{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instrument synergy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to prototype support for multiple instruments operating on the same signal incident at Earth. The instruments may have different wavebands and the data may or may not be phase resolved for each instrument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some extra data files not available on the GitHub repository are needed to run this tutorial. They can be found in the [Zenodo](https://zenodo.org/record/7094144) and should be placed in the directory `examples/examples_modeling_tutorial/model_data/` of your local xpsi directory.  These are the required files: \n",
    "- `nicer_v1.01_arf.txt`\n",
    "- `nicer_v1.01_rmf_matrix.txt`\n",
    "- `nicer_v1.01_rmf_energymap.txt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/=============================================\\\n",
      "| X-PSI: X-ray Pulse Simulation and Inference |\n",
      "|---------------------------------------------|\n",
      "|                Version: 2.2.0               |\n",
      "|---------------------------------------------|\n",
      "|      https://xpsi-group.github.io/xpsi      |\n",
      "\\=============================================/\n",
      "\n",
      "Imported GetDist version: 1.4.7\n",
      "Imported nestcheck version: 0.2.1\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.ticker import MultipleLocator, AutoLocator, AutoMinorLocator\n",
    "from matplotlib import gridspec\n",
    "from matplotlib import cm\n",
    "\n",
    "import xpsi\n",
    "\n",
    "from xpsi.global_imports import _c, _G, _dpr, gravradius, _csq, _km, _2pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Telescope(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NICER = Telescope()\n",
    "XMM = Telescope() # fabricated toy that we'll just pretend is XMM as a placeholder!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load a synthetic data set that we generated in advance, and know the fictitious exposure time for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "obs_settings = dict(counts=np.loadtxt('../../examples/examples_modeling_tutorial/model_data/example_synthetic_realisation.dat', dtype=np.double),\n",
    "                    channels=np.arange(20, 201),\n",
    "                    phases=np.linspace(0.0, 1.0, 33),\n",
    "                    first=0,last=180,\n",
    "                    exposure_time=984307.6661)\n",
    "\n",
    "NICER.data = xpsi.Data(**obs_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "obs_settings = dict(counts=np.loadtxt('../../examples/examples_modeling_tutorial/model_data/XMM_realisation.dat', dtype=np.double).reshape(-1,1),\n",
    "                    channels=np.arange(20, 201),\n",
    "                    phases=np.array([0.0, 1.0]),\n",
    "                    first=0,last=180,\n",
    "                    exposure_time=1818434.247359)\n",
    "\n",
    "XMM.data = xpsi.Data(**obs_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['text.usetex'] = False\n",
    "rcParams['font.size'] = 14.0\n",
    "\n",
    "def veneer(x, y, axes, lw=1.0, length=8, yticks=None):\n",
    "    \"\"\" Make the plots a little more aesthetically pleasing. \"\"\"\n",
    "    if x is not None:\n",
    "        if x[1] is not None:\n",
    "            axes.xaxis.set_major_locator(MultipleLocator(x[1]))\n",
    "        if x[0] is not None:\n",
    "            axes.xaxis.set_minor_locator(MultipleLocator(x[0]))\n",
    "    else:\n",
    "        axes.xaxis.set_major_locator(AutoLocator())\n",
    "        axes.xaxis.set_minor_locator(AutoMinorLocator())\n",
    "        \n",
    "    if y is not None:\n",
    "        if y[1] is not None:\n",
    "            axes.yaxis.set_major_locator(MultipleLocator(y[1]))\n",
    "        if y[0] is not None:\n",
    "            axes.yaxis.set_minor_locator(MultipleLocator(y[0]))\n",
    "    else:\n",
    "        axes.yaxis.set_major_locator(AutoLocator())\n",
    "        axes.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "        \n",
    "    axes.tick_params(which='major', colors='black', length=length, width=lw)\n",
    "    axes.tick_params(which='minor', colors='black', length=int(length/2), width=lw)\n",
    "    plt.setp(axes.spines.values(), linewidth=lw, color='black')\n",
    "    if yticks:\n",
    "        axes.set_yticks(yticks)\n",
    "\n",
    "def plot_one_pulse(pulse, x, data, label=r'Counts',\n",
    "                   cmap=cm.magma, vmin=None, vmax=None,\n",
    "                   yticks=[20,50,100,200]):\n",
    "    \"\"\" Plot a pulse resolved over a single rotational cycle. \"\"\"\n",
    "    \n",
    "    fig = plt.figure(figsize = (7,7))\n",
    "\n",
    "    gs = gridspec.GridSpec(1, 2, width_ratios=[50,1])\n",
    "    ax = plt.subplot(gs[0])\n",
    "    ax_cb = plt.subplot(gs[1])    \n",
    "    \n",
    "    # Calculate the centre of phase bins (required by pcolormesh instead of phase edges)\n",
    "    _phase_bincenter = x[:-1]+0.5*(x[1]-x[0])\n",
    "    \n",
    "    profile = ax.pcolormesh(_phase_bincenter,\n",
    "                             data.channels,\n",
    "                             pulse,\n",
    "                             cmap = cmap,\n",
    "                             vmin=vmin,\n",
    "                             vmax=vmax,\n",
    "                             linewidth = 0,\n",
    "                             rasterized = True)\n",
    "\n",
    "    profile.set_edgecolor('face')\n",
    "\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylabel(r'Channel')\n",
    "    ax.set_xlabel(r'Phase')\n",
    "\n",
    "    cb = plt.colorbar(profile,\n",
    "                      cax = ax_cb)\n",
    "\n",
    "    cb.set_label(label=label, labelpad=25)\n",
    "    cb.solids.set_edgecolor('face')\n",
    "\n",
    "    veneer((0.05, 0.2), (None, None), ax, yticks=yticks)\n",
    "\n",
    "    plt.subplots_adjust(wspace = 0.025)\n",
    "    \n",
    "    if yticks is not None:\n",
    "        ax.set_yticklabels(yticks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(NICER.data.counts, NICER.data.phases, NICER.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "veneer((5, 25), (None,None), ax)\n",
    "\n",
    "ax.step(XMM.data.counts, 'k-', label='XMM')\n",
    "ax.step(np.sum(NICER.data.counts, axis=1), 'r-', label='NICER')\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylabel('Counts')\n",
    "_ = ax.set_xlabel('Channel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instrument"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We require a model instrument object to transform incident specific flux signals into a form which enters directly in the sampling distribution of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomInstrument(xpsi.Instrument):\n",
    "    \"\"\" A model of the NICER telescope response. \"\"\"\n",
    "    \n",
    "    def __call__(self, signal, *args):\n",
    "        \"\"\" Overwrite base just to show it is possible.\n",
    "        \n",
    "        We loaded only a submatrix of the total instrument response\n",
    "        matrix into memory, so here we can simplify the method in the\n",
    "        base class.\n",
    "        \n",
    "        \"\"\"\n",
    "\n",
    "        matrix = self.construct_matrix()\n",
    "\n",
    "        self._folded_signal = np.dot(matrix, signal)\n",
    "\n",
    "        return self._folded_signal\n",
    "\n",
    "    @classmethod\n",
    "    def from_response_files(cls, ARF, RMF, channel_edges, max_input, min_input=0,\n",
    "                            translate_edges=None, scaling=None):\n",
    "        \"\"\" Constructor which converts response files into :class:`numpy.ndarray`s.\n",
    "        :param str ARF: Path to ARF which is compatible with\n",
    "                                :func:`numpy.loadtxt`.\n",
    "        :param str RMF: Path to RMF which is compatible with\n",
    "                                :func:`numpy.loadtxt`.\n",
    "        :param str channel_edges: Path to edges which is compatible with\n",
    "                                  :func:`numpy.loadtxt`.\n",
    "        :param float translate_edges: Optional translation of the energy edges (in keV).\n",
    "        :param float scaling: Optional Scaling to scale the ARF.\n",
    "        \n",
    "        \"\"\"\n",
    "\n",
    "        if min_input != 0:\n",
    "            min_input = int(min_input)\n",
    "\n",
    "        max_input = int(max_input)\n",
    "\n",
    "        try:\n",
    "            ARF = np.loadtxt(ARF, dtype=np.double, skiprows=3)\n",
    "            RMF = np.loadtxt(RMF, dtype=np.double)\n",
    "            channel_edges = np.loadtxt(channel_edges, dtype=np.double, skiprows=3)[:,1:]\n",
    "        except:\n",
    "            print('A file could not be loaded.')\n",
    "            raise\n",
    "            \n",
    "        if scaling is not None:\n",
    "            ARF[:,3] *= scaling\n",
    "            \n",
    "        matrix = np.ascontiguousarray(RMF[min_input:max_input,20:201].T, dtype=np.double)\n",
    "\n",
    "        edges = np.zeros(ARF[min_input:max_input,3].shape[0]+1, dtype=np.double)\n",
    "\n",
    "        edges[0] = ARF[min_input,1]; edges[1:] = ARF[min_input:max_input,2]\n",
    "\n",
    "        for i in range(matrix.shape[0]):\n",
    "            matrix[i,:] *= ARF[min_input:max_input,3]\n",
    "\n",
    "        channels = np.arange(20, 201)\n",
    "        \n",
    "        if translate_edges is not None:\n",
    "            edges += translate_edges\n",
    "\n",
    "        return cls(matrix, edges, channels, channel_edges[20:202,-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's construct an instance of the first instrument called `NICER`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for loaded instrument response (sub)matrix...\n",
      "Channels set.\n",
      "No parameters supplied... empty subspace created.\n"
     ]
    }
   ],
   "source": [
    "NICER.instrument = CustomInstrument.from_response_files(ARF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_arf.txt',\n",
    "                                         RMF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_matrix.txt',\n",
    "                                         channel_edges = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_energymap.txt',\n",
    "                                         max_input = 500,\n",
    "                                         min_input = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second instrument, called `XMM`, we use the some response files (those of `NICER`) with two additional arbitrary modifications (for the sake of the example):\n",
    "- add a `scaling=0.5` which reduces the sensitivity by half (see `CustomInstrument` class above).\n",
    "- add a `translate_edges=0.1` which shifts the sensitivity matrix by 0.1 keV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for loaded instrument response (sub)matrix...\n",
      "Channels set.\n",
      "No parameters supplied... empty subspace created.\n"
     ]
    }
   ],
   "source": [
    "XMM.instrument = CustomInstrument.from_response_files(ARF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_arf.txt',\n",
    "                                       RMF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_matrix.txt',\n",
    "                                       channel_edges = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_energymap.txt',\n",
    "                                       max_input = 500,\n",
    "                                       min_input = 0,\n",
    "                                       translate_edges = 0.1,\n",
    "                                       scaling = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NICER ``v1.01`` response matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8nQq4lB9fJQOCB1g4juM4ztvACwqreugpjz083q2AzmvEBdX7zM0Ai4OYYsmVOIRW5FynVyJIzuhyUZFoy/ep9r+iQsoFjnurENByVQx8OHSIXC9ahRcFpNTPJVMLh5deq2JQTNBYKAraKDpliggaFJm19m3FGrohIh5g4TiO4zjvGi8YXlEPfbtUwMcmVoev8YmV84K4oHofeeyCcGQDJD07tCJnCAZzwOLSWRUbbDJSqzUavasTKsnUaVUHqQi5qTItUw5rV3WvihpucWUJbKuwkgwp1gmVzrXLSqNgnWBzQWfFBsUmQ6eMjlbTAacIc6xhFnOsXVY51wvu0mNVUjoSVuI7Vo7jOI7zNvFdCKvjz4/wgmDnVeCC6n3nKMDiVhtgzkshsNWpjhrSNjVpb7EB5imgbUDnQuqU3AkaldRC7OtuVGqFFEE6INfJ05WwOoRX6I3Vr2VSpQnSXBMCbVwE1gyh08UGmLHB0JjRMVylAspY96kkJpjjdRpgPAqw+KZkQLcCOo7jOM6b4VULq2fgwsr5Nrigch6/YC02wKvQioMN8BDyYIVSlkLgdF0ILLFebCQbKS+FwIk6epKyFFQB1NRAjVyVAS+H65RKy/IRDnHrJQnZlkRAE3SGPJcaajFDtvp8GhVTQbX2W6kIMitFtdoAoyKWlylcRiQ/PcCi5MUKcMvo3y+kjuM4jvP6eFXC6jlwYeW8DO+doBKRz4BffNPn8VZy1GN1mFaRY91Pkpqoh2ot3U0ZTJF5rhY7M2ROlDZgrWFjQ+qU0CvztEyrVpBW9eJjQ6kx66Fa+zLUz+2wW3UttqSU62nVLFfBFWlYPp8KYQU6F8Koy7SqYPtQY9fnhAxLUMUckbCcf4w1wKKUGr9+mFil9JgV8MoGePU6+dTKcRzHcV47z9ljVQ+9ZmHlvwe817x3gqqU8hnw2eFzuYp8c6640WN1mFiVVO+TnOte0sEGWAqiWsVIbLA5ILmgk9Udp2ikTrFZmCMg0OwK2QQJYD012KKALntW1QJYp1XlUA5calBFbpewilBtgDrXYAuNkAYhtDXAIjSCzgWdDW2sCqs51D2rmGq3VQxVQMWE2FISfPhZSqHM8Wq/qr4Wt0Sug/+VynEcx3FeJy/QY1UPfcfCyn8PeK957wSV85zcYgO8SgMUReBaTOVCsXpMSoGY0FKQGK6tgLEguY6dilJj0A1ygjheX+TyEsgjS3ZFDa+o06yylAZLWqyAVmPWSxDQRVhZnXrZXFMHbcroJJgIJSgaqhUQsxq6sZQDiyolSk0CPFgBU00J5Cio4rEL621JQH5BdRzHcZzXw2u2Aj5XIqBPq95LXFA5z+bIBggsVsAlrEG0pgGmVC8gMcI81xCLxVonTUDHltAGUh+wtV0JndwoOQjZhDSCdkJaQWqX6HQgh7orhZYqrLRcnVKOsnRWFXQ6hFYINlVboA1go6EzhCHXdMAxEIaAzBkbEzLFJR1wruEVud4uMSE5wcBVOEc5RBHelgpYP7m+7cLKcRzHcV4PL2EFrIdfTFy98LTq5rk531tcUDnPx/Fo/Ti4IiuF2v9UO6sKLMEPkkO1zrGEVuSM5KZ2TaWMRiUHpekETQcRVcMsIlBEqo6TQkbqVEzrtAqphcElFCTV3qtqA7y2AuZQI9xtKuSg2FSwplx3WjWKjorOGQkKh5JgkWr/i9fJgFcdVvCUuHW3AjqO4zjOG+c5rYD1cHmpidVThZX3V723uKBynp+bNsBlWnWwAbJYAEkJKZkSI4S6T8Uc0dggMV/tKJXGkMZoGkVTTemTpQKrqiaQXO18UEUVy5SqRgsuYiZIFVGp7lLlUBMBcyPoBHmWGn4xg01CDgWbCzkItogrDYpOsZYVi1QroCnEWC2AJUMu1eYoUgMuTOprAC6sHMdxHOdt4TVZAZ+wAXpoxXuLCyrnxXmWsMql7h2VAhIRq0l6h/4qmZqrC1wJhgZDSiF3RuoMm5TUKvMIOgm5gzgLqRNyW2okeyiUpoBRxZXlKm4AMqROSRlkVnSuPVg2CDILNkHY17AKGyEMWtMB94aNoXZZ7QM6LXbAQxpgqakZkheL4xLIIZqu7YC5IMZjYR7Xr5knAzqO4zjOa+WbrIBwJYJe1groNkAHXFA534Zn9VdBtQEe91exzJ3kMH2qaYG6NyQVSDV1QiIsY6nraVWpUelIXXPKBUrLklwBaKmnISyJFTV2PQchx3pbIpRGljCLOrXKtgRYmCwTLSXoEmBhgg1NjYdPNaiihmpYnVyVpdPqYAeUvAiveu5PTK08wMJxHMdx3gy3WQHheif8sUMvPrV67tAK8InV9xAXVM6355b+KiEtYuO6v0pSqtMqWy5cZogu4mMKyFQnRNYZGgOSlDReR6OntloAUw8ShUSmHP4XrDVXXe36AlWaTMkCSYidIrOQZsG6JbSiE6yvtsDU1gALmwu5FWxQQqPofoYlrELSsgcWUw3KyAWReG0HTOkbhJUHWDiO4zjOG+MFJlb18IsJKxdV7y8uqJxXx23FwCxLRosYOcSVA4jpVQQ7ISBNQOZUO6PGBp0acqvYpMRBSG1N8Iu9kFuIs9adqa6QO6FYoTQZsYJoQZvlAlaEnDJkIUchz1Vc6STYKOgsxEGwpSg47IQwKGEwdEzImOo0LdUOLuaIzLH+TLNW8ZQSkmyxQOarHqvH7ICP9Vm5HcBxHMdx3hgvsWdVDz9bXD1VVB1/76vj/gfV7wsuqJxXz3ExsGid3GgNcBCo1jlVyjK5YtY62TnYA1NAqf/jzLMBAcmKLqXAZCGnmgIoicX2p5Sm2grLEvEuWr+faF7sh9Xul7UGWaQgFKt7VkUhm2AzIPVxCKQ+oCpIzLVvK9USY0RqzDrVuogIRTLkhCxx8mW5QErOtaj4Rp/VUwMswP9y5TiO4zivgxewAtbDL5cMCDx7WnU4F+edxAWV893wrP4qqBcVlSp0DpOrJiAxIiEg04xM7dW0ytaHaZWROiF2dcKUOurnc031y339V6zKKg0ZkUII1xew3CZKFlJS8qzkqKRR0bEGV4ReCIOQeqG9COhsi6ACTRmZMjoZMidkjMt0Ki9Tq1R/nrTsXR32yHI+LIS9eOT6Y6+n4ziO4zivlJe0Al7fJU98/sSU6vj73Caqjp/b3/PfOVxQOd8ttwRXVNJV3LpAneYsSXrEiMTaYSW2iJk5UzpDYkPqFOsVjTX9L/bVtldtgJBmozSFnITUZkqToGTUMqrX4ioUIQal5JosmDqDJORWSYOQGmE8q/1VmhTJBUmGTpkwKDJntKnnx6EcOOUaqR5rGmCJS1JgqXHx5Hy0Z/WcyYCH1+/49XQcx3Ec59XzAlbA67te8L35ae6Uq/v9Pf9dwwWV83o4tgEeDuWjHatlilMWEXWVDhgCAmgplGSEXNA5oLMixYhRkVSHXynWmD8pVUwVUQpVox2uXSJVVImUq9ulJKIZKZS6YyVGMQOBuJJaRJxrLLwksFCP2bQU/s4JCdVWSEoQtd7W+rFIFXRCvJpYwWIVNLkOsDi2AoInAzqO4zjOm+JZVkC4VVi9MM+aVoHb/98hXFA5r48nLgpHdsDEdX9VWrqe5litgHODjAEJhgwt2jZYZ9jQ0HRG6pV5rXVatRLmde2vsnW17aVWSbOS2kxqE00bMcsEK7QhIlIoTSRlJRdhXhnT0DB3gelOwEaWPHaQVLCpWgJtUsJekRiWXqtUxdWc0SlCTMgc68Tq0GeV8tJlpZRDauC3mVjd+ro6juM4jvOteZoVEJ5qB3xhnndadfN8nLeK905QichnwC++6fNweDK8Ipcaty5a7zsEVZRcgy1SqJOimChzQFIhT4ZOAUmBNAkaFYmQutpbJbOgHcSs5E7IszAXSFYobaQECJoxLZgmai9xvWBNQOpCDahYeq0kC3mEHMAmIYdaEqyx1B6r2dApQVCIddeKOVbhNGsNskhaAzWsisey7Fo9V5dV/eTx19H/guU4juM43y1PswLCqxFX3zStOjy3v9+/lbx3gqqU8hnw2eFzEfH/Zb5JboZXLDZAKVKLgHOmZFsS9vKVNVBSglKwOSBzTeCzUaugiUqcBMm1ryrP1QqYYrUCZoG8RKof/vObJkwzQQptSKgUShFSXyjz0oK+CKscqEXA7XUxsM6FYoJOBW0ETJBY6kcTJJUqolSvLIFlSQcUydepgLcJq8UKWF+fbwiw8Aut4ziO43x3PEtYwbezBD6vqDo+D+et4L0TVM5bym3CKsdqA0wZrEatcygGXpIACQFrAjq2lMawVUPYBXKrzDsj9jW4wk7rx9QL8URIbSGujLQytMmkfqINiWCZNkSakOi6mX0HOZQaq97Wc9RZSGP9aKtaEqxRsKHUwuBZCXurNsApYPuaAqhjWCyB1c4ocQmveMwKmG4pCV4unp4M6DiO4zhvB8+yA8ItbpLnFFjfZAG8+Zz+Xv9W4ILKebu4zQaoS1HwElRR8tIJBdVKd9hDigFNpVoBZ4Nc0GikWQAlTRCX4ArtICYjFUiNMgKpiTRNDcnIZYk3r19KCYXSlNpNFaqYqUKrCipJLPY/yFO1B2oU8ihkE3TOmApqipihqhRLiNagCrF8ZQWklKW368gKeKPLyq2AjuM4jvOW8E1TK3hxW+DzTKuOn9Pf798oLqict49n2QDrklOd5JSCmC7R5AmZAzJHyhyRJiBzg42B1GndseoEG7mKW9d5EVVtISUhr4QU7eo0cpaaRSHV7lfaDAoUSAZEoYQqpiRCbgWJdbcqNYLNBWuE0Ag6KyUoOl3vWMmcIBjY0lmVUk0HLIs9UJa49RfZsfJUQMdxHMd5MzyPsILnn14977Tq8Jz+Xv/GcEHlvL08pRz40F9FmeoFxKwKLFFom5qsZ1qLgY8SAfOSCDjtaznwvIa4E1JbbYBxpaSusJ0Mlmh1m+sZSKZOq6wgWihaIAixESQLZEizIFHQCLaXagMcwfbVHtjsazKgzkbYZ3TK6JSqFTDmKrCa8ERJsMRYR2YpVSvg8tqUlAC7YQX0VEDHcRzHeaN8kx3wicffIpiORdaL2gBvnoPzneOCynn7Oe6CuCoHTghGYbEBQu19ok5rxOptckZS/Z+5pFrMC9T+qixIAe3qGEpSFUWJQLEqmtpJKFooQSAvf/2RZUom1QJYS4mFFEoNwYhSLX8zZKuWP5sA5SoVEAENgpnU6VPKiCoqQonLpCrVyVW1NtbnFJXrHSsztwI6juM4ztvM806tnvi6b9l35e/1rxUXVM67wWMXhaOJFdSLTsrXVsBDcEWMSNOAGTpHtAmUNqBTS2iNuDLCoKRWsEGIayF1ME9KaWqCX9hVUVSkIJNQ0DqdWsRUnWRRPy9CyUJJQu6XadWo1WY410AMHcEmSK1hU7nqstJYe6zKaDW4YgxXUyoxve6xetqO1YvGrbsV0HEcx3FeHy86tbr6uiNhJU9xoTwNf69/bbigct49btmxEs2QlulUzpS0iJCUlx2lVJMBm2qvK41hQ8DGhtwqNipxJ8ROsPE6Er25KORQbX0laI1eL1yJKUIBzYiWGiwBUJYpVVJSl8mTIrEKKhsFnYTUg06CjYXQCzaDjYoNhk0ZHVPt14oZxrmWBMfwdCvgEidPSstLJC+WCugXW8dxHMd5PbyMuHoeAfU03Ar4neOCynl3OUoELEeTmScSAc0eTwQEmA3NmZALuTUoAcmGLjtTua32vLCD3NTnSf1yQRKtqX9LfkXtl6rPJ7IIrOUx2aTaB6NSVCkG0kJRwZr6HEVrMmA2pahQgmAqNUEwZhTqnpjGaytgTI9bAVMCqd1dh66uF7ICurByHMdxnNfPy1oCXxa3An4nuKBy3m2eElxBok5rDoEVy85RMYNpRoIhY4MMLRYM2zc0fUPqjGZXQytyEMI+kxthnhRYeqzWtceqBEh9pmQhZ0H6VIMsrCAh1vNphZSEkpU0K3Guxb5ppehYkwbDbvk4CLYvS49VJgyGxELYhxpeMTfIONep1RyXadViBYypFgDHZceqlEVkLa+LT6wcx3Ec5+3lZS2BL4NPrF45Lqic7wfHf+EpR1ZA0pIIWOq/lJCSKdmWwIoMwdCUkZjR0dDUkBolN4qNVVBJgqJKnECSoO0hMl3JcyF3QgJyI5QihOYgrjKqUEpGTev3jUo2I3eCzHVfS2ZInRA6QSdIrRJ7sKkGYuhs6JSxRtE5I5PBZIsV0K6tgEt0uiz7VWXpsJKUwORaWBnLZM+FleM4juO8VbzOqZW/378SXFA53y9uCKtbO6xKAasio+SCRK3BDikhTYBS0MbIQdEpkVtDCuRQO6wOyYC5oab7ZSALyQqlQJZCUsEMoIoqALNMSkoOQrRCnpUyF6JpDbxoqOJpqh9zU0iTUFRrgEWoRcOlyUhQVJcuq4MVMFstCk6JskympOSnWAGrkPKJleM4juO8pRynHH/X+Pv9t8IFlfP95JvKgZedI+ZYbYHzXEMrQsCGidIELBikXC2BQ4POLakzwl5JXS3vnTfVBpg6mGcjd0rujblPxCYT2kjTgGohaKaxRClCaiMpKSkpcQ7kKMyTEQdF5po6aMNSPryt4RU6QbPTWhg8ZMLekDljY0KmWK2A04zEVAMqppmyBFVIsquSYMm5vj45P2EFvH6tcGHlOI7jOG+a12kFPH4Of79/IVxQOd9vnpUIKEtv1RJgcdXvlDMSE4RqC8QMLQVTQeaM5ECaaty6ZCXGGosOUnusYiFlKI0Sc92fUsvQRlQKKlVcmRSSZVQLKSipySQLlFgTBXOj6LwEWHS1JLgY2CRYI+RG0KlQGkXHxQpoAnOqVsCl36rGrS87VpKvUgGBKq6OrICwBHv4xMpxHMdx3i7cCvjW4oLKeT84TgRMXNndSqIGOyzCaVl4qoLEtIqtEJCcMUDbgKSCToa1iqYaIhE7kCLEuYorKVotewViFnTZqQohg+YqqLTQSCFZImclZmUOiRSN1BppsBq/HpQ0UuPcmyqsbIQcFJurPdAarTtWJrXHak71Z1l6rB6zAi6pgAWu0n6urIAA2a2AjuM4jvPW4sLqreO9E1Qi8hnwi2/6PJw3wI1y4KtEQHiyHNjs6iJSzKodMNYdK9k3lC7ULqtdQ+ir1W8ajdgJcQVxL6R2SQRcK7k1xpUxdwkLmbaLmGaCJRrLSKj9UalVUhamGJjXRopGXAeY6p5V2Ck6gQ1S0wEnCHshDNUKGHaGjbnGre9jnajNCTlYAVOCaV5E45ISeNMKKEdWwCW8or5ELqwcx3Ec563hTSQD+vv9rbx3gqqU8hnw2eFzEfH/ZbyPHE2s4MgKmJVCWiY2evVYKZmigpRSAyxyRmKADJILORpFBI2CpuvgCin1dp6FWCAnIbdLSEXIFKqGM82YZtQSpoItO1exSYyaSY2RGyMqyFQDMYrWHasSauJgDbA4WAGVIILOGZ0UFIhW49ZFINU+riJVUAnxOhWQYytgqV1WJSMqV6/VcuP69fQLreM4juO8OV5XgIW/39/KeyeoHOeKm4mAy8RKitT48bQIhmViJbnUaVXbIHMAVXRu0amhNHYdWrFSbDJiBzZUq17qQGYld0rqCnNUYpdIUSn9TGOJrik0mmk0IyGSgpKLMDaBKRpzNKa+IU1KHo3UKTot3ViragVMHdhohLGQ27pjZaPdHrdutSD4Km49JySXa2G17JP5jpXjOI7jvAP4xOqN4YLKcZ4irA5cJQMue1aSUhVWpkiM0DbQBMKc0DZg+4ANgdQpcaXMe61CZy+kFdUKOAipN+YuE2fDQmbqZ7pmprFMH6olsJFCY4nUKKkI+7ZhTsY0BeZVIM5KGgzb1QCLsD3sWFVLoI2FMCphr+hcsH1Ax4ikjA6xTqxiqj1WSyJgiXGJkVfKsl8mV1bAOrG61QrI9TEvDXQcx3GcN8jr2rPy93vABZXjXHNLOTCitavqEGBxKAguBSlKgWoDTIt9Ly+7SIBGRXJACqRZoIAmQTuAWhaco5IIxLZ+PUDONcCiswRSaDWRNZOLYFKYktFYYq+FOBvRCtEMmZSitVw4tSz2v6VsWAWdS00MtGoFRKSmAkarQRWxBlkIVCEFNf3w2AoIFNJyojetgMvU6nhidfV6vr8XWcdxHMd5Y7zuLqv39P3eBZXj3ORGeAXwuB3wuMfKlh6rpctKmgYJhgxz7bJaNYRtILdK2Buxrx1Wtj/0VwnzKORWSWtltza0TXUK1cz0TaQPkUYTjRZWYSYXIWVl303MWdmNLeMUiHNg3gXiqOhYAzFsqBOruAWdhGYJsNCpEPaGjmEJsDCIuU6sglVxlTMlxqXHKl8JKzlcMI/CK4CjZECfWDmO4zjOW8ObsALefN7vOS6oHOdZ3LQDHhcEi15PrFKqwRUp1Zj1lJFglDkic1t3rGLAeiO3is5VWMUeNNZEwDQKcVJSa+zWxtQFhjay6SdaS3Qh0lntsgqLJTBlpQ+RfdswRWPftsxT7cnKXSCNgo5VuNkEaS+EXUFnaLpFXM0FC4rGjMyhWgEXG6CoHgkrXayAqU6wRJ6wAgIeue44juM4byseuf6d4ILKcZ6Hb0oFFKn2v1yq2CgFiYsAKQVpwnKskFuFEkizLJ1VEHtBcrUF5tZIRZizkJIiAqmZl0TAQqOJIJlWE2iiI9JqYmqMYJmhCcxtYNRCaquAK6rkCXIQsgk2lXosHG4LNibUFlEUFUyXzqpcp1I3rH+IUNKRTfAWK2DJhwu3WwEdx3Ec563BrYCvFBdUjvO8PK3HKrH0V2WKRsSsdjyZVhtgCBACtm/QriYC2r4ld0bqjHln1fq3EuaTmggYN0ZcKbkPbDeBfR8JbeJkPVzZADfNRJBEq5neZgDutIEhBcYU2K5bhjkwjg3TriFOiu4N2wk2CWFXgzJsLDRbwSbFpkLYGjZnZMro0DzZYxVDnVjlUkM5Dj1WN62AgEjx8ArHcRzHeRtxK+ArwwWV47wM3zSxAkrOdWJVSu19KqUGVsyGZpDZkCkgqdQI9FnRVBMBNUqNRB+FmCBPytQlLrIwtJG+nUlZ6SyyCjNBEyqFfrndW51a7UPD0ES2ITFPdXKVWyONSm4Ea6u4yo1gQ6lx66GKLJtyDbCIhg4Kc6pWwGm+tgIuRcgiS0Lg8rocurqK4lZAx3Ecx3nbcSvgt8IFleO8LM/ar8qlTq0Olj+JlJyQ1CBmNRVwCkjb1Ajz0dDJ0GSkVpEIOgtpBElKmgqpF+YixM5IqQqSuakfVwFaiwTJBMtgkT7MrGLDlI3WEmMX2HcNQ9uSRmNu6nPZRLUBdkIahGwFawthPKQDZsy0xq0viYDEVCdWIqBKsQxRao/VsbBK6fGCYFxYOY7jOM5by8taAW+z9n/j13x/rIBvnaASkc+AX7xx+ItSyqfL/bLc//cAHwB/Bvj7Syn/wes8T8e54upicEsi4FIKXBehEsxLKuA01zS9EJCxxZpAaANh15Bbo1kbca3ETpi3QuyFtKq3UxeY1g3TpsXaxMV6ZNNN9CFypx1ol+nUymZWixXwrB2YkrGLLRfrjmEO7IaOaQhMozGfhKtEwHkrtcdqXwi7xQY4GDYEbLEB6hSrFXBcUgDj8rMtgRVXVsBlkvVYeAVW7YIurBzHcRzn7eNlplVHdTOPff5NfE+sgG+doFr4C8B/4ejzdHT79wL/CPB3LY/7p4A/ISJ/ZSnl4nWdoOM8wa0TKxASRZbghuNEwBKq2ACICVLGAJ0P9sCwhFbU0t44CxRBJ0hRiTkQW2W7hFeMTaQUYd1M9DazDqCSaSSzsrlOr7T+G5tAY5ltaJnawGSF2Bl5UIpBGoXcCjmAjZCDEoKQJyWoUIKic0JVkTnV3TERyIZMs1sBHcdxHOdd59sIq5fhHX6/f1sFVSylfH7z4DKd+oeA/1kp5V9bjv2dwE+Bvx34Q6/zJB3nVm5OrBYrICVXwXCwAqaMmF5Hrc8RmSOlCcjUoGNDbgydAuHQXzXV/qq4lhq93ippVnaTMXaJmJWxM9ZN/b92bxGzQquRViMbYBMm5mxswsS2a9nPDeddxzg2zGNg6gI6KmkvpEawWUhbIe0LNpVl3yqjkxKsCiqdDCarVkBVZI7VCiipJgW6FdBxHMdx3k1edr9Kbgmjeq6ve/esgG+roPpNIvIjYKJa+v5HpZT/BPiNwKfAHz88sJSyF5E/BfxWXFA5bxNPEVZPlAM3GaYZmnCVCihTgwwN2gRsaEidkTsjDHXvaV4JYbskAm6NuFFSF7g4CWxXkbaLXKw71s3MSTty1gx0VkXVwQq4CSNTromA533Pdl7E1bqvARZDIJ4YOgphJ0sioNBsCzYoYazlwDaWGrk+JHROyBSvrYBzXKZviRLjdY/VDStgfblqSbAYngzoOI7jOG8bLyqsHvuj6PfbCvg2Cqo/Q7Xz/XngE+CfAP5NEfktVDEF8MWNr/kC+NnbvtlTdrIc5/XxzETAgjBXgVHK0mOVkJxrImBKVXTEgMb69doVJClSlDSBZEGSoD3MxchJGOYltKI15qzkIqzDxDoIK5sxKTSSMZtptZYF9zazCy2mmf3UsAstk7akVilm5AC5rUEVoYHcUrusmpoMGFQok6Iq11ZAkZoIGLV2deWlEPiGFZAlAbFaAcvVcqt3WTmO4zjOW8bLWgFFXz684vh530LeOkFVSvljx5+LyL8N/CfA3wn824eH3fgyueXY4ft9Bnz2tOcTkbf3v47z/eFpHVZwfZFJdZLDocfq0GE1zlgTqhVw7Aid0XRG2AdSp4RdtQDGvk6R4lrInTGMxti3bPuWYRNYNZGzbuCsHeqEyiZajRiZExsZc8NclLttzy62bDctD9crhjmw33XMu0CclHihhEHQcXnOsRAGaHZL3Ppo2D4hMWN7ezxuPaa6QxUjJEXSLTtWh/CKF7UCPvE6O47jOI7znfGyE6tvYwU8ft63iLdOUN2klHIpIv8B8FcA/9fl8KfArx497BOenFo5ztvJLROrKysg1ElNUki5CquUkUVcaUowBLQNyNRSWiXsA/OgpFYIgzCvhdSDjkbqjbgOPBgDF23iYtVxuepYhZkP+t2V/a/VSKczHdBpZGwC+9SwCRP72HDed1yuO8YxMPctaTB0EFKv2CjYAHGr2Ahhr4SVolMhdIaOixVwCHV3ao7IbFc/32NWwJQfE1b15XpOKyC81Rdbx3Ecx/le8m2E1YtaAY+f5y16r3/rBZWI9MBvBv4k8JeAz4FfAP7do/t/G/CPvalzdJwX5kYiICxWwCURkFKuy4GBkqsAuUoEjAkrhTIHSDURMLXVBiiR2l9VhDgviYClYe6MnJUCDEtoRWwHUhHOQgGFRhKdxPpRI43URMAuRDpL7NqGCytMbSC1gaKQx1oMXFSwFnKAYoo1BQTMqhXQRGBWRLVaAWNa7H+LFRCQZVLnVkDHcRzHecd40Q6rm8LqHe6xeusElYj8fuBfB36FOnn6J4EN8C+XUoqI/AHgHxeRPw/8ReqO1SXwr7yZM3acb8HTrICJJbRisQKqXlsBTZfgihnM0L7Ftg2lM5ptIK6M1CnTVokriCthvjRSr6S1cXESuOwTl5uOTT+xaSfu9VtOmpGTMLGxkU4jK5k5sZGE8lEXuOh69qnh4XrF+dizmxouT3riGJgHwy4MmyBshXlX492brRKGmg4YdoaNGZ0Suq/TKhlrsqHkfGuPFYedsmf0WIGHVziO4zjOW8Hx++3rsAK+Je/1b52gAn4d8H8EPgK+pO5N/Y2llF9e7v99wAr4F7gu9v1bvIPKeed5SnhFSVxPanJGzKo1rpQaUZ4zGhNMAYkZnQKpMzQacVTiADovceujEGchdcZ+MqZ1w7ZrGZNx0k5swsSH3ZbOIiudlilVwjTTtIkxB1Y2c9KMXM4dD5rIbmwZxoapaUmTkjol9XXHKrdC2oNOQtsINmVsMEKjyJzRxpBxEVdaxaOkfNVjVX9GeVxYHSZWi7A6TKzqa+aR647jOI7zVvAeWQHfOkFVSvlvfMP9hRoy8dnrOB/Hea3cWg687FfdVg5sVkuAU53waM5LL1RAcoNOio2KJCUNUouCk2CdMCdIUdh3Rs7C2AfGrl4S1mGCFlQKDdX+1xFZ23S1d3UaRnqbuWh7LruWR5aZpkBqArkJ2AjFqqjSEYpCGKsVsGhAp4yZYhph1vozzxGJqVobVa+sgEi5/tmfUhD83FbA49fZcRzHcZzvlm9rBTw+9jy8ASvgWyeoHMfhyQ6rgxXwkAgI11bAOYJN1Qo4dUgwShNo9h2hMXIfaC5rImBcK9NGSR3Mp0pcKWlVmE6Nse95tIo8PO1ZtzN3uoEP+h0bm7jb1ACLRhJ3bM+pDaSi3Gs79qllm1q+Xm/Yzi3nQ8/FrmOeAsNlg+5rcMV8UXuswr5aAW0qhKEQdou4GiI6JWROyDBX0RjT41bAvCQhPssK6OEVjuM4jvN28W2tgG95j5ULKsd5m3naxErlygp4eJyUGjghKSAp106rZrHTlYJOhiRDEqROkFSteGmUOsHqlTQpl1kYuoZxFZiysWkmxhy42+zpdOYkjNUGKDVuvdPIyiaCZLahZRVm2hDZTw2X1jN3gTwa2Qwb6nPnINgIqVluTzXYIgyKzHbVY8Ucr62AMVGigNZdsqdbAalTK7cCOo7jOM7bx7e1Ar6FPVYuqBznXeDmxOrYCpjLk1HrZlVIzQGZrO5WNYbODTbVRECdre5UddUKGFeQBmWeW+ZV4NEUmJNx2c7MK2PKxmkYUSmsbaKTSGcjuSinBmubGHLDRew5aUZ2seXrduZi6BjHhim05NFIe6EErWKug9yATUYThNxo3bNSQadFXI1ae6w0IlJ3q4pK/Tlf1gq4HL/ChZXjOI7jvF6+jRXw5rHn4Tu0Arqgcpx3iW+yAh6KgSVe31arSXrBkH2D9g2hMcK2Ia2ruJq3SuyrqJovldgrcW3sThu2XeLRSc/JemTdzny02nLW7jkLI3ebHY0kep05sYETG7hje+41W8YcuN9teDT3XM4d90/WbIeWcWwYLhpkVMJOmS9lSQc0wr5go9IsiYA2Zmwf0DkjU6xWwJyROdb4+JQoMV1ZAZ+/x8pLgh3HcRznjfNtrIDw1lgBXVA5zrvI8V91jq2AGIVUu52gdldZWqyAVsMrSqnFuhkkF7Q1pBgaFZ0FSrUC6izMRUmdMCfhPBlDN5OKMKTArmnJCCudyEFY64RKptcZk0yvhkqhs8hpMxI0cx56tm3Dhaxq3HqzWAFHyLbY/1ooKoSmBloUAY0FDUuXVcxX/VXYEtSRZTmW6+uxCKvHeqzgMSvgMydW8Fb1WziO4zjO954XtQLC41OrN2gFdEHlOO8qNzqsYJlYJUBlEVN6bQU0hRBqx5MZOkd0bCiNoWNL6IzUG2FUYifEtRAGqZOrvRD3xr5vmKbARd9z0o8MqeGsGbjb7rkbdqxtYq31H8CJDdwLDUNuuNPsueh7LmLHT7tTtlPL5b5jXLWkwUgrJe5qcEXqwQYIg9E0gk0FG5QStNoXB0OnuFgBtYZX5EyJdTInkh8rCL7NCvh4j5XvWDmO4zjOW8GLWgHh8dCuw+fPyyt4r3dB5TjfB56yY3W4wNy6YxUjNE21Ak6R0gZCG7ChqV1SK2XaLROqrTBvhNzVY9t1y67vuTjtWHczZ/3Ax6tLNjZxr91yYiO9znQ6XwmstY3sQseYA3eagW1sebhZcX+zZjc17LY90zYgoxI3iu1rcEVzYdhYCMNiBZyqDdCGWK2AY6gBFjHVsuPlZzxOBSwpX+2V3bQCwrW4ciug4ziO47wFvAor4GucWLmgcpzvE8flwKI1sOKJcmCrCYDL4yVqneDEhMRMAHSuxcAUSJMgWZFUQyQoSpyEtFJ2BeY+MEUjF+G0HcnUxL7ZDJUMkmgksZaJRhJzqVbAy9CxspmgmW3b8kAzl9YRp0DUhtwIeahWvjwIJVQroE1Co0LRagU0ATGrogrqtEoEkfikFRCetAIeRJRbAR3HcRzn7eNlJ1bw8sLqBd/nXVA5zveNpwVXHFkBi8Yr6x+mMM9ICBACOrZoE7A2YLuW0irzOhA3SmqFaSvEVf0371pS33C+6tiddbTdzNebNR+uNpyEkU/6C+6EPWudOLGBXurE6lQH5hIYusAPug2XqeOr1Qn3T9ZcTh0PNivGoWXaG/EiYKNgO6HZUjuttkKzX7qsesPGhE7VCijz0mU1znUiFVO1AuaCSKwX1mf1WL2IFfCx19txHMdxnO+El5lYwctbAV/wff69E1Qi8hnwi2/6PBznO+dGhxVUK6Borm5AuLICSi7VFhdjFRlzhDkQYqY0hswZG2s5sM5LIXAHNtUdq7QW5qlj1zWMQ8vFpmPTTVzGjrvtjrMw8mF7eWX/ayRe/2sTd3LDiY3cbfeczz0n7YZHY892bNn2PfNgxH3ds7JBCCshbutuVdMJYVRszISdVmE1pTq1SkuX1WxXe2QlZcjpGT1W9vxWwMPre/x6O47jOI7z3fGyPVYvy3O8zz+XoBKRXwf8vcBvBT6l/i72BfCngT9USvnVb3emr49SymfAZ4fPRcR/C3K+39xaDgxCoohe35+qCCmlLOEVqYqQJmA51zCIyZAcsLkQO4UMthLidF0OHJNwmYSxa0hZGfvArh0AGMJIMuVO2GFkmsUO2Muyb2UTd0PLymZOmhUXbc+Xmtl3LWPbMFsVdTkcyoGr9S83gjVCEcGajAVBhauS4CJSEw6PrYDwDT1WL2EFPH69HcdxHMf57ngZK+C3QWoS8q13lW948xeR/xzwx4CfAH+cKqQE+AT4BeBngN9ZSvnTr+6MXx8iUn6H/K43fRqO8/o4vvjIIfFO6m2Vav1TRUyhaZePDaVroAnkviX3gdwZcWO1s6oXplMh9UJcw3xaSH0hn0bCOtL1Mx+dbDnrBu62O37QXXBiI3fCjrVOtBJRMhllKoHL1HORei5TxxfjKefTikdTz9fbdS0J3rbI1tBBaS6FsK+pgM1lqQEWYyFscw2wGBI6xmoDnGKdWMU6uboKrIiPWwE5CKvD9fEgpo57rI657a9fLqwcx3Ec5/XxGsTVL+V/lVLKE0/0PBOqPwD8b0sp/+Btd4rIP7885q//NifoOM5r4llWwKyUGGuPVbal48lqiMMSXKEpI3NTp1WpRUfFRkOTErulwypKTQecG9JobPtAjMp533He9wyp4TSM3Gs7Pgg71jZeCatWIndsR6czpzbQSOKy2fOwXdFZ5HLqeNT27NqONBqlCaResKGGYdggxKHQBsEmxVolDIsVcNBq/5tr3DopISlTFvvfs62APNZjVV+3o6mVT6wcx3Ec583xuidWRzyPoPotwN/xjPv/JeDveTWn4zjOa+NWK2BCilxbAUsBiUiT6uTGDIkJmSNlCtUG2AVsDGgMhL6KmHmugkqiEMdqBRyjMK8Dw9QQs3LSjow5kDthLKFejRR6meh1pmcmqdDpzC63fND0nISJ87nnq3bD/XbNbmzZNR3zLpCGagW0QQh7oVjd8QqNUIKgUyaooHNCxoSKLMmGCYE6rXqaFTDVBMHjHqsnhJVbAR3HcRznzfIy5cCvgOcRVD8B/ibgLzzl/r9peYzjOO8iT0kFlLLsDalASmARVCHMdccqGDLNSDCsbbBdQ24DzSoQL4zUCc2FEteQemG6aEjrwLDq+NFZR+gSX2xO+cnmjNNm5Aercz5saofVvXBJrzONJD60S+6aMgfjo3DJLrfcX224v9nwaO758vSER/ue/dgyXHQwKLZTmktFRwg7odkKNhrNzghDRsdM2Dc1vGJJBGQRisxxKQlO1z1WqpDzramA8JTwinrg+rYnAzqO4zjO6+FlUwFfkucRVL8f+IMi8jcAf4K6Q1Wo4RS/APxdwD/0HZ2f4zivi1smVleJgKJ1ilNqf5WUpc+qlLpzlTJaShUnKSO5wSZFCtgsxBHIQpoEHYU5N8TeeBSVORkXXceUjX3fcBZG5mKc2sBaR9Y6YlLoZQaDbikM7nXmLHS0mliFmcuu42vNjH1DagPFatx6CfWfjVBMqy2wU4rVaZqOih6mUKrXVkBVSrzRY3UItvg2VsCr19hFleM4juN857wGK+A3CqpSyr8oIl8D/zDw3wFsuSsBfxb4PaWU//N3d4qO47xWjiZWdVpVrYBXXQ6L9Q+J17fnmgoowZAxomOktNUKGFeB1Ak26lV/lY1aAyx2ynYw9quW/dRwvuo56wb2q4Z77ZY7Yc9HgTqt0sip1LTAMzVOdWBoGj5odjzo1jyc15y0Z5yPPRfrjstD3PoqELbVCpg6Iexql1XTCGHM6Gg0jSJTRhtDB6vTqmmu4irn2mOVUhVWKT0lFdCtgI7jOI7zVvIdWwGfKza9lPJHgD8iIg3w0XL4q1LK/J2cleM4b56bVsDjHauDFfCwY2Wxig41pG2QqaEEQ/Yt1gdya4R9Q+pqh9W0rf1VcS3Ml0rqApdnDdtNz0+7yFenGz7o99zp9nzan3MWBu6FLXdsV/erZOau7UgIp7rno9Cx6zp+0J3zMK55OK34yeaM7dRysesZLlvm0ZgvjLCTJRFQCEOdXMWtYmPBhkDYVxugjk1NBTxYAeNi/5vnKqQOCYEHYSXL61TErYCO4ziO8zbyHVkBX6jYdxFQvi/lOO8Th1H508qBRSilhjigiVIyknO1ApZSb891sK2zIdGgKLakAUpeAiyKEaMwd8bDIoxzYDu35CJsm46xDaRWWJeJU93T64xR2OhEK4leZ1Qya5vY2IhK4bztaS3x0DLj0BC1oTR1v6uokNsqrooo1pYaYLFYAYspqlqtgCKLFVCX5MN8bQU8mlgdft7brICPRa27FdBxHMdx3iyv0Ar4vMW+d6nhEw+Af6sclVeJyAb4R0op//QrOSPHcd4+Hvsl/8gKmGOdVInWmHGzGuoQY00EnJujAItIaYzQNYR9Q26VuFamrdaI9a0wb4zUG/POOF+3XKxWnJ91nPYjH/R7Hq7WnIU9n7QXfBC29DJxZgONRE5lz13bMeSGoW34pD3nMvV8uT7l8/UpF3PP1ydrLncdcWhIm4DttMasrw/pgIWmF2wqhJ0RBqtx643V/bC5/oySEsS0WAG1lgY/zQq4hFccplXwnFbAJ153x3Ecx3FeKa9oYvWNgkpEfgvwS8DHgAL/noj810opv7w85AT4RcAFleO8D9wysbrVCmhVZBBCFVcpI8Eow0wzd5TGsH3AhrpjFQYl7GTplFLiWkh94GJvXK5WPFiveXiy4qQd+XJ1clUO/IPmEWsdr6yAax3pqQmBgzV8ELbcDTsuU8fn3Rlfrzdsp5YHqzXzviEORlrZVSJg3fEqNCsh7KsVMOwMnVL91xgScy0InmZYioFLjLdbAW8IK3hOKyD4npXjOI7jvC6+xZ7V80yo/qfAvwX8t4Az4J8H/rSI/PZSyn/0ws/oOM67zzeVA5Oq9S0bkqu4KrnUSVXO1RrXBIgZyWUpCQaNepUIqLOQRphLIE3Kfla+ysKub0hFidm42+4BuBN2nJYBs0IjsU6sNLPRkU0eaSSxyy2dRjZh4tHUY5q56DqGfcssLTIqpVGKCjbVHqtsgrWldloNgjUKKnVSpVpFU1wsgSLVCgjIEt5xe3hFubL8uRXQcRzHcd4yXsIK+DyC6m8EfnspZQtsgf+6iPxzwP9LRH478OiFT9RxnO8Ht1oBS80AVYGUKRoREWgaMIVpQppqBbQmoEOLNUboG/IqkDqjvbSaCNgL04WQVkZcK/Np4MFqxaOTNV+cnLDpJn5mc8qH3Y67zY6faR9xYgNnuufU9rQk1jZyanvmEvhh85D73QkXqefz9Rn3pw0PpxVfnJ6yGxuGXct80aCD1tCKndREwIs6QbOx0GwNGzM6JmwIVVxNtb9KYqqWx2VKJTFWgZRLtUTe0mP1VCtgPXB9262AjuM4jvN6eEEr4PMIqo66e370HOV/ICIC/L+B3/1CJ+g4zveTp4VXZKVQEGZK0moFzAViRGKooRVWbXQyZ7QzJDbYqIRe69RqD7YXdKqWwDgoF4OxW/UMU8P91YazbmC77rgbdnzQbPm4XFxZAFtJmMyoZpomcscaep1r5Hq7Zh0mHo0rHvQrHrUr5jEw9oF0Wa2AqRXCXrCh1B6rSbHBrqyANoalHDjDNCNzRFKm6JL4l1OdRj3LCviiPVaH19xxHMdxnO+O57ACPo+g+gvAXwf8h49/7/IPi4gC/7eXPkHHcb5f3FIODAcxwXUK3iKiSlrsf6pIzmhMMAUkZmwMpMGQZIRBmUfQVKdWOgvz3JAH41EShqlhu6qJgOdtzy635KKcWO2tYhFVGx3py0xWpdeZO2nFB2HLymYetitO2w2fW2I3NVyEFbFpqhUwaE0E7ISitccqNIsVcLnfrFoBVYSiWnu5ROp+VZblZVmsgEt/l6RUX7aX6bG6ep1dVDmO4zjOd84z3m+fR1D9X6hTqD/85Pct/30RCcDf+9In95oRkc+oIRqO43xX3OywepoV0GxJBFSY5xq1HgK2b9CuqVbAXUvujNgb86WRWmHeCPOJ1ETAU2Vat3y1WvHwbM1qPXLWj/zw5BF3moEfdOf8oDlnrSMfhkt6mWkk8oldcFd3zBifhkec5xWP1is+X9/h4bzmi7NTvt6v2Y4t24ueeVutgOHiMK0S2gtBJ2h2StgHbMrYLl4FWNSpVapWwGl+wgpYluLg26yA4D1WjuM4jvMuIOU9fwMWkfI75He96dNwnO83h1/6F1EgS9Q6KlVUyfKxCVflwDSBEozSd5TOyF0grgO5U+aNMa+F2MN8KsQVpFVhvpOgzzTriXtnO067kY/7Sz7pL/gg7PhB84hTG9joyFpGAEwyQ2mYS2CbO76Mp1ymns/HO/x0POF86vnp5Qnbfcc8BLho0L0S9rUc2AYIu0KzKzVufZvrjtWUsP18Hbc+zpBrOmCJ8cr2eBVesYR1kA/Ciisr4IHHAixum1hd3fd+X9cdx3Ec57vgl8ofpZTyhPfvhYp9HcdxXoqbE6vbotZTgqXLquQEMVQrYEyUMaBtg84tuTV0zNhYp1U21oj1uBJ0rj1WcWN8ORuP+hUXm47L2HG33bHrWz5qLrhjez4J5/Qy05I41WoNPNU9Gx0Zco1b/7A941FccdYOfD1suBg7HrZr5iGQd4Hc1h6r1MtyLoU21HM6WAF1SugYUdU6rVKtCYF52bGKUpMQj1MBX4UV8LHX3XEcx3Gc7woXVI7jvD6+yQq49DiREuhcrYAxIabQNOg4oU3AuoawbSit0mwDcVXLgaeLKqziypjv9Ex94YtNz/3TNX0/86OTu3y8uuROs+fn+gfcsT13bMuH4ZKGRK8z9+ySrMpd2/FxOGeXO362O+Wr+ZSH84ofbe7ycFhxPnTVCjgEdGuEy2oDnC9rQbCNSrNVwhDQqSFsIxozOsTrVMA5Qqw/b4kJycvtV2EFBBdWjuM4jvMacEHlOM7r52aPlWjtqSJRRGtQhVkNrwBKtnqslEWAZCxnyhwgg0YjTQooOoPOAiwCZxbmJMRVIGdliIHzricX5V67ZRdaMspaR84Y6GVGJbOWEaV2WTWS6CVyx/aYFE6akQfNmi81s+taxtAyh0AahGJLgMUARZXcFGwUEEGnjJmiJkhQUEU0QtLl55Sl0yq/+h6r49fdcRzHcZxXhgsqx3HeHM9rBTRDbNk9Uqux5NNMCYYMHbYP5NawfaiWv5UQ9krqIJwI864e3+4Cu03H1/3Mo7OeD/o997otF6ueO2HHx+GCe1aDKzY6cnZlBRz42M7ZlY6PmgsexA331xt+tLrLw2nFV7sNDy9r3PqwbrCtYQOkvoq6sC+krloBw14JrSJzxoaIDBFZrI6SEsRUf86kSLpFWB0mVvmwj/aUHisXVo7jOI7zWnBB5TjOm+dZwmqZWD22YxUjhIAEu96xagI6NuQu0HRGGAKpE+atMm3rntO8DcSNMa0afrJr+Wp1wmZ1xlenJ9xt93zSX/Az7SPu2I574ZIzrROrXmZMMxtqmMUn4Zzz1PNRc8GjuOaL1RlfbE45H3u+2mzY71qmwYibUAXVTpYAi0LYK81OlwALw8YGnRO6ryXBj1kBY6ypgAcr4HGP1auwAj722juO4ziO8zK8lKASkb8Z+HdKKcMrPh/Hcd5nbhFWohkSFFlscaUs9r9MyYaUUic5MaGlIDGjc4ACOikaC5KUOAqSaodVGpU5N0yTEmejFOGy7xhSIGbjoulJKNmqFRCFRiJG4UwH2pJqoIUk7tietU50GnnYrGgscb9Zses6BqEmFDZK0dpjlZtqBbSpUFQoIaOTYiLoFKsVUKQGV5jWSdxtVkBA4NtZAcG7rBzHcRznW/KyE6o/CfxVwF98hefiOI5TORJWJS03pVztW5FSFQJmMEewqU6sphkJRmkCzb4jNEZeBcJtceuXSlwpcRV4eKfh0Srx5eaEL05POW1HfnZ9hx9053zQbPlh8/AqAfBUB05l4JSBu7pjogqwX9d+zUVe8aPVB3x5csqDacVPTs+4GDr2u47hvEUHxXZCcyHYKDSXdWIVxkKzVXQM2JixNlzFrdOE2mM1x8d7rEyfObF6qhWwHnj89XYroOM4juO8NC8rqJ7IX3ccx3nlLLtD9Xa+3Qp4mFilVOPWQxUgkjKlCcjcoHOucetzTd9LnWITSyKgME81bn3cBD4fAvf7yKOx58v1CXfbHQ/6DR81l9xZkv82Oi4FwYmeTKuJvpnZ5h0bHfmoueRRv+JOM/D1uOHhesWX3YZxbJgvG1Jv2FhtiGEHNkLsAmHI2FRoLhWdco1bHw2OrIAS05WYErvFCng1sXpK3Do8fc/KrYCO4ziO88L4DpXjOG83NxMBuWEFzAXJuUaNH4RVSkjOVVjFVNMCJ0NStQOmVpFk2FQnRZKF1EGclTm1DH3g66hM0TjvemI2LruOD5segMH23NUdZzqgkukl0khiIxO9zJzqwEXu6SRyt9nxZXtCY4lHQ89F0zOEjjwqRY3c1HMoCqlVwlBAQEfFGq2pgHNChiMroAgisVoBAVmmduUQ4JFSFVXwhLByK6DjOI7jvFpcUDmO827w2C/332QFjDDN1RIYAjJONcBi16BdS2iMsG3InZF6ZbowYi/ENcynRuyNeBK4f9pxv098ebbhbD1wpxv4uc0H3Gu3/KA554fNA9Y6ctd2i5iKrMMFH5YtczE+DY84zz0P04YfrT/gwbzmp+Mpn2/POB86Lu6sGLYNMijhQgm7Jer9QrARwlBoLg2dM2GX0LFB5oyOc51YHayAh/CKGOskTxXJeZnwvQIr4BOvv+M4juM4B1xQOY7z7vEiVsCSKSVDNCSXaytgKeTJ0Kl2WYVRmCdFI1hfwyt0qoJrNyvj0HKx6hlT4INuzcN+xa5vuWN7duGcu7ZjrSMbJgAaSdzV3VUEeyuRD8KaszCwsplHfc8X7SkPu1W1AjYtqVdsEHIjhAHiHnJQbFJyUyPXdcoQFJkWK6ApEpe0Q5UqmiQ+o8fqJayA4HtWjuM4jvMUXFA5jvNu8k1WQKhWwKSQMmJarYCxqWIkJawJ6BiQ2JI7wwbDZiV21YY3j1Kj16MQB2W7CsSoPOp7Hq1X7FPDvXbHrm0Zmkec6gDhfIlaT/SSWDNzWgY2MrENLffsklMbeNStuNMO/KQ942Lq+DpsmFYN8z5QgpGGKuxKEGwspEarPXDKlCDoqOiUqgVwjohGRGQRTgJRkFweF1YpgcljPVZPFVZuBXQcx3Gc58IFleM47za3WQFFIcdaDHzTChhjPTbPyKHLaugoTSB0Rti25FaJa2PeVHE1XwhxXa2A87nxYL3i4XrDT89OOOlHPl5v+ZnVIz5stvxs94AP7ZJT2/Ohbmkk0UjmY9tyt+z52C74NDxiVzp+2p/x0/UZD+Y1v3ryAV8PG86Hnofna8YhIDtjvqhTq7AVmm0N1Wi2VgMsxkLYxrpjNaZqBYyp9lgdUgFjuu6xUoWcvcfKcRzHcV4h752gEpHPgF980+fhOM53wMEKeHNilZVCqT1WALp0WuUaYiFQS4NTtf9pd7AHGtYrkhWdQUdBSu20SqNymZShbxnnwBgDD7o1Yw5ctj13bEdqlI2OV/tVKoWexF3b0ZeZRiKtRO7YnqCZ0zDyoF1hmrkcOoauZbaGNNTp1MEKWAxyo1gHRcFGRYOCSY1aN12mVYZopEQBtW+wAj7eYwVuBXQcx3Gc5+FlBdU/A3z1Kk/kdVFK+Qz47PC5iPhvAY7zfeKGFbBOrJZjJV/fn1KdVtm1JZCmQecIQ0CHBh0aSqvYEIg7JXXCNMh13PrQkPrAg11ge9qx7ifOT3u+6M/4qLtklzvuhUvu2SUf2iU9kV4SpzJzKjN3tXZZDaXhXrjkfnvC/bjhg3bPg2nFV/sTvu7XjEPLdNGQVkochNgLYSeEoZAaI4yKDUbYGTolbAxIY7XLShUJBocdq6S1CPm7sAIev/6O4ziO857wUoKqlPJPvuoTcRzHeaU8ZceKHK/jw5eYcVKuwmqekbmpyYBDQPYNpQnovqHpAqlTwj7URMBemHa1SypuGubTwMO+5+JsxY83A6f9yI9P7vBRt+Xj9oKfbR9wpnvu2SV3bUdLppPEXR2BWhh8EXq2ueMHzSPuxxO+3mz41c0HPBpXfHW25uJyxTwG5vNA2FUrYHMh2ABhX8uBbQqEfSbsAzJlbD/XqdUckalaAp/LCpivLX/PbQU8vN7Hr7/jOI7jfM957yx/juO8Z9wUVqLV6ndlBaydVSVXm1+BOrFKqQqMlLBS0wElBqSATYrNutj/QKMgaRFXWbiclaFvyEW4nDvOu545Gx80W6ZiZJS1jpwx0khGKWwk0mqNXzfJnNrAqQ0EzTxsV3Qh8mVIXA4dW+2Z20DaK0UUayE3stj/hGJQNKBNBhV0jEio/VWEumNVLY+ydFodTaxYmtuvhNXjVsAneqzg6cLKRZXjOI7zHuCCynGc94OrX+6XacvBCpio4RUpUzTWLiezq4kVZsg4Y/uANgHbt+TWaLpA2AZSr8SVMJ1UQTVfGPOJkvqGz7ctX69PWK0mvjw94YN+x6f9OT/f3eeO7fi0echd3bGRmbUudkBm7ujIaI84bzp+2DzgIvf8uP+AH6/v8nBe8aOTOzzcrdgNLeOmqz1WOyWe1/6qead1WjUWmu0hvTBju1CnVVOEJtQeq5go8ww51J9d5PEdqxsTqyd6rOrBw4HHX3MPr3Acx3HeA1xQOY7zfvGycetzQEJ4Im69tEpcBWysO1Y21P2m1AvT0JDWgfNVy35o+Lw/5YvNKT9dn3Kv3fHD7g4/aB5x13Z8HM6X8IqaCriWSFcSfTMzlIa7tuOj5oJHacW9dstP16c8HFd80Z+yG1qmXUNcNVeJgHFdp1VxJYS9YlOh6QwbEzI12D7UHqs5LjHyGWatBcEHK2DKTxVWHl7hOI7jOBUXVI7jvJ/cDK84TGBIV+XA1RaXqxWwlCqwSql7SDFhqVA6Q2JBopE7RZKhM6QBKEIahTgIcxFi3zBHY07Ged8zF2UsgYvQAzDonjMduKMjJoVWMndlYi5x6baa2VpHL5GzMHC/3VRLYLfivOu41BVxMHJjFFNshKJCDlVcIdUaaEMGAZ0NGavtj5QQFWSaqxUQkCVyviy7ZpJSTQSEJ4TVE1ZAD69wHMdx3hNcUDmO837zLCtgSlUc2CER0K6TAc3QtoVgaNtgq5bSGGHbkHqrE6pLrQEWK2G6CKSVMW4afnTa8/kq8ePTMz7c7Pig2/Hr1x/wUXPJR+GCn20ecKp7TnViI5FGEqeauKsTc1F+Njzg627DRVrxy/1HfDWf8NV0wq+d3uXR0HO+7RkvOhiVcG6E3WIFPFdsUMJQaHaGjQUbGmw3I3NGpwjTXK2Ac+3sKilXK2DJFFUkL0mJN3qsnrACeniF4ziO857wTEElIv/35/1GpZS/9dufjuM4zhviGeEVJdX7a3hFRszAcr0/WBUZOSNNIOSMTgEbDImB0NcJlcZlx2ovzGNDWgUejMZuaPm6X3M5d3zUX3KvvcNF39eo9XDJh7qll8haI0bBJKE60kjiru5oJPFRuOB+u2FjE/f7NV/2J3zVbaoVsG1J24ANskynIOyF1AphLIRBCK2iU8YGQwerVsBpRmarEesHK6DdYgW86rFyK6DjOI7zfvJNE6qvX8tZOI7jvC3cnFhlBRJS5MoKyJL+J6WmA5LzIjwiGhMyNrUcOGZSb1hvaFRiX8uBNQppL8xzYJyUadVUG+Cq5+FqR8zGh+2GXe5IjXKmA7BnrZGGwkYzfZmYmeklcmE9H+Y1a5140G34vL3DJtzl0dTzVbth13fEfaAEw/ZC6oRiQhogNUY2wUalqGCm6JRqMbBWy58AYrkWBONWQMdxHMc5Rsp7/uYlIuV3yO9606fhOM7bynFSnejV9AXRWgas1f4nIYBp/dg0EIyy6iiNkbtA2jSkzogrZd7UHqv5RJg3kPrCfCdT1glbRz4423HWD3y8uuTXr+9zL2z5meYBn4QLTnXPPR1oJdMsp5IKzAj3U8+2tHwZz/jx/AH344YfDXf5Yn/Ko7Hn6/MN076h7APhkS0BGtBcsiQCFpp9RqdC2CV0TOgU0SHW8Iq5fjz0WHEcXnGwAuZ8Fb9OLo8JqMfEVT3w7Nf+PX9/chzHcd4ufqn8UUopcvO471A5juM8iyVCvN4+Cq84tgKypAOmJXK8FCSH2ucUAxYzUkCnXIMgsmGjoFHRuVoBJRtpUNJg3E/CdtWynVqmZHzQnnDR91y0Kz60S+bwiFMd6CWxlgRAQ+GOjvQl0jczjSTuhUvWOrGxiYfdikYzj7qebdcya0calNwpOQg21vCK3Bg2FYoJ1io2VtEos8EcYJyQmKr9L0aKGiLLjtVzWAGfu8cKvMvKcRzHeSd4IUElIr8T+AeA3wj8l0spvyoi/13gL5VS/o3v4gQdx3HeOI/9Un+LFTCXJcBiSctrEmWaIcY6sQqGzhFpG3QwdGzInREGIwxK7KqgiWsh9sY8duzXDcOmZTc1nPYjD9Yrztc9HzQ7tm3Hx+Gcu7YD3dNLohPoJZPI3Ckzpzqxyw337JIfNI+4nzbcbfb8dH3Cg3HNF/0J+13HvAukNlRB1QqpBxurFTAMQhiUsNgAdUqoyXXc+qxITBRdfu7jgmARJCUwubXHyq2AjuM4zveF5xZUIvJ3AH8Q+F8D/0WgWe4y4PcCLqgcx/n+85RUQClCOdyfUp3qpERZSoJlmhc7YED3LaUN2K6huTRSZ8xbZV4pqYdpq8ReSWvj8k7DRZ/48qSm+J12Iz9/co9Pugs+ai74ueY+d23HPbvkrk40FBqBexK5q5F7NvBpeMQud/xcc58v4xlfxRN+ZXOPr8cNX+/X3L/YMA2B+bIhXBg6Qnsh2N5qIuClEsaADZmwa6qwGhcrYEzINC9R8pES03WPlWrdL3ueguDnTQV87L+B4ziO47x5XmRC9XuBv7uU8n9aplIH/m3gn361p+U4jvOW8zQr4FGP1eFxUrSKrUVcVCtgwnJGUoPMGUpAYyFOCmUp5Z0FipI6ZY7K/WRs+5ZchIu542G3Iq+U87BlKA2znbORmVOdaQQUWEvBdGQjEZXMWkdObY9SOA0D6zBhUrjsWi5sRbQGGarNz5o6tSpCTQVsapeVtorZwQqYQKVOrEwRjZD0SSsg1J/7KQXBbgV0HMdx3lVeRFD9FcC/dcvxS+Ds1ZyO4zjOO8SLWAEtImbVCjg3iCoytcjQYMGwoa3hFYsVMHXKvBLCTog9xMvAfKnsVy2/smv5cnPC6Wrg65MNH3ZbPu3O+Y3dl9y1HR/bOXd1ZC2JXuBUhVMyp3rJYFt2+QGfhkc8TGs+X93hV/t73J83/Hhzh69O1+yGjmHTITvDdkpcHcWtd4JNhbBXwlbRWLCdoVOqVsDDjtXBCphLFVYi11bAZ0ys3AroOI7jvGu8iKD6MfCfBn75xvG/GfiPX9kZfceIyGfAL77p83Ac53vG88StH8eMm1W73Byvdqy0bdA2YGNNBGx6Zd4bsatpgGFbLYHzZc/upGXbr3h0Z8XJauTeasfnJ2fcbfb8uvYBP1zKgT+xS9Ya6aXQAI0U1jbTyQM+tgs+DY/4OFzwMK35UfcBP9nc4cG45vPNKee7nnHfMJy36H4Rd+u67xW2QrNWbCo0vWFjqqEbu4DM9eeSKSxWQHvcCngcXuFWQMdxHOcd50UE1f8K+J8f2f1+TkR+G/D7gM9e9Yl9V5RSPuPofEXE34Edx3l1HKyA5YawwigsiYCHJMCSKSVDNCQXSLn2WJWCzIbOodr/JkGTogniKEgSYlRSL+xLzzQFhjlQinCn2zPmwFyMe+ESgLvs2EjkVBMN0Ipwd+myWi9WwLu2pZeZtU08aNYETXzdbHjU9pwrpDaQW6WoYkPtsSpWrYkINSmwyQRAJ4WwCCHTx62AIo+HV8CSkljTCsnXU6kX6rECtwI6juM4b4TnFlSllN8nIneAPwH0wJ8ERuD3l1L+he/o/BzHcd49brMCpsN9uQqDm1bAGEENCYaMLdIErAnYviW3RtMH4tpIvTCdKPO6xq3PFy1x1XC56fj/3elp+8ivntzlVzYf8EG75+dX9/mZ9iEf2iU/Gx5wqhNrSWxUOBU4pXBXLxnKJT8XHnK/XfMwr/kN/Yd8MZ9xf97wl0/v8WBYcbHv2T7qYTSmC6O5FGyE+cII+4KNSrtWbCjYmLC+Tqt0iDDN1Qo4xxpekTISI5jW2wcxJJlSqlUQbrECPu/EyoWV4ziO85p4odj0Uso/LiL/DPBXU/ed/8NSyuV3cmaO4zjfB77BCvjYxMoSJdsyoYo1ETBlpAk1WW9uyHtF56O49WmJW9/WuPVh1fDFvuFy6DjpRx5MK75YnXGv2fKwW/NhuOSu7vjYtkuPFZgIG4GmRHq54G7es5aRj8M5X7cnbGzk/mrDl+sTftKesRsb9quOcdWgg5A6JewFG6SGV4ylxq23ik0Z7QK6X6yA01ztgDHBvIgpq1ZAcqGk9HxWwHrQd6wcx3GcN86L9lD9bcB/CfiEKqjqXxWBUsrf+qpPznEc53vDLVbAq3LglJGcKUnBcp3OWI1dl0VQldQgMZNbQ1KDTkboqhXQRsFWgiQlrYQ4KZdzDbCYorGdO+73a3IRHjVrPgrnAJzqADqxpiyiSmmlsJaZTh5xpgP37JJeZu63G+42d2g18mha8XWz5rxZkfaBEgK5FawTigppgNQIRSGPipkQRJBZa4+Vad0fU6nCarECknIVmMe7ZoeB1NNSAZ8nEfDw+juO4zjOd8CL9FD9s8A/RLX6/RjwdyfHcZwX4YYV8NBhVe87sgJe2QENmrlaAccGaQISDN11hM7IbSBcBlJ/bQWMPcSNMp+2pL7h/lnLg5MT2n7m187ucm+14+Pukt+4/oqPwgWfNo/41B6x1pm7GukXYbUx+NAGprLn03DBRW552K/5y6uPeRA3/PjsLr+2u8v52PPlxYb9tmPaG9N5IOzB9sJ8YdhYCDul2QVsyoRdQoeEzgndzzVu/dBllRIlL1bApcdKcj6yAhbAanLgy1oBn/jv4DiO4zjfjheZUP0e4HeXUv7od3UyjuM47w1PmViR6l+r5BAzfrACllwnViEgudSJ1VztcTZn0mRIriERNoHEumMl0YijMq4CX0TjYt3xcLVinxrutXe4351w3vbctR2fhkecysxGM70ICvQi3NNIL4mNzFhbeGhrTmyoVsBuQx9mvu42bIeWfdOT94bulBJqImDqhNwUbBJyI1ir2GRYUGRcrICmV3HriNQeK7slFTDnOrV62bh18PAKx3Ec55XyIoJKgf/Pd3QejuM47x+HX+oXYVUnVlrLgVluH6yAKdd/MVZhNccaYBEz2ljdU5obQmfEQetuVV8FzbwX0kqYp47zdWC37hhi4E4/8HC14nLV8VFzyVAaPrELTsvAxzbRi9AgnKqyKYVZI2u9z87O+Ticc88uud+dcLfZ8ZPuDvfHNV80p2z3LfOqZWxC3avqqpDSUcihFgTbpAQTrLE6rVIg5jqxUkFSpsxzFVLHqYAit1oBnzqxciug4ziO8x3zorHp/03eoYh0x3Gcd4KnCKs6rrrFChhjtQOqXQurJqBDS2iMpm9oVkbulOnEaiJgB/OZEVdGXDV8ebflq1XkR5s7/MrpB5x1Az+/ecAPu4d8ELb8hvZL7uqOuzpyRxPtIq4+Vkga+ag84lO75CK3/Kb2p3y+vsuDuOE/OfmIL4cTHoxrfnp+wji07C8D04VhozBdCs22WgGbSyUMhTBkwirUHqsxIkOsP9fcwBzrVGqaa49VLpQYn7QCeo+V4ziO84Z4EUF1F/jbReQXgD8HzMd3llL+wVd4Xo7jOO8fN4QVHFkBs1JI1QoItbdK6yRLkiGpCgxpApIK5IYyK2TQqKROkAI6CToJgpEGZT8ZXyTlou+Yk3G5brnX7piL8Wl4xIXtmO2ctUbWUq2AhtALoIle9jSS6GXmrm1RyZyFga/aE4JmHg09503PGDryYBS7tgIWhdTWZMCigk4ZaxRTqT1co9Zwjlh/7qu49frCPLMg+InwCnAroOM4jvOd8CKC6q/m2vL3m2/c5+9AjuM4r4pbJ1alRq0f7k8JdLECmkIIS6/TEmAxNpTG0LEl7GspbxiMeVWtgGGoH+NGmPfKw75ju+v46mTNWT/y6HTFT7q7fNRcsG2/5K5t+VB33LOZXqATpZdqTryjM/f0Abtyzod2ydftCffjCR+1n3B/2vDF5pSfrE7Zjy37dUe8DNi+ijzbQ7iKW9c6rWq1iqvBaofVnGCckGhITBQVSOlWKyAmt06s3AroOI7jfFe8SLHvb38VTygifzPwjwJ/LfBD4L9dSvnfHd0vwC8Cfw/wAfBngL+/lPIfHD2mA34/8LuBFfBvAH9fKeXXXsU5Oo7jvBW8yI7VUpiLGcwzMjVLSXBEu0BpA7avVsDUKtNOq6BaC/OlkTpjPjXun3Q8WEW+vrPm7mrgXr/jRycf8FFzySfNOT/ffM2p7rmnA6ea6p6VKHe1cErmVKsVcNsEPm0e8XU84Yv1HX55fY8H05ofn9zh4W7FMDTsz1tsq9ggNCeC7YWwF9pLxaZC2BlhH5A5Y/uAjKnujw0T5AwxVStgqbtlRRVyfr4eK7cCOo7jOK+IF+qhekWcAP8+8IeXfzf5vcA/AvxdwF8A/ingT4jIX1lKuVge8weA/ypVUH0N/HPA/0NE/tpSSvpOz95xHOd1c1NYHYmEkur9j1kBc6HkUq2ASzKezKk+LgW0NSBgk2CzILkGR0hS5iikUbnIwjQFtlNDRnjUrbjoenIR7tqOOTwis2WWxKlmjCqsTik0mliXBOEBp7pnoyONRr5qTmk18UVzyqOu56EU5qapVkBRrBVyW62ANgrZhGKCznUCpRarFRBqj5VItf+lxfoo+doKCLXTiuUtIV/b/dwK6DiO47xKpLzAm8Rtxb4HXqbYV0QugX/gMKFaplM/Bv6XpZR/Zjm2An4K/KOllD8kIneAL6mTrf/D8pifA34Z+J2llP/nC55D+R3yu1701B3Hcd4cx9MTUUSXz83qIbNq/TtYAUOAEKBtKE2gNIG8bshdIK2MeWOkTpg2wnwipB7m00LcFPIq09wZWa0mPljv+XUnD/mou+Rnuwf8XHOfu7bjh+ERpxLZqLAWuzq1uWS2JbMrwpdpxcO85kfzPX40fcDX0wm/svuAr/drzvc920crymDYVmkuFB2huYRmW2qAxa4Q9hmdMmE316j1MSHjVKPWY4J5rmIqH8WtH8Ir8mFiddT7xY249aPjT8WFleM4znvLL5U/SilFbh5/24p9fyPwKfDHDwdKKXsR+VPAbwX+ENUq2Nx4zK+KyP93ecwLCSrHcZx3jmdYAaGKhKfHrddyYEkJHQM2BHRqSJ1io9V9pk6wQYhbIfXKPCjnq5bLTc/50HHW3+XH6zt8vrrDh82Wr7uv+NjOOdOBj21PJ1xZATcovRQadtzTgbu640O75GG75sP2ks9XZ3w9bvhRf4fLoWO/6xjWDbpX4lqIlzXAIl4Wwr7uWdVzzdiY0MEWcRWRyWr63zRTYkJC/bnJhZLSi1kBv2nH6vi/g+M4jvNe87YV+366fPzixvEvgJ89ekwCvrrlMZ/iOI7zvnAoB4anWwFzpuSMmFUhEVMVVIfbcyKUgo6GzgWd7cr+ZxO1HDgrcRDSpDzMwq5vGWJgSoH73Ya5GBdNz8ehurJPdSJr4pSCitCgmGZmCr3s6CVyYT29ztyxPV81J7SauN+tud+ueSAbYm/MTaCEumNVVMgNpEEoArmt3VZBqD1cqqBybQXUeGUFZEkGfGqP1W1WwG8SVuBWQMdxHAd4e4t9b75DyS3HbnLrY0TkM2rIheM4zvePx36hX2xsSyogiSoyUqZovEoBxBTmudoCmwYbJjQY1jeEviG3RnMZSCsldkpzIaReiGtjeqTEvuWLkxVfn53Q9TO/cvYBH68u+ajd8ptWX/JROOeTcMEPwyM2EjldrIAdyonBXY3MXPDDcMHDtuVhWvOX+4/5aj7lp/Mpf/nkQx5NPV9v12wveuYhMJ8btquhFfOFEYaCDUa7tTqt2jfY0KBzRvbz0mNV/xFjFZWxiizM6seriZVcWQFfOhXwif8WjuM4zvvC21bs+/ny8VPgV4+Of8L11OpzwICPqLtUx4/5Uze/YSnlM55xziLi74CO43w/uNljJVrtf8c9VjlTstXwCstXVjiZa0KexYy2tcvKJsM6Q1NNBLRBkFgtgXEU5qlj2zf86mQ83PR80Z+yTS0ftXf4pD3nvO25qzs+sUvuaI1bX6uhInQIdzXTy8ipzLSS+CSc81Fzxlonvp43/KS9w0/bE7Zjy7btSbtA3Cu5rRMz20NuFBuFplPCbolbD4pOVu2Nw4zEUIWVCEUNcqp7Z89hBfSJleM4jvNNvG3Fvn+JKph+Afh3AUSkB34b8I8tj/mzy3P/AvCvLI/5dcBfBfybr+AcHMdx3m0OVsCjcmBItcdKtN6/dFldWQF12T+KCSarwmqyKq5iIHRKXCkaldiDTYJORlrVZMAHY+Cy75mT8fVqw/1uw7hu+ChcsGs6fhgecCozkOhFaUQ5kYa1ZE4l08s598qOe3bJRkfuxxPuNnvOmrs8nFb8JJxy0fXEVcNoTY1Y72oKoI1Cbgo5CDZJLQ7eKzonVKT2WFkN75Bln6yIPJcV8BD44T1WjuM4ztN47cW+InIC/KeWTxX4eRH5a4D7pZRfEZE/APzjIvLngb8I/BPAJYt4KqU8EpH/DfDPishPuY5N/3PAL73Az+M4jvP95ZusgAeBYOmov2qqaYBm6DhDE6oVcNdS2kDqjOakIXXCvFbmTZ1WTWeBuDZS3/GjOx2fb+6wWo/8ytkH3Gn3/HD1iN/Qf829cMkPwwM+sUtOdeaO1uCKTgI/MLhH4tOy5VPbcpEbvuxO+VH/AffThl/efMRPxxMejGs+v3PKft8y7ALzo4COQnMphG0VV+1lLTG2sRC2DTolbEzVCpgSzBEZ55r8FxcrZC719iEV0K2AjuM4znPy2ot9gb+OmhR44H+8/PuXqd1Tv49a1vsvcF3s+7ccdVAB/MNABP4I18W+v8c7qBzHcW7hNitgqra3x8MrbOl1ykunVUbSEsUeM8SAFOrkKhY0GbEDyXViFXuYcyCNyuVg/CgpD/sVl3PHPrXca7dsu46L8Ii7tuVTu1zKgTOdKIbQiS5WwIlGHtFI5MN8SS+Rs3CHr9sNJpmH3YqLrmNnPWkwcmPkpgqqEoS0q3HrxUBHJbdGMFkSAW2ZSGVEpFoB0zKNOsSti7gV0HEcx3kuXqiH6vuI91A5jvPecZieyEEkSL2tUjusZPnYhGoFbBsIVjus+o7SGHkVSKtA6pR5o8xrJXUwnQlpBXFdmO8kWCW6zcSHZ1vudAM/t3nAD7tHfNRc8BvaL/lQt9zRkQ+t0CB0EjARUimMJXJRMtusfJnXfBnP+Dqd8B8Pn/DVeMKX4wk/uThjO7QMlx1cBHSoIRphDzZAe1FqgMVYaLYJnTI6Jmxfu6yY5iW4ok7qSoxV/MxxEVK5BliA91g5juO853zrHioAEQnA3wD8PNAe31dK+cPf6gwdx3Gc18PVL/W37FjlsiQD1nJcVBdLoCJhCXcwQ4cG29VEwLBraHsj9UKzVeJKiCthvgjElTFvGn582vP5KvKT0zM+Otlyp93zGzcf8Ul7zsfhgt/QfMWpDtzV6TErYCOZO1q4my/51LbscuBnm/t8uTrjy3jKL68/4qtpwxe7U7663DAODcNFi24NG4T5XAiDYPtCeynVBrgPhF1A54wN1f4nMSPDVC2BMVFC7e2qPV7p1YVXuBXQcRzne8eLFPv+ZuBfp5bvCtWFH6gBESPggspxHOdd4pbwCtEMiavwCjGr9r+ilFKqJdCWAItcsLkmBkoM6GRQwObaYSVZ0EnQWZiTkEflURKmaDzqe3IRzvueR+2aVJQPwyWDnZPY00viVArNYgVcq2El08sMPGIjE6c6YBTuNjs2NtFq4rzveGBrxqYlD0YRI7eCtbXLysZCbhYr4KQ11GKxAtYAi4horFZAVYrU6VS1R15bActSovzS4RXgVkDHcZzvCS8yofoD1IS9v4aaxPfXAHeAf4kaHOE4juO8a9wIr6jBFTcmVinVX/7NYJlQcZhWqaJTi+4bSmPY0JA7I/ZG2OsSYCHMWyF1xnym7DcNu1XiYtfza+uBD/o9X5yc8XF7wc+0j/j17ZfctR0f6467GulFWWtDL/VN545GdvmcXTjn0/CQh3nN591dfmX1IV9PJ/za+i5f7jdcDB2X6xVxH9CdklaKjkLcSo2BnwrNTrH90mXVRnSKdc9qWLqsYoJZ646VpasdKzmIoVcVXuHCynEc553lRQTVXw/850spWxHJQCil/Hsi8nuB/wXwn/lOztBxHMd5PbxI3HpKlJzqjlWM0DaIGTJ35DZgnWFjQ+qUZqdM+7pjFXbCvKlx6+POGNYd99cb7u/XnPUDP1hd8JP1HT5otvxcc59PwyNOdeBjG+mlhlY0WC0LptDJjo/Lno/tgo/DOQ+7NZ92H/H5+g5fTRt+vLrDw33PftcxrFt0UOad0FzUAIvmstDsBJuMZqvYEGqXVRuqsJoiMoZrK2BcrICHZMBS6uduBXQcx3lveRFBJcBuuf0l8LPAXwB+jesY9P9/e38eZWm2pvVhv/fde3/fd86JiIzMrKyqW/feptsgicFM6pYXIEYBtgFP2AhoJjfSAmwkjHtJlowBdyEJZAzCYCFbBssLRAvTEmhikIGmQSwDBtQYNS3T0NDc233r1q2qnCLiDN+wB/+xvxMZGTlUZlZWVWbl+1vrrDNH7HPOl5nnyffZz2MYhmG8zFxOBOSSFfDi40pBXKIUX/cXOYeUgsZAmTw+l5oIODkkO9KgSAKNtRhYshIHJQ+O21nYLgJ99MSiXG1WDF2gL4FjtyVxh2MdWUriUMEhBBzHKnQl0clAwx2O3ZZOJw5cz3E4xEtmGVactB23dEXsA1PjQB25rz1W2VcrYBGHD4IOCgoyOlS1WgFjut8KCLXHqtRERLMCGoZhvLo8jaD6XuDHAz8A/A3gXxWRBPwa4B98DGszDMMwPi0eZgUEyKXuJ7rPCjjbAJ2rqXne1QCLocF5h28DfhMojRKWnrisVsDxTOYAC8d01tF3LV9dLbl5dMCyG/nSwXXeWp1wLWz4by0+4IY/5bpb85Y7Y6WJwzm44lAdh1Qr4FQ2fN6t+fpwk7Pc8aX2BjfjIe+Ph/yj1XXuDEvubhecHi2Ydh5dO/xaaoDFWvA7wfcQN4obCq6/F2Ahw4QMsU7oBl9TAXOuE7qUqu3veVsBH/gsDMMwjBeNpxFUvx1YzZd/C/CnqH1SN4Ff/JzXZRiGYbwo7K2AACU/3gpYap8VcU7F8/Wyi5kSHDJl3Fjj1jU64g5iBzoJqRPiTphG5W7bsO0bToaOw3bg7rTkjfaU18Kas/Z9jnXLNbflmo40IizFEXA4EZxmWhnY6kgnE7f8AW+EAw78wK3xgPcXB7zTXmHdt6wXHeMioL2SOsFvq6BKjeIG8L2QG0XHjN85tKl7rHCu7rFKCUalzPHqEquIem6pgGATK8MwjBecpyn2/bMXLv8A8KNF5Bpwp7zqZVaGYRifdR5hBTwXVhcf5/JsBcxVeMSApEzxDkkBiRnXOyQV3KD4vloBU1f3NUlUcquMUbkZHWddy5QcJ4uOW+2KqThe82dswl2yO2GpETSxFFCUpTpCSRxS6GTLsQ5cd+sqrsIBx+GYzk3cXSz5WjjkJCyYBs/kArlRUi9kV7usUqM1HXBUile8F2Sswg1fQzpEpIZXyPw+lHJ/KmBKlFk3XbQC3tdhJfrhouri52AYhmG8MDxVD9VlSim3n9dCDMMwjJeAS1ZAmIVVjg+1AopzlHGCwVcr4BCQrYfg0W1D7ny1Aq49qVNiJ4ynNYUv3vVMh46+bfnylQVfXV5hsRj58tE1Xus2vNGe8vXdLa75NZ/3d7jhNixl5Fj3HVZC5zzXNPFW6XnT/RBnOXCrXfJD3XVuxwPeOTjmnf6YO/2Srx0dst22DL1nOvG4vk6sprO5v2pbAzbcWAhrjw4JHRPazyXBUzwvCS4xVivg/v1I6QEroDjuLwjeT6vArICGYRgvEU9b7PtLgJ8NvA7oxftKKf+j57iujw0ReRv4tk97HYZhGC89H9JjJVDtbqXcswLmjKQMMaG5JuSV0UGuvVA6OiQrrq82QI1So9djYBqUaReISbm7WHB7sWSXG66FDXebJV8Mtzh2W/qy5VBHOhE6cShKELiihVYmWjkjSOKaW7PUoVoBmwNaH7nVLWvcuqt7rFKrZK+4XkgN5FCtgMUJrlfc4HBe0TEho4NeEZcQ7+5ZAaPUcAqzAhqGYXwmeZpi398F/K+p+6a+yoWwp5eJUsrbwNv76yLyUr4OwzCMF4JLVsCLPVZATcLLmZK0WgFToqR5D1JMyOgpwVcbYONJvUMnT2iqFXDq67RKR8HtPKkrbEelXzWcLVu2U8Nxu+P2csW667jqN2zDLW64Mw514pomOoEgjlYCy5I41MRhXrN1G667NTf8GbeaA47DlnfbK9waVnw1HLHetfTbhrEJaC/kVmtJ8FCFld8Jfqg2QDc4dHQ4VUrMyDhVK+Bs+yuz+DkXmWYFNAzD+MzwNBOqXwV8cynlj39cizEMwzBeUh62x2qfDFhyFQaXrYAxwuCqFXCcKN6hTcDNJcFh4YlLR2qV8UyJS4idMJ3V2/tFtQK+00V+8OCYHzi4zrV2yxeXd/hcc5cb/qxOrbTnUAaOVQmidOLpnGcqiWu64023ZVM8Xx8+4IPFER/EQ7508Bo3hwM+2B3wtaNDdn2g3zTomUf72mPld4LroVkrvi+4IeM3HjdldBfQYQ6wGGtJMClT/FSDLFK+ZwM877EyK6BhGMbLyNMIKgX+9se0DsMwDOOzwIf1WDlXJzN7K6AvlFxtcOI9pIyWUhMBU0FyQQdFskNjtdtJFtxYu6ym7IkL5W7UGlzRdfTJs1m03GlqgMU1t+a6W5PZ0krmUAuBagVcioJOLEvE+VM6nTjUHY7CFb/jKPQ4zZx2HXfCgp1viTtPca5Oq1pAqx3Q9zXAIg8Z5xTnFJ0SogqqdSInUuPW3WwFTOm+Hqv7rICP6rF63MTq/L03UWUYhvFJ8TSC6g8Av4ILdjnDMAzDeCiPsAIKewGhsxVwnlrNNkAmX+PIvaM0ARkCLjhcH/CdI7eK710NrViA65XUKnHlWG89m27B6WHH+6tDjrsdt1YHvNme8Jo/46y5Ocet91zTTDfbAK+IA+BQJ26UDRu35nV3xq284oP2iBvNGbfGA766POK91WHdY7VcMGw9ulNSV/dY+Z0Q24IfBL9TfKu4MeMahw4OmRLST8jk7qUCqkNcDa24mAqIk0f3WD3p/qqLn4NhGIbxsfFYQSUi/+cLVxX45SLyc4HvAaaLjy2l/K+e//IMwzCMl5rH7bHaWwF1X4YbERerFVDnnqdxqnushoRrHbnxuH6fCKj4rZI6iEthWnviwrE79GxXC95fTLx/dMC1xZbXug1fWV7jtXDGm/6EL4ZbHOrINd2xkmoFDOI4QFm6zEp73sg7zvxd3vQn3OoOeG9xhS8tr3N7XPHuwRG3N0u2fcOwapGdw+2EZlmj3/22EOZy4LB1uF21ArptQMYLVsCYairgdL8VsJQa2PHY8AqzAhqGYbwQfNiE6sdeuv635/Mf+fyXYhiGYXxmeZwVMCuFaocrc3ADrlBKDbSQmGqPVQp1ylMKGh06OaRAHAWdgDz3WCUlTsI0KLeL0E+e9dgSi3K3WbJtWxLKsdswuVOu6chKC0tqeIWjCqugka5EnL/Loe5Y6UCQxO2wotXIwk/cbRfclMLYBmLrQdwcWiEUBddCcTW4Ig+ZIoKGmgqoIhSXEI31NatSJIE+aAUs+5APswIahmG8cDxWUJVSftYntRDDMAzjFeChE6tyrxxYFHIG1fqYGGcr4ITsu6z6CRc8rvG4bSA3jrRQmrUjtkI4g2lVrYDTqWO9ajlbJO4cL1h1I68tr/Pl1TWuhw1f197irXCHY7fl827NoSZaUZbSEMRxhHBFI0PpOfNbvuhvczcveKe9yrvTVW5OB/yj1XXuDEvubBecHi4Zdo5x4wkHguuFsK4TKzcozUZxvceNNcCixq1HpPeQUg2vGCdKzrXHKqVq+9uLoQvhFfXtMiugYRjGp82H7qESkZ8H/F+BH19KObl03xXgvwZ+bSnlz308SzQMwzA+czxqj5XOl1NGnFa728W49ehqFHnw6OTr7Y3HDR6dCiEIbnT4HcSOGl6xcaSlsh2U7aLjdNVxMnQcNgPvLY/4wuKY1/ya280tbrhTDnXkhu7opBYEOxGWEgglERi5piOH2vP6HLd+1W95fzrk/cUhX2mPWQ8N603H2DXIoMSlEtY1bj11it9RxVUQdPLVDtj4agMcJgi+WgGn2f6YEhLnHqvnbQU0YWUYhvGReZJQit8A/K7LYgqglHIiIr8T+I2ACSrDMAzj6bgsrHLdXyUqVViVUsMrskNyFVdlLgSWmGpR8JSqGMmF0iiSwE2KGwRN4IaaCCjJkXbKMDjej47TxciQPLsUuNUcMBXHJrRcd2vwJxxK5FAnlgScCK0EVJUliU4GVhI51i2dTFzza676La2L3BpW3GombrkV0+CZfKB4h+trIXAO1Mvq8EMhe6E4qcXGTtBeqxVQpKYCqtbp3Symzq2A1mNlGIbxQvAkgurHAt/6mPu/C/jNz2c5hmEYxivJ+Rf6Cx1WF62AKVM0VoERAjitVsDRI94jwSO7EYLHdZ7QBXKjTCtPXAipFcZTIS6UuFCmI8em61gfLvna4RHLbuBLh9d4ozvjtXbND+/en0t/T3nTbVhK4Vg9QRyBhgNXrYATI2/5r3I3e+7mjh/qrvNBPOTd8ZgvH17j7rDg/fUB603H0HvG04DfSg2wOBNcX/A7rQEWo8fvAm4b0ZjR3YSMEWJCxgliTQNkmqrlL6V7XVZP0mNlwsowDONj4UkE1Q32/8I9nAJcfz7LMQzDMF5pHjqx4l54hYJMEyW7agvMhZJSnVilDFNEc4PEjIY61XJjLQeWpLgeXC9oqiJrGgP9qAxdwzAF7i4XvNcdsoktN5oz3ggnnDW3ONYtW7fjUCKdKEutUyuHB4VAZCVrGhLX3Zpjt2WpI3e6JQdh4P22xq2fhgVT54m9IweZ49YhB0VHCI0QgqBTwTtFx9kKqDpb/+bwjpwh6T0rYEoP9lhhe6wMwzA+CZ5EUH0F+HHA9z/i/h8HvPPcVmQYhmEYlydWeytgEYpotb/NwkKcqx1O874rybn2WTlXhVXnccEh0eM7JfaCm4TYCToKcRZcm0np+8DJomNInpvdilvtir4Errk1fbjDDbfhUCIw0UqdWB1IS+cSV0phKTuulZ5rbs2h9txNS674HVebHbeGJV8NVzhbtPS7hsk3pJ2QW6F4wfWQvZK94MZCUcENio4OFanCaqpTuhq3Hut7kfcWyfxkVsAnFVYmqgzDMJ6IJxFUfxr410Xkz5RSdhfvEJEl8K/NjzEMwzCM58uTWgGdq4mAItUS52cr4DghW19TAXdzImDniWtXJ1RLZVrVsIjxVIlLz2bR8Q+vLPjBxcTBsudLR9c5brZ8obvL17W3uObXfN7f4Zr2HOp4bgVsxfOac1wj80aJvOm+yll2/PDmfd7prnI3LfmB1Q3eG464PSz52tEhm13LsA2Mp6FOqzZC2FRxFda1xNiNhbDxuCGhQ6pWwJhrj9UUH9pjRZktgU9iBYSHiyubVhmGYTwRTyKofjvwi4DvF5F/G/i++fYfBfyLgAC/4+NZnmEYhmEw7xPaf8F/iBWQVMMrnKudTnsbXK5dVsSEloJMdWolOaBjDbCQJKShnusk5F4Yc2DsHXdHR0yO292SbWzY5oarfkvfBs78Gce6ZXI7VnPceif1n9VOqhWwk0yQM4JEztyCIIkj33OrWeE1c7ddcLftWLvFbAO8ZwXMXsjbGqpRFFyjuKCzFTAhXqFX8AlxWuPWXYYocxKgPJkVsN5giYCGYRjPyIcKqlLK+yLyU6jR6b+DKqCg7p36s8CvL6W89/Et0TAMwzC49IX+0VZAUqqBFap1YhOr4JCUqg2wr/1PrnH43uN3ntQKvlf8VkidoIOSFkJcKqc7z3rZcdq33BmWXGl2nK463mxPuOHP6MNNjnXHFZ245iBQp1UH4lhK4UASx7pjk7dcc2s+H25zKx5wPWz4YDzk/eGAd7ornPUtm2XHuA7oTsmtEJd1j1UKih8KvhdyUNzocL1Dvc5x666+tpRgVEpMD7UC4uSesHpaK6DZAA3DMB7Kk0yoKKV8Gfj5InIV+BFUUfX9pZQ7H+fiPg5E5G3g2z7tdRiGYRgfgcdZAS/usUppLgfW85Lgcytg8Og24BaBHBS/8TQLR+wEv1FSR00EXCup86wPGtaHC0IXeffoiOuLLdfaDT98eZPXwhk3/Clf529zqLWraqmOgCOI46o4DiRxqCNvupuc+Tu86U+43R3wQTziS4vr3J5WfHVzhQ82K7Z9w3DWIhuH2+l5SbDfVSugGwth5/Bbj44Z10e0j7O4Guc9Vg9aAe/vsXqGVMD9tOq+z8AwDOPV5okE1Z5ZQP3Nj2ktnwillLeBt/fXRcT+RTAMw3hZeagV8FJ4BdTwios9VlCtgDkjpSDBQQFNBZ0cUiCOgk4gZe6xikqMDWPnuZmUfgycdh25KHfaJSdhSW6VY7eld2fcYKSTzAHgRAjiOBQhlEgnGeWEI+05dDtUMlfDlkYjrY/cbRfccpldaImto6jHtZCDzGEVUJxStPZXFRXc3gooAlOsVkARyLkWBM9WwPMeq8tWwHliVd/KYh1WhmEYT8hTCSrDMAzDeOH4MCvg3tLm5vjxGM+nVuI9DBPiHTI0lMbjg8PvAqmt6X/jRoldtd9Nh0Jq69Tq7kHDySJyd9dx1A1cW2z52uqI15szPhfu8sVwi2O35YYOHKqwlHBuBTwQOJDIUAbOyo433Sl384JvaI/5cvcad+KSL6+u8cHugNO+5eRgxbj1TDslniquF6atENYFN0BYCH7ncGPGN64Kq2m2PqaETK5aAXNGYqy2yAfi1uvECuoeK4taNwzDeDJMUBmGYRifHR4Vt65SbYGl1PCKnO/tsdrHrceEBE/xDpkSrvGk1qGjryXBS8XvhNSC3ylxrcTOcbbzrBcLbi5W3D5actzt+NzilHcXx1z1G74YbvG6O+NQR2646Ty8IojDidCWQicTN8rIdd1y3a25m5a83pzytcUVPhgP+KHuKqd9y3rbMS5apFfcRonLOq2Ka8XvwA1K02q1AQ4Z1zhkyugYqxVw7uoiRkq6J67utwLWidXeCvhEiYBgwsowjFcWE1SGYRjGZ4+9FbDcE1aiGRIU0ZquVEqd2JRMiXoutiS5ejnmmgqYC7l1SHJoUmIrSAadBB0EKY7UK8Pg+KAIm6FhFwNDdlxrtkzF0YfAsW6BUw41MUnmUBsUxYvgRFiWRGCilTtcc2s6nTjUnqthS5DMrWbF7WbiAy2MgyeGQHFutv8J2YNrANGaDOgFBHTM4KTa/9JsAxRBXK72P33QClhI9X3cv288YSLg/DNMVBmG8SphgsowDMP4bHLJClhmjYCUe4JgtsSdWwGd1uCKEGqXVfDVCugcvvOkRajTqgNHXFRxNZ0JcSFzl5XjZLHgZLXivaNDDrqBL62u89byhGthwze0H3DDn3LdrXnLbVmpsBRHK4GFOBau4YpGJiJvuq9xN9zkbu74hvY6H8RD3h+P+IGD17g7LLi1XXFytmDsPdOZx29mK+BZ7bHyvdKsqw3Q7Tx+m5CYcbsJGWt/lQQPc0kwMd7rsZpFURVWroZX6DN0WD3wORiGYXz2MEFlGIZhfPZ5VI8V6aHhFfvSYImxTq2co6SAxExuHJICbnD4VtCouF2NW5c4pwNulc2gbBctm6Hhdl+tgKerjtebM17zZ5w1NznWLddcz/GFHisngsPjVOgkcShrOpm47tbc8GccuIFb3Yp32yt8tbnC2dBw2i6ZFoHYK6mt8e+xF3KgrrNTclOtgMUrOsxWQO9gikisyYekRIkRifN+qnmqVePW5UFhBRa3bhjGK48JKsMwDOPVYP+l/lFWwFxA5y//pSASKW62/3l/Hj/uRoekjI4e1zg0ztOqXmpJcCe4QZiSJ20d61EZBs9J1zEmx+1uxWvtAVPx3PCnbMopk1tzqGkuA/YoykIcQRJLSXQycKxjtQLKxN2w5GrYsvIjd8YF74aaDDj0DZMP5LaKvOLqtCo1UFRx43xbqImAKLUgeErVBhm1xs2L1h4raunk+d4zxcqBDcMwLmGCyjAMw3i1eIwVUMrcXeVcFQFaC4GLSrUADiN4j+sDGjwleNw2EBpHbh3NxpEaZVoI06mQWmE6aJgOAmdd5uzKgh9cjhwsBr50cJ3r7YbPdSd8Q/sBx27L5/0drrmeQykcag2uWEjDwjVcI/FGibzpvspZdtxqF3y1vcqtdMAPHlzn3f6I28OKd48O2exaxl1gOg24XZ1YjWvB9+A3StgV3OjxG48bci063oaaDDjFOq2KieKne8EVKdeo+VJqFPvDJlZmBTQM4xXEBJVhGIbx6vIEVsB6g1QrYCnVCjhPbEgJVwoyeTTm2mXVFjQqkpXUgERBp7rHakoNu94xLAJTdNxdLDiZOnYpcDVsOWs63vJ3ONKeN9yO1QUrINTpVZ1iZTpZ05A4dls6mTh0PXeaJZ2buNkecNq1nPolcedIrScHIfVCaoTcgBuE7Opeq32XlY4JHV3dSzbF8wCLog5x6bFx62YFNAzjVcUElWEYhvFq8wgr4H09VpetgDlB9IgqxISOHgZ/Hreee4cbao+VH4SpF2JXUwHjSkid4+7oWC867iwXnI0dV7stJ4sFd9sl19yaPtzmmttyrPHcCuhxHIhjKYUDSRxqzybvuObW3PCn3G2WHPmeD7pD3h8OeKe5wlnfsl22jG2D9kpq6+TM95CDknalpgI6wQ0OHRLOCTK4OQ2wRsoTI2UWQQJVXMqcEGhWQMMwXmFMUBmGYRgGPFlB8D4BLyXExWoFjAlxer8VsPW4TSA3jrD0xKWSGmU8qImAcSFMZw2pC5ytWtZXOpou8uWDq7yxXHO13fL1i1u8EU644c/4YrjFoUwc63BuBWznLqsrmjnOE2+699lk5fPhDrfSAR/EI760fI3b44r3+kPePTxkNzT065Zp7dGdEs4E3wtuB2Gt+KHge09Ye9ww2wCHCZlSPU9ptgLG8z1lpHly9SRWQBNWhmF8BjFBZRiGYRiXeVSPVVYKlyY0QMnufHIjOUPOaAaZamqgJI82BYqik+JGkCykQYiTMuWGvvPcnBxjdNxtF0zZcdp2nDRLpuK45tZs3YYbjHSSOJBAEIeiLDWgJdJJBs5YyciR9gRJ3PSHrPyAl8zJ2HHLJza+I3WO4jx5J7hGKCrkHlIQioAPQhGp06opoTrbAF2aJ3OR4jKMzMXJ9b2qQRbp4RMrE1aGYXwGMUFlGIZhGA/jkhWwJGqHFVRBIHrPCiixTqxiBHWId8gUKd6hQ0B3Ad86fO+JnSN1it9VG2BcCH47JwWuHbc3gbuLyMmu453lFa53G26uDngtrHkjnPD14SaH2nPDjRyK0oqnFc+BOA4EDiQylIFN2XHDnXG3WfBBe8SbzevciUt+aHmV91aHnPYtJ8sl4yYgvZIWNRnQ7yA3iuuVNghup7XLKjhkqGmH0k81an2e2JWU5z1WWi/v49bhPPTDhJVhGJ9VTFAZhmEYxuN40j1Ws/VNnKNEV62Ac8+TjhNsHdo3uM6Tg8P1ntwKsVPG3T4RUIhrIXWeu0eB09WCr3VHvH90yHG3443ujK8srvGaP+PNcMJb/g6HMnHdTefhFXsr4JJMJyM3ysib+z1WacXn22u8s7jK7XHFV5bH3N4t2fQNu2WH9A630SrydpBaR9gqbiz4rZsTATOurYmAMkbEuTqVm6qgvGgFLKUgmu63AjrmABATVoZhfDZ45QSViLwNfNunvQ7DMAzjJeOysBKl7PcKXbYCloKUXK2AOSMpg6+2QI0ZaevlPDp0Kkh2xBY0gkYh9dTbJqHvPO8X2IyBzdQQi3InLNnmlrE4jnVL5pRDTUySOdQGRfE4DrWhK4lOEsoZh9rT6chSR+6EFV4TqzBwp13ygRb6XUMMniI1FZAiFFcTAYsKOUhNBHT1XL2iIueJgDJHzRepYykpufZYiSClVCsgwN5CiU2sDMN4+ZHyiv/lJCLl58gv+rSXYRiGYbxsXOxUkv1+ITm3Aorbp+S52mvlFAkBmgDOUbqGEhy59aRlILXVdjctldgK06EQl5A6GI8yeZmRZeTgaMdhN/DG8owvLO9yPWz4Ye1N3vQnXHNr3nQDSxGWEmjF4+a1DWViKom7OXI7e85yww9N1/lavMLN6ZAvba9zs19xe7fk9smKtA74Ox6/EdwAYQNuV3AjhG3GDQU3Zvx6QqdcQyuG2lklU4RxouQMc4/VPm79XnhFgVwuTP0ufB95lLA6v//V/u5iGManw3eWP04pRS7f/spNqAzDMAzjufC0cetJZhGREdVqk/MONwVkyrjWkQaPjo7QCG5U4lZInaCDkhZCXDjOesdm0XGyWnD7YMVxu+OD5SFvtXe55td8fXOTa27NsY5c04kwWwEVpRXlmiqdTPS6ZSUTN/wpt8IBV/yW97sj3lsc8oPhGreXC87cirRwuF5JneD6Kq5Sp7ge/KBkL9UG2DtcH5GYYZgg+Bq3PlsBSQmJ8ugeq4dZAeub+uB7bxMrwzBeIExQGYZhGMZH4RHCSjRDgkJN/iuqs7BKFOdqObD3tccqZcrkkZiR6MmNQxK4SYkDkIU0CjrAlD2pVzajI0bltGvpk2eTGq6FFQnl1He87s5Ibs2hJlBYSoMiLLUhFMeyJBqZWOZTjnVHJxPX3IarfouTQueP+KGk7HxLbB3FOXJTkwmLE1wDqYciDjcqxSs4QaaMiqCqFJdq8uHeCji/X+epgBd7rOBBKyBYj5VhGC88JqgMwzAM43nw0FRAhRzrpEr0Xo+Vq2EV51bAYaziauvR2Qrot4HcOlLraNauJgJ2wnRWi4Hj0jEdeu4sFpwcLvnK6piDbuDLh9d4vVtzoznjG9oPqg3Qn3BDd6xUOJitgEttWNJwVRNDmXjDvc9ZuMnttuOt5g5fWV7je5rP8/7hIZux4WTdMfWB0jumteIGwe2EeCq4oeB3WgMshoLbeVyf0JjRXUDGWEM6xgni3FsV44M9VmI9VoZhvHyYoDIMwzCM58njJlZZ6zQmxntWwDJPbnJGcq5WuMmhqSCTQ0aPpIIbFD8ompTUgg6CTNWKF0dl03t2i4YxOm4tVrzfHbBetlwPG243B7UcWHtu6MChTnTiaCUA0ErgWKGTRCdbUvM+QRJnq47OTZyOC1ofOetbhtEzNC2pV1ynFFfFVWohhdqxFRrBN4qOGe8UHX0VVarne6xEpPZYJX28FdCElWEYLzgmqAzDMAzj4+ARPVYy72c+twImhZQRp1VQzNHrEhNlCqiPaGxwjSP1Dp3qtMoNik5CboVpFKZeSAvHnUnZLFtOFh19ClxrN5ykBZvcct2t6cMdbpQthxo51iqmgjiCOBYls5SIkzUNiU3XstSRddtyEK5wt1uwmRpuNUv6XcO08BQXcL2QmpoC6AbIXuvlORHQDYoGRVWRKYFzdR9ZrJOqIgo53V+YfG4FdM8mrExUGYbxCWGCyjAMwzA+Tp7KCri3AToYp9pj5T06jGjwuMbjdg1No8SuWgFTI4wrIS5rgMV41JAWgTvLjpOjJd1i5Hi143Or17ja7Phhi1t8Ltzlul/zRX+bY91xOFsBgzha8bymgN+xKe9x3a3Z5IbPt3c4iUvWqeW9g0Pujkvu9gs+ODhg7D3TJpxbAf1a8LtqBQxrxQ8FN2T8xuOmjA4J3U01wGKcaipgTBQ/3ddjRSnze/MME6uLKYwmrgzD+BgxQWUYhmEYnwTz5KVefkiPlVKnMvNjpWRK8dUKCLPIyLhSKKNDYqhWwFaRpGhU0gCShTjU8IgpC9veMQ6BMXrudD27FDhbdLwWzhiL47pbc123vOZGOhE6cQAo0MnEsdvQ6UgjiUPt2eaWpRu5E3bcDisAzpqWjc9MriGPSnb7CZVQtIZX+EZrl9WQcV5xKjVu3dXACnyq4RUxVitgrFMqRJ6PFfBFFFUv6roMw3gqTFAZhmEYxifFfV+eZ0FwwQp4LqZEHrAC4hziPTJFindoH9EhkBuH6z1+50httdelDuJCcL0jLpS09NzsPXe7JXcPO26uVlxrt9xZrni9OeWGP+Prwwcc6sixRjoRMhAkESSxKiOH2jMVx1Q8b4SOu82Kk27Byg/cGZd8sDzg1mLJMATGriFt5rj1RvC9EHshhYIbBd8rIdQ9Vi4oGtw9K+AUq5Aa9bzH6nIqIE7q+wZPP7F6UQTMfj0mqgzjpccElWEYhmF8GjzCCijMUyrR+/dYiVCcmwWGQhPQMaDBo7uA6zylUfzOk7q5HHhTkwHjUpjWgdR5bh80nByuaLuRrx5e4bXFmuvthq8srnHVb3jdn3LDnZJQznJHJxNBEg2JlY44Ctfcmt6fsM0th9pzOx5wa7HineUxp2PHzYMVZ9uOcfD0ZwG3VXQQwqqKK7crhKXixkLYOtzOo7HgthM6piquhnHeY5Uo04Tkcl4SXEpBNM0FwY/psXpZrIAvmtgzDOOpMEFlGIZhGJ8ml6yAtRy4Tl5KqvdLzlVMlVKDG5zWHqtcznusJGfK5CCDjop2DimKjqBTtQKmXpiikJKw7RzvZWU7BU66BTE7XmvXrEPHJrQ4Mn0JHLstnUygA4GESuGQaU4EnOhDYKkDSzfgNXM3LGg0cdMltm3gVBek4Mm9A1FyI7imWgHdWF+L94IbMwgUr+jkqv1viojWREBypqicWwGr6BSkFArzuOpyj9XLZgV80dZjGMYTYYLKMAzDMD5tHmMFJFFDKlKuIRaz/Y9xgjCBOqQJyOAp3iF9i2sdufH4bZ1WpU4ZNzVufToQpjNH6pRh7emXHbcWkVtHS64seq53G95anNBqBOD15pRD7XkznJB1y6H2rDTSkTmUSONvsXGBN/MJb/oTTvOC97sjvra8wt1pwbvLI077jk3fsFu1TL3DbZW0qFOraSuEbY2FDwvF7xw6FXzjzqdVshvnuHUH0df3YJrqeS73hMhHCa/4pIXMo9Zk0yrDeOkwQWUYhmEYLxIP67FKICqQLsStq9aJlUuUnCD6uscqJsrgqxVwDLidJ3cONzhSq/he8NuaCOh3QlzVouA7O8/pcskHiwPeXx3S+kjQxM3FAYe+56Rd8po/47pfM7kTllqnVCqFFROdi3Q6cS2vOXYbrvoN69Rxrdlwczjg7rjgg+6ATd8w7AL9IqCD4jdC3NS49bhWfF/LgUOjuDHjhoQ2DpkyOsZqBUy5FiHHSEkZuWgFvJwK+LRWQBNWhmE8JSaoDMMwDONF5FFWQFLtbdpbAbOr1r99MW7OSMrVClgKMiU0BiQXdFAkOTQqcQBJgk5z3HoW8qBsBkeMSgiJ4BJTdhyGnlyUqXVMxREkMtJzJAMqpZ4oHDHQuYlGEo0kNq4lSOLADdxtlnjNnDYdJ6HjTCH1jsk7ildyLzUFMIBroKjiByF7wUtNBMTJPSugSLX8uVztf/qcrICfoJARlbqeeuXR1kTDMF5oTFAZhmEYxovKh1kBVSBlikbEuSoG4txlpQ6Zu6xK8Oi2wQeH3wTSwpMbZVorcSHEVghnQlw40kKZDjxjKJRQODlY0rSR9w4PubE45nq74ebykNf8Gdf8miPt6WRiqQOdJFZEVi5yrDtGlDf9Xe42K85yxxfaq9yJS26OB3z14ArrqeHuZsFuM1sBzxxuJ7heCGvB9aUmAm5qgIXrPX6b0CmhfUTG2l8lwcNcElyDK/K9bq8X0QooUieOD9z+EFH1IoZoGIZxH6+coBKRt4Fv+7TXYRiGYRhPxWUr4OUeK2qPU4E5bj1RSkaSrxOrXGByuJjRmMlBkehxg8O3gkbF72rcug5CCZCaQhyFvvW8Nzm2q4bb3ZJdCrzWHvJ6c8ZVv+FQdxy77XmAxd4K2JFwuiNI4jDX82u+46rfsnIjd6cFH4QDbjZLdkND37SknUN3Sg6K64XUQw41XCPMt+vk8MGhw2wFdFqF1eTqPquUKDEiab78sB6rF8EKKPcmZ/XXvzjTM8MwnpxXTlCVUt4G3t5fFxH7W8kwDMN4edhbAff7q7ICqfZY7a2AIudWwJLmLqeU7u2xSgkJDokZN3hSq2hyxF5xPehUrXe5EXSqe69iEk4mZbcI5CKsY8tZ7Hi97bjidmx9S0ZZysCx29KQCZIJEgmSyTLQ6cSZ66rw0omTZsFR6FmFQ07GBTf9im3TEjvH6D1up+SdkF3dY5U9ZKdzYbDgvaBjBqVGrfs5HTDX8yKpWgEv9ViVWbN8qlbA/e963P0vSzqhYbzivHKCyjAMwzBeep6kIDhlcAlU77cCeo8MHvEO2QVcE6oVcBvIrSO1jmmtZC+kRphWkFohnnqmA8fUNbxzpeX95SGLxcjV5Y6jtudGu+Zz3QlX/JY3/AnX/ZqV1ELgIJlWEtdlx7H2TO6UG/6UTW653R7wXneFk7jgK6tjbg8rToeOW+sl/a6h33n0zOEGwW+EsBF0gLBRfO9ql9XG44aEjBm3C1VcTREZp/o+TFN9/aXANJ/nPAdZfIpWQBXI94TVfWuoN3z4Oi5iIsswPhVMUBmGYRjGy8yT9FjNVsDz8Ipc0/Akl1lwOFwp6OTRMSPZk73UCVVSUgOaBElzl1XxTKMSB884eU7blrNFyy4FjpsdQxtq6a/rueFOWelAN0+qHNV6dzjvvWokESRxxXUETRz5gdthidPMadOybVt615KHavcrXtEBihdyA26YO63mVEBUahDH6GBvBXR6zwooUi2Qc4DFA1bAT0pYqcyfy4PPvy+sot7wZIEVNrkyjE8FE1SGYRiG8bLzsInVQ6yA+w6ranurgRYSa6CFxEQJHmk9MoWavNfUfUq5Fdw472nqBJ2UuBTSQul7x9A1bBYtuylw2A6cLjreaJdcDVv6UMuB9/usOkmE2Q6ITKxk4lB3bFzLsdtyKxxwp11x3Oy4Oaw4GRa83xyw7RvGLpBbj/ZKboW0rdOqFBTfl5oK6AQ3OnRIOK91WtUrolq7rEQoLkNSJMq9dMSUwMmzCaunETEide+USJ0e7p+7/z3z79iHVjzRtOqjrMcwjI+MCSrDMAzD+Cxx/mX6IcJqf3+arYAp1ymOc/eE1eCRPoBTSnC4LlCCw688YaGkRvBbJXZCWgjTSkmdIy4C7x02fNAl3js44NrymKO25wvLu1wLG675DW+FOyy17rFayXhuBQySOdSRY7flRj5lE1o+F+5ye7HizrTiK8tj7o5Lbu+W3NksGIZAfxbQbbUCxlPBDYLrIawVPxTcUIuN3ZjRvqmpgFNChhGJqaYCTlPtscrVIvjMPVbPOq2SeUpVymz/e/B3PHRa9cBnbh1WhvFpYoLKMAzDMD6LPDS84lKPFVByruEVUCdW+0Q8VYgOzVBCxpeCRI+29efopKSpdlmlUYiTELMnD451EmJ0nLUNMSunbcdp0zEVxxW3pS+B625NN0+ogmQCGScFpzs6mXCSWenAofaoFI78wNKPeM1s2oYT7ZhCQx6UIg7XC64BZLYChmoFzIPiVWYroKIKTFVQiUqdzMUqQCTX9+z+VMDy5OEVTyNk9MLeqfl5RamiCh6YVt0nqi7zKEugCSvD+EQwQWUYhmEYn1U+zAqY58lISvemVk7PAyxEFZkixTt0CGgb8K3DDYHUKqlT/FZqaMVCmHZ1v1XcKLsDz67t2PYtHywOOGgHbq9WXGs2vN6c8blwl0O347pbz/upUrUDSuaQiUMd6d0Z29xyza85SwtuxkPeaY+5Oy14b3HE7d2S7RDYdh2xr3HrqavWRL+jXh7A74SwVXTyuK2rARZzKmCNW481rGIft37RCvgse6yeRMg8YPmbf8a+n+oRFsDz3/3Az7O4dcP4tDBBZRiGYRivAo+zAl6MW08691hpvT3nuv9oiug4Qe+RIeFbR24dfueroOoUvxNSC9NWiGtP6grD1tEvW+4sIie7jqNu4Ljd8YXlFY78js81J9zwpxy6Hce6PQ+v2E+sOpfoZKJ3get+zVW/YZ063m2v8N7ykLvjkvcXB6z7lr4PDMsG7RW3E+KZ1P6qjTJtazlwWCiuz7gx43Z+tgHeswLKRStgnEXWc7YC7sVRjXaf9zypIrOoepwF8OLzzz/aJw2wsJJgw/hYMEFlGIZhGK8ST9JjRbUColovO0VKOU8FVKAkh0QPqaCjQ6eCZCWNgqSaCphGQYoSJyGNykkW+jHQLz0Z4SgsmIpjKo6DvCB55Uh7ljrgpKDU06GOdCXOnVaRM10QJNFq5Djs8JK4E5achpZTILaOEjxFtKYAOqE4yAMgNRK+WgUFHRWnCnsr4BTvWQFVIMo9S6QIUgqFVN/Lj2IFVJ2ng3A5Av2BfVXw2AnUQwMsLLzCMD4xTFAZhmEYxqvG/ov0o4TV/ov53g4oUjuswgTqkHFCgq9WwLahBEdu5i6rRolLZVooqRWmdbUDplaZjhz9omG36LhzuKRrJ95ZXeGN5RnXmi1vtUe85s9qOIU/ZSUjS50IZJYaWRI51JHJnfKmP+GLzZLT1PH59io3pwNujyveWV3hbGw53XVszqoVcNo4po3g+tpj5XYFN0CzUdzgcEPBbzwSM9pHdJiqFXCs56QE0xziUTJMsxh5Visg874p0fv2Ul20/z1UVMH9P/txARYXwytsYmUYHysmqAzDMAzjVeVxwmounS2ke1/uS0FcopQMMSLeV5HhHW70SMqU4Gqf1Ry3rlO136VW0Cik1hEXyjgqY9uw6wProeVrzcjt1ZLXmg3XmzV3w5Jjt60n3RL2e6wuhFcEiRzqjk4nrvoNJ82ClR+4Oy243a1q3PrQsFs0jIuA9krqBL8V3MDcY+XwfSEHQaeCax2ud7XLqvcwTtUK6BzEWK2A8wSozCEeT2UFFJ2FlJx3UdWPotybGqk+KKr2PEwwXfg9Dw2wMHFlGB8rn7igEpGfDvzLwDcCbwG/upTyhy7c/4eA//mlp/31UspPuvCYFvjdwDcDC+AvAL++lPKVj3XxhmEYhvFZ5MMmVnsrYM6U7Kr1z+Ua2pDzLDYSmnLtsooZmWrin0RwYxVUkuceq1GQ7MitMk3KneTYzImA20XDaWyZOsdZ6NjklhSElYwwh1cohaVGQslMZaKTiSPtOXUdQRInYcmRH2g0ctZ13PJL1r4jdp7JeXKoYRVFZ2EVoGjdY1VcvV1HrRY/JxCruMHpPStgSgjxo1kBL7AvX36kqNpzMbTigR+ijxZVlx7zWCzEwjCeik9jQnUAfC/w78+nh/GdwK+8cH28dP/vBf7HVEF1C/g9wJ8SkW8spaTnulrDMAzDeFV4mLBKgJR7PVYpg5sTAUXOEwEZHNIExDtK79Fm7q/aBnLrSK0jbNwcYCFMZ3M64FKZDhx91/LOYcf7ywO6buKdg2Outluutxu+2N3hittxw59yza3nOPXxPivgsQ70xfG6O+M0d9xtl7zbXeUkLnhvdcR7/SGnQ8ftoyX9rmHYOaYzX+PWd0JYV3Hlt0rYFtzk8RuPGzI6JnQXaoDFFO9ZAWOs53OPFc7VtL6UHmkFlHkyhXOgDly5J3DKLLzOE//uiar7P6ZLU6s9F5IBH5sK+CQTq/1xcPnYMAzjAT5xQVVK+TPAn4HzadTDGEopX3vYHSJyBfjnqZOtPz/f9iuBLwM/B/izz3vNhmEYhvFKcVFYwWxhm3usNFeRtU8FhDkVcG8FdEjKVXhNDk21v0rGjKRCahU3KpKU1IJOgkQht8I0eaZBmbpAjI7TruNksWCXAsdhx0mz4PWw4Eh3XHNrjrQ/twIC9Vx7gkQ6mWgkceoXLN3Iwk2cti2tj9xtFmy7wM53TL0jdUrxgg5CboQc6lQte8HvMjo5vFN0SsjgUKdz3LqrkesxUkTq6y6ZAg/Grc9WwHMxI3Ju+2P/3kqZxVIVUufR6vl+0XNxanXfBOtiMuAjUgEfKa5samUYz8yLuofqp4rI+8Bd4L8EfnMp5f35vm8EAvDn9g8upfyQiPxd4KdggsowDMMwng9PagUUedAKmALiqrgqk0eCR2PGNY7UO3Sq06ppUHSk7rEalTjUAIvdpAyLwKZv2E2Bo7bnbNGxbRuu+B1jcPQucKT9uRUwSCZIZllqeMVKBza55dhtuOJ2nKQFB37kZrviZFjwvk9s+4apC4zez4JKSY3gesgeUpitgOpxg6LBgZPaY+W0To+8QyZHmWPWBR7SY1UuCKcaSHEem74fGBVBzodVev+kas9828Wp1QO2QHikte+BRMCLjz//3J9gamXCyjDOeREF1f8L+I+BfwR8PfBvAN812/kG4E1qyOjNS897b77vPkTkbeDbPsb1GoZhGMZnmw8rCIb7rYBzgAPOwTQh3lcr4BDQ4HGNx+0CpVFC54hLR2qEaTUnAnbCtFZi5+kXLe8ctbzXJt5ZXuG1g6tcaXa8tbjGjeaMK37LW+HOedz6kQznPVbXtOdYB66XDa+7Mzal4QvNER8sDjmJS95ZHXNnWHK7X3L7aMnQB4ZNYNo4tK82QL+rVsCw1poGOBT8xuHGjA4J3UUkJaSf6rQqZxgnSkzIbAUs8/6z8/JkEcTN71PKzDn1c1jFLJjyJVG156K4usB5h9X+7ot7t84/x6ecWl16zv2PMTugYex54QRVKeWPXbj6d0Tku6l2vl9AFVqPQoAH/kSXUt4G3n7kk0TsbwHDMAzDeFIe2mN1yQp4MbwiF0quxbj7HitiwpVCmRwSC5IKrlUkKxqVNILkasFLI0w5EBeOs6ikrJy1LX0KbBYNV8KSqTiuuQ2Hbkd2Z3QynRcEK+WeFbBEnC90MnGoPU4yh75n6UecZjZtw1no6H1D7hzFuZoE2EsNsOghNUJRyIPiguJU0OhQVRhrOIeIIC7dbwVMqX5J0Xmq5Vy97PTe9Ern85JrAuDDRNWj+qMuP+5y3Hr9kB45tbIQC8N4dl44QXWZUspXReQrwD823/Q1wAGvAR9ceOjrwF/+hJdnGIZhGK8eT2gFJCVwDkn7ktyITL5OrGKi+BpkoYMnNw7Xe1KndZ/VUPdYxUXtj0pdDbDYbD3brmNz2HBnueCoGThZLXitWXPVb+mbwJHrOdYth9oTJJ/bAfdWwGPd0vvANb/mbrPiTrviWnONu9OCm4sVH3QrdkND37UMW1fj1lvF9YLvITc1JdD3StjHrQeHDg4dqxXw3h4rV8VUjEhyFyZROj9ujk6XUvegKYCrYkjzPVG1Z7+36mGfiWoVsxf3VMHDe6zqh3bhZttnZRjPygsvqETkNeDzwLvzTd8NTMDPBf7o/JgvAD8K+KufxhoNwzAM45XkaYSVzsW4sxVQYqx7rMaJMgbUO3QIuMZTGq3iqq3lwON2nwgoTBslt47d2rNdddxsEzcPlxx1A1fbLV9bHnE1bHnNr3kj3GWlA8e6ZSXTuRXwUEcOGVnqwNadceo7XvNnnKQl7y2OeHdxxOm44IODFSfbRbUCrgLSu5oIuBTcCH67v1zwO4ffeXTMuF1Ax1hTAfu5x2oOrzh/z4KnOJ2DLOaOL6Dsy4OVeWqVz/dVwQVxdSkJ8OLk6r5uK3h4jxU8Uih96D6rJxVWF48Rw/gM82n0UB0AP2K+qsDXichPAG7Pp7eBP0EVUF8P/JvA+8B/AlBKORGRfw/4XXNwxT42/XuoceuGYRiGYXySPGl4BVByRnK5lw6Ycw1uiA4tBYmZMjnIoKOik4NSbYAaqxUwNTUZL8VA7BwnwDAFdlMgF+Ws7dg2DVNxXHFbklcm7elkYqkTYd4HtpKI02oBdJJZ6UCrE0ESp2GH10TjEuum5UQWTI0nNg7EkQfIfrYCjnXvUnGKGwQEiq+pgCoC+wCL8Z4Nr3hXJ1RuFp251Ih0pzUZMKX7RBVQ0wLPy34vTJoekQr4gLCCx0+tnrew2h8TF48Rw/gM8mlMqL4J+IsXrv+2+fSHgf8l8GOBXwUcU0XVXwR+cSnl7MJzvhWIwHdwr9j3V1kHlWEYhmF8inyYsMpzf1JK96ZWTmGa6tRqDEjw1QrYN7jWkRuP387TqoUybYTYCmFTJ1apVaa1slk2bLoFp0cdi3bk2nLHu4srHIcdb7V3eS2ccexqOMVSB7o5bj1IBqrQmtwpb+YT3vQnnOWOz7dXeH885O605J3lFU6GjnXfsll1xJ1Dd464qpbEaSX4bcEN0GwUN2Tc4PGtQ8aMjhHd+TqlK4XShfo+pVyF1F60lLrfDJE6rSKdpwMyR6/LQ3TMfZOrS/upLiYCPqsd8KF7rExYGQYA8kDM5iuGiJSfI7/o016GYRiGYXz2uGj9kn14hcx7iOQ8nEGcq1/wfd1fhffVEhfqeV4EcuPI7T4RUJmWe0EljIeQOkhdIR4l6BJhMXF8uGPVjLyxPOP19oyrYcvnwl2O3ZZDt+O6bs57rJRCRkgI2xzoS+BWOuBWOuAkLXhnuMrtccndccn7mwM2Q8Nu25LOAjIofiP4TZ1WhXXB9eCHQthmdCj4PqF9QmNGpkRuHAC6neYEwHwvJXAWWeRM2acDwr1JFjwoYvLcYbUXUhe/311KCnzod79HhVJc+j2PDK942Joexyv+/dN4OfnO8scppTywifGF30NlGIZhGMZLyuWJlWhN/NMMWSmU8/1DiFQrYKlJgLKPGo+pWgEnh04eyQUNimRXEwFbIAtpgDQIUhypV6ZRuZOVbdcwJUcfA3ebBVPn2IaWK7mFACsZSTqwkohSCJKrDbDEWhCsI8euJUjiil9xJ+zwkjhpFtz1kTNdEAdP9J7ilTRAUcEHyA0UUVwoFC94FUrMyKSU2eonU4Y8W/VKgTwLzVjfNymF4uaQCvahFdybWsHjUwHhkROrD7UCwkNLgh+bCLhfz4dhEyvjM4QJKsMwDMMwPl7OvzTPNsAEzK0lj7QCTmOdVM3BFeIdJXhk1+CCw288uXOk1hE2NbwitsK0nhMBF8p04Nl2LZuDjpurFYt24t2DK1zvNlxrtnyhvcMVv+W6W3PDn85x6iOBzFIjS2oq4FSU190Zp7njblry+fYqt+OK2+OKdw6usB5b7mwX7LYtY++IZx63q1bAcFZ7rPyuELa1JNgN+fztkELdG5Uy6qTuIYvznquUZ6E5T6zKPmK9UCs59zwkFfCiqLq85+pJ9ljBPXF1SSg9NhHw4uMvPOeRmLAyPgOYoDIMwzAM45PhEXusRDMk7oVXiNzrsXK5TrViFRiSct1jlQIaAzokJHpS53CtoKlOrVwv6CSk1hEHYRgcQ9swTJ6TRcetbsV62XAcdlwPG06bBUe645pbc6T9uRXQUXD7HiuJrHSgkcRVv+GKO2ThRu5OC5bhkNvNkt0Y2ISOaetJvZLDvdj3HEBHwfd1mqSxoJOrl9MsVnyur3XSWg4ss9DMOu+1qirsPvlxMRWQ+pz79lRdFi2XkgIfKqzgwS6rh3RSPTK44jHPefjjHtGvZRgvASaoDMMwDMP4ZLkkrC6WA++FlTB/wU8JKZkS9V4q4DSLi5gRr0iuU5/UKprqHis3gEQhtzUtME41wGIXlXEIbGYr4N2256ztaiKg3zIWx+QdKxlB+/P9VUuJdJKYykQIieO85Uh3LN3Auuk48gNfC4ecjR0fuMSmbZl6z+QDqa97vbKv06ocQCdwE7jRIamQk+ABmTKqFxL7RKqdL83WwCTniYns36OsnNsB5+0dkrmXBrifVj1MWD2JFfBDRFW9+TlYAW1aZbykmKAyDMMwDOPT4aFWwDpluVeAK/dsgHHuclJBpjCHWDikn3DB4+ZEwNw4UjdbARu5L8AiHihx6dl1DT941OG7idVi5CsHx1xpdrzerXmrvcuB63kr3OFIe5Y6cCQDQTKtJG7Ilkl7brgzbvhTNrnlC80BHywOOYlL3l1d4YPhgNOh49bRkn7XMO488cyjfU0odDvw/Sx+EmiqhcE6FXTM6DgHWAwOmVIVPrGeS8oQ52nePoii6JwKyH32P3hMMfCTWgEv7696lnLgi88zYWV8xjBBZRiGYRjGp8uHWQGdq5MqoFC/8J/3WCVXgxtyRmNCSkGneXqVPalRNCkaIbW1x0omQQcl5sDUOU5GT8zKWduyjQ27FLgSdgw58Jo/49DtmNyalYx0EmtwBQUkczj3WzWS6GTiitsRJLHyA7fDCqeZs6Zl3bTstCV3juIcQYXiqgVwL6iKKG4qODf3WkVFVVAnEGscuqRE0VwnVPtAjwtTqwJ1PJV4aMz6R7ICXp5WwWMnVvVz/IjCykSV8RJggsowDMMwjBeDy1bAeWIlcwBDyTX5r6hWgeUcJTqICfGuhlikhASPDA4dA65xuN7hVo7UCG5Q4gJSJ0yDkBZC3CrrwbHtWtarltOh46jtOVt03G5XXPMb+hA4cj2HuuNYdwTJBHLtsZKJlUwc6o6+BI50x92w5HZzwHHYcXtccnux5P3mgG3fMPgW1JODoLEKKkmQHbhRcEHwQdCx4JxQ5oAKHRNMAqmmItbJVEEknb931Sp5QSg9rhj4ohXwPpvfg8LqQ0VV/YAe+Eg/1Apo0yrjM4AJKsMwDMMwXiweIaweZQWUlKqwchMyTfesgEOD8w7XefwukIMSlo64mK2AGyF2VVRNayG1nu2qYXvQ8X4bee/ggGvLHYeh5wvLY66GLVf9hrfCHVY6cKg9KxnPrYBBMomRQ+05yx2b0PJGOOEkLbk5HfCVxTG3hxXvLg654w/JGw9F69QsVZGnI3MqoKvTqlFxfUFjwQ0JHavlTwZX95PFTHGzFVAVos7BFXIvETDPiYBS6sSKKooesAJe/gwuCKv7plUPSwKERwqrx1oBnzQR0ISV8QLzygkqEXkb+LZPex2GYRiGYXwIH9ZjpdSJVYx1UpNmEVEKsrcIJld7rFJBmmoPlORwjSJF0QniBGRBW5CkxBSYWsdJVsboOWtbclHO2o51qJevuC2925Ldmk4mljrhKAQyCDjd0cmEk8xKB1qdUCks3ETMymbXMuQq5iRTt5GJoB6KF5BCnoTshKIFnQpFwakgSVERNOZq/5uj1YtItQLmfTDFhUTA+5IAuX+P1SXxdO89fzC84r5pFdjEyjB4BQVVKeVt4O39dRGxP5GGYRiG8SLziB4rKfNeKqkWQOaIcXGx7ruKCXEKIVQboHfo0OCaGlzhdo7cKrFTxp2SG2qAxarGnE9bZb0MbLrE+qjloBu40vbcXq24Ena8Hs54I5xw6HZcd+t5P1U6twIupfZY9e6MN/MJ192aD8IRCzexHltua2EXF3XjUxFyq+hYp1SpkZoEOEDagcYat+6GGlzhvKCxIDHjBq2FwV7PrYA1bv2iJVDPUxMvWwEfKaweI6rOHw+PLwWGp4tat+AK4yXklRNUhmEYhmG8pDxqj9WlHiv2cespzYEWGYkRcQ6JqRYEB4+OoQqr1uEGR2oFvxWmXU0EdLs6QUqtY7NzbBcdt7sld/oFh+3A9W7Dm90RV8OW18MpN/wpKx041u194RUriTSaCSGy1IGE8IPLq6QijKM/j0CPrSOPioxaE/8i6FDFnU5VWLlB0KngW8GNBYmFEhSJBZ0SqnoeYEFKtcsrzZHzpTyRFRAuhVfs3/vH7a2CZ7YCmrAyXnZMUBmGYRiG8XIxC4N6+VKPVVbKvtx2fqyUTCm+TqxKDbao/VYFmVw95YBOikZFspIGkCTEabYCFkcclWlQ7mRltwj0cU4HbDqm7Bhy4IrbkrxyNKf/7a2ArVTxktyWa27NQRhYhom2nVAtiBR6DSTvyN6RFPIoqAO0Tq2KSg2umACh9lpNBQR0nC19gMR6WeJcDgz3TZskZwoZcA+1Aj40vOJJp1V7nsIK+JFtgJden2F80pigMgzDMAzj5eO+L8/3WwFJzKEVuZ6LgIv3uqy8r1bAYarhFcGjfcAHR249YVHj1uNKiZ0Q2zm0YiGkTpnWjk3Xsl4uuH2wYtGOfHV1hRuLNcdhx1vtXa75Ncduyw1/SjenAAbJdEwcuy3HYcdpWHDWtXQ+4jTTt55h8ozRM/SBFJU4Kql3yCj4neB6Qac6SXNDqZd3gpsUNxTcTtFY0NGhY6riakr3OqxmMSQpz/a/C1ZAqMXA824IKfn+8IoPmVbd+2guWQHhQydWTxRc8aTTqv0aDeMTwgSVYRiGYRgvN48Lr5itgPvepkIVCmVfjBsV2YsNV0t0JWZccOjkcAuHbwWNStrtk/iU1BXiLtAPjqFr2A0NJ6uOw3bg7mLBtWbD9bDhtFlwpDuO3ZalDuSiTMWTSxUJTjOtj7SungbvGZNj6xJTckyTY2o8eVSm4Gqv1gTZCa4TdIDs57j1puB9tQS6XnB7K+AQkehmK2CsrzUmcLMVcN5/dm4FlHzfPqt69dK0av++XxZbPGJq9ZQdVo+1AYJZAY0XChNUhmEYhmF8NthbAS+UA0O1ApZU75ecKUnBuSqsnKsCo5QqMHLGpUIJDnKY48q1xpq3QhxqOXAaBB2FKQq5U/pJiZNj3bVMybFuW87ajqm42mNVAtfcmozSl8BUlIygUnCS6dxEVqFxiTE5nBSmrIzB0fvAFDyTK6TgyKOAKnkQnAdUyKEKqyLgploaXAZFp0wR0JiRKc1WwDkRMM5BHvsgi70VMCtwwQp4MRHwcaEV+9tnHrnH6qPur9o/z6yAxguCCSrDMAzDMD47PIkVcJ8K6BxIBJ3AT+AUGXxNBfQO2QVK6ynB4TeB1DpSq8SNEtvaYTUdCLkVppUSV7Uc+AcPW97tIstu5KsHV7jS7nit3fBGc0qQREb4oD/gdOgYo6eEEYDORSCSvXAQBmJ2ZITN1DBEz2Zo2A2BGB3TtpYXy1DtfzoKbtxfLvje4fuCRsXvajKgThkdEjpliBkd67SKmKq4KuX8nJwpuVr+KHlODHxIh9XF/qqntQI+r+CKhzzv/seZFdD4eDFBZRiGYRjGZ5NL4RUP9FjN4RX3rIB6r8dqX5Cbcw2tSDXAQkeHZI8bFDcpmiC1IFHQSUltIcaGceGYuhpacdJ2nHYd60VDq3Wv0u1+xdnQMEbPLgSCS6iUeqLgNeM118uS6F2ot7nMEB29FGLI5OCI6tAR8ghFBG2F4uvESieh6GwDHAWnQgkZmTI4QWLdV1X2e6vmaRVZkRhrj1UCHHX8lcvjJ1b79/1JJ1bPI2p9/7wnnVjt12gYzwkTVIZhGIZhfHa5vL+KvRUw1R6rOWr9nhUw34tb3+818rXTyo0eDVVclUZxQ91nlZqawueGGreu0z7AwrGelN2iZb1o2U4NrY84ydzZLhhGT07KZg6l8JLxmmg00WjEScFLpnWRZZ7o3FT3WSXPmUv0Y2BqPJML5EmRQSmeKq684MOcDujAjZAaR1BBouLGXIVWzIgTZJL6uvb2vzSLk1lklbwXP6naAB11YnVRWD2qFHj+OfWuF6DD6vIaDeMjYoLKMAzDMIzPPs9gBTwvCHaKjKGmA3qHjPV21zrctnZZNQslLpTUCONB7Y5KnTCuA7kNbJcN24MODRl1iWnTwFQFwEkRxugZF45uDqhY+pHORRofWcwCq9XI0k/Eohw0LZupoY++7tuaHNPomXYeJsHtlNhXK+C9dMBC7BxuKrixWgElFtxYrYASMzL5utcqZ5hiFVZ7YblPBZztf+eJgHth5dw9ofSEVsCPVVg95Hn3P86sgMbzwQSVYRiGYRivFk9oBbzXY6X1cs5I9jXAIngk+5rdMCYkBySBtgJFiVOdFFGE1IJOypQCKWSS98jOoRGKQnKBXRFECqP3LMIEQC5C0ISXjM7TK4AwVwGrVGsgwOA8g8sMWsiTktRTnKLhXn+VTlJde/MEq4ZVQHHVClinVRl1AjEjQNG5swvuhVcApVyYWOHmc54svOJpgyvg6RMBP+R5Dz7OrIDGs2OCyjAMwzCMV48PswLOjxGRagVMGXFKiRGiRwaFJqBTBF87n1znyV5xvSd1dVrlBiW1kBaC2wk5KLmpseaSqx1vypCisMnQh0TfeqasLEP9mnYQBoIIXhOtiwA0Gmk0svSe1kWmxrGLgU0Tao9VE4idJ45Kbtwc9w451PAKN0AKipvANYJv6h4w12fKqEiqwkpiRqKjXIxbF6miMiXKxX1QuTzcCghP1GH1URMB613PIW7dRJXxlJigMgzDMAzj1eWRwgqEVPdYpXxvj5VEJMy3x4SMU7UCDhMyBDQ43FATAXPj8L2rcetdTeDLXsiNoBNQanCETErqhWlUpjYzNZlh6Vm3kX7h2YSW1lcbYKORME+rgmScm1AK0SurMLAMDVN2bLvAZmiYJs/QBeLgkFFJneJG0KFaAXUE30PsZytgVwuCNRZc79Cpxq3rOPdYpXTBCujOO7zO91g9zArIRxRWT1H0+0ThFY947r3HmBXQeDpMUBmGYRiGYTxuYqVyr8dqbwVUrT1WxZ/3WEkpEB1kcDlXC10p6OTQKEhWsofUVKsdVEGFCJJnO96kNYadhpxcfUwR2lQTA6uoSiwuLN1rQkuZrYGFmN355dGncythDo4kjhIUnX+v+mr5y25OCdR6Xaf6fhQn6CxSxAlMnFsBJab5RVxIBCxSRdeFDivgqcqBP6oNsN71HK2AJqqMD8EElWEYhmEYxp7HhFc80gooUq2Ak0dUkXGi9B6cQ/sGH+Zp1cqfT6hk1iJ7W2Bc3EsJzC1MvZI7x7p3DMuAD4lFO7JqJlofOQx9jVHfT6s0oRRWfiQXYQieVQiM2bNuWrZTYJg8u7YhTo44ONJCa39VX+2IbhLiTnBDjVhPjdRo+LHULqtY49Z1iEgqMNVpFbnaIfeTPPaCKBfI8wuVgpS51+ph4RWXhNVT2wDh2a2AT2IDPP9BJq6MB3nlBJWIvA1826e9DsMwDMMwXnAuhVc8YAXcx63rfPly3Pq+28k7pPFIyhSn5KBIqRHlzguaHG5UNNZOq9TWGPPUKWmcrYBNZlx4dk2kDZFdG2h9JGhi6Ue8Zjo3EaiTqYWbUMnEXPdadb5haDxrn+gnz9h6psaTRkfulNwoad5P5Yb9fiuq4JoKOey7rDIuaBVWoyJeIZUqrFKqVsCo9/ZY7a2A8hgr4GOCK57YBgjPbgW0uHXjI/LKCapSytvA2/vrImJ/KgzDMAzDeDhP02OV3TylKZQ5fl3gvMdKUwGvaHDnYi2H+mW+WuwcOs12uyJoFDQJZKmCJwl9p8Tg6hTKexofKUVoffUQOj+iFMI+EXCeYHnNjMmjUmi8p/eerbbEkIm+lgPLpBQHxQsa6mvWsC8MBjdJvV9AndYeK5XzzipRqVbA/fsm8mRWwCfcX/VENsC6kGezAloioPGMvHKCyjAMwzAM46l5QisgcN5ldW4FVDf3V02gSvF1b1Tdw+TQIZAbX9MBm3kP1VaJrRCXNcwiBSEta1lwbAuny4CGhAuJdTfRhsiqGembgc5NrPx4bgf0JFpNZC8s/ciYHX0KrNuRITm2Q0O/DMTJMe08cVBkrL+3Tqvmy2PBDQ7fKzqVej44NBa0SehUu6yYPMy9VeL207tcX3vOF8Irauy6UCdOHyas5IL17on7q+Dj6bCaP7/zdRqvNCaoDMMwDMMwnobHWAGRucdqtgIKgCZKdtUGJ4L4+euXCkSHllLjyUtBR0cetXZaTYLG2QrYCDFWcZPaQop1apWDJyfHECLD5ElZaX0gZqVxtcOqnRMwVAqti+cTK5XCmBxBM96lGrfuMjF48qhMzpGb2qdVnJAmwfU1SEOnmljo91ZAJ5Q4WwEHQaLeH7cuc9x6rkmB93qsqOf6BImAz9JfBY/tsKof4UdIBKyLMVH1imOCyjAMwzAM42l5Qivg+WPSPJlRqdObfXKe91VkzJZAaR06OSTXdEA3FTQpKdRkwDTW4IoYITWO3BRSElLjiJ2jAF2I5CIs/ESjETznoRWNRnLRagWUTPRK5yKtb9hNga1L9CEwjZ7oCrFRZKxWQB2F7PcpgLVDK/vaZVW09lipq/uhJNW4dZlFVBGpoqrofH+qVkB46P6qBxIBP0p/FXzo/qqPnAho06pXGhNUhmEYhmEYz8qHCat8wZqW5imNc/ee7lztsfIOmSKldxA82gd8cKTO47eOHIS4UGJX0/emlZCbeXK1lFreu3CcrTybJrFetHRNtQIeNOO5uFr6ESc1Yn3hJgAWbqpWwMaxbRu2U8OQHJtlwzh6ptEzLTwyKdrXJEAdBd/XhMCaAuiqJXDUc0ugjrlaAWNGJl9TAXO+0GOVL/RYzR1Xs7CSvYC5OInaT6g+BmFl0yrjo2CCyjAMwzAM46PyCGElc/gCebYCXhQI85dvAUqpYQ6S/L1pVvS4nJEcyI1CBkm1mFdyTQTUpnZY5abeF0u1AvZJiZ0yhGoDHLyn9XVy1bh0XhAM4CWDi6iU81ObHCqF3iUGn+m1kEdHco6iijb3+qu0EYrWVMAc6mU3FdQLToUyZdQJ4qRaG0Uos5CS+b25L259L6weZgV8lonVJ20DtGnVK4cJKsMwDMMwjOfFJWFVgyseYQVk3guUM7LvZoo1yEFiQryjTAGJGfWKDp40+DnC3JEaITXgxhq17gape5taIY7KNNS49WlytG2kC5GYldbVSdXST/dZAff7rRpNxKK0PjIEzy4G1r5hjPu49UAea9R6bvcR6/X3u2G2AY4FN++9kqi4QdFx7rLSGrcucbYCpnxvejefl5Rn1cjHlwj4SdgATVS9EpigMgzDMAzDeN58iBXw/GF7gZX2qXiOogIx1v1VU6xFwd6hTUB3nhIU33tSqKIm7rRa/7pa0JtnG2BcCDnAtHJMbWbbJDaLhjZEumbioBnPxVVzoRy41UgLNBoZvecwK8vQMiTPrgtsuoZpcox9YOpr3LpuFTdwIRGwFgT7ztVEwKGKKokF36caXjFlZHQQ6x4ymSIUP4vKObzC7SdW5b5y4I8lEfDjKAa2adUrgQkqwzAMwzCMj4tHCqv5Zs3VDqjUYuBS5v6qWC+Xgux7rkpBc6YkhxSQxpFjgQIaBUkKpVoBJQsShdyCFEealNwqfRFicEzJUYowOE/MWkWVUxqNOCm1y0oyzk1MUvd8VatgQqQweodqYXSBNCpJHMUr6pn7tSB7oWhBx7m/ShWNNelQXaa4jEp9D8TJeWCHiNw3sSowi6BUO6xcfa/gkg1w/34/ayLgx9lfZdOqzzQmqAzDMAzDMD5uHiKs4FKXlWjdPzX3WCERcZES/HmX1T7AogwRCR7nFR0CpVF86/CDkholdhAXNbTC9TUZMDdC7JWpzUxtYFx4QhPZNIFV09D6yMqPdH46t/95yXhXL2dqmfDCT/TJs20mNm1gjJ6+bUijI45K3jp0qFbA1NZEwNQLcVdwE/im4EZBR8UFrZOqmNE+VqEULwRXJD23BJYs9zqsZqRc2HsFDwZXzLc9cXDFh9gA6102rTLuxwSVYRiGYRjGJ8XlL9P7vVZ7O6AKJChcmKyUgkikZP/gHivvcDFTgkNbh46e3ChuUS12ORSmQUhzOqAOdY9VapVppUwhMCwadu1E8ImDbqDzkdZFDsJQhZWLKHVqtXATKoXOT3QusvCBIXk2IbIbA9PkmJowlwNXS6JOkHaC66Tu99qBG6oVMDRz3PpUcL6KJx0TMiqkUoVVTJBdPZ/tkUXyXA58IRXwKayAHxpc8VGElU2rXjlMUBmGYRiGYXxa7EuCL9kBhXS+v0qghjcAuELJ8+V97HgpSPKU5GDur5Lk0FhIjSIZYgRtQEot6JUoMCcCpiT0SYih2uum4Jh8tfl1biIjVUgxJwBqJCB1eqWJNntECt4lBu/ZSiEGTxocSSHvLX+uWgGLCNkVdBKQGmChvgoMibWnSkUocyIgIvcSAdPcc0W8UA48WwEflwgI91kBn4cN8JGYqHrlMEFlGIZhGIbxafKkXVYuVVEgAlOstkCnyDTd67LqQ+2xajyu8xSvxKWrwqkVpq2SGkjd3GUVhLQQ4sIRm8LZIrDtEj5E1l3DopnofOQwDDQu0rnpvBT4ohVw6Uf6GBiz47TpGKKnnzx915CiMu0csXfoKKQF6DBPq7ZSBdUIoZ0nVYPWCVbM6JCQySExw+ggzSLSPcIKmGbBdNEKeGE/1RNNq+CJbIDPZVJ18fM3XlpMUBmGYRiGYbwIfEiXVUnM9j+pQQ2lIGnf3ZSR5JA5el1jqoEOwSHJk1uHjoqkuq8qDSCRuq9qqkW9qS2kSWrIRHCk6OibQBsiU+dofWThJzo3PWAFbDSivtAURaXUREAf8JoZo2P0gRjqzy/BoT2kqQZX1ERAyB7cJLhG8KFaAeseMUVSQVXnPVbu0Xus9kJmjlsX5ME9Vs/ZBvjIwIonnW7ZtOqlxwSVYRiGYRjGi8TeBggX9lddSASk2gAp5X4rYMlVUHlfi3JLqZOdDJILMilSqHuWogBKmqookww61fOUtAotICchJUWkMGUlz5HvjdYi4HtR64kgmWkWVI3Gep8U+uhxLtNLqVHvAsUpZZyFjKuWQBCKr68J9NwGWISaDkhdYxVKUCSBVvsfIkiMIKUKT8cD5cCXC5XrD/wQG+DjuqguYKLq1UbKK/bhicjbwLd92uswDMMwDMMwDOPlopQLRXIzr5ygehgiUh725rwI2NqeDVvbs2Fre3Ze5PXZ2p4NW9uzYWt7Nmxtz4at7dmwtT1f9MMfYhiGYRiGYRiGYTwME1SGYRiGYRiGYRjPiAmqF5/f9mkv4DHY2j57vMjv24u8Nnjx1/ei8iK/b7a2zx4v8vtma/vs8SK/b7a254jtoeLl9GoaLy92vBmfNHbMGZ8kdrwZnyR2vBkvAjahMgzDMAzDMAzDeEZMUBmGYRiGYRiGYTwjJqgqL51X03ipsePN+KSxY874JLHjzfgksePN+NSxPVSGYRiGYRiGYRjPiE2oDMMwDMMwDMMwnhETVIZhGIZhGIZhGM+ICSrDMAzDMAzDMIxn5JUXVCLy60XkH4lILyLfLSI/7dNek/HyISI/XUT+cxF5R0SKiHzLpftFRN4Wka+KyE5E/pKI/JhLj2lF5N8WkZsispl/3hc+0RdivPCIyG8Skb8pIqci8oGI/EkR+W9feowdb8ZzQ0T+BRH5nvmYOxWRvyYiv+DC/Xa8GR8LIvK/m/9N/f0XbrPjzXjheKUFlYj8EuD3Ab8D+InAXwX+CxH5uk91YcbLyAHwvcBvBHYPuf9fAf4l4DcA/xTwPvDnReTwwmN+L/A/A74Z+GnAEfCnRMR9fMs2XkJ+JvB/AX4K8M8AEfhOEbl24TF2vBnPk68A/yrwTwLfBHwX8J+KyI+b77fjzXjuiMhPAn4N8D2X7rLjzXjxKKW8sifgrwN/8NJt3w/8m5/22uz08p6ANfAtF64L8C7wmy/ctgDOgF83X78CjMAvv/CYLwIZ+O992q/JTi/uiSrmE/A/nK/b8Wanj/0E3AZ+nR1vdvo4TvMx8w+p/2n0l4DfP99ux5udXsjTKzuhEpEG+Ebgz126689R/+fXMJ4X3wC8yYVjrZSyA/4y9461bwTCpcf8EPB3sePReDyHVLfBnfm6HW/Gx4aIOBH5pVQh/1ex4834ePgDwB8vpXzXpdvteDNeSF5ZQQW8BjjgvUu3v0f9w2oYz4v98fS4Y+1N6pTh5mMeYxgP4/cBfxv4a/N1O96M546I/FgRWQMD8O8Cv7CU8new4814zojIrwF+BPBbH3K3HW/GC4n/tBfwAnC52VgecpthPA+e5Viz49F4JCLye4CfCvzUUkq6dLcdb8bz5O8BPwE4pu5N+cMi8jMv3G/Hm/GREZF/grqv/aeVUsbHPNSON+OF4lWeUN2k/g/G5f+teJ0H/+fDMD4KX5vPH3esfY06MX3tMY8xjHNE5P9E3XD9z5RSfuDCXXa8Gc+dUspYSvkHpZT/qpTym6hT0W/Fjjfj+fKTqcfJ94pIFJEI/Azg18+Xb82Ps+PNeKF4ZQXV/D8f3w383Et3/VyqL9wwnhf/iPoX/PmxJiIdNXlof6x9NzBdeswXgB+FHY/GJUTk9wG/jCqmvu/S3Xa8GZ8ECrTY8WY8X/5T4MdSp6H7038F/LH58t/HjjfjBeRVt/z9HuCPiMjfAP4K8L8A3qL6ww3jiRGRA6rnG+oXja8TkZ8A3C6l/KCI/F7gN4vI91H/Qfgt1DTAPwpQSjkRkX8P+F0i8j71f+F+DzUu9js/yddivNiIyL8D/ErgfwLcEZH9/9SuSynrUkqx4814nojI/wH408APUUNQfhk1vv8X2PFmPE9KKXeBuxdvE5EN9d/S752v/17seDNeMF5pQVVK+Q4RuU79w/g5ao/Qzy+lfPnTXZnxEvJNwF+8cP23zac/DHwL8H+kRrv+O8BVamT/f7eUcnbhOd9K7RT6jvmxfwH4VQ/ZG2O82vz6+fwvXLr9twFvz5fteDOeJ28C3z6fn1C/mP68Usqfne+34834JLHjzXjhkFJsf55hGIZhGIZhGMaz8MruoTIMwzAMwzAMw/iomKAyDMMwDMMwDMN4RkxQGYZhGIZhGIZhPCMmqAzDMAzDMAzDMJ4RE1SGYRiGYRiGYRjPiAkqwzAMwzAMwzCMZ8QElWEYhmFcQkR+pogUEXnt017LR0FE/pCI/KlPex2GYRifZUxQGYZhGB+J+Ut7ecjp//Npr+0j8Fephe+3nvQJIvK2iHzvx7ckwzAM40XEf9oLMAzDMD4TfCfwKy/dNn6cv1BEPJDKx9BQX0oZga8975/7JIiIAlJKSZ/G7zcMwzCeDptQGYZhGM+DoZTytUun2/s754nVrxWR/0hENiLyAyLyKy7+ABH5vIj8MRG5M5/+tIj8Yxfuf1tEvldEvkVE/iEwACsR+cdF5L8UkV5E/p6I/HwRWYvIt8zP+y4R+f2XfteRiGxF5H/6sBdz2fI3/861iPzseQ0bEfmLIvIN+/uBbwN+zIUJ3f73XxGRPyAi74vI2bzWb7rwu/Y/++fPE64R+BdFZBSR65fW9TtE5L+eL18Xkf+niHxFRHYi8t+IyK9+qk/NMAzD+MiYoDIMwzA+Kf73wH8G/HjgO4D/h4j8MAARWQJ/EeiBnwH8ZOBd4Dvn+/Z8A/DLgH92/jkj8J8AEfhJwLdQhU174Tl/EPhlInLxtm8G1sCffIr1t8BvAv65eX3HwL873/cdwL8F/D2qVfBzwHeIiAB/Gvg88D8AfiLwl4HvEpHPXfjZHfBbgF8H/GjgD1Hthv/s/gHzz/pm4NsvPOdvzT/3xwC/D/i/icjPforXZBiGYXxETFAZhmEYz4P//jxluXj6nZce80dKKd9eSvkHwG+liqCfNt/3SwEBfnUp5XtKKd9HFRcHVMGwpwF+ZSnlb5VSvhf4WcA/AfyqUsrfLqX8NeBbud/S/h8DGfiFF27754B/v5QyPcVr9MC/UEr5G6WU7wF+N/CzRERLKTuqQIsXJnS7eX0/AfhF8/P+QSnltwI/wP0WSQf8hlLKXyml/P1Sygnwx4BffuEx/zTwdcAfBSilvFNK+V3z6/6BUsofmF/rNz/FazIMwzA+IraHyjAMw3ge/GXg11667e6l69+zv1BKiSLyAfD6fNM3UqdPZ3UQc84S+OEXrn+llPLehes/EvhqKeWdC7f9TaqA2v+uQUT+CFVE/TER+dHAfwd4WnvcUEr5exeufxUI1EnV7Yc+o76uJfDBpdfVcf/risDfvvTcbwd+o4j8sFLKl6ni6i/tX6uIOOB/C/wS6gSspQrOv/SUr8swDMP4CJigMgzDMJ4H23ny9DguT4MK95wSShUUv/Qhz7soVjaX7pP553wY/3fge0Tk64B/HvhrpZT/3xM87yLx0vX9732c20OB97g3ibvI6YXLw+UQilLKd4vI91Htir+bav/731x4yL8M/EvAbwT+DnVC9ju4J1INwzCMTwATVIZhGMaLwN+iWtVullLuPsXz/i7weRF5q5Ty1fm2b+KSyCml/Dci8teBXwP8CuA3f/QlP8BIte5d5G8BbwC5lPIDz/Az/wPqZOp7gRXwJy7c91OBP1lK+SNwvsfqH+fByaBhGIbxMWJ7qAzDMIznQSsib1463XiK5/8H1EnOfyYiP0NEvkFEfrqI/FsXk/4ewp+nBkH8YRH58SLyk4DfQ50mXZ5c/UHgX6EKk+94irU9KV8CfpiI/JMi8tocgvGdwF+hvq6fN7+unywiv01EHja1usy3U0Mq/nXgPy+lXJxq/X3gZ4vITxWRHwn8fqpt0jAMw/gEMUFlGIZhPA9+DjWV7+Lp//ukTy6lbIGfTg1r+I+A7wP+MHAVuPOY5+3DJlrgb8zP+e1UMdVfevh3UKdI/2Ep5exJ1/YU/AngzwB/AfgA+Oa5I+vnA99FFXR/D/gPqUEaX33Ezzln3jv1/6YmGn77pbv/Depr/i+oe9g2VGFqGIZhfILIx9CHaBiGYRifGiLy46n7sb6plPLdF25/C/hB4GeUUv7Kp7Q8wzAM4zOGCSrDMAzjpUZEfiF1OvP9wNdTLX8C/MRSShGRQO2F+u3Ajyyl/FOf1loNwzCMzx4WSmEYhmG87BwCvxP4ItUe+JeAby33/sfwn6aWBn8/8Is/jQUahmEYn11sQmUYhmEYhmEYhvGMWCiFYRiGYRiGYRjGM2KCyjAMwzAMwzAM4xkxQWUYhmEYhmEYhvGMmKAyDMMwDMMwDMN4RkxQGYZhGIZhGIZhPCMmqAzDMAzDMAzDMJ6R/z9y+bS0MJvRUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (14,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "veneer((25, 100), (10, 50), ax)\n",
    "\n",
    "_ = ax.imshow(NICER.instrument.matrix,\n",
    "              cmap = cm.viridis,\n",
    "              rasterized = True)\n",
    "\n",
    "ax.set_ylabel('Channel $-\\;20$')\n",
    "_ = ax.set_xlabel('Energy interval')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summed over channel subset $[20,200]$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "veneer((0.1, 0.5), (50,250), ax)\n",
    "\n",
    "ax.plot((NICER.instrument.energy_edges[:-1] + NICER.instrument.energy_edges[1:])/2.0,\n",
    "        np.sum(NICER.instrument.matrix, axis=0), 'k-')\n",
    "\n",
    "ax.plot((XMM.instrument.energy_edges[:-1] + XMM.instrument.energy_edges[1:])/2.0,\n",
    "        np.sum(XMM.instrument.matrix, axis=0), 'r-')\n",
    "\n",
    "ax.set_ylabel('Effective area [cm$^{-2}$]')\n",
    "_ = ax.set_xlabel('Energy [keV]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xpsi.likelihoods.default_background_marginalisation import eval_marginal_likelihood\n",
    "from xpsi.likelihoods.default_background_marginalisation import precomputation\n",
    "\n",
    "class CustomSignal(xpsi.Signal):\n",
    "    \"\"\" A custom calculation of the logarithm of the likelihood.\n",
    "    We extend the :class:`xpsi.Signal.Signal` class to make it callable.\n",
    "    We overwrite the body of the __call__ method. The docstring for the\n",
    "    abstract method is copied.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, workspace_intervals = 1000, epsabs = 0, epsrel = 1.0e-8,\n",
    "                 epsilon = 1.0e-3, sigmas = 10.0, support = None, *args, **kwargs):\n",
    "        \"\"\" Perform precomputation. \"\"\"\n",
    "\n",
    "        super(CustomSignal, self).__init__(*args, **kwargs)\n",
    "\n",
    "        try:\n",
    "            self._precomp = precomputation(self._data.counts.astype(np.int32))\n",
    "        except AttributeError:\n",
    "            print('Warning: No data... can synthesise data but cannot evaluate a '\n",
    "                  'likelihood function.')\n",
    "        else:\n",
    "            self._workspace_intervals = workspace_intervals\n",
    "            self._epsabs = epsabs\n",
    "            self._epsrel = epsrel\n",
    "            self._epsilon = epsilon\n",
    "            self._sigmas = sigmas\n",
    "            if support is not None:\n",
    "                self._support = support\n",
    "            else:\n",
    "                self._support = -1.0 * np.ones((self._data.counts.shape[0],2))\n",
    "                self._support[:,0] = 0.0\n",
    "        \n",
    "    @property\n",
    "    def support(self):\n",
    "        return self._support\n",
    "\n",
    "    @support.setter\n",
    "    def support(self, obj):\n",
    "        self._support = obj\n",
    "\n",
    "    def __call__(self, **kwargs):\n",
    "        self.loglikelihood, self.expected_counts, self.background_signal, self.background_given_support = \\\n",
    "                eval_marginal_likelihood(self._data.exposure_time,\n",
    "                                          self._data.phases,\n",
    "                                          self._data.counts,\n",
    "                                          self._signals,\n",
    "                                          self._phases,\n",
    "                                          self._shifts,\n",
    "                                          self._precomp,\n",
    "                                          self._support,\n",
    "                                          self._workspace_intervals,\n",
    "                                          self._epsabs,\n",
    "                                          self._epsrel,\n",
    "                                          self._epsilon,\n",
    "                                          self._sigmas,\n",
    "                                          kwargs.get('llzero'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if we need an additional overall phase shift parameter for additional instruments whose recorded data are phase resolved, then it could be passed to the subclass above for the signal associated with a given telescope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n"
     ]
    }
   ],
   "source": [
    "NICER.signal = CustomSignal(data = NICER.data,\n",
    "                              instrument = NICER.instrument,\n",
    "                              background = None,\n",
    "                              interstellar = None,\n",
    "                              workspace_intervals = 1000,\n",
    "                              epsrel = 1.0e-8,\n",
    "                              epsilon = 1.0e-3,\n",
    "                              sigmas = 10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n"
     ]
    }
   ],
   "source": [
    "XMM.signal = CustomSignal(data = XMM.data,\n",
    "                               instrument = XMM.instrument,\n",
    "                               background = None,\n",
    "                               interstellar = None,\n",
    "                               support = None,\n",
    "                               workspace_intervals = 1000,\n",
    "                               epsrel = 1.0e-8,\n",
    "                               epsilon = 1.0e-3,\n",
    "                               sigmas = 10.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"frequency\" with fixed value 3.000e+02.\n",
      "    > Spin frequency [Hz].\n",
      "Creating parameter:\n",
      "    > Named \"mass\" with bounds [1.000e-03, 3.000e+00].\n",
      "    > Gravitational mass [solar masses].\n",
      "Creating parameter:\n",
      "    > Named \"radius\" with bounds [1.000e+00, 2.000e+01].\n",
      "    > Coordinate equatorial radius [km].\n",
      "Creating parameter:\n",
      "    > Named \"distance\" with bounds [1.000e-02, 3.000e+01].\n",
      "    > Earth distance [kpc].\n",
      "Creating parameter:\n",
      "    > Named \"cos_inclination\" with bounds [-1.000e+00, 1.000e+00].\n",
      "    > Cosine of Earth inclination to rotation axis.\n"
     ]
    }
   ],
   "source": [
    "bounds = dict(distance = (None, None),                   # (Earth) distance\n",
    "                mass = (None, None),                     # mass\n",
    "                radius = (None, None),                   # equatorial radius\n",
    "                cos_inclination = (None, None))          # (Earth) inclination to rotation axis\n",
    "\n",
    "values = dict(frequency=300.0 )                          # spin frequency\n",
    "\n",
    "spacetime = xpsi.Spacetime(bounds=bounds, values=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"super_colatitude\" with bounds [0.000e+00, 3.142e+00].\n",
      "    > The colatitude of the centre of the superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"super_radius\" with bounds [0.000e+00, 1.571e+00].\n",
      "    > The angular radius of the (circular) superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with bounds [-5.000e-01, 5.000e-01].\n",
      "    > The phase of the hot region, a periodic parameter [cycles].\n",
      "Creating parameter:\n",
      "    > Named \"super_temperature\" with bounds [3.000e+00, 7.600e+00].\n",
      "    > log10(superseding region effective temperature [K]).\n"
     ]
    }
   ],
   "source": [
    "bounds = dict(super_colatitude = (None, None),\n",
    "              super_radius = (None, None),\n",
    "              phase_shift = (-0.5, 0.5),\n",
    "              super_temperature = (None, None))\n",
    "\n",
    "# a simple circular, simply-connected spot\n",
    "primary = xpsi.HotRegion(bounds=bounds,\n",
    "                            values={}, # no initial values and no derived/fixed\n",
    "                            symmetry=True,\n",
    "                            omit=False,\n",
    "                            cede=False,\n",
    "                            concentric=False,\n",
    "                            sqrt_num_cells=32,\n",
    "                            min_sqrt_num_cells=10,\n",
    "                            max_sqrt_num_cells=64,\n",
    "                            num_leaves=100,\n",
    "                            num_rays=200,\n",
    "                            do_fast=False,\n",
    "                            prefix='p') # unique prefix needed because >1 instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"super_colatitude\" with bounds [0.000e+00, 3.142e+00].\n",
      "    > The colatitude of the centre of the superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"super_radius\" with bounds [0.000e+00, 1.571e+00].\n",
      "    > The angular radius of the (circular) superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with bounds [-5.000e-01, 5.000e-01].\n",
      "    > The phase of the hot region, a periodic parameter [cycles].\n",
      "Creating parameter:\n",
      "    > Named \"super_temperature\" that is derived from ulterior variables.\n",
      "    > log10(superseding region effective temperature [K]).\n"
     ]
    }
   ],
   "source": [
    "class derive(xpsi.Derive):\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        We can pass a reference to the primary here instead\n",
    "        and store it as an attribute if there is risk of\n",
    "        the global variable changing.\n",
    "        \n",
    "        This callable can for this simple case also be\n",
    "        achieved merely with a function instead of a magic\n",
    "        method associated with a class.\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    def __call__(self, boundto, caller = None):\n",
    "        # one way to get the required reference\n",
    "        global primary # unnecessary, but for clarity\n",
    "        return primary['super_temperature'] - 0.2\n",
    "    \n",
    "bounds['super_temperature'] = None # declare fixed/derived variable\n",
    "\n",
    "secondary = xpsi.HotRegion(bounds=bounds, # can otherwise use same bounds\n",
    "                              values={'super_temperature': derive()}, # create a callable value\n",
    "                              symmetry=True,\n",
    "                              omit=False,\n",
    "                              cede=False,\n",
    "                              concentric=False,\n",
    "                              sqrt_num_cells=32,\n",
    "                              min_sqrt_num_cells=10,\n",
    "                              max_sqrt_num_cells=100,\n",
    "                              num_leaves=100,\n",
    "                              num_rays=200,\n",
    "                              do_fast=False,\n",
    "                              is_antiphased=True,\n",
    "                              prefix='s') # unique prefix needed because >1 instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xpsi import HotRegions\n",
    "\n",
    "hot = HotRegions((primary, secondary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomPhotosphere(xpsi.Photosphere):\n",
    "    \"\"\" Implement method for imaging.\"\"\"\n",
    "    \n",
    "    def _global_variables(self):\n",
    "    \n",
    "        return np.array([self['p__super_colatitude'],\n",
    "                          self['p__phase_shift'] * _2pi,\n",
    "                          self['p__super_radius'],\n",
    "                          self['p__super_temperature'],\n",
    "                          self['s__super_colatitude'],\n",
    "                          (self['s__phase_shift'] + 0.5) * _2pi,\n",
    "                          self['s__super_radius'],\n",
    "                          self.hot.objects[1]['s__super_temperature']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"mode_frequency\" with fixed value 3.000e+02.\n",
      "    > Coordinate frequency of the mode of radiative asymmetry in the\n",
      "photosphere that is assumed to generate the pulsed signal [Hz].\n"
     ]
    }
   ],
   "source": [
    "photosphere = CustomPhotosphere(hot = hot, elsewhere = None,\n",
    "                                values=dict(mode_frequency = spacetime['frequency']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "star = xpsi.Star(spacetime = spacetime, photospheres = photosphere)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood = xpsi.Likelihood(star = star, signals = [NICER.signal, XMM.signal],\n",
    "                             threads = 1, externally_updated = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The instrument wavebands exhibit a high degree of overlap. The energies at which we compute incident specific flux signals are based on the union of wavebands, distributed logarithmically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.plot(XMM.signal.energies, 'kx')\n",
    "ax = plt.gca()\n",
    "veneer((5,20),(0.2,1.0),ax)\n",
    "ax.set_xlabel('Index')\n",
    "_ = ax.set_ylabel('Energy [keV]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple, non-adaptive protocol to ensure that signals are not computed at very nearby energies for multiple telescopes, resulting in overhead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's call the ``likelihood`` object with the true model parameter values that we injected to generate the synthetic data rendered above, omitting background parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ll = -29322.44238519; time = 0.526\n"
     ]
    }
   ],
   "source": [
    "p = [1.4,\n",
    "     12.5,\n",
    "     0.2,\n",
    "     math.cos(1.25),\n",
    "     0.0,\n",
    "     1.0,\n",
    "     0.075,\n",
    "     6.2,\n",
    "     0.025,\n",
    "     math.pi - 1.0,\n",
    "     0.2]\n",
    "\n",
    "t = time.time()\n",
    "ll = likelihood(p, force=True) # force if you want to clear parameter value caches\n",
    "\n",
    "print('ll = %.8f; time = %.3f' % (ll, time.time() - t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-26713.601349482582"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NICER.signal.loglikelihood # check NICER ll ~ -26713.602 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2608.8410357102307"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XMM.signal.loglikelihood # check XMM ll ~ -2608.841 ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fabricate some rough prior information as the constrained support of the background parameters for XMM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "support = np.zeros((181, 2))\n",
    "support[:,0] = XMM.signal.background_signal - 5.0 * np.sqrt(XMM.signal.background_signal)\n",
    "support[:,1] = XMM.signal.background_signal + 5.0 * np.sqrt(XMM.signal.background_signal)\n",
    "support /= XMM.data.exposure_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "XMM.signal.support = support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ll = likelihood(p, force=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's confirm that the XMM background-marginalised likelihood did indeed change:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2610.121609531078"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XMM.signal.loglikelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The background-marginalised likelihood function has the following form. Subscripts N denote NICER, whilst subscripts X denote XMM.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "p(d_{\\rm X}, d_{\\rm N}, \\{b_{\\rm X}\\} \\,|\\, s)\n",
    "    \\propto\n",
    "    &\n",
    "    \\underbrace{\\mathop{\\int}_{\\{0\\}}^{\\{\\mathcal{U}_{\\rm N}\\}}\n",
    "    p(d_{\\rm N} \\,|\\, s, \\{\\mathbb{E}[b_{\\rm N}]\\}, \\texttt{NICER})\n",
    "    d\\{\\mathbb{E}[b_{\\rm N}]\\}}_{\\rm exp( \\texttt{NICER.signal.loglikelihood} )}\\\\\n",
    "    &\n",
    "    \\times\\underbrace{\\mathop{\\int}_{\\{0\\}}^{\\{\\mathcal{U}_{X}\\}}\n",
    "    p(d_{\\rm X} \\,|\\, s, \\{\\mathbb{E}[b_{\\rm X}]\\}, \\texttt{XMM})\n",
    "    p(\\{\\mathbb{E}[b_{\\rm X}]\\} \\,|\\, \\{b_{\\rm X}\\})d\\{\\mathbb{E}[b_{\\rm X}]\\}}_{\\rm exp( \\texttt{XMM.signal.loglikelihood} )}.\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "The term $p(\\{\\mathbb{E}[b_{\\rm X}]\\} \\,|\\, \\{b_{\\rm X}\\})$ truncates the integral over XMM channel-by-channel background count rate variables to an interval $[a,b]$ in each channel, where $a,b\\in\\mathbb{R}^{+}$. This is the joint prior support of the variables $\\{\\mathbb{E}[b_{\\rm X}]\\}$ rendered in the spectral plot below. The form of the prior density $p(\\{\\mathbb{E}[b_{\\rm X}]\\} \\,|\\, \\{b_{\\rm X}\\})$ is flat on this interval for each channel. This is a simplifying approximation to the probability of the background data $\\{b_{\\rm X}\\}$ given the variables $\\{\\mathbb{E}[b_{\\rm X}]\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_spectrum():\n",
    "\n",
    "    fig = plt.figure(figsize = (10,10))\n",
    "\n",
    "    ax = fig.add_subplot(111)\n",
    "    veneer((5, 25), (None,None), ax)\n",
    "\n",
    "    ax.fill_between(np.arange(support.shape[0]),\n",
    "                    support[:,0]*XMM.data.exposure_time,\n",
    "                    support[:,1]*XMM.data.exposure_time,\n",
    "                    step = 'pre',\n",
    "                    color = 'k',\n",
    "                    alpha = 0.5,\n",
    "                    label = 'background support')\n",
    "\n",
    "    ax.step(XMM.signal.background_signal, 'b-', label='MCL background')\n",
    "    ax.step(XMM.signal.expected_counts, 'k-', label='MCL counts given support', lw=5.0)\n",
    "    ax.step(XMM.data.counts, 'r-', label='XMM data')\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylabel('Counts')\n",
    "    _ = ax.set_xlabel('Channel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spectrum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectrum labelled *MCL counts given support* means the expected signal from the pulsar, plus the background count vector that maximises the conditional likelihood function given that pulsar signal, subject to the background vector existing in the prior support. The spectrum labelled *MCL background*, on the other hand, is the background vector that maximises the conditional likelihood function, but *not* subject to the prior support."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood['p__super_temperature'] = 6.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood.externally_updated = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-391383.43256154377"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-321117.06212714006"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XMM.signal.loglikelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spectrum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook thus far we have not generated sythetic data. However, we did condition on synthetic data. Below we outline how that data was generated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The background radiation field incident on the model instrument for the purpose of generating synthetic data was a time-invariant powerlaw spectrum, and was transformed into a count-rate in each output channel using the response matrix for synthetic data generation. We would reproduce this background here by writing a custom subclass as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomBackground(xpsi.Background):\n",
    "    \"\"\" The background injected to generate synthetic data. \"\"\"\n",
    "\n",
    "    def __init__(self, bounds=None, value=None):\n",
    "        \n",
    "        # first the parameters that are fundemental to this class\n",
    "        doc = \"\"\"\n",
    "        Powerlaw spectral index.\n",
    "        \"\"\"\n",
    "        index = xpsi.Parameter('powerlaw_index',\n",
    "                                strict_bounds = (-3.0, -1.01),\n",
    "                                bounds = bounds,\n",
    "                                doc = doc,\n",
    "                                symbol = r'$\\Gamma$',\n",
    "                                value = value,\n",
    "                                permit_prepend = False) # because to be shared by multiple objects\n",
    "        \n",
    "        super(CustomBackground, self).__init__(index)\n",
    "\n",
    "    def __call__(self, energy_edges, phases):\n",
    "        \"\"\" Evaluate the incident background field. \"\"\"\n",
    "        \n",
    "        G = self['powerlaw_index']\n",
    "\n",
    "        temp = np.zeros((energy_edges.shape[0] - 1, phases.shape[0]))\n",
    "\n",
    "        temp[:,0] = (energy_edges[1:]**(G + 1.0) - energy_edges[:-1]**(G + 1.0)) / (G + 1.0)\n",
    "\n",
    "        for i in range(phases.shape[0]):\n",
    "            temp[:,i] = temp[:,0]\n",
    "\n",
    "        self._incident_background = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"powerlaw_index\" with bounds [-3.000e+00, -1.010e+00].\n",
      "    > Powerlaw spectral index.\n"
     ]
    }
   ],
   "source": [
    "background = CustomBackground(bounds=(None, None)) # use strict bounds, but do not fix/derive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this same background signal, albeit with different normalisations, for both telescopes. This is simply to generate finite background contributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also in need of a simpler data object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SynthesiseData(xpsi.Data):\n",
    "    \"\"\" Custom data container to enable synthesis. \"\"\"\n",
    "    \n",
    "    def __init__(self, channels, phases, first, last):\n",
    "        self.channels = channels\n",
    "        self._phases = phases\n",
    "        \n",
    "        try:\n",
    "            self._first = int(first)\n",
    "            self._last = int(last)\n",
    "        except TypeError:\n",
    "            raise TypeError('The first and last channels must be integers.')\n",
    "        if self._first >= self._last:\n",
    "            raise ValueError('The first channel number must be lower than the '\n",
    "                             'the last channel number.')\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "NICER.synth = SynthesiseData(np.arange(20,201), np.linspace(0.0, 1.0, 33), 0, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "XMM.synth = SynthesiseData(np.arange(20,201), np.array([0.0,1.0]), 0, 180)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xpsi.tools.synthesise import synthesise_given_total_count_number as _synthesise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def synthesise(self,\n",
    "               require_source_counts,\n",
    "               require_background_counts,\n",
    "               name='synthetic',\n",
    "               directory='./data',\n",
    "               **kwargs):\n",
    "        \"\"\" Synthesise data set.\n",
    "\n",
    "        \"\"\"\n",
    "        self._expected_counts, synthetic, _, _ = _synthesise(self._data.phases,\n",
    "                                            require_source_counts,\n",
    "                                            self._signals,\n",
    "                                            self._phases,\n",
    "                                            self._shifts,\n",
    "                                            require_background_counts,\n",
    "                                            self._background.registered_background,\n",
    "                                            gsl_seed=0)\n",
    "        try:\n",
    "            if not os.path.isdir(directory):\n",
    "                os.mkdir(directory)\n",
    "        except OSError:\n",
    "            print('Cannot create write directory.')\n",
    "            raise\n",
    "\n",
    "        np.savetxt(os.path.join(directory, name+'_realisation.dat'),\n",
    "                   synthetic,\n",
    "                   fmt = '%u')\n",
    "\n",
    "        self._write(self.expected_counts,\n",
    "                    filename = os.path.join(directory, name+'_expected_hreadable.dat'),\n",
    "                    fmt = '%.8e')\n",
    "\n",
    "        self._write(synthetic,\n",
    "                    filename = os.path.join(directory, name+'_realisation_hreadable.dat'),\n",
    "                    fmt = '%u')\n",
    "\n",
    "def _write(self, counts, filename, fmt):\n",
    "    \"\"\" Write to file in human readable format. \"\"\"\n",
    "\n",
    "    rows = len(self._data.phases) - 1\n",
    "    rows *= len(self._data.channels)\n",
    "\n",
    "    phases = self._data.phases[:-1]\n",
    "    array = np.zeros((rows, 3))\n",
    "\n",
    "    for i in range(counts.shape[0]):\n",
    "        for j in range(counts.shape[1]):\n",
    "            array[i*len(phases) + j,:] = self._data.channels[i], phases[j], counts[i,j]\n",
    "\n",
    "    np.savetxt(filename, array, fmt=['%u', '%.6f'] + [fmt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomSignal.synthesise = synthesise\n",
    "CustomSignal._write = _write"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to instantiate, and reconfigure the likelihood object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n",
      "Warning: No data... can synthesise data but cannot evaluate a likelihood function.\n",
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n",
      "Warning: No data... can synthesise data but cannot evaluate a likelihood function.\n"
     ]
    }
   ],
   "source": [
    "NICER.signal = CustomSignal(data = NICER.synth,\n",
    "                              instrument = NICER.instrument,\n",
    "                              background = background,\n",
    "                              interstellar = None,\n",
    "                              workspace_intervals = 1000,\n",
    "                              epsrel = 1.0e-8,\n",
    "                              epsilon = 1.0e-3,\n",
    "                              sigmas = 10.0,\n",
    "                              prefix='NICER')\n",
    "\n",
    "XMM.signal = CustomSignal(data = XMM.synth,\n",
    "                          instrument = XMM.instrument,\n",
    "                          background = background,\n",
    "                          interstellar = None,\n",
    "                          workspace_intervals = 1000,\n",
    "                          epsrel = 1.0e-8,\n",
    "                          epsilon = 1.0e-3,\n",
    "                          sigmas = 10.0,\n",
    "                          prefix='XMM')\n",
    "\n",
    "for h in hot.objects:\n",
    "    h.set_phases(num_leaves = 100)\n",
    "    \n",
    "likelihood = xpsi.Likelihood(star = star, signals = [NICER.signal, XMM.signal], threads=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthesise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We proceed to synthesise. First we check the output path to write synthetic files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/xiaotuo/xpsi_group/xpsi/docs/source\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Free parameters\n",
       "---------------\n",
       "mass: Gravitational mass [solar masses].\n",
       "radius: Coordinate equatorial radius [km].\n",
       "distance: Earth distance [kpc].\n",
       "cos_inclination: Cosine of Earth inclination to rotation axis.\n",
       "p__phase_shift: The phase of the hot region, a periodic parameter [cycles].\n",
       "p__super_colatitude: The colatitude of the centre of the superseding region [radians].\n",
       "p__super_radius: The angular radius of the (circular) superseding region [radians].\n",
       "p__super_temperature: log10(superseding region effective temperature [K]).\n",
       "s__phase_shift: The phase of the hot region, a periodic parameter [cycles].\n",
       "s__super_colatitude: The colatitude of the centre of the superseding region [radians].\n",
       "s__super_radius: The angular radius of the (circular) superseding region [radians].\n",
       "powerlaw_index: Powerlaw spectral index."
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exposure time: 984282.620969 [s]\n",
      "Background normalisation: 1.89132784e-05\n",
      "Exposure time: 1818398.718453 [s]\n",
      "Background normalisation: 2.21412640e-04\n"
     ]
    }
   ],
   "source": [
    "p = [1.4,\n",
    "     12.5,\n",
    "     0.2,\n",
    "     math.cos(1.25),\n",
    "     0.0,\n",
    "     1.0,\n",
    "     0.075,\n",
    "     6.2,\n",
    "     0.025,\n",
    "     math.pi - 1.0,\n",
    "     0.2,\n",
    "     -2.0]\n",
    "\n",
    "NICER_kwargs = dict(require_source_counts = 2.0e6,\n",
    "                      require_background_counts = 2.0e6,\n",
    "                      name = 'new_NICER',\n",
    "                      directory = './data')\n",
    "\n",
    "XMM_kwargs = dict(require_source_counts = 1.0e6,\n",
    "                      require_background_counts = 5.0e5,\n",
    "                      name = 'new_XMM',\n",
    "                      directory = './data')\n",
    "\n",
    "likelihood.synthesise(p,\n",
    "                      force = True,\n",
    "                      NICER = NICER_kwargs,\n",
    "                      XMM = XMM_kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the normalisations, with units photons/s/cm^2/keV, are different because we require so many background counts. This detail is unimportant for this notebook, wherein we simply want some finite background contributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(np.loadtxt('data/new_NICER_realisation.dat', dtype=np.double), NICER.data.phases, NICER.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check we have generated the same count numbers, given the same seed and resolution settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = XMM.data.counts - np.loadtxt('data/new_XMM_realisation.dat', dtype=np.double).reshape(-1,1)\n",
    "(diff != 0.0).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = NICER.data.counts - np.loadtxt('data/new_NICER_realisation.dat', dtype=np.double)\n",
    "(diff != 0.0).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed in the `Modeling` tutorial, with `xpsi.__version__` of `v0.6`, the same RNG seed does not yield the same ($\\pm1$) count numbers, despite the small fractional difference between Poisson random variable expectations, for reasons that are unclear at present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.loadtxt('../../examples/examples_modeling_tutorial/model_data/example_synthetic_expected_hreadable.dat')\n",
    "y = np.loadtxt('data/new_NICER_expected_hreadable.dat')\n",
    "\n",
    "xx = np.zeros(NICER.data.counts.shape)\n",
    "yy = np.zeros(NICER.data.counts.shape)\n",
    "\n",
    "for i in range(xx.shape[0]):\n",
    "    for j in range(xx.shape[1]):\n",
    "        xx[i,j] = x[i*32 + j,-1]\n",
    "        yy[i,j] = y[i*32 + j,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(yy-xx, NICER.data.phases, NICER.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_r = (yy-xx)/np.sqrt(yy)\n",
    "plot_one_pulse(_r, NICER.data.phases, NICER.data,\n",
    "               'Standardised residuals',\n",
    "               cmap=cm.RdBu,\n",
    "               vmin=-np.max(np.fabs(_r)),\n",
    "               vmax=np.max(np.fabs(_r)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wMfVjNwCjzewAAFvcfY2Z3QDgMjNbBGAJgMsBVAC4HwDcvczM7gRwjZkVA9gM4DoA7wCY3ZHPRUREOqnmh2OapO+yttPROSEHA3g+7eefpm73ADgPwK8B5AC4BcAAAHMBnOLu5Wm/810AuwA8lIp9DsAX3X13e1deRES6Bm9d5oW+y9pIhzZC3P0FRIk3jT3uiBJJZzYRU41oAZiL2rZ2IiIizdN3WduJ4+wYERGRdtUGCajSBtQIERGRZGllToi0nTjNjhEREZEEUU+IiIgkjoZj4iHxjZDiHeGHoKyGK/ugATuo+Le29qLitxH1OX1YQxs9Nm5lJVeX/J67qPhVVT2p+N1kJnsVUZ3DBlZTZZfv4ja5rN7NdTiuruLKn5FfFRxbtZsre/127nWq2NVorl6DThm2hYp/bdMAKv64YZuCY18urL9uVNN6Z+lbrDNr5ewYaSMajhEREZGMSHxPyJBe4ZfME/py07d7kFvTvFHC9Zx8dkz4yzdncw5V9vAc7iqvfw/u2OyfG3713hKF1eE9OYsrsqmyJ/bhXie2J4S9QKupDS+/dxb3OhVkc91/75VxPSf3rBhIxbM9Ynv16xsc+04p9zqN6M3FD+zJvaeyu3FPtro2vBdqaTlX9wPyuJ7OHmTdC6u5r6LaxmfHhnEAxPGS9pP4RoiIiCRL3d4xknlqhBDKa7jxdNZZY9nfCL/aGN+Hu5JhFVf3aNfyWUYcm8FkPksZeR5kkT1iE8jXasvO+LyN6fOsT/vUo85GIqfl8Pz2fY+0tz5Z4efZAXnt+w1cQ/Yy5PdM1CKhkiY+n14iIiIdwuCu4Zg4UCNERESSxTUcExeJmh1jZjPNzOtuma6PiIhIkiWqEeLuM93d6m6Zro+IiGSG1zZ+k46T+OGYbGLBoRxycaKtZMJgJTmVk5k++VwRt/jYx4ZzU2gn5JdS8Q8uHU7F9+3BdVzldg9/rcp3ccedmQoJAPv04xZD21jNTXMd1ye8/D7duSm3967MpeIPzSensZNTOUvJpODBxBT83B5cYiq7oF9ZDXeeDe7FHcuJfcPfs29u7cfVhUzejjsHlBMSE4nqCREREZH4SHxPyJzN4Ved508qpsqeX8ItxMQufc70zBw4gLuqKiKvxrNLuSurshruKuTw/Eoq/sE17Tf382uTuKXGXy7mlhofms29Vm9u7R0cm9eD6807sWAnFc8u0LeaXL6/D9HDBXBTtZ8vDj+OAJDfi3uu28hz/vih5VR8T2Ihuk9MWEeVvaCIW9J+aQXXS7RvLretRBW5dcIeHHAtVhYLiW+EiIhI8mjvmHhIfCOEWQZ6RVl/qux+3bkr2rye3FXn4vLwq/2hvbhcgLfLuCuZgwZxeQ9T+nNXnVt2couhfXR4+NLq7CZti8jzgF1iumc37mp/VE54D9ra7dxbviCb+6TeQV5dsvk11Tu5K+ABxHYCY/tw79flFdxo9slDud680h3cebmJiO9THr6cPQCM6sPVfQWZL7OM3DqByfWReEt8I0RERJJHianxkPhGyOH54T0E7GyXV0q4+BJyE6fLppUGx/5ldT5V9mH5XM/GWyVc3sO4vtwYcDW5BT2zbPThg7ZSZb9QxOX6bNrBfditruCugKfkhvecDCFnXPTrzl1xlu8iNyIju8SPGryNiv9XUXiv1bRcbmPCkTlc5d8v5zaRZGZ4AUAekVO2gOzpXFXJ5u5wx2YJuaFe5S6uPg1RTkg8aHaMiIiIZETie0IWl4fnGkzsy10VfnsfbjbNvcuHUvHVRIb4UYO4ngc2Z2s0sVYFAMzfys1eGdOby2lZScy6qKnl2uLTcrk1VFZXcePd7Hb1A4jNv1ZXcbk1hdXc6zS0F3f1Pr4Plwf1agmXj3PM4Irg2NlF3AyvAT3J7eq3c1feFT24+F7EBnYT+nLvp2E5XC/R4N7ce6SHcT21uUSuT0PclZgaF4lvhIiISNJoA7u4SHwj5NCB4T0E7DoCVbu5ufVsHsaKivCr1N7kaq8Tcrmx93e35FHxRdVc70Of7typeurwTcGxc4q5q7CCHK5XaVg291zZj8adRE/OYHLL9A3VXC7OcPKKuYrM9Tl0YHjPBgC8UBw+C6QvmcfAromSy3VCYVRv7rXam3jPLi7jen02budySErImT0DyTWSqsjVpRtSq5yQWFBOiIiIiGRE4ntC9hsRnrfx+pYxVNn/M3UVFT9/5TAqfkDP8HHd97dxvTjjyK5Kdt+N04dxvQnrt3MzC+5fOSQ4djCZx/BqCbefysHk1Ts7E2gzsXYGewXZj+wd6E2ujbObPM9yyNk6TB7XIiI/DACqybSEz44rouLvXMrliD1fFD5D7bzx3Lof7OdHFtnJML4P93kwgczL2oNyQmIj8Y0QERFJFm1gFx+Jb4TcuzC8d2P7bu6kfeCdcVQ8Wz4T3YMceHu/lLvaf6+Mu4oc3ZsbMx5K7BgMAGcMD7/Sy83m8hjYWUzF5D48leS+GMz6EAau7DmbuY+Ift25HqssMq9iwbY8Kn54dvixmT6Ay8mau5mb9XT/Cu68+e5Bq6n451eMCI79wbvc++mIgdwHyLg+3OvK9v4NGcLlrEl8Jb4RIiIiyaOekHhIfCNkDTFcP7E/17pfWMqd5GeO4K7Eyokr5pOmrKHKvmnOBCp+31zuymojuTrsWnK31cXl4dn83cjPosHkfipryLp/5eilVPwjc8cHx44jx973quHWCSnfxV0xT+zLnfObyFWL710Zfl5euT/XI7bbuZ6QQ/O4NVGeWx7eswEApTXhnwdnDuNWON6nH1f3oh3c61RI9hauXs+tWtyQWjVCYiFRs2PMbKaZed0t0/URERFJskT1hLj7TAAz6342M//cmPLg3x/al8soz+7GjQGzO09WEjkkc5dwV1WsZRXcqXTuPmup+CXkWh7ZWeG5AIu3cTuK9ib39BjSi7vCfn7+aCq+L1EfttenN7EKJ8CvZMnmAgwjcjwA4FcHhff83L6Uu7pme0YryF6iN7dw76nPjAnPkxg1sIwq+yki3wQAThzBzQT6zybu/f3Xta3sCXHT3jExkahGiIiISDQ7JtO1EECNEDxTGL4XxadGc3kP2eQqpf3INRb6E1edG7eTeQlTuZ6KtzaEr8sBAO9sHEzF9+zGHctl5eG5DNXkFVEBuVbF4N5cHka37VyuQVF1eA/a6kpu9koVOWOL3QuG7QlhexNeL8kLjj2xgKv7ajLXZ+E2bgZZb/LTuZRYpXTt2gKq7MG9yB6od7jyzxzBHfvNO9SC6CoS3wgREZHkUWJqPCS+ETIjP/wq9Z4V3Djktp1ca33jdu5q46dENn/fHlwvzv97jRsD/ugIrqeitp0vZJiVOPuTPVB7D95CxT+6YjgVb+RnYyVx2hRkc6/T9AHhOVMA0JdYxRcAHl7N7a/EYt7fJTu4norsbtxJPL4vd+wn92NXNQ3v/TtgALfOxrwt3LpB35y8lYqfXch9tp43nqv/H9bteV9rpuia2UwAV9S7u8jdC1KPW+rxCwAMADAXwDfdfUGL/2gXlajZMSIiIm1kMYBhabdpaY9dCuASABcBOARAMYBZZsbtHJgAie8J2UW0hn959jKq7D89Hr5+AwBkGXcl9vCa8FyAk4ZyeQknckO6WFXJnUr55H4th+SXUvHDB4dfKb2wfCRV9qw13MEpquauuNh9Nyb3C+8KGdSL66nYQO7ZM4HMl/nU6M1UfBaZG/RWSfh6GBPJnoeXNoXnkwHAxu3cC3vK2FIq3ok1lLuTx5HNm9pC5CkBwOYdXPkbqri8qfocbTIcs8vdC+vfmeoFuRjA1e7+SOq+cxE1RM4CcGtr/3BXop4QERFJFo+GYxq7BRpvZuvNbKWZPWhmdVed4wAUAHj2gz/nvh3ASwCOaNPn0QUkvidkUm74FfPi17hxy5E5XMZ3FTlTYB9inYLnirhdMKeRK6B+eX9uRda5q7gdg/+6hltHYOzmvODYEeTrtKyCuwo7fgi34+f75VzvwxhiFdQN5MybUeTMnu27uI+U+1dxvQnTB7bfFfzNi7nVYT8zhjtvenTjZtNsKuPWrykhZsesrODOsf1zuV4idnfkPuRuzUvKuWPZAoPM7M20n29z99vSfp4L4DwAiwAMAXA5gFfNbCqiBggA1F8spQhA+y7Y1AklvhEiIiLJ00xztsTdD27sQXd/Ov1nM5sDYAWAcwHMqQur92vWwH2Jl/hGyKwN4dn5b3MJ3/jkKHYvCu7qgVmdchuxr0RLPLBgLBU/rjd3FdmdHDjsQcxcWEeuoZJNHsq8ntxz/cL+G6n4J4idoAcSO+4CwIubuDy6T44ppuIH9OJmXTy1nusJ+fpe4cf++GHERlIAfvUut9bNeeO58v+4nNvfZS+iZ3RiX+6zaSC50/Tdy7le431yudd1047WZhJQwy7NcvcKM1sAYBKAR1N3FwBIX3BpCPbsHUk85YSIiIi0gpllA9gbwEYAKwEUAji53uNHA3g1IxWMscT3hIiISLK0dnaMmV0L4HEAaxD1cPwYQB8A97i7m9kNAC4zs0UAliDKGakAcH/rat71NNkIMbNPhBbk7n9rfXU63pJt4SfisUO4Ra121HIdTf3I6Y3HHha+tHrVy+Ooskf34bZYf4dMoltcxg2BDOvNDaXuRUy3ZJZ4B4A+5BLWqyu5pOCHX+OGKC6cvMcswUb9eTk3vfiYwVxS7ZJSru6je3PvqX254pFl4d38PbLIugzgvsSYxFEA2L6bO+c/MamBFbka8cpaLjH8bXKxsm01XN2n9G/fxFcs3vOuVg7HjATwAIBBADYhygM53N1Xpx7/NYAcALfgv4uVneLu3Op/CdBcT8hfA8txAO2bdCAiIhID7v65Zh53RDu2z+yI+nRmTTZC3L3L54x8d9/wJEB2U6aDBnKH74gh3HLgf5g9KTj2wDzuinY5OYVvn/7kVWQed0GwoYqrz/yt4QmVA3tydV+3nRvFnJHPPdcZ+dxV5AMrws/LSX25XpzZRVwv0YgcLsHw4IHc8ttsr1I34mK3uJJ7rqNyuGns/97E9YRcftAGKn737vDPm7+v4XoBbjye29Dy2FHcwotffImbqn3TdC7ZuyHtvXWEhFFOiIiIJIp7q4djpI1QjRAzOx3AtwCMRzS+tdbMvgJgpbs/1x4VbG89iGmuA3txPRsfn7yKil+4gZvyt5y4wC7czl1B5nOrLmNMb+4Key17RUtFc9jx5cn9uOmKa8lenA3V3LXBAXnh9TlwGDeFtu+GoVT8qD7c2H5uDpl7VMpNGT5lWni+zA9mT6TKPnEo1xOybx7XS/SZf3PnwdkjhgTHbthZRpX9jee4rQ3OncD1Ll4+hTs2O3dz5Uv7M7McAEcCWJqWG9Os4M92MzsbwF8QZfqOBVDX35aFaLMeERGRTqEW1uhNmmdmd5vZN1L/3xPA64iWql+c6rAIwjS1LwXwVXd/MNX7UWcOgJ8R5cTKQ0vDW/jfP3w5Vfary7gVeoflcEtkf3Vi+FVkSTV3NV5Dblg1byu3HPhxQ7iFm/6+jpt9M5VY/Gj6IC4Xh1ngDgCeL+R6iY7iOh+wojK82+qld0ZRZe+Xy11xzl/PLbB16ECuJ+Sk0eE9GwCwfn34rI5vTuY209tGznbZspOLv+tgrv/vzqXhsQ+dVEqV/Zu3uNkx1eTMHvbzpmQH93nQEFdOSGudCuCm1P+fCaAfogXavowoIffphn/tw5izfBKA1xq4vwIAl1UkIiIindkARDsDA8BpAB5x92IADwKYEloI0wjZAGCvBu4/BgDXRZAhZjbTzLzulun6iIhIx3MYar3xmwQpBLCvmWUh6hWZnbq/L4DghClmOOY2ADelDcWMMrOjES3KMpMoJ2PcfSbS6mpmPjInvKt80RoucbRyF7d0StF2bsjkza3h3fAnDeWmiY7pz03pZfZqAYAxA0up+OrVXPfrGZPDd/W9eu5YquzzJ5VQ8ZP7cVM/a50bArljWfjb+Cf7c1Ni/13MDa+wU3TZfXuwiduThLFlJzetdHE5lzh6AjkEWUgmb2/ZEf4eXLWJe11Hk4sFFpLJ1ZW7uC/+04ZzQ6gNUe5Hq/0RwEOIOih2A6ibnHIYot2FgwSfKe7+azPLBTALQDaA5wHsAHCtu98SWo6IiIh0bu7+s9SmfaMBPOzudYu37ALwq9ByqOaqu19mZr9ANN7TDcBCd+ea9zEzvm/4lMItO7h5qxW7uMSyj05fScXXvj06OLZ6N9crk9WNu6IdlM0l1f7iLW7K33FDuOTOe94dGxw7mdh9FABeLeauxvfN5d4ih3yM2655w5/Dk03Z5be/ePQyKv4vr06g4jfv5N4jZTVcAvQpw8KvmHt043oimc8OgP/8WEYkHAPAb05YERz7r8VcgvKBA7jnuq6Ke516duM+n3aRW2I0RImprWNmxwD4h7vX/3D+M4AjQsuhFytz9yoAb7K/JyIiEget3cBOAESjIcPw3+TUOrmpx4JalsGNkNRWxN8BcCKiXQM/1BR19/1CyxIREZFOzRC15+rLBxDcdcb0hPwOwP8CeBjAq4388U7nzuXhs4u/vQ+3jsAdy/Ko+IkLhlPxczaHd3muq+SSHX84jet+ZfboAIBscrtDdmhrJZGH+/Njw7uxAeCPb3FDDves5Gaw5zzNDT316x7+2r5dyiWCPvgK91zZD4UZ+dxQVQ650zQz/HTXSm5I8beHcivnziP2MwKAaze8SsVP7ntQcOyAntxxHJvPDREWV3OL3QzP4T6fFpRxx7IhrsTUFjGzx1L/6wDuM7P0N0IWgH0RtRGCMI2QjwP4tLvPbi5QREQktlwb2LVC3dW4AdgKIL31vhPAvwHcHloY0wipAsBtpdgJnDEifDfGMRO4q4HD13Mraz61kUvROWJQ+JXYZ8dwV5xzivOp+P49uCurj4/gelpG9uemGD+6NnwK4jsrud2R39vKfXp9bCR3bPhepfAk4l1kNt6cEq4yVx+ziop/ZMFYKv7AAdwU48G9wvd3uXRv7v03i9zr6fyjiCVNAex6OTi3DwCweWd4b0JOFtfz8If3wpPgAYBcMBWXHsklQN80h+uhk7bj7l8CADNbhWh2LPdhXg/Tx/1rAN8zs/bcS0xERKRd1SWmarGylnP3n7a2AQJwPSEnAzgawGlmthD1VkRz9zNbW5lMuGt5eJtqUm4eVXYJN2SM7x4YvsAWACwqDO9peW8rl5dw+BAu/4VdgPa374fv+AkAM48uouJ/ODV8gbCiam4q5LnjudyBjeSCXDt3c+18Zhfg4wZze7X8s5CbavneGu51ZRe521TN1Wd+afhr24O8vDpsIPf5e9e/uV1683pw0+TnbQlPtJrcn3uy3cljs3MX97ruqOJ6odicsj2ZckJaycwGAvgFGp+sEvSlw7zyJQD+TsSLiIhI13QngAMRraa+AS2crMKsmPqllvwBERGRuFFiaqudCOBkd5/bmkLoxcq6mu/tEz5m8nIRl6zJJmdd9CI3Rffy/cKT9KrJLv6fv8091ykDuPJnHs1Ni71+LpeIdlh++LDDqipy/xJSaQ3Xd7x4Gzf9sCA7/ByuIVeaPGQgl8DIrszLvkfeL+eGzlZVhP+BSf257vnyXdzH57mHcft8Pj5/HBV//NDw8+COZdxzPXU4t6/OP0vCE4IB4PLXuMTXb+/N7R3zgwZ2MtFwTKsVA2j1iunBn0hmNtDMfm9mS8ys1My2pd9aWxERERHpNC4D8DMz43YXrYdpyrfJ+I+IiEgmRbNjMl2LTu9yAGMBFJvZauw5WSVoFXWmEdIm4z9xs6YyPNu+D7EeAwAcmMfGU+FYWBo+42X//FKq7KtncDNA7nqf2xDrxte54RV2fYvC6vDu4y9N57rJa3ZwQw7/WMR1NR83ZgMV/+LqYVQ8w8hrjS07uSEK9j01uV8VFZ/bo3dwbDm5nfzI3lxd7n+DO+eXV3D1+djw8Pfscq+/3UfTqmvHU/G/O4aczTZnBBX/4/+06uIbgPaOaQN/bYtCmE+MNhn/ERERkc7N3X/aFuWYB15hmtlnAXwGwLnu3iUaI2bm9x/440xXo8V2EkmGBdnhK8MCQG5PLr5/Ly7+7c15VLyISEudNf9KuP+362OvvsP8pn0bn/B5+tyr5rn7wR1SuYRjekLaZPxHREQkk9w1HNNaZlaOJnJD22OxsjYZ/xEREZFO71v1fu6BaPLKJxGtpBqEWaysTcZ/pO307Bae1McmDLLxqAhPABQRyTQuJVrqc/d7GrrfzN5CNJHl5pBytBmdiIgkjrs1epNWeR7Ax0KDgy93zawnosVJPg9gNKKulw+4e6u3FJL4YDZFA4De3bnrih279UYXkcxwqCekHX0O0V5zQZg+9ysBfBbAVQCuB/B/iBJVPweg804xEREREYqZvYsPJ6YagKEABgL4emg5TCPkMwC+5u7/NLNrAfzD3Zeb2fsATgZwK1FWRpjZTABXZLoeIiKSWVoxtdXqT1apBbAJwAvu3sBuPQ1jGiFDASxM/X8FgLzU//8TwK+IcjLG3WcCmFn3s5n55p3ho0injNhE/b15mwZS8XEyfTC3QdR/Sga0U01a5tmN4a/rhZPKqbKnTuZWg3xnEbei6ZMbuA3szp0QvvrlrA2DqLJnDCqj4v+yJo+K/+6Ba6j4J5eNpOIn9g1fRbR3911U2b94L4eKv/fj3HP99YuTqPgp/blN45LNtIFdK7XVZBWmEbIGwPDUv8sAnApgHoAZALg1vkVERKTTM7MTAExBNDSzwN1fYH6faYT8HdG0mzkAbgTwgJl9FcAIANcwf1RERCRTtIFd65nZCETtgumINrUFgOFm9iaA/3X3oE2wmHVCfpj2/381s3UAjgCwxN2fCK55zLxfFt4l987WwVTZ2Vlcd98l+3MblxVv6xMce8sSrov/1hU9qfiPDOPWFSEPDap3c/HfmlwaHHvr0lyq7GEbufhjBnMbnQ3P4fL2l5eFb2Q4LZerywOr86j4ablc3Rdu5IaHlldwk/Ce2xi+QeWnx3An2SGDuPfIhY+Po+K/NIF7rW5YFP6mevyXW6myr7x+OBV/2ECuc/zFTeGvEwAcN7iaim9IWwzHmNk3EE3SGAZgAYCL3f3lVhfcOdwEYDeAie6+EgDMbDyA+1KPfSqkEHJFqv9y9zmIekVEREQSJbWf2o0AvgHg36l/nzazKe7OJQB1TicDOK6uAQIA7r7CzL4N4LnQQqhGiJmNAnA0gCGot9CZu1/HlCUiIpIpbTAc8z0Ad7v77amfLzKz0xBNT/1h47/W5VHdocxiZWcD+COAXYim4aS/hA6gUzZCrj9nWXDs3n/gZgosOCe8mxwA7n1+IhV/1bo3gmNvmbQ/Vfba7Vzdi8je0X49mo9JV8FNXMCI/G3Bscdv60uVXePckMPf13GzKM4aw83WWVUZvmT+PvncrKfvDeHiL5vDzV45ZRg3pHHwQG635qMGhb9W3Yz7VmLPyTH9uKGkY49fT8WP6JsXHLvob9wbsLyGOzZVu7nn+unRpVT8i8V5VHx90QZ2Lf/91OKd0wFcW++hZxGlKSTBcwBuMrPPu/taADCz0Yh6h4J7Qphl238G4DcA+rv7WHcfl3Ybz9RcREQkxgaZ2ZtptwvqPw4gC0D9+fpFAAo6pIaZ920AvQGsMLPVZrYKwPLUfd8OLYRdJ+QOdydTBEVEROKlmcTUEnc/OKiYD7MG7uuSUr0fB5nZyQD2RvTcF7r7bKYcphHyFIDDAKxg/kDcLZ4bvqDYbyaMpsp+8EUu73f+Vi5b++59pgbHFvQJH54AgKUV3EygnbVc3at3c+/T0b25+AUbw+vfoxtX9sQ+lVT8y8Xc0NZVC7ghiptnhC9W9uQa7iKtOzmB4Mgh3LFk9ygqqm6/LapG9eaGej41qpSKf6Ukj4q/+q/c8Oy+ueGLlY3vW0GV3YPc6vSYCeuo+Ctf42YO7d2/9d/zrcwJKUE0M6T+G2oI9uwd6VLM7HQAvwewv7uXufssALNSj+WmekQucPdnQ8pr8lvSzD6R9uMsAL8ys6kA3gXwoTPe3f8W/CxEREQ6KXffaWbzEM0QeTjtoZMBPJKZWnWYbwG4xt33SJJ09zIz+xWA7yDKj2lWc5fq9deGB4AfNXCfIxofExERibU22kX3OgD3mtnrAF4B8DVEq4r/ofVFx9p+iGYGNeZfAC4LLazJRoi7k51wnU+/7B3BsWsKub1gFpRy/X0XTwne/RgAMJtY6OmOZdwMjW9O5oZvniYX8Momm6wnDuf27Vm9LXxxtte3cDMF3i3Lo+K/MJbr+v53SfgidABw37LwvWk+MryUKntADrfo1ONruH1y9s3lzrOqXdwQZzditGdJOfceGdCT26tlJXca4LQCbsrZ9YvDP28e/Bg362nnSm5vqMISbgiSHe7ZtKP1C405ORS45+/7Q2aWD+ByRIuVvQfgI+6+utWVi7fBaLoN5wDyQwtr9qU3s9PNbJWZ7fEtUzf+Y2anhP5BERGRrsDdf5eaLdrL3ae7+0uZrlMHWIeoN6Qx+wEInl8ecllxEdpw/CduXiMSGAf34iYGHT+U6wl5e3MeFf+1j4WvcXLaW9yVSUkVd1VYtJ3r3CzgVmnGo2uGUPEHDwhf8vrHx4cfRwB4fD6XRPfoeq5n4+uTwxNNAeCOpeHHZnZhHlX2qBxuDZVZG8J7FgFg+gDuEpjp2QCAe1aEn8f7DeQKP+xU7nUqeYxLOP7hEq5ndEj4xSdeXDSKKvvn/7OUiu81MXztGgDocQMVjs+O5XpGr6hX/TYajkmqJwFcaWZPufuHukrNrDei5TyeDC0s5BNgGoCmptz8CwC3EpaIiEgG1XrjN2nSLwDkAlhqZj8ws/9J3f4fgCWpx34ZWlhIT0ibjv+IiIhI5+TuxWZ2BKJpur8EPlhwxQE8A+Ab7h48TTmkEVI3/tNYfxw1/iMiIpJp6vBouVTy7UfMbACAiYgaIkvdndueGYC5N/1SmNmNiOY+T29k/OdNALPc/TvsH880M/P7D/xxpqshIiLt6Kz5V8LTpsOM6T3cL5t4YaPxF747c17giqnSSiE9Ib8A8ClE4z83A1iUun8fRIuWGIjxHxEREREgoBHS1uM/IiIimabhmHgIWvmnLcd/4ubTn1obHHvhb8dSZVfu4iaB3fuNVVT8C88MD47dsL0XVTa7n8oxozZS8bNWh9cdAAb15PZNnzygNDg2qxv3OuUP5vaO2byJm6L7J2LxMQD4yPA9Zs83anwBt0jV7KUjqXiRTkGzYGKDWn4w1eh4o53qIiIiIgnCrYEsIiLSyWmxsvhIfCPkwYdHB8ceTK6GMmsDd5ovepnbn+HFTeGrEn73kBVU2c8sDj8uAHD7Qm4FRmbbcQCYVcitNnnX8vBjedX0UqrshSu51Vu7Gdfve0pBORW/dFv4cM8T67k9fkb25s7hB9Zwe8HcOYPbm+aVwvAVjgHgC18uDI794x3cMNgn9l1Fxd/39ngqvncWd+x/t3ZDcOyMPtww23njw4f8AOB3S7gVmruTe8d8YWz4isiNaWZiqHSQWG1QZ2Yzzczr3QrTHrdUzAYz225mL5jZ1EzWWURERFomVo2QlMWIdiSsu01Le+xSAJcg2s/mEADFAGaZWfiWqSIiknCG2iZu0nHiOByzy9336EM1MwNwMYCr3f2R1H3nImqInAXg1pb8sQ3V4XvKf+tEbhOn393P7Ve/sJQbApnUN3xDvaISrp128ODNVPycEm6I4tBh3OZf75WNoOK/uXdpcOyja7gu/o+N4jYW27aDG0r66gJuAeLf7h0+FHYkedy//Cq3gd3xQ/Ko+PnEkCIA/JNcm3nT78PPm5eLdlJlj8rhZngtLOX6/8+fwA3LHV8WPsRyIblJIjuD7Mgh3OfNMQXce+q373OfNw3RcEw8xLEnZLyZrTezlWb2oJnVDaSOA1CAtN16Uyu4vgTgiAzUU0RERFohbo2QuQDOA3A6gK8ianS8amb5qf8HgPoLoxWlPfYhjeSYfHBrn6cgIiJxVjc7prGbdJxYDce4+9PpP5vZHAArAJwLYE5dWL1fswbuqytvJoCZjf09M/PNO8Lr99dXuOz2Z44Nz1YHgLVbuIzvsYPD14p7Y22D7bRGHTySWwT3zW3c8E2fPsSBBz9j5J0t4bNjLvniSqrsSdduouJvnrQ/FX/FuAlU/LXvh880Om0ENwPkc6O5tnpNLfcRXlrDDVleOImbTdO7e/gid3cXcudwjXNDhMcM4Y5NUXU2Ff+jGeHn8YoNA6myL32bu17917+54ZLnL6im4j86nIu/uYE1KbVYWTzErSfkQ9y9AsACAJMA1OWJ1P82HYI9e0dEREQk5mLdCDGzbAB7A9gIYCWihsjJ9R4/GsCrGamgiIh0St7ETTpOrIZjzOxaAI8DWIOoh+PHAPoAuMfd3cxuAHCZmS0CsATA5QAqANyfmRqLiEhn49BwTFzEqhECYCSABwAMArAJUR7I4akN9ADg1wByANwCYACiRNZT3J1LGEhzxKDwaXnTyemNTyznViU85/hlVPz6xeGrEo7qw2269sAibrrwtdO4l+Dyf4+l4s8Zx9V/WUX4qf2H+7m63DeVm9J70hv3UfFPHHQWFf+XT68Ljn34dS7fhDWpLzdWX1TNTV9eUZlDxef1CM8JefkM7lvpy09yHckPXsjlHr39CneevbcmPA/j1qVcvsnsy8LPMQDInzGLir+o4JNU/LYatSC6ilg1Qtz9c8087ogSTWd2RH1ERKQLcq0TEhexaoSIiIh0BE3FjYfEN0LO/HF4c7jkvvCuXQBYvI1b/jf7yKFU/KSbbgyOvWjUj6myf3IE13V8/3vjqPivTiyl4n+zkFuB8eTh4R8xB+dzdfn6PO51nTn2fCp+eQV3ibbxjfCp40cO5VamnLeJm8q5cBs3XLKjljuWldxbEJ85YXXzQSnXPz+JKvuK/bdQ8d+7byIVf2h++IrIAPDkuvDz5isTudVhN83hXqfnDzmOih81bDkVz24GKPGV+EaIiIgkixJT40ONEBERSRy1QeIh8Y2Q2b8M77Y/gJswgs+M2cb9QnX4ypcAsO0rTebxfsjj87iu3eeXhW+KBgBjenPdu9//D7dS5pNfWEHFL38/Pzh2bkkeVfb/DudmRRw5iDsP3i7lhp7+uib8tT19b26W0YCy8BlYANAri/to3y+fG9J4ozj8dQWAFUvD4/+yaRVVdk7WWCp+N/mtNyybe0/9z6jwmUYFvbmVZxcVDqLiy2q4r5Zpx3DP9Yan51HxEl+Jb4SIiEjyaDgmHtQIERGRRIlWRuWSbaV9xHrZdhEREem6Et8Tctfy8HHUCZu4aajHDeF2xfUd3DjtJ/8avovnmN5ce3MTmZ8ybUAvKn5qf26lzF6juR1O9x4UvsPw7X/kpqH++pNLqfhl73LlX79+ERX/8AHhU7s3FOdSZY/ow53DbBf3y4XcqqCPruHm6PZZF/58X/8Sd47NmcMdm37duenL/yrm4h8pfS849r1TuRVTH5s1hopfVN6Dii//A3ceLPwSt0JzznV73qfhmHhIfCNERESSR42QeNBwjIiIiGSEekJERCRRHFonJC4S3wj57j4VwbHlNdw454i+3JoM2+dx60ns1S88F4BdAnpuCZezkU2uD/G/I7n8l2/dPJaKZ3xhbPg5AADlG7jz4FfvDqDiT+/HrYWx7xXhb+N114YvYw4AG8q4NUseWcutK/LpUWVU/Ji9uWO/uir82Fz12F5U2dNyubUtHl3L7TD8w6nce3ZEztTg2Gv+xs0MWVvJvb/3H8DFT+rPfVb+4h/ca7UH13BMXGg4RkREpJ2Y2Qtm5vVuD9aLGWBm95pZWep2r5nlZajKHSrxPSEiIpI83rEDMncB+FHaz/W7gu8HMBrA6YhGiu4AcC+Aj3VI7TIo8Y2QZeV92q3su5Zz0+A+sZWbPnnkIHJLUcJRg9uvbAAorua61Y8ezHVNjyWmlm7ZyQ09bdrSl4r/2l7cVM6tO7ljs+rqjcGxYz7GdX4WP8RteP6d/dZS8X9fNpKKzyGH/Q4YED7E+dBqbpr5tFzu4/PYodyU28nDuKGzI84M3yHZd3Kv65OPca/Tsxu599SnpnDL9//ogCIq/uc3ffjnDGxgV+XuhQ09YGb7ADgNwFHu/mrqvgsBvGxmk919cQfWs8NpOEZERBLHm7i1g8+ZWYmZLTCza80sPdlqBoAKAK+m3fcKgEoAR7RPdeIj8T0hIiIi9QwyszfTfr7N3W9rYVn3A1gNYAOAqQCuArA/gJNTjxcA2OTuH7R/3N3NrDj1WJemRoiIiCROM8MxJe5+cGMPmtnPAVzWzJ843t1fqNd4edfMVgCYa2YHuftbqfsbqo01cn+XkvhGyFPrw0ekJuVyo1dTuJmZOGAwt2z0H5cMD47N68mdy73Jsffz/2c5V/7Vj1Hxqz76GSp+8F7hU4C7j+TG6s/5xTAq/pIp3BLTr5Zw02JXVIbXp+ZmqmgMz+Zyg5Zs4/Jl8ntyuT4spj7ncrsyoIZLq8Do3tyxfG55+LYMAADuLdiu2Jyy2Uu5nBNwOyc0yFv39X4DgPuaiVnTyP1vAtgNYBKAtwAUAhhiZlbXG2JmBmAwAC75pRNKfCNERESE4e4lAMIzgT9sGoAsAHUZ5a8B6IsoN6QuL2QGgD74cJ5Il6RGiIiIJIoDIDuyWsTMJgA4G8BTiBotUwD8BsB8RMmncPf3zeyfAG41s68iGoa5FcATXX1mDKDZMSIikkC13vitDe0EcCKAZwAsBnATgGcBnOTu6WORZwN4O/XYM6n/P6dNaxJTie8J+dNvw5cLXn03t7z3zl1ZVPxnXuOWUu7v4bkGz/60lCr77Ue5sf1Tfsttxf2byd+j4nMHLaPiH3lufHBsThaXl3DFAdyaBoWVvan4abk1VPzhBZuCY5ds4RKVDhrT4NIGjVpfzK11s2UHtzYH6+JFxcGxp+SOpcruzy3ngmzu4wBnT+bWXMmfEL4s/Pn3hb8/AGD/gVzl2ZyynuTl8Lmncp8HmM+FtxV3Xwvg2IC4LQC+0P41ip/EN0JERCRhvNWJqdJG1AgREZFE6aicEGmeckJEREQkIxLfE/LXX4WvEbFwG7dN+bemr6Dir50yhIp/dH34vjezH+Be6guW/IeK75XF5ZCcUMDlYTzwxgQq/vzz1wfHVr0XvqYIADz1FregRGkNN55etZvLDVpRmhccW7mbq0ufwVx+yuqV3F5Myyq4PUbYXINJ3cLX2jgsn1vbomIXdw23aBsXX1TGrRfz/L/C1w2666srqbIffJI75z97CvfZd/H9k6j4E9/jjk1DNBwTD4lvhIiISPJoOCYeEjUcY2Yzzczrbpmuj4iISJIlqhHi7jPd3epuma6PiIh0PIfDvfGbdJzED8dkdwvvlHti08bmg9L85KzwMVoA2HUV10F44tAdwbET8sqoslfdtBcV/+btXK7BVe9yOSTfnBy+ngsAnE3s71K9m8tPueVo7jx4aQ2318zzhVz7OLtbdnDsNevfaj4ozdzN06n4n5/H5QIsejGPiq/axX1kHVuwMzj2nuWDqLLzySVOjhoUXhcA+FdRHhX/8MbwNVE+/7MvU2VPePEpKv6aR7kcj3HcxwHKq1u/vkwbL0omLZSonhARERGJj8T3hIiISPKoIyQe1AgREZFEcWg4Ji4S3whZVhm+TsEzH6miyv7WNwqo+JlHrKLiX1g+Mji2cie30cUhF4XvQwEAt+zHDeoePZQbCVxQxq0/8dquucGxn+p/OFX2Te+MouJ/9smlVPwFt7xOxX914ieCY589NPycAYC/rabCsfglbu+YEQWlVPzWLdw+PBfPDd8r5yMjuG+lgmxuDZWTj+QOZvG/uLVxzhoevs7QnQe8SJU9NJtbz2VEDpffdtJoLs+qqpqrj8RX4hshIiKSMG2/W660kBohIiKSOK6skFjQ7BgRERHJCPWEiIhIoigxNT4S3wgp6BW+adUz74+myj5qMLch1uylXNIgY0EplzD4xREDqfhjXruVK3/wBVT82L7cAl6n5hwWHDucy3VENbe2GXp853Qq/uuPcAs9PV8cfmy2rQvfsBEALj1wHRV/87vcOfxlYrFAALjwdS4h8cp9w5NHuxmXaDpnM5eMXbONO4fXb+c6qi85ITwB+rHXuQ3pqsjN+phFIAHg3+uGUvFtQQujxoOGY0RERCQjEt8TIiIiyVOrxNRYUCNEREQSR8Mx8ZD4RsiAnuF5G6ecu5Uq+77bwhcPAoD3t3GbwP3s3PDNwtbN4XIBFmzmckIWn3wmFf+npdz4+OubuM2/rjtsS3Ds4i3hC1oBADfaDez+47NU/Df24V6rPn3DNzK88rWxVNk/msttwnjFgYVU/LgzuW+CO8hcgyvmh29K939TtlFlH5C3nYrPGceNfi96nEs+YvM8ROIg8Y0QERFJFgd/MSHtQ40QERFJHNd4TCxodoyIiIhkROJ7QqaPCR/Dfuk+LsfjnB9xG95dcDG37kD3M6cHx373xiKq7NtP2kDFL92QT8UXc/vjYeqAXlT8n5YNC46tIftlx/ThfuGt58LzEgBg8TZus76hvcLXt/jOVC5n4+m13PoNT6zhNm3c+x7uRBiXy+VtnDQsPPfo1mVcbtDb5Zup+PMquJyNy/fnyt9eE/5xfvPi/lTZd/2GO+4/+Ql3LCf349ZUOnYUdx5jfr2ftXdMbCS+ESIiIskS5YSoFRIHGo4RERGRjFBPiIiIJI7yUuNBjRAREUkUh2s4JiYS3wjpNzw8qe83s7jFxMb9qZSKv+VcLnn0ex8PT7o7aii38dc/FnGb9U3Lq6DiS8hd4PJ7cafqeZPCE2vH/H4GVXbhxS9T8be+xx3Ljwwvo+Lnbg5PMnx1c3jCLgCM7s29TpW7uUXoTvp0MRX/9etGUfF75YbX57zxXPLlinJuU0iAO5Zvb84jyw936jAuufrBa7mk+Sn9uc0AWcOOIMt/vH3qIa2X+EaIiIgkj4Zj4kGNEBERSRwNx8SDZseIiIhIRqgnREREEsUB1Go8JhYS3whZ9m74brEFOVxy55TZs6j4K5adS8X/6LBVwbG/m8+t1lhTy3WSHfq9HlT8bx9YQ8XPmFVOxV//4sLg2Ec/zyUYHv9/3Oqw/S/jPuz+39tccufZY8KTDA8uqKTKfruUS0j84kHLqfh3n+B2a36raiMVv++A8F2A71zWjyr74inciqZLy7hVSq9cWkLFPzwj/LxZt417rgdO4Y77394YT8U/wy3QjOPfaH0nvms4JhY0HCMiItJOzOwCM3vezErNzM1sbAMxA8zsXjMrS93uNbO8ejGjzexxM6s0sxIzu8nMuCvjGFIjREREEqe2iVsb6w3gWQAzm4i5H8BBAE4HcFrq/++te9DMsgA8CaAfgKMBfB7ApwD8pu2r27ESPxwjIiLJ0pF7x7j7DQBgZgc39LiZ7YOo4XGUu7+auu9CAC+b2WR3XwzgFABTAYxx97WpmEsB3GFml7k7t8hNjKgnREREJHNmAKgA8Grafa8AqARwRFrM+3UNkJRnAPQCEL6degwlvifk3S3hSYknDOVWPDxh6NlkbbjtrGcvHRkcO6kvV/akftwKqOWPbqLir3hlIhX//v+touIff+QTwbFjcrdQZVc9wyXJ3lnIdfCe0H8yFb+AWGD1c4dxW6DnreS2ZL/19QlUfGE1FY5fTM6h4p8kEh5v/sIyqux7/8k9188ctIKKf3QtV/6IA8KTgr/1e+51PapkEhU/LXcHFX8suaLzkNN6UfH4W/07HN707JhBZvZm2s+3uftt3B8NVgBgk6dVyN3dzIpTj9XF1F9SuwTRMrwF6MQS3wgREZHkaWY4psTdGxw+AQAz+zmAy5r5E8e7+wuB1WmoMlbv/sYq3Kmn+agRIiIiwrkBwH3NxISuQ1AIYIiZWV1viJkZgMH4b+9HIYAj6/3eIABZ2LOHpFNRI0RERBKltYmp7l6CaDikLbwGoC+ivI+6vJAZAPqk/fwagMvNbKS7r0vddzKAHQDmtVE9MkKNEBERSRxvj8m4DTCzAkR5G3ul7pqSWgNkjbtvcff3zeyfAG41s68iGoa5FcATqZkxQDTFdwGAP5nZJQDyAVwD4PbOPDMGAKyZ5Jwuzcz8/gN/nOlqdAkbqrn27PBsLlE2SXp0496TLxSHr1Z75KDOfdzH9NlOxa+u5BJZpWs6a/6VcPcPlpTt173Ap/dtfOLAi2XXzWsqJ4RhZjMBXNHAQ19y97tTMQMB3ATgzNRjjwH4lruXppUzGsDvAJwAYDuitUW+7+5cFnDMqCdEREQSxjtynZCZaHqhMrj7FgBfaCZmDYAz2qxiMaFGiIiIJEpHLlYmTVMjREREEqe2g3JCpGlaMVVEREQyIvE9IY7w7a8/Np1b8fCJeePY6nRaZ+8TOiU+8ss3RlPx+w3grlr6ZHXeq5ya2vBzEuj8yaaMJCWaMp9NAGAaXiA43DrvZ0RXkvhGiIiIJItyQuJDwzEiIiKSEYlqhJjZTDPzulum6yMiIplR28R/0nES1Qhx95nubnW3TNdHREQywVMrhTR8k46TqEaIiIiIxEfiE1OZjPJ/vDmeKvuQwVuo+LdKBlDx7YnNzH9+5XAqfkaCZnSItIRmu7QfB1Cr2TGxkPhGiIiIJI9yP+JBwzEiIiKSEeoJERGRhHH1hMSEGiEiIpIoDmgWTEwkvhHy5pbwQzC8N1d2nBJNWbVkTlz5riwqPq/Hbu4PtKPK3dyo5K/XLaTivz9iKhW/qpKrz7rK8GP5mTE1VNmXLy2i4v/9qR1U/CvvjKLi127vScXXEjPxu5FLB7FbA2gZdpE9Jb4RIiIiSeOoRXwuhJJMjRAREUkcDcfEg2bHiIiISEaoJ0RERBLF4VqsLCbUCBERkcRRTkg8JL4RcvBALR/ekCxypkCcZruw2FkOt+89jorfsJ07NtNy2fjw2PIabhbTD8Zyy/E//iYVTmNfK8bTG7jR6dO5Q4MJ/Sqp+J+/24uLPyC8/D+vGkiVfWBevD4n2ZlGEl+Jb4SIiEjSuBJTY0KNEBERSRQHUOudt/e2K9HsGBEREckI9YSIiEjCaDgmLtQIERGRxHHNjomFxDdC9h2wLTj27S3ENAQAKyq5mQgbqrgZKYcNCo/P7sa1+s84cBUV/+2nJlDxPzmwkIr/z6Z8Kv7dsvA9Rgb34o77mYdvoOLPfmgMFX/X6Wup+Gtemxgce9JQbobGlGElVPyFzxdQ8ZdO3UnF37uyDxV/7JDwL5rTh7fvlfGKcm7zqbPGcuUvLO0fHBu32S4sdvaexFfiGyEiIpI0jloNx8SCGiEiIpIoDu0dExeaHSMiIiIZoZ4QERFJGIdrnZBYUCNEREQSRzkh8WDuyc0yNjO//8AfZ7oascTuzVCQvYOKL6oOn70iIs0789CVwbHbS7nrz8cXcTO89ulfQcUv3taXil+3ncskuHzJFXD3Dz7Uenbv74P7HdJo/IbSf81z94OpPyItop4QERFJGNc6ITGhRoiIiCSKA3DXcEwcaHaMiIhIOzGzC8zseTMrNTM3s7ENxKxKPZZ+u7pezGgze9zMKs2sxMxuMrNOP66tnhAREUmYDl2srDeAZwH8A8D1TcT9DMDv037+ILHGzLIAPAlgM4CjAeQDuAeAAbiojevbodQIERGRZHF02BRdd78BAMysuUTXcndvbD+LUwBMBTDG3demyrsUwB1mdpm7h+8/EjMajhEREcm875vZZjP7j5ldVm+oZQaA9+saICnPAOgFYHqH1rKNqSdE2kQPcoO8qt1c+/fg/FIqPr9fVXDssGO4uj/xELdJ2xlnF1PxX/v1CCp+79zw6dRT+1dTZW8kp1IvKONe169O2kTFX/3uICr+O3uXBsf+fd1Aquzd5OoG03JrqPhJ5DTX216eFBxb0IvbwI7dAHNlBbdZX0+y/PF9WjuU4s0t2z7IzN5M+/k2d7+tlX+0KTcBmI9ouOVQAFcDGAfgK6nHCwAU1fudEgC7U491WmqEiIhIogTMjilpap0QM/s5gMua+TPHu/sLQfVxvy7tx3fMbBuAh8zsB+6+uS6ssV8P+RtxpUaIiIgI5wYA9zUTs6YV5c9N/TsRUe9IIYAj68UMApCFPXtIOhU1QkREJGFat1iZu5cgGg5pLwek/t2Y+vc1AJeb2Uh3X5e672QAOwDMa8d6tDs1QkREJHE6arEyMytAlLexV+quKWaWB2CNu28xsxkADgfwPIAyAIcgmsr7mLvX9aY8C2ABgD+Z2SWIpuheA+D2zjwzBtDsGBERkfb0NURJp39O/fxk6uczUz/vAPBZAC8AWIhovZDbAXy+rgCP5hN/FEAVgFcAPATgbwC+3+61b2fqCRERkcTpqJ4Qd58JYGYTj7+FqCekuXLWADijzSoWE9pFNyG76LK74lrnTrgWEfnAWfOv/NAuullZOd43Z0Kj8dsqF2gX3Q6i4RgRERHJCA3HiIhI4mgX3XhQI0RERJLFvcP2jpGmaThGREREMkI9ISIikjjN7B0jHUSNEBERSRhXTkhMqBGSEOyUW3ZKb5Zx5R+Qv5WKf6tkABUvIiLxp0aIiIgkSsAuutJB1AgREZHEUU5IPKgRIiIiCaOckLjQFF0RERHJCPWEiIhI4qgnJB7UCBERkYRxQDkhsaBGiDSIndJbS266y0657dGN+wM1tdwUY8aAnruo+PfLs6n4gl5c+SIinZUaISIikiyu4Zi4UCNEREQSxaEpunGh2TEiIiKSEeoJERGRhNE6IXGhRoiIiCTQ7kxXQKDhGBEREckQ9YSIiEjCaDgmLmLVCDGzHwL4BIDJAHYAmAPgh+7+XlqMAbgCwAUABgCYC+Cb7r6g42ssHaU91/1gbd3JvW207odIHKkREgdxG445DsDvABwB4AQAuwDMNrOBaTGXArgEwEUADgFQDGCWmfXr2KqKiIhIa8SqJ8TdT03/2czOAVAG4EgAj6d6QS4GcLW7P5KKORdRQ+QsALd2aIVFRKQTckDDMbEQt56Q+vohquPW1M/jABQAeLYuwN23A3gJUe+JiIhIs7yJ/6TjxL0RciOA/wB4LfVzQerfonpxRWmPfcDMZpqZN3Zrt1qLiIhIs2LbCDGz6wAcBeCT7l5/Qnf9BoQ1cB/cfaa7W2O3dqq6iIjEXm0TN+kosWyEmNn1AD4P4AR3X5H2UGHq3/q9HkOwZ++IiIhIw9wbv0mHiV0jxMxuRJRkeoK7L6r38EpEDZGT0+KzARwN4NUOq6SIiIi0mnmMWn1mdguAcwB8HMDCtIcq3L0iFfMDAJcBOA/AEgCXAzgGwGR3Lyf/XnyevIiItJv0IXgz+yeAQU2El7j7ae1fK4lbI6SxyvzU3WemYuoWK7sQH16s7L1GfrfJv9dWuSEqS2V1hrLiWCeVpbI6U1nStmLVCOlocT3JVZbKaq+y4lgnlaWyOlNZ0rZilxMiIiIiyaBGSNv5aQLKaktxfY5d/XjpWHWNstpSXJ9jXI+XtCENx6iLLpiOF0fHK5yOFUfHi6PjFV/qCREREZGMUCNEREREMiLpjRCNOXJ0vDg6XuF0rDg6Xhwdr5hKdE6IiIiIZE7Se0JEREQkQ9QIERERkYxQI0REREQyoks3QszsG2a20syqzWyemR3dTPw0M3vRzLab2Xoz+0lqr5pEYI6XmR1nZv8ws41mVmVm75jZlzuyvpnEnltpvzfJzMrNrKK96xgnLXgvmpldbGaLzGxH6jy7uqPqm2ktOF6nmtlrqXOrJPXe3Kuj6pspZnaMmT2W+rx2Mzsv4HcS/TkfN122EWJmnwVwI4BfAjgQwKsAnjaz0Y3E9wcwC0ARgEMAfBvA/wH4XodUOMPY4wXgCADvAvgUgH0B/B7AbWZ2VgdUN6NacKzqfq8ngAcBvNTulYyRFh6v3wD4BoAfANgHwEeQkOPWgs+ucQD+AeDlVPxJAHIAPNUhFc6svgDeA/AdANubC07653wsuXuXvCHaXff2evctBXBVI/FfB7ANQE7afZcDWI/ULKKufGOPVyNl/AXAI5l+LnE9VgCuB3AXgPMAVGT6ecT1eAGYDKAGwD6ZrnsnOV6fArAbQFbafccDcACDMv18OvC4VQA4r5mYRH/Ox/HWJXtCUlec0wE8W++hZxFdwTdkBoCX3T29Nf0MgOEAxrZ1HeOkhcerIf0BbG2resVRS4+VmX0UwBmIrrwSo4XH638ArABwmpmtMLNVZnaPmQ1px6rGQguP15uIGm1fMbMsM+sH4FwAb7h7SbtVtnNK7Od8XHXJRgiAQQCyEHW5pSsCUNDI7xQ0El/3WFfWkuP1IWZ2BoATAdzWtlWLHfpYmdkwALcDOMfdy9u3erHTknNrPIAxAD6HqNfoHAB7A3jczLrqZ1Yd+ni5+yoAJyNakGsHgDIA0xA1euXDkvw5H0td/Q1dfyU2a+C+5uIbur+rYo9XFGR2JID7AXzb3V9vj4rFEHOs7gPwe3ef075VijXmeHUD0AtRo+0ld38ZUUPkUETj+EkQfLzMrADAnQD+hOj4HAegHMBfEtBoa4mkf87HSlc9QUsQjZHWb9kOwZ6t4DqFjcSjid/pKlpyvAAAZnYUgKcB/MTdf98+1YuVlhyrEwBcYWa7zGwXoi+MPqmfL2i/qsZCS47XRgC73H1J2n1LAewC0GTybxfQkuP1TQCV7n6pu89395cAfAHAseCGU5MgyZ/zsdQlGyHuvhPAPERdlOlORpRp3pDXABxtZtn14jcAWNXWdYyTFh4vmNkxiBogP3X3G9qtgjHSwmM1DcABabefIMrkPwDAw21fy/ho4fF6BUB3M5uQdt94AN0BrG7zSsZIC49Xb0QNl3R1P3fJz/hWSOznfGxlOjO2vW4APgtgJ4CvIJridyOi7OkxqcevAvBcWnwuolbyg4imnH4CURb1JZl+LjE9XscBqARwDaIri7rb4Ew/l7gdqwZ+/zwka3YMe251Q/RF/CKiKacHpv5/DoBumX4+MTxeJwCoBXAFgEkADgLwTwBrAPTJ9PNp52PVF/9t3FchauAfAGB0I8cq0Z/zcbxlvALt+uSidQZWIUrWmgfgmLTH7gawql78NERrEVQj6hK+AgmatsUcr9TP3sBtVUfXO+7HqoHfTVQjpCXHC8AwRL1E5QCKAfwZwNBMP48YH6/PAXgr1VjZBOBxAFMy/Tw64Dgd18jn0N1NHKtEf87H7aZddEVERCQjNF4oIiIiGaFGiIiIiGSEGiEiIiKSEWqEiIiISEaoESIiIiIZoUaIiIiIZIQaISKdmJkdZ2ZuZoMyXRcREZYaISIxZ2Z3pxoabmY1qe3trzWzPpmum4hIa3TPdAVEJMhsRDvJ9gBwNIA7APQB8FAmKyUi0hrqCRHpHHa4e6G7r3X3+xEtY/7xtMf3N7O5ZlZlZm+a2UF1D5hZvpk9YGbrzGy7mS0wsy+lF25mx5jZHDOrMLOyVFn7pj1+hJm9mCp/vZn93sz6t/eTFpGuTY0Qkc5pO6JekTpXAfh/iDYv2wzgz2ZmqceyEe0rcgaAqYg2RLvVzE4EADPrDuAfAP4NYH8Ah6VidqcenwbgWQCPpR7/BKJNwv7Ybs9ORBJBe8eIxJyZ3Q1gkLufkfr5UABPAXgOwO8BPA/gNHd/JvX4kYgaFKPcfV0jZT6IaBO9r5jZQEQNl+Pc/cUGYv8EoMbdz0+77wAA8xFtKlfcVs9VRJJFOSEincNpZlaB6D3bA1HPxUUApqQefyctdkPq3yEA1plZFqJeks8CGAGgF4CeAF4AAHffkmroPGNmzyFq3Dzs7mtT5UwHMNHMPpv2N+p6WSYg2uVWRISm4RiRzuElREMgkwFku/sn6vVA1KT9f133Zt37+/sALgFwDYATU+U8iqghEv2C+5cQDcO8BOBMAEvM7NS0cu5I/V7dbX8AkwD8p7VPTESSSz0hIp1Dlbsva+HvHgXgcXe/FwBSuSJ7AShND3L3twG8DeBXZvY0gHMBPIMon2RqK/6+iEiD1BMi0vUtAXCimR1lZnsD+C2AcXUPmtk4M7s6NQNmjJkdD2A/AAtTIb8CcKiZ/cHMDjSziWZ2hpnd2uHPRES6FPWEiHR9P0fU6Hga0ayauxFN8a3LJ6lC1DPyMIBBAIpSj/8KANz9HTM7JlXOiwCyAKwA8PcOewYi0iVpdoyIiIhkhIZjREREJCPUCBEREZGMUCNEREREMkKNEBEREckINUJEREQkI9QIERERkYxQI0REREQyQo0QERERyQg1QkRERCQj/j8VaqbUEYQHJAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(diff, NICER.data.phases, NICER.data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}