BrandonBourassa_HW04.ipynb

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    "#### Part 1) Create a dataset that contains the monthly means of Sea Surface Temperature anomalies and total column water vapor from Jan 1979-Dec 2023 over the Pacific Basin (65°N to 65°S, 120°E to 60°W) masked out over land - save this to your computer. The land sea mask is available here: https://atms523sp2024.slack.com/archives/C06DPQWF1SR/p1709073677676719?thread_ts=1709065364.940859&cid=C06DPQWF1SR\n"
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       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-55823c30-da91-11ee-9b98-4c796efb5af3</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:5555/status\" target=\"_blank\">http://127.0.0.1:5555/status</a>\n",
       "                </td>\n",
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       "\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
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       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">de34f506</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:5555/status\" target=\"_blank\">http://127.0.0.1:5555/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 4\n",
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       "                    <strong>Total threads:</strong> 12\n",
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       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 7.45 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
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       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:50549\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:5555/status\" target=\"_blank\">http://127.0.0.1:5555/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 12\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 7.45 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
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       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:50577\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:50578/status\" target=\"_blank\">http://127.0.0.1:50578/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.86 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:50552\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\brand\\AppData\\Local\\Temp\\dask-scratch-space\\worker-bl1k13iy\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
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       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:50568\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:50570/status\" target=\"_blank\">http://127.0.0.1:50570/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.86 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:50554\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\brand\\AppData\\Local\\Temp\\dask-scratch-space\\worker-n2ws4_sw\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
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       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:50569\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:50572/status\" target=\"_blank\">http://127.0.0.1:50572/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.86 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:50556\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\brand\\AppData\\Local\\Temp\\dask-scratch-space\\worker-3b0oij8q\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
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       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:50574\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:50575/status\" target=\"_blank\">http://127.0.0.1:50575/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.86 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:50558\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\brand\\AppData\\Local\\Temp\\dask-scratch-space\\worker-rfveejnn\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
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       "        \n",
       "\n",
       "    </details>\n",
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       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
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      "text/plain": [
       "<Client: 'tcp://127.0.0.1:50549' processes=4 threads=12, memory=7.45 GiB>"
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     "metadata": {},
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   ],
   "source": [
    "# Import Dask so we can run it faster\n",
    "\n",
    "from dask.distributed import Client, progress\n",
    "client = Client(dashboard_address=':5555')  # set up local cluster on your laptop\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import some needed modules\n",
    "import pandas as pd\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       ".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: 516,\n",
       "                                                                lat: 130,\n",
       "                                                                lon: 181)\n",
       "Coordinates:\n",
       "  * lat                                                        (lat) float32 ...\n",
       "  * lon                                                        (lon) float32 ...\n",
       "  * time                                                       (time) datetime64[ns] ...\n",
       "    reftime                                                    (time) datetime64[ns] dask.array&lt;chunksize=(516,), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    Total_column_water_vapour_surface_Mixed_intervals_Average  (time, lat, lon) float32 dask.array&lt;chunksize=(120, 130, 181), meta=np.ndarray&gt;\n",
       "    Sea_surface_temperature_surface_Mixed_intervals_Average    (time, lat, lon) float32 dask.array&lt;chunksize=(120, 130, 181), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     European Centre for Medium Range We...\n",
       "    Originating_or_generating_Subcenter:  0\n",
       "    GRIB_table_version:                   0,128\n",
       "    file_format:                          GRIB-1\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    _CoordSysBuilder:                     ucar.nc2.dataset.conv.CF1Convention</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-02f92961-d271-4862-8edd-6382b5ff4776' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02f92961-d271-4862-8edd-6382b5ff4776' 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</span>: 516</li><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 181</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-768aa785-8445-4476-95c5-7e4ca567c931' class='xr-section-summary-in' type='checkbox'  checked><label for='section-768aa785-8445-4476-95c5-7e4ca567c931' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.75 63.75 62.75 ... -63.25 -64.25</div><input id='attrs-cef305f6-cd74-4229-954b-5e6ba6d0683e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cef305f6-cd74-4229-954b-5e6ba6d0683e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da235b32-e8c8-44f9-ade7-fea691001648' class='xr-var-data-in' type='checkbox'><label for='data-da235b32-e8c8-44f9-ade7-fea691001648' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 64.75001 ,  63.750008,  62.750008,  61.750008,  60.750008,  59.750008,\n",
       "        58.750008,  57.750008,  56.750008,  55.750008,  54.750008,  53.750008,\n",
       "        52.750008,  51.750008,  50.750008,  49.750008,  48.750008,  47.750008,\n",
       "        46.750008,  45.750008,  44.750008,  43.750008,  42.750008,  41.750008,\n",
       "        40.750008,  39.750008,  38.750008,  37.750008,  36.750008,  35.750008,\n",
       "        34.750008,  33.750008,  32.750008,  31.750008,  30.750008,  29.750008,\n",
       "        28.750008,  27.750008,  26.750008,  25.750008,  24.750008,  23.750008,\n",
       "        22.750008,  21.750008,  20.750008,  19.750008,  18.750008,  17.750008,\n",
       "        16.750008,  15.750008,  14.750008,  13.750008,  12.750008,  11.750008,\n",
       "        10.750008,   9.750008,   8.750008,   7.750008,   6.750008,   5.750008,\n",
       "         4.750008,   3.750008,   2.750008,   1.750008,   0.750008,  -0.249992,\n",
       "        -1.249992,  -2.249992,  -3.249992,  -4.249992,  -5.249992,  -6.249992,\n",
       "        -7.249992,  -8.249992,  -9.249992, -10.249992, -11.249992, -12.249992,\n",
       "       -13.249992, -14.249992, -15.249992, -16.249992, -17.249992, -18.249992,\n",
       "       -19.249992, -20.249992, -21.249992, -22.249992, -23.249992, -24.249992,\n",
       "       -25.249992, -26.249992, -27.249992, -28.249992, -29.249992, -30.249992,\n",
       "       -31.249992, -32.249992, -33.249992, -34.249992, -35.249992, -36.249992,\n",
       "       -37.249992, -38.249992, -39.249992, -40.249992, -41.249992, -42.249992,\n",
       "       -43.249992, -44.249992, -45.249992, -46.249992, -47.249992, -48.249992,\n",
       "       -49.249992, -50.249992, -51.249992, -52.249992, -53.249992, -54.249992,\n",
       "       -55.249992, -56.249992, -57.249992, -58.249992, -59.249992, -60.249992,\n",
       "       -61.249992, -62.249992, -63.249992, -64.24999 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>120.0 121.0 122.0 ... 299.0 300.0</div><input id='attrs-50e5da2b-94af-48c0-95a3-60dcbe7d9b7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50e5da2b-94af-48c0-95a3-60dcbe7d9b7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d243501-30e3-432f-8a0a-d6cc43a26cfb' class='xr-var-data-in' type='checkbox'><label for='data-8d243501-30e3-432f-8a0a-d6cc43a26cfb' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143.,\n",
       "       144., 145., 146., 147., 148., 149., 150., 151., 152., 153., 154., 155.,\n",
       "       156., 157., 158., 159., 160., 161., 162., 163., 164., 165., 166., 167.,\n",
       "       168., 169., 170., 171., 172., 173., 174., 175., 176., 177., 178., 179.,\n",
       "       180., 181., 182., 183., 184., 185., 186., 187., 188., 189., 190., 191.,\n",
       "       192., 193., 194., 195., 196., 197., 198., 199., 200., 201., 202., 203.,\n",
       "       204., 205., 206., 207., 208., 209., 210., 211., 212., 213., 214., 215.,\n",
       "       216., 217., 218., 219., 220., 221., 222., 223., 224., 225., 226., 227.,\n",
       "       228., 229., 230., 231., 232., 233., 234., 235., 236., 237., 238., 239.,\n",
       "       240., 241., 242., 243., 244., 245., 246., 247., 248., 249., 250., 251.,\n",
       "       252., 253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
       "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274., 275.,\n",
       "       276., 277., 278., 279., 280., 281., 282., 283., 284., 285., 286., 287.,\n",
       "       288., 289., 290., 291., 292., 293., 294., 295., 296., 297., 298., 299.,\n",
       "       300.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-2182f0c8-5cba-4467-8f31-f9370f559986' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2182f0c8-5cba-4467-8f31-f9370f559986' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcf4235e-9e02-498e-8e9c-37a47d60043b' class='xr-var-data-in' type='checkbox'><label for='data-fcf4235e-9e02-498e-8e9c-37a47d60043b' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(516,), meta=np.ndarray&gt;</div><input id='attrs-76ad2f14-c4c3-4683-989a-14c866ccf2bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76ad2f14-c4c3-4683-989a-14c866ccf2bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ba9bad8-9ee6-4e1d-b4fa-c1d4b157b891' class='xr-var-data-in' type='checkbox'><label for='data-1ba9bad8-9ee6-4e1d-b4fa-c1d4b157b891' 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>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></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> 4.03 kiB </td>\n",
       "                        <td> 4.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (516,) </td>\n",
       "                        <td> (516,) </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\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Sea_surface_temperature_surface_Mixed_intervals_Average</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 130, 181), meta=np.ndarray&gt;</div><input id='attrs-d83e49b8-aae5-43a0-8158-6bbb4f3f598e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d83e49b8-aae5-43a0-8158-6bbb4f3f598e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e740ad8c-eda7-4846-8153-ddd6f748975a' class='xr-var-data-in' type='checkbox'><label for='data-e740ad8c-eda7-4846-8153-ddd6f748975a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface temperature (Mixed_intervals Average) @ Ground or water surface</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>v-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LatLon_Projection</dd><dt><span>Grib_Statistical_Interval_Type :</span></dt><dd>Average</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_98-0-128-34_L1_Imixed_S123</dd><dt><span>Grib1_Center :</span></dt><dd>98</dd><dt><span>Grib1_Subcenter :</span></dt><dd>0</dd><dt><span>Grib1_TableVersion :</span></dt><dd>128</dd><dt><span>Grib1_Parameter :</span></dt><dd>34</dd><dt><span>Grib1_Parameter_Name :</span></dt><dd>sst</dd><dt><span>Grib1_Level_Type :</span></dt><dd>1</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib1_Interval_Type :</span></dt><dd>123</dd><dt><span>Grib1_Interval_Name :</span></dt><dd>Average of N uninitialized analyses, intervals = (refTime, refTime + i * P2)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        61.75000762939453,  60.75000762939453,  59.75000762939453,\n",
       "        58.75000762939453,  57.75000762939453,  56.75000762939453,\n",
       "        55.75000762939453,\n",
       "       ...\n",
       "       -55.24999237060547, -56.24999237060547, -57.24999237060547,\n",
       "       -58.24999237060547, -59.24999237060547, -60.24999237060547,\n",
       "       -61.24999237060547, -62.24999237060547, -63.24999237060547,\n",
       "       -64.24999237060547],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4af0a1cb-997b-4042-9514-4fbfa23e9517' class='xr-index-data-in' type='checkbox'/><label for='index-4af0a1cb-997b-4042-9514-4fbfa23e9517' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0,\n",
       "       ...\n",
       "       291.0, 292.0, 293.0, 294.0, 295.0, 296.0, 297.0, 298.0, 299.0, 300.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=181))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3dbaf14-057f-4dfd-b2f0-0be1c35b5612' class='xr-index-data-in' type='checkbox'/><label for='index-b3dbaf14-057f-4dfd-b2f0-0be1c35b5612' 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;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=516, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a74e3e55-7f6c-496f-9d88-e27aebaf47c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a74e3e55-7f6c-496f-9d88-e27aebaf47c0' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Originating_or_generating_Center :</span></dt><dd>European Centre for Medium Range Weather Forecasts (ECMWF) (RSMC)</dd><dt><span>Originating_or_generating_Subcenter :</span></dt><dd>0</dd><dt><span>GRIB_table_version :</span></dt><dd>0,128</dd><dt><span>file_format :</span></dt><dd>GRIB-1</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Read using CDM IOSP GribCollection v3</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                    (time: 516,\n",
       "                                                                lat: 130,\n",
       "                                                                lon: 181)\n",
       "Coordinates:\n",
       "  * lat                                                        (lat) float32 ...\n",
       "  * lon                                                        (lon) float32 ...\n",
       "  * time                                                       (time) datetime64[ns] ...\n",
       "    reftime                                                    (time) datetime64[ns] dask.array<chunksize=(516,), meta=np.ndarray>\n",
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       "    Sea_surface_temperature_surface_Mixed_intervals_Average    (time, lat, lon) float32 dask.array<chunksize=(120, 130, 181), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     European Centre for Medium Range We...\n",
       "    Originating_or_generating_Subcenter:  0\n",
       "    GRIB_table_version:                   0,128\n",
       "    file_format:                          GRIB-1\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    _CoordSysBuilder:                     ucar.nc2.dataset.conv.CF1Convention"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in dataset\n",
    "\n",
    "ds = xr.open_dataset('https://thredds.rda.ucar.edu/thredds/dodsC/aggregations/g/ds633.1/2/TP',\n",
    "                     chunks={'time':'500MB'})\n",
    "\n",
    "variables = ['Total_column_water_vapour_surface_Mixed_intervals_Average',\n",
    "        'Sea_surface_temperature_surface_Mixed_intervals_Average']\n",
    "#select the two needed variables every 4th point to get 1 degree resolution\n",
    "ds = ds[variables].sel(lat=slice(65, -65, 4), lon=slice(120, 300, 4))\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to netcdf file\n",
    "ds.to_netcdf('raw_data.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                    (time: 516,\n",
       "                                                                lat: 130,\n",
       "                                                                lon: 181)\n",
       "Coordinates:\n",
       "  * lat                                                        (lat) float32 ...\n",
       "  * lon                                                        (lon) float32 ...\n",
       "  * time                                                       (time) datetime64[ns] ...\n",
       "    reftime                                                    (time) datetime64[ns] ...\n",
       "Data variables:\n",
       "    Total_column_water_vapour_surface_Mixed_intervals_Average  (time, lat, lon) float32 ...\n",
       "    Sea_surface_temperature_surface_Mixed_intervals_Average    (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     European Centre for Medium Range We...\n",
       "    Originating_or_generating_Subcenter:  0\n",
       "    GRIB_table_version:                   0,128\n",
       "    file_format:                          GRIB-1\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    _CoordSysBuilder:                     ucar.nc2.dataset.conv.CF1Convention</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-b4970ba6-3c2f-4e05-b6d4-7faf4bc2c8be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b4970ba6-3c2f-4e05-b6d4-7faf4bc2c8be' 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</span>: 516</li><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 181</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b4c262aa-c78d-4545-b14a-a01d507d3f5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4c262aa-c78d-4545-b14a-a01d507d3f5f' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.75 63.75 62.75 ... -63.25 -64.25</div><input id='attrs-1ba194a5-49a3-4c56-a68a-30a6c9582782' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ba194a5-49a3-4c56-a68a-30a6c9582782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ddd4b8d-a5cb-4a77-aff4-91635b426bd1' class='xr-var-data-in' type='checkbox'><label for='data-0ddd4b8d-a5cb-4a77-aff4-91635b426bd1' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 64.75001 ,  63.750008,  62.750008,  61.750008,  60.750008,  59.750008,\n",
       "        58.750008,  57.750008,  56.750008,  55.750008,  54.750008,  53.750008,\n",
       "        52.750008,  51.750008,  50.750008,  49.750008,  48.750008,  47.750008,\n",
       "        46.750008,  45.750008,  44.750008,  43.750008,  42.750008,  41.750008,\n",
       "        40.750008,  39.750008,  38.750008,  37.750008,  36.750008,  35.750008,\n",
       "        34.750008,  33.750008,  32.750008,  31.750008,  30.750008,  29.750008,\n",
       "        28.750008,  27.750008,  26.750008,  25.750008,  24.750008,  23.750008,\n",
       "        22.750008,  21.750008,  20.750008,  19.750008,  18.750008,  17.750008,\n",
       "        16.750008,  15.750008,  14.750008,  13.750008,  12.750008,  11.750008,\n",
       "        10.750008,   9.750008,   8.750008,   7.750008,   6.750008,   5.750008,\n",
       "         4.750008,   3.750008,   2.750008,   1.750008,   0.750008,  -0.249992,\n",
       "        -1.249992,  -2.249992,  -3.249992,  -4.249992,  -5.249992,  -6.249992,\n",
       "        -7.249992,  -8.249992,  -9.249992, -10.249992, -11.249992, -12.249992,\n",
       "       -13.249992, -14.249992, -15.249992, -16.249992, -17.249992, -18.249992,\n",
       "       -19.249992, -20.249992, -21.249992, -22.249992, -23.249992, -24.249992,\n",
       "       -25.249992, -26.249992, -27.249992, -28.249992, -29.249992, -30.249992,\n",
       "       -31.249992, -32.249992, -33.249992, -34.249992, -35.249992, -36.249992,\n",
       "       -37.249992, -38.249992, -39.249992, -40.249992, -41.249992, -42.249992,\n",
       "       -43.249992, -44.249992, -45.249992, -46.249992, -47.249992, -48.249992,\n",
       "       -49.249992, -50.249992, -51.249992, -52.249992, -53.249992, -54.249992,\n",
       "       -55.249992, -56.249992, -57.249992, -58.249992, -59.249992, -60.249992,\n",
       "       -61.249992, -62.249992, -63.249992, -64.24999 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>120.0 121.0 122.0 ... 299.0 300.0</div><input id='attrs-79210016-d788-4c3e-af9e-627d2ab99a75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79210016-d788-4c3e-af9e-627d2ab99a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67b69199-80cb-4466-b2ac-4e81deadab8b' class='xr-var-data-in' type='checkbox'><label for='data-67b69199-80cb-4466-b2ac-4e81deadab8b' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143.,\n",
       "       144., 145., 146., 147., 148., 149., 150., 151., 152., 153., 154., 155.,\n",
       "       156., 157., 158., 159., 160., 161., 162., 163., 164., 165., 166., 167.,\n",
       "       168., 169., 170., 171., 172., 173., 174., 175., 176., 177., 178., 179.,\n",
       "       180., 181., 182., 183., 184., 185., 186., 187., 188., 189., 190., 191.,\n",
       "       192., 193., 194., 195., 196., 197., 198., 199., 200., 201., 202., 203.,\n",
       "       204., 205., 206., 207., 208., 209., 210., 211., 212., 213., 214., 215.,\n",
       "       216., 217., 218., 219., 220., 221., 222., 223., 224., 225., 226., 227.,\n",
       "       228., 229., 230., 231., 232., 233., 234., 235., 236., 237., 238., 239.,\n",
       "       240., 241., 242., 243., 244., 245., 246., 247., 248., 249., 250., 251.,\n",
       "       252., 253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
       "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274., 275.,\n",
       "       276., 277., 278., 279., 280., 281., 282., 283., 284., 285., 286., 287.,\n",
       "       288., 289., 290., 291., 292., 293., 294., 295., 296., 297., 298., 299.,\n",
       "       300.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-739b86ef-807c-42f9-8269-26f7c07284d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-739b86ef-807c-42f9-8269-26f7c07284d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cd8f16e-8146-45ac-a23f-695dd3284711' class='xr-var-data-in' type='checkbox'><label for='data-8cd8f16e-8146-45ac-a23f-695dd3284711' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2a8fe82-e8d0-4935-8c96-f57f18844b90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2a8fe82-e8d0-4935-8c96-f57f18844b90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c2fb854-7e63-4e36-b28d-20d6b92ca812' class='xr-var-data-in' type='checkbox'><label for='data-6c2fb854-7e63-4e36-b28d-20d6b92ca812' 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>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>[516 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e67b91d-0b9a-4353-9f48-9ca9a0276fe9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e67b91d-0b9a-4353-9f48-9ca9a0276fe9' class='xr-section-summary' >Data variables: <span>(2)</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>Total_column_water_vapour_surface_Mixed_intervals_Average</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-398d0f22-fd72-4a33-aef7-ceb98c411a94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-398d0f22-fd72-4a33-aef7-ceb98c411a94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05905127-c532-4894-8a10-02cdfb86fc13' class='xr-var-data-in' type='checkbox'><label for='data-05905127-c532-4894-8a10-02cdfb86fc13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column water vapour (Mixed_intervals Average) @ Ground or water surface</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>grid_mapping :</span></dt><dd>LatLon_Projection</dd><dt><span>Grib_Statistical_Interval_Type :</span></dt><dd>Average</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_98-0-128-137_L1_Imixed_S123</dd><dt><span>Grib1_Center :</span></dt><dd>98</dd><dt><span>Grib1_Subcenter :</span></dt><dd>0</dd><dt><span>Grib1_TableVersion :</span></dt><dd>128</dd><dt><span>Grib1_Parameter :</span></dt><dd>137</dd><dt><span>Grib1_Parameter_Name :</span></dt><dd>tcwv</dd><dt><span>Grib1_Level_Type :</span></dt><dd>1</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib1_Interval_Type :</span></dt><dd>123</dd><dt><span>Grib1_Interval_Name :</span></dt><dd>Average of N uninitialized analyses, intervals = (refTime, refTime + i * P2)</dd></dl></div><div class='xr-var-data'><pre>[12141480 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Sea_surface_temperature_surface_Mixed_intervals_Average</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-093a1d81-80f0-46f2-9469-869d1875eacb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-093a1d81-80f0-46f2-9469-869d1875eacb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05922c24-5c7a-4ba9-9d8e-74018349bfb1' class='xr-var-data-in' type='checkbox'><label for='data-05922c24-5c7a-4ba9-9d8e-74018349bfb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface temperature (Mixed_intervals Average) @ Ground or water surface</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>v-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LatLon_Projection</dd><dt><span>Grib_Statistical_Interval_Type :</span></dt><dd>Average</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_98-0-128-34_L1_Imixed_S123</dd><dt><span>Grib1_Center :</span></dt><dd>98</dd><dt><span>Grib1_Subcenter :</span></dt><dd>0</dd><dt><span>Grib1_TableVersion :</span></dt><dd>128</dd><dt><span>Grib1_Parameter :</span></dt><dd>34</dd><dt><span>Grib1_Parameter_Name :</span></dt><dd>sst</dd><dt><span>Grib1_Level_Type :</span></dt><dd>1</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib1_Interval_Type :</span></dt><dd>123</dd><dt><span>Grib1_Interval_Name :</span></dt><dd>Average of N uninitialized analyses, intervals = (refTime, refTime + i * P2)</dd></dl></div><div class='xr-var-data'><pre>[12141480 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56d48cd0-e22b-4db7-afe8-5cc2b9f302b2' class='xr-section-summary-in' type='checkbox'  ><label for='section-56d48cd0-e22b-4db7-afe8-5cc2b9f302b2' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1bda649a-a5d5-4168-8ac7-2a83d0c3c800' class='xr-index-data-in' type='checkbox'/><label for='index-1bda649a-a5d5-4168-8ac7-2a83d0c3c800' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 64.75000762939453,  63.75000762939453,  62.75000762939453,\n",
       "        61.75000762939453,  60.75000762939453,  59.75000762939453,\n",
       "        58.75000762939453,  57.75000762939453,  56.75000762939453,\n",
       "        55.75000762939453,\n",
       "       ...\n",
       "       -55.24999237060547, -56.24999237060547, -57.24999237060547,\n",
       "       -58.24999237060547, -59.24999237060547, -60.24999237060547,\n",
       "       -61.24999237060547, -62.24999237060547, -63.24999237060547,\n",
       "       -64.24999237060547],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcb6180f-e764-45b3-b3e0-c8bddbcd4d3b' class='xr-index-data-in' type='checkbox'/><label for='index-fcb6180f-e764-45b3-b3e0-c8bddbcd4d3b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0,\n",
       "       ...\n",
       "       291.0, 292.0, 293.0, 294.0, 295.0, 296.0, 297.0, 298.0, 299.0, 300.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=181))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9d93b664-c33d-4f84-87fd-b172e355e7e6' class='xr-index-data-in' type='checkbox'/><label for='index-9d93b664-c33d-4f84-87fd-b172e355e7e6' 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;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=516, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0247b94f-a22f-4cc1-9ccc-94ba3391963b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0247b94f-a22f-4cc1-9ccc-94ba3391963b' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Originating_or_generating_Center :</span></dt><dd>European Centre for Medium Range Weather Forecasts (ECMWF) (RSMC)</dd><dt><span>Originating_or_generating_Subcenter :</span></dt><dd>0</dd><dt><span>GRIB_table_version :</span></dt><dd>0,128</dd><dt><span>file_format :</span></dt><dd>GRIB-1</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Read using CDM IOSP GribCollection v3</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                    (time: 516,\n",
       "                                                                lat: 130,\n",
       "                                                                lon: 181)\n",
       "Coordinates:\n",
       "  * lat                                                        (lat) float32 ...\n",
       "  * lon                                                        (lon) float32 ...\n",
       "  * time                                                       (time) datetime64[ns] ...\n",
       "    reftime                                                    (time) datetime64[ns] ...\n",
       "Data variables:\n",
       "    Total_column_water_vapour_surface_Mixed_intervals_Average  (time, lat, lon) float32 ...\n",
       "    Sea_surface_temperature_surface_Mixed_intervals_Average    (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     European Centre for Medium Range We...\n",
       "    Originating_or_generating_Subcenter:  0\n",
       "    GRIB_table_version:                   0,128\n",
       "    file_format:                          GRIB-1\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    _CoordSysBuilder:                     ucar.nc2.dataset.conv.CF1Convention"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open netcdf file\n",
    "ds = xr.open_dataset('raw_data.nc')\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract variables\n",
    "\n",
    "sst = ds['Sea_surface_temperature_surface_Mixed_intervals_Average']\n",
    "total_water = ds['Total_column_water_vapour_surface_Mixed_intervals_Average']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Sea_surface_temperature_surface_Mixed_intervals_Average&#x27; (\n",
       "                                                                             time: 516,\n",
       "                                                                             lat: 130,\n",
       "                                                                             lon: 180)&gt;\n",
       "array([[[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.46020508, 271.47778338],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.52905343, 272.1062075 ],\n",
       "        ...,\n",
       "        [274.6320776 , 274.52978205, 274.54711595, ..., 274.40282338,\n",
       "         274.35033487, 274.42260109],\n",
       "        [273.96361707, 273.925532  , 273.87865637, ..., 273.50487656,\n",
       "         273.84350301, 274.14330982],\n",
       "        [273.22875428, 273.23803182, 273.17211387, ..., 271.93113842,\n",
       "         272.53514389, 272.11106355]],\n",
       "\n",
       "       [[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.46020508, 271.46240237],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.49902383, 272.05493766],\n",
       "...\n",
       "        [272.29052478, 272.35033771, 272.38305125, ..., 272.82201996,\n",
       "         272.87573224, 273.13305873],\n",
       "        [271.81396085, 271.94604217, 272.07031111, ..., 272.81152384,\n",
       "         272.90771523, 273.12548751],\n",
       "        [271.51611158, 271.55932315, 271.60155819, ..., 272.14061804,\n",
       "         272.98339908, 272.26512823]],\n",
       "\n",
       "       [[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.63061588,\n",
       "         271.61425816, 271.69018646],\n",
       "        [         nan,          nan,          nan, ..., 271.72143625,\n",
       "         271.74194454, 272.57935444],\n",
       "        ...,\n",
       "        [273.08178207, 273.04003595, 273.02221417, ..., 273.58056507,\n",
       "         273.60791078, 273.82446495],\n",
       "        [272.82275295, 272.84716704, 272.9045407 , ..., 273.54101567,\n",
       "         273.58007763, 273.73534997],\n",
       "        [272.27880338, 272.38915581, 272.51464458, ..., 272.92797226,\n",
       "         273.73242359, 273.02391908]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299\n",
       "Attributes: (12/15)\n",
       "    long_name:                       Sea surface temperature (Mixed_intervals...\n",
       "    units:                           K\n",
       "    description:                     v-component of wind\n",
       "    grid_mapping:                    LatLon_Projection\n",
       "    Grib_Statistical_Interval_Type:  Average\n",
       "    Grib_Variable_Id:                VAR_98-0-128-34_L1_Imixed_S123\n",
       "    ...                              ...\n",
       "    Grib1_Parameter:                 34\n",
       "    Grib1_Parameter_Name:            sst\n",
       "    Grib1_Level_Type:                1\n",
       "    Grib1_Level_Desc:                Ground or water surface\n",
       "    Grib1_Interval_Type:             123\n",
       "    Grib1_Interval_Name:             Average of N uninitialized analyses, int...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Sea_surface_temperature_surface_Mixed_intervals_Average'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 516</li><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-af67c90f-4ee6-44a1-a9e4-ae0e5ac2f859' class='xr-array-in' type='checkbox' checked><label for='section-af67c90f-4ee6-44a1-a9e4-ae0e5ac2f859' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 273.4 273.6 273.6 272.9 273.7 273.0</span></div><div class='xr-array-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.46020508, 271.47778338],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.52905343, 272.1062075 ],\n",
       "        ...,\n",
       "        [274.6320776 , 274.52978205, 274.54711595, ..., 274.40282338,\n",
       "         274.35033487, 274.42260109],\n",
       "        [273.96361707, 273.925532  , 273.87865637, ..., 273.50487656,\n",
       "         273.84350301, 274.14330982],\n",
       "        [273.22875428, 273.23803182, 273.17211387, ..., 271.93113842,\n",
       "         272.53514389, 272.11106355]],\n",
       "\n",
       "       [[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.46020508, 271.46240237],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.49902383, 272.05493766],\n",
       "...\n",
       "        [272.29052478, 272.35033771, 272.38305125, ..., 272.82201996,\n",
       "         272.87573224, 273.13305873],\n",
       "        [271.81396085, 271.94604217, 272.07031111, ..., 272.81152384,\n",
       "         272.90771523, 273.12548751],\n",
       "        [271.51611158, 271.55932315, 271.60155819, ..., 272.14061804,\n",
       "         272.98339908, 272.26512823]],\n",
       "\n",
       "       [[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.63061588,\n",
       "         271.61425816, 271.69018646],\n",
       "        [         nan,          nan,          nan, ..., 271.72143625,\n",
       "         271.74194454, 272.57935444],\n",
       "        ...,\n",
       "        [273.08178207, 273.04003595, 273.02221417, ..., 273.58056507,\n",
       "         273.60791078, 273.82446495],\n",
       "        [272.82275295, 272.84716704, 272.9045407 , ..., 273.54101567,\n",
       "         273.58007763, 273.73534997],\n",
       "        [272.27880338, 272.38915581, 272.51464458, ..., 272.92797226,\n",
       "         273.73242359, 273.02391908]]])</pre></div></div></li><li class='xr-section-item'><input id='section-4d3fd55a-273e-4d2b-8df4-77cba38eac9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d3fd55a-273e-4d2b-8df4-77cba38eac9b' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-6c94c299-8033-4b7e-a22b-6d786a673ba7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c94c299-8033-4b7e-a22b-6d786a673ba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-622f11ec-5a92-4978-bcd9-3b89454630ae' class='xr-var-data-in' type='checkbox'><label for='data-622f11ec-5a92-4978-bcd9-3b89454630ae' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-12-01</div><input id='attrs-e7de3f6c-4c86-4828-a9d5-8ae50ac580ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7de3f6c-4c86-4828-a9d5-8ae50ac580ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-256def3e-e9c6-40bb-9685-8013579cd339' class='xr-var-data-in' type='checkbox'><label for='data-256def3e-e9c6-40bb-9685-8013579cd339' 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>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>65 64 63 62 61 ... -61 -62 -63 -64</div><input id='attrs-9cfc1b09-9052-44b2-8483-e4cf034b7a8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cfc1b09-9052-44b2-8483-e4cf034b7a8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0dc11c6-106c-4d1a-8fd1-e7d740509ec5' class='xr-var-data-in' type='checkbox'><label for='data-d0dc11c6-106c-4d1a-8fd1-e7d740509ec5' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,  55,  54,  53,  52,\n",
       "        51,  50,  49,  48,  47,  46,  45,  44,  43,  42,  41,  40,  39,  38,\n",
       "        37,  36,  35,  34,  33,  32,  31,  30,  29,  28,  27,  26,  25,  24,\n",
       "        23,  22,  21,  20,  19,  18,  17,  16,  15,  14,  13,  12,  11,  10,\n",
       "         9,   8,   7,   6,   5,   4,   3,   2,   1,   0,  -1,  -2,  -3,  -4,\n",
       "        -5,  -6,  -7,  -8,  -9, -10, -11, -12, -13, -14, -15, -16, -17, -18,\n",
       "       -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32,\n",
       "       -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46,\n",
       "       -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60,\n",
       "       -61, -62, -63, -64])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>120 121 122 123 ... 296 297 298 299</div><input id='attrs-d6a2752b-c42d-4974-ae18-7061f329da34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6a2752b-c42d-4974-ae18-7061f329da34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88821ce6-f00c-4caf-beff-7293d885586b' class='xr-var-data-in' type='checkbox'><label for='data-88821ce6-f00c-4caf-beff-7293d885586b' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
       "       134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
       "       148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
       "       162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
       "       176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,\n",
       "       204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,\n",
       "       218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "       232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287,\n",
       "       288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3408c838-0631-4c68-a5ea-97acfc51f09a' class='xr-section-summary-in' type='checkbox'  ><label for='section-3408c838-0631-4c68-a5ea-97acfc51f09a' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1346543-3c93-4e89-9a73-c144c3fa44e3' class='xr-index-data-in' type='checkbox'/><label for='index-c1346543-3c93-4e89-9a73-c144c3fa44e3' 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;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=516, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a45dd860-a026-43a6-bcfa-9a6fca61e208' class='xr-index-data-in' type='checkbox'/><label for='index-a45dd860-a026-43a6-bcfa-9a6fca61e208' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,\n",
       "       ...\n",
       "       -55, -56, -57, -58, -59, -60, -61, -62, -63, -64],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f90bcbe-0eed-4be5-87a6-49b7eef898d6' class='xr-index-data-in' type='checkbox'/><label for='index-1f90bcbe-0eed-4be5-87a6-49b7eef898d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       ...\n",
       "       290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4749672-6f9f-4c2f-b11f-830914e3c803' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4749672-6f9f-4c2f-b11f-830914e3c803' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface temperature (Mixed_intervals Average) @ Ground or water surface</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>v-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LatLon_Projection</dd><dt><span>Grib_Statistical_Interval_Type :</span></dt><dd>Average</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_98-0-128-34_L1_Imixed_S123</dd><dt><span>Grib1_Center :</span></dt><dd>98</dd><dt><span>Grib1_Subcenter :</span></dt><dd>0</dd><dt><span>Grib1_TableVersion :</span></dt><dd>128</dd><dt><span>Grib1_Parameter :</span></dt><dd>34</dd><dt><span>Grib1_Parameter_Name :</span></dt><dd>sst</dd><dt><span>Grib1_Level_Type :</span></dt><dd>1</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib1_Interval_Type :</span></dt><dd>123</dd><dt><span>Grib1_Interval_Name :</span></dt><dd>Average of N uninitialized analyses, intervals = (refTime, refTime + i * P2)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Sea_surface_temperature_surface_Mixed_intervals_Average' (\n",
       "                                                                             time: 516,\n",
       "                                                                             lat: 130,\n",
       "                                                                             lon: 180)>\n",
       "array([[[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.46020508, 271.47778338],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.52905343, 272.1062075 ],\n",
       "        ...,\n",
       "        [274.6320776 , 274.52978205, 274.54711595, ..., 274.40282338,\n",
       "         274.35033487, 274.42260109],\n",
       "        [273.96361707, 273.925532  , 273.87865637, ..., 273.50487656,\n",
       "         273.84350301, 274.14330982],\n",
       "        [273.22875428, 273.23803182, 273.17211387, ..., 271.93113842,\n",
       "         272.53514389, 272.11106355]],\n",
       "\n",
       "       [[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.46020508, 271.46240237],\n",
       "        [         nan,          nan,          nan, ..., 271.46020508,\n",
       "         271.49902383, 272.05493766],\n",
       "...\n",
       "        [272.29052478, 272.35033771, 272.38305125, ..., 272.82201996,\n",
       "         272.87573224, 273.13305873],\n",
       "        [271.81396085, 271.94604217, 272.07031111, ..., 272.81152384,\n",
       "         272.90771523, 273.12548751],\n",
       "        [271.51611158, 271.55932315, 271.60155819, ..., 272.14061804,\n",
       "         272.98339908, 272.26512823]],\n",
       "\n",
       "       [[         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 271.63061588,\n",
       "         271.61425816, 271.69018646],\n",
       "        [         nan,          nan,          nan, ..., 271.72143625,\n",
       "         271.74194454, 272.57935444],\n",
       "        ...,\n",
       "        [273.08178207, 273.04003595, 273.02221417, ..., 273.58056507,\n",
       "         273.60791078, 273.82446495],\n",
       "        [272.82275295, 272.84716704, 272.9045407 , ..., 273.54101567,\n",
       "         273.58007763, 273.73534997],\n",
       "        [272.27880338, 272.38915581, 272.51464458, ..., 272.92797226,\n",
       "         273.73242359, 273.02391908]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299\n",
       "Attributes: (12/15)\n",
       "    long_name:                       Sea surface temperature (Mixed_intervals...\n",
       "    units:                           K\n",
       "    description:                     v-component of wind\n",
       "    grid_mapping:                    LatLon_Projection\n",
       "    Grib_Statistical_Interval_Type:  Average\n",
       "    Grib_Variable_Id:                VAR_98-0-128-34_L1_Imixed_S123\n",
       "    ...                              ...\n",
       "    Grib1_Parameter:                 34\n",
       "    Grib1_Parameter_Name:            sst\n",
       "    Grib1_Level_Type:                1\n",
       "    Grib1_Level_Desc:                Ground or water surface\n",
       "    Grib1_Interval_Type:             123\n",
       "    Grib1_Interval_Name:             Average of N uninitialized analyses, int..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Reduce the data resolution to 1 degree, and get only Pacific region for both sst and total water\n",
    "sst = sst.interp(lat=np.arange(65, -65, -1), lon=np.arange(120, 300, 1))\n",
    "total_water = total_water.interp(lat=np.arange(65, -65, -1), lon=np.arange(120, 300, 1))\n",
    "\n",
    "sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Sea_surface_temperature_surface_Mixed_intervals_Average&#x27; (\n",
       "                                                                             time: 516,\n",
       "                                                                             lat: 130,\n",
       "                                                                             lon: 180)&gt;\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.18236333,\n",
       "         -1.34094275, -1.48177076],\n",
       "        [        nan,         nan,         nan, ..., -1.34108492,\n",
       "         -1.56808052, -1.53644015],\n",
       "        ...,\n",
       "        [ 1.82249369,  1.71732675,  1.70486563, ...,  1.34342003,\n",
       "          1.29323784,  1.37194224],\n",
       "        [ 1.42362251,  1.35180877,  1.2833419 , ...,  0.77450107,\n",
       "          1.1027228 ,  1.32545044],\n",
       "        [ 1.02303687,  0.99370855,  0.91232604, ...,  0.04398174,\n",
       "          0.26648055,  0.11988314]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.18236333,\n",
       "         -1.34094275, -1.49715178],\n",
       "        [        nan,         nan,         nan, ..., -1.34108492,\n",
       "         -1.59811012, -1.58770999],\n",
       "...\n",
       "        [-0.51905914, -0.4621176 , -0.45919907, ..., -0.23738339,\n",
       "         -0.1813648 ,  0.08239988],\n",
       "        [-0.72603371, -0.62768105, -0.52500336, ...,  0.08114835,\n",
       "          0.16693501,  0.30762813],\n",
       "        [-0.68960583, -0.68500011, -0.65822964, ...,  0.25346136,\n",
       "          0.71473574,  0.27394781]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.01195253,\n",
       "         -1.18688968, -1.26936768],\n",
       "        [        nan,         nan,         nan, ..., -1.07985374,\n",
       "         -1.35518941, -1.06329321],\n",
       "        ...,\n",
       "        [ 0.27219815,  0.22758065,  0.17996384, ...,  0.52116172,\n",
       "          0.55081375,  0.7738061 ],\n",
       "        [ 0.28275839,  0.27344382,  0.30922622, ...,  0.81064018,\n",
       "          0.83929742,  0.9174906 ],\n",
       "        [ 0.07308596,  0.14483254,  0.25485675, ...,  1.04081559,\n",
       "          1.46376025,  1.03273866]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Sea_surface_temperature_surface_Mixed_intervals_Average'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 516</li><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0c52b319-24be-416d-823c-5b5ddfcf5b97' class='xr-array-in' type='checkbox' checked><label for='section-0c52b319-24be-416d-823c-5b5ddfcf5b97' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 0.7116 0.8659 1.063 1.041 1.464 1.033</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.18236333,\n",
       "         -1.34094275, -1.48177076],\n",
       "        [        nan,         nan,         nan, ..., -1.34108492,\n",
       "         -1.56808052, -1.53644015],\n",
       "        ...,\n",
       "        [ 1.82249369,  1.71732675,  1.70486563, ...,  1.34342003,\n",
       "          1.29323784,  1.37194224],\n",
       "        [ 1.42362251,  1.35180877,  1.2833419 , ...,  0.77450107,\n",
       "          1.1027228 ,  1.32545044],\n",
       "        [ 1.02303687,  0.99370855,  0.91232604, ...,  0.04398174,\n",
       "          0.26648055,  0.11988314]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.18236333,\n",
       "         -1.34094275, -1.49715178],\n",
       "        [        nan,         nan,         nan, ..., -1.34108492,\n",
       "         -1.59811012, -1.58770999],\n",
       "...\n",
       "        [-0.51905914, -0.4621176 , -0.45919907, ..., -0.23738339,\n",
       "         -0.1813648 ,  0.08239988],\n",
       "        [-0.72603371, -0.62768105, -0.52500336, ...,  0.08114835,\n",
       "          0.16693501,  0.30762813],\n",
       "        [-0.68960583, -0.68500011, -0.65822964, ...,  0.25346136,\n",
       "          0.71473574,  0.27394781]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.01195253,\n",
       "         -1.18688968, -1.26936768],\n",
       "        [        nan,         nan,         nan, ..., -1.07985374,\n",
       "         -1.35518941, -1.06329321],\n",
       "        ...,\n",
       "        [ 0.27219815,  0.22758065,  0.17996384, ...,  0.52116172,\n",
       "          0.55081375,  0.7738061 ],\n",
       "        [ 0.28275839,  0.27344382,  0.30922622, ...,  0.81064018,\n",
       "          0.83929742,  0.9174906 ],\n",
       "        [ 0.07308596,  0.14483254,  0.25485675, ...,  1.04081559,\n",
       "          1.46376025,  1.03273866]]])</pre></div></div></li><li class='xr-section-item'><input id='section-da22f7ba-4b84-4812-bbf1-66c9fb20b5e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da22f7ba-4b84-4812-bbf1-66c9fb20b5e9' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-63aec8c0-f9ff-4886-9316-a975cce22782' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63aec8c0-f9ff-4886-9316-a975cce22782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b69a0b7b-d7db-4a8b-9ce0-fef336e36a4c' class='xr-var-data-in' type='checkbox'><label for='data-b69a0b7b-d7db-4a8b-9ce0-fef336e36a4c' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-12-01</div><input id='attrs-d8d386f5-a998-4a65-adfd-04d9a3ccea8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8d386f5-a998-4a65-adfd-04d9a3ccea8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0a32bd5-24d2-49a3-af02-850c7589b2d5' class='xr-var-data-in' type='checkbox'><label for='data-f0a32bd5-24d2-49a3-af02-850c7589b2d5' 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>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>65 64 63 62 61 ... -61 -62 -63 -64</div><input id='attrs-5359832f-32b7-4653-aa69-75dd02d3f4b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5359832f-32b7-4653-aa69-75dd02d3f4b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d53fc4-09da-42f3-a34b-d67cb3dc23b9' class='xr-var-data-in' type='checkbox'><label for='data-a8d53fc4-09da-42f3-a34b-d67cb3dc23b9' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,  55,  54,  53,  52,\n",
       "        51,  50,  49,  48,  47,  46,  45,  44,  43,  42,  41,  40,  39,  38,\n",
       "        37,  36,  35,  34,  33,  32,  31,  30,  29,  28,  27,  26,  25,  24,\n",
       "        23,  22,  21,  20,  19,  18,  17,  16,  15,  14,  13,  12,  11,  10,\n",
       "         9,   8,   7,   6,   5,   4,   3,   2,   1,   0,  -1,  -2,  -3,  -4,\n",
       "        -5,  -6,  -7,  -8,  -9, -10, -11, -12, -13, -14, -15, -16, -17, -18,\n",
       "       -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32,\n",
       "       -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46,\n",
       "       -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60,\n",
       "       -61, -62, -63, -64])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>120 121 122 123 ... 296 297 298 299</div><input id='attrs-d11766b5-576f-4dc5-be2e-fe7a88560ed8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d11766b5-576f-4dc5-be2e-fe7a88560ed8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552cffc6-4547-470c-a1dc-efd001e86cc0' class='xr-var-data-in' type='checkbox'><label for='data-552cffc6-4547-470c-a1dc-efd001e86cc0' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
       "       134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
       "       148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
       "       162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
       "       176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,\n",
       "       204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,\n",
       "       218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "       232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287,\n",
       "       288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b51dbff-459e-48f2-bc66-73a951158dbe' class='xr-section-summary-in' type='checkbox'  ><label for='section-4b51dbff-459e-48f2-bc66-73a951158dbe' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7c9202cf-2f5e-424d-88dd-c4541c78c274' class='xr-index-data-in' type='checkbox'/><label for='index-7c9202cf-2f5e-424d-88dd-c4541c78c274' 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;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=516, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2898fcf-6cda-47a4-b8ba-a5b3b1569b66' class='xr-index-data-in' type='checkbox'/><label for='index-d2898fcf-6cda-47a4-b8ba-a5b3b1569b66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,\n",
       "       ...\n",
       "       -55, -56, -57, -58, -59, -60, -61, -62, -63, -64],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cea980ff-d66e-4eaf-87d9-02dd9f5189b3' class='xr-index-data-in' type='checkbox'/><label for='index-cea980ff-d66e-4eaf-87d9-02dd9f5189b3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       ...\n",
       "       290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd640a37-d559-4590-a924-64e5655d0ca1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd640a37-d559-4590-a924-64e5655d0ca1' 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>"
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       "<xarray.DataArray 'Sea_surface_temperature_surface_Mixed_intervals_Average' (\n",
       "                                                                             time: 516,\n",
       "                                                                             lat: 130,\n",
       "                                                                             lon: 180)>\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.18236333,\n",
       "         -1.34094275, -1.48177076],\n",
       "        [        nan,         nan,         nan, ..., -1.34108492,\n",
       "         -1.56808052, -1.53644015],\n",
       "        ...,\n",
       "        [ 1.82249369,  1.71732675,  1.70486563, ...,  1.34342003,\n",
       "          1.29323784,  1.37194224],\n",
       "        [ 1.42362251,  1.35180877,  1.2833419 , ...,  0.77450107,\n",
       "          1.1027228 ,  1.32545044],\n",
       "        [ 1.02303687,  0.99370855,  0.91232604, ...,  0.04398174,\n",
       "          0.26648055,  0.11988314]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.18236333,\n",
       "         -1.34094275, -1.49715178],\n",
       "        [        nan,         nan,         nan, ..., -1.34108492,\n",
       "         -1.59811012, -1.58770999],\n",
       "...\n",
       "        [-0.51905914, -0.4621176 , -0.45919907, ..., -0.23738339,\n",
       "         -0.1813648 ,  0.08239988],\n",
       "        [-0.72603371, -0.62768105, -0.52500336, ...,  0.08114835,\n",
       "          0.16693501,  0.30762813],\n",
       "        [-0.68960583, -0.68500011, -0.65822964, ...,  0.25346136,\n",
       "          0.71473574,  0.27394781]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -1.01195253,\n",
       "         -1.18688968, -1.26936768],\n",
       "        [        nan,         nan,         nan, ..., -1.07985374,\n",
       "         -1.35518941, -1.06329321],\n",
       "        ...,\n",
       "        [ 0.27219815,  0.22758065,  0.17996384, ...,  0.52116172,\n",
       "          0.55081375,  0.7738061 ],\n",
       "        [ 0.28275839,  0.27344382,  0.30922622, ...,  0.81064018,\n",
       "          0.83929742,  0.9174906 ],\n",
       "        [ 0.07308596,  0.14483254,  0.25485675, ...,  1.04081559,\n",
       "          1.46376025,  1.03273866]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate sst anomaly\n",
    "\n",
    "# Extract sst variables from dataset and calculate the mean SST \n",
    "sst_mean = sst.mean(dim='time')\n",
    "\n",
    "# Calculate SST anomalies by subtracting mean pacific sst from actual pacific sst\n",
    "sst_anom = sst - sst_mean\n",
    "\n",
    "sst_anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Total_column_water_vapour_surface_Mixed_intervals_Average&#x27; (\n",
       "                                                                               lat: 130,\n",
       "                                                                               lon: 180)&gt;\n",
       "array([[       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [8.29278451, 8.30081894, 8.27980537, ..., 7.44739259, 7.4713701 ,\n",
       "        7.51060343],\n",
       "       [8.33981749, 8.30579666, 8.23246861, ..., 7.71471487, 7.75866081,\n",
       "        7.81977094],\n",
       "       ...,\n",
       "       [6.69674615, 6.73790799, 6.78152451, ..., 7.95246895, 7.89098146,\n",
       "        7.823961  ],\n",
       "       [6.29732908, 6.34210951, 6.38569617, ..., 7.55636747, 7.45799593,\n",
       "        7.44524963],\n",
       "       [5.85582263, 5.89930424, 5.93854744, ..., 6.71759384, 6.96869709,\n",
       "        5.69090074]])\n",
       "Coordinates:\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Total_column_water_vapour_surface_Mixed_intervals_Average'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9c73a7be-27d8-45fc-8ce4-366344bcf739' class='xr-array-in' type='checkbox' checked><label for='section-9c73a7be-27d8-45fc-8ce4-366344bcf739' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 7.447 7.441 7.18 6.718 6.969 5.691</span></div><div class='xr-array-data'><pre>array([[       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [8.29278451, 8.30081894, 8.27980537, ..., 7.44739259, 7.4713701 ,\n",
       "        7.51060343],\n",
       "       [8.33981749, 8.30579666, 8.23246861, ..., 7.71471487, 7.75866081,\n",
       "        7.81977094],\n",
       "       ...,\n",
       "       [6.69674615, 6.73790799, 6.78152451, ..., 7.95246895, 7.89098146,\n",
       "        7.823961  ],\n",
       "       [6.29732908, 6.34210951, 6.38569617, ..., 7.55636747, 7.45799593,\n",
       "        7.44524963],\n",
       "       [5.85582263, 5.89930424, 5.93854744, ..., 6.71759384, 6.96869709,\n",
       "        5.69090074]])</pre></div></div></li><li class='xr-section-item'><input id='section-7d5c290e-f4fd-42bd-bd97-e2243535b5b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d5c290e-f4fd-42bd-bd97-e2243535b5b9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>65 64 63 62 61 ... -61 -62 -63 -64</div><input id='attrs-be198270-9641-4384-b4af-92cac574ab81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be198270-9641-4384-b4af-92cac574ab81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53038ef7-22af-467d-be71-0c05714be8e1' class='xr-var-data-in' type='checkbox'><label for='data-53038ef7-22af-467d-be71-0c05714be8e1' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,  55,  54,  53,  52,\n",
       "        51,  50,  49,  48,  47,  46,  45,  44,  43,  42,  41,  40,  39,  38,\n",
       "        37,  36,  35,  34,  33,  32,  31,  30,  29,  28,  27,  26,  25,  24,\n",
       "        23,  22,  21,  20,  19,  18,  17,  16,  15,  14,  13,  12,  11,  10,\n",
       "         9,   8,   7,   6,   5,   4,   3,   2,   1,   0,  -1,  -2,  -3,  -4,\n",
       "        -5,  -6,  -7,  -8,  -9, -10, -11, -12, -13, -14, -15, -16, -17, -18,\n",
       "       -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32,\n",
       "       -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46,\n",
       "       -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60,\n",
       "       -61, -62, -63, -64])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>120 121 122 123 ... 296 297 298 299</div><input id='attrs-b91b6549-a1a0-47dc-b84a-87ea4e6756b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b91b6549-a1a0-47dc-b84a-87ea4e6756b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b13fd46-9468-4cfd-b7f1-4f1ab034e857' class='xr-var-data-in' type='checkbox'><label for='data-6b13fd46-9468-4cfd-b7f1-4f1ab034e857' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
       "       134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
       "       148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
       "       162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
       "       176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,\n",
       "       204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,\n",
       "       218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "       232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287,\n",
       "       288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-543a3d7e-e621-4ccb-9c93-e36b1b5b80fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-543a3d7e-e621-4ccb-9c93-e36b1b5b80fe' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b38dc134-1e14-4cc4-b18b-69c67a414d3b' class='xr-index-data-in' type='checkbox'/><label for='index-b38dc134-1e14-4cc4-b18b-69c67a414d3b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,\n",
       "       ...\n",
       "       -55, -56, -57, -58, -59, -60, -61, -62, -63, -64],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9d6fb5e-aac2-45c7-8c3d-3dac09fa9e49' class='xr-index-data-in' type='checkbox'/><label for='index-b9d6fb5e-aac2-45c7-8c3d-3dac09fa9e49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       ...\n",
       "       290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d852ae2-5951-4d5d-a585-4602721d5177' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7d852ae2-5951-4d5d-a585-4602721d5177' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Total_column_water_vapour_surface_Mixed_intervals_Average' (\n",
       "                                                                               lat: 130,\n",
       "                                                                               lon: 180)>\n",
       "array([[       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [8.29278451, 8.30081894, 8.27980537, ..., 7.44739259, 7.4713701 ,\n",
       "        7.51060343],\n",
       "       [8.33981749, 8.30579666, 8.23246861, ..., 7.71471487, 7.75866081,\n",
       "        7.81977094],\n",
       "       ...,\n",
       "       [6.69674615, 6.73790799, 6.78152451, ..., 7.95246895, 7.89098146,\n",
       "        7.823961  ],\n",
       "       [6.29732908, 6.34210951, 6.38569617, ..., 7.55636747, 7.45799593,\n",
       "        7.44524963],\n",
       "       [5.85582263, 5.89930424, 5.93854744, ..., 6.71759384, 6.96869709,\n",
       "        5.69090074]])\n",
       "Coordinates:\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean total column water vapor\n",
    "\n",
    "# Extract sst variables from dataset and calculate the mean SST \n",
    "total_water_mean = total_water.mean(dim='time')\n",
    "\n",
    "total_water_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To plot mean sst on map extract variables\n",
    "\n",
    "# Extract lat and lon\n",
    "lat = sst['lat']\n",
    "lon = sst['lon']\n",
    "\n",
    "# Convert sst from K to F\n",
    "sst_mean_f =  (9/5) * (sst_mean - 273.15) + 32\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets create the mask with the mask dataset\n",
    "\n",
    "mask_url = \"https://thredds.rda.ucar.edu/thredds/dodsC/files/g/ds633.0/e5.oper.invariant/197901/e5.oper.invariant.128_172_lsm.ll025sc.1979010100_1979010100.nc\"\n",
    "mask = xr.open_dataset(mask_url).sel(latitude=slice(65, -65, 1),longitude=slice(120, 300, 1)).compute()\n",
    "\n",
    "# Reduce the data resolution to 1 degree, so the degree resolution matches sst anom\n",
    "mask = mask.interp(latitude=np.arange(65, -65, -1), longitude=np.arange(120, 300, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename coordaintes to lat and lon to match the initial dataset\n",
    "mask = mask.rename({'latitude':'lat','longitude':'lon'})\n",
    "\n",
    "# Remove time variable from mask to match sst and water vapor dataset ( 1 is for land, 0 is for ocean)\n",
    "mask_squeeze = (mask.LSM == 0).squeeze(['time'], drop=True) \n",
    "\n",
    "# Find where only water is on the map\n",
    "WaterOnly=total_water_mean.where(mask_squeeze, drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Au3fv5tSpU4wZMwZra2uyZcumfAc9evRQbqzPnz9Pv379eP78uXJc79q1i2bNmmFqasrChQsBEgxSU+Lly5e0a9eOYcOGMXnyZDQ0NL77u9u/fz+amppUq1ZNbXi2bNmYOnUq9erVIyoqCgcHBw4ePBhvfiGEWhINPt/Af3kTb21tjYODAwcOHEgyCEqNyMhINDQ04p17cfv2xo0bic4bHBzM9u3bqVmzJnny5FFb5pfLiKOtra0E89/q+PHjaGlpUaBAAWXYzZs3KVSoULxEbvHixZXxlStXTnKZAEWKFEl2/V5eXlhaWirHriRJkiT9KjJ7vJOcEydO4OLiQvHixVmxYgW6urosXLiQ+vXrs2nTJlq2bEndunWZPHkyo0aNYsGCBUo/Ivny5QM+xz6VKlWia9eumJqa8ujRI2bNmkWVKlX4999/UxXLxMTEcPz4ccqUKUPOnDlTNE+vXr1YunQpffv2pV69ejx69IjRo0fj7e3NlStXlLglLbRo0YJ27drRo0cPjh49qrxgd+zYMXr37o27uzsbN25k+PDh5M+fnyZNmqjNP2rUKBwdHVm+fDkfPnxg+PDh1K9fnzt37qCpqZnoep2dnZk7dy7e3t5KQtDLy4t69erh6OiISqXi1KlTykNmLy8vSpcujampKfD9cUVaxTdf09DQ4Pfff2fSpElcv36dEiVKKOPi4ri4+/vUHmeNGzemRYsW9OzZk1u3bjF69Ghu377NhQsXlGlTc+wkFCslxsPDg6ioKCXBkRoeHh4ANGrUKEXTx63Dy8uLihUrKv93cnKiYMGCWFtb4+3tTeHChZVxlpaWyt+p+Q7iyPhLxl+SlCLpXPNGkjK1L6vIxrV3fPPmTSGEEOXKlRO///67ECJ+teXEqpL6+PgIQCxcuDDB9cXGxoqoqChx4sQJAYjr168r4+Lau9y6davaPG5ubvGqZifkwIEDwsLCQgACEFmzZhXNmzcXe/fuVZtuypQpam22xtm+fbsAxMGDBxNcfkxMjIiKihJr164VmpqaibalGuft27eiSpUqSnm0tbVF5cqVxZQpU8THjx/VpgXE2LFj4y3Dzs5OdOzYUfk77vvq0KFDvGnjvr9t27YlWa7o6GgREhIiDA0NxT///KMM79y5s9DW1ha3b99OdN5v3XcJ+bopqh49eggjIyOl2nWcGTNmCEDcunVLCCHEw4cPBSBKlCihVvV5zpw5AhANGjRQm3/gwIECUGtiws7OTqhUKnHt2jW1aV1cXISJiYlS7To12wsIU1PTZI+LuONowoQJImvWrGpVjxNrHiCxph3ivvMvq3s7OTkJIF7V9u/97lxdXYWDg0Oi44OCgoSvr69au7xx7OzslPPgy88ff/wRb9q2bdvGq1KfnKSqvccdF6dOnVIbPnr0aAGI2rVrJ7rcRYsWCUBs2rRJbfi1a9cEICZOnKg23NPTUwBCR0cnVeWPc+TIEaGhoSEGDRqkNtze3j5e+89CCPHixQsBiMmTJye6zGfPngkrKytRtmzZZJs7XLZsmQDUrguSJEmSlNn9SvHOl/iq2bGKFSuKbNmyqcUZ0dHRomjRoiJHjhzKPWdKmx2L247Hjx8LQK15sJQ0OxYQECCAeE3WJubOnTsCEL1791YbfuHCBQGIUaNGKcO+bnYsLj5YtWpVvOV+HWeNHTtWAGLmzJlq05UsWVIAYufOncqwqKgoYWlpqfSpIcT/7r/d3NzU5t+6dasAku3D8v3790JDQ0N0795dCPE5ZlSpVOLw4cNCiM/N/Lq7uwshhHjy5IkAxLBhwxJc1rfEFT8ivonj7+8vVCqV6N+/vzIsKipK6eszIUkdZ3Hf1df3xhs2bBCAWL9+vRAidcdOYrFSYnr16iX09fUTbC4qTmLNjvXs2VMAyTazFSc2NlaYm5sr8UlMTIzS54gQn5s/a9asmRDif/29tGjRQm0Z3/IdyPhLxl+SlBzZ7JgkpREnJyfy5cvHypUr+ffff/Hx8Un07Yf9+/eTJUsW6tevT3R0tPIpWbKk8kZGHH9/f9q0aYO1tTWamppoa2vj5OQEfK7++iWVSkX9+vXVhhUvXjzBJrK+5ubmxpMnT9i1axfu7u4UKVKE3bt306BBA/r27atW9qJFi1KyZEm1stepUydeE05Xr16lQYMGZM2aVSl7hw4diImJwc/PL8nyZM2alVOnTuHj48PUqVNp2LAhfn5+jBw5kmLFin1z9X2Apk2bpnjakJAQ5Y0tLS0ttLS0MDIyIjQ0VG3/Hzp0CGdnZwoVKpToslKz71Jr//79ODs7Y2trq7bsuOrKJ06cUJvezc1N7S2luHLXrVtXbbq44U+ePFEbXqRIEbW3gQDatGnDhw8fuHLlyjdtb40aNTAzM4u3bcePH6dWrVqYmpoqx9GYMWN49+5dvCbR0oKZmVm8qu3f+929ePEiybdxTE1NKVCgQKJv+lWpUgUfHx+1T+/eveNNly1bNl6/fh2vlsy3atu2Lebm5nTv3p0LFy4QFBTEpk2blCbmknrTbcWKFWTNmpXGjRurDS9RogTVqlXj77//Ztu2bQQFBXH27Fl69uyJpqam2jJjYmLU9ndsbGyC67py5QotWrSgYsWKTJkyJd74pGolJTbu/fv3uLm5IYRgy5YtSW7roUOH6NOnD82aNaNfv36JTidJkiRJmVlmj3cSExoayoULF2jWrBlGRkbKcE1NTdq3b8+zZ89S1CTW69ev6dmzJzlz5kRLSwttbW3s7OwS3I605uXlBaDW5DJ8bpKnUKFCeHp6pun64porilOoUKF4TSVpaWmRP3/+BL+bL5tLg/+9EZ/c92hmZkaJEiWU4+fEiRNoamri6OgIfD5G4/ZF3L9f1rr43rgireKbhOTJkwdnZ2c2bNig1FQ4dOgQAQEBaudZao+ztm3bqv3dokULtLS04u2nlB47CcVKiXnx4gWWlpbf3LpDaqhUKpycnDhz5gxRUVFcu3aNoKAgpTlxJycnvL29EUJw/vx5wsPD49XISel38CUZf8n4S5KSI5MvkpRGVCoVnTp1Yv369SxevJgCBQoobct+7dWrVwQFBaGjo4O2trbaJyAgQEkshISEULVqVS5cuMCkSZPw9vbGx8eHnTt3AhAeHq62XAMDA/T09NSG6erqEhERkaJt0NfXp1GjRvz999+cOHGC+/fvU7hwYRYsWMCtW7eUst+4cSNeuY2NjRFCKGV/8uQJVatW5fnz5/zzzz9KImXBggUJlj0xZcuWZfjw4Wzbto0XL14waNAgHj16FK/94NRIrG+bhLRp04b58+fTtWtXjhw5wsWLF/Hx8cHS0lJtG968eUOOHDmSXFZK9923ePXqFfv27Yu37Liqul8v29zcXO1vHR2dJId/fQxZW1vHK0PcsLg+hFK7vQl9LxcvXqR27doALFu2jDNnzuDj48Mff/wBpPw4So2EyvG93114eHi8czM1TE1NKVu2rNrH1tY23nR6enoIIVJ8zifHwsKCw4cPA1CxYkXMzMzo168fs2bNAiB79uwJznfjxg0uXbpEu3btEmz+bdu2bTg6OtKiRQvMzMxwdnamSZMmlCxZUm2Z+fLlU9vfEyZMiLesq1ev4uLigr29PQcPHoy3vqxZsyrH5Jfi2gX++pgHCAwMxMXFhefPn3P06FHy5s2b2C7iyJEjNGnSBBcXFzZs2PBTgktJkiRJSg+/QryTkMDAQIQQCd4Dxt1vJXQv8aXY2Fhq167Nzp07GTZsGJ6enly8eJHz588nuB3JsbCwwMDAgIcPH6Zo+qT68LS1tU22/KmVUMyQ0Hejo6OT4HfzdT8PcfdvKdlPzs7O+Pn58eLFC7y8vChTpoySNHNycuLq1asEBwfj5eWFlpaW0rdnWsQVaRHfJKVLly68e/dOafJ51apVGBkZ0aJFC+DbjrOv4zYtLS21++PUHjup2abviYHimoZL6TkAn4+N0NBQfHx88PLywsrKSulP08nJibdv33Lr1q0EE3NxkvsOvibjLxl/SVJyZJ8vkpSGfv/9d8aMGcPixYv566+/Ep3OwsKCrFmzKj+qXzM2NgY+v5nz4sULvL29lbe/AIKCgtK03InJlSsX3bt3Z+DAgdy6dYsiRYpgYWGBvr5+on22xLUHu3v3bkJDQ9m5c6fyJg587ivkW2lrazN27Fhmz57NzZs3leG6uroJdrKZWJCR0h/n4OBg9u/fz9ixYxkxYoQy/NOnT/E6dLO0tEy2M8uU7rtvYWFhQfHixRM97hJ6UP89AgICEh0WF0yldnsT+l42b96MtrY2+/fvV7tx3717d4rLGjff18dIYgmThMrxvd+dhYXFT+kE8P379+jq6qq9tfm94vo3evToEaGhodjb23P58mWAeH3YxFmxYgUAXbt2TXB8tmzZOHjwIK9fvyYgIAA7Ozv09fVZuHAhzZo1U6bbt2+f2vf29XF89epVatWqhZ2dHR4eHkp73l8qVqwYmzZtIjo6Wq3d4X///ReAokWLqk0fGBhIrVq1ePjwIZ6ensqbmAk5cuQIjRo1wsnJiR07dijJSkmSJEn6Vf1q8Q58fpNfQ0NDrRP3OC9evACSv9e7efMm169fZ/Xq1XTs2FEZfv/+/W8qk6amJjVr1uTQoUM8e/Ys2Ze84u6/X758GW/aFy9eJFn+xO6V0zphk1acnZ2ZNWsW3t7eeHt7q3UiHpdoOXnyJN7e3pQrV065L06LuCIt4pukNGnSBDMzM1auXImTkxP79++nQ4cOyjZ8y3EWEBCg9nA9Ojqad+/eKcdMao+d1GyThYWF0ipCatWpU4dRo0axe/dufvvttxTNE5dM8fb25ty5c2rXlMKFC2NhYYGXlxfe3t7Y2NgoiZkvJfcdfE3GXzL+kqTkyOSLJKWh7NmzM3ToUO7evat2M/S1evXqsXnzZmJiYqhQoUKi08Xd2Hz9JsGSJUvSpsD/7+PHj6hUqgRvGOKqLsf96NarV4/JkyeTNWtWtU7cvpZQ2YUQLFu2LEVlevnyZYJv1XxdHoDcuXPH6yTu+PHjhISEpGhdiVGpVAgh4u3/5cuXExMTozbM1dWVdevW4evrm+BNHKR8332LevXqcfDgQfLly5fiqu3f49atW/E6Ity4cSPGxsZK56Npsb0qlQotLS215rjCw8NZt25dvGl1dXUTfNsrd+7cwOe3gb78buLeZkqJ790WBweHVAV238rf31/pNDKtxe1HIQQzZ87E1taW5s2bx5vu06dPrF+/nvLly8e7sf5atmzZlObY5s6dS2hoqFozh8WKFUt03mvXrlGrVi1y5MjB0aNHEz3uGzduzLJly9ixYwctW7ZUhq9ZswZbW1u1a3Dcjb+/vz9Hjx6lVKlSia7fw8ODRo0aUaVKFXbv3p3gG2aSJEmS9KvJrPFOUgwNDalQoQI7d+5kxowZ6OvrA59rGaxfv54cOXIonUknVkPjR2zHyJEjOXjwIN26dWPPnj3xHjJGRUVx+PBh6tevrzQDtX79esqVK6dM4+Pjw507d5TaHQmxsrJCT08vXjy1Z8+eby77j1StWjU0NTXZvn07t27dUmsRwdTUlJIlS7JmzRoePXpEmzZtlHFpEVf8yHgOPifC2rRpw+LFi5k2bRpRUVFqzV19y3G2YcMGypQpo/y9detWoqOjlea4vufYSY6DgwObNm0iODg4wYf0SSldujSurq6sWLGCFi1aJNjU2aVLl8iWLZtSS6ZIkSJYWlpy/PhxLl26pNYclkqlolq1ahw+fJjz58/TpEmTBNeb3HfwNRl/xSfjL0lSJ5MvkpTGpk6dmuw0rVq1YsOGDbi5uTFgwADKly+PtrY2z549w8vLi4YNG9K4cWMqV66MmZkZPXv2ZOzYsWhra7NhwwauX7+epmX29fWlTp06tGrVCicnJ2xsbAgMDOTAgQMsXbqU6tWrU7lyZQAGDhzIjh07qFatGoMGDaJ48eLExsby5MkTPDw8GDJkCBUqVMDFxQUdHR1at27NsGHDiIiIYNGiRQQGBqaoTHXq1CFHjhzUr18fBwcHYmNjuXbtGjNnzsTIyIgBAwYo07Zv357Ro0czZswYnJycuH37NvPnz0/1Dd7XTExMlLZRLSwsyJ07NydOnGDFihVkyZJFbdoJEyZw6NAhqlWrxqhRoyhWrBhBQUEcPnyYwYMH4+DgkOJ99y0mTJjA0aNHqVy5Mv3796dgwYJERETw6NEjDh48yOLFi5N9Yy41bG1tadCgAePGjcPGxob169dz9OhRpk2bhoGBAZDyYyUpdevWZdasWbRp04bu3bvz7t07ZsyYkeCNVrFixdi8eTNbtmwhb9686OnpUaxYMcqVK0fBggVxd3cnOjoaMzMzdu3axenTp1O8vd+7LdWrV2flypX4+fkpwXtai42N5eLFi3Tp0iXZacPCwjh48CCA0kzBiRMnePv2LYaGhmrtdf/xxx8UK1YMGxsbnjx5wsqVK7lw4QIHDhxQHk58affu3bx//z7Rt64AJQmbL18+goKCOHToECtWrGDy5MlK8i4pvr6+1KpVC4C//vqLe/fuce/ePWV8vnz5sLS0BD4nRl1cXOjVqxcfPnwgf/78bNq0icOHD7N+/XolAA8PD6dOnTpcvXqVOXPmEB0drewb+Fy7LV++fACcPn2aRo0aYW1tzahRo+LV6CtcuDAmJibJbockSZIkZUaZMd5JzpQpU3BxccHZ2Rl3d3d0dHRYuHAhN2/eZNOmTcpD77gHm0uXLsXY2Bg9PT3y5MmDg4MD+fLlY8SIEQghMDc3Z9++fRw9evSby1SpUiUWLVpE7969KVOmDL169aJIkSJERUVx9epVli5dStGiRalfvz4FCxake/fuzJs3Dw0NDVxdXXn06BGjR48mZ86cDBo0KNH1qFQq2rVrx8qVK8mXLx8lSpTg4sWLbNy48ZvL/iOZmJhQunRpdu/ejYaGhtLfSxwnJyfmzJkDqDcrlRZxxY+M5+J06dKFBQsWMGvWLBwcHJQ4HPim42znzp1oaWnh4uLCrVu3GD16NCVKlFCa0fqeYyc51atXRwjBhQsXlCbfUmPt2rX89ttvuLq60rlzZ1xdXTEzM+Ply5fs27ePTZs2cfnyZSX5olKpqF69Otu3b0cIoVbzBT4fGwMHDkQIkWCTY3GS+g6+JOMvGX9JUooISZK+2apVqwQgfHx8kpyuSJEiwsnJSW1YVFSUmDFjhihRooTQ09MTRkZGwsHBQfTo0UPcu3dPme7s2bOiUqVKwsDAQFhaWoquXbuKK1euCECsWrVKma5jx47C0NAw3rrHjh0rkjvVAwMDxaRJk0SNGjVE9uzZhY6OjjA0NBQlS5YUkyZNEmFhYWrTh4SEiD///FMULFhQ6OjoCFNTU1GsWDExaNAgERAQoEy3b98+ZfuyZ88uhg4dKg4dOiQA4eXllWSZtmzZItq0aSPs7e2FkZGR0NbWFrly5RLt27cXt2/fVpv206dPYtiwYSJnzpxCX19fODk5iWvXrgk7OzvRsWNHZbqkvi8vLy8BiG3btqkNf/bsmWjatKkwMzMTxsbG4rfffhM3b96Mt2whhHj69Kno3LmzsLa2Ftra2sLW1la0aNFCvHr1KtX7Ljl2dnaibt26asPevHkj+vfvL/LkySO0tbWFubm5KFOmjPjjjz9ESEiIEEKIhw8fCkD8/fffKdr+hPZZ3Lq3b98uihQpInR0dETu3LnFrFmz4pUzpdsLiD59+iS4rStXrhQFCxYUurq6Im/evGLKlClixYoVAhAPHz5Upnv06JGoXbu2MDY2FoCws7NTxvn5+YnatWsLExMTYWlpKfr16ycOHDgQ71h0cnISRYoUSbAc3/PdBQcHCyMjIzF9+vQkp0tIQt91Qjw9PQUgLl++nOy0ccdBQp8v95sQQvTq1UvkypVL6OjoCAsLC9G0aVNx48aNRJft4uIiDA0NxYcPHxKdZsmSJaJQoULCwMBAGBkZiapVq4rdu3cnW+44ccdlYp8vr41CCPHx40fRv39/YW1tLXR0dETx4sXFpk2bUrxPALXzPe66mtgnueubJEmSJGUWv0q887WE7j1PnTolatSoIQwNDYW+vr6oWLGi2LdvX7x558yZI/LkySM0NTXVynj79m3h4uIijI2NhZmZmWjevLl48uSJAMTYsWOV+eP26Zf3sUm5du2a6Nixo3I/ZmhoKEqVKiXGjBkjXr9+rUwXExMjpk2bJgoUKCC0tbWFhYWFaNeunXj69Kna8jp27Bjvfi84OFh07dpVWFlZCUNDQ1G/fn3x6NGjeGWP29dv3ryJt8yEvpuv760Tizni7sO+vodLzLBhwwQgypYtG2/c7t27BSB0dHREaGio2ri0iCvSIr5JTqlSpQSQYOyQ0uMs7ru6fPmyqF+/vjAyMhLGxsaidevWavGpECk/dpKKlRISExMjcufOLXr37p3oNMldY8LDw8XcuXNFpUqVhImJidDS0hK2traiSZMm4sCBA/GmX7hwoQCEpaVlvHHXrl1T7te/vAYlJKnvII6Mv2T8JUkpoRJCiBRlaSRJkiSJz9WfixYtyv79+9O7KJlKv3798PT05NatWz+kU8D27dvj7+/PmTNn0nzZkiRJkiRJkiRlHuPGjWP8+PG8efPmu/oW/V4zZ87kr7/+4vnz5wnW2sjMZPwlSVJKaKR3ASRJkiTpv+DPP//k+fPn7NixI82X/eDBA7Zs2cK0adPSfNmSJEmSJEmSJEnfok+fPpiamrJgwYL0LkqakvGXJEkpJZMvkiRJkvQTWFlZsWHDhgQ77/xeT548Yf78+VSpUiXNly1JkiRJkiRJkvQt9PT0WLdu3S/XObqMvyRJSinZ7JgkSZIkSZIkSZIkSZIkSZIkSVIakjVfJEmSJEmSJEmSJEmSJEmSJEmS0pBMvkiSJEmSJEmSJEmSJEmSJEmSJKUhmXyRJEmSJEmSJEmSJEmSJEmSJElKQzL5IkmSJEmSJEmSJEmSJEmSJEmSlIZk8kWSJEmSJEmSJEmSJEmSJEmSJCkNpXvy5eTJk9SvXx9bW1tUKhW7d+9WxkVFRTF8+HCKFSuGoaEhtra2dOjQgRcvXqgt49OnT/Tr1w8LCwsMDQ1p0KABz549U5vm3LlzlCxZEjs7O5YtW6Y2TqVSJfjZvHnzD9tuSfoVJHX+AoSEhNC3b19y5MiBvr4+hQoVYtGiRWrTfO/5K0nSZ+n9e1qxYkV69eqlNu2iRYtQqVSsWLFCbXiXLl2oXLlyGm25JGVuz58/p127dmTNmhUDAwNKlizJ5cuXlfFCCMaNG4etrS36+vpUr16dW7duqS3D19cXR0dHcuTIwYQJE5ThrVq1wtXVVW3aQ4cOoVKpGD16tNrwiRMnYmtr+wO2UEor6X2dBxk3SdK3kDGTJGUc6f1bKmMmSfp2mTVuSvfkS2hoKCVKlGD+/PnxxoWFhXHlyhVGjx7NlStX2LlzJ35+fjRo0EBtuoEDB7Jr1y42b97M6dOnCQkJoV69esTExCjTdO7cmdGjR7Np0yamTZvGkydP1JaxatUqXr58qfZp1KjRD9lmSfpVJHX+AgwaNIjDhw+zfv167ty5w6BBg+jXrx979uxRpkmL81eSpPT/PXV2dsbLy0tted7e3uTMmTPB4c7Ozmm16ZKUaQUGBuLo6Ii2tjaHDh3i9u3bzJw5kyxZsijTTJ8+nVmzZjF//nx8fHywtrbGxcWFjx8/KtP06dOH9u3bs2fPHvbt28eZM2eAz+fl6dOniY6OVqaV52Xmld7X+TgybpKk1JExkyRlHOn9WypjJkn6Npk6bhIZCCB27dqV5DQXL14UgHj8+LEQQoigoCChra0tNm/erEzz/PlzoaGhIQ4fPqwMy5Url/D39xchISGibNmy4tatW6laryRJSUvoPCpSpIiYMGGC2rDSpUuLP//8UwiRNuevJEnxpcfv6ZEjRwQgXrx4oUxrZWUlFi5cKLJnz64Me/LkiQDE0aNH02JTJSlTGz58uKhSpUqi42NjY4W1tbWYOnWqMiwiIkKYmpqKxYsXK8PKlCkjzp8/LyIjI0WDBg3EgQMHhBBC+Pr6CkCcO3dOmbZ8+fJiwYIFQkdHR4SGhgohhPj06ZPQ19cXy5YtS+tNlH4QGTdJUuYkYyZJyjhkzCRJmUdmjpvSveZLagUHB6NSqZTM1uXLl4mKiqJ27drKNLa2thQtWpSzZ88qw8aMGUOhQoUwNTWlYsWKFC5c+GcXXZL+c6pUqcLevXt5/vw5Qgi8vLzw8/OjTp06gDx/JSk9pfXvadxbKN7e3gDcvn2b8PBwOnfuzIcPH7h37x4AXl5e6OjoyCr0kgTs3buXsmXL0rx5c7Jly0apUqXUmqZ4+PAhAQEBauelrq4uTk5OauflhAkTcHFxwcDAAA0NDeV3tkCBAtja2ipva338+JErV67QvHlz8uXLp7zpdf78ecLDw+Xblb8YGTdJUuYgYyZJyrhkzCRJGUNmjpsyVfIlIiKCESNG0KZNG0xMTAAICAhAR0cHMzMztWmtrKwICAhQ/u7SpQvv3r3jzZs3zJs3L96yW7dujZGRkdrH39//x26QJP3i5s6dS+HChcmRIwc6Ojr89ttvLFy4kCpVqgBpd/5KkpQ6P+L31NDQkHLlyimBhLe3N1WqVEFXVxdHR0e14RUqVMDAwODHbqQkZQL+/v4sWrQIe3t7jhw5Qs+ePenfvz9r164FUM49Kysrtfm+Pi/d3Nx48+YNL168YNeuXWhqairjqlevrpx/p06dokCBAlhaWuLk5KR2XubMmZN8+fL9wK2VfiYZN0lS5iFjJknKmGTMJEkZR2aOm7S+ZYPTQ1RUFK1atSI2NpaFCxcmO70QApVKpTbM0NAQQ0PDBKefPXs2tWrVUhuWM2fOby+wJEnMnTuX8+fPs3fvXuzs7Dh58iS9e/fGxsYm3vn2pdSev5IkpdyP/D11dnZm27ZtwOebkurVqwMoNyvdunXD29ubDh06fP+GSNIvIDY2lrJlyzJ58mQASpUqxa1bt1i0aJHaefL1OZjQeamrq4ulpWW8dTg7OzNw4ECioqLinZdxDwO8vb2pUaNGWm6alI5k3CRJmYuMmSQp45ExkyRlLJk5bsoUNV+ioqJo0aIFDx8+5OjRo0rGGcDa2prIyEgCAwPV5nn9+nW8bFdSrK2tyZ8/v9pHW1s7zbZBkv5rwsPDGTVqFLNmzaJ+/foUL16cvn370rJlS2bMmAGk3fkrSVLK/OjfU2dnZ/z8/Hj+/DknTpzAyckJ+F8g8eTJEx4+fCibNpKk/2djYxOvWZhChQopnbJaW1sDqL2tBak/L0NDQ/Hx8cHLy0vtvPTx8eH9+/ecO3dOnpe/CBk3SVLmImMmScp4ZMwkSRlPZo6bMnzyJe6id+/ePY4dO0bWrFnVxpcpUwZtbW2OHj2qDHv58iU3b96UbSNKUjqKiooiKioKDQ31y4ympiaxsbGAPH8l6Wf6Gb+nlStXRldXl4ULFxIeHk6ZMmUAKFu2LMHBwSxZsgQ9PT0qVqyYdhsmSZmYo6Mjvr6+asP8/Pyws7MDIE+ePFhbW6udl5GRkZw4cSLF52W+fPnImTMne/fu5dq1a0oQYWNjQ+7cuZk5cyYREREywP8FyLhJkjIfGTNJUsYiYyZJypgyc9yU7s2OhYSEcP/+feXvhw8fcu3aNczNzbG1taVZs2ZcuXKF/fv3ExMTo2SwzM3N0dHRwdTUlC5dujBkyBCyZs2Kubk57u7uFCtWLMkqul8LCgqKlx0zNjaWVXYlKQlJnb+5cuXCycmJoUOHoq+vj52dHSdOnGDt2rXMmjULIM3OX0mSMsbvqb6+PhUqVGDevHk4Ojoq7adqa2tTqVIl5s2bpwQbkiTBoEGDqFy5MpMnT6ZFixZcvHiRpUuXsnTpUuBztfmBAwcyefJk7O3tsbe3Z/LkyRgYGNCmTZsUr8fZ2ZmFCxeSP39+tTe/4qrQ582bl1y5cqX59klpKyNc50HGTZKUWjJmkqSMIyP8lsqYSZJSL1PHTSKdeXl5CSDep2PHjuLhw4cJjgOEl5eXsozw8HDRt29fYW5uLvT19UW9evXEkydPUlyGxNYxZcqUH7DFkvTrSOr8FUKIly9fit9//13Y2toKPT09UbBgQTFz5kwRGxurLON7z19Jkj7LCL+nQggxduxYAYipU6eqDZ84caIAxMSJE9NicyXpl7Fv3z5RtGhRoaurKxwcHMTSpUvVxsfGxoqxY8cKa2troaurK6pVqyb+/fffVK1j1apVAhA9e/ZUG75u3ToBiC5dunz3dkg/Xka4zsu4SZJST8ZMkpRxZITfUiFkzCRJ3yKzxk0qIYRIXbpGkiRJkiRJkiRJkiRJkiRJkiRJSkyG7/NFkiRJkiRJkiRJkiRJkiRJkiQpM5HJF0mSJEmSJEmSJEmSJEmSJEmSpDQkky+SJEmSJEmSJEmSJEmSJEmSJElpSCZfJEmSJEmSJEmSJEmSJEmSJEmS0pBMvkiSJEmSJEmSJEmSJEmSJEmSJKUhmXyRJEmSJEmSJEmSJEmSJEmSJElKQzL5IkmSJEmSJEmSJEmSJEmSJEmSlIa00rsAaS0iIoLIyMj0LoYkSZIkSZIk/TQ6Ojro6emldzGkTETGTZIkSZIkSdJ/SXrETL9U8iUiIoKcOXPy9u3b9C6KJEmSJEmSJP001tbWPHz4UCZgpBSRcZMkSZIkSZL0X5MeMdMvlXyJjIzk7du3+Pv7kzVr1vQujiT95wwaNIjr169jbGyMiYkJxsbGGBkZcf78ea5fv46hoSF169alSZMmxMbG4uzsjIGBQarXc+LECY4dO8bFixe5evUqnz59QktLi9q1azNw4EAqVKgQb54+ffpw9+5dPD0902JT1Tx48ID69evz/PlzsmXLhoODAw4ODhQoUICCBQsC8O7dO969e8fbt295//49MTExVK1aFScnJyIjI5k8eTLnzp3D19c33vIbNGjAunXr0rzckpScqKgoPDw8qF27Ntra2uldHOknEEKwZ88e/vzzT16+fMmGDRv47bffAKhcuTJly5Zl7ty5yS7n3bt3HD58mP3793P8+HEiIiLIli0bzs7O1KxZE1dXV0xMTFJUpsWLFzN8+HBGjRrF8OHDU7wt79+/J0+ePGTNmpUePXrQuHFjChQokOL5v5cQgmvXrrFz50527drF06dPyZMnD3PmzKF69eppuq53796RN29eIiMjZfJFShEZN0lS+jl37hwjR45EX18fY2NjTE1NMTY2JigoiEOHDhEWFkapUqVo3rw5uXPn/uaY6c2bN2zduhUfHx98fHx49uwZAHny5KFHjx60b98eIyMjtXl8fX0pX748R44coWLFimmyvXGEEAwfPpwlS5ago6ODvb09Dg4OFCxYEAcHB6ytrQkMDFRipnfv3hEYGEj+/PmpVasWhQsXZsOGDWzevJlr167x8ePHeOu4evUqefPmTdNyS1JyZMz03/T8+XPGjh3Ltm3bqFmzJtu3b0dDQ4PNmzfTo0cPnjx5gqmpaZLLiImJ4eLFi+zfv58DBw7w8OFDNDU1KV++PDVq1MDV1ZVixYqlqDyBgYGULVsWHR0dzp8/n+y6vzRo0CBWrlxJixYtaNSoETVr1vypMcX79+/Zv38/u3btwtvbG4CePXvy559/YmhomGbrSa+YSSWEED9tbT/Yhw8fMDU15e3btzKIkKR0MGfOHEaMGMGnT58wMDCgePHifPjwgUKFCtGqVSvq1q2Lvr4+YWFhHD16FBcXl28KJL4UFRXFjRs3OHXqFIsXL8bX15fKlStTrVo19PT00NPTIyAggKVLl+Lm5sa2bdvSaGvVvX37Fk9PT27fvq18/Pz8iI6OVqbR0tLCwsICCwsLIiMj8fPzQ0NDAwMDAwwMDGjSpAnlypUjICCAP/74A/j8g7Nw4UJUKtUPKbckJSUqKoqDBw/i5uYmA4n/gH///ZcBAwbg5eVFvXr1mDVrFvb29sDnwMDQ0JDp06fTv3//VC03IiKCM2fO4OHhgYeHB9euXaN79+4sWbIkRfOvW7eO/v37ExQURJ06dWjatCnR0dF8+PBB7RMcHKz2d2BgIG/fvsXCwoKIiAhCQkIoWrQozZs3p0WLFjg4OKR6H30rIQQXLlxg5MiReHt7M3fuXPr165dmy3/37h0WFhYEBwenOKkl/bfJuEmS0o+/vz+NGjXi33//BaBw4cLo6OigpaVFw4YNadmyJfb29mkaMwG8ePGCCxcusGPHDjZv3oyJiQmtWrUia9as6Onpoa2tzebNm7l+/Tp+fn7ky5fvu9f5NSEEp06d4tq1a2px07t379SmMzExwdLSElNTU+7cuUN4eDhZsmQhKCgIV1dXqlSpQrly5Wjbti1v3rzBxsYGb2/vn/qShSTFkTHTf0tERAQzZ85k8uTJGBkZMWXKFH7//Xc0ND53qz5q1CjWrVvH06dPU7VcIQQPHjzAw8ODo0ePcvz4cUJDQ3n9+jXm5ubJzv/+/Xvq1avHuXPnyJ49O126dCFXrlzxYqaE4qe4shYtWpSbN29ibGxMgwYNaN68OXXq1PmpiYo3b96wfPlyJkyYQJ48efDx8UmzBEx6xUwy+SJJUpry9/dn0KBB7N27l9q1a7Nnz554F+q0DiTixMbGsn//fubOncujR4+IiIggPDwcDQ0NunXrxtChQzEzM0uz9SUnKioKf39/NDU1sbCwwNTUVC2J8uTJE44cOaLss2zZsgGfr2Xz5s1j+/btXLt2DW9vb5ycnH5auSUpjgwkfj33799n6dKlvH37lpCQELXP9evXyZ8/P3PmzMHV1VVtvrNnz+Lo6IiHhwcuLi7JricgIIBJkybx4sULFi9erFzfALp06YKXlxcdOnTg2bNnmJmZ0bdvX+zs7BJdXlhYGNu2bWPx4sWcP38eTU1NTE1NMTExUT5f/21iYoKFhQUdO3ZEpVJx5MgRtm7dyt69ewkJCaFEiRK0atWKli1bkidPnm/fqakghKBfv36sWLGC69evp9lDIpl8kVJLxk2SlL6io6NZvHgxo0ePBmDLli3Url1bbZofFTPB5zhkzpw5HDx4kPDwcCVuKl26NBMnTqRq1appur7kvHnzhlevXpE1a1ayZs2Kjo6OMi4iIoLTp09z/PhxKlasSIMGDZRxR44cYdWqVezfvx9nZ2f27dv3U8stSSBjpl9RVFQUq1ev5tKlS0qsFBoaSkhICI8fP+bt27cMGDCA0aNHq9Uw+fTpE3Xq1EFXV5cjR44ku57Y2Fi2bdvGggUL6NevH82bN1fG3b17l0KFCtGnTx+0tbV5//49jRo1omHDhkqiJyFXr15lyZIlrF+/ntDQUIyMjOLFSAnFT87OzpQpU4Y7d+6wbds2tm3bxs2bNzExMaFx48a0atWKmjVr/rRj/NatW5QrV45u3brxzz//pMkyZfIlDcggQpIyjv3799O0aVN69uwZ70L5dSAxYllEmqxzareUZeOFEBw4cAAjIyMO3i0LQODrB6BSYW5l/021TFK67tSIiooid+7cVKtWjU2bNqX58iUpOTKQ+HUEBAQwceJEli5dipmZGfnz58fIyEjtU6RIEbp166b2wOPdu3eMHz+ehQsXki9fPnx8fDA2Nk70OvnhwwdmzJjBrFmz0NbWRltbGz09PXbs2EG5cuUA2LhxIx06dCBbtmxkz54df39/goODadWqFX/99ZdaEqZhL1/2LCqoto5Pnz6ho6PzzTUCIyIiOHToEFu2bGHv3r2Eh4dTsWJFWrVqRdu2bbGwsEjRct68ecPRo0epWbMmVlZWKV5/WFgYxYsXx9rampMnTyYYPDXsFb8Jyq99uV9k8kVKLRk3SVLG8ObNG9q2bcuVK1e4ceMGtra2yri4mGnFMj9CQ6OTWErqeHqNTFX59uzZw5pV/ujoGBMZGUpERCCGhlZoamqrLaum85SfVq6v/f333wwfPpxHjx6RK1eu7yqHJKWWjJl+HXHJkD///JMHDx5QsmRJTExM1GKmLFmy0LVrV7Va9HFNN7u7u/Po0SOWL1+uvACWmGPHjjFixAguX75MgQIF8PPzY8SIEUycOBEtLS1iYmLImzcvgYGBZM+eHR0dHW7cuEGBAgUYM2YMbdu2BcDVpg8Ah14uUFt+dHQ0KpUKTU3Nb94fd+7cYcuWLWzatAk/Pz8sLCxo1qwZ7dq1w9HRMcXLOXjwIFmyZKFixYpJJo6+Nnv2bIYMGcLJkyepUqVKvPFx256cuH0jky9pQAYRkpSxzJ07lwEDBrB//37q1q2rDP9RyRdIWRLkr7/+4s8//wRAR8+E6KhwYmOiADC3LkjB0o3IV9yVrNYF0TPIkqbrTszx48fZuHEjVlZW5MqVi5cvX7J8+XKeP39O+/btWbt27TcvW5K+lQwkfg0PHz6kePHiaGlpMXLkSPr164e+vn6y8x0+fJg2bdoQGBgIQNasWQkNDcXc3Jxz586pPdy4e/cu69atY+nSpYSEhDBgwACGDx9OaGgoTZo0wcfHB0tLS6Vd97h/TU1N2bBhg9IE2cSJE5XrM/wvCfF1AiathISEsG/fPmbNmsWlS5do27Yt69evT9G8hw8fxtXVFZVKhaOjI02aNKFx48bkzp072XlPnDhB9erVE21+LKnkS6HSn5NTX/7myOSLlFoybpKkjOPt27cUL16cwoUL4+HhoTyY+lHJF0hZoiMsLOyLpl5UaGnpER0dDoCmhg5NmjakWbNmVKhQgRw5cqCpqfldCZiUlOn9+/fMmDGDjx8/kitXLszMzNi+fTtHjhwhS5YsnD17lkKFCn1zGSTpW8iY6dfRqVMnVq9eTd26dZk8eTLFixdPdp6IiAgaNGjA0aNHAdDX10dPT48PHz4wadIkRowYoUwbGhrK7t27Wb58Od7e3lSsWJFp06ZRtWpVZs2axbBhw9DS0sLe3p6CBQsqMVOBAgX4999/mTRpEk+ePMHc3JxXr15RP+cAZdlfJ1/SUlxfluvXr2f27NkIIXj+/LnaCwNJzVuiRAn+/fdfbGxsaNSoEU2aNMHJySnZ8yWur+R3795x7do1tRg2pYkXSP/ki9ZPW5MkSf85/fr1w8PDg06dOnHy5Mmf0r7+iGURySZBnj59ioaGFrGx0URGfFCGm1rkRs/AlCveSzh3cBoAugZZiI2OxLH+H1SoM/ib1/38+XOeP3/Oq1ev+PDhA8WLF6dIkSK8ePGCIUOGsHXrVuzt7fn06RPPnz9HV1eXtm3b0rNnT0qXLp3KvSBJkvQ/Pj4+hISEpPgGOU5ERAQVK1bE3Nxc7TNlyhSGDh3KvHnz2Lx5M+vWrePSpUtkyZKFNm3aMHLkSHLkyAGAmZkZJ0+eZP/+/dy+fZu7d+9y5coVNm3aRGhoKADZsmWjf//+tG3bVqkd87WEasCkBSMjI1q3bs1ff/0FgIWFBYGBgSlqovK3337j9OnT9OrVi9OnT3P69GkGDx5M6dKlady4MU2aNKFQoUIJvvHm5OREu3btmDBhQqr6folLvEiSJEm/DgsLC9atW4eLiwtjxoxh/Pjx3/WmckrUdJ6SbLLjxYsXAGhq6hIT80lJvACUKl2cmzdvKv1pamtro6urS/HixVGJqujqpryjZ/hf4iUsLAx/f39evXrF69evMTMzo1KlShgbG7Nq1Sqlf1E7OzseP37Mx48fKV++PCtXrqRly5Zp3jybJEn/LZ6engwaNIhZs2aleJ7Y2FhMTExo0qQJWbNmVWKmmzdvMnbsWBo3bszTp09Zt24dO3fuJCQkhCpVqrBz504aNWqkxApDhgyhVq1anDp1irt37+Lr68u6det49uwZABoaGtSsWZPx48fTpEkTtcTLj6ZSqShVqhTXr19HCEHu3Ll59uwZNjY2ybZGoFKpOH36NOPGjWPu3LksWrSIRYsWYWZmRv369WnSpAm1a9dO8OVATU1NFi1aRMmSJdm6dSsdO3YEUpd4yQhk8kWSpB9GpVKxatUqKleuTPHixRk8eDCTJ0/+4etNLgGjbT8Kx/o2hAS9QM8gC7qGWYgIDeTaieUEv32EsVl2zLPl49XTG3wKCwLgzfNb37zu+fPnJ/hwLUuWLERGRmJsbMzatWtp164dKpWK6OhoYmJi0NXVTflGS5IkfSE2NpbXr1/z/PlzvL29MTAwwMbGJlXLaNSoEY0aNYo3PEuWLHTs2JEdO3agoaGBm5sbI0aMoF69emrXLX9/fz58+ICGhgYODg4ULlwYDQ0N8uTJg46ODs+fPycgIICSJUuipZX8LemPSsAALF++nBkzZrBgwQKWLFlCy5Yt6datG5UqVUqyaryjoyNXrlxh3rx5jBkzhpCQEG7evImvry+jR4+mYMGCNGnSBFdXV8qUKaM8FIqJicHHx4fKlSunuIwy8SJJkvTrinugNmbMGA4dOsTatWt/eH9kySVgunfdSrGi7Xn/3hcNDR20tPTQ0tTF2vYDp06dQk9PjyJFiqBSqbh58yZRUVGcPXuWR4820vn3jSkuR1wZ3rx5Q+nSpZUHjXFUKhXW1ta8fPmSdu3aMX36dOWeJiwsTCZcJEn6LqGhoTx79oznz5/z8uVL7O3tUzW/gYEB27dvjzc8PDyckydPUrRoUaKjo7G3t2fYsGG0a9dO7foeEhLCvXv30NDQQENDA2dnZ2rWrEmWLFmwsbFRxtvY2GBtbQ2kX/KhadOmPH36lGXLllGhQgVKlixJjx49aNmyZZIvr5mYmDBr1iw6duxIr169OHfuHIGBgRw4cIC1a9diYGCAq6srjRs3pmrVquTMmVNJ6ly4cAGVSkXFihWBzJd4AdnsmCRJP0FERASTJ09m4sSJeHl5Ub58+R/W7Nj3iPoUxhPfE/jfPMLD28fQ1jUiT5Fa5C1Smxz5K6OhmbJ89ZfJl1u3blGmTBk6dOhA7969sba2xtDQkMuXL3PmzBmio6MZOHCgWidtkpRRyCr0mc/hw4dxd3fHz8+PqKgoZXiVKlU4depUvOkTu/4mlcCOjY1lzJgx2Nra0rJly3j3XG/evMHd3T3R5hK7devG0qVLU7I5yfZ9ktbJmICAAFatWsXSpUt59OgRFhYW1KxZExcXF1xcXJJsR/758+cMHjyYrVu3Uq5cORo0aMDDhw/Zs2cP7969Q1NTk+LFi1OhQgUMDAyYNWsW58+fp0KFCvGW9fV2J5R4kc2OSd9Dxk2SlDGdO3eOdu3a4eDgwLZt235Ys2Pfw9NrJLdv3+bQoUMcOnSIO3fuULVqVerUqYOrqyvW1tYpbn4sLvEihKBJkyacOnWK7du3kzNnTqysrHj58iVnzpzh2rVrNGnShGrVqv3ITZOkbyJjpszn3bt3DBkyhD179hAUFKQM19TUxNvbm7+af3ufu182/3Xq1Cl2795NixYtKF++vFotESEEmzdvZtCgQbx69SrRcpqbm6co4fAjmx37WkxMDIcPH2bJkiUcOHAAgHLlylG7dm1cXFyoWLFioudCbGwsq1evZtiwYURFRdG6dWtsbW3Zv38/Pj4+AFhbW1OhQgUqVKjAsmXLKFu2LFu3bgW+LfmS3s2OyeSLJP1EISEhrF69mpcvX/L+/XsCAwN5//49NWvWZPjw4eldvG/2+PFjjh07xtmzZzExMSFXrlxqH0tLSyIjIzEzM2P48OEMGzYsQyZf0sKXD8OOHz/O77//jrGxMZcvX0ZP79v7hJGk9CADicwjKCiIwYMHs2rVKmrVqkXjxo3Jnj07OXLkIEeOHFhaWsarwZHctfdb+rHat28fbdu2RVtbm8mTJ1OmTBmioqLYtm0bs2fPBmDatGkMGzYswfkDAwOJiYkhS5YsSm2YLxMRHwP9eeF/mPcvL6FnaE3fzhUoVKgQhQoVIk+ePEk20/Ly5UuuXr1KlSpVkr3Zjo2N5fTp03h4eHD06FEuXbpEbGwsBQsWxMXFhdq1a1O9enWMjY3jzXv06FEGDBjA3bt36dq1K+PHj+fVq1dcuHBB+dy5c4fatWtz+PDhePN/ub1J1XaRyRfpe8i4ScrovLy8OH36tFrMFBMTw/Lly1NdkzOjiIiI4PTp0xw7dow3b97Ei5ly5syJnp4ew4cPZ8GCBbx69Ypjx45luOQLJN0/S2oTL8HBwfTv35+1a9eyY8cOmjRpkiZllKSfRcZMmcuuXbvo1asXnz59YuDAgeTPn1+Jm7Jnz06TvO7fvY7kEiEfP36kYcOGeHl50axZMwYPHoyWlhb37t1j2rRp3LhxA319fQIDA2mUO37z9/ue/sPr168xNTX9om+u/wkJCeHAgQPs3LmTiIgIJV5ycHBQ+t1MTFRUFOfOncPMzIxixYolu63Pnz/n0KFDeHh44Onpyfv37zEyMsLZ2Vl5ga1gwYLxmid79+4do0ePZsmSJdjb2zN79mxKly6tFjP5+PgQGhrKpUuXKFmyJJA5ky+y2TFJ+kmEEHTr1o3t27eTI0cOzMzM0NTU5NKlSz+8SnlaCw4OxsvLi6NHj3L06FGlimTx4sWJiIjgyZMnhIWFKdPr6OhgampKbGwsNWvWTMeS/1hxD8LCwsIYOXIkc+fOpXr16qxZs0YmXiRJ+mEOHDhA9+7dCQkJYfny5XTu3DnZtndTkvROSR9aX1uzZg0fP35kxYoV5MqVi0uXLjFjxgwePHiAi4sLI0eOpHr16vHmE0Iwa9Yshg8fTkxMDLa2tmzfvp1KlSqxZ1FBRiyLYOs/DXl4ywNNbQMsbMphZfyEiRM9CAkJAT7/1hQoUID8+fNjY2OjVM0PCgpi9+7dnDt3DiEE+vr6NGzYkPbt21O7du0EmzzT0NCgWrVqVKtWjUmTJhEYGMjx48fx8PDgwIEDzJ8/Hy0tLQoVKoS9vT0FChSgQIEC2NvbU6JECa5fv86iRYsYO3YsW7duZdOmTfTo0YMePXoAnx98J9S0ZFziRTYxJknSf9n58+dxcXEhS5YsZMuWDXNzc+7cuUN4eHiSTUFmNLGxsVy/fp2jR49y7NgxTp06RUREBNbW1uTMmZODBw8SEBCgNk+2bNkIDAykcePGyf6Wp6fEmixLbeLF09OTTp06ERwczOrVq2XiRZKkH+bt27f069ePzZs307BhQxYtWhQvmZ9WTVq52vRJMgHz+vVrvLy8aNKkCV26dCEoKIg1a9awbds2LCwsmDJlCr169Uow8TLHuz8lS5bk1q1bqFQq+vfvz4wZM5SYZt++fbRs2ZLw8HDKlCmDhYUFmzZt4smTJ8oybG1tcXBwIEeOHErMZGpqyokTJ9i7dy+BgYEAFC9enPbt29OmTZtE+w7Nnj07Xbt2pWvXrsTExHD16lXlWaG7uzuRkZHY2Njg4OCgFjMVKFCAOXPm0LNnTwYMGICbmxtNmzZl69atNGjQAPj8OxoYGJjqF4UiHXKkavofTdZ8kaSfZNWqVXTu3JnNmzfTsmVLAAYPHszSpUuV9hszqqioKC5cuKBcQC9evEhMTAz58uVTMtnOzs5KG49CCN6/f8+TJ0+Uz9OnT6lbty5OTk6EhYX9cjVfvky8VKpUCT8/P6ZOnUq/fv0yVZAoSV+Sb3FlbDExMfTq1Ytly5bh6urK0qVLlY7uk5Laa25qEjDe3t64ubkRHv65U16VSkXTpk0ZMWIEZcqUSXS+GTNmMHToUIYMGULlypXp0qULefPm5fLly8o0ltmLYJrVjkY9NzKjdxbg8+/N8+fPuXPnDnfv3uXOnTv4+/sTEBBAQEAAr1+/RldXlzp16tC4cWPKli3Lvn37WLduHbdv3yZHjhwMGTKEbt26JfjWWGIePHjAsWPHuHHjBn5+fvj5+fH06VPibqtNTU2xt7fH3Nyc48ePky9fPm7fvp3k70FqEy+y5ov0PWTcJGVUwcHBlCxZEmtra06ePIm2tjYPHz7EwcGBkSNHMm7cuPQuYpKePn2qxEyenp68efMGfX19nJyclLipaNGiSmLl06dPPHv2TC1u0tTUxN3dnejo6AzZ7NiXvkzApDbxsnjxYnr16kWNGjVYtWpVkk17SlJGJmOmjO/kyZM0a9aM2NhY5s2bR6tWreIluH9EXyKJJWAiIyNxc3PD09NTGZYnTx6GDh3K77//jr6+foLlWXjWnTJlymBkZMTs2bPZvXs369evx9PTkxo1agCwevVqOnXqxNWrV5XaIvC5bxtfX18lbvL19VX64Hz58iXh4eEULlyYxo0b06hRI16+fMnatWvZt28fUVFRtGzZkuHDh1OiRIkUb39oaCgnT57kzJkzSsx079495WVtTU1NcufOjb29Pbdv3+bJkyfs2bNHSb4k5Mv9ktIkS9zvjqz5IkkZUHh4OI8ePeLhw4f4+/vj7+/P48ePad++fYKdECfmzp079O3bl86dOyuJFz8/P+bNm8f48eMzVOIlMDCQ06dPK9vr5+fHmTNn+PjxI2ZmZtSoUYOFCxfi4uKSaI0dlUpF1qxZyZo1K6VKlfrJW/DzffkAbOTIkfj5+XH+/PlU/ShJkiSlRmxsLF26dGH9+vUsW7aMLl26JPiGbFokt1O3jIr0mv6Moc0iiYiIQF9fHysrq2TnevPmDQCWlpZcvnyZoKCgeA/YGvxWkf1HLyuJF/j8exPXtJqLi0u85UZHRxMbG4uOjo4yrEiRIgwfPpwrV67wzz//4O7uzqRJk+jXrx99+/ZN0YPofPnykS9fPrVh4eHhPHjwQAkq4gIMc3NzHjx8Tr2ul9HWlYkRSZJ+PUIIAgIClPghLnbS1dVlzpw56Ovrp2g5sbGx9OjRg/fv33P8+HHlIeawYcOwtLRk6NChP3IzUkUIgY+PD7dv31a22cfHB19fX1QqFWXKlKFr1664uLhQuXJldHV1qek8hQP79gP74y0voVokzZr8Q5duBX7C1ny7lCZc4sRtp5+fH4MGDaJHjx4sXLhQvqwmSdIPc/bsWdzc3KhQoQIbN27EyspKuXbp3H32Q9edUALl0MsF6Ojo4OHhwfv374mIiODTp0/Y2dkptVcSSwQJIQgMDCRPnjzExMTg6emJs7MzTk5OyjRx/Um+e/dObV5DQ0NKly5N6dKlE1xuXOz2pfr16xMUFMT69euZMWMGJUuWxM3NjREjRlC1atVkt9/Q0BBXV1dcXV3V1vXixYt4MZOBgQHa2tr4+ibe52dN5ymQwWq1pISs+SJJfD75jxw5wvnz59WChhcvXijT6OjoYGdnh7a2No8ePeLGjRvxHrx87caNG/zzzz9s2LCBPHnycOnSJeXN2gYNGnDjxg3u3LmT4oDkR3ry5AmzZ89m2bJlhIaGoqenR548ecibNy+VKlXCxcWFMmXKJNmefkp9WfNlwobMf6Mdl3y5cOECFStWZPz48YwZMyadSyVJ30++xZUxCSHo3bs3S5YsYcOGDbRu3TrB6dK7VmFqasxER0crtUOjoqIYPHgwM2fOVJtmyZIl9OnThw8fPmBgYJBm5Xz8+DEzZsxg+fLlaGpqMnv2bLp165Zmy2/YyxchYlGpUvZ7J2u+SD+DjJukb/XixQu2bt3KgwcPlCTLw4cPiYj4329OtmzZyJs3L1evXqVv377MmDEjyWWGhISwZs0a5s2bh6+vr1pLASdPnsTJyYl169bRrl27H7ptKREdHc22bdv4+++/uXr1KvC5+Za8efNSuHBhatWqRY0aNeKdVylJUnxdi8TQUIsu3Qpk6JovqRW3jdWrV+fu3bs8ePAgVTVPJSkjkjFTxnX58mVq1KhByZIlOXToEAYGBklej390MiZOUk2SJVcDZ/DaRnTo0IGAgACyZcvGtWvX1F7ojo2NVfpbHjVqVJqVOSoqii1btjB16lRu3bpF9erV2bNnT5rGHrGxsYkm41Ob7P9Setd8yfxPPSXpO71+/ZrmzZvj6urK4sWLefDgAXny5KFLly6sWrWKEydOKH2Y+Pn5ceHCBaysrOjcuTOxsbHxlhcTE8OuXbuoXr06JUqUwMPDg3HjxnHmzBnlxtLDw4N9+/Yxffr0dE+83Lhxg/bt25M3b15Wr17NgAEDePjwIaGhody+fZv9+/fzxx9/UL58+TRJvHxpyqbINF1eetPU1MTCwoKpU6cyZswYPn78mN5F+mGEEHh6etKkSRMmTJjAL5THl6QMTQjB4MGDWbx4MStWrMiwiZfUlkFLS4u1a9cSEhLC7du3+fvvv+NNU6FCBWJiYtSaIksLdnZ2zJs3j8ePH9OiRQt69erF5s2buXHjBg8ePCAgIIAPHz4QExOT6mXHNSWW0sQLwJ0rj1O9HkmSpB9NCMGaNWsoXLgwI0eOxMvLC5VKhYuLC1OnTmXPnj38+++/fPz4kVevXnHu3DkmTpzIrFmzOHv2bILL9Pf3Z8iQIeTIkYMBAwZQrFgxzpw5oyReoqOjGTRoEOXLl6dNmzY/c3PjCQ0NZe7cudjb29OmTRssLCw4cuQIYWFhPH/+nFOnTrFkyRKaN2/+TYmXL6f7ngdMmYWlpSWvXr3it99+4+LFi+ldnB8qICCAcePG8dtvv/Ho0aP0Lo4k/WfcuHGD2rVrU7hwYfbv359s4gU+N2P15edHSSzBkpKmz2Z12M2LFy949uwZ//77b7yWdDQ0NChXrhwXLlxIk7LG0dbWpl27dty4cYM9e/Zw5coVOnTowOXLl7l79y5Pnjzh3bt3REREfPPzoR+ReMkIUt3s2PPnzxk+fDiHDh0iPDycAgUKsGLFCqUdbyEE48ePZ+nSpQQGBlKhQgUWLFhAkSJFlGX4+vrSuXNnHj9+TPfu3dXeEM+dOzePHz/m3LlzVKxYURk+cOBArl27hre393dsriSp27p1K3369FH+37x582TnmbRJi4pNFrNpZh0WLFhAv379AAgKCmLFihXMnz+fR48eUblyZbZs2ULjxo2Vtx8uXrzI7Nmz2bZtG9WqVUvR+n6E2NhYjh07xqxZszhy5Ai5cuVi5syZdOnSBSMjo0TnS6uHedoakZS1SJNFZShly5bl/v37TJ06lenTp7N//36uXLmS3sVKU+Hh4WzcuJE5c+Zw8+ZNAHbt2oWBgQHu7u4pWsaHDx/w8/OjcOHCafr2uiT9F4wePZo5c+awYMECOnXqlOA0GSHxEie5snxdO0ZHR4dChQolOG3RokUxMDDg4sWLKarmnlrZsmVj6dKlPHnyJNGklq6uLp07d2bhwoUJjo9LtkiSjJmkX8mLFy/o0aMH+/fvp3379vzzzz+YmZlR03kKN2/AzRthwK3//3zm6TWSwYMHs3PnTjp16sS1a9fQ19dHCIG3tzf//PMPe/fuJUuWLPTs2ZPevXsrfX4EBwezfPly5s6dy7Nnzzh9+nS6NUv14sULFi1axMKFCwkODqZVq1bs2rVLrR39tHwolNkfMKXUtm3bOHLkCEOHDqVChQocO3aMmjVrpnex0tTVq1f5559/WLNmjTKsatWq+Pv7p6h2RGxsLPfv30dLS4u8efP+yKJK0i/n7t27uLi4YGdnx6FDhzA2Nv6m6+vXCZi0rBnzPX3MuNn2TbL2TIUKFVixYgVCiASbpv4eGhoaNGjQgHXr1tG4cWP27NkTbxpNTU0sLS25cOFCgv15JfVdfN0M56/wu5iq5EtgYCCOjo44Oztz6NAhsmXLxoMHD8iSJYsyzfTp05k1axarV6+mQIECTJo0CRcXF3x9fTE2NgagT58+tG/fnnLlytGzZ09q1qyJo6Ojsgw9PT2GDx/OiRMn0mYrJekrb968oU+fPmzbto1mzZqxYMECsmXLluQ8Xz5AylWwGqWr92CI+wjs7e3Zu3cva9asUTqh2rZtG2XLlgU+14TZuXMns2bN4syZM+TLl485c+bQqVOnNL8IJic4OJg1a9awYMEC/Pz8KFmyJBs2bKB58+ZJ3gBmpAd5GdGIZRHKA0RTU1OmTJmCpaUlo0ePTueSpR0hBHPnzmXSpEm8fftWGa6trU316tXVHvx8LTg4mMWLF3P58mWuXr3K/fv3ATAyMqJp06a0bduWGjVqpHnNKkn61Zw8eZK//vqLadOm0bt37wSnyWzX67jypqSJMi0tLcqUKcPZs2cZMmTIDymPlpYWhw8f5u7du4SFhREaGqr2WbhwIXfv3k1w3tQmXvYsKvhd80sZl4yZpF+FEIL169fTv39/9PT0lE5wU/IgJG6aVatWUbJkSUaMGEHx4sWZO3cuN27coHDhwixevJh27dopL+P4+/szd+5cVqxYwadPn2jTpg2DBg366f0oCiE4c+YM8+bNY+fOnejp6dG1a1cGDhyInd3/mob8FR4Ipac6depQq1YtdHV18fPz+2WSL35+fnTr1o2TJ0+qDc+XLx8tW7ZMMpF46NAhDhw4wNWrV7l+/TqhoaEAlCtXjnbt2tGyZcsU9aUnSf9lQgg6duxI1qxZ8fDwIEuWLGl2vU6oNkxGaKrsaxUqVGDy5Mk8fPjwhyVvGzRowNOnT3n16lW8mOnOnTtMmTKFN2/exEu+JPdd1HSeopaA+RWSMalKvkybNo2cOXOyatUqZVju3LmV/wshmDNnDn/88QdNmjQBYM2aNVhZWbFx40Z69OgBfK4hUKpUKYoXL46trS3BwcFq6+nRoweLFi1S2kyUpLS0fft2evfuTWxsLFu2bKFFixbJzpPQwyynJpN4cPMIrq6uZMuWDXd3d3r27KlU+fv48SMrV67kn3/+4eHDh1SrVo1du3ZRv379NHnILITg9u3b7Nu3j3379nH16lXatGnDiBEjyJ8/v9q0t2/fZsGCBaxdu5aIiAiaNm3KihUrcHR0TDYBlNke5KWHiLAgOo15TLffYsiZMyc2NjZoa2vz6dMnVq5cSZMmTdQeuGQ2Qgj++OMPpkyZQvXq1fH29qZZs2a0b9+eGjVqJFlbCsDT05MRI0ZQvnx56tWrR8mSJcmfPz+enp6sX7+eNWvWYGNjQ7NmzahevTqOjo4yqJCkBEycOJESJUok2uFwZr5ef5nETkrDhg0ZOXIkT58+JWfOnD+kLFpaWhQtWjTBcfv27SMy8vubzIxLvHz69AldXV1lmEzA/BpkzCT9Cl6+fEnPnj3Zu3cvbdq0Ye7cuWTNmjXVDz369NrFxIkTGTZsGCqVirp16zJz5kxq1qypxCFnzpxh1qxZ7N69GzMzMwYOHEjv3r3jNaXyrT58+MCRI0fYt28fhw4dwtramlGjRtGiRQu1uCwsLIyNGzcyf/58rl+/ToECBZg1axYdO3aM1zZ8Znz4k96OHB3K3bt3CQoKwsTEhJw5c2JsbIympiaHDx+mYsWKlCxZ8qe/oJiWbt26Rc2aNZXYz9ramuHDh+Pm5kaBAgWSnb9Pnz4EBwdTp04dGjVqRKlSpQgMDGTDhg24u7szePBgXFxccHNzo0qVKhQvXly+wCZJX/Hw8ODixYt4eHhgYWHxw6/XiTVPlpZJmdQkXuBzv1omJiYsXLgw2X7XvoetrS22trbxhp85c4YpU6Z8UzcLXydb4PO9c1RUFDo6Onh6jcx0v8Gpqru7d+9eypYtS/PmzcmWLRulSpVi2bJlyviHDx8SEBBA7dq1lWG6uro4OTmptfM6YcIEXFxcMDAwQENDgzp16qitJ3fu3PTs2ZORI0cm2KeGJH2Lt2/f0qpVK5o3b06VKlW4devWNydeAHT0jGjWdyebN2/myZMnjB8/HhsbG2JjY1m9ejX58+fH3d2dypUrc+nSJU6cOEGjRo1SdHP09u1b9u3bx6dPn9SGR0ZGcuzYMQYMGEC+fPkoWrQokyZNwsrKisGDB3PgwAEKFixI27ZtuXXrc7X/Xbt2UbRoUXbs2MHgwYN5/PgxmzdvpkqVKjLx8h38b3qw9Z+GLByen38G2rB6YkUcHR3JlSsXenp63LhxAycnJ7p27YqVlRW///47T58+Te9ip5oQAnd3d6ZMmcKsWbOwt7fHysqKdevW0aBBg2QTL/C5er1KpaJnz57Mnj2bjh074ujoyJgxY/D19eXChQs0a9aMvXv30rRpU6ytrSlQoACdO3dm5cqV+Pn5xWszNDY2lqCgIF6/fi37m5H+E86ePcuxY8cYPXp0gtfuX+F6nZJt6NGjB8bGxgn2CfMzhIeHf3dfbXsWFSQ2NpZ//vmHLFmyMHbsWLVxUuYnYyYpMxNCsGHDBooUKcL58+fZuXMnGzZs+KbES5xDByKVe7p9+/ZRq1YtVCoV/v7+1K9fnypVqnD79m0WLVrEkydPmDhxYooSL3FNmPn5+cUb9/DhQ+bOnYuLiwsWFha0aNGC69ev061bN3LmzEmbNm0oVKgQq1atIioqitDQUBwdHenevTu5cuXCw8ODO3fu0K9fP5l4+Q7R0Z+447uT90FbMTIyolChQlSqVIkiRYpgYmJCjhw56NevH+fOnaN06dIULVqU7du3Z8r7++vXr1O9enWsrKxYvnw5KpWKSZMmMXDgwBQlXgBl/o0bNzJ06FBq1apF8+bN2b17Ny9fvmT+/PmEhYXh7u5O6dKlMTMzo06dOkycOBEvLy+lpkwcIQTh4eEEBATEe7YgSb+iuGZdK1asSK1atdL1ev11/zHf+klt4gXAxMSEvn37snjxYt69e/cDti5p4eHhAKmOmxJKvPj6+lK+fHkcHBwICAhIdLqMLFU1X/z9/Vm0aBGDBw9m1KhRXLx4kf79+6Orq0uHDh2UnfD1G8tWVlY8fvy/zkPd3Nx48+YNHz58wNLSMsF1/fnnn6xatYoNGzbQvn37VG1UVFQUUVFRqZpH+rXt37+fgQMHEh0dzcaNG2natCkqlSrecTJuTfwbEs0k8hNW2e1p0uTz27FRUVHcuHGDIUOGcPHiRZo1a8aECRPInj27Mh4+/xhcunQJQ0NDbGxsyJIli/Igzc/Pj0WLFrFx40YiIiKoUKEC8+bN4+rVqxw6dAhPT08+fvxI9uzZqVu3Lq6urlSrVk15a3bEiBGsW7eOOXPmUK5cOdzc3Dhx4gStWrVi2bJl6OjoqJXle7b9e2ioopV/Nf///5lNRMhL3jy5SEz0Jyyt81DSqSs57asSEvQSn2Nz2XPAi67jr1LELQC/y7vYf3ABO3eVoHT1nqxf0JtXr17x4MED7t+/z/3794mIiKBfv35Kc3UZwZkzZ5g6dSonT55kwYIFNGzYkKJFizJixAg0NTWTvM5GRERw584dbt26hb+/PxYWFhw9epR27dop02w5J2hZSUWpUqUoVaoUM2fO5Pnz55w/f175bNmyBSEEFhYWWFtbExwcTHBwMB8/flSCMktLS0qVKkXp0qWVZcmaM2kj7juWv6npb/369RQpUoR69eol+Nv1o67XP9sfy0PiDRvXUVf5v66uLoMGDWLWrFkMHz482SZD05qNjQ2+vr4JnhPamjHJzr9ptj3Pnj2jR48eeHl5Ua1aNf7++29y586tXB+3z82b4G/y174sgzxHM5bMEjOBjJskda9fv2bQoEHs37+fpk2b8vfff5M1a1aioqJoUG8WOjrf2u+KBls3v6ZdOzuioqKIiIhg1qxZzJkzB0tLS7Zu3UqDBg2U5pjijsm4DoVtbGywsrJSYpmIiAi2bt3KggULuHv3Lnp6esybNw87OzsOHz7MoUOHuHPnDtra2lStWhWHAvUxz1oAfb0sXL0M4MTFi+OZMWMGffr0YerUqeTMmZNnz55x8eJFpZmzmJgYYmJiaFBvltrWfPt+SB1tbQ3l35+1zrSmUglsbGK5c8cXTU1N2rZtS/v27dHX1+fEiRNMmTKFsmXLMnHiRE6cOMGiRYvo0KEDCxYsYNKkSVhbW6vFTI8fP6Zy5cr06NFDiYHT2/v371mwYAGLFy8mf/787N69m3HjxmFnZ0erVq2SvMYKIXj27Bk3b97E19eXFy9e8OjRIx48eBCvqR4TExO6dOnCnl3BvHt3gCtXrnD+/HnOnTvHwoULmTJlClpaWhQoUIDIyEglboqrsautrU2RIkXUYiYHB4cU9T8jJU3GTBnH/fv3uXbtGhs2bCA6OjrTXjvj7N0/+JuPq759+7JkyRLmzZvHH3/8kcYlS1rWrFnR19fn4cOH5MjxVb85iXwnX2+rEIJVq1YxatQosmfPjkqlokmTJhw8eBBDQ0MOe7jH+31OTHqfoyqRilcKdHR0KFu2rNobWf3798fHx4dz585x9uxZHB0defHihdqbKt26dePp06ccPnw42XXkzp2bgQMHMnDgQCZMmMCqVavw9fVl2LBhyXYe+eHDB0xNTdm4caPsxFmSJEmSJEn6TwgLC6NNmzYEBwfHe0Nb+vkyeswEMm6SJEmSJEmS/lvSK2ZKVc0XGxsbChcurDasUKFC7NixA/jcniVAQECAWiDx+vXrb3oLefDgwSxcuJCFCxemaj5nZ2eyZs2a6vVJaUMIQWBgIG/evEGlUmFqaoqxsTH6+vrJNnMlhODs2bOsWrWKvXv3Jlg1VldXFxsbG5YsWZJkJ99xPD096dChA9bW1qxevZpixYrFmyYlb5gmZEx7bTZs2MDYsWP59OkTo0aNonv37om+PSKEoGrVqmTJkoUxY8bw8uVLXr58SUBAAK9fv8bR0ZHmzZsrb3W9evWKM2fOULlyZeX8Sg0hBLGxsUk2dfat2/6tChXQgdhwjIO9+WhaHTT+Vw3xjt/3t6OfHoQQvHtxh0d3j6NSaZAjvyOWOYqioRF/v38IfMYL/4uYZrVjav9CmJmZAZ/frFu6dCkjRoxg3LhxDBo06GdvBqdPn2bixImcP3+ekiVLMnz4cFxdXZXz9q+//mL58uX4+/ujUqn4+PEjN27cYM+ePWzdupXAwEBKly5NuXLlKFq0KEWLFsXBwSHeQ50t5+Ln/FtW+v7X9uPeHLt69SpXr17l8uXLXL9+naCgIABKlCjBiBEj1LZJSlpUVBRHjx7FxcVFvhWXzho3boyJiQlr1qyJN+5nX8d/ti9rvsyfP58//viDli1bMn/+fOX38md59uwZRYoUUZpe/FrrQfcSnTcmNoqQe+7cuXOHY8eOkS9fPgCio6Np06YNZ86c4eDBg2odSyf13X65X9KjOQEpcZklZgIZN6W3T58+8fr1az5+/IiRkREmJiYYGRmhpZV8mP769Ws2btzI6tWrefjwISqVKl5TTVmzZqVevXrMnTs32eVFRkbStGlTTp8+zfDhwxk6dKhaDJHSt0sTsnf/YB48eMCwYcM4duwYtWrVYvr06cp1MCFbtmyhe/fubNy4EV1dXQICAnj58iUvXrzA0NCQzp07K50ICyE4evQoJiYmlCtXTil3Ssq8d/9g4PO1+Ov9/j3bnBYMDLTo8Ht+1q6+T1hY5mwtIE7cfg4ODsbLy4t///2X0qVL4+jomGDfmDExMRw9ehRNTU3y589Pzpw5le/n3r17tG7dmnfv3vHw4cOfuRnA5/64Zs6cyfLly9HQ0KB79+706dMHCwsL4PM22tnZsWDBAtq2bUtsbCwPHjzg9OnTbNiwAR8fH8zNzalevTrFihVT4iYbG5skY5SEjse4/ZpaYWFhXL9+XYmbrl27xr179xBCYGRkRNeuXenXr5+yTVLSZMyUcXh7e9OwYUNu3ryZaP+Q6X1tT61vOc8fPXpEkyZN+PDhA9u2baNUqVI/oGRJ+/3333n27BnHjh1TG56Sa9nu3bvp2LEj7u7ujB49Whl+7NgxWrRoQceOHZk1a5ZyzUzuO41bfnrFTKlKvjg6OuLrq94RqJ+fH3Z2dgDkyZMHa2trjh49qnyxkZGRnDhxgmnTpqW6cEZGRowePZpx48ZRv379FM+nra0tL3hpLDY2lnfv3vHq1atEPwEBAbx69YrXr18THR3/5lBTUxMTExNMTEwwNTVV+9fExAR9fX0OHjyIn58f9vb2jBo1io4dO2JkZKQkKF6+fMnDhw8ZNmwYjx49omrVqsmWvWbNmmzYsIFWrVpRuXJlFi1aRKdOnZTxn9uYT9WpAECf397g6toRLy8v2rVrx/Tp05Ntm3jVqlVcvHgRb29vHB0dk11Hjhw5aNmyZarLllLfuu0pVdQhkargqv+v6qfSAtX/ztVCBdXP25t3M88DRTPb4pjZFlf+FkBMAvUKDbPkxr50bgBm7YGp3T5vs7a2NkIIYmJiaNeuXbxrWGBgICYmJj+sQ8Xo6GiaNm1KYGAgf//9N0OGDIl381+lShXGjx9P48aN8fX15cGDB8DnZlI6duxIp06dcHBwSHI9608JSCCm2HIe2lX9/oRI3rx5yZs3L02bNgU+B+OPHj3Cx8eHhQsXKh1Xxv2uyCRMysjf1fQXGBiIpaVlgt9DjEi+uavMTFtbm9jYWNzd3Zk9ezYjRoxg8uTJ6XL+5smThxw5cuDp6alcZ760fX5hGvbyjTdcCMEVzz95+/QUx48fV7tWamtrs27dOmrWrImLiwtHjx5VmqD8q6t2on3hfHksyPMzY8ksMRPI6/uPEBYWpsRFyX0+fPiQ4DIMDAwSjJfi/v/06VP27t2LpqYmzZs3Z9GiRTg6OvL27Vvl5a6XL1+ydOlSzp49m6LvWEtLi7Vr19KrVy/GjBmDl5cXO3fuVB6MR0Z+W79CRz2HM3/+fIYNG4a1tTUbN26kYcOGSV7D3759y4QJE6hZsyaNGjVK0Xrq1q2r9ndK2/n/rfYMPL1GxttHGaFfF23tz/s8Kir2m/d/RhG3ny0sLGjevDnNmzdPcnptbe1Er2f58uXjwYMH9OvXL973Fh0dTUhISIIJnbSyePFipk2bRpEiRfDy8orXLKSFhQX29vb8/fffLFu2jOvXrxMaGoqGhga//fYb69ato169eqlqNi2x4zFuv6aWqakp1apVo1q1asqwjx8/cu3aNQ4ePMi8efOYO3cuffv2xd3dXSZhUkj+pqa/0NBQwsPDMTU1TfS7OHRkeIa4xqdUas/zy5cv4+bmhqmpKV5eXsqLCj+bk5MTvXr1IiwsDFNTU2V4Qr9nX35XZ8+epX379jRt2pTx48er3S+4uroyd+5cOnfuTGxsLPPnz0dDQyPZ7zRu+el1fqbqqeugQYOoXLkykydPpkWLFly8eJGlS5eydOlSAFQqFQMHDmTy5MnY29tjb2/P5MmTMTAwoE2bNt9UwO7duzN79mw2bdpEhQoVvmkZ0vfx8PCgf//+8YJIQ0NDrKyslE/58uXV/o5rh/3Dhw8EBwer/fvl/wMCAvD19eXjx4+ULVuWJUuW4OTkpHaC5cuXT3kzyt/fn2HDhqGjo8Pdu3cJDAxUPu/evePZs2c8efJE+bx48ULtLbD169cryZdv7aC4pPFuihfvhZGREceOHaNmzZpJTh8bG8v48eOZMGEC7dq1w8nJKUXryWwdKCeabEnD5WWmhExKfPkdXzwTQazQVJJ4cW98LVmyhH379tGqVSsmTJjAhg0bGDRoUKKd3X/69Inz589z9uxZWrRokeRbhXG0tLQ4cOAAnTp14s8//0QIwdChQ9WmqVy5MmXKlCE0NJT69esrbQUXKlQowTc0159KXUeZqZ0+5XKDTW68vVvg7e3N2LFjadiwIaVLl2bcuHHUq1dPJmGkDO/jx49cunSJCRMmkDNnTrUPZO62jJPz6dMnOnTowLZt25g/fz59+vRJ1/LUrFmTHTt2YGlpqbytWrBgQaWvgj2LCsZLwPj6zOX5/f1s3bqVypUrx1umkZERHh4euLq6UqtWLY4cOaLc907tppfp7gf+62TM9N/07t07Ro4cyYoVK4iN/d+DBQ0NDSwtLbGysuLp0zB0dIzQ0bYim0V+stsaMeefrhgbGxMSEhIvXlq+7ChVqzoow1++fMmHDx/Q19dn5syZtGvXDnNzc+B/D2c9vUZSsmRJ4HO/l4GBgTx69EgtZgoMDOTly5dqMdOTJ08ICwtTyn3+/Hlu3bqVohfGErNuQ0dcXV2VeHLKlCnJNnN3/fp1GjVqREhICFOmpOzh2Pc+RMtMD+Eys7TYz55eI9HU1CQyMlKtH4GnT5+yYsUKVqxYQXBwMD4+Pnh4eFCyZMkkX9h89OgRnp6e6OjopLjfrEGDBvH69Wvmz59P06ZNWbdunZJcj9OxY0eWL1+OnZ0djRs3VuImc3NzajpPYeH8tHvzPu2PXxMePnzIrFmzmDdvHvPnz6dfv34MGTJEJmGkDO/jx48ATJw4kTx58qjFTFZWVkrc7+k1MlNd+xMqa0IJmcOHD9OsWTOKFi3Kvn37Eu0z8GeoWbMmsbGxdO/enerVq1O0aFGKFy+e5Dz379+nYcOGlC9fnpUrVyb4nKZTp07ExsbSrVs3oqKiWLJkCRoaGhn6O01Vny/w+QZu5MiR3Lt3jzx58jB48GC6deumjBdCMH78eJYsWUJgYCAVKlRgwYIFFC1aNEXL/7L94jibNm2iTZs2ODk5pajPl7dv38rq82ng6dOnDB48mO3bt+Pk5ES/fv2wtbVVkiuGhobpUq7Lly8n2im5rq6ucmHNlStXvE/OnDmVcif2IGNqN71E1x0cHEzfvn1Zv349LVu2ZNGiRUqzUYn58OED7du3Z9++ffz111+MGDEixQ96M8PDlm9KuMSGYRx0jI9ZaoFG2rcznlkTNPdvHGTH/KZ07twZLS0tjhw5wuPHjylevDjVqlVj/vz5WFpa8ubNGwoXLsyOHTsICwujW7duhIeH07x5cy5cuMCpU6eU4PnAgQO4ubmlvAz371O6dGmlGYwv31BIjR+XSPk+7ap+bpIjLglz6tQpypQpw7hx46hbt65MwnwlKiqKgwcP4ubmJt/iSmdTpkxhx44dPH36lNevXyvDdXV1OXjwIDVq1FCGZYbfjtSwCVvCwIEDadu2LWvXrlWSHOnl2rVruLu7c/PmTV69egVAjRo12Lp1q9r9Z9z5s3LlSvbu3cv06dPjJbW/9vHjR9zc3Lh+/TqHDh1K8UPPd+/eYWFhIft8yUAycswEMm5KS7GxsaxYsYIRI0YQExPDqFGjKF26tBIzZc2aFU1NzSQfCCT0AOXL6ZN74/XrZcdN7+zsnOixYGlpiZ2dXaIxU7Zs2ZTrbWJlT6pcu3btolu3bmhra7N69Wrq1KmT5DYAbN26lU6dOlGwYEF27doV74F2QjLqg5a0YGioRZduBVixzI/Q0Mzd7Fha8vQaqTRtXLFiRR4/fszhw4eVBPaJEyd4//49b968QVNTkylTptC3b1/GjBnDihUraNGiBUIIjh07hr+/P/D5XDl+/HiKyyCE4Pfff2ft2rVMnDiRP//8M0XzZabj1dNrJG/fvmXmzJnMmzcPlUqlJGHk74Y6GTNlHHfv3qVv3748evSIZ8+eqXVl0KhRI3bt2qX8nZnOx8R8+TscGRmJmZkZKpWKq1evYm9vn44l+3ydHDhwIJ6envj6+hIdHY2RkRFr166lcePGatPev3+f1atXs3z5crJkycLZs2eVl0sSs3btWjp16kSHDh1Yvny50kpMUomq9IqZUp18ychkEJE2IiMjmT17NhMmTMDExISZM2fSunXrDPNQMjo6Wqlmb2Zmhrm5OWZmZpiZmaWoXxn4tsTLqVOnaN++PYGBgUr7rcmty8/Pj4YNG/LixQs2btwYrzr8t5YzvX13DZcfnHz5UmZKxIR9fMuuxa2wNPqcOClZsiTdu3enQoUKqFQqevXqxZo1a1i7di1jxozhyZMnREREUKxYMWxtbTl58iSOjo6Eh4dz8uRJRo4cyeTJk1O8fg8PD9q2bYuenh5btmxJ8O3slMioiZc4cc2bCSHw8vJi7NixnD59Gnt7e2rUqIGTkxNOTk7Y2tqmc0nTnwwkMqaIiAieP3/O06dPGTZsGFpaWpw5c0btNymj/n58i2FNwxg4cCDr1q2jSpUqLF68mCJFiqR3sYDPTeOcOnWK7t27Y2xszO7du9HW1mblypWsXbuW169fU6ZMGXr16kXnzp1TdI8SEhJC/fr18fHx4cCBAymqLSuTL1JqybgpbVy6dIk+ffpw8eJFOnbsyLRp0xLstyclD3i+fICS0jdck1q2p9dIbt68ye3bt5VYKe5jamqa4qZsU5t4+fjxIwMHDmTlypU0btyYpUuXJvu2fExMDKNHj2bKlCm0bt2a5cuXJ1tDJqmy/Spk8iVxtWrDtm3bADAxMaFNmza0bt0aY2Njbty4QcWKFWnfvj1mZmZMmzaNLFmyEB4eTtu2bdmzZw+WlpZUrFiRjRs3YmVlxYULF5JtQjxOcHAwnTt3ZufOnQwePJipU6em6D45Mx6vcef527dvmTFjBvPnzwegevXqSsxUqlSp/3ycIGOmjEkIwZs3b3j69CnHjh1jxIgRSp/KkDnPyYR8+Xu8detW+vXrR3h4OJMmTaJPnz4/rOn61IiMjMTPz48JEyawbds2xo4di7u7Ozt37mTlypWcOHFCuZaPGjUq0f56vrZx40bat29PmzZtWLVqldIiS2IvpcjkSxqQQcT38/T0pG/fvty7d4/+/fszbty4Xy6IT03iRQjB27dvmT17NlOnTqVKlSqsXbuW3LlzJ7ueDx8+kDt3bqysrNi9ezcFCxZMs7KmhzRtUuwnJl/iZKYkTGJJQCEEgYGBmJubExISwtChQ8mePTvDhw9XbvJiY2OxsrKiWrVqbNu2LUVviD9//px//vmHGTNmULt2bdavX//NVcozeuIlzpf9y8QlYbZu3cqJEye4e/cu8Lm5Q0tLS7S0tOJ9DA0NlQcY1tbWdOrUCWNj4/TanB9GBhIZ38GDB6lbty7Hjx/H2dlZbVxG+g35HnHXRC8vL3r27Im/v7/S+WJKHs79DI8fP6Zhw4bcunWL6OhozM3Nad++PZ06daJEiRKpXl5YWBgNGzbkzJkzHDhwIN53+zWZfJFSS8ZN3+f9+/f88ccfLFmyhKJFi7JgwYJEmzZKzcOd5JrM+Drhkdyyv6UviJQsP6Hlfvr0ibNnz9KtWzcCAgKYO3cunTp1StmLcSNG8PfffzNt2rQE+x1MTdl+JTL5krSkju/3798rb4Dv2bOHdevWMWHCBAoXLqxMM3XqVCZMmMCFCxcoVqxYsuuLjo7m4MGDDBo0iHfv3rFq1ap4b28nJjMfr1/u5zdv3rBixQqOHz/OmTNnCAsLw9DQkMKFC6OtrR0vZtLR0SFLlizKS7M1a9akUqVK6bg1P4aMmTK+2NhYihcvjp2dHQcOHAAy93n5tS/P08DAQEaNGsWSJUsoXbo0ixcvTrT1oJ9NCMHkyZP5888/0dbWJioqiho1atC5c2caN278TbHdtm3baN26Nc2bN2f9+vUJ1oCRyZc0JIOIb/f8+XOGDBnCli1bqFKlCgsWLEi0Lb5f5WHOl6Z20yMwMJADBw5w7949tU9wcDBaWlpMmDCBYcOGpThr/Pz5c3LkyMGSJUvo1KlTqn+E03M/p3XfLfGkQ/LlSxk9EZNUDazk3L59myJFiiTbF1HcDeLy5cs5ePAgurq6jBw5kurVq3Po0CFu3rxJYGAg2bNnp0iRIgwZMiTeD2FmSbQAFLIK5M6rpJsIjEvIvHr1ipMnT3L27Fk+fPhAdHQ00dHRxMTEEB0dTVRUFCEhIUqb6c+ePaNTp05KW/6/EhlIZHxCCMqUKYO5uTnHjh2LN/5X+M3+8pr46dMnpk+fzl9//YWjoyNHjhxJsM+p9BAaGsq8efPIly8fDRo0SFVHugkJDw+nYcOGnDt3Di8vrySDJpl8kVJLxk3fJjY2ltWrVzN8+HAiIyOxylaV7LYV0dBI/7dKfxZPr5GcPHmSy5cvc//+fSVmevLkCbGxsVSsWJH169enqM/BOK1bt+bevXscOXIkwePxV3pAlhoy+ZK2vk7WuLm5IYTg0KFDSc53//59Vq5cyerVq3n58iWVKlVi9uzZ/Pvvvxw6dIjQ0FC0tLTInj07Xbp0oXz58mrz/6rHb2xsDB8/Pqd5y9zcv39fiZm+jJsiIiIICgoiMDCQV69e8enTJ+7du/fLtTAgY6bMYcOGDbRr144rV65QqlSpX+7c/Poad/78eXr27Mnt27fx8vL6rj7c0trRo0e5fPkyLVu2JE+ePN+9vJ07d9KiRQs6d+7MkiVLlJc4vuwPD2TyJU3IICL1oqKimDt3LuPGjcPAwIAZM2bQrl27RN82+hUe4nwt7qHO0KFDmTFjBjY2NuTPn1/pANXe3p7SpUun+oIQFRWFtbU179+/R1dXl+LFi1OmTBmaN2+u1jZ/Qn7Gfv7hCZakpHPyJU5GTcIUddBVq5mRGsuWLaNnz54EBQXFq4kRHh6Op6cne/fuZc+ePbx+/ZqyZcvSrFkzwsPD2bBhA/fv38fCwoJixYqRPXt2Xrx4wdmzZ8mXLx87d+6kQIECyvIyU/IlOYWsAgEoUyDpdkUTMm/ePAYMGMCVK1eUDm5/FTKQyBx27txJ06ZNOXjwIK6urt+9vIz4W/91UvrEiRPUrFmTAQMGMHPmzHQq1Y8XGhpKrVq1uH//PqdOncLBwSHB6WTyRUotGTel3tWrV+nTpw/nzp2jXbt2PHyQA13d/9b55uk1koCAAGxsbNDX1yd//vxqcVOBAgWoXLlyqpPiEydOZMyYMQDY2dlRunRpKlSoQP/+/dHX1//lHpCllEy+pK0vH0zGxMRgYWHB4MGDGT16tNp0Qgh8fX3Zs2cPe/fu5ezZs5iamtKmTRscHBzw9vZm3759SrIxrsWAW7du8fjxY6ZOncqQIUOAXzfx8qWU1rALDg7G3t4eNzc3Vq9e/WML9ZPJmClziI6OpnDhwuTMmZNjx46lumuFjHY+p+Tci4yMxMXFBV9fXy5fvkz27Nl/QsnSx9q1a+nYsWOSze/L5EsakEFE6nh7e9OnTx+lQ6rx48eTJUuWRKfPiA9jvteXD3MaNmxIeHg4Hh4eabb8kJAQrl27xuXLl7ly5QqHDh0iT548XLhwIdF5EnqgnVSSIKkkytfzpWvC5UsZJPnypfRMxCT2vaQmAfPo0SP+/vtvVqxYQfny5Tl58qQyLjIyki5durBjxw7Cw8PJnz8/DRo0oH379pw/f56+ffuira1N8+bN6dKlC1WrVlWaK/P396dTp06cPHmSsWPHMm7cOGW5v0ryJS7xEie1CZioqCiKFy+OtbU1x48fzzD9Y6UFGUhkDkIIXFxcePjwITdv3kRfX18ZFxUVxYsXL7CxsUFHRyfFy8yIv/lfJ2Dmzp3LgAED2LBhA23atEmnUv1479+/p1q1anz48IEzZ84k2AayTL5IqSXjppQLCgpi9OjRLFy4kEKFCrFw4ULGjz2T3sX66eIe8pw6dYpq1apx/fr1RFtKSC0hBPfv31dipjNnznD27FlOnjxJ1apVM9wDr58loydfkmsmLyOJO36joqLYtGkTU6dO5c6dO5w7d46KFSsq0+3duxd3d3fu3buHvr4+tWvXplmzZjg7O1O1alUePnxI8eLF6datG61bt1aun9HR0SxatIgBAwaQJUsWAgICcK3z674c8qXUNG+4ZMkSevbsiY+PT4ZpBiktyJgp8zh8+DCurq7x4gchBO/fvycmJoZs2bIlOn9Gueal5ryL64MyR44ceHt7f3ft/Ixs9uzZDB48mBkzZihJ8C+lV8yUMdppkH6qgIAA3N3d2bBhA5UrV+by5cvJvq2dER/CfK+4hzghISEMGDCAvXv3MmnSpDRdh5GREVWqVKFKlSoANGrUiMjIyESnT+xh9rcmTTJMsiUTiNtXXyZhUrP/kkvefMt3sf6USDYBI4RgypQpjB07FlNTU0aPHk2fPn3Uplm8eDEbN25k0qRJNG7cmIIFCyoJgqNHjxIbG8u9e/fIkSOHMs/9+/eZP38+CxcuxNLSkhUrVtCxY8dUb0NG93XiBeCy3/tUJWC0tbWZOXMmdevWVX7kU9LXjiSlFZVKxcKFCylWrBgdOnTAysqK+/fvc//+fR49ekRMTAx6enqUL18eR0dHqlSpQuXKlZN84WJqN70M99s/YlmEWgKmX79+XL58ma5du+Lo6IidnV06lu7HMTc358iRIzg6OlK7dm1OnTr1zf1ySZKUckII1q1bx9ChQwkLC2PGjBn07duX32rPSO+i/XRxD3kOHjxIp06dsLW1JX/+/Gm2fJVKpdSeadWqFWfOnKFKlSoyMZiBxR0TmSEBE1fWu3fv0qBBA+7du0e9evVYsWKFWuIlODiYLl26UKxYMWbPnk2NGjWUF1pCQ0MJCAhg0KBBzJw5U4mlIiIi2LlzJxMnTsTX15d27doxadKk/0ziJbW6dOnCggUL6N69O7t37yZXrlzpXSTpP+a3336jefPmDBo0iDt37igx0/379wkKCgI+9/0aFzNVqVIFBwcH5ZzPCNe81Pbnli1bNnbu3EnVqlUZM2YM06ZN+0ElS3+DBg3i7du3uLu7kzVrVn7//ff0LhIgky//KTExMSxevJhRo0aho6PDqlWr6NChQ7IPCTPaw5e0EPfw5uLFi7Rt25YXL16wfPlyOnfu/EPXGxAQoNbJn5TxZLREV2IJmIiICDZv3szcuXO5evUqo0aNYtSoURgaGgKfa7t4enqyfft2Nm/eTOfOnRk5Mv6PdNOmTRk2bBg9e/akXP3R2OneYdWqVZw8eRJTU1PGjBnDoEGDlOX+ShJKvMRJbQLG1dWVgQMHMmzYMHbu3MmSJUvS7G1QSUqJAgUKMG7cOCZNmkSePHnInz8/jRs3Jn/+/OTMmRNfX19Onz7NypUrmTJlCiqViiJFilClShVcXV1xdXWN96ZeRk3AfGnGjBmsXbsWHx+fXzb5ApA9e3aOHj1KhQoV6Ny5M3v37k3vIknSL+327dv07t2bEydO0KpVK2bOnImtrW26P3BJD55eIwkLC2Po0KEsXLgQNzc3Vq5c+U2d4qZUQEAAANbW1j9sHdK3+/rBX0Z4GJkYT6+R3Lhxg3/++YcNGzaQP39+rl27RokSJZRp/P392bZtGxs2bCAsLIx169bFa5rH0NAQNzc31q9fT8GCBSlVqhRr1qxh48aNBAUF4eLiwsaNG3/JfiTSkpaWFitWrKBx48YULlyYiRMn0q9fvwzTf5/03zB79mzKli3LqlWryJ8/PyVKlKBp06bY29sTExPDmTNnOH36NOvXryc2NhZzc3McHR2pVq0arVq1StdrXmoTL3HKlStHq1atOHHiRBqXKOOZNGkSAQEBdO3alfLly2eIZ7DyCvcfcfnyZXr27MmlS5fo0aMHkydPxtw8+QeLGe2hS1p69eoVTk5OFCtWjIMHD2Jvb/9T1uns7PzD1yP9Or5OvLx48YJFixaxZMkS3rx5g5ubG3///Tc1a9bk06dPHDhwgG3btrFnzx6CgoLInz8/AwYMYPjw4QkuP2/evGzevJn+g0dx4MDnN79q1arFhg0baNy4sVrzRV/6VZocS0pqEjAqlYrZs2fTtGlTevToQZkyZRgyZAhjxoz5oQ8nJOlLI0eOTDDJCp87lR00aBBCCB48eMDp06c5c+YMx48fZ/HixVhZWdG+fXs6deqkdoP6dVNfkLHuDSwsLDA0NOTx48fpXZQfLjIykpCQkF+ubylJykjCwsKYNGkSf//9N3nz5uXYsWPUrFkTyDhNjfxMcQ95Bg0axNq1a5k/fz69e/f+4U2sBgQEoK2tjZmZ2Q9dj5S5JfUQMiYmhn379uHs7Iy3tzfZs2dn7Nix9O7dG1NTU+7fv8+2bdvYtm0bV69eRV9fHzc3N+bOnZtonwhz5sxh6NCh9OrVCyEEtra29Po/9u46Kqrt7+P4e0gxwELsbkywCxUbFRM7ETsQA7uwO7h2i17sBBUTwQS7kKsYiIqEIkgz8/zBw/xEOofYr7VYypyY78AAZ+/P2XuPG8fw4cPl62Lmxt8Thm1WpKhDuGHDhrx69Yr58+czbdo0bGxs2LlzJ/r6+hlYpSD8T6lSpfj69WuC201MTAAIDAzk/v37ODs74+zszIIFC7C0tKR9+/aMGTeS7t27kydP3LZSjPT+fZDa4CVG+fLluXz5cjpVk7X5+fmhra2dZda4EfOi5HABAQFMmjSJRo0aERERwd27d9m+fXuuD14Ajh8/TmRkJBcvXsyU4EUmk/Ht2zdxB5eQbH8GL2FhYYwfP55y5cqxceNG+vfvz5s3b7Czs8PQ0JC3b99StmxZunbtKl/H5cmTJ7i7u7N8+XK0tLQSfJ6IkiassnnNtFUXWH/Ug2ELHBg4cGCOD15eeyfdoH/o7p+ic7Zo0YLHjx+zaNEiNm7cSK1atXjy5EkqKxSE9CeRSKhcuTLDhw9n165dvHnzhsePH9OvXz/27duHrq4uPXr04MuXLwmeI75ARhFWmuXh06dPQPQUojmZTCbDzMyM8uXLM2fOHEWXIwg5kp2dHbq6uqxfv54FCxbw7NmzXB28xIgZbT1jxgwmTJiQKWvbeXt7o6Ojk6PW0ctJ/v55UMTPR2KdkNeuXaNKlSr07NmT8PBwjh49yvv375k9ezZaWlqMGTOGKlWqsHTpUipXrsyxY8fw8fHhxIkTtG7dOsHzli5dmn///ZdXr15x7do1Pn78yPLly3N18BIjpa9dU1OTTZs2cf/+faRSKY0aNWLu3LkZVJ0gpE6BAgVo164dixYt4urVq3z79o0dO3YQGBhIv379KFeuHMePH0/w+LSGJent48ePBAUFkYOWfo/X6dOnOXv2LJs2bUq0HywzifAlh5LJZNja2lK9enX27dvH2rVrcXV1jTWnaWJyevACYGtrS4cOHTJ0LuGLFy8yatQo+vfvj5GREaGhoejo6GTY8wk5R0zwEhoairu7O4aGhuzdu5fly5fz+fNnNm/eLL/Qj4qKYvjw4eTPn59nz57x+vVrrKysqFu3bpKN1pggRUVFlXpNu6BdonysxxPaPzdJaQCjpqbG3Llzef78OcrKyum+lpQgpLd69eqxadMmvLy8sLGx4d69e+jq6rJ//36ioqLiPUbRAcxKszyEhITQs2dPihUrxqRJkxRaT0YLCwvj48ePvH37lj59+nDp0iWkUqmiyxKEHMHT05NevXrRtWtXqlatyosXL5g/f758Qdrc3KEK0YsT//r1iwEDBmTYc/j7+zN9+nSGDBlCz549OXDggGgzZXExPxdZJXiRSqX4+vpibW1Nx44dqVixIi4uLty+fRsTExP51KonT55k586drFmzBh8fH44dO0bfvn1TNMVy9erVadu2baypsnL77wlI3degYcOGuLi4MHPmTJYvX8779+8zoDJBSB+ampqMGjWK27dv8/r1a1q2bImJiQl9+vRJ8Ma1rBLA7N27l/3797N+/focf2ODu7s7ANOnT2fFihX4+PgouCKQyHJQ5PXr1y+0tLTw9fXN1Yvz/ffff4wfP56rV6/Sq1cvNm7cSJkyZRI9JjeELX8a3/E75cqV49ChQwwePDhDnsPX15cKFSpQokQJypYtS/78+SlcuDArVqyItzGRqzq1pcEU+HmVwILtQElMyRQjJDiQKye34P3GgW/fvuHt7U1AQAAAOjo6nDlzJlaA+uHDB2xsbDh48CBv377F0dGRli1bJnj+XPUeS4HE1n75U0rWgImxcuVKrKys8PHxyZbTj0VERGBvb0+XLl3irAci5Fz+/v6Ym5tz6NAh8ufPT8OGDWnatClNmjShSZMmaGtry/dVxPXDSrM8hIWFMWLECM6cOcPdu3djzd2eU4WEhPDvv/+yZcsWnjx5QpUqVZgwYQKjRo0iNDSUokWLEhAQgKampqJLFbIB0W6K/hu3efNmFi5ciKamJhs2bMDExCRWp0Ru71C9dmM2/fr1482bNxk6knfy5Mns2bOHBg0akD9/fvLly0ePHj0YOHAgkHu/D/nyqWBqVpU9u9z5/TtS0eVkCTEdmY6OjmzevJn379/z7ds3fHx8iIyM/hpNmTKFtWvXysORmKmZDx06hJ2dHUZGRpw6dSrZHZC59f2XWqnpbA4KCkJbW5ulS5cybdq0DKgqY4k2U+51/Phxxo8fj6+vL9WrV5e3l5o2bYquri7KyspA+vweSW2Qc/PmTTp27Mjw4cPZsWNHmuvIDh4/foy1tTVHjhxBJpPRv39/zM3NKVOmjELaTCJ8yUFCQ0NZtWoVK1asoESJElhbW2NkZJTkcbkteAEo/CO6ofX9+3cKFCiQIc9haWnJ1q1bef/+PUWLFk1031zXKZ7Lw5c/pxOzcZIRFhrM1dNbsTuympDgX/Qw7k7ZsmUpXrw4mpqavHjxAm9vb8LCwihdujQ6Ojpcu3aNW7dukS9fPnr16oWZmZkIXlIpueELpDyA+e+//6hatSonT56kV69eKS1N4URDInd78OABN2/e5N69e9y9e1e+ALKhoSGjR4+mR48eqKmppfi8KbnuiG+EzbVr1xg/fjweHh7Y2NjQr1+/FNeQnclkMu7cucOWLVs4efIkpUqVYu3atfTt21eEL0Ky5fZ20927dxk7diwvXrxgwoQJWFlZxTs1RW7vdD17fhLFihVjwYIFzJo1K0Oew9PTk8qVK7NgwYJ4px1K7vfg706pnPC9y+3hS3wdjXfv3mX+/Plcu3aNunXr0rRpU3R0dNDR0eHHjx+8ePGCoKAgChYsSJkyZfD39+fo0aP8+PEDfX19hgwZgqmpKfnz509WDTnhfaQIqekk7tGjB9+/f+fOnTsZUFHGEm2m3M3Pzw97e3vu3bvHvXv3ePr0KVFRUZQoUYKRI0cyatQoypcvn+Lz/v37J6U/Vz9+/GD27Nns3LkTAwMDLl26JB/Vm1v4+fmxe/dutm7diqenJ6NHj2bHjh0ifEmL3NyIuHLlCuPHj+fjx4/MmDGDuXPnJusu69wYvABc3dGCihUrcuzYsQw5v7e3NxUrVmTq1KlJTjmUKzvFc3H48mfw8ubNG06ePMnqdVsICvClVZcRGA+dx1DDApw9e5aTJ09y9epVQkNDqVy5MpUrV8bLy4svX76gp6cnn5ohqcZDrnyPpUBKwhdIeQDToEEDXr9+Tc+ePRkyZAiGhoaxpinIykRDQoghk8n49OkT169fZ+/evTg7O6Otrc2wYcPQ1dVFQ0MjzkeBAgUoVqwYWlpace4uTc71x9/Bi7e3N9OmTePw4cO0bNmSbdu2oaurm66vM7vx8PBg7NixODk5ERoaKsIXIdlya7vJ39+fWbNmsWvXLho0aMD27dsTXeQ5t3e8mo2pwIABA3j//n2qOo6SY9y4cRw/fpz379/HuSkutcFLSo/PqnJz+PLn9/T3799cunSJPXv2cPHiRWrVqsWSJUswNjbm/v37HDt2jHPnzuHh4YGGhgbNmzcnODgYT09PlJWV6d+/P0OGDKFmzZopqiG7v38ULaUdxba2tgwYMID69eszePBgBgwYQIkSJTKouvQl2kzCn4KDg3FxceHYsWPY2NgQGBhIhw4d6NGjBwUKFIjVXsqTJw958+alaNGiaGtrx3n//Pl7KLk/UzKZjMOHDzNt2jRCQkJYvnw548aNk4/CyY0iIyOxtrbGysoKf3//TG8zZY/eHyFBX79+xcLCAltbWwwMDDh37hw1atRI1rG5IXipVf1/qe4LtzD5/8PCwjJ04SUXFxeCg4PFiBchlsEtJbx69YrDhw9z+vRpXr9+jYaGBv369aOqwVQ+/veY/evHM2OgA5GRkTRv3hwrKyu6detG1apVUzU3p3iPpb+H7v4pCmAuXLjA3r17OXToEIcPH6Z06dJs3749WSMTBSGrkEgklCtXjhEjRjBixAhevXrFrl272LdvH35+fokeq6amho6ODlWrVsXOzg51dXVWmuVJ9DokJngJDw/n1q1b2NnZsX//fpSVldm3bx/Dhg3L8fMVJ0fFihW5fPkyu3btYsyYMYouRxCyLJlMxsGDB5k+fTrh4eFYW1szduzYXN0RkRxhYWFIJJIM7aC4ceMGxYoVi7OOVVqDl5htogM9+7l2YzbBwcGcOHGCkydP4uDgQGhoKHXr1uXff/+lYsWKHD16lClTpuDp6Unx4sXp0aMH3bp1o02bNmhoaKS5BvG+STvDNitSFMD069cPDQ0NDh48yOzZs5kxYwampqasW7cuw2YrEYSMkDdvXgwMDDAwMGD16tUcO3aMnTt3Mm7cuCSPLVy4MDo6OixdupRevXql6O+Yh4cHdnZ22NracufOHfr168f69espWbJkWl9StqeiooK5uTlt2rShXr16mf78YuRLNhUVFcW2bduYO3cu6urqrF27liFDhiS7IyK3BS8xYgKY389mcOnSJf77778Mee6wsDA6d+7MjRs3OHfuHN26dQNER7hcLhv5Io2KQivAji1btnDt2jUKFy5M9+7d6dGjBzo6Ohw8eBAbGxuCgoKoWrsFE0b1pXfv3kn+kRTvp/SR0pEv8UlOGCOTyXj06BELFizA3t6eUaNGsX379izd8SPu4hKSIzIykpCQkDgfAQEBfP/+HW9vb+zt7XFwcODXr1+xFrWN73pkStcf2NvbY2dnx5UrVwgKCqJUqVL07t2b+fPnJ3ljQ27k5+cn1nwRUiQ3tZtev37NuHHjcHR0ZMCAAaxbty7Zd1Pn9g7YPfsGUKFCBU6fPk2PHj0y5DlOnTpF7969MTAw4ObNm0D6BC8xsvP3MDeOfNmzbwBbt25l9+7d/Pz5k2bNmtGzZ08MDQ25f/8+O3bs4PHjxxQvXpzevXtjYmJC8+bNU3w9nZ3fF9lNaqYg+/HjBwcOHJBf9504cSLRUYqKJtpMQnJIpVLCwsLitJl+//6Nr6+vvN00b948Fi9ezLx58+THxhdmRkRE4OzsjJ2dHXZ2dri5uaGqqkqrVq2YPn06nTp1yuyXmOUpqs0kRr5kUxcuXGDSpElAdIJnaWnJmjVr0NbWplKlSqxduzbRkR3xzaH+p5waztSqrs4LtzBat26NtbU1nz9/pnTp0uly7i9fvmBvb4+9vb28s6hkyZLkoHxTSMCfU4lB7FDk3rWjHNk8nh8/ojv4W7duTfXq1Xn79i3jxo3j69evlChRgilTpjBq1CicPpUFoGTJxINUEbxkPxKJBH19fS5cuMCmTZuYOnUqpqamNGnSRNGlCUKaqKioUKBAgUTvSnR3d+fDhw+xgpf43L+8npKj56KkpESTJk2YPXs2Xbp0oW7dumKkiyAIqdK9e3fevn0LgJ2dHQ8ePJBP7zF27NhER6LG12mYVKdtTlp7pHz58pQvX56bN2+mW/gSHh7O7du343QWFS1aFKlUSnvDVck+V1J31mfnr31OlNj3ysfHhwEDBlChwhwAKlSoQI8ePQgODubAgQPMmTOHyMhIjIyMsLKyolOnTqm+gUm8LzJXSkfAABQqVAhzc3O6d+9Ox44dWb58OSdPnsygCgUhcygpKcmnG0vIx48fmTNnDvXr14/1+N8/Q97e3tSuXRsfHx9KlChBly5dWL58Oe3atRMjxbIgEb5kU127dsXR0RFvb298fX3x8fGR/3vs2DG+f//O6dOnUVJSinOsq6srgYGBaGlpyT+KFCkSq1Pj7+lAwkIDcX90hvevrtKg7QRKVmyUKa8zteIb9fLnNoMaBgDcvHmTwYMHp+m5rl27xowZM3j8+LHoLMqF/g5eYh6LCUce3T4vD14AnJyc+Pr1K7q6upiamtKoUSM6deokv0PG6VP0cTZOsnjPHbNNSB/pMeolpSQSCT179mTq1Kn4+/tn+vMLgiI8fvw4WUO8S1VqQtGiRVFVVWXlypW0bNky44sTBCFHc3Bw4OXLl7HaTL6+vjx79ow+ffpw9+7deH8/+fn58fDhQwoUKICWlhYFCxakcOHCiU4Bcu3GbO7du8eJEydQU1Nj+fLl2X7qq9atW8tHpKRFSEgIY8aM4cyZMwQGBlK8eHG6dOnCsmXLaNeuHZqamqn6OiXUsZudv+Y5UVKd758+feLatWvyz9+/f8+vX7/Q1dWlRYsWjBkzhu7du1OmTJk01SHeF4qRmgAGoqdXrV+/Pr6+vhlQlSBkPU+ePAFIst1UtGhRWrRowenTp+nTpw9r165FTU0t4wsUUkWEL9mUsrIyrVq1infbhQsX6NatG0uWLGHu3LnyTt3w8HCmT5/Oli1b4hzTqFEjjh49GmshxT8DGKczC3l4fRsSJWXCQ4PoMzHr3nWQWPASo2jRotSuXTtdwpfZs2cTFRWFjY0NnTp1yvFTNwj/k1A4ErPNxknGiOnbGNC9Cdra2ujq6lK1alXU1ZN+j0L8AYwIXrK2vXv3snjxYlq0aEGRIkWIjIzEx8eHHz9+sGzZMho3bgxAsWLFKFGiBIMHD2b58uWYmZll6enHBCGt/P39k7UmXenKzXjy5AkDBgygTZs2nDlzhq5du2ZChYIg5FQVKlSgQoUKcR4PCQmhRYsW9OrVCycnJ0qVKiXf5uzsTN++ffn27VusY/LkycPGjRu5en0W7dqujLXt2o3ZfPr0iaZNm6KsrExUVBRDhgyhRo0a2TqAadOmDQcOHMDf35/ChZO/5t3fzp8/z6FDh1i4cCHdu3enXr16sW4UTMvX5++O3ez6tc6pktPprq+vz5UrV3Bzc0NXVxddXV2KFSuWrnWI94Vi/flzGhwcjJ6eHsWKFaNmzZrkz5+f79+/4+Pjg66uLmvXrpUfV69ePebOnUuvXr3YsGED5cqVU9RLEIQMF3NzZt68iU/Pr6yszMmTJ9myZQvTp0/n1atXXL16NTNKFFJBhC85UNeuXVmwYAGLFy9mzZo1NGrUiObNm3Pjxg1cXFywtramY8eOBAQEEBAQgLe3N3PnzkVPT4+DBw/G6uSICWD02ozn5f2jhP72590ze379+IxmofSZriu9JCd0+VPr1q2xt7dP03N++fIFFxcXDh06xKBBgxLdV3Sa5yyJBS9/7mPjVACNehPpn4z943uPiPdNxsioES/29vZIpVI8PT25ffs2BQsWRE1NDRcXF8zMzOThi4aGBk+fPmX27NmMGzeOXbt2sXXrVvl2QchpOnbsyLFjx5BKpfGOyv1TqVKluH79Oi1btmTbtm0ifBEEIUNoaGhw8uRJGjRoQOnSpalatSrNmjVDW1ubDRs20Lx5c65du4ZUKuXnz58EBARw7tw5xo4di5OTE2fPb8e4W/RNbTEdimXKlGHkyJHs3bsXgN27d7Nu3Tr5Ptmx89fAwACZTIajoyM9e/ZM9XnOnz9PnTp1WLRoEZD+HeHZ8WubG6RktEO7du1o164dIL6fOVVMAPPs2TPevHlD8eLFcXZ2JiIiAm1tbV6/fo2Pj0+sY2bPnk2lSpWwsLCgRo0azJ07l+nTpyf7hkZByE5ifgdevHiRgQMHJrqvRCJh8uTJFC9enH79+vHq1Stq1qyZGWUKKSTClxxq0aJFdO7cGWdnZ27fvs3OnTvJmzcvt27dind9gU6dOjF8+HC6deuGpaUlS5cuRUUl+u2xcLCMXbuuo5lPhdDf0fuXiTrObLP/XUgpco2YlIYuMVq3bs2WLVvw9PRM9fDlCxcuoKysTJcuXRLdT3Sg5yzJCV7+ltg0YjHbhYyX0dOM6ejoEBUVxbVr1+SjDg8dOsTQoUNp27ZtrH21tbXZvXs3pqamTJgwgSZNmjBx4kQ2b94spisUchxjY2M2btzIw4cPadiwofzxv6c5jaGiosLAgQOZNm0aP3/+pGDBgplYrSAIuUX58uXld4veuXOH27dv8/r1a6ZMmcLKlSvl7aEYRkZGGBgYMHr0aB49esSJEydidXS4urry5csX+ec2NjasXLkyWy/AXK5cOSpUqMDNmzdTHb5ERkZiZ2fHhAkT0rm6zJGT1vFJT6mZRio5xNc354sZ1TRixAiGDRsGgEwmo2TJkvLO5xgSiYR+/frRpUsXrKysWLRoEfv37+fMmTPo6upmeu2CkJHKlCmDvr4+Z86cSTJ8iWFsbEyBAgU4fvw4CxcuzOAKhdRI/NZDIduSSCQ0adKE6dOnc/r0aby9vXn//n2CCzsXKlSIM2fOsHr1atauXYuhoSFfv37l3LlzVKxYEQsLCzp37oy7uzuDBg3i4MGDsRaSX2mWJ7Nemlyt6uqpDl4A+bRtqZ3DOCIigtOnT9OiRYtEh+CLTnUhRkLvBfEeyXg1dH5kyvouY8eO5evXr5iamuLn5wdErwtVt25dtLW14z2madOmuLi4sHjxYqytrXn37l2G1ykImS3mb+WZM2fibEvoGqJXr15ERERw9uzZDK5OEITcrFixYgwcOBBra2seP35McHAwa9eujRO8xBg4cCAuLi5IJBIaNmzIkSNHCAoKomvXrjRq1Ij3799z+PBhHj58yPfv37ly5Uomv6L0l9Z1X65du8aPHz/o3r17+hWVSeILGDIqdMhORPAipEXFihXp1KkT8+bNw8HBAYBXr17x7ds3DA0N4z2mQIECrF69mqdPnxIaGhrvdPqCkBMYGxtz8eJFQkOTd5O7uro63bt358SJExlcmZBaInzJJSQSSZJ3UkskEmbMmMGNGzd4+/Yt9erVY+HChZQtWxY3Nzf2799PlSpVGDx4MG5ubjx+/DjW8ZkVwKQ1dIlRtGhR6tSpw/Xr15Pc98uXLxw7doxFixZhYmJCrVq1yJcvH5cuXaJv374JHic61YW//f2eEO+RjJVZoUuM2rVrs2vXLs6dO0f16tW5e/cuV69eTbAREUNZWVl+N+j9+/czo1RByFQqKiqYmJiwbdu2OGsoQPzXEKVKlaJ58+YcP348M0oUBEEASHJqRIAaNWrw4MEDevfuzaBBgxg9ejR2dnYcOHCAly9fMnDgQOrXr0+tWrWwsbHJhKozVps2bXj27Fmc6YD+FhUVxY0bN9iyZQvjxo3DwMAAbW1tOnXqRIUKFdDX18+kitNHYgFDbg5gRPAipIdt27ZRuXJlOnbsyNixY7ly5Qrq6uq0aNEi0eNq1qxJhw4duHfvXiZVKgiZy8TEhJCQENavX5/sY/r27cuLFy9wc3PLwMqE1BLhixBHy5YtuXv3Lt+/f8fb25tWrVpRuXJl+fZ27dqho6MTb0NCESNg0qJjx45cvHgRqVSa6H7t2rWjX79+bN++HV9fX1q3bs3GjRtxdHRk/Pjx8R4jOtVzrrR+b22cZPIPIeNkZujy0N1f/v9Ro0bh5uZG1apV6d69O15eXjx+/JigoKBEz1GkSBGqVq2Kvb09ISEhGV2yIGQ6KysrlJWVmTRpUrzb47uG6Nu3Lw4ODgQEBGR0eYIgCCmSL18+Dhw4QN++fbl79y4SiYTBgwejrKwMIP/8zJkzBAYGKrjatGnfvj0Aly5dSnS/AwcO0LZtW6ZPn87t27cpUaIEkyZN4sSJE9y7d08ebGWHTvbkBAy5OYBJKcM2K5L8EHKHmO91+fLluX79Otu2bWPHjh3Mnh398+Tk5JTkOZo0acLz589xd3fP0FoFQRGqVavGtGnTWLJkSbLDlI4dO5I/f34x+iWLksj+nDsqm/v16xdaWlr4+vpSpEgRRZeTrUVERKCmpkaBAgWwtLRk7ty5sbaPGDGCFy9e4OLiEufYjFz/JT1GvMQY3FKCo6MjrVu3xsXFhQYNGsS7n7u7O9WqVeP48eP06dMnWecWnepJkAZT4OdVAgu2A6W8iq4m1ZJa+0W8DxQnM4OXP+lX/d8UhN7e3ujr6+Pl5QVA48aNsbOzS/Tv04IFC7CysiJ//vx069YNExMTOnbsiIaGRobXHp+IiAjs7e3p0qVLtp6vXsg6bG1tGTBgAKdOnUrW2gGfP3+mTJky2NjYMGjQoEyoMHvy8/OjaNGiBAQEoKmpqehyhGxAtJvSj7m5Oba2toSFhfHjR+zrj5cvX1KrVi1u3LhB69ats10H85/hQsOGDalUqRK2trYJ7m9sbIyvry+Ojo4JTtuWnb4GSYUrqXktKQlsgoODuXLlCu3btydv3sxpMyX3NaXkdWSn77mQef58D82ZM4cVK6LfJ6qqqhw+fDjRGUY+fvxIvXr1+PnzJ3p6epiYmNC3b18qVqyY4XXHR7SZhPQWEhJCnTp1KF68OI6OjskalTtw4EBev34dZ5Yi4X8U1WYSI1+EeKmqqqKqqsrv378pVKhQnO1KSkoJXlBnFzZOMpo1a4aWlhZ2dnYJ7mdnZ4e6ujqdO3dO9nmF3CGx77V4HyiOooIXiD0CRkdHh8uXL1OlShUgejqxokWLysOY+CxZsoTXr18zc+ZMXrx4Qc+ePSlWrBibN2/O8NoFITP069ePbt26MX78+DidlPF5//49APnz58/o0gRBEFIlb968hIaGJthmArJ9uwnAyMiIy5cvExkZGe/20NBQrl69Svfu3XNE8AKJ15vRwYuiJLfG5L7+7PY9FzLPn+8NKysrzM3Ngeggw8TEhOXLlyd4bLly5fD09OTo0aNUrFiRxYsXU6lSJZo0aSJGSws5goaGBrt378bZ2Znt27cnub9MJsPDw4N8+fJlQnVCSonwRYjX2bNniYiIQCqVxtuQCAsLQ01NLVNrSs9RLzHm74+iY8eOiYYvFy5coE2bNsn6JSY63HOf+L7n4n2gOIoMXmL8GcDo6uri6uoa686txYsXJ3p89erVmT9/Ps+ePcPNzY18+fLx5MmTjCpXEDKVRCJh69atBAcH0759e54+fZrgvjKZjDlz5lC/fn26deuWiVUKgiAkT3BwMPb29qipqVGwYME428PDw4HoxXCzOyMjI37+/MmdO3fi3X7z5k2Cg4Pp2rVrvNuzayd8fHXn1OAlRnoFMNn1ey5knpj3iLKyMhs2bODkyZPyG27mzp3Lz58/Ezw2f/78mJiYcPz4cXx8fDA3N+f+/ftERERkRumCkOEMDAwYO3YsFhYWrFmzJsGbHwDOnTvH/fv3WbRoUeYVKCRb9r8FR0h3//33H0OHDqVr165cuHCBr1+/xtknPDw8RzQiILohMWzYMLy9vdHR0ZE//vPnT2bOnMn169fZuXNnvMeKTnYBxPtAiOuhu798CjJNTU2OHj3KggULCAkJoWrVqrH2i/HnlGUxSpQogY+PD82bN8/4ogUhk5QuXZqrV68ycuRI9PX1mTFjBgsWLIgzvd7FixdxdnbG3t4+WUPtBUEQMpNMJmPs2LG4u7vTuXNnXF1dCQsLi9VGiglfMvumtYygr6+Pjo4OdnZ2tGrVKta2c+fOMW7cOKpWrUrNmjVzXKd7Tns96Ul8bYS0+vs95OHhwYcPH9DU1IwVasfsF184mC9fPsLCwqhWrRpFixbN0HoFITOtX78eDQ0NZs2axb///svu3bvR09OLtU9UVBRz586lbdu2tGvXTkGVCokRLVkhlt+/f9OrVy+KFy/O4cOHMTU1xcrKCh8fn1j7aWtr8+rVq0ST16zuhVsYAJ07d0ZFRYV9+/bJt509exZdXV1sbW3ZunUrpqamiipTEIRkygqjXv70Z7AikUioVasWDRs2REtLK872+D4H+PHjB1KplODg4IwtVhAyWcOGDXn48CGLFi1i/fr11KlTh+vXr8u3u7i4MH36dFq2bEmnTp0UWKkgCEL8tm/fzqFDh9i5cyeLFy/Gy8uLjRs3xtonphPwxYsXQPYa/fA3JSUlunXrxuHDhwkJCQGi17fr168fxsbG1K9fn6tXr9Ku7UoFV5o1ZaeQIjvVKuQ8/U1207BhQ6pVqyZ/LDnvSV9fX6KioggLC8vI8gQhU2loaLB+/Xru3btHVFQUDRs2ZMaMGfL+gZ8/fzJv3jxevnyZ6FR9gmKJ8EWIZezYsbx//55Tp06hqakpX/Rszpw5sfYbOXIkXl5eiU7XlZ4yYsqxGNra2owZM4ZVq1bh5uZGv3796NGjB/Xr1+fly5eMGzdO3HErCFlcVgteYsQXqKTk8XLlymFqasr8+fPjHYUoCNmZmpoa8+bN49mzZ5QsWRJDQ0MGDBhA48aNadSoESEhIWzatAmJRKLoUgVBEGJxcXFhypQpTJw4kcGDB1OrVi0mTZqElZUVnz9/lu9XoUIF2rRpE2u+9uwcwMycORNvb2+2bNnCwYMHqVmzJtevX+fw4cOcP3+e4UNtFF1ilpYdQo3sUKOQ8/35Pkzue3L+/Pl8+PCB1atXZ1RZgqAwDRs2xNXVlWXLlmFtbU2tWrUwNTWlVKlSrF27lhkzZtC4cWNFlykkQPQoC3KhoaHY2NhgZWWFrq4uEB1MLF26lD179vDgwQP5vvr6+jRs2JBt27Ypqtw0iRn1EmP+/PlERETIGxBHjhzh/PnzlClTRkEVCoKQXFk1eImRnBEuiW1fvXo16urqTJkyJd1rE4SsoFq1aty4cYOdO3fi6OiIlpYW586d4+3bt9SvX1/R5QmCIMRx6NAhSpYsybp16+SPLVq0iPz58zNt2rRY+44bNw5nZ2eeP38ufyy7BjBVqlTBzMyMWbNmMWzYMDp37szr168ZOHCgGPGSTFk53MjKtQm5j2GbFSl6T9auXZvp06ezbNky3N3dM7AyQVAMVVVVZs2axbNnz6hcuTJXr17F0tKST58+idAxixNrvghy/v7RHX5/rkcA0aNhVq5cyb///kujRo3kj48bN46RI0fy9u1bKleunKm1pqdZu0JZaabDpk2bcHFxYenSpcmaJ1Ss8yEIipfVg5cYSQUu8Tl37hxXrlxBX1+fadOmYWlpSfv27WnRogXNmzencePGFChQIAOqFYTMp6SkhJmZGWZmZoouRRAEIUl+fn6UL18+1louWlpaWFpaYmFhgY2NDaqqqgAYGxujo6PDtm3b2Lp1q3z/azdmZ8vO7oULF/L161fMzMzo0qULIDrtUyq5X698+VQwNatKf5N/+P07+073LQgZKTAwkJkzZ1KuXDlatmyJra0t7dq1o3PnzjRr1ozmzZtTqVIlMZJayDGqVKmCg4ODossQUkCEL4JcTPhSuHDsRZ+VlZXR0NCQNyBi9OvXj9mzZ7Nw4UIOHz6cYXWl55Rjf494iREdwJgme20XEbwIgmJll9AlLWbOnMnnz5/5559/kMlkqKur8/jxY5ydnQkNDUVJSYnatWvTvHlzmjdvTrdu3UQYIwi5lEwmrksEITP5+/vHaTMB5M+fH4lEgorK/5rZampqTJgwgaVLlzJjxgwqVKiQmaUmW3JH4+jo6HD69Gn55yJ4EQQhMxm2WRHr99XRo0fZvn07BQoUIDAwEIgOw48dO8bOnTuB6BldmjdvTrNmzejYsSN16tRRSO2CIOROInwR5BIKXwBCQkLIkydPrMfy5s3L0qVLMTMzY/LkyVl6fsGEQpc/RQcweZLcTwQvgqA4uSF0iVGxYkWCgoJ48eIF79+/x8XFhdu3b3Pr1i1CQ0ORSqU8ffqUly9fsnXrVkqUKMHGjRvp27evuLNLELKIoKAg1q9fz6dPn/jx4wc/fvwgICCAqKgolJSUkEgkSCQSlJSUUFZWplixYpQoUYISJUpQsmTJWP8vVqwYkZGRfPv2ja9fv8b699atW4p+qYKQq/j7+8fbeRfTZvr777CFhQXbt2/H0tKSY8eOZVaZyZbaadBE8CIIgiL8GcBUrFgRgOXLl2NoaIiLiwv379/n1q1b/Pz5EwAfHx/Onz/P2bNnmTlzJiNHjmTlypVoa2sr6iUIgvCXS5cuceHCBXmb6cePH4SEhMRqL8X8mz9//lhtpb/bTvny5cPPz0/eXoppM3l4eCjktYnwRZDz9fUFoFChQnG2hYaGoqGhEefxESNGYG1tjbm5OXfu3Elxh196jmpJSHKClxg2TjIGt0z4NYjgRRAUIzeFLjF27txJnTp1GDp0KP369aNJkyaYmZmhpaXFmzdv+PDhA56ennz69InNmzcTFhZGv3792LVrF6tXrxZrZQiCgnl5edGtWzfevHmDrq4uhQoVolixYlStWhVlZWVkMhnh4eF8+vSJoKAgChQoQHBwMA8ePODr1694e3vHGtGipKSEVCqN9RzKysoUL148y95JLwg5la+vb4Jtpr9vWAPIly8fK1asYNiwYTg5OdGyZcvMKDNZRPAiCEJ2FBPAtG3blvHjxzNjxgwsLS2pU6cO5ubmbN68mR8/fuDm5oanpyeenp5cunQJR0dHjh49yunTp1myZAlmZmaoq2d8v5QgCPGTyWSsWbMGS0tLqlSpQsmSJSlUqBA1atRAQ0MDmUyGTCbD19eXL1++oK6ujqqqKu/fv+fu3bt8/fqV4ODgWOeMr92kpaWlsHW9RfgiyN29e5fixYvHm/7HN/IFohv9GzZsoG3bttja2jJgwIBkP19WC15i6hEBiyBkHbkxdIHoNWL0q5bmyJEjzJgxA3NzcyIjo+f6bty4MdevX6dGjRry/bt160bHjh2pVq0a79+/R09Pj169erFo0SJq166tqJchCLnWkydP6Nq1K0pKSty9ezfB6S2ePn1KvXr1Yj1WtmxZGjdujL6+Ph06dEBFRUV+x5aamholSpSgePHilChRgiJFiqCkpISfn1+y1qsTBCHtYu6cjO/va0hISLw3rAEMHjyYLVu2MHXqVB48eICSklJGl5okEbwIgpBWAZXS1q+j9S75fTZ/iwlg1qxZg7e3N9bW1vj5+QHRofehQ4fo2bOnfP+pU6fSv39/zp07R+PGjZkyZQqrV69m7ty5jBgxItY6XoIgZLyIiAjGjx/P7t27mTt3LkuWLEnw+ujvm/3z5s1L3bp1ad++PW3atEFXV5fv37/z9etXfv36JZ9RoHjx4hQvXhwNDQ2FtZkUf8UnZBmXLl2iU6dOcd7QwcHBhISEkD9//niPa9OmDcbGxlhaWhISEpKs58qM4CW5alVXz1L1CEJuV0Pnh/wjN3vo7k+nTp14/vw5v3//5vnz5+zfv58nT57Qp08fbt68Kb8rvnHjxly9epXv37+TP39+Zs+ezZMnT6hTpw4mJia8fPlSwa9GEHIHNzc3Fi5cSIsWLShevDj3799PdF7xYsWK0b59e/nnzZo1w8DAgODgYNasWUPTpk1ZuHAhACNHjmTYsGF06NCBOnXqoK2tnSU6bwUht4lZ5LZjx45xtvn5+SXYZlJSUmL9+vU8fPiQQ4cOAakPP9KDCF4EQfhbQCX1FH9k5HMmh2GbFeTNm5cTJ07g4+PD169fcXBwoEWLFowaNYrNmzfLp9hXVVXF1taW3r174+joyNSpU2ncuDHjxo2jatWq7Nmzh4iIiDS/JkEQEvf7929sbW1p3bo1Bw4cYN++fSxdujTRts2aNWsoXrw4EN2GMjIyomzZsty4cYN+/frRuXNnbt++Tdu2bRk7diy9evWiadOmVKhQIcEbYzKLRJaDVuj89esXWlpa+Pr6UqRIEUWXk618/vyZMmXKYGtrS79+/WJtO3v2LD169ODNmzdUrVqVqKgoDA0NCQ4OpmnTpjRt2hRtbW06d+7MwoULCSw2LdHnyqygIzmjXkTooiDSYAr8vEpgwXaglFfR1QhZRG4PWxKiXzX2OlzHjx9n3rx5uLu7U7lyZUaPHs3UqVNRUVHh2bNnmJiYyKc5qlevHk5OTnh6etK/f38WLFhA9erVk/3cERER2Nvb06VLF1RVVdP7pQlCjuDp6YmtrS1HjhzhyZMnaGpqMnDgQNauXYuvry8vXryINXdxzLovbdq0YdCgQSgpKfHff/9hZWXF4cOHkUqlVKlSBX19fb58+cKzZ8/4+fMnJUqUwN3dPd6O3Zi7uAICAtDU1FTAV0HIbkS7KfUGDhyIu7s7rq6usR6XyWRUq1YNAwMDdu3aBcCRI0eYP38+enp6NG3alGbNmrFixQpcXV158+aN/Oc5swMNEbxkD/nyqWBqVpU9u9z5/TtS0eUIOUB6hCWKlNgomb9/r/n4+DBhwgROnz6NsrIyvXr1Yt68edSsWZOoqCjMzc3Zvn07ampqdOnShYCAAK5cuULFihVZsGABgwYNQkUleZMFiTaTICQtPDycy5cv8++//3L27FmCg4Np3LgxK1eupGnTpty7dw8fH5847aZ8+fJhYWFBqVKlCA8Plwc1nz9/Jn/+/DRv3py8efPy6tUr3rx5A8ChQ4cYPHhwnBoU1WYS4YsAwJ49exg9ejQ+Pj4ULhy7o8/U1JQ7d+7w+vVrAC5cuEC3bt3o0aMHz58/5927d/J98+XLx8glr8lbIP6Fy7JS8AIifFEYEb4I/08ELqmjV6UQzs7O7Nq1iyNHjjBhwgQ2bdoEgFQq5fr162zdupWzZ8+iqanJlClT2Lt3L15eXrRq1YqtW7fGmrYsIaIhIQgJk8lkLFy4ECsrK/LkyUPXrl0ZMGAA7dq1w8HBgZ07d3LlyhX5/mpqahQqVIhChQqRJ08enjx5Qr169Vi7di2GhoZA9HRGt27dYvfRW7g9dcLz3bNYz7l831PKVIqe6ujPNepE+CKklGg3pU5UVBQ6OjqMGzcOKyurWNvc3NyoUaMG58+fp2vXrshkMmrWrImqqioFCxbExcWF0NBQ+f6LFy9mwYIF8s8zK9hIbvAighbFE+GLkBrZPWBJruRMV3btxmy+f//OwYMH2bp1q3xtvbJlywLw5csXdu/ezY4dO/jy5QvDhg0jMDCQU6dOUa5cOcaMGcPs2Un/zhRtJkFI3IsXL+jSpQuenp7UqlWLAQMG0L9/fyIjI9m1axf79++Xr0MukUjQ0tKiUKFCFCxYkE+fPhEcHMy0adOYOXMmBQoUICIiAldXV27dusWtW7dwdnbm169f8ucbOnQoBw4ciFOHotpMYs0XAYiecqxx48ZxghcAJyenWMPqt2zZQsOGDTl9+jQA379/5969e9y9e5fXr18jlUbFOUdmhhwieBGErE0ELmn36L8ftGzZkpYtW9K0aVPGjx/P1atX6dixIx06dMDAwIB27drh6elJp06dOHHiBE+fPsXW1pbx48ezevVq9u3bp+iXIQjZVlRUFBMmTGDHjh0sWbIEc3NzvL292bVrF+PGjeP79+80a9aMffv20a5dOwoXLoyGhkasqV3v3LnDtGnTaNeuHUOGDOHgwYMUL14cExMTwkv0BSDolz8ebi7IpFIKaBWldMVainrJgiAADx8+xM/Pj06dOsXZ5uTkhEQikYepV69exc3NjZs3b2JgYEBERARPnz7lzp073Lt3Tz51RmYSwYsgZH+5JVxJSszXIbEQJmZNmOnTpzN06FAaN25M7dq1MTQ0pEOHDnTs2JEFCxYwZ84c1qxZw5w5czh58iTz5s1j9OjRzJ8/n+HDh1OiRInMelmCkOPcuXOHrl27UqZMGS5cuEDVqlU5deoUI0eOxNHRkcKFCzNs2DCGDBlC+fLl0dTURFlZWX58QEAAq1atYu3atfKb2+rUqSOficnS0pKoqCiePn2Kl5cXGhoa6OnpAf+7nlHkNK8gwheB6DVdrly5goWFRbzbCxYsKF/L5c2bNzg4OHDw4EH59mLFitG9e3e6d+8OwKxdobGOz2qjXUAEL4KQmUTYkjEeuvujX7Uw48aNo0yZMpw+fZpjx46xYcMG1NTU0NPTo1evXhgaGrJlyxZWr17NihUrCAgIYPHixaxdu1bc7SwIqRAWFsbgwYM5ffo027dvR0tLix49enD9+nUKFSrEkCFDMDMzo1atxIOSOnXqULNmTe7du5fgXZL5NQtTp1HcdSUEQVCMc+fOoaWlRePGjeNs09LSQiaTERYWhoaGBlu2bKF27dq0atUKiF5roEGDBjRo0IDJkyfHOjYrhR1ZqRZByK1EwJJ8SYUwMQFMsWLFcHJyYv/+/Vy+fJmJEycSFRVFqVKl6Ny5My1btqRIkSKMHDmSz58/c/XqVUqWLMmuXbtijVIUBCH5Ll68SO/evWnQoAGrVq1i3759HDx4EH9/f1q3bs2RI0fo2bMnefLkSfAcWlpatGjRgt27dwPRswn8TVlZGT09PXnoAlnrekas0pnLBQYG0qVLFyIiIjAxMYl3n1KlSuHl5QWAtbU1xYoVS3DfP2XWQvYv3MJE8CIIWUwNnR/yDyHjPHSPXjyya9eu7NmzB09PT+7evYuqqir37t1j5syZWFtbU758eQoXLkxISAimpqZIpVIx8kUQUiHmuun8+fPMmTOHZcuWMWDAACIjIzl06BBeXl5s2rQpyeDFxcWF+vXrc/ToUfbs2SNvTAiCkHUdPnyYlStXMmzYsHjXAShVqhQAXl5evH//ngsXLjBp0qRYI97ik5U6B7JSLYKQk2X0Iva5UWJfu5jfbaVLl2bevHk4OTnh5+dHr1698PLyYvfu3QwbNoywsDCaNm2Kt7c3WlpaDB48mB07dhAZKab9E4SUOnz4MN27d6dly5aULVuWZs2aYWNjg6mpKW/evOHGjRsMGDAg0eAlJCSEiRMnYmRkRKNGjXj+/Hmy1rDNatczYuRLLvbz5086d+7Mq1evcHBwSPANXKpUKW7dusWvX7/Yv38/5ubmqKsnfkGQFUe7gAheBCGjiJBFcWICmBh+YfkICQmhd+/e1K5dm9u3b3P//n1mzpzJ4sWLuXbtGkZGRpw/f57p06crqGpByH58fHzo0qUL7u7uDB8+nDVr1lC3bl0uX76crDWUYvz+/ZuWLVuiq6uLvb09VapUycCqBUFID7t27WLMmDEMHz6c9evXyx+PuaMaYocvV65coWDBggwaNCjWebJaZ8CfsnJtgpCdiPBEsRIaCfPn72uIvpteXV2d/Pnzs2LFCu7fv4+LiwuXLl2icuXKDB8+HDMzM3bu3Mnjx49p2LBhpr4OIeuL7++moqe3yio2b97MlClT6NSpE/7+/jg6OrJt2zZGjBiRZH/yn+bMmcPOnTv5559/GDduXPT0rtnwekWMfMmlfH19adu2Le7u7ly7do3mzZsnuG+JEiX4+vUrT548ISgoCC0trQT3tXGSieBFEHKwP0e0iNEtWVPxEqWYNGkSFy9eZOTIkTg4OODv78/jx4+pUqUKkydPpkGDBjx58gSpVKrocgUhW3jz5g0tWrTgw4cPNG3alB07dmBiYsLNmzdTFLwA5M2blxIlStCkSRMRvAhCNrBp0yZGjx7N+PHj2b17t3we8r8b/zFrAnz58gVHR0eKFi1KREREptebGtmxI0MQFEWMWske4vue/P27bsmSJURGRvLlyxcOHTqEm5sb3t7erFy5kv379xMYGIiysjKPHz/O7PIFIVuKiopi1qxZTJkyhTZt2vD8+XM8PT1xdHRk7NixKQpeACpXroxMJmPQoEFJjiROSFYIxET4kgt9/fqV1q1b4+Xlxc2bN2nQoEGi+//69QtNTU1atmzJ5MmTmTlzJjY2NnH2s3GSZVTJaZJZ058JQk6RUMAiQpbsw8rKCiUlJfnUYsrKytSrV4+NGzfy4MED3N3d+fXrFx4eHgquVBCyvgsXLtCoUSMCAwMpWrQoV65cYd26dezfvz/RYfIJkUgkdO3aFTs7O2Sy1F07DW6ZusaHIAgps3LlSszNzZkxYwZbtmxBSSm6+RxfWPHr1y8ANDU12b17N76+vhgZGfH79+9MrTkhCXU+iOBFEGIT4UrO8+f378/feZUrV2bixIns2LGDsLDom3uLFSvGjBkzaNSoEXPmzKF69eo8evRIUaULWVhW6NTPSn78+EHXrl1Zs2YNjRo14t69e5QsWRIXF5d418pLDiMjIyIjI7ly5Yr8sZR83bPK90iEL7mMp6cnBgYG/Pz5k1u3blG7du0kj/n06RNlypRBIpGwYcMGRowYwfDhwzl16pR8n8wOXpI76kWELoKQOBGw5EwFChSgb9++HDx4MFbnroGBAT179sTW1hb4X0eRIAhxSaVSli5dSvfu3SldujQSiYSvX79iZ2eHhYVFqu++guiGxMePH3n16lWKjxXBiyBkPJlMxoIFC5g9ezaLFi1i1apV8p/5hMIKT09PAMqWLUudOnW4dOkST58+pUePHoSGhmZa7fERwYsg/I8IV3K3gErqNBj1v+kjR4wYgb+/P/b29vLHlJSU2LBhA48ePeLt27cEBAQoolQhG8gqnfuK9uLFCxo2bMjdu3fR19fnwYMH9O7dG0dHR/m0rKlRvnx5dHV1sbOzi/V4cr7uWel7I8KXXCQkJIQ2bdoQERHBrVu3qFatWoL7/hmmeHp6UqZMGSD6j9DOnTvp06cP/fv359KlSxle99+SE7yI0S6CED8RtOQeQ4cO5d27d9y+fTvW46tXr0ZNTY358+ejp6enoOqE7MrGSRbnIycKDAykT58+zJ8/H4lEgqenJwUKFOD+/ft06tQpzedv3bo1efPm5ezZs7x69Yo2bdqwaNGiJEfC/Bm8PH/+nNq1a6cqwBEEIXEbN27EysqK1atXs3DhwiSDF4i+YQ2Qt5saNWrEhQsXcHZ2pl+/fgqbgkwEL0JuI8IVITliApiaNWvSoEEDDhw4EGt7s2bNGDJkCGXKlGHt2rWKKFHIJrJSJ78inDp1iiZNmvDu3TuUlJRwdXVl9erVHDx4EA0NjTSf38jICDs7O0JCQpg2bRrdunWLXkIjka/7n9ukUilmZmZYWFikuZbUUlHYMwuZbs+ePbx//55Xr15RsWLFBPf7uyPl+/fv8qnJlixZgqurK9u2beP79+9MmzYtXToh0osIXAQBEaoIALRq1YrixYtjb29PixYt5I9XrlwZHx+fFM+3Kgi5yf79+zl9+jQFCxakfv36NG7cGEtLSwoWLJgu58+TJw+9e/dm69atrFy5ksDAQG7evEnZsmUZMWJEkqNbPD096dy5M15eXjx+/DhLXYsJQnYXFBTEsmXLGDt2LDNmzJA/nlRY4ePjA0DhwoVxc3Nj+PDhWFhYcPToUYyNjWNNmaFoIngRsjMRoAjpzcTEhPnz5yOVSuXTS0L09WBUVBSqqqoKrE7IDnJrABMREcGQIUMIDQ1FV1eX+vXrM2zYMNq1a5duzzFw4EBWr16NkZERN27cAKLX13NyckrW133GjBns3r2bevXqMXfu3HSrKyVE+JJLhIeHs2rVKgYOHJjsES8xatSowYsXL9i9ezcLFy6kQIECNGnShOrVq2dkyXEkNuJFhC5CbiRCFiExSkpKVK9enXfv3sXZJoIXQUjc2LFj6dGjh3y6sYwwefJkDh06ROHChWnWrBmBgYGYmpoyY8YM6tatS+nSpQkLCyMsLIzIyEiWLVtG3bp1+fHjB506dUJVVRUNDQ2+ffuWIfUJQm61Y8cOAgICmD37fw36hMKKPxv9NWrUAMDR0ZExY8bg4+NDv379GDFiRMYW/Fctf9dq2GZFrDpF8CJkdSJcETJbzZo1CQsLw8vLSz56EaLbU3+GMYIgxKaqqsrbt2/R0tIib968GfIcdevWxcDAgLdv36KkpMTYsWPZunUrmpqaVK9enVq1aqGsrExoaCihoaE0atSIhQsXArB+/XrWr19PzZo1FdpmEuFLLhAaGsrw4cP58uVLrEbE32ycZH915hYGoGnTpixatAhHR0fGjx/PnDlz6NOnD1evXpWPiMloCQUvInQRcjIRrghpVbFiRZ49e6boMgQh21FVVY3V+M4IDRo0oFmzZrx7945nz54RFBQEgL+/Px8/fiQiIoIvX77g4eFBoUKF5I0KY2Njvn37xp07d+jbty87duygfPnyGVqrIOQWly5dYtGiRQwfPpyyZcsCyQteAPT19VFRUaFXr15oaGjw7NkzTp8+LR89k1GdEn/Xcu3G7AQDGBG8CFmBCFeErCZmZhgPD48Mv/4ThJymRIkSGf4cU6ZMoVevXpQtW5atW7cCEBUVhZeXFxoaGqirq3Pv3j2ioqKoWrUqALa2tkybNo1Zs2ZRo0YNhg0bxooVirkOEuFLDufj44OxsTGPHz/m2LFj1KxZM979Hrr7U0Mn/nM0bdqUsLAwOnbsyKZNm1BRUeHmzZvMnj2bwoULZ2D10f4OXkTgIuQkImARMlKlSpU4c+aMossQBCEBe/bsYf78+Zw5cwZlZWXKli2Ln58fHh4efP/+naCgIMqUKYO5uTnXrl3DwsICV1dXrl27RrVq1bCxsWHSpEkMHjxY0S9FELK9bdu2MXHiRIyMjNiwYQOQ/OAFQENDg3r16vH8+XMuX75M+fLlmTp1KvXq1WP58uXo6uoCd9O97vhqSSiAEYSMJoIVITuqUKECEB2+GBgYKLgaQRD+ZmxszMqVK9m+fTsA2tra5MmTB09PT54/f45UKkUqlTJ8+HBq1KjBggULWLVqFUOGDGH58uVERETg4eHB5s2bFVK/CF9yMDc3N4yMjAgKCsLR0ZFGjRrFu99Dd/9Ez1O3bl22bNnCkCFDUFGJfsuoq6uzfv36dK85PiJsEbIrEawIivLQ3R/9qoWpWLEi/v7+/Pz5M93WqhByt6TWIhFSpnr16hw/fhxfX18OHz4sX58PkI+E8fT0ZNq0aeTJk4dSpUpx7NgxmjZtCsDPnz+JjIxUWP2CkBNERUUxc+ZM1q9fj7m5OWvXrkVZWTlFwUuM+fPnU7hwYfnPKECbNm1o06ZNqmr7+7niq+nvacUS2k8QUkoEKUJuEXONFd90zYIgKJ6SkhKWlpbMmDGDGzdusHfvXk6ePAlAWNj/btjfv38/Bw4coGjRovTu3Zvdu3cjkUj4/fs3oaGhiipfhC851Y0bN+jVqxclS5bk2rVrCU5HkVTwAjB8+HCcnZ3R19eP1ZAQhNxOhCtCVvbQ3V8+bP7z588ifBEEBQoODubnz5+Eh4cTERER60NHR4cyZcowZcoUJk+ejKurK0+ePEFbWxsdHR2KFStGsWLFyJ8/v3z9mcePHzN37lwuXryIvr4+hw8fpnfv3gp+lYKQ/fz+/ZtBgwZx/vx5tmzZwsSJE4HUhRdOTk4YGxszbtw4mjZtirKycppqS+6IFogdwIjgRUiMCFQEIX5lypTh8+fPii5DEHK1qKgo/Pz8CAsLk7eVYtpPED04wNDQEENDQ6ytrblw4QLKysoUK1ZM3m4qWrSo/BosKCiIdevWsWrVKiIiIpg4cSKrV6/O9NclwpccaP/+/ZiZmdG6dWuOHz+eYIdbcoKXL1++cP78eYoWLUrbtm35999/6dGjR/oWLAhZkAhWhJzgzuO3AOjoJDCvpCAIGWLZsmXcuXOHz58/4+npyY8fCf9NqVChAh4eHgBIJBIaNmxIw4YNY+3z48cPTp8+zZUrV7hy5Qrv3r2jatWqHD9+nN69e+Pvn/Q1nSAIsX39+pVu3brx5s0bzp07h5GREZD68GLXrl0ULlyYnTt34uXlxb///pvqdV4SG12TWAAj5D4iTBGE9OHt7U2rVq0UXYYg5CqPHj1i9erV8jbTly9fEh3Vf+LECfkNZ4UKFWLIkCGxtkulUh4/fixvM92+fRupVMrYsWOZO3cuKioqInwR0kYqlbJgwQKWLVvGqFGj2Lp1K6qqqvHum5zgBeDQoUOoqanx7NkzJk2aRO/evdmyZQvjx49Pz9IFId2J8EQQwMvzI/ny5ado0aKKLkUQcpWDBw/y5csXhg4dSunSpSldujSFCxfm9+/f7Nixg+vXr6OiosLgwYOZPTtuJ2t4eDh3796VNxxcXV2RSqVUqVKFjh070qFDB4yMjOTTwQqCkDLPnj2ja9euSKVSnJycqFevHpC8ACO+ab5+/frFiRMnmDdvHvXq1aNv374YGhrKb2JLicSClz/3EWFLzvN3kBKVRwmAX+VVCQpN20gqQRASFhERwadPn6hYsaKiSxGEXOXRo0ccPXqUtm3bMmjQIMqUKUOJEiXIkycPN2/eZPv27QQEBFCjRg1mz54d72CADx8+yNtM169fx8/Pj3z58tG6dWtWrlxJz549KVeuHAB+fn6Z/AqjiRZbDhEaGsqIESOwtbVl1apVzJgxQz41xd+SG7wAHD16lB49eqCjo4OtrS3Tpk1jwoQJfPr0ieXLl6OkFH1BmNQc8DZOsuS/GCFH+jMMiQwP5dtPqFIkABW1sIQPEgQhTT5/ek/lypUS/HsgCEL6+vjxIxs3bsTLy4s8efJgbW2NRCLh27dvbNiwga1btxIZGcnEiROZPn26vCEA0TfRODg4sHPnTi5fvkxwcDBFixbF0NCQ0aNH065du1j7C4KQOpcuXcLExITKlStz/vx5SpUqBaRs5MjfAYydnR0hISHywNXR0REjIyOaNWvGxYsXqVSpEpC8YCW5zy9kfWJUiiBkD56enkRFRcl/VwuCkLEiIiI4fvw4W7duBWDw4MGMGDECqVTKmTNnmDdvHg8fPqRBgwbMmTMHY2Njef8zRE+rvmvXLo4cOcLbt29RUlKiUaNGjB8/nnbt2tGkSRPU1NQU9fLiEOFLDuDj40OPHj149OhRrCFY8UlJ8BIcHMyzZ88YO3YsEL3A0YYNGyhbtiwWFhacOXOGKVOmMHToUPLlyyc/TiqVYmNjQ2BgIBMmTACiwxkRwGRvYiSJIGQ/gf5fxB1cgpDBoqKicHZ2ZseOHRw7dgxNTU3Mzc2ZOHEiEokET09PqlWrhoqKChMnTsTc3DzWVIC+vr7s2bOHHTt28P79e+rUqcOCBQvo0KEDdevWjdXQEAQhbbZt28akSZPo3Lkz//77L/nz5wdSF2b8GcDcu3ePKlWqULp0aQAaNGjA3bt36dSpE7q6ugwaNAhzc3Nq164d6xzv3r3D2tqamTNnUqJEiWQ/r6A4IlARhJzn3bt3QPRUsIIgZJwvX77w77//snnzZj59+kSHDh1wcHCgXbt2AIwaNYp9+/bRunVr+eMxN5LKZDIcHBzYtm0b58+fJ2/evPTv35/Vq1fTpk2bLL3GrQhfsrk3b97QpUsXgoKCuHnzJo0bN05w35QELxC9mGtUVBSNGjWK9fjUqVNp2rQp69evZ+LEicyZM4eePXvSrVs3tLW1mTlzJnfv3iVPnjyMGTNGPiWGCGCyHxG4CEL2pV+1MB4eHmKdLkHIAFKplNu3b3Ps2DFOnDjBt2/fqFixIhs3bmTEiBGxbkoJDAwkJCSEGzdu0Lp1a/njMpmMgwcPMnXqVIKDg+nXrx+HDx+mSZMmYrSaIKSzqKgoZs6cyfr165k8eTLr16+XL8aaljAjJoB58OBBnDZTxYoVcXFxYfv27WzZsoW9e/fSokULjI2Nad++PadOnWLVqlWEhYVRvnx5pkyZkqznE9KPCFIEQQDw8PBAWVmZsmXLKroUQchxvn79ysmTJzl27BjOzs6oqKgwYMAApk2bRp06dWLt++3bN7p06YKdnV2sxz98+MCYMWNwcHCgTp06/PPPPwwaNIgCBQpk5ktJNRG+ZGN3796la9euFC9enGvXrlG+fPkE901p8ALw4MEDNDQ00NXVjbOtSZMmHDt2jI8fP7Jz505Onz7Nvn37ANDV1WX58uXMmTOH169fx7rDSwQwipdQoPLau5AIWwQhh9CvWpjIyEg+fPggRr4IQjr59u0bDg4OXLp0CQcHB/z8/ChdujQDBgzAxMSERo0axTtKJSaIiYiIkD/2ZwNi8ODBrFu3jmLFiiW7lofu/uhXLZz2FyUIuUBYWBiDBw/m1KlTbN68mUmTJsm3pUeYER4ezuPHjxkwYECcbVpaWlhaWmJhYcHJkyc5cuQI8+fPZ8aMGaioqDBz5kzs7e15+PBhks8jghcRlgiCkDE8PDwoV65cgmsmC4KQfBEREdy9e5dLly5x+fJlHj16hIqKCh06dGDv3r0YGxtTqFAh+f4NRq0HwHW3Bfny5SMgIEC+LSoqCmtra+bOnUvhwoW5cOECXbp0kd+oFnNsUlx3W6TjK0w5Eb5kUxcvXqRPnz7o6+tz7ty5RIdXpSZ40a9amLULH6Cnp5foH6By5cqxbNkyli1bxrt37wgPD6dy5cqEhoYyZ84cHj58GGd4fVYSEza89i6U5D5pkdj5M1JKahfBiyCkXflwtxTt/0GtegZVAv/99x+RkZEifBGENJJKpYwYMYKDBw8CoK+vz9ixY+nSpQtNmjRJclqwmGmNNm3axN27d4mIiGD9+vUULVoUe3t7OnfunKJ6UnNdJwi5VWBgID169OD27ducOnUKY2Nj+bbUhhl/r9vy8OFDwsLC4ox8+ZOqqir9+/enf//+BAcH4+7uTvHixSlevDg/fvzg1q1biT5nTgleRHgiCEJW9OLFC9FmEoR0cPPmTXr16sWPHz8oWrQoHTp0wNzcHCMjIwoXjnvj2J/hSYNR66mdPz9OTk7MnDmT4sWLc+zYMR48eMCECRNYvnx5rJEuyQ1esgIRvmQhHh4e/PjxA319/QT38fT0xNramvXr19OlSxdsbW3R0NBIcP/kNtAjwsOx2fcPXz5/Ql0pnF+/fuHk5ISZmVmy6/9zcTKZTEa5cuV4+PAhw4cPT/Y5MkpSwUJGBw+JjTbJzOcTBCH1UhqspPac6RXI7Nu3j0KFCtGqVat0OZ8g5EYymYypU6diY2PD5s2b6devX4pGqAAUKlSIoUOH8uzZM7Zu3crPnz8ZPXo0y5YtS/FQeRG8CEL0HZXXrl3DwMAgwXZQZGQkp0+fZsmSJXz69AkHB4dYfw/TErw4Oztja2tLYGAggYGBvHv3DhUVFerVq5esc+TNm1e+r0wmo2rVquzYsYPfv3/HmrIwrbVmBBGeCIKQ03h5eXH58mU2b96s6FIEIVt7/vw5PXr0QE9Pj9WrV6Onp5foTWrxhSdOXkpUrlyZU6dO8fXrVypUqICzszPNmjVL8tisTIQvWYCPjw9WVlZs27aNqKgorKysmD17dqw36YMHD9iwYQPHjx8nX758zJw5k8WLF8vXU4lPShroB3ZvZpf1aurXr4+mpiYFChSgZ8+emJqaJnlsZGQkt27d4smTJzx58oSnT5/y+vVrIiIi8PPzS3YNGSGrhxB/15fWMCarv15ByC4yImhJ6XOnJYQJCQlhz549jBw5MtGAXhCExG3YsIHNmzezbds2xo4dm6pzKCkpceDAAfnnMpksVWu6iOBFyO1kMhmnTp1i9uzZ/Pfff9SvX58TJ07Eulv558+f7N69my1btvDp0ycMDAw4fPhwrDnF0xK8fP36lW7duqGpqUnZsmUpUKAA1atXZ/jw4eTJkyfJc3h4eHD79m2ePn0qbzf5+vqipKSEn59fnPAlI4IXEaAIgiD8z86dO9HQ0GDw4MGKLkUQsi0vLy86d+5MhQoVOHPmDJqamonun1B4UrCsLqHo8tbZApksesmKv9tN2S14ARG+KFRwcDAbNmxg1apVSCQSrKysCA4OZt68eTg7O9OyZUvCw8O5cuUKd+7coWLFiqxfv54RI0YkeadkShroH965s2frOmbMmMGKFSm7wI+KisLExITTp0+joaFBnTp1aNKkCWPGjKFu3bqJjuLJKNk5gMjOtQtCdqPIgCU50hLCHDt2DH9//1R3FguCAK6ursyYMQNLS8t4f5ZSGobE3GAh/tYLQso5OzszY8YM7t27R6dOnVi+fDmWlpbo6+tjbm6OkpISnz9/5vDhw4SHhzNgwADMzc2ZbnGJqVPsALsknyMxMVONTZo0CVVVVR49ekSRIkVSdo5r1+jcuTMRERFUqFCBunXrMnHiRHmbqefi43EPEkGJIAhChomIiGDnzp0MGTIkyc5iQRASNmzYMADs7e3R1NRMc0CSHQOWxIjwRUFOnjzJ5MmT8fHxYeLEicydO1d+Ad+kSRMmTpzIw4cPUVVVpVq1apw+fZpu3bqhrKyc5LlT2hmwbIEFJUqVYcGCBbEe9/f3p1GjRtSqVYspU6bQunXrWImjTCbD3Nycs2fPcvz4cXr27JlkfTZOshTVllyiI0MQhIRk9ZAlKeXD3VIcwNy7d4/SpUvHmg5SEISUuXfvHioqKixbtixdzieuVQQh5Xx8fDAzM+Ps2bPo6elx9epVDA0NATA0NGT06NFs3rwZNTU18ufPz9SpUxk/fjwlSpRIt1EjMcHLuXPnOHnyJLa2tnGClwULFvDvv/8yduxYTE1N46zH+ezZM3r16kXbtm05evQoWlpasbbntE4GQRCE7ODTp098+/ZNTNMsCGnk7OzMihUrKFGihKJLyZJE+JKJZDIZFy9eJCAgAFNTU9q0aYO1tTUVKlSItV+XLl3w8PBI1XOkNHiJjIzkycN7TJu7PM7UNBs2bODr16/kyZOHtm3bUqdOHSZPnszAgQPR0NBg3bp1WFtbs337dvr06ZPkc6Vn8CI6MARBgOwfrCRXSgOYvn37sn37dhwdHWndunXGFSYIOVhERAQqKiqJzlWsKNG/+5oluZ8gZFeenp5cv36dixcv4ujoyJEjR+jXr1+sn8dChQpx/Hg8o0VIv+m6YoIXAEdHR8qUKYOJiUmsfb5+/cqaNWuoXr06s2fPZuHChQwbNozJkydTrVo1PD096dKlC5UqVeL48eNxZjAQwYsgCIJiVKxYkfr162NjY0P//v0VXY4gZFsRERGoq/9vtK7rbgtxffMHEb5kkpCQEMaPH8/+/fsByJ8/PwcOHKBo0aLp9hypmQtcRUWFYsVL4ufjHetxf39/Nm/ezPjx41m9ejXXr19n48aNmJmZMWvWLIyMjDhw4ABz585lzJgxyaqthk7825KzzokIWwQh98otAUt6atOmDdWrV2fr1q0ifBGEVKpduzbBwcEcO3aMf//9F01NTfbu3ZvoenvpTfz+E3Kjy5cvM3DgQPz9o9s2y5YtY8CAAck+PrXBy59roWi9C4sVvACUL18eb29vpFJprNH+a9asQV1dnRs3bhAaGsr27dvZtm0bW7dupVOnTnh6eqKiooKdnZ0IXgRBELIQiUTChAkTMDMz4/3793FujBYEIXlq167N/v37KV26NBMmTODKlSsigPmDCF8yQVhYGEZGRty7d4+DBw/SqVMnIiIi0hS8pOeiq2XKVuDzpw+xHtu4cSMRERFMnz4diUSCoaEhhoaGvH37li1btrB3716GDx+OlZVVmmsTwYog5F6iYzH5UjL6RSKRMGzYMBYvXpzBVQlCztWuXTt69erF5MmT+f79OxDd+bpkyZIMeT7x+1AQ4NSpU/Tp04eOHTuyd+9eAgMDGbDyHKcSaLxrvQtL1fMktej838ELQKVKlQgPD8fLy4uyZcsC8O3bN7Zt24alpSUFCxb8/04GTT59+oStrS2bNm3i27dvODk50W3+v6mqVRAEQcg4AwYMYNy4cdjb2zNhwgRFlyMI2dLWrVtp3rw5U6ZM4fPnz9SoUYOwsDARwPw/Eb5kgpkzZ3Lnzh2uXLlCy5Yt03y+9AxeAEqXrcDNq/aYmJgQGRlJZGQkN27cYNy4cejoxB6uUrlyZTZt2sTatWtRUVGJtQZMRtQmCEL2IToOM15KAhgdHR1CQ0OJjIzM1Dv1BSG7CwsLw8XFhS9fvtChQwdOnTol35ae8xiL35mCEJu7uzuDBg3CxMSEI0eOoKSkRIkSJZAkMvVfUiFKajUYtR7X3RaxHotZR83U1BQtLS0iIyP58OEDampqTJkyJda+zSf8g+tuC4YOHUpkZCRNx23JkDoFQRCEtMmbNy+ampoEBgYquhRByFZkMhkfP37k4cOHKCkpUa9ePZ48eSLfrqqqqrjishjRG5MJHj9+TK9evbJU8PJnB14Ho558+fyRr1+/EhgYyO/fvylYsCBnz56lQ4cOdOzYMc7x8f0QieBFEHIe0TmYfeXLlw+A4OBgNDU1FVyNIGQ93t7e3Lt3j+/fv+Pt7c3nz5959eoVDx48ICzsf3fTq6mp0bBhQ9q2bcuIESPS9Jzid6ogJMzNzY3Q0FDWr1+fJdZa+juAqVSpEgMHDuTr1698/PiR379/ExQUhLa2NqNHj46z/kzM8SJ4EQRByNry5s1LcHCwossQhCwpKiqKu3fv8vHjR75//86XL1/48OEDd+/excvLK9a+5cqVo1WrVowbNy7Ozfq5WZqualesWIFEIsHc3Fz+mEwmY9GiRZQsWRINDQ1at27Ny5cvYx335s0bmjdvTunSpeNM3VC+fHkkEgn37t2L9bi5uXmq562XSqWpXsA+PWhqaqbLL/L0DF7+/LdRUwMGj5zI7du3efr0KWFhYdSpUweAtWvXZmptgiCkXflwt3T7ELKe5H5fYkLykJCQjCxHELIVf39/zMzMkEgkFC9enB49ejB69Gjmz5/Pjh07KFSoEKtWreLhw4f8/PmTr1+/EhAQgLOzM0uWLCFPnjypet70+p2a790j+YeQfWSXNhPA9+/fFXb3b8yNAlmpA+zPqTJUVFTYv38/AQEBuLq68vHjR4oXL0716tU5ceJErLs94zteEARByJrU1NREm0kQ/nL06FEqVaqEiooKLVu2ZPDgwVhYWLB27VquXLnCwIEDOXfuHN++fcPX15fv37/z4cMHDh48SNOmTRVdfpaS6pEvLi4u7Ny5U95JH2P16tWsX7+e/fv3U7VqVZYuXUr79u158+aNfIHBCRMmMGTIEBo2bMjYsWMxNDSkefPm8nPkyZMHS0tLHB0dU1seAD4+Puzfv58dO3bw7t07Fi9ezIIFC9J0zpQIDw/n+PHjPHv2jOrV404To4jAIr6Gf+CvAJbOm0L79u05duwYWlpaABw4cIDhw4fj4eFBxYoVYx2TlcKW5HZmJHeqnqwkPTpqMuJ1p7Wu0MhIvmXwcyf0umOOV+T7QYQaQlolZ/oxV1dXihYtira2diZVJQhZ26NHjxg5ciRPnz5NcJ9WrVrFmj4o5prob8m9Dkrt73sRruQc2aHNJJVKuXr1Ktu3b+fcuXNUrlyZGzdupOs0e0l59eoVu3fvBmKHL1ktvFi+fDlPnz7lxo0btGrVCiUlJSIjIylbtiy7du0CKim6REEQBCEZYtYM8/f3x8PDA11dXQVXJAhZw+/fv1m/fn2i/efh4eGsXr06E6vK3lIVvgQFBTFo0CB27drF0qVL5Y/LZDI2btzI3Llz6dWrFxDdga+jo8ORI0cYM2YMAD9//qR+/frUqVOHkiVLEhAQEOv8Y8aMYdu2bdjb29OlS5cU1xcQEMD8+fPZs2cPEomEvn37YmxszMKFC5HJZCxcuDA1LztF/P396dSpEy4uLrRv355FixZl+HMmJqHGf/lwN8Zt3Mvv30Hs3r07VidD3759mTx5Mnv27GHZsmWZVWqypKYzI75jsmIgk94d81m5o7905FvyZNDsh0m97qz8dRGE5EgqgLl27RqGhoZZYuoWQVC048ePY2JiEufxPHnyoKenR+PGjdHX16dHjx5Jniuh4CUtf1dE2JIzZfU2E8ClS5dYtGgRb9++pVatWqxcuZJNmzbRunVrbty4QcmSJVP56pNv27ZtTJgwAR0dHZYsWUKNGjWArBe8uLu7s3TpUmbPnh1rdJGKigojRozgn3/+4cuXL7SavF1xRQqCIAgpcuPGDWQyGYaGhoouRRAU7ufPnxQqVCjebRUrVqRRo0bo6+vTuXPnZJ0vq13LKUqqej0nTJiAkZER7dq1i9WQeP/+Pd++faNDhw7yx9TV1TEwMODOnTvyhsSSJUto3749ISEhdO3aNc6aIuXLl2fs2LHMnj2bTp06pbjjqFWrVgQGBrJkyRJGjRpFkSJFkMlkPH78mEWLFjFw4ECqVKmSmpeeJKlUyu3btxk6dCiBgYHcu3ePxo0bZ8hzJSYlHQA+Hs9o2KQlZcqUifV43rx5GTRoEPv27WPx4sWZtmBzZnaKZ+ZzJTUSQxAEIaUSGskVGBiIi4sLAwcOVERZgpDlbN26Vf7/zZs3o6GhgZ6eHrVr107RYpB+L+5QPp1qEoFLzpfV20wAo0aNok2bNhw4cICmTZsikUioWbMmRkZGLF68mB07dqTy1SfNz8+PBQsWsHXrViZPnsyaNWtQU1MDsmZj/cOHD0RGRjJs2LA420aOHMny5cs5ceIErrstsmT9giAIQlw3btygTJkylC5dWtGlCILCOTs7y/+/atUq8ubNS4UKFWjUqFGKZ9QQ10L/k+LedFtbWx49eoSLi0ucbd++RU8kpKOjE+txHR0dPn78KP+8S5cu+Pj48OvXrwS/efPmzWPfvn0cPnyYIUOGpKhGAwMDVq1aJa/j9+/fTJkyhXv37rFkyRLKlStHREREnOMiIyN59+4d5cuXR11dPUXP6ejoyM6dO7l9+zY/fvygbt26XL16lbJly8b7XADSqMgUPUdSyka4y/+fkjNXq1SRe4+fxlunqakp+/fvx87OLtYddelZ+591Q8pqz05Kh76K9/Gc+noTEyWVyf+NlEoVXI0gZH9//05WVc2Lnp4eGzdupEOHDlSqlLppUGL+LiT0d0wQsovDhw9TpUoVNm3aFKfjNLnvb//X99NUQ773sac7iyRti1D+Wbf4Gc16skObCWDXrl2YmJggkUiIjIzE0dGRkSNHoqenx8yZMxN8b33//p2IiAhKlSqVouf78eMHGzZs4OrVq7x8+RJVVVV27NjB8OHDkUgkREREYDDJGjWVrLNIa8zXoFKlSmhoaODu7k65cuVi7VO2bFk6derEgQMHGDBgAHe3TcJgkrUiyhVyEDVlifzfrPQzIQg5gZqaEhERETRo0IC9e/eydOlSZs2alapziTaTkFN07NgRU1NTHjx4wNSpU2NtS8n7O6teyynqZ1Qik8lkyd3Z09OTBg0a4ODgQN26dQFo3bo19erVY+PGjdy5c4fmzZvz5cuXWHMEm5mZ4enpyaVLl5J8jvLly2Nubo65uTlLlixh3759vHnzhpkzZ/LkyRNu3ryZ4LG/fv1CS0uLI0eOkDdv3uS+LEEQBEEQBEHItoKDgxk4cCABAQHyRcsFxcnqbSYQ7SZBEARBEAQhd1FUmylFI18ePnzI9+/f0dfXlz8WFRXFrVu3sLa25s2bN0D03Vx/NiS+f/8e586u5LCwsGDr1q2xpopIjjZt2lCkSBEAPn/+TO3atZk6dap8saBPnz5hYWHBlStXAKhQoQJ6enro6+tTrVo19u7di52dHUpKSlSpUoVFixYlOI+ym5sbjRs3ZunSpUycOBGJJOlkL613TqY3p/uuDJ0yh6dPn1K+fPk426tWrYqpqSmWlpbyx7LaaxCyl7DIKB57/qR+mYKoqygruhxByDE+qVaV/79e5UJ8+/YNXV1dVq9ejampaYrPFxERwZUrV2jfvn2KpmYSBEW6cOECxYoVo1GjRkB0R3inTp0oW7YsFy9eTPL49B7hklHUu4yW/9/Pzy9TnlNInuzSZoLY7aaRI0fi4ODA27dvyZMnD1FRUezevZslS5YQFBRE/vz5qV+/vrzd9OXLF1auXMnPnz8pUqQIbdu2Zdu2bQn+vejduzffvn3j5MmTFC9eXP54Vh8h4rhlovz/zZo1o0mTJqxfH3cqDWtra1asWIGXl5f8saz+2oSsLb+6EhPaluef6x8IChOzBQhCetJ6H865Cxbyz8ePH8/t27d5+jTl13GizSRkR97e3pw9e5bhw4ejpqaGTCZj/vz5bNmyhatXr9KwYcMccx0Tcy2nqDZTisIXQ0NDnj9/HuuxESNGUL16dSwtLalYsSLFixfnypUr1K9fH4Dw8HAcHR1ZtWpViovLnz8/8+fPZ9GiRXTr1i3Zx6mqqsp/4ZUrV44aNWpw6NAhpk2bxp49e1iwYAGFCxdm7969tG/fXt7giNG5c2eePXvGgwcPMDc3Z82aNRgbG8f7XE+fPiUkJIQuXbrI5yhOjN+LO6hkscWP85SoTEhICEFBQfH+ofD39ydPnjyxtmW11yBkL5FK0Y0HZSWJeC8JQjpSUv7fn/Vn7wPRrxo9f/GLFy/S1BD48++qIGRFfn5+FC5cGIlEQp8+fZBKpcyYMYO+ffsycOBAoqKiOHDgQJLv45Repyly3ZY/X4v4+cxaskubCWL/fjcyMsLGxoaLFy9StmxZzM3NuX//PuPGjWPixIlUq1YNZeXYN80MHTqUW7dusW/fPg4ePMjYsWNp2rRpnOcJDw/HwcGBzp07x1lnMjwy2RMxKETM10cqlRIUFISfn1+8P3NBQUFIJJJY27L6axOytnCV6PdPeJRMvJcEIZ2Fh0tj/b5u1qwZe/bsISIiItUjQkWbScjqQkNDiYyMJH/+/Dx58oSJEycya9YsDh8+zNOnT1m9ejWbN2+mWbNm2XrNlsBysQdGxPxcKurnM0XhS4ECBahVq1asx/Lly0eRIkXkj5ubm7N8+XKqVKlClSpVWL58OXnz5k31or+jR49mw4YN/PvvvyleuD4kJIRixYoRFBQEQJEiRZDJZEyaNImlS5dSoECBBI+tU6cOxYsXZ9KkSXEWt/zTixcvAOjWrRt79uyhVatWCe7r9+JOiurPDIV1m7JkZGOaNGlCvXr14mw/deoUv379ircRJQiCIGQt5cPd+KBWXf75Q3d/atWqJf9bJQg50atXr9DV1eXUqVP07NmTf/75h3HjxrFmzRrWrFlDyZIlcXZ2TnJtioSu0xQZsAjZU3ZrMwFs27aN8ePHA9CvXz8AatSogbOzM82bN0/wuIIFC9K9e3cuXrxIsWLF4m1PQPTdlWFhYZw5cwYLCwtWrlyZrBvXspITJ07g5ubGrl274mz79esXy5cvT3H4JQiCIGQNtWrVQiaT8erVKxo0aKDocgQhQ+jq6hIaGoqXlxcdOnQAom8eiRlwsGzZMiZNmpStgpe/g5asKN1vOZ85cybm5uaMHz+eBg0a4OXlhYODQ6JBR2JUVVWxsrIiNDQ02cc88/jJQ3d/Vm7cLQ9eBg0axM6dO/Hw8GDTpk3JqsfGxobQ0FDGjRuX4D4rVqzAycmJkiVL0rZtW9atW0cKltFRqCK1mnHhwgVcXFxYunRpnCnTrl+/zoABA+jfv78IXwRBELKJ8uFusT4vV00PJycnTp48qaCKBCFj/fPPPwC8fPkSiL7m+3Mqp6tXr1KhQoVEz/Fn8JLv3aNYH4oW9PBxvB9C9pYV2kwxpFKpPHhp2LAhCxcu5MaNGzx9+jTe4KXBqPWxGuVSqZTt27czatQoNDQ04n2OMmXK8P37d6ysrPjnn39o1aoVnp6eKa5VUaKioli4cCEdO3akRYsWsbaFhoZibGyMuro68+bNkz+enTouBEEQciPDNivk/9fV1aVgwYJYWlqm6m+pIGR17969w8PDgy9fvgCgpqbGokWL5NstLCyYPXu2gqpLnsBykjgf2UGKRr7E5+/FHCUSCYsWLYr1DUyJDx8+xHlswIABDBgwIMXn6tLdhEZNDdApUYoG1YokfcAffH19mTZtGkCiv3glEgktWrTg2rVrzJs3j+nTp/Pjxw+WLl2a4nozU5FazZBKpcyfPx8DAwPatm0ba7urqyvGxsa0adOGAwcOoCSmhhIEQciWTAaNwuvdMwYOHIidnR3t2rVTdEmCkK7c3KIDx5iO3AIFCnDnzh0qVKiAra0tNWrUSPT4mOAlKwQtMUS4kvNk5TaTkpISXl5eFCxYMMmpVuILFGKmSgsLC0v0WG1tbebNm0eHDh3o06cPTZs25fHjx7jutlBoUOG62yLJfY4cOYKbmxsHDx6M9XhkZCQDBgzg3r17XLlyhTp16gAieBEEQcjqAiqpo/UuDMM2K7h2YzZ58+bl7NmzdOzYkYEDB3Ls2DFUVNLcZSoIWYa7u7v8/79+/UJTU5MFCxbw/PlzPn78yLp16xRYXWzZJVRJrhzdo66iqkrxkqWRSCQ8dPdP0bHTp0+nUKFCuLq6Urp06aSfS0WFlStXsnz5cpYvX861a9dSW3aGK1KrGQCHDh3i6dOnWFlZxRr18t9//9G5c2d0dXU5efJktpsSQBAEIbf7c/SLkpIS+/fvp23btvTo0YMHDx4osDJBSD8uLi7UqVOHqKgoAE6fPi0PYGJGweTJkyfRc/i9uKOwES4JjWgRwYugCCVLlkxV8PLkyRPmz5/P1KlTk33zWaNGjbh79y5hYWGMGDECmUyWrAAkIyTneX/+/MnixYsxNjamYcOGsbaNGzeO8+fPc+LECfmIGBG8CIIgZC8xI2BatWrFsWPHOHfuHGPHjs02s9oIQmKkUinDhg1j69at8sdibiYJDQ3Fw8MDdXV1hdQW30iWnBa8QDqMfMmqoqKi+B0UiKZWQfljD9390a9aOMljv3//zoEDB2jatCkqKioEBARw/vx5unTpQuHCiR9vaWnJ9evXGTx4ME+fPqVYsWJpfSnpqkitZgQHB2NpaYm1tTUmJia0bNky1j7//PMPysrK2NnZkS9fPgVVKgiCIKQXNTU1Tpw4Qfv27Wnfvj179uyhT58+ii5LENLEy8sr1qLmPj4+1K9fn6VLl2JpaUm3bt0SXX8h9Kw16X2VI4ITITsKCAggb968iS5CmlCgsGHDBqKioqhduzYymQwHBwe0tLRo1KhRnCmN/1SqVCkOHDiAkZERmzZtwtzcPNNHwCQneLly5QojR47k169fccKlz58/s3v3bjZu3IiRkREgghdBEITsKmYETLdu3di3bx9Dhw4lICCA3bt3o6WlpejyBCHVIiIi4ozcnTRpEq6urkRERODm5oaTk1OG1pATA5WUyJHhy/u37qxfOY+Xzx7RonUHBgwbQ6OmBkgkkmQteq8MbFw8i8Xrt1KvXj3U1dUJCwtDXV2dESNGYG1tjbKycrzHKikpcfDgQerWrcvw4cO5cOFClpmyq0itZty9e5dhw4bh6enJpk2bmDhxYpz9pFIpxYoVo0iR+KdqS87XMCUy8m7T35X0MuzcgiAIWVnM6JcPatWB6MWeL126xKhRo+jbty+7d+/G1NRUkSUKQpr06NEDX19fpk+fzv79+xk9ejSHDx9m3LhxVKlSBRsbm1jXYH+v65JaImARcpK1a9eybt06dHR0mDhxImZmZhQuXDhZIUKDUes5tnAhPj4+jBw5kpEjR8q31apVi/3796Ovr5/g8V26dGHq1KnMnDmTli1boq+vn2kBTFLBy+/fv7G0tOSff/6hbdu27Nu3j7Jly8baRyqVAiQ5taEgCIKQPcQEMEOGDCFv3ryMHDmS5s2b8/Tp0wT7AAUhq1NXV0cqlXLq1Cn5DZhTp05lw4YNQPSsSH9er6X1Oiy3By3xyZHhy5TRJpQtW5Z169Zx4MABJozoTa1atWjYsCHKob+oUqEcpgN6J3qOwb260bdrRxp37Y9GnjzMXbiYL1++MG/ePAoWLMiKFSsSPLZEiRIcPHiQzp07s3HjRiwsoi/ui9Rqlu7BRUJiphaLERYWxuzZs1m9ejUNGzbk/PnzVKtWLd5jVVRUiIyMjHfb3/VnpfnR45Me9YkARxCE7Kx8uBsP3aujX7UwmpqaHD16FBcXF1xdXUX4ImR7RYoUYd++fdy7d4+HDx/y+/dvAA4fPoympmac/VNzXSDCFiEn++eff5g2bRrfv39n4cKFLFmyBCMjIxoULsyZ225o12iOumbCa2eaLD+Dq709K1euZMWKFfTp04devXoxf/58jI2NcXV1pXjx4gkev2LFChwdHenfvz+PHz8mf/78GfEyU+T27dsMGzaML1++sHnzZiZMmBDvzXQxawFERERkdomCIAhCBokJYHr37s23b9+YOHEiISEhWeLvkyCklkQioXfv3uzZswdTU1MOHDgAwMCBAxk8eLB8v5QGLyJoSZ6cGb5MmcKCBQtQU1Nj6tSp3Lx5k+3bt+Pm5oafnx+7/z2JYYsmlC9TKtHzqKup8cThlPzzIsOGoaqqyowZM6hfvz4mJiYJHtupUycmTpzIkiVLGDduHBoaGtHnyOAAxt3jA8NnLGbSpElMmDABiUTCkydPGDp0KG5ublhZWTFz5sxEFw5LKHzJigvSZob0fr0izBEEIbOVD3fD70X03yCJRIKRkREHDx5kwYIFlChRQtHlCUKa5c+fH1dXVwCWLFkSZ10GSP7fcxG2CLnJ5cuXadq0KRAdhGzbto1r167h4eFBoMdroiLDKdc88WkqG4xaj+vuWcyaNUv+WP369dHX16dPnz5cv349wTUk1dXVsbW1pWbNmuzbt49Jkyal34tLgqmpKd++fWPnzp2UKlWK0NBQFi5cyJo1a2jcuDH29vZUrVo1weNj2lMJ3bQmCIIgZE8xAUy7du1QVVVl/fr1LFiwQNFlCUKaxYSI/v7R66L/888/8m3JCV5E2JI6OTJ8mTp1qvwCXyKR0KZNG9q0aUNwcDB37tyhffv2XLzhxLih/VN0Xr8Xd5g2bRr3799nxIgR1KhRg9q1aye4/5QpU7C2tub06dMMHDhQ/nhGBTAymYwFW/bx7ds3Jk2axKVLl2jYsCHLli2jRo0auLi4ULdu3STP8/79+zgLbsYsSCuknaK/jiL8EYTcK+Zvj5WVFUePHmXmzJkcOnRIwVUJQtrFrFHXsGFDZs+eneLjReAi5FZ/hgvFihVj4cKFLFiwgPfv3zNhwgSePXuGTCZFIkl8GuXoAOZ/U3mVLFmSEydO0KpVK8zNzWMt8vq3KlWq0K1bN/bu3Ztp4YuDgwN79+5FS0uLOnXqsGjRInbs2MF///3HihUrmD59epJTzLx//x4gTrspOxGdKJmrwEexeLcgZBcxAYyFhQUrVqxgyJAhVKhQQdFlCUKa/Lmut7OzMwULFgQSDl7EdUL6yJHhS4zg4GD27t2Li4sLDx8+5PXr10ilUlRVVSlULv4ptxJzzuEGGweN4/nz5/IFJRMLXypXrkyrVq2wsbGJFb5A0gFMTAd9SjrKnd5+5+rVq5w/fx6AESNGcPHiRWbNmsWCBQtQV1dP8hxv377l1KlTWFtbx1uPkP2l9XspwhtByP6kXq9ZuXIlo0aNwszMjFatWim6JEFIk969e/Py5Ut8fX1TNCe3CF0EIZqTkxPnz5/n4cOHPHr0iJ8/fwL/v56JDEhB2zsgIIDhw4dz8+ZNpFIpZ86c4Z9//kEiSfgkI0aMoHv37rx8+TJtLyQBfwZD4eHh1KlTBwMDA06cOIGZmRmTJ0+mXr16uLq6Jtq++9OaNWuoXLkybdu2zZCaEyI6QrKvlH7vJCrR+/8uJSEwMvaxIsgRhLQLqKSO1ruwBLcbtlnB2fPzOHz4MObm5pw9ezYTqxOE9FevXj0aNWrEgwcP8Pb2jrNdXGNkjBwbvkRFRdG/f38uXbpE/fr1admyJebm5ujp6VGrVi3U1NRSPPrkhdt/PHv2jM2bN9O+ffsE10z5U40aNeTTYCTH3x3j8XWUx9f5HRIahoWFBZ07d8bIyAiJRMKrV6/w9fVN0SKQa9asoWjRogwfPjzZxwi5S3oEccooAaXI9/EFeZDGu48IeQQhY3VvWI3GjRszceJEHj16lOh0lIKQ1U2aNIkqVarQuXNn3rx5Q/Xq1RPdX4QugvA/Dg4OdOnShRIlStCwYUOmT5+Onp4eenp6GM09nOLzhYeHc+HCBQwMDJgzZw6NGjVKNHiB/y1a7+Pjk+Lnc91tkehUGX8GLwBbtmzhv//+49ixYxQtWpRTp05x//599PT0Epwe7W9ubm6cOnWKHTt2JDvwFR0aQnpKj/eTCHAEIWnG3bawfv16TExMsLOzw8jISNElCUKqlSpVivv371O7dm3s7Ozo1asXIK5RMlqO7GmRSqWYm5tjb2/P+fPn6dy5c7qcd3Dvbmzccwh1dfUkG/UxlJSUkErj71xObSf2n8fFdFBb7zuMl5cXly9fljdutLW10dbWTtG5T548ydChQ+Vr1AiCoqTm50MENoKQfEpKSiybOpr2A0axZcsWpk6dquiSBCFN9PSi/wa8fv06wes0EboIQmwvX76kT58+dOjQgXPnzsUK4lO66GoMbW1tevfuzaNHj2jdunW8i9X/LWafhNpNCYkJVhIKYP4OXr59+8bixYsZP348derUAaKnqW7SpEmKnvfMmTOoqKgwZMiQWI+LzgshO0nL+1UEN0Jusu2f/zA0NGTy5Mm0adMmW083KQgQ3W56/fq1osvINXJk+NKxY0ceP37Mtm3b0i14AShbqgTdunVjy5YtmJmZJXkHF0Q3JGSyjLswyffuEW4aJdm05xAWFhaJLgqZHM2bN+fOnfRfj0YQMkN6TY+XG0OcnD61YG78niZHPd3qTJ48mRkzZlChQgV69Oih6JIEIdW0tbUpUKAAb9++jXe7CF4EIS4jIyMqV67M16ItaDJ2c7qdd9KkSbRo0YIrV67QsWPHJPePCV9S0m76O1j5O4D5ezvArFmzUFNTY/Hixcl+nvg0b96ciIgIXF1dadGiRZrOJQjZkRh5I+QmEomEoF+18fa+R8+ePTl79ix58uRRdFmCkGqVK1fm4sWLii4j18iR4UvhwoVxdnamefPmCe6T2gXvJ02aRLt27bh16xYGBgZJ7i+RSJJ9B1ce44mxPpfJZHz79g13d3fc3d158+YN7u7uBAYGUrt2berXr0/9+vWxsrKiUOEizJ07N1Wv6U9lypTh4sWLSKXSZN2lJgg5UU4PInKj5HxPc2tAs3btWr5+/YqJiQlnz55N15sWBCEzSSQSKleunGD4IghCXJaWlpz8Txll1aTXhkyJZs2aUa9ePaytrZMVvsTc1JbcdtPfwUpISAhv375lVqey8rZTq1atKFmyJPXr16devXpIpVIOHDjAjh07KFy4cMpf1B9Kly4NwMOHD+XhSzWrDWk6pyDkNikJcERQI2SUpNZ9iZEvXzHOnz9Ply5dMDEx4cSJE8meqlIQsppKlSrh4+NDQEAAWlpaii4nx8uR4cvRo0cpUqRIgttTG7wA8gUYv3z5kuS+oaGhXL9+nVKlSiW5b0zw4unpyfz583nx4oU8aIHou8EqVKhA1apV0dbWxsHBAWtra/ndYTY2NhQoUCC1LwuAa9eusXXrVqysnetAUwABAABJREFUrETwIghCrpNQQJPTQxkVFRVsbGzo27cvvXr1ws7OLtMXDxaE9BAeHk5ERATfvn1TdCmCkG2c80j/4AWiw5Q6derw4sWLZO1vb28PQLFixZLc13W3BTKZDGtra86fP8+bN2/w9PSUt4sKFSpEtWrVqFChAp8/f8be3l7eptLT08PU1DSVrypaREQEAwcOpHz58gwbNgwQwYsgZLS0jrQR4Y2QHtq0acPp06cxNjZm0KBB/Pvvv2LdTCFb+v37NwDe3t4ifMkEue63RFqCF4Bbt24B0LJly0T3CwoKYvbs2bx9+5ajR48mum9M8PLmzRvat29PVFQUnTp1wsTEhKpVq1KtWjUqVqyIunrshlFQUBDPnj3D29s7TVPFyGQyHjx4wODBgzE0NGTWrFmpPpcgCEJOE18ok9MCGVVVVY4ePUqPHj3o1q0bN27coFGjRoouSxCSTSaTMXr0aNzd3dm2bZuiyxEEgeh2k7GxcaL7yGQyXF1dmT59OmPGjKFu3brAtQT3d91tIV/fc8uWLRgZGTFgwACqVasmbzcVKVIk1vTQUqkUDw8Pnj59SoMGDVBWVk71a/L392fOnDm4urri7OxMwYIFRfAiCNlAasIbEdgI8enUqRPHjh2jT58+jBo1iv379yu6JEFIkfv37zN58mT69etHlSpVFF1OrpCrwpe0Bi8Q3YioVKmSfKh5DJlMxpUrVzh+/DgPHjzgxYsXSKVS1q1bR61atRI8X0zw8vjxYzp27Cgf1ZKc0TL58+enWbNmqX4tnp6e2NjYcPDgQdzc3KhUqRKHDh1KU4NEEAQhN4gJZHJCCOP34g5FajVDXV2dU6dOoa+vz+bNm7GxsVF0aYKQLFKplKVLl3LgwAEOHz4s1l8QhCzg06dPfPjwgdatW8fZ5u3tzc6dO3F2dsbFxYUfP35QuXJl1q5dm+g5XXdbEBERwciRIzl8+DDbt29nzJgxSdaipKRE5cqVqVy5cqpeS3h4OBcvXuTgwYOcP38eqVTKxo0bady4sQheBCEHS25gI0Ka3MfY2JiNGzcyceJEVqxYQdGiRRVdkiAky7t37+jevTv169dn//79yVrLXEi7XBW+pIfw8PA4C2vdunWLefPm4eTkRI0aNWjevDmTJk2iSZMmSQYvgYGBbNq0iVWrVlGjRg0uXryY6JRpISEhfPjwgQ8fPlChQgWqV68e736hoaEcP36cHz9+EBISEufj3bt33Lx5kzx58tCrVy82b95M27Zt4w1eQs9aJ/OrIwiCkLvklBAmJoDR0NCgY8eOnDlzhqioKEWXJQgJ+vHjB1euXOHixYtcvHgRb29vFi9ezMCBA+Pd33dB2qYZEgQhcX+vwRIREQEQq93048cP1qxZw6ZNm1BWVqZVq1ZMnTqVRo0a0bx5c/Lnzx/vuWLcvHmT2bNn4+rqyr///ku/fv0SrEcqlfLt2zfev3/Pz58/6dy5c4LTKj948ABXV9d420yBgYFcvnwZX19f6tevz+rVqxkwYAA6OjoieBEEAYg/pBGBTPaS3HVfDNus4NqN2QD06NGDiRMn4uzsnKaZaAQhI0mlUh4+fChvM92/f5/y5ctz9uxZ+TWauJ7JeDkyfHFzc6N58+YZcu4GDRqwa9cuBg4cyNevX/Hy8uK///6jfv362NnZ0blz52Qlh8HBwfzzzz+sWrWKwMBAxo4dy9KlS+XrtshkMm7fvo29vT1v377l8ePH+Pv74+/vLz9HkyZNuHv3bpxzf/78mV69euHi4oKGhkacjzx58qCtrc3evXvp3bt3omvFxHRW5Nevn9IvlSAIQq7x59Rk2TWIiQlgjIyM2LhxI3Xq1EFPT48+ffqwdetWOnToQI0aNRRdpiBw+PBhTE1NCQsLo1atWgwdOpSuXbsmOCWsCF4EIWEx66SkRXxhScWKFSlYsCALFy7E2tqab9++8ebNG6RSKVOmTGH69OnJXvT+zp07zJ8/n+vXr6Onp8fVq1cxMDCQb/f19cXW1pYXL17w9OlTPn36hK+vL+Hh4fJ9nJyc4oyKk8lkrF27llmzZqGkpETevHljtZc0NDTImzcvw4YNY9iwYfK1P0F0VAiCkLg/AxkRxOQsMQFMqVKlqF+/PsOHD6djx44MGzaM1atXU7lyZfr06SNmlBEU7vv373Tr1o0HDx6gqalJ+/bt2b17Nz169JBfg4nrmcyRI8OXKVOm4OrqmiHn7tChA3p6enz9+pXixYtTr149WrVqhbGxcbIXqf/w4QMtW7bk27dvmJqaMnfuXMqUKQOAn58fhw4dwtramnfv3qGjo0NERIQ8dClcuDAdO3Ykb968ODk5xTm3k5MTffr0IU+ePDx8+BA9vdR3Av7ZWRH08DEgQhhBEISkZOcgxu/FHdq1a4eTkxM7duzA29sbgMWLF2Npacny5ctp0qQJZcuWjTP9piBkNJlMxpo1a7C0tGTYsGEsWbKEsmXLJnqMCF4EIXG/vT+Qt2T8I+mTI6FRKhKJhNGjR3Pjxg2UlJTQ09OjV69emJqaoqOjk+zzW1hYsGHDBmrVqiVf5FgikSCVSrl58yZbt27l7NmzSCQSSpcuzYcPH5DJZCgrK1O3bl0mTZrEqFGjYt3ABtELzY4aNQpbW1tmz56NlZVVsjvKREeFIAgpIYKYnCcmgLl58yZr1qzh5cuXAOzYsQNPT0/atWvHzJkz0dHRoXr16qipqSm4YiG3efv2LZ06deL3799cvnyZNm3aoKqqGmsfcT2TeXJk+PLy5UsePnyIvr5+up+7bNmyuLi4JGvf+Kbrimg7jG7duvH582c6deqETCZj0aJFREZG8vPnTy5fvkxkZKR8upfg4GDCw8O5cOEC6urqnDhxgh07djBixAh+/PhBVFQUysrKyGQytm7dirm5OS1atODYsWNoa2sn+3WFnrWWByyJCXr4WAQwgiAIyZQdgxi/F3do0aIFLVq0ICIiAnt7ez59+sSMGTOYNm0aAMrKyly7di3WnceCkFGuXbvGxYsXef78OQ4ODsybN48lS5YkOdJYBC+CkLTaBQJ4l8pjEwpeYqxatSrexxuMWp+sc/3zzz9s2LCBvHnz0qxZM+zs7Dh79iwRERHcv3+ft2/foqamRmRkJOrq6nh6etKzZ09WrFjB5cuX2bBhA8eOHQOiR8fEeP/+PT169ODt27ccO3aMvn37yrfFdES8mT813tpFR4UgCGkRE8SIECbrSe7UYzFiAhgrKyt5m+ndu3fcunWLQYMG0aFDByB6fZgzZ85kUNWC8D/+/v7s2LGD//77j/Pnz1OkSBHu3r1L+fLlxfWLgiVvqEY2U7JkSXbu3Jnu58337lGy1z+Jb7//vL7Tr18/Pn36hLm5OZcuXZIvNunh4cHv37/lc5UPGzaMkydPYm5ujp2dHR07dqRdu3aMGjUKiUSCsrIyPj4+6Ovr4+DgwKhRo5g4cSITJkzAwcEhRcGL7wLTZAUvgiAIQurle/coVhiTlfm9uBPrc3V1dbZt24avry/Pnz+nWrVq9O3bl8GDB/P4sfj7IWSMsLAwzM3NadeuHSdPnkQmk7F//36srKxE8CII6eTSpUtcXDEkVcfGF6Kk5JjAcpJ410r4/fs3e/fuZcqUKZiamlK4cGF27tyJra0t7u7ufP78mUaNGqGrq0uBAgU4ffo0q1evZtmyZezZs4eqVasybtw4KleuzPPnz6lZsyYTJkxg/vz5nDt3jgYNGhAUFMS9e/fiDV7+/v+fEgplBEEQUiKh339C9mLYZkXcxwwN+fTpEx4eHqxYsYKzZ89iYGDA+vXrxZqaQoZxdHSkbt26LFu2jJcvX2JsbMzt27dF8JJF5MjwpV+/fhw5coSAgIB0Od/fHWZJBTB/bv/k488yW3saTF5OnQlWODk5cfToUTZs2MC5c+coUqQIQUFBLF68mNOnT3Pq1CkMDQ3ZvXs3BgYGyGQyTExMKFu2LAcPHqRTp040btyYDRs2cO/ePfniyIcPH+bAgQNs3LgxzlCyxKSmc0IENYIgCKmXXUKYvwMYgCJFisinfhk+fDguLi60aNECe3t7BVQo5HTDhw9n27ZtbNy4EQ8PDxwcHBg2bFiSx4ngRRBSZt++fUmOYklISgKYalYb5B2Of3Y6uu62QCqVYmtrS+/evdHW1sbU1JQePXqwfft2Hj9+TPfu3QkKCqJ+/fpcvHgRPT09Xr58yaVLl+jRoweVKlXiwoULFCpUCHNzc8zMzLh+/To7d+7k3r17WFhYsGbNGoyNjWnYsCEuLi5JruEiAhhBEDKaCGGyloBK6ik+Jr4ARk1NjQoVKjBjxgx27NhBkSJFmDFjBv369SM0NDQ9ShUEuQcPHtC2bVsqVqzI69evuX//Prt376ZIkSIieMkiJLL0WGUxi/j16xdaWlq8ePGCxo0bM3ToULZu3SrfHl9HUmKS6hzLYzwxzmN/BzO9l+7A3vUF/Q0a0LNpPbot2IyGhoZ8+5cvXxg8eDA3b97EwsKCdevW4eLiQoMGDejQoQN37txh1KhRPHz4EGdnZ2rVqoWjo6N8cSSZTMaZM2eoWLEidevWTdHrS0vnhJh6TEiLUJRwpBQGeJEHqaLLEQSFy6pTkkVKpdx770eXLl3iDfaDg4MZNGgQ58+fZ8eOHZiaik5vIX3Y29vTrVs3tmzZwvjx45N9XG4KXoou2SP/v5+fH0WLFiUgIABNTU0FViVkFzHtJlNTU06cOMHLly8pVaoUDUatT1VHYFKBRFJBxq1btzAwMKBatWqMHDmS3r17U6lSJfl+MpmMbdu2YWFhQbVq1ciXLx/a2tqcPXsWBwcHOnbsSLNmzWjZsiXr1q0jMjISGxsbBg0aJD/H+/fvcXZ2ZuDAgbHWd0mqY0JMQSYoiqaKEvNqlmfpqw/8ihRtptxATEemeCmZeiyGmpoSY8dXT7DNBHD27FkGDBiAvr4+586do1ChQmktVRDw9fXFxMQET09P3NzcUnR9k5vEXMspqs2UI8MXX19fbG1tmThxIteuXaNt27ZA8sOX9Lwj2eHRK4yXbMNmxkh6N68fb2ATHh6OtrY27du35+TJk3Tt2hVjY2PMzMw4evQoJiYmREREcPDgQYyMjChevHiK68iozggRwgipIcIXQYhfVgthYsKXJhWKoKL0v8GyRWo1k/8/KiqK8ePHs2vXLj58+JDkAuiCkJj3798zdepUzp49S9u2bTl//jx58+aNd9/cFLTER4QvQlrEtJvevXtHixYt0NPT4/z580gkkkxrrP8ZakRGRtKgQQPU1dW5e/cuSkrxT9CwcOFCVq5cib6+Pu/fv8fS0pIzZ87w+/dvHjx4QPWlGwn18iQyKJD81WpmSK1/Eh0bQkYS4UvuJUIYxUppAJNU+BIzStR6VDM6derE8OHD2bhxY3qUKuRSkZGR7Nixg3nz5gGQ16g3+avpKriqrEvR4UuOnHYMYNy4cRgYGGBqakpQUBAQu7MoIek9FUwHvZp0bVQby72neOrxOd59HBwc+PXrF5aWlmzfvp23b99iZmZGuXLl6NWrFwCqqqqYmppmqeAFoqcgE9OQCYIgpI/sOCWZsrIya9asQVVVlVOnTimwKiE7CwkJYeHChdSoUYOHDx9ia2vL1atXRfAiCBlMS0uLHTt2YGdnx6FDh4DMm1brz+BCRUWFLVu28ODBA5YsWRLvtCwymYyjR4/Sq1cvdu7cSdu2bZkxYwaOjo5Mnz6d6ks3ApCnVJl0DV7+rjWxxwRBENKDmIose+reNe5UnH9Oz9mkSROGDh3KiRMnkEpFoCqkjpOTE/r6+kyaNIk+ffpQeIyFCF6yuBwbvigpKbFnzx6+f//OuHHjiBngk1gAk1EdXmtNe6OqokwTi1V07NiRp0+fxtq+a9cu6tevT8OGDfk/9u46runt8eP4ayOlFURUFOzCFgMxrt2KWNfEViwUu7tb0asYmBjXvDY2BnZ3oYgJiihKyX5/8Nu+IEPpjXGej8ce9/qpnc82ts857885p2/fvty7d49jx46xd+9etLW1U/XcGdU4IQ9hfn0IgiAIyZfZAhgTExMaN26Ml5cXGtShVsggr1+/xsHBgdmzZ+Pu7s7Dhw9p3749EonyhgcRvAhC2mrevDldunRh8ODBPHjwAFBNAFOjRg2GDx/O1KlTsbW1ZcGCBfF+U86fP8+jR4/o3bs3dnZ2bNmyBX9/f7y9vZnwUPlNbulVVhG8CIKQ3kQAozopmftFLu4cMMrmRWvfvj2BgYGcOHEixc8hZE0ymYw5c+ZQs2ZN9PX1uXTpEmfzl0Tb0EjVRUtXmjDXnsaGLwCFChVizZo1bN68mYkTJyqWKwtg0rOhyyaXOXdWTGCDuwtnz55l9erVinVv3rzh4MGD9OrVS7FMKpVSv359ypUrl6rnVYfGCRHKCIIgpExm6AUTN4AZOHAgt27d4uTJkyoskZDZXLt2jcqVK/Pp0yeuXLnCjBkzMDQ0THR7dbi2EQRNtHTpUqytrWncuDHv3r0DVBPAzJs3j4cPH1KmTBlGjBjB9+/fFes8PT0pWLAgtWvXVizLmzcvk568RZLIMGXpUVYRvAiCkFG+2khECKMiqQ1g4oYwcTk4OFCuXDkWLFiQ4uMLWU9kZCQ9evRg9OjRTJgwgYsXL9L5yDlVFyvdya9FM3sAk7puFZnA33//TUBAAKNGjSJfvnz06dMHiA1ggu9eyLCGLW0tLaqXLER4eDg1atRQLN+wYQO6urp07Njxt/trWmNDUgIYMZ+MIAhCbAijbnPBxBV89wLmdg7UrVuXsmXLMn/+fOrWrat026CgIK5cuUKDBg3iTQYoZE3+/v40bNiQQoUKsX//fnLlyvXb7TXtWkgQ1ImZmRmHDx+matWqNG3alNOnT2NsbMyjCUMzPGwoWrQoOXPmpEKFCoowNiQkhJ07dzJhwgRKzFiSoeURBEFQta82EjEPjAp8KaSX7Plf/kQikTB8+HA6d+7M3bt3sbOzU7qdj48PJUqUwNraOk2fX8ichgwZwpYtW9i8eTOdOnXKEjeC/Bq4qOKaNK1ofPgCMGLECAICAujfvz9169alUKFCQNr3dkksUJCHCKduPUIikVCvXj0AYmJiWLNmDW3btsXMzCzR42bVxoa07iEjwhxBEDIr+e+VuoYw8gBm+PDhdOnShVu3blG2bNkE240YMQIvLy+KFCmCq6srUVFR1KtXj/LlxfdzVvPjxw9at26NiYkJhw8fJkeOHL/dPqteCwlCRsqXLx+HDx+mRo0ajB07lmXLlgEZX9mNiYnh+PHj9Oz5v7/7rVu3EhkZiYuLC15rtmVYWQRBENSFCGBUI6UBjLKeM5V6LeTqmmG0a9eO0aNHM3fuXDZu3Jhgu3v37tGgQQN0dHTo3r07ZcuWRV9fn86dO6Orq5ui8xAyr7Vr1/LPP//g6elJp06dVF0cIQWyRPgikUjo06cPy5cvJygoSBG+pKXfBQXydSfOXMLCwgJtbW2+ffvGyJEjef78ORs2bEh0X9HYkHbSc7gzEewIgpAR1DmECb57gfbt2zN16lTc3d3x8fGJN2dHeHg427dvp3v37oSEhDB06FD09fUZOXIknTt3Zvr06djY2KjwDISMNH36dB4+fMjFixdF8CIIaqRMmTJUrVpVMfRYRvj1zsaHDx/y4cMHcufODcDRo0eZNm0azZo1UywTBEHIikQAoxryICUtesHIA5jRo0czaNAg3NzcqFAhft1u48aNWFhYMHz4cBYuXMi6deuIiYlhxowZzJw5k3bt2iU6N6KgWd69e4erqyt9+vSJN11FVlBs2qJ414iZtdcLZJHwBWLHxwPSJSVOaqN++zKFOfLyIpUrVyYyMpKPHz+ydOlSHB0dlW4vGhsyDxHsCIKQkdQ1hAl9dIWFCxfSvHlz9u/fT8uWLRXrtLW1MTAwwNLSknXr1hEZGYlUKmXdunVMnDiRf//9l/v371OgQAEVnoGQUXx8fHB2dlbaQyoucS0kCBkvKioKHR2dDHkuZWN4FyxYkKZNmzJkyBD27t3LyZMnqVevHsuXL8+QMgmCIKgzEcCoTlJCmKTMFVOp10L8/hnMypUrGTJkCGfPno0XphgbG/Pjxw8GDRrE8OHDAXj06BGjRo2iQ4cOXLlyhfnz56fybITMwNfXl8jISKZMmaLqoqhEZg5c4sqYmQlVKCYmhsOHDzNixAgADAwMVFYWR9vcXLlyBUNDQ4oWLcqdO3cYNGiQ0m1FY4Mg9+3ajSQ9BEHIegyfXc+wucuSqmnTpjRs2JBhw4YRHh6uWK6trU2bNm3YuXMnEHszhLa2Nn369OHkyZOEh4fz8uVLVRVbyEDh4eHcvHmTqlWrJlgXNLFnvIcgCBknMDCQsWPHcuXKlQypMyU2eaq+vj779+9n3Lhx3L9/n7Vr13Ls2DEx7r0gCIKgFr4U0ov/KBB7k7f8v0mhra3N4sWLOXfuHNu3b4+3rkOHDoSFhXH06FG0tLTQ0tKiZMmS/PfffzRu3JiHDx+m6fkI6uvSpUvY2NhgZWWl6qIIqaDxPV/GjBnD3LlzqVixIlu2bKFo0aJpevzkNnqbbZzFjRu/30c0NggpkdIARvSsEYTML24Ao+reMBKJhEWLFlG6dGmWLl3KyJEjFeuqVq3KqlWr+PHjB9myZVMsz5EjB1KplAcPHlC7dm0VlFrISC9fviQqKirNr8kEQUi5gIAAihQpgq6uLj179oz33Z1efh1OIi6pVMq0adOYNm1aupcjPUXkj1R1EdKc3isx34AgqJro/aIZ6tWrR8uWLRk5ciQtW7ZU1I8KFy6Mubk59+7dw8nJKd4+OXPm5MKFC/z8+RMtLS1VFFvIQI8ePRJ1Jg2g8eFLsWLFANiyZYvi/9NKShu7gyb2xGLq2kTXCUJGSurnWIQ0gpA5qCKIidf7xs6BEiVK0L9/f2bMmIGLiwuWlpYAiiHF/P39KVGihGIXKysrnJ2dWbBgAX369BEViQwQFRXFwYMHOXToEG/evOH9+/d8/fqVvHnzYmtri42NDZGRkbx+/ZrXr18TEBCATCajY8eO9OjRg/z586f4uQsWLIiRkRFXr16lfv36aXhWgiCklKWlJdmzZ6dZs2YsXrw4wfpHE4amy9APvwtg1JkmhipJlZRzFwGNIKQ/EcBohnnz5lGqVCkWLVrE2LFjFcsLFCjAixcvEmzv6urKxo0b2bdvH61bt87IomZZT548YcuWLTx48ID379/z/v17jIyMFHWmHDly8PbtW0Wd6e3bt1SpUoXevXvToEGDVNVty5cvj4eHBzExMUilGj94lcbS+HeuU6dO5MqVCxcXF65evarq4igoC1lE8CKos6QOfyaGQxME9SEflizuIz2Or8zkyZORSqVMmDBBsaxQoUIAnDp1KsH2I0aM4NmzZ+zevTtNyygo16BBA5ycnDh//jxSqZTy5cvTpEkTzM3NuXPnDsuXL2fDhg08fvyYHDly0KxZM2rWrMmCBQuwtbVl+vTpyGT/q/B//fqVpUuX0r17dw4dOsTPnz8TfW4dHR1q1qzJyZMnM+JUBUFIAj09PQYOHMimTZtYvHhxvGEj5dIrJFGX8bwj8kcm+SH8nnjtBCFjfLURk65ndkWKFGHgwIHMnDmTN2/eKJYXKlSIixcvJvg9rlKlCrVq1WLOnDnxrsWF9HHw4EGKFi3K4sWLCQoKIm/evDRq1Ihy5coREhLCvn37WLBgAadPnyY8PJwKFSrQtWtXnj17RpMmTShXrhyBgYHxjnnhwgV69uzJ7Nmzeffu3W+fv06dOnz69Inbt2+n52kK6Uwi06C/1tDQUExNTQkKCsLc3Fyx/PTp07i6uvLgwQN69+7N6tWrAQjfl/JJG0WjsiCkXLi2PtfKNqDirWPoRyes3CfVr71xlP1dyrf53d+s6NUjZGW/6x0THROD34tgqhYwR/v/77RJSoCj33IgAEuXLsXNzY0rV65QsWJFALp37463tzenTp2iWrVq8fZr2LAhDx484Pbt25iZmaXwjDTf27dvuXHjBgEBAQQFBeHg4ECNGjXQ1v5fh+Zv375haGgYb/LOuMaOHcu8efMICAhI1hjCX79+Ze7cuUyfPp1Ro0YxcOBAli9fzqpVq/j69SsFCxbkyZMnODs7s23btnhlimvZsmW4u7vz5s0bLCwsFMvFjShJF7cXdXBwMBYWFnz58gUTExMVlkrILJTVm8LCwnBzc2P9+vXkyZOHQ4cOYWdnl2Df9AhLkhrsJOe5RUN/5iF6yihnoi1lfElbpt/3JzQ6RtXFEdSY6AGjWrraEkY1Lsicw8+JjE76e3F1zTAAQkJCKFq0KA0aNGDz5s2x665epUaNGjg7O7Nx48Z4vR58fHxo0KABq1evpnfv3ml7MhokMjKSa9eu8eLFCwICAjA3N6d58+bkypVLsU10dDTR0dHo6+srPcbLly+xs7Nj4MCBzJo1K8nPLZPJ8PPzo0OHDmhra3P06FFu3rzJggUL8PPzw9bWlnfv3qGrq8upU6eoUEF5nTw8PBxLS0vc3NyYOnWqYrm63LiSWcivM1VVZ8oS4QvAz58/ad++PdevX+f58+dAysMXEbwIQuqkVfgipB0RQAnKRCPhBNbU5TXaJO9yQb/lQKKjoylfvjy6urp4e3tTtGhRIiIiqFevHrdv3+a///6jZs2ain1evnxJ2bJladSoEd7e3okGB1nR1atX2b59O0ePHuXOnTtA7JwIRkZGhIaGYm5uTo0aNXj//j1PnjwhKCgIR0dHNm3ahK2tbYLjnT17llq1aqV4HOHFixczdGjsRayJiQl9+vRh8ODBWFtbs2vXLjp06EC3bt1Ys2aN0vcxKCiIPHnyMGfOHMVxFOtEAJMkInwRUuN39abz58/j6OjIrl27EgxpkprKfmJhiH+3UUna33bDnBQ/t5C5iDAmlghfhKQS4YtqpTR8gf8FMOvWraNnz554enri4uKCtrY227dvp0OHDri4uODp6Rnvpqa+ffuyefNmrl+/nuZTLGRmX79+ZdOmTRw9epSTJ0/y7ds3AExNTfn69SsymYzq1atjbGzM06dPefHiBXp6eixevJiePXsqrbdUqVKFsmXLKm7kT46XL19St25dnj17BkCtWrUYPnw4TZo0ISQkhCZNmvDs2TN8fX0pXry40mP069ePAwcO8PLlS8UQZiJ8SR5Vhy8aP+yYnJaWFp8/f6ZcuXIpPoYYRkkQBE0lhpMT0lr4vuVoa2uzZs0a3rx5Q/HixWnXrh2fPn3i0KFDVKpUiYYNG3LgwAHFPjY2Nvzzzz9s375dcddXVieTyVi0aBFVqlRh8+bNVKhQga1bt/Ly5UsiIiIICQnh8uXL9OnThy9fvlCkSBEGDx7MqlWreP36NWXLlmXz5s3ExMQ22oSFhbFo0SLFnA4eHh4pKpebmxt+fn54eXkREBDAvHnzyJcvHxKJhDZt2uDl5cW6desYPny40iERLCwscHJyYs2aNQnWJzYvniAIGePr168ACepNSanoJ3e4KRG8CMqI4coEIXnE8GPq7eqaYYqQ5VeVei0EwMXFhfbt29O7d2+KFSvG9u3bad++PVu2bGHz5s20bds23hBkCxcuxNramo4dOxIVFZUh56Hunjx5QpUqVRgyZAghISGMHTuWK1euEBoaSkhICO/fv2ft2rVYWFigra1N8+bNWbp0qeJ1b926NR8/flQc78KFCwwePJjLly/j6enJq1evkl0mGxsbLl++jJeXF1evXuX06dM0a9YMqVRKjhw5OHToELly5aJ+/fq8fPlS6TF69epFYGAgR48eTfFrI6hWlun5IpPJsLCwYMiQIUycOBFIXs8X0dgoCGlH9HzRTKL3jOZJTc8XOf2WAwkPD2fjxo1MnjwZfX19fHx8yJs3L3///Tf//fcfO3fuxMnJSbFPkyZN+PbtG2fPnk2rU8mUwsPD6du3Lxs3bmTkyJHMnDkzWRM2fvnyhYEDByqCLAMDA2QyGVFRURgaGvLlyxeaN2/O/v3706X8y5cvZ9CgQSxfvpwBAwYkWC8fMuG///6jWbNmCdaLHjC/J3q+CKnxu3rT7NmzmTVrFp8/f1YMc5Ie4YcIXoTUyAq9Y0TPFyG5RA8Y1fhdz5dfQxd52PIr+XY3btxg6tSp7N27lzlz5jBy5EgOHjxImzZtqF69OocPH0ZHRweAkydPUrduXS5dukTlypXT4cwyj2PHjtG+fXssLS3Zv39/snsD7dmzh169evHp0yd0dHQwMDDgy5cvWFhYEBQUBMC1a9cSHR4sNd68eYOjoyO6urrcvHkzwRBoMpmM8uXLY2ZmxqlTp5BIJEm6NsoKv5NJpeqeL8oH4tZAd+7c4dOnT1StWjXZ+4rgRRAE4c8S+64UoUzWFr5vOfotB9KnTx8aNWpE/fr1cXR05OjRo+zcuVMxPJWenh5NmjQBYrtjT5w4kRMnTlC3bl0Vn4FqPHjwABcXF27fvs3mzZvp1KmT0u3iBhS/9hgxNTVl06ZN9OjRg6dPn/L9+3eioqJo06YNlpaW3Lp1K0FFLa0CD4upaxk4cCC3bt1i0qRJdO3aFWNj43jb1KtXj/r16zNw4ED++usvDA0NExxDBDCCkPFOnTpF5cqV440vn9Z+F7yIsEVIil97w4hGJkEQ1I2y3i5X1wxLNIABKF++PLt372bixImMGjWKT58+MWvWLA4fPky9evXo0qUL69evJ1u2bFSuXBl9fX3mzp3727kWNdmPHz9YsmQJ48aNo2HDhmzdulUxd2hSh+Z6NGEoTk5OVKtWjaNHjxIWFkZYWBhFihShRYsWPHjwADMzM/LmzavYJ62G/Xo0YSh58uThwIEDlC5dmjVr1jBw4MB420gkEubOnUvDhg3ZuHEj3bp1S9Kxk9NrNC1+Q9Ojl6qm/LZnmZ4vkydPZtGiRXz8+BFd3dg37089X0ToIgjpQ/R8ydpEGJN5pEXPFzn9lrEXkR8+fKBRo0a8ePGCW7duYWJiQuPGjbly5QoPHz6kcOHCRERE0LJlS3x9fTl69CiOjo5pcTpqLyYmhiNHjrBkyRKOHTtG/vz52bVrF5UqVVK6vbJgIjVDdqV10GExdS0BAQEULlyYadOmMXLkyATbPHv2DDs7OwYMGMD8+fPTvUyaRPR8EVIjsXrT58+fsbS0ZMmSJbi6uiqWp2UgkpQeLyKAEVJLExpsRM8XISVE75eMl5Y9X+KSz7E4ceJEpkyZwvr16+nRowdubm7MnTsXHR0d9u3bh7OzMx07dmT9+vXJ6iWfmb1+/RoPDw9Wr17N58+fGTlyJDNmzEjxnCjynhFJkZbzrcR93m7duuHj48OrV6+UBmmdOnXi6NGjPHz4kEoHxTDNSSG/FlB1zxeNn/MlODiYs2fPsm3bNpo3b64IXhIj5jQQBEFIX2L+mKxJfsODpaUlW7duJSQkhEePHmFmZoaXlxc/f/6kSJEihISEoKenx+7du6lcuTINGzbk8OHDKi59+jt//jwlSpSgadOmfPr0iU2bNvHkyZNkBS+/W/4n6RFyBE3sSZ48eTAxMSEkJETpNoUKFWLgwIF4eXml+fMLgpB00dHR3Lt3j/nz5xMdHU2rVq3S5XmSOtRYUrcThMSIOWOErErM/6Je4oYtv+vxooybmxtNmzbl4sWLAHTv3p2WLVuyePFi5s2bB0DLli3ZsmULW7ZsoUOHDkRERKRd4dXQjx8/6NWrF7a2tqxYsYJu3brx9OlTZs+enarJ6JO6T3pOdF+iRAm+fPnCz58/la5ftGgRwcHB8eZMFX5PXa4BNDp8OXr0KLlz56ZWrVr4+/vTu3fveOvFJNKCIAiqJ76Dsw55AOPn5weAlZUVAMWKFePIkSMAHDp0CIidn+TQoUPUq1ePFi1a4OHhwYsXLxK9GM3Mdu7cSd26dcmZMyfnz5/n8uXLdO7cOdEbRv4UlCQ3SEnP3iWHe7YgKCiI5s2bJ7qNlZUVERERaFBnbEHIVKKioqhRowZ2dnbMnDmTtm3bkidPnjR/nuQGKiKAEdKSCGOErEQEMOqlUq+FyQ5eIHb+xocPH5I7d27FMi8vLypUqMC///6rWNa+fXv27NnDgQMHaNy4MdeuXePr169pUnZ1EhQURL169di6dSvz58/n9evXLFy4kIIFCyq2SU048qd90zN4Adi3bx8NGzZET09P6XpLS0skEgnh4WL0mMxGYwcEDAkJoWfPntSsWZMlS5ZQuHDhRD/AgiAIgnqIG8CI4ck0y72Xbxi3YR+Wuy7j7e1Nhw4dsLOzU6xv2LAhFStWZOPGjbRv3x4tLS2yZcvGrl27cHV1VYx9a21tzY0bN7CwsFDVqaSpf//9l3bt2tGxY0fWrVv3x2uVpAYl6jJc18VX79HX1//tJKAlS5bk27dvXL16FXt7+3jrXpy8R4E6pdK7mIKQpc2cOZMrV66we/duHB0dyZkzZ7z1aTEEmAhSBHUj5owRBEGdVOq1kKtrhiGTyRgzZgz37t0jICCAT58+4e7urtjOzMyMkSNH0qFDB27dukXZsmUBaNGiBT4+PrRo0ULRc37ZsmUJ5g/JrMLDw6lZsyZBQUGcPn1aad0iLcKR9A5YEhMREcGlS5dYvHjxb7crWbIkJ0+ehKYVM6ZgQprQ2J4vbm5ufP36lfXr11OqVKkEjRnq0ighCIIgKCd6w2iWM3eecPT6fTZt2kSvXr3YtGkTEkn8u/JGjBiBj48Pzs7OxMTEjm2ura3N6tWr8ff357///uPt27ds375dFaeQ5qKiohg1ahTNmjVj06ZNaRa8qJPK+SwJDw/H19c30W0aNGiAjY0NK1euTLDO/pxfehZPELK8O3fuMH36dMaNG4eTk1O6BC8plRnnfREhU+YlesUImkb0fsl8KvVaSHh4OB4eHhw4cIDw8HCOHz9OmTJl4m3XrFkzSpUqRa1atbh8+bJiuaOjI69eveLy5cu0bNmSBQsWaEzP8tWrV/Po0SNOnTqVaPDyaw9HdXsoIw979PT0qFixIseOHfvt69CnTx/27NmDX8Nuv91OXI+oF40MX44dO8aGDRtYvHgx+fLlS7A+MzZeCIIgZFViWDLN0K9JDXo1rI5EIqFChQpKJxGUd5nft28f69ati7fOxsaGZs2a0ahRIzZv3pxRxU5Xhw8f5vnz50yfPh2p9PeXZJn12sUhvxWlSpVi7NixikDtV1paWvTp04dt27bx+fPnBOvjTiwvCELaGjZsGHZ2dowbNy7BOhG8JI+8oUM0eGR+IogRNIUIYDKfGoNWcuLECUxNTTEzM6NAgQIJtjE0NOTChQsUKVKEnj17EhUVpVhnZGSEvb09Q4cOxd/fnwsXLmRk8dONh4cHHTt2pFSphD3ibTfMyRTf14mFMvJrngEDBnDw4EHOnTuX6DG6du2Kjo4Oa9euTfR6Q1yPqB+NDF9Gjx5Ns2bNcHFxSbAuszZeCIIgCKI3TGYmlUpZ0rcd/fv3p0+fPlSoUIEVK1bw7du3eNu1aNGCbt26MWDAAKWTsLu4uODn58eoUaMy/fwv8knoS5Qo8dvtMvO1i0QiYfny5Vy8eFHp+ynXo0cPoqKi2LhxY4J1ERERRPWfzreIKD59/8HUE1cICvuRjqUWhKzj6dOneHl5JZhjSgQvyfNrA4do8NAcIogRMjsRwGQ+rqvPcfz4cR49eoSNjQ39+vXj1q1b8bYxMTFh9erVPHz4kKZNm/Lp06d462vUqEHBggXp0aMHjx8/zsjip4uQkBCldabMeM2gjO2GOXTt2pXKlSvTv3//eIFaXGZmZvz999+sXr2anz9/xrvekMlkXGnSg+DgYL59+8bGjRtZkbN0Rp2C8BsaGb6EhYWxatWqBMOZZObGC0EQBOF/RAiTOUmlUubXL86BAwfInz8/gwcPxsTEhLZt2/Ls2TPFdqtWraJr1650796dIUOGxAtZnJ2dWbBgAfPnz6dly5Z8+PBBFaeSJqKjowES9AIKmtgz3iOzq127Np07d2bkyJEEBwcr3cbKyoqmTZvGmzw0JiaGGjVqkC1bNvLkyUOBuZsotsCbZRfuUGKhNyv97hKZyQM4QVC1gQMHUrZsWWw3zIn3yEiqfG5BSI6kDCEjCIKQFvr9c5bbt2/j5ubGgQMHKFeuHIUKFWLt2rWKocTKly/P0aNHuX79Ovb29ty7d0+xv1Qq5ciRI0ilUipXrsz+/fsz9RBk0dHRSkdO0CQFN81j5cqV3L9/n2XLliW6Xffu3Xn16hVXr14FYm/4+HH7Ea+6j8HS0hILCwuMjY3p1q0bTZs2pdjWk0S9ybx1Zk2gkeHLqFGjyJMnj6qLIQiCIKQzEcJkPhKJhLrRL9i7dy+XL1/G1NSUf//9l9KlS7Nw4UJ+/vyJnp4eq1evxsPDg6VLl8ab40UikTBs2DAOHDjAuXPnFHeDyXuRZCbyCkRkpPo24Lw4eU/xSI358+cTHR3N6NGjE93G1NQ03o0zJ06c4Ny5cxgaGmJqaqpYbmmYDYCJPpcZtC/xuWQEQfizjJiIN7W9QDJDL5JfQyMRImUNIogRMgvR+yVzcpqyk6lTp+Lv70/79u15/vw5vXr1ol69eoob1+rUqcOVK1fQ19enS5cu8fYvUqQIfn5+ODo60rJlSypVqvTHOUXUlZaWllrXmdJKhQoVcHV1ZeLEiQQEBCjdRl4viltvClqzEwCt7CaKZfK65rFjx/g4aRlRH+P3jhIyjkaGL926/X7iIUEQBEGziBAm8wnft5wKFSrw/PlzQkJC6Nu3L8OHD8fBwYG7d+8ikUhwdXWlevXqSud4ady4Mc+fP2fQoEGsWrWKs2fPquAsUid37twAvH37VsUl+Z+4Ycuvgcvv1v1Jrly5mDVrFmvWrEl07Olv375hZGSk+HfFihUxNTUlW7Zs2NnZ4eLiwti/KnLQpSna0tjKhom+rtJjCYKQNHp6eul6/LQKXjJTACOCl6xJBDGCuhMBTOZUqddCtLW12bp1K0FBQRw7doznz5/Hu3GtQIECzJgxgxs3bnD//v14+5uamvLff/9x9OhRgoKCmD59uorOJHVy586tVnWm9DR9+nSMjY1xc3NTul4+bHfcepNhtXKgrYU0mz7ZypdkxIgR7Ny5E3d3dwB+/vzJ/d5j07voQiI0MnzR0tJSdREEQRAEFZCHMCKIyRzC9y0ne/bsmJqasmjRIs6fP8/Xr1+pUKEC27ZtA6BTp04cO3ZM6fBiOXLkoHz58gBUqVIlQ8ueFkqVKoWOjg5bt25VdVEAkh2oJDeE6dOnD/b29vTr10/pOMbBwcGYmZkp/p0jRw4+ffrEhw8fOHfuHOvXr2eoY1nyZzcmOiZ22IRq+XMlq8yCIGSctO7xkpkCGCFrE0GMoK5EAJM5Veq1kMp9FtNw1AbGbL+DWc0+9O7dm+HDh/PXX38RHR1N48aNMTMzY8uWLQn2l0gk1K9fn58/f2bKOhPE9gjZu3cvYWFhqi5KujM1NWXx4sXs3r2bAwcOJFgvn98nXr3p72bkXz2NPDOHYTmkKztKmdOqVSvF+mLFimFqapoprqU0kUaGL4IgCIIgQpjMIXzfcsX/V6tWjRs3blCrVi2WLFkCQLt27ZBIJEorEgA3b94ke/bs8S4+M4s8efLg5OTEhg0bVF2UVPlTACOft0ZLS4t//vmH+/fv4+joGG/iUJlMxtOnT7G2to63r1Qa/1LVYupaLKeto0WLFgA42OROi1MQhCzPv9uoeI+MIsIKQdOJeWIEdfPVRiJCmExOS0ePJUuWsHfvXnx9ffHz80NPT4927dqxefPmBDc5FZu2iMJjphMYGEiBAgVUVOrUcXV15c2bN5w4cUKxTJOvIdq1a0fDhg1p3749c+bMISYmRrHuyZMnaGtrkytX7E1o8tdBIk3YxC8PYJydnRXLRACT8bJU+GIxda2qiyAIgiBkMBHAqL+4AYyenh4dOnTg8uXLfPr0CXNzczp16sTUqVN58+ZNgn3bt29PWFgYVatWZefOnfzMRBOwf//+nePHj9OkSRNVFyXVc7okNYCpUKECZ86c4du3bzRr1kzxfp08eZLAwECaNWuWpOfbu3cvX758wW6Rd6rKLQiCcsmpmCcW2PypUSS56zW5kUXIOkQQI6gLEcBkfk2bNsXc3JwjR44A0L9/f16/fs2cOQl/L7UMDDEsUoIRI0YwfPhwpfUqdfbff/9haGhIzZo1Ac29JlAEKRIJO3fupE+fPowePVoxKoRMJmPjxo00btwYLS2tRF8H+XWZo6MjYWFhTJw4Uel6IWNofPgSEhJC/fr1adWqFa9fvxYBjCAIQhYkesGov7gBTIkSJYiJiVFUChYuXIi+vj79+vVLsF+FChU4deoUFhYWtGvXjpIlS7Jr164MK3dqbNq0ic+fPzN48GBVFyVNJDWAqV69OmvXruX169dMmzYNPz8/ZsyYQcmSJalRo4bSfcPDw9mzZw8LFy4kPDwciUSCiUnshJLi2k4Q0sa6desoWrQo3t7eyGQyRcXc1vpjvEdccSvvyQlgktpoIuZRETSZ6BUjqJoIYDI3LS0tihUrxuvXrwEoV64cY8aMYcqUKdy9ezfethKJBKu2XdC3r86aNWsoUKAAffv2JSQkRAUlT54fP36wcuVKunfvjpmZmcZfE8jPz9jYmEWLFuHo6MjChQs5cuQI69ev58aNG/Tr1y/R1+Fy4+6MGjWK69evA2BgYICOjk6C7UQAk3E0Onz5+PEjf/31F9euXePy5cuULFmSa9euiUq6IAhCFiVCGPUmD2C+f/8O/G8SwRw5crB8+XL+++8/pePeOjg44OPjw6VLlyhUqBDt2rXDx8cn4wqeAjKZjCVLltCqVSsKFiyo6uKkmaQGMFWqVKF9+/bMnDmTatWqcfXqVaZPn45EEr8R4IpjVQCWLFlC69atGT58OMuXL09wXIupa5M9B40gCP+zePFievbsiZ6eHh07dlSE3UoDlf8PYZStSyyA+fWRHJreyCIIcr+GMRF5olVdJCELEMOQZW7fv3+PN/H6xIkTKVSoEIMGDUImk8XbVktfH4u6jbEYMIqpU6eybds2OnfuHG9IK3Xk7e1NcHAwQ4YMyTLXBHHPc/z48bx+/ZrGjRvTs2dPmjRpQv/3t5Tud6e1K7ly5WLu3Lm0aNFC6TybcYkAJmNobPgSGBhIzZo1efv2LWfOnGHnzp18/fpVkeqKAEYQBCHrEgGM+grft5xv374BcOXKFUWloXXr1tSpU4fp06cnum/lypX577//qFevHn///TevXr3KkDKnhL+/Pw8ePKBbt26qLkqaBxZJCWAkEgnbtm3j8+fPnD9/nsDAQJycnOJtJw9eALZt20a7du3o3LkzK1as+O3wcvIQRtlDEIT4ZDIZU6dOZejQoYwePZrbt29TrVq1eN+fp+vOT7CfsmUAtU8MT7SXjCAIKRORJ1r0jhHSnQhgMpdKvRYC8O3bN54+faqYhF1XV5dFixZx+vRpzp07p3RfLX191kXqsm3bNg4dOsSMGTMyrNwpcfDgQapXr07hwoVVXZQMJQ9gGjZsyNu3b3n48CH379/nXruaSud38e82iv379wOwf/9+AgMD2bdv3x+fRwQw6U8jw5fw8HDq1KnD9+/f8fX1pXTp0pw5cwYjIyMcHR1VXTxBEARBDYheMOqrzOd7ODg40K5dO6pVq8atW7eQSCTUrVuXp0+fJriLKy4tLS22bt2KkZERDRs25NGjRxlY8qQ7e/YsEolEMW6xpvlT4CHvAWNoaIiDgwPGxsbx1scNXj5//szNmzf5+fMnvXv35sWLFxw+fDhdyiUIWY2XlxeTJk1i5syZzJo1i7CwMK5du/bbuah+F7z86ndDlgmCkHximDIhPYleMJlLpV4L6d27N2fPnsXW1pa5c+cSHR1NnTp1AHj69CkAjyYMVbq/29WHTJo0iUmTJrFgwYLf1rFURSaTcfbsWWrXrq3qoqhE3DlgihUrRuPL+3+7/alTpzAwMCBPnjzUqFFD6YgByogAJn1pZPiyYsUKXrx4wdGjRylSpAgQe8dk06ZN0dPTU3HpBEEQBHUiQhj1kyeHKcdHdMDHx4ewsDDs7e2ZNm0aefLkITg4mGzZslG9enXFHV6/Mjc35+jRowDY29ur5Rww58+fx87OjuzZs6u0HBkRQiTWCyVuwBLXr8uzZ8/O6tWr+e+//1i2bBllypRh586daVquP5VVBDaCppo9ezb9+/dnzJgxAOzbt4/IyEiaN2+udPvkBC/K/BrGiFBGEFJOBDFCehEhTOax47E2/v7+9OjRg9GjR+Po6MiLFy+wsrKiZ8+e5MyZky1btiQawGzBiBEjRjB8+HDatm1LaGhoBp/B7z158oSgoKBE54XMCpIz/93EiRMpWbIk1atXp2bNmpw5c4YPHz4k6XlEAJN+tFVdgPTg4eGBu7s7xYsXB+D27dvcuXOHadOmqbhkWYMmNU4UqFNK1UUQBCGDyAMYo4rlVVySrC1uEFaOm1y9epVp06YxZcoUSpUqxeLFiwGYPn06LVu2xMfHB319/QTHKVasGJcvX6Znz560adOG4cOHM2vWLLS11ePS5+3bt9ja2qq0DOrwe33FsSr25/zi/VuZ3r17I5FI6N27N7a2tvz48SNNy5HU1yK9XzNx3SFkNH19/XjDjWzcuBFHR0el30+pDV5+JzkBjP/rnKl+PkHQNHEDGL1XuiosiaBJvtpIMH6pfr0hhP/5aiOhxqotPFq8mHbt2uHi4kK5cuXo27cvdnZ2HD16FBcXFywtLXk0YSjFpi2Kt79EKmWvkRW7d++mW7duVK5cmV27dlGqlHpck7579w5A5fUmVUvqXDf58+fn7NmzFCxYEG9vbyB2dCjbDXOSFK74dxuVZebVyUjq0QKRxszNzRk/frzi3xs2bCBnzpy/7T4vJI06NNRkpKSer2gsEQTNIUKYjPe7nkdfZ7gyffpaWrRogZOTE4sWLeLQoUNUqVKFv/76ix49erBly5YEE7UDGBsbs337dhwcHBgxYgQPHjxgz5496OjopOfpJEloaCj58uVTdTEyRNxwJTXbdu7cmXHjxuHv74+9vX28dYmFNpmNsusOcY0hpKexY8cqeuC9fv0aHx8fPD09420jD1dqnxieIIBJi+AludKip4wIcARNJoIYIS3F7QEjghj18WvPpGLTFvFowlBu3rzJoEGDWLp0KVOmTMHb25tWrVrh7OzMpUuXlAYwAKPv+nP16lWcnZ2pUqUKPj4+VKtWLaNOJ1HynjgmJiYqLknaS21Pk19DEnnAki1bNlxdXZk4cSIADv+uRNvcTAQwKqSR4cuoUaMwNDQEICoqis2bN9OpUye1aGxRR79W9OWV/KwWtKRGcl8r0ZAiCOovbiAggpj0kdTh3oIm9qTy1LX4+fnRtGlTqlevzokTJ9iwYQPt27enfPnyjBgxQum+EokENzc3SpYsSdOmTenbty9r165VGtZkpNDQUJVWIjLqNz45wcuf6Ovr888//3DmzBlcXFwUyzUleElMYtdpgpAW2rZtq/j/TZs2oa+vH2/Zr+FK3ABGFcFLWkltgCPCGyGzkAcxIoQR0oK8wV+EMKrzu+Hg5AHMmjVrKFiwIOPHj+f9+/ds27aNatWq0aJFC65cuZLo/s23H+S6nx+NGzemefPmXLhwgaJFi6bHaSSZpoYvaTHEl7KQRB6wDBw4kHfv3rEtNBCtHKYJ1qfk2ELKaWT4Endel8OHD/Phw4d4lXRNl9oGFRG6pL/UvMai0UUQMp7oDZO2UjLHTtDEnuSbuhZfX18cHByYMGECBw8eZOXKlfzzzz+Jhi9yDRo0YP369XTp0oV8+fIxZcqUlBY/Tag6fMkI9uf8CA8P59WrV1y9epXr168THBxMzpw5yZkzJ4aGhmhpaaGtrU2ZMmWoVKnSH0MxJycnnJycFP/W9OBFGfk1hMVUFRdE0AhSaewUoDKZDC8vL5ydnRXfTYmFK5k5dEkrYpg0IbMRIYyQlkQIk7GSM/+OPIAZN24cRkZGuLm5MWDAABYvXkz9+vU5derUb/evsHA1F/fuxdHRkUaNGnHhwgWsrKxSewopFhoaipaWFtmyZVNZGdKasvBDJpPx/v17nj9/jp+fH5cuXcLS0pKcOXOSPXt2dHR00NLSwszMjEaNGmFsbKw4VmIBzMHK+TEmf4LnEgFMxtPI8OXZs2eK//fy8qJ8+fKULVtWhSVKeyIgybrEkCSCoDoihEmZlIQtygRN7InF1LWMGDGC7t2707p1a06fPs3o0aOVbvurzlPX8vr1a8aMGYO1tTW9e/dOk3KlRHLDl+T87v/pNyG9ryGeff/Olb/q45w/PwEBAYrllpaWFCxYEF9fXz58+EB4eDhRUVGK9UWLFqVLly507tw5y4/rLAgZRSaLbTjz8/Pj8ePHrFixAhABS1r6XVAjghkho4khyYS0JIYkSz/JCVx+JQ9g+vfvz5w5c+jcuTPBwcHkzZs3diixu/6/3b+axwZ8jhyhatWqNGvWjNOnT2NkZJTi8qSGvM6k6lEL0krc0CMsLIydO3eyfv16Ll++THh4OABaWlqUL1+e+/fv8/HjR0JCQoiOjubnz58AZMuWjVatWtGlSxfq16+faADzOyKAyVgaGb4cP36cyZMnExAQwIEDB5g/X/nkkPLGB3VruBbBipBcf/rMqNtnXBAyu1/DBBHGJJRWgcuvgib2pMNYD8aNG8eePXuYOXNmgvBFWfAiXz5qyhpevXpF//79sbOzU8lYxiEhIQQFBf1xzpeUXg/86fqmQJ1S6XKt8fBbGKteveb85xAsAt7y999/Ex4ejre3N0WLFqVEiRIEBgbi7u5O//79FftFR0dz6tQpNm3axOzZs5k4cSLz5s3D3d093jba2hp52SoIKnX58mWaNGmCh4cH+fPn56+//lJ1kbIUZcGMCGSEjCJ6wwhpSQQxKZeaoCUx8gBm8ODBjBkzhjJlynDw4EFqeXonaf/8+fNz+PBhatSoQc+ePdm+fXualzEpnj59qjHzZMrDju/fv7No0SIWLlzI58+fqVu3LuPHj8fLy4vv379jb2+PsbEx375948CBA9jY2ACxN8wEBASwdetWNm3ahLe3N+XKlePw4cOKY8tkMmJiYtDS0lLZeQoJSWTy2500QGhoKKampujr67NkyRIuXbrEgQMHePbsmdKU9tehKtKzgVoEKoI6UXUYE66tz7WyDah46xj60eEqLYsgpDVNCmKikXACa+ryGm3+fLmQXoGLMnojFvPjxw8sLS3jLU8seInLbOIqqlevzpcvX7hx40aGd2M/c+YMtWvX5s6dO9jZ2SVYnx5DaSX1ez+l1ytvwyNod+M2Vnq6TF/tibOzM/Pnz2fKlCnY2dlhbW3NwYMHAciVKxfv3r1Tepxv374xffp05syZQ506dfj27RuvXr3i3bt3NGjQgNWrVysqIHJZcegxiD+fTnBwMBYWFnz58kXjh7MT0oa83lS6dGk2bNhAxYoV8fDwUASjoueL+hBhTNZlgjbTzAozIeQpoURnyHNqShDzaMJQAKUTi8vXJ7ZOSHuaHsboaksY1bggcw4/JzL69+eaHkHL7zwc70ZAQAC5c+fGbvbyJO8n/xvatm0bf//9Nzt37qRNmzbpVcxE2dvbU6pUKby8vIA/9+hQV3F7mbi4uODt7U3v3r1xd3cnNDSUDh068OrVK+rXr8/Tp0+5dy+2Tnby5EmlN8bIZDIuXrxI27Zt0dHRoUiRIrx69YpXr16RLVs2Fi9eTJcuXVLdY0jZ6y0/l8z0XsjLrKo6k0aGLy4uLuzevZuwsDAWLFjAkCFDlG7/u8p6ejdSCIK6ychARoQvKXc98CMuO09grKfL7i6NyGVkoOoiCX+QmcOYP4UvGRm2KGMxdW28fycleJH/bhuuXke5cuWYN29eotcJ6eX+/fuUKlWKAwcO0LRp0wTrVRm+yCXn+ubl9x9Mefqc9xERDLLNz63S5Xj69Cl37txh3LhxTJw4EW1tbfr168eqVasoXbo0pUqVIn/+/NjY2JA/f37s7OziDTW2atUq/v33X6ytrbGxsSF79uzMnz+f0NBQVq9eTfv27eOVISsGMCJ8EVJDXm8yNDRET08PU1NTHj58iK5ubMOrCF/UkwhishZVhC9ymTmEeTRhKNHR0XTt2hVvb29yt+uKsV25eOvlRACjGpoWxigLXzI6ZPmdlISN8r8TmUxG69atuXbtGi9fvszw4b+aNGnCjx8/FHPVZKYGfzl5w39kZCRLlixh5MiRzJ49m+fPn/P27VuOHj1KyZIl8fb2pnjx4ty4cYMaNWqgpaWFo6Ojor6UP39+bG1tsbe3R0dHJ/bY/v64ubmhpaWFjY0NNjY2XL58ma1bt+Ls7IynpyfZs2dPUbl/F7z8bht1JMKXNCSvRDx58oRq1aqRLVs2Hj9+jL6+vtLts2JFXRCSI70CGRG+pNyIQxfwuvYQgDmNq9GjUgkVl0hIrswUxsjDlyo3DqEdk7GV/vQSN1QYpWuAiYkJe/fuzdAyyGQyihcvTrVq1RR3ccWlDuELJC2A2RL4liX+rwCoaGrCg6/fKGxnR5EiRRg0aBC1atVSbCuTyTh06BA7duzg5cuXvHr1ioCAAKKjo5FKpYwaNYr+/fsnOrTAly9f6Nu3L9u3b6dXr14sWbIEA4P/BdBZ7bpOhC9CasjrTcOGDWPhwoVs3LiRLl26KNaL8EX9iSBG86kyfJHLbCGMvMH46dOnFClSBAALCwtyDB4bb31cIoBRH5kxlPlqI0FPKmGaXQEm3H1BREzmO4dfReSPjNfIfujQIZo2bcqTJ08oXLhwhpbF09OTfv368ebNG3LlypVpGvvl5K/jixcvKFiwIAD6+vrY29tz48YNatWqRYUKFRg3bhx6enqK/d69e8fixYu5f/8+r1694uXLl4SEhABQrlw5Fi1ahKOjY6LDMv/777/07t0bExMTvL29cXBw+G05k/K6JjZHTGZ4T0T4kobklYigoCDevHmDRCJROpwHZL0KuiCkpdSGMiJ8SbkfUdGsuXKfKwEfWNCsOjkNY4dLuhTwHo8LdyhobsK4vyqhoyVVcUmFpFJ1GPO73ivRUm0ulW+iMeHLr2HChbZ/M3ToUI4fP06dOnUytCxz585l7NixXL16lXLlysVbl97hy689hiD2TqzIyEiyZcuGlpYWMpmMt2/fEhgYSOSQAehKlX+nrA0IZNWr1wCYamtT0dSEXU+fYWZmlqRy/fz5k3fv3rFu3TqmTZtGVFQUpUuXpkmTJtjZ2RESEoKxsTFdu3ZFIpEgk8lYv349gwYNwsbGhm3btlGmTJkkvw6adP0nwhchNeT1psDAQC5fvkzz5s3jjQ8uwpfMRQQxmkkdwpe4MksQIw9Y/Pz8mDVrFpd1jDEpW5FHE4by48cPhg4dyvfv33F1daVqVeXXBSKQUT8ZHcwkp+eKJoUv8rmg4H8N1l++fKFgwYI4ODiwb98+pInUC9JDcHAwhQsXplWrVqxfvz7DG/p/DRxkMhlfv35FX18fHR0dJBIJYWFhPH/+nMbHtqBtkbCXiX+3UTx48ICSJUsCoKWlRdmyZRk/fjxOTk5JLktoaCg3b97E1dWVe/fuYWpqSoMGDahXrx5SqZQvX77g7OysGE3g5cuXdOzYkUuXLjF16lRGjRqV6Fwwv76uiQUtiVH3AEaEL2kobvhibm6e6HaaVPEWBHWQ3DBGhC9pz2XnCQ4+fAlAuzKF8WhZU8UlEjSBpocvFc6cp1GjRty5c4fnz5/H60WR3iIjI6lYsSIGBgZcunQp3rr0DF/kwUtgYCDnzp3j4sWL+Pn5cePGDSIj/3/i3f+/6yoiIgKAqlWrMlMWhVEid1bJZDK+REdjqq2NRCKJFwokx+fPn/Hx8eHQoUMcOXKE9+/fo62tTXR0NJcuXaJy5cqKbR88eECHDh149OgR8+bNY+DAgUkeBkFTrgNF+CKkhrze9NfeIZxsuVjpNiKAyZxEEKM51C18iUvdgxhlc748mjAUPz8/qlWrplj2uzv5RQAjJJWmhi/wv0brgwcP0qxZM1atWkWfPn0ytExr166lV69enDlzhpo1a2ZYQ79/t1FERkbi5+eneFy8eFExd6WWlhbZsmXj27dvAOjq6mLatx0GFRN2ApC/jmFhYUgkklTVO2NiYrh27RqHDx/m0KFDXL58GZlMhlQqxdnZmR07dii2jY6OZsqUKcyYMYNatWqxefNm8ubNm+CYcV/T5AYvyo6hblQdviQrrpw1axb29vYYGxtjaWlJq1atePToUbxtZDIZkydPJk+ePGTLlo3atWsrJgqSe/ToEdWrV8fa2pqpU6fGW2dra4tEIsHPL37F3c3Njdq1ayenuEppSoVbENTJi5P34j2EjDe8RjmMdGPH/Tzy6BXOm4+Qc9o6ck5bR0DIN37GxKi4hIKgWsq+m7S0tBgxYgTv37/nw4cPGVoeXV1dpkyZwuXLl3nx4kWGPKfJ+JVs376d+vXrky9fPjp06MCBAwcoXLgw8+fPx9vbm7Vr1zJ//nzmzJnDvn372L17N35+fgQPH53ocSUSCWb/f+dXamTPnp127drh5eXFmzdv+PbtG+Hh4dja2uLh4RFv2xIlSnDp0iX69u3L4MGD0dPTw8LCgt69e3P69OnfPk9KwyFBSKrMVmdKLGQ5XXd+so4jqAdb64/YWn9UdTEEDReRPzLeQ90Um7ZIaXhStWrVeJOGe3h4IJFIkEgkzJ07VzGsDygfokwQNJmyv2V5g3rTpk0pXbo0t2/fzuhi0aNHDwoWLKgIFVIaDiTH4cotcHd3J2/evNSqVYupU6cSEhJC9+7d2bp1Kxs2bGD58uVMmTKFDRs2cO7cOapWrUrBy0+UHk/+OhoaGqb6hj+pVIq9vT0TJ07Ez8+PsLAwoqOjWbx4MXv27OHNmzeKbbW1tZk2bRonT57kyZMn2NjYoKOjQ40aNfDw8ODz58/xjp2a1zYj3pfMSvktjIk4c+YMAwYMwN7enujoaMaNG0eDBg24f/8+hoaGQOwwGgsXLsTLy4uiRYsyffp06tevz6NHjzA2NgZgwIABdOnSBXt7e/r160fdunWpXr264nn09fUZNWoUZ86cSdFJ3XJqjrFoaBQElUksgEmvOWQEsLMy54H737wN/Y6Jvi799vzv+7PCsh2Y6uvSpXwxRtQqj4FOsr76BUFjyWQylixZgrGx8W97zKaXOnXqIJVKOXXqFAUKFEjX57KYupb27duzY8cOHB0dWbt2LU2bNsXS0vK3+/n7+wNgbGyM/Tm/JN3EcsWxapIDjsS2lUqlimvLgQMHMnLkSMqXL0/fvn3Jli12uEV9fX2WLFmCk5MTd+/e5fXr12zfvp01a9Zw5coVKlasmGgglNRzEYSUyCx1prhqnxiuNGw5XXe+6AGTScUNYERvGCG9xW20VfdeMTt27ODjx4+EhIRw584dFi9eDMCoUaMYNWoUNWrUYP78+VSuXDlFE5ULQmb0uxDVdsMcVucuz/379+nWrVsGliqWRCKhXr16nDx5UrHMv9uodOtp4V20JqVLlyZ79ux06dKFTp06Ua5cuUSH7JKztLQkJCQEv0TKltbllYcd8rpR165dGT9+PK1bt2bbtm3Y2Ngo6kK1a9fm1q1bbN++ncjISI4dO8aQIUM4f/48mzdvjne81JZJnXvAqEqyer4cOXIEFxcXSpUqRdmyZVm/fj2vXr3i2rVrQGwjxuLFixk3bhytW7fGzs6ODRs28P37d7Zu3ao4TkhICOXLl6dMmTLkyZOHL1++xHuevn374ufnx6FDh9LgFAVBUBfynjEBF2Lv/gy48Ej0lElD+traFMhhgrmBPjs7NeTjhB6scoqd7PpLeCTLL97h3dfvKi6lIKiPT58+cejQISZOnKho7MxIZmZmVKhQgQMHDsRbnh49M65fv86OHTvw8vLC19eX7t27/zF4iYqKYsCAARgaGlKqVPLC86SGNEnZ1s3NjaFDhzJ06FAMDAzIkSMHjRs3VvQkqF27NgMHDmT27NnMmjULAHt7e/T09ChXrhz79u1D2Si79uf8kvUQhKTKrHUmZSGLCF40g+gNI2SkX3vFqEvPGHmIIpFIsLS0pGjRojg7OyOTyQgLC6NQoUIA+Pr6xuttKHrACJouKX+j3t7e5MqVCzc3t/QvkBJ16tThwYMHPHv2TLEsPXpa+Hcbxfjx4ylbtiyvX79m4cKFVKxY8Y/By6FDh9izZw9VqlRRWfhgamrK8ePHefXqFQUKFEBfX5/ChQuzYsUKYmJiMDc3x9XVFTc3Nw4dOkTu3Lnx9vZGS0uL0KFzGDZsGMHBwakuh+gBk1CqZkmSVwBy5MgBwIsXL3j37h0NGjRQbKOnp0etWrW4cOGCYtnUqVOpX78+BgYGSKVSGjZsGO+4tra29OvXjzFjxhAjerAIgsb7ddgyEcikndZ2hZjXxIFCOUxoUdKWl59DifopvleFrEXZd8pPmYwqVaoAKMbpVYV+/fqxZ88exV2Xcmnd2P/gwQMAnJ2dk7S9TCajd+/eHDt2jN27d2NtbZ3s5/xdqPLrut9tq6Wlxfz587l06RIbNmxg+PDhPH78mDJlysS7vgRwcnLiwIEDbNq0iUWLFmFhYUGrVq2oUaMG169fT/Y5xCUCGCGlMlOdKW7YIoKX9HW67vwMH9pNhDCCqqhLIJNYLxYDAwN8fX2pUqUKVapUoUGDBgQGBirWiwBG0FRJ+VsMPX6BDRs2EBoamgElUq5Ro0bY2NjQsmXLBENlpbX79+/TokULxTyYf+Ln50fbtm1p2rQpmwpmzByiiQUc9vb23L59m127djF//nyqVavGgAED6NSpU4JtDx8+zPbt2/H09KRPnz54enpSsGBBZs+enerrShHAxJfisWdkMhnDhg3D0dERO7vYyYTkkw7lypUr3ra5cuXi5cuXin83adKEjx8/EhoaSs6cyrtAjx8/nvXr17Nlyxa6dOmSrLLF6OqK0EYQ1FiMjo7ivzG6yrulPzuXcKxMm5ol0rVcmqqzvR2d7e3Yeecp3fb4ktvYkO4Vi/F3+aIY/v97IQjKREu14/03s1L6PSOTYWFhQY4cORg9ejRRUVEZXzBiu4c/e/aMcePGkSdPHpycnBTryp3y5XrDumnyPO/fvydnzpzo6ekl6Vz/++8/RU+Zv/76S7FPYt/ZiblUpyYVjp6It+x6w7qg5DjKto2rfPnylC9fnitXrrB69WpsbW2xsrIiKiqKqKgotLW1kUql8Rq0u3btyrJly5g1axYTJ05kz549ySr/r9LyPUmtuO+jqj6/wp+pc50JQFumBbL4d3M2OB5bYdbl93d5Cil3sNZ0xd+tT81ZND0zPkOfv2jeTwC8DLTI0OcVkkcPieK/eqm7b1Y95Y+O90+91xlTLykzYzHXRg5IsNzCwgJfX19kMhkFCxZk9OjRODk5MXToUMqWLcvd0QOpONdDyRGFrE5PKon338wgwjr2Nygp3y2GpiaE6+uzY8cOYmJiVNLeamBgwMGDB2nQoAHt2rVjz5496OnppfnvWXh4OF+/fiVPnjxJur6OiYmhZ8+e2Nvb86BZZfS10v977G5Ht9+WzdTUlObNmxMREcG1a9fIli0blSpVIioqSvH+aWtrU6xYMYoVKwbEXq86OTnRt29fpk6dSqNGjZI98sGvnnQcht3Wxak6RlqRv16qqjNJZMrGYUiCAQMGcPDgQc6dO6e4G/LChQtUr16dN2/ekDt3bsW2vXv3JiAggCNHjvzxuLa2tri5ueHm5sbUqVNZv349jx49YuTIkdy8efO3k6iGhoZiamrK1q1bUz2BkSAIgiAIgiBkBt+/f6djx458+fIFExMTVRdHiEMd60wg6k2CIAiCIAhC1qKqOlOKbmUdNGgQ+/fv5+zZs/GGobCysgJi7+aKW5H48OFDgju7kmLYsGGsWLGCFStWJGu/nGtXYSx6vgiC2oo2NCSoVz8s1vyDdlhYmh9f9JBJKCwqijKLttG2TGFmN6rGi0+hdNh2jOIWZqxvWxdpIpNSC1lbtFSba2UbUPHWMbRjov+8gxp6efZBouvKHzlOp06dOHv2LKdPn6Zw4cIZWLL4IiMjqVKlChUrVmTNmjUJ1qemt0W5wz5UrFgx0WP/6v379xQrVozJkycnGNdZ1b0+Lod8Yc+7D+Rv0BAzMzOyZ8+OmZkZJiYmhISE8Pr1a4KCgjAxMSFHjhxYW1tTpkwZSpYsib6+PgCbN29mwIDYO16LFStG/vz5uX//vmJ4kZo1a1KqVCm+f//OnTt3uH79Ovny5SN79uzMmzePqlWVD5GW0a9N3F5CaTE+s5D21L3OBLA+21Uw1OZgrenxlmd0T4ysRp1fb9EbRn2YoMV4s0JMD3lGKD9VXRy1kNa9Y+R3/0Ps3eQA7u7ubNq0iTt37mBhYcGIESPYsGEDx44do2LFigCiB4wQj55UwviStky/709ETIrub09T10YOSPAZjftZT4mIxy8IXrqJgQMHxpsTSRVWrVrF6NGjuXv3Ln2erlQsT4vfr2HhJri7u3Pv3j3y5Mnzx+379evH8ePHefToEWW3L0v18/+O/DvqV0p7l/yMJthrL/z8Sdsy9oo6U/bs2QEIDAzk9evXSKVSsmfPjoWFBSVKlMDt2SW0TI1BIuFG2wGUKFGCjx8/YmZmRsWKFfn48SMPHz4kMjISLS0tevXqRXR0NJ8+feLo0aNERUVRsmRJKlasyKJFi35fxgwkf+1UVWdKVvgik8kYNGgQe/bs4fTp0xQoUCDe+gIFCmBlZYWPjw/ly5cHYhsTzpw5w5w5yZ9wyMjIiAkTJjB58mSaN2+e5P2kkZFIRfgiCGpL+v/DzUijopBGpv14vwHHbyn+v0Cd1HWV1BQmUihkasCroE9ox0Qj/RlFxI8fHHvwiY8hlchraqTqIgpqTDsmOtOGL7/7jtHV1WXt2rVUrlyZNm3asH//fsVkqxlNR0cHFxcXxo0bx5w5cxSNs3JVTp5N0iT2v7I/58fhw4e5d+8eK1euRCcJQw1aWFhQqFAh1q1bx4gRI+KtS4/v7OSoapCNqgVt4OnDFM/BUqZMGerVq8fZs2e5efMmK1asoFq1anz69ImgoCCKFCmCJE4gfenSJTZu3MixY8cYMGAAzs7O6Ojo4O7uHm8s6JS+RykV971MyvsqZJzMUmcCiJb85GS9BQmWH6s3R8z5ko7qnx2jmO+l9onhoEb3wOS2fg+A/2vlQ90JGSfi/4cDikBGBKJ9AyDCOkLpcr1XSR8WNbE5LuS/pVWrVmXZsmV8/vwZS0tLChYsyNevX/H29lbcgKEODeyC+omIkan8syGfn+j2OLe0nfi9qA06zWsxe/ZsrK2t6d+/P1KpaoZD7NatG2PGjGHNmjUcm/6/65Xc1u8Vv13Jnd/M/3VOnnQcRqVKlahbty42NjZJ2q9q1aqsW7eO48ePp+v3dGJzqCT6HmtJMerZGoDDv9k/wbHMjIhGBsjQ1dWlQ4cOHDhwgPv376Ojo4Ofnx9RUVE8fvyYggULki1bNsX+nz9/Zt26dZw7dw4PDw+yZcuGrq4uLVq04FG3EWn7eUwm+fe7qupMyfpLGTBgAJs3b2br1q0YGxvz7t073r17x48fPwCQSCS4ubkxc+ZM9uzZw927d3FxccHAwICOHTumqIB9+vTB1NQUb2/vFO0vCELW9uLkvQSPrEgikVC7QB5uvgnCde8ZHFbuRlsqZWWrWiJ4ETRWUv7eTUxM2Lt3LyEhIRQvXhxXV1fevn2bAaVLqHv37piYmNClSxeioxOGXckNG8qePMuMGTNwcnKiWrVqODo6Jmk/PT09IiIiqFKlSrKeL6OlNOioWLEi+/fv58qVKwAEBQUBsZOhFy1aNF7wAlClShU8PDzw9PTk/fv3LF26lHHjxuHl5RVvMl5I/nskaKbMVGfa6Tgh0XUZPRl8VlP7xHC1DrhsrT8qHoKg7iLyRyb58Sf169cHYNq0aRQuXBh3d3d69OjBuHHj0vs0BCFV5MGLXFpPem7cwBGjv6owcOBAypUrx4EDB0jhTBapK4exMa6ursydO5eLFy/Gu15J6e/Wv6XqYm9vz7179xg2bFiS95NIJEilUipVqpTs50yqZAcvKdhW2XptbW3mzJnDvXv3aNmyJUFBQchkMnR0dChVqlS84AUge/bsuLu7s2vXLurVq8fGjRuZO3cuY8aMwc/Pj+ddRiR4jqwiWeHLypUr+fLlC7Vr1yZ37tyKx/bt2xXbjBw5Ejc3N1xdXalUqRKBgYEcO3YMY2PjFBVQR0eHadOmER4enqL9BUEQfpVVQ5iC5qZ8/hHBzjvPmNGwCn4DnGlTWjV3+QtCekvK37i88b548eI8efKEGTNmsG3bNgoVKsTo0aP59u1behczHnNzc7Zv387Jkyfp2LEj+/fv5/Pnz/G2+VPjfkRMDFdCvrC7xl+ULl2aSZMmMXToUE6cOJEgVEjM7du3efz4Me3bt0/xuWSUK45Vk/2Qs7GxoVSpUvTq1Ytbt2795lli1a5dm/fv3+Pj44O2tjb9+vWjSJEieHl5xdtOBDCCJtWZRAAjQMobswQhM9LX18fGxoadO3diZ2fHvXv3WLt2LZaWlgAUm7boD0cQhIyR3GAxtSQSCebdnLAa358cOXLQvHlzHB0duXjxYro/96+mTZtG5cqVcXZ2xsPDAw+r7skKgmQyGT/ehPDmwC1yeDynSpUqSKVSLl++TK1atZJ8nB07dlC7dm2srKzS5aaFtAheEts/7uNPmjRpgq+vLyNGjPjj6yyVSvHx8eH9+/fUrVuXU6dOUa1aNZo1a8bVpj1TVe7MSiJTRUyZTuQTRwYFBfHy5Uvu3r0LQK5cuahfvz5SqZTAwEDMzMwwNDQEUn7XpCAIKRdtZMSH/oOwXLkM7Qxu3PxVVhuW7O67YKJiYiifRwwlIfxZtFSbS+WbUOXGoUwz7FhKgtW4jeUhISHMnz+fRYsWUaVKFQ4dOqSYKySjrFu3jilTpvDq1SskEgnlypWjdevWjBkzBi0tLcV2Hz9+5Pnz5zx//py7d+/i6+vLpUuXiIyMxNLSkvr16+Pu7q4Y1uhPbt68ya1bt7hx4wZeXl58+PABXd3/DeOhymsmmUzGg29hPPv+AwmQL5s+efX1MNfR+WOodP1LKO4PHmOpq0v+bPrYZNPHYcIkrK2t0dLSolWrVuTMmVPxescVExODRCJBIpEQFRWFtrY2EomEr1+/EhwczLRp01i3bh2TJk1i8uTJiv1OnDjB4d49kUpAggRLXV2ss+lhky0bWmk0x1bcz21wcDAWFhYZPnmkkHnJ602vXr3C19eX6OhotLS0qFOnDrlz5yYyMpI3b95ga2sLoNY9NISMJ4YkyxgmaDPNrDATQp4SSua4Dsvs5A2d379/5/DhwzRv3jzetZAIXgRl9KQSptkVYMLdF+k27FhSw5W4jfXpOcyTTCZjde7yjBkzhnv37nH48GHq1KmTbs+nzNu3b+ncuTO+vr5ERUWRM2dOatWqxZQpUyhZsqRiu4iICF68eMHz5895+vQpfn5+nD17lsDAQLS0tKhatSrt2rXD1dUVbe0/z84RFhbG3r17yZEjBy1atGDJkiW4uromeq2U2t/MXwOYP72vP0O/4WFjz+vXr8mfPz+FChXC1tY2QU8VZcd5N2sV0e+CcCxXgaJFi1KkSBHy5cuHtbU1CxYsYO/evXh7e9OhQ4eEz/vzp6KuGhUVhY6ODtHR0QQFBeHn50fv3r3R19fn9u3birlnPnz4wL///ktMTAxSqRQTExPy589P6dKlFdv86ZyT8vrIt1FVnSlZc75kFlFRUTRq1IiPH/+XNhYtWpTWrVsze/ZsOnfuzKZNm4DYiqsIYAQh63px8l6WCmDsrMxVXQRBSBep6c12xbGqoiHbzMyM6dOn06hRI+rXr0+bNm2YMGEC9vb2GTaucY8ePejRowf+/v6cPn2akydPMmnSJJ4/f86AAQPYs2cPu3fv5sGDB4p9cuXKhaOjI/PmzaNmzZqUKVMm2eX9+++/efjwIQCdO3dWm+AFYOubdyzxf6WYFkFerTXU0qKZpQUd8liRN5GQ7HTwZyJjYihmZMDnqGg2B75lY79+8bbp1KkTMplMEb5ER0czadIkVqxYQbt27ahZsyYDBgwgOjoabW1tvnz5QrVq1bC3twcgZ87/VarOnDlDvXr10P3/Y0XGuc/JzsiQZaVKYKithSCog7Vr1zJlyhTFv3V1denWrRtnz57l0aNHvH37NsEcVEL6iDcHjJqT39UrQhhB09humIN/t1EYGBjg7Owcb50IXoSMkNoeLPLPcHrPryGRSOj77iaP/fxo3rw5LVu2xMPDg+bNm8drNE9PuXPn5sSJE3z//j12+LHTp9m2bRu1a9dmz549PH36lN27d3Ps2DFFz2BdXV3Kly9Px44dqVGjBjVr1sTU1DRZz7tr1y66deum+HerVq1+u31qe8LUPjE83jWCrXXs76+y40aF/uDhsG20/PQJqVRKTJz50B0cHHB3d6dly5ZoaWnh321UvGuOp7d1iXj0gsqVK2NlZcX58+fx9PSMd/z8+fMnGJra19eXkSNH8uTJE7Zu3cqyZcs4ePAg5ubmfP78GWNjY/r27YuhoSEGBgbxbmxs3rw5165dQ0dHh58/fxIVFQXE9ujeuXMnLVu2BEj0M62sZ1BGfP6TSyN7vmzfvp327dtz9epVypQpw7Vr15g7dy579+5FJpNRv359jh07Fm9fVTcqCEJWok49XxKTlQIZQUhMZuj5kpZDCP46XNSBAwdwcXEhODiY3Llz06JFCxo1akTBggXJnz8/ZmZmafbcf7Jlyxa6dOmCTCYje/bstGjRgiZNmlCsWDEKFCiQJnfujB8/nhkzZgBw+vRpRZd7dbhG6nvnPne/fmNkIVsaWpgTGBFB4I8I7n77xp53H/gWHc3SUsWxN0tYeToeFMz0J8/RkUopYmjAtS+hKLv4tbCwoHbt2kydOpXjx48zZMgQGjZsyJEjR4DYMeAbNmzIz58/MTExYevWrfj6+uLu7s78+f8bmsnb25uOHTuyuGQxomNk/ERGaHQ0Z4M/4/s5hDnFi/CXeY5Uvyai54uQGvJ6k52dHWXKlMHLy4vQ0FA8PT1ZvHgx79/HTrp++/ZtSpcunSkCgczs1+HdMtvrLUKY9CF6vmQsvVf/u+nk17kzRPAi/E5Ke75kxFBhGeGu8wDatGnD0aNH0dbWplatWrRs2ZIKFSpgbW1Nvnz5MuwmtuDgYOrUqcPt27eB2MDBycmJypUrU6BAAfLkyRNvJIGUePXqFTY2NgC0a9dOMaysOvx2u3+vRYsWLahXrx6bN28mKiqK58+f8/jxYzZv3syZM2do0aIFe/fuVdx0Ji/38yfGWG88ysWLF6lbty63bt1SzI0Zl5aWFhUrVqRTp0507dqVokWLkj9/fl6/fs379+8xNDRk4sSJREZGYmFhwePHj/H09MTMzIwLFy6QL18+xbGsra2pX78+7du35/v370RHR/PkyRNmzpxJ2bJluXDhQrznjhuq/GlOI2XbqqrOpJHhS7t27bh//z63b9+ON3zE+/fvKVmyJD179mTu3LkJ9leHxgVByAoyQ/gCIoARBHUOXzJq3qbyp89x/vx59u3bx759+3j+/DkQe9G5Zs0aXFxcMqQcAH5+foSFhVGzZk10dHTS/Pienp7069ePgIAA8uTJA6jPtVFQZCQLnr/kRPAn7IwMqWmeHQkS9r//yKeoSMJ+xlDCyJANZe0S3X/ly9e8CQ+niWVOChhk411EBN+if1JnVewdXadPn2b58uU0btyYw4cP07ZtW1atWoWfnx958+YlX758CYYlCwgIwNraOt7yO3fuUKZMGaXlcMplyahCtkjTYOgxEb4IqSGvN+nr67Nnzx4aNWqkWBcREcGECRNYuXIlb9++xcjISC0aFDRVYvPqZMbXXIQwaUuELxkjbugiCCnxp/BFU0KW3/HvNorXr1+zf/9+9u3bx6lTpxS9GKpXr87hw4dTPK9dcn369ImTJ09SvXp1cufOnebHj4yMJG/evLi4uDBv3jzFcnX43T5VZx4bNmxg2LBhALRp04bq1auzdetW7t69y5s3b4DYeqW8B4u83KfrzicmJkbRe8XBwYG2bdvy9etXAgICMDU1JX/+/Ny+fRtvb2/Onj1Lz5492bJlC48ePSI6OprPnz9TrFixBEOchYaGEhMTk+DmxYYNGyboHAGxvZqOHz8eb/g4SF74End7Eb6kIXklInv27IwaNYpRo+K/EZGRkVhZWdGlSxeWLFmSYH91aWAQBE0nwhdByBzUMXzJqNAlLnkjt0wmIzAwkNevX+Pp6YmXlxcDBgxg8uTJ5MiR+p4MqtasWTNevXqluFMM1O/a6Nynz+x9/5ErIV+IjImhsaUFefX10ZZIKGVkSCUlPV+SQv4eDxkyhKVLl2JqasqTJ0/iDSeWVFccq/Ly+w+QgJGWNlqS2GHSJIBZGoZmInwRUkNeb7K2tubFixcJxjifMmUK8+bN49WrV+TIkUMtGhQ0WWbv+aKMCGJST4QvGUcEMEJqyMOXkSGPiSDmzztoqLiN4d++fcPf358HDx7Qs2dPbGxsWLJkSYbPC5Mezpw5Q+3atfHx8aFevXqK5ery23267nw+fPjA3LlzOXDgAI8ePcLOzg5nZ2f09PQwNjbGxcVF6c01id0Q8qvHjx9TtmxZwsPDmTVrFqNHj05RWT9//szr168xMzPDwMAAmUyGTCbDxMQEPT29eNsmddix3xFzvqShHz9+JBiDDmLH9hs8eDBz585l/PjxKapQC4IgCIKQsVQRuMQlnxNGIpFgbW2NtbU1lStXplixYkyfPp3NmzczZcoU+vXrly49UjJK9uzZFXO+qCvHHNlxzJGdyJgYvv/8mWZhhvw9XrRoEZUrV8bS0jLFwQuAjUG2P2wpCOqhTJkySieX7d+/P7Nnz2b58uVMnDhRBSXLWn4dz10TiHlhhMxE3jNBhDDC7yTegyVjhtRSd/L5ZgCMjIyws7PDzs6OIkWK4OrqSt26dWnZsiXz5s2jSJEiKi5tyuXKlQuAr1+/qrgkysmvKebPj328f/8eCwuLBMOtKbveiHs98jtFixblzp077N69myFDhqS4rNmzZ0/SHEGJzeES9zOnzjT2GyKxN2/QoEFIJJIEkwYJgiDEJXq9CIJqvTh5T/FQB7/2AJFKpYwcOZLHjx/j7OzMkCFDsLGxYeLEibx+/VpFpUydJ0+eZJqKkK5Umqa9SCD2PZZKpXTq1In69eunaH9ByGwSm2TW0tKSXr16sWTJkniTtQrpp/aJ4RoTvMRla/0x1ZMNC0JGicgfmaqHkLmJ9zf1lDWSlytXjvPnz+Pt7c3169cpXrw4LVq04PDhw5nyGuPevdj6aWLDDKuDuNcTuXLlSlLwIpfUyeoLFy7MyJEjE/RQSWt/Kk9Sy6tKGtnzBRIPX3R1dYmOjsbAwCCDSyQIQmYSt8FXBDGCkHHUJWxR5tfGdftzflhZWeHp6YmbmxseHh4sWrSIGTNm0Lx5c1xcXKhcuTK5c+dOMFeIOpJIJOl+8azuRIAiZDWJhS8QGzIbGBhkiu8vQf2JnjBCVpCSBnrR2yb9ieAkY8i/5xNr2PePyAnje2N28QYBN1/QpEkTChQoQN++fWnYsCElS5ZEVzfz/D2o+yweybmh49ff5swQaMSl7j1gslz4cujQISIjI2nVqlXGFkgQhEzr18ZgEcYIQtpS58Dld+RDVQGUKlWKFStWMGfOHLZs2cLKlStxcnICwMLCglq1ajFq1Cjs7e1VWeRERUREcPfu3QTXR3HnFUkqEWAIQubx68SncjKZjL1799KqVSsRvmSg03Xna2Tvl7hsrT+KAEYQ4kiPYEDTAh0Rnqi/pPRwlH//G9euwqdalblYfCUrV65k0qRJjB49Gh0dHUqVKkX37t3p3bt3gknb1cX169exsLCgUKFC8ZYndb4UOXX5vdeU32R1DmA0MnzR0tLCyMgowfIfP36wfv16ypcvj62tbcYXTBAEjfDi5D0RwAhCGsisoUtccQMYAGNjY/r160ffvn15+fIlt27d4saNG2zbto3KlSvTqFEjJkyYgIODQ5KO/+3bN549e4a/vz+FChWiVKlSad4Q+uzZMwYMGEBkZCQtW7ZM9fHsz/mJAEYQMonEer4cPHiQV69eKUJkIf3JG22ySgADmtPgIwjqRoQVQkZJ7rCScbcfHfYvpzdswMPDgzt37nDz5k3OnDnDsGHDmDlzJiNGjKBfv34YGhr+8bgxMTG8evWK58+fExERQa1atdJ8xKOYmBiWLVvGwoUL6dGjR6rrZOrwe69pv8PqGsBo5JwvpqamCf4IduzYQbFixTh58iTu7u4qKpkgCJpCExqNBSGjxZ3HRZP+hpQFDRKJBFtbW1q2bMnkyZO5d+8e3t7evHr1iurVq1OiRAlcXV3ZuXMnoaGhCfZ//PgxvXv3xtzcnHLlytGqVStKly5NsWLFGDVqFA8ePEiTsv/8+ZO2bdvi6+vL2rVrKV68OOHh4Xz79i3BtjExMaxevZpp06Zx8OBB7t27x5IlS/Dy8uLjx/gVr5T0mBEEIeP9Gr4EBgbSvn17mjdvjoODAzVr1gSgvtVDVRQvy/j1btnk3j2bWYn5YARBEDKntPr+rn1iOEZGRlSrVo3+/fuzbds2Hj16RLNmzRg9ejS5c+emWbNmLFiwgLt37ybYPyIiAk9PT4oXL06BAgWoW7cuTZo0wcLCgtatW7N161aio6NTXU6ArVu34ubmxl9//cXcuXP5+fMnnz9/Vjpvzb179xg7diyenp7cvn2bgwcPMnv2bK5cuRJv+6zye5+R1HHINIlM3QepS4bQ0FBMTU2ZN28ew4f/Lz18+vQpRYoUoVmzZixcuFDpZLLiDk1ByDjRRkZ86D8Iy5XL0FbSwJeZiB4wgiaLlmpzqXwTqtw4hHZMyi5aNSlkSS5lAURMTAwHDhzg4MGDnDp1iidPnpAtWzacnZ1xdnbm7du3+Pj4sHfvXnLlysWQIUOoXbs2+fPn5+bNm+zevZt9+/YREhLC8OHDmThxYqq65F+4cIHq1asDUKJECQoUKMCRI0eIiYnBwMAAOzs7ihQpQlhYGNeuXSMgIAADAwO+f/8OgI6ODtHR0chkMkqXLs2CBQviTVYvrq/ST9zPV3BwMBYWFnz58gUTExMVlkrILOT1pvv371OiRAnFcmdnZ3x9fZk3bx5dunRBKo29V2/GvWYA+LwrrpLyarLfNbyo+o5YVdC0u3BTwwRtppkVZkLIU0JJm8ZDQRDSnh5S5poVZWTIYyLIfBPIJ0VGB+Wn687H39+fzZs3c+rUKS5cuEB4eDjlypXDxcUFa2trrly5wsaNG3n37h1OTk706NGDIkWKEBMTw/79+9m9ezeXLl2iQoUKeHp6UqFChVSVycHBgYsXLwLQsmVLrl+/TkBAAAC2traUL18eXV1d3rx5w/nz5xWdAn7+/AmAvr4+4eHhGBsb07dvXyZNmqQYtUlVv/ea+pubWO8XVdWZNDJ8CQoKwtzcXLHc29ubjh07JlguJxoGBCFjaVL4IidCGEETpTR8ycqBy6/+1APk5cuXbN26FS8vLx4/foy2tjZlypShb9++dO3aFX19/QT7REREMHfuXKZPn07x4sU5duwYuXLlSlH5YmJiWLp0KbNnzyZbtmyEhITg6uqKqakpX758ISAggBcvXmBsbEyhQoXo2LEjVatW5cmTJ3z48IESJUoQHR3NsWPHWLNmDWfPnmX48OFMnjxZMUSAuM5Ke79+rkT4IiRXYvUma2trunbtysyZM+NtL8KX9CPCF+U0tUEoOUT4IgiZg6aFL+rSIzHu72N4eDjHjh1jw4YN/Pfff0RFRZE7d26aNGnCiBEjKFasmNJjXL58md69e3Pv3j3WrFmDi4tListz584d3N3d8fX1JXfu3FhZWdGjRw+ePXtGZGQkt2/fJiYmhhw5clC/fn3+/vtvAG7evEmOHDkoVqwYFy5c4PDhwyxevBgbGxu8vb0pX748IAKYtCTCl3SUWCVixIgR/Pvvv7x48SLBPqJBQBAyniaGL3IihBE0SVLCFxG0/FlShuCSyWT4+/uTJ08e9PT0knTcu3fv0qBBA4yNjTl+/Dj58uVLbVFTRSaTsWjRIkaOHImRkRFTpkxhyJAhgLjeSkvKPk8ifBGSS1m96f3791hZWbFz507atGkTb3t5+AIigElrInz5M01sGEoKEb4IQuaQ2cIXdQlXkkLZb+Tnz5+JiIjAysoqSceIiopiwIABeHp6snz5cgYMGJDWxUy2R48e0aZNG+7evUvdunXZvHkzVlZWIoBJI+oWvmjknC/yLl1yjx8/pnDhwgm2Ew0BgiCkNU2by0IQlNHEeVvSU1KuNyQSCQUKFEhy8AJgZ2eHr68vERER1KhRg2fPnqWmmKkmkUgYNmwYT548oUOHDri5ubFmzRpAzAGTVsTrKKSnx48fAyitN8Ul5n8RMpp8boHM1GAoCIKQ0eJ+V/7ukZkoCyOyZ8+e5OAFYodJXrVqFUOHDmXgwIHMmaP6OUGKFSvGtWvX8Pb2xtfXlwYNGhAREaGyOWAy42cjM9FWdQHSw4wZM7C0tKRkyZI4OTlRoUIFFi9eTHR0NNraGnnKgiCoGXmjtOgJI2iCl2cfII2MVHUxMrX0uOHD/pwfhQoVwtfXl3r16lGjRg2OHz9OyZIl0/y5kqNAgQLMnj2bVatW8erVK8Vy+3N+4saXVBDBi5AefHx8uHLlCpMmTaJ06dJoaWlx+fJlypUr99v94gYwoidM+hC9XpSL2zikaXfqCoIgKJPVG8VT+3t4uu58JBIJCxYswNjYmNGjR/P161emTZummJdFFXR1denQoQPr1q3jxo0bREVFoaenx+m681V2DfCnz1rc391ftxW/yYnTyCRi9erVhIeHA7Bq1SoaNmzI5MmTuXz5Mg4ODkDsGOfKKrGiUUAQhLT0a88AEcYImcWLk/eI0dWF8k1UXRQhEVccq2J/zo98+fJx9uxZ6tevT82aNfn333+pXbu2Ssv29u1bAKpXr67ScgiC8Hvdu3cnPDycI0eOcPPmTapWrcqRI0fo06cPEDucIMC4UgcS7CsfikwexIgQJvkSu8NVBC9JIxp+BEHIrLJ6oJKRap8YrghgpkyZgpGRESNHjuTDhw8sW7YsWSMPpAcdHR3KlSuHkZGRSsuRFL/73CblM51Vf6c1ctixvn37AiCVSsmfPz/29vZYWFjQqlUrWrRogYODA7q6utStW5cDBw4QE/O/MRnFXYWCIKQnMVyToM7E5zPzkd80kitXLk6fPk2ZMmWoW7cus2fPJjpadePDFy5cGH19fS5fvqyyMgiCkHSFChVCW1ubJk2asH//fqpUqYKTkxO5cuXCysqKKVOm8OHDh3j7/BrI1Ld6qHgIfyaCl7SXmYfWEQRBM2ji0F+aIO5v64gRI1i7di0bNmzA0dGR58+fq7BkULp06SxTZ8qqfwca2fNl6NChLFmyhFWrVtGoUSMgtkv9jh07uHbtGnnz5mXmzJns3r2b5s2bU7RoUdzc3OjWrRsGBgZiWAxBEDKE6BUjqJoIWTSDvAdMjhw58PHxYdKkSYwZM4aVK1fSrVs3cubMib6+Pvb29pQtWzZDutcvXLiQyMhIGjRokO7PJQhCyv39999cvHiRPXv2IJFIcHd3x8TEhEuXLhEQEEDv3r0JCQlh7ty5zJo1i86dO+Pm5oadnR0QG8DIe8DElVgAI3rHxBLBS8YQPWMEQUhLiTUW68q0IKwoNnmDEAM1qy95DxiAHj16ULZsWdq0aUPRokVp06YNVatWRU9Pj1y5ctGwYUMMDQ3TvUwfPnxg9erVNGmSNUebyCq/0xoZvoSGhgKwZMkSfvz4QUhICPfv3+f+/fuULVuW1atXo6+vz8iRI7l48SILFy5k4MCBTJw4kZs3b5I3b14RwAiCkOFEGCNkBBG4aCZ5AKOlpcX06dNp06YNS5YsYfny5Xz//p2IiAgAihYtyqRJk+jYsWO6lsfT05Pu3btTpUqVdH2erET+HgtCWgoLC8Pf3x83NzdKlCihqDN9+vSJpUuXUrNmTQCmTZvG6tWrWbZsGWvXrmX69OmMGzcOSDyAUUaEMokTwUv6U9ZwqqkNPYIgJE9Wugs/K4sbwFSsWJE7d+6wfv16Vq5cyX///Ud4eDgxMTFky5aNtm3bsmjRInLkyJFu5dmzZw+hoaEsXbo03Z4jM9HU32mJTD6QrwYIDQ3F1NSUnj178u7dO+7du4e/vz85cuTAzs6OwoUL4+3tTdWqVZk8eTI9e/Zk7dq11KxZk82bN9OlSxdevnxJ/vz5FccUAYwgpL1oIyM+9B+E5cplaH/7puriZEoimBGSIrVBS4yuLu+GuGO1ZAHSSHEfV2aQWON8VFQUJ06cYPXq1ezZs4eyZctia2tLpUqVcHV1jVepuH//PgAlSpRIUS+ZwMBArK2t2bVrF61bt463TlxXpZ6y9zg4OBgLCwu+fPmCiYmJCkolZDbyelO7du14/vw59+7dIyoqimLFilGyZEkCAgK4efMmu3btYtOmTejo6LBhwwaioqIoVKgQTk5OCRoKkhrAJIcmhzJxe7+I4EX9qENjjwnaTDMrzISQp4SiuqFEBSGzyqhARVemxYAwBzwMLxAp+ZkhzymkTmI9UAGeP3/Ojh07mDdvHlKplDJlymBra4uLiws1atRQbPf9+3cuXbpElSpVMDAwSFE5OnbsyIsXL7h48aJimbgmSLrEfqv9u41SulxVdSaN7Pni7e2NRCIhf/78GBsbkyNHDrZt20bu3LlxcXGhWbNm1KpVC4AuXbpw+/Zt3rx5g7a2NsbGxvGOJXrACIKgjpQ1qotARgDRsyWrS6x3hI6ODo0aNaJhw4bs2LGDEydOEBAQwMyZM5kzZw5NmzalQoUK6OvrM3r0aH78+EHHjh3ZsmVLssvw5s0bAAoWLJjq8xESEj1ghLS0f/9+rK2tyZkzJ69evWLAgAH079+f8PBw2rVrR9OmTRXb1qxZk65du/LmzRvMzc0THCs5PWCSSpN7ysjvvhWNLOopsUZbdQhlBCGrEr1ThLQStwfMrwoWLMjo0aPp2LEjy5cv5+XLl5w7d45169ZRsWJFGjRoQM6cOfH19WXPnj0YGhpy5coVSpQokexyvHnzJl6dSVwTJE9m6SmjkeFL//79Wbp0KTt27MDQ0JDKlSszZswYvLy8qFGjBqdOnWLUqFG4u7vTvn17WrRogaurK4aGhopuZ3HH9hMBjCAImUFSGt1FQKM5RMgiJOZP1ywFgfb/33j/4cMHVq5cyaFDhzh69CgRERH8+PEDgK1btzJo0CCqVk3eNVDk//eSkkqlyS+8kCQigBHSiqGhIXXq1OGff/7B1dUVV1dXnJ2dsbS0ZNeuXQwbNoycOXMSEBDAkCFDkEgkNG/enMmTJ1OgQAG6du0a73jpEcAooymhjGhkyXx+1/irjg0+gqBuRIAiqIsk/QY3BMiHVYw1On7PKHRXjw0bNhAaGqqo84SFhTF06FCOHDmS7DJERESgpaWV9PIIf6SO3zEaGb4MGzaMw4cP06FDBy5evEhUVBQFChRQrK9QoQI+Pj4A7Nu3DxcXF1xcXKhYsSKXLl3i8+fP/Pz5E11dXXR1dZFKpSKAEQRBIyTWYC9CGfUnwhYhLckb7y0tLZk0aRKTJk1SrLtw4QLVq1cHwNTUNNnHlt/19fDhQ8qUKZM2Bc4gyQk0xHWhoAnGjx/P0KFDady4Mbly5cLU1JTs2bMDsT3mli1bBsQ2LERERNCrVy9q1KiBnp4ez549IyQkRFFn0tLSQiKRZFgAo8yvoUxmC2OEzC25DT4irBHUkTo2XAqCqkmkEiwcCvPeAQK3ByqWV6xYkevXr6d42LESJUpw9+7dtCpmhvndsG3KZPVgSSPDFz09Pby9valWrRo1a9bky5cvtGjRQum2tWvX5t69e0yaNInFixejp6dHvnz5FOuNjIzYt28fderUEQGMIAgaKzkN+yKoyRgibBHSW2K9JxwcHNi+fTudO3dmyJAhbN26FQsLiyQfV19fH4AvX76kWVkzQokjxxk/fjwGBgY4OTn9cegAcV0oaILOnTvj6+tLt27dyJEjB02aNEFHRyfBdoaGhmzcuJGuXbvSr18/IiIimDp1KlOnTlVs065dO7Zt25aiuaLSi7IeMiKQEdTFnxq5DWN04Xth8uUJJkyaNnPvicBHvYigQxAyn7hDlh09epRmzZpx7Ngx9u/fn2jbc2IiIyMJCQkByDRDkZ6uO58DBw5w+vRpKlasSPv27f844kFyzi21r0Nyg6GMIJHJZDJVFyKtyCeODAoKwtzcnJ07d9KuXTvy58+Pv7//HysCDx48YOfOnUyaNAknJydat26Np6cnz5494+nTp4rGBFHRFoTUiTYy4kP/QViuXIb2t2+qLo6QSiKMSR11DllidHV5N8QdqyULkEamTaVfUD+J9fY4efIk7dq1I1u2bOzevRt7e/skHW/06NHMmTOHCxcuUK1atXjr1PUaqsSR4zRu3Jhr166hq6uLTCbDx8eHypUrAxAdHY2npyetWrUid+7c8fZV1TnFfd9UNXmkkHnFrTfp6Ojg6OjInTt32LZtG+3bt//tvj9+/ODQoUO4u7vz6dMnFi1axMePHxkzZgzr16/HxcVFZT1fUkoEMoI6MozRpc/3Kqw2uJRm4YsgCGlPV6bFgDAHPAwvECn5qeriCBlE3sgfFhZGly5d2LNnD+7u7sybNy9JN6K8efOGfPny4eTkxL///qtYrs4BzOm689m0aRPdunUjV65cvHv3jp49e+Lp6ak456tXrxIQEICTk1OC/f90bnGDk5S8Dn8KXlRVZ9Lo8AVgy5Yt6Ovr06RJEzZv3oyTk9Nv796UyWT06dOHTZs24evrS968ecmbNy8rV66kX79+iu3UtfFAEDIDEb5kXVk5qFHnkCUxInzJuuQN+69fv6Z169a8ffuWe/fuJekitUqVKhQuXJgtW7YkWKeO10+Gq9fRs2dP7t+/z9GjRylatCh58uTh77//Zv369fz48YP27dvz33//0aFDB7y9vRMcQxXnJcIXITV+rTcFBATg4eHBxIkTuXTpEhKJhNq1a//2GDdu3KB69eq0adOGDRs20KRJE16+fMn9+/czXfjyKxHGCOpAhC+CkDmI8CXrkjf2y2QyFi9ezLBhw/D09KRXr15/3FfeYeDNmzcJbu5SxwDmqOMMFi5cyLhx4+jZsyerVq1i4MCB/PPPP7x79w5LS0v27dtHhw4diI6O5vHjx/GmAJFL7NyUBSfJeR2S0uNFVXUmjRx2LK5OnToBsb1a+vTpw7BhwxgyZAg1atQAwNramlKl/tcYKJFIWL58Obdv38bV1ZUrV65QrVo1Tp06FS98EUNNCIIgJF/cAEKTgpjMGKwIwu/IhySztrZm586d2NnZ0apVK3bv3o2Zmdlv9/306ZNizghV+90cLmFhYUycOJElZcpga2ur6Oni5eVFVFQU7u7uABw7doz//vsPJycntm/fzrhx47Czs0vwPOK6UMjM8uXLx+zZswGYP38+hw4donbt2gwaNAhDQ0O0tLRwdHRUjAQAUL58eVavXk2XLl3o0aMHvXv3xtnZmVevXqnqNNKMGK5MEARBEIQ/kQ9BJpFIGDp0KHfu3GHIkCFYWlr+cQiyz58/I5VKMTY2TnBMVfhdeHHixAlKlSqFv78/I0aMYNasWUgkEv7991+6d++OpaUlACNGjKB8+fI8f/6cadOmsW7dulQ9tzoOIZYSGh++yBUvXhwbGxtkMhkLFy5kxowZAJiZmfH582fFdsOGDWPfvn1ERUXx9u1bwsPDqV27NmvXrkUmk6nVGMaCIAiZ2e8CC2XBTFK2/3WbPx0nKQGQCFaErEoewNjY2HDw4EFatWqFg4MDhw8fxsbGRuk+L1++JDg4mJ8/VX/XX9zg5evXrwQEBJA3b15MTEzw8fGhb9++vHv3junTpzN06FC8vLxwdXXlzp07FClSRBGwFC1aFIDGjRtz48YNGjdujK2tLXp6enTt2pWuXbsqnk8EMIImaNKkCUeOHCEwMBBnZ2fF8jVr1tCzZ08A7t69S/PmzRVB640bNxR/C6dPn2Zc1wOZvvfLr0QgIwiCIAjCr+LOAbN8+XK+fPlCq1atWLJkCYMGDUp0v+vXr6Ojo0NERARGRkaKY6lC3JDj0aNHGBoaYmVlRWhoKMOHD2f9+vXUqlWL/fv3Y2hoSKtWrbh8+TIfP35UDC8WExND8eLFef78OWPGjMHd3Z1Hjx6hq6uLjY0NCxcuJEeOHCo5P1XLMuGLRCLByMiIKlWqsHjxYkJCQpg/fz6bN28GYruIubu7c+rUKd6+fUujRo3Ys2cPuXLlomjRonz48IEHDx5QsmRJFZ+JIAiC5ktu4JHY9n86jghWBOH35AFMzZo1uXjxIk2aNMHBwQEfHx+l10THjh3j8+fPTJkyRQWl/R/7c358/fqV48ePExYWxqpVqzh37hwQO3F4WFgYf/31Fz4+PhQuXBiAS5cuce3aNUaNGkWzZs1YtWoV//77L6dOnUIikaCtrc327dvx9PQkMjKSN2/e0K1bNx48eMCMGTOQSqUigBE0Qp48eYiJicHHxwctLS1CQ0MpVaqUYjLVa9eu0a1bN/z9/cmTJw8QewPbtm3bMDQ05NSpU3Tt2pVxpTQvgPmVskAGRCgjCIIgCFmJPIAxMDBg586djBgxgsGDB/PhwwemTZumdJ9t27YxZMgQxbQZqgxeLl26xIsXL3jx4gVjx44FQCqVoquri56eHp6envTo0QOpVMqdO3c4ePAgpUqVYuXKlejr6zNgwAD27NnD27dvsbOzo1+/fgQFBREYGEhkZCQHDhzgwoULHDp0SCXnqGoaGb7ccmqOcUwMEP+ux4iICMzMzDA2NubQoUN4eHiQL18+xR2M//zzD1WqVOHHjx/Ur1+fvXv3UrRoUb5+/QrAs2fPRPgiCIIgCEKWIg9gihUrxqxZs2jfvj0eHh54eHgk2LZSpUpIJBKWLVuWaEUjvdmf8+PRo0e0bNmSR48eAbE34QwePBgHBwcCAwPJmzcv7dq1i9ejef78+Rw7dow5c+YoJsr866+/WL58Oa1atcLKygqAypUrA7E37ixatIjhw4fj7+/P1q1bkUgkyQpgEut9pywYjpHJeP79B7e/fuVdRCS9L1zAwcEhWa+NICTFx48f0dLSInfu3Hz9+pWGDRuio6PDpk2buHTpEkePHsXf35+cOXNiZmZGixYtOHz4MLlz5+bmzZs8e/ZMcaysEMAoI3rJCIIgCELWIg9gpFIpc+fOZd26dUyfPp2xY8eSLVu2BNtXrlyZLVu2MHLkSMzNzeNdO2TUNcOJ2nMYNWoUc+fOVSyztrbmn3/+ITAwkJCQEDp37qy42QagdOnSjBs3jmnTptG6dWsgdvjaDh060KZNG6pWrYpUKo1XF3z69ClNmzalatWq2ExvjHGRXOl6XsHBwVy4cIErV66QM2dO+vTpg56eXro+5+9oZPiSmO/fv/Px40e6du3K5s2bkclkaGtrc+TIEcU25cuXJyIiAldXV8WyBw8e8Pr163gfNkEQBEEQhKxCHsBERUUBsXe+K1O+fHlGjx7NwoULGTJkCBYWFhlZTOzP+XHq1ClatWpF3rx5uXPnDoULF0ZXV1dx135icuTIQYsWLVi5ciWlS5fm+PHjvy2/RCJh2LBhGBoa0q9fP5ydncmdOzdSqZTvU2bw9u1b3r59y7t378iePTsjRoxQetG/YMECbt26xbdv3/j27Rtfv34lLOw7UVFRfP/+XTF/RtmyZbl16w5aWlr8/PmTh+7uXLx4MXUvmCAoERYWhq6uLp6ensyZM4f3799jYGDAjRs3OHXqFBD7+R8wYAALFixQ3Kjm5ubGhg0bkMlkqiy+2kqslwyIYEYQBEEQNEl4eDgRERFA7FCt9vb2CbZZv349hQsXZsWKFUyYMCHeuvQMYuTHHl54F82bN+fo0aPMnz+fvn37oq+vj7b2n6MCFxcXRbiybdu2BDe1/apw4cJcvHiR8uXLY+vzHbdSLdHS0kImk/HhwwdWrFjBu3fv+PTpE927d6dixYoJjnHr1i2WL19OaGhovHpTREQEP3/+VNx0B2Bubk5wcLDi3xUqVKB69epJfo3SWpYKX8zNzdm0aRPW1tZ069YNLy8vevXqxejRo7GysiIkJISCBQuycuVKcuTIQaFChbh69So3b96kfPnyqi6+IAiCIAiCylxxrMrfZy/Qs2dPpRfEcsOGDWPJkiXMnTs33l1UkDHzokyaNInixYvj4+ODiYlJsvaVX6Rra2snOThq3LgxEomEtm3bJlhnYmKClZUVL1++ZP/+/fz777/kz59fsf7NmzcMHz6cokWLUrBgQczMzMiXLx+GhoasXr1aUWmD2K7/Ojo6fP78mV69evHhw4dknZsgJJWFhQU/fvxg8ODBtG/fnlOnTuHi4sLs2bMBcHBw4MmTJ0yaNAldXV0WLFiAkZERq1evZuvWrSoufeb0u2AGRDgjCIKgSn/6jpbG6MBzB/7K9ZgYaVQGlSplxO9JxjA0NGTChAmMHTuWggULKt0mb9689OzZkyVLluDq6ppob+GUDHH6p88swNGjRzl8+DAHDx6kSZMmf9w+rk+fPin+39LSMknzo+fIkQMHBwe2bdvG7t27463T1tYmV65cSCQSPD098fDwoFevXor1MTExzJ8/n127duHg4ICRkRH58+fHyMiIT58+sWPHDsW2urq6BAcHs2nTJooXL469vT0GBgbJOr+0lqXCl5kzZxIcHEy7du1o164d1tbWirsJ+/Xrx+TJkxV/FGZmZly9ehUdHR2+fPmiymILgiAIgiCoBd/qVYiIiFAMd+Xn54e5uTlFihRRbGNhYcHYsWMZP348hQsXpk+fPvGOkZ4BTFRUFFevXmX69OnJDl4OHz7Mjh070NbW5uXLl4SGhibpGPnz5+fNmzeEhoby8+dPYmJi0NfXx8rKCkNDQwCuXr2Ks7MzZcqUwcHBgRIlSiCVSlmxYgXm5ubs37+fYsWKxTuumZkZ06ZNY/PmzeTNm5e//vqLkiVLcuTIEe7du5doRU4QUqtp06ZMmzaNv//+m4CAALy9vSlePLaCX7RoUUJDQxWfP1NTU4KDgwkODqZChQqqLLZGS0ojCohGNUEQhMQk9XtU04nfk4zz8uVLypUrh7m5OaGhoZw+fZpmzZrF640/evRotm/fTqNGjTh+/HiyhmtN7Wfaz88PKysrGjdunKz9IiIi6N+/P6ampnz58oV79+7x119/JWlfLy8vpk6dqqgzAeTMmRNzc3OkUikREREMGTKE3r17K0YjKFq0KLt37+batWuMHj2aWbNmxTtmYGAgBw8epHTp0hw9epS6dety9epVfvz4wb///guAsbFxss4xrWl8+CIfJgOgWbPYD/C4ceM4cOAAe/fupV69ehQtWhQ3Nzd69uxJy5YtAejRowefPn1iyJAh5MqVvmPRCYIgCIIgZAZ6Uil59PSYPn06ISEhjBs3ji9fvrBv3z5atGih2G7s2LG8e/eOfv36ER0dHW84V0i/AObOnTv8+PGDqlWTd+z379/TpUsXmjRpwrx583BwcKB9+/YcPnw4SftbWVkp5oRRplKlSly/fp2FCxdy+/Zt9u3bx/v37xkwYABjx47FzMwswT7jxo3j4MGDDBw4UBHi3L9/nzZt2mBsbIyLi0uyzlEQlFn6uBu6SjJGiRPExLSndevW1K9fn7///ptKlSrRunVrgoOD2bJlCwC1a9emdevWuLi4ULduXWJiYv44xJ+QfkSjmiAImkqEJxkrJb0thPjs7Ozw9PTE1dUVGxsbRo8eTaVKlbh06ZLiWsna2hofHx/++usvGjRowMGDBzNsvrxLly5RtWrVJPVaiWvs2LHcvn2bCxcusHDhQoYPH07VqlWpVKnSH/fV09OLd9OesvX//PMP9erV48CBAzx48IDdu3dTqlQpzp49S40aNRLskzdvXjw8PHBxcaFKlSo8fBj72e3Tpw9SqZTSpUurfBoRjQ9fIH4Ac/PmTWbOnMno0aNp2bIl0dHRLFu2jGbNmuHp6an40I0fPx6IHbuuW7duuLu7M3bsWIyMjFR2HoIgCIIgCKqkJZEwp3gR/nn1mkGDBinmdpg+fTply5bFxsYGiJ0PYsmSJWhrazNgwAA+ffrEuHHj4l3cy6/NfnXgwAEuXbpE3rx5qVmzJiVLlkxy+datWweQ7OFi37x5Q3BwMN27d6dkyZIMHTqUmTNnEhUVhY6OTrKOlRhzc3NmzJiR5O319PTYu3cvHh4eBAYG8ujRIx48eICOjg7Lli2jY8eOaVIuQUiMq6srUqmUHTt2oKury5kzZ3j69Cm3b9+mVKlSAJQsWZJdu3YBsQ0IefPmZdmyZVSuXFmVRRf+IDmNmKKhTRCEtCIClMxP2Xsofidi1bd6yIx7zRhX6gAQO8LSmzdv2LBhA0FBQUBsb3j5HCnyuVXKlSvHiRMnaNSoEY6OjooeMH8inyvFyMiIggUL0qJFi3jztfwuwBlX6gALb5ozdOjQZJ/nrVu3KF++PBUrVmTlypVs27aNs2fPJil8Sao2bdrQpk2bJG/ftWtXoqOjuXr1KjY2Njx+/JgXL17Qtm1bli1bpvJhxyQyDZoRMTQ0FFNTU07UqIbx/3dfisv+nB/fvn3D0dGR4OBgLl68yIkTJ3BxceH69etKK+obNmzAxcUFHR0dcubMyaJFi2jXrp1ifXqPWy4ImijayIgP/QdhuXIZ2t++qbo4giAkIkZXl3dD3LFasgBpZKSqiyOoGe83b/H+EcnAgQOZNWsW379/p3379kydOlVxR5NMJmPOnDls2bKFpk2bMmvWrETvrvr8+TOLFy9m6tSp5MyZk48fPwKxEzQWLFiQLl260KZNG/T19RPs+/PnTzw8PFi/fj2PHj3i+/fvyToXmUyGo6MjMpmM8+fPU7duXfT09JLc8yWjfPjwgcGDB7N9+3Zat27Nzp07kUqlBAcHY2FhwZcvX5I93JqQNcnrTRMvNFXa8wXA3LcZ/fv3Z8GCBbi6ulKkSBFq1arF5s2blW6fM2dOvnz5QlRUFN27d2fevHmxoWMG3L0pqAfRAJe2DGN06fO9CqsNLhEmFddhQsYTYUnSSGN0KPnchfsFvdR+zhdV0OTfhsT+RuKGJ2/evKFGjRo0adKEK1eucOnSJQoWLMiECRPo1q2bom705MkThg0bxv379zl58qTiprZfxcTEcOHCBbp27cqHDx8ICwtTrCtTpgyNGjWiV69eFClSROk1WOOIqaxYsYJ169axfPnyBCMU/MnevXtxcnLi/PnzhISE0LRpU+7evau4MUcdyGQytmzZwpAhQ5BIJBw4cICqVauqrM6UJXq+yMl7wBw6dIhq1apRp04dAgMDadOmTYLgJSYmhh8/fqCrqwvAmTNnmD17Nu3bt8fe3p4CBQoAGTNxrCAIgiAIgrr5O09u2stkVBk3jiFDhrBp0yZmzZpFxYoV2bNnD3Xr1kUikTB69GiyZcuGm5sbhQoVwt7enlKlSil6lJw5c4a5c+fi4+NDdHQ0kydPZvz48Zw6dYqLFy/y7ds3Ll++TJcuXZg/fz5Hjx6NNyRsZGQknTt3ZteuXTRq1ChFd3BJJBLGjh1Ls2bN6NGjB2fPnsXDwyPNXqu0YmlpybZt26hTpw59+/bl4cOHyeoZJAjJ0a9fP169eoW7uzsbN27k48ePitEB4oqMjOT79++YmprSqVMnihcvzqhRozAwMGD58uUqKLmgKiltqNXkhjlBSA8iFBEyk5R8XtX5dyEp5xO3B0yePHl48uQJUqkUmUzGlStXmDNnDt27d8fHx4cNGzagra1NkSJFWLFiBbVr16ZevXr4+voSHh6Ora0tACEhIcyZM4fNmzfz+vVr7OzsuHfvHkZGRqxfv57Q0FACAwPx9PRkwYIFrF27lnHd4g9hZh84mBpONcidOzeDBg2idevWyT7/Fi1aULJkSQYOHIhEIqFw4cJqVx+RSCR07tyZBg0aUL16dVavXp3sYanTUpYKX+Ty5MnDkSNHqFGjBrVq1WLDhg3x1kdERFCsWDFevnypWGZlZcXKlSvZv38/t2/fVoQvIAIYQRAEQRCyJqlEori5pX///nTp0oWmTZsyaNAg7t+/r9huyJAhjB07lj59+gBQr149GjRowPnz59m3bx8VK1ZkwYIFODs7s3btWoyMjAgPD6dkyZLMmzeP2bNnc/PmTZo2bUq1atXYsWMHlSpVIiwsDGdnZ06dOsWuXbto1apVis+lSZMmNGjQgAsXLtCiRQvatm2b2pcn3bRr146+ffty7do1tavsCJplxowZvHnzhl27dnH48GGKF4/fGLJgwQKGDx+u+HdkZCT9+vXD19eXmzdvZnBphcwqvRqS1bnxTlA9EWAIgvpSlznMUvM9oazXybhSB6hcuTK7du3C29ubjh070rFjR5o2bQpAvnz56Nq1K5MnTyZ37txIpVJmzZpFUFAQXl5e/Pjxg27dutGuXTty5cpF3bp1efbsGbq6ugwePJgFCxbg4eHB4MGDcXFx4fHjx0yZspc5j1pR7IELzTo2o0GDBuzcuZNs2bKl6LykUinTp09n4MCBFChQgOHDhyd73piMYmlpyV9//cWlS5dUWo4sGb4AlChRgpcvX+Lr60uFChUYNmyYokFg3759vHz5Eg8PD/LkyUOuXLkUSWP27Nm5c+cOLVu2jHc8EcAIgiAIgpBVyQMYIyMj9PT0ePbsWYJtrl+/zq1bt7h69Spr167Fz8+PYsWK4eXlRZcuXZBKpZw/f54pU6bQo0cP6tevz4oVK2jatClLly5l0KBBXLx4kbZt29K6dWuMjY35/v07Hz9+5NChQ9StWzdV5yCRSDh69GiqjpFRzMzMKFSoEB4eHly4cIFChQqpukiChpHfrSmRSFi/fj1Tp05l3LhxjB8/nqNHj2JkZERUVBQeHh40btyYHj16kC1bNqpVqwbETjJ78OBBNGiEayETyuyN69rRBuBfhZqWT4nWTt5wmoIgCFlBZvuej9sjxtzcHIAfP37E22bEiBFUrlwZf///Y++uw6JM3zaOf4cOGwMRWxEFURTs7o61u7trxV471+5Yxd61W9dWzMVOTMRARQVBkJ73D1/mBzIgMTDE9TkODnXmiWtG0Lmf87nu251t27Yxbtw48ufPT9OmTZk+fToWFhYEBATQsmVLQkJC2LZtG0+fPmX+/Pns37+fe/fusWbNGgoVKsTEiRM5duwYOjo63LrVjvbt27Np06ZEr2nZsmVLWrZsmahjJJeyZcvy119/MXToUMLCwrRSQ7oNX969e8f06dNZvXo1lpaWDBo0iE+fPnHq1CnOnTtHlSpV1M57Z2dnx927d7VQsRBCCCFEyhURwJQtW5aTJ0/SokULOnbsiL+/P6GhoZQsWZK2bdvStm1bZs2aha6uruouKRcXFzZt2sTGjRtxcHBg+fLlGBgY0Lp1a/r168fw4cMJCAhgzJgxuLi4cPr0aU6cOIG/vz/9+vXT6AKPqUXXrl3Ztm0bV65cYfPmzdouR6QhkecpDwsLY9++fQwbNoyAgABCQkLo1KkTefPmZefOnXz58oWtW7dSqVKlKMews7Pj69evUWYSEEIIIYRI7yICmIIFC2Jqasro0aN5/vw5hQsXxtvbG1NTU5o2bUrDhg0ZMGAAoaGh6On9uHzv5+fH/PnzWbVqlaozOaJrpk2bNpQqVYr69euzZs0anJycaNmyJbt378bDw4M2bdowZswYdHR0tPnyk13dunWxtrbm7Nmz+Pr6aqUGhTIN3Y4UsXDk6aoVyRgernabXNv/YdKkSezYsQNDQ0PmzJlDnz59qF27NpcuXaJmzZp07tyZ1q1bkzFjxmj7DxkyhNOnT0eZSiOCdL4IETehGTLwccAQcq5aht63b9ouRwgRg3ADA94PG4X5kj/RCZaFXkXcOFy8woYNG1i2bFm0G1YKFCiAvb09y5cvx8LCAoDPnz+TN29esmXLxvDhwxk2bFiUu7Hev39P7ty5gR93hhkZGSXfi0klzp07R82aNZN98UiRekWMmyZfboxBpG+ZiOBFqVSybt065s2bx/Pnz2nQoAHr1q3jypUrtG3bFgsLCzp16kSXLl0oWbJktOO/evWKAgUKcPjwYW4XWJVcL0uINEUv1ARr9048LrBNOl+ESMF0wvUp8aI7DwttIlwnRNvliFRigs1hHj16xJw5c9ixYwchIf/73jE1NaVkyZL069eP7t27qx4fPHgw69ato379+vz5558ULVo0yjGbNGnCkSNHWLFihdqGgvTuzZs35M2bN9nHTOkr7gJ69OjBiRMnmD17Nm/evGHQoEE8ePCAevXq4eHhwenTp+nRo4fa4AXA2tqaZ8+eERoamsyVCyGEEEKkfK5VK9K7d29u377NqlWr6Nu3L2FhYRw+fJhWrVpx4cIFJk6ciJubG4cPHyYoKAh9fX1sbW2jBS8AgYGBACxevFiClxiou/gtRHxF7ng5fvw4/fr1o0yZMly7do1jx45hbm7Os2fPOHz4MB4eHsybNy/G7728efNibGyMm5tbcpUvhBBCCJFqzHzQhOLFi+Ps7MzLly+pXbs29+7dw8PDg0mTJmFiYkL//v15/fo1GzZswNfXl7CwMHR0dHBycooSvESsL2NkZETevHnp37+/tl5WipbQdW4SK12FLw/9vnHmzBmWL1/O8OHD+f79O2/fvqVJkyZMnjyZ1q1b4+7uHusxrKysCAkJwcPDI3mKFkIIIYRIZf6rUgGFQkH//v1Zs2YNOjo6NG7cmAULFjBx4kQ2btyItbU1TZs2JUuWLOzdu5fTp08zePDgaMcqUKAAlSpV4vDhw2rOJIRICnPnzqVcuXL8/fffFClShK9fvzJp0iTGjx9P69at2bBhQ6zruejo6FC0aFGePXuWjFULIYQQQqQeEaFJnjx5OHXqFLa2tuTNm5exY8eyd+9eTE1NKVCgAL1792bp0qUsWrQIR0dHmjVrxsuXL6McA6Bz5868fv2ae/fuaeX1CPXSVfiy+e07ihYtip2dHeXKlSN37txYWloSGhrKgQMH+PDhA/b29mqnFDtz5gxPnz7F0tISgLdv3yZ3+UIIIYQQqUZM07EOHTqUgwcPqu7IWrJkCYcOHSI0NBRXV1e1+wwePJhTp04xevRowmOYWlYIoRnXrl3j/PnzjBkzhhkzZpAjRw6yZMnCnDlzmDp1Kt26daNfv36MHj062r6fPn1iz549KJVKLC0tefPmTbLWPsHmcJQOHpE85H0XQgghEiZyeBJZ5syZuX79OsuWLUNfX5+DBw+yd+9eXrx4gY+PD0+fPo22b+PGjcmfPz+tWrXi6dOnyVG+iIN0E74Eh4dz9rM3lpaWODo64uvry44dO9i4cSOXLl2iWbNmdOrUiW/fvqGrqxtl3127dlGnTh0GDBigmo7s06dP2ngZQgghhBCphroARkdHh6ZNm7Jy5Uqsra0ZP348y5cvZ+bMmVy9elXtcTp06MDixYu5evUqM2bMSOqyhUjXdu7cSYYMGVizZg1TpkzBycmJnTt3snfvXiZOnKiaCiNiSsAIX79+pW7durRu3Zrbt2+TKVOmZB0zRb74L0FA8pH3XQghhEicmAKYwoULM3DgQFatWsV///1Hp06dyJkzJ0+fPuW/PEujba+vr8/Zs2fJli0b/fr1w8vLK6lLF3Ggp+0CkouBjg4Vs2bm7NmztG7dmvXr15M5c2bV815eXsybN4+xY8dSrFgx1eNnz56lU6dOFC5cmDNnzrBq1Sp0dXWpVKlSlOPHdHenEEIIIUR6pu4zkqPLVRQKBS4uLvj5+WFhYYGBgYHa/RxdfgQyw4YN49u3b0yaNIny5ctTv379pC9eiHSoadOmLF68mLt37/Lvv/9Sp06dKM9PnTqVjBkzMnPmTNVjISEhNG3aFHd3d7Jly8aGDRs4fvw4AwYMAO4mec3qLvpPsDkc48UMoRnyvgshhBCaMfNBkxhvYujRowdVq1YlZ86cZM6cmVkPm8a4f8GCBdm7dy/29vZ06dKFo0ePoqOTbnovUqR09e5PKlKY+dZFGfPOI0rwAmBiYoKhoSF6elHzqH///RelUsmZM2cwMDBg9uzZNGzYkFy5cqm2keBFCCGEECLuIj47mZmZUaBAgRiDl59/P27cOOrVq0e/fv2Sp1Ah0rihVs7RpoyqVasWBw8e5Pbt29GCF4AsWbIQEhISZdFSb29vLl26xKhRo+jUqRMrV67Ex8eHgEqXk+V1qLvYLwFA0pP3XQghhNCcmP4P1dHRwcrKiixZsqgNXn7e39LSku3bt3PixAn27NmTJLWKuEtX4YuZgT7VzbKhUCiiBSampqa0b9+ejRs3EhYWpnq8d+/ehIWFceLECZo1a4ZSqaRbt26q5yV4EUIIIYSIv5g+Q6l7POIxHR0devfuzatXr/jw4UOS1idEeta0aVNy586t9rlevXrx5csX9u/fr3osZ86ctGjRgm3bttGxY8cfa77YZSFHwQzJVHHUCxYSACQfed+FEEIIzYnt/9K4/D8bsU3dunUpUKAA169f11htImHSVfjys58H971798bDw4OWLVty8+ZN4Mf8ek2aNGH58uUMHTqUatWq0bRpU7X7CyGEEEKIuPv5s1Rsn60initdujQAd+7cSbK6hBAxK1asGFWrVmX48OGsWLFCtfbLsGHDePz4MX5+fljXzEXVHoWSvbaZD5pIAKAF8r4LIYQQmpOYztLWmW7i9toC+DFuun37tiZLEwmQbtZ8iUnkQb4OMK1oYTY/ekTZsmVp0qQJkyZNwsfHh/DwcKpUqcL58+ej7SeEEEIIIRImPp+p/qtSAbvT59HX1+fevXvUq1cvCSsTQoD6wX65SXrkc67N0KFDmTVrFmPHjlVNy7zt5UQ6L3NI7jKFEEIIIdIMTdzUULhwYbZu3aqBakRipPvw5WcNcmanjlLJ8ylbmDFjBuXLlwfg9OnTqm0keBFCCCGE0I45c+agVCqpWrWqtksRIs2LaeCf1cKErON8eDjpIbNmzWLkyJGEhYVhVTUHRSplT+YqhRBCiKhOvrdOtnMZKHUpAZz9YEWwIuyX22tKXfPHyXYukTq0znRT9fv379+zevVqmjdvrsWKBEj4opaeQkGx1ct58OABu3btwtvbm1q1agESvAghhBBCaNPHjx8pUKAA5cqV03YpQqRpcbnjcnfoKJydDzNp0iR6z29Iubb5UCgUUbaZYHM4zscTAuR7RojUIDnDDaGeJv4OJMBJuwIDA/H396dt27baLiXdk/AlFjerV6a9y1XVnyV4EUIIIYTQrmrVqrFy5Uo8PT1jXBBcCJE48bnorVrYdWixaM9FXESP+L1cTBe/It8zQsSdBCAiseLyPSQBTeoQuesFIH/+/OTNm5cLFy5I94uWSfjyCxK4CCGEEEKkHNWrVwfgwoULtGvXTsvVCJH6JdXF7cgX0SM/JhfTRUzke0akNRKOiLQgpu9jCWVSNoVCQfXq1VVrlwvtkfBFCCGEEEKkGrly5SJbtmzcv39fwhchEmnpk24YZNJ2FUL8MPNBk2gBjAQvIrlIUCJE/MT2MyPBTMpQokQJdu/ere0y0j0JX4QQQgghRKpx+/Ztvnz5QrVq1bRdihBCCA2LHMBI8CLiQ8ITIVKOn38eJYxJWj9PORbh1KlTVK1aNZmrET+T8EUIIYQQQqQaR48eBSBjxoxarkQIIURSkNAlfZLwRIi0S93PtwQyScvf35/z58/Tpk0bQkJC0NfX13ZJ6ZaEL0IIIYQQItVo3bo1O3fupHLlyri5uVGkSBFtlySEEEKIn0iYIoSIjXTHJC0TExPGjh3LrFmzyJYtGytWrNB2SemWhC9CCCGEECLVsLS0JDQ0lJIlS5I3b15tlyOEiIGs3yFE2iJhihAiKcX0b4yEMvFXLO87AMzNzQFo2LChNstJ9yR8EUIIIUScFKxlw8szD+K0XVKLSx0ibRo5ciQeHh7cuHEDQ0NDbZcjhIiFrN8hROogwYoQIqWSKcviJyJ4uX//PqNHj2bo0KE0aSKfwbRJwhchhBAiDdJEABKqo8d7IH+14uiFh2rsuJqgjTok8NG+q94+rF27llWrVmFtLReKhEgNJHQRQvskXBFCpCXSJRPVbt8ytM50UxW8hISE0K1bN4oUKcLcuXO1XJ2Q8EUIIYRIJVJK8JFexfX9l5BG8x76feN5wHfWeryhTp069OvXT9slCSGEECmGhCtCCJG+u2SK5X3Hp0+fuHjxIocOHeLOnTtcuXIFIyMjbZeW7kn4IoQQQiQDCU7Sj/j+XUtYE7sjH72Y+vQFAMWLF2f9+vUoFAotVyWEiGyCzeE4d7jEZ1shxP9IwCKEEPGXHgKZCTaH8ff3p1y5crx8+RJTU1Nmz56No6OjtksTSPiiMf6hYZjq6Wq7DCGEEMlMQhWRWEn9PZSSwh11rzW2+gLDwlj08hX1spux3+M1xsbGSVmeECIBItZ0iUuo4mR9gICAAAlghIhB5IuEpuEGWAMXPhbBXydYe0UJIUQak5amLYv4HDZ37lzev3/P3bt3sbW1lZvVUhAJXzTA43sgrW/eAeB65fJarkYIIURCSZAi0qKEfF/HJbDR1M9LwVo2MZ4vMDwc39Aw+q5eI8GLEClQxIA/8p9jClXGlzhEx44d2blzJydOnGBCPQlgRPolXSwitXF/k0PbJaRYhuhAFnj1NjtBhMe4XQFLr+QrSiRIagtlIn8Oe/36NaVKlaJkyZJarEiokybDl1L7DmFmZpbo42wtXZIs+nrkMjSMdTunx09Vv8+wbiOFCxfGwMCA/6pUSHQNQgghkoYELULELLl/PiIHMI4uVwF4+/YtqyZNgus3+fTpU7LWI0R6MdTKOdHjJl9fX27evEmNGjUA9QHMBJvDbN26lZ07dwJw+vRpypYtKx0wIl2QoEVokoQgqZem/u4kxEl+KXHqssjBy6pVq7h48aJ0u6RQaTJ8iaBUKhP0jRcaGsrUqVOZefcBeQwN2FKqZIxTin0ICuJZQADtc5uz0/M9JUqUQE9Pj3379tHE5aoEMEIIkcQkRBEibShYy4bs0zao/nzv3j02btxIw4YNadiwoRYrEyLtS+i4ydXVlfbt2/P8+XO2bNlC586dAfUBzJEjRyhfvjw6OjrMmzePefPmUadOHU6ePCkBjEhTJGwRMZHgRGhCXL+PJKRJWj//W5+cYczPnccrV67Ex8eHxYsXJ1sNIu7SbPiyfv16hg8fjqOjIz169KBVq1aYmpr+cj8PDw86duzI1atXUSgUeIaE8jkkJMbw5eZXPwCW3b3HotBQ3Nzc6NWrF4cOHaJJkyY4SgAjhBAaISGLEGlb5ODl2bNnrFy5EoDffvuNfPnyaassIdK09+/f07BhQ16/fk3nzp3p0aMHJUqU+OV+4eHhLF68GCcnJwwMDAC4cuWKKnyBqAGMUqnk7Nmz9OjRgz/++AM3Nze2bdvG/Pnz8fHxkQ4YkWpJ0JL+SIAiUou4fK9KQKM5kf8/SMogJnLwEhISwqpVq3j9+jXm5uZRPoeJlEPr4cvKlSuZP38+np6e2NjYsHjxYqpWrQr8GAz06NGDO3fu0KJFC5YvX46Ojs4vjxlxJ1X79u35+PEj3bp1Y8CAARQtWhRLS0vVV7Zs2dDV1eX79+8EBATg5+fHmjVryJAhA7169WLt2rUsW7aMVoMHxxig3Pb1o0SJEmTPnh0Ac3NzHB0dcXNzU20jAYwQQvyahCtCpF/Zp21AqVRy48YN1q1bx8aNG8mdOzf//PMPrVu31nZ5QqQISTFuatasGSEhIbRq1YqNGzeyYMECChYsSL58+VRjJnNzc0xMTAgODiYgIIDv379z6dIlTp48Sa9evTh16hQFChRgzpw50Y4fEao8f/6cDx8+UK1aNQwNDbGzsyMkJIR58+bx7NkzHBwcJIARqYKELWmPhCkiPfvV97+EMwmTVEFMRPDi7e3N9u3bWbZsGU+ePKFPnz7MmDFDY+cRmqXV8OXvv/9m+PDhrFy5ksqVK7NmzRoaNmzIw4cPyZcvHxMnTsTR0ZE5c+Ywbtw4duzYQadOnX553KVLlzJv3jxGjx6NQqHA3d2d3bt38+LFC968ecO1a9fYs2cPvr6+hISEYGxsjImJCcbGxjRo0IBu3brRvHlzevbsyaBBg4CYA5TivXpzecOGKK36AQEBZMiQIcp2EsAIIURUErYIIeB/HS+jRo1i0aJF5MmThylTpjBixAhMTEy0XJ0QKUNSjZuyZs3KwYMHsbCw4M8//+Tw4cNcv36d169f4+HhweXLl/Hy8uLbt28YGBioxk1mZmYcOnSIFStW4Ovry+nTp8mYMaPac0ywOcznz58BVL/CjzETEGXc9PM0GoAEMkJrJGhJnSRMEUJzYvt5kmAmbiL+L0lsCBPxGenp06eULl2aoKAgmjRpws6dOyldunRiyxRJSKvhy8KFC+nVqxe9e/cGYPHixZw4cYJVq1Yxe/ZsfHx8qFu3LiVLlqRgwYJ8/fo1TsddunQpVatWZe/evXh4eBAYGEhQUBCZMmWiUKFC5MmTh+DgYPLly0f58uVxcHDAzMyM58+fc/bsWXr27Im9vT0rV6785dzHlStXZtasWTx//pyQkBC8vb3x8vKiUKFCiX5/hBAirZHARQgRk2/fvmFlZcXDhw/R1VU/3asQ6VVSjZvWrVvH06dPOXToEL6+vgQGBqKnp4eFhQXZsmXDysoKExMTSpcuTfny5bG2tub79++4uLiwefNm/v33X44fP07hwoVjPY+ZmRnFixfHxcWFVq1acfHiRdzd3QHIkiVLrPtKR4xIbhK6pHwSsAihfTH9HKbWUOZc7QXUOD06yY6vqRBGqVQSEBDA3r17admypSZKE0lMa+FLcHAwN27cwMnJKcrj9erV4/LlywA4OTnRuHFjOnfujKOjI3Pnzo3TsUeNGoWf34+1WExMTDAxMcHQ0BAjIyOMjIwwNDRET0+PvXv34uPjA/z40O/j44Ouri6VK1dm586dGBoa/vJcFStWRKFQ4OLiwsSJE3n79i0AZcuWjetbIYQQaZoELkKIuPDx8SEsLEyCFyF+kpTjpkqVKhEYGIiuri4ZM2aMMl6K+L2Pjw/Lly9HqVSqph8LDQ0ld+7crFmzhrp168bpXFWqVOHSpUusWbOGESNGAKBQKMicOfMv95UARiQHCV1SHglZhEh9UmMoc672AtWvSRnAQOJDmIjr2CL10Fr48unTJ8LCwsiVK1eUx3PlysX79+8BcHBw4O3bt3z69Alzc/M4H7tTp060a9eOYsWKqdZiUUepVOLu7s6tW7d4/vw5dnZ2VKpUSdUyHxISEmX78P9fTDKyDBkyULZsWbZt28aXL19YtmwZGTNmpGrVqnHaX4j0KFxfX/Wr/FykHfmrFVf7eGgy1yE0J1RHL8qvQiSFkJAQrly5wuHDh1m5cmW0z0/i1+Q9S9uScty0bt067O3tKViwIPr///lMHT8/P+7evcvNmzcxMTGhWrVqFClSBIVCwex76u+6HG39T5Q/V6lSha1bt3Lo0CFq1KjByJEjCQ8PR09PL07fw79b7WPB47Zxfm1CxNXZD1YApLRRib5SV/WrgTLt35jw6m30aze/vh1WCO0zRCfKr0I9zze5oj2WP88nLVQS1ZHqM6J8DjlZbTaNz09M8vOe9/xxk2rNXE/itH1ISAjh4eGMGTMGBwcHGjRoIGOAeNLW+6VQKpVKbZz43bt35MmTh8uXL1OxYkXV4zNnzmTLli08fhz/BNDX15fMmTOzfft2mSNcCCGEEEKkCwEBAXTs2JGvX7+SKVMmbZcjNEzGTUIIIYQQQiSOtsZMWruVNXv27Ojq6qru1orw8ePHaHd1xVeJEiUoUaJErNt8/fqVBw8ecO/ePQoVKhRru/zN+rVjPdYT/wD63XsIQO3atdm1a1eUKTPU7V/mxGnV7799+8axY8cYP348Hz9+BMDAwABHR0eqVq1KtWrVcHR0xEBNh8CvahMiJQo1NeVT7/5kX78aPX9/bZcjkklMnTEi5QrV0eNGqXqUvfMveuHSwySShk/7EZQpU4YdO3bQqFEjbZeTKkVexFykPUk5bqpZsyZmZmYxPh8WFsazZ8948OABb968oVevXpiamgLEuQslogNGqVTSokULzp07B8CZM2fiNVVzeHg4T58+Zfr06Rw6dEj1eL58+ahWrZrqK3fu3L88lnTQiAgRXS8pkUm4AT2+O7DR2JUAnWBtl6NR6rpchEitDNFhepYiTPJ5RhDh2i4nzUnK7pgj1WfE+FxydL9EFlsHTMRnqY4dO/L582dOnDiRXGWlKdoaM2ktfDEwMKBs2bKcPHkyygJBJ0+epHnz5ok6dpcuXfj3339VLffv37/HxcWFO3fuqL48PDxU21eqVCnGwf5/VSr8snHQWl+Pllkzs/3dew4fPoyzszP9+vVTu3+oUonnqLEcnTGDu3fvcvfuXV68eAFAy5YtWbp0KT4+Ppw9e5YzZ86wePFipkyZgrGxMRs3bqRdu3ZRzl3+zAX+q1Ihfm+QEFqm8/9Bok5ICDrBaWsgIWL2+tQdQNaASY30wkMlfBFJZs+ePYSHh1OnTp1Ypz0SMZP3LW1LynHT7t27GTx4MPAjaLl06RK3b9/m7t273Llzh/v37xMYGKjavlKlSlSsWPHH+itxnF1l3pOWTLA5DMCyZcsoVqwYYWFhdOnShadPn8a438OHDzlz5oxqzHTv3j0CAgLIlSsXzs7O1KlThwsXLnDmzBnOnj3LunXrAKhRowanTp2Kdf2ocSX3yRoyAoBgRZi2S4iR/v/XFqIIS9F1xteP9SDkArVIe4IIl/AlCTx5my3KnzW5dkxsn6GT+9/dEx8LA9HXgon4DPX582eOHj3K5MmT5bN/AmnrfdPqJO4jR46kS5cuODg4ULFiRdauXYuHhwf9+/dP1HH9/PyoWbMmZ86c4ebNm7Rv355v375hbm5OqVKlaNGiBUuXLlVtb22tfmG9+IQa/fJZcuOrL1n09cm2ZiX/bdmodrt3I3+ndatWZMyYkQoVKtCyZUvs7Oywt7enZMmSAFhaWmJra8uQIUMICwvj7t27/P7770yaNIk2bdqgoxN1pOPoclUCGCFEqvHyzANAQhgh0julUknrbce58NKTjh07qu6mF0JEl1TjpjFjxmBgYEDbtm1p164d//77L4aGhtjY2GBnZ0doaCi3b99WbR/TuCmuChcuzJIlS5g/f74q9IlJkyZNePnyJXZ2dpQuXZo2bdpgZ2dHhQoVVGt0Nm/eXBVAeXl5sX//fvr27cuhQ4do0aJFrMefYHNYAhghkllMC3ELIURc/fzvSGLCmBqnR3Ou9oLElqRRJ99bqwKYiOBlz549dO3aFYBWrVpprTaRMFoNX9q1a8fnz5+ZNm0anp6e2NracvToUfLnz5+o4+7atYsWLVrg4ODA+/fvadq0KatXr1Z1wgwbNgwDAwNWr17NnTt36NChQ6Jfi7GuLltKl/zlds2aNaNWrVrcunULJycnKlasiLGxcYzb6+rqYm9vz9ixY6lbty4PHz7E1tY2TjU5ulyNc/0S3gghkltECPMzCWWESD8efPCmZ8+erF27VtulCJGiJdW4qXv37vTr14/p06fj7+/PkSNHqFevHnp6ejx+/JiyZcvSoUMHHB0d8fPzI2vWrMD/LgbEJLZQY9CgQQwaNOiXtS1atIiWLVtSo0YNxowZQ548eVAoFDFunyNHDvr06cOKFSvYv3//L8MX+PXrgNhfS1L7uT4JizQv4gLXyfeJCxZF3BSw9JIARgihUYkNY9QFMDVOj050XYlx8r11lJqePXtGcHAwHh4ecZpeVaQsWg1fAAYOHMjAgQM1esyCBQty7tw5WrRoQdeuXZk5cyY6OjpcuXKFRYsWsWvXLpYuXUqPHj00et640NfXZ8+ePVStWpXatWujp6eHra0tNWvWZM6cOWrXdQEoU6YMAPfv349T+OLocpXv379z5coVQkP/N1WMvb09OXJE/7Aj3TNCiJRCQhkh0geFQoFNrqx4e3vHOj2QEOKHpBg3TZ8+nQwZMnDq1ClOnz6NlZUV3t7erFu3jsWLF5MvXz7WrVsX7840TXSVNG/enIULFzJixAiWLl2Kubk5Dg4OjB8/nooVK8a4X9myZblz5068zvXgwQPevn2r+rOZmRllypRBoVBorUNGXTAk3TpJp675YwlgkokEMEKIpBTx70t8QpjIAYy2gxd1SpUqRWhoKAEBAdouRSRAHGfqTX2KFCnC/fv3mT17NleuXKFChQpUqlSJ27dvs3r16l+2uSelLFmycPPmTW7cuMHy5cspU6YMy5YtY+bMmTHuky1bNiwsLLh3794vj1/67EXWrl1L0aJFqV27NvXr11d9WVpa0qlTJ1xcXFAqlVH2i0+njBBCJLeXZx5E+xJCpG62ucyiTGkkhEheCoWC+fPnc+vWLfLnz8+QIUOwtLRk0qRJNGjQgGPHjiV4SsC4dJX8yvDhw3n79i379++nV69evHz5klatWuHt7R3jPiVLluTBgweEhf16rvb79+/TsmVLbG1to4yZHBwcKFmyJMuXL+fr168aeS3xEdv5kruW9OTnefZF0tHkmg1CCKGO+5sc8Qp6a5wenSKDF4DSpUsDyLgpldJ650ty2LlzJ9euXWPFihX0798/2pop2qCvr0+ZMmUoU6YM/fr1I2/evMycORMrKyuyZ88OoGqr19PTo0qVKtja2nL//v0YjxmuVPJi8HA6lijBs2fP6NChA2PGjMHMzAyA4OBgDhw4wOrVq9m+fTt2dnaMGzeONm3aqO44lQ4YIURqElsAI50yQqR8/sEhfPz4UXVDyNu3b7G0tNRyVUKkT2/fvmXlypWULVuWQ4cOkStXrkQfUxOdGhYWFqq1XQYMGICNjQ19+/ald+/eqm0ixk1WVlbY2toSGBjIixcvKFq0qNpjvnjxgj/++IOtW7dSoEABNm/eTPXq1VXPP378mLVr1zJ8+HDGjh1Lnz59GDNmDZt8+iXqtcRFXMIV6YBJOjINWfKJCGCkC0YIkZQi/xuTmoLfyN04vr6+KBQKPn78CMCHDx/IkiULhoaG2ixRxJFC+XP7Qyrm6+tL5syZ+fTpkypwAPD29sbe3h4LCwvOnz+Pvr7+L4+VkAAiMZ0jwcHBVKlShf/++0/t88uWLcPd3Z19+/bx/PnzaM8HBgZSv359Lly4QJMmTZgxYwalSpVSe6zw8HBOnz7Nn3/+yYkTJ7CysqJ3797Y29tTsmTJOA20tBXQhCqVfA8LI6Pe/3LDn993CY9SvtAMGfg4YAg5Vy1D79s3bZcj0hkJZeIuVEePa/aNKH/rKHrhob/eQYh48A0Mpsj8rZSvUIFTp04xceJEli5diqurK/b29touL1X5/Pkz2bNn5+vXr2TKlEnb5YhUIKZx0/Tp05kyZQqnT5+mZs2aWqwwZlu2bKFbt27RuvgBDA0NefnyJRYWFuzdu5eWLVtG22bHjh1069YNMzMzJk+eTK9evWKc+vndu3esXr2aZcuWERAQQNeuXalVqxZ2dnZYWVnFaVwZWVwDk8SsqRMhwCeYGZVORFknRwKb+NNmCGMabkDfgPKsNbmGv06w1upILhLCiNTKEB3mZbHid58nBBGu7XLEL6SmAAbgXO0F9OvXj7Vr13LixAksLS2pUKECLVu2xNnZWdvlpSraGjNpvwUkiXh4eDB58mSuXbtG1qxZ2bFjB9evX2fKlCm/3De5gxcAAwMD/v77b0aOHKn2js979+5ha2vLixcv+KbmYvWQIUO4du0ap06d4tChQzEGLwA6OjrUrVuX48ePc/36dUqUKMGUKVOoW7cu5ubm5MqViw4dOrBnzx5CQkLUHkMbU5T5hIQw4N5DGl6/yaKXr/AOCVFbh0yfJoSIjbrpy2QKMyGSXyYjAxY3rcLdu3cpVqwYixcvxsTEJNZpWIUQmhUaGsrOnTuZNWsWAOPHj6dmzZp06tRJdXdlStOuXTs2bdpE7dq1oz0XFBSEkZERZmZmaqdrvnXrFj179qRNmzY8f/6cAQMGxBi8wI+um2nTpvHq1SumTZvGyZMn6dixI7a2tmTIkIEqVaowZcoUPDw84lR7XKcMiy0kiUuA8t8uD+ZWP0OFChU4efKkKqiSKcvir675Y9WXSFoFLL1S3UVRIUTqE9/pyLStxunRTJw4kcqVK9OoUSNq1aqFUqlk69atPHv2TNvliThIk+HLkCFDKFSoENOnT2fv3r0AVKxYkZkzZzJnzhxOnjwZ476JCV7GjRuHra0thQsXxsLCgmzZspE7d24cHBxwcnLi1KlTfP/+Xe0xpk+fTqFChVRrwGzZsoXXr1/z6tUr3NzcWLp0qeou0F69evHmzRvVvuvWrWP9+vWsXr1a7SAk1todHdm3bx9+fn64ubmxe/duBgwYwP3792ndujWWlpY4OTmp/YFO7pBj3/uP3PH7RrBSyY537/ntgRurV68mPDz6nQUSwAgh4ktCGSE0L0PZ2DtYOpa2wtXVFUtLS9U0rF++fEmm6oQQlStXpkOHDkyaNInw8HB0dXXZunUroaGhdOvWTe3n7MS6c+cODg4OFCtWjHz58pEzZ04yZsyItbU1HTp0YMOGDbx69Urtvq9fvyZPnjx069aNN2/eMHHiRG7dusWbN294/vw5np6eZM2alTJlyrBmzRp2796tCh6+fPnCb7/9RokSJVi/fj0mJiZxrjlTpkyMHTsWd3d3Pn/+zPnz5/nzzz/JlSsXCxcupECBAjRq1Ij9+/fHePNahMQEMHEJXoIDQjk66zFhYWFcv36devXq0axZM9X4UQKYhJMQJnlICCOESA6pKYTp8mQJ586dY9y4cWTNmpV+/foRHh7O169ftV2aiIM0Oe2YkZERc+bMYcKECUyePJnff/8d+DHdVqNGjbh16xZ3796NNr1WbMFLaHg4/3h+4GNwMN/DwgkIC8NAR4e/Hjwke/bsuLu7U7hwYZo1a4a1tTXGxsYYGRnx7ds3nj9/zqlTp/j48SOGhoaMGjUq2l2dFy5coGPHjnz9+pXZs2czcOBAtWvTbNq0ibFjx/Lt2zecnJyoVq0a9erVo1evXqxcuVID7+L/3Lt3j3Xr1rFlyxZ8fHyoXbs2ffr0oUWLFlHmFUyuab4Cw8I48vETe95/5FlAgOpxBwcHli5dSsWKFaPtI1OQpUwy7ZhIC9LD9GUy7ZhIDHXBy7cbt6I9ln3aBgAGDx7Mhg0bcHV1xcYm7f98aZJMOybiK2LcVLNmTSwsLPj333+jdLocP36chg0bMn/+fEaPHh2ni/4RF/WvXLnCwYMH8fPz49u3b/j7+9OxY0fVFGAdOnTAxcWF9u3bq8ZMxsbGPH36FFdXV1xdXVEqldjY2HDs2DHy5s2rOkdQUBDdu3dn586dNGzYkFWrVpE/f/5otbx8+ZIhQ4Zw5MgRqlevzsKFCxk/fjyurq7cuHFD7T4J9e3bN3bu3Mm6deu4fv06uXPnpkePHvTu3ZuCBQvGuF9STQE2weYw169fZ8WKFfz9998EBQUBYGJiwrhx4xg9ejRGRkYyBZmGJOW0ZOlt2rGYpJaLoyL9kmnH0oaUHvpGrP/y8OFDypYtS8+ePVmxYoWWq0pdtDVmSrPhy7t378iWLRsbNmygZ8+eqm0OHDhAixYtOHXqVLQukdgu1H8NCaX+9RuY6upiaWyEiY4OT/wDqFKvHkeOHGH8+PHMmTOH7t27kyNHDj59+oSHhwe6urpYWFhgbm6Onp4eHz9+ZP369bi5uVGkSJGo5/j6FScnJ1avXs3vv//O3LlzAQgJCYkyn/DXr1+ZMWMGS5YsISQkhAoVKnD+/PlYW+YT4/v37+zevZu1a9fi4uJCpkyZaNSoEc2bN6dhw4Zkzpw52j5JGXoolUq+TZ9N9+7def36NQB6enrs3buXpk2bJmstImEkfBFpUVoMYyR8EQkVW8dL5AAmInj5559/aNeuHcuXL2fQoEFJXl9aI+GLiK+IcZOTkxOBgYGcOHGChw8fqp4PDw/H2tqaQoUKcfz48XitVdK3b1/WrVuHjY0NmTNnxt/fn0ePHnHlyhXy5MmDpaUlxYsXp06dOoSFheHh4cGXL1/ImTMnFhYWmJqakiNHDmbOnEn79u1Zvnx5tPMcOnSIgQMH8u3bN9zd3cmcOTOhoaHo6OhEuYHt+PHjjBgxgsePH6Ojo8Px48epW7du4t/AGNy+fZt169axdetWfH19cXBwoHnz5jRv3hxbW9so669A0gYw8OPfhrFjx7JhwwbVcw0bNmT//v1qx44SyCSOpoMYCV+ikhBGpFQSvqQtKTWEOVd7AV++fKFWrVoEBwfj6uoary5eIWu+aNyLFy8AorVg7dixg6xZs1K1atV4HS+zvh41zbKhr6OgfJbMtLUwJ5ehIefPn+fbt2/Y2NhQpUoVbt26xb59+3j48CFZs2YlQ4YMPH78mO3btzNjxgwOHTpEeHg48+fPj36OzJlZtWoVDRs2xM3NDYDr16+TKVMmevTogZeXl2q7+fPn8+DBA0aPHs2ePXuSLHgBMDY2pkuXLly8eJGHDx8ycuRI3Nzc6NChAzly5KB+/fqsXLkyylRoSTntl0KhoFatWty+fVsVtoSGhtKsWTMOHDgQbXuZgkwIkRxkujIh4if7tA2Eh4fzxx9/0K5dO9q3b8/AgQO1XZYQ6Yq3tzc+Pj6EhITg7++vevzWrVs8ffqUFi1aAPGbKqt3794AWFlZ0adPH5o2bUpwcDCnTp1CT0+Phg0bYmRkxLFjxzh16hSBgYFYWlri4+PDqVOnWLp0Kb///jsKhYINGzbw4cOHaOdp2rQpmzdvxsfHBy8vL5RKJU2aNKFIkSIcPXpUtV2DBg24e/cuK1asYPPmzUkavACULl2aFStW8O7dO7Zu3UrBggWZO3cudnZ2FClShJEjR3L+/HlCQ3/c0JBUU4BFhChmZmasX7+eLVu2YGpqCsCxY8do2bIlgYGB0faTKckSR9aHSVoyHZkQIjlETEeWkgLfc7UXcOfOHRwdHXn9+jU7duyQ4CUVSbOdLy4uLsybN49du3Yxffp0xo8fj0Kh4MqVK1SvXp2BAweyePHiKPv/qkPCMzCIFa9ec93nKz6hoeQ00OfA+QtUqBC3zorbt28zc+ZM9uzZg76+Ph8+fCBLlizRtuvcuTNXr17l5s2b1KpVC29vb7y9vVEoFMydO5eePXuqnZIsub169YqDBw9y4MAB1SCiSpUqrF+/nmLFigFJ13USEagolUqWL1/O0KFDAdDV1WX58uX0798/2j7SAZNySOeLSG9Sa1eMdL6IhPrVWi9GzQfj4+NDly5dOHLkCNOmTWP8+PEp4vNNaiSdLyK+IsZNbdu2pV+/fjRr1oyiRYuyf/9+8ufPT1hYGPXr1+fu3bvcvHkTS0tLIO6dESb/1mbbtm3cvHkTpVLJoEGDWLJkCbq6ur/c19/fn9WrVzN//nw+fPjAtGnTmDRpUrTtbt++jb29PTt37iQwMJDu3bvj4OCAq6srrVq1YvHixaq6tSkoKIgzZ85w4MABDh48iKenJ2ZmZkycOJFhw4ahUCiSvAMG4MmTJ3To0IGbN28CP9ZEPXToEGZmZtH2kw4YzUpoR4x0vsQuJV0YFembdL6kfdoMfs/VXsD27dvp3bs3xYoVY+/evbFOaypiJp0vGvblyxd27NjBlClTmDhxIh06dCA8PJyKFSuyaNEilixZws6dO+N1zNxGhswoVoTj5cqwt0wp9jvYxzl4gR93Qu3atYv79+8zY8aMKCnlf1UqqMIBJycnXr16RZYsWbh16xbbtm3j8ePHNG3alD59+lC1alXu3bsXr9qTQv78+RkyZIhqPZtt27bh5eVF2bJl2bZtG5B0XSdBQUEMHTqU9u3bc/r0aVXYkj9/fgYOHMjnz5+j7SMdMEIIbZGOGCH+x6j5YAC6du2Ki4sLR44cYeLEiRK8CKEF3t7e1KpViytXrvD161dVeKGrq8uOHTswNDSkTZs2BAf/uPAb186IgHqncXV1xcvLi48fP7Js2bI4BS8Ai93bEdjgLC9fvmTdunUxdquUKlWKpk2b0qFDB7p3707btm25fv06O3bs4NKlSxQvXpxFixapuky0xdDQkIYNG7J69WrevHnDtWvXaN26NSNGjKBFixZ8+fIlSTtODhw4QKdOnRg3bhylS5cmX758ZM2alfv377No0SK1+0gHjGZJR0zSkE4YIURy0VY3zLnaCzh//jydOnWiVatWXLp0SYKXVCjNdr7s3LmT5s2bAzB8+HBWr16Nt7c3xsbGfPnyRXWHz/3796Ms6pqQ7ojEXtT/+ZyOLlfZvXs37969o06dOpQoUUL13Pnz5xkwYABPnjyhf//+lC9fHgsLC/LkyUOxYsWizSOc3L59+8bAgQPZsmULAwYMYOnSpejp6Wm868Ti7z1YWlpiY2NDlixZuHTpEkWLFsXU1JTbt29z6NAhmjRRf8eWdMBon3S+CJE6umGk80UkVGydL0bNB/Po0SNKlCiBs7MzXbt2TcbK0ibpfBHxFTFuqlq1KhcuXADg9evX5MuXj/nz5zN69GjVVMlOTk4MGjQoytor8VkDJj5+Pu6v9vf09GTdunXY29tTr149DA0NgR9TT0+cOJEVK1ZgZ2dH3759yZMnDxYWFlhZWaldszK5HTp0iG7dupExY0aOHDmCra1ttG1+9T7H5f3t0KEDBw8epFq1aly/fp2CBQty48YNSpcuTebMmTl37ly8zys0J7auGOl8iT/phhHaIJ0v6VNSh7/nai8Afky1+urVK+7cuaP1a76pnXS+aFjkOWxfvHhB4cKFuXr1Kh07dsTCwkL13G+//RZl24QEKYm5mK9u3/+qVKB169YMHTo0SvACUL16dW7fvs2MGTP4+++/6dq1K3Xq1KF48eLY2tqydu1aAgICElxPYmXIkAFnZ2fWrl3L2rVradOmDd+/f9d414mFhQVZs2alffv27Nu3D4AKFSpw//594MeAUgghUjLphBHp2eLFi8mdOzft27fXdilCpGtBQUGq3798+RL4sU7InDlzsLKywsnJCYAVK1awa9eueB8/Phfx1W37q/1z587N5MmTadq0qSp4gR9rZC5btozr169jYmLCsGHDaNGiBeXKlSN37tz069ePhw8fxv2FJIGmTZty+/ZtsmXLRtWqVbl48WK0bWILV+IabJUsWRI9PT2OHj1KkyZN0NfXx8jIiPv376sdM0nwkrykK0azpBtGCJFckqMbxs3NjcOHDzNy5EgJXlKxNBu+RB5I9O3bl8ePH1OrVi2uXbtGUFAQlStX5uTJkzx58oTnz59H2Te5ApjY9ontOQMDA5ycnPDy8iIgIIDnz59z9OhRihUrRv/+/cmbNy/jxo3jzZs38a5JExQKBX369OHAgQOcOHGCdu3aAQnvELrt64fH9/8FZIFh4YwfPx5fX198fHz48uULALVr18bQ0JBx48apvZjz4cMH0lCjlxAijZAQRqRF327civX5u3fvYmtri4GBQTJVJIRQJyQkRPV7BwcHHB0d6dmzJ1OnTuX79+8AXLp0idKlS3PkyJEEnSMuF/Nj2yYxYYCDgwOXL18mKCiIDx8+cPPmTSZMmMChQ4ewsbGhfv36HD16lPBw7dypnC9fPs6fP4+9vT1169blzp070bZRF7Koe+zDhw8cPXo0ynjn/PnzbNq0iZCQEL5//86XL1/ImDEjrVq1okiRItECtZkPmhAaHMb3ryE/H14kAwliNEdCGCFEckqqICbiBnN7+9jX0xQpm562C0gqkcOXJk2acOrUKXR1dbl58yYjR45k2bJl+Pv7A6idf9jR5Wq8AxVNT2elbjqynxkbG1OoUCEKFSpEw4YNefHiBcuXL2fFihXMnz+fNm3aMHz4cMqXL6/R2uKicePGLF68mP79++Pt7U3WrFnj/b66+nxl4IPHWJuasrm0Lb6hofS995A3N+8wdepUxowZw6BBg8iWLRstWrSgY8eO6OvrRzvOnj17aN26NaML5adtbnNNvkwhhNCIiAAmNUxHJkRiDRo0iC5duuDq6oqDg4O2yxEi3QoMDIwSbhw/fpyjR49SuXJlypUrR5cuXahUqRKGhoZxXrNFHW12U0Q5twHQAtzHuLNr1y4WLVpE48aNsbKyYtiwYXTt2pUMGTIka32ZMmXi2LFj5MmThz179lCqVKlo20ywOax6HTF1vFSqVIkXL15w6dIlKlWqxMKFCxk1ahSVKlVi//79vHz5kqNHj7Jw4UIGDRqEjo5OlLW2Zj5oQlhIOPNqnwGlEqfzddHRlbtstaWu+WP0Qk3APfnH8WlJ5ABGpiQTQiSHiH9rNBEAN2nShLx58zJnzhx27NiR6OMJ7UiTnS8GBgZRwheAmjVrUq1aNf7++29y5sxJSEiIat7iyAvfp3SrV6/m2rVrMT5fqFAhFi5cyJs3b1i4cCH//fcfFSpUoGHDhoSFhSVjpT/Ur18fpVLJ2rVrVX8nji5Xo3098fdn1rMXbH7zjotfvPH4Hsijb/7sev8BgJffv7Pz3XtGPHTjU3AIG0sUo2bNmqxbt46//vqLyZMnkzlzZrXBC/yY9xlgwYtXPPSTNUaEECmXdMGI9KBDhw4UK1aMKVOmaLsUIdK1TwHvo/x5lWdXOnfuzM2bN/n06RPFixdny5YtPH78GFNTUy1VGX9ubm7MnTs3xo6W+U9/o1OnTvz333+4uLhgZ2fHkCFDyJs3r9o1UJKaoaEhNWvWZP/+/bi7u6vdZoLNYcaXOMTEiRMZO3Yszs7O/Pfffzx79oxdu3bx4sULANasWcOYMWMYPXo0o0ePZseOHbi5uTFgwAAKFCjAgAED0NPTixa8ACh0FQR4BxPgE8KbFZaMLbafCTaHVV9CO6rlfCYdMRog3TBCiOSkiW4YQ0NDJk6cyN9//63qghGpj0KZhuZhilg40sjIiDVr1qhdwPXx48fUr18fDw8PsmfPzowZM+jXr5/a46WkhdkdXa5y6tQp6tati5mZGbdv38bS0vKX+4WFhbFr1y46dOjA6tWrY3ytSem3335j3759ZMuWjQ4dOtC1a1ccHR2jzFfYp08fNm3YgIFCQcBPg6SKWTJzxecrhjoK8hoZMbpQAc58/sI/nh9QKBRUr16dEydOxDp1yRHHMjRx/TEFiqGOgg0lbbDKkHoGkGlNaIYMfBwwhJyrlqH3TcIwIWKi7S6YUB09rtk3ovyto+iFh2q1FpE6ZSgbvUXeqPlgAHbs2EHHjh25ceMGZcqUSe7S0hRtLR4pUq+IcVPe4mb02xV9zONkfYARI0awbNky4Mfn+WXLlkVZOzM5u1nic+E/LCyMcuXKcfPmTWbMmMGECRNirDXycV+9ekWHDh3w9vbm7t27Md7UlVROnDhBq1at8Pf3p0aNGnTt2pXWrVuTMWNG1TZPnjyhWLFiZM2aFW9v7yj758qVC3Nzc+7cuYOFhQXNmzdn6NCh2NvbExgYSNasWdm2bRsNGzaMst/P780/v9/i7tF36Orq0rNnT9auXRvr9iLp6IWaYO3eiccFthGqF31d15PvrbVQVdoi3TBCEwzRYV4WK373eUIQ2pnGUqQO8QmAz9VeAEBwcDDFihWjYsWKbN++PalKSxe0NWZKk50vZ86coUuXLmqfs7a25urVqyxfvpxnz55pJYyIL0eXq4SHhzNy5EgqVKjA58+fOXr0aJz21dXVpX379nTt2pWJEyeqOkCS0969e3nw4AG9e/dm3759lC9fHhsbG+bMmcO7d+8ACA0NJVSppFgGU8YXLsiSEsXYZGfDqfJlWWJjzZnyZTlfwZHxRQqxy/MD/3h+YGB+S86Xd+Ds2bOxBi//ValATkMDipgYUyVrFvIbG+Pk9pRvoXIhUQiRssl6MCIte/r0KTo6Osl+gVMI8T8Fp7RV+/icx81ZsmQJ27dv59KlS+zZsydK8ALxC0SS09atW7l58yYVK1Zk06ZNQMy1Rg4S8ufPz8qVK3Fzc2PVqlXJUWoU9evX5/379zg7O6Ojo0OvXr0wNzenS5cunDp1CqVSib+/P/r6+igUCpycnDhy5AgnT57kxYsXvH37luvXrxMQEMCDBw8oWLAgxYsXx87Ojrdv3/L58+dfBi8Ao9rNBmDcuHGsW7cOZ2fnKM+n1L/39EjWiEk86YYRQiSnhHTDfPjwga9fv2JkZJSElYmklCY7Xz59+oSZmVm05yM6WeKy8HtK6XqJqPXff/+lfv36nD9/nho1arBq1aoowVFM9Ubs/+7dO4oWLcrAgQOZP39+0hceg7CwME6dOoWzszP79u3D2NiY58+fkzlzZg4ePMj06dO5efMm2fT1qZfdjGrZsmCsq8v7oCB2vnvPHb9v5DUypIdlHprkit9dKvvff2TW85eMKpifNR5vKJUpIzOsimCql/D5q0XCSOeLEImTXB0x0vkiEktd5wuAp11jihQpQu3atTly5IgEMIkknS8iviLGTTX3DyM8g160i7cRd9RH3HUZmxqnRyf5xd/4XPC3s7OjUKFCFC1alP379/P06VPVc3HpgOnbty+7du3i+fPnZMuWLeFFJ5KHhwdbtmzB2dmZp0+fMn/+fEaPHo2Hhwfz5s1j/fr1hISEUKdOHTp16oS1tTWhoaHs2rWL9evXExwcTKdOnZg1axbm5uZx7lgZknc7tra2FClShHz58vHPP/9w7NgxqlevHmU76YBJer/qfImJdMQkjnTDiPhS1/li6BHzDcLaFpQvWNsliEhiC4DP1V5Au3bt2LVrF9evX5e1MhNJOl+SWORw4lfBSkoLXuDHYpgA//zzDwClS5eO0zEiXouFhQUDBw7E2dkZbeZturq61K9fn+3bt/P8+XO+f//OqlWr0NHRoUWLFri6urLJzoZ62c04+ekzAx88psfdB4xzewYKBfOti7KrTKl4By8AzXPloHLWLGx8845RhfJz86svXe/c48k3f8KUSsL+/335eT0aIYRIaaQbRqQW327cUvu42Y0D9OjRg1OnTlGqVClOnjyZzJUJISKbYHOYk++tVV8RapweHet+Ec9H3lfdcX51bk0KDAzEwMCAjRs3RpnSMLawIPJzU6dO5evXrxw6dEijdcVXvnz5mDBhAm5ubvTs2ZMFCxYQGBhIvnz5WL58Oe/evWPVqlUEBgbSrVs3ypcvT+XKlXF2dmbYsGG8evWKv/76K17BC8Cy1x3ZuHEjZ8+excrKivLly1OrVi0mT56MUqkkJCQEkA6YlEw6YhJHumGEOoYeBjF/vflxE5HhG33VYylZrK8lHl9CM2LrhKlxejTdu3cnT548VK5cmTFjxvBNbmBOddJF+KIuTIkpYEkpwcvPChYsCMCKFSuYPHky5cuXj/O+Ea+pbt26eHl54ebmliQ1xtW4ceOoU6cONWrUIDg4mK1bt6qeUygUlMiYgZGF8nPI0Z6d9iXZXroke8uUYl3JElQ3y4ZOpLVi4kOhUDChSEGUKNn8xpO51kX5GBzCxjfvWObuQZXL1/k88Y9o+0kAI4RIiSSAEamZgb4ey5uW5saNG+TKlYt69eoxa9Ysrd4gIkR6FdHdElOXS0wBzK+CGVAfykQWcQFfkxfy8+bNy65duzAxMWHlypVA3Lo0IrbJnTs3tra2XLx4UWM1JYSLiwu//fYbjo6O7N27lw8fPnDmzBnV89myZaNv376cP3+ed+/ecffuXe7cucPr16+ZMWMG5ubmQMI6VK6aL2LEiBFMmjSJ3377jbZt2zJ9+nQuXbqEgYEBXbp0wd/fXwKYVCByECNhTPxEhDASxKQfEjTEjwQ2mhVTCDPX4DRubm5MnDiRlStXUqVKFTw8PLRQoUioNB++xBam/PxcSg1e4H/hi7GxMZMmTYr3/v9VqUDFihXR1dXlwoULmi4vzpRKJatXr+bly5c0a9aMRYsWsWXLFrXb6ikUFDIxoYipCZbGmpnbMLuBAWttSxAYHs7Qh24Eh4fTyjwn74OCCQMaNmzIyJEjCQoKirKfBDBCiJRIAhiRGsTU/QJQ3OMSZ86c4Y8//mDChAl069Yt2v/BQoiks6tK1HFFXAOYuAQvMYkIYX6+cK+pC/m5c+cG4O+//8bMzCxe4UPEttWqVdPqmAng/Pnz7Nu3D1tbW0aNGsXWrVupXbu22m1z585NyZIlsbOzw9TUVPV4YqYGy9rDjSFDhjB06FB27txJkyZNyJw5M/BjXR17e3vu3LkjAUwqI0FMwkgIk/ZIMKAdEtL8mroQptGVyUyaNIlr167h4+NDuXLluHbtmpYqFPGlp+0CksKdlk3JGB6u7TIS7b8qFVQX/TNkyIBSqSQwMBBdXd1o28VFxowZsbe35+LFi/Tt21fj9caFQqGgefPmXLlyhfnz56NIYBdLYuQ3MWaDXQlOffpCmcwZsTI1pdPlazRo0IArV66waNEi/v33X06fPk2uXLnw8/Pj/fv3OLpcTdEBnRAifYoIYJJrHRghNC3o4AqmTJlCsWLF6N69O2/evOH06dNa+YwghPgRwCQmXInrOdSZYHNYde64XByOHC5EhABbt25l/fr1GBkZJTh8qFq1KitWrOD9+/eqDpLk1qJFCyZOnEirVq1o2rQpkLzrrOjoKMjR5xm7q+4mKCiIli1bYmxszF9//UXPnj15+vQppUuXZuPGjUzofpiZD5rg9eIbmc2NMDBJk5cZ0pzIP2OyTkzcRA5gZG2Y1CO9X8xPjeLyd5bW166J/G9MAUsvapwezbnaC7h+/TotW7akevXqnDx5kqpVq2qxShEXCmUamt8hYuHI01Urxil8SUg3gzYuvsdWZ1zrMVq1jgULFrBjxw6qVKkSpWVdk0JDQ9HR0UFHJ+amqqNHj9K4cWNmz57N77//rnbb5HyfI95fX19fhg4dirOzMwAjR45k/vz5WFtb8/TpUxo1akTHtx5YZTCN7XAiDkIzZODjgCHkXLUMPZmvUgiN0XQAE6qjxzX7RpS/dRS98FCNHlukLxnK2v9yG6Pmgzl27BiNGjXi33//pW7duslQWdqgrcUjReoVMW6quX8YZ5ov1nY5CaIuiIjchZGQoCL4exjZrzRiyZIlPH/+HFdXV8qWLZuoOmMSEhKCvr5+rNvY2NhgaGjIvn37yJ8/P6Cdhe5/7m7ZvXs3AwcOxMvrx4Vof39/Oswpz8Hp99E30qH+yOLsmXITA4OoF8+0UXtqphdqgrV7Jx4X2EaoXkCynVeCmPiTICblSc7AxVBHwXTbgky6/5Kg8DRziTXVS6vhjHu3sQQGBlKpUiWyZ8/Ov//+q+2SUg1tjZnS/LRjMUnoNFLamH4qsevTmG3ejoODA+fOnWPWrFns3btXk+URFBTE4cOH6dq1K2ZmZlhYWDBo0CDOnz9PWFhYtO0bNGjA77//zrhx42jcuDEfP36Mto023udMmTKxadMmHj58yNatW5k8eTI6OjrUqFED+BEadb5zn+lPX+AVlDb/ERdCpG4vzzyQqchEihTb1GMRAg8sp0GDBpQqVYqlS5cmQ1VCCEjcFGLaUuP0aLVryES8loRe5H+6MCsjRozA0dGRa9euaTx4efr0KTNnzqRUqVIYGRlRp04d1q5dqwoxfubs7MyXL18oXbq0agynjWm+fn4/W7dujYeHBzt27MDV1RUTExMsSvyYkiwkMJzDsx5ga2vL/v37o6zlJVOUpQ4yNVn8yfowKYdMWyUipNWpzQo4z8XIyIiRI0dy8uRJHj16pO2SxC+ky/DF0eUqwcHBvHjxIsH7J7eErk/j6HKVP/74g2zZsvHo0SNGjx5NlixZ8Pb2xs3NjfBETM8WGBjIggULsLCwoGnTpri6ujJixAg6d+7MoUOHqFGjBpaWlgwePJjHj//3wU1HR4e5c+dy/Phxbty4QalSpXBxcVFbuzYUL16cTp06qeY0XrNmDVOnTlU9f+ijF61u3uHIR/lgJYRImSSAEalV0MEVDBkyhCNHjsg8xkIko9gCGA8PD/z9/ZOxmtj9XGtECBMRxCQ0TGocMoMdO3awatUqduzYQbly5QgPD+fBgwf4+vomquZr165Rs2ZNrKysmD17NjY2NqopmAcMGEDu3LmpW7cumzZtijI+c3Bw4Pbt29SuXZtWrVoxaNAgwsLCUkQAY2RkRPv27VUBlWXJLAzZX41ChQoBP4Kmli1b0rRpU758+aLaTwKY1CVyECNhTNxICKMdqf2Cukh+qTmYKeA8lzZt2pAzZ07++OMP0tCkVmlSmgxfTn/6zIynL3B+845voVGnScmxdSfjx48nb968FC1alICA+LXvKpVK/P398Ro/mb9ev+XRN/9k+yb/r0oF1VdcOLpc5cGDB2zdupURI0bw/ft3PD09uXXrFsWLF8fa2prMmTNTrVo1RowYwe3bt+Nci6urK1ZWVjg5OdG2bVvu37/Pw4cP+eOPP1iwYAHu7u5cuXKFDh06sG/fPurWrcu3n6aXql+/Pnfu3KFIkSK0atUKHx8fta8hqf3q/VQoFEyePJkNdiUoaGwEQGB4OFOfvuD4x08AvAgI4PBHL259TdzATAghhEir4tL9AtA6sy8VKlSgSZMmPH36NImrEiJ98/jnP9z+PIHvY88ojwcHB/P3339Ts2ZN8ufPz6JFi+J97JCQEF68eMG8efPYsWMHfn5+0bapcXq06isu4rrdz4HMr0ywOcykSZMoUKAALVq04MOHD7x+/ZqhQ4dia2tLlixZKFasGB07dmTVqlVqu/tj0rVrVypUqMCnT5/4+++/8fLyYvv27ao7Vj09PVmxYgVKpZIePXqwadOmKPtnyZKFXbt2sXLlSlatWsWqVatUNSe32DqKJtgcZmnz89y9e5fhw4er1u06cuQIDRo0IDg4mMDAQP7++2/y32pPkL9MZ5oaSQgTd9INkzxSy8VykbqkhlDG0NCQxYsX888//zBp0iRtlyNikSbXfDEyMsJCR8Hr74EY6uhgncGUTHp6vA8K4pF/ABkzZsTU1BRDQ0NevHgR44KuoaGhuLq6cubMGU6fPs3jx4/x9PSMFrbMty5KdbNsyfES48XR5SrDhw9nyZIl0Z4rVKgQS5cu5f79+9y4cYPLly/j7e3N3r17qV+//i+PvXr1agYMGMD9+/exsYl9jYGXL19SokQJRowYwaxZs6I9//btW6ytrenatSsrVqxQe4zkWAMmLmvrBIeHs8vzAxvfvMP3/4O965XLM+qhGxe9fdDX1+fly5fkyZMn2r7if2TNFyGSR2LXgJE1X4SmxWXtF4DPvv7UmbUJfX19bt++HetackLWfBHxFzFuMjc3x8jICHd3dwoXLkzVqlV5+/Ytt2/fxsvLi+LFi/Po0SN27dpF69atYzyep6cnp0+f5vTp07i6uvLkyROCg6NO01ulShUuXryo+rO6IOVc7QUxnkNT06Opu3D89f135tdRvybmnDlzyJUrFzdu3ODmzZtcvXqV1q1bs2XLlmhrmvxMqVSSMWNG2rZty7p169DV1Y11+y5dunDixAnc3NzImjVrtOf79evHzp07efz4Mblz5wZiDkTiEs4kdHq2uBzb1dWV33//nbNnzwLw999/Y25uTvXq1QEYO3YsGbvcT9D50wttrfkSH7I+TPyk9LVh3LuNpYDzXG2XEWcp5WK4rPkitLG+jHu3sQAsWLCAMWPGcPz48Thdz03PtDVmSpPhi5O1FdYG+oQDx70+cc/vG4++/a9VPm/evLx9+5ZevXqxdu3aGI/XvHlzDh48SMaMGalevTp2dnZYWlpy8eJFTp06hZeXF9WyZeGPooXJoKeXDK8wfhxdruLh4cG5c+cwNDTEwMBA9VW2bFmyZftfYBQQEECbNm04efIkW7ZsoV27drEe++LFi1SrVo179+5ha2v7y1qmTp3KzJkzuX//PlZWVtGeX7x4MSNHjuTatWs4OjqqPYa2Ahh15/ULDWXJSw+8goNZYmPNP57vWfDiFQBOTk7Mnj37l8dIzyR8ESJ5SPgiUqK4BjAuD55Rd8ISDhw4QLNmzZK4qtRNwhcRXxHjpoMHD1K4cGEuX77MiRMnOHDgACEhIQCYmZkREhKCv78/Hz58wMzMTO2xzp49S926dQkLC6NkyZJUqFABOzs7vnz5wt9//82LFy8wMTFh27ZtNGjQAIg9SFEXwCTlujR1zR8THqbE7fwHGmcfFWXMlCtXLkqXLh1l+3379tG+fXtq1KjBnj17yJAhQ6zHL1++PNbW1jg7O/+yFk9PT6ysrOjRo4fata++fPmCtbU1derUYfv27arHfw5R4tMVk5QBjFKp5MSJE4wcOZIlS5ZQtWpVcuXKha+vL1myZMHDw4OlHh0SdP70IDWEL5FJEBN3KTGEibiQC6SKACalBC8g4YuIWVKGMhE/s0qlkooVK2JgYMCFCxeS7HxpgYQvGhC58yUwMDDa8yY6OthXrEjp0qUpXbo0LVu2jDaICAgI4J9//mHNmjVcvXqVNWvW0KNHD758+cLmzZtZtWoVL1++pGzZsjTz9aFBDrMYO2e0Lb5TdoWEhNCzZ0+2bdvGvHnzqF69OiYmJpiYmJA1a1ayZMkCQFhYGNu3b6dr167s3LlTbVDzc9hge/IsJUqUwMLCghMnTkQbpISGhuLg4ICOjg4jRozA39+f3377jZw5c8Z63JQi354D5MmTh7CwMLJkycLr16+jvcaUWrs2SPgiRPKQ8EWkRHENXwBqj1tEWKYcXLlyJcV+3koJJHwR8fWrcVPRokWxt7endOnSVK9enUqVKkXb5tatW6xZs4Zt27ZRokQJDh06hJmZGadPn2bVqlUcPHiQDBky0L17d65XCsQwe8bkeGlJJqJjZoLNYc6ePUvz5s0pUaIECxYsIEOGDJiammJiYoK5ubmqw8Xd3V3VMeTq6hqnoMPoeE1+//131XRdP3N2dqZ79+4sWLCATJkyUaBAAerWras6dkKmI0vKAOZnPXv2ZOPGjcCPG/CGDRuW4POndaktfIkgIUz8pIQgJnLwEiElBzApKXgBCV9E/GgqkIn8c3vw4EGaN2/OxYsXqVKlikaOnxZJ+KIBEYOIXeXLEhgYxOeQED4Hh2Csq0MxU1PyGBmio1CoDSXCwsIYN24ca9eu5evXr9SrV4/OnTsTHBzMzp07OXPmDHp6erRr145BgwZRrlw5XKtW1MKrjJ/4BjDh4eGqO5N+VqZMGapWrcqhQ4d48eIFlSpVYvPmzRQuXFi1TUwBg6PLVa5cuUL9+vWxs7Pj6NGj0b7Rr169Su3atVXr8ER8GP9ZSggxfEJCmPnsJfmMjWi7ei3VqlVj+fLlTJgwAYD+/fur5mOOLCXUnhJI+CJE8pDwRaRE8Qlfjrs+oOWM1Zw5c4aaNWsmYVWpm4QvIr4ixk3Xr1/n+/fvvH//Hj8/P4oXL07JkiXJmPF/QUlE10lER8qFCxcYNWoUrq6u5MmTh169elGmTBlOnTrFrl27+PDhA7a2tgwePJiteR6ga5yyLpIlVOTpyibYHObGjRs0bNgQL6+o6zlky5aNhg0bEhgYyL59+8icOTNTpkyJc8gQFhKO62Q9Tpw4wa5du6J1/imVSho1asTx48cBsLS05PXr14l6bYkJP34VwCxevBhXV1eqVKlCjRo1MDExoVy5cnz48AFTU1Pu3btHwYIFEx3ARK4jrYQ5qTV8iSAhTNxpO4BRF75AygxgUlrwAhK+iMRJTBgT8bMbHh6OnZ0d+fPn58iRI5oqLc3R1pgpTU6gfcfvG1amJlTKmoWmuXJQJ7sZeY2N0Pn/OybVXQCfP38+CxYsoH///jx//pzt27czc+ZM+vbtS3h4OKtXr+bdu3ds3ryZ8uXLp5q7L+N7sV9HR4fFixfj7u7O7du3uXTpEidPnsTZ2ZlixYqxe/duKlasyPXr17l06VKcgpcIFStW5NSpUzx48IDatWvj6Rl1Yc8KFSrg5eWFt7c3AOfOnePQoUN8/PgxynbxDZSSws2vfpz/4s3Rj59o2rQpRYoUoXDhwnTr1g34sSaOuvBFCCGESO++3bgV523rly1BqVKl1K4ZJ4RIvIwZM1KtWjXatm1Lr169qFSpktrgJYKnpye//fYbOjo6HDhwAHd3d0xNTWnRogX79u2jU6dOXLt2jbt377KjyNM0E7zAjwvJEV81To+mbNmyPH/+nPv373P9+nXV2GXgwIHcv3+fJ0+esGLFCl6/fq26oSwunSK6+jqUnRZKs2bNaNWqFVu2bInyvEKh4MiRI/j4+DBw4ED8/f1Zs2YNd+7cISwsLN6vK7FBxa/2X7p0KSdOnGDw4MEUL16c8ePHs3XrVgD8/f1p2LAhAQEBCeqiifDzvok5ltAcdesrCfUKWHpRwNLr1xsm1fljCFliCmWEEJpj6GEQ5Ss+In52dXR0cHJy4ujRo9y+fTsJqhSJkSY7X4yMjKhiasIUq8IYxrJAa8RF/Bs3blChQgXGjBnDrFmzCAwMpG7dujx+/JiLFy9iba3+jo3U1MWQ1IHFr96LyOe/ffs2jRs3RqFQsH//fhwcHKJsGx4eTo8ePfj33395//498KMrpnz58vE6Z1I6/MGLac9e0DZ3Lor27M369ev58OEDZ86cYerUqZw/f56RI0fy559/RtkvNX3PJCXpfBEieUjni0ip4tP9ciAwB+3bt+f69esxrguX3knni4iviHFT7ty52b17t9ppxX4OXs7UnEejRo24c+cO9+7dI3v27OzcuZMOHTowbtw4ZsyYgc7/j72Sco2WlETd+jRxEacOmNBwXi3NzV9//cWYMWOYPXu2ajqzCIcPH2bKlCncvHkTgHbt2rFz506N1hFX6gIPpVJJzpw5yZ8/P82aNeP+/fvs2rULBwcHnJycVFOy+fn5qaZsjm9NsQUtqb0DJrV3vkQmXTDxo61OmJTeAZMSu15AOl9E0olrV4x7t7GEhoZStGhRypcvH6/PAumJTDumARGDiGk2xZn56DH1smdnUtFCv9xvwP1H+IWGstHOhpInTtO7d28OHjzI2bNnqVAh5ovlaf1CelwDm7i8Dz8fy9PTk5YtW3Lnzh2WLFlC/fr1yZs3r2rABuDl5aVa82XIkCEMGjSIYsWKxfvcSSEkPJzVHm849ekLnkFBqsdfvXpFhgwZGDlyJMOHD4+2QGda/56JKwlfhEgeEr6IlCo+4Yt+kwFYW1tTsmRJ9u7dm4RVpV4Svoj4ihg31ahRgytXrvD8+XPy5Mmjel5dePJ7UC0aN27MsWPHqF+/PidOnKB58+a0a9cOZ2dn1cwA6SV4iZCUAYxSqSTDqbqMGjWKBg0aMGPGDKysrDA1NY2yXceOHdmxYwdZs2Zl7dq1NGvWDAOD2C9SJlcw4X7zCyf+fMT7J36EfP/RmVOuXDmuXr3Khg0b8PT0ZOLEiVFmlohrbXHpcEnNAUxaCl9AApj4kgAmOglfRHr2qyDGvdtYVq1axeDBg3n8+DFFixZNpspSD22NmfSS7UzJaMELd/QUCgLDf912HapUcsfXDxNdXfrff8TDTJlQ6uiwe/fudB28wI/X+KsAJq7vw8/Hyp07N+fOnaNfv37069cPACMjI4oWLUrRokUpVKgQBQsWZMmSJTx//pytW7eybNkyqlevzo4dO8idOzfwI9TRxt+Fvo4OQwrkY0iBfHwPCyP7BmdMTEzImzcvjRo1ws/Pj3v37lGsWDGMjY1V74EQQiSnl2ceAIkPYYTQJl1dXcaOHUufPn149OgRxYsX13ZJQqQZnp6eBAUFER4ernospvDk/PnzAGzcuJGuXbvi5eVFrVq1WL9+fZIHL7EFHKk17Dn53vqX0zIpFAr8657i2LFjtGvXjjJlygA/1nmxsrKicOHCFCxYkDp16lCiRAmOHTtGmzZtyJkzJ7NmzaJXr17J8VJiVaBMNvptq0x4uJKvnt/pk38VFhYWHDt2jClTptChQweePn2KlZVVvI4rU4ulPnXNH0sAEw8R05AldwhTwHmuTDcmRAoUOXyMKYjp0aMHU6dOZd68eaxbty65ShO/kCbXfGlvkZvmuXLS3TLPL7fVUyiom90MfYWCXAYGjClcgD32JWnZsmWM+6Sni+iRX2tYWBg+Pj5qn4vvseBH2OLs7Mzz5885evQoc+bMoUqVKvj6+rJv3z6GDRvGsGHD+PDhAwsXLqRMmTKcP38+2how2masq4t/354UKFAAhUKBvb09ly5domvXrmTIkIF169alq+8ZIYQQQtO6dOmCpaUl06ZN03YpQqQptWrVYtmyZeTNmxeIOcg4V3sBjRo1wtzcHA8PD/r06cOpU6c4fvy4qsNCG8FLXJ7Xhi9fvsT6fMR7FXkdmdguSv+XZykeHh5cuXIFZ2dnunXrRrZs2bhx4wbz58+nV69e/PnnnwwcOJAhQ4bg7e3N2bNnY60hucMLHR0FWfOYsDt0FBkzZsTW1hYfHx/+/PNPihUrRsOGDXF3d9foOVNz14sQEbSxFszPXS4poetFCPE/Ma0PY2RkxKhRo3B2dubFixdaqEyokyanHTtdtSIZI929lVDquj7S60V0m3/P0KxZM+7evcvjx4953qxRgo8V1+nMwsLC2L17N506dSIsLAxTU1OaNWvGtm3borSlp6S/E0eXqwQEBGBra8uXL1/4+vUrABOLFKRZrpxari7lkGnHhEh+Cel+kWnHRFKJz7RjRs0HA7B+/Xr69OnDjRs3VHd/ix9k2jERXxHjpk+fPmFmZgbEHrz8iraCl+So4Vd+rnHXrl106NCBP//8k2HDhkXbPj51Ru6MOfneOsb348OHDzRp0gRXV1d0dHTIly8fu3fvpmzZsr88h7YCigk2h1mwYAFjxowhc+bMfP36FTs7O1xdXZn3JOYbISPvH5u0ELyktWnHIkj3S8JoYxoy925jU1zwkhKnHpNpx4Q2RXTBRHSsBQQEUKRIEWrVqsXWrVu1WVqKI9OOpUDJeVFfqVTi8T2QF9+/UzZzJjLppay/moZ58+AaGIyOjg5Tp06lcyKOFdc1YnR1dWnXrh1169ZFV1eXTJkyRQldUqKI1+ZkasRSr1BympqgVCrxDf31FHhCCJGUXp55INOPiVSte/fuLFiwgHHjxnHixAltlyNEmtDGZTrhGWIed8R0sT85go6Yzu3n58fFixfJlSsXZcqUUY0PztVeoPUpyM6cOUOHDh3Inj07kydPpkOHDqo1LCH+79vPF6kj7x8RzERsYzKzKhW8y3Kx1XL04jGWnGBzWCtBxcwHTfh92D5u377NgwcPCA8Px9TUlICAxIcMaSF4EeJnBSy9tDIFmRAiZfs5kDQxMWHKlCkMGDCAMWPGUKpUKS1VJiKkrCv86UxEB4ibmxsNGjRQtVkbGhrSokULRowYQfny5eN0rKQMisKUSi56f2XRokX4+voyffp0Fvj7o6+vn2Tnj7xGTLZs2WLdLiWyz5yJjaVstV2GEEIIkeoFHliOUfPB6OnpMXPmTFq3bs2ZM2eoVauWtksTIl3SZvAycOBA1q1bR2joj25MW1tbevbsycCBAzE0NEzQFGSafD1HjhzB0tKSa9euUbBgQbZs2cKoUaM0dvzIfg5mFLo6GGbPQJ3zTilyKjZ15j1pydatUTtY4hqczHzQRG33iwQvIi3TRgAjhEgdik1fhNukEQD07NmTP//8k3HjxnH06FEtVybS5JovqUHkqbeyZs2KtfWPD886OjoUK1aMu3fvUrlyZRYsWEBcZoaL61ReCRE690+USiVly5bF0dGRkJAQPD09k/z8vwpWUmrwIoQQKZF0vYiUIENZ+3hNORYh8MByAH777TfKlSuHk5NTnD4fCSES5+dgQpvBC4CNjQ2ZM2cGwNTUlLx58zJ27FiqVauGh4eHxs8XX5cvX6Z06dLkypWLQoUK8erVK40dOz7i8/ek7bAi8vnjW8vP28d1/+Re70b8j0w5lnjaWAMmJYlpoXEhxI8ABkBfX58ZM2Zw7Ngxzp8/r+WqhIQvWvBzUJEzZ06OHTvG/fv3qVWrFm/fvuXu3buMGTOGMWPGUL16dVauXMnr168JDw/n1atXUS44hIeH4+fnR8lT5/jH8z2fg0M0WuuOHTuwtLSkUqVKWFpaAvDXX39Fu+iRnAGMBC9CCBF3ErwIbUto6BJZ4IHlKBQK5syZw3///cfevXs1VJ0Q6ZeHpxnub3LE+hUx7UwB57lRHk8KvwpCBg0ahIeHB6tWrcLf35+2bdty+fJlPnz4gL29PaNGjeL8+fOEhoby+fNnfH19o+z/7ds3wsLCuHjxIocP/+8CvCYCmJcvX3L16lXatWsHgKWlJWfOnOHly5eJPnZCxCWA0fY0bRFmPmiS4BAoYr/4Bi8SwIjULL0HMEKImEUEMK1bt6Zs2bJy01oKINOOJbPIAcXHjx/x8fGhaNGiwI/pxvLly8elS5fQ09Nj9uzZVKlShUWLFjFs2DAGDRqEmZkZnz9/xsHBgcmTJ+Pv78+MGTN48OABWbNmxdvbm/3vvVhTsjgZE7lujKPLVfz8/Ni+fTt9+/ZFR0eHEiVK0KdPH6ZOnUqdOnWoUqVKtH2SYgoyIYQQQqROiQ1dIgs8sJyazQdTr149JkyYQPPmzeO1toEQImHUzfsf3wDmVxcLYwpAwsPDuXfvHkWKFMHU1JSwsDCKFi2Kjo4O/v7+ODg4cOPGDSZPnsyOHTtYuHAhmTNn5tu3bxgbGzN48GBatWrFqlWr2Lx5M6ampvj5+aFUKtm+fTvt27dXnT8xYcTatWvJmDEjTZs2BWDs2LF06NCB4cOHc+DAgQQfNzHi8noiOhEi1pBJjRLa8aKt9W7SK+l6EZoSlC842joXQgBkfBX/kMEvf8peWzohIqYgmzNnDnXr1uXAgQO0aNFC22WlWzJa1YLv37+zYMEC5syZQ0BAANmzZ0ehUODl9WNAUrNmTdW2jRs3pnHjxvj4+HD8+HHu3r1LiRIlWLt2Lc2aNQOgQYMGDB8+nCdPnmBvb8/gwYMZ+dCNVSVLoJfIBer/+usvvn37xuDBg4Ef06ItXryYdevW8eTJk2jhixBCiJRFul6EtmgydIks8MBy5syZQ5kyZVi1ahVDhgxJkvMIITQrcljzcxATU/By+fJlhg4dyo0bN9DV1SV//vy4u7sTHh5OlixZVPjot5IAANRLSURBVFM3m5mZsWLFCpYtW8bNmzc5evQoOXPmxN3dnWXLljFnzhxy5crFjBkzCA0NxdTUlJs3b9KlSxdy5cpFzZo1ExW8+Pn5sXr1avr27UuGDBmAH2O6Vq1acebMGZRKJYpEjsuSmroL46k5kBEpiwQvmifrv4i0JCGhSUo9d0oJcyICmDp16jB27FgaNGiAkZGRtstKlyR8SWbv37+nSpUqeHh4MHz4cGrVqsWVK1cIDw+nUqVKlC9fXu0C81myZKF9+/aqO7M6derE1atXMTIywt4+6sWNCxcusHXdOkLDlejpJu6H/vr16xQsWFA13RiAiYkJ1apVY8aMGbRu3ZpMmTIl6hxCCCE0T0IXoQ1JFbj8rLjHJfr378/48eNp3rw5+fLlS5bzCiE0I7YgJsIff/zB1KlTKVu2LAcPHsTT05OHDx9ia2tLpUqVsLa2Rkcn6izaOjo6ODg44ODgoHps9OjRXL58mbp162JsbKx6/Pnz52zZsoX79+8zNfxIol7P8+fP8fHxoWrVqlEeb9CgAStXrmTTpk306NEjUefQhrQWyMx80CRK94t0vSQ9CV2SlgQwIjXRZsCSnOLyOpMroCk2fRH7lizB3t6e6dOnM3PmzGQ5r4hKwpdkFKpU0qFDBwICArh37x7FihUDfnwojy+FQkHFihWjPf7u3TucnZ3pZGGOkW7il/QpW7Ys27dv5969e9jZ2ake37RpE6VKlWLEiBFs2LAh0ecRQgihORK8iOSUXIHLz6ZWK8ShQ5np378/R44cSfF3lQsh1FN34fDAgQNMnTqVqVOnMnHixGghS3xkz55dNWNAZHPmzCFHjhzMN/ShUIKP/kORIkXImDEje/bsoXnz5qrHmzZtSo8ePRgyZAi1a9dO5FlShp8vpqe2MCYigJHgJWlJ6CKSmkw9lrKll6AloWJ6f5IilGm55wQTJ05k2rRptGnThtKlS2v8HCJ2Er4ko42v33LhrSdnz55VBS+a9PXrV5o0aUKmTJlob2GukWMuXLiQRo0aUaJEiSiPFyxYkKFDh7J+/XqNnEcIIYRmSPAikpq2wpafZTIxZkm3xrSetZZt27bRuXNnbZckhNCAt2/f0q1bN1q2bMmkSZOSJFhdvXo169evZ9myZSww9Mf9TY5ELWC9fv16goKCGDp0aLTn/vzzTzZu3MiFCxcgd2KqTplSY3eMBC9JR0KX5CfdL0LbJGjRHHXvpSYCmS2hRlhbW9OrVy+uXbsma2YmM3m3E8nR5arq979aGD6wRi0yHDhAyZIlNVrD9+/fad68Obdv3yYkJITz588TNLBvoo/r5eXF27dvWbx4sdofzGLFivHhwwf8/f0xNTVN9PmEEEIkjgQvIimklLBFncblStKuXTuGDx9O/fr1yZFDLj4IoQ3u3cbG+nwB57lxPtbnz5/5+vUrFStW1Hjwsn79ehYuXMjjx48ZNmwYgwYNYsHmeUDcpkKLye3btyldunSU6c4iZM2alZw5c/LixYs0Gb6oE9MF+JQeyoiEk9BFu9JrACPdL9ojgUvyifxeJzSIUejpsWHDBipWrMiiRYsYM2aMpsoTcSDhSyJEDl4i/hxTALO7cnW2LViAlZUV4eHhGq1DqVRy4cIFypcvz4oVK7C1teU/DRz3/v37ANja2qp9PkuWLAD4+vpK+CKEEFomwYvQlJQctqizdOlSChQowKJFi5g1a5a2yxEi3flV8BKxTVwCmBs3btC6dWv09PTImjWrJsqL4uHDhzx58oQ1a9bQq1cvFAqF2triG8Tcu3cv1mk8smTJwtevX4H0PT1iXC/QS0gTNxHr1yRVJ09sx5ewRaQUEsAkPwletCfivU9ICFOuXDn69u3L5MmTGTFihHS/JCN5p+MpVKnkjq8fLxo2YVilSjRo0ICxY8diaGgIqA9gPjhNZF7TpkybNo2xY8diYKDZ/xgMDQ0xNzencOHCMQYlCeHp6QmAubn6Kcy8vb1V5xdCCKE9EryIxEptgUtkHh4efP/+PcradEKI5BFb8PLmzRsOHTrEiRMnCAkJ4dCcOTS9eTTG7UNCQujWrRuZM2fm+PHjSTJNc/78+QkLC+O3336Lso5MbOFQXIIYT09PGjZsGON5vb29/3/MFJywwtOZuFzYT+8BTUQwEvF7TQcwPx+/xunRGj2+0Lz02v0iko+ELilHQkOYx48fY2Njg66ublKUJWKQ+BXZ04FQpZKXAd856fWZRtdvMuD+I7Zs2UL27NmZPn06dnZ2nD17VrV95I6YkqfOMWzYMOrVq8fEiRM1HrwArFmzhlevXtG/f3+NHjciyInogPlZnTp10NfXZ8OGDcCPO74OHjyo0RqEEEIIkTQylLVXfaVmWbNmRUdHBy+vhK/XIISIP3XBy9u3b3n9+jXVqlUjb968DB06FG9vb168eEGZMmVo9/AL4UHqA4jly5fz8OFDNm7cmCTBi6+vL7Nnz6Zdu3aYmZnF6fVE2+ZNjihfEWxtbbl3716M+zVu3JjNmzcTHhJGeHAo7/99QMjX7wl7IQL4EdCo+0oPIgcjsT0WH5HfQ3XHOld7QaKOL0RSCsonwXZSyvhKKcFLChXfv5vs2bPz5csXQkNDk7Aq8TPpfPkFR5er7Nq1i3Zt2wKQN29e/t2zBwcHBxQKBQ8ePKB///7UqlWLRYsWMXz4cNV+ADNmzOD169ccPXo0SRaLdHd3Z/z48fTs2ZNy5cpFqftXa9D8irW1NQqFgvv371OlSpVoz1tYWNClSxcWL16Mh4cHK1euJDw8nL1799KyZcso2ya2FvHDz1PdyfsqhJCuFxFXqT1kiUnhwoXp3Lkzs2fPpk+fPhgZGWm7JCHSLUdHR1X3/Jw5c+jfvz+ZM2cmODiY+fPnM2PGDIoWLcq5c+fIli2bar8PHz4wZcoU+vfvj7190vxbNWrUKPz8/Jg/f77a5+OzLk2EiACmRIkSHD4c88Xv0aNHs2nTJpodDOL48eO8fPmS6tWroxxfBoWu3A+pSSffW0cLCtR1baTmzpmZD5pEC0hqnB5NXfUTVsQ7lKpxenSc3kOR8qTn7heZfixpSOiSOsSlE6bY9EXsmjSJUqVKsWXLFnr27Jlc5aV7Er7EIv/egwwcOJDdu3cDMH36dEqVKoWjo6NqGxsbG86fP4+TkxMjRowgU6ZMUb6Bb9y4QalSpZLk7q3Q0FA6depElixZ+PPPP6M9n9gA5vnz5yiVSgoXLhzjNqNHj+avv/7C2dmZefPmceXKFbp160aOHDmiBDaaCIPSqzClkhWvXtN3x9/RnpP3VYj0TYIXERdpNXSJbNKkSWzbto2FCxcyfvx4bZcjRLqzZcsWVq1ahaenJ1WrVqVKlSoMHz5cNT2xgYEBEyZMoEWLFtSoUYOGDRty6tQpMmbMCMCzZ8/w8/Oj7f/f8KZpu3fvZv369axbt468efNGez4hwUtkT58+jXXMZGNjQ+PGjVm1ahX169fnjz/+oFevXnT9uzCnKhVGoad++o+4rDcj/ufz9Zd0xIHvlb5jbGysevxc7QXRwoPU0CVjGm6ANXDhYxH8daLe2X/y/f8CkojXpsnXFDmAkeBFpBYSwGiWBC+pT8ZXylgDmDYHTtOqVSumT59Oy5Ytk2R9PRGdhC8xcHS5SvPmzbl48eKPD8Zdu1KyZEm12+ro6DB37lx8fX3p06cP27ZtQ09PjwwZMvDp0ydcXV158uQJVlZWGq3xyZMnXL58mX/++YcsWbLE+DoSenHewsICPT09nj17Rt26ddVuU7x4cc6dO0exYsUwNzfHz8+Ppk2bUqtWLVauXEnv3r01Ukt69jk4hK1vPdlarRqDBg1i6dKlUeaolvdVCCGEOukhdIlQpEgRRowYwYQJE/j06RPz5s2TRSSFSCbnz5+na9eu1KtXjy1bttCuXTv09fXVbmtjY8OJEyeoWbMmDg4O5M+fHz09PXLnzo2Ojg47duygRo0aGq9x48aNODo60qtXr2jPJTZ4gR9ryZw4cQKlUhnjbAfr16/Hzc2NatWqqbbp1asXVV5U4VnrquhmMI22z6/uYJdwJqqS9wwZv2M8GzduZNeuXZQqVUr1nLoAJrVL6teT1t6v9CI9d7+ABDBC/CqAcbUogu+ZMzg6OrJ//36Nrh0u1JMeZzUcXa6yYsUKDh48yNq1a5k/f36MwUsEhULBihUrmDx5MtmzZydjxoz4+vry5s0b9PX1efnypcbrLFq0KGZmZkydOpXbt2/HuN3PU1XFVebMmbGzs+PixYuxble9enXMzX/0OGfMmJGTJ0/SsmVLBg4cSEhIiEZqSc/KH/sXgPr167NixQquXbsWbRt5X4VIf6TrRcQkLazjkhDz5s1j6dKlLF26lHr16skaMEIkg9evX9O7d28qVqzIsWPH6Ny5c4zBS4QyZcpw4sQJHBwcyJw5MwYGBty6dQtjY2M8PDySpM4KFSpw69YtZs2alSTznFetWpUXL16oplxTx9zcnOrVq6uCl65du3LmzBkuXLjAiKAsCTrvz2vQqFuPJr04V3sBhoaGFCpUCKVSybRp09RuI4RI+2QNmMSTrpfULba1YAxz5CJztwGYmJhQoUIF9uzZk8zVpT9yS+BPHC5eYfLkyUyfPp1hw4bRqlWrOO+rq6vLlClToj0eHh4epVNBU/T19Tl37hydO3emXLlyTJ8+ndGjR6OrG71tPSHdEV5eXty7d49u3brFu64cOXJQoECBXw6+xK9FTNfQvn17zp49y40bN6hYsaKWqxJCaJMEL+Jn6TFsiRB4YDlGzQejUCgYMmQIdnZ2tGnTBhsbG+bPn0/Xrl2TZN09IdITdYvRP3jwgPr166Onp8fmzZvjNd6pUKECFSpEHZsolUqUyqS52DN27FgCAwOZPHkyR48eZfPmzappwn5+bQnphDl79iz58uVT3ZAWV3nz5iU8PJwiRYrg3qyZRrpwIsQUwKTlbhlDQ0MyZcpEnTp12LVrl9pt0mIHjBBCCKFOTF0wBtmy8715Rxo/uUXr1q3p0KEDf/75J7lz59ZClWmfdL78ZMOGDUyfPp05c+awaNEijQzWkyJ4iWBra8u1a9cYOXIk48aNw9ramoULF+Lt7R1t2/h2R2zbtg2Ajh07xrsuX19fMmXKFO/9RHQZMmTAysqKHj16EBwcHOtinkIIIdKf9By8RAg8sFz1++rVq3P37l3q1q1L9+7dqV69Ovfv39didUKkbuqCl6CgIGrWrImZmRmXL1+mSJEiiT6PQqFIsnGTgYEBM2fO5MKFC7x//54SJUrQtWtXrl27Fi3wUfd6Y+LebSz+/v7s3LmT7t27x7t+Pz8/gGQdN6XlLpkyZcpw+/ZtFixYwKtXr1J9B+SuyhO1XYLQgnO1F0iXlgZI94sQP8TUBaNjYMgtm/I4Oztz6tQprK2tWbp0aZJ0CKd3Er78xMXFBUdHR8aOHZtq7pI0NDRkzpw5XLt2jXLlyuHk5ESePHlwcnIiMDAwyrbxCWA2b95Ms2bNyJ49e7xrqlq1Krdu3cLX1zfe+4qoFAoF+/btw8nJCSDaVG6pUej/D3LDkujuRiHSMul6ERHS6/RiMYkcwJibm7Nt2zZOnz7Nx48fKV26NGPGjOHbt29arFCI1CemIOLp06d4eXmxbNkyLCwskrmqhKtcuTK3b99mxowZuLi4qDpwbt26FWW7uAQwEdscOHAAPz8/unfvHu96bGxsyJo1KxcuXIj3vpqUVqYu6969O5s3b8be/sf/jW/fvtVyRQkX+eL7muIDtViJSE6R/94lgEk8CWCE+B91AYxCoWDmq8+4ubnRqVMnhg8fjqOjI1evytIGmiThy/9b7u7ByIduXLp0iRIlSmi7nARxdHRk27ZtvH79mtGjR7No0SLKlCnDzZs3o24XhwDGLzSUW7du0axZswTVUqNGDcLDw3FxcUnQ/iKqEiVKMHv2bADOnDnDlClTkmxahqQUqlRy4MNHxj96CsBVbx/tFiREKiPBiwAJXWITOYABqFWrFnfu3GH69OmsWLGC4sWLs2fPnlT5f6gQyU1dAHHv3j3KlSvH1KlTAShevHhyl5VoGTNmZMyYMTx79owjR44QFBREuXLl+OOPPwgLC1NtF1sAE/m5c+fOYWtrS8GCBeNdi46ODlWrVuXcuXPx3jc5pKZQpsbp0RgYGNClSxc6dOgAQLt27ZJsHaGkdK72Ah49ekTfvn2BHzNRyIX4tE/d37H8vSeeBDBC/E9M68BUWL6JlStXcu3aNXR1dalYsSJ9+/bl8+fPyVxh2pRuwxelUslajzd8mTSVr3/MYPNbT14Ym/Ds2TNsbW21XV6i5MqVi2nTpnHz5k2MjY2pXLky27dvj7KNo8vVaF+R3fH90QJfqVKlBNVQpEgRLCwsOH36dLTnZHH4+Im8Vk/9+vUBmDZtGiNGjFC7TUoTqlRy/vMXJj95Rqsbd5j57CURTWV5jIy0W5wQQqQyErr82s8BjKGhIePGjePBgweULl2a1q1bU6dOHS5fvqylCoVIXf777z9GjBhBUFAQPXv2xN3dnf3795MzZ05y5EiZF+LjQkdHh0aNGnH9+nXGjx/P9OnTadq0KT4+Pqpt1AUwkR9TKpVcvHgxwWMm+HHT2pUrVwgICEjwMZJbSg1lItZyieh8efLkCZUrV+bly5dRttPmBe3Yzv3d8ytVzhpRq1YtbG1tuXv3LgCWlpa/3FekfurWIpL1iTRDApj4U7dOiEgbYpqGrNj0RTg6OnLt2jWWL1/OP//8Q7FixZg/fz7+/v5aqDTtSLfhy8Y371j/+i39+vVjxIgR2NnZ4eHhwblz5+jfv7+2y9MIGxsbLl26RJs2bejUqRNOTk64ubnFOG1V5FBk7/uP2NraqhaijC+FQkHjxo05dOiQ2rtLJYCJn4hwZdOmTaqFPC9cuIBSqUzRwcuHoCB+u3GbMY+f8sz/O4Y6CgoaG+MV/OPDTz5jCV+EiCvpehESvMTdzwEMQMGCBTl06BAHDx7k48ePVK5cmQYNGnDt2jUtVChE6uDh4UGTJk1YvHgxbdq0wdXVla1bt/Ls2TNOnTql7fI0wsDAgKlTp3Ls2DGuXLlChQoVuHz5Mh8/fgRi74C5cOECjx8/pkWLFgk+f+PGjQkMDOTkyZPxWm8mJUoJoUyN06OpU6cOo0f/uGj9/v17Hj9+HG07bQQZEedUd+7na87zX9cNrFixgqCgIPLmzat6rnXr1tGOIdKmyGGLBC9CiKSkLoQpNn0Rurq6DBo0iMePH9OqVSvGjx9PoUKFWLRoEd+/f9dStalbugxfzn/+wmqPN3Tp0oVXr17h6+vL/fv3uXDhAtWrVydDhgzaLlFjjIyMcHZ2ZsGCBcyfPx9ra2uKFCnCqlWrCAoKira9o8tVPL4H4uLtw6hRoxK17k2bNm14+vQp1apVU/sDKgFM/PxXpQLm5uacOXOG48eP899//+FataK2y4qVAgXvg4IpaGyET0gI7t8DCVMqafT/d0nqpJJ1lYTQNglehAQv8acugAFo2rQpd+7c4Z9//uH169dUqFCBxo0b4+rqmswVCpGy+fv707x5c4yNjVVTY2XOnJkVK1aQL18+SpYsqe0SNapevXqqMLZy5cpYWFjQtWtX3NzcYgxFFi1ahK2tLQ0aNEjwea2srChVqhRdunTh4MGDqT6AUSe5Q5kap0cza9Ys/v77b16/fk3Dhg3VbpecQcbP5/r5z9X0rTAxMaFIkSJcuXKFDx8+UK5cOYBo318SwKRtNU6PTlTwkhK60FIi6X6JP+l+SR9+DmGKTV8E/FhDc82aNTx58oSmTZsyZswYChUqxNKlS6OtLy5il+7Cl+f+AUx940mrVq3YtGkTLVu2JDg4GKVSyeTJk7VdXpJQKBSMGjWKx48fc/LkSSpXrszgwYMZMmSI2u1vN2pKBl1d2rVrl6jz1q1blwMHDuDi4sK///6rdhsJYOLnvyoV+NanB9mmT+Fm9craLueXshnoUzlrFgLCwqmT3Yz1JUuwq4wdrS1yabs0IVINCV6EBC8JF3hgebQv+DHdUJs2bbh79y7bt2/n+fPnODo60qxZs2gLbwuRHimVSnr06MHTp085ePAgc+bMwc/PD1NTUw4ePMj169e1XWKSsLKy4saNG1y8eJGFCxdy5swZypQpw7t376KFIl5eXhw5coS+ffsm6oY1gH///ZeSJUsyb948IPZum7QkKUMZfX192rZtq5o1QJtiCksiHj9Tcx7du3cnLCyMnDlz8tdff+Hp6cnSpUvjfUwhRMwkgBEiZj8HMBFfbWbu4zYlcHNzo0GDBowYMYLChQurOjXFr+lpu4DkdqlKdbJ88sbZ2RkdHR0mT55M1apVad++PZMmTdJ2eUmqaNGiFC1alDp16uDg4MDvv/9OSEgInTt3pkaNGiiVSjZv3szKlStp36MHxsbGiT5ns2bNKFGiBPv27aN58+bx3j8+Fx1fnnkQ7+MLzfoYFMy0p88JAxRFrbh37x4TJ05k/Pjx3KiW8LmwhUivJHgRErxoXuCB5Rg1HwyArq4uHTp0oG3btuzYsYOpU6dSpkwZ9uzZw2+//ablSoVIeoZv9dE1MACiXpS6ULMDBXqMY8OGDdjZ2QHQpEkT3NzcWLt2LY6OjlqpNzmYmppSpUoVqlSpQrdu3ShQoAANGjSgT58+XG/XA4BHjx6pprVq3759os+ZM2dOevfuTa9evXj//j3m5ua4dxtLAee5iT52ahRTAFPA0kuj59HWtE5KpZIFCxawdetW8uTJQ+6OucmdOzePHj2iQIECqu1+tQ7QudoLZGqqNEBdkJbQv1fpevm1oHzBGHoYaLuMVMMvvyLGRdpF2hPb33W72Qdw3biRcePGMX36dIYOHcq2bdtkHc04SFfhi6PLVZZ17UrBggUxNTUFoHTp0vj6+ib6bqXUZvDgwXz79o3NmzezadMmcufOjYmJCc+fP6dNmzZMnz5dY+dq2bIlK1euJCQkBH19/WjPa+riorrjSCCTfD4GBdPr7o/327FePfz8/GjYsCGTJk3C2NiYUS5XU/T6NEKkJBK6CJDgJSlFDmDgRwjTuXNn2rdvT9myZdm7d6+ELyLdibgY5TZphGqhb1tbW9XzBw8eTHdjpsyZM3PgwAHmz5/PyJEjGTFiBGXLlsXV1RVLS0u2bdtGjhyaudjZtGlTFAoFBw4coF+/fho5Zlqj7sJyQgMZbYYW48aNY+7cuTRv3pznz59Tv359Ll68SOXKlfHw8EBXV1drtYnkFVtXVHy+RyV0EUIkNb/8CopNX4TbpBFs2bKF2rVr06NHD9VNIyJm6SZ8iZjeysfHh0yZMkV5Lr0NIuDHwpKTJ09m0qRJXL9+nW3btvH582d27dqFvb1mL/Y4ODjg7e3Nhw8fsLS01Oixf0U6Z5LPg2/f+BAczJ07d1R3SHp5eXHw4EHVz5yjBDBCxEpCFyGSz88BDICenh7NmjVjxowZBAUFMW/ePAoWLKilCoVIfm6TRgA/xkwAGTNmVD2XHsdMANWqVaNatWp8+vSJXbt2cfz4cZYuXUrv3r0xNDTU2HmyZ89OwYIFefTokcaOmR4kJJDRdrfIP//8Q+fOndmyZYvqsV69euHi4oKOTrqbGT7d+tXUcXENYCR4iT/pfokf6X4R8KMrJnIAU69ePUxMTHBwcGDu3Ll06NBB/g+LQboIXyKCl6CgINzc3KhRo4Z2C0pBFAoF5cuXp3z58kl2jkePHpEpUyby5MmTZOfQhF91zmj6omhaC3us/7+bbPTo0dSrV48vX75w6tQplEpllCnnHF2ucqVBHW2VKUSKJKGL+Jl0vSSPiDVgIps6dSpWVlY4OTlRvHhxRo4cybhx46JchBYiLYoIXgDu3bsHkOw3TqVk2bNnZ8CAAQwYMCBJjv/9+3devnxJiRIlkuT46Yn7mxwUcJ4b49o57m9yaHwKs9jUOD06yoX2smXLcurUKUaNGoWlpSWurq7s37+fIUOGpNuQU8SfhC4iOUkAIyB6AHPv3j3GjBlD586dWbZsGUuWLEnS68upVZqPpCKCF29vb+rXr8+rV69o27atlqtKX3x9fdHT0+P79+/aLiXeCtayUX0l5bHTwoVXc0MD5syZQ3BwMJMnT2bjxo1YWlqyY8cOcubMqe3yhEiR0srPv9AsCV60K/jQSrp06cKTJ08YO3YsixYtkimARJoXOXj566+/GD58OC1atJDQMRl9+/YNpVJJSEiI6rGYwgMRNzGtmePebSzub3LE6UtTIncwODk5UbNmTfbs2YOTkxMvXrygX79+/P777xo7X3L4VeeGNqXk2iL8qqslpuc1/b2ZXkVe50wIEXcRIVyx6YsoVKgQe/bs4ezZswQGBlKlShVevXql5QpTHoVSqUwz0aWvry+ZM2fmdNWKZAwPB36EL1+/fqVChQp4eXlx4MABKleurOVK05cXL15QtGhRlixZwuDBg6M9/2lyLy1UlXokpEMmpou5Sd1tExF2hoeHo1AoYrxz60qDOnwcMIScq5ah9+1bktYkREqTmsKWUB09rtk3ovyto+iFh2q7nHRBgpeUI2JKslGjRnHo0CGePHmi5Ypi9vnzZ7Jnz87Xr1+jTa8rhDoR46YSE2fzYLqT6vFFixYxcuRI+vXrx/Lly9HTSxcTJaQYbdu25caNG7i5uUV572MKEUTcxBRiafp9jUs3zc+hQHh4eIzTtAQEBHDy5Enq1q2LiYmJ2m20NYVa5Neh7WncfpaSa1NHXVBUwHlulO8nCVuShqamHjPUUTDdtiCT7r8kKDzNXGJVS7pfRAS//D+u90XcwOPp6YmFhQUHDhygWbNm2iwtRtoaMyWo82X27Nk4OjqSMWNGcubMSYsWLXBzc4uyjVKp5I8//sDCwgJjY2Nq1KjBgwdRL/y6ublRuXJlLC0tmTZtWpTnbt26RZMmTciZMydGRkYUKFCAdu3a8enTp3jXu2LFCl6+fMmlS5ckeNGCQoUK0aFDB2bPns3Zs2dJQ3lfsvi5QyYuX/E9lqbp6OhIy7wQkaSlLjch0ouIKcmsrKx48eJFlLvRhYiL1DBmujC8j+r3X758YdKkSQwePJhVq1ZJ8KIF48aN48WLFzg5OfH161fV49IBkzixdcBoUlw6aX6uJTXOj/9zWJCSukxScm0x+TkgivgeSYoOLBGVdL/EX8QFdyEyvlKS8ZUSh94LATA3NydDhgwp+oY1bUnQ//Tnz59n0KBBXL16lZMnTxIaGkq9evXw9/dXbTNv3jwWLlzI8uXL+e+//zA3N6du3br4+fmpthk0aBBdunThwIEDHDp0iEuXLgHw8eNH6tSpQ/bs2Tlx4gSPHj3ir7/+Infu3AQEBMSr1vfv37No0SJ69uxJsWLFEvJyhQZMnz6dXLlyUatWLSpXrsyRI0ckhElBEhrmJFTeSsVUv2r6vHKRW6QU8r0o4ku6XlKmokWLEhYWxsuXL7VdikhlUtOYSalUMnPmTMLDw5k8ebLcRKMl9vb2TJo0iWXLlpE/f34mTZqkCtIkgEmc5Apg4iKmWgo4z1V9xVVyTJkmkl6N06Pj/XcvhBAphUPvhSgUCqysrCR8UUMj0455eXmRM2dOzp8/T7Vq1VAqlVhYWDB8+HDGjv3xYSYoKIhcuXIxd+5c1bzZDg4OrFixgjJlytC6dWv69etHo0aN2L9/P23atOH79+/xuuMqon3+06dPmJmZ4erqSsuWLQkLC+Pq1avky5cvsS9VJIJSqeTYsWPMnDmTy5cvU7p0aZYvX662G0mmIkt9IqY0i5h6LCafJvciUM+IG6XqUfbOvxiFBiZbbUJoWloPVmTaseQjwUvKZNR8MG/evCFv3rwcOnSIJk2aaLsktWTasdQhpYyZIOq4KUOGDAwaNIgNGzYwe/ZsnJycfn0AkaTevXvHn3/+yerVq1EoFIwYMYKJEydiaGgYZbv0cqHWvdtYjb3W5JqCLC4i16Lu/A/bDPnltGMx7RvT+RLyOn+eTs39TY5otcdlyrWkFDls+tX7GrFNevn5EbHTxNRj6WnasQgy/Zj4mev6kXTo0AFPT0/OnTun7XLUSlXTjv0soiU6W7ZsALx8+ZL3799Tr1491TaGhoZUr16dy5cvqx6bNm2a6oOEjo4O9evXB360KoWGhrJv374EdUfs3LmTtm3bUqVKFSwsLHB1dZXgJQVQKBQ0atQIFxcXzp49i5GREVWrVmX06NF8//49yrbZp23QUpUioSLu8I8tONNWqJaQqduSuvtHpE7yfSFE+mNhYYGJiYncxSUSLaWNmQBmzpyJnZ0dW7ZsYdOmTRK8pBAWFhb8+eefvHr1iqFDhzJ37lzKli3LjRs3omyXHrphIl6jpl5rSuyAiammCrtWxek4cak9Me+juo6an2uPy5RrSfkV2a/eV01/T4nUTaYeSxiZfkyoY2VlFW2KXaGBzhelUknz5s3x9vbm4sWLAFy+fJnKlSvz9u1bLCwsVNv27duXV69eceLECdVjQUFB+Pr6kiNH1P8wJ0yYwLx588iUKRPlypWjVq1adO3alVy5csVYS8QdXNmyZcPGxoaGDRsyZMiQaHcIiZQhLCyMlStXMn36dPLly8fy5cupUKFClG0+zxyspeqSnlKp5OFHb8KU4eTNnIGsxkbaLkmjzCYsj/LnyH+XQXqG3C5Zm9L3TmMYGpTcpYlYvLrwSNslJIv81Ypru4RUIVRHT9WlJp0vSSeDvZ22SxAxMGzUF4AyZcrQuHFjpk+fruWK1Pv8+TO5c+eWzpcULCWNmeB/46YiRYpQrVo1+vbtS5kyZTT4ioUmPXz4kH79+vHgwQNVp1TkMa7t9sWq3xu+0dfYeYMstbvW1f2Ow4EfXWMvX74kc+bMtHI9ChqYFi/i2D+L/F5qWyZ0mZilMDVr1sTY2PiX28dUu7rXmpJeZ3KS90Kok9h/Nw11FEwsUYAZD93TTecLQIbX6ee1il87v2wwzs7ODBs2DG9v7xQ5ha22xkyJDl8GDRrEkSNHcHFxwdLSEvjfQOLdu3fkzp1btW2fPn14/fo1x48fj9OxP3/+zJkzZ7h69Sr79+/ny5cvXLhwgZIlS6rdPmIQsX379ljbcoUQQgghhEgrAgIC6Nixo4QvKVhKGjOBjJuEEEIIIUT6oq0xU6LClyFDhrB//34uXLhAwYIFVY+/ePGCwoULc/PmTezt/zeHefPmzcmSJQvOzs7xPldwcDD29vY4ODjEuH/EIMLT0xMzM7P4vyChNWFhYaxZs4Zx48bRp08fFixYEOX5tNgBExoWTuU1e8mdwYRSubOz7fYTStjYsGnTJooWLaraLi2+dul8ESJ1kM6X5CGdLylXROdLgwYNyJ8/P2vWrNFyRepJ50vKltLGTCDjptTs8ePH9OzZEzc3N9zd3cmYMaPqubLzVmixsh803S1zv+NwFv8fe3cdF0X+/wH8tXQIIqAgBiqCnaCgYmD3Wdgdp2J3naei4lnoqSd2nXnniWcXgh2YZ4KBioGBhHTN7w9/7BekYXdnd3k9Hw8eyuzszHt2d3b4fF7zmVm9GvPnz8eMGTOwd+9ehISE4Ndff8XYsWNzfWZtbkaFKNsIiNSRL4vCXyASyRkez23tqjDKR9Ye9p2oku85iYMjXwqGI2DEVzQo/5fPiyhf8PseAd9Hvpw4cQJ9+vTB8+fPM4zWVgZitZnydmfG/ycIAsaNGwdvb2/4+fmla0QAQPny5WFpaYmzZ89KGxIJCQm4cOECli7N303NdHR0YGNjg+jo6Bzn1dbWhra27IZbk/xpa2ujQ4cOmDhxIgwNDTO8f5bzN4p2vxB50ZIAvzaphRH/+KKamREuXrqEfv36wcnJCTt27EC3bt0AqOe2J6V8/+rRTElmhy6RCtBKSeK+KkdaYINFWaX+PaKtrY2IiAil/ftSWesq7JS9zQSw3aSKatSogVKlSuHly5cZ3r//5kxEpYWrRKwOwJvMuxjye18F272e+G/kaOzZswcbNmzAmTNnsGPHDkyZMgVXr17Fjh07UKRIkRyXEzBoWqb3ALHd66m0N1+P//9b9MZDQDxSMjye29qz2sczW6Y6SL2Xiyq+5yQSGQUm8SlCoQxfdJIK3zYri6Ivvp/MXJA7F+kHxEn/H2GT/9t2aGtro0iRIoiNjUVcXJxS/n0pVk0a+XnSmDFjsHv3buzduxdGRkYICQlBSEiI9KbpEokEEydOhIeHB7y9vfHw4UMMHjwYBgYG6Nu3b47LP3bsGPr3749jx44hMDAQAQEBWLFiBU6cOIGffvopPyWTCrCxscHYsWOxdOlSHDp0KMPj5u5bRahKvn6qWh4rOjTEtltPcOjQIdy8eRNt27ZFjx49sHTpUunNU9Vx24mICChiXyfnmUh0RYoUyXVnNlEqtplIXhYsWICkpCR07doV8fHpR5EHzJ0kUlXZ032jk+Ent2p6e+H06dMoUqQIOnfujEmTJsHb2xunT59G06ZNERwcnKvlZHWDdVXuhFfl2uXhx/dYHd9zImXzzVr57u1RGKQGL7JeZupPfqSeDMF2U3r5Cl+8vLwQERGBZs2aoWTJktKfAwcOSOeZPn06Jk6cCDc3Nzg4OODdu3c4c+ZMumHRWalatSoMDAwwZcoU1K5dG05OTvjrr7+wZcsWDBgwID8lkwqQSCT4/fff4erqij59+sDf3z/DPOoYQkw+dgXLly/H4sWLMXv2bOzevRtz5szBzJkz0atXL0RGRgJQz20nIiJSBYaGhoiKihK7DFIxbDORvNSqVQtHjx7F5cuX8fPPP2d4XFkDmB9lFshkFco4ntqBc+fOISkpCY0bN0bFihVx+fJlfPnyBQ4ODvDx8cnVOrPqjC9sGD4QkSwxgFEseQQvma0jr+sxNDQEALabfpCv8EUQhEx/Bg8eLJ1HIpFg/vz5+PDhA+Li4nDhwgVUr149V8uvUKECNm3ahICAAMTExCAsLAw3b95Mt3xSTxoaGti1axfKlSuH33//XexyFGbq1KlYv349NmzYgPbt22PSpEk4ePAgTp8+DXt7e9y7d0/sEomISMY46kV1aGtrZzi7nCgnbDORPDVp0gQrV67En3/+iXfv3mV4XFUCmMxkFcq02vkPLl26BGNjYzRo0ACvXr2Cv78/atWqhVatWmH+/PlITs54b5TC7segRZ2Dl9xuK0M4IlJVigheflxfbteZelmvhISCXAhN/eQrfCGSJx0dHQwcOBDe3t6FKi0dPXo0zpw5g7t376JWrVrQ1tbGnTt3YGRkBCcnJ/z9999il0hERFQovX79GmXLlhW7DCKidPr16wcdHR3s27cv08dVOYDJSutdh3D58mW0bt0aXbp0wbx587B//34sWLAA7u7u6Ny5c6FqQ+ZWagihzsFLqpy2NTV4YQBDJFsc/aLechPCvH79GgDYbvoBwxdSSn379kVMTAwOHz6Mq1ev4uvXr2KXpBAuLi64c+cOatSogZ9++gnTp0/HgQMH0K1bN/Tq2RMbbjwSu0QiIpIBjnpRLS9evEDFihXFLoOIKB0TExN06tQJe/bswadPn3Dz5s0M86hjAGO/ajP+/vtvrFmzBrt370a1atVgZ2eHkydP4uLFi3BxccHHjx/FLlPpFIbgJVVuR7wwgKG83H+KcsYARr4UPeolrzU8f/4c2traKFOmjAIrUn4MX0gplS9fHs7Ozhg1ahQaNWqEpk2b4uPHj9IUVZ18+XVYut+tra1x/Phx7N+/H1euXEG1atWg9fQ2zA31MPfMDXyJjhWpUiIiosInISEBr1+/ZvhCREqpf//+uHfvHipUqAAnJyfs3bsXwcHBiIuLk86jjgFMlcW/Y9y4cXjy5AmcnJzQu3dvTJ8+HW3btsWtW7ewatWqTJ9XmAIIIiIiRXrx4gXKlSsHLS0tsUtRKgxfSGlNmTIFNWrUwIYNG/DhwwdYWlqiXLly0Jma+R/SquzHAEYikaBXr1549uwZZjauhZMBb6CpoYEdrs1hbqgvUpVERCQLHPWiWl69eoWUlBSGL0SklNq1a4emTZvi559/Rv/+/TFw4ECULVsWnp6eYpcmd5UWrkLp0qXh7e2NCxcuoESJEjh48CBGjBiBmTNnZjp/VveTSftD6qsw3f9GGXBkUeHE0S+F1/Pnz9lmygSjKFJaXbp0QZcuXQAADg4O6N+/P54+fYonT57A0X1rhsBC1X35dRjM3bemmxa/fCLGN6qJ0U7VoSEBNDWYlxIRESnS8+fPAQA2NjYiV0JElJGOjg78/PwAAElJSTA3N8eqVavg7+8vbmEKUmnhKgTMnYQmTZrg7NmziIuLg56eXqbz5VZeA5j4sryxsCopt3MpXg2aweBFztLeW0dZX2uGrUT5U/RFPCJsdDNMf/78OVq0aCFCRcqN4QupBHt7e9y+fRtFihTB48eP4ejoCHM1DWAyo63J0IWIiEgML168gK6uLkqXLi12KURE2dLS0oKnpycSEhLg6+srdjkKk5dgRR4Y1qgeZQ0D1EVm99ZRttecwYt8fbOWwOi1IHYZpEApKSl48eIFRo4cKXYpSoc9uqQyDAwMYG1tjSdPnkin/ThShIiISJnxkmOq5/nz56hQoQI0OPqUiFRE1apV8ezZMyQmJkqnqeN9X1RVbi59pvtGB7rvv58rq/teix3FRESk1N69e4f4+HheLSATbEWSSqlZsyb+/vtvvHz5UjqNAQwREakCBi+qidcuJiJVU7NmTSQmJuL3339PN50BjGrLdWjDe9eQyJT53jrcPxSH934pXFIv1cx2U0YMX0ilrFy5ElpaWnBycsKNGzek0xnAEBERkTw8e/aMjQgiUimNGjXCtGnTMG3aNIwfPx7JycnSxxjAFB55DWvYIU2ylBq4KFvwQkSyUfRFfLrfnz17Bg0NDZQrV06cgpQYwxdSKRUrVsS1a9dga2uLZs2a4eDBg9LHGMAQEZGy4qgX1ZSQkICXL1+icuXKYpdCRJRrEokEy5Ytg5eXF/744w907doV3759kz7OAIaykp/Ahh3alBVlCV74OSWSv4CAAFSoUAG6urpil6J0GL6QyjE3N4ePjw+6dOkCV1dXzJ8/HykpKd8fYwBDRERKhsGL6nrx4gWSk5NRqVIlsUshIsqzUaNG4dixY7hw4QIaNmyY7tLNDGBIlvIb2rBTnOSNny9x8dJjshVho7zBxtOnT9lmygLDF1JJenp62Lt3LxYvXowFCxZg7NixEAQBAAMYIiJSHgxeVNvTp08BgCNfiEhltWvXDtevX0dcXBycnZ2l12QHGMCQ8mBwQzl5NWgGXg2akat5+fkgUryAgAC2mbLA8IVUlkQiwezZs7FlyxZ4eXlh6tSpDGCIiEhpMHhRfQEBATAxMUGJEiXELoWIKN+qVKmCK1euoGjRomjevDlevXolfYwBDKm6go66YSe98ksbumQXwPC9JFKs1Pu+pCQnISgoiOFLFhi+kMobNmwY1q1bB09PT8ydO1c6nQEMERERFUTq8HmJhJdMICLVVqJECfj4+EBHRwfNmzfH27dvpY8xgCHi/W6UVWZhS9ppfC+osFHGS4/FR35BSkoKLzuWBYYvpBbGjBmD5cuXY/HixTh06JB0OgMYIiJStCL2dTjqRQ1E29TF06dPeQYXEakNKysr+Pj4IDk5Ga6urmKXQ6QWGNbIV7mdSzNMq7RwFV9PIiXi3tcRAC/VnBWGL6Q2pk6dCicnJ2zfvj3ddAYwRESkKAxdVF+0TV1E29SFIAgICAjgGVxEpFasra2xatUqXL9+XXpfKyJSHAY1uZP2dai0cJV0etr/ExVWPr6z4OM7S+wyAAC3tkzG06dPUaxYMZibm4tdjlLSErsAIlnq27cvJk+ejNDQUJiZmUmnm7tvxZdfh4lYGRERqTOGLqol2qZujvOERUQiPDwctra2CqiIiEhx2rdvD2NjY+zbtw8LFiwQuxwiykJ+A5j4sgkyrkQ+8rJ9DF2Ivru1ZbL0/z6+s9DCZYlotaQGQC9evICtrS0v1ZwFhi+kVnr27ImJEyfi33//xdChQ9M9xgCGiIhkjaGL6shN4JLWpy+hAL5fpoeISJ3o6emhW7du+Pvvvxm+EKmhwj5qhpTLN2sJjF4LYpehFtIGL6nECmDSjrwJCQlhmykbvOwYqRULCwtYWFiku4FkWrwEGRERyQqDF9WR1+AFAD59+QoAsLS0lHU5RESiq1mzJoKDg8Uug4iIiHLJYbhnhmlijnxJFRISwjZTNhi+kNoxNDREVFSU2GUQEZGaKmJfh8GLiki9f0t+fPz/kS8WFhayLImISCkYGhoiOjoagsCzkYmIiFRF2gBGGYIXgOFLTnjZMVI78fHx0NHhMFsiIpItBi6qJb+hS6pPoV9hZGQEQ0NDGVVERKQ84uLi2GYiIiJSQakBTFGR6wCA5ORkfPr0ieFLNhi+kFpJTk7G+/fvUbp0abFLISIiNcHQRfUUNHgBvt/zhY0IIlJXwcHBKF26NG+OS0REpKIibHRR9EW8qDWEhoYiOTmZ7aZsMHwhtRITE4OUlBRedoyIiPKNYYtqk0XwAgARSZq85BgRqa3o6GjEx8cjISGBI2CIiIgoX0JCQgDwUs3ZYfhCasXIyAiDBw/Gb7/9huHDh8PExETskuQmv52DUbfvyrgS1VfEvo7cXpes3ie+D0TKg2GL+pBV8GJWvSHev5/LM7iISG25ublh48aN8PLywoQJE8Quh4iIiPJB7NEv79+/BwC2m7LB8IXUzqJFi3DgwAF4eHhg2bJlYpeTJ4roAJT1OnIbIuQ24BCrE1TR6y0Mnb0MmEgZFYZ9jwrGrHpDBAUF4cKFC1i5cqXY5RARyUX16tUxfPhwLFiwAAMGDBC7HCIiIlJBO3fuhLW1NcqUKSN2KUqL4QupHSsrK0yfPh0eHh6YMWMGzMzMxC4pA3Xq/MtsW1I73X98LPV3LWh8/71WNeghRc4Vkljk8TlnoEN5pU7ft5Q9WYx6MaveEACwfPlyFCtWDMOHDy/wMomIlJW7uzv27t2LNWvWAJrKcNteIiJSN0avBbFLUHtijX559uwZ/vrrL6xbtw6ampoKX7+qYPhCasnR0REJCQmIiYkRPXwpjB1/hXGbSTHk/dliuKPa+N1TeMnqcmMA8OHDB2zbtg2//vorDA0NZbZcIiJlY2FhgXLlyuHr169AcfUNXwLmTkKlhavELoOIiEitLF26FCVKlMCQIUPELkWpMXwhtRQeHg4AKFasmGg1sBOQSPXwXkqqgd+vlJYsgxcA8PT0hK6uLtzc3GS6XCIiZRQWFibzNpMyneV8a8tkAN8DGIfhntnO+81aooiSZCZg7iQAYLBEREpLmY4H6k7Ro1+Cg4Oxa9cueHh4QE9PT2HrVUUMX0gthYWFQVNTU+FnrLJDkKhwKsi+z+Dmf/gdSnkl6+AlMTERXl5eGDduHExMTGS6bCIiZSQNX77lr4NMmTvWUoOXtL9nF8AUZFsUHdykBi+p/2cAQ0REirRp0yYYGhpi1KhRYpei9Bi+kFq6d+8erK2tIZEo5o9gdhgSUX4p8/dHEr5/hxapUxNaUN7OFSqcZB28AICmpibi4+N5w0giKhSePXuGmJgYWFtbAw9f5fp5yhy4iCWvr0lBwpq0wUvaaQxgiEiZ8Fih3pKSklCsWDEUKVJE7FKUnobYBRDJWlxcHA4cOIA+ffrIfV1F7OsodccpERGROpJH8AIAGhoasLS0xIcPH+SyfCIiZbJz504ULVoU7dq1y3Feo9eC9EdV/DjKJafLjilS2tczqx/Dd99fa8N36V/3zEIWBi9EpExU6VihTiJsdBW2rtQ2kyDwvc4JwxdSO+fOnUNERAT69esnt3UwdCEiIhKHvIKXVCVLlmT4QkSFwsGDB9G9e/dsr9WuaoHLj1IDF2UKXvIrbTCTdnschntmGeAQESkav3sKh5IlSyIuLg4RERFil6L0eNkxUjtHjx5FxYoVUblyZZkvm4ELERGReOQdvADfGxIhISFyXw8RkZgCAwMREBCApUuXZjmPunSgqUPwkpncbFde3kNF37eGiNSPuhw3KGclS5YEAHz48IH3yswBwxdSKykpKTh69Cj69Okj0/u9MHQhIiIqHPT09PD27VuxyyAikqujR49CT08PLVu2zPAYO88Kp/y87wxsiCgVjx3KIcJGF0VfxMt9PamjZsPCwuS+LlXH8IXUyrNnz/Dhwwe0adNGJstj6EJERKQcFDHqJTY2FqdOncKECRPkvi4iIjH5+fmhcePGMDQ0BMBOM8qfgnxuGNwQqQcePwqnw4cPw8TEBHXryr+NpuoYvpBaSb3Rk76+fr6XwcCFiIhIuSgieAGAY8eOITIyEv3791fI+oiIxCIIQoHaTEQFld8OW4Y2RMqDwYtykvfoF0FIwZ49e+Dq6prtfePoO4YvpFZ0dHQAAHFxcfl6PoMXIiIi5aKo4AUA/vrrLzg4OMDOzk5h6yQiEoOOjg6io6PFLoMozxjaEImPoUvh9u3bO7x+/Rr9+vUTuxSVwPCF1Mrt27cBAJUrV8708S+/Dst0OkMXIiIi5aPI4AUANDU1YWBgoNB1EhEpmiAIuH37Nrp06SJ2KUQKw9CGqOAYuhAAQKIBAGw35RLDFzn78uUL/P39ER4ejsjISHz79i3dv5qamhg9ejTq1GHnvyycOXMGVapUQZkyZTI8xuCFiIhIdSg6eAG+n7zh6+ur8PUSFXbJycm4ffs2goODM20zRUVFoUGDBhg2bBh0dXXFLlflBQYG4s2bN2jdurXYpRApvYJ2NjO8IVXHwEU1yfPSYwb65gCAp0+fol69enJZhzph+CJjiYmJuHbtGs6cOYPTp0/j9u3b0vuQaGhowMjICMbGxtJ/Q0JCsHnzZnTp0gXz589HrVq1RN4C1RUfH49jx46hd+/eGR5j8EJERKQ6xAheAKBSpUr49OkTwsPDYWJiIkoNRIVFcHAwTp8+jdOnT+PcuXMIDw+XPqavr5+uzaSrq4u9e/diyZIlmDNnDoYOHSq93DDl3aFDh6Cjo4NmzZqJXQqR2strxzXDGlIGDFwoO1pauihVqhQCAgLELkUlMHyRgZCQEJw4cQLHjx/H2bNn8e3bN5iZmaFVq1YYM2YMmjZtihIlSsDAwAASSfoDaVJSEvbu3YsFCxagdu3aaNiwIYoVKwZDQ0M0aNAAw4cPR5EiRUTaMtWyc+dOfPz4ESNHjkw3ncELERGR6hAreAG+hy8AEBAQAEdHR9HqIFJHiYmJuHLlCo4fP47jx4/jyZMn0NDQQP369TFhwgS0bt0aVapUgZGREbS0MjZTAwIC4O7uDjc3NyxevBjVqlWDgYEBLCwsMHLkSNSuXVvxG6WCYmJisHr1agwaNAiGhoZil0NEP8htpzdDGpI1Bi6UF5UqVWL4kksMX3Lp2bNn+P333xEQEICkpCQUK1YMJiYm+O+//3D79m1IJBI4OTlh+vTpaNu2LerWrQsNDY0cl6ulpYWBAweiT58++PPPP3Hu3DlER0fj06dPmDZtGhYuXIhx48Zh3LhxMDMzU8CWqqbExEQsWbIErq6uWd7vJS0GL0RERMpFzNAllZ2dHYDvQ+gZvhDlXUJCAnbt2oXTp0/j7du3KFasGExNTREXF4dz584hIiIClpaWaN++Pdzd3dGiRQsUK1ZM+nyH4Z5ZLvvWlsnYs2cP5syZg3Xr1uHTp0+IiYnBiRMnsGHDBrRr1w6zZs1C48aNFbGpKmvLli0IDQ3FzJkzpdOye92JSDnlpaOcQQ1lhYGLepPnpccqV66MixcvymXZ6obhSw4ePXoEDw8P7N+/HyVKlECtWrVQokQJfP36Fe/evYOtrS0mTpyItm3bwtzcPN/r0dbWxtChQzF06FDptNevX2PlypVYtmwZVqxYgZ9//hmzZ88u0HrU1aZNm/Dq1Sv8+++/2c7H0IWIiEi5KEPokqpIkSIwNzdHUFCQ2KUQqZTY2Fhs2bIFy5Ytw7t371CjRg04ODjg69evCA4OhiAImDx5Mjp06IA6depkepJaTgGAw3BP3NoyGVWrVsX69eul05OSknDgwAH89ttvaNKkCRo1aoRff/2V9zPJxJcvX7Bs2TL069cPFSpUAMDghagw4GgaAhi0kGxZW1uzzZRLDF9+kJKSgrt37+LcuXM4c+YMzp8/jzJlymDNmjUYOnQo9PX1FVaLtbU11qxZg7lz52LOnDlYtWoV9PX1sXjx4jwtJyIiAkWKFIGmpqacKhVPWFgYJk2ahJ07d2LIkCGoWbNmhnkYuBARESkXZQpc0nr16hW+fPmCGjVqiF0KkdL7/PkzfHx8cO7cORw9ehRfvnxB3759MWvWLFStWjVPy8ptAJAawKSlpaWFfv36oU+fPjh+/DgGDx6MLl26IDIyMtPLl2UlJSUFkZGRanu/p3/++Qdubm5ITEzEL7/8AoDBCxGll5/OeQY2yolBC8mbv78/20y5xPAljdDQUNSrVw9BQUEwMDBA06ZNsW3bNvTr1y9fN3SM+3ddvmuJtqkLs+oNAQDFixdH06ZNsXnzZrx69QpRUVG5vg+Mv78/WrRogYULF2LChAn5rkcZ3bx5E127dkVUVBS2bt2KIUOGiF0SERERZUFZA5e0Tp06BS0tLbRs2VLsUoiUmru7O+bNmwcAqFq1Kvr27YuxY8fCxsYGgHw79TNb9q0tk6GhoYGOHTtCV1cXmpqauHHjBho1apSrZSYlJaFXr164efMmgoODZV2yqJKTkzFw4EDs3bsXXbp0gZeXFywtLRm8EJFMFKSTn8FNwTFkoZzI49JjKSnJOHv2LCZPnpzzzMTwJa39+/cjODgY586dQ+PGjfMVuKQqSPCSKvThVWkA07dvX8TGxmLChAm4e/cuLl68mOPlx/777z+0adMG3759Q2BgYIHrUSaCIGDcuHEoXrw4bty4gdKlS2c6nyzeByIiIso/VQhdUp08eRINGzZE0aJFxS6FSGnFxcXB09MTQ4YMwaJFi2BlZZXucTE79SUSCa5cuYIBAwagSZMm2LJlS44naCUnJ2Pw4ME4dOgQgO83pDcwMFBEuQrh7e2NvXv3YseOHRg4cCAkEgmDFyJSCvIKDlQ11GGQQqrCfVFTNGkyF+3atRO7FJXA8OX/xcTEwMvLC+3atUOLFi0KtKyCdvin7aRIDWAkEgmGDx8OZ2dnNGnSBF26dMG5c+egp6eX6TICAgLQqlUrlCtXDlWrVsXbt28LVJOy8fPzw82bN3HixAkGL0REREpAlUKWzMQnJMDHxwdz5swRuxQipbZt2zZERERgxowZShW8pCpfvjz8/Pzg5uaGESNGoHTp0mjVqlWm8wqCgNGjR2Pfvn0YM2YM/vjjD+l9PdWBIAhYsmQJmjdvjkGDBgFQjvdIlaRe5o6vG5HqUHSIoaMFoDpQJFiAThIDFJK9nI5Ft7ZMzvY4JcvRLz6+szBnzhyYm5vD3t5eJstUd2oZvnz9+hUhISEIDAxEQEAA4uPjYW9vj3r16sHCwgIpKSl4+/YtPn78iPj4eMTFxWHVqlV49eoVdu3aVaB1hz68CuSy88HwxZ0M0zLruEg7AqZy5co4cuQIXFxcMGTIEOzZsyfdDSsFQcChQ4cwfvx4mJub4/Dhw2jXrh2MjIzyuUXKRxAEeHh4oFatWmjbtm2m8zB4ISIiki9VD1t+dOPuA0RHR/MMLipU3r17h4cPHyIgIACvXr1ChQoVUK9ePVSrVg1aWlqIjIzEq1evEB0djfj4eDx79gzjx4/HiBEjUKlSpXTLUqbOaS0tLaxfvx5v375Fjx49cOXKFVSvXj3dPMHBwZg+fTr279+PHTt2IDo6GgCQkJAgRslycebMGdy5cwdnz54FoFzvkSpIe3+hnDq2iIiI5CGnY1Hq44o4Tvn4zgLw/WoBbdq0SdcfTVlTy/ClZs2aiIuLAwAYGxtDW1sboaGhAAALCwuEh4cjPj594qerq4ujR4+ibt2sOxJCH16VaZ156bRIG8A4OTlh9+7d6NGjB3r06IHu3bsDAJ4+fYqhQ4fi2rVraNu2LbZu3YoZM2bg1atX2Ldvn0xrF9OBAwdw7tw5eHt7QyLJOJyUwQsREZFsqFvAkp2jZ31hZWWFWrVqiV0KkcI4OjoiLi4OGhoasLKywvv375GSkgJ9fX0UKVIEnz9/zvCcrl27Yv369emmKWOntJaWFg4cOIBGjRph+PDhuH79OgAgJSUF8+fPx/Lly2FkZITdu3fD1tYWjRs3xujRo1GtWjWRK5eNmJgYjB07Fg0bNkSLFi2U8j1SZmk7u9JO4+tIRESKktOx6MfH5XmcSg1eXrx4gbt372Lq1KlyWY86UsvwZfPmzbC2toatrS0sLCwAAK9fv4a/vz8ePHiA4sWLo2LFiihZsiT09PSgq6sLU1PTbK/vLevgJT/SBjAdOnQA8P2P6lQjRozAp0+fcO7cObRo0QIPHjzA7t27sW3bNlSqVAn//fcfatasKUrtsvL582eMGTMGPXv2RJcuXcQuR63EJyZCV1s73TS9n8ZK/89Qi4hIfRWmkCUzEZHf8NeRU5gybVqmJ3YQqavdu3ejatWqsLGxgY6ODqKionD37l34+/sjOjoaFStWRPny5WFkZARdXV3o6emhVKlS6fYTZeiMdhjumWkHhZGREZo0aYJLly5Jpx09ehQLFy7EzJkzMWvWLBgbG6NNmzaoWrUqVq9ejUePHqF8+fIqf9+XuXPn4u3btzh27Bi/1/Ihq8/U1fVjoa2trXT7ABERqZ/MjkViH3PWrVsHMzMzdO3aVdQ6VIlahi/t2rWDmZlZumnlypVDuXLl4OrqmuflKUPwkio1gEkdFv/vv//CxcUFL1++xOXLl3HkyBHpPWtS7wnTp08fjBw5Ert27UJQUBCsra3F3IQC2b17N6KiorBuHYMAWTl87R6mbPkHH8MisXxYN4zu0BRA+uAl9XcGMEREqqewByu5scf7OBKSkjB69GixSyFSqGbNmqVrNxUpUgSNGzdG48aNc/V8sTsA0sqqszw6Ohrv3r3D4cOH8dNPP2Hx4sVo3LgxlixZAgCIj4/HpUuX4O7ujocPH8LJyQmjR4/G77//ruhNkJmYmBhs3LgR06ZNy3B5OMq9tJ+p2NhYdOjQAZcvX0atWrVw+vRpmJqaAuCIGCIikp+0x6IfjzU//u0j7/u+fPv2Ddu2bcOYMWOgr69foGUVJrw4Ww6UKXhJFfrwKkxNTbF69Wr4+fmhYsWKGDhwIGrVqoWOHTsCAMLDw7FlyxY4Ozvj6NGj2LlzJwDg0KFDYpZeYPv370f79u1RvHhxsUtRG/GJSXgfGo5KpS0wefNBbDl9OUPwkiqr6UREJK5om7pZ/lD2kpOTse3gUfTq1QuWlpZil0OkMpSxszmzmqZNm4batWuja9euqFq1Kvz9/fHLL79IH/fy8kJsbCwaNWqEvn37IikpCQcPHkRKSooiS5epEydOIDo6GgMGDBC7FJWX+pnS0tLCixcvYGpqiqCgILRp0wbh4eHS+TIL/oiIiGTBYbhnln93pU5XxN9lO3fuRHR0NNzc3OS+LnWiliNfwgJuAcbqc4P5zHx9dA0TJkzAkCFD4OnpiXXr1mHt2rWQSCSIiopC+/bt8fjxY9SpUwcjR46Eq6sr4uLicPDgQUyaNEns8vPl5cuXuHnzJvbv3y92KWrF1bku/vC5jW/R0RjaqxvGeR2Az4d49OzZEx07doSRUfp9iSNg5IMdpIWD4Ys70v//+J6nfYwoLX4/yNe1V1/x6tUr/PXXX2KXQqRw7aZtQoqGrthlyFRmnQ+3zp2Dj48PfvnlF7Ro0QKtWrUC8L0TYdKkSbCxsYG7uzvevHmDjRs34ueff8aNGzfQoEEDRZcvE/v374e9vT1sbW3FLkUtpJ5Z7OnpiR49emDRokVYuXIl7O3tMWDAALi6uqJatWocAUNERKJQxLEnJSUFa9euRffu3VG6dGm5r0+dqGX4UlikXoJs/vz5mD9/vnT6lClTcO3aNQDAkSNH0LhxY3h5eaFXr1748uWLSNUWjCAI+OWXX2BkZCQd3ZMZhgJ5k9qh99tsI7TsPQw9evfDH3+0wM6dO9G3b1/o6enh6NGjaNmyZbrnqUsAww5NUrTsPnPy+DymBjr5XXZSSgoQFCrLkkST9jXILgSjwsWsekP8Pr45GjRogHr16oldDhHJSWrneerlmQHgyZMnGDp0KIDvJ3lpa2tj+/btMDY2BgCVbTfduHEDR44cwdKlS6XTGAgUnMNwT/hvnoQWLVpg06ZN+Pvvv7Fz506sWrUKCxYsQJcuXeDt7c0AhoiI1I6P7yycPHkSgYGB2L59u9jlqByGLyouNYBJa9iwYWjUqBHq1auHSpUqQUNDA1u2bIGPjw+GDRuGS5cuoVy5cihdurTK3Hxx/fr12LdvH/bv3w9DQ8NM51GHMEBRfuxsrFW1EuaM/xmLFi3C5s2bcePGDbx+/RqdO3eGl5dXhvBFWbETlSg9We0T0eVrQUtDfa5Uyu8KSvXo0SP4+vpi3759YpdCRHL243XRra2t4eXlBVtbW9jb28PY2BhRUVGoXLkySpQoAQB4+PAhbGxsVOa65p8/f0aPHj3g4OCAMWPGAGDwIkv1RqzCoW3b4OTkhBkzZsDPzw+bN2/G77//jhkzZuDly5eoUKECAxgiIlJK+bnvi4/vLADA2rVrYW9vr7KjgsWkPj0phdiP96WpX78+Bg4ciCpVqkDj/zvL4uPjUaJECWzduhVNmjRB2bJl0atXL0RHR4tRcp5ER0djxowZcHNzQ69evTKdh8FL7mXV6Thh2AC4ublh1KhROHbsGKytrdGnTx+cOnUKMTExCq7yf7K7jwHva0BERAWxdetWmJubo1u3bmKXQkQKkLZD3MDAAD///DNcXFyko11iYmJgZmaGsLAwdO7cGTVq1EDZsmVx4cIFsUrOk+XLlyM6Ohp///03dHR0GADIQTf3g9Kzf11dXaGhoQE3Nzfo6urC29tbOh/vAUNERKouNXh5+/YtTp06hVGjRqnMSfzKhOGLmgh9eDXbn95N6+Djx4+IiorC48ePsXXrVpw4cQLOzs74+PGj2OVn6/jx44iOjsaUKVMyfZzBS+byGlBIJBKsWbMGnTp1Qs+ePXHlyhV069YNMTExOHPmjGh1ExERyUNiYhJ2796NAQMGQEdHR+xyiEhBsgskSpQogfv37yMuLg5v377FpUuXULNmTbRs2RI7d+5UYJV5JwgCDhw4gN69e6NUqVIMXuRo2FofeHt7w8fHB8OHD4e+vj5at26dLnwBGMAQEZHqSg1eAGDXrl3Q09NDz549RaxIdTF8KURCH16FoaEhqlSpgqFDh+Lq1at48+YN3N3dxS4tWwcOHICDgwMqVKggdilKr6ChhaamJvbu3Yv69eujZcuWePDgAezt7eHu7o6EhAQZV5sewxYiIlKkPdde4/PnzxgyZIjYpRCRguUUTGhoaKBUqVJwdnbGqVOn0KdPH4wePVqp7wNz48YNvHnzhh0jCjJj333s3LkTe/bsQZcuXfDTTz/hypUrOHnyZLr5GMAQEZEqEwQB27dvh6urq3SkMOUNw5dCJu0lymrWrImJEydi27Zt+Pz5s4hVZS0lJQVnzpxB165dxS5FYfJymS15jBLR19fHqVOn0KlTJ/To0QNDhw7FgwcPsHDhQrRq1QqDBg3Cx48foffTWJmsj6ELERGJ4eLpg7C3t0eNGjXELoWIRJDbkSHa2trw9Pw+77p1yjvi/uTJkzA1NUXjxo3FLqXQWOnzAceOHcOpU6dw6tQptGnTBsOGDcPGjRtRq1YtHDlyBAADGCIiUh4RNrp5mv/y5ct4/vw5T1grAIYvhVDaAMbNzQ0JCQnSPwyVzYsXLxAVFYV69eqJXYrcKONltvT09LBx40YAQEREBH799Vd4eHjAx8cHu3btQpUqVfDu3bsCBzDR1tVlUS4REVGeJJpXwPHjxzF06FCxSyEiEeU2gDE3N0ffvn1x4MABOVeUf/fu3YO9vT00NTXFLqVQ+eXgY/Tt2xeXLl2Cl5cX4uLiMG7cOPz333/46aefpFeZYABDRESqaPv27ShfvjyaNGkidikqi+FLIZUawJiZmaF27dpKexPJ+/fvAwBq1aqV6eOqer8XZQtbMlOsWDEMHz4cS5cuxbBhw+Dg4ABBEODu7o6wsDD4+voCQJ4CGOl2M3QhIiIRRMfEwuvweQwePBhaWlro06eP2CURkcgchnvm+AMALVq0wNOnT5X2fpn3799H7dq1xS6jUJo+fTq+fPmCffv2Yf369UhMTESlSpXQr18/HDp0SDofAxgiIlIVp06dwuTJk3HgwAEMHjwYGhqMEPJLS+wCSHxNmzbFwYMHkZSUBC0t5fpIpN5nRFtbO8NjqhS8KHPIklbow6swq95Q+vuSJUtw6NAhzJ07F7t27YKjoyPq168PGxsb3L59G/379weQuwAm7YgrIiIiMbxJ0MXcuXNRpUoVzJ07F8WKFRO7JCJSAQ7DPfHvgl4AgPPnzytlcJuQkCBty+V2RA/JRrVq1TBx4kQsWrQIAQEB6N+/P0JCQuDs7IwDBw4gLi4Oenp6ADIPYPh+ERGRspk/fz4CAgLg5OSEESNGiF2OSmNsRejRowfev3+PPn36yP2m6jlJSUlBbGwswsLCsGvXLkyaNAnGxsZITExMN5+yBy/KeCmx3Eobkpibm+Pnn3/G6dOnUalSJXz+/Blt2rSBvb099u3bhzVr1iAsLCzd86OjozFlyhQ4Oztj8uTJ8Pf3B4B0oQ4REZEYTExMAADbtm3DnDlzxC2GiFTKT/MOoEmTJhgzZgxu3rwpai2CICAxMRHfvn3DkydP0KlTJ3z48AGGhobsyBfJrFmzEBsbiytXrmDXrl04efIkHBwckJSUhGHDhsHf3x+CIKR7zrlz59C4cWNUjLqJz0+uQkhJFql6IiIqTHJz3xcTExM0b94cPj4+KFmypAKqUl8MXwgNGzbEP//8gyNHjqBr166IjY3N97LevHmDlStX4syZM7maPzo6Gl5eXqhWrRr09PSgqakJAwMDmJqaYtCgQWjevDmePHmCEiVKSJ+jrMGLqoYtmUkbwFhaWkoDltQRSB4eHmjSpAmmTJmCUqVKwdXVFbNnz8bvv/+OWrVqwcvLCyVKlMCmTZswa9Ys6bIYwBARkZhMTU0BAKGhoSJXQkSqKNK6NapUqYIWLVrAz88v38uJj4/H0aNHMX/+/AwnmWXl5s2b6NOnD4yNjaGlpQUdHR0YGxujatWquHfvHv755x8ceqmX75qoYExNTaGlpYWvX79CIpFAS0sL9vb2WLFiBS5fvoz69evD3t4eY8eOxfLlyzF8+HC0atUKycnJePz4Md5c/QexYSFibwYRERGA77epYJtJNpTrGlMkmp9++glHjx5Fly5dYGlpiTZt2qBjx45o3749zM3Nc3x+eHg4Bg8ejH///RcAMHDgQLRu3TrL+b9+/YoVK1Zgw4YNiIiIQPfu3TF69Gjo6+tDT08Penp6KFeuHOzt7dM9T1mCF3UIWHKSegkyQ0NDxMTEpLssnY2NDf766y+EhIRg27ZtOHXqFPz9/fH27Vs0bNgQJ0+eRIUKFVCiRAk0bJg+cDGr3hDv7l0UY5OIiKiQY/hCRAWhqaOPmIod4aivDxcXF9StWxedOnVCx44dUbdu3VxdD/3PP//EhAkTpCc3ubm5pTvR7EdHjx7F0qVLceXKFdjY2GD69OkoXry4tM1kaGiIZs2aodnETZBIJDLbVsobiUQCQ0NDREZGpps2ZcoUTJw4EadOncLOnTtx8eJFvH79GoIgYP369Rg5ciSWLFmCV69e4f5fv6HB6LUibgUREdF3pqam+O+//8QuQy0wfCnEfry/R+vWrXHv3j0cOHAAx44dw6BBgwAAVlZWKF++POrWrYu2bduiWbNmMDAwkD4vMDAQnTp1wufPn7F48WLMmTMHJUqUQExMTLr5Uj19+hQdO3bEx48fMWLECIwfPx7lypXLsV6xgpfCELRkJvWz8eDBA9jY2GR6PyBLS0vMnj0bs2fPBvD9snGpjU5/f398/fo12xCOiIhIkfT09GBkZIR3796JXQoRqShNbV18LdMae/YMxdGjR7F69WosWLAARYoUQbly5WBra4uWLVuibdu2qFChgvR5ycnJmD17NpYtW4Z+/frh1atXePHiBaKiojINX5KTkzFjxgysXLkSzs7O8Pb2RqdOnaCpqZlhXl5qTHzBwcGIiIhA5cqVMzymqamJDh06oEOHDtJpadtNZ86cQfPmzTO9zykREZEYihcvjvfv30MQBJ7cUUC87Fgh9+NN0O3s7DB37lzcuHEDHz58wK5duzBs2DCUK1cO//77Lzp06ABzc3N06dIFO3bsgLe3NxwdHaGhoYGbN29i8ODBqFixIlasWAEzMzMMHToUb968kS7f29sbDRo0gK6uLu7fvw9PT0+lCF5+vEeLOl1CLD/ShnLXr19HgwYNcvW8tGf7HTlyBEZGRnB0dJR5fURERPnl7OyMc+fOiV2GzAiCkO5MayKSPw1NLXieD8G+ffvw+fNn+Pr6Yt68eWjWrBlCQ0MxYcIE2NjYoHr16vjll19w5coVdOnSBStWrMDKlSvx559/okmTJggNDYWNjQ1q1qyJgwcPSu8J8unTJ3Tu3BmrVq3CmjVrcPHiRXTp0oXBixK6tWUygO9tJgBwcnLK1fNS201fvnzB1atX0apVK/kUSERElA/Ozs74+vUr7t69K3YpMiPWfc4LHL4sWbIEEokEEydOlE4TBAHz58+HlZUV9PX10axZMzx69Cjd8wICAtCoUSOULl0a7u7u6R67e/cuOnbsiBIlSkgvP9WrVy98+fKloOVSJn4MYFJZWlpiwIABcHd3x+7du/Hq1Ss8fvwYCxYswOfPnzF06FB069YNTk5OuH79OipWrAgrKysEBgbiyZMnmD9/Po4fPw47OztMmTIFP//8M7p16wYXFxdcvXo13Zlg+akvK9kFKQxYcpYavLx+/RpjxozBrVu34OzsnKdlhIWFYe3atRg8eDDP4CIiIqXSsWNHXLx4EREREWKXkqOQkBCUKVMG5cuXh5OTE7p06YKff/4Zffv2RdOmTWFrawtDQ0NUrVpV7FIpF9huEkfRF/F5+skLh+Ge0NbWRrNmzTB16lSsXbsWFy5cQGhoKA4dOgQHBwd4eXnB2dkZFy9exLFjxzB58mRIJBJ4eHggNDQU3t7e0nsoOjo6Yu3atahZsyb8/f1x/PhxjBs3LsszThm8iOvWlslISkrCn3/+iVmzZsHGxgaWlpZ5WsZvv/0GPT099OrVS05VEhER5Z2zszOMjY1x/PhxsUvJlcmTJ6NEiRKoWbMmWrVqhf79+2PEiBHo0KED6tSpgxIlSuS6H1rWCnTZMX9/f2zatAk1a9ZMN33ZsmXw9PTEjh07YGdnh0WLFqFVq1YICAiAkZERAGDMmDEYMGAA6tWrh1GjRqFFixZo1KgRPn36hJYtW6JTp044ffo0TExMEBQUhCNHjiAmJqYg5VI2chNwmFVviCpVqqBKlSqYNm0aQkJC8OjRIzRr1izdWVgSiQSVK1dG5cqV4ebmhlWrVmHFihVITk7G5s2bMWzYsGyHrGVWS2pAYvjiTpaPUcGlBi8eHh6YN28eTExMsHDhQgwdOjTH565duxanT5+Grq4uPnz4gISEBOnlyIiIiJRFhw4dMGbMGJw5cwaurq5il5MtIyMjfP36FXZ2dqhatSo+fvyIO3fuoEiRIihVqhTq16+PUqVKwcTEBEOGDBG7XMoG203yk9fAJK/Li7DRzXb+zAKQW1smo2vXrujatSuSkpJw/fp1WFtbo0yZMunmc5m0Gbe2TEaXLl3g5+eHmTNnYvz48WjXrh22b98OCwuLHNdF4ri1ZTJevHiBVq1aISgoCJ06dcKiRYtyfN6zZ88we/ZsJCQkQFdXF0ePHsX06dNhbm7O95eIiJSGjo4OWrdujWPHjmHu3Llil5MjTU1NfP78Gd27d0doaCjevHmD2NhYlCxZEk5OTrCysoKZmRnGjBmj8NryHb5ERUWhX79+2Lx5c7o/MgRBwOrVqzFnzhx069YNALBz505YWFhg7969GDlyJIDvN2ivU6cOatasCSsrK+nZh1evXkVkZCS2bNkivcdE+fLl0bx583xvJMnGj/eIsbS0zPHMHiMjI/z6668YP348kpOTYWZmluM6ssOgRTE2bNiArl27Yvv27TA0NMxx/h07dmD8+PFo3rw5kpOToaenhw0bNmT6+cjrSCYiIiJZ0tXVha6uLgICAsQuJUeGhobo0qUL7t+/j23btmU5X2hoqAKrorxiu0k2ZB2y5Ge9OQUxqRyGe0ovR6WlpZXtKPLUeZs1a4Zr167h1atXKFeuHK+vruQchntiYE0tvH37Fnfv3kXt2rVzfE5ERATatm2LpKQk1KhRA2FhYejSpQsmT57M4IWIiJSGj+8sAICJiQnOnj0rcjW507dvX6xYsQI//fQT2rZtm+k8oaGhooQv+b7s2JgxY9ChQwe0bNky3fSgoCCEhISku8m2rq4umjZtiqtX/9fp6u7ujlatWsHAwAAaGhpo06YNgO8d+klJSfD29pZe85aUR347zk1MTAocvJDiaGlpwdvbG/369YO3t3eO10U8cuQIHBwccO7cOZw8eRLnz5/HwIEDM8zH95iIiMQkCAJGjRoFY2Njace2srO0tFSJS6RR1pS53VT0dUK+LrclT7K4HJi85KWWvHSmp84rkUhQvnz5LIOX1ECHlMOK/ReQmJgIV1dXLFy4EK9fv852/sePH+Ply5fYuXMnjh07hrNnz2Lfvn1oMWWrgiomIiLK/oSW1ODl0qVL2Lp1K3799VdFlVUgJUuWBAClbDfla+TL/v37cefOHfj7+2d4LCQkBAAyDJG2sLBI98dI+/bt8fnzZ0RGRqJ48eLS6U5OTpg9ezb69u2LUaNGoX79+mjevDkGDhyYYZlZSUpJQVJKSn42jXLh43+XYVpFtjdR//rkhkyXR/mTmJgIALh27RoOHjyIvXv3ol+/fjA1NcXChQvRv3//TJ9XqVIl3Lx5E0lJSVkuO+17nJwiSP/lvkqkvFL3T+6npA7eaNvh7IEDOHPmDPbs2QMTExPpcU+ZXbx4EW3bts22VlXYjsJK2dtNWloaSD0fr3hw/j9HEeV1AABFg2RwI1OdAt+WVO7Svlap256ZhqNW4cLasVk+rqMlyfW8qa55jUPTcetyWSnJU6kajWBS3Ar1i8dizZo1WLJkCVq2bIldu3ZlevWAcuXKQV9fH69evZJ+bzcdty7d5yAtHU2J9N+s5iEi8aXun9xPSVXoZPG31pFjk5GYmIj4+HiMGjUKLi4ucHNzU4m2xtmzZ6Gvr48mTZpkWa9Y2yER8niaVHBwMBwcHHDmzBnUqlULANCsWTPUrl0bq1evxtWrV9GoUSO8f/9emjoBwIgRIxAcHIxTp07laj2hoaE4f/48rl+/jsOHD+Pr16+4ePEiatSokeVzIiMjUbRoUezduxcGBgZ52SwiIiIiIpUUExODvn37IiIiAsbGxmKXQ/+P7SYiIiIiIuUgVpspz+HL4cOH0bVr13Q3WE9OToZEIoGGhgYCAgJQsWJF3LlzB3Xq1JHO89NPP8HExAQ7d+7Mc5EJCQmoU6cOHBwcsn1+aiPC59pTGBUtluf1ZKdsYmCBl/FG204my1EFBR0Zw5EwipOSkoKzF6+hfu0asGnQKtN5oqKi4ODgAEdHx3T7YHJyMjp27Ijg4GBcvXo13ZdXdu9hfFIy7gaHo04ZE+hqaWY5HxGJKyklBbdeh8HBuhi0NJT/TGSirLzRtgMA9O3cCL169cK8efNErih3QkNDYWtri2XLlmH48OHZzleyZEmGL0pGFdpNbVu7A8h65AblXVYjYXIzqiWvOAJGMVLfu+fPn+Pjx49o1KhRlvMuW7YMy5Ytw/Xr11GxYkXp9OPHj6Nv375Yt24dBgwYkKv3roiuBsY0L4c/zr9CVDxHIRMpKx0tCSa1Ko9VZ4OQkMTbJ5Dyym6E8pFj/7u0qZeXF+bNm4f3799L7yuo7CZMmABfX1/cv38fEokk3XE29TguVpspz69gixYt8ODBg3TThgwZgsqVK2PGjBmoUKECLC0tcfbsWWkjIiEhARcuXMDSpUvzVaSOjg5sbGwQHR2dq/klmlrQ0JTth+OtZtUCL0Mjh+WUS3ha4HWI7d/T5/H4QzhsbB7Azs4OjRs3ho5O5g2QuLg4vHv3DgYGBrCwsIBGmo49i5rOvD+IAjx/9QYTfl2CG3f/Q48OrfH3sfaZzqerqwstLS28fv0a2tra0ukrVqzA+fPn4efnl+GePtl11CZpfG88aGpI2KFLpAK0NDS4rxbAK53KmU7P6rj/SqeyWvxNoCxe6VSGBoDy5t87zqpXr57uWCaWqKgoREZGwsrKKst5zM3NYWtri927d2P06NFZzqcM20MZqUK7KSkpBSm8tKRM6QfEAQAibHTTTW8weq30ni2fPn2Ch4cHLCwsYGNjAycnJ5QtWzbT5QmCgM+fPyM8PBylS5dON1KJnXzyd2vLZCQlJWHFihWYP38+UlJS8OzZM1hbW2c6v5GRESIjIxEWFib9bv7w4QNGjBiBNm3aYMiQIag3YlWu1p2g9f39TUgW+F4TqYCEJO6rpLyKvohHdheHTdueuHfvHmxsbKCvry//wnIgCAKCgoJQtmzZbIOghg0bwsvLC7dv38a4rdfSPZa6bWK1mfKcUBgZGaF69erpphkaGsLMzEw6feLEifDw8ICtrS1sbW3h4eEBAwMD9O3bN8flHzt2DPv370fv3r1hZ2cHQRBw9OhRnDhxAtu3b89ruSolq86ZtJS9M+aPvd54+vQpEhISkJSUhGLFiqFnz57o378/6tatixs3bsDPzw8XLlzA9evXER///SZP2tra+PPPP9GrVy/pssyqN2QAI0cHj5/BxHlLULJEcQzt1Q3b//LGgwcPUKVKFWzevBn379/HunXroKWlhWnTpiE4OBj//vuv9PnXr1/H3LlzMXPmTDRp0kTELSEiypvcHG8VIbs65Fmjsv8tIQ/2dqbw9fUFAOnln8R07NgxjBgxAiEhIWjWrBmGDBmC7t27w9DQEDExMQgKCsLjx4+xaNEiPHjwANOmTRO7ZMoHtpsKt9Sb2aYNYRyGe+LWlsnw8fHB77//jqJFi0pvDNukSRP069cPPXr0wNevX+Hn5ydtN719+1a6jAYNGuDqVbaRFOHWlskICwtD69atcefOHUyaNAl//vknFixYgG3btuHRo0f47bffMGrUKDRq1AiPHz/GjBkzMG7cODRs2BDA96sMDBgwANra2ti0aVOugxciIiJZSf2bJLfu37+P2rVry6eYPPjy5QtGjx6NgwcPwtLSEgMGDMCQIUNQpUoVpKSk4P3793j58iX++ecf/PHHH7C1tcWUSYeA6iVzXrgCyWXs0PTp0xEbGws3NzeEhYXB0dERZ86cgZGRUY7PrVq1KgwMDDBlyhQEBwdDV1cXtra22LJlCwYMGCCPclVKTp0xYnaomFVviMjISAwdOhSrVq3Co0ePsH//fuzduxcbN26ERCKBIAgwNTVFkyZNsHTpUlSpUgVxcXH4+eefcefOnXThS+oyGcDIxzKvbXCub49tKxdBS0sTvtduYsSIEYiKisLjx48hkUhQqlQpODg4YP369Vi3bh0qV/7++QsNDUXfvn3h4OCABQsWZFg23zMiykluw4XcHNeUJUxRFbJ6vVQlxLG3MwXwvRGhp6cHW1tbUeuJiopCt27d0LRpUyxevBi7d+/GoEGDMGbMGBgZGeHDhw/SeZ2cnHDjxg3Uq1cv3TJCH16FWfWGii6d5IDtJvWXWQjz7ds3AEBISAiioqJw4sQJ7NmzB6NHj8aoUaMgCAI0NDRQt25d9OrVC46OjjAzM8OBAwewZcsWCIIAiYQ3dpan1FFKBw8exJ07d3D16lU4OjqiXLlyGD9+PBITE7Fv3z5IJBJcuHABt2/fRv/+/VG+fHnpyDVBEODh4YHz58/j7NmzaDfrTzE3iYiICpm8hi4AkJSUhEePHmHQoEFyqChvFi5ciFOnTsHLywtPnjzBtm3bsHz5cpQtWxYfP36UntBfpEgReHh44OTxeGhoaEE/H9stTzIJX/z8/NL9LpFIMH/+fMyfPz/Py6pQoQI2bdoki7IKpew6VBTRSdKyZUvs2LEDU6dORa1atVCrVi0sXrwYly9fxuPHj9GwYUNUr1493SXGAMDd3V161hfJX1DwW7x8HYzlnqtRpl5zAMByz9Xo0aMHGjduDH9/fxw8eBDu7u7Sa8x7enqidu3a8PPzw4oVKwAA586dyzBsj8ELUd4oc3CQkpwE4BreaNvJ/HKeuaXMr09hJ8v3Rh5/o7zSqSwNXoDv4Uv16tVFv26xvr4+JBIJfvrpJwwdOhRDhw5FUFAQ9u7di4SEBNjY2KBChQqoUKECSpYsmaGDlcdZ1cZ2U+GVtgOkcePG0NbWxsKFC7F48WIMHDgQAwcOREhICI4ePQorKys4OzujaNGi6Zbx6dMnbNq0CVFRUbkK6KhgBEHAiRMnUL9+fTg6fr+n6c8//4y1a9fi6NGjWLZsGTp37ozatWvDw8MDDx48gIaGBhYsWIB27drhl19+weXLlzF79mzM2Hdf5K0hIqLCID+BS1oBAQGIj49XiqsFGBgYwNTUFKNGjQLw/b5qx44dw7Vr11C2bFlUqFABNjY2KF++PDq0WwUNDeW8P41yVkVykVkniaw7O5YuXYrjx49jxIgROH36tPSGok2aNMn20lRFixZFeHi4TGuhzPlcvo5x85aiePHiaN68uXR69+7d8fz5c1SoUAESiQQhISH47bffMGfOHPTs2RPjxo2Ds7MzdHR0MGrUKMyaNQuWlpbpls0OISoodrQTFU552fdz+tslq2Xdv38f9vb2eapLHjQ1NVGhQgU8f/5cOq18+fKYM2dOjs/lcZZIPVSpUgW//vor5s+fj65du8LBwQEAYGlpiREjRmT5vNQwJjw8nOGLnEVERGDIkCE4fPgwfv/9d+l0HR0dXL16FZqamjAxMQEAlC1bFhoaGggKCsK6deuwdOlSLF26FHXr1sWJEyfwy8HH4EAlIiKSl4IGLmndv//9ZIGaNWvKbJn5VbFiRQQHByMuLg56enrQ1dVF9+7d0b1793TztXBZIlKFucPwpZD7sYOioGGMsbExNm/ejLZt22Lr1q3SURM5MTEx4cgXBfjw6TOGTZuHhg0bYufOndIGQyobGxsA369NvGXLFhgbG6NYsWJwcXHB33//jT///BOjR49GmTJlMiybHUKUFwxZiCi/8vP9ER8fj0ePHmHYsGEQBAHJycmijoCpWLEiAgIC0k27desWLCwsMj3GAjzOEqmbGTNm4NChQxgyZAhu374NHR2dHJ+T+rd7RERElt8VJBszZ87EuXPncPDgwQydPGZmZtL/+/n5ITAwECNHjkTp0qWxZMkSmJiYwM7ODl27dkW9Eat4iTgiIpIpWYYtP7pz5w6sra1RrFgxJCUlQVNTU7TjWMWKFSEIAp4/fy69X2J4eDju3buHZs2aAVD+4AUANHKehQqTVzqV0/3kR5s2bdCzZ0+sWpW7mwm+fPkSN27cgL6+fobH2NEgW79t+xv6+vo4cOBAhlEraS1cuBCHDx/G7t27YWhoCOD7GXoeHh4MXkjqx++LvPwQESnS3bt3kZCQAEdHR6xduxbGxsYYPHgwrl69CkEQFF5PgwYNcOrUKTRs2BC7du2Cq6sr6tWrh86dOyM5OTnD/DzOEqmP1E4CbW1tbNy4EQ8fPoSPj0+OzxMEAfv37wcA6OnpybXGwi76SzA2btyIRYsWZQhe0nr16hVcXV3RrFkzjBkzBsD3SwnOnDkT3bp1Q70RuWsPExERZaXoi/gMP/J0/fp1ODo6IioqCiVKlIC9vT28vLxEOWG+WrVqMDIyQpMmTTBr1iysW7cOtra2cHFxwdmzZxVeT35x5AtlK20naU6jYtLe/LVNmzb4+++/8e3bt2yHxAuCgPbt28PAwABr167NsDzKvx9vxHvu3Dns2bMHW7duzTDiJa2dO3di/vz5WLRoETp16pTjevg+KQYDCyKi/Lt+/Tr09PRQq1YtzJo1C6VKlcLFixexc+dOrF+/HqNHj1ZoPTNnzkT16tXh6emJQYMGwcrKCvPmzcOCBQuwffv2HEcOv9KpDLNs5yAiZdbCZQl8fGfBwcEBpqam8Pf3R7t27bJ9ztatW7FmzRr88ccfqFixooIqLRxubZks/X98fDyaNm2K6tWrw83NLcvnhIaGom3btihatCgOHDiQYTSlw3BPudVLRETqR96hSm4kJCTg9u3b6NGjB65cuYKwsDDUrVsX48aNw/Lly/Hy5UuF1mNubo4HDx5g7dq1WL9+PSIjIzFo0CAEBgZi0qRJuHfvnkLryS+OfKFcy82Z66kd8fXq1YMgCLh48WK2ywwPD0dAQAAWLlyIUqVKZVgO5c+PwcuhQ4fQqVMntGjRAoMHD87yeWfOnMHw4cMxYsQIzJ49O8f18H2SHY4UISKSn+vXr8PBwQGCIODKlSsYOXIkLl26BCD95WMURUNDA507d4afnx+OHDmC3r17Y9++fQCACxcuZPk8HhOI1EcLlyWQSCRwcHDA+fPncxyFd+3aNdjb22cbCFDepQ1evn79itatW+PevXvw8vLK8vKUsbGx6Ny5M0JDQ3Hq1KkMxxFVuAQKERGJI7ORLMoQvADAf//9h7i4ODg5OcHPzw+WlpY4e/YsevbsKUqbCQCsra2xYsUKPHnyBIsWLUJYWBhu376NwMBAfPjwQZSa8oojXyjfUhv/P46ICX14FdWqOaFBgwYYO3YsnJ2dpTeHTJWQkICRI0dKd5QKFSqkez7lX9rgJTk5GcuXL8fs2bPRs2dP7NixAxoamWeuvr6+6N69O9q0aYP169fneE1Hvk85YwcZEZFyuHbtGnr27Al/f3/ExsbCxcVFGnKkXi9Y0RITEzFjxgysWrUKRYsWRdeuXbF69Wq0bNkyw7w8nhCppxYuS+A29mf06NEDO3bswJAhQzLMc/r0acyfPx9fvnxBnTp1RKhSfaUNXh4+fIju3bvj69evOH/+PBo2bJjpc759+4Y+ffrg7t278PX1zTAKicELEREpS5iSV9euXYOOjg7q1KmDSZMmSdtJFy5cQL9+/USr6+rVq+jZsyfevXuHBg0aYOnSpejRowdKly4tWk15wfCFCiyzECbs8XXs2bMHtWvXhpubG3bv3i3tzE9MTETv3r1x7NgxaGtrf39uuXIKr1sdpQ1ebt68iVGjRuHevXuYNWsWFi5cmGnwIggCPD09MWPGDDRr1izTYfO3A7/meNk5YucYEZEy+vjxI968eYOnT59i3bp1MDY2Ru3atbFx40ZUrVoVJUqUUHhN79+/R8+ePXHjxg2sWrUKbm5uWd5s+3bgVwVXR0SKtH5dIIYMGYJx48ahUaNGsLOzkz52+vRp/PTTTzAwMEBYWBi6desmfayFyxKkPb0twkZXgVWrvtTgJTo6Gu7u7vD09IStrS2uX78OGxubTJ8TEBCAbt264c2bNzh06BAcHR2zDFvSdrzxvSEiUh+qGqzkxs2bN2FgYAAvLy/4+/tj8ODBePbsGd6/fy/KCWuCIGDt2rWYMmUKnJyccPHixXQn76vKCQ8MX0hmfgxhjKM/YMOGDejbty+KFy+OSpUqwdzcHPv378exY8dw6NAhlCtXDpcvX4a5ubmYpauF1ODl06dPmDNnDrZu3YratWvj2rVrcHR0zPJ5t27dwtSpU9GrVy/s3r070+AFyNv9fzILIdQ1vGHgQkSk3IyNjTFo0CD4+vrizZs36NatGzQ1NeHr64s2bdoovJ7nz5/D2dkZmpqauHDhQpZnVwMMXogKixfPysLKygo9e/bE0KFDYWRkhJSUFIwZMwYtW7bEgQMHsH37dul9YTLrbMhNZ1BhDQHSjnBJJQgC/vrrL0ybNg2fP3/G/PnzMXXqVOjqZv0ajRw5Ei9fvsTt27dRtWrVXK8/s/dGUe/FrS2Tef8ZNaXOHcA5KazfZSQ7hXn/yU7nzp3x/PlzzJgxA4IgoEWLFjh//jw0NTXRuHFjhdfj5uaGDRs2YNKkSVi6dKn0BH5AdYIXgOELyUHaEKZ1DWtMmjQJ27dvx7dv35CcnAxtbW0cOnQIHTt2BABUr15dzHLVgln1hoiMjMTKlSvh6ekJHR0d/P777xg9enSW1yoGgGfPniEiIgLW1tbw9fXFx48f0917J6tOn/wEDmmfk4Q4AP55XoaYGLIQEakmfX197NixAwDw4cMHGBkZ4e3bt3j+/DmWLFH8H+1HjhxBZGQkgoKCYGFhkeV8DF6ICg8tLV2YGLfGl68nMHPmTMTGxgIAOnTogH/++Qe6uroYO3YsgIJ1NuS3s0mVOzozC158fHwwa9Ys+Pv7o3Pnzli1alW6M2l/FBMTg1u3bsHBwQEXLlzAkSNH8hS+ZCan90IWr3nqtjOAEQc7d+VH2V5bHZ3vVxgpGpSAz2W0c5ib8kLZ3mt15+rqCldXV8THx+PTp08oU6YM5s6dCwcHBxgZGSm0lqSkJGzduhXz5s3D/Pnz0z2mSsELwPCF5Ci1s7rfqF/g6ekJQRDw+fNnJCYmpuvgF6uuzGQ3OqOgne/yGPnxLToaFwM+4PjC33Hq1CkkJCRg3LhxmDFjRpY3w0pJScGpU6ewatUqnDt3Lt1jPj4+GDhwIADFdPq81aoILR29HOdT1KgZBixEROpHEARcvXoVtWrVQsmSJQEAhw8fBgA0bdpU4fUEBgbC1tY2Q/DCsIWocDMyKgUjoxEobw2kpCQjNjYU0d/M0L6t+J3msur8UnSIkzZ4uXHjBg4fPozjx4/jwYMHcHR0xPnz5+Hi4pLl89++fYt169Zh06ZNCAsLk07fu3cvZs6cKdfai76Ih6Hh9+4a41eJ0IxOyjBPdq/nj6ETA5j8Y+cv5QU/L6TKgoKCkJCQgEqVKqFMmTIQBAG+vr4YPHiwwmt5/fo1EhMT0ahRo3TTVS14ARi+kILcDvwKezvTHK+rLuubuOe1M12ene/5DX2y8ijgOYbOcMeLFy9Qr149TJo0CcOHD8/2hlPv379Hq1at8PjxY9SrVw979uyBo6MjtLS0oK2tDSsrKwDK1wGU1WtX0FCGYQsRkfo7c+YM2rZtCz09PbRq1Qpdu3bFqVOnUL16dRQvXlzh9Tx79gy2trbppinbcZeIxKWhoQlDQ8Xfj0re8tMpmd/AJjV8SExMxNSpU7FmzRoUL14c7dq1g4eHBzp06CC9J2lm5s+fj0WLFsHQ0BAjRozAwIEDYWxsDE1NTelJbmJ3ALGTN3f4OhER5U7nzp3x8OFDVK5cGV27dkWtWrXw8ePHbE9UkJdnz54BQLp2k9jH3fxi+EIKkxrAZCW/wYs6dKD/eL+cnPx97DQmuy+HnZ2d9Aza3Fi3bh3evn2Ly5cvo2HDhpk2OFSpA+iVTuU8BTDq8FkhIqK8uXbtGszMzDB79mx4e3tj2LBhEAQB48aNU3gtgiAgICBAOsqUiIiyl1XHeW5GfXz48AE9e/bE9evXsXbtWri5uUFDQyPHdYaEhGDx4sUYPXo0PDw8Mr3UirJ3ALVwWQIf31npfi+ay+cqw2XmGJgQESmOj+8sREZG4tGjR3Bzc0NsbCw2bdqE0NBQaGlpZRh9oghPnz6Frq4uypQpA0D5j7vZYfhCCpVVAHM78CvK/TCtMHaU5xQmJCQmYt6Kddi89yAGDBiADRs2wMDAIFfLTkhIwNatWzFo0KAsvzhVKXhJ9ePnJPX1K4yfHyIiyujWrVuoV68eJk+ejMmTJ+Pjx484e/YsmjdvrvBa/Pz88O7dO1HWTUSkTtJ2zqcNC1KDlytXrqBHjx6QSCS4cOECGjZsmOtlb926Fdra2li4cKFKBi+pUgOYvNbL4IOIqPBIDerv3LkDQRAwevRoVK9eHUlJSbh06RLi4uJQpEgRhdYkCAJ2796Npk2bQlNTU2WOu1lh+EIKJ8ubuKujrEbB3I40xfRxg/Hov9tYt24d3Nzcsh0q/6Ndu3bh06dPGDVqVKaPq2Lwkhl+joiIKJUgCPD398fIkSOl0ywsLNC/f3+FrHvPnj04efIkUlJSIAgC7t69i2rVqqFFixZyXz8RUWGRGhakdiD98ccfmDhxIho0aIC//voLlpaWuV5WdHQ0/vjjD/Tp0wcmJiYAVCdsyYwq105ERPKVdoSkv78/DA0NUaVKFQCAlpaWwi439urVKyxduhTh4eEQBAGxsbG4ffs2Tpw4oRbHMYYvREoqbYiQnJyMmRN+wts3QXk+cwsAvn37hrlz56Jv376oWrVqhsfVJXghIiJK6+3bt/j06RMcHBwUut43b95g5MiROHXqFBwdHWFoaAiJRAJra2tMnjw53ckTPAYTERVcageSt7c3xo4di/Hjx2PFihXQ1tbO03JWrFiB0NBQ/PLLLwAYXhARkXpKG7wA368WULduXWhqaiqshpSUFHh5eWHGjBkwNjZGlSpVpO2k4cOHY/nSO5BIcr5cqLJj+EKkArZ5eeL+nRvY+OeRPAcvALBs2TKEh4djyZKMjQd2+hARkbry9/cHAIWGL0eOHEG/fv1QtGhRHDt2DB06dMhyXh6DiYhkJzg4GMOGDUO3bt2wevXqPF0lAADevXuHZcuWYcKECShfvjyDFyIiKjT8/f3RpUsXha0vKioKHTp0wMWLFzFq1CgsXboUxsbG0sdbuCxBHg/jSovhC5GS+++uPzb/sQzD3KaijkODPD8/ODgYK1aswOTJk1G2bNl0j7HTh4iI1JUgCDh69CisrKxgZWWlsHVOnToVTk5OOHjwIIoWzfr2xjwGExHJjiAI6N+/PwwNDbF58+Y8By8A8Msvv8DAwACzZ89m8EJERIXGw4cPERQUhHr16ilsnXv37sXly5fh4+OT4X6Y6nYMVv2xO0Rq7t7t6wCA3gN+ztPzkpOTsXXrVtSrVw8mJiaYOXNmusfZ6UNEROoqJSUFyxfNxI4dOzBjxgyFrffq1at49uwZZs+enW3wQkREspWQkIBr165h8ODBMDU1zdNzAwIC0KlTJ+zYsQMLFy5E965ecqqSiIhIufj7+6NZs2aoXr062rdvr7D1bt++HW3atFH74AVg+EKk9Np16gFNTS20blQZLZ3sYGdnh/r162PMmDEIDg7O9Dl+fn5wcHDA8OHD0bJlS9y8eRNGRkYKrpyIiEjxkhITMW/6aPy9Zys2btyI8ePHK2zdO3fuhJmZGZo0aaKwdRIREaCrq4v+/ftj0aJFMDY2RtmyZVGrVi20bt0a3t7eEAQhw3PCwsIwadIkVK9eHQ8fPsRff/2Fv/bzBDUiIiocfH190bx5c9jZ2eHixYsKO3ksICAA169fR/fu3RWyPrHxsmNESq64RUls238SD+/fxrdvETDQjMfXr19x4MABbNmyBYMHD0blypWho6MDHR0dnDx5Et7e3nB0dMS1a9fg5OQk9iYQEREpRGJCAqaNG4QrF85i0cpN+Pnn4Qpdf7Vq1bB582Z07NgRu3btQvHixbOc197OlKNQiYhkaO3atWjSpAlCQ0MRHh6OsLAwPHjwAN26dUOdOnXQu3dv6OnpQVdXF1+/fsWKFSuQkJCAhQsXYuLEiejQblW+LldGRESkao4ePQpXV1c0bNgQR48ehaGhocLWbWRkhHLlymHq1KkwNTVF165dpY/5+M5Su9EvDF+IVECV6rVRpXptAN87awBg9erVWLduHdavX4+9e/ciISEBCQkJKFOmDPbs2YM+ffqw8UBERIXKu7evce3yeQiCgEW/TMTxg9tRp04d1KlTB02bNkXFihXluv4JEyagcuXKGDBgAGrXro19+/ZlOwqGAQwRkewYGhpi8ODBGaZfuHABCxYswOLFi5GYmIiEhAQAwKBBg7B48WJYWlqqXUcPERFRdnbs2IH4+Hj4+vqidu3a0jZT3bp10aJFC2hpyS8ysLKywt27dzFs2DB069YN48aNw/Lly6GrqwtA/QIYhi9EKsrIyAizZs3CrFmzpNNSh9MzdCEiosKoXAVbnL78BAFP/kPA4wf4/DYA3t7e+OOPP1C8eHF8/PhR7sfINm3a4N69e+jXrx9cXFzQpk0bODo6on79+qhfvz7MzMzSzc8AhohIvpo2bYrz58+nmyYIgvR4oE4dPERERLlx4MABPHnyBHfu3MHdu3dx+/Zt/P333wC+X0p54MCBcl2/iYkJDh48iD/++ANTpkzByZMn0bJlS2mb6azPDLRqsVSuNSgK7/lCpGKy66CRSCS56lRiJw8REakrk2KmcGzYDAOHj4OrqytiY2NRtmxZeHt7K+zkBCsrK5w7dw6enp4QBAFr1qxB+/btYWVlhdevXyukBiIiyhqDFyIiKsy0tLRQo0YNDBo0CLNnz5beJ3ry5Mno3bu3QmqQSCQYO3Ysrl+/jsaNG+PSpUsYNmwYqlevjunTp8PHd1bOC1EBajny5fmXotCPKyb9vYpFmIjVEMne7cCv0suP5ee5RESkXJ58LJbzTHKmTn8vxcfHYc+GxVi/fj26deuGLVu2oFgxxb7GmpqamDBhAiZMmABBEDBy5Ej8888/sLS0VGgdRESUOQYvRERU2Pn4+KB///5ITk7GiRMn0K5dO4XXUKdOHWzbtg0AEBgYiEqVKsHW1lbhdciLWoYvP8qsQ0OdOhiocMpPAMPghYjSyrHDX0iEEYCAzyaARFvu9ajKsVkZghJ5yGm7VOX9AYC/ti7D1q1b4eXlhZEjR4p+Oc6kpCQcOXIE/fv3l17LmIiIxMPghYiICrugoCC0bdsWTZo0we7du1GyZEmxS4K3tzf09PQUNvpGEQpF+JKZtB0MqtSZQJQWwxQiAtQnDMjNdsjimK0ur5eiZfe6KdPfUjYlNLBlyxZMnz4do0aNErscCIKArVu34uPHjxg6dKjY5RARFToMWoiIiDJas2YNjI2NcfToURgYGIhdDj58+IAtW7agR48eKFq0qNjlyEyhDV/SYhBDRESqpDCHB4V525WZsowytrczxfLly5GYmAg3NzeFrz+t5ORkeHt7w8PDA3fv3kXXrl1Rq1atDPPl50SKJx+Lwd5OFlUSEamHFi5LMr02PIMXIiKi9Hx8ZyEyMhJbt27FuHHjRA9egoKCsGzZMmzbtg36+vqYOHEiAPU5hjN8+cGPnQcMY4iISFEYLJA6ESOQSUpKwtq1a9G3b1/R7q0iCAL27NmDRYsWISAgAC1atMD58+fRrFmzDPPmN3ghIqKMfgxg1KXThoiISFZSj5Nbt25FXFwcxowZI1otQUFB+PXXX7Fv3z6Ymppi/vz5cHNzQ9GiRdXqGM7wJQf5vQRKbq+brugGNMMkUkZPPhbjZ5PUHjtMieR/kktQUBCCg4NFvUbwkSNHMGDAAHTq1Ak7d+6Eo6NjpvMxeCEikr3UAEadOm2IiIhkIe0JCn5+fmjSpAmsrKxEqSUpKQkdOnRAREQEPD09MXz4cOkIHHU7hjN8kYH8NITFajxzZA8VRG4/t9l9rp58LAakxMAIwLPQooCGgXQ6P4+kDtg5SpR7sr70q4mJCQAgLi6uwMvKj8TEREyfPh0tW7bEv//+C4lEkul8DF6IiORH3TptiIiICurHS3OamJggNDRUpGqALVu24OnTp7h9+zbq1Kkjna6Ox3CGL4Uc73dDuZHXDp/8dhAxgCFVw85QItmRxd8kxYp9X8aXL19kUlNebd68Gc+ePcOBAwdkFrzwe4aIiIiIiGTJzMwMN2/eFGXd3759w7x58zBgwAC1D14Ahi+URmrjviCd3xxZo17E6PBhAEPKih2gRIqT379JtLS0RDuLKyIiAvPmzcOgQYNQu3btTOdh8EJERERERGIzMzMT7YS1ZcuWITIyEosWLZJOU9fgBWD4QpnI65mn2XUMFOQs1vzeb4cKjp09VNjwM0+knPLzd4SZmZnCw5enT59i9uzZiI6OxsKFCzOdJ6fghd9DRERERESkCGZmZvj69StSUlKgoaGhkHXGxsZi3759WLlyJSZPnowyZcoAUO/gBWD4QjmQZUdAbs5ize/lrRjCyIaydPxw9AvJm7J81oko93J7zFfUWVyCIOD06dNYvXo1Tp8+jRIlSmDDhg0oXbp0hnkzC174PURERERERGIwMzNDSkoKIiIipJdulpcPHz5g3bp12LhxI75+/YrOnTtj5syZANQ/eAEYvpAI5NHZwBCmYNgBROqOn3Ei9ZFTQG9ubo63b9/KvY4lS5Zgzpw5qFu3Lnbu3IlevXpBV1c3w3y7LwmoYvH9//wuIiKSPR/fWYWi84aIiCi/WrgsgY/vLOnv5ubmAIC3b9/KNXx5/fo1nJycEB0djaFDh2Ls2LGoWLGitCZ5S7vNYmH4QmqlsI6YyK4zJ7PXQxU6fwrre0myoQqfcSLKv+xOuujcuTNGjRoFb29vdO3aVS7r37t3L+bMmYN58+Zh3rx5kEgkmc63+5KQrl4iIpKt1E4VBjBERETZSxvA1K1bF6VKlcLYsWPh4+MDLS3ZRwQRERFo164d9PX1ce/ePVhYWKSrRd6UIXgBGL6QGspLB4esO/dzWrcs15fb7VTlDh8GMJRbqvw5JyLZ+vnnn3Hu3DkMGTIENWvWhI2NjczXsXHjRjRs2DBXwQsREcnHj50qDGCIiIiylxrAFC1aFPv370ezZs3w66+/wsPDQ+brunjxIp48eYIrV64U2uAFYPhChVxuOmyz6vzPT2evLC6PVtg6mRnAqIfcfm5z814Xtn2AiLKW2TFCIpFgy5YtKFOmDFavXo21a9fKfL3Ozs7YsGEDkpOT5XKWWF4kJSXhypUrotZARERERESqITWAcXZ2xsSJE7FkyRLMnTsX+vr6Ml2Pk5MTNDQ08PjxYzRs2FC6bnnLKnh59eqV3NedGYYvRDmQ1z1q8hMoFNZOZzHv6ZPVay7vAE3VA6f8flYL62eciPIvs2Pq/fv38e3bN7Rq1Uou6/zpp5/g4eGBK1euoGnTpnJZR06SkpKwb98+LFy4EMHBwaLUQEQkph+vX89RL0RERLknCAIuXbqExo0byzx4AYDixYvD2dkZ//77L4YPHy7z5edWQEAAFi1ahEOHDomyfg1R1kpEefLkYzF2SkOxr0NO60p9PC/15GX+giw/Nz+yJu/lExFl58fvnTVr1qBq1aro2LGjXNbn4OAAKysrHD58WC7Lz05yUhIun/4TVatWxcCBA1GlShXRGhJERGJLDVwYvBAREeXNjRs3cPPmTcycOVNu6+jSpQvOnj2LqKgoua0jKwEBARgwYACqVq0KX19fuLu7K7wGgCNfiESTm9Ev7MTOnLwuRSaL0RqpdcnyvZNXWJKZ7F5Xfh6JSJk9+VgM9nbf/1+6dGmcP38eUVFRMDY2lvm6NDQ00LNnT2zYsAHOzs7o3r27zNeRlcAHl7Fx8SC0bNkS+/fvh4aGBubMmaOw9RMRKRsGL0RERHlnaWkJTU1NBAYGon379nJZR9euXTF9+nT06dMH+/fvl8s6slK5cmUAwLp169CnTx+5XI46NzjyhUhEWY0S4OiBnMnyNVLWZYlBkaNliIhkLfUm99OmTUN0dDTc3d0hCLK78b0gCLh58yZ8fX3Rtm1bmJqaokePHpg3b55M15MdDc3v507FxMRg+fLlqFOnjmjXLyYiIiIiItVUrlw5DBw4EL/99hvev38v02WHhITg/PnzePXqFaZNm4Zjx46hfPnyiI+PkOl6spKcnIyqVasCAIKCglCtWjWsXLlSIev+kVqGLwHPE8QugShP2Mmdf/l9zfh6ExGpp92XBJQqVQpeXl7YvHkz7O3tsX//fiQlJeV7mSkpKTh+/Djq168PR0dHNG/eHG3btpU2Utzd3XHmzBlZbUK2KtV0xvwN1xEYGIhLly5h8+bNOH/+vELWTURERERE6mPOnDlwcnKCnZ0dJk+ejDdv3hRoeW/fvsXEiRNhbW2NFi1awMXFBUuWfB+h+vnzZ8QlnJNF2dlq4bIEmpqauHPnDnr37g1PT0+0atUKFy5ckPu6M6OW4QsAPHwaL3YJRKRAuQ1SGLgQEam/3ZcEDBkyBP/88w9MTU3Rp08f2NraYu3atYiOjs71ckJCQrBkyRLY2NigY8eOCAwMTPe4rq4uxo4diwMHDqBRo0ay3ows2VStj+fPn+PFixcYPnw4tLR4JWEiIiIiIsobGxsbbNy4EePHj8e2bdtgY2ODAQMG4L///sv1MpKTk3HixAn89NNPKFu2LLy8vCCRSNLNU7VqVezatQtbt26Fj+8sWW9GBi1clkBXVxd79+7Fx48fsWvXLpQtW1bu682M2oYvwPcAJvWHiAqHrC6XxdCFiKhw2XMZcHFxgY+PDwBAR0cHEydOhJ2dHQ4ePJjjZcL+/vtvlClTBvPnz8fXr18BANWrV8c///yD4OBgnD17FkOGDMG6deugpaWFIkWKyH2b0ipatCh0dXUVuk4iIiIiIlIvFhYWCAsLQ0REBGxsbHDs2DHUqlULI0aMQGhoaLbPjYuLQ+3atdGhQwdcv34dEokEBgYGmDx5Ml6+fIk7d+5g165d+Pz5M7Zu3YoqVaoAgMICGIlEguLFi8t9XdlR6/AlrbRBDMOYrP34OvH1InXA0IWIqHDacT5O+v/AwECkpKQgPDwcrq6uaN26NZ49e5blcw8ePIgaNWqgZ8+e0NfXx7Vr13DlyhV069YNpUuXRsuWLbF+/Xr07NkTAwYMwIsXLxSxSURERERERAXSwmVJut8rVKgAAAgICEB4eDg0NDSwc+dOVKxYEVu2bEFKSkqmy3n48CEePnyI1atX49OnT5g2bRqCg4Ph4eGB8uXLo06dOhgwYAAOHTqEq1evYs6cOdLnKiqAEVuhCV9+xIAhvZxeg6xCmdz8EBEREYlBV88AO/2S0GXwXACAra0tXFxcIJFI4OvriypVqmDatGl4/vx5huc+fPgQNWvWxIEDBzBlyhQ4OTllmEcikWDr1q2wsLDA0KFD0zVK+jeWZPghIiIiIiJSBmmDiWnTpuHevXsoV64c9PX10alTJ5iamiI8PBwjRoxA7dq1cfr0acTHp+/nffDgASQSCW7cuIHSpUvD3d090ysCODs7Y9GiRVi5ciWuXr0qna6IAEZshTZ8yUxhDQ7kvZ2F6bUkIiIi5aKhoYHuQxdgvPvfeP/+Pd69e4fTp0+jf//+EAQBK1asgK2tLaysrODm5oYzZ87g/v37CAgIwM2bN2FoaIhRo0ZlufwiRYpg27ZtuHjxIv74449sa2EAQ0REREREyiJtAFOrVi34+/vD0dERx48fx6xZs7Bp0yaUKlUKDx48QNu2bWFkZITmzZvDy8sL//33Hy5evIgSJUrgr7/+wtSpU6Gjo5PluqZMmYL69etjyJAhiI2NlU5X9wCG4UsO1DmQUfT2qNvrR0RERKqjXrPuuHr1KsLDw9G3b180b94cL1++xJgxY1C8eHF8+PABXl5eaNOmDWrXro3k5GS8efMGBw8ehJGRUbbLbtasGcaMGYMJEyagR48e8Pf3z3JeBjBERERERKQs0gYw5ubmOHPmDEaNGoWJEyfi4sWLuHz5Mvbs2QMHBwekpKTA19cXbm5uqFWrFnbs2IGPHz+id+/ecHNzy3Y9mpqa2LFjB16/fo1q1arhjz/+QExMDAD1DmAYvuSDvC6vpYjLeClDiMQAhoiIiMTwX0QN+Pv7o2nTphg0aBA6dOiAli1b4uPHjwgMDMTatWvRvHlzGBoawtLSEpcvX0aLFi1ytexVq1Zhw4YNuH//PurXrw8XFxecPHkSgiBkmJcBDBERERERKYu0AYy2tjbWrl2L7du349SpU6hcuTJu3bqFEydOIDw8HMeOHcOoUaNgbW0NiUSCSZMmYdeuXdDW1s5xPZUrV8bNmzfh5OSE8ePHo2zZsliwYAG+fPmitgGMRMisRaiiIiMjUbRoUUxZ8w5aeqZil0O5UL2yrtglkBhSYmAUfg7fTFoCGgZiV0NEWRESYRR2Et+KtQMkOf8hRaQq+jeW4ObNm5gzZw7OnTuH+vXrY/bs2ejYsSM0NTWRmJgIDQ0NaGpq5nnZycnJOHz4MJYuXQp/f39UqlQJTk5OqFmzJmrWrIkaNWrAwsICuy8V7E/wtAFOaGgozM3NERERAWNj4wItlwqH1HZTy+bzkZKS9eUhiEg8hoZaGDbCDls3ByI6OknscogoCzo6GhjlVhkb1j9FQkLmNyUnUhU/BiDfvn3DqlWrsGLFCgiCgAkTJmDs2LGwtLQEAMTHx0NXN399u0FBQfD09MTWrVsBAM2bN0eNGjWk7SY7O7tMA520QVFetkmsNhNHvpCoxB6FQ0RERIXP7ksC6tevj7Nnz8LHxwfa2tro0qULKleujHXr1iE+Pj5fwQvwfTh99+7dce3aNfj5+cHZ2RmPHz/GL7/8glatWsHS0hIWFhb478YpGW8VERERERFR/v0YbBgZGeHXX39FUFAQRo0ahd9//x3W1tYYPHgw7t+/n+/gBQDKly+P1atX482bN/jll18gCAJ2796Nvn37onr16ihSpAh69OiR4XmqNkKG4QspBYYwREREpEipI0+aN2+Oy5cv4/r167C3t8fEiRNRpkwZTJ06Ff/88w/u3buHb9++5WnZXl5eKFq0KO7du4dNmzbh5s2b+PbtG54+fYpKlSohIiICRYqay2OziIiIiIiI8i2zkSVmZmZYvnw5goODsXjxYpw/fx61a9dGy5YtsXnzZvj5+SE4OBgpKbkf/fXp0yfY2dmhd+/e6N+/P44fP47g4GB8/foVCxcuREJCAooUKZLpc1UpgNESuwCitFIDGFW5HFlmgZGq1E4kC7IOTVVl/8lqu1WlfiL6bvclQXr5LkdHR+zfvx9v3ryRXuN45cqV0nktLCzQvXt3jB49GtWrV890efHx8di/fz/GjRuH2rVrY+LEidi5cydMTEwQGRmJ0NBQvHnzBuPc/0aFyg4K2UYiIiIiIqK8yCyA8fGdBRMTE0ydOhUTJkzAoUOHsGrVKowcOVJ6n0tdXV3Uq1cPI0eORI8ePaCnp5fp8p8+fYphw4YhOjoagYGBqFGjBho0aIDIyEh8+/YNL168QIsWLbBx40a5bqciMHwhpZRTCJNdh68iOj+zW//Dp/HsgCW1oshRaT+uS5n2pdy8DgxliFRP2gAGAMqWLYvly5dj2bJlCA0NxfPnz/HixQv8999/2LVrF9avX48GDRqgbt26sLGxgaWlJfT09HD69Gn89ddfCAsLQ9euXfHXX3/Bz88PXl5e0NXVha2tLYyNjSEUb4baDdqLuMVERERERER508JliXTEiba2Nnr16oVevXohPj4eQUFBePHiBZ4/f45jx45hwIABmDRpEtq0aQMbGxtYW1vD1NQUb968wZ9//olbt26hWLFiOH78OKpUqYJ58+bh3bt3KFmyJIyNjdG7d2+MGzcuy8ua5fW+L2KSCKnRlBpIvXHklDXvoKVnKnY5pARk3eGZ105odrhmISUGRuHn8M2kJaBhIHY1BMUGLAWl6P1Klq+Nyn0nCIkwCjuJb8XaAZKMN7ojUidpA5isJCYm4vDhw9i3bx8CAwPx8uVLxMbGAvge2vTr1w/9+/dH1apVM31+6qXOZF2vWDePJNWV2m5q2Xw+UlJ0xC6HiDJhaKiFYSPssHVzIKKjk8Quh4iyoKOjgVFulbFh/VMkJOT+kktEqio3l/wKCAjApk2bcOPGDbx48QIhISEAvoc27du3x4ABA9ChQ4csR8ZkJ6/BS2q9YrWZOPKF1JqszqLPb+crR8GQslKlsOVHaWuX9/4l69dJkbUTUd7kJhjp31gbrq6ucHV1BQAIgoDo6GhERkbC0tISGhr/u52iLIMWIiIiIiIiZZD78MMcutod8OHDLCQmJiI8PBy6urrZBh+qNKIltxi+UKGSm45PeXW2sqOVCkKVwxJ5kmeYIe/XnEEMker58RJlEokERYoUyXAjSAYvRERERERE/7tcWfHixXOcTx0xfKFCS9Gd2RwFQ1lhsCIbmb2OednnxHwfGNISqY4fA5jMHiciIiIiIqLv0t4vJqvH1RXDFyIFYgBDaTF0kT9Ve405GoZINWQVwDB4ISIiIiIiyiirAEadgxeA4QuRwjGAIVULBEgcsrpnFRHJh5hBy8On8UDjvN+ckoiIiIiISCzqHrRkhuELkQgYwBQ+DFyooBjGEBU+PHYQERERERGpLoYvRCJhAKPe2GFG8laQe9zk9PnkdxORYvGYQUREREREpH4YvhCJiAGM8mJHGKkiWX1uU5ejKUlC/eLAk8AEJAspOT6P32dEecNjDRERERERkfpi+EIkMgYw4mPnF5FsyGpf4nciqTMec4iIiIiIiAoHhi9EVKiw04tI+eV2P2VIQ6qCxx4iIiIiIqLCh+ELkRJI7ZRhR2LBsYOLqPDIzf7O71USA49FRERERERExPCFSIkU5Abaqo4dVUQkD1l9txSW71ZSDB7DSFVpPf8AJGgioXJpsUshIiIiIlI7Gvl94rt379C/f3+YmZnBwMAAtWvXxu3bt6WPC4KA+fPnw8rKCvr6+mjWrBkePXqUbhkBAQFo1KgRSpcuDXd393SP3b17Fx07dkSJEiWgp6eHcuXKoVevXvjy5Ut+SyZSSQ+fxkt/1E3A80S13TYiUm5pv1sz+1EmOdWamx+SPb6+lBuq0mbSefo2yx8iIiIiIsqffI18CQsLQ6NGjeDi4oKTJ0+iRIkSePHiBUxMTKTzLFu2DJ6entixYwfs7OywaNEitGrVCgEBATAyMgIAjBkzBgMGDEC9evUwatQotGjRAo0aNcKnT5/QsmVLdOrUCadPn4aJiQmCgoJw5MgRxMTEyGTDiVSRMlyeTBadTNoaiXAwl0ExpNSe3HmNKnWtxS6DKF/UrUOdI4AKRt0+D6QY6tJmyimA4agZIiIiIqLM5St8Wbp0KcqUKYPt27dLp5UrV076f0EQsHr1asyZMwfdunUDAOzcuRMWFhbYu3cvRo4cCQAIDw9HnTp1ULNmTVhZWSEiIgIAcPXqVURGRmLLli3Q0vpeYvny5dG8efN8bSSRuknbCaSIjjN2OlFuPbnzOsvfGcQQKR+GMv/DYx3JWmFpMzGcISIiIiLKXL4uO3bkyBE4ODjA1dUVJUqUQJ06dbB582bp40FBQQgJCUHr1q2l03R1ddG0aVNcvXpVOs3d3R2tWrWCgYEBNDQ00KZNGwCApaUlkpKS4O3tDUEQ8rttRIWCPC8tw8upUHae3Hmd4Sen+YlINajC5cvyW58qbBupB7aZvuMlzYiIiIiosMrXyJeXL1/Cy8sLkydPxuzZs3Hz5k2MHz8eurq6GDhwIEJCQgAAFhYW6Z5nYWGB16//1/nWvn17fP78GZGRkShevLh0upOTE2bPno2+ffti1KhRqF+/Ppo3b46BAwdmWGZmNCRJ0JQk5WfTiFTek4C8ffar2Omkf35ggvT/mhKZlJSBxv/vn9xXVUfAvTcZpmlr5n05z++/RKXaZWVQESlC2n2VCMj5GPPjMUWm605zfAIyP0bl5hgor2OboiUmJmb6f1Ieyt5mAgBtHQ1AIx8HdBnRfvUhy8cS7UopsBIi5aOtrSH9V0cn37frJSI5S7uvEpHySW0ridVmkgj5OE1KR0cHDg4O6c7IGj9+PPz9/XHt2jVcvXoVjRo1wvv371GyZEnpPCNGjEBwcDBOnTqVq/WEhobi/PnzuH79Og4fPoyvX7/i4sWLqFGjRqbzR0ZGomjRoti7dy8MDAzyullERERERConJiYGffv2RUREBIyNjcUuh/6fsraZALabiIiIiKhwEavNlK+RLyVLlkTVqlXTTatSpQr++ecfAN+HwANASEhIuobEp0+fcn0WFgCYmZnB1dUVrq6uWLJkCerUqYMVK1Zg586d2T7vbmgjaEeb5no9RKRYWhpxqGt2BXdCGyEpRU/sciiNzEa4KAJHwygnDUkSHMwv4NaXpkgR8vUnAxHJyfxB/7svT2hoqIiVUFaUvc0EAPt+vQQkqf/3O0fRkCoyMNDCwMEVsWvHc8TEcBQykbLS1tbAsBF22Lo5EImJKWKXQ0Q/OHJsMgDx2kz5+ku7UaNGCAgISDctMDAQ1tbfb6Zcvnx5WFpa4uzZs6hTpw4AICEhARcuXMDSpUvzVaiOjg5sbGwQHR2d47wpghaS2UmkdnjjbvWh8f/7J/dV5fG//UucS488vP0u3e/cx5UL91Ui5aOtrZ3p/0l5KHubCQASE1KAhOR8rUul/Jf/k0sSKpeWYSFEuaet/b0TNzExBQkJ7NAlUnbcV4mUU2pbSaw2U756UiZNmoSGDRvCw8MDPXv2xM2bN7Fp0yZs2rQJACCRSDBx4kR4eHjA1tYWtra28PDwgIGBAfr27Zvj8o8dO4b9+/ejd+/esLOzgyAIOHr0KE6cOIHt27fnp2RScT/eqPvH39lRS5R/P+5PyoBhKxERqTq2mdSDztO3BV4GAxwiIiKiwilf4Uu9evXg7e2NWbNmwd3dHeXLl8fq1avRr18/6TzTp09HbGws3NzcEBYWBkdHR5w5cwZGRkY5Lr9q1aowMDDAlClTEBwcDF1dXdja2mLLli0YMGBAfkomFZabjmF21BLljTIGLlnh/k1ERKqIbSZKJYsApyAY/hARERGJQyIIgiB2EbKSeuPIziNuAJpFs52XHXjKT5adw3y/lYu2Rpz0PhKJvOeLwqhS4JJb3LflS1OShPrFfXDzcwtedoxIyfw24n/Hz9DQUJibmyv85pGkulLbTW3LjgQSxLnkKKkPhjvyYWioJb2PRHQ07/lCpKx0dDQwyq0yNqx/ysuOESkhH99ZAMRrMxXanpTsOiHZmSc+WXcS5/cyZXmpg58bUjbqGLb8KDfbyH2TiIiISH7kMbKHgQ5R7uR3/+M+RkTqTOxRx2kV2vAlO5l15rHzTr4U3Uksj/VltUx+dkhRCkPYkh+8RxQRKbv8fX9XknkdRETKgvfaIVWhTB18eaFsdXN/JaL8ULbvsswwfMklZR8pI69O17xuGzt/M2IoQ9nhPqN4DGOISEz83iciUoyCdMiwI1g9qEKnHH0n6/eK+zCResnqO+Lkhz8UXEneMXyRgZwa0QXt2BOzkc4OAvmR9+eGlAv3JeXFMIaI5Inf/0REqkeWHcHsBM4cgxGSJ4Y5RKolP/usKgQvAMMXhWCjm/KDI2aUF/dp9Zb2/eX+RkT5xWMFEREB+e8E1i6qA8AO2s/fQyciQbZFEVGeZLcfa+tpAqgM7cB3QFxynpfNYIfUnTzCdlUJXgCGL0QqR9kvgadu2HlWuDGIIaK84DGDiIiIiPJC0aPAGPZQQSjDqEVVCl4Ahi9EaiUvnT7sSM4eO9DoR5l9JrgfERGPF0RERESkKpSh81ydKSLcKszvoaoFLwDDF6JCi/ecSY+dZ5QfvDwgUeHEYwYREREREf2oMAcj8qaKwQvA8IWIsqCuZ/mzw4wUQV33H6LCRpmPGV++fMHDhw/x/PlzPHnyROxyiIiIiIiI5CK/wUtSUhIePXqE58+f49GjRzKuKncYvhBRrqlCh7Iyd5RR4cZRMkTKQZWPE5FfA/Hu2XF8Cr6E4hseS6fr6emJWBUREREREZF85DV4iYmJwf79+3HixAmcO3cOERERAMRrM6ll+LJtiQ3MzMzELoOIshATk4KzZ4FZfXRgYCDrL79KMl4eUeGVmJiIEyeA+YN0oa2tLXY5RDKiOseJhIQEREREICgoCMuXL4ffwYOZzmdhYYHXr1U3VCLx7L6zmO0mIiUVExODs2fP4s87i2FgYCB2OUSUhe9tphP4J3Al20xEIkhJSUFUVBTCw8Pxzz//YOnSpfj48WOG+fT19REXF6fw+tQyfCEiIiIiUjURERHYvXs3tmzZgoCAAMTGxmY6n729PXr37o1OnTqhXLlyiIqKgrm5uYKrJSIiIiIiUixBEHDlyhVs2LABJ0+eRFhYGARByDBfsWLF0L17d/Ts2RP29vZISUlB8eLFFV6vWoYvkZGRTJuJlFhMTAxiYmIQGRmJpKQkscshoiwkJiZK91UeV4nk59OnT/j999+xdetWxMfHo0OHDujVqxfCw8OxdOlSAEC5cuXQp08fuLq6wsbGRvrc+Ph4REZGilU6qTh+vxMpL7aZiFQD20xEipGSkoJjx45h6dKlePjwISpUqIARI0agePHi+Pvvv3Hjxg0AQPfu3eHq6ooWLVpAR0dH+vzQ0FBR6pYImUVDKiouLg7ly5dHSEiI2KUQERERESmMpaUlgoKCeP8XyhW2m4iIiIiosBGjzaRW4QvwvSGRkJAgdhlERERERAqjo6PD4IXyhO0mIiIiIipMxGgzqV34QkREREREREREREREJCYNsQsgIiIiIiIiIiIiIiJSJwxfiIiIiIiIiIiIiIiIZIjhCxERERERERERERERkQwxfCEiIiIiIiIiIiIiIpIhhi9EREREREREREREREQyxPCFiIiIiIiIiIiIiIhIhkQPXy5evIhOnTrBysoKEokEhw8flj6WmJiIGTNmoEaNGjA0NISVlRUGDhyI9+/fp1tGfHw8xo0bB3NzcxgaGqJz5854+/ZtunmuXbuG2rVrw9raGps3b073mEQiyfRn//79cttuInWQ3f4LAFFRURg7dixKly4NfX19VKlSBV5eXunmKej+S0TfiX08dXJywujRo9PN6+XlBYlEgq1bt6abPmzYMDRs2FBGW06k2t69e4f+/fvDzMwMBgYGqF27Nm7fvi19XBAEzJ8/H1ZWVtDX10ezZs3w6NGjdMsICAhAo0aNULp0abi7u0un9+7dG+3atUs378mTJyGRSDB37tx00xcuXAgrKys5bCHJitjf8wDbTUT5wTYTkfIQ+1jKNhNR/qlqu0n08CU6Ohq1atXCunXrMjwWExODO3fuYO7cubhz5w4OHTqEwMBAdO7cOd18EydOhLe3N/bv34/Lly8jKioKHTt2RHJysnSeoUOHYu7cudi3bx+WLl2KN2/epFvG9u3b8eHDh3Q/Xbp0kcs2E6mL7PZfAJg0aRJOnTqF3bt348mTJ5g0aRLGjRuHf//9VzqPLPZfIhL/eOri4gJfX990y/Pz80OZMmUyne7i4iKrTSdSWWFhYWjUqBG0tbVx8uRJPH78GCtXroSJiYl0nmXLlsHT0xPr1q2Dv78/LC0t0apVK3z79k06z5gxYzBgwAD8+++/OHr0KK5cuQLg+355+fJlJCUlSeflfqm6xP6eT8V2E1HesM1EpDzEPpayzUSUPyrdbhKUCADB29s723lu3rwpABBev34tCIIghIeHC9ra2sL+/ful87x7907Q0NAQTp06JZ1WtmxZ4eXLl0JUVJTg4OAgPHr0KE/rJaLsZbYfVatWTXB3d083rW7dusIvv/wiCIJs9l8iykiM4+np06cFAML79++l81pYWAjr168XSpUqJZ325s0bAYBw9uxZWWwqkUqbMWOG4OzsnOXjKSkpgqWlpfDbb79Jp8XFxQlFixYVNmzYIJ1mb28vXL9+XUhISBA6d+4sHD9+XBAEQQgICBAACNeuXZPOW79+feGPP/4QdHR0hOjoaEEQBCE+Pl7Q19cXNm/eLOtNJDlhu4lINbHNRKQ82GYiUh2q3G4SfeRLXkVEREAikUiTrdu3byMxMRGtW7eWzmNlZYXq1avj6tWr0mm//vorqlSpgqJFi8LJyQlVq1ZVdOlEhY6zszOOHDmCd+/eQRAE+Pr6IjAwEG3atAHA/ZdITLI+nqaeheLn5wcAePz4MWJjYzF06FBERkbi2bNnAABfX1/o6OhwCD0RgCNHjsDBwQGurq4oUaIE6tSpk+7SFEFBQQgJCUm3X+rq6qJp06bp9kt3d3e0atUKBgYG0NDQkB5n7ezsYGVlJT1b69u3b7hz5w5cXV1hY2MjPdPr+vXriI2N5dmVaobtJiLVwDYTkfJim4lIOahyu0mlwpe4uDjMnDkTffv2hbGxMQAgJCQEOjo6KFasWLp5LSwsEBISIv192LBhCA0NxefPn7F27doMy+7Tpw+KFCmS7ufly5fy3SAiNbdmzRpUrVoVpUuXho6ODtq2bYv169fD2dkZgOz2XyLKG3kcTw0NDVGvXj1pQ8LPzw/Ozs7Q1dVFo0aN0k13dHSEgYGBfDeSSAW8fPkSXl5esLW1xenTpzFq1CiMHz8eu3btAgDpvmdhYZHueT/ul+3bt8fnz5/x/v17eHt7Q1NTU/pYs2bNpPvfpUuXYGdnh+LFi6Np06bp9ssyZcrAxsZGjltLisR2E5HqYJuJSDmxzUSkPFS53aSVnw0WQ2JiInr37o2UlBSsX78+x/kFQYBEIkk3zdDQEIaGhpnOv2rVKrRs2TLdtDJlyuS/YCLCmjVrcP36dRw5cgTW1ta4ePEi3NzcULJkyQz7W1p53X+JKPfkeTx1cXHB33//DeD7HyXNmjUDAOkfKyNGjICfnx8GDhxY8A0hUgMpKSlwcHCAh4cHAKBOnTp49OgRvLy80u0nP+6Dme2Xurq6KF68eIZ1uLi4YOLEiUhMTMywX6Z2Bvj5+aF58+ay3DQSEdtNRKqFbSYi5cM2E5FyUeV2k0qMfElMTETPnj0RFBSEs2fPShNnALC0tERCQgLCwsLSPefTp08Z0q7sWFpaomLFiul+tLW1ZbYNRIVNbGwsZs+eDU9PT3Tq1Ak1a9bE2LFj0atXL6xYsQKA7PZfIsodeR9PXVxcEBgYiHfv3uHChQto2rQpgP81JN68eYOgoCBe2ojo/5UsWTLDZWGqVKkivSmrpaUlAKQ7WwvI+34ZHR0Nf39/+Pr6ptsv/f398fXrV1y7do37pZpgu4lItbDNRKR82GYiUj6q3G5S+vAl9Uvv2bNnOHfuHMzMzNI9bm9vD21tbZw9e1Y67cOHD3j48CGvjUgkosTERCQmJkJDI/3XjKamJlJSUgBw/yVSJEUcTxs2bAhdXV2sX78esbGxsLe3BwA4ODggIiICGzduhJ6eHpycnGS3YUQqrFGjRggICEg3LTAwENbW1gCA8uXLw9LSMt1+mZCQgAsXLuR6v7SxsUGZMmVw5MgR3Lt3T9qIKFmyJMqVK4eVK1ciLi6ODXw1wHYTkephm4lIubDNRKScVLndJPplx6KiovD8+XPp70FBQbh37x5MTU1hZWWFHj164M6dOzh27BiSk5OlCZapqSl0dHRQtGhRDBs2DFOmTIGZmRlMTU0xdepU1KhRI9shuj8KDw/PkI4ZGRlxyC5RNrLbf8uWLYumTZti2rRp0NfXh7W1NS5cuIBdu3bB09MTAGS2/xKRchxP9fX14ejoiLVr16JRo0bS66dqa2ujQYMGWLt2rbSxQUTApEmT0LBhQ3h4eKBnz564efMmNm3ahE2bNgH4Pmx+4sSJ8PDwgK2tLWxtbeHh4QEDAwP07ds31+txcXHB+vXrUbFixXRnfqUOoa9QoQLKli0r8+0j2VKG73mA7SaivGKbiUh5KMOxlG0morxT6XaTIDJfX18BQIafQYMGCUFBQZk+BkDw9fWVLiM2NlYYO3asYGpqKujr6wsdO3YU3rx5k+saslrHkiVL5LDFROoju/1XEAThw4cPwuDBgwUrKytBT09PqFSpkrBy5UohJSVFuoyC7r9E9J0yHE8FQRDmzZsnABB+++23dNMXLlwoABAWLlwoi80lUhtHjx4VqlevLujq6gqVK1cWNm3alO7xlJQUYd68eYKlpaWgq6srNGnSRHjw4EGe1rF9+3YBgDBq1Kh00//8808BgDBs2LACbwfJnzJ8z7PdRJR3bDMRKQ9lOJYKAttMRPmhqu0miSAIQt7iGiIiIiIiIiIiIiIiIsqK0t/zhYiIiIiIiIiIiIiISJUwfCEiIiIiIiIiIiIiIpIhhi9EREREREREREREREQyxPCFiIiIiIiIiIiIiIhIhhi+EBERERERERERERERyRDDFyIiIiIiIiIiIiIiIhli+EJERERERERERERERCRDDF+IiIiIiIiIiIiIiIhkiOELERERERERERERERGRDDF8ISIiIiIiIiIiIiIikiGGL0RERERERERERERERDLE8IWIiIiIiIiIiIiIiEiGGL4QERERERERERERERHJEMMXIiIiIiIiIiIiIiIiGWL4QkREREREREREREREJEMMX4iIiIiIiIiIiIiIiGSI4QsREREREREREREREZEMMXwhIiIiIiIiIiIiIiKSIS2xCyCi/4mLi0NCQoLYZRARFTo6OjrQ09MTuwwiIiLKBbabiIjEwXYTUd4wfCFSEnFxcTAxK4v4mM9il0JEVOhYWloiKCiIDQkiIiIlFxcXBxNDM8SnxIhdChFRocN2E1HeMHwhUhIJCQmIj/mMVv39oKVTROxycqVSrTJil5AnVe10xS4hT+xKhIldQp5YJzwTu4Q8MQi6J3YJeRJ97z+xS8iTVxeeiF1CrkUnJ6PTrXtISEhgI4L+r717D6spe/wH/j7dL6eSXJJ0cSuRERlKJIxcRhmGjNFFPWHcYtyGhMe4DDPuxqVB+JjR+AzFmBGNT4XBuKTc+lbIuGU8ZoaUSnXW749+9jg6qUx1Trxfz3Oex1577b3f7c5iLevsdYiISMM9e/YMhYqn6NkwADoyPXXHqZRnra3UHaFKcuzr1rjpSTOZuiNUybNmdeupLRurh+qOUCVejTPUHaFKBpukqjtCpeXmCvTscp/jJqIq4OQLkYbR0ZNDt45Mvugbmqo7QpUYGtetQYRcXqLuCFVi8sxY3RGqxNjIUN0RqkRLv27858Jzch12MYiIiKjm6Mj0oKNVN/pHCp269Z+U2np1a9ykbVC3Jl+0DOvW1y/r1LFxtIFcV90RqkRuUpfeDwp1ByCqc+pSCyciIiIiIiIiIiIiItJ4nHwhIiIiIiIiIiIiIiKqRpx8ISIiIiIiIiIiIiIiqkZckJ1IwxQ/y1V3hEorzM9Rd4Qqyc+rW2vV5ubWrfv75FmeuiNUScnTfHVHqJK8wrr1xZy5xcXqjlBpeSV16/uViIiICCgWz+rM1w8UFxeoO0KVlDwT6o5QJSUFdes7XxT5datfX5xXqO4IVVKQW6TuCFWSK6sjf5EByM2tW383EGkCmRCCLYdIAzx+/BjW1tbIza07ky9ERG8KuVyOO3fuwMzMTN1RiIiI6BU4biIiUh+Om4iqhk++EGkImUyG3Nxc3L59G6ampuqOU6GcnBw0a9aMeWsI89Ys5q1ZdTWvTFa3PjVJRET0NuK4qWYxb81i3prFvDWL4yaiquPkC5GGMTU1rRP/6D7HvDWLeWsW89asupaXiIiI6o661s9g3prFvDWLeWtWXctLRJWnpe4AREREREREREREREREbxJOvhAREREREREREREREVUjTr4QaQh9fX3Mnz8f+vr66o5SKcxbs5i3ZjFvzWJeIiIiqil17d9t5q1ZzFuzmLdmMS/Rm08mhBDqDkFERERERERERERERPSm4JMvRERERERERERERERE1YiTL0RERERERERERERERNWIky9ERERERERERERERETViJMvRERERERERERERERE1YiTL0S1aOnSpejcuTNMTEzQqFEjDB48GOnp6eXWHzt2LGQyGVavXl17IV+wceNGtG/fHqampjA1NYWbmxsOHToEACgqKsKsWbPg7OwMY2NjWFlZISAgAPfu3VNLVlWWLl0KmUyGKVOmSGVCCCxYsABWVlYwNDREz549ceXKFfWFfIGqvLm5uZg4cSKsra1haGiINm3aYOPGjWrLuGDBAshkMqWXpaWlUp20tDT4+PjAzMwMJiYm6Nq1K27duqWmxMDdu3cxatQoWFhYwMjICB06dMD58+dV1lV3m7Ozsytzf2UyGSZMmKCRba64uBhz586Fvb09DA0N0bx5cyxcuBAKhUKqo0ltrjJ5Na3NERER0T82bNgAe3t7GBgYoFOnTjh+/Li6I6lUmT6HOh07dgyDBg2ClZUVZDIZYmNjy9TRpD49x9G1i+Po6sUxNNHbjZMvRLUoKSkJEyZMwOnTpxEfH4/i4mL07dsXeXl5ZerGxsbit99+g5WVlRqSlrK2tsYXX3yBc+fO4dy5c+jVqxd8fX1x5coVPH36FMnJyYiIiEBycjL27duHjIwM+Pj4qC3vi86ePYvIyEi0b99eqXz58uVYuXIl1q9fj7Nnz8LS0hLvvfcenjx5oqakpcrLO3XqVMTFxWHXrl1IS0vD1KlTMWnSJOzfv19NSYG2bdsiOztbel26dEnad/36dXh4eMDR0RGJiYlITU1FREQEDAwM1JL177//Rrdu3aCrq4tDhw7h6tWrWLFiBerVq1emria0ubNnzyrd2/j4eADAsGHDNLLNLVu2DJs2bcL69euRlpaG5cuX48svv8S6deukOprU5iqTVxPbHBEREQHff/89pkyZgvDwcFy4cAHdu3dH//791foflOWpTJ9DnfLy8vDOO+9g/fr1KvdrWp+e4+jaw3F0zeAYmugtJohIbR48eCAAiKSkJKXyO3fuiKZNm4rLly8LW1tbsWrVKvUEVMHc3Fxs2bJF5b4zZ84IAOL333+v5VTKnjx5Ilq1aiXi4+OFp6enCAsLE0IIoVAohKWlpfjiiy+kugUFBcLMzExs2rRJTWnLzyuEEG3bthULFy5Uqt+xY0cxd+7cWk5Zav78+eKdd94pd7+fn58YNWpU7QWqwKxZs4SHh0eF9TS1zYWFhYkWLVoIhUKhcr+629zAgQNFcHCwUtmQIUOk94CmtbmK8gqheW2OiIiISr377rti3LhxSmWOjo7is88+U1Oi8lWmz6EpAIiYmBilMk3r07+M4+iawXF0zeAYmujtxidfiNTo8ePHAID69etLZQqFAv7+/pgxYwbatm2rrmhllJSUIDo6Gnl5eXBzc1NZ5/Hjx5DJZCo/EVGbJkyYgIEDB6JPnz5K5VlZWbh//z769u0rlenr68PT0xMnT56s7ZiS8vICgIeHBw4cOIC7d+9CCIGEhARkZGTA29tbDUlLZWZmwsrKCvb29hgxYgRu3LgBoPS9+9NPP6F169bw9vZGo0aN0KVLF5XLGNSWAwcOwNXVFcOGDUOjRo3g4uKCb775RqmOpra5Z8+eYdeuXQgODoZMJlNZR91tzsPDA0ePHkVGRgYAIDU1FSdOnMCAAQMAaF6bqyjv8zqa1uaIiIjeds+ePcP58+eV+hQA0LdvX7X248tTmT6HptLEPv3LOI6uGRxH1xyOoYneXjrqDkD0thJC4NNPP4WHhwfatWsnlS9btgw6OjqYPHmyGtP949KlS3Bzc0NBQQHkcjliYmLg5ORUpl5BQQE+++wzjBw5EqampmpIWio6OhrJyck4e/ZsmX33798HADRu3FipvHHjxvj9999rJd/LXpUXANauXYvQ0FBYW1tDR0cHWlpa2LJlCzw8PGo5aakuXbpg586daN26Nf744w8sWrQI7u7uuHLlCoqKipCbm4svvvgCixYtwrJlyxAXF4chQ4YgISEBnp6etZ73xo0b2LhxIz799FPMmTMHZ86cweTJk6Gvr4+AgAAAmtfmnouNjcWjR48QFBSkcr8mtLlZs2bh8ePHcHR0hLa2NkpKSrB48WJ89NFHADSvzVWUF9C8NkdERETAw4cPUVJSorJP8by/oUkq0+fQVA8ePNC4Pv2LOI6uGRxH1xyOoYnebpx8IVKTiRMn4uLFizhx4oRUdv78eaxZswbJycnlftK9tjk4OCAlJQWPHj3C3r17ERgYiKSkJKWOY1FREUaMGAGFQoENGzaoLevt27cRFhaGI0eOvHJ91JfvrRBCLfe7MnnXrl2L06dP48CBA7C1tcWxY8cwfvx4NGnSROUnfGpa//79pT87OzvDzc0NLVq0wI4dOzBixAgAgK+vL6ZOnQoA6NChA06ePIlNmzappeOoUCjg6uqKJUuWAABcXFxw5coVbNy4EQEBARrZ5p7bunUr+vfvr3L9XE1pc99//z127dqF7777Dm3btkVKSgqmTJkCKysrBAYGSvU0pc1VJq+mtTkiIiL6h6b0KSpS2T6SJlIoFAA0q0//Io6jqx/H0TWLY2iit5zaFjwjeotNnDhRWFtbixs3biiVr1q1SshkMqGtrS29AAgtLS1ha2urnrAv6d27txgzZoy0/ezZMzF48GDRvn178fDhQzUmEyImJkYAKHP/nt/Ta9euCQAiOTlZ6TgfHx8REBCgcXlzc3OFrq6uOHjwoNJxISEhwtvbu9bzlqdPnz5i3LhxorCwUOjo6IjPP/9caf/MmTOFu7u7WrLZ2NiIkJAQpbINGzYIKysrIYTmtrmbN28KLS0tERsbW2afJrU5a2trsX79eqWyzz//XDg4OAghhLh+/bpGtbmK8j59+rROtDkiIqK3TWFhodDW1hb79u1TKp88ebLo0aOHmlKVr6I+hybBS9/5ool9+uc4jq4ZHEfXPo6hid4efPKFqBYJITBp0iTExMQgMTER9vb2Svv9/f3LfArD29sb/v7+GD16dG1GLZcQAoWFhQBKP6kzfPhwZGZmIiEhARYWFmrN1rt3b1y6dEmpbPTo0XB0dMSsWbPQvHlzWFpaIj4+Hi4uLgBK149OSkrCsmXLNC5vSUkJioqKoKWl/PVc2tra0ifS1K2wsBBpaWno3r079PT00LlzZ6SnpyvVycjIgK2trVrydevW7ZV5NLXNRUVFoVGjRhg4cKBSuaa1uadPn77y/Wlvb69Rba6ivEVFRRrf5oiIiN5Genp66NSpE+Lj4/HBBx9I5fHx8fD19VVjMtUq6nNoMk3s03McXbM4jq5dHEMTvV04+UJUiyZMmIDvvvsO+/fvh4mJibR2qpmZGQwNDWFhYVGm46WrqwtLS0s4ODjUet45c+agf//+aNasGZ48eYLo6GgkJiYiLi4OxcXF+PDDD5GcnIyDBw+ipKRE+nnq168PPT29Ws9rYmKitO4vABgbG8PCwkIqnzJlCpYsWYJWrVqhVatWWLJkCYyMjDBy5EiNzOvp6YkZM2bA0NAQtra2SEpKws6dO7Fy5cpazwsA06dPx6BBg2BjY4MHDx5g0aJFyMnJkZZPmDFjBvz8/NCjRw94eXkhLi4OP/74IxITE9WSd+rUqXB3d8eSJUswfPhwnDlzBpGRkYiMjAQAjWtzQOlj3lFRUQgMDISOzj//TGtimxs0aBAWL14MGxsbtG3bFhcuXMDKlSsRHBwMoHRpAk1qcxXlNTU11bg2R0RERKU+/fRT+Pv7w9XVFW5uboiMjMStW7cwbtw4dUcro6I+h7rl5ubi2rVr0nZWVhZSUlJQv3592NjYaFyfnuPomsVxdM3iGJroLafeB2+I3i4AVL6ioqLKPcbW1lasWrWq1jK+KDg4WNja2go9PT3RsGFD0bt3b3HkyBEhhBBZWVnl/jwJCQlqyauKp6enCAsLk7YVCoWYP3++sLS0FPr6+qJHjx7i0qVL6gv4kpfzZmdni6CgIGFlZSUMDAyEg4ODWLFihVAoFGrJ5+fnJ5o0aSJ0dXWFlZWVGDJkiLhy5YpSna1bt4qWLVsKAwMD8c4776hcOqs2/fjjj6Jdu3ZCX19fODo6isjIyFfWV2ebE0KIw4cPCwAiPT1dqVwT21xOTo4ICwsTNjY2wsDAQDRv3lyEh4eLwsJCqY4mtbnK5NW0NkdERET/+Prrr6XxSceOHUVSUpK6I6lUmT6HOiUkJKjsUwYGBkp1NKlPz3F07eM4uvpwDE30dpMJIUTNTu8QERERERERERERERG9PbQqrkJERERERERERERERESVxckXIiIiIiIiIiIiIiKiasTJFyIiIiIiIiIiIiIiomrEyRciIiIiIiIiIiIiIqJqxMkXIiIiIiIiIiIiIiKiasTJFyIiIiIiIiIiIiIiomrEyRciIiIiIiIiIiIiIqJqxMkXIiIiIiIiIiIiIiKiasTJFyIiqjEREREYM2ZMtZzrww8/xMqVK6vlXEREREREVDV2dnZYvXp1jV9n+/btqFevXo1fhzTf//73Pzg6OkKhUNT6tdevXw8fH59avy4RvVk4+UJE9AZ78OABxo4dCxsbG+jr68PS0hLe3t44deqUVOfChQt4//330ahRIxgYGMDOzg5+fn54+PAhFixYAJlM9srXzZs3VV77jz/+wJo1azBnzhypLDc3FyNGjECTJk0wYsQI5OXlSfuCgoJUnv/atWsAgHnz5mHx4sXIycmpmZtFRERERFQHVNQ/DwoKqvD42NjYGsmWk5OD8PBwODo6wsDAAJaWlujTpw/27dsHIUSNXLM2de3aFZ988olS2caNGyGTybB161al8pCQELi7u1fqvEFBQRg8eHB1xVSyYsUKmJmZ4enTp2X2FRQUoF69ehr7IbeZM2ciPDwcWlq1/9+XoaGhOHv2LE6cOFHr1yaiNwcnX4iI3mBDhw5FamoqduzYgYyMDBw4cAA9e/bEX3/9BaB0cqZPnz5o0KABDh8+jLS0NGzbtg1NmjTB06dPMX36dGRnZ0sva2trLFy4UKmsWbNmKq+9detWuLm5wc7OTipbvXo15HI5jhw5AiMjozKfnOvXr5/SubOzs2Fvbw8AaN++Pezs7PDtt9/WyL0iIiIiIqoLXuwrr169Gqampkpla9asUUuuR48ewd3dHTt37sTs2bORnJyMY8eOwc/PDzNnzsTjx4/Vkqs6eXl5ISEhQaksMTERzZo1U1nu5eVVm/Hw7NmzMmUBAQHIz8/H3r17y+zbu3cvnj59Cn9//9qIp1JRUZHK8pMnTyIzMxPDhg2r5USl9PX1MXLkSKxbt04t1yeiNwMnX4iI3lCPHj3CiRMnsGzZMnh5ecHW1hbvvvsuZs+ejYEDBwIo7dDm5ORgy5YtcHFxgb29PXr16oXVq1fDxsYGcrkclpaW0ktbWxsmJiZlylSJjo4u85j2o0eP0Lp1azg7O8PR0bHMAOz50znlnd/Hxwe7d++u5jtFRERERFR3vNhXNjMzg0wmUyr77rvv0KJFC+jp6cHBwQH/+c9/pGOffzDqgw8+gEwmk7avX78OX19fNG7cGHK5HJ07d8Yvv/xSpVxz5szBzZs38dtvvyEwMBBOTk5o3bo1QkNDkZKSArlcDgD4+++/ERAQAHNzcxgZGaF///7IzMws97yqngqZMmUKevbsKW337NkTkyZNwpQpU2Bubo7GjRsjMjISeXl5GD16NExMTNCiRQscOnRIOiYxMREymQxHjx6Fq6srjIyM4O7ujvT09HKzeHl5IT09HdnZ2VJZUlISZs+ejcTERKns9u3buHHjBry8vFBSUoKQkBDY29vD0NAQDg4OShNkCxYswI4dO7B//37p6aXn57p79y78/Pxgbm4OCwsL+Pr6Kq088PzeLF26FFZWVmjdunWZzA0bNsSgQYOwbdu2Mvu2bdsGHx8fNGzYELNmzULr1q1hZGSE5s2bIyIiQmliZMGCBejQoQM2b96MZs2awcjICMOGDcOjR4+kOgqFAgsXLoS1tTX09fXRoUMHxMXFSftv3rwJmUyGPXv2oGfPnjAwMMCuXbtU3uvo6Gj07dsXBgYGZTJs27ZNGq9+8sknKCkpwfLly2FpaYlGjRph8eLFSueSyWTYvHkz3n//fRgZGaFNmzY4deoUrl27hp49e8LY2Bhubm64fv260nE+Pj6IjY1Ffn6+yoxERBXh5AsR0RtKLpdDLpcjNjYWhYWFKutYWlqiuLgYMTEx1boMwN9//43Lly/D1dVVqXzixInYvHkzdHV1ERUVhbCwsCqd991338WZM2fK/XmIiIiIiN5mMTExCAsLw7Rp03D58mWMHTsWo0ePlp7KOHv2LAAgKioK2dnZ0nZubi4GDBiAX375BRcuXIC3tzcGDRqEW7duVeq6CoUC0dHR+Pjjj2FlZVVmv1wuh46ODoDSCYNz587hwIEDOHXqFIQQGDBgQLlPQFTWjh070KBBA5w5cwaTJk3CJ598gmHDhsHd3R3Jycnw9vaGv79/meW3wsPDsWLFCpw7dw46OjoIDg4u9xrdunWDrq6uNDly9epV5OfnIzg4GDk5OdIkUkJCAvT09ODu7g6FQgFra2vs2bMHV69exbx58zBnzhzs2bMHADB9+nQMHz5caRUAd3d3PH36FF5eXpDL5Th27BhOnDgBuVyOfv36KT3hcvToUaSlpSE+Ph4HDx5UmTskJARJSUnIysqSym7evImEhASEhIQAAExMTLB9+3ZcvXoVa9aswTfffINVq1YpnefatWvYs2cPfvzxR8TFxSElJQUTJkyQ9q9ZswYrVqzAV199hYsXL8Lb2xs+Pj5lJtdmzZqFyZMnIy0tDd7e3iozHzt2rMx4EiidKDx06BDi4uKwe/dubNu2DQMHDsSdO3eQlJSEZcuWYe7cuTh9+rTScZ9//jkCAgKQkpICR0dHjBw5EmPHjsXs2bNx7tw5AKXj1Re5urqiqKgIZ86cUZmRiKhCgoiI3lg//PCDMDc3FwYGBsLd3V3Mnj1bpKamKtWZM2eO0NHREfXr1xf9+vUTy5cvF/fv31d5PltbW7Fq1aoKr3vhwgUBQNy6davMvpKSEpGdnS0UCoVSeWBgoNDW1hbGxsbS68MPP1Sqk5qaKgCImzdvVpiBiIiIiOhNFxUVJczMzKRtd3d3ERoaqlRn2LBhYsCAAdI2ABETE1PhuZ2cnMS6deuk7VeNBf744w8BQKxcufKV58zIyBAAxK+//iqVPXz4UBgaGoo9e/ao/JkCAwOFr6+v0nnCwsKEp6entO3p6Sk8PDyk7eLiYmFsbCz8/f2lsuzsbAFAnDp1SgghREJCggAgfvnlF6nOTz/9JACI/Pz8cn8Gd3d3MWbMGCGEEF9//bV0b/v16yciIyOFEEKMHj1adO/evdxzjB8/XgwdOvSVP+PWrVuFg4OD0ripsLBQGBoaisOHD0vHNW7cWBQWFpZ7ref3o2nTpmLevHlS2bx580TTpk1FcXGxymOWL18uOnXqJG3Pnz9faGtri9u3b0tlhw4dElpaWiI7O1sIIYSVlZVYvHix0nk6d+4sxo8fL4QQIisrSwAQq1evfmVeIYQwMzMTO3fuVCqbP3++MDIyEjk5OVKZt7e3sLOzEyUlJVKZg4ODWLp0qbQNQMydO1faPnXqlAAgtm7dKpXt3r1bGBgYlMlhbm4utm/fXmFeIiJV+OQLEdEbbOjQobh37x4OHDgAb29vJCYmomPHjti+fbtUZ/Hixbh//z42bdoEJycnbNq0CY6Ojrh06dJrX/f5Y9kvPiL+nJaWFiwtLSGTycrs8/LyQkpKivRau3at0n5DQ0MAUPllkUREREREb7u0tDR069ZNqaxbt25IS0t75XF5eXmYOXMmnJycUK9ePcjlcvzf//1fpZ98Ef//KXpVffyX8+no6KBLly5SmYWFBRwcHCrMWJH27dtLf9bW1oaFhQWcnZ2lssaNGwMo/d7L8o5r0qSJyjov8vLykp58SUxMlJY/8/T0VCrv1auXdMymTZvg6uqKhg0bQi6X45tvvqnw3p4/fx7Xrl2DiYmJtKpB/fr1UVBQoLQ8lrOzM/T09F55Lm1tbQQGBmL79u1QKBQQQmDHjh0ICgqSlnn+4Ycf4OHhAUtLS8jlckRERJTJaGNjA2tra2nbzc0NCoUC6enpyMnJwb179yr1/lP1RMvL8vPzVY4n7ezsYGJiIm03btwYTk5O0NLSUip71e/5+Xvh5fdHQUEBcnJylI4zNDTk+JOIXhsnX4iI3nAGBgZ47733MG/ePJw8eRJBQUGYP3++Uh0LCwsMGzYMK1asQFpaGqysrPDVV1+99jUbNGgAoHT5saowNjZGy5Ytpdfzwc9zf/31F4DSdYuJiIiIiKislydAhBAVTorMmDE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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10), subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)})\n",
    "\n",
    "# Plot SST on the first subplot\n",
    "sst_plot = ax1.contourf(lon, lat, sst_mean_f, cmap='coolwarm', transform=ccrs.PlateCarree())\n",
    "ax1.coastlines()\n",
    "ax1.gridlines(draw_labels=True)\n",
    "ax1.set_title('Mean Sea Surface Temperature (°F) 1979-2022')\n",
    "ax1.set_extent([120, 300, -65, 65], crs=ccrs.PlateCarree())\n",
    "# Adding colorbar for SST plot\n",
    "fig.colorbar(sst_plot, ax=ax1, orientation='horizontal', pad=0.05, aspect=50, shrink=0.5, label='SST (°F)')\n",
    "\n",
    "# Plot TCWV on the second subplot\n",
    "tcwv_plot = ax2.contourf(lon, lat, WaterOnly, cmap='viridis', transform=ccrs.PlateCarree())\n",
    "ax2.coastlines()\n",
    "ax2.gridlines(draw_labels=True)\n",
    "ax2.set_title('Mean Total Column Water Vapor (TCWV) 1979-2022')\n",
    "ax2.set_extent([120, 300, -65, 65], crs=ccrs.PlateCarree())\n",
    "# Adding colorbar for TCWV plot\n",
    "fig.colorbar(tcwv_plot, ax=ax2, orientation='horizontal', pad=0.05, aspect=50, shrink=0.5, label='Total Column Water Vapor (mm)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2) From the dataset shown in 1, compute anomalies by deseasonalizing the data (remove the mean monthly anomaly from the annual mean from each point), detrend, and standardize the SST anomalies. Standardization means subtract the long term mean and divide by the standard deviation, which can be accomplished using the methods described here: 6.3. Preprocessing data — scikit-learn 1.1.2 documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deseasonalize and Detrend the sst data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Sea_surface_temperature_surface_Mixed_intervals_Average&#x27; (\n",
       "                                                                             time: 516,\n",
       "                                                                             lat: 130,\n",
       "                                                                             lon: 180)&gt;\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.38342869,\n",
       "          0.34954392,  0.32953492],\n",
       "        [        nan,         nan,         nan, ...,  0.36373954,\n",
       "          0.36387786,  0.80321541],\n",
       "        ...,\n",
       "        [ 0.12777244, -0.06856424, -0.08015899, ..., -0.35416543,\n",
       "         -0.36359876, -0.25412776],\n",
       "        [-0.17031517, -0.26517393, -0.31952842, ..., -0.40553535,\n",
       "         -0.12731253, -0.0301891 ],\n",
       "        [-0.32433672, -0.34750323, -0.418057  , ..., -0.65371273,\n",
       "         -0.21520619, -0.47617535]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.38196845,\n",
       "          0.35151662,  0.33311919],\n",
       "        [        nan,         nan,         nan, ...,  0.364339  ,\n",
       "          0.34636538,  0.85265687],\n",
       "...\n",
       "        [ 0.31481135,  0.4107006 ,  0.40753237, ...,  0.34951266,\n",
       "          0.40547761,  0.62918372],\n",
       "        [ 0.02414256,  0.13141766,  0.21549724, ...,  0.40502109,\n",
       "          0.45244556,  0.48932394],\n",
       "        [-0.09853184, -0.08727549, -0.07792478, ...,  0.24707919,\n",
       "          0.33511202,  0.15685398]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -0.25884269,\n",
       "         -0.26819409, -0.26765971],\n",
       "        [        nan,         nan,         nan, ..., -0.21371787,\n",
       "         -0.27703752, -0.02584727],\n",
       "        ...,\n",
       "        [-0.00461477, -0.00999366, -0.05573728, ...,  0.02289728,\n",
       "         -0.07981238,  0.06580409],\n",
       "        [ 0.02000785,  0.00679797,  0.01798494, ...,  0.08541713,\n",
       "          0.01951389, -0.02609236],\n",
       "        [-0.04729804, -0.03649673,  0.02787443, ...,  0.13161837,\n",
       "          0.23753098,  0.23337795]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Sea_surface_temperature_surface_Mixed_intervals_Average'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 516</li><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2a75d495-dc46-477f-a658-8d1ac47a6729' class='xr-array-in' type='checkbox' checked><label for='section-2a75d495-dc46-477f-a658-8d1ac47a6729' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan ... 0.059 0.09025 0.1195 0.1316 0.2375 0.2334</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.38342869,\n",
       "          0.34954392,  0.32953492],\n",
       "        [        nan,         nan,         nan, ...,  0.36373954,\n",
       "          0.36387786,  0.80321541],\n",
       "        ...,\n",
       "        [ 0.12777244, -0.06856424, -0.08015899, ..., -0.35416543,\n",
       "         -0.36359876, -0.25412776],\n",
       "        [-0.17031517, -0.26517393, -0.31952842, ..., -0.40553535,\n",
       "         -0.12731253, -0.0301891 ],\n",
       "        [-0.32433672, -0.34750323, -0.418057  , ..., -0.65371273,\n",
       "         -0.21520619, -0.47617535]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.38196845,\n",
       "          0.35151662,  0.33311919],\n",
       "        [        nan,         nan,         nan, ...,  0.364339  ,\n",
       "          0.34636538,  0.85265687],\n",
       "...\n",
       "        [ 0.31481135,  0.4107006 ,  0.40753237, ...,  0.34951266,\n",
       "          0.40547761,  0.62918372],\n",
       "        [ 0.02414256,  0.13141766,  0.21549724, ...,  0.40502109,\n",
       "          0.45244556,  0.48932394],\n",
       "        [-0.09853184, -0.08727549, -0.07792478, ...,  0.24707919,\n",
       "          0.33511202,  0.15685398]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -0.25884269,\n",
       "         -0.26819409, -0.26765971],\n",
       "        [        nan,         nan,         nan, ..., -0.21371787,\n",
       "         -0.27703752, -0.02584727],\n",
       "        ...,\n",
       "        [-0.00461477, -0.00999366, -0.05573728, ...,  0.02289728,\n",
       "         -0.07981238,  0.06580409],\n",
       "        [ 0.02000785,  0.00679797,  0.01798494, ...,  0.08541713,\n",
       "          0.01951389, -0.02609236],\n",
       "        [-0.04729804, -0.03649673,  0.02787443, ...,  0.13161837,\n",
       "          0.23753098,  0.23337795]]])</pre></div></div></li><li class='xr-section-item'><input id='section-0f965bba-e824-4b6b-8cc8-2a180ba58cef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f965bba-e824-4b6b-8cc8-2a180ba58cef' class='xr-section-summary' >Coordinates: <span>(5)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-7e625d66-39ae-4679-936c-c3f195145478' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e625d66-39ae-4679-936c-c3f195145478' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef74f372-24fe-495f-a6b3-d7b6239c6d67' class='xr-var-data-in' type='checkbox'><label for='data-ef74f372-24fe-495f-a6b3-d7b6239c6d67' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-12-01</div><input id='attrs-f3a3dcf9-c84c-40a8-a5bd-92b0f01f3ad3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3a3dcf9-c84c-40a8-a5bd-92b0f01f3ad3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a7f0d9-2e7a-497e-a284-a2417caf94f0' class='xr-var-data-in' type='checkbox'><label for='data-11a7f0d9-2e7a-497e-a284-a2417caf94f0' 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>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-01T00:00:00.000000000&#x27;, &#x27;1979-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-05-01T00:00:00.000000000&#x27;, &#x27;1979-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-07-01T00:00:00.000000000&#x27;, &#x27;1979-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-09-01T00:00:00.000000000&#x27;, &#x27;1979-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-11-01T00:00:00.000000000&#x27;, &#x27;1979-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, &#x27;1980-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-05-01T00:00:00.000000000&#x27;, &#x27;1980-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-07-01T00:00:00.000000000&#x27;, &#x27;1980-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-09-01T00:00:00.000000000&#x27;, &#x27;1980-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-11-01T00:00:00.000000000&#x27;, &#x27;1980-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-03-01T00:00:00.000000000&#x27;, &#x27;1981-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-05-01T00:00:00.000000000&#x27;, &#x27;1981-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-07-01T00:00:00.000000000&#x27;, &#x27;1981-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-09-01T00:00:00.000000000&#x27;, &#x27;1981-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-11-01T00:00:00.000000000&#x27;, &#x27;1981-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-01T00:00:00.000000000&#x27;, &#x27;1982-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-03-01T00:00:00.000000000&#x27;, &#x27;1982-04-01T00:00:00.000000000&#x27;,\n",
       "...\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2019-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-01T00:00:00.000000000&#x27;, &#x27;2019-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-01T00:00:00.000000000&#x27;, &#x27;2019-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-01T00:00:00.000000000&#x27;, &#x27;2019-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-01T00:00:00.000000000&#x27;, &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-05-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-01T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-09-01T00:00:00.000000000&#x27;, &#x27;2020-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-01T00:00:00.000000000&#x27;, &#x27;2020-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-03-01T00:00:00.000000000&#x27;, &#x27;2021-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-01T00:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-01T00:00:00.000000000&#x27;, &#x27;2021-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-01T00:00:00.000000000&#x27;, &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>65 64 63 62 61 ... -61 -62 -63 -64</div><input id='attrs-271cfa2b-a1ad-40bc-9352-34f42b9fdcd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-271cfa2b-a1ad-40bc-9352-34f42b9fdcd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f087f89e-78cb-4656-a2b2-41b78984add2' class='xr-var-data-in' type='checkbox'><label for='data-f087f89e-78cb-4656-a2b2-41b78984add2' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,  55,  54,  53,  52,\n",
       "        51,  50,  49,  48,  47,  46,  45,  44,  43,  42,  41,  40,  39,  38,\n",
       "        37,  36,  35,  34,  33,  32,  31,  30,  29,  28,  27,  26,  25,  24,\n",
       "        23,  22,  21,  20,  19,  18,  17,  16,  15,  14,  13,  12,  11,  10,\n",
       "         9,   8,   7,   6,   5,   4,   3,   2,   1,   0,  -1,  -2,  -3,  -4,\n",
       "        -5,  -6,  -7,  -8,  -9, -10, -11, -12, -13, -14, -15, -16, -17, -18,\n",
       "       -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32,\n",
       "       -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46,\n",
       "       -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60,\n",
       "       -61, -62, -63, -64])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>120 121 122 123 ... 296 297 298 299</div><input id='attrs-1ae435d2-4978-49e0-865b-f4d148f37cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ae435d2-4978-49e0-865b-f4d148f37cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7547d1ff-006e-4e9b-801e-e2104e716fbb' class='xr-var-data-in' type='checkbox'><label for='data-7547d1ff-006e-4e9b-801e-e2104e716fbb' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
       "       134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
       "       148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
       "       162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
       "       176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,\n",
       "       204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,\n",
       "       218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "       232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287,\n",
       "       288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-0a2b31b2-901a-4b48-9f96-f98af6824e36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a2b31b2-901a-4b48-9f96-f98af6824e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c32d8c0-2f19-4f7b-8281-26a7f5ce2573' class='xr-var-data-in' type='checkbox'><label for='data-6c32d8c0-2f19-4f7b-8281-26a7f5ce2573' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec6b3517-2031-424c-97a1-1b40dc158ad6' class='xr-section-summary-in' type='checkbox'  ><label for='section-ec6b3517-2031-424c-97a1-1b40dc158ad6' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-813708b3-c2a2-4604-bbea-7358e6d45451' class='xr-index-data-in' type='checkbox'/><label for='index-813708b3-c2a2-4604-bbea-7358e6d45451' 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;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=516, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f7d3356-bc8e-49fd-ae2d-304566cab5bd' class='xr-index-data-in' type='checkbox'/><label for='index-8f7d3356-bc8e-49fd-ae2d-304566cab5bd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,\n",
       "       ...\n",
       "       -55, -56, -57, -58, -59, -60, -61, -62, -63, -64],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9dce217a-a21a-48aa-b831-325af21144a5' class='xr-index-data-in' type='checkbox'/><label for='index-9dce217a-a21a-48aa-b831-325af21144a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       ...\n",
       "       290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e285fbcf-e3d7-4813-b66a-fe22f1fcd469' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e285fbcf-e3d7-4813-b66a-fe22f1fcd469' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Sea_surface_temperature_surface_Mixed_intervals_Average' (\n",
       "                                                                             time: 516,\n",
       "                                                                             lat: 130,\n",
       "                                                                             lon: 180)>\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.38342869,\n",
       "          0.34954392,  0.32953492],\n",
       "        [        nan,         nan,         nan, ...,  0.36373954,\n",
       "          0.36387786,  0.80321541],\n",
       "        ...,\n",
       "        [ 0.12777244, -0.06856424, -0.08015899, ..., -0.35416543,\n",
       "         -0.36359876, -0.25412776],\n",
       "        [-0.17031517, -0.26517393, -0.31952842, ..., -0.40553535,\n",
       "         -0.12731253, -0.0301891 ],\n",
       "        [-0.32433672, -0.34750323, -0.418057  , ..., -0.65371273,\n",
       "         -0.21520619, -0.47617535]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.38196845,\n",
       "          0.35151662,  0.33311919],\n",
       "        [        nan,         nan,         nan, ...,  0.364339  ,\n",
       "          0.34636538,  0.85265687],\n",
       "...\n",
       "        [ 0.31481135,  0.4107006 ,  0.40753237, ...,  0.34951266,\n",
       "          0.40547761,  0.62918372],\n",
       "        [ 0.02414256,  0.13141766,  0.21549724, ...,  0.40502109,\n",
       "          0.45244556,  0.48932394],\n",
       "        [-0.09853184, -0.08727549, -0.07792478, ...,  0.24707919,\n",
       "          0.33511202,  0.15685398]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ..., -0.25884269,\n",
       "         -0.26819409, -0.26765971],\n",
       "        [        nan,         nan,         nan, ..., -0.21371787,\n",
       "         -0.27703752, -0.02584727],\n",
       "        ...,\n",
       "        [-0.00461477, -0.00999366, -0.05573728, ...,  0.02289728,\n",
       "         -0.07981238,  0.06580409],\n",
       "        [ 0.02000785,  0.00679797,  0.01798494, ...,  0.08541713,\n",
       "          0.01951389, -0.02609236],\n",
       "        [-0.04729804, -0.03649673,  0.02787443, ...,  0.13161837,\n",
       "          0.23753098,  0.23337795]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from scipy import signal\n",
    "\n",
    "# First, calculate the monthly mean anomaly for each point by subtracting the long-term monthly mean from each month's value. \n",
    "# This removes the seasonal cycle.\n",
    "\n",
    "# Handling infinite values by replacing them with NaN\n",
    "sst = sst.where(np.isfinite(sst), np.nan)\n",
    "# Calculating climatology (monthly means) and anomalies, skipping NaN values\n",
    "climatology = sst.groupby('time.month').mean('time', skipna=True)\n",
    "anomalies = sst.groupby('time.month') - climatology\n",
    "\n",
    "\n",
    "# Detrending the anomalies. This removes the linear trend from the deseasonalized time series.\n",
    "def detrend(da):\n",
    "    da_no_nan = da.fillna(0)\n",
    "    detrended = xr.apply_ufunc(signal.detrend, da_no_nan, kwargs={'axis': 0}, dask='allowed')\n",
    "    return detrended.where(~da.isnull())\n",
    "detrended_anomalies = detrend(anomalies)\n",
    "\n",
    "detrended_anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3) Perform an EOF analysis (with cosine latitude weighting) on the SST anomalies and plot a map of the first 5 EOFs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eofs.xarray import Eof\n",
    "from eofs.examples import example_data_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9jCZNmhg1a9Y0mjVrZgwaNMh44403qi3br1+/au/P5Zdfbhx88MFGTk6OUatWLSMvL8848cQTjY8//jjs882bN8/o0aOHUbduXSMnJ8cYNmyY8e9//zvssu+++67RoEEDY/369UG379mzx7j66quNvLw8o2nTpsbYsWON33//Pea2AgAAAADgZj7D4LxiwM0eeughXXXVVVq7dm3c8/Ama/To0Xr55ZdVUlKS1ucFAAAAAACA/THnNOByixcv1sSJE9NemAYAAAAAAACiYc5pwOVeeumlTDcBAAAAAAAAqIZpPQAAAAAAAAAAace0HgAAAAAAAACAtKM4DQAAAAD4//buPDyq+u77+GcSIKwZgUhCJCwqVRAtDiqCaKDVoHWnd6sPNoJVlIciRRSV2t6Al0KtilS9waWWatVHe2vdqlJoVURZBEbcxapgEBKpgBlSMZDkPH/gjJnJ7HNmzjLv13XNpTlzzszvN8sZzme+8z0AAAA5RzgNAAAAAAAAAMg5TogYoaWlRdu3b1e3bt3k8XisHg4AAMgDhmFoz549Ki8vV0EBtQMAAAAA8gPhdITt27eroqLC6mEAAIA8tHXrVvXp08fqYUS1du1ajT7xRC195RVVVlZaPRwAAAAALuAxDMOwehB2Ul9fr4MOOkhbt25VcXGx1cMBAAB5IBAIqKKiQl999ZW8Xq/Vw2nDMAz1LyhQg6SOkrY2N1PhDQAAACBjVE5HCLbyKC4uJpyGa/n9qW/j89nn9gHkp+C+xcn7i1j7x4aGA/+1a0uxZ555Rjsl/V9J90r6y1/+ogsvvNDiUQEAAABwOkpeACQlmcDZ708vmE729gFAYn+Ra/v379dl55+v0ZK6SvqBpP/7f/6PGhsbrR0YAAAAAMcjnAaQtHiBkBlhUSbhNoD8wr4id87v0EGSFCxY/76kIknnduxo1ZAAAAAAuARtPQCkhEAIyG+x9gFWtNpoPRYntPpw4v5zz549ekXS2ZIKv11WIOk0SU9I2r17t7p3727R6AAAAAA4HZXTAGzFCQETkK+y/euJRJy8f3BiMC1JZxUXq6ekIyKWHy6pXNJZPXrkflAAAAAAXIPKaSDP2DkgcXLwBLhRqvuLVNZP9/0e3M6OJ0e08/41Hdu3b9dqSRMkRTtNY5WkP0jasmWL+vfvn8uhAQAAAHAJR1VOb9u2TT/72c/Us2dPde7cWUOHDtWGDRtC1xuGoTlz5qi8vFydOnXS6NGj9d5771k4YsBe7Byc2ClgAvJdLvq/Z3r7Pp999htu7Zd/1iGH6HuS+sS4vkzSUZLOGTAgd4MCAAAA4CqOCad3796tk046Se3bt9eLL76o999/X7fffrsOOuig0Dq/+93vtGDBAt19991at26dysrKdNppp2nPnj3WDRywCTcGJwDMl8t9hRv2S26YQzTvvvuu3pH0wwTr/UDSB5LWr1+f/UEBAAAAcB2PYRiG1YNIxvXXX6/XX39dK1eujHq9YRgqLy/X9OnTdd1110mSGhsbVVpaqltuuUVXXHFFUvcTCATk9XpVX1+v4uJi08YPa0ULD+xScZcMt4YfkZz0nABuZLd9jV33Cdl4nBoaAqqstM+/PwZ6PCqRdHoS6/5D0ueSPm1pkccTrQEIAAAAAETnmMrpZ599Vscdd5x+8pOfqFevXjr22GN1//33h67fvHmz6urqVFVVFVpWVFSkyspKrVq1yoohwwbc8FNrp48/Ffk0V8Bu7Pj+c/I+PNh2xE7tR5L1z3/+U1slnZLk+qMk7ZD0wgsvZG9QAAAAAFzJMSdE/PTTT7V48WLNmDFDv/rVr/TGG29o2rRpKioq0sUXX6y6ujpJUmlpadh2paWl+uyzz2LebmNjoxobG0N/BwKB7EwAOeHUEKM1N8whE4nm77SQJxo7nsgN+c3u+53W43PC+ybaGH0++z/OktTS0qKfnXqqTpbUOcltOkqqlHTJWWdp+/79atfOMf+8BAAAAGAxx1ROt7S0yOfzad68eTr22GN1xRVXaNKkSVq8eHHYepE/JzUMI+5PTOfPny+v1xu6VFRUZGX8yL5UDvrtGG44uUIwl5z+GLUeP8854D7xPl+cUEX96KOPaq+k4SluN0xSs6Rx7dubPygAAAAAruWYcLp3794aPHhw2LJBgwappqZGklRWViZJoQrqoB07drSppm5t1qxZqq+vD122bt1q8siRC24IppE8pz5escbt1PkA+SrTkNmOn0NB06qrNUZSqhFzOx04eeIrpo8IAAAAgJs55neXJ510kjZt2hS27KOPPlK/fv0kSQMGDFBZWZmWL1+uY489VpK0b98+rVixQrfcckvM2y0qKlJRUVH2Bo6sSjXUMzsQIFS0jhN+5p/K68PM15JdHw/AbTJp1WHXNh9N3brpmD17VJbGtu0k/bWw0OwhAQAAAHAxx4TTV111lUaOHKl58+bppz/9qd544w3dd999uu+++yQdaOcxffp0zZs3TwMHDtTAgQM1b948de7cWePHj7d49MiGaAf1hHKwA6sDJ7+f9wJSE/l6sfo17CRmVVBzygsAAAAA+cgx4fTxxx+vp556SrNmzdKNN96oAQMGaOHChbroootC61x77bXau3evpkyZot27d2v48OFatmyZunXrZuHInc8pIUWuwzinPC75wA5hrN1eD3x5g0zYtapXssf7HQAAAABgDo9hGIbVg7CTQCAgr9er+vp6FRcXWz0cy9k1nIhEMA3J2sDKKa8JQj1kgxWvf7e9lu3y74/i4mJdtWePytPY9ktJswsL1dTUZPawAAAAALiUY06IiNwjbIvOKY9LPsrGc+P3u+s5d9NcYB9WBMXB96bb3qMAAAAAkE8c09YDueWUA323Vc7BXlq/D2K1EnDKe6U12iIgG+hbDQAAAABIFZXTCEMFGpzMzMA12vsgcpmT3yu815FtufwChC9bAAAAAMCZqJzOc4RTqeHxsq9Mw6lkn1u3vQaookY2BV9b2Xzf8PoFAAAAAOcinM5jbgvZso3Hy1rxWgZkEk7xvBJQI/uSfX0l837ktQoAAAAA7kE4nacI5BLjMbKPaGGUz5e/1c7ZEPkYEQDCCvHe17wmAQAAAMB9CKfzVKyDfEI8WCXWz//jBVLBICu4jVtOWAjks2gBNcE0AAAAALgTJ0REGAIAWKH1687MFh0E0wAAAAAAAPZF5TTayMUJrOwun+eeS7GC6NavwXj9kKM9Tzx3AAAAAAAAzkA4jZjiVbC6OQB089ycKlpAzfMEuFcqPeUBAAAAAM5FOI20uDE4cNt83IbnJ7fiVawDueDGzxkAAAAAQDh6TiNtdgiuzAouCECsEe9x5zmxHs8BrObz2eOzBgAAAACQHYTTcLxMAzQCOGsF+0rH+hvW4rkAAAAAAADZQlsPuEKqLQgI3OyH58S+kj0pJRWuAAAAAAAgFVROAwASilbRHu1vvmQAAAAAAADJonIaeYfwDEgf7x8AAAAAAGAWKqfhGsmEZgRrAAAAAAAAgD0QTsNV4oXPBNMAAAAAAACAfdDWA65DCA1YK/ge5ASJAAAAAAAgHiqnAQBZwRdFAAAAAAAgHsJpAEDW+P3hFyDvRbwpgv+7caPVA7POokWLNGDAAHXs2FHDhg3TypUr467/yCOP6Pvf/746d+6s3r1765JLLtHOnTtzNFoAAAAAZiKcBgDkDAE1cIBfPvlF75vHH39c06dP1w033KA333xTJ598ss444wzV1NREXf+1117TxRdfrEsvvVTvvfee/vd//1fr1q3TZZddluORAwAAADAD4TQAIGd8oowa+Y1QOtyCBQt06aWX6rLLLtOgQYO0cOFCVVRUaPHixVHXX7Nmjfr3769p06ZpwIABGjVqlK644gqtX78+xyMHAAAAYAbCaaSNbAlAqsJCOfp9AK4UCATCLo2NjVHX27dvnzZs2KCqqqqw5VVVVVq1alXUbUaOHKnPP/9cL7zwggzD0BdffKEnnnhCZ555punzAAAAAJB97aweAJyJLAkAAPMMHWr1CL4zRNKhaWy3TVJLS4u8Xm/Y8tmzZ2vOnDlt1v/yyy/V3Nys0tLSsOWlpaWqq6uLeh8jR47UI488ogsuuEDffPONmpqadM455+iuu+5KY8QAAAAArEY4jZBg4OxL8GtjgmkAmfDLd6C9B1wt8rMi0WdLPvP5pEDA6lGYo6CgQLt27QpbVlRUFHcbj8cT9rdhGG2WBb3//vuaNm2a/vu//1tjx45VbW2tZs6cqcmTJ+uBBx7IbPAAAAAAco5wGpIInAHkFgE18lU+hPTFxcVJrVdSUqLCwsI2VdI7duxoU00dNH/+fJ100kmaOXOmJOmYY45Rly5ddPLJJ+umm25S7969Mxs8AAAAgJyi5zQIpgFYgpPCAfmtQ4cOGjZsmJYvXx62fPny5Ro5cmTUbb7++msVFIT/87WwsFDSgYprAAAAAM5C5XSeI5gGYAt+f36UlOYRnk4kY8aMGaqurtZxxx2nESNG6L777lNNTY0mT54sSZo1a5a2bdumhx56SJJ09tlna9KkSVq8eHGorcf06dN1wgknqLy83MqpAAAAAEgD4XSeIpQGYAdh7T0IqIG8c8EFF2jnzp268cYbVVtbqyFDhuiFF15Qv379JEm1tbWqqakJrT9x4kTt2bNHd999t66++moddNBB+sEPfqBbbrnFqikAAAAAyIDH4DeQYQKBgLxer+rr65Pumeg0iYJpTogIIJfa9J4moEYessu/P4qLi/XAnj06NI1tt0kaV1iopqYms4cFAAAAwKWonM4zZgTL8XIjgmsAqSCYBgAAAAAgf3FCxDySi+CYXAlAsgimge9s3Gj1CAAAAAAg96iczgO5rmb2+aigBhCuTRDdZgWCaeQvPjMBAAAA5CvCaZdL9YCXfAiA2RIG00CeIpQGAAAAkO8Ip10s2YNen/ymptIcbAMIIpiGW/DZBgAAAADmI5x2qZSCaQvuF4D7EUwD4e8Dv/h5EgAAAAC0RjjtAlYGwoTRAAC0Fe3LGZ/8UQNqn/wKqCEXwwIAAAAAWymwegDpmj9/vjwej6ZPnx5aZhiG5syZo/LycnXq1EmjR4/We++9Z90gc4BgGoAdUTWNfOWTP+7rP/J63isAAAAA8pkjw+l169bpvvvu0zHHHBO2/He/+50WLFigu+++W+vWrVNZWZlOO+007dmzx6KRZpdV4bDfTzANIDbCNuSjRKF0pusDAAAAgBs5rq1HQ0ODLrroIt1///266aabQssNw9DChQt1ww03aNy4cZKkBx98UKWlpXr00Ud1xRVXWDXktNgt/LXbeADYE2Eb3IjPQAAAAADIDsdVTv/iF7/QmWeeqVNPPTVs+ebNm1VXV6eqqqrQsqKiIlVWVmrVqlW5HiYA5CV/qB7Ux8nf4ArJBNPpfCnD+wQAAAAAHFY5/dhjj8nv92vdunVtrqurq5MklZaWhi0vLS3VZ599FvM2Gxsb1djYGPo7EAiYNNr05bJCyy8flY4AsoZ9DJwkm5+/8UJov3xqkPX//gAAAACAXHNM5fTWrVv1y1/+Ug8//LA6duwYcz2PxxP2t2EYbZa1Nn/+fHm93tCloqLCtDGnw5KfDvuo2gKQPUlVhtI3ARbL5CWYKHimOhoAAAAAonNM5fSGDRu0Y8cODRs2LLSsublZr776qu6++25t2rRJ0oEK6t69e4fW2bFjR5tq6tZmzZqlGTNmhP4OBAKWBdRWZDPk0gBsI7gTZMeEHDPj85cAGgAAAABS55hw+oc//KHeeeedsGWXXHKJjjzySF133XU69NBDVVZWpuXLl+vYY4+VJO3bt08rVqzQLbfcEvN2i4qKVFRU1Gb5xo1S164H/t+tOYlb5wXAerTygFNQtA8AAAAA1nFMON2tWzcNGTIkbFmXLl3Us2fP0PLp06dr3rx5GjhwoAYOHKh58+apc+fOGj9+fEb37cZiPjfNBYA9EEjDSUKf7d++bql8BgAAAIDcc0w4nYxrr71We/fu1ZQpU7R7924NHz5cy5YtU7du3Uy5/WSrq+we/KY6PqrKACRCMA0niPd51vo1TFANAAAAALnhMQzDsHoQdhIIBOT1elVfX6/i4uKw61IJadMJqLMRAmcalBNMA4gna6G03b/lgyNF+0yLfA1bFUw3NARUWRn93x+5VFxcrAf27NGhaWy7TdK4wkI1NTWZPSwAAAAALlVg9QCcJJWsJJ1Q1+wshmAaQDZls1ra72cfhNzwyxd2AQAAAADkDuF0irJdzOfzmXMfFB0CcAMCagAAAAAA3ItwGgCQlmxVmkbeJgE1AAAAAADu5KoTIgIAci8XrRD8fn4RAgAAAACA2xBOp8Cq6j0CGQAAkCuDv72kqrvZAwEAAADgerT1SBLBNAAAzkV7GAAAAACwH8JpmzLrxIjp4iAegN2wX0K6eO0AAAAAgD3R1gNhOIAHkAmf2u5EzOxJTe9ppIrPNQAAAACwL8LpLEoUokQ7YM516MJBOwCzRAumg8vNDqglQmokxmccAAAAANgb4XSWRQuo7XKwbJdxALBGrDA51SA51u1ErmNmQC1RRY34+IwDAAAAAPsjnM4Buxwg22UcAHIjmdA41nbJBsmp3Ec2Wn5E7tcIq/MTn28AAAAA4EycEDEP+P0cuAP5yOxKZZ/8oUvwb7thX5d/eM4BAAAAwLkIp5Pk8zmzIo+DdiC/mRVQRwbRZgXT2Qi4+UIuf/A8AwAAAICz0dYjRT6fPQ6G7TAGAM7gl8+WVc7ZRk9qd+NzEAAAAACcj8ppB+KAHEAuOTnYZn/pPlTGAwAAAIB7UDntIByMI9cShZJm9zRGW6kEw2Y+H7kKpHPxGqKC2tn47AMAAAAA9yKctplYIQoH58i2dMLI4DaE1Mlr/TjHe9wyeT4AAAAAAACcgHDahiIDaoJp98t2yBsvEDUj0PTJH7rdTCt9Y23v5AA81pxiPe+EzAAAAAAAIB8QTtsUgXT+aB1Etg55c3F/Vt9u5HzNqha2U5Cd6pzyLZiOnG+2njtaewAAAAAAYD+cEBGwUCpBpE9+V7Z6yGR8iSqSrZbsOILPrV3GnSqze11n63HgSz9n4ksFAAAAAHAvKqcBC8QL36JVT0dWV0v2qg42g5sqjO08NrMFX4d++Uydd7Z+RUAFdVvB0D7dxyXd0D+V+wuu65QvGJLtLQ8AAAAA+Y5wGvhWMqGvGcFwMgGeWSGfU0LSZB+TdPpaZ5udxpJrke8DswPqbMk0jHWT1mFvqo9LpkGxm78oCL4XCKYBAAAAID7CaeS9yDAtVsVbpr2hcxnaOSEgTEey83Jq324nifUYR3vPpHLiy0S3byY3h6PJiBUuJ/O4mFXB7PTnIF7PdIJpAAAAAEiMcDpFTvlJMZKTKCBL1H5DSi6AyEaYGSsgz/fgNF5LFMKi3Ir3eDulytqtEn2W8VmXG63fAwE1WDgSAAAAALAG4XQKOFh3j1z1xs12+Ea4F12yJ0pMNiCNVUGP9PE4WsPpn2N26z2d7RZPAAAAAOB2hNNJssuBMDKXqypmggdrpPK4J7suz2Vbwdd8rMcmmd7tseSywt3pbSWS4cbPL5/P2fNinwIAAAAABxBOI29QxQyYyy2v+XwIqGEvtLUBAAAAgAMKrB4AkAuEAEDuOKVqOux+2UUgi/gMAgAAAIDoqJwGAORMuq1AcoEKamsEvxhI9bGPXN/uXzAQUAMAAABAW1ROJ4nAAgDi88uXVsCc7nbZ4PfbP+R0q0wfd7t+ThNKJ7Zo0SINGDBAHTt21LBhw7Ry5cqktnv99dfVrl07DR06NLsDBAAAAJA1VE6nIHjgS3ABIJ9lGiTbJYiOhypqayR63BNd7/QTJeajxx9/XNOnT9eiRYt00kkn6d5779UZZ5yh999/X3379o25XX19vS6++GL98Ic/1BdffJHDEQMAAAAwE5XTcD2q1gCkg5DTGtEe91Qq2u30pQKfP4ktWLBAl156qS677DINGjRICxcuVEVFhRYvXhx3uyuuuELjx4/XiBEjcjRSAAAAANlAOI2U+EI/wA+/AIAbEVBbo/Xjnupz0Hp9PqOsEQgEwi6NjY1R19u3b582bNigqqqqsOVVVVVatWpVzNtfsmSJPvnkE82ePdvUcQMAAADIPdp6wLZaBwrptgEglACQKVp8ZFeixzaVxz5WkO2TP2ftZNzwueP5r/+Sp3v31LcLBNTyl7/I6/WGLZ89e7bmzJnTZv0vv/xSzc3NKi0tDVteWlqqurq6qPfxr3/9S9dff71Wrlypdu34ZywAAADgdPyrHraQ6GDeDQf7gBs4oV90NgRDT0Jqc5n5eCaqsE41oE71C1I+pw4oKCjQrl27wpYVFRXF3cbj8YT9bRhGm2WS1NzcrPHjx2vu3Ln63ve+l/lgAQAAAFiOcBo5xcE7ACeLV0UdCrAj93Mk2lHlMpgO3WfEcxMrdI5cL/h3suvnu+Li4qTWKykpUWFhYZsq6R07drSpppakPXv2aP369XrzzTc1depUSVJLS4sMw1C7du20bNky/eAHP8h8AgAAAAByhnAapkh04N56HQDWyDRYy9eq6UjRAuq44Sh9Qdow++Hw+dLrDx5ZTZ3sr3hS2QaxdejQQcOGDdPy5ct1/vnnh5YvX75c5557bpv1i4uL9c4774QtW7RokV566SU98cQTGjBgQNbHDAAAAMBchNNIiV++uAfiuezrCSB58d6Xwevivbd5X4eLF4RGfTwJqLMu+PCmGlIHP7dSCZkJpM0zY8YMVVdX67jjjtOIESN03333qaamRpMnT5YkzZo1S9u2bdNDDz2kgoICDRkyJGz7Xr16qWPHjm2WAwAAAHAGx4TT8+fP11//+ld9+OGH6tSpk0aOHKlbbrlFRxxxRGgdwzA0d+5c3Xfffdq9e7eGDx+u//mf/9FRRx1l2jjSqcwCB/KAE0SGqgTSmUkm9HeaRPm6HT4jw8eYXIm7m54jp7ngggu0c+dO3XjjjaqtrdWQIUP0wgsvqF+/fpKk2tpa1dTUWDxKAAAAANniMQzDsHoQyTj99NN14YUX6vjjj1dTU5NuuOEGvfPOO3r//ffVpUsXSdItt9yim2++WX/605/0ve99TzfddJNeffVVbdq0Sd26dUvqfgKBgLxer+rr69v0TLTDQbddpFphyYE/YA0CZvuwqnDajM+uVMee8OSEVr4sbfphHmhokLeyMuq/P3KpuLhYa8aO1eDu3VPednMgoIFPPKGmpqYsjAwAAACAGzmmcnrp0qVhfy9ZskS9evXShg0bdMopp8gwDC1cuFA33HCDxo0bJ0l68MEHVVpaqkcffVRXXHFFSve3caPUtatZo89vBNOANQimYYZ0guREPaCTObFkuvedULoNqgEAAAAApnNMOB2pvr5ektSjRw9J0ubNm1VXV6eqqqrQOkVFRaqsrNSqVatSDqeRHk4SBViPUBqt2TWLTebEkllr1Z3KjdrxwQMAAAAAl3BkOG0YhmbMmKFRo0aFToBTV1cnSSotLQ1bt7S0VJ999lnM22psbFRjY2Po70AgkIURu0+0k0e5sb8q4DQE04jGzgF1putkvUWIXR88AAAAAHABR4bTU6dO1dtvv63XXnutzXUejyfsb8Mw2ixrbf78+Zo7d67pY8xHhNKAtQimEY9bM9asVVe3lqjEGwAAAACQlgKrB5CqK6+8Us8++6xefvll9enTJ7S8rKxM0ncV1EE7duxoU03d2qxZs1RfXx+6bN26NTsDdyGCMMA+eD8iGamEuD6fxScuTEHOs2KnPDAAAAAAYHOOqZw2DENXXnmlnnrqKb3yyisaMGBA2PUDBgxQWVmZli9frmOPPVaStG/fPq1YsUK33HJLzNstKipSUVFRVsfuZtHaewDIHkJo58pJhW8SgmOIF+hmOk4rCouj3Wfw8ynyfWPK85DMAwkAAAAAiMsx4fQvfvELPfroo3rmmWfUrVu3UIW01+tVp06d5PF4NH36dM2bN08DBw7UwIEDNW/ePHXu3Fnjx49P+f6GDpWKi2Nfnw/HotFCZ4IxWCnf+5rH+zKI96YzBD877BRSm81On4+x3hd2+aIAAAAAAPKdY8LpxYsXS5JGjx4dtnzJkiWaOHGiJOnaa6/V3r17NWXKFO3evVvDhw/XsmXL1K1bN9PH4/aCqVgBmE9+QjCbc1s1e7TXW7zXYDLhrVMfn3jj5r3pLJGfHW4JSp30mWhKQO2kCQMAAACADXkMwzCsHoSdBAIBeb1e1dfXqzhe6fS33Hpcmii8y/cKVruKFk468TnKZcjqxMcnFQTWzuGGgNppn4k+ZZBQmzzZQEODvJWVSf/7I1uKi4u1ZuxYDe7ePeVtNwcCGvjEE2pqasrCyAAAAAC4kWMqp2Evbg/0nCKZ4LFNr1UbPndWBqhuqzSPFKvnLuyHVhPmSemXBOn0WnFaCg8AAAAANkU4DTiEWeFiotvJNKhNtn2GncJStwfUcI6oJ/Wzz1vFEYLv5dbv6aR+VULgDAAAAAA5RziNqJI98RqBXm7kMsjNpGVL5Dhbv47MnEM2bjOXrWpi3Vc22rLY6UsApMdJFdXJjjNbOTCfSQAAAADgLITTiClagBYtfIxcB+awOlTMVaV2qrL9WsukDUqqc02lLUuq87b69QNzue0Eitk4qXAqJwzlMwsAAAAA7IFwOk359OvfbAZoiI5gMbZct+Cwy3OR7HvMLuNFdjmpmjoeny/zz9Nk9wdWfD4lE4gH1zl8aCAnYwIAAAAAOyGcTkM+BdOpcltIbVWlXUon88ohu5xcz+r7t1IqLUHgbmZ8Ftkh4M4koLbzZ02sXxpF4/NJAbJpAAAAAHmIcDoNrQ/mCaqj4wRzmUt0Mq9s3R8hpzPwPMEMdqnAjhdQx9o3ZeMzxowvJFN9b9rh8QcAAAAAqxBOZygbfTPdwg0BtV3Gn8vgOF7VNoEo4D52Caijab0Pbr1vyta+mf0fAAAAAORWgdUDcAu7HthbLd7BPAf6qcskkPHJH3bJ1v0AcB6/ny9ZgyL3kcG9ZrbwuAMAAADIZ4TTJiKgji7aQX1wGQF16iIrCZMJnFOpvM52EAPAvuwWlLbeF7l5v2S3xx0AAAAAcoW2HibL5MRObta6xYdZAYNVJ1+06iSJrcW632QeY36yDiCeeJ9hVnwJm4te0/Hk6gS1Gzdm/S4AAAAAwHYIp7OAPtTRxQtFo4Wq8QKIaNV0sdbPZoidal9tqwJ1ADCDnftTZyzOxHziMx0AAAAAsoFwOouook5evNYfmdxGrOujhcPpnsAxmaq+yHXSrb5OdF9URQPItuDnmtkhdbK/rjH9ZLtJToTPdAAAAAAwH+F0lnEwa0/JVHGbfdtmrW/WtgCQicjPNkdWVKc46Lz5TL/gAunQQ1Pfbts26YknzB8PAAAAANcinAYiRAuoCYEB4DvRqpwzbflhxhd1Ub9cNDk1p3UXAAAAAJiHcDqGjRulrl0dWgmGjBFGA0B0rQPgNmGwX+aHwUn8miV40sKwLxez/AGeN1XUAAAAAJBFaYfTn3zyiZYsWaJPPvlEv//979WrVy8tXbpUFRUVOuqoo8wcIwAAcIo0m1KbFfZ+V0WdfWbm34GAebcFAAAAAE5RkM5GK1as0NFHH621a9fqr3/9qxoaGiRJb7/9tmbPnm3qAAEAcBNTT+ZnZ2kkzZFhb948VgAAAACQp9IKp6+//nrddNNNWr58uTp06BBaPmbMGK1evdq0wdkBP9kFAJjN920TCreI2QrJhIAaAAAAAOBeabX1eOedd/Too4+2WX7wwQdr586dGQ/KbjI9yRN9KQEAUttK4GCvZCdp3dc57okIg+un8Rn63frJb+isRxEAAAAAIKVZOX3QQQeptra2zfI333xThxxySMaDsqNMw2Wfj2owAEBbTqygDp58MOn1/XxJCwAAAABoK61wevz48bruuutUV1cnj8ejlpYWvf7667rmmmt08cUXmz1GAABczYkBdTIiA2wCagAAAABAa2m19bj55ps1ceJEHXLIITIMQ4MHD1Zzc7PGjx+vX//612aPEQAA20kUKKfarsNOLT4i55ZoXKlWUfNLIgAAAACAlGY43b59ez3yyCO68cYb9eabb6qlpUXHHnusBg4caPb4AAAmSDVsRHzJVDqnUw2dbkAd2QPabGYH5wTUAAAAAAApzXA66LDDDtNhhx1m1lgAABlIJQxNNTh1WpgdbX5mzCEX7Tcig+B4wXO2TrDY+qSHse474/v49uYJqQEAAAAgfyUdTs+YMSPpG12wYEFag7E7Kr0AJJJOhXIqgWcyAWU2JBOQ2iHAjvdYJBtYxwp4c9kXOtp9JXv/ZgbU2eaTX22mFeeDlkAbAAAAANwl6XD6zTffDPt7w4YNam5u1hFHHCFJ+uijj1RYWKhhw4aZO0KbySSg5kRQgHWsOuGc2fdr9Ynzkg1/cxlUZ/KYxNqWNigWivFB2/ozlC+LAQAAAMAdkg6nX3755dD/L1iwQN26ddODDz6o7t27S5J2796tSy65RCeffLL5o7SZVA+KCaWB7LM6tEW4WM9HJv2UW2+f6+eb11d2RLYP+e6K7z5oY32GElADAAAAgPOl1XP69ttv17Jly0LBtCR1795dN910k6qqqnT11VebNkC7ijxYjnWATDANHJBpJSrhoDvEagGSSssKuEu8gJqKdQAAAABwt7TC6UAgoC+++EJHHXVU2PIdO3Zoz549pgzMaaL1wSSYRj5IFDon2zYB+YXnH4kQTAMAAACA+6UVTp9//vm65JJLdPvtt+vEE0+UJK1Zs0YzZ87UuHHjTB2g0xBIw21SDREJHYH4YlYKI4RgGgAAAADyQ1rh9D333KNrrrlGP/vZz7R///4DN9SunS699FLdeuutpg4QQO4RnAHmigxbs9U726mhbuvA3qlzAAAAAACkLq1wunPnzlq0aJFuvfVWffLJJzIMQ4cffri6dOli9vgAmIzgGcidREFr6+vTfW+6Jcx1yzwAAAAAAMlLK5wO6tKli4455hizxgLAZATRgLlSCZNTDVujrR/vPghzAQAAAABOl1Y4PWbMGHk8npjXv/TSS2kPCEDmCKUB88VqzZHL+wQAAAAAwE3SCqeHDh0a9vf+/fu1ceNGvfvuu5owYYIZ4wKQJIJoIPsIiQEAAAAAMF9a4fQdd9wRdfmcOXPU0NCQ0YAAJIdQGgAAAAAAAE5WYOaN/exnP9Mf//hHM28SQCs++UMXALnDe85efL4DF7jDokWLNGDAAHXs2FHDhg3TypUr466/YsUKDRs2TB07dtShhx6qe+65J0cjBQAAAGC2jE6IGGn16tXq2LGjmTeZlkWLFunWW29VbW2tjjrqKC1cuFAnn3xySrcxdKhUXJz6ffuTyC9aH1Ansz7cJ5WTnBGKAch3BNHu9fjjj2v69OlatGiRTjrpJN17770644wz9P7776tv375t1t+8ebN+9KMfadKkSXr44Yf1+uuva8qUKTr44IP14x//2IIZAAAAAMiExzAMI9WNxo0bF/a3YRiqra3V+vXr9Zvf/EazZ882bYCpevzxx1VdXR12kPOHP/wh5kFOpEAgIK/Xq/r6ehWnk04rfuAc7QCbgNp9CJQB96L/dG7lSzBtxr8/zFBcXKw1f/yjBh96aMrbbt62TQPPP19NTU1JbzN8+HD5fD4tXrw4tGzQoEE677zzNH/+/DbrX3fddXr22Wf1wQcfhJZNnjxZb731llavXp3ymAEAAABYK622HsXFxfJ6vaFLjx49NHr0aL3wwguWBtOStGDBAl166aW67LLLNGjQIC1cuFAVFRVhBz3ZFutAOtXlcB5abgDu17q9Du/37OLz0d327dunDRs2qKqqKmx5VVWVVq1aFXWb1atXt1l/7NixWr9+vfbv35+1sQIAAADIjrTaevzpT38yeRjmCB7kXH/99WHL4x3k5EqiA2yfjwpqpyOkAvJT6/c+VdXmIZh2rkAgEPZ3UVGRioqK2qz35Zdfqrm5WaWlpWHLS0tLVVdXF/W26+rqoq7f1NSkL7/8Ur17985w9AAAAAByKa1w+tBDD9W6devUs2fPsOVfffWVfD6fPv30U1MGl6p0DnIaGxvV2NgY+jvygMosfn/8A22CaQBwPp/8UQPqWMsBO3pfg/WNBqe83TZ1V0tLi7xeb9jy2bNna86cOTG383g8YX8bhtFmWaL1oy0HAAAAYH9phdNbtmxRc3Nzm+WNjY3atm1bxoPKVCoHOfPnz9fcuXNNvf9YQXOsgDra+qlWjBFuA4A9RAuiCaaRLwoKCrRr166wZdGqpiWppKREhYWFbQoIduzY0abQIKisrCzq+u3atWtTNAEAAADA/lIKp5999tnQ///9738Pq4xpbm7WP//5T/Xv39+0waUqnYOcWbNmacaMGaG/A4GAKioq0h5DopA4MqA2I5gObkNAbS2/fLT2ACCJSmnkt2RP6NihQwcNGzZMy5cv1/nnnx9avnz5cp177rlRtxkxYoSee+65sGXLli3Tcccdp/bt26c/aAAAAACWSCmcPu+88yQdqEyeMGFC2HXt27dX//79dfvtt5s2uFSlc5ATqw9iOpINh+Otl0mPzeC2hNQAAMAJZsyYoerqah133HEaMWKE7rvvPtXU1Gjy5MmSDhQRbNu2TQ899JAkafLkybr77rs1Y8YMTZo0SatXr9YDDzyg//f//p+V0wAAAACQppTC6ZaWFknSgAEDtG7dOpWUlGRlUJlIdJCTb5INuwm0zUH1NACJNh5Asi644ALt3LlTN954o2prazVkyBC98MIL6tevnySptrZWNTU1ofUHDBigF154QVdddZX+53/+R+Xl5brzzjv14x//2KopAAAAAMhAWj2nN2/ebPY4TJPoICefpFKFTVsQAPhOZLgc+aUT4TNgnilTpmjKlClRr/vTn/7UZlllZaX8/KMFAAAAcIWkw+k777xTl19+uTp27Kg777wz7rrTpk3LeGCZiHeQky2pHCNl0rojm7cfb7tcHwMm6sttZ1RPA86STNBMGA0AAAAAgPmSDqfvuOMOXXTRRerYsaPuuOOOmOt5PB7Lw+lcSjU4zXYwnS257Gcd+Rg5sao7GGQRUgPWI1gGAAAAAMCekg6nW7fysHNbDztzajDdWjaD4niPjxMDaomQGrAKgTQAAAAAAPZXkM5GN954o77++us2y/fu3asbb7wx40G5kRuC6SCfL/yS6/t1IoIyIHd4vzmfU/f1AAAAAIDUeAzDMFLdqLCwULW1terVq1fY8p07d6pXr15qbm42bYC5FggE5PV6VV9fr+Li4oTrZ7vXdPD27X6gbkZVcypzdGIVtUQFNdAaITKisfvnXbak+u+PbCkuLtYf/7hGhx46OOVtt23brPPPH6impqYsjAwAAACAGyXd1qM1wzDk8XjaLH/rrbfUo0ePjAflJKm0m/D70w9gU90213LZkzp4f04NqIF8QwiNZNn5cw4AAAAAYL6Uwunu3bvL4/HI4/Hoe9/7XlhA3dzcrIaGBk2ePNn0QdpdNgLqaLfnhCpqQuPvqqMjAzmqppGPCKYBAAAAAEAsKYXTCxculGEY+vnPf665c+fK6/WGruvQoYP69++vESNGmD5IJ0g1oM5E6+3tGFSnG1AnG77bNfyODJ998nNCRNhe6/CY1ykAAAAAAMillMLpCRMmSJIGDBigkSNHqn379lkZFBKzYyjdWuT4UgmU41WX2zWYjoWwD/mu9Zc0AAAAAAAAraXVc7qysjL0/3v37tX+/fvDrrfyRD7ZYqeWGqlWFttpzLmqLs81QmjkM34hALPY/fwKAAAAAABzpRVOf/3117r22mv1l7/8RTt37mxzfXNzc8YDs4vIkDTagbPdg1Q7Beu50HqeuXhuCOTgZJlWNrfelgppmIGAGgAAAADyR0E6G82cOVMvvfSSFi1apKKiIv3hD3/Q3LlzVV5eroceesjsMVpi48bYwWbr5XYPpltz0ljTFRlo+HzZCzl88hNMwxXSeS37v90KyIZ8+LwCAAAAAKRZOf3cc8/poYce0ujRo/Xzn/9cJ598sg4//HD169dPjzzyiC666CKzx2lbVvRGjlVVlu8H8/FC6MiTNKZbLUoYDTfzyxf2Gid8hpXy7Vc/AAAAAJCP0qqc3rVrlwYMGCDpQH/pXbt2SZJGjRqlV1991bzR2VQyB8rZrNiVorcbScQOB/jZflySGsO34VsqQTNV0nC7YBDtD73abbDDAMQXrwAAAADgZmlVTh966KHasmWL+vXrp8GDB+svf/mLTjjhBD333HPyer1mj9FWUg1WIyt2zZTs7VodBkeTzcclmnTuizAabhEtaKZCGk5CH2oAAAAAcKe0wulLLrlEb731liorKzVr1iydeeaZuuuuu9TU1KQFCxaYPUbb4MDYXGYH1LF+Ah6zd/i3gVyb55UyPThUKiEzgTSchoAaAAAAANwnrXD6qquuCv3/mDFj9OGHH2r9+vU6+OCDtWTJEtMGB/uLzHHtUFme6PZah3JRx5vrsm4gCYTJAAE1AAAAALhNWj2nI/Xt21fjxo1TcXGxHnzwQTNu0pb8fmdmlla3FUnEqj7Uce+T9AMWa937mWAaAAAAAAC4kSnhdL7J94A6WyE9eTBwQLwwunVkDQAAAAAA4GRptfVA5u0srJBKoBxrPglbZmT4k+tcdtSIO1YnfgMBV6BKGgAAAAAA5Asqp00SLct0ahsQKfZ80t3WrqKO1UkTQN6h1QcAAAAAAHCLlCqnx40bF/f6r776KpOxOJ7bMs3WlcWpzi3R+naqNG89VlolAIC9cVJEAAAAAHCPlMJpr9eb8PqLL744owHhgEwOvLPdD9qM248VLrgt4AcAmI+AOrsef1zq3j317QIB88cCAAAAwN1SCqeXLFmSrXGglUwPuM3o2xxvDOlWU0eKDBcIppHvaNUBJI+AGgAAAACcj57TNmPWgbbPl/5tJbudGWMN9uW2SzBNOAg7oc0MEJ9dPjsAAAAAAOkhnAYiEFDDCtFed7wWgcTs9AUnAAAAACA1hNNAFISCyKV0X28++UMXAAAAAAAAp0mp5zTSl0xvZXpnAvklMpSOFTJHC68j1/XJz5cqAAAAAADAUaiczoHI0DlaCE0wbT8EfcimZIPpaNfFWpcKagAAAAAA4CSE01kU76SErZcTTAP5hS8+AAAAAAAAaOthqlRD5lyE0j5fdk8UFTkHt52UKhgiJlWR6vYHA5aJDLP98kV9TRJ6AwAAAAAAJyGcNomdq5+zHVBH3pfkvlw2UegX9fnP5QMPR0m2P3S8dSID6ljrJrMO4AZ+v70/iwEAAAAAbRFOm8AJB8PkpNkT9/nngUcMiarxMw2vW6/DyRKRLwioAQAAAMBZ6DmdR+L1wIZ7+eUjmHQYs58vnn/kE7+f7wQBAAAAwCmonE6Tk0NeWiObK16lnt+vrMeCsfpiE0g6F5XOQOaoogYAAAAA+yOcToPbDnaz0Xki37pZRAtBgvOPDBnjtXPIpG9w6x7E0daJdRI92FOsgDrecwwgHAE1AAAAANgb4XSK3HqQm62AWsqfkDrZeaYTKia7DYGlu8T7MiGVkJpAG/ksct/s1s9xAAAAAHAiwukUuP2A1u3zyxetn8fIUIbqafdJ5flMpgIfcLvgfpHPPAAAAACwHuF0EjiAhRNEe51Gq4iP1yoC+YO+1sh3VFQDAAAAgPUKrB5AMrZs2aJLL71UAwYMUKdOnXTYYYdp9uzZ2rdvX9h6NTU1Ovvss9WlSxeVlJRo2rRpbdZJFQercIJ4r9NkXsN++UIX5A++lAC+ky8tqAAAAADAThxROf3hhx+qpaVF9957rw4//HC9++67mjRpkv7zn//otttukyQ1NzfrzDPP1MEHH6zXXntNO3fu1IQJE2QYhu666y6LZwA4h9WV1bFaj1g9LgDuxwkUAQAAACC3PIZhGFYPIh233nqrFi9erE8//VSS9OKLL+qss87S1q1bVV5eLkl67LHHNHHiRO3YsUPFxcVJ3W4gEJDX69WKFfXq2rWYg1QTUI2WG/Feq9l6DswOhyMD6FRbTxBWp4+qeeA7Vnz2B//9UV9fn/S/WbKhuLhYY8euUffug1PeNhDYrCeeGKimpqYsjAwAAACAGzmicjqa+vp69ejRI/T36tWrNWTIkFAwLUljx45VY2OjNmzYoDFjxqR0+0OHShYeG7pK5EE+YXV2RKv4y/ZjHS/QTDUojnZbqQamwfUJqVMX6zEjtAYAAAAAANniyHD6k08+0V133aXbb789tKyurk6lpaVh63Xv3l0dOnRQXV1dzNtqbGxUY2Nj6O9AIGD+gBEm2kn6YA47Pa5WhpqJ7pvwOnmtHyuCagAAAAAAYCZLT4g4Z84ceTyeuJf169eHbbN9+3adfvrp+slPfqLLLrss7DqPx9PmPgzDiLo8aP78+fJ6vaFLRUWFOZNDXD6fs/t6On38+a71CSBjXdAWoT4AAAAAADCTpZXTU6dO1YUXXhh3nf79+4f+f/v27RozZoxGjBih++67L2y9srIyrV27NmzZ7t27tX///jYV1a3NmjVLM2bMCP0dCAQIqHPIaVXUkYG008aP5EXrf43ojwNhPgAAAAAASIel4XRJSYlKSkqSWnfbtm0aM2aMhg0bpiVLlqigILzoe8SIEbr55ptVW1ur3r17S5KWLVumoqIiDRs2LObtFhUVqaioKP1JIGNOCXhjVUoHlzthDkhftACWwPqARK0/6GcNAAAAAACisbStR7K2b9+u0aNHq6KiQrfddpv+/e9/q66uLqyXdFVVlQYPHqzq6mq9+eab+uc//6lrrrlGkyZNsvSs9wDcizYgbUVrkhJv3US3AwBBu3fvVnV1dagVW3V1tb766qukt7/iiivk8Xi0cOHCrI0RAAAAQGoccULEZcuW6eOPP9bHH3+sPn36hF1nGIYkqbCwUM8//7ymTJmik046SZ06ddL48eN12223WTFkAHnGLx9hahoSPWbB6/kCAMD48eP1+eefa+nSpZKkyy+/XNXV1XruuecSbvv0009r7dq1Ki8vz/YwAQAAAKTAEeH0xIkTNXHixITr9e3bV3/729+yPyCYzgmtPfz+6K097D5u5E60gJre1ebzyU9YDeSZDz74QEuXLtWaNWs0fPhwSdL999+vESNGaNOmTTriiCNibrtt2zZNnTpVf//733XmmWfmasgAAAAAkuCIcBr5wSkBtUSfacSWKDRNNVQlzP5O68eCEzMC9hUIBML+NuP8HqtXr5bX6w0F05J04oknyuv1atWqVTHD6ZaWFlVXV2vmzJk66qijMhoDAAAAAPMRTsNWnBBQS84YI9whGLjme0idzPxpAQKY44knJI8n9e0MQ/J4WuT1esOWz549W3PmzMloTHV1derVq1eb5b169Qo7B0mkW265Re3atdO0adMyun8AAAAA2UE4DdtxSkANwJ5aB9kE1UBuFRQUaNeuXWHL4lVNz5kzR3Pnzo17m+vWrZMkeaIk5oZhRF0uSRs2bNDvf/97+f3+mOsAAAAAsBbhNGyJthkAzEB/aiD3iouLk1536tSpuvDCC+Ou079/f7399tv64osv2lz373//W6WlpVG3W7lypXbs2KG+ffuGljU3N+vqq6/WwoULtWXLlqTHCQAAACA7CKdha1RRA8gU7T4A+yopKVFJSUnC9UaMGKH6+nq98cYbOuGEEyRJa9euVX19vUaOHBl1m+rqap166qlhy8aOHavq6mpdcsklmQ8eAAAAQMYIp2F7BNTAgWA13/tOZ4qTKALONWjQIJ1++umaNGmS7r33XknS5ZdfrrPOOivsZIhHHnmk5s+fr/PPP189e/ZUz549w26nffv2Kisri3kCRQAAAAC5VWD1AIBk+MiPAGSBT/6wCwD7euSRR3T00UerqqpKVVVVOuaYY/TnP/85bJ1Nmzapvr7eohECAAAASBWV03AM+lADyDZ6VAP21aNHDz388MNx1zEMI+719JkGAAAA7IXKaTgOVdQAsol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      "text/plain": [
       "<Figure size 1500x1200 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-04 19:32:21,390 - tornado.application - ERROR - Uncaught exception GET /status/ws (127.0.0.1)\n",
      "HTTPServerRequest(protocol='http', host='127.0.0.1:5555', method='GET', uri='/status/ws', version='HTTP/1.1', remote_ip='127.0.0.1')\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\brand\\anaconda3\\envs\\BrandonEnv\\Lib\\site-packages\\tornado\\websocket.py\", line 937, in _accept_connection\n",
      "    open_result = handler.open(*handler.open_args, **handler.open_kwargs)\n",
      "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\brand\\anaconda3\\envs\\BrandonEnv\\Lib\\site-packages\\tornado\\web.py\", line 3290, in wrapper\n",
      "    return method(self, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\brand\\anaconda3\\envs\\BrandonEnv\\Lib\\site-packages\\bokeh\\server\\views\\ws.py\", line 149, in open\n",
      "    raise ProtocolError(\"Token is expired.\")\n",
      "bokeh.protocol.exceptions.ProtocolError: Token is expired.\n"
     ]
    }
   ],
   "source": [
    "# First, prepare a latitude array with cosine weighting\n",
    "coslat = np.cos(np.deg2rad(detrended_anomalies.coords['lat'].values)).clip(0., 1.)\n",
    "weights = np.sqrt(coslat)[..., np.newaxis]\n",
    "\n",
    "# Create an EOF solver. Assuming detrended_anomalies is 3D: time, lat, lon\n",
    "solver = Eof(detrended_anomalies, weights=weights)\n",
    "\n",
    "# Calculate the first 5 EOFs and their explained variances\n",
    "eofs = solver.eofs(neofs=5)\n",
    "variance_fraction = solver.varianceFraction(neigs=5)\n",
    "\n",
    "# Plot the 5 EOF's\n",
    "plt.figure(figsize=(15, 12))\n",
    "for i in range(5):\n",
    "    plt.subplot(3, 2, i+1)\n",
    "    plt.contourf(eofs.coords['lon'], eofs.coords['lat'], eofs[i], levels=np.linspace(-1, 1, 11), extend='both', cmap='seismic')\n",
    "    plt.title(f'EOF {i+1}, Explained Variance: {variance_fraction[i].values*100:.2f}%')\n",
    "    plt.colorbar()\n",
    "    plt.xlabel('Longitude')\n",
    "    plt.ylabel('Latitude')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Part 5) Plot the percent of variance explained by the first 10 EOFs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Calculate the variance fraction for the first 10 EOFs\n",
    "variance_fraction = solver.varianceFraction(neigs=10).values * 100  # Convert to percentage\n",
    "\n",
    "# Generate the EOF indices (1-based indexing for plotting)\n",
    "eof_indices = range(1, 11)\n",
    "\n",
    "# Plot the variance in the first 10 EOF\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(eof_indices, variance_fraction, color='blue')\n",
    "plt.xlabel('EOF Number')\n",
    "plt.ylabel('Percent of Variance Explained')\n",
    "plt.title('Variance Explained by the First 10 EOFs')\n",
    "plt.xticks(eof_indices)  # Ensure x-ticks match the EOF numbers\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 5) Reconstruct the SST field using the first 5 EOFs and plot a map of the Pearson's correlation coefficient (xarray.corr) of the reconstructed monthly time series and the \"observed\" SST time series.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets start with reconstructing the SST field using the first 5 EOFs\n",
    "pcs = solver.pcs(npcs=5, pcscaling=1) # Project the SST anomalies onto the first 5 EOFs to get their principal component time series\n",
    "reconstructed_sst = solver.reconstructedField(5) # Reconstruct the spatial field from these components.\n",
    "\n",
    "# Now lets calculate the Pearson's correlation coefficient between the reconstructed and observed SSTs using xr.corr()\n",
    "correlation_map = xr.corr(reconstructed_sst, detrended_anomalies, dim='time')\n",
    "\n",
    "# Plot the correlation map\n",
    "plt.figure(figsize=(10, 6))\n",
    "correlation_map.plot(cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Pearson Correlation between Reconstructed and Observed SST Anomalies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6) Compute a map of the Pearson's correlation coefficient between SST EOF1 and monthly mean detrended, deseasonalized, and standardized monthly mean precipitation anomalies (don't mask these over land for the plot). See anything interesting?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "                                                                               time: 516,\n",
       "                                                                               lat: 130,\n",
       "                                                                               lon: 180)&gt;\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
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       "...\n",
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       "          0.29666399,  0.29101456],\n",
       "        [-1.07364237, -1.06779193, -1.05552443, ...,  0.47964129,\n",
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       "          0.5554026 ,  0.39197063]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.50221514,\n",
       "          0.57119332,  0.64835513],\n",
       "        [        nan,         nan,         nan, ...,  0.42663204,\n",
       "          0.50522583,  0.59884573],\n",
       "        ...,\n",
       "        [ 0.65598836,  0.69052897,  0.67765038, ...,  0.28616987,\n",
       "          0.28922034,  0.29435265],\n",
       "        [ 0.76720961,  0.66831371,  0.52437184, ...,  0.30015816,\n",
       "          0.29185623,  0.27141549],\n",
       "        [ 0.32106298,  0.15798451, -0.02623758, ...,  0.39224427,\n",
       "          0.35832244,  0.27730264]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Total_column_water_vapour_surface_Mixed_intervals_Average'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 516</li><li><span class='xr-has-index'>lat</span>: 130</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a608ea31-312b-4c51-86e4-e97034281fbf' class='xr-array-in' type='checkbox' checked><label for='section-a608ea31-312b-4c51-86e4-e97034281fbf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan ... 0.2383 0.3121 0.3829 0.3922 0.3583 0.2773</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
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       "\n",
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       "\n",
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       "        [ 0.76720961,  0.66831371,  0.52437184, ...,  0.30015816,\n",
       "          0.29185623,  0.27141549],\n",
       "        [ 0.32106298,  0.15798451, -0.02623758, ...,  0.39224427,\n",
       "          0.35832244,  0.27730264]]])</pre></div></div></li><li class='xr-section-item'><input id='section-7eecc123-1456-4fc5-b77c-8a0baefc3c5a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7eecc123-1456-4fc5-b77c-8a0baefc3c5a' class='xr-section-summary' >Coordinates: <span>(5)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-83ad516c-7f68-48f2-882a-10e4124aabaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83ad516c-7f68-48f2-882a-10e4124aabaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f6f8c7-97be-451c-817c-e5b208134aae' class='xr-var-data-in' type='checkbox'><label for='data-a1f6f8c7-97be-451c-817c-e5b208134aae' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-12-01</div><input id='attrs-c7605dc2-6aa7-445f-8433-382fb9a6be7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7605dc2-6aa7-445f-8433-382fb9a6be7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f9b4ddc-47c5-4b82-afac-92555db3c959' class='xr-var-data-in' type='checkbox'><label for='data-7f9b4ddc-47c5-4b82-afac-92555db3c959' 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>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-01T00:00:00.000000000&#x27;, &#x27;1979-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-05-01T00:00:00.000000000&#x27;, &#x27;1979-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-07-01T00:00:00.000000000&#x27;, &#x27;1979-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-09-01T00:00:00.000000000&#x27;, &#x27;1979-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-11-01T00:00:00.000000000&#x27;, &#x27;1979-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, &#x27;1980-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-05-01T00:00:00.000000000&#x27;, &#x27;1980-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-07-01T00:00:00.000000000&#x27;, &#x27;1980-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-09-01T00:00:00.000000000&#x27;, &#x27;1980-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-11-01T00:00:00.000000000&#x27;, &#x27;1980-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-03-01T00:00:00.000000000&#x27;, &#x27;1981-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-05-01T00:00:00.000000000&#x27;, &#x27;1981-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-07-01T00:00:00.000000000&#x27;, &#x27;1981-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-09-01T00:00:00.000000000&#x27;, &#x27;1981-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-11-01T00:00:00.000000000&#x27;, &#x27;1981-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-01T00:00:00.000000000&#x27;, &#x27;1982-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-03-01T00:00:00.000000000&#x27;, &#x27;1982-04-01T00:00:00.000000000&#x27;,\n",
       "...\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2019-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-01T00:00:00.000000000&#x27;, &#x27;2019-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-01T00:00:00.000000000&#x27;, &#x27;2019-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-01T00:00:00.000000000&#x27;, &#x27;2019-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-01T00:00:00.000000000&#x27;, &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-05-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-01T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-09-01T00:00:00.000000000&#x27;, &#x27;2020-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-01T00:00:00.000000000&#x27;, &#x27;2020-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-03-01T00:00:00.000000000&#x27;, &#x27;2021-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-01T00:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-01T00:00:00.000000000&#x27;, &#x27;2021-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-01T00:00:00.000000000&#x27;, &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>65 64 63 62 61 ... -61 -62 -63 -64</div><input id='attrs-e33308d4-40b5-4e3d-893f-226cea6e7cf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e33308d4-40b5-4e3d-893f-226cea6e7cf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a345280-f288-41cf-89aa-7c99c67fa0bd' class='xr-var-data-in' type='checkbox'><label for='data-1a345280-f288-41cf-89aa-7c99c67fa0bd' 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>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,  55,  54,  53,  52,\n",
       "        51,  50,  49,  48,  47,  46,  45,  44,  43,  42,  41,  40,  39,  38,\n",
       "        37,  36,  35,  34,  33,  32,  31,  30,  29,  28,  27,  26,  25,  24,\n",
       "        23,  22,  21,  20,  19,  18,  17,  16,  15,  14,  13,  12,  11,  10,\n",
       "         9,   8,   7,   6,   5,   4,   3,   2,   1,   0,  -1,  -2,  -3,  -4,\n",
       "        -5,  -6,  -7,  -8,  -9, -10, -11, -12, -13, -14, -15, -16, -17, -18,\n",
       "       -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32,\n",
       "       -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46,\n",
       "       -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60,\n",
       "       -61, -62, -63, -64])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>120 121 122 123 ... 296 297 298 299</div><input id='attrs-55254a7b-5da7-473c-8797-95bedb047751' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55254a7b-5da7-473c-8797-95bedb047751' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd8f180-47f7-41d5-8817-dcc738301b2a' class='xr-var-data-in' type='checkbox'><label for='data-3bd8f180-47f7-41d5-8817-dcc738301b2a' 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>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
       "       134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
       "       148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
       "       162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
       "       176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,\n",
       "       204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,\n",
       "       218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "       232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287,\n",
       "       288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-8bbe1555-6fcb-499f-85be-dda562099280' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bbe1555-6fcb-499f-85be-dda562099280' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03947c9f-2153-43b3-b5c7-3ff287fc5d4a' class='xr-var-data-in' type='checkbox'><label for='data-03947c9f-2153-43b3-b5c7-3ff287fc5d4a' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e94bafb-2679-49d2-87d3-3af399a04c12' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e94bafb-2679-49d2-87d3-3af399a04c12' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01ebf671-2e3d-4f98-9495-d6cc0fa734ff' class='xr-index-data-in' type='checkbox'/><label for='index-01ebf671-2e3d-4f98-9495-d6cc0fa734ff' 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;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=516, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5faf8bef-7a21-4cdd-9be8-4d8d6e403c6e' class='xr-index-data-in' type='checkbox'/><label for='index-5faf8bef-7a21-4cdd-9be8-4d8d6e403c6e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 65,  64,  63,  62,  61,  60,  59,  58,  57,  56,\n",
       "       ...\n",
       "       -55, -56, -57, -58, -59, -60, -61, -62, -63, -64],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lat&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-23c73329-4834-4862-9f6b-8de4196e7cb7' class='xr-index-data-in' type='checkbox'/><label for='index-23c73329-4834-4862-9f6b-8de4196e7cb7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       ...\n",
       "       290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7cb3209b-6b2f-43c3-8040-83931f256b4f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7cb3209b-6b2f-43c3-8040-83931f256b4f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Total_column_water_vapour_surface_Mixed_intervals_Average' (\n",
       "                                                                               time: 516,\n",
       "                                                                               lat: 130,\n",
       "                                                                               lon: 180)>\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  1.69156486,\n",
       "          1.76687381,  1.83648046],\n",
       "        [        nan,         nan,         nan, ...,  1.70719223,\n",
       "          1.77791023,  1.85491071],\n",
       "        ...,\n",
       "        [-1.20569346, -1.21868761, -1.28275343, ...,  1.03237053,\n",
       "          0.94592059,  0.83827751],\n",
       "        [-1.28653597, -1.29915679, -1.30730381, ...,  0.7699034 ,\n",
       "          0.68950272,  0.57581343],\n",
       "        [-1.29088358, -1.29694364, -1.28610683, ...,  0.67567978,\n",
       "          0.61029842,  0.44382702]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  1.05969572,\n",
       "          1.2543402 ,  1.46918032],\n",
       "        [        nan,         nan,         nan, ...,  1.2221355 ,\n",
       "          1.47301193,  1.77139987],\n",
       "...\n",
       "        [-1.11240698, -1.10052391, -1.07249723, ...,  0.31876289,\n",
       "          0.29666399,  0.29101456],\n",
       "        [-1.07364237, -1.06779193, -1.05552443, ...,  0.47964129,\n",
       "          0.48087479,  0.48109677],\n",
       "        [-1.0282662 , -1.02552808, -0.99271352, ...,  0.51200736,\n",
       "          0.5554026 ,  0.39197063]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.50221514,\n",
       "          0.57119332,  0.64835513],\n",
       "        [        nan,         nan,         nan, ...,  0.42663204,\n",
       "          0.50522583,  0.59884573],\n",
       "        ...,\n",
       "        [ 0.65598836,  0.69052897,  0.67765038, ...,  0.28616987,\n",
       "          0.28922034,  0.29435265],\n",
       "        [ 0.76720961,  0.66831371,  0.52437184, ...,  0.30015816,\n",
       "          0.29185623,  0.27141549],\n",
       "        [ 0.32106298,  0.15798451, -0.02623758, ...,  0.39224427,\n",
       "          0.35832244,  0.27730264]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-16T12:00:00 ... 2021-12-16T12:00:00\n",
       "    reftime  (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2021-12-01\n",
       "  * lat      (lat) int32 65 64 63 62 61 60 59 58 ... -58 -59 -60 -61 -62 -63 -64\n",
       "  * lon      (lon) int32 120 121 122 123 124 125 126 ... 294 295 296 297 298 299\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets rename the variable so its easier moving forward\n",
    "tcwv = total_water\n",
    "\n",
    "# Deseasonalize, detrend, and standardize the total column water vapor\n",
    "\n",
    "# First, calculate the monthly mean anomaly for each point by subtracting the long-term monthly mean from each month's value. \n",
    "# This removes the seasonal cycle.\n",
    "\n",
    "# Handling infinite values by replacing them with NaN\n",
    "tcwv = tcwv.where(np.isfinite(sst), np.nan)\n",
    "\n",
    "# Calculating climatology (monthly means) and anomalies, skipping NaN values\n",
    "climatology2 = tcwv.groupby('time.month').mean('time', skipna=True)\n",
    "anomalies2 = tcwv.groupby('time.month') - climatology2\n",
    "\n",
    "\n",
    "# Detrending the anomalies. This removes the linear trend from the deseasonalized time series.\n",
    "def detrend(da):\n",
    "    da_no_nan2 = da.fillna(0)\n",
    "    detrended2 = xr.apply_ufunc(signal.detrend, da_no_nan2, kwargs={'axis': 0}, dask='allowed')\n",
    "    return detrended2.where(~da.isnull())\n",
    "detrended_anomalies2 = detrend(anomalies2)\n",
    "\n",
    "detrended_anomalies2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reconstruct for just eof1\n",
    "reconstructed_eof1 = solver.reconstructedField(1) # Reconstruct the spatial field from these components.\n",
    "\n",
    "# Compute Pearson's correlation coefficient between SST EOF1 and precipitation anomalies\n",
    "correlation_map2 = xr.corr(reconstructed_eof1, detrended_anomalies2, dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Set up the map projection and figure\n",
    "proj = ccrs.PlateCarree(central_longitude=180)  # Central longitude at 180\n",
    "fig, ax = plt.subplots(figsize=(12, 8), subplot_kw={'projection': proj})\n",
    "\n",
    "# Set the extent to focus on the Pacific region\n",
    "ax.set_extent([120, 300, -65, 65], crs=ccrs.PlateCarree())\n",
    "\n",
    "# Plot the correlation map\n",
    "pcm = ax.pcolormesh(correlation_map2['lon'], correlation_map2['lat'], correlation_map2,\n",
    "                    cmap='coolwarm', vmin=-1, vmax=1, transform=ccrs.PlateCarree())\n",
    "\n",
    "# Add features to the map for better geographical context\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
    "ax.add_feature(cfeature.LAND, color='lightgrey')\n",
    "\n",
    "# Add gridlines\n",
    "ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False)\n",
    "\n",
    "# Add a colorbar\n",
    "cbar = fig.colorbar(pcm, ax=ax, shrink=0.7, orientation='horizontal', pad=0.05)\n",
    "cbar.set_label('Pearson Correlation Coefficient', fontsize=14)\n",
    "cbar.ax.tick_params(labelsize=12)\n",
    "\n",
    "# Set the title\n",
    "ax.set_title('Pearson Correlation between SST EOF1 and Precipitation Anomalies', fontsize=16, pad=20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Part 6, the correlation seen here is a correlation between anomalies in the pattern of the sst anomalies identified by EOF1 and the cloud water anomalies in the form of total column water vapor anomalies. \n",
    "\n",
    "#### Positive correlations (values closer to 1) indicate a positive relationship between SST EOF1 and precipitation anomalies, while negative correlations (values closer to -1) indicate a negative relationship. We can see the region around the equator has a very strong correlation with precipitation anomalies as well as some sections of the western pacific. From this we can gather that precipitation anomalies appear to have a strong relationship wiuth the El Niño-Southern Oscillation (ENSO), since that entire region where the pattern operates has a high positive correlation (relatively) compared to the surrounding waters.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "BrandonEnv",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}