{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tides and winds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime\n",
    "import os\n",
    "import glob\n",
    "import csv\n",
    "import pandas as pd\n",
    "from dateutil import tz\n",
    "import seaborn as sns\n",
    "\n",
    "from salishsea_tools import viz_tools, grid_tools\n",
    "from salishsea_tools import tidetools, places\n",
    "import matplotlib.dates as md\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.set_palette('gray')\n",
    "sns.set_style('ticks')\n",
    "sns.set_color_codes\n",
    "start=datetime.datetime(2015,2,6)\n",
    "end=datetime.datetime(2015,8,4)\n",
    "times= [start + datetime.timedelta(days=x) for x in range(0, (end-start).days)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def list_wind_files(\n",
    "    to, tf,\n",
    "    wind_dir='/results/forcing/atmospheric/GEM2.5/operational/'\n",
    "):\n",
    "    \"\"\"List operational wind files in date range [to, tf]\n",
    "\n",
    "    :arg to: beginning of date range\n",
    "    :type to: datetime object\n",
    "\n",
    "    :arg tf: end of date range\n",
    "    :type tf: datetime object\n",
    "\n",
    "    :arg wind_dir: directory were wind files are stored\n",
    "    :type wind_dir: str\n",
    "\n",
    "    :returns: filesOP, a list of files in date range\n",
    "    \"\"\"\n",
    "    sstr = to.strftime('ops_y%Ym%md%d.nc')\n",
    "    estr = tf.strftime('ops_y%Ym%md%d.nc')\n",
    "    files = glob.glob(os.path.join(wind_dir, 'ops_*.nc'))\n",
    "    filesOP = []\n",
    "    for filename in files:\n",
    "        if os.path.basename(filename) >= sstr:\n",
    "            if os.path.basename(filename) <= estr:\n",
    "                if not filename.endswith('orig.nc'):\n",
    "                    filesOP.append(filename)\n",
    "        filesOP.sort(key=os.path.basename)\n",
    "    filesOP.sort(key=os.path.basename)\n",
    "    return filesOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compile_winds(j, i, files):\n",
    "    \"\"\"\n",
    "    Compile a time series of operational atmospheric products stored in files\n",
    "    at grid point j, i. First 24 hours of each file is used\n",
    "\n",
    "    :arg j: the y-index of the grid point\n",
    "    :type j: non-negative integer\n",
    "\n",
    "    :arg i: the x-index of the grid point\n",
    "    :type i: non-negative interger\n",
    "\n",
    "    :arg files: list of atmospheric operational files\n",
    "    :type files: list\n",
    "\n",
    "    :returns: wind, direc, t, pr, tem, sol, the, qr, pre, all arrays\n",
    "    wind speed, diretion, time, pressure, temperature, solar radiation,\n",
    "    humidity, precipitation.\n",
    "\n",
    "    \"\"\"\n",
    "    wind = []\n",
    "    direc = []\n",
    "    t = []\n",
    "    pr = []\n",
    "    sol = []\n",
    "    the = []\n",
    "    pre = []\n",
    "    tem = []\n",
    "    qr = []\n",
    "    for f in files:\n",
    "        G = nc.Dataset(f)\n",
    "        u = G.variables['u_wind'][0:24, j, i]\n",
    "        v = G.variables['v_wind'][0:24, j, i]\n",
    "        pr.append(G.variables['atmpres'][0:24, j, i])\n",
    "        sol.append(G.variables['solar'][0:24, j, i])\n",
    "        qr.append(G.variables['qair'][0:24, j, i])\n",
    "        the.append(G.variables['therm_rad'][0:24, j, i])\n",
    "        pre.append(G.variables['precip'][0:24, j, i])\n",
    "        tem.append(G.variables['tair'][0:24, j, i])\n",
    "        speed = np.sqrt(u**2 + v**2)\n",
    "        wind.append(speed)\n",
    "\n",
    "        d = np.arctan2(v, u)\n",
    "        d = np.rad2deg(d + (d < 0)*2*np.pi)\n",
    "        direc.append(d)\n",
    "\n",
    "        ts = G.variables['time_counter']\n",
    "        torig = datetime.datetime(1970, 1, 1)\n",
    "        # there is no time_origin attriubte in OP files, so I hard coded torig\n",
    "        for ind in np.arange(24):\n",
    "            t.append(torig + datetime.timedelta(seconds=ts[ind]))\n",
    "    numdays = len(files)\n",
    "    wind = np.array(wind).reshape(numdays*24,)\n",
    "    direc = np.array(direc, 'double').reshape(numdays*24,)\n",
    "    t = np.array(t).reshape(numdays*24,)\n",
    "    pr = np.array(pr).reshape(numdays*24,)\n",
    "    tem = np.array(tem).reshape(numdays*24,)\n",
    "    sol = np.array(sol).reshape(numdays*24,)\n",
    "    the = np.array(the).reshape(numdays*24,)\n",
    "    qr = np.array(qr).reshape(numdays*24,)\n",
    "    pre = np.array(pre).reshape(numdays*24,)\n",
    "\n",
    "    return wind, direc, t, pr, tem, sol, the, qr, pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_model_point(lon, lat, X, Y, tol_lon=0.016, tol_lat=0.011):\n",
    "    \"\"\"Finds a model grid point close to a specified latitude and longitude.\n",
    "    Should be used for non-NEMO grids like the atmospheric forcing grid.\n",
    "\n",
    "    :arg lon: The longitude we are trying to match.\n",
    "    :type lon: float\n",
    "\n",
    "    :arg lat: The latitude we are trying to match.\n",
    "    :type lat: float\n",
    "\n",
    "    :arg X: The model longitude grid.\n",
    "    :type X: numpy array\n",
    "\n",
    "    :arg Y: The model latitude grid.\n",
    "    :type Y: numpy array\n",
    "\n",
    "    :arg tol_lon: tolerance on grid spacing for longitude\n",
    "    :type tol_lon: float\n",
    "\n",
    "    :arg tol_lat: tolerance on grid spacing for latitude\n",
    "    :type tol_lat: float\n",
    "\n",
    "    :returns: j-index and i-index of the closest model grid point.\n",
    "    \"\"\"\n",
    "\n",
    "    # Search for a grid point with longitude or latitude within\n",
    "    # tolerance of measured location\n",
    "    j, i = np.where(\n",
    "        np.logical_and(\n",
    "            (np.logical_and(X > lon - tol_lon, X < lon + tol_lon)),\n",
    "            (np.logical_and(Y > lat - tol_lat, Y < lat + tol_lat))))\n",
    "\n",
    "    if j.size > 1 or i.size > 1:\n",
    "        raise ValueError(\n",
    "            'Multiple model points found. tol_lon/tol_lat too big.'\n",
    "        )\n",
    "    elif not j or not i:\n",
    "        raise ValueError(\n",
    "            'No model point found. tol_lon/tol_lat too small or '\n",
    "            'lon/lat outside of domain.'\n",
    "        )\n",
    "    return j, i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_tidal_predictions(filename):\n",
    "    \"\"\"Load tidal prediction from a file.\n",
    "\n",
    "    :arg str filename: The path and file name of a CSV file that contains\n",
    "                       ttide tidal predictions generated by\n",
    "                       :kbd:`get_ttide_8.m`.\n",
    "\n",
    "    :returns: ttide: Tidal predictions and mean sea level,\n",
    "                     the mean component from the harmonic analysis.\n",
    "    :rtype: :py:class:`pandas.DataFrame`\n",
    "    \"\"\"\n",
    "    with open(filename) as f:\n",
    "        mycsv = list(csv.reader(f))\n",
    "        msl = float(mycsv[1][1])\n",
    "    ttide = pd.read_csv(\n",
    "        filename, skiprows=3, parse_dates=[0], date_parser=dateParserMeasured2)\n",
    "    ttide = ttide.rename(\n",
    "        columns={\n",
    "            'Time_Local ': 'time',\n",
    "            ' pred_8 ': 'pred_8',\n",
    "            ' pred_all ': 'pred_all',\n",
    "        })\n",
    "    return ttide, msl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dateParserMeasured2(s):\n",
    "    \"\"\"\n",
    "    converts string in %d-%b-%Y %H:%M:%S format Pacific time to a\n",
    "    datetime object UTC time.\n",
    "    \"\"\"\n",
    "    PST = tz.tzoffset(\"PST\", -28800)\n",
    "    # convert the string to a datetime object\n",
    "    unaware = datetime.datetime.strptime(s, \"%d-%b-%Y %H:%M:%S \")\n",
    "    # add in the local time zone (Canada/Pacific)\n",
    "    aware = unaware.replace(tzinfo=PST)\n",
    "    # convert to UTC\n",
    "    return aware.astimezone(tz.tzutc())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Mesh and Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SITES = places.PLACES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Boundary Bay': {'NEMO grid ji': (380, 335),\n",
       "  'hist max sea lvl': 5.61,\n",
       "  'lon lat': (-122.925, 49.0),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': None,\n",
       "  'wind grid ji': (129, 162),\n",
       "  'ww3 grid ji': (222, 439)},\n",
       " 'British Columbia': {'lon lat': (-123.6, 49.9)},\n",
       " 'Calamity Point': {'NEMO grid ji': None,\n",
       "  'lon lat': (-123.1276, 49.31262),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': 7724,\n",
       "  'ww3 grid ji': None},\n",
       " 'Campbell River': {'NEMO grid ji': (747, 125),\n",
       "  'hist max sea lvl': 5.35,\n",
       "  'lon lat': (-125.24, 50.04),\n",
       "  'mean sea lvl': 2.916,\n",
       "  'stn number': 8074,\n",
       "  'wind grid ji': (190, 102),\n",
       "  'ww3 grid ji': (453, 109)},\n",
       " 'Central node': {'NEMO grid ji': (424, 266),\n",
       "  'ONC stationCode': 'SCVIP',\n",
       "  'depth': 294,\n",
       "  'lon lat': (-123.425825, 49.040066666)},\n",
       " 'Cherry Point': {'NEMO grid ji': (343, 342),\n",
       "  'hist max sea lvl': 5.846,\n",
       "  'lon lat': (-122.766667, 48.866667),\n",
       "  'mean sea lvl': 3.543,\n",
       "  'stn number': 9449424,\n",
       "  'wind grid ji': (122, 166),\n",
       "  'ww3 grid ji': (193, 462)},\n",
       " 'Cluster_1': {'NEMO grid ji': (241, 212),\n",
       "  'Vector Stn': '64',\n",
       "  'lon lat': (48.215, -123.099)},\n",
       " 'Cluster_2': {'NEMO grid ji': (294, 127),\n",
       "  'Vector Stn': '69',\n",
       "  'lon lat': (48.261, -123.717)},\n",
       " 'Cluster_3': {'NEMO grid ji': (376, 291),\n",
       "  'Vector Stn': '45',\n",
       "  'lon lat': (48.899, -123.138)},\n",
       " 'Cluster_4': {'NEMO grid ji': (282, 305),\n",
       "  'Vector Stn': '53',\n",
       "  'lon lat': (48.555, -122.75)},\n",
       " 'Cluster_5': {'NEMO grid ji': (344, 271),\n",
       "  'Vector Stn': '57',\n",
       "  'lon lat': (48.735, -123.135)},\n",
       " 'Cluster_6': {'NEMO grid ji': (320, 68),\n",
       "  'Vector Stn': '73',\n",
       "  'lon lat': (48.249, -124.11)},\n",
       " 'Cluster_7': {'NEMO grid ji': (504, 246),\n",
       "  'Vector Stn': '27',\n",
       "  'lon lat': (49.317, -123.801)},\n",
       " 'Cluster_8': {'NEMO grid ji': (646, 168),\n",
       "  'Vector Stn': '12',\n",
       "  'lon lat': (49.726, -124.679)},\n",
       " 'Cluster_9': {'NEMO grid ji': (423, 300), 'lon lat': (49.101, -123.249)},\n",
       " 'Delta BBL node': {'NEMO grid ji': (424, 283),\n",
       "  'ONC stationCode': 'LSBBL',\n",
       "  'depth': 143,\n",
       "  'lon lat': (-123.339633, 49.074766)},\n",
       " 'Delta DDL node': {'NEMO grid ji': (426, 286),\n",
       "  'ONC stationCode': 'USDDL',\n",
       "  'depth': 107,\n",
       "  'lon lat': (-123.32972, 49.08495)},\n",
       " 'Departure Bay': {'lon lat': (-123.8909, 49.1632)},\n",
       " 'Duke Pt.': {'in berth radius': 0.002,\n",
       "  'lon lat': (-123.89095676900132, 49.16340592936349)},\n",
       " 'East node': {'NEMO grid ji': (417, 283),\n",
       "  'ONC stationCode': 'SEVIP',\n",
       "  'depth': 164,\n",
       "  'lon lat': (-123.316836666, 49.04316)},\n",
       " 'Friday Harbor': {'NEMO grid ji': (300, 267),\n",
       "  'hist max sea lvl': 4.572,\n",
       "  'lon lat': (-123.016667, 48.55),\n",
       "  'mean sea lvl': 2.561,\n",
       "  'stn number': 9449880,\n",
       "  'wind grid ji': (108, 155),\n",
       "  'ww3 grid ji': (124, 427)},\n",
       " 'Halfmoon Bay': {'NEMO grid ji': (549, 254),\n",
       "  'hist max sea lvl': 5.61,\n",
       "  'lon lat': (-123.912, 49.511),\n",
       "  'mean sea lvl': 3.14,\n",
       "  'stn number': 7830,\n",
       "  'wind grid ji': (158, 136),\n",
       "  'ww3 grid ji': (331, 297)},\n",
       " 'Halibut Bank': {'EC buoy number': 46146,\n",
       "  'GEM2.5 grid ji': (149, 141),\n",
       "  'NEMO grid ji': (503, 261),\n",
       "  'lon lat': (-123.72, 49.34)},\n",
       " 'Horseshoe Bay': {'lon lat': (-123.2728, 49.3742)},\n",
       " 'Indian Arm Head': {'NEMO grid ji': None,\n",
       "  'lon lat': (-122.8864, 49.4615),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': 7774,\n",
       "  'ww3 grid ji': None},\n",
       " 'Juan de Fuca Strait': {'lon lat': (-124.7, 48.47)},\n",
       " 'Nanaimo': {'NEMO grid ji': (484, 208),\n",
       "  'hist max sea lvl': 5.47,\n",
       "  'lon lat': (-123.93, 49.16),\n",
       "  'mean sea lvl': 3.08,\n",
       "  'stn number': 7917,\n",
       "  'wind grid ji': (142, 133),\n",
       "  'ww3 grid ji': (261, 298)},\n",
       " 'Neah Bay': {'NEMO grid ji': (384, 15),\n",
       "  'hist max sea lvl': 4.359,\n",
       "  'lon lat': (-124.6, 48.4),\n",
       "  'mean sea lvl': 1.925,\n",
       "  'stn number': 9443090,\n",
       "  'wind grid ji': (111, 105),\n",
       "  'ww3 grid ji': (89, 200)},\n",
       " 'New Westminster': {'NEMO grid ji': (423, 363),\n",
       "  'hist max sea lvl': 4.66,\n",
       "  'lon lat': (-122.90535, 49.203683),\n",
       "  'mean sea lvl': 2.481,\n",
       "  'stn number': 7654,\n",
       "  'wind grid ji': (138, 164)},\n",
       " 'Pacific Ocean': {'lon lat': (-125.6, 48.1)},\n",
       " 'Patricia Bay': {'NEMO grid ji': (351, 214),\n",
       "  'hist max sea lvl': 4.38,\n",
       "  'lon lat': (-123.4515, 48.6536),\n",
       "  'mean sea lvl': 2.256,\n",
       "  'stn number': 7277,\n",
       "  'wind grid ji': (115, 143),\n",
       "  'ww3 grid ji': (145, 363)},\n",
       " 'Point Atkinson': {'NEMO grid ji': (468, 329),\n",
       "  'hist max sea lvl': 5.61,\n",
       "  'lon lat': (-123.25, 49.33),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': 7795,\n",
       "  'wind grid ji': (146, 155),\n",
       "  'ww3 grid ji': (296, 393)},\n",
       " 'Port Moody': {'NEMO grid ji': None,\n",
       "  'lon lat': (-122.8658, 49.28814),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': 7755,\n",
       "  'ww3 grid ji': None},\n",
       " 'Port Renfrew': {'NEMO grid ji': (401, 61),\n",
       "  'hist max sea lvl': 4.359,\n",
       "  'lon lat': (-124.421, 48.555),\n",
       "  'mean sea lvl': 1.937,\n",
       "  'stn number': 8525,\n",
       "  'wind grid ji': (117, 112),\n",
       "  'ww3 grid ji': (123, 226)},\n",
       " 'Puget Sound': {'lon lat': (-122.67, 48)},\n",
       " 'S3': {'GEM2.5 grid ji': (146, 155),\n",
       "  'NEMO grid ji': (450, 258),\n",
       "  'lon lat': (-123.558, 49.125)},\n",
       " 'Sand Heads': {'GEM2.5 grid ji': (135, 151),\n",
       "  'NEMO grid ji': (426, 293),\n",
       "  'hist max sea lvl': 5.3950000000000005,\n",
       "  'lon lat': (-123.3, 49.1),\n",
       "  'mean sea lvl': 2.875,\n",
       "  'stn number': 7594,\n",
       "  'wind grid ji': (135, 151),\n",
       "  'ww3 grid ji': (246, 385)},\n",
       " 'Sandheads': {'GEM2.5 grid ji': (135, 151),\n",
       "  'NEMO grid ji': (426, 293),\n",
       "  'hist max sea lvl': 5.3950000000000005,\n",
       "  'lon lat': (-123.3, 49.1),\n",
       "  'mean sea lvl': 2.875,\n",
       "  'stn number': 7594,\n",
       "  'wind grid ji': (135, 151),\n",
       "  'ww3 grid ji': (246, 385)},\n",
       " 'Sandy Cove': {'NEMO grid ji': (468, 333),\n",
       "  'hist max sea lvl': 5.61,\n",
       "  'lon lat': (-123.23, 49.34),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': 7786,\n",
       "  'wind grid ji': (146, 155),\n",
       "  'ww3 grid ji': (294, 396)},\n",
       " 'Sentry Shoal': {'EC buoy number': 46131,\n",
       "  'GEM2.5 grid ji': (183, 107),\n",
       "  'NEMO grid ji': (707, 145),\n",
       "  'lon lat': (-125.0, 49.92)},\n",
       " 'Sisters Islet': {'GEM2.5 grid ji': (160, 120),\n",
       "  'NEMO grid ji': (582, 175),\n",
       "  'lon lat': (-124.43, 49.49)},\n",
       " 'Squamish': {'NEMO grid ji': (532, 389),\n",
       "  'hist max sea lvl': 5.61,\n",
       "  'lon lat': (-123.155, 49.694),\n",
       "  'mean sea lvl': 3.14,\n",
       "  'stn number': 7811,\n",
       "  'wind grid ji': (162, 160),\n",
       "  'ww3 grid ji': (370, 404)},\n",
       " 'Strait of Georgia': {'lon lat': (-123.8, 49.3)},\n",
       " 'Swartz Bay': {'lon lat': (-123.4102, 48.6882)},\n",
       " 'Tsawwassen': {'in berth radius': 0.0015,\n",
       "  'lon lat': (-123.132722, 49.006165)},\n",
       " 'Vancouver': {'lon lat': (-123.1207, 49.2827)},\n",
       " 'Vancouver Harbour': {'NEMO grid ji': None,\n",
       "  'lon lat': (-123.1069, 49.28937),\n",
       "  'mean sea lvl': 3.09,\n",
       "  'stn number': 7735,\n",
       "  'ww3 grid ji': None},\n",
       " 'Victoria': {'NEMO grid ji': (302, 196),\n",
       "  'hist max sea lvl': 3.76,\n",
       "  'lon lat': (-123.3707, 48.424666),\n",
       "  'mean sea lvl': 1.881,\n",
       "  'stn number': 7120,\n",
       "  'wind grid ji': (104, 144),\n",
       "  'ww3 grid ji': (90, 374)},\n",
       " 'Washington State': {'lon lat': (-123.8, 47.8)},\n",
       " 'Woodwards Landing': {'NEMO grid ji': (414, 329),\n",
       "  'hist max sea lvl': 4.66,\n",
       "  'lon lat': (-123.0754, 49.1251),\n",
       "  'mean sea lvl': 2.215,\n",
       "  'stn number': 7610,\n",
       "  'wind grid ji': (135, 138)},\n",
       " 'YVR': {'GEM2.5 grid ji': (139, 155), 'lon lat': (-123.184, 49.195)}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SITES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_tides(ax, sdt, edt, times, ts_lines, tdir = '/data/nsoontie/MEOPAR/tools/SalishSeaNowcast/tidal_predictions/',\n",
    "               name = 'Campbell River'):\n",
    "    fname = os.path.join(tdir, '{}_tidal_prediction_01-Jan-2015_01-Jan-2020.csv'.format(name))\n",
    "    ttide, _ = load_tidal_predictions(fname)\n",
    "    ax.plot(ttide.time, ttide.pred_all,'-k')\n",
    "    ax.set_xlim([sdt,edt])\n",
    "    ax.set_ylabel('Tidal height [m]')\n",
    "    ax.set_ylim([-3,3])\n",
    "    \n",
    "    for t in ts_lines:\n",
    "        ax.plot([times[t], times[t]], [-3,3], '--', color='gray')\n",
    "    return ttide.time,ttide.pred_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "OP = nc.Dataset('/results/forcing/atmospheric/GEM2.5/operational/ops_y2014m11d18.nc')\n",
    "OPlon =OP.variables['nav_lon'][:]-360\n",
    "OPlat = OP.variables['nav_lat'][:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts_lines=np.array([ 7, 14, 21, 28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_winds(ax, location, lons, lats, sdt, edt, times, ts_lines):\n",
    "    \n",
    "    #lon, lat = SITES['East node']['lon lat'] # Giorgio: change this lat lon to Iona outfall coordinates\n",
    "    wind_files = list_wind_files(sdt, edt)\n",
    "    j=190\n",
    "    i=102 \n",
    "    #find_model_point(lon,lat,lons,lats)\n",
    "    wind, direc, t, pr, tem, sol, the, qr, pre = compile_winds(j,i,wind_files)\n",
    "    \n",
    "    datenum = md.date2num(t)\n",
    "    us0 = wind*np.cos(np.deg2rad(direc))\n",
    "    vs0 =  wind*np.sin(np.deg2rad(direc))\n",
    "    us,vs=viz_tools.rotate_vel(us0,vs0,origin='map')\n",
    "    \n",
    "    q=ax.quiver(datenum, np.zeros(datenum.shape), us, vs,scale=8*10e0)\n",
    "    \n",
    "    qk = ax.quiverkey(q, 0.1, 0.1, 10, '10 m/s')\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xlim([sdt,edt])\n",
    "    ax.set_ylabel('Wind Vectors')\n",
    "    \n",
    "    for t in ts_lines:\n",
    "        ax.plot([times[t], times[t]], [-1,1], '--', color='gray')\n",
    "    return datenum, wind, direc, us, vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mzZph7dq1qn/DgsNOnz6NgIAAjBs3DsuXL0f+/PlRq1Ytu2UqU6aMastZNDN25R5iR69r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YlffvkFDx8+xIMHD7Bu3brXtr92lSpVSnW97kdrCohIMEDp0jwWh4EDB8LHx0fQbrFYzJy5Xr16\nhc8//xwA2K5u2dnZ0Gg0egMJf8+ECRMwatQoAMDmzZtLrIOVlVWpKE/FYjFcXV0RGhqKiIgIJCQk\noEaNGli4cCETTMWBF0z8B8RvemMKH7yp4AXz0qVLsXDhQpOFIe8I16VLF8TGxmLs2LFM6CYmJmLy\n5MkAUKq9t40JJrFYjPz8fPTo0QNqtRo2NjaYNGkSdu3ahWHDhuGPP/7AunXrAGgnwzY2Nuze5cuX\n6zkDWltbm1wnHlWrVjVaP94Z7euvv0Zubi4UCgWsrKzAcRzUajWkUini4uLw7Nkz5OfnIzg4GACw\nbNkyNGzYEM2aNRPk16NHDxQWFmLEiBGYMmWK0XJDQ0Oxbds2NG3aVLA5zbhx4zB+/HjMnDkTwcHB\nGDNmTLFtmz17NmrXro2ePXuyc+fPn0e9evVga2uLXbt2wdbW1uj9Tk5O2LRpE+7duweO4/Dy5Uvc\nu3ePOY3Wrl2bjSv+/v7Izs6Gi4sLGzsePnyIFStWsO/234Yvv/wSq1atQkZGBgDtbnTOzs6YPXs2\nAG2/vX//vuAeQ1tCP378GDdu3Hgjdd6zZw/MzMzw1VdfmXxPcRNMQ/j++++xYMECHD9+nE3qbt26\nhdDQUDx//hwymQxqtRrPnz9n31xUVBRcXFwEO4za2NjA2dkZMTEx0Gg0yM3NhbW19dtDv1zRKoOU\nlBSaNm0ahYSEUEhICM2YMYNSUlLKlacxlb69vT3JZLIS7bhhYWEUFhZGv//+e7HlZGZmUm5uLjs+\nffo0s4MlJSXRlClTmBpdl9SkKNLT0+no0aNkYWHBbEBSqZR2795NkZGRNGnSJHJxcSGxWMxoO0+d\nOkU9e/akNWvW0Pr16yk+Pp6ysrJo6tSpFB8fz+K8AS2pT3Eq/YcPH1JhYWGp1GQcx1GrVq1o7ty5\n7BlcunSJfHx8SCKRlKiaN/YOHBwcSs0JUFwZRESXL1+mTZs2GWXoMpbMzMxIoVDosQHy9Jnnz5+v\nkHr6+/vTDz/8wNTDHh4edOPGDfrwww8FzF2ZmZkCtbZMJnstzo58mj17Nt2/f5+FzfLkTLa2tsyG\namFhQd7e3mRvb89intPS0sjGxsagyj0vL4+cnJzIwsKiWGrbpKQk1rbevXvTwoULiUjrwMjzVZw6\ndYqkUimFhoYK1K1EWpv/vXv3BCGVycnJNHjwYJZvo0aNKC8vz2gdePAkSHy/VyqV5OjoyBgqhw8f\nzn4Ti8U0duxYql27NhFpmUQB0NWrV0ss55/AunXrDJKPAVoiqvv379PEiRNp3LhxlJ6eTkREixYt\n0jM7qVQq8vb2LpGzgEdp1M9FiZpevHhBFhYW1KtXL0aSVVKysbExubxVq1aRnZ0dLVmyhG7evMlC\n7CZPnkyA1vxojCFUJBKxMGsA1LZtW+rSpQspFAp68OABubu7U3BwMJ08eZKWL19ucp14vPU2fCKt\nbU+XuKO8MCbwJRIJubm5mcQqx3EczZ07t9hynj9/Tr1792bH77//PnNAHD58OHl4eBCRll7x/Pnz\nevfzlKSDBg2iCxcu0MKFC5mgFolE1KdPH2rbti0BWie5JUuW0MmTJ1kbQ0NDWYcPDAykkSNH0kcf\nfUQ+Pj6Ctri6ugpsWHFxcYLjefPm0YoVKxhNrqkpICCApk+fLmjTqlWrmLAsq6DhBW1FCK3u3buT\nUqmkuLg4srKyKtW9HMfRihUrqFq1anqThS1btlBmZmapIwAMJUdHR7KxsSE7Ozvq2LEjzZ49m/Ea\nNGrUiKZNm0ZXr15l70yX6EPXSaii0/379+mnn36iGTNmkFqtps8//9zgM+InKrxQmzlzJnXu3FnP\nyXHv3r0UERFBLVu2JG9vb3Jzc6MffviBpk+fTp9++inl5eXR5s2bWTt79uxJDRs2pEGDBhGgJQa6\nc+cO1a1bl10TEBBAcrmc2rVrJ1goLFq0iNq2bUujRo0iIu2Eu2nTpqze4eHhZGlpSc7OzsXyIBBp\nnbRWrVpFSqWSrKysqFWrViSXywXcE3ySSqVUv359RtXMn/+3EvH07t1b4Ieg+92JxWJatWoVOTs7\nk0gkIicnJ9q6dStNmDCBAgIC9BwneT6NkSNHChZChmBMOF2/fp127dpFT58+pc2bN1NhYSElJSVR\nWFiYoD999tlnbJw0tT/Hx8eX+DzWrFlDVapUoWPHjlH//v1JoVBQ1apVaf78+TRw4ED2XIorR3ex\npVQqacCAAeTt7U3bt28ne3t7cnJyonr16pFCoaAZM2aUyr/grRf458+fp+DgYOrUqRMREc2ZM6fc\npDfFeelLpVLq1q2bSc5bPKmMMezdu5cAUL9+/ejOnTsEaElb+JWfnZ0dffnll2RlZSVY4cfFxdHz\n589pyJAhLCTuxo0blJ+fLyCV8Pb2Fsy+b9y4QUOHDiW1Wk2XL1+mTz/9VK/ODg4OBttcWFjIPJIj\nIyNpz549dP36dSLSsu316dOHOnfuXCqBYG9vr+fcOGrUKPZ7eYShq6trhQmumTNn6rEBlpREIhF9\n9tlnzEGrWrVqetfs2LGD6tevr3e+NFTDwH+1JXPmzKHU1FSKjo5mGxrNnz+fle3u7k4jRoygOXPm\nkKOjI1lZWZUqBK+0aeLEieTv70+WlpbUuXNnyszMLPb69PR0SklJIWtra9q9eze5urrS3r17iUhL\nU8tf5+fnx/ZTALSc5/PmzaMvvviC/P392Wo+MzOTGjZsSHK5nJRKJYlEIuYUunPnTiIi5lxZv359\n8vb2pujoaLp27RpVqVKFZDIZ3b17l1JTU6levXokkUjI09OTqlSpQrdu3aKePXsSoB3AT5w4wfow\nv6KMioqiDRs20IYNG2ju3LmkUCjI3NycnJ2dSSaTGYxI4ZkJZTIZ7d27l9q0aUPBwcG0Y8eOco1p\nrwOpqamC/Rj4b7Zom3QFnEwmoxYtWhCgpYWOjY1l+fETHI7jaObMmcUKMmPCKT09napUqUJeXl6k\nVCpJLBbT4sWLKTQ0lPz8/OjKlSuMEKu0msCSQsA3bNhAtra2FBUVRYcOHaLu3bsL7re2tiaO48jB\nwUFvUVVcGj58ODVo0IAmTpxIUqmUvLy8SCqVsuc8atQok+mm33qB37t3b0pKSqJBgwYRkVbFr7tq\nLgtKCstzc3MzSRXq5eVlMP/c3Fz6888/adKkSexaPkTK2dmZ+vXrJ/hQunTpQjk5OfTkyROaM2cO\nDR48mDp06EBt27ZlL/3nn3+msWPH6tWL/7gA0IQJE8jDw4N++OEHWrhwIXl5eZnU4aysrGjXrl0s\nr9GjR9Pw4cPJysqKrly5ItiApTQqYolEQkOHDhU8Gz5uG0CFebGXJ1lYWFBWVhYNGDCgVMKxefPm\n1KRJE1q4cKHgvO6KwtzcnGk0ypM4jiM7Ozu6cuUKJSUlkY+PD82cOZOWLVvGYr35pFQq6cyZM7R/\n/342+LyuZ9eyZUv64Ycf2LGrq2uxA92yZcvoyy+/ZF7ftWvXZvdZWFiQg4MDicViQR8Ti8V07Ngx\nNiGztLQkjuNYiFxRvgN+MmVtbU0pKSls06AxY8bQ/Pnzydramvz9/dm1O3bsYAyEdnZ21Lx5c2rV\nqhUFBQXpxaHz/ObTp0+nGzdu0Jo1awTvaNCgQUxLxGvWDE1q+ff51VdfkaurK3333XdsQM/IyKAT\nJ06QRqMpN4V4SYiPj6fPP/+cDh06JDBdxMbG0tGjRxlbplwuZ++Ep10url/w34CXlxcbt4m0mtpa\ntWqRo6MjzZ07t9goiuKE08cff6xXJq95EIvFFBQURC4uLuTs7Fwqoe/i4mK0zC1btgjMUBqNho3j\nuol/Tp6eniZrFxwdHalWrVrUqlUr4jhO7z57e3uTqXbfeoHPCwzdjlPe7WlNicPXFaTGkkQiEcxS\nMzIyaPfu3TRq1CiytbU1qnbeu3cvW5UAYGGGnTt3pmrVqrH77OzsBPfxAtyY0N2+fTvr5EXVRsW1\nw8bGhuXJf9y8naloG0y1ifHJ29ubNBoN7du3j3755RemdZBKpeUW+KXZdtdYkslkNHXqVKpXrx6Z\nmZnRihUrTBL8P//8M7m5uVGNGjVK/JjLW0dAK+guX75MVlZW5OvrS7169aJ69eqRTCYT8A2YmZmR\nv78/Xb9+nXx9fcnZ2blCyjeU5HI5+fr6CgaoomYRXYFXvXr1Encm5FNwcDBNnDiRateuTe7u7lSn\nTh3BStPS0pLatWtHP/zwg6Dv6sb9y+VyWrhwIdWsWZORFY0ZM4blwWuJRCIRe678fYB2QuPh4cHy\nGzJkCC1ZsoTZ6z09Pcne3p68vb1p9erVpNFoaPLkySSTyQQmHkP9SfeZ1alTh0JDQykuLo7OnTtH\nPXv2pG3bttGGDRvKNc6p1Wpau3YtRUVFGeUl2L59OwFgNu99+/ZRYWEh2dnZ0TfffENVqlShHj16\nlPq7B7QLhFq1agnKe/r0KVWvXp2kUilZW1vTvHnzDNbNmHA6dOgQbdu27bX1aUM7Hm7fvp2sra31\nhG52djabPBZ9xxKJpFTcJWKxuNhx5+effzbpnb/1Av+TTz6hS5cu0aBBgyg9PZ22bNnCNiopK0wR\n+Lqb5hSXbt26RTk5OVRYWCiYefbr18/olqS6ArtatWo0f/58WrZsGWMkM5ZEIhFZW1sbnTlaWFgY\n/DBN2XzG3t6e/a9QKJg9iu+85VENJyQkMAcqXWe98qrlK1pdzXEcRUdHsw2DikvW1tYm28dLS1pk\nKMnlcj1hWqNGDTp79iw5OjpSaGgoeXl5Uf/+/ZnaeOTIkRVWfkl1K7rBVNFUGluqSCQiDw8PpiI1\n5uTJC9Wi74HfnQz4r4mOJ9WZMmWK4NrSmpWCgoJo5cqV7DtzcHCgJk2aMOIUIqIePXqUiSjKzMyM\nevbsSRzHka2tbbkE/qtXr2jVqlW0aNEiUigU5OTkRCNGjKC9e/fqOQhHRESw586bWAIDA1kb27Vr\nV+YxgOM4atOmDQUFBVGdOnXozJkz9Msvvwi+DT8/P7p586agXsaE07Fjx15rf964caOgvJ07d5KV\nlRXzjSqKR48ekY+PD7Vr105vIVaa56XbD3W37tZNkyZNKpFQ6q0X+PHx8TRy5EgKDAykRo0a0ahR\noyguLq5ceZYk8PkXZYr6esOGDfTee+9R7dq1yczMjKysrKht27bUsWPHEgWCWCwWrATK64hma2vL\n7Fq6na20at2SNAKlTX369KHU1FSaO3eu4BmXhkTnTSWZTGaSIDf2URoa6N3c3PTee3nqyGtkXF1d\nqWrVquTh4cGeaWhoKG3cuJGqVq1K5ubmJJfLK2yALNo2PqKlpGgAjuOYTfufeKc9e/YkT09P6tCh\ng+B7q169OlWvXr1UDqShoaHUvXt35pvBmxg++OADNr7ExcWVuW/rfnvlEfgJCQnUoEEDvfw7duxI\njx49ouTkZPrrr79o//79tGzZMmrZsiWNHj2arK2tafz48WwrX47jaPz48WWawPBJd9OtwMBAGj9+\nvEHN07hx45hpwZhw0mg01KNHj9fWV3hHaiKiP/74g6ysrOjYsWMG6xIfH09hYWHUu3dvGjBggEGN\nY1knSYa+2ebNm9PUqVMFG08VxVsr8HmHsdeBiqDWDQsLIwAGucnd3d1NcpjSzaesgyGfh0gkYrNE\n/m/VqlUpJCSEZs6cKVDxl1SX15VeZ4hYZdImjuMoIiKCcnJyKDw83OBAXdr38KbfG+/sVh4hwydd\nemHdb0wqldKKFSsoLCxMoKEw5VkY+lYtLS3pxo0b9PHHH5Ojo2OFRGfwlMqmIi0tjaZMmUKNGzdm\nkxhTJ++8+VAqleqNafyObuVZCAwbNoyCg4Np7969NGnSJKOOqx07dqSZM2fS2LFj6cyZM3ptzMrK\nookTJ1bI8zWWGjZsSM2bNyeRSET169cvNkrj0aNH1KlTJ3Zv0XpVVAgxnxQKBSkUCmrUqBG1b9+e\nPvvsM4H/xZsW+BxRxWzl1qlTJ0gkEoSGhqJHjx7Fkl+UFlevXi0XY19YWBgAYIAJkYQAACAASURB\nVMmSJeWqR0Xk82+qSyX+HZBIJFCpVFAoFMjPz3+juytWomKhVCqRk5MDqVTKdms0MzNDbm4uZDIZ\nVCoVNBoNOycSiUDahdc/XHPDMDc3F5CPmQL+GchkMkZWJhKJBDuZvimIxWKo1ep/rHxDqFq1KuLi\n4gR10n1WfN+5cuUKgoKCKrTsCmPa+/PPPzF79mzExsYiJCQE48ePx6lTpyqsI1+6dAmXL19GVFQU\nNmzYgLFjx5Z4j5mZGXx9fWFmZgY7OztIpdIKqcu7BAsLCzg7OyMwMBBOTk5lyoNn1SsratWqhe7d\nuzPWuWrVqpUrv4qCubk5Y0R8neC3Xs3Ly/vXDvyVMA08UyYv7AHttquAlqmTH+D5cxqN5l/9zksr\n7IH/PgNdZtKKFLaWlpaoX78+atSoUSLbJs/8+W8R9oCWBhoQ1kn3Wen2nYpGhVLr1q9fHxEREThx\n4gS6dOmCbdu2oWPHjli6dGm5827UqBEaNmyI9957D7NmzcLy5ctLvMfR0RHLly9HQUEBUlJS8PXX\nX5e7Hu8CZs6ciYiICHTt2hVbtmzBnj17YG9vj7S0tGLvM7b/fHn2Cvf09IS9vT3Onz/P9pouyt1v\nb28voLc0BQqFAsOGDUPnzp3LXLfs/2PvvMOqOL7//95bgcsF6c0CUkRFFCuIBbB3jSZYwN57L7H3\nWGNMNEosMZFEY4wlSqxRo9iiRI1J7NiQoiIqHe49vz/47XxY7qVfFL/s63n2UXb3zs7Mzs6ZOXPm\nnJQUrFmzBm5ubiVOo7TkV+e54WPVi4hUBD766CMMHz4cP/30k9j2i4nBfekDOR12+/bt0adPH9Ss\nWRN79uwpdZpPnz5FSkoKgoKC8ODBgyL95tGjR+jatSsb5b148YIFXanIfPXVVxgyZAgOHz6Mrl27\nonHjxjh+/DgbZUqlUtjY2Ah+o1arWT0Wl/wCmQBATEwMzp8/j+fPnwMA3NzcEBAQwJ7PcRxevnyJ\nevXqoXbt2kV+Znp6On799Vc2mi4pWq222DEYDIlGo4GDg0OB9/DxFniqV6+OAQMGlGW2yhw+/oGI\nSF4OHz6M9u3b4+XLl3j79u37zs4HhcG/qrt372LFihUICgrCjz/+iG7duuHMmTOlTrdz587w9/fH\nzZs3IZfLYWxsDGdnZ3h6eurcm1vNk56eDqlUCnt7e6xduxaRkZHsWmhoaKnz9T4oSQQ1AGyWnJGR\ngenTp+Px48dMnbhy5Up2n0aj0Ym2VpoPi59l6wsEQ0SoXr06+9vNzQ0bNmxA586d2XUiwsWLF/Hv\nv/8WacbL8/z5c/zzzz8lznd5ITY2FgqFokhld3JywubNm9G2bdtiRTksb5QnFaxI+UEul8PJyQl3\n7txBTEwMAOgEnBIpAENZ/+3YsYN69OhBbdu2pc2bN5eJL317e3u259rc3JyGDRtG+/fv19ket3jx\nYvriiy+oZs2apFAoyNjYWOAFi3c5Gx0dXWTLYo7jiu27vSyPklq9mpmZkYmJCSkUCqpbty6LU557\nq5NEIhHs8897FKXO+KATEolEYPmqbytjq1atKDQ0lOzs7EgqldLhw4dp1qxZZVZ35cFjYHEPqVTK\nXMcWdPj4+FB6ejrNnz+fWXI3bdq0TK2kxUM83uUxduxYSktLY9uGz58/T7Vq1Sqz572v7anlelve\n9OnT6fLly4ZKTsCVK1eoatWqtHTpUho8eDABOdtBXr16Ra9evRK466xTpw7zscwH8Tl9+jRz0HD/\n/n0aMWIEqdVq6t+/P/n5+RU56tr7evH68mFsbFzivcO5yyGRSGjOnDns7/r16xMAQQyA0hx+fn4k\nkUiYi0+lUslcyPL5cHR0ZFHcrl+/Tp06ddIrlPVtpyxufuRyOW3durXYW8jKy7vv37+/Tr7ybsPr\n3bs3NWjQgF0vL3kXD/Eo7eHl5UVNmjShwYMH09SpU1lEx7Nnz773vBn6KNcCvyy5cuUKCxbj7OxM\no0aN0gmfyYc33L59e4GBCzIzM5lP8CFDhtC8efMK9ZiX9ygPHahCoRAELCntwc8AmzdvzrQipclb\n7r954err60tEOQF5Jk2aRLdv3yZ/f3+qXbs2JSYmEhHRl19+qfO7/I7cblmL865mz57NBjb6DkPs\nJy+rw9zcXEfAnzhxglxdXXXuze0pkX+fJRkkvov6KA/flHiU7/ehVqvp66+/Zn15YmIikwOHDh0q\nl3kuzVGhBT4R0e3bt+nzzz/XG7UpIyODLC0ti1RJp0+fJgA0ZcoU+uOPP6hq1ao6kZTe9ZG7Ey9O\nw+XvLW1YVd5JR6VKlYrt897a2pq5VNWX99zOT27fvk0JCQkCJyW7du2iuLg4+uGHH0itVlO7du1o\n+fLlzDtafp7n8jpmKayO+MPIyIh8fHzIwsKizN3Yvotj0qRJlJSUpNf1sYmJCQUEBNCWLVtIqVTm\nGyu9sMOQ0Q7F48M5ypsQtbS0pOTkZJ0+nY8x8H/pqPACv7CQg3///TfduXOnSGn27duX5s2bRxkZ\nGaRSqejs2bPlxrNcSfKR16tWYbMyXtWb94NevXo1hYaGEpAjRIsiEL28vGj79u16n8kHi+E1CI6O\njhQZGUkZGRmC95GdnU1XrlwR+OeeOXMmSaVSnXT5AUlp1uL/L61pS6VSWrlyJbVu3VrvdZVKRe3b\ntyeVSsUCyXyoR3kTQP/Xj/L4nRw9elSnP//nn3/ee74MfZSFwDeYlf7+/fsLPAxBYVbKXl5ecHd3\nL1Jaq1atgoODAxQKBVq2bIl//vkH/fr1K/Z+b+B/W4iKY0FeECXZ/paamiqw3jczM4OtrS0A/Vb9\n9P+t34lI4DinXbt28PDwAMdxMDU1LZITnJs3b+Ls2bOC7Xd8nZiYmKBq1apwdHQEADx79gzdu3fX\ncdYjlUrRoEED5hyJiNCwYUOoVCqBxbZCocCNGzcA6G5Hy0vurYVSqVTwfvT5DtDnmKmkOyJK+9vi\noNFoMH36dGg0Gly+fBkuLi6C6ykpKThy5AiysrJgZmaWbzrvciucVCot9h5q3ivd++Jdvc/yRHZ2\ntk57epfoq/OTJ0/qnHNzc4NEIgHHcRXyPRUVg33hkZGRiIyMxKFDh7B69WocP34cR44cwcqVK3H8\n+HFDPaZErFu3TscFraOjIwYNGgQAaN26NU6cOIHZs2cDACwtLfWmM3HiRObSNjdarRYymaxIgjq/\nNIqLvnRyd4ZpaWlMqDk4OBToZZC/z8jICF26dEF0dDSICEqlEq6uruy+gj6kbdu2YfPmzWyvvFar\nhUKhQFJSEipVqoSmTZsygfL8+XP8/PPPBZbv5cuX+PTTT/HmzRsAEAgqvpyFedp6+fIlfH19Wd6b\nNWuW771GRkbsvecejBRHwHAcB4VCwfJkSPfSReHUqVPgOA737t3T6/sgMzOzwEGSQqGAhYVFqT0n\nFgWZTKZ3652+9ymRSErkGtXY2LjE+cuNUqkEUHqPkuWF4g7s4uPjAbyfAU/e78/JyUmvXxeFQoEa\nNWrodVNclgNZGxsbmJqasmeU962wBquJVatWYdWqVTAxMcGJEyewYcMGbNq0CceOHSu3Lm35D7l1\n69b4/fff4erqii5duqBJkybFSsfU1LRcjCqrVasGIKdcS5cuZYL82bNnmD9/fqF5tLKywokTJ/Dw\n4UMAQFxcHGxsbNiouTDh165dO6xevZrFPeAd+dy8eRMWFhY4ePAg2rZtCwAIDw8vMC1ra2sEBwez\nv1NSUsBxHLKzs5lgTkhIgL29PQCgVatW7F65XA6lUgkHBwf8888/MDU1RXZ2NqKjo/HNN9/odNwq\nlQqdOnXC27dv4ePjU6CjIH0oFApUqVIFMpkM9evXBxFBKpWCiNg7eRdYW1tj4sSJkEgk8PLy0nvP\n/fv38/19eno6FAoFLC0ty7w9Z2ZmMhesQI5jJ0D/AKty5cp6vR0Wpo1LS0uDSqUqZU5zJgcKhULg\n8jS/ScG7oLQCjB+MFxV9/jP0UVaClRfmHMchIyMDW7du1et+dsuWLez/uQd7ZenToX379uA4Dvb2\n9pDJZHBzc2OTE0NpfA2Jwd/Qs2fPBB+iqakpnj17ZujHGBQvLy/IZDJcu3YNs2bNQmRkJCZPnlzk\n3ycnJ7MOKy+GmOXxA5PCePToEfz9/eHn5wcrKyv4+Piwa19//TUiIiIK/CgVCgW2bNmCRYsWsXN9\n+/aFVCoVlC8/YZCWloYuXbrg6dOnCAoKgoeHB4AcQXLixAmEhobC1dUVERERSEhIKHQAMXXqVOZf\nX6PRMAHq7e0NIGcZ49WrVwgJCcG5c+fY77KysuDj44NKlSph2rRp7IN//PgxUlNTcevWLcGoPCMj\nA8HBwRg8eDAUCgVu376N2rVr6xUW+jrKzMxMvH79GvPnz8edO3dYfjMyMvDo0aMCneYUt5OUSCT5\ndsAvXrxAYmIiYmNjcfPmTezcuRMdO3YU3KOvTFWrVgXHcayTTEpKKvDd5H7/xenUOI4TLNnkhn/P\ndnZ2LE0bGxvY2tqiVq1aqF27tk7d3759O99n8ffqc8xVHKysrPDw4UOdctapUwdOTk56f1PWSyOG\nEGDFGdDxXjD5QY6+d25lZVWmgvX27dvw8vLC5MmTYW1trXcS2bhxY1YuPlYBD98XFQe+nAVNWLt3\n745mzZohLi4O2dnZSElJQefOnWFubl4uNUIGb5nu7u7o3bs3VqxYgVWrViEkJKTcu7PlOI6p9X18\nfODn54eMjAyBBzh9VK5cmf0/MTFR70dUmH/6vFSpUkXQwJRKJaRSaZEbT2RkJJ4+fQozMzOcPn0a\nMTExCAsLQ/369REVFZXvzE+hUODOnTuoXbs2tm/fDiBn/b1mzZrQarWs8Xfs2DHfD0ClUiE7Oxtx\ncXH4888/8d1337GOPCkpCa9evcLmzZvh6OiIX375RcebX17MzMywcOFCNkuuVKkSDh06hMuXL7P1\nuuzsbGzbtg1DhgwR/PbixYvYuHEjPvnkE0yfPh3m5uaoVq0aNm3aBDMzM8yZM4d1UNnZ2dizZw/m\nzp2Lv/76i2kD3r59ywYXPPl1lG/evMGNGzeQmZnJvAR26dIFUqkUmZmZ8PT0xIwZMwS/KeqaNP9M\nuVwOqVTK6lQfd+/eRWRkJFJTUxEcHIzDhw8LBkP68l+5cmUEBwejUqVK8PDwwLBhw+Ds7JxvWfk8\nV65cGd9//32RVedEhKysLHAch969ewuu1apVC40aNcLevXvh6+sLKysrZGdng4iQkJCAV69eCSYS\nMpkMN27cyDePmZmZMDU1hUKhKLbQl0qlLF13d3dUqVIFPXv2ZNfr1auHOXPmYMmSJXp/X568BOZX\nP3K5vMB2lBteU8j3QfqWLksS7yI3FhYW6NChA5o2bSo4X6dOHchkMigUCvz999+oXr066tSpozeN\no0ePsrbJ9xl8v/XgwYMiT5x4+H6uoGA2u3btQps2bZhW7NGjR9izZw+USiUyMjLeqyZIL4a2AtRq\ntXTu3Dnatm0bbd26lU6fPk0ajaZUaZbWWvHzzz+nzz//vMB7tm3bRq1btyYiojNnzpCtrS0tW7ZM\nYCGeNwZ9kyZNaPny5exvHx8fWrx4cYGWlwXFsectkHNvMVOr1cwCP7fFfN508m7Ls7Ky0tnVkJmZ\nSV9//bVeS2eFQkGPHj0irVZL5ubmBIBsbW0pKyuLJBIJe3bz5s3J39+fgJwtMnmteO3t7dn/58yZ\nQ0+ePKH58+eTra0tTZ06lWxtbalFixZ6t1bqIz09nfbu3UsAKCAggIiIhg4dyvLo7OxMRESHDx8W\n5MPU1JTq16/P/DcMHTqUlEolTZw4kRYvXkxnzpxh1v68T4Ps7GyaM2cOc8SUlZVFP/zwg86uCb4u\nctdLkyZNqFWrVhQYGEhz584lDw8Pun//PkVHR5OZmRmpVCoiInY/x3H00UcfkZmZWZF3ZXh6epJc\nLqdq1apR9erV9e6KkMlkVKdOHVq2bBmrw5MnT5KdnR0B0Otzws/Pj169ekUmJiZkZmZGZ8+eJU9P\nT6pfvz4plUqytbXVm5/ly5eTVqulMWPGFCn/Xl5erN3s37+fXFxcWPsxMTGh58+f0507d1hb7t69\nO/vtmDFjaODAgcx7oFQqJY1GQ1u2bCnwmb6+vvT48WPBOd6LZN7vgP9boVCQpaUlATlx4bdt20YN\nGzZkdbh8+XLKzMykhIQE9tvSxJ43xJHfrpz8drIoFApKSkoiOzs7kkgkxHEc+fn5kUqlIo7jSK1W\nk4eHB6lUKlYXAPL1/WFpaUlqtbpU24PHjRtHsbGx1Lt3b0E+r169St9//z0NHTqUPD09BTt5ctOi\nRQtBPk1NTQX9U3G3luYuC1+/xsbGgp1D+/fvp6ioKDI2NmbtI/dhZ2dHdevWJalUSiqVivXtHMeR\nQqEgiUSS77sr11b6PBzHwcrKCjVr1kTNmjWhUChw6dIlQz/G4LRu3Rrnzp1DREQEGjduDDc3N6jV\naowcOTLfUXJUVBS6dOkCY2NjuLq6ombNmggMDBTMxps1a4YqVaowtWlB0P8fneYeUb59+5aNeokI\nTk5OetXKKSkpgvMvX77Epk2bBPfI5XKMHDlSr+V9gwYNWB75WW27du0gk8lgZ2eHzMxMmJmZ4cKF\nC4iIiECzZs2QmJgIOzs71K9fHz169ICjoyMaN24Mc3NzcByHr776ChKJBAsWLMDt27fRtWtXREdH\no1u3bjh79myBdcGjVCrx0Ucfwd/fH0BOsJ05c+YgJSVFEOSnVatWAlV3cnIy3NzcEBsbC47jsHDh\nQlhbW2PdunU4efIkmjdvjqioKOzYsQPW1tbw8fHBuXPnMGPGDKaCP3bsGJo2bSrQ9MhkMmabwHEc\nAgICAAD37t3DwYMHMWDAABw6dAh79uxBRkYGnJ2d4ezsDKVSibS0NGbUSETQaDRQKBRF3pXx6NEj\n1K1bF3FxcXj27Jle7YBWq8W///6LoUOHsnN37tyBq6srpFIppkyZItDQ8LseVCoVhg4dChcXF2zc\nuBFbtmzB3bt3YWtrC7VazWZaPBKJBA0bNgTHcVi7di28vb1hZGQEKyurfPOfnJwMmUyGpKQkBAcH\no0OHDszew83NDdbW1nB3d2drsR4eHpDL5fDx8cHvv/+OiIgI+Pj4QC6XQyaTYfv27UzVz3GcQJPI\nf2ve3t5wcnJCw4YNYWJiArlczpY1+Nkf74ud/1cikSA9PR09e/ZEpUqVoFarce3aNbx+/Rpt27aF\nra0t5HK5wNaBt0eQSCSlmumWlPzqPe95fomxZ8+eiI6OxoMHD7Bu3Tp4eHigUaNGiIiIwK+//opj\nx44hODgYDx48wJAhQ9i7jYiIgJubm45typs3b/D27dtSrVuHhYWxfoJvo927d8eff/6JkJAQfPPN\nN/j555/ztUOpW7cu+3/Lli2RmprKtBPGxsbFjuqZ28akc+fOUKlUGDlyJBo0aABra2s4OzvD3d0d\n3t7ekEgkSEpKYtoIHmtra6xfvx5qtRphYWHIyspimj1LS0umqcxNmdrPGHoEMXbsWGrdujWFhISw\nIzQ0tFRplsVIJzdarZZGjx7NRoPPnj2jcePGkVKppG7dutG///5Lnp6e1KJFC7K2tiYnJycyNjYm\nU1NTcnZ2ptatW9Pjx49p1KhRVKNGDRo/fjzzbNa5c2eqU6cOjRw5Uu8orkmTJqRWqwnIf89rw4YN\nycHBgSQSCY0fPz5fv9FKpZKqVavGRoympqaUkpLCypmdnU0JCQlkbm5Oc+fOFYzcc+9tHTduHNWo\nUYOGDRtGRETNmzcnjuPIy8uLOI6j7du309SpU9lvnzx5Qr/99hulpKTQ0qVLydfXlwDQunXrKCYm\nJt86Lyp3796l0NBQsrKyomHDhlFMTAz5+/uTRCKhWrVqsfvCwsLYDBAAubm5CdJZvXo1G10vX76c\n+QJITk6mixcvklarpVOnTjGvdXPnziWiHHfM/P713PvYTU1N6fz58xQZGUleXl60YcMGSk5OJhMT\nE7p//z57rr+/PymVSiIiWrBgAXvXSqWyWDEDjI2NqUuXLiSRSMjU1JT5OOCv59YMrVq1ij1/0qRJ\nNGDAADI3N6fr168zLYxcLqfKlStTtWrV6I8//qB79+6RkZERiz0xduxY8vPzY7OavPmpV68ee8bT\np0/JwcFBRxtQs2ZN1q7d3NyYgyt/f386ffo0ffPNN8RxHI0YMULwroYPH05jxoyhhg0bUnh4OJup\nDhgwgFq0aMEcCG3ZsoWqVKlCQM6sn/+WevTowTwN/vXXX7Rhwwby8PCgoKAgph3ij1mzZpFarWaa\nK2dnZ+rfvz+FhobSb7/9Rrt27WL3RkVF0fXr11k+69evz9x98xoCe3v7Yns0NIQ3w/r16+u8p5o1\na5KFhYXgnLu7O504cYJSU1NZOa5du0aZmZn5+jqZNm0aNWvWjIiIzp07R7169RKkmduhU3F9JeTW\nCm7cuJEeP37MNAmjRo0q1P8Kz2+//cbSu3PnDmvjvL8RT0/PEtetm5sbzZ49mxYtWkSJiYk0ZswY\nCg8PJ61WSxqNhrp06cLKsWHDBtbGxowZQ1qtln766SfSaDTk6upKHh4eBCDffpyvvw/C8U6PHj0M\nnWSZC3wiokuXLjFvcRqNhvr160dAjhqVKEdYLl++nB49ekTDhw+nHTt2sI47PDycZsyYQSEhIdS6\ndWvKzMwkV1dXsrGxYQF3/Pz8CABrCJaWliSRSMjJyYmpwpydnfV2FMbGxrRo0SLWKYSFhdGAAQPY\ndaVSya7Z2NjQ999/T5MmTSJ7e3s6ceIEK+OTJ0/I3d2dzMzMKCIigm7dusUaV25XxVu2bKFZs2ZR\nYGAgERET9Lkb5OrVq2nVqlUEgI4dOyaoy7CwMAJyvOoZgqysLGrUqBEBoGHDhtHz589ZQB57e3t2\nX1paGg0cOFAQByApKUmQVnh4OCvLtm3bdJ7FD/54ocGTnJxMn3zyCa1YsYICAgJYJ/L9998TUc5y\nyc8//0xarZZ69uxJ48ePp71791JiYiJ16NCBANC9e/dYIChenTd+/HidTlJfgCEAZGFhQcOGDWMC\nVK1W5ysoTE1N6d69e0RE1KlTJ5o7dy6ZmppSdHQ0eXt7E5CjbrS3t6cRI0bQp59+SkRE3bp1o7p1\n69K0adPozZs3VLVqVQoICMj3ObmF36lTp+jvv/9m70Yul9OBAweoTp06pFKpyNHRkeLi4sjY2JiW\nLFlCWVlZdP/+fZLL5bRlyxbBe0hNTaXVq1fTggULKCMjg8aMGUMcx9Hw4cPp008/pSZNmhAAWrZs\nmUAF3KRJE2rRogWtW7eOfUvz58+nRYsWkbm5ucA5UW4vlXv37qVhw4aRSqWi69ev08mTJ8nR0ZHm\nzJlDRP8bqJ08eVKQzwsXLlBWVhbNmDGDpbdw4ULq2bNnsQRKfu+8qIdSqaTvvvuOPD09qWPHjgTk\nDIA6dOig4zxrypQpFBUVVaxv8NmzZzRhwgT297Vr11h6EomEXF1d2UC5sOWNvNf5QRoACgkJYd/T\n9OnTycrKSsdJV34kJyeTRCKhNWvWEBFRvXr1yM3NTTDgLKlnTXt7e7p79y6lpaUREdGxY8fY0sLu\n3bsFg8iYmBg6d+4c1apVi7Zv3y7I4xdffEGTJk0iX19fNlDV1xZ69er1YQj8yZMnF/kFFZV3IfCJ\ncny8m5qaElGOq942bdqQXC4XCEN+Ztq8eXMmFO7fv89G0dOmTaMdO3ZQjRo1KDY2lhQKBUmlUgoM\nDGQdklwup5UrVwqEe+XKlcnNzY3GjRsn6PwtLCzI19eXnJ2d2Yw8KCiInj59Su3atWP35Y5uN3Xq\nVCIiio+Pp2vXrrG8R0ZGsns2b95MP/30E1laWpK5ublAExATE0OXL1+mypUr07Vr1+iTTz7RaZRj\nx44lIqLly5fT8uXLBfWYlJRE1apVK9YsvjBu3LhBMpmMzQQHDhzIBFtunj17Rp06daJq1aqRXC6n\nxYsXC65HRkayoDvu7u56Zw9arZZGjRpF1apV0zl/8uRJ+vzzz2nOnDnUokULJih5/vjjD5b+kiVL\naNGiRayDO3DgAGVlZQnqkR8M5O34ch98Oxk4cCBdunSJfv75ZwIgiAdgZGTEOjN+llepUiW6d+8e\nubu7s0HYy5cvmUDgO+sffviB6tevT0Q5QtvKyorMzc3pzZs3dPjwYUFcBRsbGzI1NWXP8PDw0Km/\nSZMmUa1atSggIICePHlChw4dosjISLKysiIiou7du9PVq1eJiOjNmzcEgC5duqSTTlZWFmuXUVFR\n5OTkRIGBgTRlyhT6/fffydbWliZOnEgTJkxg+evXrx/t27ePlcXMzIzatGnD6tnIyEhnBurv709E\nREuXLqV9+/ZRWloa+175NqDVaik4OJi+/fZbnXwmJSWRt7c309rExMTQ9u3bSSaTvTPvnU5OTkRE\n9N1337HZtqWlJfXt25fdY2VlRWq1mmbOnFkiu6rc2jqtVsvSrVOnDtWoUYMuXLhALi4uZG9vT1Kp\nVG88DhcXF2b7wq+p5x4AKBQKwTMPHz5M586dK3IeR44cSenp6USUo2329/dn2iGpVEpOTk4lrmM+\n3kdetFotkweVK1dm53fu3Ek3btwQ3PvmzRvau3cvCwin7zkSiYSePXv2YQj8qVOnUuvWrWny5Mk0\nbdo0dpSGdyXwExMTqXbt2uzvN2/eUIMGDXTc9V64cIGqVatGBw8eJCKi48ePM+F85MgRqlGjBv34\n44+k0WiI4zgaM2YMOTg4kFKppO7du1PVqlXJy8uLJBIJqVQqgcpt8eLF5O7uzj4ElUrFRqpKpZI6\ndepEHMcxA7thw4ax3/IzW7VarVeV/sMPPxCQ43udiGjUqFHk4OBAPj4+OvcmJSURADYjzXuMHDmS\n3fvXX3/p/H7Pnj0lewkFMG/ePBo1ahQR/U/gA6A9e/YIZiz8DB3IMW7MxMj4zwAAIABJREFUS1pa\nGs2bN4+MjY1p9+7dep+l0Who5MiRlJCQoHPt4sWL9OrVK8rOzqazZ8/qXOcj2s2bN49u3bpFQM5s\nkq8nfkAglUoFcQbyW9KRyWQkkUjozJkzbDDdv39/wYDRzMyMjIyMmL98Pk0LCwuSyWS0b98+AkCZ\nmZlMM8N3wEePHiW5XE7x8fGk1WqpXr16VLNmTfriiy8oISGBevfuTc7Ozkw4njx5kmbPnk1Lly4l\nFxcX+vvvvwXlP3fuHJ08eZIiIiJY50uU0wEmJyfT0aNHSaPRkFarpR9//JGkUinNmDFDr4/03PCz\nZolEQk+ePKEzZ85Q//796fz582wwPGHCBIFR140bN8jd3Z3++usvkkgkgjrm62/jxo1ElKMB44mK\niiKZTEZ2dnbsXGpqql63rkTElgMCAwMpPT2dvWMbGxudAEYFHXnzyAtBfffmHri0aNGCiHIGSfyy\nlpGREcXGxjLtzKJFi6h69eo0ZMiQAuu5qJiYmLCw4QsWLCAiYgafHMcJ+ib+GDRoENnb25NEImGC\nWCKRUEhICLsnd5shKt7yHz8DJyI6ePCgzvN5dXpJjoKWFq5fv04SiYTmz5/PzmVmZuoEectdHn6J\nMa9WVyKRUP/+/T8Mgf/LL7/oPUrDuxL4RDmzgtzEx8fTP//8Izh3+vRpQec0atQoqlOnDt29e5e+\n//57qlWrFmVnZ1NiYiJ17tyZ+aaXSqVkZGREtra2bKRZrVo1srKyYh/vwYMHqU+fPoKPOTg4mGxs\nbFiH9ssvv7CoUcnJyWymb2ZmxqIGuru709u3bwX5/uyzz6hTp06s4dasWZPUarXO+imPjY0N/f33\n3xQUFMTywnf8w4cPL7AeDTm750lPT2flHjp0KMsT70ueKGfQtnXrVqZiK2jgER0dTQsWLMg3rxqN\nplAhpI+kpCSqWrUqzZw5k4iIfHx8yNTUlP744w8iIrZcxL/Twjp2uVxOnTp1Egyy0tPTydrami11\n9OrViyZPnkxqtZpkMhk1aNCAtSGO42jjxo1kYmJCRETbt28nhUJBtra21LNnT/ryyy+pRYsWbHli\ny5YtVK1aNXJycqKOHTvSixcvyMbGhsaMGUM1atRgHbJWqyWtVivoZIlylr/y0/LlrWu+o3d3dy+0\nXs+dO0cAqFOnTuzclStX6OHDh8zGRJ+90KNHj5iNjZWVFRsENWjQgFxcXOjRo0d6nzdu3DgyNzcv\nNF9E/1P585qL9evXM0HMa9/yWxaRy+VsZ4iZmRlbEuGPSZMm6dgd5D1yT6pmzpzJ0iUiCgwMJIVC\nQWlpaWRra0s7d+4sUpkKIjo6mqpXr86en1vzsX37dnJ1dWXLVfxhYWFBU6dOFbRzY2NjcnZ2phUr\nVrAZf352PyWB14LxtiW57QWKevCDmsIYN26c3glAfmRkZFCtWrWobdu21LZtWwLA3v2xY8fKt8CP\nj48nIqLHjx/rPUrDuxT4xUWj0dD48ePp7du3NHbsWHJzc6OffvqJiHI6/pcvX7JtS7mjyfEvGMgx\nyuP/Hx0dTTdu3BCouX7++We6e/cuubq6kpeXFxGRYBSclZVFw4cPJ5VKRUqlko0Y864fbdq0id68\neUNERLGxsew+XgWal6ZNm9K+ffvo0KFDrLPavn07OTo60ooVK8qgNguHH6x8/vnngo+yX79+RJTz\nEbm4uLAZzoEDBwpNsywGJ6dOnWJLKytWrCAXFxfav38/ERFTyfOzUo7jBMZFeTubevXq0alTp8jJ\nyUmgiv3pp5+oSpUq5OLiQkqlkm7evEkLFy5k5e/RowdLRyaTkYWFBRERHThwgNRqNbVs2ZKWLFlC\ngwcPpqVLl7L109evX7O2wauKd+/eTU5OTjRlyhSdAXBpOHnyJAGg/v37F3pvWloayWQyVo887du3\nZ/W5fv36fH8fHx9PtWvXpsmTJ1Pfvn1p2bJl7FvVx6VLl6hdu3ZFKsf58+cFBqSpqalkZ2dHffr0\nYQKOV/nnFShSqZRq1apFHMcx257cbaF9+/aCrah52wbHcWRjY0MpKSmk1WqZwZqTkxNlZGRQWFgY\n9erVi4iIZs2axd5zaRg7diwZGxuzfmHDhg3smlarpQEDBrBol/zh7u6us2w1ceJEtgQwceJEatSo\nkY4avDR8+eWXZGdnR5GRkYLgURzHkVQqJblcrved6JtxF8arV6/yHTzmx6pVq+jAgQP0+vVrqlq1\nKjk7OzPjyHIt8CdPnkxEOaPJoKAgCgwMZEdQUFCp0n4X+/BLmk52djZptVp6/fo1ayDTp08XdMz8\nLEYul9O0adNoxowZFBUVRUDOOuu///7LDPh44XPs2DEmCOLi4ogop8Nq2LAhxcbG6uTl2rVrTEVW\nvXp14jiOqfl4cufpxx9/ZMZbeWdoPAMGDKCVK1cyK1RbW1u6cuUKXbhwgS5cuFC6yiwlx44dI3Nz\nc6pRowZbIuH57rvvCMhZHz98+PB7y+N///1HRDmzoZYtWzIjwRcvXrBOh28X/B5eDw8PtlwhlUqp\nZs2a9Oeff1JWVhY5OTkxLQFRTsfatGlTmjJlCvXo0YMaNWpE6enpFBgYSI0bN2Zp5O68jh49Shs2\nbCC1Wk3Lli2jw4cPk4+PD129epVsbGxYG5kxYwaZmpoSx3GUnp5Of/75J1tzLEhIFheNRkPOzs6C\nOOcF0aVLFx016R9//EEAqEGDBoUO3lasWEHx8fEUGRlJ6enpBd5/5coVevz4cZEGhFlZWbR27VrB\nuTFjxlCVKlUKNch0cHCgFStWkL29PZmZmbEBG7/0UqlSJdqxY4fefeRKpZJcXFyI4ziqX78+W17x\n9PSkgIAASktLo4SEBGbLEx0dTcbGxvT8+fNCy1QQvE2IRCKhunXrCnaFEBF99dVX1KVLF2a0DOgP\n4X3r1i0mhCdOnEhxcXF069atUuUtNy9fvqTg4GAiyhnsLFq0iDiOI2tra2rZsiWpVCo2kOJn8lKp\nVDC4cnBwYJobQ3P58mXWnn/99Vdq3Lgx/fnnn0RUzgX+zZs3DZWUDuVZ4PPwBnFVqlShhw8fsvOJ\niYnUqFEjkslk5OPjQ7Nnz6YlS5YQUc5H2b17dyLKsY7m18F4li5dSu3btxece/v2LT148EBvXuLi\n4sjPz48UCgVFRERQlSpV6O7du3rzO2LECPL392cOYfSxePFitjXv4cOHtHr1aramXVpnSqUlJiaG\nBg0aRL169aKlS5dSzZo1mdYjOzubatWqRUuWLKHXr1+/13zyDBw4kFavXs3+VqvVxHEcWVlZkUQi\noUWLFpGLiws1bNiQsrOzqWHDhlSpUiUaPXo0+83EiROZsSTPxYsXydzcnB48eED16tWjxYsX05Il\nSwSda24VqkwmI0tLSzIyMqJ58+YxTQ+v7uW/tfj4eDIyMiJLS0u6ffs2PXnyhOrVq0dAjvGgIVm4\ncKHAuLQg8mvPAQEB1LNnz0J/XxyD4uL2O3nTTkxM1BFyK1asYDNKlUpFKpWKatasSVlZWVSjRg0y\nNjZmDmRya/6ePn2qd7ubVCqlFi1asGszZswgopwlBW9vb7357Nixo46ALi6XLl1iebh27RqzjM99\nvXLlyrR//36SyWRUpUoVmjNnDgE5diD8EsaFCxfoyZMnOm3dkPC7hbRaLd27d4/69OlDdevWpTNn\nzugss/D2E7Vr1yaO40ilUpGdnZ1gN4ohyd3GtFqtYPm7XDvemTx5Mvz9/TFt2jQcPHgQiYmJhkr6\ng+DGjRuws7PDyZMnBU4p1q1bh+vXr0Or1eLVq1fgOI45+Jg6dSqCgoIAAIMGDULr1q0Fac6cOZNF\n8OMxNTXNN1ylnZ0dTp06hd69eyM1NRXXrl1jfrDzcurUKbx9+7ZAt8fu7u64d+8egBxXlaNGjYK1\ntTWAdxtKVR8ODg7o06cPPD09ERsbi/Xr1zM3xlKpFEuWLIGjo2OB4WDfFUeOHEFsbCwiIiKwe/du\nADlOQogIzs7O0Gq1yMjIwK+//orU1FRIpVI0b94cPj4+grgGwcHB+Pnnn5nrYwBo0qQJOnXqhGXL\nlqFz586YO3cuwsLC0KBBA3ZPbkdO2dnZSExMRHp6Ok6ePAk7OztYWVnh1q1baNu2LY4ePQoAsLW1\nRd++fdGmTRvExsaicuXKOHv2LLp06YKIiAiDuo8dMmQIc0hUGPqC6ADA/PnzixSoqCyjmeVN28LC\nQvBNV69eHdOmTWPOaXhHVXzgFRMTE2g0GsTGxgIA/v33X/bbSZMmsXDXudFoNDh//jyqVKkCIMd5\nzYMHD9C8eXPExMQAgI6DplGjRmHz5s2leoe1a9eGra0tZDIZvL290bx5c8H1unXrIj4+HvXr18cn\nn3yC5s2bY9KkSXBxccH48eNRu3ZtNGrUCElJSahcuTI2btzIHFoZGt6PPsdxcHV1xaJFi9CrVy+E\nh4fr1AHvqCgxMRFEhJSUFBw4cIC9k7KE4zj06NGjTJ9hsF776NGj+Pnnn+Hr64s//vgD3bp1w0cf\nfYTPP/8cV65cMdRjyi0xMTE4fvw43N3dBef/++8/ZGZmQqvV4unTp8wLHQD06tUL7dq1A5Djlzyv\nH2mJRFJgSFd9KJVKfPvtt6hduzYsLS3h5+enc8+zZ8/w8OFDxMXFCQLs5MXNzQ13795lf5uYmJSL\nqIBAzsfx8OFD/Pjjj9i8eTNOnz7NIucBOR66ckfQe584ODjg6NGj+P333+Hg4IA3b94wD318/e7Y\nsQO1a9fGmjVroNVq4eLiAltbW9YBaTQafPfdd4iLi8PIkSMF6S9btgw//vgjXr16BalUisePH6ND\nhw5MsPAdPi+Q+Hf477//guM4+Pj44K+//kL79u1x5MgRlu6ECRPw22+/wdvbG0lJSTA1NcW+ffvQ\nr18/REVFGax+nJycSh1oJCAgAP369TNQjgxHYGAggJxB6G+//Ybk5GSkpqZCoVDAzMwMHh4eWLhw\nIYCcWBQajQYdOnQAIPT0tmfPHhamFsjxmsm/x+zsbLx48QLGxsZ49eoVGjVqhHXr1uHly5cYMGCA\njsDv0KEDMjIy8Pvvvxcr/HNu3r59i8DAQJibm+PMmTM6E4Bvv/0WlpaWaNq0KWbNmoXp06fD0tIS\ny5Ytg7u7O7744gvs37+fTX569+6Njz76qER5KS5ubm6YMGEC61tVKhXLv1qthrW1NWxsbPDZZ58B\nyIn2yffTHzoGnaY5ODigZ8+eWL16Nc6ePYvx48fj6tWrCA0NNeRjyiVTp07VG9Qh9wy7b9++go7N\n3NxcEMXJUMKU47gCA4acPn0avr6+ePnyJdq0aZPvfe7u7oiJidGJPFVe4CPiZWVlwdXVVXAtr6vV\n90ndunXh4+MDmUyGhg0bIj09HWvXrgWQ45LU0tISsbGx+OOPP9CgQQNkZWXB2dkZDx8+xOXLlwHk\nCIxBgwYx17653fFmZWXB19cXX3/9Nfr16wdzc3NIpVIMGzYMwP9mLdWqVcOaNWuYEOJdjfr4+CAq\nKgpt2rTBxYsXWVAjV1dX1KtXD0FBQThx4gTLx9q1a/Odab9P6tev/76zoAMfwW3ZsmXw8PBgAyUL\nCwsYGRmhcuXKbHasVqtBREwI5nZTy3EcCy0N/C96JE9qaipr74mJiTh//jyAnKAxeYWxVCrF8OHD\nsWbNGixfvrxE5bKxsUFERARevnyJXbt26VyvWrUq4uPj8fTpU1StWpW5vQ0ODoanpyeaNGkCR0dH\ntGjRgpWPH+i8C9RqNUJCQtCsWTMQEbRaLWQyGftGlUolJk2ahO+++04QfOpDx6ACPzExEYcPH8an\nn36Kdu3aYevWrfD19cX3339vyMeUS/ILWcqr4fgY9eWBU6dOoU6dOtBoNOjatWu+95mZmcHGxqbA\nGOrvE47j8OmnnwLIX9VbXhg0aBDq1asHExMT2NraYt68eewa70+/VatWOHPmDF6/fo3Ro0fj8uXL\nbJYBAI0aNWIq/vT0dHbexsYGaWlp0Gq1eP78OS5cuIBbt24x7c6oUaPQpk0bxMfHY/Dgwdi/fz+U\nSiWLcsfP8G1tbeHt7Y3ff/8dqampuHz5Ms6fP49r167h0aNHgvIUNdJaRUcqlWLixImYNm0aAODY\nsWPMl3pWVpYg4qaZmRmLTCmVSgWRGo2NjbFx40YYGRmhbt26etXxT548AZCjGUxJSQEAvRq858+f\nIyIiAkeOHBFodIpbriZNmgAABg8erHO9ffv2CAgIgFKp1AmtXdToimUNx3HYsGEDa8uDBw+GWq3G\nf//9hxcvXuD48eMIDQ3FhAkT3nNODYihjAG6dOlCrVq1orVr11JkZGS+lt8loTxvyyuMCRMmkJWV\nFdsnnh/vsozOzs40ePBg5t+9IPiteeWRK1eukEajoZo1axp0725Z8OLFC8Fe6bS0NLZFKbcx1g8/\n/EBE//MzwLt25cnOzqYWLVroOAR6+/YtBQQEkIWFBWk0GkpKSqLz589T165diYioT58+NHnyZGYB\n3bNnTzI2Nqb09HS6cuUKmZqa0uzZs6l3797k7e3Ntl1u27aNAOgYC1YEDPFNxsbGMkdAp0+fJhcX\nF5LL5SSTycjT01Pg+nr48OEEgD755BOSy+VUpUoVto+cN65dsmSJwAdF3oPfzuvq6ko1atTQ6z6a\nKGdrJlA0/wf5MWfOHPLy8iKtVqu3ri5fvqzjrbI8wrtFjo6OpkOHDrHdB3ndcpcFBbWxcm20x6tq\nfvvtN+zevRv79+/XmRVUROrWrYvPPvsMkydPft9ZgVarxZMnT/Ds2TPs3LkTcrkcmzdvLvA37u7u\ngnX88oZEIsHixYvh4ODwvrNSIFZWVmyWBwBGRkYYO3YslEqlwPaAN7CbOHEiAOgYs0mlUnz//fc6\nalpTU1MW6fHOnTtsuWjGjBnIyspCSkoK7OzsUKNGDQDAokWLkJaWhvT0dIwbNw7JycnYtWsXfvvt\nN9y4cYOlO2jQIMycOVP8lkuIvb09i/x2584dREdHIysrCxzHIT4+ns3w//vvP7akd/XqVWRlZeHp\n06dME5SWlobXr19j6tSpWL9+vcAok8fY2BipqalYsGABpk6dip49e6JevXp689W1a1csWrQIcXFx\nJS6br68vBg8enO9SZKNGjTB27NgSp/+uGD9+PFq1agVnZ2d07NgRLi4uaNCgQb5a2w8aQ48gNBoN\nXb9+nb7++msaOHAgderUiXkcKykf8gz/7t27RYr29C7KePToUeratSuLAy+RSARR3fSRe2teeYOv\ns7JwnFPW8MF48P9n+C1atNAJItOuXbtiOyFJT0+nBw8eCM4tWLCA7O3tqXHjxnTs2DHSarW0bNky\nUiqV1KZNG/rll18IyHELu3nzZgIg2Gal0Who4cKFpSvwB4ihv8kGDRqwmTgfuY335Jg7GE3urXy8\nm2alUknHjx9nacXExOhsKeO9DYaHh9Pz588pOTlZx01tbjQaDfXo0aNE3iSJcrRWvKYpv7rS51q2\nPJLbQ96sWbNo+vTp7+S5H+wMn0cikcDZ2RkuLi5wdXWFRCIxqEXvh4abm1upYkQbEqVSiYMHD+L6\n9esAckb5uWO95yU6Ohqpqam4cOGCYC25vFFedg4UB5VKBS8vLwA572XmzJmoU6cOM9gCgOnTpwuM\nOouCUqnU2bZZr149xMXF4datW2jWrBk4jkNycjIyMjLw8OFDdO/eHQEBAXB0dMTQoUPRrFkzNisF\ncr5p3lZCpOTw9jwSiQTZ2dkwNzeHSqUCAHh5ebG1ZEtLSyiVSpibm8PIyAhAjs3EuXPnmBGwo6Mj\npkyZItD0EBHmzp0LqVQKa2trqFQqKJXKfPMjkUiwY8eOEm/Ps7Kygo2NTYH3lHb3xbsi926oPn36\nMMPW/2sYTOBfunQJn3/+OXr16oVWrVrhwIEDcHFxwYYNG9jeXpH3C9+58B2Lh4eHYI92Xuzs7PDN\nN9/g5s2bbI+7iOGYPHkyrK2toVarMWfOHAwcOBAff/wxux4UFFRgh11U+M6sV69ezGBq2LBh4DgO\nDRs2BMdxWLp0KRwdHSGRSBAWFgZTU1NBGh9Kx12e4Q1LtVot7t27J1AZcxzHfEYkJSXB1tYWTk5O\nSEpKglQqhYmJCTZt2oQlS5YAyNkWd/78eSas5XI55HI54uLiEBwcXOQ8qdVqgVGdCFCnTp1ys6XX\n0BhM4C9fvhxarRbTpk3D+fPnsXHjRvTr1485hBB5/6hUKigUCuY8Jzs7WzCTy4uJiQnGjRsHAMWe\naYoUjkqlwuTJk/HixQtERUXBzs6uTASrlZUVqlevjpCQEHbO2dkZ7du3Z2vBTZs2xcCBAwEANWvW\nLJbQECkaTk5OTLhaW1sL7DMkEgmbVarVaqhUKvz777/46aef0KVLF6SnpyM+Pp79Xq1WY8OGDTA2\nNoapqSk4jsPmzZvx4MGDd1+w/4MU1C9+yBhM4O/fvx9TpkxBkyZNyl1lrVu3DuvWrSsX6bzPvKhU\nKkydOhUuLi4wNTXFrFmzCv3NmDFjYGJiwoy9RAzL9OnT0bFjRwAo05lWx44d0bJlS8G5ESNGoGHD\nhuzvWrVqsf/nXloQMQxNmzZF3bp1IZPJ4Ovrq+Mngjdwe/bsGW7dusUE/6xZszB16lQAwu2/devW\nxdatW5GcnIxvvvkG7du3R9++fd9dgUQ+OEQ9XQXC2toan376KUaPHo0pU6awmX5BWFlZYejQoeIM\nv4yQSqUIDw+Hp6dnmQr8vn376lj2d+rUqczcmYro0rx5c/Tq1Qvx8fG4cOECbGxssGfPHraM06BB\nA1SvXh0ZGRmIiYnB6NGjcfz4cTRu3Bg+Pj44c+aMjuV4nz598OeffyIoKAj29vblfreKyPtFFPgV\nCH7W5u3tzbywFYWZM2cWapwjUnLMzc2xYsWKMhX4+nzI857FRN4dY8aMgZmZGQYPHoysrCyBdzmO\n4zBx4kTcv38fkZGRmDZtGotJIpfLsWbNGty8eVMnzZUrVyI1NfWDNF4VebeIX3sFZMyYMcz6tyiI\ns4ayJyQkROA9T+T/JjKZDN27d8fgwYMxePBgHePIUaNGITIyEubm5rCyssLcuXPZNTc3Nzg7O+tN\nszwEiRIp/3wwAv/q1asl/i2vtixNGoZKp6A0ipOuocr0oVPRy18cxLoqPmVVZw4ODmjZsqXe9I2M\njODt7c2uJSQklEkeDI3YvkrGu6y3D0bg6/MsVVTOnj1b6jQMlU5+aVy9erVY6RqqTB8yxa2zioxY\nV8WnLOts/vz56N69e77XeT/1Hwpi+yoZBdVbWQwEPhiBXxp4N6XlIZ3ylBcREZH3w9ChQ993FkQq\nIAb3tCciIiIiUjDlxfumSMVCFPgiIiIiIiIVAFHgi4iIiIiIVABEgV8At27dQps2bRAeHs7OxcXF\nITQ0FCEhIZg0aVKBvugLQvSIJSIiIiLyLhEFfj6kpaVhxYoV8Pf3F5z/4osvEBoaip07d6Jq1arY\nu3dvsdOOi4sT97aLiIiIiLxTKoSVPu9zvjiW7UqlEps3b0ZYWJhOOnyQi8DAQGzbtg29e/dm9+zb\ntw+XL1/Gq1evcP/+fUycOBGHDh3CgwcPsGrVKvz+++9ITU1Fo0aNEBsbi2nTpkEqlSIpKQmbNm0q\n8kCgJGUSEREREam4VAiBXxIkEoled6RExIIDWVlZsfjUuXn8+DHCw8OxZ88ehIWFYf/+/di7dy8O\nHz4MY2NjZGZmonHjxjh69Cj8/f0xatQo7N27F8+fPxdn/iIiIiIiZYIo8EsBEek97+XlBQCwsbFB\njRo1wHEcrK2tERUVBWNjY2RlZaF69erQaDQYO3Ys3rx5g2rVqsHb2/tdZl9EREREpAIhruEXE47j\nmFvb+Ph42Nra6tyTe49t7v8TETQaDQtY4u7ujoMHD6Jhw4bYvXs3Dhw4UMa5FxERERGpqIgCv5go\nlUocPXoUAHD06FE0b968WL/PyMhgSwURERG4ffs2WrVqhY8//lhvJCwRERERERFDIKr08+H69euY\nM2cOEhMTIZVKsWvXLgQGBsLMzAz79u3D7t274ejoiB49ehQr3czMTBYhy9nZGfPnz4dKpUJycjJW\nrlxZFkUREREREREBR/ktRJcjKkJghopQRkMj1lnREeuq+Ih1VnTEuioZhQXPMXSdiip9ERERERGR\nCoAo8EVERERERCoAosAXERERERGpAIgCX0REREREpAIgCnwREREREZEKQIUQ+OvWrWO+5993OuUp\nLyIiIiIiFYcKIfBFSgYRITs7+31nQ0RERETEAIiOd8qArKysfAUl7/YgLS1NcD49PV3nXEHkl44h\nISIEBwfD3NwcnTt3Rvv27VGpUqUye56IiIiISNkhCvwyYOnSpVi4cKHea3w4WxMTk1I9w1DpFJXw\n8HAYGxtj9uzZmDJlCoyMjN7Jc0VEREREDIMo8MuA2bNnY8aMGXqvbdq0CQCQmpoqOB8VFYX69esX\n+Rn5pWNI+Bl+pUqV0LlzZ7Rr106c4YuIiIh8oIgCvwyQy+WQy+V6r3EcBwAwNjYWnDcyMtI5VxD5\npWNIiAj79u1j0f1ERERERD5cKkRPzqu/y0M65SkvhcFxnCjsRURERP6PIFrpi4iIiIiIVABEgS8i\nIiIiIlIBEAW+iIiIiIhIBUAU+CIiIiIiIhUAUeCLiIiIiIhUACqEwC9P/uvLU15ERERERCoOFULg\ni4iIiIiIVHREgS8iIiIiIlIBEAW+iIiIiIhIBUAU+CIiIiIiIhUAUeCLiIiIiIhUADjiA6uXY65e\nvfq+syAiIiIiIvJOadCggUHT+yAEvoiIiIiIiEjpEFX6IiIiIiIiFQBR4IuIiIiIiFQARIEvIiIi\nIiJSARAFvoiIiIiISAVAFPgiIiIiIiIVAFHgl4CYmBh4enri77//Fpzv1asXZs2aVaI0V65cid69\ne+Pjjz/G8ePHAQBxcXEIDQ1FSEgIJk2ahKysLADA69evMWTIEEwbVY3hAAANPElEQVSYMIH9ft++\nfQgICED//v3Rv39/bN68uYSlK3sOHToELy8vJCUllTiNHTt24OOPP8bHH3+MH374AQCQnJyMESNG\noG/fvhg2bBjevHkDAMjMzMSMGTPQs2dP9vvLly/Dz88P/fv3R2hoKJYsWVK6QhmIsmhbQE7djB49\nmrWnBw8eAADOnz+Pjz/+GL1798bGjRvZ/bdu3UKbNm0QHh7Ozs2aNQtdunRhbezMmTMlzk9ZMmzY\nMDRr1qxU+RPrS0hQUBDS0tIE527duoV+/fohNDQUY8eORUZGBgBgy5Yt+PjjjxEcHCxIMyIiAj4+\nPrh3754g3ZCQEISGhqJ///5ISEgwcOkMiyH6Lp6LFy8iODgYffv2xezZs9n55cuXo3fv3ujTp4+g\nH9ixYwe8vLwE76F27dqsD+vfvz8K3XRHIsXm6dOn1KZNG1qxYgU7FxMTQ23atKGZM2cWO72LFy/S\nsGHDiIjo1atXFBAQQEREM2fOpKNHjxIR0dq1a+nHH38kIqJJkyZRWFgYjR8/nqXxyy+/CPJTnhkx\nYgRNnjyZdu3aVaLfP378mLp160ZarZYyMzMpMDCQ3r59S19++SVt3bqViIh2795Nq1atIiKixYsX\n086dO6lnz54sjUuXLgnqr7xg6LbFs379egoLCyMiotOnT9PEiROJiKhjx44UFxdHWq2W+vbtS/fu\n3aPU1FQaOHAgzZ8/n3bu3MnSmDlzJp0+fbrEeXiXlDavYn0JCQoKotTUVMG5kJAQun79OhERrVix\ngn744Qd68uQJffTRR5SdnU0vX76k9u3bk1arpYsXL9LcuXOpT58+dPfuXUG6aWlpZVOoMqC0fVdu\n2rZtS3FxcURENH78eDpz5gxdvnyZRowYQURE9+7do+DgYCIi2rdvH61fv54CAwMF78HX17dYzxRn\n+CXE29sbFy9eZH8fPXoUzZo1Y3//+uuv+OSTT9CvXz/MmzcPQM4sfPLkyQgJCUF8fDy7t1GjRvji\niy8AAGZmZkhLS4NWq8Xly5cRGBgIAAgMDMT58+cBAEuXLkXdunXLvIxlwevXr/Hw4UMMHz4chw4d\nYudDQ0OxatUq9O/fH71790ZsbCwuX76MkSNHon///rh58ya7t0qVKggPDwfHcZDL5TAxMUFKSgou\nXryINm3aABDW15QpUxAQEKCTFyqnLihK0rY++eQTPHnyBECOZuijjz4SpDlixAgMHDgQAGBhYYGk\npCQ8efIElSpVgp2dHTiOQ8uWLXHx4kUolUps3rwZ1tbWZVzSsmffvn1YsWIFACA1NRVBQUEAgLZt\n22Lr1q0ICQlBcHAwUlNTBb8T60tYX/q+la+//hre3t4AAEtLSyQlJeHSpUto0aIFpFIpLC0t4eTk\nhHv37sHb2xuLFi2CVCoVpEFE5fY7zEtBfRevtQgPD8dXX32F7OxsTJw4Eb1798aKFSv09j979+6F\nnZ0dgP/V34ULF9C6dWsAgKurK968eYOUlBS0a9cO48aN00mjuHUnCvwSIpfL4enpiRs3bgAATp06\nhZYtW7LrGRkZ2LJlC8LDwxEdHY27d+8CAJ49e4adO3eyFw0AEokExsbGAIA9e/YgICAAEokEaWlp\nkMvlAAArKys8f/4cANi9ebl8+TKGDRuGQYMG4b///jN8oQ3AkSNHEBAQgBo1aiAhIUGgwqtUqRK+\n++47dO7cGd9++y0A4M6dO9i2bRu8vLwE6ahUKgDAuXPnYGFhATs7Ozx//hwWFhYAilZf9+/fx+jR\no9GvXz82OCgPlKRtdevWDQcPHgQAnDhxAl26dBGkqVAoWFvi6/jFixewtLRk91haWiIhIQESiQQK\nhUJv3nbu3IkBAwZgypQpBlFrvgs4jtP5f3Z2Ntzc3LBz5044OTnhwoULgt+I9aX7/7yYmpoCyBkY\nHDhwAO3atdNbR8+fP8/3GwSA+fPno2/fvli7dq0Bcl92FNR35eXs2bPIysrCrl270KRJE7338vWX\nkJCA8+fPo2XLljr1Z2FhgRcvXuRbfxkZGZg6dSr69u3L+syCEAV+KWjfvj0iIiIQFxeHSpUqCV6K\nWq3GmDFjEBoaivv377OPvU6dOvmmd+LECfzyyy+YO3cuAOHHVthIrl69ehg3bhy++eYbTJgwAdOn\nTy9N0cqMQ4cOsRFsUFAQIiIi2LWmTZsCyCnLw4cPAQCenp6QyWR607p27RpWrVqF1atXA9Ctr4I6\nq2rVqmHs2LHYuHEjPvvsM8yePRvZ2dmlKpshKW7b6tSpE44dOwYAOHnyJDp16qQ33VWrVkGpVArs\nGXgKa2PdunXDlClTsGPHDtSoUQNffvllKUr4/uHdltrZ2eHt27d67xHrq2BSU1MxevRoDBkyBNWr\nV9e5XlgdTZgwATNnzsTOnTtx584d1obLIwX1XXm5f/8+6tevDwBo2bKljmaD5+XLlxg1ahQWLFgA\nc3NzneuF1d/MmTOxePFibN26FQcPHsQ///xT4P36e1KRIuHn54c1a9bA0dGRqZIBICsrC4sWLcKv\nv/4KS0tLjBw5kl3jZw15OXv2LMLCwrB161Y2ezUxMUFmZiYUCgXi4+Nha2ubb15cXFzg4uICIEdg\nvnr1qlCh966Jj4/H9evXmYFceno6zMzMmOpUq9UCEArr/Orr1q1bmDt3LsLCwpi2xNbWFi9evICp\nqWmh9WVnZ4cOHToAyFkisLa2Rnx8PJycnAxS1tJS3LZVqVIlVKlSBRcuXIBEItFb9vXr1+PVq1dY\ntmwZgJz64rUgAAqtM19fX/b/Vq1aYcGCBaUtpkF5+/YtjI2NIZPJoNVqIZVKBe0/74Auv06YR6yv\nggfAGo0GY8aMQdeuXdG9e3cAOXUUHR3N7imsjrp168b+36JFC9y5cwdt27YtaZHKjIL6rtx1xhtW\nAzmaWx59/XBycjKGDRuGKVOmwM/PD8D/+jCehIQE2NjY5JtOcHAw+7+fnx/u3LmD2rVr51sOcYZf\nCuRyOWrVqoW9e/eytXYASElJgUwmg6WlJWJjY3Hz5k1kZmbmm05ycjJWrVqFTZs2Qa1Ws/N+fn44\nevQogJx13ObNm7Nrede+tmzZgj179gAA7t27B0tLy3Il7IGcEXK/fv2wf/9+7N+/H0eOHMHr16/Z\n2jMfJOnatWtwdXXNNx2tVotPP/0UX375JRwcHNj5Zs2a4ciRIwCAY8eOFVhfv/76K7766isAOaPs\nxMREwTLL+6aobevvv/9mnUy3bt2wYMECdOzYUSe9K1eu4MaNG0x4AYCTkxNSUlLw7NkzZGdn4/Tp\n0wJbgbyMHz8et2/fBgD8+eef8PDwMFRxDcLChQtx/PhxEBEePHgAFxcXmJqaMnXqlStXipyWWF+F\n11dYWBiaNGkisBfx9fXFmTNnkJ2djfj4eCQkJMDNzU3wO/47TE5ORkhICLPuv3LlCtzd3Q1ZRINR\nUN+lVqvZQDAqKgoAULVqVWZhf+7cOWg0Gp00P/vsMwwaNAj+/v7snL///2vv3kEa6aIAjv+HgIUD\nPirBB+Kj0VpE3MLYRAUHFRwfjAYDsQkKEgxBCU4sJBJBJIVWNiJqxEKCiaVVCFZiYS2Ij4igBgRt\nTL5i2fC5u+4uLKzZzfmVM8wwHO7cM2fOHeZTds4/OzujrKyMwsLC7P7/z2Hn5+e4XC7S6TSvr6+c\nnJx8E+uvSYX/mzo7O3l4eMj2Y+BztdXa2oqu69TX1+N0OllcXMRut3/3HLFYjMfHR6amprLVbTAY\nZHJyEq/XSzgcpry8nL6+PtLpND09PTw/P5NKpdA0Da/Xi6ZpTE9PE4lESKfTLCws/KkQ/LJoNEow\nGHyzrbe3l2g0iqIoXF9f43Q6eXp6IhQKZV/rfy2RSHB1dcXc3Fw2Xh6Ph5GRETweD4ZhUFRUxNLS\nEgAOh4NkMsnNzQ2apjE2NkZXVxdut5vh4WEymQx+v//d1sFH+ZWxNT4+TiAQYH9/H6vVis/no6Oj\n45tzbW9vk0wms5/ulJaWEgqFME0Tt9sNQHd3N9XV1ZyenuLz+bi/v8disbCzs8Pm5iaGYTAzM4Oq\nqqiq+iYZ5oIv98vGxgZtbW1UVFRQXFzM2toadrv9zavVn/WpJV7vx+uLra0tKisricfjKIpCS0sL\nLpcru6BUURTm5+eBz2sZwuEwl5eXTExMUFdXx+rqKjabjcHBQVRVpaGh4btjNxe8N3fFYjEGBgbw\n+/3U1NRQVVUFgNVqZW9vD8MwaG5upqSk5M2xLy8vRCIRLi4u2N3dRVEUNE1D13UaGxsZGhrCYrFg\nmiYAy8vLHB0dcXd3h67rNDU14ff7qa2tpb+/n4KCAtrb23/YMgb5W57IEaOjo5im+dMnVPG+eDzO\nwcEBgUDgoy9FiLyWSqU4Pj7GZrNxe3uLw+H4Yc//T8mtkkbkrVxrP/xtVlZWSCQS/+TCMCH+Nqqq\ncnh4yPr6OplMhtnZ2Y++JEAqfCGEECIvyKI9IYQQIg9IwhdCCCHygCR8IYQQIg9IwhdCCCHygCR8\nIYQQIg9IwhdCCCHywH+2mrf4CJ8dXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f145a15f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig, axs = plt.subplots(2,1, figsize=(8, 6), sharex=True)\n",
    "level = 0\n",
    "\n",
    "tidetime,tideAll=plot_tides(axs[0], start, end, times, ts_lines)\n",
    "winddatenum, wind, direc, us, vs=plot_winds(axs[1], 'East', OPlon, OPlat, start, end,times, ts_lines)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "saveloc='/ocean/eolson/MEOPAR/analysis-elise/notebooks/plotResults/fullDomain398x898/exploreNowcast/NorthernNitrate/Movie/'\n",
    "np.savez(saveloc+'saveTideWindCampbellR.npz',tidetime=tidetime,tideAll=tideAll,winddatenum=winddatenum, \n",
    "         wind=wind, direc=direc, us=us, vs=vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}