source: trunk/LMDZ.GENERIC/utilities/photochemistry/Photochem_Visualizer.ipynb @ 3436

Last change on this file since 3436 was 3431, checked in by mmaurice, 2 months ago

Generic PCM:

Add photochemistry postprocessing and visualization python routines
along with an introduction notebook to utilities.

MM
File size: 168.0 KB
RevLine 
[3431]1{
2 "cells": [
3  {
4   "cell_type": "markdown",
5   "id": "e95cb3d6-faab-4db6-84e0-3ff64cb9dfeb",
6   "metadata": {},
7   "source": [
8    "# Generic PCM Photochemistry postprocessing and visualization demonstrator"
9   ]
10  },
11  {
12   "cell_type": "markdown",
13   "id": "a6b7b35c-fb19-4bda-81dd-ee03df1e4ef8",
14   "metadata": {},
15   "source": [
16    "This Notebook will show you how to use the Generic PCM photochemistry postprocessing library and how to make interactive visualization with it. For it to work, you'll need to copy the *photochemistry_postprocessing.py* file along this notebook in the directory containing the output file (*diagfi.nc*) as well as the reaction network file chemnetwork/reactfile (to become *reaction.def*)."
17   ]
18  },
19  {
20   "cell_type": "markdown",
21   "id": "ed55c2f3-5fa8-481c-b9d8-8e31f93f7993",
22   "metadata": {},
23   "source": [
24    "## Loading simulation and calculating reaction rates"
25   ]
26  },
27  {
28   "cell_type": "code",
29   "execution_count": 2,
30   "id": "cd28bac5-65f6-464b-9c2b-6cba1bde9472",
31   "metadata": {},
32   "outputs": [
33    {
34     "name": "stdout",
35     "output_type": "stream",
36     "text": [
37      "H2O2/3D/no_CO_start/diagfi120 loaded, simulations lasts 56.0 sols\n"
38     ]
39    }
40   ],
41   "source": [
42    "import photochem_postproc as pcpp\n",
43    "\n",
44    "sim_path        = 'H2O2/3D/no_CO_start'\n",
45    "NetCDF_filename = 'diagfi120'\n",
46    "\n",
47    "# The simu class is just a wrapper for xr.Dataset\n",
48    "my_sim = pcpp.GPCM_simu(sim_path,NetCDF_filename)"
49   ]
50  },
51  {
52   "cell_type": "markdown",
53   "id": "703aaead-59d4-4a93-8bfd-25ad8478dee0",
54   "metadata": {},
55   "source": [
56    "Now let's try to calculate automatically the rates of all reactions found in the reactfile"
57   ]
58  },
59  {
60   "cell_type": "code",
61   "execution_count": 3,
62   "id": "80f1c90a-7bfc-4e95-b614-c6e8432c53b3",
63   "metadata": {},
64   "outputs": [
65    {
66     "name": "stdout",
67     "output_type": "stream",
68     "text": [
69      "reaction  no + hv -> n + o seems to be hard-coded. Add it manually if needed.\n",
70      "reaction  co + oh -> co2 + h seems to be hard-coded. Add it manually if needed.\n",
71      "['o2', 'o', 'o1d', 'o3', 'h2o2', 'oh', 'h2o_vap', 'h', 'co2', 'co', 'ho2', 'h2']\n"
72     ]
73    }
74   ],
75   "source": [
76    "my_sim = pcpp.compute_rates(my_sim)\n",
77    "\n",
78    "# We can see that species list have been added\n",
79    "# to the simu object (as well as reactions dict)\n",
80    "print(my_sim.species)"
81   ]
82  },
83  {
84   "cell_type": "markdown",
85   "id": "63d2fe39-aedc-45b9-bfc8-d7670336a876",
86   "metadata": {},
87   "source": [
88    "Some reactions' rates are hard-coded and need to be added manually (you should find their rates in *reaction_rate_lib.py*). To do that we first need to define a new reaction and call again **compute_rates** with the new reaction as second argument:"
89   ]
90  },
91  {
92   "cell_type": "code",
93   "execution_count": 4,
94   "id": "e1ed253d-9186-4871-bf93-5ba0fc5f6a66",
95   "metadata": {},
96   "outputs": [],
97   "source": [
98    "# First load the parametrization for its rate\n",
99    "from reaction_rate_lib import k_JPL_2015\n",
100    "\n",
101    "# Then create the reaction objet (here for the reaction co + oh -> co2 + h):\n",
102    "hard_coded_reaction = pcpp.reaction(['co','oh'],['co2','h'],k_JPL_2015)\n",
103    "\n",
104    "# Finally, add it to the reactions of my_sim\n",
105    "my_sim = pcpp.compute_rates(my_sim,{'co + oh -> co2 + h':hard_coded_reaction})"
106   ]
107  },
108  {
109   "cell_type": "markdown",
110   "id": "042e4416-5ad3-4d1d-bbe7-896619253146",
111   "metadata": {},
112   "source": [
113    "## Now let's do some visualization"
114   ]
115  },
116  {
117   "cell_type": "markdown",
118   "id": "d5e0e556-863a-4f39-b6be-ebe1402a5f95",
119   "metadata": {},
120   "source": [
121    "### Static visualization\n",
122    "Here we use the built-in visualization methods of the *simu* class."
123   ]
124  },
125  {
126   "cell_type": "code",
127   "execution_count": 5,
128   "id": "f34cbcfa-339b-46dd-b33e-4c699a118f60",
129   "metadata": {},
130   "outputs": [],
131   "source": [
132    "import matplotlib.pyplot as plt"
133   ]
134  },
135  {
136   "cell_type": "markdown",
137   "id": "779d0619-582e-4628-8161-5c0d9d943261",
138   "metadata": {},
139   "source": [
140    "#### Meridional slice"
141   ]
142  },
143  {
144   "cell_type": "code",
145   "execution_count": 6,
146   "id": "f54ea828-ea01-4f8c-9349-e5f051ef7043",
147   "metadata": {},
148   "outputs": [
149    {
150     "data": {
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152      "text/plain": [
153       "<Figure size 640x480 with 6 Axes>"
154      ]
155     },
156     "metadata": {},
157     "output_type": "display_data"
158    }
159   ],
160   "source": [
161    "plt.subplot(131)\n",
162    "my_sim.plot_meridional_slice('h2o_vap',logcb=True)\n",
163    "plt.title('Time- and longitude-average')\n",
164    "\n",
165    "plt.subplot(132)\n",
166    "my_sim.plot_meridional_slice('h2o_vap',logcb=True,t=0)\n",
167    "plt.title('Time = 0 and longitude-average')\n",
168    "\n",
169    "plt.subplot(133)\n",
170    "my_sim.plot_meridional_slice('h2o_vap',logcb=True,t=0,lon=0)\n",
171    "plt.title('Time = 0 and longitude = 0°')\n",
172    "\n",
173    "plt.subplots_adjust(right=2)"
174   ]
175  },
176  {
177   "cell_type": "markdown",
178   "id": "3d691c4b-a690-4644-a00a-1ee77c85658a",
179   "metadata": {},
180   "source": [
181    "#### Time evolution"
182   ]
183  },
184  {
185   "cell_type": "code",
186   "execution_count": 7,
187   "id": "2328745a-55e1-40d9-86c7-41f04bea872c",
188   "metadata": {},
189   "outputs": [
190    {
191     "data": {
192      "image/png": 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193      "text/plain": [
194       "<Figure size 640x480 with 6 Axes>"
195      ]
196     },
197     "metadata": {},
198     "output_type": "display_data"
199    }
200   ],
201   "source": [
202    "plt.subplot(131)\n",
203    "my_sim.plot_time_evolution('h2o_vap',logcb=True)\n",
204    "plt.title('latitude- and longitude-average')\n",
205    "\n",
206    "plt.subplot(132)\n",
207    "my_sim.plot_time_evolution('h2o_vap',logcb=True,lat=0)\n",
208    "plt.title('Equatorial value, longitude-average')\n",
209    "\n",
210    "plt.subplot(133)\n",
211    "my_sim.plot_time_evolution('h2o_vap',logcb=True,lat=0,lon=0)\n",
212    "plt.title('Equatorial value longitude = 0°')\n",
213    "\n",
214    "plt.subplots_adjust(right=2)"
215   ]
216  },
217  {
218   "cell_type": "markdown",
219   "id": "f287c71d-83eb-4bad-a11e-dfb34e679adf",
220   "metadata": {},
221   "source": [
222    "#### Vertical profiles"
223   ]
224  },
225  {
226   "cell_type": "code",
227   "execution_count": 17,
228   "id": "4f544843-012d-41a6-8d7a-c4b40f45a5e1",
229   "metadata": {},
230   "outputs": [
231    {
232     "data": {
233      "image/png": 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eFloQ77zzDpcuXWLkyJGcO3eOpUuXsnDhwkduV716dVatWsWxY8c4fvw4AwcOzPNKrIGBgSxfvpw//vjDWHxkCwoKYurUqXzzzTecP3+ekydPsmDBAmbNmvXIfT+s3/PTPzVr1iQwMJBBgwaxatUqwsLCOHjwINOnT2fdunUAfPDBB2zYsIEZM2Zw/vx5vv/+e5kvI54KKWaegNndScAWsbfzvU2SU9ZEPnM5o0mUIFqtlh07dlC5cmX69OlDnTp1GDJkCKmpqQW6js2ECRNo3LgxXbp0ISAgAHd3d3r16lWgWDQaDatXryY9PZ1mzZrx5ptvMmXKlEduN2vWLFq2bMkLL7xAx44dad26tfEU54KoXLkyK1eu5K+//qJBgwb89NNPBAcHP3K7r7/+mnLlytGqVSu6d+9Oly5daNy4ca71+vbtS1xcHCkpKbn65s0332T+/PksXLiQevXq0b59exYuXIi3t/dD952ffs9P/yxYsIBBgwbx0UcfUatWLXr06MH+/fuNozktWrRg/vz5fPfddzRs2JCNGzfy6aefPrJvhHhSxeZ2BkUlISEBR0fHfF0OOT90Oh3r1q2jW7duRH02ntTVf7GirYbxc49hoXn0BX5OrZqO34lgDli1pNm4kvEXy705l9aL5qWlpREWFoa3t7fxw9tgMJCQkICDg0OZOcxUlnJOTk6mYsWKzJw5k9dff73M5H2vh73e9/bPG2+8YaIIC19Zeo/fq7jmnddnb7aCfH/LnJknoK1UhVTANV7ldtptKmgfPQfGplI9OAGe6ZeLPkAhhNHRo0c5e/YszZo1Iz4+nkmTJgFZ93sT0j+iZJNi5glYelYE7p6enRqXr2LG1SdrWLkS10lJuoPWzqkoQxRC3OOrr77i3LlzWFpa0qRJE3bu3En58uWL1V2ETelB/SNEcSfFzBOwyL7WTLzKrbT8XTjPobw7N3DClTvEXjpO1QYFO5NCCPF4GjVqxOHDh00dRrEl/SNKsuJz4KwEsvD0ALJHZvJ5RhMQZVEVgKTIE0URlhBCCFGmSDHzBCzc3FAVsNRDQkxkvre7ZZt1DQo15nRRhSaEEEKUGVLMPAHF0pJ0Jy0AaVH5v9ZGmnPWhaq0d84VSVxCCCFEWSLFzBPSuToBkBkdne9tzD38ACifcukRawohhBDiUaSYeULq3asAK/m9CjDgWLkeBlXB0XAHkvJ3XychhBBC5E2KmSdkfncSsMWN+Hxv4+XmwhU16zRufYzc1kAIIYR4ElLMPCHripUA0MYl53sbdwdrLlAZgIQIOaNJFK2goCAaNmxo6jCKncGDBz/yVgoBAQGMGDHiqcRT0lStWpXZs2ebOgwhAClmnpi9VzUAHG6nY1Dzd+EtjUYhJvuGk1EyMiMen6IoD/0ZPHgwo0aNYvPmzaYONRdVVQkKCsLT0xMbGxsCAgIIDQ19avv/5ptv8nWDSCFE8SfFzBMqV6UGAC7xKgnpCfneLsnx7g0nb8rp2eLxRUdHG39mz56Ng4NDjmXffPMNdnZ2uLi4mDrUXGbMmMGsWbP4/vvvOXjwIO7u7nTq1InExMQi3a9er8dgMODo6IiTk1OR7quoZGRkmDoEIYoVKWaekNarCgAOqRB3+1q+tzO41snaLvESyKXUxWNyd3c3/jg6OqIoSq5l9x9myj68EhwcjJubG05OTkycOJHMzEzGjBmDt7c3lStX5tdff82xr6ioKPr370+5cuVwcXGhZ8+ehIeHP1bcqqoye/Zsxo8fT58+ffDz82PRokWkpKSwdOnSArW1du1aatSogY2NDR06dGDRokUoisKdO3cAWLhwIU5OTvz999/4+vpiZWXFlStXch1mSk5O5rXXXsPOzg4PDw9mzpyZr/3PmjWLevXqYWtri5eXF8OGDSMpKQmA+Ph4bGxs2LAh501lV61aha2trXG9R/VtdqxTp07F09OTmjWz/hhavHgxTZs2xd7eHnd3dwYOHEhsbGyB+gdgz549tGvXDhsbG7y8vPjggw9ITv7fofPY2Fi6d++OjY0N3t7eLFmyJF99I8TTIsXMEzKztyfVKqsbb0ecz/d2dh41SVctsDSkwp0rRRWeeBKqCroUyEh+uj9P4Ub2W7Zs4dq1a+zYsYNZs2YRFBTECy+8QLly5di0aRNDhw7lnXfeITIy62KQKSkpdOjQATs7O3bs2MGuXbuws7Oja9euxlGCJUuWYGdn99Cf7C/BsLAwYmJi6Ny5szEmKysr2rdvz549e/KdR3h4OC+99BK9evXi2LFjDB06lPHjx+daLyUlhalTpzJ//nxCQ0OpUCH3fdQ+++wztm3bxurVq9m4cSPbtm3L1+X9NRoN3377LadOnWLRokVs2bKFMWPGAODo6Mjzzz+f68t/6dKl9OzZEzs7u3z1LcDmzZs5c+YMISEh/P3330DWCM3kyZM5fvw4a9asISwsjMGDBxeof0JDQ3nuuefo06cPJ06cYMWKFezatYv33nvPuM7gwYMJDw9ny5Yt/Pnnn/z444+5iiYhTEnuzVQIEp2tsIlOJSkyHJrnb5vKrg5cUCvip4RD7Glw9i7KEMXj0KXg9EOdp7/fT66BpW2R7sLZ2Zlvv/0WjUZDrVq1mDFjBikpKYwbN46EhAQ+/vhjpk+fzu7duxkwYADLly9Ho9Ewf/58FEUBYMGCBTg5ObFt2zY6d+5Mjx49aN784b8Abm5uAMTExOR4fO/zV67kv7j/6aefqFWrFl9++SUAtWrV4tSpU0yZMiXHejqdjh9//JEGDRrk2U5SUhKLFy9m4cKFdOrUCYBFixZRqVKlR8Zw7wRhb29vJk+ezP/93//x448/AhAYGMigQYNISUlBq9WSkJDAP//8w8qVKwHy1bcAtra2zJ8/H0tLS+P+hgwZYvy/j48P3377Lc2aNSMpKQk7O7t89c93333Hyy+/bMyjRo0afPvtt7Rv3545c+YQERHB+vXr2bdvn/H1/eWXX6hTxwS/G0I8gBQzhSCtvB1Ep5IWlf9bGlR2tuW4Wgk/wlGvh6LUfr4IIxQip7p166LR/G9g1s3NDT8/P+NjMzMzXFxcjH99Hz58mIsXL2Jvb5+jnbS0NC5dyrr4o729fa7nHyX7yzubqqq5lj3MuXPn8Pf3z7GsWbNmudaztLSkfv36D2zn0qVLZGRk0LJlS+MyZ2dnatWqZXwcHBxMcHCw8fHp06epXLkyW7duJTg4mNOnT5OQkEBmZiZpaWkkJydja2vL888/j7m5OWvXrmXAgAGsXLkSe3t7Y5GSn74FqFevXo5CBuDo0aMEBQVx7Ngxbt26Zbz7d0REBL6+vvnqn+PHj3P58uUch/dUVcVgMBAWFsb58+cxNzenadOmxudr165dYucbidJJiplCkFmxApy8geXqTdwZFI1TOY9HbuPlbMPvVAV2kXJ8DbZtR4FGjvoVKxZa7rx7Bgd7+xxf/E9jv0W+CwuLHI8VRclzWfaXo8FgoEmTJnnOlXB1dQWyDjMNHTr0ofudO3cugYGBuLu7A1kjNB4e//t9iY2NzTVa8zB5FT9qHofpbGxsHlok5bXN/d555x369etnfOzp6cmVK1fo1q0b77zzDpMnT8bZ2Zldu3bxxhtvoNPpgKxC6qWXXmLp0qUMGDCApUuX0r9/f8zNsz5+89O3kDUyc6/k5GQ6d+5M586dWbx4Ma6urkRERNClSxfj4an89I/BYODtt99m+PDhufZfuXJlzp3Luu1KQYpMIZ42KWYKQaP3PyNi18t4xGSw640X6bx8C5aW1g/dxsrcDEO9/iSGrsT+VijqqT9R6vd76DbiKVOUrMLC0rbMF5qNGzdmxYoVVKhQAQcHhzzXKchhJm9vb9zd3QkJCaFRo0ZA1vyP7du3M3369HzHVbt2bdatW5dj2aFDh/K9fbbq1atjYWHBvn37qFq1KgC3b9/m/PnztG/fHsgaqXF2ds61r8zMTGbOnGkseH///fdc7QcGBtK5c2dCQ0PZunUrkydPNj6Xn77Ny9mzZ7l58ybTpk3Dy8vLGM+98tM/9evX5/Tp01SvXj3P/dSpU4fMzEwOHTpkHNU5d+5cjgnEQpha2f6ELiSe1erjOHsqGeZQ7fRt/vrwxXz9pfdW12bMV3sAkLYhCDLTizhSIR5PYGAg5cuXp2fPnuzcuZOwsDC2b9/O8OHDuXo16yar9vb2VK9e/aE/2YdSFEVhxIgRBAcHs3r1ak6dOsXgwYPRarUMHDgw33ENHTqUs2fPMnbsWM6fP8/vv/9uvHZMQUYS7OzseOWVVxg7diybN282xvOoEblq1aqRmZnJd999x+XLl/ntt9/46aefcq3Xvn173NzcCAwMpGrVqrRo0cL4XH76Ni+VK1fG0tLSuO+1a9fmKJLy2z/Dhw9n7969vPvuuxw7dowLFy6wdu1a3n//fSBrnk3Xrl1566232L9/P4cPH+bNN9/ExsYmX30rxNMgxUwhqdOmB+mfvAOA7+bLrP7ynUdu4+ZgjaH5MGLUctikRGHY/3NRhynEY9FqtezYsYPKlSvTp08f6tSpw5AhQ0hNTS3QaMK9xowZw4gRIxg2bBhNmzYlKiqKjRs35pg7MnjwYAICAh7Yhre3N3/++SerVq2ifv36zJkzx3i2jpWVVYHimTRpEm3btqVHjx507NiRNm3a0KRJk4du07BhQ2bNmsX06dPx8/NjyZIlTJ06Ndd6iqLw8ssvc/z4cQIDA3M897h96+rqysKFC/njjz/w9fVl2rRpfPXVVznWyU//+Pn5sXXrVi5cuEDbtm1p1KgREyZMyHH4b8GCBXh5edG+fXv69OnD22+/necZYUKYiqLmZwihBEtISMDR0ZH4+PjH/tC9l06nY926dXTr1i3XHAOA7VM+oMJvIegVuPr5a3Qd8PFD24tP1TFr+gQmMocMCwcsR54Am3JPHGdhelTOpUFaWhphYWF4e3tjbZ11iNBgMJCQkICDg8PTnTNjQsUt54CAAAICAggKCsr3NlOmTOGnn34ynlaeH8Ut76J0b/+UpbyzlcWcofjmnddnb7aCfH8Xn4xKiXaffMO1drUxU8F16iL27l7x0PUdbSyoGDCEswYvLHUJZG7P34W6hCjtEhMTuXTpEqNGjXroej/++CMHDx40Hub58ssvee21155SlMWf9I8oC6SYKWSKohDw3XKu13BBmw66jyZy5uLeh24zqHU15lre/XA5MBfuRDyFSIUo3uzt7YmMjMTOzu6h6124cIGePXvi6+vL5MmT+eijjwo0klPaSf+IskCKmSJgZmVFiwWruF3eGtc7Kpf/byjX4h58ITBrCzNadO7Hbn1dzA0ZZIRMeorRClGyff3111y7do20tDTOnz/PhAkTjKc9C+kfUTZIMVNErMtXoPYvv5Fqo8EnUseOd/sRnx7/wPVfbOLFb/ZZV/O0DP0Doo8/rVCFEEKIEk2KmSLkXMsP11lfotdAg2MJ/Dm2Lzq9Ls91zc009Or2PGv0rQDIWD/+qdyjRwghhCjppJgpYl4dumE2+v8AaLUhkgVfD3ngNWi61HVnneubpKvmWEbshEubn2aoQgghRIkkxcxTUOf1D0jtm3UfluYLDvHb7xPyXE9RFF5/PoD/6LPWzVg/AQz6pxanEEIIURJJMfOUNAqaRWKz2ljqoeb0lfy165c812tZzYWjVd4gXtViGXcaTjz81G4hhBCirJNi5ilRzMxoOmcxiVXK45gCVh/PZM+5kDzXffd5f37I7AmALmQS6FKfZqhCCCFEiSLFzFOksbWl4cLfSXGyxuumytWRH3ImNjTXenU9HYmrO5irankskqNh3xwTRCtKi6CgIBo2bGjqMEQBBQQEMGLEiHyvv23bNhRFKdIbQIaHh6MoCseOHSuyfRQXCxcuxMnJyfhYfo8e3+DBg+nVq1eR7kOKmafM0sODmj8vRGepod4lPdtHDSI6KTrXesO71Ge2Pusu2pk7ZkJy3NMOVZQAiqI89Gfw4MGMGjWKzZuL32RyVVUJCgrC09MTGxsbAgICCA3NXdyXFIX9Zbdq1apcN44UQuRNihkTsK/fALfpUzEo0P5ACr9NGkBiRmKOdSq7aLFr+jKhhiqY65JQd8wwUbSiOIuOjjb+zJ49GwcHhxzLvvnmG+zs7HBxcTF1qLnMmDGDWbNm8f3333Pw4EHc3d3p1KkTiYmJj964FNPpsi7f4OzsnOOmm0I8Dr1ej8FgMHUYRc7kxUxUVBSvvPIKLi4uaLVaGjZsyOHDh43Pl7a/3rK5PdcD7XtvA/D8X7F8+8Nrua5B8+6ztZjFKwCoB36BW5efepyieHN3dzf+ODo6oihKrmX3jxhkD/kGBwfj5uaGk5MTEydOJDMzkzFjxuDt7U3lypX59ddfc+wrKiqK/v37U65cOVxcXOjZsyfh4eGPFbeqqsyePZvx48fTp08f/Pz8WLRoESkpKSxdurRAbZ0+fZpu3bphZ2eHm5sbr776Kjdv3jQ+n5yczKBBg7Czs8PDw4OZM2fmOoRjZmbGP//8k6NdJycnFi5caHw8duxYatasiVarxcfHhwkTJhgLj4ULFzJx4kSOHz9uHBXL3jYiIoKePXtiZ2eHg4MD/fr14/r168Z2s1+fX3/9FR8fH6ysrFBVNVeMixcvpmnTptjb2+Pu7s7AgQOJjY3Ndz+9/PLLDBgwIMcynU5HtWrVWLBgAQAbNmygTZs2ODk54eLiwgsvvMClS5ce2Ob9h2IA1qxZg6IoOZb99ddfNGnSBGtra3x8fIzvtwfJa5SxatWqxue3b99Os2bNsLKywsPDg48//jhHewEBAXzwwQeMGTMGZ2dn3N3dc93C4euvv6ZevXrY2tri5eXFsGHDSEpKemBM99qxYwcWFhbExMTkWP7RRx/Rrl27B273qP5t2bIlH3+c8+bEN27cwMLCgq1btwKQkZHBmDFjqFixIra2tjRv3pxt27YZ189+Tf7++298fX2xsrLiypUrHDx4kN69e1OhQgUcHR1p3749R44cybGvs2fP0qZNG6ytrfH19WXTpk0oisKaNWuM6zzqc0Cv1zNy5EhjjmPGjHng5UgKk0mLmdu3b9O6dWssLCxYv349p0+fZubMmTl+OUrzX29Vh42AF55Fo0LXBWeY/ceHOV50V3sr6rbtxXZ9fTSqDsMmuc3B06SqKqmZqaToUp7qz9P4xd+yZQvXrl1jx44dzJo1i6CgIF544QXKlSvHpk2bGDp0KO+8847xztMpKSl06NABOzs7duzYwa5du7Czs6Nr165kZGQAsGTJEuzs7B76s2TJEgDCwsKIiYmhc+fOxpisrKxo3749e/bsyXce0dHRtG/fnoYNG3Lo0CE2bNjA9evX6devn3Gd0aNHs3XrVlavXs3GjRvZtm1bjj+Y8sve3p6FCxdy+vRpvvnmG+bNm8fXX38NQP/+/fnoo4+oW7eucVSsf//+qKpKr169uHXrFtu3byckJIRLly7Rv3//HG1fvHiR33//nZUrVz5wPkpGRgaTJ0/m+PHjrFmzhrCwMAYPHpzv+AMDA1m7dm2OL+x///2XlJQUXnzxRSCr8Bs5ciQHDx5k8+bNaDQaevfu/UR/2f/777+88sorfPDBB5w+fZq5c+eycOFCpkyZ8sBt7h1dvHjxItWrVzcWCVFRUXTr1g1/f3+OHz/OnDlz+OWXX/jiiy9ytLFo0SJsbW3Zv38/M2bMYNKkSYSE/O+kC41Gw7fffsupU6dYtGgRW7ZsYcyYMfnKqV27dvj4+PDbb78Zl2VmZrJ48WJef/31B273qP4NDAxk2bJlOT4DVqxYgZubG+3btwfg9ddfZ/fu3SxfvpwTJ07Qt29funbtyoULF4zbpKSkMHXqVObPn09oaCgVKlQgMTGRAQMGsH37dvbt20eNGjXo1q2b8bvUYDDQq1cvtFot+/fv5+eff2b8+PE54s/P58DMmTP59ddf+eWXX9i1axe3bt1i9erV+erXJ6Ka0NixY9U2bdo88HmDwaC6u7ur06ZNMy5LS0tTHR0d1Z9++ilf+4iPj1cBNT4+/onjVVVVzcjIUNesWaNmZGQUSnuG9HT1eP+e6ulatdXt/rXVn7dOz/F8QmqG2m/iz6r+M0dV/dxBVSMPFcp+C6Kwcy6OUlNT1dOnT6upqanGZYlpiarfQr+n/pOckfxYOSxYsEB1dHTMtfzzzz9XGzRoYHz82muvqVWqVFH1er1xWa1atdS2bduqer1evX37tpqRkaHa2tqqy5YtU1VVVX/55Re1Vq1aqsFgMG6Tnp6u2tjYqP/++6+qqqqakJCgXrhw4aE/CQkJqqqq6u7du1VAjYqKyhHrW2+9pXbu3DnfOU+YMCHX+pGRkSqgnjt3Tk1MTFQtLS3V5cuXG5+Pi4tTbWxs1OHDhxuXAerixYtz9Imjo6O6YMGCB+57xowZapMmTYyP7+9nVVXVjRs3qmZmZmpERIRxWWhoqAqoBw4cMG5nYWGhxsbG5ti2ffv2OWK834EDB1RATUxMVFVVVbdu3aoC6u3bt/NcPyMjQy1fvrz6n//8x7hswIABaq9evXLkfa/Y2FgVUE+ePKmqqqqGhYWpgHr06FFVVfN+z61evVq996ulbdu2anBwcI51fvvtN9XDw+OBuWUzGAxq79691SZNmqgpKSmqqqrqJ598kuu9+MMPP6h2dnbGPNq3b5/ru8Xf318dO3as8T1+f86///676uLiYnx8f273v77Tp09X69SpY3y8Zs0a1c7OTk1KSnpkXtnu79/Y2FjV3Nxc3bFjh3Gdli1bqqNHj1ZVVVUvXryoKoqS6/fm2WefVceNG2eMG1CPHTuWY537887MzFTt7e3Vv/76S1VVVV2/fr1qbm6uRkdHG7cJCQlRAXX16tWqqubvc8DDwyPHd7ZOp1MrVaqk9uzZM88+yOuzN1tBvr9NerextWvX0qVLF/r27cv27dupWLEiw4YN46233gIe/dfb0KFDc7WZnp5Oenq68XFCQgKQNZyaPST8JLLbKIy2AFAUan4/n7P9euF6LY7bkxbwZ7lK9PTtC4C1GXQM6MCqkLa8ZLaDzH8/RX31v3DfMG5RKvSciyGdToeqqhgMBpMfX37cGLK3uX9b9e5fednLVVXF19c3xzI3Nzfq1q1rXFej0eDi4sL169cxGAwcOnSIixcv5prDkZaWxoULF+jYsSO2trb4+PgUKL/sPn9UDg9y6NAhtm7dmuedtS9cuEBycjIZGRk0b97c2KaTkxO1atXKte8HxZP9+M8//+Tbb7/l4sWLJCUlkZmZiYODQ45c7o/99OnTeHl5UbFiRePy2rVr4+TkRGhoKE2aNEFVVapUqYKLi8tD4zl69KjxUNatW7eMy8PDw/H19c3Rd3n1n5mZGS+99BKLFy8mMDCQ5ORk1q5dy7x584z7uXTpEp999hn79+/n5s2bj9xHXq/X/csOHz7MwYMHc4zE6PV60tLSSEpKQqvV5oo12yeffMLevXvZv38/VlZWGAwGTp8+TYsWLVBV1djnLVu2JCkpiYiICCpXrgxAvXr1csTl7u7O9evXjdts2bKFadOmcebMGRISEsjMzCQtLY3ExERsbW1z5XH/6zto0CA+/fRT9uzZQ4sWLfjll1/o27cvNjY2bN++neeff9647zlz5hAYGPjI/nVxcaFjx44sXryY1q1bExYWxt69e/nhhx+Mv4eqqlKzZs0c/ZSeno6zs7PxNbG0tMTPzy9H/tevX+eTTz5h9+7dXL9+Hb1eT0pKCleuXMFgMHD27Fm8vLyoUKGCcbumTZvmeL0f9Tng7+9PdHR0jt83jUZjfJ/n9b40GAyoqopOp8PMzCzHcwX5zjFpMXP58mXmzJnDyJEj+eSTTzhw4AAffPABVlZWDBo0yHg80s3NLcd2bm5uXLmS912op06dysSJE3Mt37hx40N/aQrq3uHKwmAR+Doe339DzWs69gZN4ZuXo6hhlfWGdTbAfM1LdFf3YhW5h33Lg7nu2KhQ958fhZ1zcWJubo67uztJSUnG4VJVVdn4/ManHosuRUeCklDg7dLS0lBV1VjAZ0tPT0ev1+co7BVFybGeXq9HVVXjkHNiYiKqqpKSkkJCQgJpaWk0bNiQn3/+Odd+XVxcSEhI4Pfff2fkyJEPjXHWrFn069fPWHxcunQJW1tb4/PXrl3D2dk5Vw4PkpGRQdeuXXPNh4Csz4nLly8b87k/34yMDOMyRVFy5A9Z/ZSWlkZCQgIHDx5k4MCBfPzxx0yePBkHBwdWrVrF999/b2zj/n4GSE1NzfM1MRgMxrbT09OxtrbOtU5mZqYxxuTkZLp06UKHDh2YM2cO5cuX5+rVq7z44ovcvn2bhIQEUlJSjLlqNHnPIOjZs6dxnsbWrVuxsrKiY8eOxry7d+9OxYoV+frrr3F3d8dgMNCqVSvi4+NJSEgwHqJKTk4mISGBjIwMDAZDjtiz/5/9r8Fg4OOPP6Z79+55vn4PmjuzYsUKZs+ezV9//YWDg0OO929mZmaOfWbHlZSUZCxM7u93vV5Peno6iYmJRERE0L17d15//XXGjh1LuXLl2LdvH++//z63bt0yFlv3tnH/62ttbU3Xrl35+eefcXV1Zf369fz1118kJCRQs2ZNduzYYdy3q6srCQkJj+xfgN69ezNu3Di++OILFixYQO3atfH29ja+D8zMzNi6dWuuL35bW1vj76q1tXWuqRiDBg0iLi6OL774Ai8vL6ysrOjcuTMJCQkkJCTk+V7N/n9qamq+Pwfgf++PbHm9Hve+B1JTU9mxY0eu90L2ezo/TFrMGAwGmjZtSnBwMACNGjUiNDSUOXPmMGjQION6908mU1U117Js48aNy/GBmpCQgJeXF507d8bBweGJY9bpdISEhNCpUycsLCyeuL17pdSpQ+Rbb9LyjIG1GxdTLXgJtcrVAsBQ6Rq//rcr/2f+F03j/0HtPxY0T+flK8qci4u0tDQiIyOxs7PD2toauPs+S1Swt7d/4PutOLG2tkZRlFzvcysrK8zMzIzLLSwsMDc3z7Geubk5lpaW2Nvbk5iYiL29PRqNBmtraxwcHGjevDlr1qzBx8fngb9H/fv3JyAg4KExurm5YW9vT7169XB3d2fv3r20adMGyPpQ27NnD1OnTs3372qzZs1YtWoVfn5+mJvn/n2wt7fHwsKC0NBQ6tatC2TN1bt06RIdOnQw7sfV1ZWYmBjja33hwgVSUlKM+R8/fpwqVaowadL/5q39+OOPOfo7+6/Ve2Nv1KgRV69eJT4+Hi8vLyBrtCYhIYHGjRvj4OCQ6/XJlv2aODg4cOHCBeLi4vjqq6+M7Zw9exbI+hJzcHAw/rFmb2//wP7r1KkTXl5erF+/nvXr19O3b1/j637r1i3OnTvH3Llzadu2LQC7du0CwMbGBgcHB2MRmr1PLy8vkpKSMDMzMxal58+fz9EPjRs35sqVKwU6bX3v3r0MHz6cOXPm8Oyzz+Z4rn79+qxatSrH7+WJEyewt7endu3aaDSaHH13b39aWFhgb2/PsWPHyMzM5NtvvzUWfuvXr8/Rf/f/PuX1Og0dOpSBAwfi4+NDtWrVjEcRHBwccv0RHhcX98j+hayJ2iNHjmTPnj2sXr2aQYMGGZ9r1aqVcUQlu437PehzYN++fXz55Ze8+OKLKIpCZGQkcXFxxvd4gwYNuHr1KqmpqcbYDx48mCO+/HwOeHh4cOrUKZ577jkgq5A5ceIEjRo1ynObtLQ0bGxsaNeunfGzN1t+/6gBExczHh4exuHubHXq1GHlypVA1rAgQExMDB4eHsZ1YmNjc71RsllZWWFlZZVruYWFRaF+ERd2ewCOrVqROXEisZ9+Ro9dGSz65i1GfLIad1t3+jSpTL+dAxkQv5Vycefh1ApoMrhQ9/8oRZFzcaHX61EUBY1GY/xwyx4SzV5e3GXHeH+s2R/42cuzzw7Ja73sde/9V6PR8OqrrzJz5kx69+7NpEmTqFSpEhEREaxatYrRo0dTqVIlHB0dcXR0zHe8I0aMYOrUqdSsWZMaNWoQHByMVqvllVdeyXd/v/fee8yfP5/AwEBGjx5N+fLluXjxIsuXL2fevHk4ODjwxhtvMHbsWFxdXXFzc2P8+PFoNJocfdChQwfmz59vLMbGjh2LhYWF8f1Qo0YNIiIi+P333/H39+eff/4xnuGR3Ya3tzdhYWGcOHGCSpUqYW9vT+fOnalfvz6vvvoqs2fPJjMzk2HDhtG+fXuaNWuW5+tz/2ui0WioWrUqlpaW/PDDD7zzzjucOnXKeNgmO8Z7X/+H9d/AgQOZO3cu58+fN15/SFEUXFxccHFxYf78+VSsWJGIiAjjmTUP2kfLli3RarV8+umnvP/++xw4cIBFixblyOezzz7jhRdeoHLlyvTt2xeNRsOJEyc4efJkrkm7kPV5/+KLLzJgwACee+454xlbZmZmuLq68u677/LNN98wfPhw3nvvPc6dO0dQUBAjR47MUdDe/x6/98wob29vMjMz+eGHH+jevTu7d+9m7ty5D8z1Qa/Tc889h6OjI1OmTGHSpEkP7ff89C9kFVM9e/bk888/58yZMwQGBhqfq127NoGBgQwePJiZM2fSqFEjbt68yZYtW6hXrx7dunV74OdA9erV+f3332nbti1JSUmMHj0aGxsbYz916dKFatWq8frrrzNjxgwSExOZMGGCse/z+zkwfPhwpk+fTs2aNalTpw6zZs3izp07D/wczf5dzOv7pSDfNyb9hG7dujXnzp3Lsez8+fNUqVIFyPpwcHd3z3F4IyMjg+3bt9OqVaunGuvT4vJSX+yGZI1KvbzmDlN/fxeDasBMozDsuSZ8l9kbAP3mLyD1jgkjFWWJVqtlx44dVK5cmT59+lCnTh2GDBlCamrqY494jhkzhhEjRjBs2DCaNm1KVFQUGzduzHE8fvDgwQ8d7fH09GT37t3o9Xq6dOmCn58fw4cPx9HR0fjB+eWXX9KuXTt69OhBx44dadOmDU2aNMnRzldffUXFihUJCAhg4MCBjBo1Ksdh6Z49e/Lhhx/y3nvv0bBhQ/bs2WP8oM/24osv0rVrVzp06ICrqyvLli0zntZarlw52rVrR8eOHfHx8WHFioLdc83V1ZWFCxfyxx9/4Ovry7Rp0/jqq68K1Ea2wMBATp8+TcWKFWndurVxuUajYfny5Rw+fBg/Pz8+/PBDvvzyy4e25ezszOLFi1m3bh316tVj2bJluQ75denShb///puQkBD8/f1p0aIFs2bNMn7O3+/s2bNcv36dRYsW4eHhYfzx9/cHoGLFiqxbt44DBw7QoEED3nnnHd544w0+/fTTfPdBvXr1mDlzJtOnT8fPz48lS5YwderUfG+fTaPRMHjwYPR6fY6jCQ9aN7/9GxgYyPHjx2nbtq1xDlC2BQsWMGjQID766CNq1apFjx492L9/v3HE7kHmz5/PnTt3aNKkCa+++ioffPABFSpUMD5vZmbGmjVrSEpKwt/fnzfffNPYp9kjJvn5HPjoo48YNGgQgwcPpmXLltjb29O7d++Hd2RheOQU4SJ04MAB1dzcXJ0yZYp64cIFdcmSJapWq1UXL15sXGfatGmqo6OjumrVKvXkyZPqyy+/rHp4eBjPiniU4n42U14Mer16/vVX1dO1aqtrOtRWfz+ddSaGwWBQX/lpu3pxQq2sM5v++rDIYrhXWT2b6UFnPZRmxS3n9u3bq59//nmRtHvvmULFLe+npSzmXdg5v/nmm2r37t0Lpa2i9Dh579q1SwXUixcvFllchXU2k0lHZvz9/Vm9ejXLli3Dz8+PyZMnM3v2bAIDA43r5Oevt9JG0WioGjwDvdaKmtfg1Jzp3Ey9iaIojO/ZkM/0bwCgHvoVrh4ycbRCFI3ExEQuXbrEqFGjTB2KELnEx8ezadMmlixZwvvvv2/qcArF6tWrCQkJITw8nE2bNvH222/TunVrqlWrZurQHsnkEwFeeOEFTp48SVpaGmfOnDGelp1NURSCgoKIjo4mLS2N7du34+fnZ6Jonx4Ld3c8xmYdT+29JZWf12WdoVXb3YEazbuxUt8WBRX1r+Ggf/CVNIUoqezt7Y2TsoUobnr27EmPHj0YOnQonTp1MnU4hSIxMZFhw4ZRu3ZtBg8ejL+/P//9739NHVa+mHQCsHg45379iVm7CqvDJ6k5dxP7W+ylecWWfNixJr2PvsYzhqOUu34K9s+BVqXjLwMhnoZ7L/8uxOMoje+hQYMGPXLuT3Fl8pEZ8WCKolBj+iwyLc2oGwGbvxlLhj4DR60Fb3ZtztTMlwFQtwTDnUgTRyuEEEKYhhQzxZxlpUqUHzkCgOfW32DJlrv3gvH34oxbdw4YaqFkpsD6/N1TRAghhChtpJgpAdwHDSHN1xtrHdjO+g/h8eGYaRQ+71GP8bo30KlmcG4dnPnb1KEKIYQQT50UMyWAotHg+9UPZFpoqBdm4L+zP0BVVZpWdcavYXN+1mfdA0RdPwbSS/7dxIUQQoiCkGKmhLDy8cbmnSEAtF51gY0HlwPw8XO1+VXzEhEGV5SEKNha8As/CSGEECWZFDMlSLWhw0mo5oZtOtz5Yhrx6fG4OVjz1rN+fJb5OgDq/jkQfdzEkQohhBBPjxQzJYhibo7vzB/INIP65zNY82PWDTVfb12VK86t+VvfHEU1wF8jwKA3bbCi2AgKCirQTf5E8RAQEMCIESPyvf62bdtQFIU7d+4UWUzh4eEoisKxY8eKbB95eRq5iZJNipkSxr52XfSv9QGgzqI9nDy3EytzMz57wZdJukEkqjZw7Qgc+tXEkYqn4d6b5+X1M3jwYEaNGmW8oWBxoqoqQUFBeHp6YmNjQ0BAAKGhoaYO67EVdtG4atUqJk+eXGjtiadj5cqV+Pr6YmVlha+vL6tXr87xfFJSEgMGDMDDw4MBAwaQnJxsokhLFylmSqAGHwZxy8sR+zQ4O2EUmYZMOtSuQL3atfgysx8A6uZJkBhj4khFUYuOjjb+zJ49GwcHhxzLvvnmG+zs7HBxcTF1qLnMmDGDWbNm8f3333Pw4EHc3d3p1KkTiYllexK7TqcDsm7iWJpv21Ia7d27l/79+/Pqq69y/PhxXn31Vfr168f+/fuN68yePRs7Ozs2btyIVqtl9uzZpgu4FJFipgRSLCyoNuNr9Ar4nUhg/YLPAZjwgi+/04XjBh+U9ATYMM7EkYqi5u7ubvxxdHREUZRcy+4fMRg8eDC9evUiODgYNzc3nJycmDhxIpmZmYwZMwZvb28qV67Mr7/mHN2Lioqif//+lCtXDhcXF3r27El4ePhjxa2qKrNnz2b8+PH06dMHPz8/Fi1aREpKCkuXLi1QW6dPn6Zbt27Y2dnh5ubGq6++ys2bN43PJycnM2jQIOzs7PDw8GDmzJm5DuGYmZnxzz//5GjXycmJhQsXGh+PHTuWmjVrotVq8fHxYcKECcbCY+HChUycOJHjx48bR8Wyt42IiKBnz57Y2dnh4OBAv379uH79urHd7Nfn119/xcfHBysrK1RVzRXj4sWLadq0Kfb29ri7uzNw4EBiY2Pz3U8vv/wyAwYMyLFMp9NRrVo1FixYAMCGDRto06YNTk5OuLi48MILL3Dp0qUHtrlw4UKcnJxyLFuzZg2KouRY9tdff9GkSROsra3x8fExvt+exMqVK6lbty5WVlZUrVqVmTNn5ni+atWqBAcHM2TIEOzt7alcuTI///zzE+3zUWbPnk2nTp0YN24ctWvXZty4cTz77LM5CpY7d+5Qs2ZN6tWrR+3atYmPjy/SmMoKKWZKKPdGLbnVtz0Arj+u5lrUOaqWt+X1ttX4RPcGejQQugoubjJxpCWXqqoYUlMxpKQ81R9VVYs8ty1btnDt2jV27NjBrFmzCAoK4oUXXqBcuXJs2rSJoUOH8s477xAZmXVl6ZSUFDp06ICdnR07duxg165d2NnZ0bVrVzIyMgBYsmQJdnZ2D/1ZsmQJAGFhYcTExNC5c2djTFZWVrRv3549e/bkO4/o6Gjat29Pw4YNOXToEBs2bOD69ev069fPuM7o0aPZunUrq1evZuPGjWzbto3Dhw8XuM/s7e1ZuHAhp0+f5ptvvmHevHl8/fXdi1j2789HH31E3bp1jaNi/fv3R1VVevXqxa1bt9i+fTshISFcunSJ/v3752j74sWL/P7776xcufKB81EyMjKYPHkyx48fZ82aNYSFhTF48OB8xx8YGMjatWtJSkoyLvv3339JSUnhxRdfBLIKv5EjR3Lw4EE2b96MRqOhd+/eGAyGgnXWPf79919eeeUVPvjgA06fPs3cuXNZuHAhU6ZMeew2Dx8+TL9+/RgwYAAnT54kKCiICRMm5Cg+AWbOnEnTpk05evQow4YN4//+7/84e/bsA9sNDg5+5Ht4586dD9x+7969Od7TAF26dMnxnn7vvfeYO3cuFhYWLFiwgOHDhz9eJ4gc5N5MJVir8bPZu70l5a+ncWjcMHr8ZzPvdajOqiNXWZjShTfM18M/H8GwfWBhY+pwSxw1NZXrHZ7h+qNXLVS1jhxG0WqLdB/Ozs58++23aDQaatWqxYwZM0hJSWHcuHEkJCTw8ccfM336dHbv3s2AAQNYvnw5Go2G+fPnG//qXrBgAU5OTmzbto3OnTvTo0cPmjdv/tD9urm5ARATE5Pj8b3PX7lyJd95zJkzh8aNGxMcHGxc9uuvv+Ll5cX58+fx9PTkl19+4T//+Y/xZoCLFi2iUqVK+d5Htk8//dT4/6pVq/LRRx+xYsUKxowZg42NDXZ2dpibm+Pu7m5cLyQkhBMnThAWFoaXlxcAv/32G3Xr1uXgwYP4+/sDWYXKb7/9hqur6wP3P2TIEOP/fXx8+Pbbb2nWrBlJSUn5uhlnly5dsLW1ZfXq1bz66qsALFu2jK5du+Lg4ABgLGqy/fLLL1SoUIHTp08/9g1+p0yZwscff8xrr71mjH3y5MmMGTOGzz///LHanDVrFs8++ywTJkwAoGbNmpw+fZovv/wyR4HXrVs3hg0bBmSNrH399dds27aNgQMH5tnuO++8k6MQzkvFihUf+FxMTEye7+ns9ztkvXcuXLhAbGwsbm5uuUaxxOORYqYEM7eypsKUSWS+NYYaB66x94/vaNn3fT7pVodPlr9EN7MDeNwOhx1fwrOfmTpcUYzUrVsXjeZ/A7Nubm45vqzMzMxwcXExHsY4fPgwFy9ezDWHIy0tzXgYwt7evsBzPO7/IFdVtUAf7ocPH2br1q15fplfunSJ1NRUMjIyaNmypXG5s7MztWrVKlCcAH/++SezZ8/m4sWLJCUlkZmZaSwCHuTMmTN4eXkZCxkAX19fnJycOHPmjLGYqVKlykMLGYCjR48SFBTEsWPHuHXrlnG0JCIiAl9f30fGb2FhQd++fVmyZAmvvvoqycnJrF27lnnz5hnXuXTpEhMmTGDfvn3cvHkzxz4et5g5fPgwBw8ezDESo9frSUtLIyUlBe1jFO5nzpyhZ8+eOZa1bt2a2bNno9frMTMzA6B+/frG57MPwd64ceOB7To7O+Ps7FzgeO6Vn/e0RqPJUfSKJyfFTAlXq013/nnuN3zWnYQZc0l6tj89Gnjy215PgiIHMdfya9j9LdTrBxVqmzrcEkWxscFt6xYc7O1zfPE/jf0WNQsLi5z7VJQ8l2V/mRkMBpo0aWI8THSv7C/hJUuWMHTo0Ifud+7cuQQGBho/yGNiYvDw8DA+n/3Xan4ZDAa6d+/O9OnTcz3n4eHBhQsX8tWOoii5Du9lz4cB2LdvHwMGDGDixIl06dIFR0dHli9fnmuexv0eVJzdv9zW1vah7SQnJ9O5c2c6d+7M4sWLcXV1JSIigi5duhgP8+VHYGAg7du3JzY2lpCQEKytrenYsaPx+e7du+Pl5cW8efPw9PTEYDDg5+f3wH1oNJqH9htkvUYTJ06kT58+uba3trbOd+z3yqtf8zo8+7D3dF6Cg4NzjPLlZf369bRt2zbP59zd3XOMwkDB39Pi8UgxUwq0D5rDkf0BuMZlsueToXT+aTVBPerS/ftbhOgb04kj8PeHMPgfeIpfyiWdoihobGzQaLVPtZgpjho3bsyKFSuoUKHCA0cjCnKYydvbG3d3d0JCQmjUqBGQdahl+/bteRYmD4tr5cqVVK1aFXPz3B9n1atXx8LCgn379lG5cmUAbt++zfnz52nfvr1xPVdX1xxfQhcuXCAlJcX4ePfu3VSpUoXx48cbl91/OMzS0hK9Puf1nXx9fYmIiCAyMtI4OnP69Gni4+OpU6dOvvM8e/YsN2/eZNq0acZ2Dh06lO/ts7Vq1QovLy9WrFjB+vXreemll7C0tAQgLi6OM2fOMHfuXOOX9a5dux7anqurK4mJiSQnJxsLsvvn/DRu3Jhz585RvXr1Asf7IL6+vrli27NnDzVr1jSOyjyOJz3M1LJlS0JCQvjwww+NyzZu3EirVq0eOyaRP1LMlAJ2Di6YffIBfDQLr21nOffv7/h16cfLzaoQtP812pqFYh2xB44tgcavmjpcUQIFBgby5Zdf0rNnTyZNmkSlSpWIiIhg1apVjB49mkqVKhXoMJOiKIwYMYLg4GBq1KhBjRo1CA4ORqvVPnA+Q17effdd5s2bx8svv8zo0aMpX748Fy9eZPny5cybNw87OzveeOMNRo8ejYuLC25ubowfPz5XcdqhQwfmz59PQEAAkDW/4t6/6qtXr05ERATLly/H39+ff/75J9f1Q6pWrUpYWBjHjh0z9kfHjh2pX78+gYGBzJ49m8zMTIYNG0b79u1p2rRpvvOsXLkylpaWfPfdd7zzzjucOnXqsa5BoygKAwcO5KeffuL8+fM5rj+UfZbazz//jIeHBxEREXz88ccPba958+ZotVo++eQT3n//fQ4cOJBrEu5nn33GCy+8gJeXF3379kWj0XDixAlOnjzJF198UeAcAD766CP8/f2ZPHky/fv3Z+/evXz//ff8+OOPj9Vetic9zDR8+HDatWvH9OnT6dmzJ//973/ZtGnTI4tC8eTK9p+bpUjr59/iVPusv9huB00hMymRUZ1rkWTjyUzd3Ul9IRMg+eZDWhEib1qtlh07dlC5cmX69OlDnTp1GDJkCKmpqY+cN/IgY8aMYcSIEQwbNoymTZsSFRXFxo0bcxREgwcPNhYYefH09GT37t3o9Xq6dOmCn58fw4cPx9HR0ViwfPnll7Rr144ePXrQsWNH2rRpQ5MmTXK089VXX1GxYkUCAgIYOHAgo0aNyjGXo2fPnnz44Ye89957NGzYkD179hgnn2Z78cUX6dq1Kx06dMDV1ZVly5ahKApr1qyhXLlytGvXjo4dO+Lj48OKFSsK1Feurq4sXLiQP/74A19fX6ZNm8ZXX31VoDayBQYGcvr0aSpWrEjr1q2NyzUaDcuXL+fw4cP4+fnx4Ycf8uWXXz60LWdnZxYvXsy6deuoV68ey5YtIygoKMc6Xbp04e+//yYkJAR/f39atGjBrFmzqFKlinGdR73O92vcuDG///47y5cvx8/Pj88++4xJkyYV6OyuotCqVSuWL1/OggULqF+/PgsXLmTFihWPHLEUT05Rn8Z5oCaUkJCAo6Mj8fHxj/2hey+dTse6devo1q1bruOxphZ9I4wLLzyPa7zK7Rda0OqrBfxnbziT/nucf6wnUIsr0GAg9J5ToHaLc86FJS0tjbCwMLy9vY3H8Q0GAwkJCTg4OJSZw0zFLeeAgAACAgJyfUEWRrsNGzY0Xv+juOX9tBSXvIvqdc5Lccn5aSuueef12ZutIN/fxScj8cQ8XL2JG551/Ypyf+8jZs8WBjarTHX3cnycPgQDChxfCmEPvk6CEMVFYmIily5dYtSoUaYORRQheZ1FYZBippTpPmA8h5o5AXBl3Fg0+kyCetTlqFqDpfpnslb6+0PIzP8ZEEKYgr29PZGRkfm6hoooueR1FoVBiplSxlxjTv2Js7htCw7Xkzj10wxa+LjwQn0PZugGcEfjBHEX4OC8R7YlRGm1bds2uSeOEKWIFDOlUAPvllzol3Uxrsxfl5Eed4NPutUhw8Keqel9s1baNl0mAwshhCgVpJgppbq/N4sIdzNs0gzs++JDPJ1sGBZQnT/07TmneEN6PGx9/HujlFalfD68EEIUK4X1mSvFTCnlbFse/btZ15Rx2XCY6FMHebudD57lbJmQ9krWSocXQswp0wVZjGSfpXXvhdKEEEIUrezP3Cc9U1YumleKdX1pNGuXr6ZWaDwnJ46m8x/b+PT5OryzOJV1hhZ00+yDDR/Da39BGb/ZmZmZGU5OTsZ7EWm1WlRVJSMjg7S0tGJ1KmNRMhgMZS5nkLzLUt5lMWcofnmrqkpKSgqxsbE4OTk90ZWbQYqZUk2jaKjx6WQyAz/A6+R1Dv/1K11eeJ3W1V0IvvQynawPYxG+E87+DXW6mzpck8u+X1B2QaOqKqmpqdjY2JSZO9uWxZxB8i5LeZfFnKH45u3k5FQoN92UYqaUq9uoE2ufrUWNkHMkfPUNui4D+bx7XZ775hY/6Z7nffM18O94qN4JLB7vpm+lhaIoeHh4UKFCBXQ6HTqdjh07dtCuXbtSe7HA+5XFnEHyLkt5l8WcoXjmbWFh8cQjMtmkmCkD2k74lku7uuJ+PYONP4zlhQ+/4dUWVZizpwcvW+yg/J0rsO9HaDvS1KEWC2ZmZsafzMxMrK2ti80vf1ErizmD5F2W8i6LOUPpz9v0B85EkStXoTJJrzwPQPnFIURfv8SHHWtipbVnSnrWFYPZORMSYx7SihBCCFE8STFTRrR9/wviXK1xTFbZOvUDHLUWjO5SmzWG1pygOmQkweaC34VXCCGEMDUpZsoIM0sryo/KOoxUN+Qy+w6vpb+/F76eTnyennUKN8cWQ9QRE0YphBBCFJwUM2VIrR6vcMPXA0s9XJ4+Gb2qM963aY2+ddZKG8aBXDhOCCFECSLFTBmiKAp1g77EoECjE0n8uWoK/lWd6dnQk2m6AaRhBZH74NRKU4cqhBBC5JsUM2WMS/0mJHVqBoD9T38SmRDJx8/VJt6iAj/o7l5rJuRzyJAr4QohhCgZpJgpgxqP/5IMSw3Vowys+mkkHo42vPdMdX7Wv0A05SHhKuz51tRhCiGEEPkixUwZZOFWAZvBAwFo9Ocptl8M4Y023rg5O/FFRtZyds2G+KumC1IIIYTIJylmyqjqwz4i1cUW1wQ48O1noOj49Pk6/GNozkFDbchMhU1Bpg5TCCGEeCSTFjNBQUEoipLj5957NKiqSlBQEJ6entjY2BAQEEBoaKgJIy49NNbWVBr1MQDPbrvDf3Z+QydfN9rWcCVI9yoGFDj5B0TsN3GkQgghxMOZfGSmbt26REdHG39OnjxpfG7GjBnMmjWL77//noMHD+Lu7k6nTp1ITEw0YcSlh2vPPqTXqoJNBujm/kZEYgSfd/flnOLDH5nts1baMBZUg2kDFUIIIR7C5MWMubk57u7uxh9XV1cga1Rm9uzZjB8/nj59+uDn58eiRYtISUlh6dKlJo66dFA0Gmp8NgWAgON65q0aTzVXO15rVZWvMvuRjA1cO4py8ncTRyqEEEI8mMlvNHnhwgU8PT2xsrKiefPmBAcH4+PjQ1hYGDExMXTu3Nm4rpWVFe3bt2fPnj0MHTo0z/bS09NJT083Pk5ISAAw3gX5SWW3URhtFQeW9eujeaYtbNlJveVH+LftBoa1a8/qo1f5Nq0X4yyWodkyGfNqk0pNzvlV2l7r/CiLOYPkXZbyLos5Q8nMuyCxKqpqusu9rl+/npSUFGrWrMn169f54osvOHv2LKGhoZw7d47WrVsTFRWFp6encZu3336bK1eu8O+//+bZZlBQEBMnTsy1fOnSpWi12iLLpSQzv3WLKl99iZle5dt+9jzTeDSHY61YddlAiNUYqijXOe/WnTOefU0dqhBCiDIiJSWFgQMHEh8fj4ODw0PXNWkxc7/k5GSqVavGmDFjaNGiBa1bt+batWt4eHgY13nrrbeIjIxkw4YNebaR18iMl5cXN2/efGRn5IdOpyMkJIROnTqVqtuox8z8iqSF/yHKGU7MHMK7jYfz0s/78YjezM+WX6NXLMh4axfmrtVMHepTU1pf64cpizmD5F2W8i6LOUPJzDshIYHy5cvnq5gx+WGme9na2lKvXj0uXLhAr169AIiJiclRzMTGxuLm5vbANqysrLCyssq13MLColBfwMJuz9Q833ufM6tXUfFWEpuW/oerdfoQ1MOPF+fEs0tflzZmoVhtn4zm5SWmDvWpK22vdX6UxZxB8i5LymLOULLyLkicJp8AfK/09HTOnDmDh4cH3t7euLu7ExISYnw+IyOD7du306pVKxNGWTqZ2dlR8cNRAPTbnsnMtWNo4GVPn0aVmJQ5CD0KmnN/w+Vtpg1UCCGEuI9Ji5lRo0axfft2wsLC2L9/Py+99BIJCQm89tprKIrCiBEjCA4OZvXq1Zw6dYrBgwej1WoZOHCgKcMutZz69cW8cX1sMuDZ306z4MSvfNytNtFW3vwn8+5E7HVjQF9yJpAJIYQo/UxazFy9epWXX36ZWrVq0adPHywtLdm3bx9VqlQBYMyYMYwYMYJhw4bRtGlToqKi2LhxI/b29qYMu9RSNBqqzJiJwdoS30i4/Mt33NZdYWTH6nyd+RJxOMDNc7B/rqlDFUIIIYxMWswsX76ca9eukZGRQVRUFCtXrsTX19f4vKIoBAUFER0dTVpaGtu3b8fPz8+EEZd+lpUq4TnuEwAGbM3k61WjeKmxO462WqbpBmSttG0aJMaYMEohhBDif4rVnBlRPDj164dFq2ZYZkK3JRdZeGoe/Xz0rDS045ihGmQkQsjnpg5TCCGEAKSYEXlQFIUqU2egt7OhRjTc/GU+GuurvOxfhc90g7Pu23RiOVzZa+pQhRBCCClmRN4s3Nzw+iwIgJd26tkXtpx3O3hxzbYOKzIDslZaPxoMepPFKIQQQoAUM+IhHLp3x+qZ9pgbYODamyw9/RPjn6/Dl5n9iVdtIeYkHF5g6jCFEEKUcVLMiAdSFIXKk6egd7Cjaiyk/7KYKp6x1PLx5qvMrFsbqJsnQ3KciSMVQghRlkkxIx7K3MWFihMnAdBrr4Gfl49i/As+/EFHThuqoKTdgS2TTBukEEKIMk2KGfFIdh07crthPTQq9F0RzT8Xf2JI2xp8pnsNAPXwIog6YuIohRBClFVSzIh8ievZG72LE563wGzecpr73iLGqRGr9a1RUGHdaDAYTB2mEEKIMkiKGZEvBq2WSl8EA/D8QZUVyz9m3PNVmaobSJJqDVGH4PhSE0cphBCiLJJiRuSbbZs22L3UG4D+K29w9PovNPCtzTeZfQBQNwVB6h3TBSiEEKJMkmJGFIjnx+MxuJenQjw4zltNt+Z3WKF5nosGT5TkG1m3OhBCCCGeIilmRIGY2dlSdcZMADoeU9n8x2cM6VCJoMy7k4EP/AzXQ00ZohBCiDJGihlRYLbNmuHwaiAAL6+5zc20RVwv35J1+mYoqh7WjQFVNXGUQgghygopZsRj8fhoFGplT5yToPIv/9K7zS2m6AJJVS3hyi44tdLUIQohhCgjpJgRj0VjbY33l7NQFYW2oSrn/5lG40bV+DGzBwDqxk8hPcnEUQohhCgLpJgRj82mQQPKvfUGAC+vTcDKYgnLLHpxxVABJTEadn5l4giFEEKUBVLMiCfi9t77qNWr4JAK9RbuoHPLOCZlvgqAuud7uHnRxBEKIYQo7aSYEU9EY2mJz1ezMZhpaHZeJX3b91yv2Jgt+oYoBh2sl8nAQgghipYUM+KJWdeuTfn33gXg5XXJVHb8g8n6V0hXzeHSZji3zsQRCiGEKM2kmBGFosJbb6P41sQ2HdouOUjlhqnM0z8PgGH9x6BLNXGEQgghSispZkShUMzN8f7qawyW5jQMU6m6YwHLbDtxTXVGEx8BO2eZOkQhhBCllBQzotBY+fjg/vE4APpuTsPfciUTdXcnA+/6Gm6cN2V4QgghSikpZkShcn75ZcyeewYzFfotPUdiRdisb4Ri0KH+/aFMBhZCCFHopJgRhUpRFKpP+ZJUL1fKJUPf/65kmkVPUlVLlCu74PhyU4cohBCilDHPz0pr164tcMOdOnXCxsamwNuJkk+j1eI7dwFne/ekdoSenhGr+NqtN59YrEC/4RPManYBrbOpwxRCCFFK5KuY6dWrV4EaVRSFCxcu4OPj8zgxiVLA2qca5SaOJ+XjSTy76ybzeuk5Z1aJWmlXUUM+R+n5nalDFEIIUUrk+zBTTEwMBoMhXz9arbYoYxYlRJVeL3OrR2sABq7fw5epXQBQjv4HIvaZMjQhhBClSL6Kmddee61Ah4xeeeUVHBwcHjsoUXq0/OJHrvk4YpsO/Q9sYWl6WwAy/zsc9DoTRyeEEKI0yFcxs2DBAuzt7fPd6Jw5cyhfvvxjByVKD42lJXV//JUEW4VK19NJO59GnGqPedxZ2PuDqcMTQghRCsjZTKLIuVf1JeXTdzAo0DI0isXhTQDQb50Kt6+YODohhBAlXb4mAN8rLS2N7777jq1btxIbG4vBYMjx/JEjRwotOFF6dOj9Ab/t20LT/56jw6Hz7HGsQSvnC+j+/giLV/4ARTF1iEIIIUqoAhczQ4YMISQkhJdeeolmzZqhyJeQyKfnP/+FkDPPUP98Bpn7Mkl91hybSyFw5i/w7WHq8IQQQpRQBS5m/vnnH9atW0fr1q2LIh5RijlrXXCbGkzskFFUiE9n5wEfOrU5T8bfo7Gq1gGs8j8vSwghhMhW4DkzFStWLNBkYCHuFVD3eQ6//wwZZuAVlcTZM+5YpcSQuekLU4cmhBCihCpwMTNz5kzGjh3LlSsycVM8nrf6TWPlC04AGE5qSL5uiebgz3DtmEnjEkIIUTIVuJhp2rQpaWlp+Pj4YG9vj7Ozc44fIR7F3tKe5z6YybZ6ChoVLux1RZ8KKas/AIPe1OEJIYQoYQo8Z+bll18mKiqK4OBg3NzcZAKweCwtK7Zi23v9CJ+wgqqxKmG7y1PD6jj6A/MxazHU1OEJIYQoQQpczOzZs4e9e/fSoEGDQg1k6tSpfPLJJwwfPpzZs2cDoKoqEydO5Oeff+b27ds0b96cH374gbp16xbqvoVpDG81hnde2c3wH66ivWlO7HEHnCwmYla3J9i7mzo8IYQQJUSBDzPVrl2b1NTUQg3i4MGD/Pzzz9SvXz/H8hkzZjBr1iy+//57Dh48iLu7O506dSIxMbFQ9y9MQ2uhZUTP6cx53gyAW+fsSA/Xk/jf0SaOTAghRElS4GJm2rRpfPTRR2zbto24uDgSEhJy/BRUUlISgYGBzJs3j3LlyhmXq6rK7NmzGT9+PH369MHPz49FixaRkpLC0qVLC7wfUTw1dmtMnT6vs7Z51uHKa/udsDzyD4bzm0wcmRBCiJKiwIeZunbtCsCzzz6bY7mqqiiKgl5fsAmc7777Ls8//zwdO3bkiy/+d3puWFgYMTExdO7c2bjMysqK9u3bs2fPHoYOzXteRXp6Ounp6cbH2QWWTqdDp3vyGxtmt1EYbZUURZ3zUL+hvNpjO9WvXcA3UkPUbmecXT7A9sN9YJH/G5wWNnmtyw7Ju+zkXRZzhpKZd0FiLXAxs3Xr1oJu8kDLly/nyJEjHDx4MNdzMTExALi5ueVY7ubm9tDTwqdOncrEiRNzLd+4cSNarfYJI/6fkJCQQmurpCjKnDvRlW97hTPtVx1O8Rak7EzmisMwwiv3LbJ95pe81mWH5F12lMWcoWTlnZKSku91C1zMtGzZEktLyzyfu3nzZr7biYyMZPjw4WzcuBFra+sHrnf/2VLZI0APMm7cOEaOHGl8nJCQgJeXF507d8bBwSHf8T2ITqcjJCSETp06YWFh8cTtlQRPK2f9CT2z437is2UG4sO1VNq/lTovfoziWqvI9vkw8lqXjZxB8i5LeZfFnKFk5l2QqSsFLmb69evHqlWr0GhyTre5fv06zz77LKdOncpXO4cPHyY2NpYmTZoYl+n1enbs2MH333/PuXPngKwRGg8PD+M6sbGxuUZr7mVlZYWVlVWu5RYWFoX6AhZ2eyVBUef8TqN32HltJ8uuhRK4zcCNI/ZkzBmGx6TtJr0RpbzWZYfkXXaUxZyhZOVdkDgLPAE4OjqaN954I9eygIAAateune92nn32WU6ePMmxY8eMP02bNiUwMJBjx47h4+ODu7t7jiGxjIwMtm/fTqtWrQoatigBLDQWTGkzhXWtLDlYQ0E1KCSviyZl32JThyaEEKIYK3Axs27dOg4cOMCHH34IQFRUFAEBAdSrV4/ff/893+3Y29vj5+eX48fW1hYXFxf8/PxQFIURI0YQHBzM6tWrOXXqFIMHD0ar1TJw4MCChi1KiBrlajCs0bv88IKGOHvQJZtzc9ZU0JecSWtCCCGergIfZnJxceHff/+lTZs2QNZdtBs3bsySJUtyHXp6UmPGjCE1NZVhw4YZL5q3ceNGudFlKfda3ddYF7aOXzufY/RKA8mnDNxZNQunvmNNHZoQQohiqMDFDEClSpUICQmhTZs2dOrUid9++61Qbmuwbdu2HI8VRSEoKIigoKAnbluUHBYaCz5v+Tmv3n6VgzVU/C9A3LcLcOz5AYql6U7VFkIIUTzlq5gpV65cnsVKSkoKf/31Fy4uLsZlt27dKrzoRJnVwLUB/Wv159fOy6gfrocbCjHTP8Rjwk+mDk0IIUQxk69iJvteSUI8TcMbD2dLxBaWtYth8GYD8X9sw/XNCMw9Kps6NCGEEMVIvoqZ1157rajjECIXO0s7Pmn+CSOTh9PuFPhcV7g6+v+ouvgfU4cmhBCiGMnXjN2C3nNJbgQpCsuzVZ6lfZVn+Pk5MwyKSuqhyyRt2WjqsIQQQhQj+SpmypUrR2xsbL4brVixIpcvX37soIS41yfNP+FaJS0bmmS9Xa9N+ARDWpqJoxJCCFFc5Oswk6qqzJ8/Hzs7u3w1WpJuZCWKP3dbd0Y0Gc43KVNpcVaPc1wyN7+dRYUxn5g6NCGEEMVAvoqZypUrM2/evHw36u7uXmIulyxKhgG1BvDH2f/yS+dQRq8yELdoMY69+2JVo4apQxNCCGFi+SpmwsPDizgMIR7OTGPG9PaT6XvnpbvXnlGJ/nQ8VZYtRynkizUKIYQoWeRbQJQYtZxr0bNaIL920pBuAanHT3Jn5UpThyWEEMLEpJgRJcq4lh+gc3Biebust27sjBlk3rxp4qiEEEKYkhQzokTRWmgZ3WIS65sqhFUAQ2IS16fPMHVYQgghTEiKGVHi9Kr9DNXM/LKuPYNKwl9/kbxnj6nDEkIIYSJSzIgSKbjrTKLdNPx799oz0RMnyrVnhBCijHqsYmbnzp288sortGzZkqioKAB+++03du3aVajBCfEgtStUpKW2F8vba7htB7orEdycO9fUYQkhhDCBAhczK1eupEuXLtjY2HD06FHS09OBrFsYBAcHF3qAQjzIZy+Mx91gxS+ds97GcfPnk37xoomjEkII8bQVuJj54osv+Omnn5g3b16OC+O1atWKI0eOFGpwQjyMs50VAW4jOFoDDldXQJdJdFAQqsFg6tCEEEI8RQUuZs6dO0e7du1yLXdwcODOnTuFEZMQ+fbWcwNoE+/CL53vXnvm0GHiV682dVhCCCGeogIXMx4eHlzMYyh/165d+Pj4FEpQQuSXvbUFTWsHYWeTyYq2WW/n6zO+JDMuzsSRCSGEeFoKXMwMHTqU4cOHs3//fhRF4dq1ayxZsoRRo0YxbNiwoohRiIfq92x72t+qzjp/hfAKYIiPJ3aGXHtGCCHKigIXM2PGjKFXr1506NCBpKQk2rVrx5tvvsnQoUN57733iiJGIR7K2sKMqs2D6J6UytznzDAoEP/ftSTv3Wvq0IQQQjwFj3Vq9pQpU7h58yYHDhxg37593Lhxg8mTJxd2bELkW482jfFOaMFtNwP/NlYAiJk4SSYDCyFEGfDYF83TarU0bdqUZs2aYWdnV5gxCVFgluYatK1G8n+341neToPeTCEjPJzMmBhThyaEEKKImednpT59+uS7wVWrVj12MEI8iZo+PiTusSG1vMIte3C9A7roaCw8PU0dmhBCiCKUr5EZR0dH44+DgwObN2/m0KFDxucPHz7M5s2bcXR0LLJAhXiUWu72pKZXRKOqxDqoAOiiZWRGCCFKu3yNzCxYsMD4/7Fjx9KvXz9++uknzMzMANDr9QwbNgwHB4eiiVKIfLCzMuempQ9emde56aABVHTR10wdlhBCiCJW4Dkzv/76K6NGjTIWMgBmZmaMHDmSX3/9tVCDE6Kg0srVonqGjpt36+rM6GjTBiSEEKLIFbiYyczM5MyZM7mWnzlzBoOcOSJMzNzd924xk3VGk+6aFDNCCFHa5esw071ef/11hgwZwsWLF2nRogUA+/btY9q0abz++uuFHqAQBVG+aj0qXMpk392RGZ2MzAghRKlX4GLmq6++wt3dna+//prou18UHh4ejBkzho8++qjQAxSiIGpWLM/VdCduOt4dmZFiRgghSr0CFzMajYYxY8YwZswYEhISAGTiryg2vMvbci6jIvEVsib+GhIS0CclYSbXQhJCiFLrsS+aB1lFjBQyojixNNdwR1sdd00mSdZZy2QSsBBClG4FHpnx9vZGUZQHPn/58uUnCkiIJ5XhUpvqGf9y08EKuzTQxcRgVaOGqcMSQghRRApczIwYMSLHY51Ox9GjR9mwYQOjR48urLiEeGzWFetR/YKOOAdrqsaqckaTEEKUcgUuZoYPH57n8h9++CHHVYGFMBX3KrXRnTJwznhGk1w4TwghSrMnmjNzr+eee46VK1cWVnNCPLZank6YZbgYrzWTcU2KGSGEKM0KrZj5888/cXZ2LqzmhHhslcrZkJxZmTv2WfdnSr56xcQRCSGEKEoFPszUqFGjHBOAVVUlJiaGGzdu8OOPPxZqcEI8DkVRSLCrgRUXAA0Z16JMHZIQQogiVOBipmfPnjmKGY1Gg6urKwEBAdSuXbtQgxPicRkq+FIu87+AFZobt1H1epR77icmhBCi9ChwMRMUFFRoO58zZw5z5swhPDwcgLp16/LZZ5/x3HPPAVmjPhMnTuTnn3/m9u3bNG/enB9++IG6desWWgyidLKtVA/PmxkYFCs0egOZN+OwcKtg6rCEEEIUgQLPmTEzMyM2NjbX8ri4uBx30s6PSpUqMW3aNA4dOsShQ4d45pln6NmzJ6GhoQDMmDGDWbNm8f3333Pw4EHc3d3p1KkTiYmJBQ1blDFeVarjqdNwyz7rcWaMnJ4thBClVYGLGVVV81yenp6OpaVlgdrq3r073bp1o2bNmtSsWZMpU6ZgZ2fHvn37UFWV2bNnM378ePr06YOfnx+LFi0iJSWFpUuXFjRsUcbU8nBAzajAzbunZ6dFXTVtQEIIIYpMvg8zffvtt0DW5Mr58+djd8+9bvR6PTt27HiiOTN6vZ4//viD5ORkWrZsSVhYGDExMXTu3Nm4jpWVFe3bt2fPnj0MHTo0z3bS09NJT083Ps6+f5ROp0On0z12fNmy2yiMtkqKkpizvaVCklqVRPusUb6YS6ew03V+xFY5lcS8n1RZzBkk77KUd1nMGUpm3gWJVVEfNNRyH29vbwCuXLlCpUqVchxSsrS0pGrVqkyaNInmzZsXKNiTJ0/SsmVL0tLSsLOzY+nSpXTr1o09e/bQunVroqKi8PT0NK7/9ttvc+XKFf7999882wsKCmLixIm5li9duhStVlug2ETJFnlyM9Zn/6X1IQ0XW9TC0Pt1U4ckhBAin1JSUhg4cCDx8fGPvA9kvkdmwsLCAOjQoQOrVq2iXLlyTxblXbVq1eLYsWPcuXOHlStX8tprr7F9+3bj8/ffB0pV1YfeG2rcuHGMHDnS+DghIQEvLy86d+5cKDfF1Ol0hISE0KlTJywsLJ64vZKgpOa8JOEWmivrAA0OGGjVrVuBti+peT+JspgzSN5lKe+ymDOUzLyzj6zkR4HPZtq6dWtBN3koS0tLqlevDkDTpk05ePAg33zzDWPHjgUgJiYGDw8P4/qxsbG4ubk9sD0rKyusrKxyLbewsCjUF7Cw2ysJSlrO5nblwSJr4FFJz3js2Eta3oWhLOYMkndZUhZzhpKVd0HizFcxM3LkSCZPnoytrW2OUY+8zJo1K987z4uqqqSnp+Pt7Y27uzshISE0atQIgIyMDLZv38706dOfaB+ibLDQOqDX3C1m0jJMHI0QQoiikq9i5ujRo8aJOEeOHHnoYZ6C+OSTT3juuefw8vIiMTGR5cuXs23bNjZs2ICiKIwYMYLg4GBq1KhBjRo1CA4ORqvVMnDgwELZvyjdLLWOpJv/b2RGCCFE6ZSvYubeQ0vbtm0rtJ1fv36dV199lejoaBwdHalfvz4bNmygU6dOAIwZM4bU1FSGDRtmvGjexo0bsbe3L7QYROllZedIpllWMaORYkYIIUqtAl9nZsiQIXletC45OZkhQ4YUqK1ffvmF8PBw0tPTiY2NZdOmTcZCBrIm/wYFBREdHU1aWhrbt2/Hz8+voCGLMkpro0W5e5hJk5Fp4miEEEIUlQIXM4sWLSI1NTXX8tTUVP7zn/8USlBCFAZ7GwsMZlmDj2ZSzAghRKmV77OZEhISUFUVVVVJTEzE2tra+Jxer2fdunVUqCD3vhHFh62VubGYMc8wmDgaIYQQRSXfxYyTkxOKoqAoCjVr1sz1vKIoeV6sTghTsbMyJ1NjAegx10kxI4QQpVW+i5mtW7eiqirPPPMMK1euxNnZ2ficpaUlVapUyXGlXiFMzd7KAp2ZFZCCeaaKqtejFPBmqEIIIYq/fBcz7du3B7KuBFy5cuVCOz1biKJia2VGhiarmAFQ09JQbG1NG5QQQohCl69i5sSJEzkenzx58oHr1q9f/8kiEqKQmJtpyNT8b26XIT0djRQzQghR6uSrmGnYsCGKovCoe1IqioJery+UwIQoDKqZLRnmYJkJah5n4QkhhCj58lXMZN9kUoiSRtHYkn63mDGkpZk6HCGEEEUgX8VMlSpVijoOIYqGxo4MCyANDKlSzAghRGlU4LtmZzt9+jQRERFkZOS8THyPHj2eOCghCotibk/63Xe5PjXFtMEIIYQoEgUuZi5fvkzv3r05efJkjnk02Wc3yZwZUZyYWTpkjcwA6SkJ2Jk2HCGEEEWgwLczGD58ON7e3ly/fh2tVktoaCg7duygadOmhXoTSiEKg7llOTLuluwZybnvKSaEEKLkK/DIzN69e9myZQuurq5oNBo0Gg1t2rRh6tSpfPDBBxw9erQo4hTisZhZOxpHZnSpyaYNRgghRJEo8MiMXq/Hzi5rsL58+fJcu3YNyJokfO7cucKNTognpLFxIFNGZoQQolQrcDHj5+dnvIhe8+bNmTFjBrt372bSpEn4+PgUeoBCPAlrJw/StFnzuuLmzSUjKsrEEQkhhChsBS5mPv30UwyGrJv2ffHFF1y5coW2bduybt06vv3220IPUIgn0aWVP3G+lbnhAA430jjdtwcZ4eGmDksIIUQhKvCcmS5duhj/7+Pjw+nTp7l16xblypWT+zWJYsdRa0G3t1aw0uw5Wm2Ix/NWCuf7v0j1/yzDulbuu78LIYQoeQo8MpMXZ2dnKWREseVbqRz1n1tCSA8rwiuAWXwKlwNfJvW+e44JIYQomQqlmBGiuOvpXw2HGj+wtpeG856gJKUQ/tprJO8/YOrQhBBCPCEpZkSZMapnSzItP2dFbzhZRYHUNCLefouk7dtNHZoQQognIMWMKDMszTVMGdQDXeI7LOqtcqi6AukZRL77LgkbNpg6PCGEEI9JihlRplRwsGbMwFexienD/J6wy1eBTD1RIz/izsqVpg5PCCHEY5BiRpQ5/lWdeaHTm1SJacfc5xU2NVTAYCB6/Kfc+s9/TB2eEEKIApJiRpRJr7WqinONoTSO9eXnrhr+apZ1Nt714KncnDPHeANVIYQQxZ8UM6JMUhSFqX3qE2H9Hm1uuvPbMxpWtM0qaG588y2xX34lBY0QQpQQUsyIMsvG0oy5g5qyO2kkLeJtWdnGjMXPZhU0t379lRuTv4C7V7sWQghRfEkxI8q0Ki62fDOgKTtiRtMg2Yy1zcxY1FUBRSHhjz9wX/E7qk5n6jCFEEI8hBQzoszrULsC//dsfY5HfkS1NJV/GpnxW3cNmJnhcOwYMSM/wpCebuowhRBCPIAUM0IAHzxTA/9atYmOGIa7zsBfdRVW9DbDYG5O8rZtRL7zDobkZFOHKYQQIg9SzAgBaDQKX/driJVjHfSRA3HQG1hZw8CqPuYoWi0pe/cR8cab6BMSTB2qEEKI+0gxI8RdjloL5r7ahGuGxjhHPYeFqvJ7tUy2B5ZD4+hA6rFjXBn0GplxcaYOVQghxD2kmBHiHnU8HJjWpz4nkztQPbopAN+Xv07oG9UwK1+e9LNnufLKq+iio00cqRBCiGxSzAhxn16NKjK4VVUOxPel4U1vAIIsT3Dtg2aYe3iQERbGlcBXSA8LM3GkQgghQIoZIfI0/vk6NK3ixM4bb1Mv3hlVUXg/dSOTe90kqbwlumvXuNztecL69iP2m29IOXwYNTPT1GELIUSZJMWMEHmwMNPwTf8GVLKFvdc+xC/BHo2qctRJ5cOBekIrA6pK2smTxM35iSuBr3C+RUuuvv8+t5evIONqlKlTEEKIMsPc1AEIUVxVsLdiVD09NZu2Z9O5uuhPHkWbugZLuzP82N9AZqqG+mEqDS+r1AtXcUhKIjFkE4khmwCwrFoV2zZtsG3TGttmzdBotSbOSAghSicpZoR4CEWBGm52+FYqxwfP1uBK3PP8GxpD+omz2MWtoZznEXbXSOBHS0sqX1doEKbS4LKBmlGQER5ORng4txcvRrGwwKZJE+zatMa2bVusatZEURRTpyeEEKWCSQ8zTZ06FX9/f+zt7alQoQK9evXi3LlzOdZRVZWgoCA8PT2xsbEhICCA0NBQE0UsyroqLra83a4ai957ngkjvqNGq5VY8h2dwjrSUdFiVi+FuQNgyAgzvuqjIaShQqwjqDodKfv2EfvVTMJ69uJiu/Zc+3gc8X//Q+bt26ZOSwghSjSTjsxs376dd999F39/fzIzMxk/fjydO3fm9OnT2NraAjBjxgxmzZrFwoULqVmzJl988QWdOnXi3Llz2NvbmzJ8Uca5O1ozqGVVBrWsyq3kZ9h05joXjp/FP2w9DSz3kOISxV4vKxZ1tsLljkLDyyoNwlR8I1Ssb9wgfs0a4tesAUXBum5dbNu0xq5NG2waNECxsDB1ekIIUWKYtJjZsGFDjscLFiygQoUKHD58mHbt2qGqKrNnz2b8+PH06dMHgEWLFuHm5sbSpUsZOnSoKcIWIhdnW0v6NfWiX1MvEtMC2HruBoeOnaXCpfVMZw/m2svsq2nF8gbWXNWYU/uqSoO7xU3VWJW0U6dIO3WKuJ/morGzQ9uiOXZt2mDbpg2WlSqZOj0hhCjWitWcmfj4eACcnZ0BCAsLIyYmhs6dOxvXsbKyon379uzZsyfPYiY9PZ30e24KmHD38vM6nQ5dIdz9OLuNwmirpCiLOcPj521tBs/5uvKcrytpulbsvhjHthPnsbi4jtH6vVS2PMt+Oyt2NbdmVYA1VimKsbCpH5Y1kThp02aSNm0GwKJKFbStWqFt3Qobf/8inUgsr7XkXdqVxZyhZOZdkFgVVVXVIowl31RVpWfPnty+fZudO3cCsGfPHlq3bk1UVBSenp7Gdd9++22uXLnCv//+m6udoKAgJk6cmGv50qVL0crZJMKE9Aa4kKBw+WYS7vGHeVbdT1NNKCdtLNhtY8MurTUXLSzwjiHrkNTdicRm9/yGqmZmpFatQnLNmqTUrEm6h0fWLGUhhChlUlJSGDhwIPHx8Tg4ODx03WJTzLz77rv8888/7Nq1i0p3h9Wzi5lr167h4eFhXPett94iMjIy12EqyHtkxsvLi5s3bz6yM/JDp9MREhJCp06dsCgj8xrKYs5QtHnrDSpHI++w88R5DKf/plX6LlppQrlprrDHxppdWhv22mjRZ6j4XVFpGJZ1WKpCfM52zFxc0LZqibZVa7StWmJ2d1TzcclrLXmXdmUxZyiZeSckJFC+fPl8FTPF4jDT+++/z9q1a9mxY4exkAFwd3cHICYmJkcxExsbi5ubW55tWVlZYWVllWu5hYVFob6Ahd1eSVAWc4aiydsCaFm9Ai2rV0Dt3ZrQawn8dPQMaSfX0ix+BzMSQ0G5yUkrS3a62bDDx4l5XQx43OLu6d8qfhEqVnFxJP71N4l//Q2Ata+v8do22kaNHnsisbzWZUtZzLss5gwlK++CxGnSYkZVVd5//31Wr17Ntm3b8Pb2zvG8t7c37u7uhISE0KhRIwAyMjLYvn0706dPN0XIQhQ6RVHwq+iIX8UW8EILLsYmsvDoWZJPrKVhwjaGpZ3ig9vxxGk07LGxZotfBX7ytyBZl0atq/8btakaC2mnT5N2+jRxP/+MRqtF27Jl1rVt2rTB0svL1KkKIUSRMGkx8+6777J06VL++9//Ym9vT0xMDACOjo7Y2NigKAojRowgODiYGjVqUKNGDYKDg9FqtQwcONCUoQtRZKpXsKd6F3/o4k/krRSWHTtL4rH/UvfOFromhdI9ORwDcNrSko3lK3KguhPLMm/jkKSn/t3CpkE4OKSkkLR5M0mbsycSV8auddYZUrbNm6G5e/kDIYQo6UxazMyZMweAgICAHMsXLFjA4MGDARgzZgypqakMGzaM27dv07x5czZu3CjXmBFlgpezllefaQzPNCY2YRyrjp0j/ugaasdtomV6KH43wuAGxGsUNjhW5UATL5Y3jef7tFt4x3D3LCkDtaKAKxHcvrKU20uXgoUF2kaNsG3TBrs2rbGqXRtFI7dqE0KUTCY/zPQoiqIQFBREUFBQ0QckRDFWwcGa/u0aQLsG3EkZx7rj57l1eBXVYjfRUj1F/9th9L8dhgrss/Nhb9U6nKqr8teds1ik6vC7kjVq0zAMKtzRkXLgACkHDnBj1izMXFywbd0KmxYtMUtNMXWqQghRIMViArAQomCctJb0bOkHLf1ISv+ELSfPc+PgKqrEhNCCk7RMukzLs5cBuGTtzYGqLTj3rAPrGoUyPykK99vcLWxU/CLAKi6OhLV/kbD2L6oBkX/+iV2btti2aYO2UUMUS0vTJiyEEA8hxYwQJZydlTldmvpCU1/SdOPYfeoi1w+uouK1f2munqBaWhjVzobBWYiy8uacd0fCW1TkgN8lvo45iCEjnVpR/xu1qXpdJf3MWdLPnCVu3rysicTNmxtvt2BZpYqpUxZCiBykmBGiFLG2MCOgUS1oNA6dfixHzl4mat9K3K6up5nhBBXTw6h4t7DpYulNtFdHomv5EFr3Druj97I0IRzHpKwrETcMU2kYDvbJKSRt3UrS1q1cByy8vLBt0xrbZs3Q+vtjXr68qdMWQpRxUswIUUpZmGloXrc61B2LwTCGExfDidz7J+WvrKOp/jgVM8KoeGkeXIJnsKGHfWOiKvVgq3KbhK4a5l8/QJouharX/3dF4lpRQGQkd5Yt586y5QBYenuj9ffP+mnmj8UDrgElhBBFRYoZIcoAjUahYU1vGtYcjaqO4mx4BBG7/8Qucht10o7grCThm7gb3zO76QTcCHMh3NmfS5VrcqWaOUfrnmFN3Gms0w3UvaJSL1zFN1KlSixkhIWRERbGnd9/B7JGbozFjb8/lpUqmjR3IUTpJ8WMEGWMoijU8a5CHe+PgI9ISsvg4JHdJISGUO76burqQnElDte4DfjHZd0y5IpZFcIrNCO8clUi6+rZefMYC+5cxDZVpU6kim+ESt1IqHJdRRcZSXxkJPGrVgFg7umBrb8/Nk2bYuvvj0WVKihyPykhRCGSYkaIMs7O2hL/Vh2gVQd0Oh0rV6+hsqNKxsVteMTtpYY+jCr6K1SJvkL7aMhQzbhk5Uuk57NcrVGRcL9Etlw/yH+SrmKTplL7ataoTd1IBZ9oA5nXoon/71ri/7sWAHNXV+MhKW3TplhWqybFjRDiiUgxI4TIwcbKEv9numHRpR8A165FEn5wA1zeRpX4A1RUYqmTcZI64SchHBLQcknbiCivXkRXc+VSnSj+jTnAktSbWGUo1LqaNXpTL1JDtWgD3LhBwrp1JKxbB4CZszPapk2NBY5VjRpyAT8hRIFIMSOEeChPTy88e74FvIVqMBB2MZToI+uxjNhBzeQjOCjJNErZTaNzu+EcRFOecEd/Yqp0JraiI2erX2B9zAFWZCRgodNQ81rWYal6V82pHqWHW7dI3LiRxI0bAdA4OqJt0sQ458a6Tm0UMzPTdoIQoliTYkYIkW+KRoN3zXp416wHjCFTp+Pcid3cPrkR+2u7qJEeiodyE4/49XBiPZyAixpverk0J7aqLzfcLDlR9Th/VzvCH5mpmGcqVIs2wzdCpVGUBdWuZmIRH0/Sli0kbdkCgMbODpsmjbH1zzosZV237mPfDVwIUTpJMSOEeGzmFhbUahIATQIASEtOIPTwJpLPhFA+dh8++stUN4RR/e49pNJVc85a+BLrHkCcd3VuOOk4UvEQf1c5zmqDDjM9eMeY4Rup0uSaNdUjdFgkJZG8fQfJ23cAoGi1aBs2zJpz4++Pdb16aOQKxUKUaVLMCCEKjbWtA3Xb9YF2fQBIuBlF+MH16C9uoeKt/VTgJg0yT8DVE3AV7qi2+Fs34nbFPtyuVoUY65sccjvI35VCWatmoBhUqsZmjdw0i7GlWng6lskpJO/ZQ/KePQAoVlbYNGhgPCxl07ABGmtrU3aDEOIpk2JGCFFkHMpXpP5zbwJvgqoSdyWUq4fXYxa+naqJh3FSkmmRvgsu74LLEKlWoLF9UxKrvMGdqm5EGcI44HKAf9wv8A+pKKqK1w0z/CIUmsXaUT0sDcuENONNMwEUCwus69f/36TiRg3R2NqatiOEEEVKihkhxNOhKLhU9cOlqh8wGlWvI/rMXmKPb0B7dSdVU0PxUmLxSloHoeswnFI4q3jTuFxzkn1eIKmSPZdTT3PA4QDrKkSwjiRQVSrGmVEvUkPLWEd8wlKxup1M6uHDpB4+TNzcuWBujnVdX+O1brRNmmBmb2/q3hBCFCIpZoQQJqGYWeDh1w4Pv3YAGNISuXJsEwmhITjF7MZLF44vl/G9fRkOLyPtkAUnzHxp6tqStOqDSHZXOHvnGPu1+9lQPpYN3AFVxe22GY2iLGgV64T35RSsbiaQdvwEacdPwPxfQKPBunbt/13rpkkTkJEbIUo0KWaEEMWCxtqeKi16Q4veAGTcvsbVI+tJO7cZ95v7cDbE0cxwHK4fh+sQp9rjZdGQ5p7PklGjHinlEjl+8xAHrQ+ywfkOG7gJz0L5eDOaXrOhdawTVS8nYxVzm7TTp0k7fZpbixYBYFmjBq6u5UkyN8e+RQvMXVxM2RVCiAKSYkYIUSxZlvPE59k34Nk3QFVJvZY134bLW6l45wguSiIdMndCxE6IgHCDGxW0TWjt9TJ63xokWkVxOPYAh80Ps8ExhQ11YqA9lEs0o2WMPa1inahyORmrqzfIuHCBchcuELNnLzGAZbVqaP2bom2aNanYwq2CqbtDCPEQUswIIYo/RcGmoh81KmbNt0GvI+HiXq4f24DllR1USgmlquY6VdPWwYV16M8rnMIHF3t/nvV+H0M1D25ygUPXD3JMc4x19kmsq5EErcEx2Yw2sS7Uu6RQ94aCVXgMGZcukXHpEneWrwDAokpltP7+2Pr7Y9uqFeaurqbtDyFEDlLMCCFKHjMLHGq1w6FW1nwb0hKIC93CrZP/4nBtF24ZETTgEg2SLsHJ5aSfMOcU1fF0bERXr3dJreLKTc0lDsceJFQJ5R/bW/zjndWUXYoZne5UpOV1B7wuJWB2MRLdlQjir0QQ/+dKUBS0TZpg36UL9p07y6iNEMWAFDNCiJLP2gGXJr1wadILADX+KrHH/yXpzCbKx+7DUX+LJpylScJZCF1G5ikNoXhT3a4hyZXe4nbFchy5up1btje4wAVWa6+x2vMaNIJyOhueS/SmWYwtnuduwdmLpBw6RMqhQ1wPDsamcWMcunTBvktnLNzcTNsPQpRRUswIIUodxbESbu3ewK1d1nwbQ9xlrp/cTMqFHTjFHsAl83rWyE3yJTi3EsNZhbOqFxe1DbhdcRA3KjoQq7nC4dj9xBLLUudzLHUGfKF6Wnl6R7njdyIBm7MRxtPAjYVN17sjNu7upu4GIcoMKWaEEKWboqApXw2PDtWgw9sAqLevcOP0dhLPbsP++gEqZETiq0TgmxYBl/6CS3DBUJF21n5EerbmmruWaMI5EXeEi9Z3+LLaHagGLglmPH/FmVbnFJwvxJJ65AipR45wPXgqNg0bYt+1Cw5dumDh4WHaPhCilJNiRghR5ijlqlCh9SAqtB4EgO5WJLtWzsHLPA67mAO4p1+mhiaKGhlREP4vhMMVQwVOWtbljIcPUeUtiCScC5zhP/Vu85964JxgRqvzCs9ctKFSWCKpx46ReuwYsdOmY9OgAfZdu+LQpTMWnp6mTV6IUkiKGSGEsHcnoUJzqnTrhoWFBaTc4s657dwK3YpV1D7cUy9QRRNLlcxYXojcCpFwTXXmgGUdjleoRISzhnCby/ztEM3fTVMpl2hG83Mqbc5pqBGZSerx46QeP07s9OlY169/d45NFywrVTR15kKUClLMCCHE/bTOODXqjVOjrAv4kZZA0sVd3Di1FfOre3FPOoOncoteut30igKi4IbqwGbLmhwt786V8gZ2lAtnQ9MkyiWa0eycSsuzBmpfhbQTJ0g7cYLYL7/Eul69rDk2XbpgWamSSVMWoiSTYkYIIR7F2gE7v27Y+XXLepyRQmrYPmJPbkaJ2IN7wilclQQG6A4xIBqIhjhVy1rr6hx2Ls/ZFjo2+V/FPjGTZudUWpxV8Y1USTt5krSTJ4n98ius6vri0LUrDl27YunlZdJ0hShppJgRQoiCstRiU+sZqtR6JutxZjrpEYe4fnIzathu3OKP4UIKr6ef4PXorFWuY8Mqm6ocrFuOH/1T0SXevDtio+IboZIeepoboae5MXMWSq1qlH++R1ZhU7my6fIUooSQYkYIIZ6UuRVWPq2p7NM667E+E13UMa6f3Ezm5V243j6CmyGJ/0s9w/+lZq0SrrFiZaXKrPS1Z64+kfrnUmlxVqVuhIrm3CVunPuaG7O+Jr1aRVy6vYDb872wrFrVZCkKUZxJMSOEEIXNzByLyk2pVLkpMBYMBvQxp4g9tYW0iztxuXmIqoY7fJR8AZLBAJysYM3aKh6s1tjgEZ5E87N66l5RsboURdJ3c0n6bi4JlZ2x6NieGi8Oxr5aTVNnKUSxIcWMEEIUNY0GM8/6eHjWh84jQFVRb57nxqktJJ/fQbkbB2igu0kDXRgAqeUUDgVY8weuqNfM8D2fgd8VFYeIW/Draq7+upobnraktWtM5Z4DqNWwA4qimDbH/2/vzuOqqtMHjn/uBtwryC6IrCruqYm44BJgomKZlmW55PSr1DG1ssaaasqasRrbl7HGbNLKJcslslww16QUNdw1XFABEdl3uHDP7w/zFuKCcuFyuc/79eKF53vO+Z7nOecKD9+zCWFFUswIIURDU6lQebenRVR7iPrrxeImN4Wcw1soOLaF5hm7GFCWzgDOgAdk9VOzs5+elHNueJ9S0fG0Ce/0Yli2HWXZdn5soSGzTyjuw2IJ630XLQzyvihhX6SYEUIIa1OpUHmE4Nk/BM/+D11sy08j7+gW8o5sxXBuJ8PLU6BFMUoLOB7uQHJGM0yn9QSdhVaZVbSKOwpxR/nV621+6+6JetAAbgkfRk+fnhh0BqumJ0R9k2JGCCEaI9dWuPUeh1vvcReniy5Q+NtWsg9vwSXtF4b6HUftl0tpDxVHM10oPOuEe6qWwCwI3JgNG1eT6rmax6N0VPYPY6D/QKICogh2DbZqWkLUBylmhBDCFjh749JjNC49Rl+cLs2l5MQOsg5uwufsL3QNOAIVCufT9WSkGXBI0+GfrWJ8vJFpobvZfX43b+95mxDXEKICoogKiKKjW0fr5iSEhUgxI4QQtkjvjqHLHQR2uePidHkRZad+xrR/A61SNuOZc4Lkb31oka/ixZRc1rUwsFvvyKn8U5zKP8X/Dv4PTydPgk3BuKS50C+gH44aR+vmJMRNkmJGCCGaAkdnnDoMxr/D4IvThefRJd2PMSWDgWccuVc5T4FaxU96PZsNerYZDGSXZZNNNnu27kGv1dPPrx9RgVEMbDUQNyc3q6YjxI2QYkYIIZoiFx9cboshJ+VzTAETUCaPovTXdXRN3sjgC78C2STqndhk0LPFoOc8pWw8s5GNZzaiRs2tPj2IDogiKjCKABd5vYJo3KSYEUKIJkofFgaLPqd0z15ULWfj07Ib8AxUFFN2Yjut9qzhoZM/8nx2OocddGw2GNhs0PObowN7zu9mz/ndvLH7DYKbt2FwUDTRgdF08uyEWqW2dmpCVGPVT+S2bdu488478fPzQ6VSsXr16mrzFUVh9uzZ+Pn5odfriYyM5NChQ9YJVgghbIyhRw8AypOTqcrP/2OGQzOcOg7Fb8w77O72OpXT99FywOvc6dSHzzOKWXs2jWeyc+lVWoZGUUgpOMEnBz7hge8fYODSKGbveIWf0n6ioqrCSpkJUZ1Vi5ni4mK6devGhx9+eMX5c+fO5e233+bDDz8kMTERX19fBg8eTGFhYQNHKoQQtkfr5WV+n1PJ3r1XX7B5KzwGPELQlOU0e+E0vg+uJ6rNo7xc3JJNZ9J5NTOLwcUlGEwm8o05rDj+NX/d+FciFkcwef3jrDmxhoKKgoZJSogrsOpppmHDhjFs2LArzlMUhXfffZfnn3+eu+++G4BFixbh4+PDkiVLmDx5ckOGKoQQNknfM4yKlBRK9+zBJSrq+iuoNWgDw2kVGA68BGX5RB3dzC371vJ06jZOaLN+v87GQJa2nISMTSRkbEKtqGjbrCOx7e4gts3ttHRuWe+5CXFJo71m5tSpU2RkZBATE2Nuc3R05LbbbiMhIeGqxUx5eTnl5eXm6YKCi38tGI1GjEZjneO61Icl+rIV9pgz2Gfe9pgzNO28Hbt3h29WULx7T438apW3xoBj5+G06jwcAK+ck7TZt57xx+LJKUoiwaBis0HPCQcHfis5zG9Jh3k3aS6+Km96+w1mdOdYOnl2bDTvjmrKx/pabDHvG4lVpSiKUo+x1JpKpWLVqlWMHDkSgISEBPr160daWhp+fn7m5SZNmsTp06dZv379FfuZPXs2L7/8co32JUuWYDDII72FEPZFl51NyNw3UDQajr88G0Wns1jfKqUS58ITOGYfRCk9QLJjFluaOZHk6IjpT8WLS6Uj/qa2dGoWTldDCBqVxmIxiKarpKSEsWPHkp+fT/Pmza+5bKMdmbnk8mpeUZRrVvh///vfmTlzpnm6oKCAgIAAYmJirrszasNoNBIfH8/gwYPRWfCHQmNmjzmDfeZtjzlD085bURRSPltI1YULRPq1Qh/e0zzP0nn3LsnhzsM/cvrQ9xwt2M0up0oS9E4Uass5wiGOGA/xfa6atqogwgPv4IEed+PVzLXO270RTflYX4st5n3pzEptNNpixtfXF4CMjAxatvzj3GtmZiY+Pj5XXc/R0RFHx5pPsdTpdBY9gJbuzxbYY85gn3nbY87QdPM29AyjcO06Kvbvo3lE3xrzLZa3qw9efcfi1XcsYYrC/ZnHOLVnNXtOreOAksp2gwM5GjjIKQ6e/YDPz3xAa6MrHV37MjzsIfq2brjTUU31WF+PLeV9I3E22ocFhISE4OvrS3x8vLmtoqKCrVu3EhERYcXIhBDCthjCLo7GlOze03AbVanQ+HSgbeyzjHlsC/+afJC4Pu/xuqYX9xapCa4wUqWCZId84krXMfmnMdzxSQ+e/OwRNh85TCO5AkLYCKuOzBQVFXH8+HHz9KlTp0hKSsLDw4PAwECeeOIJXn31VUJDQwkNDeXVV1/FYDAwduxYK0YthBC2RaW5+HdrVV6e9YLQOeHaaTDDOw1mOKAUZpC4awlbTq3l16pUDjlqOONYyRl2snnnfYRudaaL+zDGR02nTQsP68UtbIJVi5ndu3cT9adbBS9d6zJx4kQWLlzIrFmzKC0tZerUqeTm5tK7d282bNiAi4uLtUIWQgiboigKOV8uBsD1zjusHM0fVC6+9Bo0k17MBJOJ9FPbWb3zI7YWHeCwo5qj+mKOln3D2jVf06GiJb1CHmTcwDG46h2sHbpohKxazERGRl5zKFGlUjF79mxmz57dcEEJIUQTUvzTT1ScOIG6WTNc77nH2uFcmVqNX5vbmNrmNqaaTBw5sJyv9y5gqymdTK2GPfoM9mTMZdWXb9CeTsT0mEFs9z5oNY32SgnRwBrtBcBCCCHqLmfR5wC4jb4HjbOzlaOpBbWajt3u58Vu91NVVsCmHe+z5mQcCdpiMhzUZHCIbQcmsTDRiY7NBjB6wExuDZYXYdo7KWaEEKKJKj9+nOKffgK1GvcJE6wdzg3TODVn8KAXGDzoBYqyklmxbS7xWTvZ5wjJ+nKSTRvZsDmeDmUedPe9h3HRj+Dr2szaYQsrkGJGCCGaqEujMi6DonHw97dyNHXj7BXKxLs/YaKicPr4Jpb98h5byk+QqlOTZMglqWABa77+hPaVrRnQaQr39B2Ck04ezmcvpJgRQogmqDI3l/y4OAA8Jk60cjQWpFIRFDqIZ0IHMavSyM+Jn7Dy8FJ2qHLI0qnJ0p1ix6lnWH7kOTroehLbZyYDOzae1ymI+iHFjBBCNEF5X32FUl6OU+fO6MPCrB1OvVBpdUT0nUpE36mUF50nbtubrE37kb26Ck46VXGSncTvuo+OW53p7D6EMVGPE+ghd8M2RVLMCCFEE6NUVJC7eAkAHhMftItRCUdnH+6NfYN7gcy0vSzd/iabivZzUqdiv6GY/eUrWbvmGzqW++GtGkD/0sF42siTcMX1STEjhBBNTMG6dVReuIDW25vmQ4daO5wG16JVDx6/fwmPKwr7Dyznq72fst2URq5Wzc/aDOBrkpZ9TXulE1G3Ps7QsD7o5DZvmybFjBBCNCGmsjKy5s8HwH3cWFQOdvyQOZWKrl3H0LXrGIzlRWxIeJ+4E9+SqC3mjKOKMxxm0+FJLNmjp6NrLONvf4LWLdytHbW4CVLMCCFEE3L+tdepOH4CjYcHbmPGWDucRkPn6MzwqOeI6f83vl29kGxNIj9m7+SIg4mDhjIOGlfy43craF8RQv8u07mvzyC5G8qGSDEjhBBNRP7335P31VegUuE3dy5adxlluBKdQwv+L/Y/TNbpOHb0B77c+R5bqlLJ0ar5WZvCzyefYsUhBzo0G8S4qKfo6u9j7ZDFdUgxI4QQTUBFSgoZ/3gRAM/Jk3Du38/KEdmG9h1i+WeHWIyleazZ+jrfnVnHHodKTuorOGlay5YNPxBa7k/f0EeZ2H8Ezk5y0XBjJFc8CSGEjTOVl5P65ExMJSXoe4bhPW2atUOyOTq9G6OGvs7/JiWxduD7PKgOws9YRYlGxT5DGh+nzebuz3sxbdF0dpxIueZ7BUXDk5EZIYSwcZn//jflR46gcXen1VtvodLKj/a68Gsdzd9aR/NUeSFbfnqb1SdWs0Nn5JxjJefYQsK2zbSO9yEsaCJTBo7BvZmjtUO2ezIyI4QQNqxg3TpylywFwG/uv9H5yPUdlqJ2dCF60Eu8P+lXfhw0n8c0bWlXUYlRreKYPpMlmW9w19Le/N//HuGHw4dktMaKpJgRQggbVXHmDOeefwEAz0cfxXnAACtH1HS5BUYwZfwqVkzczaLgCYwoc8ClykSuropEzU6e3TWGO+bfxnPf/of0/CJrh2t3pJgRQggbZKqoIO3JmZiKi9G1aoX7A/fLyEBDcGhGj9tmMWfyHn4c8jnP6ToSVmZEUak445TLd3kfc/eKCB74dDzLk/ZSZZJj0hDkxKoQQtig0r2/UnboEADGtDSORw9CZTDgEBR08Ss4CIeg4Ivfg4PRuLnZxWsNGpK+VRgPjF3OAxUlHE/8hG8OfEm8rohMrZaD7ONQ0oMs+dmDW33+wsNR9+PvbrB2yE2WFDNCCGGD9Ld2x2vqXyk9eJCKlNMY09JQSkooP3KE8iNHaiyvdnX9U5FzscBRt/JHXVZmheibGAcDbfs9zrP9Hufpc/vZsPXffJu3hwS9jhOGXE4UvkPi8vdpq0QQ0+dpBt8SLK9PsDApZoQQwgapHR3xnjHDPK1UVFCRmkZFSgoVp09X+16ZkYEpP5+y/fsp27+/Wj9tgVPvf4BDSLC5yDF/DwxE7eTUwJnZNm3LrsTev5hYYxnH9nzKZwc/J15TzGmnKk6znYO7t7Bhcyva+k8mNnIorb2drR1ykyDFjBBCNAEqBwccW4fg2DqkxjxTaSkVZ85QkVK9yKlISaEqJ4eq7GxKs7Mp3b3nsk5VaFv61ixygoJw8PdHJW+dvjqdE+37PMbrfR7jb+d+5fOfXmNl4WHO6zSc98jgl6KXOL3kn7RgMB36PsrgW9vK6xPqQIoZIYRo4tR6PU7t2+PUvn21dqPRyLoVK4ns0B7T2dQaIzqmwkIq089RmX6Okp9/qd6pRoODvz+64CAcg4PRBV387hAUhLZlS1RqOY1yiWfLW3ny3uVMNZbw3c9vsvDEt5xWV7DOzYRGWUdk4rekbGyNPmgiEZHD6dTK1doh2xwpZoQQwo6Z9E44de6Mrnv3au2KolCVm/v7CM7pGqevlLKyi9OnT1O8dVu1dVWOjjgEBtQczQkORuPlZbcXIjvqDIwe+CL3DPgHO058z6e732d3+Tl+dHbiR+d0bi16Cccv/0WCKgb3PhOJ6dUFF3l9Qq1IMSOEEKIGlUqF1sMDrYcHhh49qs1TTCYqMzOvWORUnD2LUl5OefJxypOP1+hXbTDgEByMLigQXQsftN5eaLy80Hp6ofX2QuvlhcbdHZWm6Z5yUalU9G97B/3b3sGxnGN8vustfjj/C786OfGrL/gb1zFh1wq2/9iejODRdIu8hx7B9lsE1oYUM0IIIW6ISq1G5+uLzteXZn16V5unVFZiPHeu+ojO74WOMS0NU0kJZYcPU3b48NU3oFaj8fBA63WxuNF6ev5R9Hh5o/XyNM9Tu7ra9C/59h7tmTN0Pk+UXGDpoUV8dfQrUnXwmrcbPu5pPJrzT7wXvs5XTrejCZvA4P59cTM4WDvsRkeKGSGEEBaj0mpxCAjAISAALnsisamiAuPZsxeLmzNnqcy6QFVWFpUXsqjMyqIyO5uqnBwwmajKyqIqK4vy621Qp7tY7Pxe3GjMhU71okfj5Y26WeN9zou3wZsZ4U/zSPeprD6+mk/3/Zfz5PAvLw983Cp5NG8tdyV8TdKOjvzmN5LQ28bRq72/TRdyliTFjBBCiAahdnDAsU0bHNu0ueoySmUllb/fYVX5p0KnKrt60VOZlYUpPx+MRiozMqjMyLju9lVOTmg8PQnQaDi3fgO6Ft6/Fz1e5sJH83sRZK1b0g06A2M7juWedvewMnklC/Yv4HxpJv/y8uATt+Y8mneW+869RvnSd/lONwBj13EMjByKd3P7voVeihkhhBCNhkqrRdeiBboWLa67rKmi4uLITnb274XOhYtF0KWiJyuLyuwsqi5kYSopQSkrozItDT1QfObMNftWOzv/aVTH649TXl6ef5zu8vZC6+FRL7eoO2oceaDDA9wdevfFoubAAs6XXCxq/uvmzuS8XEYVbsBh7wZ+2+3PFu878Rs4kT63dECjtr/RGilmhBBC2CS1gwNqPz90fn7XXdZUUkJldjZlGRkkbthAt8BAlNy8GkVPZVYWSkUFpqIiKoqKqEhJuW7fGje3i9f0eP6p6PH2QuPp+UfR4+mJxsPjhm9Zv1JRk/l7UfOxpw+PZGdzb2Eq7bI/wrhyPtu/DSe/w/30Gjyalu4uN7QtWybFjBBCiCZPbTDgYDCg8vWlKD0d19hYdFcYUVEUBVNR0R8jPVlZVGZlX7HoqczOhqoqqvLyqMrLgyvcvfVnht69CVq08Kbiv1pR87pXc/7n48eDORVMyDlOpOkXOPwL6Yf+ySqve2g7bBq3tA26qW3aEilmhBBCiN+pVCo0Li5oXFyu+DTlP1NMJqry86m8cOGy012XCqHfi6Dz56nKz6d03z4URanTRbs1ipr9C8gszeRNV4hrNYAHit2ISdmGnymbUdnzKfliEeubDcYwYCpN+QXeUswIIYQQN0GlVqN1d0fr7g7t2l11ucItW0id8lccgoIsdvfRpaJmVNtRLD6ymAUHFvBb0Wle5jQbesfwoKoNIXu+oVX5cYaUrIH1a9hBd7Zri+k35D4cdU3r1788b1oIIYSoR5euu3EIDrZ4305aJx6+5WF+uPsHxnccj1at5eeMnUw9t5T/9I/h8J0L+M1tICZFRT+SiN79V1LndGfzkrnk5xdYPB5rkWJGCCGEqEcVp1IAcAgJrrdtuDu580yvZ4gbGcewkGEoKHx3cg0TDr/Od/0jOT1hHVv1MRTjRBvOEvXbHExvd2L7x4+Tee5svcXVUKSYEUIIIeqRMT0d4OKDBOtZgEsAcwfOZdnwZfTy7UWFqYKFhxZy3y/TWRrkSc6Ubezv/DfOq1rgripkQMZCmn98K7v+839cOPNbvcdXX6SYEUIIIepTVRVAvTyP5mo6e3VmQcwC5g2aR2fPzpRVlbGjfAejNo5jbZAOZm7lcP8PSda1x0llpNeFFbh/2pt9791L1olfGyxOS5FiRgghhGiCVCoVA/wHsHT4Uj6I/IAATQDlVeV8eeRLYlffySrnM2imLuXg7V+S5NADrcpEt9wNeH0RybG3Y8k6vNXaKdRa07qcWQghhBDVqFQq+vn1Y5LzJLzCvPjk4CfszdzLsmPLWHZsGW3d2jI49l7SCx/FfccSepf+RPuCHbB8BKcNXdDf9gQtwu8GdeN9k7lNjMzMmzePkJAQnJycCAsLY/v27dYOSQghhLApKpWK3r69WTh0If8b8j8G+g9Eq9ZyPO84H+37iGdOvsbrHTS82OcxljhHUa5oCSo5SIu1j3DhtS5kbPwAKkqsncYVNfqRma+++oonnniCefPm0a9fP/773/8ybNgwDh8+TGBgoLXDE0IIIWyKSqUi3DeccN9w8svz2Zq6lQ0pG0hIT+Bk/klOcpJvvSG+3Sh6nyrn/pwf8Tamw08vUJDwBjmd/0LQ0MdRNfO0dipmjb6Yefvtt3n44Yd55JFHAHj33XdZv349H330Ea+99pqVoxNCCCGuTVFM1g7hqlwdXRnRZgQj2oygsKKQLWe3sC5lHdtTt7M7dye73eBA+/vpfUJHVOYKAkwXaH7gPUoPfMxxv7vwjpyCb2gPsNDDAG9Woy5mKioq2LNnD88++2y19piYGBISEq64Tnl5OeXl5ebpgoKLDwUyGo0YjcY6x3SpD0v0ZSvsMWewz7ztMWeQvO0p74bOuaqwkJKff7n4b5Xaavu6Nnk7qZwYGjiUoYFDOZl/kk8Pfsq60+vYdj6eM37BBA75ip0/baPzqYV0VKVwS/pyWLKc83hyImQc4WNfrJeYa0OlKEqjfVtDeno6rVq1YseOHURERJjbX331VRYtWsSxY8dqrDN79mxefvnlGu1LlizBYDDUa7xCCCHEnznv24ffkqVUNm/Oqb89jeLgYO2QbsjpytMsK15GkVLEQ80eorWuNWWVCoXpR+iYE09P0z4cVZWsbnYfqnZ3WHTbJSUljB07lvz8fJo3b37NZW2imElISKBv377m9jlz5vDFF19w9OjRGutcaWQmICCArKys6+6M2jAajcTHxzN48OArvnG1KbLHnME+87bHnEHytqe8rZFzeXIyprx89OE9G2R7V1KXvHPKckjMSGRI8JAa84oK80k9tAPXlm1oGdTeUuECF39/e3l51aqYadSnmby8vNBoNGRkZFRrz8zMxMfH54rrODo64ujoWKNdp9NZ9INr6f5sgT3mDPaZtz3mDJK3PWnInHWdOjXIdmrjZvL20flwh8uVR13cPbxwH3CXJUKr4UbibNS3Zjs4OBAWFkZ8fHy19vj4+GqnnYQQQghhvxr1yAzAzJkzmTBhAj179qRv377Mnz+fM2fOMGXKFGuHJoQQQohGoNEXM2PGjCE7O5tXXnmFc+fO0aVLF3744QeCgoKsHZoQQgghGoFGX8wATJ06lalTp1o7DCGEEEI0Qo36mhkhhBBCiOuRYkYIIYQQNk2KGSGEEELYNClmhBBCCGHTpJgRQgghhE2TYkYIIYQQNk2KGSGEEELYNClmhBBCCGHTpJgRQgghhE2ziScA14WiKMDFV4lbgtFopKSkhIKCArt5y6w95gz2mbc95gyStz3lbY85g23mfen39qXf49fS5IuZwsJCAAICAqwciRBCCCFuVGFhIa6urtdcRqXUpuSxYSaTifT0dKKjo9m9e3e1eeHh4SQmJtZq+tK/CwoKCAgI4OzZszRv3rxOsV2+vbose7X5V2qvTZ5//rclc65NLrVd1lI5Xz5tj8f60vSPP/4ox7oB826oY335dFP5jF9vP8ixtu1jvWvXLgoLC/Hz80OtvvZVMU1+ZEatVuPv749Wq61xADUaTbW2a01fPq958+Z1/kBc3mddlr3a/Cu11zbP+sj5WrHe6LKWyvnyaXs81pdPy7Gum9rm3VDH+vLppvIZv95+kGNt28fa1dX1uiMyl9jNBcCPPfbYdduuNX2l9esjpptd9mrzbzTv+s75Rvu91rKWyvnyaXs81rXZ7s2wx2N9I/021LG+fLoxH+trza/NZ7ax/jyTY31j82807iZ/msnSCgoKcHV1JT8/3yJVvS2wx5zBPvO2x5xB8ranvO0xZ2j6edvNyIylODo68tJLL+Ho6GjtUBqMPeYM9pm3PeYMkrc95W2POUPTz1tGZoQQQghh02RkRgghhBA2TYoZIYQQQtg0KWaEEEIIYdOkmBFCCCGETZNiRgghhBA2TYoZCxs1ahTu7u6MHj26xrw1a9bQvn17QkNDWbBggRWiaxhvvvkmnTt3pkuXLnz55ZfWDqdBvPPOO3Tu3JlOnToxY8aMWr0YzdYdO3aM7t27m7/0ej2rV6+2dlj17tSpU0RFRdGpUyduueUWiouLrR1Sg9BqteZj/cgjj1g7nAZTUlJCUFAQTz/9tLVDaRCFhYWEh4fTvXt3brnlFj755BNrh1Q7irCoTZs2KXFxcco999xTrd1oNCqhoaFKamqqUlBQoLRt21bJzs62UpT1Z//+/cqtt96qlJaWKqWlpUpERISSm5tr7bDqVWZmptK6dWultLRUqaysVCIiIpSEhARrh9WgCgsLFU9PT6WoqMjaodS7gQMHKtu2bVMURVGys7MVo9Fo5Ygahqenp7VDsIrnnntOuffee5WnnnrK2qE0iMrKSqW4uFhRFEUpLi5WQkJClKysLCtHdX0yMmNhUVFRuLi41GjftWsXnTt3plWrVri4uBAbG8v69eutEGH9OnLkCBERETg5OeHk5ET37t1Zt26dtcOqd5WVlZSVlWE0GjEajbRo0cLaITWouLg4Bg0aRLNmzawdSr06dOgQOp2OAQMGAODh4YFW2+RfcWe3kpOTOXr0KLGxsdYOpcFoNBoMBgMAZWVlVFVV2cRIs10VM9u2bePOO+/Ez88PlUp1xSHxefPmERISgpOTE2FhYWzfvt0i205PT6dVq1bmaX9/f9LS0izS942o733QpUsXNm/eTF5eHnl5eWzatMkqef5Zfefs7e3N008/TWBgIH5+ftx+++20adPGghncnIb8vC9fvpwxY8bUMeK6q++ck5OTcXZ2ZsSIEfTo0YNXX33VgtHfvIY41gUFBYSFhdG/f3+2bt1qochvXkPk/PTTT/Paa69ZKGLLaIi88/Ly6NatG/7+/syaNQsvLy8LRV9/7OpPiuLiYrp168ZDDz3EPffcU2P+V199xRNPPMG8efPo168f//3vfxk2bBiHDx8mMDAQgLCwMMrLy2usu2HDBvz8/K667StVtiqVqg7Z3Jz63geXrhmJjo7G1dWV8PBwq//lWt856/V61qxZQ0pKCnq9nmHDhrFt2zYGDhxY77ldS0N93gsKCtixYwfLli2r34Rqob5zNhqNbN++naSkJFq0aMHQoUMJDw9n8ODB9Z7btTTEsU5JScHPz4+DBw8yfPhwDhw4YNV3/NR3zomJibRr14527dqRkJBQ7/nUVkMcazc3N/bt28f58+e5++67GT16ND4+PvWeW51Y+zyXtQDKqlWrqrX16tVLmTJlSrW2Dh06KM8+++wN9b158+Ya18zs2LFDGTlypHl6xowZyuLFi28saAurz31wycMPP6ysWbPmZkO0uPrIefny5crUqVPN03PnzlX+/e9/1zlWS6rPY/35558r48aNq2uIFlcfOSckJChDhgwxT8+dO1eZO3dunWO1pIb4fz106FAlMTHxZkO0uPrI+dlnn1X8/f2VoKAgxdPTU2nevLny8ssvWypki2iIYz1lyhRl+fLlNxtig7Gr00zXUlFRwZ49e4iJianWHhMTY5GqvFevXhw8eJC0tDQKCwv54YcfGDJkSJ37tSRL7YPMzEzg4t0uu3btanR5/pklcg4ICCAhIcF8fnnLli20b9++PsK1GEt+3hvLKabrsUTO4eHhnD9/ntzcXEwmE9u2baNjx471Ea7FWCLv3Nxc81/yqampHD58mNatW1s8VkuxRM6vvfYaZ8+eJSUlhTfffJNHH32UF198sT7CtRhL5H3+/HkKCgqAi6Ou27Zta/Q/z8DOTjNdS1ZWFlVVVTWG0nx8fMjIyKh1P0OGDGHv3r0UFxfj7+/PqlWrzKda3nrrLaKiojCZTMyaNQtPT09Lp1EnltoHI0eOJC8vj2bNmvHZZ59Z/TTTtVgi5z59+hAbG8utt96KWq1m0KBBjBgxoj7CtRhLHev8/Hx27drFihUrLB2ixVkiZ61Wy6uvvsrAgQNRFIWYmBjuuOOO+gjXYiyR95EjR5g8eTJqtRqVSsV7772Hh4dHfYRrEZb6fNsaS+SdmprKww8/jKIoKIrCtGnT6Nq1a32Ea1GN97eMlVx+HYuiKDd0bcu17lAaMWJEo/8lB3XfB43p/HJt1TXnOXPmMGfOHEuHVe/qmrerqyvnz5+3dFj1qq45Dxs2jGHDhlk6rHpXl7wjIiI4cOBAfYRVr+p6rC/5y1/+YqGIGkZd8g4LCyMpKakeoqpfcprpd15eXmg0mhrVa2ZmZuO/8MlC7HEf2GPOYJ9522POYJ9522POYL95gxQzZg4ODoSFhREfH1+tPT4+noiICCtF1bDscR/YY85gn3nbY85gn3nbY85gv3mDnZ1mKioq4vjx4+bpU6dOkZSUhIeHB4GBgcycOZMJEybQs2dP+vbty/z58zlz5gxTpkyxYtSWZY/7wB5zBvvM2x5zBvvM2x5zBvvN+7qsdBeVVWzevFkBanxNnDjRvMx//vMfJSgoSHFwcFB69OihbN261XoB1wN73Af2mLOi2Gfe9pizothn3vaYs6LYb97Xo1IUG3hOsRBCCCHEVcg1M0IIIYSwaVLMCCGEEMKmSTEjhBBCCJsmxYwQQgghbJoUM0IIIYSwaVLMCCGEEMKmSTEjhBBCCJsmxYwQQgghbJoUM0IIIYSwaVLMCGEHIiMjeeKJJ6wdhlXMnj0blUqFSqXi3XffNberVCpWr15t0W0tXLgQNze3OvWxZcsWc7wjR460SFxCNHVSzAghyMnJYfr06bRv3x6DwUBgYCAzZswgPz/f2qFZROfOnTl37hyTJk2ql/5DQkJYt26dRfqKiIjg3Llz3HfffRbpTwh7IMWMEIL09HTS09N58803OXDgAAsXLmTdunU8/PDD1g7NIrRaLb6+vhgMBov3vX//frKzs4mKirJIfw4ODvj6+qLX6y3SnxD2QIoZIeyEyWRi1qxZeHh44Ovry+zZs83zunTpwooVK7jzzjtp06YN0dHRzJkzh++++47KykrzcgcOHCA6Ohq9Xo+npyeTJk2iqKjouttev349Tk5O5OXlVWufMWMGt912GwDZ2dk88MAD+Pv7YzAYuOWWW1i6dGm15SMjI5k2bRrTpk3Dzc0NT09PXnjhBSzxvtxXXnkFHx8fkpKSADh37hzDhw9Hr9cTEhLCkiVLCA4OrnaqCuDbb79lyJAhODo61ugzOzubXr16MWLECMrKygCIi4sjNDQUvV5PVFQUixYtQqVS1dg3Qojak2JGCDuxaNEimjVrxs6dO5k7dy6vvPIK8fHxV10+Pz+f5s2bo9VqASgpKWHo0KG4u7uTmJjI119/zcaNG5k2bdp1t3377bfj5ubGihUrzG1VVVUsX76ccePGAVBWVkZYWBhr1qzh4MGDTJo0iQkTJrBz584aeWi1Wnbu3Mn777/PO++8w4IFC25mlwCgKAqPP/44n376KT/99BPdu3cH4MEHHyQ9PZ0tW7awYsUK5s+fT2ZmZo314+LiuOuuu2q0p6amMmDAADp06MDKlStxcnIiJSWF0aNHM3LkSJKSkpg8eTLPP//8TccuhPidIoRo8m677Talf//+1drCw8OVZ5555orLZ2VlKYGBgcrzzz9vbps/f77i7u6uFBUVmdu+//57Ra1WKxkZGdeNYcaMGUp0dLR5ev369YqDg4OSk5Nz1XViY2OVp556qloeHTt2VEwmk7ntmWeeUTp27HjVPl566SWlW7duNdoB5euvv1bGjx+vdOjQQTl79qx53pEjRxRASUxMNLclJycrgPLOO++Y21JTUxWdTqdkZ2criqIon332meLq6qocO3ZMCQwMVKZPn14j1i5dulSL4/nnn1cAJTc3t1r7xIkTlbvuuuuqeQkh/iAjM0LYia5du1abbtmy5RVHGgoKChg+fDidOnXipZdeMrcfOXKEbt260axZM3Nbv379MJlMHDt27LrbHzduHFu2bCE9PR2AxYsXExsbi7u7O3BxpGbOnDl07doVT09PnJ2d2bBhA2fOnKnWT58+fVCpVObpvn37kpycTFVVVS32QnVPPvkkP//8M9u3b8ff39/cfuzYMbRaLT169DC3tW3b1hzrJXFxcfTr1w8PDw9zW2lpKf3792fkyJG8//771WI9duwY4eHh1fro1avXDccthKhOihkh7IROp6s2rVKpMJlM1doKCwsZOnQozs7OrFq1qto6iqJU+8V8eV/X06tXL9q0acOyZcsoLS1l1apVjB8/3jz/rbfe4p133mHWrFls2rSJpKQkhgwZQkVFxY2keUMGDx5MWloa69evr9auXOUanMvbr3SKydHRkdtvv53vv/+e1NTUGutfvq+uti0hRO1JMSOEAC6OyMTExODg4EBcXBxOTk7V5nfq1ImkpCSKi4vNbTt27ECtVtOuXbtabWPs2LEsXryY7777DrVazfDhw83ztm/fzl133cX48ePp1q0brVu3Jjk5uUYfv/zyS43p0NBQNBrNjaQLwIgRI1iyZAmPPPIIy5YtM7d36NCByspKfv31V3Pb8ePHq12kW1RUxObNmxkxYkS1PtVqNV988QVhYWFER0ebR6Iu9ZuYmFht+d27d99w3EKI6qSYEUJQWFhITEwMxcXFfPrppxQUFJCRkUFGRob59M24ceNwcnJi4sSJHDx4kM2bNzN9+nQmTJiAj49PrbYzbtw49u7dy5w5cxg9enS1gqlt27bEx8eTkJDAkSNHmDx5MhkZGTX6OHv2LDNnzuTYsWMsXbqUDz74gMcff/ymcx81ahRffPEFDz30EN988w1wsei4/fbbmTRpErt27eLXX39l0qRJ6PV688jKunXrCA0NpXXr1jX61Gg0LF68mG7duhEdHW3OY/LkyRw9epRnnnmG3377jeXLl7Nw4UKgdqNbQogrk2JGCMGePXvYuXMnBw4coG3btrRs2dL8dfbsWQAMBgPr168nJyeH8PBwRo8ezaBBg/jwww9rvZ3Q0FDCw8PZv3+/+S6mS/7xj3/Qo0cPhgwZQmRkJL6+vld8Au6DDz5IaWkpvXr14rHHHmP69Ol1fhje6NGjWbRoERMmTGDlypUAfP755/j4+DBw4EBGjRrFo48+iouLi7kA+/bbb694F9MlWq2WpUuX0rlzZ6Kjo8nMzCQkJIRvvvmGlStX0rVrVz766CPz3UxXurVbCFE7KkVO2AohbERkZCTdu3ev8ayXa5k9ezarV682Pz/mZqWmphIQEMDGjRuJjIykRYsWrF27ts4X8M6ZM4ePP/7YXDRe8pe//IW8vDyLv3JBiKZIRmaEEE3egQMHcHZ2Zt68ebVeZ9OmTcTFxXHq1CkSEhK4//77CQ4OZuDAgWRnZ/Pkk0/WuDOpNubNm0diYiInT57kiy++4I033mDixInm+du3b8fZ2ZnFixffcN9C2CuttQMQQjQNzs7OV523du1aBgwY0IDR/GHGjBnmu6a8vb1rvZ7RaOS5557j5MmTuLi4EBERweLFi9HpdLRo0YIXXnjhpuJJTk7mX//6Fzk5OQQGBvLUU0/x97//3Ty/Z8+e5lGka+1TIcQf5DSTEMIijh8/ftV5rVq1kncNCSHqjRQzQgghhLBpcs2MEEIIIWyaFDNCCCGEsGlSzAghhBDCpkkxI4QQQgibJsWMEEIIIWyaFDNCCCGEsGlSzAghhBDCpv0/Xec1NzI8aWsAAAAASUVORK5CYII=",
234      "text/plain": [
235       "<Figure size 640x480 with 1 Axes>"
236      ]
237     },
238     "metadata": {},
239     "output_type": "display_data"
240    }
241   ],
242   "source": [
243    "my_sim.plot_profile('h2o_vap',logx=True,label='Time- and grid-averaged')\n",
244    "my_sim.plot_profile('h2o_vap',t=0,logx=True,label='Time=0, grid-averaged')\n",
245    "my_sim.plot_profile('h2o_vap',t=0,lat=0,logx=True,label='Time=0, equatorial value zonally-averaged')\n",
246    "my_sim.plot_profile('h2o_vap',t=0,lat=0,lon=0,logx=True,label='Time=0, equatorial value, lon = 0°')\n",
247    "plt.legend()\n",
248    "plt.grid()"
249   ]
250  },
251  {
252   "cell_type": "markdown",
253   "id": "e73804b8-98f3-4283-8c5d-153465db88e7",
254   "metadata": {},
255   "source": [
256    "## Interactive visualization\n",
257    "Now we go fancy!"
258   ]
259  },
260  {
261   "cell_type": "markdown",
262   "id": "7f745af6-fe37-4075-b433-abb03ad3492e",
263   "metadata": {},
264   "source": [
265    "#### Define widgets\n",
266    "Let's define some widgets (see [here](https://ipywidgets.readthedocs.io/en/latest/) for documenttion on jupyter's widgets):"
267   ]
268  },
269  {
270   "cell_type": "code",
271   "execution_count": 9,
272   "id": "56b07967-3900-4454-ac0d-29cfae7cc1f9",
273   "metadata": {},
274   "outputs": [],
275   "source": [
276    "import ipywidgets as widgets\n",
277    "\n",
278    "# Coordinates\n",
279    "w_lat = widgets.FloatSlider(min=-90, max=90, step=1, description=\"latitude\")\n",
280    "w_lon = widgets.FloatSlider(min=-180, max=180, step=1, description=\"longitude\")\n",
281    "w_alt = widgets.FloatSlider(min=0, max=max(my_sim.data[\"altitude\"]), step=1, description=\"altitude\")\n",
282    "w_time = widgets.FloatSlider(min=0, max=max(my_sim[\"Time\"]), step=1, description=\"time\")\n",
283    "\n",
284    "# Fields\n",
285    "w_single_sp   = widgets.Select(options=my_sim.species, value=\"h2o_vap\", description=\"species\")\n",
286    "w_multiple_sp = widgets.SelectMultiple(options=my_sim.species, value=[\"h2o_vap\"], description=\"species\")\n",
287    "w_reactions   = widgets.SelectMultiple(options=my_sim.reactions.keys(), value=[\"co2 + hv -> co + o\"], description=\"reactions\")\n",
288    "\n",
289    "# Miscelaneous\n",
290    "w_average = widgets.Checkbox(description='show average')"
291   ]
292  },
293  {
294   "cell_type": "markdown",
295   "id": "ddeec0ba-12c2-4bd9-a38f-98a0e1949f3b",
296   "metadata": {},
297   "source": [
298    "#### OH meridional slice at various longitudes\n",
299    "OH is a photolysis product with a short lifetime, so it exist only on the dayside. Scrolling through the longitudes will exhibit this dichotomy."
300   ]
301  },
302  {
303   "cell_type": "code",
304   "execution_count": 10,
305   "id": "56e81d82-fd55-464c-a746-66cf23822957",
306   "metadata": {},
307   "outputs": [
308    {
309     "data": {
310      "application/vnd.jupyter.widget-view+json": {
311       "model_id": "76979ae834af424a962ea28d4c33aec7",
312       "version_major": 2,
313       "version_minor": 0
314      },
315      "text/plain": [
316       "VBox(children=(FloatSlider(value=0.0, description='longitude', max=180.0, min=-180.0, step=1.0), Output()))"
317      ]
318     },
319     "execution_count": 10,
320     "metadata": {},
321     "output_type": "execute_result"
322    }
323   ],
324   "source": [
325    "# Slice plotting unction\n",
326    "def make_slice(lon):\n",
327    "    my_sim.plot_meridional_slice('oh',t=0,lon=lon,logcb=True)\n",
328    "\n",
329    "# Define interactive output\n",
330    "out = widgets.interactive_output(make_slice,{'lon':w_lon})\n",
331    "\n",
332    "# Build the output frame\n",
333    "widgets.VBox([w_lon,out])"
334   ]
335  },
336  {
337   "cell_type": "markdown",
338   "id": "3379c31a-95c6-4ab3-b178-20b706eed1f3",
339   "metadata": {},
340   "source": [
341    "#### Water vapor atlas\n",
342    "We can have several action widgets"
343   ]
344  },
345  {
346   "cell_type": "code",
347   "execution_count": 11,
348   "id": "fd7b4103-0436-4bda-bb39-96666c39f332",
349   "metadata": {},
350   "outputs": [
351    {
352     "data": {
353      "application/vnd.jupyter.widget-view+json": {
354       "model_id": "8d6c04b1820640ee85d1fa541fc6dad5",
355       "version_major": 2,
356       "version_minor": 0
357      },
358      "text/plain": [
359       "VBox(children=(FloatSlider(value=0.0, description='time', max=56.0, step=1.0), FloatSlider(value=0.0, descript…"
360      ]
361     },
362     "execution_count": 11,
363     "metadata": {},
364     "output_type": "execute_result"
365    }
366   ],
367   "source": [
368    "# Atlas plotting function\n",
369    "def make_atlas(t,alt):\n",
370    "    my_sim.plot_atlas('h2o_vap',t=t,alt=alt)\n",
371    "    plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n",
372    "\n",
373    "# Define interactive output\n",
374    "out = widgets.interactive_output(make_atlas,{'t':w_time,'alt':w_alt})\n",
375    "\n",
376    "# Build the output frame\n",
377    "widgets.VBox([w_time,w_alt,out])"
378   ]
379  },
380  {
381   "cell_type": "markdown",
382   "id": "4b6bb2f8-1aff-4872-9580-7c4bd9598c31",
383   "metadata": {},
384   "source": [
385    "#### Temperature profile at various times and locations"
386   ]
387  },
388  {
389   "cell_type": "code",
390   "execution_count": 12,
391   "id": "e4691cae-637b-4555-ac87-d556521a4c3f",
392   "metadata": {},
393   "outputs": [
394    {
395     "data": {
396      "application/vnd.jupyter.widget-view+json": {
397       "model_id": "df2fadeea42d4c00952e86c6814f400f",
398       "version_major": 2,
399       "version_minor": 0
400      },
401      "text/plain": [
402       "HBox(children=(VBox(children=(FloatSlider(value=0.0, description='time', max=56.0, step=1.0), FloatSlider(valu…"
403      ]
404     },
405     "execution_count": 12,
406     "metadata": {},
407     "output_type": "execute_result"
408    }
409   ],
410   "source": [
411    "# Profile plotting unction\n",
412    "def make_prof(t,lon,lat,avg):\n",
413    "    my_sim.plot_profile('temp',t=t,lon=lon,lat=lat,label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
414    "    if avg:\n",
415    "        my_sim.plot_profile('temp',t=t,label='average')\n",
416    "    plt.legend()\n",
417    "    plt.grid()\n",
418    "\n",
419    "# Define interactive output\n",
420    "out = widgets.interactive_output(make_prof,{'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
421    "\n",
422    "# Build the output frame\n",
423    "widgets.HBox([widgets.VBox([w_time,w_lon,w_lat,w_average]),out])"
424   ]
425  },
426  {
427   "cell_type": "markdown",
428   "id": "9ad5a23a-7ed6-4f56-abbf-3cf16daa087a",
429   "metadata": {},
430   "source": [
431    "#### Extensive species visualizer\n",
432    "Combining the above examples for arbitrary species"
433   ]
434  },
435  {
436   "cell_type": "code",
437   "execution_count": 13,
438   "id": "e4db2d8b-6183-4fbe-8ca1-940ef15aaa28",
439   "metadata": {},
440   "outputs": [
441    {
442     "data": {
443      "application/vnd.jupyter.widget-view+json": {
444       "model_id": "8e808114c9b444eda1b6114137896b0b",
445       "version_major": 2,
446       "version_minor": 0
447      },
448      "text/plain": [
449       "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
450      ]
451     },
452     "execution_count": 13,
453     "metadata": {},
454     "output_type": "execute_result"
455    }
456   ],
457   "source": [
458    "def make_visualizer(sp,t,alt):\n",
459    "    \n",
460    "    plt.subplot(131) # zonal average\n",
461    "    my_sim.plot_meridional_slice(sp,t=t,logcb=True)\n",
462    "    plt.plot([-90,90],[alt,alt],ls='--',lw=3,c='white')\n",
463    "\n",
464    "    plt.subplot(132) # atlas\n",
465    "    my_sim.plot_atlas('h2o_vap',t=t,alt=alt)\n",
466    "    plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n",
467    "\n",
468    "    plt.subplot(133) # profile\n",
469    "    my_sim.plot_profile('temp',t=t)\n",
470    "    plt.grid()\n",
471    "\n",
472    "    plt.subplots_adjust(right=2)\n",
473    "\n",
474    "out = widgets.interactive_output(make_visualizer,{'sp':w_single_sp,'t':w_time,'alt':w_alt})\n",
475    "\n",
476    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_alt]),out])"
477   ]
478  },
479  {
480   "cell_type": "markdown",
481   "id": "b8d39b59-07f2-4988-8e5c-558221c825a1",
482   "metadata": {},
483   "source": [
484    "#### Multi-species profiles\n",
485    "Shift+click to select multiple species (Command+click on Mac)"
486   ]
487  },
488  {
489   "cell_type": "code",
490   "execution_count": 14,
491   "id": "9dd59a1c-58c4-41f0-9730-9e97d2607c6a",
492   "metadata": {},
493   "outputs": [
494    {
495     "data": {
496      "application/vnd.jupyter.widget-view+json": {
497       "model_id": "476c88ed368e4e569b51fea78147885c",
498       "version_major": 2,
499       "version_minor": 0
500      },
501      "text/plain": [
502       "HBox(children=(VBox(children=(SelectMultiple(description='species', index=(6,), options=('o2', 'o', 'o1d', 'o3…"
503      ]
504     },
505     "execution_count": 14,
506     "metadata": {},
507     "output_type": "execute_result"
508    }
509   ],
510   "source": [
511    "cmap = plt.get_cmap(\"tab10\")\n",
512    "def make_sp_prof(sps,t,lon,lat,avg):\n",
513    "\n",
514    "    for i,sp in enumerate(sps):\n",
515    "        my_sim.plot_profile(sp,t=t,lon=lon,lat=lat,logx=True,label=sp,c=cmap(i))\n",
516    "        if avg:\n",
517    "            my_sim.plot_profile(sp,t=t,logx=True,c=cmap(i),ls='--')\n",
518    "    if avg:\n",
519    "        plt.plot([],[],c='k',label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
520    "        plt.plot([],[],ls='--',c='k',label='average')\n",
521    "        \n",
522    "    plt.legend()\n",
523    "    plt.grid()\n",
524    "\n",
525    "out = widgets.interactive_output(make_sp_prof,{'sps':w_multiple_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
526    "\n",
527    "widgets.HBox([widgets.VBox([w_multiple_sp,w_time,w_lon,w_lat,w_average]),out])"
528   ]
529  },
530  {
531   "cell_type": "markdown",
532   "id": "5fb2c987-345d-4a74-b4bf-fb368eeaac2b",
533   "metadata": {},
534   "source": [
535    "#### Species-specific reaction rates"
536   ]
537  },
538  {
539   "cell_type": "code",
540   "execution_count": 15,
541   "id": "ff21a3dc-d44a-4f0e-9aa4-211741bb592d",
542   "metadata": {},
543   "outputs": [
544    {
545     "data": {
546      "application/vnd.jupyter.widget-view+json": {
547       "model_id": "04a6123f7a8b4c7c857c03f394a2f1e0",
548       "version_major": 2,
549       "version_minor": 0
550      },
551      "text/plain": [
552       "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
553      ]
554     },
555     "execution_count": 15,
556     "metadata": {},
557     "output_type": "execute_result"
558    }
559   ],
560   "source": [
561    "def make_reaction_rate_viz(sp,t):\n",
562    "\n",
563    "    for r in my_sim.reactions:\n",
564    "        if sp in my_sim.reactions[r].products:\n",
565    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
566    "        elif sp in my_sim.reactions[r].reactants:\n",
567    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--')\n",
568    "\n",
569    "    plt.plot([],[],c='k',label='production')\n",
570    "    plt.plot([],[],ls='--',c='k',label='destruction')\n",
571    "\n",
572    "    plt.legend()\n",
573    "    plt.grid()\n",
574    "\n",
575    "out=widgets.interactive_output(make_reaction_rate_viz,{'sp':w_single_sp,'t':w_time})\n",
576    "\n",
577    "widgets.HBox([widgets.VBox([w_single_sp,w_time]),out])"
578   ]
579  },
580  {
581   "cell_type": "markdown",
582   "id": "7e0b725d-58f5-4b4e-b170-125b4707df03",
583   "metadata": {},
584   "source": [
585    "#### Profile with atlas locator\n",
586    "Here the atlas shows the column mass"
587   ]
588  },
589  {
590   "cell_type": "code",
591   "execution_count": 16,
592   "id": "b5b73171-0101-4656-b2ce-7e398070ebbb",
593   "metadata": {},
594   "outputs": [
595    {
596     "data": {
597      "application/vnd.jupyter.widget-view+json": {
598       "model_id": "60b937a2c3144c4abdec67d75cec3fc4",
599       "version_major": 2,
600       "version_minor": 0
601      },
602      "text/plain": [
603       "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
604      ]
605     },
606     "execution_count": 16,
607     "metadata": {},
608     "output_type": "execute_result"
609    }
610   ],
611   "source": [
612    "def make_sp_prof_atlas(sp,t,lon,lat,avg):\n",
613    "\n",
614    "    plt.subplot(121) # Vertical profile\n",
615    "    my_sim.plot_profile(sp,t=t,lon=lon,lat=lat,logx=True,label=sp,c='tab:blue')\n",
616    "    if avg:\n",
617    "        my_sim.plot_profile(sp,t=t,logx=True,c='tab:blue',ls='--')\n",
618    "        plt.plot([],[],c='k',label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
619    "        plt.plot([],[],ls='--',c='k',label='average')\n",
620    "        \n",
621    "    plt.legend()\n",
622    "    plt.grid()\n",
623    "\n",
624    "    plt.subplot(122) # Atlas\n",
625    "    my_sim.plot_atlas(sp+'_col',t=t)\n",
626    "    plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n",
627    "\n",
628    "    plt.subplots_adjust(right=2)\n",
629    "\n",
630    "out = widgets.interactive_output(make_sp_prof_atlas,{'sp':w_single_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
631    "\n",
632    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
633   ]
634  },
635  {
636   "cell_type": "code",
637   "execution_count": null,
638   "id": "7d391c60-7d30-421f-aa51-5f3da14b3c64",
639   "metadata": {},
640   "outputs": [],
641   "source": []
642  }
643 ],
644 "metadata": {
645  "kernelspec": {
646   "display_name": "Python 3 (ipykernel)",
647   "language": "python",
648   "name": "python3"
649  },
650  "language_info": {
651   "codemirror_mode": {
652    "name": "ipython",
653    "version": 3
654   },
655   "file_extension": ".py",
656   "mimetype": "text/x-python",
657   "name": "python",
658   "nbconvert_exporter": "python",
659   "pygments_lexer": "ipython3",
660   "version": "3.11.7"
661  }
662 },
663 "nbformat": 4,
664 "nbformat_minor": 5
665}
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