8Pr2j3mw5J+X9K8Hbzd/Xp33+7u2zdu3NjZ\niAEAwIpQI4O8YqUdIFvCdc2fwsRjzOxVkp5293u6PywAALDSsVHIypV2gLxX0tbY5S2S9rV5zMWS\nftnMHlVQdnGpmX1y8YYKAABWEjYKWbnSDpDvlnS6mZ1qZn2SrpD0pXaOcfd3u/sWd98WXvcNd3/z\nUg4eAAAsX2SQV65UA2R3r0i6WtItCjpR3OTuuyTJzG42s5PnOwYAAGCxRIExNcgrTyHtAbj7zZJu\nTrj+8lbHxG6/XdLtizA8AACwQtEHeeVKu8QCAACgJ1UIkFcsAmQAAIAEtHlbuQiQAQAAErTaKGTf\n4Uk9dXRqKYeEJUKADAAAkKDVVtPX3PRdvefzDyzlkLBEUl+kBwAA0IuqLdq8HZiY1vh0ZSmHhCVC\ngAwAANDE3RUljufaKGRiuqrJcnUJR4WlQoAMAADQJN65olqrJR4zWa6qXE2+DdlGgAwAANCkGiur\nqM5RYnGsVNFUuaZKtaZCnmVdywlnEwAAoEljBnl2gFyruabKQfb4yGR5ycaFpUGADAAA0KRVgByv\nPT5MgLzsECADAAA0iZcdJ7V5O1aKBcjHCJCXGwJkAACAJpVYhJy0UchkLEA+MllakjFh6RAgAwAA\nNIkvzEvKIE+UZvofk0FefgiQAQAAmsRLLJI2CqHEYnkjQAYAAGgSL7FI2ihksiFApsRiuSFABgAA\naBLPICd1sTgWL7Ggi8WyQ4AMAADQJJ5BTtooJGrzZkaJxXJEgAwAANAkXnecnEEOAuQTRgfIIC9D\nBMgAAABNqi1LLIIA+aQ1AzpCDfKyQ4AMAADQpGGRXlKAPB3UIJ+8epAM8jJEgAwAANCkoc1bUoBc\nrqqYN20Y6aMGeRkiQAYAAGjSaqOQyVJVg8W8Vg/16ehUObEMA9lFgAwA6IiZfdzMnjazB9IeC9At\n1TCFnM/ZHBuFVDTUV9CawaLcpaOUWSwrBMgAgE59QtKOtAcBdFO0SK8vn0vcKORYqaqhvrzWDBUl\n0Qt5uSFABgB0xN3vkHSw1XFmdpWZ7TSznfv371+CkQHHLyqZ6CvkEssnJktVDfXntXaoTxK76S03\nBMgAgCXh7te7+3Z3375x48a0hwPMqyFATiyxqGqoWNBqMsjLEgEyAABAkygo7ssnZ5CPlSoa7Mtr\nzWAQIB+hk8WyQoAMAADQJFqk1z9HicVMDTIlFssRATIAAECT+iK9eQLkwb68Vg0UJFFisdwQIAMA\nOmJmN0i6U9KZZrbXzN6a9piATrVcpFcOMsiFfE6jAwU2C1lmCmkPAACQbe5+ZdpjALqtHiDnc8lb\nTYd9kCVpzVBRR8ggLytkkAEAAJrUF+kVcrM2CqnVXFPlmob68pKkYi6ncrU26zGQXQTIAAAATWqx\nEotKU/A7Wa5KUj1AzuVMCZ3gkGEEyAAAAE0qtbnbvE2UKpKkwbDEImdKrFNGdhEgAwAANKnNs1HI\nZCnMIBfDDLLZrDIMZBsBMgAAQJP5MsjHSk0lFgTIyw4BMgAAQJP4Ir25AuTBMEDO50xUWCwvBMgA\nAABN4iUWNZc8liGul1hQg7xsESADAAA0iZdYSI0B8LFwkV68iwUlFssLATIAAECTeAZZUsNmIbPa\nvFGDvOwQIAMAADSJ1yBLjRnkienZJRa1NvcJmZiu6CP/+vCs3sroLQTIAAAATapNGeR4q7dj9T7I\nMxnk5lZwc/nC/Y/rz77yoL73+JFuDhddRoAMAAAg6ZH947ri+js1Pl2ZCZCjGuRq0iK9mQDZ2wyQ\n73rkYMNjoDcRIAMAgCX1j3c+qn/94f60hzHL9x8/ou88clA/PXBs/gxyuapi3lQMg+d227y5u+76\n8YHgMQiQe1rqAbKZ7TCzh8xsj5ld2+4xZrbVzL5pZrvNbJeZvWNpRw4AAI7HX972I91092NpD2OW\ncpglLlVrqtZcOZMKudk1yJOlqgbDXfQkydps8/bogWN66ui0pJkyDfSmVANkM8tLuk7SKySdLelK\nMzu7zWMqkq5x97MkXSDp7c33BQAAvaVSrengsZKOTJbTHsos0cK5UqWmqrvyOVOYJJ7V5m24v1C/\nnM+1V2Jx1yMH6j9TYtHb0s4gny9pj7s/4u4lSTdKenU7x7j7E+5+ryS5+5ik3ZI2Jz2JmV1lZjvN\nbOf+/b33Jx0AAFaKgxMluUtHp3ovQC6HQXCpEmWQTfmEDPKxUrW+QE9qf5HeXT8+WM88R63i0JvS\nDpA3S4r/jWWvZge5LY8xs22SzpN0V9KTuPv17r7d3bdv3LixwyEDAIDj9fRYUGLQ0xnkalXVmqsw\nZwa5Wl+gJ4V9kFt0bXN33fXIAV38nPX1x0DvSjtAtoTrmn8Fm/cYMxuR9FlJ73T3o10cGwAA6LL9\n470cIDdlkHMzGeT4RiFjU2WN9hfrl3OmlhuF7D00qX1HpvSzZ2yUGSUWvS7tAHmvpK2xy1sk7Wv3\nGDMrKgiOP+Xun1vEcQIAgC54JswgH50st90abamUwzTwdBgg53OmQi7I01UbAuSKVg021iC3CpCf\nPDolSXr2hhENFfNkkHtc2gHy3ZJON7NTzaxP0hWSvtTOMWZmkj4mabe7f3BJRw0AAI5LlEGuuTQ+\n3VudHBoyyB6UWORsdoB8dLKsVQPxDLK17GJRqgTBd18hp8G+AjXIPS7VANndK5KulnSLgkV2N7n7\nLkkys5vN7OR5jrlY0lskXWpm94f/Lk/lhQAAgFnGpys6EAbEkWfGSvWfe63MYqYGuaZauEgvKYN8\ndKqiVYOxADlnapUML4WPXcybhvrymqTNW08rtD5kcbn7zZJuTrj+8vmOcfdvKbk+GQAA9ID3feVB\n3f/YYf3Lf31Z/br9sYD56GRFWpvGyJLFu1hU6ov0wgA5jIAr1ZrGpysaHZgJoXKmll0sGjLIlFj0\nvLRLLAAAwDL19NiUnjgy1XDd/rEphTFn72aQK2EGOR4gh/XJUVlIc4lFqxrkcvjYffmcBvvylFj0\nOAJkAACwKEqVmiaa6oyfGS9py9ohSb3XC7ncVIOcbwiQg2OOToYB8mBTgNyizVs8gxyUWBAg9zIC\nZAAAsCimKzVNlqsN9bv7x6Z12sZhST2YQa7N1CBXao0BcnRbFNSvaiqxaDeDXMwHATIlFr2NABkA\nACyK6TBrOhEuSJuuVHVksqznbBqRFHSD6CXxLha1mitvMwFylCGuB8ixDHI7bd7iGeSBIiUWvY4A\nGQAALIrpShAERmUWB8aDDhbbNgzLrPcC5KjEYroyTwZ5cnYNspnVSzDmUgofO8ogU2LR2wiQAQDA\nopguhxnk6SAY3B9uErJpdECj/YWeLrGozbFRSJRBjnexyOfUctOTKIPcX8hpqK+gY7R562kEyAAA\nYFHUSyzCDPIzYYu3jaP9Wj1U1NGp3goSmzcKySdsFBJlvZsX6bXb5q1IF4tMIEAGAACLornEIsog\nbxzt1+rBYs9lkMuxNm/1rabzzRnkisyk0f74Ij1TrcVOeuVqrV6yMVjMq1z1+vOh9xAgAwCARRFl\nkMebMsjrh/u0aqD3AuRKbKOQarRIzxo3ChmbKmukv6BcbmavsqAP8vyPXarWVAyD7aG+vCSRRe5h\nBMgAAGBRlJq6WOwfm9aqgYIGinmtHiwmLtKr1VzXfXNPKsFzObbVdHXWRiFRiUWlYYGeFNQgt9PF\noi8fhF2DUYDMQr2eRYAMAAAWxUwNcrhIb3xaG0b7JWnODPJDT43p/bc8pG88+NTSDTTUUIPcvNV0\nbJFefIGeFNYgt0ghl6o19RWCsCvKINMLuXcRIAMAgK6rhFlYKbZIb6ykDSNBgBws0psdIEflGOMp\nLOCrd7FI2EmvElukF1+gJ0m5nKlFAlnleAa5GAXIvbVIETMIkAEAQFeMT1f0g31HJc1kj6VYH+SJ\naW0Y6ZMkrR4saqpcqy/kqz9GGBiPTS998FjvgxyVWDRsFDKzSK+5xCJnat3FolpTsRCVWAQZ6Clq\nkHsWATIAAOiK/3PnT/Tav/u2qjVvCJDHwxKLw8fKWjMUBMjRVs3NZRZRYDyRQoDckEFuKrFozCDP\nLrFoZ6vpKINMiUXvI0AGAABdcXiypKlyTVPlakNmeGK6InfX4cmy1g4F2deoTCHamS4yFpZdpFJi\nUa9Brs4s0mvqgzw2VU7IIAclFvNtFlKq1FScVWJBgNyrCJABAEBXRDvnTZar9Z8labxU0dGpiqo1\n19qhmRILaXYGOd0Si8YuFoWcqZALQqVqzVWrucamK7NrkMMger51eqWq1xfp0cWi9xEgAwCArojK\nKiZL1YYSi2PTFR0+VpKkmRKLKIPctFBvPNUSi8ad9HI5Uz62Uch4qSL3mfKQSJgYnrfMolSpzupi\nQR/k3kWADAAAuiIqq5gqV+s9kKWgzduhY0EgHJVYrK6XWDTVIIcZ5PE0AuRYm7dawkYh9W2mm0os\nrKkMI0m56jM1yMUgwKbEoncRIAMAgK6oZ5BjNcj9hZzGpys61JxBHpijxCLFNm/xraYrCX2Qo3rp\n5kV60THzrdMrVWoJJRYV/eTAhP7q6z+at34ZS48AGQAAdEVUd3wsVmKxfrhPE6V4icX8GeTxNDPI\nUYlFNcggN++kF5WDJLV5k+Zv9VaObTVdzAePe6xU1WfvfVwfvPWHevLoVLdfDjpAgAwAALoiyhrH\nM8jrRvo0MV3RoYmoxCLIIPcVchruy+snB441PEY9g7xIAfJ//PhgvVNGsyiDXK66ymGJRRT8Vmpe\nL/+Ye5He/F0s+gpB5tjMNFTMa7Jc1d6Dwet/8ggBci8hQAYAAF0RZZCnSjNdLNYO9WliuqrDx0oy\nm8kcS9Ivn7tZn7vvcf3wqbH6dWOLWGLxle8/oTd85E59+u7HEm+PapCj15DPmyzcLKRWm7sGuR4g\nz1ODXIplkKWgzGKyVNVjh4IA+SkyyD2FABkAAHRFYwZ5psRislzVgYmSVg0U6yULkvR7v3imRgcK\neu8Xd9VrcMfD7O5EqTpvwLlQjx08pt//7PckSQcnSonHVGozdcLHytX6Ar18zlSJlViMzupi0Uab\nt0pN/YWZsGuoL69jpaoeOzgpSXqCDHJPIUAGAABdkbRIb+1wUFLx+OHJegeLyLrhPr3rF87UnY8c\n0C27npLUWFoxUWovi9xqgZu7652fvl9SsEnHWEJ22t1VrrqGwwV01ZrXA9+8maq1Wn2RXnOAXK9B\nnreLxcxGIZI0UMzryGRZT40FgTElFr2FABkAAHRFUh/k9WGAvPfQZL2DRdyV55+ivkJO9/30kKSg\ntGKkPwhA26lDvu6be/RLf/OtWd0w4n78zITu+ckh/c5lZ2jDaF9iDXIU3A71zQS/UYBcyJmqtaBn\n83BfXoV8Y/gUtXlrtZNeX74xg7zn6fF654tokd6RybIefWZivpeMJUCADAAAumK6PLsP8rrhfknS\n3kPHZmWQpSAI3TjSr/3j06rWXBOlqk5cPSCpdR1ypVrT//72j/XA40d1zU33z1mScccP90uSLjvr\nBI32FxMzyFEHi+H+fMPYJCmfjzLIZY0OJL8GqVUXC1exocSioMcPB+UVfYVcvcTi/bc8qF/88B16\n6MmxxMfB0iBABgB0xMx2mNlDZrbHzK5Nezzo3MP7x/WXt/1I333ssKQgM3r3owdVqdbmvV9jiUUU\nIAdZ46lyrd7BotmG0X7tH5uuZ4xPigLkFhnkbz98QM+Ml/Ty527Sbbuf1odv+2FiFveOHz2jbeuH\ndMr6IY0OFBIDpHwABAAAIABJREFU5KiDRVIGOW+mqrsOT5brberiohKLuSos3F2lamMGOeqFLEnn\nbllTL7HYte+opis1Xf1P97IVdYoKrQ8BACCZmeUlXSfp5yXtlXS3mX3J3X8w3/32HZ7UH31pVxuP\nn3CdZl+ZfFybj5dwZdJ9k65sHktH4+jgdSUd2M7zFvM5DRTz2nvomHY+ekiHJ0tyD8ohJOlvb9+j\nP33N8/XVB57Ubbuf0v/4z+foLRc8K2kEkuIlFjXlrSozNQSUSSUWkrRxpE+PH56qB8QnrJodIP/k\nwIT+/t8e0btfcZaGwxKML973uEYHCvrbN79I7/7c9/VX39ij3U+O6X++7gX12ufpSlV3PnxAv7J9\niyRpdKCovYcaW8tJMx0sGjLIsUV61Zrr8LHSHAHy/F0syuFj9zUt0pOkvnxOL9iyWvd/57BqNdee\np8b1/M2r9cC+I/qDLzygD/zKC2RmOjRR0kNPjenwsZIe3j+hHz41JvegK0h84SO6gwAZANCJ8yXt\ncfdHJMnMbpT0akmzAmQzu0rSVZLUf+Jz9Pn7Hq/flpT1Sww1Eq5MOq7dx0v6i7gnHJl8XDtjO87H\nUmevYaH6Czmdd8oanb5pnSo111sueJYuOXOT/ttnv6d3/fN3VcybRvsL+rcf7m8RIEddLCoq5k39\nhVy9nlhSYomFJG0Y6dd39x6pl1SclFBi8eXvPaFPfuenmirX9IFfeaEmS1XdsutJveoFJ6u/kNcH\nXv9CPe/k1XrfVx7Ur370Ln3h7Repv5DXzkcPabJc1c+dsVGStGquDHJtdgY5l2sMkA8dK+v0TSOz\n7tuqD3IpzE43ZJCLQYC8ee2gTl4zqFKlph88cVRj0xW94SVbdenYJv3l13+kresGddFpG3TV/9mp\nw8dmaqc3rxlUMW86MllOXByY9Isf2keADADoxGZJ8aayeyW9NOlAd79e0vWStH37dt/53l9Y/NGh\nLingLlVrmirVNNiXb8huRm749Qv0kTse1iVnbtINd/1UX3ngiYbuDnHVmtczpZOlqvoLefUX8vVs\nryStGZ6jxGKkXwcnSvU2aicmlFj84ImjkqTP3LNXz9+8Wk8cmdJEqapXn3eypCCYfevLTtWz1g3p\nv/zjTv3V13+k3/vF5+qOH+5XMW+64NnrJSkssZi9SK+eQY6VPhRiAXKlnkGe/RpatXkrh5n15j7I\nkrRl7WD99f7bj56RJJ2xaURvfukpevzwpD5824/0N9/Yo1PWDelDbzxXm0b7tWXtUEM/abTP/qi9\n4wiQAQCdSEpTda95LbomKaMYBbFzGezL652XnSEpKHH49M7HtGvfEb1gy5pZx0bZYymoQR6sVIPd\n8mIlC3NnkPtUrbkeC3eVS6pB3v3EUb38uZt06FhJ7w3Lc16yba1eeur6hse67OwT9IbtW/R3tz+s\niemq/uW7+7T9WevqgfroQFHj0xW5e8N7EgXIQ/0JNcg5U6XqOnysnPgaooeZq81bPYMce6+jEout\n64ZiAXKwmPD0E0ZlZvqz1z5f05Wajk1X9ME3nKvVc7x/6D4CZABAJ/ZK2hq7vEXSvpTGgkV00Wkb\nJEnf3nMgOUAuzyzgmyzXNF2uJZRYzFGDPBoEiD8O25vVa5DDUojJUlWPPjOhV73gZL35glN02w+e\n1kufvU7P3jCcGPj/4avO1r8/fED/eOejetEpa/WuXzyzftvoQEE1DzYiiY8tKrGIZ5BzsRrkI5Nl\nVWqe+BqiQHquNm+lhAxyVMqxde1Q/ReCnY8e0oaRvvrCxmI+p7++8rzEx8TiIkAGAHTibkmnm9mp\nkh6XdIWkX013SFgMG0f7dcYJI/r3h5/Rb11y2qzbowV6UrjVdH8QIA8W88pZUH6QtMBNCjLI0kyA\nvHaoT/2FnMbDjUIeempMNZfOPmlUm0YH9KsvPWXesY4OFHXzO36mvoit+TZJGpsqNwTI9QxyrAa5\nEOticWBiWlLya4gC6bnavM1kkBs3CpGkresGtXGkXzkLjjt90+i8rw1LgzZvAIDj5u4VSVdLukXS\nbkk3uXvr9hTIpItO26C7Hz3YUE4RaS6xmK7U1F/Iy8w0HAad87V5k6RHDwQB8shAQaMDhXoG+cGw\n/visk1a1PdZVA8XEOt1oF7zmHstRm7d4SUh8kd4zY6U5X8NMF4vksZQTFunVSyzWDqmQz2lj+B6c\nccLsRYBYegTIAICOuPvN7n6Gu5/m7n+a9niweC5+zgZNlWu69yeHZ90WZZDNZraa7i8GYUZU/ztn\ngDwSBsjPBDXIw30FDfcX6jXIu584quG+vLauHer4NYyEAfLRpgC5krCTXpRBLuRbZZCD/87ZxaIy\nO4N80Wnr9ZrzNuu5JwUZ4xPDspLTTyCD3AsIkAEAQFsuePY6FXKmO8LFZHFRDfLqwWJ9q+n+MCAc\n6s8H5RZ9yQsCVw0U1JfPaXy6ouG+vPI500j/TAZ59xNjeu5Jq+oZ3U6sCgPk5k4WlfkyyGb1Dh3z\nd7FoVYM8E3Y9a/2wPvTGc+uLJKOFemcQIPcEAmQAANCW0YGitm9bq28++HT9uiNhb96oxGLNYFFT\nsRILSRrpL8yZPZaCDhtRiUGU4R0JM8jurt1PHtVZJ3UncJypQW4usZidQY5vFBJJ6mJRr0Fu6mLx\nzzsf04Hx6cQa5GYnrR6UpMQ+y1h6BMgAAKBtl5y5SQ8+OaYnj0zpM/fs1Yv/5FY9PTalqSiDPNSn\nY6WqpsvVegZ5uK8w5wK9SLRQL1o4FwXIjx+e1NhUZUH1x/MZrWeQm0ssoi4WCYv0YgFyUl2z1Uss\nZq47OFHS733me/rC/fsSSyyavfZFm/XOy06v7wCIdNHFAgAAtO2SMzfqz7/yoL7+4FP6X//6sCo1\n11NHphsyyNEivSgg/C8/c6qOlWYv7IuL6pCjDO/IQEHj+yt64PGFL9CbT7yLRVzUxSJeBpJrCpBX\nDRRUyM8OcpNKLKL34+hkeWar6YT7Rl6wZU1i+zyko60A2cxeI+kb7n4kvLxG0iXu/oXFHBwAAOgt\nZ54wqhNXDej9tzxU3/p4fLpSX6QXlSAcnSzXSyxeftYJLR93JkCeySBPTFf0nUcOaKCY0/NO7k6A\nPNwXtJ2bnUGeCWKL+aDmOIpnowB4ruzuTBeLmQC5XAl+Hp+utJVBRm9p90y9NwqOJcndD0t67+IM\nCQDQbWZ2a5jciC6vNbNb0hwTssnMdMmZG3X4WFmjYTlEECCHGeSw1vjwZLnexaIdG0abSiwGChqb\nCgLkl2xbN++Ofwsd/0j/7O2mo0V6hbzVnyufyzX8N2mBnpTcB7lUDd6Psalyvc1bcZ4MMnpLu2cq\n6TjKMwAgOzaEyQ1JkrsfkrQpxfEgwy4LM8K/9Z+CDUMmpiv1LhZRrXG15vUa5HZsDDPI9QC5r6Dp\nSk0PPjmmC569fr67LtjoQHH2Ir0w+1vMWz3TW1+kF9YYz7VVdlSiHG9iUSKDnGntnqmdZvZBMzvN\nzJ5tZh+SdM9iDgwA0FU1M6tvP2Zmz5KU3JMKaOHlZ23S//fbL9PrX7xFkjQWK7FYE1vEtpCs74bm\nLhYDM3m4C0/rdoBcmN0HOcog53L1WuF8vQY5uDxXJ46kGuQoazw2Val3sYhvNY3e1m4W+L9K+kNJ\nn5Zkkr4m6e2LNSgAQNe9R9K3zOxfw8s/K+mqFMeDDDMzPe/k1ZoMF95NTFfqWdR4GcJCMsj1GuRY\nF4vovy/YvLobw65bNVDU+HTyIr1CPINcD5CDY+bqxGEJbd4aAuTwl4f+fHfKRLD42vrkuvuEu1/r\n7tvd/cXu/m53n+jGAMxsh5k9ZGZ7zOzahRzTzn0BAJK7f1XSixQkOm6S9GJ3pwYZHRko5pSzYNvm\n+kYhsSByQTXII7P7IEvSS7atTewc0YnRsL45rlybqROeCZCD2wpRDfLg/BnkxhKL4PHGpyszNcgF\nMshZMW8G2cz+RfP8Cc7df7mTJzezvKTrJP28pL2S7jazL7n7D1odI+mhVvdN8sj+Cb3xI3d2MmwA\nyAwze1HTVfvC/55iZqe4+71LPSYsH9GCt/HpilzBDnhRBlhaWInFlrWDeuGW1Xph2OosCpS7XV4h\nBQHyj55uLrEIM8g5i5VYNGaS1w7PX4MczyBHZRXjsQzyfG3e0FtalVh8YJGf/3xJe9z9EUkysxsl\nvVrSD9o45vY27qvwtqsU/ilx5KTTFuu1AEAv+ot5bnNJly7VQLA8RQFyIWfqL+Q0UJwJiheyKG2g\nmNcXr35Z/fJzT1ylF52yRq8456SujleKFuk1lliU610scrMX6YURcKsuFo01yMHPURcLs8YNR9Db\nWgXIb5L0FUm3ufvYIjz/ZkmPxS7vlfTSNo9p576SJHe/XtL1krR9+3b/9G9c2NmoAWCBbvrN1J76\nTe6+r/VhwPEZGQj6FQ8W8+ov5Bo22lhIDXKzjaP9+tzbLu7GEGeJSizcvV4/XEnoYpFr7oM8ZxeL\n2QFylDWeKFU1VampL5+rPxd6X6tP7sclvVDSzWb2dTP7b2b2wi4+f9InpbmkY65j2rkvAKx0HzOz\n75jZn5vZJWZGi0501XCYQZ6uVDVQzGuoSwHyYhoZKKhS8/r22FJjF4to3FHtcZRJbt3FYua6KCMt\nSYcmSpRXZMy8E6W7f0fSdyT9kZmtl/QLkq4xs+dLuk/SV939pg6ef6+krbHLWzRTH9fqmHbuCwAr\nmru/wswGJF0i6TWSPmBmP5X0VQVz+E/THB+yb6QeINeCDHIxHiD3ZteG+HbTUcY7Koko5uM1yMHx\n+XxUYrHwGmRJOjhRogdyxrRapPe7TVe5pFsV7KK3TtKODp//bkmnm9mpkh6XdIWkX23zmIfauC8A\nrGhm9jeS/insYvHV8LpTJb1C0t+Y2Ynufn6aY0S2jfQX9OSRKU2Xa+ov5BtqkBfSxWIprQoXAB6d\nqmhTuIN1pVZTPmcyi7d5ay+DbIk1yDMB8oGJErvoZUyrszXa9G+VpO0K6pJPd/c/7eTJ3b0i6WpJ\nt0jaLekmd98lSWZ2s5mdPNcx890XAFD3I0l/YWaPmtn7zOyF7v5jd//bsBPRy1o9ADCfkf6gBnm6\nUlV/MShPiEpte7XEYjQMkOML9SpVVyFMBTcv0iuEWeV4+Uhc0kYhUQ2yRAY5i1qVWPxx0vVmtk7S\nbZJu7HQA7n6zpJsTrr+8jWMSrwcABNz9LyX9Zbhz3hWSPhGWXPyTpBvd/UepDhCZN9xfqO+kFwTH\npsFiXsdK1QyUWMy0eitXvZ7ljUosokV6v/LirTrzhNE5F9lFJRa1mZi4IYN8cKKkE1b1d2v4WALH\n9euMux9U8iI5AEAPcvefuPv73P08BeVor5X0YMrDwjIwGnaxmCzPBMRRHXLvZ5BnAuRKraZCvjGD\nHC3SO/vkVbri/FM0l6iLRTWhzZsUbBbS16O/LCDZcX1yzexSSYe6PBYAwCIxs6KZ/ZKZfUpBmdwP\nJb0u5WFhGRjuL6jm0pFj5XpAPNDjAfKqMIN8NFZiUa56PSBu3kmvlVx9J72ZoHg6VmIhSX158opZ\n0mqR3vc1u3XaOgXdIv6vxRoUAKA7zOznJV0p6ZWS/kNBadxV7j6R6sCwbERbQj8zPq3nnjQqSfXO\nEL1aYrFuOFhsd3CiVL+uUq2p2JRBzrXZtzhv87d5iz8msqFVP8xXNV12SQeYWAEgM/5fBfXG7wrL\n44CuigLko1OV2SUWPdrFYqCY13BfXgfGYwFyzeslFv35xhKLVpLavJUrNQ0W85qqVOUuulhkTKtF\nej9ZqoEAALrP3f9T2mPA8hYFyJI0EAbEMxnk3g0K14306eDEdP1yuVpTsanEos34OLHEolStqb+Y\nUyFnGpuukEHOGM4WAAA4bsOxAHn2Ir3eLLGQpHXD/TrQUGLhsxbpRe3bWqkv0qs19kEu5nP1BYHs\npJctnC0AAHDcogBQmskYRwFyL2dNNwz3NdYg12r1kooosG83QE6qQS5VXH35nEbC96fYw+8FZmtV\ngwwAADCnxgzyTIlFMW9tB5hpWDfcp137jtYvB32Qg/H+4vNO1LFSVRtH2utdbGHs27yTXl8hVy9B\n6SeDnCmcLQAAcNziNcj9YeZ4oJjv+ZKCoAa5VK8bDvogB2M+cfWAfuuS0+bcGKRZPmGr6VIl6IoR\nbUrCIr1sIYMMAACO20hCBvnnztjYdg/htKwf7lOpWtP4dEWjA8WwD/LxZbxnapBnrotqkKMSi14u\nN8FsBMgAAOC4DRRzyudM1ZrXA+Qd55yoHeecmPLI5rd+OCifODBe0uhAUZVqTUN9xxcWRYnmWlMX\ni75CTqPhLxBkkLOFswUAAI6bmWm4xzcGSbJuJNgsJOpkEe+DvFBRrXWt1lxiEetiQQY5UzhbAACg\nI1Gdba9uDJJkfdNuevGtphcqN8dOen35nEb6g/eGraazJTufZAAA0JOG+7OXQV4fdqiINguJbzW9\nUPWd9Bq6WHjQxYIMciZxtgAAQEfqrcwymEF+ZjxeYnF84zczmTXtpFfvYkENchZxtgAAQEeiXsi9\nvLV0s4FiXkN9+ViJRU3FDvo2581m9UEu5mcW6ZFBzhbOFgAA6EiUJc1SiYUkrR+Z2U0vvtX08ciZ\nNbR5K0U1yGSQM4mzBQAAOjLcl70MsiStG+7XM+NhDXJso5Djkcs1llhEO+lFCxjJIGcLZwsAAHQk\nypIOZKgGWQrqkONdLDopsQgyyLPbvEW1zqsG2HoiS7L1SQYAAD2nvkgvYyUW64bjJRadZZCDGuSZ\ny+Wqq5jPaeu6IX3mNy/Uy886odPhYgnx6wwAAOjISAYX6UlBDfKBiZLcXeUONgqRgt30knbSk6Tt\n29Z1PFYsrWx9kgEAQM+5+DkbdPnzT9S6sJwgK9YP96lUqWl8uhL0QT7OjUIkKZeb6WLh7ipVamwO\nkmFkkAEAQEfO2bxaf/umF6c9jAVbNxxsFvLMeEk1n9ky+njkYzXIlfC/dK7ILs4cAABYkdaPBBnv\np45OSdJx76QnBZuFRDXI5bDfG50rsoszBwAAVqQNYQb5iSOTktTZIr2cVAsj5FIlCJDJIGcXZw4A\nAKxI2zYMSZIefGJMklTosM1bVINcCjPIRTLImcWZAwAAK9LoQFGb1wxq176jkjrL+ObMVA0D5HI1\n+C+L9LKLABkAAKxYZ5wwol37jkhSZ1tN56Soy1u5Qg1y1nHmAADAinXGiaM6dKwsSR21ecsnlVhQ\ng5xZnDkAALBinXnCaP3njjLIsTZvLNLLPs4cAKAjZvZxM3vazB5IeyzAQp3RECB3tlGI0+Zt2eDM\nAQA69QlJO9IeBHA8nrNpRFHzimJHXSw0K4PcRwY5szhzAICOuPsdkg62Os7MrjKznWa2c//+/Usw\nMqC1gWJez1o/LKnDDHKsBjnqYkGJRXZx5gAAS8Ldr3f37e6+fePGjWkPB6g744QRSZ3XIM8EyJRY\nZB1nDgAwLzO7zcweSPj36rTHBnRDtFCvky4WuZzqW01P1xfp0Qc5qwppDwAA0Nvc/bK0xwAspjNO\nDAPkDgLafKyLRT2DTIlFZnHmAADAinbZWSfo93ecqfNOWXvcj2EJJRbUIGcXZw4A0BEzu0HSnZLO\nNLO9ZvbWtMcELMRAMa+3XfKcjmqG8zlqkJcTSiwAAB1x9yvTHgOQtpxJtSAuZqOQZYAzBwAA0KGc\nmar1raaD/1KDnF2cOQAAgA7lzOSUWCwbnDkAAIAOBTXIwc8l2rxlHgEyAABAhyy21XS5WpNZEDQj\nm1IPkM1sh5k9ZGZ7zOzahRxjZlvN7JtmttvMdpnZO5Zu5AAAAIF8bqbEolStqS+fkxkBclalGiCb\nWV7SdZJeIelsSVea2dkLOKYi6Rp3P0vSBZLe3nx/AACAxRZfpFeuOAv0Mi7ts3e+pD3u/oi7lyTd\nKKl569I5j3H3J9z93vDnMUm7JW1estEDAAAoCJDrbd6qVRVZoJdpaZ+9zZIei13eq9kBbjvHyMy2\nSTpP0l0Jt11lZjvNbOf+/fs7HDIAAECjnGlmo5CKs0Av4xZ9oxAzu03SiQk3vUdS0qfHmx+i1TFm\nNiLps5Le6e5HZx3sfr2k6yVp+/btzY8PAADQkVzTVtO0eMu2RQ+Q3f2yuW4zswslbY1dtUXSvqbD\n9s53jJkVFQTHn3L3z3U8YAAAgAXK56zexWK6WmMXvYxL++zdLel0MzvVzPokXSHpS+0eY8Hy0I9J\n2u3uH1zCcQMAANSZSWECWeVKjUV6GZfq2XP3iqSrJd2iYIHdTe6+S5LM7GYzO3m+YyRdLOktki41\ns/vDf5cv+QsBAAArWj4X62JBiUXmLXqJRSvufrOkmxOuv7yNY76l5BplAACAJROvQS5RYpF5nD0A\nAIAOxdu80cUi+wiQAQAAOhRv81aq1tRXyKc8InSCABkAAKBD+VxTmzcyyJlGgAwAANAhM1M12kmv\nQg1y1nH2AAAAOpTPSR7LIBMgZxtnDwAAoEM5i7d5c9q8ZRxnDwAAoENBF4twJz1KLDKPswcAANCh\noA9y8DOL9LKPABkAAKBD8TZv7KSXfZw9AACADuVzpmqYQqaLRfZx9gAAADpkZnKXajVXpeYEyBnH\n2QMAAOhQPidV3VUKmyH3FwmxsoyzBwAA0KFgkZ5ruhwGyGw1nWkEyAAAAB3KhSUW05WqJKmfRXqZ\nxtkDAADoUM6Ctm6TZQLk5YCzBwAA0KFoTV49QC5SYpFlBMgAAAAdsjCDfKxEBnk54OwBAAB0KJ8L\nAuSpMEBmo5Bs4+wBAAB0KIyPySAvE5w9AACADs1epEcNcpYRIAMAAHSILhbLC2cPAACgQ1GJxVQY\nIA+wk16mcfYAAAA6FC3Sm6lBpsQiywiQAQAAOhS1eZtkkd6ywNkDAADoUJRBZpHe8kCADAAA0KGo\nBrmeQaYGOdM4ewAAAB1q7mLRlyfEyjLOHgAAQIfiAXIxb8pFKWVkEgEyAABAh+JbTVN/nH0EyAAA\nAB2y2FbTdLDIPs4gAABAh+JdLAiQs48zCAAA0KFcrA9yf5ESi6wjQAYAAOhQvc0bGeRlgTMIAADQ\noXgXCwLk7OMMAgAAdCgKkKdKVfURIGceZxAAcNzMbKuZfdPMdpvZLjN7R9pjAtIQLdI7VqbN23JQ\nSHsAAIBMq0i6xt3vNbNRSfeY2a3u/oO0BwYspajNW7XmlFgsA5xBAMBxc/cn3P3e8OcxSbslbU46\n1syuMrOdZrZz//79SzlMYNHlYzvn9RcJr7KOMwgA6Aoz2ybpPEl3Jd3u7te7+3Z3375x48alHBqw\n6KIaZEmUWCwDlFgAAOZlZrdJOjHhpve4+xfDY0YkfVbSO9396FKOD+gFjQEy+cesI0AGAMzL3S+b\n73YzKyoIjj/l7p9bmlEBvSVWYUGAvAxwBgEAx83MTNLHJO129w+mPR4gLY01yJRYZB0BMgCgExdL\neoukS83s/vDf5WkPClhqFiux6MsTXmUdJRYAgOPm7t+SZC0PBJa5hgwyJRaZl+oZNLMdZvaQme0x\ns2uP5xgzy5vZfWb25cUfMQAAwGwNNci0ecu81M6gmeUlXSfpFZLOlnSlmZ290GMkvUNB300AAIBU\n0OZteUnzV5zzJe1x90fcvSTpRkmvXsgxZrZF0islfXSJxgwAADALbd6WlzTP4GZJj8Uu79Xs3Zda\nHfNhSb8vqTbfE7F7EwAAWEy5WERFiUX2Leoivfmayyt5UYc3P8Rcx5jZqyQ97e73mNkl843D3a+X\ndL0kbd++vfk5AAAAOpKnxGJZWdQAeb7m8mZ2oaStsau2SNrXdNjeeY65WNIvh+2EBiStMrNPuvub\nOx44AADAAhglFstKmmfwbkmnm9mpZtYn6QpJX2r3GHd/t7tvcfdt4fXfIDgGAABpiLd56yNAzrzU\nzqC7VyRdLekWBV0obnL3XZJkZjeb2cnzHQMAANArGreapsQi61LdKMTdb5Z0c8L1l7c6pun42yXd\n3uXhAQAAtIUuFssLZxAAAKBDufhOenSxyDzOIAAAQIfoYrG8ECADAAB0qLEGmfAq6ziDAAAAHaLN\n2/LCGQQAAOhQvqEGmRKLrCNABgAA6FC8xKIvT3iVdZxBAACADkVdLMykYt5aHI1eR4AMAADQoagP\ncn8h11CPjGwiQAYAAOhQvh4gU3+8HBAgAwAAdChKGtPBYnngLAIAAHQo6mLBLnrLA2cRAACgQzlK\nLJYVAmQAAIAO5SixWFY4iwAAAB0yM5kRIC8XnEUAAIAuyJupjwB5WeAsAgAAdEHOjBrkZYIAGQAA\noAsosVg+OIsAAABdkM+Z+otkkJcDAmQAAIAuCEosCK2WA84iAABAF+QosVg2CmkPAAAAYDl440u2\n6sLT1qc9DHQBATIAAEAXvOeVZ6c9BHQJfwcAAAAAYgiQAQAAgBgCZAAAACCGABkAAACIIUAGAAAA\nYgiQAQAAgBgCZAAAACCGABkAAACIIUAGAAAAYgiQAQAAgBgCZAAAACDG3D3tMSwpMxuT9FDa42hh\ng6Rn0h5EC4yxOxhjd2RhjGe6+2jag+gVzMVdwxi7gzF2RxbG2NZcXFiKkfSYh9x9e9qDmI+Z7WSM\nnWOM3cEYu8PMdqY9hh7DXNwFjLE7GGN3ZGWM7RxHiQUAAAAQQ4AMAAAAxKzEAPn6tAfQBsbYHYyx\nOxhjd2RhjEspC+8HY+wOxtgdjLE72hrjilukBwAAAMxnJWaQAQAAgDkRIAMAAAAxBMgAAABADAEy\nAAAAEEOADAAAAMQQIAMAAAAxBMgAAABADAEyAAAAEEOADAAAAMSs6ADZzPJmdp+ZfTntsTQzswEz\n+w8z+66Z7TKzP057TM3MbKuZfdPMdodjfEfaY0piZh83s6fN7IG0xxIxsx1m9pCZ7TGza9Mez1x6\n8b2Ly8JnMAvf5bQxF3cmC98DqTfnkyzMxb34vsVl4fN3PN/jFb3VtJn9rqTtkla5+6vSHk+cmZmk\nYXcfN7MCSv2/AAAIu0lEQVSipG9Jeoe7fyflodWZ2UmSTnL3e81sVNI9kv6zu/8g5aE1MLOflTQu\n6R/d/ZweGE9e0g8l/bykvZLulnRlr71vUu+9d82y8BnMwnc5bczFncnC90DqvfkkK3Nxr71vzbLw\n+Tue7/GKzSCb2RZJr5T00bTHksQD4+HFYvivp36bcfcn3P3e8OcxSbslbU53VLO5+x2SDqY9jpjz\nJe1x90fcvSTpRkmvTnlMiXrwvWuQhc9gFr7LaWIu7lwWvgdST84nmZiLe/B9a5CFz9/xfI9XbIAs\n6cOSfl9SLe2BzCX8s+P9kp6WdKu735X2mOZiZtsknSepZ8fYQzZLeix2ea96bDLJol7+DGbpu5wC\n5uIu6uXvQQ9iLu6yXv78LfR7XFiaYS09M7tN0okJN71HUlXS0+5+j5ldsqQDi5lvjO7+RXevSjrX\nzNZI+ryZnePuS1qD1GqM4TEjkj4r6Z3ufnQpxxc+f8sx9hhLuK6nMlJZk/ZnsJVe+C6nhbl4acYY\nHsNcvDDMxV2U9uevlYV+j5dtgOzul811m5n9maRfNrPLJQ1IWmVmn3T3Ny/ZADX/GJuOO2xmt0va\nIWlJJ+VWYwxreT4r6VPu/rmlGVWjdt/HHrJX0tbY5S2S9qU0lszrhc9gu9L8LqeFubg7mIsXBXNx\nl/TC569d7X6PV2SJhbu/2923uPs2SVdI+sZST8itmNnG8LccmdmgpMskPZjuqBqFRe8fk7Tb3T+Y\n9ngy5G5Jp5vZqWbWp+Az+KWUx5RJWfgMZuG7nBbm4u7IwvegRzEXd0EWPn/H8z1ekQFyRpwk6Ztm\n9j0FX+Jb3b3XWiBdLOktki41s/vDf5enPahmZnaDpDslnWlme83srWmOx90rkq6WdIuCxQw3ufuu\nNMc0l1577xJk4TOYhe8y5paF85eF70HPzSdZmYt77X1LkIXP34K/xyu6zRsAAADQjAwyAAAAEEOA\nDAAAAMQQIAMAAAAxBMgAAABADAEyAAAAEEOADAAAAMQQIAMAAAAxBMjACmRmf21m95rZS9IeCwCs\nVMzFvYsAGVhhzGxY0iZJvyHpVSkPBwBWJObi3kaAjK4wsw+Z2Ttjl28xs4/GLv+Fmf1ul59zvMuP\nt8bM3ha7vM3MHujwMT9uZk8nPY6Z7TCzh8xsj5ldG7v+d8xsl5k9YGY3mNlAeP2Amf2HmX03vP2P\n53uscPyTZnZ//HndfULBtpu3S/qr8NjBcHvQkplt6OQ1A0gPc/Gcj8lcjAUhQEa3/LukiyTJzHKS\nNkh6Xuz2iyR9O4VxLcQaSW9redTCfELSjuYrzSwv6TpJr5B0tqQrzexsM9ss6bclbXf3cyTlJV0R\n3m1a0qXu/kJJ50raYWYXzPVY4X0edvdzm557vaQhSWOSqpLk7pPhcfu69soBpIG5ONknxFyMBSBA\nRrd8W+GkrGAyfkDSmJmtNbN+SWdJus/MvmBm94S/dV8V3dnM3teUMfgjM7vGzN4c/qZ+v5l9JJyA\nGsx1TPhb+24z+/vw+b5mZoPhbX9oZg+a2a1hZuBdkv5c0mnh47w/fPh80v3b5e53SDqYcNP5kva4\n+yPuXpJ0o6RXh7cVJA2aWUHB5LkvfCx39yhTUwz/eYvHSvIHkj4gaZeCSRzA8sFcnIC5GAtFgIyu\ncPd9kipmdoqCyflOSXdJulDSdknfCyeMX3P3F4fX/Xb4G7QUTCRvjD3kGyTtDK+7OPyNuirpTfHn\nNbOzWhxzuqTr3P15kg5Lep2ZbZf0OknnSXptOBZJulbhb/nu/ntz3f9436MmmyU9Fru8V9Jmd39c\nwYT5U0lPSDri7l+Lvd58+Ge6pyXd6u53zfVYSU9qZtsUnJ9PS9qtxswSgIxjLl4w5mIkKqQ9ACwr\nUebiIkkfVDAxXCTpiII/+0nBRPya8OetCia9A+5+n5ltMrOTJW2UdEjS8yW9WNLdZiZJgwomo7iX\ntzjmx+4e1X3dI2mbgj85ftHdJyXJzP5lnteUdP9usITr3MzWKsg4nKrgfwL/bGZvdvdPSpK7VyWd\na2ZrJH3ezM6Z67HmeN4/kfTf3d3NjEkZWJ6Yi9vHXIxEBMjopqj27fkK/qz3mKRrJB2V9HEzu0TS\nZZIudPdjZna7pIHY/T8j6fWSTlSQxTBJ/+Du757nOVsdMx37uapg0k6axOaSdP/GAZi9XdKvhxcv\nDzM4rexV8D+lyP/fzv27dBWFcRx/PxENaghCQRBUQ9SWo0FTOQTR5n8QNPVfNEWzi3NQRDmEQz+Q\nIBICQSgxGh2KiMqKGrOn4bnE5aL2Fb+i2fs1Xu45nHuGzz2cX0ep5btx6kfwsal7murTW+3Cmfm1\n6b+L1M9wrbq67RylZmnORcQk1feLPbRV0r/FLDaLtUVusVA/zVFX1axk5mpmrlCHLc5Sy3zDwJcm\nkE8DY53yd6hDEBNUQM8CExFxGCAiRiLiWKdML+90PQcuR51EHgIuNc+/Awc3+9GZOdksBY72GMgA\n88DJiDgREQeo735ALeeNRcRA1DTMBWr5jYg41MxW0Oy/GwfebFBX1w3gcmYez8zjwBmctZD2IrPY\nLNYWOUBWPy1SS2YvOs++ZeYn4CGwPyJeAdc775GZS1QovsvM95n5mjrE8Lgp84S6Eqdd5q/vdGXm\nPBVaL4Fpan/dt8z8DMxFXelzc6M6ehURt6kf0qmIeBsRV5o2/ASuAY+o0L2bmUvNPrZ7wALVd/uA\nqaa6I8DT5jvnqX1vM+vV1WnHeWAwM2db/fABGIyIkX58q6RdwyzuMIu1WZG53vYYae+KiKHM/BER\nA8Az4GpmLux0u/qpOQQyk3VFUa9llqlrjT5tU7Mk6Q+zeN0yy5jFO8oZZP2vppoTyAvA/b0WyI1V\nYDg6l9OvJZrL6anrin5te8skqZjFLWbx7uEMsiRJktTiDLIkSZLU4gBZkiRJanGALEmSJLU4QJYk\nSZJaHCBLkiRJLQ6QJUmSpBYHyJIkSVLLb310gQid+BEXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = pl.subplots(nrows=2, ncols=2, figsize=(10,10))\n",
    "ax = ax.flatten()\n",
    "for i in range(4):\n",
    "    ax[i].plot(mod.spectrum['spec1'].wavelength_axis - 10830, mod.spectrum['spec1'].stokes[i,:])\n",
    "\n",
    "for i in range(4):\n",
    "    ax[i].set_xlabel('Wavelength - 10830[$\\AA$]')\n",
    "    ax[i].set_ylabel('{0}/Ic'.format(label[i]))\n",
    "    ax[i].set_xlim([-4,3])\n",
    "    \n",
    "pl.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to save the output of a single pixel to a file. In this case, just open the file, do the synthesis, write to the file and close it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = hazel.Model('conf_single.ini', working_mode='synthesis')\n",
    "mod.open_output()\n",
    "mod.synthesize()\n",
    "mod.write_output()\n",
    "mod.close_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output file contains a dataset for the currently synthesized spectral region. Here you can see the datasets and their content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['spec1']\n",
      "['chi2', 'stokes', 'wavelength']\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File('output.h5', 'r')\n",
    "print(list(f.keys()))\n",
    "print(list(f['spec1'].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we do some plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/miniconda3/envs/py36/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAALICAYAAABiqwZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pl.subplots(nrows=2, ncols=2, figsize=(10,10))\n",
    "ax = ax.flatten()\n",
    "for i in range(4):\n",
    "    ax[i].plot(f['spec1']['wavelength'][:] - 10830, f['spec1']['stokes'][0,0,0,i,:])\n",
    "\n",
    "for i in range(4):\n",
    "    ax[i].set_xlabel('Wavelength - 10830[$\\AA$]')\n",
    "    ax[i].set_ylabel('{0}/Ic'.format(label[i]))\n",
    "    ax[i].set_xlim([-4,3])\n",
    "    \n",
    "pl.tight_layout()\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Many pixels synthesis\n",
    "Synthesizing many pixels can be exhausting and time consuming if you do it one by one. For this reason, Hazel can admit HDF5 files with different models for the synthesis.\n",
    "\n",
    "### Without MPI\n",
    "The simplest option is to iterate over all pixels with a single CPU. To this end, we make use of the `iterator`. You first instantiate the iterator and then pass the model to the iterator, which will take care of iterating through all pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-09-07 08:50:51,487 - Adding spectral region spec1\n",
      "2018-09-07 08:50:51,489 -   - Reading wavelength axis from observations/10830.wavelength\n",
      "2018-09-07 08:50:51,492 -   - Reading wavelength weights from observations/10830.weights\n",
      "2018-09-07 08:50:51,496 -   - Using observations from observations/10830_stokes.1d\n",
      "2018-09-07 08:50:51,497 -   - No mask for pixels\n",
      "2018-09-07 08:50:51,498 -   - Using LOS ['0.0', '0.0', '90.0']\n",
      "2018-09-07 08:50:51,499 -   - Using boundary condition ['1.0', '0.0', '0.0', '0.0']\n",
      "2018-09-07 08:50:51,500 - Not using randomizations\n",
      "2018-09-07 08:50:51,501 - Adding atmospheres\n",
      "2018-09-07 08:50:51,503 -   - New available photosphere : ph1\n",
      "2018-09-07 08:50:51,504 -     * Adding line : [300]\n",
      "2018-09-07 08:50:51,505 -     * Magnetic field reference frame : vertical\n",
      "2018-09-07 08:50:51,506 -     * Reading 3D model photospheres/model_photosphere.h5 as reference\n",
      "2018-09-07 08:50:51,510 -   - New available chromosphere : ch1\n",
      "2018-09-07 08:50:51,511 -     * Adding line : 10830\n",
      "2018-09-07 08:50:51,512 -     * Magnetic field reference frame : vertical\n",
      "2018-09-07 08:50:51,513 -     * Reading 3D model chromospheres/model_chromosphere.h5 as reference\n",
      "2018-09-07 08:50:51,516 - Adding topologies\n",
      "2018-09-07 08:50:51,517 -   - ph1 -> ch1\n",
      "2018-09-07 08:50:51,518 - Removing unused atmospheres\n",
      "2018-09-07 08:50:51,519 - Number of pixels to read : 2\n",
      "100%|██████████| 2/2 [00:00<00:00, 13.89it/s]\n"
     ]
    }
   ],
   "source": [
    "iterator = hazel.Iterator(use_mpi=False)\n",
    "rank = iterator.get_rank()\n",
    "mod = hazel.Model('conf_nonmpi_syn1d.ini', working_mode='synthesis', verbose=2)\n",
    "iterator.use_model(model=mod)\n",
    "iterator.run_all_pixels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(npix,ncycle,nstokes,nlambda) -> (2, 1, 1, 4, 150)\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File('output.h5', 'r')\n",
    "\n",
    "print('(npix,ncycle,nstokes,nlambda) -> {0}'.format(f['spec1']['stokes'].shape))\n",
    "\n",
    "fig, ax = pl.subplots(nrows=2, ncols=2, figsize=(10,10))\n",
    "ax = ax.flatten()\n",
    "for j in range(2):\n",
    "    for i in range(4):\n",
    "        ax[i].plot(f['spec1']['wavelength'][:] - 10830, f['spec1']['stokes'][j,0,0,i,:])\n",
    "\n",
    "for i in range(4):\n",
    "    ax[i].set_xlabel('Wavelength - 10830[$\\AA$]')\n",
    "    ax[i].set_ylabel('{0}/Ic'.format(label[i]))\n",
    "    ax[i].set_xlim([-4,1])\n",
    "    \n",
    "pl.tight_layout()\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With MPI\n",
    "In case you want to synthesize large maps in a supercomputer, you can use `mpi4py` and run many pixels in parallel. To this end, you pass the `use_mpi=True` keyword to the iterator. Then, this piece of code should be called with `mpiexec` to run it using MPI:\n",
    "\n",
    "`mpiexec -n 10 python code.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iterator = hazel.iterator(use_mpi=True)\n",
    "rank = iterator.get_rank()\n",
    "mod = hazel.Model('conf_mpi_synh5.ini', working_mode='synthesis', rank=rank)\n",
    "iterator.use_model(model=mod)\n",
    "iterator.run_all_pixels()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}