diff --git a/.github/workflows/testsuite.yaml b/.github/workflows/testsuite.yaml
index baec4ce..a826178 100644
--- a/.github/workflows/testsuite.yaml
+++ b/.github/workflows/testsuite.yaml
@@ -50,3 +50,4 @@ jobs:
         if: success()
         with:
           file: ./coverage.xml #optional
+          token: ${{ secrets.CODECOV_TOKEN }} #required
diff --git a/.gitignore b/.gitignore
index 6454156..c952056 100644
--- a/.gitignore
+++ b/.gitignore
@@ -103,3 +103,4 @@ pip-wheel-metadata/
 .vscode/
 *MHz.h5
 docs/reference/_autosummary
+*.egg-info/
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 7c44e5e..4ca405a 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -34,6 +34,7 @@ Features
   a closer resemblance to the original 21cmSense v1.
 * New ``calculate_sensitivity_2d_grid`` method that makes it easier to obtain a gridded
   cylindrical power spectrum sensitivity for arbitrary bins.
+* Ability to use builtin observatories that set certain parameters.
 * ``antpos.hera()`` can now return a split-core configuration, and also outriggers.
 
 v2.0.0
diff --git a/docs/tutorials/getting_started.ipynb b/docs/tutorials/getting_started.ipynb
index f44b0a8..5fa9cea 100644
--- a/docs/tutorials/getting_started.ipynb
+++ b/docs/tutorials/getting_started.ipynb
@@ -655,7 +655,8 @@
     }
    ],
    "source": [
-    "coherent_grid = observatory.grid_baselines_coherent(\n",
+    "coherent_grid = observatory.grid_baselines(\n",
+    "    coherent=True,\n",
     "    baselines=baseline_group_coords,\n",
     "    weights=baseline_group_counts\n",
     ")"
@@ -1109,7 +1110,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/docs/tutorials/using_builtin_observatories.ipynb b/docs/tutorials/using_builtin_observatories.ipynb
new file mode 100644
index 0000000..05ccfe4
--- /dev/null
+++ b/docs/tutorials/using_builtin_observatories.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Built-In Observatories"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can create whatever observatory you like -- specifying antenna positions, beam patterns, and so on -- but there are a few built-in observatories that you can use for convenience. In this tutorial, we'll show you how to use them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from py21cmsense.observatory import Observatory, get_builtin_profiles\n",
+    "from py21cmsense.sensitivity import PowerSpectrum\n",
+    "from py21cmsense.observation import Observation\n",
+    "from astropy import units as un\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To see which observatory profiles are available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['MWA-PhaseII', 'HERA-H1C-IDR3', 'SKA-LOW1-central', 'SKA-LOW1-core']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_builtin_profiles()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, these are not simply full telescopes, but specific flavours of the\n",
+    "telescopes. More of these can be added upon request."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create an observatory from a built-in profile:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = Observatory.from_profile('SKA-LOW1-central')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SKA LOW1 central has 296 antennas\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"SKA LOW1 central has {len(obs.antpos)} antennas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SKA LOW1 has a beam width of  0.06038676654 rad\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"SKA LOW1 has a beam width of \", obs.beam.fwhm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you create an observatory, you can override parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = Observatory.from_profile(\"SKA-LOW1-central\", frequency=75 * un.MHz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "75.0 MHz\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(obs.frequency)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check out the significance of a power spectrum detection with the SKA-central configuration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sense = PowerSpectrum(\n",
+    "    observation=Observation(observatory=obs)    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "finding redundancies: 100%|██████████| 295/295 [00:00<00:00, 1438.80ants/s]\n",
+      "gridding baselines: 100%|██████████| 87050/87050 [00:15<00:00, 5686.92baselines/s]\n",
+      "calculating 2D sensitivity: 100%|██████████| 6663/6663 [00:09<00:00, 698.42uv-bins/s] \n",
+      "averaging to 1D: 100%|██████████| 564/564 [00:03<00:00, 170.10kperp-bins/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7289480861797337\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sense.calculate_significance())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see the coherently-averaged UV-coverage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f89b551d6c0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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pFDkpWEihdyqjvT2/ryMTB0EQBEHQnEh1y0x9FDMycRDyA7NALZ+XRdrTRKV+zskOCs2mAHei42DKjOb9shgW41EST5LQT8mHDkM+dBz6MsV9d4IgCIIg5BXxOAj5gSxZbZmS5UmWqaIS6DzLor/jkUVi7psscGp2ojUUsmWruNp72AS9HIfLk8SuT7Es4PcvHgihn5BSFlI9LIudqY9iRiYOgiAIgqBJ5WGpIlXkznyZOAiFgda8ydAscv0GY2m7sknsn8bj0uOOPTwNHp9dsQcEj0HoKdwzQf0zD4SKJzJeVwTABKF4kImDIAiCIGjyU1ZbPA6CkDueFuVJimnIta58ni1f8ihYfrafLPwTjW3QWH5/9kbH7YD+kCVzGwfPkuE6HLofC+RSSTnHaWIf+HlMQZTjFbMhngrhJJGEhWQvdRh6e35fp7inRYIgCIIg5BXxOPRX+JpxX19DLvS4PHQGXGv6+V5z59kkPHukt+j+eb9eOhncI3DMgeNfh3tqstwXz5bh2RRIOWMgLIvG5eE5MTEYWbJBOEUaM9NrfNzzw+irfyf6ALJUkR2ZOAiCIAiCJoneLzUU+3RWJg79DW5JnO41HzxiGoyOBK3N86yGQltcvYxpMFgeWRLmMiyGIpunw2s83HOVYzaHFWTtTC0SHiPhPN8U5WXvxeVJ8bpv4wHp4562k40vc8yJO5smT99P4bREJg6CIAiCoJGliuzIxKG/4HOuDbssCa816tMUl2JloSnU86Z+VZLtzmKZ99Tj4ZFFke7O2Y+JbfCIWbAoNsLEaLC1dq43QZ+9FEa97s+0O009bhztebQC9p92l4Kr+XtBz1M8DxwpcpUdmTgIgiAIgkbloay2KvJ0TJk49HU81iwNPGq62CyHXHUZCG1xKc/o/SJ5PnQf+vvhzrLw8Bh4WPD8fG6purImCA8lylQs7uyXf295LIQrBsWpdGn5mPKllxIpj3nwHT+Lo2i+DwR5ehK2gif3PBiKpVaMcEqQiYMgCIIgaGSpIjsycejrkEXl87Agi7UGRK5r7i4LOuXcb04oTsvSvZ/FDHjpOjBcHoZggJ1Pl3XGLrig833sT0vKqUdh3qdrzd0MKOP4vI67vi8B5/U9a3QUW1YG19/QngdPXYdiue88ItUxs1Pc0yJBEARBEPKKeBz6OsYiYtUmTxOyV53UO1Isr7/YMZaiU3HRxALEvRQzM9sKrhgJijngnh59XfI8IMiO03keFr4vFGTtuQXMYhlyVbzkljbT8SC9CXf1ziK1uLkuB//9KNb7zgPJPJTV7u35fR2ZOAiCIAiCRpYqsiMTh74OtwxOE4vB5WlwrdnTYb1fr6m7akXkuzZFX4PpPBh9B0+FyMyembTHIvMfvHRMgvYIcM8DV4D0UIr01HOg90oeAqptoZj+gFcsj5eCaCKuj/MaJqeJ/gP/HhTb9184JcjEQRAEQRA0KfiQ6uVSQ2/P7+vIxKG/UewWA1+j1mv2lqU9CsrpUeDZAF41FfqM5yFL7QlPfYFcx+2Z7eCl98DHxzw81C3LhqCjRi9Aj8/oBiScsQQmGwOZa1l4KUe6siF4doDxuHDLOpmxvUp4KK0KgiapLCR7udTQ2/P7OsU9LRIEQRAEIa+Ix0HoW1iZ19qNBUuWKFnAriqMzMLMNX/fywLNl2eCeRpcHhLlzI7wrCHgpYTIcdVy4NkXXkqR+jxqx2MVeCwDj5VgHgj4SGfEIzuCK0hyD5GrPfMkGNeHx/Pyiokods+dcMJIcGR2ZOIgCIIgCBqVh+qYSpQjBeEk4FK2c1b/NNVAPTwNBleNA7Jkbf0AE2XPruuqHkjj8FpT7ymUheD3qN0Aih3Q2RFkKHso/nndv7v6of7ouh4c7UjnwEBKj8zTQx4IkyXBsiu4p8HoJjBFylyrlrreCz0Pl+dBqj0K+SEJC8leFqnq7fl9neKeFgmCIAiCkFfE4yCcXLyyAzzWtI1l7VUd1Osy1MwobmpPRSDovB6PmWA1GnK1jHOFKyu67ocs+wCv3ZA5JuGYHcftzxWb4FWtkp6LOZ5ZOdL0w2Mc6LjrebL3wJQnzfiMJ8Xv6Cd9Ox66HekBQBB6Q0r1PkbBK7SqWJCJgyAIgiBoUnmIcejt+X0dmTgIJwevtWmCLGbtEfDyAKT7y6wk6T7uXMOnNXdjcfNqjnn2MHhidAWYpyBL1kg61oD1x6tREtyzwbNQWLVLyx+yP/PYBB5jws/jMQ7m+kxpkitQ8udtxqufg5/FfvD3nS0LQxCEvCMTB0EQBEHQpGAh1cvgxt6e39c5af6UpUuXwrIszJ071+xTSmHRokVobGxEaWkpxo4di7feestxXjQaxZw5c1BTU4Py8nJMnjwZe/bsOVnDFnqKZTk1EXx+p3eBf6bTAkHb26BSgErBFwralRTtBUfb8j1msyx7M/tM//qzPs/y+xxaBFYwkPZiwLasHRUT/X57s3zOzASlCrp+rhIJvcXdmR+OhqnMG8Py+/Wm7589J/P86HMoBCsUsj0BqZQ5j/qh5+Q6j302+HyAzwelFJRS6X6SycxxI+w90/NAMunwAlE/rvsu8PsRTh9IObK3WzFzUiYOW7ZswQMPPIDPfe5zjv133nknli1bhhUrVmDLli2or6/H5ZdfjsOHD5s2c+fOxdq1a7FmzRps2rQJR44cwaRJk5A8WS5lQRAEQRAMBZ84HDlyBN/5znfw4IMPoqqqyuxXSuHee+/FrbfeiquvvhrNzc14+OGH0dnZicceewwA0N7ejoceegh33303LrvsMnzxi1/E6tWrsW3bNjz33HOFHrpwImjLzwoETN0CwF6rP95mIIufzuMeA23hkiXr2Y59dqEtW+PZIMuWNm7Rck9KT/E6X3s2rGDI3shTwJ8LeUC4J4Q+k4eFng9Z9vp5+sJheystga+0BFZpqb2VhO2NPAf0/MljEItBxWLmcvTczfPX7Qyu9+f0NHi9P+MRoefvcb+mH/JQZXu+gtBDKDiyt1sxU/C7u/HGG3HllVfisssuc+zfuXMnWltbMX78eLMvHA5jzJgx2Lx5MwCgpaUF8Xjc0aaxsRHNzc2mTSai0Sg6OjocmyAIgiBkIwXLyE6f8FbkMQ4FDY5cs2YN3njjDWzZssV1rLW1FQBQV1fn2F9XV4cPPvjAtAmFQg5PBbWh8zOxdOlS3H777b0dvtATKKqe6QPw2hHuapWZFQ5N9kMwc7YDj+pXScpOoOqOLDuBtSfMZ65rwLIaTFXFbHhVv2T361nN00NfwVUzgq+hsmwDKxTKeJ7J2mA1JcxzIO+B0b0gvQSv2hFsP/XDsykCzvdorkv9cv0IritBw2dZNmmvDNODyHe8QzZvhsRXCKcRBfM47N69Gz/4wQ+wevVqlJSUeLbLJCCTMdiqB20WLlyI9vZ2s+3evbtngxcEQRBOS5TOqujNpsTjcGK0tLRg//79GD58uNmXTCbx0ksvYcWKFdixYwcA26vQ0NBg2uzfv994Ierr6xGLxdDW1ubwOuzfvx+jR4/2vHY4HEY4HM73LQmZYBY2ry2RbueRb2/0G7Rla3QWmMeAjsfijuNeCoRWyqmP4PI8kKVLFitZqjROrrjIJ6rcwmTH3dUvM+sxGJ0E3j9Z/uSwOTbz49huyKPgUQPDnOdhERudBPNcko6f/DrGsxOi2h9MF8NDt8G8N16rgultuHQ2zACoyiarpskVJs3zzlPwNH8v3ENE5Ot6wilHqmNmp2Aeh0svvRTbtm3D1q1bzTZixAh85zvfwdatW/GpT30K9fX12LBhgzknFoth48aNZlIwfPhwBINBR5t9+/Zh+/btx504CIIgCMKJcCqCI5cuXYoLL7wQFRUVqK2txTe+8Q1jXBPTp083wcW0XXTRRY42ucgXtLW1YerUqYhEIohEIpg6dSoOHTrUo/EWzONQUVGB5uZmx77y8nIMHDjQ7J87dy6WLFmCoUOHYujQoViyZAnKysowZcoUAEAkEsGMGTMwf/58DBw4ENXV1ViwYAGGDRvmCrYU+gYuhUKPter0CU5Lls7jsQjp6pLsF5LHMmRRJkyv5TvHZzwarNZCVnxZYjq8qnV6VffkljbdN7PUeRVK3l51R/V4yCPhdxw30HPyOWND+HM0++kyzPNj8DifPBT8+sbTYRw9LKbFy1OV8rL886wgab4vmd+zyyMlsQ7CCbBx40bceOONuPDCC5FIJHDrrbdi/PjxePvtt1FeXm7aXXHFFVi5cqX5HNKxTMTcuXPxu9/9DmvWrMHAgQMxf/58TJo0CS0tLfDr7+yUKVOwZ88erFu3DgDwve99D1OnTsXvfve7nMd7SpUjb775ZnR1dWHWrFloa2vDyJEjsX79elRUVJg299xzDwKBAK655hp0dXXh0ksvxapVq8xDEARBEIR8cSqWKug/cWLlypWora1FS0sLvva1r5n94XAY9fX1Gfsg+YJHHnnEGNarV69GU1MTnnvuOUyYMAHvvPMO1q1bh1deeQUjR44EADz44IMYNWoUduzYgfPOOy+n8Z7UicOLL77o+GxZFhYtWoRFixZ5nlNSUoLly5dj+fLlhR2ccGKQhUUGF1nEHmv6njUYjKXojKrn1RUNZPHRGjuL2ufZAQa+Rm6qZ7L7Mmv/KvNxCmEIOn+F0rECuntSgWSxE57ZEtqSN7EPZFFwz4nJRnDWijAWP7fw+XPh7fU4Ul3dzvb0HFJe2Q0p589gELng8mh44ZF9w79H+a5iarCc3xPuUUpfvzCXF04++ZSc5lIAucbftbe3AwCqq6sd+1988UXU1tbijDPOwJgxY7B48WLU1tYCyC5fMGHCBLz88suIRCJm0gAAF110ESKRCDZv3pzzxKG4VSoEQRAE4RTR1NRkYgkikQiWLl2a9RylFObNm4evfOUrjuX+iRMn4tFHH8ULL7yAu+++G1u2bMEll1yCaNRelsxFvqC1tdVMNI6ltrb2uBIHHClyJeQHD8uQH+eehnS0vt7BqyPyNX3qjjwN2iIndUPX2jOHZ1F46DeAne6KXWB6A65YgiSzUM3AnRZ2Kur0SHAPRupop2O/eQ5+Z3aIiRHgFjyPgWD7jYck7tR1AFs79XoPpn+6blzfj4l1Ydf38JyY9208WKQ3QXU7KLsmqA97eBryHWNgxqOvx2JVVKGyOYRTRj6XKnbv3o3KykqzPxdvw+zZs/HnP/8ZmzZtcuy/9tprzb+bm5sxYsQIDBo0CM888wyuvvpqz/64fEEmKYNcZBCORSYOgiAIgqDJ58ShsrLSMXHIxpw5c/DUU0/hpZdewtlnn33ctg0NDRg0aBDee+89ALnJF9TX1+Pjjz929fXJJ5+4xBiPhyxVCPlF13hQKWWss4zomgYqmbKtTlb9kmogpKtgelRb1LUUslZf1LiqOVJtBKrOSOOi8fPaEFSLgVftpPOoqiPtp/N51U2qUaGrUlLVTpNqpff7ysvgKy9LX4+Nw1Xtk9WSoOdjakPo6pd03+Z50vV17QpXzQlWe4SqZZqN1a4wVSyJeNze6DmxqqSpWBypWDzdH9WkMFU+qUpnzN7oPVNVzJNVHZO+33R9UyskaW+CcAIopTB79mz89re/xQsvvIAhQ4ZkPefgwYPYvXu30UHKRb5g1KhRaG9vx2uvvWbavPrqq2hvb++RxIF4HARBEARBcyqyKm688UY89thjePLJJ1FRUWHiDSKRCEpLS3HkyBEsWrQI3/zmN9HQ0IBdu3bhlltuQU1NDf7hH/7BtM0mX3D++efjiiuuwMyZM3H//fcDsNMxJ02alHNgJCATB6HAuPQLCJ7d4BXb4KEYyXUbvGpSZJI0B47JViAdAx0zQbEN3rEJfsc4zGGuoEj7ebAEV4r0ikFgsQKuLAkv69rLy0P7SWEyxWI1aLyUrcFjEXQAluLZLgSLGTEVNen+dLaFidnwOWMG3OPVCpb02I1C6SnSS/C6nsQ0FB2nYuJw3333AQDGjh3r2L9y5UpMnz4dfr8f27Ztw29+8xscOnQIDQ0NGDduHB5//PEeyxc8+uijuOmmm0z2xeTJk7FixYoejVcmDoIgCIJwCnEJ3jFKS0vx7LPPZu0nF/mC6upqrF69usdjPBaZOAj5xSOrwuV5IFOSFAA9lBSNJe+lnEjKhF6eBhb1b9bpSWGRVW/k1Rp57Q1zHfJQUDaERxYIH4epBUHX9dI/MNkZHrUymMfAeDosrovgUX2TxkOeAY/nlzKeHn2dIMtKIfRzSBnFSqZUqdv7tOfE9Ms9DyY7h3kWRJFROEkooNc6DsX+bZWJgyAIgiBopMhVdmTiIOSXLHoOntUz9X4FFvtgjmdWGORVGN1KkWwt38MDwRUbvWIduAVuxsGqRppxBJ2fuWeE6ym4anzovG/jGWC1IzxrajCL33hCyOPhVa2S36dXbIqH5LupNaFxVfWkGA9WPdSi4SbyXGtCEHqITByyI+mYgiAIgiDkjHgchBPDS2XMrEVnthxdWRCm5sHxVwW9siSyxjqwmAaXxU+xCFSrga+9M0vcjJcpBpr9zJNgsiPoM8UKuI5nzv4Aj/HgSo0Mc57OgjDQc2dVRl2aF8wjlK4JwWIq+HP2UOz0yr5IZ804bRdShjT3k60mhcQ+CHlGPA7ZkYmDIAiCIGhk4pAdmTgImeEeBR6zwGoucE1/r/3GguRVHy2P7ArmiaAYCFcsAR8+j3nQsQbKS2/Aq2on9UcWL9V08LKAuU5DMMs4S8LOfvVzscLaw6CrVaY8anG4LP0U87RQ9gTFGphxe2SPMA+QZywK1cZgHh0fxWRQ1gnXyaD7Js8Cr/3BruOqRsnO67EHIpsev3gwBCErMnEQBEEQBI1SFlQvPQa9Pb+vIxMHISd4vr3L0s3iaTD90HnMcndlE5j2zqqJ5vwEq+ZI+DIrS5qYABovjY97NJjehMviBl+jZ/oLDFO1U1vmJlaBPABc54F5FryUJb0sfXNdpp/ger7MY+SKNeFVRJlHyMQ46JgQX2mJ8/pe1TRNg8yeBvN+ueJmyvkcPL+HXvjoe8AUSV2xNeSJ6aXnIWsMkNBXScHqtY5Db8/v60hWhSAIgiAIOSMeh1w5XddGtaXp0l/geg2KWWpc+Y/FOJjzLOcavCsKn+sVeGRPGHg/tMbOszI8shKy4bLMeawHWcZ+p2Wetoz1+MlCNwqSOnZAexx4VojxvMSdNTHI82I8MNSeYkDimWs8GAVL6g/O2A8DjxXR92diHChGgytEag9ISsdocGXItCfIGVvhit0w98s8T+AeLxoQHNfh308r7Iwp4UqW6fYnWIMiW2xQvjwaQsGQ4MjsyMRBEARBEDQS45AdmTh40dOsgmLDeAr0j2xryUa/wLmW7Bn7AIqK182YPoJZw/eqMZHNMvUYJ1ms3HInaByu2AaehWFOYB4Xn9MTwrMMrLJSux15CpLO+yML3ugd8OvpflNap8FkYVC1TmbJ8/OMDgMNnz0/93lOy928J+65YR6SVGenoxse20EeCF8oczVUHpvhpdjpjkHRw+GlNFgWD3kaXLVQ+PPOFY+/F65aHObvSJF5Hjx/z4ViRCYOgiAIgqCRpYrsyMTBC25Ba9IWBCn4neBaaF+HWRAq4VwD5joGrih1Ot/yeE4mS8IZvW8sToJXWWQxA2a4fuYR4mvf5ropx/hd2QU8C4Ot8Zvj3NDlNSTocpQlUV6m+2EWfMAZw6GizlgFXluDV8NMx2xQloi26JlCpImpMBZ/KmM7A+lesKyQbJYk94S49CW8anyQp4jV0HDFrJgLsXGwrBGXwiersWF0LLjHJU/wWhzgWSLFgpdnlsea9CMPhCxVZEcmDoIgCIKgUXnwOMjE4XTFclqkZi04S02FosGsWZOFZu92eRrIgjSeCFIizOKR8TEFRK6HQJY2VY80sQvaguG6DFwh0WOt3yv/nyzUVLdtMftY1oMX7ufg1GVwrdkTXCmTDhv9Bu1ZAXm4WEwNeSjIA0Nr+Kb6pEesCR3XHhAfeRZYjQuXPgNlfXjUDFEe2RRpxUemZMk9GRquCOp67166Hymn5yRdi8SXuT1ZwikWo9JTy5hnDzEPJfdwFW2sg1dMhznOnpPQr5GJgyAIgiBoFHo/vyn26ZFMHLxgFgTPQ3fnZxcJLDaBVydMt3PqMPQ4y4Q/R1f/3KLNHIvAdRz4Wr0yhl3mNXZPPQETxe/0fHAPg+fafyrzGrrRPaD+6T6pP6rBYGpLaEuOYgao6iV/Pl1d9j+0B8Hye3h6tEeAqnMaTwbpGNA4SimLJO443cSysNoTNE7yNLiyL3hVzNJSx37XWrk/8/U9Yxs0WT0T5kaOn33TY/j7cCmgUlXUFGtXZJ4HTX+OBUvBMp6+3vRRzBTp/36CIAiCIBQC8Thkw8uSLnIdBy+FSHc1Qm5B0ZpzFgvDy2PDlCJdtSm4xcvz+Nlaajq421kd0jPrgmeJcEVG5mlwKVfSMPjaPbsureUrHVNBug6032RBeCgfptfo9fVZjIOBe8oSTh0FV80Ill1gPC50v51dzvas2qjRweDPT3taLB5zQgqg5v71fZB+h/HQZNZXcNU88dKx8Hrv+fo9pjV+HjvDY4JYtoUrJuhEFStPNeY5enho+xGSVZEdmTgIgiAIgialLFii43BcZOLQU4pkDdITHtvAFSO5B4Jbdlk9DZajnRXUyodciTDoVPYDs0zT18usn5C2oJ26ADya3ygSMkuQ9Ah8ei3eZA2QJc2zKQiTFUBr1syip+cVZxY29+Tw+9TPyehHWJSlwKqP0nPT9236oe49dA1cn5knAR4eH/OZnis9L37co8aG8bCw56CYfgXPTqFYBl+Js/aEweu5c12OAH3PWCxFjngpV6aH4ay2ms7qOaHL9T14LZrTJevsNEcmDoIgCIKgUSoPWRVFPn+SiUM2iv0bQDDdCsIsufI8d+URDZ+tf/poshFIwdBZTdJ4EjyqOrqUFXlsAlnMXAGR6Qy4lAy15WlqTLDzTEwD93DQcV2jIV1zQh+POxUdzXOgGALyVJDFxmtbGI8EyxoxtRacngZTPZR5DkxMhYm1CDnHRRY+j3lgNTjA1/JLWE0O7pmg69BnrmSpKBZC7+/O/H5gFCH19yPuzH7xjH3hsR9cMZIrHnL9C/53gFnYXvomimWT8NiK/hwL4IDVoHHv7z9IjEN2JKtCEARBEIScEY+DYONlGWSL8u6x0p4zNsLk+5vsAI+vJLOEjeXqqn3gVJJ0ZWfwWghcX8CjloG5T5YVYcZNNRrIkib9AcqeiGrPBVnUcZ0lQBYy9c+qZLosdvKAkF4DW/s3nyl2wHgauh39uixp7YlwvQ+mx2AFfWzcrIYGNKRQaXQwmEeJVb80yqH0HOk5kW6FF9yzwL+PPFaDWfieMTwcL88DeSZ8+r0xz4NrXNRNvvQj+hr90MPAEY9DdmTiIAiCIAgayarITkGXKpYuXYoLL7wQFRUVqK2txTe+8Q3s2LHD0UYphUWLFqGxsRGlpaUYO3Ys3nrrLUebaDSKOXPmoKamBuXl5Zg8eTL27NlTyKEL+cKy9OZzaCZYPsu2zlLK3nyWvVFkEt98PsDnQ6o7ilR3FJZl2dZrKpWOhwDS7ZNJe9PHVTIJlUzC8vtsK1gfVykFlVKw/H7b+tSbaU/X4RuN+5hzHNYv3U8sDsTisMIhWOEQUh0dSHV0pJ8HbTRe3V7FYlCxGCyfz1Z51PdlhUK294HGEfDbW0nY3sIheyNCQSAUhFVaam8Dyu2tYoC9lZTYm96PcBgIh2GVldqbPs+MMx6zN/N87eegYnHbm0PPgR1HPGFvwYC9UTvqN5GwN/3cVHe37SWh9rTp9vQerWDA9pLo69H7Mu+ToHEQdH16T+kvpr35/M66E/S9oufOPtP3Jd0+ldl7wffnIxJPyCtef4J6uhUzBZ04bNy4ETfeeCNeeeUVbNiwAYlEAuPHj8fRo0dNmzvvvBPLli3DihUrsGXLFtTX1+Pyyy/H4cOHTZu5c+di7dq1WLNmDTZt2oQjR45g0qRJSBaru08QBEEQ+igFXapYt26d4/PKlStRW1uLlpYWfO1rX4NSCvfeey9uvfVWXH311QCAhx9+GHV1dXjsscdw/fXXo729HQ899BAeeeQRXHbZZQCA1atXo6mpCc899xwmTJhQyFsQThReJZBgCnOEK5uCZS0YHQWKGeA1CTyi2alfk2XATAHqz2Rf6JoPZj+PceBr6rSGn0xlbmc5r+evGWjfZoc9MTZZExpeQ0IxBUUTO0FVS4OZs0vofgwlWi/jsJ31gYQza4CuSzEW9B5MbAZlLTC5A1Mtk7IoKGuD76fPZPWzGAcT+2JiOFisBWUhJCk2gdo7a2yYmBVeJdULL50FE6ug27m+z+x9cx0DXi2Svu+8RgtdoNhN1H6E7THobYxDngbTRzmpWRXt7e0AgOrqagDAzp070draivHjx5s24XAYY8aMwebNmwEALS0tiMfjjjaNjY1obm42bTjRaBQdHR2OTRAEQRCyQcGRvd2KmZMWHKmUwrx58/CVr3wFzc3NAIDW1lYAQF1dnaNtXV0dPvjgA9MmFAqhqqrK1YbO5yxduhS33357vm+hf+Kls1CoKTHTgzAeAFadMp1V4fQEZPMYGHhevoaf5/JM0HWpOmPKuQ7tG1Bu/4PGcVRb6JTFQP2SZUz9Ug0JXhUxxPrRFri5DkEeBfIEhLXHgK5H9689B0Z5UrdXpdqyLw052vtiul2X7QlQZSXO87TnwiILnfZT9gnFS1AWhb4/V40OtmxI2SWKZ2sY3Q7yQOjdSY9aFOa6Tk+UWymSdD6cHglXfzw7xnhE9PMKOL83RuGR12ph1XPN9515LtzKkR62mlfWhiD0QU6ax2H27Nn485//jP/4j/9wHeP/KSil3P9RMI7XZuHChWhvbzfb7t27T3zggiAIwmmDytNWzJwUj8OcOXPw1FNP4aWXXsLZZ59t9tfX1wOwvQoNDQ1m//79+40Xor6+HrFYDG1tbQ6vw/79+zF69OiM1wuHwwiTBSjYuKpRsjXaXvfPlCG5Z8FjDTk9HFobzpxfzyeJ5rOPWYIaY1maKpJ6zdkoOrKqjtSfrglhdA9IP4HVULDKy5z90Lgo9kArGioSJmSxDC6lSIopII+AtviV9jAov9MzoOg+mOVtPA2dTv2DZPUAe3+X7pcrc5KngzwQFdojQtUwCRoHvT+yuI2CJdN9YLoYxoNEMQpJp8fHFdtANTe4AqjRZWAeDKpJwmNe4s7xmVgEep/M82TGRy5nXu0SzufgJXfiqjLL9iuvmAfTkP2eiEei4IiOQ3YK6nFQSmH27Nn47W9/ixdeeAFDhgxxHB8yZAjq6+uxYcMGsy8Wi2Hjxo1mUjB8+HAEg0FHm3379mH79u2eEwdBEARBEApDQT0ON954Ix577DE8+eSTqKioMDEJkUgEpaWlsCwLc+fOxZIlSzB06FAMHToUS5YsQVlZGaZMmWLazpgxA/Pnz8fAgQNRXV2NBQsWYNiwYSbLQsgAt/i8osVPdG2Vn+fyaNBuD0+D5cwCMJY9WYZ0PrMsPcdhYijIUnRmNxiPQjyeeb+JgdDZG9qjQJ4HX9kAPT5tsWuPgC9SaX/W2QdKe3JMbQtaoy9hHjDKPiCFSJMdoZ+X9jyQB8HitSSMR0J7Lshy1s8zVaavF7D3+47qWAMd65AcWGHv77A9CqpCe1DizpgIcM9GiGVrmCwP8hQwi5meM6+qSh4Kev6sloVXdohLKZTFxnCM54E8Dbz2Bq9yyqqzmvfEq4ay7CDv3zPn9S1wjwL7vWExE67j3JDNVo1W6Dn5WGsocsdQQScO9913HwBg7Nixjv0rV67E9OnTAQA333wzurq6MGvWLLS1tWHkyJFYv349KioqTPt77rkHgUAA11xzDbq6unDppZdi1apV8GdLtRIEQRCEnpCPrIgiX6qwlCs8uvjo6OhAJBLBWHwdASuY/YRiwMfWohmu6PCeYjHPAe3mHgV2PZdlyLId0jUO/BnP47EMrsBar6h6dl1X7Qf92YyHLG/yPFBWBFncdH80XjqfPBJkCVPtCjNAHW0/oMz5OUgWLdWs8Dn2pwLO+/HF9XNJ6DV+as/et6Wfh++IbTknBtqxC4EDR+x+K3RMRVT3Rx4EVlPC4joV9DPmEnZwfqTzeLVJ058efyyzJ8j8eYpn1mvw0vvwCpxOVwFl2S/6+8KreJrvG/t+muuzbAuv3zdeFdOzSiY/zSNGwlVVs/j/jCOh4ngRT6K9vR2VlZV575/+nxiy8lb4ykqyn3AcUp3d2Pk/FxdsrKcaqY4pCIIgCELOSJGr0wWvPPRe9mc+eij98eMuC59bfpRXz9bAeVS8V16/y9NAFinFAFD/VHWR+udr7hSjQGvcppqm9miUsZgFWps3VSu1pUixCGTB0HPXWQzJKtvzQNkO8TPtz/4uu7/4ALu/QBdlddjjS4Z07IJulywPOo5b2nNBHolEpT3eYJsdsxFrjNjna0+Dy4Lw637i+ki31lmg586yEkx9DMrCoBiBuO2pMZY8PU/KUuFKm7Sfe6a4jgbtZ7Ef6d0s9oGO85ga/X2EP8g+a88Hqz5q+uff/54umxrPhT6fe+p4nQtW3dPykZJmzy4rZEeyKrIjEwdBEARBIJTV+xgFmTgI/RKzdkqa+R5ZCT3FQxmSXzfdnmVPZIlxMJY/z7/3M88C1azg+gG81gVZjmQRcws3xWIiaLwJZ/S9uR3dD3ksSIFSlds/jQIjWcY8lkF7KigmwdKxCskB9n7yNCS0ByHUblvAhwdrzwTJNuhh+bvs+w50635CPsdxKL+jn0TEvg55JOIR+35Cf7OfQ7LK/hzUMRAcyv4w9xnQnoWubmfDBHsOugaIeV6kKMk8D8bC57ECrJaFK7bBy+NEOg8UU8G/Vx4eBRfkQTGxDD5H/2Ycycy/Z2ndCv2ZngvzJHBPhCD0RSTGQRAEQRA0p6Ks9tKlS3HhhReioqICtbW1+MY3voEdO3awcSksWrQIjY2NKC0txdixY/HWW2852kSjUcyZMwc1NTUoLy/H5MmTsWfPHkebtrY2TJ06FZFIBJFIBFOnTsWhQ4d6NF6ZOBQr5hucAlQKKpGwrXP9udf9EpbP3pJJe6PPHNpPW0q54hscl0kmoZJJWMFA2psAuD8HArbVmErZm99vb5YFWFb6OI1bX9cKBe3N74fl90MlU7a1GAjYGz0n+qzPo3ZWaaltPfPnEU8A8QRUOGhvwYBtpQf8QMCPZFnIsamgHyroR3xAAPEBAUQHhhEdGEaizI9EmR/xyhDilaH0/aQUrJRCuC2OcFsciXIfEuU+HG0I4WhDCJaCvaXsLVbpt7eqEGJVIXMdf3cC/u606mSiIoRERcjcT6K6HInqciQrSpCsKIEqDdvbgFJ7KytJx20AdoxDOASUldqbfg9WMGhvJSWwSkqgUilbtTIUtDefBfgsWH6fbZXrz17fIysUsuMT6H3H40A8nv5e+Hwub4TjfP19SF+PtafP+vtM3w/LsuyNvk9e/VO/pj/7fpRStleF7s/rPPq9oPvv7e+r0HNOgeb0xo0bceONN+KVV17Bhg0bkEgkMH78eBw9etS0ufPOO7Fs2TKsWLECW7ZsQX19PS6//HIcPnzYtJk7dy7Wrl2LNWvWYNOmTThy5AgmTZqE5DEetSlTpmDr1q1Yt24d1q1bh61bt2Lq1Kk9Gq8sVQiCIAjCKWTdunWOzytXrkRtbS1aWlrwta99DUop3Hvvvbj11ltx9dVXAwAefvhh1NXV4bHHHsP111+P9vZ2PPTQQ3jkkUeMOOLq1avR1NSE5557DhMmTMA777yDdevW4ZVXXsHIkSMBAA8++CBGjRqFHTt24LzzzstpvDJxON3IV763qUbI8te9FO/Mec7j6Sh5Z349j1kwUfC0Fs6qVJqYBY8oexemeqYeP+ktUOwGrcXr7AdT48JURaQgA73mT1UlSYGRsj20DgPVNqBqlSn9q9fZaF8ndMje3x2x9weP2v111dhr676Eft462+HQp+zxlLTr8erbjQ9wPvfgUftAtNK+fnmr/bw6PlXuOO7TMgmJcn0/JCdx2P5HotJ+PoEOHcuQJP0IfX869sPSCpomq4T0EOJOfQiLak+Q9U7fF8p2CZJVzmIAWKxMiilNmhgaFoPgo2qd9H2hmAVe64LHyDC4johXrRRXLQ7KdvGqueJ1Hv89Eu9DwclnVkVHR4djf651lNrb2wEA1dXVAICdO3eitbUV48ePd/Q1ZswYbN68Gddffz1aWloQj8cdbRobG9Hc3IzNmzdjwoQJePnllxGJRMykAQAuuugiRCIRbN68OeeJgyxVCIIgCMKx5GmZoqmpycQSRCIRLF26NPullcK8efPwla98Bc3NzQBgyjVQ8Ueirq7OHGttbUUoFHIUg8zUpra21nXN2tpa0yYXxONQ7BRaUc6VvcEseWOZU54+Hx6z4Fm1SLL0jSIk03lI6wUw5UGmAGjWnT2yN1znGb0J3X+AW776vlnWRCqs9QbiLO8/aF8/McDun7In/N3aI1Dt9DR01uj7pCSJlP18/DH7OpRdkSixHD9DtNypH2e01u6g9G+631p9/bjOqii3j5OnovQTXYND6z+kSuz7DhymrAzbcvdF9fMjxUmy1CnbhYahPRBUy8J4GijLgus7BNmfJMreMJ4E8jQ5FSuN5U7jYO/VvE/2fTL6Hrwd6XhwhUqP3yd+vrlfluVhvse8iqeH0iW/P+Wl8yD0SXbv3u1QjszF2zB79mz8+c9/xqZNm1zHXEq5SmX97vA2mdrn0s+xiMdBEARBEDS0VNHbDQAqKysdW7aJw5w5c/DUU0/hj3/8I84++2yzv76+HgBcXoH9+/cbL0R9fT1isRja2tqO2+bjjz92XfeTTz5xeTOOh0wchN5hshWSQCqZzobwWZl1+z2ixU3UOot2V/EEVDxhotkpO8S0o+h3HW1P0fUUxU79prMi9Ph49Hssbm/6fsx5BEW7U5R8ZTlUZTmsI52wjnQiWVmCZGUJrHgSVjwJVRKAKgnYNScCPpO14Iun4Iun0NVQgq6GEgS6kgh0JZEIW0iELcTL/YiX+xHsUgh2KZMd4Y8p+GMKnbU+dNb6zPGjDT4cbfBB+WzvBGVVxEstxEstpIJAKggcrfXjaK3ffI4N8CE2wId4mYV4mYVQexKh9iSOnBXSWxhHzgpD+Swon4WuhjJ0NZS536fP3lJlIaTKjsl2MMft90jZFSoasyuJmuwV/bzLSmGVlZrsEZOlEww4vRAsG4eyHnh2hIrFba8EfT+DQXujrBp6/zorw6CzQUwWkukvZnsT9P3R98jcr95M1ge7fzNe+n7S95pn9eSKV/aS0HtOQVaFUgqzZ8/Gb3/7W7zwwgsYMmSI4/iQIUNQX1+PDRs2mH2xWAwbN27E6NGjAQDDhw9HMBh0tNm3bx+2b99u2owaNQrt7e147bXXTJtXX30V7e3tpk0uyFKFIAiCIBgswFW//ET6yJ0bb7wRjz32GJ588klUVFQYz0IkEkFpaSksy8LcuXOxZMkSDB06FEOHDsWSJUtQVlaGKVOmmLYzZszA/PnzMXDgQFRXV2PBggUYNmyYybI4//zzccUVV2DmzJm4//77AQDf+973MGnSpJwDIwGZOAj5hmdbeGn4c8U8P69y6VFTgEe78+wIn15b5ut12kVoxWPO/SGPaqk0PqOUSbEYei2bxqGtYV+n3W+ywr4OVa9Mhez2FNMQrSaFSNu6PNyka0h0UvaDjoUo0zENXUqfp2tTaOO4s8b+HG6zjyfDlu6PzrPbpfTtBY/Y7Y7W2+eV/M3+HND9/+0Cu2HFbh3boG+/e6C9v+SgrqVRGdLj0IqVOvbBf9T5fqxk3PFZHbHz0anaqFFqpJgDqnFBVjT99IhxMMqhOjbCVeSXx9rwGheUpWKqbzKFUtrPa1Sw6qs8y8elXGrGw6poshgHF64YhszZFV5VY0+HapnFxH333QcAGDt2rGP/ypUrMX36dADAzTffjK6uLsyaNQttbW0YOXIk1q9fj4qKCtP+nnvuQSAQwDXXXIOuri5ceumlWLVqFfzH/B1+9NFHcdNNN5nsi8mTJ2PFihU9Gq9MHARBEASBOIGlhox99KR5DhM9y7KwaNEiLFq0yLNNSUkJli9fjuXLl3u2qa6uxurVq3s2QIZMHITC4PWLYLT4TVlAvd9pkflYVLux0MjChNNTYHEPBq2Lk8Wa0tcj3YBj1q8d0LjIE0HR+yXOoCZLseuY2gP2z5Tl1JUgSz2l9QmiNfbxcIdtOR6t05Y6MzTjFZldnp0Ndr/hNl0tU8tQkIchqYfr046Arlq7XUAb9tEz9Diq7J9lH2sLVtewSJZpD4fWeYgPIE+Lbn9I15rQj4FqbxgPTVxnqejxWmfoyHLSu6CsGV3jwqgx0nM070vfSJzpdXBPBL0nHldjvmfkgWAP2Mc9XamM+42+CPdImH5Y7Qqe3ZFj9oTpl1Wz9TpLHUd9VThBTsHEob8h0TWCIAiCIOSMeByEwmAsR1oDZnoNhM+5NmzWbElngdayuYVpzmdzXy/PA0EWJbfUyMKjcdB+WoMv0ZHypCTZYa/Zp6oG2LsrnB4JX0zrJpxtr+lTTAPpMlBsQfcZ2rLXBnV3jX1l0mkItdvtOhv02r7Zb3/uqle6f63j0K5vT3seogPJJaDHlbAcnwOdup0eh/LZB0oO2eMnxUmffm7BI/q+GmzFy5K/6YGXUMyD1mMIMM8AWdLmvernqTMaqNqoeT+8ymWQqps69SIsClkJstgaFtPAPQ1pS548Q0ypkfQ+yJHCFB1dMQpeSqUenjf3OFXmzyaGwaPqponlyHx54QSQstpZkYmDIAiCIGhOpLplpj6KGZk4CAXFaDlwZUieg+6VLRFntQjIUmWeCcqaMJYj7bdYTIOJqbCc+7VFa/mcFqfJ9mAejFSpHgcZqvp47AzdD53erRUlQ3a/yZCOLTiDlAB1TATThaEYhSPn6NvSngDoy1L/yXJdiyFm9999ph7mYR3TcFRfb6DdLtlJCpRwtC/5RD8OHSuR1NkgFoUI6NiHaMTeX7ZfKyzGtAVO2TRUi4LeH1XQpOwDUtzs1K4Oim3gtUYSPJtCjyfMsjDIo8SyIChWwHi8PJJ7eqzAyD1kdJ88u4LIdpz164qx4LFAXuMt1P9UrvvxGEex/08pOJCJgyAIgiAQEhyZFZk4CCcXZklZfjKhyTPhjE5XbE1cudbKqVaFs0aA8UiYao0Bx3WNBUhr5bSmTv2ZWAkWJU86ArqWQ6LKjmGgJc1Al91/LKL71ZZ6vMzuj/QWgnaIBLp1PRqy9MnzQP0l9ePx0VK6Hl6iyr7/kr32jkQZZUPoLIha+4SQPh4+4NP9K91e32ZMW7r0lyBJ16VxOmtahHQWSLRK6zt8ovUrtIfCx2JYfEd1NU0PS5w8PCrFYk+4jobG1Kig98ksX5dFz2NisnkY6LhXLA3XdcjmSTAxECxmgren++ZZHh4eE7Of2vP+8u0BoGq25BlJcV2JIgqykBiHrEhWhSAIgiAIOSMeByEzPhZtzjlRi4bHOHhEo1ss/52i76m6YVph0HJ+pnHxrAlumfJqmXQe6UScWQ0A8HXaFrMKU+yCbh90moBUTTJaobNASM9BW+4+7Xkg5cfugUy3gcsPaM9AyQH7wNHB2lMSsPeX7ravk2w+Yv/s1lH+XVrJst3+GTtT62J0a8teX6e0VStUluvYjAit3ds/SVmS9CEoJoKqaIbbEnqcfudz0RapL6qzJcq0YmfMbq+oeiiLebG0B8m8tTjT2WDKkea9J52xBinuAWDvXbGYAW65uyx8au/hgaCqoJ6eB/JUmGqsFBtzfD0JwlVtlqq4+qgqrUfMUL48AB71MPjzKiao3ktv+yhmZOIgCIIgCITEOGRFJg79FZelnacoZ5/TsuK1JtIWRqpn16Hz2BpyOtZAW2LaskrpvH6LxT5QloVpb+6fZVEwi8tEq1M0fJA8F87sCitIAgi2ia20HgF5HpIDbV14qkFBRAfq/iipo5R0EfRtac8DxTzQHxYTa6BDAcgDQBa+8TQE9bhL7M9dQ/TnT2w9hdIG2/PQ3WY/F3+DnXXg0y6GBHkGOu3n09lk32+oza/Ha1+X9CESpc7sC8ryKD1gXz+p7y8WsX+W7bPfl/+o7WlIROxx+buOqTwJwDJZMtpiLnF6csBiUQz6e6N0fya7QtcwMZY4y2LgHob095V5IviavRmw0zPmqr3CYx94TQpqx3+PssVG8OsjswfB5Smhz+RaKlCsQ4+zUfoTEuOQFYlxEARBEAQhZ8Tj0N9gngZXlLNRwOuhxcEsn3S0NtM9SMac+7N4HviaMX32BVjeupfyHkdbdCmtA+CrGOAcD+X/pzJbdIrrRXCYngDpDqS0HoEVpah+HSOgYxqMp6HM2X+ixL5+rMJZKyKuC9oFD9s/u8/UsQYDtaUa1Zar9jQgqZ9X1B5fsNK28JNBsrTtZgMG29KRXV26NkaSakeQZWz/sLQtHB/g9DSYqH992ZgeZ/iQHl+F3cAf0+d1JPV9k+6DjrHQypnJcnscwQ77fRldB/IceSlFkkeqy35g5GHingaTJUPvlcUeIKBjThJUy+L4WRY89iJdjVNn9ZBnwdSU4HoLTh0LF0z/weWx4B4SDle4VB6eEA8PRY9h13NX4SxCz4MsVWRFJg6CIAiCQMjEISsycehvME9D+jPpHjBLqIf9mrVk7cHw6RoERsGRW0I5eh5McxMdT5YW02vIYqmRJWqxKpcm9oFqSyTjzv7IcvTr+9F6AL7yMud1aG2asi3IEtZZEioUcPTn77SvG220LelAp9Z3KKOYBrt5WNecOHKWzjrQBvORQaT86KwhQdkXPsqS+JQdwxAI2O07O+zrDayxXRYDwrYHYtcHthRkWHsk4h22pRw6w/4c04IR/i7tOejWnodKp85A8IjWcbAvi85a+0Bkl/Yk6Cqf5GEJHE06nlfsDHvcJR+02ft1NgVlo/h0rQ9VWW4/p8O2R8LUhIgypVDyrLGYBh4roFy6Dc7vI9WYSMdQ6H7ofZOnKsgUMFlWg+LVVckip/GYGhPO87J61lzZEV5ZTR56E9wj0FPPo+s6mWNC3MeF0wmZOAiCIAgCIR6HrMjEob/DLZI8WRiW32lpGYudVxM0w6DFcee4+NprWj8h5WhnLD9exZCqBBqFSG2KsyqYXPeBPBLGQqVxp7RnIkxr1myNm65D1yf9hrhWhKyxPRShA7aF3HW2HWNBnoZYpdNiJH0ECrIOddg/u+p09kTU6WnwH7HHmarVehJ+rfTYbY/LV6azPXTswuFO28MSDtj3VV1nXyCetPuJlzstY/8Z9vlljXb/0bfOAAAEO2gcFPtgf0xpg3/AbnscR7XnoaRNHds8XTsjrBUm2+3rxOsq7c97D+mGzhoWJmaEe5rIM8Bjb8iSP6o9FKV29oZ5z1S1ktrrn4qf74olYJY6xU4Y3QSPNX7+2Shfss9WZr0HV5VMHzuPMLEGQed5TG/CpeuQrxiE08mzIFkVWZGsCkEQBEEQckY8Dv0Ul2JbrpYF139IUZQ41WJga6b8Oibq3DnnpBgLEw1PMRHkeQg4v2quNWKu85DuWO931jDg55u1a/I8cL0H5jlJV1PU7QaUO8en+zP6Agn7fIppSJxhW8zBDv3ZZBXY4+2qsX8Gj9jnd9bZ1y1r1bUfBlCMgN19rEZbjGS6d9Mavt2ust6OZRhUZccMfBS2LflBEfvzl87YDQB4/C9fAgDUVtjBCSVBe3yftEYAAOEK29Nw+KB9vyWfsdul/tt2MZhqm5SFQbevdR0o9oOyRUL6/lI6JiBIWRbltmUc+sSOZYjX2dcPHNJVMY3Cp7bESVGSPErkSRqga4FQNU1TpVR7hsz31qnzwautWrz2RZasBfKguWpEGAXILDUwvBRRPWJ4jHJpNiVGU9X1+IqYZjynk6cgT4hyZHZk4iAIgiAIhMQ4ZKXfTBx++ctf4he/+AX27duHz372s7j33nvx1a9+9VQP6+STtbof+8bymAeu/+C1FsctMo/8dp53biwqP4uFYBahq7pftvtKOfvhNQSUqVnAott59DmdF87y1TeWHV1HZyF06rX7EnttnWpUxCvs/v1xUn4kS9w+PaSzKo42Ml0JHcMQ/tg+v3uI7RHw6eyJUIn9vDq0h2B/yLbIa8psC/zfmp4GAPzD8zcCAHb+/a8BAKP+65sAgOpSbanX2z8+2V0FALB0jES027bog/S6ycOgFSzDf7PbUc2Kso+1noP2mJR/bI/Tr7MgogN11cz99n2kyrSS5VH7s8uitpyeDEU1SSgG5YguI+pRa8QVE8OrqbKYF9OOZzuYjtn3nukreMUmcBT7PXTFNhiPF6uFwXUTjCeOtefjMxcST4NQePpFjMPjjz+OuXPn4tZbb8Wbb76Jr371q5g4cSI+/PDDUz00QRAEQTit6Bceh2XLlmHGjBn47ne/CwC499578eyzz+K+++7D0qVLXe2j0SiiutYBAHR0dJy0sRacbJYExSik+Jonz+9m0eTkMeCWj0uRTjcnyynbeL3y13k2BfdAEOyzcnkajm/58X5U1LZIfWaNnK21U7T/EVuxMFWtpRN1jYmktqB92sJOap0LqhZ5pFFnYZDsBYUsDHTqIsQq9fEkxTrYDQOf2OcnBtqWd7euLUE6DORBqAzZxS3+YcNsAMBnvrcFADDkgZkAgJGf/QsAoCNm30/7kVLHgFSJfb+Bg/ZzSOnXFDiq9Tu0zkS6hoZ+TFrx069/vTrPtMc3YK/dH8U4ECmtsOnXipmqRGeH/E1XHaVaFboWiFWiXRv0/eqwYzusUr2fsgeSztgZoySp4Z/NfvJIeHi6eDVKymIAq4KZzp7IXDMm/fuR2fPAPXXumKDMNh33QLg8EkKvsZCHGIe8jKTv0uc9DrFYDC0tLRg/frxj//jx47F58+aM5yxduhSRSMRsTU1NJ2OogiAIQn+H0jF7uxUxfd7jcODAASSTSdTV1Tn219XVobW1NeM5CxcuxLx588znjo6O4p08eNSYoNiFdC0Lj9PJI8BjADyqDRL8c/oCztoWLk3+hFOB0pUdwjTxfWGu9Keb0Ro2WZYUQ2Gi3jOvVVOtg7TipF5Tj9guAEvHNJgo/+64/qwVHHVMQ0oHBcQr7ecUarf7K/vE/tk+WHsKdNaBL+6MFUgMoBgIp55DvEJbnJRVoWMgIgO0B0S/1w867FiFnZMeBAAM+bXtjVt76QoAwI/+asc4tEftC8aO2M+rpNq27GPd9vgSukZGyU77uaRC2vOTcuo5hNucMRtEqN0eb7TK7o+qZKZ0Nc7g33StCXp/OgZEDbA9IOTZMe+PeQpMbELWbAOnx8zoOlBWDXm8PPpxebpcMT76c4zXasmM6/eDYDoL6doXXh453jHP3nDWyDjOgI5/XBB6QJ+fOBCuYkVKebqnw+EwwvQfhCAIgiDkimRVZKXPTxxqamrg9/td3oX9+/e7vBCnFTwf3St7gCsqZrOsmKchfTnnmm22GAdPrX4T8+Bc0+Xj4NHuPFretGPR8jza3hwnD0iZ1gXQ0fomep+Os/x4Oi9Zbk9EAx22xZ440170Dx+0PRLxSm3Bl+jaDdqQpqqYFCvQPdD+GThq749V0Zq5/SNca8cwdHdoRUitu0A6DJQdUaFrUwx7dQoAYMaXNwEA7tg9CQBwoNO+z7Mq7fieLp3t0NGqYzaM1KPOrqiz31PogH7/ejykO9FZr3UodFZFvFw/F63fED6kPTj0HFPOmJBAl7bUSW8jwWJuiBgV6XDWGHF7wpzZPea9s5om4B6vpJfrjcUosH55jA5XVHV9j3n2Ua66K67YBe6J8Mia4vfh6jdP1TJPB2TikJU+H+MQCoUwfPhwbNiwwbF/w4YNGD169CkalSAIgiCcnvR5jwMAzJs3D1OnTsWIESMwatQoPPDAA/jwww9xww03nOqhnXw8lO8889FzVKLLFsvgWhYiCy7LWi/3CKSHR7EXx1fwMxYYeS54P1SbgKLS6XpxHfZPtSfo/rpoTZ15VKhmAq+RoH8GP24HAMQaz7Avc9RuH4voLAu9dg8dwxDo1gqLWnEx0EVr2/p5awMwcISeo44heNcOKqj47CEAQKeuRREaYFvsB9vs490DdAyFrlGxbu8FAIADHbYnJHrYPq8rao+vu1OPc4CO2dCeBt9+ux1lSaiAHrfOoiCFy0Cn83PJ33SWATmQTMyHfbx0j50+QrU9Au3aA6Xv30fvi7IVyNNA709ljklwxS6Q5c9rlhDc80Z46ZRQyIBL94F5FJR+jnQfplseg+CM+UmPi46T54KymZLO/Tnium+NK4ZIyIooR2anX0wcrr32Whw8eBB33HEH9u3bh+bmZvz+97/HoEGDTvXQBEEQhGJCliqy0i8mDgAwa9YszJo161QPI3e8opzzFd3Mq0/yKpRea68cUuwjw5fpLriyIvj4+Rqu0YFwWoCuKoA81oHdl/nIsim454FGY66j9TtM1kW3/hz0iM5neg4mm6LDjoFIDdSCC3p/8GM7ZqB7kJ3V4I/a4zW1Klj35fvt4+2D7ePhvzmPk0IjZVlQ7EPnX+yYhmSpVmYcaAdJpI7a9xEL2T8Paw/D4EGfAAAqyuz7jZGHwWf3GyqxLeTUX22PRUp7FpIDdPZKXFfl1FkVpa32+ya9iQEf0Zq7/cNHDp5Oun+7fdnebt2v7ckIduhqnkH9fDrT+ip2Q4qBcX4/U0e1rgN5jMx7d9YqoeMqzt4zfW2CzqqqJhbCS8mRfk947QuNqzomy2pwe9Cc46bxpajfbNkQXnhVwc13VUxByEC/mTgIgiAIQsERj0NWZOKQb7JV34Nz7b7HGMuIXYdHYXtZHnxcTMHR87I8O4J5JJRH1DY/L0U6DkwpktZiXVX+uEfC5MEzzwQbl7kuWws3a+Slpbqdvo+w1gHo1hZyqW0xW3Hdj46BiDecAQAIHdJ6BSF6bvZPssBVud1fIqyzEbTn4XCTM+uCPAx0O7EI3Z9+PgmdfdFuj8dXZj+PlK7CWVlje0Y+OWx7ErqO6iwG7WE4ekCX39T9oEpb6t2UdWD/IA9C2T4dq6CTL0r32+OIRuyGocPO721S31/wsFbSLLP/pAQOawufYkfoa6mft8m+IE9Rt047oRgQ8iTo2AevLB/w90weha5u537ymHEFU9J/oO8xxVrw75FHDIHB/D57/P75nLE4rhifE/UQWM7fH4qx8MzKELIiMQ7Z6fNZFYIgCIIg9B3E45BvWPVJl0KcV+2IE0U5LQzv2AZ2PRPlzSwppslvcV0Iuiw/zmIbjmlo/yDFPZbfruIeU3Ne5c9Ha9uU569rHJDng2oWUJ4/xTiYmIegY7+5H4qeJ0tYr0GT5wFUzbPc9lD4j9r7k6W65kKXfTygLd3OBp2lENOxCdpC76rRCpOH9eX0MMjTkNLDCB4hfQd9XyRdG0zpx+B8Xkd22bEQJU12x6rd7jheRkqZ2tOi0x+suD3OULsz1iR0iGpm6MvpcVI2iJ9qV8Sc1ycPC5kggQ7tuqD3q2ND/Af1+ALse8JjBjj0ffQ5bRwTE0HvzyiSaoVP0uvwqFnhgvr3OWMeuO5JupaFRz9engf+++PSdeihico9j+bvAPNgiGJkz8mHZLRITguCIAjCaYLEOGRFJg6FplBrjFxhzlwus6XkHs/x12xdug0eNSxca8b02ef0tBjPBFmG7Preug5OJT86j0fD01G+1m2qKprnxExFGo+2hM2aPK2VBzP/igTa7SCFeLW2bHXVyJIDOntBKypSDQd/VGcxaCV00nHoNjoPWokxbLcr222f11WvPQ3ttuWeKLHH6eu0x5cqs493HdFZDGfa4/Jv1zEPZ+k1dR3jUL7LPr+7RmdxdDtraJCngQtmBLVnwZdQjvET4YO2Z0fp50aeGYoRSemqoz5dBZM+o0OXCyXPAcUY8KwhY+nzGAKWDcGrXZLOR7czm8OnJemNJzDujKVwXYfpPGSFeR5cHgoTw9NLz6PLwycKkb1FYhyyIzEOgiAIgiDkjHgcCoSn5Z9vDwS3sChKnUVZu2tHsH6MDgMrJubKcsg8fpcHwKOdp94E+6y4EqBHlorrurT2Te2pezrdzyxVyuagWAleNZOyL8jTonUIUuUl+j71+dR91H5e8Urb8xE8Yvcbq9SWuE4eoFoPpQfIE6E9FNVUJVPrL+hYBF+UPAM6BqJRj6/LHpf/Y5110WmPq6vJPh5oo6qe9nXJ00DVOA0UhK8fT4mphmm3o+QWyp5Q+nsS6NYehYD28JADIEa1SCg7hBQRtedCex5Qol0wUZ3NQu8/xnREzPsnj5ZT2dNY2nptWSVYNk6QsjR0zIOpNcGzjPQN0PnZsimy4RVjIJ6BvossVWRFJg6CIAiCQORhqUImDkLP4MqHvc3Tznq9zN9QrxgH135exc90y5T1jMWeJcvCSwnPFQtx/LXpYwbsvA6vSkjN0gPR49Nr+2XaY2DWzlk0P6/CmfKIRjeeDO0BiLEaBeR50MfDbfb1Omu15+GoVlis1h6CGFPspO70ZcM6y4GyL6I19vnhg1qh8S86e6LCGXMQPdP+R7hVr+2b4A99G5QdoZf86Tza79OPKV5O47D7pxgHuv9QO9XqsC35kPa0+Dt0rAN5IBLOmBfzk5QTycNDMQVcSZJXrdTVTNOxKZSOQkqhWr/BZMsw/Q5eO8VDmdSzyqUgCDJxEARBEASDLFVkRSYO+eZkRzln07rnsQHMkjLa/WTx+T08FV7a+Oy4S9+BLEuPfr3Hm/m5kcfEVWPARMfr65DyYJRMa7J4KWaCouSZpUsWaRkpLmrPRadT2dAoS+rrJ4MU/W//6K62LeBwu455GKCzIfQww232P6KV9n6qCVHyib6svjx5Bvw664JqW5DuQsknOuYhotsdtQcQ7NCxCVSKg25rn3K0N/oRHfq96ccZPGr/g7IofDrmIFlq9x8+kNDn6f7JIqfH3GZnS6gS7SHg+g0mloV5mnh2hRHmZOcbfQR6f3FHNybGgfqnGBXKsog527s8Ysp5XjoWpsj/RxBk4pADklUhCIIgCKeQl156CVdddRUaGxthWRb+8z//03F8+vTpsCzLsV100UWONtFoFHPmzEFNTQ3Ky8sxefJk7Nmzx9Gmra0NU6dORSQSQSQSwdSpU3Ho0KEej1c8DkWGVzYHeRZSpP1PWRbMg+DyGHBLzMvDQWvXtKbM1qpNTQj6bILkeVVC7VHgOg90XsKpbGnGzaoNWiQYSDoMCb1Dr4WbaP/yMsdn15o46UKY6H79k7IEdJXKQIftkVD6c5le8+9ssGMsrKTWTeiyx999BilJ6ueS0lUx63RsAIUU6J/Bwzq2gkop6GHFtRwCeSYoCyNapfUgPnZ6HuIDLEf74BHl6K/8Y/uCUZ0FEm53KhSW7Oc1OmwCh7VCp9ZtUJV2kITx1NDzIj0FUvI0eh/MA0DfX54FQ++Z3muc1zJhMRX0PdTZG6ZWhPZspEhZlHvIOPS9NFkbRW5SnsacCh2Ho0eP4vOf/zz+5//8n/jmN7+Zsc0VV1yBlStXms8h+tukmTt3Ln73u99hzZo1GDhwIObPn49JkyahpaUFfv39njJlCvbs2YN169YBAL73ve9h6tSp+N3vftej8crEQRAEQRBOIRMnTsTEiROP2yYcDqO+vj7jsfb2djz00EN45JFHcNlllwEAVq9ejaamJjz33HOYMGEC3nnnHaxbtw6vvPIKRo4cCQB48MEHMWrUKOzYsQPnnXdezuOViUOR4akYyY9TlLrW9lcei3IuD4RZo2a6Dswz4VLg88ry4DEWtJ8sSu7h8MqLN8p+2rINOqPqjUeDYh7I4xKNOc6nLAyLLGNS6DxkL+ZbA7QlTdeNO2MxUlqWIFFuW9TkYfDFdYzAQF27QStJkh4CeRrK9+rsC60o2a11HahKJXkMAp369kr17WjDnvr1dztjDoigrp2RLHV6MMIdWm9igM7a+DjhGF+wQz8nyiqJ6iqdQfa9oNoe2sJPPyemi5BgWS1x5nEgKFaBPEVIOPszNU9Ylgt9rxB0HtffW+Np8FJAZbENru9vlpgfQQCAjo4Ox+dwOIywVi3tKS+++CJqa2txxhlnYMyYMVi8eDFqa2sBAC0tLYjH4xg/frxp39jYiObmZmzevBkTJkzAyy+/jEgkYiYNAHDRRRchEolg8+bNPZo4SIyDIAiCIBAqTxuApqYmE08QiUSwdOnSExrSxIkT8eijj+KFF17A3XffjS1btuCSSy5BVE+AW1tbEQqFUFVV5Tivrq4Ora2tpg1NNI6ltrbWtMkV8Tj0d/hav8kOCGZu76HAaA6TJU5rwWS587Vjj89eSo4mJoHVrDDt2HWomqWJZmeeDzM+WgNnCoAUNW+yJGhJnnsiyPMS0FZAV7dzvNoyNrEQVK3R77zvVLlWbuy2rxvUHpRU2L5edKB9vOSg1neoo9oT9vOqftvef7TeqfRY3mr3Ey8jpUbKcrCPl2nlye4zKLbB/ln5gX3ekQbtQdD9JENaZ2K/08JOaOXKit1R3Y89vpJPqBqoPS7jedD4dWxDaoDtEQi0H7UPmPfn1H8wnz2qk4LpdJj3zGuymBol2lNE3wuuGKkx1VIpdsUrhod7rrwUS5kSq3geiod8xjjs3r0blZWVZv+JehuuvfZa8+/m5maMGDECgwYNwjPPPIOrr77a8zyllENbJ5PODm+TC+JxEARBEIRjyYO3AQAqKysd24lOHDgNDQ0YNGgQ3nvvPQBAfX09YrEY2traHO3279+Puro60+bjjz929fXJJ5+YNrkiHodihfLQs3gYiHSsgbawKd+dW/bMQnNlH1B/rv6dFh55AhSLOTA1BVgWBrpjjnbGI0EWH3kCeEwGX5vmUfkUw9BpV5Wk6HvwWhX0mdc20J99nVRrIfPzLt1nByUkKuznNOAjbalrhcWuM+37CXbqGIU46UPoqpb77fvs0sqTwS5SqrT7p+yL8n3aQ6FjGMr2Oz0NlM1ijms9BkvrMyRK/Xq8tueFPA2Bw/r566wWkz2hYxz8lFUywPbMWO266iVZMvQejDAnUxql99lFWTnHr45pPFv0Pk1sC/v+0Pcs4fSgUbVMzxgcl6IpVxhlHjDyiChzgxCEQnHw4EHs3r0bDQ0NAIDhw4cjGAxiw4YNuOaaawAA+/btw/bt23HnnXcCAEaNGoX29na89tpr+PKXvwwAePXVV9He3o7Ro0f36PoycRAEQRAE4hQIQB05cgTvv/+++bxz505s3boV1dXVqK6uxqJFi/DNb34TDQ0N2LVrF2655RbU1NTgH/7hHwAAkUgEM2bMwPz58zFw4EBUV1djwYIFGDZsmMmyOP/883HFFVdg5syZuP/++wHY6ZiTJk3qUWAkIBOH4keRJZ7bGhaPMnfFKDDFSbC1XotXEyRdBeqXrUUbi5DONxaqvq62DPkaNa99oFjNARPbYKp+6gNB9pUnizVMef5x5/V07QNjQWqL1qLsAd2N0jUULJ2lYcV09kG3fb1EZYn+bI8zUaEtY327pZ/Y1yWLP16uYxP22/0drQ/pz/b5VCWTCGkDn5QeuyM6lkFnUVBtjFRA15o4ohUty+3rlRzQtSfOcD6fYLu+fy7nQR4D7Qmyoiwrgr4fUWc2hqKYEa0kqbq6HKeZ70GpFqhgHgFLewLMeT7n9xD8/dH3jK5P2TJG54GyglhMDv/Lz/RGTHVZipExSpjkqSiQYqxkcxScU6Hj8Prrr2PcuHHm87x58wAA06ZNw3333Ydt27bhN7/5DQ4dOoSGhgaMGzcOjz/+OCoqKsw599xzDwKBAK655hp0dXXh0ksvxapVq4yGAwA8+uijuOmmm0z2xeTJk7FixYoe359MHARBEAThFDJ27FiXqN2xPPvss1n7KCkpwfLly7F8+XLPNtXV1Vi9evUJjfFYZOJQ7PAYBxb74FJ05FUx+ZeZrzX7Wf8s28LlgfBqx6tSUl6/Pj+lsx1csRb8OhTrQJYri71wRcvT2nbcmS2gqD+KqfBQjqSYB+tIp+M41WigGABSlkyW6+yDo9pjQQ6esK5loT0GlX+1sxO6a23LO9xhXy+ma15QDQyiu9r5nM/4q21Zxyq1QmLYHmep9iwkynSsxFG7n5SOpSjb26U/6+eu/0QE2u39imfRHLbvW5XZ43TV9CCLnrIdqIYIeQx4VUuuF+IF/16xaqc820Gx74uryirL3jF4xQjxGCLljMlQ+Yp1EA/DyUdqVWRFJg6CIAiCoDkVSxX9DZk4nG7waHFjoXlEl3tV0dR4eh7oeIIpBrJqhWCxCWmPQY4WG2tvsiLo+h5ZH+b+uU4EWXikA0E1DshC1rET5il1OmtkgBQTyfJmOgR+lpUSrxlgd6uzFmLVtuWeLNG6CUf0+XEar7bQ6XHrx1O+zz4/UWI/V9JhSAdh2P9I6zCQ58O+T9KloGwJv/5pJchjQDEolFXBslMI8jTo52ViWFLkqaHn5XN8Nt8v6pfeB2VbmBiJuOM49zy5Ymfimfs32RX8+0dwPQdzfx4ePK/vc289BiamgmIoqB/J3hBOHTJxEARBEARCliqyIhOH0x2ef05Lw2atmSz643fDPRHgngjuaeCY2hGZsyhMloRXFgVdx8Qw5KYo6FobJ0uXqmRSVgfpOVBWB42HYjEoCyDqzPqweI0Gspwp9qFNxw7oGIdgu30+xTwED9mei3iV3X/wsPYQaP0HqiVBhA/a7VOllFWiL6vfcyxiP9fgEbsfowj5Nx3bUKprabRRMQz2PI9qzwt5AsgTwbIVLMpS6aTsB2csgXl++n27YguMPgfpMMTZfub54kqnWfRI0gqr+niMyo46a1O4YmWyeCAUr/aaylN2BXkefPr3sdDZG6czMnHIiihHCoIgCIKQM+JxKFKMsp3P6Ukw2RRmrZRZ7twydHd8/OtxT4Vi/TFlR5c+g8XWurmHwqMmBq9VQZahYp+NDgOdz9am02vqpA/gc47P51HNUWd9GAJOD4NZi6bYCY8YDrqLwEFbmCGhYyBCe+0qe6kB2pJnMSXkUaCYBPpsqldqQtqjQbEMviOkyGmPkxQgafzGk0D3QRY8KW2a/Tpbgp6T0c/wOe9b1/xQRzsd/dF4jYeJPBYxZ7aLy9Pg4dky3wvueWB6Iy5PFPv+U82XtMeCfR/pex5XGfefMOThYdVshcIjwZHZkYmDIAiCIBCyVJEVmTgUG2RpGUOFf4OdVSG91mgtHopgqhWSJ8MZ5c0tI2PZ0fkmq0KPJ6QtPq4Eyde6qX2Q5edruAXoUrBkio+mHcv2IAvXeCqUtjDpV8SVhcHGS54I6pdZxrSWbtbgteKiKtVZGEYXwbbMU5V2TEVw3yFHu3TshG5v9AicFrevw/YU+Gh8pJ9AlizPSojq50aeHvI08CwV0tNgNSCovaUVNFPttoeEPDhWmY4RoWyLMmcNEJcOAn0v2PvmsRA828dczyNbx1UVk76/5JFgni+Xpc8+u36/Us6YkB7jcZ7X75lQAGTikBWJcRAEQRAEIWfE41Cs8GhubsmQ5UZR2T5nbAIVdzCWDtjaMFn2PPrcZA9kjjngNQXS42UWJ8+3z5J/76p1wfd71DIwnhTm+eBZGmmdB7Z2T5DlTdkCxvNAHiCmC6AtbktnJRgPhFZg9B066vhM2Q6+Dmdth1SZ7RHwUa0IbXmrEKupQZ4Geh50PWrHswq058B4HswFtceFsl8otkGnb6gjdmwGWf6UrWCyU0gJVMc4WCU6G4XGab6XTk+RqTHB9UeMxyc3vQT+faBviyumht57KjcPm/tCJ2hyKuf3hXs0uCKmkH8kxiE7BfM47Nq1CzNmzMCQIUNQWlqKc889F7fddhtiLNjpww8/xFVXXYXy8nLU1NTgpptucrXZtm0bxowZg9LSUpx11lm44447jqvrLQiCIAgnhMrTVsQUzOPw3//930ilUrj//vvx6U9/Gtu3b8fMmTNx9OhR3HXXXQCAZDKJK6+8EmeeeSY2bdqEgwcPYtq0aVBKmUIdHR0duPzyyzFu3Dhs2bIF7777LqZPn47y8nLMnz+/UMMvPrJNtJi2vpeinqfSnumG1s4pq0FbdtySs5yWvbHoTc0KFivBouZNVgTP3yfIwibPAvVLsQ4+5/np0zLX4rDArqe054NZ0jxKn3tAyCOhDtseBaN0GXDGZJgsA309f5fWO9CxDqmQjpnga/4xpndAWQsxp+KiUYrsdGZRIMY8DBqTRUFZESTwSB4TpjhKWRMma8bnzE6hfqhGiOIxF/Q8jT4EZTvocXvUUnHFwLDnn36/HrEw1E8WT4Pr9yDfhozpT2IZhL5HwSYOV1xxBa644grz+VOf+hR27NiB++67z0wc1q9fj7fffhu7d+9GY2MjAODuu+/G9OnTsXjxYlRWVuLRRx9Fd3c3Vq1ahXA4jObmZrz77rtYtmwZ5s2b5/7DLAiCIAgniCxVZOekxji0t7ejurrafH755ZfR3NxsJg0AMGHCBESjUbS0tGDcuHF4+eWXMWbMGITDYUebhQsXYteuXRgyZIjrOtFoFNFo2nLq6Ogo0B0VEV4WE1n+ZOkxCyzXqPL02ixZ8rp5jMVKGP0Eli3hkW1hPBFMLyEdC5FyfPaKljeeCRofHSZdiYQzj5+yC8x1/c6sCjOh5ZZ4lGpnOLM8TFYCr9UQ08+lQlvolDWhYxTA1uYV6S+Qh0F7KkxWRaczRgLME2DGqdv5Kiuc53MPAJFwKkJaIco+ceopmHFqj4R5rlqZ03ik6PeXVyf1Oz0NPObGYHQkMrc3o+ceLPq+cM8C1z/JtzKkF7Ike/KRrIqsnLSsir/85S9Yvnw5brjhBrOvtbUVdXV1jnZVVVUIhUJobW31bEOfqQ1n6dKliEQiZmtqasrnrQiCIAjCaUuPJw6LFi2CZVnH3V5//XXHOXv37sUVV1yBb33rW/jud7/rOJZpqUEp5djvipTnlQwZCxcuRHt7u9l2797d09s8/bAsp5dApZxWeSoJpJJQKWVbXfq45bPSEebH9qOU3nQ/+vx098rhrTCffZbTmk0p55ZMAskkrGDAGddA+0NBhzVvBQK25oDfb2+6f5VMQSVT6X5onJbP3oIBe9Pt6btt+tXXsUrCsErCUIkEVCLhamfuj64XDtlaB3q8Khazt1QKKpVKP7dQ0N78PsDvg3W40966Y84tpewtFre3eML2WtDz8vnsLZ4A4glYPh8sn8/2jCQSUN1R24sQ1VssDsTi5rklDxxE8sBB13M2G//eJFMms8N+fyl7o3b6vFQsjlQsbt6L6uqC6upK7zdfJ/t5KqUcXifL73doeNBn83fI74Pl95n+eHuDfj68/3THPmeGkMf3WSgiJDgyKz1eqpg9ezauu+6647YZPHiw+ffevXsxbtw4jBo1Cg888ICjXX19PV599VXHvra2NsTjceNVqK+vd3kW9u/fDwAuTwQRDocdSxuCIAiCkAsWjlnK6kUfxUyPJw41NTWoqanJqe1HH32EcePGYfjw4Vi5ciV8rJ7AqFGjsHjxYuzbtw8NDQ0A7IDJcDiM4cOHmza33HILYrEYQnodev369WhsbHRMUIQ8wa1lExNw/KqArqwCXqOB98tjIcyaMeXxM2VL8kJQqASPng849R7Segy6AaslYPQDos4YAKO/QN0b5Upn1UWe72+8HGxN3Og6UKyIVk40sQk8u4OyLGhcPGtClTjHS9U4PaqPKqZboWhNn31O37fzufoqK+123VoxkvQdYkyHg8cY0PtKOMfPYw1MTAPPcjCxCyxGheAKkKxmCfXno+eZrTorr4rpVXtCYg4EoXAxDnv37sXYsWPR1NSEu+66C5988glaW1sd3oPx48fjggsuwNSpU/Hmm2/i+eefx4IFCzBz5kxU6j9YU6ZMQTgcxvTp07F9+3asXbsWS5YskYwKQRAEIf/IUkVWCpZVsX79erz//vt4//33cfbZZzuOkdXh9/vxzDPPYNasWbj44otRWlqKKVOmmHRNAIhEItiwYQNuvPFGjBgxAlVVVZg3bx7mzZtXqKHnhpcSY3/Hypw9we/XS0HSrUCp8fI88FoZrJqhC24xJpnnguDVMhnGgtaeB/OZV4FMaoudskjIAqYoe/pM/TKPh8my8DOPild7UlCkLAfKAtCWszqq9R+SetxkwZOl3e2sJWHuhzwP5Jmh7AryiNBPyi4gT0aSPRfyTNDzodoVZTrrQ3tK6Lm4PAP0fvX1UlEaF6slop+Dr1TfJz1Hpsvh8nSxLAyeDePC1PpwKnt66jV4ke3vAfes5VtxUsgbko6ZnYJNHKZPn47p06dnbXfOOefg6aefPm6bYcOG4aWXXsrTyARBEATBA0nHzIrUqugp3HIw9PM1UOZpSN8fq1FhLG+uvMfy3xMez8Glx+D0GBjLk8cSsDVoly4AWws3egNk6XOLkyx8qrngikVgVSBJ8ZBZuOY4VX2k2AF23ECelDizoBm8lobrfo1ehdZXoNof7Hm4YkW0R8IQpxgJ0j3gugn+zOMgHY1SFitCMI9AinkMUvS86H0bvRC7Xx/pRtD7oedB90n37fqeOH8P07VTWMwNj1EhDwV5kui58PO84H8PjAvK+fzTnjp2frFnaBSrh/Y0RSYOgiAIgnAsMq85LjJx6CXptVDakaOF0k/w1Orna/U8G+IEUQm25u3leeAWnr5uihQYyfNA78FjjdzUUOjUtRUoNoB5Ckx/MRYrQJY2WcBsrd14HnQ1TLBaFrymhOt+PSx96kfxKpx8zd4ocerrBZ0Kma5+yAKnbATqz3y/WUxK1Kk4Sbg8M2TRa8+NT6dLp7NwnFkwrufIa5Jki21hsRQuDxGrhqpYdoeJ1cnmCdDtLK4Qyr6nLs+ceT4sO6VY6MeB6xLjkJ2TphwpCIIgCEL/RzwOPYXHArD9vbW4TxmumhSsloNHTQBeg6LXuNaKWTYCf+58bd7P9jNMNUvSQaC1eBPj4KyxYGIb2Jo/WbDGEmb6A2b4ZFkzy9xVq4LGq9ubLAmWjWCes9Gz0J9ZjIPxENBav7HwnboPZMHTcaOrQJ4VVqPCszYEy5oxz4/FwhiL35V1wbMQPLJQWEyC6/p0eoB5nFhsCI9xcJHt++zhaXDFBtFxFsORfkFFCv976PIQ9uH7l+DIrMjEQRAEQRA0slSRHZk45IrFLQcPz0N/xVhszvsjyAPhuZbrpdPQQ/hasEs5knkeSB9BJZmF6OGZMHoKHgqQhPE08JgIimnQ4zI6AzzLhPrxqJZpsjbI80CeBhYLYMbFPCLG4vU7j5vzuKVN90MxAuy5WfSXjjwQLGbCnGeeP3kEnM+d6ya49BN8zjX/FD0HiiFhHhzXc6WsHvp6mtgb53jSsQp0PfY98ai26vre5WoZcwvbeCyc2UjmvuKZY0OKDq4Lw2NTiiQW7HRDJg6CIAiCQMhSRVZk4pArHhZ1vrIJ+gzM80AWA7eYXJbViVoQXBfDeBY8PA3ckiMLkrfjMRHG4mF6D7SWzy1Sk8/PsiCYJZ2K5rb2r4wiozO7w+hN6J8pVvnVeDyU0wNgxu2l6OnxHtIeBpatEWAeBdOh8z2o7k79ma1ZM90GT9h1qZaE8YiwmABPjx7LmjCeDxoX3T/3QND1mafEne3A3jvBlFF5TBD3kPC/D8WafeWFW4GWPEN9N8ZBliqyI1kVgiAIgiDkjHgcegpZCIpZMPx4seKKDs8PPD+f59+n8/nJkmTZDF7j4UqXQaeFarIGWE2JFKv+yMdnqi/qGg2eFqqGPATm+XVHM7bjNTqM54ApWRpLm+k8kEeBK126LG2KZSDlR3pOvLYGq21hlB+5UiSvCkr4mOeExR64PDSWc79nzRKwWBLypLB+XffPY1k89CI8q7vyKpm0m/rxMU8D3VaAKV0W698Jj1gwQ289lCcDWarIikwcBEEQBIGQiUNWZOLQW/rijDkfZIl18Kx+2VNYLAHVKCCLPx2LEHO0d60Z826Z8qLPTxYfWdxM2ZArFLIocB7tz6s2cv0GM17+vLJUaeTKl8af5VJIpKqXTPmRDpNuA7fc6XlTe1Yzw2R5sCqZrpodljNmxOCRt+/ysDDPFY9l8DGdC8ViP4zHwlQXdb4vi3lYzPk8FoW/d1ctiSxVXlOZPY+85ovbs9KHLe58kq+/EycRiXHIjsQ4CIIgCIKQM+JxEI4P9zx4Hj/R/p0WqvE0kAVJa+letQNYrIkr/5+yL8h3yC1TboGamAE4jhMmBkKPiyx7wugSUJaG0Rvw0I/gypd0YfJ08OwQE2PDsi9YTAhfSzfjIs8CeQz08Fw1JZingixyt+XPsmBMfzqmg9dqyBWmy+BS9vSIWTHX59UxTa0P3Y7HIPAaGLSffz+8Yh/MhXnWj1NB0uV5KDb6gUchK7JUkRWZOAiCIAiCxlIKVi8nQL09v68jEwchNwr1i+Dh0XDlu+eY9+2l4GgsQW4Z85oBPDvAQ5/DUyGSZWlkrcFAliiLrfBUzKTL6toUKaZj4apKyZ8DWdw6RsNKOpUlTZVOxWIIyIKn7Ih45qqOWXUMWEyDS6eBPvNsCPJAxEl3gzw2Hu8n4NTpMONjnieDhwfCPBfTgZengdXqYN+jfGchCcKpRCYOgiAIgkDIUkVWZOIg9A16HSuRuYqiqVKYpfaBwWQdMMuWKzIyPQeXpU+WpymW6IxZ4NfnegXmfMU8E9wCptgIlkWQ1nnQNTVCzqwSky2hYzGMhU3ZD0FWvZMgTwPVgDDjYO1IiZHFSHDoeZgsChaT4KnnwWuAcMx70e/T6/vF379nlkUW3RavcfRhhUQhM5JVkR3JqhAEQRAEIWfE4yAUJ9wDwXQOFItdIGU/85nHMDCL2aXwyBUJTa0Ep0a/q6YB9zS4LF6nxWxiDDyUEI8ZgP1TewhUyu88n5/Hql5yvQYTY0CeCPI8mOqbLOaB6XN4xZwYjwQpP7LYBsUteqbT4KXP4DrO+/OCV7H0UsRMn8Duq8hNzdMBWarIingcBEEQBEFDSxW93XrCSy+9hKuuugqNjY2wLAv/+Z//6TiulMKiRYvQ2NiI0tJSjB07Fm+99ZajTTQaxZw5c1BTU4Py8nJMnjwZe/bscbRpa2vD1KlTEYlEEIlEMHXqVBw6dKjHz0gmDoIA27JUyaRtCasUVEpBpRSsQMDegs4NKQWkFFQ8YVvV+jOSSefmsxwR/NSvLxR0qCNalgXLssw4rJIwrJKwbdH7/eY4Uil7o/4Jn8/edHvTjvbT9aNRe1PK3pIpe9OfzXnUnvYT8bgjo0IlElCJBCy/L13fwr5RQKVc92meM6GfG51P/UEpe+P3y+4n3Y/dzjwnjsd5ZtzsPdH4LZ/lXaETsD0Rnt4IQciNo0eP4vOf/zxWrFiR8fidd96JZcuWYcWKFdiyZQvq6+tx+eWX4/Dhw6bN3LlzsXbtWqxZswabNm3CkSNHMGnSJCSP+X2bMmUKtm7dinXr1mHdunXYunUrpk6d2uPxylKFIAiCIBCnYKli4sSJmDhxYuaulMK9996LW2+9FVdffTUA4OGHH0ZdXR0ee+wxXH/99Whvb8dDDz2ERx55BJdddhkAYPXq1WhqasJzzz2HCRMm4J133sG6devwyiuvYOTIkQCABx98EKNGjcKOHTtw3nnn5TxemSoLxY1lOTeCLFpmMRpPg7Y0U7E4UrE4rFDI3vx+vdmWqq8kDF9J2FioxuLXm/FIkAVO7bUFbPrVHgb6bCx7bWkbz4C2yM1ns5HHRN+Xl2VOz4H1zy16brlT/8YDQe3086D99FzIU2PakwWv25vnTh4e8lzw65IHgtCeBXM98tSwcbmO6/dA43JBHiPyPLDvhafngd67UDTkc6mio6PDsUV5Vdkc2LlzJ1pbWzF+/HizLxwOY8yYMdi8eTMAoKWlBfF43NGmsbERzc3Nps3LL7+MSCRiJg0AcNFFFyESiZg2uSITB0EQBEEgVJ42AE1NTSaeIBKJYOnSpT0eTmtrKwCgrq7Osb+urs4ca21tRSgUQlVV1XHb1NbWuvqvra01bXJFliqE/EI1JbyssJMVdc5qCnAr00vJj0f/+8p1LQpa19dZBV76AlwXwKVPQPdP+gt+j7k7q9XAa2i4lA9dN+LMQjBZCyVhZ/+UdcHfC38+TMGSK266ngevscGrZbpqTSjn+bxfj++NZ1YF/8xqUXjWKOHj5kUtSU9DlCCFHNi9ezcqKyvN5zCrbdMTeOwOedSOB2+TqX0u/XDE4yAIgiAIx5CvjIrKykrHdiITh/r6egBweQX2799vvBD19fWIxWJoa2s7bpuPP/7Y1f8nn3zi8mZkQyYOQn7w+e3Nay270LBYBhOLEAzBCobSa/SUtWCO21kSriwHyp6gtf9g0KGmaGIKWDS/K/uC2vO1eopd6I7aG63B0xo/xUmwmAMTE0GfeUwAxVS4si7stfvU0U6kjnZCxWL2RtkV+jzab9buaaP+6DNlQ1DMAB3nsQJ8HDxbgWef0H1Tc57VwV87xVjQe9XPx3xm2Siu7BEaL8vucI2bX5fGL1kVxQfFCfV2yxNDhgxBfX09NmzYYPbFYjFs3LgRo0ePBgAMHz4cwWDQ0Wbfvn3Yvn27aTNq1Ci0t7fjtddeM21effVVtLe3mza5IksVgiAIgnAKOXLkCN5//33zeefOndi6dSuqq6txzjnnYO7cuViyZAmGDh2KoUOHYsmSJSgrK8OUKVMAAJFIBDNmzMD8+fMxcOBAVFdXY8GCBRg2bJjJsjj//PNxxRVXYObMmbj//vsBAN/73vcwadKkHmVUADJxEPIFKQGyGAGzJsyrXeZrRs7W5nhVRouECEkZkmIvTAyGswaFZ8wBr8JIMQIsViAdk+DsxygXkhVLP/V4fUxB0avqo4HFTFi6PdXGUB5VM0k50tS84LUiwM43n/V46T0HfI77cNXUcNWmYDVCeEwBq6LpihnxZX7PvDaHUbwMOGMa+HlmfD4eC+Ictau2iFfsjqmmKrUp+junolbF66+/jnHjxpnP8+bNAwBMmzYNq1atws0334yuri7MmjULbW1tGDlyJNavX4+Kigpzzj333INAIIBrrrkGXV1duPTSS7Fq1Sr4j/mb/Oijj+Kmm24y2ReTJ0/21I44/v1l1WDt/3R0dCASiWAsvo6A5VG8R+gdlvM/KA6XZD55EweP8toEn+hwiWWP67iO8zLTPLiRLs+KZJmJgy5z7Zo4GOlqNiHhEzCaOHCpazZxSEtEOycOfMKUfeIQdJzH/4x4FbXynDiYE09s4uAqfuUxcTCS2h4TB15OPdvEwRUkKUWtCkZCxfEinkR7e7sj4DBf0P8TI775UwSCJb3qKxHvxutP/KRgYz3ViMdByCv8D61L679Af1jTNQ50TQiaD/CJAvuPwQtXFU0vTFS+c6LAa02YapL0Hx3VjDDPyWPCQLCqkSZLguIoeLVKi00k2HhdWgYengMvi938x0rzCVfti8zVJKm9pSzHfaU9Ms5uPLNH2ETKTBjYc0r343OOk/frEadgtBvo+0LtaEKmTUtX9czit8eE0xiZOAiCIAiCxkqZeXev+ihmZOIg5AdTxVB/dC1NkMWW7xgHZilySzLFjjPLkVdvVB4hDlT90VRz9Kgema5iGXSeR/2wpQqLLfEonzN2ID0Apv9AGR5kefPqnHypIZVwjN+1BMJhLniL6zGQxyHBlmhyfa/GA6UtduVcuuFLKFyvwXh2uGeILw3xqpt0eePhoONOz4XneIkU8zQIxcMxAk696qOIOSl5RNFoFF/4whdgWRa2bt3qOPbhhx/iqquuQnl5OWpqanDTTTchRuuvmm3btmHMmDEoLS3FWWedhTvuuCN7eVxBEARBEPLOSfE43HzzzWhsbMR//dd/OfYnk0lceeWVOPPMM7Fp0yYcPHgQ06ZNg1IKy5cvB2AHrFx++eUYN24ctmzZgnfffRfTp09HeXk55s+ffzKGL/SEbLr9+Z7wsewIV5Cbx1o9wRUFyUNhYhMoap88CLSWzoMoqR1Zsgln8B23bHlWhrGo6XzyLFAsAg/KSzo9DTyLIr0Gz2IMeFAg4RGkqHi2g1e4uNd7ZfstP4sZMJ4AFktCz40/F+ZhsFhMCb9v7kngWRXGg0Tn8xiYLJ/TWUMU7CEGTX/nVGRV9DcKPnH4wx/+gPXr1+OJJ57AH/7wB8ex9evX4+2338bu3bvR2NgIALj77rsxffp0LF68GJWVlXj00UfR3d2NVatWIRwOo7m5Ge+++y6WLVuGefPm9VgqUxAEQRA8yYeAU5FPIAu6VPHxxx9j5syZeOSRR1BWVuY6/vLLL6O5udlMGgBgwoQJiEajaGlpMW3GjBnjkOqcMGEC9u7di127dmW8bjQadVUlE04SJ0lNjV/PqELq6pbmMCkpElzpjxQCjVKjrWDIFQtN9UZSxKTqk7zqpldVStaPgSlP0vWNIiNTanQpPHJIBZFX1dSbSwmSKSRyhU1X9cdU0rnl+l71c+Lvh4+DK2GacbFql64qmaT4yNq7n49+3rq9uU9zIrtf9pm3N++zUN9v4aSTz+qYxUrBJg5KKUyfPh033HADRowYkbFNa2urSyO7qqoKoVDIUdErU1UwOpaJpUuXOiqSNTU19fZ2BEEQBEHACUwcFi1a5NLJ59vrr7+O5cuXo6OjAwsXLjxuf7lU68pUFczrXABYuHAh2tvbzbZ79+6e3qbQ32AWn0rEoRLxtIfAHGA1GMjTQJa6hr7LaYvWWRPBwGsa8NoVzIKmWgqmVgJ5BqhmBNWu6OqC6upCqqvb3qJRpKLRtGVO4yHLmmo9eFnM3HLWlr/xLsRjUPGY+3yP59tj+Pnk+aHnr7dULI5ULO56H67uyIPi8Zm/P692Lo8LPRePzbQjTwP3OAn9H5WnrYjpcYzD7Nmzcd111x23zeDBg/HTn/4Ur7zyiqsa2IgRI/Cd73wHDz/8MOrr6/Hqq686jre1tSEejzsqemWqCga465MT4XC4V+VLBUEQhNMTCY7MTo8nDjU1Naipqcna7v/8n/+Dn/70p+bz3r17MWHCBDz++OMYOXIkALta1+LFi7Fv3z40NDQAsAMmw+Ewhg8fbtrccsstiMViCGm1vfXr16OxsRGDBw/u6fCFYoVbfaTTQFH4lEXgUipk2Qi8W6aAabwHHvoHriwA+mxqUVD2BtvPdCFc6/OkG0CmjJf171Wp0SPbhdfwKJguAX8/NB66nNGn0H+S6H559gN/Xy7JZ32eD5mP6/NSpK9hpKq1HoVLr4GUJv3Oz+Y+ivx/CEHIQMFiHM455xw0Nzeb7TOf+QwA4Nxzz8XZZ58NABg/fjwuuOACTJ06FW+++Saef/55LFiwADNnzjT63lOmTEE4HMb06dOxfft2rF27FkuWLJGMCkEQBCH/9LGy2n2RU6oc6ff78cwzz2DWrFm4+OKLUVpaiilTpuCuu+4ybSKRCDZs2IAbb7wRI0aMQFVVFebNm2eqhwmCA48qnaaGhjZxSSchW/Ekl6eB1zyg80g3wMNj4LLkKf+fMiPIQ4Ljn+dVi8FVE4Tae9RksJhsgtkfYPeXTZfjRGE6G1zBk79Hc9x4GrLYPF7Fs8xjIr0NpnzJPQrGwyQKkacLslSRnZM2cRg8eHDG9KhzzjkHTz/99HHPHTZsGF566aVCDU0QBEEQhByRWhVC/4YsQmZpp5UbPapbeihMuhQIeZVPtubuWTbbDI/FUnAFQl59kyk1co+BVwyCZzlzL+VH7pnhtRx8TkVNzxoOJwiPKXArMNJzpWqemWMxXB4Vfh2Paq1enhhXec4idzkLGchHVkSRf21k4iAIgiAIGlmqyI5MHIT+jbEImSXPo+D1Z2OBZrFUTfdm7V33G2QxECyLgsc4pKs6kkeEYghYrQmPcbk9BplrJ6jjaB44oOdB42SxH67ql9Rvb6ua0nnKef20B4G149otHh4K1/Og9szT4OXBSVfplBgGQcgVmTgIgiAIApFFfCznPooYmTgIfRNu4fJYBq98em7ResU4uCzVzFkWHFeWQ4DFAihm6XqcB5YdYPk8dBZo7T/ldd85WsouHQWnp8b0D95/irXPE9n68/A8mMPmD7MzZsHT06DfE8VquDw5gkBIjENWZOIgCIIgCBoLeYhxyMtI+i4ycRD6JkZvwGlJGgvR57QoDcbzwDwQur1rjdvoNuj2HoqEXvVRyII1MQ7MsjX7eXYG12HwsoCzWea5xh54HefPqa/BPQ9esR7weL8UA2L6cXp4VKLITcPexqYIQgZk4iAIgiAIRD6UH4t8oiYTh76O11p/kX8x01H92kLkNQx4zECWNXFX9L72RVqKx07wLAh9us6C4MqOxkPhIws4c60K7mHguGIZzAHn+L2UIV0ei2L7fnh5Hugw0+1wKWOy51OwmhynGq/vv2eMi8CRdMzsFKxWhSAIgiAIxYd4HPoq3ELgugTF6nnwiqJntQrSSoPsPK8sDH4ZrgPAr5dkCoUetS34+Aw0Tq4cSbCaE546DMzT4BqH0X/g2RFF6nnwgsW8mOSaHJU3i4ZsHinRq8iOZFVkRSYOgiAIgqCxlILVywl3b8/v68jEoa/BLW5uQbCo/aLzPOSav5/N85LN8kp36PzMsixMbAXBlR49akSY7vm4yCKOO5UiPcfn6tBZ5fE4F87SoJ/hpcdhLGiejcJiWjjF9nxY7Ifre8mzkcTzIPQCmTgIgiAIApGCax56Qn0UMTJx6KtksZhdnodiI1d9AW45+rhiYNBx2Cg0ch2AHPUcqJ0v4BG7QMPiMQ/m+jxGI8dqjIo8HdSRR4yGiYkoEk+Uh+fJPD9k0fModrJ4KNO1RqQmR67IUkV2JKtCEARBEIScEY9DX8Oj2iOvnmgsVVmzdOKhd2DwqKbo1lkgPQBmwbGqlwYWvW+BKUXS+V5R/bkqRHrdF8++yBbT0VuLyFMvo7CWlkuB83THFdujdydNWon9U/4+5I5kVWRFJg6CIAiCQIhyZFZk4tBX4Z4H2m10C8SSyIjJytAfc7RMXboOpFDppRTpEQthdB+43oPXGrzxJHhlh+RWLidbdkevPQ3ZYiryjVdWDXu+bo9Kcf/B9oSeF882kr8PPUaUI7MjMQ6CIAiCIOSMeBz6Olmi7AUPvCwtT30ImkPzLAcWO6DY+SlnzITXGrxrf65ZD1wx0j3wLKfTeNn1exs17tVvvrI5stWm4K+30J6Gvp6l4tIL6aPj7A/IUkVWZOIgCIIgCBorZW+97aOYkYmDcHpxwh4cXl3T6aHgCpMW0xfosd6Gq5rh8bNFclac7CkstsHL80HVKPOe7dBXLDeL1QDh9JVxCsJJQCYOgiAIgkDIUkVWZOIgCLlAfwi0boYrCyObpd3LNfK0R8EjBsN1PQ+9il7+QfPMaihUdsWpgmeR8BiWYr1vQXQcckCyKgRBEARByBnxOAhCT0ixmhFeHgiKbcgWe+AFr9WRTc/BFVWfp1gDj3GoRJGbVIRHtcm0givt6ONZF0LOSK2K7MjEQRAEQRAIiXHIiixVCEJvSCWBVBIqqbdE3N70Zzre6z9GdL7XdrI42dc7VVg+56ZRKeXY0gdOk+ciFIRFixbBsizHVl9fb44rpbBo0SI0NjaitLQUY8eOxVtvveXoIxqNYs6cOaipqUF5eTkmT56MPXv2FGS8MnEQBEEQBELBzrrtzXYCc8jPfvaz2Ldvn9m2bdtmjt15551YtmwZVqxYgS1btqC+vh6XX345Dh8+bNrMnTsXa9euxZo1a7Bp0yYcOXIEkyZNQrIABeFkqUIQ8oFYm8UFKY/6uDKmR7aKUDScqhiHQCDg8DIQSince++9uPXWW3H11VcDAB5++GHU1dXhsccew/XXX4/29nY89NBDeOSRR3DZZZcBAFavXo2mpiY899xzmDBhQq/uhyMeB0EQBEEgFLIvDWbd7K46OjocWzQa9bzse++9h8bGRgwZMgTXXXcd/vrXvwIAdu7cidbWVowfP960DYfDGDNmDDZv3gwAaGlpQTwed7RpbGxEc3OzaZNPZOIgCILgBcWo8M+nKsZE6Fc0NTUhEomYbenSpRnbjRw5Er/5zW/w7LPP4sEHH0RraytGjx6NgwcPorW1FQBQV1fnOKeurs4ca21tRSgUQlVVlWebfCJLFYIgCIJA5DGrYvfu3aisrDS7w+FwxuYTJ040/x42bBhGjRqFc889Fw8//DAuuugiAIDFUrKVUq597mFkb3MiiMdBKA4sK7vWgSCcKOJZOH3obWAkbQAqKysdm9fEgVNeXo5hw4bhvffeM3EP3HOwf/9+44Wor69HLBZDW1ubZ5t8UvCJwzPPPIORI0eitLQUNTU1JriD+PDDD3HVVVehvLwcNTU1uOmmmxCLxRxttm3bhjFjxqC0tBRnnXUW7rjjDij5JRYEQRCKkGg0infeeQcNDQ0YMmQI6uvrsWHDBnM8Foth48aNGD16NABg+PDhCAaDjjb79u3D9u3bTZt8UtCliieeeAIzZ87EkiVLcMkll0Ap5UgxSSaTuPLKK3HmmWdi06ZNOHjwIKZNmwalFJYvXw7ADi65/PLLMW7cOGzZsgXvvvsupk+fjvLycsyfP7+Qwxf6Mh61BARBEHrDqciqWLBgAa666iqcc8452L9/P37605+io6MD06ZNg2VZmDt3LpYsWYKhQ4di6NChWLJkCcrKyjBlyhQAQCQSwYwZMzB//nwMHDgQ1dXVWLBgAYYNG2ayLPJJwSYOiUQCP/jBD/CLX/wCM2bMMPvPO+888+/169fj7bffxu7du9HY2AgAuPvuuzF9+nQsXrwYlZWVePTRR9Hd3Y1Vq1YhHA6jubkZ7777LpYtW4Z58+YVZP1GEARBOE05BcqRe/bswbe//W0cOHAAZ555Ji666CK88sorGDRoEADg5ptvRldXF2bNmoW2tjaMHDkS69evR0VFhenjnnvuQSAQwDXXXIOuri5ceumlWLVqFfxMLj0fFGyp4o033sBHH30En8+HL37xi2hoaMDEiRMdalcvv/wympubzaQBACZMmIBoNIqWlhbTZsyYMY61oQkTJmDv3r3YtWtXxmtHo1FXGozQz6EYBrNpRT+Vsjf92QoEYAUCEvMgCEK/Yc2aNdi7dy9isRg++ugjPPHEE7jgggvMccuysGjRIuzbtw/d3d3YuHEjmpubHX2UlJRg+fLlOHjwIDo7O/G73/0OTU1NBRlvwSYOlIO6aNEi/OQnP8HTTz+NqqoqjBkzBn/7298A2MEePHCjqqoKoVDIkWaSKQ2FjmVi6dKljhSYQj08QRAEocjotYZD8QfS9njikElTm2+vv/46Uil7zfnWW2/FN7/5TQwfPhwrV66EZVn4//6//8/0l2mpgaeQZEpD8ToXABYuXIj29naz7d69u6e3KfQ1PH4ZrUAQViCYbpZIQCUSxzQQz4MgCD1AJg5Z6XGMw+zZs3Hdddcdt83gwYONhvax7pZwOIxPfepT+PDDDwHYKSSvvvqq49y2tjbE43FHmkmmNBTALYhx7HVyTXsRBEEQBCF3ejxxqKmpQU1NTdZ2w4cPRzgcxo4dO/CVr3wFABCPx7Fr1y4T8DFq1CgsXrwY+/btQ0NDAwA7YDIcDmP48OGmzS233IJYLIZQKGTaNDY2YvDgwT0dvlAs6CwKZUoIaK+CP6T36wOp/Bd4EQShiEkB6K2TssiTvAoW41BZWYkbbrgBt912G9avX48dO3bg+9//PgDgW9/6FgBg/PjxuOCCCzB16lS8+eabeP7557FgwQLMnDnTqG1NmTIF4XAY06dPx/bt27F27VosWbJEMioEQRCEvEPpmL3dipmC6jj84he/QCAQwNSpU9HV1YWRI0fihRdeMHrafr8fzzzzDGbNmoWLL74YpaWlmDJlCu666y7TRyQSwYYNG3DjjTdixIgRqKqqwrx58zBv3rxCDl3o61jOOa/xMCgd32B0HvTPIv9FFgQhT5yCdMz+hqVOAwnGjo4ORCIRjMXXEbCC2U8Q+i5c+IkgASj6OnNvVPF/zQWhqEmoOF7Ek2hvb3fUf8gX9P/EZUN/iIC/dzFyiWQUz713T8HGeqqRIldC/4ImAIrFLoiHQRCEfJBSgNXLvx+p4v77IxMHQRAEQSBkqSIrMnEQioMi/0UVBEHoK8jEQRAEQRAM+RBwKm5DRiYOgiAIgkDIUkVWCqbjIAiCIAhC8SEeB0EQBEEgUgq9XmqQrApBEARBOE1QqbQuTG/6KGJkqUIQBEEQhJwRj4MgCIIgEBIcmRWZOAiCIAgCITEOWZGJgyAIgiAQ4nHIisQ4CIIgCIKQM+JxEARBEARCIQ8eh7yMpM8iEwdBEARBIGSpIiuyVCEIgiAIQs6Ix0EQBEEQiFQKQC8FnFLFLQAlEwdBEARBIGSpIiuyVCEIgiAIQs6Ix0EQBEEQCPE4ZEUmDoKQDyzr+MeL/A+JIBQNohyZFVmqEARBEAQhZ8TjIAi9wefPuNvy2R4IZSwPHWUtngdB6NMolYLqZVns3p7f15GJgyAIgiAQSvV+qaHIDQSZOAhCT6BYBsu5ykceBrNfWxzG85A8KaM7+XjFdhT5H06hiFF5iHEo8u+/xDgIgiAIgpAz4nEQhJ6gWMwC8zyArW2qZJG6GringXlazPEit7yEIiSVAqxexihIjIMgCIIgnCbIUkVWZOIgCCcC/WHQwQtKWZmPFxs6i8TEdGgoe8Ty28eNp0U8D4JQdMjEQRAEQRA0KpWC6uVShaRjCoKQndPFotZ/EFXKjmkgzwP3QLhiHgShvyBLFVmRrApBEARBEHJGPA6CIGSH61eQ5yFb0kiRW15CEZJSgCUeh+MhEwdBEARBIJSCSbfuVR/Fi0wcBEHIDssiEQTh9KWgMQ7vvvsuvv71r6OmpgaVlZW4+OKL8cc//tHR5sMPP8RVV12F8vJy1NTU4KabbkIsFnO02bZtG8aMGYPS0lKcddZZuOOOO6CKfEYnCIIgnHxUSuVlK2YKOnG48sorkUgk8MILL6ClpQVf+MIXMGnSJLS2tgIAkskkrrzyShw9ehSbNm3CmjVr8MQTT2D+/Pmmj46ODlx++eVobGzEli1bsHz5ctx1111YtmxZIYcuCIIgnI6oVH62HvLLX/4SQ4YMQUlJCYYPH44//elPBbi5/FCwicOBAwfw/vvv48c//jE+97nPYejQofjZz36Gzs5OvPXWWwCA9evX4+2338bq1avxxS9+EZdddhnuvvtuPPjgg+jo6AAAPProo+ju7saqVavQ3NyMq6++GrfccguWLVsmXgdBEAQhr5wKj8Pjjz+OuXPn4tZbb8Wbb76Jr371q5g4cSI+/PDDAt1l7yjYxGHgwIE4//zz8Zvf/AZHjx5FIpHA/fffj7q6OgwfPhwA8PLLL6O5uRmNjY3mvAkTJiAajaKlpcW0GTNmDMLhsKPN3r17sWvXrozXjkaj6OjocGyCIAiC0BdZtmwZZsyYge9+97s4//zzce+996KpqQn33XffqR5aRgoWHGlZFjZs2ICvf/3rqKiogM/nQ11dHdatW4czzjgDANDa2oq6ujrHeVVVVQiFQmY5o7W1FYMHD3a0oXNaW1sxZMgQ17WXLl2K22+/3bU/gXivdT0EQRCEk08CcQAouKc5oaK9Fi6jsXKjNRwOO4xgAIjFYmhpacGPf/xjx/7x48dj8+bNvRpHoejxxGHRokUZ/1M+li1btmD48OGYNWsWamtr8ac//QmlpaX49a9/jUmTJmHLli1oaGgAYE8wOEopx37ehr44mc4FgIULF2LevHnm80cffYQLLrgAm/D73G5SEARB6JMcPnwYkUgk7/2GQiHU19djU2t+/p8YMGAAmpqaHPtuu+02LFq0yLHvwIEDSCaTLiO6rq7OGNB9jR5PHGbPno3rrrvuuG0GDx6MF154AU8//TTa2tpQWVkJwA7+2LBhAx5++GH8+Mc/Rn19PV599VXHuW1tbYjH4+Yh1tfXux7e/v37AcD1oAk+qxswYAB2796NiooKz8kGp6OjA01NTdi9e7cZf7Eh91g8nA73KfdYPJzIfSqlcPjwYcfSdj4pKSnBzp07XVl9Jwo3gAG4vA3HkslAzvX/q5NNjycONTU1qKmpydqus7MTAODzOcMofD4fUinbDTRq1CgsXrwY+/btMx6I9evXIxwOmziIUaNG4ZZbbkEsFkMoFDJtGhsbXUsYXvh8Ppx99tk5teVUVlYW9S8wIPdYTJwO9yn3WDz09D4L4Wk4lpKSEpSUlBT0Gpyamhr4/f6MBrKXcXyqKVhw5KhRo1BVVYVp06bhv/7rv/Duu+/iX/7lX7Bz505ceeWVAOw1nAsuuABTp07Fm2++ieeffx4LFizAzJkzzZdpypQpCIfDmD59OrZv3461a9diyZIlmDdvXp+djQmCIAhCLoRCIQwfPhwbNmxw7N+wYQNGjx59ikZ1fAo2caipqcG6detw5MgRXHLJJRgxYgQ2bdqEJ598Ep///OcBAH6/H8888wxKSkpw8cUX45prrsE3vvEN3HXXXaafSCSCDRs2YM+ePRgxYgRmzZqFefPmOWIYBEEQBKG/Mm/ePPz617/G//2//xfvvPMOfvjDH+LDDz/EDTfccKqHlpGCSk6PGDECzz777HHbnHPOOXj66aeP22bYsGF46aWX8jm0rITDYdx2223HXZPq78g9Fg+nw33KPRYPp8t95sq1116LgwcP4o477sC+ffvQ3NyM3//+9xg0aNCpHlpGLCUqSoIgCIIg5EhBJacFQRAEQSguZOIgCIIgCELOyMRBEARBEISckYmDIAiCIAg5IxMHQRAEQRBy5rSeOLz44ouwLCvjtmXLFtMu0/Ff/epXjr62bduGMWPGoLS0FGeddRbuuOOOPlP2e/Dgwa7x84IqH374Ia666iqUl5ejpqYGN910k0t6tS/f465duzBjxgwMGTIEpaWlOPfcc3Hbbbe57qG/v8tM/PKXv8SQIUNQUlKC4cOH409/+tOpHlLOLF26FBdeeCEqKipQW1uLb3zjG9ixY4ejzfTp013v7KKLLnK0iUajmDNnDmpqalBeXo7Jkydjz549J/NWPFm0aJFr/PX19ea4UgqLFi1CY2MjSktLMXbsWLz11luOPvry/RGZ/s5YloUbb7wRQP9/j8IxqNOYaDSq9u3b59i++93vqsGDB6tUKmXaAVArV650tOvs7DTH29vbVV1dnbruuuvUtm3b1BNPPKEqKirUXXfddSpuy8WgQYPUHXfc4Rj/4cOHzfFEIqGam5vVuHHj1BtvvKE2bNigGhsb1ezZs02bvn6Pf/jDH9T06dPVs88+q/7yl7+oJ598UtXW1qr58+c72vX3d8lZs2aNCgaD6sEHH1Rvv/22+sEPfqDKy8vVBx98cKqHlhMTJkxQK1euVNu3b1dbt25VV155pTrnnHPUkSNHTJtp06apK664wvHODh486OjnhhtuUGeddZbasGGDeuONN9S4cePU5z//eZVIJE72Lbm47bbb1Gc/+1nH+Pfv32+O/+xnP1MVFRXqiSeeUNu2bVPXXnutamhoUB0dHaZNX74/Yv/+/Y573LBhgwKg/vjHPyql+v97FNKc1hMHTiwWU7W1teqOO+5w7Aeg1q5d63neL3/5SxWJRFR3d7fZt3TpUtXY2OiYgJwqBg0apO655x7P47///e+Vz+dTH330kdn3H//xHyocDqv29nalVN+/x0zceeedasiQIY59/f1dcr785S+rG264wbHv7/7u79SPf/zjUzSi3rF//34FQG3cuNHsmzZtmvr617/uec6hQ4dUMBhUa9asMfs++ugj5fP51Lp16wo53Jy47bbb1Oc///mMx1KplKqvr1c/+9nPzL7u7m4ViUTUr371K6VU378/L37wgx+oc8891/ze9Pf3KKQ5rZcqOE899RQOHDiA6dOnu47Nnj0bNTU1uPDCC/GrX/3KFOoCgJdffhljxoxxqKBNmDABe/fuxa5du07CyLPz85//HAMHDsQXvvAFLF682OHCf/nll9Hc3OyoOjdhwgREo1G0tLSYNn39Hjnt7e2orq527e/v75KIxWJoaWnB+PHjHfvHjx+PzZs3n6JR9Y729nYAcL23F198EbW1tfjMZz6DmTNnmgq5ANDS0oJ4PO54Do2NjWhubu4zz+G9995DY2MjhgwZguuuuw5//etfAQA7d+5Ea2urY+zhcBhjxowxY+8P98eJxWJYvXo1/vmf/9lRU6i/v0fBpqCS0/2Nhx56CBMmTHDVUP9f/+t/4dJLL0VpaSmef/55zJ8/HwcOHMBPfvITAEBra6urUidVNWttbcWQIUNOyvi9+MEPfoAvfelLqKqqwmuvvYaFCxdi586d+PWvf23GyKuwVVVVIRQKmYptff0eOX/5y1+wfPly3H333Y79/f1dHsuBAweQTCZd766urs5Vaa8/oJTCvHnz8JWvfAXNzc1m/8SJE/Gtb30LgwYNws6dO/Gv//qvuOSSS9DS0oJwOIzW1laEQiFUVVU5+usrz2HkyJH4zW9+g8985jP4+OOP8dOf/hSjR4/GW2+9ZcaX6R1+8MEHANDn7y8T//mf/4lDhw45jLD+/h6FNEU5cVi0aBFuv/3247bZsmULRowYYT7v2bMHzz77LP7f//t/rrb0nwoAfOELXwAA3HHHHY79mWqpZ9qfL3pyjz/84Q/Nvs997nOoqqrC//gf/8N4IbzGqVg9+JN9j8CJvcu9e/fiiiuuwLe+9S1897vfdbTti++yt2Qab18d6/GYPXs2/vznP2PTpk2O/ddee635d3NzM0aMGIFBgwbhmWeewdVXX+3ZX195DhMnTjT/HjZsGEaNGoVzzz0XDz/8sAkOPJF32FfuLxMPPfQQJk6c6PBi9vf3KKQpyonD7Nmzcd111x23DbcqV65ciYEDB2Ly5MlZ+7/ooovQ0dGBjz/+GHV1daivr89YSx1wWxL54kTukaA/Vu+//z4GDhyI+vp6vPrqq442bW1tiMfjZvyn4h6Bnt/n3r17MW7cOIwaNQoPPPBA1v77wrs8UWpqauD3+zOOt6+NNRtz5szBU089hZdeeglnn332cds2NDRg0KBBeO+99wDY381YLIa2tjaHtbp///4+WZa4vLwcw4YNw3vvvYdvfOMbAGyvQkNDg2lz7Dvsb/f3wQcf4LnnnsNvf/vb47br7+/xtOZUBVf0JVKplBoyZIgrAt+L5cuXq5KSEhNA98tf/lKdccYZKhqNmjY/+9nP+mxA3e9+9zsFwETeU3Dk3r17TZs1a9a4giP7+j3u2bNHDR06VF133XU5R2H393f55S9/WX3/+9937Dv//PP7TXBkKpVSN954o2psbFTvvvtuTuccOHBAhcNh9fDDDyul0kF1jz/+uGmzd+/ePhtU193drc466yx1++23m+DIn//85+Z4NBrNGBzZX+7vtttuU/X19Soejx+3XX9/j6czMnFQSj333HMKgHr77bddx5566in1wAMPqG3btqn3339fPfjgg6qyslLddNNNps2hQ4dUXV2d+va3v622bdumfvvb36rKyso+kcK3efNmtWzZMvXmm2+qv/71r+rxxx9XjY2NavLkyaYNpWNeeuml6o033lDPPfecOvvssx3pmH35HpWyo68//elPq0suuUTt2bPHkfJF9Pd3mQlKx3zooYfU22+/rebOnavKy8vVrl27TvXQcuL73/++ikQi6sUXX8yYInv48GE1f/58tXnzZrVz5071xz/+UY0aNUqdddZZrnTFs88+Wz333HPqjTfeUJdcckmfSeObP3++evHFF9Vf//pX9corr6hJkyapiooK845+9rOfqUgkon7729+qbdu2qW9/+9sZ0zH76v0dSzKZVOecc4760Y9+5NhfDO9RSCMTB6XUt7/9bTV69OiMx/7whz+oL3zhC2rAgAGqrKxMNTc3q3vvvdc1m/7zn/+svvrVr6pwOKzq6+vVokWL+oSF2tLSokaOHKkikYgqKSlR5513nrrtttvU0aNHHe0++OADdeWVV6rS0lJVXV2tZs+e7UhJVKrv3qNSSq1cuVIByLgR/f1devHv//7vatCgQSoUCqkvfelLjlTGvo7XO1u5cqVSSqnOzk41fvx4deaZZ6pgMKjOOeccNW3aNPXhhx86+unq6lKzZ89W1dXVqrS0VE2aNMnV5lRBugzBYFA1Njaqq6++Wr311lvmeCqVMlZ6OBxWX/va19S2bdscffTl+zuWZ599VgFQO3bscOwvhvcopLGU6sOSeIIgCIIg9ClEx0EQBEEQhJyRiYMgCIIgCDkjEwdBEARBEHJGJg6CIAiCIOSMTBwEQRAEQcgZmTgIgiAIgpAzMnEQBEEQBCFnZOIgCIIgCELOyMRBEARBEISckYmDIAiCIAg5IxMHQRAEQRBy5v8HTBpxSG3IFRgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(\n",
+    "    sense.observation.uv_coverage, \n",
+    "    extent=(sense.observation.ugrid.min(), sense.observation.ugrid.max())*2\n",
+    ")\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sense",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/py21cmsense/data/antpos/hera-h1c-idr3.txt b/py21cmsense/data/antpos/hera-h1c-idr3.txt
new file mode 100644
index 0000000..d4f4003
--- /dev/null
+++ b/py21cmsense/data/antpos/hera-h1c-idr3.txt
@@ -0,0 +1,71 @@
+-1.353014802932739258e+01 -1.181450861941557378e+02 -9.566264385916292667e+01
+-1.884822099935263395e+01 -1.045396618698723614e+02 -9.560969073977321386e+01
+-2.416632062196731567e+01 -9.093424828629940748e+01 -9.555672054504975677e+01
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diff --git a/py21cmsense/data/antpos/layout_ecef_SKA_central.txt b/py21cmsense/data/antpos/layout_ecef_SKA_central.txt
new file mode 100644
index 0000000..855cbb6
--- /dev/null
+++ b/py21cmsense/data/antpos/layout_ecef_SKA_central.txt
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diff --git a/py21cmsense/data/antpos/layout_ecef_SKA_core.txt b/py21cmsense/data/antpos/layout_ecef_SKA_core.txt
new file mode 100644
index 0000000..b55f2fc
--- /dev/null
+++ b/py21cmsense/data/antpos/layout_ecef_SKA_core.txt
@@ -0,0 +1,224 @@
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diff --git a/py21cmsense/data/antpos/layout_enu_ska_central.txt b/py21cmsense/data/antpos/layout_enu_ska_central.txt
new file mode 100644
index 0000000..71a93d6
--- /dev/null
+++ b/py21cmsense/data/antpos/layout_enu_ska_central.txt
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diff --git a/py21cmsense/data/antpos/layout_enu_ska_core.txt b/py21cmsense/data/antpos/layout_enu_ska_core.txt
new file mode 100644
index 0000000..330a0ff
--- /dev/null
+++ b/py21cmsense/data/antpos/layout_enu_ska_core.txt
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diff --git a/py21cmsense/data/antpos/mwa_phase2_compact_antpos.txt b/py21cmsense/data/antpos/mwa_phase2_compact_antpos.txt
new file mode 100644
index 0000000..36b8570
--- /dev/null
+++ b/py21cmsense/data/antpos/mwa_phase2_compact_antpos.txt
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+7.936899999999999977e+01 2.719859999999999900e+02 3.762400000000000091e+02
+1.353530000000000086e+02 2.477210000000000036e+02 3.755899999999999750e+02
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+9.335500000000000398e+01 2.477429999999999950e+02 3.759399999999999977e+02
+7.936599999999999966e+01 2.477419999999999902e+02 3.760799999999999841e+02
+1.003670000000000044e+02 2.356160000000000139e+02 3.758500000000000227e+02
+8.634399999999999409e+01 2.356059999999999945e+02 3.759700000000000273e+02
+7.236299999999999955e+01 2.356100000000000136e+02 3.760899999999999750e+02
+1.493580000000000041e+02 2.477309999999999945e+02 3.754100000000000250e+02
+7.937399999999999523e+01 2.234739999999999895e+02 3.759900000000000091e+02
+1.423400000000000034e+02 2.355860000000000127e+02 3.754399999999999977e+02
+1.283489999999999895e+02 2.356100000000000136e+02 3.755600000000000023e+02
+1.143349999999999937e+02 2.355980000000000132e+02 3.757400000000000091e+02
+1.353520000000000039e+02 2.234859999999999900e+02 3.754800000000000182e+02
+1.213719999999999999e+02 2.234879999999999995e+02 3.755699999999999932e+02
+1.073569999999999993e+02 2.234619999999999891e+02 3.757699999999999818e+02
+9.334300000000000352e+01 2.234879999999999995e+02 3.758700000000000045e+02
+-3.099099999999999966e+01 1.220750000000000028e+02 3.766000000000000227e+02
+4.601599999999999824e+01 1.342210000000000036e+02 3.759200000000000159e+02
+3.200900000000000034e+01 1.341839999999999975e+02 3.760500000000000114e+02
+1.798999999999999844e+01 1.341819999999999879e+02 3.761499999999999773e+02
+3.901400000000000290e+01 1.220900000000000034e+02 3.759499999999999886e+02
+2.501500000000000057e+01 1.220550000000000068e+02 3.760899999999999750e+02
+-2.971999999999999975e+00 1.220580000000000069e+02 3.763199999999999932e+02
+-1.698900000000000077e+01 1.220699999999999932e+02 3.764599999999999795e+02
+3.995999999999999996e+00 1.099509999999999934e+02 3.762200000000000273e+02
+-9.980000000000000426e+00 1.099440000000000026e+02 3.763299999999999841e+02
+-2.399399999999999977e+01 1.099369999999999976e+02 3.764900000000000091e+02
+5.301899999999999835e+01 1.220520000000000067e+02 3.758100000000000023e+02
+-1.698699999999999832e+01 9.781000000000000227e+01 3.763600000000000136e+02
+4.602199999999999847e+01 1.099489999999999981e+02 3.758100000000000023e+02
+3.202400000000000091e+01 1.099440000000000026e+02 3.759599999999999795e+02
+1.802499999999999858e+01 1.099590000000000032e+02 3.760699999999999932e+02
diff --git a/py21cmsense/data/profiles/HERA-H1C-IDR3.yaml b/py21cmsense/data/profiles/HERA-H1C-IDR3.yaml
new file mode 100644
index 0000000..7a1b948
--- /dev/null
+++ b/py21cmsense/data/profiles/HERA-H1C-IDR3.yaml
@@ -0,0 +1,17 @@
+antpos: !astropy.units.Quantity
+  value: !txt "{{ DATA_PATH }}/antpos/hera-h1c-idr3.txt"
+  unit: !astropy.units.Unit {unit: m}
+beam:
+  class: GaussianBeam
+  frequency: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: MHz}
+    value: 150
+  dish_size: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: m}
+    value: 14.0
+latitude: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: rad}
+  value: -0.536189
+Trcv: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: K}
+  value: 100
diff --git a/py21cmsense/data/profiles/MWA-PhaseII.yaml b/py21cmsense/data/profiles/MWA-PhaseII.yaml
new file mode 100644
index 0000000..21e770d
--- /dev/null
+++ b/py21cmsense/data/profiles/MWA-PhaseII.yaml
@@ -0,0 +1,17 @@
+antpos: !astropy.units.Quantity
+  value: !txt "{{ DATA_PATH }}/antpos/mwa_phase2_compact_antpos.txt"
+  unit: !astropy.units.Unit {unit: m}
+beam:
+  class: GaussianBeam
+  frequency: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: MHz}
+    value: 150
+  dish_size: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: m}
+    value: 4
+latitude: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: rad}
+  value: -0.4660
+Trcv: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: K}
+  value: 100
diff --git a/py21cmsense/data/profiles/SKA-LOW1-central.yaml b/py21cmsense/data/profiles/SKA-LOW1-central.yaml
new file mode 100644
index 0000000..7d4c83d
--- /dev/null
+++ b/py21cmsense/data/profiles/SKA-LOW1-central.yaml
@@ -0,0 +1,17 @@
+antpos: !astropy.units.Quantity
+  value: !txt "{{ DATA_PATH }}/antpos/layout_enu_ska_central.txt"
+  unit: !astropy.units.Unit {unit: m}
+beam:
+  class: GaussianBeam
+  frequency: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: MHz}
+    value: 150
+  dish_size: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: m}
+    value: 35
+latitude: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: rad}
+  value: -0.4681814012852413
+Trcv: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: K}
+  value: 100
diff --git a/py21cmsense/data/profiles/SKA-LOW1-core.yaml b/py21cmsense/data/profiles/SKA-LOW1-core.yaml
new file mode 100644
index 0000000..8d96eee
--- /dev/null
+++ b/py21cmsense/data/profiles/SKA-LOW1-core.yaml
@@ -0,0 +1,17 @@
+antpos: !astropy.units.Quantity
+  value: !txt "{{ DATA_PATH }}/antpos/layout_enu_ska_core.txt"
+  unit: !astropy.units.Unit {unit: m}
+beam:
+  class: GaussianBeam
+  frequency: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: MHz}
+    value: 150
+  dish_size: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: m}
+    value: 35
+latitude: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: rad}
+  value: -0.4681819
+Trcv: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: K}
+  value: 100
diff --git a/py21cmsense/observation.py b/py21cmsense/observation.py
index fc45c31..41ca730 100755
--- a/py21cmsense/observation.py
+++ b/py21cmsense/observation.py
@@ -261,12 +261,8 @@ def uv_coverage(self) -> np.ndarray:
         Defined after earth rotation synthesis for a particular LST bin.
         The u-values on each side of the grid are given by :func:`ugrid`.
         """
-        if not self.coherent:
-            fnc = self.observatory.grid_baselines_incoherent
-        else:
-            fnc = self.observatory.grid_baselines_coherent
-
-        return fnc(
+        return self.observatory.grid_baselines(
+            coherent=self.coherent,
             baselines=self.baseline_group_coords,
             weights=self.baseline_group_counts,
             integration_time=self.integration_time,
diff --git a/py21cmsense/observatory.py b/py21cmsense/observatory.py
index 4c636a4..2bf0f9a 100644
--- a/py21cmsense/observatory.py
+++ b/py21cmsense/observatory.py
@@ -27,6 +27,14 @@
 
 logger = logging.getLogger(__name__)
 
+DATA = Path(__file__).parent / "data"
+
+
+def get_builtin_profiles() -> list[str]:
+    """Print available built-in profiles."""
+    fls = (DATA / "profiles").glob("*.yaml")
+    return [fl.stem for fl in fls]
+
 
 @hickleable(evaluate_cached_properties=True)
 @attr.s(kw_only=True, order=False)
@@ -139,11 +147,15 @@ def from_uvdata(cls, uvdata, beam: beam.PrimaryBeam, **kwargs) -> Observatory:
         )
 
     @classmethod
-    def from_yaml(cls, yaml_file: str | dict) -> Observatory:
+    def from_yaml(
+        cls, yaml_file: str | dict, frequency: tp.Frequency | None = None
+    ) -> Observatory:
         """Instantiate an Observatory from a compatible YAML config file."""
         if isinstance(yaml_file, (str, Path)):
             with open(yaml_file) as fl:
-                data = yaml.load(fl)
+                lines = fl.read()
+                lines = lines.replace("{{ DATA_PATH }}", str(DATA.absolute()))
+                data = yaml.load(lines)
         elif isinstance(yaml_file, collections.abc.Mapping):
             data = yaml_file
         else:
@@ -168,11 +180,42 @@ def from_yaml(cls, yaml_file: str | dict) -> Observatory:
             antpos = np.hstack((antpos, np.zeros((len(antpos), 1))))
 
         _beam = data.pop("beam")
+        if frequency is not None:
+            _beam["frequency"] = frequency
+
         kind = _beam.pop("class")
         _beam = getattr(beam, kind)(**_beam)
 
         return cls(antpos=antpos, beam=_beam, **data)
 
+    @classmethod
+    def from_profile(
+        cls, profile: str, frequency: tp.Frequency | None = None, **kwargs
+    ):
+        """Instantiate the Observatory from a builtin profile.
+
+        Parameters
+        ----------
+        profile
+            A string label identifying the observatory. Available built-in observatories
+            can be obtained with :func:`get_builtin_profiles`.
+        frequency
+            The frequency at which to specify the observatory.
+
+        Other Parameters
+        ----------------
+        All other parameters passed will be passed into the initializer for the class,
+        overwriting the profile.
+        """
+        fl = DATA / "profiles" / f"{profile}.yaml"
+        if not fl.exists():
+            raise FileNotFoundError(
+                f"profile {profile} not available. Available profiles: {get_builtin_profiles()}"
+            )
+
+        obj = cls.from_yaml(fl, frequency=frequency)
+        return obj.clone(**kwargs)
+
     @cached_property
     def baselines_metres(self) -> tp.Meters:
         """Raw baseline distances in metres for every pair of antennas.
@@ -367,6 +410,7 @@ def baseline_weights_from_groups(baseline_groups) -> np.ndarray:
 
     def grid_baselines(
         self,
+        coherent: bool,
         baselines: tp.Length | None = None,
         weights: np.ndarray | None = None,
         integration_time: tp.Time = 60.0 * un.s,
@@ -451,17 +495,19 @@ def grid_baselines(
         dim = len(self.ugrid(bl_max))
         edges = self.ugrid_edges(bl_max)
 
-        uvsum = np.zeros((len(baselines), dim, dim))
-        for cnt, (uvw, nbls) in enumerate(
-            tqdm.tqdm(
-                zip(uvws, weights),
-                desc="gridding baselines",
-                unit="baselines",
-                disable=not config.PROGRESS,
-                total=len(weights),
-            )
+        uvsum = np.zeros((dim, dim))
+        for uvw, nbls in tqdm.tqdm(
+            zip(uvws, weights),
+            desc="gridding baselines",
+            unit="baselines",
+            disable=not config.PROGRESS,
+            total=len(weights),
         ):
-            uvsum[cnt] = np.histogram2d(uvw[:, 0], uvw[:, 1], bins=edges)[0] * nbls
+            hist = np.histogram2d(uvw[:, 0], uvw[:, 1], bins=edges)[0] * nbls
+
+            uvsum += hist if coherent else hist**2
+        if not coherent:
+            uvsum = np.sqrt(uvsum)
 
         return uvsum
 
@@ -510,25 +556,3 @@ def ugrid(self, bl_max: tp.Length = np.inf * un.m) -> np.ndarray:
         # Shift the edges by half a cell, and omit the last one
         edges = self.ugrid_edges(bl_max)
         return (edges[1:] + edges[:-1]) / 2
-
-    def grid_baselines_coherent(self, **kwargs) -> np.ndarray:
-        """Get a UV grid of coherently gridded baselines.
-
-        Different baseline groups are averaged coherently if they fall into the same
-        UV bin.
-
-        See :func:`grid_baselines` for parameter details.
-        """
-        grid = self.grid_baselines(**kwargs)
-        return np.sum(grid, axis=0)
-
-    def grid_baselines_incoherent(self, **kwargs) -> np.ndarray:
-        """Get a UV grid of incoherently gridded baselines.
-
-        Different baseline groups are averaged incoherently if they fall into the same
-        UV bin.
-
-        See :func:`grid_baselines` for parameter details.
-        """
-        grid = self.grid_baselines(**kwargs)
-        return np.sqrt(np.sum(grid**2, axis=0))
diff --git a/setup.cfg b/setup.cfg
index c42e5ce..e6875dc 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -51,6 +51,7 @@ test =
     pytest
     matplotlib
     pytest-cov
+    pytest-xdist
 dev =
     pre-commit
     pytest
diff --git a/tests/test_observatory.py b/tests/test_observatory.py
index 4d130d0..7688da2 100644
--- a/tests/test_observatory.py
+++ b/tests/test_observatory.py
@@ -9,6 +9,7 @@
 from py21cmsense import Observatory
 from py21cmsense.baseline_filters import BaselineRange
 from py21cmsense.beam import GaussianBeam
+from py21cmsense.data import PATH
 
 
 @pytest.fixture(scope="module")
@@ -97,8 +98,8 @@ def test_grid_baselines(bm):
     with pytest.raises(ValueError):
         a.grid_baselines(bl_coords)
 
-    grid0 = a.grid_baselines()
-    grid1 = a.grid_baselines(bl_coords, bl_counts)
+    grid0 = a.grid_baselines(coherent=True)
+    grid1 = a.grid_baselines(coherent=True, baselines=bl_coords, weights=bl_counts)
     assert np.allclose(grid0, grid1)
 
 
@@ -228,3 +229,45 @@ def test_get_redundant_baselines(bm):
         baseline_filters=BaselineRange(bl_max=1.5 * units.m)
     )
     assert len(reds) == 2  # len-1
+
+
+def test_no_up_coordinate(tmp_path: Path):
+    mwafl = PATH / "antpos" / "mwa_phase2_compact_antpos.txt"
+    enu = np.genfromtxt(mwafl)
+
+    # Save with only EN coordinates
+    with open(tmp_path / "mwa_antpos.txt", "w") as fl:
+        np.savetxt(fl, enu[:, :2])
+
+    new_yaml = """
+antpos: !astropy.units.Quantity
+  value: !txt "%s/mwa_antpos.txt"
+  unit: !astropy.units.Unit {unit: m}
+beam:
+  class: GaussianBeam
+  frequency: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: MHz}
+    value: 150
+  dish_size: !astropy.units.Quantity
+    unit: !astropy.units.Unit {unit: m}
+    value: 35
+latitude: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: rad}
+  value: -0.4681819
+Trcv: !astropy.units.Quantity
+  unit: !astropy.units.Unit {unit: K}
+  value: 100
+""" % (
+        tmp_path
+    )
+
+    with open(tmp_path / "mwa.yaml", "w") as fl:
+        fl.write(new_yaml)
+
+    obs = Observatory.from_yaml(tmp_path / "mwa.yaml")
+    assert np.all(obs.antpos[:, 2] == 0)
+
+
+def test_setting_freq_in_profile():
+    obs = Observatory.from_profile("MWA-PhaseII", frequency=75 * units.MHz)
+    assert obs.frequency == 75 * units.MHz
diff --git a/tests/test_profiles.py b/tests/test_profiles.py
new file mode 100644
index 0000000..1533b4c
--- /dev/null
+++ b/tests/test_profiles.py
@@ -0,0 +1,34 @@
+import pytest
+
+import astropy.units as un
+import numpy as np
+
+from py21cmsense.observatory import Observatory, get_builtin_profiles
+
+
+def test_get_available():
+    avail = get_builtin_profiles()
+    assert "HERA-H1C-IDR3" in avail
+
+
+def test_load_hera_h1c():
+    hera = Observatory.from_profile("HERA-H1C-IDR3")
+    assert hera.Trcv == 100 * un.K
+    assert hera.antpos.shape == (71, 3)
+
+
+def test_load_hera_with_custom():
+    hera = Observatory.from_profile("HERA-H1C-IDR3", Trcv=200 * un.K)
+    assert hera.Trcv == 200 * un.K
+
+
+all_profiles = [Observatory.from_profile(p) for p in get_builtin_profiles()]
+
+
+def test_load_others():
+    assert len({np.sum(o.antpos) for o in all_profiles}) == len(all_profiles)
+
+
+def test_bad_profile():
+    with pytest.raises(FileNotFoundError, match="profile invalid not available"):
+        Observatory.from_profile("invalid")