notebooks/2_DC_Flawed_Steel_Cased_Wells_Short_well.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Casing Integrity - impact of the extent of the flaw\n",
    "\n",
    "[Lindsey Heagy](http://github.com/lheagy)\n",
    "\n",
    "In this example, we consider a short segment of the pipe and vary the extent of a flaw to examine if that changes the DC response. \n",
    "\n",
    "This notebook was used to produce Figure 3 in Heagy and Oldenburg, 2018. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import patches\n",
    "from matplotlib import gridspec\n",
    "import ipywidgets\n",
    "from string import ascii_lowercase\n",
    "\n",
    "import discretize\n",
    "from discretize import utils\n",
    "from SimPEG import Utils, Maps, Versions\n",
    "from SimPEG.Survey import Data\n",
    "from pymatsolver import Pardiso\n",
    "\n",
    "\n",
    "import casingSimulations as casingSim\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Parameters\n",
    "\n",
    "To examine the impact of replacing the steel cased well with a solid well, I have run a few simulations using SimPEG and simulating the DC resistivity equations using a finite volume approach on a 3D cylindrical mesh.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "simDir = 'Flawed_DC_small'  # create a simulation directory where results can be saved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The casing is 50m long, and the conductivity of the background is 0.1 S/m\n"
     ]
    }
   ],
   "source": [
    "casing_l = 50\n",
    "sigma_back = 1e-1\n",
    "casing_d = 10e-2 #10cm diameter\n",
    "casing_t = 1e-2 #1cm thickness\n",
    "\n",
    "print(\n",
    "    'The casing is {}m long, and the '\n",
    "    'conductivity of the background is {} S/m'.format(\n",
    "        casing_l, sigma_back\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = casingSim.model.FlawedCasingInHalfspace(\n",
    "    directory=simDir,\n",
    "    sigma_casing= 5e6,  # conductivity of the casing (S/m)\n",
    "    sigma_back = sigma_back,  # conductivity of the background (S/m)\n",
    "    sigma_inside = sigma_back,  # fluid inside the well has same conductivity as the background\n",
    "    sigma_air = 1e-4,   # conductivity of the air\n",
    "    casing_d = casing_d-casing_t,  # 10cm is outer casing diameter\n",
    "    casing_l = casing_l,\n",
    "    casing_t = casing_t, \n",
    "    src_a = np.r_[0., 3*np.pi/2., -0.00125],  # put the A electrode just below the surface\n",
    "    src_b = np.r_[1*casing_l, 3*np.pi/2., -0.00125] # put the return electrode at a distance of 2 x length of well away\n",
    ")\n",
    "\n",
    "# adjust the src_a location so it is connected to well\n",
    "model.src_a = np.r_[model.casing_r, 3*np.pi/2., -0.00125]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'filename': 'ModelParameters.json',\n",
       " 'directory': 'Flawed_DC_small',\n",
       " 'version': '0.0.7',\n",
       " 'sigma_casing': 5000000.0,\n",
       " 'sigma_inside': 0.1,\n",
       " 'mur_casing': 1.0,\n",
       " 'mur_inside': 1.0,\n",
       " 'casing_top': 0.0,\n",
       " 'casing_l': 50.0,\n",
       " 'casing_d': 0.09000000000000001,\n",
       " 'casing_t': 0.01,\n",
       " 'flaw_r': [0.0, 0.0],\n",
       " 'flaw_theta': [0.0, 6.283185307179586],\n",
       " 'flaw_z': [0.0, 0.0],\n",
       " 'sigma_flaw': 5500000.0,\n",
       " 'mur_flaw': 1.0,\n",
       " 'sigma_back': 0.1,\n",
       " 'mur_back': 1.0,\n",
       " 'sigma_air': 0.0001,\n",
       " 'surface_z': 0.0,\n",
       " 'src_a': [0.045000000000000005, 4.71238898038469, -0.00125],\n",
       " 'src_b': [50.0, 4.71238898038469, -0.00125],\n",
       " '__class__': 'FlawedCasingInHalfspace'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we print the parameters being used to set up the simulation\n",
    "model.serialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "copiedmodelparams = {\n",
    "    key: val for key, val in model.serialize().items() if key in dir(casingSim.model.CasingInHalfspace) and not key.startswith('_') \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "background = casingSim.model.CasingInHalfspace(\n",
    "    **copiedmodelparams\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "flaw_depth = casing_l/2.\n",
    "flaw_dz = [0.5, 0.05, 0.005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "flawed_models = {}\n",
    "\n",
    "model.flaw_r = np.r_[model.casing_a, model.casing_b]\n",
    "model.sigma_flaw = model.sigma_back\n",
    "\n",
    "for dz in flaw_dz: \n",
    "    tmp_model = model.copy()\n",
    "    tmp_model.flaw_z = np.r_[-dz, 0] - flaw_depth\n",
    "    \n",
    "    flawed_models[\"flaw_{}\".format(dz)] = tmp_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_casing = model.copy()\n",
    "short_casing.flaw_z = np.r_[-casing_l, -flaw_depth]\n",
    "# partial_flaw = model.copy()\n",
    "\n",
    "flawed_models['short'] = short_casing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mesh\n",
    "\n",
    "Here we set up a 3D cylindrical mesh, discretizing in $x$, $\\theta$ and $z$. \n",
    "\n",
    "To discretize in x, we start by defining the finest region of the mesh, ensuring that we have 4 cells across the thickness of the casing. From there, we expand the cell sizes until we reach the second uniform cell size we want to model at (`csx2`). We then use a constant cell spacing of `csx2` until we have meshed out to the end of the domain in which we want to examine data (`domainx2`). Beyond that, we add padding cells to *\"infinity\"*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hy = utils.meshTensor([(1, 2, -1.5), (1, 3), (1, 2, 1.5)])\n",
    "hy = np.ones(6)\n",
    "# hy = np.r_[hy, hy]\n",
    "hy = hy*2*np.pi / hy.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters defining the core region of the mesh\n",
    "# note that the finest re\n",
    "csx2 = 10.  # cell size in the x-direction in the second uniform region of the mesh (where we measure data)\n",
    "csz = .0025  # cell size in the z-direction \n",
    "domainx2 = 50  # go out 500m from the well\n",
    "\n",
    "# padding parameters\n",
    "npadx, npadz = 2, 14  # number of padding cells\n",
    "pfx2 = 2  # expansion factor for the padding to infinity in the x-direction\n",
    "pfz = 2\n",
    "\n",
    "# set up a mesh generator which will build a mesh based on the provided parameters\n",
    "# and casing geometry\n",
    "cylMeshGen = casingSim.CasingMeshGenerator(\n",
    "    directory=simDir,  # directory where we can save things\n",
    "    modelParameters=model,  # casing parameters\n",
    "    npadx=npadx,  # number of padding cells in the x-direction\n",
    "    npadz=npadz,  # number of padding cells in the z-direction\n",
    "    domain_x=domainx2,  # extent of the second uniform region of the mesh \n",
    "#     hy=hy,   # cell spacings in the \n",
    "    csx1=model.casing_t/4.,  # use at least 4 cells per across the thickness of the casing\n",
    "    csx2=csx2,  # second core cell size\n",
    "    csz=csz,  # cell size in the z-direction\n",
    "    pfx2=pfx2,  # padding factor to \"infinity\"\n",
    "    pfz=pfz  # padding factor to \"infinity\" for the z-direction\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1202280\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cylMeshGen.mesh.plotGrid()\n",
    "print(cylMeshGen.mesh.nC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "src_theta = cylMeshGen.hy[0]/2. + np.pi\n",
    "model.src_a[1] = src_theta\n",
    "model.src_b[1] = src_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the source location\n",
    "\n",
    "# ax = plt.subplot(111, projection='polar')\n",
    "# cylMeshGen.mesh.plotGrid(ax=ax, slice='z')\n",
    "# ax.plot(model.src_a[1], model.src_a[0], 'ro')\n",
    "# ax.plot(model.src_b[1], model.src_b[0], 'rs')\n",
    "# ax.set_ylim([0., 3*model.casing_l])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# put the models in a dictionary for convienence \n",
    "model_names = ['baseline'] + list(flawed_models.keys())\n",
    "modelDict = {'baseline': background}\n",
    "modelDict.update(flawed_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for mod in modelDict.values():\n",
    "    mod.src_a[1] = src_theta\n",
    "    mod.src_b[1] = src_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign physical properties on the mesh\n",
    "physpropsDict = dict(\n",
    "    [\n",
    "        (name, casingSim.model.PhysicalProperties(cylMeshGen, mod)) \n",
    "        for name, mod in modelDict.items()\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'baseline': <casingSimulations.model.CasingInHalfspace at 0x10f8a7b38>,\n",
       " 'flaw_0.5': <casingSimulations.model.FlawedCasingInHalfspace at 0x12d445080>,\n",
       " 'flaw_0.05': <casingSimulations.model.FlawedCasingInHalfspace at 0x12d445198>,\n",
       " 'flaw_0.005': <casingSimulations.model.FlawedCasingInHalfspace at 0x12d4454e0>,\n",
       " 'short': <casingSimulations.model.FlawedCasingInHalfspace at 0x12d42e7b8>}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 10500x1750 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the models\n",
    "xlim = 0.2*np.r_[-1, 1]  # x-limits in meters\n",
    "zlim = np.r_[-casing_l*1.25, 1]\n",
    "    \n",
    "\n",
    "theta_ind = 0\n",
    "\n",
    "plot_models = model_names\n",
    "\n",
    "fig, ax = plt.subplots(1, len(plot_models), figsize=(6*len(plot_models), 5), dpi=350)\n",
    "for a, title in zip(ax, plot_models):\n",
    "    pp = physpropsDict[title]\n",
    "    pp.plot_sigma(\n",
    "        ax=a, \n",
    "        theta_ind=theta_ind,\n",
    "        pcolorOpts={'norm':LogNorm()}  # plot on a log-scale\n",
    "    )\n",
    "    a.set_title('{} \\n\\n $\\sigma$ = {:1.2e}S/m'.format(title, pp.modelParameters.sigma_casing), fontsize=13)\n",
    "    a.set_xlim(xlim)\n",
    "    a.set_ylim(zlim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.50000000e-02,  6.28318531e+00, -1.25000000e-03]),\n",
       " array([ 5.00000000e+01,  6.28318531e+00, -1.25000000e-03]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.src_a, model.src_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up 4 sources, top casing, top & not coupled, downhole, downhole not coupled\n",
    "\n",
    "# src_locs = [25, 50, 75, 100, 250, 500, 750, 1000., 2000.]\n",
    "src_locs = [50.]\n",
    "\n",
    "src_b = np.vstack(\n",
    "    [[loc, model.src_b[1], model.src_b[2]] for loc in src_locs]\n",
    ")\n",
    "\n",
    "src_a = np.ones((src_b.shape[0], 1)) * model.src_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.00000000e+01,  6.28318531e+00, -1.25000000e-03]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## set up a DC simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_names = [\"baseline\", \"flawed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homogeneous Dirichlet is the natural BC for this CC discretization.\n",
      "Homogeneous Dirichlet is the natural BC for this CC discretization.\n",
      "Homogeneous Dirichlet is the natural BC for this CC discretization.\n",
      "Homogeneous Dirichlet is the natural BC for this CC discretization.\n",
      "Homogeneous Dirichlet is the natural BC for this CC discretization.\n"
     ]
    }
   ],
   "source": [
    "simDict = {}\n",
    "for key in model_names:\n",
    "    simDict[key] = casingSim.run.SimulationDC(\n",
    "        modelParameters=modelDict[key], directory=simDir, \n",
    "        meshGenerator=cylMeshGen, src_a=src_a, src_b=src_b\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Running baseline ---\n",
      "Validating parameters...\n",
      "      max x: 140.0, min z: -131.92749999999998, max z: 81.9274999999545, nC: 1202280\n",
      "Starting SimulationDC\n",
      "Using <class 'pymatsolver.direct.Pardiso'> Solver\n",
      "   ... Done. Elapsed time : 6.510810136795044\n",
      "\n",
      "\n",
      "--- Running flaw_0.5 ---\n",
      "Validating parameters...\n",
      "      max x: 140.0, min z: -131.92749999999998, max z: 81.9274999999545, nC: 1202280\n",
      "Starting SimulationDC\n",
      "Using <class 'pymatsolver.direct.Pardiso'> Solver\n",
      "   ... Done. Elapsed time : 5.829910039901733\n",
      "\n",
      "\n",
      "--- Running flaw_0.05 ---\n",
      "Validating parameters...\n",
      "      max x: 140.0, min z: -131.92749999999998, max z: 81.9274999999545, nC: 1202280\n",
      "Starting SimulationDC\n",
      "Using <class 'pymatsolver.direct.Pardiso'> Solver\n",
      "   ... Done. Elapsed time : 5.422872066497803\n",
      "\n",
      "\n",
      "--- Running flaw_0.005 ---\n",
      "Validating parameters...\n",
      "      max x: 140.0, min z: -131.92749999999998, max z: 81.9274999999545, nC: 1202280\n",
      "Starting SimulationDC\n",
      "Using <class 'pymatsolver.direct.Pardiso'> Solver\n",
      "   ... Done. Elapsed time : 5.814158201217651\n",
      "\n",
      "\n",
      "--- Running short ---\n",
      "Validating parameters...\n",
      "      max x: 140.0, min z: -131.92749999999998, max z: 81.9274999999545, nC: 1202280\n",
      "Starting SimulationDC\n",
      "Using <class 'pymatsolver.direct.Pardiso'> Solver\n",
      "   ... Done. Elapsed time : 6.243729829788208\n",
      "\n",
      "\n",
      "CPU times: user 55.7 s, sys: 8.75 s, total: 1min 4s\n",
      "Wall time: 30 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fieldsDict = {}\n",
    "for title in model_names:\n",
    "    print('--- Running {} ---'.format(title))\n",
    "    fieldsDict[title] = simDict[title].run(save=False)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Fields, Fluxes, and Charges\n",
    "This is a widget for interrogating the results.\n",
    "\n",
    "- `max_r`: maximum radial extent of the plot (m)\n",
    "- `min_depth`: minimum depth (m)\n",
    "- `max_depth`: maximum depth (m)\n",
    "- `clim_min`: minimum colorbar limit. If 0, then the colorbar limits are the plotting defaults\n",
    "- `clim_max`: maximum colorbar limit. If 0, then the colorbar limits are the plotting defaults\n",
    "- `model_key`: model which we are viewing\n",
    "- `view`: field or physical property that is plotted\n",
    "- `prim_sec`: primary plots the baseline (intact well), secondary subtracts the primary response from the current value (note that if you select baseline and secondary the value will be zero and an error thrown)\n",
    "- `show_mesh`: if checked, the mesh will be plotted on the right hand half of the plot\n",
    "- `casing_outline`: draws the outline of the casing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer = casingSim.FieldsViewer(\n",
    "    sim_dict=simDict, fields_dict=fieldsDict, primary_key='baseline', model_keys=model_names\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b204e53bd4b241ca8e5416ad21f671ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatText(value=0.1, description='max_r'), FloatText(value=0.0, description='min_depth')…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer.widget_cross_section(defaults={\"min_depth\":0, \"max_depth\":casing_l*1.25})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = cylMeshGen.mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_charge(\n",
    "    ax=None, key=model_names[0], color_ind=0, color=None, src_ind=0, \n",
    "    label=None, secondary=False, linestyle='-', primary_key=\"baseline\", lw=None,\n",
    "):\n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 1, figsize=(5, 4)) #, dpi=400)\n",
    "    \n",
    "    f = fieldsDict[key]\n",
    "    src = simDict[key].survey.srcList[src_ind]    \n",
    "    plotme = f[src, 'charge']\n",
    "    \n",
    "    if secondary is True:\n",
    "        f_prim = fieldsDict[primary_key]\n",
    "        src_prim = simDict[primary_key].survey.srcList[src_ind]\n",
    "        plotme = plotme - f_prim[src_prim, 'charge']\n",
    "    z_charges, charges = casingSim.casing_charges(plotme, mesh, mod)\n",
    "    ax.plot(\n",
    "        -z_charges, charges/csz, label=label, \n",
    "        color=color if color is not None else \"C{}\".format(color_ind), \n",
    "        linestyle=linestyle, lw=lw\n",
    "    )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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yY0lyHMESAAAAAEBkCotgiTFmrKThOhokkY6GPook3STpVGvtO9baMl/2qAqaLJM0VNINkgrq7FNtWFU9CG8lTV0AAAAAAABw5ylUIklJtvJppBwvO5Ykx0f7XRMAAAAAAC1RWARLJN3pMGYkHZY0ylo7z1pbEYiNbKXnJJ0rKat6uM5ldwRiLzRrXTzM5YSsCgAAAAAA4KiwrNDjfNJPHUtaebVeEh1LAAAAAAARqsUHS4wxPSWNUO1wh5FUKmm0tXZNMPa11n4habSkmh1QqruXjKiqC+HrFJdxK2lHCOsAAAAAAAAOCkoLPM4nV3jfsSQxNlrRUabB6wAAAAAACEctPlgiaYxqH0VjVPni/mPW2q+CubG1dr2kR+rsX13D+cHcG03HGNNL0sku099Zaz2/JQoAAAAAAARdgx1Lqo/CsQ0HS5Lj6VYCAAAAAIhc4RAsGeYwlifp0RDt/4SkXIfx00O0P0Lvtx7m3g9ZFQAAAAAAwFVhqZdH4XjRsaRVfHRAagIAAAAAoCUKh2BJ3xqfV3crWWytPRKKza21JZLeVv2uKSeFYn+EljGmvaQbPVzy91DVAgAAAAAA3DUULEms6liSZ5MaXCspjo4lAAAAAIDIFQ7Bkl6qDJPU9FGIa6i5X3UtvUJcA0LjEUltXOY+s9Z+HspiAAAAAACAs4LSAte5xIoKVfcgyVdig2u14igcAAAAAEAEC4ffils7jO0NcQ37HMZSQlwDgswYc648H4NzXwhrcTp+yVs//Z2x1mrnzp0BqKjxiouLZa1VVFSUoqKiZIxRSUlJk9QSCGVlZU1dAgCEPb7XolpFRUWtmzGmyX6mAcJJZmZmU5cAeMWb/1cPHTqk3UV5rvNJFUffo5TrRceSqIoj/FsDv/F9FgCCj++1ABBcfJ8NHmvr9tJoXsIhWBLvMJYV4hqc9osLcQ0IImNMZ0kvq/aRRzX901r7fghLcgpU+aSpvklZa5v9N0gAAND8Vf88wc8VgP/q/j3i7xWaK2/+37SSisqKXOeTbMVPn3vTsSQxNoq/E/Ab32cBIPj4XgsAwcX32cgVDkfhFDuMdQpxDU77OdWFFsgYEyPpNUldXS7JknRj6CoCAAAAAACeGGtVVO4eLEmu0bEk33oXLAEAAAAAIFKFQ8eSHEl1e5b2CHEN3R3GckJcA4JnrqSzPcz/zlr7vxDVUs29n2/DanU7McatCUtwNdW+AAAgvBhjfroB8E/dv0f8vUJz5dX/m8aouML9PT81O5bk1Xtaqb7kuGj+TsBvfJ8FgODjey0ABBffZyNXOARLdkjqpsoup9XOk/RiCGsYXeNzU1XLjhDujyAxxtwhaaqHS+ZYa18PVT3VrLUpvt7XGJOrqnCJMUa9evUKWF2N8f3336u8vPynr6OiohQf73SyVcsUTo8FAJorvtdGrqioyuMIoqIq3z0eHR3dZD/TAOGmuoVtU/6uADSk/ZH2DV7ToX17xRS7v+cnqUbHkgIlNLhelw5t+TuBgOD7LAAEH99rASC4+D4bHM09pBMOfTy/rfG5VWWw40JjTKtQbF61z1jVDrbUravZMsZ8bIyxLeD2XRP8t7la0kwPl3ws6c7QVAMAAAAAABqjoKzAdS6porJjSZ5NVIUXT48lx4fDe7MAAAAAAPBNOARLVjuMJUh6JET7P6j6R/FIznWhhTDGTJD0kiqDSk6+lDTBWnskdFUBAAAAAABvFZYWus4lV73DLl+JXq1FsAQAAAAAEMnCIViyQlJ5ja+ru5ZMM8akBXNjY8yZkm5R/W4l5VV1oQUyxpwr6R9yPyrqG0ljrLW5oasKAAAAAAA0hqdgSfVROHnWy2BJXHRAagIAAAAAoCVq8cESa+0BSe+rdmcJq8rHtsQYMyYY+1aFVpbW2ddU7f1+VV1oYYwxwyW9Iyne5ZIfJJ1rrc0MXVUAAAAAAKCxCss8BEts5VE4dCwBAAAAAKBhLT5YUuXJGp9XBz2sKo+oeccYc78xxi0o0CjGmFhjzAxJ/5LUus6e1WYGYi+EljFmqCr/XJ2ONpKknZJGWmszQlcVAAAAAADwRUFpgevc0Y4lbk8B1JYcR7AEAAAAABC5wuK3Ymvtx8aYFZLO19GjcGzVLVbSfZKuNcY8JukNa212Y/cwxqRI+qWkuyQdV2MP1dlzhbX2Y78eUGi9L2lPUxfhhX3BXNwY83NJ70pK8bB/mrV2dzDrAAAAAAAAgeGpY0lyRWXHkjwvO5YkxXMUDgAAAAAgcoVFsKTKDZI2SGqj+uESI+lYSX+VNNsYs1LSZ5LWS/pWUra1Nrd6oaoQSRtJJ0kaLOkXks5T5fEoNTui1HW4qo4Ww1r7SFPX0NSMMf1VGbBp63LJAVWGSn4MXVUAAAAAAMAfhaWejsKpfFon33oXLEmMJVgCAAAAAIhcYRMssdbuNsb8SpVHmUSrfrhEVV/HSxpbdfuJMUaSiiUluGzhKVBiJJVK+pW1tiV0/0AVY8yJkj6Q1MHlkkxJ51prt4auKgAAAic7O1s5OTkqKipSq1at1LZtWyUnJzd1WQAAAEFlrfUcLPmpY4l3R+EQLAEAAAAARLKwCZZIkrX2fWPM5ZJeU+Vjqw6XSPUDJk48vU2lZqCk5jE4NUMl6b7UjaZhjDlOlaGSzi6XZEsaba3dHLqqACC0tm/froIC97PnA6V9+/bq2rVr0PeB9O233+rtt9/W+++/r6+//lpZWVn1runRo4cGDx6sCy64QJdccok6deoUktr27dunzZs3a/Pmzfrxxx+1e/du7d69WwcPHlRhYaEKCytf/ElNTVVqaqratm2rfv366ZRTTtEpp5yiQYMGKSYmrH58BQAAQVJqy1Vmy1znkxvbsSSOYAkAAAAAIHKF3TPz1trFxpg0Sa9L6irnQIhT1xFvOHVAyZD0S2vtah/XRBMwxvSS9KGk7i6X5EkaY61dF7qqACD0rrvuOv373/8O+j7Tpk3T3Llzg75PJPvss8/0xz/+UenpDedc9+zZoz179mjp0qW69dZbdcUVV+ihhx5S9+5u/yw23pEjR/TFF19o9erVWrVqldasWaPMzEyv7nvw4EEdPHhQkrR27VotWrRIktS5c2ddccUVuvbaa9W3b9+A1QoAAMJPQcURj/ON7ViSEEOwBAAAAAAQuaKauoBgqAp5DJT0iirDH07H2BiHm6fxmvevHl8k6WRCJS2LMaaHKkMlx7hcUiDpAmvt56GrCgAA35SUlGjatGk644wzvAqVON1/wYIF6tu3r55//vmA1bV8+XKNGDFCd999t5YtW+Z1qMST/fv3a9asWRo8eLCmTZumw4cPB6BSAAAQjoorSj3OJ1Z1LMnz2Lz2qIS4sHwKDQAAAAAAr4Ttb8XW2kxr7bWSBkt6U1KZ6odM6t48jVfft0yV3VAGW2uvs9b6/yoJQsYY00WVoZKfuVxSJGm8tXZV6KoCAMA3WVlZOuusszRv3jxZ62tDtkr5+fn67W9/qxtvvNHvtYLNWqsXX3xRffv21YoVK5q6HAAA0AwVVbgfgyNJiRVVwRLbcMeSKCPFRYftU2gAAAAAADQo7H8rttZutNZOlNRN0lRJb0s6JOfOJG63Q1X3myqpq7X2V9bajSF+KPCTMaajpA8kneBySYmki621H4auKgAAfJObm6vRo0fr888D22Br/vz5mjZtWkDXDJYDBw5o3LhxeuWVV5q6FAAA0Mw01LEkoSpIm+9Fx5LE2GgZYxq8DgAAAACAcBXT1AWESlVnkeerbtWdK05UZeCko6RESXGSjqiya8VBSfskbbXWZjRFzQgcY0xbSe9L6udySamky6y174WuKgAAfDd58mR99dVXrvM9e/bU1KlTNXr0aPXp00fJyck6fPiwNm3apMWLF2vBggUqLCx0vO/8+fPVv3//oARMoqKiNGDAAPXr1099+vRRnz591L59e6WkpCg5OVkFBQXKzc3Vjz/+qI0bN+r999/X9u3bXdcrLy/Xr3/9a3Xv3l1paWkBrxcAALRMRbahYEmFJCnfehEsiYsOSE0AAAAAALRUERMsqasqLEJgJAIYY1IkrZQ0yOWSMkmXW2uXha4qAGjeFixYoMmTJzd1GXAxf/58LV682HX+zjvv1EMPPaSEhIRa4x07dtTIkSM1cuRI3XPPPZo8ebJWrlzpuMYdd9yhs846SwMGDPC73iFDhuiCCy7Q8OHDdcYZZ6h169Ze39daq1WrVmnGjBlatcr5pLqysjJdffXV+u677xq1NgAACF8NdSypPgonXwker5OkhFiCJQAAAACAyBb2R+EgshljkiUtl3SqyyXlkq601rq/OgcAQDNy8OBBzZgxw3V+7ty5mjlzZr1QSV1du3bV8uXLNXHiRMf5kpISTZ8+3ec6jznmGM2ZM0e7du3SV199pYceekjnnXdeo4MfxhiNGDFC//nPf/Twww+7Xrdv3z4988wzPtcLAADCi6dgSYy1iq36vNA2HCxJJFgCAAAAAIhwBEsQtowxCZKWSBrmckmFpGutta+HrioAAPzzyCOPKDs723Hud7/7XaOOr4mOjtbLL7+sQYOcm3p9/PHHWrbMt4ZeQ4YM0U033aSePXv6dP+6jDG68847de+997pe89e//lXl5eUB2Q8AALRsRRVlrnMJ1v70eYEXHUs4CgcAAAAAEOkIliAsGWNiJb0lKc3lEitpirX2/0JXFQAA/snMzNQLL7zgONetWzfNnDmz0WvGx8frhRdekDHGcf7xxx9v9JrBdNddd6lv376OcxkZGfryyy9DXBEAAGiOijx0LEmoOBosKfTmKJwYgiUAAAAAgMhGsATh6lVJF3iYnyNprTFmQBBunUL0GAEAEWbRokUqKChwnJsxY4aSk5N9WvfUU0/VpZde6ji3evVqbdy40ad1gyE2NlbXXnut6/wXX3wRwmoAAEBz5ekonARbIUk6YqNVqpgG10qgYwkAAAAAIMIRLEG4cn517KibJW0O0u3GwD4UAAAqvfLKK47jqampuu666/xa+5ZbbnGd+9vf/ubX2oE2atQo17nt27eHsBIAANBcFVv3YEli1VE43nQrkaTEWJ4+AwAAAABEtobflgEAANDMVVRUaM2aNfrPf/6jtWvX6scff9TevXuVn5+v0tJSJSUlqUOHDjruuON0yimn6Oyzz9bIkSMVGxvb1KV77ccff9SGDRsc5y6++GIlJSX5tf5jw0MmAAAgAElEQVSwYcN07LHHaseOHfXm3nzzTZ+O2QmWbt26uc7l5eWFpIb169dr6dKl+vzzz/Xdd98pMzNTBQUFSk5OVocOHTR48GCdffbZmjRpktq1a9fo9ffs2aO33npLn376qTZv3qwDBw4oJydHsbGx6tixo/r27auzzz5bl19+uX72s58F4RECANCyFVWUuc4lVh2FU+B1sISOJQAAAACAyBY2wRJjzM2Sjq0znG2t/VMQ97xPUps6w99Za58L1p4AAOCo/fv36y9/+YsWLVqkffv2uV6Xl5envLw8bd++Xenp6XriiSfUrl07XX/99brjjjvUsWPHEFbtm5UrV7rOjR8/3u/1jTEaO3as5s6dW29ux44d+uGHH3TCCSf4vU8geArR+HIc0AMPPKAHH3yw3vi1116rhQsX1hpbunSp/vSnP+nLL790XCsnJ0c5OTnatm2b3nzzTd1xxx266aab9MADD6hVq1YN1rJhwwbdf//9Wrp0qWzVu6lrKisr065du7Rr1y6tXLlS99xzjy6//HI9/fTTHgM3AABEmqKKI65zCdUdS6yXwRKOwgEAAAAARLiw6OVpjOkq6WlJt9S5xQV561hJt9bZc5Yxpm7YBAAABFBJSYn+9Kc/6dhjj9Vjjz3mMVTiJisrS0888YROOOEEPfvss0GoMrA++ugjx/GoqCidc845Adnj3HPPdZ378MMPA7JHIBw4cMB1Llghoby8PE2cOFHjx493DZU4KSkp0dNPP61Bgwbpu+++c72urKxM99xzj0455RQtWbLEMVTixFqr1157TSeffLI+/vhjr+sCACDcFXvoWFIdLClQvFdrJdCxBAAAAAAQ4cIiWCLpOknVv+WbqluhpFlB3neWpII6+yZIuibI+wIAELG2bdumoUOH6v7771dxcbHf6+Xk5OiGG27QFVdcEZD1gsUtzNCnTx+1aROYTOvQoUMbvX9T+Oqrr1znTj311IDvt3//fg0fPlxvvPGGz2v8+OOPGjZsmLZu3VpvrrCwUBdccIEeffRRVVRU+LR+VlaWzj//fK1atcrnGgEACCfFFaWuc4lV/9563bGEYAkAAAAAIMKFS7BkoqSab+u0kt6w1mYGc1NrbZak11QZKLE1apgUzH3RMGutacLbA039+AEgXH355ZcaOnSoNm3aFPC1X331VV100UUqKSkJ+Nr+ysnJ0fbt2x3nBg8eHLB9unbtqi5dujjOrV+/PmD7+Ov11193HE9MTNSwYcMCuldeXp7OP//8gPw/l5WVpXHjxik/P/+nsSNHjujCCy/U+++/7/f6xcXFuuyyy5Sdne33WgAAtHSegiVHO5YQLAEAAAAAwBsxTV2Av4wxnSWd7DD1jxCV8Jqk39T42kg61RjTxlrLs/oAgLBQXFysQ4cOKT8/XwkJCUpJSVG7du1CWsOmTZt07rnnKicnx/WaVq1a6ayzztJZZ52lPn36qF27dmrVqpUOHTqkPXv26IMPPtC7776rzEzn7Ol7772nqVOnauHChUF6FL757rvvXI9G6dOnT0D3OuGEE5SRkeFYQ3Pwn//8R4sXL3acmzx5slq1ahXQ/aZOnVovVJOQkKBzzjlHF1xwgXr37q3OnTurpKREGRkZ+vjjj/XWW2/pf//7n+N6P/zwgx566CE98cQTkqTp06fXO8ImJiZGw4YN0wUXXKATTzxRnTp1UnR0tPbv36/Vq1frzTff1LZt2xzX379/v/7whz9o/vz5/j94AABasCLbcLCk0MtgSXxsuLwvCwAAAAAA37T4YImkkTraMaTaYUn+v+3TOx9JOiSpfY2xqKq63gpRDQAABNTatWv1ww8/aPXq1Vq3bp3y8vLqXZOYmKjjjz9egwcP1plnnqkxY8aoe/fuQaknMzNTEyZMcA2VtG/fXnfeead+97vfKTU11XWd6667ToWFhXrmmWf0+OOPq6CgoN41ixYtUlpamq65pvmcbLdjxw7XueOOOy6gex133HH65JNP6o0XFBTo4MGD6tixY0D3a4yNGzfqiiuucJxLTU3VH/7wh4Dut3LlyloBkaioKE2ePFmPPPKIa2eXSy+9VI8//rhmzJih2bNnO14za9Ys3Xrrrfr000/13HPP1Zq7+OKL9eSTT+r44493vO+4ceP00EMPaebMmbr33nsdA0fPPfecbrnlFvXt29fbhwoAQNgp8ngUTlXHEhvv1VrxMXQsAQAAAABEtnB4y8XPa3xeHTD53Frr2wH1jVS1z2dVe9c0JBT7AwAQDPPmzdOjjz6qf//7346hEkkqKirS5s2b9fLLL2vKlCnq2bOnRo8erX/9618Br+fGG290DVeceeaZ2rhxo+6++26PoZJqSUlJuvfee/XJJ5+oa9eujtfcdtttysrK8qfkgNq5c6frnNtj8JWn9TwFXIKpuLhYjz32mM455xwdOnSo3rwxRn/961/Vs2fPgO5bM1QSFxen1157TS+++KJrqKRacnKy/vKXv+iuu+5ynC8tLdWTTz6p6dOn/zRmjNHs2bP19ttvu4ZKqsXGxmrGjBmaM2eO43xFRYUWLVrkcQ0AAMJdcUWZ61xiIzuWxMWEw9NnAAAAAAD4Lhx+M+7nMLY2xDV86TDGW0QBABHFWqv3339fY8eO1emnn65NmzYFZN13331Xr7/+uuPcqFGjlJ6e7lOnlMGDB+vjjz9W69at681lZWXp6aefbvSawXLgwAHXuYZCDo3lab2DBw8GdC8nRUVF2r9/v9avX6+FCxfq17/+tTp37qwZM2aosLCw3vXGGM2bN0+/+tWvglrXG2+8oV/+8peNus/DDz+sk046yXHuz3/+c63gyty5c2sFTbwxbdo0paWlOc698sorqqgISc4aAIBmqdhDx5KEqvciFXgbLIkOh6fPAAAAAADwXTgchXO8ah+DI0nfhLiGb+t8bSSdEOIaANRRWl6qfQX7QrLXkSNHan0dVxIXkn0RPN2Suyk2Orapy2ixPvvsM5166qmaOXOmbrnlFr/WuueeexzHTzrpJL311luKjfX9z6lPnz569tlnHY9XmTdvnv74xz8qIcG7FxyCyVP3FG+6tDSGp/UC1cVl1apVGjFihN/r9OjRQwsWLNC5554bgKrcTZs2TePHj2/0/WJiYjRjxgxdffXVHq+bMGGCbrzxRp9qu//++/Xhhx/WG9+7d6+++eYbnXzyyT6tCwBAS+cxWFJ1FE6h9e7nvPhYgiUAAAAAgMgWDsGSNg5joe5dX3M/q8pgSbsQ1wCgjn0F+zR28dimLgMt1LKLl6lXSq+mLqNFKy0t1a233qpt27Zp9uzZPq3x8ccfa926dY5zf/7znx27jTTWpEmTNHPmTK1fv77WeHZ2tv75z38GvROGN7Kzs13nUlJSArqXp/UOHz4c0L181a1bN02fPl033XSTWrVqFdS9UlJS9Nhjj/l8//Hjxys+Pl4lJSWO89HR0frLX/7i8/rDhw9X165da3U/qbZ+/XqCJQCAiFXkIVhSfRQOHUsAAAAAAPBOOPxm7PSKUqiDJU6vsvj/ShcAACHWpk0bXXrppXrkkUf0zjvv6LvvvlNGRoaKiopUVFSkPXv2aOPGjVq0aJGmTJni1RE0c+bM0d133+1TPS+++KLj+DnnnKPRo0f7tKaT2267zXF86dKlAdvDH26hBEkB76jiaT1PdYRCamqqnnnmGf3www+67bbbgh4qkaTrrrvOrwBTSkqKhgwZ4jo/fvx49erle4gtKipKZ599tuNc3bAUAACRpLiizHWuOlhSaOO9WisuJhyePgMAAAAAwHfh0LHE6byJUJ9B4bRfUohrAADAJykpKbryyis1adIknX766YqJcf/xoHv37urevbsGDhyoa665RqWlpXrttdf0yCOPaOvWra73e+KJJ3Tqqafqsssu87qusrIyLVu2zHHuyiuv9Hodb5x33nmO405HjDSFusdt1eTpz8sXno4W8lRHKOTk5Oj222/XzJkzdd111+nWW29Vx44dg7pnIDrWnHTSSVqzZo3j3OWXXx6Q9Z1s27bN77UBAGiJrKQi2/BRON52LImPiQ5EWQAAAAAAtFjh8JaLQoexTiGuwekVjaZ95QUAAC/cd9992rdvn+bNm6cRI0Y0OqQQGxurq6++WuvXr9eUKVM8XnvjjTc26iiVzz//3PEIGGOMLrrookbV2ZBOnTrpxBNPrDeekZGhjIyMgO7li/Lycte56OjAvtDhab2yMvd3/obS//73Pz366KPq1auXZs2aJVv1ruNAS0hI8NhtxFu9e/d2nTvjjDOCtn5OTo7fawMA0BKVGONxPsFWSJIKvT0Kh44lAAAAAIAIFw4dS3Ik1e2D3jnENXRxGMsLcQ0AADRaWlpaQNZJTEzU888/r549e+r+++93vObgwYN64okn9Pjjj3u15tq1ax3He/bsqfbt2/tcq5sePXo4dl3ZsmWLunRx+qc+dDwFfsrKygLatcRTeMRTN5PGGDJkiDZv3lxv3Fqr3NxcZWdna8+ePVq7dq1WrVrl2g2nqKhIt99+u5YvX67FixcH/Gic/v37Ky7O/0Z4bkfptG/fXj179gza+rm5uX6vDQBAS1TUQLCk+iicAuttxxKCJQAAAACAyBYOwZKdknqostNptRGSXghhDcNrfF797MWeEO4PAECzcN9992n79u1auHCh4/z8+fN17733ehUA2LRpk+O427Ef/nILq+zduzco+zWGp0BHKIMlgQhZSFJSUpIGDBjQ4HVTp06VJH366af685//rDfeeMPxuvT0dI0ZM0bvvvtuQMMlHTp0CMg6bjUFe32CJQCASFXcYMeSyqeQvO1YQrAEAAAAABDpwiFY8oOkYVWfW1UGO0aHuIYxqh1ssZI41B4AEJFmzZqlJUuWKCsrq95cbm6uli5dqkmTJjW4zo4dOxzH33vvPZkGXiwIpEOHDrnOHThwQAcOHPB7jx49eqhNmzau84mJia5zRUVFSkjw7kURbxQWOp0yWCmQ+zTGGWecoTPOOENvv/22brjhBh08eLDeNatXr9bNN9+sl156KWD7tm3bNiDruB0vFOz1PR2hBABAOGvwKJyKqo4livdqPY7CAQAAAABEunAIlnwuaXKdsU7GmPOste8Fe3NjzPmSOql2sESSnPv3AwiZbsndtOziZSHZ68iRI7W+DtS7+tF0uiV3a+oSWqw2bdrorrvu0l133eU4v2zZMq+CJfv27Qt0aT4pKipynZs3b54efPBBv/dYsGCBJk+e7DrvKXSSm5sbsIBC9XpuArmPLy688EItX75c5513nmNwacGCBbr00kt14YUXBmS/+HjvXmxqrusDABCpGgqWxFd1LCny8igcgiUAAAAAgEgXDsGSjx3GjKQnJAU1WGIq3y79uMv0v4O5N4CGxUbHqldKr5DsVVJSUutrXixEpJs0aZJrsOTTTz/1ao28vLxAluSzusGxpuB2TI8kZWdnq1evwH2vy87O9qmOUDn55JO1aNEijRs3znH+4YcfDliwBAAAtEwlUZ6DJXE/HYXj3e9t8THO3cEAAAAAAIgULf4tF9barZK21Byq+niyMeb3Qd7+Tkknq363kt3WWjqWAAAiVs+ePdWvXz/HuR07dnjsAlKtbmCrqVhb95/50OvcubPrXEZGRkD38rRep06dArqXr0aNGqWxY8c6zn322Wdav359iCsCAADNSYNH4VT9fFcs7zpN0rEEAAAAABDpwuU345dU2aWkmq36+jFjTMO99n1gjLlCld1Kar7aZKq+XhCMPQEAaEn69+/vOrdr164G7x8VFS4/pvjPU0eSvXv3BnQvT0cQ9e7dO6B7+eO6665znUtPTw9hJQAAoLk50kCwJM5aVUTFqsLLp8Xiovm5FAAAAAAQ2cLhKBxJek7SPZJSa4xZVQZnFhpjultrnwrUZlWdUB7W0SBJTYWS5gZqLwAAWipP3S1yc3MbvH9SUpLj+E033aSpU6f6XFdjNYcuHZ4CHf/9738DupfbeqmpqWrTpk1A9/JHWlqa4uPjHTvbrFmzpgkqAgAAzUWxh2BJtLWKkVQanejVWsZIsdGegyoAAAAAAIS7sAiWWGvzjDEPSpqlo91KVPV5rKQnjDHjJN1hrf3S132MMadJekrScB0NldT9+Ki1NtPXPQAACBepqamuc94cc9OhQwft3Lmz3nhRUZEGDBjgV22B8sADD+iBBx4I+j59+/ZVVFSUKioq6s1t3bo1oHt9//33juOeOtA0hcTERHXt2lU7duyoN/e///0v9AUBAIBmw1PHkviqY3DKoxO8WisuOkqmgQ4oAAAAAACEu3Dq5TlX0udVn9fsIlId+hgh6XNjzBfGmN8aY07yZlFjzElV138h6TPVDpXUXN9KWi9ppt+PBACAMJCZ6Z6zTEho+Il8t+NffvzxR59raqmSkpJ04oknOs6tW7cuYPts375dWVlZjnNDhgwJ2D6B0rlzZ8dxt8cAAAAiQ4k3wZIY7zqWxMWE01NnAAAAAAD4Jiw6lkiStbbcGPMrSV9I6qDagY/qz42kUyWdIknGmFxJ30jKkpQtKV9SK0ltJLWT1F9SStUWNZ+VqHv8jSRlSrrMWlsW0AcGAEALdfDgQdc5b45U6devn95+++164xs2bFB5ebmio6P9qq+lGTp0qLZs2VJvfMeOHcrIyFCXLl383uOzzz5znTvttNP8Xj/QiouLHcejongBCACASOYpWBLXyI4l8TGR9TMnAAAAAABOwupZd2vtTkljJOVWD+looKQ6YKIaY6mSTpd0oaQrJU2t+nhh1XhqjWul2iEV1VgrR9IYa+2OIDwsAABapK+++spxPCoqSsccc0yD9x86dKjj+OHDh7V69Wq/amuJRo4c6TqXnp4ekD08reNp/6ayf/9+x3FPxzABAIDw5ylYklAVLKnwOlgSVk+dAQAAAADgk7D77dhau16Vx97skXPHElvnZjzc3K6tGVDZKWmEtTZwfegBAGjhtmzZop07dzrOHX/88YqLi2twjTPPPFOxsbGOc4sXL/arvpZo1KhRMi4vkgTiv0dZWZmWLl3qONe/f391797d7z0Caf/+/a7BkmOPPTa0xQAAgGblSAA7lnAUDgAAAAAAYRgskSRr7deShkharPrdRiTnLiRON7drq8fekHRq1X4AAKDKwoULXeeGDRvm1RqpqamuXTJeeOEFHThwwJfSWqwuXbpoxIgRjnPLly9XZmamX+uvWLHC9fiiyy+/3K+1g+Ff//qXrHU6nVAaOHBgiKsBAADNSbGHYEn8T8GSRK/WomMJAAAAAABhGiyRJGttprX2UknjJX0j54CIV0upfshkvaTzrbWXW2v9exUHAIAws3fvXs2ZM8d1fvz48V6vdf311zuO5+fn6+GHH250bS3dNddc4zheXFysefPm+bX2rFmzHMeNMbrqqqv8WjvQKioq9Pzzz7vOp6WlhbAaAADQ3HjVsSSGjiUAAAAAAHgr7H87ttYus9YOlHSupFcl5cj52JuanOazJf1N0jnW2lOste+F5hEAANBylJaWavLkySoqKnKc79Chg84//3yv15swYYJOOOEEx7m5c+dG3JE4kyZNUocOHRznnnrqKdejYRqyYsUKffTRR45z48aNU+/evX1aN1iee+45rV+/3nHu2GOP1S9+8YsQVwQAAJqTEg/BkoSKymBJhZdH4cRGh/1TZwAAAAAANChifju21n5orb1SUkdJp0uaLulZScslrZO0VdKOqo/rqsb/WnXd6ZI6WWuvsdb+O/TVAwAQWGvXrnU9RsRX5eXlmjJlitLT012vufXWWxUfH+/1mtHR0XryyScd56y1uuqqq7Rq1apG19oQa62WLVumwsLCgK/tj6SkJN18882Oc7m5uZoyZUqj/1yzsrI0depU1/m77767UetJlUfzHDlypNH388aSJUv0+9//3nX+pptukvHwYhIAAAh/noIlP3Us8TpYws8VAAAAAABETLCkmrW23Fr7ubX2/7PW/s5aO9Zae6q1tp+19riqj6dWjd9Ydd3n1trypq4dAIBAuf766zVo0CC9+uqrKi/3/5+4vXv3auTIkXr55Zddrzn22GN12223NXrtiy66SJdeeqnjXGFhodLS0lyPcWmskpISvfTSS+rfv7/GjRvX7IIlknTHHXeoZ8+ejnPLli3TLbfc4nW4JDc3V2PHjtXu3bsd5ydOnKjTTz+90TXOmDFDJ5xwgp599lmVlJQ0+v5OSktLNXPmTF155ZUqLS11vKZXr16aNm1aQPYDAAAtV0mUexgkvurnpIqYRK/WomMJAAAAAAARGCwBAACVNm/erCuuuEI9evTQjTfeqA8//LDRIZOMjAzNmDFDAwYM0L//7d7UKyYmRgsXLlRSUpJPtT7//PM67rjjHOdKS0t1++2365RTTtFrr73W6MdQUFCgN998U5MmTVLHjh31m9/8Rlu2bPGpzlBISkrSnDlzXOfnzJmj8ePHu4ZFqq1Zs0ZDhw7VmjVrHOfbtm2rp59+2uc6d+3apRtuuEGdO3fWtddeq2XLlvkUMjl8+LCee+45DRw4UH/84x9dQyVRUVFasGCBEhK8e/cxAAAIX546lsQ3smNJjIeQCgAAAAAAkSKmqQsAAABNKyMjQ/Pnz9f8+fPVunVrDRw4UIMGDdLAgQPVsWNHpaamKjU1VcYYZWVlKTMzUxs3btQnn3yiL774wvWF/prmzp2rs846y+ca27Ztq/fee0/Dhg3T/v37Ha9Zt26dJk2apGnTpmn48OEaMWKEevfurXbt2qlt27YqLi5WTk6OsrOztXfvXm3YsEEbNmzQli1bVFZW5nNtTWHChAmaPn26a8Bk2bJlSk9P1yWXXKLRo0erT58+SkxMVHZ2tjZt2qR//vOf+uijjzzusXDhQvXo0cPvWnNycvTyyy/r5ZdfVlxcnPr376+f//znGjRokLp27arU1FS1adNGCQkJysvLU25urvbv369Nmzbp/2fvzuMkq8qDj/+eru6efWQH2YbFF0E0LCIu4MJixCUuiCYmUTDJ6xYxccurCcYlxvjGJQZRYjSE10SiRozgEsNiMIoCiqyKqMgMCMoqszJb9/P+catnerrvra6qrqrurv59P5/6zPQ59z7nDH58uurUc8+5/vrr+c53vtPUsTpnn302J5xwwrTnK0mS5r7NnSwscccSSZIkSZIsLJEkSdutXbuWK6+8kiuvvLIj8Wq1Gueccw6vfvWrpx3r4IMP5tvf/jannHIKt912W+V1Dz74IBdffDEXX3zxtMeczT784Q9zxx13cNFFF5X2b9y4kQsuuIALLrigrdjPf/7zpzvFSTZv3sx1113Hdddd17GYtVqNj370o7z2ta/tWExJkjS3bWyisGS0ycKSoZo7lkiSJEmS5GMXkiSpK/bZZx8uvfRSXvOa13Qs5qMe9SiuueYaTj311I7FnKsGBwf5/Oc/z8te9rKOxazVapx99tm88Y1v7FjMbtp///25/PLLLSqRJEk72NygFmTbjiWDi5qKNTjg0pkkSZIkSX46liRJHbV48WLe/OY3c8stt3TlaJJddtmFCy+8kH//93/noIMO6nj8gYEBTjzxRM4//3x23nnnjsfvpOHhYS644ALOPvtsli1bNq1YBx98MJdddhlnnnnmtOe13377TTtGI494xCM466yz+NGPfjStI5YkSVJ/2tTBo3CGPApHkiRJkiQLSyRJmo8uuugizjvvPF7+8pez//77dyTmYx/7WN7//vezcuVKPvjBD0670GEqp512Grfeeiuf+9znOPHEE6nVam3HWrJkCaeccgof+tCHWLlyJZdffjmnn376tGL20plnnsmtt97KG97whpb/u++///584AMf4Oabb+YZz3hGR+bz5S9/mZUrV3Luuefywhe+kD322GPaMWu1Gk972tP46Ec/ym233cZZZ53FkiVLOjBbSZLUbxoVlgxvKyxpbscSj8KRJEmSJAki6x+o54uIeBRwNHAYsA+wG7AIGAYCSOAPMnPVjE1S6qKIWAMsA1i2bBlr1qyZkXn85Cc/YWRkZNvPtVqNQw45ZEbm0gmbNm3a4ecFCxbM0Eyk9tx///3ccMMN3HDDDfzsZz/jzjvv5M477+S+++5jw4YNbNiwga1btzI8PMzSpUvZc8892XfffTnssMM46qijOOGEE7q+S8VUHnjgAS6//HKuvvpqbr75ZlatWsW99967be5Llixh2bJlLF++nP32249HP/rRHHrooRxxxBEce+yxDA0Nzej8O2XdunVccsklXHbZZdx0003cdtttrF69mk2bNrF48WJ23XVXDjnkEI4++mhOOeUUjj/++J4U0KxatYprrrmGH/7wh9x+++3cfvvt3HXXXaxdu5b169fz8MMPMzQ0xMKFC9lll13Yc889OfDAAznssMM45phjOO6441i4cMcni82181e/vY+QZotVq1YxtkYQEaxYsWKGZySV++JPv8g7v/PO0r5dt46wKEf5RcV7u7c98CC/t2Yd1x7/j7z4sqVTjvX7T9qf977wcdOarzTGPCtJ3WeulaTuMs92z/Lly1m7du3Yj2szc/lMzmeiwZmeQC9ExBOAVwLPoygmqbyUorBkysdfI+LRwOElXd/IzIfamackSTNlt91246STTuKkk06a6am0bdddd+WlL30pL33pS2d6KjNq6dKlnHrqqZx66qkzPZUdrFixghUrVvCSl7yk7RgTi/gkSZLKbG5mx5LB5nYsGRxws19JkiRJkvq6sCQinga8FzhurKnB5a1u3bIA+ELJfX8GfKjFWJIkSZIkSeqAjQ0KSxbUV3FGaguBLVPG8igcSZIkSZKgLx+7iIjFEfEp4L8pikqC7buRVL1akpk3ApeNiz32Or0D/wRJkiRJkiS1odGOJQtGRwEYqTW5Y0mtL5fOJEmSJElqSd99Oo6I/YCrKAcBAKsAACAASURBVI6+mVhQUnoLjXcyaeQD9T/Hxz88Io5qM54kSZIkSZLalAGbmtixZLS2sKl4QwPuWCJJkiRJUl8VltSLSr4JPJbGBSXjdxhpW2ZeCvyipOtF04krSZIkSZKk1m0hyAaFJcNZLBONDDZXWOKOJZIkSZIk9VFhSUQsAi4GDmB7Qcn4lYSxv98CfAJ4B/C6elvLR+GM8x9MLlA5eRrxJEmSJEmS1IbNUzxCtLBeWNL0jiUWlkiSJEmSxOBMT6CD3gccwY5FImPFJeuBTwJ/l5l3jr8pIj4+zXG/CJw5YbxjImJpZq6bZmxJkiRJkiQ1aXOD3Upg3I4lTReWeBSOJEmSJEl98dhFRBxOUdwxceeRAK4HjszMN00sKumQb1EUroxXA47rwliSJEmSJEmq0OgYHIChTIgBMpp71mpwwMISSZIkSZL6orAE+HN2/LeMfer/NvCUzLytWwNn5ihwI5OPwzm8W2NKkiRJkiSpdcMkDC6EKQpQxgx6FI4kSZIkSXO/sCQilgOnMnm3klXACzJzYw+mcX1J26N7MK4kSZIkSZKaNJQJgwuav96jcCRJkiRJmvuFJcBzgfErAkFRZPL2zPx1j+ZwY0mbhSWSJEmSJEmzyHBS7FjSpMGBflg6kyRJkiRpevrh0/FTS9puy8zP9XAO9437e1IUt+zVw/ElSZIkSZI0heH6jiXN7kMy6I4lkiRJkiT1RWHJ48b9fWy3kq/1eA4PlbQt6/EcJEmSJEmS1EBxFE7zO5YM1/ph6UySJEmSpOnph0/HB1IUk4x3ZY/nsLqkzcISSZIkSZKkWWRsx5JmDVpYIkmSJElSXxSWPKKk7Z4ez2FiYQvAoh7PQZIkSZIkSQ0MQks7lngUjiRJkiRJ/VFYUlbAUbaDSDftUtK2qcdzkCRJkiRJUoWhTAKgNtz8PQP9sHQmSZIkSdL09MOn44dL2nbq8RzKCkvW93gOkiRJkiRJqjCc9Q1n3bFEkiRJkqSW9ENhydqStt16PIfDx/19bMXhrh7PQZIkSZIkSRW2F5YsaPqeIQtLJEmSJEnqi8KSVWwv5hhzZI/n8LQJPyewssdzkCRJkiRJUoWhdnYs8SgcSZIkSZL6orDkpyVtEws9uiYilgNPpCgmGe+6Xs1BkiRJkiRJjQ2Nrdy0UFgyVOuHpTNJkiRJkqanHz4df3fc35Ni95InR8QBPRr/1UDZisRVPRpfkiRJkiRJU2jnKJxBj8KRJEmSJKkvCku+UdJWA97U7YEjYhHwJ0zerWQd8M1ujy9JkiRJkqTmtHMUTm3AwhJJkiRJkuZ8YUlm3grcOL6JYteSV0fEE7s8/LnA3uN+jvr4F2fm5i6PLUmSJEmSpCYN08aOJRaWSJIkSZI09wtL6j5BUdQxJoEh4HMRsVc3BoyIPwVewfZClvE+3o0xJUmSJEmS1J7hNnYsGQgLSyRJkiRJ6pfCkvOAuye0JbA/cFVEPKZTA0Xhw8CH2H4EzlhxSQLfzMzvdmo8SZIkSZIkTd/Q2CpOCzuWeBSOJEmSJEl9UliSmZuAN7N955CxP8eKS74XEe+JiMXTGSciTgG+D/wJk3cpARgB3jSdMSRJkiRJktR5Q23sWOJROJIkSZIk9UlhCUBmfg74PNt3DhlfXLII+Avgroj4fxHxgohYEdF4P9P67iRHRsRZEXEt8FXgyJIxxn7+68y8vtP/Nmk+yLEFPkmSpCn4vkGS1I7tR+E0v2PJgIUlkiRJkiQxONMT6LA/AA6hKP4YfzzN2N8fAfx+/QXwcEWcT0XEbhS7nQzV28avJIyPPfbz14D3dORfIc0DAwMDjIyMbPt5dHSUzGSKei9JkjTPZSajo6M7tA0M9E29vCSpi9yxRJIkSZKk9vTVCmxmbgBOBm5kx8IP2LHAZOw1djTO2M9jf38i8ChgeELf+Bg57vpvAS9JH52Umlb2BdDEL4kkSZImKnu/UKvVZmAmkqS5Znhs1cYdSyRJkiRJaklfFZYAZOaDwPHAV9hxl5Ftl0x4lYmS6yYerzN23ReBZ2Xmxk7MX5ovyr4A2rp16wzMRJIkzSVbtmyZ1OaOJZKkZgzjjiWSJEmSJLWjL1dgM3NdZj4f+GNgHeUFJo1WBsYXnEw8Ames7WHgzMw8LTM3TWe+0nw0NDQ0qW3Dhg0zMBNJkjSXrF+/flLb8PDwDMxEkjTXDG47CqeFHUs8rlWSJEmSpP4sLBmTmecCBwMfAdYz+VibsT8b7WIyvpgkgC3APwOHZObHujZ5qc8tWbJkUpuFJZIkaSplhSVl7yskSZpoON2xRJIkSZKkdvR1YQlAZt6fmW8CHgn8EfBVit1GooUXwDXA24AVmfmHmXl3L/8dUr9ZvHjxpLb169eTWXVClSRJmu9GR0dLC1EtLJEkNWN8YUk0uRNJzcISSZIkSZIYnOkJ9EpmrgfOA86LiEHgcOA3gAOAvYFlwAJgFNgA3A+sAn4EfD8z18zAtKW+NTQ0xPDwMJs3b97WNjIywtq1a1m+fPkMzkySJM1WDz744KQi1EWLFlGr1WZoRpKkuWRo7FdIk0fhDARNF6BIkiRJktTP5k1hyXiZuRW4of6SNEOWLFmyQ2EJwL333suSJUv8gkiSJO1g06ZN3H///ZPaly5dOgOzkSTNRa0eheNuJZIkSZIkFfr+KBxJs9cuu+wy6emvLVu28Itf/ILR0dEZmpUkSZptNm3axB133DFpt5JarcbOO+88Q7OSJM012wtLmt2xxMISSZIkSZLAwhJJM2h4eLj0y6ANGzawcuVKNmzYMAOzkiRJs8Xo6Cj3338/t99+O1u3bp3Uv/vuu7vLmSSpaYO0Vlgy6I4lkiRJkiQB8/QoHEmzx2677caaNWsmfVm0adMmVq1axfDwMEuXLmXZsmUMDw9Tq9U841qSpD6UmYyOjrJlyxbWr1/P+vXr2bBhw6RdSsYsXryYnXbaqcezlCTNZdt2LKkNA5umvH7AwhJJkiRJkgALSyTNsFqtxv7778+qVasYGRmZ1L9582YefPBBHnzwwW1tAwMDs664ZOLRPQMDbgglSZ1mru1fY0UlzVq0aBH77rvvrHs/IEma3YbHahWbLCxxxxJJkiRJkgoWlkiacQsWLGC//fbjzjvvLC0umaiVL556ZeKcqp6uliS1z1wrKHYq2XfffT0CR5LUsqEcfxTO2imvr1lYIkmSJEkSYGFJ0yKiBuwKjAIPpN9kSB21aNEiDjroIO655x7WrFkz09NpmU/RS1L3mWvnt1qtxu67785OO+3kTiWSpLYMZkIMwEBzxYkWlkiSJEmSVLCwpIGIeBbwMuAkYO9xXZsj4kbgIuBTmXnvTMxP6jeDg4Pss88+7LTTTqxevZp169Y1tYPJbDA6OrrtyXm/7JKk7jDXzk+LFi1i6dKl7Lzzzu5SIkmalkGA2oKmr6/5fkOSJEmSJKCPCksi4kBg4if+jZl5dxux9gU+Axw/1jThkgXAMfXX2yPinZn54VbHkVRuyZIlLFmyhMzk4YcfZt26dWzcuJGRkRFGRkZm5VE447/gjAi/+JKkLjDX9reBgQFqtRoDAwMMDw9vez/g/86SpE4ZymytsKRmYYkkSZIkSdAnhSURcTTwvZKuvwXe3mKsw4FLgL3YXlBSduzNWN8S4AMRcQzw8sycG9srSHNARLB48WIWL14801OZ0qpVq3Z4in7FihUzPCNJ6j/mWkmSNB2DmVAbavp6dyyRJEmSJKnQLwfTv5Si0GP8awQ4p5UgEbEEuBB4ZL0p2V5UMj72+L6st/02cG7b/wJJkiRJkiR1zSDAYPM7lgwMWFgiSZIkSRL0T2HJqexYBJLAlzPzrhbjvA84hMnFJGMxx4/BhL4A/jAifqvFMSVJkiRJktRlQy3uWDJoYYkkSZIkSUAfFJZExB7Ao0q6/qPFOPsCr2LHohIo37Fk7JVMLi75cEQ0v0ohSZIkSZKkrhtKoNbCjiUehSNJkiRJEtAHhSXAk0raRoGvtRjnTcDY6sJY0cjY32+hKDo5CjicYoeU/2RycQnAQcArWxxbkiRJkiRJXTRIQm24+etrFpZIkiRJkgT9UVjy+HF/H/vEf31mPthsgIgYAF7GjkfpjBWNfBk4KjM/lZk3ZOYtmfmlzHwu8GfsWIQy5vQ2/h2SJEmSJEnqksEWj8KpuWOJJEmSJElAfxSWHDjh5wRubDHGScCeJe33A6dn5paymzLzg8C/Mfk4nCdFxAEtzkGSJEmSJEldMpjAYPNH4dQGLCyRJEmSJAn6o7BkRUnbTS3GeO6En8d2ITk3Mx+a4t6/AEZK2p/e4hwkSZIkSZLUJUO0uGOJhSWSJEmSJAH9UViyL5OPorm9xRgnVLR/aqobM3MlcBXbdy0Zc2SLc5AkSZIkSVKXDCZQG276egtLJEmSJEkq9ENhydKSttXN3hwRuwGPZXtxytiqwQ8y8xdNhrm8pO2IZucgSZIkSZKk7hrMhFrzR+EMhIUlkiRJkiRBfxSWLCppW9PC/ccxebeRBL7SQoxbJtwbwN4t3C9JkiRJkqQuGhx3FE4zJSPuWCJJkiRJUqFfC0tGWrj/yRXtV7QQ466StuUt3C9JkiRJkqQuGkpgsPkdS8IdSyRJkiRJAvqjsGRjSduSFu4/vqRtC3BVCzHWl7RZWCJJkiRJkjRLFEfhDDV9fc26EkmSJEmSgP4oLCk79ma3Zm6MiIXAEyiOr4FiJ9QErs3MTdOc1/A075ckSZIkSVKHDALUml+uGXDHEkmSJEmSgP4tLDm4yXufBpQ9qvLtFuewS0lb2S4mkiRJkiRJ6rHBTAKg5lE4kiRJkiS1qh8KS26n2GlkvCc0ee8LKtr/p8U57FzStq7FGJIkSZIkSeqCoaxvVtvCUTgD1pVIkiRJkgT0R2HJ9eP+nhRFJs+MiMFGN0XEEHAa24/BGTNC64Ule48PXf/zgRZjSJIkSZIkqQsGx1Z/WjgKp2ZliSRJkiRJQH8UlvygpG0X4BVT3Pc7wO7jfg6KIpNrM3Nti3M4asLPCfy8xRiSJEmSJEnqgsGx54oGmz8KZ8CjcCRJkiRJAvqjsOS/gI3jfh7bteRvI+LAshsiYjfgfUzerQTgi23M4ZiSWLe1EUeSJEmSJEkdNtjGUTjWlUiSJEmSVJjzhSX13UUuZvsRNFAUeewCXB0Rr4iIhWMdEfF04Apgn5Jwo8C/tjJ+RCwFDi3purWVOJIkSZIkSeqOoW1H4bhjiSRJkiRJrZrzhSV1H2HHHUPGjrXZDfhnYG1E3B0Rq4FvAI8Zd9346y/MzF+2OPbzgFpJ+zUtxpEkSZIkSVIXbDsKp4UdS2oDFpZIkiRJkgR9UliSmVcB57PjriWw/VicGrAXsGzcNcmOxShbgbPaGP4lJW3rgJvaiCVJkiRJkqQOG9y2Y8lw0/e4YYkkSZIkSYW+KCypewvbj58ZXzCSFa+Ju5W8MzN/1sqAEbEEOGXceGOxrsnMrLxRkiRJkiRJPTM4tkwz6FE4kiRJkiS1qm8KSzLz18BJwM+ZvHNJ6S3j/v7pzHx/G8OeDiwqab+kjViSJEmSJEnqgqE2jsLxJBxJkiRJkgp9U1gCkJl3A0cDH2P7riRlywBj7WuBN2fmK1sdKyICeCM7FqiM+Wqr8SRJkiRJktQd24/CaX7HkpqVJZIkSZIkATA40xPotMxcC5wZER8GTgOeDawA9qxfcj9wE3ApxU4lD7U51KnAwSXtqzLzR23GlCRJkiRJUodtOwqnNtz0PeFROJIkSZIkAX1YWDImM28HPlB/dcNNwAkl7fd3aTxJkiRJkiS1YdsCmEfhSJIkSZLUsr4tLOm2zPwJ8JOZnockSZIkSZIaGxrbsWSw+aNwBtyxRJIkSZIkAAZmegKSJEmSJElSN207CmeglR1LLCyRJEmSJAkgcuyDtaR5ISJGgW2rY8uWLZvB2fSPibnUs7glqfPMtZLUXeZZzRVbR7eyaWRTS/cMkiwYTRheAjHAyGjy8JaRhvcM1wYYHvSZLHWOeVaSus9cK0ndZZ7tnrVr147/MTNzVn0gtbBEmmciwv/TS5IkSZIkSZIkSdIslZmzqmpnVlW5SJIkSZIkSZIkSZIkafYYnOkJSOq5UbYXlSWwbgbn0k8mnim0tvQqSdJ0mGslqbvMs5LUXeZZSeo+c60kdZd5tnuWAmO7lIzO5ETKeBSOJHVARKxh+y/TtZm5fCbnI0n9yFwrSd1lnpWk7jLPSlL3mWslqbvMs/OXR+FIkiRJkiRJkiRJkiSplIUlkiRJkiRJkiRJkiRJKmVhiSRJkiRJkiRJkiRJkkpZWCJJkiRJkiRJkiRJkqRSFpZIkiRJkiRJkiRJkiSplIUlkiRJkiRJkiRJkiRJKmVhiSRJkiRJkiRJkiRJkkpZWCJJkiRJkiRJkiRJkqRSFpZIkiRJkiRJkiRJkiSplIUlkiRJkiRJkiRJkiRJKmVhiSRJkiRJkiRJkiRJkkpZWCJJkiRJkiRJkiRJkqRSFpZIkiRJkiRJkiRJkiSplIUlkiRJkiRJkiRJkiRJKhWZOdNzkCRJkiRJkiRJkiRJ0izkjiWSJEmSJEmSJEmSJEkqZWGJJEmSJEmSJEmSJEmSSllYIkmSJEmSJEmSJEmSpFIWlkiSJEmSJEmSJEmSJKmUhSWSJEmSJEmSJEmSJEkqZWGJJEmSJEmSJEmSJEmSSllYIkmSJEmSJEmSJEmSpFIWlkiSJEmSJEmSJEmSJKmUhSWSJEmSJEmSJEmSJEkqZWGJJEmSJEmSJEmSJEmSSllYIkmSJEmSJEmSJEmSpFIWlkiSJEmSJEmSJEmSJKmUhSWSJEmSJEmSJEmSJEkqZWGJJEmSJEmSJEmSJEmSSllYIkmSJEmSJEmSJEmSpFIWlkiSJEmSJEmSJEmSJKmUhSWSJEmSJEmSJEmSJEkqZWGJJEmSJEmSJEmSJEmSSg3O9AQkqR9ExBJgZ2Ap8DCwOjMfmtlZSZIaiYhhYFdgGTACrAHuz8yc0YlJUomICIp8tROwCFgN/DozN83oxKYQETVgN4q5B9tz7ciMTkyS+oA5VpK6z1wrSd3lGu3cEf5vIkmti4g9gBcDzwaOAvYtuexB4CbgUuCLmXlL72YoSb0XEQcCxwCHArWyazLzXb2c03gRsRB4PvBc4EnAwUye5wbgVuAK4OLMvKKHU5QkACJiOfB0ipz6WOBwipxV9nDIw8DPgKuA7wKXZ+YdPZrqJPWF91OA5wHHA4cAwxMu2wz8BPg28BXg6y7MS+qliNgbeFz9dRCwX/21O7C4/oKiiG818GvgR8C19dc1mbm1x9M2x0rqaxHx28Bnp7jslZl5fpfnYa6VNCvV116X9GCoBzLzl90K7hrt3GVhiSS1ICL2Bd4D/C6woMXbLwPOysyrOz4xSeqxiNif4gvPsdfjgV2mui8zo8tTmyQiFgFvBc6keMqoFbcA783MCzo+MUkaJyKeCLwQOJEip5YW6DVhFPgacA5wSa+e8ImIQeC1FPl2vxZvvwP4IPBxF+QldVr9CchjgeMoviB8MsUTkdNxD/AvwHm9eIjEHCup30XELhSfv/eY4tKuFZaYayXNdhFxBcVDKN32scx8faeDukY791lYIklNiohXU3xAWDqNMAl8DHhzZm7uyMQkqcsiYnfgKRRfdI4VkuzeTqxeF5ZExHHAvwIHTDPUfwO/181qfUnzT0T8BvAK4DRgRReG+DZwRmbe1oXY20TEYRRPl/7GNEPdAPxOZv54+rOSpEJEvBD4jy6FT+CTwNsy89fdGMAcK2k+iIj/R/G+eCpdKSwx10qaC+ZyYYlrtP1hYKYnIEmzXUQMRMQngH9gekUlUJzD+XrgiojYedqTk6Te+AvgS8A7KI4Aa6uopNci4gyKDxsHdCDcCcC1EXFUB2JJ0pi/BN5Md4pKoHgy/4aIeFWX4hMRpwBXM/1FeIAjgKsj4pkdiCVJvRDAq4AfR8SzOx7cHCtpHoiIZ9FcUUm3xjfXSlIXuUbbPywskaSp/QPFQlEnPRn4ekQs63BcSRIQEb8PnAcMdTDsI4FLIuIxHYwpSd22BPhERPxVpwNHxIkUuwB08j3tcuCiiHhGB2NKUrftAXw5Il7eqYDmWEnzQUQsAT4xg+ObayWpi1yj7S8WlkhSAxHxp8D/bnDJeorz60+iWEgaAnYCnkjxZP8vGtx7LPBPnZmpJGlMRIzl16pjdxL4IsXRE/sDCyh2pHoM8Brg+w3C7wZcHBHLOzZhSap2H/BvwJ8Cz6DY2WQpxXvO3YDDgT+oX7NlilhnRcTrOjWxiDgA+AKwsMFllwEvBw4GFgGLgUdRPJH6jQb3LQIujIj9OzFXSaowCtxIcfTBe4Dfp9id7zjgyPqfzwb+GPhH4PYp4tWA8+pfUk6LOVbSPPI+urd7X0PmWknqLtdo+09k5kzPQZJmpYg4Avge1ZWUlwCnZ+avGsRYCPwV8JYGQ70qMz/Z9kQlqcsi4iPAnzRxaQI/pXg6fp/SCzKrPkh0REQsBn5I9daKP6c47/h7U8R5BfBxin9LmQsy8/fanackAUTEF4AXT2j+NXBB/fXdbPJDe0TsS7Ew3+hp+c3A4zPz5jamO36sAeBK4EkVl9wHvCIzvz5FnFOAT1N9xNq3gac1+99AkspExAspnkYH+AHwNYr88p3MXNtCnKA4Yux99T+r3A0c2krsCeOYYyXNCxHxJIp8N/EB6M8AVZ+3X5mZ53dgbHOtpDknIq4Anl7S1ZHc2Emu0fYndyyRpGrnUF1U8jngOY2KSgAyc2NmvhV4Q4PL3h8Ru7Y5R0maKQn8jCIfvhU4EdgpMx9N8UTPTPkLqj+w3AI8aaoPLACZ+WngmcC6ikt+121tJXXYLcAfAvtk5usz8zutLEBn5i8y8xX1GKMVlw0DH5n+VPlDqhfhfwk8ZapFeID6NccDVe+pjwdOb2uGkrTdHcCZwP6Z+fjMfEdm/lerhR9Z+BbwNOBtDS7dG3hT+9M1x0rqfxExTPEU+8TvqL5Fb3Z4NtdKUne5RtuH3LFEkkpMeKJpouspfultajHmPwCvruj+UGY22tVEkmZMRPwd8HzgWootCK8Frs3MhyquP5+KhZNu7lgSEXtRbFFeto3tWuDozPxZizF/h+KIiTLXZuYxrc1Skrar71hyOMURil/MzKqCkFbj/jFFkXSVY5tZwKmIvRBYCexZ0r0VeEZmXtlizOOAb1IcIzHR3cCBmbm5xalKUldFxDuBd1V0/wrYNzNHWoxpjpU0L0TEu4G/nNC8meIosj2B/664ddpP5ZtrJc1Vc2XHEtdo+5c7lkhSuaqnjxL4o1aLSureQlHxXuZVEbFzGzElqRfekpkHZ+ZLM/NvM/PyqqKSGfanVJ+N/M5WP7AAZOZnga9UdD8+Ik5uNaYkjfN+4LGZ+YVOFZUAZObHKI5trNLouJypnEH5IjzAx1tdhAeo33NuRffeFGfYS9Js8z6Kpy3L7AW0s7h9BuZYSX0uIh4LvL2k6/2ZWZVXO+kMzLWS1E2u0fYpC0skaYKIOBp4YkX3hZl5bTtxM3Md8NcV3cvwA4akWarVJy1nQkQMAX9U0X038LFphC9b8Brzx9OIK2mey8zvdzHH/nmDvt+cRtzXVbQ/DPzVNOK+G9hY0WeulTTrZOYW4LwGlxzbRlhzrKS+FhEDwKeYfPz4TygK9nrBXCtJXeIabX+zsESSJmv0BOd0z6Q/H1hT0ff704wtSfPZc4BdK/o+Pp0tZzPzZuDyqnEjYpd2Y0tSt9SLoVdVdB8SEYtbjRkRRwCPq+j+TGbe32rMMfV7P1PRfWREHN5ubEnqov9q0HdgK4HMsZLmiT+h/IG+V7e5Q3RLzLWS1HWu0fYxC0skabIXV7SvbGcrxPEycz3wpYruYyJixXTiS9I8VpW7oXphpxVVMYaB3+pAfEnqhu9UtAdwQBvxGuXaf20j3kSN8vVpHYgvSZ12V4O+ZS3GMsdK6msRcSDlO4L8c2Ze0aNpmGslqbtco+1jFpZI0jgRcSiwX0X3xR0aplGc6WxLLknz2TMr2m/MzJUdiP9lICv6zN2SZqtfNeh7RBvxqvLdr4FvtRFvov8BHmpxbEmaSRsa9K1vMZY5VlK/+wSwZELbfcBbezgHc60kdZdrtH3MwhJJ2tEJDfqqtthq1Teo/sV3YofGkKR5IyIOA/aq6O5I7q5vaXtDRbe5W9Js1bHtxCNiKXBMRfcVmTk63TEycwS4oqL72IiY+EWEJM20PRr03ddsEHOspH4XEa+k/MvGN2XmAz2ag7lWkrrINdr+Z2GJJO2o6sMFwNWdGCAzfw38tI3xJUnlup67666paN8rIvbp4DiS1ClVCzoAq1uMdRRQq+jrRa4dBI7s4DiS1AlPaND3/RbimGMl9a2I2BP4UEnXpZnZieNnmmWulaTuco22z1lYIkk7Oqqi/ZeZeU8Hx/lBRfvBEbG8g+NI0nxQlbsBruvgOFW5e6o5SNJMeXxFewIrW4xlrpWkyV5W0f4wcGULccyxkvrZOcDOE9oeBl7b43mYayWpu8yzfc7CEkna0aEV7T/p8DhVO5YEcEiHx5KkfndYRftW4PYOjlOVu6H694ckzYiIWAE8rqL7x5m5ocWQVbkWOvte2VwraU6IiKcDL67oPj8z17UQzhwrqS9FxAuB00q63pOZt/V4OuZaSfNCRCyMiH0j4tCIOCAidunR0K7R9rnBmZ6AJM0W9W0ZF1V0d/qDTqN4B9LalrmSNN8dUNF+R2Zu6eA4U+VuSZpNXtWg79I24h1Q0b4VuKONeFVW1WOWrVeYayXNChFxJPCFiu7VwN+0GPKAinZzrKQ5KyIeAXyspOsmyo/G6bYDKtrNtZLmuidExP8CjgOOBpZNvCAiHgZ+RrFzyP8AX8/Muzo8jwMq2l2j7RMWlkjSdisa9P2yw2M1indAh8eSpH5Xlb/NSPNmowAAIABJREFU3ZLmpYjYFXhdg0suaCNsVa69JzNH24hXKjNHIuI+4JEl3Qd0ahxJakdELATeCJwFLC65JIHXZOadLYY2x0rqRx8E9p7QNgq8qsNfMDbLXCupXzX6/D9mEcWupo8DXgFkRFwG/H1mfrVD83CNts95FI4kbbdHg75fdXisRvF27/BYktS3ImIp1btNdTR3Z+Zm4NcV3eZuSbPJXwM7VfRdlZlXtxGz6r1yp98nN4pprpXUMxGxKCL2jIijIuKMiDgPuAd4H9VFJa/LzM+2MZw5VlJfiYgTgD8s6fqHzLyq1/OpM9dK0nYBPBP4SkR8NyJ+Y1rBXKOdF9yxRJK2a3TO3OoOj9UoXq/Ou5OkftDL3D0Wc+cW5yFJPRMRJ9P4GJy/bCNmUF2o0q1cW2bniIjMzC6MKWkeiojjgW91INQvgFdm5mVtzMEcK6mvRMQi4B8pvrQc727g7b2fkblWkqbwJOD7EfHWzPz7NmO4RjsPuGOJJG1X9eECYE2Hx2oUr+yXoSSpXC9zd6OY5m5JMy4i9gQ+zeRF/DFfysxL2wi9DKhV9PUy1w4CS7swniS1627grcBh7RSV1JljJfWb9wCPKml/Q2Z2I681w1wrSY0NAR+JiLPbvN812nnAHUskabsFDfo2dnisRvEazUOStKNe5u5GMc3dkmZURAwCn6X8LHeAB2nu3OUysyXXQjGXtV0YU5JasRp4B/DxzByZZixzrKS+ERGPB95Y0vXlzLyw1/MZx1wrqR89BFwO/AC4Gbi13ja2Q8iu9deRwFOBZwP7TBHzzIjYkJlva3EusyXPukbbRRaWSNJ2ww36tnZ4rC1tzkOStKNe5m6ozt/mbkkz7RzgGQ36X5uZv2wz9mzJtWC+lTQ7PAI4G3h7RHwSOCcz72szljlWUl+oFzr/E5N3BlkHvL73M9qBuVZSv1gDfAb4N+C7mdkoh91Vf90IfDoihoDfAf4CeHSD+/5PRHw/M7/QwrxmS541x3aRR+FI0nZV2yECTPcJpFbiWfQnSc3rZe5uFNPcLWnGRMSbgVc3uOSjmfn5aQwxW3ItmG8lzS6PBP4SWBURb4yIqqPIGjHHSuoX/wc4oqT9HZl5R68nM4G5VlI/eA+wd2a+LjO/NUVRySSZuSUz/wU4CvjUFJd/PCJaOVZmtuRZc2wX+R9XkrZr9Eu40/myUbxGFe2SpB31Mnc3imnuljQjIuLlwAcaXHIF8JZpDjNbci2YbyV11g+Ax5W0B7Cc4qz4fYEnAMdT/WTnIuDDwHMi4kWZua6FOZhjJc15EfFoiiPCJvo+8NEeT6eMuVbSnJeZ3+hQnIeB/x0RdwLvrrhsd4qCwWaPxJktedYc20UWlkjSdo1+4fSysGRzh8eSpH7Wy9zdKKa5W1LPRcQLgPMovgAt833gBZk53Rw1W3ItmG8ldVBmbqA4j34qnwCIiKcAfwq8pOK6k4GvR8QpLRSXmGMlzWn13Zo+BSyY0DUCvCozu/GkeqvMtZI0QWa+JyIOBM6ouOS1EfHeJt/XzpY8a47tIo/CkaTtHm7Qt6jDYy1u0Lexw2NJUj/rZe6G6vxt7pbUUxFxMvA5qhdTfgickplrOjDcbMm1YL6VNIMy8zuZ+VLg+cB9FZcdB5zdQlhzrKS57nUUuzpN9JHMvK7Xk6lgrpWkcm8EHqzoWw78VpNxZkueNcd2kYUlkrTdQw36lnd4rEbxft3hsSSpn/UydzeKae6W1DMRcTxwEZOfCh3zU+DkzHygE+PVn06q2ta2l7l2S2au78J4ktSSzPwyxe4kVYvwr4yI5zYZyxwrac6KiP2AvynpWgW8s8fTqWSulaRymfkQ8H8bXPK8JkO5RjsPWFgiSds1WnjfqcNjNYrXkS8AJGme6GXubhTT3C2pJyLiWOCrVD+dswo4KTN/1eGhq748NddKmpcy80bgdxtcclYL4cyxkuaqc4FlJe1/PAsLKMy1klTu3xr0PaXJGK7RzgMWlkjSdvc06Nurw2M1indvh8eSpL6VmQ8Dayu6O5q7I2IR1dXw5m5JXRcRRwJfpzoX3Q2cmJl3dmH4qvfKnX6f3CimuVbSrJKZ/wVcXNH9pIg4qslQ5lhJc05EvAwo253p85n51V7PpwnmWkkqUV9D+FFF9wH1NdGpYrhGOw9UncUsSfPRqgZ9+3R4rL0b9N3e4bEkqd+tAh5b0m7ultQ3IuJw4FJg54pL7qUoKvl5l6awCnhcSfseETGUmVs6MUhEDAO7VXSbayXNRp8Cnl/RdzJwXRMxzLGS5qJnlbRtAT4ZEWWf0Zt1YIO+fRrEvjczG32haK6VpGo/BB5T0bc/cGsTMVyj7XMWlkhSXWY+EBFrKK90fFSHh2sUz198ktSa2yn/0LJvRCzMzI0dGsfcLWlGRMSjgcupXqB+ADg5M5tZ6GlXVZ4boFj8/0mHxjmQ6t1VzbWSZqPLgE3AgpK+JzcZwxwrqV8MURRDd8t7668y7wbe1eBec60kVWtUmFe1O8hErtH2OY/CkaQd3VLR/ugOj1MVb4TOfYiRpPmiaqvGAP5XB8dp9Lugag6SNC0RcTBFUcmeFZc8BPxmZt7U5ak0ynOdfK9srpU0p9S3/f5lRfcjmwxjjpWk7jPXSlK11Q36ygqoy7hG2+csLJGkHf2gon2XiFjRwXEeX9F+S31RSpLUvKrcDXB0B8epyt1TzUGS2lJ///kNqreNXQuckpm9yEHmWkmqdk9F+y5N3m+OlaTuM9dKUrVdG/Q1u9OIebbPWVgiSTu6pkFfs1vYNhQRe1OcSVfme50YQ5Lmma7n7ronVbTfnpkPdHAcSSIi9qUoKql637geeE5mXt2jKd1AcdRDmV7k2o1At3dlkaR2LaxoH23yfnOsJHWfuVaSqu3eoO+hJmO4RtvnLCyRpB1d3qDv5A6NcVKDvss6NIYkzRuZuRL4eUV3R3J3ROwHHFLRbe6W1FERsRdFUclBFZc8DDw/M7/dqzll5ibgyorup0bE8HTHiIgFwFMrur+VmZunO4YkdUnVcWWNthTfxhwrSd1nrpWkhqp2ARkF7mgmgGu0/c/CEkkaJzPvBH5c0f1bEVHrwDCnVrSP4i8+SWrXJRXtB0fE4zoQvyp3NxpbkloWEbtTFDtXnT+8CXhRZn6jd7PapirfLQae1YH4pwCLWhxbkmZUROxJdWHJyhZCmWMlzSmZeUZmRqdfwAkNhn1lg3vf1cS0zbWSNEFEHAasqOj+WYtFca7R9jELSyRpss9XtO9B8eGgbRGxG/Dsiu4rMvPe6cSXpHmsKncDvKID8V9e0b4O+FoH4ksSEbEzcCnwmIpLtgCnZeZ/9W5WO+h2rq2KkVOMLUkz6beAqOi7sYU45lhJ6j5zrSRNdkaDvqqdnqq4RtvHLCyRpMn+pUHfm6YZ+3XAgjbGlSQ1dgXV2zL+UUQsbzdwRDyN6u0gL8zMDe3GlqQx9Tx1CXBExSVbgd/OzK/0blY7yszbgarjd14UEQe2G7t+7wsquv8nM5vaeleSeikiBoDXNrik6d2lzLGS1H3mWknaUUTsA5zZ4JKLWwx5Ba7R9i0LSyRpgsz8GdWVjSdGRFu7ltS3x31LRfe9wGfbiStJgsxM4JyK7p2AP28nbkQE8LcNLjm7nbiSNF5ELKF4/3lMxSUjwO9l5n/0blaVqvJeDfibacR9fz1Gmb+fRlxJ6qbXAkdX9K0Erm4xnjlWkrrPXCtJQEQMAedTfYTX/cB/thLTNdr+ZmGJJJV7f4O+f4yIXVoJVv+l90/AsopLPpKZG1uJKUma5BPAQxV9b46IJ7cR8y3AEyv6LsnMH7QRU5K2iYiFFE8AHVdxyShwembOlq2zLwR+WtH32xHxklYD1u95aUX3j4GLWo0pSQAR8ZyIGO5S7BcCf9fgknPqC+utMMdKUveZayXNKRHxhPp3TJ2MWQM+BZzc4LKPZOamNsK7RtunLCyRpBKZ+S2g6onQ/YCvRERVkcgO6r/wzwaeW3HJKuAjLU9SkrSDzFwDvLOiexD4UkQ8ptl4EfE7VD+tNEL1LlSS1JT600EXAidWXJLAH2XmZ3o3q8Yyc5TG+e/8+va0TYmIp1M8IVXlTfUxJakd7wN+GhGvjoiqY2lbEhFDEfE2ivPjhyouWwV8rNXY5lhJ6j5zraQ56JPADRHxsnpByLTUj7+5HHhFg8tW0riIupJrtP0rWi+cl6T5ISL2B24EHlFxya3AGZl5VYMY+wHnUl1UAvC8zPxq2xOVpB6of+DYucnL30v1ucKPa3bMzLy52WvH1D9cXUX1cRJrKM4N/ZeqJ0jrR1K8A/gzoOppgA9k5p+1Oj9JGi8ivgC8uMElZ1MsIHXDvZl5b7s3R8QXgRdVdG8G3g58NDO3VNw/RJGP/wao2k3g3zOz6slPSZpSRFwPHFH/cTXFE+P/Dlza6tOXEbEz8BLgjcChDS4dBU7OzP9ufcbbxjLHSpq3IuIZQFUOfWVmnt+hccy1kuaECe9pf0XxUPQXgG9m5kgLcfYC3kBxnONODS7dSvF+9pvtzdg12n5lYYkkNRARp1I8RdrIN4AvURShPAQsAQ4BfpPiw8nCBvd+ODPf3IGpSlJXRcT5wOm9HDMz29riMSIOAn5AdWEgwI+AzwLfA+6heNp0P+BpwMuA3RvcezXw1KrFJUlqVkTM5Afyd2fmu9q9uf4F63XAigaXrQIuAL4L3E2xELQ38GTgd4H9G9x7O3BUZq5ud46SNGERfrzNwA+B64EbgF9SFJ48BGykOMZ2ObAn8BvAkcBTqP7icLzXZ2bLu5VMmLc5VtK81cPCEnOtpDmhwXvatRTfS91Q//M+ive0qyl2QN0F2LV+71OBY6necW+812TmJzowb9do+4yFJZI0hYj4M+D/diH0F4HfzsytXYgtSR01lwpLYNtWtP8JLOrcjAD4CfD0zPxVh+NKmofmcmEJQH3r2itovNDTjnspcu2POxxX0jzTYBG+G0aAMzPz3E4EM8dKmq96VVhSH8tcK2nW6+F72hGKIul/6FRA12j7y8BMT0CSZrvM/FuKrW6b3lKsCf8KvMyiEknqjvpWjc8BHuhg2OuAE/3AIkmFzPwRcBLFk5ydcjtFrnURXtJccgdwUqeKSsAcK0m9YK6VpG3uAp7ZyaIScI2231hYIklNyMyPUBxt8/NphlpLUfH58szcPP2ZSZKqZOYVFOd4XjHNUKPAucBxmXnXNGNJUl/JzJsocu1Ux0c24wvAMZn5ww7EkiSAO7scfzXwLuAx0zmDvoo5VpK6z1wraZ7bAHwIOCwzq3aLmhbXaPuHR+FIUgsiYiHweuBMGp+hOdFa4J+B92fmL7sxN0nqprl2FM5EEXEa8FaKs0SbNQJcBLwvM6/t1FwkacxcPwpnoog4CXg7xVOfrbgM+JvM/EYn5yNJABGxAng28CzgKcAe0ww5AnwL+Bzwr5m5bprxmmKOlTRfRMQBwBkV3V/KzOu7OLa5VtKsU38/eyJwAvB0WvtuqsrNFDvrn5eZ93UgXlNco53bLCyRpDZERA14KnAKcBRwCLArsBjYRPHU0m3ATRQfLC7p1WKTJKlaRDyWYvvFJwOHAnsCyyg+oKyl2Mb8R8A3ga9aDChJrYuIg4DnAccBhwH7UORaKHLtXcAtwLcpcu10dwWUpKbVF+aPBQ4HDqy/xvLUEorz37cAG4EHgXsojjW4Bfg+cGVmru79zAvmWEnqPnOtpNksInYDjqi/HgXsV3/tTvEd1WJgENgMrKN4P/sLirx1HfDfmdntnf0aco12brKwRJIkSZIkSZIkSZIkSaUGZnoCkiRJkiRJkiRJkiRJmp0sLJEkSZIkSZIkSZIkSVIpC0skSZIkSZIkSZIkSZJUysISSZIkSZIkSZIkSZIklbKwRJIkSZIkSZIkSZIkSaUsLJEkSZIkSZIkSZIkSVIpC0skSZIkSZIkSZIkSZJUysISSZIkSZIkSZIkSZIklbKwRJIkSZIkSZIkSZIkSaUsLJEkSZIkSZIkSZIkSVIpC0skSZIkSZIkSZIkSZJUysISSZIkSZIkSZIkSZIklbKwRJIkSZIkSZIkSZIkSaUsLJEkSZIkSZIkSZIkSVIpC0skSZIkSZIkSZIkSZJUysISSZIkSZIkSZIkSZIklbKwRJIkSZIkSZIkSZIkSaUsLJEkSZIkSZIkSZIkSVIpC0skSZIkSZIkSZIkSZJUysISSZIkSZIkSZIkSZIklbKwRJIkSZIkSZIkSZIkSaUsLJEkSZIkSZIkSZIkSVIpC0skSZIkSZIkSZIkSZJUysISSZIkSZIkSZIkSZIklbKwRJIkSZIkSZIkSZIkSaUsLJEkSf+/vTuPlvUq6wT8e0lIIJBJZJ7CPJgBFVDQhoTRDoIgygIUsUGgWxwAEXACGgFZOEUmhTbNaNpWIUBAJAoEoRmkZQpI0oC5JmEKCQESAiGEt/+oE705+b5zzzn1napz7n2eteqP7F3fu9/zVVXWStUvewMAAAAAAMAgwRIAAAAAAAAAAAYJlgAAAAAAAAAAMEiwBAAAAAAAAACAQYIlAAAAAAAAAAAMEiwBAAAAAAAAAGCQYAkAAACw7VTVEVXVQ49l98Zijb0PquqIZfe2XVTV9arqwoF79JBl97bTVNVhVfWVgXv58GX3BgAAsCyCJQAAAACws70wyWGrxt7X3Scvo5mdrLu/muT5A1N/UFXXXnQ/AAAA24FgCQAAAADsUFV19yQ/NzD11EX3shd5cZKzV43dOMkzl9ALAADA0gmWAAAAAMAOVFX7JXlZklo19frufv8SWtordPelSX5nYOpJVXX7RfcDAACwbNXtaGIAAABge6mqI5KcNTTX3at/RGfBqurYJMcOTH20u9848VpjX17dort3TbnWTlNVj0ly4sDUMd398UX3szdZCe2cmeRWq6be3N0/sYSWAAAAlmb/ZTcAAAAAwI5zbJJnDYy/OsmkwRKGVdUBGT6a5S1CJfPr7sur6veT/NmqqQdV1Q919weX0RcAAMAyOAoHAAAAAHae/5rk5gPjv7foRvZir0ryhYHx5y+4DwAAgKUSLAEAAACAHaSqDkrymwNT7+nu9y26n71Vd1+a5E8Gpu5VVfdadD8AAADLIlgCAAAAADvLY5Jcf2D8ZYtuZB9wYpJvD4w/Y9GNAAAALItgCQAAAADsEFV1tSS/OjB1fpI3LLidvV53n5/k5IGp+1bVkYvuBwAAYBkESwAAAABg5/iJJLceGH9Vdw/trMH8Xj4y/pSFdgEAALAkgiUAAAAAsHM8aWT8xIV2sQ/p7ncl+deBqUdW1fUW3Q8AAMCiCZYAAAAAwA5QVbdKco+BqU909xmL7mcf8/qBsQOTPHLRjQAAACyaYAkAAADADlcz162qW1XVbavqBlXle5+9z8+NjA+FHpjW34yMP3qhXQAAACzB/stuAAAAANi7VNXVk/znlcf3J7lVkkOSdJKLkpyT5JNJ3pHkLd19/pJa/XcrPd8jyX9K8oNJbpnkBkmulWS/JN9Icl6SzyT5UGa9v7e7v7uUhpNU1dFJfjrJfZMcmVmvu/tmVX08yduTvK67Pz3HWkeuGho7/uOwgefuyVnd/Y1NtDWoqg5Icp8kxye5U5JbJzl0ZfrLK48zkvxdkrd393lTrb0APzsyvpRgSVVdM8n9ktw/ydH5j8/61ZN8NbPP+geTnJLZvd7Q56WqKsndkjwos8/l7ZIcluSgzD6T5yT5aJK3JTm5uy+Z/68a1t3/VFVnJ7nZqqk7VdVR3X36Vq0NAACwbNXdy+4BAAAA2AtU1YFJfiXJUzILZazHpUlel+TZ3X3ubrWOSHLW0AXdXXM1upuqukWSX0vyiCTfs8HLP5/kJUle0t0XTdDLriQ3H5g6rrtP2+1535fkjzL7QX+9Osmbkvx6d39mE71t5RdIV/r7NrD2Lbp7127POzDJk1Ye633/XZ7kz5M8q7u/tM5rlqKqfijJBwamzu7uoffNZtbYlfW9B6+V5GlJnpjkOussf1aSp3b3G9bRx35JHpXkdzILea3HV5L8bpIXbVXgq6pemuQXB6Ze0N2/sRVrAgAAbAe2RAUAAADmVlXHJPlYkhdm/T/qJ8mBSR6b5JNVNbYbw+Sq6pCqelGST2f24/hGQyVJcqMkz09yZlX95JT9jamqX03y4WwsVJIkleTBST5eVY+bvLElq6o7Z/b+e0E29v7bL8kTkny6qh64Fb1N6AEj4+9aZBNVdbckpyd5ZtYfKkmSWyR5fVW9amWHoLH6RyR5T5JXZv2hkmT2Gf7jJP9QVYfu6cmbNHavx14bAACAvYJgCQAAADCXqjo+yfszO6Zisw5J8tqq+q1puhq3EkL4eJJfzixYMK8bZvaD+R9W1ZZ911JVL0hyQpID5ihzzSSvWKm1V6iqhyT5x8z3/js4yRuq6hHTdLUljh8ZP21RDazc63dmFhLZrEdn9nm5ymelqu6aWXDqbnPUPy7J31fVQXPUGHNaZrv/rHZUVd1kC9YDAADYFgRLAAAAgE2rqvsmeWNmgYUpPHcrd9RYCcG8J8PHfczrKUn+vKomO6rnClX1xCRPn7Dk06vqWRPWW4qV1/N/Z5r33/5JXlNV3z9BrUlV1fWT/MDI9EJ2LKmq45L8ZZJrTFDugUmet6r+Dyb5+ySHT1D/LpkdFzWp7j4/s91ahowFfwAAAHa8/ZfdAAAAALAzVdVtk/xNktFjLZJckuSUJKcmOTfJ+Umum+QmSX4syY/nqj9UvzTJQ7eg3/smOTlr7/jxlcx+qP/HJGcluSDJpZn1fESS+2Z2DM21R67/L0nOTvLsKXpecWSSPxwYPyezH/pPSfJvSb6U2Y/yN05ybJJHZjyMkCTPrqpPdPfrJ+x1kW6X5K9y1fffeUnentnuEp9P8uXMgic3SHLPJA/J7B4N2T/JiVV11+7+zhb0vFn3zOw4o9XO7+5/W8D6N0zy4lz1s/O5JG/N7PPyxSQXZvYevFVmQYsHZPz7x1+vqpO6+/Squm5mn81DVj3ngiR/l+Qd+Y/X8uAkN8vs3x8PTjK2M8njq+pV3f2B9f6R6/ShJEcPjB+b5BUTrwUAALAtVPfQ7o0AAAAA46pq/8yOv7nzyFM6ycuS/EZ3X7RGnUOTvDDJ41dNnZPkpoOFuze8I0hV3TqzH4QPG3nK2UlekOSV3f2tPdT6niS/ndlROkM/mneSe3X3aRvscVeGd1L5eq78g/t3kjw/yXO7+7I91HxYZq/DdUae8pUk39fdX9xgr89OMrTjyau7++c3Umsda419efX5JDfa7Z+/nOQ5SV6+1n2pqmskeVpm/Y/t5vsL3X3iJtrdElX1+0meOjD1zu6+94Tr7Mrwe/ALmYVLrnBekmcm+fPuvnyNekcleU2SO4085S3d/cCqOiWzkNkVLkrye0n+eK3PY1XdPMkrMzv+Zsj7uvtHxq7fjKr6lSR/MjD1me6+zZRrAQAAbBeOwgEAAAA24xczHir5dpIf7+5fWitUkiTd/bXufkJmOw/sHgYYDJVsRlVdLbMft8dCJa9PcnR3/+meQiVJ0t1f6e6nZLYbw9eHlkzyiqpaayeXjdg9VHJpkvt397P2FCpJku7+q8x+1B/b1eJ7MvsBfyfaPVTykSTHdPdL9nRfuvtb3f2cJI9Z42mPnaLBCY191saOZZna7qGSTyT5/u5++VqhkiTp7tOT3CfJv4w85fiqenquHCo5J8ldu/v39vR5XNmt5QGZHW815O5Vdfu1amzCx0bGb11VUxzjAwAAsO0IlgAAAAAbUlXXymy3giHfTfKo7v7bjdTs7jcl+fnMdvuY2uOT3G1k7pXd/VPd/bWNFu3uUzP7UXvoyJTbZPb3TKkzu7fv3NBF3ecmuX9mu5MMefQW/Pi+SB9Lco/u/sJGLuruVyd53cj03arqDnN3NoGqqowfabSoYMkVPpPZvf78ei/o7gty1R2JrnC1zHYKusIFSX60u8/YQP1vZnYE1bdHnvJz6621Tmvd87EAEAAAwI4mWAIAAABs1GMyfrTKK1Z2ydiw7j4pyaTHj6wce/I7I9OnZfwH73Xp7vdmPGTzlHlqD3hdd//1Zi7s7jMzO/5lSCV58qa7Wq5Lkjyiuy/e5PVPTzK268Z9NllzajfMlXet2d3ZC+zj8syCTRdu9MLu/j9J3rGOpz6uuzf8N3X3Z5P8xcj0/Tdabw9rfSXJ2PvtdlOuBQAAsF0IlgAAAAAb9Qsj41/NeIhjvX4ryYZ3D1nDw3PlI1Ou8J0kT+zuod1GNuqPknxxYPz2VfXDE9RPkm8kecacNV6Z5MMjcw9fCeHsNCd096c2e/HKzhtjO8CM7RKyaEesMXfuoppI8pru/sAc1+8pcPaO7j55C+ofOeGxVFf43Mj4EROvAwAAsC0IlgAAAADrVlW3TXL0yPSfdff589Tv7vOSvGKeGqs8dmT8td39L1Ms0N2XJnnZyPQDp1gjyf/ayPEjQ7r7u0lOGJk+JBPv7LAAlyV56QR13jYyvl2CJTdfY24s4LAVXjTn9adtcf13Z3YU12oHJLnjnLVXGwv0rPVaAQAA7FiCJQAAAMBG3G+NuZMmWmPsSIsNqarrJrn7Vq6xm78bGT9uovpT3duTk3xzZO7eE62xKO+eN2yz4uMj40dMUHsKNx0Z/2Z3f31BPZzZ3R+ds8Znk1w6MndxkrfOU7y7v5lk18j0reapPeALI+NjrxUAAMCOJlgCAAAAbMQ9RsbP6O7Tp1iguz+W5P9NUOp+Gf7u48LsefeEjfpwZsfVrHZMVc37/ctXMtuNYW7dfXGSU0emf3SKNRbovRPV+fTI+METvHZTOGRkfOj9tlXeN2+B7r484zt9/NPK/LzOGhk/dILau7tkZHzstQIAANjRtsN/HAMAAAA7x9gxOB+aeJ0p6t1lZPz0iX7E/ncr9b44MHVQ5j8e4yMrx9hM5cMSxhgnAAAMmUlEQVQj43eoqv0mXGer/fNEdcZ2/agkB0+0xjwOGhkf23lmK4y9ZzbqopHxj2xx/akDH2P3/loTrwMAALAtCJYAAAAA67ISOrj1yPS8x2RsRb2xEMynJqg95IKR8RvPWfdjc16/3nrXSHKzidfaSudPVGcsjJBsjx0oxsIKiwyWTHWvL15S/UUFS8ZCQAAAADuaYAkAAACwXtdPMrajxZkTr3XGBDWOGBl/QlX11I8kdx1Z73vn/DvGjmrZino3mnitrXThFEX2sHvNdtjBZayHSXfd2YNJ7nXGe97q+lO/jt8ZGb/6xOsAAABsC4IlAAAAwHpdd425r0681tcmqLFdQhLXnPP6saNatqLevCGYRbp02Q0syNjuGNdYYA9bfa932ms59pn+xkK7AAAAWBDBEgAAAGC91jrmYZHhhz2qqqsnOXCiXuZ1wJzXT31v1wrtzBuCYXpjYQWv1fKM3ftLFtoFAADAggiWAAAAAOu11jEPU/+gOu//+b9dQiVJUnNeP7ZjxWat9Vo5ymP7ESzZfuxYAgAA7FMESwAAAID1umyNubV2M9mMa815/Xcn6WJ7mPderHbwGnPfnngt5vflkfGDq2r/hXbCFQ4fGT9voV0AAAAsiP/4BAAAANZrrZ0uDpl4rXnrrbXLx28mOWXO+htx7pzXL/LeOspj+9k1Mn61JDdMcs7iWmHFTUbGdy2yCQAAgEURLAEAAADWa2znhCQ5bOK1Dp3n4u7uqrogyXUGpvfr7k/MU3/Bpr63a9Wz48L2s2uNuZtEsGQZxoIl/7bQLgAAABbEUTgAAADAen0pyeUjc7ebeK3bT1Bj7EfeW05Qe5HusMB6n594Lea3K0mPzI0FHNgiK8cPXW9k+qxF9gIAALAogiUAAADAunT35Uk+OzJ9p4mXm6Lev4yM33mC2ot0zMT1jh4ZvzjzH9vDxLr7W0nOHJmeOtDFnt0249+pfnSRjQAAACyKYAkAAACwER8bGb/LxOtMEf74p5Hxo6rqZhPUX5RjquqACevddWT89O4e2xmD5fq/I+NjISG2zljQ6+IkZyyyEQAAgEURLAEAAAA24j0j47evqiOnWKCqjsk0OzH8wxpzD5mg/qJcO8nxUxSqqusmOXZk+h83UGrsSKQpAzD8hw+NjB+10C5IxsM8/9zd311oJwAAAAsiWAIAAABsxN+vMfczE60xSZ3u/lTGdxD4tao6cIp1FuSRE9V5WJL9R+bWem1X+9bI+EEba4d1ev/I+G2qyj1frLEdSz6w0C4AAAAWSLAEAAAAWLfuPiPJ6SPTT6iq68xTf2VHjcfPU2OV/zEyftMkT5xwna32kKq64zwFquoaSZ46Mv2lJKdtoNwFI+M33khPrNs/Jzl/YHy/JHdbcC/7rKq6WpK7j0y/fZG9AAAALJJgCQAAALBRJ46MH57kuXPWfn6SQ+essbsTk1w4Mve8qvrhCdfaSvsn+eM5azwlyREjc6/q7rHjbYacMzJ+u6rab0NdsUcrR6ycOjJ93CJ72cf9QIb//XRRkvcuuBcAAICFESwBAAAANmqtsMbjq+phmylaVY9M8thNdzWgu7+W5Dkj09dI8saquv2UayZJVe1fVQ+duOz9quppm+znnkmeOTJ9aZIXb7Dkp0bGD05yrw3WYn3+dmT82EU2sY8bC/H8Q3dfttBOAAAAFkiwBAAAANiQ7r44ye+OTF8tyWur6viN1KyqByV5VZKar7tBL0nywZG56yf5UFU9YoqFqurgqnpyks8mefUUNVd5QVVt6KigqrprkjclOXDkKSd09+c2UrO7z01y9sj0H64cacS0/jbJtwfG71pVhyy6mX3UfUbGT15oFwAAAAsmWAIAAABsxkuSfGRk7oAkb6mqF1fVwWsVqapDq+rPMgs+XH23qbGjVjasu7+T5JFJLhh5yrWTnFRV76qq46tqQ+GWqjq8qh5dVW9Kcl6SP0pys7mavrLd70UleXlV/VVVXX8PfR1YVc/K7IiOseOFzkryvE329caR8aOSnFlVL6uqR1XVj1bVnarqyJHHtTa5/j6luy9M8paBqasneeCC29nnVNXhGd6x5BtJ3rDgdgAAABZq/2U3AAAAAOw83X3ZytE1H8osmLFaJfmlJI+pqjcnOTXJuZmFO743yU2T3D/Jjye55qprL1u59k0T9vuvVfWAJO9IMhZkOHbl8YWqek9mgYxzMjv252tJDsosoHFYkpsnudPK4zbZ2v955xlJXrqy7hV+OsmDq+rUJKdktnvIl1aec+OVv+OhGQ+UJLP7/LPdfdEm+/rTzF6nob/98CT/beWxJ8clOW2TPexrXp3kJwfGH5rkLxbcy77mQbly+O0Kr+/ubyy6GQAAgEUSLAEAAAA2pbvPqKqHJXlzxr9jOCjJw1ce6/XLST4+Z3tX0d0frKrjkrw1yVpHtdwwycNWHtvB55M8KrOgze4hjqsnecDKY6M6yWO7+32bbWrl9f+TJE/ebA027G1Jvpyrvn9/rKquJeCwpX5qZPw1C+0CAABgCRyFAwAAAGxad78ts90SvjVRyWd398snqnUV3f2hJHdO8p6tWmMrdPdbkjwuyeUTlLs8s1DJayeo9bQkfzlBHdahuy9LcuLA1DWTPGTB7ewzquo6Se47MHVmkncuuB0AAICFEywBAAAA5tLdb07yI0k+PUeZi5M8prv/+zRdjevuszM7KuYJmR0fM7XLMjue5lFTFu3u/5nkJzLbsWKzPpfk3t39yol6+k53PyLJzyQ5Y4qa7NGLM3uPrfb4RTeyD/n5JAcOjJ/Q3b3gXgAAABZOsAQAAACYW3d/OMnRSZ6R5LwNXHpZZkdJHDlV2GE9uvu73f2KJLfIbCeQD2Z2PMxmXZjk5CS/mORG3f2g7j55/k6vrLvfmuSOSV6UZCPHnnw1yfOS3KG7370FfZ3U3XfIbDeYZyT56yQfTfLFzHaz+e7Ua+6ruvvzGd4l5j9V1R0W3c8+4nEDYxckefWiGwEAAFiGEqoHAAAAplRVByR5QJL7J/mBJLdMckhmwY2Lkpyb5BNJ3pXklO7eSBBly1TVjZPcO8ldMgtv3DzJ92Z2zMjVMttV5aIkX0+yK7NjMM5I8pEkH+7uucITVbVrZc3Vjuvu0waef1iS45PcJ8lRSY5IcnCS/Vb63JVZuOPUJG/u7kvm6Y/to6qOyey1Xe2E7n7yovvZm1XVPZOcNjD1u939zAW3AwAAsBSCJQAAAADbwEaDJezbqurkJA9eNfz1JDfr7q8toaW9UlW9MbMjqHb3tSS36O4Ll9ASAADAwjkKBwAAAAB2nt/OVY8YOiSz45iYQFXdMcmDBqb+QKgEAADYlwiWAAAAAMAO092fTHLSwNSTquqai+5nL/WMJLVq7MtJTlhCLwAAAEsjWAIAAAAAO9Ozknx71dj1kjx2Cb3sVarqiCSPGJh6bndfvNhuAAAAlkuwBAAAAAB2oO7+1yR/MDD1W1V17UX3s5d5TpL9V419MsnLltALAADAUgmWAAAAAMDO9bwkZ68au0GSpy6hl71CVR2T5GcGpp7Y3d9ZdD8AAADLJlgCAAAAADtUd1+S5MkDU79WVTdYdD97iRfmqt+bntTd715GMwAAAMu2ejtHAAAAAGAH6e43VNWTkhy+auqWSb64hJZ2rKo6LMn7krx/1dTLl9AOAADAtlDdveweAAAAAPZ5VbUryc0Hpo7r7tMW2w0AAADAjKNwAAAAAAAAAAAYJFgCAAAAAAAAAMAgwRIAAAAAAAAAAAYJlgAAAAAAAAAAMKi6e9k9AAAAAAAAAACwDdmxBAAAAAAAAACAQYIlAAAAAAAAAAAMEiwBAAAAAAAAAGCQYAkAAAAAAAAAAIMESwAAAAAAAAAAGCRYAgAAAAAAAADAIMESAAAAAAAAAAAGCZYAAAAAAAAAADBIsAQAAAAAAAAAgEGCJQAAAAAAAAAADBIsAQAAAAAAAABgkGAJAAAAAAAAAACDBEsAAAAAAAAAABgkWAIAAAAAAAAAwCDBEgAAAAAAAAAABgmWAAAAAAAAAAAwSLAEAAAAAAAAAIBBgiUAAAAAAAAAAAwSLAEAAAAAAAAAYJBgCQAAAAAAAAAAgwRLAAAAAAAAAAAYJFgCAAAAAAAAAMAgwRIAAAAAAAAAAAYJlgAAAAAAAAAAMEiwBAAAAAAAAACAQYIlAAAAAAAAAAAMEiwBAAAAAAAAAGCQYAkAAAAAAAAAAIMESwAAAAAAAAAAGCRYAgAAAAAAAADAIMESAAAAAAAAAAAGCZYAAAAAAAAAADBIsAQAAAAAAAAAgEH/H8FHNkCBuoD1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2275x1750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6.5, 5), dpi = 350)\n",
    "\n",
    "primary_key = \"baseline\"\n",
    "src_ind = 0\n",
    "ylim = 8e-15*np.r_[-1, 1]\n",
    "\n",
    "plot_models = [\"baseline\", \"short\"] + [\"flaw_{}\".format(dz) for dz in flaw_dz]\n",
    "\n",
    "for i, key in enumerate(plot_models):\n",
    "    \n",
    "    label = key if key in [\"baseline\", \"short\"] else \"{:1.0e}m\".format(float(key.split(\"_\")[-1]))\n",
    "    \n",
    "    plot_charge(\n",
    "        ax=ax, key=key, color_ind=i-2 if key != primary_key else None, \n",
    "        color = \"k\" if key == primary_key else \"0.5\" if key == \"short\" else None,\n",
    "        src_ind=src_ind, label=label, linestyle = '-.' if key == \"short\" else None, \n",
    "        lw=3 if key == \"short\" else None\n",
    "    )\n",
    "\n",
    "ax.set_xlabel('depth (m)')\n",
    "ax.set_ylabel('charge per unit length (C/m)' if i == 0 else 'secondary charge per unit length (C/m)')\n",
    "# ax.set_title(\"({})\".format(ascii_lowercase[i]))\n",
    "ax.grid(which='both', alpha=0.4)\n",
    "    \n",
    "ax.set_ylim([-3e-12, 7e-12])\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('../figures/casing_charge_flawdz', dpi=350, bbox_inches=\"tight\")\n",
    "fig.savefig('../arxiv-figures/casing_charge_flawdz', dpi=200, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='8'>Sun Oct 28 12:15:48 2018 PDT</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Darwin</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.15.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numpy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.1.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>scipy</td>\n",
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       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.9.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>SimPEG</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.28.5</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>cython</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.5.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>properties</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.2.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>vectormath</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.4.0</td>\n",
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       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.1.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>pymatsolver</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>7.0.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>7.4.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>ipywidgets</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>3.0.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>matplotlib</td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
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       "    <td style= border: 2px solid #fff;'></td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='8'>3.6.6 | packaged by conda-forge | (default, Jul 26 2018, 09:55:02) \n",
       "[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='8'>Intel(R) Math Kernel Library Version 2019.0.0 Product Build 20180710 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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