{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Details about Time Series handling and Indexing and Selecting data in Xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with some data available through the xarray packages (yes the loading has a different syntax, this is valid only for data in the tutorial method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load a sample dataset\n",
    "ds = xr.tutorial.load_dataset('air_temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 244.0 244.09999 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lat' (lat: 25)>\n",
       "array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
       "       15. ], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "Attributes:\n",
       "    standard_name:  latitude\n",
       "    long_name:      Latitude\n",
       "    units:          degrees_north\n",
       "    axis:           Y"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'time' ()>\n",
       "array('2013-01-01T06:00:00.000000000', dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2013-01-01T06:00:00\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      Time"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series data\n",
    "### important to read here\n",
    "http://xarray.pydata.org/en/stable/time-series.html\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 244.0 244.09999 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'month' (time: 2920)>\n",
       "array([ 1,  1,  1, ..., 12, 12, 12])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'hour' (time: 2920)>\n",
       "array([ 0,  6, 12, ...,  6, 12, 18])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'season' (time: 2920)>\n",
       "array(['DJF', 'DJF', 'DJF', ..., 'DJF', 'DJF', 'DJF'], dtype='<U3')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.dt.season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, season: 4)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'\n",
       "Data variables:\n",
       "    air      (season, lat, lon) float32 247.01007 246.95503 246.71684 ..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.groupby('time.season').mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, datetime is an instantaneous value, so when certain operations are done on not equally spaced intervals (think about yearly average starting from monthly scale, February months are not long as March) The length of the DELTA interval is not considered. Rather it will correctly match Feb with Feb and so on, but it won't weight February differently .... Same thing when you do a decadal mean and you have leap and not leap years (this is almost never done, but in case your research requires this, have a look at the example below).\n",
    "\n",
    "A rather elaborate example of how to create these weights is here\n",
    "\n",
    "http://xarray.pydata.org/en/stable/examples/monthly-means.html\n",
    "\n",
    "This is essentially in line with doing lat and lon averages...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexing and selecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xarray.core.dataset.Dataset"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 244.0 244.09999 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xarray.core.dataarray.DataArray"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = ds['air']\n",
    "type(da)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have a dataset and a dataarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy style indexing still works (but preserves the labels/metadata when we plot it for example) \n",
    "\n",
    "Note how i have to add the \":\" for the first dimension that I am not selecting through. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3c2021c1d0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEUCAYAAAAx56EeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXe4HFX5+D/v3pLeSEI6uQESAgGSkACB0EJviggigjRBLKgUBQGlCIKICsLPBnwBBUFEiiAdlA4hJCGFJJQ0ICQhhVRS773v74+Z2Z2dndmd2d3ZnXvv+TzPfe7u1HdnzjnvOe95z/uKqmIwGAwGg5dUtQUwGAwGQzIxCsJgMBgMvhgFYTAYDAZfjIIwGAwGgy9GQRgMBoPBF6MgDAaDweCLURCGsiEiC0Xk0GrLkQRE5K8i8stqy2EwlIJREIaqICIqIjvGcN0zRaRJRNa7/g5y7W8QkRdFZIOIvJdPoYlIOxG5S0TWishSEbmo3PJWChG5WETeFZF1IrJARC727F8oIhtdz+w5z/4L7Wewxn4m7fLc6xD72W6wn/XguH6XIV6MgjC0Rt5U1c6uv5dc+/4BvAP0BH4GPCQivQOuczUwFBgMTAAuEZEj4xM7VgQ4HegBHAn8QERO9hzzJdczOzx9osgRwKXAIUADsD3wC9+biPQCHgGuALYBJgP/LO9PMVQKoyAMsSAie4nImyKyWkSWiMgfRKTe3veKfdh0u7f69QrJNAzYA7hKVTeq6sPATOCEgFNOB65V1VWqOge4AzizyHt/W0TmisjnIvK4iPR37VMR+a6IfCgiq0TkjyIixdwnCFW9UVWnqmqjqr4PPAaMD3n6GcCdqjpLVVcB1xL8HL4KzFLVf6nqJiwlO1JEhpf2CwzVwCgIQ1w0ARcCvYB9sHqf3wdQ1QPsY0bavdWcHqaI7Gcrl6C//fLce7SIrBCRD0TkChGptbePAOar6jrXsdPt7d779wD62/vzHlsIETkY+BVwEtAP+Ah4wHPYscCewEj7uCMCrnVKgeeyXQh5BNgfmOXZdZ+ILBeR50RkpGv7CHKfQx8R6elz+axjVfULYB5FPDdD9aktfIjBEB1VneL6ulBEbgMOBH4f8vzXgO5F3PoVYFesRngElnmjEauB7gys8Ry/Bhjgc53Orv3uY7sUIdOpwF2qOhVARC4DVolIg6outI+5QVVXA6tF5EVgFPCM90Kqej9wfxEyuLkaq3N4t0fGqVimqPOBZ0VkuC2T97k5n7sAKz3X7gws92wr9rkZqowZQRhiQUSGicgT9sTmWuB6rNFErKjqfFVdoKrNqjoTuAY40d69HujqOaUrsI5c1rv2Fzq2EP2xFJYj43qshtWtmJa6Pm8go6DKioj8AMt0doyqbnbJ9Lptdtugqr8CVmONMiD3uTmfg55b2GdsSDhGQRji4s/Ae8BQVe0KXI7VOw2FiOzv8UTy/u1f+CoAqOu+s4DtRcTdmx1JrqkF29a+xN6f99gQLMaa6AZARDphTZJ/GvVCInJqgecSaGISkW9hTzar6qICt/I+N+9z+ExVvaOHnGPt37oDxT03Q5UxCsIQF12AtcB6e4Lye579n2F5w/iiqq96PJG8f6/6nSciR4lIH/vzcCxvmsfsa34ATAOuEpH2InI8sDvwcIAY9wA/F5Ee9rW+DfzVdS8VlwttHu4HzhKRUbZ76PXAWy7zUmhU9b4Cz+Vjv/NE5FT7voep6nzPvu1EZLyI1NvP5WKs0d7r9iH3AGeLyC723MzPcT0HD48Cu4rICSLSHrgSmKGq70X9rYbqYxSEIS5+ApyCZVq4g1xXx6uBv9kTqyeV8b6HADNE5AvgKSyXy+td+08GxgKrgBuAE1V1OaR75+6e7lVYE6wfAS8Dv1HVZ+xjB2KZU2YWEkhV/4ulqB7GGpXsYMtRSX6JNWp52zXa+Iu9rwvWiG8V1qjmSOAoZ4Rg/+YbgRexnsVHWM8GABGZZSsg7Gd5AnCdfb29qfxvNZQJMQmDDIboiMg3gRGqelm1ZTEY4sIoCIPBYDD4YkxMBoPBYPDFKAiDwWAw+GIUhMFgMBh8adErqXv16qUNDQ3VFsNgMBhaFFOmTFmhqkFBKtO0aAXR0NDA5MmTqy2GwWAwtChE5KPCRxkTk8FgMBgCMArCYDAYDL4YBWEwGAwGX4yCMBgMBoMvRkEYDAaDwRejIAwGg8Hgi1EQhlBMWvA5qzdsqbYYBoOhghgFYShIY1MzJ932JmfcNanaohgMhgpiFIShIE12xN/ZS9ZWWRKDwVBJjIIwhEbCZww1GGLlsWmfsmbj1mqL0eoxCsJQkHKmDNmwpZGfPTqT9Zsbc/YtX7eZhkuf5NUPl5fvhoZWx9xl6zn/gWlc9M9p1Ral1WMUhKEgaQVRhgHEblc/x31vfcxtL8/L2Tf9k9UA3P36wtJvZGi1bNraBMCSNZuqLEnrxygIQ0GabQ1RDgNTU7Nm/Xd4+YPlTF+0ugx3MBgM5aJFR3M1VIa0gijjFITXauX2kDJpcNs2I3/xHOO234bbThvru9/pSJhSEj9mBGEoiNPZN5PUhkqwZuNWnp31GQB3vDKfKR+tytr/s0ffBWCzbWoyxIdREIaCxNGjz3dJ0zM0OFz31BxO+PMbvvvmr/gi6/vE+St5csaSSojVZjAmJkNB4rD4qFEDhjJz8u0TAThm92OqLEnrwSgIQ0HimIMAy631rL9OYs+GbbK2mykIQzVRVRav2cSA7h2qLUrVMSYmQ0EycxAWWxqb066GxbJhcxPXPjGbdz9dm+PWavSDwY8vNjfmeL/FwX1vfcz4G/7HDONVZxSEoTCaHkFYKuLwm19m+BXPlHTNeyd+xOPTF/vue+WD5VkTk83NyqQFn5d0P0PLpqlZGXHVs1z52Ls5+95e+HnZFMfnX2zh18+8B8ACzxxHW8QoCENBvCOIhSs3APDOx6v8TygD7onJ21+dz0m3vWlWWCeUN+et5PS7JsXau29sbgbgoSmLsrZPnL+Sr/3lTW54ek5Z7nPsra+yblPuKv+2SmwKQkTai8gkEZkuIrNE5Bf29iEi8paIfCgi/xSRent7O/v7XHt/Q1yyGaLhzEF4vVxf/XBF7PdetGoDd722ADArZ5PKefdP5ZUPlrOqhHDwqsqSNRvz7PffvtQuE698kCmLXvNnU7Py2dpwZWexKWNZxDmC2AwcrKojgVHAkSIyDvg1cLOqDgVWAWfbx58NrFLVHYGb7eMMCcDpGXp7VqkKLIs49KaXWbZuc/w3MlSVO16dzz6/+h9zl63z3b9xi/+cl+M48f5nmfO85s8bn3mPva//L8sjliMpt1dGCyQ2BaEW6+2vdfafAgcDD9nb/wZ8xf58nP0de/8hYt5QIrjlvx/6bq/E69m0tTn2exjKQyml4bW5KwH4ZJX/KGL0tc8DxTkwvPj+MsCaXzBEI9Y5CBGpEZFpwDLgeWAesFpVna7oImCA/XkA8AmAvX8N0NPnmueKyGQRmbx8ubFJV4J/v/Np3v0vvb+Mb98zuULSGJJKOWYgktQjTJIs1SLWdRCq2gSMEpHuwKPAzn6H2f/93kdOmVPV24HbAcaOHWs8Ij00NjXz2LTFHD96AKky2IDWbtpKY8DkY8oeQZx599uAZUeOdVRh3nYiKccbL3a1frnK2wuzP2NxnjmQtkpFFsqp6moReQkYB3QXkVp7lDAQcHwdFwGDgEUiUgt0A4xvY0Ruf3U+Nz7zPgqcOGZg6dd7eX7gvpTA1qaMCahZQZubaWxW2tfV5Bx/31sflSyPoXXjnui+582FBY//0T/e8d2+fnMjnduFb97O8RkBGwN3vF5Mve2RAyLSATgUmAO8CJxoH3YG8Jj9+XH7O/b+/6kJ6xmZFeusCnb9U+Vx+2vK8wpE4Kt/yrijNqvyrb9NDlwj4QRZM7ROylFbL/zn9PTnKx+bVfR1dr3qWR72uMS6w7v84P6p3PTc+0Vfv60Q5xxEP+BFEZkBvA08r6pPAD8FLhKRuVhzDHfax98J9LS3XwRcGqNsrZ5yTcjlq/QpEWZ+uib9vVmVVz7wnxcyur71k7R3/L/3rMlpvyjET8xYwq3/m5v3fBO9OEYTk6rOAEb7bJ8P7OWzfRPwtbjkMYRjn1/9lz2268EfT90DiBZUL1/7cKe9lsHQeilFPYTVLVsaw3u1PTlzCX8k2wXW/97+NzcmJrOS2uBhyZpNPDkzEzL5pfeyRwTukcnrc7MXyjV7KtrGLU1c/fgsvtjcyPRFazC0brzvPwpRGv5y4w0b7mD0g1EQhjyoak7v69onZqc/v/h+tvLwOjvd/cYC/vrGQm5/JXiiOwpL125ir+teMDFyEsZKu9NQSqSNSQvj90eJqr/MCMIoCEMAazZs9Z0kdHstedn1qmezvjc1WTWysbm5qN6YN7bPf6YvZtm6zfx9ovGGSiLNFYi0WgpBCsLogWBMPgiDL9/5+2Qmzi+tV+f0wIq1PDgB2sp1PUO8tL73YlSHGUG0Mso1LF71xdaSzr/pufe5/62PAWvyshi5gqKDmmx0yaSUOYhKUK5yo6qBsaFaG0ZBGHwpVdHc+r+56ciYxY8gsk903A4T3g61WRKvIIJMTAGFPagO3PHqfHa+8hmWrWv9kV+NgjD4ElRp8s1BBFGsf3xjU7IbHEM2SX9bURVYUB/pP9MtL7+lbSA0uFEQBl+CKse85dE9iBQrXWRUGj3KaHOjNaxP2oKstoy7w6CqLF2zKbGJncqV0KgtmTiNgmhFNDVruhENw4l/foOGS5/0bXCDhtfFWJ5UlRfmLIt8ntfE5GSyazvVM/n85aV56c+/+M9sDvzNi5x256QqShTMp6utYHxO+O9CFAoEKAgPTv6EqTFmVqw2RkG0Ii7+13T+PvHj0MdPtvM+r7UTAbkzcQUqiCI0RNFzEAEmJjOASA6fuezwr364gs0RF7wtXPEFK9ZXJiHUD+63Avu98/HqrO1Ri7S7/F3y0IyseGStDePm2ko48Dcv8pHdw46K478eJhxGMY3zM7OWRj8Ja2Gcrww+Y4jnZ3/G87OXcuOJI4u6l6E4iikPK9dvJiVCj071HPTbl6ivqXA/1SN0sQ4Zn64urr61JMwIooUy5LInufGZ99LfoyqHGYsyvSgnYqs3l68f+aK7BrEoIEtYIU667U3f7X6m5G/fM5kHJy/K3WHwZeOWJi5/dCZrN5XmzhyEY7Z8ZOoinnaFbgEY88sX0hniALYU4fhQkmye77MXr/U9Lshc61SBm5/3z7TYmjAKooWiCn9y2X+j8tTMTK/e8e5w21yD9MD8IiapDcnjvrc+4v63PuYPBSKaFiKou/CVP77ODU+/x0UPTud79031Peaa/8z23R4nDZc+yZwl2eFjguRzTFJtGaMg2ijuYbWjDGpcG6sZNeHIEX3z7nfknbloTVWDvLVknE6BNzzGlI8+55DfvcSGLeG8zoI6EtMXreEvL2c6MDMXrclxkb7r9epE+H1hzmclnZ9OgdkGFlobBdFGcZdtx/3PbQqupivp0D6d8+5fuOILlqzZyJf+8BpXPmaSEBWD83q9jdwvn5zDvOVfpHvZ7y9dx8sBOT7sK4W635f+8BrXPVmeJFbVYsOWRnsEYpmk3luaP4x4a8AoiFbOsrWbeO3DFTnb3Q2Dn4mpmoU/VaBr9ub8lcxbZpm63pi3shIitTqcgUOhZ33E71/hjLuy3VZVlcemfUpjU3OkSWr3vFelWLOxfHMsK9eXJwlXS8IoiFbOXtf/l2/e+RYbtjRm+Wu7s2UdfcurBSeoe3dpF5uMXmpShcfuTu5iY2IqkcBHHdzyPz59Mec/MI3bXpmfeJfjcsVM+uTzDXzyeev3WvJi3FxbMZc+PCP9+ccPTufpd5cy+eeH0qtzO9xt8NpNjQy/4hl6dQ5WAoO36cjydZXxV29fV7jfkhn1xC1N6yRoNXCYx+kkjVq2dlOk8BXl1CUi4Vxsy1U+9r/xxfJcqIUR2whCRAaJyIsiMkdEZonI+fb2kSLypojMFJH/iEhX1zmXichcEXlfRI6IS7aWTtj5gQfe/iT9eYad0c3pUfmtEs23YKm+tnKDzQ51NQWPcTdMGvDZEEx6DqLEkNbFuD2Xg6Hb5p+ncihXeI1C3OuTo+T/Xp3PQ1Natut1nLW+Efixqu4MjAPOE5FdgP8DLlXV3YBHgYsB7H0nAyOAI4E/iUjhlqIN4i3zYcJrOGEGVGH4FU9zy3+j+XD/7qTKLUArFOIAwO0Q406RmvCcNYnD+6infhx+nkBEIiUJqoQuuf+cvbO+l0NBTPmocF6UK/6d6yzxyyfn8JN/TS/5/tUkNgWhqktUdar9eR0wBxgA7AS8Yh/2PHCC/fk44AFV3ayqC4C5wF5xydeS8faS//3Op+HPRdm0Nbrdvl+3DpHPyUeX9sHWzTB241Wu3NjulKZJDzmdFJwylE8Vu59x9rmZz0kLuLvvjr2475y9OW/CDgCs2xQ9SKSbXz09hxP+7L9gsy1QEbuBiDQAo4G3gHeBL9u7vgYMsj8PAD5xnbbI3ua91rkiMllEJi9fnsyokXGz1VMrvUHt8pGUHvaOeUwER+/er+D51z3l7zJpFEQ08g3WwoR2jzSCCH1kaYzfsRc797Ms1+61GMVw28vR8qnPW76eg3/7EisrFF8qbmJXECLSGXgYuEBV1wLfwjI3TQG6AE43xa+o5pQpVb1dVceq6tjevXvHJXai+bUrxMY7H6/iZ4+GXwsw4bcvxSBRdGrzeCqFmYNw49YJRj+EI9RzCjE9EcWEs27jVhatKo8n0AWHDsu73ylfGyqc+e22l+cxf8UXPD+7tMV4SSFWBSEidVjK4T5VfQRAVd9T1cNVdQzwD8BR8YvIjCYABgKL45SvpTLd5U9+7r1TqihJcQzapkPeydEoCkLI9sgxCiIc6dXARUxSO+c+MWMxr8/NXWMTxPwVX7Dfr4O9gXbp1zVwn9fDbo/tegDQp2s7/n62Ne+w7w490/trU1bT5s1rHjfOupKkjNRLJU4vJgHuBOao6k2u7dva/1PAz4G/2LseB04WkXYiMgQYCiQzsHyVacmFb9qVh/HsBQfQqZ2lBL5/0A45x7SL6DHlVgrGxBSOoJXU+Xjx/WU8NXMJkxdak7Yr1m9hXRGJoILIJ8vFRwxjW9danG27tOO4Uf257bSx9OlqbR8zuEd6f22NdbFiMiCWguNg0VqSCsW5DmI8cBowU0Sm2dsuB4aKyHn290eAuwFUdZaIPAjMxvKAOk9V20ZmcBfzlq9n2y7t6NK+LvAYt9037rUJd505ljGDtynb9bp3rAcyFWnM4B7cdeZYXvlgBX99YyEAqRAL5YIwCiIa+Z60d3Rx1t1vxypLvlcnSNaq71RKuOXk0envz114ADv0zsxrpUcQFZ5Fd0RsyZ04N7EpCFV9jeDyd0vAOdcB18UlU0vgkN+9zK4DuvLED/cPPKaSjeDBw/vEcl23J9bBw/vw+RfFh0TIHkFk7zv59jdZsX4LL1x0YNHXb42E6eFWuhdc6G6n7r0dv3v+A999w/p0yfrujCDeWlDYRbWcpPs2raSjYkJtJJB3P/WPT++QxN7JnGuOLHjMLSePytnm9Li+NDLbc6lrHjdYN4vXbGL2kszz+q8nUufE+Z8zd9l6Pl65oVWnhoxKuv1y9crdK+9VqXhu116d6/Pu/8HBO4a+Vj4niDCMHNitqPPMHISh6iRxtXCH+sITy9v3ypgAvL+gXW0N0686nJlXHw7ApJ8dyk8Oz++p4oc7hPQcl+I44DcvturUkFHJTFJncK+8dx9TKfJ5Jh29e7+0WbJTiLJWW2KWujCLNX3Ps/+3FlOnURAtkEqFDyg3hcIydOtQl557aV9Xkzc2VBBuu/lRt7wa+fw2Q4FYVkpwOJNysbPHa6m+JsVtp43ha2MGZm0/aKfedG5njShvOXkUT50fbH51KGUEMW77bSjm9Hc+XpWZpG6ZVTQHoyBaIEntnfyogAmgKaLLYaFQ1H6IwNpNWzn7r/FOqLYWgtxcm5o1y0xSqFOy15DojgxepdOhvoYjRvRlp75dPMdlPh83agCDe3YqeO1S6shOfboUVfaOd41Qk1pHo2IURAskqWXPGxK8oWdH7j5zT8ba7od+Hod5/fCL6MUJ8NDkRfz3vWXRT25DfGSHrg4a1TWrZjXghQatd5w2tiR5hvXpnF5d7xXp7P2GRL6eN9pAFJTiOidQ+fzacWMURAukWr0Td53p1619+vNZ4xsAGNIrO3xGj071TBi+bTq/Q9RFS53qozvZNTarCQEegsemWWtQbw0I2qgabX1JTU30h97TnpS+9rgRPHdhxsvM6z11wLDoERPCrn8IMp0VW4a22vlJzAjCUDUWr9lUlfu+cNGBjBzUHciOpTRhp20B2G9oL576UcY+7OS4/vKo/gBZpoEw9eeoXfvy82N2jiTjrMVrsyanDYVZuOKLnG3NqixatTHrez5qIraonepruPLYESy84RhO26cha1852taRA7uHOs5tOhvcs2P6/sWOIDanFURRpwNw12sLaLj0SdZsKF82vGIxCYNaIHFnUdt1QFfe/XQtJ43NnizcoXdnHv3evry14HPqa1O8aqcyddelXfpnJh6dSnbKXttxwh4Dae8XQiNPPUylhHP2355fRsxlPP2TNZGOb+scctPLzLv+6Kxtp905KWuittAcRCpiV/PR88bnrF1wGGV3Qn5z4u7sOqA4d9MO9TUM6N4hHeY+iFWuRthdFDeFCKHvh6NIS1Fy/5j0MQCfrdtEt47BC2YrgRlBtHLyhdX2MvGyQ/jgl0elV6F+fc/tco5JpYR9dujJmME92K1Q5bVrnIjkKIco9adnp/z+8X4yGsIT1Pg3q/9nP6L2uPMpnL2378n0Kw/na2MH5Xg6RcFPORw5om/W9z2veyHnGEV5J0JejOxzM9colXvfzE1CVGmMgkgQsaxv8LnkiR43Qoe+3dpHyhzXtUPpA9AwzcqUKw7j2BAhwB2Mfig/hcJ6R33kXTvk7xnH1XOus8v3Nj6djmLXPrjRMowgPly2HvDPUldpjIJIEHEsb/C75NG79fXZmuEXXx7BqEHdGdE/f++tlLSVw+w5jLBrHaL0UIu1H7cWrn58FrdFzIOwaWt+k0qhSd8oz/y5Cw9gQPfyJqAKS509md6na/ucfeWIkuGcm8TFrMVgFEQFmTh/JQ2XPuk7KQjZhWr1Bv9sXlHxm68o1CiPHNSdf5833n/OwIdi2uOfHjWcf547LrSNOcqooK2bmP76xkJ+9fR7gfv9RgPnP/BO3mtuLjDv5S0Df/nmGN/j7j9n78C5h3KzQ++MU8T4HXtyxj6D04rMr4QcsWv+jlMYnCrcQtey5mAURIzsf+P/uPaJ2envD9sJzN9asNL3eHeZGnXN82WR4dwDts/ZFrbhL5YHv7MPkB2f30tdTYq9tw/e7yVKoz/9k+Lsx20FP9v8s7PyJ7jZ/8bgPA5gmWf+etaevHrJBN679sis0Ntu9gjYHgfDXfMXf/jGHvziuF3zelv1t0c1pbTt5ZikThJGQcTIJ59v5M7XMrGBnEVJQcPxOArVESP68vyFB2Rtc+dbGL1dOHdAP4Lk3WvINrz20wn86OChRV/bS1s3G7UEDtppWwZt05H2dTVpU46XUoPoRcEds8kpP/m8rdySfWlk/1D3+OroAfzz3HHp706VaC3rIIybawVxhvY1PpXk7xM/YupH8UQb9batndqV57U7nhp+VX5gj45luYeD+5F1qKthYwGbeT7+/NI8Vm3YwuVHR1tjkXRefH8Zd7k6JEH84B/5zUnlwK+MP/L9fUsOolesDI5iCNPRUA2vyE4YMzBrwtvRC7cELEAsxKovymNaLhdmBBETb87LNSM5q//9Ks/P//0uj7zzada25mblwbc/KWrdg5O+0aoPmfvdd87eWXMQToH+1Vd3i3yPNBXoFLordim9UFXl18+8x+2vREtGn3Tuf+tjzrr77fTalHyU2wT32Hnjc7bV+SgCJ01opThj34b0Z2cR/+n7NNDRjvnkxamXKQk/Ym1sVo/5s7SRQ9JGHkZBxMRD9nyDG2cEEbbwPT59MZc8PIM/vxTNIwXgvAlW4LzBPTtm9b7H79jL93hvgLSk4XZB7F+CB0y569+Ya5/n5oAkNpXk8kdnVuW+Z41vSK+ud1NJU1IQw/t25brjd+Xo3fqmXbJ36tuF2dccyYAeuWXo+NEDOGXv7bj4iJ0iye+e13hhTmkxwEoZGcdBnDmpB4nIiyIyR0Rmicj59vZRIjJRRKaJyGQR2cveLiJyq4jMFZEZIrJHXLJVAr+FMk15TEx+fG4PN1cV4dF0zO79WHjDMXRpX5duXJ1QAuWikp0d9yPrHGHxn5dy99BWfrElkjnh9lfm8YkdKK9czFu+vqzXi8IlRwz33e4t48VEey0Hp+49mD+dOiZnjYOfpat9XQ3XH78b3TvWR3KKCFufw3BnCBNhJYlzBNEI/FhVdwbGAeeJyC7AjcAvVHUUcKX9HeAoYKj9dy7w5xhlix+fdihoknrjFv9eQ3OBSe2wOOXXr23sYHs0RY2lA+6kM/H3Fp1K+Isvjyipdxqn+2Fzs/J/r85nw5ZG3/3L1m7i+qfe44y7J5X1vvOWVU9BBCWK8jbIQV5N1cJbp7zyeRXIOQERZVW9JqZcFgS4tfsRNshgpYhNQajqElWdan9eB8wBBmC1K47/WTdgsf35OOAetZgIdBeR8MtnE4ZfT9UxMXkbuLWb/INyPWe7HpbaQene0ZpEO8rHz/v3J4/i/EOGsnsRKRYdpVIJB6NMKkf1tW+Hxe+9vD53BQ2XPlkwbk8hnpm1lF8+OYcr/j0r4N7W//Wb/BVIS8MdmDEf3xy3HRcdFj07YJx4e/3eOuZVIEGjVvU518uX/99roeVK2BREZeYgRKQBGA28BVwA/EZEPgF+C1xmHzYAcOc8XGRva5H4vWdnBBF2SDppoZVw3emhrNm4ldcCJiHd0VW9dOtQx7QrD+OSI3PNAX26tufCw4YVFWbgtyeN5Ix9BrNnQ/zmA0e8Zi1tSO+nIO63g6OV6kW2wR4JPjw1d/6pqVnTI4tytwHyQQEVAAAgAElEQVRB725KTF5xDmHDspw2rqEkpR4H3hGzVyF4n2i+UXyh0fe6zcV1CHYpIQ5VuYj9rYlIZ+Bh4AJVXQt8D7hQVQcBFwJ3Oof6nJ5Tl0TkXHvuYvLy5cvLLu8nn29g9DXP8dHK8MNCP/x6As4cxG2vzCt4rBuncH7n3sl88863cvZ371jHJUfslPca3TvWl9VWCjCgewdr8VEFJiSdZ6CqJd1v0oLPc7Y5K9hXrt8c6VpRUppe/NB0Dv7dy5GuH5agp3HCn+POwR1O1SXNMwdyF17mKIg8CuOPp+zBcJdTRzlX7rufVLcC8aoqQawKQkTqsJTDfar6iL35DMD5/C9gL/vzImCQ6/SBZMxPaVT1dlUdq6pje/eOnkjEjw8/W8dTM5cA8Og7n7Jqw1Zu/e/ckq7pVyWcijJx/ueeYwvE2rffkvc8h+r7i8RPKj2C0JJ+75l356YifWrmUgCu/s/snH1BqGpO3ol8cj0yNePCvGbDVu59c2EriNcT7k1s2yV6bvG48fb6C9XBVEp46Lv7cPeZe3LM7v0ysZy0uPm7ILKz+FW/fMTpxSRYo4M5qnqTa9diwEkfdTDguIA8DpxuezONA9ao6pK45HNz2M2v8P37pgKw3h4O+pkJAG585j1en5tt5nn30zVM8/iW+1X+oBDHK9fn91LaEDCJ7VCOKJRJZ9suVoXs3iGah0lceM0Gq77YwtPvLg117pamZq54bBYzP13DB5+tY/JCf8VfTjZuaWLl+s1lU0qDe3bMinWUj54hAzJWEu8o1PtY/KrU2IZtmDB826z9ipZtlX9Ts/KPSRkr++oEJAyKcwQxHjgNONh2aZ0mIkcD3wZ+JyLTgeuxPJYAngLmA3OBO4DvxyibLxu3NBVcQPWnl+Zx6v9lm3mO/X+v8ZU/vp61zXcEEeCgcGYBr5a7X1/IzEW5SXD2sm3/QvlWRyeVs8Y38LuvjeTEMQP55Vd2TW/vUEJcqaZm5Ysi7cOPT8se3J53/1RemJOJZ9TcrDw3a2neBnlrk3L4za9w4l/eLEoGh8/WFc4wuPOVzzDmly8UbQ/38sODh7bojom3k9HLM8pxMtL1t1Pr5pujiJosKYi5Hm+09z9bV54Ll0CcXkyvqaqo6u6qOsr+e8rePkZVR6rq3qo6xT5eVfU8Vd1BVXdT1clxyRbE0rXRU3l6Rw4Oy9dm27PnLFmbnnT2sqLACALgL6/kLpbr1cXyThIR9t2hJzdEXA0dNtR2EqitSXHCmIGkUkKfru05ccxA/njKHiXNR1zy0AxGXPWs776rHnuXyx4JXnzmve9ijwfU/ZM+5tx7p/Ave8GkX1saNmLv1I9X8a/JnwTu/9mj74a6DsCytdHmWYJo6eYxr1nIG0ngK6MH8OJPDmL/oZYZ21vMfnbMzuw9ZBvGbd+zbHNwSTApeUmWa0GV8bMlNlz6JN+9d4rv8W/MXZEzcnBYvCa7wYgyoenHBp+en7jcTEWEk/fKzQAXxEs/OYgXLjqg8IEJ5bdfG8kxu/cr6GJ42rjBgfuCzIgAf3vzI/4x6WP++OJcGi59Mmdtg9eu7u2RfmwvhrvqMcvl1a9sBXUYvHz1T29w8UMzaLj0yYKpPwtR6UZo+5BmqErj7fV3bZ87ITykV6e0Yve+vh237cI/v7MPHetrI68D2riliSVrcl2qjYJIOEEj5mdmWbZl9wrYqx+3bMhBFHrX0z5ZTcOlT+YMKwOv57MtvQ7BtW23Ad1C+Zw39OqUXh/Rkilk5oha6aZ8tCprsdI9by4ELBdjN14PE68C6Gyb/JzQCX526ttejh4PauUXuSOAfGY270R6obhePz8mO4DhKJ8wGmF579ojefaCZHZCwk4sO4flm2eIamLa+cpn2OdX/8sJzJevqKoqDZc+ye0+loQ4MQoiJKqaFRM/X1KWDVsaaQyacLBxbNgvvR8udotf4XE6rcvWZRqN//xwP350SPnCbCedQvU8X8V2Fiz265bJLnbCn9/gZ664Rs75m7dmv09vT95rZnB6zk72vnKZ6/3KQVBobSAnrIfXzdcbR+mc/bPzh/zpVP+IN2HUrhX2O5lNTDlds9vV1nD50f4hR/LxZ0/Wv6DOzMxFazj/gWkA3JAnEVQcJPPtVRB3bzHf8P1/7xVuyDduaaLh0ifZ5cpn+ayArTdqUnO/o02OhMKOlvnagVq7YfWmv3x6ZsYbyXnGh96UvYahyVOZg96FY34otkGasSh7jsuvEcnn1bW1Kfv4v76xMPvcAmLVBimf5FlDIhHVE65QXTt8l+jZ6DZ7AvP5NT8btjTypT+8xuPTrQ5l3Mm+vLR5BeGOutroeUPPz854pawMEafd6/4ahjDXBf9JwSS4eyYdEWH7Xv528DrbNrA+j2ePE37DWza8A8R8CuA/0xcXdFX2Y+rHq/jyH7LnuPwakW/kmXtqX5ddxb0mJj9Ty9l23KGDdurdajshdaHtQtbvL1TViu0A/O2NhayzQ+341fHZi7NNhJVYlOqmdftGhsDtqeIeQdTXpvj2PRlHqjAVpWuElY9OpNawobzzmZgMwbSrTfHMBQcw7OdP5+xzesfrPLGRwnSOc0YQAS9DUR6b9qnvvkIsXZPrVeeXT7q/y0TmxWvi8Xrq+ZXrK47dhZ8cvhN1NZLzbBx6dm7Z81fu2EpeJeomM0mdv7IV01n725sfAfDOx6v4/cmjfZV/TgyoCo/c2vwIwo3b3NSrU3YFaAwRZdFv+N+jo7/SaGyK9qb9zF/vL62+n3SSuNgn3EindrWBMYOcSr+5Mbt3H8aF09tQL/V4pcQV4dYv2mc+x6ZivZ461NdQW5MKbPgOtheMtVTc6UjvPnOvwOPq7N/vHUF6KWU19WK7I+B+V15nAYdKW/batILwDt+25FECYd6/bwTXgDcaNayvX0Wf7rN4ri3jJElyc/Jeg3yOtHCU/tYmzYqw+0UIc5D3XX/uMRW694c1I3rxK3J+5SCfp1Y+BfH0+fsXdLUN6hi35EVyQFbq0955QoE4VoF1ARGXHfwsVn7rjD70WfzmmP3cnVCnU+PtSFZ6/UmbVRBL12zi6Fuz1yZszeMCuH3v4GipDn7vrrlZfUcfhXokXrYW8Ipqq+xcIOKlE6LDD6fxXLNxa2STibfhdU8eDu/bJavRfufj4lJ8+rXB3klnyD+CCCpn+w/tVfDZQeVt3pXCPfmezwvMMdEVGvH7jSC+c0C2R1hzs3LYza/kHOdc2f2u6u37ejuSiRtB2LGRgrthLRS/LG1+lc8hjOL268k1qfKb5973uVe0Bn/txurHZUkiN500Csg0ZGe68hAXYqtnzikK3nftbkhTImWK65/b6PiNCDblSVMZNIIIO/nsd1xS1zZEwT1JnU8J1oQ0MfmFuvmWPdl/iG2OCxrpLbAzArpd49vZ8yLe+1Z6LV3BWqHWmObfFZClovi9rHyNdpgFV0HD/7d9QkwP9MmJm495y0sLP95a8U7iXf3lEQVTq+46wOo5u99XVG8db1Fxz0k0q0ZeoOc3+ezHAp8w9L95NrcD4hC0Hsdp+IpZR5L0/OVhyB5BBDeDjumxqcAIvp1PB6MmJew+sFu6LAS94bW2I4C7g1pfY41Io85Vlpuw3aaJIrJnrJJUGL/3nW8OIkx991ul2tzsb6/ddUD0DG6GXIoxgLSrtSpfVAXhtv96vZj23aFX1veok8N+PVQ/kX70j3eyvn9WIH5YULl1FESh391qTUyu35UvhW3YEUTQnIyI4LTxhToN7jLjmL28Cj7q+qlSCasgJgBvisg8EZkhIjNFZEacgsVNoRGE94WHmRzyUzDehsThvokfF7yeoTDF+On79fbCXMXdSHh7/Lv0z9jzVaObAor1Nvrpw/mrYVCj1GCPsgq1/ymBPRsy+Zpv/vrIaAImFHf9rs2zJsJREGFHeDnnS+bcQmViq88k9b22K6xD4kxMNkcBO2Dlb/gScKz9v8Xi13C7X5C3YoUpH95wDM51fBfAeGLkGIrDTz/4tXk/daVb9VuNGkbPuBtxb4PufsXvf7aORauyQ1wUwq9nGEZpFcpvvcFe3e/l3AN2AAorWBHhX9/dlwk7WVFN/YLatVScjkLganFcmQwjXPe0cYPZb0drRFmTktDmRu86LIA5S7PbiURNUouI0y1aF/DXYvFrtLc2ZrZ5zUVhvIj8RhCqUGUzYqsmvZApa5v17ZrjRqS37dwvYzf3WxgVZiTiHkF4OxjeBv7W/0XLSLjLlc/mLIwL40payNlh+Tr/kC+OCSPsAMyRJYEBR4vm0J37APkVRDoxUITffe1XduXv5+xtny/phr/QNdzzDY7yyjknYSOI++3/U4DJrj/ne4tlvs+kr7uB90br9MZN8SOosoZZZGcojnw9PKcX5z4OMnMQftfJR5OrAnv92YMqv2rGZbEQr3yYnWM9TNvtVlTPX5jrXRQYI0rCzUF4ZWlF+oGbvj6SVy6e4FseHDK/u1gTU8ajLWgk4bwCdyfUmaT2nhJkso6LvKE2VPVY+/8QEdkGGAoEO5a3IC5+KNd26zTwO27bOcfmuLbAUB6CQymXGsPfUBzup+5uCP1GEGHaSacCT5y/kjteXRB4Ly9hK3Ux5cR96aF9uvCnU/egX7f2HP+nN4Dg3+XY1sNOQmd60q2nLLerrWG7Ah5vlDhySqVgS1N+LyanbPqZmNZ6FuhVui0J1bURkXOAl4FngKvt/1fGJ1a8BBVyR0HU1aRytP0lPgrFy+YABRF2zcOFhw7jyBHhokI+/L19Qx3X2qlNCV3a13LtcZk0pE50VnfP3d0O+vXoQ40g7Mr57b/5DJ4DytRrc1eErtTekWYYpeW97dG79WP0dj3SeRyC10FY/90T9sPzuq9Gt8W3Jor93aksE1P+d5Hl5mq/l6BYWJUi7CT1+cCewEeqOgEYDeQNXSoig0TkRRGZIyKzROR8e/s/XTmqF4rINNc5l4nIXBF5X0SOKPI3FSS4IbdeUH2NFDWUCxpBhF3D8K39GkLfa0T/ronN1lVJRISZVx/BKXtnIpr+4ZTR3PqN0QzaJtM7dMcUqvVTECFqQnoOwqfhDiotfhX8rPENvsd6F2oGldMw973zjLFA4YVydVlKtLy2+NZA2sRU5O+uSUlaMRQeQeR6MfmxaWsTe173Ai+4ok3HRVgFsUlVNwGISDtVfQ/IjYyWTSPwY1XdGRgHnCciu6jq150c1cDDwCP2dXcBTgZGAEcCfxKRWIKfB/XonQZ+zcatfPJ5bkrAQuRbRxGGLu3r+PHhw9ILufJRkxJuPXk0YJnEDBm6d6znyyP7Z21zN35+k5JR5iC8jX6vzvXpBmTfHXoGnn/GPoN5+eKDuOpLI3z3e0et379vakGZvBnjHJzfE+S/75iW3AriG3niVmWeTtvSEJliUdzvTkmmsxmkZJx35e4g5Av/sXj1Rpav28wvn5xdlExRCKsgFolId6wV1c+LyGPA4nwnqOoSVZ1qf14HzAEGOPvFmiU7CfiHvek44AFV3ayqC4C5QHCYxRIICqnhKI6FK6O5KDoUSucYhqF9uvDED/enY31+3Vgjkraltya7cFy4TUzv+qSKDeUx5OPJdsSIPrSrrUFRalJCn67+U3T9urXnimN3YXDP4FFfOV+jX6/Ub7+7p3raPg2B1xtij1Z7tII0tVFwovIWPQchkl51X8jE5PZiypcYyAn+WInpiFD5IFT1ePvj1SLyItANax4iFCLSgGWWesu1eX/gM1X90P4+AJjo2r8Il0JxXetc4FyA7bYLTpSSj6D4/FED6Hl55t2lhQ8qE26TiVEPhSkUrz/MXK1jrjl7vyHc+Zo1SV3rmq8Sgj2PRg3q7mvaclPOVbJi3yqoM+T83rApQX982E6M274ne28fPEJqjZRqWqtJZRRD4AjCJ5xH1/Z1dKirSec0d/O1v7xpHx9/zY8czVVVX1bVx1U1VAxjEemMZUq6QFXd4+FvkBk9gH/dynkCqnq7qo5V1bG9e/eOInqaoAdb6gjAyT5WOVpnGIQ4cJuQ/CapV28oHAzR6eFt48oVUpuSrJXTQSORMA1MlEbo3U/XsCxPmI0aH88YN5KegwhXhuprU0zYqWXngCiGKDVsx207M3JgdgidrElq13Z3GUqbmDzvyh0CxJs7HCpjOYg1o5yI1GEph/tU9RHX9lrgq8AY1+GLALcRdCAFzFjlJmqE1TiJ9O7NEKIg7hGCX695wYrCjgSNzc00NjVnrYJ3VsoqVm8zyFIVZjVtviOuOW4EVz42K/392P/3Gp19Iog6FJqDcGitsZbKxQA7qGaYeb4XLjowZ1sq5Z6DyLyL7x64Pdc/9Z51jP0KvMrcXZb8wsNUwsQUWz4Ie47hTmCOqt7k2X0o8J6qLnJtexw4WUTaicgQrDUXk+KQLaiuehWEN5k9UDBSKBCYA7ncRDUTtGXcI4i6AqG9/SojWBXyN8+9z5MzlqS31aYExSpTkiePXJjKnE+HHGKv+nWTL5e2pBud/J2efIHqDLD/0N489N190nm6o+JeKPfm/JXp7e6Skpmk9uQLd72bwdvktjuVWDQXZ8syHjgNONjl1nq0ve9kss1LqOos4EFgNtb8xnmqGj3TewiCbL1ee61fb9DxHMpHMflpi2FIr078YMKO3HH62IrcryVTyMTkJqhX3dSs3PbyfM+xKcvEhILk84YqXJnzjTLcIgWFz8g+3owgysXYhm2KrtMpyYwMfnB/JhKvu5g41/aG9naXpR8dMjTn2sUGEIxCbCYmVX2NABOeqp4ZsP064Lq4ZMrcx3+710110aqNDOjeIWtuoXP7WuprUnldWkvJTxsFEeEnPnmYDbm41zkUsrtvCEg56mfzTYm9Xe1J6kATU1hJ/XGXqTDujUFmC4DvHJjJdJYvkqmhdFIp8X0H7rmqlFgpTe+d+FHgMZ3a1XLM7v2yRq/utT9x0SZLxz4Bvup+KUe9E88pEd66/JC813fe6/Gjc5ywDFUiawThMiHls+N78a/o1tjAmYMIItQcRL4RhKsHG8Z7xRkZPDYtdxrv2N365xxniIca8Y/m6n7sKRFm+rheuwe69bWpnI5nPpfpctEmFcTuA7uz8IZjcraHmaQWoEen/L7gTqULYwoIg5+shmgETVJ3apedS9qPv3zT8qXwa5et9KJWSPfgGYjS5yCijkrzrevYzeVpc+jObc8zqZLUBI0gXJ83NzZzyh2ZFQA3nrA7AJ+tzbQf7WpTOfNFlVDtbVJBBBFmJXSYFbfpJCMlTCJVyErVZnC/N3eFdXIJj96uO/8+bzwn75lxpOvZqZ5nLtifHh2tyL7Nqpw3YYes6wpW46+a/52FcUkMOuJXX92tqHUvYQYH3xw3mI71NVx46LCQVzVEIRWQD8L9Pr355k/aM3dFe21KcuZBKmEdbNMK4tKjhnPH6WP57oFWpd/apL6VavR2GR9kpxE432fSyMFpjEwjnxzcCuK8CTumP4/ZzsqWduCw3rSvq8kyOZ1/6FCG9+2apfC7eBLmiDOCIH+PLpyJyX/7yIHdiypLYTozIsLsa47k/EODy7OheFLiP3p0z3Ply0fhICI8NGVR9rYKjCHatIL47oE7cNgufThwmLXgrrGp2XfSzmlEINPoj9+xV85xDo6NO8oLvPUb/t5RjmyG0nA3lu5wGP1sV+bMQrfccxxzTVOz5rxRkUyKUXG5NHoJkW8q0LsulcpWHu7EQr06twu8XjHpWA3lpUb8TUzvudbSeL2XwlKJ19umFYSD86Abm9V32OYe2oWpdI4ffZQX6FUEToNw4piBAPzmxN35zw/2C39BQxZB78J5tY4JyG279+ZM8DMjCfY6CHKVh5tSRhCCZCWwcpur8pmuSg0eaSidVEp83VHd5azYED9h4oeVilEQZEdT9BtB+PUq83qceHqe4WTw3+5c4mtjB2VNLhqiEeStkw7Glv6ewTnF7TLqHRU6bq5q25iCXrm3tJyxz+DcYwoEc7vYdml2l6tS5rkM8VMj/qkDytG0V8IBzSgI3Ak7mn0funvU4G0AhvftwiSP22sxit2rTJwgXSYbXXkIUhCZEYS9wacz4PxvVs0dQbhszELwKMDbi/zmuFwFEYRzz16dLe85d5kwpSPZOF5MXuVfjt5/JUyIRkGQeVmNTc3+yWTEfaz133ndXdvX0d0TAvm4UZaf+dAIeRqCegPVzijVWggKn5zyeJy5RwiOUnEriJzzRUJFYfWe69dABF3Fmz/arSAqsZrWUDwpTzBHB5FMO1Esxs21QrhT/vlpZfe2nP2SuzL3+NEDmX/90ZEURCU8EtoyhfJrpEcBrtfgnYPwNekLbNrazMovtiAioVdS+3UIApPap89xTKEZQYyFKdmkxIqZ5H1NAvz+66P4mj3HWAxmDqJCOA/609UbWbF+M384ZbTvfvDX2n4vKpUSjtqtXwQZQh9qKIKggIaZEaFVhb0rXN3bmp25Bvf5don4z/TF+ddBeL77dUSCGntnxOBMj2WNIIyGSDSOF5PfexKRksxExoupQnh7cz07tQvcH+WFuj1PCmEURHXIOB1Y331NTC4zlLcvmGV+zHMfrw3aV0EEnOvMR/kF4DMWpmSTSQaU/aKcshWm3v/AtW4n69pmBFEZvA/aqzD8TExRO24vXHRA3v1BJiaTTjRe0qODZsfN1b3PNjG55iD8bMmZz8EVNncOIveYN+at5I15K4Ds9+5EGRYfE5MZQSSbGh+l7iZMG79tV/+1LmYOokJ4X1LOknb3V49JIuxLKmQvDJqk9parP5wymgfOHRfyroZChHNzzcxB5NqS85sfHbwL5fzCR0//ZHU6Jo974d5oO5tYjY+CMPoh2aRHEJ7FcGmniBAawnnHE3bKXitlQm1UCO8IwvvKvKF5s/cVdw8vQQXF20M8dvf+jGtjeYHjxHnsze4W2SZjYsoc430ffh5ufuQ7zw/n6AsOGZZuZPyS25sRRLJxylCjp4fg6PgwzYfzju84fWxWqlITaqNC5CiIHJNTHi+m0Pcobr9ZBxEPt35jND89crjPHESGnHUQzbkmpmytEPySvecVKkeOiSlbAeWaK0zpSDZBeTkyK/cLX8M5tbYmxa++ult6eyXmLWPNSd1S8Fv85CZsLzEfxY4gTAexfDzy/X3T60q+PNLyQb/79QWAf4V9e+HnTBi+rStYX+41ix1B5Dt2wYov0ulus+dErP/Gi6nlkHZN9hQeZ3V1mFGAez6qpgyd1SiYEQT5J6UnXnaI/wiiQvWyEnln2wp7bNcjJ+aV82adp5wVFtyjNJrUZ0WsZw4ieB1EYS8mh/eXrs3McfmUPXcualX4yqj+vPbTCYHXM1SPtInJs4imv90ByGdZOHwXKw+5u+i4IwK0aDdXERkkIi+KyBwRmSUi57v2/VBE3re33+jafpmIzLX3HRGXbD6yZn93fe7brb3HU8VzbgQ74OVHD+eh7+4TSTbTQ4yX3JXUuTi9NlXNGUV4y4bzui49ajiH2RUcoHcXr+t0cLlxR4X1W7jnZUivzgzskZvU3s29Z++Vd78hHjJzEJmC85PDh6Xzb3jbnpu/PjL9eXBP6526Xav9vOziJE4TUyPwY1WdKiJdgCki8jzQBzgO2F1VN4vItgAisgtwMjAC6A+8ICLDVNU/QXAZyXVzDf5eysTQuQfsUPggDyaUQrw4FdRvJfWEnaxsa+4QF7lzCZnP7n1d29fRwRXe44+n7BF4Xo5M+K/LCGoPwgRtqxHhX9/dx6QYrTBO2dniSmd8xIi+WWlv3Ry+S9/0Z2dx5+atmXOzRhBlldSf2BSEqi4Bltif14nIHGAA8G3gBlXdbO9bZp9yHPCAvX2BiMwF9gLejEtGB3ed6dW5Pu8cRDq4W9xC2Rj9EC9pE1NaQVhbalOS9hZLueYg3L258w8ZmtUDbGpW3zmD3Qd2o6cnb0OhNRMZE1Nme1BR8HOZ9TKkdyf6detQ8DhDeXFMkkfd8mp6Wz6LRCdXwqrudiZD9+jDPQfRakJtiEgDMBp4CxgG7C8ib4nIyyKyp33YAOAT12mL7G3ea50rIpNFZPLy5cvLIp97hHDUrv3yrotwju1qZxZzhoF/P3vvou+/15BtAveZEN/xEhS+/euutI/uxXRuhX3hYcOyykpjs7LGTh+5euOWdLnxq8b52vSuHerSCst9XNBoctUXW4IvhmW2MMqhOjz97lKfreEmmk/fp4Gz9xvCtw/YPnN8a5mDcBCRzsDDwAWquhZr1NIDGAdcDDwolir0+7k5NUJVb1fVsao6tnfv8mdb+9Z+Q3LMSFmxmOyPuw3sxh2nj+XqL48AYL+hwRnmvNzzLcseXJsSpl15WF77sGPmMMRDjSe+kfN+/Wz/zX5hOV2s2biVp2ZaDcLf3liYqfw+NTlf788du8ddFoNcnu+yPbGCOH508QHhDKXhhElxkzWCcG2/5eRRWce1r6vhimN3yUqDWw6X+yjEqiBEpA5LOdynqo/YmxcBj6jFJKAZ6GVvd2frHggsjlM+B7dWtpKDe/b7+KIDHLZLn8Aw0g6DtslOaQkwys5xLQLdO9bTrjb/NQzxkZ5fyON26Bzz2drNOT2WoF59bSqVLjdRq3FjU8aQ5W4DghwWTouQW8JQWQqZiN19iONG5RhMcnDHnGzRIwh7VHAnMEdVb3Lt+jdwsH3MMKAeWAE8DpwsIu1EZAgwFJgUl3xusuYYUrkRFkuZmM6EcsiUlGLjORnKT3p04B1B+JgB7np9Qc47C2oA6moy5civItfkqd2NzYo6K21dxwXF8zlm99LyChjio74m9z13qs+MCLy5PgqRPYIoUbgw94vx2uOB04CDRWSa/Xc0cBewvYi8CzwAnGGPJmYBDwKzgWeA8yrhwQS5D72cz/3nx+xMz0719OnaPr3N+JEkh3SuB4/NP8i91NuLD+rV19ak8s5BdMiTn6KxqZlFq0JCDBIAABfJSURBVDfknBuU3D4gkrkhAVxz3K5Z378yqj99u7naAvsFh23ss9dBtGA3V1V9jeC28JsB51wHXBeXTEF4fYu9Dz5MxrAgDh/Rl8NH9M3aVomhoSEcmWRAVpfdGTm4X5G78nptyt5O/WG79OH52Z9xwaFDmTh/pXWtiC/8yZlLeGLGkpx7b/XNWFSZhsJQHN71L/vukD1XmS5vRYwgWrSba0vC62sed30z2eOSw8iB1nzQV2z7r9+7d1feu19fmLXP6/30x1P2YPn6zQzo3oFJCz63zo8ok6McvPcOmqQOMlcds1s/np3l50VjqBTedxMU1ifsCKLSk9RGQZCbECjuBx/m8lN+fqivB4ShvAzapiMLbzgmZ3vYHp3XxFRfm0rHUco3BxEW97lBLs9B5fWPp+7hu91QObzvZsOWJs9+6/+mrf6jQy+tJtRGS8KrleN+7mFebM/O7QqGTzCUnyDFcMIeA+nvsh075PNSSSuIUpwcXPKM6N+NHh1zsxQaC1NyKZSzIWrZKDa7ZbEYBUFuxMxSHnzX9oUHZcbE1PJoX5dic2NuLy9fKJR0ZS5lBJEjR+7ktgmfkVy8bUlu6tmI16vwuzYmJrI9SupqUjk9srDuqBMvOySvd4qD6fEll6A4/e1qa3wVRL6ikc+LKSxeOZas2ZR7H1OgEkudx8UsxxEt4rurdLhvoyAga6FafW2ugghLXx8TRD46FFhkZ0gO9bWprIBrDvlyhtdGSEwfRJhGwLi5JhdvUD7v24w6IDBzEFWmNpXr5lpu6mpSXH70cB49b99Y72OIzhvzLNfUByZ9krW9vjbFlgA30yCcxqGUBZFhSqJxc205eE2EUc3NrSncd4tERCqyQrGY0N+G+Jm7bD2Qu96hXUB45nw4CiJoBXQYPt+QPxAfGBNTS2LHbTtnfS9lBNHSV1K3WByt7rwMExGj7RC01qC+CDuOc07QArdJlx/Cg9/JTiDVvi77PoUitUL+sB2GZOGN3Bx10jk73HdZRMqLGUH44Lim1fnEUTG0boLeeVCCl3w452wNCJGxbdf2WelDIdcfPoz5yOiHlkHfrtHmKP1IVTjUhhlB+NC9Qz1gpXI0tC2269nJd7tXQdx+2hggv/mo0AgCcr1cvIRpAoyba8vA7zVFNQ+aUBtVonvHOlZvsJK91NemuOdbezG8X5cqS2WoND86eEde+WA5u3tWLXvnIJz4Whu3BK92z4wgSlAQIRoQMwfRMvAzJ0V9dcbNtUq8/JMJbNiaGe4fMMyVjMjE5W4zOA2295XXBjTkPzpkKJ+t3cSL7+dmN3SutdXHPTZz3fyVPMzgwAwgWgZ+7Xn0hXL5r1dujInJplvHusC0jM7EZZhV0oaWjVPpvBF8a101+Zvjtkt/7t+9A3ef5Z8R0DknnxNTXYFYDGEagUqvrjVEo6O9eNbPpTV6qI3KjiCMggiBM8d4whiTurG1k07w5B1B+OQlL3itEId1bJd/saS3Adlum9z4XMbElGzutfPV+80VRTYxVbgzYBRECJxcAbWmp9bqSY8gPArCPVcQXkFYx63eGOyqWleTYuoVhwXu945k/IqgcXNNNt06WJYHv3cX1RMpawRRgfbI2ExC4HiqmKF862envl04dOdtueDQYVnba4oYQThhOAqFct6mUz11NeLrDhtmjZ2Ybl6icXwU/MpN1CbF3UmtRGsUZ07qQSLyoojMEZFZInK+vf1qEfnUk4bUOecyEZkrIu+LyBFxyRaVPRusxS377dirwJGGlk5dTYr/O2NPdh2Q7cXknkwOW6mjLKAOskV78034udWaEUSyceYwfU1MEa+VSkn6OpUwN8U5gmgEfqyqU0WkCzBFRJ63992sqr91HywiuwAnAyOA/sALIjKsUnmp87FnwzbMuebIUJFaDa2TLBNTDBXTaePPm7ADf3xxXmaHRx9071jHolUbs7b5hQA3JIeena11VQfttG3OvlLKUouepFbVJao61f68DpgDDMhzynHAA6q6WVUXAHMBf/eQKmCUQ9ummEnqKN7RD39vX849YHt+fNhOWdtzMtbZimpwT2uyuq5GzEK5hNOna3vevOxgLj5ip5x9payGbukjiDQi0gCMBt4CxgM/EJHTgclYo4xVWMpjouu0ReRXKAZDxahNuSepc/dfdtTwHJOSM8F89G59C15/1wHdcsxakKtknAbFMSsVEyPKUHmCXOhLaeIrYVqMvXSJSGfgYeACVV0L/BnYARgFLAF+5xzqc3pOH0xEzhWRySIyefny3MVJBkMcZM9B5BbV7xy4A987yD9CbykZBIMGIY5pwjhOtGycorT/0Ojzm4XSmZaDWG8hInVYyuE+VX0EQFU/U9UmVW0G7iBjRloEDHKdPhBY7L2mqt6uqmNVdWzv3r29uw2GWHCbmJ6YkVMsfSnHAnyvickrjzEvtWyc1zvIZ31LISrx7uP0YhLgTmCOqt7k2t7PddjxwLv258eBk0WknYgMAYYCk+KSz2CIgrsCL1y5IdQ56aa9hHqcY2Ky/ztxnowHU8smneK2iHNbeiym8cBpwEwRmWZvuxz4hoiMwqo/C4HvAKjqLBF5EJiN5QF1XhI8mAwGsDyFRKwG+4JDh4Y6p5TK772GF2fuwZiYWjbO243S2DtHtmgFoaqv4V83nspzznXAdXHJZDCUgtNWD+wRzRxQiqfKeM/aG+dSzgjC6IeWjVOmohSRE8cM5IG3P2k9XkwGQ2uiUo3yq5dMCLRNO41DKRPghupTzCjzuuN342fH7GwUhMGQRML29pwJ5qj1+BdfHsHLHyxnYI9c10hHIZgAfa2D5vQIIvz7rEkJXdrXxSRRNkZBGAwh6dW5nhXrt0RunKM25Wfs28AZ+zbkPcaYlloHSc80Y1bZGAwh8SacL0Rz/hh9xWErBjOCaB1oepSZzPdpFITBEJKoldhJNdqutvxhWhzvpYS2K4aIJPU9GgVhMITEmRQMuwBui60g6mvLV80yLo5lu6ShiqS9mKorRiBGQRgMIXFGEE0h43j37doegGF9u8Qmi6Fl4zgyJPV1mklqgyEkaQURcghx2C59ePA7+7BnQ4+yySCeOYiEtiuGkBSzUK6SGAVhMITECZwatLrZi4hEntgOS0LbE0NEmhNuYzImJoMhJBkTU/VkMOsgWhfphY4J9Xc1CsJgCInjORTWxBQnziR1KWE8DNXHGZUGRe2tNkZBGAwhcSKnhjUxxYF4Opyfrt4YeKwh+TgjiCj5yyuJURAGQ0icxrk5AbX5o5Ahxw3JJq3wq1+kfDEKwmAIibPwra6M6xqi8usTduerowcwdnD5PKMM1cMxERoTk8HQwnEmhjvWl39ldFgGbdORm74+ipoaM/fQGhjQ3QrIWExGuUpg3FwNhpBccsRwOrev5djd+1dblMR6vRiiccSIPtx79l6M3yF6TupKYBSEwRCSbh3ruOyonastBmD0Q2tBRNh/aO9qixGIMTEZDC2QJEyUG1o/sSkIERkkIi+KyBwRmSUi53v2/0REVER62d9FRG4VkbkiMkNE9ohLNoOhpWP0g6ESxGliagR+rKpTRaQLMEVEnlfV2SIyCDgM+Nh1/FHAUPtvb+DP9n+DweChf/f21RbB0AaIbQShqktUdar9eR0wBxhg774ZuIRsU+pxwD1qMRHoLiL94pLPYGjJnDV+SLVFMLQBKjIHISINwGjgLRH5MvCpqk73HDYA+MT1fREZheK+1rkiMllEJi9fvjwmiQ2GZFOJhPUGQ+wKQkQ6Aw8DF2CZnX4GXOl3qM+2HEurqt6uqmNVdWzv3smd/TcYKsHXxw6qtgiGVkysbq4iUoelHO5T1UdEZDdgCDDdXkE4EJgqInthjRjcpX0gsDhO+QyGlsy86482meUMsRKnF5MAdwJzVPUmAFWdqarbqmqDqjZgKYU9VHUp8Dhwuu3NNA5Yo6pL4pLPYGjp1KTERHM1xEqcI4jxwGnATBGZZm+7XFWfCjj+KeBoYC6wATgrRtkMBoPBUIDYFISqvkaBPEn2KML5rMB5ccljMBgMhmiYldQGg8Fg8MUoCIPBYDD4YhSEwWAwGHwxCsJgMBgMvkg18+uWiogsBz4CegErqiyOH0au6CRVtiTKlUSZHJIqm5HLYrCqFlxp3KIVhIOITFbVsdWWw4uRKzpJlS2JciVRJoekymbkioYxMRkMBoPBF6MgDAaDweBLa1EQt1dbgACMXNFJqmxJlCuJMjkkVTYjVwRaxRyEwWAwGMpPaxlBGAwGg6HMGAVhMBgMBl9ajIKQhMY1TqpcSSaJzyyJMkFy5UoySX1mSZUrHy1GQQD11RagJSEie4lI12rL0YJocZW3mpjyVRQtrowlXkGIyNEi8gxwi4icVm15HETkSBF5DLhWRBKzwEVEDhSR2cC5QKIqsIh8SUQeAC4VkcHVlgfS5esx4DciclC15XEw5Ss6SSxfkNwyFobEKggRqRWRy4FfAL8HXgWOFpEvVVEmEZH2IvJX4OdYGfM6A2eLSK9qyeUgIu2B84FrVPUcVV1kb696z0VEDgWuAP6KlYfkhyJyjL2v4uVQROpE5HfA1cBfgDXAN0Rk70rL4pLJlK8iSVr5su+buDIWlcQqCFVtBOYDJ6vqM1gpSRdTRVOTWmwCHgMOVNXHgUew3IWTEN9lALBSVR8QkQ4i8lUR6Q3UQNUr8qHAE/a7vA3oAnxLRDqpanOlhVHVrcD7wDdU9Wng/4DuQFOlZXHJZMpX8SSqfEEyy1hUEqUgROQMETnMtekRYIGI1KnqOmAg0LEKcv1IRG4QkZMAVPVRVW2yvz8M7CQi14rIflWS60R701Zggi3Hv4HTsUZfV1dSLo9sJ9mb3gD2FZH2qroM2ITVsFQstayInOjpvf0Vq3zVq+pirEalZ6XkccllylfxsiWmfNlyJbKMFY2qVv0P6AE8BCwBZgA19vaU65j2WIVypwrKJcCFwOvAicAc4Eygj73/IGA3rCHt97F6CL2rJNc59r7fYfVaDrW/72w/012q+MzOAIYBd2ONBF+0P58FXO5+zzHJtC3wMtYI9N/O/TzlqwfwX6CvKV+mfLWWMlbqXyJGEKq6CngOq7BNAa70Oaw70F5V3xeRQSJyQgXkUmAC8HNVfQirYI4EjrT3v6SqM9Uyh83AGt1srJJcu4nI17F6dEOw842r6hys3lVd3HIFyHYRMArruZ0DXAX8VlXPArYAQzRmE4BaPcrHsN7bEuA7zi7XYYOBNaq6VEQGisjBccpky2XKV+myVb182XIlsoyVStUVhMtueY+qrgb+BHxVRAararOI1Nr7twe6iMgFWL2EgrHMS5TLeTaTgf0B1LJvfgDsLCLDPKccgTWsjbUC55HrPWAMsBZrgvMiERkhIlcAuwKL4pQrj2xPYz2zscAOqvqOqj5pHzcGeKtCMv0/YDZWR+QYEemnquoqXwOAGhH5IfAk0LdCcpnyVZpsVS1fHrkSVcbKQTW8R7ImsuweAWpNzqGqbwNPA9fZ3xvtQ8cA+wA7Aseo6l/KLFeNRy6n1zEXSzHtZn9/GegGdBWRehE5TURmYPUOLlXVsk5AFSHX9qp6I/B34Dys5/U1VV1ZTrmKkK2r/ee4/U3CemYPV0ImVd1ql6U3sBq7H9nbnfJ1GPAlrOd1tKreX2a5urnlS1D5iipXJctXFNkqUr7yyVXtMhYLlbJlAXsDdwCX4rKjYimplOfY7YCJwAigD9akzq7A/jHINRa4F8uddgfX9lr7/47ADVhDWWfb48B37M8HAeMTJNf3XcfWxfQui5Xte/bnocAeFZJJsINS2t9rgAOwHCAGkrH3j8O2q5dRphRWo/UE8DfPPmeereLlq0S5Yi1fJcoWZ/nKJ1fVyljcf7GPIESkRkR+hRXO9nVgD+AqEekDlvZVy5TUQUQ629s+Bh4FZgKvYKXHe1dVXy2jXCkR+QOWS9x/gX7A1bYcKbW1vqrOBd7GKpSX2qdvxkp1ilp24tcTJNd851pqudmVjTLIttDe/6GqTq2QTKqqKiLtRKSdqjap6ivALOBd4CURGaqqE1X1hXLI5KBWz3Idlmv2ANuGj4jUqj0SqHT5KoNcsZWvMsi20N5ftvIVUq6qlbHYiVsDYU1efR8YZn8fgGVDbHAdcxWWtt3d/v4NrApyIzH1gu37nAB010yv4x6g3rX/WqzFSg3AcKxeyhSsxig2z4ikypVU2ULI9Aus0UWD/f27wDLg13GWL/teOwP3YZkWHge6JOQ9JlKuJMtWQK6qlbFYf3NMD3IcGYVQ46q87ez//wbG2p93B+4n2ywwDsv7IDa5PNsPBVYDzwO/BXbBGiLeD+zoOq6z81vaglxJla0MMh3q/h6HXGRyrdRhuVyOAG4BfohlG9+vGuUrSXIlWbYi5XK3YbGUsUr/lfuhdseanV+H5enQ2eeYLsB0oL/PvpqYCqFXrk6eFz8Wa9IIrB7K9cB2rvPj6vkmUq6kylYGmSpavux9+wC32J/PBZYD/3HXjUqXr2rLlWTZyiBXLGWsWn/lnoPoBDyLpVk7YbuiedgLmKWqi0Wks4gMBcu7ScvsoZFHrgMgy4Nqsqo+ZR/7FFZD87ktV0rj86NOqlxJla1UmSpavmw+xvK4+SdwCTAVmKuq611yVbR8JUCuJMtWqlwtJoxGGEpWECJyulgRHruq6qdYk9EPYvls7y0i/e3jHF/gHsAnInIW1kTTKMhU8nIRVi4f9sBaDelMiJW1ICZVrqTKlkSZIsrVA2vNzlJgNJZteicR2bktyZVk2ZIqVxIoKie1vZahL5bdrRmYh6Vtz1c7qJiIjAdOAt5W1b+7zr0XOBX4G3Czqs4o9UeUKpdYce33xjJJLAV+rKoftHa5kipbEmUqQq7Jqnqvva2Xa39nrAn0z1u7XEmWLalyJY3IIwgRqbF7+12AT1X1ECwvpc+xNC8AarnmLQSGi0hX+2GCZd87SVXPKrNyKEaubmIF91qLtST+l6r6pTI3KomUK6myJVGmIuXayZark6quEMvdO6Wq68vc0CVSriTLllS5EomGn7ypxeqZ/Ro4EMvV62+u/YIVg+RA17bOWLFb3gY+A/qFvV+F5cqZMG+tciVVtiTKVAa5JrU1uZIsW1LlSvJfqBGEiByI5WvcA2uZ+7VkQv/uBek5hGvIDv17DJZmngbspqpLwtwvLGWUa3FbkCupsiVRpjLJNb0tyZVk2ZIqV+IJqXn3B05zff8T8D2s0MRT7G0pLJveg2QWixwHHBCXdjNytQ7ZkiiTkat1yZZUuZL+F/bhdgTakYmFcirwK/vzNOCH9uexwD8qJryRq1XIlkSZjFytS7akypX0v1AmJlXdoKqbNePjexjWIhGwknLsLCJPAP/A8g2uSPpBI1frkC2JMhm5WpdsSZUr6dQWPiSDWOFtFSvC6uP25nVYWZt2BRao5UeM2uq4Ehi5WodsSZTJyNW6ZEuqXEklqptrM1Y8khXA7rbGvQJoVtXXnAdbBYxcrUO2JMpk5GpdsiVVrmQS1SaFFcSqGXgNOLvaNjIjV+uSLYkyGblal2xJlSuJf5FXUovIQOD/t3f/Kk5EYRjGn1dBGxesrYSthIAuCxZegpWNjaIIYmOt4A0sLCza2XgL2mlvJbaKCoKIV6CogYVt9rM4KYIc0AlJdpTnV+XfDG+K8HKGyXduAI+q6mDQwStkruHGmG2MmcBcixhrtrHmGqOFRm1Ikv5/a9+TWpL0b7AgJEldFoQkqcuCkCR1WRCSpC4LQhogyekkd2ePzyR5dtSZpFXxNldpgCRngRdVNTniKNLKDZrFJIldYDPJG+ATcK6qJkluAVeA47SZPg+BE7Q/ZB0Al6vqW5JN4DFtb+N94E5VfVz/15D+zEtM0jAPgM9VdQG4/9t7E+AacBHYAfaragt4DdycfeYJbbT0NnCPti+BNEquIKTleVlVU2Ca5AfwfPb6O9pguFPAJeDp3CTpk+uPKf0dC0Janvm5Podzzw9pv7VjwPfZ6kMaPS8xScNMgY1FDqyqn8CXJFehbUiT5Pwyw0nLZEFIA1TVV+BVkvfA3gKnuA7cTvIW+EDb81gaJW9zlSR1uYKQJHVZEJKkLgtCktRlQUiSuiwISVKXBSFJ6rIgJEldvwCtN9ORJm8IVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "da[:, 10, 20].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Positional indexing using dimension names - remember it is POSITIONAL, so it won't use longitude equal 20, but the 21th value of longitude\n",
    "\n",
    "isel lookup by integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lon' ()>\n",
       "array(250., dtype=float32)\n",
       "Coordinates:\n",
       "    lon      float32 250.0\n",
       "Attributes:\n",
       "    standard_name:  longitude\n",
       "    long_name:      Longitude\n",
       "    units:          degrees_east\n",
       "    axis:           X"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.lon[20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3c20106f98>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEUCAYAAAAx56EeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXe4HFX5+D/v3pLeSEI6uQESAgGSkACB0EJviggigjRBLKgUBQGlCIKICsLPBnwBBUFEiiAdlA4hJCGFJJQ0ICQhhVRS773v74+Z2Z2dndmd2d3ZnXvv+TzPfe7u1HdnzjnvOe95z/uKqmIwGAwGg5dUtQUwGAwGQzIxCsJgMBgMvhgFYTAYDAZfjIIwGAwGgy9GQRgMBoPBF6MgDAaDweCLURCGsiEiC0Xk0GrLkQRE5K8i8stqy2EwlIJREIaqICIqIjvGcN0zRaRJRNa7/g5y7W8QkRdFZIOIvJdPoYlIOxG5S0TWishSEbmo3PJWChG5WETeFZF1IrJARC727F8oIhtdz+w5z/4L7Wewxn4m7fLc6xD72W6wn/XguH6XIV6MgjC0Rt5U1c6uv5dc+/4BvAP0BH4GPCQivQOuczUwFBgMTAAuEZEj4xM7VgQ4HegBHAn8QERO9hzzJdczOzx9osgRwKXAIUADsD3wC9+biPQCHgGuALYBJgP/LO9PMVQKoyAMsSAie4nImyKyWkSWiMgfRKTe3veKfdh0u7f69QrJNAzYA7hKVTeq6sPATOCEgFNOB65V1VWqOge4AzizyHt/W0TmisjnIvK4iPR37VMR+a6IfCgiq0TkjyIixdwnCFW9UVWnqmqjqr4PPAaMD3n6GcCdqjpLVVcB1xL8HL4KzFLVf6nqJiwlO1JEhpf2CwzVwCgIQ1w0ARcCvYB9sHqf3wdQ1QPsY0bavdWcHqaI7Gcrl6C//fLce7SIrBCRD0TkChGptbePAOar6jrXsdPt7d779wD62/vzHlsIETkY+BVwEtAP+Ah4wHPYscCewEj7uCMCrnVKgeeyXQh5BNgfmOXZdZ+ILBeR50RkpGv7CHKfQx8R6elz+axjVfULYB5FPDdD9aktfIjBEB1VneL6ulBEbgMOBH4f8vzXgO5F3PoVYFesRngElnmjEauB7gys8Ry/Bhjgc53Orv3uY7sUIdOpwF2qOhVARC4DVolIg6outI+5QVVXA6tF5EVgFPCM90Kqej9wfxEyuLkaq3N4t0fGqVimqPOBZ0VkuC2T97k5n7sAKz3X7gws92wr9rkZqowZQRhiQUSGicgT9sTmWuB6rNFErKjqfFVdoKrNqjoTuAY40d69HujqOaUrsI5c1rv2Fzq2EP2xFJYj43qshtWtmJa6Pm8go6DKioj8AMt0doyqbnbJ9Lptdtugqr8CVmONMiD3uTmfg55b2GdsSDhGQRji4s/Ae8BQVe0KXI7VOw2FiOzv8UTy/u1f+CoAqOu+s4DtRcTdmx1JrqkF29a+xN6f99gQLMaa6AZARDphTZJ/GvVCInJqgecSaGISkW9hTzar6qICt/I+N+9z+ExVvaOHnGPt37oDxT03Q5UxCsIQF12AtcB6e4Lye579n2F5w/iiqq96PJG8f6/6nSciR4lIH/vzcCxvmsfsa34ATAOuEpH2InI8sDvwcIAY9wA/F5Ee9rW+DfzVdS8VlwttHu4HzhKRUbZ76PXAWy7zUmhU9b4Cz+Vjv/NE5FT7voep6nzPvu1EZLyI1NvP5WKs0d7r9iH3AGeLyC723MzPcT0HD48Cu4rICSLSHrgSmKGq70X9rYbqYxSEIS5+ApyCZVq4g1xXx6uBv9kTqyeV8b6HADNE5AvgKSyXy+td+08GxgKrgBuAE1V1OaR75+6e7lVYE6wfAS8Dv1HVZ+xjB2KZU2YWEkhV/4ulqB7GGpXsYMtRSX6JNWp52zXa+Iu9rwvWiG8V1qjmSOAoZ4Rg/+YbgRexnsVHWM8GABGZZSsg7Gd5AnCdfb29qfxvNZQJMQmDDIboiMg3gRGqelm1ZTEY4sIoCIPBYDD4YkxMBoPBYPDFKAiDwWAw+GIUhMFgMBh8adErqXv16qUNDQ3VFsNgMBhaFFOmTFmhqkFBKtO0aAXR0NDA5MmTqy2GwWAwtChE5KPCRxkTk8FgMBgCMArCYDAYDL4YBWEwGAwGX4yCMBgMBoMvRkEYDAaDwRejIAwGg8Hgi1EQhlBMWvA5qzdsqbYYBoOhghgFYShIY1MzJ932JmfcNanaohgMhgpiFIShIE12xN/ZS9ZWWRKDwVBJjIIwhEbCZww1GGLlsWmfsmbj1mqL0eoxCsJQkHKmDNmwpZGfPTqT9Zsbc/YtX7eZhkuf5NUPl5fvhoZWx9xl6zn/gWlc9M9p1Ral1WMUhKEgaQVRhgHEblc/x31vfcxtL8/L2Tf9k9UA3P36wtJvZGi1bNraBMCSNZuqLEnrxygIQ0GabQ1RDgNTU7Nm/Xd4+YPlTF+0ugx3MBgM5aJFR3M1VIa0gijjFITXauX2kDJpcNs2I3/xHOO234bbThvru9/pSJhSEj9mBGEoiNPZN5PUhkqwZuNWnp31GQB3vDKfKR+tytr/s0ffBWCzbWoyxIdREIaCxNGjz3dJ0zM0OFz31BxO+PMbvvvmr/gi6/vE+St5csaSSojVZjAmJkNB4rD4qFEDhjJz8u0TAThm92OqLEnrwSgIQ0HimIMAy631rL9OYs+GbbK2mykIQzVRVRav2cSA7h2qLUrVMSYmQ0EycxAWWxqb066GxbJhcxPXPjGbdz9dm+PWavSDwY8vNjfmeL/FwX1vfcz4G/7HDONVZxSEoTCaHkFYKuLwm19m+BXPlHTNeyd+xOPTF/vue+WD5VkTk83NyqQFn5d0P0PLpqlZGXHVs1z52Ls5+95e+HnZFMfnX2zh18+8B8ACzxxHW8QoCENBvCOIhSs3APDOx6v8TygD7onJ21+dz0m3vWlWWCeUN+et5PS7JsXau29sbgbgoSmLsrZPnL+Sr/3lTW54ek5Z7nPsra+yblPuKv+2SmwKQkTai8gkEZkuIrNE5Bf29iEi8paIfCgi/xSRent7O/v7XHt/Q1yyGaLhzEF4vVxf/XBF7PdetGoDd722ADArZ5PKefdP5ZUPlrOqhHDwqsqSNRvz7PffvtQuE698kCmLXvNnU7Py2dpwZWexKWNZxDmC2AwcrKojgVHAkSIyDvg1cLOqDgVWAWfbx58NrFLVHYGb7eMMCcDpGXp7VqkKLIs49KaXWbZuc/w3MlSVO16dzz6/+h9zl63z3b9xi/+cl+M48f5nmfO85s8bn3mPva//L8sjliMpt1dGCyQ2BaEW6+2vdfafAgcDD9nb/wZ8xf58nP0de/8hYt5QIrjlvx/6bq/E69m0tTn2exjKQyml4bW5KwH4ZJX/KGL0tc8DxTkwvPj+MsCaXzBEI9Y5CBGpEZFpwDLgeWAesFpVna7oImCA/XkA8AmAvX8N0NPnmueKyGQRmbx8ubFJV4J/v/Np3v0vvb+Mb98zuULSGJJKOWYgktQjTJIs1SLWdRCq2gSMEpHuwKPAzn6H2f/93kdOmVPV24HbAcaOHWs8Ij00NjXz2LTFHD96AKky2IDWbtpKY8DkY8oeQZx599uAZUeOdVRh3nYiKccbL3a1frnK2wuzP2NxnjmQtkpFFsqp6moReQkYB3QXkVp7lDAQcHwdFwGDgEUiUgt0A4xvY0Ruf3U+Nz7zPgqcOGZg6dd7eX7gvpTA1qaMCahZQZubaWxW2tfV5Bx/31sflSyPoXXjnui+582FBY//0T/e8d2+fnMjnduFb97O8RkBGwN3vF5Mve2RAyLSATgUmAO8CJxoH3YG8Jj9+XH7O/b+/6kJ6xmZFeusCnb9U+Vx+2vK8wpE4Kt/yrijNqvyrb9NDlwj4QRZM7ROylFbL/zn9PTnKx+bVfR1dr3qWR72uMS6w7v84P6p3PTc+0Vfv60Q5xxEP+BFEZkBvA08r6pPAD8FLhKRuVhzDHfax98J9LS3XwRcGqNsrZ5yTcjlq/QpEWZ+uib9vVmVVz7wnxcyur71k7R3/L/3rMlpvyjET8xYwq3/m5v3fBO9OEYTk6rOAEb7bJ8P7OWzfRPwtbjkMYRjn1/9lz2268EfT90DiBZUL1/7cKe9lsHQeilFPYTVLVsaw3u1PTlzCX8k2wXW/97+NzcmJrOS2uBhyZpNPDkzEzL5pfeyRwTukcnrc7MXyjV7KtrGLU1c/fgsvtjcyPRFazC0brzvPwpRGv5y4w0b7mD0g1EQhjyoak7v69onZqc/v/h+tvLwOjvd/cYC/vrGQm5/JXiiOwpL125ir+teMDFyEsZKu9NQSqSNSQvj90eJqr/MCMIoCEMAazZs9Z0kdHstedn1qmezvjc1WTWysbm5qN6YN7bPf6YvZtm6zfx9ovGGSiLNFYi0WgpBCsLogWBMPgiDL9/5+2Qmzi+tV+f0wIq1PDgB2sp1PUO8tL73YlSHGUG0Mso1LF71xdaSzr/pufe5/62PAWvyshi5gqKDmmx0yaSUOYhKUK5yo6qBsaFaG0ZBGHwpVdHc+r+56ciYxY8gsk903A4T3g61WRKvIIJMTAGFPagO3PHqfHa+8hmWrWv9kV+NgjD4ElRp8s1BBFGsf3xjU7IbHEM2SX9bURVYUB/pP9MtL7+lbSA0uFEQBl+CKse85dE9iBQrXWRUGj3KaHOjNaxP2oKstoy7w6CqLF2zKbGJncqV0KgtmTiNgmhFNDVruhENw4l/foOGS5/0bXCDhtfFWJ5UlRfmLIt8ntfE5GSyazvVM/n85aV56c+/+M9sDvzNi5x256QqShTMp6utYHxO+O9CFAoEKAgPTv6EqTFmVqw2RkG0Ii7+13T+PvHj0MdPtvM+r7UTAbkzcQUqiCI0RNFzEAEmJjOASA6fuezwr364gs0RF7wtXPEFK9ZXJiHUD+63Avu98/HqrO1Ri7S7/F3y0IyseGStDePm2ko48Dcv8pHdw46K478eJhxGMY3zM7OWRj8Ja2Gcrww+Y4jnZ3/G87OXcuOJI4u6l6E4iikPK9dvJiVCj071HPTbl6ivqXA/1SN0sQ4Zn64urr61JMwIooUy5LInufGZ99LfoyqHGYsyvSgnYqs3l68f+aK7BrEoIEtYIU667U3f7X6m5G/fM5kHJy/K3WHwZeOWJi5/dCZrN5XmzhyEY7Z8ZOoinnaFbgEY88sX0hniALYU4fhQkmye77MXr/U9Lshc61SBm5/3z7TYmjAKooWiCn9y2X+j8tTMTK/e8e5w21yD9MD8IiapDcnjvrc+4v63PuYPBSKaFiKou/CVP77ODU+/x0UPTud79031Peaa/8z23R4nDZc+yZwl2eFjguRzTFJtGaMg2ijuYbWjDGpcG6sZNeHIEX3z7nfknbloTVWDvLVknE6BNzzGlI8+55DfvcSGLeG8zoI6EtMXreEvL2c6MDMXrclxkb7r9epE+H1hzmclnZ9OgdkGFlobBdFGcZdtx/3PbQqupivp0D6d8+5fuOILlqzZyJf+8BpXPmaSEBWD83q9jdwvn5zDvOVfpHvZ7y9dx8sBOT7sK4W635f+8BrXPVmeJFbVYsOWRnsEYpmk3luaP4x4a8AoiFbOsrWbeO3DFTnb3Q2Dn4mpmoU/VaBr9ub8lcxbZpm63pi3shIitTqcgUOhZ33E71/hjLuy3VZVlcemfUpjU3OkSWr3vFelWLOxfHMsK9eXJwlXS8IoiFbOXtf/l2/e+RYbtjRm+Wu7s2UdfcurBSeoe3dpF5uMXmpShcfuTu5iY2IqkcBHHdzyPz59Mec/MI3bXpmfeJfjcsVM+uTzDXzyeev3WvJi3FxbMZc+PCP9+ccPTufpd5cy+eeH0qtzO9xt8NpNjQy/4hl6dQ5WAoO36cjydZXxV29fV7jfkhn1xC1N6yRoNXCYx+kkjVq2dlOk8BXl1CUi4Vxsy1U+9r/xxfJcqIUR2whCRAaJyIsiMkdEZonI+fb2kSLypojMFJH/iEhX1zmXichcEXlfRI6IS7aWTtj5gQfe/iT9eYad0c3pUfmtEs23YKm+tnKDzQ51NQWPcTdMGvDZEEx6DqLEkNbFuD2Xg6Hb5p+ncihXeI1C3OuTo+T/Xp3PQ1Natut1nLW+Efixqu4MjAPOE5FdgP8DLlXV3YBHgYsB7H0nAyOAI4E/iUjhlqIN4i3zYcJrOGEGVGH4FU9zy3+j+XD/7qTKLUArFOIAwO0Q406RmvCcNYnD+6infhx+nkBEIiUJqoQuuf+cvbO+l0NBTPmocF6UK/6d6yzxyyfn8JN/TS/5/tUkNgWhqktUdar9eR0wBxgA7AS8Yh/2PHCC/fk44AFV3ayqC4C5wF5xydeS8faS//3Op+HPRdm0Nbrdvl+3DpHPyUeX9sHWzTB241Wu3NjulKZJDzmdFJwylE8Vu59x9rmZz0kLuLvvjr2475y9OW/CDgCs2xQ9SKSbXz09hxP+7L9gsy1QEbuBiDQAo4G3gHeBL9u7vgYMsj8PAD5xnbbI3ua91rkiMllEJi9fnsyokXGz1VMrvUHt8pGUHvaOeUwER+/er+D51z3l7zJpFEQ08g3WwoR2jzSCCH1kaYzfsRc797Ms1+61GMVw28vR8qnPW76eg3/7EisrFF8qbmJXECLSGXgYuEBV1wLfwjI3TQG6AE43xa+o5pQpVb1dVceq6tjevXvHJXai+bUrxMY7H6/iZ4+GXwsw4bcvxSBRdGrzeCqFmYNw49YJRj+EI9RzCjE9EcWEs27jVhatKo8n0AWHDsu73ylfGyqc+e22l+cxf8UXPD+7tMV4SSFWBSEidVjK4T5VfQRAVd9T1cNVdQzwD8BR8YvIjCYABgKL45SvpTLd5U9+7r1TqihJcQzapkPeydEoCkLI9sgxCiIc6dXARUxSO+c+MWMxr8/NXWMTxPwVX7Dfr4O9gXbp1zVwn9fDbo/tegDQp2s7/n62Ne+w7w490/trU1bT5s1rHjfOupKkjNRLJU4vJgHuBOao6k2u7dva/1PAz4G/2LseB04WkXYiMgQYCiQzsHyVacmFb9qVh/HsBQfQqZ2lBL5/0A45x7SL6DHlVgrGxBSOoJXU+Xjx/WU8NXMJkxdak7Yr1m9hXRGJoILIJ8vFRwxjW9danG27tOO4Uf257bSx9OlqbR8zuEd6f22NdbFiMiCWguNg0VqSCsW5DmI8cBowU0Sm2dsuB4aKyHn290eAuwFUdZaIPAjMxvKAOk9V20ZmcBfzlq9n2y7t6NK+LvAYt9037rUJd505ljGDtynb9bp3rAcyFWnM4B7cdeZYXvlgBX99YyEAqRAL5YIwCiIa+Z60d3Rx1t1vxypLvlcnSNaq71RKuOXk0envz114ADv0zsxrpUcQFZ5Fd0RsyZ04N7EpCFV9jeDyd0vAOdcB18UlU0vgkN+9zK4DuvLED/cPPKaSjeDBw/vEcl23J9bBw/vw+RfFh0TIHkFk7zv59jdZsX4LL1x0YNHXb42E6eFWuhdc6G6n7r0dv3v+A999w/p0yfrujCDeWlDYRbWcpPs2raSjYkJtJJB3P/WPT++QxN7JnGuOLHjMLSePytnm9Li+NDLbc6lrHjdYN4vXbGL2kszz+q8nUufE+Z8zd9l6Pl65oVWnhoxKuv1y9crdK+9VqXhu116d6/Pu/8HBO4a+Vj4niDCMHNitqPPMHISh6iRxtXCH+sITy9v3ypgAvL+gXW0N0686nJlXHw7ApJ8dyk8Oz++p4oc7hPQcl+I44DcvturUkFHJTFJncK+8dx9TKfJ5Jh29e7+0WbJTiLJWW2KWujCLNX3Ps/+3FlOnURAtkEqFDyg3hcIydOtQl557aV9Xkzc2VBBuu/lRt7wa+fw2Q4FYVkpwOJNysbPHa6m+JsVtp43ha2MGZm0/aKfedG5njShvOXkUT50fbH51KGUEMW77bSjm9Hc+XpWZpG6ZVTQHoyBaIEntnfyogAmgKaLLYaFQ1H6IwNpNWzn7r/FOqLYWgtxcm5o1y0xSqFOy15DojgxepdOhvoYjRvRlp75dPMdlPh83agCDe3YqeO1S6shOfboUVfaOd41Qk1pHo2IURAskqWXPGxK8oWdH7j5zT8ba7od+Hod5/fCL6MUJ8NDkRfz3vWXRT25DfGSHrg4a1TWrZjXghQatd5w2tiR5hvXpnF5d7xXp7P2GRL6eN9pAFJTiOidQ+fzacWMURAukWr0Td53p1619+vNZ4xsAGNIrO3xGj071TBi+bTq/Q9RFS53qozvZNTarCQEegsemWWtQbw0I2qgabX1JTU30h97TnpS+9rgRPHdhxsvM6z11wLDoERPCrn8IMp0VW4a22vlJzAjCUDUWr9lUlfu+cNGBjBzUHciOpTRhp20B2G9oL576UcY+7OS4/vKo/gBZpoEw9eeoXfvy82N2jiTjrMVrsyanDYVZuOKLnG3NqixatTHrez5qIraonepruPLYESy84RhO26cha1852taRA7uHOs5tOhvcs2P6/sWOIDanFURRpwNw12sLaLj0SdZsKF82vGIxCYNaIHFnUdt1QFfe/XQtJ43NnizcoXdnHv3evry14HPqa1O8aqcyddelXfpnJh6dSnbKXttxwh4Dae8XQiNPPUylhHP2355fRsxlPP2TNZGOb+scctPLzLv+6Kxtp905KWuittAcRCpiV/PR88bnrF1wGGV3Qn5z4u7sOqA4d9MO9TUM6N4hHeY+iFWuRthdFDeFCKHvh6NIS1Fy/5j0MQCfrdtEt47BC2YrgRlBtHLyhdX2MvGyQ/jgl0elV6F+fc/tco5JpYR9dujJmME92K1Q5bVrnIjkKIco9adnp/z+8X4yGsIT1Pg3q/9nP6L2uPMpnL2378n0Kw/na2MH5Xg6RcFPORw5om/W9z2veyHnGEV5J0JejOxzM9colXvfzE1CVGmMgkgQsaxv8LnkiR43Qoe+3dpHyhzXtUPpA9AwzcqUKw7j2BAhwB2Mfig/hcJ6R33kXTvk7xnH1XOus8v3Nj6djmLXPrjRMowgPly2HvDPUldpjIJIEHEsb/C75NG79fXZmuEXXx7BqEHdGdE/f++tlLSVw+w5jLBrHaL0UIu1H7cWrn58FrdFzIOwaWt+k0qhSd8oz/y5Cw9gQPfyJqAKS509md6na/ucfeWIkuGcm8TFrMVgFEQFmTh/JQ2XPuk7KQjZhWr1Bv9sXlHxm68o1CiPHNSdf5833n/OwIdi2uOfHjWcf547LrSNOcqooK2bmP76xkJ+9fR7gfv9RgPnP/BO3mtuLjDv5S0Df/nmGN/j7j9n78C5h3KzQ++MU8T4HXtyxj6D04rMr4QcsWv+jlMYnCrcQtey5mAURIzsf+P/uPaJ2envD9sJzN9asNL3eHeZGnXN82WR4dwDts/ZFrbhL5YHv7MPkB2f30tdTYq9tw/e7yVKoz/9k+Lsx20FP9v8s7PyJ7jZ/8bgPA5gmWf+etaevHrJBN679sis0Ntu9gjYHgfDXfMXf/jGHvziuF3zelv1t0c1pbTt5ZikThJGQcTIJ59v5M7XMrGBnEVJQcPxOArVESP68vyFB2Rtc+dbGL1dOHdAP4Lk3WvINrz20wn86OChRV/bS1s3G7UEDtppWwZt05H2dTVpU46XUoPoRcEds8kpP/m8rdySfWlk/1D3+OroAfzz3HHp706VaC3rIIybawVxhvY1PpXk7xM/YupH8UQb9batndqV57U7nhp+VX5gj45luYeD+5F1qKthYwGbeT7+/NI8Vm3YwuVHR1tjkXRefH8Zd7k6JEH84B/5zUnlwK+MP/L9fUsOolesDI5iCNPRUA2vyE4YMzBrwtvRC7cELEAsxKovymNaLhdmBBETb87LNSM5q//9Ks/P//0uj7zzada25mblwbc/KWrdg5O+0aoPmfvdd87eWXMQToH+1Vd3i3yPNBXoFLordim9UFXl18+8x+2vREtGn3Tuf+tjzrr77fTalHyU2wT32Hnjc7bV+SgCJ01opThj34b0Z2cR/+n7NNDRjvnkxamXKQk/Ym1sVo/5s7SRQ9JGHkZBxMRD9nyDG2cEEbbwPT59MZc8PIM/vxTNIwXgvAlW4LzBPTtm9b7H79jL93hvgLSk4XZB7F+CB0y569+Ya5/n5oAkNpXk8kdnVuW+Z41vSK+ud1NJU1IQw/t25brjd+Xo3fqmXbJ36tuF2dccyYAeuWXo+NEDOGXv7bj4iJ0iye+e13hhTmkxwEoZGcdBnDmpB4nIiyIyR0Rmicj59vZRIjJRRKaJyGQR2cveLiJyq4jMFZEZIrJHXLJVAr+FMk15TEx+fG4PN1cV4dF0zO79WHjDMXRpX5duXJ1QAuWikp0d9yPrHGHxn5dy99BWfrElkjnh9lfm8YkdKK9czFu+vqzXi8IlRwz33e4t48VEey0Hp+49mD+dOiZnjYOfpat9XQ3XH78b3TvWR3KKCFufw3BnCBNhJYlzBNEI/FhVdwbGAeeJyC7AjcAvVHUUcKX9HeAoYKj9dy7w5xhlix+fdihoknrjFv9eQ3OBSe2wOOXXr23sYHs0RY2lA+6kM/H3Fp1K+Isvjyipdxqn+2Fzs/J/r85nw5ZG3/3L1m7i+qfe44y7J5X1vvOWVU9BBCWK8jbIQV5N1cJbp7zyeRXIOQERZVW9JqZcFgS4tfsRNshgpYhNQajqElWdan9eB8wBBmC1K47/WTdgsf35OOAetZgIdBeR8MtnE4ZfT9UxMXkbuLWb/INyPWe7HpbaQene0ZpEO8rHz/v3J4/i/EOGsnsRKRYdpVIJB6NMKkf1tW+Hxe+9vD53BQ2XPlkwbk8hnpm1lF8+OYcr/j0r4N7W//Wb/BVIS8MdmDEf3xy3HRcdFj07YJx4e/3eOuZVIEGjVvU518uX/99roeVK2BREZeYgRKQBGA28BVwA/EZEPgF+C1xmHzYAcOc8XGRva5H4vWdnBBF2SDppoZVw3emhrNm4ldcCJiHd0VW9dOtQx7QrD+OSI3PNAX26tufCw4YVFWbgtyeN5Ix9BrNnQ/zmA0e8Zi1tSO+nIO63g6OV6kW2wR4JPjw1d/6pqVnTI4tytwHyQQEVAAAgAElEQVRB725KTF5xDmHDspw2rqEkpR4H3hGzVyF4n2i+UXyh0fe6zcV1CHYpIQ5VuYj9rYlIZ+Bh4AJVXQt8D7hQVQcBFwJ3Oof6nJ5Tl0TkXHvuYvLy5cvLLu8nn29g9DXP8dHK8MNCP/x6As4cxG2vzCt4rBuncH7n3sl88863cvZ371jHJUfslPca3TvWl9VWCjCgewdr8VEFJiSdZ6CqJd1v0oLPc7Y5K9hXrt8c6VpRUppe/NB0Dv7dy5GuH5agp3HCn+POwR1O1SXNMwdyF17mKIg8CuOPp+zBcJdTRzlX7rufVLcC8aoqQawKQkTqsJTDfar6iL35DMD5/C9gL/vzImCQ6/SBZMxPaVT1dlUdq6pje/eOnkjEjw8/W8dTM5cA8Og7n7Jqw1Zu/e/ckq7pVyWcijJx/ueeYwvE2rffkvc8h+r7i8RPKj2C0JJ+75l356YifWrmUgCu/s/snH1BqGpO3ol8cj0yNePCvGbDVu59c2EriNcT7k1s2yV6bvG48fb6C9XBVEp46Lv7cPeZe3LM7v0ysZy0uPm7ILKz+FW/fMTpxSRYo4M5qnqTa9diwEkfdTDguIA8DpxuezONA9ao6pK45HNz2M2v8P37pgKw3h4O+pkJAG585j1en5tt5nn30zVM8/iW+1X+oBDHK9fn91LaEDCJ7VCOKJRJZ9suVoXs3iGah0lceM0Gq77YwtPvLg117pamZq54bBYzP13DB5+tY/JCf8VfTjZuaWLl+s1lU0qDe3bMinWUj54hAzJWEu8o1PtY/KrU2IZtmDB826z9ipZtlX9Ts/KPSRkr++oEJAyKcwQxHjgNONh2aZ0mIkcD3wZ+JyLTgeuxPJYAngLmA3OBO4DvxyibLxu3NBVcQPWnl+Zx6v9lm3mO/X+v8ZU/vp61zXcEEeCgcGYBr5a7X1/IzEW5SXD2sm3/QvlWRyeVs8Y38LuvjeTEMQP55Vd2TW/vUEJcqaZm5Ysi7cOPT8se3J53/1RemJOJZ9TcrDw3a2neBnlrk3L4za9w4l/eLEoGh8/WFc4wuPOVzzDmly8UbQ/38sODh7bojom3k9HLM8pxMtL1t1Pr5pujiJosKYi5Hm+09z9bV54Ll0CcXkyvqaqo6u6qOsr+e8rePkZVR6rq3qo6xT5eVfU8Vd1BVXdT1clxyRbE0rXRU3l6Rw4Oy9dm27PnLFmbnnT2sqLACALgL6/kLpbr1cXyThIR9t2hJzdEXA0dNtR2EqitSXHCmIGkUkKfru05ccxA/njKHiXNR1zy0AxGXPWs776rHnuXyx4JXnzmve9ijwfU/ZM+5tx7p/Ave8GkX1saNmLv1I9X8a/JnwTu/9mj74a6DsCytdHmWYJo6eYxr1nIG0ngK6MH8OJPDmL/oZYZ21vMfnbMzuw9ZBvGbd+zbHNwSTApeUmWa0GV8bMlNlz6JN+9d4rv8W/MXZEzcnBYvCa7wYgyoenHBp+en7jcTEWEk/fKzQAXxEs/OYgXLjqg8IEJ5bdfG8kxu/cr6GJ42rjBgfuCzIgAf3vzI/4x6WP++OJcGi59Mmdtg9eu7u2RfmwvhrvqMcvl1a9sBXUYvHz1T29w8UMzaLj0yYKpPwtR6UZo+5BmqErj7fV3bZ87ITykV6e0Yve+vh237cI/v7MPHetrI68D2riliSVrcl2qjYJIOEEj5mdmWbZl9wrYqx+3bMhBFHrX0z5ZTcOlT+YMKwOv57MtvQ7BtW23Ad1C+Zw39OqUXh/Rkilk5oha6aZ8tCprsdI9by4ELBdjN14PE68C6Gyb/JzQCX526ttejh4PauUXuSOAfGY270R6obhePz8mO4DhKJ8wGmF579ojefaCZHZCwk4sO4flm2eIamLa+cpn2OdX/8sJzJevqKoqDZc+ye0+loQ4MQoiJKqaFRM/X1KWDVsaaQyacLBxbNgvvR8udotf4XE6rcvWZRqN//xwP350SPnCbCedQvU8X8V2Fiz265bJLnbCn9/gZ664Rs75m7dmv09vT95rZnB6zk72vnKZ6/3KQVBobSAnrIfXzdcbR+mc/bPzh/zpVP+IN2HUrhX2O5lNTDlds9vV1nD50f4hR/LxZ0/Wv6DOzMxFazj/gWkA3JAnEVQcJPPtVRB3bzHf8P1/7xVuyDduaaLh0ifZ5cpn+ayArTdqUnO/o02OhMKOlvnagVq7YfWmv3x6ZsYbyXnGh96UvYahyVOZg96FY34otkGasSh7jsuvEcnn1bW1Kfv4v76xMPvcAmLVBimf5FlDIhHVE65QXTt8l+jZ6DZ7AvP5NT8btjTypT+8xuPTrQ5l3Mm+vLR5BeGOutroeUPPz854pawMEafd6/4ahjDXBf9JwSS4eyYdEWH7Xv528DrbNrA+j2ePE37DWza8A8R8CuA/0xcXdFX2Y+rHq/jyH7LnuPwakW/kmXtqX5ddxb0mJj9Ty9l23KGDdurdajshdaHtQtbvL1TViu0A/O2NhayzQ+341fHZi7NNhJVYlOqmdftGhsDtqeIeQdTXpvj2PRlHqjAVpWuElY9OpNawobzzmZgMwbSrTfHMBQcw7OdP5+xzesfrPLGRwnSOc0YQAS9DUR6b9qnvvkIsXZPrVeeXT7q/y0TmxWvi8Xrq+ZXrK47dhZ8cvhN1NZLzbBx6dm7Z81fu2EpeJeomM0mdv7IV01n725sfAfDOx6v4/cmjfZV/TgyoCo/c2vwIwo3b3NSrU3YFaAwRZdFv+N+jo7/SaGyK9qb9zF/vL62+n3SSuNgn3EindrWBMYOcSr+5Mbt3H8aF09tQL/V4pcQV4dYv2mc+x6ZivZ461NdQW5MKbPgOtheMtVTc6UjvPnOvwOPq7N/vHUF6KWU19WK7I+B+V15nAYdKW/batILwDt+25FECYd6/bwTXgDcaNayvX0Wf7rN4ri3jJElyc/Jeg3yOtHCU/tYmzYqw+0UIc5D3XX/uMRW694c1I3rxK3J+5SCfp1Y+BfH0+fsXdLUN6hi35EVyQFbq0955QoE4VoF1ARGXHfwsVn7rjD70WfzmmP3cnVCnU+PtSFZ6/UmbVRBL12zi6Fuz1yZszeMCuH3v4GipDn7vrrlZfUcfhXokXrYW8Ipqq+xcIOKlE6LDD6fxXLNxa2STibfhdU8eDu/bJavRfufj4lJ8+rXB3klnyD+CCCpn+w/tVfDZQeVt3pXCPfmezwvMMdEVGvH7jSC+c0C2R1hzs3LYza/kHOdc2f2u6u37ejuSiRtB2LGRgrthLRS/LG1+lc8hjOL268k1qfKb5973uVe0Bn/txurHZUkiN500Csg0ZGe68hAXYqtnzikK3nftbkhTImWK65/b6PiNCDblSVMZNIIIO/nsd1xS1zZEwT1JnU8J1oQ0MfmFuvmWPdl/iG2OCxrpLbAzArpd49vZ8yLe+1Z6LV3BWqHWmObfFZClovi9rHyNdpgFV0HD/7d9QkwP9MmJm495y0sLP95a8U7iXf3lEQVTq+46wOo5u99XVG8db1Fxz0k0q0ZeoOc3+ezHAp8w9L95NrcD4hC0Hsdp+IpZR5L0/OVhyB5BBDeDjumxqcAIvp1PB6MmJew+sFu6LAS94bW2I4C7g1pfY41Io85Vlpuw3aaJIrJnrJJUGL/3nW8OIkx991ul2tzsb6/ddUD0DG6GXIoxgLSrtSpfVAXhtv96vZj23aFX1veok8N+PVQ/kX70j3eyvn9WIH5YULl1FESh391qTUyu35UvhW3YEUTQnIyI4LTxhToN7jLjmL28Cj7q+qlSCasgJgBvisg8EZkhIjNFZEacgsVNoRGE94WHmRzyUzDehsThvokfF7yeoTDF+On79fbCXMXdSHh7/Lv0z9jzVaObAor1Nvrpw/mrYVCj1GCPsgq1/ymBPRsy+Zpv/vrIaAImFHf9rs2zJsJREGFHeDnnS+bcQmViq88k9b22K6xD4kxMNkcBO2Dlb/gScKz9v8Xi13C7X5C3YoUpH95wDM51fBfAeGLkGIrDTz/4tXk/daVb9VuNGkbPuBtxb4PufsXvf7aORauyQ1wUwq9nGEZpFcpvvcFe3e/l3AN2AAorWBHhX9/dlwk7WVFN/YLatVScjkLganFcmQwjXPe0cYPZb0drRFmTktDmRu86LIA5S7PbiURNUouI0y1aF/DXYvFrtLc2ZrZ5zUVhvIj8RhCqUGUzYqsmvZApa5v17ZrjRqS37dwvYzf3WxgVZiTiHkF4OxjeBv7W/0XLSLjLlc/mLIwL40payNlh+Tr/kC+OCSPsAMyRJYEBR4vm0J37APkVRDoxUITffe1XduXv5+xtny/phr/QNdzzDY7yyjknYSOI++3/U4DJrj/ne4tlvs+kr7uB90br9MZN8SOosoZZZGcojnw9PKcX5z4OMnMQftfJR5OrAnv92YMqv2rGZbEQr3yYnWM9TNvtVlTPX5jrXRQYI0rCzUF4ZWlF+oGbvj6SVy6e4FseHDK/u1gTU8ajLWgk4bwCdyfUmaT2nhJkso6LvKE2VPVY+/8QEdkGGAoEO5a3IC5+KNd26zTwO27bOcfmuLbAUB6CQymXGsPfUBzup+5uCP1GEGHaSacCT5y/kjteXRB4Ly9hK3Ux5cR96aF9uvCnU/egX7f2HP+nN4Dg3+XY1sNOQmd60q2nLLerrWG7Ah5vlDhySqVgS1N+LyanbPqZmNZ6FuhVui0J1bURkXOAl4FngKvt/1fGJ1a8BBVyR0HU1aRytP0lPgrFy+YABRF2zcOFhw7jyBHhokI+/L19Qx3X2qlNCV3a13LtcZk0pE50VnfP3d0O+vXoQ40g7Mr57b/5DJ4DytRrc1eErtTekWYYpeW97dG79WP0dj3SeRyC10FY/90T9sPzuq9Gt8W3Jor93aksE1P+d5Hl5mq/l6BYWJUi7CT1+cCewEeqOgEYDeQNXSoig0TkRRGZIyKzROR8e/s/XTmqF4rINNc5l4nIXBF5X0SOKPI3FSS4IbdeUH2NFDWUCxpBhF3D8K39GkLfa0T/ronN1lVJRISZVx/BKXtnIpr+4ZTR3PqN0QzaJtM7dMcUqvVTECFqQnoOwqfhDiotfhX8rPENvsd6F2oGldMw973zjLFA4YVydVlKtLy2+NZA2sRU5O+uSUlaMRQeQeR6MfmxaWsTe173Ai+4ok3HRVgFsUlVNwGISDtVfQ/IjYyWTSPwY1XdGRgHnCciu6jq150c1cDDwCP2dXcBTgZGAEcCfxKRWIKfB/XonQZ+zcatfPJ5bkrAQuRbRxGGLu3r+PHhw9ILufJRkxJuPXk0YJnEDBm6d6znyyP7Z21zN35+k5JR5iC8jX6vzvXpBmTfHXoGnn/GPoN5+eKDuOpLI3z3e0et379vakGZvBnjHJzfE+S/75iW3AriG3niVmWeTtvSEJliUdzvTkmmsxmkZJx35e4g5Av/sXj1Rpav28wvn5xdlExRCKsgFolId6wV1c+LyGPA4nwnqOoSVZ1qf14HzAEGOPvFmiU7CfiHvek44AFV3ayqC4C5QHCYxRIICqnhKI6FK6O5KDoUSucYhqF9uvDED/enY31+3Vgjkraltya7cFy4TUzv+qSKDeUx5OPJdsSIPrSrrUFRalJCn67+U3T9urXnimN3YXDP4FFfOV+jX6/Ub7+7p3raPg2B1xtij1Z7tII0tVFwovIWPQchkl51X8jE5PZiypcYyAn+WInpiFD5IFT1ePvj1SLyItANax4iFCLSgGWWesu1eX/gM1X90P4+AJjo2r8Il0JxXetc4FyA7bYLTpSSj6D4/FED6Hl55t2lhQ8qE26TiVEPhSkUrz/MXK1jrjl7vyHc+Zo1SV3rmq8Sgj2PRg3q7mvaclPOVbJi3yqoM+T83rApQX982E6M274ne28fPEJqjZRqWqtJZRRD4AjCJ5xH1/Z1dKirSec0d/O1v7xpHx9/zY8czVVVX1bVx1U1VAxjEemMZUq6QFXd4+FvkBk9gH/dynkCqnq7qo5V1bG9e/eOInqaoAdb6gjAyT5WOVpnGIQ4cJuQ/CapV28oHAzR6eFt48oVUpuSrJXTQSORMA1MlEbo3U/XsCxPmI0aH88YN5KegwhXhuprU0zYqWXngCiGKDVsx207M3JgdgidrElq13Z3GUqbmDzvyh0CxJs7HCpjOYg1o5yI1GEph/tU9RHX9lrgq8AY1+GLALcRdCAFzFjlJmqE1TiJ9O7NEKIg7hGCX695wYrCjgSNzc00NjVnrYJ3VsoqVm8zyFIVZjVtviOuOW4EVz42K/392P/3Gp19Iog6FJqDcGitsZbKxQA7qGaYeb4XLjowZ1sq5Z6DyLyL7x64Pdc/9Z51jP0KvMrcXZb8wsNUwsQUWz4Ie47hTmCOqt7k2X0o8J6qLnJtexw4WUTaicgQrDUXk+KQLaiuehWEN5k9UDBSKBCYA7ncRDUTtGXcI4i6AqG9/SojWBXyN8+9z5MzlqS31aYExSpTkiePXJjKnE+HHGKv+nWTL5e2pBud/J2efIHqDLD/0N489N190nm6o+JeKPfm/JXp7e6Skpmk9uQLd72bwdvktjuVWDQXZ8syHjgNONjl1nq0ve9kss1LqOos4EFgNtb8xnmqGj3TewiCbL1ee61fb9DxHMpHMflpi2FIr078YMKO3HH62IrcryVTyMTkJqhX3dSs3PbyfM+xKcvEhILk84YqXJnzjTLcIgWFz8g+3owgysXYhm2KrtMpyYwMfnB/JhKvu5g41/aG9naXpR8dMjTn2sUGEIxCbCYmVX2NABOeqp4ZsP064Lq4ZMrcx3+710110aqNDOjeIWtuoXP7WuprUnldWkvJTxsFEeEnPnmYDbm41zkUsrtvCEg56mfzTYm9Xe1J6kATU1hJ/XGXqTDujUFmC4DvHJjJdJYvkqmhdFIp8X0H7rmqlFgpTe+d+FHgMZ3a1XLM7v2yRq/utT9x0SZLxz4Bvup+KUe9E88pEd66/JC813fe6/Gjc5ywDFUiawThMiHls+N78a/o1tjAmYMIItQcRL4RhKsHG8Z7xRkZPDYtdxrv2N365xxniIca8Y/m6n7sKRFm+rheuwe69bWpnI5nPpfpctEmFcTuA7uz8IZjcraHmaQWoEen/L7gTqULYwoIg5+shmgETVJ3apedS9qPv3zT8qXwa5et9KJWSPfgGYjS5yCijkrzrevYzeVpc+jObc8zqZLUBI0gXJ83NzZzyh2ZFQA3nrA7AJ+tzbQf7WpTOfNFlVDtbVJBBBFmJXSYFbfpJCMlTCJVyErVZnC/N3eFdXIJj96uO/8+bzwn75lxpOvZqZ5nLtifHh2tyL7Nqpw3YYes6wpW46+a/52FcUkMOuJXX92tqHUvYQYH3xw3mI71NVx46LCQVzVEIRWQD8L9Pr355k/aM3dFe21KcuZBKmEdbNMK4tKjhnPH6WP57oFWpd/apL6VavR2GR9kpxE432fSyMFpjEwjnxzcCuK8CTumP4/ZzsqWduCw3rSvq8kyOZ1/6FCG9+2apfC7eBLmiDOCIH+PLpyJyX/7yIHdiypLYTozIsLsa47k/EODy7OheFLiP3p0z3Ply0fhICI8NGVR9rYKjCHatIL47oE7cNgufThwmLXgrrGp2XfSzmlEINPoj9+xV85xDo6NO8oLvPUb/t5RjmyG0nA3lu5wGP1sV+bMQrfccxxzTVOz5rxRkUyKUXG5NHoJkW8q0LsulcpWHu7EQr06twu8XjHpWA3lpUb8TUzvudbSeL2XwlKJ19umFYSD86Abm9V32OYe2oWpdI4ffZQX6FUEToNw4piBAPzmxN35zw/2C39BQxZB78J5tY4JyG279+ZM8DMjCfY6CHKVh5tSRhCCZCWwcpur8pmuSg0eaSidVEp83VHd5azYED9h4oeVilEQZEdT9BtB+PUq83qceHqe4WTw3+5c4mtjB2VNLhqiEeStkw7Glv6ewTnF7TLqHRU6bq5q25iCXrm3tJyxz+DcYwoEc7vYdml2l6tS5rkM8VMj/qkDytG0V8IBzSgI3Ak7mn0funvU4G0AhvftwiSP22sxit2rTJwgXSYbXXkIUhCZEYS9wacz4PxvVs0dQbhszELwKMDbi/zmuFwFEYRzz16dLe85d5kwpSPZOF5MXuVfjt5/JUyIRkGQeVmNTc3+yWTEfaz133ndXdvX0d0TAvm4UZaf+dAIeRqCegPVzijVWggKn5zyeJy5RwiOUnEriJzzRUJFYfWe69dABF3Fmz/arSAqsZrWUDwpTzBHB5FMO1Esxs21QrhT/vlpZfe2nP2SuzL3+NEDmX/90ZEURCU8EtoyhfJrpEcBrtfgnYPwNekLbNrazMovtiAioVdS+3UIApPap89xTKEZQYyFKdmkxIqZ5H1NAvz+66P4mj3HWAxmDqJCOA/609UbWbF+M384ZbTvfvDX2n4vKpUSjtqtXwQZQh9qKIKggIaZEaFVhb0rXN3bmp25Bvf5don4z/TF+ddBeL77dUSCGntnxOBMj2WNIIyGSDSOF5PfexKRksxExoupQnh7cz07tQvcH+WFuj1PCmEURHXIOB1Y331NTC4zlLcvmGV+zHMfrw3aV0EEnOvMR/kF4DMWpmSTSQaU/aKcshWm3v/AtW4n69pmBFEZvA/aqzD8TExRO24vXHRA3v1BJiaTTjRe0qODZsfN1b3PNjG55iD8bMmZz8EVNncOIveYN+at5I15K4Ds9+5EGRYfE5MZQSSbGh+l7iZMG79tV/+1LmYOokJ4X1LOknb3V49JIuxLKmQvDJqk9parP5wymgfOHRfyroZChHNzzcxB5NqS85sfHbwL5fzCR0//ZHU6Jo974d5oO5tYjY+CMPoh2aRHEJ7FcGmniBAawnnHE3bKXitlQm1UCO8IwvvKvKF5s/cVdw8vQQXF20M8dvf+jGtjeYHjxHnsze4W2SZjYsoc430ffh5ufuQ7zw/n6AsOGZZuZPyS25sRRLJxylCjp4fg6PgwzYfzju84fWxWqlITaqNC5CiIHJNTHi+m0Pcobr9ZBxEPt35jND89crjPHESGnHUQzbkmpmytEPySvecVKkeOiSlbAeWaK0zpSDZBeTkyK/cLX8M5tbYmxa++ult6eyXmLWPNSd1S8Fv85CZsLzEfxY4gTAexfDzy/X3T60q+PNLyQb/79QWAf4V9e+HnTBi+rStYX+41ix1B5Dt2wYov0ulus+dErP/Gi6nlkHZN9hQeZ3V1mFGAez6qpgyd1SiYEQT5J6UnXnaI/wiiQvWyEnln2wp7bNcjJ+aV82adp5wVFtyjNJrUZ0WsZw4ieB1EYS8mh/eXrs3McfmUPXcualX4yqj+vPbTCYHXM1SPtInJs4imv90ByGdZOHwXKw+5u+i4IwK0aDdXERkkIi+KyBwRmSUi57v2/VBE3re33+jafpmIzLX3HRGXbD6yZn93fe7brb3HU8VzbgQ74OVHD+eh7+4TSTbTQ4yX3JXUuTi9NlXNGUV4y4bzui49ajiH2RUcoHcXr+t0cLlxR4X1W7jnZUivzgzskZvU3s29Z++Vd78hHjJzEJmC85PDh6Xzb3jbnpu/PjL9eXBP6526Xav9vOziJE4TUyPwY1WdKiJdgCki8jzQBzgO2F1VN4vItgAisgtwMjAC6A+8ICLDVNU/QXAZyXVzDf5eysTQuQfsUPggDyaUQrw4FdRvJfWEnaxsa+4QF7lzCZnP7n1d29fRwRXe44+n7BF4Xo5M+K/LCGoPwgRtqxHhX9/dx6QYrTBO2dniSmd8xIi+WWlv3Ry+S9/0Z2dx5+atmXOzRhBlldSf2BSEqi4Bltif14nIHGAA8G3gBlXdbO9bZp9yHPCAvX2BiMwF9gLejEtGB3ed6dW5Pu8cRDq4W9xC2Rj9EC9pE1NaQVhbalOS9hZLueYg3L258w8ZmtUDbGpW3zmD3Qd2o6cnb0OhNRMZE1Nme1BR8HOZ9TKkdyf6detQ8DhDeXFMkkfd8mp6Wz6LRCdXwqrudiZD9+jDPQfRakJtiEgDMBp4CxgG7C8ib4nIyyKyp33YAOAT12mL7G3ea50rIpNFZPLy5cvLIp97hHDUrv3yrotwju1qZxZzhoF/P3vvou+/15BtAveZEN/xEhS+/euutI/uxXRuhX3hYcOyykpjs7LGTh+5euOWdLnxq8b52vSuHerSCst9XNBoctUXW4IvhmW2MMqhOjz97lKfreEmmk/fp4Gz9xvCtw/YPnN8a5mDcBCRzsDDwAWquhZr1NIDGAdcDDwolir0+7k5NUJVb1fVsao6tnfv8mdb+9Z+Q3LMSFmxmOyPuw3sxh2nj+XqL48AYL+hwRnmvNzzLcseXJsSpl15WF77sGPmMMRDjSe+kfN+/Wz/zX5hOV2s2biVp2ZaDcLf3liYqfw+NTlf788du8ddFoNcnu+yPbGCOH508QHhDKXhhElxkzWCcG2/5eRRWce1r6vhimN3yUqDWw6X+yjEqiBEpA5LOdynqo/YmxcBj6jFJKAZ6GVvd2frHggsjlM+B7dWtpKDe/b7+KIDHLZLn8Aw0g6DtslOaQkwys5xLQLdO9bTrjb/NQzxkZ5fyON26Bzz2drNOT2WoF59bSqVLjdRq3FjU8aQ5W4DghwWTouQW8JQWQqZiN19iONG5RhMcnDHnGzRIwh7VHAnMEdVb3Lt+jdwsH3MMKAeWAE8DpwsIu1EZAgwFJgUl3xusuYYUrkRFkuZmM6EcsiUlGLjORnKT3p04B1B+JgB7np9Qc47C2oA6moy5civItfkqd2NzYo6K21dxwXF8zlm99LyChjio74m9z13qs+MCLy5PgqRPYIoUbgw94vx2uOB04CDRWSa/Xc0cBewvYi8CzwAnGGPJmYBDwKzgWeA8yrhwQS5D72cz/3nx+xMz0719OnaPr3N+JEkh3SuB4/NP8i91NuLD+rV19ak8s5BdMiTn6KxqZlFq0JCDBIAABfJSURBVDfknBuU3D4gkrkhAVxz3K5Z378yqj99u7naAvsFh23ss9dBtGA3V1V9jeC28JsB51wHXBeXTEF4fYu9Dz5MxrAgDh/Rl8NH9M3aVomhoSEcmWRAVpfdGTm4X5G78nptyt5O/WG79OH52Z9xwaFDmTh/pXWtiC/8yZlLeGLGkpx7b/XNWFSZhsJQHN71L/vukD1XmS5vRYwgWrSba0vC62sed30z2eOSw8iB1nzQV2z7r9+7d1feu19fmLXP6/30x1P2YPn6zQzo3oFJCz63zo8ok6McvPcOmqQOMlcds1s/np3l50VjqBTedxMU1ifsCKLSk9RGQZCbECjuBx/m8lN+fqivB4ShvAzapiMLbzgmZ3vYHp3XxFRfm0rHUco3BxEW97lBLs9B5fWPp+7hu91QObzvZsOWJs9+6/+mrf6jQy+tJtRGS8KrleN+7mFebM/O7QqGTzCUnyDFcMIeA+nvsh075PNSSSuIUpwcXPKM6N+NHh1zsxQaC1NyKZSzIWrZKDa7ZbEYBUFuxMxSHnzX9oUHZcbE1PJoX5dic2NuLy9fKJR0ZS5lBJEjR+7ktgmfkVy8bUlu6tmI16vwuzYmJrI9SupqUjk9srDuqBMvOySvd4qD6fEll6A4/e1qa3wVRL6ikc+LKSxeOZas2ZR7H1OgEkudx8UsxxEt4rurdLhvoyAga6FafW2ugghLXx8TRD46FFhkZ0gO9bWprIBrDvlyhtdGSEwfRJhGwLi5JhdvUD7v24w6IDBzEFWmNpXr5lpu6mpSXH70cB49b99Y72OIzhvzLNfUByZ9krW9vjbFlgA30yCcxqGUBZFhSqJxc205eE2EUc3NrSncd4tERCqyQrGY0N+G+Jm7bD2Qu96hXUB45nw4CiJoBXQYPt+QPxAfGBNTS2LHbTtnfS9lBNHSV1K3WByt7rwMExGj7RC01qC+CDuOc07QArdJlx/Cg9/JTiDVvi77PoUitUL+sB2GZOGN3Bx10jk73HdZRMqLGUH44Lim1fnEUTG0boLeeVCCl3w452wNCJGxbdf2WelDIdcfPoz5yOiHlkHfrtHmKP1IVTjUhhlB+NC9Qz1gpXI0tC2269nJd7tXQdx+2hggv/mo0AgCcr1cvIRpAoyba8vA7zVFNQ+aUBtVonvHOlZvsJK91NemuOdbezG8X5cqS2WoND86eEde+WA5u3tWLXvnIJz4Whu3BK92z4wgSlAQIRoQMwfRMvAzJ0V9dcbNtUq8/JMJbNiaGe4fMMyVjMjE5W4zOA2295XXBjTkPzpkKJ+t3cSL7+dmN3SutdXHPTZz3fyVPMzgwAwgWgZ+7Xn0hXL5r1dujInJplvHusC0jM7EZZhV0oaWjVPpvBF8a101+Zvjtkt/7t+9A3ef5Z8R0DknnxNTXYFYDGEagUqvrjVEo6O9eNbPpTV6qI3KjiCMggiBM8d4whiTurG1k07w5B1B+OQlL3itEId1bJd/saS3Adlum9z4XMbElGzutfPV+80VRTYxVbgzYBRECJxcAbWmp9bqSY8gPArCPVcQXkFYx63eGOyqWleTYuoVhwXu945k/IqgcXNNNt06WJYHv3cX1RMpawRRgfbI2ExC4HiqmKF862envl04dOdtueDQYVnba4oYQThhOAqFct6mUz11NeLrDhtmjZ2Ybl6icXwU/MpN1CbF3UmtRGsUZ07qQSLyoojMEZFZInK+vf1qEfnUk4bUOecyEZkrIu+LyBFxyRaVPRusxS377dirwJGGlk5dTYr/O2NPdh2Q7cXknkwOW6mjLKAOskV78034udWaEUSyceYwfU1MEa+VSkn6OpUwN8U5gmgEfqyqU0WkCzBFRJ63992sqr91HywiuwAnAyOA/sALIjKsUnmp87FnwzbMuebIUJFaDa2TLBNTDBXTaePPm7ADf3xxXmaHRx9071jHolUbs7b5hQA3JIeena11VQfttG3OvlLKUouepFbVJao61f68DpgDDMhzynHAA6q6WVUXAHMBf/eQKmCUQ9ummEnqKN7RD39vX849YHt+fNhOWdtzMtbZimpwT2uyuq5GzEK5hNOna3vevOxgLj5ip5x9payGbukjiDQi0gCMBt4CxgM/EJHTgclYo4xVWMpjouu0ReRXKAZDxahNuSepc/dfdtTwHJOSM8F89G59C15/1wHdcsxakKtknAbFMSsVEyPKUHmCXOhLaeIrYVqMvXSJSGfgYeACVV0L/BnYARgFLAF+5xzqc3pOH0xEzhWRySIyefny3MVJBkMcZM9B5BbV7xy4A987yD9CbykZBIMGIY5pwjhOtGycorT/0Ojzm4XSmZaDWG8hInVYyuE+VX0EQFU/U9UmVW0G7iBjRloEDHKdPhBY7L2mqt6uqmNVdWzv3r29uw2GWHCbmJ6YkVMsfSnHAnyvickrjzEvtWyc1zvIZ31LISrx7uP0YhLgTmCOqt7k2t7PddjxwLv258eBk0WknYgMAYYCk+KSz2CIgrsCL1y5IdQ56aa9hHqcY2Ky/ztxnowHU8smneK2iHNbeiym8cBpwEwRmWZvuxz4hoiMwqo/C4HvAKjqLBF5EJiN5QF1XhI8mAwGsDyFRKwG+4JDh4Y6p5TK772GF2fuwZiYWjbO243S2DtHtmgFoaqv4V83nspzznXAdXHJZDCUgtNWD+wRzRxQiqfKeM/aG+dSzgjC6IeWjVOmohSRE8cM5IG3P2k9XkwGQ2uiUo3yq5dMCLRNO41DKRPghupTzCjzuuN342fH7GwUhMGQRML29pwJ5qj1+BdfHsHLHyxnYI9c10hHIZgAfa2D5vQIIvz7rEkJXdrXxSRRNkZBGAwh6dW5nhXrt0RunKM25Wfs28AZ+zbkPcaYlloHSc80Y1bZGAwh8SacL0Rz/hh9xWErBjOCaB1oepSZzPdpFITBEJKoldhJNdqutvxhWhzvpYS2K4aIJPU9GgVhMITEmRQMuwBui60g6mvLV80yLo5lu6ShiqS9mKorRiBGQRgMIXFGEE0h43j37doegGF9u8Qmi6Fl4zgyJPV1mklqgyEkaQURcghx2C59ePA7+7BnQ4+yySCeOYiEtiuGkBSzUK6SGAVhMITECZwatLrZi4hEntgOS0LbE0NEmhNuYzImJoMhJBkTU/VkMOsgWhfphY4J9Xc1CsJgCInjORTWxBQnziR1KWE8DNXHGZUGRe2tNkZBGAwhcSKnhjUxxYF4Opyfrt4YeKwh+TgjiCj5yyuJURAGQ0icxrk5AbX5o5Ahxw3JJq3wq1+kfDEKwmAIibPwra6M6xqi8usTduerowcwdnD5PKMM1cMxERoTk8HQwnEmhjvWl39ldFgGbdORm74+ipoaM/fQGhjQ3QrIWExGuUpg3FwNhpBccsRwOrev5djd+1dblMR6vRiiccSIPtx79l6M3yF6TupKYBSEwRCSbh3ruOyonastBmD0Q2tBRNh/aO9qixGIMTEZDC2QJEyUG1o/sSkIERkkIi+KyBwRmSUi53v2/0REVER62d9FRG4VkbkiMkNE9ohLNoOhpWP0g6ESxGliagR+rKpTRaQLMEVEnlfV2SIyCDgM+Nh1/FHAUPtvb+DP9n+DweChf/f21RbB0AaIbQShqktUdar9eR0wBxhg774ZuIRsU+pxwD1qMRHoLiL94pLPYGjJnDV+SLVFMLQBKjIHISINwGjgLRH5MvCpqk73HDYA+MT1fREZheK+1rkiMllEJi9fvjwmiQ2GZFOJhPUGQ+wKQkQ6Aw8DF2CZnX4GXOl3qM+2HEurqt6uqmNVdWzv3smd/TcYKsHXxw6qtgiGVkysbq4iUoelHO5T1UdEZDdgCDDdXkE4EJgqInthjRjcpX0gsDhO+QyGlsy86482meUMsRKnF5MAdwJzVPUmAFWdqarbqmqDqjZgKYU9VHUp8Dhwuu3NNA5Yo6pL4pLPYGjp1KTERHM1xEqcI4jxwGnATBGZZm+7XFWfCjj+KeBoYC6wATgrRtkMBoPBUIDYFISqvkaBPEn2KML5rMB5ccljMBgMhmiYldQGg8Fg8MUoCIPBYDD4YhSEwWAwGHwxCsJgMBgMvkg18+uWiogsBz4CegErqiyOH0au6CRVtiTKlUSZHJIqm5HLYrCqFlxp3KIVhIOITFbVsdWWw4uRKzpJlS2JciVRJoekymbkioYxMRkMBoPBF6MgDAaDweBLa1EQt1dbgACMXNFJqmxJlCuJMjkkVTYjVwRaxRyEwWAwGMpPaxlBGAwGg6HMGAVhMBgMBl9ajIKQhMY1TqpcSSaJzyyJMkFy5UoySX1mSZUrHy1GQQD11RagJSEie4lI12rL0YJocZW3mpjyVRQtrowlXkGIyNEi8gxwi4icVm15HETkSBF5DLhWRBKzwEVEDhSR2cC5QKIqsIh8SUQeAC4VkcHVlgfS5esx4DciclC15XEw5Ss6SSxfkNwyFobEKggRqRWRy4FfAL8HXgWOFpEvVVEmEZH2IvJX4OdYGfM6A2eLSK9qyeUgIu2B84FrVPUcVV1kb696z0VEDgWuAP6KlYfkhyJyjL2v4uVQROpE5HfA1cBfgDXAN0Rk70rL4pLJlK8iSVr5su+buDIWlcQqCFVtBOYDJ6vqM1gpSRdTRVOTWmwCHgMOVNXHgUew3IWTEN9lALBSVR8QkQ4i8lUR6Q3UQNUr8qHAE/a7vA3oAnxLRDqpanOlhVHVrcD7wDdU9Wng/4DuQFOlZXHJZMpX8SSqfEEyy1hUEqUgROQMETnMtekRYIGI1KnqOmAg0LEKcv1IRG4QkZMAVPVRVW2yvz8M7CQi14rIflWS60R701Zggi3Hv4HTsUZfV1dSLo9sJ9mb3gD2FZH2qroM2ITVsFQstayInOjpvf0Vq3zVq+pirEalZ6XkccllylfxsiWmfNlyJbKMFY2qVv0P6AE8BCwBZgA19vaU65j2WIVypwrKJcCFwOvAicAc4Eygj73/IGA3rCHt97F6CL2rJNc59r7fYfVaDrW/72w/012q+MzOAIYBd2ONBF+0P58FXO5+zzHJtC3wMtYI9N/O/TzlqwfwX6CvKV+mfLWWMlbqXyJGEKq6CngOq7BNAa70Oaw70F5V3xeRQSJyQgXkUmAC8HNVfQirYI4EjrT3v6SqM9Uyh83AGt1srJJcu4nI17F6dEOw842r6hys3lVd3HIFyHYRMArruZ0DXAX8VlXPArYAQzRmE4BaPcrHsN7bEuA7zi7XYYOBNaq6VEQGisjBccpky2XKV+myVb182XIlsoyVStUVhMtueY+qrgb+BHxVRAararOI1Nr7twe6iMgFWL2EgrHMS5TLeTaTgf0B1LJvfgDsLCLDPKccgTWsjbUC55HrPWAMsBZrgvMiERkhIlcAuwKL4pQrj2xPYz2zscAOqvqOqj5pHzcGeKtCMv0/YDZWR+QYEemnquoqXwOAGhH5IfAk0LdCcpnyVZpsVS1fHrkSVcbKQTW8R7ImsuweAWpNzqGqbwNPA9fZ3xvtQ8cA+wA7Aseo6l/KLFeNRy6n1zEXSzHtZn9/GegGdBWRehE5TURmYPUOLlXVsk5AFSHX9qp6I/B34Dys5/U1VV1ZTrmKkK2r/ee4/U3CemYPV0ImVd1ql6U3sBq7H9nbnfJ1GPAlrOd1tKreX2a5urnlS1D5iipXJctXFNkqUr7yyVXtMhYLlbJlAXsDdwCX4rKjYimplOfY7YCJwAigD9akzq7A/jHINRa4F8uddgfX9lr7/47ADVhDWWfb48B37M8HAeMTJNf3XcfWxfQui5Xte/bnocAeFZJJsINS2t9rgAOwHCAGkrH3j8O2q5dRphRWo/UE8DfPPmeereLlq0S5Yi1fJcoWZ/nKJ1fVyljcf7GPIESkRkR+hRXO9nVgD+AqEekDlvZVy5TUQUQ629s+Bh4FZgKvYKXHe1dVXy2jXCkR+QOWS9x/gX7A1bYcKbW1vqrOBd7GKpSX2qdvxkp1ilp24tcTJNd851pqudmVjTLIttDe/6GqTq2QTKqqKiLtRKSdqjap6ivALOBd4CURGaqqE1X1hXLI5KBWz3Idlmv2ANuGj4jUqj0SqHT5KoNcsZWvMsi20N5ftvIVUq6qlbHYiVsDYU1efR8YZn8fgGVDbHAdcxWWtt3d/v4NrApyIzH1gu37nAB010yv4x6g3rX/WqzFSg3AcKxeyhSsxig2z4ikypVU2ULI9Aus0UWD/f27wDLg13GWL/teOwP3YZkWHge6JOQ9JlKuJMtWQK6qlbFYf3NMD3IcGYVQ46q87ez//wbG2p93B+4n2ywwDsv7IDa5PNsPBVYDzwO/BXbBGiLeD+zoOq6z81vaglxJla0MMh3q/h6HXGRyrdRhuVyOAG4BfohlG9+vGuUrSXIlWbYi5XK3YbGUsUr/lfuhdseanV+H5enQ2eeYLsB0oL/PvpqYCqFXrk6eFz8Wa9IIrB7K9cB2rvPj6vkmUq6kylYGmSpavux9+wC32J/PBZYD/3HXjUqXr2rLlWTZyiBXLGWsWn/lnoPoBDyLpVk7YbuiedgLmKWqi0Wks4gMBcu7ScvsoZFHrgMgy4Nqsqo+ZR/7FFZD87ktV0rj86NOqlxJla1UmSpavmw+xvK4+SdwCTAVmKuq611yVbR8JUCuJMtWqlwtJoxGGEpWECJyulgRHruq6qdYk9EPYvls7y0i/e3jHF/gHsAnInIW1kTTKMhU8nIRVi4f9sBaDelMiJW1ICZVrqTKlkSZIsrVA2vNzlJgNJZteicR2bktyZVk2ZIqVxIoKie1vZahL5bdrRmYh6Vtz1c7qJiIjAdOAt5W1b+7zr0XOBX4G3Czqs4o9UeUKpdYce33xjJJLAV+rKoftHa5kipbEmUqQq7Jqnqvva2Xa39nrAn0z1u7XEmWLalyJY3IIwgRqbF7+12AT1X1ECwvpc+xNC8AarnmLQSGi0hX+2GCZd87SVXPKrNyKEaubmIF91qLtST+l6r6pTI3KomUK6myJVGmIuXayZark6quEMvdO6Wq68vc0CVSriTLllS5EomGn7ypxeqZ/Ro4EMvV62+u/YIVg+RA17bOWLFb3gY+A/qFvV+F5cqZMG+tciVVtiTKVAa5JrU1uZIsW1LlSvJfqBGEiByI5WvcA2uZ+7VkQv/uBek5hGvIDv17DJZmngbspqpLwtwvLGWUa3FbkCupsiVRpjLJNb0tyZVk2ZIqV+IJqXn3B05zff8T8D2s0MRT7G0pLJveg2QWixwHHBCXdjNytQ7ZkiiTkat1yZZUuZL+F/bhdgTakYmFcirwK/vzNOCH9uexwD8qJryRq1XIlkSZjFytS7akypX0v1AmJlXdoKqbNePjexjWIhGwknLsLCJPAP/A8g2uSPpBI1frkC2JMhm5WpdsSZUr6dQWPiSDWOFtFSvC6uP25nVYWZt2BRao5UeM2uq4Ehi5WodsSZTJyNW6ZEuqXEklqptrM1Y8khXA7rbGvQJoVtXXnAdbBYxcrUO2JMpk5GpdsiVVrmQS1SaFFcSqGXgNOLvaNjIjV+uSLYkyGblal2xJlSuJf5FXUovIQOD/t3f/Kk5EYRjGn1dBGxesrYSthIAuCxZegpWNjaIIYmOt4A0sLCza2XgL2mlvJbaKCoKIV6CogYVt9rM4KYIc0AlJdpTnV+XfDG+K8HKGyXduAI+q6mDQwStkruHGmG2MmcBcixhrtrHmGqOFRm1Ikv5/a9+TWpL0b7AgJEldFoQkqcuCkCR1WRCSpC4LQhogyekkd2ePzyR5dtSZpFXxNldpgCRngRdVNTniKNLKDZrFJIldYDPJG+ATcK6qJkluAVeA47SZPg+BE7Q/ZB0Al6vqW5JN4DFtb+N94E5VfVz/15D+zEtM0jAPgM9VdQG4/9t7E+AacBHYAfaragt4DdycfeYJbbT0NnCPti+BNEquIKTleVlVU2Ca5AfwfPb6O9pguFPAJeDp3CTpk+uPKf0dC0Janvm5Podzzw9pv7VjwPfZ6kMaPS8xScNMgY1FDqyqn8CXJFehbUiT5Pwyw0nLZEFIA1TVV+BVkvfA3gKnuA7cTvIW+EDb81gaJW9zlSR1uYKQJHVZEJKkLgtCktRlQUiSuiwISVKXBSFJ6rIgJEldvwCtN9ORJm8IVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da.isel(lon=20, lat=10 ).plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More interesting is Label-based indexing - you don't need to know the position\n",
    "\n",
    "sel does lookup by label (label can be any datatype, in our case we have datetime64 and lat/lon that are float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(da.lat.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3c200ee940>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da.sel(lat=50., lon=250.).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you select a point, nearest neighbor is easily done\n",
    "\n",
    "Nearest neighbor lookups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3c201b1eb8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "da.sel(lat=52.25, lon=251.8998, method='nearest').plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing by integer array indices (I use isel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'lat' ()>\n",
      "array(75., dtype=float32)\n",
      "Coordinates:\n",
      "    lat      float32 75.0\n",
      "Attributes:\n",
      "    standard_name:  latitude\n",
      "    long_name:      Latitude\n",
      "    units:          degrees_north\n",
      "    axis:           Y\n",
      "<xarray.DataArray 'lon' ()>\n",
      "array(200., dtype=float32)\n",
      "Coordinates:\n",
      "    lon      float32 200.0\n",
      "Attributes:\n",
      "    standard_name:  longitude\n",
      "    long_name:      Longitude\n",
      "    units:          degrees_east\n",
      "    axis:           X\n",
      "<xarray.DataArray 'time' (time: 3)>\n",
      "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
      "       '2013-01-01T12:00:00.000000000'], dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
      "Attributes:\n",
      "    standard_name:  time\n",
      "    long_name:      Time\n"
     ]
    }
   ],
   "source": [
    "print(da.lat[0])\n",
    "print(da.lon[0])\n",
    "print(da.time[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### slice() allows you to - ahem - slice the data. It has a different behavious whether you use it in isel, or sel, inherited from Panads and Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In isel() it's not inclusive of the last value (similarly to numpy indexing:\n",
    "\n",
    "array[0:3] \n",
    "\n",
    "won't include the fourth [position 3] value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'lat' (lat: 2)>\n",
      "array([75. , 72.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 75.0 72.5\n",
      "Attributes:\n",
      "    standard_name:  latitude\n",
      "    long_name:      Latitude\n",
      "    units:          degrees_north\n",
      "    axis:           Y\n",
      "<xarray.DataArray 'lon' ()>\n",
      "array(200., dtype=float32)\n",
      "Coordinates:\n",
      "    lon      float32 200.0\n",
      "Attributes:\n",
      "    standard_name:  longitude\n",
      "    long_name:      Longitude\n",
      "    units:          degrees_east\n",
      "    axis:           X\n",
      "<xarray.DataArray 'time' (time: 2)>\n",
      "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000'],\n",
      "      dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00\n",
      "Attributes:\n",
      "    standard_name:  time\n",
      "    long_name:      Time\n"
     ]
    }
   ],
   "source": [
    "print( da.lat[0:2])\n",
    "print( da.lon[0])\n",
    "print( da.time[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2, lat: 2)>\n",
       "array([[241.2    , 243.79999],\n",
       "       [242.09999, 243.59999]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5\n",
       "    lon      float32 200.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.isel(lat=slice(0,2), lon=0, time=slice(0, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using sel, instead, slice it is inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2, lat: 2)>\n",
       "array([[241.2    , 243.79999],\n",
       "       [242.09999, 243.59999]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5\n",
       "    lon      float32 200.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index by dimension coordinate labels\n",
    "da.sel(lat=slice(75,71), lon=200, time=slice('2013-01-01', '2013-01-01T06:00:00'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note how the slicing along latitude and time included whatever is in between and equal to the start and end of the slicing. \n",
    "\n",
    "Also note, latitude is ordered in the opposite way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lat' (lat: 25)>\n",
       "array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
       "       15. ], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "Attributes:\n",
       "    standard_name:  latitude\n",
       "    long_name:      Latitude\n",
       "    units:          degrees_north\n",
       "    axis:           Y"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.lat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of this, slicing without taking this into account will give me an empty latitude dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2, lat: 0)>\n",
       "array([], shape=(2, 0), dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 \n",
       "    lon      float32 200.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.sel(lat=slice(71,75), lon=200, time=slice('2013-01-01', '2013-01-01T06:00:00'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method Nearest doesn't work when slice() is used, however you can always split up the selection if you need to use the method=nearest for one of the dimesnnions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2)>\n",
       "array([250. , 253.2], dtype=float32)\n",
       "Coordinates:\n",
       "    lat      float32 70.0\n",
       "    lon      float32 200.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.sel(lat=71, lon=199, method='nearest').sel(time=slice('2013-01-01', '2013-01-01T06:00:00'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop\n",
    "\n",
    "it is used usually to drop a variable altogether, it can also be used to drop a dimension\n",
    "\n",
    "it works for both dataset and dataarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (time: 4, space: 3)>\n",
       "array([[0.559998, 0.26724 , 0.743241],\n",
       "       [0.044651, 0.390271, 0.07231 ],\n",
       "       [0.957574, 0.552451, 0.657987],\n",
       "       [0.798716, 0.72818 , 0.454766]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
       "  * space    (space) <U2 'IA' 'IL' 'IN'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = xr.DataArray(np.random.rand(4, 3),\n",
    "                  [('time', pd.date_range('2000-01-01', periods=4)),\n",
    "                     ('space', ['IA', 'IL', 'IN'])])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (time: 4, space: 1)>\n",
       "array([[0.559998],\n",
       "       [0.044651],\n",
       "       [0.957574],\n",
       "       [0.798716]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
       "  * space    (space) <U2 'IA'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.drop(['IN', 'IL'], dim='space')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (space: 3, time: 4)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
       "  * space    (space) <U2 'IA' 'IL' 'IN'\n",
       "Data variables:\n",
       "    foo      (time, space) float64 0.56 0.2672 0.7432 0.04465 0.3903 0.07231 ..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsarr = arr.to_dataset(name='foo')\n",
    "dsarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (space: 1, time: 4)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
       "  * space    (space) <U2 'IA'\n",
       "Data variables:\n",
       "    foo      (time, space) float64 0.56 0.04465 0.9576 0.7987"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsarr.drop(['IN', 'IL'], dim='space')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Masking with where\n",
    "\n",
    "As mentioned before, where \"masks\" the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3bf73c3be0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds\n",
    "ds.air.mean(dim=['lat','lon']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3bf7353ba8>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.air.mean(dim=['lat','lon']).plot()\n",
    "ds.air.mean(dim=['lat','lon']).where(ds.time.dt.month==4).plot()\n",
    "\n",
    "#da.sel(lat=75, lon=200, time=slice('2013-01-01', '2013-01-01T06:00:00'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bf72f64e0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsmeantime = ds.air.mean(dim=['time'])\n",
    "dsmeantime.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bf72391d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsmeantime.where(dsmeantime.lat>40).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-190068777084>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdsmeantime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsmeantime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0mdsmeantime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0mdsmeantime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m220\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0mdsmeantime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1736\u001b[0m             variable = (f(self.variable, other_variable)\n\u001b[1;32m   1737\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreflexive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1738\u001b[0;31m                         else f(other_variable, self.variable))\n\u001b[0m\u001b[1;32m   1739\u001b[0m             \u001b[0mcoords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_merge_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother_coords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1740\u001b[0m             \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1582\u001b[0m                 new_data = (f(self_data, other_data)\n\u001b[1;32m   1583\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreflexive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1584\u001b[0;31m                             else f(other_data, self_data))\n\u001b[0m\u001b[1;32m   1585\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1586\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
     ]
    }
   ],
   "source": [
    "dsmeantime.where(dsmeantime.lat>40&dsmeantime.lat<60&dsmeantime.lon>220&dsmeantime.lon<300).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bf6ee8860>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsmeantime.where((dsmeantime.lat>40)&(dsmeantime.lat<60)&(dsmeantime.lon>220)&(dsmeantime.lon<300)).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorized indexing\n",
    " you can supply DataArray() objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (points: 3)>\n",
       "array([52, 60, 75])\n",
       "Dimensions without coordinates: points"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate a coordinates for a transect of points\n",
    "lat_points = xr.DataArray([52, 60, 75], dims='points')\n",
    "lon_points = xr.DataArray([250, 250, 250], dims='points')\n",
    "lat_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nearest neighbor selection along the transect, in this case the order doesn't matter, these are points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2920, points: 3)>\n",
       "array([[269.5    , 256.19998, 246.5    ],\n",
       "       [269.29   , 261.6    , 244.     ],\n",
       "       [273.69998, 262.19998, 242.2    ],\n",
       "       ...,\n",
       "       [267.49   , 263.29   , 246.68999],\n",
       "       [269.29   , 263.59   , 244.39   ],\n",
       "       [268.69   , 259.29   , 242.79   ]], dtype=float32)\n",
       "Coordinates:\n",
       "    lat      (points) float32 52.5 60.0 75.0\n",
       "    lon      (points) float32 250.0 250.0 250.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
       "Dimensions without coordinates: points\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.sel(lat=lat_points, lon=lon_points, method='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is much more to this but I am still exploring the pros and cons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assign values to a dataarray\n",
    "\n",
    "this theme is not well described in the help page, and I will try and update that. In the meanwhile below find some examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3, y: 4)>\n",
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2\n",
       "  * y        (y) <U1 'a' 'b' 'c' 'd'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "davi = xr.DataArray(np.arange(12).reshape((3, 4)), dims=['x', 'y'],\n",
    "                coords={'x': [0, 1, 2], 'y': ['a', 'b', 'c', 'd']})\n",
    "\n",
    "davi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can use numpy like assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3, y: 4)>\n",
       "array([[-1, -1, -1, -1],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2\n",
       "  * y        (y) <U1 'a' 'b' 'c' 'd'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# davi[0,:,:]= -1\n",
    "davi[0]= -1\n",
    "davi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3, y: 4)>\n",
       "array([[-1, -2, -1, -1],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2\n",
       "  * y        (y) <U1 'a' 'b' 'c' 'd'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "davi[0,1]= -2\n",
    "davi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if you want to select an area? and assign a value to it?\n",
    "\n",
    "After some researching I have found that there are two ways to do it.\n",
    "\n",
    "First, let's see what doesn't work:\n",
    "\n",
    "1) using where() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "can't assign to function call (<ipython-input-42-c4212b6122b8>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-42-c4212b6122b8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    davi.where((davi.x==2)&(davi.y=='b'))=100\u001b[0m\n\u001b[0m                                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to function call\n"
     ]
    }
   ],
   "source": [
    "davi.where((davi.x==2)&(davi.y=='b'))=100\n",
    "davi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) using isel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "can't assign to function call (<ipython-input-43-60621bb11f0c>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-43-60621bb11f0c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    davi.isel(x=0)=100\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to function call\n"
     ]
    }
   ],
   "source": [
    "davi.isel(x=0)=100\n",
    "davi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) using sel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "can't assign to function call (<ipython-input-44-974e6debed29>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-974e6debed29>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    davi.sel(x=2, y='c') =2000\u001b[0m\n\u001b[0m                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to function call\n"
     ]
    }
   ],
   "source": [
    "davi.sel(x=2, y='c') =2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) in some cases it will fail silently (chain indexing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "dafs = xr.DataArray([10, 11, 12, 13], dims=['x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3)>\n",
       "array([10, 11, 12])\n",
       "Dimensions without coordinates: x"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dafs.isel(x=[0, 1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(11)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dafs.isel(x=[0, 1, 2])[1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dafs.isel(x=[0, 1, 2])[1] +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So - what does it work?\n",
    "\n",
    "you have two options, one using loc()+dictionary of the values you want to select and assign values to, or \n",
    "\n",
    "xr.where() - this xr.where() is different from dataarray.where(), http://xarray.pydata.org/en/stable/generated/xarray.where.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 244.0 244.09999 ...\n",
       "    empty    (lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add an empty 2D dataarray\n",
    "ds['empty']= xr.full_like(ds.air.mean('time'),fill_value=0)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bf6e2d978>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#modify one grid point, using where() or loc()\n",
    "ds['empty'] = xr.where((ds.coords['lat']==20)&(ds.coords['lon']==260), 100, ds['empty'])\n",
    "ds.empty.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lat': 30, 'lon': 260}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(lon=260, lat=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bf6d5cac8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEKCAYAAAD6q1UVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGSRJREFUeJzt3X2wJXWd3/H3hxlgBByHYYCMwC64xfqwVnhwgmw0RkHlYRVICrMQ41JKalKsYdXNRjEma23ibmTdktXKZs0t0Z2tIA87SKA2CsIIErfi4PAMDoQBEcYZGXnGVZGZ+8kf3Vd6Dvfe6Xvn9Dndtz+vqq57+ne67/mevnW/53d+/XuQbSIiol/2GHcAERExekn+ERE9lOQfEdFDSf4RET2U5B8R0UNJ/hERPZTkHxHREElfkrRN0j2VsuWSrpf0QPlz/7Jckj4vaZOkuyQd22RsSf4REc35K+DkgbILgHW2jwTWlfsApwBHlttq4C+bDCzJPyKiIbZvBp4cKD4dWFM+XgOcUSn/axe+AyyTtLKp2BY39YuHaS/t7SXsO+4wIqIDnuOpx20fON/zT3rbvn7iyR21jr31rufvBX5eKZqwPbGL0w62vRXA9lZJB5XlhwCPVo7bXJZtrRXMHHUi+S9hX96oE8cdRkR0wA1e+4PdOf+JJ3dwy3W/UuvYRSsf+LntVbvzehWapqyx+Xc6kfwjIkbFwCSTTb7EY5JWlrX+lcC2snwzcFjluEOBLU0FkTb/iIgKY17wjlrbPF0DnFM+Pge4ulL+O2Wvn+OBZ6aah5qQmn9ExIBh1fwlXQq8FVghaTPwSeDTwBWSzgUeAd5THv414FRgE/BT4P1DCWIGSf4RERXG7BjSVPe2z57hqZfcxHQxv/4Hh/LCNST5R0QMmGzuPmtrJPlHRFQY2JHkHxHRP6n5R0T0jIEXerC8bZJ/RESFcZp9IiJ6x7Bj4ef+JP+IiKpihO/Cl+QfEbETsWPaaXYWliT/iIiK4oZvkn9ERK8U/fyT/CMiemcyNf+IiH5JzT8iooeM2NGD2e4be4eSXi3pjsr2rKQPz7RyfUREW0xatbYuayz5277f9tG2jwbeQDE/9VXMvHJ9RMTYGfELL6q1ddmovtucCDxo+wfMvHJ9RMTYFYO89qi1ddmo2vzPAi4tH8+0cv1OJK0GVgMsYZ+RBBkRAbnhOxSS9gJOAz4+l/NsTwATAEu1vAczbUREG9hih7tdq69jFO/wFOA224+V+4+VK9YzsHJ9REQrTKJaW5eNIvmfzYtNPjDzyvUREWNX3PBdXGvrskajl7QP8A7g31SKZ1q5PiJi7KZu+C50jSZ/2z8FDhgoe4JpVq6PiGiLHR3vw19Ht7+3REQMWV9G+Cb5R0QMmOxBb58k/4iIimJityT/iIheMeKFjk/dUEeSf0REhU0vBnkl+UdE7KT7A7jqSPKPiKgwqflHRPRSbvhGRPSM6f5CLXUk+UdEVBh4oePz9tSx8N9hRMScKPP5R0T0jckI34iIXupDzX/hf7xFRMyBLSa9R62tDkkfkXSvpHskXSppiaQjJK2X9ICky8sVD0cqyT8ioqK44buo1rYrkg4Bfg9YZfv1wCKKNc0vBC6yfSTwFHBuc+9oekn+ERE7KdbwrbPVtBh4maTFwD7AVuAEYG35/BrgjKG/jRpBRUREqbjhW7vNf4WkDZX9CdsTv/xd9g8l/RnFqoU/A74B3Ao8bXt7edhm4JDdDnyOkvwjIgbMYYTv47ZXzfSkpP2B04EjgKeBvwFOmeZQzzXG3ZXkHxFRMeQRvm8Hvm/7xwCSvgr8Y2CZpMVl7f9QYMuwXrCutPlHRAyYZI9aWw2PAMdL2keSKNYv/x5wI3Bmecw5wNWNvJFZpOYfEVFhwwuTw6kX214vaS1wG7AduB2YAP43cJmkT5VlFw/lBecgyT8ioqJo9hleo4jtTwKfHCh+CDhuaC8yD0n+ERED+jDCN8k/IqJijl09OyvJPyJiJ8Nt9mmrJP+IiAFZw3c3SVoGfBF4PcW3qQ8A9wOXA4cDDwP/wvZTTcYREVFX0dtn1/P2dF3T320+B1xr+zXAUcBG4AJgXTmh0bpyPyKiFaYGedXZuqyx5C9pKfAWyv6rtn9h+2mKoc5rysPGMqFRRMRsJlGtrcuarPm/Cvgx8GVJt0v6oqR9gYNtbwUofx403cmSVkvaIGnDCzzfYJgRES+a6u2Tmv/8LQaOBf7S9jHA3zOHJh7bE7ZX2V61J3s3FWNExEsMczGXtmoy+s3AZtvry/21FB8Gj0laCVD+3NZgDBERc2KL7d6j1tZljUVv+0fAo5JeXRZNTWh0DcVERjCmCY0iImbTh2afpvv5nw9cUq5P+RDwfooPnCsknUsx4917Go4hIqK2jPAdAtt3ANMtdHBik68bEbE7kvwjInpmyIu5tFaSf0TEgK734a8jyT8iosKG7UNazKXNkvwjIgak2SciomfS5h8R0VNO8o+I6J/c8I2I6Bk7bf4RET0kdqS3T0RE/6TNPyKiZzK3T0REH7lo91/okvwjIgakt09ERM84N3wjIvopzT4RET2U3j4RET1jJ/lHRPRSunpGRPRQ2vwjInrGiMn09omI6J8eVPxZ+B9vERFzUd7wrbPVIWmZpLWS7pO0UdJvSlou6XpJD5Q/92/4Xb1Ekn9ExCDX3Or5HHCt7dcARwEbgQuAdbaPBNaV+yOV5B8RMWBYNX9JS4G3ABcXv9e/sP00cDqwpjxsDXBGQ29lRmnzj4ioMDA5Wbur5wpJGyr7E7YnKvuvAn4MfFnSUcCtwIeAg21vBbC9VdJBux/53CT5R0RUGajfz/9x26tmeX4xcCxwvu31kj7HGJp4ptNos4+khyXdLemOqU/HNtzoiIiYjV1vq2EzsNn2+nJ/LcWHwWOSVgKUP7c18T5mM4o2/7fZPrry6Tj2Gx0REbMa0g1f2z8CHpX06rLoROB7wDXAOWXZOcDVwwu+nnE0+5wOvLV8vAa4CfjYGOKIiJhG/W6cNZ0PXCJpL+Ah4P0UFe8rJJ0LPAK8Z5gvWEfTyd/ANyQZ+B/ljZBaNzokrQZWAyxhn4bDjIioGOIoL9t3ANPdFzhxeK8yd00n/zfZ3lIm+Osl3Vf3xPKDYgJgqZb3YcBdRLSBwfV7+3RWo23+treUP7cBVwHH0YIbHRERs1PNrbsaS/6S9pX08qnHwDuBe2jBjY6IiFkNd4RvKzXZ7HMwcJWkqdf5iu1rJX2XMd/oiIiYVUcSu6R3AV+zPTnXcxtL/rYfopjHYrD8CcZ8oyMiYkZzG+Q1bmcBn5N0JfBl2xvrnpi5fSIiBgxxkFfDcfpfAccAD1JMIfF/Ja2eanKfTZJ/RMSgSdXbWsD2s8CVwGXASuCfAbdJOn+285L8IyIGyPW2cZN0mqSrgG8CewLH2T6Fosn9D2Y7NxO7RURUdasnz5nARbZvrhba/qmkD8x2Ymr+ERE7UXHDt842flsHE7+kCwFsr5vtxCT/iIhB3enn/45pyk6pc2KafSIiBs251/xoSToP+F3g1yTdVXnq5cDf1fkdtWr+U18jdlUWEdF5U/38293s8xXg3RQzJLy7sr2h7P65S3Wbfeb91SIiomva3tvH9jO2H7Z9NnAAxVT5pwGH1/0dsyZ/SedJuht4taS7Ktv3gbtmOzciorM60uYv6T9RrItyALCCYqDXf6xz7q7a/L8CfB34r+y84tZztp+cR6wRETE8/xI4xvbPASR9GrgN+NSuTpw1+dt+BngGOLv8xQcBS4D9JO1n+5HdDDwionXaMICrpocpcvLPy/29KaZ62KVavX0kvRv4LPBKivn3fxXYCPzGHAONiGg305qpG2p4HrhX0vUUkb8D+LakzwPY/r2ZTqzb1fNTwPHADbaPkfQ2ym8DERELTndq/leV25Sb6p5YN/m/YPsJSXtI2sP2jenqGRELVVeafWyvme+5dZP/05L2A26mWIV+G7B9vi8aEdFqHUn+5WIu/4WiKX4xxdqStr10V+fW7ed/OvAz4CPAtRQ3FN49r2gjItquI109gT+nWA73ANtLbb+8TuKHmjV/239f2Z3314yIiLYb9wCuOXoUuMee+9IysyZ/Sc8x/edb7a8WERGd053ePh8FvibpWxQ9fwCw/dldnbirfv67XAosImKh6VDN/4+Bn1D09d9rLidmVs+IiEHdSf7Lbb9zPidmPv+IiKqak7q15NvBDZKS/CMihqI7vX0+CHxd0s8kPSvpOUnP1jkxzT4REQPU8sVcKl4BvBc4wvZ/lvQrwMo6J6bmHxHRXX9BMfXO1HQ7zwH/rc6JjSd/SYsk3S7pb8v9IyStl/SApMslzekOdURE47rT7PNG2x+knNXT9lPU7PUzipr/hyhmAJ1yIXCR7SOBp4BzRxBDREQ93brh+4KkRZQfRZIOpOYKxI0mf0mHAr8FfLHcF3ACsLY8ZA1wRpMxRETMWXdq/p+nmNXzIEl/DHwb+JM6JzZ9w/fPKUagTQ0WOwB42vbUpHCbgUOmO1HSamA1wBL2aTjMiIiKdiT2XbJ9iaRbgRMpZl44w/bGXZwGNJj8y9nmttm+VdJbp4qnOXTay2x7ApgAWKrlHflTRETXiU719sH2fcB9cz2vyZr/m4DTJJ1KMfR4KcU3gWWSFpe1/0OBLQ3GEBExNw2055ft8huAH9p+l6QjgMuA5RRr7r7P9i+G+6qza6zN3/bHbR9q+3DgLOCbtt8L3AicWR52DnB1UzFERMzL8Nv8W9fxZRz9/D8G/L6kTRT3AC4eQwwRETMbYvJva8eXkYzwtX0T5dqSth8CjhvF60ZEzMccmn1WSNpQ2Z8o71dWzbvjS5MyvUNExKD6yf9x26tmenJ3O740Kck/IqLKQ+3t09qOL5nbJyJi0JDa/Nvc8SXJPyJiwAimdxh7x5c0+0REDGqgBb5tHV+S/CMiqtozb0+jkvwjIipEa2bsbFSSf0TEgCT/iIg+SvKPiOihJP+IiJ5pzypdjUryj4gYlOQfEdE/XVrMZb6S/CMiBqTZJyKibzLIKyKip5L8IyL6JSN8IyJ6SpMLP/sn+UdEVKXNPyKin9LsExHRR0n+ERH9k5p/REQfJflHRPSMM71DxMhct+XOl5Sd9MqjxhBJ9F36+UdE9JUXfvbfo6lfLGmJpFsk3SnpXkl/VJYfIWm9pAckXS5pr6ZiiIiYD7ne1mWNJX/geeAE20cBRwMnSzoeuBC4yPaRwFPAuQ3GEBExN57D1mGNJX8XflLu7lluBk4A1pbla4AzmoohImI+NFlv67Ima/5IWiTpDmAbcD3wIPC07e3lIZuBQ2Y4d7WkDZI2vMDzTYYZEbGTJP/dZHuH7aOBQ4HjgNdOd9gM507YXmV71Z7s3WSYEREvMsUN3zpbh42kt4/tpyXdBBwPLJO0uKz9HwpsGUUMERF1df1mbh1N9vY5UNKy8vHLgLcDG4EbgTPLw84Brm4qhoiIeenBDd8ma/4rgTWSFlF8yFxh+28lfQ+4TNKngNuBixuMIToiA7qiLTLIazfZvgs4Zpryhyja/yMi2sfOYi4REb208HN/kn9ExKA0+0RE9I2BNPtERPTQws/9zQ7yiojoomFN7CbpMEk3StpYTnD5obJ8uaTrywkur5e0f9PvaVCSf0TEAE261lbDduDf2X4txSDXD0p6HXABsK6c4HJduT9SSf4REVVDnNXT9lbbt5WPn6MY6HoIcDrFxJYwpgku0+YfEVFRDPKq3ei/QtKGyv6E7Ylpf690OMXYp/XAwba3QvEBIemgeQc8T0n+ERGD6s/Y+bjtVbs6SNJ+wJXAh20/K2k3ghuONPtERAyQXWur9bukPSkS/yW2v1oWPyZpZfn8Sopp70cqyT8iomqIbf4qqvgXAxttf7by1DUUE1vCmCa4TLNPRMROhjq3z5uA9wF3lwtbAfwH4NPAFZLOBR4B3jOsF6wryT8iYtCQFmqx/W2Ke8jTOXEoLzJPSf4REVXu/hKNdST5R0QM6vgSjXUk+UdEDFr4uT/JPyJikCYXfrtPkn9ERJWZyyCvzkryj4ioEPUHcHVZkn9ExKAk/4iIHkryj4jombT5R0T0U3r7RET0jtPsEzEq12258yVlJ73yqDFEEr1nkvwjInpp4bf6NDeff5tXrY+ImM0wF3NpqyYXc2ntqvUREbOy620d1ljyb/Oq9RERM7Jhx2S9rcNG0uY/n1XrJa0GVgMsYZ9RhBkRUeh4rb6OxpP/fFettz0BTAAs1fKF/5eIiPboQfJvdAH3tq5aHxExIwOTrrd1WJO9fVq7an1ExMwMnqy3dViTzT6tXbU+2icDuqI1TOdv5tbRWPJv86r1ERGz6kGbf0b4RkQMSvKPiOib7g/gqiPJPyKiykCmdI6I6KHU/CMi+sbp7RMR0TsGd7wPfx1J/hERgzo+ereOJP+IiEFp84+I6Bk7vX0iInopNf+IiL4x3rFj3EE0Lsk/IqJqakrnBS7JPyJiUA+6eja6mEtERNcY8KRrbXVIOlnS/ZI2Sbqg2ejrS/KPiKjy8BZzkbQI+AvgFOB1wNmSXtfwO6glzT4REQOGeMP3OGCT7YcAJF0GnA58b1gvMF+dSP7P8dTjN3jtD4AVwOPjjmcX2h5j2+OD9seY+HZfkzH+6u6c/BxPXXeD166oefgSSRsq+xO2Jyr7hwCPVvY3A2/cnfiGpRPJ3/aBAJI22F417nhm0/YY2x4ftD/GxLf72hyj7ZOH+OumW82wFV2J0uYfEdGczcBhlf1DgS1jimUnSf4REc35LnCkpCMk7QWcBVwz5piAjjT7VEzs+pCxa3uMbY8P2h9j4tt9XYhxt9neLunfAtcBi4Av2b53zGEBIPdgDouIiNhZmn0iInooyT8ioodalfwlHSbpRkkbJd0r6UNl+XJJ10t6oPy5f1kuSZ8vh03fJenYMcX3GUn3lTFcJWlZ5ZyPl/HdL+mkJuObLcbK838gyZJWlPutuIblc+eX1+leSX9aKR/ZNZzlb3y0pO9IukPSBknHleUjvX7lay6RdIukO8sY/6gsP0LS+vL/5PLyBiOS9i73N5XPHz6m+C4p/4b3SPqSpD3L8pFfwwBst2YDVgLHlo9fDvw/iiHRfwpcUJZfAFxYPj4V+DpFX9rjgfVjiu+dwOKy/MJKfK8D7gT2Bo4AHgQWjSPGcv8wihtPPwBWtOwavg24Adi7fO6gcVzDWeL7BnBK5ZrdNI7rV76mgP3Kx3sC68vXvgI4qyz/AnBe+fh3gS+Uj88CLh9TfKeWzwm4tBLfyK9hNrer5m97q+3bysfPARspRsidDqwpD1sDnFE+Ph34axe+AyyTtHLU8dn+hu3t5WHfoejLOxXfZbaft/19YBPFcO/GzHINAS4CPsrOg0xacQ2B84BP236+fG5bJb6RXcNZ4jOwtDzsFbzYV3uk16+My7Z/Uu7uWW4GTgDWluWD/ydT/z9rgRMlTTf4qNH4bH+tfM7ALez8fzLSaxgta/apKr+aHkNRazjY9lYo/jmBg8rDphs6fQgjMBBf1QcoajEwxvhg5xglnQb80PadA4e15Rr+OvBPymaJb0n6Ry2L78PAZyQ9CvwZ8PFxxidpkaQ7gG3A9RTfiJ6uVEKqcfwyxvL5Z4ADRhmf7fWV5/YE3gdcOxjfNLFHQ1qZ/CXtB1wJfNj2s7MdOk1Z431XZ4pP0ieA7cAl44yvjOWXMZYxfQL4w+kOnaZsHNdwMbA/xdf+fw9cUdZO2xLfecBHbB8GfAS4eOrQccRne4ftoylqz8cBr50ljpHHOBifpNdXnv7vwM22/8+44osWJv+yVnAlcIntr5bFj019DSx/TjUJjHzo9AzxIekc4F3Ae8uvtWOJb4YYf42ivfxOSQ+Xcdwm6R+MI8YZruFm4KvlV/9bgEmKyb/aEt85wNTjv+HFpqexDt+3/TRwE8WH5jJJUwM3q3H8Msby+VcAT444vpPL1/8kcCDw+5XDWjsFwkLWquRf1vQuBjba/mzlqWso/vkof15dKf+dsrfA8cAzU81Do4xP0snAx4DTbP90IO6zyt4WRwBHUrR1Nma6GG3fbfsg24fbPpzin+1Y2z+iJdcQ+F8UbdZI+nVgL4pZH0d6DWeJbwvwT8vHJwAPlI9Hev3KGA9U2aNM0suAt1Pcm7gROLM8bPD/ZOr/50zgm5UKyqjiu0/SvwZOAs62d5oMf+TXMGhdb583U3zduwu4o9xOpWifXEfxD7cOWF4eL4qFEh4E7gZWjSm+TRRtllNlX6ic84kyvvspe4uMI8aBYx7mxd4+bbmGewH/E7gHuA04YRzXcJb43gzcStHzaD3whnFcv/I1/yFwexnjPcAfluWvovhg3ETx7WSq59SScn9T+fyrxhTf9vI6TV3XqfKRX8NszvQOERF91Kpmn4iIGI0k/4iIHkryj4jooST/iIgeSvKPiOihJP9oNUk/2fVRETFXSf4RET2U5B+dUI7+/Ew5F/zdkn67LH+rpJskrVWxpsIlTc5YGbFQdG0B9+ivfw4cDRxFMefPdyXdXD53DPAbFFMw/B3wJuDb4wgyoitS84+ueDNwqYvZIh8DvgVMTft8i+3NLuaLuQM4fEwxRnRGkn90xWxNOc9XHu8g32gjdinJP7riZuC3y0VCDgTeQsMzpEYsZKkhRVdcBfwmxayaBj5q+0eSXjPesCK6KbN6RkT0UJp9IiJ6KMk/IqKHkvwjInooyT8iooeS/CMieijJPyKih5L8IyJ66P8DKESj/cuot3MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['empty'].loc[dict(lon=260, lat=30)] = 100\n",
    "ds.empty.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modify an area with where() and a mask \n",
    "mask = (ds.coords['lat']>20)&(ds.coords['lat']<60)&(ds.coords['lon']>220)&(ds.coords['lon']<260)\n",
    "ds['empty'] = xr.where(mask, 100, ds['empty'])\n",
    "ds.empty.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modify an area with loc()\n",
    "lc = ds.coords['lon']\n",
    "la = ds.coords['lat']\n",
    "ds['empty'].loc[dict(lon=lc[(lc>290)&(lc<300)], lat=la[(la>40)&(la<60)])] = 100\n",
    "ds.empty.plot()"
   ]
  }
 ],
 "metadata": {
  "gist_info": {
   "gist_id": null,
   "gist_url": null
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}