"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n",
+ "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n",
+ "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n",
+ "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n",
+ "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n",
+ "... ...\n",
+ "157604 POLYGON ((6.95490 46.70274, 7.00684 46.69873, ...\n",
+ "157605 POLYGON ((7.00684 46.69873, 7.05878 46.69470, ...\n",
+ "157606 POLYGON ((7.05878 46.69470, 7.11071 46.69066, ...\n",
+ "157607 POLYGON ((7.11071 46.69066, 7.16264 46.68659, ...\n",
+ "157608 POLYGON ((7.16264 46.68659, 7.21456 46.68250, ...\n",
+ "\n",
+ "[157609 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"lat_1 = 37\n",
"lat_2 = 43\n",
@@ -168,7 +389,8 @@
"x_0 = -807847.688\n",
"y_0 = -797137.125\n",
"dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,\n",
- " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, times=source_grid.get_full_times())"
+ " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, times=source_grid.get_full_times())\n",
+ "dst_nes.create_shapefile()\n"
]
},
{
@@ -187,75 +409,60 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1min 48s, sys: 1.42 s, total: 1min 49s\n",
+ "Wall time: 1min 49s\n"
+ ]
+ }
+ ],
"source": [
- "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')"
+ "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
+ "image/png": "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\n",
"text/plain": [
- "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n",
- " 32.52130788, 32.51799681],\n",
- " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n",
- " 32.55696109, 32.55364819],\n",
- " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n",
- " 32.59261692, 32.58930218],\n",
- " ...,\n",
- " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n",
- " 46.63519229, 46.63111499],\n",
- " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n",
- " 46.67081377, 46.66673441],\n",
- " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n",
- " 46.70643226, 46.70235083]])}"
+ ""
]
},
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "interp_nes.lat"
+ "plot_data(interp_nes, var_name)"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n",
- " 5.22992847, 5.27220869],\n",
- " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n",
- " 5.23384715, 5.27614727],\n",
- " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n",
- " 5.23776955, 5.2800896 ],\n",
- " ...,\n",
- " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n",
- " 7.12778439, 7.17966098],\n",
- " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n",
- " 7.13371074, 7.18561716],\n",
- " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n",
- " 7.139644 , 7.19158028]])}"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "nox_no total mass: 113.08470916748047\n"
+ ]
}
],
"source": [
- "interp_nes.lon"
+ "for var_aux in source_grid.variables.keys():\n",
+ " print(\"{0} total mass: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))"
]
},
{
@@ -267,108 +474,219 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Creating Weight Matrix\n"
+ "Creating Weight Matrix\n",
+ "Weight Matrix done!\n",
+ "CPU times: user 1min 49s, sys: 1.9 s, total: 1min 51s\n",
+ "Wall time: 1min 52s\n"
]
- },
+ }
+ ],
+ "source": [
+ "%time wm_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True, weight_matrix_path=\"CONS_WM_NAMEE_to_IP.nc\", only_create_wm=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2041: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
- " time_var.units, time_var.calendar)\n"
+ "CPU times: user 202 ms, sys: 26.9 ms, total: 229 ms\n",
+ "Wall time: 228 ms\n"
]
- },
+ }
+ ],
+ "source": [
+ "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9e5Rcd3Xn+yn6HFeXSl1Nqk0X7pZES1EnbmL5+iGQJyI4wcTm6VyYmItXSEgYQjIwiTMJkJhMwoUZSGDyILmBlQATmEwmIY+LV0yuJ5iXYexEIrKtsQxt0o7USO52uuKuoUsuVZfrNHX/2Huf366jaqklYyRHZ6/Vq6rP+5yq+v7277u/e+9Cr9cjt9xyyy23p58941xfQG655ZZbbmdnOYDnlltuuT1NLQfw3HLLLbenqeUAnltuueX2NLUcwHPLLbfcnqYWfTtPdvHFF/empqa+nafMLben1jr3svYV+KYTc53Q1wLhB1bQ1zXA6756mVe/bSGzPLs++z677aBjjlx99YAj5na+27333vtYr9d7Vnb5txXAp6amOHDgwLfzlLnl9tTavQX+6epRPsRbePcn3gv3wP/4PVkVA+NAAnSBEnBc35sl+uqXxfoaufXeogHbZo/hj913rHvvHbitt5K+Hh9wziTzfpBlj9111/nqXLZ8VlYoFL4+aPm3FcBzy+1fnBXh2Q+t8O7ie5m9GWbevf6mx4G2vs/+8JIBy7wZKMbrbHsqQD5Taw9Yth5Yn6n9WaGw7rEmgYqevws0dPlOfY2R+24TnmVJl8XAVRfg4JADeG65PRmLgA6QQBO451f7veL6Bg9TIoCwvbY5vbcL/efLXpqB8XoAn7htTzUb8MtPMNhi/esSBhk7/0YGgCc7SCwVClRH4XgLugks6TFHkIEBYEGvqemu2QYAkH1i4JVPk8EgB/DccjsL+wo7qbHEly59Ka++7X9Aq58qGeTFehsEViUCAJq13TropyNsOwPLJLMtblvv+Zf0r4YAWkOP5cHA7iN7HdkZhN2zzQz8fW/KnN+ActCzifSvqteUELzxrwPPcde0kLmXSQSQ54H5lQDKxxEKaxMQb2Ak9Nf1qUKBSI+bIM8q0nPFeg1dvcardgEr0F6G0ph7OP/41A8COYDnltuTsItZhmFgTTy9NmfuSWaBkwHH8BSK7TOISz/dOQxAE8JgYR7zqcwPGklm2UbMBpgYAT3vndvr8QH7Zfn4tltWQQC2Uob5Vv9+Cf0DkFlX9/EDpQF+ieCZ49bhtj2ODDIbsucWoAW0YH5Zr5VAEdV0Mxsk/Od5+QZnADmA55bbWdj37P9HWIQX7voyv/zSX2GE4/zS7R+ADuy/6ckde6NgXKGfrrD9Bnm4JQR4ugitkxCAI2uTwEwZSsPQVGA8vir7zhJAzsAoC3p2jYMGhy4wo+sfpt9r7xI8W/PWR/R1Sbfzy8xjf6Als4iYftCuEgD4kSSA/6Qus3ONuO0MVD0Hb7ML9BoaepydiGffmBXKJo6gVEWkRqtAGaHXOP1gl6W4HigUmBmFuHzq/XIAzy23s7DWFc+gPP5N/mn7KB0uosMYvFK8ppjCQAXJqX5sPjB5uh+lBxMDskFcdtaywN5w7/1+DeA+9Rz9jMD236SvJWB5wDk9pzxGoJSahEHAzHvk/hptAMhSSAkBeGO3zgO3XUvdHcN78iOEAc2O4WmZWNcdz9yLXd+I3hcIcJtnDaSATQdYAYaAcZickIGjfRge0ee65K7fzlFFBs5KFbgGGQgAFhloeSJPbrmdhR0rbuWx7Zs5xlbm2c4829N1V40FYLU/EGDJ/p2J+WNBOIdfl2T+H/R3KsVKNpCaXXeC4Fmvx/PHmVdP32xCgOyqAftkvVTzfJMB6wZtYzTEIDlllk4ZFBuw5Tbg2LLsvm3kOdgMZksZatt0pQ58JAi1Vpb33aO6nDAAxshAsIl+WiYe0v1XEG9+bfC9++vOLbfczsAuZR6Ai4G/zK58rEdUkNSZjcr7vKfXzSyH9UHsVDpwv03WQx00eAzyeG37bAzQOOTUE9XXJgH8vMTP/jdvuFaGVw/DA8thJgHBK/X35r1t29+OZee189j1eVrJAppRZpmd1x9/nOC5Z5+NXVOb/jhCaRQoQvUyxFO2h1UERpEAdwLtRZhfDcecKkN3DdqrQr/UgJGyvO92IK7rBZ3CcgDPLbenwPZco28MLVpwz6GNAbqXtg2iMMwGBT/XO9aWCJaTIKtjneNVCYoL7xU3UQrAHdcDqG03SUhWMpDzgd0RhK54uAWTmcAjCDA36Jf5Gddu5q/BBgZ/DTZweAlj161Dr3GKQO2MuGv1A4/tc1UZokhULgDTM4hHXQSu1YteBKYR0J4APg/tWQHpUlH2nx6S/f/68VfyTPaz7f11Kh/U4wwjlMsoxNMI17QOdWKWA3huuT0F9uK/+2uGWOPTt/2Q/NDvhNKhfr13VnmQtew67z0OMk+fZK2d9ANp1gP3yg7PyXrVht9uPSmg7TNIU549n+fwLWiYPW6F05vnuE/lOdu2/r1533aP2VmQ3dNSS85RAWpjiJteRuiNDnAQWIX2igR+Lb4wVRbwjsvAsB6/Cjdd+SkB7A8AH9WNV0k9dh4kpV/4hx4UfNGEYDmA55bbU2Cf5eXy5lUS2PzI636MQ3+0i9/91NvhTuB2uO/oqYOOEADlVOB8qiQdoxXmCYDlz1dyrxZMHETVeE7Yjj2I6lnP7D5tEFkv8Jq9Fz94DDqmvx67JpsBVAZsB8HDtsCl8c+mM89SMXYdbWBGOaNuAnEVAdl90FbuZ6kVBr0KAua1V+s/V8q2HEH4bYCfI6hW0OPZA1klp1Byy+18sd9pvJ3eXmAvPPE+eHTTdbz8yOfEm6vD0o1BoTFID36qwNsgimFQQpHx4Za27hNS7HzL7jjeS4ZAv2wCSpF49icIoJnQ7zWbJ+q5c+/Nt/UaavQrTbKec/YebDujULzkzxJujEbxSUTGn3s+fNAgkR0cQIB+fhkqkdAhcR3YBkxA95hw2SUCjz85LNz3fZ+U483cBvEEAtJXIMB9QBOAysBmvZEI2EpQtJzCNqxCKRQKQ4VC4f5CofDXbtnPFAqFrxUKha8UCoX3b/RYueV2odkBrma+eglL1VEKDSguwsv/+XO0Jp4hXtZoyBb0GZXGJduft/VUF1nrZrZLCLVEjPPOHjebrOPlisbNG3hnvXMItEg0YJm/LgNbMz9YeFAtMdjbzA4M2RmEfz+if3Yf/jn7Z+v58gqi994SScKQHa9SRoB4GTgmsr/ahMx07FpKRahOyPku3wHxNmSw9sBchEYLmk3gEkIgYnjAzQ6wM5ER3oKTcBYKhR8Afgi4vNfrfQ/wG2dwrNxyu6DsD7iF7SxykCuD1OxxWC5eTG8UUK70dDYIxAdRMD742M5s58HcwMaDvPe8zaqczEdnB45TJav4oGJW8mjLDCxr6xxrkJdcIsgEjf4Y52S55Z5pmN4B09tgLAo6bM91WzDT7xcBNaUxumsweQVUrwAuJfDfj+sOCrojwFSkgP1Gvc/H3YEtMFmUiz2BJEoR6cWX9WEM6d8pbEMUSqFQ2AK8HHgP8PO6+N8Cv97r9ToAvV5vo3V7csvtgrXf4K3wf8BWjvE9R/6R5+xf4s17fourq/dyM3/SB2rr6azNM89mOPr10E9XGNgZEJeQhJH2aqA9sjVRThWANOrBzjMzLl5kYzXooz3lY95+NkC6QP+AYZmO3rzW3bJJE+DaUYjHgTGY2xfS7Ru6fhcyq2kb16KjRFSHhSSobeyebOCYjmQw7a6J7G9/XZbFQ4i6pAzMIeCaQKMREnpi5DoaCVQOAYfknh6ow1hdEnqYAI6R8t5bNNuyfQBK2xBgv4kNUSgb5cA/ALyd/gHqu4DvKxQK79FLeWuv1/v77I6FQuFNwJsAtm3bll2dW24XlH3uQy9nz5v3857/9R/hA/Dox54JwB28nNIolFqnL4R1utolvqbIOAJOM6PQ7vTrkP3xSoRaLt5SL7QsGYQRApDeG48Q3bJtn5XvmYd8XPc1ysTUI4PUH3ZcO07sXksooI7LAZfmZLtSZv8l3bkNRModVEfhi6uDOW6jnxsJjCQhZR5kEKiOAnPQrcuzXNLBrxadrNOvIh4/Rdg/66ihsp5oVU+sSTpRNiNrWv+//tQ1UU5LoRQKhVcA9V6vd29mVQR8B5Lw+TbgzwuFk7UuvV7vw71eb3ev19v9rGed1FAit9wuKOu9Wd98DOY/Ds/+xArP/tQKbUrwaXj+6OmPcaYZnCCeaDzULwNsrwbPtzoq1EKFfimh8cCl4X6ASlUZEzA9Do0VCdiVosFg7K2OYFgDAbqdyEBTXefejH7x3HT1WsSTHVq/ANWCnus4oZbK8VZ4BjYbMdVIkxAbsOuw/0/YDisC3u3VcI/VUblvn/xUgVRRsucKmNml2ZodBLw7CHhPQOlmiLdDPCb3w14kQ2qM09pGPPC9wI2FQuFlCMtTKRQKfww8Anyy1+v1gC8XCoVvIolp/7yBY+aW2wVr7+HdFK5/N71aQSJLw/CW8f/CZ68J2wyqLeIzJU/FN3uzIk/tVaEEvIdsgNgGllcGH9e02Y3lAKJGMZQmgB0I6Nwh21e3i0dsA4CBoUkULSnH3vsyr7bcZH0emEfcsr27EB65LtTFWARzSXqYk5J2/IAynwQn144/BZpXG+4xO2A0kPuqlDWrUq9/S1muYURpkPkVuGoU4isQhYrRLg8ioH1Al12KuL4glMl1SFzEJIanKWJldloA7/V6twK3AhQKhe9HqJLXFQqFnwZeBNxVKBS+C7gIeGxjp80ttwvbXvOy/yq/vh1IrYwbBnPbUeY1dttsNKsTpNyq53vtGD7t3f73VQoNOD2dsZxAnMDUCinY1AzIO0IpRBFUpuG+Q+FaEsQrNpBuEBKMRtAA5rhwzv5eoZ+WIdEDbJPr4IuB0jH+3EDYrvu4uz+vdLm8DKUdAuJMAIvw2UP9zy5G1yOKEbOqVQvU5Ju26i+Pt6CaELp53OFOOE3qyXNQL8pmXdcBr9Lr2AD/DU+umNUfAjsKhcKDwCeA16s3nltuuZ3G/ozXc8P1fyXRpWtCivbplByD3ntbL7Bp3WmMq40z+3hQ88e3tHajEixjsoTy3gZo46SdiUpF8VIBrrpOPGafbelLtmbljSAe7OXl/uv3Kpr2UQLARSLVm9khgdSIANb+mHZuC9QafRJFwNWILlsbmHoJpP2B1ijRdZUI4suQ2ri7Zd+m0jNti/AuQncfLNUlQzOlT1p60CP6V0d4jjIC3hHygfnCMOvYGSXy9Hq9u4C79P0TwOvOZP/ccsst2Ke5kdv3XM+NB+9Miy1581K7QdSA95q9526US1YS58u2Gq9czWzjvXOv+TbQ9pxxLULAqI5430U5cGkMSiZ/0wDjlkioBmua4K/blCdtoFkPg4afbeDuf64FM3MQHxXlS5IECebMsChhss9iif5a4KZUeXgFZvYjXvCoLKzqLcXu+bQJg9KUFjSfv0f/3wHcArXrobY/PJPmIlQqULsReFD/f40+qwjxwFd0+2MIgB/R57gD2Hl6f/iMADy33HL71to7eRevvfkTLPzUJJtOtCm+Gj77aVm3njftlSIe2A3wvSY6S710gclRiItQ2wxcAkv39J/HZ0qaJ+qzGC3x5b5lmFyG2gwpeAPiZa4hQKVcrnHEDTfTMG/Z0yPmIWdppDbiuac1S6xmiNpc62Ttuh3T10ppuuUpD94B5mBuOXD2VcIAY/SMDVyNuXA/XWSDxi9Adbc+hyHoLkvQuN2C9h0ywFQm9EANJAFoWc+9VU/a0mXaom0jlgN4brmdQ7ufawQhHm3TGaVPn5fNWoR+XhgG10jxnnacWT+IoqntRVzONWgeDrSBHyiynnuWoqCMgNcaEmBs6TKtsBcXYa4ejuG9al+IatCxfRD3xaOq1tgLzb8Qr7ib9G9rkkUzX2HQBjyfjt84CvuT8HwiZLDw12Ja9S6wZGALTKtSJIpgaZ8MZt3F4K1HEURrUPqJzE2tIM+8iAD6hL5Os2HwtvvNLbfczrUdhq+8QN6++Hbkl/ke+Ow9/ZsNqlBoZtjgsy/NxpFAXG2MALajpGg9Nxe2r9OvGLHzesrDPPJlYOQIVEb1nxXd2YoyrciFdDvBG54pC80yd7S/vZl5vl4GaDaJtj6z4k53iLKmUobqtOjUWRGN9qGVfg06hJIBI8MBXB/QAOps0l+Hxfa1GcjDA551N3ElCCKoXA+VGeBjEM8g5WA1qzK+E7hft/XBSZMJHtW/YWRgMAB/Eae1HMBzy+18sJ8LWYazN8qimV+A598rQHXfOrtdTn/6u3G7XsZnf0to4SRVSyTAuNIpU05/3lYA8UWeBnn9oPG2VYTPRThfhhHwUkBvrIhyxazRCkFEC65aMasFwgzCJxaZTnvavNRhqKg0sqlJOrUJAecRAgAbGI8Ak2PAqHjc3SRw3CVCEwe7T7sOu3dfodBiB6kE0oD4dmQGsozIBG3wtTR5EGmhBTL9ctvubk7ZgSdreUu13HI7hzbFQzyX++EIVIehNiya5HmA66Fyt5YjHWAxkkBSiULyzSCPzGiPOgJMCwSRQ31FeNp4XHjlePTkFmLefIEtA7Y2Ejg8blmeV5PSKV0H3AaOTQTErTiU8eyNzHYQPOLj+tdsITOHqtATvn7M0qKsryADwhj9DYutrojtY4PcWDT4nm3QsGvKzn5KRb2/PQgFYtfSAr5EP89tnvdleqMa3E3jBQbaZwDekHvgueV2Tu07+AYl9TMrlyKa6nvkN/7ZG+DFPw68BSqflO2N2jCrWuW6GZg8DN25UCLWAnXZTMZsunq3BVMtxDvcDqWjIXjn97PzmjIjIVAgtb0I+LQQsGpJjex2p9/7tlnCAhAp2G4h9Nr0ZjMJu+YE+PIq7L0bSjUtzYqoYSwzsjQK+xcFmHcOQymBSgK1XaTdbkqrMoCYd7+U9Fd7NGD3BbxOuOu/fIeuMHS/Exb2ydvJa+XeZw/AzIw8i25dvfVx4HboHoGFFag2RCsPCPhbAweAuzemyM4BPLfczqE9l6+ymeOCFhr0u2oM9qsigmOk0rYZLSXUrIvCodmS7dPo3LB4ngbcntf1lQAvHxevu9kK69mr53qwny4wM1niJmR20AUuj1TzDEHbbNRJR45v5/bH8w0UQMCxNiwevG1n57OZBfQnGFGUczbrQUYYRTIYzQxr2QClhRp1QsMEveFBlQi92TltOysxEKMKk1VCC7QVmNwV3qemVFVsQWL9nGLtUB9Pu207CA++gVIK3nIAzy23c2hDrPEHL/i5AMTqicXWVWEO+MV+vTYI91vbTZDSaX/G2jjEDc2ErEgSSRaMiULxpDjScqlNUorBQCHrqVsThyhRXrksfHkXBKAsCUXVJj6Ymj3WSdeE3KPlrhholhCKqJs46mZN9eR1Af0uSqdodcV4SO9vGFjVWYpL/LlPr2/QNay3vIsEQCuW4m7H817zENCCeaVHmk3dvoUocyZI9ebxBH0yyDT4my3FeBrLATy33M6hlTgB70PQbpaUL51Z1op3qlCo7YPZa9Tz1SAeEQIKyDYGAtUr9DiLqjopwqTK09pzML8Y+PORMkIWr8h2TEAtkWux6nyWCGPV/SoI2B5acRmbdamDQoU0I9PTEhZcnSTgnS+sNb8a6BWjL9LgbCK9JeMONBNVkdRlgDqu5Wtj5eBPIPVJ4jFgu54gIQDustTqtvKvPr0eAtftqSO73soEkrF5WGgQErkmIAXm5kOuv6hGY5v7JUbQXZRgcX1FrnP6GuBmJPhpA8ERzshyAM8tt3Nob+M3hDv2bt+qtNgq+a4sP6n9GIegeSzQBvPqxTUI7RO36DS+3YHqdeGYRMIdT10C7YPipcZFPbcBOPK+Ng41S8YBqPdrobN8dTOB6pxst3Q0FHsyD9xTEqYysUQe87YNuKd1hlDZKteyUA966th5u82msBQLBAn19G5YOgCNw8BhVdmM6QlU2ljdJhe1cDTcx3OQGcacu9Y9L4O5O7QWyjihBGwN4qb8321J7RNWIFoMAdJkVa7bAr3G4c+uCM01vQ25+I/qySwRar1pwTqWq1Byy+18MCOuQSiIGpKhtxXxJBcRXfAHtbJgomVNGdx1J60vfRiYEy/QA0Rpl5QwtfTxVClhBxhGQGVYNNyWRm9UjiW8WF2VNtLYgJWTKRPcNv524WSKJeXt7Zq0/khcFjoiQgE40kHMHd9YpziSUqlLSLCw7/7KpHz4lFOfbELAPvW2AY7B9Dtg6t+5Z9fSZzokzyYuahA0CSqceIi04w+Ez8futQn9WauWTm/Lz8Ctzj3w3HI7h7bzyCPw+whob0VKjPpkmyPIL36rbvMxAbL5VuCFIQQtY9RzH4LmCkTqrc8lEO+TbaYU6UoTpPRJQ6fuI2WIX6XnmkNAahxqxs0OybYm4Wt3wvFJoF0XesBqm1jqfZaOKAFfRyoQen6/5BJt0HuctMBeUdUum+Xf6jJUF+GhRTc4RFqfe1nGwOooUrHpDn2mNwLvg7lFmL4OSvukUcUyMLcS9ONTZWjMyvEpi6edJFBaJKDmKDAuhbS6s7L+uOO1Ta5pAdkYDV5GhHKM9uFpOj8Aj228JmAO4Lnldi7t3yEUyl7ECztIcCXNG9sOvBrhylcki3FmWMDN+FSjKUrDIUmlhHCv0J/o01WQudyXRt0NrAqHGx/U816DAPnjBI62JWBdSmCLep7WLMGs6f73jICXP7YR2mIBof53IoB73HXLmV+WDMvSMAKWRaF+ShYDOCL3al2HUi13ES6fgAcWJYlo5qMIv5IA++REtSYsfC4MNp4Lt0BptUoIOioH/8AsTA2r5BMB7uMtpaOGXI2UFbkn/+zbiLQx1lZsTe2NmdZIOUMFCuQAnltu59YeQrqR3wT8KUGGZrK3BEGYTyLa6hZ0m0F9kaUqQDzBdkc8vUYSDmPbGri2V6DUCYDPmgbeLA0ehCrYTPASV1x6vdExBK43W0PFmwcbX6PF99jcRAD9BlDpSDzA+KEoo9SII9gzRtqwglVkVjEEY4t6Tt/Zpi73Fw+FgW9QAawkIfX07dwGuJbCzxqh52YSjrdzVDz/qh5nztVib6zC1LTsa3VcKpn2amdiOYDnltu5tP+PIB+8mdCVZU7T3odJO88cbwlYPEIAuRFCdUADnsZqwDhTWnigt3V3taCi4DLdgur1hAJUEJJLfJC1LPpvkMDl1I0wuQgNTaXvJqGGiQG6r2/SRUAa5D4qbn0bCWr6iodLiSTCNOuiFa9MIIPIqoC5zUC2lHXwWUNc8qJ0kE8fgF7zwieD5ts46ipw1bhw/V9WZU1knLcGeJu605R50HOy7Phq0IqbB39opX9QsyqKaVXEFXnGVZsV6MDc7oTr2ajlAJ5bbufSjgLvJeWbuQSZ7kdQWiGtJ2J9KwGWl/sbLPhSsabS8DTGqcx3renTlEMKNOl7AC0eBVAtktI9FjQtaYKND3ImhPZppaJkQXYJwOZpFqNBrP54CeklWfN1Q8rilXdXtL6JtS3T4CbL7h60gXB7ThKLZpEeDNWyFNV6QHXy99XDILcF9bLR422D0pxy1zY7WYXK66CyCNEdoRiXnxVZsSvz8m086HYg3kPoytOBpVYYUPawccsBPLfczqX9jr5aEozRFSbf81SGaptLdwT5na+454HwDNVoQodYNqWZNd5dRZD2cf1/1F1fi6BfRIA1VvA0EPd67lIxpKUbT+9Lv0KojZJ2Dor0vHZtVrYWUjUIHQS4NxMGHisY1RJ6ZYFQ6KqtmaIGrDaQmOc8oklAzSZUqsKBk+i5V/T8X6SP9vDB2hKa9BQFdYqtO96CqnXicXaGOTzYo8ktt9zOlc0hwPxR4M+RdPZhBNDHCBz4CgKcYzB9A0wbgFr1uxY0j0hlwIc3cNps4sp9AAf6PedxBnvxl6PXYuB+BCrjuuPjoROQ7zJkx6muhfebCNSJed41y6aMoDqjC03rXodHVDo4NSoSwRiYtHM39Jo6MHswNCq2+30OmjwzBwvLYZbisz5LaOBUqZrGqujKxzTpKR4WSud4S1LqQY4zo8HW2WVp7UYtXHflhUhxqwQayqM375GBAYCi6My7dZg+w66UOYDnltu5sp8pCFgniMKkQWiGUAFeA2h3HoqI/ngW6cGIW65JJpUqVJrwdW3K6xsXDAp2DkqZ95mI2fogZmPLMJ5obW6rKV4B5oQegH5v1v4HWFgNfP3UsPzve3Q2VkPLMlqIh62690gDhjHSQ9S49EYDRjpSSZE9wBEoaYlZSw66akc4Fmj9FYd+jyh3X0I888sbIUh7HA04rkDlsCRDVa+F5p3iXW9CaaGWyA/Ta7fSutZyLlG1j6Xja0CVlZCleaaWJ/Lklts5sJ18Rd5Yf8QGQlG03Ks22e1rN2P7QKgnbQqGzUAl1Lhez7INjM/UllFKwYKvju6Jo/5z+/cR/UHVtqMW/F/7qNvJEnEIWY42QyhFSnWodVeAV0P7gDYddvfZXoK2er8GoFYAq+0Cr0ZJNVv999I3AFqm62r/c4xRasbru0cRemdYzzsHba1cyFbSzzgualbsGVrugeeW2zmwi3gC3oHwqIuIZ20/+Ad1o32EIlOaNJIG0ToI6BuAJbpuHEbuCdzzoKzINOA5YN1GrA4sLCp1Yc2AD+r5x6DiekuW6FfxmXcPJ3e6MS+82YL2rAZLV+VeLWEIQlGt6igS8LX4gOrmrQzBc5YDv91QOmP8iChNICTdZMvsNhHP2gaYmrvuCrK8dEDqzJg6qJuoxj6RQHJyFJYOy/1Pap11iqJtB+BzOjvQgOzZIvGGdysUCkPAAWCh1+u9wi1/K/CfgWf1er3Hzu4ycsvtwrKP8JMC0PuQX+EO+gXQQ/r68wgvbgDfAvYTPHeTolkwbxmmZ2S79rJIBb0ZpXAqiuR0wJ6ChnU8OIbQOoegfVg00ok7bzbYatyzb0Lhg65NBASrZdKA6fF7ZLnVUSkZdXSZvlrMQNvRdTtCfcyUtSaMHueBuiTYxFFIuml3pJiWKXtMHTNV1gSdsiQFgerwkfszrt9UNh2ZjkEAACAASURBVBVkYAGRWPbNgrTA2KTVBieUQ3ikHgavqVM890F2Jrh/C/I1ShOqCoXCVuAHETFUbrnltkHbe9u9krhjWXhDCJh3CNTIOPB5Ao2ynrTEMjaLCFBMAMdU+nb4ZDD2lfbWs1OBeKpTNt01yCzCKTKs96RP94fg+XuFSpRZ3ydrrMOSBhzNm7XO9ryDUJMbxL1UO95SLXlHg6HW+UaLciWqumkm0gnJFDGWMVlCvPbJMTmHPQ8v3bSPwze4iKKQ/VpCwDouCl9euZ7Q07MlO5SKMmDYPmdqG+LAC4XCFuDlhNpZZr8NvB04s9BpbrldoFa4A76XLwjlcC8hSWYMUaQcRdDKOuQsEvRlVrr0agSoL9PtFwlFke5FejEukpZT9ZmGXU6vET+dV1dDvePrkN6PkAISiKa5oYHHLIWTpvy7/718roRQJLWyBPZml+HL7nrjK6B2E9Ll/S+QIO848vw2h+OYB1zSaovtRZg/HEC6iYBtF3hYS9J2Ea/a7n8ZGTwoSsDTvP8tSCEs0ESkUQHqiADEXaRHZ3WvXNvxVZi7HWbn9IZ1nyQRBc/UDpg6QwUKbNwD/wAC1DZroFAo3IjQKf+rUCisu2OhUHgT8CaAbdu2nfEF5pbbvyibgu0c4Z53Xc3e3feGxrcJ8KMICN+BeJUHEG86QbIzb9T1X9R9VmBuNgC0eYkPc3bcttnpEoDmEa33zO8LCB5a6U/I2annt3riWyLlhqNQAKub9HPIzSTQLCPDss2yetEvLsugUIkQuqaBDBgH9ASfhwcOC5hOTUgQ0ZJillqQzIaU/anhUEO8vSqyxentcuKFOckOjYEZbb7QmIN5fcbVYagNaRVI5bybwJLOCOxe23ovba1bQwSTxnOvIAPONLBfi2N5VdEZ2mk98EKh8Aqg3uv17nXLNgG/DPzq6fbv9Xof7vV6u3u93u5nPetZZ3+lueX2L8Ge2WEXhzhBaTAlMkRIZ28h8/NRxMM2xccqaYKKeZP2Z5mVp1KhPFnziTdfXjm5TniTwCePoU0Y9NWsNAxsFUqhnYRjVBE5ZHVcwLU6LEk4FU2KYRV4IwKCmvTUONovWYSTszutyXGS9Ad4E6tzcky2tUFwXjnv6qhc1wkE8JtazsDiBxYkNVqlqesaQOkG3UgDmGk2ax2hxrSBRvp3FrYRD3wvcGOhUHgZ8rWqAP8NmaCZ970FuK9QKDy/1+v909ldSm5Pxm7nBr6Xv+XiKx8XAPgg8DngEDIVv1Y31FKlP7bzw5xQJe1f8iPn4pIvTPtGkdLECX7wL++W/00T/KB7v90tW3b7/qm+KrjM10MlPZPWQX+nm6fCrNb1UutkhSMESR70d3UHKew0rfd518EwGNj+l19D2liiolruhboeJ5HO89OHCGUHgOWjQRM+vxjULUY3Xz5GCpBLh8LAYjz9XXOhObN/dvszWnLfYairyyJgegJm9TNpuP0bt2ugUo8xbq3UEkkCireHez1bOy2A93q9W4FbAQqFwvcDb+31ev/ab1MoFOaB3bkK5dtrhcK7AOj13slWjnHxo49LS6ei1H7mDmh8UTyXygcRImzsVEc8w/NPAN8DfLZLr5et6Qb8cgF+AgGmS/IwCUDvucDzPix0SEQKxowivHYE+98XtvcAae89iMRumQfsAZ/GkzLfhN3bqQDEZ3X6+iDWTHlPWZJi0iAkyHN43L0fhskJGHlIwLZaRegkfV1YdNmYOmvpLgeuPUayLiMtDGa0DsAh5asvj2BWH24V7SY/TsgAPRiSeiDMBixF/r5FKYbFsGjRG1pX3Gqm16ah8aCm0E8DHYh3IYNzwll737BxDjy389k+UaD02ufw0CXP4dJLvy79+t5P+kMwNUD8QQRMr4WxXwmu3R6+xH5e+KQuoXCnvM5dv5WdH38k6IJzO9kSJKvSAHwN+aw+Le/X025ng5EM2C67/ltlWSDeqCUE79hUGZZAE5elRKuVZy3tcDta7ROtiFgq6iDVgtJR+mcmZtpA2GRyPlhbRUvOEkDP7umRJAyQEYTuPXVJfTcvOi2ZG0lFQpMMlhKtmaLlZY1/b6/KPc4fdElFy4hktI5QKh3gY7rDrRt9qsHOCMB7vd5dwF0Dlk+d+alzO1v7EnsYY5nLen/Kdubh43DpL31dwEATHo47YWczke4s3X3yJa1+Dn579VaowY0/++dcRIeHmOLSn/k63A/dB8WLKA2H7Ld4HCl3OowE0Q4Dv9GTAMz/jlN+r0RbPBfj+yyTrlWQL6rVz9iG/OB2EwrZ73xqvPTv5Qv8X3yC5/JV1vQr/xK+8JSc61T2Gb6PIk/wwmFELQKpB95s9TdfOBVQni5F/qkwy+70VRBPZ8aJm8qxZp2GqshMcFnUIQ1EVz1pSpZtiAd8DKGT9kL8i9A+JgHEkjZvbtQ1EWYYce0t8UZnmQvLAeCM1vGAN0iZWQc4DGNHA2fve1q2gXhV6JCRSH8nKFgPSVLOzKjWYx8Syqjhbpkj0pSi3Qm/LYDS42f33c898KeZfR+f4Ta+ysX//DiHrns+LMIDy/LlmNQfSEkDP0DaN9CX8mwCzffKF/I/3fIatkRQ/XHSrL5Y22y1V8M0sZqo56PJI3OfhiM/EtRHPuuvqdxiqQilTyEu0Y9mbqSOfKttGgnw3/R4Nq20zMRR+PuXXsbNfIITbOLRz2/nr150Q8rhv7JzO+VPfVMkdYlcO2OId3MYXvWRn+GH+X+ZfHjZNektQAS/evU7WGOI9x79VfhAxG/+1lv4+Uc/FDIb9Bfy91dfxhv4QwAO8bzTfk4Xf3OBi57xBN/N1xhijc++8xXsetcoRZ6gfUj4Y9MV23OzCoO+NohP5fYe+KCON3a5BrYx/anr5gXaeU5llmQzgnjQk8Yjb4UH7ujnudfb3zjiNjD1RkQmqRI6qekqgcraKvACxCmoIw7AImmtbI4AZflOPZJAuw6TqmwZKcs1UUO+Sw3JdrRWZvZ8zcPO0kB+ILRndhxJJJocJW2OUSIk/sRjSH/Se+QeH7ZWbGtQmoG4IyDPLrh8Re/pKDIw1dUxGqJPenm2lgP409B+hP/Op9/xQ6k3U1sO2V3iYssXyEA8juSLH7svSwXnHUWEIJrW5GgSgKSLAE71E1pBbVx+1GkKtEqnrEyopTynRfHXCJmEkRz/bNpHna297Sf+H3hUzxsB7+ZJB4/Oxp49uQId7Wepy3zQrG+6z/oga0kkng7w4G6eovHAEEDbklSqBKnfIMsqOpaXIV4GDp1c/nWQWRPkkmU8Wi9JE1Mv60VcT0BWrSRo1IXJ8ey6p9QxOIFsVylrTZYa8OOIJHMMYi0AZl+3BNFt+4DiCfrlljZgVRGOuzoNbIdEJZsjZf2N2ffmTtIaJmbtVaiYW28KonG5nzQwbb9BX03ySVgO4OeRFT4Ev/bmf0/CEP/hod/knkuvZj97eC5f5QQlXn/ij/iBTd+gxAn5QVwLTEP1F/SLtFkkVW3l9ErD8sV72Em90sa3kUwBQes+7AvpxXE5fM+6SClN2y5WasWOX9IiPM0mdLWyXDeRdbGlOy9C849121HEYzIK4eMET+Rm+tPJNQmDBC5mmXHqtCnx6JbtXMIibfXAy3PfDEkwlhlS1eMeRUBhUXW5wwg/r6nou64+xBpDzGw7xOyeK9nFA5IRacFFvffnXvZVvrf4t3rNzyd5NRyvlDiuqREv4W/46m9dJee/B6b+6m6GWGOCRal7clQCbicIIOKVI7j3Bjy+bsiI28dz5JaKXguHYVaX61iebmdgaF18bPkgtYrv4win99i9vdQkflcTqi0eIdR0gdAhfhi4G/k+6GdtaeveObWEmxJaKiCCkh3/V5FZl+qpt9R79CoF+JSc85/ePkr1QeH0ehNQuFKCjV/X7jszQE0DyPymnlB14KVDUPpTRPZnjSOA7kEF9CEJenYX9X8rIWC11MtIO7xfdM8BQk2bJ4nAhd5ZZP+cre3evbt34MCB0294AVnhzQgQ/1vgP8F/ebNI+t5w25+w8Kox5pliT1PQLvLA8h+BFwI7oP0+8batjnIaZFFKpJuI59JohdhPgvzoS5FG9l0WW0oOWubfKGmAqHG0v85DbQZYhqV6GCAqmnhh1M1IWcDcSo3GkVZ7s2ssErSydeE20+mqbpskoSNNswWVvciPVnn/xkpYb/cLUBolnQV4JUG63qia3cggkNBfaN9+kEPINH8IuBJ4FTAGnTIkQ8/gJcW/4X/+8vXyzPYhqDAO/IF8Lo+0wozmBCfL53xbNPvf1ttsx4Dbyp5GA/YZYUBbMvpBeso+p6hf02wDxpk2FjCZHbr/80d18Nbej7SQzzYiLZ9q9cshBDHbHWmYbAPZVduCqsOkgTuBUnIRa0NDfJA3A/Arf/Qb9H4M9nElANdw/6kv+P0F6UFqDRkuBb4LekPwqer13PivLCKvwX9rKn0TcAOSAbtGiPPg7m9F77Gj918kfK9Afmd7SPuLpss/cmocLhQK9/Z6vZNSfnIP/DyyK968jxsKfyIfyhhMvnuZyWGF3CLCoSWyjsf1VbO9Skh/QwhFcgwEjF7xLAsImG+xk1v9DT/vHCIEHRP6vi0Jyosi21TKUBsVHe6SKgHMQ293VELlmv1VyoSCTHY+N2gYwLY78gNvAyVHC61n3SRTmlPB2+ppgMwoqlrGs2vFhQ4SpsfbOalbCgkyWAzp6ydlcfEoFPkmn+9cH2pjb7CpoVEg3guPMu9tnfdGvSbZA7h52Fvc/xC44Oy5jaLIBvbO1LLXPKvANlYXrXVl2h18GViUwbyxGq6xqZrybIOHdkdnkjq7q00Av/ME9//8DGtE/N//8Os858ceAi49PXCr3f7267mIJ3jJQ3el/PnCs8ZY5BJu50ZuLN4pg05Zddv6eXa2QdFmdlcg3xnrYlRHvrtb9T7LhPIGZfdnjsILgPsJTtJZWu6Bn0P7CD/GLc3f4erKAT7GG9j50CPMaheSKgIs1dGQhkxZKsxFEcTbEDDZgcyZIXiLCIdoHql9ARtHSHsXmrcOoVIbEf3lSU0VoS2pUtClf7n9EKdnJLXbbEo9sSVNxJi2ASghrV3NMKmcqr0UZhJ2CR6ERrShbBTJgDBShvgyoCFTWLPYyxc1bbu+EgC8FkFVEyrapp7RKTMgnpOVdLVnmhAyQ9xA011UmaZ1dI+QAcA0xA8GPfA9SmXtiYLCwVfpMzOv27fkOkHw4Ktl4dGXBjyjEuKl+uWW8u09ZTt3TKClEzRBh42bJcD4QcTeVwlNE2xGZXSebVvK7A9hvWVQRkDtBvinvxmlTo1D7OIAuznIFQB8gZecwRWfnd3Dbva+5l6hQMaQJCLtZ0kVAfA15LP3XLnNLkeRmfYVwO8jIF6D5gcuojJ0ehRfzwPPAfwc2mf4Pn7wyrtpapKCeUQQvsRT5UADtFsKNNDvTvs5rwJx1/oSbieM8tYZBDlWFKk80EBK+wCm77XWcvuLAlDHV9XrLhK4Te+ZtwLdkfLgKq1is+5n1fbGEG+ljPwoVgi9Bgl0R5LodRal84pZ1QYw65q+LDUwKmXkR3LIPSf7QTUCvRJFOoBZOdZFwuDlq+zZgNV0z8aeaeL2sYJSmesH0lrQzWNaf+MmaNwGy0l/FqWBX00DbgkC1vbszWwgMgrFZ2OWgGkXVJ5SJYUlz3hFhlem1DTBZOGoYAuEQKTRNlkz3n09D94rZyaR572c9KttyGy/Z5rwPXglsBt+9i3v52t8NwCf5kYAdvH3PPjV50li1LfBdvIVPs+L+CN+lP/wl78J98PD79nCzp94RMr7FuVaeRlS0vYg8oASvZ+i/n8IKMOX7n9+qqLaiKQ1p1DOMytcDr2Vu2nW5cfp4xmm5rBymGnAyepjWCdu5RdTDx1SYE07pnhzyo+SAZQ/sc+IW0WmuzqFTWtr2HE1SajR6OfcY38MBHz66nJYTQibSivdkyQhgMoo0hh3lZO6lNgMoqvKm+ax4JWnCUt+ULH7WwsgZpYkqimGfuSx5wz9KoEos8z+Xw7nMAqn7Xl75UcrFR1gvige+RgCtGYpbeCWWWcY6zzTXu0PPCYEfbZ5090kePE2O4h1UDjh9gOpNQL087n0SxnXy+psEJoE23V4M0oEQjxmDFVq0C/1Axl40usYRSjDMXiY72Q+Uyn7EM+DbxN4AzyXr3Irv8ZWjomDsApf47vZ+cZHJLBpjTfKwC4kKHsZfbEdQILqRTjCFHUdAp/M/CEH8HNghQ/Ja2NRPBIIgSqbKleyO3mAti9KU5a3LTg4gSRHtEIVNJqEAIu92q93CJn+eS/UsuHsh2QBKCTjzLIrm0pZeHni5JqAaTzkEoCuIHhUFUIUPlFuuxNmHqUEYmvoW3f3rBy8gXc8JIC0tCyeYaSHjoGoo6Bhz0kHtO6i9F80YDMb0etJEqhaIHca8aD8c7BgqD0T+4CsSzuawp2Ix93WoG7VPis77jDMf040xtUJ0ga5EAbCJdfVva3Pyp5not61geVVY/IcG25w8rU32m52buVLvaIkO+BOjmnlPL3PueV+IIb+LNC2W2detZ9FWjC1UiFN3mEtdKaxkIEVglquw5YylG6EG9/15ywpyH1NWimfM7udm8I/O98JO6W+Nnvh3+/9dX77nbfKg5pGFCcAEez7lSs4yBWpt73GEPvZA3xrahDlFMo5sj18if2TUmGqUQ88dLOp02zjZIuBR027WJcRkGshHK8mnaQ6VfOSl+V4FhCsbtP1VUKGR4sQVTeA8pXTLIiZpRQUGKwTOoR6GQkwqd54sxW8r/aqZslZjWYLuuqxK1v12OqJtVfUe3QetiUimcbYa4Wnx0Sx8EgimuF4SGttJBI7mFMlyMwEqVJg6WiQIU9Zr8TRwI3PqmZ7xmWlLqzA1DQCSKvheTf3K820TZ5re7X/+uPtiDrlc8jAYp+VKTXQz2abfHYMI3TNPmjPBSqqvaTPtCqZjCkdZK3FqnrMo4Q6GxNuuZWwLSM67CJCA1yh71U5UzJvsooWAdf9PirPp9kKevUKLtNylFRpkt6PZfVEiKNwVNfbc9gOPCifR20M/vdjMkR9RzpnePrZFA+xl7+lQ5Gv8V3AxpLABllOoZxndsJ8KC/VS8RzSyBwq2su40y3b2v6rrWbiifoj2avkQJuPESaFZme7xghyBJl9vVBRi1Z6jluIMikCLSAUSGVoQDI9loqOorHzoGAkKUgn8q6a6Q1pM27OwGccFRDQvAWQQaVaeshqQOM8b2NeqjHYWnXI74FWCvQLeZBLqyG43SRgHDJwFMB3IKvVj8j0esci5SqaCLB0WsJwNUgkM7ZZsUrpDOWkmmm3UyENUI/RVPAFOn/nCyjdBwB7mvceVQamhLjFrjVAQcb6MrAHaFuSXU00DLobCCliuzPOxsmKdxPCATbTHCVNM7S7ujnNwubTiix4qdLTzN7/JsjHHnGdjZxgieeosyxHMDPkR3ieRxdGOciOjz7g+ruvd9RsUOkYJqgXLD+gEveAzevpkzwqM1rHobSZgXvZfHcLCDYF4AzwLAg5hrygzuGeEsGhKbrXaQ/kqYtp5Jl8XhLBIolAUk9Bip2XkgHmMh9A5vHdKo9LOcslaHUEe85cWCNO4yZeeFmfTRKFC4VAsccrblrG5XnAw5I3D7Z7MO2D9Q6WqpL5j068BjVlBDA1g5uFJaB7yKpHr5qCoYfRQD2fudtJ4TqkjMEXbEPaq9lXu9BCkSbHM4GaQjp66Zysno1GvNIkvBcKhMEisrLWCxBB/qdExuwHIh3OxBr9uV8q182WWyJLvtdm36Jd/LrPB3tsWdMEnQ6T43lAH4ObRtL7ONKnn3dQfmyz0Hpg7rSsgkRLrPbgXhGMsAA4jkExIvAQyHhYH4uKBOuKkNJ1QX2A05UhVEyME4IHqDx5kOI57eK/PiMRzclRou0FjMgXp0VGVolDdLFEVS02wkEzxegtAsow/EvatryKCHdvggck3tqZBoGlNyfNQ7IyugMTxqL0Dwa1k3vBR6FhcOwlECUwPHDst4aIti2KdjrawLMREESmHYiX5UMQaMR7Lq8nK6Ndm3RcTqeDeuv2ksYcCP9DG6Q/6sNQuLWbNimqRdaiaCt91cy8IQAyEVE6aO0RfOYcv1lJEtyDEk68lSG5+wTUs56rh4KWbEiHH5zMRTgmpoh5CqY2snrv+cQ4B5HQFxnbO2OUIMzvl7943I9736Wkcm5rWc5gJ8HlkxAdBR4AYx8XL3NIUSWpHylURDxGCFRIDsr60gvwXZHftAlmyKvyPFKxrcOsHZLvHWQbZv3OO52lFDLAYKMEAIFY0kLGaUHUUgzjqIQoKselOMvJxAdEVBsrKhuOApyRJO7QX8gzXvgPgUdt7332pvAZEOuM11Pv9fuLvmkY4OWKEjkus2L9/tbUkxW220MhU+FT1Ue5i3bZ7mItAqztHNfYhVgu7YWA9gKJRt8txEGYrNhQmnUKFBdrCID9KOEsq02WBuv/5B7nwSVSQmY3A2syGypVNTA6KLO7HYghZ6+6B5kk+DJj5FSf9iMy2YSpk66DbgZfuAcVIx8ulkO4OfYruF+VTRIJb74lpC0wz30S/y0DRUQgl+PAz7zEM1+NIpkTbz3JEHqLZueeE2PqfUdSka9gPDsq6J4iNZ06p+t7V3VZTsIdUjmSNPaRzLbmy66Oiz8tGVXttH32qewmkDs1kMAQ1+Zz0vX/NTbWowZaJqOGaS3oa038+8r7tX2mdJU+2YdZlfVE++IBC4L1NnMSDMD9QoywHqVCVoa1SturAxqLfvrHCUF476DQxjUzcZJZzIkpLU94oSQBm5adgizMfPkh6Cr9XFsFjSCdJ9JqbtRHYhaosEHiJeQoPh1CCh/VI9vDsAeRAu9Jt/TtLzCqm5zm9xH78dhjGU+yhu5jV/nt/klcjvZcgA/X+whBAS3I1PPCKl4VoR4N2E669LXWUUQyLjebRDplHvyGt2uDIeUJrhK/8cCVKYLn3XH2CHnrxkYmJzvCCGrbBv9GughUkBIpX1GFyyHxBTL8Ivpp2l95t+C7ucL6JtZHZEks85wBwLwGqh7WsS/H+R527Jld2xrleXTu+ccQntVZha4cfsYiDdaAfibCA1RQiiJWEFxUoPSS4tQM1qkKMWwIqBmgcA64btiejwITXPHCenelmiUKB1nHrcFMU37/iiSjLId4hVZXl2G6oOEWEsVOAb3HA5fgz36vWwfhNIx90BuJqSdG22ns7Z4G8xpi7PkjnCsyw9AoQ77q3t4Jt8AoPBe6L1jwAO+wC0H8PPF9hIyE3+akJprPzQIPyAIdbQ9jaIdQIBQyW8FrtoNzQeRH/Flbn9rQ2X6cNzxR3V7X1NkGvmRL5NKGFkFGgLcg0qg+pobGzGjGbK9FCGVvfc5rB40B507GbDtRq7F0zbZmiVZ7bMH8fWOY9diA4Hdw7TJKsuEgVFnWbWItOYKwyGZBwjfBfv8OoSCZMvIgFsn1NfeQRrQjc2DLiKB0YTQxgwC726SwAQBbZUuLtwh9zOOK1OrkslKlf40cvuu/AXhw3DqltpwSGSrWHOGbcCd8Ia5P4EqTO119Rly67McwM8Te9clv8imS06wh/2U7j7Bd5ceDPK7ReSHZJ7XBMFt9QCeEGoxGKd4TKb/qVTPUuBbiFd0nR7b6BNTs0TID8mpOPrO0yFVMTRWxKPO1pA2Wx6wzL+PMtv60qjrWbbI0yBQPpVnvFHznr550zaQ+POuB+KnWhZBUJMMKocRIQFigGmoGMc9S1CI2CBsLdrMfA0XkwL6qYrZJcBcKFMwUpZZE5Ee2+8fATvC4OMHufkWzOxG4jYH6A+o+g/CHtQVwLVQ+SJU6oiKxstWLeir9zH+jqNsakrp3hOV7xjwsC5MyxN5zhMrFObo9aZ5jBEuWnuCkZUnKNyGfKn/glDwwsqfZktZGp9ZRkB8M2m6ekqDmPQLQhDSaBMQDtum4UcQSmeNwKd63bB54JoibBz3vIK5B72sJwyDgW2QN5HdL2uD6BDbp4TIiC1j0BKAZvZCY7+eM9KEn2tJYwr33RPO6bNiB7VhtPOdSb1sM1OtQCjtGgG1m3XhIkHJoYNx+uzNIzYqzGZka8jnaAOvcVVb9f0yAp4dQrLQKEEvDmHG54PVcwTnwBZ9OsQp0gQpK6M7Bku/JzLIeExLH1gteOuWZB2eLAGphXyfrD1rTa9hBna99stpO7zZN1xJ7w/Xf67/Ei1P5DnPrdebpsZR/iu7YQheUr8rBIAyNElfYSjca5nAkdsP2gJfJg8zADBP5whhytxwyysEwLYfsucuLDFFr8PqdltKu+GGL2O6UdD2oHy6L+h6Xn+CTO993e0qmhcyB9XL3MZryHOQDGeu2g3coetM54z2bLRlXjVidMRiqLwI/Ty/p1z8tadyxcRp2S2hZkWyO0GDyoQZgVUqtPovtRn3AHa7A+8jbWiRDrpH6Ndsq1QQkO+XfZ+sAzwESs4UKwj9AYS67jZD1O9R7SbSbkyxV01pjZ0UvCNkgFiFhTpM7nXnRM77u9zCi5c+wzdvLV9w4H0q2zCAFwqFIWRytNDr9V5RKBT+M1Iv7AngH4Gf6PV633hqLvPCsO9X2VSRDl+49F/xA62/kxWjiKdlP8IW4umYVhsCwFqg0bIty255Gflx1QkUjOdVIUyzvaLBH8O22U5IzT4crutM6YpBhZL8l3I9gP6WmacuHtXXYQL62mwGTpZInsastIDp0+FkqsUrbdL7XgrKoaXVkz38WHeIVN43MkwKwt0k80y1NG5DgTiKJIO0rwWfXRgEYPWDNYQcAdsmgYoleBk4Z/cx0xLJqdS0ntnWXjcTurWbqXrl+xt/RxJvpvCH3z7G4OlgZ+KB34Kwbzaj/Axwa6/XSwqFwvuAWwllXHI7C/szXs8PUOMLb32pyKnMa/ZBpbQpL/2F4iF4LOqzpwAAIABJREFU18dIG8R2Wzp1nUamzuZRdpAf3Zj+zeq+4wjqeLCy1Pwiwcs6Rmj04MrAWmNljp7Mi2f12p6esFZjZl6rvZ5qbqNmAUijU+bqENdFaVMyuqBMqKtelbR8gGmr4Wy0VYJ8NtP6N+qWb5OmGs1WOJ9RSLj7G8n8T+b/WddMYtDz69LfC3rZpfkDjP2pCwrukHurbnMHGhCMmHUUyoz1cqyRlhSmiXynOgSHYpsus/Z3liEcuX3Mo7cZoyUNoeewsr+7ZL/JIrQ/pwHRBOl1CWyvyoczT27eNgTghUJhC1J86z3AzwP0er073Sb7gB/+ll/dBWiPMRa6eECY2lqatQG4V6TY/0cJadqN0FvwcuMPEsTz9j8iUwn44lW+VoaXpxnFYjasx9Nv0fFV8Rqj1skqjkEWId6j0QAQvE3r6eixxl/GmVpEqM7X0O4ukX+Gvtrg50IfUECCvTZQJbB0GCpLEO3TxCrzRCNJlqroTOQE/Q5udrbhn02XwH55nXt2m5gwqJ1K/dJGBpJp+3z898mkh8ZBAzPjMH9QrzEifD9m9b6vIOQeFHXfxTBTSJuM2AxR1Ul0EIXLLndx5gQYHYi7Kfse2Czwo/Cl9zyf9/AfdMO/XOeOL0zbqAf+AeDtBOcha28A/mzQikKh8CbgTQDbtm0btEluzh68T6uVVQnJFllpoFmLEHBKCG2dgDkflLKAF/Rl5qX7GYDbYGGacFs/RwimZTM5XW3sLgrESUiy8TaoiP/SaqhXdDqvOnu89WwQsMVI0a94ORSdAsKAt510kFw4CJM27S8SOGN9RksEL3vKMmONQtgcvPD1gpse1AdlmWbtVIOgP54/Zhfl/232ZDMFuycbAVwsZeoKXV8jfOcahGQj04q7ZJ4YQsbrEjKlsoD7ikv796OOeeiWvGPfRXU4oojwPW3ABIssUeMmbj/FU7gw7bQAXigUXgHUe73evYVC4fsHrP9l5OP674P27/V6HwY+DKJCeVJXewHYd171laDhtapuliVnwchh+kHWAmnXkv7wpi+jP7gJgX9cJZSMtWNagk5C+NFaFTnzvCK33sw8e6R2h+dsB0n8srK7iJNrh6zXKb2mapmFwwNWOvPH8XXVre1aVT3x4y3J/KSDZL1q6dXJGwjPdpeuS6SWSzcJGZcA8WGoLgmvbMXAumsS1JsaEnmd3Xf2eZToV+t4Stg/M1PUwMlAnqWUEvrrjN1ndbeXg4c/OUMoO2sDuwUzO0iky5ZZGQVrH/Y4oXnBOJJRaQXQhhDwVwpo3lFBlRYSXLXvnZXQndPXrUh9lo7U+amaJ78MOx99hEuqiyeXjshtQx74XuDGQqHwMpRBLRQKf9zr9V5XKBReD7wCuK737dQj/gu2Z/INvu+zd/I/f+768OU22iLbMcf68ZmE0CiUDjL1HSdwtxGSxOOq3aXdQoaRTNDH6Ve4mMe/hvyYt+p5LekjcscBKi+C9h2BPoB+SaE3oweynrLnx73FwH3rALfRE+ultps1VwTQJ6eRxhE2+EQIeGzW/02MPgR8LNQMnxyXbeYPh7ogIIAdtaC+qCnzUShXawNI5K7Fc+LZ2YJ1zvH35rvS+w42ZlkPPLvM/l+2azYHwVNyLYmXWPu1NqoHR4pflbQuSlozZy3sB/TP2HSgjedk38i8bvsuZ3kxK0/8IOJETCOD6RFZd/QS4Vyy1Rxy2wCA93q9W5EAJeqBv1XB+yVI0PLaXq/39K26fp5Zh4vkzQSDixMlhIQHkw3alNWqDmozhKglHK2VSS2NEwKiEGRlIGBv1QRxr74C4TSi1Gjo9RlJbZy9plAP+lINojU8fZDt+GL1SAbRLoNsPYphoOfq64DYDMRoFJthOImmzSi6nf5rseVt5f7teq3QVXVYK/8hILbg1BUehNNBS6WBXklimalx5M6d9L0MzLNaz9pA42ioVWOladut0L0nO9CktdGNrrPKib4JiHculKIZGVZ5oSUrGTc+7I5hcRZfw9yaQr9K1v0kHwHgzsK76PXeeYq7u/DsyejAfw/5qD5TKBQA9vV6vZ/+llzVBWyHeB5fYo9M220KalF+4wXNaxxC3Kqf0u3eq+tqUHpBOGbpEtI63ubdWc9GUIVCB/GwnXKg24JkSbvaWM3qWZg/KiVRU6WDptgvqYoj4WRFSRZgs9539n+T302XxcNtrA6mVWAwYJlXvnM09NxsJjIzeKDe77FHQG0OatfogiPhOcVRUMhYlx7zgu0v0oswgF1GqkFWi1C6Plzk1EPyujQbeH/v+TR1QJwak9IEFsw1yZ/VTp+KtI0ag3/A2drl/nmAlNJdWhmwUWZ7szbask1rdk8bF+ObfVihNVeWofJKxGGw761li9p312iXGULZ2wk48lOhVvFb+L20kTG9G0990RegnRGA93q9u4C79P3Op+B6cjOrItTEPfR3TTG0sjoYHSTppEFafTDt+u631ah+4jxwSwRJ+W4NdFnndgvEVZegdIi0FOiUceUdbZmmA01Fvbql0+jBfb3txP2l16WnKkXqwa32Vw300jw7HjgwdVZfB6iyiUKxUU2EUrYgVEgFlQTqxvMDbs5LJK3dW6qdtgCzC57a+Y1isQErJiTt2HIbHOKkP3YwORzqcXv6aNCP2s8c/KwkG2jNznayt7rFdTjy9VVSqs06PkGYubUIz2IMSd+3xJ9hYBr2/fAVXPO7B2EC5pmiQ5EP8Wai9eof5wY8OQ88t6fI9pz4stQosdmidotJ22fhXn39COPDoT+qZV6PIxGtNko8JLVMwHUoR8C9NBwkYumxioQ+i5pY1G0JAJY0HbrdWp/SyNYwWc9KyiOjLdr89l6N0uVkmsS8a9NLDxowIHilkd6vAfjx1eAZVxLtTzqMAM8YzN++Pq0To1y5eaSePlhvHwXAVOe9DmadLqkpq2hZ7zOoROrtu+3Xo6r8c5uxmIqBNQRttylX7LgWEF1V6sm89iISa7DA+W6gLslr+372Ct7G+7n7H34QgN53rXMDuaWWA/h5aG/Z9Hvset0D3HLHh+HOUB+63XEdbSxYZMBjyhQLTBrVYrWfkeWl3cAx4UFBjjfiZIopdTBKqDFu3xIXgFqaE487bbScQHdFrtFP12HjdUI8oFa3yXUvqHdfQ0C9m4hX6umUQbpw7xGb9/zIANQzoH94FWqLJ69PIBQSswCwsxG3nV1/oyHPteQpr1FSSuyk2UTSr5Yxr9oSj7LXG7vtbP1xTh7YIDxPX0FxORMkTTL7mlnwNL1mVSKl9eWt4FRC6PNZRmI39j20gUkVK9QBLQv7+5f8OJ/lxRxnhDuPvgyA3rYIcuDesOUAfh7aZ7mOZca45cCHmV0WgCoBI4lMo6vDykvb9LRC6GXpemeeJEkwD90y81alEH8anGq5crRWB8VMA00G0qC1rVuhNofJ87LTcrPTBSMrBF75gcMy255UPriBcsqq1bZzrKdiqUVhYDIO3E9G7HqMjmnTz0e33WtzljRLc1BpWTuWXdOkUkzdAy5lfZyUeqi4/fzHZP+vp3e3wcaSnGBwz99N9NdN5xTHhJNBwJKYmokkP8UR1C7VlWsinWysCJef1jE/SCiJqw0bUvXTXvjKx74TgJfwNxxjJ821IiVOsMwYD7MTHtaT5qkiZ2Q5gJ+HNs+lwKV06zfTpd/j3DkstEf7MJSsfrQPJhUJKe6+VrR50yYdcxRK6oYpT54eRws0+QSiJNHiRaOio/YIatz5el+qQUqUQcvS4xG6wdgxjV4wIPYBUw/MZsZld9c7CSdL87LsUzez3LNY5hF7md9AvbJ9HonGCs6gbox/np6798Ww4sz2cea9p5VOez49aOwv0JK/ivQlczVWtMGEKXvG6Uvu4kXyOtU5wr3F3byK24C3URnq8Hrg9XagF23gwnI7yXIAP48tHoPpRDzdCgLeFevYawHMBLp19YK2018K9BhhiruCgLElZyjlUrJaKi05Di2lTgzIh0MhpHYi0rB4COJpGJkTjxwEIOsrG8sY9KDtMSKrqmhzMvDGQ7JdzfH1C6vhuKlnnIhqxDeG8OeNCF5pjFA03pvuum1Op+owgDRVDkr/JK7VUOyyZkvbIJrVpr4usFka1nZ4OsNJHGEfR3JPXq2znldds4YJPoC7JsHZpdX+bX0ikc0WKsp1V0zqFyE5BmNyb5bMhF3LGsFzbqLFNoDr4BNb/08AXsttvJBQKTa3b43lAH4+2yUCsNVD8tupVAhetWlwLanGgkIGvAcJP2Dt+tJdVIrEmsyai2lcLfojNj49AVZFdgYyiKQDiNItVQLgHF8NdIRt5kvLbsT783jtPUcQ7zsekiJUpk1OMvuORcLxGnD7RJrasILTHn0Gy26lBzyrlocGj1XLPFeXezNpIwggmhceRxoD0F6SKTUF/U2gVd9fKmoMwWXKNj1BTgDVbhIAO3vPZvacZhfD/cbWzHpIBhdrVA3KY+8i1IS3IOUoko1pTYaNx9eBPZ6Aqe0EZ2A3ob7JNkQWOARf2vp81voE4rl9qy0H8PPZxoBh2GKKDJvCRqRywXbLqUQg1G82OsTRHO1OUDz0ZVlq1cOUr1X1BxlvDQgKAvUo41GI65n6IvQXZLIg2uksq4SwAcB48Rpyr/G4BFGhv43bltN8m9ur/cHC1KxhhaGhbzGXsbHBiwfbOEEV5I63tOzah1kThBVk0M0AuFk2QxMGSwD9/41VzRi1ZBoDcwPwbOLWIoHqgTCoWT0ecx5ucRdgx7MPehyScYiWYT97OMDVAPzI4NvK7UlaDuDnsX3wr/4NQ6zx03/8cdhP+JHZD+pBiDoCaECoFw59HEVDwa66nf4mEMaVbyaUD4UQEG0EjzBCsgpNvVKtynaNRmYAUWtnXr2dSnfsLQtQC6sQaU1puz2/3lQmW6J+pUVVqYnYyuGa53gZAXh80NbJLbtrgWaatgzCNQnetldh2rTQVugpkRZ2ba3pUfL11qtyrjgSrXy3BWMrUkPGimnFQ2FGA/qc1ZZUxWJ4ud5z9DjfBuYzrYSmlO5YakFFm0dUryGUbvA1vq8BXkuoN1+F5sRFrA0NcYhdHNIyg7+v2WSHeJ6ASgXeRm5PteUAfh7bw+ykSEe8oEuR1moT9HXIiVtIhptJuExWqL/iRkNTs9Vrb7iKbxX7kZqW3HoQriKZl5dBMifZjKC0gAGSLquOCTWTJKJEaSYbD87B+h5ktoyqWZvBAhv/Ra5WoWTp32UZhBor6vW23A6WvAQBvNcIvdeAimaZput1BlIp6rHMg3X6+5S2QCR3IGn0vrYIyOfSTTS1fVkGmJLSQzYbWtL4RnUiJEoZfZSVapaQGMWCSwTyipKKvp/XQXhqBzKIbSPU8wYZaHaR1ve+f+8MR5jiXqSj13t4N0Afp/0W4LncT27fXssB/Dy2DzzwS6zuLPTL+YyfrRLoEfvhOXWJdWcx73hEOe+TvGVDQytKZaC0Dax6Z7ujgUvb19KgB9iglPmN6sCzxzFQOuP9/UCjrd8S1JvObNenzjHzo4I9E3tO5m133PPI7ne6y4sgWguUSPqZqAy0agOmXttSvf/YcRTUNVmLB1DOXQXvEaPJjOv3iTf2P0hJBr3Pzijczo18g2fyhNXpWce+ypWnXJ/bt95yAD+f7ZkJxU8Tut/4H19CCMJZ5bw1pNhUGdgD8SLEmSYMlXIAjm4C9WWY3EF/y7U1WPjNsE+kvGhkiogMPx5HIYDnPWRfsS9bGXA9ywJ+haBwHJSJGLntjD5uNjTYinib1WHtFDSODEzWcciaWdTpq3/SWIHqSgDoxkqYxVTHCSVWx5BsS9cBCWSgaOp7A2MrkdvFJUgxAIiHkUqJkHawqG2D9mzYpJ3Zx1c3bLf6ZyeWcVm7gnSwqh4jBLwjwoxrNzz82i08xsVco950kZAQnNv5ZzmAn8f20m23w68hiRF1kfNV9xC8X6sv4T3wS3X9MALsm+mvSVEWfa8FAbtIhxmA6mLgX837rTjFhSXEtFeEBrCyo5aC7zMdLdnkTLvnZL3tpltWcduYtM9j2eUaYewmIVtw0jJKDRSPkta17jq5nuedE/prqKRt4izA1yLo5LXkKW8EPg+sCq9cscCxFcByXvNSPQxoVYJ+HiBqQcVKFZi+GqFW2i35LKyzkAWOrexAqQxsDvXSR4ahYgk4IBy3lQZelP1LO4C3wGPXb+Z+pKPDTU/8Bd84tbOd23liOYCfx3bHo/9aIv6vQXTahlZWXwNCIs8qQQaG284CUj77JFPgyQ5lAA3r1+Voq0Qvcl69pX77BgWmPDmdAmU9rttf2yC2wDzaKb0vq5sCMkNYcAG/qmULWnXHoQDwdo/ZGtx9FQD9MzTgNiC3OEIHGWGsCYc9n5V+TnuQJZCWo40Tp5QZJjQ/iAYHi2OjRkwlUtEBx5oruBZ5VjK2Wgz3XWoB++HiI4/zg9HdPPNHHz35JLmdt5YD+PlspuU2b8kaLxhQu2L8gPxYnWoBr0Mu0ydrq+2QY3uFQtuVbK2VYVaVFlVgqiweXjykXqJ65snqyZpr63MJYQrvk2S8WZ6ItxIhUadUFG/4BP0p5BUUmEfdgUwTPxw89xLiDcedULSrVNRrd6DtB4kRU60YB276+ypBR2iDpgGnqXk2I4FmVzMlISThlAiVB6vufYlQ7XBOPehNh0NDhHhcqapEKCKQ70PlUvq5/M0ENdEe0m5C818M19NWnXjJBohPItRSGa78NwcBKBQm6PUuJ7fz23IAP5/NUuSLIjkDec92AiXgg5hVgpSwgzSiNYlhAwGcI7q+Kseacqn0zaamRatNmtpB+e2lVtAVL2WUEBECfO1V+TOPc2osqCYWVoM2fMpP+9cCzwwC9v8/e+8eZ1dV3v+/N3MOMyeTzIQZnCETky9JiRIFDJIaajQiIFT8ij/9YkXRKt4rivdLvdSq1S8WbK01rXhDq3il0mLBiuUiEr/EBkkhJWqQpIkMzpgZM5OczEzmHM7vj7U+ez17zT6TCYRkgP28XvOac/Z17X32/qxnfdbneR5FV1qufRgP3IoGVWbEIQdKaeDMEujwSotJAj0i4Nb3UskdJ57cVIEG8Jy3IhsVPGUpK3n1NsmVyqvhrk+jFVEdNZ+tcRSXnjXNd+7PafObaARUNqOm3eO+So6KCMc0mqIifwzD68JxFW3ZdQWhQ/ed1MRiqLUcwW7mcXvSrPRtYbPNCgCfzaayabUANClw24yDCtTQsF4mWWAPjkeX9huC6zuXdMhfMTwwZPlvcOdSuHfFAKt9iCptDmAqOU+WTlkmKGQqCkoi6wXHOavt/srImAqi6waES0D31AdbUZxgZJVkoxL1eczyNj44hW4CSJvzTkmcAlmFTlvgvyUbjNtVq2WpJB1eVioRvHwv15xCp1gv3Ba6xtz3kgkI8/ep1gcTrUewrXUJ+ziSFurw8cLzfqRYAeCz0P6Kd3EyG3mehr2dLmAnfWl34KL3bM3MEs6lW0moiTlI8LRWAl8jhEzbWoY+2q5Wg7HBrAfd1Rnl62h1OVp6VV+zCr0CLE/fdFRhm1dNDJt9FWKfl8NjshayAZZxk4/gPPNhAh3S20cmH/nAhsADpxXmW6F3NQ5IpTSpOQ4YXMekScDdJo/KpAG8LqsPv45UllmWAkXl7FRJZjGg32uEUOhgEVSGsvlD7PXvNvdHpmjZGn6kIcWIv46SV/3QRShEPUqYMd7gr7sOXcv8MnX26mhK8PvjK/yQs9Nw9wu4ivWQpnstbPZbAeCz0FbzU1aw0f0615Hmnxjd6ikLAbZeTk1k7sGBdg1Hn9RwL+6P/TYXEoJ8tvhtVch4PFAdYLjrIT8p5s9bq0F5jz/ucoIMTR2JT/AvaqWrPQS1KJIQPJ/b4raf9J59r5c4qmI8BKVGueSjEhXe7WWBvV5xsrvqrqlU8vlFxAOfC1zrzlOZCME3k8PhGsuE4hXggmZsLhRVJgK3f2nQB9X0kEZX0o8Dc3DgOUgKmpVen7993N0DzRnYQhMVU11ntObuhZZPjriOVefsUPQnOI4bd560A4s17QJ7Jdr6MfD3De5iFetZxV7fpRTh7o88KwB8Ftqz7/l/QVes4a54WZunQxOZgwS1gqgUvbiqmGLFwRM4ALc0QMlMTNaCKmIS0qH5JGYUsMQcz4aLe4AUhZJWJGd6JUZeAArAsQZMp+Qo8UWcUwmeOgVRR2qnrtFHm1rZXq7pPsMUqkJe+mgVOuTd2/ZBlpNWvhE/AiiXXK7yWs1nTPSb5Y1K1PHVVDRZ1I/OY9qZfjcedqYtmtswE9trWF9kB3yEWwHgs8zO5btcswWnV5ZMTdEsECSA/QQqYYRQmV5eWD/BG9NLvj2cZ3TYKC38fpXF3kNtwYVSK9jF1zhcthIXei3AHgn7ZkAFk8PDPGFdnThP0CZLaofyOJTr7jjlcT9hZzsccfxWVK6OyNtkzV1PSVSSTGqRCddeO1kaK2JSflp5rSPTrVKaV/Hmaeh8t9lQAC5PHNc5lkouyGis6gBcCbxqtQDivcvIpAWuDPvqN0ompd92MYFvHyTLvatzbyGkDJgg8wwU9si3AsBnoynqbosbwoN78bt6CLRJFfcy6kVejPM2BWobSBUGPMNvuz4EeewFjpV2uuaP207w5rf778sIqgYD5mkEYw0HygKPAfPZp7id9JN/tZqZKFXuFQhSvQGzfK5f10VQenTA2BZHsdhqO+Cz9fnJzN0WpL4YvOjYy7eThfHE67BP8mSjHu28JngwXkaIij3Hr7jBbNzpPk9WTYeJo0a6cPejVMqe/74t4ZzzlAMewgT0ctMQXZPV9kunLh6+G3ix+3/sGvdwbaOwR4MdMdMNkyRpSZLkjiRJ/s1/70qS5EdJkmzx/496+Jr52LDkOlhAPxe+4x8dgHoPLw2wkddZIwDvOM4TF8j6kmNp8Ek7juf23rLtsbfppfcFCJjAgWUV5+1uxXGst/k/SRCVG0R/iu4zBQyA1Issd7u/Si+ByrBKD11X3fxh1ulvNATd2KAja9NSI4fSVKHd0D5lAa65PnUuUpZYyqZkP3SS8v7p72onQK3mX6a87q24OZFlsI9W9uWWDCrskWgH4oG/Fecbyh94H3BDo9G4JEmS9/nv7z3I7XvM2dn80H0wnmXJe5eVOg7YVUpNYCiPXZNVGsKLCx0i5WJ7fcmrAR/AM1aFMRe7Qddystn19oQJwpRfxgNxByHiUIn/bbJsW3RCT0wbaTGDSQ/aZQ3t5Zlb/bfn8Md8W39TDYV2x8anVsop1fJ5dqlotL1u3ZTIT8Nvx/lGtF9FHnHdHXdyuz/nKtyoR8fxRRRUPKPsPXGGQkCRNdUoVRvEeadFPFTpXrnDYUpEbZqpEtJCC//+jtPSScoXcV1ck/kRbytZx22jz6C0lkDnDcGXP/IyzuQ/qNPC5byBAT+rfQVvPJzNPeg2IwBPkuTxwPOAjxMKJr0AOM1//ipwMwWAPyR7xTlfoE6Js+s/hNuc+qBW86oKgaNe0mWEYbLokpU4IK3hXu5fRCeY6yiZbdXAvQ5XYXmf2UapTrc64FZe7XLNcMb3ZsPfH6/iBFWfC3vcJWACAg9fgwGPHjYqc6HxUO8zEYLDUQ5rbQ9T5+m0TPy0pUIma77TanOpdKfosP1xrSIklvbZ85RbcL/FBFRaXLpeAK43vP+LCDlXpL3XZOfcbLBsShVppFSHjkWEkZb2n0vIX65zKi98iTA30Qqc4ZaPrj6SFdzBMexiNltyI/x/p3+LI5ngc/wZR1095u7XEtKRZXJpA4DGZ7L79jDAzo5OjhnwvZkfibz6fd9w78oqWL7qbrp95reLuZTPPIoylc/UA/808B7Ahmj1NhqN+wEajcb9SZLkTPtAkiSvB14PsHhxUXJ6OvsnXgfrX++KN/gkTGV504twD+QO3K+2icBxRoEbQFCfiKPudbzuWC0bUFO2+2l471PHWi/UVsuxxXHLmGG/iWDMSNnI6qDHiFQXNZe7JK3iM56fhrZZ0YLYLEjXcKOIcpP0t9MVlMg9dt3puikBbYGL310NybHKJmw9rfZjC2lY6qgHd9+VIbANR0WZAKv4XgLB+1YnoV4N0snLwZYe9jKHYw7wGmebXbvojKbrfnDji7js9Lu5bOuHHOBLylrDdaJD8MrN3+HiV/31oWnsIbb9AniSJP8bGGw0GrcnSXLagZ6g0Wh8Hvg8wMqVKxsH3MLHmu3AvYArCb+OAHqEQJHEUrkSDnyVfbAfWE3KwY59LgCy9Ti7OgnyQ6shbg3679FqfsAJ+PBsyQwnAnjeN5QF+hhwtU7AHa+L62HmrbcWe+N2290GAFOKpRbW5+1nN7PYqFQBlTY3gVlWlsKt4TyV7aaM2bMIlFY/jkIaNweXKmcLYTLXljarktICqeb7dEJmQamV1GhNYLbBcbf/Bk6Zna9c6/Ao53VdxZX/9Roa7fA+/pIW6hy1Y8w926M4h6UKLdT5+mfO4wKumnKcl53+ZYY42t2vTUyNRPb3Yj670oCl5BponPtwX+GhsZl44KuBc5MkOQef7yxJkq8DA0mSLPDe9wKCVqCwB2G3cTL7OJI1Hbjxzhbc3V5CmBxcRhhyW0D12ero8PuVgJf6df3AJqicAZPXZIFxElcpRoDYNeqL/kJGY9zRQqbUVwVPF3jpn2iX8jj0eu57eAT21kwADKEijORzEKIvIZsDJOap42o9GVqj5MP7lYtlImim55kNxStbyysUnGcWxGv4aNV7/XwA0KXAKUjzzkxOOOqJcUJx4E7CmyJg78RFyGryWFRJ3S+H8NvKTiV48FYN9Da//imzE7gB7uBJ/EHXlS5YzccjXLLuL0NksSJMtwIj8O3jX8JeKlzwusR1iHLIFzT41sBLeODX7XzlA2+k8XS//G1Jtqj3dnjmn99CiTpXcCGvOPcLwOsO2fU+nLZfAG80Gn8O/DmA98B2RoSqAAAgAElEQVTf1Wg0Xp4kyaXAK4FL/P9/fRjb+dixYcKLDsEDg8BxWrSp4QC+12/bRzZwpZ8U7C3wxaXMlPs7jUCshqo2yltSqhsQ9BSCtdEdftKt1UUzdqm2psykNVWOFRtqr7ZZL3kmFIdyjNic3mp7rErJLNeIpOQiRuVBx6llm9nYBGnnWhpySpvUOn1hBmXgkkn2ZyeLNemZ14vsMZ+lLe/HgZt+4zacyqQbtj5lARDirGarfZcX8+S1v4bv465RvP5KQgqECaAfrlj/Z1MnbL3Ve9tD1K3sVELysR7gOnjOt27l2vPP4Fi28VE+fvAv6DDZQ9GBXwJ8J0mS1+AGci8+OE16bNqp92x0L/V1foF+mSpu0qydbBDMJgK/uphUb5wCqy9KbANJFnaTetWaMARTLUf1Mb10ccxrlAWCFV90t9LqChWUSo5CGLXSBh20ixBUBKFQMi4RVqXqvu++N+yq2o/Sfk9G3K9Sxw40CUaxSarS5F8zsFLJ0UAlBdwYIM2rBg8ea2tZ2V/ZuumSDNYI0kwj9ZS+v7Idd72rCZkk7f3UPZggBG7ZtAVS67wYaIe1vAmAy2Z26YfEruQ8zuE6jpyYoP3GBzj2uRVa6vXUw06VTO04RZXmdZSaQBJXfdYIZkGTE57fgPNhFbcAsPIdG9hJN7uZxzaW+Iqejw47IABvNBo349QmNBqNIcJgprAHa/e44V7jdEjkOYB7gG0So3FCtKUmKJfjQHcYp0SxBQbuJ3jrW/0xX4VLaFWHhSpaC0EG6BM8DVSNhx55+x04L7bc4kPuR4KsbtIoTegPRXgBKt0Erl5ANA4L98A2/0Iu7CKkblURBtWk1MQhrtzZ4IjJ990eihfL5I3Paw8dkGp7lkquw5pn5X0TIWDGUi154N3spZk01XfSz5rTsPxLNVAvQIiilEetbW0HZKMoxXXrXp4Dt5zwNHbSzade/UEaX27SwMNkffRz1C/8FHY/HPWksezIw4b6+4hZhnCeuTqwNhy1tIxIxtPcQqKAR2/CgCISc5ZY8gmC91yL/vbgIhq/6QGnFeeV/xDHCdZwL7IiM2WSmwiErg+yty6Bu9dag6ciIq/VUhiqd6nCDzaqMA2uMdsPV0MF9rEhqCzBvZxWr7QUjp1LeJltulNVv7E27rz+StXpvjt8wQPqwetuFswTB8rUat5rhmkLNT8Y072Z1G844e+lBV99lzetuqRW42/zwDcj6pfDPRzHGBU+++XXAl88eBdyEOyNfI5fDj3F3d8rcI7HIuBqv4F5BjMpEu4nJOFaQnhGCkutAPDDYOfyXVqo814+yakjhGGhht0thFB1cA/tRg9MI2Sr0HgqAnATXspPYsuuaaJrJChRxka8XrpEOuFm6yyKtm1WDk2aa3ATlgoHV6GCNJ931U1elkpQ6ScbMag8HRoKy/uUxypAl4c2FL5Lvlfuc9eWViyKCkqAiXb0QUNKZlWruayEymWia8/zupuZpZfk9ddqvi2tIYXt2LihauMSdzaQSdecB9i2M1Z4/Aq3z6u3fgOWzM6Jy19ykntG/4LQIZ2Ky475WRwdCNlIY33X5zpwFlyy+m3s9mrmRw+T/eCtAPBDaEn/BK/qu4J5HMly7ubUHT4E8rN+g+MJDzOEh3fEURNpubMaMASV5TjO3MoJFaXZTwoKo55CGRv3FegBWkLOjYU9vlMY8WBmPNGu1hA5OIlTjXTgcnTsHvcqkprzhvUwdRA894VLCVGJKsKszIl7cLJJUSrdhMkqye5ECy0mLT0/uiOAbbkKXUug0gMsh8oWqAzCmKcbMiH3VU+beL57bDxEuYr/bmaWC7dUd1pFaCJQLxWNkjqhNhj2T7ltq9uXN642WtooTgsrW4br0PxM5UVLPgXA2ukv4fDamgb33LyI4373G2qtsLvDdfFHdY6FwDMIMkjdm17gFLjy4/+HXcxnHrt5H397OK5gVloB4IfJPnjSp8LwEPP/BAJnLeAcceA9iZPaVdpxw9AqzuuWp6JCDQKBHW65vMNyyU2eVXrdfgv7YGxDyNOdVttpC+BWLoU0s2PjWUrFSgBr+GLH/nsaqGPljjY3iClQkG6TF5DUTuB/zwC6XWRqhzq6DnPSjbj5gJEsFaTiDKPVALS1WvY0lnZRwQdr8uInTUdlvXy7/+6q6xAqVVPGbdyVfQOoKIq25q9ZnK+duJ6O2pHc8Hq46COf4pFie5nDfY/rppUJ9nrR6FE9Y1mlleSTkI4O6YR++lLPu7BgBYAfQmv0tXIxu/nMx97jcmjYbHwlHAJKVQAOvIYdIHSUXFj7wh6yE5x1As3QSQgC6QQ2Bbqg0gplm1cDUhWFzUld9h6QjZSsLPNZBLfCgAdbW6XHmvVO0wWSwu0h8NuKMpwgSMi6CeAlL9YGqUCoCbqRNDQ9zSHiVRm6ZoHuaNWBfnk8ZCzMK22WXm9rkExCpDTZzxuTB+rlFqjt700TddRK6MBlpm4pECb6+qGHwTRA5YV8i6s5fz8nOnx2Er+cuvCLifsfpSPOjExWw7vX/T2snp0U0eG0pNE4dDdl5cqVjQ0bNux/w0exJZ+BRkcCNxIkgzaftrwODyj33ZstNXbsGYTq8nrI15F669uGgt57FNJcUj2q4K4OopsAeh5gR0e9EsNOftZ8QIom37yNjYRKNfPasqlaf1MN7bUdgSTRJUzxZCVo2gPD630JMKkrRL0M44BcksOSk0Hqdmkis1zyXrbpXHq9NntgKKhnhg01USIkDJNqZmAoHDOeEI0nSffHl0t3XjH3breoHHVkSwnCbTtpa2tuKoJTUbPedt7qIrtexxcAHlYAv5SL6WaIE7mTOb7bfzL3HLTjX8CXmc8u1m56J4zAB1Z/iAmOBOAyPnjQzvNItCRJbm80Givj5YUHfojtaRffAn8DrDM0hSYZITORM2aCF8r4SEaFySv6zi9TuLsoDlvTYAgYGoGOEcNNe4rAgl2t5lQqmuic12ZyWNtc2n69LXqs/zHNYvGtQogAHRh01Ea5hNP+dnoNuDxwzaKqfFsN5237dnSZ6kFpjcicqj5x9Z2ZaMP352XnAbvdd3+gPqUgMYCSd4nvh6BEgTCpWcJx4D7179G3u9734lOU5enQeuBHP3AfO49YeFCO9QYu51i2Od67E37JE9NMioXlWwHgh9hWsgGud58rvTA5RJBJqcxZKWT1s1n/SlaxMBSSJw2PZDP8xSY8GbXrfa4Scevat4P9mOcoU2VHHwxvzSa+qhBA0C5vltskNR+4MbkJyhNkhxFa70cHlXbv3e8gqDpaHdVj87YMmk7QLpfnLY7aAnKX0RlnJi/NNqWSUb6Q/QxNgn8MpZKaJpVHcTdfGQbV0SjfiX1TowhYgLns5mes4m5ey0UHUUb4I57JPPZw6lUb6TnvT+hlkCfWf8W+liPTbZKfEsLYH4ItYgeL7x+k1g6lxzVyMp8UFltBoRxq+5sEPuGSPckWdrsX3uaJruGoEzmjJbIAMTYecG1eWzb4xE4oziE72ZgnCxSoW5o1tpLZNs5FIrphd9V7vL5Ntq0QqApZnCBL4N5lKJmKlR2KXtExasBdpJ75WH8ozGxFHfqeTrC2hTbZe5qesz10jrVaPm2iiVErvczz3HW9mQ5CAU0lwo+rYs3g0tHa4tMK4NJFrPDbtfnrB+67tpu7fameIY7m/FRk/eDtPK7kI3yYOexlyeX3u5HfMI7eaoH73tPNJ3g///DTdxwUAC+suRUUyiyw2ziZUyeAt8K8TwTQFQDYNKvSUgssy2bYXioFACmXsp0BeG+9SRt0fAvk2jYG+zyTplttSXOQGNVGialabG1rAc2CK8CxXlEzNuQ8ewiSwRqwUMqNcYIXKjkiIZDIYnx8jTq37Ux0LammuxdqNoNhlMyrXAp5U1L6Jj5fDnCLBx/rjyZGlQ7BJjBRrpM8eaPotp5wYQvfMsTCZbdy08V/lG52Iv/Jprv/kCO6qzzwJUfwN96fc7xp7MlX/drle7nRtOdVbt0+jqSbIT709A9QqLIPjxUAfqhN/HEXKR2xLad4AQSw7ep0gJHm8p7mV7MpXCGfsij7U0+azxWCx2736WoLANrstNPlCpmyzFupFAAtj5ce63dAWm4xnY0H78mJiCqqh4RWM7HJWpjQFChn9jXcvkLvIeRmsR78g7WMwkXl13bgaKIaIRDL5gER9WIDvDRs6gdaXdrUGi1wSwJrfvag23ceVzKP3a6z3ESQehqO7Rae+ahKDPVItALAD6Gd+r6NcI3/omAN4Fjlge6DMRUDqEKv50fvu9dREj0+CMWmlB0dduvGxp2jZIsuVPzfGN6Lh9wpoRLQ2w6VZwGb4L7t2ao2Mc1QaQtURa5SoxZ5vPZzDsimIDkCtPgAJZ9FsdIJlaWkRREmfR6UuOqOtZg+AZ8oa5VbOLwuG0Gp/6NV7013hPwukFW2SBcvusXqxXUvNNLo6Aj5WWq1sG2lNfLcW4ABT9tsxhWfEGXSjhtt+Go9KUcuE6COAtvh5D/dDC93+9zCs3jzkz7LBK18/43Pj3aY3n7K05nPLhrnQLIOR520Oqrm8f+wE4AzTrmWV87oaIU9XFYA+KG0SxqwIXHezA5Cfoc20mT+leUwucW/3J4aWOjrWI5WPfWgqD8FiPjDL2uHLUbCJ+9ac3KxQsJOYO6uwrzrfKCQ2U6TfJD1RptZpTU7MSgw12nHak5BktIR/phlmx9kOHi75VYcsPs84+VS4PzB5P1uzx5DMkdRJeUWGL097KNOadRQJVo3sC546PqP7yQrrcDpoSOsWKnfRk//+Ou3ybVKcYcobbeWl8yoYoCpE5VtBK5c+eFtXnGrFV/rPh+1aIznL/k+/fRBF1zFBczU+sXndDWgJ3HPZwssPG2IxqsS3vWqj7GRk2d8vMIeHisA/HBYG+5FlBZ7CW6Crtv9lWvQZVKKpjpsQiCKnQC06pJl7a7mpTWpLeQhSgd9n18/j+Cpz2GqydvMk+spKyE4qif1VFsC16zOIMUqr6ap1Ty/LCATxz0SOOZ5uG1THXqnqxk5bFIOWI31gViGnrHLS0Yp0gol31lW2jz4Kop0BpbbNls0w4K5flN77JL5U3pVlVyzvJaNXpT9Ap655Ces52nsYv7MGpxj3/vIczl74oeU6g/QuhxYD72vGnSSv8IOqxUqlENsV3Iex7KNOezl5Ps3u8Rx34UxH6Tym2oWVKxCZNkyJ9kDr2QY9lLDekjItMV7ZbsJnreVCWpSdDhnXSb6EuPZCnBsEMmIS9SknOHgQ9CtVM+AiaVOrNdbw3n8qSyx1SeokiKjDYa3B/4/vS9meyvlSzsZr5tOiw6THX0obYDNYRKvAyivBDY7qqpDqQ9eSqhAL0qjFccVD4bfJLb0mLqf9oLsMEXtVy4QFUW2+1levE4I0Oohzcy/7qxT+ClPZz2r+CVP4C7+cGqjZmJrE/iwP+45OM268tecVURHHgorVCizxPrpo8Je5rGbRiskzwKWQeU2GF4L3cZjzYS4+19K6hMmnOxwshYiCEdH8/lfcMulcplj1i9sg23jgTNXhyFQLJecEgRM0IynFlSlJ52MNDxvnLrVcuWxV64JyHLJX3N/li/uWky2Zmc17F9WrhTcPl1KM+C3rajKjgmfTwOIdG+iAKAOpSWANGCq3IIDR1XZEUBrO5WXqzkteqWWDcSqiMvWpKUmKS1VojYJvNtwXvcIjnKzubHtJKrSI7ThgHW5O9ZGVnA3T+KnPJ37714CT+LB2UUNuNGVM/vv5/8B/Szg5VzJAEWR8sNtBYAfYns3n0k//13XG3hrz+fTElkCR/0v+9SnmszMWC1oruXt1Wppwr406tGaneAEB+QdHWTAQJLFjh6myNjE7U7Wg7dejsBPgTsVgJwozmYRjJlLq4XOolbz3Lbyvei8VX/c9sCXT2rSbzFpfpR0AtbMFZSV9KvmOoByNfKYVQ2mRFqHslQiBN3YMmddBJ22p3h030p5VIgq1Ech8ZDzvUaIuLWmAh/apgSc7Y49cbZb3Pb1Bo3Xm30eLHjL/tl52k/2fwPTblzYobICwA+jPZub2X58D4u3DEKXq3LOZR78eqFyK+5l34jLAzLs8nSA8yLFDuwdD171KMHTlixwL1N/aIH5fYNZIcNC5UwRYLZCh/fAaXXHHR4M3r81Gw2qwKR44lSTipM53jgE8LafJz1tVGmF8mJgFSF5VzV4071DhIRg7biAF09NVKpQkVxzFDff0EqawbFjhJDZUSXQ9NflQX+R/26TTV3vlDFWFlj2k87KzaJrKamtkv9Z2sRy5X7CVuHy7gBkve7FOG+7D+58xxMAlyxKh8mAd2GPWisA/DDaHazgaIZYvOwGB0Z9QCe8dtVneSFX87zv3OA23I6rTrInaMJjT9bmPpF8UCBtPW+7bBLYBpzmudWxqgdIFZTwFMDAUJgIFSCVS1PB11Z4F6haGsVqte36vKCXyejzGFAeh657CYUhlvh7plwpEzhAFt0i79t76ikFMWy26cFx11XgNr9ewUF1QsrfEmlRjEzNyprj7FMqRxkWce3r0Gfto+IUqmdpKKC0vbqeOtkJTalsFgEnA8fD3531eoY4GoCTKOyxZgWAH0Z7Jd92H44HPh2Wz+evuInTeF7phgBMpmoOeL6X4IVbZw6yk5/kLF+4GLZsh4WQqj8y8jYvj5MCZjSa6MvTf9tq8s34b8jK+MpN6JVmlYAyNoID8Jr5k5et808QALxu9tO5bGFdAakt69ZG4K4zgnbzfyJarwlFdRSa6LRgbOkSedxertjU6xaF9GbShFdv5XIKe+xaAeCz0FrZRx/98Frgm2S4aIWod3U6b7iSk4GwixAtaFUeHWfB6PUeKBdB76DP7y11w4g5yDgw19EpXSPAsANxnStODCXlhhQdFpgnffKrri6y2uea46+lyqiII27zKhuftCvj8aqTEb0h4Jb3q2o/ugaBqy0sPEQavbm7GuYcUjXPUkBFn8UxtxKqHMl7huCtmzzuk36b2ojP5dLtz6/iDZ6CmRS/j99mhzmf2qxOoQ034ljpz/W4Qv1R2AwAPEmSNuAWgl9yVaPR+HCSJCuAzxHUu29qNBoPPna3sKl2A24STcmL/ITaZM3lPykRZIF6zyv+8/A4maRTY+MwfI3hvte5/2VgdJOftBQIlRyw7h4M5yuXXLSmzKpN0krvJleIJgU7unAV6W2Nx1L4X15CKEIhCqFGdmghpYcN9onrSsoEkgJaAXwbwcOuhzSzmZwk6nS6CJy02mZpDR1T7dC2Xs6nkUg6ohiJ2urPUxsP92xgMMxj9C4meN/abwVwItzzhsezlWN5DoUVNjMPfAI4vdFo7EmSpAzcmiTJD4CPAh9pNBo/SJLkHOCvgdMevqY+dqyPfroZcp7aIKkHWl4GtQ3ZHCaxlFi5UOKMgdbsNik/PuLLrrV4wMVpwFXNB4zGmmx0pqiX3YYiyICjpQssL62ep2T+amR7JHm/EFIItETLtZ01ecmiJuSNk5U7yvOW9w0471sc+KA5lj2HvPwuMgFI4No1WTXXL8CPrtMqX5qKc3SMKjAAOzmaXRzVbOvCHmO2XwBvuEgfCaf0zjf8nwQMKuZV2EOwpH+C5/Zdy25ewk+uPwvWkyYpYgIYCAyHzW/S4TlmCwI2ojINi8dPMNp1eKleH5RP8Acdh7JXnpRFFZic27RBh/dKx+4iBdIuW4gAwiSheGkbiOKrtjNKiDgaNOexVdq1fj2u3qVRqUzJ6ofTwwuY03Xmu42OtNukIC7apuaKI6eh/kaSmPLRom70WZO0cX1PdUqSOtahQ9px/AhokCznresHNyIZhlP/bCOn/uMdFFYYzJADT5KkBbgdOA5Y22g01idJ8jbgh0mSXAYcAeRmBE6S5PXA6wEWLy6E//uzk/GV6t9F4HjFB9eDvjuOnCwRcA7yJwEt/Vwy23QtwYHrIA5QlKbUepWyTgIPXYeKJt4gBJ+04OgeAbhtQDvZ4BXM92UEmqLFt0dFLjxvPWoUOHFeFo0A4vwtscUFi7VdqnFfjIu09JPG8+yNI+jOwejRrY7c1vKU9FDFpuNRSY3ww7WbZTLlC++DIm6msNhmBOCNRqMOrEiSZD5wdZIkJ+BA+e2NRuOfkyT5E+BLwJk5+34e+Dy4UPqD1vJHoTX6Wnki53EaN7kX177IKkDA1IyCsc461mfvJT/HSQVPf3STBRZprAWcWicu1yow8sz2ElEATlqQedCcKw+4Sjjw9pOSCtGfLhAo5rNnUj4ttrLoFs91T0ne1ez8OR0FMLWivLjtmk+OpY4K3O8QWy/uHr4fqsuOALK3s7DHth2QCqXRaOxKkuRm4I+BVwJv9au+CwexjtNj2H7JSZzNNn506zN4zttudQBwTQgWsSNyG2qf4oqG8P7rJNDbFrIWWnlhuQQdLyZMEoIDF69AYRxHcQicrAxOYBPzwtpfbRk06zsJwSmiMaqEvCKdOM27ZHfjPj2AvyarIU9zdOflHOkMHLQF/FgZEy/rECG4npQDnxKRGk1+pkoSi6p1sty17fBqTm8PJo/MRAj0sVZWqb2zoNEH7a0Pokcq7FFtM1GhPA6Y9OBdwXnZn8SxhM8CbgZOJ9VIFPZQ7d+HX0ByI+7ufhOn1uh04Luw0yVWGjDywTyn0DrBHT6/9X2D/nu7KVU2iAPNfoLnLX5aFes1edhG0FBLX+05cyAAvDhuyILfFtf2mLuWZbzmnGAlm8AqD7jTHN2e9imVfA4SrzpJeekqVHr8NUrtouvF8fopX14zlI3mEkQT6VprMDlISGfbbu5JyZ1vtBrUOhXP81d0f3D7Tk5EHc6pbl++D8lWeOYbfgTATwoNSmHeZuKBLwC+6nnwI4DvNBqNf0uSZBfwd0mS6BUugncPkiWbcYB6q19wCi5aEFxq1ZYQSWkLBaf5ScyvWhKY1oLXPTbhAQwcIGuSUZ6jdNaQfUIsTWAkhylNoMK8Mk1rS4GRVx7MmAXluM6k9aan875nYmPjPqze0kHj2e/qDCyg1gzYztg8VaIRUEe770jUmZigonJUezPNg74Czn3Ddw7gpIU9VmwmKpQ7YWrm9kajcSsOWgo72KbkSIuBzcAv/PJluKIPE3BsFdgOA1sClZL+rwXNdqUX1wXj8n1UluMmxNQhKHcIhPwgJdIKLJlQ7+MJyZWUH6WGy9Miz3NH0I1PGu/VJtzSfxtAE1slnug01mwSMnffpcCIn2w0mQmpTh0FlH078kL7M6ZsgJYqIZu6dl57tq0qDFFSJyH9eAchCrTmC0QAYwMwug46jgf64GmsNzm9Cw+8MGcHxIEXdmisetYRtK94wFUn30pQcohfnsBxo63Q203qQY/uMDmnJcczEYiVlxIAuIsAHJZOkapE+00AZ/h9qmZ/KVSqgdOdNADWDPyaUSeQBfm87fLyi+d54xUMX+5Ls41F/HKs/bYJtmzbreefdgwl0qr1Y9UQfZpW8TG0TaXV/xbdhBFJiTA3cDwukyCECd2roXK6j0xdCbe85mnczkp2u/IWhRWWWgHgs9DaW+swnKR5r22OkYyqoU6Q7tU8taLhvc14B1ntdR4FYKMLrRZZ25ZwkaFRIifq2SjMB2OxB76/sm0z3aZW8zr2ev55tCyPLpmJTeHoLS9ez1JZVE3BCY1sSmRlm+042eEG3MhBk5iFFdbECgCfhXYc/8099wLLfZ4Q5a8eJguuXbgJyCVufaVE4LO7/X49ZBUSyt6nlKitBKlgD0x6aqVW8/RLF3AtDgSH3SRkM0sLS5j82nkBNLurUz3nZl63zK7PU5NY04QhZD3v2NPXMkWYpp621DJE1ed1H/c4EC6rGpCCmvK29Zrw0X5/fBWdOMuv/3iOsnZ19usa/1dYYbEVAD5bTdXpO4FTcXTFMC6TvnJFQ+CtF+OAXMCxmKx+WxzwXQTPXNuqys3WrFc52g/0B5BTWL0KKkMA6Ipya9egoyVEQ5ZK2ZJn1sotM/fcm4H7/sA8rRRfDzLBSsnJDDMetKFSAOcJt0HlXh2IrPRRn83IJaVYRGvVnPyz3OkSiV36r28B4D2/ckU9Gk+Y7ooLK2z/VgD4LLOLuZSf1z/oviiKT0Bjw7mrOABuIcj8BNriuJV4qd0cR0mebKY77+GXgfKI87LjCEY7+QhT149NQHkwuyzj6cYThjmBL9ZD3h8wgwNMTZQ2A++yL59WgTB6ac/mgZlCTYnvV+cSd3gjhLJtthPQBHA/DsTbPYXTTUhIBsx9/E7/6ej8CyussBlaAeCzzI5kgo7b9jmw6PULlXtDqUk1gWiV94txKhUBjbILglOMbCeExW/HdQQn+G2H/f6LgBuhNuj5dA+iFRM6L2VKWly5051nsj7VwwYvWfSf5d2WDFhmCj7Up35O623WswCfBt004a2VwpYu3D1bhMupCdnoT59/e3Io8vIV+i6zwTqebqIbNyLS/IIH6cnPucRjnIj7DbrhCx95BfPYzRu5gnfnhcUWVtiDsALAZ5ldtvZDzoNrw00alggFCOUVmqhAIID2dWQ9RemvR3CFbt9LACKp93fgOgJ5ilt9bpTt/hiSGfr6nGV/3ErORGgzT3nMthkyVXgqbVPzm0znnaefReF4/rpUy9Ix6fY7cB2UnXxVjnBIFThlmxYWQl5xWyXHyzlHq77M3CBh/qANd88UmTnhv78T6IFv8DJ2M483Tr20wgp70FYA+GyzfoKnuwyn15YmG5zXLJ14XFjAZMPLFDBY7pY3FkG95P5aalDvhGuWPJ/6mhZaqNPLAGs2/CxoxCFURa87egXIzREy09wjlVYykYyTEbDHFkv64mUaGShAKbZJKVEg+7TrGKKhpAYB11GqUzNh8Jl8LOOh/WXpwXNGA3+15p3sYR6rWM8lfHjqBoUV9hAscdliD42tXLmysWHDhv1v+Fi21yUOSN5IyiWd25sAACAASURBVM3ecdxyADawkgtHv0ZpBBfg0wn3repmDnsBOOrksWwIO0AJfvv/nJv5Mf6CX/JEAP6D5zVtwgV8meXczQdv/JQL5YcwgSpvWmqXnBzb0DxjYLnFhPHjwt4n62GCFKbSJel+i935xgaMIsXot+35U4qlbjThtvCCRimWFukynwXQmzx/r4rzWu8nKTPyTN2TpbiOdjVO9XNWkcPtYbdbEtatOYUW6sxjNwBDdHMidzFvdIydHZ3sYj7f5nxOxqXjPZcfHs4WH5AlSXJ7o9FYGS8vPPDZZl+Y+rIrDPYC7qC3Y4CejkFWtW8k6Wq4mpayO6AxnJB46dxEN7ReA9/nXOaxmzs5cUZ5NK7k1SSfgeddfB0ns9lJDsdxXuk6nIfqK6/T76rPw/496NT7bvMKF2PxvhUTaj5Zd+A8tiVQLALrWi149GVbhk0dWTuU7UglPSEhZW1M2QyRqnvKcY5zrbff90SdyjIciJ8Nv19QKcovzNS+lDj1VQ34RoPk/fD+T/wFi9jBXubwDtYCMLE34chxSNbCmR/6N/5j3f8GYPVVt7uOswRvOO7TvJvLmD885t4H36F/eOsnoQRrF70G3pKkI9U7L3sCJ/3iV9AKO5fMZQMr6acPgFdzZW5z/5MT+cPb3XD13FNcqoNrePHDdHPyrQDwR5Ddzckgr6Erf5uhrrlU2vdSqj/AzjndcD68jn8C4PwDOFfjYndGTg/LruQ8LvjDf3Zfqm4is9I6s6Ca6SyP8wagzUV3akJVcsVyN1R8p5GZOFW1C5td0c4TEH2um/8tBJrKJquSl91qtreBVUbBkiptFrm/3y7oZIijCwA/QPvvb/wB+3gScPf0G9pc9d/EddLXAlW4vP62kGfma3DM10c4ZutImmHzovEvTc1L/2DtLg5bUpGCQinsgOwL/Cnz2M2pyb9QBuZFL4GlTuJIRXnPmrjU9l0ecIdHjJful43GOvQ2HECqXmfVA6cAtw1HWwjYNX/Qhhs5iO/W/iMGeFf5/0oXAK5TUFUgAYbJq6LJ3VrNh9ADPxp4Br/mOADeyBX5N7KwrJ2WwCtwE/HvhztPeQJ3sIKj2MUC+hljDmtO/pnL0PkB3O/7Kdwk9RDud9a8j54HO1lt5zy0zQLz2cZPrID/XvMHDNHNBhxrIe8fgP9KYAtUn38E7ZseCOmQ3/DwYWkzCqUA8MIOzHoTxqrZCUsbYQnZ8H/roZcVBWonB41NToT95FkrZ3Zae7MLlztEAU0qMnEKIbrUAvooDtBX4LLWg3txpZmXdy3lSA03v3ALIUNhJ3A63PlpF3nzT7yCHSxiHnv44sveHM7ps+NvPWUBS4oKg1zC23kJ32bJPfenv9e6NaewkRV8i5fwk5ed5e77chxw16B2HZQ+6bblrVDtOoL2Dz8QfruzcL+PVEKx2RGTOlw7wdxNAHo7/4H53AH0wt9d8nrqfuU7rvoH9zz04wLrthDoHnXm/3roAbygUAo7MJvrIxmnCamXzWt3dEem5Fgr2YLGxksqG29eCakqGIpGibZ6CBTSCPsfCgvMF+Fevi4CgOuYKoNWw/HXKmihyc1nwQ991qkfcja/Hj2Ol3V8Y/83obCZmZ/TKH3KLHsvtI8/EHLV6/eZLmeNANwqkiyAq8Pvifapm88+E+hbt37eOQVRIjR+iIvRsIVLDpMVHnhhB2Z/nbiH/XOkD/aoecDTfCKiQvr8CoG4wFT5RjRhCCF6VBkXFbBkAboDWApr/+k1ALx79FIA9nbMjGm+hLdzGjfTzwLqlLiLE/koH5+y3TWczZHs48zRm9ndUeEuTmSCI3kOP5nReR4NdhNPZwH9HL/pf5y0VN7mVjJ5zKXKmfgKtP4Y6IcfveYZHM0QJ9+z2XHEvsP/xWv+F7/mOJ73vhucF9uKC3i6wZzYqoVqBHWPtWbet9pknxnRKba9Sj8BoWOw6RIUSNdCCKiTfNRP3hNFHvPvBYVS2Cy3s7kGgGdzE+/r/TT3DQYePOa8K61eTbLML6jigNvK9WyirXbcSyFgr+E4zi6/7aeBPrhwyT+ylwrf5pUH+eoemzZn9Pes6ljPTbc8F/4CWAK/vaKTeRO7aR/0HK+NDdhOtuBHOwFklT/eesACVo2kNNqxIKx5DetlWxCOvW/tayex62RBWsFZGmk1i1MwWv+Mt17HOQxzyVZg0hxJrG76VUGhFDbLbf2+p9F65D6OZicshoW+uMGYmZSEbLRlauKarSduc44s9n81nMcmOaC8+PWw8x1z2cEi9uaWaS7sYNkxF44EIJbXaX8zTzOkXim438qCqEDT0mUCb5lNEAZTk6zJlLtHx9KxVVzDHk8qojYCiA8zPYhvJ2TqNCmI08+DOEdkwrfLjhIPI4oWAF7YAdmuI4/xn14Nz3qNG0qvh0q/U4RoQrMiXhnccHMpbvJwIyHSFEJxCkjVHtWXHsHLW6/k6u+/FNbBjy55BruZx8+8TGS6IKTC9m9zRn/P3i1dabqE57/wK6ziZwyf4b53DeJGSnZEZekFccZbCaBHtL32iQEz5pM7SPPZZ/aD7MSy5kqs5FTHtp66TRURz3Fon5Zof1l/tExzNZKlKvdQk0n4w2EFgBf2oG3tZa+hwhivPukb0AIdi4C6k/5VIBuevgf3wuuFFLe4BNb+o+OzL+Vd7KOVF3I1PQxwz/Mfz3FLfpPyzi86pFf36LEL+RzP5CdcOPwNkiq8adHH4Hs4r7Idvr3+VQ7MVxPAOaY9IAC1NPPyRiELasqOaUHO8swyn7FxCmBbfTcEr1ydiM3QqfZa3b5Vn2iZBfk482ReSmN7PKWugGyxanuMgcMTbXvEYTlrYY8Ku4gvUqclvETLgUXQ0UV4ASwfqjzayqzYAXTBRf/3S+xmHv/zpuO5/01LWMs7uJy3chw74IQiDP1g22Vf/5ADpRFcp6oi2va3sikCYhMYitawvHMJ11nv8cu0foIsvQKhSMkIIXmatRpTO5KYW1c74va1k28xDSQKyEcIp3M0nbl7h+3yAsMOg+13EjNJkjacKlbTAVc1Go0P+3VvAd6Muw3XNhqN90x3rGIS81FqP0icimA7Tne9GfdEdBI8J1vpvhPHny6Hxhtgd+eRAHS0PIS6bIVl7Dj+mx4G+Rbns55VPJubOPqqPe532UD4reStSjIJ2Um/vCjZPPCywGw/xzRFjaz+Wpz1kFkf7ysaRCZeWoFZduIxDuTRMW10LbhrHzD7qLOKrydWr8TXqP/3PbyOxkOZxJwATm80GnuSJCkDtyZJ8gPcKPkFwEmNRmMiSZKeaY9S2KPXntuAoSQESTwf+CjupVTxA/2VgJW4l2URvKDrO/zeV1t/7Aj0Hl5bxS2cxl0czU4WXzXI4pHvu7mHu2g+6VYyywV01vsWT20B1nq5mpyOjx/noImBdZxsUI49pvadINAyEMDb1mu1lge+dkJWOnA5GDYBXAzgdgTQiuP8h8hXwRwG2y+AN5yLrgGRCpk0gD8DLmk0GhN+u1gVWdhjyV7egMsT90Rtx71w8Quh3NntwLOguuoIbtn3TEZ2eg1335SjFvYg7YuXv9nRI/0ECkPgaDluWV6ul+k+QwiKKeWsaxZwYytK5XnvduJSOm1F2KpjaTffLfjGqhVx9bZt2wnzMirsoVzuEGISSkwF51h1Yid2D5PNiL1JkqQFuB04DljbaDTWJ0nyBOCZSZJ8HHep72o0Gv+Zs+/rgdcDLF68OF5d2KPJfC6IL/CnnPOR6wBY+OEhuJ6pE1M/hvrqEltblrKhT5mACh98praSdXQzxG7m8VOenVn3RH7pQG8L2Qk9gZnAU5PJshjsIOuVx3nP62Z9XlRirOiwZj1teeQaocWecw9ZoKySD5xqm01KZs9dxwG02twdds3cD3VI6qAss6dOotn8wCG2AwrkSZJkPnA18BbgW8CNuAwQfwh8G1jamOaABQf+2LK/4AN8dOsnHJDU4cvPfRnncC3HrB8JXtDlBM/74mLCckb2mYTXXvxZjmYnOzmaL915ES876ctcef9rHMC8l+B1C8At2FgKIwYhC9D6XjLLpYG262zxkGbTGBYYrc7aJiGLuWvbNk18W8lhlSw4xyONHnNMW37Qhs3b/2q/Ojvx8jZthJ7b2Pu+dvZy4Kk1Go1dSZLcDPwx8Bvgex6wf5YkyQO4Kq2/OwjtLexRaK9e+41s+LzolI5pdyssx7749Tdz08v/iL3M4UtclF1ppX0WXDX8twEtMZBitreg3WwiTxOESoEg0/c8RYiAfIKpoGzbpElUq8VuMccTnx1PvooW6SSU0rPUja5VvuQJ5ty6hiGywUWia+x9aDbJewhtvwCeJMnjgEkP3hXgTOCTOF78dOBmT6ccCexsfqTCHhP2pMQ9/CfCmf/xNKc1lkRNiYGGSF+sr17xJ+xgEQAfPBztnYX2Lv6KVfyMOexlPrvY68tCjzGHFuo8715gBHoYdOv+HuZ/YVc2zW0e4EKIhoz552Zqk5jHtiX88s6RF+moggoC0bhDqQL34mSoEDp0myI2z55ANhIUsp48OI+5n5C+QfdHwWSSN+raYu/cqqh0P2IZ4cPsfU9nM/HAFwBf9Tz4EcB3Go3GvyVJciTw5SRJNgH7gFdOR58U9hgxacLHYc0zfpY/pFa9T2AJ2w5Z02arXcuZHMs2nnzLrwF44ppXcCxbmcMYc9ibpg3Y5dU6qhb05Mt/DZuh0Z44oe8VZKWBFvzEe1ugzlOktBPAOZ7EiwHbUhlKTqbi2BA0/xBUH5HaZfPfOTVfB1DZAstfao7bxdRz2WuwE5V5pn36CJ3WiWZftUmh9gOEFA66B1WzXtfR5/+GeUSoUO4kVPWyy/cBL384GlXYI9jkwWgiyg6tLU/qOck1t/yMp6/4mfvS8e1D1MjZba9739cC+HQTogtX4gDxXoKUThTBRtx9tZWa6kyNOoSs2iTmgeWh27wjsVzPqjtshGS7aY/AVfprO2HoNW0//wpM4nBzIU7eljlP7Knb9lvVjLVmHZSqNXUROpUunFpKWvQlft0Xo2uV7FDXaWmnZgFDh8gOYwxRYY8mezuXcBFrOW7cF3SYbmMbvjwCJQ1rNyaw5rE1iLuDJ9FDC330B/CzNTetBz2Ic1XVKeZ5f4NMTRBlj5M3QallNvrQgnYsCZRE1KpQxIP3mGU2sZQ9j8lEeA/OkV1m5XuymNbJC6SJLeb0BeI2UEnHWmq+34aj+F6M865HyGrbY9rJ8vKH0QoAL+yg2JO4m0V7fwOdvjBD3pOlpPyWVvkmzntcBCyGH/FMzrr3Fm5cuppn89OHv+HG/oaL6KOf8+//F+5Y4AjZk/dXl/Eh2nx2seR197voVQHuKEwOhcLNAB3tuMm2xX67EbJ5su0oR1yzpTXkWUvxIxCtmfVxkE672d6mkz3e728FZT2EzH+6DksxqK0lHFDW4NgSPNVXOxpzqlMqok1sJx91+GCuUale85Ql8phbCWC7Hbjc5bDvUJrjVtjir2XZGbiybbGHH3Px8fUfJisAvLCHbOfwPa77+tfwqcLdw65UsDZ4JO9p24ID8BrwTnhO/63c+PLVAHyLF3ITp3G5apU9Ciy5t8Erln6Rd3GZA+/1Pv7N3p9FUO6BstIRQHOwzvNEx81/G7RTw3n5SiIFjloQGMpbVtRiD6FogTrfceAXBA9c59+KA71TyXrdMttOX5O0a7E/bjdUnoHroLpxIG0nY+1EqyYRa+QXsdZ9kX0PJjdB+VRgD2xe5/qVUWD4Lkfh1IBjgTFg8P1u91V9wPv9MSbMuWXbmRXoOQuaUNijwvoJL5QNxpAnB9kEQu1MHSJfD3TBs9v+H9897/kPuUlv5xKezU3MYzcA9/hCw6/jnzLbfY4L6WGAPlrpZQC2w4IFB7+mpSuGEV2X6ipaa0YPtJONOIx54bw0qbrfKk4AwRuHrDpDNMF2gtJCv1mcQtVGKkozrZqlNmGUbYNsESGFsOwEc0xbPEHnaCbXi3l+mbZfihs93OW+3ocD7TG/uuybttmfbtT/P2kEKjfivHGdR8f8hf8/CwJ5CgAv7CHbJfw5XAdjG6AiXlGpRrcQQMImJNJQWKAEzhP3IPPs824K2657GxMnwzVznk+dFr7NS2ihzhP5JfPZRTdD9NHP2cM3A1AvwUBHN3N4ExX2UvGv6xz2AjBab2Vvi5PmHXP1CPNe+H+Ywxgvet4P3PlOhGMGXQOTTmiseWj357ucSx/9PInzeObSW7iAb7Dk6vvd/bmeEAFoOWKZAKyPLDUCYXJT+8STbLFZ6irWUHeY75IjCsh1LlER9/plyvF+IgHMRgkTlp2EwtNDhIlW0Runk530th1TyWwnq+HcZ68qGV4LXV3An5Ct8mQnT1dBeYVf/xU4s90dY9uQa+ryTih3wnA//LwGP8eFm/9HFVZfA11nkZ2EHSTb+bzj8M7ZFABe2EO2k3p/xVgVKqrQYr3vTsLwv04An0EC4HSa/QA2wdEv2AMlOP/F/4KXiQOwgjvYwSJaqHMydzCfXSygn2N2jLjK5VUo1WBhzxAf/8XHXJZE71meumwjvAJGzzoy0/4LTvtnBwxWI9wJyQWTsA3u5IncwQq2sYQ6LXx00ydSj2zdCS4NwAV8nf/pd292o6+VrZ48XnLh/Tz7irkc/bs9rP707e74P8Zpnm1JLgu6cQi41sdqHgv0cfkx7R8Huthz2X1GCcEr1lsWGOrcIzgALeGkoFJ1aJ9Wpo4g9Bsv9+tuMO2KuWv72QK4HxCt9zTdsD91eRCeOmSudcS3Z7Fp8xZ/DSfiPOo+R5kMvNEX3V7kOoIz2+DMIeC1bvtRRbTWCblSpLQZIlsY+TBZAeCFPWg7kf/kUt7DH9cJ5dOsesJGxglUpEywQRp6EUb88n6CZ3mX+9xah5PX3EHNoNqdnMga5U95L6EAskKndQyBxDDwHej43j46+vaFC4mH+OPAHpicX+aeNf+LfQ9xrJy22b5t4v1l6swgC6wWzGxZMbuf1jUzWxcS3G/R1nzzzLFl0ocP4XjuLkLRX3nPOk9M6ygyUr/zDWQ11rbzahY8tIkUkO/C0SDg1U4CWc0jWK/+XoJDoeyD/h72ftFvZ2kpk2mw4zKz3kaHyimZBen7CgAv7CFZH/2M+RerbCu22GQ/Aoth3FC9hvPe4jBtBUt0Eryn75K+PMfxG2iB40t/7xbE8jAbZWhBWf+3EHJiLDBti4ON+oEdULoOjl/+P9AO847bzWJ2ODC+MVznyqXOqz55zkaO6tvllr/sj1jyKb/NJjjmtBGn4b7NLxPdobkCyJYBy5Pu9Zj7ZRUdnWQByO5jgdHK4J5h9lGbluJogk6CtylwrZL1suWFV3GabhWiXoLzfBXhqG3V6dyAm+xUTrsus51NhqVO2IDtFk/b3IOjOE5bietI7ASnOqlu37Yuc51SnIgiqptz22CgbkK6B623ybREJU3JSnJ4rKhKX9iDsmfyI37ygbNc0EPda7/tkNIGeYgekbbW8pvtZDPm6SW04c0WoGX7KzQQe3FxcQDLy4/484ya7SWLs8fWOW2JLcnsOs16W9lmgHCdAtm8qMjYlYr3sfdLNFQPrkOSh2k5dG1vNdnyJDvNMl3jZv+/jexoSXncBbBLCWBpOwUVBbZcvdpUIs1HPrkJyu/E0UgrTJt0/PU4ykyAi7++78J9Q7AXeHw7VM4mSAQlndS1qNOIc7iIVukm23nkubFxQYs4wZbO9apDg59FVfrCDr5tgdFRV0KtHIOdJtjiSLXY281TXNgXJ+8JnU6lAVML58b8cpx3vJVsxaAJQlCNzAKuLUAgrt/mxraKjZLZJ+aiIQuyMQ8cX6fd1kZcKmlUHGiS14lJUQL5neA42SAepT3Y7P/LK4+TOymQJ07wpHZsBiZ8J98PnEuIKDUjpMntUK4SfiP9pqfDwni00oLTgds2t5KVT1qz0j97//IChuwoQvdznBB6P0usAPDCHpRdxD/ADhgehw7r+dmq4BDSc0J4GS21YTldBYFYwLWZCmNvFLJAb1UMmG31wvUQJuTUhvujbe3+zbx40Qc2lB2cZ2ertMdmr1ueoz1X3n7inmUtOCCyWfUEpNbDF5Wgc9p2xp5nO1mwWkq2Yk0fTuYX0zO2k7Gh5qVoO3AcdgturmIzsAF+/jU36NkG/A8unH4h0DsCT11FlmISv91O8M7VcdnRzyacp91NCLQR/21liepw7PNjR391c19sp2c7j1lgBYAX9qDs/K/8C/TAsUtxPKhASVIvPeCSX+V5k/FnvaAQ6Aybi9nysLFZULdceKs5Vj9TQUYvbNyRxGC6x6y3IewWhKs4jrfDXIelE2IPMj6P9Xyt4sR6kxIqN+sE1T4pMmwhjbhsme1sT2BqUV87CW0jNe1IRWYBzda8rAHrzPpBv+zHHqz94ucACxcDK+A+BYTlZVbsIoxm1OH3E2ih5eZcq3GArlgEec96jrqY2umLjtF5NdIQL68RSDOp5iG2AsALOyD7Fi+kTgsXALyQbDixna2fzmIqwcrdmgE0TPWQIetFxRTEgTzdcYcyXX3lvM7Hmp0gs9s0a2eeyau2sjyBtVWQWM/Znk9/FkzVsXaaZW240Hh7Xhu804brNGpMpajy7vE4oVPYQkgQZTs/D8SVQdg27nB0nqFjFr7CXJv9zdXZ6liagNYkK0z1jE8gS3nEz9h0unltbztpe44XHv68PQWAF3ZA1q2U739Ddqa+mVnFhMzqirUuHpbGWeXywDteZ48n9YSsh6xnqfOPEjoQC3YCMeUAUc4NcBSDzmvDuCWDlIeolKV51Iw88ryJTAvYoziQsZVghk17BfDyNO0Io5Us12trQYIbHSk5lnJ2q8iwgNZ6qLb0GWRzqFj6Rvd40B/rRH/PlKTLq5A6znARj6lpItJWz9G12HPqnunZkrzSZjyEUFBbdEjec6T/MYhb0G5psn4W2CxpRmGz3V7CV+llkM/80a3ugbaluvK44ukmHy2fmBeAIptOpWGXx+fPm8QTndCF41B1vAGCfM6+sOLe15CtLmPPIQDWyy3FBoRc1lvM9nE7Y28xVo5ILSMgFwAvYqqH30PoPKyHnTepKFPgzijOU/4uWb4YnFIEghc7gbtnkOXVLSCqgIeejR+S7Vx6o/3VPjsZqXulSdQJsz4GX1FkVl0EsINw/1fnXH98HPvZ5nJZa753Ac+joFAKe4TaWbikVdaTmY72iM0+cfsLKLF5qw+WjeJyWQjkfHEEJgjRoSbyM+VB7Qghztw3nYkaiCcAY/DOU95Yasny+tYLtbSGnS+wHUF8bAs+Skv7Hbhz0OUIWfUKQij9MrJyUGg+2aoO2MpCt5rPEPKmxPdDJtrIShVlcQfaggNp6wjYa9W8RZwdM36eYk/bgvc1BA5dHdJ2Zo0VAF7Yfu3tXMK5bKCHAefRKHBDD7UsD9SbeTxab+V1AtL98dd5fG+8TLU2tU7abXG7esmXEVLcqi2LzPmtJwjZiDypPeK0rDYCdAFTJw+tN5p3TVbqqMhVmxTsx4TJNXnl4wQliD2W1YjrXg2a774NmwedEmQvMPw1t6wXeGoNeJE53jghAtFqy9sJkZmb/TYqvABBUtpJ6GjsvZMpRr7PXC84b72dQEkJZNXZWhpOn882++dRJ3lS1Zjv1rXHz9cssVnUlMJmqy1iB7uYzwVn/nMAb8gvKjCTJyqexLPUSgyccWeQ5/nGy3qYGphj9bt2InCQAJCKEOwnqA7EkQtQBThSZ4iaqZnjWE24pHy2mruOFc8D6L/AOr6X8nC7CJGuaqdoCSuRFMVjaa6S+T4CP7jNqUHuwYWnlwnFOJ76IkJNSUtv9ZHlvxXqHudRsaZtu8kWfdD++q02+PXnkOXg1VENk50P0DXr8wRTCylPR5HkPa83+OMocWQnIfjqQCfHH2Y74nA3oLDZbcn3oEVvpSRcB2IHQq88mO3zzKo99GejCucSXl5L02ii05aDm05ZYzsERTjGoFDK+dM+MFWSpgm5IRzXvCfnvEtxIKoQb4GyvPJmJiqhk0wipvsIA5RMJaXjyXaa6rTiTlujkTyeH7L3Qxx7F9lIylZC8eHOnP3sdrJmwV7N2mBHes3AG4L+3ZqldVqYSoEdJptFfUlhs82Sd7v/b/3N592HH0cb5HG5seU9YXme9HRP4kzpFJnNqaLUn3rxdjBVPbMYB4h7cNc0SDa7nf5bD1JUgoJq7OTeUqYmdrJpBiwA2glXSwfpvKIlYi9aJg91sfmu8P24g9F+Go2cCM/1fO66/lDoQHV/GSV49DaEXO0bJfDBOp/tQKxnbDtVUU9W9SN6RfsoHYE6BpvCNdaft5hlsWQwbwI373nSdltwtEwvUyN4ZyFazsImFTZb7B8vvZBj2QZP8gsUthyb9Sr3x09r6B8rTppxjHbIOlPv3Fd5yUzwKRJQlMMJpj2QDftfba5F3rhqTbYQ6IMqWWAWjSF5W0xbiBfWeaRssdclTbPaqXZsJdwztd3eX+uhlsz+18NkP5SXwJaNbtPlyleyBBfS3gmrbVZBgeUwweNWpkdFoFolkUztsdGOapMooQlcR6kIU61TR1jFdUYjhGjQFrKdct68hE0Gpns717RLv4l+IwhzCc8wx7d1MmV6BuaaZU85/BpwmAGAJ0nSBtxCeDSuajQaHzbr3wVcCjyu0WjsfLgaWtihtze+7ivuwbZZ22QCvrzyXnoxYwDPA+n9gfL+ntA4ShCmStEgyMvyvNjpTB6gDT/XtdsOYpzggWPaZBNX5Z2z2b2x24sntpSQtvN87eQGGB5xYemrVpg26hijsBtfiUZtG8KBtTpUHbffbKMO1042Wlopj76IaRfrCeteqoJPv18mQI+DdyA7GdrM7ChB1JidKLfe9EacPFJ0oEZkeaMcdaLqyGaShvcQ2kwe4wng9EajsSdJkjJwa5IkP2g0GrclLyNmhAAAGJ9JREFUSbIIFwU7i4Q1hR0UuzCBG2F00CWrAqZWdIHwssZALA+QnHV6ISAMfwV21qzmVzlV4mN1El7E+OWz257gj7+DkLMF8gsKyCx1YgFO+4ovH/H/62a9zQgIYeLSXoPukb1X8oJFJeS9WTaQZwts2xLoj/uAVap7CfzcZxnsGnFFhCutDugna7BtEFZVcV64DdixckRwap5W3CjA0iI2ZXB8H8W1E62XFy4g132yFZqkD5c3bCedpV2v4qiOvOIX9rnbjvvNbbTpHpw88Fz//Qayo7aY/1YHOgttvwDecPlmNZWiSWqNH/4WeA/wrw9L6wo7LFYbTSjtAFqhQxNl+ws3tv+l0oBs+lKYSqEIIMF5RdJji1fOC0mH4FHLM7J8ddXsZyWByvUB2SG5hu+2gG9eJyDA3kqQUMZh89o/3vcuHDUgBQlkOy+bUdAChgUTeZVDQD8MvB9uxuHO6nYYrjov++eD0DsIC7vhqSsICaD8NVX8OXqvJ+RlV2ejACarMukn0BSt5t61mW26o+OIatJ2VvVheXNLsUAYsQDcYe6Bqj1Zmsb+zqLAuv3xNUpoxXUEdmJyKY4Ouc1vv5wQ7dos4jKmumaJzUiFkiRJS5IkG3GP+I8ajcb6JEnOBe5rNBr/tZ99X58kyYYkSTb87ne/OwhNLuzhsou5FP46CQsWw6RNHGQ5xpkoLWKz3rTMetmS5M3E24nbElMO+msz28ae1UMxdS7xhKRARhN6OnecMMteBzS/nzILgj4ASsq7SVx+7M1ABUeTqGINfQT1jZIySZHTR/A6LTWQJ2FsMcs1cZg3EZ0HcsowGXuycaFkmR2haH8dw1dLSq/HPj925CKbIAC6zCa10uSpRkhxdkII1MosnDGcUZMajUYdWJEkyXzg6iRJTgI+gIvL29++nwc+D66gw0Noa2EH2Vayjn/lBSz8/hC0QstZ/9fVlLwQ6HeTX2MTUB7CTf4o2i4GnemeolgFkEe1yOvRJF/8EsrsviPRf3s+gVwNx3fK9NJaGd0goR6m5XhtCtqa2U7XAc1HB1aCqGOuyVnWjPKRGiOmZTDL/eYv6QHOAJbBmbYIgqrlnGqOHXc6zyLr+bYQwFI01zKzfogQrq9jCujE/8e/sc4lz75OdgRitfN2f2nYdX6NyvDXpLZOkPXEd5hj2zb8glAxqB3ndWu7mTge060/jHZATWo0GruSJLkZeAHudvxXkiQAjwd+niTJ0xqNxm8PeisLO/j2vIQnXvsl/p63cMltfwnA3373zx3vbXJcVKwcTp6lzFZSgeYZ4fD7LsLRDxa4bN6RQbM8j8LIe1pjKWOdLIcbV1/XkH8jWdBuIejnFpCdBNxDtqzYhDnWIoKkTtevnODjUVviziumpuJsf7FXLgrIH2f5GQTdtJ1XsLy5OrDF5jpkvX7ZBM4TV4Qm/lyitOz2qmozQfBk28h6ubp2y13rGuyEpp4rjbzi3/1+HNXRhfttlElRlI5C5ZcQPGg7TzHqj9tCGGlY5yCOhG2GhnaOYpbkQJHtF8CTJHkcMOnBuwKcCXyy0Wj0mG22ASsLFcrst98ynzolFgJX/tFrspNtreQHjsDUBzyv4glNlk1nzYbd8QSoXnwbyq6hfB4tIS845uVjT8oqKnSNdUJxW/HUtp32c9VsZ8PGLYhpHxtSH7fBAks7UycSLY01jgMxm7MlVonIbK1Je85ewu8dr8v7Lhme9OZWGhgXcdD90voq+Uhjw/w7o2Uyeebb/TVuxHnlK8mOUGTWg4fQuQmtFDhlR1nxddvfzCpb7P9ZYjNpzgLgq0mStOA48+80Go1/e3ibVdjBsOQzOC/qfvjYS95FK/t4LfuAfe7FGMRVX/cPe9k/DR2WK16E82Rib1uW5y3HkYVat4XACwuM4wRMecePP1sQVzQfZAsVDJtlUpHovOJOlxDAFoIHqorjVj7ZR5CjaYJO+mR1DNJP/4JQ1ktmiydbLbNkkDGA61p0DJuWVhOz2s+G98cWA5xS+Q6Y773meiD7e1qvGtwI6mRC0QqVn+sgdGbav5OQMye2eIK60yyzk95V8wfuN1BnEkdl6lnU+i7f3n4CFWOvyT4jAmvbeeie2qCjCeC42cME7xfAG43GnbifbLptjj1YDSrsoVvr8CgTazthdYO5K3eyZ+d8zuQG5vN7jnrdmAtq8fUIJ80Daz/LyrEnaC1+euKscuAe/DxgsdVpYh67RlCZ2Hwmeolsm2IggKynLoAXaPYRvEi1f5hQ77CGe+lbouOUcOqFfhxAnOOX7yDtCLndtEEeqiiEzQT+3ZZD07nrBCpJJdvsfbGTyTvIdpaiCPr9sez1CcyktxatIa/Zptm1pvug9AB2xGF/hyU4cLS0jdLdalQSjw7kZQt0Le1h8+vo3tsJ0+W+rdtxnZrUL+pIdhAUN5v9teq3AufBd5HNp2KjPpUADZzUUI7LOfDbF3amo9fZYrNsQFDYQ7XW4YB4jW8m8DngjbgIj824h9x7kjFgl/OehpnMvsf8oM0VEYO3XgibtjOezGt2DgtI+zN1HGqT5a1FmwzhAGqQzORgxhMTt6r83gI+FXqwaUZtdZy8IXqe2d9AbR0lC3gwNb+LlgnsJIOLj2kr7MjkqVrqY5jQ2eneWYmj9crlrc81y8X7K32sMkvmXWse5WW3z6O54uyXKqVm501k6oxjqkr3Ko43aGba36t3fs1xLjJ5FlkB4I9AS7bX6H78AHOP2M0StnHTPc91OuMqHPkiL9VUYv9NBO9oE2kukDxve2wcKtNJ7Syo2Mkpy+3abWLOXMvzIjvt8SHrectsBRZ7TKs3lsUTVJbr1jJVj7GaYlEsaotA0aYREK+r88QqBeutajtxzvJ0lT3qRMJEnq7PBrXILHdrJwLtPRPgiB5RkFEM6KIELJUzEB1DbSwRvHDRG8oeKO+3jTA66SKMkAT8MUeep77RPtKb299yhGwnGUcEa4QFbkTQ7duxmKyVcJRi3gS5TLx6BzTe6Rbt7jySoZZuhnB/hQde2EOyzmN20n2Ee5wWscOB9zpgEHZ/+HFuIzt59Tf+s1GXWG9bYF6xCXvsTH3ezHsMvlapovPGL0gsFSPn2HEq0LyUq2qXKAALzsqlrfJaQ+Z4scpF17acLIhYmZyPZGQ5gUfvMcfqI3iueaMDy2t3EvhmS63I5OXrOLaTEFAt9ustz2zbDEFKZ5NLxblBRnAdu0BOx7HZEUX3aKQiJYcoIGmx9SwIZNWR2+hSHRuyz4Xyy9jr0LVpf80/xLnia+ZP5xD11IL7jUQdiTob8Z/tsx4/y56SqpfcX+vEPubP2cUT+CWzzQoAfwTariOP4egH6vzy/U9xD6hAoITj8OS9xl6nvKecYXsufWKXxXzw/nJCzOTJil8+cZ0WtC3VYr1qecml6HOeKqWZ1xrn8xDQj+Rsa3OR2GPZ/Ztdo/5biiWe5NRxrLdqO5o4W198bjvZaNuWp0ixnbIF8fg65KnLw+3A3Ws9b/LAISt9jO91MyrJXqeOow6hnrPOtktmfxOZUia04vjwzbgRgkZX+zOfHrfk+f56CXYxnzkuk8yssgLAHwGW/AoaX04cWPsMbTuVA0Q8YN5wUC+TXi69kDY5fd2AtwU/q/jI81CsvMp625ZOgSxwNRu6tjglTNlOVtlt1S4d03qVEwSFgYbaKkKAudY2s98I2VqQnn5K6z9C8AhFM6i2otqhc/biPNSYqpBpcjLOvxLL5WSWiomBXGYLRVsAjTuwvOPnLVdqAHAjjc0E791O9gkA1RHcZa4jTvAVd062I9PoyFJSkh6q/XMJGnWY+gzBVBmpzdmtidk2XA1Lmc0o2Mx8ceeaGQn27b0/fJkzg2McIisAfJbbS/gqncee7WbP+wlgajniPH7RfpZHGXszAkf7stnAl3gfbRu/nFbKZ4fJGgFYlYH1rMxooFwyx7ZSMtEKJXPNSwlg3U1WVlYnqEjieyDAV9UW5UER7ymQ1oRZyRzrHELNzD5cHg1wwSZKtypbSehYuwkcu/hpW5HH/nZ5Sb3sJGm/WTbK1CAhlRzLC4qx35X5DwIFZI/TY9qie9JKFqxLZGkI5SCJKS9dq3KL21QJ6hwlP7T5azD3IY/Cy5tfsWoScM+FlDQ6pxJY5Xn21kkoQYtfX5qAhnViCgAv7EBs160LwoPfDKzzvsvyZHx56+MZe3lG8Tqi7fIyD1rQb7ZvG80Dh7SvRhpW0TBCc040buv+PMK8CVUbpKJj9ePA2iZeglDcwU4I2t/BKkD24Ib2S8lOZuqampnA2Zq8T6stVwdm+ez4/trRlqicWI2iiM0Jgrbd1vnUttaDtu23EaVWmWSTX9lnK/5t8mSh9voF0LFCJzZLQY3jRkv9ZDsWO8pp8qwKvOslKHXMHg04FAA+u+2/Ej73lEoY8lslwkwsz8uYbr39PgGTkdyqbF8ieaMdTAU/G50YdwIxH+q98Mmaz7vSAqUSlPO4TVne5JWW6zyyufz/7Z1rrFxVFYC/xUx7MRcKfVBoAbXERgV+CN4A8jBEAYEY8YEG/0CCicFAoj9MKKmpGH6hURMTxCga0BAwKihBiEWCIZiA3kIfIRRuW6CWkhZreKq33mH5Y+/Vs2Zz5s5p750zZ3R/yWTOnOeatc9ZZ++11167u1a7h8J4pnN62nF+EmE7vw3LTsPj/PyOZsS83tKoHB8x4zv6fGiluS0sOdMMwSinIZF+cJCfp7Isc967Kdwi1mKxloH9V4tWshaJ17+FNloNHYp7wLtVcMdYh2KHwuVk17XWWdra8O4vfz7vNvO5w32qB5L/7QfvWM08jeYxnaT6SnzlnYZayoaK9f/L2TzCmTzB91+5AW6DxVP/6h4kcTAG2db59WmJl/1OzrHAbnRzV/iHxR5Eqy1bE9uHkJnR9obcnyMapneNwYLldDfxLdoDiofVG1ardXrDOM47Izk8VguDYOBTv6g19b2/9lR3zjcpOv582Xi/u/3P9LwWh2/f3vVktX7/IrAQPnP1QDBM6QCmKYparu+X8DVM043VPu36plMLbbT9T3L/z9YvpTsfuUX9nEiRktefI70fxwn6M3eYvQxMDp9ozMrVBjuZgbdOVF9xKMvhPeV+21R4iyhm5SmLrV8e/pMugf2HBzdKa6Yw4E0zmE2TJ+OZoshVMRcO9vhYa17QDjXj/8zEyW698a5y/jQCoayWFO/ABVA8xL7jtMz/2asDsCqWO3olhcHsUBjOFkUHmF3ncYqwOesAhW6ftifteMT9LpPdXBremPgBRZa1cYbipWZGzb0E34G9FCyRk/dBW2z6UXTlC+dhJ5MZYeuLMANr+9qw/lWEcQbWSW7Hl0XrrCToz7vufPIwu2Ya+WPr09zhZWMXrHVi1/wQxQvW+ghabl/vpnEGu9OG6bHD6LTbjE3vL7nQcMkGvGEs4+9cyy2hlrGDA7mfgbkb8n5445FwwIiX+cvtgbWh4P7BKMMbaHsorQbvz5u2Hky+3ZTLaTHWPva76qg7O7/Jbw+4rfO+3JTX6c7ct5RgGD9IkY/azzXp8W4bH3WRTgqRRg/5472MfmKINiFlrDFFUesepzDKpntr2fjIk/hSnbk+1ERlkiIu+3mKMl4arzUDrI/r7IXoW2TGqXG9tXTSTJa2r9XoLZ2xj2f3LTp7kViLwc7tsdGiaaSU6SD692dWlrtMpscWVhpcWyfZgDcAeRTWf/Q8Ltz6GPftBG4HJqErd/JslLlIehn72TpB/fZoZLv83r7W4mvB/mEzw7GE7gT76Wg8Y4zuZrzvoPL+Xf+/rOPNd4jZ6Dubw9POcxJFmJ9hnXwmr3WI+ub8crcfFPNEWm3VR4TYdlu2yBWTfwlFPLKRRobsc7rw/RzHUvjwnZE5gI8n935mczc8EH9bGRwFfNadf6fbf6L7P+mZIHcBe6G9jq5a+vS5wGkwFmvrM+PQ3kKohZ9D4XraQcgRY//XD9qxAULeKvr7xF409sK3+G6T3VLb7qV4aVu8vB1vZTAZv8901/p38t2O/3UGpleHWjdAp9Wi1enVxBku2YA3jSkK4526EcqMrzds5n+ejdRwpB2Fac++dbzZS2IRhQ80pWzwRbpf+tJ4rlm9+o3lK3GmJN8qMNI4ees83EnhHlhNkf9lH0XCrLL7ILZwZILQilhKuC/HCbXft2DsNZi2FpMZQHONtAkjg3fwzvsxdV34YfD2P7xc5sYyt5HV1v09Nua2+WRbvo/AruvvRx8F5V8aHei024023EY24ENkLevo0OKIiWu5cMNjobb0K8oNdtmoPY/5Qf0w7bLSNUP/YjacI8WtAyqvD5Ss+5Jb/liy7dfxRTJJmAko0mlD51hYeBSIH3xj92JZP4EfvAPdHa7+28IAbZ29bNKZovw8qum9b62q1AWVRqDEF1JrZoZOq9V4I54N+BA5kjcAeGPdMeGBsPSXVirTwPZsaDMN4vLifhwDuMQtQzHI5fJDOPfd0t3xPUmona+imALtLULL4h9x2x6K2HcbOWpT5FmfQ9raMHwr0VoB0Q01/re32f++7t0XtdIA9eEjYdL5epiYmNDJycn+O2Yymcyh8E0pooosisdXoFt0ZySEor8gmYBiZiW8uugIWvEEi/nngIXvjYhsUNWJdH2ugWcymf8dvjXHCukGOeA7f3VRCHfp9B3KPDxqrYGLyCvAi3M8zTKg6XNvNl3GLN/caLp80HwZs3wHx3tU9Zh0Za0GfD4QkcmypkSTaLqMWb650XT5oPkyZvnmh8OGLUAmk8lkDo1swDOZTGZEGUUD/uNhC1CBpsuY5ZsbTZcPmi9jlm8eGDkfeCaTyWQCo1gDz2QymQzZgGcymczI0kgDLiKfF5GnReRtEZlw6y8UkQ0isiV+p5kabL8bReQlEdkYP5fWIV/cdoOIbBORZ0XkEz2OXyIiD4nIVPxePJ/ylVzvl04XL4jIxh77vRB1u1FEahsyW7W8ROTiqNdtIrKmRvm+IyJbRWSziNwrIkf32K9W/fXThwR+ELdvFpHTBy1Tcv0TReQREXkmPi9fLdnnfBF5zZX9upplnLXMhq3Dvqhq4z6EzAfvB/4ETLj1pwEr4/KpwEs9jr8R+PoQ5DsZ2EQYxLsK2A60So7/NrAmLq8Bbq5Rt98F1vXY9gKwbAjl3be8CIOgtxMSxC6Mej65JvkuAtpx+eZe5VWn/qrogzAV84OAAGcBT9RcriuA0+PykcBzJTKeD9xf9z1XtcyGrcN+n0bWwFX1GVV9tmT9U6pqmZifBg4XkdpzrPeSD7gMuFtVp1X1eWAbcEaP/e6Iy3cAnx6MpN2IiABfAO6q43rzzBnANlXdoar7gbsJehw4qrpeVf3cPCfUcd0+VNHHZcDPNfA4cLSIrKhLQFV9WVWfjMtvENK1HV/X9eeJoeqwH4004BX5HPCUqvZKEXZdbPL8bNAuCsfxhJTzxi7Kb9hjVfVlCDc5xVQCg+Y8YI+qTvXYrsD66J76ck0yGf3Kq6puB83VhBpZGXXqr4o+mqIzROS9hBb0EyWbPyIim0TkQRE5pVbB+pdZY3RYxtCSWYnIH4HjSjatVdXf9Tn2FEJT9qIeu9wK3EQonJsIboOra5BPStbVEqdZUd4vMnvt+xxV3S0iy4GHRGSrqj46aPmoVl4D1W0V/YnIWkJ+uzt7nGZg+iuhij6Gdj92CSFyBPAb4Guq+nqy+UlCno83Y9/Hb+meW2jQ9CuzRuiwF0Mz4Kp6waEcJyInAPcCV6rq9h7n3uP2/wlwf03y7SLMzmecQDH5lmePiKxQ1Zdjc2xvyT4HRT95RaRNmEzrw7OcY3f83isi9xKa6fNigKrqc5byqqrbQ6KC/q4CPgl8XKNztOQcA9NfCVX0MVCdVUFEFhCM952qek+63Rt0VX1ARH4oIstUtZZEUhXKbOg6nI2RcqHE3v/fAzeo6p9n2c/7qD5DmKmvDu4DrhCRMRFZRahJ/KXHflfF5auAWVsc88QFwFZV3VW2UUTGReRIWya0bmrRW8Xy+iuwWkRWichC4AqCHuuQ72LgeuBTqlqaFHoI+quij/uAK2MkxVnAa+a6q4PY5/JT4BlV/V6PfY6L+yEiZxBs0r6a5KtSZkPVYV+G3Yta9iE8xLsImXn3AH+I679BmFtjo/ssj9tuI0aEAL8AtgCbCQWwog754ra1hOiAZ4FL3Hov31LgYcJMgw8DS2rQ6e3ANcm6lcADcfkkQiTDJkIH8doay7u0vLx88felhEiG7TXLt43gB7V77kdN0F+ZPoBrrJwJzf9b4vYtuIipmvR2LsHdsNnp7tJExuuivjYROojPrlG+0jJrkg77ffJQ+kwmkxlRRsqFkslkMpmCbMAzmUxmRMkGPJPJZEaUbMAzmUxmRMkGPJPJZEaUbMAzmUxmRMkGPJPJZEaU/wIuZCjLGSosxQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_data(interp_nes, var_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Weight Matrix done!\n"
+ "nox_no total mass: 1.4103307834025396\n"
]
}
],
"source": [
- "wm_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True,\n",
- " weight_matrix_path=\"CONS_WM_NAMEE_to_IP.nc\", only_create_wm=True)"
+ "for var_aux in source_grid.variables.keys():\n",
+ " print(\"{0} total mass: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Flux conservative interpolation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.0 Using previous example to put the data as flux"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "nox_no total mass: 1.4103307834025396\n",
+ "nox_no total flux: 8.809495172462768e-08\n"
+ ]
+ }
+ ],
"source": [
- "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)"
+ "# Mass to Flux\n",
+ "cell_area = interp_nes.calculate_grid_area()\n",
+ "\n",
+ "for var_aux in interp_nes.variables.keys():\n",
+ " print(\"{0} total mass: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))\n",
+ " interp_nes.variables[var_aux]['data'] /= cell_area\n",
+ " interp_nes.variables[var_aux]['units'] = 'm-2.kg.s-1'\n",
+ " print(\"{0} total flux: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.1 Interpolation"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 19,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Flux units: m-2.kg.s-1\n"
+ ]
+ },
{
"data": {
+ "image/png": "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\n",
"text/plain": [
- "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n",
- " 32.52130788, 32.51799681],\n",
- " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n",
- " 32.55696109, 32.55364819],\n",
- " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n",
- " 32.59261692, 32.58930218],\n",
- " ...,\n",
- " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n",
- " 46.63519229, 46.63111499],\n",
- " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n",
- " 46.67081377, 46.66673441],\n",
- " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n",
- " 46.70643226, 46.70235083]])}"
+ ""
]
},
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "interp_nes.lat"
+ "source_grid = open_netcdf(path=source_path)\n",
+ "source_grid.create_shapefile()\n",
+ "source_grid.keep_vars(var_name)\n",
+ "print('Flux units: {0}'.format(source_grid.variables[var_name]['units']))\n",
+ "source_grid.load()\n",
+ "plot_data(source_grid, var_name)"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 6min 12s, sys: 2.3 s, total: 6min 15s\n",
+ "Wall time: 6min 16s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', flux=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "nox_no total flux: 8.805639456704551e-08\n"
+ ]
+ }
+ ],
+ "source": [
+ "for var_aux in interp_nes.variables.keys():\n",
+ " print(\"{0} total flux: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
+ "image/png": "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\n",
"text/plain": [
- "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n",
- " 5.22992847, 5.27220869],\n",
- " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n",
- " 5.23384715, 5.27614727],\n",
- " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n",
- " 5.23776955, 5.2800896 ],\n",
- " ...,\n",
- " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n",
- " 7.12778439, 7.17966098],\n",
- " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n",
- " 7.13371074, 7.18561716],\n",
- " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n",
- " 7.139644 , 7.19158028]])}"
+ ""
]
},
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "interp_nes.lon"
+ "plot_data(interp_nes, var_name)"
]
}
],
diff --git a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb
index 8ebefeef0f2536b9f35f135f3a3cdc3085aa81d7..98c4665cccc3e7bca1d5badb56e54ce5828c98e2 100644
--- a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb
+++ b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb
@@ -16,6 +16,7 @@
"from nes import *\n",
"import geopandas as gpd\n",
"import pandas as pd\n",
+ "import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.options.mode.chained_assignment = None"
@@ -82,7 +83,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2666: UserWarning: Shapefile does not exist. It will be created now.\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2814: UserWarning: Shapefile does not exist. It will be created now.\n",
" warnings.warn(msg)\n"
]
}
@@ -219,7 +220,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Add data of last timestep"
+ "### Save timezones data as variable"
]
},
{
@@ -228,7 +229,7 @@
"metadata": {},
"outputs": [],
"source": [
- "grid.keep_vars('O3')"
+ "timezones = grid.shapefile['tzid'].values.reshape([grid.lat['data'].shape[0], grid.lon['data'].shape[-1]])"
]
},
{
@@ -236,13 +237,565 @@
"execution_count": 9,
"metadata": {},
"outputs": [],
+ "source": [
+ "grid.variables['tz'] = {'data': timezones,}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable lmp was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IM was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable JM was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable LM was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IHRST was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable I_PAR_STA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable J_PAR_STA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NPHS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NCLOD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NHEAT was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NPREC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NRDLW was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NRDSW was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NSRFC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AVGMAXLEN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDRMINout was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDRMAXout was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDIMINout was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDIMAXout was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IDAT was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DXH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SG1 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SG2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DSG1 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DSG2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SGML1 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SGML2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SLDPTH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISLTYP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IVGTYP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NCFRCV was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NCFRST was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FIS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable GLAT was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable GLON was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable VLAT was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable VLON was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ACPREC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CUPREC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MIXHT was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PBLH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable RLWTOA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable RSWIN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable U10 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable USTAR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable V10 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable RMOL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable T2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable relative_humidity_2m was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable T was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable U was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable V was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SH2O was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SMC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable STC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_ACPREC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_CUPREC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_DEPDRY was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_OPT_R was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_SW_TOA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_SW_SFC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_LW_TOA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_LW_SFC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ENG_SW_SFC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ADRYDEP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable WETDEP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PH_NO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HSUM was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable POLR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_optical_depth_dim was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_optical_depth was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable satellite_AOD_dim was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable satellite_AOD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_loading_dim was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_loading was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable clear_sky_AOD_dim was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable clear_sky_AOD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable layer_thickness was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable mid_layer_pressure was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable interface_pressure was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable relative_humidity was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable mid_layer_height was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable mid_layer_height_agl was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable air_density was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable dry_pm10_mass was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable dry_pm2p5_mass was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QG was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_002 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_003 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_004 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_005 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_006 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_007 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_008 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_002 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_003 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_004 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_005 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_006 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_007 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_008 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_002 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_003 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_004 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_005 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_006 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_bc_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_bc_002 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_so4_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_no3_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_no3_002 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_no3_003 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_nh4_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_001 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_002 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_003 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_004 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_005 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable O3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NO3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable N2O5 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HNO3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HONO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PNA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable H2O2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NTR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ROOH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FORM was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ALD2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ALDX was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PAR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MEPX was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MEOH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FACD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PAN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PACD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AACD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PANX was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable OLE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ETH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IOLE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CRES was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable OPEN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MGLY was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISOP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISPD was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TERP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SULF was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ETOH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ETHA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CL2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HOCL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FMCL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HCL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BENZENE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SESQ was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NH3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DMS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_I was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_T was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_F was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_A was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable O was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable O1D was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable OH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XO2N was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MEO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HCO3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable C2O3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CXO3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ROR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOLRO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CRO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYLRO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISOPRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TRPRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SULRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CLO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOLNRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOLHRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYLNRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYLHRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BENZRO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BNZNRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BNZHRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SESQRXN was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_dim was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_1 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_4 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_5 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_6 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_7 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_8 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_SALT_total was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_OM_total was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_BC_total was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_SO4_total was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_NO3_total was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_NH4_total was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_1 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_4 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_5 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "grid.to_netcdf('grid_with_tz.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid_with_tz = open_netcdf('grid_with_tz.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid_with_tz.keep_vars('tz')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid_with_tz.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n",
+ " 'Asia/Aden'],\n",
+ " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n",
+ " 'Asia/Riyadh'],\n",
+ " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n",
+ " 'Asia/Riyadh'],\n",
+ " ...,\n",
+ " ['America/Pangnirtung', 'America/Pangnirtung',\n",
+ " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n",
+ " 'Asia/Tomsk'],\n",
+ " ['America/Pangnirtung', 'America/Pangnirtung',\n",
+ " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n",
+ " 'Asia/Tomsk'],\n",
+ " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n",
+ " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]]],\n",
+ " dtype=object),\n",
+ " 'dimensions': ('rlat', 'rlon', 'strlen'),\n",
+ " 'grid_mapping': 'rotated_pole',\n",
+ " 'coordinates': 'lat lon'}"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grid_with_tz.variables['tz']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Add ozone data of last timestep"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid.keep_vars('O3')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
"source": [
"grid.load()"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -251,7 +804,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -390,7 +943,7 @@
"[95121 rows x 3 columns]"
]
},
- "execution_count": 11,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -408,7 +961,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -444,7 +997,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -478,7 +1031,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -514,7 +1067,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -536,7 +1089,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -549,7 +1102,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -614,54 +1167,54 @@
"