1-magnetic-inversion-raglan-reproduce.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "qRtkY1ZV0H5EWNEJOHXM",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "# 3D Magnetic inversion of Raglan Data\n",
    "\n",
    "In this notebook, we inverted the magnetic data acquired at [Raglan deposit](https://en.wikipedia.org/wiki/Raglan_Mine) located in Northern Quebec, Canada to obtain a three-dimensional susceptibility model of the subsurface. Magnetic module of an open-source geophysics software, [SimPEG ](https://www.simpeg.xyz)was used for this inversion. About 20 years ago, this data was inverted using the [MAG3D code](https://mag3d.readthedocs.io/en/latest/content/overview.html) developed by [UBC-GIF](https://gif.eos.ubc.ca/about) group, and a 3D susceptibility model was obtained. This was sort of the first time that field magnetic data was inverted in 3D, and made a significant impact on locating drilling location for a mineral exploration. By revisiting this historical data set with the latest SimPEG code, we aim to archive this field example and make it reproducible such that it can be used as an educational resource."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "fGeLrs950fsUhJ35PQpf",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "<center> <img src=\"./figures/mag3disco.gif\"></img> </center>\n",
    "\n",
    "![](block:FesuZy9nERkRH9r67vBe/MgG4npa3Bdz1K9oqS01m/1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "iOQiiQGbIRruyhisHj5b",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import tarfile\n",
    "\n",
    "from discretize import TensorMesh\n",
    "\n",
    "from SimPEG.potential_fields import magnetics\n",
    "from SimPEG import dask\n",
    "from SimPEG.utils import plot2Ddata, surface2ind_topo\n",
    "from SimPEG import (\n",
    "    maps,\n",
    "    data,\n",
    "    inverse_problem,\n",
    "    data_misfit,\n",
    "    regularization,\n",
    "    optimization,\n",
    "    directives,\n",
    "    inversion,\n",
    "    utils,\n",
    ")\n",
    "import pandas as pd\n",
    "from ipywidgets import widgets, interact\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "aSpLw62yhupdk0DZ6aw3",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Load Data and Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "4WMjX0CWjQhIJiiNZYEt",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def read_ubc_magnetic_data(data_filename):\n",
    "    with open(data_filename, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    tmp = np.array(lines[0].split()[:3]).astype(float)\n",
    "    n_data = int(float(lines[2].split()[0]))\n",
    "    meta_data = {}\n",
    "    meta_data['inclination'] = float(tmp[0])\n",
    "    meta_data['declination'] = float(tmp[1])\n",
    "    meta_data['b0'] = float(tmp[2])\n",
    "    meta_data['n_data'] = n_data\n",
    "    data = np.zeros((n_data, 5), order='F')\n",
    "    for i_data in range(n_data):\n",
    "        data[i_data,:] = np.array(lines[3+i_data].split()).astype(float)\n",
    "    df = pd.DataFrame(data=data, columns=['x', 'y', 'z', 'data', 'data_error'])\n",
    "    return df, meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "fd8qpddeKWXx77YRwK7m",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "data_filename = \"./data/Raglan_1997/obs.mag\"\n",
    "df, meta_data = read_ubc_magnetic_data(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "abbsc6MxLpNsiK3i5uwv",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "VxaqhRixSkl0mwRbAFUi",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'inclination': 83.0, 'declination': -32.0, 'b0': 60000.0, 'n_data': 1638}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "R6Xvh3ZSQQVOrK5vJzEO",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "TES5YUyw3p0KmSchX151",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>data</th>\n",
       "      <th>data_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>509.0</td>\n",
       "      <td>42958.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.90</td>\n",
       "      <td>5.8580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>504.0</td>\n",
       "      <td>42858.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>88.32</td>\n",
       "      <td>6.7664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>499.3</td>\n",
       "      <td>42758.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>86.11</td>\n",
       "      <td>6.7222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       x        y     z   data  data_error\n",
       "0  509.0  42958.0  40.0  42.90      5.8580\n",
       "1  504.0  42858.0  40.0  88.32      6.7664\n",
       "2  499.3  42758.0  40.0  86.11      6.7222"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "MR10Asrw7Y4R3lRT1VWk",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
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     "outputId": {
      "block": "lV9xDgMmK3Ihtx2Zzlpt",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Down sample the data\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "nskip = 2\n",
    "receiver_locations = df[['x', 'y', 'z']].values[::nskip,:]\n",
    "dobs = df['data'].values[::nskip]\n",
    "# Plot\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "vmin, vmax = np.percentile(dobs, 0.5), np.percentile(dobs, 99.5)\n",
    "tmp = np.clip(dobs, vmin, vmax)\n",
    "ax1 = fig.add_axes([0.1, 0.1, 0.75, 0.85])\n",
    "plot2Ddata(\n",
    "    receiver_locations,\n",
    "    tmp,\n",
    "    ax=ax1,\n",
    "    ncontour=30,\n",
    "    clim=(vmin-5, vmax+5),\n",
    "    contourOpts={\"cmap\": \"Spectral_r\"},\n",
    ")\n",
    "ax1.set_title(\"TMI Anomaly\")\n",
    "ax1.set_xlabel(\"x (m)\")\n",
    "ax1.set_ylabel(\"y (m)\")\n",
    "\n",
    "ax2 = fig.add_axes([0.9, 0.25, 0.05, 0.5])\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "cbar = mpl.colorbar.ColorbarBase(\n",
    "    ax2, norm=norm, orientation=\"vertical\", cmap=mpl.cm.Spectral_r\n",
    ")\n",
    "cbar.set_label(\"$nT$\", rotation=270, labelpad=15, size=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "gxTRkyTi8crjI1jCgy7j",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Assign Uncertainty\n",
    "\n",
    "Inversion with SimPEG requires that we define data error i.e., standard deviation on the observed data.\n",
    "This represents our estimate of the noise in our data. \n",
    "For this magnetic inversion, 2% relative error and a noise floor of 2 nT are assigned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "o4B8UEwAqiVOQkj2WkcB",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "standard_deviation = 0.02 * abs(dobs) + 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Wzh206pv5WvgN66aI5RY",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Defining the Survey\n",
    "\n",
    "Here, we define a survey object that will be used for the simulation. \n",
    "The user needs an (N, 3) array to define\n",
    "the xyz locations of the observation locations and the list of field components\n",
    "which are to be modeled and the properties of the Earth's field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "1ckCTPBEKAAaJaSjGmtf",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define the component(s) of the field we are inverting as a list. Here we will\n",
    "# Invert total magnetic intensity data.\n",
    "components = [\"tmi\"]\n",
    "\n",
    "# Use the observation locations and components to define the receivers. To\n",
    "# simulate data, the receivers must be defined as a list.\n",
    "receiver_list = magnetics.receivers.Point(receiver_locations, components=components)\n",
    "\n",
    "receiver_list = [receiver_list]\n",
    "\n",
    "# Define the inducing field H0 = (intensity [nT], inclination [deg], declination [deg])\n",
    "inclination = meta_data['inclination']\n",
    "declination = meta_data['declination']\n",
    "strength = meta_data['b0']\n",
    "inducing_field = (strength, inclination, declination)\n",
    "\n",
    "source_field = magnetics.sources.SourceField(\n",
    "    receiver_list=receiver_list, parameters=inducing_field\n",
    ")\n",
    "\n",
    "# Define the survey\n",
    "survey = magnetics.survey.Survey(source_field)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "tvdlWJXlPbY0wx3e7WXF",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Defining the Data\n",
    "\n",
    "Here is where we define the data that is inverted. The data is defined by\n",
    "the survey, the observation values and the standard deviations.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "0ilUUAtZ37JPhGvv2mbt",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "data_object = data.Data(survey, dobs=dobs, standard_deviation=standard_deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "pmjLnrDiBqWle3682tjW",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Defining a Tensor Mesh\n",
    "\n",
    "Here, we create the tensor mesh that will be used to invert TMI data.\n",
    "If desired, we could define an OcTree mesh.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "iHgpup2yTu0Mj7lCqEsW",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "unvn0z3GAlZtU2vLtvBf",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:6: UserWarning: Warning: converting a masked element to nan.\n",
      "C:\\Users\\sgkan\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy\\ma\\core.py:717: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh = TensorMesh.readUBC('./data/Raglan_1997/mesh.msh')\n",
    "susceptibility_ubc = mesh.readModelUBC('./data/Raglan_1997/maginv3d.sus')\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "mesh.plotSlice(susceptibility_ubc*np.nan, ax=ax, grid=True)\n",
    "ax.plot(receiver_locations[:,0], receiver_locations[:,1], 'r.')\n",
    "ax.set_xlabel(\"Easting (m)\")\n",
    "ax.set_ylabel(\"Northing (m)\")\n",
    "ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Ne89kliyBxHX9gSKNsQZ",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "GIAeJhVPzMtdIiGeVqHN",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "mesh.plotSlice(susceptibility_ubc*np.nan, ax=ax, grid=True, normal='Y')\n",
    "ax.set_xlabel(\"Easting (m)\")\n",
    "ax.set_ylabel(\"Depth (m)\")\n",
    "ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "rGIVDWKhXkDBNFhfkj22",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Starting/Reference Model and Mapping on Tensor Mesh\n",
    "\n",
    "Here, we would create starting and/or reference models for the inversion as\n",
    "well as the mapping from the model space to the active cells. Starting and\n",
    "reference models can be a constant background value or contain a-priori\n",
    "structures. Here, the background is 1e-4 SI.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "lJeZjRvy64nf1ukBT8XV",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define background susceptibility model in SI. Don't make this 0!\n",
    "# Otherwise the gradient for the 1st iteration is zero and the inversion will\n",
    "# not converge.\n",
    "background_susceptibility = 1e-4\n",
    "\n",
    "# Find the indecies of the active cells in forward model (ones below surface)\n",
    "ind_active = surface2ind_topo(mesh, np.c_[receiver_locations[:,:2], np.zeros(survey.nD)])\n",
    "\n",
    "# Define mapping from model to active cells\n",
    "nC = int(ind_active.sum())\n",
    "model_map = maps.IdentityMap(nP=nC)  # model consists of a value for each cell\n",
    "\n",
    "# Define starting model\n",
    "starting_model = background_susceptibility * np.ones(nC)\n",
    "reference_model = np.zeros(nC) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "7mp2PjQqPE6exXFH4DKy",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Define the Physics\n",
    "\n",
    "Here, we define the physics of the magnetics problem by using the simulation\n",
    "class.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "w9Gs27cjctKjJ8lw0aZ7",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define the problem. Define the cells below topography and the mapping\n",
    "simulation = magnetics.simulation.Simulation3DIntegral(\n",
    "    survey=survey,\n",
    "    mesh=mesh,\n",
    "    modelType=\"susceptibility\",\n",
    "    chiMap=model_map,\n",
    "    actInd=ind_active,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "PjLWRNUJRbslk1oZg2ja",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Define Inverse Problem\n",
    "\n",
    "The inverse problem is defined by 3 things:\n",
    "\n",
    "    1) Data Misfit: a measure of how well our recovered model explains the field data\n",
    "    2) Regularization: constraints placed on the recovered model and a priori information\n",
    "    3) Optimization: the numerical approach used to solve the inverse problem\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "biSNevd7G3QLa1R54kPI",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define the data misfit. Here the data misfit is the L2 norm of the weighted\n",
    "# residual between the observed data and the data predicted for a given model.\n",
    "# Within the data misfit, the residual between predicted and observed data are\n",
    "# normalized by the data's standard deviation.\n",
    "dmis = data_misfit.L2DataMisfit(data=data_object, simulation=simulation)\n",
    "\n",
    "# Define the regularization (model objective function)\n",
    "reg = regularization.Sparse(\n",
    "    mesh,\n",
    "    indActive=ind_active,\n",
    "    mapping=model_map,\n",
    "    mref=reference_model,\n",
    "    alpha_s=1,\n",
    "    alpha_x=1,\n",
    "    alpha_y=1,\n",
    "    alpha_z=1,\n",
    ")\n",
    "\n",
    "# Define sparse and blocky norms p, qx, qy, qz\n",
    "reg.norms = np.c_[0, 2, 2, 2]\n",
    "\n",
    "# Define how the optimization problem is solved. Here we will use a projected\n",
    "# Gauss-Newton approach that employs the conjugate gradient solver.\n",
    "opt = optimization.ProjectedGNCG(\n",
    "    maxIter=20, lower=0.0, upper=np.Inf, maxIterLS=20, maxIterCG=30, tolCG=1e-4\n",
    ")\n",
    "\n",
    "# Here we define the inverse problem that is to be solved\n",
    "inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "MYDZcQH0NLqnprNNx01e",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Define Inversion Directives\n",
    "\n",
    "Here we define any directiveas that are carried out during the inversion. This\n",
    "includes the cooling schedule for the trade-off parameter (beta), stopping\n",
    "criteria for the inversion and saving inversion results at each iteration.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "jC5Pl8mpNY6r2eBmycRM",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Defining a starting value for the trade-off parameter (beta) between the data\n",
    "# misfit and the regularization.\n",
    "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=1)\n",
    "\n",
    "# the cooling schedule for the trade-off parameter.\n",
    "beta_schedule = directives.BetaSchedule(coolingFactor=2, coolingRate=1)\n",
    "\n",
    "# Options for outputting recovered models and predicted data as a dictionary\n",
    "save_dictionary = directives.SaveOutputDictEveryIteration()\n",
    "\n",
    "# Updating the preconditionner if it is model dependent.\n",
    "update_jacobi = directives.UpdatePreconditioner()\n",
    "\n",
    "# Setting a stopping criteria for the inversion.\n",
    "target_misfit = directives.TargetMisfit(chifact=1)\n",
    "\n",
    "# Add sensitivity weights\n",
    "sensitivity_weights = directives.UpdateSensitivityWeights(everyIter=False)\n",
    "opt.remember('xc')\n",
    "# The directives are defined as a list.\n",
    "directives_list = [\n",
    "    sensitivity_weights,\n",
    "    starting_beta,\n",
    "    save_dictionary,\n",
    "    beta_schedule,\n",
    "    update_jacobi,\n",
    "    target_misfit\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Lgvf2F8PMgsWydT1e5Xz",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Running the Inversion\n",
    "\n",
    "To define the inversion object, we need to define the inversion problem and\n",
    "the set of directives. We can then run the inversion.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "8snLNJ3cQYqc4uq40eeb",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "wR8khh1jA6CmBGyOSOaL",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "        ***Done using same Solver and solverOpts as the problem***\n",
      "((819,), (16000,))\n",
      "Computing sensitivities to local ram\n",
      "[########################################] | 100% Completed |  8.4s\n",
      "model has any nan: 0\n",
      "=============================== Projected GNCG ===============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  4.26e+06  3.24e+05  3.96e-06  3.24e+05    7.16e+05      0              \n",
      "   1  2.13e+06  7.42e+04  9.96e-03  9.54e+04    2.01e+05      0              \n",
      "   2  1.07e+06  4.30e+04  1.79e-02  6.21e+04    1.07e+05      0   Skip BFGS  \n",
      "   3  5.33e+05  2.82e+04  2.72e-02  4.27e+04    1.08e+05      0   Skip BFGS  \n",
      "   4  2.66e+05  1.73e+04  4.03e-02  2.81e+04    5.61e+04      0   Skip BFGS  \n",
      "   5  1.33e+05  1.04e+04  5.78e-02  1.81e+04    4.63e+04      0   Skip BFGS  \n",
      "   6  6.66e+04  5.56e+03  8.17e-02  1.10e+04    2.93e+04      0   Skip BFGS  \n",
      "   7  3.33e+04  2.72e+03  1.09e-01  6.37e+03    1.49e+04      0   Skip BFGS  \n",
      "   8  1.66e+04  1.27e+03  1.38e-01  3.57e+03    1.04e+04      0   Skip BFGS  \n",
      "   9  8.32e+03  7.01e+02  1.64e-01  2.07e+03    1.19e+04      0   Skip BFGS  \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 3.2414e+04\n",
      "0 : |xc-x_last| = 3.4050e-01 <= tolX*(1+|x0|) = 1.0126e-01\n",
      "0 : |proj(x-g)-x|    = 1.1938e+04 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.1938e+04 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =      20    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Here we combine the inverse problem and the set of directives\n",
    "inv = inversion.BaseInversion(inv_prob, directives_list)\n",
    "\n",
    "# Run the inversion\n",
    "recovered_model = inv.run(starting_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "oTOuI0F9a3mdifs9QIbz",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def plot_tikhonov_curve(iteration, scale):\n",
    "    phi_d = []\n",
    "    phi_m = []\n",
    "    beta = []\n",
    "    iterations = np.arange(len(save_dictionary.outDict)) + 1\n",
    "    for kk in iterations:\n",
    "        phi_d.append(save_dictionary.outDict[kk]['phi_d'])\n",
    "        phi_m.append(save_dictionary.outDict[kk]['phi_m'])\n",
    "        beta.append(save_dictionary.outDict[kk]['beta'])\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(12,5))\n",
    "    axs[0].plot(phi_m ,phi_d, 'k.-')\n",
    "    axs[0].plot(phi_m[iteration-1] ,phi_d[iteration-1], 'go', ms=10)\n",
    "    axs[0].set_xlabel(\"$\\phi_m$\")\n",
    "    axs[0].set_ylabel(\"$\\phi_d$\")\n",
    "    axs[0].grid(True)\n",
    "\n",
    "    axs[1].plot(iterations, phi_d, 'k.-')\n",
    "    axs[1].plot(iterations[iteration-1], phi_d[iteration-1], 'go', ms=10)\n",
    "    ax_1 = axs[1].twinx()\n",
    "    ax_1.plot(iterations, phi_m, 'r.-')\n",
    "    ax_1.plot(iterations[iteration-1], phi_m[iteration-1], 'go', ms=10)\n",
    "    axs[1].set_ylabel(\"$\\phi_d$\")\n",
    "    ax_1.set_ylabel(\"$\\phi_m$\")\n",
    "    axs[1].set_xlabel(\"Iterations\")\n",
    "    axs[1].grid(True)\n",
    "    axs[0].set_title(\n",
    "        \"$\\phi_d$={:.1e}, $\\phi_m$={:.1e}, $\\\\beta$={:.1e}\".format(phi_d[iteration-1], phi_m[iteration-1], beta[iteration-1]),\n",
    "        fontsize = 14\n",
    "    )\n",
    "    axs[1].set_title(\"Target misfit={:.0f}\".format(survey.nD/2))\n",
    "    for ii, ax in enumerate(axs):        \n",
    "        if ii == 0:\n",
    "            ax.set_xscale(scale)        \n",
    "        ax.set_yscale(scale)\n",
    "        xlim = ax.get_xlim()\n",
    "        ax.hlines(survey.nD/2, xlim[0], xlim[1], linestyle='--', label='$\\phi_d^{*}$')\n",
    "        ax.set_xlim(xlim)\n",
    "    axs[0].legend()\n",
    "    plt.tight_layout()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "qGxgkUKOU6TYYGB9IBfJ",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "onrFGjCzqtEL9A7Tc0yx",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_misfit.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "7N2FEFNOn3Jba0o84N0t",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "bvG76IZ5fOCgCYnJCm87",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07981cb519c04b5db7a0abd37882d2b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='iteration', max=10, min=1), RadioButtons(description='sc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_tikhonov_curve(iteration, scale)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(\n",
    "    plot_tikhonov_curve, iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), step=1),\n",
    "    scale=widgets.RadioButtons(options=[\"linear\", \"log\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "5H2X5fwrwFqXtxOnpkoe",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def plot_dobs_vs_dpred(iteration):\n",
    "    # Predicted data with final recovered model\n",
    "    dpred = save_dictionary.outDict[iteration]['dpred']\n",
    "\n",
    "    # Observed data | Predicted data | Normalized data misfit\n",
    "    data_array = np.c_[dobs, dpred, (dobs - dpred) / standard_deviation]\n",
    "    vmin, vmax = dobs.min(), dobs.max()\n",
    "    fig = plt.figure(figsize=(17, 4))\n",
    "    plot_title = [\"Observed\", \"Predicted\", \"Normalized Misfit\"]\n",
    "    plot_units = [\"nT\", \"nT\", \"\"]\n",
    "\n",
    "    ax1 = 3 * [None]\n",
    "    ax2 = 3 * [None]\n",
    "    norm = 3 * [None]\n",
    "    cbar = 3 * [None]\n",
    "    cplot = 3 * [None]\n",
    "    v_lim = [(vmin, vmax), (vmin, vmax),(-3,3)]\n",
    "\n",
    "    for ii in range(0, 3):\n",
    "\n",
    "        ax1[ii] = fig.add_axes([0.33 * ii + 0.03, 0.11, 0.25, 0.84])\n",
    "        cplot[ii] = plot2Ddata(\n",
    "            receiver_list[0].locations,\n",
    "            data_array[:, ii],\n",
    "            ax=ax1[ii],\n",
    "            ncontour=30,\n",
    "            clim=v_lim[ii],\n",
    "            contourOpts={\"cmap\": \"Spectral_r\"},\n",
    "        )\n",
    "        ax1[ii].set_title(plot_title[ii])\n",
    "        ax1[ii].set_xlabel(\"x (m)\")\n",
    "        ax1[ii].set_ylabel(\"y (m)\")\n",
    "\n",
    "        ax2[ii] = fig.add_axes([0.33 * ii + 0.27, 0.11, 0.01, 0.84])\n",
    "        norm[ii] = mpl.colors.Normalize(vmin=v_lim[ii][0], vmax=v_lim[ii][1])\n",
    "        cbar[ii] = mpl.colorbar.ColorbarBase(\n",
    "            ax2[ii], norm=norm[ii], orientation=\"vertical\", cmap=mpl.cm.Spectral_r\n",
    "        )\n",
    "        cbar[ii].set_label(plot_units[ii], rotation=270, labelpad=15, size=12)\n",
    "    for ax in ax1[1:]:\n",
    "        ax.set_ylabel(\"\")\n",
    "        ax.set_yticklabels([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "T0BeCSGrILtcOihG1lyd",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "G1mYEKMGtPTwigL9qtL4",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34a8be613c414bf79d58fa16592165dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='iteration', max=10, min=1), Output()), _dom_classes=('wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_dobs_vs_dpred(iteration)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(plot_dobs_vs_dpred, iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), step=1, value=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "NCgDJZsySu8Ekeiwwsuu",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "D0C7T3d3j55vsMOJehsb",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a5c0b5ea5c34a51bed24953ec90f814",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='iteration', max=10, min=1), FloatText(value=2000.0, desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_recovered_model(iteration, xslice, yslice, zslice, vmax)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_recovered_model(iteration, xslice, yslice, zslice, vmax):\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    mesh.plot_3d_slicer(\n",
    "        save_dictionary.outDict[iteration]['m'], clim=(0, vmax),\n",
    "        xslice=xslice,\n",
    "        yslice=yslice,\n",
    "        zslice=zslice,\n",
    "        fig=fig,\n",
    "        pcolor_opts={'cmap':'Spectral_r'}\n",
    "    )\n",
    "interact(\n",
    "    plot_recovered_model, \n",
    "    iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), value=0),\n",
    "    xslice=widgets.FloatText(value=2000, step=100),\n",
    "    yslice=widgets.FloatText(value=41000, step=100),\n",
    "    zslice=widgets.FloatText(value=-500, step=100),\n",
    "    vmax=widgets.FloatText(value=0.07),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "hRE67wg0YfCRzGSCPL22",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Comparing the historic model with the recovered model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "96iZsmnlbnTTOAPDtzin",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "def plot_3d_with_pyvista(model, notebook=True, threshold=0.04):\n",
    "    pv.set_plot_theme(\"document\")\n",
    "    # Get the PyVista dataset of the inverted model\n",
    "    dataset = mesh.to_vtk({'susceptibility':model})\n",
    "    # Create the rendering scene\n",
    "    p = pv.Plotter(notebook=notebook)\n",
    "    # add a grid axes\n",
    "    p.show_grid()\n",
    "    # Extract volumetric threshold\n",
    "    threshed = dataset.threshold(threshold, invert=False)\n",
    "    # Add spatially referenced data to the scene\n",
    "    dparams = dict(\n",
    "        show_edges=False,\n",
    "        cmap=\"Spectral_r\",\n",
    "        clim=[0, 0.1],\n",
    "        stitle='sus', \n",
    "    )\n",
    "    p.add_mesh(threshed, **dparams)\n",
    "    p.set_scale(1,1,1)\n",
    "    cpos = [(-5090.61095767987, 35424.20054814459, 5280.45524943451),\n",
    "     (2298.051317829793, 40974.692421295964, -864.2486811315523),\n",
    "     (0.4274014723619113, 0.35262874486945933, 0.8324547733749025)]\n",
    "    p.camera_position = cpos\n",
    "    p.show(window_size=[1024, 768])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "CxVlgqC3E9wUuzh0UUh6",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "### Recovered susceptiblity model from SimPEG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "XLuc32zWuC8IM9NhO15o",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "rNXqNFtT2nBIcYoLKPXO",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6k4DUbW5Nxe3r/MHP36IuqCaQevdfS5ff9ue+M6dJBipEAQBQlMpTToUHrilzxX/R9UY/uMuxyZ2hQTbqNbemRB1m2ZOWqgs7frYr9pFTR/8fXjOhLkRvCrj8tj/ry64duwkoHwUAQP5ciP7lz0Al0b+0qZSwn3F29Qp29Zpbj0QdZrYJzPTCbvDor49kn6WIJmCmf1Ot3wIgCwoAgJw5crzIBk+0nFc4Yirur8CuV7Cr19yacpnzXNK/ydMEwtK/qb5vAZAOBQBAbhwZ7t/giZa/4j9CLTKT+1stTLV4SQOlm/Pco7/H8gdPisiqB6/YeN296prLfve72Hu532+B7CgAAHLQwugvTdzTN91UahGy65Xq0uVpF47UpGfgjL96QvQtk6b/Zz1/9pzZ37tyn83tX/T38zvGrDpuhYjoGmCjvmUMsFH9wgJArdUl+hdxfHR3hvvndfqn6NNaVX7Y+xzfxOwzWXLaLu1jkBdvAfDtAPCbrz2gLry0c2j0Q+no7xHWBMzoH+hxV/wo7E8RryFNAE3i0MICQL3UJfrr2+cyq24O98/47AoKl7kHpiICbuqZrPAYUxIytzUtAFpgEwhL/yazCcSmf5O/Cdi8hjQBNABDgAAkVvlIj8Yf37O0yRU6lVoMoqjFTGr1mlvT7lt2jT12qf6nP/2LyFf629QF1QRsor+ib7n0wKlEc/WWvzjiwp8nPvxofd8FQKMAAEjAhehf/gwQ/TOq0U4C4vZManXMoLtvmT0bgNkEAqkm8LPfDLzl0Q+1fHDPcKDY7QCXbL3T8pEj1PFdABSHNhcCcFnlqSjjDKTbaFCX6J/02RV9+9hHUxdcHnUTO5NOjbcR9zKoZwjQ7pt2mP/c9oeZ6Ls/5MgB85/RTSBit+DAJqDTv2f1f/b31LV3AQjDFgAAMeoe/bNMt+S9PNNNLtEdK383pSbrTWsxk6Z+v68+CY7McP/QIzrbUp7c1+/C3/xJXfA0gdgjAn3ts7MvgmoCuaz4j1C7jw1aiwIAIFTlYbHC6F/mdJs35sdSLUbdBEY611b/m9x5VfuHHiEiOdYAWdgELI8Hqnzts/2Tb/rr00Qu+/8uzXF+wtAE4DgKAIAAlUcHon/tppJFLQ7S7490jnPkVVU1QG76ZY6P+bYnHyoyc/LfDojIVV+IGU2knHzTX6sLp/3L6eoCTQBt5u46DACVqDwsFjQDsats6xv98zp+f+pJ5C4sKrmz3v36m96pLjzt8ec4Mkti8UlQF0qY4dPf8Feea/ZcuaAAJN0HwPS2JwccLTSiBujoH+aJR8++m+V8wGgCcIErC1MAlWtq9NcPHn1+nzpGf/2AYUeFz2tCVSVvz1NwoQDo6G962uPPEQfynP1ZI9SF4mbYUwA86V8yFIDA9G/yNIHY9G/6i8e9q4Q38ffrXqIvP+Ks/xIHPjlooeoXpgAqV3n0l+KzXURKrsWevokes7SOUQ5zyE21n9LA9G+qsAlEv0fmnD/1mA/ou6gLuc9wEQUgNvp7XPWFmUTpX0Q2b/7+RRd8LdFdEjGjvwdNACWjAABtV/la1dLGwRedkmNnoKDJeZ5aOR2jEhWOnYiN/qZCa8CvfrXB/OdjHrNaXQh7j8LmXNcAKeCFjS4AselffAVgcLi3+pjD7Gdg9KiR+am/8sTY22/e/H0RqSr9m2gCKAc7AQPtVfmKf/b0remEKmEG3JKbwKtPfY6InLnq+NhbXrblR1Lk3ree9B94jSmit+g/PfWYD7hz4CAlcPzPhptuVxdim4CZ/kVk9IpvqwthTUCl/0JZpn8R+d15LxaRyw9+xuvv+aE48F6gqZxYqQOgZJX/zJc/A+Yh0hsW/UtIwy5sAQich3JeYVUAtLAmoNK/iFyy6Srz+lzeoOigb1LbBBJtslByHBrk2QLQu+HG8fvmHyrF+J/B4Z7/Zv4m4In+YcwmYKb/IrYArOseqy787bkHWd7lO+duN/9JE0ARql+mAyhTC6N/VdMt84giRe8+Yf6zqg9P7G7cUti8eQqAppuAjv6KpwBoqefTPv0rXzn6cyf88vn2t1fz75/t1DPsLwD68vh9/XRDgCJurJqAZfrXPvr9P+174iPMa3IvADr9myKagCf6e9AEkCOGAAFt0fLoX+akyxzz0zfO/FrE40vQsTj9V1Yr96FBlqvPL9h4rYiMjg5bPmy6kTavP/2Fb1tjm+a/cvTn1IVrnnCluhDbBDztxVTEmKslB3bkD/E3+/OtsyUh4nig2oabbn/RxBFPPSrBbPjTf+4C07+IfOFt96gLniYQnf5F5BMH/aUwOgg5oQAAzafTRlUnMKowMurVxuU89yYN9w9b4+7I6aUC5TKWPcXgmaSSvobnrp9N89FNQKd/U0QTiIj+HkmbwKWbv+w/FUA6qgkccVTo9/dFE0eoC9dfMakuPPWVi6If86Pf/1Mu8xYmLPp7mE0gNv2bPnHQX35p7x1ufgdRFxQAoMkqX1lbbfQvc9INi/6xk3Btt1GPdC2lhOhvSrF+PawJBEZ/D08TsE//Jvt5vnTzl9WF7E3AJv2bIppA0dFfrNO/STeBA1dYpbIv7b3ji3v+UNU+8WgGCgDQTC7EMkfOHlXOFNsT/T1qtEFAImev5PRvMufzJzeerS6bh+n0M5uATfo3XfOEK1/xqUe8/WkrPvyTe/WVYfsthLFvgOYOADkKjP4eniZQQvq/7b8v+Z+/fr26/G9HfSLp3e+7d1pdCGsCX9p7h/9KmgBSoAAATeNI9K9kHiqJ/uVMsYQTpUmGJ+J4BIlNq3o3X5tjfRZHp3+xLiTnrr/y9/1tIvLSjtV5sl7xqfmB729/2goRMWtACpU0QJv0b/qni2499IkxgefiTV+/eFP6b9lt/32J55rcm0Bg+jc5/jWEUygAQHMQ/Zsa/YueUI7tou5Dg9R+vVJ6EwiL+xddcH30HX9/2U3qwlcsaoCZ/jVVA/7wmb9/2Ks/azOrgeyj5+9umE49FeUZdx62Q2ZEZNny+P2DReQLO7bZ3CxLk/Gnf1MuTeD7I/eY1ycaoRd7e7QQBQBoAhfClgt7+kZQ+0DnMnsNi/4FTaJeQ4Ne9fpne25j2QT8hwD6hzcGHzM0Qo6jj1QNEF8TCIz+Hn/4zN+rC/k2gdc/ZcHa+kceN/zb6/ale/Bn3LngwP87Hpg9UlBEE7BM/xdv+vpFF4ikis7R6d+kmsBrHv0By8012tX9O5eIVdvxoAkgTLfqGQCQSafTUbm22j19q5oHPemSJ1famJ/iJlTOc+nP6cwpblop6NmLuM0FG6/tdGyzl0r/v7j5A+p/sbd/9anPKWjfg6/0t6n/iV36N63+n0/OPgMRr+0jjxtW/0v0gJ70b9rxwIz6n3nlF3Zsi0j/5jFAAw//b/nRPeMNfx0/64ZNF39TFr470a7u33l1/84lo2nSv8nxbyLKxxYAoK5Y61/ypEtrGvUa82PJ8aFBsXQH6PeDT2IVuOJfd4AnHh2a8gsddPS3ix4iIl86/fci8teXPtzmLpu+9OvcZyOM6gARGwQ+eeXLROQb//MGywfUHeCbnbss7xJ78q/YlejnrPsvdeHstS+OfiiV/k1hW2yUq/t3Bj7OF/f8IXpCEdgmAIUCANRSVQfYMWdA2hT9y5lircf8WHJ5aJCNwCYQO+xHNQFdA27+1XkicvRjzjJvo5pAjjVApX9N1QAJbwIFRf+BJx4184uoR9abAjxNQKX/3i9uTTrFr07esXhx/ACHpOf9zdIE/NHfw98EwtJ/XmgCLVdxhgCQVJYAl+Mg+DpG/xSzXYvob/m8Ko/+gezDR6GfOn0UoJBJhw7AWLP6ufZTmenPiMjigRHP9R867yv+G6smYL8TsMkT/QN5aoAn/W/4t5/GPoKlM059gbpw5tqTRGTj66NGzL/rM+8Qkdc86r2B6f+qD9wfPa2vTs4eJ8dTAPyHADr/mt+Y/0z36Yod/6OagCf937Dxh7GPvHjhCG3PECDzJAA5ogm0CgUAqI3sAS57hKr1QX4SPf0yn2nG98Xm7pVvMooV+4K7WQA6c0Ft9eoTIh4hLPqbAmvAkp17ppeviLiXvwDYpH/TH/5qyn9l7gVApX+Tvwmo9N+/9RcSt+I/sAno9C9xBSCX9C92OwAsHV3UFzFH3GcvAP+xO/FWkURoAm3g+k8CAMk1jKb+qXP8ID/5Pk69hvtHz62bK/7DRCSPqgpA9B7AHd+xNPxNwCb9m8wmsGTnHn05sAmYBeATvzn7K4//pOVUlH375l/SbS+fP0BnXgXgjFNf4I/+HqoJqPQvd/5GRGauu9ny8XUTiEj/YhQAT/RXyiwAKdK/LCwABa3+D0QTaDAKAOC03NOby8Ngip605ZryHKdYzoTCnle9or+Hf+brUgC01atPSBr9tZFtfxKRd13xC7MAKJ4aoAvAJ35ztnl9bBMwo7/H2t6Prec0xtZbzrW85dh9s8e5t0//yoXfvPV31+w1rwkrAIHpX4ovAOY/MxaA/9h9ayVb82gCzcNOwICjXEhvjYn+rk2xhJ9w98f8RCt9X2EduXp5PeKGDdf0pbdy1XPOW3/1WWueZ39Hlf5F5P2vfKKIfPDiH5h/HXxg9sS9ugl4or/y0htfK+E1ICL9i8i67rGSuQbYR39l94EHfe1xnxeRvz33IPt7+dN/mLD0Xw7PKKBEzPSf1/wkxR7DzUMBAJxD9C950iVH/6Kn5cLnJy+e2FFKq1FNIIcasGr1/FaF89ZfrS5ENwEd/U3/9A/PUBcCm0Bg+tdUDZCFTSA6/WuqBkjaJrBuwzdFZO3qmPE/mkr/IvKFt81uCohuAhd+0zYNP/SUJ6y/4IuWN3ZWhenfRBNoDAoA4BAXoluror/Ubbh/5ZMon+dJlfUcM20QMKO/R0QTCEz/Jk8TePsHT7WfJdUEvnDU5fZ3UU785QtvuOnd6vJTjnlf0rurGiCRTUBHf4+IJpAo/VvesmipNwI4Ev09aAJ1V+9txEBjlDn0PGwq1cbH8kfF1HG4f9jjqxN8Nm95Hr17g+T3qr761BPD/tTtDohIvx/cBDz7AKxa/ZyhzvwJbqf6+y7Y+L3oSZ+15nmx0T/Ajt0i0jvmafb3SJr+T/zlC8P+ZNMEXnf6CwKv9zSBsPQfSDUBT/r3DwHS+wCo9B+7+r+gY4CKbwcASb4PwKNXPnnTxi/qf7r8NacJ1AtbAICKObLWtsLflcaP+am219VU9HtU/trHTqcr4TVAeduakz3XmGUgzMitvxcRWTRkOys7duuL3Zt+IhY1IN2K/4i/5rVNIFH6F5E3nXXjY184ZnNL+xX/qb87WzZ/yaYDaLt+/SsRecTpj1f//N2lN8be5dErn5xixqrCNoF6oQAAlXEn+lc1G42P/kVPS0+i0+k48nHKhX0mK3lokKoB4msCnhX/lt7zt8apuCbnDsYf3QSM9K+pGiAhTSD39G9STSBFDThz1bOm+pPPu+mvrj7my5Z3sT8z7kNPeYL6HKTe79bSls1fUhdim4BK/zMPOUxfo5qAvwao1f/1iv4eNIFaoAAAFXAkqxH9az0tMyU35hc39UuX5ahBnvE/b1/7IhH58LqvRc5nV0TecuZxF55/nX/Fv40F6d8U1gSCor+HpwmkiP6/eN3oylXHJ7rLpVuue8om75Vh43+UM1c9S19+3k1/pS5ENwH79P+oM/7C8pY52rL5S2e85PEiIocd6f+rSv+BAjcImOnfHP9TOzVdCrUEBQAoFdG/wj19dTosdEJSSvQPm0rpO8vmKftApoxFSEV/z+VzN1wZeOO3nHncYHdRtxN6HoAwodHfQzUBVQMs0r+pe9NPPv9y7xmCY13dv/MgecT5G69V/7RpApduuS7RJMzo7xHWBKKjv7kDwCNPe3yimSnE7XP7J8w1gYj0b9JNYHThbgN1/CKjFigAQHkcGahd1WxUFf1Lm2g5+zGL3dMp/Tj6meT+NqUoQmb6N71t9fPVBd0E3nLmcSJy0KLZ4Rw2B7rRbNO/Njk1ceSj5FBZfIttoE8X/f1XRjeBpNFfItO/yWwCnhmL2AHgUac9PuwwO9Vk6NtvFZFdmR/G80UG8kIBAMrgwlqcarNg+a2j2WN+LLm/QaDoGbMpQmHR30M1gfGZnTr6e8Qe+X5ox/YPXn6DiPzT655iM8WJIx81f/mxx6gL0U0gr/RvUk3ArAHFpX9ty+Yf/N5u2M+jFq7494/+r1cZDmQ+BUdWJKHuKABAsVwIXuY8lL8mieH+LkzFzQxUWpSJGBqU9IS1B+3tyt5tIiL7Hxp4A5sNAjY1wEz/C64PaQJFRH+T3iAwOmp9wCIREXnH6LiIyJarRGTvGVb7S2zZ/IP4G4nIwvQfvWhrxn4ybn6RUUcUAKAoLkR/adnxPaXc5+vUmB8b7mwQqGoGzFdARX/7ORnbtXAg/vZt0bdft+Gb71j7knesffGH1v1X4A1UDRBfEwiL/h6qCagakCL9i8jzOock6gASkv4j8uhs+p8zsuUqdSGiCdin//5jHyNTU54rPTXAfxIAfxPIxZav3igis7sCBzEPARRm1HfegEDNKDOoFgUAyF/l6ary2WjDcP+ip1XoJKpdj1j5GIZf/3qjZ8V/7EvhTf8W3rH2JXMXXiwiYTVAFjYBy/SvTTz2mK8c/bmk86Y9r3OIJNwU4OcvlkP79q3dfybiLoFNIFn0z0yfPi/HL4KqARLZBOzFHgKIJoDUKABAnoj+RP+8plLC0yl/g4ALX5Cbf3VexF/9c5gl+i+88sXr3nVF9B3f94VbRW5925rnW04oS/Q3qRogIr9Ift9LNs0fJUkXy1+faXssUd0Ezu+PWt7Fn/7nd//tDuhrRGRD3DmAZ29cwBch3yYQiyaApCgAQD5cSDaVzwZ7+tZlKh4lbBBw4QsSHf1N+qUY3Zn4OC6B6T+Rc9fPRuroJpBX+jc98fI96sIvXmcbx/3s07/2pU0TD5cJEfn9Ww6MvmXgun//UB7L6O998AK+CLoJnHqG1b7mWrozANAEYIkCAGTlQrKpfDYY7p/LJKTSD1JxGwQqH/MjIq87/QVnrXme/e1H90yIiAzMDXmf8Y4198se/T3CmkAR0d8jXRNY1z3W/Odfv3Vx7F2+tGnC/OfDL7xPXfA3gcc+b8ST/gd8OwAo/nH/iRSUoT++ZfbUcjZNIPv5v2gCiFb9Qhmor8oTW4rZyD2K1WjMT7rn3qQxP4nkkhuKePV+fvP7ReQvjn6X5e1v++9LROSfz/0PfU1sE5hN/36+GvDPl84fELPbGfzHNVFnwI0dAjS97ICIv6oaUEL699M1wL8TsB4C5En/prAm4En/gdT+CY993kj/8Ud7/hRQALoDidK/5feuiAwdWAPUTsCB6T/7IoImAJNzPzlALdQx+pv3ymXOaxT99X0T3bHBY34SSZ0bimg1Kv2bopuASv+ysAAogTUgNPp7zEzJwvQvIt3O/Eb1wCaQsQAc/5871YX77p22mskC/PbNy81/qvQfEf1Nnhpgk/5Nt6/0HkWnOzVlDv4Z705fdMHXEj1mumWCFNMEItK/5PptoglAKABAUo7EtTJzcL5Tr3Ci9k+c6B/IfoaLW/Efxl8Dbrvlwvl/DA75C4Cmm4Bt+p/T/+/fvPebd6nLZvrXPDUgSwHQ6d9UbRNIlP4zurp/52NWPdVzpVkAxrvTIlJ0ATDvqC7UehQiTaDNKACALXfiWsZfgix3r2n0149jGV7LOR6OC5+lFKJDQ0GfkOj0b1JNYEH6FxGRf97wlYh7/eOaFyzZsyfRLPX/+zf68nu/eVdgATAfPzb9S0gBCIz+HhU2gaQOPmR+ENHdd8bvWSFzo4D8BUAPAVLpXxIWgFy+g2UeQau4qdAEWoidgIF4TkX/Cuekksxa8p6+wor/OBH7Cpcz5ieaP/rHUuvpx0dnh7nHNgEz+ivvOelB7//WfRF3+cj6b7zp3S8Ukc3v+3qiebNJ/yJy4IpBcb4GmNHfc01YE4g+QUFfZKJb8VOu9pQaeSn/iMCoHAUAiOLO0rDy6F/+1DnEp+M80UcKeGo33fzhQRkSkWmxWlW8f2d5oscPHKkf3QT86T/Wm946v+r6DUlqgGX61w5cMehsB/Cnf/9fPTUg9vRknvSfdPxPjhpzyJ1m9BnYoAAAoRwZpEH0L2Fydd+CX6G+cTrVHJ/jTTd/2PxnbA1YNnxQN+SgkGGij9gjc01A14AU0V8Wpn9N1QBZ2AQ843+2/cvN/0/k70a9e76GcTb6S1z699/s7junYtP/dG9f1tkqQEFNoOSlR2P6DCJQAIAAjqypJfo3Y3KOfJyKYD61vNYdeqK/SdUA8TWBZcMHiUhvaEhEbGpAbPQ3zdaAvlWENQVGf4/AJmD6f3tuVxeim4Cz6d8y+pv+c/e2Pf2ZiBuo6N+LvE3lmpGhm/EsEIgCACzgSFZrYfQXhvvXSthTyziYOCL9m3QTGB1e7vmTqgES3gQ60jl3/ZXRJ9k16ejfeeLs4W76v/hx7L1s0r/pDe9+4YqJwfdu/kHYDVQT8NeA0qL/V/rbXto5NNFdru7f2d01Gxz/91KrTRn/uXtb4PV6D2A3V/xHaEaGbsazgKmZm6SBFBzJaiXMRnTOrmpPXynrZFvNOGpHtZIeUFUsXvCtt3wk0cvlj/6BdA3QRwHqyPxOC7E1IGLFv6cGmDsB75sZF5GVq463mUMRWTHhXRn33s0/2PYvN0fcRTWBFOn/6v6dE9JLFOXVaJwJ6al/Wt5X3as71vFcH9EEdPrfs8u7dl8VADP9+7cAlHYM0IxSLIIcXJjQBOrOuY8UUL72RH89oTIP4Bg7M6VNtMxXuJwJlS/1U4u+49ZbPqIv2zy4Zfqfn/ofb37vZ389e1m8kVSCmoD9mB/VBPwFQImtAf70r7zhOZdF33Fib0+s47iSIsrrsfj6XlrY3c3h+/4CoJlNwLPi318AHnnmEzzXZCwAlUfqRAG68rmNQBOoKXc/UkAJ3AlqJY9+8Uyr8dG/tMm584kqQl6nkBPjJTKjv0fgtHrSE5Glw1FnzPVO9I8LVqW/77Oh+/LqGpBixP/7Ns6nT7MAaP4mEBb9FcsCoMRG+YgcH3F3M8oH3tF/d8/OuxEFQPnfSw/zD/tJVwAu3vR1sf72uROpbQK0O3MbgSZQLzX4SAFFcCeolT8n5m9Jq4b7Fz3RWvxIp5P750Q9oM2If3OiPV8GjW4CnuivRBQAZVAGzl79wtgZm3f/nxc8/qd+GVgAFF0DotO/JCwAWmCUt8zx5t39B+GJvqOILJau/8rYAiAiSzreO6YoAHr1v2UMdfDbGjbnDs5qNJpALdTsUwVk1+bor6db5lB4/9TLnK6n7RQ0XXc+VLkr6KlFrPgPNBN5yJfAGhCY/sWuAOjL8U1gYfpX3vmJH0XfaclM5+w3nxBxg9j0LyEFQAuL8rE5XoKifLp7SU4F4HErj53sT3huYxaAwME/0R9dl1O1J0C7PKuoL44ChBZxJ6VVPicN3tO35MlV/lYWqqDPyS9v/uBAZyD+dnOi07+I7Np3v7qgmkBY9E/hnA1fl7AaEBT9EzzyR69RF6KbQGpf6W+TkFDuFH/693jcymMj/hox7r++57TyH3IHyB0FAK3gTkqrfE4Y7p/vhFz4UBWhoNfwlzd/UF3QmT66CcRGf49d++5fNrwi97dE1QAxm0C29L/gwT96jRRTAypP/zar//3M1f9h6V+t/rfZ67fWx6/UHabyHw40DwUADefOcrPyOTGHwpc/0YZNrvJ3s1BFFBsd/T1UxA+sAWPj0++++NtrVj3XchLLhleoC53Dj1EX+n+8KfGMRprdIPDax+f7sFL8BoHaOXrlsXo55R//I8kP+lnr1er13ZoBZ1EA0FjuRLTK56QNe/qWNrnK381CFb3iP4xng8DY+Pzh7ddv/Ja6EN0EdPo3qSaQfw245PvqwtlvfGa+jyxzTWBib2/xiOujd4pzdOSwn+nevks2XZn6wfXxAGr3Xa711gy4hgKABnJqsV7tKJH27OlbwuSc+lwVocwV/2Fm+jPLZExkl4i8++Jvm39STcBfAwKjvynnDQITO/XFwCYQuwew7XT29kQkrAZE7wGcReUDhx638tiIT2GOZwKuxWr1wG8lTQDZUQDQNO4My642LxL9c5+QI5+rIhT0Mt6w9T0iMtRZZHn7ZTI2e2nx0rDbeDYIxKZ/U+fwY2TXve950yHvvei7YbcxDwFkTzWBIjYI6KBfuw0Cj3vTMfd96Td3PzBlf5eDX3eUuhA2RifH9K/VOkzXeuZRLQoAmqO0w73bz0mFMxAx9YJeogZH/3ImVIlCo78y1Z9UF6KbwHz6t7N+47cWdUfPPuslCe6z6171/+9507NFJKIGpKNqwOI/3jnxyMNib5xUtU3g0a99jIj85pO/srz94940u9Xl4OWzp1SLbQI6/Sv9hTWgiOjvUeswXeuZRyUoAGgCdyJa5XPCcP98pyJufK4KUtDLaKZ/k2oC/hqQNPqbzjnvq+pCTBOYi/4mVQOkgCaw+Le3i0hYDVgyk2kn1CyDf2wO5x8hogaYA4d0+jepJnDAU0d+dfWOgL8uTP9Sevo3VX6exCxqPfMoEwUA9ebOMq7yOWHMT+4TcuFzVZASVvyHMTcIZIn+HqoJBNeAoPRvKmiDgKoBEt4EakrVAAlqAoHR3+Mxz1smIroG+KO/x56ZndE3KE4tdhIIU+uZRwma/AuHZotIMOWvjQ6bE2dnIJeXqHbR3/JZV/6GFq3kFf9hlg0dJCJD06HrpN+98UvRj7CoOxr2p9kmEBf9A+wbP+eyn0bdYCImj/b/eGfYn1QTsNkC8JtPxZyu2EZeJ/Q9/LWPjr7Lbz75q8XS9af/+77kfRYHPHVERO49cH7PjdHRYc9tRrqztTAs+mc5BJCS7itQSZjO69tKE4BHzXYqAmTurCj9fr/yBVnlc1LVDJQ/3XImV/kbWjT9BPN92N/8+oJXn3bi+Rtt16AvGzpIpX8RmRrsqv/lO0vnnPfVeycTnqtr37jsGxeRs0978tmnPTnf+VEW//Z2vVmgSR792sfYpH/FTP8Rilvxn/or0J/TmZP7vHnk+G0tf+bhOIYAoU7cWTVb+ZxUOANN3cDCmJ90fvPrC/Rl3QFWrnp22O119PdQHSBig4C9N7z1L9SFe4dnhxut2Bd3JKJ9454rdAeI2SCQ3PZv3Kou7P+CIwNvUNDq/0KpD1Z0qOy94PDYLTJq9X+Fw35s1HqcPbsLQ6EAoB6cWs5WnhSrmoHajflxbUJVKegD86tfbVAXur7z+Kom4KkBYdHfpDcFpG4COv2bVBMIrgG+6O+hmkDuNUDmmkBYDWiY3gsO91/pH//jePT3qPU4e5pAy1EA4DqnwlnlM8OevjWdUFWKe4I6/YtIb+48vh7mBgGb9L/A9OT73vKCd1/4Dft7BEZ/U8AGgbj0r81vEPjoNfazZCN2g0At6HX//aDtAIHp32/37snR0aEc56ocdU/Std6ggdQoAHCXUwujymemJdFfSty+UfmWnKIVveLf0lmrTxaRvdM7Rwb3s7zL0MRsLn/fW16gLvibgGcP4Nj0b1JNoN+fOWhfkpHQ01NinO1LnwY4L7oJVCjjwCH/p23kbx8lIrv3TMbed/fu+NtIHnsAF6cZTaCmM4+kKABwUeVp21T5zLQq+pczxcrf06IV9ASTRn+ZS//K3unZ0R0RTUBHfw/VBMI2CIwnHDfSn9tkcc9oX0QO2mNRA6a957FSTSD3GlBrntdRpX8RGRtdJOE1wDL6i8jmC6/afGENenutm0CtZx72avBFQqvkFVzqeJjLMmcg9vVp5J6+bfg9K2fMj985677sucaM/oH8NSAs/fu9+8JvmFsAdAGI2PlY64cMWJKwJuCL/oF0E4g4DKjywG+mo29wz51WU4wWuC4/9jCggfeKPQzo0hHvUH4RGR/0fgh37Z5QF/Q3cXR02JP+w4YAmSv+M36RK9n0V9XBmnPRhiVnC7ny8QLyDS4ZF52VR38peOkf8eDNHvOjLjR1uVfQK3nTzR/Wlwe7AVFPfAUgNv2bRgb3s4/+83bdd86nb1EX/VsAAptARPQ3LagBdulfO+eS7ztbANKdBODBDx0eOulh0fcaGxnuGKP/9w7OiEjH91C6AEQILABhw37SLawqDNaJFkHuFACt8YvQVnHu44V2yn1Jl/oBHYn+Rc9D4OvTnjE/LrzL+SruGZnp3+RpAroAJIr+ypLBMREZnrAdByK77vNc8c7LfxR2W90ELNO/dtmWH73j756S6C4yMH80pA/847+G3aqqAvCBG09Z+/hPJrrXgx86fMef9ul/hm0KGJvbAtCZS/+SXwGIHfRfl1R9480ffPzR/6RnQ12ImBkHC4BGE2gAdz9eaImCgkuKRacLobCqY963J/pXMg9Fs/y0X3/Tu556zPvtHzYs+pt0DdAFQB0SdM2q59pMQkV/j5gm4Ev/ElkARET6vTNXHW8zP9plW37U2z6bVvVevzEGvMdClaAaEJv+xaIAPOghQyJy15+jbuaJ8h+48RTzn2FNwLzXgx86LCJmAdA8TWDpyLD6/I0Pzres7AUg6f6+LqfqG2/+oPlPmybgcgHQGrMUbaEafLzQVIUuOJIuOitf1JZ/nE1zdXgjo7/YPbVar8qyfzGvv+ld5j9jm4BN+jd9ZMPs7rnmOQGia0Bg+tcCakBQ9FdiC4C+GNsELtsy+1C6AGihTSAo+nvoJpBjAVDCaoCO8p7o7+FpAupeKvorgQXg6JftLyK7l88e2nXpyPDeQe8GFk8B6PVnhgZmj776wM7dgTOTJf0vmHRk7S//m+5J/5quARI0z5X/Ktmr9VK0tWrz8UKTVDXEpaqZcXAG1OvT+OH+SUtg0rtUKHX0NwXWgKTRf+nQcn35nR/5f/6TgomvCURHf4/ZJhCe/iVJAVDCaoBO/xJUABRvDbBI/6a1/+NfYm+TqABoniagonx0+p+fq7kasFi6ZvqXoAKg0r+IdA6bP7r/3q73ZroA6NNE6AKg+GuALgC5HOszMJWWn6rD0r8pcINAjQoA6oiPF0pV1RCXamcmeh4qmYF2jvkp4e7lsP/YRKR/k24CN2x9j75yUWdJ7B3N9K+8+9zPh91Y1YBE6V9EBjpDIjJw920Rt0laADSzCZjpX8ILgHb2/4k/4pDX6AHq/9c+/6NhN7HZASCwACiqBiyWrmX0N1225UeTX/SeiyCsAJjpX8ILgHmSOE8BEJGOdERk+85d6p+jo0NFHOa/qlRtE/09Eu0kAGTBeQBQEqdylQszU+3R/cuctGtjfqI5fiqcXFb8B9640/GO2J7sj0t4DfBH/1hT/UkRmZqa3G/oQMu7qPQvIjMHHzF7ja8JxKT/SBdsvFZERkeDD20U7ZyLviciZ7/pWbZ3mEv/IrLuyjerCxFNIB3VDYbtzrxr8vSfCJ7oL0HpXwk7RfTs48ydNuAzH/+25aTT8R/YvgQp0r+I/J+VL/nY+V9Vlx1fFqHuKAAonAtpW3NhZiqcB52Py/khLDn65zgtN48XZF9vXn3qCfa7vfqjv0nVAFnYBFKk/7eunB85s3PqPhGJrgE6+nuoJhC9QaBMqgZIdBMwor+HagK51wCZ6zZisduDzKV//+p/Pz3+J1q/3+/LgvTvX/0vIps2fsnm0fKitgCUEKlvu3aNOsnFzgNtu66IXLLl+zr9a24ui9AAFAAUyKkFlgszU230NyddwqZwl4f72zP7Ur12FLHMf9Hp36SawIrhB9vPg2JGf5OqARLUBMLSvxaxQaAqoU0gPP1rxW0QkLlPgs1uD9Eso7+I7N69L+x8XqaS07+EHP1Mwr9Z5mY0+yNo3XbtGn15v/tmP+TRTeCSLfHnk2aDAPLFPgAohAuByakVJ+5Ef/P64g7BFDjF+k7InFyZU5S0z/HVp57guSYw/9mnf2Xp4HyWXdQNWKHr2QEgLPoHUjUgNvr79aX/zx/5t8hbxJwAK3AIUOw+ABL56s3WAIv07zE+1HnjY6NOuiyR+wAoEUOAInZ7CNwCoPYBMNN/9BCg3bv3SdDh/D1bAC7c6D11dAnCFnqBX+3AQXSxNcBM/4H8TcBM/3oLQOzymSaAjCgAyJkLaVvPiQsHUnAw+uu/FnH6hYgp5j6tqt5Zp/ZlD+QvAIoOf/sNrXj/eV9UlwNPmuthRn+TpwaYBSBR+le6MrBs+CD72/fF++IEN4EqCoCIyNASETl7pe2Z0caHvAPzAptAbPoXi30Azlx1vH/df1gB8K77P+xwz4yqAqCivxJdACpJ/2Kdqvv9fuwuNP4mEBv9TboGeNb92xcAjSaAdCgAyI070V9xYX5cPshP7vPWjDE/jsxGxgcPKwAi8o6zXiIig51hXQC0sCYQlv41XQN0AVB7f9pUC6Ur3oNpxjYBf/rXvDWg0gKgxNYAf/o3mU0glwIgQc/aXwD2f8powD2DCoCZ/iW8AHz0/K84smU4TKK952WuCSRK/9q5//4rzzUpCoDmyIIRdUEBQA5cW+64sEbEhT19c7mZ5RSliWN+bBTxYcv+1oQVAJX+lQ+t+6/A25ipPTb6e+jzAZuHf4mtAf70rwXWgIjo7zHbBBwoAJq/CURHf5OqAbkUgMCn7CkA+590iDywy3+zzmGH90X0TPdF7tkbekR/bWhgkVrxX+HyOfablTT9zz1sV0RW3B98drMwF3zmhskRb79SBSDLEsCFnz/UAgUAtsJOqiIuLWjM+XF57bsLk87l9WnJmB8bubwUeb2e/gJgRn8lrAAoK1c9O2n6X9SfParEu9f/R+DxH/1NICL6e+gmYJ/+tX/+8P+LvoEnDb/jtOfry+ecF76jaqoCoOgaYJ/+tXWnfmzXbTGVJnsB2P+kQ6Qjst1bADw7AIx3p0Vk125vWfIXAP8B/l07EeEf/u21InLvYx+a8DEXfAxsasCmz9ygLkwsLABZVv8HzRhNAFGc/kFFUhHHdsx39WQRD5uR/7ek/LxYl+iv75JlVut7iM9CZfnRTXJ6r3eqC0895gOBN/AUAH/6l8gCcNbq+bXUiweCBoH46PSvvHNd6OnAdA2wT/9Krz+9fNEhie4yObPH/OeHzvtK4M3MNGymf1NAE8hQAJTprrxtTfDkwly0+Tt7f3yTuhxRA7IUgP1POkRkbg1/SAFQH9OJ7vTsnEQWgOhze5WZUyO+Yir9m2KbQPQ+9GFNYNNnblBz0Cm4AGg0AQSiADRKxFKjwQuU8o9yYz8PLk8641Zmon+0pFtj7G+s07/J0wR0AQiM/kpYATDTvxZRAzzRX4koAEpHOqtWnRB9G1OvP23+06YJeNK/5q8BOg2HpX9tQQ3IowAoNjXgos3fURd0AdA8TSDdDgAiMvnFW/d/3iGi86lEFYBc0v+CRy74ZyVsqeWP/qawGmB/BC2zCah1/3oAVTkFQKvv4hRFoAA0R+x3O98hH/oYO9ETLVT5R7mpdkL5TjrdfRnzk4jNtzL6BqbA6G/SNUAVgLVrnjcUdLBOxV8AAqO/h6cJBKZ/sSsA6oJNDfCkfy2sBoRFf5OuASoKx0Z/j3PWBW9MWMC6AGhhTUCnfwkqAIquAan3AF6w7l99JP37ABx2uI7+yuLu6D0771v44LMFwD79mwpayAQuT6LTv8lsAkmPnysiK+7ffcFnbjC30U+E7AAgBS/6nFp/hwrV/vcVpuilRr5DPqo90H4lR7lJNxvOTjrp68OYn9TCfnFTjPmx8dRjPvCLm71Dg/xNwCwANtHftHhgNCz6K/YFQAtsAmHR38NsAjbp37TpoiuTpv+pxbPJ/twPhJ9/IC79S1ABUMwaYEZ/JawAaJOPOSx20p4C0Lt759jI0IJ1/yEFYOLwQz3XLO6OTsuUurx9504RGR0dShf9TbnnVP/XzT79m+47+ogU97psy3VLxiddKAAaTaDlKACNEvF9zpio/Msj91d7l7ASpabRXz9OEQNUMmpY9PcwN6BJfiv+PYa6i8P/NF8DdAFImv7F2A6QfQuAh1kDLNO/tmRwaaLbi8je6Z3qwkG9/SzvotO/KaAJZCgA2thYwAac2AIwNjy7c8V9Dw89bbNZAHp37xSRsSVDC96ToALQP/JR/n6lC8BMf0pf+bHzo3YxTySvnOpZ4qVL/5//u5vVhRNvfJHlXS7bcp2ILBmf9Fw/We74nwg0gXaiADRQ2K7A+b7XlRSA8o9yk3EeXJ60Cz2qwmlVKOn7+OpTnyMhp/L1iIj+vlsu0gWg2xkQkTWrnmtzx8CdAfw1IHUBUM5c+SybmTFduuW6yfHZxzxrzfNs7qLTvym6CQSmf21BDXCgACiBNUAXAJX+xaIA9I98lARtYFncHZ3s7/VcmWMB0PJajXXbTRv1lf1bfmF5dx39PaKbgEr/ElkAdPT3zGf5aAKt0oqfWxSh5IVUukV/Ebs+O77dI8cHZMV/7jyHqVVXxj5rVQCUsBpgH/210cHl7z3332WuACgRNSD2cEBmDchYAPRrstKi9lw6l7F0AVAiakBg9Df5a0B09Pc49wP/lmIHAL9cCsDsvR60//jo/IdkdHRYR//Ze43M7bmrBwLN7QGsor/iLwCLBkb29cY9VxZRAJSkIfXW315y5CPfKHNLPDP9m6KbQFj6N/mbgE7/IjLiKwBqCJAn/Ysb60FaskBuueo/Z6ipWhwHJq+ZrDz6FzH16GNGFTHFyqdVoYinGfsKmAVAW9gEOhI0yj/M6OBy85/+UwKLrwlYHgxUWdQfjC4A0elffK9GRA241MhYngKgeZpAbPo3qSaQKP0rO6bu2bzhRxE3SFcAYtO/BBWAvQ/aX19WNWDJHu+he8IKgJn+JagA+Lc5F5f+A6cb8d259beXmP/sTniLip+/Cdikf03XADP9S0gB8Kd/caMAKGwQaDZXPmeonVrsYpvXgY8aFv31g4flUcb85MvmaUa814EFQDnTd2qt2BrgSf8SUgCUNauemyj6K8MDi0XkXR/+XNgNkhYAzdMELl2YscIKgHLWmucliv7KZVuum55Y9Pa1L7S/y46pezzXBDaBqgpA6L3UTsDmS7h9V2z6l+oKgGcG/J+ZFOlf0zUgUfrXfvcW7wvuKQBh6V+cXCrSBBrJuc8ZaqS45VT5+7kWOhtuTjpwx+6iJ1rJtCqU9GkG/tCGFQB/+tcCa4A/+ivRBUBdsK8BKv2b/E0gdQFQVq463hP9legCICLTvUmx25tCUStxpyfmX8zYJuBP/5qnBjheAGaWHWSODVPcLACe2VAfHjP9J4r+2szXrxaRL7wt9A0NdHX/ThE58q2P9VzvKQAbLv2OhG8SdHbBSBNoEnc/Z3BfEcupSvZzLXo23Jx0VQdybUn0l2xfEPNV8heAiOjvoZtAWPoXuyFASnQN8Ed/k1kDMhYAWXiqKc2yACixNUAP4TALgBJYAyKiv4dqAgXtASy+AtA9ZL8dSw8WkYGFR+v33kvvBNyXmeUHycKdQxRPAVg0MCIiZe4AYMOz4l9EPvSh/zx7VbIjvar0b7JpAir9i68AeNL/+i3XqAv+PO1y+je1ZzHeYPX4qMFN7u9im2IOKx/uX/Lwm5Kfb11+3jLKcROWiLzq9Qvivn36V5YMLFVBLYy/AEQfESiwBkSnf9O7Pvw5FwqA5m8CntHb/gKg6SZgn/61j50ftYeABBWAU09/mr584emXh97RKADdQ/YTEVUAtMAmoAvAzLKDZu8bWQD0h8qpAuBP/yLyoQ/9p75s0wT86V+LqAE6/Ut4AdDR38PcilKjJSQ1oNZa8WOMguQY5oo7ZGfS0ReNHO7vwhRb8lNRxNPUWwDevvbFH173NfsRLCKyZGD+0PhhNcAsAJYHA1VUE7CP/lpHuu/68GcjblBmAVD0q3qZb3BRRAFQ/uHMp0TfwO+yLdfN7Jp9jtMDwdsCzAJgRn+Tvwbo9K+iv+IpAJpuAmr8j1rxr0UUAPOz5CkAFab/6296l7pw4OKH6CvN9K9F1ICI9G/yNIGI9C9zBSAs/ZtaspyECygADVTOeQAkv11spfT9XLPcMneVLPHLHMrZnp+0gj5FqgC8fe2LReTD676mroytAWb09/A0AV0AEqV/Zai7aMngmP3tO7Ig7IbVgPILQMTDxhaAyd5sLF5psXFGFwxdAGan4qsBugCEpX+TbgKqAJjpX8ILgDKw876xkSG94l8LLAD+GmkWgDLT/8+2vu9Jj3u3/qdO/6YDFz8ksABoniZgmf5NX3jbPWb6l5ACYJP+ZeGGWWnHYhNVoQA0TZajDaaYluO72GY8+krRKoz++lD0JRzKqQ0LmULfyl/c/AF9ed36BQElrAZEpH9NJ7n3n/fFt5z5lyIy3E1wsEv/fsaxTcCT/k2eJlDTAqBE1ABz84KnAMxPbq4JqAJgk/61C0+/fGx4wJP+Ja4AiMjYaMBz9BeA7vRkb3DYc6UqACWv+P/Z1vfpy+YZiP22bLze5gHf/liLnbJ99HaA6C0AF1/wDcsH9CwwaQIoTit+m1slOm/lPmrf8V1sY1+NcmYjbOrlT9r/61LcPLDiPxdm+hdfAdB0E7CJ/h7jM7s818Q2gYiDjQbWgIjob9I1IPr1DIzp4kwB0Mwm4B9ZFFYAZic60D1zVeLTIW/e/P3FdwcMUk9RAALTv4gEFgBn079YF4D+rb8TkXe8+FGxt9QC9we4un+npwDYp3+JPDrz7Hy2YImKclAAmqbMyOv+LrbRC9MWjvnxH/ezoF0v/NNqpNJW/GthBUBEVq9+7nDC8wH7o78psAZYnm7MrAGW6d/0zg/9S8Rf/TH97W99ub783o98IeyO0QUgrFekLgDKylXP9qd/iSsAIjLZ7YnI6tUnRN9M27z5+yJSRAFQ0V8cSP9m9BeL9C8JC4AW2wRiDwr0u7fsnyj6i/ObrNEwFIAGih4FVNVhdqpabAVuUSX66z/l/nkIm1bDFP1MA9O/hBeA1avnh+/b1ICRof3um/iz5czoJmB/smGl2+kuHkiwh4CITBkZ/b0f+ffA23iSupn+jft6a0BBq/8lrgCIyNioNzeLdQFQYmuASv8SVABi079EFgCd/sVXACZn9l50wddiHzwvnvS/YcO3bHaIT1cAtMAmYH9ygLW9H1veUpL/nko7FrYoCAWgmcL2A5YqjrNZeebW492rnY3ypx47UUeGhNVOmWN+PPwFwIz+HmFNIHrFf6Ch7qJEewiISLczv+LfsgZMBWV0fw0wk3pg+l9499kmUPL4H5O/AMSmf1lYADR/E9DRX8m3AJjpXxYWgMmZvSJSWgHwpH8R2bDhW+pCdA3IWAAUswYkPTWYYtMEUm9UF5oAkmvLDzYKErvAqjwU6oMqVDgbLgz3T30by2lJO36BCn2mN9w0f0iTwW7AOmPxFYCI9K+ZNWBkaL93f+hziQ4kKgtX/NvUADP6e4Q1gcDo76GbgErqsdF/4X2/kK4AvPVNz9+w4TsR9yq5ACi6BnjSv+RXAAb3bJfF3v1JdAFQ6V/KKgCnnPZcEVm16gTzSl0ANP8HO5f0rx11zXKbm/ld3b/z6/0/xt4sr1MHAjYGq54BNJY7y6N2HuKznIm68y6XoNAPkpn+RWS6t0/Ca4DYRX9lX29CRIa7i0eGZg8Oc8HGa8XiQKISNOZHH/MxrAlEpH8RmZjZLb4aYJP+ReQ9//hymasBidK/iLznH/92977tIvKR9V+3vMtb3zR7gMjVq2fPwxDdBMIEjv/JaMOGayR8p4XsBvdsF5G+BJ+wTaf/km3ceI264GkCmuUH+01rnnHR+h8knXq69O85QmhxzA3d0pplMrJgC0BjBY4CKudUAI6EwmoXhbWI/tkP5Fr5u1yO0lb8BzJrgNoCYJ/+lSVG4D7nvC95/hoWmGxG/HtqQHT691g8MGYZ/U06fY4OLbO8i4r+psAaYOZpnf79PDWgoB0AJHILgBJYALxbAJYt7//hzyKy8/AjI2dy/r1W6V9E+ouXen5FxjvTnjuWuQXA1O/HvDgicuaq4/1bAN605hn6sq4BsVsAMqZ/m9X/kve5NdWFliyikUJbfr9bJeybn9cSwVxIObWLbeBslB9SaxH99b0cP5CrC8pc8R9tsDusCkCn05Xw9aAeSxaubvcXAMWsAUl39h3uLkkU/ZXp3uwhXMJOUeznX/ccWwP86d9kNgGdpyPSv0k1gRL2AA7jLwD+9N8R6f1hwd7e/iYQnP5FOouXqrlUvxxTAx39lmkuFwARWXLPdhGRgw5V/zTTv+ljb/5MxINkX/df9Pif6IeV1iyrkQgFoIFKOBWAXqY4soutf8bMa8o86qhU8SJkyfGOH8i1WtWu+PdTI2c+vO6/OkbajqgBS4IG3IcVAOXMVccnTf8iMjA3mnRowOq+/hwpcTUgdtiJvwlER3+TqgGjo0OW0d/0oXUxB8H0F4A3vGH+AP8Xrbs28F4ZC0Bn2XKZG8PjKQAiMrN9Ys8T549VrwqAjv6KpwBMDXQk6I2rRwEQedOHXxF748AakMvInwoLgH5wdaEli27YoAA0UGmnAjBHGbnwQQobj1TaaPhKor9kePGTzjNjfvLy85vfv379t1Za74zrGTQfOILF0wQC079EFgDzxFURux+YBoJ2JIuuAYHp3+RvAvaDznUNsE//WrfrPfVVrIs3f3dy72wZG5/ZGXgbswCY0d/krwFZCoBK/yKiPr79oAIgIoNHP0REdgztNza6yJP+5x5uqcxFf6WmBcAm/ZtUE8iyy6/nmsoLgDkVdaElS3JEYCfgBqr8oDfl4xCfDZtctUoY8zPQHRKR8zfOxr7oJmB5DE21i+SqVSeERf9oZvoXi72QJST9i8jUzOzIfn8TiE3/Mhf3VQ1Iur/pnqkd4zO7FndHE93rki3fE5E9e2bn7aw1z4u9y8Wbv+u5ZsnAfhJeAyQ8/YvIm9bOfgDCNgjY0+k/mkr/IrJsaqfsEBn0vZW+9O9X5kkAUkua/kXk/3z01Vcf8+V0kyttr990zOG7nmvQNhSAZlIdIOKvGR/fP8i+wtEvlUy32qm3YVxTJYp+snrYz8zCHKyagL8GJD2LlogMdganehNDSU4J7In+prAaEBb9PVQTUDXAJvqbJmf2TvUnhzrJxiOpUxxMzI3Lt2kCKv2bzlt/tUTWAH/611QNEF8TiEj/JtUE0tWAbmfAk/7Vb4D/0zyb/vWf525hjvgXX/r3v4PlLIj8q//tTR68IsW90qV/x6O/h6cJtGQJDxMFoLEKHU3of/Dy9wSwnFZB20NaUnhatSmp8p19PTUgafo/a/V8YJ3qTagLsU0gIv1rqgbIXBOwTP/zMzMz2ZPeQCfhvfqT+r8iYtME/Cc4U00gogb407923twpF8wmEBH9PfQGAcvobxpcOjC1V0SkFz7QxTP+R5+418O/A0DnYQ+RyYnYeYhe96+Ya5ocXFAsuWf75MErNm64Rv1zVdzZlJXc07/lIYCqwvFDW4sCgGSiQ5JT0b/QGWhD9C9zctUqbcW/jfM3XrtmTeJVnmb6N6kmEFgDbKK/Rz9gbXKMnsxG2Jn+7BEkY5uADv3+K8NqQPS5jQM3CEREfw+9QcA+/WtLBvZTAdQyfYrIls3zh6hXx1aKqAFzN7Pae6H7sIeo3YL75qYBI+erizNjB4iI9L1H/PRQ439qNKTE5o0oYt2/zeKl8lUtNXofkRcKQGNF7KGbZVlT+Wl9q50Hhvs3jFPRX0TM6B8debWw6G8KrAHnb7zWfi9kmdtvQUSmZXYoyKDEnIhKp3+TagJhNSAw/fv/ar4s0enfpDcI2Kd/7bz1V4+NpT+ll2UNMNO/pg+x6m8CltFfRLoPe4iEnNjLNJv+IwUO/S9zSMlAZ0g6Entq50BhGwSKG/Rfr1XsDA1qDwpAM3nG6Fe+diEX1T6LNgz3L39y1Sr6yb761BMTJezAFf/RNcAm/c8/VG9CRN71tv/x/nP/Q0XBsH0PPHT091BNILAGBEZ/k78GREd/D3VjPTbJ3mVbrhtMfpzTvEQMRwmM/h6eDQJJ0/88c92/8Q1Inf4XPHZZeVe9j+lqgCx8L0rY5bd2wbpevQUptOjHvlX8ySbi7F25Tyt3JR/sMt+puzxdz6ei6Mm5o5wn++pTT1QXAhO2PgSQhET/QKoJfGT919V5u4aT7CO7aGB+AMz7z/2i/waB8xmW/j3MGhCb/v16MpP0Lls2/2DX7nEJP5Ox32VbrpO54OihDwEUwb8FQB8DNMLkzO6wP+ka4E//43tjXpDRoPOLLZqIH9zf3zfhLwC9/Q/23KznGwI03ZtKcdif7N81vZjSewMPdAI+k/4mYFmQnvSp3SIyPZNsDu2jf+A+AJ5g7f6aF5pA87j+mUM60UfEr1EByCWoZZnDxg/3b+FivbT3VBcAxROvdQFIOuJ/ycDY7ukHzGtsaoCZ/iWkACh6Pi2jv0dXkh1N31zxb7mjsI7LqgBoEU1ARX8lXQEIHP+TsQAogVE+lwLQH9m/s9d7gP/+5IR0jBX/Hekt96Z/CSoAmzZGnUIuWurljP/besppzw0sAIpZA2wKgEr/Cx7BogkkOuBP9E7AtVsCt2pVUbMxBKiZAg99Yx6xoS7rG9o25qeSiTr+SchRtR8q/3ibM1c9y/J8W9pMf9qT/kVkX39SwmuAJ/pbzmeKHZEVtS7fsgZ4hv3Y7CgcMVTmgo3XSlANMNN/yWLTf6DY9B/In/5t7hWY/j2yRP/ZmUm+pTHslp++7FuvO/0FYffS7c5maJA//YvIoDrncXgNyPdwn/rHuvKfPEvuzyEsUQAaK3CBEn1+gCwTyneTgrQv+ksVw/0l5HPSPO48R73i/8xVzxK7820p/hX/HvvmwrTZBJKmfxFZufrZkuSgPYFsakDEoP+wHYVtBspfMP8KHy+Vpv+S9fWgnujo35n9b0T0N1f/Z0//JsuR5dkXhu846yUfWRdwwmwlMPqbBueOguppAknTf+wxQPUzZcw9SkYBaLLAJYhnKeOa1u7pW+Z0PZMr/xwOJXNte5eK/ibPgfb9Alf8h1FNYOlg/A6dfir9eyYt2WqA+JqA5f6+5qRtor/HBRuv9RwsP8KZq551wcZkhwZ6/WlP05cv3nRDovsWJ8cV/5J3+teiNwhk/MLGnsg5Nv2bzCaQ+6m+AjfU6z9JQxfIcAQFoKUcXKy09py+JU837Me1kTXAwafjT/8m/waB2BX/gRZ3RyLOABDIH/1NWWqALNwgkOhoP2rSH9/y43TTtXTaGU+XubfGpgaY0V/5h7c+RVLVgMCh/Ol0gtK/3ixgXuXf5devoOjvnRffam+n0r+phPRvYoMAikYBaLKI1fxOLU0qT2lVrSGuasxPxG2aVAPcX/EfRteAJQNjInL+xu+K9Xm7FndHzH9anhI4Ov1rGccFJY3+MjeApyPx+9qmo6K/KbYG+NO/pmqAFL9BwF8bpnuTw/51/749gCXogD9+5aR/zTxideyNI3YAiE3/Z+3eIy/vXP3vyRYL3+jlH/3FbgHr2SBgeS/ABgWgsSIOBOTOmQHKCZoRa5VasuI/6eTqvvLJwQJjn/4Vdc7aqZn5xGxTAzzp3xS2QcAy+nuoQ9HrE1TZmIk7s6xf0cP3/elf0++XpwlEpH9T6g0CynRvX6K9w8N2ePWk6cnFi8XiV//8DV+wn3Re/IckloRfYU/639fzHhH1rN2zJ4R+3stnH9+mCRSR/lMsmhgahNxRANqoiF2Bk6p8KUb0j1XHnxwHZ/WsNSeLyHnrrxTrI9ar9B9I1QDxNYGI6G/y1IAzVz2z358RkY71KaXMlfGWNaBe0d9DbxCwjP6mwcWTk3t8Z9uyE7tbiHFLq+0qKv3Hqjz9S6rV3oHpX59mYfB9Xw28l2oCETXAkfRvqvvaGbiDAtBkjqzm96g8orUk+kt+H4C6jAty8AO/cvWzzXEvYcep1CKiv4e5QcAy/ZvOXPVM85+WNSBwKI5xYtqAv9Y6/WtLRrobN14jIqtWnWB5l0u3mHstp6wBEnecqBzTfyXRXyK/tvbrIM5bf7XM1QDPuv+w9K+FbRDInv49hwDKcQHF0CBkRwFoLD3UR/9THFj3X3lEY7h/ai7XAAfn6oat7wkbXRNWA+zTvzYYfkakMEPdxTun7gn8U0QNsBmF79kgkCL6S/Hpf7C7aOOGa8RYPRzLPACRTQ1YGP1NWWuAGE0gXfQPKxJVrfiXJEPhzZ+zwD0B/MN+YtO/yWwCDq77D1TH7bRwBAXAIbl/gc2Fppn7c19G2By6ofLFU0tW/Bc9OQdrQOWt0uOGre+xuZlZA1JEfxFZu/okdWG6P3sW29g+MD6za3xmV/Rt/DUg0T64qgb0pWd/FyVR9D/tjOP05Y0bvh14G88xQD0nALapAWHHHo2oAeHpX9OL4vRNYLpndV/LYT/7ZvammJOMUnxtA8OubgL+3WwSpX/tkpvv+J3vw9u3e8HDlLCMYmgQkqIAVMwfpAqqARWqPCy2JPpLiVHYkRpQ+Qz4WaZ/7YKN165e/dyp/r6hToKdPnX091BNILAGRKz4D6RqQDf5FgYpN/2LyKrVJ0p4DVA86V+LqAGxZx5QNUCMJmCR/k15bowdGVzuueb+XTvGLI4xWpf0b/KE3dee9nx/+j//FVtEZO0THpLokS+5+Y7Uc+Vx6ENnvztlrqFgaBDsUQCq589Sle+hm5fK85nnZLclT7ruY35iVVgDKv9o+SWN/iKyevVz9eWp/j4RsakBYelf89eApOlfSZH+i47+4kv/WkQNCEv/mqoBYjSBROcdU03AJm2X4/5dO2xupqP/RReEnjG3CPnumyQirzntpI0brglscet++Wd1waYJ5JX+zegvFS2mGBqEWBSAhqvqVAAuLHQqHO4vDRrzE6v8Tc+ujfkRkZ9ufe/5G79reah+xUz/WnQNiI3+JlUDlgwsVf+M3f/YVIsV/4FUDZC5JhAb/T1UE8jx5Fwp7No9nuXuSdN/mYpYWOkhQNFjulQTiKgBuad/qfoX0JwHNgjAjwJQPZ1mcv9mRixtczkVQNjjV57PGPNTvnJWOFXedgKdctpzV656jlifsSsw+ptUDZCFTSBR+p+9e3fR9NxDKaoGSGQTKGfFvxSQ/k2rVp/4iUt/4r8+Nl4Hpv/dexKfwkxE9uxJsxt0Fo6n/xK+uXpjTuCqr7ANAkWk/0/88Xe5PGYu2CAAPwpAxTzH6pG8x/9EHF4t+4T8A5YqX7i0KvqXPEVLxY0Lcqft+J2/8TvG5agaEJv+TVP9fYsGlqiHihjf7zcUt+Y7cIOAmyv+Tz392KSPf+mWH0ra0xXXlyf9Bw5JqiT6S5Ff3ssv/cZrTkvcjc0NAp70/7utKefETP/OyrhBwOWFMJJq1/LRTZ6vU+0yk16gVLtoiE6cNscpyjJpor9HvjWgFk/Zw3/GrjNXPWsgyZ6+IrJoYMn4zG7zGpsD/sSmf82sAenSfwrp0n+vPy0iXYtMr9J/ydLtAOA5TlHqe02MdyYkft1/L9WBWbMrdAkZeDxQS+t++eexwfgjXNkcAqgW6V9jX2EIBaDZAk8FIHlvZDAfv237g7Z5zI+NXGpAvZ6y3/kbv7t29UnqKCUz/X0iYlMDVPT3pH9T4AYB++hvyhj99UFCYzcFJN3f17/uX0fYsCZQSfrP0cTe/v777Sci23futL3LuNXyPCz9F7oHcB2ru0fGA4A6jqFBbUYBqIyONWFxPN/jJHgGGuXy4J5FhucZlbMoacmevpVMMS+pPxX1fcqmM1c+c2rh+Ylia4B/xX8YswakS/+rkgxJiqaaQFgNyHfYj3+DgGvRf3J8YPl+S0TkgZ0xJ17QJvbOf9Qta8CSwWVLlsr2XTE3qyr9V/Xl7fzgN7k8TrPTv4l9hVuIAlAZ/QUr88DtOYpYuJcW/UubVrXTbUYOTvQD04ynvHb1SVO+U5NqgTUgdsV/oIG0S/Ic07/mrwHZV/yH0TWg0PS/b7x7wH7LReT+nQ9Y3mVyfP40asv3WyoWNcBM/5qqAUP7pu6e8O67vGRw2fzNlu4nElwDIob9NCP9FzSuqT3R3xQ9NKjuG2PhQQFASs4O9y960uUPcGrSMtdmi3MznrJ/xX+gmbmj9Ax0hhcNLNk7vUMWnoU31qLOEnWh15tRF7rd+LsXEf1NelxQonNjpdjfV7ULy1es1585YL/9ReT+ndstH3/f+PwYccsaYKZ/TdUACWoCgdHf4+DFS0TEqAEB240PXLJYRO7Zu1tvGGl8+i9IO9O/iaFBbUABqB6Z0l7bhvuXPMUyBY4LasZTPnPlM5PeZVF3ZLK3d+/0bBlQZ+GNDbU6+nuoJhBRA4pO/1q/P3Pa6U8Xkcsu/VHsjQc6Q4lOViAJty30+jP6sk0NMKO/SdUACWoCgdHfQx2baGYumtukfxHZMzAjFicouGfvbpnL/Tt3Twfumlz0ab9q9PsSuAdwLunfqWOAZhE9UBm1RgGonn/f2RxjUNj3to5D/Rju3zzNG3i6dvVJ69ZfJUlSrEr//uuja0BY+tcCa0C66N+ZW9/cF9s3qG+kbRGJrQEDxo7INicrECP9xzal3sKZ0VQNkKAmEJb+TUPdJSIy1ZtdK2+T/jVVAxKl/1gq/Udjl99ov70xzczX6xBAKeiU0qRlNSgATvCfDSCvb5cOWJ6RfDlOogSM+WmJWj/3lSuPF5Hp3uxafJuV2Sr6B6Z/TSdpHXNjo79J1YCPrP+apEr/nYVDTdQ/Y2tAPyRwqxogC5vAQPgxiMJew9Qr/sMMdReLiB6yZZP+jfsuEZFJy7P3Jv90b9+5K/ZoofftvN/modoz7Gf4SYfv+9kfE9/ruCPlxt9nnPTyZ+6f8REcpN5c8y2uXYqAn0PfWBTarcOO2JPvJIp4TMlvY4jjh6Bp4SLV/5Tr+CLcsPU9YsyweVIwJbAGhK34j7a4O5b0LoPd4b0ztgeiMXWCz6Y6z98EwqJ/oMsu/ZEn/e/YFboOW7+G/vQftgVgoDPoT/+LBkb9t4w9PfDYmPcIS3v2TC8dXbRjd9Q7ODa2YATO7t2TY6PzjxM4UfMu2+f2FvAXgCWD++nLGzd8XkROOS2g3ekhQM0e9uM/Edii8fndbyKagDkEaNFxR4rI1kvTFAC9BUCn/w2fvT7F47jJn/49f6pkrpAdb54TPNG/uAykH9n9ApD7i2A/e0T/ckS8IzV6QW646d2ea/wFQNERNl30H+6O6MtdsV1FnS79x0Z/k64BidK/cumW6wa785E3ogAogevCAwvA3j3TgTf2F4DJmd1LBpbGRfkFBWDPnmkRWTqX5sPuq9P87t2Ts9eMeouEpwbou2w39hWOKAAq/WueGqAKQLPTv0k1ATP9a4E1QBUAFf2VLAXAXPffmAJgHt3bkXcZeWEIkCvMr1Zx37e67NBT0HOPfdiqTmTWtgVr7Otci30DPCv+Y6kBLW9b8wKxGyBkMtO/iPSkJ3E1QEX/fTOT9nOoJEr/6vZ96adL/0nvYmnvHttDQ07OHXF12diIhEd5kyf9h93Xn/4DLR1bcv/OBzzncNhud+oAT/oXkU9f9i1ZWANaNej/U5d9U0TOeGXAXvjDTzpcXfA0ATP9Z9HgkT9VzwWKQgFwQuB3rLgvXkGPnMuGhbYN9xeXfj7LYf86uzze1L/iP9bq1SeJyLRxZEabvV090d/UmzvWvr8JpFvxP9AZ6PVjzubrp7YA6NXwlk2goPSvo3/suHkx0r+moryENIE9kb1i2djIjt17RMQ8Rmd0+temerM3277T6vb+6G9SNaBoNU2HqgmoGlBQ+m/G6n/P+1vH9xrRKACtUOjJhvPCIT4bL/WzLnpoXCI3bH3P+Ru+u9J65b2i0n+YsA0CEenfZG4QGOwOT/T2pFjxP9AZEJFuZ7ZLWDYB/24AqglE1ACnVvwr6jmYS0nzWajxP9Hpf6G+WEf/FC7ZdGVBj2zP2fR/xt8fJxbnwRh+0uFLLSriUc8c+vX3p6Jvc+BBDTwEkLPvL3JEAXBUjoMf/N9kc0+AXCaREdG/8XJ51o7UgPM3fldEzt94rYjY1IDo6G8ya4Bl9Df1pDfUXTTR2yNzOd5+df6Abxh97CNEHwsorAY4mP4j6M0aSdJ/Ag/s2iUiS8cWi8iu3fGnjRPSf5E8OwAc9cz4ZH/gQUMPOqlpg3+a+v7CgwLgliKOBOpnRv9q9wdoVfSXti5Y833WjtQAJbYG2Kd/7YKN165Z87yk9xrqLvIfUdRmdb4/+vsfwX93yxMCmDUgMPqbewCnZh/9Rfr+9L9kYKn/dvuNLtq5Z3YVvmX637l7XEV5sUvzKv3roUphNUDfwJHoLw587yJs+ex1gfsAJKXSf/Tqf9b9o9YoAE7wpPCWfP0qXNAw3L8cxT1r92tAiugvIjr692R23XlX4sczqPQfcYOwHB+d/gPvbn8uMK3TGbh0yw+T3svSuN25tERk957JwDPj+u2Z3jE6uGy/0UUiomtAhJ2+A3rGrtRX6d9P3XFJd+TuhYf2dyT9V/5dK0fsuv/o6O/40QsitOcthlAAKhd4HPTc15j6l0fVrvtv1Yp/R0Jq+Ur4LXHqYEHnz+3RKyIrVz2n15/udpItYANX/KsmEFYD1NFjLA8qauZ4y+jvv7uIzCQ84I9l+p/pT++3dFREdu7aY/nIwwNLxsXque+2yPHKnukd5j+Xji7qRNYAf/qfv+/Y4u07d3q2coRFf4/PfPzbIvLqU08U0n8SGVf/Ww77ib6By0cviFCXtxh5oQA4wfziFRHNdU6qfFNDq6K/tHWRWvJL7dTPrbkdoNefFhGbGhA75iewBsSu+A/U7XQHZWhG0g9qV+XBpgbYR3/zn2E1wDywz/DA7OmQly0dEZEdu0Jfh9TRX1FLTLU1YMfu8U5nwQGXItK/pk8OPdgdtkz/6zf+u7qgakDlWrIcyyX9m4cAcmoNRQQXlpwoHwWgYp7sIkV+Cav9ele7iNHnMixzilL1a16+ap91teOC1qx57kwvYMRwbA2wH/Gva4Ba8b+vF58+PQZlNr4MzC38UzeB2BqQLv1ribYGhNWAjOnfr9/viUin07WJ/h66CUS76IKvJX3k4rRnOfbAz28bG445v97RLz/wru/ujLhB4AFAnVpD4deSdgc/CoArPMsIN5cUscJOBVDtcH9hzE8pHPkhqaQGrFx1fGD613pzMddsAil29hWRwe6Qjv7qpF2W4/J1+jepJpBvDfBH/37IXshm+u8GjUrab+noYGdYRO7fuT12ZvpzJ0boSFes079l9F8wIbvDK52/4QvqwmtOs3qjnYr+4sw3OpEtV3zfZhSQ5xigD/z8tti7HP3yA/1XmocAij38v4MbBOr4FiMvFADnmE0gxxATsfK70O8/Y37awMHOU1oNCFvxH0Y3gVWrn9vr97qdmJWOHsPdxfqkUZo+d29YEwiM/qZcaoCIzPRnitjfd3Jmt82JvbS+9CzT/67dk5aPvH7Dv4lIp9M59fSXBN5An/pX0elfRD512dXqQkQTIP3nZcsV31cXLPcHUOnfv/rfPASQSv8Rq//tT/7lzgaB+r7FyAVvfytEfM9zWQaZD6Kn5Wb0L26RV/nSvCru/4rk+9a8+rQTdcxOejowZVF3ZF/fG09jm0Bg9A/kqQGx6d/DrAFTvYmh7mL7+073p0Tksi0/8s5S8CrzfsdY6x+4BWDnrj2BAV3vAzB/y90T6iA/O4NO4qt4jgK0a/ekhJwzeHRwmb68YePn/adS8dcAXQDM6B/IrAFq6qT/4gTWAL0FQK/7jygAet2/vwCoLQBZTv1b1QaBJr3FSIctAG2Xy8h4z5ZNF4b7lzxFaWv0lzo88SIa6cpVxw90h9av/5YkqQEq+vvTv8wdnCesBtinf1k4LmhQhiZ6uxd3xyzvKyKDnSERGZ+Z3V11qjchIrE1QEV/5bQzni5BNWCh+DfC/nBAO40Dbu43NiKRNUDmon+svshGX/qXhStxPU0gNv3L3AYBVQNci/7SuGg489/b1IWBRx3q+VPsyJ/AYT8eWdK/VLRBoGFvMdLhQ9AK0UuWfJcFbq74N2+T77y1c0lal+jvl3F9W6fTuf7Gd+l/mof+lLgmELjiP5BZAxJFf5PaUXjvwgHusU1AvT46/Qc9bEATMNO/32VbfrRwC4B51OPgLQBm9I/dAqDTf+Bh/s0moG7gSf8RWwDUsJ9YqgaMjQ3bpH+X1fd7HeHUY480/6lqgG4Fmn8LwMAh+3mu8W8BuOLu3+Uwi4YSNgi08zcLfnwO2iLwHMP5LmvMRyv5h8R+cjku+xr5Y2mjGb8fSd8+dXsz/YuvACj+GmAf/U3dTjdL+t8bvm9rWA3odDoR0X/h48/WgOjob7p0s9o3wDOWJqAAeFb8RxSAnQvPtBVYAHbs2iNzb9/Y6LB/3X/g42++8KqkH3Kndu5MoRnfaz9PAQjjKQDjy0fGFnuHSHgKQO7p31TQ70tT32WkwEehdYo4FUDgPgBS1rIm0VRymaU2R39p1hO3fEaeFf9aYAFQdA1Il/4XD4zkuOI/eBJGDYhd8R+ok/xsYpdu/kHYI6gCEH34f214YMlO33l2IwpABP/jp0j/pjp+TZqdC206gFkAxpePiEhEASg0+ptyX0nX4HcZSfFpqF70N9z9b6xnDqP/mfukJeGSMfv8uP+OFKTBTzx6r3HxrfjXIgqAiKxdc5I+9HuirJwl/dtE/wXT6o6lS/9Koud12ZbrPHsDm3ff7Qv0ij+gT4wH77bkLwAfPf8rMnc+3TCex8/rtLs1qgEN/mp7RDQBXQBU+pfwAlBa+jelaAJl/hajjvhAVC9wcI7nr/V6m0pY0KR+WbLMWx3fi1y05In7n2bYin8togCY6X/hVKIScwkr/v26nSER6VkP5lHUPr6nv+EZ1re/TnyHA9KvRvb0L74CoNK/KbAJ6ElkXPEfyPFxQS35apvOeOnjZ+7e7bnS8fRvSjfelfQPP44CVD3z5FnFLY79A/Q985D7FAtS1dH9y5+oI9rzy+E5mJWIXH/ju1TET3S4TxX9w0772u/PSEgNyJj+x2e8scaGSv+SsAboI/wMdoZsdgNQ6T9MR2Tp2OJdIR3AdMmmq9SF157+/Iib+aO/8pmPf1tCakAR6V9cOui7X3u+2h4DB88OfvM0AZ3+w1Se/sX6bGKkf8TiY+EEz3dVfKshcxm2HrE4yP1AQMUda7/8ATytXXo6GFnKoX9cX/X6E8zr/TXAvwUgbMV/yIRma8DigRERSZf+F3VH9swsWPHfX3he3jA6+vtF1wDz+J7/8MbnqAthNcAT/T1bAPbunfEdm39BDdCr53X0N3lqgHqosPTvoWqAevy8hv3Ecuc71cLFmnrxT3/JMZ7rZ+7erSuB5t8CsOHzPytu3lIL+0SZaxXb9kbDEp8MJwQmcgnarTaXSdSxAOT1w5l0j+FcJlo7PHH1z8BVxWYNMAtAouhvWjK4NHX0FxFP+teia0BE+tf8NcB/aH9dADSzCfhX/McWAEXXgLmAHpD+TaoJjI0OW6Z/rZJ4VPm4oBaGQv2Uz3jp421u7ykAbqZ/zf+JUs+3hW807DEEyFHqq1tcLq/RciHfMGq5TGxtApZafTbyZfnEAwcFpUv/iwfGJL8V/x5q80JgDbBJ/+IbFBR3Yq9Z6jxil2yO2j061oHLDlDH1H/jW0+OTf8i8slLE6/Cr/A7XuG4oHYu2cyv9pav3KguWDYBcT79S8gnqrVLcliiADgh8FvqH5GccRKqUehr8nrk4lT1W9Xa5WahL/ht3111xLM3FvHI2aV44nrd/8rVzzavsd9bYPHA2HQ/zRaD6BX/HnqUkWoCltHflOIumzd/3+Zme/eGbqPQZ9SySf8pOPIdL3rXLw9HnnXJwp61agKxNcD99G/KNzag2dq4OIDEHXoo+4OXPFYnr0du5+oxpdBwcNt3V+nLrtWAiCceffjI1atP7Ml8hD1/w3f15egaoFb8p07/ltHfb6izyP68XWG2bP6e+U//ECCV/vvSkyBqCJAZ/T1DgC664OsZ5zCWmzm46HFBbj7rotk/a08TGFs8WK/or3k2Akhbf9EQq41LhJbzrBso6IhDWR626GVW4Oy1eUFZ9Ir/wOtdqAGxTzysAHiiv2IWACWwBmRc8T/R2zPTn05636HOIvOfOdYAswCYK/4jCoBnxb9ZAIpO/7X4mhcxk6R/S6oG1Dr9+4e5Vr7PCdzUxoVCawXuJ6T/6sJOwOX8PAcuHNv5RSj6uYelf63CGmDzKQ0sAIHpX4IKgGLWgNTpf8nA0r0zO81r7GuAJ/1rudQAXQA8w37CCsCePQsmWnL6r9HXPMfDHuTyOLVTr7c7Fxz5B4nwKWmRiGMN5f4j4fLRNmu6M3TuShvzE6vkGmD/afcXAD3i3y+sACj/uOYFU3PRv9vpxs/lnCUDS0XEk/616BoQFv1N2WuABB7tJ6QAXHzBN0TklNOeq/6pCkBrh/3EyrjutqbPOqN2dh5+15AUn5IWKeFgoxHTir6xlLi81htJy5yoUyoZ8xOrnBqQ6JNpFoCwFf9aRAEw079mUwOi078WWANs0r8pYxPo9ac/cen1+p+BBUClf9Obznwhw35spHgW7UyBPGv/P4FAfEpaJPAnpKCNhpaPVslvczMCQWrurPj32PSZn66/9Ds5zoxHivddF4DVq0/csPHbIrJyVbItAIHR3xRRA/zDfqKZNSBp+lc6IlPJa0BvYf1QNcBfAPzpvwQNS0L2n+GGPXFLPGvAHocBbRH/kUCVVqVwvfq/hQvNol/zM175jLPf8PQUd9z0mZ/mPjMe6d7uM1c9U6QjIjNzsfj8jd+VyBpgik3/ItLr98RXAyxX/HsMdAZFZLi7eLqXZl2+Wi4MdYYkSQ3o+TY+vP70p4rIxy/9sXkl6T8XnoM8Bj671q7gaN7bbaOdzxq5oAC0iyNLikqWWebvYtsOkVbaMz1n849EJFENKDr9p37u19/0zrlU7BVbA/5xzQtEJDb9a2YNSLri3zTcXSwig90hEbGvAf4naVkD/OlfO/X0Y9WFpx7zfsvZyFGzv9qBZ33S1zT1WUdr5xNv57NGXigAKJVTWxtaUgPK/5GwrAEXfOanRZ+uJt1zn4v+MXMXVgNsVvwHUiv+x2d2deYm3RfbmVfR32RZAyKeZEQNiIj+Htff9C4ptwa0JxX5z/rUkifu0Z533NTOZ40cUQBaJ/AcgSUsR6od8xNxgwbXgGqfVHQNuMC34j/fuSxixX8gswasXXPSgAxO9PaIyEDCE+iODi4bn9nluVI1gdga4E//WkQNsHySQ3NPRDcB+/SvXX/Tuy7bcl1Bp/U1tTAVmUuwFj79Fj5laeuzRr4oAO0Sth9wLkkx7EEqjP72021eDXDkF8JfA7KM+fnDf572sL+5LPZmha74DzTcWbR2zUlTvckpmVTXqN0GbGrA6OAyEfGnfy2iBkREf5O/BqR4kkOdoZ70pnqTye8acJzQ3DXpy5uU5xCQ0o7XoT3P1MORZTvqjgLQOmEjYSTzYsWToQNPSViO1D8MzagBDs6/qgEiMjYypKKnP4DGRtI//Odp+r9hNSDLiv8LNn7vzKBz90Z72+qTe9ILS8axNSA2/Wv+GmCZ/jVVA0RkJtVewj3pichQd5GIJKoBKv0Xuvq/tZHI/4G32VG4Adr8jrfziSN3FADkrPLt0dknWt8aUMd5tqFCv/8aTw3I8tZfsPFakc4FG68VEfsaoNJ/bBoOrAH20d+ka0DS9K+oIwUNDAyKyL6Zcfs79hYe1tOyBpSw4l9aHIkinnizNwjwjhc9FXWhnS9ye1AAWidsCRK4b0DqSeT1UCmmm9cyq3Y1oBY/ip5PRt+4MmzW/enf86eH/c1l+b5NNjUgesV/ILMGpEv/yuKB0bkHTDYWX6V/bXhgiVjUgF7IOX1lrgZISBNg2E+hLL/vtVuOxarFgq4IpaV//Zlp7UvdEhSAdvEfNcL/14w8ZxYr54enuKnU4ufTkdnbcsUPznjlM3J8wIjo77nZrV881WbfgEQiaoDliv9AM/2ppUMH7Jneof5pcz5gk07/YgT62Cbgif6m6BoQkf5Nng0CrPgvWtLn3phxQa1900t74noq+ucbTUUBaKOCliOBMbSEAF3CktHlGlDfX8T+wnX/np8ay/TvuX3RNeBtq08WkZ701q2/OsUOA0uHDhCRvdPzK/4DTwQWyIz+Hirfh9WAiPSvBdYAy/SvqRpw8SUFns5Zq+/HPqMsS6G6jwtq85te5hPXk6tqLz6UgwKA3EQsJsqsHMVxrQa4Myf5Shr9TatfddyG/5v/6ucLNl67evVzRWRfb8K8UpLsMLB06AAz+ptia0BE+tcCa4BN+tdUDRCRiZk98Tfuzt54X2++Nkz39p1+xl+KyKVbfmg/3USa+rG3kVcac21RFqtGs5qvyp84HaDBKABtZC5TanoSmQoXiy78dlb+q5CLTtC6/zNe/pR/OuVJ6R5w07/dlH2u/FT0l5Dj39vUAP+K/0BhNcAm/Wu6BiSK/v4p7pmePxvxQGfA/KuO/uY/984NalIKqgFtziK5P/e6jAtq7Zte8qB/jdDfEhSA1jEP06muyeswoKVxYT7N/RxKnrQLT78I6y+dHTrywU//TEQS1YCCor/Mpf/YU1+pGiBBTSBixX8gVQNEpNvpJor+piWDS0Xk/olt+pqxof3t726mfxGZ6c+oCwOdAU/610YGl6kLZhNQNUByagJN/eTbKO65Oz4uqLVveplPPLoD0Aeaije1daJPGZPv9zz3pYaDP1FlzpKDTz9QxK7AS0cWHApTPZMNl86PGj/j5U8xb2BTAzzpP90QoFef+hzPbgjRK/7VCYDDqBpgueI/zNjQchHpzYVvS3oYj5n+jceMrwGe9K8tGRiznw3PBgHlKce8z/4RTHX55Beh5Ofu1Evd2txZ1aB/z5VS6aouFC3ZASjQJHoLQC329NeHJHNtMaRmSc1eoRNy8+kH2nLFD7Zc8QPLG5vp3++Dn/6Z2iAQaNO/3VTosJ9efzp23X+gCzZeq+LyOed9OcXdx4aWq/QvIt3OQHfhCJwIKv3fP7EtMP2LyO6p7buntofdfc/0zlzSv4iMDC7T2wS0G2569w03vTvR40itPvm5K/+5mwu0an8aWhs6q9qw7Lkm+piBaACGALWR/yvt8pfcqTVSYQrdMaAWr4Cf6gARWwPWR0Z/U+CgoEpG/Nt4+9oXqQvTvX0isnHjNSKyatUJlnfX0d+kOkD01gCd/mMnoTqAZ2tAWPSX5OlfGxlc5t8U8Ma3Pv+STVdaPkJrU6BU+twrHxfU2ve9qiceOM6H1f/NRgFoo/7Cw/M7nv5rtOgpogbU6xXwC6sB9ulf0zUgS/S/+Vfn6ctHP+ashX/snLnqmTP9fZvO/4HMvYMrEx7rU6d/D5saEBj9TWE1IHrYTxizBhSR/i/e/B0Rec2pf6GvuTTJ+QFq2nvz4sgXv5JjHjjy3Mvn5hN3cJaQCwpAS5lfac8+AFXMToD6/vzn9ZNZ31fAz6wBKaK/6YOf/tnYkqH42wUx07/+p6oBYYfxOX/jtWJdA8LSv6ZqgAQ1gdj0r3lqgP2K/0BqM0WE89ZfJSJnrTk50cOq9G/S6d9m9b+bSagcDn7xSztekIPPvTSVf+YD1wy2871oCQpAG1W+oInWjOVOlmVoM14BP/sdA4rgSf+x15tia0Bs9PfwbBCwT/9atzMw3B8SkZ7I9sm7kt5d0UcZWtRdrC5MGic6EJGPnj979J5ENSAi/dtwfAFVKJefe9Hjglx+7kWr9rkHjgVo7XvRHhSA1ilnj9XUGvYbkKIGNOwVcIFNxFd7rAYeu0YLqwFJ07+2ceM1/7T2pQPdNMthlf7vm75bpkVEOnM7CvftjhoUdoBR1QQ8NUCLrQHZo7+0OHnU5bvPWMd8ufDc/ccHR+NRANrIs6xx5EfXkdkoguXvZYNfAUudTuf0v31yvo9pk/41/4Fr/M6fO+S/ZIj+yj+tfamIzPSmRSRRDZhP/z6qCUTXgNjTC9jUAPE1AX/6T8SFGFSh2j39HMcF1e6558id585R/9uGAoD5bQJVfedbEnyja0DLl7n6ZfGcByCLRNHf9Pa1L7p483cf2Lk77AZvW3OyiOzrTUzM7JGEZ+pVVPQ3WdYAFf0lJP1rETXAfm71uKAw5gaBsPT/D2989lRv4rItP45+KD7/NX362ccF1fe5Z+fOcw/bLRANRgForxK29NmsS3BnCVgOfw1oSf+JYH4Gtvz7Dbl0gNTpX0Qu3hx1kq+3rTl538JV40lrgD/9a9E1IGLFfyBPDUh9XuFo562/amxsOOyvU70JETntjGPVPwObQNsWAqbGfP3TbRBo+Vvf2ucOF1AA2ihwGa13DCjzWG9lTs4pRH8l91dAnwb4vPVXi8hZa56X6O6x0V9E9oUMjLGpARHR3+SvAZYr/gOpGhC7Or8cp51x7PU3vfOpx3xA/bPlX4HmRcBEGwSa9/Tttfm5wxGcCbil+v2+/5QfSglbBpw9rW+Z9Cvg8j7ZhQr7DGz59xvSPaBO/9p5669WTcBGlvSvTczsUU3AzzL9azO9adUE9Ir/FOlfWTIw1u0MdjuurPG5/qZ3Xn/TO1u+EGh2BIxeuFU76LRybX7ucIcrvwdoD5Z9nnVjLdwaEPtkVQewHwvkj/4mm60BEenfMvqb/B0gafrXhmc6ItPb+w90O10R6fV7ie7uOY2X6gA2JzkeKn6LwU9uPNvcGtAqLVkMBo4LaslzD9Pypw938EFsqYhlUPbFk/kInsvSmowbJvrlbcNLlPQD5q8BnhOBBab/153+gsBH89eAsOivdgJOkf61ZcMHi8jeuZPsDieM1Evmhv1s7z/g+ZNlDYg9iW9YE9Dp/8PrvmYzoYh9AE459Qk2j9CeGtCG73iYNj93hfQPd7AFoKUKHerTX3hCQWG5LyJ2L0Kztwake14RWwOiV/wH8mwNiFjxv3r1iTP9KRE5d/1VgacAW9FfJiL3doJPHeBJ/zLXIixrgEr//uivxG4NiI3+c48TsEGghHX/Hpdt+dFTN5U8zWqQ//y/Di3RzmcNl1EA2qvQJZEZZPnNS7rob2QNyPgx8NeAFOlfUzUgYr21iKj0P9kbl6BTgKn0L0E1wB/9TbE1IGLFv0dYDbBM/8bjzNaA3KO/zer/y7b8KN+JOqvlS0L99HM8gUBdtPyth5soAChDmxd/qZ97Y2pAjk8h9f7BuVA14AMrA0bz6xoQnf61sBoQveI/kKoBMtcEkqZ/bfHgUhGZ6U2ZV7597YssRwF5EP092rwMlKCn357twy1/6+EsCkB7hQ0BymVR5TnOfTs3++bylOteAxr24xeY/rUV/WUyObljYNLy0Tw1IEX6N40NLheRGYsdfP2GB5aoCwPdIVlYA9TZjhPVgNNOP05d2NfbG3ab9qT/+n558xK9EKj7Ii5awxaAaBIKQEtFnI82+4I4cJHX7KW8X77L/Tq+evWa21jR0d+0bGaRiCSqAcu6y6Tfkwzpf2RgP3VhoDMoSWqAjv6msBogFk1Ap38RGe6OSFANaFX6b8xXIAX7hUAjxwW1/N2H4ygA7RW4YPIshVNo88oepbhnV6NXr2G/fPbpX7OvAcu6y0RkV3+PiAzKkIhMy1TMfRbS6V+zrAGB6X/+QbqzeyNYbhAwo/+CqSysAaT/lkjx9Js0Lqjl7z7cRwFAzhKt7GnY8rGcHy3Ha4CzM5ZaivSvfeqS617zxuBYLHPRX0R29XaZ19vXAH/0N0XXgOj0v+BxFm4QmJjZs3LVs0Xk/I3zx1AKS//zk+uObN7yPc8+Bg3WvOVbIhmfvuNLuVgtf/dRCxSAlooYlF/aWWkbtmNAyUt8N0uUa/OTmjoFr4gc1F+e+kE2XfqdiL/OrvhfGP1NqgZIeBOITv+avwbYR/8Fj9MdEpE9Uw/oa3QNiE3/IrJ5y/dSTLSOmrRYSyev5UBNxwU1ZjGIZqMAtFfEaJ98F16NHxFU4fy7U6IcmY1c6PQvIuecf5WInL3y5ESPoKN/2MsRm/5N/g0CltHfpGtAuvQvIhPTuyWoTqgaEE2l/zas/if85f4K1GtcEB8A1AUFoO1cWFTVugZUvrh34dWr/EXwUye3Uke4t2dGf5OqAWLXBKLTf9iwn1i6BqRI/8rQwKIhWSQhZxDbcdb/FZFl570q8L4q/Wv2uxq3Z8W/OPlFKFmhr4ALy7pofABQI3xY4RaXF+4eDs5q+bPk4IugvOa0k8x/BjYBz4nAPOl/1+7xM1cdv2XjT/x3DKsBgWN+7h3fKyJrVj1XEq74D7R0+EARmZwJPbxmmKGBRf4rdRNQ6V/z1ABP+veIqAGe9B+xBeCSTVdFTKIWWh7+Sl4UODguqOUfANQOWwDaK3asf1VjWsThWKm5uawv+dVz80UIFL1BIGzF/wUbr10iASfHDRwXFDPif+gAdWFff58ErH+Pp6K/smhgRJLUgMD0L3MnEdu+9tOe6/XWgOjor4RtDWDdf3uU//RdGxfU8g8A6ogC0F5u7kWquFwD3JwrUwmvnvsvQqDAGhCW/mOZNeCCufSvWnXfuCwi73vbK0RFfxERWTQ4IiKT0wnW4pvpX7OpAWHRX/FHf5OqAYs+9DKbORyYe1Vn+tNti/5Sw+9Cjqr9EXHhx8LNn1EgGp9auL7wcmf26vhLX8Q8u/OORPAMAfLrdgbHxoYj0v8Zb3iGiFy0/vux0xobnR9K5C8Ab33Li8VI/x42NSAw/XsfJ6gGZEn/HpY1QEQu2XxtT2YC/9S8IUC1+C4UyqlXoJJxQU69AoA9tgDA9UTryIFuarqUz3f1mAtvRF56/emZXjeXh/KPpfNcE5b+ZW5rgIQ0AZvoP/s4vq0BOaZ/EZl8xxfFogZcsvnaRA9bazVdJuTItVeg/HFBrr0CgD0KAGqg2o28DUi9ubyA/NSlE5H+Tf5xQfbpf/5BBkZEJGwFvJI0+psm3/HFe/40fNj/fbH/T62K/sLXwe1XoJyfDJdfASAWBQC1UUkNaNIiPvUL2IAKVBeqBgx3Ux6qX0Rkep/ertEbHPD8MUv6F5F7/jQsIre/6r/UP3UTaFX65+sgNVkwFnceMT4DaAAKABZwf7FeWg1o6iI+6Qvo/keiYYYfuF9d2DW2WJJuB5hesLWhOz0jC2vA+Tffri6sPPqwRHOlor+HagJfOX6peWX09oe64+tQuwVj7uOC+AygGSgA8KrF0q3QGlC7X7gUbF7ANrwOBUn9knnSv4js2nefiCwdPrB/zddFpHPCC4PvOR060MhfA2SuCVjWgMD0r7302l3iqwGNVItlY6Fq/Qqk2yDgecq1fgUAEwUAMVxe3hVxJFOXn2/uImpAq16HQEOdqNRr6dR/eNa+mQkRGR4IOJ+AyR/9tbEf/ES/E6oGiKcJhKd/TdUAD5saEJ3+tdga0JWBGQk+CtDmC6++ZJPNRKrEN6IZr0CWDQLNeAUAhQKABTyrOiqcE3t5HSaotSu8PTWgta+DadXQuHz8ayIieydkZLnNXfrGwX86Iq//h2eZf42uAdHpP3hyeoOARfqPFlYDLKO/SdWALx0/Yl7ZFe+uCB6Of+pcnrfSNC/7MhgSLUcBQDAHT7QewXJRHrEEZ+Huwvl0srvtt5eqC4v27BGRydHRpI+wamjce9XeB0QktgaYddmT/jV/DUgR/U2zNeBZz429ZSxPDUiR/rW/vnav7gCx6V9x9uPHkkEa/SJEjwvST7zBrwBaiwKAAOZSz7xSX3ZzUZguQziYOSphvg41fU10+teS1oCA9K/Z1YC+yKkh6V/TNSBj+p+f6Pe+JbnWgFcufXjqR7jizttFZKk8WqzTv+bax4/YJ+14EaLHBbXhFUALUQCwgCcFBi4WHR8aZJ8h3MkZlfP8wrmWw2L5o7/JpgZERX9TZA0IW/EfaN/MxL6lI2O79mZM/1ouNeD0Qx8mIiI9Edm7K/GJ0lT6l+TR31Tc0RsTIfZJ+14E/6Kvba8A2iOfE2GiSQIXdv1+v167B6gZ7nQ6YbOqFuss2dVLFPGmR7yGjohO/9qiPXtUE/CzTf/a3gdmm4CIzB3zJ1H613YvHel05pfDYz/4Sbr0r2z66m8j/jrRidlbYC79zxpZ2htZ2rOfuk7/iVyy6arA6/tzyv8ERnwp2qPNL4L+4FU9I0CB2AKABcylXsSivy6/CoFrsmu0YrtoNj/wjm8NsEz/mn9rQOL0r81tDYgd8xOt2xmQzoCIzPSmPvjvv/qnlz8m3eNEpP/Y6C++9K+pDhC7NSDf9G8q+RPY2tRr4kXQHN9DHUiNAgAvvbxrzG+AGSAa86QySvqT5mANSBr9TaoGrFqexymr9j6wbsM3164+Kd29u535oTID3SER+eC//0pEktYAnf7vm7lXRA4cWKH/lHTFfyC9KcDfBIqL/qZyPoEsH4QXQUTmXoQG7BkFhKEAIIGIJSALxxpJ/QPvzg9hlvSvffja20Tk7ccfkf2h1m34pogkqgFm9PezrwGBK/51Dcgl/Zs8GwTKSf9aoZ9Agq/wIohIyIvALgFoGAoAgvkPgxB4vYcL0dDPPMJ9y7fn5vLcXfghPOe8r4jI2We9NPtDVVIDotO/FlsDogf9qxowOrhf2A2Spn9N1YBL/3tbivumTv9a7jWgzcsEE+lW4g79yeuDxqAAIIZN9PcfL8gd/oW4O6uxS5bvr7sLVaqONcAy+psCa0B09Df1+r25Sc+P3kkd/ZVP/vHPIrLw5AdWsqd/La+DBZF6FV4H4UVAm1AAECDR/rKexO/U0jNiad6qGlDQ03TkNaxRDUiR/jWzBlim/yUDY/ryyOB+IvL+/3qXiPSlJyJ/Ou2KdHMyl/6TufD8L6ebXLSMO/cT+MSBr7AjAo+GDDQVBQBR9DH4YheFbv6E1P0QN7koOuI48hq6XwOypH/tg//+q7HFVsttf/pXVPoXkYde9kp1IVETcCr9m1J8Dkn/woswh9cBbUMBQIzYn1VnB//46d0A/NeLAxE2d2U+I0dew9xrwMDDH5T9odZt+GZHOmetOTn7Q1kKTP86+nuoJhBbA9JFfykl/Wv2n0MCn/AiiIgDSy2gEhQAWDHPD1Dr34zYkxvU+tmZKnki7tSAvT+5Y+RpD87+UPd/+y4ROeDEHGrAeeuvEpGia0Dsiv8w0TXA2RX/gaI/h5V/Ph3RmGVdFrwIaC0KAGwF/o42b9Hpwr6tGVU1/9ff9E4RGRlc9pMbz37qMR9IMRtbbzlXX37cY9+WcX72/uQOEWlnDZCE6V8LrAGB6X9qMnSL30H/46h91aV/LbAGNHKplQKvg/AioN349CM9z9KzYQvTmtaAjO/CDTe9W114yjHvS3TH629658jgMvOavdM7EtUAM/1r/hqg5nDzhb+IfrS93/y5+c/AGjDz+7ti52rHbd5r/DVgamyZ90Y+nZAD5niawMZVn4h9qOh9AE7/0CliMezHxp9OuyJixX9YATjofxwlIus/+tXU0y1C9oMFNUnDltXp8CKg5dgCgEyiD5lca46MZrGXfVZ1+teXbWqAXvHvuX5kcNnNvzrPZmtAYPQ3/6RrgJqrviR+jm5uDZACNgikW/Ef6EffnXisHHjLw+6zv4tK/w7qGyc4l5p8owvSvGV1CrwIAN8BZGLu+Nvgz5L7oSGvFf+BImqAf8V/oIitARHp32OqPzndmz2v7ZYLfxk/0YUbATRdA9JtATCpGpBlC4DprDUnZ94C0N998JEicvZZL82Y/j//6I+b//TXAM8WAE/0d2oLgP+D5/43ugjtfNZ+5aR/NjrBcRQAwJabP5/5rviP4KkBYSv+I+yd3tHpDDz1mPeJSL/ft4/+C/XHZ3ZnKQDKyNMenL0AKEv/6tGxt7EpACIyePsfY8+uFV4A+iKiCoD+5z+d9RKb6Xp40r9m1gCzAPhX/LtTACLSnpvf6IKwzlsp7XVo8OZxNAOfSyAZp0JDoSv+A6kaYLniP1BHuntndi7ujqa7++itvxeRD/7nHbG3jC4AInL772YecexQ9G1sCsDgcF9ElrwgavRLkgIwd48QQQVg/jPgKQCaZRMIi/4mVQN0ATDTf39uvh0pAPU9h0m+yKBKma+DOS1efziIfQCAZBxZqVPain+P3/7mQkm44t802l8sIr1s6V9E+r//tYh0Hp51xPnvfjwlIrE1wMb4N34tcTUgCfXO2tQGq8/AB8/7qsTVAJv0LyKP/cOBIvLLB9/vWfHft5vd0lh+SWu3t09SlS+sHFHh6xB2ChqgQt2qZwCoJX2O5Eqmrn5LUv+cvPrUE9Ol/2XDB4nIOed95bz1Vye972h/sUr/IrJuwzfVKXKTPcJc+tf6v/+1agIZ/e7HU6oJZDf+jV+rJpCTfly+T/YZ+OB5X1VNwM8y/WtPuOOAB190j3mNO+nf8vzlJvWFqvBLXRByp1L+62CePAdwEIsGIJOSVxzmNblXn3qiiKxcdbz9XVT6l7mz7SpnrXmezX119Ff+ecP8I6xdfVL83X3R/5yN13iu8W8NsBkC5L/SszXAfgiQn7k1IPkQIL/ZR5gbAhQ80bAhQH56a0DS6B/ojjcdpKe3odLxP9mjXmO2BpD+lapeh2YfKRt1xxAgIJMyxw/k/vtx/sZrxaIG6OjvpzYFRNcAT/r3UJsCImqAP/0HasCgoMj0LwsHBeXwMVCbAh596Z3ZH0pE1NaAbW8K/aiUI5fviPmllto2AeKmUuYxfzwTYuQPXMZHE8hNcTUg90dWWwBMYTUgMP2bGwE0fw0Ii/7mFgCTpwZERH//FgCTqgHptgCYHnHsUJYtAKaRFzwm/nFiCsCs6BOB2W8BUH6z5aci8tLOoTY3trS29+McH81eQWGrdhsEajfDxSn5iJ8S9LKzNzDcxD4AqIdajM0taBhxxhH/ls7feK3aIKAtGz4oYt2/n2fHgOgV/4HMfQMsV/wHynHfgHvvymffgImrb5m4+pZcHqoIX+lv+0p/W16Ptq577LrusXk9mo0Ug/7t1Wv3gHIWF7VQZtqOfs1r8clB21AAUAP1WnrmGBcKjTWBdAdIFP2189Zffd76q839fVNYt+GbF7z731LfXbv9dzOx6/ht3HsXNSCN0mpAOZG3FjWAFcxaJS9F4I6/5ieHdwfuoADAaWELzc6cSubKhl7oR98s4gZVrck7f+O1F2+6IcsjnLPh6+ds+HrG2fjg5Td88PJMs6Hk0gGEGpBW0TWg5FDlcg0gX2rlfyo8Uw+8De8OnEIBgLs8I1nNYZTi21HPTemyggvrij687usfXpcpxLtTA/LaFCDUgLQKqgFVfU0crAGVLzHcUe1L4V/1486HBDBRAOCoiJSvV6XU4gcvaVZwaggvNSAQNSCFq/v5HG5Iqzzyml/takNe5S+FI1xYdaLYbP4FqsVhQOEoT2h2YZmehc3RQp09dofqAG9f+8LUj6A6wNmr0z+CiKgO8E+ve0qWB1Ed4LBHDGR5EEV1gBUPyuGAoaoDjD0m5QmSlTesGBMR6d0jIuu7eR6OU3WALEcKyjf9O/VNCdwyWSZHIm/lnH0d3JwrgAIAd9VikE8iETXA2V8vzakakFFemwJkrgYc8tAclqX3Xr9XRFY8dSTFfWfT/5w1vXvW3z8pIrLiIdlnTElXA5q34j+MTcnPl1NFqFrOfioAZ1EAUA9huwJHrE139vfAU2zK/xX/+c3vT31fmxFBUzI9FL5sOWfD14ceuG9q+YGp50FEtt84KSL7P35RlgfJ0Z1/mpbqaoAn/S98uD+LVFYD2pP+tdJqgPsvRWmceimatMYKzUYBQA2EHWMhcKFvBmt3fhX8yl9fqKj0P9RdNNWbTP0gQzN9EZkaSP9TN/TAfSKSugYccPyK+6+9lxoQFf0XPFwFNaCF6V8r+ttdo5eiaK69FHr0v1NzBfhRAOA6czFquUuAOSrX2aWw+VzKmU/Piv+h7iIRCawBkzN7Fw3Er4GuvAYora0Btul//uFKqgFFRH+pYaIqqAa4vFgrmZsvhYOzBPhRAFAbNr+jbv4e+On51E+q6IgTNuwnogZYogYEKroGJE7/8w9XSA2QuSaQPf3f9uN3i8gRx75P/bMuX+ow+daAur8aOeKlALKgAKBmosf9R/zTEWE5oNAxA7GD/qkBBSmiBqSP/gseLucaICJf6W9bnO3Q0ir6m5cf9vT3NyPkefb8SfekiLwaLwWQEecBQG3oo+NHHHXb85Pg4C9E9DH+cz/B0M9vfr/9Lr9D3UWqCaSmakC85Q8KfYQH7lNNIIvtN06qJuCIO/80ve0POZ2C4Pq95/7k9lweSmSuBrjBTP/aH370rsDra6o/J+l33J0j3LuAlwLIjgKAmnHnJFnFyasGpDvaT8YaMDTTt60BUfpTW3MeR165bX+YyasGnPuT2/OsAQ6ITvm3/fjdTaoBkvA77tTJAStH+gdyQQFALXlOviM1Gf+TSMYa8JrTTtq44Zqwv7511TOj796d6XVneimmO2v7XbL9rvR3FxGR5nUAoQYEsQz37awB5F0TrwaQF/YBQHO4P/7HL3Ym9aindE9HdYBVq09IcV8RUR2gN5B2TYHqAPuHDviJpTrA0OMOSf0IblId4NCH5XBCYtUB3va0w1Lc9zdfyFrSMkoR6D27CDdArc8PWCZeDSBHbAFAvQUeJLR5vxMpNgV86rJv6ssbN1wTsTUgVuVbA6a23snWgGjubw34ev+PnmuyrM5vw9aA5i3HsuDVAPLFFgA0RL77zjoo+2GCsm8NSL8pQNgaEMqRrQElyxjfL7j8uo7I+mPzmh1XeA4WBIX0D+SOLQBojr6h6nkpSvb9g6vcFCBsDQi17Q8z9987nctDnfuT2997tUOH9wl0zseuTX3fCy6/Lsc5cVPuBwSrL9I/UAS2AAD1k3FrgOoAA52hdFPvzvRkfKcs2S/d3UVEtt8188f7Bo5If8j/qa133ntXf8WDUj4FZ6kOcMCKHJbMu396r4iMPXlF9ofK3RmvOVHmOsDZ/+f4RPfddPl1DU7Eni91oacHqQXSP1AQCgBQVxnDwUB3WERmevvSTHtkf9m7XUSy1ICZ2+4TkSw14N67pkSEGhDB5RqgJKoBmxq97j8s7LazBrTt+QIlowAA9VZlDRCR8Z0i1IBCUAM8VPrvizRyC0Dsqu5W1QBW/ANFowAATZDxaKHUAGdRA2Ru0L8n96+/+MqSZqt49t/cNtQA0j9QAnYCBpoj416DA91h1QRSGt852wTSUjUgi3vvmlJNoGHuv3c6r12Ed//0XtUEqhL7+TznY9eauwirQf/6Pik+3LfdfP5tN5+f/H4lSZF3G7yLMOkfKAdbAIBGMVcQnnLqc1M8wmwH6KWN0Xl0gCybAmRua0DzqA6w/0NyWHFTVQdQ8U7tBBxNdYClI5k26Zi5X10+4uiVWR4wd1nyrueYoQ3IzaR/oDRsAQAaSK0g/PTHv5X+IWYmM83AA7v7D+xOP/Hb7rvvDpZOwX73i97vfpHtYKxz9j9wcP8Dy1sNVHK8C1zr79TWgFxeEH3s47pvECD9A2ViCwCQics/WnoFYbpNAbMdYGBR+hl4YHdn+Vjqu6sOcOCD8wm7DaM6wCOemENNUh1g+335DDEK40L69/y1wq0BRQzir/XuAS4vSIFGogAA6dVifVu1NUBtB6AGFKRGNSDnsBv5V8t1/FXVgELDbh1rAOkfKB8FAEijFtHflLQG9BeNdSaNMTzUAIfVqAaUIOkIn5JrQDlht0Y1gPQPVIICACSmf7Fq99NlXwMu2/Ll0/17alIDHFZ5DVhc4k5lgf07y+D+cmpAyUsM92tA7RahQGOwmx2QgNrNznPwDf8NSp+vZHLYPzhuF+HOQctDp55t/2ARue+OLrsIh3FzF+GjX3dELo+jnfKWZ//i5g/84uYP6Guy79q7afM1GR8hWlVh19xF2KmlE+kfqBBbAID0zF8vp35ZY+kOc9oZf5XyISb2iYgsTnnegH3bZ0RkeP+BlFNHpHu29UTkoEPz2Rpw0MmH/OZzt6e7e2D0Hx/sL5m2+r6oL1hn4eVT3vJsfQPVAZ549DvP2XDl2aufn24mpbnpX9NTd2SDQOUvCNByFAAggYhj7dVxUBA1oNlyrAGP/t+HiUjSGhCx4n98sC8iljVAe40R/U2qBpyz4UoRSVEDGp/+TS6MC3LqBQHaiQIAJBMx/ieCyz94+dSAtB1AqAEFq6oG2Az7ia0B5h/C0r9HohpQQvQXB1a3+1VYA1xeGJag5U8f7qAAACn5F+JhS3b1K+tsFFCy1oBsmwKEGlCwkmtAokH/NlsDTrFL/5pNDWjViv9A5dcA91+ToqnNyC1/EeACCgCQWIrFt/6hdXzRn277xrw8agAdoDi51wDxNYHU+/v6a8DSkSF1ITb9f2zDDwKvj6gBpH+ttBpQo9ekUHQAuIAjaQCJRazmD7xe314djqPAOcuJmslLP/XtlPef2DfbBFLZt31GbQ1AQe7Z1lNNIBeP/t+H6TKQ/Wg/44N91QS0pOv+/c7ZcKVqAtoFpH8f82BBuT+4++s+CuU/BJN6qSucJYAtAEBuAgcFifODfzz03HY6nUs/9e2AswHY6Y9PiUhnyVC6u+/e2RORsf1YSVGUe7b1Dj+6c/+f8nmFH/2/DxtL+177qQ7wfzJHf5PeGrBp8zWFJq9aJ13PNsBcnkitX5C8+I8SwXYAVIsCABTIc+g993l+kPSPFjWgqQ54aE9E8qoB+dqw4RoRWb36hBwf85wNVy4dTT9ELVYzIl2OxwxtxgtSEDoAKkQBAHIQuxCvxYI+bA6LqAGLjxiZuG2v5d2pAUUrugZs+MS16e+74Rp1IZcmUFz6r9eGPktJdw/wLEPcX+iVj9cEjqAAADkIXGFWrwW9TYcRtgY0mstbA6SYDQKm9Rd8MfV96/VlTyrdXsLNfk1ieUb76Os9K4NqsW4IjUQBAPIUthx3fwiQ5S/Qghqw44E0ExqfSt0BhBpQvKJrwKtPPSHL3TPWgL5xbgHzchYtCXCJakBLXpNo/hdB/ZPQDxdQAICS1G5xHzHD8zXgZU9K88jjU+lnS0TmagCK0/KtAfbaluQiaoB+Kdr2moQh6MNlji7cgWbwH/2tSfr9/pb/+Gnqu89Md2Yiz/2Eyh3w0J5qAll0xvbPZWb8Nmy4Ru8hYKkj0hfpL7zcF+lPTfz85vcnnYHWxruIY4a29jXx0Ok/9ieAlwuV4IsKFKsNP4eBmwJ6d++IvtfebdP68sCg9yX68+9jXrRer+GvanZDwzH96vCjrQrYwJOPFhHZ+0D0zfyHAdXRf/2mL3n+ZDMQqNux3Ua9aLong4ttbrl0dFinf5lrAjI10RF59Zufqf71F0e/y+ah2vDVtuFJt21+TTwbRvyfEPMgy21+oeAChgABxWrDUj7jiCARUZsCzBrwkId3YjsAok3t64tFDbA1slwkvgbMi1zx/5mPXyOZ9wdYYHpCRGxqQMdzeWpCdBMQERG1KSC6BhDgtNod7LggNuFebxDgw4PKUQAA5KOIGoDsqqkBdsN+KqwBs3zpXwurAY083Gd2ZvyVtr4+nn18I46qDFSOAgAgT7oGnPqMh6d7BMsa0O12GAVkr8wakHTQf5k1oG8M/ulMTagrI14UTw1g3W0gPdhd0h4ztAEijvUJOIidgAHkr9/vX/b93338B79P/Qgz0/x85m9qX181gXyMLJ9tAobUu/x+5uPXqCaQm+mJ2SbgNzWh03/sy7Fl8w/UBSJdoMCXRe8lXMkslUw/Tc/zbcnTR02xBQBAUbJvDWhPhihTQVsDMh7t53f/fZGI/PPb/9cjHvWm4rYGdET6UxPi2Qk4hI7+N938YRG5ceuHcpurpoguRe3pS/5DoLLsguPYAgCgWGprQJa7tydGlGlqX79vMYZq9hBAsXybAhJR6V9f/ue3/6+itgZMhWwT8NHp33TTzR9WZQCyMP2zeUR8hYcOAJdRAACUIeOIIGpAQfo9qxpg9VAWw2kCmenfvHLRdG/RdK5nfJueXfevQ1knZAeAwPSvUQOExL+QuQsEoR+1QAEAUJLsOwZQA3L08CfMx5Qca0BSgenfFFsDci4JvvT/pjc8J/Bmbe4A/vTPF5MOgHqhAAAoFTXAWeXXgNj0r+W4NUBvqej7rpG4df+mS7Z872db35fLLNUIh7H3C9wHusHngEczsBMw0EyOH4ZP7zD39w87PMsjIHeqA3S6ybKLPsOupdjo/5FzPu+/UnWAycGUq66Wjgzry565PeXNzxTr9H/Jlu/py6oDPOlx7043S/VC9PfwvCDm0T95oeA4CgDQQI6nfy17DUBBUtSA+ePrL7z+rac/57Yb1x/x+DX6GvsV/4Ey1gD/U1LXpEj/mt4U0OAmQPq3wRkAUBcUAKBR6jgIlRrgrH6vL+M7ZMky+7v407+6cNuN60XkiMevyZj+tSw1wNNS+tv+nCX9m5q6QYBQK0HH+gTqi30AgObQ558P+31yuRX0+/1/ufW2qucCQcZ3yPiORPfoi7z19Ofo9K/dduP6D6/7Wn5zJoume3LnnYnu0l94ub/tz5Z3jE3/yqVbbPciqAvyrkf0EH9eK9QCWwCAJogd8+Ny9Df9y623sSnAUaoD2G0NONMX/U2qA7x97YvymC0RkdkOcMghie9I+o9TWvrXyyhnA7RnuL+wSzTqjAIANI3/B8n9X1aT/mU9ofOQiJtd0//zcyTqBihESA0wB9X4V/wHqqoG6PnMuO7/1NOP9VxD+s9lQi5H6sC9fuuyzxVgogAATaBDc4q/usmyBqAa4VsDLNO/VlUNYORPrAqDuMsdwI8dA1BHFACgOfS+v4EbAer440QNcJqvBiRN/1o5NUB9ATrW6X/J3r0iMjm6NPaWpP+MPAfQdHZtRcQeVnVcwKLNKABA09j/Dpm/si7/elEDijAzKQOL8nig8R0icubKV9jc9gMfvSbir2XUgG1/tvygq/Rvg/SfO8cPpunyvAGWKABAc4StNotYnVaLcbcKNSB3M5Mikk8NOOf8q0Tk7JUnZ38oVQPyXAOsa4D1sB/L9N+86C/VLQo8od/ZDuDyBgrAHocBBZpDHQM07E+ea9z8cY3V7/ev6dvGONiYmZxtAtmdc/5Vqgnk4N575N578nkoxfpooRnT/0UXfN12ltxT/jF/XNaZ47ne7AB1XIoCwhYAoPFq8UObSB33aXafm1sDZjvAioNyeCg7nvQftgPAol53vHEfwJLTv39yLqz19x/nJ/BmLswqkAUFAGi+2NX/tQvTav7VbHMw0By1vAZYrvtf1GvgxnNHjvhZo2CtF0G1mFvAgwIAQKQ+v2GeX1y9NYAakKN21gDSf9VzMavaQfZhJ1Hx/ynwGqAuKABAw9VoN99Y0QfgowYoQ8MdEZnal/W9VjUglx8Jx2tA56ADSP+lTc5/xE8Hg7WaK886/gYsQgGNAgC0jmftWo1+1aLnkxpgyqsGyAO7RESWxx8LP9Y551/V2ftAf788sruuAda79o4tHgi8fs8tu8cOOkD/sx9+AKLhxqX/qkawREzRkcWRbiYuzAxQBAoA0Bae4+upy7Ub/R+LGmBysAZ0dt4jInnVgL2/3TPyyNHUD7Dnlt2Wt7RM/zU6BJAjUdspga8JLxQaiQIAtFSzD2OnSg4dQBka7uTQAcTRGrD3t3tEJEUNCEz//kJsOeynRtFfnAy1LqxxDxyexM6+aCQKANBGzfglY0SQvdw2BUhDaoDluv+w9D/e2Wf+k/SfUeWbIsN2RXChlgBFaNqIRgAw9fv9a/q3f0c4d5iIyNBwRzWBHKgakAdVAxZc8+c/pHuovb/do5pAtMD07494vT/d3ffdxn8z0n9GLqxfV1m/8h4ClIYtAACaj60Bppw7gGObAiRua4Dluv/en+72XBOYT0n/uSh5rgIrh15KmK+Smy8XkB0FAEBbUANMA0MiIjNTmR9I1YAlyzI/UOE1IDb666zXn0v/ZlXy1ybSfy4qmauIM44x6B9tQAEA0C7UAFN+NeAeEZHlOWT3zs57ZrbtHDh0v+wPJUYNsD/gT9+37j8Q6T8X1ab/sKDv5msF5IgCAKCNqAEmB2vAzLadIpJjDRgdiNrnzb/uPxrpvxn00H/PUZJ5udB47AQMoL36/T77B2sDQ7NNIKsH7pltApnNbNupmkA5POm/++hHlTbp4hBnA3lG+bP7L9qGLQAAWo1NAR5ptgYsWRRwpatbA/z61iv+a4f0H8EcBSTGa8UrhjagAABAo2rAgx7avetPvYwPomrA4EMz79pbhxpgmf77QfsBu4z0r0S8Dp59AHjF0B4UAKAe+GUqQWNqwIMe2hWR7DVA1Ecu++gIh2vA3X+Whx1pdUv9KtTie8gSI5Z/uD+vGNqDAgAAC1ADvHKsAXvGZf8csnteNeDuP8vDnrU4+/y4hvQPIBoFAEAF3D/MNjXAK68asH2niORVA+69o7/iwSln6e4ku3+rT2rH+K+zSP8eHNgH8OMoQED91PqU9Xrma/F73O/3r+nfXvVc5OBBD+2qJpBVvy+p37g94/OXt++cbQKZ3XtH/947YmbJfwzQROlf0V+59Zu+lPjOZSHpajbLSXMnYKBV2AIA1EyN0rOfnvl6/ehe07+9AZsCJN+tAbm8g9m2Bvz51/PfAtUBLLcGWKZ//3fM5W9drZcMRbA8vievGNqJAgDUSX1/480596ykNH+hnX1qjowIOuKorojc9utMCX5uU0C2GpDXiCDJc1CQTQ2wTf8TM+ajOF5YWfEfRn9zeYkAEwUAqI36/oBF9BZPMXD8OTapBgwetlREpm/flWlWcnyziq8B9sN++hMzEhT63awBjn9rHKE3PPJaAUIBAGrE5nfL5SjgOe2OZp6SsxZDg6gBXr2+iEg3v60BefDUAPsV/3nNQDlc/sq7xhwUxIuGlqMAADWQKBY7GAjMFfxi/PTWIu6Hsa8B35E/F1cV8qoBWTuA5FkDpvf2RWRwJIeHUjVgj90jkf6byjwQEK8YIBQAoC7sV/87m6r1vJmpxbMzQO1+m93ZGpCoA4w8atRzTT6bAsTRGmDDMv3vkfH4G5Wijt8XAI7gMKCA6ywDvePpX+v3+0lTi/uHPc3laKFHPi7TczziqK7aGpDF4GFLVRPIqtefbQKZTe/tqyZQKH/67z7hMf6bkf4BNAMFAHCdTWIOW6fuFE+IjzgWUNjdHa8B1/Rv/44kP7y84cjHdbLXgJEnrsjyCFJ1DZj8w27/lYXWANb9A2gbhgABtVeXNBA7k2GHCTL3GXD8yeYyIkh1gFu3pn+aqgPs/cW9qR9B1KCgAw+Q++7P8iAirg8KCkv/nmmQ/gE0CQUAqDc9ql6CAnRdskLYqn3P9bV4LtKsGiAHHiAi+dSAbld6mc9BJjK9t59XB9g3aTU/pP8G4HUDTBQAoMb8B9Gveo4S8OyxEPHzXNPD9lmeiDRa02pAtysi2WtALsOBbNK/O9FfSP8A8sM+AEBdeWJxop1rHakKsfst9OeUOFM5UzOffd+AjLMx8sQV2fcNkAMPmG0CGXW7s00gm6l9/al9KT8bKv0/6pmLIm5D+gfQVGwBAOoqRdx3cEfhRM/Cndm259lKk3pQkOoAvelMM9PIrQGqAwwN23akjMN+Ltl0leWEclTTDz8AZ7EFAGg4HUBrcfotPW+eEU11TD/+XZazHy100VOPWPTUIzLO2MgTV8jhj8z4IDXdGmCZ/rsPXpZ5jnJD+geQOwoA0Bz+42yqC4mOtlktT1zWlx2fbT8V2gJzW/ajheZSA3LoAJJrDchDYA1Y0hlQF0j/AKAwBAhoDnO3Wr3i3x+dzU0BLmcL/+rzCmcmqei5zeUwQaoDTF5/W+pHmO0Af/xt+kdQ9l8h27MNK5LcRgRJyKAg0j8AaBQAoFHMI89ERwfzzMEOhgw1b4EbMRqjUTVg/xUikksNGBzrTu/OuQaQ/gHARAEAGigsN+hIYQZrZ0fXNCb9WG4NOFIOSz2JhtWAwbGuiETXgNt/Z/XxmNrXH5Dc0n85ewA7W8sBNAYFAGgj+9zPashy6Brw2sdlrQEyvjf9fNStBuTFqfTPNw5A0SgAQMNFjPiP2FFVWA1ZhVxqwPjypUse2JVpPhLVgOElwdfXpAYERv+9By33XEP6B9AkHAUIaB3Pkeljb4OS9fv9T27NdLTQ8eVLx5cvzTofhz9y7OBDsj7I/itmm8DkviwPMzjWVU0gX5aD/kn/ABqGLQBAW/izRfRAILJIhfLaFCAiGbcGqA6w++47szyI7L9C+n25645MD5L31oCDHzxkczPSP4DmoQAAbWQeAijsryXPEvyaVgMe9GARyasGiMxkeRDSP4A2owAArRN9qFB/KyCdVMupGpC1A0huNeDAgwdF5L67p1Pcl/QPoOVY7gBYIGxf4armB6ZOp3P6K58VfZvxRQPRN1jywC5ZYXXmgbHRRYHXL6gBYTsBewR+hIJqwMzdu20eb9cd8001ugYsXri3m5n+93/m/oF32fDZn5SzGwxfLgCVYAsAACsR5+QixJRGbw2IrQERxpcvHV003puMCe5h6X/Dhn9TF8541fGp52FW6VsDbNL/xMErJMk59VLjiwOgKhQAAPMSHRSo2afpdVkuNaBr0QE8dPRXtvzfa0XkjNc/P/U8zDJqgOXq/0CxNcBm5I9K/1pxNYD0D6BCHAYUwAKWKT/6HAIoQb/fv/SK72V5hO6i8e6iccsbe9K/tuUTV275xJVZZmOWqgGZHXjwoGoCHinSv6Y+6p1OJ6/TZpP+AVSLLQAAQsUeJNTMMeaNCTflyGtTgIhEbw0IS//alk9c2el0Tn/dyalnQ0TkQQ8e2H/PzK+3ZXoQEfFtDciS/jX/Zz4d0j+AylEAAASzOV9YuhvXSC1OiFZ0DYhN/7Iw1GYcFDRw1KEiklcNGB2I2SVaiU3/mj5+rs2ngl3qAbiJAgAgWGxM8QT95sWaejUZXQNe9frnpHsEfdTLM97w1/rKpOlfRNSIIHdqQCz79K+k3jGA9A/AESyMAMwLCyiB1+srPX9twFpPne1qsQXAL7YGjI56h8T4j3m/evX/TJH+PeZrgOVruG+P/7rAGmAeBjRCxBYAdRSgwPR/0QVfs3lwiawBsd8LAKgKyyMAaXjG/EQEndpl6Oinpi+7/4wiaoCnAKQ+45VNqJ3tABkKgOKpAbkUgIzpXws7TJa5h4z7HxgA7cEQIAAp2Yz4V7mnRmNpwnZrFl8xcD/S2QwKynKyW8tXYHZEUMb9gwsYFJR05E+EiEFBtSvAANqAw4ACyCpsHXmNcr/JPNqjJ7fpf9Yoz/X7/f/7ie8E/qmE9K9tufyqLZenn5w2cNShA0cdarn6P8L+xy7NPjMe/qOFcrRcAG5iCwCANCIyTewuki6vEzVX8JvX17TMKIGbArIM+5G0b5/qANm3Bix/0piIPPCzNGcNKyL6m0o4hTAAZEQBAJAbmyHyYbsOO0UPWzJnsu47dJo1oNBB/7EqrAFFp38PagAAN9XvNwyAU2wOBpro4EKO8O8MYFkAXH5Smk0wLedoTt4aEL4T8AI7dnmuCKsB5k7AnvQ/8ZBD/LdPsQewn971ha0BANxUgx8qAO5LEQ09qcip3JzuuEa1OzFCdDAt8yiW8zUgbQGQkA6gC4B/3b+/AOSY/sX6YwMA5XPoFxdAq9ToKOmWs1rTwz5G9JmSn84Zrzs5SwFQPDVAFYDy07/U6jMAoG1YPAEoVdh+Ai6npejhQIHXu/x0AkWUnJKfyxmvfEb8jcILgKJrwOjAQOC4f08ByD39+/8JAO7gMKAASqWPilijbKSP7Rg9iqNGz8jPc/xKrfwUu+WKH2R/kOVPGlO7CNvs9VtE+gcAl7HAAlCG2JWj7uenRAc2df/phHFnT4aoTQFxWwC0scXBB7vTWwBI/wBaiGUWgDKk2622Lhr2dMSlpxBcA3IqAKR/AO3EYgtAGcxA2Yz15RGj5Ov4dEz6DXK3BtgVgLD0LyITDzmE9A+gtVhyAShJxGnC6p6ibPYSrgvPMezFpWc0XwMyF4ANn/9Z9vmp++cWQGux8AJQMXfyZWqNOfhjxHma3XmbznjlMzIWANI/gJZj+QWgYg0IUhEbN2rEpsa482ad8dLHx94msACQ/gGARRiAKjUpSLXkubizKUDiaoC/AJD+AUAoAAAq5FSUhCk24zr13oXVAE8ByJ7+y3zWTr3CABqGAgCgGuSbBnDnTQzsALoA1G7FfzMGlQFwFgUAQAUYRNEkztYAVQBql/7Fbn8MAEiNxQoAIAcO1oCxxYN1TP/+KdIBAOSLZQoAIDdO1YAtX7kx++NUFb7pAACKwwIFAJCzxqTVCp8IBQBAcbpVzwAAoGnUuYTNPVnrqKq9fhX1Gob9EwCyoAAAAPLX7/drXQMcWeNe01cPgOMoAACAotS0BlSS/gM3ApQ8DwBawok1HACAxnNn/+Bo1aZ//6Q5JwCA3LEFAABQhlpsDahw5E/YdPX1pH8AeWELAAA0RMRaZNc4MsLew4Vj/rj5ygBoGLYAAEATqOCoQ2TVsxPDwU0B1SZvczW/+bI49RIBaAwKAAA0Qe0Gijg1Ioj17gBahQIAAKiMCzXAtfRvbgRwasYANIZbSz0AQEaeg+3UKEpWcpgg19K/5uyMAWgAli8A0BD+rG9G6roEyjJrQF1eEwDIF0OAAKDG/AeJ9yTa2gXccgYFqcev3YsDALmgAABAvXmCsv6n53rP4WUcp2tAEQ9uHjEJAFqIAgAArVCj9K8VsSmAFf8AQAEAgBozs2zs6Pk6Bt98RwSR/gFA2AkYABog7FA//lFA9vd1UMb9g0n/AKCwNASABvKE3Yjsq/5UySE400k3q6R/ANAGq54BAECxIgbP6Fhco0OF6lkV6xpQi+cFAKWhAABAk0UEZX8xqFFKtq8BpH8A8GCxCADNFDu4338OgZqKHeBU8vwAgONYMgJA29VoB4AwgU+B9A8AgTgMKAC0RdjOAA1Iyf6jhZL+ASAM+wAAQOs0YJV/oBT7BwNAC1EAAKAtWpKJW/I0ASA1hgABQOtEny8MANBsDJEEgJZqzFGAAACJUAAAAACAFmEIEAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAOC0X/3qVyeccMLY2Nixxx7705/+VF3Z6XR+9atf+W/Z6XQi7gUAQgEAAMBl/X7/b//2bxcvXvzxj3/88MMPf/nLX97r9Qq6F4CWGKx6BgAAQKg77rjjlltuufLKKw877LCTTjpp7dq1O3fuXL58eRH3AtASbAEAAMBdBxxwwMjIyKc//emZmZkVK1Z88pOftMnx6e4FoCUoAAAAuGvx4sX/8i//8rGPfeyoo47asmVLv98v7l4AWoICAACA0/7mb/7mv//7v9/4xjeeffbZZ5xxhrpyeHh4YmLCc8udO3dG3wsAhAIAAIDLZmZmHnjggSVLlqxdu/Yb3/jGpz71qd27d4vIoYceetttt3lurI8LFHYvABAKAAAALnvggQf233//O++8U0T222+/qakpteL/Wc961oUXXjg5OalvuX379vXr10ffCwBEpMO4QAAAXPb0pz/94IMPPuOMMy677LJt27b95Cc/EZGbb7756U9/+mMf+9hXvepVRx555C233HLRRRcdfvjh1157rfplD7wXAAhbAAAAcNwVV1yxe/fuv//7v7/vvvuuuOIKdeXRRx/9wx/+8NGPfvTHPvaxV7ziFZs3b37d61730Y9+NPpeACBsAQAAAABahS0AQOts3br1ZS972aGHHrps2bIXv/jFP/vZz1LfONFDIbsc37sf/OAHL3zhC/fff/9DDz307/7u7zy7kz784Q/vLPTzn/883WzAL8f3Mcs7xfuYXV5v5WMe85iOz1/8xV/oG/CVLEGil/Hqq6/W74Xe+d7yoVx4vygAQLv86Ec/Ou6440Rk/fr1l1122eLFi5/xjGdcf/31KW6c6KGQXY7v3VVXXXXCCScMDw9fdNFFF1544cTExBOf+MQ//elP6q979uy59dZbt2zZ8kPDox71qBSzAb8c38cs7xTvY3Y5vpWf+9znfrjQcccdd+SRR6q/8pUsQdKX8WlPe5p6I5I+lCvvVx9Am5x88sn/3//3/83MzKh/9nq917zmNSeffHKKGyd6KGSX43t31FFHvexlLzP/+opXvOKVr3yl+qfaW/Suu+7KPhvwy/F9zPJO8T5ml+Nb6XHbbbcNDAxceeWV6p98JUuQ+mUUkVtuucX+oRx5vygAQItMTU11u92f/vSn5pVq4+PExESiGyd6KGSX43t33333ichXv/pV86/f+MY3Dj/8cHX5k5/85LJly3q9XsbZgF+O72M/wzvF+5hdvm+lx9vf/vbHPOYx+p3lK1m0LC+jpwDU5avHECCgRe66665er/eIRzzCvPKII44QkbvvvjvRjRM9FLLL8b3bsWPH3/zN3zzpSU8y/7pkyZJ77rlHXd66detRRx3V7/e3bdvmOXsU73tGOb6PkuGd4n3MLt+30jQ+Pn7ppZe++c1v7nQ66hq+kkXL8WWsy1ePAgC0yK5du0Rk6dKl5pXLli0TkT179iS6caKHQnY5vndHHnnkF7/4xQc/+MHmX7/85S8/+clPVpe3bt165513HnHEEQ95yEOWLVv2l3/5l7/85S9TzAb8cnwfJcM7xfuYXb5vpemzn/3svn37TjnlFH0NX8mi5fgy1uWrRwEAWkevVVK63ajlQPSNEz0UssvxvTP98Ic/3LRp03ve8x71z61bt+7YseMDH/jAnXfeecsttzztaU97+tOfvnXr1nSzAb+83seM7xTvY3a5fyX7/f6FF1742te+1syIfCXLkePL6P5Xb7D8SQIA3PGLX/zixS9+8fvf//6TTz5ZXXP55Zcfcsghj33sY0XkQQ960KZNm6amplauXHnVVVdVOqfw4p1qnu9973s///nPP/e5z5lX8kYjdxQAoHV6vZ65vqHX66W+caKHQnY5vnfK1q1bTz755L/6q79au3atvvLEE0/03OzNb37zk5/85H379qWYDfjl9T5mfKd4H7PL/St54YUXPv/5zz/qqKPMK/lKliPHl9H9rx4biYAW2W+//URkx44d5pU7d+4UkeXLlye6caKHQnY5vnf6mq1bt5544oknnHDCJz7xCb1J+jvf+c53vvMdzwMefvjh+/btu/vuu3nfM8rxfczyTvE+ZlfEV/L222//whe+8Ja3vMW8GV/JEuT4Mtblq0cBAFpkxYoVg4ODv/3tb80rf//733e73RUrViS6caKHQnY5vnfqnzfffPNzn/vcZz3rWVdcccXg4PzW4O9973tveMMb+v2+ed9f/vKXS5cuPfTQQ3nfM8rxfczyTvE+Zpf7V1JELr744sMPP/xFL3qReTO+kiXI8WWszVevzGOOAqjci170Is8pSF7/+tfrU5Ds2rXry1/+suWNo/+K3OX43m3duvXggw9+6UtfOjk56ZnK1q1bBwYGPv7xj+trpqamTjzxxFNOOcXmkRErr/cx4zvF+5hdjl/Jfr8/Pj6+YsWKdevWeabCV7Icid5Nk/hOBFaLrx4FAGiXG264YdGiRX/913/9uc997l//9V///u//vtvtXn311eqvt9xyi7leIPrG0X9F7vJ67377298+6EEPesQjHvHtb3/7hwup+65bt67b7b71rW/9r//6r8985jPHHXfcYYcdps9CyvueUY7fwSzvFO9jdjm+lf1+//LLL1+yZMl9993nnxBfyRIkejdN/gJQi68eBQBoneuvv/5FL3rRfvvtd9BBB73kJS/5xje+of/kX8ZF3Dj2r8hdLu/dZz/72dhtwl/+8pePP/74sbGxhz3sYaeeeuq9995rORuwkeN3MMs7xfuYXV5vZa/Xe9KTnnT66aeHTYivZAkSvZuavwBEP1TsX8vR6S8cVQYAAACgwdgJGAAAAGgRCgAAAADQIhQAAAAAoEUoAAAAAECLDMbfBADmvOZln4n46+Ti0EXK5JKopU3EX+PuOJTujgNLQo9/sHjJdMQdFy+ZSXnHkeiHDf3rksWhUxSR0fBnGfGn6L+ORN8x9CWPmeLIYOjp7peE/0lElgxE/jX8vhFTjL7jcHcg4o6D3eHQP3VC/xRzx/A/RT/sQCfyRZ+aSPnX1HecTnnH/uRk1B0nwv8a8ScRmdiX9mGj7tgP/2t/z1TEHXt7Q//a3xu1iIi8Y9QUIx52aqITccepyah1xFMToX+NuWP4XyMeU0QmJ0IX2uN7o77pE+F/HR8v5I6vHL864q+OYAsAAAAA0CIUAAAAAKBFKAAAAABAi1AAAAAAgBahAAAAAAAtQgEAAAAAWoQCAAAAALQIBQAAAABoEQoAAAAA0CIUAAAAAKBFKAAAAABAi1AAAAAAgBahAAAAAAAtQgEAAAAAWoQCAAAAALQIBQAAAABoEQoAAAAA0CIUAAAAAKBFKAAAAABAi1AAAAAAgBahAAAAAAAtQgEAAAAAWqTT7/erngcAAAAAJWELAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFqEAgAAAAC0CAUAAAAAaBEKAAAAANAiFAAAAACgRSgAAAAAQItQAAAAAIAWoQAAAAAALUIBAAAAAFrk/wdzgtu/QKZsUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x22FFEC6ADD8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3d_with_pyvista(recovered_model, notebook=True, threshold=0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "s8tXwmOYi3MtdFTZ9GkI",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "### Historic susceptiblity model from MAG3D (UBC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "XEfbILGk3uh1cyJz6Vqo",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "TnL6MXOxDHyH8kOwzL8E",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x22FA3F71EB8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3d_with_pyvista(susceptibility_ubc, notebook=True, threshold=0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "iooxa": null
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "iooxa": {
   "id": {
    "block": "XhgscyNd1MQaxeG6XN2P",
    "project": "FesuZy9nERkRH9r67vBe",
    "version": 2
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}