From 1a4411eae91c92ff78f76b2bcfa4cbf0913d605c Mon Sep 17 00:00:00 2001
From: "github-classroom[bot]"
 <66690702+github-classroom[bot]@users.noreply.github.com>
Date: Wed, 10 Sep 2025 19:35:41 +0000
Subject: [PATCH 1/5] Setting up GitHub Classroom Feedback


From 3cb628b68f28dc3fc924be987d6b1ea9cf11b8b3 Mon Sep 17 00:00:00 2001
From: "github-classroom[bot]"
 <66690702+github-classroom[bot]@users.noreply.github.com>
Date: Wed, 10 Sep 2025 19:35:44 +0000
Subject: [PATCH 2/5] add deadline

---
 README.md | 1 +
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diff --git a/README.md b/README.md
index 39399da..35e4b43 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,4 @@
+[![Review Assignment Due Date](https://classroom.github.com/assets/deadline-readme-button-22041afd0340ce965d47ae6ef1cefeee28c7c493a6346c4f15d667ab976d596c.svg)](https://classroom.github.com/a/p9TKJWzX)
 # ATMS 523 Module 1 template repository
 
 A GitHub template repository for short exercises on **xarray** with a small, cloud-hosted climate dataset.  

From 30bc5831df90c8b37db557fc8525bfc38c8545d7 Mon Sep 17 00:00:00 2001
From: Patrick Anthony Trierweiler <pat6@illinois.edu>
Date: Fri, 12 Sep 2025 00:12:29 +0000
Subject: [PATCH 3/5] initial commit

---
 notebooks/Month-mean.nc | Bin 0 -> 35054 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 notebooks/Month-mean.nc

diff --git a/notebooks/Month-mean.nc b/notebooks/Month-mean.nc
new file mode 100644
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From 92bfe458ffd9bcb6d4d7898c609faccc206925c2 Mon Sep 17 00:00:00 2001
From: Patrick Anthony Trierweiler <pat6@illinois.edu>
Date: Fri, 12 Sep 2025 00:15:42 +0000
Subject: [PATCH 4/5] initial_commit

---
 notebooks/SST_Range.nc   | Bin 0 -> 35054 bytes
 notebooks/SST_STD_Dev.nc | Bin 0 -> 35054 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 notebooks/SST_Range.nc
 create mode 100644 notebooks/SST_STD_Dev.nc

diff --git a/notebooks/SST_Range.nc b/notebooks/SST_Range.nc
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From 5201a76fc1f8688a2208621118c715445dc140f5 Mon Sep 17 00:00:00 2001
From: Patrick Anthony Trierweiler <pat6@illinois.edu>
Date: Thu, 16 Oct 2025 21:48:01 +0000
Subject: [PATCH 5/5] finisheded code

---
 notebooks/01_xarray_oisst_exercises.ipynb | 1894 ++++++++++++++++++++-
 1 file changed, 1879 insertions(+), 15 deletions(-)

diff --git a/notebooks/01_xarray_oisst_exercises.ipynb b/notebooks/01_xarray_oisst_exercises.ipynb
index 4ce03ae..e7a58c7 100644
--- a/notebooks/01_xarray_oisst_exercises.ipynb
+++ b/notebooks/01_xarray_oisst_exercises.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4b28dcd7",
    "metadata": {},
    "outputs": [],
@@ -52,12 +52,1065 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "bef6e720",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 31 files for 198201. Example: ['noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/198201/oisst-avhrr-v02r01.19820101.nc', 'noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/198201/oisst-avhrr-v02r01.19820102.nc']\n"
+     ]
+    },
+    {
+     "data": {
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+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 514MB\n",
+       "Dimensions:  (time: 31, zlev: 1, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
+       "  * time     (time) datetime64[ns] 248B 1982-01-01T12:00:00 ... 1982-01-31T12...\n",
+       "  * zlev     (zlev) float32 4B 0.0\n",
+       "Data variables:\n",
+       "    anom     (time, zlev, lat, lon) float32 129MB dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;\n",
+       "    err      (time, zlev, lat, lon) float32 129MB dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;\n",
+       "    ice      (time, zlev, lat, lon) float32 129MB dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;\n",
+       "    sst      (time, zlev, lat, lon) float32 129MB dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;\n",
+       "Attributes: (12/37)\n",
+       "    title:                      NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
+       "    source:                     ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
+       "    id:                         oisst-avhrr-v02r01.19820101.nc\n",
+       "    naming_authority:           gov.noaa.ncei\n",
+       "    summary:                    NOAAs 1/4-degree Daily Optimum Interpolation ...\n",
+       "    cdm_data_type:              Grid\n",
+       "    ...                         ...\n",
+       "    metadata_link:              https://doi.org/10.25921/RE9P-PT57\n",
+       "    ncei_template_version:      NCEI_NetCDF_Grid_Template_v2.0\n",
+       "    comment:                    Data was converted from NetCDF-3 to NetCDF-4 ...\n",
+       "    sensor:                     Thermometer, AVHRR\n",
+       "    Conventions:                CF-1.6, ACDD-1.3\n",
+       "    references:                 Reynolds, et al.(2007) Daily High-Resolution-...</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-4ebfe6d2-d7cb-4973-bed9-0135f955b15d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4ebfe6d2-d7cb-4973-bed9-0135f955b15d' 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>: 31</li><li><span class='xr-has-index'>zlev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad0848b9-a899-42f3-9dcb-c597ff638de8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad0848b9-a899-42f3-9dcb-c597ff638de8' 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'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-4351fcc4-aa5e-47e7-a1e3-04c14a6984fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4351fcc4-aa5e-47e7-a1e3-04c14a6984fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bd618c5-d1be-46cf-8db4-01cbf5a1737a' class='xr-var-data-in' type='checkbox'><label for='data-7bd618c5-d1be-46cf-8db4-01cbf5a1737a' 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>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      shape=(720,), 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'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-3f8d41d1-0a25-419f-a8e0-fcd439b3c704' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f8d41d1-0a25-419f-a8e0-fcd439b3c704' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3f697a-8c79-4f1f-945f-549d59b185a3' class='xr-var-data-in' type='checkbox'><label for='data-ce3f697a-8c79-4f1f-945f-549d59b185a3' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
+       "       3.59875e+02], shape=(1440,), 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'>1982-01-01T12:00:00 ... 1982-01-...</div><input id='attrs-c857121f-4a7e-4db7-a862-e105f2b84acf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c857121f-4a7e-4db7-a862-e105f2b84acf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64fef246-2d71-4168-af93-6c5b7ef5dea8' class='xr-var-data-in' type='checkbox'><label for='data-64fef246-2d71-4168-af93-6c5b7ef5dea8' 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>Center time of the day</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-01T12:00:00.000000000&#x27;, &#x27;1982-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-03T12:00:00.000000000&#x27;, &#x27;1982-01-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-05T12:00:00.000000000&#x27;, &#x27;1982-01-06T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-07T12:00:00.000000000&#x27;, &#x27;1982-01-08T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-09T12:00:00.000000000&#x27;, &#x27;1982-01-10T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-11T12:00:00.000000000&#x27;, &#x27;1982-01-12T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-13T12:00:00.000000000&#x27;, &#x27;1982-01-14T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-15T12:00:00.000000000&#x27;, &#x27;1982-01-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-17T12:00:00.000000000&#x27;, &#x27;1982-01-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-19T12:00:00.000000000&#x27;, &#x27;1982-01-20T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-21T12:00:00.000000000&#x27;, &#x27;1982-01-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-23T12:00:00.000000000&#x27;, &#x27;1982-01-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-25T12:00:00.000000000&#x27;, &#x27;1982-01-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-27T12:00:00.000000000&#x27;, &#x27;1982-01-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-29T12:00:00.000000000&#x27;, &#x27;1982-01-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-31T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-0fa54b0f-2938-4835-b002-aad488893ad3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fa54b0f-2938-4835-b002-aad488893ad3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6c19a8c-9436-4f0a-8d72-49975a0e3cd0' class='xr-var-data-in' type='checkbox'><label for='data-d6c19a8c-9436-4f0a-8d72-49975a0e3cd0' 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 height</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>actual_range :</span></dt><dd>0, 0</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb9c56cf-c148-43b3-8af6-b61c98bed798' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb9c56cf-c148-43b3-8af6-b61c98bed798' class='xr-section-summary' >Data variables: <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>anom</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-b7a2a416-f929-4642-a98b-bf1fa07a0a0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7a2a416-f929-4642-a98b-bf1fa07a0a0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74e51221-260b-47ad-9943-cf8c2a703d87' class='xr-var-data-in' type='checkbox'><label for='data-74e51221-260b-47ad-9943-cf8c2a703d87' 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>Daily sea surface temperature anomalies</dd><dt><span>valid_min :</span></dt><dd>-1200</dd><dt><span>valid_max :</span></dt><dd>1200</dd><dt><span>units :</span></dt><dd>Celsius</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> 122.61 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
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+       "                        <td> (1, 1, 720, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-bd6bc75c-d204-4684-98ad-06dd85e6b9fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd6bc75c-d204-4684-98ad-06dd85e6b9fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88a400c4-ca99-41f1-aab7-dabc205f4597' class='xr-var-data-in' type='checkbox'><label for='data-88a400c4-ca99-41f1-aab7-dabc205f4597' 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>Estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 122.61 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-16c85f78-1c58-4a16-9ee5-2c5a9b27810e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16c85f78-1c58-4a16-9ee5-2c5a9b27810e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75725ee6-33ce-440e-8f58-65fcc444881b' class='xr-var-data-in' type='checkbox'><label for='data-75725ee6-33ce-440e-8f58-65fcc444881b' 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 ice concentration</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</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> 122.61 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 1, 720, 1440) </td>\n",
+       "                        <td> (1, 1, 720, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 63 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-b3364bd7-773a-41fd-a6e2-ef75ec6f579e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3364bd7-773a-41fd-a6e2-ef75ec6f579e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7b453ad-2a12-42e7-95ef-3a99a07b4f53' class='xr-var-data-in' type='checkbox'><label for='data-d7b453ad-2a12-42e7-95ef-3a99a07b4f53' 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>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</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> 122.61 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 1, 720, 1440) </td>\n",
+       "                        <td> (1, 1, 720, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 63 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2747708d-b797-42ff-b2a5-bf15fbc48633' class='xr-section-summary-in' type='checkbox'  ><label for='section-2747708d-b797-42ff-b2a5-bf15fbc48633' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a888b59e-3902-4e44-81d2-c086154b51f0' class='xr-index-data-in' type='checkbox'/><label for='index-a888b59e-3902-4e44-81d2-c086154b51f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
+       "       -87.875, -87.625,\n",
+       "       ...\n",
+       "        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,\n",
+       "        89.625,  89.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-87fbc578-c1e6-4ac6-93a9-bff6b0b37a75' class='xr-index-data-in' type='checkbox'/><label for='index-87fbc578-c1e6-4ac6-93a9-bff6b0b37a75' 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([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,\n",
+       "         2.125,   2.375,\n",
+       "       ...\n",
+       "       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
+       "       359.625, 359.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cbb17436-ca59-41aa-8fa0-35a80043c574' class='xr-index-data-in' type='checkbox'/><label for='index-cbb17436-ca59-41aa-8fa0-35a80043c574' 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;1982-01-01 12:00:00&#x27;, &#x27;1982-01-02 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-03 12:00:00&#x27;, &#x27;1982-01-04 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-05 12:00:00&#x27;, &#x27;1982-01-06 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-07 12:00:00&#x27;, &#x27;1982-01-08 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-09 12:00:00&#x27;, &#x27;1982-01-10 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-11 12:00:00&#x27;, &#x27;1982-01-12 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-13 12:00:00&#x27;, &#x27;1982-01-14 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-15 12:00:00&#x27;, &#x27;1982-01-16 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-17 12:00:00&#x27;, &#x27;1982-01-18 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-19 12:00:00&#x27;, &#x27;1982-01-20 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-21 12:00:00&#x27;, &#x27;1982-01-22 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-23 12:00:00&#x27;, &#x27;1982-01-24 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-25 12:00:00&#x27;, &#x27;1982-01-26 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-27 12:00:00&#x27;, &#x27;1982-01-28 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-29 12:00:00&#x27;, &#x27;1982-01-30 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-31 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlev</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c59a9e8-0a84-4aac-afd4-69435d7beb75' class='xr-index-data-in' type='checkbox'/><label for='index-8c59a9e8-0a84-4aac-afd4-69435d7beb75' 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([0.0], dtype=&#x27;float32&#x27;, name=&#x27;zlev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39424630-0f1a-4b2f-8ec7-9a4262ea464a' class='xr-section-summary-in' type='checkbox'  ><label for='section-39424630-0f1a-4b2f-8ec7-9a4262ea464a' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final</dd><dt><span>source :</span></dt><dd>ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR</dd><dt><span>id :</span></dt><dd>oisst-avhrr-v02r01.19820101.nc</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>summary :</span></dt><dd>NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs.  A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.</dd><dt><span>date_modified :</span></dt><dd>2020-05-08T19:05:13Z</dd><dt><span>date_created :</span></dt><dd>2020-05-08T19:05:13Z</dd><dt><span>product_version :</span></dt><dd>Version v02r01</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>creator_url :</span></dt><dd>https://www.ncei.noaa.gov/</dd><dt><span>creator_email :</span></dt><dd>oisst-help@noaa.gov</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Earth Science Keywords</dd><dt><span>platform :</span></dt><dd>Ships, buoys, Argo floats, MetOp-A, MetOp-B</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments &gt; Passive Remote Sensing &gt; Spectrometers/Radiometers &gt; Imaging Spectrometers/Radiometers &gt; AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>time_coverage_start :</span></dt><dd>1982-01-01T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>1982-01-01T23:59:59Z</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/RE9P-PT57</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>comment :</span></dt><dd>Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.</dd><dt><span>sensor :</span></dt><dd>Thermometer, AVHRR</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>references :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST and Navy AVHRR SST. Ice data: NCEP Ice and GSFC Ice.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 514MB\n",
+       "Dimensions:  (time: 31, zlev: 1, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
+       "  * time     (time) datetime64[ns] 248B 1982-01-01T12:00:00 ... 1982-01-31T12...\n",
+       "  * zlev     (zlev) float32 4B 0.0\n",
+       "Data variables:\n",
+       "    anom     (time, zlev, lat, lon) float32 129MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>\n",
+       "    err      (time, zlev, lat, lon) float32 129MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>\n",
+       "    ice      (time, zlev, lat, lon) float32 129MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>\n",
+       "    sst      (time, zlev, lat, lon) float32 129MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>\n",
+       "Attributes: (12/37)\n",
+       "    title:                      NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
+       "    source:                     ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
+       "    id:                         oisst-avhrr-v02r01.19820101.nc\n",
+       "    naming_authority:           gov.noaa.ncei\n",
+       "    summary:                    NOAAs 1/4-degree Daily Optimum Interpolation ...\n",
+       "    cdm_data_type:              Grid\n",
+       "    ...                         ...\n",
+       "    metadata_link:              https://doi.org/10.25921/RE9P-PT57\n",
+       "    ncei_template_version:      NCEI_NetCDF_Grid_Template_v2.0\n",
+       "    comment:                    Data was converted from NetCDF-3 to NetCDF-4 ...\n",
+       "    sensor:                     Thermometer, AVHRR\n",
+       "    Conventions:                CF-1.6, ACDD-1.3\n",
+       "    references:                 Reynolds, et al.(2007) Daily High-Resolution-..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "yearmonth = \"202001\"  # TODO: try other months like \"201507\" or \"201909\"\n",
+    "yearmonth = \"198201\"  # TODO: try other months like \"201507\" or \"201909\"\n",
     "prefix = f\"noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/{yearmonth}/oisst-avhrr-v02r01.*.nc\"\n",
     "\n",
     "fs = fsspec.filesystem(\"s3\", anon=True)\n",
@@ -94,10 +1147,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "602af4dd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(['anom', 'err', 'ice', 'sst'], ['lat', 'lon', 'time', 'zlev'])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "list(ds.data_vars), list(ds.coords)"
    ]
@@ -118,10 +1182,673 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a4963450",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       ".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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 31, zlev: 1, lat: 52, lon: 72)&gt; Size: 464kB\n",
+       "dask.array&lt;getitem, shape=(31, 1, 52, 72), dtype=float32, chunksize=(1, 1, 52, 72), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 208B 18.12 18.38 18.62 18.88 ... 30.38 30.62 30.88\n",
+       "  * lon      (lon) float32 288B 262.1 262.4 262.6 262.9 ... 279.4 279.6 279.9\n",
+       "  * time     (time) datetime64[ns] 248B 1982-01-01T12:00:00 ... 1982-01-31T12...\n",
+       "  * zlev     (zlev) float32 4B 0.0\n",
+       "Attributes:\n",
+       "    long_name:  Daily sea surface temperature\n",
+       "    units:      Celsius\n",
+       "    valid_min:  -300\n",
+       "    valid_max:  4500</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'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li><li><span class='xr-has-index'>zlev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 52</li><li><span class='xr-has-index'>lon</span>: 72</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e388bc24-bb92-4780-9f2c-265bec8f1b9d' class='xr-array-in' type='checkbox' checked><label for='section-e388bc24-bb92-4780-9f2c-265bec8f1b9d' 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>dask.array&lt;chunksize=(1, 1, 52, 72), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 453.38 kiB </td>\n",
+       "                        <td> 14.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 1, 52, 72) </td>\n",
+       "                        <td> (1, 1, 52, 72) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 64 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"427\" height=\"152\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-86ceee6d-b68c-45a4-8f03-b260903a54f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-86ceee6d-b68c-45a4-8f03-b260903a54f4' 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'>18.12 18.38 18.62 ... 30.62 30.88</div><input id='attrs-bdac87f6-b56e-4d4b-9cbf-f274cfaacc0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdac87f6-b56e-4d4b-9cbf-f274cfaacc0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-030b8154-e29b-460d-90d5-5f415dd3bba3' class='xr-var-data-in' type='checkbox'><label for='data-030b8154-e29b-460d-90d5-5f415dd3bba3' 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>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875, 20.125,\n",
+       "       20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125, 22.375,\n",
+       "       22.625, 22.875, 23.125, 23.375, 23.625, 23.875, 24.125, 24.375, 24.625,\n",
+       "       24.875, 25.125, 25.375, 25.625, 25.875, 26.125, 26.375, 26.625, 26.875,\n",
+       "       27.125, 27.375, 27.625, 27.875, 28.125, 28.375, 28.625, 28.875, 29.125,\n",
+       "       29.375, 29.625, 29.875, 30.125, 30.375, 30.625, 30.875], 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'>262.1 262.4 262.6 ... 279.6 279.9</div><input id='attrs-b53b1bce-3d04-4389-9948-be5d84754054' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b53b1bce-3d04-4389-9948-be5d84754054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26d55afd-fee8-49a1-9110-f0b0ccbb1ac0' class='xr-var-data-in' type='checkbox'><label for='data-26d55afd-fee8-49a1-9110-f0b0ccbb1ac0' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([262.125, 262.375, 262.625, 262.875, 263.125, 263.375, 263.625, 263.875,\n",
+       "       264.125, 264.375, 264.625, 264.875, 265.125, 265.375, 265.625, 265.875,\n",
+       "       266.125, 266.375, 266.625, 266.875, 267.125, 267.375, 267.625, 267.875,\n",
+       "       268.125, 268.375, 268.625, 268.875, 269.125, 269.375, 269.625, 269.875,\n",
+       "       270.125, 270.375, 270.625, 270.875, 271.125, 271.375, 271.625, 271.875,\n",
+       "       272.125, 272.375, 272.625, 272.875, 273.125, 273.375, 273.625, 273.875,\n",
+       "       274.125, 274.375, 274.625, 274.875, 275.125, 275.375, 275.625, 275.875,\n",
+       "       276.125, 276.375, 276.625, 276.875, 277.125, 277.375, 277.625, 277.875,\n",
+       "       278.125, 278.375, 278.625, 278.875, 279.125, 279.375, 279.625, 279.875],\n",
+       "      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'>1982-01-01T12:00:00 ... 1982-01-...</div><input id='attrs-8ff929ac-f31a-4af4-886d-2c9e6cf06d10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ff929ac-f31a-4af4-886d-2c9e6cf06d10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f5c4798-0530-4798-8fa7-52daebc4839a' class='xr-var-data-in' type='checkbox'><label for='data-3f5c4798-0530-4798-8fa7-52daebc4839a' 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>Center time of the day</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-01T12:00:00.000000000&#x27;, &#x27;1982-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-03T12:00:00.000000000&#x27;, &#x27;1982-01-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-05T12:00:00.000000000&#x27;, &#x27;1982-01-06T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-07T12:00:00.000000000&#x27;, &#x27;1982-01-08T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-09T12:00:00.000000000&#x27;, &#x27;1982-01-10T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-11T12:00:00.000000000&#x27;, &#x27;1982-01-12T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-13T12:00:00.000000000&#x27;, &#x27;1982-01-14T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-15T12:00:00.000000000&#x27;, &#x27;1982-01-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-17T12:00:00.000000000&#x27;, &#x27;1982-01-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-19T12:00:00.000000000&#x27;, &#x27;1982-01-20T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-21T12:00:00.000000000&#x27;, &#x27;1982-01-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-23T12:00:00.000000000&#x27;, &#x27;1982-01-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-25T12:00:00.000000000&#x27;, &#x27;1982-01-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-27T12:00:00.000000000&#x27;, &#x27;1982-01-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-29T12:00:00.000000000&#x27;, &#x27;1982-01-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1982-01-31T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-3b04566b-d017-400d-a28a-efdf8f9c9e2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b04566b-d017-400d-a28a-efdf8f9c9e2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8150f14b-b059-449b-9941-2a27c97114a0' class='xr-var-data-in' type='checkbox'><label for='data-8150f14b-b059-449b-9941-2a27c97114a0' 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 height</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>actual_range :</span></dt><dd>0, 0</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f5efd63-3a69-46fb-9ff6-e1e31c3b8dff' class='xr-section-summary-in' type='checkbox'  ><label for='section-5f5efd63-3a69-46fb-9ff6-e1e31c3b8dff' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-05350ce8-9587-4bae-88bd-3ddecb8c563c' class='xr-index-data-in' type='checkbox'/><label for='index-05350ce8-9587-4bae-88bd-3ddecb8c563c' 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([18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875, 20.125,\n",
+       "       20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125, 22.375,\n",
+       "       22.625, 22.875, 23.125, 23.375, 23.625, 23.875, 24.125, 24.375, 24.625,\n",
+       "       24.875, 25.125, 25.375, 25.625, 25.875, 26.125, 26.375, 26.625, 26.875,\n",
+       "       27.125, 27.375, 27.625, 27.875, 28.125, 28.375, 28.625, 28.875, 29.125,\n",
+       "       29.375, 29.625, 29.875, 30.125, 30.375, 30.625, 30.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-eac5c06e-9e2c-4122-98fa-252c0cc7f34f' class='xr-index-data-in' type='checkbox'/><label for='index-eac5c06e-9e2c-4122-98fa-252c0cc7f34f' 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([262.125, 262.375, 262.625, 262.875, 263.125, 263.375, 263.625, 263.875,\n",
+       "       264.125, 264.375, 264.625, 264.875, 265.125, 265.375, 265.625, 265.875,\n",
+       "       266.125, 266.375, 266.625, 266.875, 267.125, 267.375, 267.625, 267.875,\n",
+       "       268.125, 268.375, 268.625, 268.875, 269.125, 269.375, 269.625, 269.875,\n",
+       "       270.125, 270.375, 270.625, 270.875, 271.125, 271.375, 271.625, 271.875,\n",
+       "       272.125, 272.375, 272.625, 272.875, 273.125, 273.375, 273.625, 273.875,\n",
+       "       274.125, 274.375, 274.625, 274.875, 275.125, 275.375, 275.625, 275.875,\n",
+       "       276.125, 276.375, 276.625, 276.875, 277.125, 277.375, 277.625, 277.875,\n",
+       "       278.125, 278.375, 278.625, 278.875, 279.125, 279.375, 279.625, 279.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cbcae486-792e-4745-b437-30b2fa7c2d0d' class='xr-index-data-in' type='checkbox'/><label for='index-cbcae486-792e-4745-b437-30b2fa7c2d0d' 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;1982-01-01 12:00:00&#x27;, &#x27;1982-01-02 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-03 12:00:00&#x27;, &#x27;1982-01-04 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-05 12:00:00&#x27;, &#x27;1982-01-06 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-07 12:00:00&#x27;, &#x27;1982-01-08 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-09 12:00:00&#x27;, &#x27;1982-01-10 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-11 12:00:00&#x27;, &#x27;1982-01-12 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-13 12:00:00&#x27;, &#x27;1982-01-14 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-15 12:00:00&#x27;, &#x27;1982-01-16 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-17 12:00:00&#x27;, &#x27;1982-01-18 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-19 12:00:00&#x27;, &#x27;1982-01-20 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-21 12:00:00&#x27;, &#x27;1982-01-22 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-23 12:00:00&#x27;, &#x27;1982-01-24 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-25 12:00:00&#x27;, &#x27;1982-01-26 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-27 12:00:00&#x27;, &#x27;1982-01-28 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-29 12:00:00&#x27;, &#x27;1982-01-30 12:00:00&#x27;,\n",
+       "               &#x27;1982-01-31 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlev</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3a14f09a-7761-40af-98f7-78f7ae8353ca' class='xr-index-data-in' type='checkbox'/><label for='index-3a14f09a-7761-40af-98f7-78f7ae8353ca' 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([0.0], dtype=&#x27;float32&#x27;, name=&#x27;zlev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a73b920f-edab-410a-a590-a43ddead6ac3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a73b920f-edab-410a-a590-a43ddead6ac3' class='xr-section-summary' >Attributes: <span>(4)</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>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'sst' (time: 31, zlev: 1, lat: 52, lon: 72)> Size: 464kB\n",
+       "dask.array<getitem, shape=(31, 1, 52, 72), dtype=float32, chunksize=(1, 1, 52, 72), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 208B 18.12 18.38 18.62 18.88 ... 30.38 30.62 30.88\n",
+       "  * lon      (lon) float32 288B 262.1 262.4 262.6 262.9 ... 279.4 279.6 279.9\n",
+       "  * time     (time) datetime64[ns] 248B 1982-01-01T12:00:00 ... 1982-01-31T12...\n",
+       "  * zlev     (zlev) float32 4B 0.0\n",
+       "Attributes:\n",
+       "    long_name:  Daily sea surface temperature\n",
+       "    units:      Celsius\n",
+       "    valid_min:  -300\n",
+       "    valid_max:  4500"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "var = \"sst\"\n",
     "\n",
@@ -164,7 +1891,56 @@
    "execution_count": null,
    "id": "7c7b9f30",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Month mean SST (°C): 23.382261276245117\n",
+      "Month std SST (°C): 0.3922087550163269\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warmest day: 1982-01-03 24.211030960083008\n",
+      "Coolest day: 1982-01-16 22.84658432006836\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_12277/4235597154.py:15: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  print('Warmest day:', pd.to_datetime(ts.time[imax].values).date(), float(ts.isel(time=imax).values))\n",
+      "/tmp/ipykernel_12277/4235597154.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  print('Coolest day:', pd.to_datetime(ts.time[imin].values).date(), float(ts.isel(time=imin).values))\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pat6/envs/xarray-climate/lib/python3.13/site-packages/dask/array/numpy_compat.py:57: RuntimeWarning: invalid value encountered in divide\n",
+      "  x = np.divide(x1, x2, out)\n",
+      "/home/pat6/envs/xarray-climate/lib/python3.13/site-packages/dask/array/reductions.py:291: RuntimeWarning: All-NaN slice encountered\n",
+      "  return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n",
+      "/home/pat6/envs/xarray-climate/lib/python3.13/site-packages/dask/array/numpy_compat.py:57: RuntimeWarning: invalid value encountered in divide\n",
+      "  x = np.divide(x1, x2, out)\n"
+     ]
+    }
+   ],
    "source": [
     "ts = dss[var].mean(dim=['lat','lon'])\n",
     "\n",
@@ -186,10 +1962,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "829c560e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max anomaly (°C): 0.8287687301635742\n",
+      "Min anomaly (°C): -0.5356791615486145\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "month_mean = dss[var].mean('time')\n",
     "anom = dss[var] - month_mean\n",
@@ -221,10 +2016,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "17a5f06f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CwYMH0a9fP3Tq1AlNmjQxvufyyy/HmjVrMHjwYHzyySc4//zzcfLJJ5fkpETZtWtXyZfU/3jxqnv37vjll19Qo0aNiPIvvvgCZ555JkaOHIklS5Zoba5atQq5ubkR5+KuvPJKZGdn44EHHoj4gtkDDzyA9evX4y9/+QteeOGFknw1evRo45fKbrvtNnz22WeYMmUKRowYUVIeCATQt29fTJo0CZdeemlUmXfNmjUAIJ67BA5/6crtdmPevHkIBALIzs6OyIG/b+Pcc88tdXqSzMxMpKWl4d133y3T+4moHCX0935Ev8Fvt8pU6vDtk1wul9q6dWvJ6xdccIGqWbOmys/PV6NHj9ZuZ+g4jmrVqpUCoObOnRvR9r/+9S8FQLVs2VKFw+GS8pycHAVA1ahRQ7tV0hVXXKEAqJdeeimiPJpbZQJQPXv2VIcOHSop/+WXX1StWrVUrVq1VFFRUdTjUtxe7969I37C/u677yoAqnbt2urUU09V+/btK3lt06ZNyuPxqI4dO0a0tWLFCgVAnX766RH1lfrfbQ5uu+22iPJNmzaJt7AaM2aMAqD+9a9/RZSXZUxNHnvssZK27rrrLvX222+r3bt3H/E9l1xyiQKgmjdvrh544AG1bt06lZ+fH9X0lIrvVpl5eXnK5XKptm3bGuv89NNP6tJLL1VpaWnq+OOPV/fcc4+2PjRv3lwBUG+//XbMfVBKqc8//1wBUAMGDCjT+4mIiGKxfv165fF4lGVZ6uqrr1YLFixQW7ZsOeJ7ynKM/714bs+Ul5dXkq/OOuss9dxzz6mvvvpKhUIh43tKu1XmvHnzFACVlZWlCgoKSsr37NlTclwvr1tlFgsEAsrtdisA6vvvvy8pP+ecc5RlWerFF1+MqL9v3z7VoUMHlZSUpHbs2FFSfv755ysAasOGDdo0WrdurTweT1TLhjmIiIgocV566SXVoEGDkgwBQKWnp6u+ffuq1157TavvOI66++67VVJSUsR7mjZtqgYPHqw+//zzI06vvG6VOXDgQJWTk6PGjh2rBg8erOrUqaMAqMsuuyzi/F1pLrroIuXz+SJyRXF+a9KkiZjzGjdurOrUqRNR1qJFC2Xbttq8ebNWf8KECdo87969W7lcLnXqqaeK/SrOJXfddVdU83Hccccpj8cTVV2Tnj17KgDqySefLHMbxedXf59riejo44U7qhR+f+Fu3bp1CoAaN26cUurws0hs21Y333yzUkqJF+7WrFlTclFK0qVLFwVArV69uqSs+CLT6NGjtfrFF7nuuOOOiPJoL9x999132mvXXnut8cRIae1t3LhRe61Zs2YKgFq+fLn2WteuXZXL5YoIJ8XPBvzqq6/EaXXs2FFlZGRE1a/du3crAOr666+PKC/LmJqUJUju3btX9e/fX1mWVVLftm3Vvn17NXr06IgTVZJ4LtwVP7ulR48eMb/39/x+vwKg/vvf/5bp/Tt27FAA1J///Oe4+kFERBStqnay6IsvvlAdO3aMmLbf71dnn322evzxx7XnfZR24e68885TALRn+f2+r+V94U4pperXr68AqA8//FAp9b+TQ3989luxhQsXKgARz0Ypvuh45513RtT9+OOPFQDVr1+/qPrMHERERJRYRUVFatmyZeqee+5RvXv3VmlpaSWZ4tprrxW/lL1v3z71r3/9S912223q7LPPVj6fTwFQLpdLzZgxwzit8rpwJ/1dd911xve9/vrrqnfv3qpBgwYlX2D6/d/27dtL6hbntz59+ohtnXnmmcq27ZJ/HzhwQOG3ZwhLVq1apc1z8TPpTjvtNJWTk6P9jRo1quTL+NHwer2qXr16UdU1yc7OjvsZdWeccYYCEPGDCiI6+nirTKp0/vznP6Ndu3aYOXMmxowZg2effRaO45Tc1keyfv16AId/Oi/p3r071qxZg88++wxnn312xGvST9CLn4O2b9++mPtfq1YtZGZmRtVmbm6uVu+6666LuP1mWloaWrRoodVr2LAhNm/ejE6dOmmvNWrUCOFwGDt27ECjRo0AAB988AE8Hg9efvllvPzyy9p7ioqKsGvXLuzZs6fk9gj5+fmYNm0aXn31Vfzf//0fDh48GPGcPtMz68pjTC3LwqRJkzBixAgsW7YM69atw/r16/Hhhx/i2WefxaxZs/Dkk09GrBe1a9fGggULsGXLFixbtgyffPIJPv74Y3z55Zf48ssv8eSTT2Lp0qVRP6slFnv27CnpQ3mQnvkTjfT0dADA7t27y6UfREREpbnsssvQr18/rFy5siRvrVmzBgsXLsTChQtx7bXXYvbs2SXHtrIc48tT+/bt8dlnn+GTTz7BypUr8emnn2LdunV499138e6772LGjBlYuXJl1Mf09evXw7Zt8Xkk0rOQy0txJise1w8++AAAcODAATFj7tq1CwDw3//+t6SsX79+qFWrFubOnYvJkyeX3Dp0zpw5AA7n0mgwBxERESWWx+PB+eefX/Jcs3A4jAULFmDQoEH45z//iX79+kXc8hs4fL7p8ssvL3nMSn5+PiZPnowJEybgb3/7Gy6++GLUr1+/wvq8cuVKdO3aFcFgEP/9738xfPhwzJ49Gy1atNCeDffoo49i2LBhqF27Nnr06IHGjRsjOTkZlmVh4cKF+OKLL8RnC6elpYnTdrvdcByn5N+//vorABjnVyovzj8ff/wxPv74Y+N85uXlGV/7Pb/fj0AgEFVdk4YNGwIAfvrppzK3UVBQUNIfIkocXrijSmnIkCH4+9//jqVLl2LWrFno1KkTTj75ZGP9AwcOAIDxWRjF5fv379dekw7ibvfhTSMcDsfY8yOHgj+2OW7cOK1e165dIy7c1apV64jtSa8XvxYMBkvK9uzZg1AoJE7z9/Ly8lCnTh0Eg0F0794dH330Edq2bYvLL78cGRkZJQ8IHjdunBiKgPId07IEyaZNm+Kmm27CTTfdBOBwYBk6dCgWL16MIUOG4PPPP4+pD9EoDjTxhqzjjjsO33//PX766Se0bNky5vczYBERUSJUxZNFp556asSXjT766CMMHDgQX3zxBcaNG4dHHnkkqnYOHDiA9PT0koz0ew0aNCiv7kYIBALYu3cvACAjIwPA/04evf3223j77beN7/39ySO/34/LLrsMzzzzDN566y307NkTwWAQL774IjIyMtCzZ8+o+sMcREREVLm4XC5cdtll2LBhAyZMmIAVK1ZoWeyPUlJSMH78eKxatQpr1qzB2rVr0b9//wrvq8fjQfv27bF48WK0bt0aOTk56NWrV8l5wFAohJycHDRo0ADr16/Xzv0Vf3kpHjVr1gQA/PLLL+LrUnnx+bjhw4fjoYceirsP9erVw3fffYdgMCjmymh06dIFM2fOxPLlyzF+/PgytbFnzx643e6SL0QRUWLYie4AkeSaa66B3+/HTTfdhG3btuHGG288Yv3ig+WOHTvE13/++eeIepWFOny72oi/ivpmdq1atVC7dm1xmr//K36A8aJFi0pOYG3YsAEzZszAxIkTkZubW3JBLBGKg2SXLl1QWFiItWvXlvqe448/Hv/617/g9XrxxRdflJzYKk/16tUDgLjbLv62/vLly8v0/uLpF/eHiIgoEYpPFg0fPhwAsGLFilLfU5ZjfHnq3Lkzpk+fDiC6/harVasW9u7dG/GFqWKmbBqvNWvWIBQKoX79+iVf+CrOudOmTTti1ps1a1ZEWwMHDgTwv1/Zvf7669izZw+uvPLKqE8aMQcRERFVTjVq1ACAiLsnVcR7ykNycjKmTJkCx3EwYsSIkvLdu3dj//79OOOMM7SLdnl5eSV34YpHzZo10bx5c2zbtg1btmzRXl+zZo1W1rlzZ9i2jffeey/u6QOH7woBAN9++22Z27j00kuRnp6ODz74AO+8884R60pfxs/Pz8e2bdvQvn37Mt8BgYjKBy/cUaWUlpaGSy+9FD/99BNSUlJwxRVXHLF+8bdwVq1aJb5eXH7KKafE1a/i2weV5Zd4iZaVlYV9+/bh66+/jqr+xo0bAQCXXHKJ9trq1avLtW9lEWuQ9Pl88Hq9Fdaf4447DhkZGXEFLAAlF6lnzJhh/KZXMSlkffPNNwCAjh07xtUPIiKi8lCVThaZpl1a/jvllFPgOI54QseUTePhOA4mTpwIALjyyitLyrOysgAg5pNHZ555Jk488UQsWrQIBw4cKLmAV3xBLxrMQURERInx4osv4u2334647WOxHTt24JlnngGAiMfGPPDAA8ZzQ2vWrMHKlSvhdrtx+umnV0ynj+Cyyy5D+/bt8c4772DlypUADn8hJzk5GZ9++mnEnQOCwSCGDRtWbrfIvvbaa+E4Du6+++6ILLh161bxTgz16tXDVVddhU8++QTjx49HKBTS6mzatAmbN2+OavrFX+Rft25dmfoPHM6yjz76KADg8ssvx7Jly8R669atE5fvRx99hHA4jG7dupW5D0RUPnjhjiqtCRMm4NVXX8WyZctKTqKYnHnmmWjZsiXWrFmD+fPnR7w2f/58vPvuuzjppJPEZ4/EovjZbz/++GNc7SRC8TfehwwZgu3bt2uv5+fnR4SD4m9v//GE0/fff49//OMfFdbPYrEGyfz8fIwfP954kueRRx5BXl4eWrduXbIcy5NlWTj77LOxe/fukoueZXHmmWdiyJAh2LNnD7Kzs/Hdd99pdRzHwYsvvohrrrlGe614GTJkERHR0VDVThZ99NFHmD17dsktFX8vGAxiypQpWn9r164Ny7KM+e/6668HAIwePTriVpF79+7FhAkTyrP72LlzJ/7yl79g1apVaNy4MUaNGlXy2qmnnoqzzjoLr7zyCmbOnCm+f8OGDdi5c6dWPnDgQAQCATzxxBN488030b59+yPepv6PmIOIiIgS48MPP8T555+PRo0aYcCAARgxYgRGjBiBAQMGoHnz5ti4cSP69OmDSy+9tOQ98+bNQ9u2bfGnP/0J119/PUaNGoVhw4bhvPPOw9lnn41AIIApU6aUPC/taLIsq+QRL6NHjwYA2LaNv//97/jhhx/Qrl073HbbbRg6dCjat2+P119/vdyO+yNGjEDHjh3xr3/9C506dcLIkSNx8803o2PHjiXnE2078lT69OnTkZWVhbFjx6J169YYNGgQ7r77bgwcOBCdO3dGZmYmPvzww6im37dvX7hcLuPFtmhdddVVeOaZZ5Cfn4/s7GycfPLJuOWWWzBmzJiS+Tn99NOxdetW7b1vvfUWAPlL/ER0dPEZd1RpNW7cGI0bN46qrmVZmDNnDnr06IHLL78cffr0QatWrfDtt99i4cKFqFGjBv75z39qB9hYnXvuuXj55ZfRv39/XHjhhfD7/WjSpIl44qCyOffcczF58mTcfffdOPHEE3HhhReiWbNmyMvLww8//IDVq1ejS5cuWLp0KQDgoosuQmZmJh566CFs2LABJ598Mn788Ue8/vrr6NWrV4VfvJw3bx5GjBiBVq1aISsrC8cddxzy8/Px9ddfY8WKFVBKYerUqSVBMhgMYuzYsRg3bhw6d+6Mjh07onbt2ti7dy/Wrl2LDRs2ICUlBU899VSF9fmSSy7BggULsGzZMmRmZpa5nccffxwulwtPPfUU/vSnP6Fr167o0KEDfD4ftm3bhhUrVuCnn36KCN7F3nrrLaSlpaF79+7xzAoREVFUPvzwQ0ybNg0NGjRAly5d0KxZMwDA5s2b8cYbb6CgoEA8WRTLMb48bd++Hddffz1uvfVWdOnSBa1bt0ZSUhJ+/vlnLF26FDt27EBmZibGjh1b8p7U1FT8+c9/xnvvvYerrroKJ510ElwuFy6++GK0b98eV1xxBV566SW89tpraNu2Lfr06YNgMIj58+fjtNNOw6ZNm8rU19zcXACHL1Tt378fX3/9NdasWYOioiJ07twZ8+bNQ926dSPe88ILL6B79+644YYb8Oijj+LPf/4z0tLS8NNPP+HLL7/EV199hQ8++EC7leS1116LsWPHIicnB8FgMKZf2xVjDiIiIjr67rjjDpx44ol455138OWXX2LZsmUIBAKoU6cOunbtiiuvvBJXXnllxG0PZ82ahTfeeAMrVqzAqlWrsGPHDiil0KhRI1xxxRW4+eab4/7iezz69u2LTp064YMPPsDixYtx0UUXYfz48cjIyMCzzz6Lp59+GrVq1UKPHj0wYcIE5OTklMt0/X4/Vq5cibFjx2L+/Pl4+OGH0axZM4waNQpnnXUWFi5cWPIsvGI1a9bE6tWrMWPGDLzwwgtYsGABAoEA6tevjxNPPBEPP/wwevToEdX0jz/+eFx00UVYvHgx9u3bh9q1a5d5XgYPHowLLrgA06dPx9tvv4158+YhPz8faWlpaNu2LR5++GEMGjQo4j2O42Du3Lno0KFDQn5tSUR/oIgqAQCqUaNGUdUdPXq0AqBmzZqlvfbNN9+oq6++WjVo0EC53W7VoEEDddVVV6lvvvlGq5uTk6MAqJUrV2qvbd68WQFQAwcOjCgPhULq7rvvVs2aNVNut1sBUOecc07J602aNFFNmjQR+32k6Zkcqb1zzjlHmTbhgQMHKgBq8+bN2mvvvfeeGjBggDruuOOUx+NRdevWVR06dFDDhw9XH3/8cUTdH3/8UV155ZWqYcOGKikpSbVu3VpNmTJFBYNBbd5Lm0fTmJqsX79ejR8/XnXr1k01bdpUJSUlKZ/Pp5o3b66uvPJK9d5770XUD4fDasmSJWr48OGqc+fO6rjjjlNut1ulpqaqdu3aqWHDhonj8XvF4yatW9EoLCxU9evXV507dy7T+/9o3bp1atCgQerEE09UKSkpyuv1quOPP1717dtXvfTSSyocDkfU//bbbxUANWzYsHKZPhERUWl+/PFHNX36dNW3b1910kknqRo1aiiPx6MaNGigevbsqZ5//nnteBXrMf6PZs2aFVOm+L1ff/1VvfDCC+q6665T7dq1U3Xq1FEul0vVrl1bnX766eq+++5Tv/76q/a+7777TvXu3Vulp6cry7K0vFBYWKjGjRunmjVrprxer2rSpIkaNWqUCgQCYmY6EgARf16vV9WpU0edcsopavDgwWrJkiXamP5xHidOnKhOOeUUlZKSopKSklTTpk3VhRdeqJ5++mmVl5cnvu/cc89VAJTb7VY7duyIur/FmIOIiIioupoxY4YCoJ566qkKnc7atWsVAPXQQw9V6HQkr732mgKgnn/++aM+bSLSWUol4OERRETV1H333YdRo0Zh/fr1Md1iqjzccccdmD59Ov773/+iefPmR3XaRERERMxBREREVJVt375du+vD1q1bceaZZ+Lnn3/Gli1b0KhRowrtw2WXXYZ3330X33//PZKTkyt0WsWUUujUqRNcLhc++uijiF9oElFi8MIdEVE5CgQCaNmyJdq3b4/Fixcften+/PPPaNGiBYYOHYoHH3zwqE2XiIiIqBhzEBEREVVlp59+OoLBIDp16oS0tDRs2bIFr7/+Og4dOoT77rsPI0eOrPA+/Pjjj5g5cyYGDBiANm3aVPj0gMNZ6umnn0bfvn3RsWPHozJNIjoyXrgjIipn7777LlauXIk777wTKSkpR2WaH3zwAd566y0MGzYMaWlpR2WaRERERH/EHERERERV1RNPPIHnn38e3333HQ4cOIDU1FScfPLJuPXWW9G/f/9Ed4+IjiG8cEdERERERERERERERERUCdiJ7gARERERERERERERERER8cIdERERERERERERERERUaXAC3dERERERERERERERERElQAv3BERERERERERERERERFVAu5Ed6Ay2b9/P1avXo0TTjgBPp8v0d0hIqIqpLCwEFu3bsU555yDtLS0CpvO9u3bsW/fvnJpq3bt2mjYsGG5tEUUDWYtIiIqq6qWtZiz6GhjziIiongwa1UuvHD3O6tXr0bfvn0T3Q0iIqrCFi5ciD59+lRI29u3b0erlifgYJ5TLu3VqFED33zzTbUOOlS5MGsREVG8qkrWYs6io405i4iIygOzVuXAC3e/c8IJJwA4vHJmZmYmuDdERBSLmy54QCtz0lPFugUN/FpZfgP57tFFteTpBWtGhozgrt3Y9czskmNJRdi3bx8O5jlYMKsBWjTzxNXWps1BXHL9Duzbt6/ahhyqfJi1iIiqpuw5c8Ryd74llifvUFqZf2dIrKvcehuhZD2XBQ7uxv+9VzWyFnMWJQJzFhFR1dXj1efEcs9OPY/U2KrnLACAodgRspYrqFcO/LobG1fNYtaqJHjh7neKbyWQmZmJNm3aJLg3REQUixreulpZOLmmWNeqlaLXrSNfuLPS5elZteVvBx2N29K0aOZBm5beCp8OUXlj1iIiqpq8DRqI5Z6D8oU7f6F+MiilwHDhzqO3EUyRcxnArEVkwpxFRFR1eT+Ss5bX0i9u+fNivHAnZC2XkNWKMWtVDrxwR0REVMU4ykFYxXdbASfO9xMRERFVV/FmLeYsIiIiIjNmrdLxwh0REVEV40DBMX2VKoY2iIiIiEgXb9ZiziIiIiIyY9Yqnfn+E0RERERERERERERERER01PDCHRERURXjlNP/iIiIiEjHnEVERERUcRKZtT777DP07dsXDRs2RHJyMlq1aoV7770Xhw4dKsc5jB9vlUlERNVC6Lg0rSyQIT9Qt6Cu/r2Volpyu0VphjBQuyjy3/nBI3WvXDkKCKs4b5VZ/e8qQEREROXEt9cSy2v8IAeKtP8c0AstuY38xqlaWcin1w175PdXhHizFnMWERERxcK7zSOWp32nh4qamwvEuvnHJ4nlh2oKuUrIWsFg9c9a//nPf3DGGWegZcuWeOSRR1C3bl28++67uPfee/Hpp59i0aJFZe5TeeOFOyIiIiIiIiIiIiIiIqq2XnjhBQQCASxYsAAtWrQAAHTv3h0///wzZsyYgX379qF27doJ7uVhvHBHRERUxcT7EN/iNoiIiIhIF2/WYs4iIiIiMktU1vJ4Dv+ysVatyNtupaWlwbZteL3eMvepvPEZd0RERFWMA4VwnH88oUREREQkizdrMWcRERERmSUqaw0cOBBpaWm4+eab8f333+PgwYN4/fXX8fTTT+OWW25BSkpKOc9p2fEXd0RERERERERERERERFRlbNy4USvLyMhAvXr1xPpNmzbFBx98gH79+pXcKhMA/v73v+ORRx6pqG6WCX9xR0REVMUU31Ig3j8iIiIi0iUqZ61YsQKDBg1Cq1atkJKSgkaNGqFPnz749NNPS33vK6+8giuuuAKZmZnw+/1o2rQprrrqKnz33Xda3a5du8KyLO0vOzu7TP0mIiIiikV5Za2+ffuibdu2EX9PPPGEcbpbtmzBRRddhDp16mD+/PlYvXo17r//fsyePRuDBw8+WrMfFf7ijoiIqIpxlEJYxfmMuzjfT0RERFRdxZu1ypqznnzySezZswfDhg1D69atsWvXLkydOhVZWVlYtmwZunfvbnzvlClT0KBBA4wePRrNmzfH1q1bMWnSJJxyyilYt24d2rRpE1G/efPmmDdvXkRZWlpamfpNREREFIvyyloLFy5EZmZmxGsZGRnG940cORK//vorPv/885LbYp599tmoW7cuBg0ahGuvvRbnnHNOmftVnnjhjqiKy657o1a2dPeMuNs9t/t9WpnjkX+kG/bJ5cplxdWH9169M+q6pw5+SCy3wnJ9V1AoK5QPGHZIL4+lb3R0FNXSHyAbqO2S66bqZaFkuV0nWV6JatUsiJxWauDIHSxHzm9/8bZBRERH9v4PzcXyM5p8H3fbTWc8oJUlbZM/nqX+pGeRpH3ynjzkl/NX2KeXfzzz9iN1MUKrnIfFckuIT0HhOAsAwbpCAAPgTtHLN10+Ouq+UeK4igwnXCx9fVMeOZd5D4SEMr2enSevPxUh3qxV1vc+/vjj2q2dsrOzkZmZiUmTJh3xwt3ixYu193bv3h1NmzbFww8/jGeffTbiNb/fj6ysrDL2lIiofAxdf7VY/sQpc+Nuu/PSUVrZ7n1ySLF/9GtlKT/L7TrC4UzJhzh8df9wY//+qOkzejY83Lhe5E2Xzz80y9ijlZ1c+yex7uT286PuG1U8V6FcHkzVM1VBPZ9Y1zZEpdTtejKxwvqKZf9qOIlaAcora2VmZmpfTjqSzz//HK1bt9aeZXfaaacBAL766qtKc+GOt8okIiIiIiIiIkow6XksqampaN26NbZu3Rrzexs2bIjjjz++1PcSERERHQsaNmyIr7/+Gnl5eRHlH3zwAQDg+OOPT0S3RLxwR0REVMWEocrlj4iIiIh0lSlnHThwAOvXr4/p2+TFvv/+e/zwww/iezdt2oT09HS43W60aNECo0ePRkFBgdAKERERUflKVNa67bbbsHv3bvTo0QP//ve/sWLFCkyaNAm33347WrdujZ49e5bznJYdb5VJRERUxTgKEO5qEHMbRERERKSLN2sV56yNGzdqr2VkZIi/jjO55ZZbkJ+fj9GjY7t9bCgUwg033IDU1FQMHx55q7YuXbrg8ssvR6tWrVBQUIAlS5bg/vvvx5o1a7By5UrYNr/jTURERBWnvLJWrC6++GIsX74ckydPxrBhw3DgwAGccMIJuOmmm3D33XfD69Ufw5MovHBHRERERERERFTO+vbtq5Xl5OQgNzc3qvffc889mDdvHh577DF06tQp6ukqpXDDDTfgvffew4IFC3DCCSdEvD5hwoSIf1944YVo2rQp7rzzTixatAj9+vWLelpEREREVUm3bt3QrVu3RHejVLxwR0REVMXE+xDf4jaIiIiISBdv1ip+78KFC5GZmRnxWkZGRlRtjBs3DhMmTMDEiRNx6623Rj1tpRQGDx6MuXPnYs6cOejTp09U77v66qtx5513Yt26dbxwR0RERBWqvLJWdcYLd0RERFWMAwthWHG3QURERES6eLNWcc7KzMws07Ppxo0bh9zcXOTm5mLUqFFRv6/4ot2sWbPw3HPP4eqrr4552rxNJhEREVW08spa1Rkv3BFVET0bDJVfCOvfMbgg6Sqx6rLAPK2sxxkThJoAklxakeOVP8SFfXK549HLlGG/qmz9hVNuetgwPaEwJbYdth0SyorkNtyF+o2Ts65+SKxrCTdZ/uCFO2LqW7wuODlHLF/22bij2o+jbfWbI7SyDrcZ1iG/Xub45Btku/zCygIgPflQxL/zkwKl9JCIiCqz6z++Xitr5m8r1r3jrZFi+QfnT9bKTnxZzlr2oWStzL9b7lvyrrBW5iqUv2dqKT3DHa6vH+fOuGyqWFfKcCkphrwnfKJ054lV4T4kNAwgWENvpOkTD4p1PRn68fa7AWPkCVaQjrfK+eLz6cPF8urgPxPkeTNlLWXX0MqS9sqZyrtXX6b2rwVaWVHhr0fqYrUxfvx45ObmYsyYMcjJkXO9RCmFIUOGYNasWXj66adx/fX6Pu1I5syZAwDIysqK6X1ERNFqNvc+razLiUli3Se+1W9jN7TlSrHu2cvvEsv359XUyqzt8vRq/KiXeX+VzxFI56SUJZ9P6vB3+TjpCI/R8ifLp+lDemREsFAoBPB/Ab2NX/L0YzIALPnxHrHc59Zz50fZk8S6FeVYzFr/HS/PW5u79bFQlpz3vQfldda/W89g3v1FWln4UOGRukhHWaX7KtXnn3+OXr16oXHjxvD7/UhPT8fpp5+OuXPnanXXr1+P8847D6mpqUhLS0P//v3x/fffJ6DXRERER4+jyuePjk3MWkREREeWqJw1depUjB07FtnZ2ejVqxfWrVsX8VfshhtugNvtxg8//FBS9ve//x3PPfccrr/+erRr1y7ifZ999llJvffeew/Z2dl4+umn8fbbb2Px4sUYOnQoRo0ahe7du+Oiiy4q87jRYcxaRERER8ZzWqWrdL+4279/P0444QRcccUVaNSoEfLz8zFv3jxcc8012LJlC8aMOfxtym+++QZdu3ZFx44d8e9//xuBQABjx47FWWedhc8//zzq+8YTERFVNbxVJsWDWYuIiOjIEnX7psWLFwMAli5diqVLl2qvK3X4LFU4HEY4HC759+/fO3PmTMycOTPifU2aNMGWLVsAAMcddxxcLhfGjx+P3bt3w7IsnHjiibj33ntxxx138FaZ5YBZi4iI6Mh4q8zSVboLd127dkXXrl0jynr37o3NmzdjxowZJQFn7Nix8Pl8eP3111Gz5uGfPnfq1AknnngiHnzwQUyZMuVod52IiKjaW7FiBebOnYv3338fW7duRVpaGk499VSMHTsWnTp1iqgbDAbx2GOPYdasWdi4cSN8Ph9at26NBx98EGeccUaC5oCYtYiIiCqnVatWRVVv9uzZmD17dkRZ8YW50mRmZuKNN96IrWMUE2YtIiIiileV+SpV3bp14XYfvs4YCoXw+uuv45JLLikJN8Dhb5F169YNr776aqK6SUREVOHCv30zKd6/snjyySexZcsWDBs2DG+++SamTZuGnTt3IisrCytWrPhfH8Nh9OvXD/feey+uuOIKLFmyBPPmzUN2djby8/PLayioHDFrERERHZaonEXVG7MWERHRYcxapat0v7gr5jgOHMfBvn378PLLL2PZsmWYPn06AGDTpk0oKChA+/bttfe1b98eb7/9NgKBAJKS5AeOEhERVWUOLDgqMbfKfPzxx1GvXr2IsuzsbGRmZmLSpEno3r07AOCxxx7DkiVLsHbtWmRlZZXU7dWrV9k7TeWKWYuIiEgWb9Y6Fm7fRKVj1iIiIpIxa5Wu0l64Gzp0KJ5++mkAgNfrxaOPPoqbbroJALBnzx4AQHp6uva+9PR0KKWwb98+HHfcccb2d+7ciV27dkWUbdy4sby6T0REVGES+Yy7P160A4DU1FS0bt0aW7duLSmbNm0azj777IiLdlS5MGsRERHJ+NwVKg8VmbWYs4iIqCpj1ipdpb1wN2rUKAwePBg7d+7E4sWLceuttyI/Px933nlnSR3LMi+gI70GAE888QTGjRtXbv0lqmhLdjxRIe2+/f4Ysbxrz/u1slCSfHfdolR5e3M8ernxyxRC0yHDlwvDQnnYK9dVhr2c5QhdKJLrugv0TrsCcl1XoV522vUPiXU/nnW73EgMzjtnklZm+T1y3bMnamXKJS/T5Svvjq9jlYRjuCG0EsqVS4l1vUkhsbx2UkHEv11eYeEfIw4cOID169eX/Npu69at2LJlCy666CKMGjUKzz33HPbs2YOWLVtixIgRGDhwYIJ7TACzFtEfzTptVtR1x7aNvt3vBshZq+2Ih7WyGj/JxxzfXj2kOG55G/QGhZADAMJhTtnRZzhvnnxQDUt5z9BuyC+XF9bSywMZLrluqh7uznz7H2LdtT3ifz5Uuzv05eQ2DHHHW/W6poy64aHh8XSr0sg3fX9DCGHeA4bjRsgwoERVXEVmLeYsqoo2X10x5xnePfcBsbzFVP1cTOpWebuquSUYVx+UK7aLCNL5taKachtSuTI8/Sr8s37C7FCKfHKtqG5YLLdS9LFovTBXrPufvnJ5tNqM1LMTAAjxEgBw8i3RZ60vH64eWaswTS+TzmkBgH+vfF5LWj8t4TODFZLfT4lRaS/cNW7cGI0bNwYAXHjhhQCAu+++GwMHDkSdOnUA/O8bSr+3d+9eWJaFtLS0I7Y/dOhQDBgwIKJs48aN6Nu3b/ydJyIiqkCH7+cd32Nqi7/ZJH0zNyMjQ/xlncktt9yC/Px8jB49GgCwbds2AMCcOXNw/PHHY/r06ahVqxaeeeYZXHfddSgqKsKQIUPi6j/Fj1mLiIhIFm/WOhaeu0Klq8isxZxFRERVGbNW6Srthbs/6ty5M5566il8//336NSpE/x+PzZs2KDV27BhAzIzM0u9D3i9evViOilJRERUWSgV/zPu1G/vlz7c5+TkIDc3N6p27rnnHsybNw+PPfYYOnXqBODw8zwAIBAI4M0330STJk0AAD169MCpp56Ke++9lxfuKiFmLSIiosPizVoqzpxG1VN5Zi3mLCIiqsqYtUoX39f1j6KVK1fCtm00b94cbrcbF110EV555RUcPHiwpM6PP/6IlStXon///gnsKRERUdWxcOFCfPXVVxF/Q4cOjeq948aNw4QJEzBx4kTceuutJeXF3yBu1apVyUU74PDtfi644AL89NNP2LlzZ/nOCMWNWYuIiIio4jBrERERUbQq3S/ubrzxRtSsWROdO3dG/fr1sXv3brz88st46aWXcNdddyEjIwPA4ZOFp512Gnr37o2RI0ciEAhg7NixqFu3Lu64444EzwUREVHFCcf5EN/iNgAgMzMTbdq0ifn948aNQ25uLnJzczFq1KiI11q0aIHk5GTxfUodvme6bVeZ7w5VO8xaRERERxZv1joWbt9EZsxaRERER8asVbpKd+Hu9NNPx6xZszBnzhzs378fqamp6NChA55//nlcffXVJfVatWqFVatW4R//+AcuvfRSuN1udO/eHQ8++GBJCCIiIqqOHGUjbHoacQxtlNX48eORm5uLMWPGICcnR3vd7XajT58+mD9/PrZs2YKmTZsCOHzRbunSpWjRogXq1q1b5ulTfJi1iIiIjizerBVPzqKqj1mLiIjoyJi1SlfpLtxdf/31uP7666Oq26lTJ7zzzjsV3CMiIiIqNnXqVIwdOxbZ2dno1asX1q1bF/F6VlYWgMMX95YsWYLs7Gzk5uaiZs2aePbZZ/HFF1/g3//+dyK6Tr9h1iIiIiKqOMxaREREFK9Kd+GOiIiIjsyBBSfOx9Q6ZbytwOLFiwEAS5cuxdKlS7XXi2+F2aJFC7z33nsYOXIkbrzxRgSDQXTs2BGvvfYaevfuXfaOExEREVWweLNWWXMWERER0bGAWat0vHBHFaZno7/JL5iea5Tk04pUkkesqtzCquuSN1glTO+tj8aKdbPbjZb7ZultK6+8+Sz7JFduo5I6t/t9YrmTqs9fMFke42CKXO549TJl2K8ql14W8st1Q8Kjs5wkJffBLZdL7CK5c+58vdyTL7fhPqTXtYNy3VP++rBY7irU++wqkufDm6ZvI7ahrsT0y/Iu/R8Uy9e8cmfUbVdqwqJWhnXF5wmJ5TU9gYh/O57CuLsVLaccnnFX1pCzatWqqOu2bdsWr7/+epmmQ0Sla/HSRLHcsvT9mW3L+7hgYfQfByxDG5Lvrxglln/8Y1Ot7JvCBmLd9w60FMtnnDon6n5UBi2mPiSWZ2zXy/zb5IARStVDVdgvLzvPQTl42AVFhh7qHJ+eL8Ip8vRcQk52PPIxxnLk4CGW23IboRR9LLa70sS6TZ67Xyvz7pQ/X6QIywMAUvYJuSzgiHWl+Tbl50436jnw0xnD5U5UYo5X3i9Yjj7f7oKwXDekZy3lE9Y3dfROX8SbtY6Fk0lEx4KBH90glqd5DkXdxv8drKeVbd5dR6wb+DlFLHcV6PuUTXfeLta98ZOBWtnWQ2li3Y2/6Ldk/W7AGLFuZdb08aliefp3er6o8aOck6RjVJFwvgUAvPv0TGWHDDlZyeUer34SzJMnnBgD4Nuvz0coWc5UIZ++roQM56+S9sjTK6ytl+c3kus2nfGAVuY+INdN+UnvW8oBeXxM58Ac4dxvUU35mNthmJ61vphWBbOWTx8LZTgH7smXM6onT89aVlhf56WyisKsVbrqfzNQIiIiIiIiIiIiIiIioiqAv7gjIiKqYsJxPsS3uA0iIiIi0sWbtZiziIiIiMyYtUrHC3dERERVzOF7gSfmVplERERE1V28WYs5i4iIiMiMWat0vHBHRERUxRy+F3h83y46FkIOERERUVnEm7WYs4iIiIjMmLVKV/1/U0hERERERERERERERERUBfAXd0RERFWMUw7PuHOOgfuBExEREZVFvFmLOYuIiIjIjFmrdLxwRzHpeeII+QWPviqptJpyXZf8U1bl82hljk9eRR2vvnEqy/ATWWE77nb+FLlrtZPlNoS2wz6XWLXLJQ9qZWsW3Cm3WwkEa8hjXFRDn7+iGvIYB1PltsM+vUzJwwZHX/wIJyu5rj+sFyY5csOW3AaE4nCRoXO2VG5Yj4X1zQ4auhaSyx2PsL4VydMLe/Vyl2l6YWGmDZuNsuUXOg98SCv7aM7tciOVwIaHhovlmfc/rBf65HUoxVcklqd5DkX8O+wOxNa5ODiw4cR9q8zqH3KIqqKWr9wrljthfZt1QobjliDkGHb4BXIbdkDYR4QNxz63fnxpNvc+sW7zRv21skNBr1h35145SzZfqx+Lvh9WeY9Ftb6Tx63m/x3UyuxD8jFH1UrSywyL1ArLxzO7QA8IpvxsS+Wmu9EIdaWsDgDK8DnADunZzl1gqBsUMuoufXwAwC7Uy3wH5Gzo3yPkSwDeA3pgM42xcunzHfLL25jj0+t2u0D+jLJy2T/E8srg++Hyttf5On07NXFSpfVbyMO2vK+oCPFmLeYsosrrT6+O08ocw0E11d9ALK+XkqeVhRx5u/9pX5pWFtjjF+t698ltSKc1mk2fKtZF7VZake0ynFvZrZ+0aXO38FkZwNf3yZ+tK4M6n8vjlrpdzz7+rQfEuvmZtbWysHBuBgAsRx9P168FYl3llc+5SW1AycvJk6eXh/1yu8FUPXc4v5pO/MjFSXv1+km7hZN2kM93uQsMWWuvnqm8++UTWFZQzlpOkj5/wV8N5zVT9fXizAHydrP25TvE8srg+9v0rNVmpLyd2kWGhSpwkoTz8M7Ru1TErFW66j+HRERERERERERERERERFUAf3FHRERUxYQVEDb91CKGNoiIiIhIF2/WYs4iIiIiMmPWKh0v3BEREVUxDmyEeatMIiIiogoRb9ZiziIiIiIyY9YqXfWfQyIiIiIiIiIiIiIiIqIqgL+4IyIiqmIc2HAUf3FHREREVBHizVrMWURERERmzFql44U7IiKiKsaBVQ63yozvGXlERERE1VW8WYs5i4iIiMiMWat0vHBH6Nnob/ILyX6tSCX7xKrhVL3c8bnEuo5H3ijDSXp52CNvhI5XLzc+z9LSX7Ac+QmWVij6TcLxyOVhnz69jrc+LNZV0hDF+HBNy4muDAAsoW2rvjzPwVS9rKim3G4oWS53PPoElVueQSXUhT8s1nUlhfQytzzTljTTAJywvr4FDeuQIzQdEhceoFx6I7be3cN9M5SHhXI7KNe1hU7HUte8rkS/Ip46+CGx/JNnb4+6jaPN8Qrz55IHw+OS10OPHVnfbRpMIjrmtXhpolju9uj7F9tw3LKEfVQwZAgjBfoxyi6SD3KeA3Iu8+3Vy1xF8uSUkLWKaiWJdbf+eILQgNyuy1AuHedaj5KzlvdXvcwdiP4YF0g3ZFHD0EvTS90lH0essF4ujSUA2CF9+UuZ43C5vEyVV898ym3I5Sleva5t6FtQOP6ZsrbxKfJ6295fDcffPGHcDJ8v7CK9b64COYCJ8xGrgN43d55hw5EY8ld2h3vE8qVfjI++7aPMky99DpDXoaJawvomLNOgV69HRAQATf85RSuz3fJxpHZtfb/8x892xX7+qbZYvtuqpZVZHsNx5KB+/PXtkc8nuAJyE+5DUl352Ofs1jNYSD+9d7gfBXqZ56Bc9+Rb5KwlZSLbcOhzFenHhoI68rEhqA8xvPvldoXTcIenF9CXiVUo54CkXYVyI1K7+4QF4pGXadCQiR3hfKfplII7Xw+/UsYBAFdAb1fumTmXJe/Qx8jUN0c4ttshud2wcJ445Jd7ZyXJ5Z4D+nLy7BVWZAB+KSsbsvYFp+ZqZcs+0csqC/8ueYxN60VhbX1DDfn1c/mH9vuBz+PqGpUjXrgjIiKqYsLKQtj4bYXo2yAiIiIiXbxZizmLiIiIyIxZq3S8cEdERFTFKNhx389bHQP3AyciIiIqi3izFnMWERERkRmzVul44Y6IiKiKCSsb4Tge4lvcBhERERHp4s1azFlEREREZsxapav+c0hERERERERERERERERUBfDCHRERURXjAHBgxflHRERERJL4s1bZrFixAoMGDUKrVq2QkpKCRo0aoU+fPvj000+jev/OnTtx3XXXoW7dukhOTsbpp5+O5cuXi3XfeecdnH766UhOTkbdunVx3XXXYefOnWXsOREREVH0EpW1qhJeuCMiIqpinN9uKRDPn3MM3FaAiIiIqCzizVplzVlPPvkktmzZgmHDhuHNN9/EtGnTsHPnTmRlZWHFihVHfG9hYSHOPfdcLF++HNOmTcOiRYtQv359ZGdnY/Xq1RF1V69ejZ49e6J+/fpYtGgRpk2bhnfeeQfnnnsuCgsLy9R3IiIiomglKmtdd911sCzL+Ldu3bpyntOy4zPujjHZdW/UyqwaqWJdJ9WvlYVqJ4l1gzX0VSmYLG9AjscSy8PeGOp69DLlEqsCUhNKbtcyXa5XhvIop2fsmzQpuWsw7Y+ktk3Tc4QtXhn2AiG/PtPhZHmAlE8ut1zCwNnyYNpuvQ23LyzW9XlCWpnLZeibYdk5jj6gliVXDgrljmk5ufR27aBhfQsZyoXZtvVZ/q1toczwWdsl1JWmdbhc7pst1De1UZl9f9vtWlnzFyeJdYtC8kaSF4rcaR2SdkxEdEwx7Ue8huOZVzieHSoQAhGAUIG+j3Htlvc7njx9H+45KFaFb7987PPv1vvsLoj+e40hvyEHuvW+hQyZMZAmH4scKTMa8ox03JKOnQDgOaSPhe+APD5hn6FvQgZzXHLdYLqetd15cufsQn1dCSfJgc/xyONpu4Vyy3C8L9IHTso4hyco5T25btgX/XrhKpTXN8+v+hhZpsAnlFvB2IKL8ujjrNzy2Cthk7TCct8sadwMdeGO5cNI5bD25Tu0svPOnijWtYR1qzBFH2PTZ8Pq5PHHH0e9evUiyrKzs5GZmYlJkyahe/fuxvc+99xz+Oqrr/D+++/j9NNPBwB069YNHTp0wIgRI/Dhhx+W1L3rrrtw0kknYf78+XC7D+9AmzVrhjPPPBMzZ87EzTffXAFzRxS/dq/liOW+VGFfbTi5ciggZy2Je58cMPw79bZN52Gk3OHJk+tKWeRwG3p5yB/9+QRT36Q8I00LAAprydOT2jadI5DypatQ7lxhvj69sE9uVzqfCAAh4VhiN6gp1pWO16ZMhTQ9w5kyldQHALCL9JxjyjNKyJJijoCcwaX3A4Bvv3yiSRoLV36RWFfKl45P3m6kfjiGbKhs0zFfXwl8AcN8hPQxVlIeBuAkVa3zOZ88p5/TAoBzu8qfR6X1Iq+hPsYBb/XPWvfccw/++te/auUXXXQRfD4fTjvttAT0Ssav2xMREVUxYdjl8kdEREREukTlrD9etAOA1NRUtG7dGlu3bj3ie1999VW0bNmy5KIdALjdblx99dX46KOPsG3bNgDAtm3b8PHHH+Oaa64puWgHAGeccQZOOukkvPrqq2XqOxEREVG0EpW1WrRogaysrIi/wsJC7N69G9dffz1crhh+fVPBeNaOiIioilHKghPnn+kbqERERETHunizVnnmrAMHDmD9+vVo06bNEet99dVXaN++vVZeXPb111+X1Pt9+R/rFr9OREREVFEqU9Z67rnnYFkWBg0aVG5tlgfeKpOIiIiIiIiIqJxt3LhRK8vIyBB/WWdyyy23ID8/H6NHjz5ivT179iA9PV0rLy7bs2dPxH9NdYtfJyIiIqruDhw4gPnz5+Pcc89Fs2bNEt2dCLxwR0REVMWEYcV9q8uw+ABQIiIiIoo3axXnrL59+2qv5eTkIDc3N6p27rnnHsybNw+PPfYYOnXqVGp9y/CMJek1U90jtUFERERUHsora8X7JakXX3wRBQUFuOGGG8rcl4rCC3dERERVjFI2HBXfhTsV5/uJiIiIqqt4s1Zxzlq4cCEyMzMjXsvIyIiqjXHjxmHChAmYOHEibr311lLr16lTR/y13N69ewH87xd2derUAQBjXemXeERERETlqbyyVrxfknruuedQp04d9OvXr8x9qSi8cEdEREREREREVM4yMzNLfTadZNy4ccjNzUVubi5GjRoV1XvatWuHDRs2aOXFZW3bto3474YNG3DhhRdqdYtfJyIiIqrs4vmS1JdffolPPvkEw4YNg8/nq4juxYUX7iqhnsfdIpYv+flxrayHPUCsaycny+XptbUyJ72GWLeojt5GYZq8ygRT9dtphPzyLTYcj1gMR2haKgMAx6uXKZdcV7p4b3p+paXkcgjlVliuakvlpnalqob5MI1bWNivOEmO3IZX74jyyHUtodx2G+q65Bm0hAG1bbmuy6W37fWExLo+tz7IHpe8QKQ+ABAfYlrkkQf/kEdf4QJuud2wW19pw4XyN0iskGFFFNc3ua5dqJe7TdtCofB+eYhhGcqVUG5Y/Djt+oe0so9n3S5XrgSckLycDhXJG9++opSIf/8aFAa4ghy+pUB8tzHirTLpWNZiqr5/AoBNd+j7qKbPPCA34tWPW5ZL3q6CQXnHHMjTD+L2Xnmfk7xLbztlu7wD9uTrfXMH5GO4J0/e4Xv2FuiFYUP4cenz53XL+1QllAdryh+S7KAcBItS9TakPATIeU3MagBchUIWORAU6yrTsq6h99mShx5WUFiHgnLnQql6FnHchmwgd1kcexj7ZhgkqV2X3q5jWP6mPithUSvbFNj1orBPXleUMD27SJ5pu9AQfgSm+XC8+rZgKUNODkX/AcE2ZMnzO9+rlb310dio2z3a7IC8XoWFbTrR4s1a8bx3/PjxyM3NxZgxY5CTkxP1+/r164ehQ4fiww8/xJ///GcAQCgUwty5c/HnP/8ZDRs2BAA0atQInTt3xty5c3HnnXfC9ds+fN26dfj2229x2223lbnvRMWazLxfK/th0Aixbr+18jmwXQUpQqlfrFt0SM9P1h45HKhD+vbpOShWRe1f5H21mKsMt5l1CXV9++RjjuXI07OFzBBKlo990nGnME3Ol+FCva7jiW3/JeUcU/aRjp/JO+XgYim9z0HDecak/Yace1AfZ2ksATl3SMd1U7ldJB/jpOUPAK5Cvb4pX8bSN7Gu4dxqUU3DOiSsh3bAkIlt/RguZVwA8OzXz5lYSjjBCyCUHP38hWoliXXFZWLKZUKfpZwFVO6sZdovSOfGxXPrhnPPFaG8slZZvyQFHP61HQAMHjy4zP2oSLxwR0REVMU4sOK+VabDC3dEREREonizVllz1tSpUzF27FhkZ2ejV69eWLduXcTrWVlZAIAbbrgBc+bMwaZNm9CkSRMAwKBBg/D4449jwIABmDx5MurVq4cnnngC3377Ld55552IdqZMmYIePXpgwIABGDp0KHbu3ImRI0eibdu2uP7668vUdyIiIqJoJSprFSssLMTcuXPRuXPnSnu3AV64IyIiqmKccvjFHS/cEREREcnizVplzVmLFy8GACxduhRLly7VXle//VIgHA4jHA6X/BsAfD4fli9fjhEjRuBvf/sbDh06hI4dO2LJkiU455xzItrp2rUr3nzzTYwdOxYXXXQRkpOT0bt3bzzwwAOV8lZRREREVL0kKmsVW7hwIfbu3Vtpf20H8MIdEREREREREVHCrVq1Kqp6s2fPxuzZs7Xy+vXrY86cOVG10aNHD/To0SOG3hERERFVD8899xxSUlLwl7/8JdFdMeKFOyIioirGUXb8t8qM8/1ERERE1VW8WYs5i4iIiMgs0Vnrrbfeiuv9RwMv3BEREVUxjrIR5oU7IiIiogoRb9ZiziIiIiIyY9YqXfWfQyIiIiIiIiIiIiIiIqIqgL+4S7Bzu9+nlbkb1BXrZrcbrddt3lSsq3xesTxUO1krC9SV6xbWcunvT5Yf/BhKEsr8YlU4HrlcCWuj0rvwWxtKKzPVVW6hrun5laZyvQlYjlzZCkl1De0KHMNW6XiFTgBQSWGtzOUXOgHA69Prul16GQBYMTzjU8ldE7lsubLLpQ+S19C3JE9QK/O55Hl22/Lgu4WF4hhWjEM+faX91Sus9AAOevTyYEBeqCokf3dChfV+WCG5b8oltGHJ7UpVlT6Uv01PLpfqm7a99U/dLr9QSW25ZqRY3n7xWLH8UChyvQiEj94h1UH8D+KNYbdEVGU1m6vnLABQKXIYafHQQ1qZ5Y1+X+14Dftqw/7evUvvR9JOuY3Un/XjZ/LPRWJdy9HrWmF5q7eDhr2Bo5dbIUPdIv14bYxUXv2g4ZYOUAB8hq8Y2kG9jWCKXNkO6WNhm459YaGuMG8AYBXJB0o7YAi6UhtCgArV9Il1i2rp7dpF8vJwHZJn0AoK82IIfNKyVm7DtuAS2jCsAK6gIc/KwywKpepjEUqWw0jYo3fEFpYzAPj2yZ0Wx9mW6zpuaSwMY+yS1k3D+HjksX/7/TFieWX11kdypjr9iql6oTBsYcM+tiLEm7WYs+hY0eJfk+QXhBM/TZ59QKyalF5fLLctfZ9o+szu8grnRX41ZKofhT7skw9EnvwYtmbDiRF3gdC3fDnDmY4Zjkc/zrkK5T5LQyRlQwCwg3pld0Ce52CKfKy1hVlxBwxjUSgd++TpSTk3bDjeW0LeAwBHOn4aTmCEaujrrGM415G0q1ArM2VGFTCcXBGWdShZzpFKCMWOlL8gz55tOr9jyNohv96InWI4d5wulBvWY99efZm6D8jbgl0kn18JpurlUn8B+aKH6XywsLvBO6tHyZUrsdVvjhDLTx76sF4obXpHMcAwa5WOF+6IiIiqGN4qk4iIiKji8PZNRERERBWHWat01X8OiYiIiIiIiIiIiIiIiKoA/uKOiIioinGUZbxNTCxtEBEREZEu3qzFnEVERERkxqxVOl64IyIiqmLCsBGO80fz8b6fiIiIqLqKN2sxZxERERGZMWuVrvrPIRERUTWj1P++nVTWP8Oz00u1YsUKDBo0CK1atUJKSgoaNWqEPn364NNPPz1CfxXOPvtsWJaFW2+9tYxzTURERHR0xJu1ypqziIiIiI4FzFql44U7IiIiitqTTz6JLVu2YNiwYXjzzTcxbdo07Ny5E1lZWVixYoX4nscffxwbN248yj0lIiIiIiIiIiKqenirzAQL1PFqZVaaR6xrxXAlOeyV7/NaVEMvl8oAIOzTyxy5a1DCmiS9/3Ab8oxIbSiXqa5QbhvqCm1YhnYRw+1xjYtDesF0311poRr65vI4YrnHG9LKkpOKxLo+t17XZcvthh39un4oLF/rDzousdxx9Pm2YliRbUNdWxhkt2E+klz6PB8uD+ptWHIbaT59PtJ8AbHuPq9fK9sf0MsAoKBQ3qBCQX08w0XyGEurVlisCShbr2y75XXTlodN3k71oaxWTOvsH9fPWNbteDmw4cT53Zuyvv/xxx9HvXr1Isqys7ORmZmJSZMmoXv37hGvbdmyBXfffTf++c9/on///mXuL1GZmI7rYfkFd55erlymnKRv845wvAAAKySXe/fr5Z5DYlWEhUNGoK4hM4b1vtkhQ74olMv1hArYBfIOX1nC8T4sH42USz6eSdwFpiOazrQLlo6TrqBcWRo34/QC8oHSLYyR8sofuUI19bAc8svjI/XNnS/3wbXPsBIJX0tVPrlv0vQsYTkD8jdBpcwBAC5DuePRyx3D5xlLyJd2kZzhHJfeO8e0TXvl46K0fkv9BQAl5apYHr8RwzpYnYT8wr4wX1gHo98lxC3erBVvTiOqKo6ru18s3xaqrZV5NyeJdYsCKWK5ShWOc4bzPsjTj2f+A3JV6bxWYS15mzUei4SumfKFlCVNmcMKG45n0jHKcEyVpucKyO1KqUM8lgFI3S7vhKXzj64ief6kU0eOWx57794Crcyz3zDGhqzlePS2lZANAMASsrLbMB/uvfliudiHFMPJUSFfuPPlc3nK1vscrCWldTnbmz4HhJLksQj7hPzkkzNqUQ2h3JB93Pl6XXHdhnkbsYXtzDGss1Kudhkyo3KqdwaTMpRvv17mHKzwrvxvWsxapap0cxjtLbiUUnjmmWfQqVMn1KxZE3Xq1ME555yDN954I0E9JyIiOjocWAir+P6cmM4k/s8fL9oBQGpqKlq3bo2tW7dqr914443o0aMH+vXrV6bpUflj1iIiIjqyeLNWWXMWVQ/MWkREREfGrFW6SnfhLtpbcOXk5ODGG29E586dsWDBAsyePRs+nw+9e/fGK6+8ksA5ICIiOrYcOHAA69evR5s2bSLKn332WXz00UeYPn16gnpGEmYtIiIioorDrEVERETxqnS3yoz2FlwzZ85Ely5d8OSTT5bU69GjBxo0aIA5c+bwdlxERFRtFT+MN942AIjPnsvIyBB/WWdyyy23ID8/H6NHjy4p27ZtG+68807cf//9aNiwYVx9pfLFrEVERHRk8WateHMaVW3MWkREREfGrFW6SnfhLtpbcHk8HtSqVSuiXlJSUskfERFRdXU44MT5jLvfQk7fvn2113JycpCbmxtVO/fccw/mzZuHxx57DJ06dSop/+tf/4oOHTpgyJAhcfWTyh+zFhER0ZHFm7WOhZNJZMasRUREdGTMWqWL6cLdxRdfXKaJPPLII2jevHmZ3gv87xZcxd9KAoBhw4bhzjvvxHPPPYf+/fsjEAjggQcewIEDB/D3v/+9zNMiIiI6lixcuBCZmZkRZRkZGVG9d9y4cZgwYQImTpyIW2+9taR8/vz5WLp0KdasWYMDByKfCl9UVIT9+/cjJSUFHo/wZPhjHLMWERERUcVh1iIiIqKqIKYLd6+//joaNGgAn88X9Xt+/PHHqL+1byLdguu2226D3+/HLbfcgsGDBwMA0tPTsXjxYpx55pmltrlz507s2rUroky6XRgREVFl48BCOM4H8RY/yDczM1N7Nl00xo0bh9zcXOTm5mLUqFERr3311VcIhULIysrS3vfMM8/gmWeewauvvir+2u9Yx6xFRESUePFmLSfOnEYVp7pkLeYsIiKqypi1ShfzrTIXLlyIzp07R1U3FArB6/XG3KnfM92Ca9asWRg2bBhuvfVW9OzZE0VFRfjnP/+JPn364JVXXsEFF1xwxHafeOIJjBs3Lq6+xaLD3x+WX2io/yTUcclVHWEolWEJOobysJBNHZ+S2/Do5ZZcFdKvU5XwfgBQbrnccgnlLifqupZtaFfom2Ub2jVs81LbLkMbtlDXNgycJZS7DfPsNkzP6w5pZT6XXnakNiQhR183gy555bSCchtF0Os7jmnHqo9F2FA3JPyUOtafSEvLxO+SZ8Rn6+NZ15sn1q0jfADck5Qi1t1VIJcfDOhtFLjkfWlQmA/HsJyUWxi3kDxudlAudwX0cmXYZ1UXfo+8PSX9YTsrtMNHozsAyvcZd2Uxfvx45ObmYsyYMcjJydFev+6669C1a1etvFu3bujbty+GDRuGtm3blnn61R2zVtk8+39naWU+fw+xbugn+fZTvv16Wdjww1C3R9+GHKEMAFwBuQ1peu4CU9jSi0zTs4T9sitoyCJhQ15z6ceMcLJhXbOlIGjIl14hG3jlW6RYIbkNW5gXd370+2Dp/QDgKtTbsALyMcBy5EylbOFYmyQHc8ejj4VliGruQ3o/3AcL5cqFhnJHyM+G+ZDq2oZlqkLC8jOEajsoT8/x6G2EUg3j5tbbdhXI7XryhPXC0DdXUfQ5GTGcKFDS9gHAEjdqQxvV/LyEtN9zFQnroGHbrQh87kr1Vh2y1tHOWQDw8Y9NtbJaXvkXjD/l6QEqaZdQEYCrUM4BRTX1cQ/75R2lb4/eRtJe0/FeL1cueZs1bcpSsSlTSfsuK2w4JxUynGcScpnpvJ/0ud9E6psyHosM4yllV8Ox1pLyhWGexbEw5S+vPBhSnjVlVFeBnrVcQv4CABQK546kaQGwgoaMKsyLZRq3Qr0fyl1DrhvWx8K0broC8niG/cL5QEMuk84HefJN24I+vXCSfEIpnCKPhZQDTZ+J3IHo1007phxY9STtF9Y3aZYPHL1xYNYqXUwX7jp06ICUFPlEs8S2bXTo0AHJyckxdwww34Jr3759Jd9IevDBB0vKe/bsia5du+Kvf/0rNm/efMS2hw4digEDBkSUbdy4kd/+JyIiOoKpU6di7NixyM7ORq9evbBu3bqI17OystC0aVM0bdpUfH+jRo3Ei3p0GLMWERERUcWpLlmLOYuIiKh6i+nC3WeffRZT47Ztx/yeYke6Bde3336LgoICnHbaadr7Tj31VKxevRp5eXlITU01tl+vXj3xgcFERESVnaPsuB7iW9xGWSxevBgAsHTpUixdulR7XRm+wUjRYdYiIiJKvHizVrw5jSpOdclazFlERFSVMWuVrlLOYWm34GrYsCEAaN/yV0ph3bp1qF27dkzfoCIiIqpKFA7fzzuev7JeXlu1ahWUUsa/I/ZbKUyfPr2MU6byxKxFRERkFm/W4teYiFmLiIjIjFmrdDE/406ya9cuFBQUaOWNGzeOua1obsHVuHFj9O/fHzNmzIDP58OFF16IwsJCzJkzB2vXrsX48eON9wUmIiKq6sLKQjjO+3nH+346upi1iIiIjp54sxZzVtXDrEVERHT0MGuVrswX7g4ePIjhw4fjxRdfRCAQEOuEw9E/JL5YtLfgmjdvHqZPn47nn38eM2fOhMfjwUknnYS5c+fiyiuvjHm6RERERJUJsxYRERFRxWHWIiIiosqqzBfubrvtNrzwwgu44YYb0L59e/h8vnLp0KpVq6Kql5SUhDvvvBN33nlnuUyXiIioqlCI/xl3qnLeLZt+h1mLiIgoMeLNWsxZVQOzFhERUWIwa5WuzBfu3njjDUyePBnDhg0rz/5UC+3ueFgrc5LluiHhluWhZPkurWG/oxd65LqmX4taHr0Nyy20C8Cy9LaVIzeshAnaLkO7hu1Kqu8ytCGV20J/TaR5O1K5y9bLvS75m3duodxtRT/Gpvlw23IbUtuxjIVjWlmE5RR25IXnMvTNJbStYtgpO4h+fQsZ+lYUdonltuXRykzLKcVdqJXVdh8S69bzHtTK0jz6LVcAIMkVFMt32DW0sn2GxVQgrJsht2Ef4tbHyAnJ46YKDeXCBuw2LKeW9+r7wm/HDhfrVmbrLrhPLP/rp9dE/FsZlnNFcJRl3nZjaIMqN2YtmbPjJLF8VyhTKwsclE/Ape6R23YJX7b3/mrIWrawDRkOv+6A/IL3oJ4Z7CLD9NxC1gpGf7x358vHHNchuRxhvW3Hrx87AcDxCscX4ZgDAJbwLEzLMWSf/CKx3A6E9ELDMzaVrfdD6gMAQPpVhRwNoHyGsRDGKOyXP3Ipl75MXQXCvAFwSfNsoFLkDx7SfCvDcoJwezZpLA93TqjrMiz/Ijk/e/L0rKXc8nwUZehjHDKMsW+fkMsN24IdkPumPNGv39IyjeV8hPR+ALANi79Hl4la2dtrRkc/wUrigxfv0MrO6XW/VmYHDRtkBYg3azFnVQ3MWjJT1lpf0EQr+25nhljXv03/HO6Wf9Ro5M4XCg0nlPy79GOcf5ch+xTp+5JQknzewPBRV2zDs18/lh1uXNh3Gfb3puNy2K/3z3QsioV03LGFeQMAW5oPyFlSGW7zasXwC1ZpLJRHzl/hJEPWEvIzDOc1pTzqGNYLu4Zff3+RfLBWHrkNZevzImUOAIB+igiWkNUBwPdLnt6uYV0pqms4WS0Ie+Vx8+/S59u3V94WpM9P0ueIw3Xl8mCqsF6YNgWhyyFhWwIA3z69TMpZQNXMWu+/pGetMy99UKh59J4cx6xVujLv5QOBANq1a1eefSEiIiKi3zBrEREREVUcZi0iIiKqrMp84e7CCy/Ee++9V559ISIioig4sMrljyo3Zi0iIqLEYM46NjBrERERJQazVuliulXm3r17S/7/mDFjcOmll6JGjRq46KKLUKdOHa1+enp6/D0kIiKiCErFf1sA053iKLGYtYiIiBIv3qzFnFV5MWsRERElHrNW6WK6cFe3bl1Yv7tPsVIKd911F+666y6xfjiGexcTERERHeuYtYiIiIgqDrMWERERVQUxXbgbO3ZsRMAhIiKio89RNhzjE5ijb4MqH2YtIiKixIs3azFnVV7MWkRERInHrFW6mC7c5ebmVlA3iIiIKFqOsuK+VWa876eKwaxFRESUePFmLeasyotZi4iIKPGYtUoX04W73xs0aBDuueceNGvWTHvthx9+wLhx4zBz5sy4OldVBWvoZeEkuW4o1dHKVGpIrGv79Fs0uNz6+4/Edun1XXb0bYQd+Wq2imFjsS35JrQetz5/UhkAuCy9zxX5pTmPS++H1yUvJ69Q1y30FwDcwtibxsdUHouQsPxChm8olMcO0IbeZ5cd/XxI7wfkvgUdV/QdA8SHmJqWkzS9JDso1q3lOhRV2eHpyeu3tKxNy/9XW9+5HHJ5xbpBtz5GoSJ53BxLPjw4jj4WprvHuKr5g2L/uEzKYxsl+j1mLdmUvZli+QsbT9PK/Bvl/WGt7+Udl2+ffmx3Fcp1lUvfx1kh+ThiF8qZQboxv3LJ+2XLEdoOx3BMDRTJLwQK5XKPRy8T5hkAHK+eJSxD36ywkH2C8rhZhrG3CoR5kcYHgGVH/01MJRwnVbK8DjleeTmFk/TjpzQ+JnbIsEyFoOv45b6pVJ9cbgtteCrmm6rBVEMuM0QD3x59mXr2BQxt62N8qK48PSnO+g3rpuugvI3YhXrmMy1/KyyMp2GRKrewPIRlBAB2kWFbcKpv9rCDQh427Eqrm4MHD2L8+PH4/PPP8dlnn2H37t3IycmJ6oJT165dsXr1auPrP//8Mxo0aHDEuhdccAGWLl1a5v5XJcxasnWG3PLOntZaWfDnZLFurZ/0bTjt/+TPxaZjkbSPk45lAODOE/bhMTwAyZVnOI64DcfJGD7q2vn68cxJMZwkNJyltYSs5DJlBjFfyvNhC9nVmFtNpw6F6Ym5FYDj0/Ol4zMdU4XzpYb5CCcZlp9Q3RWI/hyo45HbLayfqpVJYwkAIUPfpFxtOq0lrfemHxu5D+n50FUo981zQP4cUFRD366LUg2fA4T87C6QV2TXIfk8miiGbcxVKG8LRSnR51ypDc+v1TdnAYAnX18v3Idiu85AFavMn9Rmz56NXbt2ia/t3r0bc+bMKXOniIiIyEzBghPnn6rmF1arA2YtIiKixIg3a8WTs/bs2YMZM2agsLAQffv2jem9TzzxBD744IOIv+XLl8Pj8SArK6vkol2x5s2ba/UfeeSRMve9qmHWIiIiSoxEZq2qosy/uDuSvXv3wueTv+lJRERE8eGtMolZi4iIqOIk8vZNTZo0wb59+2BZFnbv3o1nn3026ve2bq3/ImrOnDkIBoMYPHiw9prf70dWVlaZ+1qdMWsRERFVHN4qs3QxXbh79913sWrVqpJ/P/vss9otFAoKCrBo0SIxMBIREVH8HMQfUngDhMqJWYuIiCjx4s1a8eQsq5yfAfHcc88hNTUVl19+ebm2W1UxaxERESVeIrNWVRHThbuVK1di3LhxAA6HSdM3v5o0aYLHH388/t4RERERHUOYtYiIiKi8fPfdd3jvvfcwePBgpKbqz2TatGkT0tPT8euvv6JJkyb4y1/+gjFjxsDv9yegt0cHsxYRERFVBTFduBsxYgRuvfVWKKVQr149LFu2DKecckpEHZ/PJwZCIiIiKh+qHG6VqY6B2wpURcxaREREiRdv1irOWRs3btRey8jIQL169crcdiyee+45AMANN9ygvdalSxdcfvnlaNWqFQoKCrBkyRLcf//9WLNmDVauXAnbto9KH482Zi0iIqLEK6+sVZ3FdOHO7/fD7/cjEAjgySefRIMGDVCnTp2K6hsREREJ+Iy76otZi4iIKPHK67krffv21V7LyclBbm5umduOVigUwpw5c9CmTRvxOXYTJkyI+PeFF16Ipk2b4s4778SiRYvQr1+/Cu9jIjBrERERJR6fcVe6Mn2FKikpCcOGDcPevXvLuz9ERERExzxmLSIioqpv4cKF+OqrryL+hg4delSm/eabb2LHjh0YPHhw1O+5+uqrAQDr1q2rqG5VGsxaREREx641a9bgwgsvRO3ateH3+3HiiSdi/Pjxie5WhJh+cfd7zZs3x44dO8qzL1VO5v0Pi+WqptLKwn7DIxNTwlqROykoVnW59TZsS58WYP65qGWoL9cV+mDL8xHLz1NtW+6Dx62Phdellx2pH/EyjY/b0qdn6luSK6TXteW60vKzhWmZ6pqEHJdY7sTwoPOQo1/XD6nYrvWL61AMjw81dVda34JhwzzHsG6G3NHPn8+Wt9N0d55WZppn0zKV+mxcpjHM3yF49ULD24OGdh2hy46hb9WdC5GDYSP6bTReDiw4poUXQxtUuTFrAc3m3aeVqfA5Yt2kTfo+rs5/5eNv8s8BsdwuEuo7huzj0vd9lqGuFZT7obzRR3GpDSsgH4tgC9t3UM8nRywP6/NieeT+2sJ8KI/crCu/SC9Uhv2n4bCsfPr0rFAM2VCYN0BeHo5HPsYplyFrm+ZFakOYP8djmGm3Pj3HZ8gGhr6JDHWlvoW9ct+KUvU2CmvL7YaFKAIAyTWTtLLa/5XXTe9+fb0vSJf75gjjZoXlZWQVydOTlqm4rwDgCuj9kPoAAEooN8Y6aZuGeZ9THdhBYR8Uy3Yep3izVvF7MzMz0aZNm/LqVkyee+45eL1eXHPNNTG/t7reJvOPmLWAbivu0MqU6i/W3by5vlaW/o28rtT4qVArMx3j7CJDfpK2ecNxy84XppdsOOgIzdpBIZ8AgGFbcPxC0DHtkwN63+ygnOFUzRS5GwHhGGXIHEroszLlGa+QZw37WqtA7rN0nBTHB3J2CSXLfbNDwtjH+JFbOtaGk+Rl6hLWw6KacvZVwnHZHYjtGBUS+lFUQ16/gylCDjR8jLBD+ni6C+SBq/2NnGc8eXp5QR15OVnSSSIDKYOZtn9XkbycvL/q9UN+U07Sp2c6tWqHhLoxzFtV5CrUl7OUvypKeWWtsnjhhRdwzTXX4LLLLsM///lPpKamYtOmTdi+fXuZ26wIZb5wN2zYMEyePBk9e/ZEzZo1y7NPREREdAR8xt2xgVmLiIgoMar6c1d27NiBN998E/3794/pNpBz5swBAPHWmtURsxYREVFiJCprbdu2DTfeeCNuuukmPPHEEyXl3bp1K3NfKkqZL9x9/fXX2L17N5o2bYru3bvjuOOOg/W7n8dYloVp06aVSyeJiIiIjjXMWkRERMemJUuWID8/HwcPHgQA/Oc//8H8+fMBHH4WXXJyMm644QbMmTMHmzZtQpMmTSLeP2fOHIRCIeNtMt977z1MnDgR/fr1Q/PmzREIBLBkyRLMmDED3bt3x0UXXVSxM1hJMGsREREdW5599lnk5+fjH//4R6K7UqoyX7ibPn16yf9/5ZVXtNcZcIiIiCpGvA/xLW6DKjdmLSIiosSIN2vFm7Nuvvlm/PDDDyX/fvnll/Hyyy8DADZv3oymTZsiHA4jHA5DCbepmzlzJpo2bYrzzjtPbP+4446Dy+XC+PHjsXv3bliWhRNPPBH33nsv7rjjjmPmVpnMWkRERImRqKz17rvvIj09Hd988w369OmDr776Cunp6ejfvz/uv//+SvUL/DJfuHOq8f30iYiIKjNHxX9CqJrfrr1aYNYiIiJKjHizVrw5a8uWLaXWmT17NmbPni2+9u233x7xvZmZmXjjjTfK0LPqhVmLiIgoMcora23cuFF7LSMjA/Xq1RPft23bNhw6dAgDBgzA3XffjUceeQQff/wxcnJy8NVXX+G9996L+PV9IpX5wh0RERERERERERERERHR0da3b1+tLCcnB7m5uWJ9x3EQCASQk5ODkSNHAgC6du0Kr9eL2267DcuXLzfeseBoi/vC3fLly7F8+XLs2bMHdevWxbnnnovu3buXR9+IiIhIoBD/rTIVKsc3iKh0zFpERERHV7xZizmramHWIiIiOrrKK2stXLgQmZmZEa9lZGQY31enTh189913uOCCCyLKe/bsidtuuw3r16+v+hfuioqKcMkll+DNN9+EUgputxuhUAiTJ09Gr169sGDBAng8nvLsa6Wj3PL9L8I+odwfFuu6fCG9zC3frsG24r+vmYppg4h+epbQN9OvSt0uw1hY+nxL7R6pXCKNm22YN5ctj71X6HOSS192AOC19bpeW64byzK1hfEBgJDjirouoNc17SRjWVdM4xbL/EnLNKbxiXGZSssv2VUk1vW7glpZil0ot2vpdb2WvPzruPLE8gMev1a215Mi1v016NPKAiF53+tx6+um8EiMw+VeefkHHb1c+QzrkLButpj6kFh30x23yx2pxB47ZV7Ev7/2fY0X8eZRmbZSVoz7c7kNqtyYtQDXtiStLGW7XLfGVn0f598RkNvNl/f3CAsZxaXvywAAtr4DVaZnAhn2qY5HOC575TaUVzjeew1RPiRkKrfcrhWSj1HSfd6skJzh7KBwfDHcfswq1I+TMMYWw35KCJmOL/ptwTL0TSVF/9HIVSCPm5L6HMM981yF8hgjLCwPQ7u2bRg3oTzsk9fvcLK+vhTWktsNpOvlgQy5b45X7poV1ttITte3fwDw7te33+TdhnETuA3bv/WrnMvg1sfISpX7Ji0/O2jY/oVt3TGt8wbSPufcbveJdZevvDumthNN6u/XX3+Ntm2PzvPG4s1azFlVA7MWsGV7Xa3M/kX/jAkAtbbp63XNH+XjoWdvgVZmCccywJANIO/jlCH7KCFTmU9KCZ9pDRFObNfEND2X0HjYkJMO6uMGAKqGfo7AlDulPlumD/5CP5QhM6oa8nrhCBnVMeSLULLQt5DcN2k3ahvqGucP+rwY10OhbTto6JvwOUDKagCgPIbzJUJxyC/XLRIeueUYYqsSyj2/GtqtLQcz7x79fFeN7fJ6ERY+50jbPwBYh/R2PcLnCACwwvL65hLWb1dA7pudKm0LYlW48/R+SOs2AJzf+V6x/K2PxsqNV1LLV1SPrJWZmYk2bdpE/b727dtj3bp1QnuHV47K9JzfMvfk3nvvxbJlyzB58mT88ssvKCoqwi+//IIpU6Zg2bJluPdeeSUmIiKi+DiwyuWPKjdmLSIiosRgzjo2MGsRERElRqKy1iWXXAIAWLJkSUT5m28e/iJ+VlZWfDNWjsr8i7sXX3wRo0aNwl133VVSlpGRgTvvvBN5eXn45z//ifHjx5dLJ4mIiIiONcxaRERERBWHWYuIiOjYcv755+Oiiy7CvffeC8dxkJWVhU8++QTjxo1D79690aVLl0R3sUSZf3H3008/4ayzzhJfO+uss7Bt27Yyd4qIiIjMlDp8L/B4/ngLp8qPWYuIiCgx4s1azFlVA7MWERFRYiQya7300ku47bbbMGPGDPTs2RNPPvkkhg8fjvnz55fjHMavzBfuMjIysGHDBvG1DRs2HPEhgERERFR2xfcCj/ePKjdmLSIiosRgzjo2MGsRERElRiKzlt/vx+TJk/Hjjz8iGAzihx9+wKRJk+Dzyc9WTJQyX7i7+OKLMXbsWLzyyisR5YsWLUJubi769OkTd+eIiIiIjlXMWkREREQVh1mLiIiIKqsyP+Nu4sSJWLt2LQYMGICUlBQ0aNAAv/zyC/Ly8tCuXTtMnDixPPtJREREv3EU4MT5TW5HlVNnqMIwaxERESVGvFmLOatqYNYiIiJKDGat0pX5wl3t2rXx0UcfYfbs2Vi5ciX27NmDU045Beeeey6uvfbaSvfTwoqw6fbbxfKmTz+oF8awHpqqWlb0a6Tp56KxtCG/P/q6tmFaLlsuj6XtWKbnscNamdellwGAV6hrKk9yBcW6bqGux3LEuhLHsAaEY9iZOUr+MW3I0ctNO0lpXfHZIbGuaeylcrdhLKRlYloe0hjbiL4PgDwvaZ5DYt36nl+1smS7SKzrtfR2PZY8H8l2oVheww5oZSkuuW6SS5ieYf0OC8tfGXYJjlAXAMJuffmF3XJd5RIad3jboPJQHrdg4i2cKj9mLWDjiOFaWZdLhJwFwP+zvu90HSwQ61ohw3E5rJcrj6Fzwr7PtFUpw37SSXJpZSG/XmbiCsh1XYXCcVIeClher/xCUMg5hoOGFdCPicY8my8czxz5uAXDOq6ShIXikqcojb3jluc57NPH0yqHT4R2yJCJ8/SxsA7Jx3sU6WNs10gWqyqfvNKGU/T5DiXL62agtl4eSJfHuKiWXub4DLmsUG5DjJjGZaqXW4Yxdrz6fIST5I/AdnpNsdwK6uun8hhuXCNtIzGsQnF+VAMA2ML2T7GLN2sxZ1UNzFrA5qvv1so63PawWDdtk76z9v+of1YGACsg5AjHkL/chuwjHMOVR64rlUs5CzDkMsPx3hWQz4FIWdIqkuuKwoZ9tUs+vojj6ZXnzy4U6goZFwCcGkl6mU8+TprybLCmnjuKUk3nCPR9o/uQ3DdXoVAeNmRRQw7wSlnLsJxsoa5dN1WsK41FYW05f+U3kMcz5I+uDAAcoQnHEOGl04GGU4RwhEwFQAzypsOa1EY4We6cS9gHKMPJYLvIsI0I9U15XSo3ZTgpM9pCBgSAcHKZL6fQ7zBrlS6uNc3n8+Gmm27CTTfdVF79ISIiIqLfMGsRERERVRxmLSIiIqqMeImYiIioilGw4r5Vporlp+BEREREx5B4sxZzFhEREZEZs1bpDD+WLV0wGMSECRPQunVrpKSkwOVyRfy53bwmSEREVBGUKp8/qtyYtYiIiBKDOevYwKxFRESUGMxapStzCrn77rvx8MMPo2fPnujbt+8xce9vIiIioqOFWYuIiIio4jBrERERUWVV5gt3//73vzF27Fjk5OSUZ3+IiIioFA4sOHHeFiDe91PFY9YiIiJKjHizFnNW1cCsRURElBjMWqUr860y9+3bh7PPPrs8+0JERERRUMoql7+yWLFiBQYNGoRWrVohJSUFjRo1Qp8+ffDpp5+W1AmHw3jooYeQnZ2N448/HsnJyfjTn/6EkSNHYv/+/eU0CtUfsxYREVFiJCpn0dHFrEVERJQYzFqlK/Mv7s4++2x8/vnn6NatW3n2p3oICSuOE/3KZFnyTVpddvQ3b7UsJ+q6pnvCWkKXbUPf5PfHdrNZqR+xbIQ25Om5bX0svHZYrJvqKRTL/a6g0EZIrOsy9EMSFr4dEHJcYt2gksulbxiElHxNXnrop2mZJrn1+TONW5JLHotktz6eNd0Bsa5UXsMl17WF9TuWcTe1kWLLyz/ZLoq6rmk9lHgtw7gJ05PWQUBeJj7D8gg7+noRNmxjQWG7AQDbpZc7LnmelVsvN+2aWkx9SCy3wnr/No4YLjdyDFEqvof4FrdRFk8++ST27NmDYcOGoXXr1ti1axemTp2KrKwsLFu2DN27d0dBQQFyc3NxxRVXYPDgwahbty7Wr1+PCRMmYPHixfjkk0/g9/vj6v+xgFlL5jkoH4tc+fp+2QrI+07Yhu+uSeHHZdhWpOAivR+A45GnF/Lrx/aQP/q+OV7D8b5Ar+s2NRuW9+HWIaEwJI+95USfO1FQEH1dr1csVtLyM4y9cutjHBbGHQAcYVlbMWT4w/XFQBt9A0J/AQCuJGFict+soLyc3Af0XOU1rEOhJI9W5smTp+fSYwuwS243aY88Fqk/6X3z7MkX6yqf3rdgDb0MAIIpQvbxyLfCC6XIH409eXquMm17SlgmlmH5O+7o1y2pXRPT9tj93MlaWVGaPM9rFtwZ9fSqq3iz1rFwMqk6YNaS+fYZzkkFhOOLKQMI5covH9fLg+PX92dFaYZjQ7K+Dzd9TnUH5DY8B/SM6TLsq12F8udzUUA+zwBL6rOh04XCgdlQVzyaKfk46RiWn7S7U7HkZwMlHCcd4fwAAFimsS/Qp2cXyJ8PnBp61iqqJc+zq1Afz+RtUngG7HCyWB5IEzKfadiE8pBfruz7Ve+b/xd5nj175VyufEJ+9snZJ1BbKFfyZ313QF+3DKcvkbRL3hbCSYasLDDlNYmY1wzrlR2S1+MzB0zVygpryW188uztUfetumLWKl2ZL9w9+uij6NOnD5o0aYLevXvDa/hgTURERNXH448/jnr16kWUZWdnIzMzE5MmTUL37t3h9/uxefNm1KlTp6RO165d0bhxYwwYMAALFizA1VdffbS7XuUwaxERERFVHGYtIiIiqqzKfOGuY8eOCAaDGDBgACzLQnJy5DcILMvCgQMH4u4gERERRVIqth9wmNooiz9etAOA1NRUtG7dGlu3bgUAuFyuiIt2xTp37gwAJfXoyJi1iIiIEiPerBVvTqOjg1mLiIgoMZi1SlfmC3eXXHKJ8afIREREVHEU4r8tQHlmnAMHDmD9+vXo3r37EeutWLECANCmTZtynHr1xaxFRESUGPFmrWPgXFK1wKxFRESUGMxapSvzhbvZs2eXYzeIiIgoETZu3KiVZWRkiL+sM7nllluQn5+P0aNHG+ts27YNI0eOxKmnnorevXuXqa/HGmYtIiIioorDrEVERESVVfRPaYxDOByGy+XC+vXrj8bkiIiIqjWlrHL5A4C+ffuibdu2EX9PPPFE1H255557MG/ePDz88MPo1KmTWGfv3r248MILoZTCSy+9BNs+KvHjmMKsRUREVH7KK2dR9cGsRUREVH6YtUpX5l/cxUodCzceJSIiOgocZcGJM6QUv3/hwoXIzMyMeC0jIyOqNsaNG4cJEyZg4sSJuPXWW8U6+/btQ48ePbBt2zasWLECzZs3j6vfZMasRUREVD7izVrx5jSqnJi1iIiIygezVumO2oW7Y4kd0leccFhemSxLD35ulyPWdQnltvB+AAg78vSkq9FhR/7lg9S21F8TU91Y2oiFy5bHzWuHtbIkV0is63cFDW3o9T2W3i4AOKpifkliW/L8OY5LKwsZlqkDffm7De0mu/WxSPUExLppngKxvK4nTy9zHxTrprv1ujVtuV1buJOxNG9HEhZ+cOyCPBbS9Excwni6TO83rENJtj72UhkAJAnrrNv2yX0TthGXYXs07YfCbr3c8Rq2BWE/ZNo+bOMBt/J+OH74v+dH/HvHJn0drgoyMzPL9My5cePGITc3F7m5uRg1apRYZ9++fTjvvPOwefNmLF++HO3bt4+3u0RQLsP+QnhGjUqW94eOzyO3IbStXIbjunTyzvCcHFMbVlhvww7J+z2lH+5hihzKo7/geIQGACi3oW+OcBwoLJQnKNU1UEH9uGW55L4ZCcvJ8cpthJL1jzuh5OizmitgmDfDaigezgzrhePV+2aH5WOqJawXyrBMTdODsL559svLtOaBImF68rhJ26TUXwBw78sXy50ft+ntGubDPqGhXpjhl9sVPu2GfHK7YZ/80djx6vNtOYbtVGjaLjJkGVvY35jWK9N+T2JYZe2Q/oK0D6oszs+6VyvLO7QzAT0hOvYYThHACgqfBVOT5LpJ3ugnaMoRQrFlOk6G9WOi6VgkzZ90vACAUJIhJwWFNxiOW5bSj1H2ftM8y+dAkC8cPw13MRGzlmXIPl59OdmHDLklSc7PsZziE06tmQnt2kF5YqFUORO5CvXlZBk+B4ST9XLlkceiyK+36xjqmtbD1J+EDGY43NuF+npvhQ3nrw7pGc76ebdYV4XkBaJObKyVhU35SdjUi2rIdQvT9HFzF8jj4yqU9yGFafqyNq0X0jbpNmR7V5GQk4TsBAB2oTxuSbuF7UwZPndWAuedM0kry8v/JQE9IZNKd6+qFStWYNCgQWjVqhVSUlLQqFEj9OnTB59++qlWNxgM4qGHHkK7du3g9/uRlpaGM844A++//34Cek5ERHSUqMPXDOL5i+ea6Pjx45Gbm4sxY8YgJydHrFN80e7777/HW2+9hZNPPrnsE6RyxaxFRERUigTmLKr6mLWIiIhKwaxVqkr3i7snn3wSe/bswbBhw9C6dWvs2rULU6dORVZWFpYtW4bu3bsDOHx/8X79+mHNmjUYMWIEzjjjDOTn5+PTTz9FvvRNFCIiomqiPO7nXdb3T506FWPHjkV2djZ69eqFdevWRbyelZWFgoICXHDBBfjss8/wyCOPIBQKRdTLyMhAixYt4uo/lR2zFhER0ZHFm7WOheeukBmzFhER0ZExa5Wu0l24e/zxx1GvXr2IsuzsbGRmZmLSpEklAeexxx7DkiVLsHbtWmRlZZXU7dWr11HtLxER0bFk8eLFAIClS5di6dKl2utKKfzyyy/4+OOPAQDDhg3T6gwcOBCzZ8+u0H6SGbMWERERUcVh1iIiIqJ4VboLd38MNwCQmpqK1q1bY+vWrSVl06ZNw9lnnx0RboiIiI4FifzF3apVq0qt07RpUyjp+V9UKTBrERERHRm/BU7xYNYiIiI6Mmat0lW6Z9xJDhw4gPXr16NNmzYAgK1bt2LLli1o164dRo0ahfr168PtdqNNmzaYM2dOgntLRERUsVQ5/REVY9YiIiL6H+YsKm/MWkRERP/DrFW6o/KLO9u2MXDgQNStW7dM77/llluQn5+P0aNHAwC2bdsGAJgzZw6OP/54TJ8+HbVq1cIzzzyD6667DkVFRRgyZMgR29y5cyd27doVUbZx48Yy9Y+IiOhoUiiHX9yh+n876VjCrEVERFR+4s1azFnVT2XLWsxZRERUlTFrla7MF+62b9+OgwcPomXLlgAOP1R36tSpWL9+Pc4//3wMGjSopK5lWZg1a1aZpnPPPfdg3rx5eOyxx9CpUycAgOM4AIBAIIA333wTTZo0AQD06NEDp556Ku69995STyY98cQTGDduXJn6VBorHH1d29avD3vccgNeV/QNBy35x5RhRyp3xLqxbDyWpc+HFeP2I9WX2gUAt6X32W3L8yGNm9cOGdqVx9gTw0K1hb4ZKX15OIb3uww7JFsYI9NYSLy2PG+1vAVaWbpHfkB2XU+eWF7fc0Ary3D/KtatY+ttJ1nycpIElLw7CxvGLSyMvYkj/Dg5lnZdhvXH1IdwnBdkbMP3TqRyj2G/YuqDtF8w7StCQjccw2HHtMZatt52s8eminU3/+0OQysV44/buhXLtk8UBWYtmeOR9zlOkr5/UW6XWDfsk8sdb/THBnEXbvjanyHOQNp92kG5snT3WcdVDh9WwobOFRbpfTikZwMAUMGgVma55DEWmeqawmQMt+K1pLqxhFRTXUfugyWUO4b1TXmFcjlqidNzfB65XbchMxbpx3zLsPztgL78rf36cgYAlST0w7SMAoViseWO4WNpWPgcEJDzjKtIH2OVZNiHyMOJkF+qH/065DbuAITPT4YoYYqt0rolRCfj9Ez7m84DH9LKPppzu6HhiqFsfaaVceaIyqYqZ62KPKdlSx/kIOeZsJC/TEzHJ8swvbBP2PkZjst2SN+BmvapriJheobPtKZjgxLyqBOWd9aqHPKac0A4j2I47ycdCKwaqXJdj5Cfhf0vALjy9WxwuAnhvJZpWUv5yXCYdAWEZWrIF7a0TAE4Qt/chjak+fMYjjvBmvqKEU6Sx83xyOXSOus+II+x2G6SIT87Xq3IleQTq1oBuQm7SD8X5y6QN6ikffr8ydkJcITdhWkbC6TL8xdM1qfnKYg+a9mhGM51Bw3nyw3biJi1lTyDHW99WCv7fPrwqPtWHoKp+gIJqRg+w1GFK/OtMm+66SY8+uijJf8eP348Ro4cibfeegtDhgzB3Llz4+7cuHHjMGHCBEycOBG33nprSXmdOnUAAK1atSoJN8DhIHXBBRfgp59+ws6dO4/Y9tChQ/HVV19F/C1cuDDuPhMREVU43ivzmMCsRURElCAJzFkHDx7EiBEjcP755yMjIwOWZSE3Nzeq986ePRuWZYl/O3bs0Oq/8847OP3005GcnIy6deviuuuuK/X4Xp1U5azFnEVERFUaz2mVqswX7tavX49u3bqV/PuZZ57B8OHDsXfvXtx44414/PHH4+rYuHHjkJubi9zcXIwaNSritRYtWiA5OVl8n/rtarptuPpdrF69emjTpk3EX2ZmZlx9JiIiOhqUQsmDfMv+l+i5oNIwaxERESVG/Fmr7NPes2cPZsyYgcLCQvTt27dMbcyaNQsffPBBxF/xhaJiq1evRs+ePVG/fn0sWrQI06ZNwzvvvINzzz0XhYXyr2Orm6qctZiziIioKktk1qoqynzhbs+ePWjQoAEA4L///S9+/vlnXHfddQCASy65BN9++22ZOzV+/Hjk5uZizJgxyMnJ0V53u93o06cP/vvf/2LLli0l5UopLF26FC1atCjzfceJiIiIKgNmLSIiomNPkyZNsG/fPqxevRr33Xdfmdpo27YtsrKyIv48nsjbdd1111046aSTMH/+fPTo0QNXXXUV/v3vf+Orr77CzJkzy2NWKj1mLSIiIqqsyvyMu1q1apX8bP/dd99Feno62rVrB+DwT/uLiqK/L+/vTZ06FWPHjkV2djZ69eqFdevWRbyelZUF4HAIWrJkCbKzs5Gbm4uaNWvi2WefxRdffIF///vfZZ0tIiKiyk/F9IgnYxtUuTFrERERJUi8WSuO91qxPiy+DLZt24aPP/4Y9913H9y/e8bkGWecgZNOOgmvvvoqbr755grvR6IxaxERESVIArNWVVHmC3edO3fGlClT4PF4MG3aNJx//vklr33//fdo2LBhmdpdvHgxAGDp0qVYunSp9nrxLQNatGiB9957DyNHjsSNN96IYDCIjh074rXXXkPv3r3LNG0iIqKqoPjWAPG2QZUbsxYREVFixJu1Ep2zevfujV27dqFWrVro2rUr7r33XrRt27bk9a+++goA0L59e+297du3x9q1a49aXxOJWYuIiCgxqnrWOhrKfOFu/Pjx6NGjB/r06YPatWtj9OjRJa8tXLgQnTt3LlO7q1atirpu27Zt8frrr5dpOkRERESVGbMWERFR1bZx40atLCMjA/Xq1auQ6TVo0ACjR49GVlYWatasiQ0bNmDy5MnIysrC2rVr0aFDBwCHbxEJAOnp6Vob6enpJa9Xd8xaREREVFmV+cJdx44d8cMPP+Cbb75BZmYmatasWfLa0KFDceKJJ5ZLB6siJTw50HI7Yl23O6yV+dwhsa7Hpdd1DFeXLUv+vWhI+B1pEC6xbry/OXXZ8jy7DH2T6kvzbCpPcsnj5rX1cp+hrseS+yyxDfMhLRNTXUCY5xinF47hGwaOrdf12vIY13AHtLLannyxbl33QbE8zXVIL7P1MgBItoN634TxAYAi4fGctmF9jWV8TMLQ2whLGzoAR6gbUPKutshQ7gjzZ5qexLSuSNuY1F8A8Bq2PYlpPyQtkqAj13WMm54w34bNqenTD2plW26609Rw3Fx/WD9dR/N3+so6/BdvG1SpMWvJ3Pny/sk+WKiVqWSvWNfxyvtUK6xvx47PsL9369uQabOyHHn/YAeFcsOuRIoopmOfFdQr20HDuBXqx18AQEAfT4TlNixbGCOXKV8KPIb04zIc+6TpGW4tp6Rywz1ZlJCTTMzLVB975TasbyF9PK1C+bZsKtWvlQVryuPmKjQcVB3hs4RPziK2cGC2CuS+ieVhQx+EeT48weiXqdSGXSi36yoUtmmXIYsYVkNpW7dDhm1P+IhhPNwK82eZ1k2PvA6FhTaU0F9A3r8Z9zdCebvbHxbrbnhouNxInKyQsA5K81BR4s1av723b9++2ks5OTnIzc0te9tHkJ2djezs7JJ/n3322ejVqxfatWuHsWPHYtGiRRH1TbflPBq366wMmLVkvv2GfeohPTM4Xvl4Lx0nHdO+zC8fi0LJetumj8XuAqEPhn2GVG4ZYosdMmwjwnFAygAAYEnHKMPx3sTy6plWmT9E60z3oxOynWXIX6bjp7S/dgWi75tpOdlFQt9M+cvUNyGPWIbsG6xfQys71NAn1pUOD948eZ7D8scRcf125ctjbxfofTadIbLzhQwfNOR9t2H7FbKdJ0/eLxTW1HsSw+krCKdsAQChJMMyFdYB42cwKfoY6oZ9wv7GsF6Jny8g57hYPl+cNEHOWv83pmKylrSdStm5wpRT1qrOynzhDgCSk5NxyimnaOW9evWKp1kiIiI6AoX4n3F3DNwOvFpg1iIiIjr64s1axW9duHAhMjMzI17LyMgoe8Nl0LRpU3Tp0iXiOWt16tQBAPGXdXv37hV/iVddMWsREREdfeWVtaqzGK6B63bt2oW7774bp59+Ok466SR8/fXXAICnn34an332Wbl0kIiIiOhYxaxFRERUdWVmZqJNmzYRfxV1m8wjUUrB/t0vW4ufd7dhwwat7oYNGyKeh1fdMWsRERFRZVTmC3ebN29G+/bt8eijj8KyLGzatAmFhYd/jvvll1/i0UcfLbdOEhER0e+ocvqjSo1Zi4iIKEGqUc7avHkz1q5di6ysrJKyRo0aoXPnzpg7dy7Cv7tV3rp16/Dtt9+if//+iejqUcesRURElCDVKGtVlDLfKnPEiBGoXbs2Pv30U9SrVw/e391vuUuXLsjJySmXDhIREVEkpSyoOO/nHe/7qeIxaxERESVGvFkr3py1ZMkS5Ofn4+DBw88S/89//oP58+cDAC688EIkJyfjhhtuwJw5c7Bp0yY0adIEAHDeeefh7LPPRvv27VGzZk1s2LAB999/PyzLwvjx4yOmMWXKFPTo0QMDBgzA0KFDsXPnTowcORJt27bF9ddfH1f/qwpmLSIiosRIdNaqCsp84W758uV48skn0bBhw4hvaAHAcccdh+3bt8fdOSIiIhKUx7eLjoFvJ1V1zFpEREQJEm/WijNn3Xzzzfjhhx9K/v3yyy/j5ZdfBnD4V2JNmzZFOBxGOByG+t0DYtq1a4eXXnoJDz74IAoKClCvXj10794d99xzD0466aSIaXTt2hVvvvkmxo4di4suugjJycno3bs3HnjgAfh8vvhmoIpg1iIiIkqQBGetqqDMF+4CgYDxgcX5+fkR908nIiIiotgwaxERER2btmzZUmqd2bNnY/bs2RFlDz/8cEzT6dGjB3r06BHTe6oTZi0iIqJjy6pVq9CtWzfxtQ8++CDi1uKJVuYLdy1btsQ777wjhrx33333mHiY8UkT5VCsauqXfG1vWKgJJHlDWpnPpZcBgMt2tDLH8LNQ24r+srPLluua2o52epahD9J8AIDb0ss9LsO4CWMklQGA19bbcFtyu7bQh8Pl0Y+nxzB/EpfYrvz+oCN/aHDs+H4anOoqEstruQu0smRbrptkB8XyZKtQK/MYxtgrzHcY8rxJ62ZQyeMTVPJuTmo7bGjDEeoWGdqNRVC5YiqXSOus27SNxbBumrZ/l7Aeug3bqeMIY+yR+6CEuoDhCzSmfZOwL2v6/GSx6pZrRsptxCD8h8fEmtbXisBbZR4bmLWAMy99UCvzh6Lfl1lFhkyVZ8hPHn0f54TkfXLIr5c7Hrldw6FP5jLsD4VDlKldV5H+gh2Q99Uoko/hKizUt0z7X71zlstwLJNyy+9uTRbRB7fhhKmjz58VMmS7In16ttuQL4RuWI6cAU3jaRfK4ym3IayfHjlfBNP9elkNQxYx5dygPm6G6AM7T89w2P+rXFlswLAeS+uVgWUYC7ijz0niNmJYjY2HRWEVcAUMn3MCwrppWIeUNEYxHpodr/4GZbjQIO0XXIXyTsRVKHyeNWS1VjnyZ+Jvxg0Xy6PlygvofSgU1ssKwts3HRuYtYAzLp+qlXmV4RyRV9//uvLlcwRKyDOukLx/EveHAGyfXl9q9/AL0bcr7WuF00aHGXKnu0B/g+uQnAHsfH1/hiJ53Izc+jHRjiVrmS5CC8coZch7VtiUifT5dgmZ+nAjettWWB5jaXrGXFZkyD4Fet+UzyPWDdbUy4tSor94bzgtJh5TAcC3Sz+muXfslxuR1hfDdiou69QUsaryGTK4Xx+LULLpM5GwTA2fUdxCfjLV9RwyLGvp451hLFxFQp4JGsZNaCOcZNjGDOuhtHOR8hcAWIZzvJJm0/X99OZb74j6/SbePYe0sqKAfi64oiQ6a02aNEm7gFfZjvtlPvM8ZMgQ3H777WjYsCGuuuoqAEBRURHmz5+PJ554AtOnTy+3ThIREdHv8FaZxwRmLSIiogTh7ZuOCcxaRERECZLgrHXiiSdWql/XScp84W7o0KH4/PPPMXz4cNxxx+GrvF26dIFSCkOGDMHAgQPLrZNERERExxpmLSIiIqKKw6xFRERElVVc93qbMWMGBg0ahNdffx07d+5E3bp10bt3b5xxxhnl1T8iIiLSWIj5flpiG1TZMWsRERElQrxZizmrqmDWIiIiSoTEZq1bbrkFf/nLX5CcnIzTTz8d99xzD7p06RJXm+Ut7oc0ZWVlVfqfFRIREVU7vAXTMYNZi4iIKAGYtY4ZzFpEREQJUA5Za+PGjVpZRkYG6tWrJ9avVasWhg0bhq5du6JOnTrYuHEjHnjgAXTt2hVvvPEGLrjggvg7VU7ivnC3bNkyrFq1Crt378Y999yDxo0b4+OPP0bTpk2RkZFRHn0kIiIiOmYxaxERERFVHGYtIiKiqqlv375aWU5ODnJzc8X6J598Mk4++eSSf5911lno168f2rVrhxEjRlSPC3eHDh1Cnz59sHz5cljW4Z8m3nzzzWjcuDEefPBBnHDCCXjwwQfLraNERET0m3gf4lvcBlVqzFpEREQJEm/WYs6qEpi1iIiIEqScstbChQuRmZkZ8VKsX7pJS0tD79698dRTT6GgoAB+vz+OjpWfMl+4Gz16ND755BMsWLAAPXr0QM2aNUteO//88/HYY4+VSwcrgzYjHxbLVS25vvI5Wpk3KSTW9XuCWpnHFRbrum29XUfJ93N1lF4XAOwYtgjLiu/Thm14vzQfAOC2hHEzjIXX1suTXPpYAoDP1sfeI0wLMPfZFedYmNgQlins2NoQ+maeP73c7yoS6/psfTyTLHmMXcJ8mJjq2sKqHDYMe1i4j3FYyeNWpFxiuSO1EcPYm5ZT2LBNSoJK3gUXCeWmbV1a1tL2AQAhO/r5CzvycgoL26/bJfdNKb0fpvlQYdO+TCg3rW4uYSz88jp7+lsjtbKGqb+KdRsmHRDLT0jyRPxbWm4VRgGIYV0ztkGV2rGUtU6+Rc5ayUKZadV3UnxamRWU94emcrtIzwx2kXwcsUL6Nq9chv2sqc9uvX44ybA/FPa1plynLKEN0yHAZZg/r0cvjGU/5zbU9QjlPmFaAGA4bimP3mfHa5gPYYhsQ8BQjj5uroC8rrjyAmI5AnquMg29EpZ/sH4NsW5BXa9WFvLL64oVlqcorcvKLbfhqql/WLULDPNcWChMTO6DZcoiScLyc8vLFML6bRcZtnVHX9aOYdUM++Sx8B7U84Vvv5wvLGHdsoJycLGU0DePPD6hZLnTYZ9e37S+WQX69FwF8mdU7wF9LIIp8nbqSCEeQOtR+n7dkhcTUrfrfUtL1td5ZRn2FRUh3qzFnFUlHEtZq+1dhqwlHEtchuxjB4UVW8ocAKywvu8T8wkAd558TkJiylriPlU4zgLyx0nT+S9pvw4AtpAP7IC8T0VQOGYI4wPAeOwTc5nHsE8UxsJ4TBUymBL2vwDgCPkLAOxCff5Mp9CUsHM0HsND0Z9nMtUN10jSygJCpgKAYIpwTDUsf9PxTFJYy5DLQvpnl9TdhuVUKPQjNUWsqoRMrAy5XPkM0xPYIcM53oCQfQyLTjg9C0+e4Rz4oejzkzJkEVvIYGHDPCshg4W9hv2b8JkBANz5wn4h33A9IEmfnmn/Fjyol5uuVbiEWA4AtbbofUuRPhMbcmuFKKeslZmZiTZt2sTfnd/WLctwrEqEMp9lfPnllzF+/Hj069cP4XDkgm7cuDF+/PHHuDtHREREdKxi1iIiIiKqOMxaREREtG/fPrz++uvo2LEjkpL0C/6JUuYLd7t27TJezbRtGwUFBWXuFBEREZkpJX+RMtY2qHJj1iIiIkqMeLMWc1bVwKxFRESUGInKWldeeSUaN26MU089FXXr1sV3332HqVOn4pdffsHs2bPL3qEKENs9+X6nUaNG2LBhg/jal19+iWbNmpW5U0RERHQEqpz+qFJj1iIiIkoQ5qxjArMWERFRgiQoa7Vv3x7Lli3D4MGDcd5552H06NFo3bo13n//fZx33nnxzVM5K/OFu/79+2PixIn47LPPSsosy8IPP/yAhx9+GAMGDCiXDhIREdEfKKt8/qhSY9YiIiJKEOasYwKzFhERUYIkKGuNHDkSn332Gfbv349QKISdO3filVdewWmnnVbOMxi/Ml+4y8nJQcOGDdG5c2eceuqpsCwL119/Pdq2bYt69eph5MiR5dlPIiIiomMKsxYRERFRxWHWIiIiosqqzBfuatSogffffx/jx49HamoqWrRogeTkZNx9991499134ff7y7OfRERE9BsLgKXi/Ev0TFCpmLWIiIgSI+6slegZoKgwaxERESUGs1bp3GV5U0FBAW644QYMHToUI0eO5LeQiIiIjqbyeHYKn71SqTFrERERJVC8WYs5q9Jj1iIiIkogZq1SlenCnd/vx6JFi/DXv/61vPtTKX09ebhY3mz6VPkN/rBe5AuKVZPcernbdsS6bksoj/HystfW+2ZiW/oWIJUdqTyWurawxXldIbGu19bLXYZ23THMs0lYuG+uaXqxcIQfvUrTAgBHyT+QDTmuqKfnEdYhjyWPj6lcUqTkPgSURysLGuoGVXzLSRrLw+XyeIZj+MFx2DD2Emm9MC1T07g5cT4Tw7TOe13Rz4fjqpjvroTCch/Cbnm/50j1bcO27tP3C/6kIrFuqrdQL3PrZQDgd8lt/HEb8UCeB6KyONay1mePy1mr2/lTtDJXQM4GVjD644gVlutKbVj/z96dh0lR3esDf6t6mxkGZBtENIA6ImFVUSRoFDEomxG48lOIuYiKUVxwQaKoMIgaRIghRkxAWRIQDagYFPCKgDtet+SCxAUFJbiwCsMwS3fX+f2BjAz1/c509TLdPfN+nmce5fTpU+fUcurtru7qcjnDWWXS/CTPccavzH0Ngq4yx+8+dwIAAu552fErmUGoa5RzgPHL5yLLJ5Q7HuY5ZV3AEvrsKJlK2aZWIPbsY2whw5XK+5AddvfZd0De/iiTzw1yH5SMkuve/pE8+eWZI+wWWjzRIqqv3L0+TUTeh4yUA0Lu/gIApONJ2n8AwK+8/BS2k8oIr1H2l4lVfaU57qfbyj6vrDc74n5A24fEvilzllQXIfn4t3I8vGzXxlHhPn4tpW/+oHsdhfZqx528I4YbCNtU3TfdD0jzpjaPEcWjvmWtDQ/JWavnpe73taR5DwAs4XxtlyrnQ+ncp+QhcT4EENy+31XmNAjJTUjnrVx5TjXCOUp8PgBElYlLqq6MQ6Sd97Tzp5arJFLW0s6/Ql0ttzpBpQ+2Ox/49suvraV1b2tZ+4C7DZMnb/9wszyxvKypex+IhuR1b0vdUDZTaI/wnqRwngUAf7m8TX2l7vpOQ3duOdg3d+eMkCMBwJEyg7RPAHACyusD4Xwb2lEq963CvU3KG8v7W0W+kMvL5L5JuRWQs71l5HVvhPE5QXl5kRzh/Vmlrl/YdhrfATlrNdgmvAe+T56zypu496GyxvK285fJ81Bgv9APad5L0XuBFJ+4k+8pp5yCDRs2JLMvREREFItk/IhvgheIKfWYtYiIiNKEOateYNYiIiJKE2atGsV94W7KlCmYOnUqXn311WT2h4iIiGpikvRHGY1Zi4iIKE2Ys+oFZi0iIqI0YdaqUVy3ygSA0aNHY//+/ejduzeaNGmCY445BtZhX721LAv/+te/ktJJIiIiovqGWYuIiIgodZi1iIiIKFPFfeGuWbNmaN68eTL7QkRERLFIxqeLsujTSb1798bMmTPRvn1712Offvoprr32WqxevToNPUstZi0iIqI0STRrZVHOSoZszWrMWkRERGlSB7JWqvNP3Bfu1q5dG/dCiYiIKAH17MLd2rVrsW/fPvGx4uLiOnt7I2YtIiKiNKkDbybVpmzNasxaREREaVIHslaq80/cv3FHRERElG7ffPMN8vLy0t0NIiIiIhIwqxEREVF9k4z8E/c37l577TX1Mdu20bhxY7Rv3x5+f9yLyBgn/HG6WO7kOWJ5KDfsKssNuMsAIOiLusr8ltyu33aX25Z8edn2cNlZbUPoRzKW56UfUh8AIKCUJ8oxllgu9g2J98HxcO08bOS6Dtx91raHl23qCMsLG59Yt9wExPJiJ9dVlmPJx4LUjyDcx4dG2x7auoi9ZcAn7W9ednlL3nY+Zd1Hhf1CKtNoc0iOz73utbraflGmlMfKBOVjzGjHnk+aF+Q+NMgtd5U1zT0g1m2ZW+wqOzokf0qmeWC/WJ5nV11ejl0h1ksNC1DWmac24rB69WosWLAAb731FrZu3YrGjRvj9NNPx4QJE9CtW7cqdT/44AOMGzcO69atg9/vR+/evTFt2jSccMIJNS7n+eefx/PPP1/578mTJ6OgoKBKndLSUqxduxannnpqXGPJdPUpa51z8UNieU6x+7jy7SsT6xor9n3aMspcFhHODmH5vGXl5rj7oE3VfuUBqc8+D8emNiUL7VqOXNkKR+Q2HOncpyzQJ+QDnzZmoVxpV+1b2L08W1lvxhbOI+XyNhXXm1bXKDkwGHSXKdtf2l+006yv3EO2jyh1hfVsaXWFdWFyhbEBsCLCdpL2ierKpf0tqqzjslJ3ma1krbJ8d9WwnFvV41eqq+3fwnHmBOU5WpqHnFy5rhNSXgcEhO2kdS0oHXvyunCEfdZXIe8rgf3K6w7h0NH249Be9z5kRYX9VZnHUiPRrJVoTst8dSGr1aes1fNS+X2tnJ3ug9VXLr9atoScZEXkudoS8pMTkucc8TwCyOetCqWuMG9ZIWX+FeYXjfbWk5HO7VoWlc59Xvcp6byjtSHlKq1v2rlWoM7BQrldLOd1+IV1IeVvAChzv76PtmgkVg3nK9taGF6gRB6z4xfeW1Ne4vvKhPfWwvI4/MoqFnNAjnyMoGlDoQF5m2rHpMRWM7h74No2lZZX3ljoLyCeGo0tj0PMLQAcIfNHc+S6Uq7WllfeyN1GpIFYFb5SOc+Ky/O5XzMCgCO8NxZVxizFkWCxvO20vOYrde+fTo472ztKNkyN7MxatZl/4k4fvXr1qvKjvZL8/HzceuutmDhxYryLISIioiNYRn9z10sb8Xjsscewa9cujBkzBh06dMCOHTswffp09OjRAy+99BJ69+4NAPj444/Rq1cvnHLKKfj73/+OsrIyTJgwAT//+c/xz3/+0xVsjrRx40YsXrz4YF8tC6tXr4Z9xBuzoVAInTt3xowZM+IbTIZj1iIiIkqPRLNWojktG9SFrMasRURElB7ZmrVqM//EfeFu2bJluPHGG3HyySdj2LBhOProo/Htt99i0aJF+OSTT3Dvvffi9ddfx+TJk9G0aVPceOONCXWUiIiIfpDG37h79NFH0aJFiyplffv2RWFhIR544IHKC3cTJkxAKBTCCy+8gEaNDn4yslu3bjjppJMwbdo0PPjgg9Uu584778Sdd94J4OAnntesWYPu3bvH1+ksxaxFRESUJnXgd1dSrS5kNWYtIiKiNMnSrFWb+SfuC3crV67EOeecg3nz5lUpHzFiBEaMGIE33ngDs2bNAgDMnj2bAYeIiKgOOPKiHXDwk8gdOnTA1q1bAQCRSAQvvPAC/vu//7vyoh0AtGnTBueddx6ee+65Gi/cHc6RbqFWDzBrERERUTbI1qzGrEVERETxSnX+8XAn/6qefvppDBs2THxs+PDhePbZZwEAAwcOxGeffRbvYoiIiCjD7d27Fx988AE6duwIAPj8889RWlqKLl26uOp26dIFmzZtQlmZ8rsHgq+//hqffPJJ5b8jkQimTp2Kyy67DHPmzEl8ABmKWYuIiIiyQbZmNWYtIiIiileq80/c37grKSnBjh07xMe+++47HDhwAADQsGHDOvFDvkRERJkimb9xt2nTJtdjBQUF4jfrNNdffz1KSkpw1113AQB27doFAGjatKmrbtOmTWGMwZ49e3DMMcfE1P4111yDNm3a4NFHHwUA3Hfffbj33nvRuHFjLF68GMFgEJdffnnM/c0WzFpERETpka2/u5Iu2ZrVmLWIiIjSoy5krVTnn7i/cXfWWWfhnnvuqXJVEQA+/vhjTJgwAWeffTYA4IsvvsBxxx0XdweJiIgodQYNGoROnTpV+Zs5c2bMz7/nnnuwcOFCPPzww+jWrVuVxyzLUp9X3WNH+vDDD3HeeedV/nv27Nm45ZZbsHv3blxzzTWVIamuYdYiIiKibJCtWY1Zi4iIiOKV6vwT90eG/vCHP+Ccc85Bx44d0alTJxx99NH47rvvsGHDBjRp0gR/+MMfABz8yuCIESMS6mS6OX75Eq4VjIrlOaGwu8zvLgMAv+W+F6qtXDKWyqXnA0DQjsTchq204fNw6dqu5V+E1PoscYz7zWEH8hvGtlIegHtbh+ET63pZb1GhbxFHbtcx8nV2qQ1lGAgbd9vljjwNBKyAe1lKw2XGXRcAKoy77YAl75s5lnCMKB8t8MG9/X3aPuFh14wq69gLqR+eth3k7RQRygB9X5ZI80KuT56bcnzy8iqE/bPMr2z/qLuu9HwAKA/FfjoK+eR9qFGw3FXWLFgi1j06tM9V1jxQLNZtaMu3dMyxq667Bj738lPGWAf/Em0DwNKlS1FYWFjloYKCgpiamDRpEu677z7cf//9uOGGGyrLmzVrBuDHb94dbvfu3bAsC40bN465q7t27ULLli0BAP/+97/xzTff4IorrgAA/Nd//ReefvrpmNvKJvUpa9nl8hxuH6hwFxbLx7XlE+Za7dPxQXne8sL43cszQXl5TkhenvHFfhz7KtwnNCsin+R8Ze7cYlXIcycqhHUMANL9+kMhua60PgPyujAB4TxgK+df5TcDnByhbeXDAHbYvS7E/UpTGvttfVURZf8WtpNP2o8BwLi3ta9Crusvls/tdrl7HxC3BwBju9entn9bwaCwMHl7GGVbWxXCOnLk11ood28/Y5TXM8XuuqF98n5cfpTcN8fvHks4X14XdjT24Cmt42iO3IdwntK3gLsNLY5Eg8KcpRw30ZBQV8vl0rYDENjvXheB/cq+KRwLaZdo1ko0p2WZbM1q9SprKZkhsNd9nrOEcyegZImIcvwK5y311XZUnkdM0H2OcnLlTOVIdYV5D5DnTo2lDM8Szu1q1ioTJuaIUlfNWu5zrfHL53B4yKhSFg03Es7r0Kc1/wFhLML2ByBv633y63Cprn1APo8EAkq+ELoRyZPXhf+A8F6OcD4EAF+5kOH2HhDrOvk5Ynk05N5+UlYD5LzmKO+h+IR1ZCnZ11IOSqtMWM/SfgzANG3gKtPmGyvq3om0t1DLj1JeVwnHbyRH3jmlDKdltYh7GIgoh6M2L3jJdlFhHMJbqGq7/jJ5HMG98j4k7Z9GeI1q1eZv1taBrJXq/BP3u9QdOnTAhg0bcOuttyInJwdffPEFcnJycNttt+H//u//8NOf/hTAwU/i33HHHQl1koiIiA5jkvQHoLCwEB07dqzyF8ttMidNmoSioiIUFRVh/PjxVR478cQTkZubi/Xr17uet379ehQWFiInR34BIznqqKOwfft2AMBrr72Gpk2bonPnzgAOfnOvQrvwkOWYtYiIiNIkSTkrHsXFxRg3bhwuuOACFBQUwLIsFBUVxfTcZ599FsOGDUNhYSFyc3PRtm1b/OpXvxJ/n61Xr16wLMv117dvX899ztasxqxFRESUJmnMWsmS6vyT0E26W7ZsialTpybUASIiIsoukydPRlFREe6++25MnDjR9bjf78dFF12EZ599FlOnTkXDhg0BAF999RXWrFmDW265xdPyunfvjgcffBCBQAAzZszABRdcUPnYF198gVatWiU2oAzGrEVERFS/7Nq1C7NmzULXrl0xaNAgPP744zE/98EHH0TLli1x11134YQTTsDWrVvxwAMP4LTTTsO6devQsWPHKvVPOOEELFy4sEqZl7siHJLNWY1Zi4iIiOKR6vyT8K/r7t27F+vWrcPOnTvRv39/NGnSJNEmiYiIqCZp+nTR9OnTMWHCBPTt2xcDBgzAunXrqjzeo0cPAAe/kXfGGWdg4MCBuOOOO1BWVoYJEyagefPmuO222zwt895770WfPn1w8cUXo0mTJrjrrrsqH1u6dCm6d++e+MAyGLMWERFRGqQpa7Vp0wZ79uyBZVnYuXOnpwt3y5Ytc905oXfv3mjbti0efvhhV1u5ubmV2S0R2Z7VmLWIiIjSIAO+NZeIVOefhC7cTZ48GVOmTEFpaSksy8K7776LJk2a4Pzzz0efPn14KwEiIqIUsIx+L3gvbcRj2bJlAICVK1di5cqVrsfND79n0L59e6xduxa//e1vcckll8Dv96N3796YNm1azL+hd8ipp56Kr776Ch9//DEKCwvRqFGjysdGjx6Nk046Kb7BZAFmLSIiotqXaNZK6LnKbw/GQrrdeatWrXDcccdh69at8XeqBtmc1Zi1iIiIal86s1aypDr/xP0bdzNnzsSkSZNw1VVX4cUXX6x8ow4ABg4ciBdffDGhjhEREVHmWbt2LYwx6t/hunXrhlWrVqGkpAR79+7Fc889hxNPPNHzMlevXo0XX3wRp512Gho1aoTvvvsO/fv3R8uWLfH000+jdevWyRpeRmHWIiIiokR98cUX+PLLL123yQSAzz//HE2bNoXf78eJJ56Iu+66C6WlpZ6Xka1ZjVmLiIiI4pXq/BP3N+7+9Kc/4dZbb8XUqVMRjUarPHbSSSeJP35MRERESZCMH+LNgE8nxWrChAno06dP5b/HjRuH119/HX369MGSJUtw0kkn4Z577kljD1ODWYuIiChNEs1aPzx306ZNrocKCgrEb8alQiQSwVVXXYX8/HzXbwyfffbZuPTSS9G+fXuUlpZixYoVmDp1Kt544w2sWbMGth3757yzNasxaxEREaVJkrJWOqU6/8R94e6LL77AhRdeKD7WsGFDfP/99/E2nXFMyBHLAzkRsTwvGHaV5fjlun7b3bZj5Ftj2MIeaVty3/x2VCwPCPW1ul74UnS0RBH7bUK09ZYMjnG/aLEteb2FHXdd6fmAPL6I45PbVdqQ2naUzSHtQ6UIypUFAUeeMkK2vH9LDjghudx298Nn5P3bJ+zH0r4NAEFtOxn3enbi/xLyj2r5xBEV9nttXpC2U76/QqxrQ25DWm9e9lntWHCUY90vbD9tf8v3lbvL/GVi3aN87k8SN7TlTxdr+1DAqtqPINxzf8rUswt3n376KX77298COPgG1HPPPYcHH3wQo0ePxrRp0zBnzpyMfDMoUfUpa/nK5TnHqhCO9zL3sX6wEWF+yZfPW8Yvz0WWMMdpTI77vBXNC4h1naAy90n9iMoHpx1xl/vK5fnJrnCXWxF5HUO7JZv0hq20jjVKuybo3iaOUHawDbnYCbq3kxVVMoM0bm3MEWF9hpWMo7URcC9P3I8BWEbY1j7ldUBYWPdK4PPtl899Up/VY0HZXeQFCm1IYwPU8SEg7ANaG7bQRoXcYXu/+9we3Jsj1jW2fPyG89zjK2sm77O2EAWkY1cr117O+CqUbS2U22G5rnQ7IScgL9AJusu1fcJfqrxW3ueeq317lW9yCceeyXFvD6s8i7LWD88dNGiQ66GJEyeiqKgogcZj7IIxuOqqq/D666/jmWeewU9+8pMqj993331V/t2/f3+0bdsWY8eOxfPPP4/BgwfHvKxszWr1KWtZSr6Q5lqrVH6NiFLl/CIJCe8zaOdU6RwAwGngfu/ACcl1oyHh9b1fyzhCuZa/wsr5Rcha6nlLouYvpVxqW4llJuBeF9Hc2DNxJE/Ow5aSO5ywsO4b5cqdE9gV8nlZ2t/s74vFqoGIvG+ZkHxuF+sK617L2r597vOZl2MMAHxl7j4b5QMTltAPS8nlRshalrBPAADCyvvB0ustZRzSawx/idyulHPKG8vjqGiorAupae3tYOmwicqVA8Xuyrk75Wa1bS2NT3m7DI6wa2q3fpSyVrBYXsfB3XLWssrcGcr4hXlB249ToQ5cuEt1/on7XeqjjjoK3333nfjYli1b4v4E2erVq3HllVeiffv2aNCgAY499lhcfPHFeP/999XnGGNwzjnnwLIs3HDDDXEtl4iIKFscuhd4on/ZYt++fWjcuDEA4P3330dJSQl++ctfAgC6d++Or776Ko29Sx1mLSIiovRIVs5aunQpNmzYUOVv9OjRKe+/MQZXX301FixYgHnz5uHiiy+O6XmXX345AGDdunWelpetWY1Zi4iIKD3qwntaqc4/cV+4O//88zF16lSUlJRUllmWhUgkgscee0z91FJNHnvsMWzZsgVjxozB8uXLMWPGDGzfvh09evTA6tWrxec8+uij4i0oiIiIKPu1aNGi8lZFq1atQps2bXDccccBAIqLixEIxP5JymzCrEVERJTdCgsL0bFjxyp/qb5N5qGLdnPnzsXjjz9eeTHOCy+3yQSyN6sxaxEREVG8Up1/4r5V5r333oszzjgDHTp0wODBg2FZFv70pz/hww8/xFdffYW///3vcbX76KOPuoJs3759UVhYiAceeAC9e/eu8tiWLVtw55134q9//SuGDBkS73CIiIiyh7H0+2l5aSNL9O3bF+PHj8dHH32EefPmYcSIEZWPffzxx2jbtm36OpdCzFpERERpkmjWSlPOMsZg1KhRmDt3Lv7yl79g5MiRnp4/f/58AECPHj08PS9bsxqzFhERUZpkadY6XKrzT9zfuCssLMSbb76Jn/70p5g5cyaMMfjrX/+K5s2b4/XXX0fr1q3jalf69Fl+fj46dOiArVu3uh675ppr0KdPH0/3XyciIsp6JsG/LPLAAw/glFNOwezZs3Hqqafi7rvvrnzsySefRM+ePdPYu9Rh1iIiIkqjNOasFStWYMmSJVi2bBkAYOPGjViyZAmWLFmCAwcOAACuuuoq+P1+fPnll5XPu+mmm/DEE09g5MiR6Ny5M9atW1f59+GHH1bWe/3119G3b1/85S9/wcsvv4xly5Zh9OjRGD9+PHr37o2LLrrIU3+zNasxaxEREaVRlr+nler8E/c37gCgQ4cOWLlyJcrLy7Fr1y40adIEubmx/wBprPbu3YsPPvjA9amkxx9/HP/7v/+LjRs3Jn2ZRERElBmaN2+OlStXio+tWbMGOTnKD5rXAcxaRERE9c91111X5YLc4sWLsXjxYgDA5s2b0bZtW0SjUUSjURjz47tXhy70zZkzB3PmzKnSZps2bbBlyxYAwDHHHAOfz4fJkydj586dsCwLJ510Eu69917cdtttnm+Vmc1ZjVmLiIiI4pHq/JPQhbtDQqEQWrVqlYymRNdffz1KSkpw1113VZZt27YNY8eOxdSpU+Na9vbt27Fjx44qZbyfOBERZYVk/BBvhnxCKVGNGjVKdxdqBbMWERFRLUo0ayWYsw5dYKvOvHnzMG/ePM/PAw5+0+zFF1/03rE4ZEtWy7asxZxFRERZLc1ZK9WSkX88Xbi79957Y65rWRbuuecezx060j333IOFCxfikUceQbdu3SrLr732WnTt2hWjRo2Kq92ZM2di0qRJMdW1AvKeEAhExfLcQIWrLMcXEev6LcdVFvFwB1Nb2cN9SrnfdvfZl4Q93RbG4ZVj3OP20jdtzMkgjc9R7qUbNr6Y60aEulGtruOuCwAO3PVtZb2FhXUMJ/ZpwFH2Y3jY/MVWWCzPs92fagxY8jEWELaHT+lEjrK8qLDeNGGT2GccbKVvQWV8De0yV9l+n7sMAML+2OeLfF+5u8wvt5tjKdvaAy/r2Attv5C2tVrXlup6G/OR+0UkOZ+FiU0ybg2Q4SGnvqqvWctX5uH4Cyo/8Cx8O8CEgmJVp0FIbiPqPjCssDyPOEH3edn45HkvGlDmaqG6FqksqW9C2cGOCOWWMidrP5gttRFWtlNU6HRAmROldhXa+nSE9Wkrw5O2EyLymO2IsK21/U0aMyCOz5LaVera2vqJuJentuuT9zcnTzgetP1CHIc8ZiO0YZW5Xw8BgKWtN6nPjrIuhGPdylGOaWEc/mK5b5EcOWtHct3Li4aU9SYU28pxKh2/vjK5rh2W15u6PmOkzTf+UuH1RYW8v9kHlG1dJmTwCrmuuL9J9coTz6cxSzRrMWdlrLqStbzkLAAIfi+/LpbmVISVutI5KihnLdNA+JaBdh4JypkhmuMujypztRMScqA2VQvdsJVzjjrPCuvCBOS+WbnCuihzvzYHoGctoX+WX16eNBLtPAKh3G4gbw/p7SQAiOYI5+VG8rdMfCXufcvky99wtXzC+CrkfdMqUdankFFsZTtJ1HOc8PpAe30RzZOzpJSr/Hvl92ekfctXLNcVj2mF5Sj7hVSuZXupC+HYT4LRoHygasevX9gkwlurahvCW0EAgECpu8/B7+Xso739LC3PUuYWKWv5KuTt4TvgnhdsKWcBsJTX1SbHvR9aFe662mvflGDWqpGndxmLioqq/NuyrCq3ZThUdkiiAWfSpEm47777cP/99+OGG26oLF+yZAlWrlyJN954A3v37q3ynIqKCnz//fdo0KABAtobEQBGjx6NoUOHVinbtGkTBg0alFCfiYiIiOLFrEVERESUOnUlazFnERER1W2eblzuOE7l3yeffILjjz8e999/PzZv3ozS0lJs3rwZkydPxvHHH4+PP/44oY5NmjQJRUVFKCoqwvjx46s8tmHDBkQiEfTo0QNNmjSp/AOA2bNno0mTJjXe9qFFixbo2LFjlb/CwsKE+kxERFQbLJOcP8o8zFpERETpx5xVd9WVrMWcRURE2YxZq2Zx39drzJgx+O///m/ceeedlWVt2rTB+PHjEQ6HcdNNN2HFihVxtT158mQUFRXh7rvvxsSJE12PX3HFFejVq5er/LzzzsOgQYMwZswYdOrUKa5lExERZTzeKrNeYNYiIiJKE96+qV5g1iIiIkoTZq0axX3h7vXXX8dtt90mPnbWWWdh2rRpcbU7ffp0TJgwAX379sWAAQOwbt26Ko/36NEDbdu2Rdu2bcXnH3vssWL4ISIiqjN44a5eYNYiIiJKE76ZVC8waxEREaUJs1aN4r5wFwqF8N577+H88893Pfbee+8hqPxAbU2WLVsGAFi5ciVWrlzpevzIe48TERER1UXMWkRERESpw6xFREREmSruC3eDBw/GpEmTkJ+fj+HDh6NJkybYs2cPFi5ciHvvvRe/+tWv4mp37dq18XaJ4YeIiOoFC4nfz9tKSk8olZi1iIiI0iPRrMWclR2YtYiIiNIjk7LW448/jlGjRqFBgwbYv39/EltOTNwX7n7/+9/j888/x4033oibbroJfr8fkUgExhicc845+P3vf5/MfqaZvBfZtlIu7HVSmcZL3frKtpyEnu8YO+E+hI1PLI847vKwsjypHxGPfXOMe6rS9iFpeY5SNypMgVFhWQAQhrwubOFFR5kJiHWLo7mushwrLNZtYFW4yoLKOHxWRCxPVNTDdvIp+2uOLY+vMQ64lwd5eXm2e11ox4e0Phv6ymKuW13bkmQcZ1546ZsPsdcNG/lUeeS+XGHkdUYUr/qUtaK5WiTNcxc1FsoAOAHhHBeSz09WVMlajpDhKuS5zPjd5canvHzQyqV2Pbxhpy7Pib1vlqW0YQvrTttMQhvGVtZbwN2uE1Tq+pT8FHAvzwnJnbOEtv3CttNYjvJtC0c+j4j7lrZNI1H388NKbokKy1PWj8mRs5YTdK8j45e3v13m7pvGksan9E1dF9L4jHKu9gvb2i8f6xKrzJ2dAMBXHpLrC9vUcpTjSZhDfKXyOAL73bnBVyz3zS6R85q0D6mk9ak9X9pO5XLfVNI2yXXnfQCAtM96GRtRnOpT1jJCTgKA0mPcuSraNl+s60jTrzLH5X5bGnvnNLYw1yq5RXmrQiS9deD1LSYx5wgZBwCssJSplPOW9vaFMC9r2U7qm5HWpdKG8JbWwXJlH0Ig9r5J50kxRwAwQm6xypQV5OGtB7ukXH5AynBKZpT6Fm0gZ8aoknNtKT8ry7MqYn9fy5Iyg5LLcUA5Tn0eclXYfb72l8iZoeIoIYsqXVMPaWEz5eyRD2D/AXe5XSHvb15ymaftobxmEOeyCuU9JamuMhc6jeSsJb1W8u8ULlBF61/+2rZtG8aOHYtWrVph79696e5OFXFfuGvYsCFWr16NlStXYu3atdi1axeaNWuG8847DxdccIH+JgARERER1YhZi4iIiCh1mLWIiIjqt2uvvRbnnHMOmjZtiiVLlqS7O1XEfeHukL59+6Jv377J6AsRERHFItEf8T3UBmUFZi0iIqJalmjWYs7KKsxaREREtSwDstaCBQvw6quvYuPGjbj77rsTbzDJEr5wR0RERLXLMkn4jTu+oUREREQkSjRrMWcRERER6dKdtbZv346bb74ZU6ZMwXHHHZdYYyni6QeIunTpgg0bNsRc33EcdOnSBf/+9789d4yIiIiovmHWIiIiIkodZi0iIqK6Y9OmTfjoo4+q/G3fvr3G540ePRonn3wyrrvuulroZXw8feNuw4YNKC2N/QdmjTGen0NEREQ14K0y6yxmLSIiogyQAbdvotRg1iIiIsoAScpagwYNcj00ceJEFBUVqU995plnsGzZMnz44YcZ/Xu2nm+VOWjQIIRCoZjrZ/LgiYiIshIv3NVpzFpERERpxgt3dRqzFhERUZolKWstXboUhYWFVR4qKChQn7Z//35cf/31uPHGG9GqVSt8//33AICKigoAwPfff49AIIAGDRok0Lnk8HThbsSIEXEtpHnz5nE9L2MoGc1nO2K5Ldxk1Vb2RKmuttOKdT1yjHswvqS06+muq1lHGp+0LgHA0XaYGGn7ilpf2t+UbRqwo64yv+UuA4CAUu6FtI4OROUXSNLycpywWLeBXR7T8wEgRym3LXfbtlHWm+NuI2z5xLpRaV9R9gmfVSEvzxdxleXY8roo8wVcZbYlz01BYV0ELPeyACCAxLe/RFsXUW93bhb5II871uWFjXxKDPONF6pF9TVrlTdxz2UAEAi4j1Xl9Asj1VWmFrtCPrAtRyhX3qxzPCzPS0xSpnAYn9APeQqHJZ3PtHb98vnMsoXlReW6kJYn9RcQ16caRaNyp/2lHiZmYZvaFbGf44xf3nhWVB6fETagFVaWFxHKhcwBALCFdW8rO5ayz1qOsD4deZtKx5MjZA4AsIV2LaPsK9KYASAs5JzSMrmuRMlwCLsPEvE4BxDYK49POvYcv5Ltyt3rIrBXHoe994C7sHi/3IcD8jd9jDA+GOVgt4T9RTrONcp6s4LyerN8Oe5CZb4xQXcGEy+UKPsrkRf1NWvtax0Uy52gMMdph5pwWAZK5HkkKM2pylxth2N/Hae1YWkBUaLMZ2K7Ue0NOqFMOcWZgHuFWpYwRwL6eVI6hyuk7KLmGWHdB0rkPkgZB5BzpxVRtpOwPEdYPwfbELapvBvDisihWM1gAiPlKm29+4Q+q/umskBpcSElawl9sw7I7yeJOVDLESVCFgFghYQV7ZffL7H2FLvKfCXyOHKCwrFg5PcItXnIf8C9TYL75PfLfELWsiLKNi1zv88o5UgAQLm87o2QwbTNbwn7kInGvr9aTZvIfVCOdancaZTrrleqzE0ZrLCwEB07doy5/s6dO/Hdd99h+vTpmD59uuvxJk2a4OKLL8bSpUuT2Mv4eLpwN3fu3FT1g4iIiGKU6I/4HmqDMg+zFhERUfolmrWYszIXsxYREVH6pStrtWzZEmvWrHGVT5kyBa+++ipWrFiRMR/W8XyrTCIiIsoAfEOIiIiIKHWYtYiIiIhSJw1ZKycnB7169XKVz5s3Dz6fT3wsXXjhjoiIKMvwG3dEREREqcNv3BERERGlDrNWzer2D5MRERERERERERERERERCebNm4f9++Xfm04XfuOOiIgo2xgkfkuBevDpJCIiIqK4JJq1mLOIiIiIdMxaNeKFOyIiomzDC3dEREREqcM3k4iIiIhSh1mrRrxwFwPb78jltryH+G13fdvDjVe1urYltFuH9lJpfMngmMTvCBuFFXO7UeOuqxG3aexPVwWUdem3o0Jdd9nBfrj3LZ/HGwhHhbvxho1PrHvACcZUBgBlJiAsq1ys60NELA8IYwmiQqwbFNZbBeRxhIX9IqrsK45yt2JpfwtA3k45PneftXnBJ+wXWrsarc+xksYGABe23ZhQuwCwesvJMdf1srwnN50pltuouj4tpGYOI6oPKvLluUU6pQpT8sG6QhOOX55z/NrxKizQCSpzuNS2kedf45P7YUXd9bVTrSW0bUWUcYjtKg0rfTPCfG9FY5/ntL5ZZe7zsq9CPlfDUvpmC9tEGYfYrLI8KyrsXI7HYCase2jrzZGWp2wnZV+WaOOzhX3AyVPWsU/IMzly9pH2e7tEzmVWWNnWEfe6MBXhmJen7t8e2MVlYnko7O6bto5RJoz7QKlY1ZS6l+dIzwdgKS8QrFDIXRiQX+Kv3DlLLI9Vv+Nukh+QjhsAK755NOa2L+w20V0oziFJeKFEVE+VNZOPn2hO7G3YwrRslHN1NCTkCI8vl7T8FCtteVK5HVZyi4fso+UWCLnFKIHPcrROC20rVW3hvAWpDIBV5n4/wYoI5xYATm7sbyFrOVDsm3YOl873yvqxKpQXCFrukNowQts+JfsI29Sn7a9qnnWXGyXvGSGPWgGlb+XC+1raa5QK+T0wqdzKkfcLMYs48r7i3+vOPnaZsm8q+6wt7LPqdpZylbR+AMAvrE9t+yvrYuXWGXL9GPX7yRj5gbB78l3x6YOe2j6/1wOuMmn+1uZ0So+E3oHdsWMH7rzzTvzsZz9Du3bt8NFHHwEA/vKXv+DDDz9MSgeJiIjoCObHH/KN968Ofe6jTmPWIiIiSgPmrHqDWYuIiCgNmLVqFPeFu82bN6Nr16744x//CMuy8Pnnn6O8/OCV7P/7v//DH//4x6R1koiIiA5jkvRHGY1Zi4iIKE2Ys+oFZi0iIqI0YdaqUdwX7saNG4fGjRvjs88+w2uvvQZz2Nduzz77bLz55ptJ6SARERFRfcSsRURERJQ6zFpERESUqeL+jbtXXnkFjz32GFq1aoXoEfe1P+aYY/D1118n3DkiIiJS1INPF9V3zFpERERpxKxV5zFrERERpRGzVrXivnBXVlaGpk2bio+VlJTAln40noiIiBJWeU/vBNugzMasRURElB6JZi3mrOzArEVERJQezFo1izuFnHzyyVi1apX42GuvvYZOnTrF3SkiIiKi+o5Zi4iIiCh1mLWIiIgoU8X9jbtRo0bh1ltvRatWrfCrX/0KAFBRUYElS5Zg5syZ+NOf/pS0Tqabz++I5QE7KpbbHr7naVty23LdenAp+QiOif3achSW0oZcnihHWZ4XPmGbavuPtq9I+0XAUvZNoa7Uh4P9iH3f1EjrXltv0rYucwJi3eJorqssxwqLdYPKcZonrKOQsi4CVsRVFjZyuxWWexxhZT8OG59YLu3LUQ+fs/B5mFe8+nmbTSlrO1G9236SknaHF74TU72PKj7CeNTSi/tk/BBv/TulZJ36lLWU6R7RkHDOqIi9XTVGKPu/VN/xyectqW9WxFs28EfcHbEcuXOWUNfLcWyUcVhaTrKFxn3KCo0I58SoklvK3BvQ+L19ltDyuc+f6vjC7r5JZQDkcWi8fAvDKBvKFnKAX9kewphhK3WVdQ9hHRlL279jX8cw7peUVkTug1Um5zWUlbubPXBAritkLSjjkNaR5VdeAh8olRdX4u6HEcoAwEjjiMhjtnPdefZ/Kp6U+5YBVvznjylr+6X3J8VU76OPPkKnTrNT1o8qEs1azFlZoT5lrUgDuTwadJfZ7pe/AADpZaZwClBpucwE5AeMdJ7T5nvpmNMylVCu5S9teZ76JjWrZAPjC8lPiMY+qVjlwnlH65t0ulbP98p7R2Xu/GRLfQAAR1ig9vaFdLrX1oPUrsZoOUl+f0YkZEarTD5w7KB8kIhZS8l2kQbuA9WvbFPfASlTyRnHKS0Ty6WsbeUo+6a03nKEiQVyBveVKi/ulKwl7cva+Kyg+0Xmih1/ltvNACu2zkhZ26+sHR9TvYNZ69GU9aMKZq0axX3hbvTo0fjnP/+JW265BbfddhuAgz/ea4zBqFGjMGLEiKR1koiIiH7EW2XWD8xaRERE6cHbN9UPzFpERETpwaxVs7gv3AHArFmzcOWVV+LFF1/Ed999h+bNm2PgwIHo2bNnsvpHRERER0rjN+6Ki4sxefJk/POf/8SHH36InTt3YuLEiSgqKqravDF4/PHH8ec//xmfffYZAoEAOnXqhHHjxmHAgAEJdr7+YNYiIiJKA34KvN5g1iIiIkoDZq0aJXThDgB69OiBHj16JKMvRERElOF27dqFWbNmoWvXrhg0aBAef/xxsd7EiRMxefJkXHvttZgyZQrKysrwyCOPYODAgXjmmWcwZMiQWu559mLWIiIiqj9i/ZCUZvv27Rg3bhxeeOEFHDhwAF27dsV9992H888/31V31apVuOeee/Cvf/0LeXl5GDhwIKZOnYoWLVokeVSZjVmLiIiIMo23H5QgIiKi9DNJ+otDmzZtsGfPHrz66qv43e9+p9abM2cOzj77bDz22GPo06cPLrroIvzjH//AUUcdhfnz58e3cCIiIqLakKacBfz4Iany8nIMGjTI03PLy8tx/vnn45VXXsGMGTPw/PPP4+ijj0bfvn3x6quvVqn76quvol+/fjj66KPx/PPPY8aMGVi1ahXOP/98lJe7fyOJiIiIKGnSmLWyhadv3Nm2DcvDj61Gox5+4J2IiIhiYv3wl2gbcT0vxhwQCARw1FFHVSnLycmp/CMZsxYREVH6JZq1EnnuoQ9JWZaFnTt3qnc3kDzxxBPYsGED3nrrLfzsZz8DAJx33nno2rUrxo0bh3feeaey7u2334527dphyZIl8PsPvjV0/PHH46yzzsKcOXNw3XXXJTCKzMWsRURElH7pzFrZwtOFuwkTJlQJOHPnzsX+/ftx0UUXoWXLlvjmm2/wwgsvoEGDBrjyyiuT3lkiIiLKDmPGjMHYsWPxxBNPYMiQISgrK8NDDz2EvXv34qabbkp39zIWsxYREVH95uWi0pGee+45nHzyyZUX7QDA7/fj8ssvx/jx47Ft2zYce+yx2LZtG95991387ne/q7xoBwA9e/ZEu3bt8Nxzz9XZC3fMWkRERJQNPF24O/ye6tOnT0fLli2xatUq5OfnV5YXFxfjF7/4BfLy8pLWyXQLBCJiedAvf/LKbzuuMttylx0sd3+v01a+6ynVTYaoco3alwHfOdXWm2Pcd3l1jDwOsa4yZm3de+HzsE39tnsf0ta7VFdfXuz7m0Zbn6kSFbbTASck1v1e+NSjtq9o6zNglQhlct0c4cWzVjdg3H0LK9sjasnbVDomw8L68eq01l8l3IZk3L+GiuWOcDfmA9GAWLc4LH8Da3/EvQ9EHHldLPv5I1oX66YkTdGbNm1ylRUUFCT82yY333wzcnNzcf311+Pqq68GADRt2hTLli3DWWedlVDbdVl9zVq2HLUA8c1Leee3HOF8GFaWF5bbcILu5Tm2fD6UTpPqe63K8SqduuyokgPD7sqW8TARyKciwJEfsKR+COtYpX1DwXGvJCui1NVWqHD+tHzKeVJqu6JCrhsV1oXWrl95GWV7OF9rbUukbR1RNqqyX1gR9/q0w/K6NwF334SI80MbQj+U/Rh+n7w8YX9xtO0k9UFZl1aOkCXV/UopF9anFZTzzMo9sX8zyouuNz0slgcOuPvmL5XXfbDYvZ1ytpeKdX173eUr/q3fHrvOSv/LYc82bNiAn//8567yLl26AAA++ugjHHvssdiwYUOV8iPrvvnmm6ntaBrV16ylkbKI9laAVK69TJVeLmvtOr7Y52UxnwDwCRlFyoYAYEmZSjqXATDKucH4hYEr52Wxzx7PRUZYR5aW4ZRzu9iuMA6rQg7mdlQ+h9tl7vO1VZqErOXlQwzqOVzKKEp+kjKjh6yt9dbKlTMDQu71qb1l5yt2r09b2U4mR1ielsul9QPASPuytt6E10ri8QHIOVmpa/nyxfJU5ZHuV/zeVRbYr7zPWKG8b1viXs+BvWViXavU/UJ15Uf3V9fFuikLs1Ztivud4JkzZ2LcuHFVwg0ANGzYEOPGjcPMmTMT7hwRERG5WSY5fwAwaNAgdOrUqcpfMs7hc+fOxZgxY3DDDTdg1apVWL58OS644AJcfPHFeOmllxJuvz5g1iIiIkqPZOWsTZs24aOPPqryt3379pT1e9euXWjatKmr/FDZrl27qvxXq3vo8bqOWYuIiCg9kpW16jJP37g73LZt26rcUqFKo34/vv3227g7RURERLVj6dKlKCwsrFJWUFCQUJt79uyp/KbdtGnTKsv79euHXr164dprr8XmzZsTWkZ9wKxFRESU3QYNGuQqmzhxYpVvfSVbdbfaPPIxrW4it+vMJsxaRERElKnivnD305/+FL///e/Rr18/BAI/fg23oqIC06dPR/v27ZPSQSIiIjqCQeK3FPjh+YWFhejYsWOiParik08+QWlpKc444wzXY6effjpeffVV7N+/3/XpZqqKWYuIiChNEs1aPzw3FR+Qqk6zZs3Eb8vt3r0bwI/fsGvWrBkAqHWlb+LVRcxaREREaZKkrFWXxX3h7r777sOgQYNwwgknYMiQIWjZsiW+/fZbPPvss/j222+xdOnSJHaTiIiIKiXxwl0qtGrVCgCwbt06jBgx4sdFGoN169ahSZMmaNCgQeo6UEcwaxEREaVJkt5MSsUHpKrTuXNnrF+/3lV+qKxTp05V/rt+/Xr079/fVffQ43UdsxYREVGa8MJdjeK+cDdgwACsXLkSd911Fx599FE4jgPLstC9e3fMnTsXv/jFL5LZTyIiIsoQK1asQElJCYqLiwEAGzduxJIlSwAA/fv3R+vWrTFkyBDMmjULoVAI/fv3R3l5OebPn48333wTkydPrje3YEoEsxYRERF5MXjwYIwePRrvvPMOzjzzTABAJBLBggULcOaZZ1Z+uOrYY49F9+7dsWDBAowdOxY+nw/AwQ9dffLJJ7j55pvTNYRaxaxFREREmSruC3cAcP755+P888/HgQMHsGfPHjRp0gR5eXnJ6hsREREJkvFDvIk8/7rrrsOXX35Z+e/Fixdj8eLFAIDNmzejbdu2WLhwIf70pz/hb3/7G+bMmYNAIIB27dphwYIFGD58eGKdr0eYtYiIiGpfolkr0ZxW04ek8vLycNVVV2H+/Pn4/PPP0aZNGwDAlVdeiUcffRRDhw7FlClT0KJFC8ycOROffPIJVq1aVWUZDz74IPr06YOhQ4di9OjR2L59O+644w506tQJI0eOTGwAWYRZi4iIqPalO2tlg4Qu3B2Sl5fHYENERFRb0nyrzC1bttRYJycnB2PHjsXYsWPjXxBVYtYiIiKqRWm+fVMsH5KKRqOIRqMw5seFhUIhvPLKKxg3bhxuvPFGHDhwAKeccgpWrFiBc889t8oyevXqheXLl2PChAm46KKLkJeXh4EDB+Khhx5CKBRKbABZiFmLiIioFvFWmTWK+8LdvffeW+3jlmXhnnvuibf5jBIMRMXygC2XSxxji+W25aUN923FbOXyclSoe5DPVeKH0gehCZ+Ho8K2nJjrHqzv5Yhzt21LHVbrJs7vob8BZTsHbHff/EpdnzAOwOt6cwsb9z6hcTyuOVvoc1RpQ+yHugvF/oIqxwqL5Q3tMqFMWcdimby/+YRiWzluHKU8LBy/jrK8CuGYPqP1FrFuMlzz3ghXWcgOinWlOUCbQxr4y8Xyv535uIfe1S/14dNF9V19ylpaZHCEU4MVkOv63NM6fBH5QPGVyedaK+qe8e0KuQ2/X5iXo3JdWyuvEDJKWF4ZVkTos7LeLONenlR2sF2lkXLh/Gl7uMWtl9vhRr1lRrEfjtKG1La0LgEgKpUrL5dsZRJORsiUSNtP2aZwlHJpXWjrTWrbJw/O+IVyqawaVsidJezcXLmysJ0sn5xnLb+w/YJyblH37/IKV9GK7x6T6yaow/iH5Qfy5WIjBE8nII8jGnSXv/aPCTH3rT5KZ9aK5UNS8+bNw7x581zlRx99NObPnx/Tcvr06YM+ffp47F3dUZ+yVjhf2aGlKUPNF+7K1n65rvSWlJb3LCUnWUZ4gofjUstU0nlSy0lGOzdIWUs5/4q5rCIiN6ucPy1bKPeStbTMIKx7S8s42nuH0vYLy+/DyJlPC/fCmNVxKH3TMl+stMwg7ZsBb2+x28LrFCeo5CfhfK/tK3aJsO7z5Ezlk/YrQMyHYqYCAKHcBORcZoS8ZivHwop/3ycvL0Gn3CBnLTtXyFTaOlZ2b18Dd/3Vr9wRe+fqIb6vVb24L9wVFRVV+3hdCjhEREREtY1Zi4iIiCh1mLWIiIgoU8X9mVDHcVx/O3fuxOOPP45OnTrF9AkxIiIiioNJ0h9lNGYtIiKiNGHOqheYtYiIiNKEWatGSb2ZS9OmTXHllVdi+PDhuOmmm5LZNBEREf3g0I/4JvpH2YdZi4iIKPWYs+ovZi0iIqLUY9aqWUp+haF79+545ZVXUtE0ERERUb3HrEVERESUOsxaRERElE5x/8Zddf71r38hP1/5BW0iIiJKTDJuC1APPp1UlzFrERERpVCiWYs5K+sxaxEREaUQs1aN4r5w99e//tVVVl5ejv/7v//DnDlzcPnllyfUMSIiIlLwwl29wKxFRESUJnwzqV5g1iIiIkoTZq0axX3h7oorrhDLc3JycPnll2PatGnxNp1xgr6oWO6zHbHcrsWbrDrGUh6R74Lqs9xjcYxc1xbq1jafh3UZsiJiub6OEqNt54Cw3rzU9VnyfqX2w8NMFTY+T23HKqqs4yjcy/Mlob/lTuxTV55dLpa38BVLSxTrBqzY7yrseBhfmZHrlgnjLjPymKOIff/+08e9XWV7o3li3T2RBmK5bQVdZV62h63s3wGl/J71g11lRwf2inVbBfa4yhraZWLdPsf/W+siUcaoT1lLI54G5KkaluOeU+0KeZ61K+SMY0Xdc5HxKecAYfq1y5XspJ0abKER5dwAYZq01LpCZa1uROlzRMhV2rrwCRvKVupKY9ZYHupq4zMe1oUXUrsAIGQflbSOvKwfYX892IZcbELu87UTCoh1nYB7HNFQ7HnIKOPwR5TXTyF3vrCC7jKNFZTHgYBQ7le2kbZ/S8eCom/Hu1xl0ca5Yt2SY93lwaPlPlQ0kpfnCMOr0GJZvnubdL71YbFq3g73MdJoc6lY1/+dnMtWfDZV6QhR5qhXWUs5vRi/+3i3Il7O1Uq7wnlAympANXlGKPaVKkFQbDjxHKG9PSONxVLOcQgLWSssn1usqHwekM7h2vi0c7vILyxPWxfK+CwpdyrbWuyzln28bD+Nds6XSJlBI7yPaoLyspyg8l5OyF3f+JVtGnBnIl+pvA8ZIV/6yuT3xSwvY/an5KZ9nnP5hWdMcpVF8+RxhBu5y4PN5O0UbuBe945Pea9TiahSG6df/XuxboOv3dsv55sSsa5dVuEqW/Hv38mdoDol7qNu8+bNrrKcnBwcffTRCXVo9erVWLBgAd566y1s3boVjRs3xumnn44JEyagW7duAIBoNIoZM2bgf/7nf7Bhwwbs3r0bbdq0wcUXX4w77rgDjRs3TqgPREREmSwZP8RbH37IN9sxaxEREaVHolmLOSs7MGsRERGlB7NWzeK+cGdZFo455hgEhKvzkUgEX3/9NVq3bu253cceewy7du3CmDFj0KFDB+zYsQPTp09Hjx498NJLL6F3794oLS1FUVERhg0bhquvvhrNmzfHBx98gPvuuw/Lli3De++9h9xc+ZONREREdUI9CCn1HbMWERFRGjFr1XnMWkRERGnErFWtuC/cHX/88Xj77bfRvXt312P/+te/0L17d0Sj3m+1+Oijj6JFixZVyvr27YvCwkI88MAD6N27N3Jzc7F582Y0a9assk6vXr3QunVrDB06FM888wzvRU5ERERZjVmLiIiIKHWYtYiIiChTxX3hzlRzD9poNAorznsRHxluACA/Px8dOnTA1q1bAQA+n69KuDnkUNg6VI+IiKgusozRfwvCQxuU2Zi1iIiI0iPRrMWclR2YtYiIiNKDWatmsf/CuEAKMeXl5VixYgWaN2+eSNNV7N27Fx988AE6duxYbb3Vq1cDQI31iIiIsppJ0h9lPGYtIiKiNGDOqjeYtYiIiNKAWatGnr5xN2nSJNx7770ADoabHj16qHWvvvrqxHp2mOuvvx4lJSW466671Drbtm3DHXfcgdNPPx0DBw6ssc3t27djx44dVco2bdqUcF+JiIhSLdEf8T3UBmUeZi0iIqL0SzRrMWdlrrqStZiziIgom6Ura/3zn//EXXfdhfXr12PHjh3Izc3FySefjOuvvz7jblHt6cJd9+7dMXr0aBhjMHPmTFxyySU4+uijq9QJhULo3Lkzhg8fnpQO3nPPPVi4cCEeeeQRdOvWTayze/du9O/fH8YYPP3007Dtmr9IOHPmTEyaNCmmPtjKnuAY+bYJUrmnuoj9dgxa37SrzlFheZ7G5+FOEVHjE8sDVuz3iLfhyOUe+mHbqXnV5FP6FrC9jC9zX9E5whdypf0HqG7/drfhWPJ6Kzfu6Siq7HA+D+vtKN8BsbzEBF1lYVMm1nWEYyTg4QvL2nbWxieVVyjHk7SdVm85Wax7dCDfVaYdj7aynUJWjqvs/i7PinVHvjvSVZarHB9+pVzqX44dFuv6hO0UVMb3zpfHu8rU7SHsx5LN2ypiqkdUnfqatayoPE9Kn4I3SggwPqGuknSdgNx/O+Luh6X8to3lCHUj8twJ5TYeTtBDFBe6bJR5y1LOy6Ko0mdHKRcXKHROy1/StvbL5zgTw35W2QUv/dVuqyJsU7Wutt6k844WXIVyE5DXBaR1oY5D7psJBWLum3Q8QSoDEA3EXtcuF/oAwN4vlGu3qJPG7aVuWM4RKFPW2959rrIL80eIde0WBa4yn7Iucne6t7UdkdfPG4+MFctPvf5hV5l2+Efd0Vd/w0Now/Fr+7F8nPY79kahE95/K+xwxZHdCT2fCKg7WctLzgIAX5l8DEfy3GVeYoQ2jRhhzjDSebaaRuwK95yhZUYjnQeUU6qYRZTVreVOKQdqxIyi5QiN1GdtfAITUrKWksFEypht4TxnR5T5PhJxl2nnBmmbatknLLQLAEHh5OdTNrZU7uF2ucZLrod8m79oUHnfRyh2/HJm8Av7rC/X/T4OAGC//H6ZuJ49vBFrH1DeG5HWZ5lct9/R18ltNz7KXahkX2kOscuFfQLAmv/5ravstN+4cxYA+MtiP/592ttE0u6tHacV7n2z30nj5HbLyuXyCndHjHDc7I/ukZ9fh3z//ff4yU9+gmHDhuHYY49FSUkJFi5ciF//+tfYsmUL7r777nR3sZKnWaVfv37o168fAKCkpAQTJkzA8ce73/xMlkmTJuG+++7D/fffjxtuuEGss2fPHvTp0wfbtm3D6tWrccIJJ8TU9ujRozF06NAqZZs2bcKgQYMS7TYREVFqJeO2AJn7uYF6jVmLiIgoAySatZizMlZdyVrMWURElNXSlLV69eqFXr16VSkbOHAgNm/ejFmzZmXvhbvDzZ07N5n9cJk0aRKKiopQVFSE8ePHi3X27NmDX/ziF9i8eTNeeeUVdOnSJeb2W7RoIf5gMBERUabjrTLrB2YtIiKi9OCtMuuHbM5azFlERJTNMi1rNW/eHNu3b09uownydOHutddew2mnnYb8/Hy89tprNdY/55xz4urU5MmTUVRUhLvvvhsTJ04U6xwKN1988QVefvllnHrqqXEti4iIiChTMGsRERERpQ6zFhERETmOA8dxsGfPHixevBgvvfQS/vSnP6W7W1V4unDXq1cvrFu3Dt27d0evXr3E3x0BAGMMLMtCNI572E+fPh0TJkxA3759MWDAAKxbt67K4z169EBpaSkuvPBCfPjhh/jDH/6ASCRSpV5BQQFOPPFEz8smIiLKGvwkd53ErEVERJQhmLXqJGYtIiKiDJGErLVp0yZXWUFBQY3fSh89ejT+8pe/AACCwSD++Mc/4je/+U3iHUoiTxfu1qxZgw4dOgAAVq9erQacRCxbtgwAsHLlSqxcudL1uDEG3333Hd59910AwJgxY1x1RowYgXnz5iW9b0RERBkhCbfK5JtRmYlZi4iIKAMkmrWYszIWsxYREVEGSFLWkn7bdeLEiSgqKqr26ePHj8fVV1+N7du3Y9myZbjhhhtQUlKCsWPHJtCp5PJ04e7cc8+t/P8jf8QvWdauXVtjnbZt28IYJmEiIiKqW5i1iIiIiFKHWYuIiKjuWLp0KQoLC6uUFRQU1Pi81q1bo3Xr1gCA/v37AwDuvPNOjBgxIqbn1wZPF+4O17t3b8ycORPt27d3Pfbpp5/i2muvxerVqxPqXKZwjHbrBLm8wvG5ymztErITez/kNuQG1OV5kGgb2npzYIvlATsScx98wri99NdneVjxAOwUfWTSQeyf7osaeb15GYu0TbTtFPVQ11H6FhXGF3bkaUdqI6yO2b09Qsa9/wBAmQnIfROXF/v2sD1sO0f5FKfPyNvOJ+xvQUu7RYu7XDsWGlgVrrLG9gGxbuNovlj+vS9P6Yfb3DPcP/Z+z/rBYt2AMr6j/O7+5dnucQBAjjA+28MkK613APApfasw7rm+1hgk/kluvk+Q8epT1hIiwEEePgTv+NyVLZ+8o0dzlOO3XMgXFbHfIsso873leMgd2pi9RZfY+ZV1IeRZRJVOOMI60rapLQzQJ5/vLeUNTWk9G1tuAzlCeW5QXl5EGN+BUrndiLJfSOtCyxdBd0YxAXl7aOUSK6qsN7+wLpR1LLbhyHVtYch2hfIapULZMaR9S7k9nYkIbWjHmM/DuVpZnnPAnUWMUtf6fq+7THg+AATLwq4y/96c6nro8uGjt7jKzh4yTawbyXNvfyVqI7DfvT4tZfsbZQ6xhP0bRnn7QZo7K9zrB47S4VRINGsxZ2WF+pS1LGX6tYVDzUsb0jkAAIyQy4yUAaorF9pwlPOhlBmMli+8hCrtVqpC01qEk86/VlQ5P2lZUsodjrJEqY2oMn8K7ydp20PMcACcnNjzjJS17N375eWFveycHvockM9FYk7y8I1cLX/ZYSVfBGM/p0nHAiqU9y8OCAeqkuGNklEsS+hbWbnaP1e72i2GpdypZDgx7wGw/e7tZ5eWycsTtr/v+9iz1gd/cecsADi3/1Sx3PgT+wa3E5L3Tem4scLKpK69tvPnutuQ1lsk7ktF3iUpaxUWFqJjx44Jd6d79+7485//jC+++CL7L9ytXbsW+/btEx8rLi7Gq6++GneniIiISGcl4VaZSfh8B6UYsxYREVF6JJq1mLOyA7MWERFRemRa1lqzZg1s28YJJ5yQ3IYTkJLLqN988w3y8mL/VgYRERERxY5Zi4iIiCh1mLWIiIjqnmuuuQaNGjVC9+7dcfTRR2Pnzp1YvHgxnn76adx+++0Z8207wOOFu+effx7PP/985b8nT57sGkxpaSnWrl2LU089NTk9JCIioqqMUW9v5qkNyjjMWkRERBkg0azFnJWxmLWIiIgyQJqy1s9+9jPMnTsX8+fPx/fff4/8/Hx07doVf/vb33D55ZfH358U8HThbuPGjVi8eDEAwLIsrF69GvYRvycRCoXQuXNnzJgxI3m9JCIiokoWknCrzKT0hJKNWYuIiCj9Es1azFmZi1mLiIgo/dKVtUaOHImRI0fGv+Ba5OnC3Z133ok777wTAGDbNtasWYPu3bunpGNERESkSPRHfA+1QRmHWYuIiCgDJJq1mLMyFrMWERFRBmDWqlHcv3HnOE4y+0FEREREh2HWIiIiIkodZi0iIiLKVHFfuKtP/rfvA2L5eatvE8sd4/6yZsSxhZoAhGJb+56oWCy36yhtOMZdX6sbcXxyPxIU9fA9WB/kIC2to4AVjb1dy1tAtz3Ul9ZxVCjTaHWl/eogD20LdR1tHxKWJ40NAB45bWHMfcgU737V1lVme/ietePhox228gXuHEsuj9rufdn2cO/mHGV/9Ql9bmhXiHUb2WVi+T5fjqvspc0dxLolTshVdnyogdw3pc95drnQt1K5ruUei9dj3Ysj2/YyTyTKcg7+JdoGUaZ462k5U509ZFrMbdgR9xxnRZW5U5l/jXAiMH7lPCu1bSvL8yknGKlYm+61NiTauCV+Je+ZgLvMTtHE4Wj9lcstL9vJ5x6fCShjFs6/VlhYD0A1fY6tDwBgpG1qy+P4n3UTYl9eBrig+71iuV3iPq8DACqEPKJlH+H4NVH5dYDl5bcv/PJLY7thQ3dhJCK3IfTDyFELVvF+97JK5fzVr/XNciOChg1y5a41dJeboLxvWhEhi5aG5brahY+AcOwoc6+4raNCuyl6fSpJNGsxZ1Gm+WTCLWL5ab952FVmlHcKfWXuY9WvTOvS4WoFlNe/Ht7T0F5zGWEeMT65XctDppKyIQDYYXc/xPM6AEvKIu6Xyj9Ujr1vVkReF1ZYOCd6mX+VLKKdM5yAu762ji1hm6hbXzgXqeNQtrWXHLjyX5O1nmSkX5wrv1ctnpeVfIGwnGccIedYwaDchpR9tGwgnNutoJy1rZB8kJgDwvtB2rGel+cuLJHfT+rXYby7UMoiAIRW1X5EGsu1jd+9L/tKlXwpHafaazitXDqepHYrlO2cAsxaNYv97ChYsGABTj/9dDRo0AA+n8/1R0RERClgkvRHGY9Zi4iIKA2Ys+oNZi0iIqI0YNaqUdwX7v7xj39g5MiROPXUU1FaWoqRI0di2LBhaNCgAU466SRMmJBdnwolIiIiyiTMWkRERESpw6xFREREmSruC3dTpkzBrbfeij//+c8AgNGjR2PBggX49NNPEY1G8ZOf/CRpnSQiIqIfWSY5f5TZmLWIiIjSI505a//+/bj55pvRqlUr5OTk4JRTTsFTTz1V4/N69eoFy7LUv2+//bbGun379o2/41mIWYuIiCg9+J5WzeL+jbtPPvkEkyZNgvXD/YUjP9z/tmXLlrj77rvx0EMP4corr0xOL4mIiOgwRv/tHy9tUEZj1iIiIkqXRLNW/M8dMmQI3n33XUyZMgXt2rXDk08+iWHDhsFxHAwfPlx93syZM7Fv374qZQcOHEDfvn3RrVs3tGzZsspjJ5xwAhYurPpb5Y0bN46739mIWYuIiChd0pe1skXcF+6i0SiCwSBs20aDBg2qfHqrdevW+OKLL5LSQSIiIqL6iFmLiIioflm+fDlefvnlyot1AHDeeefhyy+/xO23345LL71U/d21Dh06uMrmz5+PcDiMq6++2vVYbm4uevTokdwBZBlmLSIiIspUcd8q8/jjj8fXX38NAOjatSsWLVpU+diSJUtwzDHHJN47IiIicuGtMusHZi0iIqL0SFfOeu6555Cfn4+hQ4dWKR85ciS+/vprvPPOO57ae+KJJ5Cfn49LL700vg7VccxaRERE6cH3tGoW9zfuzj//fKxatQrDhg3DmDFjcOmll+Ldd99FMBjEJ598gilTpiSznxkpauTrno5xhDIr1d2JidSPsCN/Ys+23OPwwqccQclYFz6hb1KZRhubT/marVTfUbY/hLravuKF7WFGmtz5uYSXV9flWBFXWUD9mnXs+2zUw1e1tb0iR9jWAUTFutKerLXrE4Zhiy0AAV+5WJ5nh11lB5yAWFdaxw3tMqVvcj9yLPfyAgnOTQAQ9nBMavPCkf3wJ6FfMTNI/K4A9SDkZDtmLcDxuyeuQKl8rNnl7nI7LNeNhpQ5QCh2gkpdoWlLudWHFVXKhf5Zjtxn4xfymrY8291nddrzK20YOR+KpH5E5fMWHKEjXudPad0rVY0lPOLItY10oswNinVXfjZVWSIBgO/7EvmBsPu8rvLLL1XF40zazgorKOcW5ObI5UFhH9D2b6kfwvEIAPAJ5RGlXW29lQl5bU+FvLhiYZtofZPYyjpWtpO4LrTbIUWFg1qqm/Btwj1INGvF+dwNGzbgpz/9KfxHrNcuXbpUPt6zZ8+Y2vrss8/w+uuv4+qrr0Z+fr7r8c8//xxNmzbFvn370KZNG1x22WW4++67kZubG1/nsxCzlnwKFl6CqXW149IJxD4v29r8IpyXnYg8b1mOux9WJPYD0QiZs9r6Qpawoh7a8DL/AuJ6FjMO5CxplcrnBks6FymckHz+tELuzCjmVsjZ1zQIiXVXfjEt5r7VR74D8oFqVbjfh1EzVX4DufFyIV9UaBODez+0G+TJdaWsZSuvOZR5wYSEXBaQ23Ckvh2Q3+sSx6flPSm3AGK282vZReib8WvvMwt1g0r+0l4fSuXCvFkfslY2ifvC3f3334/yHw7koUOHwufzYeHChbAsC+PGjcMVV1yRrD4SERER1TvMWkRERNlt06ZNrrKCggK0aNFCrL9r1y6ccMIJrvKmTZtWPh6rJ554AgBw1VVXuR47++yzcemll6J9+/YoLS3FihUrMHXqVLzxxhtYs2YNbK8XFbIUsxYRERFlqrgv3IVCIYRCP34qYsiQIRgyZAgAoLS0FF999RVat26deA+JiIioqmTcFqAefDop2zFrERERpUmiWeuH5w4aNMj10MSJE1FUVKQ+1armG6TVPXa4SCSC+fPno2PHjuLv2N13331V/t2/f3+0bdsWY8eOxfPPP4/BgwfHtJxsx6xFRESUJknKWnVZSj5GtXz5chx//PGpaJqIiIiMSc5fHIqLizFu3DhccMEFKCgogGVZ6ptP4XAYv//979G5c2fk5uaicePG6NmzJ956660EBk8AsxYREVFKJSlnLV26FBs2bKjyN3r0aHWxzZo1E79Vt3v3bgA/fvOuJsuXL8e3336Lq6++OuYhX3755QCAdevWxfycuoxZi4iIKIXS9J5WNon7G3dERESUHsn4Id54n79r1y7MmjULXbt2xaBBg/D444+L9aLRKAYPHow33ngD48aNQ8+ePVFSUoL3338fJSXKbx8RERERZYBEs9ah5xYWFqJjx44xP69z585YtGgRIpFIld+5W79+PQCgU6dOMbXzxBNPIBgM4te//nXsnf5BfblNJhEREaVPsrJWXcYLd0RERBSzNm3aYM+ePbAsCzt37lQv3D3yyCNYsWIF3nzzzSq3aBowYEBtdZWIiIgoqwwePBizZ8/GM888g0svvbSyfP78+WjVqhXOPPPMGtv49ttvsXz5cgwZMgTNmjWLednz588HAPHWmkRERERUu3jhjoiIKBul6dNFsf62yowZM3DOOefwzR8iIiLKTmnIWv369UOfPn1w3XXXYd++fSgsLMSiRYuwcuVKLFiwAD6fDwBw1VVXYf78+fj888/Rpk2bKm3Mnz8fkUhEvU3m66+/jvvvvx+DBw/GCSecgLKyMqxYsQKzZs1C7969cdFFF6V8nERERET14XfqEsELdwl47fyHxPILX73ZVWYr39+Uym1lrxXrWk41PXRzILzhqh4kid0iw2dF5Va1dQH3WAK23EZAaFsqO7g8d7s+dR3Hvj4dbcUZn7uu1q5xr2NbGYe47QAUdXpebpsAAF/85xixvKGwOnOUCxK2su4l6n7hQUBYnk/pQoWHezpHk3BCDArHKeywWDdg5H1Zoh2TAeHYiXrYHmHhGAPk48lLu+mWzltlxmLr1q3YsmULLrroIowfPx5PPPEEdu3ahZNPPhnjxo3DiBEjUrdwqlPe+vttrrLze/9OrGtF3POFpU18yuHuBNxzhrFjnxssR8kXjpJRpD5r87owHxrtQrrQhuXIWcQqrZDbCAtzeyT2eR3ardbcMQnQmvUpbUjrSOub1I+Q3K60Pl96f5LSOTqk3/G3ugu17aHt31F3fSsYiL0N7VjwCy93c0Jys6Gg3EbAvdOqx56waxnh+QBghH3TDitzRUm5vLwK93Fq9nu4HbVP7huEec/S6obk9Smue2UuRDjiLhP2CUSEeimSzts3Pfvss7jrrrswYcIE7N69G+3bt8eiRYtw2WWXVdaJRqOIRqMwwvEwZ84ctG3bFr/4xS/E9o855hj4fD5MnjwZO3fuhGVZOOmkk3Dvvffitttu460y65n3Z9/iKutx+e/FulLOsTwclk5AnjsdZbq3osLr4rB8cPnK3DnHNsr7MB5e9mk50EhvrWmZUchgUm492K6H3Onl952UHIjSMneZkr9sZXmOz30eiOYobzcH3Bv7f/53glyXKvU9xb2OtD3FKhcOSi1TB+TtJL5uUDOD0HaDPLGqyRWylsdzjpSfnDxlEpH45eVZB9yviSwhZwEAKkqVcuF1ldZGXq57ecL7yYCeJUXK3CJmc+mYrsXfjeOtMmvm6cLdBx98EFO9L774Iq7OEBERUe3atGmTq6ygoAAtWrSIu81t27YBOPiJ7+OOOw5/+tOfcNRRR2H27Nm44oorUFFRgVGjRsXdfl3GrEVERFS/5efnY8aMGZgxY4ZaZ968eZg3b5742CeffFJt+4WFhXjxxRcT6WJWY9YiIiKibODpwt3pp58e0y2yjDEx30qLiIiIPHLMwb9E2wAwaNAg10MTJ05EUVFR/E3/8Am9srIyLF++vPIWTn369MHpp5+Oe++9lxfuFMxaREREGSDRrJVoTqOUYdYiIiLKAMxaNfJ04W7u3Lmp6gcRERHFyiDxe4H/8PylS5eisLCwykMFBQUJNd2sWTMAQPv27av87oplWbjwwgvxu9/9Dtu3b0/oW311FbMWERFRBkg0a9X995KyFrMWERFRBmDWqpGnC3f8TRoiIqK6pbCwEB07dkxqmyeeeCLy8pT72v9wz3T+foqMWYuIiIgodZi1iIiIKBvwXTMiIqIsc+hHfBP9SxW/34+LL74Y//73v7Fly5bKcmMMVq5ciRNPPBHNmzdPXQeIiIiIEpDJOYuIiIgo2zFr1czTN+6IiIgoQ5j0pZQVK1agpKQExcXFAICNGzdiyZIlAID+/fsjLy8PkydPxooVK9C3b18UFRWhUaNGePzxx/Gvf/0Lf//739PWdyIiIqKYpDFrEREREdV5zFrV4oW7FPBbjqss6IvGXNcWyjR2Ei4vO1B+cNlD037bPT5baSBgyetCKvdSV1tvUl0f5Lpe1mdYe8D4Ym4jYEdcZT5lvY3rsCLmduujr7e1EsvzlB8U9wnltnIs+ITyaC3fTFn7enRQGIfWt6hQHPtsc6jt2H+gXdqXtWMs6KEn2jEi9c3RtpM0hyhr2THymI8sN0q9uui6667Dl19+WfnvxYsXY/HixQCAzZs3o23btjjxxBPx+uuv44477sA111yDcDiMU045Bf/4xz8wcODAdHWd6gBfqfvcCQBWmbvchORzshVRjteAex4wfg/Hdliecyzlh7Mt6YWKo8yHwhzjadbRXhRFleVFhAwWlXOZSDn/iozWBw9nKZ+SvwLu8mheQKy66tXxsS+vHurX9hb5gbJyV5EpLZPrKrdJtvzCy1Jb2YekfUvb/n6hPBm3avbLbRih3AnG/trAeH3zIiLMex6OU0tZb+L20Naxtp2k41qZC6W5xVQIr7ai8vxPRMkV3CfPI9JLOUeZGqJBIVMp069R5hEr9ukTtpDtnKhyzhFeGBslt6gv8aT6WvaRXogr872YDQF5/tTmX6mulvekc4aWRQNyfpLOfdEceeOtXflbuW0CAPRrfbNYLm5pL9tfOq8D6jYV29D2i2BQaFdZnpDBjJDVAcBomdFDVpJeg2nHulgalt/5NeXu7HuwvjunWPkNtO6529XypZDBtPWgrh9hLKa01F0vomR4SgteuCMiIsoyybgtQCLPP/z2l9Xp1KkTXnjhhfgXRERERJQGiWat+nD7JiIiIqJ4MWvVjBfuiIiIso2Bp29Fq20QERERkVuiWYs5i4iIiEjHrFUjXrgjIiLKMpYxnm4RobVBRERERG6JZi3mLCIiIiIds1bNknCDfyIiIiIiIiIiIiIiIiJKFL9xR0RElG2cH/4SbYOIiIiI3BLNWsxZRERERDpmrRrxwh0REVHWSfxWmfXihuBEREREcUk0azFnEREREemYtWrCC3cp8OI5f3SVDVt3jVjXtrJrJ9P66xPK/XZUrBuw5UviUtu2pdV1l/uUA9YnXIIPWHLffMryosZ9V1kHVsx9C4g1lbpK36h6IUu+869P2U61KZrC+y7Lo1bGLB2/Ste87IXqsScsT6sb8DAXhk3s2zSgzSHCOvIp2ylqycuLHrH2rSybz4my1f+8fY9Y3rfzXe5CnzJfBH1isbHd9R25qljXVs5FpkIudwLuxi1H7rPxudswyvhsYT6zIsod8v3KABF0Fzkezg6OMicm45xou/tsQkJ/AUQbuMsrjtKSGVXH7C+Ry8vK3YVR+fxrBeSXn0Y411o+ZTv5hTZsZf8W9hUo53XtjQPjCGNRjlNEhXGEY/84sFURkR8oLROLTVQ4JrVjTBq3MI+pdX3KXCGtY41R5hBhfzEVFe4yJxz7sogobq/943axvPf5U9yFefIcIB3tUnY6WC73Q5rNLOVdzGhAaFuZq8W4ppwbtJeeTiD2Xx6yc9ydNsqpwZLOOYA8t0fl+d6KCucSbYERoW5QzlRaZnRC7vFFlP2CaiCc+wDI52AP+ULNBv4cuVzK1WHlHCztW9rxJOU1pW/asWCE1092hZwvxFwVUTJqsTvnqtlX2U52bq67UM1PwrpQ8qyYUbW5IqxkSalcKpOyJaUNL9wRERFlG4PEP1zE64xEREREskSzFnMWERERkY5Zq0a8cEdERJRtjEn8Wysp/CYoERERUVZLNGsxZxERERHpmLVqFPt3u4mIiIiIiIiIiIiIiIgoZfiNOyIioixjGfknC722QURERERuiWYt5iwiIiIiHbNWzXjhjoiIKNvwVplEREREqcPbNxERERGlDrNWjXirTCIiIiIiIiIiIiIiIqqzVq9ejSuvvBLt27dHgwYNcOyxx+Liiy/G+++/n+6uufAbd0RERFnGMoDlJN4GEREREbklmrWYs4iIiIh06cpajz32GHbt2oUxY8agQ4cO2LFjB6ZPn44ePXrgpZdeQu/evePvVJLxwl0tWdRjllj+q3dGucpsZEbK99tRV1lAOaKkuiE7ItYNWWGxPGC52/B5WBe20jdbOJJ9Wl1teUL9sPGJVaU++4SxAUDAcq8jaT1QVTu+PtZVZsNK2fKiHvbDqIevamvnp0S/Cq09Pxl7lrR/28qql+oG1LryA/K6l9exNG5H2RwBYV7QtkeFtkaPaMLLfJUw3iqTyGXl+vtdZX163ifWNX75uDZCseNXJi7LXa7NI9GQnBmkScqOyMszPne50U59Qt+k5wMAAvLLAcuv9FkSEc4wUhkAGGEtWco8q55g3H1zGobEqhWNg66ysiYexlZP9T3qSnehdt6ICttU2s4ATFTeL6xgwF3oV16qSvumrWxTaf/2Kce/cNx4ZUnrKKK87qhwvw6wDpTLDVfIr5/UcoElHDeWdux5oR2nUgiLyK8PjVQurcvazC68fRORy+pX7nCVnfPLh+TKwtQg5SwAUN5aEV/2RbUXlCJ5gXZEaFiLe2q4c7dhlPnQBIQBau1qK0k4l1hReU4V5x/pXA0AtrC8HDlTRRvlyF3Lc59rI7mpe3+mrrgwf4SrzAq6cysAQHl/VWJJ+Uk7J2l5XaLlMoH2WssTJZdZ0rEQVvKlVB5W1qXjbtdUVMjtCpkKgJwxtZwklWvbSVoX0rEL6Fmr3J0xrVzhmI6EgBK56aRLU9Z69NFH0aJFiyplffv2RWFhIR544IGMunCXcbfK9PJ1xQ8++AC/+MUvkJ+fj8aNG2PIkCH44osv0tBrIiKiWmSS9Ef1ErMWERFRDZizKAHMWkRERDVIU9Y68qIdAOTn56NDhw7YunVrfI2mSMZduHvsscewZcsWjBkzBsuXL8eMGTOwfft29OjRA6tXr66s9/HHH6NXr16oqKjA3//+d8yZMweffvopfv7zn2PHjh1pHAERERFR5mLWIiIiIkodZi0iIqLssXfvXnzwwQfo2LFjurtSRcbdKjPWrytOmDABoVAIL7zwAho1agQA6NatG0466SRMmzYNDz74YK33nYiIqDZYRrkdl8c2qH5i1iIiIqpeolmLOat+Y9YiIiKqXrKy1qZNm1yPFRQUiN+s01x//fUoKSnBXXfdFXd/UiHjvnEXy9cVI5EIXnjhBfzXf/1XZbgBgDZt2uC8887Dc889V2v9JSIiqnWH7gWe6B/VS8xaRERENWDOogQwaxEREdUgSVlr0KBB6NSpU5W/mTNnxtyNe+65BwsXLsTDDz+Mbt26pWq0ccm4b9xJDn1d8dCnkj7//HOUlpaiS5currpdunTByy+/jLKyMuTkyD+cSkREREQ/YtYiIiIiSh1mLSIiouRbunQpCgsLq5QVFBTE9NxJkybhvvvuw/33348bbrghFd1LSFZcuDvy64q7du0CADRt2tRVt2nTpjDGYM+ePTjmmGPUNrdv3+66Z7j01UoiIqKMYwA4SWiD6AfMWkRERIdJNGsxZ9ERkp21mLOIiCirJSlrFRYWxvXbdJMmTUJRURGKioowfvz4BDqSOhl/4e7Q1xUfeeQR19cVLctSn1fdYwAwc+ZMTJo0KSl9TMTCM2e7yn79ztUxP99Ows3zA3ZULPdb7vKAJR9RIV8kpucDQMh21z3YD3d5QGnDFvrhU472oOVu11ZeSfmU8cG47yqrrfsA5PGJdYXx5djhmJ9fX0WlW89Uf8i7+Lw+4QiOh1fj2nkoqraRWN9SyRa65lPGERDqBjyPzV1fW2/SbBFQjlPpPtGO0jU7xiThT/hKWuwsY5LwG3d8R4kOqstZ6+W37hbLz73oIbHc8QtjUsYpRANo83c0JJdbUZ9QKM8lRpiAteNYqgufsCwAVkBuw/EJbSjTnBUQ2nbkylbUvTwjLQuAFVHWRdD9EiaaI7+sCee7+xbJy9zzbMaQ9peIknGl/c2St4cVCMReHgzKywsJdbXjVNo3tblLPZ6Eg10qg7wva/uxVSGsT+W40ZZnhULuMrkFGOMhp0jbI8e9LACAT/n1jdIyd5k2PoE4tmgQEJpNhUSzFnMWHS4VWSsTchYAvPaP28XyHr+a7iqTsxNgtGFK9ZW6jvRiTpm2pLynvbXmq1CyiNQPJc84Qfe5SHvrCVHlnCE2rKzQiFCuzeECkyOfq52QfG6Xcq6WfelHlpS1tHOHlBmUbG+E9yTFZVVHyloKKWuJ+csrLTMIxWKmAuT1qc2rfnef7SaNvfVNaruG18yx1DV+4ZhW9hXLUjKjXzh+G+a7y8rLgF1qD5MqnVlr8uTJKCoqwt13342JEyfG3U6qZfSFO+3ris2aNQPw4yeUDrd7925YloXGjRtX2/bo0aMxdOjQKmWbNm3CoEGDEu43ERERUTZg1iIiIiJKnVRlLeYsIiIi76ZPn44JEyagb9++GDBgANatW1fl8R49eqSpZ24Ze+Guuq8rnnjiicjNzcX69etdz1u/fj0KCwtrvA94ixYtxB8MJiIiyniH/RBvQm1QvcasRUREpEg0azFnEVKbtZiziIgoq6Upay1btgwAsHLlSqxcuVJoNnMynPK96vSq6euKfr8fF110EZ599lkUFxdXln/11VdYs2YNhgwZUpvdJSIiql2HAk6if1RvMWsRERFVgzmLEsSsRUREVI00Za21a9fCGKP+ZZKMu3AnfV3x8L9DJk2ahAMHDmDgwIFYsWIFnnvuOQwYMADNmzfHbbfdlsYREBEREWUuZi0iIqLMtX//ftx8881o1aoVcnJycMopp+Cpp56q8Xnz5s2DZVni37fffuuqv2rVKvzsZz9DXl4emjdvjiuuuALbt29PxZDqHWYtIiIiSlTG3Soz1q8rtm/fHmvXrsVvf/tbXHLJJfD7/ejduzemTZuGgoKCWu0zERFRrXIg/iiz5zaoXmLWIiIiqkGiWSuB5w4ZMgTvvvsupkyZgnbt2uHJJ5/EsGHD4DgOhg8fXuPz586di/bt21cpO/R7aoe8+uqr6NevHwYMGIDnn38e27dvx29/+1ucf/75eO+99xAKheIfADFrERER1SSNWStbZNyFu7Vr18Zct1u3bli1alXqOkNERJSBLBhYCX6F30Jm3QKAag+zFhERUfUSzVrx5qzly5fj5ZdfrrxYBwDnnXcevvzyS9x+++249NJL4fP5qm2jU6dOOP3006utc/vtt6Ndu3ZYsmQJ/P6Dbwsdf/zxOOusszBnzhxcd911cfWfDmLWIiIiql66slY2ybgLdwQ4sMRy28MOaVvyZeeAUO63o2LdkB0Rni/XlcoDSrs+pW++BA8424r9+VofvNQPwr1+AKBCOKx8yscApHZHnPSWp77VZVu3HSOWB5RjJFNFPe7bXuvH3K7QbDI+oKLdc9knbCef5XHbCSdxn9aEhxO+1Ib2FoittXvE8RvyOK8kxCDx306p+xmHSGWUict4mKKkNrR2rajcsON3lxtbbsTYsXfOCrrb0KYoOxyIeXnaCytLOMHYFXIOtMJCuTaf+eRyE3DP2MYnrzdp3B88dou8vHqoX+ubxXIr6N4vtNOG5XdnX3w2sE0AAD6dSURBVCugvMwMBuXy3Bz38nKUfVPY/tCOGzU0uEn7scb4PfzihLJ/GyETqetNy0/Kfi824Qj9UOYV0yDPXRaU+2Y58uRilQltC/sKAFjS9pPqhkuBfWITyZdo1orzqc899xzy8/MxdOjQKuUjR47E8OHD8c4776Bnz57x9wvAtm3b8O677+J3v/td5UU7AOjZsyfatWuH5557jhfuKGHS2zPqWzZKuZTLHG1eF49Xpa40FSlVIznyq0TLuMvtsDwQf6mQy7SXmBH5AV+5Oz/5DijvHUZif10qzeFGmau1LCqN5d05t8bch7qu37E3iuWWkIlMVM7PEHKZpX2IQ9p+2reoldwhvR7RcoDJcZdLGQeQ9zc1f2mvOxz3OlLzniNkLfU9KSGjajlLylSAnKuU5TkN3NtEy5fS6ydLO86114xSBvcL+1C0Fn9VLU1ZK5tk3G/cERERERERERHVNxs2bMBPf/rTKhfUAKBLly6Vj9dk4MCB8Pl8aNq0KYYMGeJ6zqF/H2rzyOXEsgwiIiIiSi1+446IiCjbGJOEb9zVg48nEREREcUj0az1w3M3bdrkeqigoAAtWrQQn7Zr1y6ccMIJrvKmTZtWPq5p2bIl7rrrLvTo0QONGjXC+vXrMWXKFPTo0QNvvvkmunbtWqWNQ20euZzqlkFERESUFEnKWnUZL9wRERFlG164IyIiIkqdJL2ZNGjQINdDEydORFFRkfpU/TZe1T/Wt29f9O3bt/Lf55xzDgYMGIDOnTtjwoQJeP7552Nqq7plEBERESUFL9zViBfuiIiIiIiIiIiSbOnSpSgsLKxSVlBQoNZv1qyZ+I233bt3A5C/JVedtm3b4uyzz8a6deuqLAOQv723e/duz8sgIiIiouTjhTsiIqJs4/zwl2gbREREROSWaNb64bmFhYXo2LFjzE/r3LkzFi1ahEgkUuV37tavXw8A6NSpk+euGGNg23blvw+1sX79evTv379K3fXr18e1DCIiIiJPkpS16jK75ipERESUWQwsk9gfUPdvK0BEREQUn/TkrMGDB2P//v145plnqpTPnz8frVq1wplnnumpvc2bN+PNN99Ejx49KsuOPfZYdO/eHQsWLEA0Gq0sX7duHT755BMMGTIkrr4TERERxY7vadWE37jLQAvPnC2W//qdq2NuI2DJl539dtRVlucLi3VDlrvctuSDwicsL2C5l+W13FbGIdX1KZfabQ8Hsu3hcr2tXPcOWhEPbdT9SSYRPzn2G7H8222tEm47msHrPpq5XYNPWG8+5WcwpCPEhlzZp5SLc46Rj1OtH7HS9gmt3SPXRVCZH4ko87z2/O1i+dn/Nc1dqN47P/ZJx/ErvyMUcs+UlnYS8DDHGaGyEqlgtI/xCb9xZLT53sOJS/ztJKVdy5HbNT53p50gP48YjxVf/UEs79dytLvQ1n4PS1j3gYC8wJyQWGxy3PWdnKBcN+iT206QseX9zZLmAGVesCLuA80Ky699xHa13xazlf1bWs9aXeG4MQF5XTp57nVvlO1vl8mvJcXaat+Efgj9hVP3j/N+/fqhT58+uO6667Bv3z4UFhZi0aJFWLlyJRYsWADfD+vqqquuwvz58/H555+jTZs2AIBf/OIXOOecc9ClSxc0atQI69evx9SpU2FZFiZPnlxlOQ8++CD69OmDoUOHYvTo0di+fTvuuOMOdOrUCSNHjqz1cVPd8/aTt7nKzvz17+XKSowQc4cyTUaDUr5Q5mphWtbykPYSLyq8m2orLxx9FUK5knG0r1c4fvcDtjanCkxQeftX6LN6bgjIy9PyIR20YtsjYnm/o69zlVlQMo5fKNeyVkg4h+cqmSokt2GE/c0k4/dPpdcjWqZS8pNYV3stEhUWGFbes5XeZzLycWPy5Dwrvn5SjqdIAynDKXOIsI7sAxVyHzRirhLWmzY3UVrwwh0REVG2SfRHfA+1QURERERuiWatBJ777LPP4q677sKECROwe/dutG/fHosWLcJll11WWScajSIajcIctpzOnTvj6aefxrRp01BaWooWLVqgd+/euOeee9CuXbsqy+jVqxeWL1+OCRMm4KKLLkJeXh4GDhyIhx56CKGQ8oYkERERUbKkMWtlC164IyIiyjaOSfyTUPwkFREREZEs0ayVwHPz8/MxY8YMzJgxQ60zb948zJs3r0rZww8/7Gk5ffr0QZ8+feLpIhEREVFi0pi1sgUv3BEREWUbgyR84y4pPSEiIiKqexLNWsxZRERERDpmrRrV/ZvEExEREREREREREREREWUBXrgjIiLKNofuBZ7oXxyKi4sxbtw4XHDBBSgoKIBlWSgqKqqhuwbnnHMOLMvCDTfcENdyiYiIiGpNmnIWERERUb3ArFUjXrgjIiLKOskIOPGFnF27dmHWrFkoLy/HoEGDYnrOo48+ik2bNsW1PCIiIqLal56cRURERFQ/MGvVhL9xl0X+dubjrrKR744U69qWI5YHrKirzC+UAUDAdpfbykHhE5an9cFLudRfAAhakZjr2nC367MSP7gdoV0A8HloY8RJbyXcj/rIZ1np7oInPsj9jWbASUb79IZ+rLvLAsr4Apa7dW1dSHUBwBY+QeNl+4eV4zQqfjInsf1KG1td06ZNG+zZsweWZWHnzp14/HH3uelwW7ZswZ133om//vWvGDJkSC31kig+bzwz1lV29pBpYl3bHUXgBOR2jZK4HeOeN7QpTowuXj5lqE1Rtoe5SzoJAIgG3OXC0A42IS1PqWuUlRENuc8ZTlCu++bi2+TGqXpR4fyp/AC8sd11LVtJGH45KZuAu9z45TaMtB8qx4IVcZdb2nETVdpwhHXhoa4Vll+jiOvY6yeHhfVsQvKEY0LuCcoR1vvBdoW5SeovAKtCmAwBeXwaaV7wCX2Tyogoa7zzt1vF8h6X/14sN8Ih7yinF6muUaZf6S0p5W0qOX95bEPKSVr4sQLK+SUklCnnIjscdJVp870lhNSKxsLCAETy5JUv5WeKgZhzlR1OylU58nYyAfc2lTIAADhaZhCylhVR3sstC8dcV16YMmYt20n7vZY5pAwXUXKLlEXC7rEd7ESOvLhc93o22mstaftr842QJbXXSVZUm/iEzCjl8gizVibhhTsiIqJs4xj1DVRPbcTB8njh/JprrkGfPn0wePDguJZHREREVOsSzVqJ5jQiIiKiuoxZq0a8cEdERJRtjHPwL9E2Uuzxxx/H//7v/2Ljxo0pXxYRERFR0iSatWohZxERERFlLWatGvHCHRERUT0m/fZcQUEBWrRokVC727Ztw9ixYzF16lS0atUqobaIiIiIiIiIiIjqC164IyIiyjYG3n8HR2oDwKBBg1wPTZw4EUVFRQk1f+2116Jr164YNWpUQu0QERER1bpEs1bdv3sTERERUfyYtWrEC3dERETZxiThN+5+CEhLly5FYWFhlYcKCgoSanrJkiVYuXIl3njjDezdu7fKYxUVFfj+++/RoEEDBALyj2QTERERpVWiWSvRD1gRERER1WXMWjXihTsiIqJ6rLCwEB07dkxqmxs2bEAkEkGPHj1cj82ePRuzZ8/Gc889J37bj4iIiIiIiIiIqD7jhbss57O8XV22hfoBKyrXFb5z6rPkH360hXKtXS/lQSsSc12tXR8S/7HKKOyYlycZXvhOwn2oj77dJv8uls+yXGU23GUA4FPKU0ZanPIpEG0P8qWoy17WhXuP/6END+s+ILQiPf9gG/IS7QTXRUA5/suEte8o37OPxvgpHm2dpYQxSbhVZuo+nXTFFVegV69ervLzzjsPgwYNwpgxY9CpU6eULZ8omeyoMjeIB708aRllMnP87rYtZZ60hE8jWo6HSVKpqkVJI9R3/Mr4hBOXNmYo45OE831yeZ67jf+df2vM7dKP+h13k1huIkIGjyrJRdqmjpK/lU/VWsJxZmnnKemYVNq1w0Kflb5JfQAACG1YXsanjUMq93puFo4z45OPG+k4VZstd29/S1qXAKwKZb/wMhZpHxLnilrM9YlmrXrwKXCiZLHD8vESCUlznNyGVB4NeshU2inOw9tJ0aBcLmYiZTozSk7yhd1ldlReGVbUfVcT45NfrUaErFXaTG733TnMWvHod9I4+QHhPGGUrGVJLzyScZ5RM4q7yFbO92I+0Nr1cDx5ylra6w4p22l1I8I48nLFqiagTEQCab4BAF+ZkLWV9SatYyssv1/uhbiOTeLvoceMWatGvHBHRESUbdJ84W7FihUoKSlBcXExAGDjxo1YsmQJAKB///5o27Yt2rZtKz732GOPFS/qEREREWUMvplERERElDrMWjXihTsiIqJsk+YLd9dddx2+/PLLyn8vXrwYixcvBgBs3rxZvWhHRERElBX4ZhIRERFR6jBr1YgX7oiIiMiTLVu2xPU8Uw+CFRERERERERERUSJ44Y6IiCjbOEb//SAvbRARERGRW6JZizmLiIiISMesVSNeuCMiIso6SbhVpvSL00RERESExLMWcxYRERGRjlmrJna6O0BERERERERERERERERE/MZd1nv89HkJt1G04eKY69qW/BVWn3CVWyoDgIAVFcuDViTmulJ5QHj+wX64+zzkxA/Ful48vemMhNuoj9768gSxvGebL2Juw4blKvMJZZ7bsGJvw1Y+FeJI+71y3Hj5cIj25XHp0xfaOKQxa7T16WW92R4+G+Klb17Y8InlAWHli9sOgG3FtqH8Vi1+FibRH/E91AYR1ei1529PuI0ev5oulhsP04Zl3POkseXj2NjuunZUOW/J0ySMz92G45fn6mjQXf7O326TG/ag26iHxfJIXsJN12na/rZuobBNbGUnFM7tRrmVjeUTdqKInMsRlsstn7sflpIvLGHf1M5pVkToszYO5RhBRHg9ot3WR7plj1HqeskNwjENAEbaftL6AcRtamlZwEtGELYdACAv112mjEPqm0ibsFIh0azFnEUUs7eeTjwzdL3JnRlMQK4bEeYcX7mSk5R3TR0h+2i5TjplqBlQmTocYSzrFiSeUU+/6veustLmqXltXpecNdSdtd5crOzHWtbyQspaCuncbpWUy5VNMPZ2S8NyubSDa+d16dyo3e7Qyy0Uw0pdKY9q5+ege12YHHkSkV4nAQCELOnkypOIXeHOl2JuVZiQ3K4VlreTmMGiwvK0PJwKzFo14oU7IiKibOOYxO/nXQ/uB05EREQUl0SzFnMWERERkY5Zq0a8VSYRERERERERERERERFRBuA37oiIiLKNcWC0W295aIOIiIiIBIlmLeYsIiIiIh2zVo144Y6IiCjbGCR+W4C6f1cBIiIiovgkmrWYs4iIiIh0zFo14q0yiYiIiIiIiIiIiIiIiDIAL9wRERFlG2OS80dEREREbsxZRERERKmTxqxVXFyMcePG4YILLkBBQQEsy0JRUVHyxpYkvFUmoajT8zHX/d3G/mK5bbnvKyuVAYAPcrktfMd1xElvxdy32nZp4bvp7kJWyrEiYvln/2nlKmtoyW344H7AFsqq47OkNuTPMkht25Z8ggibqKssoLWrtOGFl3UhjdkraR0FLF/C7aZKVLnntdRnadsBse9b/tr8LIzjAMoc66kNIqoV6xbeFnPdnw2bLpYbYSqytPlJKHb8ct23nhkba9dq3fuzb0l3F7KSLUctnN/rAVdZ0O/h5aDy4thE3edPK6qcYyoqYl6crS3PL5xvlcVZUhta34RxHGxbaEP7TQ2tbblhd5FPyRJWEl62RxPLnSYg5z2j5EtLWJ9aG5bUt7C0PRLPsjFLNGsxZxHVqn/9MfbMcMqND7vKosHYMxUAOIGYF4d//SFz88x7T9ya7i5kJTvsnuP7nHWfWNdvKzuR7eH9g4gQ7rTMYcd+/rFKw3K5lJ/CSsD0wktO8pK1tAs40rlYe19My2Biu3KxCSb43piW1YQ+W+XK9rCUcUh51hGylpaHUyGNWWvXrl2YNWsWunbtikGDBuHxxx+Pvx8pxAt3RERE2SYZn+TmJ8GJiIiIZIlmLeYsIiIiIl0as1abNm2wZ88eWJaFnTt38sIdERERERERERERERERUTpYSbgjWW3ghTsiIqIsY4yBSfAWTIafBCciIiISJZq1mLOIiIiIdMxaNeOFOyIiomzDW2USERERpQ5vlUlERESUOknKWps2bXI9VFBQgBYtWsTfdobghTsiIiIiIiIiIiIiIiLKGoMGDXKVTZw4EUVFRbXel2Sz090BIiIi8sgxyfkjIiIiIrc05qz9+/fj5ptvRqtWrZCTk4NTTjkFTz31VI3Pe/bZZzFs2DAUFhYiNzcXbdu2xa9+9St89tlnrrq9evWCZVmuv759+8bdbyIiIqKYJSlrLV26FBs2bKjyN3r06DQPLjn4jTvy5M4Oy8Xyh/99gatsTPtVqe4OZaGAJd+/2Af3i1ufJX+2wIb7R0R9Hn9Y1BY+tyC1q/bDyOMIWD5XmQO5rp2EW+hI45bGdrA88R9fldaFtrzaJq1nbR+Stl/ISuyUGFT27ZQwjroPemqDiDLO24tuE8t7XjrdVfbm3+W6RBJLih12an6Y3USjSh+U7COdk8IRuY2olMu0doVypW+IejgvauvNy/qUsqvPnSMBAH6lXIo52rqQ6ir52fELmUgbW1BenhUV+qysYisSdpdJv3lSm9kl0ayVwHOHDBmCd999F1OmTEG7du3w5JNPYtiwYXAcB8OHD1ef9+CDD6Jly5a46667cMIJJ2Dr1q144IEHcNppp2HdunXo2LFjlfonnHACFi5cWKWscePGcfebKFv885FbXGWnXv+wWPfDR911iWwhSljaBza0fGG7T8xWQHlPQjpfR+SchFAgtuejmlwWEQZYWhZ737TfLfPyvp2Xumr2EcJPQFg/XpendcOXYJ+1t9aMu12To4xDyYxWabm7UNqHtJycCknKWoWFha6MU1dkxrutRyguLsa4ceNwwQUXoKCgAJZliV9vNMZg9uzZ6NatGxo1aoRmzZrh3HPPxYsvvlj7nSYiIiLKEsxaREREmWf58uV4+eWXMXPmTPzmN7/Beeedh9mzZ6NPnz64/fbbEa3mDbVly5bh+eefx8iRI3Huuefi8ssvx6pVq1BeXo6HH3ZflMjNzUWPHj2q/LVv3z6Vw6s3mLOIiIgoURl54W7Xrl2YNWsWysvLxfuUHjJx4kRcc8016N69O5555hnMmzcPoVAIAwcOxLPPPlt7HSYiIqpFxgDGMYn98U6Z9RqzFhERkS7hrBVnznruueeQn5+PoUOHVikfOXIkvv76a7zzzjvqc1u0aOEqa9WqFY477jhs3bo1vg5RXJiziIiIqpeurHXIihUrsGTJEixbtgwAsHHjRixZsgRLlizBgQMHkjDCxGXkrTLbtGmDPXv2wLIs7Ny5E48//rhYb86cOTj77LPx2GOPVZb16dMHLVu2xPz58zFkyJDa6jIREVHtMQ7U+015aoPqK2YtIiKiaiSateLMWRs2bMBPf/pT+I+4XWmXLl0qH+/Zs2fM7X3xxRf48ssvxYtHn3/+OZo2bYp9+/ahTZs2uOyyy3D33XcjNzc3rr7Tj5iziIiIapCmrHXIddddhy+//LLy34sXL8bixYsBAJs3b0bbtm0Taj8ZMvLCnRXjfWUDgQCOOuqoKmU5OTmVf0RERETkxqxFRESUeps2bXKVFRQUiN+OAw5+U+uEE05wlTdt2rTy8VhFIhFcddVVyM/Pxy23VP2drrPPPhuXXnop2rdvj9LSUqxYsQJTp07FG2+8gTVr1sCWfheIYsacRURElNm2bNmS7i7UKCMv3MVqzJgxGDt2LJ544gkMGTIEZWVleOihh7B3717cdNNN6e4eERFRShjHwFiJ3RfA8F6ZFANmLSIiqo8SzVqHcpb0TbeJEyeKv3d2SHUXfWK9IGSMwVVXXYXXX38dzzzzDH7yk59Uefy+++6r8u/+/fujbdu2GDt2LJ5//nkMHjw4puVQYpiziIiovkpW1qrLsvrC3c0334zc3Fxcf/31uPrqqwEc/CTasmXLcNZZZ1X73O3bt2PHjh1VyjZu3AhA/lQcVe/bz/e7yj5yPkpDTyjTbfomLJbnWu4fWm+gfNIzKHyV2o7tNeyP9YWf+LQhN2Jb7rqO8pVsB+4Th6N89dtJwjlGGrc0toPlHleSuLzY11ttk9a9WjcFt4n8fPPBfbu8vDzpbR/pgClO+LYAB1CSpN5QXcaslTkO7P3WVfbRR8xa5CbtKwDglFS4yvwV++RGosWuIuO4nw8AEC4kWNGAXDcclMull6U+n7I8IeeoL9yF8qg7cwLwFsxivHhSfRtCmTZmRymPuNeFicp1TVgoV8ZhpGItbCvr3ooK5coqtisi7sKwu+xAxR4A2ZG1DuWspUuXorCwsMpjBQUF6vOaNWsmfqtu9+7dAH785l11jDG4+uqrsWDBAsyfPx8XX3xxTH2+/PLLMXbsWKxbt44X7moJc1bmKN0tnzuZtUhSUuzeX8IH5Jxklyu/lRUudZdpGUU6X1tK1ioPxfb86kSE81+4TK4rte0o508v/fBSV8uBUhtGuRQi5ie5rmPkde9oeU1gV7i3tRWRt78lZVQttkrbDgAqhP1T2N8OhL8HkF1Zq04zGW7Hjh0GgJk4caLrsTlz5phQKGRuu+02s2rVKrN8+XJz2WWXmby8PLNy5cpq2504caLBwd2cf/zjH//4x7+k/c2bNy9FZ0Rjtm3bZho2bJi0vjZs2NBs27YtZf2l7MCsxT/+8Y9//Mumv2zJWvHkrFGjRpn8/HwTDoerlC9atMgAMG+++Wa1z3ccx1x55ZXGsiwzZ84cT8v+9ttvDQBzxx13eHoeVY85i3/84x//+Jdtf3U5a2WTrP3G3Z49eyo/lTRt2rTK8n79+qFXr1649tprsXnzZvX5o0ePxtChQ6uU7du3D59++ik6d+6MUEj4hMJhNm3ahEGDBomfoKsL6vL46vLYAI4v29Xl8dXlsQEHP+H6//7f/0O7du1StoxWrVrh448/xp49e5LSXpMmTdCqVauktEV1D7NWatXl8dXlsQEcXzary2MD6v74si1rxZOzBg8ejNmzZ+OZZ57BpZdeWlk+f/58tGrVCmeeeab6XGMMRo0ahblz5+Ivf/kLRo4c6WnZ8+fPBwD06NHD0/MoPunOWUDdnjPq8tgAji/b1eXx1eWxAXV/fPUha2WTrL1w98knn6C0tBRnnHGG67HTTz8dr776Kvbv34/8/Hzx+S1atBB/EPpnP/uZp34UFhaiY8eOnp6TTery+Ory2ACOL9vV5fHV5bEBQKNGjVLafqtWrep0MKHMwaxVO+ry+Ory2ACOL5vV5bEBdX98dTlr9evXD3369MF1112Hffv2obCwEIsWLcLKlSuxYMEC+H64nepVV12F+fPn4/PPP0ebNm0AADfddBOeeOIJXHnllejcuTPWrVtX2W4oFMKpp54KAHj99ddx//33Y/DgwTjhhBNQVlaGFStWYNasWejduzcuuuii2h94PZQpOQuo23NGXR4bwPFlu7o8vro8NqDuj68uZ61skrUX7g5t3HXr1mHEiBGV5cYYrFu3Dk2aNEGDBg3S1T0iIiKirMasRUREVPueffZZ3HXXXZgwYQJ2796N9u3bY9GiRbjssssq60SjUUSjUZjDftdn2bJlAIA5c+Zgzpw5Vdps06YNtmzZAgA45phj4PP5MHnyZOzcuROWZeGkk07Cvffei9tuuw228jvjlFzMWURERFSdjL1wt2LFCpSUlKC4+OAPo2/cuBFLliwBAPTv3x+tW7fGkCFDMGvWLIRCIfTv3x/l5eWYP38+3nzzTUyePBlWMn40nIiIiKgOYtYiIiLKPPn5+ZgxYwZmzJih1pk3bx7mzZtXpezQhbmaFBYW4sUXX0yghxQL5iwiIiJKRMZeuLvuuuvw5ZdfVv578eLFWLx4MQBg8+bNaNu2LRYuXIg//elP+Nvf/oY5c+YgEAigXbt2WLBgAYYPH56urhMRERFlPGYtIiIiotRgziIiIqJEZOyFu1g+LZaTk4OxY8di7Nixqe/QEQoKCjBx4kQUFBTU+rJrQ10eX10eG8DxZbu6PL66PDag7o+P6h5mrfSqy+Ory2MDOL5sVpfHBnB8RJkk03MWULePqbo8NoDjy3Z1eXx1eWwAx0e1yzKH3xSdiIiIiIiIiIiIiIiIiNKCvzpMRERERERERERERERElAF44Y6IiIiIiIiIiIiIiIgoA/DCHREREREREREREREREVEG4IU7IiIiIiIiIiIiIiIiogxQ5y/crV69GldeeSXat2+PBg0a4Nhjj8XFF1+M999/31U3HA7j97//PTp37ozc3Fw0btwYPXv2xFtvvVVZ59NPP8XYsWPRrVs3NG7cGE2bNsVZZ52FJUuWxNSfLVu2wLIs8e+pp55K+/gAqP2bMmVKTH0Kh8OYNGkS2rZti1AohPbt2+ORRx7xPLZUjG/evHnq+GIZY7q2X3V9bt++vavdRx55BO3bt0coFMLxxx+PSZMmIRwOx9SnZG2/ZI8tW489L9suG4+9WMeXScderGMzxmD27Nno1q0bGjVqhGbNmuHcc8/Fiy++KLabCccdUTowa3kbH5Cd832s48vG+T4bc1Yqxpetxx6z1kHZeOwxaxHFhlnL2/iAzJnv63LO8jI+Zq2DsvXYy8asVZdzlpfxMWtlN3+6O5Bqjz32GHbt2oUxY8agQ4cO2LFjB6ZPn44ePXrgpZdeQu/evQEA0WgUgwcPxhtvvIFx48ahZ8+eKCkpwfvvv4+SkpLK9v7nf/4HL774In7961/jjDPOQCQSwdNPP42hQ4di0qRJmDBhQkz9uvHGGzF8+PAqZSeddFLax3fIJZdcgttuu61KWevWrWPq0+jRo/G3v/0NkydPxhlnnIGXXnoJY8aMQXFxMcaPH5/W8Q0YMABvv/22azkTJkzAyy+/jMGDB8fUr9reflKf33nnHdx8882uPt9///245557cMcdd+CCCy7Au+++i7vvvhvbtm3DrFmzauxTsrZfsseWrceel20HZN+xF+v4MunYi3VsEydOxOTJk3HttddiypQpKCsrwyOPPIKBAwfimWeewZAhQyrbzJTjjigdmLWYtTI1a9XlnJWK8WXrscesdVA2HnvMWkSxYdbK3qxVl3OWl/Exax2UrcdeNmatupyzAGatesPUcd99952rrLi42Bx99NHm/PPPryx7+OGHjW3b5u233662vR07dhjHcVzlAwYMMHl5eaasrKza52/evNkAMA899FCMI6hessdnjDEAzPXXXx9XfzZs2GAsyzIPPPBAlfJRo0aZ3Nxcs2vXLk/tpWJ8R9q/f7/Jz883Z599do1107X9JFdccYWxLMt89tlnlWU7d+40OTk55pprrqlS9/777zeWZZmPPvqo2jaTuf2SPbZsPfYk0viMyc5jT6KN70jpOvZiHduxxx7r6ltpaak56qijzC9/+cvKskw67ojSgVnrIGYtXabP95JMz1nGMGtVh1nroEw/9pi1iGLDrHVQNmatupyzjGHWqg6zlneZ/J6kJBuOPWat7Fbnb5XZokULV1l+fj46dOiArVu3VpbNmDED55xzDnr06FFte82bN4dlWa7y7t2748CBA9i9e3finfYg2eNL1NKlS2GMwciRI6uUjxw5EqWlpVi5cqWn9mpjfE8//TT279+Pq6++2vNzExXr+I5UXFyMxYsX49xzz0VhYWFl+cqVK1FWViauf2MMli5dWm1/krn9kj22bD32jqSNL1HpOvaO5GV86Tr2Yh1bIBDAUUcdVaVeTk5O5d8hmXTcEaUDs9ZBzFq6TJ/vj5QNOQtg1tIwa/0o0489Zi2i2DBrHZSNWasu5yyAWUvDrBWfTH5PUpINxx6zVnar8xfuJHv37sUHH3yAjh07AgC2bt2KLVu2oHPnzhg/fjyOPvpo+P1+dOzYEfPnz4+pzTVr1qCgoEA8cCRTpkxBMBhEXl4ezj77bPzjH/+IezxHSsb4nnzySeTm5iIUCqFbt26YO3duTMvesGEDCgoK0LJlyyrlXbp0qXw8Ucnefk888QQaNWqEoUOHxtyH2tx+kqeeegolJSWuk8Oh9du5c+cq5ccccwyaN29e4/pP9fZLZGyaTD72JDWNL5uOPYmX7ZdJx540tjFjxmDlypV44oknsGfPHnzzzTe49dZbsXfvXtx0002V9TL9uCNKB2YtZq3DZfp8f6RszVkAsxbArHW4TD/2mLWI4seslb1Zqy7nLIBZC2DWypZjT5KtOQtg1qqTavsrfpngV7/6lfH7/ea9994zxhjz9ttvGwCmUaNGpkOHDubvf/+7eemll8wll1xiAJhZs2ZV297s2bMNADNjxowal/3111+bUaNGmb///e/m9ddfNwsXLjQ9evQwAMzs2bMzYnzDhw83CxcuNK+99ppZsmSJ6devnwFg7r777hqX3adPH3PyySeLjwWDQddXbtMxvsP9+9//NgDMb37zm5iWnY7tJznzzDNN48aNTWlpaZXyUaNGmVAoJD6nXbt25oILLqh22anefomMTZLpx56kuvFl27EniXX7Zdqxp43tz3/+swmFQgaAAWCaNm1qXn755Sp1Mv24I0oHZi1mrUOyZb4/XLbmLGOYtYxh1jokW449Zi2i+DBrZW/Wqss5SxqfhFnrR5l+7EmyNWvV5ZxlDLNWXVTvLtzdfffdBoB55JFHKsvefPNNA8AEg0GzZcuWynLHccxpp51mjjvuOLW95cuXm2AwaC655BLxPsWxqKioMKeeeqpp1qyZCYfDcbVxSLLHd8jAgQON3+8327dvr7Zenz59TPv27cXHgsFgzBOaJtnjGzt2rAFg3n333bj7lOrtd6QNGzao94weNWqUycnJEZ/Xrl07c+GFF1a7/FRuv0THdqRsOPaO5GV8h2TysXckL+PLpGNPG9ucOXNMKBQyt912m1m1apVZvny5ueyyy0xeXp5ZuXJlZb1MPu6I0oFZi1nrcNkw3x8uW3OWMcxaxjBrHS4bjj1mLaL4MGtlb9aqyznLGGYtY5i1JJl67B0pW3OWMcxadVW9unBXVFRkAJj777+/SvnHH39sAJguXbq4nnPnnXcaAOKPPq5cudLk5OSYAQMGmPLy8oT6NmXKFAPAbNy4Me42kj2+wz311FMGgFm+fHm19S677DJTUFDgKt+/f78BYO68884YRiJL9vgqKipMixYtTNeuXePu0yGp3H5HuuWWWwwA8+GHH7oeu+OOOwwAU1JS4nqsefPmZtiwYdW2nartl4yxHS5bjr0jxTq+w2XysXekWMeXSceeNrbdu3eb3NxcMbCde+65pm3btpX/ztTjjigdmLWYtQ6XDfP9kbIxZxnDrHUIs9ZB2XDsMWsRxYdZK3uzVl3OWcYwax3CrOWWqcfekbIxZxnDrFWX1ZvfuJs0aRKKiopQVFSE8ePHV3nsxBNPRF5envg8YwwAwLarrqqXXnoJgwYNwrnnnotnnnkGwWAwof5py4lVsscXb73OnTtjx44d+Pbbb6uUr1+/HgDQqVOnap+vScX4XnjhBWzfvj0pPyKayu13uIqKCvztb39Dt27dcMopp7geP3Qv4kPr+5Bvv/0WO3furHH9p2L7JWtsh2TTsXe4WMcXb//Scewdzsv4MuXYq25sn3zyCUpLS3HGGWe4nnf66adjy5Yt2L9/P4DMPO6I0oFZi1nrSNkw3x8uG3MWwKx1CLPWj7Lh2GPWIvKOWSt7s1ZdzlkAs9YhzFqJ9S8b3pMEsufYY9aqA2rpAmFa3XvvvQao/n66w4YNM4FAwGzevLmyzHEcc8opp5gTTzyxSt2XXnrJ5OTkmF/84hcx3a+4JhUVFeaUU04xzZs3N5FIxPPzkz0+Sf/+/U0gEDA7duyott6GDRuMZVlmypQpVcp/85vfmNzcXLNr164al3WkVI1vwIABJicnx+zevdtznw5XG9vvkMWLFxsAZubMmeLju3btMjk5Oebaa6+tUv673/3OWJZlPvroo2rbT/b2S+bYjMnOY++QWMYnyfRj7xAv48uEY6+msX355ZcGgOtYchzHnHXWWaZJkyaVt7LItOOOKB2YtZi1JNkw3x8u23KWMcxah2PW+lE2HHvMWkTeMGtlb9aqyznLGGatwzFryTL52Dsk23KWMcxa9UGdv3A3bdo0A8D07dvXvP32266/QzZt2mQaN25sTj75ZLNo0SLz4osvmsGDBxvLsszixYsr673++usmNzfXtG3b1qxevdrV3t69eyvrzp8/3/h8PjN//vzKsltuucXccMMNZtGiRWbNmjXmr3/9qznjjDMMADN37ty0j2/q1KnmiiuuMH/729/MmjVrzNNPP20uuOACA8AUFRVVWbY0PmOMufrqq00oFDIPPfSQWbt2rRk/fryxLKvGryTXxvgO2bZtm/H5fGb48OHqsjNp+x3St29fk5uba77//nu1zfvuu89YlmXGjx9v1q5dax566CETCoXMqFGjahyfMcnbfskeW7Yee7GOL1uPvVjHd0gmHHuxjm3IkCHGtm0zZswY89JLL5l//OMf5r/+678MADN58uQqbWbKcUeUDsxa3saXrfN9NmatupyzUjG+bD32Yh1fth57sY7vkGw69pi1iGLDrOVtfJk039flnOVlfIcwa2XnsRfr+LLx2It1bIdk27HHrJXd6vyFu3PPPdcAUP8Ot379ejNgwADTsGFDk5OTY3r06GGWLVtWpc7EiROrbW/NmjWVdefOnes6AJ944gnTvXt307RpU+P3+02TJk3MhRdeaF566aWMGN8//vEPc/bZZ5uCggLj9/tNw4YNzc9//nOzaNEi17Kl8Rlz8BMDEydONK1btzbBYNC0a9fO/PGPf8yI8R1y//33GwBm9erV6rIzbft99dVXxrZt89///d81tjtjxgzTrl07EwwGTevWrc3EiRNNRUVFjeMzJnnbL9ljy+ZjL5bxZfOx52XfzIRjL9axlZaWmoceesh06dLFNGzY0DRt2tT06NHDLFiwQPzh6Ew47ojSgVnL2/iyeb7PtqxVl3NWKsaXzcces9aPsunYY9Yiig2zlrfxZdJ8X5dzltfxMWtl97GXbVmrLucsL+Nj1spuljE/3EyViIiIiIiIiIiIiIiIiNImvl8+JCIiIiIiIiIiIiIiIqKk4oU7IiIiIiIiIiIiIiIiogzAC3dEREREREREREREREREGYAX7oiIiIiIiIiIiIiIiIgyAC/cEREREREREREREREREWUAXrgjIiIiIiIiIiIiIiIiygC8cEdERERERERERERERESUAXjhjoiIiIiIiIiIiIiIiCgD8MIdERERERERERERERERUQbghTvKGPPmzYNlWXjvvffS3RXR2rVrYVkW1q5dW1m2fPlyFBUVpWR5RUVFsCwrae1dccUVsCwLlmWhU6dOMT3HsqyUjY9it3HjRhQVFWHLli2ux26++ebK7Zqfn1/7nSMioqzBrFUVsxYdwqxFRETJwKxVFbMWHcKsReQdL9wRxei0007D22+/jdNOO62ybPny5Zg0aVIae+VNy5Yt8fbbb+PJJ59Md1fIg40bN2LSpEliwLnlllvw9ttvo3///rXfMSIioiRi1qJ0YdYiIqL6gFmL0oVZi8g7f7o7QJQtGjVqhB49eqS7GwkJhUJZMYYDBw4gLy8v3d3ICm3atEGbNm1QUFCQ7q4QERElhFmr9jBrxY5Zi4iI6gpmrdrDrBU7Zi0iGb9xR1nnjTfewPnnn4+GDRsiLy8PPXv2xIsvvlilzqHbE6xZswbXXXcdmjdvjmbNmmHIkCH4+uuvq9QtLy/HbbfdhpYtWyIvLw/nnHMO3n//fbRt2xZXXHFFZb0jbylwxRVX4NFHHwWAyq90W5aFLVu2YMuWLbAsC/PmzXP1X/qa/osvvohTTjkFoVAIxx9/PKZNmyaO3RiDmTNn4pRTTkFubi6aNGmCSy65BF988YW3lXiEffv2YdSoUWjWrBny8/PRt29ffPrpp2Ldzz77DMOHD0eLFi0QCoXw05/+tHI9HO6jjz7CBRdcgLy8PBQUFOD666/Hiy++6LotQ69evdCpUye89tpr6NmzJ/Ly8nDllVdW9mvs2LE4/vjjEQwGceyxx+Lmm29GSUlJXOvlww8/xMCBAyv73qpVKwwYMAD/+c9/PK2v9957D7/85S/RtGlT5OTk4NRTT8Xf//73KnV27NiB0aNHo0OHDsjPz0eLFi3Qu3dvvP766672HnvsMXTt2hX5+flo2LAh2rdvj/HjxwM4uC8PHToUAHDeeedV7mfSvkVERJQMzFrMWsxazFpERJQ6zFrMWsxazFpENeE37iirvPrqq+jTpw+6dOmCJ554AqFQCDNnzsRFF12ERYsW4dJLL61S/+qrr8aAAQPw5JNPYuvWrbj99ttx+eWXY/Xq1ZV1Ro4ciaeffhrjxo1D7969sXHjRgwePBj79u2rti/33HMPSkpKsGTJErz99tuV5ccccwy++eabmMf0yiuv4OKLL8bPfvYzPPXUU4hGo5g6dSq+++47V93f/OY3mDdvHm666SY8+OCD2L17N+6991707NkT//rXv3D00UfHvNxDjDEYNGgQ3nrrLUyYMAFnnHEG3nzzTfTr189Vd+PGjejZsydat26N6dOno2XLlnjppZdw0003YefOnZg4cSIA4JtvvsG5556LBg0a4LHHHkOLFi2waNEi3HDDDWIfvvnmG1x++eUYN24cHnjgAdi2jQMHDuDcc8/Ff/7zH4wfPx5dunTBRx99hAkTJmD9+vVYtWpV5b3SY1kvJSUl6NOnD44//ng8+uijOProo/Htt99izZo1KC4ujnl9rVmzBn379sWZZ56JP//5zzjqqKPw1FNP4dJLL8WBAwcqQ/Hu3bsBABMnTkTLli2xf/9+PPfcc+jVqxdeeeUV9OrVCwDw1FNPYfTo0bjxxhsxbdo02LaNTZs2YePGjQCAAQMG4IEHHsD48ePx6KOPVt7S4sQTT4y5z0RERLFi1mLWYtZi1iIiotRh1mLWYtZi1iKKiSHKEHPnzjUAzLvvvqvW6dGjh2nRooUpLi6uLItEIqZTp07muOOOM47jVGlr9OjRVZ4/depUA8B88803xhhjPvroIwPA/Pa3v61Sb9GiRQaAGTFiRGXZmjVrDACzZs2ayrLrr7/eSIfR5s2bDQAzd+5c12MAzMSJEyv/feaZZ5pWrVqZ0tLSyrJ9+/aZpk2bVmn77bffNgDM9OnTq7S3detWk5uba8aNG+da1uFGjBhh2rRp4ypfsWKFAWBmzJhRpfz+++939fXCCy80xx13nNm7d2+VujfccIPJyckxu3fvNsYYc/vttxvLssxHH31Upd6FF17oWofnnnuuAWBeeeWVKnV/97vfGdu2XfvDkiVLDACzfPlyY0zs6+W9994zAMzSpUuVNRSb9u3bm1NPPdWEw+Eq5QMHDjTHHHOMiUaj4vMikYgJh8Pm/PPPN4MHD64sv+GGG0zjxo2rXebixYtd6+1II0aMMA0aNIh9IEREVO8wazFrHY5Z60fMWkRElAzMWsxah2PW+hGzFpF3vFUmZY2SkhK88847uOSSS5Cfn19Z7vP58Otf/xr/+c9/8Mknn1R5zi9/+csq/+7SpQsA4MsvvwRw8JNOAPD//t//q1Lvkksugd+f+i+klpSU4N1338WQIUOQk5NTWd6wYUNcdNFFVeq+8MILsCwLl19+OSKRSOVfy5Yt0bVr1ypf0/dizZo1AIBf/epXVcqHDx9e5d9lZWV45ZVXMHjwYOTl5VXpQ//+/VFWVoZ169YBOLheO3XqhA4dOlRpY9iwYWIfmjRpgt69e7vG26lTJ5xyyilVlnXhhRdWuS1BrOulsLAQTZo0wW9/+1v8+c9/rvzkjxebNm3Cxx9/XLmujlwH33zzTZV98M9//jNOO+005OTkwO/3IxAI4JVXXsG///3vyjrdu3fH999/j2HDhuH555/Hzp07PfeLiIgoGZi1mLWYtYiIiFKHWYtZi1mLiGLFC3eUNfbs2QNjDI455hjXY61atQIA7Nq1q0p5s2bNqvw7FAoBAEpLS6vUP/Kr+H6/3/XcVNizZw8c5/+3d/8xUddxHMdf58Xtzpu7SASFipARf1xgP8xzURAsUhxtrjY3W6UouVFbhbBWze02bIKllWuzH2snLH+1glX4V25aWWHpbNzQqLmJ/ViwhbOBWxTx7g/H5XEHQgKe9nxsjO3z/Xw/n8/3c8C9ts+Hzw1p7ty5MddGlvX09MjMlJaWpqSkpKivw4cP/+c3xt7e3rjPO7L/3t5eDQ4O6vXXX4/pf9myZZIUGUNvb2/c4w1GO/Ig3mva09OjcDgc09esWbNkZpG+xjsvPp9Pn332mW699Va98MIL8vv9Sk9PVzAY1F9//TWuuRo+5qG2tjamryeeeCJqDl555RVVVVUpEAioublZhw8f1pEjR7R06dLIz58kPfroowqFQjp9+rQeeughpaamKhAIaP/+/eMaEwAAk4WsRdYiawEAMHXIWmQtshaA8eIz7nDFSE5O1owZM+Kesz38wbwpKSkTanP4Tb2np0cZGRmR8sHBwZiwNBHDu4wGBgaiyke2mZycLIfDoe7u7pg2RpalpKTI4XDo0KFDkaB2oXhl4zF79uzI814Yckb2n5ycHNkF9uSTT8ZtKysrK9JmvLPM4z2npMiZ3hdKSUmRx+NRKBSKe8/waz2RecnLy9PevXtlZgqHw2psbFRdXZ08Ho+ee+65uP3E6/P555/Xgw8+GLdObm6uJGnnzp2699579cYbb0Rdj3fueEVFhSoqKnTu3Dl9/vnnCgaDKi8v1w8//KDMzMyLjgsAgMlA1iJrjbw+/J2sBQDApSNrkbVGXh/+TtYCMBILd7hieL1eBQIBtbS0aMuWLfJ4PJKkoaEh7dy5U9dff71uvvnmCbVZWFgoSXrvvfciH44qSR988IEGBwcvev+FO52GxyOd34HjdrsVDoej6n/00Ucxz7Ro0SK1tLTo5ZdfjgSjvr4+tba2RtUtLy9XQ0ODfvnll5gjEC5FcXGxXnrpJe3atUtPPfVUpHz37t1R9WbOnKni4mJ9++23ys/Pl8vlGrXNoqIibdmyRSdOnIg6VmDv3r3jHld5ebk2bdqk2bNnR4LTaPUmOi8Oh0MLFizQq6++qsbGRh07dmxc9+Xm5ionJ0ft7e3atGnTRfsYGbjC4bDa2tp0ww03xL3H6/WqrKxMf/75p5YvX67jx48rMzMzZkcdAABTgaxF1hqtHlkLAIBLR9Yia41Wj6wFYCQW7pBwDhw4oK6urpjyZcuWqb6+XqWlpSouLlZtba1cLpe2b9+ujo4O7dmzJ+4Ol7H4/X6tXLlSW7duldPpVElJiY4fP66tW7fK5/NpxoyxT5PNy8uTJG3evFllZWVyOp2RN/9HHnlEoVBI2dnZWrBggb755puY0CBJGzdu1NKlS1VaWqqamhr9/fff2rx5s7xer86cOROpV1BQoHXr1qmiokJHjx5VYWGhvF6vfv31V33xxRfKy8tTVVXVhJ5fku6//34VFhbq2Wef1blz57Rw4UJ9+eWXevfdd2Pqbtu2TXfffbfuueceVVVV6aabblJfX59Onjyp1tZWHThwQJL0zDPPKBQKqaysTHV1dUpLS9Pu3bvV2dkpSRed1+E2mpubVVhYqOrqauXn52toaEg//vijPvnkE9XU1CgQCIx7Xvbt26ft27dr+fLlmj9/vsxMLS0tOnv2rEpLS8c9X2+99ZbKysq0ZMkSrV69WhkZGTpz5oy+++47HTt2TO+//76k88Fr48aNCgaDKioq0vfff6+6ujplZWVFhefHH39cHo9HBQUFmjdvnrq7u1VfXy+fz6c777xTknTLLbdIkt5++23NmjVLbrdbWVlZ03LsBQDg6kPWImsNt0HWImsBACYfWYusNdwGWYusBfxnBiSIHTt2mKRRv06dOmVmZocOHbKSkhLzer3m8Xhs8eLF1traGretI0eORJUfPHjQJNnBgwcjZX/88YetX7/eUlNTze122+LFi62trc18Pp9VV1ePee/AwIBVVlbanDlzzOFwRI3z999/t8rKSktLSzOv12sPPPCAdXV1mSQLBoNR4/r4448tPz/fXC6X3XjjjdbQ0GDBYNDi/YqGQiELBAKR58/OzrbHHnvMjh49Oub8rlq1yjIzM+NeO3v2rK1Zs8auvfZamzlzppWWllpnZ2fcsZ46dcrWrFljGRkZlpSUZHPmzLG77rrLXnzxxah6HR0ddt9995nb7bbrrrvO1q5da01NTSbJ2tvbI/WKiorM7/fHHVd/f79t2LDBcnNzzeVymc/ns7y8PKuurrbu7u4JzUtnZ6etXLnSsrOzzePxmM/ns0WLFlljY+OY8xZPe3u7rVixwlJTUy0pKcnmzp1rJSUl9uabb0bqDAwMWG1trWVkZJjb7bbbb7/dPvzww5jXoampyYqLiy0tLc1cLpelp6fbihUrLBwOR/X52muvWVZWljmdTpNkO3bsiLq+atUq83q9E34WAMD/B1mLrDUSWetfZC0AwKUia5G1RiJr/YusBUyMw8xs8pcDgSvbV199pYKCAu3atUsPP/zw5R7OpFi9erU+/fRTnTx5Ug6HQ06nc9rHsG7dOu3Zs0e9vb1jHkmA8RsaGtLQ0JDWrl2r5uZm9ff3X+4hAQBwUWStqUHWmnxkLQDAlYisNTXIWpOPrAXEx1GZ+N/bv3+/2tradMcdd8jj8ai9vV0NDQ3KyckZ9YNar1SnT59WUlKS/H6/Ojo6prSvuro6paena/78+erv79e+ffv0zjvvaMOGDYSbSbR+/Xpt27ZN0vmzxAEASDRkralB1poeZC0AQKIja00Nstb0IGsB8fEfd/jf+/rrr1VTU6MTJ06or69PKSkpWrJkierr6zVv3rzLPbxJ09XVpd9++02S5PF45Pf7p7S/+vp6NTY26ueff9bg4KBycnJUWVmpp59+esJntk+H4R0+Y7nmmsTb6/DTTz+pp6dHkuR0OnXbbbdd5hEBABCNrDU1yFrTg6wFAEh0ZK2pQdaaHmQtID4W7gBA549caGpqGrMOfy4BAAD+G7IWAADA1CFrAVcXFu4AQNE7t0azcOHCaRoNAADA1YWsBQAAMHXIWsDVhYU7AAAAAAAAAAAAIAHMuNwDAAAAAAAAAAAAAMDCHQAAAAAAAAAAAJAQWLgDAAAAAAAAAAAAEgALdwAAAAAAAAAAAEACYOEOAAAAAAAAAAAASAAs3AEAAAAAAAAAAAAJgIU7AAAAAAAAAAAAIAGwcAcAAAAAAAAAAAAkABbuAAAAAAAAAAAAgATAwh0AAAAAAAAAAACQAP4BLf9VPXpxwSgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1800x480 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mean_sst = dss[var].mean('time')\n",
     "std_sst  = dss[var].std('time')\n",
@@ -247,18 +2053,76 @@
     "Write a NetCDF with your regional mean time series and month-mean/variability maps.  Upload with your committed repository."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5ad936f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/pat6/module_2/module-2-check-in-ptrierweiler/notebooks'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "04d97552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_sst.to_netcdf(\"Month-mean.nc\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "8167b72e",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "std_sst.to_netcdf('SST_STD_Dev.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "979134d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "range_sst.to_netcdf('SST_Range.nc')"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,