code/influx_figure_using_composites.ipynb

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    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import shapely.geometry as sgeom\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from dask.distributed import Client, LocalCluster, get_task_stream\n",
    "import xarray as xr\n",
    "import glob\n",
    "from aostools.climate import PlotEPfluxArrows#, set_size\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.path as mpath"
   ]
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Constants"
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    "#sns.set_style('whitegrid')\n",
    "#plt.style.use(['seaborn-whitegrid','seaborn-poster','plotstyle.mplstyle'])\n",
    "mpl.rcParams.update({'font.size': 18})\n",
    "xr.set_options(keep_attrs=True,display_style='html')"
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    "timescale = 20\n",
    "DJF_bool = '_DJFonly'\n",
    "root_path = '/mnt/nas4.meop2/meop40.data.model/CMAM/0A.daily/'\n",
    "root_path = '/mnt/data04/model/CMAM/0A.daily/'\n",
    "cesta = f'{root_path}composites_woSSW{DJF_bool}/'\n",
    "name = 'himalayas'\n",
    "lag = 0\n",
    "a0 = 6376.0e3 # [m]\n",
    "g = 9.81 #[m/s2 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af25cad-dc96-4482-ae13-fe428c0a1e07",
   "metadata": {},
   "source": [
    "# Functions"
   ]
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   "source": [
    "def cosphi(lat):\n",
    "    return np.cos(np.deg2rad(lat))"
   ]
  },
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   "source": [
    "# Load data"
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     "text": [
      "/mnt/data04/model/CMAM/0A.daily/composites_woSSW_DJFonly/acceldiv-calc_anomalies_comp_himalayas_20days.nc\n",
      "/mnt/data04/model/CMAM/0A.daily/composites_woSSW_DJFonly/ep_p_anomalies_comp_himalayas_20days.nc\n",
      "/mnt/data04/model/CMAM/0A.daily/composites_woSSW_DJFonly/ep_phi_anomalies_comp_himalayas_20days.nc\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (lag: 21, lat: 48, plev: 63)\n",
       "Coordinates:\n",
       "  * plev               (plev) float64 1e+03 925.0 850.0 ... 0.0015 0.001 0.0007\n",
       "  * lat                (lat) float64 -87.16 -83.48 -79.78 ... 79.78 83.48 87.16\n",
       "  * lag                (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 ... 3 4 5 6 7 8 9 10\n",
       "Data variables:\n",
       "    acceldiv-calc_wn1  (lag, plev, lat) float64 0.2569 3.677 ... -0.4105 -2.483\n",
       "    ep_p-wn1           (lag, plev, lat) float64 -11.24 -44.84 ... -0.0001523\n",
       "    ep_phi-wn1         (lag, plev, lat) float64 0.1493 0.383 ... -3.656 8.949</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-35f6a8bc-7bf7-410e-9814-0df99ed45fe8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-35f6a8bc-7bf7-410e-9814-0df99ed45fe8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lag</span>: 21</li><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>plev</span>: 63</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f560fd51-c9f4-4c7e-b94e-16ee857e70cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f560fd51-c9f4-4c7e-b94e-16ee857e70cd' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 925.0 850.0 ... 0.001 0.0007</div><input id='attrs-5fabcae5-722a-46c9-807e-fe17091d03f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fabcae5-722a-46c9-807e-fe17091d03f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6225ed16-fce7-4013-9776-c0b77d2e4aca' class='xr-var-data-in' type='checkbox'><label for='data-6225ed16-fce7-4013-9776-c0b77d2e4aca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hPa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1.00e+03, 9.25e+02, 8.50e+02, 8.00e+02, 7.80e+02, 7.50e+02, 7.00e+02,\n",
       "       6.50e+02, 6.00e+02, 5.00e+02, 4.50e+02, 4.00e+02, 3.50e+02, 3.00e+02,\n",
       "       2.85e+02, 2.50e+02, 2.00e+02, 1.70e+02, 1.50e+02, 1.30e+02, 1.15e+02,\n",
       "       1.00e+02, 9.00e+01, 8.00e+01, 7.00e+01, 6.00e+01, 5.00e+01, 4.00e+01,\n",
       "       3.50e+01, 3.00e+01, 2.50e+01, 2.00e+01, 1.50e+01, 1.00e+01, 7.00e+00,\n",
       "       5.00e+00, 4.00e+00, 3.00e+00, 2.00e+00, 1.50e+00, 1.00e+00, 7.00e-01,\n",
       "       5.00e-01, 4.00e-01, 3.00e-01, 2.00e-01, 1.50e-01, 1.00e-01, 7.00e-02,\n",
       "       5.00e-02, 4.00e-02, 3.00e-02, 2.00e-02, 1.50e-02, 1.00e-02, 7.00e-03,\n",
       "       5.00e-03, 4.00e-03, 3.00e-03, 2.00e-03, 1.50e-03, 1.00e-03, 7.00e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-ac3d77cc-e021-4a2a-9da2-ecf6f2b91fe2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac3d77cc-e021-4a2a-9da2-ecf6f2b91fe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96f01465-30b5-41c2-8ef9-e6811e185564' class='xr-var-data-in' type='checkbox'><label for='data-96f01465-30b5-41c2-8ef9-e6811e185564' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lag</span></div><div class='xr-var-dims'>(lag)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-10 -9 -8 -7 -6 -5 ... 5 6 7 8 9 10</div><input id='attrs-623b1ff6-80e2-445b-b4fe-e363efc3dbbf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-623b1ff6-80e2-445b-b4fe-e363efc3dbbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63a30104-a81a-4cef-8549-3dec430ac43c' class='xr-var-data-in' type='checkbox'><label for='data-63a30104-a81a-4cef-8549-3dec430ac43c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,\n",
       "         4,   5,   6,   7,   8,   9,  10])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6cf9d669-ffff-4720-8840-49fb35af2599' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6cf9d669-ffff-4720-8840-49fb35af2599' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceldiv-calc_wn1</span></div><div class='xr-var-dims'>(lag, plev, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2569 3.677 ... -0.4105 -2.483</div><input id='attrs-9dc47ccb-a377-4216-a592-a77bd1ac04a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9dc47ccb-a377-4216-a592-a77bd1ac04a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f739ce-9e92-44ff-a564-a3f38a6a3199' class='xr-var-data-in' type='checkbox'><label for='data-85f739ce-9e92-44ff-a564-a3f38a6a3199' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 2.56912833e-01,  3.67652733e+00,  2.65758488e+00, ...,\n",
       "         -7.43403932e+00, -3.14145141e+00, -4.36499750e+00],\n",
       "        [ 2.19743150e-01,  2.19826269e+00,  1.69029044e+00, ...,\n",
       "         -1.37060098e+00, -1.17113887e+00, -1.27672107e+00],\n",
       "        [ 2.85124760e-01,  1.03327251e+00,  9.19538652e-01, ...,\n",
       "          2.18412693e+00,  7.04999500e-01,  1.01820437e+00],\n",
       "        ...,\n",
       "        [ 8.41254269e+00, -6.72671128e-01, -5.35636449e-01, ...,\n",
       "          3.37714905e+00, -1.25007243e+00, -4.33187901e+00],\n",
       "        [ 4.18555058e+00,  1.11553148e+00, -4.85324828e+00, ...,\n",
       "          2.22769940e+00, -3.94386381e-01, -6.95963555e-01],\n",
       "        [-1.24043398e+01,  1.11432105e+01, -1.01994467e+01, ...,\n",
       "          7.69450418e-01,  6.33169750e+00,  7.36840569e-01]],\n",
       "\n",
       "       [[ 2.65440304e-01,  3.01355701e+00,  1.93420206e+00, ...,\n",
       "         -5.43871207e+00, -3.95968158e+00, -1.53030159e+00],\n",
       "        [ 2.67436330e-01,  2.13180524e+00,  1.43991511e+00, ...,\n",
       "         -4.05977838e-01,  6.64134345e-03,  1.06078050e+00],\n",
       "        [ 3.07007188e-01,  1.49707395e+00,  1.14867414e+00, ...,\n",
       "          1.41070042e+00,  1.69960661e+00,  2.37009887e+00],\n",
       "...\n",
       "        [-6.43625349e-02,  5.50463206e+00,  1.79422427e+00, ...,\n",
       "          1.70011840e+00,  2.72019705e+00, -3.49024689e+00],\n",
       "        [-6.35046838e-01,  3.62690600e+00,  3.38210017e+00, ...,\n",
       "         -5.05149093e+00, -7.34553790e-01, -6.34591406e+00],\n",
       "        [ 1.25379662e+01, -8.98677762e+00, -5.05052635e+00, ...,\n",
       "         -4.50032052e+00, -6.13068022e+00,  1.31169784e+00]],\n",
       "\n",
       "       [[ 7.60137651e-01,  6.28661848e+00, -1.53202205e+00, ...,\n",
       "         -7.12927762e-01,  1.17434628e+00, -1.96189556e+00],\n",
       "        [ 7.45270011e-01,  1.99326100e+00, -1.33513706e+00, ...,\n",
       "         -1.00806216e+00, -1.98232411e+00, -1.76928203e+00],\n",
       "        [ 4.83811578e-01,  1.02095702e+00, -3.61037326e-01, ...,\n",
       "         -1.24530896e+00, -2.59341420e+00, -7.95850529e-01],\n",
       "        ...,\n",
       "        [ 5.40740768e+00, -3.48015943e+00,  2.74515915e+00, ...,\n",
       "          4.03338644e+00,  3.80958035e+00,  4.81475797e+00],\n",
       "        [ 1.50415296e+01, -3.14771280e+00,  3.58034890e+00, ...,\n",
       "          2.73433810e+00,  2.28037965e+00,  8.03917829e+00],\n",
       "        [-7.14254505e+00,  7.17405988e+00, -6.67167917e+00, ...,\n",
       "         -2.31927879e+00, -4.10524420e-01, -2.48251955e+00]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ep_p-wn1</span></div><div class='xr-var-dims'>(lag, plev, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-11.24 -44.84 ... -0.0001523</div><input id='attrs-ff74ca7c-cceb-4fa2-b9f0-21282587d793' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff74ca7c-cceb-4fa2-b9f0-21282587d793' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca4ac6ba-1965-4d1b-a152-e90b4ab54630' class='xr-var-data-in' type='checkbox'><label for='data-ca4ac6ba-1965-4d1b-a152-e90b4ab54630' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.12384037e+01, -4.48434581e+01, -4.72112371e+01, ...,\n",
       "          6.96971108e+00,  1.84095326e+01,  1.06384332e+01],\n",
       "        [-1.03257398e+01, -1.94419183e+01, -2.98810739e+01, ...,\n",
       "         -3.20434529e+01, -1.51070768e+00, -1.15991181e+01],\n",
       "        [-9.09496836e+00, -9.11646187e+00, -2.15801465e+01, ...,\n",
       "         -3.40874153e+01, -1.12634980e+01, -8.29743278e+00],\n",
       "        ...,\n",
       "        [-3.04311675e-04, -1.72957498e-04,  2.20525275e-04, ...,\n",
       "         -4.97227099e-04, -6.74057028e-05,  2.78601998e-04],\n",
       "        [ 1.86305496e-04, -1.64588137e-04,  7.26932476e-05, ...,\n",
       "         -2.47479440e-04, -1.29587527e-04,  2.81447711e-04],\n",
       "        [-1.59968306e-05, -3.35340286e-05, -1.67772545e-04, ...,\n",
       "         -8.89976085e-05, -2.01466463e-05,  1.60299226e-04]],\n",
       "\n",
       "       [[-2.30030034e+01, -3.91114524e+01, -3.29072131e+01, ...,\n",
       "          1.33951953e+01,  4.18112049e+01,  1.73038705e+01],\n",
       "        [-2.13275057e+01, -1.56769709e+01, -1.91422659e+01, ...,\n",
       "         -1.64048566e+01,  1.29613962e+01,  1.28632200e+01],\n",
       "        [-1.91276446e+01, -2.83711405e-01, -1.07974483e+01, ...,\n",
       "         -9.24029466e+00,  1.94107469e+01,  2.23639672e+01],\n",
       "...\n",
       "        [ 9.13928598e-04,  3.46678039e-04, -2.96008563e-04, ...,\n",
       "          2.96692339e-04, -3.35580565e-04,  5.26883354e-04],\n",
       "        [ 2.41684595e-04,  4.08803795e-04, -9.11130203e-05, ...,\n",
       "          8.41305463e-05, -2.13254681e-04,  4.39921019e-05],\n",
       "        [ 2.20208280e-04,  1.90084238e-04, -1.19070664e-04, ...,\n",
       "          7.18648543e-06, -1.85027489e-04, -1.81950639e-05]],\n",
       "\n",
       "       [[-4.07583989e+01, -8.18909398e+01,  2.06486756e+01, ...,\n",
       "          3.22063568e+01,  9.85281034e+00, -1.55019859e+01],\n",
       "        [-3.74214290e+01, -4.62529565e+01,  1.03153454e+01, ...,\n",
       "          2.16946483e+01,  2.15880820e+00, -3.47442688e+01],\n",
       "        [-3.47983358e+01, -4.96081308e+01,  6.11035561e+00, ...,\n",
       "          1.66540081e+01, -1.70956759e+01, -4.33843071e+01],\n",
       "        ...,\n",
       "        [-1.38671394e-03, -4.69061373e-04, -2.78233258e-04, ...,\n",
       "          2.60374945e-05,  2.79130435e-04, -7.77850416e-04],\n",
       "        [-1.77645588e-04, -6.33922264e-04,  9.83864335e-05, ...,\n",
       "          1.79723948e-04,  1.88019559e-04, -2.97399392e-04],\n",
       "        [ 2.68656095e-05, -4.19825164e-04,  1.00102939e-04, ...,\n",
       "          1.16806563e-04,  7.84773709e-05, -1.52342110e-04]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ep_phi-wn1</span></div><div class='xr-var-dims'>(lag, plev, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1493 0.383 ... -3.656 8.949</div><input id='attrs-81fc83a9-882f-41a6-a555-8db2af1869ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81fc83a9-882f-41a6-a555-8db2af1869ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99fdeb6b-e3f9-4732-a660-c562415567ab' class='xr-var-data-in' type='checkbox'><label for='data-99fdeb6b-e3f9-4732-a660-c562415567ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.49341900e-01,  3.82997290e-01,  4.63444122e-01, ...,\n",
       "         -3.84936955e-01, -1.04198875e+00,  6.91998049e-02],\n",
       "        [ 1.49341876e-01,  2.95883664e-01,  6.05678398e-01, ...,\n",
       "         -1.67145903e+00, -7.45002839e-01, -1.17660042e-02],\n",
       "        [ 1.49341878e-01,  5.34487325e-01,  7.28662166e-01, ...,\n",
       "         -1.47794619e+00, -1.00862152e+00, -2.95962075e-01],\n",
       "        ...,\n",
       "        [-4.15085669e+00,  4.62656647e+00,  9.91835818e-01, ...,\n",
       "          4.57292032e-01, -1.42168991e+00,  1.22585178e+00],\n",
       "        [ 5.48545884e+00,  1.10705111e+00,  2.34062913e-01, ...,\n",
       "          1.73365230e+00,  4.34818616e-02, -3.21255029e+00],\n",
       "        [ 2.64801960e+00,  6.72277809e+00, -3.86889863e+00, ...,\n",
       "          6.47613167e+00, -8.99799786e+00, -7.30778198e+00]],\n",
       "\n",
       "       [[-3.17853757e-01,  5.19931630e-01,  6.98037471e-01, ...,\n",
       "         -1.96403311e+00, -8.69400941e-01, -4.43301529e-01],\n",
       "        [-3.17853718e-01,  4.65331640e-01,  6.75249186e-01, ...,\n",
       "         -1.77809821e+00, -1.66489075e+00, -9.17129810e-01],\n",
       "        [-3.17853709e-01,  6.20106546e-01,  7.07806126e-01, ...,\n",
       "         -1.05597741e+00, -1.88177944e+00, -8.18206866e-01],\n",
       "...\n",
       "        [ 4.16663196e+00,  5.13633529e+00, -7.43965002e+00, ...,\n",
       "         -4.80709478e+00, -1.06273850e+00, -6.59019436e+00],\n",
       "        [ 6.28265458e+00,  1.37578048e+01,  2.07987105e+01, ...,\n",
       "          8.18414808e+00,  6.10289507e+00, -3.87088674e+00],\n",
       "        [-1.53915987e+00,  1.41096041e+01,  1.46705386e+01, ...,\n",
       "          2.27815911e+01,  1.22626884e+01,  3.23970655e+00]],\n",
       "\n",
       "       [[ 1.28724229e+00, -6.16277113e-02, -2.94784802e-01, ...,\n",
       "          1.34058458e+00,  1.96148875e+00,  5.38711336e-01],\n",
       "        [ 1.28724236e+00,  1.34289156e-01, -7.58385510e-01, ...,\n",
       "          1.56955745e+00,  1.98193825e+00,  1.59251952e-01],\n",
       "        [ 1.28724237e+00,  9.22448901e-02, -8.15954105e-01, ...,\n",
       "          1.22928188e-01,  6.12643448e-01,  1.67239019e-01],\n",
       "        ...,\n",
       "        [-5.28538240e+00, -1.10004952e+01, -1.72330078e+01, ...,\n",
       "         -6.73017967e+00, -1.16231021e+01,  2.20318158e-03],\n",
       "        [ 1.70481175e+00, -6.77360507e+00, -1.56846154e+01, ...,\n",
       "         -1.50510902e+01, -9.76787531e+00,  4.59824223e+00],\n",
       "        [ 1.21362239e+01, -1.05404433e+01, -9.73293369e+00, ...,\n",
       "         -1.35152985e+01, -3.65645449e+00,  8.94938502e+00]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43ea9d60-206a-482a-872f-769c8a9e3cfa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43ea9d60-206a-482a-872f-769c8a9e3cfa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (lag: 21, lat: 48, plev: 63)\n",
       "Coordinates:\n",
       "  * plev               (plev) float64 1e+03 925.0 850.0 ... 0.0015 0.001 0.0007\n",
       "  * lat                (lat) float64 -87.16 -83.48 -79.78 ... 79.78 83.48 87.16\n",
       "  * lag                (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 ... 3 4 5 6 7 8 9 10\n",
       "Data variables:\n",
       "    acceldiv-calc_wn1  (lag, plev, lat) float64 0.2569 3.677 ... -0.4105 -2.483\n",
       "    ep_p-wn1           (lag, plev, lat) float64 -11.24 -44.84 ... -0.0001523\n",
       "    ep_phi-wn1         (lag, plev, lat) float64 0.1493 0.383 ... -3.656 8.949"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timescale = 20\n",
    "num_waves = 3\n",
    "wave_range = list(range(1,num_waves+1))#+['45678']\n",
    "e_type_ls = ['himalayas']#, 'eastasia', 'westamer']\n",
    "titles = ['Himalayas']#, 'East Asia', 'Rocky Mountains'] #'West America']\n",
    "noe = [37,37,25]\n",
    "var_ls= ['acceldiv-calc','ep_p','ep_phi']#,'acceldiv-calc_wn','ep_phi-wn','ep_p-wn','accelogw']#, 'accelogw'] #'ua'\n",
    "what = 'anomalies'\n",
    "inserted_wave = 0#'all'\n",
    "\n",
    "ds_ls = []\n",
    "for var in var_ls:\n",
    "    da_ls = []\n",
    "    for i,e_type in enumerate(e_type_ls):\n",
    "        if var == 'accelogw':\n",
    "            infile = f'{cesta}{var}_{what}_comp_{e_type}_{timescale}days.nc'\n",
    "            print(infile)\n",
    "            da_wave = xr.open_dataarray(infile)\n",
    "            da_wave['plev'] = da_wave.plev/100.\n",
    "            da_wave['plev'].attrs['units'] = 'hPa'\n",
    "            #print(da)\n",
    "        elif var == 'ep_p':\n",
    "            infile = f'{cesta}{var}_{what}_comp_{e_type}_{timescale}days.nc'\n",
    "            print(infile)\n",
    "            da_wave = xr.open_dataarray(infile)*-10000\n",
    "            da_wave = da_wave.expand_dims('wave')\n",
    "            da_wave.name = var+'-wn1'\n",
    "            da_wave['wave'] = [inserted_wave]#'all']\n",
    "            \n",
    "        elif var == 'ep_phi':\n",
    "            infile = f'{cesta}{var}_{what}_comp_{e_type}_{timescale}days.nc'\n",
    "            print(infile)\n",
    "            da_wave = xr.open_dataarray(infile)\n",
    "            da_wave = da_wave.expand_dims('wave')\n",
    "            da_wave.name = var+'-wn1'\n",
    "            da_wave['wave'] = [inserted_wave]#'al\n",
    "        \n",
    "        elif var == 'acceldiv-calc':\n",
    "            infile = f'{cesta}{var}_{what}_comp_{e_type}_{timescale}days.nc'\n",
    "            print(infile)\n",
    "            da_wave = xr.open_dataarray(infile)\n",
    "            da_wave = da_wave.expand_dims('wave')\n",
    "            da_wave['plev'] = da_wave.plev/100.\n",
    "            da_wave['plev'].attrs['units'] = 'hPa'\n",
    "            da_wave.name = var+'_wn1'\n",
    "            da_wave['wave'] = [inserted_wave]#'all']\n",
    "            \n",
    "        else:\n",
    "            da_wave_ls = []\n",
    "            for i in wave_range:\n",
    "                infile = f'{cesta}{var}{i}_{what}_comp_{e_type}_{timescale}days.nc'\n",
    "                print(infile)\n",
    "                da = xr.open_dataarray(infile)\n",
    "                #da['wave'] = ['#'+str(i)]\n",
    "                #da = da.squeeze()\n",
    "                da = da.reset_coords(drop=True)\n",
    "                if i == '45678':\n",
    "                    da = da.expand_dims('wave')\n",
    "                    da['wave'] = [i]\n",
    "                    \n",
    "                #da['plev'] = da.plev/100.\n",
    "                da_wave_ls.append(da)\n",
    "                \n",
    "            da_wave = xr.concat(da_wave_ls, dim = 'wave')                \n",
    "            #print(da)#.data_vars.keys()[0])\n",
    "        \n",
    "            #print(da_wave)\n",
    "        da_ls.append(da_wave)\n",
    "    da_conc = xr.concat(da_ls, dim = 'e_type')\n",
    "    da_conc['e_type'] = e_type_ls \n",
    "    ds_ls.append(da_conc)\n",
    "    \n",
    "ds_all = xr.merge(ds_ls).squeeze().reset_coords(drop = True)#, compat='override')\n",
    "ds_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9663965b-2bdc-4505-9f48-50c6dad36fa9",
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    "execution": {
     "iopub.execute_input": "2021-11-24T15:57:05.843638Z",
     "iopub.status.busy": "2021-11-24T15:57:05.843143Z",
     "iopub.status.idle": "2021-11-24T15:57:05.906836Z",
     "shell.execute_reply": "2021-11-24T15:57:05.906037Z",
     "shell.execute_reply.started": "2021-11-24T15:57:05.843584Z"
    },
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (e_type: 3, lag: 21, lat: 48, lon: 96)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
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       "  * e_type   (e_type) &lt;U9 &#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;\n",
       "Data variables:\n",
       "    ptrop    (e_type, lag, lat, lon) float64 dask.array&lt;chunksize=(1, 21, 48, 96), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-22a034b9-dd36-4731-a404-a858d8fa25fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22a034b9-dd36-4731-a404-a858d8fa25fd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>e_type</span>: 3</li><li><span class='xr-has-index'>lag</span>: 21</li><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-921eefb1-0594-43b2-af64-4121cb815a20' class='xr-section-summary-in' type='checkbox'  checked><label for='section-921eefb1-0594-43b2-af64-4121cb815a20' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-95f74c47-4cf9-46d2-a1ae-e7e5f784b559' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95f74c47-4cf9-46d2-a1ae-e7e5f784b559' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2434f241-9899-4472-bf22-6cb355f6b254' class='xr-var-data-in' type='checkbox'><label for='data-2434f241-9899-4472-bf22-6cb355f6b254' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-81202b97-fa6e-434d-a3ac-d98af6e677e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81202b97-fa6e-434d-a3ac-d98af6e677e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69cec9f1-54b4-40fa-8ed4-15991edf8694' class='xr-var-data-in' type='checkbox'><label for='data-69cec9f1-54b4-40fa-8ed4-15991edf8694' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lag</span></div><div class='xr-var-dims'>(lag)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-10 -9 -8 -7 -6 -5 ... 5 6 7 8 9 10</div><input id='attrs-c498409c-30e7-4235-b9ce-fea2cdd30afb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c498409c-30e7-4235-b9ce-fea2cdd30afb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa6f846-8314-408b-b66a-9578c50224cb' class='xr-var-data-in' type='checkbox'><label for='data-9fa6f846-8314-408b-b66a-9578c50224cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,\n",
       "         4,   5,   6,   7,   8,   9,  10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>e_type</span></div><div class='xr-var-dims'>(e_type)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;</div><input id='attrs-276bbfe0-ba49-4cde-9f49-84dc4011c05f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-276bbfe0-ba49-4cde-9f49-84dc4011c05f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff01ffee-316c-4f7d-9c7c-41400aeef8a0' class='xr-var-data-in' type='checkbox'><label for='data-ff01ffee-316c-4f7d-9c7c-41400aeef8a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;himalayas&#x27;, &#x27;eastasia&#x27;, &#x27;westamer&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0590fe60-cd7e-4dc3-8db0-f6d45198da9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0590fe60-cd7e-4dc3-8db0-f6d45198da9d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ptrop</span></div><div class='xr-var-dims'>(e_type, lag, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 48, 96), meta=np.ndarray&gt;</div><input id='attrs-f682f06f-d4a1-4206-8097-859136c9599a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f682f06f-d4a1-4206-8097-859136c9599a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4c03cf-a6fe-48b0-a896-094fe3b1dd53' class='xr-var-data-in' type='checkbox'><label for='data-9b4c03cf-a6fe-48b0-a896-094fe3b1dd53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.21 MiB </td> <td> 756.00 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (3, 21, 48, 96) </td> <td> (1, 21, 48, 96) </td></tr>\n",
       "    <tr><th> Count </th><td> 15 Tasks </td><td> 3 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (e_type: 3, lag: 21, lat: 48, lon: 96)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "  * lag      (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "  * e_type   (e_type) <U9 'himalayas' 'eastasia' 'westamer'\n",
       "Data variables:\n",
       "    ptrop    (e_type, lag, lat, lon) float64 dask.array<chunksize=(1, 21, 48, 96), meta=np.ndarray>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infile_name = f'{cesta}ptrop_absolute_comp_*_{timescale}days.nc'\n",
    "infiles = glob.glob(infile_name)\n",
    "#print(infiles)\n",
    "e_types = list(map(lambda x: x.split('comp_')[1].split('_{}days'.format(timescale))[0], infiles))\n",
    "da_trop =  xr.open_mfdataset(infiles, concat_dim='e_type', combine = 'nested')\n",
    "da_trop['e_type'] = e_types\n",
    "da_trop['ptrop'] = da_trop.ptrop/100.\n",
    "da_trop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b8f4d7-f828-495b-a442-8a1e56efab81",
   "metadata": {},
   "source": [
    "# Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "544a2edf-54a2-44a9-9ba9-5bef8c2da211",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:57:12.195551Z",
     "iopub.status.busy": "2021-11-24T15:57:12.194814Z",
     "iopub.status.idle": "2021-11-24T15:57:12.217519Z",
     "shell.execute_reply": "2021-11-24T15:57:12.215953Z",
     "shell.execute_reply.started": "2021-11-24T15:57:12.195487Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vert_const = a0\n",
    "#vert_const = 2*np.pi*a0/g\n",
    "lat_slice = slice(50,70)\n",
    "pres_slice = slice(70,10)\n",
    "lag = 0\n",
    "\n",
    "def integrate_fluxes(ds_all, lat_slice, pres_slice, vert_const = a0):\n",
    "\n",
    "    cosphi_arr = cosphi(ds_all.sel(lat = lat_slice).lat)\n",
    "    influx_vert  = (\n",
    "        (\n",
    "            ds_all['ep_p-wn1']\n",
    "            .sel(lag = lag, plev = pres_slice.start, lat = lat_slice)\n",
    "            *cosphi_arr\n",
    "        )\n",
    "        .assign_coords(lat = cosphi_arr)\n",
    "        .integrate('lat')\n",
    "        #*vert_const#*a0\n",
    "    )\n",
    "\n",
    "    outflux_vert = (\n",
    "        (\n",
    "            ds_all['ep_p-wn1']\n",
    "            .sel(lag = lag, plev = pres_slice.stop, lat = lat_slice)\n",
    "            *cosphi_arr\n",
    "        )\n",
    "        .assign_coords(lat = cosphi_arr)\n",
    "        .integrate('lat')\n",
    "        #*vert_const#*a0\n",
    "    )\n",
    "\n",
    "    sel_lat_dict = dict(lat = lat_slice.stop, method = 'nearest')\n",
    "    cosphi_arr = cosphi(ds_all.sel(**sel_lat_dict).lat)\n",
    "    influx_hor = (\n",
    "        ds_all['ep_phi-wn1']\n",
    "        .sel(lag = lag, plev = pres_slice)\n",
    "        .sel(**sel_lat_dict)\n",
    "        .integrate('plev')\n",
    "        *cosphi_arr/vert_const\n",
    "\n",
    "    )\n",
    "\n",
    "    sel_lat_dict = dict(lat = lat_slice.start, method = 'nearest')\n",
    "    cosphi_arr = cosphi(ds_all.sel(**sel_lat_dict).lat)\n",
    "    outflux_hor = (\n",
    "        ds_all['ep_phi-wn1']\n",
    "        .sel(lag = lag, plev = pres_slice)\n",
    "        .sel(**sel_lat_dict)\n",
    "        .integrate('plev')\n",
    "        *cosphi_arr/vert_const\n",
    "    )\n",
    "    \n",
    "    #total_div = outflux_hor - influx_hor +  influx_vert/1e4 - outflux_vert/1e4#.load()\n",
    "    total_div = outflux_hor - influx_hor +  influx_vert- outflux_vert#.load()\n",
    "    print(f'outflux_hor {outflux_hor.values} - influx_hor {influx_hor.values} + influx_vert {influx_vert.values} - outflux_vert {outflux_vert.values}')\n",
    "    print(f'total_div {total_div.values}')\n",
    "    return influx_vert, outflux_vert, influx_hor, outflux_hor, total_div\n",
    "\n",
    "def concat_fluxes(ds_all, lat_slice, pres_slice):\n",
    "    influx_vert, outflux_vert, influx_hor, outflux_hor, total_div = integrate_fluxes(ds_all, lat_slice, pres_slice)\n",
    "                                 \n",
    "    ds_q_ver = xr.concat([influx_vert,outflux_vert], dim = 'plev')\n",
    "    ds_q_ver = ds_q_ver.expand_dims('lat')\n",
    "    ds_q_ver['lat'] = [(lat_slice.stop+lat_slice.start)/2]\n",
    "    ds_q_ver.name = 'vertical'\n",
    "\n",
    "    ds_q_hor = xr.concat([influx_hor,outflux_hor], dim = 'lat')\n",
    "    ds_q_hor = ds_q_hor.expand_dims('plev')\n",
    "    ds_q_hor['plev'] = [(pres_slice.start+pres_slice.stop)/2]\n",
    "    ds_q_hor.name = 'horizontal'\n",
    "\n",
    "    #norm = np.sqrt(ds_q_hor**2+ds_q_ver**2)\n",
    "    scaling_hor = -1#*norm# because of negative `dp`  np.abs(ds_q_hor.mean())\n",
    "    scaling_ver = -1#*norm# because of negative `Fp` np.abs(ds_q_ver.mean())\n",
    "    ds_q_pert = xr.merge([ds_q_hor/scaling_hor, \n",
    "                          ds_q_ver/scaling_ver])\n",
    "    #print(ds_q_pert.mean())\n",
    "    ds_q_pert = ds_q_pert.fillna(0)\n",
    "    norm = np.hypot(ds_q_pert['horizontal'],ds_q_pert['vertical'])\n",
    "    ds_q_pert = ds_q_pert/norm\n",
    "    \n",
    "    return ds_q_pert, ds_q_ver, ds_q_hor, total_div\n"
   ]
  },
  {
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   "execution_count": 11,
   "id": "f6afe8a1-a736-4b87-baa8-50184a8b3177",
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    {
     "name": "stdout",
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     "text": [
      "outflux_hor 1.236930065689078e-05 - influx_hor -1.1521853380991632e-05 + influx_vert -1.127379122966249 - outflux_vert -0.04628523193572163\n",
      "total_div -1.0810699998764897\n"
     ]
    }
   ],
   "source": [
    "lat_slice = slice(50,70)\n",
    "pres_slice= slice(70,10)\n",
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         lat_slice,\n",
    "                                                         pres_slice)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "id": "a70c0c1e-5a8d-4033-bf0b-7a6b0b49f70e",
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outflux_hor 1.236930065689078e-05 - influx_hor -1.1521853380991632e-05 + influx_vert -1.127379122966249 - outflux_vert -0.04628523193572163\n",
      "total_div -1.0810699998764897\n"
     ]
    }
   ],
   "source": [
    "lat_slice = slice(50,70)\n",
    "pres_slice= slice(70,10)\n",
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         lat_slice,\n",
    "                                                         pres_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d0071d6-cda0-4098-a014-3c7ac34cd03e",
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outflux_hor 1.236930065689078e-05 - influx_hor -1.1521853380991632e-05 + influx_vert -1.127379122966249 - outflux_vert -0.04628523193572163\n",
      "total_div -1.0810699998764897\n"
     ]
    }
   ],
   "source": [
    "lat_slice = slice(50,70)\n",
    "pres_slice= slice(70,10)\n",
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         lat_slice,\n",
    "                                                         pres_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64a7b3db-3899-4236-bfa6-796d3087a6e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:57:24.182397Z",
     "iopub.status.busy": "2021-11-24T15:57:24.181750Z",
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     "shell.execute_reply": "2021-11-24T15:57:24.215533Z",
     "shell.execute_reply.started": "2021-11-24T15:57:24.182336Z"
    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outflux_hor 1.236930065689078e-05 - influx_hor -1.1521853380991632e-05 + influx_vert -1.127379122966249 - outflux_vert -0.04628523193572163\n",
      "total_div -1.0810699998764897\n"
     ]
    }
   ],
   "source": [
    "lat_slice = slice(50,70)\n",
    "pres_slice= slice(70,10)\n",
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         lat_slice,\n",
    "                                                         pres_slice)"
   ]
  },
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   "execution_count": 15,
   "id": "4a69d152-ea9d-4a61-a057-509b0749caba",
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     "iopub.execute_input": "2021-11-24T15:57:26.279721Z",
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     "iopub.status.idle": "2021-11-24T15:57:26.297372Z",
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     "shell.execute_reply.started": "2021-11-24T15:57:26.279677Z"
    },
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       "array(-15.63188833)\n",
       "Coordinates:\n",
       "    lag      int64 0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'acceldiv-calc_wn1'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f102fd9d-baa7-41e5-9f50-a5d9c7b5fb06' class='xr-array-in' type='checkbox' checked><label for='section-f102fd9d-baa7-41e5-9f50-a5d9c7b5fb06' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-15.63</span></div><div class='xr-array-data'><pre>array(-15.63188833)</pre></div></div></li><li class='xr-section-item'><input id='section-3b8b2259-7cd4-4bfb-8990-8eaf0063915e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b8b2259-7cd4-4bfb-8990-8eaf0063915e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a1a0f7be-244d-43f9-b73a-cfa1b23346be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1a0f7be-244d-43f9-b73a-cfa1b23346be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-889f7f43-2c04-4e67-a984-686adc678fa2' class='xr-var-data-in' type='checkbox'><label for='data-889f7f43-2c04-4e67-a984-686adc678fa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c298a13-3505-4808-b3c6-5b4e96dc6e9e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c298a13-3505-4808-b3c6-5b4e96dc6e9e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'acceldiv-calc_wn1' ()>\n",
       "array(-15.63188833)\n",
       "Coordinates:\n",
       "    lag      int64 0"
      ]
     },
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   "source": [
    "cosphi_arr = cosphi(ds_all.sel(lat = lat_slice).lat)\n",
    "conv_int = (\n",
    "    ds_all['acceldiv-calc_wn1']\n",
    "    .sel(lag = lag, lat = lat_slice, plev = pres_slice)\n",
    "    .assign_coords(lat = cosphi_arr)\n",
    "    .integrate(['lat','plev'])\n",
    "    \n",
    ")\n",
    "conv_int"
   ]
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       "array(-3741641.96342748)\n",
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       "    lag      int64 0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vertical'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-26cd8eae-69be-4066-ae7f-f72f4f49b6b1' class='xr-array-in' type='checkbox' checked><label for='section-26cd8eae-69be-4066-ae7f-f72f4f49b6b1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-3.742e+06</span></div><div class='xr-array-data'><pre>array(-3741641.96342748)</pre></div></div></li><li class='xr-section-item'><input id='section-4f43f21d-7030-476c-962a-c59fc9b10fe9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f43f21d-7030-476c-962a-c59fc9b10fe9' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-30f26302-b3dd-4e36-ad54-c9b47efe70be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30f26302-b3dd-4e36-ad54-c9b47efe70be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80e15c09-0479-4d4c-839c-7cc1f076f2c0' class='xr-var-data-in' type='checkbox'><label for='data-80e15c09-0479-4d4c-839c-7cc1f076f2c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-baf11be0-d363-4575-8576-6b342b7ff0d2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-baf11be0-d363-4575-8576-6b342b7ff0d2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;horizontal&#x27; (plev: 1, lat: 2)&gt;\n",
       "array([[-73.46333716,  78.86666099]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 68.65 50.1\n",
       "    lag      int64 0\n",
       "  * plev     (plev) float64 40.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'horizontal'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>plev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0cb39f78-96ec-4a2b-9de5-9496dc4d9da4' class='xr-array-in' type='checkbox' checked><label for='section-0cb39f78-96ec-4a2b-9de5-9496dc4d9da4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-73.46 78.87</span></div><div class='xr-array-data'><pre>array([[-73.46333716,  78.86666099]])</pre></div></div></li><li class='xr-section-item'><input id='section-ed7c06d0-73ca-49db-a4ac-c213d2dc7949' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed7c06d0-73ca-49db-a4ac-c213d2dc7949' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.65 50.1</div><input id='attrs-a90539ba-d087-40d9-bdf4-064c8a66c479' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a90539ba-d087-40d9-bdf4-064c8a66c479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd9e851-ea63-45c9-aed4-f622bda051f5' class='xr-var-data-in' type='checkbox'><label for='data-bfd9e851-ea63-45c9-aed4-f622bda051f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([68.652017, 50.099453])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ace18099-97e3-4073-8a34-8338cef2073b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ace18099-97e3-4073-8a34-8338cef2073b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda38cec-2739-48a8-86e3-40dc64cf1884' class='xr-var-data-in' type='checkbox'><label for='data-eda38cec-2739-48a8-86e3-40dc64cf1884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.0</div><input id='attrs-0110fbc8-6cd1-4e76-8c6d-db5973b9b211' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0110fbc8-6cd1-4e76-8c6d-db5973b9b211' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-322c9091-2f4c-4049-a856-e60f03c07980' class='xr-var-data-in' type='checkbox'><label for='data-322c9091-2f4c-4049-a856-e60f03c07980' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([40.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b3ba299-4e9c-42e3-9d9f-7cbc93d2abfa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b3ba299-4e9c-42e3-9d9f-7cbc93d2abfa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'horizontal' (plev: 1, lat: 2)>\n",
       "array([[-73.46333716,  78.86666099]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 68.65 50.1\n",
       "    lag      int64 0\n",
       "  * plev     (plev) float64 40.0"
      ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vertical&#x27; (lat: 1, plev: 2)&gt;\n",
       "array([[-7188169.2880328 ,  -295114.63882216]])\n",
       "Coordinates:\n",
       "  * plev     (plev) float64 70.0 10.0\n",
       "    lag      int64 0\n",
       "  * lat      (lat) float64 60.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vertical'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>plev</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c2b5dd7d-cd43-42b6-9e48-17d9f00b9b2e' class='xr-array-in' type='checkbox' checked><label for='section-c2b5dd7d-cd43-42b6-9e48-17d9f00b9b2e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-7.188e+06 -2.951e+05</span></div><div class='xr-array-data'><pre>array([[-7188169.2880328 ,  -295114.63882216]])</pre></div></div></li><li class='xr-section-item'><input id='section-43731d30-7dc9-43e5-86c9-bca6550b5a69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43731d30-7dc9-43e5-86c9-bca6550b5a69' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>70.0 10.0</div><input id='attrs-ecba72a7-26a8-4b1d-9e82-8db14f212677' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecba72a7-26a8-4b1d-9e82-8db14f212677' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-effe0777-77c2-4a8f-9aa0-c63d68cefa89' class='xr-var-data-in' type='checkbox'><label for='data-effe0777-77c2-4a8f-9aa0-c63d68cefa89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hPa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([70., 10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-5d163339-7647-4bbc-b104-d9c2358a6043' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d163339-7647-4bbc-b104-d9c2358a6043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0f15d7d-f47f-4098-bdc3-7b79b055a580' class='xr-var-data-in' type='checkbox'><label for='data-b0f15d7d-f47f-4098-bdc3-7b79b055a580' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-1869c2c8-ace2-405c-8a14-3ba5c4d9e3de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1869c2c8-ace2-405c-8a14-3ba5c4d9e3de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-795fd00d-e807-4b09-a027-e0df554e4055' class='xr-var-data-in' type='checkbox'><label for='data-795fd00d-e807-4b09-a027-e0df554e4055' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4132ce53-e812-4f56-addb-029318b0b910' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4132ce53-e812-4f56-addb-029318b0b910' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'vertical' (lat: 1, plev: 2)>\n",
       "array([[-7188169.2880328 ,  -295114.63882216]])\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;horizontal&#x27; (plev: 1, lat: 2)&gt;\n",
       "array([[-73.46333716,  78.86666099]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 68.65 50.1\n",
       "    lag      int64 0\n",
       "  * plev     (plev) float64 40.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'horizontal'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>plev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f98bda32-2cc2-4b17-8068-2a64c9761cc3' class='xr-array-in' type='checkbox' checked><label for='section-f98bda32-2cc2-4b17-8068-2a64c9761cc3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-73.46 78.87</span></div><div class='xr-array-data'><pre>array([[-73.46333716,  78.86666099]])</pre></div></div></li><li class='xr-section-item'><input id='section-f9f671cb-1f69-4377-946e-c0eeea386996' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9f671cb-1f69-4377-946e-c0eeea386996' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.65 50.1</div><input id='attrs-7680bb2a-b974-41ca-9e08-6dbe0681b4b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7680bb2a-b974-41ca-9e08-6dbe0681b4b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f9adc85-1c29-40de-8a02-19815b8208d7' class='xr-var-data-in' type='checkbox'><label for='data-9f9adc85-1c29-40de-8a02-19815b8208d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([68.652017, 50.099453])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-84a04ff1-4275-4d51-b9a1-100a9cf14633' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84a04ff1-4275-4d51-b9a1-100a9cf14633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09c76fa0-2005-4e23-a53d-482787ee9b24' class='xr-var-data-in' type='checkbox'><label for='data-09c76fa0-2005-4e23-a53d-482787ee9b24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.0</div><input id='attrs-8528e61c-458d-4aad-95ec-f6a230c8bece' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8528e61c-458d-4aad-95ec-f6a230c8bece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37e68620-443d-43b2-a739-1da387af03a1' class='xr-var-data-in' type='checkbox'><label for='data-37e68620-443d-43b2-a739-1da387af03a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([40.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3226b94f-7796-4dbd-ae6e-21b098574e7b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3226b94f-7796-4dbd-ae6e-21b098574e7b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'horizontal' (plev: 1, lat: 2)>\n",
       "array([[-73.46333716,  78.86666099]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 68.65 50.1\n",
       "    lag      int64 0\n",
       "  * plev     (plev) float64 40.0"
      ]
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       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vertical&#x27; (lat: 1, plev: 2)&gt;\n",
       "array([[-7188169.2880328 ,  -295114.63882216]])\n",
       "Coordinates:\n",
       "  * plev     (plev) float64 70.0 10.0\n",
       "    lag      int64 0\n",
       "  * lat      (lat) float64 60.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vertical'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>plev</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9a3ffa3f-8cdf-41ae-89b5-7b507c44f70b' class='xr-array-in' type='checkbox' checked><label for='section-9a3ffa3f-8cdf-41ae-89b5-7b507c44f70b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-7.188e+06 -2.951e+05</span></div><div class='xr-array-data'><pre>array([[-7188169.2880328 ,  -295114.63882216]])</pre></div></div></li><li class='xr-section-item'><input id='section-d046b124-5ac5-4cff-b60a-c2bc2b7a1309' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d046b124-5ac5-4cff-b60a-c2bc2b7a1309' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>70.0 10.0</div><input id='attrs-9944e307-b7ff-4493-834d-d920db7ad2fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9944e307-b7ff-4493-834d-d920db7ad2fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1710cd95-e385-446e-8004-89801662c140' class='xr-var-data-in' type='checkbox'><label for='data-1710cd95-e385-446e-8004-89801662c140' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hPa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([70., 10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-89127fa3-7d56-4fcc-b5ab-4795689305a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89127fa3-7d56-4fcc-b5ab-4795689305a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-477caad0-87aa-4da2-8c6c-7aec8004a314' class='xr-var-data-in' type='checkbox'><label for='data-477caad0-87aa-4da2-8c6c-7aec8004a314' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-2c357b60-86fe-4e3a-a253-63a2ddbee416' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c357b60-86fe-4e3a-a253-63a2ddbee416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cbe4baf-4be9-48d1-9179-a7fac120f36c' class='xr-var-data-in' type='checkbox'><label for='data-0cbe4baf-4be9-48d1-9179-a7fac120f36c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-562dc4d0-746e-4a7d-a9b1-3ef9a250e554' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-562dc4d0-746e-4a7d-a9b1-3ef9a250e554' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'vertical' (lat: 1, plev: 2)>\n",
       "array([[-7188169.2880328 ,  -295114.63882216]])\n",
       "Coordinates:\n",
       "  * plev     (plev) float64 70.0 10.0\n",
       "    lag      int64 0\n",
       "  * lat      (lat) float64 60.0"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_q_ver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "d4d86104-9458-42d7-9055-1d14bd8e62d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-07T22:20:06.234133Z",
     "iopub.status.busy": "2021-07-07T22:20:06.233544Z",
     "iopub.status.idle": "2021-07-07T22:20:06.722310Z",
     "shell.execute_reply": "2021-07-07T22:20:06.721550Z",
     "shell.execute_reply.started": "2021-07-07T22:20:06.234066Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.0, 90.0)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = dict(units ='inches', scale = 200000)\n",
    "kwargs = dict(\n",
    "              units='width',\n",
    "              pivot='mid', headwidth= 10,\n",
    "              minshaft=3          \n",
    "             )\n",
    "#kwargs = dict(units ='inches')\n",
    "p = ds_q_pert.plot.quiver(x = 'lat', \n",
    "                          y = 'plev', \n",
    "                          u = 'horizontal', \n",
    "                          v = 'vertical',\n",
    "                         **kwargs)\n",
    "plt.yscale('log')\n",
    "plt.ylim(1000,1)\n",
    "plt.xlim(10,90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "77e47e65-9e5f-4d67-97bd-3f1cbcc959ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-24T10:40:17.823290Z",
     "iopub.status.busy": "2021-09-24T10:40:17.822681Z",
     "iopub.status.idle": "2021-09-24T10:40:17.831156Z",
     "shell.execute_reply": "2021-09-24T10:40:17.830305Z",
     "shell.execute_reply.started": "2021-09-24T10:40:17.823223Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/data04/model/CMAM/0A.daily/'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c80e0be9-63d0-421b-8aa2-1e57947b7e79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-24T10:42:34.670257Z",
     "iopub.status.busy": "2021-09-24T10:42:34.669705Z",
     "iopub.status.idle": "2021-09-24T10:42:34.687146Z",
     "shell.execute_reply": "2021-09-24T10:42:34.686023Z",
     "shell.execute_reply.started": "2021-09-24T10:42:34.670197Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds_ua_clim = xr.open_dataset(f'{root_path}ua/ua_climatology.nc')\n",
    "ds_ua_clim['plev'] = ds_ua_clim.plev/100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "db00439f-332e-40b2-9d97-4ca95a3aa4a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-24T11:40:46.561914Z",
     "iopub.status.busy": "2021-09-24T11:40:46.561309Z",
     "iopub.status.idle": "2021-09-24T11:40:46.947793Z",
     "shell.execute_reply": "2021-09-24T11:40:46.947020Z",
     "shell.execute_reply.started": "2021-09-24T11:40:46.561848Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7fb0c2d1d1f0>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_ua_clim['ua'].where(ds_ua_clim['ua'] > 0) \\\n",
    "                .sel(month = 1) \\\n",
    "                .mean('lon').plot.contour(x = 'lat',\n",
    "                                          yincrease = False, \n",
    "                                          cmap = 'Greens',\n",
    "                                          levels  = 21, \n",
    "                                                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "ffd9ae0b-dd47-447f-8329-0c8d5fb58e17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-24T14:16:56.587242Z",
     "iopub.status.busy": "2021-09-24T14:16:56.586500Z",
     "iopub.status.idle": "2021-09-24T14:16:57.068327Z",
     "shell.execute_reply": "2021-09-24T14:16:57.067606Z",
     "shell.execute_reply.started": "2021-09-24T14:16:56.587175Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-220-b277535914db>:66: UserWarning: Setting the 'color' property will override the edgecolor or facecolor properties.\n",
      "  el = mpatches.Ellipse((70, 150), 35, 235, facecolor='None',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb0c2790f70>]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aU6dONTg2YcKEBm1qtZqoqCjKy1u3XOpzFmqRUs7pbhtuNVLP6ViwrIrE4RaOH+w8de38zCC2vDeE2x5JY+k30tj0ZhI2a9ve+tKlJGNPFEr7UYKHRaKapqH02FWc1uYvvDficjkwGq9QuTMNWwREz70T7ZyxKDZfgILWOQt6fTRabQQqlfa6GIRrD5VKCyhwuWqwp1upDiyhvI8/NeNnohg5BV12PuqMy1SVXGjVfA6Hlby8PeTl7QGodxwCAuI79eLb2MqCK/QKMjQL9al7EI3EZxcVHWHXrkdxOq0MGHAPc+e+R0TExFZdeLxN4LCTRMz6mqz3vtWlGREmU16r+qnVahYtWsSiRYsA9136+vXrefzxx3n33XdZuXIlK1as8DinLtXyvvvuq53LxO9+9zt+/etf89xzzzVYap8/fz5vvfUWO3fuZPXq1ezatYtZs2ZRV+AqJiaGwMBAduzYwQsvvNDmeIWKigrAnfLYFqKjvbd9VVrqjmPatm0b27Zta7JfdXVDzb622FEXVxET03j8VV173d/keq6Pr7gelUqF09m6LC2fs+Cj2yguUFFarCRpZOc6C+BOp/z6/cEsfvAitz+axqa3knDY2raaUVMTTvWVchwmG6FjY4mc0Z+S41exV1ibPU9KF1VVlykqOuS55F8M1uI84pY+RP9VT1N0YCtlpw+CdG8XKBQqDIah6PUx6HRR6PVRqFRtj/wPAfoWgLHSTn6omcpB8SgG9CPUuIQgoxJNSTk1pgLM5kKMxstYrc3vsxcVHaGoyJ1aFx4+jqioqWi14W22qyVqahruhzsG7QabDmXWlEbt2rJlOTNnvkr//iu7xUG4Hkel++6vJ6ZPNoZSqeTee+8lOTmZX//61+zcubOBs3Aj/v7+/OpXv2L37t3s37+fAwcOeAQzzps3r95ZGDlyJMXFxQ0yLRITE9m7dy9Op7PN8Qp1F8Hc3NZv2QFefW/UZZS88sorPP/8851mR908BQUFjR6vS13tLI0In7PgoxsRpJ3XMWlGNRo/F7aazl0izk4LYftHg1h4/0VueySNr95JxGFvvcNgtbrvAqxFJooOZBE+MY7IqfGUncnHktdwVcDhMFFScorc3B1NjllTnM+VD/5G7KJ7iZq1lIgxc1BkmNEa9ShE25wZl0ty5Uo2aWlXyMy8SlFRCUajCZfLhVbrR3h4KP37xzFqxgiUo4MpD7BREiwRsX4EmQdhqB5OdPUSNE5BTU0FRmMGpaVnqK7OaXLOkpKTlJScJCAgjpiYOQQFDcBb2xR1f+86pK4cZ59TqC7NQTg996aLio6yZcty5sx5m/j4nlFt0V5lANzOgjWv6eDBnkZgoFuIrC3xHU2dU3fRr3MW4Fq8Qh2JiYkcP36cjRs3kpKSQkJCAgkJCa2aNykpCYPBwNmzZ8nLy2vTVkRbqFsJaewufMoUt+O6b9++NjsLbSExMRG9Xs/p06cpLy9voIy5a9cuAI8UVm/Ss3OnfNz0pCbrUKlgUFLzd+fe4sr5UHZ+MpDY/kYWP3gRhbL1arwOR0D9Prqj2kbRgSxsFVbCxvUhaEjdnbWkujqby5c/4syZ/2vWUQAIDR1JQtwdhOXEorlgQ6nSwZgIbKN0OAPadtH9wx/+zSuvvM2WLXtITb1MWVkldrsDp9OFyWQhKyuXPXuO8Pff/Idjr+xhVLqBxJwgIiu0WDROsqKrOTOojAvxlZTG+KGPHc2QxG8wZsyPGTBgFSEhTQfqVVdf5eLFNZw69RtKSk7icrVte6YxrFbPyHHHgH0gJMrLsxr03bfvm0yf/vce4ygAOKqDemT65AcffMC2bdtwNRLwWlBQwOuvvw7ArFnX/s4vv/wy58+fb3S8/fv3s2vXLlQqFVOnTvU4FhMTQ1JSEpmZmbz55puEhoYyatQojz51cQt1QXltUW1UKpV861vfwmKx8M1vfpOamhqP4zabrT5moCPUZX1kZ2c3ODZhwgRmzpzJp59+yptvvtno+cnJyRQVFXXIBo1Gw4MPPkh1dXWDAMbLly/z17/+FbVazcMPP9yheZrCt7Lgo1vJuaLBbFKQNMLKhTNdI2166Uw4SqVk7qoMFq2+yNfvD8blbI3fLLBao/D3d28luGxOio/kEDIiiqAh4TjVJg5++BIOW9OOjxCC6OgZBAcn4u8fi8ddeJkLZbkNR5QSe7yKmjF+KAsdaC46WnWvbrW2zuGSEg4cOIFKpeTuu28jyKyhb7HEonFSEWCjIsBGboSZ3AgzfjYFIdV+RPolERIyHCmdGI1ZVFSkUlx8rMHYLpeDrKyNZGVtJCZmFhERE1Gr277F5HRqsduvLd1LTTWOhAMo8kahMHtueVRWXsJszich4e42z9OpSAX2KgPqoJ7lLBw5coRXXnmF6OhoZsyYUX8Xf+XKFb788kssFgt33nknq1atqj9n7dq1/PCHPyQpKYkpU6YQExODyWTi/Pnz7Ny5Eyklf/zjHxu9s58/fz6pqakkJydz1113oVB4ftaioqKIjY0lOTm5vn9b+PnPf86RI0fYuHEjQ4YMYdmyZQQGBpKTk8PXX3/Nyy+/3CAToK3Mnz+fl19+maeeeopVq1YREBCAwWDg2WefBeD9999n3rx5PPHEE/z1r39l8uTJGAwGrl69ytmzZzl37hyHDh0iMjKyQ3b87ne/Y9++ffz973/n2LFjzJ07t15nwWg08ve//73VqzJtxecs+OhWXC5B2jktQ0dbUGtc2G1ds9iVdjICpdrFrDsz2+QwWCzR9c4CgNNu5cLWj9D182f4wtVMWf0jjnz4f9itJo/zQkKGERU1FX//PjRbGEm6paJVxU7s/VQ4YlW4Cp0oq9qeeREQoCcwMICqKiMmk6XB8b17jzFv3nRCQoIQCPQ2FfoyFbFlemxKV63jUENBiIWCEAshRg1RFToCRQJBQQOIi1tIWVkyOTmbcbkaLs/m5+8lP38v0dEziImZiULRej0N96qC++/k0pdgm/EvUNWgTlvYoG9GxsckJNyFQtHzSrM4jMGoApsW/OkOvv/97zN48GC2b9/O2bNn2bp1K1arlbCwMObMmcPq1atZvXq1x376W2+9xZdffsnOnTvZvXs3BQUFSCnp06cPDzzwAP/1X//FjBkzGp1v/vz59YWiboxXqGPu3LmsXbsWIUSDbYqW0Gg0bNmyhVdffZV3332Xd955ByklsbGxrFy5skm72sLixYv54x//yOuvv86f//xnbDYb/fr1q3cW4uLiOHHiBH/7299Yv349a9euxel0Eh0dzbBhw3juuedalQraEqGhoRw6dIjf/va3fPrpp/zpT39Cp9MxadIkfvCDH9QHq3YGoivzjm9WfIWkOka/gTU8/lwx698L4eyJrpXuHTapkFkrMslKNfD1+4NxOpp3GAICLtK//1pcLgfFxce4evWalGvssCmMW/ktzOWF7H/7V9jMVcTFLSQsbAwqVdtXTaQSLJP9UOU50WS2vKz/0kt/wWazM23aeCZOHEVUlPsO3OWS7N17hE8/3drgnAcfvJPJk8c0O26NykmRwUqxwYpTKVE5BAEWNYEWNQEWFf5WJcaqK+Tn76a6+mqT4/Tvv4KwsFG0JqahsHAexcWzcBmyqJn+KiicaA4+g7J0YIO+69ePY+rUPxMbe2Pmc/cTMedLdHGZZK/5dpfNWVdIqqSkdVkR3clrr70GuHUPfPRMfvOb3/DSSy/93rey4KPbyc7QUFaiZOxkc5c7CxeORiGlYPbKKyx+MJ2ta4c06zAYjXEUFZ0hJ2dDg2N5Fw5TY6pk6oM/Zs7jv6fsaAGyA7WjhBMUlS6cIQrIbLn/nDlTmDJlLFqtZ/CfQiGYM2cK+/cfp6jIU666qqphOteN+DmU9C3xJ7ZUT0WAjUp/G9U6OxWBNvf4LgiwhBOQ8ABhFSaqM49QWnSiwTiZmRvIzd3GgAH3ERDQsBqoS0hq1E5q1C6KQ6uwR3+KY8B+RE0gmr3/1ahao5SS8vJzREU1zI7oCTgtepQ6M+4aKD1DGdOHj/bgcxZ8dDtSCk4f82febVUEhzioLO/at2XKsUhcLpiz8gpLHk5jy3uJDRwGKV1cvryOnTsfYN486NtI5euoqClEBk2h/FQhYRPiCBuvouTY1Q7VylKWu7APUOPyEyhqmh9ozpzmL5iNpWnd6Fg0a4sUhBn9CDO6z7GpnBh1Dqp1dow6O3lhZggXiIFT0FmmIYoLMGceg+J8hK0GKQQ2tSC1fAt69RACokbi0KrrHQS7+rqAu7gt4FShKB2I5uijiJrGUw8dDhMKhRqlsvWvoytxWvwRSicKTQ0um/crrDaFb8XYh7eQUiKl9DkLPnoGZ47pmXdbFWMmmdmztetz0tNORCJdgrl3Z3Dbo2lseXcIDrsSKSU5OZvZtu1unE53pPXVq57OQnj4WGJj56JWu9PHrEUmypMLCB0dQ8joGMpP5zc2ZatQlrmwDwBnqAJFfvuXKVJTL1NY2FCmun//uHaPqXEoCTMq650Hh8JFtc6BUWenWm/HFB+J7LcUAGmuBj8d1KagmQCTy4HSZkXn1BFsVuNnV6KxK6gpHU5Z1nKwBjUqvHQ9NltV/d+9J+K0uFfKlHpTlzkLarU/1htiZnz4aC/V1dUuh8Nh9DkLPnoEFWUqMtL9GDPRxN6vA5Gy65ds009FICXMXeV2GD75pz9fbb6foqLDHv2ys2HyZAgLG0qfPgvx8wtpMJY5pxKln4rgpAicVgdVqe1L31JYJcLswhmqQN1OZ+Hq1QLeeWd9g/ZBg/rRt2/bqnE2h8qlwGDSYDC5AxldQmLS1joPCjvGrHPIqjKorgJTFZirkS4XFgH9hz6DTufeZrhydSbCamj1vD25eqbTXOss6EzYK5ouuuRN1OoghFCRk5ND38aWwHz4aAMnT540AVk991Pm45bj1BE9oeFO4gfYus2Gi6cj2PFRAjH9qph591eYreca9AkNnUd09N8YMODeRh2FOoyXSqnOLCdoUBgBCU33awllmQtXsALZjvDZS5ey+Nvf3m6QDREQoGf16jvbbVNrUEhBoEVNbJmeISUxjBNz6e8YhCi8iqiuqi9sJSVcuPBvCgsPYrFEYjL1HgGjlnBY3IGt7riFrkEIQULCCj75pKGD6MNHWzAajezZs8cP+NLnLPjoMaSc1WG1CsZO7r4l1NLSs7z430P5n2d+xsgJI/lw93uMmuhOeQoLG83KlcdYtmwHNTWrWhjJTcW5Qsz5RoKHRaKLad9yubLMCQqB09C2j+vFi5n8619rsFg8hWr0eh3/9V8PER7efgemPQihIDx8LGPG/Jjo6GkNjl+9uo2PPvpzq2sb9AauX1noSuLjV/PHP/61zTLIPnzUIaXkxRdftGm12r1SynKfs+Cjx2C3Kzh/Ss/w0Rb8tK1XVvQGDoeFw4f/m/XrR1NTU8bXG7bz5LJncLlcvLHxX/zyzzu5++7TRES4q7dZrdGYTK0TPyk7lYetzELomBg0Ibo226YwSnBInKGt/7iWlVXwxhsfYbd7plyGhATzve894dXth7aiVPrRp89Chg37Lw9tBKsVTp++xNq1cVy58mm32edNXDU6pEug1HetsxAXt4C+fZ9h+vS5bNmyBbvd3qXz++jdXLp0iWeeecb+xhtvZFVWVj4AvmyIDiOEWA484XRW+/KivMCxA/6MmWTirgfL+PDNsC6JXcjN3cGXXy5o0J584jy//NYBfv/2Q0xZEMQnN1SYLSycw4ABVxqc1wCXpOT4VWIXDUYXHYCtvKFAUnMI6c6KcBmUSFqn5rhjx0EsFk9FR71ex/PPP0pYWNeuKDSFThfJ6NE/5urVrykuPsbZs1CnQLxt290MHPgAs2a91i4FyB6DVCDtGhSampb7eplRo36EThfDN7/5S8rLH2TUqPEEBwc3UFBUqO0gJC67psnMHaF0IpSO2j4tvwOlBH0rpEXS09Ox2+32jRs3do3eu49mMZvNroyMDEVhYaFUqVQfV1dX/1hKWQI+Z6HDSCk3CiGClMqAhoL1PtpM/lUNWz4zsHRVBfOXVrF9U+dUUAOoqSlnz54nycxseBerUum54459hIePQ+13kuKMhisCZnM/ysvHEBJyuuXJpHsv2VnTviBFZYULZ4QSqRcIc8tpcRkZDYs/zZ49qcc4CnUoFCri429HrZ5CaurfPI5dvvwBly9/wMqVx4mIGN9NFnoBhQvp7B69tsGDH2Hw4Eeors6hvPw8VmsV13sEQmUjYsoWzDkDMF8c0eQ4moh8DKOPUnZ0Jo6q0GbndDrBbIbvfa9l+/7+97+zf//+w+fPn/9Ha1+Tj07FChQAx6X0VInxOQs+ehxH9/sTGWNn5gIjxYUqzhzzvlBTYeFhPv98aqPHJk9+mZEjX0ChUKH1txMQbKckv3EbCgoWEhSUhlLZ/GqB0s99sXDVtK/AkqLcCahxGhQozC07HP7+OiIjPaPvY2Ojmujd3QjM5h/y8MMvsm3bPeTn7/Y4+tlnE1i4cD0JCXc1fnpPR7hAdu+Ob0BA30aFsAKHniZidi5XP34M28CGold1qILLiF9spUgzneq0UU32A3A4oKIC7ruvZbs2btzI/v37s6SUH7Xc20d34nMWfPRABJvXGwiLcHDHfeWUlajIueId0R0pXZw58weOHv1Jg2NhYWNYuPBTgoKuxSKEx7j3mkvyGl9TdTr9KShYSJ8+XzQ7r8LP/VFzWtvpLNhwp1CGKFDntewsPPvsI+2apzsoK5uAxRKHVgvLlu0kNfUN9u3zlP/dtu1uJk78f4wZ85NGxaV6MkLhQrp6ZnhYwODz2MrDsJU270g6qgy4HEo0IQ21OnzcGvTMd7CPWx6XS7Du7TAqy1Xc/3gphtCOlzy2WIr59NPxjToK8+a9z113nfRwFADCY90pb6VNrCwAlJePpays+WVyZZ2z0M6VBXBvRbiCFK3ZMu41mM19yc9fXP9cCMHQoU9x771pKJWeWz/Hjv2UHTvuw+nsvtTatiMRCtmqff6uRulfhTYmh+qLw2hRiloqsFeEoQkpbb6fj5sW38qCjx6Lxaxg7ethPPXdIh54spT/vBKBraZ9/m1e3h42bZrToD0wsD933HGgtlx0Q8JjTRjLNdRYmvuoCPLzb8fPrxR//8xGeyi1XnAWyl3uKpRBCpSVzWeLvPTSXygr86x22JqiUV2J3R5MVtb9SNnwb2swDOGRR4r46qvbKCjYX9+ekfExJSWnWLHiMFpt14gcdQjhjg/oiSsLAYNSEAKqLw1rVX97eTh+0U0XCvNxc9Pz3sE+fFxHaZGadW+FERFl5+6HyxCibZr3LpeT48dfbNRRGDv2p9x336UmHQWAsBgzJXktx0xIqSQ7+15qahq/gCn8VEinC2lvf0qootIFLukuLNXLcTq1ZGWtxuls+m+rVgewbNluRox43qO9quoS774bTkVFemeb2XFErfBUj3QWLmAtisFR1bqgV1t5OOrAKoSqN63s+PAWPe8d7MPHDWSka9nymYGkEVbm3V7V6vPM5nzWrUvi5MlfNTi2bNlOJk78tUee/42oNE4MYVZK8ltXXtrp1JOR8TgWS58Gx5R+qnZnQtQhXG7NhbaKM/U07PYgrlz5BlZrywGXCoWSadNeYebM1xocW7cukcLCA51hotcQilrnsJsDHG9EbSjFL6KwdguiddjK3CXPfVsRtyY96x3sw0cTHN0fwPGD/sxaaGRgYssp2bm5O1izJpaqqkse7SEhI3j44UJiY+e2OEZYtBmhoFUrC3U4nf5cufIoRuNgj3aln7LdmRAe45Q7kQEKpLrDQ3ULNTURZGQ80SpH4XqGDn2KpUu3N2jfvv1eHI626VZ0KYqeubIQMOgCUoLp8tBWn2MvdzsL6tD21Tnx0bvpWe9gHz6aYctnwRTlq1j5YBn+AU3fpV+48GqjIksTJvyaVavOoNNFtmq+8NjaTIhWrizU4XJpyM6+n5KSa6mZCq2qQ/EK9eNUuC8+vXF1oaoqiYyMx7Hb26ed0afPfO69N6VBu81WQWrqGx01r1Oo3zbrUc6CJGDwBSy5/XCaWy94Za/PiPCtLNyK+AIcffQa7HYFH78bxtPfK2Tlg2WsfS3cQ+HR5XJy4MC3SUn5d4Nz77jjQKP1CJojPMaMxaTCVKlps61SKikoWIzROISYmK0o/VRtVm5sDEW1BLvEaVCiKm46/uGll17o8Fzewm4PorBwHhUVo2kx6r4FDIYkHnmkmM8+m4TReE09c+/ep3C5nAwb9kwHrfUuddsQsgdtQ/hFFKAOLqfi5JS2nSgV2CtCfemTtyg95x3sw0crKMpX8/XnBgYPrWHKrOr6dru9ms8/n9LAUQgLG80jj5S02VEAd3BjaZ6ejlzgTKYELmc8hUKjwm5uu9NxI4LaFEqDoill3h6D06knP38x6enPU1Exho46CnVoteHce28KffrM92jfv/+bnD//T6/M4TXqYhZ60MqC/6ALSKcS05XENp9rL4/oEdsQQgi9EOKKEEIKIf7eyPFEIcQGIUS5EMIkhNgnhJjXHbbeLPScd7APH63k6H5/UpO1LFheSViknerqHN56K5Di4uMe/ZKSnmLlyhPtSrETCklolLlJ5ca2oNHbEALyc2aQnX0vRmNih+40lRUupJ9A+ve83H0Aszme3NzlpKV9h9LSqY2mRnYUpdKP227bQkzMbI/2Awe+zblzDa4d3Yaoy4boQToLAQPSsFztj8umbfO5tvKwnpIR8UsgvLEDQoiBwEFgKvAH4AdAALBVCNFwf9JHq/A5Cx3EV0iqOxB89ZkBlQoiotN5//34Bj2mT/8Hs2a91my2Q3NICU6HAo22YxkMAHaLBptZQ3hCIVVVw8jKeoDU1O+Tl3d7o5kTLaEsdYJDYuvXc3YRbbZQCgvnkpb2HTIyHqe8fDwul3dUN5tCoVAxZ867KBSe8xw8+BzJya906tytRalzi3q5atp+Ye4sbJWhaGOz0YQWtflcTUgJTqsW6eqeWhcAQohxwAvAz5vo8lvAACyWUv5WSvlPYCaQB/xD9DYJ0B6Cz1noIFLKjcB/lMqAnr4qfFNRUabEYnZirmlYBOr227cyfPi3OjaBFBRkBRLTv/Wpmk3hcirJOjGEyMF5BIS7hZKcTn/KyiZx+fJTpKc/S3HxLOx2Q6vGEw5Q5zhwhSq7NdDR6dRRVjaBjIwnSE9/juLi2djtXVuoSqFQodWG0q/fHR7thw69wNmzf+pSWxpDFej+fzuqDN1ryHUU71iGy+ZH1OJPUWhaX+xRaKzoEy66RZy6yVkQQiiB14EtQIMPvxDCH7gD2C2lPF3XLqWsBt4AhgATu8TYmwyfs+Cj1yGl5MSJX3Hh3BmGJHmmKN5zz3ni4hZ5ZZ78zEBCIq1o/e0dHiv7xGAcNiUJkxuL5g+nsHAeaWnfIS3tO+Tk3E1p6WQslj5Nbleo8pwIiwvbAFWXKQnX1ERQXj6W3Nw7uHjxW6Sk/IC8vGWYzX3xVjxCe1m48FP691/p0Xb48Pc5c+blbrLIjSqoAgC7sfOqp7YVpyWAwq9XogqoInLBF/XCUS0RMCANhcpBdXrT1Sm7gO8CScCzTRwfBfgBhxo5drj2p89ZaAc9Zx3Th49W4HI52L79PjIzPyU99f+Yt2gOAFptBPfccx6dLsJrc+VfCQQgpr+RK+ebL8vbEnarH7lnB9B37CUu7h2J1dhYLITAbg+hsjKEysqR7hZhR6fLR6+/ik53Fa22CLXaiEJRg/qKA9swDY4YZauKS7UWKVXY7UHYbKGYTH2xWOIwm/vgcvWcpfQbUSiULFjwMTt3PkBGxsf17UeO/BAQjB79391ilyqw0r1sb+/cLZm2UlPYh5L9C4mYvZWQCfspPzarxXMChiRjKw+lpijG2+YMFkJcH3D0mpSygQqXECIB+AXwSyllphCifyNj1cmx5jZyrK6t7Xt/PnzOgo/eg9NZw6ZNcyksdN80pKdc5IFH7mP4iLuZPGkNKpV3L2bFef7YbQqvOAsAmccS6TvuEv0nppO6c2yrzpFSjdkcj9nsGZehUNhQqaqICNqM7J9PZf5QRIkGlaoKpbKmNrDOhRDuhzvAToGUCqRU4nKpcTgCsdsDsduD6n93OAJxOrV090pBe1AolMyf/yFCKLl8+cP69iNHfoC/fx8GDXqgy21SB1bi6EGrCtdjTBmDX2Q+IeMPUlMcjTlzSJN9VYEV6GKvUnZkFp3w3rgopXy4Ff3+BVwBmttfqhNFqWnkmPWGPj7agM9Z8NErcDjMfPrpBCoqri3jp6e6awPcv3oNOZnev+t1ORUU5QR4JW4BwFIZQMGFeOLGXObywWHYre2/23S5NNhs4eRtvY+YZR+inZxGweZ7seb0q++jsJrRXjqHyliOIzgMa79EXP7u1RJVeTEBp/ZRMW9ph19XT0IIBfPmrUUIFZcuralv37lzNXp9LLGxs5s52/uoAivrZZJ7HoLS/YvQhBUTOW8TuZ8+ir2i8cyhgCHnkBKMF4d3sY1uhBAPAYuAWVLK5vYFzbU/G/twaW/o46MN+GIWfPR4bLYqPvxwkIejABBmcAcxRkZ3XBmxKfIzAwmLMaPx884cGUeGotI46Dv2UsudW4G0+1Gw+V4cVSFEL1mPX2QeAJqrl4la+yeCD35FQPJhgg9sJuqDV4hY9w/CP3udiI//if5Sslds6GkIoWDu3HeIj/d0hDZtmkNZ2fkutESi6sErCwDSqaJw60qkU0XU4vUIdWM35JLAIeew5vbDWd31r0UI4Yd7NWEzUCCEGCSEGATUecbBtW0G3BkP0PhWQ11bY1sUPlrA5yz46NFYrWW8914UZnO+R/uiRRsINdyNrUYQ0cnOgkIBUf2qW+7cCqqLDRRfjqHfhHQUKu/Y7arRkb/pPpwWPdG3r0MdUow+7RTC6cQeHot5yGicAe4veVVlKerifBCC6hGTvDJ/T0QIBQsXfkpQ0ECP9k8+GYHJlNfEWd5FqTOhUDl6tLMA4DQFUfj1CtTB5UTO2wQ3yH35ReWiDq7A2H2BjTogAlgKXLzusbv2+EO1z58EknFvQUxtMArUSVYeb+SYjxbwbUP46LGYzYWsWRPdoP32278mLm4hUkJxoYrImI5nKzRFYXYATqcgpn8VOekGr4yZcXgokx/cSdyoDLJPNr1P3Bac5kDyN91P7Io1xCz7iJKsGVgThmEdcK2qoKq8GE1BNghBTWwCzqCuTXPsapRKDXfddYK33zZ4tK9d24fHHqtEownq1PlVQe60yZ6UCdEU1vx4Sg/NI3z6DiLmbKbs2CycJveWVdDQM7jsakxXvPNebQcm4J5G2iOAf+JOo/wPcFZKWS2E2AjcJYQYLaU8AyCECMDtTFwEjnaN2TcXvpWFDuITZeocqqtzGnUU7rhjH3FxC+uf52T6ET+gBq2udelfbcVhV1KUE0D8kEqvjVmeE0FZdgSDZp5DrWt9nntLOIwG8jfdB0giv7MLxXiB/txRgg5tRX/hOC6NFvPQ8ZiTxiGcDgw7G2pU3GxoNMGsXp3ToP2zzybhdHauCqGmVha5qTiAnkZV8gQqTk8iYMg54le/SvjMLQQkniUgMRlj6qhuy+iQUtqllJ/c+AC+qu1yubYtvfb5T4BK4GshxI+FEN8C9uHehnhOSunTxGkHPmehg/hEmbxPZeWlRlUZV648TnT0DI+200f1qNUwYmznxSxlpRgIjzXjH9zYfm57EJzfOgGVxk7SvNNeGtONvTyC3E8fxbZLjeHP627JmIUbCQiIY9Wqsx5tlZVpbN9+D5153fALL8BZ49ejBJmaR1B2eB45HzyDMXUUgUnJRM7djKMqhLIjXRsY2hGklJeA6bh1FX4M/B/u1YklUsqt3Wlbb8bnLPjoUZSXX+CjjwY3aF+1KpmIiPEN2vOvqinMVzFmUuc5C5mp7uX6fkkVXhvTVBrMlSNJ9BmZSWh8odfGBfcetOXNSLCBYpjAMS3ylotZuJHQ0JEsXbrDoy0r6wuOHPlRp83pF1FATXE0vS0N1WE0ULJvMdnvf5O8DQ+S+9nDSEfHi6B5GyllppRSSCkbCDRJKVOklHdKKQ1SSr2UcoaUcnt32Hmz0KSzIIRwduDh8FX48tFWSkvP8PHHDVOz7rvvIqGhTQVXCc4c9advfxthkZ0Tu1BRrKWyxI/+SeVeHffyweGYK/wZtugEQuE9USUAU9JEyhauwvrjKQT8vRLxVl9K7n2CyhlLqZy1jKL7nqNq2m1enbOn06fPPObOXePRdvbsy6Sm/sf7kykcaMKKsBV7XcCoy3CaArEW9MVl9ckS+Gg+wFHg3ufJaOOYWuDedlvk45akoiKV9evHNGhfvTqLgICGWxLXc+aEngXLKxkz0cyOLzsjmEyQmRrCiCmFqDROHDbv6OK7HCoufD2eCffuJWFyKhmHvJfDbotNAMC6fxj2yhDCpu1A+VgVhVtW4TQHeG2e3sbgwQ9iNF7h+PGf1bft3fskYWGjiYiY4LV5NKElCKWrdmXBh4/eT0vZEP+WUr7flgGFEOHAfe03ycetRlXVFdatG9qg/aGH8tHrW/6yra5ScilVy+gJZnZuDuqUcsBZKSGMnlFA3KBKMi90XM2xjpKMWApS4xg47QL5F/phqfTehbxOlIlD5ZRvG0Xw0+fps/Jd8r9ahbwsakWZ7vLafL2FsWN/SmXlRS5efLe+7bPPJvLIIyXtKmfeGH4RBQA+Z8HHTUNzMQtbuSZw0RZqas8taZdFPm4pTKZcPvxwQIP2Rx4pbpWjUMfpo3qCQ5wkDPZWEKInBVkB1FiUJAzz7lYEQMr2cUiXYPiSY60u6tMSN4oy6TYex3anFccjZUR+/u8uC3D0i8wjYu5GYle+izqkZ3wlCCGYPfs/+Pt76vZ88cUMXC7vbAf5ReTjtGp7vMaCDx+tpUlnQUp5m5Ryd1sHlFIaa88923Lv3o8vdbL9WCzFrF0b16D9oYfy0WrbJpGbdl6H0wkDhnSOs+ByKbh4JoxBo0sxRFi8OnZNtZ7UnWMITyhk6PxT3CiK0x4aFWWSApntQqa6EAqomT6wCcW+DiJc+A9MIXbFe/S56138Ey6ijcojesn6NpVE7kwUChUrV57waKuoSOXo0Z94ZXxtbDbWwj70tuBGHz6awifK1EGklBuFEEFKZUDLZdt81FNTU8F770U2aF+9OqtNKwp1OOyCglw1feI7L3f++I44Bo8uZdrtWWx+J8mrY189M4iAMCP9J6Vhrgwg61hih8YzDx3fpCiTwq+G4MdOEzzsKoGOv2HJHkD15STMWYM6FPWu8LMQOPQMwSNOoAowYq80ULJ/Aca0kWjCiold/j6RC76g4KtV0ETp7a5Er4/ijjsO8MUX0+vbzp59mZiYWfTrt6zd46qCytEYyqlK9l4MhA8f3Y3PWfDR5djt1bz/ft8G7ffdl95iMGNz5GZrGDXBjBCyU+IWrCY1J3b2YdrSbOKHVJDtJUXHOlJ3jkEbZCJp3imsVXoK0xr+jVqLLTYBhdWM/txRj0JS5qHu9FPr54MI/t1W+EEU/gPS8R+QjsuhapfjoDaUEDzyOAFDzqFQO7Bc7UfJvkWYswfWOwU1BXHXSiJP3Ef50Z6Rtx8dPY2pU//CoUMv1Ldt3bqc+++/1EAqurXo+14BwHw1wRsm+vDRI2iTsyCEGAh8F5gMhNBwG0NKKdv3CfNxS+BwWFm3bhh2u2ethVWrkgkObqiv0BZyszVMmmEiLNJBSaG6Q2M1xbnDUQydVMS0pVlcvRyEy+nNO2TB2U1TmBiwi1HLD3GsWkdFbvsqFmquXiZs6wfgdCIAKQTBB7fgCApBqjWoi/MQQN6Bhyk9uABt9FX8B6TiPyDtBsdhKOasgY04DhJd3wyCRx5HH38Fl0NJ9cXhVCVPwFbWcMUIwJgyFr+IQkLGHcJWGonpcsOg1u5gxIjnycnZzNWrX9e3ffjhIB5/3IRK1fa0QV3fDOyVBhyVN7ecto9bi1Y7C0KIkcB+3KU/04ABwHkgDIgGLgNXO8FGHzcJTqeNDRsmYzJ5yu+uXHmsGR2F1pOb5b6g9Ym3dZqz4HIqOPRlP25/LI0RUwo5e8C7efQuh4qT62cx5eFtjLt7L4ffW4C5vO01DOpiFmzhsThCI9DkZ6E0GVFVlrpDIoSgevhEd2epwJof764P0JjjYFdhzh6IKSMJS248AQPTCBpxHE1IGQ5TAGVHZ1F1YUyr8vFL9i9AE1pMxJzN2MvDmnQsuhIhBAsXfspbb3lmomzffh+LF3+BEG1YpVI40fXJwpg2El+8go+bibasLPwSsAGTgFKgCPiOlHKnEOIp4DfAnd430cfNgMvlZPPmhZSVeca9Ll++12v57SVFKqxWQVy8jTPH/L0yZmNkpxvITgtm/Lxc0k+HYzV51zGxW/w4sW42Ux7ZzoR793Do3YXYLdo2jdFczEKzhaQacxwGpuKfkEbAwLT6btaiGAq3L8eUkQSuNuhOuFQUfr2SPne/TdSS9eSufwxXja5Nr60zUKv9ue++dD766FqxpOzsTVy48E+GD/92q8fRRl9FobZjyfFtQfi4uWjLGuoM4DUpZRrXwrUFgJTyddxFPX7nXfN6Pr5siJaRUrJ9+yry8/d6tN9++1ZiYmZ6cR5BXraGPv06t0AQwMHN/VBpXExa2DmLaeaKQE58MhO/ACvjV+1rczlrW2yCh6MA4AiJqC8k1aqKk7WOQ+n+RWSv+TZ5n6+m7NgMcj97mLxPH8F0aXjbHIVanOYACrfehcq/msgFn3stXbSjBAcPZtGiDR5tBw48S1HRsVaPoe+bgXQqsOS1P/bGh4+eSFuchUDcWw3gXmEAuP727QBuh+KWwldIqmWOHv0JmZkbPNoWLfqMuLhFXp8rN1tDVKwdlapz/x0VxTrOH4pi6IQiwmJMnTJHZV44ZzZOJTi2lFHLD7f5oloX4FhXdVJhMtYfU5UXt63qZK3jUHFiBjVeSAmsKYqlZO8i9H0zCZ28u0NjeZP+/e9kxIgXPNo2bJiE3d66/7Gu7xWsBXHdVqHRh4/Ooi3bEIW4YxOQUhqFECbg+gLnIYB3dHB93DSkpb3NmTO/92ibO/c9+vdf0Snz5WZrUKkgqo+N3KzO/cI+vrMPg8eWMH1pFl+8MZTO2KMuSo8jdfs4hi48SdK806TuGNeq81ob4NidCo7GtNFoIgoxjDlKTUk0pkvDWj6pC5gy5f/Iyvoco/FKfdu+fc8wb96aZs4Cpd6IX3gRpYfndLKFPryNEOLFDg7xrpQy0xu29FTa4iycBiZe93wP8B0hxFHcKxTPAme8Z5qP3k5u7k727PmGR9uUKX9k8OCHOm/OLHf8QFy8vdOdBZtVxdFtccxekcmAEWVknPOOVPCNZJ0Ygi7YRP9JaVgq/ck63rIGQ5sCHLuR0oPz3QGPszdjLw/FVtr98sgKhZI77tjP2rXXFB4vXVrLoEEPEh/fdPEtXW3KpC9eoVfyErWfjHacK3EH/2d60Z4eR1u2Id4HwoQQddFIPwOCgV3ADsAA/I9XrfPRaykvv8CXX873aBsy5BuMGvW9Tp23qlJJVYWCfgM7R8nxRlKPRVKSr2fa0iy0/p1T9RLcGgwFqXEkzT9FVGJOi/3NQ8dTtuAeSu56ioo5Kyh64DsU3/1Mz6s66VJS+PUKXDU6ohZtQCib/xu6XHb27HmySVlms7mQQ4e+32Gz/P1jWbDgY4+2LVtux2ptWrLav/9FHCZ/bKXdn+Hho118F0ho42M8t0jaS6udBSnlR1LKWVJKS+3zU8Bw3H/g54FRUsr9nWNmz8UX4NgQs7mgQanpiIgJzJr1ehfMLrhwRk/iCAs6vXfLPjeGlIJdnwxAq3ew4N5LCNFZsRJuDYaK3DBGLTuMoU/zdRZssQnYYvt7xCy4NNr6AEfhdLQtZqETcVn9Kdq5DHVwBYZxh5rtK4SKiooUMjLWNXr8zJk/4HJ5x2kbMGAVfft6OlRbtixHyob/Y1VAJfp+l6hO96VM9mJKpJRZbXkA2d1tdFfRorMghFAJIe4WQvxICPF4bVVJAKSUOVLKv0op/yGlbGsp65sCX4CjJ3a7iTVrYj3aFAo1y5fvQaHompCWk4f1qFQweoK5S+Yrzfdn/xf9iRtcxfj5uZ02T50Gg9WoZ9zde9GHVDXZ98ZCUsEHNhP1wStErPsH4Z+93mWFpFqLNa8fxrThGMYcRm1o2hESQjB+/EucPPnLBqsLZnMB6elvMWbMj7xm1/z5H3g8Lyo6THr62w36BY04CUDludbFlPjocUzFXQCxrVTUntv6lJleSrPOghAiBDgBrAN+C7wOpAkhxneBbT56GS6Xs1Zn39NvWr06p11KeO2lMF/D1Sw146aYGtjSWaSeiCT1eAQT5uUSn+j9ypR12C1+HF83CykFE+7dg0bfeGGmRgtJAarKUtTF+e6YhRGTOs3O9lB6aB4uu4bwWVtp7v/Wp88C/PxCG6wunDnzMoMGPdSgmmRH0GiCWb58j0fbnj2PU1V1LfhRqGwEDj2N6UoiTlPbBbR8dD9SyiNSytJ2nOesPdfYcu/eTUsrC/8LjAS+BJ4D/g4EAK91sl0+ehlSSvbs+QalpZ4xrvfccwG9PqrL7Tl52J+oWAd94jsvjuBG9n3Rn5I8PfPuuUxIZOetalgqAjn5ySz8AqyMu7txDYZeE7NwHS6rP2VH5qCLzSFgyLkm+zW2utAZqwp1xMTMYtgwT2GmDRsm1c8dMPg8Sr8aKpN991A+bl5achaWA1uklHfUbjV8B/gxMEYI0bC28C2IL2bBzZkzL3Px4nsebcuW7SYkpHv0/8+d1GOrEbWrC12D06Fg69rBOB0Klj2eSlBo55VjrswP48wXbg2GsXftR6n2dIq8IsrUDRhTRmMt6EPY1J0o/JouBV63upCdvRHonFWF65k69Y8ez63WEk6f/g0gCR55gpriKGoKfF+JNxtCiIVCiG8JIX4mhHjxhsfPutu+rqQlZ6EvsPmGto24I3j6dYpFvQxfzAJcufIZR4963tHNmfMusbHdV1mwpkbB+dM6Ro4zo9F0nUKgsVzLpjeTUCgly59IwT+487Iyii7Gce6rSYT3L2TS6l0NtiS8KsrUZQhK9i5G4WcldMrupnvVri4kJ/8Zl8vZaasKdSiVfqxa5Rnjcfz4i2hHrkMTWkLl2Yl0RWCjULjQBpkQys4P3r2VEUIkCSFSgC24V9R/gTu98sbHLUNLOgt+QNkNbeXXHeuxCCF+AozDndqSAGRJKfs30z8R+D0wG9AAJ4GfSyl3dr61vZeiomNs2+Yp7DN27E8ZMuThbrLoGicO+zN2splhYyycPtp5tSJupLxIz5dvJbH8yRSWP57K568Pw1LdOYWtcs8OwGbyY/SdB5ny8DaOr5uNuTyoV4gyNYWtLJLKs5MwjDmCMW0ENQWNl+ru02cBGo0BozGTYcP+q9NWFeoIDR3BpEm/r3eM5z0xl9jpV6g8P5Lqi8NbONs7JM45Q/9J7hodNSY/rEa9+1Glq/+9xqjDUqWnxqjH5fTp5LWTf+O+WX4B2Me1694tS5tKVN9AT7+T/g1uR+ckbg2IJqktvX0QcAB/ACqBp4CtQojbpJTbO9fU3onZXMCGDZ5Bcv363cGECb/qJos8ybmiobhQxfgppi51FgBK8vz56u1Elj6eytJvpLLxjaHUWDrycWua4st9OPbBPMat2suUh7dz4pNZiB29Q5SpKcqPT8d/YAqRc7+k4KtV2CsaluoWQjB69A/ZsePeTl1VuJ7Ro/+b8+f/xtgV/Vj07UWc3HSS9M8qmTRxaedPLlzEDMui/Go4JRnRaIPMaAMt6IOrCYkrRqNrWBPFZvbDatRhrdJTnBFDzumBIL1ZVv2mZRLwOynl37rbkJ5Ca769vi+EuP+652rcXzf/TwhxY46TlFL2lMqTA+vSOYUQ53AHZjbFb3E7FOOllKdrz3kXdwnufwghkmRjydXcujELTqeNjz7yVBIMCOjHwoWftK2kb6ciOHnYn8V3VhIeZe+0stVNUZAdyJb3hnD7o2nc/lgqm/4zFLutc+70KvPDOPLeAsbft4dJD+ziTF5/ytpTdbKHIB0ainYsJ3rxp/S5+x1K9i+kupGyzwMH3kNIyLlOX1WoQwgF33jxbeIWHuPstmQ++cV6XE4XQwY/isGQ1Klzh8SV4BdgJWX7OApSGxaqUqrtaAMt+AWa0QWZ8Qu0oA2sdShCqhm++ASxw7JI3jypXWXPbzFKgebFTG4xWuMsjK193MiURtp6zGpDa3UfhBD+wB3A7jpHofb8aiHEG7hLc08EjjYxz0YhRJBKpZ+lUhU3OofLpcXlCqx9ZkelqmjSHofDgNsfA4XCiELReJCclCqczmtf9k3NDeB0BiClW3hTCAtKZXUz80fU/65UliNE49UODxz4LnZ7Ve3cKgwGAytW7ESjqehRryn5hIv5S2H6PCOffxDa7GvqjP9TQaaKbR8MYtHqi9z2SBpb14ThdDR+Z9fR/5OtGo6vHcOwxccZ+kwKl7YOpvLLvSiqLDiCw7D2G4BtpPsioyorImjXVxgXLmzza+qq956zREv++jsJm7Kb8Amb0EWkUnF8Ok5bkMf/KSxsAI278t4nYEgykfOOUXhOyUc//QiX0x0Ps3HjbB56qKBTHeXopBycdiXFl2MaPe60qzGVqTGVNeYISGJHZDJ0wUmmP76Vi/tGknlsiG+VoWk+BFYA/+hmO3oMzToLUt4S76RRuOMvGpOOO1z7s0lnoQ61uloZGfnPRo+ZTOOorFxe26+YiIimlQyLi5/CbneLGgUG7sbf/2Sj/Wy2GEpKnq5/3tTcABUVyzCb3WldOt0FDIZNTfbNy/t5/e8hIR+j0eQ32m/AgCrOn3f/HhERwTPPPAO812jf7n5Nh/d8h2lzqzl+IIAac9OvqbP+T5kpT7Pz44HMv/cyYaEfN9qvra+puf9T/iHgEvDBFYJq4+DcMQsCEepyR+Tkudt1D15s12vq0vfeBbBdADUpRISmYLEPp7x4FQB+flmo1VcxmSbXOyWdhf+g80TM2Yz5aj9MB+9G8AvAnW1jsRSRlvYmSUlPdMrcQriISsyh+HIMTnt7VsgEeecSKL0SzbAlx0mad5rQ+CJOfjITn+Jko/wU+EQI8RnwV9x1HxpElUopbxkFx87ZRO1d1MkNNia9V9fW6BqnEOJp4Gkg1Ol0Yrc33FcFcLmu7YBIqWqyX93x689rqq/TafB43tyYLpfW4/fm+t44h91uv6HNgtGYSXX1tTvEiRP/D7u96fif7n5Ne78OYtR4M8vvLWfdm8ENXtP1tl1vszf/T5fOhuOncxIQ4o/VrGo04NGb/yflySoUThuqgUrsIQk400pQmqqol50R4BqvxWX3fM1teU11dNV7T6Gyowwwoo+8jEkWYCuJRq8/iU6XgssVWO+UdAaBSWcIn/0V1rx4CrfcDS4NK1Yc5pNPRtb32bv3Sfr1W45O5/3aEOEDCtAGWMm/0LEktBqTjlPrZzBgSgpD5pwlYmAexZe7Zgunl2HHvQ39A9wrz01xy0SQiia24m8q6mIWGsuGEEI8DLwLPCGlfPOGYwOAy8ArUsoXmhn/wYCAwe+sXp1+U79xzOYC1qzxXAIdOfK7TJ36p26yqPUMHWXh/sdL2bohmIO7A1s+oZOYsTyTEVML2fXJANJORrR8QjvR5F1BaTMx6qcFBMWUcez9eVSfl70mZqEp1IYSopeuQ+lnpWDrXfhVJ+Pvf9JjBcPbBI04TviM7ZizB1C4dSXSec3RO3jwu5w795f65336zGfpUu/HQ49ZuZ+QuGJ2/+NOpKvjC75C4WLGk5txOZQceGuxV7cjVDozxQUa1n3Y8r3oQw89xNq1a9dIKbs/feo6hBAvA98DTuGuKNno3ZCU8hddaVd30qaVBSFEPPAMMBgIo+H6lZRSzm9wYs+mTmqvsVRQ7Q19blkaC2g0GIYyefLL3WRR20g5qyU1Wcvc26o4f0ZHZXn3LKod+LIfhnALs1ZcobJUS0FW5zgutlh3meSTnw5myiPbGHv3Pg6bFmIO6d0qg/aKcPI2PEzM7euIuX0dxi3RjSwOe4/gMYcIm7IHU8YQCrffAS7P982kSb/xcBZyc3dw9eo24uIW4i00/hYiB+WSeSzRK44CgHQpSN8zirErDxI7PIu8c94pq63W1jDpwd1UFfvjzkLvtTwCfCalXNXdhvQUWv3OE0LcBqQDPwEWAwNoWK5zQCfY2NnU7t42utVQ19Z51YF6Cbt2PVIf0FjHnXce7LLiUB1HsHm9AQksXVVBd8XiSpfg6w8GYyz3Y/FD6QQaOk+0SWE1oz52hgvPuKj4uITRc7aj8nOn1/VcUaaWcZoCyfv8QayFsejisjppFknIxL2ETdmD8eIwCretaOAoAKhUugYrCZs3L8Lh8N79RZ8RmSiUktyz3v16LUzrS0VeKINnJqPwgsiTQuVg3Kp96A3VnN/VPcqtXkQPfN3dRvQk2uKm/hZ3KskkKWWwlDKhsUcn2dmZJAM1uCuH3UhdxsfxrjOn55GS8hoZGR95tN1zz3n8/AzdY1A7qaxQsWtzEInDrQwb3bSUcGdjs6r46t0hKBSSJY+kodZ4/9b4+qqTfsdPUfCrfHJWnCH2i78Q/tlrPa7qZFtx2bQUfHkf9ip3gSx9/zS85wBKQqfuJGT8QapSRlO8c1mzy/R9+swnLm6RR9uRIz/2mi1xozIoywlvIsuhIwjSd49GF2wmfvzFlrs3O5SL0XccwtCnhNMbplKU4f24jS7mMNA1Slu9hLY4C0nAX6SUN9WFU0pZjVvCeo4QYnRduxAiAHgSuEgLmRA3M4WFh9m37xmPtoUL1xMSMqyJM3o2R/YFkJej5ra7KvDTdp0M9I1UlurY9sFgQiIszL/vEkJ4d6WjsaqTUipwXLWgKemZVSfbinSqseT2B0Aff4WIOZtB0VHHSxI+cyuG0ceoTB5PyZ4lrdrPnzt3jcfz8+f/RllZx52xkLhi/MOMXD0zsMNjNUZZdhTFl2MYMPVC/apT25EMW3iSqCG5pGwfR35q44qb7UUIkSiEWCuESBFCVAohzEKIVCHEn4QQDfJIa/tvEEKUCyFMQoh9Qoh5bZz2+8B9QoieJ3HaTbRl47YYaO+7qcupDVysCx2OADRCiP+tfZ4lpbw+z+8nwHzgayHEn4Eq3AqOfYClTQky3eyYzQV8/rnngsvIkd8lIaH3fn5cLsHGdSE89d0i5i+tZPP67gvyu3opmANf9mPmHVlMWpTDka0NhXbai3noeKyNiDLFBR4iYnAexYrx5F/uuQqOrUa6w6ZMWYMInp2MUmemcNudSIem7WMJFxFzviQw8TzlJ6dSfnQWrU0r1OkimD37Tfbseby+7bPPJvH44yaEaH+cQdzoDBw1KgrTvHsBvp70PaOY/vhWEiancHHv6JZPuIEBUy8QP+4SGYeTyD4xpBMsJA6IAT4DruJW2h2JOxPtfiHEGCllEXhVjfcvgBH4WAiRC1yhYXRMb4zRazdtcRbeA+7GnXPaG3iChhE2dTrEe7hOFEBKeUkIMR34He6qmnW1IZbcqlLP7oBGzw9+bwpobI68HA1H9wUwaWY1Z47ryc3qvjIn5w9HERplYezsfMqLdKSf8k6GhC02ob6QlMpYXivKlEimajlBIw7RX3MBx2fnyB37CLIX1w+orFzu1sbIAxunCJ/5NTHLP6R4zxKEkAiVA6Gyo1DZG/yuUNs82tSGMrTRuZQdnUXFyWlttmXIkMc4duynmM1ufQin08r58/9kxIhn2/XaVH42opNyyD3XH6e98wJyjUUh5J3rR/+J6WSfHExNtb7V5/YZmcGQ2cnknutH+u62OxqtQUq5A9hxY7sQYi+wDngMt2MAHVDjvYEBuPe16nQUvOfJ91KafAfWZj5cz9vAXCHE58ArNO5p9RiRCinlnDb2TwF6ilR1t3PgwLPY7UaPtt4V0Ng8OzYHMXS0hTvureDff4zE5eouYRrBgY39CA63MnvlFSwmNTnphg6P2lwhqbzP1JQUuy9oM/+5mbRdo2vvXHu3OI8xZSxOiz+RCz6n771vtnwC4HKokA4V0qHGZVdTsm8hVefblzEihGD58j0eTvbBg88xaNBqtNrQNo8XMzQbpdrZaVsQ13Nx30iih+YwaMY5zm9p3fZU+IA8ht92jJKMaM5tnkQ3vH/qoltDoONqvNfTXNHBW5Xm3NVMGkYM1b0bljVz3s1xNbmFycraRGqqp3phbwxobA5bjYLN6w088EQpU2dXc2BX92kvuFwKvl4zmOVPprD4wXS+fCuJ/MyOBbPVxSw0VUhKCoFzynCcNhVjVx6k/Go4qTvGUpkf5qVX1T2YM4eQu/4b+IUX4HKokXa12xFwqK85Bdf97u0LXHDwYMaO/SmnTv2/+rZDh15g7tx32zxW3OjLVBUZqCro/K0yS2UA2ScH0W/8RTKPJrUYTBkUXcqYFQcwFhk4tWE60tX5X/tCCC3uGj9aYBjuKsEAm2t/ekWNt3YutZSycfW2a336SClvmUy55pyFX9KDaj346Bqqq6+ydetyj7Z58z7otQGNzZGarCMlWcucWu2FirLuEzS11aj48u0k7nzqArc9ksbG/wylOLe52mfN01TMwo2iTEVvuYgbeYXBs5KZ+ug28s7Hk75nNNaqrq3S2V4CAvai1aZSXT0Tq9WdrmcvD8de3jqlyM5g7Nj/9XAWLl58jzFjfkJISOvTCQMjywmOKefCtnF01R375YPDiBuVweDZZzn92Ywm++kNRsbfsxebWcuJj2fhtHW4QNtgIcT1gfOvSSlfa6Tfk8D1VSAzgYeklPtqn7dbjbcR1gL3NnWwNrByJ5DYVJ+bjSa/HaWUL3WhHT56AC6Xg08+GeHRlpCwikGD7m/ijN7P5vUGnv1JIUvvrmDt643pjHUdVpOaTW8O5c6nL3D7Y2l88fpQyotav398PXWiTNfjCInAEXJDTIRUcPXsQPJT40mYnELCpDSiEq+SdSyRy4eGeeNC0KkolZVoNPkoFD1HN02l0rJgwTq2b792rdm6dTn333+p1WPEjc7A6VCQf75j8s5twW7RknFkKENmJWOILaEir6HDpdFbmXDfboSA4x/NxmbySj2Oi61UcNwApOJeXRiLe8vh+jd03YelMfES6w19WmKFEOIVKeV3bjwghIjE7Sj07mW4NnIrFIry0UoOH/5vbLZKj7b2LJ/2JqoqVOzcHMSQ4VYmzTB1tzmYqjRsejMJl1Ow7PFUAkMar/zYGuoCHIMObUV/4TgK07UYlBtFmZw2NZf2jWLfa7dTkBrPgKkpzHz6S6KTsvEtMLadhIRVqNXXlvKrqi6Tnf1Vq871CzTTZ8QVCtPisFu7Nvg261giNdVahi851iCV0hBbwpSHt+EXYOXEx7O6vMy1lPKqlHK7lHKDlPLnwKPA74UQP6nt4k013qeA54QQP7q+UQgRAewCooFFjZ14s9KksyCEiK3dI2oTwk2sEKJn35L48ODq1a85d+4Vj7Z7701BpercSn49gSN7A0g9p+W2uyoYMqz7xJrqqCrTsunNJJQqybLHU9EHtj1j+XpRpoDkwwQf2EzUB68Qse4fhH/2epOiTFajP8mbpnDw7YXUGHWMWXGQCffuQW8wNjKLj6YQQrBs2U6Pti1bbsflanYbHJCMXHoEhOTSvpEt9PU+TruKMxun4B9mZNyqfShUDoRwMWhGMpMe2oFQSI59OLdHxLZIKc/irt3wrdomr6nxSinfAf4X+I0Q4iEAIUQosB13KudiKWXjZVlvUppbWcgB2pNQH1p77sx2WeSjyzGb89m8ebFH2+zZb2EwJHWTRV2LlIL174ZSkKtm1aNlxMR1v5xIeZGeL99KRBdgZ9njqfjpWrrIeNKYKBOAqrIUdXHLokxVBWEcenchF74eh6FPCdOf/IqB084hvCALfKsQETGe+PilHm3nzv292XP6TUgnvH8hqTvGYq7onqDbsqxozm6cQkhcMWNWHGTSgzsYNOM8+RfiOfDmEipyuy8epBF0uK854GU1Xinlb4BXgf8IIe4HtuFOqbxdSnnLCfU1F9ElaP/6Y+/OwbqFcLmcfPqpZ6pY3763kZj4WPcY1E3YbArefz2cJ79bxOqnSnj9z5FUVXRvBffi3AC2vJvI7Y+lsvQbaWz8TxL2mtbZ1NoAx2aRCrJPDqEwvS9J804xeNY5YoZncXHvKArT4vB9zFtm1qzXWbMmtv754cPfY8iQR9BqG96Z+4dVMmTOGYouxXZJumRzFKTGo/G3MmzhSexWNac/n0pBStfFT1yPECJaSlnQSPtcYASwG+pTJDcCdwkhRkspz9T264ga77O4BaHW4t7CuF1KeaC9r6U309I3zxohxJoW+jSGb5Ozl3D8+P/Wi8jUsWDBx91kTfdirFKy9rVwnvhOEQ8+Xcqbr0RQU9O9YT15V4L4+v3BLH7oIrc9nM6XbyfhdLRsU1OiTOahbsewLmahYl7Li4c11TrOfDGN3OQEkhacZOzKAxiLg8g4NIz8lHivlje+2dDrYxg//iVOnHipvu3gwReYN+89j35C4WT0HYdw2tTdpVnQgOwTQzCXBWIsCabG2L5AWy/xr+uyD7Jwxx+MB+7HrbL4/ev6tkuNVwjxSDPzb60d8zMgQQhRHz0spby5g7quQzQlZiWEeKuDY/9eSpnawTF6BUKIBwMCBr+zenV6r9KYyMvbw6ZNczzaVq06S2ho1++V9iQGJlp58OkSMtL9eP/18FrBJolSaUatrkapNNX/VCjsCOFCCBfg/imlAlAgpfvhcvnhcPjjcARgtwfgdPrjdGppywVh0KgS5t97maw0A1+vHYyrhVLFjYkyIRQ4gkKQag3q4jwEkPf0z9v2xxEuopNyGDjtAoERlZjKAsk4NJS88/29Vj65Leh0Z9BosrFYRmGzdc+db0s4HFbefNMz9ueee857pCMPnnWGgdNSOLl+BkUX47raRK/icEBFBaxf33Lfhx56iLVr165pLhtCCHEv7mDGUbizHyRup2Eb8PKNQoBCiKG41Xhnc02N96Xm1HiF+wMsaZuXJqWUveo7vyM0lzr5ja40xEfXYrEUN3AUZsz41y3tKCgUNWi1RZQXF7B/m53ZS+L5xrdPcGjHXoSornUIvIOUShyOgHonwmYLxWKJxmqNpqYmghu/gy6dDUejdTJrRSZz78lg57qBSNn091pLokwIQfXwdtSGkAoKUvpRkBJP1JCrDJx+npFLjzJw+nkyDg0l91xCl8pHWyyjsVg6R2bYW7hTKT9m+/Z76tu2bFnG/fdfRgiBIa6YAVNSuXomodc7Cp2BlHIdblnn1vZvjxrv3Db2v+Xo3k1ZH92ClC4+/9xT+z4mZjZDhz7TxBk3HypVFTpdPlptAVptITpdARpNWf3xoqtw6uAwxk6bhsZvGge3b8fp8J6zIIQTtboStbqywTEpFdTURGC1RmO1RtU6ETFcOBqFRutkypIcbFYl+z7vT1M3Ql6JWWj+FVCY3pfC9DgiBuYxcPp5Rtx2nIHTz3Pl8FCunh2Ay+H7eqkjIeFuNBoDNlsFAEbjFXJyviJh0EJGLTuMpUpPyo5x3WvkLYyUck9329DT8X2ab0FOnfp/VFV5CsQsXvwFQnT/PmlnIYQTvT6HwMCLBAam4+dX3OI5ly5cwG63M3HmTGbddhv7t27Fbuv8TAkhXGi1hWi1hde3YjbHkZsymLMBEYyaUYTNqmyyUmWrRZk6bi3Fl/tQfDmWsP4FDJx+nmGLTjJg2gUyjyaRc2pQpxZBUiorUCjMOJ3BuFw9V3WyLpXy00+vOQRbtizllU8PoQsyc2TtvB4vgOXj1sbnLNxiFBef4PjxFz3a7rrrBBpN1wqsdAUqVTWBgRcJCLhIYOBlFIrGhN2aJ+viRRx2O1PmzmXy3Lns37q1EyxtDRK9Pge9PoeqbMhNG8PY2WA1lpN8OAmXq6EOjcJqRnvpnEeAo8vfnY6nKi8m4NS+VgU4tg5BaWYMpZnRhMYXMXDaeZLmnSZuVAZH1s7HbukccaGAgH34+5+komIZZnP7CkB1FeHhY4mPX0529kYAJs4fT98xmVw+OJSKXG87cT7aghBiNXBASpnVYmfP8zTAKmB7XZnsmxWfs9BBhBDLgSeczuoef1vucFj57LMJHm1TpvyJ8PCbZ/lTCDvBwSmEhJzA379Nn/smyc3MJPn4cUZPnkx4VBQlhYW4XJIrV7JJS7tCZuZViopKMBpNuFwutFo/wsND6d8/jkmTRtO3b4xX7LieKydOExiqZcICf7D8mfLSBMrKxlNdPRAQzVadvD7A0XvOQh2CsuwoyrKjCE/IZ+zd+xi/ai9HP5jr25YAZs16jTVrYggOD+a/fvsUGeczSd29HKUvoaS7eQ94mGuVLFtLYO25C3Fna9y0+D69HURKuVEIEaRUBszqblta4tChFzyeBwcnMnLkC4327W34+RUTGnoCg+EMSqX3VRgvX7jAkJEjGTFhAru//JI//OHf5OUVNtrXZLJgMuWSlZXL3r1HmDZtPPfcczsKhXevCJeOnmPsbdMZMH4wFw8nExSUgt1uoKxsHM6deZ0T4NgGSq7EcOaLaYxdcYAxKw5ycv2MWz7NUq+PZvqcH3Pf/4Sj89fy1+//g2iDYMyYH7V8so/ORABJQoi2fo8H0xPyXLsAn7Nwi1BQsJ+UlH97tC1fvquXxym4CA4+T1jYMfT67Ja7dwCn00nKqVOMmz6dqD59sFpbV7NBSjhw4AQqlZK7777NqzaZK6vJuZBB/IhBVBSUUpyZh1pdQVTUTmyLBeYJsVhiFmAyueMXvBvg2DqK0uO4sG0cwxefYOC0C1w+MKLlk25idMHVfPOPUxCqUn752G/JTr9KNj9m2LD/uim3AnsZP619tIWOiBf2KtrlLAgh/IBwoFhK2f3auD6axW6v5osvPNW358//EL3e+8vjXYMkMDCN6OgdrQpU9BZX0tJIHDWKEeMb7o0HBOgJDAygqsqIydRwZWPv3mPMmzedkBDvXhCyky8RHBnKwInDMZZWYDW66+RoEiWq6lz8Tr5DTV4wRr+RGGMnt0uUqaPknBqMoU8pg6afpyw7kvKcyE6fsyfiH1bJxPt3oVC6WPPLUlKOpdUfO336t0ya9NtutO6Wp6NSAee9YkUPpk3OghBiHPB/wAxASe0+TW3Jzg+A3zYnfOGje9iz53GP51FR0xk48L5usqZj+PtnEhW1Hb3+apfP7XK5uHDqFBNnzUKpVBEQoGfatPFMnDiKqKjw2j6SvXuP8OmnnoGQUkrS0zOYPHmMd42SkrQDZxh723SSpo/lzNeHkC4XthSofh1wAFThL/bjrziAIygEl0rXiTELjXNh63gMMaWMvuMQB95cjN3S5hp1vZqgqDIm3Lcbl1PB0ffnExmwBPhh/fHTp3/HqFH/3agMtDcIDLHicglMlV1bxbK3UFs4ykcztHoDUQgxBtgHDAQ8JC5ro0B1uFW2fPQgcnK2kpHhKd+8ePHn3WRN+1EqTfTr9z4JCW93i6NQR9bFixgrK3ny24/z4ovPs2zZvHpHAUChEMyZM4XIyIZf+lVV1Z1ik81iJf3wWQJCg0gY5y7+ZTsCOATKeNBMkihCACFRVZSjLslrsZCUt3Ha1Zz+fBpqXQ2jlh3hFlm5BSAkroiJD+zCaVdxZO18qkuCUan0TJv2V49+x479b6fMH9GnmnueS2bVt88RFNr+kuc+bm3asrLwS9wlQMfi1uZ+/IbjO4B7vWSXDy9QU1POV18t8WhbsuTLTrt76Sy02nz69fuwUQGjzkPicjlwOq04nVYcDmv974d2S57/4fN8/v7vyUjfjVKpQ6n0q38oFH5I6WjkdXTeXV15XjFXUzKIGzoAU1kVtmlXUY+R+I251sdZAPYM90XaER9NcfWM2pWHrsFYFELazrEMW3SCwbOSubh3VIfHrK6eidk8Hqcz2AsWehtJ3OgMhi44iaVKz7EP53rUWBg69BkOHny+/nlKyquMG/cz/P1jGxusXYRGm1n6jVSsZjVqPyfLn0xh65ohlOT1XE0KHz2TtjgLM3FvM1TXxizcSDbgvXe5jw6zbdvdHs/79buD+Pjbu8ma9hEcnEyfPl+gULStRHPrkNTUVGCxFGKxFFBRkYrZ3HiGw/VkZX3OtHkr6Tc4kUP7GpYczsuD4kZCKSyWzZw5s5OQkOH4+/dBp4tCq41AofCOGE/WmXT0wYEMmjQCh81O6VXP16KMdj/c5DPI8RpZWfdjsfTxyvytIfvkIAIjyxk47QKOGjVXjgzt0HhOpwGn0+Ad47yIys/G8CXHiBmaQ8mVKM5unIrN7Ln1olRqmD37LfbsubZdfujQd1mw4COv2GCIsLD88RQcdgUb/5OERuvktofTWfHMefZ8OoCLZ3pUqWkfPZy2OAtaoLlbO18obw8iI+MT8vJ2ebTNndueAqLdh8Fwhri4z7wyltNprXUKijAaMykvv9DusVwuJ+dPb2HMxDtRqbU47NeWdsvKYO/ehudERUFYmFvrorj4BMXFJzyO63SRGAxJ6HRR6HRR+PmFIETb0gylS5K67xQj5k0kcfoYzn5xlOKt5bhKQBkJmpGgqL0Bd+aD9WsjAx55i8uXn8Bq7apgV8H5rRNQaRwkzj2Do0ZNzulBXTR31xAcW8LoOw6hDTKTtnsUVw4PpansusGDH/ZwFjIy1lFV9VuCggZ0yIbAECvLH09BAhv/MxRjudtR+eQfI1i0+iLz77tMeB8Th7fEI13ezYgKizbjkCrcNZx83Cy0xVm4jLssaFPMA9r/DezDa5jNhR5FawCWLduNRhPYTRa1HbW6ktjYze0+3+EwU1V1mZKSExiN3hFnup7kk18yeeaDDBk6iwtnvwagoAB27gT7DYsgWi1Mn978eBZLERaLpwCcv38MERGTCQ4ehErVumVjl9PJ+d3H6aMdwtVnq3DV1F4IhMS8HhThIPzAmQ0gCHjUQVzcZ1y+/DRSdlEmtVRwdtMUlBoHwxYfx2FXkX++f7uG6llVJyUJU1IYPCsZa5WeI2vmU5nX/N27QqFsUGRq376nWbq0/XHi/sE1LH8iFaVa8sXrQ6ksuVbx0mpSs+k/SUy9PZvRMwoIizGz/YNBWM0dX90KizYxfl4uA0aUc2R7NNDd/w8f3qQt3w7vAz8TQqwDTtW2SQAhxPeBJcB3vGuej7YipWTz5oUebUOGfIPY2NndZFH7iIra3kZ5ZonFUkhFRRp5ebvbPa9S6UdAQF80GgNqdQBKpbb+oVJd97taT43VyuwFP8ZPOZ60tEvs3PkZdrtnEIBOp+b++0cRHq7EaMxs4BA0h8mUj8m0of55bOzc2tWHCJrTgXHaHaS8mYqrRhI0OhBHoAXrBTuuKnDVTS/Ab5Y7fkGrLSIs7CglJdOaHNPbSJeC0xumMf6evYxcegSnTdWuiosaTTb+/iex22O71VnQ+FsYteww4QmF5Kf05fyWiThqWndnnZDguV2Ym7uDsrLzhIYOb7MdugAby59IxU/vYOMbQykr1Dfo43IpOLCpP8W5/sxacYW7v32OLWuGUJrfvjiG8FgT4+fmkjC8nBqrkmPbY9m3KYZv9s6EKx9N0BZn4f9wp0puBVJxOwp/FkJEANG4a4v/0+sW+mgT6elvU1aW7NE2Y8Y/usma9iGEg6Cg1Bb7uVw2qqoyKCs7S3l5SpvnCQoaSFDQAHS6aHS6SNTqgDadn5eVRVxCAlVVVt5+e2MDRyEkJJhvf/vhBpkRLpcdq7UYi6UQkymX8vLzOBwtR6nn5e2q31oKCxtNaOhIAgP7IUTDj7F6sgvdBDWTf+JeDEzecQTTRTP2jNrjiaC8zqygoAtd6iwAuBwqTq6fycT7dzHmzoOc+GQWpZnRLZ/YwwhPyGfkssOoNA7ObZ7I1bMDaIuonxCC227b4hGMvGvXg9x99+k22eGns7Ps8VT8A21seiupxSDG9FMRlBfpWPzgRVZ+8zx7NySQfiq8lbZLIvtWM25OHv2HVlBjUXJsex+SD0ZjrlZhNbfJ9B6HEMIJPCylfL+J4/cB78sba8nfxLTaWZBS2oQQC4HngAcBKzAEuAj8CXhFSum9Gr69hJ5UG6K6OruBpsKKFUdRqXRNnNEzUSqtTQY0OhwWysqSKSjYi91uatV4KpWWsLBx+PvH1MYDhLU5HqAxSouKSEhMZM/eY1gsnhd7vV7H888/SlhYQ5VEhUKNXh+LXh9LWNhY4uOX1b42ExZLIWZzIUVFh7HZqpqeu/QMpaVnAPD370OfPvMJDOxP3Re9Zgi4qu0c/p8DhOiiCB3Uj+BRJopic5AuiTMfqt+BgNpkZ7W66bk6E6dNzYl1s5m0eifjVu3l7BdTKUzv2y22tA1JeEIB/SelEp5QiLEomKPvT8NU2r6sjLi4RajVgdjtRsD9/y0qOkZkZOskuZUqF7c9kk5wmJXN7yRSmN26Lcfi3ADW/3MECx+4yLx7Mhg6sYgjW+MpyGr8fJXGyeDRpQybVEhEHzNWs5Kj2+I4dzAKW81NJQjc0vd5t3/fdzWt+u/WZj9MBvKllH8G/typVvUiekptCCldbNgwxaNt5MjvtfrLpifhcPhjtwdflyopqa7OIS9vF0ZjZqvGMBgSCQsbQ1DQABSKzgm0ioyJocZiITX1UoNjs2dPatRRaA6Vyp/AwAEEBg4gKmpq/cpJaelpKirSmjzPZMolPd0tfRIffzthYaNxpGlqRZlsVHIVFCBUAn1fHXZs2C47AAGPurciLJbuS2SyW/04+v48xt+zlzErD5CybTzZJwd3mz3NIZROYodl0X9iGoGRlViNWtJ3jyLz+JAOFcpyry5s9lBa3bHjPh54IKMVJ0vm3XOZqL7VfP3BYPIy2uawWKrVbPzPUJLGFzNh/lVWPHOBzBQDR77uS3ntNkZolJlhkwsZMqYEjdZFab6OvZ/35+KpcOy2W+bm+nriAWN3G9GVtPbd7cSto/B93CsJPnoY5879FbM536Ot98rHCgoL5xIT8yGlpafJyWldWeiYmNkYDIno9dF0tuMvFApi4uO5euUK/npdg62G2NioDs+hUGgwGJIwGJIAicmUR0VFCgUFB5o8Jzt7M9nZm4k7/CRqRxzKeIkyWuK4BK4qMGVawAUoBLo5CsCJlCqKi7u3Dprd6sfRD+Yy5s6DDFt0Ar8ACxf3jqSn3MCptTX0HXOZ+AnpaAOsGIuCObtpMvkX4pEu71wso6NnEBg4AKPR7SAYjVcoKTnZYlXYyYtyGDiyjEOb47lyPrRdc0uXIOVYJBdPhzFiaiFjZ+dx73PJXEoOIyC4hpj+1TjsgsvJYVw4GklhdgA95X/jLYQQdwJ3Xtf0tBBiQSNdQ4EFwP4uMayH0CpnQUrpEEIUcLO9O24SKirSOXToux5tq1Ylo1T2vtQlKSXFxUfZsuVXhId/yciRTfcNCOhLRMQEgoIGoVI1DOTqTCJjYlBrNORmZvLss490wYwCf/8+tVsOC7DZqqisTCc/f3ej2zEl/bajiNbhTCwgPv52DEEDcRaA44ogJDaCwfcmoo3TcOnoeU4dmNmtKwt1uBwqTn06g2GLjzNw2gX8Aiyc3zIR6eq+SpW64Gr6T0yjz6gMVBonJRnRJG9KrI2t8P7X4bx5a/n886n1z/fufYq77jrRZP+hE4sYOzuf84cjObO/4/EeDruS03tjSTkWydjZeYyYWkB1pR8Hv4wn7WQ4NRbvaIL0UMYAj9X+LoFZtY8bqQYOAs92iVU9hLasm30M3CuE+NutGJvQU3G5HHzyiWclvwkTfkVoaO+q7mezGbl0aQ3793+rvi07G0pLYeJE8K+N1VIqNcTHLyckJKnRwL6uok///jjsdgrz8rplfo0miIiICURETMBqLaGgYH99DAOANSbT/UsNXLy4FoC+fRcTNmksZmUR6RdMDAqawNCZY1EG+LHrE4mU3X8vIKWC81smYjXqGTzzHP6hVeSdS6A0KwpzecO7WaczGJstBpfLm86ixNCnlP4T04gachUpBfkX4rlyNInqYoMX52lIVNQUtNpwrNYSAEpKTlJenkJISEPxqrjBFcy84wpZqQb2b+qPN52XGouKw1viOfp1HC4poAe8NzobKeUvgF8ACCFcwENNBTjeirTl2/YNYC6wTQjxF9zbEQ1iXqWUnVsr2IcHJ0/+CpfrWjCgSuXPmDE/6UaL2obJlMuRIz/k0qXGP5NZWZCbC7fffhvTpw9Cp2vfMqs3SUhMZEBSElkXL+JyOnnppb9QVuapV/bgg3d6v2hUE2i14fTvv4K+fW+r3bbZAoDCqsf/ynDUxlDswaXkmQ+So99KZOQSRMn3uPibMob/KYGRUwtJPhhNcW7bskE6D8HlAyOwVukZOP08w5ccB8BSqacsO5LSrChKs6KoMeqprp5FdbV3tlCE0klMUjb9JqQTHFOO3armypEksk4Moaa664KE58//iC+/nF///MCBZ1m2bIdHn+AwCwvvv0RZoZ7tHw7yurBSHa5uXNXpZhKAritp2wtoi7NwDvfSjADmNNPvlox26Q5KS89w8uQvPdpWrTqDQtHz/wVmcyFHj/6E9PS3muwjhIrZs99kwIBVqFQ6MjNNBAefx2BIRq/P6UJrrzF4xAjGTJlCfk4OJw80HTvQHSiVfkRGTiYychIVJ/JxfhIFTiUCgRQuQo8soUpVRoXdTiwHEAiKr7oD6mosPS+SPTd5ALnJCfiHGgntV0hYvyIiBubRZ2QmAKbSwFrHIZKy7CjslpZrbyiUTnTBJnSGavSGavSGa7/rDNWoNE6qS4I4v2UCeef747R3/d8lNnYu7q9Zd/BpXt5Oqqoy6lUd1X4OFj+cjssl2LJmyK0aYNipSCm9r+TWy2lrIalbp1RcD8fprGH9+jEebdOm/Y2goIHdY1ArsVpLOX7851y40LT2w6BBDzFmzI8abKU4nf6UlU2irGwSanU5BsM5goPPo9UW0hVvzWHjxjF83DhyMjI4sns30tVTd+MEquRYXE6BjBEUiVI0eYIAAgmyhxMESCRHOELUhcvM49uYjT11L1pgKgvCVBZEzqnBgCQwsoKwfoWE9isidngm8ePc2ShVhQbKap0Hu8XvOifAVOsYVKMNsniM7rQrMVcEYKnwpzQriuLLMZ0Wj9BahBAsWbKRLVuW1bcdPvwDFi1aX5/5YAizsunNoVRX+EpOdxZCiKm44xIGA2E0fFNIKWXP/sL1Im3RWXipE+3w0UaOHPmhx/PAwP4MH/7tbrKmZWpqKjh16v9x9uz/Ndln1qw3GDjwftTqlpXk7PYQiotnUlw8E6XSgl6fjb9/FjpdPlptPkql90rxCiEYNWkSQ0aO5Ep6Oif27UPKnuc3OxwBWCyxWCyxWAf4YQ8LwJLgjhC12E2cPvpnqs67a21kkEEFFfx31AtUG0189OEE5s//CINhSHe+hFYgMBaFYCwKIfNYEgbDF+j151GFjiNqhIa+4y7Sf5JnmqnVqMNc6wyYKwJqnYMAzBX+2ExaemLcdt++ngXfMjM/xWS6yvxVThKGVbB/Yz/yrvjK8XQWQohHgLcAO5COu1DiLU3PW3vsZXSHKFNp6RnOnfurR9sdd+xHiJ73pWezGTl79mVOnvxVk33mzHmXQYNWt3v7xOnUYTQmYjQm1rZINJpy/PyK0WjK0WjK8PMrQ6MpQ62uwB271DJ+Oh0DEhMZOHQoOn9/Lp47x+nDh9tlo7eQUkVNTSh2ewg1NaHYbO6H1RqJw3GdkE4oKPRm9OeOojKW4wgOI2nMdzCP/yaHDn0f9cUc7uIuho5OojC3kNLS06xbl8igQQ8ydeqfayWlez51QZklGbFknxuPQunE0KcEpdrhdgoq/Tukf9BdCCFYsGAd27ffC4BKpWTcgh2MmZnEuUNRnDvU8dRcH83yUyANWCCl7J4o5h5Gqz9FQohWRRFJKRupuXfz0tWiTC6Xs8H2w5w57+Dv33VlhluDw2EmOfkvHDv20yb7zJr1OkOGPOq1Es3XEPUX0QZHhBO1uhKNphyVyoRSaUKluvZQq03E9Q9g4NC+xPXvi0KppCAnh+P79lFw9Wqjs7300gsdttjp1OJ06nE4/OsfTqf/dc8DqKkJrXUIWnYKNVcvE7b1A3A63bvfQhB8cAuOoBDi1DNQMwABjJ06hv/76TWNtUuX1nLp0lrGj/8Fo0f/oNepf7qcSsqyb44Laf/+dwGg89fx+zf+HzMWJnF4q4HTe/rRE1dDbjL6AT/wOQrXaIvLvZvWbQz7om06kXPn/uLxPDAwgcGDH+4eYxpBShcpKa+xf/9/Ndln+vS/k5T0VLfoQEipbNSRCA5xMHayibGTzBhCnZhNCo7s13HioIby0mAUikQUCjtC2FEobCgU9trnLtwfC4kQkroYYPcdr/vhcimRUo3Lpcbl0tT+dP9e1+7tL3992imE04ktPBZHaASa/CyUJiOqytJaEwXhdw/CVCX5bM2mBuefOPFzTpz4OStWHCYycrJXbfPROhQKJUvveJcHnneSOHIIv/7eb0k/2Y9p017pbtNuBa4CvoCQ62iLs/CNRtpUwEDcQhaZwL87bpKPpqiuzubw4f/2aFu6dEeP2X4oK0vms88m4nQ2Xi1y8uSXGT782z3mblWplCSOsDBuiomBiW6bM9L9+PqLYFKTdTidPePv2h7MQ8djTRiGdcCw+jZVeTGagmwQAv9xUUz736sc3tKXh1YbychYx44d9zcYZ8OGKYwY8QKTJ/++V4p89WaCwyw88P0RaLSVfO/hH7Lva3f2zfjxv8DPz9C9xt38vAo8KIT4s5TS2d3G9ATaEuD4TlPHhBAvAye9YpGPRnGXnl7s0TZx4m8ICkroJouuYbebOHz4v0lJebXR4xMm/IqRI7/bqsDFriAi2s64KSZGTzDjH+CiolzJ3q8DOXXUn4qy3re/3Ri22IbvC0dIBI4QdyzCzLvSqbEoOX8kEiEEAwfeR//+K0hOfoWjR3/kcd65c3/h3Lm/sGpVcq8T++qtRPY1ctsj6QD84+eZ9Y4CwNmzf2TixKZjgHy0nUa22Y8DdwNHhRD/AK7gLnvgwa207e6Vb0YpZbkQ4g3gh0CTToWP9pOR8TEVFZ5lm0eP/kE3WXONrKxNbN26vNFjo0b9gHHj/heNpvujtjUaFyPGWhg31UTf/jYcDkg7p+PkYX8up/l1SL1QYTWjvXSuPpDQ2i8Rl7872FBVXkzAqX1UzLvLWy+lw3YljS8iXJnDnvvzsQ+fUN9fqfRjzJgfkpj4GJs3L6G09JTHeJ98MpIJE37JmDH/0yu0PHor/ZLKWXD/JcxGNV++lYRWMZzrF3ZPnfo1Y8b8qM0l1X00y24abrPXfSm80cQxyS207e7N26hyYIAXx/NRS01NOTt23OfRdtddJ1Aouu8uuLr6Kl9/fSclJQ0XlPz9+7Bs2W6Cgwd1g2WehEXamTanmpHjzPhpJUX5KrZsCObMMT1mU8c/580FEkq1BnVxHgK63Floyi7CghFHnew6Xas4ObzhuTpdJHfddYL09HfYs8dz9/H48Rc5ffr3rFp1pkdoelgso7DbY7HZ4rvbFK8wbFIhM+7IpCTXn83vJmI1qVGpYNKk33us+Jw//0/GjPlhMyP5aCONbbP7uA6vXG2EEFrgYaDAG+P58GTPnic8niclPdliJbrOwuVykJz85wY6D3XMm/c+Awfe341xFJKYODtDR1pIGmUhKsaB3Q7JJ/ScOOzP1UwN3gwmbE0gYfXwri8TfqNduqIrKEzVyLIyKkvczoOpGbuEECQmPkafPvPYsGEqZvO1oHCHw8SHHw5i+vS/M2zYt7o1ZsZm64fN1q/b5vcWQiGZuiSbUTMKyEo1sO2DQTjs15zZ4cO/7eEsHD36I0aOfOGWiCMRQgwBHgIW4Y6R0wKXcdcr+ouU0nRD/0Tg98BsQIN7i/znUsqdTc3R3Da7DzdtSZ18s4lDocBUIALo/nXxLqazdRby8/eRmfmZR9vUqX/pjKlapKjoGBs2TGr02IAB9zBz5mvdEnglhCR+gI2hoywkjbQQEurE5YKsDA1ffRrMudN6qqs6Z7WwpUDCmtgEnEEhnTJ3y3YNJXRBHJNn5dN3SAylyWbOf+gg97KB6vBBrbIrICCeBx/M4dy5vzaobHrgwLOcPftH7rhjX49L3e1NaLQOFtx/ifghlSQfjOLg5n4Naj2o1f6MG/czD72Sy5c/ZMiQrqh42u08Dnwb+AJYi1soaS7wa9zFDadIKS0AQoiBuCtCOoA/AJXAU8BWIcRtUsrt3WD/TYForRKdaFrJpgy3wtXfb9UKXUKIBwMCBr+zenW6V69ITmcN//mP1qNtyZLNxMff5s1pWsRmM7J//zebLPbUHel1KpVkwBArQ0dZSBxhxT/Ahd0OGWlaUpJ1pJ3TemWboTX0uJgFIUkYVs6IkVeoOXqZyjQrFTKWLDEeqzqk3XZVVKTzyScjPAqX1bFkySbi45d67SW0Fo0mC5WqBJstHoejdwhJXU9wmIUlj6QTFFLDvi/6k3o8ssm+VmsZ774b5tH21FOuHpMNVYfDARUVsH59y30feugh1q5du0ZK2WT+txBiAnBRSll5Q/uvcYsnPSel/Htt2zrcgYnjpZSna9sCgPOAFUiSrbjo1So4NocELLiVHU9KKR0tjdnbaUs2xC1bfqy7OHbsZx7Po6NndrmjUFR0lA0bGncEJk36LaNG/XeXxU74+bkYPNzK0JEWBg+14qeVWC2C9AtaUs7quJSqxVbTtW/TnhSzoFC6GDymhDGz8rGfu8qRJSeRNrePL0UmIeJwh+wyGIbw+ONmTp78ZQNFzi1bljF+/C8YN+5nXXrx0unO4u9/koqKZb3OWYgbVMnCBy7icgk2vZlEfmbzgcBabSgDB97P5csf1rfl5++uLTx18yKlPN7EoY9wOwsjAIQQ/sAdwO46R6H2/OraAPxfAhOBo62Y9m08gxrr3tQ3tkmgVAjxUynl660Yt9dyc+SJ3YSUl6dw9uzLHm0LF37SZfNL6eLkyV9x4sRLDY6Fho5kyZJNBAR0TVBZsMHBlDnVjJ9iwk8rMVYpSD6pJ+WsjisX/bpVD6EnxCyoNU6GTipi9PR8/IPtFOfqOfrbSqTN5XW7FAoVEyb8kv797+LTT8d6HDtx4ufk5+9myZIve4yWRk/ET+dg3JxcRk4roLxIx5b3EjG2siDU+PG/8HAW9u59mvvvv9hZpvZ04mp/Ftb+HIVbSOlQI33rdNpb6ywsBH6He5v9VdzSzwBJwDNACfBb3DEU3wZeFUKUSSlbsZ7SO2lLzIIS8JNSmq9rMwBP4P6DfiilTPa6hbcgUrr47DPPL/JZs15Hp2t6idKbmM35fPrpBI+gtjoWLdpA//53dokdUbE2ps+rZsRY91vu/Ckdxw4EkJOp6VCqozfpypgFtcaJPsiGf5ANfaAd/0A7QWFWBo0qxU/n5OrlIHatH8jVS0FoYgNQhI7pNLvCw8fwxBNWtm27h+zsjfXteXm7ePNNPQ8+eNUXx3ADCqWL4ZMLGT8vFz+tk9QTERz8sl+bSkwbDEMwGJLq06irqi5RUZHeCwqANctgIcT1qwevSSlfa+6E2uvRi7hjE+r2R2Nrf+Y2ckpdW2vflNNxOx4jr7/mAV8IIf6J2yEZIaX8tRDi38AZ4HuAz1nArc44hWtLPmpgP1D3bfQ9IcTU65d/bgU6I8DxwoVXcTiuBfhqteEkJj7RzBneIzv7S4/SuHWEhY1h6dIdaLUN6y14F0nC4BqmzzMyeGgNNVbBkX0BHN4dQGVFz1sIa0n8qGkkKrULlcaFWuNErXHhp3OgD7LjH2hzOwWBdo+fGr+GYUM1FiVXLwdxZm8sRVev5d23367Wo1T6sXjx5yQn/6mBsujatXE+qeh6JANGlDF5cQ7BYTXkpAdzaEs8ZQX6do02Y8arbNo0p/758eP/y4IF67xka7dwsbmYhSb4C+7r0f9IKevu+uv+oI1JyFpv6NMSTwB/vcFRAOq3Nd4GngN+Xfv8XeD7rRy7V9KWb98ZwKfXPV+F21H4NnAK+BD4MdBQM/YmxtuFpMzmfA4c8Cw1fccdBzp9H9jprGHv3qe4ePG9BsemTfsrw4c/26k2CCEZNtrC9HlG+sTbMVYp2L4piGMHArBaelK4jKy/sPvpHfjpHGic1YjkVBTlFWj7BRI0ow+6GD/UGhf23DKK371A4ssT650CtcaJSu1CNPOyHHaBqUqD2aihJF+POc2A2ajGZNRgqlJjrv1pr1HSVCpoVwReCiEYNer7hIaOZvPmhR7HNmyYwuzZb5GY+FiH5ujNRPU1MvX2bKL7VVOar2PTW4lcvWjo0JgxMZ5fNRkZH2O1lqLVhjVxxs2FEOJXwLO4VyB+e92hugt7Y3s62hv6tEQkzQsuqYDrK5blcZNv67flxcXglrysYylwXkr5LwAhxGu493J8dIBDhzyd07Fj/7fTlxgrKlJZt25oo8c6W+JXpZaMnWRi2lwjoeFOSopUfPGhgTPH/XE4um6rQaF0ERBsIzCkpv4REGxDW+sQ1DkGfjonSuW1GKeiXSUcfegkLpsLJFiVUPGWQBfvj0KnovpcBQDG/5mBw6bAblNgtylrf1ditylw1P60WVWYqtSYqjTYrE07Aa2hqwMv4+IWcN99F/noo8Ee7Xv2fIPCwgPMmPHqLaX6GBRqZfLiHAaOLMNUpWb3+gTSTkZ4ZftMCMHcue+xa9e1m/Fz5/7KhAm/6PDY11NXO0Wnc+FwChz22kft704HOBy1bQ73w+kQWC3U3lh4//MrhHgJ+F/gLeCbNxyu2zdtbKuhrq2xLYrGSAeeEEK8KqWsusGGYNwrD2nXNScARa0cu1fSFmdB4OlpzcFzpSEftzfmowNUVV3yeD5u3M+a6NlxpJS1FSJv/MzBgAH3MmfO250WqKbTO5k0w8SkmdUEBLrIydSw9XMDaee0nRKPoFC6CDTUOgOGGgJqf9Y5Bv6Bdo87fZcLTFUarCYVNRYVpgI9VrOKGouSGovq2uPNDKhx4YiOxm6IRJ2bjdJkxJRhqg8kNA2fyNY1Xbun3B2Bl8HBg3jssUo+/3wq5eUX6ttTU98gL28XK1cev+kLIPnpHIybm8uIKYW4XIJj2/twZn8MjjbEJbSGAQPu9XAWTp78JWPH/tQrIk1+fi7GTzMxdbaRIENTGfPN88l7wUBgh225HiHEz4GfA+8CTzaSApmMewtiaiOnT6n92VRmxY38ElgHpAkh3sLtPAAk4i6cGAncV2uXAveK+oGGw9w8tMVZuAIsxh31OR33SsOu647H4hbA8NEBpk79M/v2fROXy87ChR93mkJbTU0FX321hKKiIw2OLVr0Gf37r+iUeYOCHUyfV824KSY0fpK081oO7AgkK8O7yor+QTZiB1TSZ2AVsQlGAg01ns6AE6or/TBWaMi9FIyxwg9j+bWHqUqNy9Xy9oem7xQUEaN7qChT14tFaTRB3H33WQ4e/A4XLvyjvr2q6jLvvBPCvfemYjAkem2+ysrlVFY2XpukK1Gq3MGL4+ZeC148tj0Os7FzPr9KpYbx43/BiRM/r2/rqEiTWu1i+rxqpswxotNJMtL9+PzDQIryVajUoFRJVHUPtfunUkX973UPoZBcTvdudWchxIvAS8B7wDeklA28mNrYgY3AXUKI0VLKM7XnBgBPAhdpXSYEUsr1QojVwJ9wb69fTz7wkJSyLj1NCdwGFLf5hfUi2iLK9ALuP9wF3Es6VmBgXQCIEOILIEhKOadTLO2h1AY4fleni5n98MN5PWlzvUmKio6wYcOUBu1+fqHcffcZAgLiGjmrYwQZHMycb2TcVBNCuOWXD+wKpChf7ZXx/XR2+gysos+AKmIHVhES4Y5nsppV5GUEUlqgdzsCFXXOgKaBSl576XGiTD3ErosX17Jr10MN2leuPE5ExPhOm7crUShdDB5dyoT5VwkMsXU4eLEteE+kyR0vtHhFJYYQJxfOaNm3PYi8nPY5Op0gyvRt4O+4BZB+BtzoKBRKKbfV9h2E2yGwA38GqnArOI4Elkopt7bltdRmXYzHvc0gcN80H78Vy1a3RZTpL0KIQGAF7oDG/7nOUQjDvczzf51hZE/G2wGOnU1Kyuvs2/d0g/YxY/6HCRN+6fV95WCDg5kLjYyd7M7uOH3Un33bAztcClqtcRLT30ifge7Vg7BoM0IBthoF+VeCSDkWSe7lIEoL9NCJaZY9SZSpp9k1ePCDGAyJDdKAP/tsAnfcsZ/o6OmdNndnExZjIml8MYPHlKLVOyi66s/uTweQe/n/t3fe4XFc191+72xf9N5IVDaQBHtvYlGXqN4t2bJsK26xndhxbOeL49iO5RLHJbZly022WmTJalSjJEoUSbH3BlYQBNHrLhbYvnO/P2YBAiDKLgoBkvM+zzwzO3PvnTuLxcyZe3/nnISL1ofhCNKUnhnghjscFE7yUVNl4u9PJVNRNryjAsNAxw8ol96zGn8IvAsgpTwVHvn+IdqIQEduiOsHE+o5bBTsJMIRicuZqO7YUsrvARckUpdSNqHrFcY0oZCfDz74OGVlz19w7JZbPiIzc8mwni8xOcjyq13MWqAZCft2aEaCs2WwRoIkfVw7eVNayClqJW1cOwaDJBQU1FbEsmvDOKpOx9NQGRPR9MFwMRaCMo3lfqWlzePBB6t5+unsbvtfe20ZN920gZyc1UNqPzZ2E1brMdraluP19i7SHS4stgATZzUxeU4DaTluQkHBmaNJHNuTRuWphBE1Svti3rzvDipIk9WmsuqGVuYvbcPnVXj9hUR2b40ZM/FLuiKlfBhNJxBp+VLg4gSDuYIYsquHECIVSJJSXrFhxMY6bncdzz8/iUCgm6iXpKTp3HLL5mEVncXGh1h9QyuzFrQjJezdFsPmDXG0DjJGgtEcYvLsRqYuqCMly4OqQkNlDAc2Z1F1Op7as3GEgqM3+zO2E0mNjX7Z7Vk8/LCTZ5/Nxe8/L2t64401Q84pYTA4MZtrUJRIPeKiREjGFbUyZV49BVNbMBglDVV2Nr+Wx6kDqfg8o+stl5AwkcTEYhyOUiCSIE2S2QvdXLPWic2usvujGN5/Kx6P+8rxVOkNIcQZtOmNKVLKgBCiLIJqUko5+nnaLxLRRHD8OLBMSvlol32PAV8Pb29HG+pxDXsvxzAjnXVyqPSVKXLGjH9h4cIfIvpz9o8CISRzF7dz9VonRqNk90cxbNkQR6tzcDfTmAQfJYvrKJ5fj8UWoqHKzocvF3D6UDJ+79hxZ74YwY8Gw1jrl9kcz8c+VsULL0ylra2ic//bb9/M1Vf/jcLCu0elX31hjQkwZU4DxQvqSUjx4Wk3cmRHBsf3pNJUGzPa3evGsmWPRxSkyWpTueXeFqbN8nD2tJk3X0qkturyT3EdIWfRxtw6RHwVXbZ1iG5k4R/o4lcazgT2r8Am4Bia3+k/A8Pr7DvGGcuahcrK9y4IlAPDH7I5LSPA2ntbyCv0c/q4hddfSKK5cXAP9PRxbcxYVkPhtGYAyo4kc/CjTOrPDa8bFoBiCGGy+TDbfdra5u+y7cNo9aMGDQR9JoJ+k7a+YNtIyBHAeOQkSouTYEKqLnDsA5MphnvuOc7LL8+npeVw5/733tPcdCdN+sRF60vvSLIKXExdUE/htGYMRkl1WRy73h1H2ZFk1NDY1C9HEqQpJ9fP3Q83EZ8QYv2rCWzbGDsmpxxGi57C/CtNqB8J0dzRJwAvdPl8N1p66mullH4hhATuYQSNBSHEN4E5nFennpVS5vdTfjLwI+Aqzgtd/kNK+X4vZRXgy2hGUT6aG8zfgG9LKdt7lr8UOHbswvDqd999lKSk4ZnbNVtUlq5ysexqF36fwkvPJHFgl51oXSCFIimY2syMpbVk5rXh8xg4+FEWh7dn0BZhgp3eMJgDJI1rIHl8A9Z4N2abr4tx4Mdo7jurbMCrGQOKQcVoCWAw9S5+dm1uo/wz1ciA1N5DFGDHmxiybEijGbXMoZW7bi2hwPB4fkTCWBA49obRaOXOO/eybt0q6urOu6Vv3PgwgUAb06Z9oZ/aw4/BqJKV30ruJCd5U1pISPXh8xg4siODozvTcTSM/YRYvQVpKi19gtmzvwloOVY+/vkGPO0Kf/xlGlVnx5yAUecSIBpjIYHucRTWAO9JKf3hz7uBC/2khpcfoBkoe4HE/goKIYqArWiJRn6M1vfPAOuFEDf0ooz9GfAl4GXgp0Bx+PNsIcTVvfn1jnUKC++lrOxFQJKYOIXbbtuO2Tw0tbbRJJlY7GX6bDeTpnkxmyUHdttZ/0oC7W3RzXsqBpUp8xqYvaKauCQ/ziYLW9blcXxPWlTJdc4jictwkD6hirTCGuKzmlEUiRpS8Lba8Hss+NqttDUlEHBb8HvM+N0WAh4Lfk947bYQ8JqRPUSSQlExmgOa4WAOYrQEMJoDmJ5/D+GXiPwklHFxqMcbkA4voUo3SDcokPLxRGZ89e+4HTG46hNpPptB09kM2hrjGYkodzB2BI69oSgm1q79kPXrb+HcuTc793/00RcJBFzMmtXTrX14MZpD5E9poWhGM+MmODGZVYIBQXVZPHs35nD6UDLBwKU1h98zSNOuXd9i5syvk5gsefDRRnwehT/+Mh2X89K6rtFGCFGA9qzLAJ6RUpYLIcxAJlDb5fl32RONsVALTAQQQqQBs9BCbnYQC4y072mRlLIs3IfD4XP2xWNoBsXcjuRW4WQfR4BfCyGmdEQAE0JMQ0sK8pKU8s6OBsKil1+iRed6ll4Yy5qFwsI7ufPO/fj9TjIzlw5an2AwSIome5k+28OUEg8Wq6TNpbBvh50Du+1Rv6kIIZk4q5F5a6qIT/ZRezaWj17P4+yxpKiHRhVDiOS8OtInVJNWVI0twY2U4KxO4cy2Ypoq0nFUpaIGh6ZzkKpCwGsh4O1+reac5Sgp884LCad2FRKCUpxOcKKgfZOTuFQnCdlNZEzSIs5626ydhkNTeQbe1uGbCx9LAsfeUBQD11+/jvfff5DTp5/r3L9z5zfx+1uZP/+/hjUXicGoMn6igwkzm8ib4sBkVmlzmji+J42K44lUn4m75AyErhgMZmbM+Jduae2bmt7hi9+cjdki+eMv03RDIUqEED9Cm1o3oJnY24BytDwTR9HCTv98lLp30YnmDvo+8AUhRDOwCu3Le6PL8clEHnd7UHQYCgMhhIgBbgE2ds2CGY7w9Qe0UJ5d85rfj/aK9/MeTf0ezV/3QfowFoZLsyClRBu86LoOIaWKopgwGLo/pDyeeoJBT7iMtqhqCFBR1SCqGkRKbZ2QMOkCQ6Gi4k38fkeXsqHwOTWNjxCSlLQA2eP93P/Jq5gwKQt3u+DwPjuH9tp4a91fCQa15G6KYgov5i7b55f09IWYzXGAJG+KgwXXVhAIncXdlsvm1yZz7kQC0bxhm+1e0oqqSZtQTWpBLUZzkKDfQFN5Jqe2TKfhdDZ+t3XghoYBf3YBiteN/fDObtoAd7EWdMjY3IDxt5sp6zLcb0toIyWvjpT8OlLya8medhaAtqY46o6Np/b4eFz1iQxl1GHAfrU0kPj+S6OipehACIXVq5/BbI6ntPR3nfv3738Mv9/J0qW/GtBg8PtzAQgGUy9sX5HkFDmZMKOJgmktWKwhPO1GTuxN5eTBFGrPxo2Ku+NIUVLylU5jwWw289BnfaSkBXn6d6nDFvzsSkEI8Q/Av6C9LL4OvNNxTErZGg5CuBbdWOiVbwNL0Ib0QUvNWQ4ghDACdzJ2cnnPQMs8tq2XY9vD667Gwnw0t5lugTeklF4hxH7OBwXpk7a2U4Ynn4zR5LNSICWdN7ri4s+xaNGPu5XfuPGTnDz5VKdh0B8L53yfWVO/gpAqSImQKu9vuJuq+k0DdQuA62b8mOKsmxFqCCFVhBpi5/Yv0Owuj6h+ftLvOdIiqdjjRga1vu7c9VW8obaI6j+w9FUK50xi/v1uMiYFaKqEG2fdiqKYiYvLJz6+kLi4QuLjC4mPLyIurpC4uIKwgaGhGIOMn3WazCkVJOY0IQR4Wm1UH86n/lQ2zWczUEMX/81pMNoAjzOWyoOxVB4sAiSxaU5S8upIn1BF4eJSipYepb0pjppj4zm3dyK+9ujnzceqZqEnQgiWLXscszmRAwd+1Ln/6NHfoChmliz5Wb/1PZ6ZeDwzu+yRZIxvY9LsRgpLmrHFBPF5DZw5ksSpgylUnU4YtsidY42YmGzS0ubR2LiH/3n8JyxePo+nnvBz5uTFMZwvMz4PvCyl/Eo46GBPDqJlvrxiiCaCY2V4uH4q4JRSVnQ5bAceBQ4Mc/8GS0cEmN5GOjr2dc1Mlg00Sil7y4NeBSwRQph7zk8JIR5Fu+5kkPj9vft6lyTt4yv5v0M63aitblSnm+r2HZyIMGLovON/5ZGqjzpOihCw13Ek4mGc5TXPcIdnE4pWHQSsCzbRHGH97CefYHlcmmaohO+zv5bezgTxA3HXyu3kfzkdmtsQ/7WZmJd2AaCqfpzOEzidJ3qtZ7WmER9fyCe+8gtm31iLNd6DszaJU1umU38qB1ddIiM15x8pQ9cGCNoaEmlrSOTs7smY7V4yJlWSOaWCosWl5M8/Qdm2Ysp3TY5qKmUsaxZ6IoRg4cIfYjbHs2vXv3XuP3z451itKcyZ8/+6lfd4Gti8+VGamg6wYsUfyMlZTVySl0mzG5k0q5GEVB8Bv8LZY4mcOpDCuZOJoxqL42KyaNFPWbRqE3fefwc//M6PeeuVNlau/BNSSurqtnL06OPU1W2lqOheFix4bOAGr1wmAY/3c7wBuHA46zIm2giOIbTMXj33twKvDlenhoGOwOy9Pfy9Pcp0bPdWtmf5bsaClPIJ4AkhxMeAp/vqjHlCJklf6R54xv7oCXi6tNs+IQSKoiAECAQGRUERkH3tFEpmzcLQ0ITiDwAw4f0TVNd4MSgKBiE6F0UITIqCUVEwCoFRUZg1K4vi3Gyk0UAwK53AuEyuf8vI9HY3IsaO0WzCYDBg8Hox1jZibGxBUTU9pwDmTM8nN6n7/PY/iKm4g0EkEFRVAqqKP7zuWDxWM67kWKx3LSR1RynZ63dj8AdpW51J6nNWGr39mxtebwM+XyPz7jiJ/4yJw7/KpLZ1OoGEZEpLf09BwR3YbMMZN0AijAEUixeD1YMwBQi2JhJyx9CXUTLc2gC/28q5/RM4t38C9kQXk1YdYNJVhxg/6zQnPpxBzdG8Pvsykv26GMye/S0MBivbt59P0757979jsSR1ekm4XGd5881rcTpPEBsfS/bE97j6ZgvjJ6tYbGaqyuLYuzGHsiNJBHxjJx7HxUHy8UdnsGxNIb/52W/5xY//F4C0tLmUlj5Bc/PBzpIHDvyYefO+h6Jcad9RxHiB/kREeYDj4nRlbBBxIinoTKrxMeBaNHXo16WU+4QQSWjzNxuklCOqW+jSl8NAbG+uk0KIO4EXgc9LKR/vcWwqmsjxMSnlt8L7DgHpUsqMXtr6G5qbqKUv5asQ4mNFsbF/OXD33YaOb7Pr92pUFKwmE9JqQbVZkXYrfrMJabMi7TaE3Ya025A2C2rH2mYjlBSPmtTde0FxujA0NGNsaMbQ0BReN2NsbEYEz49UqBYzwZxMAuMzCeRkEhyXSTAjDQzaG5bweDFV1mKsrMFUWYepsgZDXeOwvKcH7RYqb1pIy+wJWOtbGP/SR8RWXJjq3eX3U+ZyUdba2m19wt1GhbOVYDBEekYym+6/j4QPy3B7FZwtkpdFMt+reQZFGMnNu5lJkz5Jbu4NKEov87JCxZJegzG2FcXiwWD1amuLtlas3s5tg9WLMFw42hPy2PA3p51fmtLwt6QiAxfHBS05t47Jq/eTkNmCoyqFYxtm46i+fF9qdu36d/bt+363fatWPU1KykzWv3MjsxfncePd13PV9cs5tvUkZ49WEpe+jKozy2hzXrlugSuvd7Lqehdvv3aGT97Xv4TKbs/mYx+rHFYRaQfDnUhqNBBCrAdipJTLwtMQDcDVUsr3hRBWoBTY21UQf7kTTQRHO5rIYwnQjvam3fFq0oomBPwTmkJ0tKkOr3N6Odaxr6tRUw1MFUJYepmKyEGboujXRUYBYkz9iIikRHi8KB4vNEOkciNpMhJMTSaUlkwwPUVbpyXjmz4RNW72+YKqRHE4MTY5CCXGE0pLPt83pwtTZS2WwyfCBkIthmbHsA/gS6BlZiFVNy8kZDGTuWEfGRsPoIR69zqNM5uZmZLCzBRtSrA11c6RqwqpnZiGyeEh8Y2D2PafY7I7CPM1IZsaVPnJm++ELzlIefkrlJe/gs2azoSJDzJ58idJTi3Gll1BTOFx7PknMNq7Tw+pAROq10bIZ0X1WfG3pKD6rKi+8D6vDdVnQQ2aMCW0YE5pwJzcQNyUgyimQGc7gdYEzXg4mwAb3cgqFb8tB29u8bAGP2quyGDbX64hZ3o5E1ccZNHH36PmaC7HN87s14NirAVlipR5876Lz9fE0aPn7fzalu+z4LrbePl7vyM5LZmWxhZe+uur1J9soSAvj3MnEnG7r1xDYdmaVlZd72LjO7V88yvfHLB8dvbqETEULiN+guZm/xTacw0gUwhxHVosoXHAA6PVudEgmjGo7wDzgNvR4hfUdRyQUoaEEC8B1zE2jIVDaNMKi3s51pGbeXeXfbvQRksWAJs7doYtyFloUSpHBREIYqqpx1Rz4Zu5arWEjYfzRkQoJRFjVR22nQcwVdZohoFr5GNKqUYDFXcuo2VmEfaKenJf3oKtzhFZXUVwbGk+xxfnYfSHmPphGUW7z2EMqJDSXVukGBVuLC7irPSxta7zJ4jHW8+hQ//DoUP/Q86U8cy7bTYlafPxVpfQfmYS/qY0VJ+VkM8KauQ/e8+5rp8kxjgn5pR6zMmaAWEsO4ft20c6J6jsykHktrcJJcajipjhExJKhapDhdQeG0/BomMULDhG+sQq9r20jMYzWRcUv1QEjr0hhGDp0l/j9TaRkXeWz37jUUrmTsPn9fHh25t584W32bphG8FgiLVr11KQlzfaXR5VFiyr55q1fl578W0+9/A/oKoDh4TJyVlzEXp26SKlfE8I8TngF5w3Cp4Kr/3AZ6SUvQnoL1uiMRbuBp6QUr7ahzr0FHDv8HRraIRdJNcBdwghZkopDwAIIWKBTwMn6e758DzwLeArdDEW0II42YFnLka/o0Xx+lDO1WA6VzOq/fDH2znz4Brc2alkrd9NxqZDiAint1xJNvasnUpLdjy5B2uY/sFpLJ5Av3UenDiRBydO5LjDwZ9PnODJ0yep62IQVR07R9UPz7Hux2+zevUzFBYOVzZCQdCVSNCViLtcS9STuOFF7P5mAplpyBwbprN1CKcPY7MTKZ0goG3ahbk5BksoYOLU5hIqDxQy587NzLlzM/teWUrDqe6DaJeSwLE3kjM8/PzZ/2DcBBc152p47F9+wlt/X09ba2QeOBcLKVVqaz+iqmoD9fXbcTiO4fHUI2UQkyme+PgiMjIWM2nSJ0hNnT1wg1Gy4ppW1tzk59UX1/HFR74UkaEADDnb55WAlPKJsIvk3cAUNLHQSeBvF2u6fSwRjbGQTf/eDm5g+AP4d0EI8RCasAQgDTALITpGMs5KKZ/qUvybaJG33hFC/AxtquQzaNMKN8kuogIp5SEhxK+BL4ZHSN7kfATHD+kjxoIOtI9LpezBq1EtJgqffo+EY+cGroT2vCqfmcWhNRNRQioLXj5MzvGGiM8bNAj8C8cx5VNT+XKyifLNxznx3E627j2BPzztIVU/SypO02I9gzczH6EM/7DrBULCuWBqrSGObdhzyzAuDyCUJgJ7zuCpzGe4vDe8rTHsem4V8+7dyJw7tnB840zKd07ubP9SFDgCmCxB5q2uYvqSOnwePz/6xi/5+19eJhjoOzT3aPL3v8/uJhzsis/XRENDEw0NOzl8+JcUFz/K0qW/RlGGw8VXsubGVlZc6+LVF97lC4/8I6FQZN5V8fETiI3NHYY+XP5IKWuB/x3tfowFojEWmuhdA9DBNM5rBUaKT6HleejK98LrDzk/TISU8pQQYimaluIbnM8NcX0voZ5BG1UoR3OFvAloRPuRfPtSDPU80gRirTQuKqZueQkml4cJf3474mkHn83EvhsmUzMpjbTyZua+XoqtLbKoqa4YA/umxbO/OA6PzUBqs58btjQz9ZQN8+wVNBcv4LnTp3nyxAniFSOfj9tE3b6N7LKvZM+8fyAQE0sw6MZsjh/C1Z+nr+BHzTF30OIKEr/1I2J3byPrR8/jrcumZc9SPBWFDIfREPBa2Pnsakpu3sGU1fuJTXVyZP08ZMhwSQRl6o5k4qwmFl1fgT02wIdvlvHdr34eR5NjtDvWL11TbvePpLT0dxgMFpYs+cUQzyq57jYnS1a2sXtrDO++Npv4+Cm0tByJqHZOztVDPL/OlUg0xsIG4JNCiP/ueSAcP/sRujysR4JoM4FJKUuBiNIrht1CfxpedPqhZUYB525dQshiJuHoWXJf+Qijuy/P0+54Y8xs+thsPPFWpm84xYRd5yJ6bEpgz/R4PlyYRMggmHDWzdxDreRWe7vVT7Za+cK0aXxh2jTaAwFiTCbSgipp+zcx5YNdfNuazo6GdSxb9jh5eTcP5vK7EYk2QAINN95K4pytZN34Aq1HZtG45VqQQ/f9DwVM7H95KROWHWbCMu1hcfjNhZeUZkExqKy8o4xJs5uoPxfDT77+Oq+9+B+j3a1BYbWmYbNl4HbX4PM1XXD8yJFfMWPGvxAbO26QZ5DccLuTRVe1sX1TLG+9lEBKShJ33rmPI0d+w+7d3yYQaO23BV2vEBlCiMVogZcmAilcaOFLKWXRRe/YKBGNsfCfaKLAXcBzaPfv64UQ1wCfRRMU6lE+LmNCFhOVaxfRPGci9op68v6+GWtDpG9W2ojClvtn4Ysxs+y5faRU9X9T60AV8N7SFPZNj6eo3M3VHzWR6Bp4WLrDO0UxKmTNG0fTmVq2vvc7vFJl/fq1FBXdx5Ilv8BmS4/4GnoSqTbAVToL1/ESkudvJnH2dgwxbdS/dysyOBxheAWntpQghKRo6VHqTowjsOHS0CxYbEGue/AE2QUudr47jp3vJbLu79+NuP6mTZvw+ZZRWDhlBHvZP1ZrGsXFjzJx4oMkJmr9kFLl8OH/Zdu2r3QrK6VKdfWGQaXjNpokN93ZwpxFbrZujGX9K+fDpCuKiZKSLzNhwv3s3PlNjh//Ux+tCLKzV0V97isNIcTH0XIfBYATQEX/NS5/oongeEoIsQbNjaTjv/lr4fVh4CEpZWQT1jqXHO3j0yi/5yr8SbFkbthH5gf7EWrkMTr8FiMf3TcTd4KVJX87GLGhEDAK1q1J42RBDAv2O1i5vWXQA/j15hBxFjPecDCo06f/j8rKd1m84jGKZl4P9lak1aktNgeYPCgNEzFUzUb4e89ZFpU2QDXQvGMlwbY4Upa9S9ba56h96y5Ur73XtqPl1EfTSJtYzfTrd7Fj+4wxr1mIT/Zy4yeOE5fk473/K+LUwVSMBigouIuysucjasPpdFJf30J+/vAl4YqGkpJ/YvLkR7qFJgct70VJyZc5evRxnM7j3Y653dELklPSAtz9cDNZOQE+XB/H+2/1nrHUZkvnqqv+yJQpn2bLls/T1LS/2/HU1NlYrb3p03V68G/AcbTYCiM9vX5JEG0Exz3ATCHEdDQBoABOSin3jUTndEYfKQR1K2dQs3o25tZ2Jv7+LWLP1g1csQsBs4Gt987ElRLDohcPknrOEVE9t1XhxRsyqEm3cPWWJuYejszA6ElZkqA6Dtonj+c7K+7jyVe3smurFmLa52ti47uPcsKRy6IvrSQmPXzT98YiVBOh8XsJzHoBpW4qhop5GGpmIELmzrb92QUXnC+YlEYwqe/Ikq1H5hJsjyX96nVk3/YUtW/eQ7B16A9uqRo49PpCFn/iHSY+0siB15ZE1a+LSUaui+sf0v4G6/5YrCV1CrNmzXPMn/89mpr209R0oHPd3l7Za1vBYKSBx4efkpIv93u8t1gG0aaJnzbbza33thAMCp76XQqnSgfOFZKRsZjbb9/F0aOPs2vX/+ucmhg//saozn0Fkwf8i24onCciYyHscngA+F8p5c+llIfRRhN0LmNCZiOnP3Et7QWZJB04zbhXt2H0Rpe+XVUE2+6egSMjloUvHyajvCWieu02hWduy8YVY+D29fVMKu8970Z/OCzwu/kGjmSc1wbY/XbWFF7N9CUTefVPH9LcrLniVe+qYN0jf2f+0m8zddJXUDAjkciEKkK5uwiO24O68DCBoBnDubmYDt+C8GsPuMEEP3KXT6ZmXQyZN7xI9m1PUfPaAwQcQ4/K6KpP4vRH05i44jA1e9JpW9885oIy5U5u4doHTtLmtPDWXybjbOqe6EgIQULCRBISJlJYeHfnfre7lvr6HeFlOw0Nu5gxYyaLF5cAZ/H7x1a8hcrKd3E4jl2wPz19US+lL8RgkFx3m4OFy9upKDPzwl+TaXVE/n6nKEamT/9HCgvv4uDBnxIMepg5818irn+FU4mWjFAnTES/vHDcghRgbDk564woNdfMpb0gk9wXNpGy79Sg2iifmUXT+ETmrjtK1qkLBV998d7SFFpjDNz3ei3jaiMTT3alyQY/XG6kxQYPHAgxu0Yl0QuWTu+yHFy33MW/7drFr44cQQKBQDtbN/4rp0tf4+qrnycmJgfhHIdyaBzGQ7eipp4ilLubUO5OQlmHMO+7D9vOuEELCX1146h++SGyb3+K1OXvULOuI1P60CjbPpUE10GM33+WhIAcUwLHuCQva+49TXOdnTf+PBmfJ3LNht2eSX7+reTna5plKSWJia8TE7MXh6NxTBkLjY372bDh/gv2Z2VdFVG8hdi4EPd8som8Qj8fvR/Le68noA4yW6bdnsWiRRfo0nX657fAx4QQPwuL3694opmG2I4WwfEPI9QXnTFE+7g0GhZPJXV76aANhYDZQOmyAlIrWhh/JPKpi9O5No5NiGX5zpZBGQp1MfCj5UbazfD1LSEmNfWurYgzm/nl0qXcV1TEpzZt4pjDAUCo5SQW2V1HIFAwNE7C0DgJ9dRK/POewr/4DyS9kTckIWHAmULLrhWkLn8He/7JzmBPQ0GqCvVPeSAgCWVk4E/IHBMCR0VRufq+UyDh3WcnRmUo9MZYDVdcU7OJ9etvucCt0mpN46qr+hIenicn18+9jzRhs6n87S/JHNk3PJoWnb4RQvRMprEbuBPYGY7Bcwa4wGiQUo5adN+LTTTGwjeA94UQO4AnuwY10rm8kIqg4valmFxustfvHrhCH5xYlIs/xsz0Fw5G/L7sNwreWZ5CSrOfhfsdUZ+zKk4zFIIKfHNTkPwImliSmcn+O+/kv/bt44f79/OrrDySN/4Tb039Ku2FJReUV1qzsXzwNYKT38F557u0rbITsC7GUDsdiF5I2Hp0FvHT9pKyZAOeyoJh8ZBwFixm2sNbqLMsp35H8aD6NdzMv6aSjPHtvPPsBFyOy3OEt7p6I2+9dSOhkKfbfoslmRtvXE98fGG/9WctaGftPS24nAb+8Is06qrN/ZbXGTY2opnRXem4bf2hj2MSGI4IW5cE0RgL/wO0oH1xPxZCnEaL2tgVKaXUnXgvceqXTMOblUzhX9/F4Os/9HJfeGPMnJo/nnFHakmqdUVcb9ucRFrjTDzwSjWGKENhNdrhB1cZUVT41qYg46LQQ1oMBr47bx6fLS4mOyYGZ4WDrENfY9u5a9g785PsOfM7Jk16mJiYbACENGA6dgNq4nS8k/5CzN4nsZwoQA0ujD74kVRo3Hwt2bc+S+KcrbTs7Bl3LHpcCVMJjisn5sPtxG+rGPWgTNmFTmZfVcORHemUHb481fgu11neeef2CwyF2NhcbrzxXRIT+x41EkJy/W1a/ITTxy288JdkPO4r5jk0FvjkaHdgrBONsVCIZkl1+JtekM5Z5/LAk5WMydEWcejm3mgcn4BqMjBhZ3RtnM6zk3/OzfhBTD9sG6fgsggeeydATuT2STeyYzQXvITcRGKz40nd/wF165/nt87d7N/3GHPnfYfp07/UmQ7bethPyk+BUByCRqR4c1DaAG9NLu6zRcQUHB8WY8FceZrqO/ZASCVWVoy6ZmHe6ipcLWa2vjF2dAXDzYEDP8Hvd3TbZ7Ekc/PNG4mPv9BrpgOTWeWujzczZbqXrRtjefe1wesTdAaHlPIvo92HsU40cRbyR7AfOmMIg9ePao7Kq/YC2pK1eda4psi9GFQBzQlGCs5F7/kAcDRdMM4pB20o9MRgVEiancXjpRsBCATb2L79axw//ieWLv0V2dmrOoMy+TJT8ZWcw7Y7CaMzMChtgLcum+S80wiTDxkY2jC9/fg+CKrYSiy0KgWIstpR0yykZLWTXehi65u5hIJDj1o5Vqmr++iCfdOnf6lfQyEmNsQDn2kke3yAN15MZOeW3uN56OiMNkN7Iuhclhi8fkJWs/ZMGWQbbcl2bE4vxmDkcwmtsUZCRoVkR/RTH34FTqQKVpUNbxoPi8HAU6tX8cWPPqI0LIBsaTnK66+vprDwXlYV/DPegql4CovxXftdxMdtxLz2yKC0Ab6GTO2cqXV4a4aW6MddPBdZUsCM/znJwdcXUH24YNQ0C5NmNxIKCo7tHv4YD6FQAn5/Fqo6+iJAqzWFhITJ3fYlJ8/os3xKeoAHH20kNl7l//6YwvEjA8dP0NEZLaI2FoQQFmAl2rQEQBnwoZRy9CKj6AwrBo8fFAXVbMTgH1y2v7ZkO7Et0Y0QNCVpQ/spgzAWTqUIAgbBtIbh192uzsnhwF138cvDh/nO7j20BbX+lZU9T0XF68yZ821KQhMwVM4lOGU9gUwLwaS5UZ+n01hIqxmyseDPLiAg8lBDp4lN0cQboxOUSVI4vZlzJxPwe4f/3aStbQVtbT2F7KPDTTf1lp+udyZM8XLnQ02oquDJX6VRVaELGXXGNlGNCYbjZVehpXD+dXh5E6gSQjw87L3TGRUM4cBLIdvghsIl0JZsIzaKKQiApsSwsdASvbFwJF2gqJIpI2AsAJgUha/OmMHxe+/h/qLzuWOCwXYO7/xPTj5zJ+ZflxH/hgXitnce7xASRoLqjSHoiseSVjssfRYeL9W/a0d9cTf2o7tR2s/Pz0TTr6GQPq6duEQ/ZYeTR/xclwKKIrn+dgcPfbYRV6uBP/w8XTcUdC4JIjb1hRD3Ak+iCRz/GziKNko9FS2R1B+FEB4pZWRB3S8ThBBrgU+1hUKXjSLJWu8AoHXyOFJ3Hu+/cB8YAirtSdENq5qC2oO+IdlMbk10A1XWgBYtcleOYMXZkfPqzY6J4dk1a3i0uJgvfvQRnpYkHuABDF4D4mQL8nQsiO0E405GLSRUzF4Usw+pDn1evyPrZHNIcw1PEFWjInAsnN5MKCgoLx3dPBQXg2efzaet7Wy3fVdd9WcmT34YAItV5Z6Hm5gwxcf2D2N5d10CweBlc9vQucyJ5q70LeAYMENK+UMp5WtSylellI8BM4CTaMk3riiklOuAP8YaDJdN3ImYinrs5xqoXzYdOYjANwIo3FtFfWEKzrTIE/xMP95GbHuQzfOTLnBqHogbTqpMq1P58xwDx1JH/ga8MjubfXfeySMpV2PESI1Sg3OZC2k3gFQwOpswNWhh5V0RCgnjS3ajWHw4Dy4Ycv86hJe2EgvK0jxCsVo+Aq1fNZrAcfrQz9M/kqKSJipPjcwUBEBCwjqys/8Tu33PiLQ/XCQmB/nUl+spmOjj5WeTeOvlRN1QiAIhxDeFEC8IIcqEEFIIUT5A+clCiFeEEC1CiHYhxGYhxOqL1N3Lkmj+gycD/y6lvMB7XUrpFEL8GfjOcHVMZ/QQQMamg5z52Boc0/JIOlwedRsF+6o4vjiXkwvGM++NC+Pj94YpJFm818G7y1MpH2eloDLy0QWjhC/uCPHdVUZ+ucjAf3wQJKM96m5HhUFRyLnXwmZTHZ64Gr5Z70M2HOZoUwYnjNM4rLp5qf4XuE9KMl3LyMxcQXb2VaSmzul0vexAmL0kzNhF+5mJ+JuG7pXsLp6LZUkGE/+ziu1Pr8ExLe2iCxzTctqJS/Kze8O4ET3PWCcn18/9n27EaJQ89dtUzpy0DlxJpyc/AJqBvUBifwWFEEXAViAI/BhwAp8B1gshbpBSRi4u0ekkmpGFWvoXx6tAdOkIdcYsCUcrsDQ6qbtqRtRv+QBmb5D8gzVUTs3AExe59mFGqYt41+BGF2IC8E9bg0gBP1tixD2Cvj4OC/xouYH3b1WZnyv44sYijm9LoymtmPkLFT5e9BGBwJ8xYeJm/xoqKt5g585/5ZVXFvHkk4m88cY17N37PaqrPyQY9JJQshuDxUfLnqXD0j9/dgHxy03UP+lAfUHTLKhmK+7iubinzEGEgiOuWSgq0aYgzhy9/Kcg+iJ7vI+Hv9hAwC/4w8/TdUNh8BRJKVOklNcAA2WCfAzNoLhOSvmYlPI3wPJwvV+LsRonfIwTze30SeBhIcRvpJTdEkoJIeKBR4A/D2PfdEYRISXpmw5x7o5luIqyiT8dfabWol2VnJ4zjtNzxzF94+mI6hhVWLKnhbdXpnE618aECs/AlbqQ2QZf2hbix8sN/HqhgX/eGmK4J4iOpQp+vcCAxwS3P6dw5Klk3g0BCISQKCKBpPgcCnwFfJ5EBIKXOP9gDgbdVFW9R1WV9oKjKGausV3NgpRP4W/M7CwnpYoQg9MvmCtP0/7nd2gPSGKpG4WgTJoXRNXp+BGbgrgUWLi8ndoqE8/9IQV3ux6RcbBIKcsiKSeEiAFuATZKKfd3qd8mhPgD8F1gPrBzJPp5ORPNf/Fm4GbgkBDiN2j6BYkmcPwc0Ahs7pmQ40pKtHG5kbzvFDVXz6F+RcmgjIUYp5ecY/WcmZXN5K3lmPyRJW+bfqKN7bMT2bwgiaIKT9SxHoobJZ/YF+JPc408O0Py0IHhib2gAm9MVnhxmkJGG3x9S5Adm2MJhQTZqX7SkoOcrTHjajfQ5DRikIkgYOKEJv6QtYJNNTVsqq2l3NU9apSq+klMi8H0Oz9J7i344lLxx6fy3Lur8PsdxMUVdFnyiYsrID6+ALs9B0Xp/QEUc2IvBCRKQSJtpryLnkgqNdtNfLKPPR9kj9g5xhoPPFAOaB4PN93lYN6Sdg7ttfGX3yQTDOgvsxeJGWippbf1cqzDTUk3FgZBNMbCu122f8T5xBod/wV5PcpccYk2LjeUkEr6R0eovmE+bfkZxJZHP8s0cec5qqZmUDYnh8nbKwauABhUWLrbwRtr0jg6MYZpJ6MXH6wsl1TFh1g/0UCMH1adUYnzadqGaFHRAj69PlnhYKbCwnMqj+wNYQtCsNjN1AIvUwvP6ysaWoxU1JoRAgqyfSTFh4ApfGrKFAAq2trYXFPDhzU1bGiso6yxhZsCsVzb9Bc8PgV3rYLPo/LHlqP4UGlvr6S2dvMF/RLCSGxsLrGx41i16mliY8d3HlNW5MKNBzlLCb7maVitN2N2tlw0zcKEGU2EQoLyo1eWy6Q9JsSdDzUzYYqPTe/E8f5b8UipGwoDMFEI0TVj3RNSyicG2VaHdVrVy7GOfTmDbPuKJhpjQU+0cQWSuvMYDYuKOfXwdeT9fTNJh85EVT+p1kXmqUZKVxQQ4/Ay7lh9RPWmnmpjT0k8b65MwxCSTCmLPgT0/QdVmm2CV6YaeGWqZrPaApJYP8T6wms/xPq7bHfulwgJu3MUtuYqNMYILEHJx/eFWFOmdlrIBdn+C86blhQkLanvYFa5sbHcXjKZxM8spmhKHMYzzdzzfiuWeecDMTV4PPif6n9ERMogLlcZLlcZ83f8HmnPx2lIQebEkvGF03zhxu8D3+8sb7EkY7WmYbOlYa1Iw2JJxmJJxGxOZPbsf+uW8jkY9ODx1GOxJGIyxUU1HZKZ66JkSS3lR5Pwea6cKYgJxR5uu78Fm13l5WeT2L8zck+gK5yTUsqHhqmtjlCevSWX8fYooxMF0eSG0BNtXIEYfAEmP76OMw+spvz+VbizU8h+Zw8iigzl8189ytZ7ZrD7lmJAMu5Yw4B1FAn3vl7L32/I4NVr0vFsbmL20eiSPihoHhLnjoU4kaLQZia8CNos2nZdrKDNLHCbe3/7E1IyrV5y55EQ86olll5mUtxehcOnrLS4jKQkBJmc5yUuRnvQN7QY2bwvljtWOwBtqO3wpFg+WJyMz6ywaK+DJXtbO2NMdJBms+H91KeoaGvjjMtFucvFmS5LuctFnUfTcwjgwXGHkL59+CwKB//1Rk7UN2DHzjSmkUwyTTRx3Hccp+84Tudx0khjDst5iT9iVmxcY1xK0GQjZLETMtuocB3g5c13d3wLmM0JnYvJFIPBYMNotGE02omPL2LBgscAiEnwce2DJzi8p4onf7ID5JZwWStCGBHCgKIYEOL8YrWmkpRU3O36PZ4GfL7msJEiwoaMEl6LzrXfn4bLdRWK0j3ipd/fSigUmTeN0RiDydT9we7zOVBVzRCUnb/1rn+j89s2ezw33eFn4Yp26qqNPPV4KuVlTnpxHOsFBYslEYOhuwg4FPIjhBL+jvSRiSjoeKvoTVVt7VFGJwquHLN/hJE2q/DMn4E0m5BmM6rFpG1bzAh/AFN1Pcbqeox1DYhgZHP3YwVTm4cJf3yLqpsXUn/VDDyZyeQ/vxGj98K36t4wBkIs+dvBsMEwFTgakcFg9avc83otr12TxjsrUnHbDCzZ44hKwyCAXCfkOvt/Sw8JaDeDq8OYMIPPCFMbJIn9PHNOV5p5bn0KoS4Cx7e3JpAUH8RsklQ3mADBHasdNCWaeGd5ChU5NnJqvVz3YSNp/USrNBsMTEhIYEJCQq/H3cEg5S4XDR4PqdnpBMwGttw/i0BqDHk/2c9X+WcUDAgEKirXcz3NNBMgQDbZncLLVIPkc+0/IRBUCIQEwSC811rDy51nkvj9jgsyKnYw3jqOqcEiAlYbc3+Zi0kYee3/bebDfT/r9zvvoCBjDTfP/z0oClIooCjsPfrf7D7+84jqT5vwCCvm/xQDDq0+sG37Fzhe9nRE9efP+H/MLfl6t33vv38X52o2RFT/Oz/4KQtX3MP2d01s+LuFUNDDm2+uoslxJKL6N6x8gbzcG5EIULTrf/bZ8Xi8HaNwImw0KF3W5/8Lbr11K8nJJZ2fVTXAk08m9nImgaIYEcKIohg7t2+/fSc2W3pnKY+nnrffXhs+bsBgsGAwWDEabRgM1m6L0Whl7tzvdBt58vkcuFy1wJSIrn+Y6RBX9TbV0LGvtykKnQHQjYXhIilBcX7s1u77/AGE3480m8Ec9qsPqRjrmzDW1IWNh0aMdY0YGlsQ6vAmQRpOlJDK+Fe3YatupnLtIk58fi35z32AvaY5ovoXGAyilHGlA09JmEKS29fX89bKVLbMT6I50cRVO5qJbxteg8sgId6nLUThtLnvuL1PgSMShIDZM9rZND+RnbMSMQZVrvuwkZmlrkEn6erAbjQyNSkJkpIImA1sv7MEZ3osi146zJaj8RgwdvarvNqMy62QIdPoeFG2Z1Xwo/ELsBgMjC8Z363tU+V+cj6Kwen30xboP/x2lrmdBxJeo+mfb8UzwUTSvz/DrLr3uhgb/VPoOsKjR76k9Utqb/LulmPsHrCmxtTq9/jMxo8B4b+chGrnQSKNPTqv7G98un5vuLb2VznQfIBIk6vHmT00feLn5G0p5ZHwvvfaKmiKsP6N+3/E4tI/ne+8hP/ztXDeD0giZZC+BvNu3vAlJpo0g1IgCUqVPwQjf3le+96XSTDGEEJBVYzUhdw81RCp/k9wA4uQRjOqwYRqMOFqO0mbuxT4YcR9GEYOoU1BLO7l2KLwOtKflk4XdGNhmBANTaHU7/2vQfg0A0EEAojwP7cUglBaMsGsdALZGQSz0wnk5eCdM/18A6EQhoZmjHVNmgFR34jiaEXx+hBeH4rHh/B6EaHRNShSdx3HWtfCmQdWc/wLt5C2rZTst3ehRNCvToPh7hnsunUa7QlWJm2vGPChqUi48YNGElqDbJ+TyLHCGEqOu1i0z0mia3CJroaLuf0IHJuSTDiuNXNsYRKqQTD1ZBurtzYR4xnev2FLRqz2fSbamPd6KZmnm5hbbI5AeGkEZvXa5q35+dyanw9AUFVp9ftxhBdPMKgtoRDuYJDY+Fgc3/gEnqJsst/aRYb0s2x2EZ9NEHhCITzBIN5QiKCqEpKy+6KqLMzKZPqC7l4TUw82MbG0EVVKpJRI0Lah87NEi7oTOzWDcfPzSQue/y3kb60g7VRjRN/fhOnJzJnRPXDU+PeOcar6vId4x5t852/VYEDG2EBK0rfsZFp6Jqw938b41+NwtfT/2+y4punLMpmb3f36LU8ZUbxB1Aim+0qWJzIzOaXzc0BV4a8DVutk6XyVRJMPVIlEUtbqgsrI6tqE4IuhXxIKCkJSIaQKdrqaOCb8jIaxEHaRXAfcIYSYKaU8ACCEiAU+jRZpWPeEGARCRjH3rNM7QoiPTYyN/cuJBx6IyvNDtZgJpacQzEwjmJFKMD2FYEYqodRkMPQhKAsEUbxehMeH4vYgPF6Udo+27faiuN2Yy85hrKwd8ptrfwStZmqunUvjomJiz9SQ/+wHmNojmyMOGRX23jiFyqkZjDtSx6z1xyN2q2yNMbBjdiIHiuNQBUwua2dmqYu8Ku+IXm9/dGgWml1GzNnAKgOnFsbQkmDCVBrE+r9+7lnRTOogsmn2R9CkcGpBLseW5GFt8zNv3VFSK50X9CsSLcVgaZ2QTcUdywjGWBn/6lZS9p4aUnvRsi4hgb0xMdzscDDXfXGmon0T83F86h4UVzvJv34KgyMSbcLgkFKiSokKhMLGVk8Dwm40YlCUbnXcwQsNFTVsoAWlJKiq2iIlubGxKF2mNbzBIAeamwmqKgFVxa+qeMMGn7eL8ecNhZDAN2bN6naeg/X11DU0cM2WLQNe34MPPsgzzzzz9EACRyHEQ2gedwD/CJiBn4Y/n5VSPtWl7AQ0gyAA/AxoRYvgWALcJKVcP2DHdC5AH1kYIkNJJKX4/CjnajCdq+m2XxoUQqnJhOJikFYL0mpBtVqQNmu3bdVuRdptBFKTUe02pM0KitYNQ2ML1gOlWPcfxXiuZtgfpEavn/GvbSOmvI6KO5dx/Au3UPjMBuxVAw++GoIq8147SnxDO0eXF9CcE8+8146SUj3wTTe+PcQ1W5pYtM/BzhkJHJ4cy7EJsSS0Big51kbJcRfx7RdHE6IK2O2KYf3f4lGDAAKOAO+DOTNEDH5azhlwYSB1xvAZCqoiKJ+ZxbGl+fhiLeSU1jFr/QnM3vMPiGi0FIMhaDVTfcN8muZPxtLgYNLv3sBeHenA+6WLZ/4MnPfdjLGuiaTfPYvBGZ3oNlqEEBiEwICW+TTSOjEm08AF+8BqNLIwPX3ggn0wIzlZ014ML58Cruqx73vh9YdAp7EgpTwlhFiKNrTxDTTDYi9wvR7qefDoxsIQkVKuE0LExxoMKwYuHRkipHZqGaLqixCosXZ8UyfgnTWV9pULaV+zBENTC5YDpVgPlGI6Wz2shkPywTKsjU7OfGw1Jx69KeK3SwFM3naW1AoHu28pZvODs5mypZzJ2852Tt/0R1x7iDXbmrlqZwsn8u0cLI5jy4IktsxPpOCch5mlLiacdWMYxhH/kAI16RbOZVmpyLZSnWGl7T9CqEEV02RISAvhPgK+VoVAJTikASFg/rS2gRuPAAlUFadzdEUB7Ul2Us45WPjyYVKqLjSyItFSDLZfzsnjOXfbEgJxNjI2HiDz/f0ol5hoN1ok0H7dctpuWIn5eBmJf34Rxdubd57OSCClXBll+VLg1gEL6kSMbixcRggpMbjase84gH3HAVSbFW/JJHwzp+JesRD36iUozY7wiEMppoqqiB7MA2GvbmLyr1/jzP2rqLhrBe6cVMa9sQOhDtx4SpWT1X/axf5rJ1G6opD6gmTmrTuKvTWyG7ExJJl6up2pp9txxBk5NDmWg1PieOW6DOyeENNOtFFyzEVKlymAntfcl/EUNAiqMzTj4FyWlaoMC0GT9saU2uxn2ok2LLkB4m4KMicnPAQ+ta+gTINHAvUFyRy5qhBnZhzx9W0s/tsBMsqa++x7f1qKwfYraDNTefMiWmZPwFrTTOHT70U0knSpIxWF1ntvwrNwFtadB0h4/vVR1w7p6FxsdGPhMkbxeLHvPIh950FUmwXf9Ml4ZxbjXj4f96rFKC3O84bD2cohGQ5Gt48Jf15P9XXzqF9egiczmYLnPsDUNnBuB5MvxPx1pWSUNXPg2km8/8h8Zq0/EZG3RFcSXUGW73awdI+DM+NsHJwSx57p8eya2bvrYb/I8Ot3eDu9yc/MUhe51V7G1Xqxe8MPC8EFTloDBWWKhubseI5cVUhjXhJ2h4e5644y/kjdgKNDgwkW1R/OSeM6tQmZG/aRsfFARKLWSx3VYsbxybvwTyki5u0PiX1706hpY3R0RhPdWLhCUDw+bLsOYtt1ENVqwTdtEt5ZxbiXzsO9chGKqx3b9n3YP9yJoW1wuZ2FKsl5axe2qiYq7ljGsS/eSt6Lm4g/FVleidwjdaRUOtl1y1R23TqNqslplLx/KuJRhg4UCUXnPBSd89BuVTiXbaUp0RzupLbqtIt6fu56PRIyG3yMq/Vh9ff9YBxuIWHIoFA9KZXyWdk05iVhafcz450TFOyvRolgtGY4++WPt1N93TxtNKGuhaK/vBOxu+ylTiA7A8fHbyeUlkz8s69h33lgtLukozNq6MbCFYji9WHbcwjbnkOoFrNmOMwspn3NUtpXLsK28wAxH2zD2NgyqPaTD5ZhrXdw9r6rOP3I9aRtPUL227sjmteOcXpZ8fQ+Ti7K5fiSPOqKUpi0vYKJOyowBKN/k43xquFQ0SOjlB9OIWFrip3yWdlUTM8kYDNhd3iYuvE0RXuqMAaimzIYar9Uo4H65dO1FOVCkPHBfjI/OHDZaxMApAD3ioW41q5GafeQ9NtnsZwsH+1u6eiMKrqxcIWj+PzY9h7GtvcwwbRk2lctxrNwJp7Fs7EeKCVmw1ZMlbVRt2uvbWbyr16j+vp5NCyZhqsom7y/fRjRW6kiJZO3nWX8kVoOryqidHkBZ0syKdlwiqyTjWNqGHiwQkIpoDU1hubseJpzEmjOjqctNQYRUsk+0Uj+gWrSylsGfa2D7hfgmJ5P9Q3z8SfFkXj4DNlv7cLSMjwizeFmrdPJWqdz4IIREoqPxXn/WvzFE7AcOk7C/61DaY8uTbqOzuWIHmdhGBhsnIWxSig+FveKBbiXzkXarJiPnSZmw1bMJ8sH9fBqnZjD2TuXE7JbyHp3D+lbjkSVW6IhN5GDV0+kNT2WhDoXGaebyChrJrmqFWWUf79nqs14vMqAQkKf1UhL2ChozomnJSueoEWz1c1uP8lVraRVtDD+cB0Wz9DdLCPtV1fcWclU3byQtoIsrDXNjHt9O3FnojcUL1W80ybivH8t0mwm/pV3sG3dO6YM00uGYBAcDvj73wcsGmmcBZ3RRzcWhoHLzVjoQLVacC+di3vFAtSEOIwV1cRu2Irl4LGoHvYAQbuFituW4pyeT2xZDbkvbsbiiPxtVRWC8llZVBZn0DwuHqkomLwB0s62kHrWQVpFC3GN7lG5uffUBuRPDqEW2XFkxFHptVG6Lkj2b7RER0JVia9vJ7nKSXJ1K8lVrcQ4PCPS70g1C76kOGpXzaR5zkSMHi9Z7+4lZdeJqP/Glyqq3Ubr7dfinT8DY1UtiX99OWq3ZZ0u6MbCZYluLAyRcFCmf8qy2a6qfuihYY9EMhaQRgOeeTNoX7OYUFoKhvomYt7fhm3XQUQo8jlsCTTPmUjlzQuRikLGlkOkbzqEwR+dQj9gMVCfn0xdYTINeUm4E20AmNv9pFU4SK1oIbXCQVzT8BsPEvDFmHHHW3EnWDhdYWbTryVqx2CAAsIA5jwzil3Bc1B7s7/vT0ZSqlpJrG3FGBh5L4LeNAuK4ALNwiffKqBpzkSEqpK64xiZ7++POEHYWGBTbCzHrFaWt7VR7I0sgmgHEvDOmorrzutR7VZi3vuI2He2RPWb1ukF3Vi4LNE1C0NkJIIyjTVEMIR9+z5sO/bjmzGFtjVLaL3vZtpuuIqYjduxf7gzoiRYAkjZe5K4smqqrp9P7erZNM6fTNaGfaTsPhFRXAbQXC1zjjeQc1zLXNmeYKUxN5GGvCQacxOpKtaiz1nafKRVOEisdWF3eM67QoaRItwrEfaGCH+WHR4SisATZ8GdYA0bB1Y88RZU4/kBpLNfqkYNuIidYiJuvMC5P4C3ReI/7euiDWhn8vaRCwncG/1pFrRrFRR8KpfmWUWk7Sgl48ODmFyX3ty802CgxmzGHWXEwFB8LK133YBvxhSMFdUk/eZpTDXRuerq6FxJ6MaCTsQIKbEeKMVyoBT/xHzar16K69Zr8MyeRuLTr2CsjyxAj9nRTsH/baR9yxGqbpzPuduW0rBkKtlv7SL+eGXUowExTi8xh2rJO1SrRdpLtNKYm0RDXiKNuUlUTUlDDiH8rKXNh73VR2JdG1knG7E7vdrS6qXBGMJ/Nee1AQXDH5RpMPQWlOmcOZWjlvF4clJJW5pIXl0FGf/9wiVpJAwWCXgWzsR127VIg4G4V9/D/uH2iA1VHZ0rFd1Y0IkaAVhOlmM5WY53xhSc99xE49c+Q8Lzb2DbcyjidmIqG5j4xJs4p+ZRff08yj5xLfZzDaRvPkTikbODmjMXQKzDS6yjhvyDNdpQc6wZv93UGUxBdKQC7rLq3CfDAwwybBy1+TH0E3woIR3oEUZ/OIMyDZaOoEwhsxFHSQGN8yfjzk0ny+Mjdfsx0p7fjKktumH7SxkJ+KZOpO36FQRzszGdOkvC869jbLgyYkbo6AwV3VjQGRLWg8cwlVfi+PgdOB+6jUBeNnGvvBvRtARoD+bEo2dJOFZB07xJ1C+bTvkDqzE3tZL+0RFS9pxAiTLGQM/2bW1+bG0jNw9/MbI7RoME3DmpVE2awOHTVtr3+kisamZ68mlyT5/C4AuE+5V4Ufs1GkjAN20ibddpRoKhqYX4Z1/DtuvAsIQ619G5UtCNBZ0hY2htI/k3T+O6ZQ3ulYsI5GSS+OSLGFyRR4IUqiR153FSdp3AOTWXuuUlVN6ymJo1s0nbXkrq9tKIU2BfTEY6u2M0BK1mWmYV0TRvEmePS3Y+uBfVpxktQkgOCEiKT7zo/RoNNCNhkjaSMD4LQ2Mz8c+9hm3XoYgNWR0dnfPoxoLOsCBUlfhX3sVUUY3zvrU0fe0zxD//OtajA2eg7NaOlCQeOUvCkbO052VQv3w6tWtmU7eihOR9p0jefxpbdVPUHhQjxUhmd4wEqQhcRdk0zyrCMS0faTZiq2qk8Sf7UX3qqPVrtJBC4Js+ibZrl2tGQkMz8c++im33Yd1I0NEZArqxMETCrpOfaguF9PgtgG3vEYw1DTg/fgeOR+/Hsv8o8S+tx9Aa3YNJALFn64g9W4c3NZ76ZSU0z55A04IpoEosza3YqpuwVzVhq2nCXt2E0X3xUwaPRHbHvpBC4E1LwJOdgjsnFXdOCp6sFFSLCYPHR8q+k6TsOoG9uglLupnZV0cXlOlSJNevTS8lGxTaVy3GvWwuoZQkDI3NJDzzKtY9h3Txoo7OMKDHWRgGLtegTENBGhTaVy+h7drliGCQuNff1yLiDeH3FrSZac9N1x6W2Sl4slPwJ8V1Hjc52rBXN2lGRHhtah35QE0joVm40DBIxZOVjGoxASD8Qew12jXGna4h/vi5C7JAjjUtxUgQyM7AvXw+nrnTwWzCdKqcmE27sBw+rhsJo4UeZ+GyRB9Z0BkRREgl9t0tWPcdofXuG2m9+0Y882cQ/7c3MVXXDapNo8dPwvFKEo5Xdu4L2szdjAd3dgrOKbmgaCaCsc2jGQ0uN4o/iMEXQPEFMPjDa18AxR/ssh3oLCNC6oCGRjSaBakIVJMR1WhAmgyd26rJiDQZ8MfH4MlJwZ3d3TBQ/AFsNc2k7DmBraoJe3Uj1gZnvw/DsaSlGG6kouArmUz7ivkEivLAH8C2+yD2zbv1WAk6OiOEbizojCjGxhaSHn8G79zpuG67lqavfpqYjduJWb8JxT/0HAhGj5+40zXEna7p3BcyG/FkJmsP3qwUPFnJeNMTUS0mQmYTGCKMuRBSEaqqPZSlPL+tSm2ERJXs+MoxQqFGkqbHEF9ko2F3K976AE2tJqQEFCh8eBz7vndtROdV/AFs1V0Mg6qwYRDliMxoaylGglCsHc/iObiXzkVNjMfQ2IL6wlvIA6UY3R5MuiZBR2fE0I0FnRFHALY9h7GUnsK1dg3ta5bgWTAD657D2HYeHPRIQ18Y/EFiK+qJrbjwLVOiha8OWUyoZmOnAaFaTOF9JlSLsXOfVARSKKAIpCK0tVC0bSGYutJM0bws8lbGI4UAJQ1nuY/6/W0oqiSnxER8CiibDqEEg4hACCUYQgmEtwPBzs8Gt29QhkFvXEwtxUjjz83GvXw+3tlTwWjEfOw09hfexHL0FK/Hx7M3PoGbVclc98ikIdfR0dGNBZ2LiOL2aoGbdh6kfeUi3Mvm4165CGNNPdZ9R7HuOzLiQXIEWvhqJRiCyD07+ySvY+O5PgrsGPo5BkNHUKaujIVgUf0hjQZCyYmEUhIJJScSTEnEP7GA4PgshNeHfete7Ft2RxwpVEdHZ/jQjYUhontDRI/5zDnMZ86h2m14Z0/FM2cabddfRduNKzFW1mLddwTrvqMYmx2j3dWIGKtCwrHWLykEakKcZgikJhJKTtIMg7BxoCbGd68QCGKqriPuxbew7TqI4rt0Elzp6Fxu6MbCELkSEkmNFIrbg/2jPdg/2kMoIQ7vrGK8s6fRtnYNbWvXYDpbhXV/KebSUxhrG0Yl/fRAjFUh4Wj3SzWbCGanE8zJxJ8/jkBeDqHkROiShAtVojhbMTQ5sBwvw9DswNDkwNDUgqHJgeJq06Ms6uiMEXRjQWdMYHC6iPlwJzEf7iSYnIB31lS8s6fhuvVquPVqFEcrlmOnMZVXhR8mLRgcraPuHjdWhYQXs1+qzUpgXCbBnEwC47QllJ4C4eRdSmsbpjOVWA8d7zQEDE0ODC1OPR20js4lgm4s6Iw5jM1OYt/fRuz72wglxOGbUoR/ShHeGVPwLJp9vmAo1O1t1NjoOG9INLZclGHrsSokHIl+SUBNiCOQkxE2DLIIjssglJLUWUZpcWKqrMW6/yimc7WYqmpRHK1jclRIR0cncnRjQWdMY3C6sO/Yj33Hfm3OOzGeYEoioZQkQqlJhFKSCKYmERifhYyxd6urOF0YGlswNjZjaGzRDIuwcTFcQ9wF2X7cXoWdh+3dtAFzizVlfkOLkZfev/gJm4ajX1JRCOZk4C8YT6BgHP6C8d10BYb6Jkxnq7Ft3YvpXA2mqjqUdt0jQUfnckQ3FoaILnC8eAgpMbQ4MbQ44dTZC46rNotmRKQkEUxLJpSWTDA1Cd+UItSEuO6FA8HzxkPYgDA2nf8s2j0RvQ2PtjZgOPul2qwE8sfhLxhHoGA8gdxspMUMgNLswFxWgelMJaaqWoxVdWNCcLi8rY25bjcJ+nSGjs6IohsLQ0QXOI4dFI8PpbIWU2XtBcekyai55SUnEkpO0EYnwmr8QG4OMsbWrbzweDE0NHcxKJzhRfvcEVDq0tUsCOassuJau4ZgZhqBrDTU5EStckjFWFWLbfs+TGcqMZ85h8HpuujXEAmJoRCJuqGgozPi6MaCzhWBCAQx1jVirGvs9bhqtXTz8Q+latMbwax0fNMmgan7v4poc2NodjC55CwFVU6Kp4S0qY4mB81nWjhXZRo1zYJqtTBrqZmJK8xMXJlFKCmBUFICDc0Gqk4GkDYLOauyic+Poz0YxFjXhPlMJcatezGVV2KqqB6W6Jo6OjqXD7qxoKMDKF4fSnVdr9EkpQA1NqbbyIS2JJK5dhKh5ARajd3/lXJUFYIh/IEg9cEgIhiEYAgRDCKCIQgEO7dFIAjBoJZCub/ojX0cklYLofgY1LhY1PhYpNVCLBALtAIEQxgcTlLiW0mP9WCsr8ew4QDG2gYMTS2j7lEyFA7YbFSYzczweMjzj/60iI7O5YpuLAwRXbNw+SMkGFztGFztcLbqguPtXoVDVUk0e60kFtgpXJ5CTIYVaTTQVBVkx98auf7bE8BoQBqNSKMBaTIirRbU8GeMRmR/uSNE3z8v4Q+gtLowVdaiuNowtLRiaHHirXJybLsHR0OI1D6DMl26hgJAhdnM3pgYsgMB3VjQ0RlBdGNhiOiahSub7kJCidjahvJs2wVCwvuKXh2lflkZS8JLHR2dS5MI0++NDEKIbwohXhBClAkhpBCifIDyk4UQrwghWoQQ7UKIzUKI1X2UVYQQ/ySEOCaE8AohzgkhfiqEiBlq2zo6HZwXEgaYOclNQqymT2hyGqlp0HQLC6aPpsBxbPVLR0fn0mS0RxZ+ADQDe4HE/goKIYqArUAQ+DHgBD4DrBdC3CClfK9HlZ8BXwJeBn4KFIc/zxZCXC2lVIfQdtd+6dMQVzBXUlAmHR2dK5fRNhaKpJRlAEKIw2iarL54DM2gmCul3B+u81fgCPBrIcQUKTV1mBBiGvCPwEtSyjs7GhBCnAF+CdwHPDuYtnuiT0Nc2VzOQZl0dHR0OhhVY6HDUBiI8NTBLcDGjod5uH6bEOIPwHeB+cDO8KH70bIR/7xHU78Hfgg8SNhYGETbOjqdXE5BmXR0dHT6YrRHFiJlBmABtvVybHt43fWBPh9Q6fGAl1J6hRD7w8cH23ZvTCxvazNMfuoptZ8yOpchVl+OMKrxIiS8qIpPGkN2IaQRHOdnpfyGZvnsU3UX1e1grPZruPGYTMJvMIgnAwFpDoUu6Wu5nJCKIoOFhQMG66ivrzcBky9Cl3SGyKViLGSH1xf6rZ3fl9OjfKOU0tdH+SVCCLOU0j+ItjsRQjwKPApYAnDghMfzk36uQecyZDZxxVasMVvl3t2EJQBFFGXnkjsJ4BSnjpwLnWvAo/drRPB4AFYBH4xyT3R6cuZMJKWygHEj3BOdYeBSMRY6MgT19vD39ijTsd1b2Z7l/YNouxMp5RPAEwBCiN1Symf6OKeOjs4IIYT4Jynlp0e7HzqDQwixe7T7oDMwo+o6GQUdqewsvRyz9ijTsd1b2d7KR9u2jo6Ojo7OFcWlYixUh9e9TQd07Os6jVANpAohejMActCmKPxdykbTto6Ojo6OzhXFpWIsHEKbJljcy7FF4XXXoaxdaNe2oGtBIYQVmNWjbLRt98UTEZTR0dEZfvT/vUsb/e93CSD6CB9w0emIsyClzO/j+AvAHcAcKeWB8L5YtFgIPmBylzgLJcAB4OUecRb+ES3OwkNSyqcH07aOjs7YRghh6UPcrKOjM0hG1VgQQjwE5IU//iNgRou2CHBWSvlUl7IT0NwXA2jRGVvRoiyWADdJKdf3aPt/gS+iRXB8k/MRHD8CVveI4BhV2zo6OmMPIUQ68AKwHG3E8C4p5cnR7ZWOzuXBaBsLG4Gr+jj8oZRyZY/yxWhBla5CMyz2At/pLRyzEMIAfAXNtTEfaASeB74tpbwgKH40bevo6Iw9hBB/Rnsp+CHa/71VSqnnd9HRGQbGzDSEjo6OzlAIT2V+Q0r5engqcpuUsr8Q8jpjECGEBP4ipXx4tPuic55LReDYL0KIleGslX0twdHuY1eEEPlCiO8IIWaNdl90dEYDIUSyEOK/hRCnwllhG4QQHwghlvcoF0022FPAQ+HEcJ8Jf+7aVn6Xe8L3++hXedjo0ImAPu69bUKIPUKIL4dHeHUuAy6VoEyR8hyaPqEnYy0Mcz7wH0A5sH80O6Kjc7ERQuQBG9ESx/0ROAEkoIVez+lSLtpssP8PeBfNSGgFbuunG/8khPi1lLJmGC5J5/y9V6BFxX0YLTfPNLQpIZ1LnMvNWNjb1cvhSkQIIYCY3nQZOjpjhKfR7j0zBnhYR5UNVkp5OCxWng6ckFK29NHubmAe8B3gH4Z2KTph9vbwMHscKAU+LYT4dyll3eh1TWc4uCymIaJBCGEVQvxECFEthPAIIXYKIa4VQjwZnivrWrY8LMLs2UbH0NvDXfbFCSG+L4TYIYRoFEL4wkOsPxRC2LuUe5jzcez/3GXobmOXMjFCiMeEEKfD7dQKIf4afiPrtR9CiC8IIY6ihaj+2tC/KR2d4UcIsQJYBvxYSlkjhDB1/f/oUq7PbLDAH4BJdE8I13G8XUq5ox9DAWAHmpfUI0IIPYnRCCClbEVLzieAQiGEUQjxr0KIo+FppyYhxMthbUlECCGuFkK8I4RwhNs4KIT47IhdhE43LreRBbsQIrWX/f7wjxe04bLbgHXAeqAIeAmIKOtJP+QAnwb+jpb+OojmWfF1YDZwXbjcJuAHwLfQgpFsDu+vAxBCGMP9Wgq8iOZKOhH4HHCtEGKelLKyx7m/AqSgpeCuBc4N8Vp0dEaKG8PrCiHEOuAGwCCEOAl8t8vb6XBkg+2Pb6IZI4+hxVjRGUbCI5wTwh8bgWeAe9CmiR4HMoEvANuEEMullPsGaO9R4Ldof/v/AtqBa4DHhRBFUsp/GZEL0TmPlPKSX4CVgOxneT1c7trw5yd71L+to2yP/eVobzZ9ne/hLvvMgKmXst8Ll13QX/0uxz4TPvbjHvtvCu9/qpd2moH00f476Iu+DLSgvdFLoB4t5snHgEeAw+H9nwyXuzP8+XO9tDE1fOwHUZ47P1zvV+HPT4Q/L+pSphw4PNrf06WydLkHfRtIBdLQDL3fh/dvQ3uoSzTXddGl7gy0l6rNPdrsdo9Gy0zpBZ7t5fy/AEJA0Wh/F5f7crlNQzyB9sPsufxb+Pht4XW3VNJSyleA40M5sZTSL6UMgDY6IIRICo9ydIiwFkbY1O1ogszHerT/BpoY8lYhRM+/21+llPWD7ryOzsUjLrx2AauklM9IKf+EFkjJAfwg/PsedDbYKPgPtCRxPx5iOzrwn0ADmhF4AM0AfA3tnnt7uMx/yfATHkBKeRB4HVgmhEjrp+270EaZ/iiESO26oI0QK8CaYb4enR5cbtMQJ2X/QZQK0R7EJ3o5VgoMaf5SCPF54LNoCuCeD/SkCJspAKpl73OuR9ByW6Si/VN20Nv16OiMRTzh9XPyfDI3pJQtQojXgI+j/R+OeDZYqWkmfg58SwixVkq5bijtXeE8gRY9U6JNEZyQUjYDCCEK0O67pb3UOwzcinbfa+ij7eLwur97e8Yg+qwTBZebsTAQIspjfUWsuuB7E0L8M5q+4B20/BPVgB9Ny/AkkYtJ++tjX+gptHUuFTr0NrW9HOvwjEji4mWD/RGaR8RjQog3hqG9K5X+XtQGc0/rrf7HOf8b6UnZEM+hMwBXmrFwGk23MAntLb0rU3op3wwk97K/sJd9D6HNd94gu+eduL6Xsv2FzTwNXC+ESJRSOnocm4rmP97YT30dnbHMTrTRt3G9HOvYV49mTAxHNth+kVK2hgM0/Qz4xFDb0+mV02gC72LgYI9jU8Pr/gTmHfk9GgcYOdYZQS43zcJAvBped1POCiFuo/cpiBPAFCFE10AxFjQVb09CaEaA6FLWCHyjl7IdMRB6M0ReQfu7dKsnhLgBzavita7GiI7OJcYraHqFB4WW2RUAIUQW2vz2SSnlKam5SK4DVgohZnYpF4vmdXSSwXtC9OQ3aIb+f9L7tIfO0HglvP5m2EsCACHEdDSPlC1Syr6mIAD+hmY4/qcQwtbzoBAiIXxf1hlBLreRhTlCiAf7OPaKlHJ92F3rE0KIZOBtNNfJf0CbO5veo86vgPuA94QQv0XzeHiI3of9X0QTJb4lhHgJiAceQMtk2ZOjaDfMzwsh3GjCrnop5ftoUxafAP5VCJGP5mo5Afg8mnvltwb4DnR0xixhbcLXgN8B24UQf0L7v/pceP3FLsW/iSZce0cI0TUbbA5aNthhSWwjpfQLIf4d6Mhy2zQc7epoSCnfFUL8De1emiSEeJ3zrpNetGzA/dWvFEJ8Di2+RqkQ4ingLJrnRQmakTkVzeDTGSlG2x1jOBYGdp2UwIRwWRuatqAWTWy1C22I7El6uE6Gy38CzVPCjzZU9nVgNRe6ThrQbm6n0Kzgs2gq6+Jw2e/0aPdGtMyW3vDxjV2OxaAZHmXh89aj3cjy+rjuh4fy/emLvlzsBS22wXY0MZwLTeuztJdyxWgjgg40I30LcPUgz5lPF9fJHscEsC98XHedjPw77bgHfW2AckbgX9FEjj60Kd5XgJJeyl7g3h7evxTN9bY+fF+sRgtw91W0DKOj/n1czouedTKMEOJJ4BNSyqGKcXR0dHR0dC4rrjTNgo6Ojo6Ojk6U6MaCjo6Ojo6OTr/oxoKOjo6Ojo5Ov+iaBR0dHR0dHZ1+0UcWdHR0dHR0dPpFNxZ0dHR0dHR0+kU3FnR0dHR0dHT6RTcWdHR0dHR0dPpFNxZ0dHR0dHR0+uX/A98a7PXU3hnDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = dict(units ='inches', scale = 200000)\n",
    "kwargs = dict(\n",
    "              units='width',\n",
    "              pivot='mid', headwidth= 10,\n",
    "              minshaft=3          \n",
    "             )\n",
    "lat_start = 0\n",
    "fig, ax = plt.subplots()    \n",
    "da_trop2=da_trop['ptrop'].sel(e_type='himalayas', lag=0).mean('lon')\n",
    "lat = da_trop2.lat.values\n",
    "# tropopause line\n",
    "ax.plot(lat, da_trop2, color = 'k', \n",
    "        linestyle='--', lw = 3)\n",
    "# wind climatology\n",
    "ds_ua_clim['ua'].where(ds_ua_clim['ua'] > 0) \\\n",
    "                .sel(month = 1) \\\n",
    "                .mean('lon').plot.contour(x = 'lat',\n",
    "                                          yincrease = False, \n",
    "                                          cmap = 'Greens',\n",
    "                                          levels  = 11, \n",
    "                                          ax = ax)\n",
    "ax.fill_between(lat, da_trop2, color = 'blue', alpha = 0.6) # stratospheric cooling\n",
    "ax.fill_between(lat, da_trop2, y2=1000, color = 'red', alpha = 0.6) # troposheric warming\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim(1000,1)\n",
    "ax.set_xlim(lat_start,90)\n",
    "ax.set_ylabel('pressure [hPa]')\n",
    "#ax.set_xlabel('latitude [deg]')\n",
    "ax.set_xlabel('')\n",
    "ax.set_xticks([0,60,90])\n",
    "ax.set_xticklabels(['Equator','60˚N','Pole'])\n",
    "ax.set_title('')\n",
    "fmt = mpl.ticker.FormatStrFormatter(\"%g\")\n",
    "ax.yaxis.set_major_formatter(fmt)\n",
    "\n",
    "# BDC \n",
    "style = \"Simple, tail_width=0.5, head_width=12, head_length=8\"\n",
    "kw = dict(arrowstyle=style, color=\"k\", lw = 3)\n",
    "a2 = patches.FancyArrowPatch((5, 70), (80, 150),\n",
    "                             connectionstyle=\"arc3,rad=-1\", **kw)\n",
    "ax.add_patch(a2)\n",
    "a3 = patches.FancyArrowPatch((10, 90), (60, 150),\n",
    "                             connectionstyle=\"arc3,rad=-.5\", **kw)\n",
    "ax.add_patch(a3)\n",
    "\n",
    "# SSW threshold\n",
    "ax.hlines(10,lat_start,60, color = 'gray', linestyles='--', linewidths = 2)\n",
    "ax.vlines(60,1000,10, color = 'gray', linestyles='--', linewidths = 2)\n",
    "ax.annotate('SSW criterion',\n",
    "            xy=(60, 10), xycoords='data',\n",
    "            xytext=(75, 60), textcoords='offset points',\n",
    "            size=20,\n",
    "            bbox=dict(boxstyle=\"round\", fc=\"w\"),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"angle,angleA=0,angleB=-90,rad=10\"),\n",
    "            zorder = 1000)\n",
    "\n",
    "# wave dissipation regions\n",
    "wave_center1 = 35\n",
    "el = mpatches.Ellipse((wave_center1, 30), 25, 25, facecolor='y', alpha=0.5)\n",
    "ax.add_artist(el)\n",
    "wave_center2 = 45\n",
    "el = mpatches.Ellipse((wave_center2, 1), 25, 5, facecolor='y', alpha=0.5)\n",
    "ax.add_artist(el)\n",
    "el = mpatches.Ellipse((70, 150), 35, 235, facecolor='None', \n",
    "                      color = 'k', fill = False)\n",
    "#ax.add_artist(el)\n",
    "\n",
    "rectangle = mpatches.Rectangle((55, 100), 30, 150, edgecolor='orange',\n",
    "facecolor=\"None\", linewidth=4)\n",
    "#ax.add_patch(rectangle)\n",
    "rx, ry = rectangle.get_xy()\n",
    "cx = rx + rectangle.get_width()/2.0\n",
    "cy = ry + rectangle.get_height()/2.0\n",
    "\n",
    "# question marks\n",
    "ax.annotate(\"?\", (cx, cy), color='black', \n",
    "            weight='bold', fontsize=30, ha='center', va='center')\n",
    "ax.annotate(\"?\", (wave_center1, 30), color='black', \n",
    "            weight='bold', fontsize=30, ha='center', va='center')\n",
    "ax.annotate(\"?\", (wave_center2, 2), color='black', \n",
    "            weight='bold', fontsize=30, ha='center', va='center')\n",
    "\n",
    "ax_h = ax.twinx()\n",
    "ax_h.get_shared_y_axes()\n",
    "ax_h.axis([lat_start, 90, 7*np.log(1013.25/1000), 7*np.log(1013.25/1)])\n",
    "ax_h.set_ylabel('height [km]')\n",
    "ax_h.tick_params(direction='out', which='both')\n",
    "\n",
    "# waves\n",
    "color = 'purple'\n",
    "style = r'$\\sim$'\n",
    "t = np.arange(0,22,1)\n",
    "ax_h.plot([wave_center1]*t.shape[0], t, linestyle='None', \n",
    "         marker=style, color=color, markersize=10)\n",
    "t = np.arange(0,40,1)\n",
    "ax_h.plot([wave_center2]*t.shape[0], t, linestyle='None', \n",
    "         marker=style, color=color, markersize=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "56561be8-d3a9-4b17-93a1-54226f20de19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-24T11:02:44.671345Z",
     "iopub.status.busy": "2021-09-24T11:02:44.670694Z",
     "iopub.status.idle": "2021-09-24T11:02:44.685731Z",
     "shell.execute_reply": "2021-09-24T11:02:44.684728Z",
     "shell.execute_reply.started": "2021-09-24T11:02:44.671277Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "c0, c1 = el.get_extents().get_points()\n",
    "x0, y0 = c0\n",
    "x0, y0 = c0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "5db0c8e3-0d2e-47cc-9c94-50554e5049b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-24T11:05:31.231745Z",
     "iopub.status.busy": "2021-09-24T11:05:31.231144Z",
     "iopub.status.idle": "2021-09-24T11:05:31.241649Z",
     "shell.execute_reply": "2021-09-24T11:05:31.240923Z",
     "shell.execute_reply.started": "2021-09-24T11:05:31.231679Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 42.5, 177.5])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum([(x/2, y/2) for x,y in zip(c0,c1)], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba3f56b4-1e42-4854-aa48-75f647bb6ab5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T16:07:20.353331Z",
     "iopub.status.busy": "2021-11-24T16:07:20.352739Z",
     "iopub.status.idle": "2021-11-24T16:07:21.107286Z",
     "shell.execute_reply": "2021-11-24T16:07:21.106523Z",
     "shell.execute_reply.started": "2021-11-24T16:07:20.353266Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outflux_hor 1.236930065689078e-05 - influx_hor -1.1521853380991632e-05 + influx_vert -1.127379122966249 - outflux_vert -0.04628523193572163\n",
      "total_div -1.0810699998764897\n",
      "outflux_hor 0.00024121876318439463 - influx_hor -3.539032182321483e-05 + influx_vert -3.186412205894702 - outflux_vert -1.5864079931130766\n",
      "total_div -1.5997276036966177\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (9,9))\n",
    "lat_start = 18#-20\n",
    "\n",
    "cmap = mpl.cm.PuOr_r#RdBu_r\n",
    "levels = np.arange(-7.2,7.6,0.4)\n",
    "plot_kwargs = dict(yincrease = False, add_colorbar=False, \n",
    "                   cmap = cmap, robust = True,\n",
    "                   levels = 21)\n",
    "temp = ds_all['acceldiv-calc_wn1'].sel(lag = 0, plev = slice(None,1))\n",
    "temp.plot.contourf(x = 'lat', **plot_kwargs)\n",
    "\n",
    "da_trop2=da_trop['ptrop'].sel(e_type=e_type, lag=0).mean('lon')\n",
    "lat = da_trop2.lat.values\n",
    "ax.plot(lat, da_trop2, color = 'gray', \n",
    "        linestyle='--', lw = 3)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.hlines(70,lat_start,90, color = 'green', linestyles='--', linewidths = 3)\n",
    "box = sgeom.box(minx=70, maxx=102.5, miny=20, maxy=40)\n",
    "ax.vlines(list(box.bounds)[1::2],90,50, color = 'green', linestyles='--', linewidths = 3)\n",
    "\n",
    "color = 'red'\n",
    "kwargs = dict(scale_units='y', pivot='mid',\n",
    "              headwidth= 12, \n",
    "              minshaft=3, color = color)#, scale = 10000)\n",
    "kwargs = dict(units='width', pivot='mid',\n",
    "              headwidth= 12, minlength = 2,\n",
    "              minshaft=3, color = color)\n",
    "\n",
    "# upper box\n",
    "lat_slice = slice(50,70)\n",
    "pres_slice= slice(70,10)\n",
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         lat_slice,\n",
    "                                                         pres_slice)\n",
    "ds_q_pert.plot.quiver(x = 'lat', \n",
    "                          y = 'plev', \n",
    "                          u = 'horizontal', \n",
    "                          v = 'vertical',\n",
    "                          ax = ax, add_guide = False,\n",
    "                         **kwargs)\n",
    "num = ds_q_ver.sel(plev = pres_slice.start).values[0]*-1 # /1e4\n",
    "prec = 2\n",
    "ax.text(66, pres_slice.start+20, f\"{num:0.{prec}f}\", color = color,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = ds_q_ver.sel(plev = pres_slice.stop).values[0]*-1 # /1e4\n",
    "ax.text(66, pres_slice.stop-2, f\"{num:0.{prec}f}\", color = color,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = ds_q_hor.sel(lat = lat_slice.start, method = 'nearest').values[0]*-1e5#/1e4\n",
    "ax.text(lat_slice.start-6, pres_slice.start-40, f\"{num:0.{prec}f}\", color = color,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = ds_q_hor.sel(plev = lat_slice.stop, method = 'nearest').values[0]*-1e5#/1e4\n",
    "ax.text(lat_slice.stop+6, pres_slice.start-40, f\"{num:0.{prec}f}\", color = color,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = total_div.values#/1e4\n",
    "ax.text(60, 30, f\"{num:0.{prec}f}\", color = color,\n",
    "            ha='center', va='center')\n",
    "lat_slice_diff = lat_slice.stop-lat_slice.start\n",
    "pres_slice_diff = pres_slice.stop-pres_slice.start\n",
    "rect = mpl.patches.Rectangle((lat_slice.start, pres_slice.start), \n",
    "                             lat_slice_diff, pres_slice_diff, linewidth=3, \n",
    "                             edgecolor='red', facecolor='none',\n",
    "                             linestyle='--')\n",
    "ax.add_patch(rect)\n",
    "\n",
    "#lower box\n",
    "pres_slice = slice(300,100)\n",
    "color = 'k'\n",
    "kwargs = dict(units='width', pivot='mid',\n",
    "              headwidth= 12, minlength = 2,\n",
    "              minshaft=3, color = color)\n",
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         slice(50,70),\n",
    "                                                         pres_slice)\n",
    "ds_q_pert.plot.quiver(x = 'lat', \n",
    "                          y = 'plev', \n",
    "                          u = 'horizontal', \n",
    "                          v = 'vertical',\n",
    "                          ax = ax, add_guide = False,\n",
    "                         **kwargs)\n",
    "# upper and lower edges\n",
    "num = ds_q_ver.sel(plev = pres_slice.start).values[0]*-1 # /1e4\n",
    "prec = 2\n",
    "ax.text(66, pres_slice.start+100, f\"{num:0.{prec}f}\", color = color ,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = ds_q_ver.sel(plev = pres_slice.stop).values[0]*-1 # /1e4\n",
    "ax.text(65.5, pres_slice.stop+20, f\"{num:0.{prec}f}\", color = color ,\n",
    "            ha='center', va='center')\n",
    "\n",
    "# lateral edges\n",
    "num = ds_q_hor.sel(lat = lat_slice.start, method = 'nearest').values[0]*-1e5#/1e4\n",
    "ax.text(lat_slice.start-6, pres_slice.start-40, f\"{num:0.{prec}f}\", color = color ,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = ds_q_hor.sel(plev = lat_slice.stop, method = 'nearest').values[0]*-1e5#/1e4\n",
    "ax.text(lat_slice.stop+6, pres_slice.start-40, f\"{num:0.{prec}f}\", color = color ,\n",
    "            ha='center', va='center')\n",
    "\n",
    "num = total_div.values#/1e4\n",
    "ax.text(60, 200, f\"{num:0.{prec}f}\", color = color ,\n",
    "            ha='center', va='center')\n",
    "\n",
    "\n",
    "ax.axis([lat_start, 90, 1000, 1]) # 0.1\n",
    "ax.set_yticks([1000,100,10,1]) # ,0.1\n",
    "fmt = mpl.ticker.FormatStrFormatter(\"%g\")\n",
    "ax.yaxis.set_major_formatter(fmt)\n",
    "ax.set_xticks([-20,0,20,40,60,80])\n",
    "ax.set_ylabel('pressure [hPa]')\n",
    "ax.set_xlabel('latitude [degree]')\n",
    "# Create a Rectangle patch\n",
    "lat_slice_diff = lat_slice.stop-lat_slice.start\n",
    "pres_slice_diff = pres_slice.stop-pres_slice.start\n",
    "rect = mpl.patches.Rectangle((lat_slice.start, 300), \n",
    "                             lat_slice_diff, -200, linewidth=3, \n",
    "                             edgecolor='k', facecolor='none',\n",
    "                             linestyle='--')\n",
    "\n",
    "# Add the patch to the Axes\n",
    "ax.add_patch(rect)\n",
    "\n",
    "ax_h = ax.twinx()\n",
    "ax_h.get_shared_y_axes()\n",
    "ax_h.axis([lat_start, 90, 7*np.log(1013.25/1000), 7*np.log(1013.25/1)])\n",
    "ax_h.set_ylabel('height [km]')\n",
    "ax_h.tick_params(direction='out', which='both')\n",
    "plt.savefig('influx_analysis_lag0.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "26086f4a-654a-427d-9dfc-9a50554be2d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-29T09:36:53.433818Z",
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     "shell.execute_reply.started": "2021-07-29T09:36:53.433751Z"
    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outflux_hor 0.0002262850865694671 - influx_hor -9.27216783377089e-05 + influx_vert -0.5887569797199934 - outflux_vert -3.186412205894702\n",
      "total_div 2.597974232939616\n"
     ]
    }
   ],
   "source": [
    "ds_q_pert, ds_q_ver, ds_q_hor, total_div = concat_fluxes(ds_all, \n",
    "                                                         slice(50,70),\n",
    "                                                         slice(500,300))"
   ]
  },
  {
   "cell_type": "code",
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    "1538.0108340637003 +225.64869194481778  -20316564.22478462/1e4 +10114937.364088977/1e4\n",
    "\n"
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       "-67"
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   "source": [
    "pres_slice_diff"
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