{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0077bd1",
   "metadata": {},
   "source": [
    "## 1. Load segment data. Refine segment as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7bfa27",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0efb11a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:32:51.380837Z",
     "start_time": "2022-06-02T13:32:50.722127Z"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import metview as mv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0340ecfd",
   "metadata": {},
   "source": [
    "Load segment file (csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f11f47af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:32:54.311084Z",
     "start_time": "2022-06-02T13:32:54.306717Z"
    }
   },
   "outputs": [],
   "source": [
    "# Read the CSV file containing segments\n",
    "csv_name = \"segments_mc.csv\"\n",
    "patho_csv = \"/bog/amuttaqin/Datasets/\"\n",
    "\n",
    "with open(patho_csv+csv_name, \"r\") as file:\n",
    "    csvreader = csv.reader(file)\n",
    "    segments_whead = list(csvreader)\n",
    "    segments = segments_whead[1:]\n",
    "    [j.pop(0) for j in segments]\n",
    "    \n",
    "segments = [[float(y) for y in x] for x in segments]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7811fc",
   "metadata": {},
   "source": [
    "Find the intended segment. Will start with segments over Malay Peninsula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07ff6ccc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:32:59.201158Z",
     "start_time": "2022-06-02T13:32:59.196710Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14.9, 97.8, 13.6, 98.3]\n",
      "[14.9, 97.8, 13.6, 98.3]\n"
     ]
    }
   ],
   "source": [
    "# Manual by looking into the segment names and coordinates\n",
    "# Automatic by finding lat1,lat2,lon1,lon2 with certain range\n",
    "\n",
    "segments[23]\n",
    "s = 23\n",
    "\n",
    "lat1 = segments[s][0]\n",
    "lon1 = segments[s][1]\n",
    "lat2 = segments[s][2]\n",
    "lon2 = segments[s][3]\n",
    "line_segment = [lat1, lon1, lat2, lon2]\n",
    "\n",
    "print(segments[s])\n",
    "print(line_segment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9185cb0d",
   "metadata": {},
   "source": [
    "Display segment on map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dba8bce3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:34:21.314738Z",
     "start_time": "2022-06-02T13:34:20.637904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mv.setoutput(\"jupyter\", plot_widget=False, output_width=1200)\n",
    "my_view = mv.geoview(\n",
    "    map_area_definition=\"corners\", \n",
    "    area = [0, 90, 15, 110],\n",
    "    subpage_y_position=25)\n",
    "my_coast = mv.mcoast(\n",
    "    map_coastline_colour=\"charcoal\",\n",
    "    map_coastline_resolution=\"medium\",\n",
    "    map_coastline_thickness=2,\n",
    "    map_grid_line_style=\"solid\",\n",
    "    map_grid_latitude_increment=5.,\n",
    "    map_grid_longitude_increment=5.,\n",
    "    map_label=\"on\",\n",
    "    map_label_latitude_frequency=1,\n",
    "    map_label_longitude_frequency=1,\n",
    "    map_label_height=0.5)\n",
    "geolines = mv.mvl_geoline(*line_segment,1)\n",
    "line_graph_segments = mv.mgraph(\n",
    "    graph_line_colour  = \"red\",\n",
    "    graph_line_thickness = 5.0,\n",
    "    graph_line_style = \"solid\")\n",
    "title = mv.mtext(\n",
    "    text_font_size=1.0,\n",
    "    text_lines=[\"Segments\"])\n",
    "mv.plot(my_view, my_coast, geolines, line_graph_segments, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c81d57",
   "metadata": {},
   "source": [
    "Refine segment coordinates if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1018833",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:34:39.733542Z",
     "start_time": "2022-06-02T13:34:39.731469Z"
    }
   },
   "outputs": [],
   "source": [
    "# new_lat1 =\n",
    "# new_lon1 = \n",
    "# new_lat2 = \n",
    "# new_lon2 = \n",
    "# refined_segment = [new_lat1, new_lon1, new_lat2, new_lon2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc4a919",
   "metadata": {},
   "source": [
    "Display refined segment on a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1e85554",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:35:24.661461Z",
     "start_time": "2022-06-02T13:35:24.658872Z"
    }
   },
   "outputs": [],
   "source": [
    "# area_segments = [0, 90, 15, 110]\n",
    "\n",
    "# mv.setoutput(\"jupyter\", plot_widget=False, output_width=1800)\n",
    "# #mv.setoutput(mv.pdf_output(output_name='/bog/amuttaqin/Figures/segments'))\n",
    "\n",
    "# my_view = mv.geoview(\n",
    "#     map_area_definition=\"corners\", \n",
    "#     area=area_segments,\n",
    "#     subpage_y_position=25)\n",
    "# my_coast = mv.mcoast(\n",
    "#     map_coastline_colour=\"charcoal\",\n",
    "#     map_coastline_resolution=\"medium\",\n",
    "#     map_coastline_land_shade=\"off\",\n",
    "#     map_coastline_sea_shade=\"off\",\n",
    "#     map_coastline_thickness=2,\n",
    "#     map_grid_line_style=\"solid\",\n",
    "#     map_grid_latitude_increment=10,\n",
    "#     map_grid_longitude_increment=10,\n",
    "#     map_label=\"on\",\n",
    "#     map_label_latitude_frequency=5,\n",
    "#     map_label_longitude_frequency=5,\n",
    "#     map_label_height=0.5)\n",
    "# geolines = mv.mvl_geoline(*refined_segment,1)\n",
    "# line_graph_segments = mv.mgraph(\n",
    "#     graph_line_colour  = \"red\",\n",
    "#     graph_line_thickness = 8.,\n",
    "#     graph_line_style = \"chain_dash\")\n",
    "# title = mv.mtext(\n",
    "#     text_font_size=1.0,\n",
    "#     text_lines=[\"Segments\"])\n",
    "# mv.plot(my_view,my_coast,     \n",
    "#     geolines, line_graph_segments, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f11b67",
   "metadata": {},
   "source": [
    "## 2. Convert segment into transects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74dee6fd",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6f0b671",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:35:27.773109Z",
     "start_time": "2022-06-02T13:35:27.756850Z"
    }
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import LineString\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4a7469",
   "metadata": {},
   "source": [
    "Convert segment into transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c55c7191",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:13:29.293238Z",
     "start_time": "2022-06-02T15:13:29.284422Z"
    }
   },
   "outputs": [],
   "source": [
    "lat1 = segments[s][0]\n",
    "lon1 = segments[s][1]\n",
    "lat2 = segments[s][2]\n",
    "lon2 = segments[s][3]\n",
    "\n",
    "dist2coast = 0.4 \n",
    "dist2trnsc = 0.3\n",
    "\n",
    "# Create a line aligned with coastline. Let's call it \"segment\"\n",
    "segment = LineString([(lat1, lon1), (lat2, lon2)])\n",
    "\n",
    "# How many possible transects on this segment?\n",
    "n_trnsc = np.floor(segment.length/dist2trnsc)\n",
    "\n",
    "# Create two parallel lines on the left and right side of the segment. \n",
    "# One will be over landmass, the other will be over ocean\n",
    "segpl = segment.parallel_offset(dist2coast, 'left')\n",
    "segpr = segment.parallel_offset(dist2coast, 'right')\n",
    "\n",
    "# Split segement at a specified distance\n",
    "excess = segpl.length - (dist2trnsc*(n_trnsc-1))\n",
    "start_dist = excess/2\n",
    "distances = np.arange(start_dist, segpl.length, dist2trnsc)\n",
    "\n",
    "ptsl = [segpl.interpolate(distance) for distance in distances]\n",
    "ptsr = [segpr.interpolate(distance) for distance in distances]\n",
    "\n",
    "list_ptsr = []\n",
    "list_ptsl = []\n",
    "for pr,pl in zip(ptsr,ptsl):\n",
    "    list_ptsr.append([round(pr.x,4), round(pr.y,4)])\n",
    "    list_ptsl.append([round(pl.x,4), round(pl.y,4)])\n",
    "    list_ptsr.sort()\n",
    "    list_ptsl.sort()  \n",
    "\n",
    "trnscx = []\n",
    "list_trnsc = [] \n",
    "for i in range(int(n_trnsc)):\n",
    "    trnscx.append([list_ptsl[i], list_ptsr[i]])\n",
    "    list_trnsc.append([item for sublist in trnscx[i] for item in sublist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "169f2f9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:13:31.431156Z",
     "start_time": "2022-06-02T15:13:31.428558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 4)\n",
      "[[13.6864, 97.8382, 13.9736, 98.5849], [13.9664, 97.7305, 14.2536, 98.4772], [14.2464, 97.6228, 14.5336, 98.3695], [14.5264, 97.5151, 14.8136, 98.2618]]\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(list_trnsc))\n",
    "print(list_trnsc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9473caaf",
   "metadata": {},
   "source": [
    "Display transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d0624baa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:13:33.582862Z",
     "start_time": "2022-06-02T15:13:33.165651Z"
    }
   },
   "outputs": [
    {
     "data": {
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A36D0AYBufP78edOmTcPsid2HcQeqAQDgX0HpAwDdUFFR4eTkfPHixe8OqK9HgoORyZOR8eORsjIqJgMAoKSioiI7OxvtFHQGSh8A6Mn69esjIiJqa2tjY2P7JiH29iIPHiAWFoiYGGJri7CwIGfOINzc6CYFAFBDUVHR7t276+rq0A5CT6D0AYCezJw5s7293dzcPCUlZefOnV++NPr5IQoKyIIFyJMniJUVkpGBvHuH2NoiBALaWQEAlKejo7NgwYJDhw4RiUS0s9ANKH0AoCdYLDYiIuLmzXvS0huqq5VNTA66uRHl5ZGYGKS8HAkKQiZORDsiAIC6tmzZUlhYCJe9Ro5h1/UBgCHl5CBXrnCGh9dKSGzDYol8fM1nzsRs2bIC7VwAANSwsLCIiYm1tbWhHYRuQOkDAB2or0diYpALF5CMDISdHTEwSOPlVbtwwScm5jqRCLe7A8DscDhcT08P2inoBpQ+ANCu3l7k2TPk8mXkxg2krQ2ZPBk5cQJZuxbBYnVMTX1YWHDW1tZoZwQAoA+DwcBcn5GD0gcAWlRSgkRHIxcuIIWFCD8/snYtsmVL/3k8fOrq6q9fv543bx56GQEAtIKFhaW7uxvtFHQDpjkDQFs+fkQMDBAZGWTfPkRVFYmLG3r+8vz58x88eDB8V21tSGQkkp9PubAAAJrAzs7e3NyMdgq6AaUPALSFhwf59Qvx9kaKi5E7dxBzcwSPH+KwWbNmpaenNzQ0DNnJ+/fIli2IuDiydi1y7RplAwMAUKepqZmSkoJ2CroBF7wAoC1ycsjXr38+jJOTc+rUqc+ePTMzM+trHDAbetEixMYGmTOHgmkBALRgxowZ58+f7+npweFwaGehAzDqAwC9mj179uvXrxEE6e1FnjxB1q1DJCQQW1sEi0VOnEB+/UJiYhBDQwSDQTsoAIDCREVFRUVFs7Ky0A5CH6D0AYBejR8//suXPD8/RF4emTsXuXMHWbsW+fgRSUtDduxABATQzgcAoKIZM2a8evUK7RT0AUofAOhPezsSG4ts2iRZVla7f3+rgsJ/V3PW0EA7HAAADTNmzHj58iXc4j4SUPoAQE8+fEB27EAkJRELC6SoCMvPL/PgQcHjx8jy5UPPhgYAMAk1NTV2dvY3b96gHYQOQOkDAB2oq0OCg5FJkxAtLSQ4GDEwQB4/Rr5+RYyNNT99eoh2OgAATbCysgoLC0M7BR2A0gcA2tU3f3nMGMTWFsHhBs5f3rRp04MHD378+IF2UgAA+ubMmVNfX5+eno52EFoHpQ8ANKqrC1FURObORR49QuztkZycIeYv8/Lybty48fjx48N3lZeH7N+PuLlRNjAAAF1YLHbZsmV/XOwUQOkDAI1iZUVsbZGbN5GSEiQwEFFRGfqw5cuXV1dXv3jxYvBDra3I5cvIrFmIkhLi54cUFFA0LwAAfXJycj9//kQ7Ba2D0gcA2rV7N2JqirCyDncMDodzcnI6fvx4Z2dnXyNpNrSUFGJlhZSWIh4eSF4ecvUqxQMDANAlJSUFpc8fwWrOANA9bW1tBQWF27dvGxgsjY1Fzp9HPn78n9WcYVVDAJiEmJhYdXV1Z2cnHu75/D0Y9QGA7vX2Is3N3KGhrGJiQ8+GBgAwCRwOp6SklJmZiXYQmgajPgDQsZISJDoaOX8e4eAorK83X78esbeHVQ0BYGrTp09/+fKllpYW2kFoF4z6AEB/SKs5z52LyMggbm6IoiIiIFCYnS0LqzkDAJYsWXLv3r3Gxka0g9AuKH0AoCek+ctjxiAWFkhxMeLhgRQUINHRVQQCm6AgD9rpAADoExER0dHRGfKuT0ACpQ8AdGDAas5z5iCPHyNfviCenoi0NFJQUCArKzvy3pqaKBYUAEADYCvT4UHpAwAd2LcPsbVFODmR0FCksnLg/GUsFtvR0UH6+uHDhxYWFvX19YM7Ia0NbWGBiIkhZWXUig4AoLpp06a9f/++/4IXoD8ofQCgA7t3I58/I2/eIJs2IdzcAx/V0NAoKiqqq6tDECQ7O7uzs/PgwYP9D/j+HXFzQ6SlkblzkaQkxN4ewcKvPgCMi4+PT15e/sOHD2gHoVHw9w8AOiAriygr//ZRVlZWbW3tlJQUBEGKioqsrKw+f/6M9JsNPW4c4ueHKCsjMTFISQkSEICIilItOwAABXDNaxhQ+gDACMaOHfvr1y8EQQoLCzU1NevrG7Zv7xkwG/rxY2T58j+sDQ0AYAwzZsxISkpCOwWNgnV9AGAEPDw8FRUVFRUdFRXVS5ZIEok84eH1CxYIwmrOADAhHx8fISGh2tra3t5eLFzeHgRKHwDoXm8vUlLC/fBhnpdXiZSUJDs7joMDyc5GZGTQTgYAQEN9ff2dO3ekpKSg7hkSlD4A0LHv35GoKCQiAmlo4JGQaDQxKeTikgkORgwMuvj54coWAEyKhYUFg8HIycmhHYRGQekDAP1pb0du30aCg5GnTxEsFpk9G1mwgOfz58apU4va2mQQBOno6GBhgd9uAJgUCwvL1q1bTUxM0A5Co2AoDAB6UliI2NggoqKIhQVSUoL4+iI/fyKPHyNmZvzl5WU/fvyQkZFBEERLSysyMhLtsAAAdOBwOAKBwMMDK7wPDUofAOhMVBRiZIQ8fozk5iK7dyNiYgiCIGPHjpWVlX369Cmp9HF3d4+Li4PdmwFgTiwsLD09PWinoF1Q+gBAT2RlkZqagas5kzg7O8vLy5N2tBASEtq7d6+Hh0draysqOQEAKGJhYenu7kY7Be2C0gcAOsPOPnS7rKxsdHR03xD3rFmzJk+eHBgYSL1kAADaAKXP8KD0AYBhOTk5ffz48dmzZ2gHIaf8/Pza2lq0UwBA0zg5OZtgm+Lfg9KHuSQnJ3t4eCQkJKAdBFADJyfnwYMHfX19q6qq0M5CHjExMZs3b7aysvrx4wfaWQCgXRoaGunp6WinoF1w+yuz+Pbt28mTJysqKgwMDOLi4kxNTdFOBKhBTU1t+fLlnp6eZ86cwdDVos61tbW1tbUVFRW1tbWVlZU1NTUFBQV1dXWXL1/Ozs7evHmzurq6hobGpEmTVFRU8Hg82nkBoCETJ050dXXt6OhgY2NDOwstgtKHKcTExFy8eHHz5s1mZmbFxcUMdgUEDG/jxo2bN29OSEgwMzNDO8twGhoa7t69++zZs9LS0rq6Om5ubgEBAREREUFBQREREVlZWR0dnSlTpnBwcIwZM0ZHRycjI+Pjx4/Hjh0rKChQUlKa+P+4B29tDwCT4eTkVFRU/PTpk46ODtpZaBGUPoyvsbExJCQkLCxMSkoKQRAODo729na0QwHqweFwtra2586do9nS59u3b5cvX37z5s2MGTNsbGxkZGQEBARYh91nlZ+f38DAwMDAAEGQtra2rKysjx8/Xr169cCBA8uWLVu/fj0nJye14gNAiyZPnpyRkQGlz5Cg9GF8ly5dMjAwINU9CILw8fE1Nja2t7ez/+5OIcBwtLS0ysrKfv36NWbMGLSzDHTv3r0TJ05s2LBh9+7do1uBjYODQ0dHh/QnvqKiYv/+/ZycnOvXrydzUADoCisra29vL9opaBRMc2ZwVVVVCQkJ1tbWfS1FRUUsLCww+Z+p4HA4Q0PDhw8foh3kfxCJxODg4ODg4AsXLqxatYosK8+Kiory8PD0FfoAMK3q6mohISG0U9AoKH0YXExMjImJibCwMOnbwsLCHTt2uLu797UAJjFv3rxHjx6hneJ/xMXFZWRkXLp0iYybLPb29mZlZamqqpKrQwDoVE1NjYCAANopaBSUPgyuqalJUlKy79v79++rq6vPnj0bxUgAFerq6k1NTfn5+WgH+Y+2trYvX76cOHGCl5eXjN2+e/dOTExMjLS7BwBMrKamRlBQEO0UNApKHwbHysraf03PNWvWfP78OS0tDcVIABVYLHbevHm0s6TT169fJSQkyH7nbUZGxvTp08nbJwD0qKamBi54/Q6UPgyOlZW1q6ur71tubu69e/cePHiwfyNgEmvXrn306FFWVhbaQRAEQXJyckjbjZGXpKRkaWkp2bulL9XV1deuXXv58uWPHz86OjrQjgPQMWbMmLdv36KdgkbBHV4Mrre3l0gk9m+ZNGlSfX39gEbADAQEBHbv3u3p6RkdHY36Qmc4HK6srKy3txeLJecHMHl5+ZiYGDJ2SHe6u7t3794tKCjY3d398+fPsrKyyZMnm5ub6+np4XA4tNMB6nFxcbGzs5s9ezbM+BkMRn0YXGZmprKycv+WV69eTZo0CVa/ZWwxMTEXLlwY3G5gYKCsrHzmzBnqRxpg+fLlXV1dp0+fJm+3Y8eOLSoqYuZ7ej9//lxXV+fv73/8+PHY2NinT5/OnTv30qVLixYtCgoKqq+vRzsgoBI5OTl9ff27d++iHYQWQenDyFpaWvLy8iZOnNi/8cGDB0ZGRiglAlTy+vXry5cv379/f/BDu3fvfvr06devX6mfqj8cDrdixYqnT5+S9wJcY2MjBwcHfW3ZQV7jx4+vra1tbm4mfcvGxmZiYhIWFnb69Ony8nIHBwdY0ZR5GBkZ0dp9nTQCSh9GlpaWNmHChP4DPFlZWV+/foU7vBhea2urjY3NxYsXBz/Ew8MzderU3Nxc6qcagIODw8TEhLzbqrx9+1ZXV5eZSx88Hj9p0qR3794NaJeXlz9w4IC8vLynpydc72YSkydPrq6uLi4uRjsIzYHSh5G9e/duypQpfd/W1NT4+flt27aNg4MDxVSACjo7O7W1tTs7OysrKwc/KioqOmQ79c2cOfPFixdk7JBU+pCxQ3o0e/bs333W37dvX2VlZUhICJUjAVRgsVgaXMuUFkDpw8hSU1P7Sp9r165ZWFhMmTJl/vz56KYCVMDHx9fQ0MDOzj7kst3CwsI0UvqMGzeup6eHXKsN9fb2pqWlwaZFhoaG79+/H3JaDx6PDwwMTExMhOsgTMLIyAhKn8Gg9GFYbW1tP3/+JK3oX1tbGxwcfOXKlW3btjHztQDmISgoWFNT09HRMeRObbQz6oMgiIyMDLlKn9zcXDExMVjLhIuLa8aMGbdu3RryUQEBgWPHjgUEBJSUlFA5GKA+VVXVrq4uWrjATVOg9GFYHBwchoaGN27cQBDk2rVr8+bNk5CQQDsUoJKxY8e+e/euvb19yJvYRUREKioqqJ9qsJqampycnGnTppGlt7q6OhEREbJ0Re9sbGyuXLlSW1s75KNKSkorV64MCgqicipAfRgMZvXq1YGBgcx82+NgUPowms+fP8fHx3d2diIIwsXFxcbGVlFRER8fv27dOrSjAeqxsLDIysqqq6sbctRHQkKivLy8ra2N+sEGuHPnjoGBAScnJ1l66+joQH29IhohKSk5f/780NDQ3x1gaWn5/v37b9++UTMVQMWyZctYWVkjIyPRDkJDoPRhKE1NTdu2bbt27dqDBw8QBPny5cu4ceOuXLkyY8YMGPJhKuzs7Hv27GFnZx+y9OHk5FRTUxt8ExCVEYnEhIQEMzMzcnUIpU9/mzdvfvbs2e+WdeHg4LCysoKBH2aAxWJtbW2fPHmCdhAaAqUPQ+no6GBlZRUVFRUSEiLNHiWNbKekpKSmpqKdDlDVtGnTnj9/zsIy9IrtM2fOTEpKonKkAQoKCtjY2Mi4y3p7e/uQpR5z4uXlDQ0NDQ0NDQ4OHvKAZcuWffnyhUY2NgEUhcPhfvengDlB6cNQODg4Wltb8/PzZWVlnz59KiEhwcnJKSkp6e3tvX//ftrZtRtQxzAbF+jr679+/Rrdy/8fPnwwNzcnY4cw6jOApKRkcHDw8+fPfXx8+hY57IPH4zdt2nT+/HlUsgFq6unpgW1M+oPSh6GQSh8+Pr6SkpLAwMBDhw6R2jU1NW1tbY8dO4ZuPEA7xMTEeHl5f/z4gVaApqamvLw88i610NnZCTu0DCAsLBwSEtLT02Nubh4bG9vT09P/0cWLF5eVlX38+BGldIBKoPQZAEofhoLFYvF4vKWl5YEDB3x9fZWUlPoemjRpEu3czwxoAR6PR3HU58WLF0pKSjw8PGTss6amhrwdMgYCgeDu7n7mzJnnz5+vXLny/PnzDx8+JE1wZmFhWbNmDUyAZXhQ+gwApQ+jiYuLk5OT4+bm1tTU7N/Ox8dXV1eHVipAg7q7u1G8/P/kyZPJkyeTt8+kpKSpU6eSt0+GoaSkdO7cORcXFxwO9/Llyx07dpBuhjA2Nv706VNhYSHaAQEFQekzAMx7YjTi4uJYLHbwGr58fHyNjY29vb1YLNS7AEEQRFxcvKioSF5envqnrq6urqurk5GRIWOf3759IxKJ/Uc6wWA6Ojqk1a7v3bv37Nmz+fPns7Oz29vb29nZ6ejo1NfX19TU8PHxzZkzx8DAgI+PD+284J/cvn07KirK398f3c85NAj+LxiQqKhoR0dHfX19/79cWCyWi4ursbER/pwBEi0trbS0NAMDA+qfOisrS0lJibwLi6emppJ3GImxPydMnz49ICCANDvKzMxMQUGhuLiYn5+fj4+vvLz84cOHCQkJly9fRjsmGKWenp79+/cXFRVpamqePHnS1NR0wDQvJgelD2OSl5fPz88fcM2Ln5+/rq4OSh/mERERwc3NPXfu3CFnwGhpaXl4eFA/FYIgBQUF0tLS5O1TX1//0qVLdXV1/Pz8pJaWlpZHjx5VVVXJyspKS0vLyMj8cePezs7OrKystLS0tLS0nJwcFRWVVatWzZ49m/FqIF5eXkVFxeTk5FmzZiEIMmHChAkTJpAeUlFRmTVrlrGxcUlJCWknHEB3jhw50tbWdunSJSKRaGFhUVNT8/37d7RD0RAofRhQR0fHr1+/eHl5B7Tz8/MPuaMhYEhdXV3BwcGzZs06c+bMypUrbW1tBxygpKRUXFyMyki4kJAQ2e8qkpaWXrhwoaOj4549e1paWm7duvXq1SttbW1ZWdlnz54VFRUVFRUJCAjIyMjIyMjIysrKysrKyMiIiop2dnbm5OSQyp3c3FwFBYXJkydbW1uTVn28evXqiRMnNm7cSMalF2nEhg0bDhw4QCAQtLS0BjyExWJnz5799OnT9evXoxEN/JNbt27l5uaGhISwsrIiCOLm5ubh4VFbW1tVVSUsLIx2OpoApQ8Dun379oQJEwbP4YCZzkylrKxMWFjYx8enpqZm6dKla9as4eLi6n8AFovF4XA9PT3UL30kJCQocb/hjh07bt26tW3bNgEBgSVLluzatatvBAhBkN7e3rKysqKiosLCwry8vKdPnxYWFra2tmIwmLFjx06ePNnKymrixIn9d9UwMDAwMDDIzc318vLKy8tzcnJipOGfqVOn+vr6urq6urq6Dr7uOW7cuMzMTFSCgX/EwsJCWtSN9K2Ojs7WrVsPHjz4+fPnmTNnopuNRkDpw4Cys7P19PQGt8OoD1MRFBSsr69vb28XFBQ0MjLy8fFZunTpgGugpNKH+tnExcWrqqrI3i0WizUzM/vd8AwWix0zZsyYMWP675ba3NyMwWAGFIUDKCsrh4aG7tmzx8rKauvWrbq6umTOjR5NTc0zZ87s3Lmzrq5u6dKl/R8iEonknYwFqGbmzJkBAQEtLS19P9iLFi3i4OBQVlZGNxjtYJxPMKBPUVHRkBMpoPRhKlxcXMrKymlpaQiCbN++HY/Hu7q6DjgGi8WisrSPqKhoXV0dLWwlTSAQhq97+g47c+bM+vXrjx07ZmZmFhQUVFxcTIV4VKCkpBQSEhIbG2thYREdHd03MCwmJvb27dvk5GR044FRIBAImpqaA3aqMTQ0FBERQSsSrYHShwHV19cLCgoOaOzp6Xn79i1sYspUpk2bRnrr4uLiWrt27YAZ7sXFxXg8/o8zfymBhYWFl5e3oaGB+qceNQwGM2fOnJiYGG9v75aWFmtr6/v376MdijzGjBlz7do1Nze379+/L126dPfu3XFxcZWVlWvWrDl16tTq1avLysrQzgj+zty5cx8/fox2CtoFF7wY0JCrV128eJGHh8fIyAiVSAAVIiIipEV7EQSpqakZUBDfvHlz0aJFaC10JiIiQl+lTx8VFRUVFRVTU9MdO3aUlJTY2NignYg8Jk6cOHHixNbW1kePHn3+/Lmzs/P169d2dnZpaWnv3r0zNTVFOyD4CzNnzvTz82tsbIT1zYcEpQ8D6n+RnjTYk56efufOncjISLh4z1SqqqqEhIRIXycmJvbdvYwgSGdn5927d8PCwlCKhoiIiND1pHs5ObmLFy/u2LGjsbHRycmJYX6zODk5TU1NSYVOYWGhu7v7r1+/KioqjI2NSbcLAbrAycmpra396tUrY2NjtLPQIrjgxYD6j/o8ffrU39+fk5PzwoULcKGX2VRVVZFe9NevX2dmZm7YsKHvofb29u7ubhTXthcREaH3mWciIiIhISE5OTkhISFoZ6EIWVnZsLCw2bNnl5SUwE4XdOTp06cvXrzQ1taGa16/A6UPA+q/Ci0LC4uSkpK1tbWcnBy6qQDZ/fz5c9OmTTY2NmvXru27sNVfdXW1kJBQc3Ozr6/vgQMH+k/r4eHhWbNmzfHjx6mY938wQOmDIAiBQDh69Ojdu3fv3btXW1sbGBjY0dGBdihyYmVldXd3v3//vqKiItpZwEgdOnTo+vXrZ86c+fz5My3cTECDoPRhQBgMprW1lfQ1Hx8fnc6oAMMrKiqysbGZO3eura1tR0fHkDcckbbKcnd3nzFjxoDb2hEEWbNmzZcvX9LT06mSdyBhYWEGKH0QBBEQEDhx4sSJEycCAwNv3Lhx6dIltBORH1zqoi94PN7b2/v58+fXrl1jpJWoyAj+UxiQubn57t27Se+FfHx8jPEGAwb4/v07Ly/vkiVLqquru7q6htyxXFRU9Nq1a+PHj9++ffvgRx8+fNje3t5/BT9qIhKJDPNHeezYsd7e3l++fDlz5kxMTExpaSnaiQBTY2Vl7erqwuFwAgICaGehUTDNmQFt3ryZn59/y5Ytd+/ehVEfRmVgYPD27dtNmzZVVVVt3LjR19dXVVXVwsKifz1x6NCh3z392bNnYWFhwcHBsrKy1Ig7SF1dHYFAQOXUlKCtrR0fH48giKWl5fHjxwMCAtBOBJgXKytrZ2cn2iloGoN86gIDLFu2rLm5uampiYeHp6GhAS73Mh4sFuvu7r5s2TJXV9eQkBBNTc34+PiRL0B3+/ZtW1tbtOoeBEEaGhpGspYg3VmzZk1eXt6A1eQAoCY8Ht/V1YV2CpoGpQ+jKS0tffbs2ZMnT7i4uMrKylhYWNjY2FpaWtDOBSjC3Ny8s7NTWVnZzMwMg8GMfHwbg8HExcX5+PhERERQMuBvNTQ0MNKoTx88Hu/l5XX48OHY2Fi0swAmRbrghXYKmgalD0OpqqrasGHDgwcPnjx5smDBAjk5ubKyMnZ2doZ8jwEk2traX758KS0tbWxs7FvF548CAwNXrFjByckZHx9PJBIpmnBItbW1jPpjqa6uHhYWFhcXd+jQof7XHU6dOoXiQkqAeUDp80dQ+jCUqKio+fPn+/v7+/r6btu2jYWFJTIy0sDAgGHWWwODCQkJrVq1ysPDg4eH582bNyN8FhaLNTQ0ZGdnnzNnDio/HqWlpYy3zmxra2tWVlZ3d7ekpGRYWFhbW9uaNWtycnIQBCkuLr59+/aLFy98fHzgAjSgKDweD3N9hgelD+NobW29c+fOqlWr+lo+ffr0/Plze3t7FFMBKrCyspo5cyYOh1NQUPirJz579mzOnDkUSjWMT58+NTY2ojjTiELOnDnj5OS0YsWK+vp6Tk5OHx8fa2trR0fHU6dOHTp0aP369RcuXCguLj5z5gzaSQEjg1GfP4LSh3G8fv1aQ0NDTEyM9G1ra6uHh8e+ffsY77M1GICVlXXt2rXR0dH9t6oYbPBgAwsLCypXu8LDw83NzRnm5vY+3d3dmzdvNjAwcHJyIn3snjdv3rVr1yoqKoSFhVetWsXJyenv7//06VNYZhdQDpQ+f8Rof3qYWVVVlaSkZN+3N27cUFdXnz59OoqRAO14+/bttGnTsrKy+jfq6uq+ffuWykm+ffv27du32bNnU/m8VKCsrJybm2tvby8qKtp3fzs/P7+3t7ePjw+p1OPh4QkICPD39//x4weqYQHD4uTkrK2tRTsFTYPSh3H035q7p6fn8ePHCxYs6H/Ap0+ffHx8YC8e5vTr1y9RUdF9+/Y1Njb2NWppaX369ImaMTo7O/38/NatW8eQCwSrqKjk5ORgMJj9+/enpKR8+PBhyMOUlJQcHR1dXFyampqonBAwAxMTk6ioKJhSNgwofRhHUVGRqKgo6Wtvb28hIaEpU6b0PfrmzRsXF5f29nZG3WoRDE9cXFxGRmbWrFlOTk59u15wc3P37XlCHd7e3sLCwitWrKDmSalGXl6+rKystbWVnZ3dwcHh5MmTv7ueuGDBgilTpgQHB1M5IWB4LS0tsbGx5eXlT58+RTsL7YLSh0HU19enp6fr6emRvn379u327dv778vNw8PDwsKyYcOG5ORkmPzPhMTFxcvKynbs2DFz5sxNmzYdP368s7OTjY2NmttthoeHFxQUeHl5MeothywsLFpaWgsXLpw/f76Pj09eXl5ZWdnvDjYxMUlLS6NmPMAM8vPzX716JSMjQ/1r2XQENrJgBKmpqcePH583b17f8rh6enpRUVGurq4sLP95iSdMmLBkyZIdO3bg8fiUlBR9fX308gIUiImJlZWVYbHY1atXCwoKHjp0aNWqVdevX5eWlqZOgHfv3sXFxYWHh7OxsVHnjKg4duxYU1NTc3MzHo/PysoSFxf/3ZHjxo379etXc3Mzo65vBFBx7do1RUXFgoKCv73fk6nAqA/da2xs3L9/v62traura1/j1q1bi4uLo6Oj+x9pa2u7a9euzs5OWGWfCXFycrKzsx84cGDJkiVhYWHHjh179uxZYWHhgQMHqBPg6dOn69atExYWps7pUMTNzS0uLi4oKDhr1qxhxrdYWFiUlZUzMzOpmQ0wtl+/fr1//z4oKEhaWlpXVxftOLQLRn3oXnR09IwZM2bNmtW/kY+PT1dXd/AkytmzZ48bN44hZ5iCPzpw4EBNTc2mTZtkZGQQBLl58+aKFSs4ODioc/bPnz8vXryYOueiF9OnT7927Zquri7j3ecPUPHjxw8VFRUeHp6oqCi0s9A0+H2je0pKSpmZmYNXccjNzZWXlx98vISEBDN88mYSnz9/trS07O7uHsnBM2bMMDU1JdU9CIJ8//6dakPinZ2dRUVFSkpK1DkdvVi9enVnZ+eFCxfQDgIYRGlp6TDXWEEfKH3onoGBARaLzcvL69/Y09Pz4cOH/nd4AUb17du3Bw8eDHNAY2NjZmZmUlJSfHx8SEjI69evSY01NTVUW0/5+/fvMjIyeDyeOqejFzgc7siRI/fv33/+/DnaWQAjUFJSSk5OhvUM/whKH0YgIyPz8+fP/i2ZmZmSkpL8/PxoRQLUwc3NjcViL126NHgNj2fPnt28eRNBEGdn58OHDyckJHz58qWrq8vT07OxsbGrq4uVlbXvOktNTc2xY8cot8Tw58+fVVRUKNQ5XePn5/f39z9y5EhJScmnT5/y8/PRTgTomKamppycXHx8PNpBaB3M9WEEUlJSJSUl/Vvy8vLgnYYZ8PDwEAgEHh6eyMjIdevW9bW3tLR4eHhISEiYmZkpKCiIiopaWVmRHqqrq3vw4IGFhQUWi3337t2ECRMiIyOvX78+f/78wMDArq6uBQsWkPfmcyKR+Pr1awMDAzL2yUiUlZWXL19+4cKFd+/e4fH40NBQCQkJtEMBemViYvLw4UNGXTqLXGDUhxFIS0t/+/atf0tXVxdcXGAGBAKhpaXFyckpISGhf/uLFy8UFRVJi9lPnz69/wofY8aMKS8vRxBk+/btR48enTNnTklJSWRkpIuLi7e3t5eX171798gbMiwsrKGhYf78+eTtlpGYm5s/e/aMi4vLyspq586dI5y8BcBgWCy2paUF7RS0DkZ9GMGcOXPOnDlTWFjYN3Wju7u7b0UfwMBwOJy0tHR0dPSAi5sPHz4cP348afCGk5Oz/7qFpaWlpOnGJiYmJiYmLS0tpOWgkpKSfH19lyxZMnPmTDImfPr0aUJCQkREBGMv5zO83t5ed3f3rKysgICAcePGDT5AUFBw3759eDx+3rx5ERERtbW1IiIi1M8JGEBiYqKRkRHaKWgdjPowAgKBYGlp2X9RfCh9mMfZs2e/fPlSWlra11JfX5+VlSUmJka6gUtBQSE/P7/vg2B5eXn/e0BIdU9aWpqvr+/hw4fd3NzIuMJeXV2dj4/P0aNH+3aXY07p6ek/fvwwMTG5fv36744xMTGZN28egiBcXFzwqR2MTmZm5tevX6H0+SMofRjEypUrP3z48P37d9K3OTk5/XdxBwxMREQkODjY0dGxr+XatWtz5879+fMnaXSHm5tbQ0PjzZs3pEcH3/7a2tp68OBBd3d3TU1N8maLjIycN28e3NOekZGhoqLS0tIyklvquLi4qLyxGmAALS0thYWFhw8fdnJyYuYR1hGC0odBcHBwWFlZBQUFIQhSWVn58ePHuXPnoh0KUImAgEDfy93U1HTjxo3169fn5OT0XVuZM2fOgwcPamtriURieXm5mJhY/6dfv3594sSJU6dOJW+qhoaGhISEvunVzGzOnDkZGRm3bt0aSenDw8MzYK0KAIZRUVFhYGCwcOFCJycnbW3tOXPmoJ2IDsA1EcaxbNmyyMjInJyc169fGxkZUW2VXkBTXrx4oaWlhcfjy8vL++7ymzVrVlxc3LJly1RUVNjZ2Tk5Ofs/hUgkDiiGyCIqKmrOnDmU6JnuyMnJRUZG3rx5cyTjavb29jt27JCVldXQ0KBCNkDvcnJyNDU1AwMD0Q5CT6D0YRx4PN7a2trJyQmLxYaEhKAdB6CjsLBQSUkpMTFx6tSpOByO1MjLy3v58uWOjo67d+8O3rmWQCBUVlaSN0ZjY2NcXNyVK1fI2y394uLiWrNmzUiOVFZWPnjw4O7duy9cuDB27FhKBwP0Li8vT05ODu0UdAZKH8Zx+/bto0eP2traTpo0acyYMWjHAegoKSnh5OR8//59aGjogIfY2NjMzc0HP6W2tlZAQIC8MaKjo2fPng3r04yOrq6uo6Ojg4PDxYsXYdgMDO/Hjx9wketvwVwfxlFbWzt58uSIiAjYoouZ8fHxPXjw4PDhwyPfyqeoqEhaWpqMGbq6umJjYzds2EDGPpmNkZHR0qVLDx8+jHYQQOuouRkfw4DSh3F8/fp1ypQpPDw8sIELM3N1db18+fKkSZNG/pRfv36R937ArKwsSUlJGHr8R7DrGfijmpqahoYGqm3GxzCg9GEQMTExubm5JiYmvb29/devA8wGi8X+7c3k7e3tAyY+/6N3797B1rmj9vLly/Pnz3d1dX358kVZWRntOICmZWRkaGho9G3GB0YI/r8YhKysbHd399KlS2VkZGBqJPgrnZ2drKysZOwwNTVVW1ubjB0yj9bWVk9Pz7i4uLKysry8PFgSCQzv48ePfzXEC0hgmjOD0NHRiY+PLy8vl5KSQjsLoDOkXdzJ1Vtzc/OPHz/gxuzR+fHjh7S0NAaDqa+vr6ioGPmELcAkkpOT8/LyysrKSktLy8rKysrKwsLC0A5Ff6D0YRysrKxQ94BREBUVHbzO4ailpaWpq6vDJJXR6erqIhKJXFxc9fX1lZWVcMsC6O/06dNJSUnTp08fO3bstGnTxowZIy4uDku4jQKUPgAwu7Fjx+bn50+cOJEsvaWmpuro6JClKyY0btw4AoHQ1NSkqanZ2trKx8eHdiJAE4hE4vHjx9PT00NCQuCn4t9B6QMAs2NjY6upqSFXb+/evfPx8SFXb8yGi4vr3LlzCIKUlpYKCQlhMBi0EwH09fb2Hjly5MePHxcuXCDj7sLMDEofAJhaaWnpo0ePrl69Spbe8vPz29raFBUVydIbM2tsbOTm5kY7BUBZbW3t27dv7927193dfebMGfLeicnMoPQBgKmdOHHC0tKSXHNK4uLiTE1N4VbbfycoKEjGoThAX2pra2/cuPHmzZvi4mIdHR0jI6P58+fD/DkygtIHAOb16dOnr1+/kmvJ4La2tgcPHpBrAInJCQoKNjY2/vHmu9bWVkdHx+rq6r17906ePJlq8QDl5Ofn79y5c9q0adu2bZs4cSILC7xNkx98OAOAeeXl5enq6pLr0+Tjx48nTZokIiJClt6YHBaLFRQUrKqqGuaYxsbGLVu2yMrKbtu2zdPTMyAgoKenh2oJASW8f//ezs5uy5Ytrq6uWlpaUPdQCJQ+ADCRO3fuODs7933b2tpKxtkDubm5cG8XGYmJiZWWlg5zgI+Pj5qa2p49e2bOnHn16tWfP39u27atpaWFagkBed25c2ffvn2+vr4LFixAOwuDg9IHAGZBJBIvX7788ePHvpbW1lYyLgry69cv2KqdjCZOnJiamvq7R79+/frp06dt27aR7gIjEAjHjx/n5ua+fv06FTMC8uju7j516lRISEhwcLCmpibacRgflD4AMItXr16xs7O3t7e3tbWRWsg76gOlD3lNnz79zZs3v3v0/PnzGzZsYGdn72vBYrEbNmxISEjo7e2lSkBAHj9//ty0aVNBQcGlS5dgI1LqgNIHAAbR2dl55MgRJyennz9/DnnAs2fPpKSkuLm5y8rKEAQhEonZ2dnkWsS5t7e3vLwcdmsnowkTJpSVlVVXVw9+6NmzZz9+/DA1NR3QPn78eD4+vpSUFGrkA+Rw7969DRs2LFy48NixY7BWIdVA6QMAg/Dx8ampqZk0adKOHTuam5sHH2BnZ8fGxmZgYCApKYkgyO3bt3t6egwMDMhy9urqam5ubjY2NrL0BhAEweFwurq6ycnJA9rv37/v5+fn7+8/5Px0MzOz+Ph4qgQEo9TS0pKbm/vw4UM3N7eIiIhz586tWLEClq+kJpg9Tt8qKirY2NjgswKIjo5OTk52dnbW19fPy8uLiYnZuHHjgGPExMQ8PDxIX9fV1Z05c+bMmTPkWoMHrnZRwqxZswIDA2NjYzk4OFhYWBobG4uLiwUEBM6dOycvLz/kU4yMjE6fPl1VVQX7f9Ga8vLyW7duJScnFxQUSEpKysjIKCoqenh49L9qCagDSh961dDQEBQUdPfuXWlp6StXrqAdB6Ds6dOnvLy8UVFRsbGxu3bt2rlz56pVq4aZwnz8+HETExMlJSVyBUhJSYFFnMlu3rx5ioqKpOlZ3d3dXFxc0tLSw6/yzMnJqaam9unTJ0NDQ6rlBH9EJBL37Nkzbty47du3a2howF3r6IL/fbpUWVm5detWLS2tmzdv2tjYpKenw00BTO7ixYsIghCJxICAAD8/P1VV1bi4uDVr1gx58OfPn9PS0sh4WaSwsDA+Pj4qKopcHYI+Y8eO/avju7q6srOzPT09KRMHjNKLFy+6u7v37t0LF7ZoAcz1oT+lpaW2traLFi3as2ePgICApaUlvOUAEgwG4+LiwsfHJyUlFRUV1dnZOeRhFy5c2LRpE7mG2YlEop+fn42NDSxmSAvev38vLy8vICCAdhDwX729vefPn9+6dSvUPTQCSh86QyQSd+3atWbNmnXr1pFajI2N379/33e7MmByGAzG1NT0+/fv48ePf/DgweADMjMz8/PzFy9eTK4zJiQktLa2Ll26lFwdggE6OzuDgoKKi4tHcvDLly9nzpxJ6Ujgr9y5c4eXl3fatGloBwH/AaUPnfn8+XN3d3f/txk2NjYFBYXc3FwUUwGaMn369O/fvxcXF/Py8g54qKOj48yZM5s3bx5+Z6iRq62tPX/+vLu7O2xZSjltbW3h4eGWlpbXrl0bZs2e/Px8X1/fx48fz549m5rxwPA6OztDQ0O3bduGdhDwXzDXh84ICAgMXqheVVU1OzsbpvsAEjwe7+PjU1hYqK+v37+9qqrK1tZWWVnZxMSEXOcKDAw0NTVVUFAgV4dgMF5eXjs7u5iYmMTExN7eXktLywEHdHd3Hzx48P3792ZmZjExMUJCQqjkBCSdnZ3Z2dm/fv0qLS399etXXl6egoKCuro62rnAf0HpQ2fExcVZWVmLi4ulpaX7Gpubm2GaBehPR0dnwHZaPT09Tk5OixcvXr9+PbnOkpycnJube+DAAXJ1CH5n3bp1qampnz59Iq3J1F9PT8/+/fs7Ojpu3bpFrp1oweh0d3cnJiZevHhRWFhYRkZmzJgxOjo6pqamKioqaEcD/wNKH/ozadKkjx8/9pU+ZWVlr169cnR0RDcVoHEtLS0FBQVWVlbk6rCtrc3X13f//v2wjCEVYLFYX1/f8vLywesRfPjwobi4OCIiYsi6p6ury8vLS0lJae3atTDH9ncSEhJqa2sHL4U1cr29vffv3w8ODpaSkvL391dVVSVjPEB2UPrQH3Z29q6urr5vr1y5Ym5uPvxSHwDw8PCQtrAg18KDQUFBkyZNgq3aqYaHh4eHh2dw+8+fP1VVVX833vPixYuSkpL8/HxBQUFjY2MKZ6RLbW1tZ8+e7ejoWLFiBRcX198+vbe398mTJ8HBwQICAgcOHICJB3QBSh/6U19f3zd9tba29uHDh7GxsehGAnRBQUEhLy+PLKXPly9f7t+/D5uE0wJeXt4PHz5UVFSIiooOeKiqqio6Onr16tVSUlI7duz49u2blJSUpKSklJSUmJgYDodDJTCtKSkpERIS4uLi+vLly+TJk0f+RCKRmJSUdOHCBTwe7+zsrKurS7mQgLyg9KE/jY2NlZWV7e3t7OzsoaGhxsbGsIYHGAlFRcW8vLwBc59HJzQ01NbWFnZQoQVz5sypqqravHnz4cOHSXNpe3t7U1JS4uPj09PT58+fP3v2bFZWVl9f3+zs7G/fvj179qykpKSmpkZMTEzy/8nIyEyZMoU5lxiurq4WFhaWlZV9+/btyEuf/Px8Ly+vrq6uLVu2kOV3ClATM/6g07ulS5dev379woULsrKyzc3NsIsFGCFFRcWXL1/+ez89PT3p6elubm7/3hUgi5UrV4qIiOzdu3fSpEljx469desWHx+fmZnZwYMHOTk5Scdoamr2vxbT2dlZWlr68+fPkpKSkpKSR48eRURE+Pr6CgoKovSPoKzKysrTp0+/efMmKChowI4r1dXVQkJClpaWmzZtGjdu3Ny5c//YW2Ji4unTp7du3bpkyRKYQUWPoPShP3Pnzp07d25bW1tmZqaCgsIoLk4D5qSoqBgaGvrv/eTm5oqKisJYI00xMDCYOnVqZGRkdXW1v7//+PHjhz8ej8fLysrKysqSviUSiSEhIWvXrvXz85swYQLF41JRZ2dnZGRkdHS0ubn5rl27nJycIiIi+v/0kkofMTGxkydP2tvbJyYmTp48WVNTU01NbcBqVb29vR8/foyNjS0oKAgODv7bPUYA7YDSh15xcHBMmTIF7RSAnsjIyJSXl3d0dPzjPVmpqana2trkSgXIhYODY/PmzaN7LgaDsbGxUVFRcXJysrOzMzc3J282FJ07dy4yMlJBQeHjx4+CgoLz5s1zdnYmTdAhHVBdXU0qARUUFEhXCd+/f+/m5jZp0qRDhw6RjqmoqLhy5cqjR4+EhYXnzJnj6ekJNzbSNViAFQBmwcLCIi0t/ePHj3/s5/3793BjF90hEokJCQlbt251c3NrbGwc8hg9Pb3Q0NDr168fOnTodxvA0Z3169cHBATY29uvX7/++fPnvb29YmJiDg4OeXl5pANycnKkpKRIXxMIBFVV1bq6uvLy8pycnMuXL5eWlvr4+FhaWrKxsUVERERFRW3cuBHqHnoHoz4AMBHSTV7/ssBaU1NTTk4O3MFLX8rLyw8fPtzU1GRtbZ2SkuLq6nrq1KkhJzVLS0uHh4cfOnRo8+bNAQEBDLBWKh8fX9/OHoqKiitXroyMjHzz5s3WrVtnzJjR2dnZ0tJCGkHv6ek5cuTI06dPW1tbNTU1PT09d+zYERERsXz58vj4+MHbwgD6BaM+ADARRUXF79+/j/rp9fX19vb25ubmfZNnAY27e/eugYHBihUrtLS0wsLCZsyY4ezs3N7enpyc/LuncHJy+vj4zJkzZ926denp6dRMS2nCwsIGBgbPnz9ftmxZXFyciIjI+PHjL168SJrTU15e/ujRIwkJCXV1dW9vbwkJiejo6Dt37mzZsgXqHgYDoz4AMBEODo729vbRPbe+vt7GxmbWrFn29vbkTQUoR0FBAYPBhIeHy8nJkVowGMy8efNevXo1zC3ZGAxm3bp148eP37t3744dOxYuXEitvBTX0dHBzs6OIAiBQLCxsen/kLCwsJCQ0KxZszZt2kQqhlhZWcm10S+gKVD6AMBEioqKZGRkRvfc0tLS5uZmOzs78kYCFDVu3DhbW1tnZ+fQ0NC+25qIROJIZqvo6OgEBQU5ODg0NzdbWFhQOCk52dvbV1ZWCggIkG5FFBUV7erqamlpaW5ufv369a5du4Z8Fh6Pj4+Pp3JUgAq44AUAE/mX0kdFRUVMTOzVq1fkjQQozcLCYvbs2bt27WptbSW1NDU1jXDrG1lZ2eDg4GvXriUmJlIyI5lpaWnV19cXFxcLCwuLiIiUl5c3NDSws7PLysoGBgby8/OjHRCgDEofAJhIYWHhqEsfBEE0NDSKi4vJmAdQh4ODg4yMzPnz50nftrS0jHw9MAkJib179964cYNi6Uajra1tmEc3btx4+/btdevWPXjwIDU1dfz48VZWVhs3brSwsIAZ+gCB0gcA5tHZ2VldXT1mzJi+FtLtWiPvgYODY/i3HECbMBiMtbX106dPiUQigiCfP3/+q+X4NDU1y8vL79+/jyDIly9fTp8+vXv37j179vzuJnlK+/z585IlS4b/0eXg4LC0tExISDAyMnrx4sWSJUtsbGyuXbvW09NDtZyAZkHpAwCzaGxsZGdn71t3Pz093djYeMOGDXV1dSPsgZ2dfdSzpAG6pKWlubi4cnNzKysr8/Pz/2plJhwOd/bs2YsXLy5cuHDv3r04HM7IyEhAQMDV1RWVSkJZWVlKSsrOzm6Y+9RI8Hj8woUL/f39Hz16tH79+uTkZBsbm4qKCurkBDQLtdInICBAXV0dh8P5+vr2NdbW1i5evJiTk1NBQeHu3bukxnfv3unr6y9atKikpITUoqCgICYm1nfd2tXV1cHBgcr5AaA7QkJCoqKiWVlZCIK0tbUdPHhw9uzZmpqaI5/6UF9fD1uW0q+ZM2e+fPny8ePHpA1N/+q5ioqKV65cOXXq1M2bN+3t7efMmePi4sLCwnLy5EkKpR0GBoPZvXt3R0eHo6Nj3zvF8PB4/LRp006ePKmhoXH06FFKJ2RUDPPGjVrpIyMj4+fnN2CjOAcHB1ZW1vLy8sDAwJUrV5aXlyMI4uHhER8ff+jQoYMHD/Yd2dnZ2XfdGgAwQgYGBg8ePEAQ5OzZs+rq6tXV1X+1ZUFVVZWwsDDF0gHKmjlz5osXL968eTO6ncY5ODgUFBT6vsVisd7e3ikpKQsXLrS3t/f396+vrydb1j8ZN26ch4dHb2+vp6fn5cuXR/iszs7O8vJy2Pdw1Ebyxl1WVobQ/Bs3aqWPhYXFggULCARCX0tbW1tcXJyXlxcPD4+pqammpubNmzcRBCESiRgMBovFkq5Skzg6OgYEBPTVjwCAkVi6dOnjx48TExOfPXu2cuXK79+/z5o1a+RPr6yshNKHfqmqqjY0NPT09JA+Vf47bm7u69evh4aGrlu3TlBQ8Pbt297e3l1dXWTp/I9MTEw8PT0xGMzZs2ePHj3a29s7/PHl5eWbNm1iYWFxdXWlTkLGM5I3btKMeBp/46ahuT55eXk9PT2qqqqkb9XV1XNzcxEE8fLyMjU1dXV1PXDgQN/Burq6GhoaNFI/AkA7GhsbHR0djYyMpkyZMvhGdAEBgXXr1h08eHDPnj3Pnz9fuHBh3yaOI1FVVcUAOxswLSwWq6+vn5mZScarlhgMRkJCQldXd9OmTStWrKipqTl27Bi5Ov+jhQsXenl5sbKy3r9/f//+/cNUXa2trdu3b583b97BgwdhBy4yGvzGTZp+TuNv3DS0pGFzczOBQOibg8nDw0MaN5s6deqQS4mQ/me3bNkyZG9lZWUfPnygXFpAZfCCjkR1dXVgYKCGhoazs/PRo0fz8vIG7zgxbtw4JycnDg6OqKgoNze3v/pfLSgo+PnzZ1VV1b9HLSkpKSgogNeUyiZNmsTHxycoKEiJ//mampp169YdOHCAjY1txowZ/9jbr1+/ODg4+pZh/B0REZGVK1deunQpJSXF0tJyx44dHBwcA44hEoknT54UERFRVVWFH7mRy8vL6z9mM6TBb9xFRUXIaN+4qYaGSh8CgdDc3EwaJUMQpLGxsf+o2mDD1491dXX5+fkUCQrQAC/oHzU0NJw7d27WrFmkRXgxGAwHB8eQ/2ksLCx3797l5ORsa2sb+f9qW1tbZ2fnr1+/yJK2vLy8qqoKXlPqExYWptB/e11dXWlpqbGxcXR0dP81FEbn+vXrnz59UlFRmTZtWt8uHEMSFRWdP38+adFFNze39evXD1iw8devX1+/fnV0dISft79SXl7+x9KHvG/cVENDpY+CggIOh8vJyVFTU0MQJCsra8WKFcM/hVQ/DrnCuoqKyvLlyykSFKAhJycHXtDh3bhxY8mSJQYGBkFBQSIiIsePHx9mYD8pKcnZ2Xn+/Pkj7z8/P//OnTvkehXy8vL4+fnhNWUkpF9SIpF469YtIyMjHh6ef+ltwYIFpKk5r169UlRUHH4+/vLly2fOnBkQEKClpZWYmOjv76+kpNT3aHp6+ufPn1euXPkveZhQUlLSmzdvhj9m8Bv30qVLh3/KMG/cVIPaXJ/u7u729vbe3t6+Lzg4OMzNzb28vJqamhITE9PT083MzIbvhFQ/RkVFUSczALQsMzOzpKTk+PHj9vb2Z86cGabuKS0t/fLli4GBwV/1D3e2g5FoamrCYrH/vt4PgUA4ceJEXV2dpaVlWFjY7du3hz/e0NBwz549nz59mj179tatW4ODg/um/rS3t8MupGQxkjfuZcuWDd8JLbxxo1b6uLq6cnBw3Lx5093dnYODIyYmBkGQM2fOtLe3i4iI7Nq1Kzo6WkxM7I/9eHl51dbWUj4vALQuNzc3Pz//+PHj06ZNI+07/TsJCQkLFiz4qwnOCIK0tbUNnkUBAElzc3NWVpatre2iRYv09PR4eXn/vU9xcfHAwMDw8PBt27adP3+etC7DMAwMDFxdXe/du+fu7p6bm7tu3brk5ORnz54dOnRIT0/v3/OAkbxxi4uL/7Ef1N+4UbvgFRgYGBgYOKCRdHvkH5+bl5fX97Wuru4fL0YCwAz27t1LIBD+uEVXT09PYmLiKK61Q+kDfufx48fHjx9vaWnZtWuXjo7O31bVw1BRUdm3b5+/v//+/fsPHjzIyso6Z86cYY6fPXs2Doc7fPjwiRMnioqKwsPDW1tbjxw5MnHiRHJFYmYM88ZNQ3N9AAD/YoT7MmZmZoqIiPzVFk4kUPqAIR04cODLly++vr7x8fGUGFyZNWtWaWnpqVOnfH199+zZw8LCMnPmzGGO19fX379//86dO6OiohYsWED2PIAB0NC6PgAAKkhPT9fS0hrFE8lb+hw6dMjDw2PJkiWVlZXk6hNQX2Nj48uXL69cuaKurk65s1haWk6aNOnixYvHjh3z9vb+49ZdpC0UgoODKRcJ0DUofQBgLh8+fJg8efIonkje0kdQUHDu3LmLFi06cuQIufoE1Pfu3TtNTU0yXuH6HRcXF1ZW1vj4+FOnTnl6ev6x+lm/fv3Lly/hbnYwJCh9AGAiXV1d2dnZo5v3wMLC0tbWRq4kixYtSk5OHjdu3JcvX8jVJ6C+lJQUXV1dKpwIi8UePnw4Nzc3NTU1ICDA09MzPT19mOO5ubnXrl0bFBREhWyA7kDpAwATyc7OlpWVHd32jdra2ikpKeRKIiUltWTJEg8PD9gUjK5RrfRBEISTk/PEiRPXr1+vrq4+cuSIq6trRkbGMMfPnz//06dP1MkG6AuUPgAwkVFP9EEQRFFRsa2traSkhFxhxo8fHxwcvG/fPnJ1CKgsPz8fh8NJS0tT7YwiIiLHjh3z9fWVk5M7fPjwnj17goKCuru7hzxYUFCwoaHhd48CZgalDwBMZNQTfRAEwWAw06dPT0pKImMeRUXFcePGkbFDQE0pKSlTp06l8knHjRu3YMGC8+fP6+joREdH5+bmbtiwYcg5Pc+ePePn54fVT8BgUPoAwCz+ZaIPiZmZ2fXr13t7e/8xSWpq6osXL3Jych49elRQUPCPvQG0/OOP06ht3rz55cuX379/FxISOnHixNKlS21tbSMjI/v/ZMbHx/v7+584cQLWcQaDwbo+ADCLt2/fjh07dnQTfUhUVVXFxMSePHkyb968UXdSUFCwf/9+JSWlwsLC+vr68vLymzdvjro3gKL6+npBQUHqn5ebm3vDhg3nz58/duwYgiCmpqY6Ojqenp4PHz4cM2ZMd3d3TU1NS0vLxYsXpaSkqB8P0D4ofQBgCvX19X5+fu7u7v/Yz9q1a0NCQv6l9Pn+/buWltbGjRtTUlIWLlwIO5jSr/r6+n/co3TUzM3NIyMjs7KyJkyYgCCIhITEhQsXkpOT29vbcTgcFxfXhAkTYAVO8DtwwQsApnDkyBEjI6N/vxlHT0+vvb39w4cPo+6hqqqq766ujIwMFRWVf4wE0NLY2EiWvbpGAY/Hr1ixov94IRaL1dPTMzQ0nD17to6ODtQ9YBhQ+gDA+Kqqqt6/f29nZ/fvXWEwmDVr1ly+fPlfeqiurkYQpKKiIioqavbs2f+eCqCioaEBrdIHQZCqqqo/7lgHwJCg9AGA8T169GjWrFnkWnJ3/vz53759G/U6uaamphkZGfv27bty5Yqenp6JiQlZUgEq6+zs7O7u5uTkROXsXV1dT58+nTZtGipnB/QO5voAwPju37+/fft2cvWGx+NVVVU/f/4sJyc3iqdzcnL6+/unpaUtXLjQysqKXKkAlaE45FNfXx8cHDx27FhFRUVUAgB6B6M+ADC4goKCmpqaUa9kOFhTU9OHDx/09fVH3YOampqenh4OhyNXJEB9DQ0N1J/j/PPnTz8/P3Nz887Ozn+fsw+YFoz6AMDgHjx4MG/ePCyWbJ9zPn78qKamNsK3vdbWVrSuiQCKamxs5OPjo9rpsrKyrly5kpGRYWZmFhsbi8pN9YBhQOkDACMjEokPHjzw8/MjY5+lpaXDL5dCJBJzc3NfvHjx6tWroqIiXl5eDQ0NDQ0NIyMjAQEBMiYBKKLOne29vb2vXr26cuVKZWXl6tWrvby84NYt8O+g9AGAkVVUVHR0dIwfP56MfZaXl3d3d/f29g4YSero6EhNTU1KSnr16hUPD8/MmTPd3NxUVVXLy8s/fvz44sWL9PT0gIAAMiYBKKL0ne2dnZ137tyJjIwkEAhr166dM2cOGUcuAZOD0ocBlZaWHj58uKam5uTJk2JiYmjHAWgqKSkh++6SxsbGfn5+y5YtW7NmjZaWFoFAeP36dVJSUlpamrKy8owZM9avXz9mzJi+4yUkJCQkJAwMDBYtWvTz509JSUny5gGooNw05/r6+tjY2NjYWFVV1f3792tqalLiLICZQenDaLq6uuzs7CwsLAoLC+/evbtp0ya0EwE0lZSUkH0tfwUFhZCQkPT09Bs3bkRERDQ2Nk6bNs3Q0NDDw2OYKyDs7OxmZmbR0dG7d+8mbx6ACkqUPj9//oyKinr48KGBgUFQUNDYsWPJ2z8AJFD6MBoikVhdXW1paZmWlnb69GkofZjcz58/KbSNkaam5t9+HF+2bNmqVat27doFO0oygObm5v5je/+INIs5PT3d3NwcZjEDSoPSh9Hg8fjJkyd7eHiQ935mQF96e3tv3LjBzc1dUlIyf/58tOP8h4iIiJyc3Pv372ElOgaAwWD+vZPe3t7Xr19fu3bt58+fS5cu9fDwIBAI/94tAMOD0ocBHTp0KDIyUk9Pb86cOWhnAeh49epVZGRkV1cXLy8vTW1ebWBg8OLFCyh9GAAOh+vp6Rn103t6em7dugWzmAEqoPRhQHx8fA4ODminAGh6+fLlunXrTp061dTURFPTipWUlJ4/f452CkAGWCy2t7d31E8/efJkdnY2zGIGqIDSBwAGVFZWlpGRMWbMmNraWppaUVBcXLy0tBTtFIAM/qX0SUpKev78eWRkJIq7nwJmBqUPAAzI1dW1urq6o6MjLCwM7Sz/Q1RUtLa2tru7G+0g4F+NuvQpKSk5fPjwiRMnoO4BaIFrqwAwIBkZmcmTJ1dVVdHU1S4EQXA4nJCQUHl5OdpBwL8aXenT0dGxZ8+ezZs3q6ioUCIVACMBpQ8ADOvt27fq6upopxhIQkICrnkxgNFNc46MjJSUlFy+fDklIgEwQlD6AMCYqqqqUlNTjYyM0A4ykISERFlZGdopwL/CYDBEIvGvntLa2nr9+vWtW7dSKBIAIwSlDwCMKS4ubsGCBVxcXGgHGQhmOjOGUYz6XL9+XVdXV0ZGhkKRABghKH0AYEDd3d2JiYlLly5FO8gQxowZA6UPA/jbUZ+2trarV69aWVlRLhIAIwSlDwAM6NmzZ7KysnJycmgHGYKEhMSvX7/QTgH+1d+O+sTFxU2ePFleXp5ykQAYISh9AGBAsbGxy5YtQzvF0GCuD2OQlZW9e/fuhw8f/nhkb29vVFTUpUuXNm/eTIVgAPwRrOsDAKOpra398ePHzJkz0Q4yNCEhoYaGhq6uLrSDgH8yZ84cLi4ud3f3FStWDHMZq7S01NPTk0gkXrp0SUJCgpoJSVpaWvz8/MTExOzt7al/dkCbYNQHAEZTX18vKCiIw+HQDjI0LBYrJiZWWVmJdhDwr3R1dadMmZKXl/e7A27fvm1lZTVjxoygoCBU6p6cnJzVq1enpaW1t7dT/+yAZsGoDwCMpqmpiZubG+0UwxEXF4fShwFERkZ+/fo1NDR0yEdv3LgRGRkZFBSE4pwzd3d3QUHBpqYmISEhtDIAGgSlDwCMprGxkYeHB+0Uw5GRkSkoKEBlGACQS1JSUnR0dHh4+JCbxN2/fz88PDwkJATdV9nT0/PJkyd6enra2tooxgC0BkofABhNU1MTjZc+c+bM8fT0XLFiBdpBwCh9+/bt0KFDJ0+eFBUV7Wvs7Ox8//7958+fc3JyMjMzz507h3p1q66uToMLmgPUQekDAKNpbGyk8QtekyZNampqgp286NepU6fY2dkTEhJSUlLY2dm/fPmSk5OTlpZWVVWloqJiaGi4Z88eQUFBtGMCMDQofQBgNLR/wQuLxVpaWoaEhGzevBm276ZH7u7u379/r6ioKC8vr62t1dLSsrKyunz5speXF9rRAPgzKH0AYDRdXV1DTr+gKQYGBq9fv3ZxcTl79iwrKyvaccDfERUV7X+piwSDwaASBoC/BTe3A8Bo+Pn56+rq0E7xZ3PnzuXn5/fy8iISiZmZmTt27Ghra0M7FACA8UHpAwCjERQUrKmpQTvFn2EwmIMHD5aWlp49ezYzMzM9Pf3w4cNohwIAMD4ofQBgNPRS+iAIwsbGdvz48efPn1++fNnZ2bmoqCgmJgbtUAAABgdzfQBgNGPHjv327VtnZycej0c7y5/x8vJeuHChpKREU1NTTU1t27Zty5Ytw2LhUxkAgFLg7wsAjEZQUFBRUTElJQXtICMlLCysqamJIIi8vDyBQMjOzkY7EQCAkUHpw4zi4uJWrVrV2dmJdhBAKfPmzXv06NHg9vz8/KCgoBUrVri7uzc2NlI/2B8ZGho+e/YM7RQAAEYGpQ8zioiI6O3tffDgAdpBAKUYGBi8efNmwJaNJSUl1tbWHR0d+/bt4+fnX7ly5bt379BK+DuzZ89+9uwZkUhEOwgAgGFB6cN0amtra2tr7e3tr169inYWQCn8/Pxqampv3rzp33j//n0TE5Pt27erq6s7Ojp6eXl5eXmdPHmyu7sbrZyDKSoqsrCwfP36Fe0gAACGBaUP0+Hn5zcyMoqPj8fhcPfu3RvmyJqamtbWVqoFA+Q1d+7chw8f9n3b2NiYkJBgYmLS16KtrX316tWsrKz4+Hg0Av6Wrq5ueno62ikAAAwLSh+mg8Fg9u3bx8bG9uXLl+Dg4N8dVlpaumLFivnz53/79o2a8QC5GBgY5OXleXt7t7a2EonEAwcOzJ07V0lJqf8xvLy8Dg4O169fp6kLTKqqqjk5OWinAAAwLCh9mBEOh/P3909OTo6IiPjdMZ6enpaWliwsLLAHIZ0iEAjHjh3r6OgwNDRcuHBhU1PTtm3bBh82ceJETk7Ot2/fUj/h70DpAwCgKFjXh3nh8fhh1n3Jz8+3trbm5+eH0odONTY22traqqmpJSUllZaWioqKsrAM/fu+cuXKa9euTZs2jcoJf0daWrqhoaGhoQF2NgUAUAKM+oCh8fLyCggI1NTU0OYt0OCPUlNTFRUVv3z5EhQU1NzcTCpziUTio0ePmpqa+h85b968b9++FRQUoJR0ICwWq6ysDAM/AAAKgdIHDE1OTi4/P19dXT0jIwPtLGA0vn37pq6ufvr06Z6eHnd3dwcHBwRBurq6vLy8Vq5cmZqa2nckKyurmZlZbGwsemEHgmteAADKgdIHDE1RUfHjx4/Nzc09PT1oZwGj8e3bNyUlJTk5ue3bt2/YsIGLiwtBEDweP3HixJkzZ7q4uFRUVPQdvGzZsqdPnzo5OaWmptLClGdVVdXPnz+jnQIAwJig9AH/lZSU5OLiEh0djSCIpqZmbGzshAkTZs6ciXYuMBrfvn0bN24c6eukpCR9fX3S19ra2lgslpWVtf9ML0FBwVu3bk2fPv3YsWPr16/Pzc1FIXE/KioqMOoDAKAQKH3Af/z8+fPgwYP6+vrXrl179OiRlpbWy5cvd+3ahcPh0I4G/lpDQ0NbW5uYmBiCIF1dXampqX2zmHV0dJKTk1tbWwdMImZnZzc3N7969aqFhcXOnTs9PDzq6+upn5xERESEhYWltLQUrQAAAAYGpQ/4j5SUFAUFhUWLFh07diwwMDA/P5+TkxPtUGCUvn79qqioiMFgEAT5+fMnHx+fgIAA6aHx48c3NjYKCAgMuTs6BoMxNjaOjY3l4uKysrJCsfiA6T4AAAqB0gf8x+LFi5uamm7fvq2goGBqago7fNG1kpISKSkp0tdjxoypqqrqm7OFxWInT55M2in9d3h4ePbs2WNlZWVra1tcXEzxuENRUVGB6T4AAEqAdX3Af+Dx+H379jk7O3d1dVVVVTU0NKCdCIze2LFj7969S/oaj8cTCITW1lZubm5Sy5EjR7q6uv7Yibm5OQ6Hs7OzS0hIGGYJKApRVVUNDQ2l8kkBAMwARn3Af6moqGzYsOHLly/8/Pyurq5oxwGjp6qqmpeX19nZiSBIU1NTW1tbX92DIAgWi2VjYxtJP0uWLGloaOjt7aVU0N9TUVH58uULKqcGADA2GPUB/2P58uVoRwBkwMbGJisrm5ub+/Dhw3v37qmoqIyun46ODiKRyM7OTt54I0EgEERFRX/8+KGoqEj9swMAGBiUPgAwJnV19cOHD3NwcMTFxfHw8Iyuk8bGxlE/d9RnRBCEdFLSTGcofQAA5AUXvABgTKamptra2idOnBAUFGRlZR1dJ93d3V1dXS0tLeTNNqTq6uqTJ0+ampquXLkyPz8fgZnOAADKgFEfABiTgoLC7t27/7ETcXHxuXPn7tmzR1tbG0EQHA63ePFiSowDvXnzxt3d3djY+OrVqxkZGfb29ufPn1dTU0tISCD7uQAATA5GfQAAw3F0dJw0aVJjY2NjY+PHjx/d3NwoMfW4oKBg8eLFTk5OoqKi8+fPX7Ro0b179xQVFUtKStrb28l+OgAAM4NRHwDAcPB4/KZNm0hf9/b27tq169SpUzt37iTvWXA4XP/d4qSkpD5+/MjKyiovL//ly5eJEyeS93QAAGYGoz4AgN8qKirasWNHbW0t6VssFnvo0KGkpKR79+6R90SsrKz9lxr69u0b6fZ7mO4DACA7KH0AAL+Vn5//7t27wMDAvhYeHp6AgIDjx49XVlaS8UR4PL5vMvWrV6+SkpK2bNmCIIiamlp2djYZTwQAAFD6AAB+i0gkTp8+PTk5uf/q3vLy8suXLz969CgZT6Srq/vmzZumpiYEQR4/fjxlypTU1NTi4mLYyQsAQHZQ+gAAfktISKigoEBJSWlA/WFlZfXu3TvSGjxkISIioqend/PmTQRBFixY0NTU9OjRIxsbm9evXzc2NqK4hzwAgPFA6QMA+C11dXUpKanv378PuOrExsamra39+vVrMp5r9erV165da21tnTp1qq+vr7+///Hjx2NiYpSVlWG6DwCAjKD0AQAMx83NTVZWdvB2tvr6+i9fviTjicaNG6enp+ft7d3XoqysTCAQ2traYLoPAICMoPQBAAxHVFT04sWLzs7OA9pnzJiRmppK2iGVXJydnQsKCuLi4vpapk+f3tvbm5aWRsazAACYHJQ+YKQaGhr27NkTHx+PdhBAbVgsFoPBDGjk4+NTUFAgb1GCx+P9/PyCgoK+fPlCapGRkRkzZkx5eTlc8wIAkAuUPmBEbt++vWrVKkFBwQsXLmRmZqIdB9CEWbNmXbp06efPnwPa/2XPLykpqd27d+/du7e5uRlBkLFjx5aUlKxZsyY8PPyfsgIAwP+D1ZzBn2VmZgYFBfn7+8vLy2dnZ1dXV6OdCNCEZcuWtbS0rF+/fsqUKTIyMhwcHN3d3ffv329ubr579+7ggaIRMjQ0TE9PP3TokJ+fn4yMTFFR0eLFi8PCwgoKCsaOHUvefwIAgAnBqA/4s7S0NHV1dXl5+R07digqKs6aNQvtRIAmsLGx2djYJCQkTJo0CUGQmpqa2tpaXV1dRUXFUdc9JDt37iwrK7t27RoXFxcXF1djY+OSJUtIt74DAMA/gtIH/JmFhUV2dva6deukpaX37duHxcKPDfgvAoGwbNkyGxubnTt3Ojk5VVVVGRgY/GOfeDze19c3LCzsx48fioqK58+fnzJlyoMHD7q7u8mSGQDAzOA9DPwZgUDw8/MzMzPrq3taW1vv3LnTNxcVAJLOzs6UlBR9ff1/70pCQsLa2vr48eMHDx7k4+NzcXFpbGx88+bNv/cMAGByUPqAEVFWVra0tEQQJCMjw8vLy9jY+MmTJ9u3b09KSkI7GqAheXl5HBwcBAKBLL0tXbq0oqIiJydnx44dsbGxq1atEhMTI0vPAABmBqUPGKnOzk4rK6sjR47Iy8vHxcWdOHFi//79V65cQTsXoCHKyspqamrk2t4Lh8M5OjoeOHDgxIkThYWF48ePf/Hixfnz5zs6OsjSPwCAOUHpA0bq6tWrQkJCMTExa9asERAQQBBEV1f3+/fvg9f5BUwLg8EcOHAgPT3dwMDA3Nw8MTHxHzucOnVqREQECwvL6dOnX7x40dvbW1JSYm1tTd594wEATAVubgcj9e7du7Vr1/ZvwePx2trab968WbhwIVqpAK3h4uK6fv16U1NTcXGxh4dHeXm5jY3Nv3QoKSnp4ODQv+XKlSvr168/cuSIhobGv4UFADAjGPUBI4XBYAbf2zVjxgyY7gMGwGAwPDw8ampqfn5+9+7dI3v/a9eudXd3d3FxoUTnAACGB6UPGCkcDtfT0zOgUU9PLyUlhbwbOQGG0dTUJCIiQomep06dGhwcfPbsWdhZBQDwt6D0ASPFwsLy4sWL4uLi/o0CAgJycnLp6elopQK0rKKigkKlD4IgsrKyoaGhV65ciYqKotApAAAMCUofMFIODg4sLCwbNmyor6/v3w7XvMDvVFVVCQsLU65/cXHxCxcuxMfHe3h4wP4qAIARgtIHjJScnNzu3bvnzJlz7dq1/u2ioqK1tbVopQK0rLKyknKjPiSioqJRUVGSkpIrVqwIDg7u6uqi6OkAAAwASh/wd6ysrGJjY0m7apOkpKTo6OigGAnQLEqP+pCws7Pb2NiEh4dnZ2evXr06NTV1mIMbGxs/fPiQmZlZUlLS2tpK6WwAABoEN7eDvzNmzBhdXd34+Ph169YhCEIkElNTU+3s7NDOBWhRVVUVpUd9+khLS586dSopKcnb21tZWXnXrl2ioqI9PT01NTUVFRX5+fmfPn3Kzs6uqKhQVFTs7e0lbbaqqKhoZGQ0d+5c0lJVAABmAKUP+Gvr1693cHBYsWIFGxvb9+/fubi4JCQk0A4FaJGCgkJgYKCPj4+kpCR1zqivr6+rq3vp0qVVq1bh8fiGhgZ+fn4RERFpaekJEyasXLlSQUGhb42Gnp6ed+/ePXjwICgoyMHBwdzcnDohAQDogtIH/DVFRUUVFZXbt28vW7bszp07c+bMQTsRoFF79+6NjY3duHGjk5OTkZERdU6Kx+M3b95sYWHR1dUlICAweDGqPjgcbtq0adOmTSstLbW3t29ra1u9ejV1QgIAUARzfcBobNq06fLlyykpKY8fP16xYgXacQCNwmAwFhYWFy5cCAsL8/DwoObcGl5eXiEhoWHqnv4kJCQuXryYmJh4+vRpSgcDAKAOSh8wGmpqamJiYk5OTt7e3oKCgmjHATRNTk4uPDwch8NZWVnl5+ejHWdogoKC58+fT05OPn/+PNpZAACUBaUPGCVXV9cjR45oamqiHQTQAU5OzgMHDqxdu3b79u00uxSCgIBAUFDQvXv33r17h3YWAAAFQekDRklOTk5fXx/tFIBGvX792tjYeMAqO4sXL168eLGzszPN7nzCw8Pj7u7u7e0N970DwMCg9AEAkF9cXFx9ff3g4ZPNmzeLiooeOXIElVQjoaOjo62tffLkSbSDAAAoBUofAACZVVRUZGZmWltbP336dMBDGAzG3d09NzeXljddd3R0fPPmTUpKCtpBAAAUAaUPAIDMbt++bWRkZGxsnJSU1N3dPeBRTk7OY8eO3bhxo6CgAJV4f8TFxeXh4eHj4wOXvQBgSFD6AADITFhY+NevXyIiIjw8PDk5OYMPkJCQcHZ2jo2N/fnzJ/XjjYSOjs7kyZPPnDmDdhAAAPlB6QMAILMFCxZ8+/bNx8eHm5tbWVl5yGNUVFT09fVdXFza2tqoHG+EnJycXr58OfyOYAAAegSlDwCAzPB4/NatW+vq6vz9/fF4/O8O09XVVVNT8/DwIBKJ1Iw3QgQCYe/evYcPH4bLXgAwGCh9AADkZ2JiEhAQICYm1tdSW1s7+OKXi4tLTU1NWFgYddONlJ6e3sSJEw8ePNje3o52FgAA2UDpAwCgiNLS0tTU1Nzc3Pb29qtXr1paWu7atevRo0f9j8Hj8QEBAfHx8R8/fkQp5h+4ubmxsbFt2LChuLgY7SwAAPKA7UsBAOT3/v17JycnVVXV0tLS+vr6KVOmnDp1qrOz08nJSVtbm5+fv+9IQUHB1atX3759e+LEiejl/S12dnYvL6+7d+9aW1vv3r3b0NAQ7UQAgH8FpQ8AgPxIu4eeOHGClZW1rq6ub6M3Y2Njf3//AUsaGhoaXrx40dXVlZWVFY2wf2ZsbKygoLB79+7U1NTdu3ezsMBfTgDoGFzwAgCQn4KCgqqqqoWFRV5eXv8NbvX09AbfMyUiIiIjI5OWlkbdjH9n3Lhx0dHR9fX1dnZ2VVVVaMcBAIwelD4AAPLDYrGHDh2ysrIiDfA0Njbu27cvKCjI09PTw8Nj8PHGxsbh4eG9vb1UT/oXuLi4/Pz8pk2btm7dOhov1AAAw4DSBwBAKaamph0dHUFBQfb29tzc3M3NzTY2NjNnzhx8pJmZGSsra2hoKPVD/hUMBrNx40Zvb28PD4/g4GAar9UAAEOCK9YAAErBYrF79uyJjo42Nzc3Nzcf/shDhw6tXr1aU1NTS0uLaglHR1NT89KlS3v37s3MzDQyMlJXV5eWlsZgMGjnAgCMCJQ+AAAK0tDQ0NDQGMmRAgICXl5eHh4ekZGRAgIClA72j4SFhS9cuJCYmPju3buQkJCWlhZ1dfUJEyYYGhpKS0ujnQ4AMBwofQAAtEJHR8fExMTHxycwMBDtLH/GwsLSN5pVXV2dlZWVkZGxadMmLS2tHTt29F/OEQBAU2CuDwCAhlhbW2dmZpaUlKAd5O8ICQnNnj3b0dHx1q1b4uLiNLs7BwAAgdIHAEBT8Hi8iYnJzZs30Q4ySpycnA4ODt3d3bdv30Y7CwBgaFD6AABoi7m5+e3btzs7O9EOMkpYLNbNze3MmTO1tbVoZwEADAFKHwAAbZGUlFRUVHzx4gXaQUZPQUFh0aJFx48fRzsIAGAIUPoAAGiOubl5XFwc2in+iY2NTVZW1rt379AOAgAYCEofAADNmTVrVlFRUVFREdpBRo+NjW3Pnj1Hjhzp6OhAOwsA4H9A6QMAoDksLCw6OjqfPn1CO8g/mTp1qqqqKu0vUQ0As4HSB9Cxrq6u+vp6tFMAipCRkaG7W9wHc3JySkhI+PbtG9pBAAD/BaUPoFe9vb1r1qxZvHjx0qVL/f39X716hXYiQE7S0tJ0fcGLREBAwN7e3tvbG3b7AoB2QOkD6BWRSMThcDt37vT19RUXF/f3909JSUE7FCAbGRkZBih9EAQxNTXF4/EXLlxAOwgA4D+g9AH0CofD+fj4nD9/npWVde3atbq6ur9+/UI7FCAbaWnpnz9/MsBgCQaDOXLkSEpKyoEDB7q6utCOAwCA0gfQM1lZWXt7++3bt58+fTo1NXXSpEloJwJkw87OzsvLW15ejnYQMhASEgoNDe3u7ra1ta2rq0M7DgDMDkofQN/MzMwOHjzIzs6+bt06OTk5tOMAchISEmKYBZHxePyhQ4cmTZq0ceNGxriQBwD9gp3bAd2bOHHixIkT0U4ByK+6ulpYWBjtFGSDxWK3bdsmIyOzefNmb29vbW1ttBMBwKRg1AcAQIt6e3vr6+uFhITQDkJmixcvDggI2L9//40bN9DOAgCTgtIHAECLqqqq+Pj4cDgc2kHIT0NDIzQ09OrVq4GBgQwwjxsAugMXvACDKC8vT0pKqq+vZ2Nj4+DgYGNjExUVlZSUFBcXZ8i3T4ZXUVEhKiqKdgpKkZKSCgsLc3FxcXR09PLy4uXlRTsRAEwESh9A36qqqm7evPny5cvKysoZM2aIioo2NjZWVFS0t7eXl5f//PmzurpaRkZGV1dXV1d30qRJeDwe7chgRBi79EEQhJeX9/z58xcvXly9erW3t7eGhgbaiQBgFlD6ADoWHR0dFhY2f/58FxcXdXV1LHaIC7hdXV1fv35NSUkJCQnJz8+3trZesWIFjAPRvq6uriFfUEaCw+FsbGxUVFR27969fPlyS0tLTk5OtEMBwPgY/C8LYGyXL18OCgpydnaeOHHi794mWVlZ1dTUrK2tQ0NDIyIiXr9+vW7dus+fP1M5KvhbKioq2dnZaKegBj09vcuXLxcUFCxevPjs2bONjY1oJwKAwUHpA+hYZ2cnDw/PyI+XlpY+d+7c6tWrd+3aFRkZSblg4N/JyMg0NzfX1NSgHYQaREVFvb29L1++XFNTY2dnB9UPABQFpQ+gY3p6es+ePfvbZy1cuPDKlSt379719/eH+2toSnt7e0JCAmmwB4PBTJgwISsrC+1Q1CMhIeHh4aGnpwfVDwAUBaUPoGO6urqje2sUEREJDQ0tKiravXt3e3s72YOBUejs7LSwsEhKSnJzc7OxsSkqKlJXV8/MzEQ7F7XZ29tPmzZty5YtUP0AQCFQ+gA6RiAQRv32wMXFdfLkSQKBsGXLFthWiRbU1tZ2d3cfO3bs5s2bhoaGtra2ysrKTDXq08fBwWHy5Mk7d+6E7U4BoAQofQAde/369b9sWcrCwuLp6Tlr1qz169fDtkqoq6+v5+PjQxAEh8NNmDABj8dPmDDh69ev3d3daEdDwa5duwQEBIKDg9EOAgADgtIH0Ku2trYnT54sWbLkH/uxsrLauHHj5s2br1+/Dh+yUVRXV8fPz0/6+urVqxYWFtzc3BISEt++fUM3GCowGMz+/ftv376dkZGBdhYAGA2UPoBeZWdnjx07VkBA4N+7WrJkydmzZ5OTk5ctW/bgwYOenp5/7xP8rb5Rn9ra2tevXy9evBhBEHV1dea85oUgCB8f3969e728vFpbW9HOAgBDgSUNAb36+vWrvLw8uXpTVFQ8efJkenr6uXPnfHx8VFVVNTQ01NXV5eTkxMTERthJb29vXl7ep0+fWltb2djY8Hg8Dw+PqqqquLg4uXIysI6Oji9fvly5ciUjI2PZsmWkZQsmTJiQkpKyYsUKtNOhY+bMmU+fPj1z5szu3bvRzgIA44DSB9ArPT09GxubrVu3knH/I01NzdDQ0KampszMzE+fPkVGRhYWFjY3N8vIyEhLS8vJycnIyMjIyIiJiXFzc5Oe0tnZmZubm5GR8fHjx48fPwoJCWloaPDx8XV0dHR2dtbX1/v7+4uLixsaGhoYGIwZM2bI87a2tpaXl9fU1BAIBFlZWQ4ODnL9i+jIwoULOTg4srOzxcTENm/eTGpUV1cPCQlBNxi6XFxcVqxYYWJioqKignYWABgElD6AXsnKys6bNy8oKIjsH4i5ubmnT58+ffp00rctLS1FRUWFhYWFhYWPHj369evXr1+/2traeHl5CQQCaY+wSZMmLV682MPDY/AFuN7e3rS0tKdPn27atAlBEDU1NRUVle7u7vLy8qqqqoqKioqKit7eXjExMUFBwaamprKyMmNj482bN//Vao0MAI/HGxkZGRkZ9W+UlpZubW2trq4WEhJCKxi6uLm5ra2tT5w4AVOeASAXKH0AHbOxsVm2bNnSpUvJeOVrMC4uLhUVlQGfubu7uxsaGhoaGsTExIbfdwmLxero6Ojo6Ozdu7esrCw7Ozs3N5ednV1DQ0NYWFhMTExERIRAIPQdX1NTExISsnHjxlOnTklISFDqX0UnMBiMqqpqVlbW7Nmz0c6CGlNT06tXr6ampuro6KCdBQBGAKUPoGM8PDwbNmw4efLkqVOnqHxqFhYWQUFBQUHBv3qWuLi4uLj43LlzhzlGUFDQ1dU1JiZmy5YtoaGhwsLC/5aU7pFmOjNz6YPFYm1sbM6cOXPp0iUMBoN2HADoHm3d4WVoaIj5f7q6uqTGd+/e6evrL1q0qKSkhNSioKAgJibWd9eDq6urg4MDOokB2iwsLMrLy5OTk9EOQmYWFhZLly51cHCAJX2ZbTuLIRkaGmKx2FFs2wIAGTHMezRtlT4Igly9erWpqampqen58+ekFg8Pj/j4+EOHDh08eLDvsM7OzvPnz6OUEdAQHA63bdu248ePM97Cd+vWrZs+ffr27dvb2trQzoImNTW1r1+/MvmSSxgMZsuWLWfPnoWVFwC6GOM9muZKH3Z2dgKBQCAQ+m5yIRKJGAwGi8USicS+wxwdHQMCAmC5C4AgyIwZM8TExG7evIl2EPLbtm3b2LFj3dzcGK+wGzlOTs4xY8Z8//4d7SAomzJlipiYWGJiItpBAFNjjPdomit9duzYISMjs3jxYtLuzQiCeHl5mZqaurq6HjhwoO8wXV1dDQ0NWi4qATXt2rUrJCSE8a4NkZb0ZWVltbe3r62tRTsOarS1td+8eYN2CvRt3bo1NDS0o6MD7SCAeTHGezRtTXPeuXOnlJQUKytrUFCQoaHhly9f+Pj4pk6d+urVq8EHk/67t2zZMmRXT548yc/Pp3BeQD2vX78efov1jo4OMzMzbW1tqkWiGiwWW1paqqOjM3PmTIaZ9VxXV1daWtr313N41dXVSUlJP378oHQq2ldTU7N06VI1NTW0gwzhj7+kgL5UVVUNGG8e8B6dm5vLz88/uvdolBFplYKCwu3bt4d8SF5e/vHjx0Qicd68eYGBgXv27Nm6dWv/A4KCgoKCgqiRElDLgQMHhj+gpqZm9uzZVVVVVImDglevXs2dOzc+Ph7tIOTx/fv3K1eujPz4pUuXZmdnUy4PvSgqKjI0NGxoaEA7yBD++EsK6MvLly8XLlz4u0cVFBQSEhKGfOiP79Goo7kLXn1YWFj+OL/By8uLlq8mAmoSEBBYuHBhTEwM2kEoRU9PLzQ09Nq1az4+Pkw453f+/Pn3799HOwX6pKWl9fX1IyMj0Q4CmB1dv0fTUOnT1NQUExNTXl5eWVl58ODBsrKyadOmDf8U0tXEqKgo6iQENM7S0vLmzZs0+GtGLtLS0uHh4fX19ba2ttXV1WjHoSpjY+NHjx4x83TvPra2tjdu3KisrEQ7CGAug9+j9fT0hn8Kzb5H01DpQyQST506NW7cOAUFhcePH9+9e1dEROSPz/Ly8mLm6Z+gPwkJCW1t7bi4OLSDUBAnJ6efn5+Wltb+/fvRzkJV4uLi0tLSKSkpaAdBn4iIyOLFi8PDw9EOApjL4PdoUVHRPz6LNt+jaWiaMw8Pz+vXr0dyZF5eXt/Xurq6xH431AEmZ2dnt2nTpvnz5zPMdODBMBiMnZ3d6tWrX7x4MWvWLLTjUM/ChQvv3bv3xw+azGDdunVmZmYuLi5YLA19fAWMjZHeo+HXBjAUaWlpMzMz6u9rQWVYLNbJyen48eOdnZ1oZ6EeQ0PD5OTk5uZmtIOgT0BAQFBQsLCwEO0gANAlKH0Ao9m4ceOnT5/evXuHdhDK0tLSkpGRefjwIdpBqIeHh0dLS+vFixdoB6EJampqI1waAAAwAJQ+gNGws7O7u7t7eHj0bSjDqIyMjEY4/swwSNe80E5BE1RUVD5//ox2CgDoEpQ+gAFpa2vb29vv2rWL8dZ37m/atGmpqalMddOTnp7e169f4eYmBEFUVVVzcnLQTgEAXYLSBzCmJUuWTJs2zdXVlYFnw/Dz80tJSWVkZKAdhHrweLyBgQFTXeb7nXHjxhUWFjLwjzcAlAOlD2BYO3fu5OHhcXZ2ZuC3hxUrVgQEBDD24NYAM2bMeP/+Pdop0IfH4wkEQn19PdpBAKA/UPoAhoXFYn18fPj5+R0cHBh1nUNjY2M9PT07O7u6ujq0s1AJGxtbT08P2iloAicnJ6P+YANAUVD6AEaGxWIPHDggKSm5Y8cORn2T2L59u5GR0ebNm6uqqtDOAqiKi4urpaUF7RQA0B8ofQCDw2Kx7u7uioqKDg4OjPo+YWVltXjxYltbW5j/y1QIBAKj/kgDQFFQ+gDGh8FgXFxcVFVV7ezsGhoa0I5DEevWrVu1apW1tfXPnz/RzgKohJOTE0ofAEYBSh/AFDAYjJOT09SpU7ds2cKoM0OXL1++fv36LVu2FBcXo50FUANc8AJgdKD0AUzE3t5+5syZmzdvZtRtz83Nze3t7e3s7H78+IF2FkBxUPoAMDpQ+gDmYmtra2JiwsDTYhYsWODg4ODg4MBUd7wzJ5jrA8DoQOkDmI6VldWSJUvs7OwYtfpZuHChsrJySkoK2kEAZbGwsHR1daGdAgD6A6UPYEZ9k4J//fqFdhaKmDJlCqNu4DpmzJj8/Pze3l60g6CvqqpKWFgY7RQA0B8ofQCTIk0KtrOzY8hdTidOnJiZmYl2CoqQlJTk4eGB7asQBCkvLxcTE0M7BQD0B0ofwLzMzc03bdpkY2OTn5+PdhYyKywslJWVRTsFpcyePfvFixdop0AflD4AjA6UPoCpmZqabt261d7ensG2hfrw4cPkyZPRTkEps2bNev78Odop0FdRUSEqKop2CgDoD5Q+gNmZmJj4+PgcOHAgODiYSCSiHYc80tLSGLj0GTduXHd3d1paGtpB0NTY2IjD4bi4uNAOAkYjJycnLCysubkZ7SBMCkofABBNTc2IiIi3b986OTkxwB+j9vb20tJScXFxtINQCgaD2b9/v4eHBzNvWwZXu+hUZ2enl5fXvn37EhISMjIy0I7DpKD0AQBBEERERCQoKEhCQmLdunX0PvWHnZ198eLFZ86cQTsIBeno6JiZme3bt49pd3GH0oceVVRUWFtbd3R0XL16VVdXlyHvsaALUPoA8B94PN7Z2dna2trOzu7p06dox/knO3bsePXqVWpqKtpBKGjTpk3s7Oxnz55FOwg6ysvLYaIPffnw4YOVldW8efN8fHw4ODhMTEwiIiKKiorQzsWMoPQB4H8sXLjw9OnTp06dCgwMpN8RBS4urj179hw8ePDBgweMuuodFos9fPjwkydPPnz4gHYWFFRWVoqIiKCdAoxUfHy8m5vbwYMH16xZQ2pRV1ffsWPH9u3bGXVXQVoGpQ8AA40bN+7SpUt5eXkHDx6k34nP+vr6rq6uiYmJixYtCgkJqa2tRTsR+fHw8NjZ2YWGhqIdBAXl5eUMPJ2LkXR0dLi7u8fHx0dEROjo6PR/yNjY2MDAwM3NjX4/ZdEpKH0AGAIfH9/JkyfLysr8/f3RzjJ6enp6586dCw4ObmhoWLZsmaurK+Otczhr1qzc3Nzv37+jHYTa4M52evH48eOKioqLFy8OWapu27YNj8cfPXqU+sGYGZQ+AAyNjY3t2LFjWVlZwcHBaGf5J9LS0s7OzgkJCSoqKm5ubjY2NgxzX0lhYeHq1at1dHQkJCTQzkJtMM2ZXrS3t8vJybGxsQ35KBaL9fb2TktLu3nzJpWDMTMofQD4LQKBcPr06Tt37tD7rGcEQXh4eNatW5eYmGhqanrgwIGdO3fS+0hJb2/vxo0brays/P39mW15m5aWloaGBpjrQxdERES+ffs2zAFcXFy7du168OAB1SIBKH0AGA4/P39gYKCfnx+93/FOgsViFy5cGBcXN3XqVAcHBx8fH/qdzITFYidNmlRZWYl2EBSkpqZqaGiwsLCgHQT8mZ6eXl1d3fDXmnl5eVtbW6kWCUDpA8AfKCkp7dq1y9nZmQFWOyRhZWVdsWLFzZs3v379mpCQgHac0du7d++NGze+fv2KdhBqe/v27dSpU9FOAUYEi8WuWrUqIiJimI8ZBAKhpaWFmqmYHJQ+APzZggULpkyZ4uHhQb9jJINxcnK6ubmFhIT09vainWWUhISEHBwcvL29GfUG/t95+/bttGnT0E4BRmrRokU1NTUHDhyoqakZ8gBWVtaOjg4qp2JmUPoAMCKOjo719fXh4eFoByGncePGCQsLP3z4EO0go7do0SIxMbHAwEC0g1BPQUEBBoORlZVFOwgYKQ4OjuDgYHZ29uXLl69cufL48ePJycnt7e19BxQXF8P9etQEpQ8AI8LKyurn53fr1i0PDw9GWiPH1dX1+PHjKSkpaAcZJQwG4+npmZGRcevWLbSzUElycjJc7aI7bGxsbm5uT5482b9/Pw8PT1hYmKmpad8o8qVLlxYtWoRuQqYCpQ8AIyUsLHzt2jUhIaGVK1eePn36xYsXDLB9prKycmBgoIeHR3JyMtpZRomTkzMgIODs2bMvX75EOws1vHv3TldXF+0UYDSwWKySklJnZ2dbW5uioiIGg0EQJDU1tby8fPHixWinYyJwgwAAf4GDg2P79u0mJiZPnz5NSEjw8fFhYWFRVVVVUVFRU1NTUVGhx7us1dXVfX19PT095eXlHRwc5OXl0U7012RkZM6ePevo6FheXr5ixQq041AcHo9HOwIYjZKSEjc3N3FxcScnJw0NDQRBiETi2bNnt2zZgsPh0E7HRKD0AeCvycnJycnJkb4uLS3NycnJyckJDg7++vWruLj4hg0bjIyMSJ/n6IWmpmZcXFxsbOyWLVt0dHTWrl07btw4tEP9HUVFxYsXL+7cubOkpMTR0RGLZdghbTExsfLycrRTgL9WWVm5Zs2azZs3923jhSDIgwcPenp6DA0NUQzGhKD0AeCfSEhISEhIzJ07F0GQ3t7ejx8/Hj9+PC4uzsXFRUlJCe10f4GVldXS0tLU1PTmzZuOjo4yMjLr1q2bMmUKHdVwIiIiFy5ccHZ2Ju0TyahDI2JiYr9+/UI7Bfhr3Nzcenp68fHxcnJyTU1N7969e/fuHQcHh7e3Nx39ljEGhv1gBAD1YbFYTU3NS5cuLVy40MHB4c2bN2gn+mucnJyrV6++ffv2ypUrz507Z2lpGR8f39nZiXaukeLh4Tlz5gwLC4u9vX1jYyPacShi5syZd+/ehRXw6A6pytm5c+fp06cfPXo0fvz4c+fO3bhxg+5GWBkAlD4AkBkWizUzMztz5syhQ4fS0tLQjjMaWCxWX1//8uXLe/bsefXq1eLFi4ODg5uamtDONSJ4PP7QoUPjx4/39PREOwtFyMvLT5069cqVK2gHAaOhr69/9erVo0ePWlhYyMjIoB2HSUHpAwBFKCkpHTlyxM3NLTc3F+0sozdx4sTjx4+fPHmyuLjYzMyMXm4gx2AwO3bsKCoqoseBt5GwtbWNiYlhpEUWAKAmKH0AoJRJkya5urru3buX3peoHzdu3OHDh0NCQq5fv+7m5kYXG3qwsrLu3r07MDCQjq7WjZy4uLixsXFYWBjaQQCgS1D6AEBBBgYGurq6hw4dQjsIGYwdOzYiIkJAQGDVqlWfPn1CO86fTZkyRV5ePioqCu0gFLF+/fr4+Hj63YQEABRB6QMAZTk6OhYXFycmJqIdhAzweLyzs/O+ffv27t0bHBxM+++7jo6OUVFRZWVlaAchv66uLl5eXga+hx8AyoFfGwAoC4/HHz58+PTp0wUFBWhnIQ9dXd3IyMjMzEx7e3saX89aQkJi48aNzs7OjHc/1I8fP+hx8UkAaAGUPgBQnJyc3NatW11dXRlmc2YBAYHTp0/PnDlz3bp1r169QjvOcCwtLVVVVffu3Uv7Y1R/JS8vD0ofAEYHSh8AqMHU1FRRUfHkyZNoByEbDAazatWqwMDAo0eP0vhsYhcXl/b29jNnzqAdhJwKCgr6lhQHAPwVKH0AoBI3N7eUlJTnz5+jHYScVFVVo6KiamtrN27cWFxcjHacobGysgYEBLx48SI+Ph7tLGRDJBJhCWAARgdKHwCohJOT8/Dhw0eOHCktLUU7CzlxcXH5+PiYm5tbW1snJiYSiUS0Ew2Bh4fn1KlTwcHB79+/RzsLeUhISDDYDxIAVAOlDwDUo6KismbNGnd3956eHrSzkJm5ufmFCxfi4+NtbGx+/PiBdpwhSEpK+vj47N+/n2ZHp/6KpKQk7OQFwOhA6QMAVa1du5aDgyMiIgLtIOQnJycXHh5uamq6devWwMBAGlzIUVNT087ObteuXV1dXWhn+Vfi4uIMedM+AFQApQ8AVIXBYA4cOHD9+vXPnz+jnYX8MBiMsbFxTEwMgiDLly+/e/cu2okGMjMzIxAIWVlZaAf5VzgcjsHuWftbzc3N9vb227Zte/36NdpZAJ2B0gcAahMWFnZ0dPT09GSAsYch8fDwODs7+/r6RkVFbd26tbq6Gu1E/0NXVzclJQXtFP+qu7ubhYUF7RRoevToESsrq6Gh4bFjx9DOAugMlD4AoGD+/PkSEhLXrl1DOwgFqaurX7lyZcKECdu2baOpbb+g9GEMY8aM6ezsXLRoUXNzc1NTE9pxAD2B0gcAdDg7O1+6dInWRkTIC4fD2dnZaWtrOzo60s7CPxMmTCgsLKT39Z17enqYfBcLRUXFL1++fPz4kZOTs6GhAe04gJ4w9W8OACiSlJRcvHjxuXPn0A5Ccbt27RITEztw4ACNzE1hYWGRlZXNy8tDO8g/6enpYfJRHwEBAQ8PD09Pz9LSUib/rwB/C0ofAFCzadOm1NTUN2/eoB2EsjAYjLu7e2NjY0BAANpZkK6uLl9fXxYWlm/fvqGd5Z8w3gWv2tra0NDQxsbGkT9l9uzZsbGx586dExMTo1wwwHig9AEANVxcXL6+vl5eXgy/Nh0rK6u/v/+nT5+uXLmCbhJvb+9v374VFhbSe+nT09ODw+HQTkFOOTk5V65cWbVqVXZ29sifxcbGpqWlRblUgCFB6QMAmtTU1DZs2ODs7MwwO5v+DhcX14kTJ2JjYx8+fIhWBiKR+OLFi1mzZgkICPzV6AINYrzSh0AgSEpK7tmzx9HRce/evTk5OWgnolEfP348duwY4y2LSk1Q+gCAslWrVsnKym7bto0xVhkehoiIyIkTJwIDA9FahhiDwRAIhDt37nR3d5ubm6OSgVwY74KXhoZGWVmZmppaQkLChAkTNm7cCJOXBysrK9u9e3dsbCwNLhlKR6D0AUyntbWVRubb9jl8+PD8+fM3bdoUHBzMqIv9kMjJyW3YsMHDwwOtlyA+Pt7BwYFAIOjo6KASgFx6e3tp7cf4H2GxWGFh4bq6Ok5OTlVVVRkZGV5eXrRD0Zbu7u59+/atX7+eQCB0d3ejHYeOQekDmEttba2Jicm8efM8PDxoZxFYLBZrbm5+6dKlrKysdevWMeRCz31WrlyJxWLv37+PytnxePylS5esrKxQOTsZaWpqvn37lqYWTPp3vb29WCy2s7Pz4sWLJiYmaMehOSdOnBAUFFywYAE7OzuDjflRGZQ+gLlcuHDBxMQkMjJSXV09MDCQphYVlJCQOH369KZNm3bt2hUYGNjW1kb2UxCJxMuXLy9ZsmThwoXLly/fv3//vXv3qLzCDRaL3bdvX0xMTHt7OzXPS0IkEsvLy3/+/En9U5OXhISEnp4eac+QUcvIyKCp7eT4+fljYmI2b97My8u7cuVKtOPQnM+fP/f09Gzfvn3hwoU8PDxox6FjUPoAZpGfn29tbZ2VlWVtbS0mJrZs2bILFy5ERUUlJiaiHe1/GBoaXr16tbKy0t3dneydFxYWXr161c/PLyIiws/PT0tL6+nTpxYWFlQeAJOVldXS0nr79i01T0qCwWDCw8OfPXvm6OhI76NrVlZW165dq6+vH93Te3t7Dx06dPHixcLCQnLG+gc7d+5kZWW1trY+ePAgHo9HOw7NuXDhwvjx42fOnLllyxa0s9A3KH0As7h3756EhERUVFTfpyUxMbGzZ89euHDh8ePH6GYbQEBA4PDhw5mZmWSf+MzBwdHd3S0iIiIiIiInJ2dqanr06FFPT8+jR496eHhQYpzpd1asWPHu3btRv23/CxERkdDQUG1t7d27d58/f576AchFTk5uyZIlLi4uo5sf9vLlSwKBsGLFilu3bpE92+ioqKg4OjrOmDEDg8GgnYUW4fF4Gxsba2trtIPQPSh9ALPQ1NQsKysbsPa/tLT0qVOnAgMD9+zZk5ycTDvzRvF4/IIFC8h+H7iYmNiiRYtOnjzZv1FLS+vatWusrKw2NjY1NTXkPePviIiITJw40dfXlzqnG4CNjW3VqlWRkZGPHz++efMmKhnIYsuWLfz8/Hv37h3FJlZXr15dv369qanp3bt3KZENAJoFpQ9gFlpaWjU1Nc+fPx/QrqCgEB8fP2XKlODg4EWLFt27dw+VeINNnjw5KyuL7N1aW1unpaVlZGT0teTm5qampu7bt2/27NkbNmyg2j32hoaGxcXFKA458PHxnTx5MigoKDk5Ga0M/wiLxR4+fFhERMTS0jI9Pf2vnovH48vKysrKyhhsrjQAfwSlD2AWeDze09PTz8+vtrZ2wENcXFzm5uYREREBAQEXL16Mjo5GJeEAampqWVlZZB+I4uTk3L17t5eXF2ldkPDwcBcXl3Pnzu3du3fNmjWk9RWpMwGZhYXF29v7zJkzKM41kZKSOnbsmKenJ/0u7ozH43fv3r1///79+/cHBgaO/OKXvb39kydPzp8/7+npScmAANAcKH0AE1FXVzcxMTly5MjvDlBRUTl79mxkZGRSUhI1gw1JQECAh4enpKSE7D3PnDlzypQpvr6+ycnJN2/evHTp0uXLl/F4/JYtW4yMjFRUVI4ePUr2kw5p7NixW7ZscXV1RXFfdxUVlT179mzduvXcuXMVFRVoxfhHU6ZMiYqKKi0t3bBhwwhLSRUVlfDw8LCwsHnz5lE4HQC0BUofwFxI01nmzp27Y8eOly9fDj5ATEwsICDAx8fH398f9QsBMjIylCh9EARxdHT89u2bq6vr/v37BQUFWVlZDx48OHbsWHd3dxcXl7S0NKoVf+bm5tLS0r6+vkQikTpnHGzOnDlhYWE9PT2rV6+2t7dPSkpCMcyo8fPzHzt2bNWqVdbW1levXqXHfwIA1AGlD2AueDw+LCwsOjp68eLFx48f9/HxSU1NTU9P77+BqKqqakxMTHd39/Lly1HccApBEHFx8fLyckr0zMbGFhERER4e3reoMQaDcXV1bWpqunTp0oEDB44cOUK126+8vLx+/frl7e2N4jRzKSmpbdu23bt3z9jY+Pz580uXLr106RI97vNlbGx8+fLlx48fb9u2raqqCu04ANAiKH0AMxIWFp4zZ05UVBQLC0tERMSFCxfWr1/v6+vbd5sMDw+Pm5ubv7//pUuXtm7ditbuWqKiopS7BMPBwSEvL//27du+qd8sLCz+/v4PHjyorKycP38+1W6/4uDgOHHiRHFx8ZEjR9Adq8Dj8cbGxlevXt23b9/nz59NTU2PHTtGd1uLSEhIBAcHq6iorF27lkKlMwB0DUofwLy4uLh279597ty54ODg+Ph4FhYWCwuLu3fv9r37Tpgw4cqVK3p6ehs2bDh9+jT156OIiYmVlZVRrv+2tradO3f6+PicO3eO9K/m4+M7evSov7+/ubl5fn4+1VY84uDgOHnyZFFRkY+PDy1cqZk8ebKfn9+1a9d+/fp16NAhWoj0V1hYWOzt7fX19Z89e4Z2FgBoDpQ+ACAIghAIBGdn54CAgKioKAcHh75hHhwOt2rVqqtXr5aUlKxcuZJqy96QiImJUfRTOwcHx5IlS5SUlNLS0nbt2vXhwwcikaioqLh06dKwsDBPT8/AwMDBN8RRLgyp+kF97KePiIjIkSNHfv78SacrH06dOhWVJbMBoHFQ+gDwX2pqaleuXJk+ffrGjRtDQkL6hnlERET8/f319PQuXrxIzTySkpLFxcUUrQOcnJxqa2vnz58/derUo0ePmpmZHTt2LD09vaenR0VFZcmSJe7u7qTb4KmAg4Pj+PHj379/DwwMpJHqB4/HHz9+/MmTJ/Hx8Whn+Wu6urrfv3+n3/v2AaAQKH0A+B84HM7S0jI6Ovrbt2+rVq16//5930MbNmy4d+8eNW/7EhER4ebmpuhbFxsb25EjR0JCQrS1taOjo48cOSIgIEDa2RRBEBsbGykpqbVr13758oVyGfrj4uI6ffp0VlZWeHg4dc74R7y8vKdOnQoJCXn06BHaWf4OBweHtbX1gMW7AQBQ+gAwBBERkYCAgB07dnh5efV93Ofn59fU1KTyTp/Tp09/8+YNRU8hKyu7ffv2vXv3dnR0KCsrr1+/ft68eaTNI1lYWFxdXe3s7LZv3061YQ8CgXD06NG4uDhaWF2JRFJS8tSpU2fPnj1x4kRPTw/acf6CmZlZeXk5XPYCoD8ofQD4LX19/aCgoJCQkL65ouPHj6fy0sPTpk2jwjYLixYtUlJS8vf3H/JRVVVVHR2diIgISsfoIywsfOTIEV9fX9SXVuqjqKgYHR398+dPOzs7Kk/5+hc4HG7btm0nT56knf3pAEAdlD4ADGfMmDGBgYGnT59eunSpm5vbnTt3xMTEqBlAU1Pzx48faWlplD7R3r17v3//7ujoGBYWFh8ff+nSpfPnz7u6ui5cuHDTpk3CwsKXL1+mdIb+1NXV9fT0goKCqHnS4XFxcQUEBOjp6a1bty4zMxPtOCM1a9YsAoEAe5QC0IcF7QAA0DpVVdWbN29mZ2cXFxdbWlqOHz+emmfH4/H+/v779u1bv379qlWrKHciTk7O8+fPP3ny5OfPn+Xl5dzc3BwcHPr6+tu2bRszZgzlzjsMBwcHCwuLOXPmTJw4EZUAg2EwGCsrK3l5eScnpzVr1lhZWaGdaER27drl4uIyd+5cdnZ2tLMAgD4ofQAYETU1NTU1NVROra2tHR4e7uzsnJWVNXv2bBkZGSkpKQ4ODrKfiIuLa8mSJWTvdtR4eHg8PDz2798fGRnJx8c3/MHNzc2RkZE5OTltbW2zZ882NDQUFRWlUDA9Pb2wsDAXF5cfP364ubnRfj2hqqqqrq5+9erVDRs2oJ0FAPTBBS8A6ICEhERYWJicnNzjx489PDzmzJljamrq5+f39u1bFDf+pIJp06bNnz9/3759wy+p/OvXrw0bNlRWVi5fvnzTpk2FhYWrV6/etGnT9+/fKRRMSkoqPDy8p6fH2to6Pz+fQmcho61bt0ZFRdHj1hwAkB2M+gBAJb29vcePH3/37h03N7e8vLy8vPysWbNGPjLBzs5ubW3d11VhYeGrV69CQ0MPHz68efPmRYsW4XA4imVH05YtW/bu3bt3715fX18WlqH/ZJ08eXL+/PmbNm0ifTt16tQ9e/bcv39/69atXl5eU6dOpUQwDg4Ob2/v2NhYOzs7XV3dzZs3S0lJUeJEZCElJaWvr3/jxo2NGzeinQUAlMGoDwBU0t3d/fHjx/r6ejU1NSUlpe/fv1taWt66dWsUXWGxWDk5OSsrq4sXL/r7+z98+NDCwoJRb2DG4XCknU3d3Nx+d8NXdnb2ggUL+rewsLAsWrQoICDA09MzISGBcvGWL19+69YteXn59evXu7q6lpSUUO5c/8jKyur69esdHR1oBwEAZVD6AEAleDz+8uXL+/fvT09Pv3nz5sqVK8PDw8+cOfOPd8urqqqeP3/eycnJz89v7969lZWVZMpLQ1hZWX19fQUEBFasWPHq1avBBzQ1NfHz8w9u19DQCAkJuXz58tmzZym3PDQHB4eVldXt27eVlZU3btzo4eFRWlpKoXP9CxkZmQkTJiQmJqIdBACUQekDAPVgMBh9ff3Lly+vWrXKwcGhu7vbzMyMLHcdT5s2LSYmZuzYsStXrgwODu7u7v73PmkKHo93dXU9dOjQsWPHLCwsvLy8+v+/cXBw9G27NoC0tHRYWFh6+v+1d+fxUK7//8BnxtjGGGuoJCHV2EKSJaWUFkvJWkJoEJOElArJFoWyFOfoiGQJkY46aF8oHR8SlXYtFFKRJZr5/TGPM1+/LKnM3Ma8n3/pmst9v7lNXu77Wqp27949mnFR5eXlOTk5v7AKDg6Hs7e3z8vLk5SUtLOzCwsLa2lp+dmDMNqmTZvS09NZa1VGAMYcRB8AmA2NRhsZGfn6+rq7u3d1dX38+HFMDsvFxUUikdLS0mpra1lr4ZnRU1NTy83NDQkJUVZWPnXqVHR0NO1eDplMDg4OHi7wCQoKHj16lEqlbtu2bYRQ2NXVFRYWFh4efunSJRKJ1NTU9AsVEggEEomUm5vLz89vaWkZFhY2rtY/VFBQkJCQKCsrQ7oQAJAE0QcAZCxdupRMJufk5KDR6DE8rKSkZFxcnKurq7+/f0BAwFjlqvGDg4NDXl5+7dq1x44dq6mpoaUfY2NjUVHREbb94uLiCg0N5eXlPXDgwMD2O3fubN682cvLKygoaP369f39/ZmZmUePHl20aJGnp+cv79sqKChIJpPz8/NpASguLm78TK2yt7dPTU0dJ7vDAoAIiD4AIGbVqlW0xzdjfmQ9Pb3Tp09PmjTJwsIiMzNzQm5iwM/Pn5CQUFtbS9vm3dfX9/Tp0yP0x2Aw+/fvr6urO3XqFK3lyZMne/bsMTc3X7t2rbq6+u7duwMCAvj4+NBo9MaNG1VUVH5z408hISEymZyRkdHR0WFlZTVO5sBra2ujUKiKigqkCwFIev/+fX5+fldXF9KFIAOiDwBI0tXVlZOTY8SReXl5yWRyQkJCSUmJi4vL06dPGXEWZOHx+Pj4+Pr6+sjISHFx8c7OzpFH8+BwuOjo6NTUVNrQ8nPnzpmbmxsaGurp6RkbG2toaAzsvG3btlu3blVVVf1mkRISEv7+/j4+Pu7u7kzeAG5IaDTazs7uxIkTSBcCEEClUu/evbtjxw4bG5usrKzMzEykK0IGRB8AJjJ5efnjx4+vWbNmy5YtBw8enHh/5NHST3Nzs7W1NRaL/eEENwkJCUtLy5MnT6JQqC9fvoiKig7Xk4+Pz8rK6uLFi2NS59KlS93d3T08PMbD5C9DQ8Pm5uYJORoMDKerqys/P9/GxiY8PFxVVbWoqCgyMjIrK6u7uxvp0hAA0QeACQ6NRq9evfr06dNfv341NzefeNtY8vHxxcTEeHt7q6urj2Z/DwsLi0uXLn348OHLly94PH6EnqKiosNNHPsFRkZGdnZ27u7uiM/8wmAwNjY2tPwHJry3b98eOHDAyMiosrJyx44deXl5NjY2OBxOWlpaVVX115YWY3UQfQBgCwQCwd/ff//+/ampqb6+vhNv+wttbe2YmBgREZEf9hQQEFixYkVWVlZXV9cI2291d3cfO3ZsbLeMtbS0tLCwcHFxQXzal6mpaXV19fPnz5EtAzBaX1/f1q1bcThcdnZ2eHi4mprawFcdHBwyMjIm3loYPwTRBwA2oq6ufurUKTQaHR4ejnQtSFq/fn1BQcGsWbOGXCCRprCwkEgk0gYFj+2pDQ0NyWQyspPveHh4LCwsMjIyEKwBMEFmZua0adPIZPKkSZMGv0okEmfOnBkfH8/8wpAF0QcA9sLJyRkcHFxbW3vp0iWka0GMpKSktLS0tLT0xYsXh7sB8+TJk+8GPo8VFxeXhQsXrl271t/f/9atW0jNv7O0tLx06RLiT98A47S0tKSlpXl7e4/QJygo6O7du1u3bv21haxYFEQfANgODw9PUFDQgQMH2tvbka4FMatWrbp8+fKyZcuGmxL/4sULaWlpBp3dzc2tsLBQVVU1OTl59erVR44cefnyJYPONRwBAQEjIyO2nePDDlJTU9esWSMpKTlCHwKBcOLECTU1tY0bN2ZlZU3IhTAGg+gDADtSVFRcsWJFdHQ00oUgZtmyZZWVlSYmJvn5+T09PQNf6u3tTUhIaGxsZNC6AzQEAsHCwiI1NTUxMRGDwWzevDk4OLi5uZlxZxxsw4YNhYWFHR0dzDwpYI6vX7/+888/FhYWP+zJwcHh4OBw/PjxS5cuOTk51dbWMqE8ZEH0AYBNubm51dXVXblyBelCkMHHx2dgYJCSkqKlpeXp6VlRUUGbGH/79m0rK6u3b99mZWURCAQmVDJjxgwPD4+CgoLp06fb2tqGhYV9+PCBCedFoVDi4uILFy7Mzc1lzukAM126dIlIJIqLi4+yv5SUVFJSkomJSUBAAG3Vn4m3FgYdRB8A2BQPD8+ePXsiIyPZ9o9+Pz8/fn7+xsZGdXX1tLQ0W1vbxYsXh4WF7dixIzQ0VFhYmJnF0HY/zc7O5uDgsLS0PH78OHMWXLG1tc3NzYV9LSYYCoWSm5tramr6U5+FRqPXrl2bn5/v7e1dV1eXnp7OoPIQB9EHAPalpqamqamZk5ODdCHIwGKxQUFBGhoaJ0+ebGtrW7x4saOjo7u7Oy8v7+vXrxEpSURExM/PLz09vbm52dTU9MSJE4xehkBOTo6Li+vx48cMPQtgpq9fv+7cuZObm3vRokW/8OloNHrevHn79+8nkUhjXts4AdEHALZmb2+fk5Mz8Zb5GSU0Gr1ly5bLly8HBgbKyMi8efOmpKQkISGBRCJt3LgRqU2OJk+e7O/vf+zYsQcPHqxbty4/P5+hg091dHRu3rzJuOMDZuru7vb29qZQKDExMVgs9ncONbY7K48rv/V9AQCwOmlp6dmzZxcXF69ZswbpWhDDwcFBJBKJRCK9hUKh3L59+8yZM/Hx8du2bTMxMRnY/9mzZ25uboKCgpqamgsWLFBVVR3NKtI/S0ZGJiIiorKyMj4+/syZMx4eHpqamr9zwDt37vz7779ubm7ftevo6KSkpGzatOl3Dg7Gg46ODg8Pj1mzZu3cuRODgVsbw4JvDQDsztbWNj09nU0mtY4SBoPR0tKKjIxMSUk5efLkvn37Bs4CO3funKmpaUBAgKCg4IkTJwwNDV1cXI4fP15fXz/m30YNDY3U1FQHB4fIyMgtW7bU19f/8qHevHmTkpKSnZ39Xbu6uvrjx48/f/78e5UChFEolN27dysoKOzatQtyz8jguwMAwp4/f/7DTTcZSkNDA4/HwyOPIc2YMSM9PZ2Dg8PW1vbZs2e0RhwO9/Llyzlz5jg6OiYlJf3zzz92dnYfP37ct2+fgYGBn59ffn7+GG5Tikajly5dmpOTs2zZMh8fH39//18bAT116lQpKamcnJxDhw59+/aN3s7FxaWmplZRUTFWBQNEHD58+Nu3b97e3hP4QdVYgegDAJLev3/v5OS0fv36v/76C8EBN7CZ5Qi4ubn37NmzadMmV1fXkpISFApla2vb3t4eERFBmxjFy8uro6Ozffv27OzsnJwcPT296urqTZs2rVmzJjw8/M2bN2NSBgcHB232DTc3t5+f3y/su4TD4QgEQmpq6tOnTxMSEga+BMN9WF1xcfGVK1dCQ0M5ODiQroUFQPQBADFUKjUtLW3FihVpaWl1dXXW1tZI/fpZtmzZ69evnz59isjZWcLq1avj4+OPHj0aFhaGwWBiY2Nfvnxpbm6ekZExcBkeUVHR1atXBwcHX7hw4eDBgxgMJiYmZgzLoC1JQKVSs7KyRu7Z1NRUUlJSXV1NfwaHRqOpVCo/P39ISEhBQcHAHTy0tbXLy8thijuLam9vP3jwYExMjKCgINK1sAYY5gwAMj5+/BgcHNza2nrw4EExMbGDBw+Wl5fHxMQ0NzevW7eOycVwcHDo6+tfvXpVVlaWyadmIfLy8unp6fv27XN2do6KikpKSqqpqcnPz09OTkahUOLi4uLi4vPmzTM2NhYWFkaj0XJycp6eniYmJp8+fRIQEBirMjg4OEgkUmBg4IYNGwY/2rh9+/bp06dra2spFIqoqGhXV1dXV5eOjo6goOCzZ89oU36EhYWpVOrA2wNCQkITeP26Ca+0tFRHR0dGRgbpQlgGRB8AmOHbt29kMnng4oHv3r0zMjKKjIykT0DV0tKaPn26g4PDzJkzlZWVmVyhnp5eYmKio6Mjk8/LWvB4fGRkZFpamqur659//qmioqKiooJCoTo7O9+/f9/U1HTx4kVzc3MtLa2tW7eKi4vz8PCIioo2NzePYfRBoVBKSko8PDy3bt3S0dGhN9JuImZmZuJwuM7OTmlpaQUFhVevXj18+PDFixdEInHlypVz586ldZ42bdq1a9foM9devHghISEBY0RY1NWrVy0tLZGugpVA9AGAGdBoNIFA6Ojo2LVrF+0XDB8fn5SU1HfdpkyZEhAQ4O/vn56eLiQkxMwK1dTUGhsb29raREREmHleloNGo+3t7fv7+93d3ZOTk2mbXeDx+GfPnoWFhWlqaqamppaUlGzatCkmJmbWrFl4PL6zs3PMy3B2dj569Ki2tjbtx6mnpyc4OPjNmzeqqqoiIiJkMpmbm5vWs62traSkJDs7+8WLF+rq6rTGPXv2bNu2raWlZdWqVVevXj158qSdnd2YFwmYgEKh1NXVqaqqIl0IK4GxPgAwAwaD2b9/v6CgYGFh4Zw5c+bMmTM499Do6uquWrXK39+fybPNsVispqYmjHUdJScnp/nz5/v4+NAv08GDBx0dHQUEBJycnISFhX19fT08PGJiYvr6+hgRffT19Tk4OEpLS1EoVFNTk6OjIxcX144dO6qqqlxdXem5B4VCiYiI2NjY5OfnT58+3d/fn9YoLy+flpZ2+/ZtW1vbhoaGoKAguG3Aop48eTJp0iTm7Dc3YUD0AYBJODk5o6KiHj9+TBsaMgJXV1csFpuUlMScwugWLlx47do1Jp+UdXl5eVEolIKCAhQK9fXr10ePHpmZmXl6eiYlJaWlpXFwcJw8eRKNRjc2NvLx8Y352dFotLu7e0hIyKpVqywtLY2NjYOCgrKysohE4uBRO52dnRkZGXfu3LG2tqY3ioqKJicnl5WVBQQEzJs3b8wrBMxRU1PD/OfjrA6iDwDMw8PDEx0dXVJSkpmZOUI3DAYTGhp6/vz5q1evMq02FAqlq6t79+5dtt3U4meh0ehdu3YdO3bs48ePXFxcWCy2t7cXhULJyMgEBARERETgcLht27aVlpYyKFjMnz8/Ly/vr7/+KikpsbGxQaFQGzduFBYWtra2dnV1LSws7OjoePXqVVRUlImJyePHj8PCwgwMDAZ/FYyoDTDNvXv3aAPOwOhB9AGAqQQFBRMTEzMzM2krxAyHQCCEhYWFhoaO1aowo0EgEGbOnHn37l2mnZHVycrKLl++nHYbT0BA4NOnT7R2NTU1HR2dyMhICoXC0HV1J02aJC4uTt9GQ15efu/evRcuXLCysrp586axsbGjoyMfH192dnZwcPCsWbMYVwlAyr1793h4eDIzM6Oiop4/f450OawBog8AzCYmJhYbG0ubzT5CN0VFRUdHR19fX9q9BOaAZ14/a9OmTRcuXOjt7ZWSknrx4gW93cvLq7W1lflPLVEoFBcXl76+fmRkZHFx8blz57Zs2TJp0qTfOSCyq42DEVAoFAKBEB4efujQocLCwpaWFqQrYg0QfQBAgIyMTFRUVGBg4IMHD0boZm1tLS0tHRkZ2dfXx5zC9PT0rl27BkvbjZ6IiMjs2bNv3rwpLy/f0NBAb8fhcBYWFvS9LxCBw+EGjnemGbiFxWBv377Nz88PCQk5dOhQSUlJfn6+jY2NlZUV3Asct5SUlDg5OdetW3fq1Kn58+cjXQ5rgOgDADJUVFS8vb33798/8kyuPXv2tLS0mJiY/PHHHwNXDWYQaWlpHh6ex48fM/pEE8mKFSsuXLggJyf33fdNUFCQ/gjsO/fu3QsPD9++ffvRo0eZcFnpiouLjY2Nk5KS3r9/n5iY+Pr1a/pLVCo1JyfH3t6+trZ2zpw5kyZNKi0traio8PLy0tXVfffuHdOKBKNEoVBCQkIeP35cUFCwa9eu4SaNgsEg+gCAmOXLl/Pw8Jw/f36EPjgc7siRIwkJCS0tLba2tkwY+qOrq8vk4dWsTl9fv6KiQl5evqamZmD7p0+fcDgcCoVqa2tLTk4eOPEqOTkZh8OZmpp++vTJ3Nz8wIEDo9/utLS09NSpUz9bZH19vZmZWUREhJ+f34MHD9asWVNQUEAvuK2tzdPTs7i4OCUlJTAwcN26dXZ2dlFRUZGRkfPnz586dWphYWFTU9PPnhQwVHx8fHNzc1xcHO3HDIweRB8AEINGo7dv356QkNDT0zNyTxkZGX9/f3d3d3d394FbLzGCnp7e9evXGXqKCYafn19SUpJCoXByctbX19Pb7927p6ys3NraSiKRKioqXF1daTd4KBTK/fv37e3tFy1atHPnzry8PCEhITs7Oy8vr4GfPqSioqL4+PiUlJSfDSIVFRWampoFBQWLFi2KjY29ePEiHo+n7VvS1ta2fv16IpGYkpIy5J0DZ2dnbW3tjRs3vnz58qdOChjqxo0b27Zt4+HhQboQ1gPRBwAkKSoqKikpjTzXnW716tUmJiZkMpkRS+TRqaqqvn79Gka2/hQFBYW6urply5bl5ubSG+/fv6+oqPj333+rqKgcP35cR0fH2dn59evXz549ExERoe80KSQkRCKRzp49q6am5uPjQxsfPeRZ2traIiIiYmNjV69enZaWNvryaA+zDAwMhIWFaS3Xr1/n5eWlzfm6f//+nDlzXF1d6bt6ff36deDgeiwW6+DgYGhoeOXKldGfFDBUb2/vmzdvZsyYgXQhLAmiDwAIc3FxOX369CjXbnZ0dFRXV9++fTvjVt/h4ODQ0tKCZZ1/CpFIfPDgga2t7Z07d+gT99rb28XExF6+fKmoqIhCoVxcXGxtbR0dHcvLyzs6Ouzt7YODg8+fP097EIbD4TZu3Ehb7NvOzu5///vf4LMICQkpKChcvnzZ2dm5vLx89PEUjUaLioq2t7fT/tnV1XX48GE/Pz/aoj5CQkIfPnxoa2u7fv16XFyck5PTkiVLNm3a9N1odyKRCIPAxo8nT55IS0tzcnIiXQhLgugDAMJkZGQEBARqa2tH2d/Ly0tERCQ2NpZxJS1dupS2QwIYJTU1tVu3bqHR6N27d4eEhDQ2NqJQqL6+PiwW29jYOH36dFo3MzOzhIQE2n0gFxcXRUXFkpKSVatW7dq1izbXj5OTk0Qi7d27d8eOHYMffmEwmJCQkJycnPDw8N7e3levXo2+Qn9//9DQUCcnpyNHjkRERGhoaNCXAJaWlv706ZO1tXVmZiY3N7erq2tpaWl/f/938au9vR32dxs/Hj16BAs1/TKIPgAgz8DAoKysbJSdMRjMrl27SktLab9fGWHhwoUNDQ3wzGv0pk+frq+v7+3traqq6uLi4uzs7OPjIy4u3tvb+/z5czk5OXrPmTNnpqWlSUpKBgYGdnZ2hoSE0B51eXp6/vHHH7SZ51paWr6+voGBgYOXdBITE0tPT9fT0zMxMZGXlx99hUQisbi42NXVlTYkduvWrfSXCARCYWFhaWlpYmIiiUTS0NB4+/btx48f8Xj8wCO8e/dOXFz8F745gBEaGhp+6gcADATRBwDkLVu2rKioyNLSMjIy8odDnlEoFBaL7evro4/bGHOcnJz6+vojrzcNvrNjx47p06d7eHgYGBhEREQoKSnFxcWlpKQ4OTkJCAgM7MnFxeXp6ZmSkvLw4UNjY+P4+HglJaWMjIx79+6RSCTaUOjly5fPnDkzISFh8IkmTZq0cuVKNzc3fn7+n6qQl5dXQ0PD2dk5ODi4oaHB29t7uCHzb9686evrGzjava+v7+rVq7BX1Pjx6NEjiD6/DKIPAMiTkpI6d+5cREREZ2eng4PDwEWBh3T9+nVVVdXv/igfWytXrhx51j34DhqN9vPzk5eXJ5PJs2bNsre35+bmvnfvnp6e3pD9paSkwsLCTp8+LSEh4evr6+PjY2BgoKGh4ejoSLuf5+fnV1ZWVlVV9QvFfPv2beSlCxMTE7FYLIlEqq2tHTzOTE9PLzw8/OzZs/SW/Px8WVlZ2qAlMB48ffoUos8vg+gDwLiAx+NlZGSCg4NtbGw2b948cuy4efOmvr4+Q+tRVVXt6Oh4+vQpQ88ywaDRaF9f39mzZ7u5uX3+/JlKpX779m3kdZBFREQcHR0LCwtJJFJZWVlJSYmenp6zs/O5c+cIBMLOnTujo6O/+5Surq6ioqIRFvimUqmWlpaOjo4jbGsgISEhJCS0YcOGkJAQIyOjwcsqqqiodHV1PXr0CIVCdXd3//XXX25ubj/+FgCm6OjowGAwDP3jZ2KD6APA+GJqanr06NGUlJSwsLDhpn01NTVJSkoytAw0Gr18+fILFy4w9CwTDxqN9vHxUVJS8vDwoK1kmJGR4eLicv369REeZWIwGB0dnbi4uB07dpw9ezY0NDQ7O3vdunVJSUlTpkz5rnNISEhycrK3t/dwm7tRqdRXr14tXLhw06ZNHR0dQ/YJDAysqanp7u7Ozs6WlZWtqKj4rgMvL6+vr6+rq+vOnTv9/Py0tbXhHsP48eHDBxhy/jsg+gAw7sjJyaWlpdXW1g7+hUTT2tr6mxtSjsaqVavOnz8/yln3gI6WfhYvXmxnZ9fe3p6VlWVqanry5ElDQ0MPD4+TJ0+OsLHXggULlJSUuru7T5w4ERkZ6e7uHhISMrDD169fb926deLECQKBsHfv3iF3W8NgMNra2jdu3BAWFs7LyxvyRHx8fLGxsRkZGevXr3/79q2uru7gPsuXL8/Ly9PR0Zk9e/bOnTt/8tsAGKitrQ2iz+/AIl0AAGAIOBzO0tIyLy9PW1t78KstLS2ioqKMrkFGRoZAIFRXV6upqTH6XBMMGo12dHScPXu2r6/vkiVL9PX1ExMTe3t7Kysry8vLvby8uru7RUVFaVufKikpKSoq0getKykp1dbW6unpycnJDZwaRnP//v2pU6cKCwsHBAS4urpmZmauX79+cAExMTFFRUWxsbGXL192cHAYskhxcfFjx441NjZqa2tjMEP/GSwsLGxsbPzr3wjAGF++fIHNK34HRB8AxqmVK1fGx8c3NTVNnjx5YHtHRwcWi+Xl5WVODefPn4fo82u0tbXT09OLi4sTExNfvXpFJBLFxcUlJCRIJBIPDw83N3d/f39DQ0Nubm5QUBA/P7+SktLcuXNlZWUvXrw43DHl5OTa29trampUVFSCgoKcnZ3XrFkz+LcgGo02MTHR1dX9+PHjCBVKSUnBnpesiI+P78uXL0hXwcIg+gAwTvHw8BgaGhYVFZFIpIHtzHnaRbNixQorKytfX18uLi7mnHGCERMTc3BwcHBwaG1tbWhoaG5ubm5uvn37Nu2D1tZWAQGByZMnk0gkdXX1hw8flpaWVlVV2dra0o9AoVCqq6uxWCyRSMRisbSxz/v27Tt16pSkpKSgoGBlZeWiRYuGPLuwsDDjVkAACCIQCJ8/f0a6ChYG0QeA8cvCwsLNzc3R0RGL/b+3KnOedtFMmjRp9uzZ5eXlw/1yBaMkKio6+KpRKJTW1taXL19mZmamp6c7OTkdOnTo7t27Kioq9D7p6elnzpzh5OScO3fu7t27USiUnp5eWVlZQkICDocTFBTU0dFh6lcCxgF+fv7hBrCD0YDoA8D4NWPGjGnTpl27dm3JkiX0xra2NqZFHxQKNX/+/H///ReiDyNgMBgxMTExMTENDY26urojR47cvHnzwIED9KTb0tKSnp5+4sQJISEha2vr8vJyLS0tFArl4+NjbW0tKCiYkJAwMBYDNgHR5zfBDC8AxjUtLS3a7k40VCr1ypUr06ZNY1oBKioqNTU1TDsd21JQUEhISKBSqf7+/vTVCCsrK+fPnz916lQcDhcQEBAaGkrbtpZAIOTk5Bw/flxISAjRqgEycDhcX1/fCGs7gZFB9AFgXJOSksrOzt66dWtKSkpVVVVcXFxra6u9vT3TCiASic+ePRvN9hrgN2Gx2IiIiJ6eHvqE9pcvX0pLS9M+VlNT+/jxY39/P+2feDyeh4cHkTrBeIDH4zs7O5GuglXBnVIAxjUDAwM1NbV79+5VV1fHx8dTKJTDhw9zc3MzrQBubm45Obn6+nqY58UEXFxcUVFRa9euffr0qays7OvXr8XExOrr6zs7O9+9e8fPzw9TmgENgUDo6OiA236/BqIPAOOdsLDw4sWLFy9ejFQBKioqsLoP03Bzc5uamp45c8bHx8fS0vLPP/+8e/cuLfRs3769pKSkvb1dRERESkoKlldmZwQC4ePHj7A2wa+BB14AgB9QVlaG4T7MpK+vX1paikKhVFRU4uLi0tPTAwMDcThcSEjIhQsXGhsby8rKfHx8NmzYUFxcjHSxABkSEhLNzc1IV8Gq4K4PAOAH5s6dGxIS0tfXx8nJiXQtEx+FQomLi6OvodzV1ZWWlpabm2thYXH+/Hn6Ay8KhVJZWRkdHf3kyRMymYxGo5ErGSBgypQpTU1NSFfBquCuDwDgB4SFhefMmUO7DwEYikKhxMfH9/X1bdmyhUKhnD171tzc/O3bt6dOnXJxcRk40AeDwWhqav7xxx9VVVXh4eGw1Rq7kZCQgOjzyyD6AAB+bMOGDenp6UNulgnGBJVKvXTpkpWVVX19fVhY2OPHj+3t7YuKig4ePBgcHCwmJjbkZxEIhMTExNevX+/Zs4c+JR6wg8mTJ0P0+WUQfQAAP6alpdXf319ZWYl0IRNTZWWlg4PD8ePHt2/ffvjw4ZMnT5LJZCsrq+TkZCKROPLn4nC42NjYL1++BAUFwb0f9jFlypQXL17Q1nkCPwuiDwDgx9BoNJlMDgoKevPmDdK1TCgPHjxwd3cPDw/fsGFDamrqhw8fLC0t3717l5WVZWRkNMoRPLQp8a2trWFhYXBnjk3MmDFDQUFh8+bN79+/R7oW1gPRBwAwKnp6es7Ozu7u7q2trUjXwvJ6enpKSkq8vLy8vb2XLl2akZHR19dnaWlZWFgYGBgYGhr6s9uOcnFxRUdHP3/+PDY2ljElg/EFg8GEhYWZmpra2dnduXMH6XJYDMzwAgCMlpmZWXt7u5ub2969e5WVlZEuh/V8/fq1vLy8pKTk5s2bysrKSkpKmpqa5eXlR44cIRKJu3fvVldX/+WD8/LyHj58eP369QsXLpw3b94Ylg3GLTMzMykpqT179tjZ2a1fvx7pclgGRB8AwE9wcnKSlJTcuXPnggULyGQyLCY7SjU1NQUFBVeuXKHtOikgIHD37t2GhgY1NbUlS5bs2bNHQEDg98+Cx+N9fHwiIiIyMzN/fyWCrq6uQ4cOzZkzx8zMDIOBRwTj1Lx5806cOOHu7s7JyWlhYYF0OawBog8A4OcYGhouXLgwKSlp3bp1hoaGZmZmM2fORLqo8e7Bgwf9/f0WFhYSEhKT/8OIDUn09PQKCgrS09MdHR1/81A9PT03btw4e/ZsQ0PDrl27YOmgcUtcXPzIkSNOTk5iYmKLFi1CuhwWANEHAPDTcDicl5fXxo0b//77b29vbwEBgbVr165cuZKXlxfp0sYpa2trpp3L19fX1tZ23bp1v3knSVhYODw83NPT89atW4cOHfLx8RmrCsGYmzJlSlRU1Pbt26dNmyYjI4N0OeMd3MMEAPwiUVFRe3v7goICMpl8586d1atX0xakQboudjd58mRNTc2LFy/+/qHU1NQcHR15eXmrqqpiYmJ+/4CAcRQVFV1cXEJCQmCNgx+C6AMA+C0YDGb+/PkRERGnTp0SEhLaunUriUR68eIF0nWxNUNDw3/++WdMDuXg4DB16lRZWdnbt2+np6ePyTEBg5iZmXFzcx85cgTpQsY7iD4AgLEhISHh5uZ27ty5FStWkEikK1euIF0R+9LW1n769OmYrPiCRqODg4NfvnyJQqESExOfPXv2+8cEDIJGow8cOHDr1q3o6Oi+vj6kyxm/IPoAAMYSBweHmZlZbGxsTk4O/OeLFE5OThKJ1N3dPSZHIxAIx48fNzIy4uLiys7OHpNjAgYhEAjJyckPHz4sKipCupbxCz0hl/5MTk6urq7W1NREuhAwZsrKygwMDJCuAoyZtra2xsZGVVVVpAsBP+HLly8YDGa4wezwJh0/ent7ubi4fnNS3rNnz+7evfv333+PVVXjx8SMPigUKi4uDukSAAAAABamo6OjpqaGdBVjb8JGHwAAAACAwWCsDwAAAADYCEQfAAAAALARiD4AAAAAYCMQfQAAAADARiD6AORVV1fr6Ojg8XhFRcVr167RGg0MDND/WbBgAa3x9u3benp6xsbGr169orXIycmhB9i5cycyXwNAoaKiopSVlTk4OCIiIuiNHz58MDExweFwcnJy9FmyQzbCxR2HRn9N4Q0LWAkVAET19vZOnz49MjKyt7f39OnTQkJC7e3tVCp16dKlmZmZHR0dHR0dXV1dtM7Lly9vaWn53//+5+zsTGuRlZU9ffp0x396e3uR+kJAdnZ2cXGxoaFheHg4vdHGxsbMzOzTp09nzpzB4/Fv374drhEu7jg0+msKb1jAQuCuD0BYfX19a2urj48PFxeXubm5pKRkQUEB7SUeHh48Ho/H4+lLqFGpVDQajcFgqAMWZaB3w+PxXFxczP8SAI2lpeXKlSvxeDy9pbu7Oy8vb9++fQQCYc2aNWpqarm5uUM2ouDijkujvKa0l+ANC1gFFukCALuj/v8rS1Gp1Lq6OtrHnp6enp6eKioqYWFhioqKKBRq3759a9as4efnT0pKQqBW8JOePHny7ds3BQUF2j+VlZXr6uqGbETBxWURw10+FLxhAeuAuz4AYUQiUUhIKCoqqqenJycnp66u7suXLygUatu2bWfPnj1//vyMGTMMDAza29tRKJSWltb169eLi4unTZtGP8KGDRtE/3PmzBnEvhIwSGdnJx6Pp6+mTyAQOjs7h2xEwcVlEcNdvu/esLR3MVxTMD7BXR+AMG5u7jNnzpDJ5JCQED09vdWrV0tKSqJQKCMjI1qHw4cPFxcXX7t2zdTUdMgjHDlyZOnSpbSPRUREmFM2GA08Ht/Z2Ul76oFCoT5//kx7zDG4cbgjwMUdb4a7fN+9Yevr6zU0NIY8AlxTgDi46wOQN2/evPLy8s+fPxcUFNy/f3/w/5hYLLa/v3+4TxcREZH8z3AbKwJEyMnJcXBw0B+I1NbWKigoDNk43BHg4o43o7l8WCyWQqEMdwS4pgBxEH0A8ioqKt6/f9/Y2Ojh4SEuLr5s2bKOjo6cnJzm5ub3798HBwc3NTXp6uoO9+l9fX09/+nr62Nm5WCg/v7+np4eCoVC/4CXl9fMzGzfvn0dHR1nz56tqqoyNzcfsnG4Y8LFRdYor+ngN+zs2bOHOyZcU4A8hGaWAfB/fH19+fn5+fj4zMzMmpqaqFTqp0+fdHR0CAQCPz+/rq7ujRs3hvtcWVnZgT/P9vb2zKsb/P+8vb0HXovMzEwqldra2mpkZMTDwyMjI1NUVETrOWTjYHBxETfKawpvWMBaYOd2AAAAALAReOAFAAAAADYC0QcAAAAAbASiDwAAAADYCEQfAAAAALARiD4AAAAAYCMQfQAAAADARiD6AAAAAICNQPQBAAAAABuB6AMAAAAANgLRBwAAAABsBKIPAAAAANgIRB8AAAAAsBGIPgAAAABgIxB9AAAAAMBGIPoAAAAAgI1A9AEAAAAAG4HoAwAAAAA2AtEHAAAAAGwEog8AAAAA2AhEHwAAAACwEYg+AAAAAGAjEH0AAAAAwEYg+gAAAACAjUD0AQAAAAAbgegDAAAAADYC0QcAAAAAbASiDwAAAADYCEQfAAAAALARiD4AAAAAYCMQfQAAAADARiD6AAAAAICNQPQBAAAAABuB6AMAAAAANgLRBwAAAABsBKIPAAAAANgIRB8AAAAAsBGIPgAAAABgIxB9AAAAAMBGIPoAAAAAgI38P9f/V8GpR4yqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mv.setoutput(\"jupyter\", plot_widget=False, output_width=1200)\n",
    "my_view = mv.geoview(\n",
    "    map_area_definition=\"corners\", \n",
    "    area=[0, 90, 15, 110],\n",
    "    subpage_y_position=25)\n",
    "geolines = []\n",
    "for i in range(len(list_trnsc)):\n",
    "    lns = mv.mvl_geoline(*list_trnsc[i],1)\n",
    "    geolines.append(lns)\n",
    "line_graph_transects = mv.mgraph(\n",
    "    graph_line_colour  = \"blue\",\n",
    "    graph_line_thickness = 3.,\n",
    "    graph_line_style = \"solid\")\n",
    "title = mv.mtext(\n",
    "    text_font_size=1.0,\n",
    "    text_lines=[\"Transects\"])\n",
    "mv.plot(my_view, my_coast,     \n",
    "    geolines, line_graph_transects, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1d1dde",
   "metadata": {},
   "source": [
    "### 2.1. Determine transect over land vs ocean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd596d0",
   "metadata": {},
   "source": [
    "Load land-sea mask data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70256bcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:40:15.350992Z",
     "start_time": "2022-06-02T13:40:14.020791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "  \n",
       "                <table>\n",
       "                <tr></tr>\n",
       "                <tr><th style='text-align: right;'>shortName</th><td style='text-align: left;'>lsm</td></tr><tr><th style='text-align: right;'>name</th><td style='text-align: left;'>Land-sea mask</td></tr><tr><th style='text-align: right;'>paramId</th><td style='text-align: left;'>172</td></tr><tr><th style='text-align: right;'>units</th><td style='text-align: left;'>(0 - 1)</td></tr><tr><th style='text-align: right;'>typeOfLevel</th><td style='text-align: left;'>surface</td></tr><tr><th style='text-align: right;'>level</th><td style='text-align: left;'>0</td></tr><tr><th style='text-align: right;'>date</th><td style='text-align: left;'>20131129</td></tr><tr><th style='text-align: right;'>time</th><td style='text-align: left;'>0</td></tr><tr><th style='text-align: right;'>step</th><td style='text-align: left;'>0</td></tr><tr><th style='text-align: right;'>number</th><td style='text-align: left;'>None</td></tr><tr><th style='text-align: right;'>class</th><td style='text-align: left;'>od</td></tr><tr><th style='text-align: right;'>stream</th><td style='text-align: left;'>oper</td></tr><tr><th style='text-align: right;'>type</th><td style='text-align: left;'>an</td></tr><tr><th style='text-align: right;'>experimentVersionNumber</th><td style='text-align: left;'>0001</td></tr>\n",
       "                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsmfname = \"/bog/amuttaqin/Datasets/ERA5-Land/land-sea-mask/lsm_1279l4_0.1x0.1.grb\"\n",
    "lsm = mv.read(lsmfname)\n",
    "lsm.describe('lsm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c02f05",
   "metadata": {},
   "source": [
    "Determine transect: over ocean vs land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8953e755",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:13:38.036318Z",
     "start_time": "2022-06-02T15:13:37.869579Z"
    }
   },
   "outputs": [],
   "source": [
    "trnsc_land = []\n",
    "trnsc_ocean = []\n",
    "for i in range(len(list_trnsc)):\n",
    "    loc1 = [list_trnsc[i][0], list_trnsc[i][1]]\n",
    "    loc2 = [list_trnsc[i][2], list_trnsc[i][3]]\n",
    "    \n",
    "    loc1_val = mv.nearest_gridpoint(lsm, loc1)\n",
    "    loc2_val = mv.nearest_gridpoint(lsm, loc2)\n",
    "    \n",
    "    if (loc1_val > 0.5):\n",
    "        trnsc_land.append([loc1[0], loc1[1]]) #trnsc_land.append([loc1_val, loc1[0], loc1[1]])\n",
    "    else:\n",
    "        trnsc_ocean.append([loc1[0], loc1[1]]) #trnsc_ocean.append([loc1_val, loc1[0], loc1[1]])\n",
    " \n",
    "    if (loc2_val > 0.5):\n",
    "        trnsc_land.append([loc2[0], loc2[1]]) #trnsc_land.append([loc2_val, loc2[0], loc2[1]])\n",
    "    else:\n",
    "        trnsc_ocean.append([loc2[0], loc2[1]]) #trnsc_ocean.append([loc2_val, loc2[0], loc2[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c2abdc91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:12:04.070423Z",
     "start_time": "2022-06-02T15:12:04.066616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[13.7223, 97.9315],\n",
       " [14.0023, 97.8238],\n",
       " [14.2823, 97.7161],\n",
       " [14.5623, 97.6085]]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trnsc_ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ac73471d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:12:06.910239Z",
     "start_time": "2022-06-02T15:12:06.906866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[13.9377, 98.4915],\n",
       " [14.2177, 98.3839],\n",
       " [14.4977, 98.2762],\n",
       " [14.7777, 98.1685]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trnsc_land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb298615",
   "metadata": {},
   "source": [
    "NEXT. Write transect into a file. Add transect ID, region (R1, R2, R3, R4), mainland (Malay_Peninsula, Sumatra, Java, Borneo, Philippines, Sulawesi, Papua, Australia, Others). Also add coast coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc66e42a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:47:04.802893Z",
     "start_time": "2022-06-02T13:47:04.801313Z"
    }
   },
   "outputs": [],
   "source": [
    "# with open(\"/home/amuttaqin/Datasets/transects_mc.csv\", \"a\", newline='') as csvfile:\n",
    "#       coordwriter = csv.writer(csvfile, delimiter=',', quotechar=\" \", quoting=csv.QUOTE_MINIMAL)\n",
    "#       coordwriter.writerow(list_trnsc[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e599abfa",
   "metadata": {},
   "source": [
    "## 3. Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813d96f8",
   "metadata": {},
   "source": [
    "### 3.1. Skin temperature difference (point)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e196098",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "42fb4e83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T14:13:12.952873Z",
     "start_time": "2022-06-02T14:13:12.231444Z"
    }
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from cdo import Cdo\n",
    "cdo = Cdo()\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19348eb",
   "metadata": {},
   "source": [
    "Define bounding box for memory-efficient data loading based on transects' coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ed17307d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:47:47.427218Z",
     "start_time": "2022-06-02T13:47:47.424058Z"
    }
   },
   "outputs": [],
   "source": [
    "# For Malay Peninsula region, specifically for segement #1\n",
    "box_lat1 = 0\n",
    "box_lat2 = 15\n",
    "box_lon1 = 95\n",
    "box_lon2 = 110"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d75767",
   "metadata": {},
   "source": [
    "Area-subset dataset into a temporary file. Remember to delete this file at the end of the script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dad90730",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:51:20.462941Z",
     "start_time": "2022-06-02T13:48:35.141444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/bog/amuttaqin/Datasets/Temporary/tmp2.grib'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin1 = \"/bog/amuttaqin/Datasets/ERA5/skto/skto.grib\"     # Data size: 20.5 GB\n",
    "fin2 = \"/bog/amuttaqin/Datasets/ERA5-Land/skt/skt.grib\"  # Data size: 24.7 GB\n",
    "\n",
    "ftmp1 = \"/bog/amuttaqin/Datasets/Temporary/tmp1.grib\"    # 1.98 GB; subset of 15 x 15 deg longitude by latitude \n",
    "ftmp2 = \"/bog/amuttaqin/Datasets/Temporary/tmp2.grib\"    # 4.91 GB; subset of 15 x 15 deg longitude by latitude\n",
    "\n",
    "cdo.sellonlatbox(box_lon1,box_lon2,box_lat1,box_lat2, input=fin1, output=ftmp1)\n",
    "cdo.sellonlatbox(box_lon1,box_lon2,box_lat1,box_lat2, input=fin2, output=ftmp2)\n",
    "\n",
    "# execution time: 5m 52s\n",
    "# latest execution time: 2m 45s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "16206f8c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:53:00.928267Z",
     "start_time": "2022-06-02T13:52:59.854486Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['File format : GRIB',\n",
       " '-1 : Institut Source   T Steptype Levels Num    Points Num Dtype : Parameter ID',\n",
       " '1 : ECMWF    unknown  v instant       1   1      3721   1  P16  : 235.128',\n",
       " 'Grid coordinates :',\n",
       " '1 : lonlat                   : points=3721 (61x61)',\n",
       " 'lon : 95 to 110 by 0.25 degrees_east',\n",
       " 'lat : 15 to 0 by -0.25 degrees_north',\n",
       " 'Vertical coordinates :',\n",
       " '1 : surface                  : levels=1',\n",
       " 'Time coordinate :',\n",
       " 'time : unlimited steps',\n",
       " 'RefTime =  1991-01-01 00:00:00  Units = hours  Calendar = proleptic_gregorian',\n",
       " 'YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss',\n",
       " '1991-01-01 00:00:00  1991-01-01 01:00:00  1991-01-01 02:00:00  1991-01-01 03:00:00',\n",
       " '1991-01-01 04:00:00  1991-01-01 05:00:00  1991-01-01 06:00:00  1991-01-01 07:00:00',\n",
       " '1991-01-01 08:00:00  1991-01-01 09:00:00  1991-01-01 10:00:00  1991-01-01 11:00:00',\n",
       " '1991-01-01 12:00:00  1991-01-01 13:00:00  1991-01-01 14:00:00  1991-01-01 15:00:00',\n",
       " '1991-01-01 16:00:00  1991-01-01 17:00:00  1991-01-01 18:00:00  1991-01-01 19:00:00',\n",
       " '1991-01-01 20:00:00  1991-01-01 21:00:00  1991-01-01 22:00:00  1991-01-01 23:00:00',\n",
       " '1991-01-02 00:00:00  1991-01-02 01:00:00  1991-01-02 02:00:00  1991-01-02 03:00:00',\n",
       " '1991-01-02 04:00:00  1991-01-02 05:00:00  1991-01-02 06:00:00  1991-01-02 07:00:00',\n",
       " '1991-01-02 08:00:00  1991-01-02 09:00:00  1991-01-02 10:00:00  1991-01-02 11:00:00',\n",
       " '1991-01-02 12:00:00  1991-01-02 13:00:00  1991-01-02 14:00:00  1991-01-02 15:00:00',\n",
       " '1991-01-02 16:00:00  1991-01-02 17:00:00  1991-01-02 18:00:00  1991-01-02 19:00:00',\n",
       " '1991-01-02 20:00:00  1991-01-02 21:00:00  1991-01-02 22:00:00  1991-01-02 23:00:00',\n",
       " '1991-01-03 00:00:00  1991-01-03 01:00:00  1991-01-03 02:00:00  1991-01-03 03:00:00',\n",
       " '1991-01-03 04:00:00  1991-01-03 05:00:00  1991-01-03 06:00:00  1991-01-03 07:00:00',\n",
       " '1991-01-03 08:00:00  1991-01-03 09:00:00  1991-01-03 10:00:00  1991-01-03 11:00:00',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '..................',\n",
       " '2020-12-29 12:00:00  2020-12-29 13:00:00  2020-12-29 14:00:00  2020-12-29 15:00:00',\n",
       " '2020-12-29 16:00:00  2020-12-29 17:00:00  2020-12-29 18:00:00  2020-12-29 19:00:00',\n",
       " '2020-12-29 20:00:00  2020-12-29 21:00:00  2020-12-29 22:00:00  2020-12-29 23:00:00',\n",
       " '2020-12-30 00:00:00  2020-12-30 01:00:00  2020-12-30 02:00:00  2020-12-30 03:00:00',\n",
       " '2020-12-30 04:00:00  2020-12-30 05:00:00  2020-12-30 06:00:00  2020-12-30 07:00:00',\n",
       " '2020-12-30 08:00:00  2020-12-30 09:00:00  2020-12-30 10:00:00  2020-12-30 11:00:00',\n",
       " '2020-12-30 12:00:00  2020-12-30 13:00:00  2020-12-30 14:00:00  2020-12-30 15:00:00',\n",
       " '2020-12-30 16:00:00  2020-12-30 17:00:00  2020-12-30 18:00:00  2020-12-30 19:00:00',\n",
       " '2020-12-30 20:00:00  2020-12-30 21:00:00  2020-12-30 22:00:00  2020-12-30 23:00:00',\n",
       " '2020-12-31 00:00:00  2020-12-31 01:00:00  2020-12-31 02:00:00  2020-12-31 03:00:00',\n",
       " '2020-12-31 04:00:00  2020-12-31 05:00:00  2020-12-31 06:00:00  2020-12-31 07:00:00',\n",
       " '2020-12-31 08:00:00  2020-12-31 09:00:00  2020-12-31 10:00:00  2020-12-31 11:00:00',\n",
       " '2020-12-31 12:00:00  2020-12-31 13:00:00  2020-12-31 14:00:00  2020-12-31 15:00:00',\n",
       " '2020-12-31 16:00:00  2020-12-31 17:00:00  2020-12-31 18:00:00  2020-12-31 19:00:00',\n",
       " '2020-12-31 20:00:00  2020-12-31 21:00:00  2020-12-31 22:00:00  2020-12-31 23:00:00']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.sinfo(input=ftmp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5d51186f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:53:51.738078Z",
     "start_time": "2022-06-02T13:53:50.325729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['File format : GRIB',\n",
       " '-1 : Institut Source   T Steptype Levels Num    Points Num Dtype : Parameter ID',\n",
       " '1 : ECMWF    unknown  v instant       1   1     22801   1  P16  : 235.128',\n",
       " 'Grid coordinates :',\n",
       " '1 : lonlat                   : points=22801 (151x151)',\n",
       " 'lon : 95 to 110 by 0.1 degrees_east',\n",
       " 'lat : 15 to 0 by -0.1 degrees_north',\n",
       " 'Vertical coordinates :',\n",
       " '1 : surface                  : levels=1',\n",
       " 'Time coordinate :',\n",
       " 'time : unlimited steps',\n",
       " 'RefTime =  1991-01-01 00:00:00  Units = hours  Calendar = proleptic_gregorian',\n",
       " 'YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss',\n",
       " '1991-01-01 00:00:00  1991-01-01 01:00:00  1991-01-01 02:00:00  1991-01-01 03:00:00',\n",
       " '1991-01-01 04:00:00  1991-01-01 05:00:00  1991-01-01 06:00:00  1991-01-01 07:00:00',\n",
       " '1991-01-01 08:00:00  1991-01-01 09:00:00  1991-01-01 10:00:00  1991-01-01 11:00:00',\n",
       " '1991-01-01 12:00:00  1991-01-01 13:00:00  1991-01-01 14:00:00  1991-01-01 15:00:00',\n",
       " '1991-01-01 16:00:00  1991-01-01 17:00:00  1991-01-01 18:00:00  1991-01-01 19:00:00',\n",
       " '1991-01-01 20:00:00  1991-01-01 21:00:00  1991-01-01 22:00:00  1991-01-01 23:00:00',\n",
       " '1991-01-02 00:00:00  1991-01-02 01:00:00  1991-01-02 02:00:00  1991-01-02 03:00:00',\n",
       " '1991-01-02 04:00:00  1991-01-02 05:00:00  1991-01-02 06:00:00  1991-01-02 07:00:00',\n",
       " '1991-01-02 08:00:00  1991-01-02 09:00:00  1991-01-02 10:00:00  1991-01-02 11:00:00',\n",
       " '1991-01-02 12:00:00  1991-01-02 13:00:00  1991-01-02 14:00:00  1991-01-02 15:00:00',\n",
       " '1991-01-02 16:00:00  1991-01-02 17:00:00  1991-01-02 18:00:00  1991-01-02 19:00:00',\n",
       " '1991-01-02 20:00:00  1991-01-02 21:00:00  1991-01-02 22:00:00  1991-01-02 23:00:00',\n",
       " '1991-01-03 00:00:00  1991-01-03 01:00:00  1991-01-03 02:00:00  1991-01-03 03:00:00',\n",
       " '1991-01-03 04:00:00  1991-01-03 05:00:00  1991-01-03 06:00:00  1991-01-03 07:00:00',\n",
       " '1991-01-03 08:00:00  1991-01-03 09:00:00  1991-01-03 10:00:00  1991-01-03 11:00:00',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '..................',\n",
       " '2020-12-29 12:00:00  2020-12-29 13:00:00  2020-12-29 14:00:00  2020-12-29 15:00:00',\n",
       " '2020-12-29 16:00:00  2020-12-29 17:00:00  2020-12-29 18:00:00  2020-12-29 19:00:00',\n",
       " '2020-12-29 20:00:00  2020-12-29 21:00:00  2020-12-29 22:00:00  2020-12-29 23:00:00',\n",
       " '2020-12-30 00:00:00  2020-12-30 01:00:00  2020-12-30 02:00:00  2020-12-30 03:00:00',\n",
       " '2020-12-30 04:00:00  2020-12-30 05:00:00  2020-12-30 06:00:00  2020-12-30 07:00:00',\n",
       " '2020-12-30 08:00:00  2020-12-30 09:00:00  2020-12-30 10:00:00  2020-12-30 11:00:00',\n",
       " '2020-12-30 12:00:00  2020-12-30 13:00:00  2020-12-30 14:00:00  2020-12-30 15:00:00',\n",
       " '2020-12-30 16:00:00  2020-12-30 17:00:00  2020-12-30 18:00:00  2020-12-30 19:00:00',\n",
       " '2020-12-30 20:00:00  2020-12-30 21:00:00  2020-12-30 22:00:00  2020-12-30 23:00:00',\n",
       " '2020-12-31 00:00:00  2020-12-31 01:00:00  2020-12-31 02:00:00  2020-12-31 03:00:00',\n",
       " '2020-12-31 04:00:00  2020-12-31 05:00:00  2020-12-31 06:00:00  2020-12-31 07:00:00',\n",
       " '2020-12-31 08:00:00  2020-12-31 09:00:00  2020-12-31 10:00:00  2020-12-31 11:00:00',\n",
       " '2020-12-31 12:00:00  2020-12-31 13:00:00  2020-12-31 14:00:00  2020-12-31 15:00:00',\n",
       " '2020-12-31 16:00:00  2020-12-31 17:00:00  2020-12-31 18:00:00  2020-12-31 19:00:00',\n",
       " '2020-12-31 20:00:00  2020-12-31 21:00:00  2020-12-31 22:00:00  2020-12-31 23:00:00']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.sinfo(input=ftmp2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957f57f8",
   "metadata": {},
   "source": [
    "Regrid skin temperature of land 0.1 by 0.1 to 0.25 by 0.25 as over ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "24a816e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:55:09.499870Z",
     "start_time": "2022-06-02T13:54:42.963170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/bog/amuttaqin/Datasets/Temporary/tmp3.grib'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftmp3 = \"/bog/amuttaqin/Datasets/Temporary/tmp3.grib\"\n",
    "cdo.remapnn(ftmp1, input=ftmp2, REMAP_EXTRAPOLATE='off', output=ftmp3)\n",
    "# execution time 27.7s\n",
    "# latest execution time 26.5s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0306b899",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:55:29.866961Z",
     "start_time": "2022-06-02T13:55:29.118405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['File format : GRIB',\n",
       " '-1 : Institut Source   T Steptype Levels Num    Points Num Dtype : Parameter ID',\n",
       " '1 : ECMWF    unknown  v instant       1   1      3721   1  P16  : 235.128',\n",
       " 'Grid coordinates :',\n",
       " '1 : lonlat                   : points=3721 (61x61)',\n",
       " 'lon : 95 to 110 by 0.25 degrees_east',\n",
       " 'lat : 15 to 0 by -0.25 degrees_north',\n",
       " 'Vertical coordinates :',\n",
       " '1 : surface                  : levels=1',\n",
       " 'Time coordinate :',\n",
       " 'time : unlimited steps',\n",
       " 'RefTime =  1991-01-01 00:00:00  Units = hours  Calendar = proleptic_gregorian',\n",
       " 'YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss',\n",
       " '1991-01-01 00:00:00  1991-01-01 01:00:00  1991-01-01 02:00:00  1991-01-01 03:00:00',\n",
       " '1991-01-01 04:00:00  1991-01-01 05:00:00  1991-01-01 06:00:00  1991-01-01 07:00:00',\n",
       " '1991-01-01 08:00:00  1991-01-01 09:00:00  1991-01-01 10:00:00  1991-01-01 11:00:00',\n",
       " '1991-01-01 12:00:00  1991-01-01 13:00:00  1991-01-01 14:00:00  1991-01-01 15:00:00',\n",
       " '1991-01-01 16:00:00  1991-01-01 17:00:00  1991-01-01 18:00:00  1991-01-01 19:00:00',\n",
       " '1991-01-01 20:00:00  1991-01-01 21:00:00  1991-01-01 22:00:00  1991-01-01 23:00:00',\n",
       " '1991-01-02 00:00:00  1991-01-02 01:00:00  1991-01-02 02:00:00  1991-01-02 03:00:00',\n",
       " '1991-01-02 04:00:00  1991-01-02 05:00:00  1991-01-02 06:00:00  1991-01-02 07:00:00',\n",
       " '1991-01-02 08:00:00  1991-01-02 09:00:00  1991-01-02 10:00:00  1991-01-02 11:00:00',\n",
       " '1991-01-02 12:00:00  1991-01-02 13:00:00  1991-01-02 14:00:00  1991-01-02 15:00:00',\n",
       " '1991-01-02 16:00:00  1991-01-02 17:00:00  1991-01-02 18:00:00  1991-01-02 19:00:00',\n",
       " '1991-01-02 20:00:00  1991-01-02 21:00:00  1991-01-02 22:00:00  1991-01-02 23:00:00',\n",
       " '1991-01-03 00:00:00  1991-01-03 01:00:00  1991-01-03 02:00:00  1991-01-03 03:00:00',\n",
       " '1991-01-03 04:00:00  1991-01-03 05:00:00  1991-01-03 06:00:00  1991-01-03 07:00:00',\n",
       " '1991-01-03 08:00:00  1991-01-03 09:00:00  1991-01-03 10:00:00  1991-01-03 11:00:00',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '................................................................................',\n",
       " '..................',\n",
       " '2020-12-29 12:00:00  2020-12-29 13:00:00  2020-12-29 14:00:00  2020-12-29 15:00:00',\n",
       " '2020-12-29 16:00:00  2020-12-29 17:00:00  2020-12-29 18:00:00  2020-12-29 19:00:00',\n",
       " '2020-12-29 20:00:00  2020-12-29 21:00:00  2020-12-29 22:00:00  2020-12-29 23:00:00',\n",
       " '2020-12-30 00:00:00  2020-12-30 01:00:00  2020-12-30 02:00:00  2020-12-30 03:00:00',\n",
       " '2020-12-30 04:00:00  2020-12-30 05:00:00  2020-12-30 06:00:00  2020-12-30 07:00:00',\n",
       " '2020-12-30 08:00:00  2020-12-30 09:00:00  2020-12-30 10:00:00  2020-12-30 11:00:00',\n",
       " '2020-12-30 12:00:00  2020-12-30 13:00:00  2020-12-30 14:00:00  2020-12-30 15:00:00',\n",
       " '2020-12-30 16:00:00  2020-12-30 17:00:00  2020-12-30 18:00:00  2020-12-30 19:00:00',\n",
       " '2020-12-30 20:00:00  2020-12-30 21:00:00  2020-12-30 22:00:00  2020-12-30 23:00:00',\n",
       " '2020-12-31 00:00:00  2020-12-31 01:00:00  2020-12-31 02:00:00  2020-12-31 03:00:00',\n",
       " '2020-12-31 04:00:00  2020-12-31 05:00:00  2020-12-31 06:00:00  2020-12-31 07:00:00',\n",
       " '2020-12-31 08:00:00  2020-12-31 09:00:00  2020-12-31 10:00:00  2020-12-31 11:00:00',\n",
       " '2020-12-31 12:00:00  2020-12-31 13:00:00  2020-12-31 14:00:00  2020-12-31 15:00:00',\n",
       " '2020-12-31 16:00:00  2020-12-31 17:00:00  2020-12-31 18:00:00  2020-12-31 19:00:00',\n",
       " '2020-12-31 20:00:00  2020-12-31 21:00:00  2020-12-31 22:00:00  2020-12-31 23:00:00']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.sinfo(input=ftmp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ccd02afa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:55:43.986062Z",
     "start_time": "2022-06-02T13:55:43.792969Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#',\n",
       " '# gridID 1',\n",
       " '#',\n",
       " 'gridtype  = lonlat',\n",
       " 'gridsize  = 3721',\n",
       " 'xsize     = 61',\n",
       " 'ysize     = 61',\n",
       " 'xname     = lon',\n",
       " 'xlongname = \"longitude\"',\n",
       " 'xunits    = \"degrees_east\"',\n",
       " 'yname     = lat',\n",
       " 'ylongname = \"latitude\"',\n",
       " 'yunits    = \"degrees_north\"',\n",
       " 'xfirst    = 95',\n",
       " 'xinc      = 0.25',\n",
       " 'yfirst    = 15',\n",
       " 'yinc      = -0.25']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.griddes(input=ftmp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b4bed474",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:55:46.474397Z",
     "start_time": "2022-06-02T13:55:46.367537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#',\n",
       " '# gridID 1',\n",
       " '#',\n",
       " 'gridtype  = lonlat',\n",
       " 'gridsize  = 3721',\n",
       " 'xsize     = 61',\n",
       " 'ysize     = 61',\n",
       " 'xname     = lon',\n",
       " 'xlongname = \"longitude\"',\n",
       " 'xunits    = \"degrees_east\"',\n",
       " 'yname     = lat',\n",
       " 'ylongname = \"latitude\"',\n",
       " 'yunits    = \"degrees_north\"',\n",
       " 'xfirst    = 95',\n",
       " 'xinc      = 0.25',\n",
       " 'yfirst    = 15',\n",
       " 'yinc      = -0.25']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.griddes(input=ftmp3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b04721b4",
   "metadata": {},
   "source": [
    "Convert grib to netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "49b59c24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:56:28.429426Z",
     "start_time": "2022-06-02T13:56:09.642055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/bog/amuttaqin/Datasets/Temporary/tmp3.nc'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc1 = \"/bog/amuttaqin/Datasets/Temporary/tmp1.nc\"\n",
    "nc3 = \"/bog/amuttaqin/Datasets/Temporary/tmp3.nc\"\n",
    "\n",
    "cdo.copy(input=ftmp1, options='-f nc', output=nc1)\n",
    "cdo.copy(input=ftmp3, options='-f nc', output=nc3)\n",
    "\n",
    "# execution time: 18.8s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea846c95",
   "metadata": {},
   "source": [
    "Load area-subset data. Assign land skin temperature and ocean skin temperature into variables (time, lat, lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b16a4b54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T13:57:27.685165Z",
     "start_time": "2022-06-02T13:57:26.394388Z"
    }
   },
   "outputs": [],
   "source": [
    "ds1 = xr.open_dataset(nc1, chunks={'latitude':5, 'longitude':5, 'time': 24*365})\n",
    "ds2 = xr.open_dataset(nc3, chunks={'latitude':5, 'longitude':5, 'time': 24*365})\n",
    "\n",
    "# execution time: 1.29s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "bc2ad68a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:25:15.423860Z",
     "start_time": "2022-06-02T15:25:15.404225Z"
    }
   },
   "outputs": [
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
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       ".xr-icon-database,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 262992, lon: 61, lat: 61)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * lon      (lon) float64 95.0 95.25 95.5 95.75 ... 109.2 109.5 109.8 110.0\n",
       "  * lat      (lat) float64 15.0 14.75 14.5 14.25 14.0 ... 1.0 0.75 0.5 0.25 0.0\n",
       "Data variables:\n",
       "    var235   (time, lat, lon) float32 dask.array&lt;chunksize=(8760, 61, 61), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Thu Jun 02 08:56:09 2022: cdo -O -s -f nc -copy /bog/amutta...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a4ae8938-1626-40a8-a86e-c8a3066967fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4ae8938-1626-40a8-a86e-c8a3066967fc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 262992</li><li><span class='xr-has-index'>lon</span>: 61</li><li><span class='xr-has-index'>lat</span>: 61</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-83a1f185-1bc9-418a-85ca-385e0bf08083' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83a1f185-1bc9-418a-85ca-385e0bf08083' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-59207cf5-bb12-446d-ad4f-d54682cb7c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59207cf5-bb12-446d-ad4f-d54682cb7c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-641d5774-a7a7-4829-9239-e41755aec7f1' class='xr-var-data-in' type='checkbox'><label for='data-641d5774-a7a7-4829-9239-e41755aec7f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>95.0 95.25 95.5 ... 109.8 110.0</div><input id='attrs-d0a534c0-9bc8-4147-9dd5-0c54ac629d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0a534c0-9bc8-4147-9dd5-0c54ac629d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa30eb3d-3c79-44c1-aaa5-f3d66e9274ae' class='xr-var-data-in' type='checkbox'><label for='data-aa30eb3d-3c79-44c1-aaa5-f3d66e9274ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 95.  ,  95.25,  95.5 ,  95.75,  96.  ,  96.25,  96.5 ,  96.75,  97.  ,\n",
       "        97.25,  97.5 ,  97.75,  98.  ,  98.25,  98.5 ,  98.75,  99.  ,  99.25,\n",
       "        99.5 ,  99.75, 100.  , 100.25, 100.5 , 100.75, 101.  , 101.25, 101.5 ,\n",
       "       101.75, 102.  , 102.25, 102.5 , 102.75, 103.  , 103.25, 103.5 , 103.75,\n",
       "       104.  , 104.25, 104.5 , 104.75, 105.  , 105.25, 105.5 , 105.75, 106.  ,\n",
       "       106.25, 106.5 , 106.75, 107.  , 107.25, 107.5 , 107.75, 108.  , 108.25,\n",
       "       108.5 , 108.75, 109.  , 109.25, 109.5 , 109.75, 110.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 14.75 14.5 ... 0.5 0.25 0.0</div><input id='attrs-b01ed582-62bd-4dba-b2f9-c0d42f0bde6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b01ed582-62bd-4dba-b2f9-c0d42f0bde6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de6db1cd-cabf-47b3-9aa0-edc7791ea0eb' class='xr-var-data-in' type='checkbox'><label for='data-de6db1cd-cabf-47b3-9aa0-edc7791ea0eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
       "        0.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9cd7996b-dbac-4eaa-97b9-f27076964d5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9cd7996b-dbac-4eaa-97b9-f27076964d5f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>var235</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8760, 61, 61), meta=np.ndarray&gt;</div><input id='attrs-f05a5340-b28c-4df0-9854-ea917f5331f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f05a5340-b28c-4df0-9854-ea917f5331f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-585c1bd8-91b5-41af-9146-60619bc91670' class='xr-var-data-in' type='checkbox'><label for='data-585c1bd8-91b5-41af-9146-60619bc91670' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.65 GiB </td>\n",
       "                        <td> 124.34 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (262992, 61, 61) </td>\n",
       "                        <td> (8760, 61, 61) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 32 Tasks </td>\n",
       "                        <td> 31 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 262992, lon: 61, lat: 61)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * lon      (lon) float64 95.0 95.25 95.5 95.75 ... 109.2 109.5 109.8 110.0\n",
       "  * lat      (lat) float64 15.0 14.75 14.5 14.25 14.0 ... 1.0 0.75 0.5 0.25 0.0\n",
       "Data variables:\n",
       "    var235   (time, lat, lon) float32 dask.array<chunksize=(8760, 61, 61), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Thu Jun 02 08:56:09 2022: cdo -O -s -f nc -copy /bog/amutta...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de..."
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     "end_time": "2022-06-02T14:00:07.018759Z",
     "start_time": "2022-06-02T14:00:07.001066Z"
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       "    table:    128</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'var235'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 262992</li><li><span class='xr-has-index'>lat</span>: 61</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a6d50b2-f2b2-43dc-87b9-09fd84b49207' class='xr-array-in' type='checkbox' checked><label for='section-6a6d50b2-f2b2-43dc-87b9-09fd84b49207' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(8760, 61, 61), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-3f5d8be6-2ad4-4bdd-9126-3ce1fbf61148' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f5d8be6-2ad4-4bdd-9126-3ce1fbf61148' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-79a602af-7b94-406f-9603-575b2f6558fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79a602af-7b94-406f-9603-575b2f6558fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea5b573-a5b6-45e4-afeb-2792fa5480f5' class='xr-var-data-in' type='checkbox'><label for='data-bea5b573-a5b6-45e4-afeb-2792fa5480f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>95.0 95.25 95.5 ... 109.8 110.0</div><input id='attrs-cfbbeb6d-5894-429e-905a-d3cb1e6ff841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfbbeb6d-5894-429e-905a-d3cb1e6ff841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47d951f5-9fff-440c-a8b0-e791543739c6' class='xr-var-data-in' type='checkbox'><label for='data-47d951f5-9fff-440c-a8b0-e791543739c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 95.  ,  95.25,  95.5 ,  95.75,  96.  ,  96.25,  96.5 ,  96.75,  97.  ,\n",
       "        97.25,  97.5 ,  97.75,  98.  ,  98.25,  98.5 ,  98.75,  99.  ,  99.25,\n",
       "        99.5 ,  99.75, 100.  , 100.25, 100.5 , 100.75, 101.  , 101.25, 101.5 ,\n",
       "       101.75, 102.  , 102.25, 102.5 , 102.75, 103.  , 103.25, 103.5 , 103.75,\n",
       "       104.  , 104.25, 104.5 , 104.75, 105.  , 105.25, 105.5 , 105.75, 106.  ,\n",
       "       106.25, 106.5 , 106.75, 107.  , 107.25, 107.5 , 107.75, 108.  , 108.25,\n",
       "       108.5 , 108.75, 109.  , 109.25, 109.5 , 109.75, 110.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 14.75 14.5 ... 0.5 0.25 0.0</div><input id='attrs-65e25fed-d6c7-42e6-a0df-d806fc63de57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65e25fed-d6c7-42e6-a0df-d806fc63de57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9d783ea-236c-4392-990d-d4dc92d7b78e' class='xr-var-data-in' type='checkbox'><label for='data-d9d783ea-236c-4392-990d-d4dc92d7b78e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
       "        0.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac5beceb-6454-4f1e-9768-c81fb9aa7372' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac5beceb-6454-4f1e-9768-c81fb9aa7372' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>table :</span></dt><dd>128</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'var235' (time: 262992, lat: 61, lon: 61)>\n",
       "dask.array<open_dataset-375909669535d6fde1746bc4932596cevar235, shape=(262992, 61, 61), dtype=float32, chunksize=(8760, 61, 61), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * lon      (lon) float64 95.0 95.25 95.5 95.75 ... 109.2 109.5 109.8 110.0\n",
       "  * lat      (lat) float64 15.0 14.75 14.5 14.25 14.0 ... 1.0 0.75 0.5 0.25 0.0\n",
       "Attributes:\n",
       "    table:    128"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds2['var235']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436c99cc",
   "metadata": {},
   "source": [
    "Extract land skin temperatuer and ocean skin temperature at each transect's tip (time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "09123330",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:19:41.751206Z",
     "start_time": "2022-06-02T15:19:41.743526Z"
    }
   },
   "outputs": [],
   "source": [
    "# transect-1\n",
    "t = 2\n",
    "\n",
    "ts1 = ds1.sel(lat=trnsc_ocean[t][0], lon=trnsc_ocean[t][1], method='nearest')\n",
    "ts2 = ds1.sel(lat=trnsc_land[t][0], lon=trnsc_land[t][1], method='nearest')\n",
    "\n",
    "# ts1 is time series of skin temperature at the tip of each transect over ocean\n",
    "# ts2 is time series of skin temperature at the tip of each transect over land\n",
    "# execution time: 8ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f50383f",
   "metadata": {},
   "source": [
    "Subtract land and ocean skin temperature at each transect's tip (time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9e67f611",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:19:45.001870Z",
     "start_time": "2022-06-02T15:19:44.995244Z"
    }
   },
   "outputs": [],
   "source": [
    "ts3 = ts2['var235']-ts1['var235']    # land - ocean \n",
    "# ts3 is time series of skin temperature difference between land and ocean at each transect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "de7d120d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:19:46.933256Z",
     "start_time": "2022-06-02T15:19:46.920621Z"
    }
   },
   "outputs": [
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       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;var235&#x27; (time: 262992)&gt;\n",
       "dask.array&lt;sub, shape=(262992,), dtype=float32, chunksize=(8760,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'var235'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 262992</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dcd074ff-4907-48fe-b33f-0a2dddf54b5b' class='xr-array-in' type='checkbox' checked><label for='section-dcd074ff-4907-48fe-b33f-0a2dddf54b5b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(8760,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
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       "            <table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-cf858a6c-3a89-43b7-9eb8-31817db8d56e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf858a6c-3a89-43b7-9eb8-31817db8d56e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-03f86e6d-da88-4276-9b00-49ac93a49154' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03f86e6d-da88-4276-9b00-49ac93a49154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9d5c687-7abb-4b58-b503-94ad75bca837' class='xr-var-data-in' type='checkbox'><label for='data-a9d5c687-7abb-4b58-b503-94ad75bca837' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-299ecf09-25bb-427a-9b77-230d60bca4fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-299ecf09-25bb-427a-9b77-230d60bca4fc' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'var235' (time: 262992)>\n",
       "dask.array<sub, shape=(262992,), dtype=float32, chunksize=(8760,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2465c427",
   "metadata": {},
   "source": [
    "Groupby time.hour and calculate hourly mean and standard deviation: <br> \n",
    "resulting mean and standard deviation at each hour (whole period of 30 years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "0d592a39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:19:49.711258Z",
     "start_time": "2022-06-02T15:19:49.228236Z"
    }
   },
   "outputs": [],
   "source": [
    "ts4 = ts3.groupby('time.hour').mean(dim='time')\n",
    "ts5 = ts3.groupby('time.hour').std(dim='time')\n",
    "\n",
    "# ts4 is the hourly mean of skin temperature difference over the whole period\n",
    "# ts5 is the hourly std of skin temperature difference over the whole period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5f652cb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:19:56.341622Z",
     "start_time": "2022-06-02T15:19:51.147664Z"
    }
   },
   "outputs": [],
   "source": [
    "dtmp_mean = ts4.compute()\n",
    "dtmp_stdv = ts5.compute()\n",
    "\n",
    "# execution time: 5.23s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df78ae52",
   "metadata": {},
   "source": [
    "Plot hourly mean of skin temp difference with standard deviation shading (whole period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "521106b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T15:20:44.565978Z",
     "start_time": "2022-06-02T15:20:43.869356Z"
    }
   },
   "outputs": [
    {
     "data": {
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YY4wxoywxMMYYY8woSwyMMcYYM8oSA2OMMcaMssTAGGOMMaMsMTDGGGPMKEsMjDHGGDPKEgNjjDHGjLLEwEw78biiqvkOwxhjClJJLLtszK5EY3H6g1H6AxEGQ1EAKvxeqsq8VPq9VJZ5qfB78xylMcbknyUGpmSFo3H6gxH6AxGGwzGSGwkC4RiBcGz0ughUlu2YLJT7LFkwxkwvlhiYkhKMxOgPROgPRgiE42ndVhWGQzGGQ68lCx4PVJX5nETBTRbKfNYDZ4wpXZYYmKI3HI7SH4jSH4wQiqSXDOxKPA6DwSiDwejoNq9HqHJbFppryvF6JKv3aYwx+WSJgSk6qspQ+LWWgUh0agcSxuLKQDDKQDBK93CYOQ2V1FX4pzQGY4zJFUsMTNEIRWO094cYCEaJxQtjVkEkqmzoHKa+0s+chgp8XutmMMYUt7QSAxFZAswCKoBuYI2qDuQiMGMSBcIx1ncNEY0VRkKQrM+d7TC7voLG6rJ8h2OMMZM2YWIgIh7geOAs4DigERjpUFUgLiLPA9cDV6rqxhzGaqapgWCEDV3DO80qKDSxuLK5J0BvIMKchgqb0WCMKUrjtnuKyPuAl4C/4yQB3wWOBfYF9gBeD5wJ/Ac4FVgjIpeKyLxcB22mj56hcFEkBYkGg1Fe2T5Ix0DICikZY4qOjPfGJSLPAj8BrlPV0C5PJLIn8DngVVX9aTaDzLUVK1boypUr8x2GSdLeH2R7/y5fegWtsszDvMYqK55kjCkoIrJKVVeMtW/crgRV3S+dO1HVl4BPphmbMTtRVdr6gnQPhvMdSsYC4Thr2gdpqSmntbYcj01tNMYUOJuVYApKPK5s7B5mIKFuQLFThY6BEH2BCHMbK6kpt387Y0zhmmiMwXIR6RKREyY45gQR6RSR/XMTnplOorE4r3YOlVRSkCgcjbOuY4jNPcMFM93SGGOSTTTp+ivAw6p663gHuPseAL6Y7cDM9BKKxljbMbTD2gWlqmcowsvbB+gLRPIdijHG7GSixOAtwN9SOMfVOLMVjJmUQDjGqx1DhKPZLWdcyKIxZWPXMBu6hojEps/jNsYUvokSgxZgcwrn2ALMyE44ZroZCEZY2zFYsIWLcq0/EGVN++C0aCkxxhSHiRKDbmBuCueY6x5rTFqKsUZBLkRjyqudgwyFSnNshTGmuEyUGNwHfDSFc3zEPdaYlLX3B9ncE5j2ScGIeBzWdQ7RH7RxB8aY/JooMfg/4CgRuUxEmpJ3ikiDiFwCHAX8KFcBmtKiqmzuGS76wkW5oAobu4bpGSr++g3GmOI1UYGjp0XkTOBy4EwRWQlsxCmPPB9YAUSB96rqM1MQqylypVijINtUYXNPgJgqLTXl+Q7HGDMNTbhGrKreAOyJ0yIQAg4EDgLCwA+BPd1jjJlQqdcoyLatvUG29wfzHYYxZhraZQk2Vd2Ks4CSMZMSiyuvdg4Riti0vHS094eIxpW5DZX5DsUYM41M2GJgTDa09QYsKZik7sEwG7uGbZVGY8yUscTA5FRfIELvsI20z0RfIML6rmHiVkbZGDMFLDEwORONxdnSE8h3GCVhMBjl1c4holYl0RiTY5YYmJzZ0huwxYKyKBCOsa7TSigbY3LLEgOTE73DYfoDNgMh24KROGs7BglFrYSyMSY3UloYXkTmT7A7DvSran92QjLFLhKLs6XXuhByJRJV1rYPsailmsoyb77DMcaUmJQSA2A9TmGjcYnIRuA3qvrLTIMyxW1zT4C4tXbnlDMFdJAFzdXUlKf6b2yMMbuW6jvKe4EfA88BNwEdOCsqngTsg1PsaAXwExHBkoPpq2swxKAVMZoS8Tis7xxit6Yq6iv9+Q7HGFMiUk0M3gTcpKqfTtr+JxH5LfAGVT1LRAaBTwCWGExDoWiMrX1WrW8qqcKm7mHiDZU0VpflOxxjTAlIdfDhacC/xtl3E07LAcBtwIJMgzLFyVZLzI+R9RW6Bm1hKmNM5lJNDILA4ePsO9zdDyDAUKZBmeLTMRBiOGQj5fNpa1+QwZB14xhjMpNqV8JFwDdFpBm4mR3HGHwC+IF73BsAW2lxmglGYrbgTwEYWbZ5aWsNZT6biWyMmZyUEgNV/aaIdANfBs7DmaEgwDbgywmDDa8BLstFoKYwqSqbe4atC6FAxOLKhq4hlsyoweORfIdjjClCKc9zUtVfisivgfnATJykYJOqxhOOeT77IZpC1jEQIhC2uYmFJBiJs7knwPzmqnyHYowpQmm1N7pJwAZgE7AlMSkw008gHKN9wAa8FaK+QIR2694xxkxCyomBiJwgIo/hDDTcCOznbr9IRN6fo/hMgbIuhMK3vT9EX8BWtjTGpCelxEBEzsKZlvgicE7S7V4BPpr90Ewh29YfJBixBqNCt7lnmGDEZosYY1KXaovB14GfquoHgb8m7Xse2CurUZmCNhSK0jkQzncYJgXxOGzoGrZVLo0xKUs1MVgA3DnOviBQl51wTKGLx5XNPbZAUjEJR+Ns7B5Grd/HGJOCVBODTcAB4+xbAazJTjim0G3tDxKOWhdCsRkMRtlmgxGNMSlINTG4FPi2O8iw0t0mInIc8BXg4lwEZwrLQDBC96B1IRSrzoEwvcP2/BljJpZqYvBj4C/AFUC3u+1h4HbgGlX9TQ5iS4uI7CUid4nIsIi0ich3RcQWq8+SWFzZ0mtdCMVuc0+AQNgGIxpjxpdq5UMFPiUiv8BZabEZJ0G4W1VfzmF8KRGRRuC/wGqcMs1LgJ/jJD7fyGNoJaOtN0Akan3UxU4VNnQ7lRH9XiubbIzZWcqVDwFUdS2wNkexZOITOF0cJ6tqP3CniNQBF4jIT9xtZpL6AhF6h20+fKmIRJWN3cMsbqlGxMomG2N2NG5iICJHpnMiVb0/83Am7Xjg9qQE4GqcLpCjcBZ+MpMQjcXZUkKzENpWPcSa264l2NNJRWMLS48/nTkHjbdwaOkaDsXY0htgXqOVTTbG7GiiFoN7eW2xJNzfR0jSdYB89ucvA+5O3KCqG0Vk2N1nicEktfUGS2YOfNuqh1h93SXEI84AvGBPJ6uvuwRgWiYHPUMRKv0hmmvK8x2KMaaATJQY7Jvw+2ycVRP/A9wAtAOtwCnAW4GP5CrAFDUCvWNs73H3mUkIRmIFU1J3Mt/0g5EYnYMhOgfDdA6EiPzzKryRHUflxyNh1tx27bRMDAC29gUp93upKU+rV9EYU8LGfTdIXClRRH4IXKmqyQP5/iMi3wc+hzP4L5/G+lo7VsuGs0PkHJzyzsyfPz+HYRWv9v7CWCBpvG/6fYEw0UUHOh/+AyE6BkM7JAIDoegO5zlvuHfM8wd6Ornrhe2sWNhEfaU/1w+noKjCxq5hlrbWUOazwYjGmNQHHx4HXDjOvvtwEoN86gEaxthez9gtCajqRcBFACtWrCiNtvIsKqTWgjW3XTuaFIyIR8I8f9PVXDH/tQ/y2nIfLbXltNSUsWxWLTNqyt3r5cyoKeel3zQT6u3a6fxD/lr+fNcreAT2nFXHIQubOGRRE7s1Vk6LwXmxuLKxe4jFLTV4PKX/eI0xE0s1MejGmQY4Vlnkd/NabYN8eRFnLMEoEdkNqHb3mTR1FMhyyqpKsKdzzH11sUG+d9I+tNSU0VJTToV/4mEu8RPO2KHlAcDjL+PQU9/PfvNfx+PrunhsfTdXPLKeKx5Zz+z6Cg52k4S9Z9fhS5jeV2qDGAPhOJt7AsxvtsGIxkx3qSYG/wdcKCILcVZZHBljcBLOjIDzchJd6m4Dviwitao64G47AwjgtGiYNISj8YJoLdjWF+QP961lX28NdbHBnfZXNLbwut0aUj7fyAf3eB/oS1treO/rF9A5GOKJ9d08tq6b257byk3PtFFd5uWgBY0cvLCJBT0vsvaffy65QYx9gQjtA0FaayvyHYoxJo8k1YVVROQk4HycNRN8QBR4Gvihqv4zR/GlxC1wtBp4DmeK4mLgF8CvxhgXsZMVK1boypUrcxtkEWnrDdCVx9LHkVicG5/awjVPbMLrEc5q7aLisX/s9E1/r9POzvkHcSAc4+nNvTyxrpsn1nfTG4jwwU1/oS46dqJy5Dd+ndN4psKiGdU2GNGYEiciq1R1xVj7Uv7vV9V/Af8SEQ8wA+hQ1YJYTUdVe9x1Gy7EmZrYC/wSuCCPYRWlSCxO91D+koLntvTx+3vXsKknwOFLmvnYEYtprimnbX5jXpruK8u8HLa4mcMWNxNX5eXtA2z46c5JATBul0ex2dobYGlrzbQYX2GM2VnaXwvcZGB7DmLJiKquBo7NdxzFrmswTD5W5+0LRPjzQ+u468V2WmvL+fbb92LFwqbR/XMOOjzvzfQeEZbNqqO9sWXMJCBe3UAkFi/6UsPBSJyuoTAtVt/AmGlp3HcwEblURHZP9UQi4heRj4jIB7ITmplqsbjSNTS1gw7jqty5ehuf/Osq7n25g9MOmsfv3nvgDklBoVl6/Ol4/GU7bIt5fNxReRAf/+sq/rt6e9EXhWrvDxGNFUSDoDFmik3UYjAMPCMiq4DrcVZTfE5VR+vjisgC4CCcAYjvArbg1gYwxadrMER8Cj8LNnQN8Yf71vJ8Wz97za7j3KOXsKC5euoCmKTxBjHObt2bKx5Zz6/vfoUbntrM+w9dwGGLm4uyST4WV9oHQsxpqNz1wcaYkjLh4EN3yt8ngA8A83CKBQWBEFCHU0AohrP88qX5HoQ4WTb4EOJx5cVtA1PyTTcYiXHNE5u48ektVJV5+cgbFnHs8lY8RfgBmkxVeeTVLv766AY29QTYvbWGDx62kP3TmD1RKEScmRq7mgZqjCk+Ew0+TGdWwh44rQOzgAqc2gUvAY+r6nCWYs0LSwycugXb+oJZP2/yfH/vwSdw2bYm2gdCHLeslQ8fvqgkqw3G4so9L7Zz1RMb6RgIsf+8es46bCF7zKzNd2hpqanwsail8FtxjDHpyUpiUMqme2Kg6rQWRGPZfS0klzIGiIiPp+e/mXee8g72nVuf1fsrRJFYnNue28o1T2yiPxjlsMXNfODQBezWVDyFhBa0VFFXUXrJmzHTWVamK5rS1T0UznpSAGOXMvZrlDf2Pc6+c9+f9fsrRH6vh3fuP5c3LZ/Jv55u48antvDYui6O2bOV9x4yn+grqwq+guLW3iC1M31FOVbCGJM+SwymOVWlYzA3MxHGm9c/1noFpa6qzMeZh8znhH1nc/2qzdzyvza2rHqI47ruwxNzqkwWagXFcDRO52CYGbU2fdGY6aC4J1ybjPUOR4hEc9Od5K0be8phRWNLTu6vGNRX+vnoGxfxp/ev4OiBJ0aTghEjy0AXmvaBoE1fNGaasMRgmstVa8GWngB316wgKjs2Snn8ZSw9/vSc3GcxmVFbjj/QN+a+QqygGI/Dtv7sD041xhQeSwymsb7hCKFI9r8Fdg+F+dZNz7GxYRkL3/Wh0RaCisaWKVnfoFiM13ISr2ooyAJJPUMRAuFYvsMwxuSYjTGYxjoGs/8NcDgc5Ts3P09/MMIP37Uvu8+shTcek/X7KQVLjz99p1kbMY+fO6sO4vbrnuGzx+3OwgKbKri1L8DiGTX5DsMYk0MZtxiIyCsisjYbwZip0x+MEAhnt7UgEovzw1tfYEP3MF9723InKTDjmnPQ4ex12tk7tKjsf8bZnHzaO+kYDPH5a5/m749vJFJAfftDoRh9w/lfktsYkzvjthiIyHxgq6ru6l3gfqxLouh0DGR3bEFclV/f9QrPbO7j82/anQMXNGb1/KVqrMWh5gD7zq3n4gde5arHN/Lw2k4+e9weLG0tjG/q2/qD1Fb48Hhs+qIxpWiiD/R1wAEAInK3iCwb6yBV/aiqfjgXwZncGApFGQ5lt6/48ofXc9/LHZx12AKOXTYzq+eejuor/XzpLXvyjROX0x+I8sXrnuaKh9cTjua/9SAcjdM5xYttGWOmzkSJQQAYKc92NM7aCKYEtGe5teBfT2/hxqe28PZ9Z3PqgfOyeu7p7vWLmvnd+w7kuOUzuf7JzXzm6qd4YWt/vsOivT9UUF0cxpjsmWjw4VPAr0XkTvf6p0Vk63gHq+pXshqZyYlAOMZgMJq18z3wSgeXPLiOw5c0c/YRi606Xg7UlPv4zLG788alLVx4zxr+3z+e5R37z+EDhy7I2wJHqrCtL1hUpZ2NMakZd60Et+vgZ8CewGJgO86qimNRVV2ckwinwHRaK2FD1xD9gewkBs9u7uXbNz3PnrNq+e4796HMZ0NNcm04HOWKRzZw6/+2Mquugk8fu5T95jXkLZ4lrdVUldnkJmOKTcaLKIlIHDhUVR/PdnCFYLokBsFIjFe2D2blXOs6B/nqDf+jpaacH5+8HzUV9uEwlf63pY/f3v0KW/uCnNawnQXr7iPU2zXl6y1UlnkLZlCkMSZ1EyUG437FSxpw+GGcwYimiGVrJkJ7f5ALblpNVZmX77xzb0sK8mDfufX85j0H8N7mDpqfvml0/YmR9RbaVj00JXEEwjF6h8O7PtAYUzQmavs9Amhwf78MWJTzaEzOhKIx+gKZzz/vD0T49s3PE4rFuOAde9NSYwvr5EuF38vctffi1x27hqZ6vYVt/UHiBVip0RgzORN91dsEnCYig4AAi9zfx6Sqq7MdnMmejoEQKfQaTSgYifG9W1azvT/I907ahwXNhVWVbzoab12FqVxvIRJ1VuicWVcxZfdpjMmdiRKDHwG/Bz4HKHDVOMeJuz8/w6PNLkVicXozrFYXiys/u+MlXto2wFePX8bec+qzFJ3JREVjy5hJwKC/lufb+qbseeoYCNFYVWYDUI0pAeP+F6vqxcA84CicD//zgGPHuBzj/jQFqnMws9YCVeUP963lsXXdfPzIxbxhyfRdNrnQLD3+dDz+sh22ia+M52cfzvk3/o9/PrWFVAYYZ2pk+qIxpvhNOGpMVbcD20XkO8C/VLVtasIy2RKNxekaTH9wWNuqh1hz27UEezqJVTXwatVBnHb0sZy435wcRGkma2T2wchzNTIr4fB9X8+v/vsKlz60jhe29fPZ43bP+bTCvkCEoVCU6nIbjGpMMUtpumKpK+Xpitv7g7T3pzcboW3VQzut+hf3+NnvjLOZs+KN2Q7R5Iiq8s+nt3D5w+uZXV/J145flvNxIZVlHpa22uJZxhS6iaYrTrSI0rXA11R1rfv7hFT19AxiNDkQiyudg+lPUVxz27U7JAUAnniENf+5zhKDIiIivPuAeezeWstPbn+RL173DOcds5Sj92zN2X0GwnG6h8I0VZft+mBjTEGaaKTQDMDv/t7qXp/oYgpM11CI+CTK2RfCSHeTPfvMredXZxzA0tYafn7ny/zxvrU5Xedge3+QmE1fNKZojdtioKrHJPx+9JREY7ImHlc6ByZXeGa8ke4VjTbosFg1VZfx/ZP24cpHN3DjU1tY0z7I/3vbMmbUZr8ORTSmdAyEmFVv0xeNKUY2t6hEdQ+HJ/2treGIk4jIjjmjx1/G0uOtt6iY+bwePnL4Ir52/DI2dg/z2Wue4qmNPTm5r87BEMFIdpf2NsZMjYnGGHwrnROp6nczD8dkQzyuky5/HFflb50t+GYfy5uHVhHum/r6+ya33rCkhQVN1fzothf49k3P877Xz+e0FbvhyeLKmKqwpTfAkhm2joIxxWai1RU7kjZVAiNrrA4CI//xw8CwquZuRFOOldqshI6B0KTnlP939XZ+ffcrfObYpbx5r1lZjswUkmAkxu/uXcO9L3WwYkEjH5jRyeb/3rDDtMdMk8HZDRVWNtuYAjSpRZRUdcbIBXgn0A68H6hS1TqcJOED7vaTsh+2mYxMWgv6AxEue3gdy2fXcdzymVmOzBSaCr+XL7xpDz551BIGVz/Oi9dfNjq2JFuLMW3rCxKKWpeCMcUk1TEGvwF+qKpXqWoQQFWDqvo34P+A3+UqQJOezsHQpMcWXP7IeobDMc49aklWm5VN4RIRTth3Nm8LPokvB4sxqcKWnkBG5zDGTK1UE4N9gPGqHm4BlmcnHJOJWNxZzGYyVm/t587V2zlp/zksbLHFkaabWH/3mNuzMUV1KBSja5KvS2PM1Es1MXgZ+IKI7NBZKCIVwBeAl7IdmElfx8Dk6hZEY3F+f88aWmrKec/B87MfmCl4401FzdYU1a3WpWBM0Ug1Mfg0cCiwWUSuEpFfichVOEszHwp8JlcBmtREY/FJVTkEuOmZNjZ0D/PxIxdTWWaLZE5HYy3GFBUfc487OSvnty4FY4pHSomBqt4P7A78GZgNvNX9+Wdgd3e/yaOOSa6g2D4Q5O9PbOSQhU0curg5+4GZojDnoMPZ67SzR1sIvHVN3DvjGC7e2kg0S1USrUvBmOKQ8jJoqroV+EoOYzGTFJnkCooAFz/wKnGFc45cnOWoTLGZc9DhO0xP9L7Uzs/vfJmLHniVc49empX72NYfpLbCT5nPaqsZU6jsv7MEtA9MrrXg8XXdPPpqN2cePJ+ZdVa+1uzo6D1bOfmAudz23DZue25rVs4ZjzuFj4wxhcsSgyIXisboGUq/tSAYifGn+9eyW1MVJ71uTg4iM6XgrMMWcuD8Rv50/6s839aXlXMOBqN0T+I1a4yZGpYYFLn2/sm1FlzzxCbaB0Kce9QS/N7CfxmIQH2ln5baMhqq/NRU+Kjwe/B6rN5CLnk9wpffuiez6ir40W0v0j4wuYqaybb2BQhHc7fCozFm8lIeY2AKTzASoy8QSft2G7qGuPHpLRy3rJV95tbnILLsEIGach8NVX7qKvx4xkkCVJVoXInGlGg8TjSmROJxYu62SMz5PRJTWw54EmrKfXz9xOV86bpn+MGtL/Djk/ejwp/Z7JV4HNp6A1Yzw5gCZIlBEZtMa4Gq8of71lLp9/LhwxflJrAMVZV7aaj0U1/px5dCa4aI4PcKzmfVxB9YI0nEQDBK91CIQNi+taZit8YqvvyWPfnuv1fzm7tf4ctv2RPJsDrmQDBKz1CYxuqyXR9sjJkyKbchi0i5iHxSRC4VkTtEZHd3+xkiYpUPp9hkWwvufrGd59v6+dAbFlJf6c9BZJNTWeZhVn0Fe86qZcmMGpprylNKCtLlJBEemqrLWNpay9LWGhqr/XgKvzcl71YsbOKswxbywCudXL9qc1bO2dYXIJKl6ZDGmOxIqcVARPYA7gTqgVXA0UCtu/sI4ETgrBzEZ8YxmdUTB4IRLntoHctm1fLmvfK/SFKZz0NDldMykGnT9GRVlnmZV1bFnHqlNxCxVoRdOOXAuazrHOIvj25gQXM1hyxqyuh88bhT+Mi6FIwpHOksorQRWIhT3CixDfE+4I3ZDctMZDgcZSAY3fWBSa54eD2DoSjnHr00b4sk+bxCS20ZS1tr2HNWLTPrKvKWFCTyeGSHVoSmmjJrRRiDiPDpY5eyeEY1P7vjJTZ1D2d8zoFglN5hm6VgTKFI9a3vCOBHqtoLJPdqb8epgmimyPb+9KvHvbC1n9tXb+ed+89h0RR/OxOBxmo/i2ZUs3x2HbPrKwu69HJlmZe5DZUsn1XH3MbCjjUfKvxevn7CXpT7PHz/ltUMTiJJTdbWG7QuBWMKRKqJQRCoHGffXKA3K9GYXRoMRdN+I47Fld/fu4aWmjLOPGRqF0nyeYVFLdXMa6yipry4xrq+1opQY60ISWbUlvPV45fRPhDip3e8mPFsj1hcabPCR8YUhFTf5u4EzheRxLlt6q62+Gng1qxHZsa0vT/9sQU3P9PG+q5hPnbEYqrKpu7DuabCx+6tNVQXWUIwFmtF2Nnec+r5xFFLeHJjL1c8sj7j8/UHrEvBmEKQ6jv2l4GHgDU4SYIC3wL2BsqA7CzBZiY0EIwwHEpv6dqOgRB/e3wDKxY0ctgULpLUWldekmWWR1oRmqrLCEZidA6G6B2OTKrIVCl4696zWNc5xI1PbWFhczXHLmvN6HxtvUFqyn05mZFijElNqqsrbgL2B/6IMwBxLc64guuAg1R1W64CNK+ZTGvBxQ+8SjwOHz9qScbzzlPh9QgLW6pKMilIVuH3Mq+xyhk30VBBuX96fpid/cZF7Du3ngvveYWXtw9kdC6nSyE71RWNMZOzy3cyEakQkYuBPVX1m6r6BlXdQ1UPVdWvq2rXFMQ57fUFImlPo1u5vptHXu3ijIN3Y9YUfFBXlnlZ2lpDbUXh1EeYCl6P0FJTzh4za1k0o5q6Sh95mvSRFz6vh//3tmU0VpXxg1tfyHgdhL5AhL7h9Gt0GGOyY5ddCaoaFJH3AH+bgnjMONpTbC1oW/UQa267lmBPJ0P+Wg6d80befcAbchwdNNWUMae+YkpaJQpZTbmPmnIf4WicnuEw3UNhorHS72eor/TzjROX8+Xrn+WyK/7BwV2PEOrtoqKxhaXHn77Dcs6p2NIboLrca10KxuRBqv91dwPH5DIQM77e4TDByK5bC9pWPcTq6y4h2NMJQHVkgIO3/JeOpx/JWWwisFtTJXMbKqd9UpCozOdhZl0Fy2bVsltTJVXlpT9YcVFLDefN72f5q/8h1Os0JAZ7Oll93SW0rXoorXPF4srWSRTxMsZkLtXBh78DLhGRapwZCNtJqmegqquzHJvBqe2fat2CNbddSzyS1IwbjbDmtmvT/saWinK/h/lNVQVRoKhQiQgNVWU0VDmDFbuGwvQMhUt2sKLnydvw647TaeOR8KReg73DEeoqIwVVutuY6SDVxOA/7s8vuJfEtzVxrxfEp4OIfA74JfAPVT01z+FkrGc4kvLytCMtBaluz0R9pZ95jZXjrnhodlbhd6Y8zqqrGO1mCKXQElRMsv0abOsNUO7zWPJpzBRKNTEoim4EEWnFmUbZke9YskFVaR9IvTm1orFlzDfgisaWrMUkArPqK2ipKc/aOaebkcGKLTXlDIaidA+G6Q+WxpTHbL8GozFlTfsgcxoqabJVGI2ZEiklBqp6X64DyZIfAbcAu+U7kGzoGgoTiab+abH0+NN55uqL8MZfa8r1+MtYevzpWYnH7xPmN1VNaZGkUjcyWDEai9MzHKFnuLhbEZYefzqrr7tkhy6tqPhoOepdkz6nqrPQ0mAwytzGSrzWSmVMTqU0+FBEqnZ1yXWgKcR4MHA68NV8x5IN8bjSMZDemggdM/fmzqajiFc3AM63tL1OOzsr4wtqKnwsnVFjSUGO+LweZtQ6Ux4Xz6imocpflFMe5xx0OHuddvZoC4G/vomHZx/Lr9bXZlzVsC8QYU37IMPhzNdmMMaML9V3+UF2XjwpWd46AcUZDn8h8BNV3VIKo+O70pzmpqpc+ch6embvw7Hv/xBlvuxN85pZV07rNChYVCiqy31Ul/uYE1f6AhG6h8IEwulVvMynOQcdvkMyOmfbAOf/839899+r+eG7981ovEA4GufVjiFm1lUwo9a6s4zJhVQTg4+wc2LQBLwF2Av4XjaDmoQPA7OAn+U5jqyITaK14NF13by8fZDzjlmataRABOY3V1E3zQoWFQpvUvnl7qEwvcORjBcsmmp7zqrlK2/dkx/e+gI/uf1Fvn7CXhl1B6jCtr4gQ6Eo8xorrdaBMVmW6hiDy8fZ9UsR+T3OmglZ4y7WtMulnFX1RffYHwKfUdWUl2cTkXOAcwDmz5/aFQd3pWswlNabfyyu/PXRDcxtqORNy2dmLY65DZWWFBSICr+XOQ2VzK6vGG1FGEpz3Yx8ev2iZj5+5BL+cN9a/njfWs49OvMS3QPBKK+0D7JbU/Gt3GlMIcvGf9MNwDXA57NwrhGnARencJwA5wObgDtEpMHd7gP87vUBVd3pHVRVLwIuAlixYkXBfAWLxZWOwfRaC+57uYON3cN85a17Zm1gVmtdOY02CrzgJNZFCEVj9A5Hiqa64gn7zqZjIMT1T25mRm05p6/IfIxwNKas7xyitda6u4zJlmwkBgcD6X2S7YKqXgJckuLhewIrgJ4x9vUARwAPZim0nNvaFyCexqD0SCzOVY9vYPGMag5fmp1piQ1V/mmxCFKxK/d5mVnnpbW2nIFQlN6hSMFPe/zAYQvoHAzxl0c30FJTxrHLMm/hUoXt/SEGQ1F2a6rCb10LxmQkpcRARH4yxuYyYDlwHPCrLMaUrm+Mcf+/AvqAbwP/m+J4Jq2tN0DPUHqLx9zx/Da294e44B1L8WRh0GVVuZd5jZUZn8dMHRGhrsJPXYWfeFwZCEXpD0QYCEYLbjyCR4TPHLc73cNhfnP3GhqryjhgfmNWzj0UivHK9kHmNVkXmDGZSLXF4HR2HnwYBDYDn8Ftks8HVX0ueZuI9AKdqnrvlAc0SVv7AnQNpjedKxiJcfXKTew9p44D5zdkHEOZz8OCpipb86CIeTxCfaWf+ko/qspQOEZ/wGlJSKcmRi75vR7OP345X73hWX5024v8+JR9WdRSk5Vzx+LKhs5hWmrLmFVni3oZMxkptbmp6kJVXZR0Wa6qb1bV36uqTSzOwNa+AJ0D6c/xvvnZNnqHI5x12MKM3wC9HmFhS5WN8C4hIkJNuY85DZUsm1XH0tYaWuvKqfDn/zmuLvfx7XfsTVWZlwtuXp1Whc9UdA6EWdsxRChaPAM0jSkUqRY4+paIzBln32wR+VZ2w8qMqh5dLOskbOsLTiopGAxG+ceTm1mxoJG9ZtdlFIMILGiuotxn9ehLWWWZl5l1Few+s5Y9ZtUwq74ir6s+ttSUc8E79iYYiXHBzasZDGb3+0UgHGNN+yB9w+l1zxkz3aX61eHbwLxx9s1x95s0be8Ppl2vYMQNT21mKBTjrMMWZBzHvMZKqm2617RS7vMyo7acJTNqWDa7lrmNldRU+Ka82uLClmq+fsJytvYG+MGtq4nEslsOOh6Hjd3DbO4Zzvq5jSlVqSYGIysojmUeY88IMBPY3h+kPcXllJP1DIW56Zk2jtx9RsZ9s6115TRU2bTE6czv9dBUXcailmqWz65jZn35lCYI+81r4LPH7c5zbf386r8vE8/BtIqeoQgvbRtgU/ewlVQ2ZhfG/ZooIh8EPuheVeAPItKfdFgFsC9wR27CK03tGSQFANes3EQkFud9r8+sMJNNSzTJvB6htbaC2nI/m3qGp2xBp6P3bKVrKMzlD6+npaacDx++KOv3oQq9wxF6hyNUlnlpqSmjvtJvAxSNSTJR+/Ew0OX+LjjT/7qTjgkDtwG/z35opam9P8j2DJKCbf1Bbn9+G2/eaxZzGiY/rdCmJZqJVJZ5WTqjhra+9KfQTtbJB8ylYyDEDU9toaWmnHfsP+awpqwIhGNs6g6w1RscLTtt9Q+McYybGKjqdcB1ACLyZ+C7qrpuqgIrRe0DmSUFAH9/bCMeEc48ePJV42xaokmFxyPMa6yitiLClp5AzmsiiAgfO2IxnYMhLn7gVerankVW3Uawp5OKxhaWHn96VlYKTRSNKe39IToGQtRX+mmuKbMVRM20l+p0xQ9bUpCZjoEQ2/sySwo2dA1xz0vtnLjfbJprJreynE1LNOmqr/SztLWG6imYweD1CF96y54c6dnE4F1/J9jTCUCwp5PV111C26qHcnK/I90Ma9uHWNM+QM9QGC3kEpLG5FDKqbGILATeD+yBM7ZgB6p6evbCKi0dAyG29WU+T/uvj22gwu/l1APHmyAyMZuWaCarzOdh8Ywa2gec8TG5/Mys8HtZ0fkI4aTyKPFImDW3XZv1VoNkgXCczeEAW/uCNNdYN4OZflItiXwQcB/OYkV7AM8C9cBCnOqHa3IUX9HrHMxOUvDy9gEefbWb9x4yn7rKyZV7tWmJJlOttRXUlPvY1B0gHM3dwMRwX9eY20daEKZCLP5aN0NdhdPNYP8/ZjpINQ3+KfAPYB+cgYgfVdXFwBtxZiyMtZbCtNc5GGJrb3Yqul35yHrqK/2c9LrJDciyaYkmW6rKfOzeWkNDVe7WI6hoHHtBMG9dU87uczyq0BeI8GqH082wrS/IQDBCvMDWoTAmW1JNDF4HXAWMfEWoAFDVh4HvAP+X9ciKXFcWk4JnNvXyzOY+Tjto3qQGRtm0RJNtHo+wW1MV85uq8OSglX3p8afj8e+YyEbFx61lB/DH+9YyFMpPLYJAOE7HQIj1ncOs3trP2o5BtvcHGQxFLVEwJSPVTxkFwqqqItIOLAAedvdtAnbPRXDFqmswRFuWkgJV5cpHnbndx+8zO+3b27REk0v1VX4qy2rZ1DPMcCh76xKMjCNYc9u1o7MS5r/5FDaH5nLL/7by8NpOzn7jYo7YvSVvs2tUYTgUcx93CBGoKvNSU+6jutxHVZnXZv6YopRqYrAaWALcAzwCfF5EVuLUMfgKsDY34RWf7qFw1pICgEdf7eLl7YN85tillPnS+2pm0xLNVCjzeVjcUk3HQIj2gewNTJxz0OE7DTQ8Bzh22Ux+d+8afnrHS9z5wnY+edSSjGp6ZIuqs/TzkCUKpsilmhhchDPQEOB8nEqHL7rXh4CiWLAo17qHwmzpCWTtfLG48pfHNjK3oZJjl81M67Y2LdFMJRGhta6C6nIfm3qGc7rE89LWGn526v7857mtXPnoBs77+5OcdtBunHrQvIKaPTBWolBd7qO63Et1mY8KvxevxxIFU3hSSgxU9S8Jv78gIsuBw4BK4FFVbc9RfEVjOBzNalIAcN/L7WzqHub/vW1Z2m8g85oqbVqimXLV5T52b62lrTdAbw5XNfR6hBP3m8NhS1q49MFXuerxjdz3cgefPGoJ++/WkLP7zYSqsyqqs4qkU9PE5xUq/F7KfZ4dflrCYPJpl4mBiFQANwE/VNV7AVR1ELgzt6EVl0gsu9+QIrE4f3tsI0tmVPOGJc1p3ba63EtdRe5GjBszEa87MLGmPMyW3kBOax40VZfx5bcu47jlPfzxvrV841/PcdQeM/jo4YtorC78WTjRmDIYizKYtN3nFcp9Hsr9XioSfloLoJkKu0wMVDUoIgcD9vVzCt3+/DbaB0J86uileNLsl5xVbzMQTP41VpdRWeZlY3fuF2M6cH4jvz3zAK5ftZnrV21m5fpuzjpsIW/de1ZRfvuOxpRobKQb4jVej1Duf611odznwe/1UOb14CnCx2kKU6pjDG4C3gXclbtQzIhgJMY1Kzexz5w6DpjfkNZt6yv9VuvdFIwKv7MY05Ycdy0AlPu8vO/1Czhqjxn84b61/OG+tdz14nY+MKOL/gdvyumaC1MlFteEmRA78noEv1fwez34fR78XqHM6yQOPvd3G/xoUpHqJ8jtwE9FZDZwK7AdZwrjKFW9NcuxTVs3P9NG73CE849fntY/sohTyMiYQjJS86C6PExbjrsWAOY1VvH9k/bhvpc7uOvf/2HLY3fhd8srj6y5ABRtcjCeWFyJxZXgBK0zPjdxKHOThZHf/T7B6xF8Hk9RtrCY7Eo1Mfir+/Nk95JMsa6GjLWteohXbr2Gut4uzimvo75NYHbqb14NVX4q/PY0mMLUVF1GVZmXDV3DOS2nDM4siaP3bEWuXUkoT2suFKKRLooA49ecEMFNEgSf14PPIztcH/ndaaGwRKIUpZoYLMppFIa2VQ+x+rpLiEfCCFAe6k/rm40IVt3QFLwKv5elrTVs6QnQF8ht1wJAqHfsNRcCPZ1s6Qkw14p/7UR1JIFQSGFsSGIi4XUvHnntdxHwutc9Hnnt94RjSlksrkRicWJxHX3cPvdvUahSna64IdeBTHdrbruWeCS8w7Z0vtm01JQX1BxuY8bj9Qjzm6ucsuF9wdyu1NjYMubCS4O+Gj75t1UcuriZUw6cx56zanMXRInbIZGYJI/HeV14xfnA9IjgEfCIk1g4151tssM257on4ZjEfR7J3QfwyAd+NK5EY3EiMSUajxONKeGY8zMSi4/7+h5JqLwJLTIeEXze15Kn1/Z58HiYsq6edJZdLgc+AqwAdgM+paqviMgZwLOq+kKOYpwWxls1LpXV5DwemFFrYwtMcWmuKaeqzMfG7tx1LSw9/vTRlrgRHn8Z+530Xk6T3bjlf2088moX+8yp45SD5nHQ/EYboJcH8TjE40qE3GSJIu4FweN5LXGAnRMQSUo2BNwPfyUS3/UHfqomm1DNa6zM+VTcVJdd3gOnbkE9sAo4GhhJsY8ATgTOykF800a8ugHPUO9O28dbZS5Ra21FyTfHmdJUWZbbroWx1lwYmZWwO3DKgXO5Y/V2/vX0Fr5z82oWNldx8oHzOGJpi9UMKCGquB/kSizu/DTjS7XF4DfARuAdwCDOGgkj7gN+nOW4ppX1nUPcVb2CYwP34o2/NlDK4y9j6fGnT3hbv09oLoJCLsaMZ6RroXMwxLYcdC2MtebCiKoyH+963VxO3Hc297/cwT+e2sIv7nyZvzy6gXe9bi5v2WumDeg1006qicERwGmq2isiyf8l24H0l/0zAISjcX52x0v0zdiLPY5czJa7bkhrvnVrbUVBD2IxJlUtNeVU57hrYTx+r4fjls/kmGWtrFzfwz+e3MzFD7zK1Y9v5MT9ZvP2/eYwtPrxMVsejCk1qSYGQZx1EcYyF+jNSjTT0JWPrGdD9zDffsdeLFnQxJLDjkr5tuV+D41VVvrYlI6RroXNPcP0B6K7vkGWeUQ4ZFEThyxq4oWt/fzjyc1c/cQmnrnvHo7tvBdPzOnuKOV6CMak2ol2J3C+iNQnbFN3QOKncYoemTQ9vamXfz3Txon7zmbFgqa0bz+zrsIGSpmS4/UIC5qrmd1QQT5f3stn1/GNE/fi9+87kKP6Hh9NCkaMzBoyptSkmhh8GZgBrAH+gjNy41vA/4A5wNdzEl0J6w9E+OV/X2a3xko+9IaFad++qtxLfaW1FpjS1VJTzuIZ1VSW5XcQ4G6NVfiDfWPuC/R08symXmJxG8xmSkdK/3GqugnYH/gjsBBYizOu4DrgIFXdlqsAS5Gq8rt719AfiPCFN+85qcFNs6yYkZkGqsp8LG2tZUFLVV4ThPFmBw36avnGv57jo1c8wZ8fWsf6zqEpjsyY7Eu5joGq9gDfdC8mA3e/2M7Da7v44GELWdpak/btayt8VJfbQklm+qir8FNX4ac/GKG9P0QgPH5J31wYrx7CwSe/j9bGPbn3pQ7+9UwbNzy1hYXNVRyzZytH7TGD5hqrL2KKT1qfLiLSAOyD01rQBjyvqr3ZD6t0besL8qf7X2WfOXW8+4C5kzqHLatspqt8JQgT1UOYDxyx+wz6AhEefKWDe17q4M8Pr+fyh9ez/24NHL3HDA5b0jy66mnbqodsdoMpaKIpTBoWER/wA+BTQFXCrmHg98DXVTX3hc9zZMWKFbpy5cqMztEXiLCxa3jCY2Jx5Ws3PMvG7mF+c+YBtNam/wHfUOVnt6aqXR9ozDQwEIywPQ8tCLvS1hvgnpfaufelDrb1BynzeTh0UTNvkPUE7r56p5aHvU4725IDk5JsVT4UkVWqumKsfam2GPwCOAf4LnAD0A60AqfgdC1UAJ/JONISd/2qTbywbYAvvnmPSSUFtlCSMTuqrfBTW+FnIBihfSDEcKgwEoQ5DZW87/ULeO8h83lx2wD3vNTOg690suiVG6iLTX5NFGOmQqqjeT4AnK+qP1TVF1W12/35A5wZCR/IXYil4eXtA1z1+EaO3H0GR+/ZOqlzNFWXUeazMq3GJKut8LNkRg2LZlRTVV44lQpFhOWz6zj36KVc8ZFDqIsNjnlcoKeTjoHQFEdnzNhSbTGIA8+Ps+85rPD0hALhGD+/4yWaqsv55NFLJnUOjwdabaEkYyZUU+6jZkYNg6Eo7f1BhgqkBQGc6orjrfY44K3hI1c8wfymKlYsaOSgBY0sn11nK6aavEg1MfgLcDZw+xj7Pgb8NWsRlaBLH1rH1r4gP3jXPtRMcjbBjJpyW9TFmBQVaoIw3uyGPU84g4/ULmTVhh5ucmc3VPq97L9bPSsWNHHQgkZaEmY42ABGk0upfkptAE4RkeeBm3htjMFJOKss/lxEznWPVVX9Q9YjLVKPrevi9ue3ccqBc9l3XsOkzuHzyg5vCsaY1IwkCEOhKNsLIEGYaHbDvsC7D5jHcDjKs5v7WLWhh1Ube3j01W4AFjRVsWJhI8uHXmHgrr+PJhdWntlkW6qzEtJZ0URVtXA6+VKQq1kJPUNhzvv7k7TUlPOz0/afdLPgnIYKmw9tTBYUSoKQKlVlY/fwaJKwuq2f962/csyxChWNLRz5jV/nIUozlQpmVoKqWht2mlSV39z9CsFInC++Zc9JJwVlPg9NtqyyMVlRXe5jcQG1IOyKiLNuxILmak4+0GlNePBrvx/z2EBPJ9et2sRes+tY2lpDua+ovp+ZAmLl83Lk1ue2sXJDDx8/cjHzM6g7MLOu3BZKMibLii1BGFFV5ht3AOOwv5YrH9kAgM8jLJlRw/LZtSyfXcfy2XU0Vu38BcPGKpixpFv5cE+cZZZ3mkyvqrbComtT9zCXPbiOA+c3cuK+syd9nsoyDw1j/DMbY7KjGBOE8QYwvv60D3DsXofw0rZ+Vm8d4IWt/dzyv6388+k2AGbXV7B8Vp2bKNTiW/cUL1x/qY1VMDtJKTEQkX2BvwPLgbG+vipg7VZAJBbn53e+RIXfw+eO2z2jb/tWzMiYqVFMCcJEAxgBDlnUzCGLmgHn/Wht+yCrt/bzwrZ+Vm3s4e6X2gH48Ka/UhO1YktmZ6m2GFwGRIC34yy9HJ748Onrqsc2srZjiPNPWJ7RAJHqci+1FbassjFTqVgShDkHHZ7Sh7ff62HZ7DqWza4DnLFPW/uCvLC1n+glA2PeJtDTyZWPrGdRSzWLWqqZXV+J12PdmdNJqonBcuAUVR2rjsG098ID93D/369ksKuDam8Np+z7Fg5b/MaMzjm7vjJL0Rlj0pWYILQPhBgMRvMdUlaICHMaKpnTUMn944xVCJTVcsNTW4jFnRlr5T4PC5qrWNTsJAoL3YRhZFEosLEKpSbVxOBxYH4uAylWLzxwD3dcdCHRsFPOtC42SMPz/6Zt1axJ/2PUV/qpLLOeGWPyrbrcx6JyX8klCDD+WIVDTv0AJ77uMDZ2D7Ouc4h1nUOs7xzi4bVd3L56++ixM+vKWdRSzbKhV6hdeSNEnXX0bKxC8Us1MTgH+LuIDAP3AL3JB6jqxEsLlqgHrr5yNCkYkUk/nQjMrLeaBcYUksQEoWMgxEAJJAi7GquwZEYNS2bUjB6vqnQNhXm1Y4h1Xa8lDHs+eyvEdlxcNx4J8/SNV/Fc5RLm1Fcyu76CpuqyXY65spaHwpBqYtAJrAeunOCYafkVd6Br56Y4YMwmulQ0VpfZ/GNjClR1uY/qch/BSIyOgRB9gQgp1IgrWKmOVQCnG6KlppyWmnIOWdQ0uv2OL469MJQ30Mtv714zer3c52F2fYXTlVFfyeyGih2Shq1PPrxDC4a1PORPqonBX4HDgJ9hgw93UNvcwkBnx07bKxpb0j6XiC2UZEwxqPB72a2pitZojM7BMD1D4aJOEDIxXl2FyoZmLv7ACtp6A2ztC9DWF6StN8CGrmEeW9c9OoYBnKTh/Rv+QlVk51kSL99yDbMOfAOeNGZ4WctDZlJNDI4BPqaqV+UymGJ0xHvO2mGMATj9dEuPPz3tc82oLbfV1IwpIuU+L3MbKplZW07nYJiuoRDxdArIl4DxxiosPeEMZtVXMKu+Amjc4TaxuNIxENohaah8ZexZEqG+Lk75w8M0VZfRXF1GU005ze7vzSO/15TRXF1Omc9D26qHCq7lodgSlVQTg/XAtBxDsCvLjzgGYHRWwmSfdJ9XmGHrIRhTlHxeD7PqK5hRW07XUIiuwTDR2PRoQtjVWIWxeD2yU9Jw/31jtzxodQMnvW4OXYNhuobCrOsYZOX6MKHozhlYbbmP018du+Vh9c1/Jzh/f+or/dRV+lP6EpaND/RsJSqvxdJFbUsLR7znrNHPn2xLdRGlE4DvAKep6vqcRJJHuVpEKR22UJIxpUNV6R4K0zkYJjzGB5jZWfIHKDgtD3uddvZOH6CqylA4RtdgiK6hMN1ua03XUJglN3973Cp8Fy765Oj16jIvdZV+GtxEoT7pUrbhaYbu+juaUARKfGVUHXcmuvhAAuEogUiMQCROIBwjEIkRdH8GIrHRbW969iKqIzu3hgTL63jpqM9RW+FzVgF1L9VJ12vKffQ8+wirE6pUAvjKynnLOedNOjnIeBElnKRgPvCyiKxn7FkJh0wqOkO53xZKMqaUiAjNNeU0VZfRF4jQORgiELYEYSLptDyIyOiH5oLm6h323f/g2C0Pvtomvvq2ZfQHI/QOR+gPROgLRugLRNjeH+Sl7QP0ByKMDH344MZ/UBfbseVBo2G2/vcfXDF/x/VvfB6h0u+lssxLpd9Lhft7U3UZVWMkBQDloX5eaR9gMBhlKBwlPsF39A9t+gu1SVUqo+EQD1x9ZU5aDVJNDJ5zLyYHZtZV2EJJxpQgEaGhqoyGqjIGghHaB0IMF2g1xUKQziyJ8Yw35mH5O97DnKUTDwqPqzIUitIXiLD6+38Y85i62CC/fc8Bo0lAZZl3wm6J+58cZ3BmYwsXfWDF6P0Oh2MMhqJOohCKOr+7l5q/jT3zY7xZcZlKddnlD+fk3g1V5V7qK630sTGlrrbCT22Fn+Hwa7UQputMhlyazJiHER6R0efp1XFmW1Q0trCwpXqMW49t3MGZCQPUPQktINTtfI77bx07ltrm9Ge/pSLd1RUFmAfsBjyjqkM5iWoamV1vCyUZM51UlflY0OwjHI3TMxyme2j6DFScKrlseUh3xlkmicpEsfjKyjniPWelFUuqUk4MRORc4BvALJxxHAcDT4rIDcD9qvqrnESYWmx1wHeBdwGtwAbgT8CvNZXRlXlSX+nfod64MWb6KPN5mFlXQWttOf2BKF1DoYJdtGk6ysYHeuK5MklUdowl97MSUl12+cvA94Af45REvjth973AmcCvshxbOi4HjgTOxynAdAzwC5wlon+Zv7DGZ6WPjTHgjEOor/JTX+UnGInRMxymZyiyQwEgkx/ZaHnIlpFY5jVWZrRybypS/br6KeBbqvoTEUmu1/sSsEd2w0qdiFQBJwGfU9WL3M13i8jewHso0MSgyUofG2OSVPi9zK6vZGZtBX2BCF1DYQJha0UwUyvVxGAWsGqcfXEgnx3lPsAD9CVt78UZD1FwPB4rfWyMGZ/HIzRWl9FYXUYgHKN7eHqXXTZTK9X6u2uAo8bZdySwOjvhpE9V+4Frga+IyOtEpFZE3g6cDvwuX3FNZEZNOT4rfWyMSUFlmVN2efnsOuY0VFDht/cOk1upthj8Cvi9iISB691trSLyUeALwMdyEFs6zgL+BjzlXlfga6p6Rf5CGpvP66xQZowx6fB6nKJJzTXlDIWidA+Fi351R1OYUq1jcImINALfwqmCCHArzvoJF2R7cSURqQdmpxDXi+6vvwReD3wYeBV4I3CBiHSq6qXj3Mc5wDkA8+fPz0bYKZlZV4HHY8WMjDGTN7L885y40hdwqvcNhawugsmOlNZKGD1YpBZn+eUWoBt4RFWT+/YzD0rkbODiXR2nqiIi+wHPAG9R1TsTzvEj4BNAs6pOWIt0qtZKqPB7WNpaY1UOjTFZF43F6QtE6A1ErLpiCcvWrISM10oQkbOAW1S1C7gjaV8T8HZVvTLjSF2qeglwSYqHL3N/Pp20/SmgAWgGOrISWIZm1lvpY2NMbvi8ntGuhnA07rYkhG2NBpO2VEex/BlYMs6+Re7+fNng/jwwaftBwBCQm2LSaaou91JXYaWPjTG5V+bzMKO2nKWttew+s4aZdeWU26BFk6JUBx9O9DW3GejPQiyTtdK9XCYi3wLW4Ywx+BwFVPlwdn1lvkMwxkxDFe5qf611FQTCMbe7IUwkWhBvjaYAjZsYiMhJOIWDRnxTRJKb5CuAI4AnchBbSlQ1JiLvAL6PMzhyBk4rwgXAz/MVV6KGKj+VZVbMyBiTX5VlzmqAs+orGA5H6R12Bi7aWg0m0UQtBq3AvgnXl+AUOkoUxhlz8P0sx5UWVd0GnJ3PGMYjAq11Nj3RGFNYqsp8VJX5mF1fwXA4xkAwykAwQjBiYxKmu3ETA1W9GHdmgIjcA3wyYXqgSZGVPjbGFDIRGZ3+OKu+gkgszkAwymAwykAoQtzyhGkn1ToGuVnCqcRZ6WNjTLHxez00VZfRVF2Gqo62JgyGIjbDYZqwNX9zaEatlT42xhSvxNYEcFoTBoNRp9vBWhNKliUGOeL3CS3V1lpgjCkdfq9ndHEna00oXZYY5MjMWit9bIwpXWO1JgyFogyFYwTCUYKRuJVoLlKWGORAZZknKyUrjTGmWPi9Hhqqymiocq7H4spwOEogHGMoHGM4HLWuhyJhiUEOzKyryHcIxhiTV16PUFvhpzah4mswEmMoFGU4HGM4HCMctUyhEKWcGIjIqcDJwDycwkY7UNVDshhX0aqp8O3wj2CMMcYxUoWx2b0eicXdJCHKUChGMBKz7ocCkOoiShfgVBV8BliNU9jIjGGWtRYYY0xK/F4P9ZUe6iudL1PxuBKIxAhEnCQhGIlbspAHqbYYfBT4P1U9P5fBFDsrfWyMMZPn8SQOaHxNOBonGHWShZCbLISiNrgxV1JNDGqBu3IZSLETsbEFxhiTC2U+D2U+zw4r1KoqoWjcSRSir7Uw2LiFzKWaGFwNvA1LDsZVW+5DxKYnGmPMVBCR0TEL9byWMMTjTsIQjMQIx5xEIRR1fsbi1sSQilQTg7uAH4tIC3An0Jt8gKremsW4io4lBcYYk38ej4yuIpksFlcisdcShZHEIeL+tK4JR6qJwTXuz4XAB8fYr4B1rhtjjClYXo/g9TitDGMZSRYiCUlDOBZHs5QxxOJOchJXLegkJNXEYFFOozDGGGPybGQsA1NQzT4W19EkIRZXonElHldi6vyMJuxP3OeZgtbpVFdX3JDrQIwxxpjpwmm9KMwu6HETAxGpUtXhkd93daKRY40xxhhTvCZqMRgQkcNU9XFgEGccwURsjIExxhhT5CZKDD4CrE34vYCHShhjjDEmG8ZNDFT1ioTfL5/oJCJiizEZY4wxJcCTykEi8v0J9lUCN2UtImOMMcbkTUqJAfAZEfl68kYRqQH+A+yV1aiMMcYYkxepdgG8E7hFRIZV9ZcAItII3AE0AEfmJjxjjDHGTKVU6xjcKyInA/8UkQDwT+C/OAMSj1DVbbkL0RhjjDFTJdWuBFT1duAM4FfAKmAYONKSAmOMMaZ0TFTg6IQxNkeBq4B3AL8ADhtZPGi6L6JkjDHGlIKJuhL+jdNVMF7NxqsSfrdFlIwxxpgSMFFiYAsnGWOMMdPMRAWObOEkY4wxZppJu2Khu6DSR4FlwDbgSksijDHGmNIw0eDDnwPvUNU9ErbVAk8AuwM9QD3wRRE5RFVfznWwxhhjjMmtiaYrHgP8NWnbl4A9gI+pagswB1gPfDMn0RljjDFmSk2UGCzEqVeQ6BRgtapeBqCqHcDPgcNzEp0xxhhjptREiYEPCI5cEZEmYDlwd9Jx64FZWY/MGGOMMVNuosTgZeDohOtvd3/ennRcK9CdxZiMMcYYkycTzUq4ELhYROqB7cBngHU4CyclegvwXG7CM8YYY8xUmqiOweUiMhv4FM4Kik8Cn1LVyMgxIjIDOAn4To7jNMYYY8wUmLCOgar+CPjRBPs7sPEFxhhjTMlIeXVFY4wxxpQ+SwyMMcYYM8oSA2OMMcaMssTAGGOMMaMsMTDGGGPMKEsMjDHGGDPKEgNjjDHGjLLEwBhjjDGjLDEwxhhjzChLDIwxxhgzyhIDY4wxxoyyxMAYY4wxoywxMMYYY8woSwyMMcYYM8oSA2OMMcaMssTAGGOMMaMsMTDGGGPMqIJPDETkDBG5QUS2ioiKyIfGOOZNInKNiGwQkWEReU5EzhMRbx5CNsYYY4pWwScGwKnAQuDfExxzDlANfAM4Abga+Dnwk1wHZ4wxxpQSX74DSMEZqhoXkRrg7HGOOVdVOxOu3ysiVcDnReR8VQ3lPkxjjDGm+BV8i4GqxlM4pnOMzU8BFUBd1oMyxhhjSlTBJwYZeAPQqaod+Q7EGGOMKRYlmRiIyF7AJ4Df5TsWY4wxpphM+RgDEakHZu/qOFV9cZLnbwT+ATwL/HCC487BGbQIMCgiL03m/pK0AGN1axSzUnxMUJqPqxQfE9jjKial+JigNB/XgvF25GPw4WnAxSkcJ+meWEQqgH8B5cA7VTU83rGqehFwUbr3sYv7X6mqK7J5znwrxccEpfm4SvExgT2uYlKKjwlK93GNZ8q7ElT1ElWVXV3SPa9bs+AqYG/geFXdnvXgjTHGmBJXDNMVU/V74G3Am1Q1G90CxhhjzLRT8ImBO5BwL5yphwArRGQQ6FDV+9xjzscZL/AjIC4ihyacYrWq9k9RuFntmigQpfiYoDQfVyk+JrDHVUxK8TFB6T6uMYmq5juGCYnIBcC3x9h1n6oe7R5zL3DUOKc4RlXvzUVsxhhjTKkp+MTAGGOMMVOnJOsYTCUR2UtE7nIXb2oTke8W++JNInKqiDwsIl0iEhSRl0TkGyJSlu/YMiEiPhH5qoi8IiIhEdksIr/Md1yZEpF3iciz7mNaJyJfyHdM6RCRpSLyJxF5RkRibgtg4v7ZIvJTd/+giGwSkStEZE6eQk7Jrh6Xe8x6d3G4xMu2PISbkhQf02wR+bOIbHGfr6dE5H15CDclInKaiNyUEO8qETkz6ZhzReQW9z1RReTo/EQ7NQp+jEEhc2sm/BdYDZwELMFZvMmDs6BTsWoG7gF+CvQChwAXALOA8/IWVeb+DBwHfAd4EdgNZ/xK0RKRw4EbgMuALwGvB34sInFV/VU+Y0vD3jiLnz0KjJV8HgS8G7gEeAyYifN6fFhE9lHVwSmKM127elwjrgJ+m3B93GnWBWDCxyQiHuAmnPeQrwDbcBbC+6uIDKvqjVMYa6q+AKwDPo9Tq+AE4CoRaVHVkeflLECB24EzxzxLCbGuhAyIyNdwXvwLRgY4ishXcD9Ep3DQY86JyA+ATwGNWoQvGhF5G3AzsL+qrs53PNkiIrcDlap6ZMK2XwAfwnkNFvKHDOB8mIysiSIi1wMtI+OH3G0NwKCqRhO27QG8BHxIVa+Y2ohTs6vH5W5fD1yvql+a+gjTl8JztQx4AaeOzM0J258EXlHVM6Y45F1yE4DOpG1XAYep6iL3usddzG8f4H+U+Ng160rIzPHA7UkJwNVAJeMPhixWXUz8rafQfQS4u5SSAtfrcFqtEt0BNAKHTXk0k7CrhdJUtTcxKXC3vQwMA625jC0TqSwAV2xSeEx+92df0vZeJlG0bipMsAhfa8IxJfdcTsQSg8wsw2mSHqWqG3HesJblJaIsEhGviFSJyBuBzwB/KMbWAtfrgZdF5EIR6XfHhNxQ6P3UKahg56bnkWXGl09xLFNGRPYDqnC68YrdR0QkLCJ9InK9iIxbqrYIPIfT3fNdEdldROpE5EPA4cAf8xpZet5Aaby2JsXGGGSmEScTTtbj7it2QzjlpQGuBL6cx1gyNQunef0Z4D1ALfAT4EYRObSIE541wMFJ2w5xfzZNcSxTwu3H/jXwCk7rSDH7F05//WacRO7bwAMisq+qJn/rLniqqiJyPM7jetndHAE+rKp35y+y1InIcThjxj6S71jyxRKDzI31gSLjbC82b8D5VnYI8C3gQuDcvEY0eeJeTlLVLgAR2QrcBxwL3JXH2DLxR+APIvIx4Hqc5+qL7r5Y3qLKrR/hdJMcpaqRfAeTCVX9bMLVB0TkYeBp4MPAr/IRUybcpO0vOIMPzwDacQbzXSoiXar6n3zGtysishBnMOi/VPXy/EaTP5YYZKYHaBhjez1jtyQUFVV90v31QRHpBK4QkZ+r6tp8xjVJPcCrI0mB60GcZvi9KN7E4DJgf+APONXZhoH/hzPKveTWCxGRc3Fars5U1cfyHU+2qepz4qz0emC+Y5mktwMnAnuo6ivutntFZDecFrqCTQxEpAm4DdgIvD/P4eSVjTHIzIskjSVw/wGqSRp7UAJGkoRFeY1i8l4YZ7sARTuwSFVjqnoeMAPYD2cq36Pu7kfHvWEREpFTcBKer6jqNfmOJ8eKtcVxGTCckBSMeApnOndBEpEq4N84A6xPVNWhPIeUV5YYZOY24K0iUpuw7QwggNNEXUoOd3+uy2sUk/dvYD8RaUnYdiTOKOpn8hNS9qhqj6r+z53Tfy7wsKqWTHLqFpT5G3Chqv4sv9Hkjjsdbk9gVb5jmaQNQJWI7Jm0/SBg/dSHs2si4gOuA3bHWZm3Pc8h5Z11JWTmjzij9W8QkR8Di3FqGPyimGsYiMh/cKbAPY/TT304Tr/1NUXajQBOM/tngJtF5Ic4gw9/DPxXVR/Ma2QZEGfBsDfi9EvX4RRfeau7rSi439ZOcK/OBepE5FT3+q3AAuCfOK1w18iOi6R1FOprMoXHdQxOk/W/gTacb9vfwGnKvnxKg01RCo/pVpz4/yki3wU6cLoWTsepg1KIfo/zmD4LNCW9vp5S1ZCIrAAW4hRFAzjK/ZKxXlVXTmm0U0FV7ZLBBad/+m6cVoKtwPcAb77jyvAxfQ9n2tEgzliJJ4FPA/58x5bh41qK88Y1hDPm4HKcgk15jy2Dx3QQ8IT7XPUDtwD75juuNB/DQpym87EuC3Fmk4y3//J8x5/B49oPZ2xLB87I/W3ua3JOvmOf7GNyj1mK8w28zX1dPgN8HLegXqFdcFoydvWYLi+2118mF6t8aIwxxphRNsbAGGOMMaMsMTDGGGPMKEsMjDHGGDPKEgNjjDHGjLLEwBhjjDGjLDEwxhhjzChLDIwpcSJyuYgUdBEWEfmiiNyTcH3cmN2lie91f9cULke7xy4RkUtFZJO7zHGHe65DE859i4h8M6cP1pgCZ5UPjTF5JSI1OAs/fWASNz8s4fdKnGJj38cp9DRitYgcjlPc6hWclULX4qwvcTLwkIg0qbPM8f8BN4nIb1W1dxLxGFP0LDEwxuSciFSqamCc3WcCIeCOdM+rqqMLRbkJBsDapO2VwDU4FSJPUNVwwin+ISKX4FQeRFUfEJEunCTlt+nGY0wpsK4EY6YJEXmziDwrIkMi8qCI7J20v0pEfiMi20QkKCJPiMhbko5ZLyI/S9r2IbfJvsa9frR7/a0icpOIDAIXThDaB4EbNHdlWE/Dqev/+aSkAABVvUdVhxM2/QM4K0exGFPwLDEwZnqYD/wU+AHON/RW4FoRkYRjLgY+7B7zbmATcIuITHZBpktx6uS/0/19JyJSDbweeHiS95GKo4A2Vf1fisc/DBwkIo05jMmYgmVdCcZMD03A4ar6CoCIeIAbcZb4fVFEluMkDB9W1SvcY24HngW+ibNiY7quU9VdDeTbH+d96LlJnD9Vc3FW/EvVM4AAK4A7cxKRMQXMWgyMmR7WjyQFrtXuz3nuz4NxPgyvGzlAVePu9cm2GNyy60OY5f7snOR9pCqdboqRWGZNeJQxJcoSA2Omh96k6yN97RXuz9nAYFJfO8B2oEpEyidxn9tTOGbk/kNJ26OAd5zbeN39qdqC05WSqpFYKiY8ypgSZYmBMQZgK1AjIlVJ22cCw6o68mEZBMqSjmka55ypfEvvdn82JG3vYPxv7LOB9hTOPeJeYG7yYMsJjMTSPdFBxpQqSwyMMeBM5VPg1JEN7sDEU4EHE47bDCxPuu2bM7jfl9yfi5K2PwDMEpFDEjeKyDzgIHd/qq7HaTX4pYj4k3e6sygSE6KF7s+X07gPY0qGDT40xqCqL4jI34ELRaQOWAN8DFgGfDLh0BuB34rI+TjJxMlAqt/Ex7rfdSKyFefD/p6EXf/BmR3wbxH5DvACsAD4BrAB+Esa9xEQkTOA23CKGf0OeBVoAd4FvA9oTrjJCqAPeH6SD8uYomYtBsaYER8DrsCZhfAvnA/it6tqYovBRcCvgM8A1+KMVfh+hvd7A3B84gZ34OMJwNXAV4HbcaoSPggcqaqD6dyBqj4EHIgz++EHOBUSLwLqgDe7VQ9HvA240Y3BmGlHcldTxBhjdk1EDsBpfZinqtvyHEs9zqDJNyUlRMZMG9ZiYIzJK1V9CqdF4Lx8x4LTbfKoJQVmOrPEwBhTCL6IMxMh3/pwukmMmbasK8EYY4wxo6zFwBhjjDGjLDEwxhhjzChLDIwxxhgzyhIDY4wxxoyyxMAYY4wxoywxMMYYY8yo/w/d7chaPXSCtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = dtmp_mean.coords['hour'].values\n",
    "y = dtmp_mean.values\n",
    "y_err = dtmp_stdv.values\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,5))\n",
    "ax.plot(x, y, '-')\n",
    "ax.fill_between(x, y - y_err, y + y_err, alpha=0.2)\n",
    "ax.plot(x, y, 'o', color='tab:brown')\n",
    "ax.set_xlim([-1,24])\n",
    "ax.set_ylim([-12,4])\n",
    "ax.set_xticks([0,3,6,9,12,15,18,21])\n",
    "ax.set_yticks([-12,-8,-4,0,4])\n",
    "ax.set_xlabel('hour (UTC)', fontsize=15)\n",
    "ax.set_ylabel('Skin temperature diff. (deg C)', fontsize=15)\n",
    "ax.set_title('Skin Temperature Difference: Land-Ocean', fontsize=15)\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "fig.savefig('/bog/amuttaqin/Figures/segment-1_transect-3_d2c-0.4.png', format='png', dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8557ca56",
   "metadata": {},
   "source": [
    "Groupby time hour at each season (DJF, MAM, JJA, SON) and calculate mean and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1100a01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ts3_djf = ts3.sel(time=ts3['time.season']=='DJF')\n",
    "# ts3_mam = ts3.sel(time=ts3['time.season']=='MAM')\n",
    "# ts3_jja = ts3.sel(time=ts3['time.season']=='JJA')\n",
    "# ts3_son = ts3.sel(time=ts3['time.season']=='SON')\n",
    "\n",
    "# ts3_djf_mean = ts3_djf.groupby('time.hour').mean(dim='time')\n",
    "# ts3_djf_std = ts3_djf.groupby('time.hour').std(dim='time')\n",
    "\n",
    "# ts3_mam_mean = ts3_mam.groupby('time.hour').mean(dim='time')\n",
    "# ts3_mam_std = ts3_mam.groupby('time.hour').std(dim='time')\n",
    "\n",
    "# ts3_jja_mean = ts3_jja.groupby('time.hour').mean(dim='time')\n",
    "# ts3_jja_std = ts3_jja.groupby('time.hour').mean(dim='time')\n",
    "\n",
    "# ts3_son_mean = ts3_son.groupby('time.hour').mean(dim='time')\n",
    "# ts3_son_std = ts3_son.groupby('time.hour').mean(dim='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b124413",
   "metadata": {},
   "source": [
    "Plot hourly mean of skin temp difference with standard deviation (DJF, MAM, JJA, SON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb142edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = ts4.coords['hour'].values\n",
    "# y = ts4.values\n",
    "# y_err = ts5.values\n",
    "\n",
    "# fig,ax = plt.subplots(figsize=(8,5))\n",
    "# ax.plot(x, y, '-')\n",
    "# ax.fill_between(x, y - y_err, y + y_err, alpha=0.2)\n",
    "# ax.plot(x, y, 'o', color='tab:brown')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82da84b8",
   "metadata": {},
   "source": [
    "Append output at each transect (whole period + each season)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79f4b6f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:08:17.941308Z",
     "start_time": "2022-06-02T01:08:17.939510Z"
    }
   },
   "outputs": [],
   "source": [
    "da_whole = da1, da2, da3, da4, dan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b43035f",
   "metadata": {},
   "source": [
    "Transect-composite of hourly mean (whole period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f726b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# xarray.concatenate or merge\n",
    "# da_composite = da_whole.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13178326",
   "metadata": {},
   "source": [
    "Transect-composite of hourly mean (DJF, MAM, JJA, SON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "796d362a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:14:37.730215Z",
     "start_time": "2022-06-02T01:14:37.728311Z"
    }
   },
   "outputs": [],
   "source": [
    "# xarray.concatenate or merge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3c080a",
   "metadata": {},
   "source": [
    "Plot the transect-composite of hourly mean (whole period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04fba839",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = ts4.coords['hour'].values\n",
    "# y = ts4.values\n",
    "# y_err = ts5.values\n",
    "\n",
    "# fig,ax = plt.subplots(figsize=(8,5))\n",
    "# ax.plot(x, y, '-')\n",
    "# ax.fill_between(x, y - y_err, y + y_err, alpha=0.2)\n",
    "# ax.plot(x, y, 'o', color='tab:brown')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa464673",
   "metadata": {},
   "source": [
    "Plot the transect-composite (DJF, MAM, JJA, SON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38ef56fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = ts4.coords['hour'].values\n",
    "# y = ts4.values\n",
    "# y_err = ts5.values\n",
    "\n",
    "# fig,ax = plt.subplots(figsize=(8,5))\n",
    "# ax.plot(x, y, '-')\n",
    "# ax.fill_between(x, y - y_err, y + y_err, alpha=0.2)\n",
    "# ax.plot(x, y, 'o', color='tab:brown')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caeb0807",
   "metadata": {},
   "source": [
    "### 3.2. Sea breeze intensity (point, line-transect, line-segment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7d53bc",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f08bd8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# metview\n",
    "# cdo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c5bba0",
   "metadata": {},
   "source": [
    "Use the bounding box info from previous analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0ea245e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Malay Peninsula region:\n",
    "# lat1 = 0\n",
    "# lat2 = 15\n",
    "# lon1 = 90\n",
    "# lon2 = 110 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7dead9",
   "metadata": {},
   "source": [
    "Area-subset dataset into a temporary file. Remember to delete this file at the end of the script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad5fddae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp data = CDO sellonlatbox\n",
    "# Limited area (20 degree longitude by 15 degree latitude) of 30-year hourly data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d613868",
   "metadata": {},
   "source": [
    "Load area-subset data. Assign horizontal winds into variables (time, lat, lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb25f285",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Xarray.open_dataset(temp_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7d59b1",
   "metadata": {},
   "source": [
    "Groupby time.hour and mean and std over time dimension. CDO dhourmean and dhourstd over whole period (30 years) with a file output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd6678e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:17:58.541130Z",
     "start_time": "2022-06-02T01:17:58.539297Z"
    }
   },
   "outputs": [],
   "source": [
    "# cdo dhourmean dhourstat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d19daf",
   "metadata": {},
   "source": [
    "Load cdo dhourmean and dhourstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa1bba2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:18:20.855846Z",
     "start_time": "2022-06-02T01:18:20.853994Z"
    }
   },
   "outputs": [],
   "source": [
    "# mv.read(ftemp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8a6e6c",
   "metadata": {},
   "source": [
    "Extract projected wind intensity/values at each transect (cross section), wind_parallel=on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c2dffa7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:18:50.433401Z",
     "start_time": "2022-06-02T01:18:50.431620Z"
    }
   },
   "outputs": [],
   "source": [
    "# line = [lat1, lon1, lat2, lon2]\n",
    "\n",
    "# xs = mv.mcross_sect(line=line, data=f, wind_parallel='on')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2565099a",
   "metadata": {},
   "source": [
    "Convert cross section data into xarray DataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9575d1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# xsa = xs.to_dataset()\n",
    "# xsa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f7ba20",
   "metadata": {},
   "source": [
    "Plot projected wind, x-axis as longitude/latitude, y-axis as wind intensity/values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71c4a380",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = uv_land.coords['hour'].values\n",
    "# y = uv_land['u'].values\n",
    "\n",
    "# fig,ax = plt.subplots(figsize=(8,5))\n",
    "# ax.plot(x, y, marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49528351",
   "metadata": {},
   "source": [
    "Average over land, hourly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb361d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:22:54.068823Z",
     "start_time": "2022-06-02T01:22:54.067035Z"
    }
   },
   "outputs": [],
   "source": [
    "# split array in the middle \n",
    "# avearge on one half that over land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d0cac21",
   "metadata": {},
   "source": [
    "Plot projected wind, x-axis as time 24 hour, y-axis as wind intensity averaged along transect over land each hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a5ef621",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T01:22:57.167840Z",
     "start_time": "2022-06-02T01:22:57.166057Z"
    }
   },
   "outputs": [],
   "source": [
    "# x = uv_land.coords['hour'].values\n",
    "# y = uv_land['u'].values\n",
    "\n",
    "# fig,ax = plt.subplots(figsize=(8,5))\n",
    "# ax.plot(x, y, marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c4c94f",
   "metadata": {},
   "source": [
    "### 3.3 Combination between temperature difference and wind breeze intensity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e2375a1",
   "metadata": {},
   "source": [
    "Scatter plot, x-axis is the temperature difference range, y-axis is the wind intensity range, each point on the scatter plot represent transect at certain hour?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6096a83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d41419f5",
   "metadata": {},
   "source": [
    "### NEXT 3.4. Precipitation (point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5131105d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "17c10546",
   "metadata": {},
   "source": [
    "### NEXT 3.5. Surface fluxes (point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f55da37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8f9f9e6d",
   "metadata": {},
   "source": [
    "## NEXT 4. Append analyses outputs to a dataset for each transect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5767b440",
   "metadata": {},
   "source": [
    "Append to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46ad6b71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c08e6537",
   "metadata": {},
   "source": [
    "Delete temporary files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2958228f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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