3_composites.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Composites\n",
    "\n",
    "In this notebook we'll take a closer look at how `pre_et_look` works internally. To do so, we'll be using a couple of functions that you'd normally not need to touch, because they are called automatically when you run `pywapor.pre_et_look.main`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll install pywapor in case it's not installed yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pywapor --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can define some of the basic parameters that were explained in the introduction notebook. In this case we'll look at the Fayoum region again, for a period of two months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "import pywapor\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "project_folder = r\"/Users/hmcoerver/pywapor_notebooks_3\"\n",
    "latlim = [28.9, 29.7]\n",
    "lonlim = [30.2, 31.2]\n",
    "timelim = [\"2021-06-01\", \"2021-08-01\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the first things happening when you run `pre_et_look`, is the creation of a more precise definition of the composites (from here on also referred to as time_bins) for which `pre_et_look` will prepare the data to be used by `et_look`.\n",
    "\n",
    "The default value for the `bin_length` in `pywapor.pre_et_look.main` is `\"DEKAD\"`, which creates three composites per month (of roughly 10 days each), just like much of the WaPOR data. We'll go with this default value for now. The variable `bins` contains the edges off these bins.\n",
    "\n",
    "Normally you wouldn't have to run this code yourself, its just shown here to explain what happens inside the `pre_et_look` code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Composite 1 starts at 2021-06-01T00:00:00.000000000 and ends at 2021-06-11T00:00:00.000000000.\n",
      "\n",
      "Composite 2 starts at 2021-06-11T00:00:00.000000000 and ends at 2021-06-21T00:00:00.000000000.\n",
      "\n",
      "Composite 3 starts at 2021-06-21T00:00:00.000000000 and ends at 2021-07-01T00:00:00.000000000.\n",
      "\n",
      "Composite 4 starts at 2021-07-01T00:00:00.000000000 and ends at 2021-07-11T00:00:00.000000000.\n",
      "\n",
      "Composite 5 starts at 2021-07-11T00:00:00.000000000 and ends at 2021-07-21T00:00:00.000000000.\n",
      "\n",
      "Composite 6 starts at 2021-07-21T00:00:00.000000000 and ends at 2021-08-01T00:00:00.000000000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bin_length = \"DEKAD\"\n",
    "\n",
    "bins = pywapor.general.compositer.time_bins(timelim, bin_length)\n",
    "\n",
    "for i, (start, end) in enumerate(zip(bins[:-1], bins[1:])):\n",
    "    print(f\"Composite {i + 1} starts at {start} and ends at {end}.\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides defining the time_bins, `pre_et_look` also downloads data. In this example we'll look solely at NDVI data (from MOD13 and MYD13). So instead of using the default (`\"level_1\"`, `\"level_2\"` or `\"level_2_v3\"`) `sources`, we'll define some custom values here. Like this, `pre_et_look` will only process `ndvi`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sources = {\n",
    "    'ndvi': {'products': [{'source': 'MODIS',\n",
    "                            'product_name': 'MOD13Q1.061',\n",
    "                            'enhancers': 'default'},\n",
    "                          {'source': 'MODIS', \n",
    "                          'product_name': 'MYD13Q1.061', \n",
    "                          'enhancers': 'default'}],\n",
    "            'composite_type': 'mean',\n",
    "            'temporal_interp': False,\n",
    "            'spatial_interp': 'nearest'}\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run `pre_et_look`. Since we'll only download and process `ndvi`, this should go pretty quick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--> Collecting `ndvi` from `MODIS.MOD13Q1.061`.\n",
      "    --> Applying 'mask_qa' to `ndvi`.                  \n",
      "    --> Saving merged data.\n",
      "        > peak-memory-usage: 11.6MB, execution-time: 0:00:02.425868.\n",
      "        > chunksize|dimsize: [time: 5|5, y: 403|403, x: 506|506]\n",
      "        > timesize: 5 [2021-06-02T00:00, ..., 2021-08-05T00:00]\n",
      "--> Collecting `ndvi` from `MODIS.MYD13Q1.061`.\n",
      "    --> Applying 'mask_qa' to `ndvi`.                  \n",
      "    --> Saving merged data.\n",
      "        > peak-memory-usage: 9.3MB, execution-time: 0:00:02.093121.\n",
      "        > chunksize|dimsize: [time: 4|4, y: 403|403, x: 506|506]\n",
      "        > timesize: 4 [2021-06-10T00:00, ..., 2021-07-28T00:00]\n"
     ]
    }
   ],
   "source": [
    "dss, sources = pywapor.collect.downloader.collect_sources(project_folder, sources, latlim, lonlim, timelim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dss` is a dictionary with the paths to the downloaded netCDF files for each product we've requested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('MODIS',\n",
       "  'MOD13Q1.061'): '/Users/hmcoerver/pywapor_notebooks_3/MODIS/MOD13Q1.061.nc',\n",
       " ('MODIS',\n",
       "  'MYD13Q1.061'): '/Users/hmcoerver/pywapor_notebooks_3/MODIS/MYD13Q1.061.nc'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll define a function that can plot some values in these datasets at a point of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_timeseries(dss, latlon, composites = None, var = \"ndvi\", unit = \"-\"):\n",
    "    fig = plt.figure(figsize = (6,4), dpi=150)\n",
    "    ax = fig.gca()\n",
    "\n",
    "    if not isinstance(composites, type(None)):\n",
    "        x = composites.time_bins.values\n",
    "        y = composites.sel(y = latlon[0], x = latlon[1], method = \"nearest\")[var].values\n",
    "        width = np.diff(bins) \n",
    "        ax.bar(x, y, width = width, align = \"edge\", color = \"lightblue\", edgecolor = \"darkblue\", zorder = 8, alpha = 0.8)\n",
    "\n",
    "    for product, ds_fh in dss.items():\n",
    "        ds = xr.open_dataset(ds_fh, decode_coords = \"all\")\n",
    "        y = ds.sel(y = latlon[0], x = latlon[1], method = \"nearest\")[var].values\n",
    "        x = ds.time.values\n",
    "        ax.scatter(x, y, label = \".\".join(product), zorder = 10)\n",
    "\n",
    "    ax.legend()\n",
    "    ax.grid(zorder = 0)\n",
    "    ax.set_facecolor(\"lightgray\")\n",
    "    ax.set_xlabel(\"time\")\n",
    "    ax.set_ylabel(f\"{var} [{unit}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latlon = (29.32301, 30.77599)\n",
    "plot_timeseries(dss, latlon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll run the functions that turns these seperate measurements into composites (again, normally this is done internally when you run `pywapor.pre_et_look.main`). Note that we pass an empty list here for the `enhancers` argument, see the notebook on enhancers to learn more about this argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--> Compositing 1 variables.\n",
      "    --> (1/1) Compositing `ndvi` (mean).\n",
      "        --> Using `MYD13Q1.061.nc` as reprojecting example.\n",
      "            > shape: (403, 506), res: 0.0020° x 0.0020°.\n",
      "        --> Saving `ndvi` composites.\n",
      "            > peak-memory-usage: 58.6MB, execution-time: 0:00:02.029245.\n",
      "            > chunksize|dimsize: [time_bins: 6|6, y: 403|403, x: 506|506]\n",
      "--> Didn't find any products for `is_example`.\n",
      "--> Creating merged file `et_look_in.nc`.\n",
      "    > peak-memory-usage: 28.0MB, execution-time: 0:00:02.101097.\n",
      "    > chunksize|dimsize: [time_bins: 6|6, y: 403|403, x: 506|506]\n"
     ]
    }
   ],
   "source": [
    "enhancers = []\n",
    "ds_composites = pywapor.general.compositer.main(dss, sources, project_folder, enhancers, bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll see two messages in the output. They indicate that no example dataset was found. An example dataset is used to make sure the pixel-size and dataset bounds are the same for all variables and products. When no example dataset is found, the function will automatically search for the dataset with the smallest pixels and use that dataset as an example. Since here we are using only two products with a very similar pixel size, this is not very important here.\n",
    "\n",
    "The output looks like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  --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",
       "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.Dataset&gt;\n",
       "Dimensions:      (time_bins: 6, y: 403, x: 506)\n",
       "Coordinates:\n",
       "  * x            (x) float64 30.2 30.2 30.2 30.21 30.21 ... 31.19 31.2 31.2 31.2\n",
       "  * y            (y) float64 29.7 29.7 29.7 29.69 ... 28.91 28.91 28.9 28.9\n",
       "    spatial_ref  int64 ...\n",
       "  * time_bins    (time_bins) datetime64[ns] 2021-06-01 2021-06-11 ... 2021-07-21\n",
       "Data variables:\n",
       "    ndvi         (time_bins, y, x) float64 dask.array&lt;chunksize=(6, 403, 506), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e9410938-b4fa-4483-be69-0a793d6fafc0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9410938-b4fa-4483-be69-0a793d6fafc0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_bins</span>: 6</li><li><span class='xr-has-index'>y</span>: 403</li><li><span class='xr-has-index'>x</span>: 506</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-44645999-33e2-4176-b1a4-dfbf9fe64035' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44645999-33e2-4176-b1a4-dfbf9fe64035' class='xr-section-summary' >Coordinates: <span>(4)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.2 30.2 30.2 ... 31.2 31.2 31.2</div><input id='attrs-766ec904-9866-4c5a-a16b-2932d4ac8332' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-766ec904-9866-4c5a-a16b-2932d4ac8332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-764c471b-e774-4a31-96a1-df32e08f4ce6' class='xr-var-data-in' type='checkbox'><label for='data-764c471b-e774-4a31-96a1-df32e08f4ce6' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([30.199116, 30.201099, 30.203082, ..., 31.196461, 31.198443, 31.200426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.7 29.7 29.7 ... 28.91 28.9 28.9</div><input id='attrs-de7a116d-6011-4c67-8193-4b9ff5690452' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de7a116d-6011-4c67-8193-4b9ff5690452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57da6740-3d26-4421-beca-b31b679e0784' class='xr-var-data-in' type='checkbox'><label for='data-57da6740-3d26-4421-beca-b31b679e0784' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([29.70005 , 29.698067, 29.696085, ..., 28.906933, 28.904951, 28.902968])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b72a146-fe19-45ef-a51d-2ee79e5e0883' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b72a146-fe19-45ef-a51d-2ee79e5e0883' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dd4d436-29ca-42e4-9d5e-5429d6439de2' class='xr-var-data-in' type='checkbox'><label for='data-2dd4d436-29ca-42e4-9d5e-5429d6439de2' 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>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>30.198124670407314 0.001982792281130139 0.0 29.70104166399938 0.0 -0.001982792281130138</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_bins</span></div><div class='xr-var-dims'>(time_bins)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-06-01 ... 2021-07-21</div><input id='attrs-f45b78b3-3ee9-4fb8-9672-8d13c33895f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f45b78b3-3ee9-4fb8-9672-8d13c33895f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91782dc1-c672-465b-8a7f-5d2c9751886e' class='xr-var-data-in' type='checkbox'><label for='data-91782dc1-c672-465b-8a7f-5d2c9751886e' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-06-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-21T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-11T00:00:00.000000000&#x27;, &#x27;2021-07-21T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-806a603e-f795-474d-936e-bcaae5c0a73f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-806a603e-f795-474d-936e-bcaae5c0a73f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ndvi</span></div><div class='xr-var-dims'>(time_bins, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 403, 506), meta=np.ndarray&gt;</div><input id='attrs-44310a13-9b1e-493f-9ef6-5e86c289a838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44310a13-9b1e-493f-9ef6-5e86c289a838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-292a55d6-84d5-40a8-9469-06c05febb6f3' class='xr-var-data-in' type='checkbox'><label for='data-292a55d6-84d5-40a8-9469-06c05febb6f3' 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'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 9.33 MiB </td>\n",
       "                        <td> 9.33 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (6, 403, 506) </td>\n",
       "                        <td> (6, 403, 506) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"195\" height=\"161\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"95\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"111\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
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       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"111\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <text x=\"85.439388\" y=\"131.012511\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >506</text>\n",
       "  <text x=\"165.439388\" y=\"63.225949\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,165.439388,63.225949)\">403</text>\n",
       "  <text x=\"7.719694\" y=\"123.292817\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.719694,123.292817)\">6</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7bda89c-ec76-4065-bec2-6c6026b99301' class='xr-section-summary-in' type='checkbox'  ><label for='section-c7bda89c-ec76-4065-bec2-6c6026b99301' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b99afccd-0578-4282-a88f-c2e2d28055a5' class='xr-index-data-in' type='checkbox'/><label for='index-b99afccd-0578-4282-a88f-c2e2d28055a5' 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(Float64Index([ 30.19911606654788, 30.201098858829013, 30.203081651110143,\n",
       "              30.205064443391272, 30.207047235672402,  30.20903002795353,\n",
       "              30.211012820234664, 30.212995612515794, 30.214978404796923,\n",
       "              30.216961197078053,\n",
       "              ...\n",
       "               31.18258103798843,  31.18456383026956,  31.18654662255069,\n",
       "               31.18852941483182,  31.19051220711295,  31.19249499939408,\n",
       "               31.19447779167521,  31.19646058395634,  31.19844337623747,\n",
       "                31.2004261685186],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=506))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd3e401d-a4d4-4ebb-8ca6-e22284394290' class='xr-index-data-in' type='checkbox'/><label for='index-cd3e401d-a4d4-4ebb-8ca6-e22284394290' 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(Float64Index([29.700050267858813, 29.698067475577684, 29.696084683296554,\n",
       "               29.69410189101542, 29.692119098734292, 29.690136306453162,\n",
       "              29.688153514172033, 29.686170721890903, 29.684187929609774,\n",
       "               29.68220513732864,\n",
       "              ...\n",
       "               28.92081290137467,  28.91883010909354, 28.916847316812408,\n",
       "               28.91486452453128,  28.91288173225015,  28.91089893996902,\n",
       "               28.90891614768789,  28.90693335540676, 28.904950563125627,\n",
       "              28.902967770844498],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=403))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_bins</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-22728b12-acad-4de0-8e32-c43be55c1b1a' class='xr-index-data-in' type='checkbox'/><label for='index-22728b12-acad-4de0-8e32-c43be55c1b1a' 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(DatetimeIndex([&#x27;2021-06-01&#x27;, &#x27;2021-06-11&#x27;, &#x27;2021-06-21&#x27;, &#x27;2021-07-01&#x27;,\n",
       "               &#x27;2021-07-11&#x27;, &#x27;2021-07-21&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_bins&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9776d74c-c2c3-4374-baeb-136607d83c55' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9776d74c-c2c3-4374-baeb-136607d83c55' 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.Dataset>\n",
       "Dimensions:      (time_bins: 6, y: 403, x: 506)\n",
       "Coordinates:\n",
       "  * x            (x) float64 30.2 30.2 30.2 30.21 30.21 ... 31.19 31.2 31.2 31.2\n",
       "  * y            (y) float64 29.7 29.7 29.7 29.69 ... 28.91 28.91 28.9 28.9\n",
       "    spatial_ref  int64 ...\n",
       "  * time_bins    (time_bins) datetime64[ns] 2021-06-01 2021-06-11 ... 2021-07-21\n",
       "Data variables:\n",
       "    ndvi         (time_bins, y, x) float64 dask.array<chunksize=(6, 403, 506), meta=np.ndarray>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_composites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make a plot like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j8QJAYR2q10cOS+EG6DgsfjpUv7eHIvIO208UbZjYb8fNH16WG5KRMuZksk3ztxwtVJ15W47qVHLRxnZ6woABA5SSkqKvv/46a5tzyFFeH66dH9xiY2PzPYazzKUf9gojJiZGkrRr1658y/bo0UNVqlTR+fPn1apVKw0aNEiTJ0/W5s2blZFRvLtSFotF/fv316FDh/Tzzz9nbXe+XwMGDHBZz263648//pBU+t6vgvruu+80bNgwDRkyRJ07d1abNm108eJFTZw4UXXr1s0q54y5Xr16qlChQr7tFve1FsThw4clKddV0JzbneUkZZ2voKAgtW7dWsOGDdPkyZP1wQcf6LHHHtO1116rNWvWeCxmACgMW1BlHa7XS5IK9JwRSTpU7w7ZA90/isCbpKQVbfW1otYrCSQjZczcTTnniOQn3WFo7uYjHoqo8Pr06SOr1arZs2dnbZs9e7aqVauW7RvvyzmH1VSpUiXfYziH6pw+fbpIMTonT589ezbfsuHh4VqwYIEaN26s1NRUzZ8/XyNGjFDbtm1Vq1YtDR8+XEeOFP39v3yoVmpqqr7++mu1aNFCjRo1clknKSkp605K1apV8z2Gu96vvFbJKqzffvtN06dP1+zZsxUfHy+r1aq33npLgwYNylauMP3i0nJFfa0F4bzrd/nSyE7O7ZfeHXT2tYkTJyopKUmzZs3S0aNHtXXrVvXv31+nTp1S//79dfRo4b6MAABP2RH7vE5Xua5Azxk5XeU6/d78+ZIIq1QL9i/aR/ei1isJpTcyeMT6A0X7sLcu0X0fEourQoUK6tq1q1asWKFjx45pw4YN2r17t/r165c1JyAvBVnN2lmmqOuIF7Z+bGysNm7cqHnz5unhhx9W8+bN5e/vr3Pnzum///2vbrjhBu3evbtIsVx99dWKiYnR/PnzZbfbtWjRIp07dy7PeTOXvkel8f0qiOeff14pKSk6c+aM1q9fr3vvvVdPPPGE7rzzzqzlGS89dkFXOXeWK+5dq4LI7f1wFasznvT0dP33v/9Vz549FR4erkaNGmnq1Klq0aKFkpKSNGXKFI/GDAAF5fAL1LobJ+tA/TtzHbLlsPjpQP07te6mKXL45r0gTFlwbVXXzzHLT5NqRatXEkhGypgL9qJ9gCpqPU+56667lJGRoTlz5mR945/bkCOnihUzn9h68uTJfNt3Tip31iks57fmBRn24+Tr66tu3bpp/PjxWrNmjQ4dOqRJkyapUqVKOnHihEaMGFGkWKTM9yspKUmLFy/WrFmz5Ofnl+fSsxUrVsz6IFyS71dR6+clMDBQ0dHRevfdd/XII4/o+++/1wcffJC133lXpiCv89Jyly8d7E6hoaGSpAsXLrjcf/HixWzlLv13jRo11Llz5xx1nA8DXb16tVtjBYDicPgFanvL0Vr+j2Xa2eRxnazWRkmVmupktTba2eRxLf/HMm1vOZpE5H9uaRiqwj4yxM9H6tIgNP+CJiEZKWNCrEVbDaKo9Tzl1ltvVfny5TVjxgzNmTNHUVFR+c5taNKkiSS5fNbC5ZxlnHMZCss5n+DSZ1UUVrly5TRkyJCsb7J/+uknpaSkFKmtfv36ycfHR5MnT9bSpUvVsWPHPIdfWa3WrNg3bdqUb/ul4f0qCGfCeumDFJ39IjExsUDDxJyv9fKlkt2pVq1akjKfQeOKc7uznKSs597k9pA45/6CJl0AUJLsgZWVcM0wrevwkeI7z9C6Dh8p4ZphZX6OyOUqBPmqU/3CJRad64eW2mV9JZKRMqdlnYJ/U3+pVnWLVs9TnM/Q2Lp1q06cOJHvXRFJWSs2ffnll/mWdZbp0qVLoWPLyMjIWtL3pptuKnT9y914441Z7RZkDoorERER6tChg1auXCm73Z7nEC0n5/v11Vdf5VkuLS1N8+fPl1S09+vcuXNZT353x/uVF+fcFuedHClzDkhsbKwMw8j3tf7xxx/67bff5Ofnp44dPbfOvTOpy+1hlM7tzkRKUtYDI3NLqJxzYwr63BwAQOk07LoKurZqwe4UXVs1QMNaun/UgTuRjJQxfZpHyM+ncOPy/Xws6hNbw0MRFd2gQYNUqVIlVa5cuUDJyN13362KFStqw4YNWatJuTJr1ixt2LBBlSpVyjHZuSDeeustHTp0SDVq1FCvXr3yLZ/fXIV9+/ZJyrxbUZxnkdxzzz2qVKmSatasmeuT5S/10EMPyWq1at68eXmuwvT222/ryJEjuuqqq9S1a9dCx/X888/rwoULatGiRa7PPHEX5xCly5/k/vjjj0uSXn/99WyJyqUcDoeee+45SZnD3go64b0ounXrJklZSd6ltmzZov379+vqq6/OtirYzTffrJCQEO3bty/bKltOzqWIc1suGADgHQL8fPRSx6rqlseQLT8fqVvDUI3tVE1WX/fNx/QEkpEypkpogO5oFlGoOr2bRahyqDX/giWsbdu2OnTokA4ePJjr0JRLhYWFaeLEiZKkRx99VJ9//nmOMjNmzNCjjz4qSZowYYLCwsIKHM+xY8c0YsQIjR07Vr6+vpoyZUqOh+u58uGHHyouLk47duzIse/IkSNZH5S7deuWrb0PPvhAzZo104svvlig+JxL/O7ZsyfXVZouVbduXY0dO1aGYah///5ZD+JzysjI0DvvvKOXX35Z/v7+mjJlinx8Cn5J2b9/v+655x59+umnCgkJyTaPo6hOnDih9957z+UdpOXLl2vUqFGSlOPJ5f3791f37t11/Phxde/eXTt37sy2/+zZsxoyZIiWLVumBg0a6N///nexY5WkBx54QM2aNcu2TLUk/eMf/1DdunX122+/acKECVnbL1y4oKeeekqSsj2ZXcpcYeuRRx5RWlqaHn/88WzzTZYsWaIZM2bIYrFo6NChbokdAGCeAD8fxbWupKl31NK9zcorNiJQV1W2KjYiUPc2K6+pd9RSXOtKpT4RkaTCPW0GV4R/dmukxNMpBXrwYcs65fXPW10v/+qNevXqpSlTpujxxx/XQw89pFdffVXNmzeXlDkX4MCBAwoICNCUKVPyvKsxcuRIhYaGyjAM/fXXX0pMTNSOHTuUkZGhatWq6cMPP1SnTp0KFFNaWpo++eQTffLJJ6pbt66io6MVHBysI0eOaP369bLb7apbt67GjRuXrd7p06e1e/duHTt2rMjvR34ef/xxXbx4Ua+88or69Omjq666Stdee63sdrvWrl2rEydOKDw8XJ9++qlatWqVazvDhg2TlHl34fz589q7d6927dolwzDUsGFDTZ06Vddee22Oet9//73eeOONbNvsdnu24VzPP/+8br31VkmZE7tHjhypsWPHqnnz5qpZs6YuXLiQdTwp80O8q3P72Wef6b777tPChQt13XXXqWXLlqpTp47OnDmj+Ph4paSkqEmTJvr8888VHh6eo/7rr7+uxYsXS5L++usvSdL69euzxfrFF18oIuLvLwMOHTqk3bt353ggob+/vz755BN1795dzz33nObMmaPatWvr559/1rFjx3T77bfnSKgk6Z///Kd+/vlnLV68WE2aNFHLli118uRJrVu3Tg6HQ2PGjFHLltmferx582Y9+eSTWf/t7E+9e/eWv7+/JOm+++7TkCFDchwPAGCuCkG+6ndtuPpdm/PvkrcgGSmDAv199eGgpnp98R7N2+L6uSN+Phb1bhahf97aSAF+pXfSU1Hcc889uvHGGzVx4kQtW7Ys66nhtWrV0qOPPqq4uLhsw19cWbBggSTJx8dH5cqVU9WqVdW7d2/dcsst6tOnjwIDC76E3r333quaNWtqyZIl2rx5s9atW6ekpCSFhYUpNjZW3bt310MPPVSouzTu9Nxzz6l79+764IMPtHLlSn333Xfy9/dX3bp1de+99youLi7fZ5FMnz5dkuTn56dy5copIiJCgwYN0m233aYePXrIz8/1pejUqVNav359tm2GYWTbdvn8j1dffVWrVq3SH3/8oU2bNsnhcKh69eq68847df/992fNwblcUFCQZs+erUWLFumzzz7TunXrtHHjRqWnp0uSWrRooR9//DHrA/rl9u/fnyPWc+fOZdt26ZLC+WndurVWr16tV155RatXr9a2bdtUv359PfHEE4qLi3N5FyowMFDff/+93n33XX3xxRdasmSJAgMDddNNNykuLi4rabvUX3/9lSNuKft8laLMBQIAoCAsRkEX10exREdHy2azZX2IdcUwDKWlpSkgIEB169Yt1JCX/Dg/BF0+bOhUsl1zNx/RusQkXbBnKMTqq1Z1K6hPbI1SOTQLnpFb/4CUkJCgTp066cSJE3r77bf1yCOPmB1SqeRwOJSYmCibzSZ/f/88nxlz8OBBSbmv/AWUlT6SmpqhwYPXym7x1Zufd1RAIN8Rl1a21HQ9e/dyWeypmjWrvQIDr6wvaourV69eCggIcDnkPD/0+jKucqhVD7Wvq4fa1zU7FKBUatCggebNm6du3brpmWeeUfXq1XXHHXeYHRYAAFcEkhEAyEfz5s01f/58rVixQgkJCbLb7dxFAgDADUhGAKAA2rZtq7Zt25odBgAAVxSSEQAAADdJupihJXuTtf1EqlLSHAr291GTaoHq0qB0PwUbMAvJCAAAQDHZ0h36cEOSlu9LVroj+77NR1M1c9tZda4fqmEtK3rFsx+AkuLVDz1MTU3V6NGj1bhxYwUGBqpGjRoaOnSoy6cP52batGmyWCz5/nz22WcefCUAAMBb2dIdGv3jCf2wN2ci4pTukBbvTdaLy4/LllshoAzy2jsjqamp6tSpk+Lj4xUREaGePXsqMTFRU6dO1bfffqtffvlFDRo0yLedhg0bavDgwS73nTt3Lmsp3nbt2rkzfAAAcIX4cEOStp+wFajs9hM2fbQhSXGtK3k4KsA7eG0y8tprryk+Pl5t2rTRkiVLFBoaKkkaP368nn76aQ0dOlQ//fRTvu20a9cu10Tjgw8+0IIFC9S2bVvVr1/frfEDAADvl3QxQ8v3JReqzrJ9yRrUtLyCGK0FeOcwrbS0NE2YMEGSNGnSpKxERJJGjBihmJgYrVq1Shs3bizWcZxPjb7nnnuK1Q4AALgyLcljaFZu0h3S0oTCJTDAlcork5E1a9bo7NmzatCggWJjY3Ps79u3ryRp4cKFRT7G/v37FR8fL6vVqn79+hW5HQAAcOXafiK1SPV+O160esCVxiuTka1bt0rKfBCZK87tznJF4bwr0r17d1WoUKHI7QAAgCtXSlrRJqMXtR5wpfHKOSMHDx6UJNWqVcvlfud2Z7mimDFjhqTCD9GKjo52uT0hIUHVqlXLN6aKFSvKarXKbrfLx8d9uaJhGJIku93utjZx5aB/oLgcDocMw5DdbtexY8fyLJuWliapeNdoeJ417awiT65QxfN/yC8jVem+gTpT7hodqtJBdv/yHj22N/URS7pRxHp2HTp0SHa7XYY1UHZ7miw+JCilld2eIRmZfy8PHTqkgACv/D7fY9LS0hQQEFCkul6ZjCQnZ46zDA4Odrk/JCQkW7nCWrdunXbt2qUKFSqoe/fuRQsSAIBSKjNxdP0h2sdhV8yfn6lO0ir5GBnZ9lU5/5sa/TlHByrcpG0175HDx+qR+NL+d9fA4Sj9H87rh0o7zxZ+Jnr9UEM2m0OGIcmQ83+AMscrkxHnt7gWi+tffuf+onIO0erfv7+s1sJdaHfs2OFye3R0tGw2m2rXrp1rXcMwlJaWJovFIqvV6tY7I85vvAv7elA20D9QXA6HI+vaFRkZmev1Wfr72+68rofwrNTUDA0evDbH9gCfNE1u/7HqVd2Xa10fI0P1zvyokzsT9fDqB2Rz+Ls9Pm+6JjkCfKTO1SWfQiQkDkMr/3tMK1Iz9OefdlWpFSB/f39Zre5/L+EehiNdsmR+9oyMjFRgoK/ZIZUq/v5F77teeY8pLCxMknThwgWX+1NSUiQp2ypbBZWenq7Zs2dLYhWt0iQ4ODjrZ+3anH9AnebOnZtVLioqKtdyhw4d0nPPPafmzZuratWqqlKlipo3b65nn302z2EBUVFR2WIJCwtTzZo11axZM913332aPn26UlNzn5TorH/gwIEc+zZs2KD77rtPjRo1Unh4uCIiItS0aVPddddd+uCDD3Tu3Llc273UgQMHsuILDQ3Vn3/+mWvZ8ePHKzg4WOXLl8+6C5icnKyrrrpKwcHBWrx4ca51U1JSdPXVVys4OFjfffedJOnzzz/P9v4EBwercuXKql+/vrp27apRo0bp999/z7XNjIwMzZ07VyNHjlTnzp1VuXJlBQcHa/jw4bnWSU9P1yuvvKI77rhD11xzjapWraoKFSqoSZMmevLJJ3Xo0KE8368LFy5o/Pjx6tChg2rVqqUKFSroqquu0tChQxUfH59rvVOnTmnq1Kl67LHHdP311yssLEzBwcH66quv8jxefmbMmKH27durSpUqqlmzpnr16qVff/0133rz58/X7bffrsjISFWoUEGNGzfWgAEDXL6GRYsW6cUXX1T37t1VvXp1BQcH6x//+Eex4oZ3slt8s/08G/uNWlXdp/y+0zMMqVXVfXo29pscbbjjx7AGZg5d8kDb7v5Jt1tkOXTx7zcmvzdOks+hi0pLs2S+VrG+L8o2r7wz4vw2LbcnrTu3F+VbtyVLlujEiROqX7++brjhhqIHCY/54osvdP3117vcN2vWrHzrz5w5U8OHD9fFixcVGRmpjh07ymKxaPPmzZo4caI+/vhjTZw4UQMHDsy1jV69eik0NFSGYej8+fNKTEzU3Llz9eWXX2rUqFGaMmWKunbtWuDXNG3aNMXFxcnhcKhBgwa65ZZbFBQUpP379+u7777T119/rRYtWqhVq1YFblPK/Lb6q6++0pNPPuly/xdffJFjW2hoqN577z317t1bTzzxhDZu3OgysX/ppZd04MAB9enTJ8dwxkt/f+x2u06fPq2tW7dq9erVGj9+vAYMGKB3331X5cqVy1bvr7/+KvSXAKmpqXrttdcUGhqqa6+9VrGxsbLb7dq2bZs+/PBDzZ49W4sWLXK58t7WrVvVp08fHTlyROHh4WrdurXCwsK0Z88effHFF/riiy/08MMPa9y4cTnuVMbHx+uxxx4rVKz5ee655zRhwgQFBQWpU6dOSk1N1Y8//qjly5dr+vTp6tmzZ446GRkZeuCBBzR79myFhISoTZs2Kl++vA4dOqTFixerWbNmOa5l999/f4GTW1z5Xv7oJlkDfRWQekq3LnlWMqQ8bmxJ+nt/vwYbFPbIq7IFuvcBfnZ75pwRb7lTYE936KNVB5VwMiXvghaLGlQJ1rC+UfL39VHyObuG3bqoZIIESimvTEaaNm0qSdq0aZPL/c7tMTExhW7bOUTr7rvvLmJ0XubCSfltnSGfQ7/IYk+WYQ2Vo/YNSo8ZKIVUMTu6bAICAlS/fn3NnTtXb731lvz8snff06dPa+nSpWrWrJm2bNniso1vvvlGDz74oKxWq6ZMmaK7774723CS6dOna/jw4XrwwQcVFham22+/3WU7r7/+uurUqZNt27Fjx/Tmm29qypQp6tOnj+bNm6dbbrkl39f1559/6qmnnpJhGHr//fc1ePDgbDGdOnVKs2bNUnh4eL5tXapOnTo6f/68Zs2a5TIZ2b59u7Zv3+7y/erWrZv69eunL7/8Ui+++KLGjx+fbf/GjRv1/vvvq0KFCho3blyOtm+44QZ9+OGH2bYZhqHvv/9eTz/9tL744gv9+eef+vbbb7Pd2vX399fAgQPVokULtWjRQlu3btUTTzyR5+sMDAzU8uXL1bJly2x9IiMjQy+99JLGjRunJ598MsdDUA8ePKhbb71VZ8+e1aOPPqqXX35ZQUFBWfvXrVune++9V5MnT5a/v7/efPPNbPWrVaumYcOGZcU6fvx4zZw5M89Y87Jy5UpNmDBBlSpV0ooVK9SwYUNJ0tq1a9W1a1c9/PDDuvHGG3Os7jd69GjNnj1bXbt21X//+19VrFgxa19SUpJOnz6d41g9e/ZUVFSUWrRoob/++itrOXSUTdZAXwUE+qnhvq/lY6QXqq6Pka4GR75WwjXD3BqTcyK31eodH1MCJMXdUl9z1h/Rr3uTlOHIeYfE18ei1g0r6M5WNeTvm/nlhi01I0c5oKzxymFabdu2VXh4uBISErR58+Yc++fMmSNJ6tGjR6HaTU5O1tdffy2pDCQjaRflv/j/FPh+c/mvfkO+iT/J58hG+Sb+JP9Vr2duX/yMlF661kHv37+/Tp06paVLl+bYN2fOHKWlpemuu+5yWTc5OVlxcXFZH/rvueeeHOPa7777br3//vsyDEOPPfZYrkMBXalevbreeecdvfjii3I4HHrooYdks9nyrffDDz/IZrOpTZs2uu+++3LEVLlyZQ0fPlxXXXVVgWORMpO3Xr166bfffnM5NMp5Fym39+utt95SpUqV9OGHH2YbJpSenq7HHntMGRkZeuONN1StWrUCxWOxWHTbbbfpp59+UkREhFavXq2PPvooW5mQkBB9/PHHeuSRR9SqVSsFBgbm266fn5/atGmTIzn19fXViy++qMDAQK1fvz7HuYyLi9PZs2c1ePBgjRs3LlsiIkmtWrXSwoULFRQUpIkTJ2rdunXZ9l9//fV69913dc899+iaa64p9hyv9957T1Lm3RFnIuI8zgMPPKBz587ps88+y1Zn7969eu+99xQZGakZM2ZkS0QkqUKFCtnacpo8ebKefPJJtW/fvkjDWXFlqnhiQ5HqVTqx3s2ReCern48GtqmlV/pG6fbY6oqKCFW9KsGKigjV7bHV9UrfKA1sUysrEQGQySt/I6xWq+Li4iRlfqC49EPG+PHjtW3bNrVr104tW7bM2j5x4kRFRUVp5MiRubY7b948paSkqHXr1mrUqJHnXoDZ0i7K+tVA+W2dLosjzWURiyNNfls/l/XLu6S0iyUcYO4GDBggi8XicnjRF198odDQ0FyT0BkzZujUqVNq2bJlrh/ApcwP59ddd51OnTqVdaesMJ555hnVrl1bx48f17x58/Itf+rUKUmZSYe7OV/n5e+Xw+HQl19+qfr16+c65K1KlSp688035XA49Oijj2ZNKH333Xe1bds23XzzzUWaV1W1alW98MILkqT333+/0PULw2KxyMfHRz4+PtmSld9//13Lli1TYGCgXn311VzrN2rUSI888ogMw9DEiRM9FmdqaqpWrlwpKXMI4OXuuOMOSZlzPS41bdq0rGFaua0uCBSUX3o+Q4zcXO9KVS7IX91iqmr4LfX1f7c11PBb6qtbTFWVC/KOIWdASfPKZESSRo0apeuvv17x8fFq1KiR+vfvr9atW+vpp59WpUqVNHXq1GzlT506pV27duno0aO5tun84HmlT1z3X/6CfA/9ku8igoYk30O/yH/5iyURVoHUrl1bbdq00XfffZdt6ebExEStXbtWPXv2zPENt9MPP/wgSerXr1++x+nfv7+kzDlEheXr66vevXtLUo6hQa7UrFlTUuYwnb179xb6eHlp27atIiMjNXv27GyrzK1evVp//vln1uvMzcCBA9W5c2ft3LlTb775phISEvTaa68pODi4WB/O+/TpIx8fH+3bty/XuV/FZRiGxo0bp5SUFHXo0CHb+ufO89qlS5ccdxMu5+wvS5cuLfZKfbnZtWuXbDabqlSp4vL5Sc2aNZOUObTuUitWrJAkdezYUYmJifr3v/+tuLg4vfjii/rxxx89EiuuXOl+RUtoi1oPACQvTkYCAwO1YsUKvfDCCwoODtaCBQuUmJiowYMHa/PmzS6HJuTl6NGj+vHHH+Xv75/vBzSvlnxCvr9lrhaW3/odzv2+v30hXTjp0bAKY8CAAUpJSckaUif9PeQor3O3bds2SXI5kflyzjLOOoXlnK+0a9eufMv26NFDVapU0fnz59WqVSsNGjRIkydP1ubNm5WRUbzxxBaLRf3799ehQ4f0888/Z213vl8DBgzIt40JEyYoJCRE48aN0913363U1FS98MILqlevXpHjCgsLy6q/c+fOIrdzuVGjRmnYsGEaMGCAmjRpopdeeklXXXVVjsSpMH0hOjpaVqtV586dU2JiottivZRzxa8aNWq43B8SEqLy5csrKSlJf/31V9b2P/74Q5K0fv16xcbGasyYMfrkk080btw49ejRQz169ND58+c9EjOuPGeqXlekeqertsy/EADkwmuTEUkKCgrS2LFjtXfvXtlsNh07dkzTpk1TZGRkjrJjxoyRYRiaNm2ay7YiIiKUnp4uu92uSpXcuypIaeK3bWauQ7NyY3GkyW9b0SfmulufPn1ktVqzlmCWpNmzZ6tatWq6+eabc6135swZSZnDj/LjHDLlavJvQTj70NmzZ/MtGx4ergULFqhx48ZKTU3V/PnzNWLECLVt21a1atXS8OHDdeTIkSLFIeUcqpWampq1OldBhiPWqVNHo0ePVlpamrZu3arY2NisYZLFUZj3qKAWLFig6dOn65tvvtG+ffsUHR2tzz77THXr1s1WznleC9IXfH19syaNO4fUuZtzqGleQ62c+5x3BFNTU7OWkX722WfVvn17rVu3TsePH9e3336runXr6scff3TLuULZcKheHzkshZsw7rD46VD93h6KCEBZ4NXJCArP59AvRat3MPfnLZS0ChUqqGvXrlqxYoWOHTumDRs2aPfu3erXr598ffN/CFFBhtrk92BNd9ePjY3Vxo0bNW/ePD388MNq3ry5/P39de7cOf33v//VDTfcoN27dxcplquvvloxMTGaP3++7Ha7Fi1apHPnzuU5b+Zyjz76qKpWrSop8+5DQd7n/BT3PXZl+/btSklJ0cGDB/X111/LarWqbdu2Oeb+OI9d0GFXznKeehp0Qd6Ly2O99K5ZjRo1NGfOHF177bUKCwtTx44d9eWXX8rHx0dz5851+/A/XJlsQZV1uF4vSfk/C9y5/1C9O2QPdP98NwBlB8lIGWOxJ+dfyI31POWuu+5SRkaG5syZk/WNf35DjpxzA06ezH/ImfMb8PzmE+TG+c375cuw5sXX11fdunXT+PHjtWbNGh06dEiTJk1SpUqVdOLECY0YMaJIsUiZ71dSUpIWL16sWbNmyc/Pr1DLufr4+GStbBUSElLkOC5VlPeooCpXrqwuXbpo0aJFioiI0BNPPJFtborzzldB+kJ6enrW3RtP3TV1rmiV1+ptFy9ezFY2JCQkawWvAQMG5HhS9bXXXqvmzZvLMAytXr3aE2HjCrQj9nmdrnJdgYbxnq5ynX5v/nxJhAXgCkYyUsYY1qIt41nUep5y6623qnz58poxY4bmzJmjqKiofMf/N2nSRJJcLgd9OWeZojyrRvp7TkJeT4HPT7ly5TRkyBBNmTJFUuZk+JSUoq1a069fP/n4+Gjy5MlaunSpOnbsmHWnwwznz5/X/v37JRXvPcpPeHi4br31Vl28eFHLly/P2l6YvrBjxw7Z7XYFBwfnGO7lLs6hpbkNx7tw4YLOnj2r8uXLKywsLGu788GuuT3g1fksnIIkXYAkOfwCte7GyTpQ/85ch2w5LH46UP9Orbtpihy+AS7LAEBBkYyUMY7INkWrV7t0PY3e+QyNrVu36sSJEwWaiO18AOGXX36Zb1lnmS5duhQ6toyMjKwlfW+66aZC17/cjTfemNVuUedXREREqEOHDlq5cqXsdnuhhmh5wty5c2UYhho1apTrpG13cd7NuHS+h7MvLFu2LGsuUW6cfaFjx445nmXiLo0bN1ZAQIBOnjzpcnUx50Mpr7322mzbnQ+ATUpKctmu87XxLBEUhsMvUNtbjtbyfyzTziaP62S1Nkqq1FQnq7XRziaPa/k/lml7y9EkIgDcgmSkjEmPGSjDp3BrnRs+/plPZC9lBg0apEqVKqly5coFSkbuvvtuVaxYURs2bMhaTcqVWbNmacOGDapUqZIGDRpU6LjeeustHTp0SDVq1HD5zIjL5TdvYd++fZIyn69TnGeR3HPPPapUqZJq1qyZ65PlS8KJEyf0yiuvSMqci+Jpa9askSTVr18/a1t0dLQ6deqk1NRU/etf/8q17p49ezR58mRJ0vDhwz0WY1BQUFbiumDBghz758+fLynzjuClunfvLsn1EtJ//fVXVhLjXBoYKAx7YGUlXDNM6zp8pPjOM7Suw0dKuGYYc0QAuBXJSFkTWlUZTTKXvy3oBMWMJgOkkPxXHSppbdu21aFDh3Tw4MFch6lcKiwsLGuJ10cffVSff/55jjIzZszI+oA8YcKEbENi8nPs2DGNGDFCY8eOla+vr6ZMmZJjHL8rH374oeLi4rRjx44c+44cOaLHH39cktStW7ds7X3wwQdq1qyZXnyxYM+BcS7xu2fPHlMekGcYhhYvXqwOHTro6NGj6tChg+6///5it/vtt9/qhx9+yJHUpaSkaPTo0Vq9erWqVauW4y7XhAkTFB4erk8//VTPPPNM1pwMp3Xr1un222/XxYsX9dRTT6l9+/bFjvXPP/9Us2bNXCYHzvP85ptvZptwvnbtWv33v/9VuXLlNHjw4Gx17rzzTtWpU0fLli3LNkk/PT1d//d//6ekpCRFR0erTZui3REFAMDTPDPmAKVaWqeXZTmTIN98VtaySMqIbKO0zi+XTGAloFevXpoyZYoef/xxPfTQQ3r11VfVvHlzSZnzBw4cOKCAgABNmTIlz7saI0eOVGhoqAzD0F9//aXExETt2LFDGRkZqlatmj788EN16tSpQDGlpaXpk08+0SeffKK6desqOjpawcHBOnLkiNavXy+73a66detq3Lhx2eqdPn1au3fv1rFjx4r8fnhKfHy8hg0bJkmy2+06c+aMtmzZkjVUauDAgXrnnXdcDnt64oknsr7Rd5ZfuHBhtme+XHonYMuWLXrttdcUERGhpk2bKjw8XMePH9e2bdt05swZhYeHa/r06TmGKtWtW1ffffed7rzzTk2aNEnTp09XmzZtFBYWpj179mjLli2yWCwaNmyYxo4d6/J1XjoMzzkHZuzYsVlJb7NmzfTee+9llUlPT891VbSOHTvqscce06RJk9S6dWt17NhRdrtdP/74oxwOh6ZOnZpjQYWAgAB9+umn6t69u4YNG6b3339fderU0ZYtW3TgwIGsB8BevkrX66+/rsWLF0tS1nNL1q9fn+31fPHFF4qIiHAZKwAA7kIyUhb5B8l+50z5L39Rvr994fK5I4aPvzKaDMhMRPwCTQjSc+655x7deOONmjhxopYtW5b1NO5atWrp0UcfVVxcXL4TlZ1DaXx8fFSuXDlVrVpVvXv31i233KI+ffpkrTxVEPfee69q1qypJUuWaPPmzVq3bp2SkpIUFham2NhYde/eXQ899FCh7tKYbd++fVnDy4KCghQeHq6oqCi1atVKAwcO1DXXXJNr3Z07d2r9+vXZtp04cUInTpxwWb5nz55KTk7Wzz//rE2bNunMmTMKCgpS/fr1NXToUD3yyCO5fqhu3ry5tm7dqsmTJ+ubb77Rr7/+mm1ezr///W899thjucZ6eZySlJCQoISEBEkqVD+QMof4xcTEaPLkyVkPYe3QoYOef/553XCD63lbrVq10i+//KLXXntNP/74o3bs2KGqVatqyJAhev75510+d2n//v05Yj937ly2bXa7vVCxAwBQFBajoAvto1iio6Nls9lcjgd3MgxDaWlpCggIUN26dbOW7XQH5weLHMOGLpyU37aZ8jkYL4s9WYY1VI7aN2TOESmFQ7PgGbn2jzLqgw8+0NNPP62qVavqxx9/zDbfBK45HA4lJibKZrPJ398/z2emHDx4UFLuq4DB81JTMzR48FrZLb568/OOCggsXd9NlpVr0vmzNg3puFBVa4XqndmdFBhUuDmdKDm21HQ9e/dyWeypmjWrvQIDi/+8rStJr169FBAQ4HLIeX5K19UHJS+kitLbPCG1ecLsSIBS45FHHtHRo0c1btw49ezZU8uXLzd1KWQAAK5UJCMA4MLYsWMVERGh06dPa+3ataauQAYAwJWKZAQAcvHII4+YHQIAAFc0lvYFAAAAYArujAAArmhJFzO0ZG+ytp9IVUqaQ8H+PmpSLVBdGoSqQhCTUAHATCQjpRSLnAHwJqXxmmVLd+jDDUlavi9Z6Y7s+zYfTdXMbWfVuX6ohrWsKKtv7qt/AQA8h2FapYhzKUzDMGSz2UyOBgAKLi0tLSshyWtZ35JiS3do9I8n9MPenImIU7pDWrw3WS8uPy5bboUAAB5FMlLK+Pj4KCMjQ+fPny+V3zQCgCsXLlyQw+Fw6/ORiuPDDUnafqJgX+psP2HTRxuSPBwRAMAVhmmVMj4+PkpPT1dycrIkqVy5cgoICCj2N40OhyPb/wOXon+gqBwOh5KTk3Xu3DllZGTI19f8ORhJFzO0fF9yoeos25esQU3LM4cEAEoYyUgp4+PjIz8/P6Wlpen8+fO6cOGCW4Y8lKbhEyh96B8oDofDofT0dFkslnz7UOYw1MykNzU1wyPxLNp5PtehWblJd2TW63N1OY/EVNqkpmZk/t5bJIm78ADMQzJSCjkTEofDIbvd7pY2ne1YrVa3tIcrC/0DxWGxWOTj4yMfH598kxGbzaFnn02UJFmtRzwSz8XWlaQqgYWuN3vlCX3zxh8eiKj0cTgMJSZeUJVaYWJEMAAzkYyUUs4/7JJ7Vqk5duyYJCkyMrLYbeHKQ/9AcRTljpphDZTdQ3fiDL+izVsx/Hxkt5SNYVoOiyFD3AkFYD6SES/gzqEzDMNBXugfKEkvf3STrIHu//A/eeUB7T5+odD1GkVV0MMPN3N7PKVR8jm7ht26yOwwAIBkBABgDmugrwIC3f9n6KoaoUVKRq6qEeaReEojm4fm6wBAYZWONRgBAHCTNo0qytencHf5fH0suqFRBQ9FBADIDckIAOCKEh7kr9YNCpdYtG5YQeWC/D0UEQAgNyQjAIArTt9WNdSoWkiByjaqFqI7W9XwcEQAAFdIRgAAVxyrn48e7VxPbRvnPmTL18eito0r6rEu9eTvy59DADBD2ZipBwAoc6x+PhrYppZ6NKum+D1J2nMsWbZ0hwL8fNSoeqhuaMTQLAAwG8kIAOCKVi7IX91iqqpbTFWzQwEAXIb70gAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABM4dXJSGpqqkaPHq3GjRsrMDBQNWrU0NChQ3X48OEitbd37149+OCDqlu3rgIDA1WlShXdcMMNeuutt9wcOQAUnm/qGVX541PVXfWk6v84THVXPakqf3wq39QzZocGAECR+JkdQFGlpqaqU6dOio+PV0REhHr27KnExERNnTpV3377rX755Rc1aNCgwO3Nnz9fAwcOlM1mU2xsrNq0aaPTp0/rt99+05QpU/TMM8948NUAQO4sGTZFbHlX5RMXycdIz7Yv9MR6Vfn9E52t211Hmz0hwzfApCgBACg8r01GXnvtNcXHx6tNmzZasmSJQkNDJUnjx4/X008/raFDh+qnn34qUFtbt27VgAEDFBYWpqVLl6pdu3ZZ+xwOhzZt2uSR1wAA+bFk2FR39dMKObU51zI+Rroq7v9aAX8dVGL7t0lIAABewyuHaaWlpWnChAmSpEmTJmUlIpI0YsQIxcTEaNWqVdq4cWOB2hs+fLjsdrumTZuWLRGRJB8fH1133XXuCx4ACiFiy7sKObVZRj7lDEkhpzYrYst7JREWAABu4ZXJyJo1a3T27Fk1aNBAsbGxOfb37dtXkrRw4cJ82/rjjz+0evVqNW7cWD169HB7rABQVH6pp1U+cZEkyZJPWef+8onfMYcEAOA1vHKY1tatWyVJzZs3d7nfud1ZLi/Lly+XJHXp0kWpqamaPXu2NmzYIIvFopiYGPXr10/lypVzU+QAzGQYhmw2h9lhFFjEnm9yzBHJj4+RrrA93+hYo3s8FFXxpKZmyDCUeSsn3/s9AIArnVcmIwcPHpQk1apVy+V+53Znubzs2LFDkhQUFKRmzZpp165d2faPHDlSc+fO1Y033lig2KKjo11uT0hIULVq1QoUkyekpaVJKth7grKnrPQPm82hZ59NNDuMApvacaVqRhS+XuLKFRryYl23x+MODoehP/+0q2qkVTZbmiw+JCRmSLOn/e9fhuz2NPn4lq7zYBiZ8djtdpMj8azSfh7wN7s9QzIy++ahQ4cUEOCVg4s8Ji0tTQEBRZuv6JXJSHJysiQpODjY5f6QkJBs5fKSlJQkSXr33XdVoUIFzZs3Tx07dtTx48f10ksvaebMmerVq5d27NihiIgifCoAUOoY1kCzQyiQEGta/oVyqVdaX6PhMCRL4e72AACuXF6ZjDi/MbFYXI+idu4viIyMDElSenq6pk+frltuuUWSFB4erhkzZmjPnj1av369Jk2apFdeeSXf9px3Wi4XHR0tm82m2rVrFzg2d3J+423W8VG6lZX+kZqaIav1iOwWi17+6CZZA33NDilPNeK/kk4eKHy9qAj9e2gnD0RUfMnn7Bp2a+Y8GKvVX1arv8kRlU3+VuffSUupPA/OOyJWq9XkSDyrtJ8H/M1wpEuWzM+ekZGRCizlfz9Kmr9/0fuuVyYjYWFhkqQLFy643J+SkiJJ2VbZyq+tmjVrZiUilxoyZIjWr1+vlStXFjFaAKWRNdBXAYGl+xJ4tnpLVTv5a6HrJVVvVWpfmy01w+wQAACliFcOeHN+e5vbk9ad2wvyLW/dunUlSXXq1Mlz/4kTJwoZJQAUz6F6feSwFC6pcFj8dKh+bw9FBACAe3llMtK0aVNJyvVhhM7tMTEx+bblXBr4zBnXS2GePn1aUsHusgCAO9mCKutwvV6S8l93yrn/UL07ZA+s7MmwAABwG69MRtq2bavw8HAlJCRo8+acTyWeM2eOJBXouSGdOnVSSEiIEhISdOjQoRz7ncOzcltGGAA8aUfs8zpd5boCPWfkdJXr9Hvz50siLAAA3MIrkxGr1aq4uDhJUlxcXLa5I+PHj9e2bdvUrl07tWzZMmv7xIkTFRUVpZEjR2ZrKzg4WMOHD1daWpoeeeSRbG0tXrxYn376qSwWi4YNG+bhVwUAOTn8ArXuxsk6UP/OXIdsOSx+OlD/Tq27aYocvkVbWhEAADOUzhmOBTBq1CgtW7ZM8fHxatSokdq3b68DBw5o7dq1qlSpkqZOnZqt/KlTp7Rr1y4dPXo0R1ujR4/W6tWr9d1336lRo0a6/vrrdeLECf36669yOBx69dVX1apVq5J6aQCQjcMvUNtbjtbuJo8pct88VTqxXn7pKUr3C9bpqi11qH5vhmYBALyS1yYjgYGBWrFihV5//XXNnDlTCxYsUIUKFTR48GC9/PLLioyMLFRbP/74o8aNG6fp06fr+++/V2BgoG6++WY99dRT6t69uwdfCQAUjD2wshKuGaaEa7hTCwC4MnhtMiJlPjV97NixGjt2bL5lx4wZozFjxuS632q16p///Kf++c9/ujFCAAAAALnxyjkjAAAAALwfyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU5CMAAAAADAFyQgAAAAAU/iZHQCAsifpYoaW7E3W9hOpSklzKNjfR02qBapLg1BVCPI1OzwAAFBCSEYAlBhbukMfbkjS8n3JSndk37f5aKpmbjurzvVDNaxlRVl9LeYECQAASgzJCIASYUt3aPSPJ7T9hC3XMukOafHeZB0+n6aXOlZVgB8jSQEAuJLxlx5AifhwQ1Keiciltp+w6aMNSR6OCAAAmI1kBIDHJV3M0PJ9yYWqs2xfspIuZngoIgAAUBowTAsoAYZhyGZz5F/QJM7YUlM98+F/0c7zOeaI5CfdkVmvz9Xl3BZHamqGDMOQLJJkuK1dAABQNCQjQAmw2RwaPHit2WHkym63S5Ks1iMeaf9i60pSlcBC15u98oS+eeMPt8XhcBhKTLygKrXCZJCLAABgOpIRoATZLaVz2VrDmpko2C2eWcHKKOJEdMPPx63vmcNiyBCrdAEAUFqQjAAl7OWPbpI1sHQlJXZ7miTJavX3SPuTVx7Q7uMXCl2vUVQFPfxwM7fFkXzOrmG3LnJbewAAoHhIRoASZg30VUBg6frVs/hkTuiwWj0T11U1QouUjFxVI8yt75XNQ3NiAABA0bCaFgCPa9Ooonx9Cjc8ytfHohsaVfBQRAAAoDQgGQHgceFB/mrdoHCJReuGFVQuyDPDxgAAQOlAMgKgRPRtVUONqoUUqGyjaiG6s1UND0cEAADMRjICoERY/Xz0aOd6ats49yFbvj4WtW1cUY91qSd/Xy5PAABc6UrXLFoAVzSrn48GtqmlHs2qKX5PkvYcS5Yt3aEAPx81qh6qGxoxNAsAgLKEZARAiSsX5K9uMVXVLaaq2aEAAAATMQ4CAAAAgClIRgAAAACYgmQEAAAAgClIRgAAAACYgmQEAAAAgClIRgAAAACYgmQEAAAAgClIRgAAAACYwquTkdTUVI0ePVqNGzdWYGCgatSooaFDh+rw4cOFaqdu3bqyWCy5/uzcudNDrwAAAAAou7z2Ceypqanq1KmT4uPjFRERoZ49eyoxMVFTp07Vt99+q19++UUNGjQoVJuDBw92uT08PNwdIQMAAAC4hNcmI6+99pri4+PVpk0bLVmyRKGhoZKk8ePH6+mnn9bQoUP1008/FarNadOmeSBSAAAAAK545TCttLQ0TZgwQZI0adKkrEREkkaMGKGYmBitWrVKGzduNCtEAAAAAPnwymRkzZo1Onv2rBo0aKDY2Ngc+/v27StJWrhwYUmHBgAAAKCAvHKY1tatWyVJzZs3d7nfud1ZrqDeeustJSQkKCAgQNHR0brjjjtUpUqV4gULAAAAwCWvTEYOHjwoSapVq5bL/c7tznIF9eyzz2b776eeekr/+c9/dP/99xe4jejoaJfbExISVK1atULH5C5paWmSCv+eIG/WtLOKPLlCFc//Ib+MVKX7BupMuWt0qEoH2f3LZ5Wz2Ryy2+0yrIGy29Nk8XGYF7QLhmFIkux2u8mReFaaPe1//zJkt6fJx9cwNZ6y6O9zIM6BiUr77wLXJJQ2dnuGZGT2zUOHDikgwCsHF3lMWlqaAgICilTXK5OR5ORkSVJwcLDL/SEhIdnK5ecf//iHbr75ZrVo0UJVqlTRvn379Mknn+i9997TAw88oEqVKqlXr15uiR1XBh+HXdcc+FS1Tv0kHyMj274q539Toz/n6HDlDvq9zr1y+FhNihIAAKB088pkxPmNicViyXN/Qf3nP//J9t/R0dF6++23ddVVV+mhhx7Sc889V+BkZMeOHS63R0dHy2azqXbt2oWKzV2cd0TMOv6VxJJhU93VTyvk1OZcy/gYGap9crkqGWeU2P5tXUzzk9V6RHaLRVarv6zW0vWr5/z20Wq9shMnf6vz2uA8D/6mxlMW/X0OxDkwUWn/XeCahNLGcKRLlszPnpGRkQoM9DU7pFLF37/ofdcr7zGFhYVJki5cuOByf0pKiiRlW2WrKB544AFVrVpVu3fv1v79+4vVFq4cEVveVcipzcov5TUkhZzarIgt75VEWAAAAF7HK5MR57f7uT1p3bm9uHcBfHx8sh6cePTo0WK1hSuDX+pplU9cJElyfV/ub8795RO/k5/tjEfjAgAA8EZemYw0bdpUkrRp0yaX+53bY2Jiin2spKQkScW/y4IrQ4X938rHSC9UHR8jXZUPfuehiAAAALyXVyYjbdu2VXh4uBISErR5c85x+3PmzJEk9ejRo1jH2bFjh3bt2qXg4GBFRUUVqy1cGUJO5j5PJC9hp4tWDwAA4ErmlcmI1WpVXFycJCkuLi7b3JHx48dr27ZtateunVq2bJm1feLEiYqKitLIkSOztfXDDz+4fFL7tm3bdOedd8owDD3wwANX/CQ6FIxPekqR6vmmX3RzJAAAAN6vdC3pUwijRo3SsmXLFB8fr0aNGql9+/Y6cOCA1q5dq0qVKmnq1KnZyp86dUq7du3KMffjl19+0UsvvaQ6deqoQYMGqlKlivbv369NmzYpPT1dN910k15//fWSfGkoxRx+rpeTzk+GX5CbIwEAAPB+XnlnRJICAwO1YsUKvfDCCwoODtaCBQuUmJiowYMHa/PmzWrYsGGB2unatauGDh2qcuXKaevWrZo7d6727t2rdu3a6aOPPtLy5ctzfZ4Jyp4LVWKLVO+vSkWrBwAAcCXz2jsjkhQUFKSxY8dq7Nix+ZYdM2aMxowZk2N7mzZt1KZNGw9EhytRUr0eqvL7J4WaxO6w+OlU7e6S9nguMAAAAC/ktXdGADOkB1bS2bq3SVKBnjMiSWfrdld6QEWPxgUAAOCNSEaAQjra7EldqBxboOeMXKgcq6PNniiJsAAAALwOyQhQSIZvgBLbv60z9XrKYXE90tFh8dOZej2V2P5tGb4BJRwhAACAd/DqOSOAWQzfAB1p8ayORz+givsXKuTkZvmkp8jhF6wLVWJ1pt7tyghkaBYAAEBeSEbKAMMwZLM5JEmpqRkmR3OlCdeFendL9e7OueuS9zo1NUOGYWSO3cp3tgkAAEDZQDJSBthsDj37bKIkyWo9Ym4wZZTDYSgx8YKq1AqTQS4CAAAgiWSkTDGsgbJb8pt2DU9wWAwZ+U55BwAAKFtIRsqYlz+6SdZAX7PDKHOSz9k17NZFZocBAABQqpCMlDHWQF8FBOY87ecvpil+zxntOXZBtnSHAvx81Kh6qG5oVEHlgvxNiPTKYmOuDgAAQA4kI2WcPd2hOeuO6NeEJGU4sk9m2Hk0WYu2HlfrhhV0Z6sa8vdlJWgAAAC4D8lIGWZPd+j9Zfu15/iFXMtkOAz9vPuMTpyz6dHO9WT1IyEBAACAe/DJsgybs+5InonIpfYcv6A561mJCwAAAO5DMlJGnbuYpl8TkgpV59e9STp/Mc1DEQEAAKCsIRkpo37ZcybHHJH8ZDgMxe8pXAIDAAAA5IZkpIzac6xgw7Ny1kt2cyQAAAAoq0hGyihbuqNE6wEAAACXIxkpowKKuCpWUesBAAAAl+OTZRnVqHpIEeuFujkSAAAAlFUkI2VUm0YV5etjKVQdXx+LbmhUwUMRAQAAoKwp8kMPDx486LYgateu7ba2UDDhQf5q3aCCft5zpsB1WjesoHJB/h6MCgAAAGVJkZORunXrymIp3DfrrlgsFqWnpxe7HRRe31Y1dOK8rUAPPmxULUR3tqpRAlEBAACgrChyMiJJVatWVVRUVJHr79y5UydOnChOCCgGq5+PHu1cT3PWH9Gve5NcPnfE18ei1g0r6M5WNeTvy6g+AAAAuE+xkpFbb71Vn3zySZHrDxkyRJ999llxQkAxWf18NLBNLfVoVk3xe5K051iybOkOBfj5qFH1UN3QiKFZAAAA8IxiJSPFZRiFewI4PKdckL+6xVRVt5iqZocCAACAMqLIycjmzZtVsWLFYh385Zdf1lNPPVWsNgAAAAB4pyInI02bNi32wSMjIxUZGVnsdgAAAAB4H2YkAwAAADCFR5ORN998Ux07dvTkIQAAAAB4KY8mIzt37tRPP/3kyUMAAAAA8FIM0wIAAABgCpIRAAAAAKYgGQEAAABgCo8mI1FRUbrxxhs9eQgAAAAAXsqjychzzz2nFStWePIQAAAAALwUw7QAAAAAmKLIyci2bdt0+PDhYh388OHD2rZtW7HaAAAAAOCdipyMxMbGavTo0cU6+AsvvKDY2NhitQEAAADAOxU5GTEMQ4ZhuDMWAAAAAGWIX3EqL168WB07dixy/Z07dxbn8AAAAAC8WLGSkWPHjunYsWPFCsBisRSrPgAAAADvVORkZP/+/e6MAwAAAEAZU+RkpE6dOu6MAwAAAEAZ49XPGUlNTdXo0aPVuHFjBQYGqkaNGho6dGixlxzes2ePgoKCZLFY1K1bNzdFCwAAAOBSXpuMpKamqlOnTho7dqySk5PVs2dPRUZGaurUqWrevLkSEhKK3PZDDz0km83mxmgBAAAAXM5rk5HXXntN8fHxatOmjXbv3q3Zs2dr7dq1evvtt3Xy5EkNHTq0SO3+97//1YoVK/Tggw+6OWIAAAAAl/LKZCQtLU0TJkyQJE2aNEmhoaFZ+0aMGKGYmBitWrVKGzduLFS7J06c0DPPPKPOnTvrrrvucmvMAAAAALLzymRkzZo1Onv2rBo0aODyCe59+/aVJC1cuLBQ7T7++OO6ePGiPvjgA7fECQAAACB3XpmMbN26VZLUvHlzl/ud253lCmLRokWaPXu2/vnPf6phw4bFDxIAAABAnor10EOzHDx4UJJUq1Ytl/ud253l8nPhwgU9+uijuuqqq/Tcc88VK7bo6GiX2xMSElStWrUCx+RONptDhmFIhmS3p8ni4yjxGMq6NHva//5lyG5Pk4+vYWo8lzOMzHjsdrvJkXhWaT8PZcHf50CcAxOV9t8Frkkobez2DMnI7JuHDh1SQIBXfp/vMWlpaQoICChSXa9MRpKTkyVJwcHBLveHhIRkK5efUaNG6cCBA/rxxx9ltVrdEyQAAACAPBU5GRk7dqwsFosee+wxVaxYUWPHji1wXYvFohdeeKGoh876xsRiseS5vyA2bNigCRMm6N5779XNN99c5JicduzY4XJ7dHS0bDabateuXexjFFZqaoYslkQZFslq9ZfV6pU5qFfztzr7pOV/58Df1Hgu5/z28UpPxkv7eSgL/j4H4hyYqLT/LnBNQmljONIlS+Znz8jISAUG+podUqni71/0vlvkT6VjxoyRxWJR//79VbFixaz/LkgiUNxkJCwsTFLm8CpXUlJSJCnbKluupKen68EHH1R4eLjGjRtX5HgAAAAAFF6Rk5GpU6dKkiIiIrL9d0lw3l3I7Unrzu353YU4fPiwtmzZourVq+vOO+/Mtu/s2bOSpHXr1qlDhw4KDQ3Vt99+W8zIAQAAADgVORkZPHhwnv/tSU2bNpUkbdq0yeV+5/aYmJgCtXfs2DEdO3bM5b6kpCT99NNPCg8PL0KkAAAAAHLjlUsBtG3bVuHh4UpISNDmzZtz7J8zZ44kqUePHnm2U7duXRmG4fJnxYoVkqSuXbvKMIysOyUAAAAA3MNtyUj79u01ZcoUnTlzxl1N5spqtSouLk6SFBcXl23uyPjx47Vt2za1a9dOLVu2zNo+ceJERUVFaeTIkR6PDwAAAED+3JaM/Pzzz3r00UcVERGhnj176ssvv1Rqaqq7ms9h1KhRuv766xUfH69GjRqpf//+at26tZ5++mlVqlQpxxyWU6dOadeuXTp69KjHYgIAAABQcG5LRtauXavhw4ercuXKWrhwoe666y5VrVpV9913n5YuXSqHw70P2gsMDNSKFSv0wgsvKDg4WAsWLFBiYqIGDx6szZs38xR1AAAAoJRzWzLSsmVLvfvuuzp8+LCWLl2qwYMHy9fXV5999pm6deummjVr6qmnntL69evddUgFBQVp7Nix2rt3r2w2m44dO6Zp06YpMjIyR9kxY8bIMAxNmzatQG136NBBhmFo8eLFbosXAAAAwN/cPoHdYrGoU6dO+uSTT3T8+HHNmTNHd9xxh86dO6f33ntPrVu31lVXXeXuwwIAAADwMh5dTctqtap3796aM2eOjh8/rocffliGYWjv3r2ePCwAAAAAL1Dk54wU1O7duzVz5kzNnDlTCQkJkqSAgABPHxYAAABAKeeRZOTYsWP64osvNGPGDG3atEmGYchisejGG2/UoEGDcjztHAAAAEDZ47Zk5Pz585o7d65mzpyplStXyuFwyDAMxcTEaNCgQRo4cKBq1qzprsMBAAAA8HJuS0aqV68um80mwzBUp04d3XXXXRo0aJCio6PddQgAAAAAVxC3JSNBQUEaPHiwBg0apHbt2rmrWQAAAABXKLclI8ePH5efn8fnwwMAAAC4QrhtaV8SEQAAAACFUeQM4rPPPivWge+9995i1QcAAADg3YqcjNx3332yWCyFrudc5pdkBAAAACjbipyMvPjiizmSkb1792rGjBkKDQ3VLbfcotq1a0uSDh48qCVLlig5OVl33323GjRoULyoAQAAAHi9IicjY8aMyfbfu3bt0vXXX6/77rtP48ePV/ny5bPtP3funEaMGKF58+bpl19+KephAQAAAFwh3DaBfeTIkapSpYo+/vjjHImIJIWHh+ujjz5S5cqVNXLkSHcdFgAAAICXclsysmrVKrVq1Uo+Prk36ePjo1atWmnVqlXuOiwAAAAAL+W2ZMRms+ngwYP5ljt48KDsdru7DgsAAADAS7ktGWnRooXi4+P15Zdf5lrmq6++0s8//6wWLVq467AAAAAAvJTbnlT40ksvqXPnzrrrrrs0depU3Xnnnapdu7YsFosOHDigr776SkuWLJGvr2+Oye8AAAAAyh63JSM33XST5syZowceeEA//PCDlixZkm2/YRiqWLGiPvzwQ3Xo0MFdhwUAAADgpdyWjEhSz5491alTJ3311Vdas2aNjhw5IsMwVKNGDbVr10533nmnwsLC3HlIAAAAAF7KrcmIJIWGhmrIkCEaMmSIu5sGAAAAcAVx2wR2AAAAACgMt98ZSUlJ0YYNG3T06FHZbLZcy917773uPjQAAAAAL+LWZOTFF1/UO++8o5SUlFzLGIYhi8VCMgIAAACUcW5LRv7973/rlVdekZ+fn3r06KHGjRsrNDTUXc0DAAAAuMK4LRn56KOPFBQUpNWrV6t58+buahYAAADAFcptE9gPHTqkm266iUQEAAAAQIG4LRmpXr26u5oCAAAAUAa4LRkZMGCA1q1bp6SkJHc1CQAAAOAK5rZkZMyYMbrmmmvUu3dvJSQkuKtZAAAAAFcot01gv+222+RwOPTzzz8rKipKdevWVa1atWSxWHKUtVgsWr58ubsODQAAAMALuS0ZWblyZda/MzIylJCQkOsdElcJCgAAAICyxW3JyP79+93VFAAAAIAywG3JSJ06ddzVFAAAAIAywG0T2AEAAACgMEhGAAAAAJiCZAQAAACAKUhGAAAAAJiCZAQAAACAKUhGAAAAAJiCZAQAAACAKUhGAAAAAJjCq5OR1NRUjR49Wo0bN1ZgYKBq1KihoUOH6vDhwwVuIz09XWPGjFH37t1Vv359hYWFKTAwUI0aNdJjjz2mgwcPevAVAAAAAGWX1yYjqamp6tSpk8aOHavk5GT17NlTkZGRmjp1qpo3b66EhIQCt/PSSy9p1apVioiIULdu3dS1a1fZ7Xa9//77iomJ0aZNmzz8agAAAICyx2uTkddee03x8fFq06aNdu/erdmzZ2vt2rV6++23dfLkSQ0dOrRA7QQGBmrNmjVKSkrSzz//rK+++kpff/219u3bp5EjR+rcuXN69NFHPfxqAAAAgLLHK5ORtLQ0TZgwQZI0adIkhYaGZu0bMWKEYmJitGrVKm3cuDHftvz8/NS2bVv5+fll2+7r66uxY8cqMDBQa9eu1YULF9z7IgAAAIAyziuTkTVr1ujs2bNq0KCBYmNjc+zv27evJGnhwoXFOo7FYpGPj498fHxyJCsAAAAAiscrk5GtW7dKkpo3b+5yv3O7s1xRGIahN954QykpKerYsaMCAgKK3BYAAACAnLzy637nCle1atVyud+5vbArYT333HM6fvy4zp8/r23btikhIUFRUVH68MMPC9xGdHS0y+0JCQmqVq2aKatz2WwOGYYhGZLdniaLj6PEYyjr0uxp//uXIbs9TT6+hqnxXM4wMuOx2+0mR+JZpf08lAV/nwNxDkxU2n8XuCahtLHbMyQjs28eOnRIAQFe+X2+x6SlpRX5i3uvTEaSk5MlScHBwS73h4SEZCtXUHPnzs22Cte1116rGTNmqF69ekWMFAAAAEBuvDIZcX5jYrFY8txfWHv37pUknTp1Shs3btS//vUvtWjRQh9//LEGDx5coDZ27Njhcnt0dLRsNptq165dpNiKIzU1QxZLogyLZLX6y2r1ytPu1fytzj5p+d858Dc1nss5v320Wq0mR+JZpf08lAV/nwNxDkxU2n8XuCahtDEc6ZIl87NnZGSkAgN9zQ6pVPH3L3rf9cp7TGFhYZKU6wpXKSkpkpRtla3CqFy5srp27arly5erRo0aeuSRR3To0KGiBQsAAADAJa9MRpx3F3J70rpze3HvQoSHh6tHjx66ePGili5dWqy2AAAAAGTnlclI06ZNJSnXJ6M7t8fExBT7WJUrV5YknTx5sthtAQAAAPibVyYjbdu2VXh4uBISErR58+Yc++fMmSNJ6tGjR7GP9dNPP0mSGjRoUOy2AAAAAPzNK5MRq9WquLg4SVJcXFy2uSPjx4/Xtm3b1K5dO7Vs2TJr+8SJExUVFaWRI0dma+ubb77R999/n2PSe0pKiv71r3/pp59+UvXq1dWtWzcPviIAAACg7PHaZZVGjRqlZcuWKT4+Xo0aNVL79u114MABrV27VpUqVdLUqVOzlT916pR27dqlo0ePZtu+adMmvfTSS6pRo4ZiY2MVHh6uY8eOacuWLTpz5ozCw8P15ZdfFnkyPAAAAADXvDYZCQwM1IoVK/T6669r5syZWrBggSpUqKDBgwfr5ZdfVmRkZIHa6d27t/766y+tXr1a69ev15kzZxQUFKSGDRvqoYce0vDhwxUREeHhVwMAAACUPV6bjEhSUFCQxo4dq7Fjx+ZbdsyYMRozZkyO7TExMXr77bc9EB0AAACAvHjlnBEAAAAA3o9kBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApSEYAAAAAmIJkBAAAAIApvDoZSU1N1ejRo9W4cWMFBgaqRo0aGjp0qA4fPlzgNs6ePauZM2dq4MCBuuaaaxQSEqKwsDBdf/31eu+995SWlubBVwAAAACUXV6bjKSmpqpTp04aO3askpOT1bNnT0VGRmrq1Klq3ry5EhISCtTOuHHjNGjQIM2ePVvBwcG6/fbb1apVK23dulVPPvmkOnbsqJSUFA+/GgAAAKDs8dpk5LXXXlN8fLzatGmj3bt3a/bs2Vq7dq3efvttnTx5UkOHDi1QO6GhofrnP/+pgwcPasOGDfriiy+0fPly/fbbb6pdu7bWrFmjV155xcOvBgAAACh7vDIZSUtL04QJEyRJkyZNUmhoaNa+ESNGKCYmRqtWrdLGjRvzbev555/Xq6++qpo1a2bb3qhRI73xxhuSpFmzZrkxegAAAACSlyYja9as0dmzZ9WgQQPFxsbm2N+3b19J0sKFC4t1nKZNm0qSjhw5Uqx2AAAAAOTklcnI1q1bJUnNmzd3ud+53VmuqPbt2ydJql69erHaAQAAAJCTn9kBFMXBgwclSbVq1XK537ndWa6o3nvvPUlSz549C1wnOjra5faEhARVq1at2DEVhc3mkGEYkiHZ7Wmy+DhKPIayLs3uXJXNkN2eJh9fw9R4LmcYmfHY7XaTI/Gs0n4eyoK/z4E4ByYq7b8LXJNQ2tjtGZKR2TcPHTqkgACv/D7fY9LS0hQQEFCkul75TiYnJ0uSgoODXe4PCQnJVq4oJk+erGXLlql8+fJ6/vnni9wOAAAAANe88s6I8xsTi8WS5/6i+umnn/TEE0/IYrHok08+UY0aNQpcd8eOHS63R0dHy2azqXbt2sWKrShSUzNksSTKsEhWq7+sVq887V7N3+rsk5b/nQN/U+O5nPPbR6vVanIknlXaz0NZ8Pc5EOfARKX9d4FrEkobw5EuWTI/e0ZGRiow0NfskEoVf/+i912v/FQaFhYmSbpw4YLL/c7ngly6ylZBbdu2Tb169ZLdbtd//vMf3XHHHUUPFAAAAECuvHKYlvPuQm5PWnduL+xdiISEBHXt2lVnz57VmDFjNHz48OIFCgAAACBXXpmMOJfc3bRpk8v9zu0xMTEFbvPIkSPq0qWLjh07pieeeEKjR48ufqAAAAAAcuWVyUjbtm0VHh6uhIQEbd68Ocf+OXPmSJJ69OhRoPaSkpLUtWtX7d+/X0OGDNE777zj1ngBAAAA5OSVyYjValVcXJwkKS4uLtvckfHjx2vbtm1q166dWrZsmbV94sSJioqK0siRI7O1lZKSottuu03bt29Xv3799NFHH+U6MR4AAACA+3jlBHZJGjVqlJYtW6b4+Hg1atRI7du314EDB7R27VpVqlRJU6dOzVb+1KlT2rVrl44ePZpt+7/+9S/9+uuv8vX1lZ+fn+6//36Xx5s2bZqnXgoAAABQJnltMhIYGKgVK1bo9ddf18yZM7VgwQJVqFBBgwcP1ssvv6zIyMgCtZOUlCRJysjI0MyZM3MtRzICAAAAuJdXDtNyCgoK0tixY7V3717ZbDYdO3ZM06ZNc5mIjBkzRoZh5Egqpk2bJsMw8v0BAAAA4F5enYwAAAAA8F4kIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABMQTICAAAAwBQkIwAAAABM4dXJSGpqqkaPHq3GjRsrMDBQNWrU0NChQ3X48OFCtfPTTz/ppZdeUvfu3VWlShVZLBZFRUV5KGoAAAAAkuRndgBFlZqaqk6dOik+Pl4RERHq2bOnEhMTNXXqVH377bf65Zdf1KBBgwK19cQTT2jr1q0ejhgAAADApbz2zshrr72m+Ph4tWnTRrt379bs2bO1du1avf322zp58qSGDh1a4LZuueUWvfrqq1qyZIk2bdrkwagBAAAAOHnlnZG0tDRNmDBBkjRp0iSFhoZm7RsxYoQ+/fRTrVq1Shs3blSLFi3ybe/f//531r8TExPdHi8AAACAnLzyzsiaNWt09uxZNWjQQLGxsTn29+3bV5K0cOHCkg4NAAAAQAF5ZTLinN/RvHlzl/ud25kHAgAAAJReXjlM6+DBg5KkWrVqudzv3O4sV5Kio6Ndbk9ISFC1atVMiclmc8gwDMmQ7PY0WXwcJR5DWZdmT/vfvwzZ7Wny8TVMjedyhpEZj91uNzkSzyrt56Es+PsciHNgotL+u8A1CaWN3Z4hGZl989ChQwoI8Mrv8z0mLS1NAQEBRarrle9kcnKyJCk4ONjl/pCQkGzlAAAAAJQ+XnlnxPmNicViyXO/GXbs2OFye3R0tGw2m2rXrl3CEUmpqRmyWBJlWCSr1V9Wq1eedq/mb3X2Scv/zoG/qfFczvnto9VqNTkSzyrt56Es+PsciHNgotL+u8A1CaWN4UiXLJmfPSMjIxUY6Gt2SKWKv3/R+65X3hkJCwuTJF24cMHl/pSUFEnKtsoWAAAAgNLFK5MR592F3J607txuxl0IAAAAAAXjlclI06ZNJSnXBxQ6t8fExJRYTAAAAAAKxyuTkbZt2yo8PFwJCQnavHlzjv1z5syRJPXo0aOkQwMAAABQQF6ZjFitVsXFxUmS4uLiss0dGT9+vLZt26Z27dqpZcuWWdsnTpyoqKgojRw5ssTjBQAAAJCT1y6rNGrUKC1btkzx8fFq1KiR2rdvrwMHDmjt2rWqVKmSpk6dmq38qVOntGvXLh09ejRHWx9//LE+/vhjSZLNZpMkHThwQK1bt84q8/777+f6kEUAAAAAhee1yUhgYKBWrFih119/XTNnztSCBQtUoUIFDR48WC+//LIiIyML3Nbhw4e1du3abNtSU1OzbTt//rzbYgcAAADgpcO0nIKCgjR27Fjt3btXNptNx44d07Rp01wmImPGjJFhGJo2bVqu+/L66dChg+dfEAAAAFCGeHUyAgAAAMB7kYwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMIVXJyOpqakaPXq0GjdurMDAQNWoUUNDhw7V4cOHC93W2bNn9eSTT6pOnToKCAhQnTp19MQTT+js2bPuDxwAAACA9yYjqamp6tSpk8aOHavk5GT17NlTkZGRmjp1qpo3b66EhIQCt3X69Gm1atVK7733nvz8/NSrVy+FhYXpP//5j1q2bKnTp0978JUAAAAAZZPXJiOvvfaa4uPj1aZNG+3evVuzZ8/W2rVr9fbbb+vkyZMaOnRogdt66qmntGfPHvXu3Vu7du3S7NmztX37dg0fPlx79+7ViBEjPPhKAAAAgLLJK5ORtLQ0TZgwQZI0adIkhYaGZu0bMWKEYmJitGrVKm3cuDHfto4dO6YZM2bI399f77//vvz8/LL2vfXWW6pSpYpmzJih48ePu/+FAAAAAGWYVyYja9as0dmzZ9WgQQPFxsbm2N+3b19J0sKFC/Nt6/vvv5fD4dCNN96oatWqZdsXEBCg22+/XRkZGfr+++/dEzwAAAAASZJf/kVKn61bt0qSmjdv7nK/c7uzXHHb+uSTTwrUljewp2aYHUKZlGb7+323p2bIYrGYGE1OdntmfIYj3eRIPKu0n4eygHNQOpT288A1CaUNn588xyuTkYMHD0qSatWq5XK/c7uzXEm1JUnR0dEut+/cuVO+vr7q3r17gdpxJ4fDV8eP91G1OvX07L1LSvz4kBwOQw7DkGE49Oy9S1Xa/t4Y//v/UhaW25X281AWOM+BwzA4ByYq7b8LXJNQGhmSjh84oDvuGC8fH5KTSx09elQBAQFFquuVyUhycrIkKTg42OX+kJCQbOVKqq28WCwWBQQEZJvfUpJOnpygk4ekBg0amHJ8SOGl+K13rj5XFvpHaT4PZcUpJejU4bLR30qz0vy7wDUJpVFCQoJ8fKRy5ThplwsICMj6zFxYXpmMGEbmdya53c507i/ptiRpx44dhSpfUpx3bEprfDAX/QMlif6G/NBHUBrRLz3DKyewh4WFSZIuXLjgcn9KSookFeguhDvbAgAAAFBwXpmM1K5dW5JyfdK6c7uzXEm1BQAAAKDgvDIZadq0qSRp06ZNLvc7t8fExJRoWwAAAAAKziuTkbZt2yo8PFwJCQnavHlzjv1z5syRJPXo0SPftrp16yYfHx+tXr1aJ06cyLbPZrNp4cKF8vHx0a233uqe4AEAAABI8tJkxGq1Ki4uTpIUFxeXbb7H+PHjtW3bNrVr104tW7bM2j5x4kRFRUVp5MiR2dqKiIjQXXfdJbvdrkcffVTp6X+vaf7ss8/q5MmTGjhwoKpXr+7hVwUAAACULRajsMtFlRKpqanq0KGD1q5dq4iICLVv314HDhzQ2rVrValSJf36669q2LBhVvkxY8bopZde0uDBgzVt2rRsbZ06dUqtW7dWQkKCGjRooOuuu047duzQ9u3b1aBBA/3666+qXLlyCb9CAAAA4MrmlXdGJCkwMFArVqzQCy+8oODgYC1YsECJiYkaPHiwNm/enC0RyU/lypW1fv16DR8+XHa7XfPnz9e5c+cUFxendevWkYgAAAAAHuC1d0YAAAAAeDevvTMCAAAAwLuRjAAAAAAwBckIAAAAAFOQjAAAAAAwBckIAAAAAFOQjAAAAAAwRZlPRlJSUrRgwQLdf//9iomJUbly5RQSEqKmTZtq7NixSk5OzrXuZ599platWik0NFQVK1bUbbfdpvj4eJdld+7cqTfffFOdOnVS7dq1FRAQoOrVq6t3795avXp1rsf49ttv9c9//lOdO3dWeHi4LBaLunXrVuzXHR8fr9tuu00VK1ZUaGioWrVqpU8//TTfehs3btSgQYNUs2bNrNdw8803a+rUqSUSh6fej9zQP8zpHxcuXNDnn3+u4cOHq1WrVgoICJDFYtEbb7yRZz2LxZLnT2pqaqHiKGn0t/z725gxY/I9zxaLRatWrfJoHFLJX48k+ohZfaSsXpMKin5Z8L+Vs2bN0k033aTy5cvL399fNWrUUL9+/bRx48YSi8OMa1eejDLuo48+MiQZkozo6GjjzjvvNLp27WqEhYUZkoyoqCjj+PHjOeo99dRThiQjKCjI6Nmzp9G1a1fDz8/P8PX1NebNm5ejfM2aNQ1JRrly5YwuXboY/fr1M6699lpDkmGxWIx33nnHZXzh4eFZ8Tl/unbtWqzXPG/ePMPX19ewWCzGTTfdZPTp08coX768Icl46qmncq03YcIEw9fX1/D19TVuuOEGY8CAAcbNN99sVKhQwejUqVOJxOGJ9yMv9A9z+sfmzZtzvC5Jxuuvv55nPUlGSEiIMXjwYJc/dru9UHGUNPpb/v1t/vz5uZ7fzp07G5KM4OBg46+//vJoHIZR8tcjw6CPmNVHyuo1qaDolwW7ZgwfPtyQZPj7+xudOnXKFr+fn5+xcOHCEonDjGtXXsp8MvLpp58ajzzyiLF79+5s248cOWLExsYakoy77ror277ly5cbkoxKlSplqxcfH29YrVYjPDzcOHPmTLY6Xbp0MWbOnGnYbLZs2ydPnmxIMnx9fY0dO3bkiG/o0KHGW2+9ZaxcudJYuHBhsTvMmTNnsjrh3Llzs7YfO3bMaNiwoSHJ+PHHH3PU++677wyLxWLUr1/f2L59e7Z9drvd2Lx5c4nE4e73Iz/0j0wl3T/27t1r3H///caUKVOMTZs2Gf/6178K/Ie/Tp06hTpWaUJ/y5Rff8vNs88+a0gyBg0aVCJxlPT1yDDoI04l3UfK6jWpoOiXmfLql1u2bDEkGRUqVDB27tyZbd/48eOL1Fe86dqVlzKfjOQlPj7ekGQEBARk6/i33XabIcllBv74448bkoxx48YV+Di33HKLIckYM2ZMnuVWrFhR7A7z73//25Bk9OzZM8e+efPmGZKMHj16ZNuenp5u1K1b1/Dx8TG2bNlS5GMXN47LueP9KA76RyZP9I/LjR49usz/4ae/5c3hcBiRkZGGJGPx4sUlHofZ1yPDoI/kpzh95HJckwqOfplpwoQJhiTj4Ycfdtmm826GqztI7ozjcqXh2lXm54zkpWnTppIkm82m06dPS5JSU1O1fPlySVLfvn1z1HFuW7hwYaGPc+TIkWLFWxDffvutJNexd+/eXYGBgVq2bFm2caxLlixRYmKiOnfunBWrGXGUNvSPTJ7oH8iJ/pa3lStX6tChQ6pevbo6d+5sWhxmoo/krTh9BEVHv8wUEBCQb7u+vr4KDw/3aBylEclIHvbt2ydJ8vf3V8WKFSVlTp6y2WyqUqWKatWqlaNO8+bNJUnbtm0r9HGqV69e3JDz5YzLGeelrFarrr32WqWmpmrXrl1Z250XjC5duujcuXP64IMP9Oijj+rJJ5/UjBkzZLPZSiSO0ob+kckT/aM4Lly4oFdffVUPPfSQnnrqKX322Wd5Tp70FvS3vE2fPl2SdNddd8nX19e0OMxEH8lbcfpIcVyp16SCol9muvnmm+Xn56fZs2fn6K/vvPOOzp49q/79+xcoaSlOHKURyUge3nvvPUlSt27dsjrHwYMHJcnlL48khYSEqHz58kpKStJff/2V7zESEhKyMtt//OMf7gg7V+fPn9fZs2cl5R6/c7vzdUrSjh07JGWulnHNNdfo0Ucf1QcffKD33ntPd999t66++mr9/vvvHo+jtKF/ZHJ3/yiuU6dOadSoUfrwww/17rvvavDgwapbt66+++67EovBE+hvuUtNTdXcuXMlSffcc49pcZiNPpK74vSR4rpSr0kFRb/M1LBhQ40bN05nz55VkyZN1LlzZ/Xv319NmjTRs88+q0GDBmnKlCkej6M0IhnJxaJFi/Tf//5X/v7+evnll7O2O7/NCA4OzrVuSEhItrK5SU9P13333Sebzab+/furRYsWbog8d5fGk1v8rmJPSkqSJL388ssKCgrSsmXLdP78eW3dulWdO3fW/v371aNHD128eNGjcZQm9A/P9Y/iuPfee7V48WL9+eefSk5O1ubNm3XPPffo9OnT6t27t9atW+fxGDyB/pZ37N98843OnTun6OhoxcbGmhaHmegjnusjxXGlXpMKin6ZPfYnnnhCM2fOlL+/v5YvX64vv/xS27dvV+3atdWxY0eFhoaWSBylDcmIC3/88YfuvvtuGYaht956K9s4eMMwJGWuHZ4bZ5n8DB8+XGvWrFH9+vX1/vvvFy/oAihIXK7KZGRkZO1buHChOnXqpLCwMMXExOjbb79VrVq1tH//fs2YMcOjcZQW9I/s3N0/iuPTTz9V165dVaNGDYWEhKhZs2b67LPPNHLkSNntdo0aNcrjMbgb/S1/zuE3RfnG29uvRxJ9pCCK00eK40q8JhUU/TLntqeeekp33XWX7r33Xu3evVvJyclau3atatSoofvvv1//93//5/E4SiM/swMobQ4fPqxu3bopKSlJI0aM0BNPPJFtf1hYmKTMMaC5SUlJkaQ8M9yxY8dq8uTJqlatmn744YescZTFdd999+XY1qtXL/Xq1SsrdmeM5cqVy1HWVezOeq1bt9bVV1+drXxAQIAGDhyof//731q5cqUeeOABj8VRGtA/PN8/POG5557LisFut8tqtXrkOO5Gf8s/9tOnT2vx4sXy8fHRwIEDTYvDLPQRz/cRT/DWa1JB0S9zxv7pp5/q3XffVa9evfTBBx9kbW/VqpUWLVqkq6++Wu+8844eeOABRUVFeSyOUsnTy3V5k5MnTxpRUVGGJGPIkCGGw+HIUcb54KMqVaq4bCM5OdmQZJQvXz7X40ycONGQZISHhxfq+QsFWX5NyvlQptGjR2ftd65H7WodbsMwjOuuu86QlG2J1iFDhhiSjIEDB7qs8/777xuSjC5dung0jsuV9HJ09I+S6x+XK+gymnmJiIgwJBl//vlnkdsoSfS3gl0HJk2aZEgybr75ZlPjMGN5TPpIyfWRy5XFa1JB0S9d98uOHTsakozp06e7rHP//fcbkowpU6Z4NI7LlYalfbkz8j9//fWXbr31Vu3cuVO9e/fWRx995PL24VVXXaWAgACdPHlShw8fzjFpaNOmTZKkmJgYl8eZMWOGhg8fruDgYH333Xdq1qyZW1+Hkc8tuaZNm2rVqlXatGmTrrnmmmz70tLStH37dgUEBOiqq67K2h4bG6upU6fqzJkzLtt0LtV3aebtiTjMRP8o2f7hbg6HQ+fPn88RR2lFfyv4daAgw2+utOuRRB+RSraPuJu3XZMKin6Ze788fPiwJLm8g3Hp9kv/ll6J1y5XmDOizLWve/bsqQ0bNqhr166aNWtWrsv+BQUFqWPHjpKkOXPm5Njv3NajR48c+xYtWqT77rtP/v7+mj9/vtq2bevGV1Ew3bt3l+Q69m+//Vapqanq1KmTAgMDs7bffvvtslgsWr9+vctbqitXrpTkemk5d8ZhFvpHppLsH+62ePFiXbhwQQ0bNsz1D0FpQX/LVJDrwL59+/TLL78oKChIffr0MS2OkkYfyVSSfcTdvOmaVFD0y0y59UvnksMbNmxw2eb69eslSXXr1vVoHKWSWbdkSov09HTjjjvuMCQZ7du3Ny5cuJBvnaVLlxqSjEqVKhm7d+/O2h4fH28EBAQY5cqVM06fPp2tzpo1a4ygoCDDz8/PmD9/fpFidcettNOnTxvlypUzJBlz587N2n78+HGjYcOGhiRj2bJlOeoNGDDAkGQ8+OCDRlpaWtb2jz/+2JBkBAYGGgcPHvR4HJcqiVuL9I9MJd0/LleQIRFfffWVsWvXrhzbV65cadSoUcOQZIwfP77IMZQE+lumgl4HXnrpJUOSMWDAgCLH4I44DKPkhjrQRzKVdB+5XFm5JhUU/TJTXv1y/PjxhiQjNDTUWLt2bbZ9zqezh4WF5XjN7o7jcqVhmJbFMLxkqr2HvPfee3ryySclSXfccUeu31CMGzdOlStXzvrvJ598Uu+9956Cg4PVpUsX2e12LV26VA6HQ19++WWOb2AqVKigs2fPql69errxxhtdHqNdu3ZZE3ydXn755ay1yM+fP68//vhD4eHhWZObJGn+/PmKiIgo8GueO3eu+vXrJ8MwdNNNN6ly5cpatmyZzp49q8cffzxrTfBLnT59WjfccIN2796tevXqKTY2VomJidq0aZN8fX01derUQq9SUpQ4PPF+5IX+YV7/uOOOO3T06FFJmbe3//zzT0VGRqpGjRqSpIiICM2fPz+r/H333adPP/1UjRo1Ur169RQeHq49e/Zoy5YtkqQBAwZoxowZ8vEpvTeE6W8F629OV111lXbv3q3vvvtOt912W4GP6a44Svp6JNFHzOwjZfGaVFD0y/z7ZWpqqrp06aI1a9bIx8dHbdq0UY0aNbRjxw79/vvvV/RnqXyZlQWVFs5vN/L72b9/f466U6dONVq0aGEEBwcb4eHhRteuXY3Vq1e7PE5BjjF48OAc9QYPHlyk2PKzZs0ao1u3bkb58uWN4OBgo0WLFsYnn3ySZ51z584ZzzzzjFG/fn3DarUalSpVMv7xj38YP//8c6GPX9Q4PPV+5Ib+YV7/qFOnTp6vq06dOtnKL1q0yBg0aJARFRVllC9f3vDz8zOqVq1q3HrrrcZXX31VpBhKGv2t4P1t7dq1hpQ5AfbSu3HFUdqvR4ZBHzGzj5TFa1JB0S8L1i9tNpvx9ttvG61atTLCwsIMPz8/IyIiwujbt6/xyy+/FPr4RY3DjGtXXsr8nREAAAAA5vD+e4MAAAAAvBLJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCAAAAABTkIwAAAAAMAXJCACgVKtbt64sFovZYQAAPIBkBABgqsTERFksFnXo0MHsUAAAJczP7AAAAMjL8uXLlZaWZnYYAAAPIBkBAJRqDRo0MDsEAICHMEwLAGCaMWPGqF69epKkn376SRaLJevnvvvuk+R6zsilQ7suXLigESNGKDIyUkFBQWrevLkWLlyYVfarr75Sq1atFBISomrVqunxxx/XxYsXXcaTnJyssWPHqkmTJgoODla5cuV00003acGCBR55/QBQ1nFnBABgmmbNmqlPnz6aO3euqlWrpm7dumXta9euXb717Xa7OnXqpISEBLVu3VrJyclatWqV7rjjDi1evFi//fabnn32WbVs2VK33HKLVq9erQkTJuj06dOaMWNGtraOHz+ujh076vfff1fNmjXVpUsXpaSk6JdfftEdd9yh119/Xc8//7zb3wMAKMsshmEYZgcBACi7EhMTVa9ePd10001auXJljv1169bVgQMHdOmfK2cdSerQoYPmzZunChUqSJKmTZumIUOGqGHDhjpz5owWLFig9u3bS5KOHDmi2NhYnThxQgkJCapfv35Wm7fddpu+//57Pfvss3rllVfk7+8vSdq3b59uueUWJSYmatOmTYqJifHUWwEAZQ7DtAAAXsvX11cfffRRViIiSffee6+qVKmivXv3Ki4uLisRkaQaNWpo0KBBkqRVq1Zlbd+yZYu+//573XDDDXrjjTeyEhFJql+/vt5++21lZGTo448/LoFXBQBlB8kIAMBr1a1bVw0bNsy2zcfHR3Xq1JEkdenSJUcd54T4o0ePZm1bunSpJKlnz54un2niHDK2fv169wQOAJBEMgIA8GI1a9Z0uT0kJCTX/c59Npsta1tiYqIk6bnnnss2id75U7lyZUnSqVOn3Bk+AJR5TGAHAHit/J7MXtAnt2dkZEiS2rdvn20eyeWcSQkAwD1IRgAAZV6tWrUkSX379tXjjz9ucjQAUHYwTAsAYCqr1SpJSk9PNy2Gzp07SxLPEwGAEkYyAgAwVeXKleXv76+EhISs4VIlrXXr1urUqZNWrFihp556SsnJydn2OxwOLVmyRGvWrDElPgC4UpGMAABMZbVa1a1bNx07dkxNmzbVvffeqwceeEBTp04t0ThmzJihmJgYvfvuu6pTp446deqkAQMGqH379qpevbq6du2qDRs2lGhMAHClY84IAMB0H3/8sf7v//5PS5cu1cyZM5WRkaH09HQNGTKkxGKoVq2afv31V02ePFmzZ8/W+vXrZbfbFRERodjYWPXs2VP9+vUrsXgAoCzgCewAAAAATMEwLQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACmIBkBAAAAYAqSEQAAAACm+H+xtl0fm/1gegAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_timeseries(dss, latlon, composites = ds_composites)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graph we have just created, you should see a couple of things. First of all, there are blue-dots and orange-dots that show available NDVI measurements at our POI, coming from MOD13 and MYD13 respectively. Then there are blue bars that show the composite pixel values at this location. Since our composites are dekadal, the width of the bars is about 10 days (check the x-axis).\n",
    "\n",
    "Each composite value is the mean of the available measurements within the respective `time_bin`. For example, most of the bars match with some of the measurements. While the first bar, ranging from June 1st till June 11th is exactly in between two measurements, i.e. it’s the mean of the two available measurements.\n",
    "\n",
    "The composite type is defined using the keyword `composite_type` in the sources variable. Possible values are `\"mean\"`, `\"min\"` and `\"max\"`.\n",
    "\n",
    "Now let’s adjust our composite_length to create 6-day composites (instead of dekadal) and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Composite 1 starts at 2021-06-01T00:00:00.000000000 and ends at 2021-06-07T00:00:00.000000000.\n",
      "\n",
      "Composite 2 starts at 2021-06-07T00:00:00.000000000 and ends at 2021-06-13T00:00:00.000000000.\n",
      "\n",
      "Composite 3 starts at 2021-06-13T00:00:00.000000000 and ends at 2021-06-19T00:00:00.000000000.\n",
      "\n",
      "Composite 4 starts at 2021-06-19T00:00:00.000000000 and ends at 2021-06-25T00:00:00.000000000.\n",
      "\n",
      "Composite 5 starts at 2021-06-25T00:00:00.000000000 and ends at 2021-07-01T00:00:00.000000000.\n",
      "\n",
      "Composite 6 starts at 2021-07-01T00:00:00.000000000 and ends at 2021-07-07T00:00:00.000000000.\n",
      "\n",
      "Composite 7 starts at 2021-07-07T00:00:00.000000000 and ends at 2021-07-13T00:00:00.000000000.\n",
      "\n",
      "Composite 8 starts at 2021-07-13T00:00:00.000000000 and ends at 2021-07-19T00:00:00.000000000.\n",
      "\n",
      "Composite 9 starts at 2021-07-19T00:00:00.000000000 and ends at 2021-07-25T00:00:00.000000000.\n",
      "\n",
      "Composite 10 starts at 2021-07-25T00:00:00.000000000 and ends at 2021-07-31T00:00:00.000000000.\n",
      "\n",
      "Composite 11 starts at 2021-07-31T00:00:00.000000000 and ends at 2021-08-06T00:00:00.000000000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bin_length = 6\n",
    "bins = pywapor.general.compositer.time_bins(timelim, bin_length)\n",
    "\n",
    "for i, (start, end) in enumerate(zip(bins[:-1], bins[1:])):\n",
    "    print(f\"Composite {i + 1} starts at {start} and ends at {end}.\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then recalculate the composites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--> Compositing 1 variables.\n",
      "    --> (1/1) Compositing `ndvi` (mean).\n",
      "        --> Using `MYD13Q1.061.nc` as reprojecting example.\n",
      "            > shape: (403, 506), res: 0.0020° x 0.0020°.\n",
      "        --> Saving `ndvi` composites.\n",
      "            > peak-memory-usage: 80.9MB, execution-time: 0:00:02.039768.\n",
      "            > chunksize|dimsize: [time_bins: 11|11, y: 403|403, x: 506|506]\n",
      "--> Didn't find any products for `is_example`.\n",
      "--> Creating merged file `et_look_in_.nc`.\n",
      "    > peak-memory-usage: 51.3MB, execution-time: 0:00:02.143718.\n",
      "    > chunksize|dimsize: [time_bins: 11|11, y: 403|403, x: 506|506]\n"
     ]
    }
   ],
   "source": [
    "ds_composites = pywapor.general.compositer.main(dss, sources, project_folder, enhancers, bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then create a new plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_timeseries(dss, latlon, composites = ds_composites)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the new graph, you’ll see that the blue bars have become smaller (i.e. less wide), since our time_bins are now shorter. As a result, some composites could not be created, because no NDVI measurements were available during that respective epoch. For example, the fourth composite (ranging between 2021-06-19 00:00:00 and 2021-06-25 00:00:00) is missing.\n",
    "\n",
    "We solve this by applying a temporal interpolation between the available measurements. This is especially suitable for this data, because the MOD13 and MYD13 datasets itself are 16-daily composites, i.e. their measurements can be considered valid for a 16-day period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--> Compositing 1 variables.\n",
      "    --> (1/1) Compositing `ndvi` (mean).\n",
      "        --> Using `MYD13Q1.061.nc` as reprojecting example.\n",
      "            > shape: (403, 506), res: 0.0020° x 0.0020°.\n",
      "        --> Saving `ndvi` composites.\n",
      "            > peak-memory-usage: 82.5MB, execution-time: 0:00:06.065150.\n",
      "            > chunksize|dimsize: [time_bins: 11|11, y: 403|403, x: 506|506]\n",
      "--> Didn't find any products for `is_example`.\n",
      "--> Creating merged file `et_look_in__.nc`.\n",
      "    > peak-memory-usage: 51.3MB, execution-time: 0:00:02.173311.\n",
      "    > chunksize|dimsize: [time_bins: 11|11, y: 403|403, x: 506|506]\n"
     ]
    }
   ],
   "source": [
    "sources[\"ndvi\"][\"temporal_interp\"] = \"linear\"\n",
    "ds_composites = pywapor.general.compositer.main(dss, sources, project_folder, enhancers, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S7969r1lGXFyc4uPjtXPnTq1du1bdu3f3SKw7d+6UlPtTnoYNG+rQoUPauXNndnfBVatWSZLatWuntLQ0zZs3T1u3bpXJZNLNN9+snj175uhaBgCecLRmT8Xu+iBfg9htpgAdrdXDrXFYLTb9cehPmWRo2LBN13xy/7+xtcfdGoOrrjQvJ1UIzvd5c1ef1jdv/p7977zGxhRHPBkpYc6kWLRw+4l8nbNg+wmdTSlY/09P6Nevn1JTU/X1119nb7N3Oerbt2+u59k/4DVq1CjPa9iPsZ+TX/Hx8ZKkvXv35nls165dVaFCBV26dEnNmjXTwIEDNXXqVG3btk2ZmYV7KmUymdS3b18dPXpUP/30U/b2q7toOWO1WvX771k3t+L2ernqu+++07BhwzRkyBB16NBBLVq00JUrVzR58mTVqFEj+zh7zDVr1sxzpi6p8HV1xbFjxyT9L1H9K/t2+3GSst+vkJAQNW/eXMOGDdPUqVP1wQcf6PHHH9fNN9+s9evXeyxmALCzhJTXsZrdJcmldUYk6WjNe2UNdn8PAUmqUDVC6X4Bspr8c/0xzMEyzMHXPMaTP0YBB6IbAX7ZZfgqnoyUMPO35hwjkpcMm6H5247rkTY1PBNUPvXs2VP/93//p7lz52rgwIGSpLlz56pSpUq64447dObMGafn2bvVVKhQIc9r2LvqnDt3rkAx2gdPX7hwIc9jIyMjtWjRIg0ZMkT79u3TwoULtXDhwux9vXr10ujRoxUdHV2gWPr376/x48friy++UOvWrZWWlqavv/5aTZo0UZ06dbJnmbpacnJy9qPeihUr5nkNd71e15olK79+/fVXh252wcHB+te//pXdZuzy0y6uPq6gdXVFSkpWv+GrZ5G7mn27/Tjpf21t8uTJKlOmjD7//HO1bdtWp0+f1uuvv665c+dmP/GLioryWOwAIEm7Gz2vsD+TVO7M5mseZ5J0rsIt+q3x8x6NZ9z02xQUkvvHXqs1a+p2sznQo3HkZurqw9p36nK+z4uNK6Ohg+v7zNgYZ3gyUsJsOlywD3sbk9z3IbGwypQpo06dOmnVqlU6efKkNm/erH379qlPnz7ZYwKuxZX+lPZjXB2MXdjzGzVqpC1btmjBggV69NFH1bhxYwUGBurixYv6z3/+o5YtW2rfvn0FiuXGG29UfHy8Fi5cKKvVqiVLlujixYvX7D529WtUHF8vVzz//PNKTU3V+fPntWnTJj3wwAN68skn1bt37+zH8Vdf29V+tvbjCvvUyhW5vR7OYrXHk5GRof/85z/q1q2bIiMjFRsbqxkzZqhJkyZKTk7WtGnTPBozAEiSLSBYG2+bqsO1estmcp4E2EwBOlyrtzbePk02/2tP9lJYeY+L8f/vj/vGrOTn54bo8ALV64boCLdOPuANJCMlzGVrwT5AFfQ8T+nfv78yMzM1b9687MHZ/fr1u+Y5Zctmreqa25OTq9kHldvPyS/7t+audPux8/f3V+fOnTVhwgStX79eR48e1ZQpU1SuXDmdPn1ao0aNKlAsUtbrlZycrKVLl+rzzz9XQEDANaeeLVu2bPYH4aJ8vQp6/rUEBwerXr16eu+99/TYY4/p+++/1wcffJC93/5UxpV6Xn3cX6cOdif7WKjLl51/S3blyhWH467+e3R0tDp06JDjnPvvv19S1nghACgKtoBg7Wo6Riv/tkJ76j+hM5VaKLlcA52p1EJ76j+hlX9boV1Nx3g8EfEFLWLLyt8vf1/I+fuZ1DLW9c8ZxRXJSAkTZi5Y9lzQ8zzlrrvuUunSpTV79mzNmzdPcXFxeY5tqF+/viQ5XWvhr+zH2Mcy5Jd9PMG1VoHPS6lSpTRkyJDsb7LXrFmj1NTUApXVp08f+fn5aerUqVq+fLnatWt3ze5XZrM5O/atW7fmWX5xeL1cYU9Yr15I0d4ukpKSXOomZq/rX6dKdqeqVatKylqDxhn7dvtxkrIHsue2YJd9v6tJFwC4izW4vBJvGqaNbT9UQofZ2tj2QyXeNMxjY0R8UWRIoJrXzl9i0bxOGZUK8U63MnciGSlhmlYvWAbdrEbxyrzta2js2LFDp0+fzvOpiKTsBQi//PLLPI+1H9OxY8d8x5aZmZk9pe/tt9+e7/P/6rbbbssu15UxKM5ERUWpbdu2Wr16taxWa54zfEn/e72++uqrax6Xnp6ePcalIK/XxYsXtXz5cknueb2uxT62xf4kR8oaA9KoUSMZhpFnXX///Xf9+uuvCggIULt27psL/6/sSV1ui3fat9sTKUnZC0bmllDZx8a4um4OAKBo9WoWrdhKrt2jYyuFqXezgo0lLW5IRkqYno2jFJDPx4ABfib1bFT8GvzAgQNVrlw5lS9f3qVk5L777lPZsmW1efPm7NmknPn888+1efNmlStXLsdgZ1e8/fbbOnr0qKKjo12a+jWvsQoHDx6UlPW0ojBrkdx///0qV66cqlSpkuvK8ld75JFHZDabtWDBgmvOwvTOO+/o+PHjuuGGG9SpU6d8x/X888/r8uXLatKkiW699dZ8n58f9i5Kf13J/YknnpAkvfHGGw6JytVsNpuee+45SVnd3lwd8F4Q9umR7Une1bZv365Dhw7pxhtvdJgV7I477lBYWJgOHjzoMMuWnX0hxtymCwYAeJc5wE/DO9RUq7q5d9ny9zOpVd2yerxjTQX6Xx8f46+PWsBlFcKDdG/D/M2k06NhlMqHm/M+sIi1atVKR48e1ZEjR3LtmnK1iIgITZ48WZI0fPhwffrppzmOmT17toYPHy5JmjRpkiIiIlyO5+TJkxo1apTGjRsnf39/TZs2Lcfies5Mnz5dI0aM0O7du3PsO378ePYH5c6dOzuU98EHH6hhw4Z66aWXXIrPPsXv/v37c52l6Wo1atTQuHHjZBiG+vbtm70Qn11mZqbeffddvfLKKwoMDNS0adPk5+f6LeXQoUO6//77NWvWLIWFhTmM4yio06dPa+LEiU6fIK1cuVIvvPCCJOVYubxv377q0qWLTp06pS5dumjPnj0O+y9cuKAhQ4ZoxYoVql27tv71r38VOlZJeuihh9SwYUOHaaol6W9/+5tq1KihX3/9VZMmTcrefvnyZT399NOS5LAyu5Q1w9Zjjz2m9PR0PfHEEw7jTZYtW6bZs2fLZDJp6NChbokdAOB+5gA/DWhRVa/2itM9jSorLipcNSuEKi4qXPc0qqxXe8VpQIuq100iIjG1b4n0j86xSjqX6tLCh02rl9Y/7or1fFBFpHv37po2bZqeeOIJPfLII3rttdfUuHFjSVljAQ4fPqygoCBNmzbtmk81Ro8erfDwcBmGoT///FNJSUnavXu3MjMzValSJU2fPl3t27d3Kab09HR9/PHH+vjjj1WjRg3Vq1dPoaGhOn78uDZt2iSr1aoaNWpo/PjxDuedO3dO+/bt08mTJwv8euTliSee0JUrV/Tqq6+qZ8+euuGGG3TzzTfLarVqw4YNOn36tCIjIzVr1qxcV3KXpGHDhknKerpw6dIlHThwQHv37pVhGKpTp45mzJihm2++Ocd533//vd58802HbVar1aE71/PPP6+77rpLUtbA7tGjR2vcuHFq3LixqlSposuXL2dfT8r6EO/svf3kk080ePBgLV68WLfccouaNm2q6tWr6/z580pISFBqaqrq16+vTz/9VJGRkTnOf+ONN7R06VJJ0p9//ilJ2rRpk0OsX3zxhcO0ukePHtW+fft06dIlh7ICAwP18ccfq0uXLnruuec0b948VatWTT/99JNOnjype+65J0dCJUn/+Mc/9NNPP2np0qWqX7++mjZtqjNnzmjjxo2y2WwaO3asmjZ1XBl527Zteuqpp7L/bW9PPXr0UGBgVl/kwYMHa8iQITmuBwDwjFIhgeocX1Gd4/OeXt/XkYyUQMGB/po+sIHeWLpfC7Y7X3ckwM+kHg2j9I+7YhUUULwGrxfW/fffr9tuu02TJ0/WihUrtGzZMklZg4GHDx+uESNGOHR/cWbRokWSJD8/P5UqVUoVK1ZUjx49dOedd6pnz54KDnZ9FdUHHnhAVapU0bJly7Rt2zZt3LhRycnJioiIUKNGjdSlSxc98sgj+XpK407PPfecunTpog8++ECrV6/Wd999p8DAQNWoUUMPPPCARowYkedaJPb1PgICAlSqVClFRUVp4MCBuvvuu9W1a1cFBDi/FZ09ezbHOiiGYThs++v4j9dee01r167V77//rq1bt8pms6ly5crq3bu3HnzwwewxOH8VEhKiuXPnasmSJfrkk0+0ceNGbdmyRRkZWSsIN2nSRD/++GP2B/S/OnToUI5YL1686LDt6imF89K8eXOtW7dOr776qtatW6edO3eqVq1aevLJJzVixAinT6GCg4P1/fff67333tMXX3yhZcuWKTg4WLfffrtGjBiRnbRd7c8//3S61szV41UKMhYIAABXmAxXJ9dHodSrV08WiyX7Q6wzhmEoPT1dQUFBqlGjRr66vOTF/iHor92GzqZYNX/bcW1MStZla6bCzP5qVqOMejaKLpZds+BeubUL/E9iYqLat2+v06dP65133tFjjz3m7ZCKJZvNpqSkJFksFgUGBjqskXLkyBFJuc/0hZKJduFdaWmZGjRog6wmf731aTsFBV9/309fumDRkHaLVbFquN6d217B15h5ypd/H1rSMvTc/T/KbGRq1qxbFeyFdUe6d++uoKAgp13O83L9tTzkS/lwsx5pU6PYrK4OFDe1a9fWggUL1LlzZ/39739X5cqVde+993o7LAAArgskIwCQh8aNG2vhwoVatWqVEhMTZbVaffLbMwAAihuSEQBwQatWrdSqVStvhwEAwHWFZAQAACAfkq9katmBFO06nabUdJtCA/1Uv1KwOtYOV5mQ62vSF8DTSEYAAABcYMmwafrmZK08mKIMm+O+bSfSNGfnBXWoFa5hTcvK7J+/BYaBksqnV0xJS0vTmDFjVLduXQUHBys6OlpDhw51uvpwbmbOnCmTyZTnzyeffOLBmgAAgOLMkmHTmB9P64cDORMRuwybtPRAil5aeUqW3A4C4MBnn4ykpaWpffv2SkhIUFRUlLp166akpCTNmDFD3377rX7++WfVrl07z3Lq1KmjQYMGOd138eLF7Kl4W7du7c7wAQCAD5m+OVm7TltcOnbXaYs+3JysEc3LeTgqwPf5bDLy+uuvKyEhQS1atNCyZcsUHh4uSZowYYKeeeYZDR06VGvWrMmznNatW+eaaHzwwQdatGiRWrVqpVq1ark1fgAA4BuSr2Rq5cGUfJ2z4mCKBjYozRgSIA8+mYykp6dr0qRJkqQpU6ZkJyKSNGrUKM2aNUtr167Vli1b1KRJkwJfx75q9P3331+4gAEAgE8wDEMWi2MXqyV7LuXaNSs3Gbas83reWMqN0blPWlqmDMOQTJLE+tfwHp9MRtavX68LFy6odu3aatSoUY79vXr10s6dO7V48eICJyOHDh1SQkKCzGaz+vTpU9iQAQCAD7BYbBo0aIPDtivNy0kVgvNd1tzVp/XNm7+7KzS3stkMJSVdVoWqETLIReBFPpmM7NixQ1LWQmTO2LfbjysI+1ORLl26qEyZMgUuBwAA+B6r6X/dq4yAgs33YwT4OZRTnNhMhgwx4xe8zyeTkSNHjkiSqlat6nS/fbv9uIKYPXu2pPx30apXr57T7YmJiapUqVKeMZUtW1Zms1lWq1V+fu6b7Mz479ceVqvVbWXC99Eu4C42m02GYchqterkyZMO+9LT0yUV7p4MzzGnX1DMmVUqe+l3BWSmKcM/WOdL3aSjFdrKGljaY9ctru3CYrHJarXKMAfrpQ9ayRzkr//89If2n07Nd1l1biitB4c6/1zgbSmXrBrxt+WSDFmt6fLzv/4ej6Rb0//7t7zr6Mu/D63WzOz779GjRxUUVPST5aanpysoKKhA5/pkMpKSkjWILDQ01On+sLAwh+Pya+PGjdq7d6/KlCmjLl26FCxIAACKMT+bVTcdnqWqZ9fIz8h02Ffh0q+K/WOejpVvq9+qPyCbn/m/H3bc94E1PT1rEIbNVrymwLVYbFndlgzJHOSnoGB/xVYOLVAyEls5VEHBxfPJiDWteMaFkscnkxF79moyOX+8aBSy86O9i1bfvn1lNpvzde7u3budbq9Xr54sFouqVauW67mGYSg9PV0mk0lms9mtT0bsmX5+64PrG+0C7mKz2bLvXTExMQ73Z/s339e6/6FomTItqrHuGYWd3ZbrMX5GpqqdWalyxnkltXlHV9IDcoylKIziev+x2Qz98YdVFaoGKTAwUGZzoFrHVdAPu84p0+b65wt/P5PaxFWQ2RzowWgLLtBsr4tJZnNgsY2zMPJTx+LaHl1h2DIc7r/BXkiAAwML3n58ctHDiIgISdLly5ed7k9Nzfr24upZtlyVkZGhuXPnSmIWreIkNDQ0+2fDhtx/Gc6fPz/7uLi4uFyPO3r0qJ577jk1btxYFStWVIUKFdS4cWM9++yz1+wyEBcX5xBLRESEqlSpooYNG2rw4MH67LPPlJaWluf5hw8fzrFv8+bNGjx4sGJjYxUZGamoqCg1aNBA/fv31wcffKCLFy/mWu7VDh8+nB1feHi4/vjjj1yPnThxokqXLq3Q0FB16tRJUtYTxRtuuEGhoaFaunRpruempqbqxhtvVGhoqL777jtJ0qeffurw+oSGhqp8+fKqVauWOnXqpBdeeEG//fZbrmVmZmZq/vz5Gj16tDp06KDy5csrNDRUI0eOzPWcjIwMvfrqq7r33nt10003qWLFiipTpozq16+vp556SkePHr3m63X58mVNmDBBbdu2VdWqVVWmTBndcMMNGjp0qBISEnI97+zZs5oxY4Yef/xx3XrrrYqIiFBoaKi++uqra14vL7Nnz1abNm1UoUIFValSRd27d9cvv/yS53kLFy7UPffco5iYGJUpU0Z169ZVv379nNZhyZIleumll9SlSxdVrlxZoaGh+tvf/laouOFbora/p7Cz2/KcQ8mQFHZ2m6K2T8zeZjX5u+XHMAfLMAe7rTx3/vx1LEVkSKCa187f+NHmdcqoVMj19wEfcDeffDJi/3Ytt5XW7dsL8i3csmXLdPr0adWqVUstW7YseJDwmC+++EK33nqr032ff/55nufPmTNHI0eO1JUrVxQTE6N27drJZDJp27Ztmjx5sj766CNNnjxZAwYMyLWM7t27Kzw8XIZh6NKlS0pKStL8+fP15Zdf6oUXXtC0adOyP9y7YubMmRoxYoRsNptq166tO++8UyEhITp06JC+++47ff3112rSpImaNWvmcplS1rfVX331lZ566imn++2J99XCw8M1ceJE9ejRQ08++aS2bNniNLF/+eWXdfjwYfXs2TNHd8ar//9YrVadO3dOO3bs0Lp16zRhwgT169dP7733nkqVcpzy8s8//8z3lwBpaWl6/fXXFR4erptvvlmNGjWS1WrVzp07NX36dM2dO1dLlixxOvPejh071LNnTx0/flyRkZFq3ry5IiIitH//fn3xxRf64osv9Oijj2r8+PE5nlQmJCTo8ccfz1eseXnuuec0adIkhYSEqH379kpLS9OPP/6olStX6rPPPlO3bt1ynJOZmamHHnpIc+fOVVhYmFq0aKHSpUvr6NGjWrp0qRo2bJjjXvbggw+6nNzi+hOQdk6lk5ZIUp7Dl+37Syd9p4A6Q7K3v/Lh7TIX8ttX63/78xe3b+RTLlo17K4lObb3ahat05cs2n/K+RehV4utFKbezaI9ER5w3fHJZKRBgwaSpK1btzrdb98eHx+f77LtXbTuu+++AkbnYy6fUcCO2fI7+rNM1hQZ5nDZqrVURvwAKayCt6NzEBQUpFq1amn+/Pl6++23FRDg2HzPnTun5cuXq2HDhtq+fbvTMr755hs9/PDDMpvNmjZtmu677z6H7iSfffaZRo4cqYcfflgRERG65557nJbzxhtvqHr16g7bTp48qbfeekvTpk1Tz549tWDBAt1555151uuPP/7Q008/LcMw9P7772vQoEEOMZ09e1aff/65IiMj8yzratWrV9elS5f0+eefO01Gdu3apd9++00NGjTIMfNc586d1adPH3355Zd66aWXNGHCBIf9W7Zs0fvvv68yZcpo/PjxOcpu2bKlpk+f7rDNMAx9//33euaZZ/TFF1/ojz/+0LfffuvwaDcwMFADBgxQkyZN1KRJE+3YsUNPPvnkNesZHByslStXqmnTpg5tIjMzUy+//LLGjx+vp556KsciqEeOHNFdd92lCxcuaPjw4XrllVcUEhKSvX/jxo164IEHNHXqVAUGBuqtt95yOL9SpUoaNmxYdqwTJkzQnDlzrhnrtaxevVqTJk1SuXLltGrVKtWpU0eStGHDBnXq1EmPPvqobrvtthyz+40ZM0Zz585Vp06d9J///Edly5bN3pecnKxz587luFa3bt0UFxenJk2a6M8//1SvXr0KHDeyOFuboriK2v+N/IyMfJ3jZ2So9MHFMowbJJNkDvZTUHDhPkKY/LJeL7O5eH0UsaRlOt1uDvDT8A41NW/Tcf1yINlply1/P5Oa1ymj3s2iFejvk51PgCJXvO4ALmrVqpUiIyOVmJiobdu25fjGc968eZKkrl275qvclJQUff3115JKQDKSfkWBK1+U/69zZbKlO+zyT1qjgPXjlVm/n9I7vCIF5H9udU/p27evxo4dq+XLl+uuu+5y2Ddv3jylp6erf//+TpORlJQUjRgxIvtDf//+/XMcc99998nf318PPvigHn/8cbVr1y57QoS8VK5cWe+++64qVaqkcePG6ZFHHtGePXvynF3ihx9+kMViUcuWLTV48OAc+8uXL3/Nbkq5CQoKUvfu3TVjxgz99ttvuummmxz2258i9enTx+k02G+//bZWrlyp6dOnq0+fPmrevLmkrG5Rjz/+uDIzM/Xmm2+qUqVKLsVjMpl0991365ZbblGLFi20bt06ffjhhxo+fHj2MWFhYfroo4+y/7137948yw0ICFCLFi1ybPf399dLL72kyZMna9OmTbp8+bLDezlixAhduHBBgwYNcppQNWvWTIsXL1bz5s01efJk9ezZ0+HJ1K233urwhK6wY7wmTszqBvPcc89lJyL26zz00EN6//339cknnzgkZwcOHNDEiRMVExOj2bNn55jUo0yZMk6nJp86dWr239euXVuouJHF2doUxdX0NmtUpXL+z/vjp7U6dKhqiV6XwhzgpwEtqqprw0pK2J+s/SdTZMmwKSjAT7GVw9Uylq5ZQH75ZNpuNps1YsQISVkfKK4eOzJhwgTt3LlTrVu3VtOmTbO3T548WXFxcRo9enSu5S5YsECpqalq3ry5YmNjPVcBb0u/IvNXAxSw47MciYidyZaugB2fyvxlfyn9ShEHmLt+/frJZDLpiy++yLHviy++UHh4eK5J6OzZs3X27Fk1bdrUaSJi179/f91yyy06e/Zs9pOy/Pj73/+uatWq6dSpU1qwYEGex589e1ZSVtLhbvZ6/vX1stls+vLLL1WzZs1cu35VqFBBb731lmw2m4YPH549uO+9997Tzp07dccddxRoXFXFihX14osvSpLef//9fJ+fHyaTSX5+fvLz83N4avLbb79pxYoVCg4O1muvvZbr+bGxsXrsscdkGIYmT57ssTjT0tK0evVqSVldAP/q3nvvlZQ11uNqM2fOzO6mldvsgiha3h7r4MpPaGDBpi4NCbCyLsV/lQoJVOf4ihp5Zy393911NPLOWuocX5FEBCgAn3wyIkkvvPCCVqxYoYSEBMXGxqpNmzY6fPiwNmzYoHLlymnGjBkOx589e1Z79+7ViRMnci3T/sHzeh+4HrjyRfkf/VmGrt1f2JDkf/RnBa58Semd3y6i6K6tWrVqatGihb777julpKRkj2VISkrShg0bNHDgQIeuNlf74YcfJGU9CchL3759tXnzZi1btkyPPPJIvmL09/dXjx499N5772nNmjXXTHwkqUqVKpKyuukcOHDA4VvxwmrVqpViYmI0d+5cvfzyy9ndv9atW6c//vhDzz777DXPHzBggL744gutWLFCb731lgYMGKDXX39doaGhhfpw3rNnT40YMUIHDx7UsWPHcl0zqDAMw9D48eOVmpqqdu3aOTyhWrZsmSSpY8eODt2anOnTp48mTJig5cuXyzCMXGfxK4y9e/fKYrGoQoUKTl+Lhg0bSsrqWne1VatWSZLatWunpKQkffnllzpy5IjKli2rtm3bql27dm6PFXlzx3gKT4pO+FI6k3MSjbxUji3A4xQAyINPPhmRsvqJr1q1Si+++KJCQ0O1aNEiJSUladCgQdq2bVu+P9CdOHFCP/74owIDA9W3b18PRV0MpJyW/69Zg5ZdHbjo/+sX0uUzHg0rP/r166fU1NTsLnXS/7ocXeu927lzpyQ5Hcj8V/Zj7Ofkl328kivdjLp27aoKFSro0qVLatasmQYOHKipU6dq27Ztysx03nfZVSaTSX379tXRo0f1008/ZW+/uotWXiZNmqSwsDCNHz9e9913n9LS0vTiiy+qZs2aBY4rIiIi+/w9e/YUuJy/euGFFzRs2DD169dP9evX18svv6wbbrghR+KUn7ZQr149mc1mXbx4UUlJSW6L9Wr2Gb+io50PeA0LC1Pp0qWVnJysP//8M3v777//LknatGmTGjVqpLFjx+rjjz/W+PHj1bVrV3Xt2lWXLl3ySMzInTnYX0HBAcX250LlpnlXwokz5W9x8ysFAD6cjEhSSEiIxo0bpwMHDshisejkyZOaOXOmYmJichw7duxYGYahmTNnOi0rKipKGRkZslqtKleunIcj956AnXNy7ZqVG5MtXQE7Cz4w19169uwps9nsMBPU3LlzValSJd1xxx25nnf+/HlJWd2P8mLvMuVs8K8r7G3owoULeR4bGRmpRYsWqW7dukpLS9PChQs1atQotWrVSlWrVtXIkSN1/PjxAsUh5eyqlZaWlj07lytJe/Xq1TVmzBilp6drx44datSoUXY3ycLIz2vkqkWLFumzzz7TN998o4MHD6pevXr65JNPVKNGDYfj7O+rK23B398/e9yFvUudu9m7ml6rq5V9n30x17S0tOxppJ999lm1adNGGzdu1KlTp/Ttt9+qRo0a+vHHH93yXuH6crRmT9lM+esYYTMF6GCVnLO5AUBh+XQygvzzO/pzwc47kvt6C0WtTJky6tSpk1atWqWTJ09q8+bN2rdvn/r06SN//7y7RriyKGZeC2u6+/xGjRppy5YtWrBggR599FE1btxYgYGBunjxov7zn/+oZcuW2rdvX4FiufHGGxUfH6+FCxfKarVqyZIlunjxYp7dx642fPhwVaxYUVLW0wdXXue8FPY1dmbXrl1KTU3VkSNH9PXXX8tsNqtVq1Y5xv7Yr+3qAqn24zy1UrQrr8VfY736qVl0dLTmzZunm2++WREREWrXrp2+/PJL+fn5af78+Tpw4IBH4oZvsoSU17Ga3SXJpXVGJOlozXuVFnT9flEHwHtIRkoYkzWlSM/zlP79+yszM1Pz5s3L/sa/X79+1zzHPjbgzJm8u5zZvwHPazxBbuzfvDubySg3/v7+6ty5syZMmKD169fr6NGjmjJlisqVK6fTp09r1KhRBYpFynq9kpOTtXTpUn3++ecKCAjI13Sufn5+Cg7OmlXN1dnF8lKQ18hV5cuXV8eOHbVkyRJFRUXpySefdFiXyP7ky5W2kJGRkf30xlNPTe1jn3JbyFWSrly54nBsWFhY9gxe/fr1y7Fq8M0336zGjRvLMAytW7fOE2HDh+1u9LzOVbjFpe665yrcot8aP18UYQEogUhGShjDnP9V6QtznqfcddddKl26tGbPnq158+YpLi4uz/7/9evXlyRt27Ytz/LtxxRkrRrpf2MSrrUKfF5KlSqlIUOGaNq0aZKkNWvWKDU1tUBl9enTR35+fpo6daqWL1+udu3aZT/p8IZLly7p0KFDkgr3GuUlMjJSd911l65cuaKVK1dmb89PW9i9e7esVqtCQ0NzdPdyF3vX0ty6412+fFkXLlxQ6dKlFRERkb3dvrBrbgu82tfCcSXpQsliCwjWxtum6nCt3rl22bKZAnS4Vm9tvH2abP7XnqIcAAqKZKSEscXkXI/BpfOqFa/V6O1raOzYsUOnT5/O86mIpOwFCL/88ss8j7Uf07Fjx3zHlpmZmT2l7+23357v8//qtttuyy63oOMroqKi1LZtW61evVpWqzVfXbQ8Yf78+TIMQ7GxsbkO2nYX+9OMq8d72NvCihUrsscS5cbeFtq1a5djoU13qVu3roKCgnTmzBmHJzh29nVzbr75Zoft9gVgk5OTnZZrr5v9aQpwNVtAsHY1HaOVf1uhPfWf0JlKLZRcroHOVGqhPfWf0Mq/rdCupmNIRAB4FMlICZMRP0CGX/7mQTf8ArNWZC9mBg4cqHLlyql8+fIuJSP33XefypYtq82bN2fPJuXM559/rs2bN6tcuXIaOHBgvuN6++23dfToUUVHRztdM+Kv8hq3cPDgQUlZ6+sUZi2S+++/X+XKlVOVKlVyXVm+KJw+fVqvvvqqJDkseOgp69evlyTVqlUre1u9evXUvn17paWl6Z///Geu5+7fvz97gcCCLDzpqpCQkOzEddGiRTn2L1y4UJJyLPTZpUsXScqxurwk/fnnn9lJjH1qYMAZa3B5Jd40TBvbfqiEDrO1se2HSrxpmKzB7l/7CAD+imSkpAmvqMz6WdPfujpwMbN+Pyks71mHilqrVq109OhRHTlyJNduKleLiIjInuJ1+PDh+vTTT3McM3v27OwPyJMmTXLoEpOXkydPatSoURo3bpz8/f01bdq0HP34nZk+fbpGjBih3bt359h3/PhxPfHEE5Kkzp07O5T3wQcfqGHDhnrppZdcis8+xe/+/fu9skCeYRhaunSp2rZtqxMnTqht27Z68MEHC13ut99+qx9++CFHUpeamqoxY8Zo3bp1qlSpUo6nXJMmTVJkZKRmzZqlv//979ljMuw2btyoe+65R1euXNHTTz+tNm3aFDrWP/74Qw0bNnSaHNjf57feesthwPmGDRv0n//8R6VKldKgQYMczundu7eqV6+uFStWOAzSz8jI0P/93/8pOTlZ9erVc7pCPQAAxYHPLnqIgktv/4pM5xPln8fMWiZJmTEtlN7hlaIJrAh0795d06ZN0xNPPKFHHnlEr732mho3biwpa/zA4cOHFRQUpGnTpl3zqcbo0aMVHh4uwzD0559/KikpSbt371ZmZqYqVaqk6dOnq3379i7FlJ6ero8//lgff/yxatSooXr16ik0NFTHjx/Xpk2bZLVaVaNGDY0fP97hvHPnzmnfvn06efJkgV8PT0lISNCwYcMkSVarVefPn9f27duzu0oNGDBA7777rtNuT08++WT2N/r24xcvXuyw5svVTwK2b9+u119/XVFRUWrQoIEiIyN16tQp7dy5U+fPn1dkZKQ+++yzHF2VatSooe+++069e/fWlClT9Nlnn6lFixaKiIjQ/v37tX37dplMJg0bNkzjxo1zWs+ru+HZx8CMGzcuO+lt2LChJk6cmH1MRkZGrrOitWvXTo8//rimTJmi5s2bq127drJarfrxxx9ls9k0Y8aMHBMqBAUFadasWerSpYuGDRum999/X9WrV9f27dt1+PDh7AVg/zpL1xtvvKGlS5dKUva6JZs2bXKozxdffKGoqCinsQIA4C4kIyVRYIisvecocOVL8v/1C6frjhh+gcqs3y8rEQkI9kKQnnP//ffrtttu0+TJk7VixYrs1birVq2q4cOHa8SIEXkOVLZ3pfHz81OpUqVUsWJF9ejRQ3feead69uyZPfOUKx544AFVqVJFy5Yt07Zt27Rx40YlJycrIiJCjRo1UpcuXfTII4/k6ymNtx08eDC7e1lISIgiIyMVFxenZs2aacCAAbrppptyPXfPnj3atGmTw7bTp0/r9OnTTo/v1q2bUlJS9NNPP2nr1q06f/68QkJCVKtWLQ0dOlSPPfZYrh+qGzdurB07dmjq1Kn65ptv9MsvvziMy/nXv/6lxx9/PNdY/xqnJCUmJioxMVGS8tUOpKwufvHx8Zo6dWr2Iqxt27bV888/r5YtnY/batasmX7++We9/vrr+vHHH7V7925VrFhRQ4YM0fPPP+903aVDhw7liP3ixYsO26xWa75iBwCgIEyGqxPto1Dq1asni8XitD+4nWEYSk9PV1BQkGrUqJE9bac72D9Y5Og2dPmMAnbOkd+RBJmsKTLM4bJVa5k1RqQYds2Ce+XaLkq4Dz74QM8884wqVqyoH3/80WG8CZyz2WxKSkqSxWJRYGCgw9OYI0eOSMp91q/rQVpapgYN2iCryV9vfdpOQcHX33d9ly5YNKTdYlWsGq5357ZXcEj+xh/+VXG9/7i7nsVVSahnfupYXNujKyxpGXru/h9lNjI1a9atCg4u/Fpg+dW9e3cFBQU57XKel+vvbon8CaugjBZPSi2e9HYkQLHx2GOP6cSJExo/fry6deumlStXenUqZAAArlckIwDgxLhx4xQVFaVz585pw4YNXp2BDACA6xXJCADk4rHHHvN2CAAAXNeY2hcAAACAV/BkBABw3Ui+kqllB1K063SaUtNtCg30U/1KwepYO1xlQop+UCcA4NpIRoopJjkD4Eu8fc+yZNg0fXOyVh5MUYbNcd+2E2mas/OCOtQK17CmZWX2NzkvBABQ5OimVYzYp8I0DEMWi8XL0QCA69LT07MTkr8usuhplgybxvx4Wj8cyJmI2GXYpKUHUvTSylOy5HYQAKDIkYwUM35+fsrMzNSlS5e8/k0jALjq8uXLstlsbl0fyVXTNydr12nXvsDZddqiDzcnezgiAICr6KZVzPj5+SkjI0MpKSmSpFKlSikoKKjQ3zTabDaHPwGJdoHCs9lsSklJ0cWLF5WZmSl//6Idl5F8JVMrD6bk65wVB1M0sEFpxpAAQDFAMlLM+Pn5KSAgQOnp6bp06ZIuX77sli4P3uo+geKNdgF3sNlsysjIkMlkytGWsrqdZiW7aWmZbr/2kj2Xcu2alZsMW9Z5PW8s5bY40tIys/4/mSSJp9oA4CqSkWLInpDYbDZZrVa3lGkvx2w2u6U8XB9oF3AHk8kkPz8/+fn55UhGLBabnn02SZJkNh93+7WvNC8nVQjO93lzV5/WN2/+7rY4bDZDSUmXVaFqhOhhCwCuIxkppuy/2CX3zFJz8uRJSVJMTEyhy8L1g3YBd3DlyZphDpbVA0/gjICCjVExAvxkNbmvm5bNZMgQTxgBIL9IRnyAO7vQ0B0HztAuUBRe+fB2mYPdO05j6urD2nfqcr7Pi40ro0cfbei2OFIuWjXsriVuKw8ASgqSEQBAkTAH+yso2L2/dm6IDi9QMnJDdIRbY7F4YDwMAJQETO0LAPBZLWLLyt8vf0/2/P1MahlbxkMRAQDyg2QEAOCzIkMC1bx2/hKL5nXKqFRIoIciAgDkB8kIAMCn9WoWrdhKYS4dG1spTL2bRXs4IgCAq0hGAAA+zRzgp+EdaqpV3dy7bPn7mdSqblk93rGmAv351QcAxQUD2AEAPs8c4KcBLaqqa8NKStifrP0nU2TJsCkowE+xlcPVMpauWQBQHJGMAACuG6VCAtU5vqI6x1f0digAABfwrBoAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeAXJCAAAAACvIBkBAAAA4BUkIwAAAAC8gmQEAAAAgFeQjAAAAADwCpIRAAAAAF5BMgIAAADAK0hGAAAAAHiFTycjaWlpGjNmjOrWravg4GBFR0dr6NChOnbsWIHKO3DggB5++GHVqFFDwcHBqlChglq2bKm3337bzZEDQN78086rwu+zVGPtU6r14zDVWPuUKvw+S/5p570dGgAAbhHg7QAKKi0tTe3bt1dCQoKioqLUrVs3JSUlacaMGfr222/1888/q3bt2i6Xt3DhQg0YMEAWi0WNGjVSixYtdO7cOf3666+aNm2a/v73v3uwNgDwP6ZMi6K2v6fSSUvkZ2Q47As/vUkVfvtYF2p00YmGT8rwD/JSlAAAFJ7PJiOvv/66EhIS1KJFCy1btkzh4eGSpAkTJuiZZ57R0KFDtWbNGpfK2rFjh/r166eIiAgtX75crVu3zt5ns9m0detWj9QBAP7KlGlRjXXPKOzstlyP8TMyVPbQ1wr684iS2rxDQgIA8Fk+2U0rPT1dkyZNkiRNmTIlOxGRpFGjRik+Pl5r167Vli1bXCpv5MiRslqtmjlzpkMiIkl+fn665ZZb3Bc8AFxD1Pb3FHZ2m4w8jjMkhZ3dpqjtE4siLAAAPMInk5H169frwoULql27tho1apRjf69evSRJixcvzrOs33//XevWrVPdunXVtWtXt8cKAK4KSDun0klLJEmmPI617y+d9B1jSAAAPssnu2nt2LFDktS4cWOn++3b7cddy8qVKyVJHTt2VFpamubOnavNmzfLZDIpPj5effr0UalSpdwUOQBkMQxDFovNYVvU/m9yjBHJi5+RoYj93+hk7P3uDM9t0tIyZRjKepST5/MeAEBJ45PJyJEjRyRJVatWdbrfvt1+3LXs3r1bkhQSEqKGDRtq7969DvtHjx6t+fPn67bbbnMptnr16jndnpiYqEqVKrkUkyekp6dLcu01QclBu/Aei8WmZ59Nctg2o91qVYnKf1lJq1dpyEs13BKXu9lshv74w6qKMWZZLOky+V2fCUm6Nf2/fzNktabLz//6q6e762gYWedbrdZCRuZeJeG9lEpGPfNTx+LaHl1htWbKMAxZrVYdPXpUQUFF3/EpPT1dQUEFG7/ok8lISkqKJCk0NNTp/rCwMIfjriU5OVmS9N5776lMmTJasGCB2rVrp1OnTunll1/WnDlz1L17d+3evVtRUQX4lAAA12CYg7P/HmZOv8aRuQszpzuUU5wYNkMy5e9pDwCg5PDJZMSevZpMzntV2/e7IjMzU5KUkZGhzz77THfeeackKTIyUrNnz9b+/fu1adMmTZkyRa+++mqe5dmftPxVvXr1ZLFYVK1aNZdjcyf7N9/euj6KJ9qF96SlZcpsPi6ryaRXPrxd5mB/RSd8JZ05nO+youOi9K+h7T0QZeGlXLRq2F1Z42DM5kCZzYFejsgzAs323zum67ae7q6j/Rtos9lcyMjcqyS8l1LJqGd+6lhc26MrDFuGTCaTzGazYmJiFBzsX+QxBAYWvP34ZDISEREhSbp8+bLT/ampqZLkMMtWXmVVqVIlOxG52pAhQ7Rp0yatXr26gNECwLWZg/0VFBygC5WbqtKZX/J9fnLlZgoKLp63c0taprdDAAAUYz45m5b9W9zcVlq3b3fl294aNWpIkqpXr37N/adPn85nlACQP0dr9pTNlL+kwmYK0NFaPTwUEQAAnuWTyUiDBg0kKdfFCO3b4+Pj8yzLPjXw+fPOp8Y8d+6cJNeesgBAYVhCyutYze6S8p53yr7/aM17ZQ0u78mwAADwGJ9MRlq1aqXIyEglJiZq27acqxTPmzdPklxaN6R9+/YKCwtTYmKijh49mmO/vXtWbtMIA4A77W70vM5VuMWldUbOVbhFvzV+vijCAgDAI3wyGTGbzRoxYoQkacSIEQ5jRyZMmKCdO3eqdevWatq0afb2yZMnKy4uTqNHj3YoKzQ0VCNHjlR6eroee+wxh7KWLl2qWbNmyWQyadiwYR6uFQBItoBgbbxtqg7X6p1rly2bKUCHa/XWxtunyeZfsKkUAQAoDorniEcXvPDCC1qxYoUSEhIUGxurNm3a6PDhw9qwYYPKlSunGTNmOBx/9uxZ7d27VydOnMhR1pgxY7Ru3Tp99913io2N1a233qrTp0/rl19+kc1m02uvvaZmzZoVVdUAlHC2gGDtajpG++o/rpiDC1Tu9CYFZKQqIyBU5yo21dFaPeiaBQC4LvhsMhIcHKxVq1bpjTfe0Jw5c7Ro0SKVKVNGgwYN0iuvvKKYmJh8lfXjjz9q/Pjx+uyzz/T9998rODhYd9xxh55++ml16dLFgzUBAOesweWVeNMwJd7Ek1kAwPXJZ5MRKWvV9HHjxmncuHF5Hjt27FiNHTs21/1ms1n/+Mc/9I9//MONEQIAAADIjU+OGQEAAADg+0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBX+PTUvgCuT4ZhyGKxeTsMj0pLy5RhGJJJkgxvhwMAgFeQjAAodiwWmwYN2uDtMDzKZjOUlHRZFapGyCAXAQCUUCQjAIotq8nf2yF4jM1kyMh6LAIAQIlFMgKgWHvlw9tlDr7+kpKUi1YNu2uJt8MAAMCrSEYAFGvmYH8FBV9/typLWqa3QwAAwOuYTQsAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeAXJCAAAAACvIBkBAAAA4BXX3+T9AIql5CuZWnYgRbtOpyk13abQQD/VrxSsjrXDVSbk+lvUEAAA5I1kBIBHWTJsmr45WSsPpijD5rhv24k0zdl5QR1qhWtY07Iy+5u8EyQAAPAKkhEAHmPJsGnMj6e167Ql12MybNLSAyk6dildL7erqKAAeo8CAFBS8FsfgMdM35x8zUTkartOW/Th5mQPRwQAAIoTkhEAHpF8JVMrD6bk65wVB1OUfCXTQxEBAIDihm5agA8xDEMWiy3vA11kLystzf0JwJI9l3KMEclLhi3rvC41w2QYhmSSJMPtsQEAgOKBZATwIRaLTYMGbXBbeVarVZJkNh93W5l2V5qXkyoE5/u8uatPa9HrZ5WUdFkVqkbIIBcBAOC6RTIC+CCryT1T4Rrm4P+W5/5ZrIwCDkQ3AvxkNfnLEDNrAQBwvSMZAXzUKx/eLnNw4ZISqzVdkmQ2B7ojJAdTVx/WvlOX831ebFwZ3df/Jg27a4nbYwIAAMULyQjgo8zB/goKLtx/YZNf1qAOs9n9t4IbosMLlIzcEB2hwCAWQQQAoCRgNi0AHtEitqz8/fLX1crfz6SWsWU8FBEAAChuSEYAeERkSKCa185fYtG8ThmVCnF/lzEAAFA8kYwA8JhezaIVWynMpWNjK4Wpd7NoD0cEAACKE5IRAB5jDvDT8A411apu7l22/P1MalW3rB7vWFOB/tySAAAoSRjADsCjzAF+GtCiqro2rKSE/cnafzJFlgybggL8FFs5XC1j6ZoFAEBJRTICoEiUCglU5/iK6hxf0duhAACAYoI+EQAAAAC8gmQEAAAAgFeQjAAAAADwCpIRAAAAAF5BMgIAAADAK0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVPp2MpKWlacyYMapbt66Cg4MVHR2toUOH6tixY/kqp0aNGjKZTLn+7Nmzx0M1AAAAAEoun12BPS0tTe3bt1dCQoKioqLUrVs3JSUlacaMGfr222/1888/q3bt2vkqc9CgQU63R0ZGuiNkAAAAAFfx2WTk9ddfV0JCglq0aKFly5YpPDxckjRhwgQ988wzGjp0qNasWZOvMmfOnOmBSAEAAAA445PdtNLT0zVp0iRJ0pQpU7ITEUkaNWqU4uPjtXbtWm3ZssVbIQIAAADIg08mI+vXr9eFCxdUu3ZtNWrUKMf+Xr16SZIWL15c1KEBAAAAcJFPdtPasWOHJKlx48ZO99u3249z1dtvv63ExEQFBQWpXr16uvfee1WhQoXCBQsAAADAKZ9MRo4cOSJJqlq1qtP99u3241z17LPPOvz76aef1r///W89+OCDLpdRr149p9sTExNVqVKlfMfkLunp6ZLy/5rAOXP6BcWcWaWyl35XQGaaMvyDdb7UTTpaoa2sgaU9dl2LxSar1SrDHCyrNV0mP1uhyjMMQ5JktVrdEZ7bpFvT//s3Q1Zruvz8Da/G4wkloY7S1fVUCann9ft+uruO3H+8qyTUMz91LK7t0RVWa6YMw5DVatXRo0cVFFT0HZ/S09MVFBRUoHN9MhlJSUmRJIWGhjrdHxYW5nBcXv72t7/pjjvuUJMmTVShQgUdPHhQH3/8sSZOnKiHHnpI5cqVU/fu3d0SO3ybn82qmw7PUtWza+RnZDrsq3DpV8X+MU/HyrfVb9UfkM3P7KUoAQAAfINPJiP27NVkMl1zv6v+/e9/O/y7Xr16euedd3TDDTfokUce0XPPPedyMrJ7926n2+vVqyeLxaJq1arlKzZ3sT8R8db1rwemTItqrHtGYWe35XqMn5GpamdWqpxxXklt3pHhX7BvCXKTlpYps/m4rCaTzOZAmc2F+y9s/wbIbC5eiVOg2f5/2F7PQK/G4wkloY7S1fVUCann9ft+uruO3H+8qyTUMz91LK7t0RWGLUMmk0lms1kxMTEKDvYv8hgCAwvefnxyAHtERIQk6fLly073p6amSpLDLFsF8dBDD6lixYrat2+fDh06VKiy4Puitr+nsLPblFeqa0gKO7tNUdsnFkVYAAAAPssnkxH7t/u5rbRu317YpwB+fn7ZCyeeOHGiUGXBtwWknVPppCWSJOfP4/7Hvr900nfyTzvv0bgAAAB8mU9202rQoIEkaevWrU7327fHx8cX+lrJycmSCv+UBZ5lGIYslsIN5r6WqP3fyM/IyNc5fkaGIvZ/o5Ox97stjrS0rEFqWRnP9TfYEAAAlCw+mYy0atVKkZGRSkxM1LZt23KsNTJv3jxJUteuXQt1nd27d2vv3r0KDQ1VXFxcocqCZ1ksNg0atMFj5U9vs0ZVKuf/vMNrVmvYq3XdFofNZigp6bIqVI1QPodGAQAAFDs+2U3LbDZrxIgRkqQRI0Y4jB2ZMGGCdu7cqdatW6tp06bZ2ydPnqy4uDiNHj3aoawffvjB6UrtO3fuVO/evWUYhh566CGfHNBUEllN/h75CQ0s2FR/oYFWt8di5NlRDAAAwDf45JMRSXrhhRe0YsUKJSQkKDY2Vm3atNHhw4e1YcMGlStXTjNmzHA4/uzZs9q7d2+OsR8///yzXn75ZVWvXl21a9dWhQoVdOjQIW3dulUZGRm6/fbb9cYbbxRl1VBIr3x4u8xunkkiOuFL6czh/J8XF6W3hrZzWxwpF60adtcSt5UHAADgTT6bjAQHB2vVqlV64403NGfOHC1atEhlypTRoEGD9MorrygmJsalcjp16qSjR49q06ZN2rFjhy5evKhSpUqpdevWGjhwoIYMGSJ//6KfIg0FZw72V1Cwe5v2hcpNVenML/k+L7lyM7fGYknLzPsgAAAAH+GzyYgkhYSEaNy4cRo3blyex44dO1Zjx47Nsb1FixZq0aKFB6LD9eRozZ6K3fVBvgax20wBOlqrhwejAgAA8G0+OWYEKGqWkPI6VrO7pLznsLLvP1rzXlmDy3syLAAAAJ9GMgK4aHej53Wuwi0urTNyrsIt+q3x80URFgAAgM8iGQFcZAsI1sbbpupwrd6ymZz3cLSZAnS4Vm9tvH2abP5BRRwhAACAb/HpMSNAUbMFBGtX0zHaV/9xxRxcoHKnNykgI1UZAaE6V7GpjtbqQdcsAAAAF5GMAAVgDS6vxJuGKfGmYd4OBQAAwGfRTQsAAACAV5CMAAAAAPAKkhEAAAAAXsGYESj5SqaWHUjRrtNpSk23KTTQT/UrBatj7XCVCWH1eQAAAHgGyUgJZsmwafrmZK08mKIMm+O+bSfSNGfnBXWoFa5hTcvK7J/X6hoAAABA/pCMlFCWDJvG/Hhau05bcj0mwyYtPZCiY5fS9XK7igoKoFcfAAAA3IdPlyXU9M3J10xErrbrtEUfbk72cEQAAAAoaUhGSqDkK5laeTAlX+esOJii5CuZHooIAAAAJRHdtEoAwzBksWQNCklLy9SSPZdyjBHJS4ZNWrLnknreWMoDERZeWlqmDMOQTJJkeDscAAAAuIBkpASwWGx69tkkSZLZfFxXmpeTKgTnu5y5q0/rmzd/d3N07mGzGUpKuqwKVSNkkIsAAAD4BJKREsQwB8tqMsko4EB0I8BPVlPxnOrXZjJkiBm/AAAAfAnJSAnzyoe36+Nfjmnfqcv5Pjc2rowefbSh+4Nyg5SLVg27a4m3wwAAAEA+kIyUMOZgf90QHV6gZOSG6AgFBRfPJmNJY3A9AACAr2E2rRKoRWxZ+fvlr0uTv59JLWPLeCgiAAAAlEQF/pr7yJEjbguiWrVqbisLeYsMCVTz2mX00/7zLp/TvE4ZlQoJ9GBUAAAAKGkKnIzUqFFDJlPhBwybTCZlZGQUuhzkT69m0Tp9yaL9LnTXiq0Upt7NoosgKgAAAJQkhRoAULFiRcXFxRX4/D179uj06dOFCQEFZA7w0/AONTVv03H9ciBZmbac8+H6+5nUvE4Z9W4WrUB/evQBAADAvQqVjNx11136+OOPC3z+kCFD9MknnxQmBBSCOcBPA1pUVdeGlZSwP1n7T6bIkmFTUICfYiuHq2UsXbMAAADgOV6dGslgdbpioVRIoDrHV1Tn+IreDgUAAAAlSIGTkW3btqls2bKFuvgrr7yip59+ulBlAAAAAPBNBU5GGjRoUOiLx8TEKCYmptDlAAAAAPA9jEoGAAAA4BUeTUbeeusttWvXzpOXAAAAAOCjPJqM7NmzR2vWrPHkJQAAAAD4KLppAQAAAPAKkhEAAAAAXkEyAgAAAMArPJqMxMXF6bbbbvPkJQAAAAD4KI8mI88995xWrVrlyUsAAAAA8FF00wIAAADgFQVORnbu3Kljx44V6uLHjh3Tzp07C1UGAAAAAN9U4GSkUaNGGjNmTKEu/uKLL6pRo0aFKgMAAACAbypwMmIYhgzDcGcsAAAAAEqQgMKcvHTpUrVr167A5+/Zs6cwlwcAAADgwwqVjJw8eVInT54sVAAmk6lQ5wMAAADwTQVORg4dOuTOOAAAAACUMAVORqpXr+7OOAAAAACUMD69zkhaWprGjBmjunXrKjg4WNHR0Ro6dGihpxzev3+/QkJCZDKZ1LlzZzdFCwAAAOBqPpuMpKWlqX379ho3bpxSUlLUrVs3xcTEaMaMGWrcuLESExMLXPYjjzwii8XixmgBAAAA/JXPJiOvv/66EhIS1KJFC+3bt09z587Vhg0b9M477+jMmTMaOnRogcr9z3/+o1WrVunhhx92c8QAAAAAruaTyUh6eromTZokSZoyZYrCw8Oz940aNUrx8fFau3attmzZkq9yT58+rb///e/q0KGD+vfv79aYAQAAADjyyWRk/fr1unDhgmrXru10BfdevXpJkhYvXpyvcp944glduXJFH3zwgVviBAAAAJA7n0xGduzYIUlq3Lix0/327fbjXLFkyRLNnTtX//jHP1SnTp3CBwkAAADgmgq16KG3HDlyRJJUtWpVp/vt2+3H5eXy5csaPny4brjhBj333HOFiq1evXpOtycmJqpSpUoux+ROFotNhmFIhmS1psvkZyvyGDwt3Zr+378ZslrT5edveDUeT3F3PQ0j63yr1VrIyNyrJLyfJaGO0tX1VAmp5/X7fnL/ub6UhHrmp47FtT26wmrNlGEYslqtOnr0qIKCiv5ZQ3p6uoKCggp0rk8mIykpKZKk0NBQp/vDwsIcjsvLCy+8oMOHD+vHH3+U2Wx2T5AAAAAArqnAyci4ceNkMpn0+OOPq2zZsho3bpzL55pMJr344osFvXR29moyma653xWbN2/WpEmT9MADD+iOO+4ocEx2u3fvdrq9Xr16slgsqlatWqGvkV9paZkymZJkmCSzOVBms0/moNcUaLa/56b/1jHQq/F4irvraf8GqLgl4SXh/SwJdZSurqdKSD2v3/eT+8/1pSTUMz91LK7t0RWGLUMmk0lms1kxMTEKDvYv8hgCAwvefgr8qXTs2LEymUzq27evypYtm/1vVxKBwiYjERERkrK6VzmTmpoqSQ6zbDmTkZGhhx9+WJGRkRo/fnyB4wEAAACQfwVORmbMmCFJioqKcvh3UbA/XchtpXX79ryeQhw7dkzbt29X5cqV1bt3b4d9Fy5ckCRt3LhRbdu2VXh4uL799ttCRg4AAADArsDJyKBBg675b09q0KCBJGnr1q1O99u3x8fHu1TeyZMndfLkSaf7kpOTtWbNGkVGRhYgUgAAAAC58cmpfVu1aqXIyEglJiZq27ZtOfbPmzdPktS1a9drllOjRg0ZhuH0Z9WqVZKkTp06yTCM7CclAAAAANzDbclImzZtNG3aNJ0/f95dRebKbDZrxIgRkqQRI0Y4jB2ZMGGCdu7cqdatW6tp06bZ2ydPnqy4uDiNHj3a4/EBAAAAyJvbkpGffvpJw4cPV1RUlLp166Yvv/xSaWlp7io+hxdeeEG33nqrEhISFBsbq759+6p58+Z65plnVK5cuRxjWM6ePau9e/fqxIkTHosJAAAAgOvcloxs2LBBI0eOVPny5bV48WL1799fFStW1ODBg7V8+XLZbO5daC84OFirVq3Siy++qNDQUC1atEhJSUkaNGiQtm3bxirqAAAAQDHntmSkadOmeu+993Ts2DEtX75cgwYNkr+/vz755BN17txZVapU0dNPP61Nmza565IKCQnRuHHjdODAAVksFp08eVIzZ85UTExMjmPHjh0rwzA0c+ZMl8pu27atDMPQ0qVL3RYvAAAAgP9x+wB2k8mk9u3b6+OPP9apU6c0b9483Xvvvbp48aImTpyo5s2b64YbbnD3ZQEAAAD4GI/OpmU2m9WjRw/NmzdPp06d0qOPPirDMHTgwAFPXhYAAACADyjwOiOu2rdvn+bMmaM5c+YoMTFRkhQUFOTpywIAAAAo5jySjJw8eVJffPGFZs+era1bt8owDJlMJt12220aOHBgjtXOAQAAAJQ8bktGLl26pPnz52vOnDlavXq1bDabDMNQfHy8Bg4cqAEDBqhKlSruuhwAAAAAH+e2ZKRy5cqyWCwyDEPVq1dX//79NXDgQNWrV89dlwAAAABwHXFbMhISEqJBgwZp4MCBat26tbuKBQAAAHCdclsycurUKQUEeHw8PAAAAIDrhNum9iURAQAAAJAfBc4gPvnkk0Jd+IEHHijU+QAAAAB8W4GTkcGDB8tkMuX7PPs0vyQjAAAAQMlW4GTkpZdeypGMHDhwQLNnz1Z4eLjuvPNOVatWTZJ05MgRLVu2TCkpKbrvvvtUu3btwkUNAAAAwOcVOBkZO3asw7/37t2rW2+9VYMHD9aECRNUunRph/0XL17UqFGjtGDBAv38888FvSwAAACA64TbBrCPHj1aFSpU0EcffZQjEZGkyMhIffjhhypfvrxGjx7trssCAAAA8FFuS0bWrl2rZs2ayc8v9yL9/PzUrFkzrV271l2XBQAAAOCj3JaMWCwWHTlyJM/jjhw5IqvV6q7LAgAAAPBRbktGmjRpooSEBH355Ze5HvPVV1/pp59+UpMmTdx1WQAAAAA+ym0rFb788svq0KGD+vfvrxkzZqh3796qVq2aTCaTDh8+rK+++krLli2Tv79/jsHvAAAAAEoetyUjt99+u+bNm6eHHnpIP/zwg5YtW+aw3zAMlS1bVtOnT1fbtm3ddVkAAAAAPsptyYgkdevWTe3bt9dXX32l9evX6/jx4zIMQ9HR0WrdurV69+6tiIgId14SAAAAgI9yazIiSeHh4RoyZIiGDBni7qIBAAAAXEfcNoAdAAAAAPLD7U9GUlNTtXnzZp04cUIWiyXX4x544AF3XxoAAACAD3FrMvLSSy/p3XffVWpqaq7HGIYhk8lEMgIAAACUcG5LRv71r3/p1VdfVUBAgLp27aq6desqPDzcXcUDAAAAuM64LRn58MMPFRISonXr1qlx48buKhYAAADAdcptA9iPHj2q22+/nUQEAAAAgEvcloxUrlzZXUUBAAAAKAHcloz069dPGzduVHJysruKBAAAAHAdc1syMnbsWN10003q0aOHEhMT3VUsAAAAgOuU2waw33333bLZbPrpp58UFxenGjVqqGrVqjKZTDmONZlMWrlypbsuDQAAAMAHuS0ZWb16dfbfMzMzlZiYmOsTEmcJCgAAAICSxW3JyKFDh9xVFAAAAIASwG3JSPXq1d1VFAAAAIASwG0D2AEAAAAgP0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABe4dPJSFpamsaMGaO6desqODhY0dHRGjp0qI4dO+ZyGRkZGRo7dqy6dOmiWrVqKSIiQsHBwYqNjdXjjz+uI0eOeLAGAAAAQMnls8lIWlqa2rdvr3HjxiklJUXdunVTTEyMZsyYocaNGysxMdHlcl5++WWtXbtWUVFR6ty5szp16iSr1ar3339f8fHx2rp1q4drAwAAAJQ8PpuMvP7660pISFCLFi20b98+zZ07Vxs2bNA777yjM2fOaOjQoS6VExwcrPXr1ys5OVk//fSTvvrqK3399dc6ePCgRo8erYsXL2r48OEerg0AAABQ8vhkMpKenq5JkyZJkqZMmaLw8PDsfaNGjVJ8fLzWrl2rLVu25FlWQECAWrVqpYCAAIft/v7+GjdunIKDg7VhwwZdvnzZvZUAAAAASjifTEbWr1+vCxcuqHbt2mrUqFGO/b169ZIkLV68uFDXMZlM8vPzk5+fX45kBQAAAEDh+GQysmPHDklS48aNne63b7cfVxCGYejNN99Uamqq2rVrp6CgoAKXBQAAACAnn/y63z7DVdWqVZ3ut2/P70xYzz33nE6dOqVLly5p586dSkxMVFxcnKZPn+5yGfXq1XO6PTExUZUqVfLK7FwWi02GYUiGZLWmy+RnK/IYPC3dmv7fvxmyWtPl5294NR5PcXc9DSPrfKvVWsjI3KskvJ8loY7S1fVUCann9ft+cv+5vpSEeuanjsW1PbrCas2UYRiyWq06evSogoKK/llDenp6gb+498lkJCUlRZIUGhrqdH9YWJjDca6aP3++wyxcN998s2bPnq2aNWsWMFIAAAAAufHJZMSevZpMpmvuz68DBw5Iks6ePastW7bon//8p5o0aaKPPvpIgwYNcqmM3bt3O91er149WSwWVatWrUCxFUZaWqZMpiQZJslsDpTZ7JNv+zUFmu3vuem/dQz0ajye4u562r8BMpvNhYzMvUrC+1kS6ihdXU+VkHpev+8n95/rS0moZ37qWFzboysMW4ZMJpPMZrNiYmIUHOxf5DEEBha8/fjkmJGIiAhJynWGq9TUVElymGUrP8qXL69OnTpp5cqVio6O1mOPPaajR48WLFgAAAAATvlkMmJ/upDbSuv27YV9ChEZGamuXbvqypUrWr58eaHKAgAAAODIJ5ORBg0aSFKuK6Pbt8fHxxf6WuXLl5cknTlzptBlAQAAAPgfn0xGWrVqpcjISCUmJmrbtm059s+bN0+S1LVr10Jfa82aNZKk2rVrF7osAAAAAP/jk8mI2WzWiBEjJEkjRoxwGDsyYcIE7dy5U61bt1bTpk2zt0+ePFlxcXEaPXq0Q1nffPONvv/++xyD3lNTU/XPf/5Ta9asUeXKldW5c2cP1ggAAAAoeXx2WqUXXnhBK1asUEJCgmJjY9WmTRsdPnxYGzZsULly5TRjxgyH48+ePau9e/fqxIkTDtu3bt2ql19+WdHR0WrUqJEiIyN18uRJbd++XefPn1dkZKS+/PLLAg+GBwAAAOCczyYjwcHBWrVqld544w3NmTNHixYtUpkyZTRo0CC98soriomJcamcHj166M8//9S6deu0adMmnT9/XiEhIapTp44eeeQRjRw5UlFRUR6uDQAAAFDy+GwyIkkhISEaN26cxo0bl+exY8eO1dixY3Nsj4+P1zvvvOOB6AAAAABci0+OGQEAAADg+0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAAADgFSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeAXJCAAAAACvIBkBAAAA4BUkIwAAAAC8gmQEAAAAgFeQjAAAAADwCpIRAAAAAF5BMgIAAADAK0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAAADgFSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeIVPJyNpaWkaM2aM6tatq+DgYEVHR2vo0KE6duyYy2VcuHBBc+bM0YABA3TTTTcpLCxMERERuvXWWzVx4kSlp6d7sAYAAABAyeWzyUhaWprat2+vcePGKSUlRd26dVNMTIxmzJihxo0bKzEx0aVyxo8fr4EDB2ru3LkKDQ3VPffco2bNmmnHjh166qmn1K5dO6Wmpnq4NgAAAEDJ47PJyOuvv66EhAS1aNFC+/bt09y5c7Vhwwa98847OnPmjIYOHepSOeHh4frHP/6hI0eOaPPmzfriiy+0cuVK/frrr6pWrZrWr1+vV1991cO1AQAAAEoen0xG0tPTNWnSJEnSlClTFB4enr1v1KhRio+P19q1a7Vly5Y8y3r++ef12muvqUqVKg7bY2Nj9eabb0qSPv/8czdGDwAAAEDy0WRk/fr1unDhgmrXrq1GjRrl2N+rVy9J0uLFiwt1nQYNGkiSjh8/XqhyAAAAAOTkk8nIjh07JEmNGzd2ut++3X5cQR08eFCSVLly5UKVAwAAACCnAG8HUBBHjhyRJFWtWtXpfvt2+3EFNXHiRElSt27dXD6nXr16TrcnJiaqUqVKhY6pICwWmwzDkAzJak2Xyc9W5DF4WrrVPuuZIas1XX7+hlfj8RR319Mwss63Wq2FjMy9SsL7WRLqKF1dT5WQel6/7yf3n+tLSahnfupYXNujK6zWTBmGIavVqqNHjyooqOifNaSnpysoKKhA5/rkk5GUlBRJUmhoqNP9YWFhDscVxNSpU7VixQqVLl1azz//fIHLAQAAAOCcTz4ZsWevJpPpmvsLas2aNXryySdlMpn08ccfKzo62uVzd+/e7XR7vXr1ZLFYVK1atULFVhBpaZkymZJkmCSzOVBms0++7dcUaLa/56b/1jHQq/F4irvraf8GyGw2FzIy9yoJ72dJqKN0dT1VQup5/b6f3H+uLyWhnvmpY3Ftj64wbBkymUwym82KiYlRcLB/kccQGFjw9uOTn0ojIiIkSZcvX3a6374uyNWzbLlq586d6t69u6xWq/7973/r3nvvLXigAAAAAHLlk9207E8Xcltp3b49v08hEhMT1alTJ124cEFjx47VyJEjCxcoAAAAgFz5ZDJin3J369atTvfbt8fHx7tc5vHjx9WxY0edPHlSTz75pMaMGVP4QAEAAADkyieTkVatWikyMlKJiYnatm1bjv3z5s2TJHXt2tWl8pKTk9WpUycdOnRIQ4YM0bvvvuvWeAEAAADk5JPJiNls1ogRIyRJI0aMcBg7MmHCBO3cuVOtW7dW06ZNs7dPnjxZcXFxGj16tENZqampuvvuu7Vr1y716dNHH374Ya4D4wEAAAC4j08OYJekF154QStWrFBCQoJiY2PVpk0bHT58WBs2bFC5cuU0Y8YMh+PPnj2rvXv36sSJEw7b//nPf+qXX36Rv7+/AgIC9OCDDzq93syZMz1VFQAAAKBE8tlkJDg4WKtWrdIbb7yhOXPmaNGiRSpTpowGDRqkV155RTExMS6Vk5ycLEnKzMzUnDlzcj2OZAQAAABwL5/spmUXEhKicePG6cCBA7JYLDp58qRmzpzpNBEZO3asDMPIkVTMnDlThmHk+QMAAADAvXw6GQEAAADgu0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAAADgFSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeAXJCAAAAACvIBkBAAAA4BUkIwAAAAC8gmQEAAAAgFeQjAAAAADwCpIRAAAAAF5BMgIAAADAK0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAAADgFSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeIVPJyNpaWkaM2aM6tatq+DgYEVHR2vo0KE6duxYvspZs2aNXn75ZXXp0kUVKlSQyWRSXFych6IGAAAAIEkB3g6goNLS0tS+fXslJCQoKipK3bp1U1JSkmbMmKFvv/1WP//8s2rXru1SWU8++aR27Njh4YgBAAAAXM1nn4y8/vrrSkhIUIsWLbRv3z7NnTtXGzZs0DvvvKMzZ85o6NChLpd155136rXXXtOyZcu0detWD0YNAAAAwM4nn4ykp6dr0qRJkqQpU6YoPDw8e9+oUaM0a9YsrV27Vlu2bFGTJk3yLO9f//pX9t+TkpLcHi8AAACAnHzyycj69et14cIF1a5dW40aNcqxv1evXpKkxYsXF3VoAAAAAFzkk8mIfXxH48aNne63b2ccCAAAAFB8+WQ3rSNHjkiSqlat6nS/fbv9uKJUr149p9sTExNVqVIlr8RksdhkGIZkSFZrukx+tiKPwdPSren//ZshqzVdfv6GV+PxFHfX0zCyzrdarYWMzL1KwvtZEuooXV1PlZB6Xr/vJ/ef60tJqGd+6lhc26MrrNZMGYYhq9Wqo0ePKiio6J81pKenKygoqEDn+uSTkZSUFElSaGio0/1hYWEOxwEAAAAofnzyyYg9ezWZTNfc7w27d+92ur1evXqyWCyqVq1aEUckpaVlymRKkmGSzOZAmc0++bZfU6DZ/p6b/lvHQK/G4ynurqf9GyCz2VzIyNyrJLyfJaGO0tX1VAmp5/X7fnL/ub6UhHrmp47FtT26wrBlyGQyyWw2KyYmRsHB/kUeQ2BgwduPTz4ZiYiIkCRdvnzZ6f7U1FRJcphlCwAAAEDx4pPJiP3pQm4rrdu3e+MpBAAAAADX+GQy0qBBA0nKdYFC+/b4+PgiiwkAAABA/vhkMtKqVStFRkYqMTFR27Zty7F/3rx5kqSuXbsWdWgAAAAAXOSTyYjZbNaIESMkSSNGjHAYOzJhwgTt3LlTrVu3VtOmTbO3T548WXFxcRo9enSRxwsAAAAgJ5+dVumFF17QihUrlJCQoNjYWLVp00aHDx/Whg0bVK5cOc2YMcPh+LNnz2rv3r06ceJEjrI++ugjffTRR5Iki8UiSTp8+LCaN2+efcz777+f6yKLAAAAAPLPZ5OR4OBgrVq1Sm+88YbmzJmjRYsWqUyZMho0aJBeeeUVxcTEuFzWsWPHtGHDBodtaWlpDtsuXbrkttgBAAAA+Gg3LbuQkBCNGzdOBw4ckMVi0cmTJzVz5kynicjYsWNlGIZmzpyZ675r/bRt29bzFQIAAABKEJ9ORgAAAAD4LpIRAAAAAF5BMgIAAADAK0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAAADgFSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSEYAAAAAeAXJCAAAAACvIBkBAAAA4BUkIwAAAAC8gmQEAAAAgFeQjAAAAADwCpIRAAAAAF5BMgIAAADAK0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAAADgFSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXuHTyUhaWprGjBmjunXrKjg4WNHR0Ro6dKiOHTuW77IuXLigp556StWrV1dQUJCqV6+uJ598UhcuXHB/4AAAAAB8NxlJS0tT+/btNW7cOKWkpKhbt26KiYnRjBkz1LhxYyUmJrpc1rlz59SsWTNNnDhRAQEB6t69uyIiIvTvf/9bTZs21blz5zxYEwAAAKBk8tlk5PXXX1dCQoJatGihffv2ae7cudqwYYPeeecdnTlzRkOHDnW5rKefflr79+9Xjx49tHfvXs2dO1e7du3SyJEjdeDAAY0aNcqDNQEAAABKJp9MRtLT0zVp0iRJ0pQpUxQeHp69b9SoUYqPj9fatWu1ZcuWPMs6efKkZs+ercDAQL3//vsKCAjI3vf222+rQoUKmj17tk6dOuX+igAAAAAlmE8mI+vXr9eFCxdUu3ZtNWrUKMf+Xr16SZIWL16cZ1nff/+9bDabbrvtNlWqVMlhX1BQkO655x5lZmbq+++/d0/wAAAAACRJAXkfUvzs2LFDktS4cWOn++3b7ccVtqyPP/7YpbJ8gTUt09sheES65X/1sqZlymQyeTEaz3F3Pa3WrPIMW0ahynG3kvB+loQ6StTzesL95/pSEuqZnzoW1/boCl//bOeTyciRI0ckSVWrVnW6377dflxRlSVJ9erVc7p9z5498vf3V5cuXVwqx51sNn+dOtVTlarX1LMPLCvy6xcFm82QzTBkGDY9+8ByXYf3VEnur6fx3z+L28tVEt7PklBH6X/1tBlGiajn9fx+cv+5vpSEeuanjsW1PebHkSOHdO+9b8rPr+iTkxMnTigoKKhA5/pkMpKSkiJJCg0Ndbo/LCzM4biiKutaTCaTgoKCHMa3FKUzZybpzFGpdu3aXrl+UYi8fqvmwJ31tM86VxzbRUl4P0tCHSXprBJ19ljxbGfuVBLeT+4/15eSUE9X61ic26OrIupIUohXrh0UFJT9mTm/fDIZMYys/DW3x232/UVdliTt3r07X8cXFfsTm+IaH7yDdoGiQDuDM7QLFCe0R+/xyQHsERERkqTLly873Z+amipJLj2FcGdZAAAAAFznk8lItWrVJCnXldbt2+3HFVVZAAAAAFznk8lIgwYNJElbt251ut++PT4+vkjLAgAAAOA6n0xGWrVqpcjISCUmJmrbtm059s+bN0+S1LVr1zzL6ty5s/z8/LRu3TqdPn3aYZ/FYtHixYvl5+enu+66yz3BAwAAAJDko8mI2WzWiBEjJEkjRoxwGO8xYcIE7dy5U61bt1bTpk2zt0+ePFlxcXEaPXq0Q1lRUVHq37+/rFarhg8froyM/80v/eyzz+rMmTMaMGCAKleu7OFaAQAAACWLycjvdFHFRFpamtq2basNGzYoKipKbdq00eHDh7VhwwaVK1dOv/zyi+rUqZN9/NixY/Xyyy9r0KBBmjlzpkNZZ8+eVfPmzZWYmKjatWvrlltu0e7du7Vr1y7Vrl1bv/zyi8qXL1/ENQQAAACubz75ZESSgoODtWrVKr344osKDQ3VokWLlJSUpEGDBmnbtm0OiUheypcvr02bNmnkyJGyWq1auHChLl68qBEjRmjjxo0kIgAAAIAH+OyTEQAAAAC+zWefjAAAAADwbSQjAAAAALyCZAQAAACAV5CMAAAAAPAKkhEAAAAAXkEyAgAAAMArSnwykpqaqkWLFunBBx9UfHy8SpUqpbCwMDVo0EDjxo1TSkpKrud+8sknatasmcLDw1W2bFndfffdSkhIcHrsnj179NZbb6l9+/aqVq2agoKCVLlyZfXo0UPr1q3L9Rrffvut/vGPf6hDhw6KjIyUyWRS586dC13vhIQE3X333SpbtqzCw8PVrFkzzZo1K8/ztmzZooEDB6pKlSrZdbjjjjs0Y8aMIonDU6/HX9EuvNMuLl++rE8//VQjR45Us2bNFBQUJJPJpDfffPOa55lMpmv+pKWl5SuOokI7y7udjR07Ns/312Qyae3atR6NQ+L+Y3e9touSdv9xFe3R9d+Hn3/+uW6//XaVLl1agYGBio6OVp8+fbRly5Yii6Oo7lNuZZRwH374oSHJkGTUq1fP6N27t9GpUycjIiLCkGTExcUZp06dynHe008/bUgyQkJCjG7duhmdOnUyAgICDH9/f2PBggU5jq9SpYohyShVqpTRsWNHo0+fPsbNN99sSDJMJpPx7rvvOo0vMjIyOz77T6dOnQpV5wULFhj+/v6GyWQybr/9dqNnz55G6dKlDUnG008/net5kyZNMvz9/Q1/f3+jZcuWRr9+/Yw77rjDKFOmjNG+ffsiicMTr4cztAvvtItt27blqJck44033rjmeZKMsLAwY9CgQU5/rFZrvuIoKrSzvNvZwoULc31fO3ToYEgyQkNDjT///NOjcRgG9x+767VdlLT7j6toj67dH0aOHGlIMgIDA4327ds7xB8QEGAsXry4SOIoqvuUO5X4ZGTWrFnGY489Zuzbt89h+/Hjx41GjRoZkoz+/fs77Fu5cqUhyShXrpzDeQkJCYbZbDYiIyON8+fPO5zTsWNHY86cOYbFYnHYPnXqVEOS4e/vb+zevTtHfEOHDjXefvttY/Xq1cbixYsL3ajOnz+f3VDnz5+fvf3kyZNGnTp1DEnGjz/+mOO87777zjCZTEatWrWMXbt2OeyzWq3Gtm3biiQOd78euaFdZCnqdnHgwAHjwQcfNKZNm2Zs3brV+Oc//+nyh4Hq1avn61rFAe0sS17tLDfPPvusIckYOHBgkcTB/SfL9douStr9x1W0xyzXao/bt283JBllypQx9uzZ47BvwoQJBWojxf0+5U4lPhm5loSEBEOSERQU5PCf4+677zYkOc3Sn3jiCUOSMX78eJevc+eddxqSjLFjx17zuFWrVhW6Uf3rX/8yJBndunXLsW/BggWGJKNr164O2zMyMowaNWoYfn5+xvbt2wt87cLG8VfueD0KgnaRxRPt4q/GjBlTYj8M0M6uzWazGTExMYYkY+nSpUUeB/efLNdTu/irknz/cRXtMcukSZMMScajjz7qtEz70wxnT5DcGcdfees+lV8lfszItTRo0ECSZLFYdO7cOUlSWlqaVq5cKUnq1atXjnPs2xYvXpzv6xw/frxQ8bri22+/leQ89i5duig4OFgrVqxw6OO6bNkyJSUlqUOHDtmxeiOO4oJ2kcUT7QL/Qzu7ttWrV+vo0aOqXLmyOnTo4LU4ihrt4toK0y6Qf7THLEFBQXmW6+/vr8jISI/G4atIRq7h4MGDkqTAwECVLVtWUtYAK4vFogoVKqhq1ao5zmncuLEkaefOnfm+TuXKlQsbcp7scdnjvJrZbNbNN9+stLQ07d27N3u7/abSsWNHXbx4UR988IGGDx+up556SrNnz5bFYimSOIoL2kUWT7SLwrh8+bJee+01PfLII3r66af1ySefXHNgZXFHO7u2zz77TJLUv39/+fv7ey2Ooka7uLbCtIvCuN7uP66iPWa54447FBAQoLlz5+Zop++++64uXLigvn37upS0FCYOX0Uycg0TJ06UJHXu3Dm7AR05ckSSnP4Hk6SwsDCVLl1aycnJ+vPPP/O8RmJiYnb2+7e//c0dYefq0qVLunDhgqTc47dvt9dTknbv3i0pa0aNm266ScOHD9cHH3ygiRMn6r777tONN96o3377zeNxFBe0iyzubheFdfbsWb3wwguaPn263nvvPQ0aNEg1atTQd999V2QxuBPtLHdpaWmaP3++JOn+++/3WhzeQLvIXWHaRWFdb/cfV9Ees9SpU0fjx4/XhQsXVL9+fXXo0EF9+/ZV/fr19eyzz2rgwIGaNm2ax+PwVSQjuViyZIn+85//KDAwUK+88kr2dvs3HaGhobmeGxYW5nBsbjIyMjR48GBZLBb17dtXTZo0cUPkubs6ntzidxZ7cnKyJOmVV15RSEiIVqxYoUuXLmnHjh3q0KGDDh06pK5du+rKlSsejaM4oF14rl0UxgMPPKClS5fqjz/+UEpKirZt26b7779f586dU48ePbRx40aPx+BOtLNrx/7NN9/o4sWLqlevnho1auS1OIoa7cJz7aIwrrf7j6toj46xP/nkk5ozZ44CAwO1cuVKffnll9q1a5eqVaumdu3aKTw8vEji8EUkI078/vvvuu+++2QYht5++22H/vCGYUjKmlc8N/Zj8jJy5EitX79etWrV0vvvv1+4oF3gSlzOjsnMzMzet3jxYrVv314RERGKj4/Xt99+q6pVq+rQoUOaPXu2R+PwNtqFI3e3i8KYNWuWOnXqpOjoaIWFhalhw4b65JNPNHr0aFmtVr3wwgsej8FdaGd5s3fFKci339x/rq2ktovCuJ7uP66iPebc9vTTT6t///564IEHtG/fPqWkpGjDhg2Kjo7Wgw8+qP/7v//zeBy+KsDbARQ3x44dU+fOnZWcnKxRo0bpySefdNgfEREhKat/aG5SU1Ml6ZpZ8Lhx4zR16lRVqlRJP/zwQ3Zfy8IaPHhwjm3du3dX9+7ds2O3x1iqVKkcxzqL3X5e8+bNdeONNzocHxQUpAEDBuhf//qXVq9erYceeshjcXgT7cLz7cITnnvuuewYrFarzGazR67jLrSzvGM/d+6cli5dKj8/Pw0YMMBrcRQl2oXn24Un+Nr9x1W0x5yxz5o1S++99566d++uDz74IHt7s2bNtGTJEt14441699139dBDDykuLs5jcfgsT0/X5UvOnDljxMXFGZKMIUOGGDabLccx9kWRKlSo4LSMlJQUQ5JRunTpXK8zefJkQ5IRGRmZr3UYXJmiTcq5YNOYMWOy99vnrHY2V7dhGMYtt9xiSHKYqnXIkCGGJGPAgAFOz3n//fcNSUbHjh09GsdfFdWUdbSLomsXf+Xq1JrXEhUVZUgy/vjjjwKXURRoZ679v58yZYohybjjjju8Ggf3nyzXU7v4q5J0/3EV7dF5e2zXrp0hyfjss8+cnvPggw8akoxp06Z5NI6/8pWpfXky8l9//vmn7rrrLu3Zs0c9evTQhx9+6PQR4w033KCgoCCdOXNGx44dyzGwaOvWrZKk+Ph4p9eZPXu2Ro4cqdDQUH333Xdq2LChW+th5PHYrkGDBlq7dq22bt2qm266yWFfenq6du3apaCgIN1www3Z2xs1aqQZM2bo/PnzTsu0T+d3dXbuiTi8gXZRtO3C3Ww2my5dupQjjuKGdub6/3tXuuJw/6FdFCQOd/OV+4+raI+5t8djx45JktMnGFdvv/r35fVyn3IHxowoa37sbt26afPmzerUqZM+//zzXKcEDAkJUbt27SRJ8+bNy7Hfvq1r16459i1ZskSDBw9WYGCgFi5cqFatWrmxFq7p0qWLJOexf/vtt0pLS1P79u0VHBycvf2ee+6RyWTSpk2bnD52Xb16tSTn08+5M46iRrvIUpTtwt2WLl2qy5cvq06dOrn+kvA22lkWV/7fHzx4UD///LNCQkLUs2dPr8VRFGgXWYqyXbibL9x/XEV7zJJbe7RPObx582anZW7atEmSVKNGDY/G4bO89UimuMjIyDDuvfdeQ5LRpk0b4/Lly3mes3z5ckOSUa5cOWPfvn3Z2xMSEoygoCCjVKlSxrlz5xzOWb9+vRESEmIEBAQYCxcuLFCs7njcdu7cOaNUqVKGJGP+/PnZ20+dOmXUqVPHkGSsWLEix3n9+vUzJBkPP/ywkZ6enr39o48+MiQZwcHBxpEjRzwex9U8+fiRdpGlqNvFX7nSTeKrr74y9u7dm2P76tWrjejoaEOSMWHChALH4Em0syyu/r9/+eWXDUlGv379ChyDO+IwDO4/dtdTu/ir6/3+4yraY5ZrtccJEyYYkozw8HBjw4YNDvvsq7NHRETkqLO74/grX+mmZTKM62g4fgFMnDhRTz31lCTp3nvvzfXbi/Hjx6t8+fLZ/37qqac0ceJEhYaGqmPHjrJarVq+fLlsNpu+/PLLHN/OlClTRhcuXFDNmjV12223Ob1G69atswf62r3yyivZ85RfunRJv//+uyIjI7MHQEnSwoULFRUV5XKd58+frz59+sgwDN1+++0qX768VqxYoQsXLuiJJ57Injf8aufOnVPLli21b98+1axZU40aNVJSUpK2bt0qf39/zZgxI98zmBQkDk+8Hs7QLrzXLu69916dOHFCUtaj7z/++EMxMTGKjo6WJEVFRWnhwoXZxw8ePFizZs1SbGysatasqcjISO3fv1/bt2+XJPXr10+zZ8+Wn1/xexBMO3OtndndcMMN2rdvn7777jvdfffdLl/TXXFw/8lyPbeLknT/cRXtMe/2mJaWpo4dO2r9+vXy8/NTixYtFB0drd27d+u33367Lj8nuZW3sqDiwv7NR14/hw4dynHujBkzjCZNmhihoaFGZGSk0alTJ2PdunVOr+PKNQYNGpTjvEGDBhUotrysX7/e6Ny5s1G6dGkjNDTUaNKkifHxxx9f85yLFy8af//7341atWoZZrPZKFeunPG3v/3N+Omnn/J9/YLG4anX469oF95rF9WrV79mvapXr+5w/JIlS4yBAwcacXFxRunSpY2AgACjYsWKxl133WV89dVXBYqhqNDOXG9nGzZsMKSsQbFXP4UrDO4/tIu/Kkn3H1fRHl1rjxaLxXjnnXeMZs2aGREREUZAQIARFRVl9OrVy/j555/zff2CxlFU9yl3KvFPRgAAAAB4h+8+NwQAAADg00hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4BckIAAAAAK8gGQEAFGs1atSQyWTydhgAAA8gGQEAeFVSUpJMJpPatm3r7VAAAEUswNsBAABwLStXrlR6erq3wwAAeADJCACgWKtdu7a3QwAAeAjdtAAAXjN27FjVrFlTkrRmzRqZTKbsn8GDB0tyPmbk6q5dly9f1qhRoxQTE6OQkBA1btxYixcvzj72q6++UrNmzRQWFqZKlSrpiSee0JUrV5zGk5KSonHjxql+/foKDQ1VqVKldPvtt2vRokUeqT8AlHQ8GQEAeE3Dhg3Vs2dPzZ8/X5UqVVLnzp2z97Vu3TrP861Wq9q3b6/ExEQ1b95cKSkpWrt2re69914tXbpUv/76q5599lk1bdpUd955p9atW6dJkybp3Llzmj17tkNZp06dUrt27fTbb7+pSpUq6tixo1JTU/Xzzz/r3nvv1RtvvKHnn3/e7a8BAJRkJsMwDG8HAQAouZKSklSzZk3dfvvtWr16dY79NWrU0OHDh3X1ryv7OZLUtm1bLViwQGXKlJEkzZw5U0OGDFGdOnV0/vx5LVq0SG3atJEkHT9+XI0aNdLp06eVmJioWrVqZZd599136/vvv9ezzz6rV199VYGBgZKkgwcP6s4771RSUpK2bt2q+Ph4T70UAFDi0E0LAOCz/P399eGHH2YnIpL0wAMPqEKFCjpw4IBGjBiRnYhIUnR0tAYOHChJWrt2bfb27du36/vvv1fLli315ptvZiciklSrVi298847yszM1EcffVQEtQKAkoNkBADgs2rUqKE6deo4bPPz81P16tUlSR07dsxxjn1A/IkTJ7K3LV++XJLUrVs3p2ua2LuMbdq0yT2BAwAkkYwAAHxYlSpVnG4PCwvLdb99n8Viyd6WlJQkSXruueccBtHbf8qXLy9JOnv2rDvDB4ASjwHsAACfldfK7K6u3J6ZmSlJatOmjcM4kr+yJyUAAPcgGQEAlHhVq1aVJPXq1UtPPPGEl6MBgJKDbloAAK8ym82SpIyMDK/F0KFDB0liPREAKGIkIwAArypfvrwCAwOVmJiY3V2qqDVv3lzt27fXqlWr9PTTTyslJcVhv81m07Jly7R+/XqvxAcA1yuSEQCAV5nNZnXu3FknT55UgwYN9MADD+ihhx7SjBkzijSO2bNnKz4+Xu+9956qV6+u9u3bq1+/fmrTpo0qV66sTp06afPmzUUaEwBc7xgzAgDwuo8++kj/93//p+XLl2vOnDnKzMxURkaGhgwZUmQxVKpUSb/88oumTp2quXPnatOmTbJarYqKilKjRo3UrVs39enTp8jiAYCSgBXYAQAAAHgF3bQAAAAAeAXJCAAAAACvIBkBAAAA4BUkIwAAAAC8gmQEAAAAgFeQjAAAAADwCpIRAAAAAF5BMgIAAADAK0hGAAAAAHgFyQgAAAAAryAZAQAAAOAVJCMAAAAAvIJkBAAAAIBXkIwAAAAA8AqSEQAAAABeQTICAAAAwCtIRgAAAAB4xf8D5V4NZkC/1pgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_timeseries(dss, latlon, composites = ds_composites)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two composites that were previously missing are now there! Note that although here we are looking at one pixel, these interpolations are done for the entire domain.\n",
    "\n",
    "Possible values for temporal_interp are `False`, `\"linear\"`, `\"nearest\"`, `\"zero\"`, `\"slinear\"`, `\"quadratic\"` or `\"cubic\"`. But be aware that some of these can be computationally heavy."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('pywapor')",
   "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": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "30267a9963ef4e891f35edf7c2db672f8b38daceac54cc246d7b09d3d93c63b2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}