{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculates and plots the *NIWA SOI*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NIWA SOI is calculated using the Troup method, where the climatological period is taken to be 1941-2010:\n",
    "\n",
    "Thus, if T and D are the monthly pressures at Tahiti and Darwin, respectively, and Tc and Dc the climatological monthly pressures, then:\n",
    "\n",
    "```\n",
    "SOI    =   [ (T – Tc) – (D – Dc) ]  /  [ StDev (T – D)  ]\n",
    "```\n",
    "So the numerator is the anomalous Tahiti-Darwin difference for the month in question, and the denominator is the standard deviation of\n",
    "the Tahiti-Darwin differences for that month over the 1941-2010 climatological period. I then round the answer to the nearest tenth\n",
    "(ie, 1 decimal place)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import ma\n",
    "import urllib2\n",
    "import requests\n",
    "from matplotlib.dates import YearLocator, MonthLocator, DateFormatter\n",
    "from dateutil import parser as dparser\n",
    "from datetime import datetime, timedelta\n",
    "import subprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### defines a function to get the BoM SLP data for Tahiti or Darwin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_BOM_MSLP(station='tahiti'):\n",
    "    url = \"ftp://ftp.bom.gov.au/anon/home/ncc/www/sco/soi/{}mslp.html\".format(station)\n",
    "    r = urllib2.urlopen(url)\n",
    "    if r.code == 200:\n",
    "        print(\"streaming MSLP data for {} successful\\n\".format(station))\n",
    "    else:\n",
    "        print(\"!!! unable to stream MSLP data for {}\\n\".format(station))\n",
    "        sys.exit(1)\n",
    "    data = r.readlines()\n",
    "    r.close()\n",
    "    fout = open('./{}_text'.format(station), 'w')\n",
    "    if station == 'tahiti':\n",
    "        data = data[15:-3]\n",
    "    else:\n",
    "        data = data[14:-3]\n",
    "    fout.writelines(data)\n",
    "    fout.close()\n",
    "    data = pd.read_table('./{}_text'.format(station),sep='\\s*', \\\n",
    "                         engine='python', na_values='*', index_col=['Year'])\n",
    "    subprocess.Popen([\"rm {}*\".format(station)], shell=True, stdout=True).communicate()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set up the paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# figure\n",
    "fpath = os.path.join(os.environ['HOME'], 'operational/ICU/indices/figures')\n",
    "# csv file\n",
    "opath = os.path.join(os.environ['HOME'], 'operational/ICU/indices/data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### set up the plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years   = YearLocator()\n",
    "months  = MonthLocator()\n",
    "mFMT = DateFormatter('%b')\n",
    "yFMT = DateFormatter('\\n\\n%Y')\n",
    "mpl.rcParams['xtick.labelsize'] = 12\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['axes.titlesize'] = 14\n",
    "mpl.rcParams['xtick.direction'] = 'out'\n",
    "mpl.rcParams['ytick.direction'] = 'out'\n",
    "mpl.rcParams['xtick.major.size'] = 5\n",
    "mpl.rcParams['ytick.major.size'] = 5\n",
    "mpl.rcParams['xtick.minor.size'] = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set up proxies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proxies = {}\n",
    "#proxies['http'] = 'url:port'\n",
    "#proxies['https'] = 'url:port'\n",
    "#proxies['ftp'] = 'url:port'\n",
    "\n",
    "### use urllib2 to open remote http files\n",
    "urllib2proxy = urllib2.ProxyHandler(proxies)\n",
    "opener = urllib2.build_opener(urllib2proxy)\n",
    "urllib2.install_opener(opener)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preliminary: wet get the date for which the next update is likely to be made available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Next SOI update expected: Monday 4, May 2015\n",
      "==========\n",
      "\n"
     ]
    }
   ],
   "source": [
    "url = \"http://www.bom.gov.au/climate/current/soihtm1.shtml\"\n",
    "r = requests.get(url, proxies=proxies)\n",
    "urlcontent = r.content\n",
    "date_update = urlcontent[urlcontent.find(\"Next SOI update expected:\"):\\\n",
    "                         urlcontent.find(\"Next SOI update expected:\")+60]\n",
    "date_update = date_update.split(\"\\n\")[0]\n",
    "print date_update\n",
    "print(10*'='+'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the data for Tahiti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "streaming MSLP data for tahiti successful\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tahitidf = get_BOM_MSLP(station='tahiti')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the data for Darwin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "streaming MSLP data for darwin successful\n",
      "\n"
     ]
    }
   ],
   "source": [
    "darwindf = get_BOM_MSLP(station='darwin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### defines climatological period here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clim_start = 1941\n",
    "clim_end = 2010\n",
    "clim = \"{}_{}\".format(clim_start, clim_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calculates the climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tahiti_cli = tahitidf.loc[clim_start:clim_end,:]\n",
    "darwin_cli = darwindf.loc[clim_start:clim_end,:]\n",
    "\n",
    "tahiti_mean = tahiti_cli.mean(0)\n",
    "darwin_mean = darwin_cli.mean(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculates the SOI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "soi = ((tahitidf - tahiti_mean) - (darwindf - darwin_mean)) / ((tahiti_cli -  darwin_cli).std(0))\n",
    "\n",
    "soi = np.round(soi, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec\n",
       "Year                                                            \n",
       "2011  2.0  2.0  2.1  2.3  0.3  0.1  1.1  0.2  1.1  0.7  1.3  2.2\n",
       "2012  1.0  0.2  0.3 -0.5 -0.1 -1.0 -0.1 -0.5  0.2  0.2  0.3 -0.7\n",
       "2013 -0.1 -0.3  1.1  0.1  0.9  1.5  0.9 -0.0  0.3 -0.2  0.8 -0.0\n",
       "2014  1.2 -0.1 -1.2  0.9  0.6 -0.1 -0.3 -1.1 -0.8 -0.8 -1.0 -0.6\n",
       "2015 -0.7  0.0 -1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soi.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### writes the CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "soi.to_csv(os.path.join(opath, \"NICO_NIWA_SOI_{}.csv\".format(clim)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stacks everything and set a Datetime index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts_soi = pd.DataFrame(soi.stack())\n",
    "\n",
    "dates = []\n",
    "for i in xrange(len(ts_soi)):\n",
    "    dates.append(dparser.parse(\"{}-{}-1\".format(ts_soi.index.get_level_values(0)[i], ts_soi.index.get_level_values(1)[i])))\n",
    "\n",
    "ts_soi.index = dates\n",
    "ts_soi.columns = [['soi']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>soi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            soi\n",
       "2014-11-01 -1.0\n",
       "2014-12-01 -0.6\n",
       "2015-01-01 -0.7\n",
       "2015-02-01  0.0\n",
       "2015-03-01 -1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_soi.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### choose the period of display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts_soi = ts_soi.truncate(before=\"2012/1/1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 months rolling mean, and some data munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts_soi[['soirm']] = pd.rolling_mean(ts_soi, 3, center=True)\n",
    "\n",
    "dates = np.array(ts_soi.index.to_pydatetime())\n",
    "widths=np.array([(dates[j+1]-dates[j]).days for j in range(len(dates)-1)] + [30])\n",
    "\n",
    "### middle of the month for the 3 month running mean plot\n",
    "datesrm = np.array([x + timedelta(days=15) for x in dates])\n",
    "\n",
    "soi = ts_soi['soi'].values\n",
    "soim = ts_soi['soirm'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plots the SOI, lots of boilerplate here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10cd7f890>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mR8TP4divl9jtqwS9rDVjt68celTRM7gQleY43QscChwLXAZsCzxpZtuUsG5t\nGewZEqlWJehlrZmlVrk0Y9fLWjNLrRy1GesNiVRLevHplUOz2vRqM9aD7GzMSqdcmllqlUszdr2s\nNbPUKpdm1nq1GesNiVSrIJU+OMSuwIvAP5xzxyTmD6HhAbyk9SwTQgghhBBCVDCXpqYn4pyr6B/w\nPLCohHKuDNseEqNWJejFfvwq4RjGvs9lsm9steyv9OLSqwQbK0BvbKznJPbrJXb7KkFP5yQTvbGx\n2leGfXX55ldaqF4+OgNr2toIUTpmtpeZ1QGbtbUt1UDueJtZ37a2pTmY2e/M7Im2tkMIIYQQ1U1F\nOE5mtnmB+QcDuwCPZby9seFF86LU/CFhfk8Al8FY9mFbfy2mZWZdzGy0mU03s2VmtsDMnjazE1Pl\ntjKzG4HxZrbCzOaa2Y1mtlW+7ZZqZxb7mofnSy1oZhPN7LpCy8thX9aaTdEzs+Fm9llWeqWS1AzX\nep2ZHZCyrZ2ZvR+WHd9K9uViimdnqJnXRjPrb2afFTv++bTCeaszs7fz6H4lLCtJt7nHz8w2NrPr\nzGyamS01s/fM7Hpgap5y481sUfiNM7MeqTLXmNmLZrbczOp9ZDzsb23iOkn+Dm+q3W1Z39pKs9r0\nyLj+QnY2xn69xG5fJehlrRm7feXQoxWewTFoNUZFOE7AH8zsOTO73Mz+y8z+28zG4UfWmw+cl/H2\nHLAc+KmZbZKxdnP5A/B14L+BAcBhwARg41wBM+sHvIQfqn0YfvCMU4GdgRfNbOuEnmPdy6gQhXgP\nODM17yv4ES2h9a4ha5WNmHUE/gQ8SfP3bTnQw8wOSs0/C388y33Mtgy/n+Iblk4FDgLuSJW7HdgD\nGAocAQwCxqfKGDAWuJXG7R4KbJH4qYdQCCHEekelOE63Ax8BpwFXA7/EP+SvBXZ3zs1qZN3m8gTe\ns764UIHQA7WTmT1oZovNbL6Z3Z7rITOzAWb2uZkNS6xzROgJ2sfMRuIdnCMTLbXpl60cRwOjnXN/\nc86955yb7Jz7g3PufxNlfg+sxo886Jxzc4MHfihQF5avNYVGXkYTLcmDcvsa/taZ2XGpMieF3q9l\noZX7sJTWEWb2Zlj+FLB9WLRPWN7TzO4wszmhhfw1MxueWH8s/sXvu4nj1Dcsyx3/Jenj38i+bWVm\nfzKzj8PvATPbLizb1MzmmdmIxD7vFlrcjw/TI83sVTM7O7TmLzWze82sV2o7Z5jZG2G/3zIfcmaJ\n5T3M7H9WuposAAAgAElEQVTN7INQ5g0z+7qZfQkYA2yY2N8RYZ2OZnZFOFbLzOyFdOt+I8e7KNZw\nOM9bgf80sw0T887Cv0yn1+0bjsPi8HvSEj2dieN2opnNCGXqHTfzvVlXJs7Nb4F2ic3Uhv37Z1i+\n0Mz+bmY7JDQet1TvpJl1D+fp2CL7ewUwGbiLJjprCa01eAfkzMSyTYAj8cczeQ0UvPbN31+GmdlH\n5h265LZuM7P78tnhnHvdOXe8c+4B59xM59xTeCfqUDPrGtbfEe/snOOc+5dz7nngv4CjzGz7hNYP\nnHO/B6anj0fq2H3snPsw8VtFE8lzLlpE1nrl0Kw2PTQceRRa5dKMXS9rzXLaF94Pbg7P8jfN7CYz\n697IerPN7O7E9Almdkt4jgw3H4XQw8wWJsrsG94vtkxsM7n8h+E9Irnd2sTyWjN7NY8tp5tZ70L7\nZ4lop9jPRz4qwnFyzt3lnDvaOdfXOdfZOdfFObeLc+6nzrkFZdik4R2N84FvWeHhznsCT+FDYPYG\nvgx0Be4zM3POvQX8CLjOzPqZ2ab4F85fhBeV3wB3Ao8Ax+Fbap8rsK1/A18pVHHMhw8OBX7vnFue\nXOacWwZcH9bvkW/9FvJrvEO7O35f7ktUxD7AX4B/hOXXhfLJ1utO+J6yI/G9ZdcAN5jZIWH5D/DH\nZQzrWrTnhoqZO/7fInX88xlqZl3wTvFSvDO2DzAPeNTMOofr6XTgQmAnM+uMb6m/zTl3T0KqFjgZ\n79AeCvQP9uW2803gcuAiYAfgXOAk4DthuQF/Aw4Ehocy/w2sAJ4FfhhszO3vlUH6lrDOScAZ+Bfx\nv5rZbk043jkHuJSRJqcC04BvhPU2w/dO3JLSqwHuAzbFj2h5MLBJsCVJLfCfwDHA4cDAcJxynAuc\nDZyDPzft8Mc5Z/9koAtwFb7OfQn4NByDDqHMjcDJKWfjJGAxUDA81cyOxF+D36flPVxjgOMTDudp\nwDPAzFS5Ytf+nfj7dHLU0B74TzLc1AR7euB7CZeG6X2Bz51zyfvNs8CSsKyp/Nl8w8XT1kj4pqh6\nJre1AULEjpm1xz9jb3bO7eCc2wF/vx8TnrWFGBQaxaBhhIBzzn0KfJAosx8wCdg/TO8D/Cuxzkms\nez/NUUodHo6Pelg/yXIEiph/NGFUPbxzc3/4/3HgjvD/ELxD1TNMXwY8mlp341Bm78S8P+Nf/B8E\n/kkYBj6xrb+WYNOB+DCflcDL+BfiQxPLvxi2e0yB9b8Wlu9VynbxL7h1wKDU/DrguFSZCxLLDXgL\nGBWmRwNvpjQuDOv1bWT7dwB/TEw/AVybKlPS8U8tPxN4OzWvHb5H8z8T834LzMDfvN4GuiSWjcT3\n7H0hMW//sN1tw/R7wCmp7fwQeD38fxi+Z2JAATuHA5+l5m0b1umTmv8XvMNc8vHGO0PfKXLN1eFv\nmN8Cng7zfgI8nOdaOCwck+Q2+gV7D0kct2VAt0SZnwPTE9MfFLieHm/Ezg3DtvcL0xsAC4BvJMr8\nC/h1IxpbAu/nrpt8x7+UX3I9fA7f2eH/1/AOYFFdGl771wEPJaa/HY5TTYk2bYTvMbo6ddxn5Ck7\nAzgvz/yfALPyzO+FbxwajI8CuDSci1NKsU0//fTTT7/6P3xaxlH4BrXJwAv4d7ijSbynpNaZBXwX\nmBCmTwBuCf8PB64L/98AnBX+vxsfyv3bMH0pcGH4f1u8U7UP/pM/+bZZC7yamncC8BnwZli/E76x\ndRr+3fUaSnjnjeHHejyqXrnItTifhw9VGpSnzJ7AQRYSyc0nfedyGJK9VGfjeyQOBE514Yw0Befc\nP4PmIfhW6O2Bh83sD03VKgNrW63Dvv0LyLVo7EjDQSDqTYfwrAvNbGoIS/oM/8Lep8h2Sz3+6XX6\npdZZhH+5TK5zHr6F/jT8S+DSlM77zrm5iekX8I7EjqFn8QvAjant/DKxjYHAPOd7JUtlEP66fCOl\n+9WEbtHjDeCc29E5d32J270dGBhCuM4Ebs5TZkfgA+fce4ltzMK/4O+UKPeucy45OMI8wuiKoSel\nXq9r4npKhrdtaz4k8x0z+xTfG1sD9A3rrCARKmdmO+N7p/LZnWM88L/OuRcbKdNUbgbONLMvAlsB\n96QLlHjt/xE4LNeLi9+vW51zdcUMMB+a91dgDvCzlu1OQ5xzC51zv3XOveCcm+ScuwSfj5n5toQQ\nokoYhM/hvwQ4wDk3GHgA3/i+ZyPr3YXvddq2kTLP4HuawL833AXsFab3w0ceAJwI3Ol8dNR2Idqk\nKM65u/FRFCc753LvzTcCRznn9sQ/4ys6v16OUxHCi9Q95Al3wre2PoAPiUr++uMv8By7At3xLeFf\nyLeZUmIznXOrnXNPO+eucM4NxedfnWM+3+edYN/OkDfWc6ew/J1i2wnkXsosp5cIhSpGOsypUNjT\nPuHvT4Af4/NLDsEfw7/gj1ex7eSO/zkUPv5JavAtOOlztj2+cufoB2xN6EUqYke+bYDPGUlu40zC\n+WkmNfhzuBf193kH1uXTOFoQZpbvOnTOLcb3mt6Ad3LubaJWst6kc18cxe9DSadpCP6c98Lv/2C8\nE7oaSIbm3QR8OYQungk8m89JTdh4MHCJma0ys1Vh/Q3D9NlF7Etr5fg/YDe8w3x7cOjSFLz2c3rO\nuan4lrszzGwX/INzTB6ttD1d8eGgdfjWy/0Si/+ND6tMljf8+f13Me1QfkiBRS/i62CTyDo+vRzx\n7rHbWG16WWrGbFvWWuXSjF0va80y2mf4UPeZzrnPAZzPG200Nx0f4fEb4ALCczePjc8B+5lZLTA7\nPJcshJUPYl2o3ol4pwr8M+k/C+gV3JXwdwd8tMKMMD2Bhs/0TFCOU1z8HN9bdERq/nT8qFXvOZ+E\nnfx9DmBmG+Fbs3+DzzMab2bdEhorgfbNtGta+NvVObcQn9fynZCXs5aQ1/Nd4G/OuUWJRY15/bnc\nsWSc6h4Fyq7NiQgvX4MTtr2BDyNMsk9q+gB8aORt4SVxFn7kwKR9+Y7TJMLxx/feNDj+eXgZ2A5Y\nmOecfRL2oQO+l+VpfFL99eEFPMlWZpZ0ggfj69M059x8fE/Ldkn9nI0J23tbYlCDFCupPygCwCv4\nG07vpF74zQtlplH8eDeHm/H5RLc751bmWT4N2NLqj9zYG3/9vFHKBpyPv55H/uspdy10x18bo51z\njwdnqDupa8M59wb+AXAOcArFHY1dqO/kjsCHFe6OD2doMsHhvBt/3Ar1dpVy7YPvdRqOH5jjaefc\n9Ma2He4xf8dfL1/N02P6HNDVzJL5TPviwx6fpWXsgb/+hRBCNJ1J+PvxthYGWQgRGUfi32EK4fDv\nmwdRIGInPDs2wof95e71L+MbGGc755aa2a74xq9HzX+G4kR8vlNTKPR+2Soj5JaVto4hbK0fTc9x\n+mtq3nX4F6lkjlNv/HDo9+Bf7rbBDxRwA96ZAT+08Yv4l+COwBRgXEL3AvyL//b4Fob2BWyaiH8J\n3BMfV/pVfAzp64ScqbD9f+O7Yg/GV5whYfp9YOvG9jHPNp/FOw874VurnyR/jtO7wPH4F75r8Ano\nW4YyffDDM18dlp8Q9ndtzg1+4IM5+DyhHfCj/y0CnkjYcgO++3frcJyslOOfZ586h+M2EX9z6Rf+\nXol3dAB+hR9RsXuYfgifY5U7ziPxMbyP4V+s9wVeJeTFhTJnhePww7Dfu+BHUDw/LLdwfF/HD5LQ\nD58ndExYvl84RoeG/e0c5o8Pth0f9ncvfK/F10o93qHcm8B3i5z/tec6TPcEOjayfBL+etkz2PUc\n8EJi+UgaxkMPJ5Hzgw/x+oT619OnuWsB75x+CNyGd4C/hA+TXAkMy6O9Aj8oxIZNvF/Us6u56+F7\nTTduZHmha//xlG7XcM0tB04vYkO3cOxfC8coOUx4h0S5v+EH/9iHddfwfSmt7fCO0FX4e8juYbpD\nWH46/oG6YzhfPwnH/L+beuz0008//fRbm3d9H/798JnwjLsUH+2RN7cV3+iWezf9dnjujwnTwwk5\nTmH6Xnz00f5h+kR8fus1YXo0qVxX/MBGfVPzatPP9DD/fmBI+L8T/h1xmzB9BxWe49TmhrX1AShQ\n9hYSL8Fh3qbhBWxN7uIM87fDd2d+jH9RfhP/stcBnx/zObB9ovzOodzXw/Qm+J6inPZBBWw6Hz+w\nxAK8AzcL7yBslSr3BXzI2Vz8y+T7YXrLYvuYZ5s74F+El+AdvgPI7zidFCr3MnzPw9CUTs7JWxb2\n4eSwrznHaSO887MY7wj9Cv8C+XhCoz/rRv1Krlvw+DeyX5vheyDm419EZ+JDs3rhX8RXJs8DsHko\ne16YHol/yTwbf3Nair8R9Upt50R8S86yYN9TufMelvcI5+bDUOY14ITE8uvD+a4DRoR57fFxzzPw\nL6jz8N3oA0s93qHMWs1GjlM9x6jYcrzTdm84j4vDOd0ysfwSYGpKYziwODHdDv+S/kn4XROOw+Nh\n+RB8o8CrYf+m4h3Pz2joOHUOdtzUjPtFPbtS1/uwpqxXZH+LXvuJsmPwTlXnIrYPCXauCX/rEtMH\npbY9Hu+YfgqMIzQWJMo8kVo/9zdX/4bhnf/Pg8YL+Nj2Nr3f6xfnj/AypZ9++jX+w78f3Bye5dPw\n7yjdGyk/k3WOU0f8u1/OcTqdxOBa+Aau5cAGYTr3bPtGmJ5B4r01zPsffATOkMS8Wvz70pzE7wR8\nnm5ycIihrBsc4mrWDb62F4mBkGL7Iccp/wFooeaQGLVaS48CI++tD8evMU3y9JzEck5i0iyTfWOb\nUHZLfE7VvlnYiHfYlgK1bXF+8T2fN8RyPqpNrxJsrAC9sbGek9ivl9jtqwQ9nZNM9MbGal8Z9tXl\nm9/c3BohhIiS8A2MTfDhBpNc/W8VtYSvAL9yzs3OSK8kzGxjfI7lYfjBJoQQQgjRBuRyNtZ7zMw5\n5yo/KS0iwqgsM/DfvpnUtta0HuHDscc75/QS28qY2Ujn3MgiZYbgv7/2Nj40cmormFY2zGw2Pqzu\ncufcb9rYHCGaTSn1VwgRL9VUhwv5DepxEs0mtLynR35b73HOXYpP1BQR4pybyHo0YqhzrratbRBC\nCCHEevRy0RbEPP587HpZa8ZuXyXoZa1Zpm8q1GYpFvP+Si8uvXJoVpseGddf0HecYtKMXS9rzdjt\nK4ceVfQMLoQcJyFEJTG5rQ0QQjQb1V8hKpuqr8PKcRJCCCGEEEKIQCG/QT1OQgghhBBCCFEEOU4t\nIObYzNj1staM3b5K0MtaM3b7staL2TbpxakpvXg0Y7Yta61yacaul7Vm7PZVm55ynIQQQgghhBAi\nEpTjJIQQQgghhBAB5TgJISqe1uqKF0Jkj+qvEJWN6rAcpxYRc2xm7HpZa8ZuXyXoZa1Zphvs8CzF\nYt5f6cWlVw7NatMj4/oLynGKSTN2vaw1Y7evHHpU0TO4EHKchBBCCCGEEKIIynESQlQMZjbSOTey\nre0QQjQd1V8hKptqqsPKcRJCCCGEEEKIZiLHqQXEHJsZu17WmrHbVwl6WWuWKd64NkuxmPdXenHp\nlUOz2vTIuP6Ccpxi0oxdL2vN2O0rhx5V9AwuhBwnIUQlMbmtDRBCNBvVXyEqm6qvw8pxEkIIIYQQ\nQoiAcpyEEEIIIYQQopnIcWoBMcdmxq6XtWbs9lWCXtaasduXtV7MtkkvTk3pxaMZs21Za5VLM3a9\nrDVjt6/a9JTjJIQQQgghhBCRoBwnIYQQQgghhAgox0kIUfG0Vle8ECJ7VH+FqGxUh+U4tYiYYzNj\n18taM3b7KkEva80y3WCHZykW8/5KLy69cmhWmx4Z119QjlNMmrHrZa0Zu33l0KOKnsGFkOMkhBBC\nCCGEEEVQjpMQomIws5HOuZFtbYcQoumo/gpR2VRTHVaOkxBCCCGEEEI0EzlOLSDm2MzY9bLWjN2+\nStDLWrNM8ca1WYrFvL/Si0uvHJrVpkfG9ReU4xSTZux6WWvGbl859KiiZ3Ah5DgJISqJyW1tgBCi\n2aj+ClHZVH0dVo6TEEIIIYQQQgSU4ySEEEIIIYQQzUSOUwuIOTYzdr2sNWO3rxL0staM3b6s9WK2\nTXpxakovHs2Ybctaq1yasetlrRm7fdWmpxwnIYQQQgghhIgE5TgJIYQQQgghREA5TkKIiqe1uuKF\nENmj+itEZaM6LMepRcQcmxm7XtaasdtXCXpZa5bpBjs8S7GY91d6cemVQ7Pa9Mi4/oJynGLSjF0v\na83Y7SuHHlX0DC6EHCchhBBCCCGEKIJynIQQFYOZjXTOjWxrO4QQTUf1V4jKpprqsHKchBBCCCGE\nEKKZyHFqATHHZsaul7Vm7PZVgl7WmmWKN67NUizm/ZVeXHrl0Kw2PTKuv6Acp5g0Y9fLWjN2+8qh\nRxU9gwshx0kIUUlMbmsDhBDNRvVXiMqm6uuwcpyEEEIIIYQQIlDROU5mtr2ZXWZmz5vZh2a22Mxe\nMbOfm1mXtrZPCCGEEEIIsX5TEY4TcCbwQ2A6cCnwE+At4BfAs2bWqS2Mijk2M3a9rDVjt68S9LLW\njN2+rPVitk16cWpKLx7NmG3LWqtcmrHrZa0Zu33VptdaOU7tW2MjGXAXcLlz7rPEvBvNbDpwIXAW\n8Ps2sUwIIYQQQgix3lPROU5mtiswBfiDc+47Rcoqx0kIIYQQQgjRKBWd49QIXwh/57epFUKIVqG1\nuuKFENmj+itEZaM6XMGOk5m1Ay4GVgG3t5ENQ2LUqgS9rDVjt68S9LLWLNMNdniWYjHvr/Ti0iuH\nZrXpkXH9BeU4xaQZu17WmrHbVw49qugZXIhKyXHKx9XAPsAFzrnpbW2MEEIIIYQQYv2lInOczGwU\nflCIG5xz386zfAgwJDX7EvyIfGkmOucmlqih8iqv8m1bfggwsQnly22Pyqu8ypdefgiqvyqv8pVc\nfgjr6nAM9pSzfD6/YWLFOU5mNhIYAYxxzp3dhPU0OIQQFY6ZjXTOjWxrO4QQTUf1V4jKpprq8Hox\nOETCaRrbFKepXMQcmxm7XtaasdtXCXpZa5Yp3rg2S7GY91d6cemVQ7Pa9Mi4/oJynGLSjF0va83Y\n7SuHHlX0DC5ExThOZjYC7zSNc86d2db2CCHahMltbYAQotmo/gpR2VR9Ha6IUD0z+y5wHfAefiS9\ntNH/ds49WkRDoXpCCCGEEEKIRinkN1TKqHp74Z2lPsCteZZPBBp1nIQQQgghhBCiuVREqJ5z7gzn\nXLvwq8nzO6Qt7Io5NjN2vaw1Y7evEvSy1ozdvqz1YrZNenFqSi8ezZhty1qrXJqx62WtGbt91aan\nHCchhBBCCCGEiISKyHHKAuU4CSGEEEIIIYqxXgxHLoSoblqrK14IkT2qv0JUNqrDcpxaRMyxmbHr\nZa0Zu32VoJe1ZplusMOzFIt5f6UXl145NKtNj4zrLyjHKSbN2PWy1ozdvnLoUUXP4ELIcRJCCCGE\nEEKIIijHSQhRMZjZSOfcyLa2QwjRdFR/hahsqqkOK8dJCCGEEEIIIZqJHKcWEHNsZux6WWvGbl8l\n6GWtWaZ449osxWLeX+nFpVcOzWrTI+P6C8pxikkzdr2sNWO3rxx6VNEzuBBynIQQlcTktjZACNFs\nVH+FqGyqvg4rx0kIIYQQQgghAspxEkIIIYQQQohmIsepBcQcmxm7XtaasdtXCXpZa8ZuX9Z6Mdsm\nvTg1pRePZsy2Za1VLs3Y9bLWjN2+atNTjpMQQgghhBBCRIJynIQQQgghhBAioBwnIUTF01pd8UKI\n7FH9FaKyUR2W49QiYo7NjF0va83Y7asEvaw1y3SDHZ6lWMz7K7249MqhWW16ZFx/QTlOMWnGrpe1\nZuz2lUOPKnoGF0KOkxBCCCGEEEIUQTlOQoiKwcxGOudGtrUdQoimo/orRGVTTXVYOU5CCCGEEEII\n0UzkOLWAmGMzY9fLWjN2+ypBL2vNMsUb12YpFvP+Si8uvXJoVpseGddfUI5TTJqx62WtGbt95dCj\nip7BhZDjJISoJCa3tQFCiGaj+itEZVP1dVg5TkIIIYQQQggRUI6TEEIIIYQQQjQTOU4tIObYzNj1\nstaM3b5K0MtaM3b7staL2TbpxakpvXg0Y7Yta61yacaul7Vm7PZVm15r5Ti1b42NCCGEWH8YOurB\nlwaeMqLb0FEPfpaV5sBTRnQDBmSlJ4QQQmSNcpyEEEI0iaGjHnwJ2DFj2Wn/uPjIvTLWFEIIIZpM\nIb9BPU5CiIrBzIY45ya2tR1iLdMy0snaCRMRovorRGWjOqwcpxYRc2xm7HpZa8ZuXyXoZa1Zpnjj\n4VmKxby/sestnDkl0+fHgrde6JalXuz1oxr1yLj+gnKcYtKMXS9rzdjtK4ceVfQMLoQcJyGEEEII\nIYQognKchBAVg5mNdM6NbGs7qp1EjlOWoXrKcVrPUf0VorKppjqs7zgJIYQQQgghRDOR49QCYo7N\njF0va83Y7asEvaw1yxRvXJulWMz7G7uecpyk1wxqM9ZTjlNEmrHrZa0Zu33l0KOKnsGFkOMkhKgk\nJre1AUKIZqP6K0RlU/V1WDlOQgghmoRynIQQQqzPKMdJCCGEEEIIIZqJHKcWEHNsZux6WWvGbl8l\n6GWtGbt9WevFbFs59JTjJL221stSM2bbstYql2bsellrxm5ftekpx0kIIYQQQgghIkE5TkIIIZqE\ncpyEEEKszyjHSQhR8bRWV7wQIntUf4WobFSH5Ti1iJhjM2PXy1ozdvsqQS9rzTLdYIdnKRbz/sau\npxwn6TWD4RnrKccpIs3Y9bLWjN2+cuhRRc/gQshxEkIIIYQQQogiKMdJCFExmNlI59zItraj2lGO\nk2gOqr9CVDbVVIeV4ySEEEIIIYQQzaR9WxtQyZjZEOfcxNi0KkEva83Y7asEvaw1y2EfUJulWMz7\nG7vewplTanpts3tWciHH6cjM9GKvH9WmN3TUgy9ttuO+tUNHPXhUFno5Frz1QrdJEy4d0FKd2K+X\n2O2rBL2sNWO3rxx6VNEzuBBynIQQlcTktjZACNE8evTdsRs+LDNL5masJ4QoTNU/g5XjJIQQokko\nx0k0lTJcM6DrRghRJpTjJIQQQgghhBDNRI5TC4h5/PnY9bLWjN2+StDLWjN2+7LWi9m2cujpO07S\naypZXzOQ3XUT+/USu32VoJe1Zuz2VZuevuMkhBCiYnF1dcx96e8bT3903BYrl3zarq3tEUIIIVpK\noRynLBOfMskrMrMLgEHAnvhRPd51zvVrwvrKcRJClIWQv5E5seZuFMtXWb1iac2U//tV7cJ3Jm0M\n0Lln7+V7nTH6rc49Nl1dQFK5Kus5ynESQlQShfyGShpV73JgITAJ6EG2zp0QogJoreFGm0nWo4Vl\n+YLZaiz56P2Or9x+2XZLP3q/c27eso/ndXrxpp/tsNcZo9/q0rP3qra0T7QdWQ9hL4RoXSJ/BrcK\nlRSqt41zblPn3FBgXlsbA3HHZsaul7Vm7PZVgl7WmmWKNx6epViWNob8jWkZ/aLP+cmXr/LR9Eld\n//XHc3dMOk25oIPlny7Y4MWbz9vh8w/f2yCfXuz7Ww7NatP7YNKjmTfWKscpHs3Y9bLWjN2+cugR\n8TM4uhynuro65syZw2uvvcbcuXOpq6srp10NcM7NbtUNCiGEKJl3n/3LJq/cdun2q5d93h7Aatq5\nAUec/e4ux/1ohtW0cwArPvu444tjzh+w+IN3OrWttUIIIUTTadRxWrp0KWPHjmXo0KF0796drbfe\nmt12242+ffvSvXt3hg4dytixY1m6dGlr2RsVWXZXZt31Gbte1pqx21cJellrlqk7f3aWYlna2Gub\n3TNtTdp0wODPstTL+nzk9rduzWpeu/fqPm/9/aatXd0aA2jfuevqgadc8vbW+x370ZZ7HLJot//8\n2TvWroMDWLV0cYeXxl44YNF70zon9WLf33JoVpte5403zzzEPqvrJvbrJXb7KkEva83Y7SuHHhE/\ng1srhLCg43TNNdfQr18/rrzySvbaay8mTJjASy+9xNtvv81LL73E+PHjGTRoEFdeeSW1tbVce+21\nrWGvEEKISFjx+aJ2L445v/8Hrzy6WW5el022WrbPOVdN26T/oM9z8zbfef/Fe5z087drOmxQB7B6\n+ZL2L48bMWDhzCkbtoXdQgghRHMoGG/81FNP8dBDDzFo0KC8ywcNGsTXvvY1fvnLX/Lyyy8zevRo\nfvCDH5TN0CzIeuSrBW+90G3ShEsHZKGVdcJd7HpZa8ZuXyXoZa1ZpiTS2izFsrQx68R3n7txZGZ6\nWZ+P9yc90m7GE7fvuPzTBWtzlnptN+iT3b9x/uz2G3Rp0Pu26fZ7fz7wlBFvT779F/3XrFzWbs3K\nZe1eue2y7Xf/+nnvbDpgcPT7Ww7NatNb9sn8zEe2zeq6if16id2+StDLWjN2+8qhR8TP4NYauKKg\n43TPPfeULLLnnns2qXy5CQliQ9Lz33lswm7peRv323VNvhCbhTOn1Hwy69UG3x5JlZ9bbJvAxHwn\nMk/52jCv1PKN6gN7FEiUy0Q/o/K1Zpa3e1X7W5L+8Dz7vD7v7xBgCzMb2Ux7ylZ+632P2XLl0sXt\n61at6JYvdGjBWy90+3jmlAZJ7D232f2zfOUXzpxS8/6kR3qZXTayNexvavlNBwzut2D6yxtQt2Zt\nma33O/aD7Q8/c57V1DS6v3sOu+ytSRNGbr96+ZL2datW1Ez+0y/7b3vwSasXvz892v1NsAjIUr82\nVYfX2/3det9jtlyx5BN766E/bpmbX+j6b2p9ifh+VTXnl8rY3wZlW6hfbfsL9Z/Bse1v1tczqfcN\ngIkt+o7TE088wc0338yECRMaK5Z5C5OZvQZ0cc5t04R13OGXPfByhmbo+xFCCKAs36iJ8v5iZgac\nC/ab3GOipn3Huh2P/s6srQYeuqhUncUfzOj08riLt1+1dHEHAKtpz87Hfn/Gq/dctV15LBdtjb7j\nJN8CZrcAACAASURBVISoJKzAd5yaPBz5vHnzGD16NNtttx2HHXYYc+bMycZCIYQQ0WJmnYBbgbVO\nU8euG6/ca/jlbzbFaQLovuW2y/c+81dvbdCt50oAV7ea1+69ZlszOyVzw4UQQoiMKMlxWrNmDffd\ndx9HH300ffv25eKLL2bYsGHMmTOHJ598stw2RkuW3x2Jfez+2L9XELt9laCXtWbs9mWtl++7Ri0h\npu8amVlvfAjEabl5G27ap26fb109baO+Oy5rjmbXzfqu2PusK97s1GPTFQC4OoDxZvbN5tqZpNqu\nv0rQy7qOgL7jFJNm7HpZa8ZuX7XpleN85KPRm9j06dM5//zz6dOnD2eddRb9+/dn6tSp1NTUcMIJ\nJ9C7d+/WsBEAMzvNzC4ys4uATYGNctNmdmqrGSKEEFWEme0FvAR8MTdv850P+GjAEWev7NS91+qW\naHfp2XvV3mf/+s0NN+ubCw834EYz+2FLdIUQQohy0GiOU01NDQMHDuTcc8/lhBNOoGPHjgB06NCB\nKVOmsNNOO5W0jUwMNXsC+FLSvoT2ROfcIUXWV46TEKIsrK85TmZ2EjAGyH2wtg740WGX/nWYmWW2\nvyuXLN7xxTHns2TBe10Ssy9yzl2ehb5oe5TjJISoJArlOBUcVQ9g11135bXXXuP+++9n880358tf\n/nL5LCyCc+7gNtu4ECIKWmu40fWNpn6KwdXV8ebfbtgK2CI3r13Hzmt2Oe7HMzffad9h+BfWTgUF\nmkjHDbt3GvzN3yx/6Zaff/7ZvBldw+xf9P3iUd/b4chvve/HpGg6eqGOi6yH7I+ZrD9/ArqeRduj\nZ3CRUL0pU6bwz3/+kx49enD88cez3XbbMXr0aAr0UlUdynGKRzN2+ypBL2vNMsUbD89SrJpynBbO\nnLIz3uFp9Ld6+ZIdX7l91B5zXnhwrdPUZZOt3D7f+u3KzXfad6tQrtPHs6ZmaR6fzZvRae+zflXT\ns99uaz8PMeeFB7d488EbdnfOFbU7/cv6+EH8dTh2vQ8mPdpoY21ziDnHqdQ6V8pP13N8mrHbVw49\nIn4Gt1aOU9Gb2ODBgxk8eDBXXXUVd911FzfddBN1dXWcffbZnHzyyRx//PGtmuskhBCi2TQaJvX5\ngvc6vnLbqP7LPp639ht2G2+9y6e7n/TzWR27dF+TKDqwHMa179h52sBTRtikCZdu+8nsV3sAzHnh\ngfYrl366aNcTfvJuTU2DT+sVYsdy2CdEM8giNFHXsxCRUHIL6YYbbsjw4cN5+umneeONN9h///0Z\nNWoUffr0Kad9UZPvQ3zNJeuuz9j1staM3b5K0Mtas0zd+bOzFMvSxnwf0m4JWd5foHH7Vi1fUjPr\nqbs2feHGn+y07ON5a0PwvrDXEfP3POPyd1JOEwA9+zX4nniLyOm169jJDRp26YxN+u/5SW7Z/Nf+\nucmUOy7vV7d6Vckxe1kfP4i/Dseu13njzTMPV8nqPJfjfpXlPUHXc3yasdtXDj0ifga3Vghho47T\niBEjePLJJ1m1alW9+TvssAO/+c1vmDt3LnfeeWdZDRRCCFEePps/e4PX7r26z1NXnr7b9Edv7bt6\nxdJ2AFbTzu1w5Ldm7/Qf35vbhF6ezGjXvqPb4+SLZ262034Lc/MWvPVCz0m3XbrNmlUrMv+ouhBC\nCFEKjTpOY8aM4eCDD6ZHjx4ceuihjB49mueee441a3zjY4cOHTjuuONaxdAYUY5TPJqx21cJellr\nlineuDZLsSrLcaoBP/DDv1/7Z48Xbj6v/3O//94uH7zy6GZrVi5f6x116NJ91aBhl73V94tHLSys\nBlnnOKX1atq1Z/evnz+7925DFqwtM2PyRi/fevF2a1YuL+o8KSckPr1ln8zP3OmNPMcps3uCruf4\nNGO3rxx6RPwMjiLHae7cuUyfPp0nnniCJ554guuuu46LLrqIrl27cuCBB3LwwQdzyCGHMGjQoNaw\nVQghJre1AZXKmhXLmPHEHZu9P+nhzZZ/umCD9PJOG2224gt7Dv2wz+CvftShc7dMww6bi9XUsMvx\n577XrmOnurkv/X1zgEXvvdH91Xuu3Hr3Ey+c3dzR9tZHho568KWBp4zoNnTUg5mFdA08ZUQ3YEBW\nel1794viuhJCNJuqfwYXHRyif//+9O/fn3POOQeAadOmMXHiRCZOnMioUaM477zz1vZAVRvKcYpH\nM3b7KkEva80y2Xd1xnoTs9KKNcfJzHbeYrchfRdMe77TmlXLU0mpxsb9dvm07+CjPtxsx30XW03p\nDeTlynFKY2bsePR357brsEHdu8/d1xvgw2nP95o58Y6l2x588oeF9KoxJ2TTAYO/kLFelt9cot/+\nx7Xog8n5UI5T84n9eq7SZ1ymmmXQi/YZ3Fo5Tk0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K1vRTzqUjbrG/fuSnkMuaF2gGY41x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\niEgP/x9zik4yzMcnewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a817250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,7))\n",
    "fig.subplots_adjust(bottom=0.15)\n",
    "\n",
    "\n",
    "ax.bar(dates[soi>=0],soi[soi>=0], width=widths[soi>=0], facecolor='steelblue', \\\n",
    "       alpha=.8, edgecolor='steelblue', lw=2)\n",
    "\n",
    "ax.bar(dates[soi<0],soi[soi<0], width=widths[soi<0], facecolor='coral', \\\n",
    "       alpha=.8, edgecolor='coral', lw=2)\n",
    "\n",
    "ax.plot(datesrm,soim, lw=3, color='k', label='3-mth mean')\n",
    "\n",
    "ax.xaxis.set_minor_locator(months)\n",
    "ax.xaxis.set_major_locator(years)\n",
    "ax.xaxis.set_minor_formatter(mFMT)\n",
    "ax.xaxis.set_major_formatter(yFMT)\n",
    "ax.axhline(0, color='k')\n",
    "\n",
    "#ax.set_frame_on(False)\n",
    "labels = ax.get_xminorticklabels()\n",
    "for label in labels:\n",
    "    label.set_fontsize(14)\n",
    "    label.set_rotation(90)\n",
    "labels = ax.get_xmajorticklabels()\n",
    "for label in labels:\n",
    "    label.set_fontsize(18)\n",
    "labels = ax.get_yticklabels()\n",
    "for label in labels:\n",
    "    label.set_fontsize(18)\n",
    "\n",
    "ax.grid(linestyle='--')\n",
    "ax.xaxis.grid(True, which='both')\n",
    "ax.legend(loc=3, fancybox=True)\n",
    "ax.set_ylim(-3., 3.)\n",
    "ax.set_ylabel('Monthly SOI (NIWA)', fontsize=14, backgroundcolor=\"w\")\n",
    "ax.text(dates[0],3.2,\"NIWA SOI\", fontsize=24, fontweight='bold')\n",
    "ax.text(dates[-5], 2.8, \"%s NIWA Ltd.\" % (u'\\N{Copyright Sign}'))\n",
    "\n",
    "textBm = \"%s = %+4.1f\" % (dates[-1].strftime(\"%B %Y\"), soi[-1])\n",
    "textBs = \"%s to %s = %+4.1f\" % (dates[-3].strftime(\"%b %Y\"), dates[-1].strftime(\"%b %Y\"), soi[-3:].mean())\n",
    "\n",
    "ax.text(datesrm[8],3.2,\"Latest values: %s, %s\" % (textBm, textBs), fontsize=16)\n",
    "ax.text(datesrm[0],2.8,date_update, fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saves the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(fpath, \"NICO_NIWA_SOI_{}clim.png\".format(clim)), dpi=200)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}