diff --git a/examples/jupyter/expansion-filters-zernike.ipynb b/examples/jupyter/expansion-filters-zernike.ipynb
deleted file mode 100644
index f29137e2dc..0000000000
--- a/examples/jupyter/expansion-filters-zernike.ipynb
+++ /dev/null
@@ -1,1142 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, Zernike polynomials provided in OepnMC to represent continuous functions of space or angle can be reconstructed from the tallied moments.\n",
-    "\n",
-    "In this example, we will determine the spatial dependence of the flux along the radial direction $r'$ and / or azimuthal angle $\\theta$ by making a Zernike polynomial expansion. Let us represent the flux along the radial and azimuthal direction, $\\phi(r', \\theta)$, by the function\n",
-    "\n",
-    "$$ \\phi(r', \\theta) = \\sum\\limits_{n=0}^N \\sum\\limits_{m=-n}^n a_n^m Z_n^m(r', \n",
-    "\\theta) $$\n",
-    "\n",
-    "where $r'$ is the position normalized to the range [0, r], and the azimuthal lies within the range [0, $ 2\\pi$]. \n",
-    "Since $Z_n^m(r', \\theta)$ are known functions, what we need to do is to determine the expansion coefficients, $a_n^m$. By the orthogonality properties of the Zernike polynomials, one can deduce that the coefficients, $a_n^m$, are given by\n",
-    "\n",
-    "$$ a_n^m = k_n^m \\int_{0}^r dr' \\int_{0}^{2\\pi} d\\theta Z_n^m(r',\\theta) \\phi(r', \\theta).$$\n",
-    "$$ k_n^m = \\frac{2n + 2}{\\pi}, m \\ne 0. $$\n",
-    "$$ k_n^m = \\frac{n+1}{\\pi}, m = 0.$$\n",
-    "\n",
-    "Similarly, the problem reduces to finding the integral of the flux times each Zernike polynomial."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import openmc\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To begin, let us first create a simple model. The model will be a pin-cell fuel material with vacuum boundary condition in both radial direction and axial direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define fuel \n",
-    "fuel = openmc.Material()\n",
-    "fuel.add_element('U', 1.0, enrichment=5.0)\n",
-    "fuel.add_nuclide('O16', 2.0)\n",
-    "fuel.set_density('g/cm3', 10.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define surfaces used to construct regions\n",
-    "zmin, zmax, radius = -1., 1., 0.5 \n",
-    "pin = openmc.ZCylinder(x0=0.0, y0=0.0, r=radius, boundary_type='vacuum')\n",
-    "bottom = openmc.ZPlane(z0=zmin, boundary_type='vacuum')\n",
-    "top = openmc.ZPlane(z0=zmax, boundary_type='vacuum')\n",
-    "\n",
-    "# Create three cells and add them to geometry\n",
-    "fuel = openmc.Cell(fill=fuel, region= -pin & +bottom & -top)\n",
-    "geom = openmc.Geometry([fuel])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the starting source, we'll use a uniform distribution over the entire box geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "spatial_dist = openmc.stats.Box(*geom.bounding_box)\n",
-    "settings.source = openmc.Source(space=spatial_dist)\n",
-    "settings.batches = 100\n",
-    "settings.inactive = 20\n",
-    "settings.particles = 100000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Defining the tally is relatively straightforward. One simply needs to list 'flux' as a score and then add an expansion filter. For this case, we will want to use the `SpatialLegendreFilter`, `ZernikeFilter`, `ZernikeRadialFilter` classes which multiplies tally scores by Legendre, azimuthal Zernike and  radial-only Zernike polynomials evaluated on normalized spatial positions along radial and axial directions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a flux tally\n",
-    "flux_tally_legendre = openmc.Tally()\n",
-    "flux_tally_legendre.scores = ['flux']\n",
-    "\n",
-    "# Create a Legendre polynomial expansion filter and add to tally\n",
-    "order = 10\n",
-    "cell_filter = openmc.CellFilter(fuel)\n",
-    "legendre_filter = openmc.SpatialLegendreFilter(order, 'z', zmin, zmax)\n",
-    "flux_tally_legendre.filters = [cell_filter, legendre_filter]\n",
-    "\n",
-    "# Create a Zernike azimuthal polynomial expansion filter and add to tally \n",
-    "flux_tally_zernike = openmc.Tally()\n",
-    "flux_tally_zernike.scores = ['flux']\n",
-    "zernike_filter          = openmc.ZernikeFilter(order=order, x=0.0, y=0.0, r=radius)\n",
-    "flux_tally_zernike.filters  = [cell_filter, zernike_filter]\n",
-    "\n",
-    "# Create a Zernike radial polynomial expansion filter and add to tally \n",
-    "flux_tally_zernike1d = openmc.Tally()\n",
-    "flux_tally_zernike1d.scores = ['flux']\n",
-    "zernike1d_filter          = openmc.ZernikeRadialFilter(order=order, x=0.0, y=0.0, r=radius)\n",
-    "flux_tally_zernike1d.filters  = [cell_filter, zernike1d_filter]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The last thing we need to do is create a `Tallies` collection and export the entire model, which we'll do using the `Model` convenience class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tallies = openmc.Tallies([flux_tally_legendre, flux_tally_zernike, flux_tally_zernike1d])\n",
-    "model = openmc.model.Model(geometry=geom, settings=settings, tallies=tallies)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running a simulation is now as simple as calling the `run()` method of `Model`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011286925958112105+/-6.687581616485285e-06"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that the run is finished, we need to load the results from the statepoint file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint('statepoint.100.h5') as sp:\n",
-    "    df1 = sp.tallies[flux_tally_legendre.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We've used the `get_pandas_dataframe()` method that returns tally data as a Pandas dataframe. Let's see what the raw data looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>spatiallegendre</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.543425</td>\n",
-       "      <td>0.000599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000635</td>\n",
-       "      <td>0.000523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.055989</td>\n",
-       "      <td>0.000342</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P3</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000053</td>\n",
-       "      <td>0.000284</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000749</td>\n",
-       "      <td>0.000230</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P5</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000111</td>\n",
-       "      <td>0.000149</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000692</td>\n",
-       "      <td>0.000177</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P7</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000064</td>\n",
-       "      <td>0.000152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P8</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>0.000142</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P9</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000201</td>\n",
-       "      <td>0.000119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>P10</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000051</td>\n",
-       "      <td>0.000112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    cell spatiallegendre nuclide score      mean  std. dev.\n",
-       "0      1              P0   total  flux  5.43e-01   5.99e-04\n",
-       "1      1              P1   total  flux  6.35e-04   5.23e-04\n",
-       "2      1              P2   total  flux -5.60e-02   3.42e-04\n",
-       "3      1              P3   total  flux -5.27e-05   2.84e-04\n",
-       "4      1              P4   total  flux -7.49e-04   2.30e-04\n",
-       "5      1              P5   total  flux  1.11e-04   1.49e-04\n",
-       "6      1              P6   total  flux -6.92e-04   1.77e-04\n",
-       "7      1              P7   total  flux -6.41e-05   1.52e-04\n",
-       "8      1              P8   total  flux -1.39e-04   1.42e-04\n",
-       "9      1              P9   total  flux  2.01e-04   1.19e-04\n",
-       "10     1             P10   total  flux -5.05e-05   1.12e-04"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the scaling factors for expansion coefficients will be given in Python API, thus, we do not need to multiply the moments by scaling factors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a_n = df1['mean']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Loading the coefficients is realized via calling the OpenMC Python API as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phi = openmc.legendre_from_expcoef(a_n, domain=(zmin, zmax))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot it and see how our flux looks!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux [n/src]')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = np.linspace(zmin, zmax, 1000)\n",
-    "plt.plot(z, phi(z))\n",
-    "plt.xlabel('Z position [cm]')\n",
-    "plt.ylabel('Flux [n/src]')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A rough cosine shape is obtained. \n",
-    "One can also conduct numerically integrating using the trapezoidal rule."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.543424143829605"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.trapz(phi(z), z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cases show how to reconstruct the flux distribution Zernike polynomials tallied results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint('statepoint.100.h5') as sp:\n",
-    "    df2 = sp.tallies[flux_tally_zernike.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>zernike</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z0,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.543425</td>\n",
-       "      <td>0.000599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z1,-1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000236</td>\n",
-       "      <td>0.000398</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z1,1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000126</td>\n",
-       "      <td>0.000325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z2,-2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000104</td>\n",
-       "      <td>0.000206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z2,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.064291</td>\n",
-       "      <td>0.000404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>61</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z10,2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000130</td>\n",
-       "      <td>0.000099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>62</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z10,4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000057</td>\n",
-       "      <td>0.000092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>63</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z10,6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000048</td>\n",
-       "      <td>0.000109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>64</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z10,8</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000056</td>\n",
-       "      <td>0.000092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>65</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z10,10</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000046</td>\n",
-       "      <td>0.000098</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>66 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    cell zernike nuclide score      mean  std. dev.\n",
-       "0      1    Z0,0   total  flux  5.43e-01   5.99e-04\n",
-       "1      1   Z1,-1   total  flux -2.36e-04   3.98e-04\n",
-       "2      1    Z1,1   total  flux  1.26e-04   3.25e-04\n",
-       "3      1   Z2,-2   total  flux -1.04e-04   2.06e-04\n",
-       "4      1    Z2,0   total  flux -6.43e-02   4.04e-04\n",
-       "..   ...     ...     ...   ...       ...        ...\n",
-       "61     1   Z10,2   total  flux -1.30e-04   9.86e-05\n",
-       "62     1   Z10,4   total  flux -5.70e-05   9.21e-05\n",
-       "63     1   Z10,6   total  flux -4.79e-05   1.09e-04\n",
-       "64     1   Z10,8   total  flux -5.59e-05   9.23e-05\n",
-       "65     1  Z10,10   total  flux -4.58e-05   9.76e-05\n",
-       "\n",
-       "[66 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot the flux in radial direction with specific azimuthal angle ($\\theta = 0.0$)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux')"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = np.array(df2['mean']) \n",
-    "zz = openmc.Zernike(z_n, radius)\n",
-    "rr = np.linspace(0, radius, 100)\n",
-    "plt.plot(rr, zz(rr, 0.0)) \n",
-    "plt.xlabel('Radial position [cm]')\n",
-    "plt.ylabel('Flux')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A polar figure with all azimuthal can be plotted like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = np.array(df2['mean'])\n",
-    "zz = openmc.Zernike(z_n, radius=radius) \n",
-    "#\n",
-    "# Using linspace so that the endpoint of 360 is included...\n",
-    "azimuths = np.radians(np.linspace(0, 360, 50))\n",
-    "zeniths = np.linspace(0, radius, 100)\n",
-    "r, theta = np.meshgrid(zeniths, azimuths)\n",
-    "values = zz(zeniths, azimuths)\n",
-    "fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\n",
-    "ax.contourf(theta, r, values, cmap='jet')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sometimes, we just need the radial-only Zernike polynomial tallied flux distribution. \n",
-    "Let us extract the tallied coefficients first."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint('statepoint.100.h5') as sp:\n",
-    "    df3 = sp.tallies[flux_tally_zernike1d.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>zernikeradial</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z0,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.543425</td>\n",
-       "      <td>0.000599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z2,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.064291</td>\n",
-       "      <td>0.000404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z4,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000601</td>\n",
-       "      <td>0.000223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z6,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000454</td>\n",
-       "      <td>0.000227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z8,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>0.000166</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Z10,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000102</td>\n",
-       "      <td>0.000161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell zernikeradial nuclide score      mean  std. dev.\n",
-       "0     1          Z0,0   total  flux  5.43e-01   5.99e-04\n",
-       "1     1          Z2,0   total  flux -6.43e-02   4.04e-04\n",
-       "2     1          Z4,0   total  flux -6.01e-04   2.23e-04\n",
-       "3     1          Z6,0   total  flux -4.54e-04   2.27e-04\n",
-       "4     1          Z8,0   total  flux -1.15e-05   1.66e-04\n",
-       "5     1         Z10,0   total  flux -1.02e-04   1.61e-04"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A plot along with r-axis is also done."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = np.array(df3['mean'])  \n",
-    "zz = openmc.ZernikeRadial(z_n, radius=radius)\n",
-    "rr = np.linspace(0, radius, 50)\n",
-    "plt.plot(rr, zz(rr)) \n",
-    "plt.xlabel('Radial position [cm]')\n",
-    "plt.ylabel('Flux')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can also re-construct the polar figure based on radial-only Zernike polinomial coefficients. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9SSL1zgRiPKx25kMp/YQQUjzhtfAWMBNANHYMDRjTsD9HhcFguMZsrnp4dPT34paWgqkvToIE0kUAYpZF2YgL1cNDqHIOw+D1okepxIEsNb7R6fFyYXFMT9S35Nwfcwxm8byo77t/4JbJf0gIhmQZGJJlYG+29ruXx3s0JSMu1A0N4oed7ZCyLFpUWWhQa7A3OxsDGfKY4+aS0OeBdyGEPtuTCIFhZNiz51ektvaF5QzzYjshZDGltCueqggh+QBOA7AewE3TXC7G2OgAixi/2zOVOdcjAIBxEbwV6hGMv7YewMUY6yYeSym1j/cIXgXQBaAHY72DvePXXwLgZwCep5RuirJeSX5+/tMKRcVFbW3ryZR5guaCAMaf+OsGB7FgaBAG3yias9SoV2ejQaOBPUMe1QRtPA3+ZNRsnoe9xzRwVh4wjTAASFkGpS4X5jmHUTM0CHUggEa1Bru0OajXZGNUktrntaT0EKbpHRQWboHHc69vYKDjWIZhvoy1eELIKwA2AMjCeC9/vEewCoAPwIcAbqeU+gghEgDPAygC8NO5cLaCIILDf3YHADml9FeEEDUAllI6QghZC+AhSmlFnHVqjcayb4zG40r37LmGTHlw/CyWgIiymDc8jGUDDtQODcEml2OXNge7srWwyRVTNvxcNviTwYcIIjGVHKQsg0qnE3WDA6gdHoRbLME2nR5bc/Tol6eut5BqIWRnd0KpvI3xeq3X9vf3PxFtkYSQ0wGspZReEz7cSwixALACkAH4M4Dm0BzgXEMQweE/KwLw9iQ/awOwjFLqiLG+eXp90dda7bVZYymjp4BnCaRCAGKWRe3QII6y96FkxIVGtQZbdXrszc5GQBR5bD8ZjX4kkiWCiUwlBp3Xi2UDDiztd0DJMNiq0+MLvRF9CkUSI/wO3oUwhQykUjdKSu6kw8Pdr9ps+8+nlDLTFUcI2QDg/wEIApADUGNs4cdFYdccg0nmA+cCggjG/l5BKW0a//P1ANZQSs8hhJgB2CillBByBMYmj4piWcEgFouP1OmKNmdkrJd2ddVMfTGPEki6AChFtXMY3+uzoco5jD3ZWnxuMOJAlhp0kqf+VDX+4aRKBBOZTAzKYADL+x04yt4HORPEFr0RnxmMGJZlJDlCnoUw5VARi8WL/0i7uz/Z3dd3YAWl1BttsRN7BJTSXkIIAfAgAC+l9PYEI5+RzDkREEJeAHAMAD0AG4BfAVgLoApjk0PtAK6ilHYTQq4DcDXGniRGAdxEKf0i2rpUKt0Ps7IML/r9D4gHBvInv3AW9QK0Ph+OsfVilcOOVpUKnxjNqNdkR2z806Hhn0i6iGAikcSQ5fdjpcOOo+02uCRS/NdsxnatDowoeXsYUtk7qKv7O3p7X+6y21sWRLv5bIIIPgJgwNiE8A6Mfe/nZDbUOSeCZKHTFf0sM1O7qb//IZHHo538wlnQCyCUYvFAP07q7UYGy+JjoxlfGgzwiQ+f5EzHxj+cdBVBOJGkkO9241hbLxYMDWCHVof3LXlwJHE+IVW9g/Ly9zA09Nigw9FWSynt4S+I2Y0gAo4hhBCjsfwPWVnG69vaNhGGUU5+MU8SSJYAMgMBHGvrxeo+G/apNXgvNw89ykNXQqV7wz+RmSCCcCZKQcyyOKLfgZN6u+GWSPCuJQ97srVJSY+RKhkUFHyJ0dENHoejfQmltJG/IGYvggg4hBAiMpkqX83JKT+zoeHXhNIp1rzPYAkYvKM4o6sTlc5hbDZZ8LHJfNgSx5kmgBAzTQQhIvUSikdcOKWnG4XuEbyXm4fPDKakDBvxJoQpZGCx1CMQuMM/nsFUSEsRI4IIOIIQIjGZKj42m1ccuXPnzZh0eegMFkChewRndnZA7/PirbwCbNXpDxv7n6kCCDFTRRDORCmo/X6c2tOFpQMObDZZ8KHZEnHYjktSIQO9vh1i8U3MwED7yX6//0N+ApidCCLgAEKI1Gis+MJsPnbprl3XkEk3I85QCRSNuHBeRxvELIs3CoqwT605ZKhhpjf+4cwGEYQTLgV5MIgTrD1YY7PiM6MJ7+bm8SqEVMhAo7FBqbyeGRhoO83r9b7HTwCzD0EECTImgfKvLZaTFu7ceeXkA7EzUAIF7hGc294GGcvg5aISNGcd+sWeTQIIMdtEECJcCFKWwQm9PTjW1ouPjWb8x5LHWa6mSPAihClkkJXlgEp1DTM83Hmm2+1+m/vKZx+CCBKAECK1WCq/Xbr0mNq3374GlCavJ8CnAHReLy5ob0G234+XC4vRqMk+5OezUQAhZqsIQoQLQcYwOKm3G6v7rHjPko+PzJZJ93gkSrJlUFFhRTB4I7XZOs4QZDA9ggjihBAiMRrLt+Tmnry4uvpcotMF8eij+sNlMIMkoAgGcXZnO2qHBvFicQl2anWH/Hw2CyDEbBdBiHAhKIJBnNXVjgWDg3ipqAQ7tDm8rTLiXAgRZJCf78fNN9vx2GNiuN03Mg5H52k+34gwTDQFggjiYEwCFV+Yzccv27XragJQXHDBEA6TwQyRAKEUJ1h7cGJvN97JLcBmk/mQJ8O5IIAQc0UEIcKFoPN5cX5bKzR+H54trUB35hRJEROATxmEJLBpkwFdXbKDw0QOR4swgTwFgghihBBC8vPzX9frT/r+jh03hj02TZDB+Qs5r5sPCZS5nLi0uQn1mmy8Vlh0yOThXBJAiLkmAuDwVUZlLicuaTmARrUarxQW8zKhzMdQUf6nWw+RQAiNxga5/BrGZjtwpLC0NDKCCGIkPz//j1rtimv37PllhAyi4zJYrsaj3ywC5SiVOR8CyAwEcGFbM/ReL/5aVole5Xcb3+aiAELMRRGECBcCoRTHWXtxcm8XXisoxld6Ay/DRVwJIV/txM0rt2HTL7MOkUAIg6EDwA1+u721Tth0djiCCGLAbDav02jmb2hq2kQonWSVxQULcEFtA3QKLycy4FwClGKFw44fdrbj9YIifDnhCz6XJQDMbRGECBeCKhDARa3NyAoE8JfySgxmcJ/cLlEZHJTAV0vR5VRPOok8tunsdo/D0V4hpKM4FEEEUaLVai/UaMqf6+x8TMSyk3wZDs4JUE5kwLUEsv0+XHZgPzwSCZ4rKT/ktK+5LoAQNZvn4fSj/+eQ1zaK16UomtQSLoQFgwO4qLUZ71vy8KHZwnnvIF4ZHCaBEJPIYCwdxX2DDkdbabSJ6uYCggiiQCaTHW8wVLzvcDwh8vsn+cAeNjGcmAy4lsBRfTac2dWBv5WUHrIaaLYKYB2zMa77tn66AcuOviOue2erMEJCyGAYnN/WgjyPG49XzuP8SM1YZTCpBEJMIoOKincxNPREp93eUhlLCuvZjCCCaSCELDaZyr8eHX1M4nSaIl806eqg2GXAtQAyAwFc3rwfASLCX8sqDskJNNMlEG9jPxWJiGAyZoMgwnsH1UODuLTlAN7IL8QXxkm+E3ESrQymlQAwbQprq/XVXX19B5ZEc7jNbEcQwRQQQnL1+uJmieRBudVaGvmiaZeIRi8DriVQMzSIS1oO4NWCImwxGA++PhMFwEejHwk+RBCJmSiHiXsPftK8HyIKPFVeAY9kigSLMTKdDKKSQIgpZLBo0R+ow/GfZzs7O/9n0ovmCIIIJoEQIjcYStuzsm41trQcGfmiqPcJTC8DLiVAKMW57a2odDnxaGX1wQm+mSaAZDX+4SRLBBOZSWIIF8Iquw1ndXbgiYoqtGRxtyR0MhnEJIEQk8qARWXlTdTh2Hl1LGcgz0YEEUSAEELM5srPcnLOOrK+/sLIF8W8WWxyGXApgWy/D9c17kODWoNXC4sPbgybKRJIReMfTqpEEM5MkEK4DEyjo7iusR6fGU14z5LH2UTyRBnEJYEQk8hAKnXDYLiMsVobj2YY5st4Y53pCCKIgNFY/juDoe6W+vq7I3+i494xfLgMuJTA/KFBXNrShOdKyrFHm3Pw9XSWQKob/omkgwjCSXcphIQgZRlc3NIMdcCPP1XMg1fCzSa0kAwSkkCISWSQnd0JieQ6n8PRVk4p7Yo31pmMIIIJZGZmnmEwzPtnZ+cTIpY9fGNK4mkjvpPBkxeXcpPki1Kc0tONlY4+/KG6BkOy9B4KSrfGP5x0E0E46SqF8N7BUX02nNbdiYfm1cCmUHBSvvHnSFwCISaRQWHhFng89zocjrYiSqknsUpmHoIIwiCEVBuNZTs9nielIyO6yBdxkj+I4qJbdkLn9+HxinkJyUDCsrj8wH5QAjxVVong+AlU6SiBdBZAiHQWQTjpKIWQEEpGXLhqfwOeLynD7rCeaTzkud24sbEeD1XNR+s6CxdhTiqD2tp/wG5/5RubrWkFnWMNI//n1s0QCCEqvb74K4nktzxLAJA94sJLRSXol2XgqqYGkDg/cxq/H7/YvQMtqiw8UV6VthJYx2ycERKYSaTjexr63LWqsrC+diHO7mzH2u5OIM7Pd7gEujMzeT+Aac+e82EwLFhmMJTey2tFaYjQIxjHbK76zGA476g9e86NfAFnEgj7MFOK89pb4+oZ5HrcuKGhHs+XlKXtfEC6NVTRMFN6BBNJpx5CqGcgZln89EAjvGIxnimtiOnzPVEC4XCSn2iSXoFY7IfZfCnb3b131VxKUCeIAIBOp7vSaDziTw0NGzmeHD6UiE80ccggtKHnj1Xz0TX+JUkXAaRr488wUgR8Gvh9Gvj9ajBBJYJBBZigAiwrBWXFoFSE3s7jYCn8EIQwEIkYiMU+iCWjEEtGIZWOQCpzQpYxBKlsBISk33cnXYRwcN6AUvywow2l7hE8XDUfvihOQptKAgCHmUsnkYFO1wpCbvQ4HO15cyUNxZwXASGk2mSq2DU4+IzE71dFvogDEUzZrY1BBqvsNqzt7sKm+bVpNSmcDgLw+7LgGcmHZyQX7pE8eD1G+Lw5oFQEkdgPWcYwZDInpBlOSCQeiCWjkEhGIRIFQAgDImLQtOdSlNc8C0oloKwYDJMBJqhAMKhAMKCC36dGwK9BwJ8FSgkkklHIlQ4olFYoVV1QqnqgzOyFSBxM6XuRDkIIn0RebbPiBGsPfl9dC5cswiKMcaaTQAi+ZVBZ+SaGh5/babPtXzwX5gvmtAgIIQq9vrhHJLo3u6+vJvJFfEsgRBQyOKG3G0c47Ng0v/ZgjvhUSyBVAvD7s+AcqIRzuBwjw8XwjuohyxiGUtWNTFU3lKpuKJQ2yOQDEInYqMuNdWgoGFDAO2qAx22BZyR3XERjk5qZWZ3I0rRCrd0Plbotpji4ItVCmJi47oK2FmyaX4v+CHmKopVACL5lUF19F+3r2/ELh6Nt1s8ZzGkRmM1V7+v1Z524dy9Xm8YOJ6YJrilkcGZnO8pdLjw0b37aTAonUwJ+XxYG+2sxaK+Da7gEEsko1Dn7oc5uQpamFRnyfk72MXE1R8AyErhdBXANl2J4sAojziJIpCPI1tUjx7ALWZrmpIohXYRQ4RzG5Qf24w/zag45AyNWCYTgUwZisRdG4yVMb2/DMkrpDm4qSk/mrAiys7MvNJkW/9/+/Q9GOGAG/M4LTMVEGQD4cVsLNAE/nqioAktSL4FkCIBSAtdwKRy2ZRi0L4BIFIBWvwda/W5kZTdDJOInTxifk8V+nwaD/fMxaK+Dc6gMikwb9Kat0Bm/hSzDxUudE0mlEEIyKHSP4LrGfXi0shrtKlXcEgjBpwxMpv1gmFtHHI42M6XUzU1F6cecFAEhxKzXF7f7fM/KXK7syBcluzcQTkgGPi9cUhkyWAZPl1WmPF0E3wKglGB4sBJ93UdhaKAaKnUb9KatyDHuhESSnGzByVo1RCngGcmDw7YU/X1LAABGy1cwWL5EhnyY9/pTJYSQDMyjHty0by9eLijGuZ1tcUsgBJ8rierqnkdf37/e6u1tOCPxStKTOScCQggxmSp3q9VX1TQ1HR/5olRKIATL4je7tkMVDODmJUeATfFwEJ8ScI/kwtp5LPr7FkKd3QJj7ufI1u1NyZh6qpaP+rwa2HtXoq93FUSiIMz5n8Bg3gKxxMdbnamWwcL+ftzasAcPVc7HNwZDQmXyO1/Aorj4p2xHx7bjGIb5mJuK0os5JwK12nhZbu6yJxsb7+NtqWjCEqAUF7U2I4Nh4JJIoHAIwX8AACAASURBVAv48XjFPNys25RwbLHClwAYRoa+7qPQ27UGEskozAWboTduS/lqm3TYR+D16GHtWgO7dQWysg8gt/ADqLNbeKsvFUJ4ofNq3NhYj78VleLC9hY8UlWNjsxJVu1FCZ+9Ao2mE1LptR6Ho904G4eI5pQIQkNCLtdzMp9Pc/gF6SABAOe2t0Lr9+HJ8ipQAOe1t2IN2YbWRe1I8AjkqOFLAKMeA7rbTsKAfSGMuV/BUvBfZMgHeakrHtJBBCEoJRjqn4/utpPh92uQV/Q+DJaveJkfSaYM5E45yraV4Bflx6A7M/PgMNGDEyaQ44FPGVRX/x0u199e7OzsPD/xStKLOZNiYjy19AcazQ2RJZAmnNrdiVyPZ0wChACEoHDxa/DL/SjZUQQkwdt8SMA1XIy9396IfTuuhTq7GcuOvgPFFa+llQTSDUIotPq9qF32AOYvfhhuVwG2fnofOppPRzDI7TGRyVoBFpJA89JWXFDwJwCAVaHEI1XV+FnDXuh8ic0FcZKGYpIHwn37zodEYjlXLBYfk3gl6cWc6RGo1cbLCgqOeLK+/t60HRJaY+vFKnsffj9/AZiJcwIUyGuwQOaV8doz4LpBGB6oRFvTORCJAigsewOanP2cls816dQjiEQwmIHejuNg7ToWOtNWFJS8DamM25EKvnoH4RLwqr9r8MOXlv6kuQn31tRNueksGhLuGUzSK8jJ6YRIdK3H4Wg3UUpHEqskfZgTIiCE6A2Gkm6P5xmZ2x1hlVAaSGDxQD/O7GzHvbUL4R/fhn/YxDCPMuBaAM6hMrQ2ngux2I/iypehUndyWj5fpLsIQrCsGNau1ehuOxV68xYUlPwbEukoZ+VzLYPJJBAiJIO6wQGc09GG9bULo0pHMRV8yaCu7u8YHPz73zo6Oi5KrIL0YU6IwGKp/jA7+6LjGiKt/koDCZSMuHBlUwN+W7sII9Kxs18nXR3Egwy4lIDHbUJLw4/BMjKUVL2ILE0bZ2VziXqdP+Lrmxfdj2N23HLY686NiT2h8gXLitHbcRy6209EbuGHyC36gNM5BC6EMJ0EQoRksNpmxYp+O+6vrk34vA5+ZMCgoOBSprNz12JK6e7EKkgPZr0ICCFHFBcv+7Kt7UkRXxvHEhGBzuvFuvpd2FS94OBBHtMuEeVQBlxJIODPRFvTOXAOlaO06gVo9fWclBsvkzX00zGZCKIhlbJgghnoaDkdDttylFS8DJ1pG1cnRiYkg2glECIkgx+2t0IdDOCvpRUJHX3JV6/AYtkFhrmnt6/vQN5syEU0q0VACBEbDKU24Hc6u73i8AtSLAFFMIhf7t6Bv5ZVoEk9NoEd9T4BDmTAhQQoJbB2rUZny2koLHsLprxPk5qVM94GfzISEUEkki0HnzcbLQ0XIuBXoaLmGSgybZyUG48MYpUAcGjW0mv2N6BNpcK/8wpirjscvmQwf/7dtLPzg8ucTudfE6sg9cxqEeh0uhtzc095cM+edbxMECciAUIpbqnfg8+MRnxpMAGIY7NYnDLgqhfgduVh/+7LodK0oaTyJU7HqKeC68Y/HK5FMJFkiWFooAoH6i+GzrgdReWvczJcFIsM4pFAiPDzDO7csxOvFxQlfNIZHzJQKgehVF7iG5845n87OI/MWhEQQnQGQ0nP8PDzsojppVMoAQA4r60FYlC8UFwGIIEdwzHKgAsJsKwYHQfOQn/fYlQu+Avv8wB8NvwT4VsE4fAtBZYVoav1NPT1rEJl7VNQa5sTLjMaGSQigRAhGaj9fvxiz05sqq5N6Axk/noFL2Jg4LV/9vbuOzuxClLLrN1HUFhY+LROd1lkCaSYFY4+lLhH8I+iUgAJpo0gQPe83qj2GXAhgRFnAbZ/cQ+IKIjFR/6KNwmo1/kP/pqt8P1vFIlYFJa9ifmLH0Zzw4/RvO9CsIwkoTKn+wxxIQHgu++EUybDnyrn4caGvZAH4991nvD+gkkeHOvrz4FEIj2DELIgsQpSy6wUASFkISH60xsaTot8QQp7A7keN87ubMcfq6oTXhFxkChkkKgEKCXoaD4djbuuxLyFj6Oo/A1edrjO9sZ/MviUglJlxaKVv4UsYwjbv7wbbld+QuVNdl4yVxIIEX4G8tt5Bbi6qSHu8485IWK7IYZIdIvYZKp4K+nxcMisFIHJVPkacAMvq4QSkYCMYXBd4z48VlkNj2SaZaKxMoUMEpWA36fBzq/vQMCvxuJVdyMzqyvBYA9nrgogEny8F4RQFJS+jaq6J7Bv59Xobjsx4TY1/HPFtQRChL4fnxtNcEqlOLm3O+6yONl1HIGOjkXQaosKxGLxCbxUkARmnQgIIWs0mtzi9vbFnJed6AfpJ81N+MCSezC5FueZRCPIIFEJDDrmY+eWO1FY+ibKqv/OaVK4uTD8kwh8vDcqdScWr7obI85i1G+/MeFUFeuYjbxJIEToe/JcaTm+12dDiSv+sxv4GiJyOK4jOl3xi4SQGdmmzsigJ4MQQozGslcGBq6P/O/i6LCZeFhts0JCWXxkGjvGkLd00mEy+P52JyiNb/iJUqD9wJloazoHdUdsQI6Bu30zQuMfG1y/X2JxAFV1T0Jv2oodX/4Kblde3GW5XXk4clsmbxIIJyAS45Gq+biyqQHKYIDXuqYkQjvicJTAYFikVSqVP0pBRAkzq0SQkZFxtk5Xo3M4yjkvO5EnCdPoKNb2dOLJ8sqENsdEDQEurPgZMuT9aNh1VcwyYBgZ6rffCL8vGwtX/JazxHCCABKD6/fPlPc5qhc9ivrt18NuXRbz/W5XHuq334j5ix/CjZn3cBZXJEIPTjaFAm8UFOKyA01xl8XXEFFb29VEqTQ8SQiR8lIBj8waERBCJFlZlme6u6/hLalcPIgoi6v378OT5VVJO3B+HbMRhAAllS/FLAPvaA52fHkXcgw7UFHzLCeHwwgC4BYu38/MrC4sWvkb9LSfiLams6OeNwiXQGbW2Lg93xlMQ9+bLw0mUAKssse/WY6PISKPxwiz+djMnJycaxIrPPnMGhFkZ2dfkZt7VJbTaeG87EQ+NGd1dmB3thbNWWPrmJMhgRCxysA1XITd39yO8vnPwVLAzUFMggD4gyshSGVuLFi+EX6vFo27rgLLTp3sLZIEQiQrnfVTZZU4q7MDOQmmreaa5ubLIBJlbSSEJHawQpKZFSIghEglEs2m1tafRr4gRb2BEpcLdYMDeL2wCEByJRAiWhn09y1Ew66rUbN0EyepooVeQPLg4r0WiVhU1D4NZVYndn+zDsFA5M1bU0kgGYS+Q6MSCZ4pLcdV++NfUsrHENHoqBp5eWdkmEym1JwDGiezQgQqle4yi2WNYtKD6BMg3g+LmGVxeXMjnqiYBzYJCwmmehKbTga2nlVob/ohFq5YDyUHuWkEAaSGRN93QoDC0rdhKfgIO7++A35/1iE/j1YCyRoi2petRbcyE8fZeuMuKyEZTPKA2dDwYzCMfB0hJCP+wpPLjBcBIUSkUKh/39JySeQLUtQbOLOrA1/rDAeP3uOzNxDNF28yGfS0H4/ejuNRt+JeyGTxL8sDhF5AOsDF+2/M/QrFFa9i15Y74B0dy/ETa08gWUNE/yguxck93dD6fEmpLxp8vkyYzSfItFrtlZF+Tgg5hRDSSAg5QAi5PcLPLyWE2AkhO8Z/Xc53zDNeBFKp/AcGw5JMt1t/+A9TtHksz+3GkoF+vDWeNTHVEggxUQadLaeiv28JFizfCIkksbFWQQDpAxdC1hl3oqLmGez+5jYM2GviGg7iUwah75RPLMbzJWW4vHl/aoaIJmljmpsvJmKxegMh5JCcHoQQMYBHAZwKYD6ACwgh8yMU8SKldNH4r7/EH2B0zGgREEKIVpv3RE/P/3C+JjPeDwehFFccaMRfyisPHjeZToRk4PNmo7P1dMxf8iDE4vjXZAu9gPQl0f8XTc5+FJa9gd3f3I6y6mfjmhNIhgx2a3Pgkkix0mHnra5YGR3Nhtl8pEKhUEw86P4IAAcopS2UUj+AfwA4M/kRHkr6tVSxcVxOToV2aChCvvIUDQmtsVnRkpWFNtXY+Gq69AbC6W4/GWJRAKa8T7B/zxVxbzoTBJD+JCJqtysPHc1nY97Cx9C87/8dHCaKlWQMEz1fUoYfdLYjg4lv5zsfvYLOzkuISmV+hJBDNg/lAQg/t7Vr/LWJ/JAQsosQ8gohJLEDGaJgRovAbK58dnDwJ2nTG8gMBLC2pxMvF5YASE8JWLuORr9tMWqWPojSqhfj3nQmSGBmEev/V/icgCnvS1TWPo09W285bAI5WviSQeg7NiKV4j1LHs7paOelnngYHrbAaFyglkgk4dkvI33RJo5pvQmgmFJaB+ADAM/yFWOIGSsCQsgylcposdmqUh3KQX7U3oo38oswKkks1e90xPulcliXoqfjeNQufRAiERP3pjNBAjOTaP/fIk0Ma3L2o6TqRez+5tZJl5amipAMPjJbUOUcQp7HHVc5fPQK+vp+QnJyip8Ke6kLQPgTfj6AnvB7KKX9lNLQ7PeTAJbGH1h08Nti8YjFMv8xlr2Q85xC8X4YCt0jyPe48XTZ2JGYfO8ZiJXhgUq0jS8RFUu+W2ERkkHr/vPQsOsqzKt7fNKjJmeqAFgihifDCLfCArfCAk+GEX5pFvySLAQkKjBi2cFnMpcyD5sXbRp7bqOAlHFDFnBBGhyB3D+ITK8VmaO9yPT2QhZwJnJcdEpQr/NPeSDOVKuDdMadYIJK7P32f7Fg+caY05CvYzYmdP7xdFBC8GxpBS5pbsK9tQuTk85lGuz2EpSXm/WEkMWU0u0AvgFQQQgpAdAN4HwAPw6/hxBioZSG1sR+H8A+vuOckSeUEUKMZnNlt9X6ooTrVNPxiuDne3biheJStPI8NxBPb8DjNmHvtpuxYPlGyBX9Ea+hFGjdfx58Xl1EGcwUCbBEDGdmMQazKjCoqoAzsxgELJTevoMNuNJrQ0bACWnQBVnQDTH73b8t/IQyCoKgWAm/VAW/RAWvLGdMJvIxofikGsiCLmhd+5HtaoLWtR8KPzd5mfgmkgyiXSLa0Xw6PCP5qKp7PK62li8ZhE41u7axHp8ZTNiZo4urnIROM4twkll5+Sdwu//0WU/P3qMBgBCyFsAfAIgBPE0pXU8I+TWArZTSfxFCNmBMAEEAAwCuppQ2xB/U9MxIEVgslt8ZDBffunv3hYf/MAUSWDjQj6PtNjxSNbYKLJ0kEPBnYseWX2Je3Z+QpZl6/HQyGaS7BNxyE2zaJejLWQqvTAv1SBu0rv3Qupqg9rRDRKN/co31qEqfVI0hVcWYeLIq4JNpkePcB+PAt9AP74aETd/3LlwGsewToBRo2vMTyOSDKK54Pa66+ZSBzuvFzfv24BeLlsS1mZNrEQAsTKbzgjZbk5lSGvlJLMXMuKEhQohEpyu6fv/+s1IdCoCxpHI/am/FpupaXuuJRwIsK0L99htRUvnytBIAIg8TaW5Pn4064bgUeeg0HoO+nCWQ+wZgGvwWCw78GZm+vqTGkRFwwjS4DabBbQAAlkjQr56HPu1SNBRdgIzAMPL7PoGl/ytI2PR6L0PDRLFuFiMEKK95Bru/WQeHdSn05m1JiDZ6+uVy7NZqcYzNio/MuTHfL3vEGb8MLqiLIAMRTKYzJIHAK7cCOGwDWTow4yaLCSGnWizLM3y+TE7Ljbc3sNpmw95sLfrlYwd8pNPcQEvDhdDk7IPeFP0XNXwCueWpK0DTaBTcJ8lCc+5p+GThfdhbcik07lYcvfMOrKxfj5Led5IugUiIaBCG4T2oaXsWx+y4BXXNT8KtMOOzhRuwrfJG9GUvTKv3FPcY0fD2DTFvFhOJWMxf/DBam86N6zwDvlcR/TO/CKf0dEHKcH+cajw0NZ0NkYhcn64H16RlUFNhNlc97HCclxappsUsi7U9XfhnfiGv9cTzpbF1HwnvqB5F5bF33QkBFj79POT+fuyouDblDZdTmY8d5dfgq9q7QACsqL8XK+vXI8/xxSHj++lIpteKeR0vYs32m1DS+y569avw8eL70Wo+GUHR5JO2ycCpzMe2qpuxtHETLL9tjfl+qcyN+YseQf2O6xM+6YxrRiUSfGo046Q4j7bkegXR6KgaJtMCBYBj4y+YP2aUCAghRVKpvMBq5XbJaLz/6cfYrPhGp4dbyvH5w2HENTk8YkFHy/fHx/ljr1O9zg8CYF77CymVgV2zAF/W/BJ7S/4HuY7PsXrHrSjteRsZAX4OFuETAiDH1YiFBx7Hkbt/hYAkE58u+h32Ff0YPqkm6fGES0Dtif8M6sysLhSWvonGXVfGnOGB717Be5Y8HGOzIiNCr4D98AP4j1gK/7JFYP7wwOSFdbwCvECA/q0JxzUycg6xWKofTrggHphRIjCbzdep1adFTpae5N6AhGVxcm8X3s7LB5A+Q0IMI0X9juswr+5PkEhHY74/fGI4VTIYVFXgi9q70WE+HjWtz2DV3t/AOLQznQZUEkIWHEFl12tYs/0WZI724Mvau9BQeD4C4uSsz59MAvEuCjDlfQ6JxIOe9hNjvpfPXcd+sRj/NVtwSs+hoqMMg+BtN0P60iuQfvE12NdeBW04fFGOdEM3sP9hQLci9sojtEft7YsAsJWEkPi2aPPIjBEBIYQEg/Krm5tP5bTceHsDx1p78ZXeCI+Ev1Pp4vmSNO+7CJaCzVFNDk8kUkOQTBm4FHn4unodGgvPQ03LX7G08Q9Qezqnv3GGIqJBFPZtxuodtyHDP4jPFm5Ac+4ZYAh/azim6wnEK4PymmfQ23UM3K78REPkhNCD2X/MuTjKbjukV0C/3QZSUgpSXAIik0F09g/AvvP2YWUwG9YD1bcBYq6GvQgMhuMlWVlZP57+2uQyY0QAYGlOTplidDTCFvck9wZElMWJ1h68mzs2SZYuvYH+voXwjhqQW/ifmO+dqgHgWwaMSIb6oouwo+I6lHW/iZX166HxpE+qAL4RUQYl1vdw9I51YMQZ+HThRjg03K9Ci3Y4KB4ZiMUBzKt7HA07rwLLxCYyPnsFAbEYnxrNOM4atnm3twckL2yCOzcPtPfQMw3YXTtBu7sge301p/G0t58BpdJyF6eFcsCMEYHFUv3LQOC0tIh3pd2OnVptWvUG/P4stDRciHl1T/CyoZIvGfRlL8KnC++DPDCI7+26EzpnPSflzkQkrA+Vna/giH334UDemdhecR38kvhy+0wk1jmBeGSgUnfCkPslWvf/KOZ7+ZBB6AHtA3MujrX1QsyOn78daTIj7EtDWRbML34OyW/WJxZAhAfU4eFcyOWqHEJIUWKFc8uM2EdACJHo9UUnW61Hc1puXMNClGJtT9fBfQPpMkF8YO+lKK54GbKM4ZjvjfZLH5JBQ9EF2FFxLRY1PQpyWL6s6AiIFdhddgWCYgVW7F0PhZ/ffTa7NkV33ej90V9bd3P88UyF0mfHivr16NWtwucLfo2qjn8gt39L3OXFOzE8XTqKSBSU/Bs7vvolnINlUGubYw2VF0YlEnybo8dR9j58YjKP9QC6w1YT9XSDmM3f/X3EBbqvHoHvnz7292Eb8On3gaP/BeiWJRyPQnGK2GTqvhLAzxMujCPS4gk7Co43mZbKKI3woUzysNCCoUF0KjMxmJE+p9A5bIvBshIYLN/EfG+sT35c9AwGVeX4vO63MAzuwBH7NnIugV2bDv/FB3zWQQDk9n+Jo3b/El3GY7Cz/EowcSw3TXR1UMyfD0JRteDP2L/nMrBs5HUdk8HnENE7ufljk8aUgixeAtrSDNreBur3g339NZBT1x68lqg1kDW1QrZjN2Q7doMsW86ZBACgre0UMIzy+gnpqVPKjBBBbm7t+pGRtZy+afFOEq/t6cK/8/ibEIv1yxAMytHaeAEqa5+Oua54JwbjlQEFQVP+2dhT+hMs3/d7FNg/iav+SPDd6KcqBllwBMv3bYRmpBWf1f0WTmX0e1a4WiIaK0qVFcbcL9F+IPW7/0M99mGZDJ2ZmagZHgKRSCDZeD8C5/4AgVXLITrzLIjmVSO4YT3Yd/7NbQARHlS9Xg1ycoqUABZyW1n8pL0ICCHyQMBd194eIRNrknsDxtFRyBgGnZkqAOkxSdy2/1zkFb8b15BQIsQqg6BIjm+qb4NfqsZRu+9CpteacAzp0PhPBpexEQDF1vexeP8fsb3yevToVk17D5cSiOeBIb/kbQz0Lcao2xTTfUnpFQAQnXgSZF9/C9m2nRDffCsAQHLHnRCF9QxCSP/1NmQvVHIai0h0iqigoOBGTgtNgLQXAYDjzOYjImcZTTIn93bjvdz06Q2MOAvgHCqHpeC/MdfFRSK5aGUwKtPjiwV3w+L4CjWtz0JE4ztFKkS6Nv6TwZUU1J5OHLn7V2g3n4DGgnMmnZ3hoycQ6+dFJGJRNv//0LT30niPEuaM0ANbmyoLqmAQem9i53NzQXPzGoyOis5LdRwhUt+6TkNe3oLbnM5jUz4sJGMYLBgawFbdWGrbVPcGKAUO1F+CippnJj0/YDK4zCY6nQwGVeX4quZO1Lb8FQX2jxOqa6YJIBKJxi9lPFixdz38Ug2+rfwZGHLoyrVUDQdFIjunEVLZCPptsZ2rwmev4H1LLk6MM+1E3EQYuQgElNBo8jMIIWlxslZai4AQQrze4ZUdHREmaZI8LLTCYccWnSGutLbREOuH32FbBrnCjixNbDli+EgpPZkM7Nl12Fl+FVbUb0COqzHu8meDAMJJ9N8jAosFLU8hx9WILTU/R1A0tnCBbwnE89kpnfc3tO4/N+aJY64JPbh9rTNg8WD/d0tJYyCh/EORypMdLdbpdLGvteWBtBYBgCV6fZWUUu7W68e9k9jWi80mC2dxJALLitHWdA5Kql5MdSgHmSgDq3Yp9hVdiFV7fwNlAllBZ5MAJpKoEEp630GBbTO21NyJAVVZUnoCscogQz4EvXkrejpOiOk+vnoFjEiE3dk5WDQ4wEv5sdDVdTwkkpzrUx0HkOYisFgsF1O6JuUxmkY9CIpEvKWajvVD39txPPTGbciQD8V0H98HzIRkEBTLsb3yeqzYux4ZgfgmsWdbL2AqEvm3Ftg/hqn/G3xRtx51Bx5L+XBQJApK30RP+/ERzzr+4F0WS+f7sajKjwc2Hp4c7tvHt+HJBY/jL4v+jOe+9wzs9fa44wh9bzebzDjO2jvN1RwTYQTD5TJCIpFlE0LiO0qNQ1LeyE5FMKi4tL19TarDwDE2K/5rMk9/YRJgGCm6209AQdmbMd2XrFPG+rRL4ZXloMD2EepLLo55n8FcEsBE4vl3O5X56DQdh/mtz6K+5H8ODhPxSayfJYnEh7yiD9DVeuiKHIahuPmGIF55S4qvd0vx6ossGuq/m+9ax2xEzY9rccXuq3D5jp9i5W2r8OFNsadPmUhnpgpZQT+y/bEfFMT18JBWe5Q4IyPjDE4LjYO0FQEhpECp1Gb6fBFOCopzfiDencRLBxzYqtMDSH1voKf9JJjzP4VEkvqVDxMZyKpAQ9H5WFG/ATVtz8W8z2CuCiCcWEQYPidQ2vsOinvfxdZ5t4Al/I/HxyoDS8FHsFtXIBBQHnxt29cUpWUEJaUEMhnBD84T4e1/HTp2n6H+TmwBdwBcpbn6zGDGkfbUH2TU338s0enK+Tm3MwbSVgRKpfL07OxVqZ1hAlDhcqJVlYWAKOWhgGFk6O1cg7yi92O6Lxm9AZciFzvLr8HyfRshC7pi3mcgSOBQpns/Ik0MF9g/gX54z/j7nV6IxEHkFb+LzpbTD77W0wPkFXz3ucjLB3p7Do18HbMRWx/9Bo+VPYKPbvsQJz18ckJxhB7kvtIbsNIR/zBTXER4gO3tnYdAwF1GSBLsPQVpKwKLxXJhf//0G2f45nt2G74wGFMdBgCgt+NYmPI+g1iSXmff+qQabJt3C5Y2/gFKn+Pg69HKQJBAZCZ7X6ZaHVTe/QZkARf2FV3Ie3yxPmCY8z+Gw7rs4Glm0+R+O8iya5fjmubrcNzG4/D5bz+LJ9TDcMpk8IpEMHhjP7OD2+EhEfT6eWIAizgsNI4o0hBCCBkaCizr7q5ObRyUomZoCHs0WgCpHRZiWTF6O49HXlFsY6R89wZYiLC16ibMb30O6gipo6eSwVyeD4iWie9PNEtEa1qfgSuzED26lUmIMHpEIgaWgs3oaR9bQZSXB3R3fmeD7i7AbDncBKHvyfzza7H/n/EvQ57IFwZTWgwPAUtE2dl5Kc3HkZYiAFCenV0koTRCbymJ8wPVw0NoVGvAiFL/NvX1rEKOYUdcp47xyb7ii6Af3g3j0I5Jr4kkA0EA0RN6r6LdJ0BAsaTxYewvPBcuRS6vscU8V1D4Iaxda8CyYixZTtB8gKKtlcLvp3jtJRZrzzhUBM1N34niwNtN0FYkfrjXwT0Fej2W9zumuZpjIrRfPT2roFRqLk5uIIeS+hYuAkql8oSMjCUpH5RfNuDAN+OTxFwT6yRxd/tJyC95J6Z7+O4N9OhWYUSRh8rOV6e9NlwGH/muBU2fxIszgq0vxrZZTMq4sXj/H/Ft1f8iKEqfg+UlEh90pm9h710JiYTg/ock+MHaAJbXBnDWOSJU14iw/ldB/PvNsUnjPz/GYEVdAH9Z9GdseeArnPHs9zmLxSORYlQshs4X+8ILLoeHhocL4fd7LITweDTdNKSlCHJyyq4eGIjjnFAuCQ0LZfMzLBQLw4PlkCv6kSEfTFkME/FkGLC/8Fws2f9w1GcSEAD+G16ApL8fjmsFGUSLPz8f9ptvRtbtsW0W07jbUNLzNnaVXcFjdLE/cOQVvY/u8fONT1orwrf7ZNi5X4Zbfz727HfnPRKsPWOsadr4oARbdknRtG0QF/33YhhquJ2v26rTY2k/v2dhREOq5wnSTgSEEDI66qy0WuelNI58jwc2uQJBHoaFYu4NtJ2KvOJ3Y7qH9jvssAAAIABJREFUz94ABbCj4hosaP4LpIw76vt2bRqTQfYLggyiJSQBw6ZNkHV1xTykVtC3GUGJAtYcbnLpc4Fc0Q+ZzAXXUEnKYgg92G3L0WPZQJKHhyIgEi0RabVabg9kj6X+VFU8BeU5OQUSILXzA8sGHNimS/mGPwT8mfCMWKDRcjdJlihtllOgdnfEdKxkeAMmyCA6JkogRCwyIAAWHngC+4ouhF+i4j7IcWJ98Mgtfi/mtBN84JDLoQoEoAjGnhGXy+Ehh2MF5HLz5ZwVGCNpJwKxWHykUrkg5fMDdYMD2KFNfaZRW/fRMOV9GtM5xHz2BtxyE9pNJ6K6/W8JlSPIYGomk0CIWGSQERhGVcdL2F2WsnbmMLS6vXAOVYBhoj91ja/8Q3uytZg/HFu6Fq7p6ytCIOAxp+rUsrQTgdk8/4Lh4dqUxpDBMJAzDIZlsR8NOB2xfpit3UfDlM/N2mku2F16ORa0/AViNnrZTNZoCTKIzHQSCBGLDHL7vwRLpLBnR+5Vv9sKVD0NlD8F3BfheOQHtgLz/wrUPQsc/zLQHuFhOJYHEEIo9OYtsPembi4w9IC3K1uLuqEkJqGLOLJBoFZbxABScqh92okgGBxd3tu7IKUxzBsewj5NdkpjAMYOnpHLx8ZTo4XP3oBNuwRSxg2dc1/U90zXWKVaBoxKBX9BAbzV1fAsWgRWqcTowoXwlZcjYDKBSpK7kCNaCYSIRQY1rc+gvvgisBO+9gwLXPsh8M4PgPpLgRcagfoJ86eLjcDWi4BdlwDnVAC3JXa0BADAkv8xrF2rEy8oQRo1GlQ5k3vCXySUymoxgNgOb+CIlC1XigQhRGw0lmb5fJrDf5jE+YG6oUHsGl8tlEpsPd+DMe/zVIcBAGCJGPuKLsSK+nujvifaRiokg6ELLoDj2muhf/RREB6OtQpqtfDW1cFXWQlfaSmoTAaR2w3xwADELheIzwdWLsfowoVgMzPBqNVgdDpQiQQSux0ZTU3IaGiAfN8+EObwTJmJEqsEYkXps8M4uB3tlpNR0vvdUuSvrUB5NlA6/uxzfhXwxgFgftgU2bFhRyWvtADPT/IsoF7nh3NjdD1pudIBlpXC59UgQx5dQ7yO2YiNYm5T8wREYrgkUuT4vBjISN1SW4+nGgUFBScCmH49NseklQgAVGZnF4n6UrzZr8o5jJeKxlY0pGp+gFJgoG8xiiteSUn9E2m1rIWlfwsUfn6W2vElg6Bej5E1a+A54ggQnw+K7duh/PxzaP/v/yCKcGTh6KJFyHnuuUNeowCCRiN8FRVwH3UUBi67DJKeHqg++QTKbdtA4phonEgiEti1Cai7ObprKzpfw6cL70Oe/VPIgiMAgO4RoCDru2vys4AtU2RpfmoPcCpHC36Mlq9g712J/JL3uCkwTvZka1EzPIRPjanLMmy3L4RKlXn69FdyT1oNDRFClspk81I6UZzBjH2pfWLuw4hlfsA1XIrMrE6IxYGo7+FrWCgoykCH6TiUd/8z6nvi2TnM1TARBTBaVwfrL34B+w03QDw0BPM998By113Ifv11KOrrI0pgqrikfX1Qff459E88gdz//V9kv/wyfPPmofv++zH44x8jmB3/UCIXPYFo328J60Npz5s4kHfmwdci5vyZ5P7n64GtNuBWjlajGixfwt6bulQYoQe9RrUGVcOxDw9xuXLI5TIjEPAaUjFhnFYiyM2tOd/truGsvHj+kyqcTjRlRUh9nWQctmXQm75OdRgAxpaLFto+inqCOJH0EYnKYLSuDr0bNmBk9WrkPPccLHfdhawPP4TI44k/qAgxZrS1IefZZ5F3yy2Q9PTA9vOfo/8nPwGjju2zw/dwUCQK+j6GLWcZ/JJMAGM9gM6waaguF5AbYaXpB+3A+i3Av84CMqYYS4jlgSRDPgwKEfy+rOkvHoeP1UPNqiyUjkQ/F5cwkwx1q9VmMYCC5AUyRlqJIBj0r+jrS+1EcZVzGI3qCHMUSWagbxFyjDujvp6v3gAjkqHTdCyKrLGlvk6EeGQQMBphvfNOuE44AYaHHoLhkUcg6+jgP9ZgEFmbNyN33TpkNDXBevfdGD7tNNAoNiJyLYFoBSyiDEp630FL7tgoxHIz0DQEtA4Dfgb4RyPw/bJD79luA678z5gEjMoIhSaAzvgt+vuWcFtojATEYgRFBMpg9D1wPlCp5qVkwjitRMCyAbXbHaGLncSD6qtczoMiSNX8gNejh1TmSovDZ9rNJyKv7xNI2OhSX3OVTC5aGVAAw6edhr7bboPmn/+E8YEHILVauQkiBgilUH36KSy33w5Go0Hvb3+LQO7kCd/46glE+/4X2D5Cr24lAmIFJCLgkeOAk18Fqv8KnFcJ1OiBuz4H/nVg7PpbPwFGAsC5bwKLngO+/zpnIUNv2op+W+p3PjdlaVDh4vYEslgJBEphNBqPSHa9aTNZTAjJKSxclNqNZJQi2+/DYAb3x/3F0p0dsC9EjmHybJ7JgoKg3XQCvrfr5ympf7oJZEalguP66yHp64PljjsgCqT2aQ4ARH4/cv7+d/hKStB3881Qv/kmsjZvPuSaVAwHTURMgyi0fYQO03Eo63kba0uBtaWHXvPro7778wfnxlZ+LKuHlKpueNwmsKwIIhE7/Q080azKQqnLhZ3a1GUUcDpLIZOZTwJwRzLrTaceQYVKVZjSHUU6vw/9KVw+FmLAUYccw65UhwF79kLkuBogZaJLfc1HaunJegaB3FxY77kHqg8/hO6pp9JCAuFktLbCcuedGF26FP2XXnpwqCgdJBDi/7N33vGVVnX+f5/ba3rvmZIM04GhCoroqrvCui6Cyyq/tazrIqK4oOiKutjRwaVa1gJuQURRsSGKMNKGAaZnSjIlmfSb3JSb3N7O74+bO4TMTXLL89znZiaf14vXi0me55yTW877+Z5vaxx5ir6qN2rezUwIKCo5xrRn5eIXz0gNP0G3w0mr15vxfUo6jEdHWwmHfXkvwlQwIBBCtAnRpOl6Wr3TdNvVq8eSjqQU+L112BwDad+jln+gu/ZttAyl5xtQs7/AXBgE29oY+eQnqbznHuwvFYZDPZV0wSCVd96JLhhk5JZbCDU35wUC6b4XpqiPIn8v40VrVVtLuiqt2M/EaP6OgGcreQQ8bLVSk0XHsqyV4sg7HHYgZdyev0UkVDAgaGxsvNjv1yS7+qRavV66HelHL6SrTJ5efNONOJy9GdUWUkMBUylhYxHFvm5tFzKjJAwQAtdtt1H11a9iOnFqR7RCkwBKH34Y48AAQ1/7GhV33625JTBbLUNP0FP7FlXGzuQBpbTiAJPj2nYklELg1+uxa2xdWizFOiFEXs+nCgYERqNxy9TUisUvTFPZmGtNPi8nZiwCrRzFnvF2isu0rzTaV/VGmlxPab2M1yjS3Ey4pQXH008z+e53L5naROGGBgJbtlD0m9/gufrqtCKKclW6VkHpdCdeaz0RvVXdBS0ik9lDJFxEPK7tltRrd9DgT7+0uhpyOJp0wOp8zlkwIPB4YmsmJ5sWv1BFVYaCjFq09RF4JtZkBAK1joWGy8+nduzFtK7NR9vJaFkZozfdRNXWrZQ98MCSKVQ32ydQ9pOfYOzrY/x975v3+ueBvwWuAH6Y4vc7gXcD5wCZda9OLQHUjL+Mq+w8BUbLTY6iE/imtD0VGLDZaFAw5yQbGQyNOiFEWz7nLAgQCCGEEDpzypK0eQod1cfjxBGabyy+6QbsTvXj3xeS31yFIRY4WYJAa0mDgZGbb6b8+9/HODSkeaG6dJXKMVzy8MPEysrwvuENp1wfA74KfBv4JfAH4Nica2qALwHpdDBJF9C17hcYrLgovYtVVFFpJ56J9Pc/NRzGA1YbdYHMQaCkwzgSaaG6uj2vzRoKAgRAidVarOlaqoJBXBZtzeNI2I7eEEAIbeM4BisupM69Pa1r82ENjF93HfYdO7AcfLURTqHDYL7oIAFU3HcfniuvJFxf/5p7OkiklDYARuBtwLY549YDbSj7xS3y9+M3VxHVKR82nYnF6iw+zrSCx8OZKHkUPGCzU6/x0ZDPV4vBYMqrB79QQFBrtVZo+k2uC/gZtCmcMpmhvFMtOIu1d84mjoVSFKXXQIG1a4k0NVH061+f8rtChcFiIaK6YJCKb387seZZ/oIREk/8SVUBLtVXm1D1xE5GSrXN7rU7+/BN5b26wms0YTJRGlavlPspSnHiMTFRQywWmT8bUQUVCgjqDIZyTb/FVcEAIyr4BzIqNDfVgqOoJ+3r1fAPRHUW4sKIOaJ9fXZpMDD+wQ9S/u1vz1sErdBgkG6egPn4caz79zP19ref/FkqOzDXvyZdi61qYs+8TWvypWSBxXhMwzxXIYgLoUoZ9HTl9ZYRiQTyWvCsUEBQK2WlxiAIMqpxMpl/JnRUS40XraEszcYzah8Lea64Avtzz2EcHV3wukKBQabJYsWPPor3jW88WaiuGphdHGOEhFWQD5VMdzHhzGugSkrZ7EME/NqVggYYN5kpC6dXUkUd6RFCl1caFgQIysvLV0ajFYqNl43jpmJWxJBWoaN+bx1Wx6AmcyflLllPhadD0zUAxOx2fG94A8W/+U1a12sNg2wyhnXhMMWPPsrk1Yn6DeuAXqAfiJBwFp/qUlZHehnFGPMTMmpbcNHmHMDnrV/8whmp4TB2W8xUZlCmXA0ZDEa9EEL5XrnzqCBAYLXWnx8OV2q6hvJQiHGT8s6ydCUlxGIWDAYtn0RgrGgt5Z70W1Gqpakrr8T5+99n1PRFKxjkUjbC/sILBNesIVpSgoFEgZnrgb8D3gKsAu7nVadxB/BXwB9JRA+9M4050rXcKjwduIuVKwOfVCZHmDb7AP4MQKCGRswWKkPagsBmqxC81mWkqgoCBFLK1kAgX0ZwaumkJJaHRJ/5FI3YMRi1DdeMCz0xnQljTNuoibjJhP+CC3A+/XTG9+YbBrnWDhJSUvyb3zB1RaIk9KXAb4DfAR+aueYG4LKZ/19PIn9gB/AMiTBTpVQ6dZgJZ7uCI2Yum2OIgE/joyGzhbKQtg9kFkuFAPLmMC4IEESjwUqvNwUI8pRDIKTU3MkYDFRisS18Fq62vNZ6nIH0NjM1/QO+Sy7Btn171i0g8wUDpQrI2V94Af+WLUiDtsWAi30nmLJrm9BlsY4SDCh3TJyNPEYjRRqXmZgJnjmzLIJYLGL2+ZSv8ZOunJEI00ajZvMDBP2VWKzaNmuesrdQ5NO+fo/38stxPvlkTmOoDQMlq4iKaBTbrl34t2hbk98c8WjuIzAYA0Qj2oZxe4wmSvIZQppCsZhTAHl7MwoCBEIIvZZLKY6E8WgNgkAFFqs77evVCB2dsjdR5NM2ailaVgbxOIbx8ZzHUgsGapSStj/7LL6LL1ZkrFRKuyJpZIqQUdtWrUJIpMy/hZ4MEvGYTBRFtAaBFYvFUpqv+QoCBCA0XUdRJMKUUXkHfSYRDeFwCSbzpOJryETTtiacfm0tAv8FF2DboVwym9IwUKufgKm7m0hjo+bHQ0X+E0zZtK35ZTBNE4loVw7eZzDgyOJYUskyE7GYg6KiorxF0BQECITQFgS2aBS/xl/ASKgYk1nbNnkBUxnWUO5P4jmtYeNGbLt3KzqmUjBQs6mMAMyHDxNarW0svy04SsCsbQSfyTRFJKTdEZUUAqFxu55o1IbFYjlzQCCEMOh0ek09tdZYDL9e2y6Z4VAxRpPG2bxCINCuVaAUgkhtLYZB5XMpcoVBPjqLWTo6CKxfr8rYaa8hPEbQrF2rRkiUpA5rCIJCUCRix2g05u2N0BwEgMNo1NY5ZI1FCeq1tQiiETtGk3bhozFhQBdPzxxWK2IoWlWF0eXKuazCfMoWBvlqL2k+coTwqlWqjZ+OLOFxAqYyTddgMHqJRvLepKugFInYEUKU5Gu+QgCB02SyaW4RBDS2CGIxEzqddg6qoKkMS1jbY6FwczOmnh5V58gUBvnsMWwYGSFaqe2xjDU0TtCk/INoJsENBkOQaFTjSsBChyGunXUcjToIBKwN+ZpvURAIIX4khBgRQnTM+tlmIcSLQog9QohXhBDnz/xcCCHuEUIcFULsE0KcM+ueTwghdgkh3j1nCofBYNUUBOZYjJBeeyZqmcoQNhZhimjro4jU1WEcSL9Xc7ZKFwb5bjQvSISSaukwNkUmCRu1C+UG0BsCxDQGQUivxxyLaTZ/PG4jHo+mZRYJId4mhOic2Xc/PfOzdUKI7UKIH6fjg01n93uQRGn02foGcLuUcjPw+Zl/Q6JfxuqZ//4F+M7MohzAecD5wD/OGcug1/hYRi8lMW391ZorpjOhj2ubTRkrL0c/NpaXuRaDQb4hkJR+YoJYSd5OBE6dPx4hpstbiZvUa9AHicW0LQAZEwK9hhVIYzEDUspFN8ZE6D33k9h71wLXCiHWAv9GotndKySqlSyoRXc/KeUzwNwzAwkkg42LgaR37x3Af8uEXgRKhBC1vFpNN9Urq9P6hCoBgsKoZa+V4joT+rjGsdMlJegnJvI233ww0AoCMAOC0ryFj58igdTWNAWELoaMp39Uq0bhubyCIEUFhXhcD6SVTHE+cFRKeVxKGQYeJrEP60nst3HSqGae7Q58E/BNIUQfsJVErSxINFDqm3VdP1AvpZwG9pOg009PXYP2IIhqDQKNp4/pzZqDQJrN6PJc9XEuDOImk2YQABChEHGzdsUPC0FCxIhLjX12mlsEOkhvY0y55wJ3kyhZdRGJGoULKtsd+HrgE1LKRuATvNpnO9V2JgGklF+TUp4tpXzo1DVouwvqpSSuNQg0VkxnRBfXtr5K3GRCaFDjJQmDuMlEtKGBim99SxMIAPjPP5/gWnW6FKYb7eVxrFRl/nTlm25kuO8yTddwwZibCg0rkOp0UYaHO9MpupRyz5VS7pZSXiClfK+UclFnh5BpUE8I0QL8Vkq5fubfHqBESimFEALwSCmLhBDfA7ZJKX8yc10ncJmUcmiBsc+56KK3vXLVVR879Q8qy85hJJoy8/ZXhIJMG4yEZiKHavTDi9yRnmpIf5xE0/r0Nx9dv7JPKxGDnZjOhCW8+NFMQKX+iZHaWgwjIwgNnHTSaCRSUwMmE7rpafQjI5o8nkQrK9H5fOj8mTdQT0fW6sWvSdSc6lF87nhDeq9oJGInFrVmVHJlWKH6bMOxxDh1fj+jFguRDCsSy94sn63HA6/5ZzjsY+vWG4NjY0MLboJCiIuA/5BSvnXm35+BxIN3JtNn66UdJNEzYxtwOXBk5ue/Bj4qhHgYuIAEIOaFwIziHo9O3nJL/amfkiyrj2aa6v1Px47wUnkFh0oSZ7NKNaa5IoOzy5ef+TpbLv3M4hfOSOlaQ8Ol5zJR1M5ZJ+YabKdKrTwC1623UvbAAxhH8lt8L+kTqL/5ZkZuugn7zp1Ey8upuP/+vLcsHPvnf8b+/PNYDinfE2LjzcBi30Zg29l3ctmeWxSff+ra9JzQ7uFzmZ5qpbXt52mPfYf+1myX9RptHU+UA//kwf38aOVqxjLsWhj+YZZ1mn6y7zX/dDgmcTiKp9O482VgtRCiFRgA/oFTA3IWVTrhoz8BtgPtQoh+IcQHSZRKv1MIsRf4KokIIYDfA8eBo8D3gY+ksYY4GmazAkR1AoOG54GFIH08rHm0iM7rJe7Mb+jiXMew1p3OYg4HOq92iYWF8C2ISwNCpG8VKgWB2dLH4/nzG86BAIBOJwGx6NshpYwCHwWeAA4Bj0gpD2S6hEUtAinltfP86twU10oSfTQyUVxKbUEQEzp0yyAgrtO2AqthbIxoWRnmY8fyMt980UFJGExeey3uG27Iq2UQKy9Hr0Dl1WwlM8gwV0vxmBGdTlt/lU7jkHK9PgppPiFLKX9P4iE8axVC8Lw/Gg1qugtHhcCkYRZhIcgQCxDRa5vWbxgdJVqdxiG2AlosRFQryyDucKDzadchLqK3YogFFr9QRcViVgwGbddg1DiSUKcLotcb8uatLgQQeCMRbd/0gF6PJa5dFiGAEHHice3eDkt4nKBZ2xozpt5ewk3ql0BON08g3zCIW62IQEDTGLqguVyVUiNTd6R/7BiLWtBrDAJLLHYyeEQL6XQ+rNag+mn2yfnyNdECmg6HfZpaBAGDAWtUWxAYjH5iUe2K7xmjXiKG9CyCjTertIbeXiLN6rZKzDRZLJ8wCLe2YjqhbT+IoKkci8alyKMRm+YWgUDb9rUmkw8gbw1KCgEEwWhU29IGAb0ea0zbc1GjaZpwWLvOUIWQRaELhUBK4lZ16sxkmzGcLxgE167FciBjP5+iCpjLsITzU+ZjPkXCRRg17s2htYxGH7FYLG9p9pqDIOFg1tZbHNAbsGpYYArAZJ7UtBkHJP0E2hb7shw6pEpCVa5lI/IBg8CGDZqDIGgqx6p1FdpQsebd+rSWweAlFAqN5ms+zUEAIDUOG5o2GnFqkNE6W0bTlObNOBz+Qbz5q3ybUtaXX8Z3wQWKjqlU7SA1YRBzOJBmM4Y81lpKJa+tHoc/b0fTKRUOF2E0aWcRmGIxwrrM/QPhjypn0RsMAbxeb94SapZBAEwZjao0q84kvtlsGScUTN9Zm4nzLV05/SeYsqt7Rr+YLIcPE2pvR2aY0TmflC4gpxYM/Oefj+2llxQZK5XS9et4LXXYA8p3iMtE8ZgJvV67B7PiSBiPUdtQap3Oy9TUVN7O6AoCBEKIiNGonZ/AYzRRrAIIMpHFNkIwoG1TkmKf9iAQ8TjWPXvwn3dezmOpVUVUDRh43/hG7M8+m/viclBMGBDE0GmY4BmP6xFCm/m3jieyqYsiETxGbZMrDQY/QDqZxYqoIEBgMFg9TmeKuiIpMu7UUFivxxTTNo/AYnUT9GsLgqIMQKBW5BCA84knmH7LoiXUF5TapaSVhEG4oQERDGIczduRcEp5bQ04/doU20sqFCzDbNXWWV0cDuMxaQsCmJDAmeUj0OuN/TZbfuvLzJUA0DC72GwZIxTUuGl4dJqwwYnU+GNhGhwEKQm1tGR1f776CSgFA88730nxb3+r7OKy0KRjFcXebk3XEPRXYdV4LygNh5nU2CIIhdxxXu3zoroKAgRSundaLNq++dNGI46odiGkOl0cKQUyrV4U6ikTq0BNlfzsZ0xefXXG92nRXjIXGESqqojU1mLZu1edBWYgd/E6yj3KRy1l4s/yeRuw2bX1UVSEgrgt2nZI8/vdkrRKBCqjggCBy+U6bDAoZwVl470fNVuonKk/njwrzLcK4XiowtOBu3i9pmsAsHR2Ik0mgm1tad+jVWexXGAwcd11lD78sKp5HOkc40kS5aeLVSg/nYn83jpsjvSjltQoOFcVDDKaYdVRpRUKeSXgydd8BQGCaDQ6oNenX3tcDbktFirz3B1rrmzOAXzeek3XkAkI1PQTAJQ9+CDj73tfWhurlu0lITsYBNeuRZpMWPflxxe2kPzmKmyhUYTGlYD93npsTm3DVytCQUYt2naJkzIelek0i1FIBQECYFBKt6afwJFZFoFWchT14J1qSft6NUJIbcFh/JYq4gXw0TANDGA5eJCpK65Y8DqtIZBUJjCIm0yMffCDlP3oR/lb4AJyl2xU5VgoE0kJ4VARJlPegmVSKlFnKNtWLbnLaPQhhD6vm5H23/aEhkIht6b1hoatVmoCyneFysR0dRZ14/W0Kr6GTCSAsunDjBWv03QdSZU+/DDeSy8l3JA60a1QIJBUujCYeO97cT75JEaXSu3eZpSu1TZUfgE14y+rupbFFAqWY9E4YiiRTJbHbTFFZGRp6TBGozWvIWSFAgKXzzemKQgGrTbqVWoPmK6sdhcBvzIt93JRrXs7gxUXpXWt2sdDIhql8v77Gf34x4nPceAVGgSSWgwGvosuIlpdjfMPf9BmgXMU0VsJmUpwqJBIlonVOu1ZgaNY26iluoCfQZt2xR8BioqGAdGTzzkLAgRSykg0Go6m7MOQp1wCn8ZRQwBCSAzGacIh7YrPAVR4DjBWvA5ZEKXowHTiBMW//S2jN954clMtVAgkNR8Mws3NTL7rXVTcc0/e22DOJ1fZFqrHX9F6GUxNrKKoJP2mREo6ipMBIvV+P4NWbUFgNvcyOdm/LZ9zFgQIAIxGy4Tdrm2xK5/BgF3jmkPFZV14JtrTvl4NP4FOxiidPsJ4UfrrUFuOv/wF08AA4x/4AKECh0BSc2EQqapi9KabqLrzTvR5aD6TrrU2WHERde4X1F1MGvJMtFNc2qnpGur9PvptmTdoUrLOkE7XJ32+8T2KDZjOnPmcbCHZbJEdJSXHNV1Dv81Gg1+77lAAxaWH8YxrvwE3jD5DX9XlaV2r9vFQUiUPPUS0rIzhL3+ZigKHQFJJGOgCAQa3bqX8O9/BOKhtnPxshQxOAqYKnP4+TdcRj+uJxSwYTdr1awZo9PsZ0PhoyO/viwNd+ZyzYEDgcrmedziUOx/MhtA9dictvsQHUclcgkxM2OLSLjwT6cfOq6WKyf1MOldpXpZ6tiINDUTq6rDu34/3rW/VtHFIJorW1BBctw7bSy8x/Za35GXd6cK5r/pymkaeVuUQMCP/wORKnMXaPggCVAcDuCzafua93kEJ5LVDUcGAwOv1dphMvZoemh53OFnh1TZ0zWBMtCqMRLR9KhFI6keeob/qDWldr7ZVkPQJVN15J5V33okIBBj51KeIm7WN915MgXXrGLn1ViruvZeK++7Lew/khSSB/qrX0zDyF62Xwrh7A2UV+zVdgz0Swa/Xa/zexIjFImEpZV4dlgUDAqArEDihaS5Bn91Oo4aNw5MqqTjApDv98E01/AQATSNP0Vt9OVq7NOc6hgVQ9tBD2HbsYOhLXyLc2KjxCk+VFALPO97BxHveQ/UXv4i5uztvnc7ShbK7eAMl08cwxrSNlgOYcG+gpKJDk7mT1n+Lz0u3w5m/iVMEwpSXD2AyOfJeb6etr12RAAAgAElEQVSQQHBienpI0z0nOhM/bNS4kX1ZxT7G3Zs0XQOAOTKFwz+gaabxQtFBzm3bqLzvPkZvvBHP29+uWA+DXBWpqsJ1223Eioup/fznX9NsJp89kBfT8foraBl6XJWxM3k4iUYtyLgho0QyNUpLrPBO0+NwZHyfko7ikpLjmEy+vNcjL4xvDiCljMVi0ZBOp23UTrfDyYrpxAdSKz9BUWkXnvE2LYuhnlRb36N0NWZe/E0JpRMiaurtpXZm0x36ylcIrV6d51W+qrjRyOQ738nIpz5F8S9+Qdl//zciRUiymjBIF8YeeysSHSU+beP2AcZHNlNaqX2ZjbapKbqc2nYJtNmOMzAw8Hy+5y0YEABYLM7uqqojp/4iT7kEAIeLimmfylutp5TS6WI4nH2al5sAcAb6MUW9jBWtSet6payCTPIEdOEwZQ89RMX99zN59dW4br2VcHP+KqhKg4HpN7+ZoW98A4C6T38a6yK9h7W2DDqbrqat7+d5nXM+uV1bqKjWOI9BSmoDfoas2jqKo9GueCQS2ZXveQsKBNPT7l+WlCh3TpiNydZZVEz7lHb9UpMqr3kF93DuXbqUUFvvIxlZBbnCINtkMVN/P9Vf/SrFv/414+99L8Nf+AK+889HGtSpGxOtqGDi6qsZ2LqVaEUFNbfdRskvf5nSCkglpWGQ7us+ZWskqrdRNq1OzH4mDyXxuA7vVHNGEUNqHAtVB4OJaCGNnfgTE8fiQN695gUFAo9n6Cm9/rCmByIjFgtVwYCmTWoAyiv3MDZyjqZrSKrYfwJ9PMxY0VrV51IiY9hy6BA1X/kKZT/4AcG1axnYuhX39dfjO++8U8pUZCIJRGpr8VxxBUO3387oxz6GYXycuk99itKHH84qSUwpGGQC38PN/0h77yNZzaO0JsfWU1J2SLP9N3n82z7lobMojxn9KU459PoAsVg0IKXMe/VL7UrspdbuycnOGFquSwgGbYm6QwN2O1vHb+GWsq2KDH2H/lZujd2R1rUGYwCL1Y13qhFHUXrJPlN3mCi6VZ3ey2u7f8yu9pu4dO+/p1WqeOPNsO/OzOZQumyEaWCA8gcfROp0BNesIXDOOXiuugqp02Hu6cHY14dheBjD+Dg6rxddMIjU6YiWlhK324kXFRGpqiJaU0O4tZVoVRWGkRGsO3dSee+9GNzKlE5PwmDy2mtx33ADFfffr1r5idHiDQgZp3zqoCrjZyrX4OuobXxK62VwlmeSp2pqM75PSUdxTc0hzGY0SfEuKBBIKT2Vla1hiBq0XNq+kjI2Tk4wYM881VxJVdU9x8jgxTiKfqrpOgAcwWHKPQfprb6cZteTad2TCQzUrB0k4nGsBw9iPZjY/OImE+GWFiJ1dYRXrSJQWkrMbkdaLESrq3F/5CPofD70Xi+GkRFMx47heOopDCMjqlVfygUG6VoDcXQcbLmO8w4r82CTShkdC8UMTE+2smZj+km0ahwLISUrvVN836ltImdRUQc9PUO/02LuggIBJBzGlZVH142OznFO/mQfXLsx4/HCHy3CdF9mZ/77S0p5//EjPF6fuvRxvlRevYsTR6+itf0RhEhvU1DTKmjr+xnPbfwqde4XFI09z3cBOV04jKWrC0vXqRvQwNat1HzlK6qvIZWygUEmR0Inat9C1eQebCFt28Im5R45l/KqPWl/ttVSTTDAqMVKXGh9Un44HggEdmgxs9Z/+Sny+fp/WlmpbYbhiNVKeSiEPq5ttya9PkJRyREm3Nq3jgQwxgKsHHiMg63XpX3PYhtVoVcRzbcy8RlkAoGAqZSemreyuu8XuS9SIQ31vZHaxqc1mz/pH9gwMcG+klLN1pHUxMSROKBJHG3BgWBiYuJZvf6A5hH0R5xFrJ5OWBJa5RMA1DY9xVDfGxWbP1c1jmwjYKpgpCR962y+DWsZAqmVDgwygYAE9q76COu6H8AQV88PmcmxUChQSixqweZIvz+7KsdCwKbJcfaVlKkydkqlcBTbbF5isahfC0cxFCAIgB2jox3apvYCu8rKOXdc2z7KAM7iYwR8NYRD6ae+q5VTAIlNatPRb3Og9f0ZFaSbu3EtQ2BhKRla2lt9Odawm6pJ7ZO2khrqu5zaxm1aLwNjPEZlMJhV/oCSjuLa2lew2+PqpHmnoYIDgZQyIKWYtNtTbMJZJpZl84btLyllw6zSAEoqkycbIZJWQXolofMha3iCVf2/omPFBzK6LwmDZQikp/lgkNmRUAXddVew7viP1VnkjDLLHdAzMnQhVXV5T6A9qaSVv35yko6SUs3zB2y2nfT09Dys1fwFBwIAvd77k5qalzRdQ1ivZ8xsoVbj9pUANfXP4hp4HfF4+m+XmlYBQMPoX4jpLfRVvj6j+1o+twyBTDQXBhtuTn/DigsDO9d8gg3Hvq/qkVCmcg+fT3nVHvR6bcvJAGwZc/NKWbnWy2BiYncUeEar+QsSBIODg780m1/R3E/wSnkFW2aOh5T0E0BmVoHeEKKscn/BZBpDYoPa3HU/x+uvxGNLr5zDlK2Bne03c8nYMgQyURIGdW1j7Fl9Q9otRDtWvJ9a94uUTx1SdX2ZPnT097yVuuY/ZnSPGv4BISVtUx46izOvL6TksZBe7ycU8gWklJq1aCxIEAAvjo/vT+0nyOPx0Ctl5Zw3pr2fAKCh9ff0db89o4Rnta0CQzzIOZ13sbv9Y0T0C+dcJCGw5fCdOAP9eetqdrpo082w5sRPsITTg0Ff1WWEjUWsGPxNnlaYnibH2zFbJrDaRjVbQ/KhbvX0FMedzvyGjabYv5qaXsZksmlmDUCBgiDhJ2DS6dR2E542mQjq9VQFApquA8BiHcNmdzE5ln6fAlAfBs7AAG29P2Nn+03E5/k4zYVAUsswSE/J10mQHgwmHCs5Xvd2Nnfdr1oCXFKZfr56j72DppWPZXSPWtFCrxtx8UJllSpjZyKH4xUGBjq+r+UaChIEAHq996GGhhe1XgbPV1ZxsTuRgKPl8RBA08rHOHH07xRdgxKqG3uR8qmD7F19/SlNbOaDQFLLMJhfG28+9fVZDAZeSw17Vn+U8w59s6D8AgDeqUZkXI+zuEfrpaCTcdZ5Jukozjx/QMljIYCJid0xIO89CGarYEEwODj4qE73ouZ+gpfLKzjfrZ0ZO1t2Zz8Go5/JsfRKQieltlUAsKr/lxhiQQ43/+Or8y4CgaSWYXCqFnpN5oNB0FjCK2d9knO67s5L9nCmn6vurmtoacus9LXS1kDyYW7t5CSHiouJ5bOZUYpjIYdjklDI79XSPwAFDALgBZdrX4RUBc7y6CfwG4yMmc00qtDUHjL/oLe2/YyeI+9SdA1KSADrj/8Ir7WO47V/nTYEkkr19HumKp3XYS4MwnobL639NOuOP0Cxr0ftJWYMgWlPK/G4keLSFP1GNNCloy5eqKzWehnU1W1DCKFZ2GhSBQsCKWXUbC46UFenfRLMtupa3uhKPwNSTdmd/ZhMHsYzLDuRD6tAIDmn8y4GK17H9vX/kTYEZutMhkGmMEzCwBid5qkt32ZV/6+o9GjT93cxdXdeQ2vbz7ReBgCWaJQWr5dDRdpGCwEI8Wx8ZOTofyk6aBYqWBAADA52fL2sTLtaJEntLitjw8SE5rWHkmpt/yndh/8BKbVNgkkln7WGsMGO09+Pq+zcrMY4E62DbP/esLGI8aK1lE/uw1W2Je3Q0lyU6UPFhHsdOn2YopJjGd2n1rHQRe5RXqyozG8SWYpTDCHCuN1dEWB3/haSWgUNAinlE2Nj29Nr95SmsiF6XOjYU1ZeEDkFAFb7CMVlnbgGLsnoPrWtguRx0HmH7+TCA19i0rGSzsZrTnEgp6szAQa5QC9oLOXFdZ9jzYn/Y0vnt9IOLc1FmX6GpBQcP3wtK9c8pNKKMtcbXENsq86894DSam5+CYul6Hkpte9OXugg8EjJSElJiuOFPPYxBniqupbLhwvjeAigedUv6Dt+JbGoOaP71ILBXJ+ATsY4t/MuAuYK9q66nrjQZzXu6Wod5Pp3eWzNbF//edYdf4CqyX1ph5bmW66BSykqPYLV7sroPrVCRuv9PgIGAxPmzL43oPyxkMPxFzkw0KFec4gMVNAgAPD7PffV1/9Z62UwZLOhl5KaQKLkhNZWgdHko67pSU4cfaei68hG8zmGBXE2Hf02jsAgL677HGGDI+s5ThcgKPF3DJedx+62j3He4W9SMavTmNowyPQhIhKx0XvsCloKwDeQ/L6+dXCAP9bW5XfylA+tcVyuV6KA9mffLAEQTE0NP+TzPaNoNdJsyf5EbT1vHRxQcik5qa75T0y41+P3ZmbmKmkVLBYdJIBVA4+xYvB3vLD+dqatuX0JlyoQlFi3BI7Wv4PjdW/n4o7P4wgMnnKNWjDI5jPT03kNTSt/i9GYWb0utawBazRK+5SHPaXa1xaqre3CaLT0aFV2eq4KHgRSyhN+/8RUUVGKWP48Hw+9Ul7BOs8k5piibouTyvQLIIRk1bof09XxgYxKT4AyMMgkRLRm/GXO7rqHne0301t1WdZ+g6SSG2uhQ0GpNYYNTl4669P4zZVceOBLmKK+ea8thGOiaU8L3qlmqus1rZwAvGoNXOYa5pmq6qxKeit9LFRZ+XtGR4/8h6KD5qCCBwFAOBy8q7lZ2Vae2byxUgieq6ziDa7EeafSx0OQOQyKS49gsw8x3H9ZxnPlAoNM8wQAiv0nuGTfZ3EXb2BX+00Z9TNYSIUGBaXX4y5ex/Mbvkiz689sPP4DdHJxA1lJGGT6OYnH9XTt/2faNvwg48ActawBIaU2TuJ5joWGhp6NRCKRX+V3MfNrSYBgcnLge+Pjf1LnMTxD/bmmjjcND6bdWDwfWnHWQ/R3/w2hYEle5ssGAkkZ4kHOOXIv1eO7eH7jl3EXrVV0bVpAYfacSs4b05k42PxeOhuv4cIDX6Jm/OWM7lcCBtk8LPQdv5Ly6l3YndofoyYf1raMueksLsZnNGq8Imho2InJZNsnpdS+xv2MlgQIpJSueDw6VFbWfeov83w85DMa6Sgp5cKZshOFYBUYDEFWrPk/uvZ/SPUjolwgMFsNo89w/sE7ONrwTnavviEnR/J8mrtB57pRKz3eQhop2cizm76GOTLJxR23Yw1nV4EgFxhkAwHvVCPu4S0ZF5YD9awBpOTKgT5+W9+Y1e1KHwsVFf1ODgx0/Luig+Yog9YLSFfj492fW736Nw+Mj39MsQPP8EeLMN03lfF9v61v5OZDHWzPd1LKAiqv2ovbtYWh3jdR15xZlNXUHSaKbg0vfp1CEEjKFhrhgoNfYbDiYp7f8CVWDTxGw8g21U+0F9q8x6u1PWIKGos50Pp+YnozFxz4KtbwWM5jJmFwuPla9qy+gc1H7kcs4qXJBgLxmIHD+/6VNRu/i06XWXyHGhBIPqSd5ZlkxGJh1KLMUWTamieJzOXaFQaeyu9iFtaSsAgAQqHQoyMj26JK1h7KVhNmMwM2GxsnE60sC8EqAFi19n8Y6P0r/L7Ma6gs9sVXGgJJCaDe/QKX7PssE45VPLfxa4yWbFRs/KWiiN7K4aZ/4MX1n6fO/QLnH7pDEQgklQ8HcnfXu6muewFHUZ/iY+eid/T38lhDU1b3Km0NrFz5F0wm2xNSyoI46k5qyYBASuk1GCwHmpv3KDputm/0rxqa+fveHjI+i1FRen2Y9g3/xaE9NxCPZW7szQcDtSAwW8aYn43Hf8A5XXfRW305L6z/AhOOVarMVUiKCQPH6q7kuY1fxRye4PV7PkXtuDptWtOFQTbWwPjoRqanmmlo/X3G96ppDaya8hDT6eizK3/0mI30+t/Hh4YOfVHrdczVkgEBwODggc8UFf069c6bZ6tgyGbDZbWyaTJxdlsoVkFRyXGq67Zz9NB1Wc05dxPIBwRmyx50cW7nXazr/jFdTdewfd3nGSnZnHO4aaEpbHDS1fgunjl7KzGdiUv3fprW4SfSigjKRYvBIBsIhAKlHD34XtZuvh8hMnunVPMLzOjq3h4eaWrN6t6crIEU+5HTOcnY2HE/sCv7gdXRkgIB8KehoV1Bs3n+GOpslO0b/mhjC1f1nlDVKsjmi1Lf8jjhYCkjgxdmNWdyM8g3BGar2NfDBQe/yvrjP2Kw4iKe2fxNTlS/mahO/SqqamraWs/elR/mhfX/gSkyxev3fIq2/kcxxEN5W8N8MMjKLxDXc3DPjaxe/wAms0fhlWan5ENZu2eSoF7PCUdhWAOtrb8iFot+qxBqC83VkgKBlDJmMITuXbWqMPqwjlit9NtsnDueOMtVwyrIRkLAmk3foffYO/BOZRcpMXRbq2YQmC1noJ/NR7/DhQe+RNBUyrOb7mDX6hsZLd5QMPV0FlPI4OR47d/w7MavcWDF+6kZf4U37LmFluE/oo8v7qRXQ3Nh4Pl65rV3AI4e/H+UVe6htPxQxveqag1IydW9PfysQKwBiDMy8tvoxET/3dkPrJ6WFAgAhoeH75qcfCxGqsOCPB8PATza1MJVvT3opHolqrP5whiMAc46+14O7bmBSHjhxvJz5Zuu5+Duj7Pm7fdoCoHZMkemaO/7GZft/gTNrj8xWPk6tp39LTpa389IyUZiorAC4AKmMk5Uv5kda/+dHes+ixQ6zjv0dS488GWqJ3YuGrWTDyVhIC6d5PC+f824rPlQ32VEwk6aVv4647nVgkDyYWzTxDgTJhP99sw++2qptfUldDrTLq07kc2nJQcCKeVQPC4PNTUpW8I72yeAMbOF/aWlXOYaBtSzCrL54tgdg7S2/5QDu24iHk+v+mcSAmvPvhu7cyAvDW0ykQDKpw6z6eh3ef2eT1I5uZfhsvN5dvM32LH2M3TXvg2PvZV4nj/aYYODkZLNHGi5jr9s/ga72z5GxGBjbfeDvH7vp1k5+FsskcI4Opmt6TtMtLY9gtkylhEMJsfbGTzxZtZs+m6hRFCflJCSd5/o5uHmFVndr7w1AGbzI/HBwQOfyH5gdSUK8LhqUQkhXn/WWW/ddujQN1J/BK/NLvwwm5wCSBSzun3fLj636RxC+sST6S1l6lSXvTV2R8b39B3/G7xTzazZ9J0Fv7RzITBb6eQZaC2vpZaR0s1MONuZtjdhiAUomT5Cka8HR2AIe3AYU8Sz4IHSts1buWzP/DCPCwN+SxU+Sy1eax2TzpVM25owRP2UTh+hbOoglZP7C65xfCrNhryUiZ7CoWA5azZ+d0Gnr99bw4Fd/8aG876OxZr5A67a1sAbhwepCQT4SevKrMZRGgRlZUPo9R+ZGB09Xl6I/gFYQgllc/Ss231kurh4pMjjqVJs0GwTzAIGA0/W1PO3/X38rDm7M0k11dD6e44ceD89R66ite3RlNcsBAFIP+lMSzmCQziGhmDocQDCBjuTjtVM2xrpq2rDZ60lbCwGKTHGfJgi0xijXkzR6cRZvYSgqTTRTEfoiBhsRAwOwgYnEYODmN6MkFFswRHsgSHswSFW9f0Sp78PXar8lgLWXEtPCGhte4TurmtOJoWlgkE47OTArps4a/N9WUFAbZljUf56sJ8vbDxH66WcVGPjT+XRo65bCxUCsEQtAoCioqIbWlvffe++fTee+oCXpUUA2VsF+nicL+3dxX+ete5kBmMhWQVSCjp2/htllXupb37yNb9bDAKzVegwSEcSMbPJOwkbHISNTuIz0Ugdre9jffeDs2DhxRidxhT1aubYVVoLHfctZBlEoxb27fh3Wtp+Tllldv44ta2Bf+w+xqjFwp9q67MaR2lrwGAIUVJyddjtPlEipQxkP7i6WnI+gqSmp6d/NDj4p0jKWuc5OI2z/SDEdDr+p3Ul7zt+NOu501U2XyYhJGvPvofRoQtxDVx88ueZQADUb3eZDwkkpqgPe3CYUu9Rqid2Uzu2g9qxHRijvsT/j79EhecARf4TWMPjpwUEpu4wLfr+JS2DuT6DWMxIxyu30ND6eMFCoN7vY83UJE/W5LnxzAJas+YXGI3h7xYyBGAJgyDxwurub29XvvtRtjA4VFJKUKfnbJV6G+cqvT7C+i1bGeh5G27XuRlDIKl0NpRlFZYyeb/mwiAWM3Bw101U1W6nqm57VvOrnTiGlLzv2BF+vGJ1Vv0GQHlrQIgoIyM/iw4NDRVcJvFcLVkQALjd3be7XL+MCJHiaU2DUFKA/21dyT/0dGOMqZshmu0Xy2AIsuG8O+juvIZ9L306YwjM1jIMloayeZ+SMDCZx9mx7W5KyjsyLmaYlJoQSD5sXeQeZcRi5ZhT2dpAuait7XFMptBPpJTKFY1SSUsaBFJKj15veqS9PfM45sWU7dPBhNnMUzW1XNPbA6hrFWT7BQuHSpBSj8HoJ+CvyWkNyzAoXOVqucm4Ad90EzZHP97plozzDCA/ELBHIryz7wQPtWQXLgrqJJBNTf1vrL+/v6DKTc+nJQ0CgOHhzpsnJh6OQooncI2sgj/W1rNqeooV0wnHcyHBIHkctO6c/2TzhV+k9+g7cA1elNMalmFQeMr1PYlFzex/5ZOUVe5j0/lfyzjPIJ/6p+NHebSpuSCaziTV2roNEM9LKQsjI3MRLXkQSCldOp3xz6tX/0nxsbN9SpBC8F+r2vngsS70cfXDCtOFwVyfgNHkY+MFX2Wo93IGTrw5pzUs+w0KR7m+D5Gwnb0vfYbq+udoaHliXgfyYsqHNbBpfAxrLMaOiuzDyNVIIIvFfhwbGjr0oewHzq+WPAgAhoYO/msg8N+xQuhVkNSQzcbL5ZW8s+8EoL7jeLEv3XyO4YTP4BuMj26iu+uqnOvnLcNAOykB42CgnL07PkvTit9Q0/DsyZ9nCoN8QMAajfKenuP8cNXqrMdSut8AwIoVO4hEQgellF2KD66STgsQSCl7otHY9jVrnlz84gyVywfl1w2NrJ+cYGUejohg/i/fYtFBen2E9ed+i0i4iMN7r0+7HMV8WrYO8i8lXu9pTwv7X76V1esfoKJm5ym/TxcGqkcIzegDx47wWEMTk6bsCublrHl8A7HYd+NDQ1nWgddIpwUIAIaHD7/H4/lBDFI0/snRKsgWBnGh4ztta/jQ0U7MM1FE+YZBuiGiQkja1j+Ao6iHfS99mkg499K9y0BQX0q9xm7XuRze96+sP/dOikuPzHvdYjBQGwLJ78+FoyPoZZznqzLvxpeUOtbAXwiF/DullHsVH1xFnTYgkFL2Ssmv2tvnaZqt0RGRy2rjj7X1vLdb/USzpJJfxmzyBBpXPE5D6+/Z8+JtWZewnqtlGCgvpQAgpaDnyN8z0PMWNl/wJax216L3zAeDfEGgNBTi7/tO8MOVbVmPlTMEUu4nMYLB78WGhw+/O7fB86/TBgQAw8OdH56Y+N+ITqd8k49cPjhPVddSGg6zZWwUyE+iWbbJYgAV1btZe849HNp7Q9bNbeZq2TpQRkq+jtGIlQM7P0E0YmPDeXdgNKXf8GkuDO7Q5ec4SEjJDV2H+PGKVQUVJQSwevXvkJI/SCm7tV5LpjqtQCClHNPp/HetXftQ6gs0sgoQgu+sXsM1J3qoDCYyzdWEgWXKwtO7v5JTspjdMcjZF97OyODFdHW8P6seyKm0DITspPTrNu1pZveLX6Cybjur1v4vOl3m0W1JGOy0ttO6pzllixCllPy+vKu3h86iYg6UlGY9lhrWgE4XxuN5MDo0dOgDuQ2ujU4rEAAMDw/fPjz8y5DVOq342Ll8gHxGI99f1caNnYdOhpSqAQPLlIWVO1s5tqWb+0rem9NYBmOAded+C5t9iN0vfh6/L/vz2LlKbmzLUFhYSr9GUsJAz1/Rtf9DrDv7bqqzLBmR1DcMtzKwZoiwJawaDJLfkw0T47RPefh5U0vWY6nhFwBYu/angHhASjmiygQq67QDgZTSF4tNfaat7QepL9DIcQxwpKiYHRWVXNd9LKc1zKfZEAg6E/Xwcz23FQIaWv/A6nUPcnDXTQyeeJPiLZqXgXCq1HhNwqFi9r98Kz5vA5svuh2bYyin8U5+tgSqwSAJgZJwiOu6j3Fv+1lZ1xJSRCn2D6vVx/Dwz8MjI0dv1mBFiui0AwHAxMTEfQMD26ZLS/u0Xsop+l1dA6XhEJcq3NEsFQSSUsKJV1RynLMv/gLeqRY6XvkkoWBJzmPO1ZkOBDWtpNGh89m747PUt/yBtvUPoNdHchrvlM+UCjBIfjcM8Tg3HTrAgytW4ckhVFQdBzGsXv1d4vH4l6SUyh9D5ElLth/BYjIajW9ubX3dH48cuUfRLmZJZdu3ABLNMz6/fw8/XNnG8ZkiWbn0LlgIAnOVTS+DuRob2cTxw++hoeVxahqfVrVVYb77HyzWoUxpqQ2+ULCYIx0fRKcPs3rdgxhN3pzHXPDBQkL94VpMQRPdm0+wYDu4BXTyAUlKPnykkx6HgyfqGrIbDIWOhFKAoKLiGELcPDE62l0ppVS30qSKOi0tAoBIJPLk9PTwvhUrtqkyfi4frJDewF1r1vGvRzopDic2umwtg0wgAMpYB+VVezn74s8zPdXC3h2fxe+tzXnM+XS6+hLU/pukFAz2Xs6+lz5LbePTrD37PvUhAIpbBm8dSgQ7PJFloxnFNE/ymNN5R3x0tPuKpQwBOI0tAgAhREN19eput/v/DLFYCpMyR6sAcrMM1k+M867eHr6yYRMRnT5jqyBTCMyWEpYBgGdiNUcP/BMlFQdoWfUL9AblQ3fnkxrWgloWQT5BNj3ZypED76eotIuWtp9jMCjTPzmjh4gcLIPZzuGrenv48obNRHXZP7OqdyT0ONPTDzw3NHT40twm0F6nNQgAampqvlxZ+Xf/3tHxkYI7IgJ409AgGyYnuHvNWqQQacMgFwgkpRQMpBQMnngzg71/ReOK31Bd/9yCzc/VkhJgUAIEWlkvoWAp3Z3XEAxUsnrdg9idyhS+zNqKzAIGSQg0+rzc0HmIr67fxJQpt392tIsAACAASURBVNdTDRAYjT5KSt4TGR3tbliqkUKzddqDQAhhqqhoGYvF7ndMTKQ4Y9TYKgC4tvsYOuD/WlcCi/sLlIDAbCkFhEjYzomjf49nvJ3WNQ9TVtGhyLi5KhNAZAKCQjmuikYt9B27EvfIubSu/hnl1TsV89vkfJSYAQxmZw5/+sA+7lqzjiGbLafp1bIG1q//FkNDv/202+1W5sujsU57EAAYjca3tLZe/IeE4zjFJ1FjGAgp+VjnQQ4WlfCnusRZ6HwwUBoCSSkFA4CAr4ruzncTCpXQ2vYzSsoPKza22nrl2a+x5dLPaL2MtBSLmunveRuugddR3/wktU1/RqdT7qhasZIRacAgCQFLNMpnO/byUOtKDhXnFpmmFgSqq48Si9086Xb3VEopUxQ3W3o6I0AAUFu75qXS0n8679Cht6e+QGMYGOMxbj2wnz/X1LG9MlFbfS4M1IJAUkrCABJlLnq6riYSsdO06jFKyztUjTBSQksBBNGIlYETb8E18DpqG5+mrvnJnMNBZ0uVmkELwCAJAWM8xqcO7Oepmlq2V+aWvKhWlJAQMRob3x/v7d17sZRyR+6TFIZO26ihuRoe7vzr0dHvhex2t9ZLSamITs/Ws9bz14N9bBpPtDidHUmkNgRA+Q3A7hxg3bl3sXrdg7j6X8+uF77IyOCFxONnzMdOUYUCpRw79B52b78dvSHIuZfcRuOKxwsfArBoNJFOxvn44YO8WFFVsBAAWL/+QWIx9wOnEwTgDLIIAKzW4nc1NW15pKvrP1VxHEPu/gJnJMxnOvbx4xWr6JwxjW8z3Kc6BOZKaesAEk1P+rv/hvHRDVTXv0Bt058xmQorB6fQLAIpwTO+hoGetxEKllHf8gSVtduzqg20mPLSR2COZbB14haElHyk6zD9NhuPNTbnPIVaR0JlZcfR6T4x7Xb3VEkp8/NFzJPOKBAA1Nae9ZLT+U/nHTnyN6kvKAAYlIZC3HpgH99f3U5Qp+fjnQeZumBH3iCQlBowgMTZtmvgEgZ734TV7qK28SlKKzo0iTSaq0IBQTjsxNV/Ka6BS7A5BmloeZyiUnVKk+SrkcxJSejd/feUh0N8b1U7Hzh2BJ/BwE9mgiVykXrWQOJIqK9v70VSypdyn6SwdMaBQAhRVlHRMhgI/NDs81WkvkjjkFKA8mCQz3bsRScl31y3gQGbPafs41ykFhCkhGnPSob63sjURBvlVbuoqnseu7NPM1+CliCIxYyMuc5hZPASQsFSqhuepbruuYzKQ2eqvEOAmSNPKbmm5zgXjY2ys6yC/21dSa5vurpHQj9iYuLnP+jv718yfYgzkTK1hZeQpJTjNpvt2qamLz/a2fmfqaOIclT4o0U5w8ASiyGBuBBYZnU30wIGyc1CaSAIAUUlxygqOZbYBEfOoafraoKBSsqrdlFR8wqOou6CdzDnomjUwvjIZtzD5+HzNlBetYuWtkdwFKlbJ0sLAMCrfi8BOKNRvAYjzkgYQW5JyGpCoLr6KMPDj0253QM35j5JYeqMswiSqq1ds72y8h8v3L//71JfoOERUb3Px8c7D3L3mrUEdXo+eXA/D6xcfdJnoJVlAOpZB7MVjVgZH92M27UF71QjxWVdlFXso6T8gKpPx6C+RSAl+L0NjI9uZHx0E9GInbLKvVRUv4KjOD/Q0xwCUvKho5349Qb+t2UF1/T2UB4O8d3Va7KqLKpYaekUINDrI9TVfSDe17fvtDwSSuqMBYEQoriionlQyrttY2OtqS/SAAazITBgswMJn8EnD+7np82t7C0rB7SFAeQHCADxuJ6piTbG3RuZdK9DIigu7aK49DDFZZ2YzLkfw82W0iCQUuCdasIzsQbPeDu+6UbsjgFKK/dRVrkXi3VMsbkWk1YAgFchoI/HubHzEH02G482tSTMQim55kR3VjBQEwIAGzZsxeV68T6Xq+u0tQbgDAYBgBDivPr6dduHhh7Qx+PzlLfNIwxSQSApZyTMJw928Hhdw7x5BvlWvmAwW9GIFc9EG56Jdjzj7UQjTqw2F47i4ziKTmB3DGCxjWTteM4FBNGoBb+3Dt90I15PK9NTLcSiVuzOPorLOikuPYzd2Z93p7iWAIBXIWCOxfjEoQ72lpbzeP2cLP8sYKA2BFpatuH339M9MnJslZRS+TCtAtIZDQKAmpqaz1VUvP72AwduUy2kFBaHwUIQSMoSjXLLoQ52lFeezECGMxMISUkpCPir8HpW4J1qxuetJ+ivRAiJ2TKOxTqKxTaK2TKO0TyJyTSFyexBbwikzMJNBQIpIR4zE4nYiYSKCYeLCYdKCAYqCPorCQYqiUYc6A0BbPZBbM5+nEXdOIp7MBr9+XopTpHWAIBXIeCIRLjl4H6eqqnjmeqa1BdnCAM1/QJO5zBm87+E3O4TLVLK4dwnKmyd8SAQQoiqqlUHS0r+ZU1X11tTX6QyDNKBQFLGeIyPdB3GbTbzUMvKk18WrWEA2gJhruJxHeFgGcFAYqMOBcsIh4oJh4qJhIuIRq3IuP61NwnwT9djS/Z5lq/+XK8PYTD4MZknMZk9mMwezFY3FusIFqsbg9FXME7tQgAAvAqB6oCfTxw+wMPNK9gzc7Q5r9KEgZoQECJKU9OH4gMDHX8diYT+mPtEha8zHgQAQojyiorm/nj8fsv4eGPqi1SCQSYQSEpIyT+cOE5NIMB97WcR0SU2tEKAQVKFBIVMVCh5BNmoUAAAr0Jg9ZSHDx3t5NttZ9HjcKZ38yIwUPtIaOPGe6XL9cyDw8OdS7IRfTZaBsGM9Hr9JfX16/8yOPhDXSw2T1VJhWGQDQRm601Dg1w6Msx/nrUez0yp3kKCASw9ICxFEBQSAOBVCFw06uLK/j7uXLueMbMls0HmgYHaEFix4nmmp+8cGB091nK6FJRLR8sgmKXa2trby8sv+tyBA/+hqr8AoPWOoZwgkNT6iXH+X/cxvru6/WTbS1gGQrZaSiAoVAAIKbm25zj1AT/3tZ1FwJBlutIcGIRuLFZmofOWkOhDp/toyO3uWSmlHFBmsqWhZRDMkhBCVFe3PVdZ+Y6LOzrem/oiBWDQUDTFzRfu5J43r8kJAklVBQJ8vPMAf6ht4NlZjrhCgwEUPhAKHQSFtvknlYSALRrhY4cPcsxZxM+bWrLKC3iNZmBQ8hfJ/S9vRuaaADoPBIxGH5WVH4wND3deGovFtuc2ydLTMgjmSAhhqaxs7bHZbq0+ceKi1BflAIMkBO7cvoX+aaci5SgAzLEoH+k6zKTJxP+0rjrZ2q8QYZBUIUKhEEFQqJs/vLZCbrPXy/Vdh/hFUwsvVVQqNkf4o06uXX+YcmswNxjMAwGIs3r1J+TY2L7rx8bGvpf1QpewlkGQQkKIuoqKlmOx2L2WiYmm1BdlAYO5EEhKKRggJX8z2M+F7hHubV/LqMUKFDYMkioUKBQSCAoZADALAlLypuEhLncNcl/b2py7is3Wqz4BmRsM5oUArF9/rxwdfebR4eHOq7Nf6dLWMgjmkRDivNraNdvHx3+oD4UcqS/KAAbzQSApxWAArJry8KGjXfy8qYWXZz2ZLQUggLZQ0BoEhb75w2utAHMsyr8c6SKs0/HAytWE9foF7sxMpzqGc4DBAg3oPZ4fHB0ZOXrWmeQcnqtlECyg0tKGD1dWrvjO0aN3Cynn+YCnAYPFIJCUkjCwRyJcf+Qwk0YT/7NiFSF94YWYpqt8giHfIFgKG/9szYbAqikP/3ysi9/VNb7GN6WE5o8OygIG80CgoeEAgcBnpsfGTjRJKSezX+3S1zIIFlFNTfv/VFef/d59+z7DvJVKF4BBuhBISkkYICWXu4Z4y9AA31/VzrECjirKRGqCQU0QLLVNf66SENDJOFf1nmCtZ5LvrF7DiNWq2BzphYdmAIMFIoT0+hsjo6PdG6WUS6eptkpaBsEiSmQer95WXf1Xl+7f/+H5P3EpYJApBJJSFAYkMjuv7zpMR0kpv2xsJqZ7tVXkUgbCXCkBCCVBsNQ3/qRmWwF1fh8fPtLJntIyftXYnHtU0CxlliOQBgzmgYDNNk5R0YdjIyNHz8gIoVRaBkEaEkIYq6pW7S8tvba9s/Nd8184CwbZQiAppWGgk3H+tr+P88ZG+eHKtoLOOVBamQAiXRCcLpv8YppdNfRv+3s5d3yMH65qozvdLOE0lV2i2AIwmAcCer2f2toPx8fHj/69z+d7LLvVnn5aBkGaEkLYKitbjxUXf7Tm6NE3z3/htRtzhkBSSsMAoNbv51+OdnLU6eSRplYis5x7pzsQ0tG6bWs4cNkZf1LwGiug1TvNB492sbOsnF83NL3GolRCuWULp4DBvDWEIrS23hifmjry0dHR0e/kMOlpp2UQZKBETaKWY3b7bcUnTpyX8pqGhjA3f2k6ZwjMltJAEFLylqEBLh8e4uGWVnaXvbZl55kMhDMdBLMBYI1GueZEN63eab6/qp0Be+7Jj3OlTMmIWTC4WYeUqY6r4px11r9Lt7vrrpGRo/+mwKSnlZZBkKGEEI0VFS1dJtM3LIOD7a/5XUNDmJtvHuXOOyvpv3SLovOqYR2UhENcd/wYlliMB1euOpl3AGcuDM5UEMwGAFJy6aiLK/t7+V19I89U1SjqC0hKsbpBAEiu5RnKy6Pcf3/FHBhINm3ayvDwjl+4XEfeJZc3vVO0DIIsJIRYW1nZuhu+ZRodXQHMgUD/TNE6BWsTgTowADhrcoLruo+xu6ycXzc0EtK/WhvmTAPCmQaC1wAAaJ2e5rruo/TZ7DzS3IrPaFRlXmUhwMxxkOTaaydPgcHGjfdJl2vbdpfryKWne4OZbLUMgiwlhNhUWdn6Mvyn0WyuPxUCSSkMA1AHCPp4nMuHh/ir4QGeqG3g6Zoa4uL0jC5aSGcKCOYCoCIY5Nqe4ziiEf6vdSW99nmSKHOU4gCAOT6B18Jg48bvyeHhJ19xuY5cfCYnjC2mZRDkICHEOdXVK3Zcf/1thh/8YMOpEEhqicAAEufC7+jvZePEOI82tbCzrJzZHVdOdyCcCSCYDQFHJMI7+ntZNznBT1ta2Vu6SOOYHKQ+BJJKwGB6+v94+eVtu12uI+cvQ2BhLYMgRwkhtlRXr9gej3/LMDraOv+FSwgGAOWhIFf19tDg9/FoYwt7S8vOCCCcziCYDQBbNMIV/X1sGR/jd/UNqvkBQCUAwIL1gzZt+p4cGXmyY2io61wpZUSdBZw+WgaBAhJCnFNZueJFuNOY9Bmk1BKDASRKXF/V10N1IMAvmlrYV1J6WgPhdATBbADYIxHeNjTABe4RnqhtYFt1jeLhoLOlBQQ2bLhfjow8vcvlOnLRMgTS0zIIFNKMz+Alo/HrpsHBNfNfqAIMQH0g1AT8/F1fLw1+H7+rb2BHReVrfAhwekDhdAHBXB9AaSjEFQN9rJ+c4MnaOp6urj1Zqlwt5e8oKKk4mzffIwcHn3tpZOTIJcvHQelrGQQKSgixtqKiZafdfptlvjwDYMnCAF67ofy5po6/VFe/JsoIljYQljII5m7+AI0+L28f6F8Q4EpLCytAiChr135Zut0HnnS5uv5aShlTZxGnp5ZBoLBm8gz2l5TcWLxYBrJaygcQ7JEIbxoe5JLREQ4Ul/BEXT3D1tfWoV+KQFiKIJgLAJ2Mc57bzVuHBgjq9Txe18D+OUd6akkLCOj1IVas+JT0+bruHRwcvGk5TyBznXEgEEI0Av8N1ABx4L+klHcLIb4JXAmEgWPA+6WUk0KIFuAQ0DkzxItSyn+dGesyYCvwlJTyU7PmKK+sbO0oK3tvzYK1iWBJWweQyFI+d9zNWwcHiAvBU9W1vFJeccq581KBwlIBQaqn/4pgkMtcQ5w/Nsq+kjL+WFuvaGXQhaQaAGBBCJhMU9TW3hQPBPo+7XK5vpn8uRDCAjwDmAED8HMp5ReEEB8FbgJWApVSSvfM9ZcBjwHdM0P8Qkr5xZnf/QPwKeC/pZR3Kf3nFYLORBDUArVSyl1CCCewE/g7oIHEhh4VQtwBIKW8dQYEv5VSrk8x1k+B9wFfBr4/u5ytEMJWVbVqV3X1W9r27/8XMW8Ja1jy1kFStX4/b3QNcfb4GB0lpTxVU0vfPPHohQqGQgZBqs3fGI9xntvNZa4hdMBfqmvYUV6paIOYxaQVBIqLXdjtH4/5fIP/b3Jy8qHZvxNCCMAupfQKIYzAc8DHgRAwAf+/vTsPj6M+7wD+fffQrvaS9pBWkrEtC1/gI7jchgT6YKCQBiiQ4KTk4YEQQh8/IW7jAgkkaXkCISmhT0pDWp4AdYshUK4SCmmAYKAYMLahPiRbsq0DW9Z6V3tK2nPm7R87grVindbe7+d55kEazezOAvt+53fMDDYDOGNMEGxg5j8f+z5E9CKAawBsAnAzMw/NxkcrJYbJN6kszHwEwBHt5xgRdQCYw8y/z9nsfQCTnMoDAHQAGNmWxTGVnplHiGgFQK+vXBn4wq5dd2Lch9s8tTNvYTD6JS1EIByxWPDkgpPxm9YFOC0YxFd6u+FOJrHV04AtnsZjzk5zi1qphkIpOF7x16sqlodDOM9/FK3DQ9jucuPRhUvgK9DZ/6i8BgAwYQg0Nx+EomzI+P19l6ZSqT+M/bvWPTRasI3awsz8EQDQ9LrJRjdmTHhGV76qLghyaWf7qwB8MOZPNwF4Ouf3BUT0EYAogLuZ+R1t/a8BbAHwJjN3jH19Zk4T0YVEuo0LF37n+r6++2ncx17mMQyA7Je2UK0DlXTY4fZgh9uD2kwGZw368Y0DnahVMtjm8uBDd8Mxz7WVUPjM8Qo/kD3zXx4O44zBABZHI9hd78RrzS3osjsK0vc/VjFDoK3tXUSjP0sEAj2rRwv78RCRHtkW/0IAv2Tmsd/zsc4lov8D0I9s62CPtv55ANsAPMHMsel8jHJRdV1Do4jIBuAtAPcy8/M56+8CcAaAq5mZicgEwMbMg0R0OoAXASxj5mlVVbfbfZPJ1PhIIvEP+lBo3sQb5zEQgMJ2F+Wyp1I4PTiIM4MBuBMJ7HI6scPlQafdMe5c9kIHQ6G7hsYr/ABQl0ritFAQZwwOojERx+56J7a53NhbV5+3i78mU8wAAIBTT30Cfv9zPr//4CqtdT8pIqoH8AKAbzPzbm1dD47tGnIAULWupMsB/IKZF53AJykrVRkEWp/hywD+h5kfzFl/A4BbAVzEzCPj7LsZ2bOFbTN43zM9nta3rdY7zL29qyfeOM9hABQvEADAqChYEQ7h9OAgFsaiCJpM2FnvxM56Fw5bLJOe5eYrIPIZBBMVfQAwKQpOiYSxIhzCKZEw4no9djpd2ObyTOnfST7lPQCACUNAp0th6dIfczDYsWVgoHMNMyem89JE9CMAw8z8gPZ7D3KC4DjbT/j3SlN1QaANIm0EEGTm9Tnr/wzAgwAuYGZ/zvoGbVuFiNoAvANgBTMHZ/j+LQ0NC3Z4vdd6d+++fuKNKzwMcjUk4lgZCmFFOISW+DACJjP2Oeqw11GHg3Y70rqpDX6eaECcaBBMVuxzOZNJLI1GsCQawcJY9r/DXkcddjmd6HDUF3TAdzzFDgAAsFoD8Hj+Vh0ZiTzg9x+4cyrTQ7XvbVqb+VcL4PcAfsrML2t/78GxLYImAD6tF+AsAM8CmF8tU1GrMQjOR7aY70J2kBcAvg/gn5CdajaorXufmW8lomsA3AMgA0AB8CNm/u0JHoPZ6138RkPDqed2dNxFijLOzeqAgoQBUDqBAABgRkMykS2SkQjahrLdsp9Yrei22tFts+GQxZqXWyT/5KNt+N6qM44JlOkU9+MhZjQmEpg3PIS2oRgWDMXgTiYRNJnQqYXdfrv9jy7MK6aCBAAwaQi0tLQjk7k7HYsFrh0ZCb801ZclopXInvDpkZ3U8Qwz30NEtyE7FbQJwFEArzDzzdq00r9C9nseB/A3zLxlRp+pDFVdEJQKIqKmpqa/Mxob7x4evk8XDM6deIdqDIQcBlXF3JFhLNAK6ZyREdgyGQwbDDhca8GR2lr4zbXwm8wImE2IGYwz6koZDYLp0qsq3MkkGpIJeJIJNCYSmDMyAm8iDgLDb6pFn9WKg7ZskAVrTEXt6hlPqQQAwFi+/D8xMLApGgj0nJ07NVvMPgmCItPr9ee5XHNfd7nWmTs7L5144wKFwahSDYVc1nQaLfERNMXjaEgmsksiAVs6e6+xtE6HSE0NIsYaxIxGxPV6JPR6jOgNSOj1SOt0UIg+Xb7VtQ+PLFoCPTMMrEKvMkyqCrOSQa2ioDaTgUVRUJdOwZFKwZ7JgJihEmHQZIbfbILfVIujZjP6ay04ajbn9aZus6VgAQBMGgI1NUNoa7uHw+HudwcGOi+rxHn7pUaCoAQQkdvrXfSOx3Pa0o6O20lVJ+gqAiQQpsGoKKhLp1GXTsGeTqNWUWBWssXcnMnAwAwDM/SsQs+M1f6j+N8GL1QiZIigEiExJjxGDAZEjDWIGI0YMhqLNoNnNhQ0AIBJQ8Dr3Q3gR5lkMrAuFAo9UpiDEhIEJYKIqKHh5B/X1JjvSCbv1QcCEzzbACh4GADlHQhTNdOuoXJTagEAqFi2bBN8vmfDgUDPudIVVFgSBCVGm2K6ubn5RsuuXVdi0gsZJRBmVSUHQcGL/6hJQsDhCKOp6Yc8MnLghUOHDl3PzPECHZnQSBCUICKqa2pa8qrD0XzO4cM/oOFhz+Q7FSEQgMoLhUoMglINAABYtOh1hMP/nEqlgjeFw+FNBTgqcRylM19NfIqZIwBWWyx111itN21qafmWqavrixPvlOdbVIynkPcyElNXtOI/apIQMJkiaG29j8Phg3v8/u41zOwr0JGJ45AWQYkjIldT05JX7faWM/v77y7p1sGocg6Fcm4RFL34A1NqBSxc+AYikYdSyeTgzdFo9IlquWirlEmLoMRpVzCfbbFYrrZav/Hk/Pm3mNrbL8eEYwejX8YiBUJuQSrnUCgHJVH8gSkFgM0Wxty593MweKDd7+++SFoBpUNaBGWEiFxe75JXHA7vWYHA7RQKTXIRGlD01sFYpR4M5dIiKKcAAFQsXfoyBgcfSyWTg9+MRqP/Ia2A0iItgjKitQ7O0ev1a1yuvudXrLjE1t5+MymKafyditw6GEtaCzNTMoV/1JQCAGhs3Ifa2p+qkcjwm35/93XMPDj5XqLQpEVQpoiopqmp6YfMtjus1m8bDh68cGo7lkggjFUqoVAKLYKSK/q5phgANTVDWLz4l+zzvT/o9x+8nJk/zPORiRMgQVDmiOikpqYlL9ts3pXB4O006T2LRpVoIOQqRjgUIwhKuvCPmmIA5HYDZTLpu0KhQw8yszr5fqKYpGuozDHzIQCn6fX6i1yuvhc+97kL7J2dNyMen6S4lFiX0fFMVCBLpQUxXWVR9HNNOQCAefN2gOghNRwe+YPf371WuoHKhwRBhVAU5Q0icqtqdJ3Vuvn+k0++ytTe/pdQ1QnGD4Bjv+glHApjjVdQixkQZVfkJzKNAPB4uuB0PqTGYgMHBwb2XcPMU99ZlATpGqpARGRtaFj4E0C51e2+3rh371WYVuaXUSDMtgcu3owNr11Y7MMorimGgMPRj5aWhzkYbA8cPXrgWmZ+O89HJvJEgqCCEZG7qWnpvwLqlfX13zTs3bsG2Wd0TFEVBkLVBsE0WgAORwitrY9wf/+WoWi0/4ZUKvWiTActb9I1VMG0PtpriWge0WNPzJnzb6stlq/ru7ouQfbBTZMog3EEcQKmUfwBwGr1o61tI44ceTve0xP662g0+mtmVvJ0dKKAJAiqADP3AfgCEbU2N2/8ldf76Jr6+rWGzs4rwDzJsw+Ash1HEMcxzeIPAE7nJ2hpeYx9vh3DPT2D62Ox2L8zczoPRyeKRIKgijBzD4DLiMhrND5+j8fzxI1e718Y9+37MtJpy9ReRFoJ5WkGAeD17oXT+RgHg/uDnZ19t6TT6RdlKmhlkjGCKkZEdV6v93ZFMX+3peXimt7e6ygSaZz+C1VQKFTUGMEMij+goq3tPeh0T6pDQ0f7BwY6bwDwpowBVDYJAgEiqrXb7TeazZ773O5Fjnj8q9TbuwqTPhRnPGUcDGUfBDMq/oDJNIzFi19AIPBfGSLD9v7+9nXMvH2Wj06UKAkC8SkiIgDnNzef8jARLa2vv8Jw4MAVSCatM3/RMguFsg2CGQZAc/NeuFzP8MDAh0mDIfkLn8/3c2b2z/LRiRInYwTiU1rz/x0AK4ioSVWD6222p25bvPhPzLHYl6inZxWmNf0UkIHmfJhh0R9VWxvDwoWvIhz+rZLJpPra2/fdxsyvygyg6iUtAjEhItIDuLilZdl9mUxieWPjBYb+/i9RMNh24i9egsFQki2CEyz8AECUQmvrWzAaX1GDwYMjen38YZ/P9zAz987CEYoyJy0CMSHtLPF3AH5HRLZIZODLTueWv58zR9/icl2i7+39IqLRhpm9+NgCV4LBUBSzUPizVLS2bofV+t88MLA9pSh4tru77x8B7JDBX5FLgkBMGTMPAXgcwONE5B0ePnyD2fzSnS6X02G1fl7f338RpvSwnPGMVwArOSBmrehnEaUxb95W2O1vsc+3NZNKWT/u6dl9F4A3AwHOzOqbiYohXUPihBHRfIfDcbXF0rKBiBvd7nMMweCfor9/JaZ0BfNM5SEg8tI1NMvFfiyTKYL58zeD6B01ENiXNpsd7x0+vPtnyE77TOT1zUVFkCAQs4qIHER06YIFC9ZFo8q5Xu9Kg6qerTt06FzEYp7iHNQ0AmNaQZDnAj8eIgVz5rTD7f4A4fC7yshIeIhI98zRo/v/BcBH0u0jpkuCQOQNERkArG5pablaVW1fY2an271Cn0yeTgMD52B4uEjBMIEHHjiMDRvmFPswxlDQ3NyB+voPkErtUMLhvozZXLd3cLDrV4lE4hVm/qTYRyjKm4wRiLxh5gyAt7VlPRHV+nxdoHYkowAAA/9JREFUZzc27vya3b7xKrudnQ0Ny/WKcir5/Svg9y+C/C+Z7eppbt6Juro9GB7epYRCfRmLxfhhT0//png8/jqAA3LWL2aTfOtEwTBzHMBmbbmFiGoHBrrOqq/fusZmc13l8cTaTCZbTX39Er2qLqVweDl8vkVQVWNRjzufbLYQGhp2wWbbg0RinxIO96o6nT5KlHlj586el5n5fQD7AwEp/CJ/JAhE0WjB8Ja2/ADIjjEcPrx7ldPpPN/lcl3mcimn6fUGk93eTGbzPF0mM5eGhtoQDrdhaMiNaV/gVgR6fQpOZy/s9oOwWHrB3KfGYn2cSEQzOp0xlkrVvLd7956ntVs6dMmFXaLQJAhESWHmKD4Lh3uB7EVtPt/+eQAWW63WZV6v93y7veYcsznhNBhqDBaLi0wmD+l0Hp2qepBONyKZbMTISAOi0Qak05M8rnPGVNhsMVitPtTWHkVNjR9GYwBAgDOZgBqPBzgejzLAiZoa2+FEIrO1u7vjNQCdyBb8UJ4OTIhpkcFiUdaIyAigEUALgGaDwXSS2z1/ldFYe4qqpuem0/E6ZjYSkY6IdADpDAYTjEYLjMZaMhjMpNPpAOjArIPLlaFQSK/dbZmhqhmk0yOcXeJQVYWZWc1uwIpeb0zY7bxfUZTuSCSyLxgMHgBwBEC/9s+I9OeLUidBIKqKdmM9EwA7ABsAC7L9S7mLAkDVlgyAIQAxAMPaALgQFUWCQAghqlzpj7QJIYTIKwkCIYSochIEQghR5SQIhBCiykkQCCFElZMgEEKIKidBIIQQVU6CQFQ8IppLRG8SUQcR7SGi72jrnyaij7Wlh4g+ztnne0S0n4j2EdGlOevXEtEOIlpfjM8iRD7IvYZENcgA+C4z7yAiO4DtRPQaM183ugER/RxARPv5VABrASxD9tYVrxPRYu1mcGsBnAlgExHZtMd3ClHWpEUgKh4zH2HmHdrPMQAdAD59+ox224mvAHhKW3UlgN8wc5KZuwHsB3DW6OajL5vzsxBlTYJAVBUiagWwCsAHOas/D8DHzF3a73MA5D716xA+C47nAWwDsE0LFSHKnnQNiapBRDYAzwFYr93uetRX8VlrADj+mT4DADNvBLAxbwcpRBFIEIiqoN2u+jkAm5j5+Zz1BgBXAzg9Z/NDAObm/H4SsreVFqIiSdeQqHjaGMCjADqY+cExf14DYC8zH8pZ9xKAtURkIqIFABYB2FqYoxWi8KRFIKrBeQC+DmBXzhTR7zPzK8jOAsrtFgIz7yGiZwC0IzvjaJ08PlJUMnkegRBCVDnpGhJCiConQSCEEFVOgkAIIaqcBIEQQlQ5CQIhhKhyEgRCCFHlJAiEEKLKSRAIIUSV+38W7weMKgX9VAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = np.array(df3['mean'])  \n",
-    "zz = openmc.ZernikeRadial(z_n, radius=radius)\n",
-    "azimuths = np.radians(np.linspace(0, 360, 50))\n",
-    "zeniths = np.linspace(0, radius, 100)\n",
-    "r, theta = np.meshgrid(zeniths, azimuths)\n",
-    "values = [[i for i in zz(zeniths)] for j in range(len(azimuths))]\n",
-    "fig, ax = plt.subplots(subplot_kw=dict(projection='polar'), figsize=(6,6))\n",
-    "ax.contourf(theta, r, values, cmap='jet')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Based on Legendre polynomial coefficients and the azimuthal or radial-only Zernike coefficient, one can easily re-construct the flux both on radial and axial directions. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7fbd4845da10>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# reconstruct 3-D flux based on radial only Zernike and Legendre polynomials\n",
-    "z_n = np.array(df3['mean'])  \n",
-    "zz = openmc.ZernikeRadial(z_n, radius=radius)\n",
-    "azimuths = np.radians(np.linspace(0, 360, 100)) # azimuthal mesh \n",
-    "zeniths = np.linspace(0, radius, 100) # radial mesh \n",
-    "zmin, zmax = -1.0, 1.0 \n",
-    "z = np.linspace(zmin, zmax, 100) # axial mesh \n",
-    "#\n",
-    "flux = np.matmul(np.matrix(phi(z)).transpose(), np.matrix(zz(zeniths))) \n",
-    "flux = np.array(flux) # change np.matrix to np.array\n",
-    "#\n",
-    "plt.figure(figsize=(5,10))\n",
-    "plt.title('Flux distribution')\n",
-    "plt.xlabel('Radial Position [cm]')\n",
-    "plt.ylabel('Axial Height [cm]')\n",
-    "plt.pcolor(zeniths, z, flux, cmap='jet')\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One can also easily re-construct the 3-D flux distribution based on Legendre and Zernike polynomial tallied coefficients,  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define needed function first \n",
-    "def cart2pol(x, y):\n",
-    "    rho = np.sqrt(x**2 + y**2)\n",
-    "    phi = np.arctan2(y, x)\n",
-    "    return(rho, phi)\n",
-    "\n",
-    "# reconstruct 3-D flux based on azimuthal Zernike and Legendre polynomials\n",
-    "z_n = np.array(df2['mean'])\n",
-    "zz = openmc.Zernike(z_n, radius=radius) \n",
-    "#\n",
-    "xstep = 2.0*radius/20\n",
-    "hstep = (zmax - zmin)/20\n",
-    "x = np.linspace(-radius, radius, 50)\n",
-    "x = np.array(x)\n",
-    "[X,Y] = np.meshgrid(x,x)\n",
-    "h = np.linspace(zmin, zmax, 50)\n",
-    "h = np.array(h)\n",
-    "[r, theta] = cart2pol(X,Y)\n",
-    "flux3d = np.zeros((len(x), len(x), len(h)))\n",
-    "flux3d.fill(np.nan)\n",
-    "#\n",
-    "for i in range(len(x)):\n",
-    "    for j in range(len(x)):\n",
-    "        if r[i][j]<=radius:\n",
-    "            for k in range(len(h)):\n",
-    "                flux3d[i][j][k] = phi(h[k]) * zz(r[i][j], theta[i][j])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us print out with VTK format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/mnt/d/Dropbox (MIT)/MIT_WORK/pebble-depletion/triso-method/report_excel/zernike_expansion/rectilinear.vtr'"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# One should install pyevtk as the pre-condition\n",
-    "from pyevtk.hl import gridToVTK\n",
-    "import numpy as np\n",
-    "#\n",
-    "# Dimensions\n",
-    "nx, ny, nz = len(x), len(x), len(h)\n",
-    "lx, ly, lz = 2.0*radius, 2.0*radius, (zmax-zmin)\n",
-    "dx, dy, dz = lx/nx, ly/ny, lz/nz\n",
-    "#\n",
-    "ncells = nx * ny * nz\n",
-    "npoints = (nx + 1) * (ny + 1) * (nz + 1)\n",
-    "#\n",
-    "# Coordinates\n",
-    "x = np.arange(0, lx + 0.1*dx, dx, dtype='float64')\n",
-    "y = np.arange(0, ly + 0.1*dy, dy, dtype='float64')\n",
-    "z = np.arange(0, lz + 0.1*dz, dz, dtype='float64')\n",
-    "# Print out \n",
-    "gridToVTK(\"./rectilinear\", x, y, z, cellData = {\"flux3d\" : flux3d})\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use VisIt or ParaView to plot it as you want. Then, the plot can be loaded and shown as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f7c6e4dbb50>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f1 = plt.imread('flux3d.png')\n",
-    "plt.imshow(f1, cmap='jet')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}