{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additive Bias, Multiplicative Bias and Percent Bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Bias measures how well the mean forecast and mean observation correspond to each other. It can tell us whether there is an over or under-forecast tendency and informs how a forecast system could be easily recalibrated.\n",
    "\n",
    "`scores` has multiplicative bias, additive bias and percent bias implementations for use on continuous forecasts.\n",
    "\n",
    "Additive bias is often called the \"mean error\". Since it does not tell us the average *magnitude* (i.e. average of the absolute value of the error) of the error, it is possible for there to be little or no bias even when there are large positive and negative errors.\n",
    "\n",
    "Multiplicative bias is well suited for forecasts and observations that have 0 as an upper or lower bound (e.g., significant wave height, or wind magnitude) - or to causes of error which are multiplicative in nature. This could be useful for a forecaster who wants a simple method to bias correct a wind speed forecast as wind speeds can never be negative.\n",
    "\n",
    "Percent bias is used for evaluating and comparing forecast accuracy across stations or datasets with varying magnitudes. By expressing the error as a percentage of the observed value, it allows for standardised comparisons, enabling assessment of forecast performance regardless of the absolute scale of values. Consider three stations with average streamflow values of 5, 25, and 50 m³/s. Assuming an additive bias of 1 m³/s for each station, the calculated percent bias values are 20%, 4%, and 2%, respectively. Assuming a threshold of 5% for acceptable performance, the first station (flow value of 5) is considered less accurate, while the other stations demonstrate acceptable performance.\n",
    "\n",
    "**Note:** In this tutorial we use the forecast and analysis grids that are downloaded or derived in `First_Data_Fetching.ipynb`. Please run through this tutorial first to fetch data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from scores.continuous import additive_bias, multiplicative_bias, pbias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcst = xr.open_dataset(\"forecast_grid.nc\")\n",
    "obs = xr.open_dataset(\"analysis_grid.nc\")\n",
    "\n",
    "# Let's select the forecast for the same timestamp as the analysis\n",
    "fcst = fcst.sel(time=obs.time.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additive bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x138f3d2e0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate additive bias and preserve the \"lat\" dimension\n",
    "bias = additive_bias(fcst.temp_scrn, obs.temp_scrn, preserve_dims=\"lat\")\n",
    "bias.name = \"additive bias\"\n",
    "bias.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how biases become larger towards the poles, but are relatively unbiased in the tropics.\n",
    "\n",
    "## Multiplicative bias\n",
    "\n",
    "Let's imagine that our data has a lower bound at zero and we want to calculate the multiplicative bias. To avoid downloading more data, we will convert the temperature data to be degrees Celcius and clip the data to have a minimum value of zero. Readers may wish to explore the data available from the NCI server and download a wind grid instead for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x138fd7fe0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert to Celcius and clip data to have a minimum value of zero\n",
    "fcst_clipped = (fcst.temp_scrn - 273.15).clip(min=0)\n",
    "obs_clipped = (obs.temp_scrn - 273.15).clip(min=0)\n",
    "\n",
    "bias = multiplicative_bias(fcst_clipped, obs_clipped, preserve_dims=\"lat\")\n",
    "bias.name = \"multiplicative bias\"\n",
    "bias.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's worth noting that for a latitude slice, if the mean forecast and mean observation is zero, then the multiplicative bias for that latitude slice will be `NaN`. If the mean forecast is greater than zero and the mean observation is zero, then the multiplicative bias for that slice is infinite. \n",
    "\n",
    "We can see below in our dataset that we have `np.inf` values, and `np.nan` values close to the poles where values are negative (remember that this data is temperature data converted to degrees Celisus and clipped to have a minimum value of zero). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;multiplicative bias&#x27; ()&gt; Size: 8B\n",
       "array(inf)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'multiplicative bias'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-297bcce9-0a8f-41a7-b5a3-a1b94c74c715' class='xr-array-in' type='checkbox' checked><label for='section-297bcce9-0a8f-41a7-b5a3-a1b94c74c715' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>inf</span></div><div class='xr-array-data'><pre>array(inf)</pre></div></div></li><li class='xr-section-item'><input id='section-b268520a-e839-4139-b2ea-beb15f2a8002' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b268520a-e839-4139-b2ea-beb15f2a8002' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-107d1bb0-3494-4a47-bf56-3c961ff71bb6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-107d1bb0-3494-4a47-bf56-3c961ff71bb6' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7ef60907-14d0-4a4c-bd63-648a28426906' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7ef60907-14d0-4a4c-bd63-648a28426906' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'multiplicative bias' ()> Size: 8B\n",
       "array(inf)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bias.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;multiplicative bias&#x27; (lat: 1536)&gt; Size: 6kB\n",
       "array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 12kB 89.94 89.82 89.71 89.59 ... -89.71 -89.82 -89.94</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'multiplicative bias'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1536</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-953e1517-68de-4d58-83e8-36db451e72d5' class='xr-array-in' type='checkbox' checked><label for='section-953e1517-68de-4d58-83e8-36db451e72d5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-2f2409a3-952d-4376-a8a1-f22124163927' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f2409a3-952d-4376-a8a1-f22124163927' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.94 89.82 89.71 ... -89.82 -89.94</div><input id='attrs-81bedb19-c91d-45d3-b9cf-e8e69bcc1706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81bedb19-c91d-45d3-b9cf-e8e69bcc1706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-592db138-b013-4465-bcaa-ac3aaf23b6fc' class='xr-var-data-in' type='checkbox'><label for='data-592db138-b013-4465-bcaa-ac3aaf23b6fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitudes</dd><dt><span>type :</span></dt><dd>uniform</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 89.941406,  89.824219,  89.707031, ..., -89.707031, -89.824219,\n",
       "       -89.941406])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a86bdeed-1056-438d-8ff6-f0d0d3523ed9' class='xr-section-summary-in' type='checkbox'  ><label for='section-a86bdeed-1056-438d-8ff6-f0d0d3523ed9' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e8e495a-73dc-4b60-a180-1f315150a3d2' class='xr-index-data-in' type='checkbox'/><label for='index-5e8e495a-73dc-4b60-a180-1f315150a3d2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([         89.941406,  89.82421850032573,  89.70703100065145,\n",
       "        89.58984350097718,  89.47265600130291,  89.35546850162864,\n",
       "        89.23828100195436,  89.12109350228009,  89.00390600260582,\n",
       "        88.88671850293154,\n",
       "       ...\n",
       "        -88.8867185029406, -89.00390600261485, -89.12109350228914,\n",
       "       -89.23828100196343, -89.35546850163769, -89.47265600131195,\n",
       "       -89.58984350098623, -89.70703100066052, -89.82421850033478,\n",
       "       -89.94140600000904],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=1536))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f248c1f2-5606-4880-9291-ee286c00145e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f248c1f2-5606-4880-9291-ee286c00145e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'multiplicative bias' (lat: 1536)> Size: 6kB\n",
       "array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 12kB 89.94 89.82 89.71 89.59 ... -89.71 -89.82 -89.94"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Percent bias\n",
    "\n",
    "Let's imagine that `fcst.temp_scrn` represents the forecasted streamflow values and `obs.temp_scrn` represents the observed streamflow values. Now, we want to create another dataset with significantly higher values by adding 1000 to both the observed and forecasted streamflow values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGxCAYAAACOSdkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACcFklEQVR4nOzdd3gUVffA8e9ueu8VQkLvvfcgKCKiKCKir1jxp4LSbCiIiK/4WhA7Igp2FEUsSK9K751AAiSUFEp6z+78/pjsJEs2IX1Tzud58iQ7Mzt7N9vOnnvuvTpFURSEEEIIIeohvbUbIIQQQghhLRIICSGEEKLekkBICCGEEPWWBEJCCCGEqLckEBJCCCFEvSWBkBBCCCHqLQmEhBBCCFFvSSAkhBBCiHrL1toNqOmMRiOXLl3Czc0NnU5n7eYIIYQQohQURSE1NZXg4GD0+uLzPhII3cClS5cICQmxdjOEEEIIUQ7nz5+nYcOGxe6XQOgG3NzcAPUf6e7ubuXWCCGEEKI0UlJSCAkJ0T7HiyOB0A2YusPc3d0lEBJCCCFqmRuVtUixtBBCCCHqLQmEhBBCCFFvSSAkhBBCiHpLaoSEEKKaGAwGcnNzrd0MIeoEOzs7bGxsKnweCYSEEKKKKYpCXFwcSUlJ1m6KEHWKp6cngYGBFZrnTwIhIYSoYqYgyN/fH2dnZ5mcVYgKUhSFjIwMEhISAAgKCir3uSQQEkKIKmQwGLQgyMfHx9rNEaLOcHJyAiAhIQF/f/9yd5NJsbQQQlQhU02Qs7OzlVsiRN1jel1VpPZOAiEhhKgG0h0mROWrjNeVBEJCCCGEqLckEBJCCFEuYWFhzJ8/v8RjdDodK1asqJb2VLdz586h0+k4ePAgAJs3b0an09Wr0YGvvfYaAQEB2uP88MMPM3LkSGs3q0wkEBJCCFFlYmNjGTZsmLWbUavUloDqxIkTzJ49m88//7xWP84yasxKYq5mcO5qOqE+zoT6uFi7OUIIUSUCAwOt3YQ6KycnB3t7e6vdflRUFAB33nlnra6BqzUZoblz59K9e3fc3Nzw9/dn5MiRRERE3PB6y5Yto1WrVjg6OtK+fXv+/vvvamjtjX2w4TTjvtrNyiOx1m6KEEJYFB4ezsSJE5k4cSIeHh74+voyc+ZMFEXRjklNTWXs2LG4uLjQoEEDPvnkE7NzXN819uKLL9KiRQucnZ1p0qQJM2fONBvxc+jQIQYNGoSbmxvu7u507dqVvXv33rCt0dHRjBgxAi8vL1xcXGjbtq32fm/KsKxZs4bOnTvj5OTETTfdREJCAqtWraJ169a4u7tz//33k5GRoZ1z9erV9OvXD09PT3x8fLj99tu1D/+KKq69586dY9CgQQB4eXmh0+l4+OGHgYLHY/Lkyfj6+jJ06FAAjh49yrBhw3B1dSUgIIAHH3yQK1eulPp+mLr4fv75Z/r374+TkxPdu3fn1KlT7Nmzh27duuHq6sqwYcO4fPkyoHaJjRgxAgC9Xl9sIJSdnc2zzz6Lv78/jo6O9OvXjz179mj7u3XrxrvvvqtdHjlyJHZ2dqSlpQFw4cIFdDodkZGRFf2XF6vWBEJbtmxhwoQJ7Ny5k3Xr1pGbm8stt9xCenp6sdfZvn07Y8eO5bHHHuPAgQOMHDmSkSNHcvTo0WpsuWW+bmoUfyU1x8otEUJUN0VRyMjJq/afwgFMaX399dfY2tqye/duPvjgA+bNm8eiRYu0/e+88w4dO3bkwIEDvPTSS0yaNIl169YVez43NzeWLFnC8ePH+eCDD/jiiy94//33tf0PPPAADRs2ZM+ePezbt4+XXnoJOzu7G7ZzwoQJZGdns3XrVo4cOcL//vc/XF1dzY557bXX+Pjjj9m+fTvnz5/n3nvvZf78+fzwww+sXLmStWvX8tFHH2nHp6enM3XqVPbu3cuGDRvQ6/XcddddGI3GsvwLy9TekJAQfv31VwAiIiKIjY3lgw8+0K739ddfY29vz7Zt21iwYAFJSUncdNNNdO7cmb1797J69Wri4+O59957y3w/Zs2axYwZM9i/fz+2trbcf//9vPDCC3zwwQf8888/REZG8uqrrwLw3HPPsXjxYkDt/oyNtfyl/oUXXuDXX3/l66+/Zv/+/TRr1oyhQ4dy7do1AAYOHMjmzZsB9XXxzz//4Onpyb///guon/0NGjSgWbNmFf6fF6fWdI2tXr3a7PKSJUvw9/dn3759DBgwwOJ1PvjgA2699Vaef/55AObMmcO6dev4+OOPWbBgQZW3uSR+rg4AXE7Ltmo7hBDVLzPXQJtX11T77R5/fSjO9mV72w8JCeH9999Hp9PRsmVLjhw5wvvvv8/48eMB6Nu3Ly+99BIALVq0YNu2bbz//vvcfPPNFs83Y8YM7e+wsDCee+45li5dygsvvABATEwMzz//PK1atQKgefPmpWpnTEwMo0aNon379gA0adKkyDFvvPEGffv2BeCxxx5j+vTpREVFacfec889bNq0iRdffBGAUaNGmV3/q6++ws/Pj+PHj9OuXbtStas87fX29gbA398fT09Ps+s1b96ct99+2+w+de7cmTfffNOsnSEhIZw6dYoWLVqU+n4899xzWpZp0qRJjB07lg0bNpj9z5YsWQKAq6ur1rbiuj/T09P57LPPWLJkiVY/9MUXX7Bu3Tq+/PJLnn/+ecLDw/nyyy8xGAwcPXoUe3t7xowZw+bNm7n11lvZvHkzAwcOLNX/tLxqTUboesnJyUDBE8aSHTt2MGTIELNtQ4cOZceOHcVeJzs7m5SUFLOfquDnlh8IpWZVyfmFEKIy9OrVy6zbo3fv3pw+fRqDwaBdLqx3796cOHGi2PP99NNP9O3bl8DAQFxdXZkxYwYxMTHa/qlTp/L4448zZMgQ3nrrrVJ3RT377LNaoDNr1iwOHz5c5JgOHTpofwcEBGjdc4W3mZZsADh9+jRjx46lSZMmuLu7ExYWBmDW3vIqTXst6dq1q9nlQ4cOsWnTJlxdXbUfUxBp+t+V9n5c//8BtEDNtK3w/+dGoqKiyM3N1QIpUBdK7dGjh/Yc6d+/P6mpqRw4cIAtW7YwcOBAwsPDtSzRli1bCA8PL/VtlketyQgVZjQamTx5Mn379i0xKo+Li9MeTJOAgADi4uKKvc7cuXOZPXt2pbW1OJ7OatdYSmZeld+WEKJmcbKz4fjrQ61yu9a0Y8cOHnjgAWbPns3QoUPx8PBg6dKlvPfee9oxr732Gvfffz8rV65k1apVzJo1i6VLl3LXXXeVeO7HH3+coUOHal1cc+fO5b333uOZZ57RjincxabT6Yp0uel0OrPuohEjRhAaGsoXX3xBcHAwRqORdu3akZNT8ZKG0rTXEhcX88E1aWlpjBgxgv/9739FjjWtv1Xa+3H9/8fStsroFizM09OTjh07snnzZnbs2MHNN9/MgAEDGDNmDKdOneL06dOSEbJkwoQJHD16lKVLl1b6uadPn05ycrL2c/78+Uq/DQBXB/UNKT1HAiEh6hudToezvW21/5RnZM+uXbvMLu/cuZPmzZtr6zrt3LmzyP7WrVtbPNf27dsJDQ3llVdeoVu3bjRv3pzo6Ogix7Vo0YIpU6awdu1a7r77bq0W5UZCQkJ48sknWb58OdOmTeOLL74o1fUsuXr1KhEREcyYMYPBgwfTunVrEhMTy30+S4prr2kkmCnrVpIuXbpw7NgxwsLCaNasmdmPi4tLtdyP4jRt2lSrZzLJzc1lz549tGnTRts2cOBANm3axNatWwkPD8fb25vWrVvz3//+l6CgIFq0aFGl7ax1gdDEiRP566+/2LRpEw0bNizx2MDAQOLj4822xcfHlzic08HBAXd3d7OfqmDqp0/PvvETXQghrCUmJoapU6cSERHBjz/+yEcffcSkSZO0/du2bePtt9/m1KlTfPLJJyxbtsxsf2HNmzcnJiaGpUuXEhUVxYcffshvv/2m7c/MzGTixIls3ryZ6Ohotm3bxp49e4oNrAqbPHkya9as4ezZs+zfv59NmzaV6nrF8fLywsfHh4ULFxIZGcnGjRuZOnVquc9XlvaGhoai0+n466+/uHz5sjaCypIJEyZw7do1xo4dy549e4iKimLNmjU88sgjGAyGKr8fJXFxceGpp57i+eefZ/Xq1Rw/fpzx48eTkZHBY489ph0XHh7OmjVrsLW11br1wsPD+f7776s8GwS1KBBSFIWJEyfy22+/sXHjRho3bnzD6/Tu3ZsNGzaYbVu3bl2RPm1rcNECIckICSFqrnHjxpGZmUmPHj2YMGECkyZN4oknntD2T5s2jb1799K5c2feeOMN5s2bpxXcXu+OO+5gypQpTJw4kU6dOrF9+3Zmzpyp7bexseHq1auMGzeOFi1acO+99zJs2LBSlSsYDAYmTJhA69atufXWW2nRogWffvppue+3Xq9n6dKl7Nu3j3bt2jFlyhTeeeedcp+vLO1t0KABs2fP5qWXXiIgIICJEycWe57g4GC2bduGwWDglltuoX379kyePBlPT0/0en2V348beeuttxg1ahQPPvggXbp0ITIykjVr1uDl5aUd079/f4xGo1nQEx4ejsFgqPL6IACdUp7xlFbw9NNP88MPP/D777/TsmVLbbuHhwdOTk6A+oJt0KABc+fOBdQ07MCBA3nrrbcYPnw4S5cu5c0332T//v2lrvhPSUnBw8OD5OTkSs0OXU3Lpusb6wGIevM2bPS1dzIqIUTxsrKyOHv2LI0bN8bR0dHazSmT8PBwOnXqdMNlNISwlpJeX6X9/K41GaHPPvuM5ORkwsPDCQoK0n5++ukn7ZiYmBizuQz69OnDDz/8wMKFC+nYsSO//PILK1asqPCwx8rg4lBQp54hdUJCCCGEVdSaUWOlSVyZhtsVNnr0aEaPHl0FLaoYB1s9NnodBqNCRo4BN8cbTxgmhBD11bBhw/jnn38s7nv55Zd5+eWXq7lFJatt7a3Pak0gVNeoo0ZsSM3KIy07j4AbX0UIIaqVpS+X1rJo0SIyMzMt7itpPjlrqW3trc8kELIiVwdbUrPyyJCRY0IIUaIGDRpYuwllUtvaW5/VmhqhusjZXp2HI01GjgkhhBBWIYGQFbnmF0xLsbQQQghhHRIIWZFpUkXJCAkhhBDWIYGQFbnkL7ORkSM1QkIIIYQ1SCBkRaa5hGR2aSGEEMI6JBCyIllvTAhRm4WFhd1w1mmdTseKFSuqvC1LlizB09Ozym9HqLZt20b79u2xs7Nj5MiRbN68GZ1OR1JSkrWbVmYSCFmRrEAvhKjrYmNjGTZsmLWbUWuUJrisCaZOnUqnTp04e/YsS5YssXZzKkQCIStyloVXhRB1XGBgIA4ODtZuRqnk5ORYuwmlYjAYMBqNVm1DVFQUN910Ew0bNqz1mTgJhKzIVWqEhBA1WHh4OBMnTmTixIl4eHjg6+vLzJkzzZY8Sk1NZezYsbi4uNCgQQM++eQTs3Nc3zX24osv0qJFC5ydnWnSpAkzZ84kNzdX23/o0CEGDRqEm5sb7u7udO3alb1795a57VFRUdx5550EBATg6upK9+7dWb9+vdkxYWFhzJkzh3HjxuHu7s4TTzwBwBdffEFISAjOzs7cddddzJs3r8iH/e+//06XLl1wdHSkSZMmzJ49m7y8G7+XK4rCa6+9RqNGjXBwcCA4OJhnn30WUP/f0dHRTJkyBZ1Oh06nLsZt6vb7448/aNOmDQ4ODsTExJCdnc1zzz1HgwYNcHFxoWfPnmazgV+9epWxY8fSoEEDnJ2dad++PT/++KNZe8LDw3nmmWeYPHkyXl5eBAQE8MUXX5Cens4jjzyCm5sbzZo1Y9WqVQCcO3cOnU7H1atXefTRR9HpdMVmhH799Vfatm2Lg4MDYWFhvPfee9q+jz/+2GzdzxUrVqDT6ViwYIG2bciQIcyYMeOG/9MKU0SJkpOTFUBJTk6u9HN/vzNaCX3xL+WxJbsr/dxCiJohMzNTOX78uJKZmVmw0WhUlOy06v8xGsvU9oEDByqurq7KpEmTlJMnTyrfffed4uzsrCxcuFBRFEUJDQ1V3NzclLlz5yoRERHKhx9+qNjY2Chr167VzgEov/32m3Z5zpw5yrZt25SzZ88qf/zxhxIQEKD873//0/a3bdtW+c9//qOcOHFCOXXqlPLzzz8rBw8evGFbFy9erHh4eGiXDx48qCxYsEA5cuSIcurUKWXGjBmKo6OjEh0drR0TGhqquLu7K++++64SGRmpREZGKv/++6+i1+uVd955R4mIiFA++eQTxdvb2+zcW7duVdzd3ZUlS5YoUVFRytq1a5WwsDDltddeu2E7ly1bpri7uyt///23Eh0drezatUv7f169elVp2LCh8vrrryuxsbFKbGysdt/s7OyUPn36KNu2bVNOnjyppKenK48//rjSp08fZevWrUpkZKTyzjvvKA4ODsqpU6cURVGUCxcuKO+8845y4MABJSoqSnt8du3aZfYYu7m5KXPmzFFOnTqlzJkzR7GxsVGGDRumLFy4UDl16pTy1FNPKT4+Pkp6erqSl5enxMbGKu7u7sr8+fOV2NhYJSMjQ9m0aZMCKImJiYqiKMrevXsVvV6vvP7660pERISyePFixcnJSVm8eLGiKIpy+PBhRafTKQkJCYqiKMrkyZMVX19fZcyYMYqiKEpOTo7i7OysrFu3rsT/p8XXV77Sfn5LIHQDVRkI/XnoohL64l/K6AXbK/3cQoiaweIbdXaaosxyr/6f7LQytX3gwIFK69atFWOhAOrFF19UWrdurSiKGkjceuutZtcZM2aMMmzYMO3y9YHQ9d555x2la9eu2mU3NzdlyZIlZWqnohQNhCxp27at8tFHH2mXQ0NDlZEjR5odM2bMGGX48OFm2x544AGzcw8ePFh58803zY759ttvlaCgoBu287333lNatGih5OTkWNwfGhqqvP/++2bbFi9erABmAWF0dLRiY2OjXLx40ezYwYMHK9OnTy/29ocPH65MmzZNuzxw4EClX79+2uW8vDzFxcVFefDBB7VtsbGxCqDs2LFD2+bh4aEFNYqiFAmE7r//fuXmm282u+3nn39eadOmjaIoimI0GhUfHx9l2bJliqIoSqdOnZS5c+cqgYGBiqIoyr///qvY2dkp6enpxd4XRamcQEi6xqzIPX/F+ZTM3BscKYQQ1tGrVy+tiwagd+/enD59GoPBoF0urHfv3pw4caLY8/3000/07duXwMBAXF1dmTFjBjExMdr+qVOn8vjjjzNkyBDeeustoqKiytXutLQ0nnvuOVq3bo2npyeurq6cOHHC7LYAunXrZnY5IiKCHj16mG27/vKhQ4d4/fXXcXV11X7Gjx9PbGwsGRkZJbZr9OjRZGZm0qRJE8aPH89vv/1Wqi41e3t7OnTooF0+cuQIBoOBFi1amLVjy5Yt2v/MYDAwZ84c2rdvj7e3N66urqxZs6bI/6DweW1sbPDx8aF9+/batoAAdVnwhISEG7bT5MSJE/Tt29dsW9++fbXnjk6nY8CAAWzevJmkpCSOHz/O008/TXZ2NidPnmTLli10794dZ2fnUt9mecmiq1bk5qj++1OzpEZIiHrFzhlevmSd27WiHTt28MADDzB79myGDh2Kh4cHS5cuNasdee2117j//vtZuXIlq1atYtasWSxdupS77rqrTLf13HPPsW7dOt59912aNWuGk5MT99xzT5GCaBcXlzLfj7S0NGbPns3dd99dZJ+jo2OJ1w0JCSEiIoL169ezbt06nn76ad555x22bNmCnZ1dsddzcnIyC0jT0tKwsbFh37592NjYmB3r6uoKwDvvvMMHH3zA/Pnzad++PS4uLkyePLnI/+D629XpdGbbTLdb2QXa4eHhLFy4kH/++YfOnTvj7u6uBUdbtmxh4MCBlXp7xZFAyIrcnSQjJES9pNOBfdk/gK1h165dZpd37txJ8+bNtQ/fnTt3FtnfunVri+favn07oaGhvPLKK9q26OjoIse1aNGCFi1aMGXKFMaOHcvixYvLHAht27aNhx9+WLteWloa586du+H1WrZsyZ49e8y2XX+5S5cuRERE0KxZszK1ycTJyYkRI0YwYsQIJkyYQKtWrThy5AhdunTB3t5ey7aVpHPnzhgMBhISEujfv7/FY7Zt28add97Jf/7zH0ANZE6dOkWbNm3K1e6yaN26Ndu2bSvSnhYtWmjPnYEDBzJ58mSWLVtGeHg4oAZH69evZ9u2bUybNq3K2wkSCFmVl7M9AKnZeeTkGbG3lZ5KIUTNEhMTw9SpU/m///s/9u/fz0cffWSWwdm2bRtvv/02I0eOZN26dSxbtoyVK1daPFfz5s2JiYlh6dKldO/enZUrV/Lbb79p+zMzM3n++ee55557aNy4MRcuXGDPnj2MGjWqzO1u3rw5y5cvZ8SIEeh0OmbOnFmqjMYzzzzDgAEDmDdvHiNGjGDjxo2sWrXKLBvz6quvcvvtt9OoUSPuuece9Ho9hw4d4ujRo7zxxhslnn/JkiUYDAZ69uyJs7Mz3333HU5OToSGhgLqSLatW7dy33334eDggK+vr8XztGjRggceeIBx48bx3nvv0blzZy5fvsyGDRvo0KEDw4cPp3nz5vzyyy9s374dLy8v5s2bR3x8fLUEQtOmTaN79+7MmTOHMWPGsGPHDj7++GM+/fRT7ZgOHTrg5eXFDz/8wF9//QWogdBzzz2HTqcr0rVWVeST14o8neyw0asvrmvptWP+CiFE/TJu3DgyMzPp0aMHEyZMYNKkSdowc1A/8Pbu3Uvnzp154403mDdvHkOHDrV4rjvuuIMpU6YwceJEOnXqxPbt25k5c6a238bGhqtXrzJu3DhatGjBvffey7Bhw5g9e3aZ2z1v3jy8vLzo06cPI0aMYOjQoXTp0uWG1+vbty8LFixg3rx5dOzYkdWrVzNlyhSzLq+hQ4fy119/sXbtWrp3706vXr14//33tWCmJJ6ennzxxRf07duXDh06sH79ev788098fHwAeP311zl37hxNmzbFz8+vxHMtXryYcePGMW3aNFq2bMnIkSPZs2cPjRo1AmDGjBl06dKFoUOHEh4eTmBgICNHjrxhGytDly5d+Pnnn1m6dCnt2rXj1Vdf5fXXX+fhhx/WjtHpdPTv3x+dTke/fv0ANThyd3enW7du5eq2LA+dohSaEEIUkZKSgoeHB8nJybi7u1f6+Xv8dz0Jqdn89Uw/2jXwqPTzCyGsKysri7Nnz9K4ceMb1o/UNOHh4XTq1KlWzHRclcaPH8/Jkyf5559/rN0UcZ2SXl+l/fyWrjEr83F1ICE1mytp2dZuihBCCODdd9/l5ptvxsXFhVWrVvH111+bdemIukW6xqzM11WtE7qSJl1jQghRnGHDhpkNEy/88+abb1bqbe3evZubb76Z9u3bs2DBAj788EMef/zxUl33+++/L7adbdu2rdR2isohGSEr83NV1+CRjJAQoqYpvFyDtS1atIjMzEyL+7y9vSv1tn7++edyX/eOO+6gZ8+eFveVNDxeWI8EQlbm7aJmhKRYWgghitegQQNrN6FU3NzccHNzs3YzRBlI15iVmeYSkkkVhajbZFyKEJWvMl5XEghZmUv+CvRpsgK9EHWSqTvkRksvCCHKzvS6qki3o3SNWZmbKRDKktmlhaiLbGxs8PT01NZpcnZ2NpucTwhRdoqikJGRQUJCAp6enkWWGSkLCYSszDV/vbH07BtPqS6EqJ0CAwOBsi1aKYS4MU9PT+31VV4SCFmZqWssVbrGhKizdDodQUFB+Pv7k5sr2V8hKoOdnV2FMkEmEghZmatWIyRvjkLUdTY2NpXyxi2EqDxSLG1lbtI1JoQQQliNBEJWpmWEZPi8EEIIUe0kELIyU41QjsFIdp5khYQQQojqJIGQlZkyQiDdY0IIIUR1k0DIymz0Opzt1eJJ6R4TQgghqpcEQjVAwRB6GTkmhBBCVCcJhGoA0+zS0jUmhBBCVC8JhGoA0+zSMpeQEEIIUb0kEKoBXOzzu8akRkgIIYSoVhII1QCy3pgQQghhHRII1QBussyGEEIIYRUSCNUALjK7tBBCCGEVEgjVAAXF0tI1JoQQQlQnCYRqAFmBXgghhLCOWhUIbd26lREjRhAcHIxOp2PFihUlHr9582Z0Ol2Rn7i4uOppcCkVBELSNSaEEEJUp1oVCKWnp9OxY0c++eSTMl0vIiKC2NhY7cff37+KWlg+BYGQdI0JIYQQ1cn2xofUHMOGDWPYsGFlvp6/vz+enp6V36BKotUIZUnXmBBCCFGdalVGqLw6depEUFAQN998M9u2bbN2c4qQrjEhhBDCOmpVRqisgoKCWLBgAd26dSM7O5tFixYRHh7Orl276NKli8XrZGdnk52drV1OSUmp8na6yvB5IYQQwirqdCDUsmVLWrZsqV3u06cPUVFRvP/++3z77bcWrzN37lxmz55dXU0ECg+fl0BICCGEqE71omussB49ehAZGVns/unTp5OcnKz9nD9/vsrbVLhrTFGUKr89IYQQQqjqdEbIkoMHDxIUFFTsfgcHBxwcHKqxRQWBkFGBzFwDzvb17mERQgghrKJWfeKmpaWZZXPOnj3LwYMH8fb2plGjRkyfPp2LFy/yzTffADB//nwaN25M27ZtycrKYtGiRWzcuJG1a9da6y5Y5Gxvg04HiqJmhSQQEkIIIapHrfrE3bt3L4MGDdIuT506FYCHHnqIJUuWEBsbS0xMjLY/JyeHadOmcfHiRZydnenQoQPr1683O0dNoNPpcLW3JTU7j7SsPPzdrN0iIYQQon7QKVKUUqKUlBQ8PDxITk7G3d29ym6n99wNxCZn8cfEvnRo6FlltyOEEELUB6X9/K53xdI1lcwlJIQQQlQ/CYRqCBeZS0gIIYSodhII1RBuMpeQEEIIUe0kEKohTF1j6RIICSGEENVGAqEawhQIpUogJIQQQlQbCYRqCKkREkIIIaqfBEI1hKlGSLrGhBBCiOojgVANIV1jQgghRPWTQKiGkK4xIYQQovpJIFRDaF1jORIICSGEENVFAqEawlUyQkIIIUS1k0CohnCRGiEhhBCi2kkgVEPIhIpCCCFE9ZNAqIbQltiQrjEhhBCi2kggVEOYusbScwwYjIqVWyOEEELUDxII1RCmrjGQkWNCCCFEdZFAqIZwsNVjZ6MDpE5ICCGEqC4SCNUQOp1OJlUUQgghqpkEQjWILLMhhBBCVC8JhGoQGUIvhBBCVC8JhGoQmV1aCCGEqF4SCNUgro7SNSaEEEJUJwmEahDpGhNCCCGqlwRCNYh0jQkhhBDVSwKhGsTRzgaArDyDlVsihBBC1A8SCNUgTvZqIJSZY7RyS4QQQoj6QQKhGsTRNj8QypWMkBBCCFEdJBCqQZzs1YcjWwIhIYQQolpIIFSDmGqEJCMkhBBCVA8JhGoQrVhaAiEhhBCiWkggVINIRkgIIYSoXhII1SBOWkZIRo0JIYQQ1UECoRrE0U59OKRrTAghhKgeEgjVIE5SIySEEEJUKwmEahCpERJCCCGqlwRCNYij1AgJIYQQ1UoCoRrEVCMkGSEhhBCiekggVIOYaoRy8owYjYqVWyOEEELUfRII1SCmrjGQFeiFEEKI6iCBUA1iFghJnZAQQghR5SQQqkFs9DrsbaROSAghhKguEgjVMDKpohBCCFF9JBCqYbS5hHIkEBJCCCGqmgRCNYyTvRoIZUuxtBBCCFHlalUgtHXrVkaMGEFwcDA6nY4VK1bc8DqbN2+mS5cuODg40KxZM5YsWVLl7awIR1tTRkiKpYUQQoiqVqsCofT0dDp27Mgnn3xSquPPnj3L8OHDGTRoEAcPHmTy5Mk8/vjjrFmzpopbWn6O9rLemBBCCFFdbK3dgLIYNmwYw4YNK/XxCxYsoHHjxrz33nsAtG7dmn///Zf333+foUOHVlUzK8TRVkaNCSGEENWlVmWEymrHjh0MGTLEbNvQoUPZsWOHlVp0Y06SERJCCCGqTbkyQjExMURHR5ORkYGfnx9t27bFwcGhsttWYXFxcQQEBJhtCwgIICUlhczMTJycnIpcJzs7m+zsbO1ySkpKlbezMFONkARCQgghRNUrdSB07tw5PvvsM5YuXcqFCxdQlIK1sOzt7enfvz9PPPEEo0aNQq+vvYmmuXPnMnv2bKvdfkFGSIqlhRBCiKpWqojl2WefpWPHjpw9e5Y33niD48ePk5ycTE5ODnFxcfz999/069ePV199lQ4dOrBnz56qbnepBAYGEh8fb7YtPj4ed3d3i9kggOnTp5OcnKz9nD9/vjqaqtHmEZKMkBBCCFHlSpURcnFx4cyZM/j4+BTZ5+/vz0033cRNN93ErFmzWL16NefPn6d79+6V3tiy6t27N3///bfZtnXr1tG7d+9ir+Pg4GDVbj6ZWVoIIYSoPqUKhObOnVvqE956663lbsyNpKWlERkZqV0+e/YsBw8exNvbm0aNGjF9+nQuXrzIN998A8CTTz7Jxx9/zAsvvMCjjz7Kxo0b+fnnn1m5cmWVtbGinCQjJIQQQlSbCg2fv3LlCrt27cJgMNC9e3eCgoIqq10W7d27l0GDBmmXp06dCsBDDz3EkiVLiI2NJSYmRtvfuHFjVq5cyZQpU/jggw9o2LAhixYtqrFD56Gga0xqhIQQQoiqV+5A6Ndff+Wxxx6jRYsW5ObmEhERwSeffMIjjzxSme0zEx4eblakfT1Ls0aHh4dz4MCBKmtTZXOyk1FjQgghRHUp9fCutLQ0s8uzZ89m9+7d7N69mwMHDrBs2TJeeeWVSm9gfSM1QkIIIUT1KXUg1LVrV37//Xftsq2tLQkJCdrl+Ph47O3tK7d19ZCMGhNCCCGqT6m7xtasWcOECRNYsmQJn3zyCR988AFjxozBYDCQl5eHXq+v8Qua1gaO0jUmhBBCVJtSB0JhYWGsXLmSH3/8kYEDB/Lss88SGRlJZGQkBoOBVq1a4ejoWJVtrRcKRo1JsbQQQghR1co8BfTYsWPZs2cPhw4dIjw8HKPRSKdOnSQIqiSmjFC2ZISEEEKIKlemUWN///03J06coGPHjixatIgtW7bwwAMPMGzYMF5//fViZ2sWpedkL6vPCyGEENWl1BmhadOm8cgjj7Bnzx7+7//+jzlz5jBw4ED279+Po6MjnTt3ZtWqVVXZ1nrBIX/R1cwcCYSEEEKIqlbqQGjJkiX8/fffLF26lD179vDtt98C6oKrc+bMYfny5bz55ptV1tD6omDRVQmEhBBCiKpW6kDIxcWFs2fPAnD+/PkiNUFt2rThn3/+qdzW1UMys7QQQghRfUodCM2dO5dx48YRHBzMwIEDmTNnTlW2q94yjRrLMRgxGIufRVsIIYQQFVfqYukHHniAW2+9lTNnztC8eXM8PT2rsFn1l2lmaVC7x1wcKrQcnBBCCCFKUKZPWR8fH3x8fKqqLQJwzC+WBgmEhBBCiKpWqq6xJ598kgsXLpTqhD/99BPff/99hRpVn+n1OuxtZQi9EEIIUR1KlW7w8/Ojbdu29O3blxEjRtCtWzeCg4NxdHQkMTGR48eP8++//7J06VKCg4NZuHBhVbe7TnOysyEnzygF00IIIUQVK1UgNGfOHCZOnMiiRYv49NNPOX78uNl+Nzc3hgwZwsKFC7n11lurpKH1iaOdnuRMGUIvhBBCVLVSF6AEBATwyiuv8Morr5CYmEhMTAyZmZn4+vrStGlTdDpdVbazXnGShVeFEEKIalGuSlwvLy+8vLwquy0in6O28KoEQkIIIURVKvOiq6LqyaSKQgghKsv5axmcv5Zh7WbUWDI2uwYyzSUkGSEhhBAVkZVroP/bmwA49cYwbVSyKCD/kRpIaoSEEEJUhitp2drfSZk5VmxJzSWBUA3kKIGQEEKISpCUkav9nZKZW8KR9VeZA6HMzEwyMgr6GqOjo5k/fz5r166t1IbVZ5IREkIIURmupRdkgQoHRaJAmQOhO++8k2+++QaApKQkevbsyXvvvcedd97JZ599VukNrI8cTKPGcqRYWgghRPklZhQEQsmSEbKozIHQ/v376d+/PwC//PILAQEBREdH88033/Dhhx9WegPrIycZPi+EEKISXE2TjNCNlDkQysjIwM3NDYC1a9dy9913o9fr6dWrF9HR0ZXewPrIyV59WKRrTAghREVIRujGyhwINWvWjBUrVnD+/HnWrFnDLbfcAkBCQgLu7u6V3sD6yNlendUgIyfPyi0RQghRm5nVCEkgZFGZA6FXX32V5557jrCwMHr27Env3r0BNTvUuXPnSm9gfWTqGsvIkYyQEEKI8iscCMmoMcvKPKHiPffcQ79+/YiNjaVjx47a9sGDB3PXXXdVauPqK2d7GTUmhBCi4swCoSwJhCwp18zSgYGBBAYGmm3r0aNHpTRIgJO9ZISEEEJUXOEaoZw8GYlsSbkCob179/Lzzz8TExNDTo75TJXLly+vlIbVZ9I1JoQQojJcSy/IAmVLIGRRmWuEli5dSp8+fThx4gS//fYbubm5HDt2jI0bN+Lh4VEVbax3TMXSmRIICSGEqIDMQoNuJCNkWZkDoTfffJP333+fP//8E3t7ez744ANOnjzJvffeS6NGjaqijfWO1jWWK6PGhBCiouKSs3j9z+OcvZLOon/O8PDi3fWmBrNwFkgCIcvKHAhFRUUxfPhwAOzt7UlPT0en0zFlyhQWLlxY6Q2sj0zF0jKztBBCVNzLvx3hq21nefDLXbyx8gSbIy5z+0f/En013dpNq1IGo0KeUdEu5xjkM8WSMgdCXl5epKamAtCgQQOOHj0KqMttFF6DTJSfNrO0zCMkhBAVti3yCgAXEjO1bZEJaQx8Z7OVWlQ9rs8AZefVjyxYWZW5WHrAgAGsW7eO9u3bM3r0aCZNmsTGjRtZt24dgwcProo21jvOWteYAUVR0Ol0Vm6REELUXs72NsUWCucZjNjalDknUCtcHwhJ15hlZQ6EPv74Y7KysgB45ZVXsLOzY/v27YwaNYoZM2ZUegPrI1ONkKKo/buO+RkiIYQQZfPYkj0klrDGVmxyFiHeztXYoupzfQZIAiHLyhwIeXt7a3/r9XpeeumlSm2QKBg1BuoQegmEhBCi7BJSsthwMqHEY/q/vYlvH+tB/+Z+1dSq6nN9FkyGz1tWqkAoJSVFW0csJSWlxGNlvbGKs9HrsLfVk5NnJCMnD28Xe2s3SQghap39MUmlOm7Tycv1IhCSjJBlpQqEvLy8iI2Nxd/fH09PT4s1K6ZaFoNBirEqg7O9DTl5xnozxFMIISqbaVTY8PZBDGnjz5SfDlk8LuZa3Rw9JjVCpVOqQGjjxo1al9imTZuqtEFC5WRnQxK5Mru0EEKU07n8QKipvyvD2gXx4+7znLiUwu8T+xLm48IX/5xh7qqTXErKsnJLq4apRshGr8NgVKRrrBilCoQGDhxo8W9RdWS9MSGEqJizV9RAKMzHGUc7G37+v95m+/s194VVcDkt2xrNq3KmDJCboy1JGbnkGIwyEtmCcq01lpiYyJdffsmJEycAaNOmDY888ohZIbWomIJJFSUQEkKI8oi+qs5tF+brYnG/n5sDAFfTsjEYFWz0dStAME2g6OqgBkKmbQ62MgCnsDJPnrB161bCwsL48MMPSUxMJDExkQ8//JDGjRuzdevWqmhjveRsp8aokhESQoiyy8wxEJusdnk19rEcCPm4OKDXgVGBoxeTq7N51SI715QRsivYJt1jRZQ5EJowYQJjxozh7NmzLF++nOXLl3PmzBnuu+8+JkyYUBVtrJdMXWOZUiwthBBlFp1fAO3uaIuns53FY2z0OkwrUNz5yTYURbF4XG1lygi5ORR0/kjBdFFlDoQiIyOZNm0aNjYFqTUbGxumTp1KZGRkpTbOkk8++YSwsDAcHR3p2bMnu3fvLvbYJUuWoNPpzH4cHR2rvI2VQZtdWpbZEEKIMjt3Re0Wa+zrUuqamGd+PFCVTap2pmJpBzs99vmzZ0sgVFSZA6EuXbpotUGFnThxgo4dO1ZKo4rz008/MXXqVGbNmsX+/fvp2LEjQ4cOJSGh+Amz3N3diY2N1X6io6OrtI2VxSU/gk/PloyQEEKUlWnEWHH1QSb3dG2o/f3X4Vim/Wx5iH1tZAp67G302NnozLaJAqUqlj58+LD297PPPsukSZOIjIykV69eAOzcuZNPPvmEt956q2pamW/evHmMHz+eRx55BIAFCxawcuVKvvrqq2JnuNbpdAQGBlZpu6qCa34glJZd/NTwQgghilIUhR92xQAQWkx9kMkbI9vRMcSTmSvUBcRXHrnEe/dW7Zf66mKqB3Kw05OeX2+6dM95XhrWyprNqnFKFQh16tQJnU5n1n/6wgsvFDnu/vvvZ8yYMZXXukJycnLYt28f06dP17bp9XqGDBnCjh07ir1eWloaoaGhGI1GunTpwptvvknbtm2rpI2Vyc0xPxDKkq4xIYQoi91nrxFzTe0aa+bvWuKxjnY2PNgrlM4hntz+0b/k5BnJzjPUiZFVhTNCJgu2REkgdJ1SBUJnz56t6nbc0JUrVzAYDAQEBJhtDwgI4OTJkxav07JlS7766is6dOhAcnIy7777Ln369OHYsWM0bNjQ4nWys7PJzi6YU+JGS4pUFVNGKDVbAiEhhCgLU7cYwC1tAko4skDbYHe8nO1IzMglIi6VDg09q6h11UfLCBUK6kZ0DLZWc2qsUgVCoaGhVd2OKtG7d2969y6YQKtPnz60bt2azz//nDlz5li8zty5c5k9e3Z1NbFYrpIREkKIcrmWrpYUjOrSsNSLVut0OsJ8XUiMSSIuOYsOlr8rV4qjF5NZtvc8k4a0qLK1JHMNRt5ZEwGArY2OGcNb88bKE9SxqZIqRZmLpa3F19cXGxsb4uPjzbbHx8eXugbIzs6Ozp07lzi6bfr06SQnJ2s/58+fr1C7y6ugRkgCISGEKIvEjBwAvF0sD5svjnv+fDspVfwF9PaP/uXrHdG89sexKrsN02SSoHYPap8p8uW6iFoTCNnb29O1a1c2bNigbTMajWzYsMEs61MSg8HAkSNHCAoKKvYYBwcH3N3dzX6sQasRkkBICCHK5Fq6Ggh5lTHb4u6UHwhlVs8glWOXqm4Sx4TUgvXTHugZqo1Els+Uosq1xIa1TJ06lYceeohu3brRo0cP5s+fT3p6ujaKbNy4cTRo0IC5c+cC8Prrr9OrVy+aNWtGUlIS77zzDtHR0Tz++OPWvBul4uqgviAlehdCiLJJzA+EvJ3LFgiZvoCmZFVPIFSVa35dTlVrXXs18cbeVq9lhNJlbroialUgNGbMGC5fvsyrr75KXFwcnTp1YvXq1VoBdUxMDHp9QZIrMTGR8ePHExcXh5eXF127dmX79u20adPGWneh1KRYWgghyudaRvkyQh75GaHC3UqVLSGlela6NwVC/m7qJMKmWqmsXJlH6HplDoSaNGnCnj178PHxMduelJREly5dOHPmTKU1zpKJEycyceJEi/s2b95sdvn999/n/fffr9L2VBUZPi+EEOWjZYTKGAj1CPPmM6LYeeZqVTQLgP98uUv7uyrrlk2BkGlhWUc7NUkgC3kXVeYaoXPnzmEwFP1HZmdnc/HixUpplCjICGXmGsgzSAQvhBCldSWtfIFQy0C3/OtnV9m6Y6fi07S/q3K1+wQtI2QKhNSMkGnZDVGg1BmhP/74Q/t7zZo1eHh4aJcNBgMbNmwgLCysUhtXn7kUWiQvPduAh3OtqWsXQgirycjJ0wqCTUFAafm4qoFTrkEhOTMXzzLWGJVVVhUuqn3ofBIAoT7OADhJ11ixSh0IjRw5ElCLux566CGzfXZ2doSFhfHee+9VauPqM3tbPQ62erLzjKRm5+JRzOrJQgghCpi6hJzsbLTMemk52Nrg7mhLSlYel1OzKz0Quj7LFH0tg7TsvDK380YMRoUzV9RJJbuGegOFa4QkI3S9UqcZjEYjRqORRo0akZCQoF02Go1kZ2cTERHB7bffXpVtrXdkCL0QQpSN1iXk7lCuUVm++Vmky2nZNziy7ApnY9wcbVEUOBlb+asXFP7McHdSP0dMNUJ5RoVcKbcwU+b+lrNnz+Lr61sVbal/btAHLRNgCSFE2SSk5BcJu5atW8zEdD1TnVFlMgUoOh20yq9HMgVulSkjf4i8nY1OW16j8AzbkhUyV6583IYNG9iwYYOWGSrsq6++qpSG1XknV8KWt+HOTyCwncVDTMtsyBB6IYQoncv5Ewn6u5cvEDJlhKpimLspEHK1t9XqQNOr4P3ddE5n+4KPeAfbgrxHVq6R/FH1gnJkhGbPns0tt9zChg0buHLlComJiWY/opSOLIPYg/Bv8cP7JSMkhBBlE5sfwPiX85O+ef5q9ccuVUGXVf57uaujLS75QUpGFQxnT8tWz1m49kin02ndY5IRMlfmjNCCBQtYsmQJDz74YFW0p/7oNQGO/Qan14IhD2yKPhSm2aVTJRASQohSuZiYCUBDL6dyXb9zIy8ADsRU/hf71Gx1xmoXB1uc7dWuqqqY6flUXGr+7ZgvOOtoZ0NWrlECoeuUOSOUk5NDnz59qqIt9UuDLuDgAdkpEHfI4iEFxdLVM927EELUdheTKhYIdWyoTg1z7mpGpXdbpRfK1Ji6xjKyKz8oeeHXwwCcuZxutt3RVobQW1LmQOjxxx/nhx9+qIq21C96Gwjrp/59dqvFQ6RrTAghSu9kXAoHYpIAaODpXK5zeDrba3PuXKnkkWOmL7VujgUZIVPdUEZOHvPWRnC8Ervk8ozmA3Kc8m8zSyZVNFPmrrGsrCwWLlzI+vXr6dChA3Z25vPbzJs3r9IaV+c1HgARK+H0eug3pchuNymWFkKIUsnJM3Lr/H+0y+XNCAH4utlz/lomV9KyCfVxqYzmAZCYrgZCrg62BYNh8r/ofrDhNJ9vOcOHGyM599bwct+G0Vj8aGRTwbQss2GuzIHQ4cOH6dSpEwBHjx4121eVK+nWSa1ug9UvQvQ2uHwK/FqY7XZzVIPM5EzpGhNCiJIcvpBkdtmzApPQ+ro6cP5aJnHJlZsROpg/23PLQDe88ydrTMxfIHbfucqpSSqpplQmVbSszIHQpk2bqqId9ZNnI2h5G0T8DfuWwK1vmu32yV8nx7SAoBBCCMtOJxSs4bVqUv8KfTFvFejOgZgk9kUnMrxDUGU0D4NR4WSc2u3VMsBNW2fsav77u7GS1jZLyiz+80IbNZYnNUKFlXsBq8jISNasWUNmplqYVlUL1NV5HcaovyPXFdnllR8IXcuQjJAQQpQkIn+k1CN9w2gd5F6hc/Vt5gPAH4cuVcoszFm5Bm56b7O24Kq3i722IOyZy2nkGoyU0KNVJumFiq+b+Jp362nrjUnXmJkyB0JXr15l8ODBtGjRgttuu43Y2FgAHnvsMaZNm1bpDazzmoSDzgaunILEaLNd3i5qalcyQkIIUbKdZ64C0D3Mu8Ln6t/cD1CLpf+78kSFz3fXp9uJvpqhXfZysad1kDsu9jakZuUV6darSNdV4ULoLx7qZrbPNb/cQupOzZU5EJoyZQp2dnbExMTg7FxQlT9mzBhWr15dqY2rF5w8IaSH+nfkerNdXvl9yNckEBJCiGJFJqQSEa9mhFrmL11RER5OBfVFS7afq/D5Tly3npiXsz0uDrZ0CVXnLHpr1UltsViAc1fNh72XhSmIaubvSlM/V7N97vkF2ilSd2qmzIHQ2rVr+d///kfDhg3Ntjdv3pzo6OhiriVK1Gyw+jtyg9lmHxd1qve07DyyZbijEEIUsS86kSHztmpLNwZ7lH+0WGEvDWul/Z1XyYuU+rqqX3JNgcqec4na/EcAkYXqncpi/vpTjP96L1BQD1SYu5NM0mtJmQOh9PR0s0yQybVr13BwKN/aLvVesyHq77NbIK8g++PmaKsV1CVJnZAQQhTxx8GL2t9eznbaXDkV9UT/Jtpw8wuJmTc4ungZhWaOnjG8NVueD9cKuZv6WR6aX55AKCvXwPz1p0nPr/8xTZ5YmGlKlpQs+TwprMyBUP/+/fnmm2+0yzqdDqPRyNtvv82gQYMqtXH1RmBHcPGDnDQ4v0vbrNfr8MofAnq1ClZCFkKI2u7QhWTt7wX/6Vpp59XrdTTOLzY+eyWdrFxDmeo1jUaFPeeucfaK2s3lbG/DY/0am81L1NDL8qSP5QmETuV3DZoUXm3exDQlS6oEQmbKPHz+7bffZvDgwezdu5ecnBxeeOEFjh07xrVr19i2bVtVtLHu0+uh6WA4vFStE2rcX9vl7WLPlbQcba4JIYQQBUxBw/qpA2jmX/H6oMKa+LlwMi6V0wmpzPrjGBcSM1j5bP9SjUr7flc0M38/hn1+VinA3bHIkH5ThsYkxNuJ89cyyxUInb9mnrWy1DVmynDlyPB5M2XOCLVr145Tp07Rr18/7rzzTtLT07n77rs5cOAATZs2rYo21g+m7jEpmBZCiFLJyMnTlqgIcC/favMlMWWENp28TMy1DIwK7MofnVYco1Hhpz0xzPz9GFAQdPi5FS0dMWVoTGYMbwOU7/3+YlKG2eVLSVlFjtECoUqueartypwRAvDw8OCVV16p7LbUb01vAnQQfxRSYsFdncTLNNeEBEJCCGEuIUUdaeVkZ6OtzViZGvuqxcw7CgU/0dcyijscgD8PX+LFX48U2d6zcdFh/YUzQq/e3karGSouUEnOyGXt8ThubRdYJIi6eF0dU7Bn0aJxOxvJCFlS5ozQ4sWLWbZsWZHty5Yt4+uvv66URtVLLj7qivQAUQWjx7wkEBJCCIsu5y+K6u/uUCVLPDX2LVrMfH3AYaIoClm5Br7fFWNxfzcL8xsVDoQGtvS7YaAy6acDPP/LYWauUJe3+n5XNIv+OcORC8ks33/R7Ni37+lQ5Pr2EghZVOYQeu7cuXz++edFtvv7+/PEE0/w0EMPVUrD6qXGA+DiPji/Gzr/Byi0zEYl1Ahti7zCigMXcXGw5cVbW1Xa6AohhLAGU0bIz7VqRiw383ctsi02uWiXE8DzvxxmzbG4YuuH2ljY7upgS/cwLzJyDIT5uJCQqp67uEBlc8RlAFYcvMQtbQN55bejRY75/MGuDGkdoI04LsxUr5QtgZCZMgdCMTExNG7cuMj20NBQYmIsR8KilII6qb9jD2mbTDVC3+yI5s5OwXQNLd+sqQmpWTywqGBEWhM/F8b1DitvS4UQwupMgYO/e9UEQoUnVjQ5cjEZRVGKZKB+2XcBgN1nr1k8l6UaIZ1Ox8//1xtFUUepmTI2eUYFo1FBbyGYMXn6+/0WtzfxdbEYBEFBIFQZy4bUJWXuGvP39+fw4cNFth86dAgfH59KaVS9FdxJ/Z1wXJtPyFQjBDDqsx3FXnXd8XhunreFvw5fsrj/6MVks8vlnbBLCCFqinP5Q9MraxLF0jIFPSWZc2fbUp1Lp9NpAY8pUIHyFzQ38Cr+f2EvxdIWlTkQGjt2LM8++yybNm3CYDBgMBjYuHEjkyZN4r777quKNtYfnqHg6AmGHDUYArrmT8F+I1/+e4bTCWlM/OGAxf2F17kBOH+Dgj8hhKjJFEVhV372pV0Djyq7nY/GdgbArVAx9vO/HCa90HpdltYGa+zryuKHu+PtYs8X47oV2W+JqUYIigYrpV3Y3Nm++I4eqRGyrMxdY3PmzOHcuXMMHjwYW1v16kajkXHjxvHmm29WegPrFZ0OgjqqM0zHHoLgToR4W55w63qn4wsyPJZSqqZ1bBp5OxNzLYOEQuvaCCFEbXM8NoWTcanY2+oJb+lXZbczomMwg1v742xvS9hLK7XtX+84x9PhzQDLM/8HeTrS1M+VfTOGlLqQ275wIHRdsJKSeeNlMf6Y2Lfk88s8QhaVKSOkKApxcXEsWbKEiIgIvv/+e5YvX05UVBRfffUV9vb2Nz6JKJmpeyz2oLbpvdEdgZKzQw6FUqoHr1vJGNRVlAFaB7mZXRZCiNro5eXqEPWbWvrj6Vy1nz2mLMvW5wtWTzB1ywHaXEaFmbrryjKaTa/XYWejHn99Hc/lG7xnzxrRhg4NPUs8RjJClpUpI6QoCs2aNePYsWM0b96c5s2bV1W76q8gNejh0kFtk0/+An2W0q+gPi5XCw2v33P2Gl0amQdNV/KX6Ggd5M6aY/FcTcu5YTGeEELURBk5eRzOr3sc2blBtd1uIx9n/NwcuJyajU+hkWqF1xMDNfNS3lG59jZ6cg2GIsHK9bdxvbu7NCxxP4CDXc0bNXY1LRtvF/sqmf6gtMqUEdLr9TRv3pyrV0ueWVNUgGnkWPwxMKjpVtOaMZnFBEIpWXlmT+wTsSlFjjFlgFoGqBmhPKNCUqasNyPqn4ycPH7cHUP01fRS112ImuVEbCqKog4/v7VdYLXe9oO9QgHz7rD0bPP35h4W5gwqLbtiuq8y8hdTbernotUtgVrucOS1WyyOcNOkXYb0Kzjlf5bkGZUaMXLs+13RdH1jPbP+OGbVdpS5WPqtt97i+eef5+jRovMXiErg3QQcPMCQDQkngIJAKDvX8hM3IcV8XouLSUUn/DLVCAV7OmkvGOkeE/XN0YvJtHl1DdOXH2HgO5vpPGcdPxQzAZ6ouTaciAegf3Pfar9t0/tncmZBFv76bE1F3lu17iuD5YyQi4MtPZsUBFotAlyLzDJtJisZPusNn/XFiYLPiuK+WFen1UfjAHV6mOs/x6pTmQOhcePGsXv3bjp27IiTkxPe3t5mP6KCdLqCOqGL+wC0KN5S11h2noFtkVfMtp2/lkl8oSeVoija6vW+bg7afBbbr7ueEHVZQmoWjy7ZY7YtKSOXWX8c5filollUUXOZJhYsdTYoNwsO/6wGBVejIHID5JUvWPF0VoMOs4xQjvl784u3tirXuaH4gmZTRsjJzgZ/t4J11a4fEVzE4Z8h/TKkxWF/Zj2maojMnPIFQsWVaJRH4YTshxtPV9p5y6rMo8bmz59fBc0QZhp2V0eOXdgL3R7RAqEMC0/cWb8fY+me84D6zeBUfBpxKVn0fHMD3z/ek77NfEnLztO+XXg722uF1TvOXOXhvkUnxxSiLprz1wkSUrNp4ufCO/d0YOeZa7yzJoJcg8KUnw7y+8S+WvZV1GzH87v/2wbfYNi8osDuL2DV8+pl1wDITFSnKAEI7AA3zYRmg0FfusfelBEy7xpTszWDW/kzd1R7s0ClrIoNhPK731zyh/EPaOHH1lOXGd3tBrVB+wuWvtKdXoez/UjSsvPKHAjlGow0f2UVAPPHdKqU2izTfXS002sLzlpDmQMhWUKjGjTsrv6+oH57dXdSH6bMXAPTlx/hzbvaaYVlpiAI1DeFc1cytKDng/Wn6dvMl8R09QXrZGeDk70N93YLYdYfx4hPka4xUfflGowciEniz0OX0Ongw/s6066BB11Dvbm7SwNuencLEfGpPPvjAT5/sKtVizbFjZm6UwCCPW8QcOxbXBAEAaTFm++POww/jIa+k+Dm10t1+6YRasn5NZbZeQZtCSRPZ/sKBUFQfNdYen7XmKkI+7MHurA/JpFeTUqYyDg3C+IKlbFci8LRzoa07DyLX6xLcrbQKLnJPx2slEAoKb978cuHulv1S0iZu8YAoqKimDFjBmPHjiUhIQGAVatWceyYdQue6oyG+ZNvXYmAzCSzVZV/3B2jzQp9/ZDNloFuBHoUvAhNs1JfTc82u2wahn+hmMUDhagrjlxIpu2sNdz7uTor+71dQ8wm3wvycNIWp1x7PL5UMwYL6zpwPlH7u6TJA4nZCWteKbjc6T/gXExN0bYP4NPe8M88SI23fEw+T61GKJfkzFz6vrWRt1dHAODrWvFh/MUtg2EKvNzz64FcHGzp39zPbBLGIhLPAYX6n66dwdm+5ME3xUnNKvi8CaiEJU3yDEatWy/Io2LBY0WVORDasmUL7du3Z9euXSxfvpy0NPVD+dChQ8yaNavSG1gvufiCV36X1cW92F73RD8RlwpA7HVF0f2a+ZqtMWNK4Zq+rZgCIVMfd0qWjBoTddtnWyLNuhge7htW5JgRHYMZ3199vf1vdYQ2sEDUTKZZ8afd3KL4g2J2wldDITcDGvWBVxNh5Cfw3Cl4ORZeS1Z/ZiUVTFmScBw2zFavl5Vc7KlN759p2Xks+ueMNjUJgG9pF381GiEzyeKu4ub6MXXFeTmXUBh9vWtR6m/vpurv9Ms0tVGTF2XtGkst9Hlhqy9XDsXM+cRMsvOMONnZEObjUuHzVUSZ781LL73EG2+8wbp168wmULzpppvYuXNnpTauXtO6x/YW2bXuuPqN5VKhVZDbBLnTJsidMJ+Cmagz8iP+a/ldY175gZCbg/pCyskzkp1n/ZEDQlSF2ORM1hwr+Hb/1t3ti10ZfOrNLfFzc+BKWjaPfb1HhtXXYKZAtYlf0ZXhNXu/Kvj7rgVg+uDW24B9odn6dToY/TW0uwda36FuSzyrZoaK4eZoh6n39KONkWb7fEqTEcpMVIOt/4VC1KYiu4tbIT4p/wutV2knj8xMhKX3q38HdQTURv8n52fgxvMSXS+lUEaoMj43ruX3VPi7O1h9PrsyB0JHjhzhrrvuKrLd39+fK1dkFFKlua5OaEjrAG3X7rNXMRoVLSPUr5kvv03og16v48HeodpxphdOYv5ki9753yRcHQvSyWlZZXsxCFFbzFt7CoNRoWdjb869NZz7ejQq9lgnext+HN8TG72OwxeSi11BXFifqYvIs7jMSPpVOP67+vdj68Er1PJxJt6N4Z4vYcy3cMt/1W17FkGW5ZGENnqd2bpjhZUqI/TjWLiwW/3753HmQ6covlg6MT8j5FHajNCGQjVPzW+BFrcC0NgYDZS9a8z0eQLFT+VSFqbaVc+S5j+qJmUOhDw9PYmNjS2y/cCBAzRoUH0zfNZ5IYUCIUMen/2nC9teuglXB1viU7LZceaqlhFq5OOMg63a73tTqwAW5S/wl5L/hmGaddrbRX2R2uh1Wj+xpanhhagNcvKMfLvjHGuPxZltVxSFx7/ey7L8ep8pJXWhFNLM341780fgTP/tiMVhwlfSsvl0cySPf72H//t2L099t0+moahmpi4iixMIxh+Hz/pAXpY6OW3D0i12qun1NLg3hJw0WDO92MOKW9LjhhmhjGsQs6PgcnYKnDHPCplqfnIN5gGSaX64UtXTJMUUZMVGfw2dxsJQNchrmBeNDYYyd40V/nKQXQmTMZpKNjyqeHmU0ijzqLH77ruPF198kWXLlqHT6TAajWzbto3nnnuOcePGVUUb66eAduDsAxlXIXIddi2H0cDTiZta+fPHoUt8vvUM/vnzAQVf98LQ5rnID4RMqeTCL1JXB1sycgxmBXBC1FRGo8Jbq08SmZCGt4s9g1r6M+GH/dp+vQ6a+7thUBRtMAHA8A5BJY+quc5Lt7Zm/YkEzlxO57PNUVoQFXM1g+d/OaStdl7Y5ojL/PJU7xsP5RaVwpQRMguE8rLh3D/w3aiCbQNfgLKOANTrYfi78ON9cOA7sHOBIa+Zd6ehjlaLuVZ0/h7THG1FGHLh7FbY/pF62ckLmoTDsd/U7FXTm9TRXd/dzW2OI1nHQHIKdT8piqLVRoV636CeRlFgfvuCy21Hqr+9GoOdC/a56YTp4sqUEVIUhX9OFwT8OXlGFEWp0AhLLbNXGzNCb775Jq1atSIkJIS0tDTatGnDgAED6NOnDzNmzKiKNtZPNnbQcaz69/5vtM39mqmjHraeuszR/LV2gvIX9zMxBUKmJ1pCqpo5CnQvCJjcnaRgWtQeW09fZuHWM2w8mcAv+y6YBUEARgUi4lOLBEFvj+pQptvxcLbj1dvV+Uy+3nGOrFwDp+JTGfvFTi0IcrTT08DTSVscMzPXwLSfD0ldUTXIyjVotTNaF9GRX+CtUPMgqNfT0PK28t1Iy2HQ9RH1792fw4J+kJOhZnNiDwMUWcvRxO/6rrHkC2A0wHd3qz+m7E+vp6HV7erf+5bA201gQV9Ii+euK5/jRyKLt5/TTpOaXbCMkn9JI7ZOr4PVhTJZ3k0K/tbrIUB9brfVRZdp+PzltGzt88SkouuVmTJSzuVck60ylTkjZG9vzxdffMHMmTM5evQoaWlpdO7cWRZgrQpdxsGOj+HUGkiJBfcgRndryAu/qi/Gk/mjx4I9zQMhn/wusKSMXH7ee564/C60AFMgpCi459cJpch6Y/VaRk4e2yOv0tDbiVaBlguJa4JlxQxrnzCoKafj0wjycESn07H6aBzJmblMvKkZTw1sWq4izNvaB/G/1Se5kJhJ9/+u17KmId5OvHNPRzqFeGpzniSm59DnrY2cjEul6xvr+fWpPjT2te4ImLrM9GGs1elkJsIfz0Je/gjaht3hoT/BzqmEs5TCbe+CvYv6/nstCt4MKth3z1c09u2hXZwypAVX07MZ2kiH7tw/4BYEfzxj3gVWmHdT6PMM6G3h0FKIXKdm/guZa7eIx68+T3JmLh5Odlotp5dNFo6Hvob296r3MSkGslPBPRiOLjefMwngvh/NL4f0hAt76Kc/QkwZAiHTVCumBWdBDYQqMvdPVn7GqyZMYlrmQMikUaNGhISEAMgEZFXFryWE9ILzO2FeK3hsPbqQ7rQNdudYoSUBmvq5wOUItcCv3T14NepJq0A3TsalsnDrGeJTsvAihW7rRsH3B8HZhx5u09lPcJEoX9QfGTl5tJu1BmN+IqNVoBu/PtUHo6KUvHZRNTsVn8qqI2pd4l/P9KNtsDuHLiTj42JPiLd5l8Vrd7Qt3UlzM8HW0WLXiY1ex8u3tebp7/drQVC7Bu58Ma5bkeyrl4s99/UIYfG2c1xLz+GxJXt4pG8Yn2yKonMjT/Lyi7VHdwspeVFMUSqm+iB3R1t0F/fD2lcgNx08GsGYb8C/LdhWQs2Jja1aU9P8FvjmDvN9vzzKML/OvM4EUnEmzNeZSYObwecD1Akai9P2LhgyWy15MAVqd34M77UsOKbNSDi+gj7644BCckZ+IJRfy/mR3Ufw1wH4a8qN70OvCeB/3VIfIT1gBzTXXySiDF1j8aZ6VG9nrqRloyimkWPlf05n5RdcO9hVfCh+RZWrBV9++SXt2rXD0dERR0dH2rVrx6JFiyq7bRZ98sknhIWF4ejoSM+ePdm9e3eJxy9btoxWrVrh6OhI+/bt+fvvv6ulnZWma6GZvNfNBOCD+wpWHnaxt8HPSYHfnoTdC2HJbXB6PZ/ep34gRCakkZqVx1O2f+KYcFC9UsZVnrk8G1vyJBCqx5btvaAFQaBmGNvOWkP719Zqi1rWBAs2R2FU1G7hdg080Ol0dArxLBIEFSs3E/Jy1G6DXx6Ft5vCfwNhtie82QC+vUvtfk6+qF3ltvZBzL6jLQ08nbi1bSBfPdS9SBBkMmN4G54drGbEz1xJZ+bvx4hLyWLV0TjWHY/njZUn6Dh7LUcuFD83jSgd0/vVw7br4Ktb1KyL3g5ufx+CO1dOEFRYk4Fw85wim10vH+BBm7UAdA7xUtcvuz4I8gor+Lv9aBj1pTqCzaHQsH+3QLj7Cxg+D2YkwN0LAR3Oumx8SNHub2pWLsFcoR8HbtzmYW+rAdegl4vuy29TI118mbrGErU5jOxxtVXfNKb9fIhfKzABaVaugRBdPLdFvwN/ToaUS+U+V0WVOSP06quvMm/ePJ555hl69+4NwI4dO5gyZQoxMTG8/nrppikvj59++ompU6eyYMECevbsyfz58xk6dCgRERH4+/sXOX779u2MHTuWuXPncvvtt/PDDz8wcuRI9u/fT7t27aqsnZWqw31qMd2p1eqLfsXTNDYN8UThbeaj+++YguONefD9KJoA3XWvskdphTNZjLXZqO7v/B848B0uxlTesfucyMzi58uoLKb5impSlqG+UxSFb3acA6BjiCeHzieZ7V+y/RyDC03ZUFXyDEa+3hFNkIcjoT7OnIxNJTY5k5NxqQxpHcCCLVFaF/ATA5rc4GyFGPIg9hDs/RIO/6S+LizJSYOojeoPqF0r7UdDx/t4qE8YD/UJs3w9owF0eshKxsbRg6k3t6B/c1/Gfbm72CLUER//y5HXbpHXQQUkpGYxUv8vk3IWqhuaD4Vhb5nXwlS2vs+qX0gd3NWZmj/sBMCzgce4p2k7Gn39AqRcFxD4NINn9qmFy2nxasBTnA73ml92D4aUi4ToLms1nKlZeQy0OVRyOzv/Bwa+BJ4hxR/j0wwjenx0qdhlxAPtiz+2kKTMHJzJYu7FcfjZXORN41gWnh7BP6evMKrrDdY6K0ZWrpHZtl/TMe4gxKGO8Ov8n3Kdq6J0Shkr/Pz8/Pjwww8ZO3as2fYff/yRZ555pkrnEurZsyfdu3fn448/BsBoNBISEsIzzzzDSy+9VOT4MWPGkJ6ezl9//aVt69WrF506dWLBggWlus2UlBQ8PDxITk7G3d1KNRQ5GeZ91M6+RDa8k++P5zHLtmBBPXpPhJN/5U+rDnuNLRid8yr32mzhf3ZfqG8WE/ep/d7rZpKr2PBWh7+ZOapXlTb//77dy7bIq6x8th+hVp5BVKh2nrnKfQt3EmqfzKr7A3B2cWdXqi/TV57j7NU0bG1s2DfzZm06/6ry5b9nmfPX8RscpTClyUUmOa6C9Cvg7AWOHnDlNFw7o9ZyuAWDIRt0NuqCmmkJapdJYfau0PlBaHMn+DQFdJAWBxGr4dQquLgfbTkCW0foeB+E9VfrMK6dgdRYtVYvNRYyC40ecw1QP8wC2pHm24HoHE9aOiZyWe9HkLs9sUnpDPrkIKYBml1DvXhhaEt6lmE0m1C9+fcJ7t45mlb68+DfBv7vH7UbqzqlX4V3m4NiIeDtO1ltV7u71QEv5fHVMIjZzqK8YTQY8z7D2gexfP8F/H4bQ3+bozDoFXVE3PE/1JFtw/6nzoVUSknvdsEzLYpPAucw4clnS3WdN/8+Qdj2l7nfVv3CEK940jP7UwDOvTW87PcReOfL73j+/AT1gmsgDH8PWt9ernMVp7Sf32V+BuXm5tKtW9G5Gbp27UpeXtUNxc7JyWHfvn1Mn15QEa/X6xkyZAg7dlguStuxYwdTp0412zZ06FBWrFhRZe2sEvbOanr24A/qrKcZV2h26ktmmR49Fz8Y+CJ0e1R9k//2bshJpZv+FGcdC0XYXR9RRw70fZbkf7/AIzOGoKs7UZSebNx/gvbNQvH3qLxAJeHKVRxd3bl2fAuj9NF8/68fL9/ZpdLOL8pHMeTy8bJVvG67ggf0G7FZqr6h9wQ2AnmONizMG866g00Z1atliecq3Q0qaveUnZNWk5ORncN7364g6swpXrU9goIOBbAjDzudAU/SOKcEEqS7yiDva3heOlH8+TNz1KLZ6zl4qKuK93paXbbGLQjsrpuDxdUPAtvDwOfVLxDH/4CD38Plk+ponn1Lbnz/0uK1YdGugKlKKajQ74POXqzObM0ZYzA5F2z54ssGnBo6moOHDtKqVSvGD+nE5YSLOLj54O5cwULfOiApI4fnPv6BwOAQ3vjPYG274eIBWunPY9TZon/k7+oPggBcfNQPbNOkjSYOHurirc7eFTu/T1OI2c7jtqv4PekFIIhrFyO52+YoRnTo296tHtfmDvWnjJK92uOZFkVwxslSX0cfuU4LggACdEn8ZP86Kw09gfIFQi1T1ZUoYoKG0uj/fi7XOSpLmZ9FDz74IJ999hnz5pl3qSxcuJAHHnig0hp2vStXrmAwGAgIME/XBwQEcPKk5Qc0Li7O4vFxcXEWjwfIzs4mO7tgraGUFMuzi1a7vs+qP9mp8O/78M976vaAdjB+U0HfeEgPmH4eNs4pOAbUb8zdH9MuxgUNwuPM1/S+uoJzP5xl0KmvSdW5ogx9GV3PJwumpC+L+ONw4g+4coqrl87if00d4rzMNNrzwNfQ/nd1/gxQ59ZIjQP3BgW3Z0pQmopYFUXthlAM6m9jHiSfV/8P/q3V+UNSLqprAwV2qPibEKhtSrmkns8ab7SVTVHUiTkj/ibnciTZERv5jvSCV7+9q9pFlM8WA0/b/kHC2h0omY+hiz8KHg3VYLu0/19Tl8CJP0ncMB+v7Auk4YRTuxHkXDqC/mokM3W5UJqSjmTAxkF9/ja9SQ16MhPVotPADmqGxpCrBjl5WYAOnDzVCfX0ZRiR4hWmvsb6PAPn/oUjyyD+mPpt26eZ2mXhFgzuQWoWyGhQs1ERq9SM0tVINUuVW3R+GcecREbabIfCzdn4Hg8CbIOsf+3w0+WSgQM5jXpg3/U/6ms7L0v939s5qVmwmi75ohr0+jar0GkOrvmaRRnPkXjalcR9H+HVuBPYOjHz4lMAXAvqj6+T5SHs1eL2+ep7REosjPpCfa4HtKuc95/+U+HAtwD4nF8LdKHp2R8AiPPoSHAF/7dpPu3h/ArCsiJKd4Udn/LSNbU+NbHFveSe349/ZiQ99SfpqT+JYpyPrhyfF8FZ6jpoST5dKH7O9+pRrnf5L7/8krVr19Krl9qlsmvXLmJiYhg3bpxZBub6YKk2mDt3LrNnz7Z2M4rn4AaDX4Ue/wdnNkOzIUULBHU69ZjWI2DvYvUNvPN/1DftfFeajablma9pm30ATh8AHXiQqs6menIl9JkIzW4uXSBgyIO/Jqlp2nzFJf3zfpuA7dPb1LqNre9A+mV10rKAtuqHV/QOdSis0aDeD6UMc1XY2EOLoer8HG1GFv32b6Io6geWrb2a5k65oHZ9JF9Q60rylzXBxU/NovV6qvg3uOQLcGaLmgZ3yV/Z2sZBLdw05EBWkvoh7dkIstPUeUQuHVA/1BRF7c7xb6POXWJfyd2G187Ctvnq9Aup6qgr+/yfXMWGiy6tCRv9FjTurx6fnQopsaSf3U3Sylk0MF6GLW8VnG/PIuiUv3ZR3BH1fhpyICddDUo9G6kB6bUzcOkg5Ki1PaaPK1cy4ejPOIHaK6U4ku4YiEdIWxz9m6pBrb1z/mguvfp/CmgHvs3V4Nm1aB0gAH6lmzm61HQ69X9i+r/cSIfR6g/kL6Z5TZ0wLzdD/R+BOtlfzA5IjSMzM52ME+vw0aWSp+ix1Rlx1Km1IM5kQ8w/6s/1GnZXA7CMq/lBWZAaGIb2KdtwcUMeHPgGks5DzyfB7bpaMEVRuxZ1Osv/85wM9TGKO6z+nF6ndtWkJcDSB9TXb8+n1Of09o/UYHLEB9D85vzbz1WfSzq9GkCE9FCPNcm4RqcT6pc4L10a/PmI2c1nKXYkD3qLYtaRrx7O3vD4evV/Vdmjpr2bsCFoPINjv6DfqblwpBndk9RBPlEtHie4gqfPDOgKQOecvbDlHTUbWpzki2YzbOcNfIljG3/GP6rgfcGwdwm2PR4tczv889Ti6CyPKqzvKqUy1wgNGjSodCfW6di4ceONDyylnJwcnJ2d+eWXXxg5cqS2/aGHHiIpKYnff/+9yHUaNWrE1KlTmTx5srZt1qxZrFixgkOHLBeeWcoIhYSEWLdGqArsi07k3y+m8LTtnyjo+DB3JHY6A/9n86f2poxPM3hktdp9UJwjv8CqF7R5MI47duZwmgfuugz66I+Rgx0f5t3FfmNz/nawMIqhPOzdwNFdzQTp9AXfztMTCo5x8lKDREOu+oF0+aT6weEWCOf3QOoNRijYu2kf5ICatWrYTa0ZaRKufuAf+00dqWfIKfY0peYaoGYi3ILUzEL0NjVT03oEOPuqHxjNbyn6oWWiKGqWJCkGLu5TP3hP/KkVCRttHPg9pztHjWEcNjZh4kMPMLBV8QWcz/+wjabHP6OVLoZ4xYtBNgfx1yWV7T7p9MTZNeST9JtYZ+jKXTbbCNXFcVwJZZ+xBaOG3cKjAyo5iKklftx+mtX/7iK0aRuiYi/jRSqhjZuzZccuhrKNcQ5b8LDJUzOhFjJMRXg0gka91FFJWSnqSuqJ0Wpw7eihBpMpF9Xn2aWDcLlQV6NPMzVIt3NSXx9ZKZBwTN037B2wdcifZdlRHX13fhdaLVUVuqK4s87QlTscD+KSV9D1+VTOJN6Z9Squxaz3VRcsWfE3Dx80r8O9pHhzbPQ2bm5XsVBo95mr2CwZSlf9aXUuo4l7LBeb56STd2YrtkvvA+CsMYCgmSf4YXskeetm84TtyoJjp55UM6WlpShkvh6Ek5LJ9ltX06dX7wrdp+KUtkaozIGQNfXs2ZMePXrw0Udqf7zRaKRRo0ZMnDix2GLpjIwM/vzzT21bnz596NChQ+0qlq4C569l0P/tTdhgwKjToyjqt5qGugT+7HkCr2Pfqm/ALYbBfd9b7mKIOwIL+mN6U9zvfxd3x9yDaZVjE0c7PVm5Rgbr9zHf7lPcdJng6KkWd/d+Wv3WEb1NPTignZoZ0unVN3GdjXrbehvt77h02HL6Mrc2dcLO3p5lhxOJvprBc+1ScT63Ua3xSD5f8j9AZ6NmmxzcwK8VeDRQR4X4teRq6HCOJNsxwLAb/ab/wtXTJZ8rsIPa5vQrarszrhUEWnbO6jZT15N/G/UDKyc9v2ZGD6fXQ3JMybcB6ptWy2HqhGzOPmoWxreFmjnZ+g7EHy16nSbhKB3v5+ndPqyKUgP854e2ZMKgktPrq4/G8eR3+7TLrmTwec/L9LU5qd62b3O17Y4e6vDlc1vVLhF0aiat+RAu2jdmwLztGIwKb97Vnpd/O6Kdb9G4bgxpU/Wj0mqbveeucc8CteZx6RO9uJiYSY9gB0IcM9QlGhLPqRk3G3u10PviAcgux7B8e1f1+Zd+uWINvq5bNTZwENccG9H23Nfq86PNSLW9sQfLdNprNj48lDGFI0oT3B1t2fdEA078/CqT44dxRgkud4FubfHF1jO4rZ3CfbabtW2v5D7K3U/MpGtoxbrfTsWnMvT9zax2fIWWREPjAfDg7wXlCYnR6pxIWUlm1+uX/QH/vPkQH2w4zfz1p+mpO8FPDvlTCwx/D7o/XvpGZFyDt9UC73/uPUr/NiWMdKuAKiuWtqapU6fy0EMP0a1bN3r06MH8+fNJT0/nkUfU1Om4ceNo0KABc+fOBWDSpEkMHDiQ9957j+HDh7N06VL27t3LwoULrXk3agTTNO0GbEBRl98I8HDk0HnY2fRWhrXoq66MfGqVOmOpKfVvkpYAP94PKGDnwpV7/+CeryzPPdM9zJvh7YN4aTncpP+SPS/0BQd3dp5LZOnyUzwxoCltuqmPocGooNepSyacu5rO4QtJdAv1xtHOhtPxqVxMyuR/q09yJS2H7xt6kJqVx9kr6uig6Kv+LHroJXQDX4DI9WrK3s5JDTqaDlIzN0nn1eLY0L5qYKHTm9VDZeTkMfqjfzlzOZ1xvZvz+jN71UxL3FF1xejjf6gZG3TqOTvdr67qXDg9rijqB4zORi2sNHU12LuYzyFikpetdhXu+VLNAAR2UAON3Ey1/sSYpwZtl0+qWZ6SOHnlLzbZHVreCg26sv54PKui9gKwbsoAmge4lXwOILyleRYwDWfeje1I36eftnyF5kPMLu4+e43xX+zGYFTo3cSH+3s2ItDDgf+tiuDx/o0lCCpGtzBveoR5s/vcNe5buFPb3irQjYUPjqJRl+vmTspJh6O/qn8nX1CfZ47u4BGiZnpSY9XRcBlX1YyQezA06gPtRqnPlcsnIOGEGrBnXFE/oBzc1BmId34GB79Ts6O9nlKf044e6si7zGvq66dBF0g4CUuGY3Rw45ZzD5CKM5ueeZnGboqaJTAa4cjPcGyF+n7S+UH1g9PWQf3ysOZl9fnv0xzu+Ag8GjD6i9NEpauT+KVk5dH8w2hArXHs0LAW1EpVkLuTLW/m3W8WCP1l6MVjlbBAqbeLPQp6tuW1pqVttBpgf94fHlkFp9fCr48Vuc4DOdO5oPih0+m4vUMQ89efZpfSmjjFi0BdolorVRZJ0QAkKJ7YOFh/cECtCoTGjBnD5cuXefXVV4mLi6NTp06sXr1aK4iOiYlBX+hDrU+fPvzwww/MmDGDl19+mebNm7NixYraM4dQFXKwtaFlgBsR8Wr3T5tgdzyc7Dh0PokTcakMG3KH+oEcd1idY6VwIKQosOzhgizGAz/za7QXRiWe7mFeTL+tNccupTBzhZqhaBngRr/mao9+co4exdGDxIxc/u/bfSRn5rLl1GW+fLg77o52PPTVbm2V5Rs5fN0EdRtOJrD2eDxD2waqtUIthpb5//LflSc4c1kNrL7dGc3DfcJo4udVUDPSf9qNT3J9bYVOV3yXFqgfCF3GqT/XM02Kpihql8SuzwuCu8sRcCVCDbj6TbFYy3TsUjLjv1GDoP8b0KRUQRCo095vmDaQrFwDzva2DHp3Mwdiklh9NJaBLfz53+qTtAhwIyMnj6MXkxnVtSH9m/uhKAqz/zzOkvx1kuxt9bxwqzry7KZWAdzUSgKgG/lP71B2nzNf3PVkXCrvrz/F3Lvbk2dUCrqF7F0sP28KM9V1WRLQVv2xZOQn6uzKdk7qc7Q4/q1g2km2nb5M6hJ1wr9DCQYaN2jAvHWn2ByRwFt330ab++8rel0XX3USwbsLvpxm5xk4e03NHo7v35gv/jlrdpXCE8rWVe6OdiTjyr3ZM/m05UGmnGpLMq54u1Q8EDItcvqV4VYetV2tbow/qk7Ya2GU5FZDe7YZCz4zm/m7sW/GELq+sZ6v84byot1StUu+LPKPv6D44mBr/Zmla1UgBDBx4kQmTpxocd/mzZuLbBs9ejSjR48uerBgdLeGvLFSrRW4q3MDMnMN/HbgIn8cvMiUIc3R9ZuszsR76Af1w3zA8+oImu0fFnRl3fkJhPVjzxa1wPjWdkF0aeRFl0ZenLuSzv6YRJ65qTmO9uqTPcdgJCkjl80RCdqsqYkZudz96fZStdnDyY6uoWr57caTCbg62LL86T78sCuGJdvPMW/tKfo28y1X/cCmkwl8v0t9gTrY6snOM/L++tN8NLby3njzDEZ2n71GAy8n/N0cScrMKXbGYjM6ndql1ui6OZ/SEgoyT9dRFIVHFquPi4+LPRNvKttok6Z+avbKaFS07s0nv9uv/W8KW3s8nvH9m/DTnvPEpajf5Ae08OP5W1rSvh58g69Md3QMJsTLibjkLLZFXeG7nepz8rcDF/ntQMHs1//p1YhXb2+LfTk+SPaeu8aBmCQe7huGnU0J13fyLN0JbezYc76gi2zlkVh6NfHhww1qt/LPe8+bLX+SlJFDnlHh7yOxtA320F7TAOeuZGBUwM3RlpeGtSY5M5ef9xZMWFgf1nIzLceyW2nNk5l92GtMRK+jUub1srXR08jbmZhr/vzs8Rj3Jn+p7rg+CJp6koStXzLl3yZcX+7gk7+47Eklv0vrUilmvC5MC4T8CCvp+VdNal0gJCrPo30bE+rjQlM/F5r4uZKencdrfxzj3NUMdp29Ri/fQoWsB79Xf7waq3MZAXQfr80Eapr9t21wQT/szPxVvE28nO1IzMglPjWLZflvbPd1D+FCYib/RhZMxHlb+0B8XByYMKgZgR6ORF1Ow9vZHldHW+1NW1EUjl1KwdvFnmBPJ54d3Jxle88TEZ/KfQt38PuEftiUcsHNIxeSeWTJbq6k5Wj/l3u6NuS2D//hz0OXePm2VsUGKylZuSzYHMW5q+m8dGtrPJzt1HWQLIwkSc3KZfSCHdr/yuTuzg14+54O2JbnDaG4kVSoH5wJ+Qskvntvx3LPaKzX6/jyoe48sGgXYHnV6YwcAx9sKKilGtI6gEUPFZ1vTJRO5/zVzYe1D2LOne14dMkeNkWY1/N8tzMGX1cHJg1uXqb1HpMzc7U6JAc7PeN6h5WrjSsOXGTyTwcB+PzBruwtlMVadzyejoUC4KjLBUFSdp6B2z/6V1vIE8wn5TudoL4+mvm7YqPX8dbdHcwCofqg8ONper15OduXaxFhSyYOasYLvx5mgfEO7n1pFrx13QD2gS+CexAXOkzg6r+Wv6T6utpzIC3/y9XV02pw41nKgfCFAqHmNSAQsn4LhNXo9TpubhNAk/xv/i4OttzZqQEA766JQPFvq3a56AvFy6YgyMEDbnoFULMcpje1kr6t+bupw9lXHo5lxxl1lNl/eoXy3r0daRPkTqiPMz890YtPH+jKnJHtCPRQj2/q54qXi73ZN1edTke7Bh4Ee6oBireLPV8/2gMnOxuOXkwpca0sRVH4dmc0vx+8yPhv9jLi43+1IMjORsekIc1pE+yufUtdf7z4c034fj+fbo7i7yNxDHhnEx1nr6Xf/zax88xVfj94kZkrjvLDrhiycg38tOd8kSAIYPmBi3y+9Uyxt1Fei7edA9TMwaCWxQdMpdG3mS/PD1W7uNwdbfn1qT7sfmUw+2YMYdNz4epK4Pn6N/flrVGlm7pf3JhOp+Orh7sza0SbIvvmrz/N22tKOR9MvrXHCuZRW38iocj+rFwD89ef4psd5zAai46luZqWTXxKlhYEAfzft/vYc1133rtrT2l/b4+6SmK6+hr7Zd8FsyAI1KUzTCIT1KCpWf77kl6vY8bw1gBMu7l+jDJs6l/wPnruqtpVb3qvqww9Gqtd6JeSMlEc3NUVB9Cp7/WTj2hd8tm5xU9f8p9eoSThxiXH/GCo8Lx1N1IoECoxI1lNJCMkzEwa3JzfDlxgb3Qiyw9cYtSQ12DIa+polX1LIPawOltvt8e0eXpm/XFMu76fa/G1BC0D1ZqkjzZGAnBTK3/aNVC/Nf49qZRztpSgW5g3j/QN49PNUXy8KZKb2wRo36wURdH+Xrbvgla/ZOLtYs/NrQMIb+mnpaWHtg1gX3QiM38/RlN/V3o38TH7phZ1OY1/ThddUuZiUqZZoStgNmJq5u1tyDUY6dLIi8MXknhj5Qm+3n6OJwY0Mct4leVb/vXikrM4cjEZnQ6mDKmcD4+nw5syrF0gAe6OuBQKfHxcHdj0fDgOtnpc7G0r7VurKKDT6Xikb2Me6h3G30djCfNx4ZkfD3D2Sjqfb4miW6gX4S39tSzoqfhUPtscxUN9wugU4ml2rj8PFxS2bj11menLj5BnMBLe0p9b2wUy/pu92vPazdGWuzoXrCW188xVHvxyF7mGogFSrkHB3dEWN0c7rc4v2MORHIORK2k5dJ6zjuduaWEWIJmsOHCRAzFJRF/N4HisOolti0L1bI/3b8IjfRuXOstb2/m7OTK2RyN+3B1Dav7aLK0CS1ffVxqmL5lZuWqpgpdvM3UYfWaiWVZHXWFedV9385FdzvbqSOLVPg/x6MWZsO9rde23VrfduAFSIyRqskAPR/5vQFM+2HCa//59ghEdg9UaBK8wNSCyYF1+xsTPzaHED8HeTX3441DB/D13dqro1GBFPdQnjC/+OcPhC8l8tiWKp8Ob8eehS7z062F8XB2YPqwVc/82X65hTLcQ3ry7fZE32dFdQ1iw5QzX0nO4/4tdDGrpx4IHu+Jgq74BLN+vpusHtfTjo/u78NW/Z2nu78obK08UW/DdpZEn43qHagFPpxBPFmyJIiE1m40nExjaNpDLqdmMXrAdfzdHFj/S3SzoKI1cg5G3V5/Uzu9TQnBaFjqdTsseXs+3km5DlEyv13F7B/V1s3bKALq9sZ7kzFwe+3ovD/cJ45XhrbmQmMkt728F0GqKvnm0BysOXuRKWg5bT5l3sf24W/1QWmZhJfF/Tl0hxMuZZ388QICHI2cup5sFQcPaBbLqaEGGqUuoF++O7ki3N9YDMO2Wlqw9HseaY+p7hKUgCODNv81XB7DR6xjRMbjItvqkcJkBFIz0rQyOdjb4utpzJS2Hi0mZeLnYqyNVr2PqlnOys+GNkeaDjJzs1felXQ69ebTrw+oX5V8fg0dXQ1DH4m/caFQnewXOK/41IiNk/RaIGmfCoGZ4ONlxLT2HU/FFu3IKS0jN0upQ/pzYr8Rjb2sXpHWdTR/WSntDr0wB7o48c5P6gn57dQRhL63kmR8PkJ5jIOZaBk99v5/EjFya+bvy+YNd+XBsZ/57VzuLb7JeLvYsfLArXUO90OlgU8RlLZOUkZPHL/kfHKO6NsTVwZZnBzdnWPsgfnu6Dw/0bMTorg05Onso21+6ifCWfozv35gfxvcye+Hb2+q5p6v6TeubHec4FZ/K/327l3NXM9h97hofb4rEYFQ4f60Uk+rle3v1SZbnfwA+0rf0izGK2sXORs8DPQu+vS/Zfo7mr6xi0Lubixw77qvdLN9/UQuCejT2ZuWzJb9eQc16zvrjGJeSszgQk6QNcDAZ3iGIW9sWTMzZKtAdX1cH7urcgDZB7gxtF0hvC4vLPtgrlM3PhdOlkafF2/3q4e5a1qK+crQzn7vNlKmuLKa6x7jkrGKPMQVCnUI8i9QwOue3b83xBOL7vQFNB6tzz/01tWCpJEsSz0JeJtmKHdFKAHY21g9wJSMkirC31dPM35V90Ymcu5qudV9Z0uO/GwC1cO5Gb1weznZsnDawQl0+pfHMTc34dHMkWcX0b9vZ6Fjwn64087ec3SisW5g3vz7Vh0X/nOGNlSf4ee8F7u8Zyo+7YohPycbbxZ4hrc2HhPu7O/LfuwpqZFwdbFnySI9ib+OBno1Y9M8ZtkVe1b7Jm3y2OYqf9pznWnoOM4a35vH+xU9HH3U5jW93RGtD198f05E7OlZ+sClqjqfCm3LwfBJ7oxPJKVTEbmej49en+vDm3yfYeca8dsfX1YH/jmxH8wA3dky/iU83RdEtzIu3V0domcx37unA878c5tB1U1QAPDu4OU+HNyUlKxd/N0f2RRfM+hzqo85z9P6YTtq2fs2Lzkz/cN8wwnxd6Nfcj/0xSdr24R2C+OR+WZgZ1IloC6vsQMjdSf34T88pfrH0tPxuOReHohPqFt62aPt5Xhn5GXzQES7uhVOrzZdNKeykOiP1IaUJRvTlGvVY2SQQEhaFeDmxLzqRSyXM6ZOUUbC0RPew0s12WtVBkOk2nh/aijl/Hde2zbmzLUPbBfL19nN0bOhZqiCosMf7N+F4bArL919k5CfbtO0fje1c5JtbWYV4O3N/z0Z8syNa2/bVw9346t9z/Bt5hWv5Raafbo7iwd6hWtdcYX8eusQzPxYMYb2lTYBZbYeom9wc7fhhvDqlwsrDseyPSeS29kE08nbGz82BpU/05p/Tl3nwy92AWuf19KBm2vQSQR5OzMnv8jANlAC1NuSl5Ucw5BdL927iw7mr6bg52jJpcHNs9Drted+gUBGvKRAqrJm/K4vGdePx/PmsoGBqhrs7N9CG2AN8WA/mCCotR9vrM0IVn0OoMKf8xy8zx1DsMdfS1Wy/pfmL2gQVfEG2tdGrc6X1/D91fcMf71NXDrhppvmaj4oCB9UFZFcY1IxkTegak0BIWOSV/8RPzMgt9pgz+TM6g/k3wJrg0b5h9GzsTRM/F1Iy87Rs1fNDW5X7nNNuaclfh2LJMajfvPs396Vvs8pZ+nHiTc1YdTQOo1Fh2ZO9aeLnSttgDx7/ei+xyVlcScvmWnoO05cfYd69ncyuG5mQyou/HtYuD2kdUGTqAlH3De8QxPAORdd76t/cjz2vDOFiUmaRwuniONja0LWRlzax47ODm9MpxBO9vmitTiPvguAnzMfyqNEhbQJ4bUQb3vz7JEse6V5wvK8Lh169hds//oeODT3rXR1QSZzsq7ZrzCE/EMrKtRwIHbmQrGXrvCwEQo0KBb3Bpt6AfpPh0I/q2og7PlaX4+lfsBA7iefg8gkUvR1/GXoCEgiJGswrfyr3pBICocv5tUGdG3lWOCtS2UzD6wGc7Svnad7A04mP7+/Mz3svEObjzNM3WK+rLPzdHNnyfDi2+oJUcYC7I38+o35rWn00lie/28/y/RcJdHfk7i4NtazWq78fIyPHQOdGnnz3WM8yF1eLus/PzQE/t7IV284b05H/LNqFXqeja6hXsV0Y4S39GdJaLXoNKqF7/OG+jbm/Z2iR83g427H1+UHVki2uTa4PfCo7ENIyQhZKCOKSsxjx8b/aZZ9iZrS+o2Mwfxy6VFBA7+QFY3+EL29Rlwba86U6BYvpsb20H4A8v7akRKvzRNWE4FfeMYVFns7qi65w99f1TF023pWw/k1tcUvbQG4pVBxamUoK2G5tF0TPxt7sOnuNTzdH8enmKOaP6USPxt5sj7qKXqd200kQJCpLQy9n1k0diF5X8oeVva2eRQ91L3b/9cdaIkFQUT6u5u+rHs6VGwiZapAsZYRWHjFfO8zbxXIQbZtf6JxrKBRMNegKL0bDey0h5QJ8fw/c9yPY2qvr3gHZ/h0hGuxrQDYIZNSYKIZpPqBVR+PMn+SFaIFQJax/I27splbmkyJO/ukgfd7aCED7hp409CpanyFERdjZ6GvEN/b6yOu6L5ielZwR0ucHn4VnhAd10EXh+koAbxfLt20KZIp8Rji4wm3vADp1AeyD36nb85fiyPBTh9fXhBFjIIGQKEa3QsXP/5y+bPEYCYSq1yN9G/PKba354L5OtGtgPsdIryalK1YXQtQOhcsNXB1stQkMK8vfR+Isbv84f8LbworLCNlpgZCF4fKd7ochs9S/j/8OuVlw6SAAab7qqNqaMGIMpGtMFMPPzQFXB1vSsvO0WqDrSSBUvext9YwfoA6fv7NTA/678ri2MvdNFVxCQwhR8xyadQvnr2UQ7OlU6d2HrYPc+Oe0+t6eazBqQc31S6UA+BdTX2ZXXEbIpNXtsP41iN6eHwylg3sD0t2aA5drRKE0SEZIlODmNur8ONdPomZyVQIhq3r5ttbMvbs9/72rnbZ2kBCi7vBwsqNdA48qeY8tvHbdk9/u0/4OtrDAdHFF8Ha2FmqECvNpBp6hYMiBvyar29reRU7+4ZIREjWeaZRCSmbRCbeupGVrs9RKIGQdOp2OsT1KudqzEEIU0sCzoKZww8kEElKyOJ+YqU2ZUFhx2Sg7fQldY+oVofktsOcLddZpgHajyMlSIyHJCIkaz91RjZMtZYR+LbQuUctKXAxQCCFE1bt+5uqzV9IZ9dn2Mp3DFMjkFJcRAmg3quBvrzAI7qxlkCQQEjWes0PxU7BHxKlrkDXydpbRSkIIUctcn+U5n2h5FQGnEuaI07rG8koIhEJ6QoNu6t+9ngadTguEpGtM1HgO+U/SHAtP8qj8WaWnDyv/TM1CCCFqhqtp5oNi3hvdkS/+OcNrd7Qt9jqm4fN5xhIWWdXr4ZFVkBQDvuoktFogVEOGz0sgJIplX0wgpCgKUQlpAGVes0sIIUTN0DXUS1s0N/pahtm+/i18GdW15PUKTZ8R2XnFr1cGqJMp+hbMxG9a1V66xkSNZ4r2s68LhFKy8kjLVrvLQrylW0wIIWqj7x7rqQ24OB2faravpC6x64/JKGHhVktMxdUSCIkaz7Qo3/UZoSv5KVQ3R9sat8aYEEKI0nGyt6FNkDrY5VR8mtm+0ry3m5YFysguayBUs2qEakYrRI1UkBEyf5KbJlg0LcMhhBCidvLMX8rj+tHBpcnWmGa7zsgtOqCmJKYv17LWmKjxtGJpg+WMkK8EQkIIUatdv6ZZWWiBUJm7xkw1QjWjWFoCIVEsUyB09GKKWfeYlhEqZtp1IYQQtYNnBVa1L2/XWI7MIyRqi8L9t4v+PUNe/pN35eFYAHxdZUZpIYSozQLczZfP6BjiyV/P9CvVdZ20jFDZusZy89RiaakREjVeeqF059urI3hnTQQAkZfVojovWVpDCCFqNT83BxoVGv07plsI7Rp4lOq6Lg5qIJSZW9aMkHq8ZIREjdfUz8Xs8udb1ayQqahuVJeS55gQQghR843sFKz9bQpuSsPZTu0ayzUoFifeLY5p+LxkhESN19DLmV+f6m227UpaDooCNnodDTyLrlIshBCidvEv1D3mYl/6eZZNXWMAmWUomJZRY6JW6RrqzYZpA7XLj3+zB4BgT0f0+ppR8S+EEKL8/AsNfHEuQ0bI3lavjfwqyxB6KZYWtU5TP1e88kcWHL2YAsCglv7WbJIQQohKElwou+9chowQFMwunV6GkWOmRVpNi7ZamwRColSGtQ8yu/zMTc2t1BIhhBCVqfCakWUNTUyBU1m6xgoWXa0ZIUjNaIWo8WYMb03HEE8Amvi5yBxCQghRRzja2fBo38b0b+5L22D3Ml3X3UkNhM4nZtzgyAI1rVhaVp8XpeJsb8tvT/VhxcGL9Gjsbe3mCCGEqESvjmhTruv1auLDqfg09kcnctt1PQfFqWmrz0sgJEpNr9dxtwyZF0IIkS/IQ60vupaRU+rr5EqxtBBCCCHqAtNAmuSM3BscWUBWnxdCCCFEnWBavT6xHBkhe1l0VQghhBC1mWnNyYT8xbhLI6eG1QjVjFYIIYQQotZp5KOuU3YpKbPUy2zk5I8ak0BICCGEELWan6sDzvY2GBW4UMoh9FIjJIQQQog6QafTaavXR18tXSAkXWNCCCGEqDMa+7oAEHU5rVTHy8zSQgghhKgzWgS4ARARl1qq47V5hGStMSGEEELUdq2D1EDoZCkDoexcNRBysC39SvdVSQIhIYQQQpRbq0B1fbKI+FTyDDceOZaVpy7Q6mhXM0KQmtGKUrh27RoPPPAA7u7ueHp68thjj5GWVnJ/ZHh4ODqdzuznySefrKYWCyGEEHVfI29nXB1syckzEnmDOiGDUdEWXXWUjFDZPPDAAxw7dox169bx119/sXXrVp544okbXm/8+PHExsZqP2+//XY1tFYIIYSoH/R6He0aqFmhwxeSSzw2Oz8bBOBQQzJCtWLR1RMnTrB69Wr27NlDt27dAPjoo4+47bbbePfddwkODi72us7OzgQGBlZXU4UQQoh6p30DD3aeucbxSyklHpeVW9B1JhmhMtixYweenp5aEAQwZMgQ9Ho9u3btKvG633//Pb6+vrRr147p06eTkVG6eQ6EEEIIUTqNfNQh9JeSMks8zpQRsrfRo9fXjFFjtSIjFBcXh7+/v9k2W1tbvL29iYuLK/Z6999/P6GhoQQHB3P48GFefPFFIiIiWL58ebHXyc7OJju7YM2UlJSSo1shhBCivgtwcwAg/gZrjmVpI8ZqTh7GqoHQSy+9xP/+978Sjzlx4kS5z1+4hqh9+/YEBQUxePBgoqKiaNq0qcXrzJ07l9mzZ5f7NoUQQoj6JsDdEYD45KwSj8vKVTNCDnY1o1sMrBwITZs2jYcffrjEY5o0aUJgYCAJCQlm2/Py8rh27VqZ6n969uwJQGRkZLGB0PTp05k6dap2OSUlhZCQkFLfhhBCCFHfBHqogdDltGwMRgWbYrq9MnNr1tB5sHIg5Ofnh5+f3w2P6927N0lJSezbt4+uXbsCsHHjRoxGoxbclMbBgwcBCAoKKvYYBwcHHBwcSn1OIYQQor7zcbFHr1OHx19Nz8bfzdHicckZuQB4OttVZ/NKVHNCshK0bt2aW2+9lfHjx7N79262bdvGxIkTue+++7QRYxcvXqRVq1bs3r0bgKioKObMmcO+ffs4d+4cf/zxB+PGjWPAgAF06NDBmndHCCGEqFNsbfT4uubXCSUXXyeUmJEDgJezfbW0qzRqRSAE6uivVq1aMXjwYG677Tb69evHwoULtf25ublERERoo8Ls7e1Zv349t9xyC61atWLatGmMGjWKP//801p3QQghhKizTN1j8SnF1wkl5WeEPJxqTkaoVowaA/D29uaHH34odn9YWBiKomiXQ0JC2LJlS3U0TQghhKj31O6wZOJKDIQkIySEEEKIOijAXe0aSyghEErMzwh5SY2QEEIIIeqSQNMQ+pQb1wh5SEZICCGEEHWJaS6hkrvGJCMkhBBCiDrIVCxd0jIbSZlSIySEEEKIOijE2xmAC4mZZoOXCktMl3mEhBBCCFEHBXs6otOps0dHXU6zeIxp1JinZISEEEIIUZc42NpokyoOmbeVi9d1keXkGUnPUZfYkBohIYQQQtQ5lwutPn8wJslsX3Jmrva3u6MEQkIIIYSoY6bd3EL7O/pautm+jJw8AJztbdAXsyirNUggJIQQQohKMWFQM1oFugGQcN18QqaV553sbKq9XSWRQEgIIYQQlUKv1zGqS0PAvCsMIDO/PsjJXgIhIYQQQtRRHvmF0KZZpE20QEgyQkIIIYSoq0yTJZrWFTMxdY05S0ZICCGEEHWVabLE5OsyQhn5GSFHyQgJIYQQoq7y0rrGJCMkhBBCiHrGw0ntGkvJysVgLFhqIytXiqWFEEIIUceZusYUBVIKjRzL0Iqlba3SruJIICSEEEKISmNno8fDSQ2GEgrNNF0wfL5mhR41qzVCCCGEqPWCPZ0AuJiUoW0rqBGSjJAQQggh6rAQLzUQWnM0XtuWKaPGhBBCCFEfjOqqzi69/kQ8iqIWTJtqhGTUmBBCCCHqtEEt/bHV67iankNschZQaNSYZISEEEIIUZfZ2+pp7OsCwOmENEAWXRVCCCFEPRKUXzB9OX/kWEZOHiDzCAkhhBCiHvB1USdWvJKmBkKZuUZAMkJCCCGEqAd83RwAuJKfEcrMzwhJsbQQQggh6jzTpIrJ+bNLm2qEHCUQEkIIIURd5+aoTpyYlq1mgjJz1K4xyQgJIYQQos4rGgjlF0tLjZAQQggh6jpXB7VrLCUrD6NRIUOW2BBCCCFEfeHqkJ8RysolNSuP/AmmtdqhmkICISGEEEJUusJdYylZasG0k50N9rY1K/SoWa0RQgghRJ1gCoRSs/K0kWPuTjWrWwwkEBJCCCFEFXBzVLvAMnIMJGbkADWvWwwkEBJCCCFEFXBxKBgddikpEwB3RwmEhBBCCFEPONgW1ANdTFQDIckICSGEEKLecM+vE7pgyghJICSEEEKI+sI0hF4yQkIIIYSod1zzM0IXtRohGTUmhBBCiHrCLX926YvSNSaEEEKI+saUETLNKi2BkBBCCCHqDTcH864wqRESQgghRL1xfQZIAiEhhBBC1BuezuaBj0yoWAH//e9/6dOnD87Oznh6epbqOoqi8OqrrxIUFISTkxNDhgzh9OnTVdtQIYQQQgDgeX1GyFkCoXLLyclh9OjRPPXUU6W+zttvv82HH37IggUL2LVrFy4uLgwdOpSsrKwqbKkQQgghoGjgUxOHz9e8FhVj9uzZACxZsqRUxyuKwvz585kxYwZ33nknAN988w0BAQGsWLGC++67r6qaKoQQQgjA08le+1uvK5hgsSapNRmhsjp79ixxcXEMGTJE2+bh4UHPnj3ZsWOHFVsmhBBC1A+FM0IeTnbodDortsaymheaVZK4uDgAAgICzLYHBARo+yzJzs4mOztbu5ySklI1DRRCCCHquMKjxHxdHazYkuJZNSP00ksvodPpSvw5efJktbZp7ty5eHh4aD8hISHVevtCCCFEXVG4WNrLxb6EI63HqhmhadOm8fDDD5d4TJMmTcp17sDAQADi4+MJCgrStsfHx9OpU6dirzd9+nSmTp2qXU5JSZFgSAghhCiHwhkhg1GxYkuKZ9VAyM/PDz8/vyo5d+PGjQkMDGTDhg1a4JOSksKuXbtKHHnm4OCAg0PNTN8JIYQQtYmtTUHHU3N/Vyu2pHi1plg6JiaGgwcPEhMTg8Fg4ODBgxw8eJC0tDTtmFatWvHbb78BoNPpmDx5Mm+88QZ//PEHR44cYdy4cQQHBzNy5Egr3QshhBCifvn8wa7c0iaAl4a1snZTLKo1xdKvvvoqX3/9tXa5c+fOAGzatInw8HAAIiIiSE5O1o554YUXSE9P54knniApKYl+/fqxevVqHB0dq7XtQgghRH01tG0gQ9sGWrsZxdIpilIzO+1qiJSUFDw8PEhOTsbd3d3azRFCCCFEKZT287vWdI0JIYQQQlQ2CYSEEEIIUW9JICSEEEKIeksCISGEEELUWxIICSGEEKLekkBICCGEEPWWBEJCCCGEqLckEBJCCCFEvSWBkBBCCCHqLQmEhBBCCFFvSSAkhBBCiHpLAiEhhBBC1FsSCAkhhBCi3rK1dgNqOkVRAHUVWyGEEELUDqbPbdPneHEkELqB1NRUAEJCQqzcEiGEEEKUVWpqKh4eHsXu1yk3CpXqOaPRyKVLl3Bzc0On01m7OVUuJSWFkJAQzp8/j7u7u7WbU63q632vr/cb5L7Xx/teX+831L/7rigKqampBAcHo9cXXwkkGaEb0Ov1NGzY0NrNqHbu7u714oViSX297/X1foPc9/p43+vr/Yb6dd9LygSZSLG0EEIIIeotCYSEEEIIUW9JICTMODg4MGvWLBwcHKzdlGpXX+97fb3fIPe9Pt73+nq/oX7f95JIsbQQQggh6i3JCAkhhBCi3pJASAghhBD1lgRCQgghhKi3JBASAGzevBmdTmfxZ8+ePQCcO3fO4v6dO3daufUVExYWVuQ+vfXWW2bHHD58mP79++Po6EhISAhvv/22lVpbec6dO8djjz1G48aNcXJyomnTpsyaNYucnByzY+riYw7wySefEBYWhqOjIz179mT37t3WblKlmzt3Lt27d8fNzQ1/f39GjhxJRESE2THh4eFFHt8nn3zSSi2uPK+99lqR+9WqVSttf1ZWFhMmTMDHxwdXV1dGjRpFfHy8FVtcOSy9n+l0OiZMmADU3ce7ImRCRQFAnz59iI2NNds2c+ZMNmzYQLdu3cy2r1+/nrZt22qXfXx8qqWNVen1119n/Pjx2mU3Nzft75SUFG655RaGDBnCggULOHLkCI8++iienp488cQT1mhupTh58iRGo5HPP/+cZs2acfToUcaPH096ejrvvvuu2bF17TH/6aefmDp1KgsWLKBnz57Mnz+foUOHEhERgb+/v7WbV2m2bNnChAkT6N69O3l5ebz88svccsstHD9+HBcXF+248ePH8/rrr2uXnZ2drdHcSte2bVvWr1+vXba1LfjImzJlCitXrmTZsmV4eHgwceJE7r77brZt22aNplaaPXv2YDAYtMtHjx7l5ptvZvTo0dq2uvp4l5sihAU5OTmKn5+f8vrrr2vbzp49qwDKgQMHrNewKhAaGqq8//77xe7/9NNPFS8vLyU7O1vb9uKLLyotW7ashtZVr7fffltp3LixdrmuPuY9evRQJkyYoF02GAxKcHCwMnfuXCu2quolJCQogLJlyxZt28CBA5VJkyZZr1FVZNasWUrHjh0t7ktKSlLs7OyUZcuWadtOnDihAMqOHTuqqYXVY9KkSUrTpk0Vo9GoKErdfbwrQrrGhEV//PEHV69e5ZFHHimy74477sDf359+/frxxx9/WKF1le+tt97Cx8eHzp07884775CXl6ft27FjBwMGDMDe3l7bZsoeJCYmWqO5VSY5ORlvb+8i2+vSY56Tk8O+ffsYMmSItk2v1zNkyBB27NhhxZZVveTkZIAij/H333+Pr68v7dq1Y/r06WRkZFijeZXu9OnTBAcH06RJEx544AFiYmIA2LdvH7m5uWbPgVatWtGoUaM69RzIycnhu+++49FHHzVbK7OuPt7lJV1jwqIvv/ySoUOHmq2z5urqynvvvUffvn3R6/X8+uuvjBw5khUrVnDHHXdYsbUV8+yzz9KlSxe8vb3Zvn0706dPJzY2lnnz5gEQFxdH48aNza4TEBCg7fPy8qr2NleFyMhIPvroI7Nusbr4mF+5cgWDwaA9hiYBAQGcPHnSSq2qekajkcmTJ9O3b1/atWunbb///vsJDQ0lODiYw4cP8+KLLxIREcHy5cut2NqK69mzJ0uWLKFly5bExsYye/Zs+vfvz9GjR4mLi8Pe3h5PT0+z6wQEBBAXF2edBleBFStWkJSUxMMPP6xtq6uPd4VYOyUlqtaLL76oACX+nDhxwuw658+fV/R6vfLLL7/c8PwPPvig0q9fv6pqfrmV536bfPnll4qtra2SlZWlKIqi3HzzzcoTTzxhdsyxY8cUQDl+/HiV35eyKs99v3DhgtK0aVPlscceu+H5a+pjXloXL15UAGX79u1m259//nmlR48eVmpV1XvyySeV0NBQ5fz58yUet2HDBgVQIiMjq6ll1SMxMVFxd3dXFi1apHz//feKvb19kWO6d++uvPDCC1ZoXdW45ZZblNtvv73EY+rq410WkhGq46ZNm2b2bcCSJk2amF1evHgxPj4+pfrG37NnT9atW1eRJlaJ8txvk549e5KXl8e5c+do2bIlgYGBRUaTmC4HBgZWSnsrU1nv+6VLlxg0aBB9+vRh4cKFNzx/TX3MS8vX1xcbGxuLj2lNfDwrw8SJE/nrr7/YunWrWZbXkp49ewJqhrBp06bV0bxq4enpSYsWLYiMjOTmm28mJyeHpKQks6xQXXoOREdHs379+htmeurq410WEgjVcX5+fvj5+ZX6eEVRWLx4MePGjcPOzu6Gxx88eJCgoKCKNLFKlPV+F3bw4EH0er02eqh379688sor5Obmav+TdevW0bJlyxrZLVaW+37x4kUGDRpE165dWbx4MXr9jcsGa+pjXlr29vZ07dqVDRs2MHLkSEDtNtqwYQMTJ060buMqmaIoPPPMM/z2229s3ry5SBevJQcPHgSo1Y+xJWlpaURFRfHggw/StWtX7Ozs2LBhA6NGjQIgIiKCmJgYevfubeWWVo7Fixfj7+/P8OHDSzyurj7eZWLtlJSoWdavX19st9GSJUuUH374QTlx4oRy4sQJ5b///a+i1+uVr776ygotrRzbt29X3n//feXgwYNKVFSU8t133yl+fn7KuHHjtGOSkpKUgIAA5cEHH1SOHj2qLF26VHF2dlY+//xzK7a84i5cuKA0a9ZMGTx4sHLhwgUlNjZW+zGpi4+5oijK0qVLFQcHB2XJkiXK8ePHlSeeeELx9PRU4uLirN20SvXUU08pHh4eyubNm80e34yMDEVRFCUyMlJ5/fXXlb179ypnz55Vfv/9d6VJkybKgAEDrNzyips2bZqyefNm5ezZs8q2bduUIUOGKL6+vkpCQoKiKGpXYaNGjZSNGzcqe/fuVXr37q307t3byq2uHAaDQWnUqJHy4osvmm2vy493RUggJMyMHTtW6dOnj8V9S5YsUVq3bq04Ozsr7u7uSo8ePcyGn9ZG+/btU3r27Kl4eHgojo6OSuvWrZU333xTqw8yOXTokNKvXz/FwcFBadCggfLWW29ZqcWVZ/HixcXWEJnUxcfc5KOPPlIaNWqk2NvbKz169FB27txp7SZVuuIe38WLFyuKoigxMTHKgAEDFG9vb8XBwUFp1qyZ8vzzzyvJycnWbXglGDNmjBIUFKTY29srDRo0UMaMGWNWB5OZmak8/fTTipeXl+Ls7KzcddddZl8CarM1a9YogBIREWG2vS4/3hUhq88LIYQQot6SeYSEEEIIUW9JICSEEEKIeksCISGEEELUWxIICSGEEKLekkBICCGEEPWWBEJCCCGEqLckEBJCCCFEvSWBkBBCCCHqLQmEhKgFwsPDmTx5co05jyUPP/ywtnZXVQgPD0en06HT6bT1kSzZvHkzOp2OpKSkKmtLfRUWFsb8+fNLPMb0GBVezFSImkwCISHqoOKCgeXLlzNnzhztcmk+2GqS8ePHExsbS7t27azdlDptyZIl5Q5kYmNja9VzSghZfV6IesTb29vaTagQZ2dnAgMDrd0MAHJzc7Gzs7N2Mypdbm5uha4fGBiIh4dHJbVGiKonGSEhaqFvv/2Wbt264ebmRmBgIPfffz8JCQkAnDt3jkGDBgHg5eWFTqfj4YcfBsy7xsLDw4mOjmbKlCladwbAa6+9RqdOncxub/78+YSFhWmXDQYDU6dOxdPTEx8fH1544QWuX7bQaDQyd+5cGjdujJOTE//f3r3HNHW+cQD/1kFLBeqmq7NIKZsMB4ngKnMBw5CBIlm8x2QOA1EJU8asGJmMbVIwbM5AxBiD8oegpESSOYSpdXeUi5uIUBao5SayQc10Ml0FJsjz+8Nw4rGo8NtFGc/nL895r+ftiTx5z9NTf39/fPbZZ0J5d3c3oqKioFQqIZfL8eKLLyIvL2/Ua3Hy5El4e3tDLpcjNDQU7e3tdnUqKioQHBwMuVwOtVqNTZs24datW0K51WrFG2+8Ablcjueffx6FhYV2u2USiQQ5OTlYsmQJnJ2dkZGRAQAoKSmBVquFk5MTXnjhBaSlpWFgYEBo9/vvvyM2NhZKpRIKhQKvv/46TCaTUG4ymRAaGgpXV1coFArMmTMH58+ff+R1D+3afPnll/Dx8YGLiwsWLVoEq9Uq1BkcHER6ejrc3d0hk8kwe/ZsnDp1Sihvb2+HRCJBUVERQkJC4OTkBIPBgLVr1+LGjRvCfaHX64U2PT09WLduHVxdXeHh4YHc3NxHzpWxJxkHQoyNQf39/dixYwdMJhOOHTuG9vZ2IdhRq9U4evQoAMBiscBqtWLPnj12fXz++edwd3dHeno6rFar6A/oo2RlZSE/Px8HDx5ERUUFrl+/juLiYlGdTz75BIcPH8b+/fvR0NCAxMRErFmzBqdPnwYAfPTRR2hsbITRaITZbEZOTg6effbZUa3Dzz//jBUrVmDx4sWoq6tDbGwskpOTRXVaW1uxaNEirFy5EvX19SgqKkJFRQUSEhKEOtHR0ejq6kJZWRmOHj2K3NxcIbC8l16vx/Lly/HTTz9h3bp1KC8vR3R0NHQ6HRobG3HgwAHk5+cLQRIArFq1Cr/++iuMRiNqamqg1WoRFhaG69evAwCioqLg7u6O6upq1NTUIDk5ecQ7TT09PcjMzERBQQHOnDmDjo4ObN26VSjfs2cPsrKykJmZifr6ekRERGDJkiVobm4W9ZOcnAydTgez2YzQ0FBkZ2dDoVAI98W9fWZlZSEgIAC1tbWIj4/Hxo0bYbFYRjRfxp5Ij++H7xljIxUSEkI6ne6B5dXV1QSA/vjjDyIi+v777wkAdXd3P7QfjUZDu3fvFtVJTU0lf39/0bndu3eTRqMRjlUqFe3atUs47u/vJ3d3d1q6dCkREfX19dHEiROpqqpK1M/69etp9erVRES0ePFiWrt27YMv+j7DrcH7779Pvr6+onPbtm0TXfv69espLi5OVKe8vJwmTJhAvb29ZDabCQBVV1cL5c3NzQRAtDYAaPPmzaJ+wsLC6OOPPxadKygoIJVKJYyjUCior69PVGfGjBl04MABIiJydXWl/Pz8kS3CPfLy8ggAtbS0COf27dtHzz33nHDs5uZGGRkZonavvPIKxcfHExHRpUuXCABlZ2fb9T1p0iS7MTUaDa1Zs0Y4HhwcpKlTp1JOTs6I2jP2JOIcIcbGoJqaGuj1ephMJnR3d2NwcBAA0NHRAV9f33907Bs3bsBqteLVV18Vzjk4OCAgIEB4PNbS0oKenh4sWLBA1Pb27dt4+eWXAQAbN27EypUrceHCBSxcuBDLli1DUFDQqOZiNptF8wCAwMBA0bHJZEJ9fT0MBoNwjogwODiIS5cuoampCQ4ODtBqtUK5l5cXnnnmGbvxAgIC7PqurKwU7QDduXMHfX196Onpgclkgs1mw5QpU0Ttent70draCgDYsmULYmNjUVBQgPDwcKxatQozZswY0fVPnDhRVFelUgk7WTdv3kRXVxfmzZsnajNv3jzRo7nhruth/Pz8hH9LJBJMmzZt2N0zxsYKDoQYG2Nu3bqFiIgIREREwGAwQKlUoqOjAxEREbh9+/Zf7n/ChAl2+T6jTaC12WwAgBMnTmD69OmiMplMBgCIjIzE5cuXcfLkSXz99dcICwvDO++8g8zMzL8w++Hn8vbbb2PTpk12ZR4eHmhqahpxX87OznZ9p6WlYcWKFXZ1nZycYLPZoFKpUFZWZlc+9K0svV6Pt956CydOnIDRaERqaiqOHDmC5cuXP3I+9z9Ck0gkdp/dSNx/XaMdcygQZ2ws4kCIsTHm4sWL+O2337Bz506o1WoAsEuulUqlAO7uTjyMVCq1q6NUKnHlyhUQkZBAfe97eyZNmgSVSoUff/wRr732GgBgYGBAyH8BAF9fX8hkMnR0dCAkJOSB4yuVSsTExCAmJgbBwcFISkoaVSDk4+OD0tJS0bkffvhBdKzVatHY2AgvL69h+5g5cyYGBgZQW1uLOXPmALi7o9Xd3f3I8bVaLSwWywP71mq1uHLlChwcHETJ5vfz9vaGt7c3EhMTsXr1auTl5Y0oEHoYhUIBNzc3VFZWij6DyspKzJ0796Fth7svGPuv4mRpxsYYDw8PSKVS7N27F21tbSgtLRW9GwgANBoNJBIJjh8/jqtXrwo7NPfz9PTEmTNn0NnZiWvXrgG4+22yq1evYteuXWhtbcW+fftgNBpF7XQ6HXbu3Iljx47h4sWLiI+PF72zyNXVFVu3bkViYiIOHTqE1tZWXLhwAXv37sWhQ4cAANu3b0dJSQlaWlrQ0NCA48ePw8fHZ1RrsWHDBjQ3NyMpKQkWiwWFhYXIz88X1dm2bRuqqqqQkJCAuro6NDc3o6SkREiWfumllxAeHo64uDicO3cOtbW1iIuLg1wuFwLBB9m+fTsOHz6MtLQ0NDQ0wGw248iRI/jwww8BAOHh4QgMDMSyZcvw1Vdfob29HVVVVfjggw9w/vx59Pb2IiEhAWVlZbh8+TIqKytRXV096nV4kKSkJHz66acoKiqCxWJBcnIy6urqoNPpHtrO09MTNpsN3377La5du4aenp6/ZT6MPZEea4YSY2xE7k8ULiwsJE9PT5LJZBQYGEilpaUEgGpra4U66enpNG3aNJJIJBQTEzNsP2fPniU/Pz+SyWR0738HOTk5pFarydnZmaKjoykjI0OULN3f3086nY4UCgU9/fTTtGXLFoqOjhaSpYnuJtJmZ2fTzJkzydHRkZRKJUVERNDp06eJiGjHjh3k4+NDcrmcJk+eTEuXLqW2trYRr8GQL774gry8vEgmk1FwcDAdPHjQLlH83LlztGDBAnJxcSFnZ2fy8/MTJRF3dXVRZGQkyWQy0mg0VFhYSFOnTqX9+/cLdQBQcXGx3finTp2ioKAgksvlpFAoaO7cuZSbmyuU37x5k959911yc3MjR0dHUqvVFBUVRR0dHfTnn3/Sm2++SWq1mqRSKbm5uVFCQgL19vY+cB2GDJeQXFxcLPoc79y5Q3q9nqZPn06Ojo7k7+9PRqNRKB9Klr73vhmyYcMGmjJlCgGg1NRUIho+ud7f318of9jcGHtSSYj+jwfKjDH2L5s/fz5mz579r7y1+JdffoFarcY333yDsLCwf3y8/5r8/Hxs3ryZf+aEjQkcCDHGxoT58+ejqqoKUqkUZ8+exaxZs/62vr/77jvYbDbMmjULVqsV7733Hjo7O9HU1PSffHv0P8nFxQUDAwNwcnLiQIiNCZwszRgbEwwGA3p7ewHczZP6O/X39yMlJQVtbW1wdXVFUFAQDAbDYw2CIiMjUV5ePmxZSkoKUlJS/uUZjcxQYv1TTz31eCfC2AjxjhBjjD2BOjs7hcDvfpMnTx7zvxvH2JOCAyHGGGOMjVv89XnGGGOMjVscCDHGGGNs3OJAiDHGGGPjFgdCjDHGGBu3OBBijDHG2LjFgRBjjDHGxi0OhBhjjDE2bnEgxBhjjLFx63+4lQ2tUHq/dgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fcst_flow = fcst.temp_scrn\n",
    "obs_flow = obs.temp_scrn\n",
    "# Calculate percent bias and preserve the \"lat\" dimension\n",
    "bias = pbias(fcst_flow, obs_flow, preserve_dims=\"lat\")\n",
    "bias.name = \"percent bias (%)\"\n",
    "# Generate second dataset with large flow values\n",
    "fcst_flow_large=fcst_flow + 1000\n",
    "obs_flow_large = obs_flow + 1000\n",
    "# Calculate percent bias for large flow values and preserve the \"lat\" dimension\n",
    "bias_large_flow = pbias(fcst_flow_large, obs_flow_large, preserve_dims=\"lat\")\n",
    "bias_large_flow.name = \"percent bias (%)\"\n",
    "# Compare the percent bias for small and large flow values\n",
    "bias.plot(label='pbias_small_streamflow')\n",
    "bias_large_flow.plot(label='pbias_large_streamflow')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the percent bias is higher for small forecast and observation values compared to large ones, although the absolute additive bias is the same for both datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What to try next?\n",
    "Have a look at Isotonic Regression in `scores` to see how conditional biases can be calculated.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "\n",
    "Additive Bias (Mean Error)\n",
    "\n",
    "- https://www.cawcr.gov.au/projects/verification/#meanerror\n",
    "\n",
    "Multiplicative Bias\n",
    "\n",
    "- https://www.cawcr.gov.au/projects/verification/#multiplicative_bias\n",
    "\n",
    "Percent Bias\n",
    "\n",
    "- Sorooshian, S., Duan, Q., & Gupta, V. K. (1993). Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model. *Water Resources Research, 29*(4), 1185-1194. https://doi.org/10.1029/92WR02617\n",
    "- Alfieri, L., Pappenberger, F., Wetterhall, F., Haiden, T., Richardson, D., & Salamon, P. (2014). Evaluation of ensemble streamflow predictions in Europe. *Journal of Hydrology, 517*, 913-922. https://doi.org/10.1016/j.jhydrol.2014.06.035\n",
    "-   Dawson, C. W., Abrahart, R. J., & See, L. M. (2007). HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. *Environmental Modelling and Software, 22*(7), 1034-1052. https://doi.org/10.1016/j.envsoft.2006.06.008\n",
    "-   Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. *Transactions of the ASABE, 50*(3), 885-900. https://doi.org/10.13031/2013.23153"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}