diff --git a/README.md b/README.md
index 40d4b73278e4285b98aa816228b408bc9f9eb344..1d4cd57b03feea5852e41e0c4056f8883bef1858 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
 # Keshtgar et al. (2022). Cloud-radiative impact on the dynamics and predictability of an idealized extratropical cyclone
 
 Code repository for the ICON simulation run scripts, scripts for deriving baroclinic life cycle initial conditions, postprocessing of model output files, and the analysis scripts.
-The associated data for the analysis will be available via KITopen of Karlsruhe Institute of Technology.
+The post-processed data used in the analysis along with a copy of the Git repository are published at the LMU open data server (https://doi.org/10.57970/h1y02-bjv70).
diff --git a/analysis_plots/Figure15.ipynb b/analysis_plots/Figure15.ipynb
index 8378deb616f91ca835323168b2d5fd184ef32e36..7c28631302246eca0f200561a0226073cec55a3c 100644
--- a/analysis_plots/Figure15.ipynb
+++ b/analysis_plots/Figure15.ipynb
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -96,7 +96,7 @@
     "    ds_list = []\n",
     "    for sim in list(res): \n",
     "        print('Working on loading data for', sim)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/pverrorgrowth/'\n",
+    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output_3/pverrorgrowth/'\n",
     "        # model output 'pv_icon' used for calculation\n",
     "        fname1 = path+'pverror_diag_1x1_'+sim+'.nc'\n",
     "        ds_var1 = xr.open_dataset(fname1)\n",
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,12 +200,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6EAAAIWCAYAAACm44tTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzddXzV1f/A8de5sbvugrHRXQNGKt0hISoCklIK+LPFFgP92kEooRICFgJKNyjS3c02GKx7u3l+f1w2GWywjY1tcJ6Pxx7bPvfzOfd9F/d8Tr2PkFKiKIqiKIqiKIqiKHeDpqQDUBRFURRFURRFUe4fqhGqKIqiKIqiKIqi3DWqEaooiqIoiqIoiqLcNaoRqiiKoiiKoiiKotw1qhGqKIqiKIqiKIqi3DWqEaooiqIoiqIoiqLcNaoRqigFJIRoKYT4RQhxWQhhEkLECSHWCSGGCSG0185pJ4SQ131YhBDhQojpQgivG8qrdO2cUXk8399CiM134aXd+Lx9hRDP53I867W1u+7Y5sLGKIQYfq28SoWNtRDP2UQIsVoIcUkIkSmEuCKEWCmEaHm3YlAURSmLVB1Y9uvAXGL47loMC0oqBuX+oxqhtyCE+EoIsaKk41BKDyHEs8A/gDfwCtAJGAmcAmYAvW645BmgJdAFmA+MAebdpXDvVF/gpgoY2If9Ne27q9EULU/gDPAC0BWYeO3YFiFEs5ILS1HuDiHEO9duOnW3OKeRECJdCBFyN2NTSi9VBwL3Rh2YTQjRChgMJJd0LMr9Jc/KRwHgI+CcEKKDlHJjSQejlCwhRBvgc2CqlPKZGx5eJoT4HHC54fhxKeWOa19vFEL4A6OEEIFSyivFHHKxkFImAztue2IpJqXcAGy4/pgQYjUQCwwBdpVEXIpSmkgp9wsh1gHvAcNKOh6lZKk60O5eqAOzCCH0wEzgA2BsCYej3GfUSOgtSCmjgD+BF0s6FqVUmATEAy/n9qCU8qyU8tBtysjqOS3SkYXrpjMNv+F4XtOG/hZCdBJC7Ls20nFECNH3unN+xH7TGXTddKoLeZVZgDirCCFWXHvOGCHEV4Ahl/MeF0JsvHZOqhBivxBi2A3nHBZC/JHLtVnxdS1geGmAETAX8DpFuZd9BwwSQpQv6UCUEqfqwDzKLECcpa0OfAnQAp8V9LUoyp26LxuhQohqQoj5QojzQogMIcQ5IcSMG9cpXLMY6CqECL7bcSqlh7Cvc2kHrJVSZt5BUZUAK3Ahl8c0QgjdjR938Fy3UhX4Cnuv9sNAFPCbEKLatcffA1YCMdinHbUE+t3JEwohHIB1QCNgPDAcqAy8kcvpVYDfsE8R6ou9M2i2EGLcdefMAHrlcnM8FjgPrM1HTBohhP7adMOp1w7PzudLUpR7QW0hxKZrN8VRQoh3hRDX3xusxT5Nb3jJhKeUBqoOvPfqQCFE1WvP/bSU0lTAl6Mod+x+nY5bHogEngUSsP+zv4b9DefGxCRbsTfWOwPf370QlVLGF3ACLhbwOs21StQJ6Ag8BXwppYzO5dzvrn3kZksBn/d2fIE2UsrTAEKIfdgr4ceAKVLKs0KIGMB03VSqOzUM+/9ay6wyhRCrgMM3niilnJL19bUb4s1AOew/v2+vPTQf+5T5J7HfMCCE8MV+Q/G2lFLmI6ZfgP7Xvo4GekgpjxX0hSlKGbYUe932Ifb10W8CNuAdACmlRQjxL9ANmJJ7Ecp9QNWBd6601YHfAkuklJsK/5IUpfDuy0aolHIr9sYlAEKI7diTlGwTQjSSUu6/7txYIUQk0ALVCFUKbs0N36/APv0lN+8Dy3I5Xhwjc6ezKl8AKWW0ECKaIp4idYOWQMT1FbqU0iaE+IVrN7xZhBDVgXeBNkAg/83aMF53bYqwZ/IbJYT4QEppA0YAAvghnzG9DPwPCMbeM/2XEKKTlHJPIV6fopRFs6SUH137eq0Qwh14QQjxpZQy8drx/cBLQgjNtf8zRckvVQf+p9TUgUKIJ4CmQK07fVGKUlj363RcByHEa0KIE0KIDOxrwLZde7hmLpfEYB89Ve5fcUAGULGA143H/kbfCfgZ6Il9pCE3F6WUe278AFILG/QtxOdyzAg4FsNzZSkHXM3leI5jQghX7FOWGmJfg9Qa+8/we25eOzMd+01DDyGEwJ558Q8pZW7PcxMp5Tkp5W4p5RKgO/bR0Pfz/YoUpez75YbvFwOuQL3rjsVg/9/zvltBKaWOqgPvXKmoA6+V/zn2DthMIYSnEMITe5tAf+17fcFfnqIUzH05Eop92tFE7L1M24EUoAKwhNzfgDKwTyVR7lPXpqRtBjoLIQxSSuPtrrnmVNaomhBiIxAAvCaE+EFKGVGEIWat0XG44bhPET7HnYoC6uZyPOCG71tiv9FpLaX8O+tgbmuDpJRHhBDbsK+ByQSqUcgMf1JKkxDiEBBamOsVpYy68WY16/ug645lXPus6sH7lKoDi0RpqQN9AT/s0+tvnGIfjH1Kcj/sU/UVpdjclyOhwOPAPCnl+1LKjVLK3UDiLc73xr51g3J/+wh7hfZJbg8KISoLIRrkdfG19RnPYq8kJxVxbFex9+LWu+F4zzso00jR3nT+CwQLIVpkHbi21uWxG85zvvbZfN15XkCfPMqdjn0U8x3sNzyF2k5JCOEMhAFnC3O9opRRN94AZ31/6bpjWSOgqh68v6k68M6UljrwCtA+l4+rwPprX/+d59WKUkTu10aoMzdvwzAitxOvZYQLBk4Wd1BK6XZtLfHzwAQhxDohxGAhRGshRO9radaPYM90d6syDgK/A0/mktHuTmKT2Kc6PSmEmCCE6CyE+BJ7NsPCOgZ4CyGeEkI0FULUv8Mw5wLngCVCiOFCiB7Ye1rdbzhvO/ZsnNOEED2FEI9hT0qR1w3w79cee4C8k1rkIIT4TgjxoRCivxCirRBiKP8lflDTcZX7yY03wI9jn/545LpjlbGvZctAuW+pOvDeqAOllJlSys03fmAfSb167XvV4aQUu/u1EboaGCaEeFoI0UUI8S3QKo9z62HffHlrHo8r9xEp5ZfAg9hHzj8FNgI/ArWxT4H5Mx/FvAXogVeKOLz/wz6l/B3slbEj9mnnhTUb+/qwKcAu8vfa8nQtBXxn4AD2ntu52NPIv3/DeTHYpwJpsaeo//BaLAvyKNeMPZmF8VqZ+bETaIt9k+41wNvYk5OFSSm33epCRbnHjL6WI6GzEOJTYBTw6XVJiQCao+pABVUH3klwpawOVJQSJ/K3i0HpJoRYjT21/AdSytz2W7rxfF/sewJmbeS7EvgS+5vMCCnlj9ed+zowAQiWUlqKNnJFUe7UtXUyZ4BtUsohJR2PopQFQoh3sHe+1Ae+wZ4BPgmYhX17B9u184Kxb8vRW0r5V8lEqyhKXlQdqJRVZT4xkRBiIPYMYvl2bZrB47kVl8uxwcDse60Bem0dwnvYp4HskVKq3jOlTLm2lUQ9YBD2KfOflWxEilJ2SCnf4b9tIdrf4tRBQDiwqphDuqtUHaiUdaoOVMq6Mj0d91pK6S+wr1EojvL7YE/SUGr+sYUQ3wshooUQR3J5rJsQ4qQQ4owQ4naL/vtgz35oBiKLI1ZFKWaNgX+AR4H/k1IeKNlwFOXeIoRwxD7F8S0ppbWk4wFVByrKdVQdqJRpZXo6rhBiJlBVStlRCCHJ53TcApT/OJAkpSw1PcBCiDbYk0bMk1LWu+64FjiFfb1BJLAbGIh9TcGHNxQz8tpHgpTyOyHEb1LKR+5G/IqiKErZIISohb2x9rEsJTcLqg5UFEW5N5TZ6bhCiAeBoRRwKm5BSCkXF1fZtyOEaArMAZphr0R3AQOklFuFEJVyuaQZcEZKee7a9YuBPlLKD4FeuZQfCZiufVsqergVRVGU0kNKeQI4URLPrepARVGUe1uZbIQKIfTY01B/KqW8J7dOkVLuFkIsx541zQlYIKW8afrRdYKA6zd+jsSe0TAvS4BvhBCtUVkPFUVRlFJE1YGKoij3tjLZCMWe1tsJ+CA/JwshxgBjAOrUqdPk6NGjxRha0TEajTRt2hRHR0e2b98O9jThnD9/nl69egFkT4/65ZdfWLNmDcCTAPPmzWPXrl0A43MrO5eZVVOLOn5FUZT7TG7J7UqcqgNvpupARVGUIlegOrDMJSYSQoQArwNvAgYhhOe1BEVc9732+muklDOllGFSyjAnJ6e7HHHhxcfHk5qaSkpKCpmZmbc8t0KFCkRE/NcJHBkZSfnyRbYPtKIoilJGqTpQURRFKW3KXCMUqIJ9A+IFQMJ1HwAvXvu6fsmEVrTGjBnDe++9x+DBg3nllVvv6dy0aVNOnz7N+fPnMZlMLF68mN69e9+lSBVFURSlaKk6UFEU5d5VFqfjHiD3Pc02YW+YzsG+aW+ZNm/ePHQ6HYMGDcJqtdKqVSs2btzIrFmz2Lx5M7GxsVSoUIHJkyfz5JNPotPpmDp1Kl27dsVqtTJy5Ejq1q1b0i9DURRFUQpM1YGKoij3tjK9Rcv18rtFS1hYmNyzZ89dikpRFEW5j5TKNaHXU3WgoiiKUkzu7TWhiqIoiqIoiqIoStlVFqfj5kpKWep7oBVFURRFURRFUe53aiT0Dpw6dYqWLVtSo0YNWrZsyenTp28655133sHf35/Q0FBCQ0MZPz7XbPGKoiiKUmao+k9RFEW5E6oRegfGjRvH+PHjOXXqFOPHj2fs2LG5njd06FAOHDjAgQMHmDZt2l2OUlEURVGKlqr/FEVRlDuhGqGFFB0dzb59+xg4cCAAAwcOZN++fcTExJRwZIqiKIpSfFT9pyiKotype2ZN6N0WERFBUFAQWq0WAK1WS/ny5YmIiMDPzy/HuYsXL2bt2rUEBgYyefJkWrZseVN5M2fOZObMmQCcOHGCWrVqFf+LUErc6dOnuXz5Mi4uLiUdiqIoSr4Udf0Hqg68H504cYJVq1bRsmVLdDp1O6oo9xv1X1/Mxo0bx+uvv45er2fdunX06dOH48eP4+Pjk+O8MWPGMGbMGADCwsJQKfTvD9WrV2fatGm0bt2aZs2aZd/UKYqilHX5rf9A1YH3o9DQUC5fvszUqVPp0qULtWvXRgiVY1JR7hdqOm4hBQcHc+nSJaxWKwBWq5XLly8THByc47zAwED0ej0AnTt3Jjg4mCNHjtz1eJXSycPDgxEjRnD27FmmT5/OqVOnuFf27lUU5d6k6j+lKOh0OgYMGECfPn3YsmULc+fO5cqVKyUdlqIod4lqhBZSVsa/RYsWAbBo0SIaNWp001SkS5cuZX994MABLly4QM2aNe9qrErp5ufnxxNPPEG3bt1Yu3YtP/30k1pbpShKqaXqP6UoVa5cmbFjx1KvXj0WLFjA8uXLSU1NLemwFEUpZmo67h349ttvGTZsGO+++y5eXl7MmzcPgB49evDuu+8SFhbGa6+9xt69e9FqtTg4ODB//nwCAwNLOHKlNKpevTpVqlRh9+7d/PDDD9SvX5927drh5ORU0qEpiqLkoOo/pShpNBrCwsKoV68eW7ZsYfr06TzwwAM0b95crRdVlHuUuN+m/oWFhcnSvtZErYe5f+T1u05PT2fTpk0cO3aMtm3bEhYWhkajJi4oSilX6he0qTpQKS1u9XuOi4tjzZo1xMbG0qVLF2rWrKnWiypK6Vegf1J1V6sopZCzszM9e/Zk2LBhnDhxghkzZnD27NmSDktRFEVRip2Pjw+DBg2iZ8+ebNiwgfnz53P16tWSDktRlCKkGqGKUor5+/szZMgQOnXqxIoVK1i4cCFxcXElHZaiKIqiFLuqVavy1FNPUatWLebNm8eKFStIT08v6bAURSkCqhGqKKWcEIKaNWvy9NNPU6lSJebMmcOaNWvIzMws6dAURVEUpVhpNBqaNWvGhAkT0Gg0TJs2jR07dmRnZ1YUpWxSjVBFKSN0Oh2tWrVi/PjxmEwmpk6dyp49e7DZbCUdmqIoiqIUKycnJ7p3787w4cM5c+YMM2bM4PTp0yUdlqIohaQaoYpSxri4uPDQQw/xxBNPcOTIEb777jvOnz9f0mEpiqIoSrHL2tasa9eurFmzhgULFqhtzRSlDFJ5rxWljAoMDGTYsGEcP36c5cuXExAQQJcuXfD29i7p0BRFURSlWKltzRSlbFMjoYpShgkhqFOnDuPHjycoKIjZs2ezbt06jEZjSYemKIqiKMVKq9XSokULJkyYgM1mY+rUqezatUstU1GUMkA1QhXlHqDT6WjdujVPPfUU6enpfPPNN+zbt09VxIqiKMo9L2tbs6FDh6ptzRSljFDTcRXlHuLm5kafPn24fPkyq1evZvfu3XTr1o2KFSuWdGiKoiiKUqwCAgIYMmQIJ0+eZMWKFfj6+tK1a1d8fHxKOjRFUW6gGqGKcg8qX748I0aM4OjRoyxZsoSgoCC6dOmCp6dnSYemKIqiKMVGCEGtWrWoVq0au3btYs6cOTRs2JC2bdvi6OhY0uEpinKNmo6rKPcoIQT16tVjwoQJBAQE8N1337FhwwZMJlNJh6YoiqIoxSprW7Onn35abWumKKWQaoQqyj1Or9fTtm1bnnrqKZKSkvjmm284cOAAUsqSDk1RSoXUdCuvfB7Btz9Hk2lUN6iKci9xdXVV25opSimkGqGKcp9wd3fn4YcfZsCAAezZs4fZs2cTERFR0mEpSokymW28Pe0S+0+k89u6BMa8c4EdB1NVJ41y1/yzP4XRb5/n2NmMkg7lnpa1rVnbtm1Zvnw5ixcvJj4+vqTDUpT7lloTqij3mQoVKvDkk09y+PBhfv31VypWrEinTp3w8PAo6dAU5a45cT6DTbtSOHI6g5MXMnl1VDl8PHV8Mf8Kb3xzifrVnXjrqfJ4uatqUik+Ukp+WhFPg5rOvD3tEl0f8GBobx8c9LmPEUTHm9m0M5mzkUaeeswfLw/191kQWdua1ahRg3///ZfZs2fTqFEj2rRpg8FgKOnwFOW+okZCFeU+JISgQYMGTJgwAW9vb7799ls2b96M2Wwu6dAUpdht3p3Mc/+L4K8tiViskv97IoCOLdwJreXMnMmV+b8nAjh5IZP3v7uM1apGRJXic+R0BmnpVp5+3J+Zb1ci4oqJp9+7yI5DqRw4kc7eo2nsOJTKX1sSeeGTcMZOvkBktBlPNx3PfRxOTLx6zy6M67c1S0tLY+rUqWpbM0W5y1QXmqLcxxwcHGjfvj2NGjVi/fr1fPPNN3Tq1In69esjhCjp8BSlyFy4ZOSrBVdJSbNyMcpEnapOvDchCHdXbY7zdDrBQ+08MTgIPv7+CrOXxDD2Uf8Silq51/26NoGHO3uh1Qi8PHS883R5NuxIZtHKeLQa0GkFOq3AzUVDv45eNKvvkj1K6uOh5flPIvjkhWACffUl/ErKJjc3N/r27au2NVOUEqAaoYqi4OnpySOPPEJ4eDirV69m165ddO/enaCgoJIOTVGKxIxfojkXaaRRbWeaN3BlaG8fDA55Twbq0sqDY2cz+HVNAr3beVLOz+EuRqvcDyKvmjh6JoNXR5fLPiaEoFNLDzq1vP3yiAHdfTAYNDz/cTgfvxBMhQD1N1pYN25rVqFCBTp37qy2NVOUYqQaoYqiZAsJCWH06NEcPHiQxYsXU6VKFTp27Ii7u3tJh6YohXb4dDp7j6Yz+hE/BnTzzvd1j3X15q8tSfx7MI2HO6kbfKVo/b4ugZ5tPHAyFH5lVN8OXjjoBM9+FE7rJm40redCaC1nnB3VaquCytrWrGbNmmzfvp3vvvuOpk2b8uCDD+LgoP7/FaWoqXcpRSkhK7cmlnQIuRJCEBoayoQJE3B3d+fbb79l69atar2oUmbNXRaHl7uWPu09C3RdeX8HKpZ3YPuB1OIJTLlvJaVa2bQrmT4dvO64rB5tPPn0xWACfXT8sSGBAS+c4aVPI4hNUO/ZhXH9tmaJiYlqWzNFKSaqEaood5nNJpm7LJYFf8WVdCi3ZDAY6NixI6NHj+bKlStMmzaNo0ePqopYKVOOnc3gwIl0BnTzxrEQI06tQl05dCqdlDRrMUSn3A9S0qxEXjHlOPbX5kQeCHXFx7NoJqRVCjIwoLsPn7wQzC+fV6NuNSfemnZZ7Xt7B67f1mz37t1qWzNFKWKqEaood1F6po13pl9m/4l0pr1eNhIfeHl58dhjj9G3b1+2bdvGjz/+SFRUVEmHpSj5survJBwdBD3aeBbq+lahrthssOtwWtEGptwXYhPMTPjgIs/+z57ZdtHKOMKjjCzflED/LvmfGl4QTgYNw/r4EByg59Mfr6iOwztUoUIFRo0aRfPmzfn111/5/fffSUpKKumwFKXMU41QRblLLl018cyUi3i5a/nkheAyt79bpUqVGDNmDA0aNOCnn35i2bJlpKaqaYpK6ZVhtLFldwptwtwKvUauZiVHvD20/KOm5CoFFBNv5oVPIujR2oNfPqvK+IH+RMdbeO5/EVQJdqRKheLbl1IIwQvDA7kSa+anFfF5nmexSFZuTWTwK2dZvimh2OIp69S2ZopS9MrWXbCilCEJyRaOn8vkxPkMTpzL5NTFTEb28+Whdp5ldvsTjUZDkyZNqFu3Llu3bmX69Om0atWKFi1aoNOptxOldNm2N4X0TBvdHrx9ptG8aDSCVqGurN+RTGq6FVdn7e0vUoqVlBKzRZJplGSabDgZNLi5lK7fS0y8mRc/jaBHG8/sZFgNajjToIYzEwb6Y7UV/+ikg17D5PFBTJhykYrlHGjdxC37MatNsnl3CvOWxeLvo2d4X1+++yWGtk3d8XAtXT/L0uTGbc2mTp1Kp06dqFevXpmt1xWlpKi7RkUpYvFJFn5YGsu2vSnUrOxI7cpOPNzZi1qVHfF0uzf+5RwdHenSpQtNmjRh7dq17N27ly5dulCrVi1VESulxpp/kgjy11O/utMdldOjtSd/bUli3fZk+nW680Qyyq1lmiRLNyZw/GwGx89lEpNgyfG41SbRagQGB4GjQUOm0UbH5u483t0bP++S3y8zOt7Mi59E0KudJ491vXnKrVYr0Grvzvukj6eOyU8HMenLSBauiMNkkZjMkrQMGxUC9Dw3NJDQWs4AHD+XybzlsUwcFHBXYivLctvWrFu3bmpbM0UpgHvjjlhRSgGT2cbv6xL4dW0CXVu5s+CjKvf8qImPjw8DBw7k3LlzOSrigAB1E6OUrNhECwdPZjC8j88dd4zUqORI7SqOLN2UQJ8Onmg0qqOlOF2NNXEu0khobRcG9fShnF/OhqVGCHS6/34HCUkWflkbz5h3LtC+uTsDunkT4HP3G6NSStZuT2bWbzEM7OFN/87Fs+azoGpUcmT6GxVJTrXioBf2DwcNPh7aHP8bw3r78ORbF3iorSeVgopvqvC9JGtbswMHDmRva9apUyfc3Nxuf7Gi3OdUI1RRisDBk+l88sMVqgYb+ObVEILus03Dq1Spwrhx49i7dy/z5s2jVq1adOjQARcXl5IOTblP7T5iTyTUKtS1SMrr29GLD2dFsfdYOk3rqb/r4lSxvIHnhwbm+3wvDx1jH/Xnsa7e/LomnrGTL+DtoaNhTWca1nSiYU1nvNyL5nZH2myY955EV7siGlfn7ONRMSa+nH+VpFQrHz5XgeohjkXyfEUl0FdPoO+tG+YebjoG9fRhxi/RfPRsBTWrJZ+EEDRq1Ig6deqwbds2ZsyYQYsWLWjZsiV6fcmPzCtKaaUSEynKHbBaJT8ujeWDmZeZOMifyeOD7rsGaBaNRkPTpk2ZMGECer2eadOmsX37dqxWtbWFcvftPpKGj4eWykWU/KVNEze83LX8uiZeZRstpbzcdYx51J/fv6zGq6PKEeSvZ8OOZEa+cZ7v/4i5o+1KrDEJpH7zKzEPjCFh7IckjPwAabYgpWT5pgTGfxBOo9rOTHu9YqlrgBZE73aexMRZ2HmoeLNB7zt+72WbNhgMdOrUidGjRxMVFaW2NVOU21AjoYpSSFfjzEyZFYVBL/j2rUp4l7Fst8XFycmJbt26ERYWxpo1a7LXi9aoUUP1rCt3hdUq2XcsjQcauRbZ35xeJxjQzZtvf4nh34NpRTbCqhQ9rUZQvaIj1Ss68kgXb2ITzHz7Swyj3r7A04/75/m7s16Jw3zoDOaj55CJqdjSMpBpmdgSUjAfPIVj91Z4Tn8JfYNqJIx4n4RXZzC7dn9Ohxv5elIIFQLLfgekTicY97g/0xZdpUldF/S6on/P3nM0jQ9n3bvbfHl5eTFgwAAuXLiQY5lKuXLlSjo0RSlV1F2zouTCYpFEx5uJijUTFWP/iE+yYDTZMJolJpPk/CUjj3Tx4rGu3mqNWC58fX0ZPHgwZ86cya6Iu3btir+/f0mHptzjjp/LIDXdRrP6RdtQ7NvBi9V/JzF9cTRN6jhjcFCTicoCXy89b4wtz77jaUz9KZq/tiQyrLcPVa1xGDfvw7jtAOaDp5FmKw4Nq6GrVwVNkB86F0eEsxPC1QmH5nXRuP83DdvywXNc7v4i1WI9mPjdEzgZ7p2/hWb1XFgW4MDH30fxf08EFGlug11H0vh4ThSTx5dn+N9FVmyplLWt2f79+/npp5+oXr06HTt2xNVVdWApZVNahpWNO1PYfiCVZvVd6N3eE+0d3P+qRqhyX0pIsnA6PJPkVCspaTaS06zEJlq4EmNveMYlWvD20FLOz4FyvnrK+ekJKeeMwUFg0GtwcBAE+ujv26m3BVGtWjWeeuop9uzZw9y5c6lbty7t2rXD2dn59hcrSiHsPpqORkDj2kX7N6bTCSYMCuDFTyOY+WsMEwb5q9H9MqRxbRemj3Hh+Gs/YZm6n3NCom0dSrl+7XD/YBzaIL8cv8/0TBuHz2Rw5HQ6rE/HxdmIq7MGAcxdFsvjr7zAA59/gNhaCzo3A+xrRm2XY9H4eCCcym5ynzfGlmf2bzGMfvsCzw0LpFkh1kGbzDb0OpH9M915KJVPfrjC5PFB1K12Zxmrywq1rZlyL7h42cjv6xPYuDOZTKPEx0PL7iNpbNiZzAfPVCj0tk7qP0C5r0gpWbUtiTlLYqlW0YCHqxY3Fy3uLlpqVHSkbZgb5Xz1+Pvoi2Ua0v1Kq9XSvHlz6tevz+bNm5k2bRpt2rQhLCwMrfbeziCs3H27D6dSu4pjsewdGVrLmYc7ebFkfQI2CRMH+auZEGWANJlJm7mM1BlLqDa0O7rX3mNbvAeL1ySgOyAof9mMo8MVDA727VNOXczkwiUjNSo6Ur+GMw56QWKKhcirNtIzbLw4ohxN67lgqvMaCcPeJbNDEyynI7GcjkA4GdB4u+P145voKpcv6ZdeKE4GDRMHB/BgY1c+m3uVRrWdGfuo3y1HRU1mG4dOZbDrcBo7D6dyNdaMzQYODgJHBw1SwvvPBFG7yv3RAL2e2tZMKatOnM/g5c8isdkk7Zu507OtBzUrObJxZwofzYli6YYEhvXxLVTZhW6ECiH8pZTRhb3+Dp73EWAgEAb4A+HAEmCKlDLlbsejFC+rTbJldwrLNyfioBP4eenw8dLh56UnKEBPSDnDTWnm8xIVY+LzeVdJy7DxyYvBVCmihCVK/jk7O9OjR4/s9aJ79uyha9euVKtWraRDU+4RUTEmTl00Mqp/4SrF/HhqgB86LfyyJoEgfz2PdCkdW3EouTNuO0DS69+iqxiI74rP0FWyr83rAnRq5cHh0xkkpVjJNNkwmiRmi6RNEzdqV3G87ZRrhya18Jz9GtZzl3Ee2gNd9WA0Hq6k/biCuD4v4znjZQwPNLgLr7J4NKrtwsx3KjHz12iGv36eR7t40buDV47px2fCM/ljfQLb9qdSubwDzRu48ubY8lQNNiAlGE2STJMNB73Axen+7nRU25opZcm5iExe/SISd1ctX7wcnGMf5o4t3Nm8O5k/NycysIc3DvqCL0m4k5HQPUDIHVxfWC9ib3i+BkQCjYB3gPZCiFZSysKnv1NKDZtN8ve+VOYuj8XZUcPj3b3R6zXEJZiJTbRw6mImm3YlEx5lwmyRBAc64Oupw91Ni6erFvdrUwMyTRKjyUZquo0te1IY0M2bRzp73bWNwpXc+fv788QTT3D69GlWrVqFt7c3Xbt2xde3+BoOyv1h0257X2S7pu7F9hxCCEY/4sfxc5ks35TIw5281GhoKSRtNlI/XUj6LxvweH8shq7Nb+qw1GgEDWve2bRtQ4t60KJejmMuw3uiqxpE4lMf4/rSYFyGdL+j5yhJzo4anh0SSL+ORuYtj2PYa+d4rKs3gb56/tiQwKVoM73bezJvSmU83XLeVgoBTo4CJ8d7Z81sUbhxW7PatWvTvn17ta2ZUmpYbZL/fX8FB73gkxcq5GiAZunf2ZsXP41gw45kurf2LPBz3LIRKoTofYuHSyoH+UNSypjrvt8ihIgH5gLtgI0lEtV9yGS28c/+VFZtSyI1w0aHZm50aO5+R1liU9OtbNqVwvJNCej1GsY+6kfTei63HOlMSrUSEWUkPslKUqr9IzreAoDjtWlAnm5avn41hApqDWepIYSgRo0aVK1alV27dvH999/ToEED2rZti5PT/TddSykam3YmU6eq4233RLxTQggeaufJlFlR7DueTlhddfNYmsj0TBKf+xJrVBy+q79A6+t512MwtA7FZ+n/iB/2HuZ9J3F/exQaz7KblKZieQNvjivPuYhM5i2PY9u+VPp28KR1Yzd0avlKgWVta1avXj22bNnCtGnTaN26Nc2aNVPLVJQSt+HfZM5GGHl9TDnK+eV+79ywphNVgw38vi6Bbg96FHhq+e1aC38AW4DcSnUr0DMVkRsaoFl2X/scdDdjuV9kGG3ExJvJNEoyjDYyjDYOnkhn3b/JVA4y0KONB+4uWtbvSGb+n+epU9WJutWcSEmzkphsITHFik1CtWAD1Ss6UqOiI+X99ViskoxMGxlGSVSMibXbk9l+IJXGdZwZ86g/YXWd8/UH7eGqxaO6SnJTVmm1Wlq2bEmDBg3YtGkTU6dOpV27djRp0gSNRvWeK/l34ZKR85dMjB94dzIwP9jYFQ9XLX9tTlSN0FLEejWehBHvoa0ShM8vHyAcS67zUVclCN+Vn5My5UdiOjyN+7tjcOz5QJleB1gl2JF3xqvbraJy47Zme/bsUduaKSXKaLLxw9JYalay50rJixCCPu09+XzeVc5FGqkaXLDxyds1Qk8DT0opz+fyxBEFeqbi1fba5+MlGkUpJS1WhK5gvWqZRhu7DqexaXcye4+l4+WuxcmgwcmgwdGgoUoFA19NCsmRHbZJXRcyjDb+2ZfC+UsmfD11VAsx4OmmQ0rJmXAjW/emMGdJDNHxFnRagaNB4GTQ4Omuo31TN8Y86oeXu8qXdT9ycXGhV69eNG3alNWrV7N79266detGlSpVSjo0pYzYtDsFjeCWlWZRctBr6PagB7+ujSc2wYyvV/GOviq3Zzl3ibjH3sD5ia64/t+AUnETr3FzxuPDp3Hq25bEl74h47dNeEx5Cm15tfxA+U/WtmanT59mzZo1alszpcQs3ZhITIKFSaPK3XapSbP69g7Y/cfTi7wROhfwBW5qhALfFuiZiokQIgh4F1gvpdyTxzljgDEAISElsYy1YNIzbew5mobNJrHZwGyRpGXYSMuwkpZhX9+YkGwhIdlKfJKFDKONqhUM1K7iRK0qjlQLNpAZk0T6ih3Itf/gePAo1lpV8XisPS792qD18ch+LptNEpNg4VK0iUtXzVyONhFxxcTh0xnUrORI+2ZuPDckMHuN5e04GTR0aumR62PX79lntUq1LlPJVUBAAEOHDuXEiRP8+eef+Pv706VLF3x8fEo6NKUUM5psrNqWSKM6zrkuCUhISODHH39kwoQJ6PVF11js1dbeCF28Kp4Jg0pncpGyVgcWlvVKHPGD3sLtuQE4D+5W0uHcxKF5XfzWfUPq178Q2+1ZPD7/Pxw7NS3psEqElLKkQyi1qlevTpUqVdS2ZkqJyDDa+HVNPGF1nfO1Xt7XS09woAP7jqcXOEnfLRuhUsoPb/HYBwV6pmIghHAFlgEWYERe50kpZwIzAcLCworsne/UqVMMGzaMuLg4fHx8mDdvHtWrV89xjtVq5ZlnnmH16tUIIZg0aRKjRo26ZbnGS3Fsn72T6KDKSAc9Oq3AxUlz7UOLv7eOmpUc8XTX4nMlAodtu0g8mkzy5jQS4zPYn5BISGIk54Jrc6FeM+I6jkR76ATVZu6kwfvziKtYlTSDK6Y0I9YMEwZhJcM/AGPtmgQ0qUWdB8rxwvDAXEckbXFJJE2ahjRZcH9vDLqQwEL97O61Bqjl/GWM2w7g1L89Gpfc1zMWZkT6fiWEoHbt2lSvXp0dO3YwZ84cQkNDadOmDY6OJbUcXSnNVv2dRHySlddG595ZsWjRIp5//nlcXV0ZPXp0kT1vOT8Huj/owV9bEkttI7Q46sDiqv8Ky5aUSvwTb+M8qGupbIBmEQY9bi8NxtAmlISnP8HUry1urwxB6O+vGUDG1TtKOoRSTW1rppSUvzYnkphiZWjv/M/UaFTbmbXbkwr8XAV61xNCBEkpLxX4WYqBEMIRWA5UAdpKKSPvdgzjxo1j/PjxPPHEEyxYsICxY8eycWPOvEg//fQTZ86c4fTp08TFxdGoUSM6depEpUqV8izX3Ukw6OSfWP6MQN+wGvqw2ugCQ9BVDUJXtQIAGUu3kLFwLdaYRJz6tsEjLBjhbEA4OSI8XXFoXpcqORpD1TCZe3DuZCLmlXvwFGa8fJ3w8nXEwUmP5VQ4pt2HMH/0Mxgc0PZujXVkL7QV/psGYtx+iMSJn+PUtw0aL3diuz+P6/j+uIzpe982rmxxSaR8uZiMJVtwCK1O6lc/4/bqMJweboe4tp7RfOQcqdN+I3Pldgwdw3Ad2xd9s7qlYppYaafT6XjwwQcJDQ1lw4YNTJ06lfbt29OoUSO1XlTJZjLbWLwqnnrVnWhYM/dOoKNHjwLw/vvvM2zYMBwcim6d4BO9fFi7PbnIyisLiqv+KwyZaSJhxPs4tKyPy8RHi7Ts4uLQvC5+a78i8ZnPiev/Kh7/G4/GzxPh6IBwNNzTdao0W0j+4MeSDqNMUNuaKXdTptHGz6vjaVzbmTpV858gMrSWM8s3JRb4+URBpkQIIaKllCU+OV0IoQeWYl8L2klKme8utbCwMLlnT66zdgskOjqaGjVqEBcXh1arxWq14uPjw+nTp/Hz88s+r2fPnowYMYJHHnkEgAkTJlCxYkVeeumlW8XInj17sKWmY9p9HPPeE1jORGI5ewnruUtICY4dmuA0qAuGto0QRdgrJqXEciaSjJ9Wk/7LBgztmuAyqjfG9btIX7QOzy+exdCuMQCWC1EkTZqOLTYR16cfRuPvjcbb3f7h45Fnz66UEkwWhKFsrp+SFivWC1FkrvqX1G//wKlPa1yfH4jW1xPTnuMkvzUThMB5eE8y/9iC+cQFXEb3xal/ezJXbidt1lI0Hq64jO1H6/eepyj+Hu8XUVFRrFq1CpPJRLdu3Yr8ZlYpW8wWyd/7Uti2L5Wte1L433MVaJJHgqB27dqxb98+UlJS+Pbbbxk7dmyRxvLdL9GMfcy/1PcsFUUdWJz137UY8/2+KM0WEsb9D6HX4Tn9pezOv7JC2mykTfud9PmrkBlG+0emCY2vB86DuuD8RPd7bu1o2ryVZP71N13PrVX1XwFIKTl16hRr165V25rd5ywWCzExMZQrV67IypRS8tWCq/y1JYkvXgmmfgESfianWun/3BnWzapZoDqwoPM/SryCFUJogJ+AjkDPgjRAi1JERARBQUHZ0yK0Wi3ly5cnIiIiRyUcHh5OxYoVs78PCQkhIuLmnE4zZ85k5syZAMTE2BMAa1ydcWzfBMf2TbLPkzYbZJoQzsUzJVEIgb56MPp3RuP6/CDSF64hcdz/0FUPxnftV2j9vLLP1VUqh/eid8n8YwuZq//FlpCCLS4JW3wytoQUNL4eaCsEoK3gh8bVCWtkDJbwq1gvRSO0Whz7tsFl5EPoa1cqltdSlMyHzpA67Xcsp8OxXIhCG+CNvkktfJd/gq7qf1kCHcJq4/PXZ2Qs2UzGLxtw6tsWrx/ezG5wuwzvifOQbhjX7SZt9rKSejllVrly5RgxYgTHjh1j6dKllCtXji5duuDl5XX7i5V7ipSSj7+PYtOuFBwNgp5tPGhcJ+9K8+jRozz22GMcOXKEKVOm8OSTT6LTFd0UyMe7F2wtTFlW1PUf5F4H3or52Hkyfllvn4XSrA6eXz1f5hqgAEKjwXXio7heN4Kb1Rmc/uMKYjpNwPBgQ1zG9sOhSa0Si1PabEXy87WlZZD6xWK8574FI9cWQWT3DyEENWvWpFq1auzcuVNta3Yf++STT/jggw+IjIzE09OzSMpcvimRv7Yk8Xh37wI1QAHcXbVUCzYU+DkLWgOXhpXk04BHgQ+ANCFEi+seiyyJablFYcyYMYwZMwaw9wLnRWg0UEwN0Btp3F1wHfcwruMezjseIXB6uB1OD7fLcVxarFivxGGNuIo1MhqZmoGhUzO0IQFoK/gj0zNJX7Ca+EFvoateAad+7ZBGM7aYRGyxCUijGedhPXBoVLN4X2Q+ZG7cQ9Izn+P6wiBcJz6KrmoQwinvfzah0eD8SAecH+mQ++NaLY7dWuDYrQWErSuusO9ZQgjq1q1LjRo1+Pfff5k1axaNGzemdevWGAwFfxNUyqZlmxLZtCuFYX18GNTD55brzKOjo4mNjaVu3br06NGD/v37s2rVKh566KEii8fD7f5a01fU8lMH2uKSyPhjC+m/bEAmJOP0aEd8ln2MrnL5uxlqscvqDPb4YBxuk4aS8dtG4oe8g9/GaWgD726CNuvlWNJ/WkP6ojXoalTE+/vX76gTPO3bP3BoVR99AzWdtLC0Wi2tWrWiYcOGbNy4UW1rdh9atGgRaWlpbNq0iX79+hW6HJtN8s3CaP49mEpsgoWWDV0Y2a9wo+uNahc8cVZZrDW7X/v8+rWP600G3rkbQQQHB3Pp0iWsVmv2dKTLly8THByc47yQkBAuXrxI06b2DHg39gzfq4ROi66CP7oKeczednHC7bmBuI5/hMwV28lcswONhwsaXy90daogM40kjJqCQ+OauL08BF314NzLKWbpi9eR8uFcvH58E4ew2iUSg5I7vV5PmzZtaNSoEevXr2fq1Kl06NCB0NBQtd72HiWlZP2OZHYcTOOf/Sm0aODC4J4+t00hf+zYMQDq1q1L+/btCQgIYPbs2UXaCL2fFHf9ZzkZTuLEz9A3qIa+QVVsiWmk/7Ie0z+HcOzSDPe3RuLQqn6ZHPksKI2bMy4jemENv0rqVz/j8eHTd+V5zYfOkPLlYkw7juDUty3e898hdcYS4kd+gPePbxZq71VrdAJp3/+J76oviiHi+4+LiwsPPfSQ2tbsPnPmzBkOHz4MwNq1a++oEXr0TAZ/bk6kWT0XHunsRc82nretT/PyaNeCzwYqc41QKWWlko4BwN/fn9DQUBYtWsQTTzzBokWLaNSoUY6pSACPPvoos2bN4uGHHyYuLo6lS5eydevWEoq69BEOepz6tcWpX9ubHnMZ0p20H/4irt8rGLq2wO2FQfleGyOlvKOGiJSS1K9+JmPROnx+/whdtQqFLkspXm5ubvTr149Lly7lqIjv5a0o7lc/LI1l4Yp4fDx1dG7pwZhH/fJVYWYlJapbty56vZ7hw4fz6aefcvnyZcqXv7dG0e6G4q7/tCEBOLSsj/nwGTKWbkEYHHB6pAOeXz6Hxu3+3KbCdcIjRLcZh8u4h9FVLFxW+vwyHztP/OC3cX1xMJ7fvJCd8d3zy+dInPApCaM+wGvOGznyOtjikrCcu4xwdULj5oxwc0a4OuXIWZH6+UKcH+tY6Kz6Su4CAwMZNmxYjm3Nunbtirf3/bNE4H7yxx9/ANCoUSPWrr2zKe3rdyTj6CB4c1x5nBzz16mXkpJCy4aN6dapEx9O+zp7y7PcdtS4nTKZmOhOFFViIoATJ04wbNgwEhIS8PLyYt68edSsWZMePXrw7rvvEhYWhtVqZcKECdl/KK+88kr2lKNbxKgW61/HlphK6vTfSP9pDc4DOuM64RE03u7Af2tnTFv2YTkdgTUi2r7u9HIM+rpVcB7cFcferdHkY/qQNTIa486jmHYcwfTvEYSLI97z3kYbUHxv5Op3XbSklBw5coT169cTHBxM586d8fDIfd9apXSRUpKYmHjT+t6zEZmcumgkKtrEwpXxdG/twfNDAwrUyfT000+zcOFCEhISEEJw+vRpatSowZQpU3j11VeL8mWU+iH4oqoDi6v+uxajel/MRcqnP2ENv4Ln1y8U+FrzqXCskdFgNCNNZrBaMbRtjMYn5/ujNTKa2D4v4/7WSJz6tLmpHGmxkvj0x8hME+5TnsK4fjeZK//BfPAMumoVkOmZ2JLTkSlpyHQjwsmAcHdG4+aCLTYRv63fZtff6vdc9CwWCzt27GD79u1qW7N7VKtWrTAajYwcOZIJEyZw5swZqlatWuByTGYbj71wlub1XXh1dP47Y//8eCq9X5loj8WjHCu3bMKjYfbSuQLVgQVthO6VUja5/ZmlV1E2QouLemPOnfVKHKlfLibjz39wGdodW1wSxk17AXBo1xh97UpoQwLt607L+WL69zDpC9di2nUUp4ceRFuxHLbEFGRiKrbEFPtHfAq2xFRkYgrC2RGHFnVxaF4PhxZ10dWqWKSZh3OjftfFw2QysX37dnbu3EmzZs144IEHinRLDqVomc1mnnzySX7++Wf+3X2MmBRfOrVw5/TFTJ7/JAKT2V5PtWzowjtPBxV4n+G2bdtiNpvZvn179rF27doRGRnJqVOninId1X3TCC1O6n0xd7aUdGJajcb7tynoa+Z/WU/6gtWkfLwAff2q4KBHOOiRZjPmPSdwe3UoTo93RgiBLSGFuL4v4/xEN1xG98mzPGm2kPjUxxi37sfQuRlOvR7A0LbxTWtFpc2GTM1AJqdhS01H4+GKttx/s5nU77n4pKamsmHDBk6fPq22NbuHXLp0iQoVKvDee+/x2GOPUbNmTWbMmMG4ceMKXNY/+1N4e9plpjxbgWb1cs8qfyNptfJCteZ8fWEfUwaO4pVFs/i2bmfG7Pkra4p+8TVC7wWqAi77LOcukfb9X+gqBmJo1xhttQq3HBWxXokj47dN2OKS0Hi5Ibzc0Hi6ofF0tX++dkw4O971tYTqd128kpKSWL9+PRcvXqRTp07Ur19frRctZSwWC3379mXFihUAPDZ6FnG21tSq4sjVWDMGBw3vTwxCpxOU99MXeL2KlBI/Pz/69evHrFmzso8vWLCAIUOGsHHjRtq3b19UL6fU/3GpOrBsS52xBNOe43jPuTElxs2kzUbK/+aT+dffeC+YfFMCJ/PhsyS9MhVhcMB98miS3vgOh7DauL81Ml+xSKv1jjpq1e+5+F2+fJnVq1erbc3uAUePHqV///6cOXOGo0ePUqNGDSpXrkzjxo1ZsmRJgcqy2iRvfn2JUxcz+fnTqrft2F23bh0Gg4GwKBOdhw4gvYIvu04cwcPNjcHacnz63CQ83h8LBawDy9yaUEXRVQnK+mPPF22gD64THinGiJTSysPDg/79+xMREcGqVavYtWsX3bp1o0IFtca3tNi2bRsrVqzgnXfe4Z133mHvvqP0H9SVUxcysUr49MUgKpYvfNbjmJgY4uLiqFu3bo7j/fv3Z8KECcyePbsoG6GKUqxchvckbdZSTAdO4RBaI8/zpNFM4vNfYg2/gs/yT9H63LwsQV+/Kj5/fkr6vFXEPfoahk5NcXtjeL5jKe6ZQsqdK1++fI5tzcqXL0/nzp3VtmZlyD///MP06dNZsmQJ7u7urFu3jpo17dNf27Vrx5o1awpUXnyShQ9nRbH/RDoj+vretgFqtVoZMmQIer2ef/zbsd+SzJM9BqHX62nStCkHz18i/fs/cRnWo8BJRAvcCL22T6dGSmm57lhXoB6wUUq5v6BlKoqiFKfg4GBGjx7NwYMH+fnnn6lcuTKdOnXC3d29pEO77x08eBCAp556im+mzSEt8TTPDwvE4CAwmiQBPvrblHBrJ06cAKB27ZzZrZ2cnHjiiSeYPXs233zzjUrioZQJwsmA67OPk/TyNBya1sYWm2jf2iwxBXQ6hKMDwsmALSYBbeXy+Pwy5dZbimm1uIzohVP/9ggXx/si4/D95sZtzWbOnElYWBgPPvig2taslIuNjaV9+/a4uroyfPhw3nzzzRzJ9KpVq8bcuXPJyMjI916xH86K4ti5DF4aEUiXVre/B/r333+5evUqAF8m7iTDZuHBBx8EoEWLFny962uMPrXIWLYVtxcHF+j1FWYkdBFgBIYCCCHGAdOvPWYWQvSUUq4vRLmKoijFRghBaGgoderUYdu2bcyYMYMWLVrQqlWr7Oxuyt138OBBAgIC8PD0RedSBZl5/o4bntc7c+YMYK+sbzRq1CimTZvGTz/9xMSJE4vsORWlODkP7IJMy0TotGjCaqPx80Tj5QZWGzLDiMw0gZQ4tG6Y79FKjXv+1oQpZVfWtmahoaFs2LCBqVOn0rFjRxo2bKiWqZRSa9aswWw2s2bNGpo2bUpsooW5y2J5rKs3To6a7OnV4eHh2aOjt3I5xsT+E+mM7OdL1wfyl7Txjz/+wMHBAU8HR6am2uvTBx54ALA3Qj/99FNO1/Yn89e11CpgI7QwXV4tgJXXff8SMBvwAJZw896diqIopYaDgwMdO3ZkzJgxREdHM3XqVI4cOcL9tj6+tDh06BANGzZk5dYkDG5VSYg5i9VqzfVco9HIm2++md2wzI+zZ8+i1Wpz3bInNDSUevXqsXz5csC+PvXFF18kPDy8cC9GUe4Codfh+tTDuIzug1O/thgebIi+bhX0Darh0LwuhraNMLRrrKbLKrlyd3enX79+DBgwgD179jBr1iz1nldKrVixAn9/f5o0seeE/XrBVeb/GceCv+IAshuhFy5cyFd5a/9JRiOgc8v8zQKTUrJkyRI6derEE97VMSMJCQnJXtLUvHlzAA4GOfHe4U0FeGV2hWmE+gOXAIQQ1YDKwFQpZQrwA1C/EGUqiqLcVV5eXjz66KM8/PDD/PPPP/zwww9cvny5pMO6r1gsFnuChZr1+HFZLHXr1MFkMnLu3Llcz1+4cCHvv/8+gwYNyrOheqOzZ89SqVKlPEe7GzdunL2P6L59+/jss8/46aefCveCFEVRyogKFSrw5JNP0qJFC3777Td+++03kpKSSjqs+16G0UZquhWr1crq1avp3r07Go2GHYdS2X4gFV8vHb+viyfyiim7EXrx4sXblmuzSdZuT6JxHWf8vPM32+jgwYNcuHCBPq078HimBxqhyR4FBfvfUFBQEPP2bmNhRkSBX2thGqHJgM+1r9sBsVLKQ9e+twJqQyJFUcqMihUrMnr0aEJDQ1m4cCFLly4lJSWlpMO6L5w6dQqj0UhcZmUyTTbGDbX3qh47dgyA9PR0+vTpw8SJE5FS8vnnn+Pp6cnu3bv56quv8vUct9tDrW7dukRFRZGQkMChQ/aq7MCBA3f2whRFUcoAIQQNGjRgwoQJ+Pj48O2337Jp0yZMJlNJh3ZfslgkL3wczvDXz/Pr0i0kJCTQs2dPEpIsTFsUTUigA1+/GoKDXsP/vo9i6wEHdHp9vkZC959IJzreku9puABLly5Fo9HQWedDBa0Ti6Z/xzvvvJPjnBYtWnD42FECndwK+GoL1wjdDkwSQvQCniXn1NxqQGQhylQURSkxGo2Gxo0bM3HiRFxcXJg+fTrbtm3DYrHc/mKl0LKSEp2NDaF/Jy/at24I2BuhRqORfv36sXz5cqZOncqQIUM4cuQIX3zxBQ899BBvvPEGly5duu1znD179paN0Dp16mQ/5+HDhwHYv1/l11MU5f7h4OBA+/btGTduHPHx8UydOpVDhw6pZSp32e/r4jl10YhNwptTfkGj0ZKuDePJty8Qm2Dh/4YE4O+tZ+xjfpy8kMn3SxPQOwVy4lTus4eut2R9Am7OGh5o5ArY9+e+3YyiAwcOUKdOHdz/OY6udiUeGzeKGjVyZuVu2bIlAB8/P6nAr7cwjdCXAW9gOfZRz3eue2wA8G8hylQURSlxBoOBzp07M3r0aC5dusTUqVM5duyYqoiLyaFDh9Bq9fj4VWFgDx/c3NwICQnh4MGDDBgwgLVr1zJ79my6du3KTz/9RGBgIAMHDuTjjz8mIyMjey1nXuLj40lMTMw1KVGWrK1bjh07lj0SeubMGTUarijKfSdrW7NHH32UHTt2MGfOHCIj1djS3XDpqom5y+N4sJEr371VkYTLf+Pu34j5K02U89Xz7VsVaVjTGYCebTxZ/W0N5k2pjJNrEPsO3DpPwtEzGew8lMZj3bxx0Nubfm3btuXll1++dUyXLhHkF4Bp1zEce7TK9ZwxY8awdOlSnnjv1QK/5gI3QqWUp6WUNQA/KWU1KeWF6x7+P+yNVEVRlDLL29ubxx9/nD59+rBlyxbmzp3LlStXSjqse87evQdw9qxClwd9cXOxJ1GpU6cOP//8M8uWLeObb77hySefZMGCBTRp0oR3330Xg8FAzZo1qVSpEmvXrr1l+VkJjG41ElqxYkWcnZ05evQohw8fJigoCCll9qiooijK/SZrW7OwsDB+/vlnlixZQnJyckmHdU/7cVksOq1gwuAAvNwFyXGnGDGoPfM/rMzXr4XctF+2RiMo7+9ArZqViYoK51ykMddypZTMWRKDl7uWvh3t+8NmZmayc+dO9u3bd8uYIiMjKWfRgJQ49m6d6zlubm706dOnUBmWC70hlJQyLpdjh6WUMYUtU1EUpTSpXLkyY8eOpV69esyfP5/ly5eTmppa0mHdM/bsO4CLZ036dvhv4/T69e257T788EMmTJgAgK+vL3v27GH06NGAfR1Tly5d2LhxI2azOc/yz549C9y6EarRaKhduzbr168nLi6OJ554AlDrQhVFub9lbWs2ceJEPDw8mDFjBlu2bLnle65SOLEJZrbuTaFHaw98PXWcO3cOs9lMwwZ1KefngFaTdwOv/QM1MaXHMGNRRI5ZW0aTjSXrE5g84zKHTmUwqKcPTgZ7s+/kyZPYbLZbZkU2m81cvXoVv6tp6GpXQl89uOhe8DVqV2JFUZRb0Gg0hIWFMXHiRAwGA9OnT+eff/5R60Xv0Nq160mIu0Kdeo2pFPRfD+/zzz/PsmXLmDTp1utLunTpQnJyMrt27crznKxGaJUqVW5ZVp06dbIz5Hbv3h0fHx+1LlRRFIWbtzWbNm2a2tasiP25JQmbDXp38AT+S85Xu3bt215bs4a9fvtn1xl2HkoDID7JwgufRjB9cTSnL2bSo40HPdv8l5Aoq76LjIzEZrPlWm5UVBRSSvyvpuD40IOFfm23oiuWUhVFUe4xjo6OdO3albCwMNasWcPevXvp0qULNWvWVBt9F1BycjJPDB2Bs0cl3nr1qRyPBQYG0rt379uW0aFDBzQaDWvXrs2RMv56Z86coXz58jg7O9+yrKx1oWAfiQ0NDVUjoYqiKNfJ2tbs4sWLrF69ml27dtGtWzfKly9f0qGVaSazjRVbEmnR0JXyfg4AHD9+HMhfIzRrmxY3fTTTF0eTkm7l+yWxJKdZeefp8jzY+OastVmNUJPJRHR0NIGBgTedk5X4r5zGEaeHcp+Ke6fUSKiiKEoB+Pj4MGjQIHr27MmGDRuYP38+V69eLemwypTnnnue2OjLdB/wGW2a+RWqDC8vL5o1a3bLdaG3y4ybJasRWqFCBby9vQkNDeXw4cNqtFtRFOUGN25rtmzZMrVM5Q5s3pVCYoqVhzt6Zh87duwYFSpUwM3t9tueZDVCm9RI4nKMmf/NuYK7q5YvXg7JtQEK/zVCASIict/fMyshVVD1KuiqBuXz1RSMaoQqiqIUQtWqVXnqqaeoVasW8+bNY8WKFaSnp5d0WKXezJkz+f77OYTUf5KXJ3a9o1Hkzp07s2vXrjwz2Z49e/aWmXGzZG3TkrUeNTQ0FKPRyMmTJwsdm6Ioyr3q+m3NnJ2dmT59On///bfquCsgKSVLNiRQqbwDobX+m7Fz/Pjx7HrpdsqXL49Op0OYrzD+cX9eG12OGW9WpEYlxzyvOXbsWHbdmNe60Mgz9m1fKnVtk9+XU2CqEaooilJIGo2GZs2aMWHCBDQaDVOnTmXHjh233XvrfrV582bGjx9Pucpt6PnoJJrWvfU02dupXbt2nskVUlJSiIqKytdIaKVKlShXrhytW9unHNWqVQv4L7uuoiiKcrOsbc1GjRpFZGQk06ZN4/jx42q9aD4dOZPBmXAjfTt6ZXfI2mw2Tpw4ka+puABarZaQkBDOnz9Pv05edGjujuYWiYwyMzM5e/Ys3bp1A/JuhF7cdQBHNJTr2baAryr/btkIFUJUFkJsEEKcE0J8LoRwvO6xvLNBKIqi3EecnJzo3r07I0aM4MyZM8yYMYPTp0+XdFjFKjMzs0B7aZ49e5b+/ftTIbgqNR78jAHd/e54LW1QkH2KUNbalesdPHgQgIYNG962HI1Gw8mTJ3nppZcA+3QzgAsXLtxRfIqiKPeDrG3NevfuzebNm9W2Zvn0x4ZE3Jw1dGrhnn0sIiKCtLS0fI+Egn02z+7du/N17okTJ7DZbLRu3RpnZ+c8p+OGHztBOb0zDk1q5TuOgrrdSOh0YAnwKOANbBBCZE0w1hdbVIqiKGWQn58fgwcPpkuXLqxevZoFCxYQE3Nv7lo1YsQImjdvnmdmPYDffvuN3r1789VXX/HQQw8B0HXgLAL8vWjV0PWOY7hVI3Tv3r0ANGnSJF9lubm5odPZc/X5+vri7OzMxYsX7zhGRVGU+8X125otWLCAP//8k7S0tJIOq1S6cMnI3/tS6NHGE0fDf82xgiQlytKtWzfOnDmTr87vrPWgdevWJSQkJNeRUGmzcTnyEuV9/RE6bb7jKKjbNUIDpJTTpJR7pZTDgb+wN0Q9ADXWriiKcgMhBDVq1ODpp5+mWrVq/PDDD6xatYqMjIySDq3IxMTE8Ntvv3H8+HE2btyYffzKlSuEhYUxfPhwJk2axKOPPsr27dt59tlnOX36NN/OXsTZaH96tPZAp7vzjMJZWRlza4Tu27ePwMBAypUrV+ByhRBUrFhRjYQqiqIUUNa2ZhMmTMDBwYFp06axffv2MrdMZdu2bbRo0aJYtus6f8nIi59G4Ommpd+1hESXLl1iyZIl2bN4CtII7d69OwCrVq267blHjx5Fp9NRvXp1QkJCch0JNR8+y2VjKhWq3Xp7szt1u0ao4fpvpJQfAr8AG4Dbp2xSFEW5T2m1Wlq0aMGECROw2WxMnTqVXbt23XLksKxYuHAhFosFZ2dnZs6cmX180qRJHDp0iCVLlvC///2PgQMHEhkZyZEjR/j3339JtDZCCHLsV3YnnJyc8Pb2znMkNL+joLmpVKmSGglVFEUppKxtzUaOHMmFCxeYNm0aJ0+eLBPrRXfs2EGPHj3YuXMngwcPztGJfOR0Opt2JWO2FO51bNmTwvMfh6PTCT5/KQRfL/vE0meffZb+/fszadIk/Pz88PX1zXeZVapUoWbNmqxcufK25+7atYsaNWrg4OBAcHBwriOhGet3cdVqJDi0bi4lFJ3bNUJPCSE6X39ASvkpsBC4fbYHRVGU+5yzszM9e/Zk6NChnDhxghkzZnD27NmSDuuO/PDDD4SFhTFu3Dj++OMPrl69yr///svcuXN54YUXuHLlCvv372fBggU4OjpSt25datdtxF9bEnmgkWt2pVsUgoKCbmqEpqenc/z4cRo3blzoctVIqKIoyp3z9fVl0KBB9OjRg/Xr1zN//nyio6NLOqw87du3j27duhEQEMCPP/7I8ePHmTRpEgBrtyfxwqcRfDAziideOcvaf5LyXa7VKvlw1mXe+/Yy5fwc+PzlYCoE2vcFTUxM5M8//6R79+506NCBxx9/vMBx9+jRg82bN98yS//Ro0fZsGFDdvkhISFcuXIFo9GY47zL67djwkZI9eJt6ulu83iuPwUp5edCiJ+LIZ77XkJCQkmHoChKMQgICGDIkCGcPHmSFStW4OvrS9euXfHx8Snp0ArkwIEDHDx4kKlTp9KxY0c+//xz+vfvT0REBEFBQbz++us4OzsTGhqa47o/NyeSlmFjYPeifb0VKlS4qRF68OBBbDbbHY+ExsfHk5KSkq+92pSicf78+ZIOQVGUYlCtWjUqV67M3r17mTt3LnXq1KF9+/Y4O99ZlvSiIqXkyJEjdO7cGU9PTzZu3EhISAi7d+/m66+/Bu9HOBrhT6PazvTr6MXPq+P5+IcrXI4xM6yPD0IIMjIy+PXXXxk0aFB2joGssqcuimbDzhSGPOTDE7180Gr/W5Ly66+/YjQaeffddwkLCytU/N27d+eLL75g06ZN9OzZM9dzPv/8c5ycnHjqqacACA4OBuxTgatUsU+9taWkE7HvCPBf3oXicstGqJTSeIvHbp7/pNwxd3d3zp49S61atbBYLFitViwWCzabDa1Wi06ny/XznWaZvNtiYmLw8yvcJvX3EjXScn8RQlCrVi2qVavGzp07mTNnDg0bNqRt27Y4Oua9p1dp8tlnn2EwGBg4cCDe3t4MGDCAv//+m4oVK/Lee+/h6npzwiGjycZvaxMIq+t8y73LCiMoKIh9+/blOJb1/Z2OhAJcvHiRv/76i0WLFrF7924cHBwKH6xyW4GBgZw9e5aaNWtm138WiwUpZZ713/U3e2WFqgNV/Xc/0mq1NGvWjPr167N582amTp1KmzZtaNq0KVpt8SXAuRVpNBM/dDKm3cfoFbUBJzfH7AYoQFCdJ4FpbFi3lAkTX+HJ/n7odYJm9Vz4csFVFvwVh4NeMKinDz///DMjRowgIyODsWPHApCRaWPhyjj+3JzIY129GNbn5mm28+fPp2bNmnfUcdqmTRucnZ1ZtWpVro3QqKgoFixYwKhRo7Kn+ma9xvDw8OxGqGn7YaLM9tHUChUqFDqe/MjXO7cQosMtHrYAV6WUalfvIqDVanMdDbVYLCQmJhIfH098fDwJCQnZXyclJeHs7Iy3tzdeXl54e3vn+Lo03tyGhYWxZ8+ekg5DUUqETqfjgQceoGHDhmzcuJGpU6fSrl07GjdujEZTerdv3r59OwsWLOC1117D29sbgMWLF9/2upVbk0hMsTKoZ9GP+gYFBREdHY3ZbEavt0/z3bt3L35+fndUgVaqVAmw3yj//PPPHDp0iO+//55x48YVRdhKHpycnHKtA41GY3a9d339l5CQQEpKCu7u7jfVfVlfZ/1dlCaqDlTuZ1nbmoWFhbFmzRr27NlD165dqV69+l2LITMzk48++oiM9bt59pwgvFN9wuf/wecVGlH52vv/uYhMNh1wJqRqE/RpGxk34LPs67dt20zfB4NJz3BlwV9xtG/mxoEDBwB4663JmF27kZiqZ+/RNFLSbXRq4c6o/jd3PF24cIFt27bx/vvv39GAksFgoGPHjqxcuRIp5U1lffnll5jNZp5//vnsY1kjodcnJzJu2ccVrQUo4ZHQ66zHng33xp9O9qpcIUQU8KqUcn4RxaZcR6fT4evrm+tCZZvNRnJyco7K+ciRI9nf63S6XBun3t7euLi4lLlRVEW5V7i6utK7d2+ioqJYvXo1u3fvplu3blSuXLmkQ7uJ1WrlmWeeISgoiFdffTXf1yUkWZi7PJZGtZ2pX92pyOMKCgpCSklUVFR2r+6+ffto3LjxHb23ZTVC9+3bx4EDBxBC8MEHHzB8+PBS2bF3rzMYDAQGBhIYGHjTY1mdtNc3Ti9cuEB8fDyJiYnZCaxya6A6ORX936SiKPmTta3Z6dOnWb16NTt37qRr167FPkvg5MmT9O71EKfOnEYDPPXKVxyobID50CxJYNy0l9QmjfhqwVXcnLWMGzWQ1159kdOnT1O9enU+//xzXnjhBXr37s3sH35j15HzTF8czc5d+3F18yY6Ooqvv/6aFh3G06iOC4909qJO1Zvfay6bbKxYsQKgUOtAb9SjRw/+/PNPTp06Rc2aNbOPX716lalTp/L4449Ttep/6zyDg4PRaDTs3LmTIUOGAJCwaQ/fmyIJCgrK9f22KOW3Edr+Fo9pgfLAI8CPQogEKeVfdxyZkm8ajQZPT088PT1vekxKSVpaWo4G6tmzZ9mzZw/x8fFYLJY8G6ju7u6lelRGUe4V5cqVY/jw4Rw/fpxly5YRGBhIly5dskcbS4Ply5ezd+9eFi5cmOuU27zM+CUao0nyzOCAYunwyuqpjYyMJCQkBIvFwrFjx+jatesdlevv74/BYGDu3LkAvPvuu7z55pvMmjWLiRMn3nHcStG5XSdtSkpKduM0Pj6eY8eOZX+t1WrznEXk6uqqOmkVpZhlbWtWtWpVdu3axQ8//ED9+vVp165dkXcSWaMTSHnve97843uios7zmlstpqScYHtdb7atWU1gYCCV/YLY+crPvBfqDsDLIwKpHfIYr736IrNnzyYzM5Ovv/4aR0dH9u/fj5+3niG9fJj5Wwx79h7Ev3IXgh0TiDw+i6EfdKdTp05IKfn777/5/vvvGTx4MB07dmRfqpWXLmbiefAobm5u2dNh70TWVi0rV67M0Qj96KOPMBqNvPPOOznOd3JyYsSIEXz33XdMmDCBaq7evHNoPafSY1n3+7pinyKdr0aolHJLPk5bIIT4BXgJ+36iSikghMDV1RVXV9fsUYLrZWZm5ug9vnz5cvYoanp6Oh4eHrlW0F5eXne0DmfMmDF38rIU5Z4jhKBOnTrUqFGDf//9l1mzZtG4cWPatGmDwWC4fQHFbP369bi5ufHoo4/e9lyzRbJxZzIHTqSz8VoihuDA4llLmdUIzUpOdO7cOcxmM3Xq1LmjcjUaDRUrVuTUqVO4urryyiuvsGzZMhYtWqQaoWWIRqPBw8MDDw+Pm2YYSClJT0/PMbX33Llz2V+bTKY8G6geHh531Emr6kBFyUmr1dKyZUsaNGjApk2bmDp1Km3btiUsLKzIBkRSP/2JuKWbWHnlNI/Wa86bP85hZpcHWblmNVu2bKFNmzYcNLQjdP1SXh4WR3DPRtSu4gR40KJFCz7++GOEEEyYMIEKFSowadIk4uLi6N/Zm/i4y2z8Pomnhj3A0MH96NGjB127duWhhx7i6NGjnDlzxh5DaiodO3ZkQ5J9yuvuYyepWbNmkXR4VaxYkTp16rBy5Uqee+45wF43zpgxg2HDhlGjRo2brpkyZQq//fYb48ePp7JRw9z0cJ57cgydOnW643hup6hX8y8CfiziMpVi5OjoSLly5XLd0N1sNudYhxofH8+ZM2ey16G6urrmqJyvr6Bvd9OsKmBFyZ1Op6N169aEhoayYcMGvvnmGzp06EBoaGiJzkzYunUrDzzwwG07nzKNNt799jK7Dqfh5qKhQ3M3BvYovhHdGxuhx44dA7jjRiiQ3Qht06YNer2emjVr8u+//95xuUrpIITAxcUFFxeX7LVR1zMajTkaqFmdtAkJCaSmpmZ30ubWUL3d/4mqA5V7WVJSEvv27SMiIuKmTLG34+LiQq9evWjatCmrV6/OXi96/TTSwog4cATtz+vY2tiftOVmhn31Po6NatK9e3d+++03MjIyqF67Bd+da8QX7puo/+EUHA8/gHXKU2h9PXnjjTeYP38+kyZNIjQ0lPXr1wP2bOwdOnQg2NO+r3S71k0ICQlhx44dPPXUU2zdupVGjRrx4osvsnTpUg4dOoRFSv5JseCmheizp2jYri1SSqyA7g4bo927d+ebb74hNTUVV1dX5s6di9Fo5I033sj1fH9/fyZPnsyzzz6LDsGTNZvx4bSv7yiG/CrqRmgKoFIH3iP0ej1+fn65zs232WwkJSXlmOYbGRmZ/bWDg0Oe03ydnZ3VFCdFuQ03Nzf69u3L5cuXWbVqVfZ60aysrXdTbGwsR44cYdCgQbc8L9No47WvIjl8OoP/eyKAXm09iv1/3cfHB4PBcFMjtFatWndcdta60I4dOwL2yro072+nFC2DwZBnJ63FYiEhISHHTKKsUdTExERcXFzybKCqNcXKvSw6Opp69eoRExMDgIeHB3369ClwOQEBAQwdOjR7WzM/Pz+6dOlSqG3NTpw4Qb0mDXnMsQLJaTUJCgqidevWgH0d5fz59nQ2hy/VwinAE/+tM7DN/ZPUGb9jPnoO75/epWfPnjmyzjZs2BCwb1vWoUMHDh48CECDBg0Ae86HrHKzXL58mbVr17I9OoUUq5bnvSwsuxJJTLmqjDuXSYzZxpvBjjRyKfw02B49evDZZ5+xfv16+vbty+o1a2jQqPEtc02MHz8e8e9Rmm48TZOfZ+Jwl2ZfFXUjtDlwsYjLVEohjUaTPS33RlJKUlNTczRQT58+nf11ZmYmGzZs4PTp0zg6OtKoUSM+++wzvL29cXNzU+tQFeU65cuXZ+TIkRw9epQlS5bQuXNn6tWrd1dj+PvvvwF7Cvi8SCn5bO4VDp/OYNKocnRs7n5XYhNCEBQUlN0IPX78OMHBwUWyt2dWI7RDB3uCeH9/f1JTU0lPT79pb7uzZ8/ecU+9UnbodLpbdtJmJQvMqgezlrlcvXqVNWvWcO7cORwdHalfvz4ffPBBdkNVJQtUyrolS5YQExPDvHnzGDlyJDt27ChUIxRy39ZsxIgRBUpcZDp4mrWTP8Zqs7EoPRw2hPPCCy9kr3ds+UAnhEaLVueKwb0ar4wMxMXfBV4ajKF9Y+KHvUdc75fw3TgVra9ndrl+fn4EBQVlZ8Q9ePAglStXxt395rrvQJqVJXFmVvvUwGazMX/3YVyqhuIXdRaAtArVSLdJ3LWCly9k8kw5Bx7yLlxW7+rNWqH38Ob52T9xsvYD/L19OyHD/o9/ki084K7DbJNIwEHz3/uMxmThscOpOHRti0PDu5ehuEgaoUIIHdAbeAX4sijKVMouIQRubm64ubnlOmrz9NNPU7NmTaZNm0ZCQgLnz59n8+bNxMfHk5GRgaenZ669x56enmVyPzjl7vvyyy9JTEws6TCKnM1mY+3atXe9Ebp161YcHR3z3ERbSsmCv+LYtCuFJx/2vWsN0CzXN0KPHTtG7dq1i6TcIUOG4OjomN3j7e/vD9h7+rMaqFn+/vtv1QhVgJzJAm9MNjJx4kSaNGnC4sWLSUhI4OzZs9nLXBISEm5KFnh9XaiSBSr5UdL139y5c/Hx8eHMmTP4+fmxePHiIstrYLVaWbBgQfZ6x9sx7TxKXP9X2Z5wAB8HJ4aPGMHX38+iZ++B/LUlkbR0K8s2JeEX3I7a1QOYN6Uqjob//sccwmrj8+sHxHb5P1K//gWPd3NOow8NDc3RCM2qK65ntknejchEKwStQhuyG/h7/yGGhYZx7uApAF57oB6PVnPCaIMPIo18GWVCAr1v0xC1SYlF5mxQ/pEiCOzxCOFL5jF/SVekxUKD9p2ZEmlkuL/k9zgzEngn2EBtZ3tDPHPJZmRCMq4TH8vXz7Wo5Hef0Aiu247lBlrA91pZ64H3iyY05V6UmprKwoULiYyMzM6wef3oitlszjG9KTY2llOnThEfH09ycjJubm55VtBqE3klS2Ji4k1Z4O4VJfG6tm7dSosWLXK9kYhPsvDVgqv8sz+Vjs3deLz73c/oGxQUxK5du7DZbJw4caLI1tsFBwfn2FPtVo3QqKioInlO5d6VmprK/Pnzs+u/ihUrEhoamuOczMzMm5a5HD58+KZkgTd21KpOWiVLSdZ/0dHRvPfee7z22mtMnjyZmJgYFixYwFtvvXVHHSjr1q1jz549hISEcOjQoXxdY0tKJXHCp2iD/Tns6kKLag2Y9OGX+Dd5jv/9ZMVivQpAkL+eLZv+pFbl3DPx6utUxunxTqTPW4nL6D7oggOyHwsNDWX16tXZM/4GDhx40/X/plpJssKUEAfCqtVmprMzHheP8YiPjp9OnkQIQd8GtdAJgU4Lk4MNTI408lWUCReNoKNn7v/XUkrejjByKsPGZ5UcqWDQEG+2sTLBQt9BQ/hm0Uxiv3oDFxcXZvZty3OXbHx71UQ1Rw2pVslzFzIZ4a+nr5eOtB9XoKtTGX3TounAza/8vmNtIO9GqAWIBjZJKdcXSVTKPevs2bP4+PgwefJkNm3ahKurK++//z4PPvggYF+H6u/vn32zdz2r1XrTOtTw8PDsdTkGgyHPdahOTk5qipOiFEJycjL79+/PNanB1Tgzz/4vnKQUK2Mf9ePhzl4l8n/WuHFjFi9ezKpVq0hPTy+ykdAbXd8IvdHly5eL5TmVe8ft6j+wJwssX7485cuXv+n6rE7arPovLi7upmSBN9Z9Wd+XhgzbSuknpWTOnDnYbLZbdualp6czd+5cDh48iJubGy+//DJ+fn788ccf2Gw2HnnkEQCaNWvGjBkzOHHiRKGTxZnNZgYPHpy9xtTPz49XX301120JAWR6Jpnrd5P+4wqsV+LQLniTY+3mUKVBH4a8eh4pJd0e9OCRzt54e+pwdBBoNLeut9yeH0TGks2kfrwAz29eyD4eGhqK1WqlR48eSCmz15leb1WCBV+dIMxVi1YI6terh8v5Y1Rz0nLixAkqVqyYYysavUbwVgUDz1/I5NurJlq7a3OMdGZZEm9he4oVBwEvXsjkxSADW5MtWCW81Kkla2vW5OTJk/Tq1YtAF0c+dkvg2LkYOjSsQLqjE59cNjLzqpk/ojIYrXemxaB2d73+zu8WLcOLOQ7lPmGxWDh37hyNGjXik08+YefOnTz00EOcOXMm13n018vazy23vROllKSkpOQYRT158mT291LKPJNEuLu7qwaqctctXryYK1eu8Oyzz5Z0KLe0bds2bDZb9owFKSXR8RZS0qy8/91lMjJtfPVqCP7lDVBC/0YjR47krbfe4v/+7/+AosmMm5tbNULVSKhyO3dS/8GtO2mvTxaY1VEbGRmZ/bWDg0Oe282oZIEKgMlkYsKECcyaNQuwr4nv0qVLrue++OKLzJgxAy8vL5KTk5k1axb9+/fnn3/+oXr16tnJeZo3bw7Arl27qFOnDlJKfv/9d9asWUN0dDSDn3iC3U170cdbz4PuuTdJ1q5dS0xMDIsXL8bR0ZGHH37YnnBn9eqbknxJKbk46HUydhzBy8cb8dpoJq86D8Dl1BoM7OHOE718CPQt2HpLbXlfXEb3Ie2bX9E3q4PLEPt+nFkzGXbu3Mlnn32WncQuS4zZxp5UKwN99Wiv/Y/Vr1+fpUuXIqXk5MmTOfbzzOKgETwZ4MCLFzLtI5s+9njPZ9r4PtqEg4B/Uqy0dNMywl/PCxcyeeViJgAdPLQEO2oZNmwYr732Gl27dsVy7hIOj75Og6hYYh0d0FUJ4v9sNrq0b8ns1q357P1X+LiSI3d3oU/+p+N+D/wopdxazPEo97iKFSui0+mypyw0b94cX19fTp06led6s/wQQuDu7o67u/tN61CllGRkZORooIaHh3PgwIHsRElZlXFu61CLe7Ne5f6zf/9+hg4dil6v5+mnny7wVPLwKCMnL2Ry9nw0GRZXUtMlfTp40qCG8+0vLqANGzZgMBho1aoVUTEmPvnhCodOZQBgcBB8/Hww6T56Bp5KJ9RFy3shhuzKtrCsUjI1ykSCVfJGBcNtU9b7+PjwxBNPMHv2bIBiGwnNSoahGqFKYRRX/Qc5kwXeuDb5+mSBWY3SrGUuCQkJ2Gy2PGcRqU7assVqtXLkyBFefvllrFYrkyZNws/Pj8zMTPbs2cP27dvZvn179rYikyZNonp1eyKacePG8cMPP/DKK6/w559/MmzYMA4dOnRTEqDExETmzp3L0KFDadnzY9ZtOkTEwS/488+VJCcnMnT068z8NYZWjVypW7Mm7u7u7Nq1i+7duzNmzBiWL1+Or68vZrOZQydOUvn37uiFyLMROn/+fHx8fOjXrx8ODg707duXJUuWMHnyZD788MMc5xo37mH06gXsEqkMGPQd4QdqcHbfDIQQ/PTNQ9Sqmv+ERjdye3EwlmPnSX51BlpfTxy7t6RKlSoMHDiQDh06MGrUqJuuWRJnwQZ08/rvtTVo0IA5c+YQFRWVvQVYbkKdNdRz1vBznJmeXjrSbfBGeCapVom7TlDNUcNL5Q146ARzqjpx3ijRALWc7NOeR40axbFjx+jfqh1xD08CixXPaS9h2n8S68UroNFQ8+eVvLp4FR/M/pTXow08ZDER6qKhiYv2rvzf53c67gBgmBAiHJgHzJdSnim+sJR7la+vL+3bt2fdunV06dKFU6dOER0dTbVq1YrtOYUQODs74+zsnL2n4PVMJlN2AzUhIYHo6GhOnDhBfHw8KSkpuLu751pBe3l5qXWoSoGlpaUxaNAgpJSkp6ezZ88eWrVqleu5qamp7N27l4CAACpXrozQ6Nl/PI0n3zxPxLFFnNoxhWqNh9Gg9SS27Emhd3tPhjzkg5e7jri4ONLS0ggJCSlUnFJKtqdYWbt+Ay1aPsAfG9NZtDIOjZBMaAfefk5Uru5BnJ+ON8IzcdYKdqZamXbFxMRAh0JXYCab5INII3+nWAH47oqJ8eVuP5XwmWeeYfbs2fj7+xcqhX9+ZO0pqRqhSmGURP0Ht08WmJGRkWOZS0REBIcOHbopWWBu61BVJ23JSUhIIC0tjaCgoOz32/fee4/ff/8dg8GA1Wpl/vz5dO3aleXLl5OcnAxAtWrVqF+/Pj/99BM//vgjn3/+OcHBwfzwww+89tprfPDBBwwaNIhmzZrx9NNP8+uvv+Z43h9//JH09HTadRvNT2sT8fOugnudL2hwbQLKuQy4sC6BX9cm0KmlO9VrNmbFilX89ddfxMTE8Omnn/Lss8/y3XffMX78ePzPHONgjbqYbPKmaadJSUksW7aMJ598Mvt+q379+jg7u/DFF18wYuRo9p/1ZOehVKpVcKDpZ9+yyRiD1OqZ9cVQOvYaT4DjEfS1at1RAxRA6HV4fjeJ+EdfI2H8J/gsfh+HZnVYuHDhTefapGROtJlf4sx08tBS3uG/9bBZo8QffPABaWlpuY6Egv3/drCvnlfDjbx4MROTDeIski8rOVLLOef/nY9eg88Ng7t+fn7Mnz+f5Pe+Jy0hBd91X6OvEYJTv7bZ50izBa99J/mklgefxAt+iTWzKBYaOmv4v/IGKhqKNxFafhuhAcCjwFDgDeANIcS/wFzgFyllUjHFp9yDvv32W0aOHMkLL7yAXq9n/vz5ec7tvxscHBwICAggICDgpsesViuJiYk5KugLFy5kr8txdHTMNUlS1jpUpWyy2WwFTqIQFxfH4MGD0Wq1LFmyJM81WG+88QYnT55k8eLFDBgwgM2bN2c3QrMaqD4+PgQGBjJz5kzi4uIA8PMP4oF+czl0MpUAzw84+e8CAgICOLPve2Z9PpKjl6uxdEMCa/5JwottLJ3/EhaLmeH/9zONGofRuokbcYkWNm0/TeVKIVQIcCAmwYIA+nTwxEH/3+uVUrIp2crbByM4evgQdVo+z+JfwhnCEVof24x1aQwbe3ZizkNdiKwcjP/lq7z93qeseXoIy+o34ESGjfbuOvp669DfZq1NhlWyLskeR7JVsizeQpxFMiHQgSiTjd/jLWgFNHDREuaS+9oYsN+Y9O7du9iTs+S2V6iUUjVClXwpbfUfgJOTE0FBQbl20t6YLDAmJuamZIF5TfNVnbRFw2q1cvLkSQICAvD29kYIwbJlyxgyZAgpKSl4eHjw2muv8cgjj/DRRx9Rt25dDhw4wIkTJxg5ciRLly6lf//+9OvXj5YtW2ZP575y5QpPPfUUzz77LA4ODjRp0iQ7oVGDBg148803eeONN1i3bh2dO3cGYNveJN6d8hVVajTlr52BVAvR8fWrIYRHmbgcY0argSB/B/x99Py4Ko4/EizEVm9E+O7NuHsH8Pff/9CkSWOMJhsVa3UFIYhf+weu1etyJN1GY9ecjavff/+dzMxMhgwZkn0sNsFMrMNozNZf6dBzAhUbvUiFAC3+my+w8+xerEiee20xR08tZe3PUwEYMWJEkfwuNM6OeM17m7g+LxE//F18/vgf+po3d+ysSLCwONbMQ146JpbL+X/QqFEjPDw8mD59OmDPaZCXpq5ahvnp+SfFygWjjefLO9zUAL0d4/rdOLSoh77GzR3SQq/DoXldygGfe0CmTbI+0cLsaBMTzmXwU3Vn3HXFNyIqpMwr31AeFwgRDAwBngBqAZnAMuwjpGuklLaiDjKPGL4AOmNfhbQeeFZKGX67a8PCwuSePXuKOULlfiClJDk5OUcFff3XQog816G6ubmpKU4FlJ6ezv79+2nVqtVtf3bvvPNOobIDnj9/nrFjx3Ls2DGOHj2Kh4dHvq47e/Ys3bp14+LFi5jNZgYNGsT8+fNvasgeOXKE0NBQRo0axbfffkuDBg0IDAxk7dq1gH3a0dChQ/Hw8CApKYnaDTviX/UxYmPjObXrYxwcHHFx1hJ9JZJXX32Vl19+mQYNGuDq6sq4ceM4dOQMq9Zs4nL4ETz86mE2JmIxpdCk51ycPWtx9dxKjmx6nqBaA6n1wNvZcTWu7cxbT5XnSqyZS1eNPDW6J2n1G+FWP4yjrwzn4w7PMPj0BYTFSnjvDnz35GAuOrtSMzmRthEXefDsOVyirpK2/G/W9e3G9t6dORsQwAPCxFs13dFdGy0x7TyC+ch5cNDh2CGME17efHTJyGXTf/VQYxcNA30daOyqxSIlkyOMbL82KtrQWcNHFR3zbIharVaEEMW6jUWLFi1wd3fP/p2Bvbfe09MTKWWp/6dWdaBSVK5PFnhj/ZeYmHhTssDr68LSnizQbDZz7tw53Nzc8PHxKfLETkajkStXruQ6Mn29U6dOMWzYMHbs2AGAu7s7ISEhHDlyhLCwMIYNG8aqVatYuXIlfn6BpKSm8ECHJwjr9gZXTBJtogWr1YaUggAfPQ80cqVedScqBDhgcNBgtVp55ZVXmDdvHlu2bMmxlCEzI4P6DRqg0Wj4ZelOft+QxpLfF3F080s06PgZwTV7Me31ilQsb//ZWKW9EzHDJvHQCubFmEmwSHzCT7N1+sdUHvAqNU664uKo4eJlI+kawb4dIzEkxtJw2R4e9tYzNjBng61///7s27ePc+fOIYQgKsZEx4depHG7Z4k/PZ1VSz4DQCM0/OHTgpn6WPaLVDafvsD48ybcT+/H8OsMRo17mr1VwnDTCuo4a3jQ7c6mm1rCrxDX+yWk0YTn1Bdx7Ng0x+NvhWdy3mhjXrXc/84zMjKIjo7GarXetI1TXmxSoilgzJbwK8S0GIX75NG4jM7/Xq2nMqw8dS6TCYEO9LtxiPXWChRggRuhOS4Wohn20dHHAB/sWXJ/klK+WOhCb/+czsBBwIh9VFZi3xbGGWggpUy71fWqAlbuhqyplnk1UE0mU/b6nRsraE9PT7UX3HWMRiPz58/n7bff5vLly4wcOZIZM2bcspf9do1Qo9GIXq/P8XPevn07nTt3RghBWloaU6ZM4dVXX81XjN27d2fHjh2sWLGCzZs38/rrr990vZSSDh06cOjQIU6dOoW3tzfjxz/Dj3O/Z/+RK1htOp4c1pvz507Rd8wmjp+NxdXNk6b1XKhU3gDGUzw3rmf2Ng+PP/44AKtXr+ahhx7CYrFgMBho2bIlvXr1YuLEiVy6dInWrVuTkJDAw49P4NefvsbZxZmE+Hhef+tjKlcqx449F7hg6g9osOkEMWlbOTx/NABB7v5ctWYw6X+/8kJsBJv7dedbnSdeOsHTgQ43VeSW85dJ/foXjBv3sPLBVix4ehjN/t3N6GOH8bwai3HT3uxzL4XV440P38Bbr+GlIAMhDgIb4Ke/+W8/3SrZmGThiygT7d21vFbBUODKuKj07t07e015luPHj2cl3Si9d9XXqDpQuRuykgXeWPdlfQ/kuQ71bnbSpqWlMX/+fFxcXGjSpAlJSUn8+++/fPXVV4SH/zeu4e/vT3BwMCEhIUycOJH27dtnP2Y2m/ntt99Yvnw5sbGxDBkyhEcffTR7NlRKSgobNmxg27ZtuLq6YjabmTNnDtHR0dSuXZsXX3yRkSNH3hTb4cOHs7fGeuedd7BarZw7d45z585Rq1YtPvjgAxJTNcxaHcfcb97k7K45VGv6ElKbSfVP3sXkpafS+QwqXTKhEXA23Ej4FRMAGgGNajvTtbkLTb3T0UVcwnr+MrYrcchMM5azkZj2HGe1O4w8+AeBVXtTI2wUu5cPokGDevy2bjMH0wXd/fVohCDeInk/IpOD6f+NQ1U2CF4OMlDDScvKeDOfRZkIOZpGwCWjPTFQbRdmLJ3FqQ9eYOBfu3GuWYfZ1XLmNahcuTLNmzdn8eLF7D+exmdzr7Jn0xesXfopvm5mvh77PIbD5/ns2D84u7pw2ZJhHzV98XNOZdpj+a6KI3vTrMy8akYvwCxhlL+egX457yFsUrIxyUpNJw3B+ZiGagm/QsKTU7AcO4/n9Jdw6vNf8r5HTqbT1FXHpAolm5U67fs/SX7jO/z+mYmu8s0Zt29l3NkMbNh/fgX4f7x7jdDsQoTQAx8CzwFIKYttkYAQ4v+Az4GaWetShRCVgdPAy1LKz291vaqAldLAaDTmWId6feWcNb0mr3Woen3BsrqVVVFRUXz11VfMmTOH2NhYWrRoQdOmTfnmm2/o2LEjf/75501TnqWUJCYm8uWXXzJ58mQsFgvh4eFUqlQpu8EZGxtL06ZN0el0vPHGGwwePBghBI0bNyYxMZFt27YxduxY9u7dy4ULF3B2ds5R/h9//MHBgwfRarU899xzXL16lerVq/Pa62/RodcLxCaa+eajEezduYEV6w9RtXIFXJw0fP7ph3w45W16D/4QB/9HSE6zEnV2LYc3PEOTXgtxcgvm78VtqdRgNO16vcIjXbxo38wdZ8f/KsPz58/zxRdf8PXXX+d43bGxsQgh8PLyuqkD4/LlywwaMpQtGzfgElSR0J82cnXy05zZtCb7nDe+XcK5hh2IcdWya3hXTFGRBEktZ6LOU69VBwJnLsdFCylW+/SgV4PsyRDyIm02LKfC+TkyjR8DK6KxWmmxfTcPu0H9rk0wXbzCM5EWYgL9mVPVCR+/22cGBVgca2LWVTMdPbS8WN6Q54hocRo1ahQrV67MsSXLxo0b6dixo2qEKko+ZCULzK3+u1WyQG9vbzw8PO5oHWp6ejpHjx7l9OnTnD17lhkzZuQ6lb5NmzYMHToUi8VCdHQ0ERERhIeHZ+/ZumrVKtq1a4fRaOSRRx7hr7/+wt/fHzc3N86ePYuLiwudOnUiJSWFbdu2YTabMRgMmEwmpJT06tWLdu3asXDhQg4cOMCxY8duWhvYt29fNm/ezJEjR6hQoQIAFotk3al0NiRZuCAgwaCBa+sN0y+eIahSVY5On0K18a9T31nLzlQrHT20PFfOgJNWEHnFxJnwDBLW7cfw10aqRZ/A5OoEUuITG49Zp8eocSDe0YML3hUJi9zPN8kn+CrpKFoE7s4u7N29m2nOFdmfZqOlm5YH3bTMumoiwwbPlXegpZuOKJONSgZN9nIMKSWvXMzkSLqNd0McaeKi4b1II9vPXWZN++qUa/kotWfO4bkMGy2rObHqQgbbLsQws19Vwjq+TPnnXiL1ipmgMxm4pc9h2lcfkPzOLNJmLkNXqyJ/1/fh4a/fA+DlX1awp05bnivnwNQrJjp66NiVaqWSQfBhRUc+jDSyNdnKxxUdaXRt+u8Vk42PLxk5mG6jnF7wXVUnXLS3fzuX6ZnEPTwJW3wyfn9/h3DQc8loY+iZDJ4r50Av75K9X4sf9BaW8Kv4//1dga9dFm/m6ygTM6o4UsMp3/9zBaoD72jxjBCiGvaR0CeAikAK8OstL7pzvYEd1ydGklKeF0L8A/TB3kBVlFLNYDAQGBhIYGDgTY9ZLBYSExNzVM7nzp0jISGBxMREnJyc8pzme6frUM+fP8/y5cv5+++/OXnyJLVr16Zdu3Y5kgIUhb179/L5559z9uxZhBB88sknPPjgg6SkpLDmz79YtvhXflm9AovVQp/evRk9eCgd6zXCFn6V2uGpjF/2I4Natmfhl9PRNKnJz3/8zrRp0zh06BCZmZkEBARgNptZuHAhFy9exNPTky5duvHRR1MYP348ly9fpnatWgwfPpz58+fTsWNHDh06xI9zFxGV6M2DXZ9h9eoeDBrzOZ4VB6HVCupUdeLCyW1M/fAxhBD2dZN/H8PB0QuNRsf2i53YOdt+M2Pzn4jRtJohT75GjRavc2b3p0Qem09A1V7YPPrStJ4L3h46kmU3XtgALkGHKe8ZAdJG1w9HM6hlBVp76G7qfaxcuXKuWxT5+vrm+bOO9QggcNpS6v35K51atqBq1SB+n/I9rkt+4LUuD/Ds8CeYOfcb2szsSIN/17Fu73be9m1Id88Qensn89SooYS7avHXC+o7a+ngob1t9luh0aCvVYknakEHk40/Yk2sateSf2xQPVlDgH9lzjlZGf+/b9B6G+DL5/L1dzPAR49VwvfRZmLMmUwJccRJK9ifZuVImpXBfvpiHyH19/cnJiYmx7phtR5UUfLv+mSBWY2r612fLDA+Pj7XZIG51X95JQu0WCxs2LCBL774grVr13L94EurVq34+eef8fDw4ODBg/j4+FC9evXsjLE3io2NpW3btvTq1Yvhw4dz8uRJ1q9fz+dffE2rDsO5eNnE/r1/s3v7cnbsXI+rmxtDR0ykXftu1A9tjl4vwJZJgJ8XjgbBgMcHU7NGVd59911++umn7OfZ9s8uli1bxsixr/PXcWcOHYtHXjISbrSR2dIDXLVoYswEp9roUMMJL1ctKf61uWC0cUWn4avKTlQ2CBbGmvkx2syJjAyauGjZFZeJb1Q4Lc/sYc+I9nze8iXktfcxpwQTrtEWgrSCYJtEm2EjwjGJN5cuonp0U946upWJlVtx4ItV7H92DC21ZnamwL8pVmo5aXixvIHK1zpNq9/QaBFC8FoFR166kMkb4ZlUd9RwLMPGQ9WDqD7xeaZ+/Sk+ax9iSpUGJC38h4ARg0mIPw6Ae4dmpFZyQhviyFeD/PnyIx3JC9eRNnMZziN64f7+WPoJwZCECyxbvZZd1VvSyUNLTy8dh9KtrE60APDatUzrL5Y3cM6YweTITF4ob8Ag4INIIxIY7KtnUayZr6KMvFYh5/Yvuf4tOzviNmkI8YPeJv3n9bgM6c7RDPvykboFXLtZ1GxpGRi3H8JlRK9CXd/RQ8e3V0z8FGOmm5ckxiw5lWGjtbuW5m5Fk3uhMGtCvbFnyx0KNMM+HXY99iRFf0gpM4sksryf/wqwTEo59obj04FHpZS3TH91u15gi8VCUlLuM3qllCDETc387J/htZsfcePx6w/m+pjIcTzHqULkOF/keOi/x/4LIWdZecnrsdyOS1l0Zd14/MYz7qSsmx6zFWFZRRlXXtcg8/j5249brVbMZjPJyUkkJSaRmJhAYmICSUlJ1z4SEUJgMBgQQoPNZsNqteDq6oanpwfJySlciozkxInjXLkShaubG44GRyxWC1arFWNmJuHhFwEICalIrVq1OXHiOOHhF6lVuw4ff/w5rdu0Q6PRcOrkCZJTknFydMLRyYnMzEx++/VX/v57C75+5SgfVInAchURQETEKUxGIy4u7gibhsvhF1i59nfcnFypWb4qEbGXuJIYQwOPchxLvoJZ2nAXOvo4lWeMRw0qa5zQ2P6b4mPV6vjGGMnHMXupqXMlwppBurRSyT2Qpn41cbRoWH55F0nmNEK8a1G9XCtMMUfYFbsfs82CDcmH7vUY5FqZWdpU/he5FbO0UtutIsNavs7RynWwoGX/woFcyYjhnZFziKvelMPhFrYvH0tSzGEeGLCB8wdmc37/VIRGT5U6XXj5re95sLEbIeX0nEm28emrz7Fo3rd4ePqSEB9NuwFP0eSVj3i6ujNBzlq+ijKxJtHC3sEdSTq4E42DAeeqtWj329+kS0H16KuEaiyUL++F5ewlYsySkyHB/DPnC4a99hZNXLQ0cdWSYJGczLDR0EWL23U9t1Yp+SnGzPwYM4EOgtcrGKh17cbgeLqVNyOMCIuVK59P5u/vP2N6vb78cHILJ62pHJnwAeWeHYymYmCRTQ1PtUrWJFrYnGTheIaNB9y0PP/jQtK/+8Oeta9O5XyXtSHRwpRLRgb46HnCT8+wMxnEWyTD/fQM8b99Z4ktKZW07/8EkwWX8f3RuOZ/a5svv/yS5557jri4uOwOgU8//ZSXXnrpnhgJTc/IJCPNeNPxrPemvN+jAP6ri3Jck33g5vrkv8dyef+TWZ/kdV/nEtd119lyi1P+99l2U/w5z7/pUVvO13Tjc+f22H9l5XzQdn2ddF36jBtf97UfZa7H7Y9lf3XD897888jxGDdfdn1MN8Z7y/otl9/Vfz/7XM6/qfTrTrz26fqEIvb6zoTFYsFiMWOxWDCZTZhMJpKSEkhIiL9W/yWQnJxESkoy6enpSAQZmRkkJyeBRiARXIm8gNlkxMPHnxY9B1Chdig+IdVw9gtC4+KKGbBJ+4eU9jhtegfQanExWXE02tCaNdjSrRjTrVyOu8KfM/+P6LP7sZgyaNjpTfwqDcBmzfXHdf2PKNdhogv7PuPsgdk8/cYGgkNqcS4ykyXfjyDp6gFaTfobXZ+KcN300Go2yRsVHQnOY0uTG5ejHEyz8mFEJklGC3V3HySieiVifX1w1kB3Lx0VHTSk22Brsv29WQJaoJOnjoe8dHjrBM9dyOSq2f6LMphMlL8QyeRnXif8yf7EdmpBR2HEIdgfXcjNHevXS7ZIXg3PJNosGeyrp4eXDixmHnjgAY4cO4bRaERarQyZ8TumK6f5efIkHtx6npbBAexNs/HUsr9YN/1jon5dSZeDBxn/ZAeEXoeUkq8j0/k9IoG+lfz4v3IOaIXgYJqV5y9kUttJwzeV/5tWetlk490II6evTdmtYtAwOcRAeQcN86NN/BhjZpS/nsd99bediiqlJK73S1gvx+K/fRZfxNnYnGRhaS3nIu0UtcYkoPXzyvf5GUs2kTjhM7x/m4KhVYNCPecnl4zZjXgABwEWCW8GG2iT+99f0U/HvTbdthf2hmd3wAE4hj0Z0QIp5eVbXF6khBAm4HMp5aQbjr8PTJJS3rJ5frsKeM2aXXTr1rxIYlWUUkUIdK7uOAWUx+ATgJQ2kKA1OKI1OKLR6XGvXpfAdj1xDqoEEgwZRuK2rmLP1LdJi7uCwdkVodWRmZKYS/GCal5BJNrMxKfEYbXa37h0Do5oHZ0wpiaBzYZWp6dx8y70bPcodS5GEXj0ON9c3MV+YwJVA+pQxacxrr5NcNFJ0hr7Eh/iR7qHK36n4/C6mEacXzAWvQP71n3GlRPraOjsQ3OvECo2DGN3+1Yca1SX81PfJ6xtHyqmmnBKS+ditUpESCMnpr2Pg4cX9Z7/EI2UaM1mEg7v4djcL6j+4hQc6oVmvx7fP5fy++tD6eNcgalu9TkrMml9eQMvVmjEC151SDOaeThlD4eizvH261/j2/xBIqtU5KTWQLIV/FLj2D6gLZWqV6fciOeIbWhfL+KogQC9INwoedRHTzuSWPLhpyz69VeGeFRjYIojW7u2Y03/nkQFBWK9lu1VWG1UOnueA4um4/vaR5j1epw1kLUEx0kD3Tx1VHPUkGyFlQlmIkySjmkJjD5+GM9W9dBVq4AQAsvFK5z8bQtvNG9NnMbGzvbVsVhMaDVa5nz3HUNGFk0mwbykWiWOGtAkpRHdahQOjWvivWBygcr4+JKR9YkW2rhr2ZRspaGzhoPpNiYHG7L3mzuWbqWyQYOT9r8Ou/R5q0j5aC4yKQ2EQBvsj8f/JmBo2yhfz7tw4UIGDx7M8ePHqVWrFgAvvPAC3377LWlpaWW+ETrx+U+Z+sVLdzEiRSlaem9fDAFBgEBazDh4++Lg449LtdrovXzRe3ih8/BC7+6F3tUDFycX9I7OCCEQ1xrYQoLZQY/JMe81fVJKbCYjWoMjOrOFSimpVHfVovH1IM4K5zNtGGxQziKJEXBRCHz+n727Dq+yfAM4/n1OrjuI0SlIt9IlICogqCC2lKDiz8YAUbEbBMUEgzQBQVIJQRAQQTo3cl1nceL5/XHGZLDBNtbcn+vate3N+z31nPt9SmuqOjQ+do1HhgtruovTx8/w9pjWeIZVp/boZzm9aiEnV3xH/YcnEjHicepaFU95ZXAkMQ27MtCjTghGs+nCWE7H4Th0nMnvv83z4x9DmYxkbt5N5qZdpG/ZjSPZhv+w6/B69m72KwvVrQZ8zmtymu7SHMtw8WuCgyXxDjK0O6vwNMCrNTxYnejglwQHr/tkUO3rRdg+X4ROtmXvb6xRCd+n78Lzxk55Pm7OrLzjbIsa+/5Idn37E3d8MIVu3box69dFDBo0iMzMTH5ZtYanNh9kfKiBuzedIujYSbYvnYXlqdcAGBNu4eZgE/NjHXx0OpObg02MCbfkqKT5+LSdLv7G7JuwZzm0Zn6MnWiHZkS4Bc+spsNOrZkSlcGaJCcDg0w8UMlyyWQyY+124m59FmuP1jz+7GOEeph4tcala1Lzy/btryQ++j4BUx/Dc1DXfO0T0/9RXAkphP4+HVXIm8lOrTmeqbE5Nf4mhb9R8fSxdHbbXDxQycKNQabzH5tiSUJjgQAgDpgDfKm1LpVOJVlJ6Fta66fPW/4y8GRuSahSaiQwEqB69eqtjh49mufx9+2N5MEJUy4WQR5xuX+fV4/JhfWmlziUusg+5z7R+vxVKpf7ojl3ye/KXOp6swK72LXkdaz8bX9u7Llev7rYObLrngsQ1zk1xjk2z+PcOf/IY31u58jtsTw3rvNeL+fXmJ8Tl1IGDAYDymA452/jef8bMJutePr44enjj4eXLy6nE4c9A6PZQkZaKmmpSaSnJGGzuX+n2ZLJTE/Dw8sbD28/vLz88PLwwsPqhTEoBB0SSqrJwMG/NxC1fQPY7dSp1Rgv/yCSTYpUBWkWEz6de2MNqwyAITOT1JhTGO0Oanr6EZJiw8vhIMXbk1g/XzKsFmxmE+nqwg9GDwXXBZjYlurkWKYm1KSwGOB4psbbAFaDwtMAzb2NWBSsT3ZyJusObZhZ0dXPxE9vTqb5A09zPM1BilbU9jJR1cNdALnIuusNODW4sh5lf6MiKCMd331Hic10sqxuPf5490WOfPQ6ncZMIHnXNnZuWM6yMa+wtXMXltdvgC0hloStGwjteRMmh5Oqx45T6/RpagV4sKBeQ1wuTZrZjFd6Orf+vZ32PkamVa/HAS9fHt6+hVY7/8Wx9yj2v/ZiqBKKR6+2GKsEY+3SEtPVtUnbvJvo/cextGqIX3gQxj93MmnKSzxorMG2oBD+7t2ZiMY1aNysJssSHaxJdGbXJjQyuej/+3qaTpz63+vL04qhUhDOwyfBYCBu9BD+ueNGtk1/hdmzZjFv3jw6duyY+wu6mKRM/47kFz/De/QgfMYNJuP3bTgjz+B5Sw+M4Rc2PT4r3qG5a7+NVBf09DfyvypWHjmSzv40F6MrWTiS7mJJgoMGngZeC3Ri3r4X2+eL3MPVd2qO33P3om3pJIx/B+eRk1g6NsX/tXGXHLxhxYoV9OrVi99++y17kvFhw4bx559/cuDAgTKZhBakDJw/bzWfzJ2X15Hy9Zl+fil4ic3P2ySPc+RS/qnc1l1ked6Lz7Ziyr0suWhNSC6to3JEoc5fqi7YJEdM51ybRuVy7v9iy/PMuXwfUDlOcuH2uYSU55Lz98m5PNfFWSsU50d2wTWcbVGmFCaTGaPRhDH7twmT0YTRaMJssWKxeGC2WLP/tppMWJTCqjVm7cLsyvqtNSaXE1dGGhmpSdhSkkhLTSQ1NYmUtBSSbCnYXQ4CfHwJ9PEl0MubQG0iQJmwBIegA4NINJmwmc1keljwPBOP74Fj2AP9SAkPJi4hlVPayP7G9TldpRImuwO/jHSqJSWS5OvL/tBQwlNTaRIfwwlfX/b7B5JqytlXMHrVIvZPeYz0U1EYjCauHTqGfj0GUfPkGa6Zuwjzmbj/NvawYqoejvK0ojytYDbh2H8M1yn3Nm8m7+Mx3/rZmxtrVcbSpjGeg7pi7dw8jyfoQkkOzZZUJ/+kOukZYMpuXurUOjuBdCXbcOw9is6049hzFNvcFTj2HCV4/hQsbRtlH0trTfri9WSs+gscDowRYXgO6oZtwSpSP5jvTvy9PdGpaYz3imJ55F4ClYW6XgF81fJGdKad+e3b891dQzg07WUmT5rEwXQXa5Od2YMNdfEz8mwRDVrn0poZpzNZGOtgfGULN+Sjb2fqrCWcfGU2o+d9zJBFy7j5j00ETH30krXDl5Kxdjtxt08EhxNz66sI+emNS+6TuW0vsdc/it+Lo/C+74bLOv/5Up2aF6My2JzipKW3gWcjPM4dI6JYktAfcDe3XaS1thc04KKklDoN/FBczXGFuBI5HI7suU/PHyQiMTERLy+vPPuheni47/YlOjQxDhdhZgO+RkWmy31LJq95Il1acyjdxcEMF3Z3pSweBtic4mR1opMQs+LRKlZa+xjRWvNXqpPfk9ztneIdmu2pThwaWvsYaetjpJGXkVpWhUGpQk/Rci6n1myNT2NQt05E7dgKQKWbbqfRyx9hwF3r2MbXSHUzBJiNeMUnYl/6B+k/ryVzw05OVK3Ex888RP1Tpxm8YSPWP/9BJ6SgAYfZhNnhxBDoi7FuBB492uB9/43uLxSXMGnSJCZOnEj6kg0kvzYb54EoTFfXwdKuEXa7kxO/78CVYSf85GkAvO67Ae97+pO5cReOvUdxHDuNpVVDPAd0xhjhni/ubJPv0hiVWWfYSZwwnbRvf825wmLCfHUdXKfjMLe+Cv/XxmLfto/EZ6bjOagbPg/fwrIkF7Oj7bxby4NQs4FUp+aV4xn8kTWlSw9/I2sSHNQ4cJj6f/+LwWhgWHUfKt3R57875emZ2L5aSvKbX2O+qiZB37160aRjx44dNGvWjPnz5zN48GAAunbtitPpZO3atWUyCT2XlIFCXOjcwQLPHzApJSUFf3//PPuhms1mdKYd57HT2Hcfwb51D479kTgjz+BKSkVnZKLT7ZCRmd0E2eblSWxYCDFhIdgtZoIz0gk5fZqlB7fT2DeU5jVqozzcCaapQXXMV9XEEBKAzrRj33EA14kYdHoGOi0DnZ6JsWZlLC0aYKpfnZfmfcGzQ+5C29Ixt2xw0Zt5Rc2VkEJM//+hE1PxuKEj2pYOBoP7Zuu2fRiC/FDenjiPR0NWNxvP23rh+9jtGAJ9SX77W7776FPuiVwHwOMte/BUky7o9Ezi+3dhVIsOnJkxhS3vvYQCliY4OJmpsRpgWIi5SAer01rzvyPpHMtw8WU9rwtqjc86nuEizKwwGxTL/zrMqx5hTFywkPpzl6A8LATNfRlzvWqFiyE9k9Ot7sIYFohH32tIeXcOIaun5To36bkSHnyL9GUbCfvrSwy++e9uku+4tGZxvINppzKpbjXwRg2Ps/OJFn0SmmMHpUKAlOLu+3mR868CLFrrjuctX4P7erpcbH8pgIUoGJfLRWJiYp4FtNlsznUUw8DAQLy9vQs11H6iQ+Np4KIFikNrXDr3bYoiCT3Lbrdz8OBBjkVGEtykFVEWX1p5G4m4yBDurvhkMBow+HlnL9MuF66TsWA1Y/DyAE9roR6bc69NO52kLVxD6ic/4TxyAp1px3NgVzz6dsB59BSGKiF4Xn9tgc9RGjI37yZ9yQas3VphjAgldeaPOPZHYgjyI/2XPzCEBeE6HYchwAdXXBKWa5oQ+PlzKJ+c87C5tOanOAcRVkUrKyx5cibTbr8FrBYcykAli2JKdY8Lnr/UzxeR9MwMgr55AWvXVnnGeerUKSpXrsy0adN44IEHAGjQoAHNmjVj3rx5koQKUcGcHSwwt+lmzt6kzWu6mbM3aSGrb6zD6U5KM+yQaQeDAeXtUaB+6ZdSlOVfYTgORBF35wu4ElNQnh6gXSgvT3weGITnLT1QRiPOEzGk/fQ7prrV8OiZc47NtLQ0wsLCSElJ4fvvv2fAgAHZ6+bF2Fn99otMnzK5RK7l7HyZtwabGXneHKZpLs0npzP5Ic5BWx8jz0RYGXEwDS8DfFTHE73nKHG3PQsmE6FrZ7jL/QLKWPc3cbc8Q+CXz2Nu2YAzLe/C+57r8Zs0Is99nGfiOdPmHrzu6Iv/S6Py3K4obElx8OyxDGpa3XN4B5gK9qUmX8MbKaUMwPPAeMAXcCqlfgbu01onFDDmy/UT8KZSqrbW+lBWfDWBa4GnLrajEKLgDAZD9h3f8ydV1lqTmpqaIzk9cOBA9t8OhyPXu8dBQUH4+fnlWfN2sek/zjIpVcB7boVjNptp2LBhdv+/vNOT/xgCfS9YpgwGjFUv2lCjwJTRiNctPfC6pQfgTkrVZUxfUJosba7C0ua/idL9X3kg+++MjTtJGPsGHjd0xP/NB0n/eR2Jj75P8itf4j9lTI7jGJRiQNbk2inTv6PlnMXM79UM777XsNPm5Plj6Yw7nMbkah409f7vsfK6/TpSp39H8quzsXRpmecNgrMjEZ85cyZ72cmTJ+nTp8/lPwhCiDLHZDIREhKS6yjkLpeLpKSkHAnqrl27sv82Go0X3Jw9+7d3kF+JzYdakkx1IwjbMPOi2xirhOAzelCu6zw9Penfvz9z5syhRYucffVvCTHzr6XkWuzU9zTSy9/Ed3F2ansY6BngTpuSHJr/HUnjSIamrY+RP1OcjDiYRrRd814tD/f3k6tqEvjx08QOfJLUDxfi+9jtBT5/xu/bwGTE0uFqDD5eePRpj23+anyfugvlkfsgfLavloLdUehRcQuitY+JF6rBpMgMHj6cxpf1CnYzJb9j7I7BnYSuATYDtYGBQBJQvCNYXGgmMA74USn1LO5WfC8CkUDBJ8IRQhSaUgofHx98fHyoXr36BevT09NzJKhRUVHs2LGD+Ph4bDZbdhOn8wvngIAATKaiGQL8SlJeE9BLsba/mrAtX2R/YfO6rRf2XYewfbYIz5u7YWnV8IJ9nGfiSXnza6y92+HVpwMAV3sZmVrbkwlH03niaDr9Ak0kOjSVLQYGBJnweXQYiY+8S8avf+JxXe4D1JlMJoKDgzl92t3cOTU1leTkZCpXrlxMVy+EKKsMBgMBAQEEBARcsO7sTdrzp1vbsmULcXFxOByOXOdDDQwMxN/fv1S6R5QVEyZMoEGDBrl+ryhpYypZOG138crxDLalOrkh0MT005lEZWpeqWGljY+Jj05lMi/Wzk1BphxTs1jaNcbjxk6kfPgdXkN7F/hGdObavzG3bJBdU+51Zz/Sf15H6qwl+IwccMH2OtOObfYvWLu1wlSn6mVdd3618zXxRk3FM0cL3kA2v9/yRgAzz+2HqZQaBUxVSo3SWmcW+MyFpLVOVUp1B94BZuOuC1kJjNdap5RUHEKIS/Pw8KBKlSpUqXLhYC92uz1HP9TY2NjsWtTExER8fHzy7IdqtV6676SoWM6vMfB98g7Sl/xB4uMfEPLr+yhTzgQ8dfpCdIYdv+fvzbFvFYuBD2p78lJkBovjHYSaFL8nOZkfa6dp22upMj6F4+mexOy3MSLcwrW5DENfuXLl7LlBz/6WJFQIca5zb9JWq3Zhn8D09PQcCeqJEyfYuXMncXFx2Tdpc2tFFBgYWOFv0jZp0oQmTZqUdhiAu2XWGzU9+PS0ne/i7NlTljwX4U5AAUaEm2nmbaCl94U3gn2fvYf0ZRtJmvwZgR89me/zuuKSsO84gM+jw7KXWa9tirVrS1Le+Ravwd0xBPnl2Cd9yQZcp+PwevPBwlxqoV3tZeS9WgWfpz6/r+LawGPnLZsLTAdqAPsLfObLoLU+BtxckucUQhQts9lMWFgYYWFhF6w72w/13L43UVFR2TWqFoslz2a+Xl5F3wlflD0GHy/8XrifhJGvkr54PZ43dc5e5zwTT+qXv+B5c1dMtS+8G+xrVLxW0wOX1hiU4kSmix/j7GxPdbG9X0/Co05gdjiZGJnBHaEuqloMpDg1JzJdWAwKr7DK7I08zienM9my/TAAWzxCuKvErl4IUd55eHhQuXLlXG9g2e32C/qhHjhwgPj4eBISEvDx8cmzH6ooeialGFXJwm0hZn5LcuBvVHTx/y+FMihFe9/cUypTRBg+D91Cyhtfkz6wCx592ufrnBnrd4DWWDs1z7Hc9/l7ien5EMnvzsF/8kjAPeZE5vodpLw7F2Otyli75afjUNGq6VHwmvv8JqE+uJvenis56/eFnZ+EEOIynNsPtU6dOjnWaa1JSUnJ0cx337592f+bTCaczkvMGi4qBI9+12CsE0HKtIV43Ngpu8YzdfpCyLTj8/CtF93/7FD+VSwGxlRy165nRp4h9rpHMT8yjA9uGcis6P8GhPdQ4ACO+YUTv2sX82LsJB2JBGCHt9SECiGKhtlsJjQ0lNDQC5tv5jZYYFRUVPb/27dvL/mArxD+JsWN+Ziu5Xw+44aQvngDiU9/iKX91RgCfC65T+ba7SgfT8wt6udYbm5YE69hvbF9vgjrtc2wtLmKuDsmYd+2D+XvTcDbDxd6XtCSVpD6/KpKqXNHJTGeszzh3A3PDhgkhBBFTSmFr68vvr6+1Khx4TDlDoeDqVOnlurogMUpt75HVyplMOAzZhCJj71P5tq/sXZujv2fg6R+tgjPwd1yrQW9FEu1MKzdWmL/5hcmPjyEk+EWXICXAYJMigwND9aN4POfT/NjfSvvL4pmE/Bzl3pFfn1CCHG+Sw0W+Pbbb1fY8g/KZxmozCYC3n6YmOv/R/Jrs/F/Zcwl98lY9zeWDk0u6GoC7tpQ+65DxI9+FWPVUJzHo/F/6yE8B3bNc8CisqggSeiCPJb/kMuyijk6hhCizDOZTIwfP760wxAlxPPmbiS/PpvkV7/EEORH/ANvYAjxx+/5+wp/zGHXkXH/FOwbd1L1vKZQHgpa1KrGJ04nibExREZGEhAQgJ+vNAoSQpQupRSPPvpoaYchcmFuWtddg/nNMnzG3pw9T3dunKdicR45iddd/XJdb/DxImj2JGJvfgpn5BmCvnoB67VNiyv0YpPfJLSkR8AVQgghLklZzfg+czeJT0wlpvdDoBRBc1+6YMCGgrB2bQlmExm/bbugPw6QPdDWiRMniIqKIiIiotDnEkIIcWXwefAWbHNXkPL+PPxfH5fndpmbdwNgadsoz20MQX4EL34bnWzDGF4++wLnKwnVWn9Z3IEIIYQQheE1pAfWri2xfbkEY+VgrB2bXdbxDF4eWNpcRcZv2+DZC+/BShIqhBCioIwRYXgNuw7bV0vxHjcYU/VKuW6X+ecu8LBivrpOruvPMnh5gJdHcYRaIspHz1UhhBDiIoyhgfg+djtet/cpkuNZO7fAsesQzpiEC9ZJEiqEEKIwfB4cAgZF6vTv8twm889/sbRqgDJX7Kl4JAkVQgghzmPp3ByAzN+3X7AuPDwcpRSHDx/m9OnTVKtWDeeZ+JINUAghRLljrByC583dsc1dgTM28YL1rhQbjl2HsbTJuyluRSFJqBBCCHEec5M6qEBfMn7fduE6s5nw8HA2b94MQEREBLbPF5V0iEIIIcoh79EDIT0T2xeLL1hn37oPXK6L9getKCQJFUIIIc6jjEasHZuR8fs2tNYXrK9SpUp2Elo1JIzUWUtKOkQhhBDlkLleNaw922D7fBE6LSPHusw/d4HBgLlVw1KKruQUOglVSg1XSt1ZlMEIIYQQZYW1Rxtcp+LI3LTrgnVVqlQhKSkJgJBdUej45JIOTwghRDnl/cDNuOKSsM1fmWN5xm9bMTWuhcHXq5QiKzmXUxP6edaPEEIIUeF49r8W5e+N7fMLm0ydHZwIIPDHjZib1yvJ0IQQQpRjlnaNMbeoT+pH36OdTgAch45j/2svnjd1LuXoSsblJKG9s36EEEKICkd5eeB1W2/Sf9mA82RMjnVnk1BfkwXPYzF4jx1cGiEKIYQoh5RSeI8ehPPwSTJ+3QRA2oLVYDDgOahr6QZXQgqdhGqtV2utV156SyGEEKJ88rqrHzhd2L5almN5eKYCoLLRg4Cpj+HR75rSCE8IIUQ55dGvA8bq4aR8uBDtcJK2YBXWzs0xVgou7dBKhAxMJIQQQuTBVLMy1h6tSf34B9J+/B0A+96j+M5aAUDNa1rjOagrSqnSDFMIIUQ5o4xGfMYNwf7XXqI7jcIZdQbPwd1LO6wSI0moEEIIcRH+rzyAqWF1Esa8zulWdxHTfRyVvHwBqFa7VilHJ4QQorzyvP06AqY/gc50oAJ98ejTvrRDKjGShJYD/cfu49+DaSVyrlMxdnrev5foOHuJnE8IIco6Y9VQgr97Dd+n78TS/mp8HhvGVV+/CrjnCBVCCCEKQymF502dCVv3EaG/TUd5eZR2SCXGVNoBlGX/e/0Yuw+lYzSCwaCoHGJm2PXBdGnte8l9l61P5OtFscx6pXa+z7d9j40n3o7k148b5Fi+aFr9AscuhBCi6CizCZ8Hb8n+3xf44IMPuOGGG0ovKCGEEBWC8rRi9LSWdhglqkBJqFKqFtAC+Edrvb94Qipbbu8fzPD+wTidmh9WxTNl5gnqVqtF1XBLaYcmhBCiFI0bN660QxBCCCHKpYsmoUqpRcB9WuvTSqkbgU+A7UBjpdSjWus5JRBjmWA0Kvp1DmD63GgORmZQNdzCT6vj+W5FPHFJTqpXtjBqcChN6nvx78E03p19GodT03/sPgBeejCChrU8ePWTk+w6mEZGpqZKmJkRN4fSqrE3MQkOJrwXhctF9j4PDQun97X+9Lx/L+88WY0m9dwT1/7+VzJf/RzLqVg7lYLN3HljMB1bumtnz9bADuwRyNxlcaRnuOjS2peHhodjNLgHznjjs5Ns3W0jxeYkNMjM7f2D6dHOrxQeVSGEEEIIIcSV5lI1oTW01qez/n4C6KS13quUqgwsAa6YJNTu0Py8JgGAiHAzqzYl8cWPsbz8UFXq1/Dg1w2JPP1uFJ++WItGdTwZf0f4Bc1x09JddGzpwxP3VcZiUixcEc8L008w65VahASYmPJwBE+8HXnR5rf/HkzjlZkneWFsFVo18mbzrlRe+PAEbz9h4qrangCcjrMTn+Rg1pRaRMc5GPfyUZrW96JHe3eieXU9T0beEoaPl4HftyTz2qcnqVvNSo0qV1YzACGEEEIIIUTJu9TAROdmJRat9V4ArfVJwFhsUZUh3yyO5aYH93P9mH18/n0Mj94VTu1qHixbn0j/zv5cVdsTo1HRt1MAtSKsrNyUlOexPD0M9Ozgj5eHAZNJcWufIEwmxd7D6fmOZ+n6RDq18qFtEx+MRkX7pj50bOnD0nWJ2dtYzQbuGhCCxWygariFFld5sffIf+fo2ykAfx8jRoOiW1s/akdY2b7XVrgHSAghhBBCCCEK4FI1oYuVUlOBp4DlSqm7ga+AvsCZYo6tTBh2vbtPaHKqk7e+OMW2PTb6dgogOs5B1zbmHNtWCTUTHefI81gZmS4+XhDNph2pJKU4UcpdO5qY7Mx3PNFxDurXzDlyVuVQMweOZmT/H+BrzG56C+BhNZCW7gLA5dLM+imWNZuTiEtyooD0zILFIIQQQgghhBCFdakk9AngFeA4EA9Ux90vdBlwX/GGVrb4ehv5312VuHPCIdZvSyY0yMTJmJzTmJyMsdO+mbvyOLd5yxf8Gs+OvTbeeDSCSiFmlFIMGn8AnbXekI8Jc0KDTJyKzsx53mg7oUH5G2Nq9Z/J/LI2gVf/V40alS0YDIoHXjyC1pfeVwghhBBCCCEu10XTHq21XWv9GFAJuAFoDYRrra/XWh8tiQDLEj8fI4N7BfLZ9zH0vtafxb8lsudQGk6nZtn6RA5GZtC9rbvfZZC/iYRkJ6lp/9Uw2tJdmM0G/HyM2B2a2T/HkGL7b32QnwmXC06el2Se67pr/Fm7NYXNO1NxujR//pPCuq0pXHetf76uITXNicGoCPAxojX8si6Rg1EZl95RCCGEEEIIIYpAvqrPtNZpwD9Kqapa69hijqlMG9gzkIXL43E4NHfcGMwrn54kPslJtXALLz/kruEEaNHQi5aNvBj+1CFcLpg8riqDewWy/2g6tz52EB8vI4N6BlIp+L8mvRGVLNzYLYBxLx/D4dSMGxZGrw45k8vGdT154t5KfDT/DGdiHYQFm3j6/so0quOZr/h7X+PP9j027pxwCKvFQK8Oftmj7gohhBBCCCFEcVO6AO0wlVJntNZhxRhPsWvdurXesmVLaYchhBCi4smlI0bZImWgEEKIYlKgMjAfvRALf3AhhBBCCCGEEOJcBU1CZfgaIYQQQgghhBCFVtAkVAghhBBCCCGEKDRJQoUQQgghhBBClBhJQoUQQgghhBBClJiCJqGRxRKFEEIIIYQQQogrQoGSUK11q+IKRAghhBBCCCFExSfNcYUQQgghhBBClJh8J6FKKU+l1Hil1Gql1GmlVGbWz+msZeOVUl7FGWxZFHf7RFKmLSjtMIQQQgghhBCiXDDlZyOlVDVgFVATWA8sAOIABQQCjYDXgbFKqR5a62PFEm0Ji735KTL/2oMym8GgMAT6YmnTCO8RN2FuWheAoK9fKOUoL59t7gpS3ptL2IaZpR2KEEIIIYQQooLLVxIKvAukAfW01kdy20ApVRP4AXgHuPnyQysbfMbfhu/42wBwRJ0h7etlxPR/lMCPnsSj7zXFem5td6DM+X2KhBBCCCGEEKLsy29z3J7AM3kloABZ657P2rZCMkWE4fvkHXgO7k7isx+htSb25qdIfncOAPEjppA0MWdtom3Ocs50uB+tNQCZm3YSc9MTnGp0G2c63E/KjO+y12Vs2MHJajdiW7CKM+3v43Sjoe59tu4l+rqHOVVvCDE3PUHy299ypu292efQtnSSXviUM+3u41Sj24gb9jyOwyey18fe/BRJL3xC/P1TOFVvCGc63E/60o3uY2/ZTeJT03AePcWpuoM5VXcwGRt2FN+DKIQQQgghhLii5TcJ1QU4ZkG2LZc8b+qM62QszgNROZff1ou0havRdkf2srR5K/G8pQdKKex7jxI3/AV8xgwi/J+vCZo1Edvni0lbsOq/gzhdZKz6i5Bf3yf879m4klKJu2MSnjd1Jvzfb/F/aRS2r37Jcd6Exz7AcTCK4EVvEr59NuYWDYi7c3KOOGzzVuE9cgDhe+fidU9/Esa/g7alY2l9Ff6vjsVYoxKVDiyg0oEFWK9pWjwPnBBCCCGEEOKKl98kdAXwslKqVl4bZDXHfRFYXgRxlWmGKiEAuOKTcyy3dm0JFhMZK/4EwHHkJJmb/8XrFnflsO3LJXj0vxaPPu1RRiOmetXwuud60uavynEcvwl3YfDzRnl5kL78TwxeHniPGYQymzA3qYPXbb2yt3XFJpL+w2/4TxmDMTQQZTHj8+hQXGfisG/dm72d542dsLRthDIY8BreB52UmqO2VAghhBBCCCFKQn47HI4HVgP7lFIbgZ1APO5azyCgMdAeOAI8UuRRnkMpVR8YC3QDagPJwGbgOa3138V57rNcJ2IAMAT65ozNaMTz5u7Y5qzAo+81pM1bgaVjM4xVQwFwRp4mY/0O0n/545yDuTBWCf3vf4MBQ9X//nedjMVQNRSlVPYyY9Ww7L8dkacBiO75YI5YtN2JMytOAEN44H9/e3m4t0lJK9B1CyGEEEIIIcTlylcSqrWOUko1BUYCNwADcCef4E5GdwGPAzO11rZiiPNcvXEnoF8CW4EA4Algk1LqWq31X8V8ftJ+WouhcjDGuhEXrPO6tSfRPcbhPB1H2vxV+D5zd/Y6Y0QYXrf2wv+VMXkfXJEj4TRUDsZ1PBqtdfZy5/HoHMcECF3/McZg/8JdkEFdehshhBBCCCGEKAL5nidUa52mtX5Pa91Ta11Za23N+qmkte6Rta64E1CAOUAzrfVbWuvVWuvvgT64R+99uDhP7DweTfIbX5M2bwV+k0fmSBbPMtWNwNy0LomPvocrNS3HCLped/Uj7affSf91E9ruQDuc2PcdI+OPf/I8p0fPNrhS00j96Hu03YF91yFsc1dkrzeGBOAxsAtJT3+I86S75tOVmEL6LxtwpeavptMYGogrJgFXckk8fUIIIYQQQogrWbmb/0NrHZPLskSl1D6galGfL+XdOaROWwgKDIF+mFs3JPinN7C0aJDnPl639STx8al43X09ymrOXm5uWJOgL58n+fXZJDzyHmgXppqV8X4g7xltDP4+BM2aSOKEGaS8+Q2mxrXxurUHaT+ty94m4I0HSflgPrGDn8Z1JgHl542lXWMsXVrm6xotHZth7dyCM+3vA6eLwM+fxdqhSb72FUIIIYQQQoiCUGenBymSgynVGZikte5eZAfN33mDgEjgc631uItt27p1a71ly5aSCayYJE35AvuOgwTPebG0QxFCCPGfMt+3oSKUgUIIIcqkApWB+W6Om0+hQJciPmZ+fID7wt/NbaVSaqRSaotSakt0dHRum5RpGb9tw3k6Du1ykblpJ7avl+E5oHNphyWEEKIcKO9loBBCiIonX81xlVLV83m80EtvcsGxe5K/aV1+01p3zWX/p4FhwH1a6wO57ai1/hj4GNx3gQsaY2mz7zlCwkNv4UpOw1gpCJ/Rg/C8pUdphyWEEKIcKO9loBBCiIonv31Cj+CejuVSVD63O9cG4Kp8bHfBqDlKqdHAFOBZrfVnBTxvueEzaiA+owaWdhhCCCGEEEIIcdnym4SmAb8DCy6xXWvc07jkW9aIunsKsg+AUuoO4EPgLa31ywXdXwghhBBCCCFEyctvEvo34NRaf3qxjZRSCRQwCS0MpdRA4HPgE631Y8V9PiGEEEIIIYQQRSO/SehfwOB8blusowNmjcD7LbAD+EIp1f6c1Rla623FeX4hhBBCCCGEEIWX3yT0VS7dFBet9UKKfsTd83UHrEALYP15644CNYv5/EIIIYQQQgghCilfCaPW+rjW+rfiDiY/tNaTtNYqj5+apR2fEEIIIYQQQoi8FXetpRBCCCGEEEIIkU2S0DJi2D4byxMc+d5+eYKDYfv+m7XmqaPpzInJzNe+21Od9NqVWuAYL6UgMQghhBBCCCGuTPntE3rFuX73f0ma3eX+bT4nZV98lXee+/bYlcq7NT1o4m0srvAu8GoNjxI7F+R+jSUdgxBCCCGEEKL8kSQ0D+cmmW8ez8AJPFnVWnoBCSGEEEIIIUQFIEloIRxMd/HhqQwOpLnwNSr6BJoYGmLGqBQjDqQB8OTRdJSCbn4mHqtqZWGsnZ/j7MQ4NL5GRQ9/E/eEuffJjz02J++dzORYpou6HgZanVfL+r/DabT0MTI81ALA68cz2JriJMWlCTUrhodY6BGQ8+lelmDnizN20lyaDr4mHqpkwdPojueT05msSXQQ79QEGhUDg83cHGwGyPMaz4/hVKaLj05nstPmIsOlqWk18GJ1D/xNxTqLjxBCCCGEEKIMkyS0gFKcmieOpDEgyMwr1T04adc8czQds4JbQyzMrOtJj12pvFYjZ1PVULPilRoeVDIrDqS7eOpoOuFmxQ1B5nyd86lj6dwabGZwsAcHM1w8eywDy0Vyuau9DIwKt+BjhN+SnLx2PIM6HgZqerjbFLuAP5KdzKzjSaaGicfSmX46k/9Vcdf21rAaeLeWB8EmxfZUFxOOpVPdqmjjY8rzGs+V7tI8diSdNj5GPq/riacB9qS5MEv+KYQQQgghxBVNBiYqoE3JTsxKMTzUjMWgqGE1cGuImSXxFx9UqLOficoWA0op6nka6RVgYluqM1/n3JjsxNOguC3EjNmgaOhppG/Axe8f9As0429SGJWiu7+J2lYDf9tynm9kuAUfoyLIpLg7zMKvCQ5cWgPQK8BEiNkdbwsfI+18jWxNceUr3rMxZ2gYV9l9DqNSNPYy4mWULFQIIYQQQogrmdSEFtAZu4twi0Kd04y2isVAtF1fdL9ViQ4WxNg5YXfh0mDXcJVn/u4BRNtdhJlznrPSRapBXVrzZbTd3ZzW4Y4r3QWJjpwxhp9TLVnJrLBrSHRCoAm+i7WzJN5OtF2jgUwN3f3zn0CesruobFb5bm4shBBCCCGEuDJIElpAYWYDZ+wOtNbZSeHJTBeh5yR056ddZ+wuXonKYFI1K219jJgNihmnMtiXlr+axZBcznkqM++kd1Wik1/iHbxWw4MaVoVBKcYcTOP8PU7bNVWyktlTdo1Zgb8RdtqczDydyZs1PWjoacCoFJMi03Psf6nUspLZwCm7A6fWkogKIYQQQgghsklz3AJq52sk06X5JsaO3aWJzHAxJ8ZO38D/8vkgk+J45n8JZprL3QczwKQwKfjX5izQnKAdfI2kuTRzY+04tGZfmpOlF9nf5tIYFfibFBr4Jd7OwfQLE95PTmeS6tTEOzSzojPp5W/CoBSpTo1Bgb9RoYCNyQ42J+dsynv+Neb2OJkUTD+VSYpT49Saf21ObM6L1xgLIYQQQgghKjapCS0gH6PitRoefHgqk3kxdryNij4BJoYE/zfA0L1hZr44Y2f6qUy6+Jv4XxUrd4Waee5YOg4NzbyNdPc35ZoY5nXOl6t78MHJTGZH26nrYeCGQFOeiWjvrP6md+63YVXu/p1NvHPebzDgThTvP5iGzekeHXdMJfeotm18jPTyNzH2UBpKwTW+Jq71yzkAUW7XeC5Pg+LNmh7MOJXJXftt2DXU8jDwYjWZS1QIIYQQQogrmdL6yqqZat26td6yZUtphyGEEKLiKfN9D6QMFEIIUUwKVAZKc1whhBBCCCGEECVGklAhhBBCCCGEECVGklAhhBBCCCGEECVGklAhhBBCCCGEECVGklAhhBBCCCGEECVGklAhhBBCCCGEECVGklAhhBBCCCGEECVGklAhhBBCCCGEECVGktAi0LdvX15//fXs/7ds2ULTpk3x9fVl/PjxALz66quEh4fj4+PD5s2bSynSK9fo0aMZN25cvrdXSrFu3bpijKj0TZo0iZ49e5Z2GEIIIYQQ4gojSehFdO3aFavViq+vL/7+/tSuXZs77riDv/76K8d2v/zyC0888UT2/xMmTKBPnz4kJyfz7rvvEhUVxYQJE1i9ejUpKSm0adOmpC+lTCquJKhmzZp89dVXOZbNmDGDqVOnFvm5isOVkAALIYQQQogrlyShl/Dcc8+RnJxMYmIiq1evpkaNGrRv357vv/8+z30OHTpE06ZNs/8/cuQIBoOBRo0aFToOu91e6H1FxSOvByGEEEIIUV5JEloANWrU4KWXXuLOO+/kwQcfRGsNuGtMX3rpJQACAgI4dOgQ999/Pz4+PsydO5devXrhdDrx8fGhTp06ANhsNh577DFq1apFUFAQffr04cCBA9nn6tq1K+PHj2fAgAH4+fnx1ltvATBz5kyuvvpq/P39adGiBb/++mv2PpMmTaJHjx5MmDCBsLAwwsLCmDhxYo5r2LFjB3369CE0NJSgoCB69eqVve7YsWMMHjyYypUrU7lyZUaOHElycnKej0fNmjWZPHkyHTt2xMfHh9atW+doauxwOJg8eTK1a9cmKCiIHj16sHPnTgDmzp3LlClTWLNmDT4+Pvj4+HDo0CEA1q5dS8eOHQkKCqJOnTq89dZb2Y/1mjVrMJlMzJ07lzp16uDv788tt9ySHecNN9zAsWPHsh//3r17A3D33Xdz//33Z8c2YcIEateunf2cvPvuu5d8/s+1c+dOrrvuOkJCQqhevTpPP/10dmJ45MgRlFLMnj2bRo0a4evrS+/evTl58mT2/u+//z61atXC19eXqlWrMmHCBACaNWsGQO/evfHx8cmO+exj3a1bN7y9vVm4cCE2m42HH36YatWqERISwoABAzh27Fj2Oc6+hvr374+Pjw+NGzfml19+yXEdWus8Xy/t2rXjnXfeybH9888/T48ePQr0WAkhhBBCCJGD1vqK+mnVqpXOry5duugXX3zxguW//vqrBvTu3btz3a5GjRp69uzZ2f+vXr1aG43GHMcYOnSovv766/WpU6d0RkaGfv7553WDBg10ZmZm9jF9fX31ypUrtcvl0qmpqfqjjz7SderU0du3b9dOp1MvXrxYe3t76/3792uttZ44caI2mUx6+vTp2m63640bN2qTyaTXrVuntdb6xIkTOiAgQE+ZMkWnpKTojIwMvXz5cq211mlpabpOnTr6ueee0zabTcfFxem+ffvqe+65J8/Hp0aNGrpy5cp6y5YtOiMjQ7/yyis6JCREJyYmaq21njJliq5Tp47evXu3Tk9P1xMnTtSVKlXKXj9x4kTdo0ePHMfcuXOn9vHx0T/88IN2OBx69+7dumbNmvrLL7/MfiwBfe+99+rk5GR96tQpXbduXf3SSy/l+fhrrfVdd92l77vvvuz/Z8+erY8fP65dLpdeuXKl9vDw0EuXLs1eD+i1a9fmet2nT5/WQUFBesaMGTojI0NHRUXpVq1a6RdeeEFrrfXhw4c1oK+//nodHR2tExMT9TXXXKPvv/9+rbXWe/fu1Z6ennrnzp1aa63j4+P1H3/8cdFz16hRQ0dEROitW7dql8ulbTabHjlypG7btq2OiorSKSkp+r777tNNmzbVDodDa+1+Dfn4+Ohff/1V2+12/dVXX2mr1aoPHz6c/fhf7PXy6aef6saNG2fH4HQ6dbVq1fS3336b6+MihCj9Mu5SPwUpA4UQQogCKFB5JDWhhRAREQFAbGxsofaPiYnh22+/5cMPPyQ8PByLxcLEiRM5efIkmzZtyt5u8ODBdO/eHaUUXl5evP/++zz//PM0a9YMg8FAv3796NatG3PmzMnep379+owePRqTyUS7du1o3rw5W7ZsAWD27NnUrVuXp59+Gm9vbywWS3afzEWLFqG1ZvLkyXh6ehIYGMiLL77I119/jdPpzPNa7rvvPlq1aoXFYuHJJ5/E09OTRYsWAfD555/z5JNP0rBhQ6xWK88//zxGo5HFixfnebzp06czZMgQbrrpJoxGIw0bNmTcuHHMmjUrx3avvvoqPj4+hIeHM2DAgOxrzK/hw4dTpUoVlFJ0796d66+/npUrV+Zr31mzZtGsWTNGjRqFxWKhatWqPP300xfEOHHiREJCQvDz82PYsGHZMZpMJrTW7Nq1i5SUFAICAmjfvv0lzztixAhatGiBUgqr1cqsWbN46aWXqFq1Kt7e3rz77rvs3r2bP//8M3ufAQMG0KtXL0wmE7fffjutW7fmm2++yV5/sdfLbbfdRmRkJBs3bgRg2bJl2Gw2Bg4cmK/HSQghhBBCiNyYSjuA8igqKgqA4ODgQu1/+PBhgBz9RsHdzy8yMjL7/5o1a16w39ixY3nooYeylzkcjuykGKBy5co59vH29s5uqnrkyBHq16+fZ0zHjh0jICAgx3KlFKdOnaJq1aq57ndujEopqlevnv34REZGUrt27ez1BoOBmjVr5rjG3OJYtWoV3333XfYyl8tFtWrVsv83Go2Ehobmeo359f777zNz5kyioqLQWpOWlsawYcPyte/hw4dZv359jsdKa31Bsn7uc3FujLVr1+brr79m+vTp3H///TRt2pTnn38+u+lwXs59rKOjo0lPT8/x+Pr4+BAWFkZkZCQdOnS4YJ+z/599fs6P8fw4vby8GD58OJ988gnt27fnk08+4c4778RqtV40TiGEEEIIIS5GktBCmDt3LlWrVqVBgwaF2r9GjRoA7N+/P0cydT6DIWdFdY0aNXjhhRcYMmRIoc5bs2ZNFixYkGdM9evXZ9euXQU65pEjR7L/1lpz7Nix7KS4WrVq2Qk3uJPJI0eOZCeU51/f2Tjuvfdepk2bVqA4zpXbcc+1fv16nnzySVauXEm7du0wGo0MHjw4u9/ppdSoUYOePXtetEb3UgYNGsSgQYPIzMxkxowZ3HTTTcTGxuLl5YVSKtd9zr2u0NBQrFYrhw8fzu5nnJKSwpkzZ3Ik7Oc+P2f/79evX77jHDVqFNdeey0TJkzg559/Zvv27fm/SCGEEEIIIXIhzXELIDIykokTJ/LFF1/w3nvv5ZksXEpYWBjDhg3jgQce4Pjx4wAkJCTw/fffk5KSkud+jzzyCJMmTWL79u3ZtXfr1q1jz549+Trv8OHD2bt3L6+99ho2mw273Z7dBLV///7Y7XamTJlCcnIyWmuOHz9+0VGAAT777DO2bt2K3W7njTfewGazcf311wPuwYBef/119u3bR2ZmJi+//DIOhyN7faVKlTh27BiZmZnZx3vggQeYM2cOP//8M3a7HYfDwb///stvv/2Wr2s8e9z9+/fnuT4pKSm7NlUpxeLFiy8YsOdi7rzzTrZs2cJnn31Geno6LpeLQ4cOsXTp0nztv3fvXpYuXYrNZsNsNuPv749SKjvJvFT84E5I77zzTp577jlOnDiBzWbj0UcfpWHDhrRt2zZ7ux9++IGVK1fidDr59ttv2bx5M7fddlu+r7Vp06Y0btyYwYMH07Zt28sa4VkIIYQQQgiQJPSSXnzxRXx9ffHz86Nz584cOHCADRs2cPPNN1/WcWfOnEmDBg3o2rUrvr6+NGnShPnz5180sR0xYgRPPPEE99xzD4GBgVSvXp0XX3wx39N1VKlShTVr1rB8+XIiIiIIDw/ntddeA9xNL1euXMm///5Lw4YN8ff3p0ePHpes+Ro5ciQPPfQQgYGBzJ07l8WLF+Pv7w/A448/ztChQ+nduzfh4eGsWrWKX3/9FT8/PwCGDBlCtWrVqFSpEgEBARw+fJirr76aRYsW8e6771K5cmXCwsK4++67iY6Oztc1Ajz77LN89dVXBAYG0rdv3wvWX3fdddxxxx20bduWkJAQFixYUKB+jpUqVWL16tX88MMP1KxZk8DAQAYOHJg9uu+lZGZm8sILL1C5cmUCAgJ4//33WbhwIR4eHgC8/PLLPP/88wQGBjJq1Kg8j/POO+/QunVr2rRpQ/Xq1Tl58iQ//fQTRqMxe5v77ruPt99+G39/fyZPnsx3332XowlvfowaNYpt27YxYsSIAu0nhBBCCCFEblR+myBWFK1bt9YFHcRG5K5mzZq89NJLDB8+vLRDEbno2rUrPXv25Nlnn72s46xZs4YBAwZw4sQJvLy8iig6ISqkwjWPKUFSBgohhCgmBSoDpSZUCJGn9PR03nzzTUaMGCEJqBBCCCGEKBKShAohcvXdd98RFBREQkICzzzzTGmHI4QQQgghKggZHVcU2vkjr4qyZc2aNZe1/6BBg7DZbEUTjBBCCCGEEFmkJlQIIYQQQgghRImRJFQIIYQQQgghRImRJFQIIYQQQgghRImRJFQIIYQQQgghRImRJFQIIYQQQgghRIkp90moUmqoUkorpaJKOxYhhBBCCCGEEBdXrpNQpVQA8A5wqpRDEUIIIYQQQgiRD+U6CQVeB/4GlpV2IEIIIYQQQgghLq3cJqFKqWuB4cDY0o5FCCGEEEIIIUT+lMskVCllBj4G3tBaHyjteIQQQgghhBBC5I+ptAMopCcBK/BKfjZWSo0ERmb9uwu4Oh/77ATSCxtgKQgBYko7iAIob/FC+YtZ4i1e5S1eKH8xl7d4d2qtL1m+lLSCloHlsPyD8vdakXiLX3mLWeItfuUt5vIWb4HKQKW1Ls5gLh2AUj2B5fnY9DetdVelVF3gH2Cg1npp1jG+AHpqrSOKMK4tWuvWRXW84ibxFr/yFrPEW7zKW7xQ/mKWeEtHebyO8hazxFv8ylvMEm/xK28xV/R4y0JN6AbgqnxsZ8v6/T6wCtiYNTougAVQWf9naK3TijpIIYQQQgghhBCXr9STUK21DdhTgF0aATWA+FzWxQPvAeMvPzIhhBBCCCGEEEWt1JPQQrgN8Dhv2VNAK2AIEFVE5/m4iI5TUiTe4lfeYpZ4i1d5ixfKX8wSb+koj9dR3mKWeItfeYtZ4i1+5S3mCh1vqfcJLQrF0SdUCCGEEEIIIUTRK5dTtAghhBBCCCGEKJ8qRE2oEEIIIYQQQojyQWpChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElChRBCCCGEEEKUGElC86CU+lEpNa204xBlj1Kqg1JqnlLqhFIqUykVq5RarpS6SyllzNqmq1JKn/PjUEodU0p9qJQKPO94NbO2uT+P861TSq0pgUs7/7wDlFL/y2X52Wvres6yNYWNUSl1d9bxahY21kKcs+Z5z8+5PwElFYcQZc2572WlVAullE0pVb2UwxJliJSB5b8MPOfc7ZVSS5VSCUqpVKXUP0qp20o6DnFlkiQ0b5OAEUqp+qUdiCg7lFLjgfVAEPAk0BO4F9gHTAf6n7fLQ0AHoDcwGxgJzCqhcC/XAOCCAhjYivuatpZoNMXjFdzXcu5PcqlGJEQZobXeBiwHXiztWETZIGUgUEHKQKXU9cDvwClgGHATMBPwKM24xJXDVNoBlFVa621Kqe3AeOCB0o1GlAVKqc7A28BUrfVD563+USn1NuB93vLdWuuNWX+vUkqFAfcrpSpprU8Vc8jFQmudBGy85Iblw6Fznh8hxIU+wv359rTW+kRpByNKj5SBbhWhDFRK+QKfAx9qrcefs2pF6UQkrkRXdE2oUqqZUur7rKYkaUqpvUqpp8/ZZA5wu1LKs7RiFGXKU0Ac8ERuK7XWB7XWOy5xjLN3Tou0eds5zZnuPm95Xs2G1imleiqltmY1t9uplBpwzjZfAHcBVc9pTnUkr2MWIM7aSqnFWeeMVkq9B1hz2e42pdSqrG1SlFLblFJ3nbfNP0qp73PZ92x81xU0PiGuJFnvsz1KqQyl1C6l1MBcNvsVSALuLtnoRBkkZWAexyxAnGWlDBwChAJvFfQahCgqV2wSqpRqC/wB1AEeAa7HfYcv4pzNfgf8cDe7EFcw5e7n0hX4VWudfhmHqgk4gSO5rDMopUzn/1zGuS6mDvAe7tf8IOAksEApVTdr/YvAEiCa/5qp5vYFNd+UUhbcTftaAGNxf6mtBTyby+a1gQXA7bibRP0MfKKUGn3ONtOB/kqpKuftOwo4jPvL86W8otx9lRKVUj8ppZrk/4qEKL+UUj2Bb4D9uD8D3sD9mdDg3O201g7cZWWfko5RlB1SBla4MrAj7hsKTbKSWYdSKlIpNTHruRai2F3JzXHfBGKB9lprW9ayVedt8zfgAtrnsk5cWUIAT+BoAfczZBWinkAPYAzwrtb6TC7bfpT1k5vfCnjeSwkBOmut9wMopbbiLoRvAaZorQ8qpaKBzCJsrnoX7oK1w9ljKqV+Af45f0Ot9ZSzfyulDMAaoDLux29G1qrZwKvAfWT1WVNKheD+QjFRa60vEksG7sf6V9xfMhoCE4ANSqm2Wuvdhb5KIcqHF4A9wE1aaxeAUmo37maGe8/bdhvwuFLKcHZbccWRMvDylaUysArghftG1IvAX7j79z4HBOCunBGiWF2RSahSygu4FnjjnAT0Alpru1IqEfebVYjCWHbe/4uBx/PY9iXgx1yWf1KkEbntP1v4AmitzyilzlDETaTO0wGIPLdA11q7lFLzcA8Elk0pVQ+YDHQGKvFfq42Mc/ZNVkp9hbt/0ctZX47vARTuvi550lqfBM69o7xWKbUU2AU8Awwv1BUKUQ5k1XS0AV49N6nUWm862+TwPNG4mwwGATElEqSoKKQM/E+ZKQOzjucBPKO1fjtr2RqlVDAwVik1SWudWMjrFCJfrtTmuIG4rz0qH9um4b6DJ65ssbhfCzUKuN9Y3F/2egJzcTf7fi6PbY9qrbec/wOkFDboi4jLZVkGxTsqXmXgdC7LcyxTSvngbrLUDHcfpE64H8PPuLDvzIe4vzT0U0op3CMvfq+1zu08F6W1jgTWZZ1LiIosBDCTj/djlrSs31IWXrmkDLx8ZakMjM36vfy85b/i/mxofIn9hbhsV2RNKBCPu5lt1XxsK3d+BVprh3LPAdZLKWXVWmdcap8s+7IKUZRSq4BwYIJS6vOspKeonO2jYzlveXARnuNynST3gi38vP874P6i00lrve7swtz6Bmmtdyql1uLuA5MO1M36u7AUcLEmTEJUBDGAnQvfe2QtO7/JZdA5+4krkJSBRaIslYG7zh7ivOUq67c0uxfF7oqsCc1qgrsOGK4uMvKtUqoS7rti5/ePEVemV3EXaG/ktlIpVUsp1TSvnbP6Z4zHXUg+VcSxncZ9F/fq85ZffxnHzKBoaz7+AKoppdqfXZDV1+WW87bzyvptP2e7QNxzmOXmQ6Av7uZM+7TWheq/rZSqjruZ/qbC7C9EeaG1dgKbgcFZ70EAlFLtcA8cc75auJsRpuWyTlw5pAy8PGWpDPwh6/f5A45dhzuZ3ZmPYwhxWa7IJDTLY7g/TP9QSt2hlOqmlLpPKfXBOdu0y/r9e8mHJ8oarfXvuCeuHqeUWq6Uul0p1UkpdWPWMOs7cX9Zu9gx/gYWAvflMqLd5cSmcTd1uk8pNU4p1Usp9S7u0QwL618gSCk1RinVpghGjv0SOAR8p5S6WynVD3dB6HfedhtwTwkxTSl1vVLqFtyDUuRVC7Mwa9215D2oRQ5KqbeUUu8opW7Jeu+PBtbivvs75RK7C1ERTMQ9INcPWe+zu4F5uCeuP187pBy84kkZWHHKQK31TuALYLJS6gnlnq7mVeB+4DWtdXE0gRYihys2CdVab8b9ho0EPsA9FPfj5Own2h/4S2t9oOQjFGWR1vpd3EObJ+AeYXkV7g/yq3A3gfk5H4d5HnefiyeLOLyHge9w3w2di7sW/8HLON4nuOfKnQL8Sf6uLU9a60ygF7Ad953bL3EPI//SedtF4x4K34h7iPpXsmL5Ko/j2nEPZpGRdcz82IX7efwId5+YScB6oJ3WWlo+iApPa70C9/QPDXB/bjyOu5Yqx+tfKVUNd9+0OSUcoiiDpAwsvDJWBoL7+Xob92O0BPeouv/TWk8qwDGEKDR18RGcy4esUS2vA17WWuc231JhjumBu/3+Y1rrT4vimGVJVhOQF3HfgduitS7IB5cQZUZWP5kDwFqt9R2lHY8QFYlS6knc00LUyWrGWyFIGSgqCikDRXlV7mtClVJDcd+lLWqjgDMU7K5SsVNKfaaUOqOUuqC9vlKqj1Jqr1LqgFLqUv0tbsI9MJOd/I0SLESZopTyU0pdA7wLVAPeKt2IhKhYsm7GPgw8X1YSUCkDhXCTMlCUd+V6dFylVADwDu5Jdb8p4sNnAHdrrR1FfNzL9QUwFZh17sKsed+m4W7qEQVsVkr9hLs5xyvnHeNe3E2w/tBaf6SUWgCsLOa4hShqLYHVuG8WPay13l664QhR4dQE3gNml3Ic5/oCKQOFACkDRTlXrpNQ4HVgl9b6W6VUkSahWusZRXm8glJKtQE+BdriLkT/BG7VWv+ulKqZyy5tgQNa60NZ+88BbtJav4K7b+v5x48CMrP+LRN3uIUoCK31Gv4bTl4IUcS01nuAPaVxbikDhbg4KQNFeVduk1ClVEfgToqnKW6p01qfvYv7Eu4hwr/KGs0sL1VxD7J0VhT/je6bm++AD5RSnZBRD4UQQpQhUgYKIUTFVi6TUKWUGfeolm/mZyRLpdRIYCRAo0aNWu3atesSe5QNGRkZtGnTBg8PDzZs2ABZU0ccPnyY/v37wzmTDM+bN49ly5YB3Acwa9Ys/vzzT4CxuR07lwGpphZ1/EIIcYUpk7USUgZeSMpAIYQocgUqA8vrwERP4r4z+nJ+NtZaf6y1bq21bu3pWZTzDhevuLg4UlJSSE5OJj09/aLbRkREEBn5303gqKgoqlQpsim4hBBClFNSBgohhChryl0SqpSqDjwDPAdYlVIBWQMUcc7/xlILsAiNHDmSF198kdtvv50nn7z4dFpt2rRh//79HD58mMzMTObMmcONN95YQpEKIYQQRUvKQCGEqLjKXRIK1MY9AfFXQPw5PwCPZf3dpHRCKzqzZs3CZDIxbNgwnnrqKTZv3syqVasYOnQoHTp0YO/evURERPDpp+4pTE0mE1OnTuW6667jqquu4pZbbqFx48alfBVCCCFEwUkZKIQQFZvKpV9EmZZV69k8l1WrcSemn+KeeDolt/1bt26tt2zZUmzxCSGEuGKVyT6h55IyUAghRDEpUBlY7gYm0lonAGvOX66UAjiaNWS1EEIIIYQQQogyqDw2xxVCCCGEEEIIUU5VmCRUa6201s+W5Dn37dtHhw4dqF+/Ph06dGD//v0XbDNp0iTCwsJo3rw5zZs3Z+zYXEeLF0IIIcoNKf+EEEJcjgqThJaG0aNHM3bsWPbt28fYsWMZNWpUrtvdeeedbN++ne3btzNt2rQSjlIIIYQoWlL+CSGEuByShBbSmTNn2Lp1K0OHDgVg6NChbN26lejo6FKOTAghhCg+Uv4JIYS4XJKEFlJkZCRVq1bFaHRPSWo0GqlSpUqOybLPmjNnDk2bNqV379788ccfJR2qKKN+//13jhw5UtphCCFEgUj5Jy6X0+lk6dKlxMTElHYoQohSUu5Gxy1vRo8ezTPPPIPZbGb58uXcdNNN7N69m+Dg4Bzbffzxx3z88ccA7Nmzh4YNG5ZGuKIInI61YzErAv0u/fbas2cPn332Gb169SIwMLAEohNCiJKR3/IPpAy8Epw4Y8fby4C/j/vmxYEDB5gwYQJNmzalS5cueHp6lnKEQoiSVO7mCb1cRTVH2pkzZ6hfvz6xsbEYjUacTifBwcHs37+f0NDQPPdr1aoVb7/9Nl26dLlYjMg8buWT06m55dGDTHu2BpVCzJfcvlWrVrzzzjv88ccftG7dmo4dO2K1WksgUiFEMbgi5gktzvIvK0YpAyuY07F2Rk46wrdv1MHLw90Ir3Xr1vz222+sWrWKPXv20LVrV1q1aoXBII30hCinClQGyju9kM6O+Pftt98C8O2339KiRYsLCuDjx49n/719+3aOHDlCgwYNSjRWUXL+3mcjPMScrwQU3PPbdu7cmTFjxpCUlMTUqVPZvn07V9rNISFE+SHlnyiI1DQnz31wnGHXB2cnoGd5e3tzww03cMcdd7Br1y5mzJjBoUOHSilSIURJkua4l2HGjBncddddTJ48mcDAQGbNmgVAv379mDx5Mq1bt2bChAn89ddfGI1GLBYLs2fPplKlSqUcuSguv29JpnMrnwLv5+fnx8CBA4mKimLp0qX8+eef9OnTh+rVqxdDlEIIcXmk/BP54XBoJk8/QaM6HtxyXd5dTipVqsRdd93Fnj17+PnnnwkPD6d3794EBQWVYLRCiJIkzXHLIGmKVD45XZpbHz3Ie09Vp2q4JV/75PZca635559/WLFiBdWrV6dXr174+/sXR8hCiKJ1RTTHLW5SBlYMWmve+vI08UkOJo+titGY8+2R1/PscDjYuHEjGzZsoEWLFnTu3Fm6qQhRPkhzXCFKw879aQQHmPKdgOZFKUXTpk0ZN24cwcHBzJgxg9WrV5OZmVlEkQohhBDF6+tFsRw8ls6zI6tckIBejMlkomPHjowZMwabzcYHH3zA1q1bcblcxRitEKKkSRIqRBH5/a9kurT2LbLjWSwWunXrxujRo4mLi2Pq1Kns2LFD+osKIYQo03bss7F4bSIvPRSBp0fhvmr6+vpy0003MWzYMLZv387HH3/M0aNHizhSIURpkSRUiCLgcmnW/ZVM51ZFl4Se5e/vz80338yQIUPYuHEjn376KVFRUUV+HiGEEKIoLFuXyM09AwkOuPyhR6pUqcI999xDx44d+e6775g3bx7x8fFFEKUQQmvN3iPpfLUolqQUZ4meWwYmEqIIbPnXRoCfiYhKl9cU92KqVavGiBEj+Pvvv5k7dy61atWiZ8+e+Pn5Fds5hRBCiILIyHSxfnsK9wzKe7qeglJKcfXVV9OgQQM2bNjAxx9/LNOaCXEZXC7Num0p7mbzkRkAZGa6uLcI37eXIjWhQhSBBcviuLlX3iP/FRWlFM2bN+fBBx/E39+f6dOn89tvv2G324v93EKUBKdLE5PgKO0whBCFtHFHKvVqeBBSBLWg5zObzXTp0kWmNRMin7TWpNhy1nA6XZqn3oli8vQTZNo1D90eRqvGXvyyLhGHo+TeS5KECnGZDkamc/RkJt3allyNpMVioUePHowcOZLTp08zdepUdu7cKQWxKPc++Po0dzx1iENRGaUdihCiEFZuTKJHu+ItD89Oa3brrbeyZcsWZs6cybFjx4r1nEKUNy6X5p3Zpxn08AE++Po0yanuZHTJ74ls3W1j5JBQPplckxu7BTKwRyDxSU42bE/hh1XxPP5mJBmZxTsYmCShQlym+b/GM7BHAGZTyc/OEBgYyC233MLAgQNZt24dn3/+OSdOnCjxOIQoCsdOZrDk90TsDs0bn50s0TuyQojLl5TiZPteGx1bFny+7MKIiIjgvvvuo3379ixYsIAFCxaQmJhYIucWoixzuTRvfXGKJb8n0rSBFz+vSeCeZw/z/cp4PlkYTYuGXgzpHYjR4P7u2uZqb8KCTEybc4ap35xh2x4bq/5MLtYYJQkV4jLExNvZ9HcK13cOKNU4atasyciRI2nevDnffPMNP/74IykpKaUakxAF9dn3MXhYDYy/I5z9xzL4cO4Zok5lSg2/EOXE738l07qRFz5exhI7p0xrJsSFNmxPYdmGJO64IZg3H6vGh8/VoGqYmWnfnnE3wR0ejlL/VZ4YDYp+nfyJTXDQvqk3Nata+GFlfLGWvzIwkRCX4fuVCfTs4Ievd8kVuHkxGAy0bNmSxo0b8/vvv/Phhx9yzTXX0L59e0wmeauLsm3/0XTWbU3hrpuC6d8lgN2H0vhpdQI/rU6gST1Pnrq/MuHB5tIOUwhxEas2JZXI+Ai5OTutWcuWLVm+fDlTp06lZ8+eNGnSJMeXbSGuBCv+SCLQz8jw/sEA1K3uwTtPVmfN5mSsFkW1XAbSvLl3ECGBZrq382X5H0m8M+s0O/al0ayBV7HEKDWhQhSSLd3FL+sSGdSzdArcvFitVnr16sX9999PVFQU06ZNY/fu3VKbJMq0DdtTMCgY2MP9fnrs7kp88XItRt8SysHIDEZOOsKPq+KLvY+KEKJwTsfaOXw8gzZXe5dqHP7+/gwePJjBgwdnT2t2/PjxUo1JiJKUnOpk0z+pdGvrh9H43w0Yg0HRvZ0f17bIfTpBT6uBPh39sZgN9Gjnh6+3ge9WFN90SJKEClFIS9cl0ryhF5VDi29alssRFBTEbbfdxo033siaNWv48ssvOXXqVGmHJUSuduyzUbe6NbsZn1KKiHALg3sHMWNiDWpHWPngmzMMf+oQe4+kl3K0QojzrdqURKdWvljMZeOrZfXq1RkxYgStW7dmzpw5fP/99yQlJZV2WEIUu7V/JWN3aHq0L/wAYR5WAzd1C2T9thT2HEorwuj+UzY+KYQoZ5xOzcLlcQzpXbZqQXNTq1YtRo0axdVXX81XX33Fzz//TGpqammHJUQ2u0Oz+1A6Tern3uSnSqiFt5+oxpuPVcNoVLw3+xQul9TsC1FStMOJtl986qSVm4p/VNyCOndaMz8/P2bMmMHvv/8u05qJCm3FxiQiws3Ur3F5c+je0ieIAF8j0+dFF0trOklChbgIrTUZ63cQd+9LxNzwKPYdBwD4ZV0i4cFmrqrtWcoR5o/BYKB169aMGzcOi8XCtGnT2LBhA06n89I7C1HM9h1JJ9OuaVIv7/eTUormDb0YMTiUfUcz+HWD1GgIUVISHnqb6GtHkvbdarTrwibxB46lk5rmuuh7uDSdndZsxIgRnDp1imnTprFr1y7ppiIqnPgkBzv2pdG9nd9l94X28jBwz8AQdh1IY+1fRT/YpSShQuRCZ9ixffMrMb0eJGnCdKxdW+J1Rz/ihk8i+sUv+WrhKcYODSvtMAvMw8OD6667jnvvvZcjR44wbdo09u7dKwWxKFU79tkAuDofX2C7t/WlUR0PPlkYzUfzz/DLukR5/QpRjDLW/IV96x78Xx9H6qc/E9P3ETLW/Z29PsXm5NVPTnJb3yAMhrI9ANDZac0GDBjA2rVrZVozUeacinH3rS6svYfd3VWaNyyawYT6dPSnRhUL836NK5LjnUuGzBTiPPZ/D5Mw7k0MYUH4PXsvli4tsu8mWbu04K/b3+GFuA1Uv/9xqFa3lKMtnJCQEIYNG8aBAwdYtmwZmzZtok+fPoSFlb/EWpR//+xLo0YVCwG+ly6SlFI8eHs4L3x4gh9WJmB3aDytiq5tylYzQCEqAp2WQeKEGfhNGYO1a0ssXVqQ/vM6Eh//AGOVEKzD+/HS/mo0a+DFjV0DSjvcfDs7rdn27dv55ptvqFevHj169MDHp2TmNxUiN/GJDh5+5SjxyU7uuCGYYf2CcwwslB97j6RjUFCvukeRxGQ0KLq18eXLn2KJT3IQ6Fd0qaPUhAqRRbtcpHz0PXG3PIP36EEEfTsZa9eWOZoz7En25K1mdxH+5K3E3T4RZ0xC6QVcBOrWrcvo0aNp2LAhX375JYsXL8Zms5V2WOIK4nRpdh5MK1AzvnrVPfjq1dos+rAedapZ+Xh+tIyaK0QxSPlgHuYmdfDo3hpw3wTyvLETob9Nx/OOfhx480fun/kMdx75Bdep2FKOtmDOTmv24IMP4uXlxYcffsi6detwOC7e91WI4uB0al6eeZKUNBfXNPPhyx9jeW7qcTLtBSvb9h5Jp3plC54eRZfitWvmg9aw+Z+iHU9EklAhAMeRk8Td9izpizcQvPhtvG7pcUFbeqdT8/7Xpxl1SxgBt3bHc3B3kid/WkoRFx2j0Ujbtm0ZN24cBoOBqVOnsnHjRukvKkrEoagMbGmuPAcluhijQTHm1jDOxDmYv6zomwoJUVGlfrGYpMmfkTJ1AbZvfiV96UZcsYk5trHvj8Q26xf8Xhhxwf7KYuYna2Om936QSj+8CkkpxN05uVw2jZdpzURZ8O2SWLbvsfHw7eFMGluVh4eH8+c/qUyadiLfiajWmn1H0mlQs2hqQc+qW81KcICJjTuKtl+oJKHiiqW1JmPd38Td/SIx1z+KtVNzgr97FVONSrlu/+PqBPy8jXRr655fyeexYWT+sZOM9TtKMuxi4+npSd++fbnnnns4cOAA06dPZ//+/aUdlqjg/tnnHvq9sAOaNG/oRedWPnzxYyzvfXWaFJvcPBHiYlI+/gHb54swBPvhik8ic/O/2L76hTMdR5Lw6PvYdx9Ba03SU9PweWQoxkrBFxzjz39S+HF1Ai8/VBWfxtXxe+UBcDjI/H17vmJwRJ0p4qu6fGenNbvhhhtYvXq1TGsmSszJ6Ey+XhxH1za+9L7WH4AbugbwvzvD+XNnKl8vzt9N1jNxDhKSndSvVbRJqFKKdk292fKvDYej6G7OSJ9QcUVKX7GZ5Jc/Bw1e991AwIePY/DK+00bm+Dgq0WxvPtktewaUoO3J34vjiLxqQ8JXfEBymouqfCLVWhoKLfffjv79+9n6dKlbNq0ieuuu47Q0NDSDk1UQDv22agUYiYsqPDvn8fvrUxIYAw/rIznUFRGjvepEOI/aT+vI/Wj7wn58Q2METnHAHDGJGD7ailxtz+PIdAPZTbhdXe/XI/z9eI4xtwaRkig+32rDAa8x9xMyrQFWLu0uHQcXy+7/IspJrVr12b06NFs3bqV2bNn07BhQ7p37463t3dphyYqqBnzojEaYNSQnN+z+nUOYP32FJauTeDOGy7dP/TsoEQNahRtEgrQrok3S35PZOeBtCIb9EhqQsUVxzZ/JYmPvY/v8/cRsnoa3nf0vWgCCrBgeRw92/tRvXLOOZc8+rTHVKcqKdMXFmfIJU4pRf369XnggQeoU6cOn3/+Ob/88gtpacUzYbG4Mmmt2bm/YP1Bc+NpNfDAbWGMGxbGrgNp/PWv9GsW4nwZG3eSNGE6QbMmXpCAAhhDAvAdfxthGz/F5+FbCZj2OMpovGC7/cfSORNn59rmOQfx8RzQGcfB49lTmeVF2x3Y5iy/vIspZmenNXvwwQdlWjNRrLbvsbF+WwrD+gcTmsvN2H6d/IlNdPJnPvpj7j2SjskItatd3vyguWnZyBuzSbF2a3KRHVOSUHFFSf18EcmvziZo/hQ8urXKV21JWrqLpesSGdQzMNf1/i+NInXmTzgOV7xh3o1GIx06dGDs2LE4nU6mTp3Kn3/+iSuXeeKEKKjIU5kkJDtpWr9o5hbs09Gf0CATs3+KweXSbNmVSnKqfGkUwr7vGAkjXyVg2uOYG9e+6LbKYsbzxk6Y6lTNdf0PK+O5oWvABbUyymLGe+RNpHx48ZuyGSv+xJhHt5eyRqY1E8Vt3dZkPCyKwb1y/47ZrokPgX5GlqxLzHX9ufYdSad2hBWLuejTO0+rgY4tfVjxRxJpGUXzHVCSUFHmaa1Jnf0LmVsub6CAlA/mkfrxDwR//yrmetXyvd/S9Yk0b+hFpZDcmwsaI8LwGTeYpGdmFDq2ss7b25v+/ftz5513snv3bmbMmMHBgwdLOyxRzu042x+0EIMS5cZiNnBb3yB2HUznnmcP89Q7UYybcpQTZzKL5PhClEf2fw8TN/R5fJ+/F2vn5pd1rMRkB+u3pdCvk3+u671uv46MtX/jOJp3X0rbV8vwGt7nsuIoaWenNevXrx8rVqxg9uzZnDlT9vq1ivJn+x4bjet55pk4mkyK3tf4s2lHCpGnMsm0u/jg69OMeuFI9qjwKTYn0+ee4e99NhrXLZqburm5qVsAqWkuVm5MKpLjSZ9QUealf7+G1A8XoixmtC0dj/4d8eh3DRgUztNxuE7F4opJQPl6Y6wa6v6pHIIrIRnHgSgcB6Kw/70f59FTBH//Wq6DLOTF6dJ8tyKep+6rfNHtvO+/EdvsX8j44x+sHZpc7iWXWeHh4dx5553s3buXxYsXExoaSu/evQkOzv9jKsRZ/+yzEeRvpGpY0fWn7tvRn/nL4nG6YMTgUOYujeOhV47xyiMRRTZvmhDlRcbv20kY+wZ+L43C86bOl328X9Ylck1znzzn9DX4eOE1/DpSZ3yP/ytjLljviDxN5vb9BH4yAV697HBKXN26dalVqxZbtmzhyy+/pFGjRnTr1g0vr6K5kSauLPGJDo6cyKRH+4vPc319Z39+XBXPiImHCQ8xc/y0HYA//0mlUytfnv3gOLsOpNGvkz933RRSbPE2rutJnWpWflqdwPWd/S977AVJQkWZ5opPJmnyZwR+/izm5vVx7DlK+k9rSXx6GspixhAejLFSEIaQAFynY7Fv3YPzeDTOk7EY/H0w1Y3AWDcCj/7X4tGrHYaAgk1EvfHvFPx9jDSqc/Evr8pswueR20h542ssC1+p0IOiKKVo2LAhdevWZdOmTXz66ac0b96czp074+EhX/JF/u3Yn8bV9byK9P1iMRv45IWamE0Ko1FxbXMfnng7kifeiuTNx6pRp5q8RkXFo7W+4H1km7+S5Bc/J2Dm01jbX33Z53A6NT+tTmDSA7k30z3L+74bie46Bp/HhmEMzlljmvbNr3je3BXlWfR91kqK0WikXbt2NGnShDVr1jBt2jQ6depEmzZtMObSh1aIvGzf6x6/oMUlBvqpEmbh0xdrMeeXODbuSOGZkZWZ9u0ZVm9OJjzYzM79aYy+JZTBvYOKNV6lFDd2C+CdWafZeSCNJvUu7+aLJKGiTEt68TM8+nfE0qIBAOaramK+qia+T95RIudfuDyem3sF5utLsufArqS8N4/MdX9j7dS82GMrbSaTiWuvvZZmzZqxatUqpk6dSteuXWnZsiUGg7T0Fxe3dXcq0XEO7uhf9DUIHtb/Xn8RlSy8+Xg1Hn0jksfejOS+QaH07eh/yVEGhSgvbAtWkfT0dJSfN6Y6VTHViQA0Gav+ImjhKwXqfnIxf/ydQkigifqXmIPQGBaIZ/+OpE7/Dr9n78lefnZAoqC5LxVJPKXNy8uLfv360bp1a5YtW8aWLVu47rrrqFevXmmHJsqJv/fa8PIwUC8fo9mGB5t5eHg4DxMOwD/701i2PhGjATwsij4dc28iX9S6t/Pjg69P88f2lMtOQuWboiizMv74h4zftpVYwnm+fUfSORljp1NL33xtr0xGfB8dRvLrX11Rgxb4+Phw4403cvvtt/PPP//w0Ucfcfjw4dIOS5RhWms++y6G0CATPTtcvBlSUagSauGtx6tRvZKFd2ef5qFXjuF0XTnvUVEx6Uw7iROmk/LuHIJ/fJ3g71/Fe8wgTHWqoLw9Cf7pjSJLQAF+WJXAgO65D55yPu+HbiF96R/Ej3oVZ3Q88N+AROb61YssprIgLCyM4cOH07t3b5YuXcrXX39NTExMaYclyoFte2w0re9ZqJuiXdv4kpGpWf1nMt3a+uHjVTK18J5WAw1qeWaP6XA5JAkVZZLOsJP4xDT8XxqFwbd0+losXB7HgO6BmEz5/3DwuLEjOtlGxpqtOZY7T8WS9PIXuFIr7hQnlStX5u6776ZLly78+OOPzJ07l/j4+NIOS5RBf/ydyp7D6dzRP7hYRvHLTZVQC+8+VZ1RQ0LZeySdfw9W3PeiqFi0LZ3UT34ibdE6HPsj0Q4nzlOxxA6egPNEDCFL3sHcqBam6pXw6NYK7/tvwu/ZezCGF13TvMPHM4g8mUGnVvm7KWuKCCN0+QcYa1QipseD2OatxDZ7abkbkCi/zp3WrHbt2nz22WcsXbpUpjUTeYqOs3P8tL3Qc25eXdeT4AB3g9b+XQOKMLJLa1bfk31H07GlX94ouZKEVjCOQ8dJfvNr7LuPlHYolyXlg3mY6kXg0bdDqZw/Jt7On/+kcn3ngjVvUEYjPo8OI+WN/2pD01f/RUyf8WT8to3Ex6dW6FpSpRSNGjVi3LhxVKlShZkzZ7JixQoyMjJKOzRRBiSlOFm4PI73Zp+iariZ3teUTPOhs5RS9Ovsj9mkWLc1pUTPLSomrTUZf/yD48jJYjtH6qc/kbZwNWkL1xB312RO1R9CdJcxWLu3IvCzZzD4eRfbuc9auDye67sEYC7ATVnlacVvwt0EfTWJ1Jk/krl9H57XX1uMUZa+c6c1czgcMq2ZyNP67e4yqHXjwr1/DQbFkN6BdG7tS4NLNJEvas0aeOFywa4Dl3eTRfqEVhDOkzGkvDOHtMUb8Lz+GuKGPoe5VUN8x9+GuUmd0g4v37TWpH29DNusXwhZ+m6pxfHtkjh6XeNfqOYNHtdfQ8p7c0j/5Q/sW/eS9v1vBEx/AkuzesTc+Bi2LxbjfU//Yoi67DCZTHTq1InmzZuzcuVKpk6dSvfu3WnevHmFHrRJ5JSamsqOHTsIrdqCmQuj2b7HhtMJjet4MObWsAK1Migq3p5GWlzlxfptKYy+JVRejxWM1hrHnqPopBRcqeno1HRwOjHWqISpTkSRJWyupFTS5q0k9cslKJMB55kEPG/siM8jQzGG5a/Jar7Ok5hC6kc/EPz9a5iymtZqWzquxBSMlYtvFMxznY61s35bMl+8fPH5RfNiblqXkCVv4zwZU64HJCqIs9OatWnThqVLl2b3F61Tp/x8HxPFa+XGJGpHWKlZtfDvieIeiCgvjep6YjTCjn022lxd+M9USULLOW13kPzabGzfLMNraG/C1s7AEOSH36T7sX29jLi7XsB8dR08buiItVPzHNOTOKLOkL54PRkrNmNuUR/vkQMwhgSU3rXY0kmcMB37jgPuqVSqlEwBe76jJzJYszmZz16qVaj9lcGA76O3Ez/iFazdWhLy63vZIwQGzpxA7I2PY25WD0vLBkUZdpnk6+vLgAEDOH78OEuXLuXPP/+kT58+1KhRo7RDEyXg008/5eGHH2bYQ0tIU/UY3CuIbm19qVvKU6V0bOHD2/+c5mBkRqnHIoqOtqWT8L/3yNyyB2PVUAzeHihvD1AKx5FTOA9GoXy9MNWvjrVnGzz6dsBULTx7f1eyjYy127Fv/hfPob1z7buo0zNJfv0rbHN+xdq5BQFvPoi5bSN0fDIpH8wjutsDeN/ZD+8Hbi6SriSpH/+ItWeb7AQUQHl5YPQqudftvGVx9OsUgL9P4fucKbMJU/VKRRhV+XDutGaLFi0iLCxMpjUTHD+dye5D6Yy4uXS+514uT6uB+jU82JE1um9hSRJajmmtSXzsfZxn4gldOTXHXVHl5YH3iJvwuqMvad+vIePXTSRNnIkxPAhL20bY/zmI4+hpPPq0x/ve/mT8vp3ozqPxGtID79EDS+wO61mOwyeIHzEFU8OaBC96C0MJFrDn+2h+NEP7BV1WgWvt056guS9h6XA16pyRYk21quD/+lgSRr1aqjW9Ja1q1arce++97Ny5k++++46IiAh69epFQEBAaYcmitG+ffsAWL30U9559yNu7Vs2vnh1aO6DYfZp1m9LkSS0gnBGnSHu3pcxN6hO2NoZuda4aZcL18lY7LsOkb5sE7Hvz8NQJRRrx2bYdx7Avm0/5tYNMTeuTdygp/B5YjgZN/Vi54F0alS2UDX1DPFjXsdUuwqhq6bluKmrgvzwm3g/XvfdSPKUL4kd+CRB307GGFr4WlFXbCKpXywi5Jd3Cn2MyxWX6GDVxiQ+e7FwN2WFTGsmLrTqzySUgm7tin9gvuLStL4XC5bHkZbhwtNauN6d5S4JVUoNBoYCrYEw4BjwHTBFa51cmrGVtORXvsRx8DhB817OM2lTHha8hvbGa2hvtNOJfccBMv/8F4/rO7oTJLP7JeDR9xp8Hr6V1BnfEd1jHN5398fnkduy1xcHZ0wCGb9tI3PNVtJX/YXv47fjdVe/Um0et2VXKsdPZ/LC2IvPg3YpSims1zbNdZ1H32vI3LKH+DGvX9Y5yhulFE2aNKFhw4asX7+ejz76iDZt2tCxY0csFktphyeKwdlRkk8fXETLBo5SjuY/gX4mrq7nydJ1ifTt5E9YkLm0QxKXIXPTLuJHv4b3yAF4jx6YZxmiDAaMVUMxVg3F2qste0bdy99z/iJjwz8kVOuA89GRVK7uS6UQMycDW9HwjQ85/f5aNt98F95//sVNf/9M5ODB1P/fDQRWyr0JnSkijIBpj5Hy1jfEDnyK4DkvYowIK9R1pXy4EM8bOpZqDeKCX+Po0d6PQP9y93WxzMltWrNu3brRokULmdbsCqK1ZuXGJJrW9yzXZU+zhl7MXRrHP/vTaFvIJrmqsIOkKKXCtNZnCrXzZVBKbcSdeP4IRAEtgEnAHuAarfVFe3+3bt1ab9mypbjDvCytW7fmUjGmfvozqZ8vIuTH1zEEF+3gHs5TsSQ+9j6umET8P3i0QEO86/RMMrfsxnn0FCrAB4O/D4YA92h6jiMncB4+gePQCey7DuM8dgrLNU2wdm2JR/fWhS6oi4rTqRn1whHuGRjCtS3yNwJgYWmHk+SXPqP7oo8u+VxXVImJiaxYsYKjR4/So0cPmjZtKv3zKpjGjRsTeSqd5LhDvPbaazzxxBOlHVK2PYfTeOLtKAJ8jLz5eLWi+jJQ5l/AFaUMPCv9lw3ukdTfewSP7q0vum2Kzcm+o+n8sy+NVZvcNRE92/txbUtfUm1Ojp3MJPJUJiej7dSKsNKqrpmIufNIn7scQ+VgYp56mJWxgfz+VzK+XkZaNPKiVSNvmtb3zHX8gJQZ32H7bBFBc17EVLtgNzadp+OI7vbABa2cSlJiipO7Jxzio0k1i+XLckGe54ro5MmTLF26lIyMDPr06UPNmjVLOyRRAjZsT+H5qcd57O5KJTa3Z3HIyHRx8yMH6NXBn4eHZ3drKFAZeDlJ6DGtdYlP9qSUCtVaR5+37E7gS6CH1nrVxfYvbwWw1hpXTALK04ry8kAZDKT9vI6kSTMJ/uH1HP1ZipLWGtvsX0h5/St8xt+Gx/XX4IyKxnn8DM6oaHR6hjseb0+UlxXXqTgy1v2NfeteTA2rY6pbDZ1sw5WQjCshBVwujDUqY6pdxf1TrzrmFvWLtaa1oBb9lsDqP5N487FqJZYMXemFMEBkZCRLly4FoG/fvkRERJRyRKIoaK3x8vIhtM4QQjwPkZJ4Krt5blnx78E0nno3iuqVLLz/dHUMhst+30sSWgTy+7mYsWYrCQ++RdDXL2BuWveC9Wfi7Gz918b2vTb2Hk4nJt5O3eoeXFXbgy5t/Khfw5qvz3r7vmOYIsJQWS2OXC7NwcgM/vo3la3/2th9KI3rrvVn1C1hF4wea/t6KclvfUPQF8/nGmNeEp/9CGUy4DdpRL73KWpf/hhDTIKDR+8qnppYKf/cn5P//vsvy5cvp3LlyvTu3ZvAwKIb2EqULQ6HZsSkIwDMnFST1NQk/P3LbyL6wvTj/HsgjW/fqHO2/CxQGXjRDEApdeNFVpdKQ/bzE9Asm7N+X14byjLGlZRK4uMfuOecdGl0WgbKywpmE8FzXy62BBTcTSe97+yHtWMzEv73HikfzMMYEeb+qRqG8rTiik5AHz2JtmWg/H3wvvcGLDOfLpHh4otais3Jlz/GMGV8hNTGlbBq1apx//33s2PHDubNm0eNGjXo1asXfn7lt6+EgFOnTpOebiOsUnW6twznvffeRWtdpt5fjep4Mm5oGK9/dorlfyRx3bXuLwOxCQ7mLo3Dlu4i0M/I3QNCMF5+gioKIT3DxZk4O0aDwmRS2ZO62zftQv/vTdJffYJozyokbU8hKcVJYoqTUzF2tu1OJTnVRYurvGje0ItbrguiRmVLoSaFP3+AIoNBUa+GB/VqeHBb32CSU528/vkpHnn9GM+PrpKj1tDr9j4oPx/ihj2P56098Xl02CXHPMj881/Svl9D6G/TCxxrUUlNc/LT6gQ+mFDidQ1XFKUUjRs3pn79+vzxxx/MnDmTli1b0qlTJ6zWK2Mk4SvJ4t8TiDyVyeRxVfnjj3V07dqVJUuWcN1115V2aIXSsYUva/9KYc/hdBrV8Szw/peqhvoe+I3cM9viba9YMF2yfu8u1SiKUOb2fSSMeR1r11aE//0VysOCdjrdw80bDRi8C/5kF4apdlVCfqjYfRcPRabz3tdnaN/Mh3oySEmpUErRrFkzrrrqKtatW8f06dNp164d1157LWZz+e0zcSWbv3gXAAP6NMbsPEJmZiY2mw1v77J1k6pnez9+XpPAzAXRXNvCh9Q0F0+8FcmZOAc+Xgbik5zUq+FB51Zlqcir+P7cmcqqjUn88XcKAX5GXC5wODVOpyYiLpIxaz5iXpc7OPpXANZ/ovHzMeLnbcTfx0jVMDP9OlehToS1KGq3L8nX28gLD1Rh3rI4xr18lCfvq0yrRv+9zj1v6IilfWOSJ39GTJcx+L04Co8+7S84jis1jeTXZpP+8zoC3hlfqqPV/7g6gdaNvagSJv31S4LZbKZz5860aNGCFStWyLRmFZDTqflqUSxN63vSoZk3Q4dOw+Vy8eyzz9K7d+9y+Ty3beKN0Qjrt6UUSxK6H7hPa334/BVKqcgCn60YKKWqApOBFVrrCtGuwxWTQPwdL+D3yhg8+3fMXq6MRlQ5rGUsq5JT3bWfazYnc/eAEPp2Kr9NIioKi8VC9+7dadmyJcuXL2fq1Kn07NmTq6++ulx+QF9Jft+STFiwiYa1PElOdTLnx50A3Ni7EZs2JQEQFxdX5pJQg0Exblg4414+ypD/HcRkdC976/FqNKjlwb3PHmbOL3F0aukjr8EScjAyg1k/xtCjnR8j2tjxOnIEnZLmnh8zxYZt5RL8pj3EM/2uKe1QsxkMitv6BtOwlievzDzBbf2CGdjjv2aVxtBAAj54lIx1f5P49HRS3p+LpU0jzC3qY27RAOfRUyQ+8QGWdo0JXTkVQ1DptQRJTHGy8Nd43ntKakFLmq+vLwMHDswxrVnfvn2pXl2ei/Juxz4b8UlOHrw9kNjYWL7//ntq167Nli1bWLx4Mf37l7/54329jTRv4MX6bcncf3NIgcvISw3H9SWQV4/4GQU6UzFQSvngHqDIAdxzke1GKqW2KKW2REfn1pq3cPbt20eHDh2oX78+HTp0YP/+/Rds43Q6GTt2LHXq1KFu3bp88sknlzyuKz6Z4EVv4dm/Iw6HJiPzomMtiQLKtLv4cXU89z53GIdT8+mLtejfJUCa25UhAQEBDBkyhEGDBrFhwwY+++wzjh8/XtphiTxs3Z3K5BknGPfyMZ56J5LhTx0i+tRRAGrXrkVQkHtC7fj4+NIMM08Nanrw6vgIBvUMpHNrX958vBqN6nhiNCiGXBfEviPpbNtzefOhlabiKAOLq/wDqB5q5LWIXXR67yUcd04gfckGMrfuxRF5GjId+L8zHs8ylICeq3lDL96fUIMFv8bx85qEC9ZbOzYjdOUH+D5zD4aQANJ+XkfswCdIfPQ9/KaMIeC9/5VqAgrw9aJYurTxJaKS1IKWlrPTml1zzTUsXLiQ+fPnk5CQUNphXRESExMZOHAgmzdvvvTGBbB2awpWi6LN1d7MmjWLzMxMFixYQO3atZk0aRKFHaOntHVs6UvUaTt7D6cXeN+L1oRqrV+5yLqXC3y2IqSU8gB+AmoDXbTWUXltq7X+GPgY3IMyFFUMo0ePZuzYsQwfPpyvvvqKUaNGsWpVznGRvv76aw4cOMD+/fuJjY2lRYsW9OzZ86KjoJ32DWP0p2nEJx0gJc2J1WygS2tfbuweUCzNRaPj7Pj7GrGY874n4XRpTsfaicwaPTA+yUl6pov0DBfpGRofLwON63pydT1PKoeYy2SNQVqGi8W/JTD/13jqVbcyZXyENL8t42rUqMGIESP4+++/mTNnDnXq1KFHjx74+krTyLLik08/Y86vULVOV7q2dTdtbdHQC6+4JBKOhODj45M90EZcXFwpR5u3Vo29adX4wlra3tf4MeunGKZ+c4ZOrXyJjrOzaUcq3dr6Mm5Y8fXLL0rFUQYWV/kHwIFjZP7xDz7jb8PapSXKVPg5m0tDeLCZNx6txqNvRGI2qQtGwFQWM9Zrm+aYxqus9Jc+EZ3Jio1JfDq5ZmmHcsWTac1Kx9NPP80PP/xAaGgobdq0KZJjulya9VuTaXO1Nx4WxSeffEL79u1p0aIFjzzyCA8++CAHDx6kbt38D15WVnRv58vMhdEsWB7Ps6MK1iS3QEOTKqWqaq1LvTpCKWUGFgJtgZ5a639KOoYzZ86wdetWli9fDsDQoUMZN24c0dHRhIaGZm83d+5cRowYgcFgIDQ0lAEDBjB//nwef/zxPI8d6G9mwojKBPqb8PMxkpTi5Jd1iTw/9TihgSb6dw6gbVNvAnwv/fTZHZo9h9KoXsWKv0/OgvxUjJ0vf4xh/fYUvDwM3NoniH6d/LFa3Mlopt3F+m0pLPotgd2H0gnwNVKtkoVqlSwEB5gICTThYTXgYVHEJznZuCOVTxdGo4Frmvtw540hBJXy3GJaa/YfzWDdtmSW/J5I0/qevPRQVUk+yxGDwUCLFi1o1KgRa9euZfr06XTo0IEOHTpgMpWd0ZWvROvXr2fkiPvx8K3O2j9uoVUjH+4Z4G480/vHY9nJxtma0LKchObFYjYwdmg4MxdE8+3iWLw8DVQJNfPDqgQ6tfKlWQOv0g6xxBVn+QdgblCdwBlPFus1FLcqYRZef7Qaj70Zidms6HGJSenLQgIK8Nl3MQzqGUign3y2lhVms5muXbvm6C8q05oVj7NjUlgsFpYsWVJkN4f+PZhGbKKTTq18mTdvHrt37+aLL74AoHnz5gAcOHCgXCah3p5Gru/sz8LlBW/pVNBPmW1AqU7mqJQyAF8DPYDrtdYbSyOOyMhIqlatitHoTuyMRiNVqlQhMjIyRyF87NgxatSokf1/9erViYy8eHdaL08Dtav9lyQF+pkY1i+YW68LYuOOFJauT2LqnDNEhJlp1dib5g29CA82E+xvwtPDgNaaXQfSWLExibV/pRASaOJkjJ261ax0aOZD0wZerPgjkRUbk7ipWyDfvFabE2fszF4Uy5wlsQzuHURiipOl6xKpVdXKjd0CeekhbzytF2+9PahnIFprTsbYWbQmgfsnHmFw70AG9wq8aC1rcdh7JJ1l6xP5Y3sKFovimuY+vPV4NWpUkdHmyiur1UrPnj1p1aoVv/76K1OnTqV3795cddVVUhCXoJWbkli5MYnH7gzkzjvvBgykJR3F7DgE/Fezc+TIkezCtaw3x72ULq196dLaF4dDowxgt7uH2X971ilmTqpZ4p9vpa04yz8AytDUXZejWiULrz0SwRNvR6KA7pdIREvbnkNp/LM/jUfvLp4pWcTl8ff35+abb86e1mzz5s306dNHpjUrQmPHjqVGjRo88sgjjB8/np07d9KkSZPLPu7arSmYTYqra2ta3fwoLVu2ZPjw4QDZieeBAwcu+zylZWCPwBJJQsvCN71pwBDgZSBVKXXuEHNRF2uWW5Z9/PHHfPzxxwDk1WfHaFRc28KXa1v4Yndodh9KY8vOVL76OZaYBAdxiQ4Myn3nPsDPSI/2fnz4XA3Cg81kZLrYtsfGhu0p/LQmgfZNvfnsxVrZdzvr1zTy4riq7D+WzvxlcQT4mnjnyepUK2CfEKUUVUItjBwSxvWdA5i5IJp7nj3Mg7eH076pz+U9SPn025ZkPvj6NDf3CuTVRyKoXtkiSUoFEhgYyK233srhw4dZunQpmzZtok+fPlSuXLm0Q6vwjp7I4K0vTpFp19xy5xscOnSA5j3fZseqx1iwYAF169Zl0qRJDBs2jKNHjzJw4ECgfNeEnsuUNQek0aoYf0c4T74dxejJR7m1TxC9r/GTz5nLkJ8ysDyqWdXKq+MjePaD4xw5kcHdN4XkGLE3xeZk+twzbPw7lZpVLdSt7kHd6lZqVrES4GvEz8eY3TqpOGmt+XhBNHfdGHzJG86idJ0/rVnNmjXp2bOnTGuWDzabjS+++IL+/ftfMNhTTEwMO3bs4PXXX2fIkCGMHz+eJUuWFEkS+teuVJo28OTdt1/l+PHjzJ8/P/smXnh4ON7e3uU6CQ0LMtOldcG7SRU0CS0LvWb7Zv1+JuvnXC8Ak0oiiGrVqnH8+HGcTidGoxGn08mJEyeoVq1aju2qV6/O0aNHs9uVn39n+KyRI0cycuRIwD2B86WYTYqm9b1oWv+/pmBaa2zpLmxpLkICTTm+EFktBto39blkIlivugcTRlS55Pnzo2q4hUljq7J9j40XZ5xgyvgIGtQs3mawi39PYNZPsbz2vwjqVJMmtxVZrVq1GDVqFNu2bePrr7+mfv36dO/eHR+fkrnZcaXJtLuYMvMknlYD3dtYmTB2NqE1ezF+3B184fiJBQsWkJCQwAcffMCHH35IZmZmdnNcb29vTCZTuU9Cz9WqkTcTx1Thq0WxvPH5KQwG6NXhyhhhu6jLPyh4GVie1K7mwbRnazB5+gkmTjvOU/dXxtvTyOadqbw96xTtmngz7dkaRJ7K5MCxdDb+ncr8ZfEkpjhJSnFiNECAn4mq4WYiwi1EhFuoV93K1fWKrin4xr9TSUpxZs+VK8q286c1mzFjBu3ateOaa66Rac0uYuHChYwdO5aHHnqIESNG8OGHH2Z/V/77778BaNGiBVWqVKF58+YsWbKEJ5+8sGuAMyOTW1pdS2ufcMYNHorX3dfnOaVSRqaLY6cyaVbXzuNPv8Odd95Jhw4dstcrpahTpw4HDx4skmvUWjNlyhTuuOOOEh1V+Y4bggu+k9Y63z/AmYJsXxZ/WrVqpYtKly5d9OzZs7XWWs+ePVt37dr1gm0+//xz3bt3b+10OvWZM2d01apV9cGDBy963KKMsaz4bXOSvv3JgzopxVFs5/h2SYy+/cmDOupURrGdo6hVxOe6NKSlpelly5bp1157Ta9bt07b7fbSDqnC+Wl1vO5x3x69bmuS/u77HzWgOw38WNvSnHrq1Kka901KPXz4cF2rVi0N6F9++SV7/7CwMD1q1KhSvILi4XK59N3PHNIPTjmidRko4y71U1SfOcVV/mldcT8XM+0u/c6sk/qeZw/p1z87oYc+fkBv2Zly0X1cLpe2pTl15KkMvfHvZL3g11j9zqyT+vYnD+qp35zSdrvrsmLKyHTqr36O0QMf3q+37Lp4LEWtoj7PpSE+Pl7PmzdPv/322/qff/7RLtflvS4qqmeeeUYbjUY9bNgwDehdu3Zlr3vzzTc1oM+cOaO11nrChAnaaDTqhISEHMdwZWTq1zvcoAHd2CtIn6jSX8cMfjrPx3zP4TTd4749+oHxUzSgd+zYccE2gwYN0g0bNiySazx48KAG9MMPP1wkxyugguWVBdpYktAcdu/erdu2bavr1aun27Ztq/fs2aO11rpv37568+bNWmutHQ6HHj16tK5du7auXbu2/uijjy553Ir6wfzB16f0cx9EXfaHo8vl0r+sTdBf/BCtP114Rn8077SePP24vu+5Qzo6LrOIoi0ZFfW5Li0xMTH6m2++0e+9957evXu3FMRF6MEpR/Sw/23SLpdL33zzzTo0NFRHx6VprbU+fvy4Vkrpq666SttsNh0ZGamffPJJnZqamr1/w4YN9ZAhQ0or/GI1f1ms7nHfHq3LQBl3qZ+i+swprvJP64r/ubj4t3g9fc5pnWIr/E3ZpBSHfuqdSP3oG8d0fFLhbrpt/TdF3/3MIf3s+5H6ZHTJl50V/XkuDUeOHNEzZszQn3zyiY6KiirtcMqcIUOG6Hr16ulDhw5pQL///vvZ64YPH66rVq2a/f/69es1oL/44oscx/hnxCTto0zabDRpg8GgT0yfr09Uvl7bFq7K9ZyLf4vX3e/drWvVrquvvfbaXLd54okntMVi0Q7H5VfUrF27VgO6Ro0apfEdqEDlUcXo/V9KGjZsyKZNmy5YvmTJkuy/jUYj06dPL8mwyqyRQ0J55LVIFi6PZ3DvoEIdQ2vNR/Oi2bbHxjXNfbBaDHgZISTQzMPDw/HzKV9D+YuiFRwczNChQzl48CDLli3L7i8aHl4+ptIoaRs2bOCVV15h/vz5eHjk3Xw96nQmK5fO4d/fn8InZSQ///wzY8aMISTQvU+VKlWYO3cuLVq0wNPTk4iICF599dUcxwgMDKxQzXHP1esafz79Lqa0wyhRUv4VXr/OAZd9DF9vIy89VJXPv49h7EtHeW5UFTysBg4fz+DoiUyiTmfi0mAxK8wm94/DqcnM1GTYXSQmOzkda2fs0HCuaS5dGCoKmdbs4vbt20e9evWoVasWtWrVYsWKFTz44IMAbN++PXswPYAOHTpQt25dPv/8c+666y4A7HuO8PxXH+M0Gvhg6vuMHj2af2r60LJFfZImfYq1RxsM/jnfTwcjM0iL/ZPDhw4w+YWJucZVt25dMjMziYqKyrPLQn6dPHkSgKNHj7Jjxw6aNWt2WccrTgVNQvMxrJ0QubOYDTw3ugrjXj5KozqeNKpTsPmEtNZ8PD+av/faePOxfheEGgAAhaFJREFUavh6S8IpclenTh1Gjx7Nli1bmDVrFldddRXdunXD2/vCeSCvZDNnzmTRokUsXryYm2++Oc/tVm5MIvLf2fj6+mUPHnO2UD5ryJAhFz1XUFBQduFY0fj7GOnUSr7Ii5JlNCjuvzmUutWtPP1uFP6+RmpWsVKzqpUOzX0wKPc0bWd/TEaFxaywWtxTqzVt4CWDEFVAMq1Z7lwuF/v376dbt24A9OzZk7lz5+JwOHA4HOzevZubbrope3ulFPfeey8TJkzInj4l8sWZLE4/xagRIxk6dCgPPPAAGzZupNurY4np+wgpb3+L3wsjso/x2Wef8cIzb2BLiSEoKIjBgwfnGludOnUA9wi5RZWEAvz4448VJwnVWrcqrkDElaFSiJlH76r0f/buOrzK8g3g+Pc5vQ62wYKN7u5SQEpEREFBVMRWFLsb+2cXJiKoKAYiICoICEhJSXfD6LGuk8/vj7NNBttYj+n9ua5dbO95z/ve5zj3nPuJ++GRNw8RUcNMbKSFuCgrdaMttGniW+jep7kJ6Lrtmbz2gCSg4twMBgOdOnWiZcuWLFq0iA8++IALLriATp065VWl+y/TWjN37lwApk6dWmgSqrXmu5+Wkpawhffff5/69euzcePGfD3GxREaGsqWLVsKvP5XX33FxRdfTEREle4AViaX9gyu6hDEf1SvjoH06iiVUUV+uduatWvXjnnz5v3ntzU7cuQImZmZNGrUCIA+ffowYcIE1q5di8lkwu12n9WuXX/99Tz11FNMnjyZZ4aNYtrsWTi0hxtuv5XAwEBatmzJvAVLePSxZ/C9pj8Zk2bjO2ogpgbebXPeeOMNMtJTaNSkI4/cd22hM45yt2nZs2cPffr0KfPrNJvNdOjQgRkzZvDMM8+U6XoV6b/bJSKqTNc2/vz4TgPijzs4eNT7tWBlGm9/eZzonL1PWzXyxaAg2+Eh26HZuCOTHfuzee2B2jLlVpSIj48PAwcOpEOHDsydO5c1a9YwYMAAGjZs+J9siHNt2rSJo0ePEhUVxezZs0lJSeHPP//EZDIxcODAvPO27M5iw4pvsNp8GTVqFEFBQfkeL67CpuP+9ddfjB49mlq1ajFlypQSNcDZ2dls3bqVdu3alTie8tayYclmdgghRGUIDQ2Vbc3wTsUF8pLQiy66CIAFCxbkLdk5MwmNjo7m4osvZvKEz7hpxm5+cB2jedOmtGnThpkLk8g0tGDzyp+4/bndvDh6OJaZf5J8/zsoHyu7/lrDtkPbuLLe5dz01iQGXhBcaGwxMTFYrdZy2abl6NGj1KpViyuuuIJHHnmEgwcPVmqV3JIo8TwMpZRBKWU649gApdSDSqm25Rea+DezWgzUr22jd6dARg8J44Wx0fz4dgPuGBGBAr797RTfzUlkzrJU/tqQjsmoJAEVZRIeHs51113HxRdfzO+//86UKVP+VfsRllTuKOh7772H3W5nzJgxXH755dx///35zvv5j8Mc3/sLI0aMICio9Ns3hIaGkpqaisvlynd8//79gHfk+uKLL+bIkSPFvub7779P+/btmTFjRqnjKi//5Q4NIcT5L3dbs5YtW/L1118za9Ys0tPTqzqsSrNr1y7gnyQ0PDyc1q1bM3XqVH766Sf8/f2pV68eew9l8+jbh3jry2Os3ZrBnf2HcPTEcS7ZO5c1mQmMGn0D70w5wftfn6Beo464nZkcObidsR+n4LzpKpxrt+Pae4SFjbyzEx5MS6FR0v4iYzMYDNSrVy9fEnrbbbcRGxtLVFQUP/30U7Ff59GjR4mMjOSSSy4B4I8//ijJ21SpSrMYYCrwee4PSqk7gN+A14G/lFJ9yyk28R9jytn79Kah4bz5cCyvPVibF8ZG8+RtUdx9rRQdEuWjYcOGjBkzhoYNGzJp0iR+/fVXsrKyqjqsSjd37lxatGjB0KFDqVOnDlOnTsVoNLJz5868DyYOp4eZs//A7crkxhuuL9P9QkO9xciSk5PzHT948CAAH374IS6XK6+3urivAeDGG2/kwIEDZYpPCCH+7QwGAx06dGDs2LFYrVY+/PBDli1bdlbn4L/Rzp078fHxITo6Ou/YPffcy+7du/nll19o27YtC1enc/crB9l9wM7Clak8+uYhwiZu4NPYiziIA4PBwNaEnvz6ZwojLwnlo1cuA+CiVvtwuTQzontQ47e3ifjrMxaQRFR0I4L8ahI4+bvcXUYK1aBBg7wkNDExkYkTJxIdHU1mZmaJk9CoqCiaNm1KSEgIy5YtK8W7VTlKk4R2AX497eeHgc+AIGA68GQ5xCWEEBXGaDTSpUsXxo4di9aa8ePHs3LlStxud1WHVikyMjJYsmQJAwYMQCnFY489Rs+ePfn000/RWrNx40YA/tqYwfFD6zCaTHTq1KlM9wwJCQE4a0ruoUOHCA4OpkmTJgAcPny4WNfLyspi6dKlXH755bjdbm644YYyxSeEEP8VNpuNAQMGcPPNN3Pw4EE+/PBDtm/ffs5EqTrZsmULx44dy/s5tzKuweBNfdxuze7Ufgy+fRVTvpnO/Y+9y6ufHaVxHRsTnqvDtLcb8HK748QmHOBIk+tpNfB7Wvb5EIM1gsdvieTmoeHUqVOHevXqsXTxXLq08mPx2gyMLRqQmpHO4sWLiajbm786DsD11yYcSzcUGW/jxo3zOoH/+OMPPB4Pb775JhdccAF///13sV/3kSNHiIyMxGAw0K1bN5YuXVq6N7ASlCYJjQAOAyilGgB1gfFa6zRgEtCy/MITQoiK4+vry6BBgxg9ejQ7duzg448/Lpc1Gee7P//8E4fDwYABAwC4/fbbWbRoEf369QPIa/Dmr0glM3EjbVq3xtfXt0z3zB0JTUpKynf84MGD1K5dO693Oj4+vljXW758OXa7nVtuuYWHH36YRYsW/Wu3gBFCiIqQu63ZoEGDWLBgAV999RXHjx+v6rDKLHfLsAcffDDv2M6dO/Om4gJMnH6SVZszSMu2smRXW75dEEB0TQsv3RNDUFoihu17qTtrOsbYmoyZMoJPX+nNV+NHMfH5OvTp4p1qq5TisssuY8GCBXRuYSA5zc367ZnMnTsXl8uFMehCgm8YiCEyjIyPphcZ85AhQ7Db7cyYMYPff/+dwMBAOnXqRLt27di2bRuZmZnnfN12u53ExMS89b7du3dn+/btnDp1qjRvY4UrTRKaCtTI+b4XkKC13pjzsxsofLM5IYQ4D0VERDBq1Cj69u3Lr7/+yjfffENCwr9338fFixdjMpno3r17vuNRUVGEh4fz999/s2ZLBn9tSCHl5Ea6dOlS5nvmJqEFjYTGxsbi7+9PUFBQsZPQ+fPnYzKZuPDCC+nRowdAgftWCiGEKFr9+vUZM2YMTZo04csvv2T27NlkZGRUdVilMmXKFEaOHInT6WTbtm0AOJ1O9u7dS6NGjXC6NFN+TuD7uUlc1juYZ8dEseeQnbRMD0/fHome9jsnOt9MwsX34dq0B//7RxIWbqNVI19aNvLFYMi//n/IkCE4HA5Sji7D18fAHytTmThxIsEhtQiNbEOvHjXwGdwd+4pN6MzsQuPu1q0bcXFxTJkyhd9//50+ffpgMplo164dHo+HDRuKHkkF8kZ+o6KiAPLa+OXLl5fqvaxopUlClwOPKaUuBe4j/9TcBkDxPkEIIcR5RClF48aNufPOO6lTpw6ff/45c+fOJTu78Eajulq8eDEdO3bMG93ccyibX/5MxuWGdu3asXT5Gp4Zf5hA0wHs2RnlkoQWNh339Mp9MTExxZ6Ou2DBAjp37kxAQAAdO3bEYDDw119/lTlOIYT4L8rd1mzs2LEYjUY++OADVqxYUe2Wqbz11lu0bt2aW265hV27dqG1Zv/+/bhcLkLC6nDH8/uZPPMUF3YIYMyICLq09mfcnVG8cHc0ETN+JuWR8Vgvak/IpKcJnfoCPsOLrtjeo0cPQkJC+OWXn7mgrT+//bGR33//neimV9O5VSAhgSasvdqB3Yl95dnblOUyGAxce+21zJ07lwMHDtC/f3+AvOrvxZmSm1vYL3cktGPHjpjN5vN2XWhpktBHgFBgFt5Rz3GnPTYCWFH2sIQQomqYTCa6devGXXfdhcPhYPz48axZswaPx1PVoZWLjIwM1qxZg2+N9uw6mM2+w3YefP0Qb395nFuf3UeyswG7d26jVqiHC5ruBygwCc36ZRnpnxS/WEJBI6EZGRkkJiZSu3ZtwJuEFmckNCkpiTVr1tC3r7cOnr+/Py1btpQkVAghyih3W7Mbb7yRPXv28OGHH7Jz587zar1ocnJygfFkZ2ezadMmBg4cSKtWrUhPT+f48eNs3rwZgJ//CiU9w82L90TzzB1RmE3eUc3ubQNovOAX0l75AtsVPQn5/ClsAzpj7dn2nJXPTSYTgwYN4pdffmFAd3/2rP8KZTATUudK+nX1VpS3dG4BNguORUUnktdee23e97lJaExMDOHh4axdu5Y//viD6OjovKryZzp69CjwTxLq4+ND+/bt/z1JqNZ6l9a6ERCutW6gtd5/2sP34k1ShRCiWvPz82Pw4MFcd911bN68mU8++YR9+/ZVdVhltmLFClwuFwmOVtz14gEeeO0gVouBR26shUEpTH5N0NrFTZeksmH9asLCwqhfv36+a7j2Hib57jdJe24i9hWbinXf3JHQ09eEHjp0CCBvJDQ6OjovCY2Pj2fr1q0FXmv58uVorendu3fesS5durBy5cp/TWeBEEJUpTO3Nfv666/Pi23Ndu/eTWRkJJMmTQK87UHPnj1JT09n/fr1uFwuOnXqRMOGDQHv1iy/zlsLKGrHNeHdx+Po0so/3zUzPv+ZtFe+xDa0F8HvPYAym868bZEuu+wyTp06xSvjbiPxwCzadLqMNs1j6NLaDwDlY8XSuTn2cyShzZo1o127djRo0IB69ep5n6sU7dq1Y82aNTzwwAMcOXKEr776qsDn5yahudNxwTsld/Xq1eflrK7SjIQCoLU+a5Wr1nqT1rrqf0OFEKKc1KpVi9GjR9OzZ09mzZrFt99+W+kFcJYvX867775bLtea9PXvoAzcdXM/BnQPwsdq4H/3x9C/exCfv1iXL94eCMCSPxewdOlSunTpkq8nWHs8pDz8PspixhgdTuqTH6Nd556uZTKZCAgIyPfe5W7PcvpI6LFjx3A6nYwdO5ZBgwYVeK1Nm7yJ7+kbi3ft2pWUlBS2b99esjdECCFEoXK3NWvQoAGTJk3it99+q9JtzV577TWys7OZMmUK4N3r+s8//2TOnDmsWrUK8E5DzU1CZ/y6gVlz1hAUGsf4pxtTK8yc73ra6SLtzW+wXNiW4HfuRxlLvh3g4MGDuemmm5g7dy6ZmRl8+v5jvP9EHBbzP2mWtVc7XLsO4T5cdJr0ww8/MGvWrHzH2rVrx6ZNm9iwYQNBQUFMnTq1wJHgI0eOYDQaCQ8PzzvWr18/7HY7v/32W4lfV0UrdRIqhBD/FUopmjVrxl133UV0dDSfffYZ8+bNw263V8r9X3/9de677z727NlT6mu43W5OJDqYO28xEVHNufnKOB4cXYtvXqtP3Whr3nn16tUjOCiIJ554gl27duUV/cmVOWUOjhWbCXzmZgKfvw3X9gNkTv6lWDGEhobmS0LPHAmNiYlBa82xY8dYtWoV+/fvL7BS4+bNm6lduzZBQUF5x3KnDMuUXCGEKF+nb2vm8XgYP348q1atqvSZJ4cPH2by5MkEBASwePFi9u/fz88//wzAzz//zOrVq4mMjCQ6Opq4uDiMJhPTf9uIK303PXu0ITjg7BFOx7IN6KQ0/G68FGUq3X70NpuNiRMncvLkSeLj4+nQocNZ51h7edd2nms0tF69ejRt2jTfsfbt2wPQunVrXn75ZbZt25a3ldrpjh49Ss2aNfO2oQHo06cPkZGRTJ48uaQvq8JJEiqEEMVkMpm44IILGDNmDJmZmbz//vv8/fffFd4Q5/bu5k4/KimPx0ODBg2oE1uLxKN/M+TSi86q8JfLuW4nEwPb8lZsV3566lXGXDiQ7Lkr0Vl23IdPkvbiJCw9WuMzsh/Wi7tg6dycjEk/F2u9UGhoaL7puAcPHkQplbc9S+6/q1evzptWtHr16rOus3nzZlq0aJHvWKNGjQgJCWHFCilLIIQQFSF3W7Prr7+e7du389FHH5Wpc7Sk3nzzTTweD19++SUej4dbb72VzMxMGjZsyC+//MJff/2Vt6f18VMebP618WTuIfnUPtq0aVXgNbNmLUUF+GLt2bbM8RmNxrz1mGcyNYrFEB6MY1XhxYkK06NHD+rXr8/bb7/N8OHDMZlMTJ069azzjh49mm8qLng/t1x33XX8+uuv58V06tNJEiqEECUUEBDAkCFDuOaaa1i/fj2ffvopBw4cqJB7HTlyhCNHjmAymZg8eTIul6vE19iyZQv79+/H6l+fOvWac+vN1xZ4nmPdDhJHPk2P6Ppc17gjnSf+ScawJ0i68QUSBj1A8j1vgttD0Bt3o5RCKYXPVX1w7zuKc+O591etWbNmvnW1hw4dIjIyErPZOz0qJiYGIK9nG2DNmjX5ruFyudi2bdtZSahSig4dOrBu3brivSlCCCFKpWbNmowaNYo+ffrwyy+/8M0331T4XpRut5uJEycyYsQIhgwZQv369Zk/fz6RkZE899xznDp1it27d9OxY0fWbs3g0bfj8Q+O48Sh5Xg8Hlq2bHnWNbXDSfacFdgGdEbZLBUav1IKc8v6OLfsLfFza9asye7du+nduzdhYWH079+/wCm5R44cKTAJHj16NC6Xi2+++abU8VcESUKFEKKUoqKiuPHGG+nRowfTp0/n+++/zzfSVx5yRwLvu+8+Dh8+zNy5c0t8je+mLwJg8LVvsmvHOjp27HjWOZ6UdJJufglDaCA1pr1C2G9vETzhcUImPU3wJ4/hSUjBsWIzAY9fjym2Vt7zbJd0A7OJ7Bl/njOOvn37smXLlrxE9ODBg3nrQeGfkdDZs2ejlKJOnTpnjYTu3r0bh8NxVhIK3rVLe/eWvIEXQghRMkopmjRpkret2cSJEyt0W7OtW7eSmprKwIEDUUoxbNgwAIYPH84ll1yCyeSdapvobMSjb8VjNED/Xi3y4imozbAv3YBOTsd2aY+zHqsIphb1ce08hLY7y3SdAQMGcPDgwbNGNo8ePVpgEtq8eXPat2/PF198Uab7ljdJQoUQogyUUrRo0YKxY8dSs2ZNPv30UxYsWFBu60VXrVqF0Wjk6aefJiIiosRTcuevSGHS1wvw8Qvjtce7YCxkGm7qi5PwnEgm+ONHMUaHo4xGfAZ1xzagMz6DexC24H2Cxz+I742X5nueIdgfa692ZM1agj7HtOQhQ4YA5BVdOHToUN56UPBO17XZbCQkJNC4cWN69erF6tWr8/X25hYlKugDRb169UhKSir3jgAhhBAFy93W7M4776zQbc1y1/t37twZgFGjRhEWFsaNN95IUFAQvXr1AuDvPbH06RzAhOfq0LlDEwCsVisNGjTIdz2tNZmTf8mZituuXGMtjLl5PXC5ce0o28yp3HYzt64CQGZmJidPnszXpp7u6quvZt26dXl7iZ4PikxClVJ1lVILlFJ7lVJvKaVspz22quLDE0KI6sFsNtOzZ0/GjBlDSkoK48ePZ/369WXeW2316tW0bNmSwMBAhg0bxty5c3E4HOd8ntaar35O4H8Tj5GRuJ6+F/WgVljB043syzaS9fVc/G6/HEvrhgWeYwwPwWdo7wIrB/oMuRDP0QScqwveUiVXgwYNaN68OTNmzEBrzcGDB/M1mEqpvCm57du3p0OHDpw8eTJfQ7t582aUUmcVbgDytpKR0VAhhKhc/v7+Fbqt2cqVKwkNDc1LJlu0aMHJkydp3bo1AE888QRd+t5LcGgod42sicVsyKuQ26xZs7yR0lxZ0xdhn78a//uuRlnzV8ytKOYW3m1XSjMl93S57WZuhXkgb0lQnTp1CnxOnz59APjjjz/KdO/ydK6R0A+B6cBVQCiwQCkVkPNY5fwXE0KIaiQwMJChQ4cyYsQI1qxZw4QJE/I1FCWhtWb16tV502cvvvhi0tPTWb58eb7z3B7NL38m88S78Xz+wy5ef28aj751iC9mnqJzMzsppw5w4YXdC71P+vs/YIwOJ+ChgteKnot1QGewWcnKmfZblMsvv5wlS5Zw//33k52dTbt2+Xugc6fktmvXLu91nz4ld/PmzTRo0AAfH5+zrp27r1plFsoQQgjxj4ra1mzlypV07tw535ZhuewOD8eyWuEXN4YbhoQR6O/tLM1NQs9cD+o+mkDqUx9j7tgMv9uGlDm24jLG1UL5+eDcXLYkNHcZy+kdtPv37wegbt26BT6ndevWhISEVKsktKbW+gOt9Vqt9Q3AbLyJaBBQtu59IYT4F4uJieHmm2+mS5cuTJs2jWnTppGSklKia+zevZvk5OS8an+9e/fGZDIxZ86cvHP2xtsZ8/wB3v7yOHsOZvPYw3fyyL1XMXPq89x+VRjt63gLBnXvXnAS6j52CsfSDfgM74vysRZ4zrkY/HzwGdSNrJlL0FlFT0MeMmQIbrebd999lxtuuIGRI0fme/z0kdDWrVtjNpvzFScqqDJurtwkVEZChRCi6pT3tmapqals2bIlbyru6bbuyeKaR/YycXoCbZv4cmnP4LzHYmNj6dixI5demn8ZScYnP6Gz7AS/fW+p9gUtLWUwYGpWt8wjoWFhYdhstnwd3LmjzoWNhBoMBnr37s2CBQvweDxcccUVvPHGG2WKo6zOlYTm+0SitX4F+B5YAAQU+AwhhBCAtyFu1aoVY8eOpUaNGnz88ccsXLiwWNNp4Z8RwNwRwYCAAHr06JFXnGjBylTufvkAyWkunrkjisdHpXPywHzq1G3EzrUT+fbT2/jmm2+wWq1njTjmypqxGDwefIb2KtNr9bm6Hzo1g+zfit4ipX379jRt2pS+ffvyySefnNWrHRcXh8FgoG3btlitVlq2bMnatWu9sWZlsXv37gKrHIJ3OlhERIQkoUIIcR44c1uz8ePHl2pbszVr1qC1PisJ1VrzwdQTmM2KNx+uzWsPxmA0/tOmGI1GVq1axVVXXfXPczwesn5eirV3B0z1osv2AkvB3KIerq37zllDoShKKWrXrn3WSKjFYqFWrVqFPq9Pnz4cPHiQV155hRkzZjBlypQS39vj8ZCQkFDmpUZw7iR0p1Kq3+kHtNZvAN8A9ct8d3GW4n44FUJUHxaLhd69e3PHHXdw6tQpxo8fz8aNG8/5R3z16tX4+PjQvHnzvGMDBgxg/fr1TJ25g1cmHKVRnI2Pnq7DhR0CeOmllwgMDOTvtX/x0ksvMXfuXH788Ufat2+P1VrwKGfWj4swt22EqX7ZGmNL1xYYa9ck87v5RZ5nMBhYu3Ytv//+OxbL2WtU7733XubOnUtgYCDgXfezZYt3X7WtW7fi8XgKHQkF72ioTMetvlJTU6s6BCFEOcvd1mzkyJGsX7+eCRMmFLitmcfjOWtbLvBOxQXyZgXlWr4+nR37sxk9JIzWjX0LnKp7JufqrXiOnsLnsgtK+WrKxty8Hjo9C/eBY2W6TkFJaG4nbmEuuugiAJ566inAW+gvPT292Pe8+uqrCQgIIDw8nO+//76Ukf/DdI7Hry7ooNb6LaXUd2W+uzhLdnY2O3fuJC4uDpfLhVIKk8mE0WjM96/JZCryF+18d/LkScLDw6s6jCqXO4df/DcEBQVx5ZVXcvDgQebMmcOqVasYOHBg3jrIM61atYp27drlK6hw8cUX8/jjj/PwY08TExWBb80QPvvEn4MHDzJt2jSeeuopQkJCeOKJJxg7dizTpk2jVauCN+l2bt+Pa8teAl+4vcyvTRkM+IzoS/qb3+CKP4EpJqLQcwtaz5krIiKCvn375v3cokULvvzyS5KSktiwYQNAXiGKgtSvX59ly5aV4hWI80FKSgo7duwgNjYWt9uNwWDI1+6d3hZKG1i9Sfv335O7rdmWLVuYPn06MTEx9OvXj+DgYACmTZvGiBEjmDNnDgMGDGDnzp1MnDiRH3/8kUaNGhEaGpp3rdR0N5N+SiCmppn+XQOLHUPWjCVgs2Lt3+ncJ1eA04sTmepGlfo6sbGxzJs3L+/n/fv3FzoVN1fjxo2JjIzk6NGjPPjgg7z55pusXr2a3r17n/N+R48e5bvvvuOSSy5h7ty5bNq0iREjRpQ6fjhHEqq1LnTyttb6cJnuLAoUGBiY1xOstSYzM5PExEQSExNJSkrK973T6SQkJITQ0NC8f3O/DwoKOq8b6A4dOhTY2yXEf0FsbCy33norGzZs4Ntvv6VevXr06dMnb/QPwOVysW7dOm6/PX+CGB3bDB//KA5v/4Hju02sWuhGa01gYCB9+vTh/vvvzzs3MDCQm266qdA4sr7/A0xGbEPKp0fYZ3gf0t/4muzpi/C/Z3i5XDN3FHjLli1s2LABX1/fvCq4BalXrx5Tp07F4XAUONIqzm+1a9cmLS0N8LaBqampBbZ/iYmJGAyGAtu/0NBQ/P39izUqUlWkDRT/VbnbmjVu3Jjly5fz6aef0qFDB3r06MGiRYsAeOONN+jduzeDBw9m3759NGjQgLvvvhuAjCw3T713mE27sgB45o6ofFNwi6JdbrJ/WYatbwcMfoV3hlYkU6NYMBlxbt6LTxn2J61duzZHjx7F6XRiNpvZt28fQ4cOLfI5SinuuusuDh06xBNPPMGbb77JX3/9VawkdMmSJQCMGzeOjRs3Eh8fX+rYc51rJBQApdRFRTzsAo5rrXeUORqRj1IKPz8//Pz88m3onstut+drkI8cOcKWLVtITEwkIyODoKCgAhvokJCQs0pVCyEql1KKNm3a0LRpU5YuXcrHH39Mly5d6Nq1K2azmS1btpCVlZW3HjTXB9+epOvQH3jp7hp0bNsApRSZmZn4+fmV6EO3djjJ+mEBtv6dMYYFl8trMsVEYO7QlKxZf1ZYEtqyZUuMRRSSqF+/Ph6Ph4MHD561L5yoXpRSBAUFERQUdFbFx9xO2tOT03379rF27VoSExNxOBwFJqehoaHnfSetEP8FuduatW3blvnz5/P++++zYMECDAYD8+fP584772Tnzp3Mnj2bQYMG5T3vp/lJbNqVxbWX1qBzSz+a1S9+MulYthFPQjK2KpqKC6BsFkwNa+Mqh21aPB4PR44coUaNGiQkJJxzJBTgySefzPu+cePGrFhRdB2HXEuWLMHPz4+2bdueNRW4tIqbiczHWw33zE84eQualFJHgce11l+VOSpRLFarlcjISCIjI896zOVy5W3anttA79mzh8TERFJSUvDz8yu0gS5s7ZgQovxZrVb69OlDu3btmDdvHh988AH9+vVj1SrvVsynr4FZvj6dpX+nc8uIxnTpUCPvuL+/f4nvm/37SjynUvC5pn/ZX8RpfC7rQeozE3DtOoSp4dmdZyUVGxuLv78/mzdvZuPGjfkKTBTk9G1aJAn99zq9kza3ovLp7HZ7XvuXlJTEsWPH2Lp1a14nbWBgYIHtX3BwMGaz7EAnRGXJ3dZs27ZtPPzww1x00UUsX76ciRMn0q9fPy655JK8c1PT3Xz/exLd2vhz4+VhJb5XxqTZGEIDsfWtmqm4ucwt6mFfsr5M1zh9m5bcdZ3FSUJP17VrV2bPno3W+pyd2EuWLKFLly6YTCZq167NunXrShX36YqbhBY1TmsEooArgclKqSSt9ewyRybKxGQyER4eXuCaE4/HkzfFKbeBPnz4cN73JpMpr0E+s4H29S3ewu9zue2228p8DSH+TUJCQhg+fDj79+9nzpw5TJ06laCgoLypp5nZHt7/+jh1oi1c1T/0HFc7t6xvfscQGYa1Z9syX+t0tkt7kPrsZ2T9vJSAB0ae+wnnoJSiefPmzJ07l6SkpCLXg4Js0yK8rFYrtWrVKrBSpMvlIjk5Od9Mor1795KUlERycjK+vr6FTvO12WzlEp+0gULkd+jQIbTWXHXVVTidTpYuXcq4cePyfeac9nsimVkebhhSo4grFcy1/yj2eavwv2c4yla1SzVMzeuR9cMfuBOSSz0TKTY2FoCDBw/mbf9W0iS0S5cuTJ48mb179xa5zCU5OZmNGzfy7LPPAt4EeNasWcVKXotSrCRUa724GKdNUUp9DzyMdz9RcZ4yGAwEBwcTHByc94Etl9aajIyMfAnq7t278753uVwFJqchISEEBgYWe4qTNMBCFKxOnTrcdtttvP3220RERDBr1iz69OnDl7MzSUh28fQdsZhMZesIcsWfwL54Hf73XV3ue6QZa9XA0rk52bOWlEsSCt7iRBMnTgQotMhSrsjISGw2m1TIFYUymUyEhYURFnb2SEpuJ+3ps4g2b96c97PJZMrX9p3eFpZkSry0geJ8lZ2djcfjwdfXt1Lvu3z5cgwGAyNHjuTqq69m+vTpLFmyBIfDQffu3UlKg+nzk+jVMYB6tUveGZQ5aTYYDfiOvuTcJ1ew3OJErs17MfYqePu0czl9JDQxMREo3UgowIoVK4pMQpcvX47Wmgsu8E5jjomJITs7m1OnThX4d7S4ynth4FRgcjlfU1QipRT+/v74+/vn9bKcLjs7O1/vcXx8PJs2bSIxMZHMzEyCg4MLbKCDg4NlHar4z9i8eTMfffQRc+fO5dVXX2XYsGEler7dbmfPnj1cc/09fPxDCh9/9T8S7M0YeWXPEq1/KUzmxFlgUPiO7Hfuk0vBNrgHqU9+jHPbfsxN65T5eqdvUXOuJNRgMNC+fXsWLFhQ5vuK/57TO2kLWoeakZFR4DKXxMREXC5XoctcStJJK0RVuuqqq9izZw9r1qyp1ER02bJltGzZMq9A34033khycnLeetHdia1we2pzy7CSV5X2pGWS+e08bIN7YKxV8lHU8mZullMhd/NerKVMQv39/QkODubQoUPYbDZsNluRe4QWpHnz5litVjZu3FjkeUuWLMFkMtGlSxfgnwQ4Pj7+vEpC0wApR/gvZrPZiIqKIirq7LLSTqcz3zrUhIQEdu3albcONSAgoMAGOiQkRNahivPGsWPHWLJkCVdccUWpOk48Hg/9+vUjJSUFHx8fxo0bx9ChQ0s0ZeXXX3/F7XYTn9aYkOhuZGY0xXxsOa4T33PkyIgC//8rdnwp6WROmYvtsgswFrGNSlnYLruA1GcnkPX9fMzP3lLm6+UmoXXr1s1XQbgwV155Jffffz87d+6kUaNGZb6/EJC/k7agYoHZ2dn51qEeOXKEzZs353XSFlQsMHcdqnTSivPB3r17mT3bO5nxqaee4q233qqU+7rdbv766y+uv/76fMeDg4O58sor+WX+diaM+57ubQPwt90KlGzddsaHP6LTMvG//YpyjLr0DMH+GGMicJZDcaKDBw9iMBiIi4sr8dRYo9FI3bp1z7l8ZcWKFbRr1y6vU+L0Udg2bdqUKnYo/yS0M3D27rPiP8FsNhMREUFExNkfbD0eDykpKSQmJnL06FFeeeUV1q5di8FgICoqiuHDhxc6zbe81qGK/4533nmH5OTkUj3322+/ZceOHURERDB48OACi54U5eTJkxw7dozLLrsMpRQzZ85k9OjRZ019L8zhw4f54osvCA4JZ++udVwUup/mkRZ0LThyOIXPP/88b6Pp0sj88jd0Rhb+Y0o2OlsSxhpBWPt1ImvaQgKeuAFlLltT06JFC6Do/UFPl5uE/vDDD/kqAQpRkWw22zmLBR49epRx48axcuVKDAYDsbGxDBw4ED8/v0LXoUonrSiJsrR/uTNImjZtyttvv01SUhJxcXEluobT6WTmzJlERETQvn17/Pz8zvmcXbt2kZ6ezvHjxxk3bly+x+KPO1i2Lh2TEUzOY3z44Yc88sgjxY7HfTSB9E9mYLu8J+ZW50+xOlOLemVOQmvXrp1XWOjyyy8v1TXq1at3ziR0586dDBw4MN99gTJXyC2XJFQpZQIuAx4F3imPa4p/F4PBkLc9zLvvvkvDhg35+eefUUpx7Ngx/Pz88k3z3blzZ97PHo+nyHWokqCKMyUnJ5/VkBXHpk2beO6557jiiitYs2YNM2bM4NChQ/j4FH8K7GeffQbA66+/TmxsLHFxcSQlJRUrnlOnTtGkSROiY2Joc/HXREVFMf7J2Hy/46V5Xbl0lp2MibOw9Gybtyalovhe3Y+k31Zgn78a28CuZbpWZGQk3bt357LLLivW+TExMXTv3p3vv/9eklBxXsgtFvjCCy8QFxfHjz/+iFKK48ePEx4eTkpKSr5pvrnFAhMTE7FYLIVO85VOWnGm0rZ/TqeTTz75hEGDBvHtt9/SsmVLVq9ezYQJE0o0Uv/222+zZcsWtmzZwooVK5g7dy4XXnhhvnNWr17N7bffTuvWrfn4449p27YtDRo04Jtvvsm3v/O03xNZ+v1JLrjYylO3RxIbaS32a3Nr7wYeaa9/DW43AY+Nyvf4nmw3c5Jc+BoVN0ZU/iROc/N62OeuxJOZjcG3dAXPRowYgdPp5IorruDqq68u1TXq1avH0qVLCy0ylJmZydGjR/N1pEdERGAymSonCVVKHeK07VjOYATCcq41H3ixTBGJf7X09HS+/PJL4uPj837Zc+ewBwQEFNjjlpWVlS9BPXjwIBs2bCAxMZGsrKy85PbMBjo4OLjI/QSFONMrr7yCv78/n332GRs3bqR3795MnTqVm266iaysLKxW6znXdS1btozw8HAaNmyIUoo77riD559/nm3bttG0adMinzthwgQSEhK46/HvWLo1lJuuCCvXD5hp73yL50QS/h8/Wm7XLIy1d3sMESFkfje/zEmoUoqlS5eW6DnDhw/n3nvvZfv27TRp0qRM9xeiPBTU/tWsWRMgrx07V7HAxMTEvGKBiYmJeDyeItehSoIqimv27NkcO3aM2267DX9/f9566y2GDh3KJ598wl133QV46x089dRTjB07lr59+551jaysLF577TUuuugiPvjgAwYPHsyoUaPYuHEjgYGBrF69mokTJ/LZZ58RFBTEunXrWL16Ndu2bWPmzJn5EtATiU4mzUigSys/nhkThcVcvDXVWmv+SHEz/pidLu4srv9uPn63XoYp9p/1kl+fdPD5CWfezxcGmqhvq9w12+bm9UBrXFv3YelQ9GeDwowaNYpRo0ad+8Qi1KtXL68YW2jo2ZX39+3bB5CvcJHRaCQ6Opr4+Pgy3bu4XRsLKDwJdQEngIVa6/llikb86+3Zs4caNWrw3HPPsXDhQvz9/XnxxRfp0aNHoc/x8fEhOjqa6Ojosx7LXYea2yCfPHmSHTt2kJiYSGpqKgEBAQVWMQwJCcn3x06IrVu38t133/HQQw8RGhpKz549admyJe+99x6dO3emZ8+eWCwWhg0bxgsvvJBXEGDKlCncfffdeXt1Ll26lG7duuV9+Lvzzjt59913ufXWW1m8eHGhHSMul4sPPviAzl17sWxbNBf3CKJ983NPYyou59Z9ZHw0HZ/hfbF2aVFu1y2MMhnxGdabjE9n4ElJxxBU8r1My+LKK6/kgQceYNSoUUybNq3EU8qEKG+laf+KWywwt6M2Pj6ejRs3kpSUlFcssKBZRCEhIdJJK/Kkp6fzyCOPUKdOnbx9OS+//HJ69+7NM888wzXXXIPNZmPEiBFs3bqVmTNnMmrUKCZPnpyvY/aTTz7h2LFjfPfddzRp0oQpU6bQvXt3Lr30Uk6dOsW2bduw2WzceuutvPrqq7zxxhu8+OKL9O3bl8GDB+eL6fOfEvB4YOw1NQtMQLXdSdbPS7B0aoYpthZaazypmbx11MEctwU/AyxwW7gsLoqa9/4zSrgu3c2kE056BRq5uaaF2/Zk8X2Cg8djymf7peLKnRrs3Li71EloeTh9W7OCktDcqbpndpDVrl27ckZCtdY3lOkuQuRwuVzs3buXtm3b8vrrr7Ny5UoGDx7M7t27i1Vw5ExFrUN1u91561BzG+gDBw6QmJhIcnIyVqu10Gm+Pj4+0oP8H3Lo0CEGDhxIaGgoDzzwAOD98HfPPfdw66230qNHD2w2G127duXDDz/EarXyxhtv8MQTTzBlyhSmTp3KzJkz8fX1Zffu3fm2X6hZsybvvfceo0eP5t133827fi73ySTsC9bww1dfEx8fT5PWDxEVbuauq8uvaJDWmpRHx2MI8ifwmZvK7brnYr2gNRkfTfdWAOxedFXb8hYVFcW0adMYPXo07dq1Y82aNWdVOy2t8ePH06JFC3r16lUu1xP/DeXd/kHJiwXu3LmTpKQkUlNT8ff3L3S7Gemk/ffSWvP777/nFbTp2rUrb731Fnv27GHhwoV5U2+VUrz99tu0a9eOzp0707RpU7Zu3cqsWbNYvHgxb775JsOGDWPIkCGAdznJK6+8Qu/evfOm33bu3Jlx48bx9NNP0717dyZMmMBVV11FUFAQAM8//zytWrWiW8NmJN/xKuYW9bANvoCtjiDmr0jl6oGh1Ao7uwiRdrtJvvsNsmcvA8DYIAbPiSR+vrgPc269lsE//coAH83d/Qey5Mk7aRDkzy+JThJcml+TXMRYFA9FWfExKgaFmJh+ysVNER5qWipvNNQQWQNDRAjOjbsr7Z4FyU0u9+zZQ4cOHc56PHe7szOT0JiYGFatWlWmexd3Ou7nwGSt9Z9lupv4z4uLi8NkMjFypHf/wM6dOxMWFsbOnTsL/OUvC6PRmNewnklrTVpaWr5pvtu3b8/7HiiwimFISAgBAQGSoP5LLF68mMWLF/PFF1+QnJzMwoUL86bHAVxzzTU8+uijuFwu5syZQ+vWrRkxYgQTJ05k7NixfP/991x00UX8/fffdOrUiZG33gFAZvPOrMtw08bXgFKKUaNG8eOPP/L444+zfft2Lu7XjyOrNxK3+xQd1h7B43DyfspK4oy+fLruD/SgxvjYSrdm84jDw0G7hy4B//x5d6zYhHPtDoJeH4shtHQfdkvD1MI7fce5aXelJ6Hg7clfsmQJrVu35pdffmHs2LGFnnvY7qGGWWEznP3/9s8//8zGjRt58sknSUhI4N577+Wyyy6TJFSUSGW2f1C8TtrTZxLl7jeYlJSE1WotdJqvdNJWb5988gljxow56/hjjz1Gz5498x1r3bo1v/32G/fffz+zZs3ivvvuY/DgwQwcOJCffvqJl156KW+t/pgxY0hKSuLtt9/Od40nn3ySMWPGUKOGd2sUx5ptZKxbhPK1YWpYm6Fde5Iw4klSElKw/ryUtFe+xONbg5vrtmHwgLvyXct18BjOnQdJeeBdsmcvw//Ba1AWE47V29h05cV8d1FfeqQkcO2a1bhWbaVNrTjmtm/B7oPZrM3wABBhVjxd25uAAgyrYeanUy4e3J9NgFFxSYiJwaElq75bGkopzK0a4Fy/q8LvVZTcztnCihPt3buXwMDAvP9+uWrXrs306dPxeDyl3n6quNNxRwCjlVIHgS+Br7TWVZu6i2opLCyM3r17M2/ePPr378/OnTs5ceIEDRpUbsUypRSBgYEEBgaetbmv1vqsdaj79+9n3bp1JCYmYrfbC12HGhQUJFOcqgGtNc8//zzjxo1DKUXTpk357bffaNcu/35dvr6+zJs3Dx8fn7z1nHfffTfff/89l112GQ6Hg7tee5fGfmb69OvH+6+8iMFiZVFkC/7cn00LXwMXBppoaDPw2cSJPPrAg0yZ9AUTJkwAQAGfXX4zm4IMrP3iFN07PoQKM2B78QOyalrxGdq72K/JozU/JbqYeNyBXcMLtaFboPdPfObns1EhgSW6XnkwhgVjiAzDuWlPpd73dC1btiQ8PJx169YVes6vSU7ePuKgvs3Aa3E2Ak3/fMBesWIFV155JQ6Hg2HDhrFixQo8Hg+7dlXtBwdR/Zwv7R/k76Q9c5N6rTXp6en5ZhHlFgtMTExEa11gchoaGiqdtOe5ZcuWcc899zBw4EAmTJhAVlYWf/zxB4cOHeLpp58u8Dn9+/dnw4YNLFu2jG7dugHeQluPPvoot99+O7/88gv79u3jhx9+4JVXXsmrYm73aJwa/I2KGjVqoB1O0l7/mowPf4ScokGnwkL54Ml72f/+qzisVi7ddwrnO79zoX03vbYsQE+oBfd7O21cB4+R0Psusk9sIivgBH53DiPgwWsAb/v31Z4sojU81iUW2/T/kTV9EcMa1uIpN6zL8PBwlIUBwaazfj8jzAZuqmlmbbqbVDe8c9QBUCmJqLl1Q+wL1uDJyMLgV/Y9wEvD39+fiIiIQpPQPXv2UK9evbPet9q1a+NwOEhISCiwo6s4ipuE1gSuAq4HngKeUkqtAL4Avtdap5Tq7uI/6eOPP+amm27iwQcfxGw289VXXxEcHFzVYeVRSuHr64uvr2+B23M4HI58vcfHjx9n27ZtJCYmkp6enm8d6pmJqtlc8X/Uzie//vord999NxaLhZdffpnLL78cpRQej4dvv/2Wjz76iN27d7N161ZCQkIqLI4jR46wbds2OnToQHx8PC+//DLffPMNN9xwA++99x4BAQGFPvfMxLR79+60adOG9evXE921Fx/Y6hIANJr0O1k3XkLbLPjyg49Z0rYNs9q05sPMYADiTqRztW7Co2G9OXhBI2oP7Mkdn7/LrbMm4fF4iGl6Df/7+GnimlhIHP08yfe8jQoOwHZR8UZIvk1wMvGEk87+Rk65NG8csfOZj4Gg4wlkz/kL011Xgq0KKgC2rI9rc9nK0JeFUoq2bdsWmIRmuTVfnXTy3SknTX0M7M728PCBbF6MtRJuNnDkyBGGDh1KVFQUhw4dYvLkyWzZsgWA3bt3l6kHWPw3ne/tH3j/nwkICDhnscDcBPXgwYOsX7+epKQksrOzC12H+l8rFujxePj44495+umnadGiBa+//jqdOnUCvBVHP/30Uz755BP8/PxYuXJlhb83hw8fZtiwYcTFxfHNN9/k/d4VpxPEZDKdNUo6evRonnvuuby1nBdeeCF3PfAQU086mJ7oItHlTTSvdqdyTfw+0l/+Avfew/heO4CAh6/jSLaLlxONpKG41GBnqcHKL4GBdBlyMZ3uiiLlnrdIf3Mq1m6tMHdqxvGnPuW15x9h75/fEz7uCUyR/4zM/ZXmZp9d81i0FR+DAhS+V15EZ60ZecJJaz8DHf0LT3muDrNwdRg4PZpxh+y8c9RBpEXRoYjnlAdzm4be4kSb92DpXPxaDR6tSXNDkKl8OnyK2qZl7969NGvW7Kzjp2/TUqFJqNY6HZgETFJK1QZGAdcBnwDvKqVm4h0hnau19pQqkhLIieFtoB/ewYT5wH1a64MVfW9RdvXq1WPRokVVHUapWSwWatasmW/aZi63201ycnK+Bnr//v1561B9fHyKXId6vkhISOCXX36hVq1aREVF5a0fatWqVYG93Nu3b2fNmjWkpqYyevRobDYb99xzDx9++GHeCOLQoUO57rrrmDRpEvfffz/jx48nJiaGY8eOMWPGDG688cZyfx12u51XX32V//3vf2RlZaGUQmuNxWLh6aef5rnnnivw9bi1ZkOGh1MuTW2rwggkuzWNbEaCTIrr7xzL+ttuIXzErdxe08LOdAcZiel85tuRwItbYdxzmL7b99N/9jySzBY2xMUyfdilvPrYPbz/0NX0a+2d/jJrWD/69h/MoYQgHnjsdXq0806VDZn8NKeGPELyna9TY+ZrONftxHMqBb9CNto+ZPfw5UknFwQaeTbGygG7ZszeLF474uCxL2dzKiyUZ4YMJWpfNg9FWdmS6WZOsgunhgAjDAg20zPQiLmAqahlZW5ZH/u8VWUqQ19Wbdu25a233sLhcOStdVuW6uKtI3aS3TAw2MS9kRbWZ7p55qCdG3dnMSrczN4vv+TYsWNs3LiRp556ismTJ5OcnExYWBgJCQkcOnRICh6JEqnu7R8UXSwwt5M2t6P2xIkTecUC09LSCAgIKHAW0flWLHD37t0sXbqUunXrEhoaSlJSEjExMQXu96y1ZtWqVWzatAkfHx9GjhxJWloaw4YNY8GCBfTo0YPt27fTuXNnXnvtNe6++24GDRrEokWLaNSoEWvXrmXGjBkMG1Zxezfb7XauvPJKMjIyWLBgQbE7PrTLjWv7AYwxERiC8xeXs1qtfPrpp/z2229ccskldOt9EWM2J3PU6kO7Uyfot2glh4KC+bbvBazK8iPllaexBvryWcsQtIKndmeRadO8EWejgcXAyinH0R0C6HZlOAaDgaD/3Ynz7x2cGvkM1i7NmdCxM5vbtGDvspm8p/1pmOhkn91DtwATUxKcRJoVFwXlT+QNSnFLzeL/XpkNimdrWxm5M5PfklwVn4TmFCdyrN9VoiT0w2MOZiS66B1kZHS4hRhr2TpD69evz7Jly8467vF42Ldv31lFo4C8QZr4+Hjat29fqvuW+N3VWh8CXgZeVkp1wjs6Ojzn64RS6mut9UOliqYYlFK+wB+AHRiNt2rvi8BCpVQrrXVGRd1biHMxGo3UqFHjrLnz4G2oUlNT803z3bp1a973BoOh0M3K/f39K2SKU27juXLlSg4cOIDL5SIzM5NvvvmGzMzMs86vU6cOjz/+eL7CO/PmzWPAgAHonOk17733Hg0bNmT27Nk88MADvPzyyxiNRl5++WWeffZZ1qxZw/bt27n//vt54403qF+/Pt9///05k1C3R5Oa7sbPx1CsUu0Oh4OhQ4fy66+/MrTTBQxN82X9oX34KSNX1WlFVEIA6W9NxX0kAdfueCxtGmK9uAurmzXhraMOUtxnX9PXAP2CTSzoNpxuX9bm3R5taTpzDj0//BHPiUSCxz+Ez5D8+6GFAQ2BAW7NVTsy+SMiitw+xQy7jdg+k2hqNjBmxD/l4w1+PoRMeoqEi+8nofc/a2Lsf67HE5mN+1QKhpAAlMGAW2vePGLHZoB7allQSlHHprgjGN5LcjMl3cCu/z2GHcUhu4eb92QB0MBmIMykOOTw8MphO9+fMvBRPRvGcv49M7es7+3p3bIPS8eqqQDYtm1bnE4nW7ZsoW3bthxxeHj5sJ0Yi4EXYi008/V+cOnob2JiAwMfHXPw6XEnWZt3ER4eTsuWLbnxxhuZNWsWAPcENOB1EtixbZskoUKc5lydtKcXC0xMTMwrFpiUlISPj0+R61ArgtvtZt68eWzatIn4+HgMBgOHDx/mxx9/xOM5e0ylbdu2vPbaa/m2J3n99dd59NF/tr2aNGkSp06dYvPmzXz66afccsstpKenc+utt/LII4/w+eefs337dr788kuuueYamjZtyssvv8zQoUMLbeezsj1s25dFSpqbxnVtRIaZi/WZICMjg59++olJkybx119/8XmrS6hx5fMcc7hQwf4YatbAVT+OjOAa2I4cQXk8HOndB4/FQu3f52BYthadlglmE9aebfG/dwSW9v9seTVo0CAGDRoEwPh5WzkaFcfDT7xC69XrMbduiO+dw/g5+Si/1o+hfrAvqzI8zEx04WeEeIfmhVgrjX2MTJx+ksNLUwht58/0DDeRGW5qWmyET32BlA+mMdNpYUn/XlxXw8hPvkZ+SXIBYFYwM9H7/QORlnJpvywGRfdAE3+kuHB4NJYK6JzNZQwPwRAVjnND8Vc4rkxz8VOiixa+BpanuVmfkc3XDX3KFGfdunWZOnUq2Q4HttM6g44cOYLdbi+w8yW3E+rw4cOlvm+ZUnyt9SpglVLqfuAV4P6crwpLQoFbgXpA49x1qUqpjcAu4HbgrQq8txClppQiKCiIoKCgs6p0aq3JzMzMN8137969eT87HI5CE9SgoKBiTQncunUrv/32G/v27SM5OZmQkBBWr17NypUrAW/vtsViweVyMXToUO655x6ysrI4duwYQUFBHD58mIkTJ3L77bcTGRnJ4MGDcbvdPPDAA9StW5fp02eyYfMBHn7wVmbPns3YB17hqtFjycg2EhJo5LHHnyIgIIAHHniA66+/njfeeAODwcDw4cN56623OHXqVF7ynpntYe+hbHbsz2bFqm0s+XMu/pGXokzeino1a5i4akAoA7oFYbEojGf88c1KSOLqbr35de1y3uxyKSMPaMztm3DFE/eBxYz995U4V28le8ZiVEgAmS0bYPvyVw7MWMr/Jr5JLaed27duJuZUIkdCQ3GlZWDdd4Q53Tszs0UzGu/ezysfTyfs0fdIBcydmhE8/kGsXVsW+v4HGBXdA4zMT3FxW00LmRluHnk/nuTLwjAFGsk2Kk4fJzTVrkno50+S8flsfEf2w33sFCef+ZTUuZs48f12DFHhJF8/iHe7XcAOH38ermUi1GzAk5ZJxsc/0WnSz/S4bTQ/XX8VAA/UstApwMjMRBctfQ108jd6p0ZrzS9JLt456uCPFDf9gsu359d8WnGiqkxCAdatW0erNm149bAdI/BCrJWIMzo0oiwGXoi18ekxB4/t3kNobe//q5dccgnhVl88ThfXtuzK6wu3sOntyfQbMEDWwAlRDMUtFpiblG7fvj3vZ6VUobOIirMO1ePxsHz5chYvXsyBAwfIzs4mKCiI3377La/6Z+51TCYTDz74IKNGjeLYsWMkJycTHBzM5s2b+eijjxg6dCirV6+mcePGHD9+nBdeeIGBAwfy2hvvMePn33n5uYfRwEtvfkeHHgPIyPLg5+fPpMlf4ePjw+TJk3nnnXfy9nd89NFHueWWW/j999/pfVE/EpJdxB9z8OfaFOb8vgyPIRhljUKpf5bz+NoMRNQw0TjORufW/tSuZSHA10BoUE4dgGwPB49mMXrkQFatXEK4xZfnA5vRxxbN0dbNScoGR0IalsOniNmyjABnJqdsQVjcTmLmevdkzjTZWBvThqQeTehoOEHUquWcGvwQPsP78vttI5ltDuDFWBuxVgPbF21kZs069Nm4iQHTxnm35aoRhFKKEXgLywA8diCbqQkObAZFEx8DXf2N/LUxnam/JjLowiDaxdp4Id7OQ/uzAahhCsR9040ku6Gdj+L6mlYO+Bu5qq6NQKOiplmxMNXFrixPubZdFwR6E9016e682goVxdKmIc6NxasxcNLp4Y0jDupaFa/H2diU6eGRA9ksTHUxILh0y71SXZr1wbF4PB7eXbeHRzv/004Xtj0LQEREBEajsUxJqModvSjVk5VqgHck9DogDkgDftBa31rqi577ngsAm9a6+xnHFwNorXsW+MQcHTp00GvWrKmo8ISoEHa7PV+CmpSUREJCAidOnCAlJQWbzYafnx8BAQG4XK68kVVfX1927tzJ0qVL2bRpEwBBQUF504tq1qzJ3XffzZVXXklERARag8fjXW/g9oDHo3G5NBlZHtIzPZxKSueGa/pxOH4vj7/wLTt3bOHLjx/m4pEfQeBFOLTG6UwlOyWegJBmKACtsbns2NzZmI2gPCcIUX7YUtMIcaRxMmU37y1/jg79XqRhm6vJyvawc8OPJB75i6y0eFKOrwWgXqMOvPjGT7iVD2u2ZLB5V1be+xMbaeGSC4KwntzI+0/dw4L9m/AATwc0YUyNJgQ+cQO+Nw9G5STrB+0e/AwQ6nIyO8vAO0cdtLKCMymNfQYLL9/6EBGJSWirBRxOVFAA5ia1MVgtHLX5UBM35vAQTI1qY2nTCFPL+sVKRFakuHgq3k67PVnsmJdEdjt/3J0CMStoZDPwRh1bob2Zbq25a2sKy957mV+69ebw1oO8eOUwDG43N7w/kb51Qgl89mYSr34ax1+bsV7cBct9V/NkQDQ1zYrHo62FxujRmtv2ZOEGJtb3wVCOSZXWmhOtrsPatyPBb99XbtctCY/HQ1BQEDfccAO9x73FB8ccPBZtLfJDi9aaGnF1sbTqzO/ffUPjtZuYevmtmIdfxDUf/Q9/q41rzFG88eDjBD59E8psAu/ykPOatIGiuimoWGDuljMnTpwgIyMDX19f/P398fPzIzs7m6SkJCwWC2azmY0bN7Jo0SIOHvSu2IqIiMDHx4fExERatmzJ/fffT79+/QgMDMST2wZ6NB6PdwaO05nbBrrZs/cA117Vg6DgcB546gumfzeepQumMuzOuSRmR6NNiuz0I3icDnz941CA0h58XNlY3XasZoV2HiMUf2ypKQTb03AozRtLHsHoE0b7S7/FaLKSeHg5u1f9j7TEnYC3E9vPP5DOXS7g7scmE3/cxbEEJ5t2ZZGe6UFrD8d2zyK0RhymPbMYHtSZlXtm8sWRhbwQ1IorAxvxXbPLWBrdAa0MBPgZqBttpW6MldgoC34BHpTLjDsjmxMnD5BpUNQKjGKZy8raaAt6WyZRfyXwqGspxw8d4JUXH0cbDYSmpzP09z+Y2bUL2QF+TG7iR3BQ4ftb78xyM2avN8H8X5yVWuke7n75ADVrmHn/iVgsZgNHHB6OOzXxdg+bMt24NfQPNtHB34hRKcaNG8e4ceMq9HfO6dFcuSOTboEmuvgbWZji4rEYa4HV08sq/f3vSXvlS2pu+7bI/bS3ZLp57pCdDI/m/bo+1LMZ0Fpz854szErxcT1bvjY+w635PsHJgBATUYVsPZPm1ty5N4vtK5awZvTFtPtkBjNGD6Z2zvTeyZMnc+ONN7J79+6zCpiBd11onz59mDx5cu6hEr1BJU7vlVKheDs1rgc64Z0OOx94EvhJa51d0muWUHNgZgHHt+AtnlQmq1dt4+q77irwMa11wW9vvkRenXb4jARf5T1Q+PULUegjhV2rsGcUeriIzoii4i3w/SjsMiV7Pwp8zjkUen5h9y7wOarIuAp/bws7vwiFvrcePC4nHpcLj9vl/d7twuP0/utyZONxOoq6ch6D2YJfrdrEDbyaWl0vIqhmLH5WP3wtfvg5Pew/lcL7E9agPBqldd6/BrcH35R0AhJTCTt0gpCkJPycmTzp35E7nbt46n7vhtaxcc2IGdKNhNqBOIICMANmmmFwewjIyCAi/ih2q5XDcXUISkym8eZk3CZNlm84kWuOcMUfu5hp9MW49D1GB/oxL+sQM/58i4iAEOpHRnHZrU8TUyeO2267jS8/vJWff/6ZaweFsn57Jtv2ZuN0aQ4t3s7sW+/i24NzsSoDMWH1iXr0PVYHNedooov6Bl9ifknE38/IrmwPc2OsKA1101zsCTbBMQebQ814fAOIWJPKE20ewK5McNofdLNJ0aaJL327BNK4QwCmYhYESExxMeOPJLbvzWbbgWy4tibrLYqO3QP5u6U/FwQauTDQxPPxdj445uD+KGuB1/nplItdmMkymXi2W09OddKEKs2rplT86tUgc+LPOFZvw7V5j3da8NBeALyj9TkTZINSXBNu4aV4O0vT3FxYSM9vllvza7KLvdke2vsb8TfAmnQ3bqChzUDvIBPWMxpppRTmlvWrtEKuwWCgTZs2/L1uHccTnLT2NdA3qOgiIE6nk5TDh2hy6dW8f9TOi29NZWDd5kS88wJKKRo1b8aBNCeZn83CtWkPIROfrNTtbyrK2+98y/ivPz3reN7fySLbQHXG4eL/zdeFHM99VBf22aai28BztU8V2AaWZpCgWG1gke3faSdUdBtY1HvrceN2OdFuF+6ctvCf7524Hdl4XK5Cn386o48v/tF1aXDVrdTs2IuA4PCcNtAX/ywHq/elsP79pfnbQI/G6HLhl5xOUEIyYYeOE5SWSrg7mwfj+jNu43fcd0tnADr0uBy/PlFkRtcgy9cHG95piiaXi6CUNMIPHyOpRjAnIhsQeegIDXYkkOlnxGOoQYMlu+m4dBX1rHHccGoN4fNGEeIXyIJdS6kTVIPXRtyGuVEsB7Gze88evvnmG267ZQF3jRwOgNut2bY3i+k/TefZzx8DwAcDGWEr+Dspnj41GzG83zUk33wtl4aHcJXNQEQNM1Z/AxsyPaxKdzMp1UWaG5r5GDBGmNlU07tgxM8AGR4INylOtvIn22bg3j0DMd8cTGRWJmOm/cT/rhzKZ0MvIyoljZuCLLi1FYfTU+iSmUY+Ri4ONpHk0tR3a+59Nx6TUTHuzqi850RZDERZoK2fsVIq1BbEbFB0DTCxMMXFvGQXGuid5qZnUPmPipqaeWfbuHYeKnS2ULzdw4P7swkzKV6t60Ndm/e9UkpxRaiZd446mHLSyboM72ymgSFmZiY6mZLg5KdEJw9HW7mggHb9vaN2jjs0b/VuRy+DgbT1K3i3f39ei7NhUIo9e/ZgMBiIjY0tMK7o6OiKn46rvHMALsWbeA4ELMBW4HFgitb6SKkjKLlQIKmA44lAgeU1lVK3AbcBhb6RubKyXSQmFPFyCmkHVaEPFPbhr+DjRX5WLOTBkt670FsUcfPCP8SW9HUUdn4JOk9U7j/neN06/7HC7qFUAWHlft4qLK4i3o8CHynBf1elvccMJhMGoxmjyYzBaMJgMmM0GjGYTBjNNkwWKyaLFaPVisliw2gyYzQY8fENILRWLFarDx67A7PJiCMzDUd6GvbEVDKOribJnkF8VgYuHxuuWpFQIxxTSCimwFDMQcGYgkIw+vjle/0GtxtPTvW+tqceJ3HlItK3biDm8utwW2203r6T6KQkLCYjbqOBTJeH5KBATsRFE2g20uHUUY5bfdjRoyM2pTEaDXzToTXf3jGKuI/r8uf4l7h+xzck7dpEe2sNfvTrRHxkA0IXHCeqTzRpFw7l/jnTePfSUVx71bXUPJVC7b3xeLbu46uNy/kiZRO9Yptx0XtfM2nu90QM7EmG1uwFjq9MI2PmKe+izmsiMGV7MKe42BNlxXbCwcWJLnZuyGB3tptIi4G+l9YkLMREoJ8Rs0mRmu5m/xEHy9al8cpnR/l1STLPjIkmyP/sREZrzerNGRw65iAlzc2MP5LJdnioH2Plog4BZNgUC2vbWFnbu57l1poWalkMXJXl5odTLnoFmWjr573uAbuHHVlu/A2KyScddPY30iDIxAGnpoZJ8WZdHyLM/uhxt+A+cAz7/NX43zciLwH1/noV7/+tnoFGJlsUX55w0D3AeNbamp1Zbh45kE2a2/vhZE6y9wOgRYFBwQwPrM/w8FjM2Um0uXVD7ON/QGdmo6qwONGEzz/HanfxSLTfOd+XgwcP4vF4GNyiIavtmrm1ohl2RU9UToXhhg0bsm7dOoLHP0jyg++ReMMLhM16vTJeSomVpA1MSkkrvA0s4i0rjzZQFXWPotqnCm4Di/5dqd5tYIHt32nPrfA2sKD2D1AGhcFkyWn7TBiMpn/aQqPJ2/5ZbRgtp7WFRm8b6BcUSkjN2pgNRrTLhVEpnOmp2NNSyD58lPS9uzmZlUGmIxNXQCCuyEgMoWGYgkMxBQZjDgr1toHW/H+rDG4PHqP3w3+ng4+TtGoxWQf2EDz6Xqwp6XRbt5GaaamYLGaceEeiEiPCSIiuRX3locfJw+wL8WNL764Eak2W2cSnXTvwxSN30inhBL3ffo4fZn4JwIiYFtzZZiA7ajWgy09LGOSwQ+0IVgaE8uLt99Dntx2Y60Ti8bGxvWYUM95/ntpmPx4NasKb/ilss9iwBTfE96s5nGgWRfcAI6lu+Oqkg7UJDg4d9nYl2AzQPcBIHauBOckukl2aB6Ms1LUa+CbBSYzFwE0RZt4/5uCXRr7QyBdnuptj09P51NKfjOlpEJzFkb3ZvKZTgOMA+FgVvToFMnJgKCaTIiXNjUd728fBGrbsyeL+hckkprh486HaRIafP0WpcvUKMjIvxUWPACObMt0sTXOdMwndkeVmY4aHK2qYMBXz/2lTI+/fZNfOA4UmobMSnWjgnbo2ws5I8PsFm5h4wsHkk06MwF67g+4BJn5OctHUx4AHGHfIzsux0Pm0/cP/SHHxR4qbGyPMXBAeTteuXTm+fB7rMp7miYN2Ho6ysGXLFurWrVvozg7R0dFs27atWK+zwNdezPOOAcF4E70JwBda66qcz1NE110BJ2v9KfApeKciFXXhCy9sSdK+7WWLTohqQGtNRkZGvulNiYmHObVvE6cSE3E4XViCglGBoWT5B+MJ8JbYjwqvQePYGEI7jcZi8PaSequqNipxDIfsHuanuJg/9nFa1Ixm+0sPYvALoN2sZXweUZOlDiMGj4fmf2+itcNMi32beXL5HGY++wbmwGCCk1No+9UEnl++jd69LuLCz2eyKFMRs8jAN018sWt454idZV0Cqd8ziMMOb8P7Xl0b9W0GtmW4qdfUt9hTbO4YHs78Fam8/dVx7nxhP20a+xIWaqZOlIWIUDMOp4dp85JYufGf+midWvpx54gIYmpZ8t73K7I87M32UNOsqJUzTeaGCAvL09y8edjOhYEm1mS42ZP9T2EMi4KxkRY+tRh4or4PwUaVV55dGY0Ef/wojhWbsPYuXZU6o1LcEOEdDZ2fcvb6ksknnBiA8XVtNPIxsD3LQ6ZH08rXiFl5q/XNTHRxY4SZmmdM/TG3awxuD46Nu7F2KbgCoNaa1HIsOX+mNm3bkp2RQdjhnbRvee73KHed2MDmDcg+eIhvb72W1vX9aZ3zeKNGjZg+fTqmwT0ItphJvv3VCom7PJSkDXz+2Vt5/tkKW1EjxHnD4/GctQ414dQ+ErevJTEpCY8yYA0KwRMYQoZvEMagUEJCQ4gLC6XhhU0JvKg5VgXhZlWqJQxaa7ZmeZif7GKRsSbWF96jmZ8Fd73mpI28hee095q/3DSCMUuX0HHFGu6p24F7N/7OvDV/0XZdOOMfGsNKHw/r4nfR7/r72PHIM+gPXueWh5/mhggL7x+18+whO3FWRaJLk+mGDv5GegcaaO1npJmPIa8q+tVhZu/04ZzX8kLsP52s90ZaaOVrpLZVEebw8EO7AHYeyObKzoG0beLLySQXSaku0jLcpGV4OHLSwfwVqfy2JKXQ19+kro17rqtJk3rnz84Ap+sSYOKjeor6NgPvHHGwKLXwQkXZOVu7rE73VjSsZVH5Rh49Wud7bzdmuJmb7OLvDDe+hgDCxz3ILfFHKWiznCyPZk6yiwsCjGcloAA2g+LZGBupbk1Ni+Kuvdk8cTCbE07N2FoWOvgbuXtfNv87bOeT+gYizAaWpbp447Cdpj4GRoZ52/pLLrmEJ598kqfUKb7LqMFtuzL4Y+FChg0dmu9+2uUme9YSrH06EB0dzfz580v5Dhc/CV2Cd0/Q2VprZ6nvVj6S8I6GnimEgkdIhRAFUErh7++Pv79/3n5Pp8tdV/NPA32CxK3bOZqYyJ7MzLy1pWcWiggODsZkKt6fltpWAzdGWLgh3IznyTs4fP0glqQ4mWSoxUEn3BBuxqlhYdfWTOnQGp9OTbFf1R3X/+6jTrsO/DF3DtNXLMI3rgHq5S9Ylqm4JcLM9kAjfkaFHzCutpUvTzpZne6mf7CRfsEmGvh4G9dmJSy/bjAo+ncPIqaWhU+nneTvbZmcSnbhOe1jvc2qGDMinH5dgzCbFD62/I2GUormvkaa++YfRbUZFA9GWXlwfzY/Jjpp4mPgzloW2vsZSXFr/I0qb11HXAHl2A2+Nmx9Opbo9ZypV6CRaT4GJh130ivwn6m1+7M9rEx3MzrcTNOcuM+Mf3iYmVmJLn445WRsZP7RUEu7xgA4124vMAnVWvPeUQe/JLn4qL6N+rby2y9vf7aHxw5kc6i2d5P10NXzUQPPvfdqbkGG+nXrcvvVL/G/157m8SM2XjSZaOdvpGHDhrjdbvbt20ejS3vgee5UucUshKh4BoPhnMUC869DPUTS/o3sTkxkm9NJSEhImYoFnt4WjI30dlIav57Aziw30045aWAz0srPwLtHHLzWszcjh/XnlmDNS/Ub8KA6RpsvpuA2Wwh6eSwWH1+ctz9IptVKWz8jr8Z56wt8VM+HhakuZiS6aGRTjKllzZvKedb7UUQibVSKvrlr6H2M3DEi/76Q0QVsgzJ6SBiLVqfh52MgOMD4TwFBBZFhZuIKWXpyPmmU81mhR6CRX3OSxi4BZ39uWJ3uZnW6m+vCzfyc6OTPVBcXBJrQWvNnqpsJxx34GhWPRltZkOziu1NOfA3Q0d+IW8Padq14gFY8nuai6xnXX5jiIsMDlxUxNbntabOyLggwsiTNTS2zokvOrKanY6yM2ZvF/fuyibUaWJPuppGPtwhf7qynQYMG8eSTT+JZuYDxI0dz06wlpCQn0+2ivni0ZkOmh8Uns2jy+XRaf/Y9PiP7Ex0dTWpqKunp6fj7F76etTDF3Sf08tzvlVJhQHolrP0szBa860LP1AzvFGEhRDmw2WxERkYSGRl51mMulyuvYc79d/fu3SQlJZGSkoKfn99ZyWnuz1br2Q2PUt69OGNjY7kWaJnhJtikiM1Jtm6qaeG4w8PxOp14/+ab+eyzz1gxaxp169blhiefo8XVN+MXFsaFgSaa+RoZd/o04pwRvhtKt5dygZrV9+GdR71TaBxOD4eOOTiV4sZiUsRGWvIqFJZUaz8jXzX0IdikcjbcrlwGpbitpoUH92fz6XEHd9bylrz/4ZQTq4IhRTSCEWYDfYJN/JbkYlS4Jd+IpqFGEMa6kTjW5p9lkuXWnHJ5e3ln5ZTcn5Xo4v6o8klCPVrz9lE7dq25tVVdjrZszdb5v8Izj53zuXv27MFqtRJ2MgPj/sP8L+kAT9cKZdyhbD6s50OjRt6R/507d9KoUSP8bhlSLjELIaqeUgo/Pz/8/PwK7KS12+352r8jR46wZcsWEhMTycjIICgoqND9UAvqpD19+UMjHyNPxPzzN/C9ujbeP+ZgaoKTGYkQ8eArbH5gFA0/ep5hF3Thwe+/5uabb2Z812hv4R4/Y95ondmg6B9spn8pK6eWRUSomeEDChozqn7a+hnxM8CS1MKTUF8DjAo3k+TS/JHiwu7RTDju4KdEF3WtilNOD7flbJF2WYiJO2pZ8jp6t386hde7ducZHxuvxam8pNKlNTNznt/St3h7gd4QYWFFehZDa5jzfq9qWw08E2Plu1NOTjo99A02cU+kJd/njFatWhEdHc0vv/zCjTfeSOttS1gMfB3XlW+3ZuIBlEcz+6oruNliodenU6l5fz8AHlu9l9SYBnzZ0LdE72tx14QagGeA+4AAwK2U+hm4WWudXKI7lt0s4A2lVD2t9d6c+OoA3YFzf7IQQpSZyWQiPDyc8PDwsx7zeDykpKTkG0WNj4/P+9lisRS63Yyvry9KKVr5nZ2E1LQYqGmB8ePHc/fdd1O7dm1CQgpcBl6pLGYD9WvbqH/255RSiSykil1laeNnZEioiRmJLvZle4ixGliQ4uKSENM5p8qOqGFmfrKLe/dl8USMNa8XWWsNnZrj/GMNOqdQ0sYMN08ezCYzZ8bxwGATLg0LUlzcUdOCj7HsSfjcZBebMz08HGXh4hAze4YM5uWXXyYxMbHAbSJOt3fvXurVq4dzyQZQiprdWvBSgI3b92bx3CE7zzVoCMCOHTu49NJLyxyrEKL6sFqtRXbSJicn55vmu2fPHhITE0lJScHX17fQ7WZstrPXzJsNivsjLTSyGViX4eaxm6/m613L+fijd/jzU0XXrl156aWXyn2PZ/EPi8E7qrg01cVdtSz4ntY+aa1Zk+6mrZ8Rk1JcmLO9y5cnnfyU6OLSEG/Cl+qGz447aOZjYNAZHboxtYJ54t5neOmXyTwXn807dXyoaVY8F29nd7anyOr2Z6pjM/BNQx9CzmivOwWY6FRAAp1LKcUll1zCt99+i8PhYOOiBTRu1Yah9SPxNShqJyUSN+x+Pv/sf3x2xWBmdu1M0lbvyswtB+Pp2aDkS7KK210/Bm8SughYjXefziuAVODGEt+1bCYAY4GZSqmn8K4PfQE4BHxSybEIIc5gMBjypimdubeU1pr09PR8CequXbvyvvd4PIVuVh4YGIhSCqvVSqtWraro1f033BNppZHNwPtHHezJ9tDez8i1YefuSa9jM/BaHRv/i7dz975snqttpaO/kecP2dk5ahQvz1yCO/4EW0PDePxANhEWxTVhZkJNBtr4edeZzktx8UeK66xGuqSyPJpPjzto4Wugf840sksvvZQXX3yROXPmcM011xT5/D179lCvXj0ci9dhblkfQ40gagKPR1t58qCdl9L8qFGzVt7WR0IIAd5O2rCwMMLCws56zOPxkJqamm8UdfPmzXk/m0ymQhPUQSF+XJrzd7HDW2+xY/t2oqKimDBhAj4+5+e6yn+ToaFmFqS4+fGUk1ERFlxaYwAOOzTHnZqRYd5O1zZ+RgKM8G2CkzCT4vaa3hlFISZ4OLrgKcimxrH4ZmbxbPph7vOL5uY9WeR2Rz8QZflnKnQx1SikOvE5X+PQoUyYMIExY8awfPly7r333rzlNekz/yItNY3n43z42dfCpgMZ7HJ67zNaJXBD7ZIXHSzuq7oVmKC1vj33gFLqdmC8Uup2rXXx9oooB1rrDKXURcDbwFd4CxItAO7TWqdXVhxCiJJTShEQEEBAQECBVTqzsrLyTfONj49n48aNJCYmkpWVRXBwcKHrUI3G8ltHKODiEDN9gkwYVdFrhc7U1s/IhPo+PHIgmxfi7XT1N7I0zQ1WG79eOZhL1u/lqQb+RFgUb8bZCD2tsWzmY6CuVTEt0UmU1QAa5iQ7qWszcHVYyaonLk5xkeqGmyMsefF37NiR8PBwZs+enZeEnjhxApfLRVRUVN5ztdbs3buXnt264/hlO353XJH3WOcAE49Fe3u0df3m/LpmPRsy3LQuYPReCCFOZzAYCA4OJjg4+KzHcosFFrTMJTExEZfLla/te/311wkJCcFut2O1Wou1DlWUXhNfI90DjPxwyklTXwOvH3YQZ1V0yKkt0SFnCq1JKXoEmPgt2cWdZ4yaFia3Qm74jv28N6wBK9PcJLk1HfyM+dZ7VrSLL76YRx99lFdf9Rba69u3b95j9oVrMTWsjaV2BMOAy9tFsv/2x/gGOHakdJukFDcJrQc8dMax74CPgDhgV6nuXkpa64PAsMq8pxCi4vn4+ODj45MvIcjldDrzJagJCQns3Lkzbx1qQEAAoaGhhIeH4/F4Cri6KClzKdelBpoUL8VaGbsvm4Wpbi4LMZHs9PDL8MGsz/JWDn4lNn8CCt5OitERFl6Ot/PQfm/ZAbOC+SlutPYmgCtzCjfUsRnYmunmz1QXMRYDHf2N+ary/pbsIsaSfx2NwWBg0KBBzJgxg8zMTCwWC926dWPPnj106tSJN998kx49enDy5EnS09OJxQYuN9ae7fLF2TfYxIWBRka0a82sT8Zz3640uoXYeCmuarafEUJUf6cXCyyok/bMYoGHDx9m06ZNJCYmknlascCDBw9WQfT/DTdGWLh1TxaPHrATYlKszfCwPsNBtEXlW0ozKtxMUx8DFwYWL4E01o4AmxXXzgPUthqoXUDxwcryyiuvkJWVxcyZM+nRowcAOjMbx1+b8Rt9Sd55xlo1qDGsL0Hv/0783n2luldxk1B/vFNvT5eW829Aqe4shBAlYDabiYiIICLi7ApDbrc7bx1qeno6wcHBjBs3rvKDrAQF9aCfj2qYDbweZ2N5mpuhNUwcc2iWpTjZ61ODZ6KseVvTnOmCQBPTmxhZm+7GqaFLgJG3jtj57ISTz054i7N/cdJJtwAjS1K95fA9QLARpjbyxWJQHLJ72Jzp4ZYI81nraG655RYmT57Mxx9/TK1atdizZw833XQT8+fPZ/jw4WzZsoV169YBEJsFmIxY2jc5K06LQXFll3bMGO/gkrR9/Gkt+XoYIYQorqKKBTqdzrx1qFu2bPnXtn9QtW1gXZuBYTVMHLRrHom2MjfZyYTjTjqeMVpZ02JgUGjxE0llMGBqGINr56HyDrnElFK8++67vP3223mj6/a/NoPdibVX/u3N/O64nFofPM2Bv9aV6l4lmWQcrZQ6fYGX8bTjyaefmFswSAghKoPRaMybmgvQunXrczxDVIYYq4HhOT26MVbFTX+vIX3rfi58tehSAj4GRY/T9lh7NNpKuMlJsEnROcDI5BMOFqe6uTjYxJhaFtaku3kh3s7aDDddA7wVeg3AgALW0XTv3p0+ffrw6quvEhERQbNmzZgwYQLr16+nc+fOjBw5kjVr1hATE0P7bAumBjEoW8FTgXPXJkcf2sYXXduU7k0SQogyMpvNecUCX3755aoO519tTK1/1nWOqGEmzmqghW/Zp8yaG8ViX7G5zNcpL6dP77Yv+htsVixnbLFmblKHqPAI4nfvRWc7Cm0rC71HCc6dhnfabe5Xbq39GWccr9SpuUIIIaqHwYZsBk2ZjufwyRI9z6QUt9WyMDzM2+A/W9vGtMa+PBxtxd+o6B7gLQSxOMVNplszJ9lJlwDjWdN9cz377LOcOHGCzZs38/DDD2MwGGjXrh1PPvkkc+fOxcfHh4ULFxKw9wSmpnUKjatJkyZYLBY2bNhA6DkqBwshhPh3UUrRNcBEQDlUczc1qo3nyEk8aZnlEFn5si/8G2vXFgUmmbXbtuSYPZ2s6YtKfN3ijoRWdgVcIYQQ/zLmxt51Ts6dBzHGlG3j1tPLz5sNiu4BJpakuggzK1LcFFnN94ILLqBfv37s2LEjX5XcJ554An9/f4YNG0ZcjQiOx5/A9/pLCr2O2WymWbNmbNiwoUyvRQghxH+bqVEcAK5dh7C0a1zF0fzDdeg47j3x+I0eWODjtdu24MSvs0j+aBq+1/Qv0bWLlYRqrb8o0VWFEEKIM+Q1sjsOwkUdyvXaPQONzEl2MTXByYWBRpqcY3rUjz/+SFZWFhbLPz27FouFhx7y1uBzrNzijblpXJHXadWqFfPmzStj9EIIIf7LTI28m427dhw4r5JQ+6K/Ac5aD5orLi4OD5o9O3ZydknJokk9ZyGEEJXCEBKAISLEm4SWs3b+3im5BuCmiHOvSwkICCiwyFUu57b9AJib1i3yOq1bt+bo0aOcPFmyKcZCCCFELmNsTbBZzoviRKezL1yLsXZNjPWjC3y8f3/v6OdcS1qBjxdFklAhhBCVxtQoFtfOA+V/XaW4JcLCHbUs5VLe3rVtHyrYH0NkjSLPyy2C9cknn5T5nkIIIf6blNGIqX4Mrl3nzxY72uHEsXQD1l5tz6o0nysuLo7OnTsz25Bc4utLEiqEEKLSmBrH4tp5CF0Be7leGmpmWI3C14KWhHPrfsxN6xTa8Obq2bMnQ4YM4emnny6X+wohhPhvMjWqXSEzhUrLsXY7Oj2r0Km4uYYPH86GQyXfGEWSUCGEEJXG3DgOnZmNu4QVciuT9nhwbT+A6RxTcQFMJhPTp0+XbRGEEEKUialRLO7DJ/Gknx8Vcu2L/vbuld2j6G3vrrzyylJdv9RJqFLqOqXU9aV9vhBCiP8eUyNvhVzX9vKfklte3IdOoDOyMBexPcvpDAYDjz/+eMUGJYQQ4l/NnNs+7o6v4ki8nOt3YW5WF0OAb5HnxcbG0qVLlxJfvywjoZNyvoQQQohiMeVs03I+TTk6k2vrPgBMzepUbSBCCCH+M/I6ac+T9tG182Bem30uV199dYmvX9x9QgvSH5nOK4QQogQMQf4YaoXi2nH+joQ6t+0HpTA1Lnp7FiGEEKK8GONqgcWEa2fVJ6Ge5HQ8xxPzEuNzueeee0p8j1InoVrrhaV9rhBCiP8uc9O6eVugnI9cW/dhrBuJwddW1aEIIYT4j1AmI6YGMbjOg/YxNxEubmfsuYr4FURGMoUQQlQqU9M6uHYdQjucVR1KgZzb9hV7PagQQghRXqwXtMW+eB325RurNI5/ktDijYSWhiShQgghKpW5WV1wunDtOVzVoZzFk5GFe/+xYlXGFUIIIcqT/8PXYqwbSfI9b+NJSa+yOJw7DqB8bRijwyvsHpKECiGEqFS5BX9c2/ZVbSAFcO04CFrLSKgQQohKZ/C1Efz+g3iOnyLlyY+rLA7XjoOYGtVGGSouVZQkVAghRKUy1Y8Biwnn1v1VHcpZchNjUzMZCRVCCFH5LG0b43//SLKnLyJr5p9VEoNr16FiFyUqrRIloUqpukqpoUqphhUVkBBCiH83ZTZhahibtxXK+cS5dT/Kzwdj7YiqDkUIIcR/lP89wzG3b0zKYx/gPpJQqfcuaWXc0ioyCVVKzVZK1cz5/jJgJXAHsEgpVfINYYQQQgjA3KzOeVkh17VtP6YmcRU6BUkIIYQoijIZCX7vQXSmnYyJsyr13rlbqFVkUSI490honNb6eM73jwAXaK37Ax2ARys0MiGEEP9apqZ18BxPxH0qpapDyaO19lbGzVmzKoQQQlQVU90ozG0a4Vi5pVLv68xLQit2r+xzJaHW0763aK13AGitjwLGCotKCCHEv5q5WT2A82pKrvvwSXRKBqYmdao6FCGEEAJLl+Y4N+7Gk5ldaffM/mU5hqjwCq2MC+dOQn9RSo1XSvkD85RSNyilTEqpwcCJCo1MCCHEv1ZuhVzn5r1VG8hpnKu2AmBp36SKIxFCCCHA0rk5uNw4126vlPu59h3BsWQ9vtcNQClVofc6VxL6CJANHAauBT7P+fkO4OYKjUwIIcS/ljEsGGPdSBx/ba7qUPI4Vm9F+flIZVwhhBDnBUuHpmAwVNqU3Mwpc8BowPfqfhV+L1NRD2qtncBDSqmngQaAGTigtT5V4ZEJIYT4V7N0bUn27GVotxtlrPoVHo5VWzF3aIIyVX0sQgghhCHQD1PzupWShGq7k6zv5mPt3xljrRoVfr9ilf/TWmdprTcBxyUBFUIIUR6s3VqhUzPOiym5nuR0XNsPYOnUrKpDEUIIIfJYOjfHsXYH2uGs0PtkTV+IJzEVv1EXV+h9cpW0Bv26ColCCCHEf46leysAHMs3VXEk4FizDbT2rr8RQgghzhOWLi0g245z3Y4Ku4fOzCbt9SmY2zbC0rNdhd3ndCVNQit2haoQQoj/DGPNUIz1Y3As31jVoXinOplNWNo0qupQhBBCiDzW7q1QIQGkPv852uWukHtkfDYLz7FEAp+5qcILEuUqaRKqKyQKIYQQ/0nW7i1xrNxSYQ1rcTlWbcXcqj7K11alcQghhBCnMwT5E/TSHTjX7STjo+nlfn1Pcjrp43/AOqALls4tyv36hSlpEiqEEEKUG0u3Vuj0LJwbd1dZDJ7MbJwbdmLpKFNxhRBCnH9sQy7ENqgbaW9+jTu+fHfJtP+xBp2ehf/YK8v1uuciSagQQogqY+3eCgwG7PNXV1kMjmUbweHC2qty1sEIIYQQJaGUIuCx0eBwkf37ymI9x30kAfviddgXrS3yPPuitRhCAzG3rdzlKJKECiGEqDKGGkFYOjUje+5fVRaD/Y+1KF+bFCUSQghx3jLVj8ZYNxL7H2uKPM+5ZS+Jo5/nRIcbSBz5NInXPIujkKJG2uPBvvBvLL3aoQyVmxaW9G6HKiQKIYQQ/1nWi7vg2rYf14FjlX5vrTX2hWuwdG+Fspor/f5CCCFEcVn7dMS+fBM6M7vAx93xJ0i49EEcq7bg/+A1hH7zPMrHSuY3vxd4vmvzXjynUrD2bl+RYReoREmo1rryIxRCCPGvZhvQBaBKRkPde4/gPni8ShpgIYQQoiRsfTpCtgP7soKrymf+uBDsTsJ+e4eAB6/B2qsdtiEXkj3jTzwZWWedn50zqmrt2bZC4y6ITMcVQghRpUxxtTA1rYO9CpJQ+8KcBvgiSUKFEEKc3yxdWqB8bQVOydVak/XDAixdW2CqE5l33Pea/uiMLLJn/HnWc+wL/8bcqgHGsOCKDLtAxU5ClVI+Sqn7lFILlVLHlVKOnK/jOcfuU0r5VmSwQggh/p1sA7rgWLkV96mUSr2v/Y+1GOvHYIqtVan3FUIIIUpKWc1YLmiDff5qtM6/c6Zz7Xbce4/gc1WffMfN7ZtgahxLxoSZOHd5V1ZqrUn/eDrONduw9utUafGfrlhJqFKqNrAReB1QwDTgVeC1nO/J+X6DUiq2AuIUQgjxL2a7rAd4PGT/tLjU18j4/GfSXv2q2HuOehJTsS/dgK1/1TTAQgghREnZLu6C+/BJnGu25zueNe0PsFmxDeqe77hSioBHrsN96DgJve4kYeD9nBr8EGnPf45tUDf87xxWmeHnMRXzvHeALKCh1np/QScopeoAM4C3gap5NUIIIaolc5M6mFs1IPP7BfjdclmJn+8+mkDq8xPB4cK5dR/BHz2CwddW5HOyf10OLjc+l/csbdhCCCFEpbIN6kbqEx+R+d08LB2bAqAdTrJmLcF2SVcMAWdPTLUN7Eb4quZkTvwZx9870OmZ+D9yHf73DK/0qri5invXvsCThSWgADmPPZNzrhBCCFEiPsP74Nq8B+eWvSV+bsbHP4Hbg/99V2NfsIaU+98953OyZiz2TsVtUa804QohhBCVzuDvi21wD7JnLcGTUyXXvnQDOjkdnyEXFvo8Y40gAh65jhrfvkDY7DcJuO/qKktAofhJqD73KaU6VwghhADwjkiaTWT9sKBEz3MnJJPx1Rx8hvUm4JHr8H9gJNk/L8G+ZD3a6cK+aO1ZU3TdRxNwrNiMz+UXopQqz5chhBBCVCifEX3R6VneGT1A9qylqEA/rBdWfpXb0ipuEjofeEkpVbewE3Km474AzCuHuAqllGqklHpXKbVRKZWulDqqlJqllGpdkfcVQghRsQyhgdj6dyJr2kJ0lr3Yz8uc+DPYHfjfPRwA/zuHYYyrRcqTH3Nq6GMkXvMsGR/+mO85WdMXgdYyFVcIIUS1Y+nSAmOdSDK/+BWd7SB77gpsAzpXq/2ui5uE3gf4ADuVUkuUUh8ppV5WSr2U8/2fwM6cc+6voFhz9Qd6A18Ag4E7gXBgpVJKauwLIUQ15nvTYDyJqWR+PadY52uHk8yv52Lt1wlT/WgAlM1C4Lhbce+Ox7XjAKZmdUn/YBqexFS01mRMmk3a/77E0q1l3nOEEEKI6kIphf/dV+Fcu51TI55Cp2Rgu7RHVYdVIsUqTKS1jldKtQJuw5v4XQ6E5jycBGwBHgYmaK0zKyDO030LfKBPq0uslPoD2A/cC1xfwfcXQghRQaxdW2Lp1pL08dPwvfZilI+1yPOz563Ck5CM76iL81+nfyeCP3kMc+sG6MxsEvrcTerzE/GkZWL/bQXWfp0IHv9QRb4UIYQQosL4juyPa/sBMibMrHZTcaH41XHRWmcB7+Z8VRmtdUIBx1KUUjsB6dIWQohqzv/Ba0gc9jiZU+bgd+uQIs/N/GoOxuhwrL3a5TuulMJn8D+9wj5XXUTW9wtQvjYCHh+N351DUUZjhcQvhBBCVIaAZ25Cu9wYo8Or1VRcKEESej5TSoUCLYBJVR2LEEKIsrF2bYmlRytSX/kSQ40gfIb2KvA814FjOP5ch//D154zoQx4+iZM9aLxGd4HY60aFRC1EEIIUbmU0UjQS3dUdRilUq51eZVSF+ZMja1s7wMK736mZ1FK3aaUWqOUWnPy5MlKDUwIIUTJBX/4CJa2DUke+wZp735X4DkZE2eB0YDviH7nvJ6xRhD+9wz/Tyag0gYKIYQ435T35jDhQIlKDSql+iqldDG+FhXy/MeBa4CxWuvdBZ2jtf5Ua91Ba90hPDy8pK9JCCFEJTOGBRP67YvYhvYi/bUp2JduyPe4+0QSmVPm4HPlRRijwqooyupB2kAhhBDnm2JNx1VKxRbzeqVp3ZYDTYtx3lkFj5RSdwAvA09prT8vxb2FEEKcp5TZRNBrY3Fu3E3y3W8SNv99jDWCAMj4aDo4XfjfM7yKoxRCCCFESRV3Teh+QJ/rJLxTYotzXp6carrbS/IcAKXUKOBD4E2t9Uslfb4QQojzn8HXRshHj5Aw6AFOdr8N35H9UL4+ZH75Kz5De2GqG1XVIQohhBCihIqbhGYBfwLTznFeB7zbuFQopdQVeIsQfaa1lhr7QgjxL2ZuXo+wWW+Q/sGPZEyYBVpjrB+N//0jqzo0IYQQQpRCcZPQDYBbaz2xqJOUUslUcBKqlLoQmApsBCYrpbqc9rBda72uIu8vhBCi8plbNSDkk0fxpGeirBaU+V9R3F0IIYT4TypuK74WuLKY56pSxlJcFwFWoC2w7IzHDgB1Kvj+QgghqojB37eqQxBCCCFEGRW3Ou7/gKvPdZLW+ketdXlX3D3zHuO01qqQrzoVeW8hhBBCCCGEEGVTrJFQrfVh4HAFxyKEEEIIIYQQ4l+uQkcthRBCCCGEEEKI00kSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0kgSKoQQQgghhBCi0lT7JFQpNVIppZVS8VUdixBCCCGEEEKIolXrJFQpFQy8DRyr4lCEEEIIIYQQQhRDtU5CgdeADcDcqg5ECCGEEEIIIcS5VdskVCnVHbgOuKuqYxFCCCGEEEIIUTzVMglVSpmBT4HXtda7qzoeIYQQQgghhBDFUy2TUOBRwAq8UtWBCCGEEEIIIYQoPlNVB6CU6gvMK8api7XWvZRSDYAngSu01tnFvMdtwG05P24BWhTjOZuBYl3/PBEGJFR1ECVQ3eKF6hezxFuxqlu8UP1irm7xbtZan7N9qWwlbQOrYfsH1e93ReKteNUtZom34lW3mKtbvCVqA5XWuiKDOXcASvkCscU4NVNrfVAp9SuggWtPe+xDoCfQHLBrrbPKIa41WusOZb1OZZF4K151i1nirVjVLV6ofjFLvFWjOr6O6hazxFvxqlvMEm/Fq24x/9vjrfKRUK11JrC9BE9pBsQBSQU8lgS8C9xX9siEEEIIIYQQQpS3Kk9CS+FqwHbGsceA9sBVQHylRySEEEIIIYQQoliqXRKqtf7rzGNKqRvwTsNdVI63+rQcr1UZJN6KV91ilngrVnWLF6pfzBJv1aiOr6O6xSzxVrzqFrPEW/GqW8z/6nirfE1oeVBKTQb6aq1jqjoWIYQQQgghhBCF+1ckoUIIIYQQQgghqofquk9ohVBK1VZKTVNKpSilUpVS05VSxancWyWUUjFKqfeVUiuUUplKKa2UqlPVcRVEKXWlUupHpdQBpVSWUmqHUuoVpVRAVcdWGKXUAKXUH0qpY0opu1IqXin1vVKqWVXHVlxKqTk5vxcvVnUsZ1JK9cqJ7cyv5KqO7VyUUpcopf5USqXn/K1Yo5S6qKrjOpNSalEh77FWSs2p6vgKopTqrpT6XSl1Iue9/VspdVNVx1UYpVRvpdTSnL9riUqpr5RSNas6rpKS9q9iVbc2UNq/ildd28Dq0v6BtIGVoSxtYLVbE1pRlHermD8AOzAa7zYwLwILlVKttNYZVRlfIRoAw4G1wBKgf9WGU6SHgIPAE3iLR7UFxgG9lVLdtNaeKoytMKF439sPgZN4txJ6DPhLKdVSa32gKoM7F6XUSKB1VcdRDPcAq0/72VVVgRSHUup2YHzO1wt4O/PaAL5VGFZh7gQCzzjWFXgLmFX54RRNKdUKmA/8BdwKZAJXAhOVUlat9UdVGd+ZlFIXAL8Dc4FhQA287cYCpVR7rbW9KuMrLmn/KkV1awOl/as81aYNrGbtH0gbWKHK3AZqreXLOyX5XsANNDjtWF28fwweqOr4ConZcNr3t+D94FCnquMqJNbwAo5dnxPzRVUdXwleR+OcmB+s6ljOEWcwcAwYmRPvi1UdUwEx9sqJrW9Vx1KCmOsAWcB9VR1LGV7DRLzJRmhVx1JAbC8DDsD/jON/ASuqOr4C4p0P7AZMpx3rmPN7fWdVx1eC1yHtX8XHW+3bQGn/yj3OatUG/hvav5zXIW1g+cVbpjZQpuP+4zLgL6317twDWut9wDJgSJVFVQR9/vWcFkprfbKAw7k9f9GVGUsZncr511mlUZzba8AWrfXUqg7kX+YmwAN8XNWBlIZSygfvVlY/a60TqzqeAljw/r+VdcbxZM7P5SNdgHla67yRC631arx/J66osqhKTtq/CvYvaQOl/ftvq9btH0gbWAHK1Aaejy+oqjQHNhdwfAtQbdZAVDM9c/7dVqVRnINSyqiUsiilGgKf4O1h/baKwyqUUqoH3h72O6s6lmL6WinlVkqdUkp9cz6vQwN6ANuBq5VSe5RSLqXUbqXUXVUdWDENBQKAL6o6kEJMzvn3PaVUlFIqWCl1K9AHeLvqwiqUG2+v9ZnsQItKjqUspP2rGud9GyjtX6WoLm1gdW//QNrA8lamNlDWhP4jFEgq4HgiEFLJsfzrKaWigeeB+VrrNVUdzzmsBNrnfL8b79SpE1UYT6GUUma8HxTe0FrvqOp4ziEFeBNYDKTiXSP1BLBCKdX2PH2Po3K+Xscb6x68varjlVImrfW7VRlcMVwPnAB+q+pACqK13qyU6gX8xD8fIp3AHVrr8/GD7w68PcF5lFJxQCTn/2jR6aT9q2TVqA2U9q/iVLc2sLq3fyBtYHkrUxsoSWh+Be1Xoyo9in85pZQ/MBPveqMbqzic4hiFd2F7PbzFJeYppXporfdXaVQFexTwAV6q6kDORWu9Dlh32qHFSqk/gVV4CzU8VSWBFc2Atxf1Bq319Jxjf+RU5XxcKfWezlkUcb5RSkUBfYF3T586cz7JGW35Ee8I3B14pyQNAT5WSmVrrb+uyvgK8C4wJaf65nt4k7lP8U5Zq1bTRZH2r9JUszZQ2r8KUg3bwGrb/oG0gRWkTG2gTMf9RxLeN+9MIRTcQyxKQSllw1uRrB4wQGsdX8UhnZPWepvWemXO+pI+gD/eKoHnlZwpPE8CTwPWnGkcwTkP5/5srLIAi0Fr/TewE+/C9vNR7pqoeWcc/x2oibf373x1Hd6/+efrNCTwFmVwApdqrWdrrRdore8BvgfeVUqdV21WzgeCF4EHgePAVuAw8CtwtApDKylp/ypJdWsDpf2rXOd5G1id2z+QNrDclbUNPK9eTBXbgnddzJma4X1TRRnlTJX5EegEXKK13lTFIZWY1joZ75SkBlUcSkHqATZgCt4Pjrlf4O3BTgJaVk1oJaIoeFTmfLClkOO5I0bn8+jX9cAGrfWGqg6kCC3xxnjmNJ5VeEu/R1R+SEXTWj8NhAGtgEit9UigIbC0SgMrGWn/KkF1bwOl/as052sbWJ3bP5A2sEKUpQ2UJPQfs4AuSql6uQdyphh05zzcS6i6yem9+RpvT+oQrfVfVRxSqeRswNsE71qI8816oHcBX+BtmHvj/QBx3lJKdQAa4V2HdD76KeffAWccHwDEa62PVXI8xZLzvjbn/O4BBm/RkzZKKcsZxzsD2XjXKJ53tNYZWutNWuvjSqmL8f6NqE4VJKX9q2D/hjZQ2r+Kd563gdWy/QNpAytaadtAWRP6jwnAWGCmUuopvL1QLwCH8C50Py8ppa7M+Ta3cMBApdRJ4KTWenEVhVWQD/AuYH8JyFBKnb6QOf58nJKklPoJ+BvYiLdoQCPgfrzreN6swtAKlNNLvejM40opgANa67Meq0pKqa+BfXjf42S8RRkexzuV4/2qi6xIvwILgU+UUmHAXrwbSffn/F7bdT3e39tvqjqQcxgP/AD8rJT6EO96mMvw7vf3tta6oCp8VUYp1RYYiPd3GLzVIx8GXtNaL6+ywEpO2r+KV63aQGn/Kl41bAOra/sH0gZWiDK3geW1Yem/4QuIxTtVJhVIA2ZwHm9+nROzLuRrUVXHdkac+4uIdVxVx1dIzI8Ca/E2Dpl4q4B9cr7/ThTyO3LebdaNt7HdiLdCoBPvB95P8U7nqPL4iog7EO8HyuN4S5NvBK6p6riKiNcMnMS7L1qVx1OMeAfi/TB5Mufv8Hq8VQKNVR1bAbE2xzvlKBnvh4W/gRurOq5SvhZp/yo21mrVBkr7VymxVbs2sLq1fzkxSxtYcbGWqQ1UORcRQgghhBBCCCEqnKwJFUIIIYQQQghRaSQJFUIIIYQQQghRaSQJFUIIIYQQQghRaSQJFUIIIYQQQghRaSQJFUIIIYQQQghRaSQJFUIIIYQQQghRaSQJFeJfSCl1g1JKn/aVoZTar5T6SSk1XCkl/+8LIYT4V5I2UIjzn6mqAxBCVKirgHjAincz+kHAVOC2/7d3N6FWVWEcxp8/gUQ5SBIrMqIQm0VIgZMKQtCwIkwoCkLIizXog2ZBRY6CBkUTcxJFYkS3wILCCO6Nikioi4pFRSAUlYQVXcRrX7wN9r54uigd5Xxxz/ObbNbZa62z1+Ccl3fttddOcmtVzQ3z4iRJ6iNjoDSiTEKlxW1/VX3bUd6VZBKYBJ4BHhzOZUmS1HfGQGlEuRxBGjNV9SbwFjCR5DyAJNuTzCT5PcnRJFNJ1s63SXJxkj+TPLywvyRPJTmeZFlbXp/kk7avY0m+TvLkoMYnSdLpGAOl0WASKo2nd2mWJ13bli8FngNuB7YAPwMfJrkaoKqOAHuAbZ2dJDkHuA94vap+S3Il8DZwGLgTuA14Fji/r6ORJKl7xkBpyFyOK42n79rjJQBVtXX+RBtU9wJf0ATX+ZnfHcB0kuur6qP2s43ASmBnW14DLAEeqKrZ9rOpfg1CkqSzYAyUhsw7odJ4SnssgCTrkkwn+QX4G/gLWA1cNd+gqj4AvuS/M8HbgINV9Wlb3t+2fS3J5iQr+jkISZLOgjFQGjKTUGk8XdYef0qyhmZp0jGaWd+1wHXAAeDcBe1eADYnuTDJ5cAGTs4A024AsZ7mv2UXcCTJviQ39nMwkiSdAWOgNGQmodJ42gicAD4H7qCZ+d1UVXuqal9VfQYsO0W7V2hmebcAE8AcsLuzQlVNV9UG4AJgXVv/nSTL+zMUSZLOiDFQGjKfCZXGTJJNNJslPF9Vx9vdAf+hXZbU1rmJ5p1qhzvbVtVskt00S5CWAq92PPfCgrp/AFNJltLsRHgFcLQPQ5IkqSvGQGk0mIRKi9s17ezrEpqAegvNy7vfBx5r6+wFHgFeTvISzXMwTwA/nKbPHZx8JmZn54kk9wM30Cxt+h5Y3n7Pj8ChnoxIkqTuGAOlEWUSKi1uk+3xBM2W8zPAXcAbVVUAVfVekoeAR2mWJR0C7gUeP1WHVXUwyTfAbFXNLDh9ALgZeBpYAfwKfAzcU1VzvRyYJEn/wxgojai0v0FJ6kqS1cBXwERVvTjs65EkaVCMgVJvmIRK6kqSlcAqYHt7XOXMriRpHBgDpd5yd1xJ3dpK89Lti4C7Db6SpDFiDJR6yDuhkiRJkqSB8U6oJEmSJGlgTEIlSZIkSQNjEipJkiRJGhiTUEmSJEnSwJiESpIkSZIGxiRUkiRJkjQw/wLDHo1BBWs37wAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1224x576 with 4 Axes>"
       ]
@@ -271,7 +271,7 @@
     "        \n",
     "        axins.tick_params(labelsize=11) \n",
     "        axins.ticklabel_format(axis='y',useOffset=False) \n",
-    "        axins.set_title('x10$^{-7}$',fontsize=10,loc='left')\n",
+    "        axins.set_title('x10$^{-6}$',fontsize=10,loc='left')\n",
     "        ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
     "        \n",
     "        ax.set_xticklabels([])\n",
@@ -302,7 +302,7 @@
     "        \n",
     "        axins.tick_params(labelsize=11) \n",
     "        axins.ticklabel_format(axis='y',useOffset=False) \n",
-    "        axins.set_title('x10$^{-7}$',fontsize=10,loc='left')\n",
+    "        axins.set_title('x10$^{-6}$',fontsize=10,loc='left')\n",
     "        ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
     "        \n",
     "        ax.set_xticklabels([])\n",
@@ -339,7 +339,7 @@
     "        #axins.yaxis.set_major_formatter(formatter)\n",
     "        axins.ticklabel_format(axis='y',useOffset=False) \n",
     "        #axins.ticklabel_format(useOffset=True)\n",
-    "        axins.set_title('x10$^{-7}$',fontsize=10,loc='left')\n",
+    "        axins.set_title('x10$^{-6}$',fontsize=10,loc='left')\n",
     "        ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
     "        \n",
     "    elif i == 3:\n",
@@ -370,7 +370,7 @@
     "        \n",
     "        axins.tick_params(labelsize=11) \n",
     "        axins.ticklabel_format(axis='y',useOffset=False) \n",
-    "        axins.set_title('x10$^{-7}$',fontsize=10,loc='left')\n",
+    "        axins.set_title('x10$^{-6}$',fontsize=10,loc='left')\n",
     "        ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
     "\n",
     "    i = i + 1     \n",
diff --git a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_part01-checkpoint.ipynb b/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_part01-checkpoint.ipynb
deleted file mode 100644
index 8be5eb6bf9446793db7a1dff57f5dc1c2a0563dd..0000000000000000000000000000000000000000
--- a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_part01-checkpoint.ipynb
+++ /dev/null
@@ -1,1348 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnosing the growth of PV differences, Part 1\n",
-    "---\n",
-    "@ Behrooz Keshtgar, KIT 2022\n",
-    "\n",
-    "Adapted from the original code by Tobias Selz, LMU "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1- load python packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "from datetime import datetime, timedelta\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "from scipy.interpolate import interp1d\n",
-    "from numba import jit\n",
-    "import windspharm\n",
-    "import metpy.calc as mpcalc\n",
-    "import metpy\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
-    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For reference, print package versions to screen:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xarrary:    0.16.0\n",
-      "numpy:      1.19.1\n",
-      "metpy:      1.0\n",
-      "matplotlib: 3.3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('xarrary:   ', xr.__version__)\n",
-    "print('numpy:     ', np.__version__)\n",
-    "print('metpy:     ', metpy.__version__)\n",
-    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary for loading simulations\n",
-    "simdict = {\n",
-    "         #'LC1-channel-4000x9000km-2km-0002' : {'res':'2km', 'radiation':1, 'rh':0.8}, # No radiation\n",
-    "         'LC1-channel-4000x9000km-2km-0003' : {'res':'2km', 'radiation':1, 'rh':0.8}, # Only cloud radiation\n",
-    "         #'LC1-channel-4000x9000km-2km-0004' : {'res':'2km', 'radiation':1, 'rh':0.8}, # 2xOnly cloud radiation\n",
-    "          }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2- Loading datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0003\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load 3d datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        # loading remapped datasets (1x1 r)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        if sim == 'LC1-channel-4000x9000km-2km-0011':\n",
-    "            fname = path+\"icon-atm3d*.nc\"\n",
-    "        else:    \n",
-    "            fname = path+\"icon-fg*.nc\"                                                                                  \n",
-    "        ds_var = xr.open_mfdataset(fname)[['u','v','temp','pres']]\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_fg = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0003\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load PV \n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        # loading remapped datasets (1x1 r)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"icon-atm3d*.nc\"                                                            \n",
-    "        ds_var = xr.open_mfdataset(fname)[['pv']]\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_atm3d = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0003\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load temperature tendencies datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        # loading remapped datasets (1x1 r)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"icon-ddt_temp*.nc\"\n",
-    "        ds_var = xr.open_mfdataset(fname)\n",
-    "        # adding cloud-radiative heating rates and total physic tendency\n",
-    "        if sim in ['LC1-channel-4000x9000km-2km-0002','LC1-channel-4000x9000km-2km-0003']:\n",
-    "            ds_var['ddt_temp_radsw'] = ds_var['ddt_temp_radswnw'] - ds_var['ddt_temp_radswcs']\n",
-    "            ds_var['ddt_temp_radlw'] = ds_var['ddt_temp_radlwnw'] - ds_var['ddt_temp_radlwcs']\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_ddt_temp = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0011\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load wind tendencies datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"icon-ddt_wind*.nc\"\n",
-    "        ds_var = xr.open_mfdataset(fname)\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_ddt_wind = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3- Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Constants\n",
-    "\n",
-    "#Gravitation\n",
-    "g=9.80665\n",
-    "#Adiabat-coef (R_L/cp)\n",
-    "kappa=287.04/1004.64\n",
-    "#Reference pressure (Pa)\n",
-    "p00=1e5\n",
-    "#Gas constant\n",
-    "Rd=287.\n",
-    "#lapse rate\n",
-    "alpha=0.0065*Rd/g\n",
-    "#Level list to interpolate (K)\n",
-    "thlevs = np.arange(310,352,2) \n",
-    "\n",
-    "# functions for deriving the PV diagnostics for PV error tendency\n",
-    "#----------------------------------------------------------------\n",
-    "# Potential temperature\n",
-    "def calc_theta(temp, pres):\n",
-    "    return temp * (pres/p00)**(-kappa)\n",
-    "\n",
-    "def calc_ddttheta(temp, pres):\n",
-    "    return (temp * (pres/p00)**(-kappa)) / 1.4 # Cp/Cv factor related to temp tendencies in ICON\n",
-    "\n",
-    "# vertical derivitive on model levels\n",
-    "def ddtheta(field_ml, theta_ml):\n",
-    "    ddth_field_ml = np.zeros(theta_ml.shape) * np.nan\n",
-    "    ddth_field_ml[1:-1,...] = (field_ml[2:,...] - field_ml[:-2,...]) / (theta_ml[2:,...] - theta_ml[:-2,...]) \n",
-    "    ddth_field_ml[0,...] = (field_ml[1,...] - field_ml[0,...]) / (theta_ml[1,...] - theta_ml[0,...])\n",
-    "    ddth_field_ml[-1,...] = (field_ml[-1,...] - field_ml[-2,...]) / (theta_ml[-1,...] - theta_ml[-2,...])\n",
-    "    return ddth_field_ml\n",
-    "\n",
-    "# Fill nan values\n",
-    "def fill_nans_hint(field):\n",
-    "    \"\"\"Fills the nans in the 2-d lat/lon-field with linear, horizontal interpolation.\"\"\"\n",
-    "  \n",
-    "    #Get dimensions\n",
-    "    nlat, nlon = np.shape(field)\n",
-    "    #mirror the field in east-west-direction\n",
-    "    nmir = 25\n",
-    "  \n",
-    "    fieldc = np.zeros((nlat, nlon+2*nmir), np.float32) * np.nan\n",
-    "        \n",
-    "    #copy field\n",
-    "    fieldc[:, nmir:-nmir] = field\n",
-    "    fieldc[:, :nmir] = field[:,-nmir:]\n",
-    "    fieldc[:, -nmir:] =field [:,:nmir]\n",
-    "    for ilat in range(nlat):\n",
-    "        for ilon in range(nlon):\n",
-    "            #Interpolation required?\n",
-    "            if not np.isnan(field[ilat,ilon]):\n",
-    "                continue\n",
-    "            #search for valid point in x direction on both sides\n",
-    "            jlon = nmir+ilon-1\n",
-    "            while np.isnan(fieldc[ilat, jlon]):\n",
-    "                jlon-=1\n",
-    "            westval = fieldc[ilat, jlon]\n",
-    "            iwestlon = jlon\n",
-    "            jlon = nmir+ilon+1\n",
-    "            while np.isnan(fieldc[ilat, jlon]):\n",
-    "                jlon += 1\n",
-    "            eastval = fieldc[ilat, jlon]\n",
-    "            ieastlon = jlon\n",
-    "            #Set new interpolated value\n",
-    "            we_val = (eastval-westval)/(ieastlon-iwestlon)*(nmir+ilon-iwestlon)+westval\n",
-    "      \n",
-    "            #search for valid point in y direction on both sides\n",
-    "            jlat = ilat-1\n",
-    "            while np.isnan(fieldc[jlat, nmir+ilon]):\n",
-    "                jlat -= 1\n",
-    "            southval = fieldc[jlat, nmir+ilon]\n",
-    "            isouthlat = jlat\n",
-    "            jlat = ilat+1\n",
-    "            while np.isnan(fieldc[jlat, nmir+ilon]):\n",
-    "                jlat += 1\n",
-    "            northval = fieldc[jlat,nmir+ilon]\n",
-    "            inorthlat = jlat\n",
-    "            ns_val = (northval-southval)/(inorthlat-isouthlat)*(ilat-isouthlat)+southval\n",
-    "            \n",
-    "            #Set field to the mean of both values\n",
-    "            field[ilat, ilon] = (we_val+ns_val) / 2\n",
-    "\n",
-    "# Isentropic interpolation\n",
-    "def interpol_on_th_fast(field_ml, theta_ml, thlevs, fillnans=False):\n",
-    "    if theta_ml.ndim!=3:\n",
-    "        raise Exception('theta_ml has to be 3 dimensional')\n",
-    "    nmlev, nlat, nlon = theta_ml.shape\n",
-    "    ndimfield = field_ml.ndim\n",
-    "    if ndimfield==3:\n",
-    "        nvar = 1\n",
-    "    elif ndimfield==4:\n",
-    "        nvar = field_ml.shape[0]\n",
-    "    else:\n",
-    "        raise Exception('field_ml has to be 3 or 4 dimensional, ie. lev, lat, lon.')      \n",
-    "    field_thl = np.zeros((nvar, len(thlevs), nlat, nlon), dtype=np.float64)\n",
-    "    for ithlev, thlev in enumerate(thlevs):\n",
-    "        for ilat in range(nlat):\n",
-    "            for ilon in range(nlon):\n",
-    "                nint = 0\n",
-    "                for imlev in range(nmlev-1):\n",
-    "                    if (theta_ml[imlev, ilat, ilon]>=thlev and theta_ml[imlev+1, ilat, ilon]<thlev)\\\n",
-    "                                or (theta_ml[imlev, ilat, ilon]<=thlev and theta_ml[imlev+1, ilat, ilon]>thlev):\n",
-    "                        a = (thlev-theta_ml[imlev+1, ilat, ilon]) / (theta_ml[imlev, ilat, ilon]-theta_ml[imlev+1, ilat, ilon])\n",
-    "                        field_thl[..., ithlev, ilat, ilon] = (1-a)*field_ml[..., imlev+1, ilat, ilon] + a*field_ml[..., imlev, ilat, ilon]\n",
-    "                        nint += 1\n",
-    "                if nint!=1:\n",
-    "                    field_thl[..., ithlev, ilat, ilon] = np.nan\n",
-    "    mask = np.where(np.isnan(field_thl), True, False)[0]\n",
-    "    if fillnans:\n",
-    "        for ivar in range(nvar):\n",
-    "            for ithlev, thlev in enumerate(thlevs):\n",
-    "                fill_nans_hint(field_thl[ivar, ithlev, :, :])\n",
-    "            \n",
-    "    if ndimfield==3:\n",
-    "        field_thl = field_thl[0]\n",
-    "    return field_thl, mask\n",
-    "\n",
-    "def calc_pv_cd(u, v, sigma):\n",
-    "    r_e = 6371200. # earth radius \n",
-    "    #f = 2*2*np.pi/86164.1 * np.sin(np.deg2rad(45.0))                         # constant Coriolis\n",
-    "    f = 0.0001031260914097046                                                 # Coriolis at 45° N\n",
-    "    #Calculate gridlength\n",
-    "    dx = 2*np.pi*r_e / 360. * np.cos(np.deg2rad(45)) # change the denominator according to the grid size\n",
-    "    dy = 2*np.pi*r_e / 360.                                                       \n",
-    "\n",
-    "    def ddx(f):\n",
-    "        return (np.roll(f, -1, -1) - np.roll(f, 1, -1)) / (2*dx)\n",
-    "\n",
-    "    def ddy(f):\n",
-    "        ddyf = (np.roll(f, -1, -2) - np.roll(f, 1, -2)) / (2*dy)\n",
-    "        ddyf[..., 0, :] = 0.\n",
-    "        ddyf[..., -1, :] = 0.\n",
-    "        return ddyf\n",
-    "\n",
-    "    def rot(u, v):\n",
-    "        divf = ddx(v) - ddy(u)\n",
-    "        divf[..., 0, :] = 0.\n",
-    "        divf[..., -1, :] = 0.\n",
-    "        return divf\n",
-    "\n",
-    "    return 1/sigma * (rot(u,v) + f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4- Changing to numpy arrays"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Common variables\n",
-    "lat  = ds_list_fg[0].lat.values\n",
-    "lon  = ds_list_fg[0].lon.values\n",
-    "time = ds_list_fg[0].time\n",
-    "\n",
-    "# pressure, temp, wind feilds at model levels\n",
-    "pres = ds_list_fg[0].pres.values\n",
-    "temp = ds_list_fg[0].temp.values\n",
-    "u    = ds_list_fg[0].u.values\n",
-    "v    = ds_list_fg[0].v.values\n",
-    "pv   = ds_list_atm3d[0].pv.values\n",
-    "\n",
-    "# temp tendencies\n",
-    "ddt_temp_pconv = ds_list_ddt_temp[0].ddt_temp_pconv.values\n",
-    "ddt_temp_radlw = ds_list_ddt_temp[0].ddt_temp_radlw.values\n",
-    "ddt_temp_radsw = ds_list_ddt_temp[0].ddt_temp_radsw.values\n",
-    "ddt_temp_gscp  = ds_list_ddt_temp[0].ddt_temp_gscp.values\n",
-    "ddt_temp_mphy  = ds_list_ddt_temp[0].ddt_temp_mphy.values\n",
-    "ddt_temp_turb  = ds_list_ddt_temp[0].ddt_temp_turb.values\n",
-    "ddt_temp_drag  = ds_list_ddt_temp[0].ddt_temp_drag.values\n",
-    "ddt_temp_diff  = ds_list_ddt_temp[0].ddt_temp_diff.values\n",
-    "\n",
-    "# wind tendencies\n",
-    "ddt_u_gwd   = ds_list_ddt_wind[0].ddt_u_gwd.values\n",
-    "ddt_v_gwd   = ds_list_ddt_wind[0].ddt_v_gwd.values\n",
-    "ddt_u_pconv = ds_list_ddt_wind[0].ddt_u_pconv.values\n",
-    "ddt_v_pconv = ds_list_ddt_wind[0].ddt_v_pconv.values\n",
-    "ddt_u_sso   = ds_list_ddt_wind[0].ddt_u_sso.values\n",
-    "ddt_v_sso   = ds_list_ddt_wind[0].ddt_v_sso.values\n",
-    "ddt_u_turb  = ds_list_ddt_wind[0].ddt_u_turb.values\n",
-    "ddt_v_turb  = ds_list_ddt_wind[0].ddt_v_turb.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5- Deriving potential temperature tendencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temperature\n",
-    "theta = calc_theta(temp,pres)\n",
-    "\n",
-    "# physical temperature tendencies\n",
-    "ddt_theta_pconv = calc_ddttheta(ddt_temp_pconv,pres)\n",
-    "ddt_theta_turb  = calc_ddttheta(ddt_temp_turb,pres)\n",
-    "ddt_theta_gscp  = calc_ddttheta(ddt_temp_gscp,pres)\n",
-    "ddt_theta_mphy  = calc_ddttheta(ddt_temp_mphy,pres)\n",
-    "ddt_theta_drag  = calc_ddttheta(ddt_temp_drag,pres)\n",
-    "ddt_theta_diff  = calc_ddttheta(ddt_temp_diff,pres)\n",
-    "ddt_theta_radlw = calc_ddttheta(ddt_temp_radlw,pres)\n",
-    "ddt_theta_radsw = calc_ddttheta(ddt_temp_radsw,pres)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6- Static stability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we also need 'sigma' static stability to create a mask to avoid exceeding values\n",
-    "sigma = np.zeros(theta.shape) * np.nan\n",
-    "for t in range(len(time)):\n",
-    "    sigma[t,:,:,:] = -ddtheta(pres[t,:,:,:], theta[t,:,:,:])/g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7- Potential vorticity based on remapped datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# deriving PV based using central differences\n",
-    "hei = ds_list_atm3d[0].height\n",
-    "pvcd = np.zeros(pv.shape) * np.nan\n",
-    "for t in range(len(time)):\n",
-    "    for h in range(len(hei)):\n",
-    "        pvcd[t,h,:,:] = calc_pv_cd(u[t,h,:,:],v[t,h,:,:],sigma[t,h,:,:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 8- Vertical gradients"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Calculate theta gradient of pv and thetas\n",
-    "\n",
-    "ddth_pv = np.zeros(theta.shape) * np.nan\n",
-    "ddth_pvcd = np.zeros(theta.shape) * np.nan\n",
-    "\n",
-    "ddth_theta_pconv = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_radlw = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_radsw = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_gscp = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_mphy = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_turb = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_drag = np.zeros(theta.shape) * np.nan\n",
-    "ddth_theta_diff = np.zeros(theta.shape) * np.nan\n",
-    "\n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    ddth_pv[t,:,:,:] = ddtheta(pv[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_pvcd[t,:,:,:] = ddtheta(pvcd[t,:,:,:], theta[t,:,:,:])\n",
-    "    \n",
-    "    ddth_theta_pconv[t,:,:,:] = ddtheta(ddt_theta_pconv[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_radlw[t,:,:,:] = ddtheta(ddt_theta_radlw[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_radsw[t,:,:,:] = ddtheta(ddt_theta_radsw[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_gscp[t,:,:,:] = ddtheta(ddt_theta_gscp[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_mphy[t,:,:,:] = ddtheta(ddt_theta_mphy[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_turb[t,:,:,:] = ddtheta(ddt_theta_turb[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_drag[t,:,:,:] = ddtheta(ddt_theta_drag[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_diff[t,:,:,:] = ddtheta(ddt_theta_diff[t,:,:,:], theta[t,:,:,:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Save all fields in a list and their labels\n",
-    "fieldlist = []\n",
-    "labellist = []\n",
-    "\n",
-    "for var in [u,v,pv,pvcd,sigma,ddth_pv,ddth_pvcd,\n",
-    "            ddth_theta_radsw,ddth_theta_radlw,\n",
-    "            ddth_theta_pconv,ddth_theta_gscp,ddth_theta_mphy,ddth_theta_turb,ddth_theta_drag,ddth_theta_diff,\n",
-    "            ddt_theta_pconv,ddt_theta_gscp,ddt_theta_mphy,ddt_theta_turb,ddt_theta_drag,ddt_theta_diff,\n",
-    "            ddt_theta_radsw,ddt_theta_radlw,\n",
-    "            ddt_u_gwd,ddt_v_gwd,ddt_u_pconv,ddt_v_pconv,ddt_u_sso,ddt_v_sso,\n",
-    "            ddt_u_turb,ddt_v_turb]:\n",
-    "    \n",
-    "    fieldlist.append(var)\n",
-    "    \n",
-    "for name in ['u','v','pv','pvcd','sigma','ddth_pv','ddth_pvcd',\n",
-    "             'ddth_theta_radsw','ddth_theta_radlw',\n",
-    "             'ddth_theta_pconv','ddth_theta_gscp','ddth_theta_mphy','ddth_theta_turb','ddth_theta_drag','ddth_theta_diff',\n",
-    "             'ddt_theta_pconv','ddt_theta_gscp','ddt_theta_mphy','ddt_theta_turb','ddt_theta_drag','ddt_theta_diff',\n",
-    "             'ddt_theta_radsw','ddt_theta_radlw',\n",
-    "             'ddt_u_gwd','ddt_v_gwd','ddt_u_pconv','ddt_v_pconv','ddt_u_sso','ddt_v_sso',\n",
-    "             'ddt_u_turb','ddt_v_turb']:\n",
-    "    \n",
-    "    labellist.append(name)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9- Isentropic interpolation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\n# Interpolation\\nfieldlist_th = np.zeros((len(fieldlist), len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\n\\nmask_thint = np.zeros((len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\nnvar = len(fieldlist)\\n\\nfor f in range(nvar):\\n        print('working on field:',f)\\n        for t in range(len(time)):\\n            #print('Working on time step:',t)\\n            fieldlist_th[f,t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[f,t,:,:,:], theta[t], thlevs, fillnans=True)\\n\""
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''\n",
-    "# Interpolation\n",
-    "fieldlist_th = np.zeros((len(fieldlist), len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "mask_thint = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "nvar = len(fieldlist)\n",
-    "\n",
-    "for f in range(nvar):\n",
-    "        print('working on field:',f)\n",
-    "        for t in range(len(time)):\n",
-    "            #print('Working on time step:',t)\n",
-    "            fieldlist_th[f,t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[f,t,:,:,:], theta[t], thlevs, fillnans=True)\n",
-    "'''    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "working on field: 0\n",
-      "working on field: 1\n",
-      "working on field: 2\n",
-      "working on field: 3\n",
-      "working on field: 4\n",
-      "working on field: 5\n",
-      "working on field: 6\n",
-      "working on field: 7\n",
-      "working on field: 8\n",
-      "working on field: 9\n",
-      "working on field: 10\n",
-      "working on field: 11\n",
-      "working on field: 12\n",
-      "working on field: 13\n",
-      "working on field: 14\n",
-      "working on field: 15\n",
-      "working on field: 16\n",
-      "working on field: 17\n",
-      "working on field: 18\n",
-      "working on field: 19\n",
-      "working on field: 20\n",
-      "working on field: 21\n",
-      "working on field: 22\n",
-      "working on field: 23\n",
-      "working on field: 24\n",
-      "working on field: 25\n",
-      "working on field: 26\n",
-      "working on field: 27\n",
-      "working on field: 28\n",
-      "working on field: 29\n",
-      "working on field: 30\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Another way of interpolation (faster)\n",
-    "\n",
-    "def isentropic(field_ml,theta_ml,thlevs):\n",
-    "    \n",
-    "    nvar = len(field_ml)\n",
-    "    \n",
-    "    field_thl = np.zeros((nvar, len(time), len(thlevs), len(lat), len(lon)), dtype=np.float64)\n",
-    "    \n",
-    "    for f in range(nvar):\n",
-    "        print('working on field:',f)\n",
-    "    \n",
-    "        for t in range(len(time)):\n",
-    "\n",
-    "            for i in range(len(thlevs)):\n",
-    "\n",
-    "                field_thl[f,t,i,:,:] = metpy.interpolate.interpolate_to_isosurface(theta_ml[t,:,:,:]\n",
-    "                                                                           ,field_ml[f][t,:,:,:],thlevs[i],True)\n",
-    "\n",
-    "    return(field_thl)\n",
-    "\n",
-    "#------------------------------------------------------------\n",
-    "fieldlist_th = isentropic(fieldlist, theta, thlevs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nmask_thint = np.zeros((len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\n\\ntemp = np.zeros((len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\nfor t in range(len(time)):\\n        print('Working on time step:',t)\\n        temp[t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[-1,t,:,:,:], theta[t], thlevs, fillnans=True)\\n\""
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''\n",
-    "mask_thint = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "temp = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "for t in range(len(time)):\n",
-    "        print('Working on time step:',t)\n",
-    "        temp[t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[-1,t,:,:,:], theta[t], thlevs, fillnans=True)\n",
-    "'''        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on time step: 0\n",
-      "Working on time step: 1\n",
-      "Working on time step: 2\n",
-      "Working on time step: 3\n",
-      "Working on time step: 4\n",
-      "Working on time step: 5\n",
-      "Working on time step: 6\n",
-      "Working on time step: 7\n",
-      "Working on time step: 8\n",
-      "Working on time step: 9\n",
-      "Working on time step: 10\n",
-      "Working on time step: 11\n",
-      "Working on time step: 12\n",
-      "Working on time step: 13\n",
-      "Working on time step: 14\n",
-      "Working on time step: 15\n",
-      "Working on time step: 16\n",
-      "Working on time step: 17\n",
-      "Working on time step: 18\n",
-      "Working on time step: 19\n",
-      "Working on time step: 20\n",
-      "Working on time step: 21\n",
-      "Working on time step: 22\n",
-      "Working on time step: 23\n",
-      "Working on time step: 24\n",
-      "Working on time step: 25\n",
-      "Working on time step: 26\n",
-      "Working on time step: 27\n",
-      "Working on time step: 28\n",
-      "Working on time step: 29\n",
-      "Working on time step: 30\n",
-      "Working on time step: 31\n",
-      "Working on time step: 32\n",
-      "Working on time step: 33\n",
-      "Working on time step: 34\n",
-      "Working on time step: 35\n",
-      "Working on time step: 36\n",
-      "Working on time step: 37\n",
-      "Working on time step: 38\n",
-      "Working on time step: 39\n",
-      "Working on time step: 40\n",
-      "Working on time step: 41\n",
-      "Working on time step: 42\n",
-      "Working on time step: 43\n",
-      "Working on time step: 44\n",
-      "Working on time step: 45\n",
-      "Working on time step: 46\n",
-      "Working on time step: 47\n",
-      "Working on time step: 48\n",
-      "Working on time step: 49\n",
-      "Working on time step: 50\n",
-      "Working on time step: 51\n",
-      "Working on time step: 52\n",
-      "Working on time step: 53\n",
-      "Working on time step: 54\n",
-      "Working on time step: 55\n",
-      "Working on time step: 56\n",
-      "Working on time step: 57\n",
-      "Working on time step: 58\n",
-      "Working on time step: 59\n",
-      "Working on time step: 60\n",
-      "Working on time step: 61\n",
-      "Working on time step: 62\n",
-      "Working on time step: 63\n",
-      "Working on time step: 64\n",
-      "Working on time step: 65\n",
-      "Working on time step: 66\n",
-      "Working on time step: 67\n",
-      "Working on time step: 68\n",
-      "Working on time step: 69\n",
-      "Working on time step: 70\n",
-      "Working on time step: 71\n",
-      "Working on time step: 72\n",
-      "Working on time step: 73\n",
-      "Working on time step: 74\n",
-      "Working on time step: 75\n",
-      "Working on time step: 76\n",
-      "Working on time step: 77\n",
-      "Working on time step: 78\n",
-      "Working on time step: 79\n",
-      "Working on time step: 80\n",
-      "Working on time step: 81\n",
-      "Working on time step: 82\n",
-      "Working on time step: 83\n",
-      "Working on time step: 84\n",
-      "Working on time step: 85\n",
-      "Working on time step: 86\n",
-      "Working on time step: 87\n",
-      "Working on time step: 88\n",
-      "Working on time step: 89\n",
-      "Working on time step: 90\n",
-      "Working on time step: 91\n",
-      "Working on time step: 92\n",
-      "Working on time step: 93\n",
-      "Working on time step: 94\n",
-      "Working on time step: 95\n",
-      "Working on time step: 96\n",
-      "Working on time step: 97\n",
-      "Working on time step: 98\n",
-      "Working on time step: 99\n",
-      "Working on time step: 100\n",
-      "Working on time step: 101\n",
-      "Working on time step: 102\n",
-      "Working on time step: 103\n",
-      "Working on time step: 104\n",
-      "Working on time step: 105\n",
-      "Working on time step: 106\n",
-      "Working on time step: 107\n",
-      "Working on time step: 108\n",
-      "Working on time step: 109\n",
-      "Working on time step: 110\n",
-      "Working on time step: 111\n",
-      "Working on time step: 112\n",
-      "Working on time step: 113\n",
-      "Working on time step: 114\n",
-      "Working on time step: 115\n",
-      "Working on time step: 116\n",
-      "Working on time step: 117\n",
-      "Working on time step: 118\n",
-      "Working on time step: 119\n",
-      "Working on time step: 120\n",
-      "Working on time step: 121\n",
-      "Working on time step: 122\n",
-      "Working on time step: 123\n",
-      "Working on time step: 124\n",
-      "Working on time step: 125\n",
-      "Working on time step: 126\n",
-      "Working on time step: 127\n",
-      "Working on time step: 128\n",
-      "Working on time step: 129\n",
-      "Working on time step: 130\n",
-      "Working on time step: 131\n",
-      "Working on time step: 132\n",
-      "Working on time step: 133\n",
-      "Working on time step: 134\n",
-      "Working on time step: 135\n",
-      "Working on time step: 136\n",
-      "Working on time step: 137\n",
-      "Working on time step: 138\n",
-      "Working on time step: 139\n",
-      "Working on time step: 140\n",
-      "Working on time step: 141\n",
-      "Working on time step: 142\n",
-      "Working on time step: 143\n",
-      "Working on time step: 144\n",
-      "Working on time step: 145\n",
-      "Working on time step: 146\n",
-      "Working on time step: 147\n",
-      "Working on time step: 148\n",
-      "Working on time step: 149\n",
-      "Working on time step: 150\n",
-      "Working on time step: 151\n",
-      "Working on time step: 152\n",
-      "Working on time step: 153\n",
-      "Working on time step: 154\n",
-      "Working on time step: 155\n",
-      "Working on time step: 156\n",
-      "Working on time step: 157\n",
-      "Working on time step: 158\n",
-      "Working on time step: 159\n",
-      "Working on time step: 160\n",
-      "Working on time step: 161\n",
-      "Working on time step: 162\n",
-      "Working on time step: 163\n",
-      "Working on time step: 164\n",
-      "Working on time step: 165\n",
-      "Working on time step: 166\n",
-      "Working on time step: 167\n",
-      "Working on time step: 168\n",
-      "Working on time step: 169\n",
-      "Working on time step: 170\n",
-      "Working on time step: 171\n",
-      "Working on time step: 172\n",
-      "Working on time step: 173\n",
-      "Working on time step: 174\n",
-      "Working on time step: 175\n",
-      "Working on time step: 176\n",
-      "Working on time step: 177\n",
-      "Working on time step: 178\n",
-      "Working on time step: 179\n",
-      "Working on time step: 180\n",
-      "Working on time step: 181\n",
-      "Working on time step: 182\n",
-      "Working on time step: 183\n",
-      "Working on time step: 184\n",
-      "Working on time step: 185\n",
-      "Working on time step: 186\n",
-      "Working on time step: 187\n",
-      "Working on time step: 188\n",
-      "Working on time step: 189\n",
-      "Working on time step: 190\n",
-      "Working on time step: 191\n",
-      "Working on time step: 192\n",
-      "Working on time step: 193\n",
-      "Working on time step: 194\n",
-      "Working on time step: 195\n",
-      "Working on time step: 196\n",
-      "Working on time step: 197\n",
-      "Working on time step: 198\n",
-      "Working on time step: 199\n",
-      "Working on time step: 200\n",
-      "Working on time step: 201\n",
-      "Working on time step: 202\n",
-      "Working on time step: 203\n",
-      "Working on time step: 204\n",
-      "Working on time step: 205\n",
-      "Working on time step: 206\n",
-      "Working on time step: 207\n",
-      "Working on time step: 208\n",
-      "Working on time step: 209\n",
-      "Working on time step: 210\n",
-      "Working on time step: 211\n",
-      "Working on time step: 212\n",
-      "Working on time step: 213\n",
-      "Working on time step: 214\n",
-      "Working on time step: 215\n",
-      "Working on time step: 216\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Generate a mask where sigma>siglim\n",
-    "siglim = 1e4\n",
-    "\n",
-    "mask_siglim = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    print('Working on time step:',t)\n",
-    "    \n",
-    "    for i in range(len(thlevs)):\n",
-    "\n",
-    "        #mask_siglim[t,:,:,:], _ = interpol_on_th_fast(np.where(fieldlist[labellist.index('sigma')][t]>siglim, np.nan, 1.), theta[t], thlevs, fillnans=True)\n",
-    "        mask_siglim[t,i,:,:] = metpy.interpolate.interpolate_to_isosurface(theta[t,:,:,:]\n",
-    "                                                                           ,np.where(fieldlist[labellist.index('sigma')][t]>siglim, np.nan, 1.),thlevs[i],True)\n",
-    "        mask_siglim[t,i,:,:] = np.isnan(mask_siglim[t,i,:,:])  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10- Helmholts decompostion of wind field"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Helmholtz-decomposition using windspharm\n",
-    "# NOTE: the latitude direction has to be reordered from N to S, also the time has to be the last dimension\n",
-    "reorder = lambda f: np.moveaxis(f[:, ::-1, :], 0, -1)\n",
-    "reorder_back = lambda f: np.moveaxis(f, -1, 0)[:, ::-1, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on time step: 0\n",
-      "Working on time step: 1\n",
-      "Working on time step: 2\n",
-      "Working on time step: 3\n",
-      "Working on time step: 4\n",
-      "Working on time step: 5\n",
-      "Working on time step: 6\n",
-      "Working on time step: 7\n",
-      "Working on time step: 8\n",
-      "Working on time step: 9\n",
-      "Working on time step: 10\n",
-      "Working on time step: 11\n",
-      "Working on time step: 12\n",
-      "Working on time step: 13\n",
-      "Working on time step: 14\n",
-      "Working on time step: 15\n",
-      "Working on time step: 16\n",
-      "Working on time step: 17\n",
-      "Working on time step: 18\n",
-      "Working on time step: 19\n",
-      "Working on time step: 20\n",
-      "Working on time step: 21\n",
-      "Working on time step: 22\n",
-      "Working on time step: 23\n",
-      "Working on time step: 24\n",
-      "Working on time step: 25\n",
-      "Working on time step: 26\n",
-      "Working on time step: 27\n",
-      "Working on time step: 28\n",
-      "Working on time step: 29\n",
-      "Working on time step: 30\n",
-      "Working on time step: 31\n",
-      "Working on time step: 32\n",
-      "Working on time step: 33\n",
-      "Working on time step: 34\n",
-      "Working on time step: 35\n",
-      "Working on time step: 36\n",
-      "Working on time step: 37\n",
-      "Working on time step: 38\n",
-      "Working on time step: 39\n",
-      "Working on time step: 40\n",
-      "Working on time step: 41\n",
-      "Working on time step: 42\n",
-      "Working on time step: 43\n",
-      "Working on time step: 44\n",
-      "Working on time step: 45\n",
-      "Working on time step: 46\n",
-      "Working on time step: 47\n",
-      "Working on time step: 48\n",
-      "Working on time step: 49\n",
-      "Working on time step: 50\n",
-      "Working on time step: 51\n",
-      "Working on time step: 52\n",
-      "Working on time step: 53\n",
-      "Working on time step: 54\n",
-      "Working on time step: 55\n",
-      "Working on time step: 56\n",
-      "Working on time step: 57\n",
-      "Working on time step: 58\n",
-      "Working on time step: 59\n",
-      "Working on time step: 60\n",
-      "Working on time step: 61\n",
-      "Working on time step: 62\n",
-      "Working on time step: 63\n",
-      "Working on time step: 64\n",
-      "Working on time step: 65\n",
-      "Working on time step: 66\n",
-      "Working on time step: 67\n",
-      "Working on time step: 68\n",
-      "Working on time step: 69\n",
-      "Working on time step: 70\n",
-      "Working on time step: 71\n",
-      "Working on time step: 72\n",
-      "Working on time step: 73\n",
-      "Working on time step: 74\n",
-      "Working on time step: 75\n",
-      "Working on time step: 76\n",
-      "Working on time step: 77\n",
-      "Working on time step: 78\n",
-      "Working on time step: 79\n",
-      "Working on time step: 80\n",
-      "Working on time step: 81\n",
-      "Working on time step: 82\n",
-      "Working on time step: 83\n",
-      "Working on time step: 84\n",
-      "Working on time step: 85\n",
-      "Working on time step: 86\n",
-      "Working on time step: 87\n",
-      "Working on time step: 88\n",
-      "Working on time step: 89\n",
-      "Working on time step: 90\n",
-      "Working on time step: 91\n",
-      "Working on time step: 92\n",
-      "Working on time step: 93\n",
-      "Working on time step: 94\n",
-      "Working on time step: 95\n",
-      "Working on time step: 96\n",
-      "Working on time step: 97\n",
-      "Working on time step: 98\n",
-      "Working on time step: 99\n",
-      "Working on time step: 100\n",
-      "Working on time step: 101\n",
-      "Working on time step: 102\n",
-      "Working on time step: 103\n",
-      "Working on time step: 104\n",
-      "Working on time step: 105\n",
-      "Working on time step: 106\n",
-      "Working on time step: 107\n",
-      "Working on time step: 108\n",
-      "Working on time step: 109\n",
-      "Working on time step: 110\n",
-      "Working on time step: 111\n",
-      "Working on time step: 112\n",
-      "Working on time step: 113\n",
-      "Working on time step: 114\n",
-      "Working on time step: 115\n",
-      "Working on time step: 116\n",
-      "Working on time step: 117\n",
-      "Working on time step: 118\n",
-      "Working on time step: 119\n",
-      "Working on time step: 120\n",
-      "Working on time step: 121\n",
-      "Working on time step: 122\n",
-      "Working on time step: 123\n",
-      "Working on time step: 124\n",
-      "Working on time step: 125\n",
-      "Working on time step: 126\n",
-      "Working on time step: 127\n",
-      "Working on time step: 128\n",
-      "Working on time step: 129\n",
-      "Working on time step: 130\n",
-      "Working on time step: 131\n",
-      "Working on time step: 132\n",
-      "Working on time step: 133\n",
-      "Working on time step: 134\n",
-      "Working on time step: 135\n",
-      "Working on time step: 136\n",
-      "Working on time step: 137\n",
-      "Working on time step: 138\n",
-      "Working on time step: 139\n",
-      "Working on time step: 140\n",
-      "Working on time step: 141\n",
-      "Working on time step: 142\n",
-      "Working on time step: 143\n",
-      "Working on time step: 144\n",
-      "Working on time step: 145\n",
-      "Working on time step: 146\n",
-      "Working on time step: 147\n",
-      "Working on time step: 148\n",
-      "Working on time step: 149\n",
-      "Working on time step: 150\n",
-      "Working on time step: 151\n",
-      "Working on time step: 152\n",
-      "Working on time step: 153\n",
-      "Working on time step: 154\n",
-      "Working on time step: 155\n",
-      "Working on time step: 156\n",
-      "Working on time step: 157\n",
-      "Working on time step: 158\n",
-      "Working on time step: 159\n",
-      "Working on time step: 160\n",
-      "Working on time step: 161\n",
-      "Working on time step: 162\n",
-      "Working on time step: 163\n",
-      "Working on time step: 164\n",
-      "Working on time step: 165\n",
-      "Working on time step: 166\n",
-      "Working on time step: 167\n",
-      "Working on time step: 168\n",
-      "Working on time step: 169\n",
-      "Working on time step: 170\n",
-      "Working on time step: 171\n",
-      "Working on time step: 172\n",
-      "Working on time step: 173\n",
-      "Working on time step: 174\n",
-      "Working on time step: 175\n",
-      "Working on time step: 176\n",
-      "Working on time step: 177\n",
-      "Working on time step: 178\n",
-      "Working on time step: 179\n",
-      "Working on time step: 180\n",
-      "Working on time step: 181\n",
-      "Working on time step: 182\n",
-      "Working on time step: 183\n",
-      "Working on time step: 184\n",
-      "Working on time step: 185\n",
-      "Working on time step: 186\n",
-      "Working on time step: 187\n",
-      "Working on time step: 188\n",
-      "Working on time step: 189\n",
-      "Working on time step: 190\n",
-      "Working on time step: 191\n",
-      "Working on time step: 192\n",
-      "Working on time step: 193\n",
-      "Working on time step: 194\n",
-      "Working on time step: 195\n",
-      "Working on time step: 196\n",
-      "Working on time step: 197\n",
-      "Working on time step: 198\n",
-      "Working on time step: 199\n",
-      "Working on time step: 200\n",
-      "Working on time step: 201\n",
-      "Working on time step: 202\n",
-      "Working on time step: 203\n",
-      "Working on time step: 204\n",
-      "Working on time step: 205\n",
-      "Working on time step: 206\n",
-      "Working on time step: 207\n",
-      "Working on time step: 208\n",
-      "Working on time step: 209\n",
-      "Working on time step: 210\n",
-      "Working on time step: 211\n",
-      "Working on time step: 212\n",
-      "Working on time step: 213\n",
-      "Working on time step: 214\n",
-      "Working on time step: 215\n",
-      "Working on time step: 216\n"
-     ]
-    }
-   ],
-   "source": [
-    "udiv_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "vdiv_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "urot_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "vrot_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    print('Working on time step:',t)\n",
-    "    uvwin = reorder(fieldlist_th[labellist.index('u')][t,:,:,:])\n",
-    "    vvwin = reorder(fieldlist_th[labellist.index('v')][t,:,:,:])\n",
-    "    \n",
-    "    vwobj = windspharm.standard.VectorWind(uvwin, vvwin, gridtype='regular')\n",
-    "    udiv_th, vdiv_th, urot_th, vrot_th = vwobj.helmholtz()\n",
-    "    udiv_th, vdiv_th, urot_th, vrot_th = map(reorder_back, (udiv_th, vdiv_th, urot_th, vrot_th))\n",
-    "    \n",
-    "    udiv_th_t[t,:,:,:] = udiv_th\n",
-    "    \n",
-    "    vdiv_th_t[t,:,:,:] = vdiv_th\n",
-    "    \n",
-    "    urot_th_t[t,:,:,:] = urot_th\n",
-    "    \n",
-    "    vrot_th_t[t,:,:,:] = vrot_th\n",
-    "    \n",
-    "    del uvwin,vvwin,vwobj,udiv_th,vdiv_th,urot_th,vrot_th"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Accuracy check of the Helmholtz-decomp\n",
-    "for t in [50,100,150,160,170]:\n",
-    "    for ilev in range(len(thlevs)):\n",
-    "        u = fieldlist_th[labellist.index('u')][t,ilev]\n",
-    "        v = fieldlist_th[labellist.index('v')][t,ilev]\n",
-    "        err_rot = np.sqrt(((u-udiv_th_t[t,ilev]-urot_th_t[t,ilev])**2).mean()) / np.sqrt(((u**2).mean()))\n",
-    "        err_div = np.sqrt(((v-vdiv_th_t[t,ilev]-vrot_th_t[t,ilev])**2).mean()) / np.sqrt(((v**2).mean()))\n",
-    "        if err_rot>0.1 or err_div>0.1:\n",
-    "            print (t)\n",
-    "            print(ilev)\n",
-    "            raise Exception('Helmholtz-decomposition error too large.')   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 11- Creating a dataset and save to nc file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# saving all the results as a dataset\n",
-    "ds = xr.Dataset(data_vars={\"u\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('u')]), \n",
-    "                           \"v\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('v')]),\n",
-    "                           \"sigma\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('sigma')]),\n",
-    "                           #\"sigmainv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('sigmainv')]),\n",
-    "                           \"pv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('pv')]),\n",
-    "                           \"pvcd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('pvcd')]),\n",
-    "                           \"ddth_pv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_pv')]),\n",
-    "                           \"ddth_pvcd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_pvcd')]),\n",
-    "                           \"ddth_theta_pconv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_pconv')]),\n",
-    "                           \"ddth_theta_gscp\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_gscp')]),\n",
-    "                           \"ddth_theta_mphy\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_mphy')]),\n",
-    "                           \"ddth_theta_turb\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_turb')]),\n",
-    "                           \"ddth_theta_drag\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_drag')]),\n",
-    "                           \"ddth_theta_diff\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_diff')]),\n",
-    "                           \"ddth_theta_radsw\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_radsw')]),\n",
-    "                           \"ddth_theta_radlw\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_radlw')]),\n",
-    "                           \"ddt_theta_pconv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_pconv')]),\n",
-    "                           \"ddt_theta_gscp\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_gscp')]),\n",
-    "                           \"ddt_theta_mphy\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_mphy')]),\n",
-    "                           \"ddt_theta_turb\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_turb')]),\n",
-    "                           \"ddt_theta_drag\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_drag')]),\n",
-    "                           \"ddt_theta_diff\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_diff')]),\n",
-    "                           \"ddt_theta_radsw\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_radsw')]),\n",
-    "                           \"ddt_theta_radlw\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_radlw')]),\n",
-    "                           \"ddt_u_gwd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_gwd')]),\n",
-    "                           \"ddt_v_gwd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_gwd')]),\n",
-    "                           \"ddt_u_sso\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_sso')]),\n",
-    "                           \"ddt_v_sso\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_sso')]),\n",
-    "                           \"ddt_u_pconv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_pconv')]),\n",
-    "                           \"ddt_v_pconv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_pconv')]),\n",
-    "                           \"ddt_u_turb\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_turb')]),\n",
-    "                           \"ddt_v_turb\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_turb')]),\n",
-    "                           #\"mask_thint\":([\"time\",\"lev\",\"lat\",'lon'],mask_thint),\n",
-    "                           \"mask_siglim\":([\"time\",\"lev\",\"lat\",'lon'],mask_siglim),\n",
-    "                           \"ipvcd\":([\"time\",\"lev\",\"lat\",'lon'],ipvcd),\n",
-    "                           \"udiv\":([\"time\",\"lev\",\"lat\",'lon'],udiv_th_t),\n",
-    "                           \"vdiv\":([\"time\",\"lev\",\"lat\",'lon'],vdiv_th_t),\n",
-    "                           \"urot\":([\"time\",\"lev\",\"lat\",'lon'],urot_th_t),\n",
-    "                           \"vrot\":([\"time\",\"lev\",\"lat\",'lon'],vrot_th_t),\n",
-    "                           \n",
-    "                           },\n",
-    "                coords={\"lat\": ([\"lat\"], lat), \n",
-    "                        \"lon\": ([\"lon\"], lon),\n",
-    "                        \"time\":([\"time\"],time),\n",
-    "                        'lev':([\"lev\"],thlevs)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds.to_netcdf('/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/LC1-channel-4000x9000km-2km-0003_remapped_1x1/pvdiag_calc_1x1.nc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pycrh",
-   "language": "python",
-   "name": "pycrh"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_part02-checkpoint.ipynb b/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_part02-checkpoint.ipynb
deleted file mode 100644
index 13124765f0604ad460cecf3a6ff54f5b4064b346..0000000000000000000000000000000000000000
--- a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_part02-checkpoint.ipynb
+++ /dev/null
@@ -1,387 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnosing the growth of PV differences, Part 2\n",
-    "---\n",
-    "@ Behrooz Keshtgar, KIT 2022\n",
-    "\n",
-    "Adapted from the original code by Tobias Selz, LMU "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1- load python packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# loading libraries\n",
-    "\n",
-    "import sys\n",
-    "from datetime import datetime, timedelta\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "from scipy.interpolate import interp1d\n",
-    "from numba import jit\n",
-    "import windspharm\n",
-    "import metpy.calc as mpcalc\n",
-    "import metpy\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
-    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For reference, print package versions to screen:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xarrary:    0.16.0\n",
-      "numpy:      1.19.1\n",
-      "metpy:      1.0\n",
-      "matplotlib: 3.3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('xarrary:   ', xr.__version__)\n",
-    "print('numpy:     ', np.__version__)\n",
-    "print('metpy:     ', metpy.__version__)\n",
-    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary for loading simulations\n",
-    "simdict = {\n",
-    "         'LC1-channel-4000x9000km-2km-0002' : {'res':'2km', 'radiation':0, 'rh':0.8}, # No radiation\n",
-    "         'LC1-channel-4000x9000km-2km-0003' : {'res':'2km', 'radiation':1, 'rh':0.8}, # Only cloud radiation\n",
-    "         #'LC1-channel-4000x9000km-2km-0004' : {'res':'2km', 'radiation':1, 'rh':0.8}, # 2x Only cloud radiation\n",
-    "          }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2- Loading derived datasets from part 01"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0002\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0003\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Function to load simulations\n",
-    "def load_simulations():\n",
-    "    \n",
-    "    ds_list = []\n",
-    "    \n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        \n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"pvdiag_calc_1x1.nc\"\n",
-    "        ds_var = xr.open_dataset(fname)\n",
-    "        # deriving ddt_theta_totphy\n",
-    "        if sim == 'LC1-channel-4000x9000km-2km-0002':\n",
-    "            ds_var['ddt_theta_totphy'] = ds_var['ddt_theta_mphy'] + ds_var['ddt_theta_turb'] + ds_var['ddt_theta_pconv'] + ds_var['ddt_theta_diff'] + ds_var['ddt_theta_drag'] \n",
-    "            ds_var['ddth_theta_totphy'] = ds_var['ddth_theta_mphy'] + ds_var['ddth_theta_turb'] + ds_var['ddth_theta_pconv'] + ds_var['ddth_theta_diff'] + ds_var['ddth_theta_drag'] \n",
-    "        else:\n",
-    "            ds_var['ddt_theta_totphy'] = ds_var['ddt_theta_mphy'] + ds_var['ddt_theta_turb'] + ds_var['ddt_theta_pconv'] + ds_var['ddt_theta_diff'] + ds_var['ddt_theta_drag'] + ds_var['ddt_theta_radsw'] +ds_var['ddt_theta_radlw']\n",
-    "            ds_var['ddth_theta_totphy'] = ds_var['ddth_theta_mphy'] + ds_var['ddth_theta_turb'] + ds_var['ddth_theta_pconv'] + ds_var['ddth_theta_diff'] + ds_var['ddth_theta_drag'] + ds_var['ddth_theta_radsw'] +ds_var['ddth_theta_radlw']\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Excluding boundaries\n",
-    "ds_list[0] = ds_list[0].sel(lat=slice(10,80))\n",
-    "ds_list[1] = ds_list[1].sel(lat=slice(10,80))\n",
-    "\n",
-    "# common variables\n",
-    "lat  = ds_list[0].lat.values\n",
-    "lon  = ds_list[0].lon.values\n",
-    "time = ds_list[1].time\n",
-    "lev  = ds_list[0].lev\n",
-    "\n",
-    "# adding missing variables to the no_radiation datasets\n",
-    "ds_list[0]['ddt_theta_radlw']  = xr.zeros_like(ds_list[0]['ddt_theta_pconv']) \n",
-    "ds_list[0]['ddt_theta_radsw']  = xr.zeros_like(ds_list[0]['ddt_theta_pconv'])\n",
-    "ds_list[0]['ddth_theta_radlw'] = xr.zeros_like(ds_list[0]['ddt_theta_pconv'])\n",
-    "ds_list[0]['ddth_theta_radsw'] = xr.zeros_like(ds_list[0]['ddt_theta_pconv'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3- Helper functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# divergence\n",
-    "def div(u, v):\n",
-    "    divf = ddx(u) + ddy(v) \n",
-    "    divf[..., 0, :] = 0.\n",
-    "    divf[..., -1, :] = 0.\n",
-    "    return divf\n",
-    "\n",
-    "# curl, vertical component\n",
-    "def rot(u, v):\n",
-    "    divf = ddx(v) - ddy(u) \n",
-    "    divf[..., 0, :] = 0.\n",
-    "    divf[..., -1, :] = 0.\n",
-    "    return divf\n",
-    "\n",
-    "# Calculating gridlength for deriving horizontal derivities\n",
-    "r_e = 6371200.\n",
-    "dx = 2*np.pi*r_e / 360. * np.cos(np.deg2rad(45)) \n",
-    "dy = 2*np.pi*r_e / 360.\n",
-    "\n",
-    "# zonal derivitive\n",
-    "def ddx(f):\n",
-    "    return (np.roll(f, -1, -1) - np.roll(f, 1, -1)) / (2*dx)\n",
-    "\n",
-    "# meridional derivitive\n",
-    "def ddy(f):\n",
-    "    ddyf = (np.roll(f, -1, -2) - np.roll(f, 1, -2)) / (2*dy)\n",
-    "    ddyf[..., 0, :] = 0.\n",
-    "    ddyf[..., -1, :] = 0.\n",
-    "    return ddyf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4- PV error tendencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#list of variables needed\n",
-    "varlist = ['pv', 'ddth_pv','pvcd','ddth_pvcd', 'pres', 'u', 'v', 'urot', 'vrot', 'udiv', 'vdiv',\n",
-    "           'ddt_theta_gscp', 'ddt_theta_radsw', 'ddt_theta_radlw', 'ddt_theta_turb', 'ddt_theta_pconv', 'ddt_theta_drag', 'ddt_theta_totphy',\n",
-    "           'ddth_theta_gscp', 'ddth_theta_radsw', 'ddth_theta_radlw', 'ddth_theta_turb', 'ddth_theta_pconv', 'ddth_theta_drag', 'ddth_theta_totphy',\n",
-    "           'ddt_u_turb', 'ddt_v_turb', 'ddt_u_pconv', 'ddt_v_pconv', 'ddt_u_sso', 'ddt_v_sso', 'ddt_u_gwd', 'ddt_v_gwd',\n",
-    "           'mask_thint', 'mask_siglim','ddt_theta_mphy','ddth_theta_mphy','ddt_theta_diff','ddth_theta_diff']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check whether masking is required \n",
-    "\n",
-    "mask1 = np.logical_or(ds_list[0].mask_siglim,ds_list[1].mask_siglim)\n",
-    "if mask1.all==True:\n",
-    "    print('mask1 is required')\n",
-    "    \n",
-    "#mask2 = np.logical_or(ds_list[0].mask_thint,ds_list[1].mask_thint)\n",
-    "#if mask2.all==True:\n",
-    "#    print('mask2 is required')    \n",
-    "\n",
-    "#for t in range(len(time)):\n",
-    "#    for l in range(len(lev)):\n",
-    "#        for i in range(len(lat)):\n",
-    "#            for j in range(len(lon)):\n",
-    "#                if mask1[t,l,i,j]==True:\n",
-    "#                    print('mask1 is required at',t,l,i,j)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#based on equation 3.9 of Baumgart et.al 2019 (with out spatial integral and normalizing)\n",
-    "\n",
-    "#Parameterization categories for the diabatic term   \n",
-    "pcatlist_temp = {'mph': ['mphy'], 'conv': ['pconv'],'rad': ['radlw','radsw'], 'diff':['diff'],\n",
-    "                 'gsp':['gscp'], 'turb':['turb'], 'drag':['drag'],'phy': ['totphy']}\n",
-    "\n",
-    "pcatlist_wind = {'mom': ['turb','pconv','sso','gwd']}\n",
-    "\n",
-    "pvtend = {}\n",
-    "for pvar in ['P', 'rot', 'div','bnd']+list(pcatlist_temp.keys())+list(pcatlist_wind.keys()):\n",
-    "    pvtend[pvar] = np.zeros((len(time),len(lev),len(lat),len(lon)), dtype=np.float32)\n",
-    "    \n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    for l in range(len(lev)):\n",
-    "    \n",
-    "        #data1 = ds_list[1].isel(lev=l,time=t) # forecast (with CRH)\n",
-    "        #data2 = ds_list[0].isel(lev=l,time=t) # analysis (without CRH)\n",
-    "        \n",
-    "        data1 = ds_list[0].isel(lev=l,time=t) # forecast (without CRH)\n",
-    "        data2 = ds_list[1].isel(lev=l,time=t) # analysis (with CRH)\n",
-    "        \n",
-    "        #mask_siglim = np.logical_or(data1['mask_siglim'], data2['mask_siglim'])\n",
-    "\n",
-    "        ddtP = {}\n",
-    "\n",
-    "        dpv = data2['pv']-data1['pv']\n",
-    "        mpv = 0.5*(data2['pv']+data1['pv'])\n",
-    "\n",
-    "        ddtP['P'] = 0.5*dpv**2\n",
-    "\n",
-    "        ddtP['rot'] = -dpv *((data2['urot']-data1['urot'])*ddx(mpv) + (data2['vrot']-data1['vrot'])*ddy(mpv))\n",
-    "        ddtP['div'] = -dpv *((data2['udiv']-data1['udiv'])*ddx(mpv) + (data2['vdiv']-data1['vdiv'])*ddy(mpv))\\\n",
-    "                   +0.25*dpv**2*div(data2['udiv']+data1['udiv'], data2['vdiv']+data1['vdiv'])\n",
-    "        \n",
-    "        ddtP['bnd'] = -0.25*div(dpv**2*(data1['u']+data2['u']), dpv**2*(data1['v']+data2['v']))\n",
-    "\n",
-    "        for pcat in pcatlist_temp.keys():\n",
-    "                ddtP[pcat] = np.zeros(data1['pv'].shape)\n",
-    "                for par in pcatlist_temp[pcat]:\n",
-    "                    #temperatur tendency contributions\n",
-    "                    if f\"ddt_theta_{par}\" in varlist:\n",
-    "                        ddtP[pcat] += (-0.5 * (data2[f\"ddt_theta_{par}\"]-data1[f\"ddt_theta_{par}\"]) * (data2['ddth_pv']+data1['ddth_pv'])\\\n",
-    "                                    -0.5 * (data2[f\"ddt_theta_{par}\"]+data1[f\"ddt_theta_{par}\"]) * (data2['ddth_pv']-data1['ddth_pv'])\\\n",
-    "                                    +(data2[f\"ddth_theta_{par}\"]-data1[f\"ddth_theta_{par}\"]) * mpv\\\n",
-    "                                    +0.5 * (data2[f\"ddth_theta_{par}\"]+data1[f\"ddth_theta_{par}\"]) * dpv)*dpv\n",
-    "\n",
-    "        for pcat in pcatlist_wind.keys():\n",
-    "                ddtP[pcat] = np.zeros(data1['pv'].shape)\n",
-    "                for par in pcatlist_wind[pcat]:                \n",
-    "                    #Wind tendency contributions                   \n",
-    "                    if f\"ddt_u_{par}\" in varlist:\n",
-    "                        ddtP[pcat] += (0.5*(1/data2['sigma']+1/data1['sigma'])\\\n",
-    "                                    * rot(data2[f\"ddt_u_{par}\"]-data1[f\"ddt_u_{par}\"], data2[f\"ddt_v_{par}\"]-data1[f\"ddt_v_{par}\"])\\\n",
-    "                                +0.5*(1/data2['sigma']-1/data1['sigma'])\\\n",
-    "                                    * rot(data2[f\"ddt_u_{par}\"]+data1[f\"ddt_u_{par}\"], data2[f\"ddt_v_{par}\"]+data1[f\"ddt_v_{par}\"]))*dpv    \n",
-    "\n",
-    "                        \n",
-    "        for proc in ddtP.keys():\n",
-    "            pvtend[proc][t,l,:,:] = ddtP[proc]\n",
-    "            #pvtend[proc][t,l,:,:][mask_siglim==True] = 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5- Create a dataset and saving the results to a nc file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.Dataset(data_vars={\"dpe\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['P']),\n",
-    "                           \"ddtrot\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['rot']),\n",
-    "                           \"ddtdiv\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['div']),\n",
-    "                           \"ddtbnd\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['bnd']),\n",
-    "                           \"ddtrad\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['rad']),  \n",
-    "                           \"ddtgsp\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['gsp']),\n",
-    "                           \"ddtmph\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['mph']),\n",
-    "                           \"ddtcon\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['conv']),\n",
-    "                           \"ddtturb\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['turb']),\n",
-    "                           \"ddtdrag\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['drag']),\n",
-    "                           \"ddtdiff\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['diff']),\n",
-    "                           \"ddtphy\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['phy']),\n",
-    "                           \"ddtmom\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['mom']),\n",
-    "                                                  \n",
-    "                           },\n",
-    "                coords={\"lat\": ([\"lat\"], lat), \n",
-    "                        \"lon\": ([\"lon\"], lon),\n",
-    "                        \"time\":([\"time\"],ds_list[1].time),\n",
-    "                        \"lev\":([\"lev\"],ds_list[0].lev)})\n",
-    "\n",
-    "#-------------------------\n",
-    "# total diabatic\n",
-    "#ds['ddtdia'] = ds['ddtphy'] + ds['ddtmom']\n",
-    "ds['ddtdia'] = ds['ddtrad'] + ds['ddtmph'] + ds['ddtcon'] + ds['ddtturb'] + ds['ddtdrag'] + ds['ddtdiff'] + ds['ddtmom']  \n",
-    "ds['ddtrhs'] = ds['ddtdia'] + ds['ddtdiv'] + ds['ddtrot']\n",
-    "#-------------------------\n",
-    "ds.to_netcdf('/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/pverrorgrowth/pverror_diag_1x1_02_03.nc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pycrh",
-   "language": "python",
-   "name": "pycrh"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_restart_sim_part01-checkpoint.ipynb b/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_restart_sim_part01-checkpoint.ipynb
deleted file mode 100644
index ed18563ee3d4d0ce1e4d0eab0fb34a896b773b4a..0000000000000000000000000000000000000000
--- a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_restart_sim_part01-checkpoint.ipynb
+++ /dev/null
@@ -1,973 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnosing the growth of PV differences, Part 1\n",
-    "---\n",
-    "@ Behrooz Keshtgar, KIT 2022\n",
-    "\n",
-    "Adapted from the original code by Tobias Selz, LMU "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1- load python packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "from datetime import datetime, timedelta\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "from scipy.interpolate import interp1d\n",
-    "from numba import jit\n",
-    "import windspharm\n",
-    "import metpy.calc as mpcalc\n",
-    "import metpy\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
-    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For reference, print package versions to screen:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xarrary:    0.16.0\n",
-      "numpy:      1.19.1\n",
-      "metpy:      1.0\n",
-      "matplotlib: 3.3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('xarrary:   ', xr.__version__)\n",
-    "print('numpy:     ', np.__version__)\n",
-    "print('metpy:     ', metpy.__version__)\n",
-    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary for loading simulations\n",
-    "simdict = {\n",
-    "         #'LC1-channel-4000x9000km-2km-0005' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 3\n",
-    "         #'LC1-channel-4000x9000km-2km-0006' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 4\n",
-    "         #'LC1-channel-4000x9000km-2km-0007' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 5\n",
-    "         'LC1-channel-4000x9000km-2km-0008' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 6\n",
-    "          }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2- Loading related datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0008\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load 3d datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        # loading remapped datasets (1x1 r)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"icon-atm3d*.nc\"                                                                                  \n",
-    "        ds_var = xr.open_mfdataset(fname)[['u','v','temp','pres','pv']]\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_atm3d = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0008\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load temperature tendencies datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        # loading remapped datasets (1x1 r)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"icon-ddt_temp*.nc\"\n",
-    "        ds_var = xr.open_mfdataset(fname)\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_ddt_temp = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0008\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load wind tendencies datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        # loading remapped datasets (1x1 r)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "        fname = path+\"icon-ddt_wind*.nc\"\n",
-    "        ds_var = xr.open_mfdataset(fname)\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_ddt_wind = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3- Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Constants\n",
-    "\n",
-    "#Gravitation\n",
-    "g=9.80665\n",
-    "#Adiabat-coef (R_L/cp)\n",
-    "kappa=287.04/1004.64\n",
-    "#Reference pressure (Pa)\n",
-    "p00=1e5\n",
-    "#Gas constant\n",
-    "Rd=287.\n",
-    "#lapse rate\n",
-    "alpha=0.0065*Rd/g\n",
-    "#Level list to interpolate (K)\n",
-    "thlevs = np.arange(310,352,2) \n",
-    "\n",
-    "# functions for deriving the PV diagnostics for PV error tendency\n",
-    "#----------------------------------------------------------------\n",
-    "# Potential temperature\n",
-    "def calc_theta(temp, pres):\n",
-    "    return temp * (pres/p00)**(-kappa)\n",
-    "\n",
-    "def calc_ddttheta(temp, pres):\n",
-    "    return (temp * (pres/p00)**(-kappa)) / 1.4 # Cp/Cv factor related to temp tendencies in ICON\n",
-    "\n",
-    "# vertical derivitive on model levels\n",
-    "def ddtheta(field_ml, theta_ml):\n",
-    "    ddth_field_ml = np.zeros(theta_ml.shape) * np.nan\n",
-    "    ddth_field_ml[1:-1,...] = (field_ml[2:,...] - field_ml[:-2,...]) / (theta_ml[2:,...] - theta_ml[:-2,...]) \n",
-    "    ddth_field_ml[0,...] = (field_ml[1,...] - field_ml[0,...]) / (theta_ml[1,...] - theta_ml[0,...])\n",
-    "    ddth_field_ml[-1,...] = (field_ml[-1,...] - field_ml[-2,...]) / (theta_ml[-1,...] - theta_ml[-2,...])\n",
-    "    return ddth_field_ml\n",
-    "\n",
-    "# Fill nan values\n",
-    "def fill_nans_hint(field):\n",
-    "    \"\"\"Fills the nans in the 2-d lat/lon-field with linear, horizontal interpolation.\"\"\"\n",
-    "  \n",
-    "    #Get dimensions\n",
-    "    nlat, nlon = np.shape(field)\n",
-    "    #mirror the field in east-west-direction\n",
-    "    nmir = 25\n",
-    "  \n",
-    "    fieldc = np.zeros((nlat, nlon+2*nmir), np.float32) * np.nan\n",
-    "        \n",
-    "    #copy field\n",
-    "    fieldc[:, nmir:-nmir] = field\n",
-    "    fieldc[:, :nmir] = field[:,-nmir:]\n",
-    "    fieldc[:, -nmir:] =field [:,:nmir]\n",
-    "    for ilat in range(nlat):\n",
-    "        for ilon in range(nlon):\n",
-    "            #Interpolation required?\n",
-    "            if not np.isnan(field[ilat,ilon]):\n",
-    "                continue\n",
-    "            #search for valid point in x direction on both sides\n",
-    "            jlon = nmir+ilon-1\n",
-    "            while np.isnan(fieldc[ilat, jlon]):\n",
-    "                jlon-=1\n",
-    "            westval = fieldc[ilat, jlon]\n",
-    "            iwestlon = jlon\n",
-    "            jlon = nmir+ilon+1\n",
-    "            while np.isnan(fieldc[ilat, jlon]):\n",
-    "                jlon += 1\n",
-    "            eastval = fieldc[ilat, jlon]\n",
-    "            ieastlon = jlon\n",
-    "            #Set new interpolated value\n",
-    "            we_val = (eastval-westval)/(ieastlon-iwestlon)*(nmir+ilon-iwestlon)+westval\n",
-    "      \n",
-    "            #search for valid point in y direction on both sides\n",
-    "            jlat = ilat-1\n",
-    "            while np.isnan(fieldc[jlat, nmir+ilon]):\n",
-    "                jlat -= 1\n",
-    "            southval = fieldc[jlat, nmir+ilon]\n",
-    "            isouthlat = jlat\n",
-    "            jlat = ilat+1\n",
-    "            while np.isnan(fieldc[jlat, nmir+ilon]):\n",
-    "                jlat += 1\n",
-    "            northval = fieldc[jlat,nmir+ilon]\n",
-    "            inorthlat = jlat\n",
-    "            ns_val = (northval-southval)/(inorthlat-isouthlat)*(ilat-isouthlat)+southval\n",
-    "            \n",
-    "            #Set field to the mean of both values\n",
-    "            field[ilat, ilon] = (we_val+ns_val) / 2\n",
-    "\n",
-    "# Isentropic interpolation\n",
-    "def interpol_on_th_fast(field_ml, theta_ml, thlevs, fillnans=False):\n",
-    "    if theta_ml.ndim!=3:\n",
-    "        raise Exception('theta_ml has to be 3 dimensional')\n",
-    "    nmlev, nlat, nlon = theta_ml.shape\n",
-    "    ndimfield = field_ml.ndim\n",
-    "    if ndimfield==3:\n",
-    "        nvar = 1\n",
-    "    elif ndimfield==4:\n",
-    "        nvar = field_ml.shape[0]\n",
-    "    else:\n",
-    "        raise Exception('field_ml has to be 3 or 4 dimensional, ie. lev, lat, lon.')      \n",
-    "    field_thl = np.zeros((nvar, len(thlevs), nlat, nlon), dtype=np.float64)\n",
-    "    for ithlev, thlev in enumerate(thlevs):\n",
-    "        for ilat in range(nlat):\n",
-    "            for ilon in range(nlon):\n",
-    "                nint = 0\n",
-    "                for imlev in range(nmlev-1):\n",
-    "                    if (theta_ml[imlev, ilat, ilon]>=thlev and theta_ml[imlev+1, ilat, ilon]<thlev)\\\n",
-    "                                or (theta_ml[imlev, ilat, ilon]<=thlev and theta_ml[imlev+1, ilat, ilon]>thlev):\n",
-    "                        a = (thlev-theta_ml[imlev+1, ilat, ilon]) / (theta_ml[imlev, ilat, ilon]-theta_ml[imlev+1, ilat, ilon])\n",
-    "                        field_thl[..., ithlev, ilat, ilon] = (1-a)*field_ml[..., imlev+1, ilat, ilon] + a*field_ml[..., imlev, ilat, ilon]\n",
-    "                        nint += 1\n",
-    "                if nint!=1:\n",
-    "                    field_thl[..., ithlev, ilat, ilon] = np.nan\n",
-    "    mask = np.where(np.isnan(field_thl), True, False)[0]\n",
-    "    if fillnans:\n",
-    "        for ivar in range(nvar):\n",
-    "            for ithlev, thlev in enumerate(thlevs):\n",
-    "                fill_nans_hint(field_thl[ivar, ithlev, :, :])\n",
-    "            \n",
-    "    if ndimfield==3:\n",
-    "        field_thl = field_thl[0]\n",
-    "    return field_thl, mask\n",
-    "\n",
-    "def calc_pv_cd(u, v, sigma):\n",
-    "    r_e = 6371200. # earth radius \n",
-    "    #f = 2*2*np.pi/86164.1 * np.sin(np.deg2rad(45.0))                         # constant Coriolis\n",
-    "    f = 0.0001031260914097046                                                 # Coriolis at 45° N\n",
-    "    #Calculate gridlength\n",
-    "    dx = 2*np.pi*r_e / 360. * np.cos(np.deg2rad(45)) # change the denominator according to the grid size\n",
-    "    dy = 2*np.pi*r_e / 360.                                                       \n",
-    "\n",
-    "    def ddx(f):\n",
-    "        return (np.roll(f, -1, -1) - np.roll(f, 1, -1)) / (2*dx)\n",
-    "\n",
-    "    def ddy(f):\n",
-    "        ddyf = (np.roll(f, -1, -2) - np.roll(f, 1, -2)) / (2*dy)\n",
-    "        ddyf[..., 0, :] = 0.\n",
-    "        ddyf[..., -1, :] = 0.\n",
-    "        return ddyf\n",
-    "\n",
-    "    def rot(u, v):\n",
-    "        divf = ddx(v) - ddy(u)\n",
-    "        divf[..., 0, :] = 0.\n",
-    "        divf[..., -1, :] = 0.\n",
-    "        return divf\n",
-    "\n",
-    "    return 1/sigma * (rot(u,v) + f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4- Changing to numpy arrays"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Common variables\n",
-    "lat  = ds_list_atm3d[0].lat.values\n",
-    "lon  = ds_list_atm3d[0].lon.values\n",
-    "time = ds_list_atm3d[0].time\n",
-    "\n",
-    "# pressure, temp, wind feilds at model levels\n",
-    "pres = ds_list_atm3d[0].pres.values\n",
-    "temp = ds_list_atm3d[0].temp.values\n",
-    "u    = ds_list_atm3d[0].u.values\n",
-    "v    = ds_list_atm3d[0].v.values\n",
-    "pv   = ds_list_atm3d[0].pv.values\n",
-    "\n",
-    "# temp tendencies\n",
-    "ddt_temp_totphy = ds_list_ddt_temp[0].ddt_temp_totnwpphy.values\n",
-    "\n",
-    "# wind tendencies\n",
-    "ddt_u_gwd   = ds_list_ddt_wind[0].ddt_u_gwd.values\n",
-    "ddt_v_gwd   = ds_list_ddt_wind[0].ddt_v_gwd.values\n",
-    "ddt_u_pconv = ds_list_ddt_wind[0].ddt_u_pconv.values\n",
-    "ddt_v_pconv = ds_list_ddt_wind[0].ddt_v_pconv.values\n",
-    "ddt_u_sso   = ds_list_ddt_wind[0].ddt_u_sso.values\n",
-    "ddt_v_sso   = ds_list_ddt_wind[0].ddt_v_sso.values\n",
-    "ddt_u_turb  = ds_list_ddt_wind[0].ddt_u_turb.values\n",
-    "ddt_v_turb  = ds_list_ddt_wind[0].ddt_v_turb.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5- Deriving potential temperature tendencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temperature\n",
-    "theta = calc_theta(temp,pres)\n",
-    "\n",
-    "# physical temperature tendencies\n",
-    "ddt_theta_totphy = calc_ddttheta(ddt_temp_totphy,pres)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6- Static stability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we also need 'sigma' static stability to create a mask to avoid exceeding values\n",
-    "sigma = np.zeros(theta.shape) * np.nan\n",
-    "for t in range(len(time)):\n",
-    "    sigma[t,:,:,:] = -ddtheta(pres[t,:,:,:], theta[t,:,:,:])/g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7- Potential vorticity based on remapped datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# deriving PV based using central differences\n",
-    "hei = ds_list_atm3d[0].height\n",
-    "pvcd = np.zeros(pv.shape) * np.nan\n",
-    "for t in range(len(time)):\n",
-    "    for h in range(len(hei)):\n",
-    "        pvcd[t,h,:,:] = calc_pv_cd(u[t,h,:,:],v[t,h,:,:],sigma[t,h,:,:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 8- Vertical potential temperature gradient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Calculate theta gradient of pv and thetas\n",
-    "\n",
-    "ddth_pv = np.zeros(theta.shape) * np.nan\n",
-    "ddth_pvcd = np.zeros(theta.shape) * np.nan\n",
-    "\n",
-    "ddth_theta_totphy = np.zeros(theta.shape) * np.nan\n",
-    "\n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    ddth_pv[t,:,:,:] = ddtheta(pv[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_pvcd[t,:,:,:] = ddtheta(pvcd[t,:,:,:], theta[t,:,:,:])\n",
-    "    ddth_theta_totphy[t,:,:,:] = ddtheta(ddt_theta_totphy[t,:,:,:], theta[t,:,:,:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Save all fields in a list and their labels\n",
-    "fieldlist = []\n",
-    "labellist = []\n",
-    "\n",
-    "for var in [u,v,pv,pvcd,sigma,ddth_pv,ddth_pvcd,\n",
-    "            ddth_theta_totphy,ddt_theta_totphy,\n",
-    "            ddt_u_gwd,ddt_v_gwd,ddt_u_pconv,ddt_v_pconv,ddt_u_sso,ddt_v_sso,\n",
-    "            ddt_u_turb,ddt_v_turb]:\n",
-    "    \n",
-    "    fieldlist.append(var)\n",
-    "    \n",
-    "for name in ['u','v','pv','pvcd','sigma','ddth_pv','ddth_pvcd',\n",
-    "             'ddth_theta_totphy','ddt_theta_totphy',\n",
-    "             'ddt_u_gwd','ddt_v_gwd','ddt_u_pconv','ddt_v_pconv','ddt_u_sso','ddt_v_sso',\n",
-    "             'ddt_u_turb','ddt_v_turb']:\n",
-    "    \n",
-    "    labellist.append(name)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9- Isentropic interpolation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\n# Interpolation\\nfieldlist_th = np.zeros((len(fieldlist), len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\n\\nmask_thint = np.zeros((len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\nnvar = len(fieldlist)\\n\\nfor f in range(nvar):\\n        print('working on field:',f)\\n        for t in range(len(time)):\\n            #print('Working on time step:',t)\\n            fieldlist_th[f,t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[f,t,:,:,:], theta[t], thlevs, fillnans=True)\\n\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''\n",
-    "# Interpolation\n",
-    "fieldlist_th = np.zeros((len(fieldlist), len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "mask_thint = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "nvar = len(fieldlist)\n",
-    "\n",
-    "for f in range(nvar):\n",
-    "        print('working on field:',f)\n",
-    "        for t in range(len(time)):\n",
-    "            #print('Working on time step:',t)\n",
-    "            fieldlist_th[f,t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[f,t,:,:,:], theta[t], thlevs, fillnans=True)\n",
-    "'''    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "working on field: 0\n",
-      "working on field: 1\n",
-      "working on field: 2\n",
-      "working on field: 3\n",
-      "working on field: 4\n",
-      "working on field: 5\n",
-      "working on field: 6\n",
-      "working on field: 7\n",
-      "working on field: 8\n",
-      "working on field: 9\n",
-      "working on field: 10\n",
-      "working on field: 11\n",
-      "working on field: 12\n",
-      "working on field: 13\n",
-      "working on field: 14\n",
-      "working on field: 15\n",
-      "working on field: 16\n"
-     ]
-    }
-   ],
-   "source": [
-    "# test another way of interpolation (faster)\n",
-    "\n",
-    "def isentropic(field_ml,theta_ml,thlevs):\n",
-    "    \n",
-    "    nvar = len(field_ml)\n",
-    "    \n",
-    "    field_thl = np.zeros((nvar, len(time), len(thlevs), len(lat), len(lon)), dtype=np.float64)\n",
-    "    \n",
-    "    for f in range(nvar):\n",
-    "        print('working on field:',f)\n",
-    "    \n",
-    "        for t in range(len(time)):\n",
-    "\n",
-    "            for i in range(len(thlevs)):\n",
-    "\n",
-    "                field_thl[f,t,i,:,:] = metpy.interpolate.interpolate_to_isosurface(theta_ml[t,:,:,:]\n",
-    "                                                                           ,field_ml[f][t,:,:,:],thlevs[i],True)\n",
-    "\n",
-    "    return(field_thl)\n",
-    "\n",
-    "#------------------------------------------------------------\n",
-    "fieldlist_th = isentropic(fieldlist, theta, thlevs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nmask_thint = np.zeros((len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\n\\ntemp = np.zeros((len(time), len(thlevs),len(lat),\\n                         len(lon)), dtype=np.float64)* np.nan\\nfor t in range(len(time)):\\n        print('Working on time step:',t)\\n        temp[t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[-1,t,:,:,:], theta[t], thlevs, fillnans=True)\\n\""
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''\n",
-    "mask_thint = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "temp = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "for t in range(len(time)):\n",
-    "        print('Working on time step:',t)\n",
-    "        temp[t,:,:,:], mask_thint[t,:,:,:] = interpol_on_th_fast(np.array(fieldlist)[-1,t,:,:,:], theta[t], thlevs, fillnans=True)\n",
-    "'''        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on time step: 0\n",
-      "Working on time step: 1\n",
-      "Working on time step: 2\n",
-      "Working on time step: 3\n",
-      "Working on time step: 4\n",
-      "Working on time step: 5\n",
-      "Working on time step: 6\n",
-      "Working on time step: 7\n",
-      "Working on time step: 8\n",
-      "Working on time step: 9\n",
-      "Working on time step: 10\n",
-      "Working on time step: 11\n",
-      "Working on time step: 12\n",
-      "Working on time step: 13\n",
-      "Working on time step: 14\n",
-      "Working on time step: 15\n",
-      "Working on time step: 16\n",
-      "Working on time step: 17\n",
-      "Working on time step: 18\n",
-      "Working on time step: 19\n",
-      "Working on time step: 20\n",
-      "Working on time step: 21\n",
-      "Working on time step: 22\n",
-      "Working on time step: 23\n",
-      "Working on time step: 24\n",
-      "Working on time step: 25\n",
-      "Working on time step: 26\n",
-      "Working on time step: 27\n",
-      "Working on time step: 28\n",
-      "Working on time step: 29\n",
-      "Working on time step: 30\n",
-      "Working on time step: 31\n",
-      "Working on time step: 32\n",
-      "Working on time step: 33\n",
-      "Working on time step: 34\n",
-      "Working on time step: 35\n",
-      "Working on time step: 36\n",
-      "Working on time step: 37\n",
-      "Working on time step: 38\n",
-      "Working on time step: 39\n",
-      "Working on time step: 40\n",
-      "Working on time step: 41\n",
-      "Working on time step: 42\n",
-      "Working on time step: 43\n",
-      "Working on time step: 44\n",
-      "Working on time step: 45\n",
-      "Working on time step: 46\n",
-      "Working on time step: 47\n",
-      "Working on time step: 48\n",
-      "Working on time step: 49\n",
-      "Working on time step: 50\n",
-      "Working on time step: 51\n",
-      "Working on time step: 52\n",
-      "Working on time step: 53\n",
-      "Working on time step: 54\n",
-      "Working on time step: 55\n",
-      "Working on time step: 56\n",
-      "Working on time step: 57\n",
-      "Working on time step: 58\n",
-      "Working on time step: 59\n",
-      "Working on time step: 60\n",
-      "Working on time step: 61\n",
-      "Working on time step: 62\n",
-      "Working on time step: 63\n",
-      "Working on time step: 64\n",
-      "Working on time step: 65\n",
-      "Working on time step: 66\n",
-      "Working on time step: 67\n",
-      "Working on time step: 68\n",
-      "Working on time step: 69\n",
-      "Working on time step: 70\n",
-      "Working on time step: 71\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Generate a mask where sigma>siglim\n",
-    "siglim = 1e4\n",
-    "\n",
-    "mask_siglim = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    print('Working on time step:',t)\n",
-    "    \n",
-    "    for i in range(len(thlevs)):\n",
-    "\n",
-    "        #mask_siglim[t,:,:,:], _ = interpol_on_th_fast(np.where(fieldlist[labellist.index('sigma')][t]>siglim, np.nan, 1.), theta[t], thlevs, fillnans=True)\n",
-    "        mask_siglim[t,i,:,:] = metpy.interpolate.interpolate_to_isosurface(theta[t,:,:,:]\n",
-    "                                                                           ,np.where(fieldlist[labellist.index('sigma')][t]>siglim, np.nan, 1.),thlevs[i],True)\n",
-    "        mask_siglim[t,i,:,:] = np.isnan(mask_siglim[t,i,:,:])  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10- Helmholts decompostion of wind field"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Helmholtz-decomposition using windspharm\n",
-    "# NOTE: the latitude direction has to be reordered from N to S, also the time has to be the last dimension\n",
-    "reorder = lambda f: np.moveaxis(f[:, ::-1, :], 0, -1)\n",
-    "reorder_back = lambda f: np.moveaxis(f, -1, 0)[:, ::-1, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on time step: 0\n",
-      "Working on time step: 1\n",
-      "Working on time step: 2\n",
-      "Working on time step: 3\n",
-      "Working on time step: 4\n",
-      "Working on time step: 5\n",
-      "Working on time step: 6\n",
-      "Working on time step: 7\n",
-      "Working on time step: 8\n",
-      "Working on time step: 9\n",
-      "Working on time step: 10\n",
-      "Working on time step: 11\n",
-      "Working on time step: 12\n",
-      "Working on time step: 13\n",
-      "Working on time step: 14\n",
-      "Working on time step: 15\n",
-      "Working on time step: 16\n",
-      "Working on time step: 17\n",
-      "Working on time step: 18\n",
-      "Working on time step: 19\n",
-      "Working on time step: 20\n",
-      "Working on time step: 21\n",
-      "Working on time step: 22\n",
-      "Working on time step: 23\n",
-      "Working on time step: 24\n",
-      "Working on time step: 25\n",
-      "Working on time step: 26\n",
-      "Working on time step: 27\n",
-      "Working on time step: 28\n",
-      "Working on time step: 29\n",
-      "Working on time step: 30\n",
-      "Working on time step: 31\n",
-      "Working on time step: 32\n",
-      "Working on time step: 33\n",
-      "Working on time step: 34\n",
-      "Working on time step: 35\n",
-      "Working on time step: 36\n",
-      "Working on time step: 37\n",
-      "Working on time step: 38\n",
-      "Working on time step: 39\n",
-      "Working on time step: 40\n",
-      "Working on time step: 41\n",
-      "Working on time step: 42\n",
-      "Working on time step: 43\n",
-      "Working on time step: 44\n",
-      "Working on time step: 45\n",
-      "Working on time step: 46\n",
-      "Working on time step: 47\n",
-      "Working on time step: 48\n",
-      "Working on time step: 49\n",
-      "Working on time step: 50\n",
-      "Working on time step: 51\n",
-      "Working on time step: 52\n",
-      "Working on time step: 53\n",
-      "Working on time step: 54\n",
-      "Working on time step: 55\n",
-      "Working on time step: 56\n",
-      "Working on time step: 57\n",
-      "Working on time step: 58\n",
-      "Working on time step: 59\n",
-      "Working on time step: 60\n",
-      "Working on time step: 61\n",
-      "Working on time step: 62\n",
-      "Working on time step: 63\n",
-      "Working on time step: 64\n",
-      "Working on time step: 65\n",
-      "Working on time step: 66\n",
-      "Working on time step: 67\n",
-      "Working on time step: 68\n",
-      "Working on time step: 69\n",
-      "Working on time step: 70\n",
-      "Working on time step: 71\n"
-     ]
-    }
-   ],
-   "source": [
-    "udiv_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "vdiv_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "urot_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "vrot_th_t = np.zeros((len(time), len(thlevs),len(lat),\n",
-    "                         len(lon)), dtype=np.float64)* np.nan\n",
-    "\n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    print('Working on time step:',t)\n",
-    "    uvwin = reorder(fieldlist_th[labellist.index('u')][t,:,:,:])\n",
-    "    vvwin = reorder(fieldlist_th[labellist.index('v')][t,:,:,:])\n",
-    "    \n",
-    "    vwobj = windspharm.standard.VectorWind(uvwin, vvwin, gridtype='regular')\n",
-    "    udiv_th, vdiv_th, urot_th, vrot_th = vwobj.helmholtz()\n",
-    "    udiv_th, vdiv_th, urot_th, vrot_th = map(reorder_back, (udiv_th, vdiv_th, urot_th, vrot_th))\n",
-    "    \n",
-    "    udiv_th_t[t,:,:,:] = udiv_th\n",
-    "    \n",
-    "    vdiv_th_t[t,:,:,:] = vdiv_th\n",
-    "    \n",
-    "    urot_th_t[t,:,:,:] = urot_th\n",
-    "    \n",
-    "    vrot_th_t[t,:,:,:] = vrot_th\n",
-    "    \n",
-    "    del uvwin,vvwin,vwobj,udiv_th,vdiv_th,urot_th,vrot_th"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nfor t in [50,100]:\\n    for ilev in range(len(thlevs)):\\n        u = fieldlist_th[labellist.index('u')][t,ilev]\\n        v = fieldlist_th[labellist.index('v')][t,ilev]\\n        err_rot = np.sqrt(((u-udiv_th_t[t,ilev]-urot_th_t[t,ilev])**2).mean()) / np.sqrt(((u**2).mean()))\\n        err_div = np.sqrt(((v-vdiv_th_t[t,ilev]-vrot_th_t[t,ilev])**2).mean()) / np.sqrt(((v**2).mean()))\\n        if err_rot>0.1 or err_div>0.1:\\n            print (t)\\n            print(ilev)\\n            raise Exception('Helmholtz-decomposition error too large.')  \\n            \\n\""
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Accuracy check of the Helmholtz-decomp\n",
-    "'''\n",
-    "for t in [50,100]:\n",
-    "    for ilev in range(len(thlevs)):\n",
-    "        u = fieldlist_th[labellist.index('u')][t,ilev]\n",
-    "        v = fieldlist_th[labellist.index('v')][t,ilev]\n",
-    "        err_rot = np.sqrt(((u-udiv_th_t[t,ilev]-urot_th_t[t,ilev])**2).mean()) / np.sqrt(((u**2).mean()))\n",
-    "        err_div = np.sqrt(((v-vdiv_th_t[t,ilev]-vrot_th_t[t,ilev])**2).mean()) / np.sqrt(((v**2).mean()))\n",
-    "        if err_rot>0.1 or err_div>0.1:\n",
-    "            print (t)\n",
-    "            print(ilev)\n",
-    "            raise Exception('Helmholtz-decomposition error too large.')  \n",
-    "            \n",
-    "'''            "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 11- Creating a dataset and save to nc file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# saving all the results as a dataset\n",
-    "ds = xr.Dataset(data_vars={\"u\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('u')]), \n",
-    "                           \"v\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('v')]),\n",
-    "                           \"sigma\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('sigma')]),\n",
-    "                           #\"sigmainv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('sigmainv')]),\n",
-    "                           \"pv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('pv')]),\n",
-    "                           \"pvcd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('pvcd')]),\n",
-    "                           \"ddth_pv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_pv')]),\n",
-    "                           \"ddth_pvcd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_pvcd')]),\n",
-    "                           \"ddth_theta_totphy\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddth_theta_totphy')]),\n",
-    "                           \"ddt_theta_totphy\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_theta_totphy')]),\n",
-    "                           \"ddt_u_gwd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_gwd')]),\n",
-    "                           \"ddt_v_gwd\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_gwd')]),\n",
-    "                           \"ddt_u_sso\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_sso')]),\n",
-    "                           \"ddt_v_sso\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_sso')]),\n",
-    "                           \"ddt_u_pconv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_pconv')]),\n",
-    "                           \"ddt_v_pconv\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_pconv')]),\n",
-    "                           \"ddt_u_turb\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_u_turb')]),\n",
-    "                           \"ddt_v_turb\":([\"time\",\"lev\",\"lat\",'lon'],fieldlist_th[labellist.index('ddt_v_turb')]),\n",
-    "                           #\"mask_thint\":([\"time\",\"lev\",\"lat\",'lon'],mask_thint),\n",
-    "                           \"mask_siglim\":([\"time\",\"lev\",\"lat\",'lon'],mask_siglim),\n",
-    "                           \"ipvcd\":([\"time\",\"lev\",\"lat\",'lon'],ipvcd),\n",
-    "                           \"udiv\":([\"time\",\"lev\",\"lat\",'lon'],udiv_th_t),\n",
-    "                           \"vdiv\":([\"time\",\"lev\",\"lat\",'lon'],vdiv_th_t),\n",
-    "                           \"urot\":([\"time\",\"lev\",\"lat\",'lon'],urot_th_t),\n",
-    "                           \"vrot\":([\"time\",\"lev\",\"lat\",'lon'],vrot_th_t),\n",
-    "                           \n",
-    "                           },\n",
-    "                coords={\"lat\": ([\"lat\"], lat), \n",
-    "                        \"lon\": ([\"lon\"], lon),\n",
-    "                        \"time\":([\"time\"],time),\n",
-    "                        'lev':([\"lev\"],thlevs)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds.to_netcdf('/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/LC1-channel-4000x9000km-2km-0008_remapped_1x1/pvdiag_calc_1x1.nc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pycrh",
-   "language": "python",
-   "name": "pycrh"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_restart_sim_part02-checkpoint.ipynb b/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_restart_sim_part02-checkpoint.ipynb
deleted file mode 100644
index 2419e67d12f8cf979ecd7f0897b2cb7cb6d88fe0..0000000000000000000000000000000000000000
--- a/analysis_plots/processing_scripts/.ipynb_checkpoints/PV_diff_growth_restart_sim_part02-checkpoint.ipynb
+++ /dev/null
@@ -1,391 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnosing the growth of PV differences, Part 2\n",
-    "---\n",
-    "@ Behrooz Keshtgar, KIT 2022\n",
-    "\n",
-    "Adapted from the original code by Tobias Selz, LMU "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1- load python packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# loading libraries\n",
-    "\n",
-    "import sys\n",
-    "from datetime import datetime, timedelta\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "from scipy.interpolate import interp1d\n",
-    "from numba import jit\n",
-    "import windspharm\n",
-    "import metpy.calc as mpcalc\n",
-    "import metpy\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
-    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For reference, print package versions to screen:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xarrary:    0.16.0\n",
-      "numpy:      1.19.1\n",
-      "metpy:      1.0\n",
-      "matplotlib: 3.3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('xarrary:   ', xr.__version__)\n",
-    "print('numpy:     ', np.__version__)\n",
-    "print('metpy:     ', metpy.__version__)\n",
-    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary for loading simulations\n",
-    "simdict = {\n",
-    "         'LC1-channel-4000x9000km-2km-0002' : {'res':'2km', 'radiation':0, 'rh':0.8}, # No radiation\n",
-    "         'LC1-channel-4000x9000km-2km-0003' : {'res':'2km', 'radiation':0, 'rh':0.8}, # Cloud radiation\n",
-    "         'LC1-channel-4000x9000km-2km-0005' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 3\n",
-    "         'LC1-channel-4000x9000km-2km-0006' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 4\n",
-    "         'LC1-channel-4000x9000km-2km-0007' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 5\n",
-    "         'LC1-channel-4000x9000km-2km-0008' : {'res':'2km', 'radiation':1, 'rh':0.8}, # CRH disabled at day 6\n",
-    "          }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2- Loading derived datasets from part 01"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0002\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0003\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0005\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0006\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0007\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0008\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Function to load simulations\n",
-    "def load_simulations():\n",
-    "    \n",
-    "    ds_list = []\n",
-    "    \n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_1x1/'\n",
-    "            \n",
-    "        fname = path+\"pvdiag_calc_1x1.nc\"           \n",
-    "        ds_var = xr.open_dataset(fname).sel(lat=slice(10,80))\n",
-    "        # deriving ddt_theta_totphy\n",
-    "        if sim == 'LC1-channel-4000x9000km-2km-0002':\n",
-    "            ds_var['ddt_theta_totphy'] = ds_var['ddt_theta_mphy'] + ds_var['ddt_theta_turb'] + ds_var['ddt_theta_pconv'] + ds_var['ddt_theta_diff'] + ds_var['ddt_theta_drag'] \n",
-    "            ds_var['ddth_theta_totphy'] = ds_var['ddth_theta_mphy'] + ds_var['ddth_theta_turb'] + ds_var['ddth_theta_pconv'] + ds_var['ddth_theta_diff'] + ds_var['ddth_theta_drag'] \n",
-    "        if sim == 'LC1-channel-4000x9000km-2km-0003':\n",
-    "            ds_var['ddt_theta_totphy'] = ds_var['ddt_theta_mphy'] + ds_var['ddt_theta_turb'] + ds_var['ddt_theta_pconv'] + ds_var['ddt_theta_diff'] + ds_var['ddt_theta_drag'] + ds_var['ddt_theta_radsw'] +ds_var['ddt_theta_radlw']\n",
-    "            ds_var['ddth_theta_totphy'] = ds_var['ddth_theta_mphy'] + ds_var['ddth_theta_turb'] + ds_var['ddth_theta_pconv'] + ds_var['ddth_theta_diff'] + ds_var['ddth_theta_drag'] + ds_var['ddth_theta_radsw'] +ds_var['ddth_theta_radlw']\n",
-    "        \n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list = load_simulations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# common variables\n",
-    "lat  = ds_list[0].lat.values\n",
-    "lon  = ds_list[0].lon.values\n",
-    "lev  = ds_list[0].lev"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# adjusting time steps / filling the initial time steps for restart simulations with datasets from 0003 simulation\n",
-    "\n",
-    "temp_1 = ds_list[1].sel(time=slice(20210101,20210104)) # sim 0007 day3\n",
-    "temp_2 = ds_list[1].sel(time=slice(20210101,20210105)) # sim 0004 day4\n",
-    "temp_3 = ds_list[1].sel(time=slice(20210101,20210106)) # sim 0010 day5\n",
-    "temp_4 = ds_list[1].sel(time=slice(20210101,20210107)) # sim 0008 day6\n",
-    "\n",
-    "\n",
-    "ds_list[2] = xr.merge([temp_1,ds_list[2]])\n",
-    "ds_list[3] = xr.merge([temp_2,ds_list[3]])\n",
-    "ds_list[4] = xr.merge([temp_3,ds_list[4]])\n",
-    "ds_list[5] = xr.merge([temp_4,ds_list[5]])\n",
-    "# new time steps\n",
-    "time = ds_list[2].time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3- Helper functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# divergence\n",
-    "def div(u, v):\n",
-    "    divf = ddx(u) + ddy(v) \n",
-    "    divf[..., 0, :] = 0.\n",
-    "    divf[..., -1, :] = 0.\n",
-    "    return divf\n",
-    "\n",
-    "# curl, vertical component\n",
-    "def rot(u, v):\n",
-    "    divf = ddx(v) - ddy(u) \n",
-    "    divf[..., 0, :] = 0.\n",
-    "    divf[..., -1, :] = 0.\n",
-    "    return divf\n",
-    "\n",
-    "# Calculating gridlength for deriving horizontal derivities\n",
-    "r_e = 6371200.\n",
-    "dx = 2*np.pi*r_e / 360. * np.cos(np.deg2rad(45)) \n",
-    "dy = 2*np.pi*r_e / 360.\n",
-    "\n",
-    "# zonal derivitive\n",
-    "def ddx(f):\n",
-    "    return (np.roll(f, -1, -1) - np.roll(f, 1, -1)) / (2*dx)\n",
-    "\n",
-    "# meridional derivitive\n",
-    "def ddy(f):\n",
-    "    ddyf = (np.roll(f, -1, -2) - np.roll(f, 1, -2)) / (2*dy)\n",
-    "    ddyf[..., 0, :] = 0.\n",
-    "    ddyf[..., -1, :] = 0.\n",
-    "    return ddyf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4- PV error tendencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#list of variables needed\n",
-    "varlist = ['pv', 'ddth_pv','pvcd','ddth_pvcd', 'pres', 'u', 'v', 'urot', 'vrot', 'udiv', 'vdiv',\n",
-    "           'ddth_theta_totphy','ddt_theta_totphy',\n",
-    "           'ddt_u_turb', 'ddt_v_turb', 'ddt_u_pconv', 'ddt_v_pconv', 'ddt_u_sso', 'ddt_v_sso', 'ddt_u_gwd', 'ddt_v_gwd',\n",
-    "           'mask_thint', 'mask_siglim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check whether masking is required \n",
-    "mask1 = np.logical_or(ds_list[0].mask_siglim,ds_list[5].mask_siglim)\n",
-    "if mask1.all==True:\n",
-    "    print('mask1 is required')\n",
-    "    \n",
-    "#mask2 = np.logical_or(ds_list[0].mask_thint,ds_list[1].mask_thint)\n",
-    "#if mask2.all==True:\n",
-    "#    print('mask2 is required')    \n",
-    "\n",
-    "#for t in range(len(time)):\n",
-    "#    for l in range(len(lev)):\n",
-    "#        for i in range(len(lat)):\n",
-    "#            for j in range(len(lon)):\n",
-    "#                if mask1[t,l,i,j]==True:\n",
-    "#                    print('mask1 is required at',t,l,i,j)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#based on equation 3.9 of Baumgart et.al 2019 (with out spatial integral and normalizing)\n",
-    "\n",
-    "#Parameterization categories for the diabatic term   \n",
-    "pcatlist_temp = {'phy': ['totphy']}\n",
-    "\n",
-    "pcatlist_wind = {'mom': ['turb','pconv','sso','gwd']}\n",
-    "\n",
-    "pvtend = {}\n",
-    "for pvar in ['P', 'rot', 'div','bnd']+list(pcatlist_temp.keys())+list(pcatlist_wind.keys()):\n",
-    "    pvtend[pvar] = np.zeros((len(time),len(lev),len(lat),len(lon)), dtype=np.float32)\n",
-    "    \n",
-    "for t in range(len(time)):\n",
-    "    \n",
-    "    for l in range(len(lev)):\n",
-    "    \n",
-    "        data1 = ds_list[5].isel(lev=l,time=t) # forecast (with CRH) (changing restart simulations from 2:5)\n",
-    "        data2 = ds_list[0].isel(lev=l,time=t) # analysis (without CRH)\n",
-    "        \n",
-    "        #mask_siglim = np.logical_or(data1['mask_siglim'], data2['mask_siglim'])\n",
-    "\n",
-    "        ddtP = {}\n",
-    "\n",
-    "        dpv = data2['pv']-data1['pv']\n",
-    "        mpv = 0.5*(data2['pv']+data1['pv'])\n",
-    "\n",
-    "        ddtP['P'] = 0.5*dpv**2\n",
-    "\n",
-    "        ddtP['rot'] = -dpv *((data2['urot']-data1['urot'])*ddx(mpv) + (data2['vrot']-data1['vrot'])*ddy(mpv))\n",
-    "        ddtP['div'] = -dpv *((data2['udiv']-data1['udiv'])*ddx(mpv) + (data2['vdiv']-data1['vdiv'])*ddy(mpv))\\\n",
-    "                   +0.25*dpv**2*div(data2['udiv']+data1['udiv'], data2['vdiv']+data1['vdiv'])\n",
-    "        \n",
-    "        ddtP['bnd'] = -0.25*div(dpv**2*(data1['u']+data2['u']), dpv**2*(data1['v']+data2['v']))\n",
-    "\n",
-    "        for pcat in pcatlist_temp.keys():\n",
-    "                ddtP[pcat] = np.zeros(data1['pv'].shape)\n",
-    "                for par in pcatlist_temp[pcat]:\n",
-    "                    #temperatur tendency contributions\n",
-    "                    if f\"ddt_theta_{par}\" in varlist:\n",
-    "                        ddtP[pcat] += (-0.5 * (data2[f\"ddt_theta_{par}\"]-data1[f\"ddt_theta_{par}\"]) * (data2['ddth_pv']+data1['ddth_pv'])\\\n",
-    "                                    -0.5 * (data2[f\"ddt_theta_{par}\"]+data1[f\"ddt_theta_{par}\"]) * (data2['ddth_pv']-data1['ddth_pv'])\\\n",
-    "                                    +(data2[f\"ddth_theta_{par}\"]-data1[f\"ddth_theta_{par}\"]) * mpv\\\n",
-    "                                    +0.5 * (data2[f\"ddth_theta_{par}\"]+data1[f\"ddth_theta_{par}\"]) * dpv)*dpv\n",
-    "\n",
-    "        for pcat in pcatlist_wind.keys():\n",
-    "                ddtP[pcat] = np.zeros(data1['pv'].shape)\n",
-    "                for par in pcatlist_wind[pcat]:                \n",
-    "                    #Wind tendency contributions                   \n",
-    "                    if f\"ddt_u_{par}\" in varlist:\n",
-    "                        ddtP[pcat] += (0.5*(1/data2['sigma']+1/data1['sigma'])\\\n",
-    "                                    * rot(data2[f\"ddt_u_{par}\"]-data1[f\"ddt_u_{par}\"], data2[f\"ddt_v_{par}\"]-data1[f\"ddt_v_{par}\"])\\\n",
-    "                                +0.5*(1/data2['sigma']-1/data1['sigma'])\\\n",
-    "                                    * rot(data2[f\"ddt_u_{par}\"]+data1[f\"ddt_u_{par}\"], data2[f\"ddt_v_{par}\"]+data1[f\"ddt_v_{par}\"]))*dpv    \n",
-    "\n",
-    "                        \n",
-    "        for proc in ddtP.keys():\n",
-    "            pvtend[proc][t,l,:,:] = ddtP[proc]\n",
-    "            #pvtend[proc][t,l,:,:][mask_siglim==True] = 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5- Create a dataset and saving the results to a nc file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.Dataset(data_vars={\"dpe\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['P']),\n",
-    "                           \"ddtrot\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['rot']),\n",
-    "                           \"ddtdiv\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['div']),\n",
-    "                           \"ddtbnd\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['bnd']),\n",
-    "                           \"ddtphy\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['phy']),\n",
-    "                           \"ddtmom\":([\"time\",\"lev\",\"lat\",'lon'],pvtend['mom']),\n",
-    "                                                  \n",
-    "                           },\n",
-    "                coords={\"lat\": ([\"lat\"], lat), \n",
-    "                        \"lon\": ([\"lon\"], lon),\n",
-    "                        \"time\":([\"time\"],ds_list[2].time),\n",
-    "                        \"lev\":([\"lev\"],ds_list[0].lev)})\n",
-    "\n",
-    "#------------------------- \n",
-    "ds['ddtdia'] = ds['ddtphy'] + ds['ddtmom']\n",
-    "ds['ddtrhs'] = ds['ddtdia'] + ds['ddtdiv'] + ds['ddtrot'] \n",
-    "#-------------------------\n",
-    "ds.to_netcdf('/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/pverrorgrowth/pverror_diag_1x1_02_08.nc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pycrh",
-   "language": "python",
-   "name": "pycrh"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/analysis_plots/processing_scripts/.ipynb_checkpoints/diabatic_PV_tendencies-checkpoint.ipynb b/analysis_plots/processing_scripts/.ipynb_checkpoints/diabatic_PV_tendencies-checkpoint.ipynb
deleted file mode 100644
index a31856677e4a256722f033cc74eacb6a3fb44546..0000000000000000000000000000000000000000
--- a/analysis_plots/processing_scripts/.ipynb_checkpoints/diabatic_PV_tendencies-checkpoint.ipynb
+++ /dev/null
@@ -1,391 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diabatic potential vorticity tendencies\n",
-    "\n",
-    "-----------------------\n",
-    "\n",
-    "@ Behrooz Keshtgar, KIT 2022"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1- load python packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
-    "import xarray as xr\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import colorlegend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For reference, print package versions to screen:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xarrary:    0.16.0\n",
-      "numpy:      1.19.1\n",
-      "matplotlib: 3.3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('xarrary:   ', xr.__version__)\n",
-    "print('numpy:     ', np.__version__)\n",
-    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary for loading simulations\n",
-    "simdict = {\n",
-    "         'LC1-channel-4000x9000km-2km-0002' : {'res':'2.5km', 'radiation':0, 'rh':0.8, 'mphy':1}, # No radiation \n",
-    "         'LC1-channel-4000x9000km-2km-0003' : {'res':'2.5km', 'radiation':1, 'rh':0.8, 'mphy':1}, # Cloud radiation \n",
-    "         }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2- Loading related datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on loading data for LC1-channel-4000x9000km-2km-0002\n",
-      "Working on loading data for LC1-channel-4000x9000km-2km-0003\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load 3d datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        path = '/work/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_0.5x0.5/'\n",
-    "        # datasets\n",
-    "        fname1 = path+\"icon-fg*.nc\"\n",
-    "        fname2 = path+\"icon-atm3d*.nc\"                                                                          \n",
-    "        ds_var1 = (xr.open_mfdataset(fname1, combine='by_coords',parallel=True))[['u','v','w','rho','z_ifc','pres','temp','pres_sfc']]\n",
-    "        ds_var2 = (xr.open_mfdataset(fname2, combine='by_coords',parallel=True))[['pv']]\n",
-    "        ds_merge = xr.merge([ds_var1, ds_var2])\n",
-    "        ds_list.append(ds_merge)\n",
-    "        del ds_var1,ds_var2\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_atm3d = load_simulations()\n",
-    "#----------------------------------\n",
-    "# load temperature tendencies datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        path = '/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_0.5x0.5/'\n",
-    "        # datasets\n",
-    "        fname = path+\"icon-ddt_temp*.nc\"                                                                     \n",
-    "        ds_var = (xr.open_mfdataset(fname, combine='by_coords',parallel=True))\n",
-    "        # adding cloud-radiative heating rates and total physic tendency\n",
-    "        ds_var['ddt_temp_radsw'] = ds_var['ddt_temp_radswnw'] - ds_var['ddt_temp_radswcs']\n",
-    "        ds_var['ddt_temp_radlw'] = ds_var['ddt_temp_radlwnw'] - ds_var['ddt_temp_radlwcs']\n",
-    "        # deriving total NWP heating rates\n",
-    "        if sim == 'LC1-channel-4000x9000km-2km-0002':\n",
-    "            ds_var['ddt_temp_totphy'] = ds_var['ddt_temp_mphy'] + ds_var['ddt_temp_pconv'] + ds_var['ddt_temp_turb'] + ds_var['ddt_temp_diff'] + ds_var['ddt_temp_drag']\n",
-    "        else:\n",
-    "            ds_var['ddt_temp_totphy'] = ds_var['ddt_temp_radlw'] + ds_var['ddt_temp_radsw'] + ds_var['ddt_temp_mphy'] + ds_var['ddt_temp_pconv'] + ds_var['ddt_temp_turb'] + ds_var['ddt_temp_diff'] + ds_var['ddt_temp_drag']\n",
-    "        ds_list.append(ds_var)    \n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_ddt_temp = load_simulations()\n",
-    "#----------------------------------\n",
-    "# load diabatic wind tendencies datasets on model levels\n",
-    "def load_simulations():\n",
-    "    ds_list = []\n",
-    "    for sim in list(simdict.keys()): \n",
-    "        print('Working on loading data for', sim)\n",
-    "        path = '/work/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/'+sim+'_remapped_0.5x0.5/'\n",
-    "        # datasets\n",
-    "        fname = path+\"icon-ddt_wind*.nc\"                                                                     \n",
-    "        ds_var = (xr.open_mfdataset(fname, combine='by_coords',parallel=True))\n",
-    "        # deriving total diabatic wind tendencies\n",
-    "        ds_var['ddt_u_tot'] = ds_var['ddt_u_gwd'] + ds_var['ddt_u_pconv'] + ds_var['ddt_u_sso'] + ds_var['ddt_u_turb']\n",
-    "        ds_var['ddt_v_tot'] = ds_var['ddt_v_gwd'] + ds_var['ddt_v_pconv'] + ds_var['ddt_v_sso'] + ds_var['ddt_v_turb']\n",
-    "        ds_list.append(ds_var)\n",
-    "        del ds_var\n",
-    "    return ds_list\n",
-    "#----------------------------------\n",
-    "ds_list_ddt_wind = load_simulations()\n",
-    "#----------------------------------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3- Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Constants\n",
-    "#Adiabat-coef (R_L/cp)\n",
-    "kappa=287.04/1004.64\n",
-    "#Reference pressure (Pa)\n",
-    "p00=1e5\n",
-    "\n",
-    "# Calculating gridlength for deriving horizontal derivities\n",
-    "r_e = 6371200. # earth radius\n",
-    "dx = 2*np.pi*r_e / 720. * np.cos(np.deg2rad(45)) \n",
-    "dy = 2*np.pi*r_e / 720.\n",
-    "\n",
-    "# Potential temperature\n",
-    "def calc_theta(temp, pres):\n",
-    "    return temp * (pres/p00)**(-kappa)\n",
-    "\n",
-    "# potential temperature tendencies for ICON physical heating rates\n",
-    "def calc_ddttheta(temp, pres):\n",
-    "    return (temp * (pres/p00)**(-kappa)) / 1.4 # Cp/Cv factor related to temp tendencies in ICON\n",
-    "\n",
-    "# vertical derivitive on model levels _ compatible with data arrays\n",
-    "def ddz(field_ml, z_ml):\n",
-    "    mid  = (field_ml[:,2:,...].reset_index(\"height\") - field_ml[:,:-2,...].reset_index(\"height\")) / (z_ml[:,2:,...].reset_index(\"height\") - z_ml[:,:-2,...].reset_index(\"height\"))\n",
-    "    low  = (field_ml[:,1,...] - field_ml[:,0,...]) / (z_ml[:,1,...] - z_ml[:,0,...]) \n",
-    "    temp = xr.concat([low, mid], dim=\"height\").transpose('time','height','lat','lon')\n",
-    "    up   = (field_ml[:,-1,...] - field_ml[:,-2,...]) / (z_ml[:,-1,...] - z_ml[:,-2,...])\n",
-    "    ddz_field_ml = xr.concat([temp, up], dim=\"height\")\n",
-    "    return(ddz_field_ml)\n",
-    "\n",
-    "# horizontal derivitive in zonal direction\n",
-    "def ddx(f):\n",
-    "    return (np.roll(f, -1, -1) - np.roll(f, 1, -1)) / (2*dx)\n",
-    "\n",
-    "# horizontal derivitive in meridional direction\n",
-    "def ddy(f):\n",
-    "    ddyf = (np.roll(f, -1, -2) - np.roll(f, 1, -2)) / (2*dy)\n",
-    "    ddyf[..., 0, :] = 0.\n",
-    "    ddyf[..., -1, :] = 0.\n",
-    "    return ddyf\n",
-    "\n",
-    "# zonal vorticity \n",
-    "def avor_x(w,v,z):\n",
-    "    avor_x = ddy(w) - ddz(v,z) \n",
-    "    return avor_x\n",
-    "\n",
-    "# meridional vorticity \n",
-    "def avor_y(u,w,z):\n",
-    "    avor_y = ddz(u,z) - ddx(w) \n",
-    "    return avor_y\n",
-    "\n",
-    "# vertical vorticity \n",
-    "def avor_z(v,u):\n",
-    "    avor_z = ddx(v) - ddy(u) + 0.0001031260914097046 #f at 45°N \n",
-    "    return avor_z\n",
-    "\n",
-    "# rotational wind \n",
-    "def rot(v,u):\n",
-    "    rot = ddx(v) - ddy(u) \n",
-    "    return rot\n",
-    "\n",
-    "# diabatic PV tendency\n",
-    "def pv_diab(rho,ddt_temp,u,v,w,z,pres):\n",
-    "    #deriving potential_temp\n",
-    "    ddt_theta  = calc_ddttheta(ddt_temp,pres)\n",
-    "    pv_diab = 1/rho*(avor_x(w,v,z)*ddx(ddt_theta)+avor_y(u,w,z)*ddy(ddt_theta)+avor_z(v,u)*ddz(ddt_theta,z))\n",
-    "    return pv_diab\n",
-    "\n",
-    "# diabatic PV tendency only vertical component\n",
-    "def pv_diab_z(rho,ddt_temp,u,v,z,pres):\n",
-    "    #deriving potential_temp\n",
-    "    ddt_theta  = calc_ddttheta(ddt_temp,pres)\n",
-    "    pv_diab = 1/rho*(avor_z(v,u)*ddz(ddt_theta,z))\n",
-    "    return pv_diab\n",
-    "\n",
-    "# advective PV tendency\n",
-    "def pv_adv(u,v,w,pv,z):\n",
-    "    pv_adv = -(u*ddx(pv)+v*ddy(pv)+w*ddz(pv,z))\n",
-    "    return pv_adv\n",
-    "\n",
-    "# advective PV tendency only vertical component\n",
-    "def pv_adv_z(w,pv,z):\n",
-    "    pv_adv = -(w*ddz(pv,z))\n",
-    "    return pv_adv\n",
-    "\n",
-    "# momentum tendencies\n",
-    "def pv_fric(rho,temp,ddt_u,ddt_v,z,pres):\n",
-    "    #deriving potential_temp\n",
-    "    theta  = calc_theta(temp,pres)\n",
-    "    pv_fric = 1/rho*(avor_x(ddt_v*0.0,ddt_v,z)*ddx(theta)+avor_y(ddt_u,ddt_u*0.0,z)*ddy(theta)+rot(ddt_v,ddt_u)*ddz(theta,z))\n",
-    "    return pv_fric\n",
-    "\n",
-    "# momentum tendencies only vertical component\n",
-    "def pv_fric_z(rho,temp,ddt_u,ddt_v,z,pres):\n",
-    "    #deriving potential_temp\n",
-    "    theta  = calc_theta(temp,pres)\n",
-    "    pv_fric_z = 1/rho*(rot(ddt_v,ddt_u)*ddz(theta,z))\n",
-    "    return pv_fric_z"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4- Deriving height (z) and vertical velocity (w) on full levels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(len(ds_list_atm3d)):\n",
-    "    w_fl =  (ds_list_atm3d[i].w - (ds_list_atm3d[i].w.diff('height_2')/2)).values\n",
-    "    z_fl =  (ds_list_atm3d[i].z_ifc - (ds_list_atm3d[i].z_ifc.diff('height_3')/2)).values\n",
-    "    \n",
-    "    w_fl = xr.DataArray(w_fl, dims=('time','height','lat','lon'))\n",
-    "    z_fl = xr.DataArray(z_fl, dims=('time','height','lat','lon'))\n",
-    "    \n",
-    "    ds_list_atm3d[i]['w_fl'] = w_fl\n",
-    "    ds_list_atm3d[i]['z_fl'] = z_fl\n",
-    "    \n",
-    "    del w_fl,z_fl"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5- Deriving the PV tendency equation terms (3D & only vertical component)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on diabatic PV for sim 0\n",
-      "Working on PV due to friction for sim 0\n",
-      "Working on avdective PV for sim 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# diabatic PV tendencies\n",
-    "for i in range(len(ds_list_atm3d)):\n",
-    "    print('Working on diabatic PV for sim', i)\n",
-    "    for var in ['ddt_temp_gscp','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_totphy','ddt_temp_pconv','ddt_temp_mphy','ddt_temp_turb']:\n",
-    "        # 3D\n",
-    "        ds_list_atm3d[i][var.replace('ddt_temp', 'ddt_pv')] = pv_diab(ds_list_atm3d[i]['rho'],ds_list_ddt_temp[i][var],ds_list_atm3d[i]['u'],ds_list_atm3d[i]['v'],\n",
-    "                                                                      ds_list_atm3d[i]['w_fl'],ds_list_atm3d[i]['z_fl'],ds_list_atm3d[i]['pres'])\n",
-    "        # only vertical component\n",
-    "        ds_list_atm3d[i][var.replace('ddt_temp', 'ddt_pv_z')] = pv_diab_z(ds_list_atm3d[i]['rho'],ds_list_ddt_temp[i][var],ds_list_atm3d[i]['u'],ds_list_atm3d[i]['v'],\n",
-    "                                                                      ds_list_atm3d[i]['z_fl'],ds_list_atm3d[i]['pres'])\n",
-    "\n",
-    "# PV tendencies due to friction\n",
-    "for i in range(len(ds_list_atm3d)):\n",
-    "    print('Working on PV due to friction for sim', i)\n",
-    "    # 3D\n",
-    "    ds_list_atm3d[i]['ddt_pv_fric'] = pv_fric(ds_list_atm3d[i]['rho'],ds_list_atm3d[i]['temp'],ds_list_ddt_wind[i]['ddt_u_tot'],ds_list_ddt_wind[i]['ddt_v_tot'],\n",
-    "                                              ds_list_atm3d[i]['z_fl'],ds_list_atm3d[i]['pres'])    \n",
-    "    # only vertical component\n",
-    "    ds_list_atm3d[i]['ddt_pv_z_fric'] = pv_fric_z(ds_list_atm3d[i]['rho'],ds_list_atm3d[i]['temp'],ds_list_ddt_wind[i]['ddt_u_tot'],ds_list_ddt_wind[i]['ddt_v_tot'],\n",
-    "                                                  ds_list_atm3d[i]['z_fl'],ds_list_atm3d[i]['pres'])\n",
-    "# advective PV tendencies\n",
-    "for i in range(len(ds_list_atm3d)):\n",
-    "    print('Working on avdective PV for sim', i)\n",
-    "    # 3D\n",
-    "    ds_list_atm3d[i]['ddt_pv_adv'] = pv_adv(ds_list_atm3d[i]['u'],ds_list_atm3d[i]['v'],ds_list_atm3d[i]['w_fl'],ds_list_atm3d[i]['pv'],\n",
-    "                ds_list_atm3d[i]['z_fl'])    \n",
-    "    # only vertical component\n",
-    "    ds_list_atm3d[i]['ddt_pv_z_adv'] = pv_adv_z(ds_list_atm3d[i]['w_fl'],ds_list_atm3d[i]['pv'],\n",
-    "                ds_list_atm3d[i]['z_fl'])\n",
-    "    # left-hand side of the equation\n",
-    "    ds_list_atm3d[i]['ddt_pv'] = ds_list_atm3d[i]['pv'].diff(dim='time', label='upper')/3600 #PVU/s\n",
-    "    # relative vorticity \n",
-    "    ds_list_atm3d[i]['vor'] = ddx(ds_list_atm3d[i]['v']) - ddy(ds_list_atm3d[i]['u']) + ds_list_atm3d[i]['u']*0.0 \n",
-    "    #/ r_e * np.tan(np.deg2rad(lat)).reshape((1, len(lat), 1))    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save the datasets \n",
-    "ds_list_atm3d[0] = ds_list_atm3d[0].drop(['u','v','w','rho','z_ifc','temp'])\n",
-    "ds_list_atm3d[1] = ds_list_atm3d[1].drop(['u','v','w','rho','z_ifc','temp'])\n",
-    "\n",
-    "ds_list_atm3d[0].to_netcdf('/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/LC1-channel-4000x9000km-2km-0002_remapped_0.5x0.5/pv_tendencies.nc')\n",
-    "ds_list_atm3d[0].to_netcdf('/work/bb1135/from_Mistral/bb1135/b381185/output/LC1_Limited_channel/icon-v.2.6.2.2_2km/sim_list_output/LC1-channel-4000x9000km-2km-0003_remapped_0.5x0.5/pv_tendencies.nc')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pycrh",
-   "language": "python",
-   "name": "pycrh"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}