Changeset 3511 for trunk


Ignore:
Timestamp:
Nov 12, 2024, 5:52:49 PM (7 weeks ago)
Author:
mmaurice
Message:

Generic PCM:

Python postprocessing: change the implementation of some routines,
add automatic traceur.def reader and possibility to export files
readable by the chemical pathway analyzer chempath.

MM

Location:
trunk/LMDZ.GENERIC/utilities/photochemistry
Files:
2 edited

Legend:

Unmodified
Added
Removed

  • trunk/LMDZ.GENERIC/utilities/photochemistry/Photochem_Visualizer.ipynb

    r3431 r3511  
    2727  {
    2828   "cell_type": "code",
    29    "execution_count": 2,
     29   "execution_count": 1,
    3030   "id": "cd28bac5-65f6-464b-9c2b-6cba1bde9472",
    3131   "metadata": {},
    … …  
    3535     "output_type": "stream",
    3636     "text": [
    37       "H2O2/3D/no_CO_start/diagfi120 loaded, simulations lasts 56.0 sols\n"
     37      "H2O2/3D/start_no_CO/diagfi61 loaded, simulations lasts 60.541668 sols\n"
    3838     ]
    3939    }
    … …  
    4242    "import photochem_postproc as pcpp\n",
    4343    "\n",
    44     "sim_path        = 'H2O2/3D/no_CO_start'\n",
    45     "NetCDF_filename = 'diagfi120'\n",
     44    "sim_path        = 'H2O2/3D/start_no_CO'\n",
     45    "NetCDF_filename = 'diagfi61'\n",
    4646    "\n",
    4747    "# The simu class is just a wrapper for xr.Dataset\n",
    … …  
    5959  {
    6060   "cell_type": "code",
    61    "execution_count": 3,
     61   "execution_count": 2,
    6262   "id": "80f1c90a-7bfc-4e95-b614-c6e8432c53b3",
    6363   "metadata": {},
    … …  
    149149    {
    150150     "data": {
    151       "image/png": 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    152152      "text/plain": [
    153153       "<Figure size 640x480 with 6 Axes>"
    … …  
    190190    {
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0bNgwDR8+XJI0depULVq0SNOnT1dmZqYkad26dWX+faNGjdSxY0c1btxYknTDDTdow4YNbgnGDz/8UNddd52qVaumcePGlZlPX331lTp06ODW85EAAMxzNZsk/80nsgkA/E9V23Yqj6v5ZLqpKElPPfWUxo0bpy1btqi4uFiXX365atWqVZFJAQDOE7w7WEGhwZWaRtGpYkmyB1GJ8ePHKyMjw9S0CgsLtW7dOj3yyCMOw1NSUrRy5UqXptGxY0f9/vvvOnz4sKKiorR8+XKNGDHCVB1lSUlJsf+/vHxKTU3Vhg0bONwMACqosvnkzmySrJ1PZBMAeAfbTuacm0/lcTWfKtRUlKTw8HB16NChon8OAPCC3bt3KzIy0n6/IntCHDhwQEVFRYqJiXEYHhMTo5ycHJemUb16dU2aNEmdO3eWYRhKSUlR7969TdfiirLyyTAMjzwfAMAcd2ST5F/5RDYBgPVVxW2nsriaTy41Ffv16+fyE8+bN8/lcQEAnhUZGekQjJVhs9kc7huG4TSsPBc6TKAizOQTAMAa3JlNkvXyiWwCAP8U6NtOnuDS1Z+joqLst8jISH3++edau3at/fF169bp888/V1RUlMcKBQD4Rt26dRUUFOT0y1pubq7TL3DeRj4BQNVl1XwimwCg6rJqNnmKS3sqzpw50/7/v//977r99tv1yiuvKCgoSJJUVFSktLQ0t/7iCACwhuDgYCUmJio7O1u33HKLfXh2drZuvvlmH1ZGPgFAVWbVfCKbAKDqsmo2eYrpcyq++eab+vLLL+2hKElBQUFKT09XcnKynn32WbcWCADwvOPHj2v79u32+zt27NCGDRsUHR2tJk2aKD09XQMGDFCHDh2UlJSkGTNmaNeuXRo5cqQPq3Z0oXwyc7gBAMAa/D2fyCYACDz+nk2ucDWfTDcVz5w5ox9++EEtW7Z0GP7DDz+ouLjY7OQAABawdu1adevWzX4/PT1dkjRo0CBlZWWpf//+OnjwoCZMmKB9+/YpISFBCxcuVNOmTX1VspML5RMnwwcA/+Pv+UQ2AUDg8fdscoVbL9RyriFDhmjo0KHavn27rrrqKknS6tWr9fTTT2vIkCFmJwcAsICuXbteMDjS0tKUlpbmpYrM69+/vwYOHKgBAwbo+uuvV+3atR3yacqUKb4uEQBgkr/nE9kEAIHH37NJkvbv36+tW7fKZrOpRYsWqlevnsPjx44dc2k6ppuK//rXvxQbG6vnn39e+/btkyQ1aNBAY8eO1UMPPWR2cgAAVEp+fr7+9re/6a233lJRUZGmTZumadOmSSKfAAC+QTYBAKyoJJ/efvttFRUVSfrjtBwDBw7USy+9pPDwcFPTc+nqzw5/UK2axo4dqz179ujIkSM6cuSI9uzZo7FjxzqcKwQAAG9IT0/XsmXL9PHHH+vo0aM6evSo3n//fcXHx+vmm28mnwAAXkc2AQCsqCSfPvroI3tP78MPP9SyZcsq9GOX6T0Vz8UVywAAvvbBBx/ov//9r7p27Wofdtttt6lOnTq6/fbbNX36dN8VBwCoksgmAIAVlZZPN9xwg8LCwiqUT6abivHx8eVeBeaXX34xO0kAACrsxIkTiomJccqn06dP6+DBg7r44oslkU8AAO8hmwAAVlSST+erX7++Tpw4YXp6ppuKo0ePdrh/+vRprV+/Xp999pkefvhh0wUAAFAZSUlJGj9+vO677z7VqFFD0h/Z9Pbbb+v06dM6evQo+QQA8CqyCQBgRSX59NZbbyk0NFSSdPLkST3xxBNKSkoyPT3TTcVRo0aVOvzf//631q5da7oAAAAq44UXXtD111+vL774Qm3btpXNZtOGDRsUGhqqzz//XEuXLiWfAABeRTYBAKyoJJ8aNWrklE+LFi0yPb1KnVPxXKmpqXr00Uc1c+ZMd00SAIALSkhI0LZt2/TOO+/oxx9/lGEY+stf/qK77rpLYWFhCgsLI58AAF5FNgEArOhC+WSW25qK//3vfxUdHe2uyQEA4LKwsDDdc889pT5GPgEAfIFsAgBYUXn5ZJbppmL79u0dTjZsGIZycnK0f/9+TZs2zS1FAQDgquLiYlWrVq3UfNqzZ48OHTpEPgEAvIpsAgBYUUk+lTb8t99+U5MmTUxNz3RT8eabb3YIxmrVqqlevXrq2rWrWrVqZXZyAABUSF5enoYPH66PP/5YkZGRatWqlbp27WoPyRMnTujZZ5/Vli1byCcAgFeQTQAAKzo/n0aOHKnHH39cQUFBkqT9+/crPj5eRUVFpqZruqmYkZFh9k8AAHC7xx57TN99953efvttHTlyRBMnTtS6des0b948BQcH6/fff9ezzz7LRhsAwGvIJgCAFV0on6Q/9qY3y3mfxwsICgpSbm6u0/CDBw/aO5wAAHjaggUL9Oqrr+q2227T8OHDtXv3bu3bt099+vRRQUGBJMlms5FPAACvIZsAAFZ0fj6tW7dOBw4ccMons0w3FcvqXBYUFNi7m66aPn262rRpo8jISEVGRiopKUn/93//5/BcGRkZiouLU1hYmLp27arNmzebLRkAEIAOHDigpk2b2u8bhqH3339fx44d0w033KATJ05IMp9PZBMAoKI8lU0S+QQAqLjz86lOnTrKzs52yiezXD78+cUXX5T0R+fy9ddfV61ateyPFRUVafny5aZ342/UqJGefvppXXrppZKkWbNm6eabb9b69et1xRVXaPLkyZoyZYqysrLUokULTZw4UT179tTWrVsVERFh6rkAAIGlcePG+uGHH/Txxx9L+iOf5syZo1tuuUXTpk3TNddcI8MwdN9995nKJ7IJAFBRnsomiXwCAFRcST7Fx8fbh0VERGjx4sVKSUnRLbfcUqHputxUfP755yX98QvYK6+84rC7fnBwsJo1a6ZXXnnF1JP36dPH4f5TTz2l6dOna/Xq1br88ss1depUjRs3Tv369ZP0R3DGxMRo9uzZGjFihKnnAgAElpSUFM2cOVNr166V5JhPhmHo4MGDMgxDJ06cMJVPZBMAoKI8lU0S+QQAqLiSfLrhhhschteqVUuLFi1Sz549KzRdl5uKO3bskCR169ZN8+bNU+3atSv0hGUpKirS+++/r/z8fCUlJWnHjh3KyclRSkqKfZyQkBB16dJFK1euJBgBoIp74okntHfvXl1xxRWSnPPp+PHjWrdunbp06VLh5yCbAABmeCObJPIJAGBOST6VJiIiQv/73/+0bt0609M1ffXnJUuWmH6S8mzcuFFJSUk6deqUatWqpfnz5+vyyy/XypUrJUkxMTEO48fExOjXX38tc3oFBQX2k0xKf1w2GwAQeGrXrm3fSPv888+VlJSkhx9+WMXFxQ7jVWTDzd3ZJJFPAFAVeDKbJLadAAAVc34+ff7558rNza10PrnUVExPT9eTTz6pmjVrKj09vdxxp0yZYqqAli1basOGDTpy5Ig++OADDRo0SMuWLbM/fv7VZwzDKPeKNJmZmXriiSdM1QAA8E/p6ekKCwvT008/rXr16jmc7/fccSRz+eTubJLIJwCoKjyVTRLbTgCAynniiSc0YcIEdejQQQ0aNKjQFZ/P5VJTcf369Tp9+rQk6dtvv630k54rODjYfrLhDh06aM2aNXrhhRf097//XZKUk5OjBg0a2MfPzc11+gXuXI8++qhD4zMvL0+NGzd2W70AAOtYv369tmzZoqysLL3xxhul5tP69etNT9fd2SSRTwBQVXgqmyS2nQAAlfPKK68oKytLAwYMcMv0XGoqnnvI89KlS93yxGUxDEMFBQWKj49XbGyssrOz1b59e0lSYWGhli1bpmeeeabMvw8JCVFISIhHawQAWMOSJUtUp04dJScnuy0YS1PZbJLIJwCoKryVTRLbTgAAcwoLC5WcnOy26VUz+wdDhw7VsWPHnIbn5+dr6NChpqb1j3/8QytWrNDOnTu1ceNGjRs3TkuXLtVdd90lm82m0aNHa9KkSZo/f742bdqkwYMHKzw8XHfeeafZsgEAAWr48OGaPXu22/KJbAIAVJa7s0kinwAAlVeST+5i+kIts2bN0tNPP62IiAiH4SdPntRbb72lN9980+Vp/f777xowYID27dunqKgotWnTRp999pn9UtZjx47VyZMnlZaWpsOHD6tTp05avHix03MDAKqWcw/VKi4u1owZM3TkyBHZbDZFRUU5jPuPf/zDVD6RTQCAivBkNknkEwCgYkrLp//9739q06aNatSo4TCu2XP9utxUzMvLk2EYMgxDx44dU2hoqP2xoqIiLVy4UPXr1zf15G+88Ua5j9tsNmVkZCgjI8PUdAEAge3cc1GdOXNGCQkJ+vLLL7V582anYDSbT2QTAKAiPJlNEvkEAKiY88/j265dO0nSpk2bHIZX5PopLjcVL7roItlsNtlsNrVo0cLpcZvNxpXDAABece65fqtVqyabzaZq1appzZo1TuOuWrWKfAIAeBzZBACwonPzyd1cbiouWbJEhmGoe/fu+uCDDxQdHW1/LDg4WE2bNlVcXJxHigQAoCzkEwDAasgmAEBV4HJTsUuXLpKkHTt2qEmTJhXaLRIAAHcjnwAAVkM2AQCqApeait9//73D/Y0bN5Y5bps2bSpXEQAALiKfAABWQzYBAKoKl5qK7dq1k81mk2EY5Y5ns9lUVFTklsIAALgQ8gkAYDVkEwCgqnCpqbhjxw5P1wEAgGnkEwDAasgmAEBV4VJTsWnTpp6uAwAA08gnAIDVkE0AgKrC5Qu1nG/Lli3atWuXCgsLHYbfdNNNlS4KAICKIp8AAFZDNgEAApHppuIvv/yiW265RRs3bnQ4V0jJFc04LwgAwBfIJwCA1ZBNAIBAVs3sH4waNUrx8fH6/fffFR4ers2bN2v58uXq0KGDli5d6oESAQD+buvWrWrXrp39FhYWpgULFrj1OcgnAIBZns4nsgkAYJY3tp3cxfSeiqtWrdIXX3yhevXqqVq1aqpWrZquueYaZWZm6oEHHtD69es9UScAwI+1bNlSGzZskCQdP35czZo1U8+ePd36HOQTAMAsT+cT2QQAMMsb207uYnpPxaKiItWqVUuSVLduXe3du1fSHyck3rp1q3urAwAEnI8++kg9evRQzZo13Tpd8gkAUBmeyCeyCQBQGZ7adnIX003FhIQEff/995KkTp06afLkyfrqq680YcIEXXzxxW4vEADgecuXL1efPn0UFxcnm81W6u7106ZNU3x8vEJDQ5WYmKgVK1ZU6Ln+85//qH///pWs2Bn5BACBx9/ziWwCgMDj79nkTqYPf/7nP/+p/Px8SdLEiRPVu3dvXXvttapTp47mzp3r9gIBAJ6Xn5+vtm3basiQIbr11ludHp87d65Gjx6tadOm6eqrr9arr76q1NRUbdmyRU2aNJEkJSYmqqCgwOlvFy9erLi4OElSXl6evvrqK82ZM8ft80A+AUDg8fd8IpsAIPD4eza5k+mmYq9evez/v/jii7VlyxYdOnRItWvXtl/FDABgDXl5eQ73Q0JCFBIS4jReamqqUlNTy5zOlClTNGzYMA0fPlySNHXqVC1atEjTp09XZmamJGndunUXrOfDDz9Ur169FBoaamY2XEI+AYB/cDWbJP/PJ7IJAPxHVdp2chfTTcXSREdHu2MyAABJEbsNBQUblZpGUeEff9+4cWOH4ePHj1dGRoapaRUWFmrdunV65JFHHIanpKRo5cqVpqb1n//8R/fee6+pv6kM8gkA3Key+eTObJL8N5/IJgBwH7adfMstTUUAgDXt3r1bkZGR9vtl7QlSngMHDqioqEgxMTEOw2NiYpSTk+PydI4ePapvvvlGH3zwgekaAACBwx3ZJJFPAAD3YtvJPJqKABDAIiMjHYKxMs4/TMswDFOHbkVFRen33393Sy0AAP/lzmySyCcAgHuw7WSe6as/AwCqlrp16yooKMjpl7Xc3FynX+AAAPAW8gkAYDVVLZtoKgIAyhUcHKzExERlZ2c7DM/OzlZycrKPqgIAVHXkEwDAaqpaNnH4MwBAx48f1/bt2+33d+zYoQ0bNig6OlpNmjRRenq6BgwYoA4dOigpKUkzZszQrl27NHLkSB9WDQAIdOQTAMBqyKazaCoCALR27Vp169bNfj89PV2SNGjQIGVlZal///46ePCgJkyYoH379ikhIUELFy5U06ZNfVUyAKAKIJ8AAFZDNp1FUxEAoK5du8owjHLHSUtLU1pampcqAgCAfAIAWA/ZdBbnVAQAAAAAAABgCnsqAgAAAAACztFLQiRJUT8X+LgSeEvJa17Cm6/9uc/New5VBU1FAAAAAEDAOnpJCE2eAHd+M7G04Z56D5T23LznUFXQVAQAAAAABJTzGz3+2uQpq1nmj/NSmsruWVjW8rnQuO5Yfhd6bn99zwFm0FQEAAAAAMACXG2S+XvDqrJ7FpppJpb39xVZhmYbmf78OgEXQlMRAAAAABAwymtYWbHBU9kGmb8x0zg9V9TPBW5fVhea3rnvl4o+t1Xfd4A7cPVnAAAAAEBAcOWQ1EDhjnk5ekmI/eYNlXkeX7x27lo+gfS+A85FUxEAAAAAYJcXTwPEW3zZrCrtvJOeVNUba1V9/hGYOPwZAAAAHlPZjSgOGQN8wx8P2fS38xG6q8lUkfnxZoOLZtpZVnnvAe5CUxEAAABu5c4NSFemxQYaAH/jiXMDurouLO+53d30oqHojMYiAkmVaSrmxYcoKJgPLwAAvlDWRgW5HDh8ueFY2sn8AVSePzU/zK6D/Gne3MmV5eSuZUNDEQh8VaapWMJffu12xwrYCvMBAChfXnyIonf7ugrPcHXDRfJOZpVWD1lZeVbcaDy3Jl5joHICuflW2cOGK7pcPLXevND8mHneyr7uVswGKwnkzxWqlirXVHRFoKwA2SsEAPyDNxtrnuSOk8V7+5Ard2W+1V47V+erInX72/ckmslA5VW0AeKtBr+vrlxsxXMZllVTRZ7XivMXSGgsIhDQVKyCPPHl2tfh4Y7zh7BCB+Br/tpc9NT588wsB1/nkC+ev2T5uPPKn+6artXxfQAwz2wDxB0NuIo8T0X+3pW6ynoed53L0J3Or6myOWG1+QskNBbh72gqQpL/B4A76vfVBqEvN0RRPldfm6JCQ/rSw8WgSqlIc9EXh3t6Y28LlM2Th89VZZ7cy7Oy2OsSvuZKA8SbFwHxlgutF6w8X77aixOus/L7B7gQmoqAD/kqfCvyvIEWdHzxgT+o6PvUzN9xWBNQMVb5LJRXBz96wRMqe3itOxso7vocuuOQYXeez9Ad3H0UQVnzZpV1ob+z8tEqnKcY5aGpCMAlfGEAAhOfbQCAWe445Nhq/PXIJ28J5HmzEqsvZ3fUxw9egaWarwsAAAAAAAAA4F9oKgIAAAAAAAAwhaYiAAAAAAAAAFN82lTMzMxUx44dFRERofr166tv377aunWrwziGYSgjI0NxcXEKCwtT165dtXnzZh9VDAAIdGQTAMCKyCcAgNX4tKm4bNky3XfffVq9erWys7N15swZpaSkKD8/3z7O5MmTNWXKFL388stas2aNYmNj1bNnTx07dsyHlQMAAhXZBACwIvIJAGA1Pr3682effeZwf+bMmapfv77WrVunzp07yzAMTZ06VePGjVO/fv0kSbNmzVJMTIxmz56tESNG+KJsAEAAI5sAAFZEPgEArMZS51Q8evSoJCk6OlqStGPHDuXk5CglJcU+TkhIiLp06aKVK1eWOo2CggLl5eU53AAAqCh3ZJNEPgEA3IttJwCAr1mmqWgYhtLT03XNNdcoISFBkpSTkyNJiomJcRg3JibG/tj5MjMzFRUVZb81btzYs4UDAAKWu7JJIp8AAO7DthMAwAos01S8//779f333+u9995zesxmszncNwzDaViJRx99VEePHrXfdu/e7ZF6AQCBz13ZJJFPAAD3YdsJAGAFPj2nYom//e1v+uijj7R8+XI1atTIPjw2NlbSH7+6NWjQwD48NzfX6Re4EiEhIQoJCfFswQCAgOfObJLIJwCAe7DtBACwCp/uqWgYhu6//37NmzdPX3zxheLj4x0ej4+PV2xsrLKzs+3DCgsLtWzZMiUnJ3u7XABAFUA2AQCsiHwCAFiNT/dUvO+++zR79mx9+OGHioiIsJ/rIyoqSmFhYbLZbBo9erQmTZqk5s2bq3nz5po0aZLCw8N15513+rJ0AECAIpsAAFZEPuFYU8fD2CN+NXxUCeCs5P3J+7Jq8WlTcfr06ZKkrl27OgyfOXOmBg8eLEkaO3asTp48qbS0NB0+fFidOnXS4sWLFRER4eVqAQBVAdkEqzp/Y9JVfLkHAoM38+lYY5uCQste55y/XnF1/cT6yHWuLFOzucDyhzuV9f471tTGe60K8WlT0TAu/Eaz2WzKyMhQRkaG5wsCAFR5ZJPrSvsy6S9fIivSoHP3vFW0SWjV53EXdyxnX8+zv3wO4F+slE8V/YyxJ5Mzb66vynsud70m3ngOd/JEAywQ3+dm36eBuAxQOktcqAUAAHifJzZkLjTNin659HWTyCo1VAWBsJw9NQ9snCFQnPsZqQrva39Yr1X0h0Iz82bV5eDOxuK582jlH1+9/cOmVeYb7kdTEQDgFc8//7xef/11GYah6667Ti+88IJsNmt+ufSm8w8xq8yXLqt+WT+XP9QIWJXZz0/RKT5vriCffMuVBqM/HuYbCHlX1jkcA2HezueOxqInDlk/lzf2JvUUmovm+Us20VQEAHjc/v379fLLL2vz5s2qUaOGOnfurNWrVyspKcnXpVlOIH5RBwCrIp+sxV0Z6I7pVKT5EegZXhXmz8pHVFS2MWeF1+9YU5vCt/m6Cuvzp2yiqQgA8IozZ87o1KlTkqTTp0+rfv36Pq4IAADyCaVz9RBtKzRq4D4VaSx6+z1gtrlotffoscbWqseq/CWbqkxT8UJXMAOAigqEw8uWL1+uZ599VuvWrdO+ffs0f/589e3b12GcadOm6dlnn9W+fft0xRVXaOrUqbr22mtdmn69evU0ZswYNWnSRNWrV9fIkSN1ySWXeGBOAACBhHyCFVitKQPPulBD2SrvB6vUURWRTWdV83UBAADfy8/PV9u2bfXyyy+X+vjcuXM1evRojRs3TuvXr9e1116r1NRU7dq1yz5OYmKiEhISnG579+7V4cOH9cknn2jnzp3as2ePVq5cqeXLl3tr9gAAfop8AuBLx5ranG4A2XRWldlTEQCqory8PIf7ISEhCgkJcRovNTVVqampZU5nypQpGjZsmIYPHy5Jmjp1qhYtWqTp06crMzNTkrRu3boy//7999/XpZdequjoaEnSjTfeqNWrV6tz586m5wkA4N9czSaJfAIAeA/bTubRVAQAi4ncUaDq1Sv3K+iZMwWSpMaNGzsMHz9+vDIyMkxNq7CwUOvWrdMjjzziMDwlJUUrV650aRqNGzfWypUrderUKdWoUUNLly7Vvffea6oOAIBvVTaf3JlNEvkEAGDbyddoKgJAANu9e7ciIyPt98vaE6Q8Bw4cUFFRkWJiYhyGx8TEKCcnx6VpXHXVVbrhhhvUvn17VatWTT169NBNN91kuhYAgP9zRzZJ5BMAwL3YdjKPpiIABLDIyEiHYKwMm83xF0DDMJyGleepp57SU0895ZZaAAD+y53ZJJFPAAD3YNvJPC7UAgAoV926dRUUFOT0y1pubq7TL3AAAHgL+QQAsJqqlk00FQEA5QoODlZiYqKys7MdhmdnZys5OdlHVQEAqjryCQBgNVUtmzj8GQCg48ePa/v27fb7O3bs0IYNGxQdHa0mTZooPT1dAwYMUIcOHZSUlKQZM2Zo165dGjlypA+rBgAEOvIJAGA1ZNNZNBUBAFq7dq26detmv5+eni5JGjRokLKystS/f38dPHhQEyZM0L59+5SQkKCFCxeqadOmvioZAFAFkE8AAKshm86iqQgAUNeuXWUYRrnjpKWlKS0tzUsVAQBAPgEArIdsOoumIgAAAADArrBxoaqFlX/6/ZBdwSpoUmhquiG7gitTFkxw5bXh9XC/85c7yxiBjqYiAAAAAMAUsw3Fc/+mMo0WV5/Xl82ciiwbyfs1e7MBVtFlcj531uiO92Np0ytrmNUajGZeE3fWXtjYPe8FWANNRQAAAD/hro2y8lhto8cb3L1cq+IyBMwoaFJo+nNi9nPqjfWlu1VkuZQ1ncr8nTeavpWZvruXkTum6cp8m23ieqLpa4n3xskKTwIWRFMRAIAqxCobWYHQdLHKsnQ3X86XLzeY3OlCNQTC+x+oLFebFFb4TPsTdyyvijSQvPk6VbbBVdYehe6cnif+rjJ7Pbrr9bH6ewPeR1MRAIAA4G9f2Mqq15cbl+U9t78tX39VVZZzefNJwxFVTVlNiqqyPjifu/bEc4cLNbF8/RqZXVYXqtcbe9C6i6sNRk/Vx48CKEFTEQAAH3LlZPhViS+/fPLFF1bAXo6oqlgHn1XRxqInl+G5TSQrvVbubm6ZWfZWWQ7nNxitsNeoVZYNPI+mog/wAQMCS/FJPtMAAO8w8z2SfAL8l7v3wnMXq27LurMuq++hWR4r7DmJqqXKNBXZEwQAAAAAALiKZhlQPrpsAAAAAABYjJlDdgHAF2gqAgAAAABgQa5cXAQAfIWmIgAAAAAAAABTaCoCAAAAAGBRZe2NyF6KAHyNpiIAAAAAABZ2fgORhiIAK6CpCAAAAAAAAMAUmooAAAAAAFhcyd6J7KUIwCpoKgIAAAAA4AdoKAKwEpqKAAAAAAAAAEyhqQgAAAAAAADAFJqKAAAAAAAAAEyhqQgAAAAAAADAlOq+LgAAAAAAALhXs0b7K/y3O3+r58ZKAAQqmooAAAAAgIBUWmOtKjTMKtNQvNDfV4XlZwWeeO9W9H3Ba46y0FQEAMDCSr78We3LXGU3VuA/zLz3zL4vfPW+ttr712qfbyAQlPc5d+c6wIqfX0+v45o12u+2+XZHra7UYuZ5vPWaVmTefZVf5z5vRZdPyTTO5Bdot1uqghVUmaZik7gDql4zxNdlAAhABCMqw9V8sloTBFWHJ997vK//UJHlYMVGBqoWdzQZSpumO6blzXWLJ5ZDRXl7vn2115ynplPe9Nz92vpz/p1fe1nLxp/nEa6rMk1FAAAAIFC4srHGj17wlPPff642Gcr7m/OHV6SJ4+smhi8bjL6Y94o2Fn39OlWEVY8csQJ/fD3hPjQVAQAAAAAucaWB4I4mg9kmjtUaG95qMPp6vs02Fn1db2VZaQ9NwAqq+boAAAAAAABK06zR/gs2YazepHFlHio6XStwtQ6r1OtLLAMEGp82FZcvX64+ffooLi5ONptNCxYscHjcMAxlZGQoLi5OYWFh6tq1qzZv3uybYgEAVQb5BACwGitkky8bIqU15jzVrPMUd55D0GrzfaGarFZvZQTSvACV5dOmYn5+vtq2bauXX3651McnT56sKVOm6OWXX9aaNWsUGxurnj176tixY16uFABQlZBPAACrIZv+UNK88tfGTmVq94f59vfGr6v86crNgCf59JyKqampSk1NLfUxwzA0depUjRs3Tv369ZMkzZo1SzExMZo9e7ZGjBjhzVIBAFUI+QQAsBpfZxMNEfdy9ZyR/rjcS8476I+1m2Hm/IqBvixQdVn2nIo7duxQTk6OUlJS7MNCQkLUpUsXrVy5ssy/KygoUF5ensMNAAB3IZ8AAFbj6WyiIeI5Ze3Z5+97+Plz7WZUlfkEymLZpmJOTo4kKSYmxmF4TEyM/bHSZGZmKioqyn5r3LixR+sEAFQt5BMAwGrIJv8WCE1ElI3XFYHMsk3FEjabzeG+YRhOw8716KOP6ujRo/bb7t27PV0iAKAKIp8AAFbjiWyiIQKUr6pcoAYojWWbirGxsZLk9Mtabm6u0y9w5woJCVFkZKTDDQDge//61790xRVXKCEhQe+8846vy6kw8gkAAksg5JOnsomGCOAaPitwN3/JJss2FePj4xUbG6vs7Gz7sMLCQi1btkzJyck+rAwAYNbGjRs1e/ZsrVu3TmvXrtX06dN15MgRX5dVIeQTAASOQMknsgnwvdLOjwlUhD9lk0+bisePH9eGDRu0YcMGSX+cYHjDhg3atWuXbDabRo8erUmTJmn+/PnatGmTBg8erPDwcN15552+LBsAYNIPP/yg5ORkhYaGKjQ0VO3atdNnn33m67LKRD4BQNXgT/nk7WyiIQJUHJ8fVIY/ZZNPm4pr165V+/bt1b59e0lSenq62rdvr8cff1ySNHbsWI0ePVppaWnq0KGD9uzZo8WLFysiIsKXZQNAwFm+fLn69OmjuLg42Ww2LViwwGmcadOmKT4+XqGhoUpMTNSKFStcnn5CQoKWLFmiI0eO6MiRI/riiy+0Z88eN86Be5FPAGAN5NNZZBNgfTQTqway6azqvnzyrl27yjCMMh+32WzKyMhQRkaG94oCgCooPz9fbdu21ZAhQ3Trrbc6PT537lyNHj1a06ZN09VXX61XX31Vqamp2rJli5o0aSJJSkxMVEFBgdPfLl68WJdffrkeeOABde/eXVFRUerYsaOqV/dpBJWLfAIAayCfziKbAMAayKazrFkVAMAt8vLyHO6HhIQoJCTEabzU1FSlpqaWOZ0pU6Zo2LBhGj58uCRp6tSpWrRokaZPn67MzExJ0rp168qtZcSIERoxYoQkafjw4br00ktNzQsAIDC4mk0S+QQA8B62ncyjqQgAFhP8015VrxZcqWlUKy6UJDVu3Nhh+Pjx403vwVBYWKh169bpkUcecRiekpKilStXujyd3Nxc1a9fX1u3btU333yjV155xVQdAADfqmw+uTObJPIJAMC2k6/RVASAALZ7925FRkba75e1J0h5Dhw4oKKiIsXExDgMj4mJUU5OjsvT6du3r44cOaKaNWtq5syZlt2FHwDgWe7IJol8gmt6xv6o7JxWvi4DgB9g28k8a1YFAHCLyMhIh2CsDJvN5nDfMAynYeUx88scACBwuTObJPLJE5rEHZBUsWavlfSM/dH+L41FABfCtpN5Pr36MwDA+urWraugoCCnX9Zyc3OdfoEDAMBbyCeUp6ShWNZ9APCEqpZNNBUBAOUKDg5WYmKisrOzHYZnZ2crOTnZR1UBAKo68gllKauBSGMRgKdVtWzi8GcAgI4fP67t27fb7+/YsUMbNmxQdHS0mjRpovT0dA0YMEAdOnRQUlKSZsyYoV27dmnkyJE+rBrwjbI2St15aF3JcwTK4XqubMgHyryWKG2eA20evYF8glkXWt9wKDSAyiKbzqKp6Kf4lS1wmf2S4+73woWen/ees1PHT+srXxdRSWvXrlW3bt3s99PT0yVJgwYNUlZWlvr376+DBw9qwoQJ2rdvnxISErRw4UI1bdrUVyUDpnl6/XXu9CuywVpafReq2cobxmaXtzfm1dcZdv7zW/n1swryCWa4+hmnseifKpuzgLuQTWdVmaZit5ifFFqrhq/LAC7Iahs8qBq6du0qwzDKHSctLU1paWleqqjq8HU+VYUv5b5Yr7m64VPZ2rwxb77+scvT0/Ul9ma8MPIJrgrEdQTOKu8cmaw34W1k01lVpqkIAACcBeqXcittXFqplorwVoMUfwi0Q98Bq2JvRf/hyiHtJXhNAe+iqQgAACSVvdeUN5tFZjYGaGJ5H8vce9yxrAPh9Bzwf95olFf080IzyvoqcioNX7+WzRrt9+nzA95EUxEAAJTJ200kmlYAEDjOXad7otnjzsxgL2HrCfTvBLznEAiq+boAAAAAAEDg6Bn7Y4UuPmX2OTwh0BtZ3lLWe8DM3wey8xvugaCyrzn8E3sqAgAAAADcwpXz31V2zyxPNy7Yg6ziyrqgiqvLsio0pcpquPvz+62sJqk/zxNcw56KAAAAAIBKMbOXkr/swVYVGlzuVN7ycuX94Y7lbfUm1oWWkT+q7OsO/8aeigAAAACACqtI06Aie2b5ojnBXosXZuZ1KWsvtqrQeHJlHv1tj8WK/JDwfz/He6oc+ABNRQAAAACAaZVtBJlpoPi66eRvzR5v8Ze9Tn2tIo1Xq7/fKvr6dYv5SV+5uRb4Doc/AwAAAABMcVdDyNW9t6zAKnVYBcvDNRVdTlZevlauDd7FnooAAAAAAJe5u6FQ1l6AVmxccBEKa74uVuXNvXm9hdcf56KpCAAAAABwiacaCuc3T/yhceFqjVZrClWUP7wmVuLOvXkD5T2EwENTEQAAAABwQZ5uKgVq08oXeze6+7x8/vLaWOV8hP6yvMwK1PlCxXFORQAAAABAuWgmuEfP2B+92px1x3P542vviUP0vfHaXej5AathT0UAAAAAQJloZrifJ/ZeLOt1qujee/7+urvrsOHzl8P5989/Dk8uN18eCu3v7wd4BnsqAgAAAADgI5XdC87VvzUz/UBpILnjQimujOPNPQkD5bVBYGBPRQAAAABAqWhgeJfZvQrNvj6u7OkWaK+5N/fU9HZjsSJ7LZZWo6/PQQn/RVMRAAAAAOAk0JpL/uRCh0dX5rUpqyEV6K+3mUOH/WVZuGueLtSk9JflAe/j8GcAAAAAgAOaCNZx/uG17npt3H1BF3/g7sPErcCd88QFYWAWeyoCAAAAAOy6xfwkqYavy0ApPHFV46qmrL37/HlZuHue/HlZwLtoKgIAAAAAgCrj3MN9A6WBdm5jMVDmCdZHUxEAAAAAAFQ5gdZ8C7T5gfXRVAQAAAAAAGW6LfJb+///m3elDysBYCU0FQEAAAAAgJNzm4nnD6O5CICrPwMAAAAAALvbIr8ttaF4/jgAqjaaigAAAOdxZWMKAIBAYzb/yEqgauPwZwABwRNfaDikA/A/7l4XlDU91g8AgEBDgxCAWVWmqdg34jvVimDHTACuc/WL1XFbsSZ6uBYA1trYcXct5TUpPTXfNEb9E+cyA+Bulc2Z2yK/ZZ0EVFFVpqkIAADcx5UNEFc3MKzULPQVXywDXzynpzY6q+J7yJV55kcvABdSFdefANy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",
    193193      "text/plain": [
    194194       "<Figure size 640x480 with 6 Axes>"
    … …  
    225225  {
    226226   "cell_type": "code",
    227    "execution_count": 17,
     227   "execution_count": 8,
     228   "id": "e2aa2f5b-c527-44f8-98a5-ebde0e62f01f",
     229   "metadata": {},
     230   "outputs": [
     231    {
     232     "data": {
     233      "text/plain": [
     234       "'kg/kg'"
     235      ]
     236     },
     237     "execution_count": 8,
     238     "metadata": {},
     239     "output_type": "execute_result"
     240    }
     241   ],
     242   "source": [
     243    "profile = my_sim.get_subset('h2o_vap',)\n",
     244    "profile.units"
     245   ]
     246  },
     247  {
     248   "cell_type": "code",
     249   "execution_count": 9,
    228250   "id": "4f544843-012d-41a6-8d7a-c4b40f45a5e1",
    229251   "metadata": {},
    … …  
    231253    {
    232254     "data": {
    233       "image/png": 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",
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OHQNg+PDh/PjjjyxZsoSLFy/y3//+l3PnzmWrXoV4VpLIvAjh/yYtHqW4o/47RkYSGVGAlChRgv3796PT6WjdujXVqlVjxIgRODs752gOi5kzZ+Li4kKDBg3o0KEDgYGBRuMyssPe3p7169dz5swZatasyaeffvrILq+H/fTTT7i7u9O4cWNee+01Bg0ahIODQ44nQKxfvz4LFy5kzpw5+Pn5sXXrVsPA6SdZvHgx9+/fx9/fnz59+vD+++9TrFixLNv17t2b4OBgSpYsScOGDY3WTZo0iXHjxjF16lR8fX1p06YNGzZswNvb+4nHzk69P61+FEVh48aNNG7cmAEDBuDj40OPHj24fv26YUxV9+7dGTduHGPGjKFWrVpcv379iYOwhXgRFDW3r/M0sbi4OJycnIiNjc32zL4bN26kXbt22ZqMJyU9hV+7+lM/RCW871ssTFD51XIyuFWE9468iCLkupyWuSB4uMwpKSlcvXoVb2/vfDWzb07o9Xri4uJwdHTM1xNo5cSTynzz5k08PT3Zvn07LVq0MFGEL96LOs/5qX7kvZ1/y/yk797s/n7LAIDndD3uOsViM3JBt3IVuXPy35lWpUVGiDzln3/+ISEhgWrVqhEeHs6YMWPw8vKicePGpg4tT5D6EfmVJDLP6VrcNYrFZPzfrYI3d04kZzxJiYH0VDDPersEIcTLp9Vq+eSTT7hy5QoODg40aNCA5cuXF5pWyKeR+hH5lSQyz+lGeAil/53rqWgFbxLMwkhVzbFS0jNaZZxLmzZAIQQAgYGBBAaa5nL6/EDqR+RX+bdjLY+IvnoBAK2DDRYO9hRzsOYOzhkrE7JOfy6EEEKIF0cSmeeUFHYVALV4UQDcHa25I5dgCyGEEC+FJDLPQVVV9LcjALDyzOhC8nC0/v8l2PERpgpNCCGEKBQkkXkO91Lu4XwvFQBHr4ybq3k4PdgiI11LQgghRG6SROY5XI29SrHYjP/beJYBMhIZmd1XCCGEeDkkkXkO1+KuUTQmYw4Zi1KlgH+7lgyDfSWREUIIIXKTJDLP4VrM/1tkLEtl3I3W/cExMpLIiDygf//+dO7c2dRh5DmKorBu3brHrr927RqKohjdyVpk2LVrF4qiEBMTY+pQhJBE5nlE3r6EtRZUBcxLlACMu5ZUSWRELlMU5YmPCRMmMGfOHJYuXWrqULOIjo6md+/eODo64uzszMCBA0lISHhpxw8PD6dt27Yv7XhCiNwhE+I9h8TrVwBQi7hgZmkJZF615JyxQUIUqCrk8O66QmRXeHi44f+//fYb48ePJyQkxLDM3t4ee3t7gBzfZTq39e7dm/DwcLZt24ZWq2XAgAEMHjyYFStW5Opx09LSsLS0xMPDI1ePk5u0Wq3MuCvEv6RF5hlpdVq4nXFVkuW/42MAbCw1pFm7AaDo0iD5vkniE4WDh4eH4eHk5ISiKEbL7O3ts3QtNW/enGHDhjFixAhcXFxwd3dn4cKFJCYmMmDAABwcHChfvjybNm0yOtbZs2dp27Yt9vb2uLu706dPH+7evftMcZ8/f57NmzezaNEi6tWrR6NGjfj2229ZuXIlt2/fzvZ+0tPTef/993F2dqZIkSJ89NFH9OvXz6i87du3N5TXzc3NMHvtw11LR44coWbNmlhbW1O7dm1OnDjx1OPfu3ePnj17UrJkSWxtbalWrRq//vqrYf0PP/xAiRIlsiSRnTp14q233jI8//PPP/H398fa2pqyZcsyceJE0tPTDesVRWH+/Pl07NgROzs7Jk+ejE6nY+DAgXh7e2NjY0PFihWZM2dOjutHr9czdepUw378/Pz4448/jPazceNGfHx8sLGxoVmzZly7du2pdSPEyyKJzDO6EX8Dt5iMLyfb0t5G64o4OXBfzfgrWC7BzsdUFdISTfPI5ZvSL1u2DDc3N44cOcKwYcMYMmQIb7zxBg0aNOD48eO0bt2aPn36kJSUBEBMTAzNmzenZs2aHDt2jM2bNxMZGUm3bt0M+5wyZYqhBehxj7CwMAAOHjyIs7MztWvXNry+ZcuWmJmZcfjw4WyX48svv2T58uUsWbKE/fv3ExcX98hxLz/99BOWlpbs37+fBQsWZFmfkJBA+/btqVy5MkFBQUyYMIHRo0c/9fgpKSnUqlWLDRs2cPbsWQYPHkyfPn04cuQIAG+88Qb37t1j586dhtdER0ezefNmevfuDcDevXvp27cvw4cPJzg4mO+//56lS5cyefJko2NNmDCB1157jTNnzvDWW2+h1+spVaoUq1atIjg4mPHjx/PJJ5/w+++/56h+pk6dyk8//cSCBQs4d+4cI0eO5M0332T37t0A3Lhxgy5dutChQwdOnjzJ22+/zccff/zUuhHiZZGupWd0Ne6q4a7XD7bIALg7WXPnvhMuSgIkRECxSqYIUTwvbRJMKWGaY39yGyztcm33fn5+fPbZZwCMHTuWadOm4ebmxqBBgwAYP3488+fP5/Tp09SvX5/vvvuOmjVrMmXKFMM+fvzxRzw9Pbl48SI+Pj785z//MUpsHqXEv2PJIiIiKFasmNE6c3NzXF1diYjI/kSS3377LWPHjuW1114D4LvvvmPjxo1ZtqtQoQJfffXVY/ezYsUK9Ho9ixcvxtramipVqnDz5k2GDBnyxOOXLFnSKOEZNmwYW7Zs4ffff6du3bq4uLjQtm1bVqxYQYsWLQD4448/cHNzo1mzZgBMnDiRjz/+mH79+gFQtmxZJk2axJgxY/jvf/9r2HevXr0YMGCA0fEnTpxo+L+3tzcHDx5k1apVtGnTJlv1k5qaypQpU9i+fTsBAQGG4+/bt4/vv/+eJk2aMH/+fMqVK8eMGTMAqFixImfOnOHLL798Yt0I8bJIIvOMrsX+/67XFg8lMh6OVkSpzvhwS1pkRJ5UvXp1w/81Gg1FihShWrVqhmXu7u4AREVlvH9PnTrFzp07DeNtHhQaGoqPjw+urq64urrmcuT/FxsbS2RkJHXr1jUs02g01KpVK0tXjr+//xP3df78eapXr461tbVhWeYPe6YqVapw/fp1AF555RU2bdqETqdjypQp/P7779y6dYu0tDRSU1OxtbU1vK53794MGjSIefPmYWVlxfLly+nRowdmZhkN4qdOnWL//v1GLTA6nY6UlBSSkpIM+3qw9SrT3Llz+fHHHwkLCyM5OZm0tDRq1KiR7fq5fPkySUlJtGrVymi/aWlp1KxZ01A39erVM1r/cN0IYUqSyDyjBG0CbmkZLTLaKOOrkzxdbLmtZoyTUUP/Qan+5L9SRR5lYZvRMmKqY+fm7h8aKKooitEy5d8B6pk/eAkJCXTo0OGRf4UXL14cyOhaerDF5lGCg4MpXbo0Hh4ehiQpU3p6OtHR0bkyCNfO7vlbtzZu3IhWqwXAxsYGgK+//po5c+Ywe/ZsqlWrhp2dHSNGjCAtLc3wug4dOqCqKhs2bKBOnTrs3buXWbNmGdYnJCQwceJEunTpkuWYDyZWD5dh5cqVjB49mhkzZhAQEICDgwNff/11jrrmMq8S27BhAyVLljRaZ2Vlle39CGFKksg8o07lOjG71o/43E4jav48nNp3MMwl06VWKYbvbEk3dsOplVD/XShe/Sl7FHmOouRq905+4u/vz+rVq/Hy8sLc/NFfGznpWgoICCAmJoagoCBq1aoFwD///INer8/y1//jODk54e7uztGjR2ncuDGQ0ZJx/PhxQ6tEdvn6+vLzzz+TkpJiSB4OHTpktE2ZMmWyvG7//v106tSJN998E8hI/C5evEjlypUN21hbW9OlSxeWL1/O5cuXqVixolELkb+/PyEhIZQvXz5HMe/fv58GDRrw7rvvGpaFhoYa/p+d+qlcuTJWVlaEhYXRpEmTRx7H19eXv/76y2jZw3UjhCnJYN9n5OXkRc1+IzlbWsEsVcv1/36K+u8AzZLONvjVb8lfugAUVNStn+X64E0hctPQoUOJjo6mZ8+eHD16lNDQULZs2cKAAQPQ6XQAuLq6Ur58+Sc+MpMgX19f2rRpw6BBgzhy5Aj79+/nvffeo0ePHoZkJzuGDRvG1KlT+fPPPwkJCWH48OHcv3/f0KKUXb169UJRFAYNGkRwcDAbN25k+vTpT31dhQoV2LZtGwcOHOD8+fO88847REZmnT+qd+/ebNiwgR9//NEwyDfT+PHj+emnn5g4cSLnzp3j/PnzrFy50jCG6UnHPnbsGFu2bOHixYuMGzeOo0ePGm3ztPpxcHBg9OjRjBw5kmXLlhEaGsrx48f59ttvWbZsGZCRoF66dIkPP/yQkJAQVqxYkSfnJRKFlyQyz+HNyn3Y29MXrQbS9x8mbssWw7p3m5bjO6UXqao5ytXdcGmbCSMV4vmUKFGC/fv3o9PpaN26NdWqVWPEiBE4Ozsbxnrk1PLly6lUqRItWrSgXbt2NGrUiB9++MFoG0VRnvij+dFHH9GzZ0/69u1LQEAA9vb2BAYGGnXJZIe9vT3r16/nzJkz1KxZk08//TRbg1k/++wz/P39CQwMpGnTpnh4eDxyFuXmzZvj6upKSEgIvXr1MloXGBjI33//zdatW6lTpw7169dn1qxZj2wBetA777xDly5d6N69O/Xq1ePevXtGrTOQvfqZNGkS48aNY+rUqYYEc8OGDXh7Z1yNWbp0aVavXs26devw8/NjwYIFT+1CFOJlUlS1YDcVxMXF4eTkRGxsLI6Ojk/dXqvVsnHjRtq1a5etCaeuxF7h19Gd6LI3Ha2rA1W27EDj4ADA7O0Xsd41kf+Y/43qVhFlyAHQ5L3evJyWuSB4uMwpKSlcvXoVb2/vHP8I5hd6vZ64uDgcHR2fOfl4ma5evYqPjw/BwcFUqFAhW6/R6/X4+vrSrVs3Jk2alO/K/CI8qcwP109BIec5/5b5Sd+92f39zr+lzyPKOpWlxJBh3HYFi+h4rk+falj39itlWWH5BtGqPcrdEDjxkwkjFSJ/2bhxI4MHD35iEnP9+nUWLlzIxYsXOXPmDEOGDOHq1atZWj0KK6kfURhIIvMC9KnxFtu7lQMg5fe1JJ06BYC9lTn9mvsxJ70rAOrOKZASZ7I4hchPhg4dyty5c5+4jZmZGUuXLqVOnTo0bNiQM2fOsH37dnx9fV9SlHmb1I8oDPJeP0c+ZG5mzsB+s9mxvzONz+i4+PFI/NZvRTE3p3e90gTufZUryVsomxgB++dAi3GmDlmIAsHT05P9+/ebOow8S+pHFAbSIvOClHcpj9XwwcRbg9XVcMIWzwfA2kLD0FaVmZbeEwD14HcQe9OUoQohhBAFhiQyL9CbDd5lR8eMWX5j5y4g7WZGwtKlZkmuFGnKYX0llPQU+OcLU4YphBBCFBiSyLxA5mbmdBnxHedLK1ik6Tn9yfuoqoq5xozRgRWZrP13/ohTv8LtkyaNVQghhCgIJJF5wSq6ViRh+Jukm4HdkfPc3rAGgMAqHigl/Vmna5CxoUySJ4QQQjw3SWRyQc+2H7KveVEAwidNIj0+HkVR+KhNJb7WdidVtYBre+HiZhNHKoQQQuRvksjkAgszC5p9OpcIFwW72FSCJn0AQIPybniX9+VHXZuMDbeOA53WhJEKIYQQ+ZskMrmkUvFqRLzbEQD7v/YScWwfAB8GVmReeifuqQ5w7xIELTVhlKIw6N+//yOnzRd517Vr11AUhZMnT2b7NS/jPE+YMCHHN+TMr5o2bcqIESMMz728vJg9e7bJ4snPFEVh3bp1ubZ/SWRy0eu9JnHS3wkz4NLYD1DT0/HzdKZR1bLM/neSPHZNhZRYk8Yp8i9FUZ74mDBhAnPmzMmTN/mLjo6md+/eODo64uzszMCBA0lISDB1WM/sRf7QeXp6Eh4eTtWqVV/I/oQoyCSRyUUWGgv8Pp9FgjW43Yjj4DcZE+F90Loiv+mbE6ovDkn3YN8sE0cq8qvw8HDDY/bs2Tg6OhotGz16NE5OTjg7O5s61Cx69+7NuXPn2LZtG3///Td79uxh8ODBpg7L5NLS0tBoNHh4eBjuFi7Es0pLSzN1CLnO5InMrVu3ePPNNylSpAg2NjZUq1aNY8eOGdarqsr48eMpXrw4NjY2tGzZkkuXLpkw4pypXD6AsD5NAbBZ+id3r56nfDF7XqvlzdT0jPudqAfnQcwNE0Yp8isPDw/Dw8nJCUVRjJbZ29tn6XJo3rw5w4YNY8SIEbi4uODu7s7ChQtJTExkwIABODg4UL58eTZt2mR0rLNnz9K2bVvs7e1xd3enT58+3L1795niPn/+PJs3b2bRokXUq1ePRo0a8e2337Jy5Upu376do30tWrQIX19frK2tqVSpEvPmzTNaHxQURK1atbC2tqZ27dqsXbvWqNtm6dKlWRK9devWoSiK4XloaCidOnXC3d0de3t76tSpw/bt2w3rmzZtyvXr1xk5cqShNSzT6tWrqVKlClZWVnh5eTFjxgyjY3l5eTFp0iT69u2Lo6MjgwcPztK1pNPpGDhwIN7e3tjY2FCxYkXmzJmT7TqKi4vDxsYmyzldu3YtDg4OJCUlARl3y/bx8cHW1payZcsybtw4tNrHj+N7uPsFoHPnzvTv39/wPDU1ldGjR1OyZEns7OyoV68eu3bteuw+ly5d+tjWRci4WeLnn39OqVKlsLKyokaNGmze/P8LJzLrbs2aNTRr1gxbW1v8/Pw4ePCgYZt79+7Rs2dPSpYsia2tLdWqVePXX399Si3+31tvvUX79u2Nlmm1WooVK8bixYsf+7on1e/FixdRFIULFy4YvWbWrFmUK1fO8Dzzc+jo6IiPjw99+/Y1+hw2bdqU9957jxEjRuDm5kZgYCAAM2fOpFq1atjZ2eHp6cm7776bpQV04cKFeHp6Ymtry2uvvcbMmTOzfDb+/PNP/P39sba2pmzZskycOJH09HTD+kuXLtG4cWOsra2pXLky27Zty0aNPh+TJjL379+nYcOGWFhYsGnTJoKDg5kxYwYuLi6Gbb766iu++eYbFixYwOHDh7GzsyMwMJCUlBQTRp4z7d+fxTUvG6zTVI59PASA4S0rsMesNof0vii6VNjxuYmjFA9TVZUkbZJJHrl9U/ply5bh5ubGkSNHGDZsGEOGDOGNN96gQYMGHD9+nNatW9OnTx/DD1xMTAzNmzenZs2aHDt2jM2bNxMZGUm3bt0M+5wyZQr29vZPfISFhQFw8OBBnJ2dqV27tuH1LVu2xMzMjMOHD2e7HMuXL2f8+PFMnjyZ8+fPM2XKFMaNG8eyZcsASEhIoEePHvj6+hIUFMSECRMYPXp0jusrISGBdu3asWPHDk6cOEGbNm3o0KGDoTxr1qyhVKlSfP7554bWMMhIorp160aPHj04c+YMEyZMYNy4cVm6+qZPn46fnx8nTpxg3ListzDR6/WUKlWKVatWERwczPjx4/nkk0/4/fffsxW/o6Mj7du3Z8WKFUbLly9fTufOnbG1tQXAwcGBpUuXEhwczJw5c1i4cCGzZj1fi/F7773HwYMHWblyJadPn+aNN96gTZs2j/2DtHv37katir/++ivm5uY0bNgQgDlz5jBjxgymT5/O6dOnCQwMpHPnzoSGhhrt59NPP2X06NGcPHkSHx8fevbsafjBTUlJoVatWmzYsIGzZ88yePBg+vTpw5EjR7JVprfffpvNmzcbzjPA33//TVJSEt27d3/s655Uvz4+PtSuXZvly5cbvWb58uWGm3w++Dk8cuQIf/zxR5bPIWR8vi0tLdm/fz8LFiwAMu679c0333Du3DmWLVvGP//8w5gxYwyv2b9/P//5z38YPnw4J0+epFWrVkyePNlov3v37qVv374MHz6c4OBgvv/+e5YuXWrYTq/X06VLFywtLTl8+DALFizgo48+yladPhfVhD766CO1UaNGj12v1+tVDw8P9euvvzYsi4mJUa2srNRff/01W8eIjY1VATU2NjZb26elpanr1q1T09LSsrV9dp09skk97VtJDa5YSd23Yoaqqqo6af059dWPv1HV/zpmPG4GvdBjZldulTkve7jMycnJanBwsJqcnGzYJjEtUa26tKpJHolpiTku05IlS1QnJ6csy/v166d26tRJ1el06v3799UmTZoYfe7S09NVOzs7tU+fPoZl4eHhKqAePHhQVVVVnTRpktq6dWuj/d64cUMF1JCQEFVVVfXevXvqpUuXnvjQarWqqqrq5MmTVR8fnyyxFi1aVJ03b162y1yuXDl1xYoVRssmTZqkBgQEqKqqqvPnz1ddXV3VxMT/1+f8+fNVQD1x4oSqqo+ut7Vr16pP+3qsUqWK+u233xqelylTRp01a5bRNr169VJbtWpltOzDDz9UK1eubPS6zp07G21z9epVoxgfZejQoWrXrl0Nzx8+zzqdLkuZ7O3tDXURGxurWltbq5s2bXrsMb7++mu1Vq1ahuf//e9/VT8/P8PzJk2aqMOHDzd6TadOndR+/fqpqqqq169fVzUajXrr1i2jbVq0aKGOHTv2scfNdPnyZdXV1VX96quvDMtKlCihTp482Wi7OnXqqAMHDlR1Op2h7hYtWmRYf+7cORVQz58//9hjvfrqq+oHH3zw2LI9fH4rV66sfvnll4bnHTp0UPv37//UMj3o4fqdNWuWWq5cOcPzkJAQo7gf/Bxmnufr168bfQ6bNGmi1qxZ86nHXrVqlVqkSBHD8+7du6uvvvqq0Ta9e/c2+my0aNFCnTJlitE2P//8s1q8eHFVVVV1y5Ytqrm5udH53rRpkwqoa9eufWQcj/ruzZTd32+TdsD+9ddfBAYG8sYbb7B7925KlizJu+++y6BBgwC4evUqERERtGzZ0vAaJycn6tWrx8GDB+nRo0eWfaamppKammp4HheXcbdprVb7xCbSTJnbZGfbnPCp0YLN7WtQ/q+TKLMWE9m8K4MaleHXoxVYo2tEF80+9Fs+QffmX/BAs/TLkFtlzsseLrNWq0VVVfR6PXq9HsDwryk8GEdOXvPgv5lUVTU8Mp9Xq1bNsJ2iKBQpUoSqVasalhUtmjEPUkREBHq9npMnT7Jz507s7e2zHPfSpUuUL18eZ2fnbI3F0ev1hlgeVcbslj0xMZHQ0FAGDhxo+M4ASE9Px8nJCb1ez/nz5w3dOpn7rFevntFxHlVvDy9LSEhg4sSJbNy4kfDwcNLT00lOTub69etGr8t8D2U6f/48HTt2NFoWEBDA7Nmz0Wq1aDQaAGrVqvXY42f+f968eSxZsoSwsDCSk5NJS0ujRo0ahvWPOs8P7rNNmzZYWFiwbt06evTowapVq3B0dKR58+aG7X777Te+++47QkNDSUhIID09HUdHR6NjPFxXDx8nMwa9Xs+pU6fQ6XT4+PgYnbvU1FRcXV2feJ5jY2Np37497dq144MPPkCv1xMXF8ft27cJCAjIUqcnTpwwiuXB97O7uzuQ8X728fFBp9MxdepUVq1axa1bt0hLSyM1NRUbG5unli3z+cCBA1m4cCGjR48mMjKSTZs2sX37dvR6PUOGDDFqWcn8HXpa/Xbr1o3Ro0dz4MAB6tevzy+//IK/vz8+Pj7Z/hwC+Pv7Z6nb7du38+WXX3LhwgXi4uJIT08nJSWFhIQEbG1tCQkJoXPnzkavq1OnDn///bdh2alTp9i/f79RS41OpzPsJzg4GE9PTzw8PB77eXtY5vfBg5+HTNn9TTJpInPlyhXmz5/PqFGj+OSTTzh69Cjvv/8+lpaW9OvXj4iICOD/b8JM7u7uhnUPmzp1KhMnTsyyfOvWrYbm0+zIjX699DodubP3DEXv69g8pi/OXUbRuKjC9JvdaKc5jHXYQY7++gURzrVe+LGz42X0ZeY1mWU2NzfHw8ODhIQEw+A4VVXZ+upWk8SlTdISp8Tl6DUpKSmoqmr40jTsS6slPT2d+Ph4IOOL5+HtVFVFp9NleW1iYiJxcXHExMTQpk0bwziFB7m7uxMXF8eMGTOe2g1x8OBBPD09cXJyIjIy0uh46enpREdH4+TklCWOR4mKigJg9uzZRl1UABqNhri4OMMXYWbZAcO4gMyypaWlGX4kM2X+P/PfkSNHsmvXLiZNmmQYp9KvXz8SEhIM2+j1elJSUoz2o9PpSE1NNVqWnJxs2LdGo0Gv1xvifVyMq1ev5sMPP2TSpEnUrVsXe3t7vvnmG4KCgoz+WHvwPD9Y5kwdO3bk559/pl27dvzyyy907tzZ0H145MgR+vTpw8cff8wXX3yBo6Mja9as4bvvvjMcIzU11eh9otfrH1k+Ozs74uLiuHPnDhqNhp07d2b5kcrc5lF0Oh3du3fHzs6Or7/+Osv5SEpKMnrtg+c5s+7S0tIM22Quyzxfs2bN4rvvvmPKlClUrlwZOzs7xo4da7Tf9PR0o308fH47d+7M2LFj2b59O0eOHKFMmTL4+fkRFxfH6NGjeeeddwzxxcXFZat+bW1tady4McuWLaNy5cosX76ct956y7A+O5/D9PR0LCwsjOonLCyMjh078tZbb/Hxxx/j4uLCoUOHGDZsGPfu3SM9Pf2R79WHv1MSEhL4+OOP6dChQ5bjp6WlkZKS8tjPUnJy8iPPd1paGsnJyezZs8dorE3mec4OkyYyer2e2rVrM2XKFABq1qzJ2bNnWbBgAf369XumfY4dO5ZRo0YZnsfFxeHp6Unr1q1xdHR86uu1Wi3btm2jVatWWFhYPFMMT3LOKhXGfEntI1Hc7X2PKf170mLWXhantmWo+V/Ujf2b9O4fg+bFH/txcrvMedHDZU5JSeHGjRvY29tjbW1t2M4JJxNGmTPW1tYoipLlfW5hYYG5uTkODg7Ex8ej0WiwtLQ02s7MzAxra+ssr7WxscHR0ZG6deuyZs0aqlat+tgraYYPH06fPn2eGKOXlxfm5uY0a9aM2NhYLl26RK1aGYn71q1b0ev1NG3aNFufVUdHR0qUKEFERMRj5zapXr06v/32GxYWFtjY2AAZgyUh44fU0dERT09PEhIS0Gg02NnZARkDLzOPAXDs2DEGDBhgGKuQkJDAjRs3jOrR2toaCwsLo9irVKnCsWPHjJadOHECHx8fw1jAR9V95l/cmTGeOHGCBg0aGH233bx5E41GY3jdw+fZwcHBaNAxQL9+/QgMDOTGjRvs2bOHKVOmGF5/+vRpypQpw+ef/3+83rx584zeU1ZWVkbH9PDw4N69e4bnOp2OkJAQihcvjqOjIw0aNECn05GUlMQrr7zy6BP5CMOHD+f8+fMcOXKEYsWKGZZnnvOTJ0/Stm1bw/KjR49So0YNHBwcstQd/L8FydbWFkdHR4KCgujUqZOhJU+v13P16lV8fX0NrzE3Nzc6vw+fJ0dHRzp16sSqVas4dOgQb731ltG6h2WnfgFDstO3b1+uXbtGv379DOsf/BxqNJpHnueH4wYICQlBr9fzzTffYGaWMTQ2c+C3g4MDjo6O+Pr6cvr0aaPXnTlzxig+f39/rl+//tjPW40aNbh16xaJiYkUL14cwDDIOvO75GEpKSnY2NgYBgg/KDt/0ICJE5nixYtTuXJlo2W+vr6sXr0ayPiQAERGRhoqJfP54yrSysoKKyurLMstLCxy9COd0+2zq0bH/uxY9wclDoSSMnUWyt+deK9ZBWb+3ZGe5rtwjQ7F4tQvUO/lX4aaW2XOyzLLrNPpUBQFMzMzwwc9v8mM++H4H7zy48Hnj9ru4WWZ9fHee++xaNEievfuzZgxY3B1deXy5cusXLmSRYsWodFocHNzw83NLVuxVqlShTZt2vDOO++wYMECtFot77//Pj169KBUqVLZLvPEiRN5//33cXZ2pk2bNqSmpnLs2DHu37/PqFGj6NWrF5999hnvvPMOn3zyCdeuXWPmzJlGZQsICMDW1pbPPvuM999/n8OHDxsGC2fWR4UKFVi7di0dO3ZEURTGjRuHXq83qjMvLy/27t1Lz549sbKyws3NjdGjR1OnTh0mT55M9+7dOXjwIHPnzmXevHlGdf1w3T94Ls3MzPDx8eHnn39m27ZteHt78/PPP3P06FG8vb0N2z7qPD98Pps2bYqHhwd9+vTB29ubgIAAwzofHx/CwsL4/fffqVOnDhs2bDBMYvbgMR583qJFC0aNGsWmTZsoV64cM2fOJCYmxnDsSpUq0bt3b/r378+MGTOoWbMmd+7cYceOHVSvXp1XX301yzldsmQJ8+fPZ+3atWg0GkPLW+aA8Q8//JD//ve/lC9fnho1arBkyRJOnTrFggULjMr84Gf5UfX5xx9/cOjQIVxcXJg5cyaRkZFUrlz5iefl4eeDBg2iffv26HQ6+vfv/8TvjuzUL8Drr7/O0KFDGTp0KM2aNTP6PDz4ORw9ejSWlpZERETw+++/Gz6Hj4rTx8cHrVbL3Llz6dChA/v37+f77783qpP333+fxo0bM3v2bDp06MA///zD5s2bjfY1fvx42rdvT5kyZXj99dcxMzPj1KlTnD17li+++ILWrVvj4+PDgAEDDC1pmYPXH/fdamZmhqIoj/z9ye7vkUm/sRs2bEhISIjRsosXL1KmTBkAvL298fDwYMeOHYb1cXFxHD582OgDmN/Un/I9SdZmlA5PZ+N3o+ldvzROzkWYqc2cJG8KJN4zbZBCPKBEiRLs378fnU5H69atqVatGiNGjMDZ2fmZE7/ly5dTqVIlWrRoQbt27WjUqBE//PCD0TaKojxxMr+3336bRYsWsWTJEqpVq0aTJk1YunQp3t7eQMaP36+//srZs2epWbMmn376KV9++aXRPlxdXfnll1/YuHGj4TLch5vuZ86ciYuLCw0aNKBDhw4EBgbi7+9vtM3nn3/OtWvXKFeunGGMkb+/P7///jsrV66katWqjB8/ns8//9zo8uTseOedd+jSpQvdu3enXr163Lt3j3fffTdH+4CM+uzZsyenTp2id+/eRus6duzIyJEjee+996hRowYHDhx45BVUD3rrrbfo168fffv2pUmTJpQtW5ZmzZoZbbNkyRL69u3LBx98QMWKFencuTNHjx6ldOnSj9zn7t270el0dOzYkeLFixse06dPB+D9999n1KhRfPDBB1SrVo3Nmzezbt06o0uUn+azzz7D39+fwMBAQ3L3LLMit2zZkuLFixMYGEiJEiWeuG1269fBwYEOHTo88hw9+Dls06YNDRs2ZNSoUU/9HPr5+TFz5ky+/PJLqlatyvLly5k6darRNg0bNmTBggXMnDkTPz8/Nm/ezMiRI41aSQIDA/n777/ZunUrderUoX79+syaNcvwm21mZsbatWtJTk6mbt26vP3221mufMoVTxwKnMuOHDmimpubq5MnT1YvXbqkLl++XLW1tVV/+eUXwzbTpk1TnZ2d1T///FM9ffq02qlTJ9Xb2/uRI5wfJa9ctfSwoDkT1OCKldTddX3VqNhwdXXQDbXsR3+qIeOrZlzBtO7dXD3+g+SqpSePnC8oHnc1S1515coV1dzcXL148eIz7+NRZc7OFUH5WX47zy+CqcocHx+vOjo6qqtXr36px1XVl1Pmt99++4lXFr8IL+KqJZO2yNSpU4e1a9fy66+/UrVqVSZNmsTs2bONstAxY8YwbNgwBg8eTJ06dUhISGDz5s1Z+tLyG7+3PyTBwZyisSo7F02gc42SVCnlyti0ARkbnPgFwg6ZNkghTGjjxo0MHjyYChUqmDoUIYzo9XqioqKYNGkSzs7OdOzY0dQhvRDTp0/n1KlTXL58mW+//ZZly5Y983jVl8nk81+3b98+ywyJD1IUhc8//9xogFRBoLG1Re3ZCX5YTbFVe4l7J5px7SvzxoJYVuqa0UOzE/4eCe/seakDf4XIK4YOHWrqEIR4pLCwMLy9vSlVqhRLly4tMLeSOHLkCF999RXx8fGULVuWb775hrffftvUYT1Vwaj9fMr/nY85uXwd7vd1/PPj53QZNodXqxdn2uketLM4jmNUMByaBw2HmzpUIQoMLy+vXJ85WRRsBfU9lN2ZovOa/Hl5RgFhbmdParc2ALis3E5iSjwft6lEkrkTk1L/neZ61zS5D5MQQgjxGJLImFjdd8eTaGOGxz09/yz9Ak9XWwY28uYPXWNOmVUGbRJsegn3qhBCCCHyIUlkTMzCwZHErs0BsFu+kRRtMu82LUcRextGJ/dHr5hDyAa4sNHEkQohhBB5jyQyeUC99yaSZK1Q/E46u36ehoO1BaNb+3BJLcUS9d8JozaNgbRE0wYqhBBC5DGSyOQB1s6uxHTOmL7bYtlatOlpvFHbE9/ijkxP6cR9Sw+IvQG7vzJxpEIIIUTeIolMHlFv2OckWymUiNSy99cZaMwUxr3qSzLWjEl8M2Ojg99BZLBpAxVCCCHyEElk8gj7Iu7cbZ9xu3P1x9/Q6XU0KO9Gq8rubNP5c9ymAejTYcMHUAAv+xO5p3///s80/bownWvXrqEoCidPnsz2a17GeZ4wYcJj73OXm+Q9LJ5EEpk8pO7wz0mxhBLhqRxc9Q0An7TzxUKj8N79Hug0NhB2AE6uMHGkIq948EaBj3pMmDCBOXPmPPF+RaYSHR1N7969cXR0xNnZmYEDB5KQkGDqsJ6Zl5cXs2fPfiH78vT0JDw8nKpVq76Q/YmXIzvv6YULF1KmTBlq1qzJ4cOHTRRpwSKJTB7iXMyT8DY1AUhZ+DN6vR5vNzv6BnhxGzcWm/87t8zWzyAp2oSRirwiPDzc8Jg9ezaOjo5Gy0aPHo2TkxPOzs6mDjWL3r17c+7cObZt28bff//Nnj17GDz45d/1Pa9JS0tDo9Hg4eFRYGaMLSye9p4OCwvjq6++YuXKlXz66acMGDDAhNEWHJLI5DH1RnxBqgWUvJnMsXUZdwJ+v3kFXGwt+Cq2Offty0NyNGz/r4kjFXmBh4eH4eHk5ISiKEbL7O3tszTLN2/enGHDhjFixAhcXFxwd3dn4cKFJCYmMmDAABwcHChfvjybNm0yOtbZs2dp27Yt9vb2uLu706dPH+7evftMcZ8/f57NmzezaNEi6tWrR6NGjfj2229ZuXIlt2/fztG+Fi1ahK+vL9bW1lSqVIl58+YZrQ8KCqJWrVpYW1tTu3Zt1q5da9Rts3Tp0iyJ3rp161AUxfA8NDSUTp064e7ujr29PXXq1GH79u2G9U2bNuX69euMHDnS0BqWafXq1VSpUgUrKyu8vLyYMWOG0bG8vLyYNGkSffv2xdHRkcGDB2fpWtLpdAwcOBBvb29sbGyoWLEic+bMyXYdxcXFYWNjk+Wcrl27FgcHB5KSkgD46KOP8PHxwdbWlrJlyzJu3Di0Wu1j99u0aVNGjBhhtKxz585Gd/dOTU1l9OjRlCxZEjs7O+rVq8euXbuyHfujpKam8v7771OsWDGsra1p3Lgxx48fN6zftWsXiqKwY8cOateuja2tLQ0aNCAkJOS5jvsk2XlPx8XF4ezsTPXq1alVqxbJycm5Fk9hIolMHlOkRFlutMpoTo77fhGqquJka8HIVj6kY87IxH9v4HX8J7mpZC5TVRV9UpJJHrk9/fmyZctwc3PjyJEjDBs2jCFDhvDGG2/QoEEDjh8/TuvWrenTp4/hBy4mJobmzZtTs2ZNjh07xubNm4mMjKRbt26GfU6ZMgV7e/snPsLCwgA4ePAgzs7O1K5d2/D6li1bYmZmlqPm9uXLlzN+/HgmT57M+fPnmTJlCuPGjWPZsmUAJCQk0KNHD3x9fQkKCmLChAmMHj06x/WVkJBAu3bt2LFjBydOnKBNmzZ06NDBUJ41a9ZQqlQpPv/8c0NrGGQkUd26daNHjx6cOXOGCRMmMG7cuCxdfdOnT8fPz48TJ04wbty4LMfX6/WUKlWKVatWERwczPjx4/nkk0+yPaW8o6Mj7du3Z8UK427p5cuX07lzZ2xtbQFwcHBg6dKlBAcHM2fOHBYuXMisWbNyWl1G3nvvPQ4ePMjKlSs5ffo0b7zxBm3atOHSpUvPvM8xY8awevVqli1bxvHjxylXrhxdu3YlOtq4pfrTTz9lxowZHDt2DHNzc956660n7rdKlSpPfP+2bdv2sa/Nznu6atWqVK9eHScnJ6pUqcIXX3zxzHUg/k/aLfOg2iM+J2prF0peT+TkhmXUbN+fXnVL89PB6+yKKsfJUh2ocXc9/D0K3tktN5XMJWpyMiH+tUxy7IrHg1D+/XHJDX5+fnz22WcAjB07lmnTpuHm5sagQYMAGD9+PPPnz+f06dPUr1+f7777jpo1azJlyhTDPn788Uc8PT25ePEiPj4+/Oc//zFKbB6lRIkSAERERFCsWDGjdebm5ri6uhIREZHtcvz3v/9lxowZdOnSBQBvb2+Cg4P5/vvv6devHytWrECv17No0SJsbW2pUqUKN2/eZMiQIdk+BmTUl5+fn+H5pEmTWLt2LX/99Rfvvfcerq6uaDQaHBwc8PDwMGw3c+ZMWrRoYUhOfHx8CA4O5uuvvzZqtWjevDkffPCB4fm1a9eMjm9hYcHEiRMNz729vTl48CC///77U+s8U+/evQ3Jqa2tLXFxcWzYsIG1a9catsl8T0BGS9Ho0aNZuXIlY8aMydYxHhYWFsaSJUsICwsznPvRo0ezefNmlixZYvR+yq7ExETmz5/P0qVLDYnFDz/8wLZt2/jxxx+NYp08eTJNmjQB4OOPP+bVV18lJSUFa2vrR+5748aNT2yBsrGxeey67L6nFy9ezFdffYWtre0T9yeyTxKZPKh4aV+ONPfBZ+tFoufNR321H+YaMz571Zf+S44yKLwDhxz2oYk6B4fmQ8P3TR2yyGeqV69u+L9Go6FIkSJUq1bNsMzd3R2AqKgoAE6dOsXOnTuxt7fPsq/Q0FB8fHxwdXXF1dU1lyP/v8TEREJDQxk4cKAhAQNIT0/HyckJgAsXLlClShWjH66AgIAcHyshIYEJEyawYcMGwsPDSU9PJzk52dAi8zjnz5+nU6dORssaNmzI7Nmz0el0aDQaAKO/4h9n7ty5/Pjjj4SFhZGcnExaWlqOriBq164dFhYW/PXXX/To0YPVq1fj6OhIy5YtDdv89ttvfPPNN4SGhpKQkEB6ejqOjo7ZPsbDzpw5g06nw8fHx2h5amoqRYoUeaZ9hoaGotVqadiwoWGZhYUF/v7+nD9/3mjbB9/nxYsXBzLe06VLl37kvsuUKfNMMeXUs5ZdPJokMnlUjRETidnRkxJX4gje9jtVWnenacViNPEpyu6L8LPD2/RP+TrjppJVu4BTKVOHXOAoNjZUPB5ksmPnJgsL41Y8RVGMlmWO8dDr9UDGD3mHDh348ssvs+wr8wdiypQpT/0LOzg4mNKlS+Ph4WFIkjKlp6cTHR1t1KLxJJlXgyxcuJB69eoZrctMELLDzMwsS1few3+Vjx49mm3btjF9+nTKly+PjY0Nr7/+Omlpadk+zpPY2dk9cf3KlSsZPXo0M2bMICAgAAcHB77++uscdcNZWlry+uuvs2LFCnr06MGKFSvo3r27YUDxwYMH6d27NxMnTiQwMBAnJydWrlyZZUzPg55WdwkJCWg0GoKCgrKck0clxS/ak97Tj1KlShWuX7/+2PWvvPJKlnFGmV7Ee1o8G0lk8qgyZWsQ1Ngb351XifjuG6q0zrhi6bNXfdl3+S4Tb/jRpXRtHKOOZdxUssdyE0dc8CiKkqvdO/mJv78/q1evxsvL67FX0uSkaykgIICYmBjDQFyAf/75B71enyUpeRx3d3dKlCjBlStX6N279yO3qVSpEj///DMpKSmGcSCHDhmPLStatCjx8fEkJiYaEoqH52/Zv38//fv357XXXgMyfqAf7v6xtLREp9MZLfP19WX//v1Z9uXj45OjZGv//v00aNCAd99917AsNDQ026/P1Lt3b1q1asW5c+f4559/jMZoHDhwgDJlyvDpp58alj3pRx0y6i5zPBBkDEo+e/YszZo1A6BmzZrodDqioqJ45ZVXchzvo5QrVw5LS0v2799vaEHRarWcOHEiy8DjnHqerqUX8Z4Wz0YG++Zh1UaMR6uBEhejubTzTwAquDvQq25pVMz4KKU/qpk5XPgbQjabOFpRkA0dOpTo6Gh69uzJ0aNHCQ0NZcuWLQwYMMDw4+3q6kr58uWf+MhMgnx9fWnTpg2DBg3iyJEj7N+/n/fee48ePXoYkp3smDhxIlOnTuWbb77h4sWLnDlzhiVLljBz5kwAevXqhaIoDB48mODgYDZu3Mj06dON9lGvXj1sbW355JNPCA0NZcWKFVkG41aoUIE1a9Zw8uRJTp06Ra9evbL8Ze/l5cWePXu4deuW4WquDz74gB07djBp0iQuXrzIsmXL+O6773I84LhChQocO3aMLVu2cPHiRcaNG8fRo0dztA+Axo0b4+HhQe/evfH29jb6ga1QoQJhYWGsXLmS0NBQvvnmG6PxM4/SvHlzNmzYwIYNG7hw4QJDhgwhJibGsN7Hx4fevXvTt29f1qxZw9WrVzly5AhTp05lw4YNOY4fMlqvhgwZwocffsjmzZsJDg5m8ODBJCUlPXUw79OUKVPmie/fkiVLPva1L+o9LXJOEpk8rELF+lxskNFldOOb/zfvjmzlg4O1OZuiXLng1Sdj4cYPIS3JFGGKQqBEiRLs378fnU5H69atqVatGiNGjMDZ2Rkzs2f7Glm+fDmVKlWiRYsWtGvXjkaNGvHDDz8YbaMoyhMn83v77bdZtGgRS5YsoVq1ajRp0oSlS5fi7e0NZHRf/Prrr5w9e5aaNWvy6aefZukec3V15ZdffmHjxo1Uq1aNX3/9lQkTJhhtM3PmTFxcXGjQoAEdOnQgMDAQf39/o20+//xzrl27Rrly5ShatCiQ0ZL1+++/s3LlSqpWrcr48eP5/PPPjQb6Zsc777xDly5d6N69O/Xq1ePevXtGrTPZpSgKPXv25NSpU1lasTp27MjIkSN57733qFGjBgcOHHjkFVQPeuutt+jXrx99+/alSZMmlC1b1tAak2nJkiX07duXDz74gIoVK9K5c2eOHj1qNE7laef5YdOmTaNr16706dMHf39/QkNDWb16NS4uLtneR27IzntavHiKmtvXeZpYXFwcTk5OxMbGZmvQmlarZePGjYaBcaZ27uwu9N2GYK4H20WzKdMoEIBFe6/wxYbzlLJT2WP3EWZxN6HRSGg5IcfHyGtlfhkeLnNKSgpXr17F29v7sVc05Hd6vZ64uDgcHR2fOfl4ma5evWq4yqdChQrPtI9HlfnatWt4e3tz4sQJk0y3n9vkPOf9Mr8IBaXMT/ruze7vd/4tfSFRpWpTztfLGCh2Zc7//5LsG+CFVxFbbiYqrCs+PGPhgW8h6vyjdiNEvrNx40YGDx78zD9uIn+Q8yyelyQy+UDZYaPRKeBxJpxrezPGwliam/FJO18APj7nSbJ34L83lRwtN5UUBcLQoUOZO3euqcMQuUzOs3heksjkA7VqtuNs/Yx5PW5+Opb05IyxMK0qu9OgXBHS0vV8oe8H5jZwfR+cX2/KcIXI07y8vFBVtUB2KwlRGEkikw8oikKDSfO4b69QJCqFfRPfMywf36EyGjOF5SFww3dgxgu2jYP0VBNGLIQQQrwcksjkE6VLVeb++xlzdBT98yBX928BoJKHI33qZ8yl8O61V1DtPeD+NTj8valCzdcK+Nh3IYTIU17Ed64kMvlI2z7jOVenKGYq3Bz7Mdp/u5hGtvTB1c6SM3d07C397z1k9nwNic92Z+LCKPNqrcybJAohhMh9md+5z3PFrMzsm4+YKWbUn/Y9Nzp1xS0qhT0Th9Ji2hKcbC34MLAiY9ec4b3gihx3r4555GnYOQXazzR12PmCRqPB2dnZMMW4ra2tYUrzgkKv15OWlkZKSkq+vlwzJ6TMUuaCKr+XWVVVkpKSiIqKwtnZOUczXT9MEpl8plRJX8693xPnKSvw+PMQlzttpnxAG7rV9mT54eucvRXHYtu3eYf3IWgJ1B0ExXxNHXa+kHk/lIfvl1JQqKpKcnIyNjY2BS5Jexwps5S5oCooZXZ2dn7ue1FJIpMPte7zGeu2bqfSsShufzyW0lsaY2lty4QOVXh9wUGmXXCjh08gTte3wJZPoc8aU4ecLyiKQvHixSlWrNgT77eSX2m1Wvbs2UPjxo0L1cSHUuaCT8qcP8tsYWHxXC0xmSSRyYcURaHelz8Q1vE1ikamsGviu7SeupTaXq68VrMka0/cYmz8G8w1+wcldAdc2gYVWpk67HxDo9G8kA9XXqPRaEhPT8fa2jrffvHllJRZylxQFcYyP07+61gTAJQoWZHE4Rn3Simx7jAXD2wE4OO2lbC11LDxti2XvP+9l8qWT0FX8FoYhBBCCElk8rHmfT7hUi13NCrc/uQT0lIScXe0ZljzjKm+37neHL1NEbgbAkFLTRusEEIIkQskkcnHFEWh3lcLibNVcI9IZcfnGZdev9Uo4z5MVxPM2VZsQMbGO6dAcozpghVCCCFygSQy+Zx7yQokj+gDgOfao5w/8DdW5hrGd6gMwPDLNUhz8YHk6Iy5ZYQQQogCRBKZAqBJn4+54u+BRoXwTz8jNTmR5pXcaV6pGCk6M74x75+x4eHv4V6oSWMVQgghXiRJZAoARVGo+/VC4m0Vioensm3SfwAY174yFhqF7254cdfjFdBrYdt4E0crhBBCvDiSyBQQRUuWJ214PwDKrDvG2YPr8XazY2CjsgB8EPsGqqKBC3/D1b2mDFUIIYR4YSSRKUAa9h3DtZoemOsh8pNxpKQk8F7z8hRzsGL3fTfOFX8tY8Mtn4BeZ9pghRBCiBdAEpkCRFEU6k5fTIKNQonwVLZM+g/2VuaMbVcJgHduBqK3coSI03DqVxNHK4QQQjw/kyYyEyZMQFEUo0elSpUM61NSUhg6dChFihTB3t6erl27EhkZacKI874iJcuiG5FxyXXZtUGcOvgnnWuUxL+0M7fS7PjLsVfGhjs+h9QEE0YqhBBCPD+Tt8hUqVKF8PBww2Pfvn2GdSNHjmT9+vWsWrWK3bt3c/v2bbp06WLCaPOHen1Hc6NGccz1EPXpeJJT4pnYsSqKAmNuBJDiUAYSImH/bFOHKoQQQjwXkycy5ubmeHh4GB5ubm4AxMbGsnjxYmbOnEnz5s2pVasWS5Ys4cCBAxw6dMjEUedtiqJQZ8YiEm0USt1OY/Pk/1CtlBM96niShgXT9f/euuDAtxBzw7TBCiGEEM/B5DeNvHTpEiVKlMDa2pqAgACmTp1K6dKlCQoKQqvV0rJlS8O2lSpVonTp0hw8eJD69es/cn+pqamkpqYansfFxQEZdwrNzh2NM7fJ73c/ti/mSfqwfvDVUiqsOcGRwD8Y3rwtG06Hs+heFd4uXguP+0Hot09A2+5bIP+XOScKynnOCSlz4SBlLhwKQ5mzWzZFVVU1l2N5rE2bNpGQkEDFihUJDw9n4sSJ3Lp1i7Nnz7J+/XoGDBhglJQA1K1bl2bNmvHll18+cp8TJkxg4sSJWZavWLECW1vbXClHnqWqmP34FeUv3udacXPi3/uEw3fsWH1NQ23zK6wyH4eCym6f/xJjV87U0QohhBAGSUlJ9OrVi9jYWBwdHR+7nUkTmYfFxMRQpkwZZs6ciY2NzTMlMo9qkfH09OTu3btPrIhMWq2Wbdu20apVqwJxa/TY21e53qkztikqp7v60XHcMjrOO8ilqETWFP8Z//ub0JWsw99F36NV69YFoszZUdDOc3ZImaXMBZWUuWCWOS4uDjc3t6cmMibvWnqQs7MzPj4+XL58mVatWpGWlkZMTAzOzs6GbSIjI/Hw8HjsPqysrLCyssqy3MLCIkcnO6fb51VuZXwIGz4QvlyE77pTnGr3FxM7NqPXosO8G9GeA3a70Nw6SgmLw1hYvFogypwTBeU854SUuXCQMhcOBbnM2S2XyQf7PighIYHQ0FCKFy9OrVq1sLCwYMeOHYb1ISEhhIWFERAQYMIo85+a/UcRUb0EFjqI+/RzKntAu2oeRKgu/GbVFYAqt38HbbKJIxVCCCFyxqSJzOjRo9m9ezfXrl3jwIEDvPbaa2g0Gnr27ImTkxMDBw5k1KhR7Ny5k6CgIAYMGEBAQMBjB/qKR1MUhVqzfiTRxgzPiHS2fDaAce0rY29lzsR7LUi0csc27S5mRxaYOlQhhBAiR0yayNy8eZOePXtSsWJFunXrRpEiRTh06BBFixYFYNasWbRv356uXbvSuHFjPDw8WLNmjSlDzrccS5bB+pMRAFTdfJng3Yv5MLAiKVgxMaU7AGb7Z0N8hOmCFEIIIXLIpInMypUruX37Nqmpqdy8eZOVK1dSrtz/r56xtrZm7ty5REdHk5iYyJo1a544PkY8WdU3BhHR2BczwHLyfFp4p1PD05nfU+sRYlYORZsI/0wydZhCCCFEtuWpMTIi9zWYvpj7rpa4xeo5+NFAJr9WBXMzMz5O7puxwYnlcPukSWMUQgghsksSmULGytEF9ymT0QNVj0RxZdPXvNWwDCfUCmwxewVQM+6OnXeuyhdCCCEeSxKZQqhc0/bcff0VANy/W0M7z2iKWKlMSOqGVrGC6/vh/HoTRymEEEI8nSQyhdQr474lytMeh2QI+2wUXb1TCacI87XtMjbY+hmkpz55J0IIIYSJSSJTSJlZWVFx9nzSzKHCxQRcT/1EJ7/iLEjvwF3FFWKuw6H5pg5TCCGEeCJJZAoxjyq1SR78OgAB2y7S2SMUS1sHpqR2y9hgz3RIiDJhhEIIIcSTSSJTyNV7byK3KxfDMh20kybxYYsSrNU34oxaFtLiYedkU4cohBBCPJYkMoWcYmaG36yFJNgoeN7WYrl6HPXLFmVi2psAqMd/goizJo5SCCGEeDRJZAROJbwJ7dwSgMqbLtDd5SinNZX5W1cfRdXL5dhCCCHyLElkBAA2NVpws1EFzFRwnbGAd+rYMi29J6lYwNXdELLJ1CEKIYQQWUgiIwwCpi7kvosFRWP0lFk7AeuiXixKb5uxcuunkJ5m2gCFEEKIh0giIwysnF1xmzIJPVDlUAT9LLYwL70Td1QniL4CRxeaOkQhhBDCiCQywohPs05EvhYAQMUf/6RDRZWv0jPujq3umgaJ90wZnhBCCGFEEhmRRZMJ84gqaYdDskrAn1+z27op5/RlUFLjYNcUU4cnhBBCGEgiI7LQWFlTfta3aDXgcyGet9NWMym9DwDqsSUQdd7EEQohhBAZJJERj1SyegBxAzsBUHftQVyK2LBJVwdF1aFulsuxhRBC5A2SyIjHajhiCrd83bBKh84bFzKb10lVzVGu/AOXtpk6PCGEEEISGfF4ipkZ/nN+JNFGofStNLrc28gSXRsA0jd/AjqtiSMUQghR2EkiI57ItXQF+PAdABrtvMQpPLmrOmIefQmO/Wji6IQQQhR2ksiIp6rdazg3GpTFTIUeO/5mRtqrAGh3TIakaBNHJ4QQojCTREZkS6MZS7nvbE6x+zp8Q88RrPfEIi2WtC3jTB2aEEKIQkwSGZEtti5FcZk8Ab0CdU7fZfWdigBYnvoFru4xcXRCCCEKK0lkRLb5tujKjW4NAGi/L5jFcRkzACevHgppSaYMTQghRCEliYzIkZafzedaBUestVD6WARh6a7YJISRumOyqUMTQghRCJlnZ6O//vorxztu1aoVNjY2OX6dyNvMLSyp8s0PhHftSamoVP65UJr+VaOxODwP/F6HEjVNHaIQQohCJFuJTOfOnXO0U0VRuHTpEmXLln2WmEQeV9rbjwtj+uE4YSn1zkbxh0tlXi8ZTMLvQ7Afthc0FqYOUQghRCGR7a6liIgI9Hp9th62tra5GbPIA1p1H8PJ1l4AeB2N5WaCA/Yx50nZPcu0gQkhhChUspXI9OvXL0fdRG+++SaOjo7PHJTI+xRFofXkZVwpZYFdisq5Y0VR9aDZ+zXcvWTq8IQQQhQS2UpklixZgoODQ7Z3On/+fNzc3J45KJE/FHEohtO0CSRYQ+mIFLae88ZCTSP2t/+AXm/q8IQQQhQCctWSeC6Nanfh9MBXACh9LpWwCAec7hwj+dBCE0cmhBCiMMjWYN8HpaSk8O2337Jz506ioqLQP/SX9/Hjx19YcCJ/6Pmf2fwa1IwGh+KIPOxE8daJKNsnQJVXwamUqcMTQghRgOU4kRk4cCBbt27l9ddfp27duiiKkhtxiXzE1sKWOl/M5XqvvpSJ0nPkSAkavHKTe7+9R5FBa0HeI0IIIXJJjhOZv//+m40bN9KwYcPciEfkU9VL1eb4R71w/3g5ruF6Qi84Ud5sJ0knfsPWv4epwxNCCFFA5XiMTMmSJXM08FcUHr3bfsSWrqUBSD1jR0KUJfoNYyDxnokjE0IIUVDlOJGZMWMGH330EdevX8+NeEQ+ZmFmQY+R37O/mjlmKlw87IZ1UhwRq0aaOjQhhBAFVI4Tmdq1a5OSkkLZsmVxcHDA1dXV6CEKNy8nL5w/HcNtV7BJhEtHXHG/+idJ5zaZOjQhhBAFUI7HyPTs2ZNbt24xZcoU3N3dZbCvyKKr35v8d/BWun59DMtbVty5aIfFuvexLX8crKRbUgghxIuT40TmwIEDHDx4ED8/vxcayLRp0xg7dizDhw9n9uzZQMal3h988AErV64kNTWVwMBA5s2bh7u7+ws9tnixFEXh/e6zmHO6Db02JhJ1ypFyRe9y84+xlOr9nanDE0IIUYDkuGupUqVKJCcnv9Agjh49yvfff0/16tWNlo8cOZL169ezatUqdu/eze3bt+nSpcsLPbbIHW42bjR9fxqHfRQ0eoUrB11xD15O4uX9pg5NCCFEAZLjRGbatGl88MEH7Nq1i3v37hEXF2f0yKmEhAR69+7NwoULcXFxMSyPjY1l8eLFzJw5k+bNm1OrVi2WLFnCgQMHOHToUI6PI16+Fl4tCXuvA1FOYBav4fYxJ+J//w+kp5o6NCGEEAVEjruW2rRpA0CLFi2MlquqiqIo6HS6HO1v6NChvPrqq7Rs2ZIvvvjCsDwoKAitVkvLli0NyypVqkTp0qU5ePAg9evXf+T+UlNTSU39/w9lZnKl1WrRarVPjSdzm+xsW1DkZpmHNfqYj7sfZtjCSJKu2+B0/i5XVv8Xzy6TXvixckLOc+EgZS4cpMwFU3bLluNEZufOnTkO5nFWrlzJ8ePHOXr0aJZ1ERERWFpa4uzsbLTc3d2diIiIx+5z6tSpTJw4McvyrVu3Ymtrm+3Ytm3blu1tC4rcKnPlMq+xsskP9N6l59ZxJ8oUWcx2bUnSHErnyvFyQs5z4SBlLhykzAVLUlJStrbLcSLToEEDLCwsHrnu7t272d7PjRs3GD58ONu2bcPa2jqnYTzW2LFjGTVqlOF5XFwcnp6etG7dGkdHx6e+XqvVsm3bNlq1avXYchY0L6PM35ZK5+T1xdS4CrcPOlHZ7WdKfLgHzDS5crynkfMsZS6opMxS5oIiu8NVcpzI9OjRgz/++CPLZdeRkZG0aNGCs2fPZms/QUFBREVF4e/vb1im0+nYs2cP3333HVu2bCEtLY2YmBijVpnIyEg8PDweu18rKyusrKyyLLewsMjRyc7p9gVBbpZ5WO33GdRnH2VmXMAl1gKLAxFc3zyb8p0+zpXjZZec58JBylw4SJkLluyWK8eDfcPCwnj77beNloWHh9O0aVMqVaqU7f20aNGCM2fOcPLkScOjdu3a9O7d2/B/CwsLduzYYXhNSEgIYWFhBAQE5DRsYWIWGgs+a/s1CzpZoQdir9jhsv5bku/dMHVoQggh8rEcJzIbN27kwIEDhu6b27dv07RpU6pVq8bvv/+e7f04ODhQtWpVo4ednR1FihShatWqODk5MXDgQEaNGsXOnTsJCgpiwIABBAQEPHagr8jbyruUp0XXkayvn9Gad/e4HVdXjDVxVEIIIfKzHHctFS1alK1bt9KoUSMg427Y/v7+LF++HDOzHOdFTzRr1izMzMzo2rWr0YR4Iv960/dN+nfcSL0Lp/GI0eCydQ8xnc7hXLqKqUMTQgiRDz1T5uHp6cm2bdtYvnw5devW5ddff0Wjef5Bm7t27TLM6gtgbW3N3LlziY6OJjExkTVr1jxxfIzI+zRmGj5r+jlL2mT0fd6/bEv4ArmppBBCiGeTrRYZFxeXR95TKSkpifXr11OkSBHDsujo6BcXnSiQKrpWpE6nQew9PZ9XgsFi5xUiTu/Eo3ozU4cmhBAin8lWIvNgK4kQL8I71d+h/2ubqHnlKvb3LYid/iEey46C3IRUCCFEDmQrkenXr19uxyEKGUuNJaNbf8Gy4334zyY9uuMJXPl7CWU7vGXq0IQQQuQj2Rojk9N7KMXHxz9TMKJw8Xf3p+gbPThfCpR0hbQ509GnF9zptoUQQrx42UpkXFxciIqKyvZOS5YsyZUrV545KFF4jKg9klUd3Ug3A/WmSsiMT0wdkhBCiHwkW11LqqqyaNEi7O3ts7XTgnwTK/Fi2Vva85/XvuDPk0PoekBFv3I9aW9/iGWRYqYOTQghRD6QrUSmdOnSLFy4MNs79fDwKLBTJosXr4lnE/58owXh57dT/L5CyMi3qfbTX6YOSwghRD6QrUTm2rVruRyGKOw+bTqBD47s46NfU9AcuUjMwb04B7xi6rCEEELkcS92Kl4hnlERmyJ06P4Zu6sqKChcGzMSVboohRBCPIUkMiLP6FKxC4cCvYi3Bqs7idyaO9vUIQkhhMjjJJEReYaiKHzebQG/Nst4W8YsWkLazVsmjkoIIUReJomMyFNKO5WmaLMOnCsNmnSVy2OGo6qqqcMSQgiRR0kiI/KcMa9+wcYWVmg1oBw/R9ymTaYOSQghRB71TInM3r17efPNNwkICODWrYym/59//pl9+/a90OBE4WSuMad703H8GZBx36XrEz5Dl8PZpYUQQhQOOU5kVq9eTWBgIDY2Npw4cYLU1FQAYmNjmTJlygsPUBROgfW6kuRXjFuuYBGXzM2vp5k6JCGEEHlQjhOZL774ggULFrBw4UKjSe8aNmzI8ePHX2hwonAb9Opc1rfM+H/SqrUknThh2oCEEELkOTlOZEJCQmjcuHGW5U5OTsTExLyImIQAoGzZKtQs6c/O6hldTFfGfihzywghhDCS40TGw8ODy5cvZ1m+b98+ypYt+0KCEiJTx64zia6tJc4GNNduEbE4+7fKEEIIUfDlOJEZNGgQw4cP5/DhwyiKwu3bt1m+fDmjR49myJAhuRGjKMRc3dyp796Fdc0ynt+bO4+0sDDTBiWEECLPyNa9lh708ccfo9fradGiBUlJSTRu3BgrKytGjx7NsGHDciNGUcg1eP0zEsNXc9rLkurXdFz+bAy+y35FURRThyaEEMLEctwioygKn376KdHR0Zw9e5ZDhw5x584dJk2alBvxCYGtnQMeFYZx6ZVU0jSgHDlF/LZtpg5LCCFEHvDME+JZWlpSuXJl6tati729/YuMSYgsanUcQle9yj9+Ga0wcYcPmjgiIYQQeUG2upa6dOmS7R2uWbPmmYMR4nHMLSxJsSjPfaebACTdjTRxREIIIfKCbLXIODk5GR6Ojo7s2LGDY8eOGdYHBQWxY8cOnJycci1QIRKdyqNY6QFIi75r4miEEELkBdlqkVmyZInh/x999BHdunVjwYIFaDQaAHQ6He+++y6Ojo65E6UQAMV8Mbu+HdCgu3/f1NEIIYTIA3I8RubHH39k9OjRhiQGQKPRMGrUKH788ccXGpwQD7IrVQ1Ly4wWGTVG7r0khBDiGRKZ9PR0Lly4kGX5hQsX0Ov1LyQoIR7FvVx1bCx0AGjiEk0cjRBCiLwgx/PIDBgwgIEDBxIaGkrdunUBOHz4MNOmTWPAgAEvPEAhMnm4uqBaWgNgnqZDn5yMmY2NiaMSQghhSjlOZKZPn46HhwczZswgPDwcgOLFi/Phhx/ywQcfvPAAhchkZqaQblGEdLN7mOtBd/++JDJCCFHI5TiRMTMzY8yYMYwZM4a4uIxxCjLIV7wsFtaexNnewzUB0u/fx6JECVOHJIQQwoSeeUI8yEhgJIkRL5Odkw9xthn/192PMWksQgghTC/HLTLe3t5PvMfNlStXnisgIZ6kqIc/8TarABXtPZlLRgghCrscJzIjRowweq7Vajlx4gSbN2/mww8/fFFxCfFI5crX5ei/w2Lib17AhU6mDUgIIYRJ5TiRGT58+COXz50712i2XyFyQ1mPouzNuHCJmBsXKG3acIQQQpjYc42ReVDbtm1ZvXr1i9qdEI9kZ2VOqk3GZIwJ4TdNHI0QQghTe2GJzB9//IGrq+uL2p0Qj5Vmk9Eko4uR2xQIIURhl+OupZo1axoN9lVVlYiICO7cucO8efNeaHBCPEq6vROQgJKQbOpQhBBCmFiOE5lOnToZJTJmZmYULVqUpk2bUqlSpRcanBCPYuZaHLiFeXI66PVg9sIaFoUQQuQzOU5kJkyY8MIOPn/+fObPn8+1a9cAqFKlCuPHj6dt27YApKSk8MEHH7By5UpSU1MJDAxk3rx5uLu7v7AYRP5jW7I8cAyLFAViw8DFy9QhCSGEMJEc/ymr0WiIiorKsvzevXtGd8TOjlKlSjFt2jSCgoI4duwYzZs3p1OnTpw7dw6AkSNHsn79elatWsXu3bu5ffs2Xbp0yWnIooApXrosAFYpkB5+zsTRCCGEMKUct8ioqvrI5ampqVhaWuZoXx06dDB6PnnyZObPn8+hQ4coVaoUixcvZsWKFTRv3hyAJUuW4Ovry6FDh6hfv35OQxcFRFmvcgBo9AqxIUEUqfyqiSMSQghhKtlOZL755hsAFEVh0aJF2NvbG9bpdDr27NnzXGNkdDodq1atIjExkYCAAIKCgtBqtbRs2dKwTaVKlShdujQHDx58bCKTmppKamqq4Xnm/aC0Wi1arfapcWRuk51tC4r8VmYXe3diLcEmDZKunMbxGeLOb2V+EaTMhYOUuXAoDGXObtkU9XFNLA/x9vYG4Pr165QqVcqoG8nS0hIvLy8+//xz6tWrl6NAz5w5Q0BAACkpKdjb27NixQratWvHihUrGDBggFFSAlC3bl2aNWvGl19++cj9TZgwgYkTJ2ZZvmLFCmxtbXMUm8ibUtVU3Kf+l2KxoGmj4XyzyaYOSQghxAuWlJREr169iI2NfeJ9HbPdInP16lUAmjVrxpo1a3BxcXn+KIGKFSty8uRJYmNj+eOPP+jXrx+7d+9+5v2NHTuWUaNGGZ7HxcXh6elJ69ats3WDS61Wy7Zt22jVqhUWFhbPHEd+kh/LvH3WBIhVUbRxtGvXLsevz49lfl5SZilzQSVlLphlzuxReZocj5HZuXNnjoN5EktLS8qXLw9ArVq1OHr0KHPmzKF79+6kpaURExODs7OzYfvIyEg8PDweuz8rKyusrKyyLLewsMjRyc7p9gVB/iqzAqgoaLHQaJ75Euz8VeYXQ8pcOEiZC4eCXObslitbicyoUaOYNGkSdnZ2Rq0djzJz5sxsHfhx9Ho9qamp1KpVCwsLC3bs2EHXrl0BCAkJISwsjICAgOc6hsj/zMiYyyhdAbSJYOVg2oCEEEKYRLYSmRMnThgG3Rw/ftxoQrznMXbsWNq2bUvp0qWJj49nxYoV7Nq1iy1btuDk5MTAgQMZNWoUrq6uODo6MmzYMAICAuSKJYGSmcigQGqCJDJCCFFIZSuRebA7adeuXS/s4FFRUfTt25fw8HCcnJyoXr06W7ZsoVWrVgDMmjULMzMzunbtajQhnhCKmpHIaM2AtATTBiOEEMJkcjxG5q233mLOnDk4OBj/BZyYmMiwYcP48ccfs72vxYsXP3G9tbU1c+fOZe7cuTkNUxRwmge7llLjTRuMEEIIk8nxCMlly5aRnJz1Zn3Jycn89NNPLyQoIZ7G0LWkKJLICCFEIZbtFpm4uDhUVUVVVeLj47G2tjas0+l0bNy4kWLFiuVKkEI8LHOwr1ZRpGtJCCEKsWwnMs7OziiKgqIo+Pj4ZFmvKMojJ6ITIjeY/TuNY0bXkiQyQghRWGU7kdm5cyeqqtK8eXNWr16Nq6urYZ2lpSVlypShRIkSuRKkEA8ztMiYKZAmXUtCCFFYZTuRadKkCZAxw2/p0qVf2CXYQjwL5YHBvunJcTkftS6EEKJAyNb3/+nTp42enzlz5rHbVq9e/fkiEiIbMkepaxWF9OR4SWSEEKKQytb3f40aNVAUhafdX1JRFHQ63QsJTIgneXBmX11y9u7HIYQQouDJViKTecNIIfIK5d+cWqso6FJkjIwQQhRW2UpkypQpk9txCJEjDyYyqswjI4QQhdYzDy0IDg4mLCyMtLQ0o+UdO3Z87qCEeJrMoeZpZgqqtMgIIUShleNE5sqVK7z22mucOXPGaNxM5lVMMkZGvAyKYR4ZFUUmxBNCiEIrx7coGD58ON7e3kRFRWFra8u5c+fYs2cPtWvXfqE3lBTiSTJbZLRmCopWEhkhhCisctwic/DgQf755x/c3NwwMzPDzMyMRo0aMXXqVN5//31OnDiRG3EKYSTz7tdpioJGm2jiaIQQQphKjltkdDqd4c7Xbm5u3L59G8gYEBwSEvJioxPiMZR/uzTTFQVNuiQyQghRWOW4RaZq1aqcOnUKb29v6tWrx1dffYWlpSU//PADZcuWzY0YhcjKxgpiEkm/Y4FS9B7E3QZHuUWGEEIUNjlukfnss8/Q6/UAfP7551y9epVXXnmFjRs38s0337zwAIV4lKJ9BwAQuE9hrbkDKUeWmDgiIYQQppDjFpnAwEDD/8uXL8+FCxeIjo7GxcVF7r8kXprifQcQvmYldpdukXrUnnsWSyjZbAxoLEwdmhBCiJcoxy0yj+Lq6ipJjHipFI2GStPmoFOgbghsitKjv7DB1GEJIYR4yV5IIiOEKdhVqULq620A8NlrxfEdc0wckRBCiJdNEhmRr9UcO5loJ0uKxsH5Q7dQo+TKOSGEKEwkkRH5mpmtLVZjxgLgf0LDthVjTRyREEKIl0kSGZHv1e7ag3OVXDFTgbXnSY2/Y+qQhBBCvCSSyIgCwfWj70i0As9I2P7FW6YORwghxEsiiYwoEJrVr8GxeiUBKLHxMlGh50wckRBCiJdBEhlRICiKgnPPL7leUsVaC6c/+o/hzuxCCCEKLklkRIHRoYEfN2sXJ90MSp69y4U1S00dkhBCiFwmiYwoMOyszFFqvsuZWukAJHw5i/S4OBNHJYQQIjdJIiMKlJYt2+JR1oZwV7CP03Li8w9MHZIQQohcJImMKFDKuNlz3e11whsmA2C7YR+xQUdMHJUQQojcIomMKHAqtOhPS7tUDlUFMxUuffwBqlZr6rCEEELkAklkRIHT0NeTvRYtKOYXS5wN2N24S9j335k6LCGEELlAEhlR4CiKgnm9t2mrS+SfJnoA4r5fRFpYmIkjE0II8aJJIiMKpJavNOKIWo1Xi0VzykvBXKvn8icfytwyQghRwEgiIwokeytzrpfrSfW0NC43TiXNHJRjpznauA4nJ44m+ewZkKRGCCHyPUlkRIHVpEM/Dmv8eUcXzV8t9KSag8OdRKx+3cCtnr3xmDaRy1PGk3z6tLTUCCFEPiWJjCiwPFzs8R+7nQsBS2nu7oXujXscCEzlaEVIsQDHmBT4dR3XunXnVOP6XJ88keSTJ1H1elOHLoQQIpvMTR2AELnJwlzDKy07obboyMnTJym9/Rv8XTZyvJ7C2bv2OF61xP+yivWdOJJ+Xsm1n1eS7uaMa9tXcWnbDpsaNVDMJN8XQoi8yqTf0FOnTqVOnTo4ODhQrFgxOnfuTEhIiNE2KSkpDB06lCJFimBvb0/Xrl2JjIw0UcQiv1IUhZp+NWn1wRJiB54g1fUt3nBQea1mOOf7xLGyo569lRWSLMH8bgxxPy/neq/enG/SiIjJk0k6dkxaaoQQIg8yaSKze/duhg4dyqFDh9i2bRtarZbWrVuTmJho2GbkyJGsX7+eVatWsXv3bm7fvk2XLl1MGLXI78p6liBw4ET2VP2a/eUnUzmpFBNtI2hZK5ID/RJZ8Jo5e6ooJFmBcuc+93/+hetv9iGk8StEfD6JxCNHUHU6UxdDCCEEJu5a2rx5s9HzpUuXUqxYMYKCgmjcuDGxsbEsXryYFStW0Lx5cwCWLFmCr68vhw4don79+qYIWxQQdpYa2nQehI532PzPFiyOzuc/cfvRWMVyrI4VvzVx4/4dc/wvpFPnoord3Wjur1jB/RUrMCviimPr1jgGBmJbuzaKufTSCiGEKeSpb9/Y2FgAXF1dAQgKCkKr1dKyZUvDNpUqVaJ06dIcPHjwkYlMamoqqamphudx/979WKvVos3GNPWZ22Rn24KisJfZwsKCFs1boW/akv2nzhKzex5NEzbyVcotks0VNtVzZm670uiv3qfeBR11LqrY34sm5teVxPy6EjMXF+xbtMC+dWts6uTdpKawn+fCQspcOBSGMme3bIqaR6471ev1dOzYkZiYGPbt2wfAihUrGDBggFFiAlC3bl2aNWvGl19+mWU/EyZMYOLEiVmWr1ixAltb29wJXhQ44XGpmN3cT8vkLZQ1CwcgQqNhiXNF9tiZU+R6NPUvqNQNUXFI+f/r0m1tSaxShfjq1UgqVw40GhOVQAgh8rekpCR69epFbGwsjo6Oj90uzyQyQ4YMYdOmTezbt49SpUoBz5bIPKpFxtPTk7t37z6xIjJptVq2bdtGq1atsLCweM5S5Q9S5seX+fb9RA5s/R2vS8sIUM4alu9zqsTu8lX5J/4y7hfvUv+CSr0QFcfk/7/WzMkJu+bNsW/VEtv69VFMXLdynqXMBZWUuWCWOS4uDjc3t6cmMnmiDfy9997j77//Zs+ePYYkBsDDw4O0tDRiYmJwdnY2LI+MjMTDw+OR+7KyssLKyirLcgsLixyd7JxuXxBImbMqU8yZMm8OJj5lAGv+2YFl0Pe0St9Do9gLNAq6wDsWxTjq/yq7mii8f2MvZa8mG1pqnGNjiV+7lvi1azFzdMSheXMc2gRi16ABZpaWL7GUxuQ8Fw5S5sKhIJc5u+UyaSKjqirDhg1j7dq17Nq1C29vb6P1tWrVwsLCgh07dtC1a1cAQkJCCAsLIyAgwBQhi0LKwdqCLu3akB7Ymh3HzhK9az6tkv7GTRtF2+NLaK5Yc6vCa5zu6M/6+of58fYRKt3QE3BBpd5FcI6LI3bdOmLXrcPMwQGH5s1wCGyDXcMGmD0i8RZCCJE9Jk1khg4dyooVK/jzzz9xcHAgIiICACcnJ2xsbHBycmLgwIGMGjUKV1dXHB0dGTZsGAEBAXLFkjAJc40ZgfWqo9adx9HL41i/ZRH1o37H1+wGZS/+itfFldT3aEr6KxPZXOsef11Zz4/3r1DpJtS/oCfgooJzfDyxf/5F7J9/YWZnh33z5jgGtsauUSPMrK1NXUQhhMhXTJrIzJ8/H4CmTZsaLV+yZAn9+/cHYNasWZiZmdG1a1dSU1MJDAxk3rx5LzlSIYwpikLdCiWoW2E8oVEjWbz5D8peXkYzsxN4ROyEVTt5zd6HXq+8x9WA6vx1bTPrKmxiaUo0Prc01D+vp9ElDU6xicStX0/c+vWY2dpi37QpDm0CsX/lFcxsbExdTCGEyPNM3rX0NNbW1sydO5e5c+e+hIiEyLlyxRwo13cA9xJ6seyfPdieWEh7/U6KJFyETe9TxsKVobUGMrrdb+yPDeGv0L9YUXoXP7VMo/wtDQEhKo0vWeB4P4m4jRuJ27gRxdYW+yaNcQwMxL5xY8zkijshhHikPDHYV4iCoIi9Ff06tiKlbXM2HA4mes/3tE/dQHFtNBz6Gu3h2VT36UrT5sOJDfgvW69vZX3oen4qdYKfmqdT/raGRpfMaHzRAvt7ScRv2kz8ps0o1tbYN26MY5tA7Js0wczOztRFFUKIPEMSGSFeMGsLDV0bVUPf4Ft2X/iYP7b+ROPoVfiZXcE1ZCWErCTdvQFdm4/gjcCl3Ei4xd9X/uYvh79YWvImS5ukUjZCQ4vLNjQMUbC9E0/81q3Eb92KYmWFfeNXcAhsg33TpmjsJakRQhRuksgIkUvMzBSaVS5Fs8qfcPbmu8ze+hcVr/1Ma+UoRSIPwK8HuG/rhVKpGwOrNuU/HfpzKiaj62mz1WYWFo9nYSMV70gN7a+5Ujs4DZvIWOK3bSd+23YUS0vsXnkFu4AAbKpXw7pSJRQTXtYthBCmIImMEC9B1VLOVH2rL7dj3mDBzgM4nFzMa/yDS9I1XI5/Bce/Qos5bva+9C1Zj6GVPuC4rcJft3ayz2wf33rch3oqZaI01L+gp/FFc4reTSNhxw4SduwAQLGwwKqyLzbV/bCpXh0bv+pYeHqiKIqJSy+EELlHEhkhXqISzjYMfa0F8W0b8/fRENRTv+Fx7zDV9OcpqsRRKuEMhJyBkEW0AvwsS3PL3Y8gd1dCrLScc7zOb+43+K2xHs87GupeVKlwO+PhkKwl5dRpUk6d5v6/x9O4uGBdvRo21atjUaUKZklJpiy+EEK8cJLICGECDtYW9HylKrxSFVVVuX43kaPBp0i4tA/7yGNUSD1HBbNbFEsLo9iNMGreyHhdnJkzV4tU52qJstzyciC4xj12RgcTnXwP9xgo/29SU/62inckcP8+ibv3kLh7DwDlgevLlmHr54d19erYVPfDuqKPdEkJIfItSWSEMDFFUfAqao9Xk4bQpCEA9xPT2H3xCnfO78X81mE8E05TlVAc9TH43dmD352MxCQNS8IdKhNRoikR/iW5XF/PmZhL/HYvGG1KEmWiMLTYlL+tUvw+aK9dJ/badWL//Cvj+JaWWPv6Yu2XkdjY+FXHolQp6ZISQuQLksgIkQe52FnSpGYlqFkJGERquo5zYVGEnT1I+rUDuEWfoLp6AVclgTLxJykTchJCMl4bYeVNvHst7nv7cqOWA+eSI9l+9yzz7l/EJjGd8uGZiU1GC45DShrJp06RfOoU9/kZAI2rKzbVqv0/ualeDU02broqhBAvmyQyQuQDVuYa/MsWx79sF6ALqqpy5U4Ch88dz+iOigqiYto5yppF4JF6FY+wqxAGdYGWGlfuFfEn3bMbe83i0DSoQHBsCMvvniMs7joe9zG02Bi6pKKjSdi9m4Tduw0xWHp7Y1O9uiG5sa7oY/I7egshhCQyQuRDiqJkzChcrAk0awLAvYRUdoVc5u6FvVjcOkzphDNUUa7gpIvGKWo7RG3HB0gJtSLSoSqqZ30saw7kqqM9Z+KucPbuWf68e4a4hHt4Rf4/ualwW8UjBtKuXiXt6lVi//wzIwYrK6x9fbHxq54x3sbPD4uSJaVLSgjxUkkiI0QBUcTeiqa1qkCtKsB/SNHqOH09kptn96G7fpCi90/ip17AiSTKxAXBuSA4Bx4oeFuXo5VHbRwrvI3W25ezadGcvXuWg3fPsOjeOczjkgwtNhVuQ/lwFfuUVJJPniT55ElDDJoiRbCpVu3/yU016ZISQuQuSWSEKKCsLTTULl+C2uW7Ad1ITU1j6eoNlCxqT8qVgzjeCaJiWjBlzKIokXIZrl2GaysBsDEvSvki/nQv24AijYdxw9aOs9HBnL17lrV3z3Ap+iJF76UbXSXlFQXcu0fCrl0k7NpliMOybFnDvDbW1atj7SNdUkKIF0cSGSEKCTMzBQ87MwKbNsWiVSsA7sSnsvNCCNEX9mBx+yheiaeprFzDJf0OLpFbIHILHISSii0WjtWoWboeRau8g8bTjwuJNzl79yxn7p5h+92zhN+/jlek8SXgHjGQduUKaVeuELtuHfBvl1Tlyg8kN35YlCwhXVJCiGciiYwQhVhRByua1akOdaoDkKLVceLKbW6d24cadohiMSeorl7EgSS8Yg/DmcNw5ht0mFHUpjwBxevStuIruLZ4nzhre87dPceZu2c4ffcsy++eIS367gOJzQNdUidOkHzihCEOTZEihsTGpnp1rKtVQ+PgYKpqEULkI5LICCEMrC001KnoSZ2KPYGe6PUqlyNj2Xv2KEmh+3C6E4Rv+nlKKXcpmXwRrlyEK7/AJtCae+Du5o9XuYYMrDIYjXtlIlPucObuGc7cPcO2u2eZc+csjlFJRnPblMnsktq5k4SdOzMCURSjLimb6tWxqlBBuqSEEFlIIiOEeCwzMwWf4s74FG8F/3ZHRcWnsDM4mPshe7G8fRTvpDNUUq7jlh6BW8RGiNgI+yFRsSPZqTrlSwfQsEpjbKv9B525FVdjr3Lm7hnO3TvHz3fPcDUyhFIR6f9PbsJV3GNU0kJDSQsNJXbtWgAUa2ujLimb6tUxLyFdUkIUdpLICCFypJiDNcXq+UM9fwCS03QEhd4gPHgvatghPGJPUU29iB2JeMcchJiDcHom6WiIsK0IxevQxLcxr1UdDPbFSNWlciH6gmG8zbq7Z7kffi1Ll5RdSgrJx4+TfPy4IRZNkSLY+P3/JpnW1aqhsbc3VdUIIUxAEhkhxHOxsdRQ19cLfL2APuj1Khcj7nPlzGFSQvfjdO84VdKD8VDuUyopGEKDIXQZ/A13LEoQW7Q2buUa0K1yY3pX7AlmZsSmxnLu3jnO3j3L/rtn+D7yNJbh96hw699LwMNVSmd2Sf3zDwn//JMRjKJgWa6s4Q7gFn7VQVVNVzlCiFwniYwQ4oUyM1OoVMKVSiXaAm0BiIxN5p/gs8SE7MU6/Chlk8/go9ykqPY2RW//Bbf/gr2QpNgQaV+Z9OK1qFAhgIBKHVGqD0ZVVSKTIg2tNmvuniUk/Awetx6c30alWKxK2uVQ0i6HErtmDQBerq7cDT6PU+tW2NSsiaLRmLB2hBAvmiQyQohc5+5kg3tAHQioA0BiajpHQ8OIOLcHbhyiRNwpqqih2JKMd3wQxAfBxR9gA9zTFCPapTqa0rWp79uIltXeAUtbdHod1+KucebuGc7ePcvWO6eJuHGBsrd1hon7Kt1QsYyOJuann4j56SfMXJxxaNYch5YtsGvQADNraxPXjBDieUkiI4R46eyszKlXuSxULgv0R69XuRIVy7XgYyReOYzNnRN4JZ+nvHKLIrooitzdDne3w3HQYUa4dTkSi9bE1rse7So3onPZjmBmRlxaHEERQRyJOMJvEYcJi7yI31WVOhdVal1Wsb8fQ+yaNcSuWYNqbYltgwY4twrEvmkTzF1cTF0tQohnIImMEMLkzMwUyns4U96jJTRvCWTMaXPq2i3Czx8kPewoLvdPUTH9IsWUGEqlXIIbl+DG77An4wqpCPsqpBf3p4pPA5pWfgul7kdExkeycPNCIl5N54vwY9gEX6XOxYzEpmhcGsn/7CL5n13ozRTU6pUo1qYDzi1bY1mqpIlrRAiRXZLICCHyJGsLDTUrlKZmhdJAdwDuxaew/+J5oi8eRHM7CI/4s/iqV7AjkXLxRyD+CFxcAH9DlLkH952rUV9fjNrVeuBc92Mi2sZwNOIoW28f4taJfXifvkOdiypeUSqcPM+9k+e5N+0rEsoUxaZ5E7zbd8e2chW5xFuIPEwSGSFEvlHEwZqGtWpCrZoAqKrKtahYrgYfJfHKIWyjTuKVEkw55TbF0iModjeCSgArl6PFnDTrclQoWoPq3vXx6NaHqP62HI44wqozu2DvYSoHJ+B7A+yv34ElfxC25A/iXK1Jrl8Vj7adqNC0IxoLSxPWgBDiYZLICCHyLUVR8HZ3xtu9FTTLmLAvNV3H6as3CD9/AO31ozjeO0FlNRQ3JY7SKSFwIwRu/AZ7wFmxp4ZDFaoW98f5zXEklCxJUNgJ7u3YgtOREKpc1uIYnYLjxmOoG49x3GY8t/1KYNG0IRXbdKesu6+01ghhYpLICCEKFCtzDdUreFG9ghda7Rts3LgRfZMWHAoNMeqSqqRexYEEHOIOQ9xhCJkPgL15Ce67VIcBbxLvUZxr586h23MYz1OROCSr+By6BYd+J2H67/xezor4+r4UbdWOWhWbUcqhlIlLL0ThI4mMEKLAc7Gzon6tWlCrFpDRJRV2J4Zr544YrpIqk3yesko4Hum38bhzG+5sBiANc25XqkBsw4aEp9mSHnIDh6ArON9LpXpIKoScRP/TSQ6XmsIvVV0wa1yfyn7NqetRl2K2xUxZbCEKBUlkhBCFjqIolCnmQpligdAsEIC0dD3nrt8gPHgf6WFHcY4+jU96CK5KAl4p5+HW+YwXu0JMK0duppfn7h0rrEKjcb4ZT+UbUPnGfdi0ibCim/ihgsIt/1KUrPUKdYvXo45HHVys5RJvIV40SWSEEAKwNDejSrkyVClXBugNQGxiGkcunOb+v11S7vFn8VGv4qzE4Wx+HIoDxUGbqOFahAfREdbY30ym9B0ofUeFAze467CCYxV+ZamPQrpfJWqXqkc9j3r4u/vjYOlg0jILURBIIiOEEI/hZGdJ3Vq1oVZtIKNL6uadGK6dO0zilcPY3jlB6eTzeNlFUKHcLSgHulSFhHBr7t6yJTncCrd4aHNcpc1xlYR1wZwod55fKixjTDkN5UpUpa5HXeoWr0vNYjWxMbcxcYmFyH8kkRFCiGxSFAXPYi54FmsDzdoAoNXpOX8tzNAl5RJ9igplLlLO6x56HSRGWpFw05rY29bYp2h45ZzKK+dUtBo9Z7xOctTnFH+UX0SiowV+Rf2o55HRDVW9aHUsNXKptxBPI4mMEEI8BwuNGb7lvPAt5wW8CUBcchpB508RffEAGpcgPEqepVz6NXTRGuJvWRN/0xoSzPEPVfEPVRkEXCqp46jPUdZWOMa8IgrWGmtqFqtJ3eJ1qedRD98ivpibyVe2EA+TT4UQQrxgjjaW1PKvA/4ZN8lUVZXb92K4fvZgRpdU5HFKRV7A7lYS8besSYm2pOItqHhLz5s74XYR2FMlie01DnAw/CAA9hb21HavTcOSDWlcqjEl7EuYsohC5BmSyAghRC5TFIWSbi6UbNoOmrYDIF2nJ/TaNcKD96MP3kORcydxCLtPaqQ5Je4p9Nijp9s+lYsVVVbWtSC4RAK7bu5i181dTD48mfLO5Xml1Cs0LtmYGsVqSGuNKLTknS+EECZgrjGjYrmyVCxXFjr0ASAhJY1Lx/aT9PfvOOw/jMWdZCqdV5hwXodaVEuIn451lS04aWvF5ZjLXI65zJKzS3CwcKBByQY0LtWY+sXqm7hkQrxcksgIIUQeYW9tSc1GzaBRMwDijp/kxvy5KPsPoNyxoNJ2Cz7br8PG5z5nq+rZ5WrNfhtrYohny7UtbLm2BQWFkpqS3DpziyZlmuDr6ouZYmbikgmReySREUKIPMrRvwZVFi5EGxVFzMrfuP/bb6Tfu0f8KQdKn1V4u3Q6430iuehhxh5bG/ba2HDBypKbupvMPzOf+Wfm42ZdhFdKNaZxqcYElAjAzsLO1MUS4oWSREYIIfI4i2LFKPr+MIr85x3iN20i+qefSTl3Du1VDdevFkMpU5z65c153fUyGss77LW1Zo+NDYdsrLmbco+1l9ey9vJazNHgX6wGjUs3o3Gpxng5eslNL0W+Z9L2xj179tChQwdKlCiBoiisW7fOaL2qqowfP57ixYtjY2NDy5YtuXTpkmmCFUIIEzOztMSpUye8/lhFmV9X4Njuf+3deVxU9f7H8dcMDMwMgywu7IsrgiJoihu5hSimUubN0nuv7ctt0WvWzW6/tqt1r9fMe7ve2152M9vMJUtLzQVFwSVQExRMEFld2IZ1YM7vD65ThDszDMvn+Xj4yPM9Z8583gzJh+/5zpxJ4OiIU3Y+rltzqNgVzFnVXOprpjOjIpLPs6t4M7+I35aWEWgyUUc9yUUHWLJ/CVPXTiV21RheTvwLibmJ1NTX2DueENfFrjMyFRUVREREcM899zBt2rQm+xcvXsw///lPVqxYQffu3fm///s/JkyYwNGjR9FqtXaoWAgh7E+lUqEfOBD9wIF0KyykeNUqSj79jLr8fNQfraK/RoP71Kl4/HYR1ZgYfmgzN+Yk4lT9A2n6GnbqtezXaikwnWdVxmesyvgMJ0VNhCGE2L5TGdM9Bm8Xb3vHFOKq2LWRiYuLIy4u7qL7FEVh2bJlPPvss8THxwPw4Ycf4uXlxdq1a7njjjtaslQhhGiVNF5edJs7ly4PP0zZ199w7sMPqU1Pp2z1aspWr0Y/bBgDf/dbDNPnYEZFp4wjBKduZebpBM7VH+awvo4EnZYzjo7sq0hj34E0Fh34GwGKgcGeN3DzwDsZ7DcMB7WDvaMKcVGtdo3MyZMnKSgoICYmxjLm5ubG0KFD2bNnzyUbmZqaGmpqfp4iLSsrA8BkMmEyma74vBeOuZpj2wvJ3DFI5nZOrcZlymQ0E2LZ9fY79D2RSeX326jcu5fKvXtx9PPF7c47CbzlVrpPexR4FHO9mf4n0rjp8BYKCraRQwYHdXDY2YkclZGc4h2s+X4HBrOKMLyI8o5m8uC76Obeuj6Mr0O9zv/TETJfbTaVoiiKjWu5KiqVijVr1nDLLbcAkJiYyMiRI8nLy8PHx8dy3O23345KpeLTTz+96HleeOEFXnzxxSbjH3/8MXq93ia1CyFEa+RYXIL73r24JSfjUFkJgFmjoeyGQZSMGEGtl1ej481mhcqyImqLD3Gm/jA52kIO6Bwod/h5OaVaUehZ40APkzeBThH4eQ7GUePcorlEx1BZWcnMmTMpLS2lU6dOlzyu1c7IXK8FCxYwb948y3ZZWRkBAQHExsZe9gtxgclkYvPmzYwfPx6NRmPLUlsNySyZ2yvJrIFZMzFXV2P85htKVn5M7fHjuO9Nwn1vErrhw3GfNRP9jTeiUjd974diNvNT5mH2HvqUoyVJpDue5aSTAxlaMxnaPCAPr9IN9K/W0sc5jBt6TSFsYCxavcG+mTuAjpD5whWVK2m1jYy3d8NCs8LCwkYzMoWFhURGRl7ycc7Ozjg7N/3tQKPRXNOLfa3HtweSuWOQzB1Do8waDc4zZuB5++1U7ttH8X8/onzrVqr27KFqzx40gYF4zpqJ27RpOLi6NjpP336D6dtvMNDQ2Oz/cTtbj3zCEeMh0h2NFDo6UGgwsZVUNFkp3JD2LH1r3ejlMoQeIZPoOWgMehfXX5dn+8wdRHvOfLW5Wm0j0717d7y9vdm6daulcSkrKyMpKYmHH37YvsUJIUQbpFKpcImKwiUqitrTuRSv+piSz7/AdOoUha/8lTP/+Cdd5zyO5+zZF3+8Ws2Q8HEMCR8HQFVtJRtTvmBHxlccqsnkrEMde/Va9uprgF0Ep28j5kA1/at90QZMovuNd+Ib2LMFE4uOwK6NjNFoJDMz07J98uRJUlJS8PT0JDAwkLlz57Jw4UJ69+5tefu1r6+vZR2NEEKI6+Pk74fXk0/S9ZFHKP1qA8Uf/ZeajEzOvvnWJRuZX9M56ZkW9XumRf0eRVH4qeQEXx36nN0535NRV0CWk4Z3nDRAOTeUryD+s38TUBNEVdAkgqPvJCC4l21Dig7Bro3M/v37GTt2rGX7wtqW2bNn88EHH/DUU09RUVHBAw88QElJCdHR0WzatEk+Q0YIIaxErdfjMeN2DKNuJHPsOOrLy1EU5Zo/8VelUtHToxdzRy9gLgsory1nR84OvjjyMQdLDnNAp+WATovOXMb4kve45ZPXKa7rQWnQzQRG30FQ9942SijaO7s2MmPGjOFyb5pSqVS89NJLvPTSSy1YlRBCdDzqC2tjTCaUmhpUzfyF0dXJlck9JzO552QKKgrY8NMGVqd/zunKPNa7GljvasDPVMrUc2/TfeVr/Kj04WzQzQRGz6B7jz5WSCQ6CrklqhBCCNR6PfxvFsZsNFr13N4u3twXfh/fTN/Ef+P+y229b0PvoCNX48h/PNyIC/Dj1a7nKCn6D17/jeLIX4axbcVLnMg8ftlfdoWAVrzYVwghRMtRqdWoXVwwG43Ul5fj2KWL9Z9DpSKyWySR3SL5U9Sf2HpqK+sy15GUn8Q+nZZ9Oi2LOnsQW1HILfn/osdHr3LEIZQzgZPwHzGD3r36yE0uRRPSyAghhAAaLi+ZjUarz8hcjM5Rx+Qek5ncYzL5xny++ukr1mWu41T5Kda5GljnasDfZGKqMZf4nGX4rnyVw+pQCgPi8B95ByG9pakRDaSREUIIAYCDwUAdYC4vb9Hn9TH48MCAB7g//H5+KPqBdSfWsenkJk5Tyb893Pm3hztDq6qJN2Zz06nX0Gcv5ZA6lMKAifiNuINe3Xu0aL2idZFGRgghBPDzgt/6ctvPyFyMSqVikNcgBnkN4k9DfnHpqSCJJJ2WJJ0WXWeIqzASX/4TMdmvocp+jVRVXyq0g/mxZw8i+vWXmZoORhoZIYQQAKhdG24tYDa27IzMxeg1eqb0nMKUnlPINeay/sR61mWuI9eYy5euBr50NeBTp+K28mKmlmcQUZUOX3xE6uq+5PtNxHfEDPr3DUWtlqamvZNGRgghBAAOhoYZmZZYI3Mt/Ax+PBzxMA8OeJCDhQdZd2Id32Z9Sz5V/MvDneUe7vSvUjOzvIhxlceIOJ0Ony0jVdWXPL8JeA+7g4gwaWraK2lkhBBCAD/PyBQteZWSdevQhoaiDQ1DGxaKc58QHAwu9q1PpWaw92AGew9mQdQCNmdvZt2Jdewr2MdhnZkFui44oCa0VsVY4zmGVf9E7Ol/oPr8nxxU96egezx9xsykT6CfXXMI65JGRgghBACdYmMp3/Qt9SUl1BxNo+ZoGqV82bBTpcIpMBDnsFC0fUPRhoWiDQ21ydu0r4Zeoye+VzzxveLJKs7ite9e47jjcU4bT3PECY54uvM64GKG4VWVDKs6yfBTf6Xru39ju9MwKvveRuSY2/Dt3Mku9QvrkUZGCCEEAC4jRtB7TyKm3Fyqjx6lOi2NmrR0qtPSqCsspDY7m9rsbMo3brI8xqFrl59nbkL7og0NRRMQgErdcp+36mfwY5x2HH+P+zsF1QXsydvDnvw9JOcnU24qZ4uLni0u+oZjTXUMq/6RYT8doPrIc2zSj0EdMYOh0bG46Z1arGZhPdLICCGEsFCpVDj5++Pk70+n2FjLeN3581QfTaMmPY3qo2lUp6VRm5VF/ZmzVJxJoGJnguVYtYsLzqF9G2ZuQhtmb5x79kTlZNtGQaVSEdgpkMBOgczoO4M6cx1Hzx21NDapZ1LJ1cBqjYHVrgZUikLf2mSGp+2g6oCeev1NdB76e4YNHoyzo4NNaxXWI42MEEKIK3L09MQQPRJD9EjLmLmykupjxxrN3NQcP465ooKq/Qeo2n/g5xNoNDj36tXQ2PTt29Dc9O2Lg8Fgu5rVjgzoOoABXQfwYMSDVJoq2V+4nz15e9ibv5fMkkzSnJ1Ic3YCd3A2b+eGH75l1x5XOhvGMGjEQwwO7SOLhFs5aWSEEEJcF7Vej37gQPQDB1rGFJOJmp9ONpq5qU5Px1xWRk1aGjVpaZT+4hyaoMDGMzd9+6Lp1s0m9eo1ekb5j2KU/ygAzlSeYW/+XvacTmB3zk7OU0GiXkeivg7YgkfSt/TcocffMJy4EQ8yold/m9QlmkcaGSGEEFaj0mjQhvRBG9IHt/h4ABRFwZSbR3XaUWrS0qi+sO6moABT9ilM2aco//ZbyzkcunRpNHOjDQ1FExho9XU3XfVdLZ9VoygKJ0pOkHjyO7alr+dITS7FDg7sd6lhv7Kdtbu303W7hh4uEcRF3k5sz2hcnVytWo+4PtLICCGEsKmGdTd+OPn7wfjxlvG68+cbLkelp/+87ubkSerPnqUiIYGKhF+su9Hrce7bt9HMjXPv3qittO5GpVLRy6MXvTx68ftBf8BUb2J/+jo2H/yIH6sySHdScUZj4kztfpKS9/NSEgQ4BzOu13jGBEYT3jUcjVpjlVrEtZFGRgghhF04enpiGDkSw8jG625qjh9vuCT1y3U3lZVUHTxI1cGDvziBI869euHUpw8edSbK1Wq0fv5ofH1w7NoVlcP1L9jVOGgY3m86w/tNB7OZvLRNbEt6m4yKI+zXOZCt0ZBdm8X7R9/m/aNv46zWEuUTRbTfSEYHjMbPIJ9V01KkkRFCCNFqqPV6dJGR6CIjLWNKXR01P/3UaOamOj0dc2kpNenp1KSn0xUo/GbjzydycMDRqxsabx803t4NzY23Dxrfhm1HHx8c3N2v7r5MajW+/SYxq98kMFVz7of1ZOz7gJyqwyTrNCTptBRTTULuThJyd/JK8iv0cu/FmIAxjPYfTXiXcBzU8i4oW5FGRgghRKumcnRE26cP2j59cJs6FWhYd1OXl0d1WhoVR46QvTeJbmo19QUFmIqKoK6Ourx86vLyqbrUebXaxk2Ojw8an4YmR+PT0PCo9frGD9Jo6Rx1O52jbmdo5XmiE1dR98NHGE0Z7NFpSdDpOKh1JrMkk8ySTN45/A6eWk9G+Y9ijP8YhvsOR6/RX7wgcV2kkRFCCNHmqFQqNH5+aPz80I4eTVL37gyaNAmNRoNSX0/d2bPU5edjys/HlF+AKT+fuoKf/15/7hxKdTW1WVnUZmVd8nkc3Nx+bmwuNDm/mNnxHv0AqphHqCs4isv2d5ie8SWKcpYEnZYdeh07dS6crz7P2sy1rM1ci0atIconijH+YxgTMAZvF++W+6K1U9LICCGEaFdUDg5ovLzQeHk1ukT1S+aamoZ3TV2kyakryMeUl4+5ooL60lLq/3cJ66LUahy7dm24XOXrQ5XXgygU069gP2NNh1HriznkpmGLi4Hv9G6c15jYnbub3bm7WZS0iBCPEMYENDQ1YZ3DUKta7hOR2wtpZIQQQnQ4amdnnIKCcAoKuuQx9eXlmPIu3uSYCgqoKyhAMZmoKyykrrAQUlMbPf4UDfeh6uQAM3R1/FZfSrXBzDE3LQc6u/CTZw1n3NJZUZDOm4fepIuuC6P9RzPafzTDfIehc9TZ9GvQXkgjI4QQQlyEg6srDiGuENLnovsVs5n6c+cwFRT83PD8r8kxFTSsz6k7exbqFUxGR0xGRyiC3kBvKhudq8IZznYq4FynzzjU6TN2ujniHtSbXn2GMjA8Fp/gMJvf4qGtkkZGCCGEuA6q/11WcuzaFV14+EWPUWprMRUV/bxe5/Qpqn/cS3XmEdSlRkwVDphNalxqwOUMBJ1R/vdIE3AUOEop71OiApO7C3q/QHR+gTj1C8PJbEZRlIs+b0cijYwQQghhIyonJ8tNOH/2aMN/zv9ESeIKOLAKXck5TJUOmCodyK/wILuuG471CrrSUlyLa3CqB6fiCuqK0yg/kgbffkswkPXxKgzR0RiiR+IyYgQO7u4tH9LOpJERQggh7MGzB+6TX4RJz2HO3Eb57vdwO7WZMCWXMHKpVRzYptxAsn8s+Ok4fXoLJacy8CqGftkK/U+Bc1ERpV9+SemXX4JajS48HJfoaAw3RqMND2/WhwK2FdLICCGEEPakdkDdJ4YufWKgqpiqg59RmfQBncuOMkGVzIS8ZApz3dmiGUvB4Pmc8TzJ66e/xVRdQd8chUFZakbk6PHILaMqNZWq1FTOLl+O2s0Nw8gRuIyMxiU6Go2XbW7GaW/SyAghhBCthc4D3cgH0Y18EAp/5GzCe+jSvsCrvoRZdWsgdQ0HzL0J7hLLPkMdxQNPsKL7cVZQiWeZA7GF3RiX54HH4RzMpaWUfbORsv994rFzSAgu0SMx3HgjukGDrHafKnuTRkYIIYRojbz60WX6q1D3CrVpGzm3+z26FezkBnUGN5zPYPY5J7bkDCW99+843e0cux228kmnM3zS+wwuY7XMUm7ipnxPdAeOUX34MDXHjlFz7Bjn330PlV6PS1QULjdGY7jxRpwCA+2d9rpJIyOEEEK0Zo5OOIXH4xMeD+WFnN/zIeaDH9GlOospSgJTjieQle5FuMdEcvoFkVK5k59Kf+ItdvCWN0TeFcn9gX8nIgsqdu3CuGsX9WfPYty+HeP27RQCrhMm4PvXV1Dr2t5n10gjI4QQQrQVrl54xj6Jacwctn3+Or1MaXTO/oZgdSH3la6gZreGrY4jON7vXk555LL99FZSzqTwyJkUenv05t4H7iV24YvUZ5zAuGsXFQm7qDxwgPJvv+VUQQH+//k3jp6e9k55TeSzkIUQQoi2RqWizNAL71n/Qb8gg5KYVynQh+CsMjGpfgdzDz3Pfdu/44+1N3NL0O3oHfVkFGfwdMLTTFk3lXWqQ7je83uCPlxB0IoPULu5UZWaStadd1KbnW3vdNdEGhkhhBCiLXNywT36PryfTKJ69nec9JtKLRrCVFn8LuefPLnt3ywo6MUtXePxcPYg15jLwqSFTPhiAu8cfof6ASEEf7wSja8vpuxTZN1xJ1W/ut1CayaNjBBCCNEeqFRouw+l+/3/RfPkMbIGLaDI0ZdOqkpuKd/AX5JfZ3FmHdO1o/HWe3Ou+hz/OPgPYr+I5c3Sr+n80Ttow8KoLy4me/ZdGBMS7J3oqkgjI4QQQrQzKpfOBE99mm7P/EhR/CqOuY+iXlExrO5Hnk/7L2//mMXvGEKwIRijycjbh99m6s7fs/fZyehvjEaprqbwr3+zd4yrIo2MEEII0V6p1XQbOImQuV9R82gqh7rfRwmuBFPEUydX89GhfTxcHUKQ3p+SmhJePryUZ4afAqD2VDaK2WznAFcmjYwQQgjRAei7BjFg9qsYnk4nNfIFTqn9cVNV8Yf8zaw+soe7yzvj7mjgiCqXehVgquNQ+g57l31F0sgIIYQQHYij1kDELX8k4NlDHB7zLoecB+GsUph39gc2ZaYx3ejAOTcVAP/68FEe//5xjhcft3PVl9YmGpnly5cTHByMVqtl6NChJCcn27skIYQQok1TqR0IHzOdAQu2cezWb0nsFIeD2ZH/O5dFoF85AHfuMJNw8numr5/O0wlPk1OWY+eqm2r1jcynn37KvHnzeP755zl48CARERFMmDCBoqIie5cmhBBCtAshEcMYMe8Tzj+YwvcBj+LY1xkHbT0+xfDgDzUoKHz909dMWTuFFxJfIM+YZ++SLVp9I7N06VLuv/9+7r77bsLCwnjjjTfQ6/W899579i5NCCGEaFd8ff0Zd+8iPJ/9kdKhIwCIz6rhk9x8RlZWUa/UszpjNZNWx/Hn7a1jhqZV36KgtraWAwcOsGDBAsuYWq0mJiaGPXv2XPQxNTU11NTUWLbLysoAMJlMmEymKz7nhWOu5tj2QjJ3DJK5Y5DMHYOtMzs5qgkePpazO5I412Uche5uPFe4gSLnAv7l4U6STsv67K9Zn/013VSuPDrsSSZ3n2zVGq42m0pRFMWqz2xFeXl5+Pn5kZiYyPDhwy3jTz31FDt27CApKanJY1544QVefPHFJuMff/wxer3epvUKIYQQ7YVbUhJeX67BGBZG3uzfU1dvpupMJl3PJWPmMOs86titb7jJ5HT9dCKdIq36/JWVlcycOZPS0lI6dep0yeNa9YzM9ViwYAHz5s2zbJeVlREQEEBsbOxlvxAXmEwmNm/ezPjx49FoNLYstdWQzJK5vZLMkrm9aonMdVFRmG69FQc3NyK7d2+yf1BRLhnHdlHsoSWq1414aq17s8kLV1SupFU3Ml26dMHBwYHCwsJG44WFhXh7e1/0Mc7Ozjg7OzcZ12g01/RiX+vx7YFk7hgkc8cgmTsGW2bW+Pig8/G55H4vv2C8/IJt8tzAVedq1Yt9nZycuOGGG9i6datlzGw2s3Xr1kaXmoQQQgjRMbXqGRmAefPmMXv2bAYPHkxUVBTLli2joqKCu+++296lCSGEEMLOWn0jM2PGDM6cOcNzzz1HQUEBkZGRbNq0CS8vL3uXJoQQQgg7a/WNDMCjjz7Ko48+au8yhBBCCNHKtOo1MkIIIYQQlyONjBBCCCHaLGlkhBBCCNFmSSMjhBBCiDZLGhkhhBBCtFnSyAghhBCizZJGRgghhBBtljQyQgghhGizpJERQgghRJvVJj7ZtzkURQGu/nbgJpOJyspKysrKOsxdVCWzZG6vJLNkbq86QuYLP7cv/By/lHbfyJSXlwMQEBBg50qEEEIIca3Ky8txc3O75H6VcqVWp40zm83k5eXh6upKVFQU+/bts+wbMmSIZfvC38vKyggICCAnJ4dOnTo167l/ef7mHHupfRcb//XY5bYls2S+XpL5+o+VzJcfl8yS+cJ2cnIy5eXl+Pr6olZfeiVMu5+RUavV+Pv7A+Dg4NDoBf/l9q/3derUqdnfHL8+5/Uee6l9Fxu/XMZfb0tmyXy9JPP1HyuZLz8umSXzhW03N7fLzsRc0KEW+z7yyCOX3P71Pls83/Uee6l9Fxu/XMZfb0tm65DM13+sZL78uGSWzL/e7oiZr6TdX1q6VmVlZbi5uVFaWtrsLretkMySub2SzJK5veqImS+lQ83IXA1nZ2eef/55nJ2d7V1Ki5HMHYNk7hgkc8fQETNfiszICCGEEKLNkhkZIYQQQrRZ0sgIIYQQos2SRkYIIYQQbZY0MkIIIYRos6SREUIIIUSbJY3MNbr11lvx8PBg+vTpTfZt2LCBkJAQevfuzTvvvGOH6mxvyZIl9OvXj/79+/PRRx/Zu5wW8dprr9GvXz/CwsJ4/PHHr3gDs7bu2LFjREZGWv7odDrWrl1r77Js7uTJk4wdO5awsDDCw8OpqKiwd0k2FxwczIABA4iMjGTs2LH2LqfFVFZWEhQUxPz58+1dis2VlJQwePBgIiMj6d+/P2+//ba9S7I+RVyTbdu2KevXr1duu+22RuMmk0np3bu3cvr0aaW8vFzp06ePcvbsWTtVaRuHDh1SBg4cqFRVVSmVlZXK0KFDleLiYnuXZVNFRUVKjx49lKqqKqWurk4ZMWKEkpiYaO+yWkx5ebnSuXNnxWg02rsUmxs1apSyc+dORVEU5dy5c4rJZLJzRbYXFBSklJeX27uMFvfMM88ot99+u/LEE0/YuxSbq6urUyoqKhRFURSj0agEBwe3u59NMiNzjcaMGYOrq2uT8eTkZPr164efnx8Gg4G4uDi+++47O1RoO2lpaQwfPhytVotOpyMiIoJNmzbZuyybq6uro7q6GpPJhMlkolu3bvYuqcWsX7+em266CRcXF3uXYlM//vgjGo2GG2+8EQBPT08cHdv9reg6pIyMDNLT04mLi7N3KS3CwcEBvV4PQE1NDYqitLtZ5XbVyOzcuZMpU6bg6+uLSqW66HT48uXLCQ4ORqvVMnToUJKTk63y3Hl5efj5+Vm2/fz8yM3Ntcq5r5at8/fv35/t27dTUlJCcXEx27dvb/GMv2brzF27dmX+/PkEBgbi6+tLTEwMPXv2tGKCa9eS3+efffYZM2bMaGbFzWfrzBkZGRgMBqZMmcKgQYN4+eWXrVj99WmJ11mlUjF69GiGDBnCypUrrVT59WuJzPPnz+eVV16xUsXN1xKZS0pKiIiIwN/fnyeffJIuXbpYqfrWoV39ylFRUUFERAT33HMP06ZNa7L/008/Zd68ebzxxhsMHTqUZcuWMWHCBI4dO2b5LTsyMpK6uromj/3uu+/w9fW1eYbmsHX+C2tExo0bh5ubG8OGDcPBwcHmuS7H1pl1Oh0bNmwgKysLnU5HXFwcO3fuZNSoUTbPdikt9X1eVlZGYmIin3zyiW0DXQVbZ66rqyMhIYGUlBS6devGxIkTGTJkCOPHj7d5tktpidd5165d+Pn5kZ+fT0xMDOHh4QwYMMDm2S7F1pn37dtHnz596NOnD4mJiTbPczVa4nV2d3cnNTWVwsJCpk2bxvTp0/Hy8rJ5thZj72tbtgIoa9asaTQWFRWlPPLII5bt+vp6xdfXV3nllVeu6dzbtm1rskZm9+7dyi233GLZnjNnjrJy5cprL9xKbJn/gnvvvVfZsGFDc8q0Kltk/uyzz5Q//OEPlu3Fixcrf/vb36xSrzXY8nX+8MMPlVmzZlmjTKuyRebExEQlNjbWsr148WJl8eLFVqnXGlri/+f58+cr77//fjOqtC5bZH766acVf39/JSgoSOncubPSqVMn5cUXX7Rm2c3SEq/zww8/rHz++efNKbPVaVeXli6ntraWAwcOEBMTYxlTq9XExMSwZ8+eZp8/KiqKI0eOkJubi9FoZOPGjUyYMKHZ57UWa+UvKioCGt7Zkpyc3Koy/po1MgcEBJCYmEh1dTX19fVs376dkJAQW5XcbNb8Pm8tl5WuxBqZhwwZQlFREcXFxZjNZnbu3EloaKitSm42a2SuqKigvLwcAKPRyPfff0+/fv1sUq81WCPzK6+8Qk5ODllZWSxZsoT777+f5557zlYlN5s1MhcWFlpe59LSUnbu3Nmq/w27Hu3q0tLlnD17lvr6+ibTaV5eXqSnp1/1eWJiYkhNTaWiogJ/f38+//xzhg8fjqOjI6+++ipjx47FbDbz1FNP0blzZ2vHuG7Wyh8fH09paSkuLi68//77rXpBpDUyDxs2jEmTJjFw4EDUajU33XQTU6dOtUW5VmGt17m0tJTk5GRWr15t7RKtzhqZHR0defnllxk1ahSKohAbG8vkyZNtUa5VWCNzYWEht956KwD19fXcf//9DBkyxOq1Wou1vrfbEmtkzs7O5oEHHrAs8n3ssccIDw+3Rbl203p/CrVSW7ZsueS+qVOntuofctZgjdmrtmbRokUsWrTI3mW0KDc3NwoLC+1dRouKi4vrMO9kAejRowepqan2LsNu7rrrLnuX0CKioqJISUmxdxk21WEuLXXp0gUHB4cm/zgXFhbi7e1tp6paTkfML5l/JpnbF8n8M8ksOkwj4+TkxA033MDWrVstY2azma1btzJ8+HA7VtYyOmJ+ydxAMrc/krmBZBbQzi4tGY1GMjMzLdsnT54kJSUFT09PAgMDmTdvHrNnz2bw4MFERUWxbNkyKioquPvuu+1YtfV0xPySWTJLZsksmTs4O79ryqq2bdumAE3+zJ4923LM66+/rgQGBipOTk5KVFSUsnfvXvsVbGUdMb9klswXSGbJ3BZ1xMzWplKUdvZZxUIIIYToMDrMGhkhhBBCtD/SyAghhBCizZJGRgghhBBtljQyQgghhGizpJERQgghRJsljYwQQggh2ixpZIQQQgjRZkkjI4QQQog2SxoZIYQQQrRZ0sgI0QGMGTOGuXPn2rsMu7jrrrtQqVSoVCrWrl0LQFZWFiqVipSUFKs+1wsvvEBkZGSzzvHBBx9Y6u2or5kQ10IaGSEE58+f57HHHiMkJASdTkdgYCCPP/44paWl9i7NKiZOnEh+fj5xcXFWP3dVVRUuLi6NbvzXHDNmzCA/P1/ubizEVWpXd78WQlyfvLw88vLyWLJkCWFhYWRnZ/PQQw+Rl5fHF198Ye/yms3Z2Rlvb2+bnHvz5s0EBQXRq1cvq5xPp9Oh0+lwcnKyyvmEaO9kRkaIDsJsNvPUU0/h6emJt7c3L7zwgmVf//79Wb16NVOmTKFnz56MGzeORYsW8dVXX1FXV2c5bseOHURFReHs7IyPjw9PP/10o/2X8tZbb+Hr64vZbG40Hh8fzz333APAiRMniI+Px8vLC4PBwJAhQ9iyZUuj44ODg/nLX/7CnXfeiYuLC35+fixfvrwZX5UG9fX13HPPPfTt25dTp04BkJ6eTnR0NFqtlrCwMLZs2dLo8tQF69atY+rUqRc974kTJ+jRowePPvooF+7P+/bbbxMQEIBer+fWW29l6dKluLu7NzuDEB2VNDJCdBArVqzAxcWFpKQkFi9ezEsvvcTmzZsveXxpaSmdOnXC0bFh4jY3N5dJkyYxZMgQUlNT+c9//sO7777LwoULr/jcv/nNbzh37hzbtm2zjJ0/f55NmzYxa9YsAIxGI5MmTWLr1q388MMPTJw4kSlTplgaiwv+/ve/ExERwQ8//MDTTz/NnDlzLpvjSmpqavjNb35DSkoKCQkJBAYGUl9fzy233IJerycpKYm33nqLP//5z00eazab2bBhA/Hx8U32HTp0iOjoaGbOnMm//vUvVCoVu3fv5qGHHmLOnDmkpKQwfvx4Fi1adN21CyEARQjR7o0ePVqJjo5uNDZkyBDlT3/600WPP3PmjBIYGKg888wzlrFnnnlGCQkJUcxms2Vs+fLlisFgUOrr669YQ3x8vHLPPfdYtt98803F19f3so/t16+f8vrrr1u2g4KClIkTJzY6ZsaMGUpcXNwlzzF79mwlPj6+0djJkycVQElISFBuuukmJTo6WikpKbHs37hxo+Lo6Kjk5+dbxjZv3qwAypo1ayxju3fvVrp162bJ8PzzzysRERHK7t27FQ8PD2XJkiVNar355psbjc2aNUtxc3NrUvfo0aOVOXPmXDKXEKKBzMgI0UEMGDCg0baPjw9FRUVNjisrK+Pmm28mLCys0eWntLQ0hg8fjkqlsoyNHDkSo9HI6dOnr/j8s2bNYvXq1dTU1ACwcuVK7rjjDtTqhn+GjEYj8+fPJzQ0FHd3dwwGA2lpaU1mZH69CHb48OGkpaVd8fkv5s4776SiooLvvvsONzc3y/ixY8cICAhotK4mKiqqyePXrVvH5MmTLRkATp06xfjx43nuued44oknGh1/7NixJue52HmFEFdPGhkhOgiNRtNoW6VSNVmzUl5ezsSJE3F1dWXNmjVNHtMcU6ZMQVEUvv76a3JyckhISLBcVgKYP38+a9as4eWXXyYhIYGUlBTCw8Opra21Wg2/NmnSJA4dOsSePXuu6/Hr169vsj6ma9euREVFsWrVKsrKyqxRphDiMqSREUIADTMxsbGxODk5sX79erRabaP9oaGh7Nmzx7JoFWD37t24urri7+9/xfNrtVqmTZvGypUrWbVqFSEhIQwaNKjRue666y5uvfVWwsPD8fb2Jisrq8l59u7d22Q7NDT0GtM2ePjhh/nrX//K1KlT2bFjh2U8JCSEnJwcCgsLLWP79u1r9NiMjAyys7MZP358o3GdTseGDRvQarVMmDCB8vLyRuf99Xl+vS2EuDbSyAghLE1MRUUF7777LmVlZRQUFFBQUEB9fT0Af/jDH8jJyeGxxx4jPT2ddevW8fzzzzNv3rxGl1YuZ9asWXz99de89957jWZjAHr37s2XX35JSkoKqampzJw5s8mMETQ0PIsXL+b48eMsX76czz//nDlz5lx39scee4yFCxcyefJkdu3aBcD48ePp2bMns2fP5tChQ+zevZtnn30WwHJpbd26dcTExKDX65uc08XFha+//hpHR0fi4uIwGo2W5/rmm29YunQpGRkZvPnmm2zcuLHR5TohxLWRRkYIwcGDB0lKSuLw4cP06tULHx8fy5+cnBwA/Pz8+Oabb0hOTiYiIoKHHnqIe++91/ID/mqMGzcOT09Pjh07xsyZMxvtW7p0KR4eHowYMYIpU6YwYcKERjM2FzzxxBPs37+fgQMHsnDhQpYuXcqECROalX/u3Lm8+OKLTJo0icTERBwcHFi7di1Go5EhQ4Zw3333Wd61dGGm6nJvuwYwGAxs3LgRRVG4+eabqaioYOTIkbzxxhssXbqUiIgINm3axB//+Mcms19CiKunUn45TyyEEK1YcHAwc+fOvaaP7r/rrrsoKSlp8vkv12r37t1ER0eTmZmJm5sbPj4+nD59Gi8vr2ad9/777yc9PZ2EhIRG42PGjCEyMpJly5Y16/xCtHcyIyOEaPc2bNiAwWBgw4YNV/2YNWvWsHnzZrKystiyZQsPPPAAI0eOpGfPnpw/f56lS5deVxOzZMkSUlNTyczM5PXXX2fFihXMnj3bsn/lypUYDIYmjY0Q4uJkRkYI0WynTp0iLCzskvuPHj1KYGBgs5/nemZkioqKLO8e8vHxwcXF5aoe9+GHH7Jw4UJOnTpFly5diImJ4dVXX6Vz587XU7rF7bffzvbt2ykvL6dHjx489thjPPTQQ5b95eXllkXG7u7udOnSpVnPJ0R7J42MEKLZ6urqLvoOowuCg4MtnxAshBDWJI2MEEIIIdosWSMjhBBCiDZLGhkhhBBCtFnSyAghhBCizZJGRgghhBBtljQyQgghhGizpJERQgghRJsljYwQQggh2qz/By4WWNV7Xh0HAAAAAElFTkSuQmCC",
    234256      "text/plain": [
    235257       "<Figure size 640x480 with 1 Axes>"
    … …  
    269291  {
    270292   "cell_type": "code",
    271    "execution_count": 9,
     293   "execution_count": 10,
    272294   "id": "56b07967-3900-4454-ac0d-29cfae7cc1f9",
    273295   "metadata": {},
    … …  
    302324  {
    303325   "cell_type": "code",
    304    "execution_count": 10,
     326   "execution_count": 11,
    305327   "id": "56e81d82-fd55-464c-a746-66cf23822957",
    306328   "metadata": {},
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    309331     "data": {
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    312334       "version_major": 2,
    313335       "version_minor": 0
    … …  
    317339      ]
    318340     },
    319      "execution_count": 10,
     341     "execution_count": 11,
    320342     "metadata": {},
    321343     "output_type": "execute_result"
    … …  
    345367  {
    346368   "cell_type": "code",
    347    "execution_count": 11,
     369   "execution_count": 12,
    348370   "id": "fd7b4103-0436-4bda-bb39-96666c39f332",
    349371   "metadata": {},
    … …  
    352374     "data": {
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     376       "model_id": "36798865efc74c7aba687cf67cb26d54",
    355377       "version_major": 2,
    356378       "version_minor": 0
    357379      },
    358380      "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,
     381       "VBox(children=(FloatSlider(value=0.0, description='time', max=60.54166793823242, step=1.0), FloatSlider(value=…"
     382      ]
     383     },
     384     "execution_count": 12,
    363385     "metadata": {},
    364386     "output_type": "execute_result"
    … …  
    388410  {
    389411   "cell_type": "code",
    390    "execution_count": 12,
     412   "execution_count": 13,
    391413   "id": "e4691cae-637b-4555-ac87-d556521a4c3f",
    392414   "metadata": {},
    … …  
    395417     "data": {
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    398420       "version_major": 2,
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    400422      },
    401423      "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,
     424       "HBox(children=(VBox(children=(FloatSlider(value=55.0, description='time', max=60.54166793823242, step=1.0), Fl…"
     425      ]
     426     },
     427     "execution_count": 13,
    406428     "metadata": {},
    407429     "output_type": "execute_result"
    … …  
    435457  {
    436458   "cell_type": "code",
    437    "execution_count": 13,
     459   "execution_count": 15,
    438460   "id": "e4db2d8b-6183-4fbe-8ca1-940ef15aaa28",
    439461   "metadata": {},
    … …  
    442464     "data": {
    443465      "application/vnd.jupyter.widget-view+json": {
    444        "model_id": "8e808114c9b444eda1b6114137896b0b",
     466       "model_id": "6089ba159a1d4814b8807b78a65fa19e",
    445467       "version_major": 2,
    446468       "version_minor": 0
    447469      },
    448470      "text/plain": [
    449        "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
    450       ]
    451      },
    452      "execution_count": 13,
     471       "HBox(children=(VBox(children=(Select(description='species', index=3, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
     472      ]
     473     },
     474     "execution_count": 15,
    453475     "metadata": {},
    454476     "output_type": "execute_result"
    … …  
    463485    "\n",
    464486    "    plt.subplot(132) # atlas\n",
    465     "    my_sim.plot_atlas('h2o_vap',t=t,alt=alt)\n",
     487    "    my_sim.plot_atlas(sp,t=t,alt=alt)\n",
    466488    "    plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n",
    467489    "\n",
    … …  
    488510  {
    489511   "cell_type": "code",
    490    "execution_count": 14,
     512   "execution_count": 16,
    491513   "id": "9dd59a1c-58c4-41f0-9730-9e97d2607c6a",
    492514   "metadata": {},
    … …  
    495517     "data": {
    496518      "application/vnd.jupyter.widget-view+json": {
    497        "model_id": "476c88ed368e4e569b51fea78147885c",
     519       "model_id": "e3d87cbaec0147bca8596046cdaad974",
    498520       "version_major": 2,
    499521       "version_minor": 0
    … …  
    503525      ]
    504526     },
    505      "execution_count": 14,
     527     "execution_count": 16,
    506528     "metadata": {},
    507529     "output_type": "execute_result"
    … …  
    516538    "        if avg:\n",
    517539    "            my_sim.plot_profile(sp,t=t,logx=True,c=cmap(i),ls='--')\n",
    518     "    if avg:\n",
     540    "    if avg: # just for the legend\n",
    519541    "        plt.plot([],[],c='k',label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
    520542    "        plt.plot([],[],ls='--',c='k',label='average')\n",
    … …  
    538560  {
    539561   "cell_type": "code",
    540    "execution_count": 15,
     562   "execution_count": 17,
    541563   "id": "ff21a3dc-d44a-4f0e-9aa4-211741bb592d",
    542564   "metadata": {},
    … …  
    545567     "data": {
    546568      "application/vnd.jupyter.widget-view+json": {
    547        "model_id": "04a6123f7a8b4c7c857c03f394a2f1e0",
     569       "model_id": "525673a453824ea4bcdc5591d796dcb5",
    548570       "version_major": 2,
    549571       "version_minor": 0
    550572      },
    551573      "text/plain": [
    552        "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
    553       ]
    554      },
    555      "execution_count": 15,
     574       "HBox(children=(VBox(children=(Select(description='species', index=2, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
     575      ]
     576     },
     577     "execution_count": 17,
    556578     "metadata": {},
    557579     "output_type": "execute_result"
    … …  
    565587    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
    566588    "        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",
     589    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--',label=r)\n",
    571590    "\n",
    572591    "    plt.legend()\n",
    … …  
    589608  {
    590609   "cell_type": "code",
    591    "execution_count": 16,
     610   "execution_count": 18,
    592611   "id": "b5b73171-0101-4656-b2ce-7e398070ebbb",
    593612   "metadata": {},
    … …  
    596615     "data": {
    597616      "application/vnd.jupyter.widget-view+json": {
    598        "model_id": "60b937a2c3144c4abdec67d75cec3fc4",
     617       "model_id": "4ac76b3211434eb5b46298f1de86d554",
    599618       "version_major": 2,
    600619       "version_minor": 0
    601620      },
    602621      "text/plain": [
    603        "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
    604       ]
    605      },
    606      "execution_count": 16,
     622       "HBox(children=(VBox(children=(Select(description='species', index=2, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
     623      ]
     624     },
     625     "execution_count": 18,
    607626     "metadata": {},
    608627     "output_type": "execute_result"
    … …  
    613632    "\n",
    614633    "    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",
     634    "    for r in my_sim.reactions:\n",
     635    "        if sp in my_sim.reactions[r].products:\n",
     636    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
     637    "        elif sp in my_sim.reactions[r].reactants:\n",
     638    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--',label=r)\n",
     639    "\n",
     640    "    plt.legend()\n",
     641    "    plt.grid()\n",
     642    "\n",
     643    "    plt.subplot(122) # Atlas\n",
     644    "    my_sim.plot_atlas(sp+'_col',t=t)\n",
     645    "    plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n",
     646    "\n",
     647    "    plt.subplots_adjust(right=2)\n",
     648    "\n",
     649    "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",
     650    "\n",
     651    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
     652   ]
     653  },
     654  {
     655   "cell_type": "markdown",
     656   "id": "239adb94-d3b7-4a9a-81fc-297e72e63639",
     657   "metadata": {},
     658   "source": [
     659    "#### Species-specific reaction rates with atlas locator"
     660   ]
     661  },
     662  {
     663   "cell_type": "code",
     664   "execution_count": 19,
     665   "id": "c849fda1-3969-4709-bb17-fdcaff5cbf86",
     666   "metadata": {},
     667   "outputs": [
     668    {
     669     "data": {
     670      "application/vnd.jupyter.widget-view+json": {
     671       "model_id": "7e6bc22e2111489c88cbae0c2b6eea97",
     672       "version_major": 2,
     673       "version_minor": 0
     674      },
     675      "text/plain": [
     676       "HBox(children=(VBox(children=(Select(description='species', options=('o2', 'o', 'o1d', 'o3', 'h2o2', 'oh', 'h2…"
     677      ]
     678     },
     679     "execution_count": 19,
     680     "metadata": {},
     681     "output_type": "execute_result"
     682    }
     683   ],
     684   "source": [
     685    "def make_sp_rate_atlas(sp,t,lon,lat,avg):\n",
     686    "\n",
     687    "    plt.subplot(121) # Vertical profile\n",
     688    "    for r in my_sim.reactions:\n",
     689    "        if sp in my_sim.reactions[r].products:\n",
     690    "            my_sim.plot_profile('rate ('+r+')',t=t,lon=lon,lat=lat,logx=True,label=r)\n",
     691    "        elif sp in my_sim.reactions[r].reactants:\n",
     692    "            my_sim.plot_profile('rate ('+r+')',t=t,lon=lon,lat=lat,logx=True,ls='--',label=r)\n",
    620693    "        \n",
    621694    "    plt.legend()\n",
    … …  
    632705    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
    633706   ]
    634   },
    635   {
    636    "cell_type": "code",
    637    "execution_count": null,
    638    "id": "7d391c60-7d30-421f-aa51-5f3da14b3c64",
    639    "metadata": {},
    640    "outputs": [],
    641    "source": []
    642707  }
    643708 ],

  • trunk/LMDZ.GENERIC/utilities/photochemistry/photochem_postproc.py

    r3431 r3511  
    1515warnings.filterwarnings("ignore", message="The following kwargs were not used by contour: 'lat'")
    1616
    17 M          = {'co2':44,  # Molar masses
    18               'o':16,    # TODO: automatic parser
    19               'o1d':16,
    20               'o2':32,
    21               'o3':48,
    22               'h':1,
    23               'h2':2,
    24               'oh':17,
    25               'h2o_vap':18,
    26               'ho2':33,
    27               'h2o2':34,
    28               'co':28,
    29               'cho':29,
    30               'ch2o':30}
    31 
    3217background = 'co2' # background gas of the atmosphere
    3318
    … …  
    4631    Methods
    4732    -------
    48     get_profile(field,**kw)
    49         Get profile of a field (either local or averaged)
     33    get_subset(field,**kw)
     34        Get pa subset at fixed given coordinate of the data
    5035    plot_meridional_slice(field,logcb,labelcb,**kw)
    5136        Plot a meridional slice of a field
    5237    plot_time_evolution(field,logcb,labelcb,**kw)
    5338        Plot time evolution of a field (as a time vs altitude contour plot)
     39    plot_time_series(field,lat,lon,alt,logy)
     40        Plot time series of a field (at a specific location (lon, lat, alt), averged, or a combination thereof)
     41    plot_atlas(field,t,alt,logcb,labelcb)
     42        Plot atlas of a field
    5443    plot_profile(field,**kw)
    5544        Plot a profile of a field
    … …  
    8069        self.data[field] = value
    8170
    82     def get_profile(self,field,**kw):
    83         """ Get profile of a field (either local or averaged)
    84 
    85         Parameters
    86         ----------
    87         field : str
    88             Field name
    89         t : float (optional)
    90             Time at which to select (if nothing specified use time-average)
    91         lat : float (optional)
    92             Latitude at which to select (if nothing specified use area-weighted meridional average)
    93         lon : float (optional)
    94             Longitude at which to select (if nothing specified use zonal average)
    95 
    96         """
    97        
    98         if self['latitude'].size == 1:
    99             # 1D
    100             return self[field][:,0,0]
    101         else:
    102             # 3D
    103             if 'lat' in kw:
    104                 if 'lon' in kw:
    105                     return self[field].sel(latitude=kw['lat'],method='nearest').sel(longitude=kw['lon'],method='nearest')
    106                 else:
    107                     return self[field].sel(latitude=kw['lat'],method='nearest').mean(dim='longitude')
    108             else:
    109                 # Latitude-averaged profile: need to weight by the grid cell surface area
    110                 if 'lon' in kw:
    111                     return (self['aire']*self[field]/self['aire'].mean(dim='latitude')).mean(dim='latitude').sel(longitude=kw['lon'],method='nearest')
    112                 else:
    113                     return (self['aire']*self[field]/self['aire'].mean(dim='latitude').mean(dim='longitude')).mean(dim='latitude').mean(dim='longitude')
     71    def __area_weight__(self,data_array):
     72        return self['aire']*data_array/self['aire'].mean('latitude')
    11473
    11574    def get_subset(self,field='all',**kw):
    11675        """ Get a subset at fixed given coordinate of the data
     76
     77        Can also average over a given dimension. In this case,
     78        the meridional average is area-weighted.
    11779
    11880        Parameters
    … …  
    13395            Altitude of the slice. If nothing
    13496            specified, use time-average
    135 
    136         Raise
    137         -----
    138         Slice direction not provided
    139         """
    140         if len(kw) == 0:
    141             raise Exception('Slice direction not provided')
    142 
     97        """
    14398        if field == 'all':
    14499            data_subset = self.data
    … …  
    147102           
    148103        if 't' in kw:
    149             data_subset = data_subset.sel(Time=kw['t'],method='nearest')
     104            if kw['t'] == 'avg':
     105                data_subset = data_subset.mean(dim='Time')
     106            else:
     107                data_subset = data_subset.sel(Time=kw['t'],method='nearest')
     108        if 'lat' in kw:
     109            if kw['lat'] == 'avg':
     110                data_subset = self.__area_weight__(data_subset).mean(dim='latitude')
     111            else:
     112                data_subset = data_subset.sel(latitude=kw['lat'],method='nearest')
    150113        if 'lon' in kw:
    151             data_subset = data_subset.sel(longitude=kw['lon'],method='nearest')
    152         if 'lat' in kw:
    153             data_subset = data_subset.sel(latitude=kw['lat'],method='nearest')
     114            if kw['lon'] == 'avg':
     115                data_subset = data_subset.mean(dim='longitude')
     116            else:
     117                data_subset = data_subset.sel(longitude=kw['lon'],method='nearest')
    154118        if 'alt' in kw:
    155             data_subset = data_subset.sel(altitude=kw['alt'],method='nearest')
     119            if kw['alt'] == 'avg':
     120                data_subset = data_subset.mean(dim='altitude')
     121            else:
     122                data_subset = data_subset.sel(altitude=kw['alt'],method='nearest')
    156123
    157124        return data_subset
    … …  
    178145            raise Exception('Trying to plot a meridional slice of a 1D simulation')
    179146
    180         meridional_slice = self[field]
    181        
    182         if t == 'avg':
    183             meridional_slice = meridional_slice.mean(dim='Time')
     147        meridional_slice = self.get_subset(field,t=t,lon=lon)
     148           
     149        if logcb:
     150            plt.contourf(meridional_slice['latitude'],meridional_slice['altitude'],meridional_slice,locator=tk.LogLocator(),**plt_kw)
    184151        else:
    185             meridional_slice = meridional_slice.sel(Time=t,method='nearest')
    186         if lon == 'avg':
    187             meridional_slice = meridional_slice.mean(dim='longitude')
    188         else:
    189            meridional_slice = meridional_slice.sel(longitude=lon,method='nearest')
    190            
    191         if logcb:
    192             plt.contourf(self['latitude'],self['altitude'],meridional_slice,locator=tk.LogLocator(),**plt_kw)
    193         else:
    194             plt.contourf(self['latitude'],self['altitude'],meridional_slice,**plt_kw)
     152            plt.contourf(meridional_slice['latitude'],meridional_slice['altitude'],meridional_slice,**plt_kw)
    195153           
    196154        plt.colorbar(label=field if labelcb==None else labelcb)
    … …  
    216174        """
    217175
    218         time_evolution = self[field]
    219        
    220         if lat == 'avg':
    221             time_evolution = (self['aire']*time_evolution/self['aire'].mean(dim='latitude')).mean(dim='latitude')
     176        time_evolution = self.get_subset(field,lon=lon,lat=lat)
     177           
     178        if logcb:
     179            plt.contourf(time_evolution['Time'],time_evolution['altitude'],time_evolution.T,locator=tk.LogLocator(),**plt_kw)
    222180        else:
    223             time_evolution = time_evolution.sel(latitude=lat,method='nearest')
    224         if lon == 'avg':
    225             time_evolution = time_evolution.mean(dim='longitude')
    226         else:
    227             time_evolution = time_evolution.sel(longitude=lon,method='nearest')
    228            
    229         if logcb:
    230             plt.contourf(self['Time'],self['altitude'],time_evolution.T,locator=tk.LogLocator(),**plt_kw)
    231         else:
    232             plt.contourf(self['Time'],self['altitude'],time_evolution.T,**plt_kw)
     181            plt.contourf(time_evolution['Time'],time_evolution['altitude'],time_evolution.T,**plt_kw)
    233182           
    234183        plt.colorbar(label=field if labelcb==None else labelcb)
    235184        plt.xlabel('time [day]')
    236185        plt.ylabel('altitude [km]')
     186
     187    def plot_time_series(self,field,lat='avg',lon='avg',alt='avg',logy=False,**plt_kw):
     188        """ Plot time series of a field (at a specific location (lon, lat, alt), averged, or a combination thereof)
     189
     190        Parameters
     191        ----------
     192        field : str
     193            Field name to plot
     194        lat : float (optional)
     195            Latitude at which to plot (if nothing specified use area-weighted meridional average)
     196        lon : float (optional)
     197            Longitude at which to plot (if nothing specified use zonal average)
     198        logy : bool (optional)
     199            Use logarithmic y-axis
     200        matplotlib plot keyword arguments
     201        """
     202
     203        time_series = self.get_subset(field,lon=lon,lat=lat,alt=alt)
     204
     205        if not 'label' in plt_kw:
     206            plt_kw['label'] = self[field].units
     207        if logy:
     208            plt.semilogy(time_series['Time'],time_series,**plt_kw)
     209        else:
     210            plt.plot(time_series['Time'],time_series,**plt_kw)
     211           
     212        plt.xlabel('time [day]')
     213        plt.ylabel(field+' ['+self[field].units+']')
    237214
    238215    def plot_atlas(self,field,t='avg',alt='avg',logcb=False,labelcb=None,**plt_kw):
    … …  
    254231        """
    255232
    256         atlas = self[field]
    257        
    258         if t == 'avg':
    259             atlas = atlas.mean(dim='Time')
     233        if 'altitude' in self[field].dims:
     234            atlas = self.get_subset(field,t=t,alt=alt)
    260235        else:
    261             atlas = atlas.sel(Time=t,method='nearest')
    262            
    263         if  'altitude' in atlas.coords:
    264             if alt == 'avg':
    265                 atlas = atlas.mean(dim='altitude')
    266             else:
    267                 atlas = atlas.sel(altitude=alt,method='nearest')
     236            atlas = self.get_subset(field,t=t)
    268237           
    269238        if logcb:
    270             plt.contourf(self['longitude'],self['latitude'],atlas,locator=tk.LogLocator(),**plt_kw)
     239            plt.contourf(atlas['longitude'],atlas['latitude'],atlas,locator=tk.LogLocator(),**plt_kw)
    271240        else:
    272             plt.contourf(self['longitude'],self['latitude'],atlas,**plt_kw)
     241            plt.contourf(atlas['longitude'],atlas['latitude'],atlas,**plt_kw)
    273242           
    274243        plt.colorbar(label=field if labelcb==None else labelcb)
    … …  
    294263        """
    295264
    296         profile = self[field]
     265        profile = self.get_subset(field,t=t,lon=lon,lat=lat)
    297266       
    298         if t == 'avg':
    299             profile = profile.mean(dim='Time')
     267        if logx:
     268            plt.semilogx(profile,profile['altitude'],**plt_kw)
    300269        else:
    301             profile = profile.sel(Time=t,method='nearest')
    302            
    303         if self['latitude'].size > 1:
    304            
    305             if lat == 'avg':
    306                 profile = (self['aire']*profile/self['aire'].mean(dim='latitude')).mean(dim='latitude')
    307             else:
    308                 profile = profile.sel(latitude=lat,method='nearest')
    309             if lon == 'avg':
    310                 profile = profile.mean(dim='longitude')
    311             else:
    312                 profile = profile.sel(longitude=lon,method='nearest')
    313            
    314         if logx:
    315             plt.semilogx(profile,self['altitude'],**plt_kw)
    316         else:
    317             plt.plot(profile,self['altitude'],**plt_kw)
     270            plt.plot(profile,profile['altitude'],**plt_kw)
    318271           
    319272        plt.xlabel(field+' ['+self[field].units+']')
    320273        plt.ylabel('altitude [km]')
     274
     275    def to_chempath(self,t,dt,filename_suffix='_chempath',lon='avg',lat='avg',alt='avg'):
     276        """ Create files redable by the chemical path analyzer chempath (DOI: 10.5194/gmd-2024-163)
     277
     278        Parameters
     279        ----------
     280        t : float
     281            Initial time
     282        dt : float
     283            Timestep
     284        filename_suffix : str (optional)
     285            Suffix to append to the files being created (species.txt,
     286            reactions.txt, model_time.dat, rates.dat, concentrations.dat)
     287        lat : float (optional)
     288            Latitude at which to plot (if nothing specified use area-weighted meridional average)
     289        lon : float (optional)
     290            Longitude at which to plot (if nothing specified use zonal average)
     291        alt : float (optional)
     292            Altitude of the slice. If nothing
     293        matplotlib's plot / semilogx keyword arguments
     294        """
     295
     296        # Save pecies list in chempath format
     297        with open(self.path+'/species'+filename_suffix+'.txt', 'w') as fp:
     298            for sp in self.species:
     299                fp.write("%s\n" % sp)
     300
     301        # Save reactions list in chempath format
     302        with open(self.path+'/reactions'+filename_suffix+'.txt', 'w') as fp:
     303            for r in self.reactions:
     304                current_formula = self.reactions[r].formula
     305                current_formula = current_formula.replace(' ','')
     306                current_formula = current_formula.replace('->','=')
     307                fp.write("%s\n" % current_formula)
     308
     309        # Save rates, concentrations and times in chempath format
     310        rates   = np.array([],dtype=np.float128)
     311        conc    = np.array([],dtype=np.float128)
     312        conc_dt = np.array([],dtype=np.float128)
     313        times   = np.array([t,t+dt],dtype=np.float128)
     314       
     315        out    = self.get_subset(t=t,lon=lon,lat=lat,alt=alt) 
     316        for r in self.reactions:
     317            rates = np.append(rates,out[f'rate ({r})'])
     318        for sp in self.species:
     319            conc = np.append(conc,out[f'{sp} vmr'])
     320       
     321        out    = self.get_subset(t=t+dt,lon=lon,lat=lat,alt=alt) 
     322        for sp in self.species:
     323            conc_dt = np.append(conc_dt,out[f'{sp} vmr'])
     324        conc = np.vstack((conc,conc_dt))
     325
     326        times.tofile(self.path+'/model_time'+filename_suffix+'.dat')
     327        rates.tofile(self.path+'/rates'+filename_suffix+'.dat')
     328        conc.tofile(self.path+'/concentrations'+filename_suffix+'.dat')
    321329           
    322330class reaction:
    … …  
    603611    if reactions == 'read':
    604612        reactions      = read_reactfile(s.path)
     613        s.tracers   = read_traceurs(s.path)
    605614        s.species   = []
    606615        s.reactions = {} # reactions dict will be merged at the end
    … …  
    609618    for r in reactions:
    610619        for sp in reactions[r].reactants:
     620            if not sp in s.tracers or not s.tracers[sp].is_chim:
     621                raise Warning(sp, 'is not recorded as a chimical tracer')
    611622            if not sp in s.species:
    612623                s.species.append(sp)
    613624        for sp in reactions[r].products:
     625            if not sp in s.tracers or not s.tracers[sp].is_chim:
     626                raise Warning(sp, 'is not recorded as a chimical tracer')
    614627            if not sp in s.species:
    615628                s.species.append(sp)
    … …  
    622635    for sp in s.species:
    623636        # volume mixing ratios
    624         s[sp+' vmr'] = s[sp] * M['co2'] / M[sp]
     637        s[sp+' vmr'] = s[sp] * s.tracers['co2'].M / s.tracers[sp].M
    625638        s[sp+' vmr'] = s[sp+' vmr'].assign_attrs({'units':'m^3/m^3'})
    626639        # molecular densities
    … …  
    678691    return s
    679692
     693class trac:
     694    """ Tracer class
     695   
     696    A class to store useful informations about simulations tracers
     697   
     698    Attributes
     699    ----------
     700    name : string
     701        Tracer's name
     702    M : float
     703        Tracer's molar mass [g/mol]
     704    is_chim : bool
     705        Is it a photochemical tracer?
     706    """
     707    def __init__(self,name,M,is_chim):
     708        self.name    = name
     709        self.M       = M
     710        self.is_chim = is_chim
     711
     712def read_traceurs(path):
     713    """ Read the traceurs of a simulation
     714
     715    Parameters
     716    ----------
     717    path : string
     718        path to simulation
     719
     720    Returns
     721    -------
     722    dict
     723        dictionnaries of all tracers
     724    """
     725    tracdict = {}
     726    with open(path+'/traceur.def') as tracfile:
     727       
     728        for iline,line in enumerate(tracfile):
     729           
     730            # Empty line
     731            if len(line.split()) == 0:
     732                continue
     733               
     734            # First line
     735            elif iline == 0:
     736                if not '#ModernTrac-v1' in line:
     737                    raise Exception('Can only read modern traceur.def')
     738                continue
     739               
     740            # Second line (number of tracers)
     741            elif iline == 1:
     742                ntrac = int(line)
     743                continue
     744           
     745            # Commented line
     746            elif line[0] == '!':
     747                continue
     748
     749            # Regular entry
     750            else:
     751                line    = line.split()
     752                name    = line[0]
     753                is_chim = False
     754               
     755                for param in line[1:]:
     756                    if param[:4] == 'mmol':
     757                        M = float(param[5:])
     758                    elif param[:7] == 'is_chim':
     759                        is_chim = bool(float(param[8:]))
     760                       
     761                tracdict[name] = trac(name,M,is_chim)
     762               
     763    if len(tracdict) != ntrac:
     764        raise Exception('Mismatch between announced an read number of tracers')
     765       
     766    return tracdict
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