diff --git a/docs/notebooks/Gallery.ipynb b/docs/notebooks/Gallery.ipynb
index f56b6e2a..53987052 100644
--- a/docs/notebooks/Gallery.ipynb
+++ b/docs/notebooks/Gallery.ipynb
@@ -176,7 +176,9 @@
     "| Transform-on-load | How to transform and adjust data at load-time | [Using Built-In Operations](./data/UsingTheInBuiltOperations.ipynb) | 18 Aug 2025 |\n",
     "| Applying data transforms | -- | [Applying Data Transforms](./data/Transforms.ipynb) | 18 Aug 2025 |\n",
     "| Geospatial subsetting | -- | [Region Cutting](./data/RegionCutting.ipynb) | 18 Aug 2025 |\n",
-    "| Remote Zarr Loading | Demontsrates loading data in a remote zarr archive | [Remote Zarr Loading](./data/Remote_Zarr_Loading.ipynb)  |2 June 2026 |\n"
+    "| Remote Zarr Loading | Demontsrates loading data in a remote zarr archive | [Remote Zarr Loading](./data/Remote_Zarr_Loading.ipynb)  |2 June 2026 |\n",
+    "| Building an accessor | A demonstration of how to build a data accessor for PyEarthTools, using publicly available Met Office data | [Building a Data Accessor](./data/BuildingAnAccessor.ipynb) | 22 September 2026 |\n",
+    "| Building a site archive  | A demonstration what is required for a site archive, using the `third_party/aws_site_archive` package.  | [Building a Site Archive](./data/BuildingASiteArchive.ipynb) | 22 September 2026 |\n"
    ]
   },
   {
@@ -214,7 +216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/data/BuildingASiteArchive.ipynb b/docs/notebooks/data/BuildingASiteArchive.ipynb
new file mode 100644
index 00000000..f137c199
--- /dev/null
+++ b/docs/notebooks/data/BuildingASiteArchive.ipynb
@@ -0,0 +1,2287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de2318dd-65aa-4c3f-8a6f-f4b16bf9c185",
+   "metadata": {},
+   "source": [
+    "# Building a Site Archive\n",
+    "\n",
+    "A key element of the the PyEarthTools user experience is enabling a central repository or catalogue of data that is specific to the site or platform in questions. This abstracts away the details of how the data is stored and organised for a particular site and instead providing a consistent interface to the data. Platform managers and data curators can optimse the storage and loading of the data for the specifics of the platform without requiring additional training for the users of the data.  This tutorial is aimed at those who would like to create a new site archive for their particular platform. The tutorial builds on the [Building an Accessor tutorial](BuildingAnAccessor.ipynb) tutorial.\n",
+    "\n",
+    "## Components of A Site Archive\n",
+    "\n",
+    "There are a few components that typically go into \n",
+    "\n",
+    "* Paths to datasets - These done have to all be in the same location, though typically all on the same platform.\n",
+    "* Dataset Accessors - For each of the datasets to be included in the site archive, there should be an accessor.\n",
+    "  * This doesn't have to be a bespoke accessor, so long as it is is suitably parameterised to be able to adapt to the target platform. For example, one could use the same ERA5 accessor for several sites, so long as the root directory of the data is passed as a parameter and the organisation of the data on disk is the same.\n",
+    "  * Accessors should include call to the `register_archive` function so that they can be accessed through the `pyearthtools.data.archive\n",
+    "* A list of accessors - This is included\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e61976a5-b3a1-42fe-998f-b799b5da5846",
+   "metadata": {},
+   "source": [
+    "### Loading the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a0b8c748-1ca3-464a-80cc-31782d660d6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "016591ac-f529-482f-8404-008777a51667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e8000402-055e-48d1-9e10-997d24ffd586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot\n",
+    "import cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6242a7de-8a03-4ca8-9881-d16ad525d50d",
+   "metadata": {},
+   "source": [
+    "When we initially import the pyearthtools data library, we will see only the generic classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "50932f07-5b7f-497e-af98-c32cda2ad1f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyearthtools.data.archive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2fedb688-66c8-4032-9152-e2d92ddfb3b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ERA5_ARCO',\n",
+       " 'ZarrIndex',\n",
+       " 'ZarrTimeIndex',\n",
+       " '__all__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'config_root',\n",
+       " 'extensions',\n",
+       " 'get_indexes',\n",
+       " 'get_root_directories',\n",
+       " 'load_root_directories_from_config',\n",
+       " 'print_indexes',\n",
+       " 'pyearthtools',\n",
+       " 'register_archive',\n",
+       " 'registered',\n",
+       " 'reset_root',\n",
+       " 'root',\n",
+       " 'set_root',\n",
+       " 'set_root_directory',\n",
+       " 'sys',\n",
+       " 'utils',\n",
+       " 'zarr']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(pyearthtools.data.archive)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f14e777b-4ae0-4702-89af-c0909384eb0b",
+   "metadata": {},
+   "source": [
+    "To access the data in the site archive, import the site archive library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "96e06dc3-6d14-48c1-900e-e667d851167b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import site_archive_aws"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "433028f7-ff49-4cf7-ba10-22a8d3a389e5",
+   "metadata": {},
+   "source": [
+    "Once that is done the data is then available in a standardised, portable way i.e. site specific code is not required beyond the initial import. We can do a listing of the `data.archive` module to see the additonal data index classes that are now available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c0b74ce8-f343-4144-beb3-e05a6f930ca5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ERA5_ARCO',\n",
+       " 'MOGlobal10km',\n",
+       " 'MOUKV',\n",
+       " 'ROOT_DIRECTORIES',\n",
+       " 'ZarrIndex',\n",
+       " 'ZarrTimeIndex',\n",
+       " '__all__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'aws',\n",
+       " 'config_root',\n",
+       " 'extensions',\n",
+       " 'get_indexes',\n",
+       " 'get_root_directories',\n",
+       " 'load_root_directories_from_config',\n",
+       " 'print_indexes',\n",
+       " 'pyearthtools',\n",
+       " 'register_archive',\n",
+       " 'registered',\n",
+       " 'reset_root',\n",
+       " 'root',\n",
+       " 'set_root',\n",
+       " 'set_root_directory',\n",
+       " 'sys',\n",
+       " 'utils',\n",
+       " 'zarr']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(pyearthtools.data.archive)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a6ab0f3-c584-4e3b-8023-e7d4c7f6d5a7",
+   "metadata": {},
+   "source": [
+    "### Load data from an accessor - UKV\n",
+    "We can now load the data from a specific data index class in the site archive. These are configured for the particular dataset and platform. In this case the data is public and so should work from any platform with the correct libraries installed. To start with we will load analysis data created by the [Met Office high-resolution UK forecast model known as UKV](https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting). We will load a subset of variables for a particular time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0f3e45c9-6eef-45e4-b103-193deb0f194e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var_list = [\n",
+    "    'temperature_on_pressure_levels',\n",
+    "    'relative_humidity_on_pressure_levels',\n",
+    "    'wind_speed_on_pressure_levels',\n",
+    "    'wind_direction_on_pressure_levels',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78430489-4610-4304-a2c1-3279eed99858",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_accessor = pyearthtools.data.archive.MOUKV(var_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b28d54ac-1678-414d-b7b6-06be3d42b31d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_time = datetime.datetime(2026,3,1,12,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a716b3d3-ad6e-4382-900f-c807a041d3a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MOGlobal10km_AWS': 's3://met-office-atmospheric-model-data/global-deterministic-10km',\n",
+       " 'MOUKV_AWS': 's3://met-office-atmospheric-model-data/uk-deterministic-2km'}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_accessor.ROOT_DIRECTORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9d095010-d766-4a84-a767-3e50f3c00b66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['MO_UKV_AWS_DIR_TEMPLATE',\n",
+       " 'MO_UKV_FNAME_TEMPLATE',\n",
+       " 'ROOT_DIRECTORIES',\n",
+       " '_InitialisationRecordingMixin__get_items_for_repr',\n",
+       " '__abstractmethods__',\n",
+       " '__annotations__',\n",
+       " '__call__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__matmul__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_abc_impl',\n",
+       " '_data_wrapper',\n",
+       " '_desc_',\n",
+       " '_get_interval',\n",
+       " '_get_preprocess',\n",
+       " '_initialisation',\n",
+       " '_mf_args',\n",
+       " '_open_args',\n",
+       " '_property',\n",
+       " '_pyearthtools_initialisation',\n",
+       " '_pyearthtools_repr',\n",
+       " '_repr_html_',\n",
+       " '_round',\n",
+       " '_skip_transforms',\n",
+       " '_variables',\n",
+       " 'aggregation',\n",
+       " 'base_transforms',\n",
+       " 'copy',\n",
+       " 'data_interval',\n",
+       " 'data_resolution',\n",
+       " 'exists',\n",
+       " 'filesystem',\n",
+       " 'get',\n",
+       " 'initialisation',\n",
+       " 'load',\n",
+       " 'make_catalog',\n",
+       " 'preprocess_transforms',\n",
+       " 'range',\n",
+       " 'record_initialisation',\n",
+       " 'retrieve',\n",
+       " 'safe_series',\n",
+       " 'sample',\n",
+       " 'save_index',\n",
+       " 'search',\n",
+       " 'series',\n",
+       " 'set_interval',\n",
+       " 'to_repr_dict',\n",
+       " 'update_initialisation']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(ukv_accessor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "11a594a9-5ad5-4f29-a1de-7561bf45c3d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260301T1200Z/20260301T1200Z-PT0000H00M-temperature_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260301T1200Z/20260301T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260301T1200Z/20260301T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260301T1200Z/20260301T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc']\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_data_ukv = ukv_accessor[sample_time]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d8569524-4acd-4ecb-8233-989683c44a2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\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",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\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-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\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",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > 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",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B 00:00:00\n",
+       "    forecast_reference_time       datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB ...\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB ...\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB ...\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-03-01T13:18:47Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0</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-2e7a123a-b69b-496b-8c77-acfa2636e977' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-2e7a123a-b69b-496b-8c77-acfa2636e977' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>pressure</span>: 33</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 970</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 1042</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-1197eb6a-91f7-4b0c-922e-cf14ec1e373c' class='xr-section-summary-in' type='checkbox' checked /><label for='section-1197eb6a-91f7-4b0c-922e-cf14ec1e373c' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-03-01T12:00:00</div><input id='attrs-97a2d503-fea9-4948-910f-b2a429082aeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97a2d503-fea9-4948-910f-b2a429082aeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2682fef2-d136-4725-97dc-97f6fc5a44d7' class='xr-var-data-in' type='checkbox'><label for='data-2682fef2-d136-4725-97dc-97f6fc5a44d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-03-01T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-776c67c6-b74f-443a-91d2-bf2f57aa1b2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-776c67c6-b74f-443a-91d2-bf2f57aa1b2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f03f18-2473-413e-bb7d-d16a0f0651e5' class='xr-var-data-in' type='checkbox'><label for='data-f9f03f18-2473-413e-bb7d-d16a0f0651e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.036e+06 -1.034e+06 ... 9.02e+05</div><input id='attrs-1402c983-73a0-45cf-acd1-41325c0fb390' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1402c983-73a0-45cf-acd1-41325c0fb390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fd84b98-b4bc-46b6-8261-1e6109c92e4d' class='xr-var-data-in' type='checkbox'><label for='data-1fd84b98-b4bc-46b6-8261-1e6109c92e4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.036e+06, -1.034e+06, -1.032e+06, ...,  8.980e+05,  9.000e+05,\n",
+       "        9.020e+05], shape=(970,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.158e+06 -1.156e+06 ... 9.24e+05</div><input id='attrs-6501372f-d642-4788-8e26-5d6d7fc1a425' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6501372f-d642-4788-8e26-5d6d7fc1a425' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d601dd4-bfdc-4f4e-bd00-086dfc1e9da4' class='xr-var-data-in' type='checkbox'><label for='data-8d601dd4-bfdc-4f4e-bd00-086dfc1e9da4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.158e+06, -1.156e+06, -1.154e+06, ...,  9.200e+05,  9.220e+05,\n",
+       "        9.240e+05], shape=(1042,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-28d78233-376e-42b0-8150-b4af0809b3ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28d78233-376e-42b0-8150-b4af0809b3ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4041a3d5-0319-4c5c-9ea3-2757405bf1ad' class='xr-var-data-in' type='checkbox'><label for='data-4041a3d5-0319-4c5c-9ea3-2757405bf1ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-03-01T12:00:00</div><input id='attrs-feeb8b28-41c8-44a1-b5a3-16d872ad1bd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feeb8b28-41c8-44a1-b5a3-16d872ad1bd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc60b24f-159c-4ed0-9356-d5a82d9f679c' class='xr-var-data-in' type='checkbox'><label for='data-cc60b24f-159c-4ed0-9356-d5a82d9f679c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2026-03-01T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65bd70eb-af8b-4418-a298-d4049b0c820e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-65bd70eb-af8b-4418-a298-d4049b0c820e' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(8)</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>air_temperature</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>285.9 285.9 285.9 ... 208.2 208.2</div><input id='attrs-bd172592-6043-4f96-bc74-52a40bad97df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd172592-6043-4f96-bc74-52a40bad97df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-422c7525-2d2f-4c3c-b147-626372a4f96a' class='xr-var-data-in' type='checkbox'><label for='data-422c7525-2d2f-4c3c-b147-626372a4f96a' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[285.9375, 285.9375, 285.875 , ..., 284.0625, 284.0625,\n",
+       "          284.125 ],\n",
+       "         [285.9375, 285.9375, 285.9375, ..., 284.125 , 284.125 ,\n",
+       "          284.125 ],\n",
+       "         [285.9375, 285.9375, 285.9375, ..., 284.1875, 284.1875,\n",
+       "          284.1875],\n",
+       "         ...,\n",
+       "         [278.6875, 278.6875, 278.6875, ..., 273.0625, 273.0625,\n",
+       "          273.    ],\n",
+       "         [278.625 , 278.625 , 278.625 , ..., 273.0625, 273.0625,\n",
+       "          273.0625],\n",
+       "         [278.5625, 278.5625, 278.5625, ..., 273.    , 273.0625,\n",
+       "          273.0625]],\n",
+       "\n",
+       "        [[284.9375, 284.9375, 285.    , ..., 282.875 , 282.6875,\n",
+       "          282.8125],\n",
+       "         [284.875 , 284.875 , 284.9375, ..., 283.    , 282.9375,\n",
+       "          283.0625],\n",
+       "         [284.875 , 284.875 , 284.875 , ..., 283.0625, 283.125 ,\n",
+       "          283.25  ],\n",
+       "...\n",
+       "         [221.6875, 221.6875, 221.625 , ..., 209.375 , 209.375 ,\n",
+       "          209.375 ],\n",
+       "         [221.75  , 221.6875, 221.6875, ..., 209.375 , 209.375 ,\n",
+       "          209.375 ],\n",
+       "         [221.75  , 221.75  , 221.6875, ..., 209.375 , 209.375 ,\n",
+       "          209.375 ]],\n",
+       "\n",
+       "        [[220.3125, 220.3125, 220.25  , ..., 217.625 , 217.625 ,\n",
+       "          217.625 ],\n",
+       "         [220.3125, 220.3125, 220.25  , ..., 217.625 , 217.625 ,\n",
+       "          217.625 ],\n",
+       "         [220.25  , 220.3125, 220.25  , ..., 217.625 , 217.625 ,\n",
+       "          217.625 ],\n",
+       "         ...,\n",
+       "         [218.6875, 218.6875, 218.6875, ..., 208.3125, 208.25  ,\n",
+       "          208.1875],\n",
+       "         [218.75  , 218.75  , 218.6875, ..., 208.3125, 208.25  ,\n",
+       "          208.1875],\n",
+       "         [218.75  , 218.75  , 218.75  , ..., 208.3125, 208.25  ,\n",
+       "          208.1875]]]], shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_azimuthal_equal_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647</div><input id='attrs-6d91818d-76da-426d-acfc-8b3c081d4728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d91818d-76da-426d-acfc-8b3c081d4728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fc14315-6dea-43cb-8468-c3cbea602cf3' class='xr-var-data-in' type='checkbox'><label for='data-0fc14315-6dea-43cb-8468-c3cbea602cf3' 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>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>-2.5</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>54.9</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.037e+06 -1.035e+06 ... 9.03e+05</div><input id='attrs-9f70afcb-41dd-4e5e-bcd4-a89c6f3e0f03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f70afcb-41dd-4e5e-bcd4-a89c6f3e0f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66c6daca-0245-4c6f-9357-a00455f89154' class='xr-var-data-in' type='checkbox'><label for='data-66c6daca-0245-4c6f-9357-a00455f89154' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.037e+06, -1.035e+06],\n",
+       "        [-1.035e+06, -1.033e+06],\n",
+       "        [-1.033e+06, -1.031e+06],\n",
+       "        ...,\n",
+       "        [ 8.970e+05,  8.990e+05],\n",
+       "        [ 8.990e+05,  9.010e+05],\n",
+       "        [ 9.010e+05,  9.030e+05]]], shape=(1, 970, 2), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.159e+06 -1.157e+06 ... 9.25e+05</div><input id='attrs-9a9b45df-2625-4877-bb8c-1dad345c8477' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a9b45df-2625-4877-bb8c-1dad345c8477' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c904bab0-2321-4d3c-882f-ca3c7ca5ea00' class='xr-var-data-in' type='checkbox'><label for='data-c904bab0-2321-4d3c-882f-ca3c7ca5ea00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.159e+06, -1.157e+06],\n",
+       "        [-1.157e+06, -1.155e+06],\n",
+       "        [-1.155e+06, -1.153e+06],\n",
+       "        ...,\n",
+       "        [ 9.190e+05,  9.210e+05],\n",
+       "        [ 9.210e+05,  9.230e+05],\n",
+       "        [ 9.230e+05,  9.250e+05]]], shape=(1, 1042, 2), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-15f6badc-713d-4aee-92c4-233bb3a1fc57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15f6badc-713d-4aee-92c4-233bb3a1fc57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0996f0b-8788-424f-89e9-71d6ca252a3a' class='xr-var-data-in' type='checkbox'><label for='data-e0996f0b-8788-424f-89e9-71d6ca252a3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd></dl></div><div class='xr-var-data'><pre>array([[[[0, 0, 0, ..., 1, 1, 1],\n",
+       "         [0, 0, 0, ..., 1, 1, 1],\n",
+       "         [0, 0, 0, ..., 1, 1, 1],\n",
+       "         ...,\n",
+       "         [1, 1, 1, ..., 1, 1, 1],\n",
+       "         [1, 1, 1, ..., 1, 1, 1],\n",
+       "         [1, 1, 1, ..., 1, 1, 1]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 1, 1],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 1, 1, 1],\n",
+       "         [0, 0, 0, ..., 1, 1, 1],\n",
+       "         [0, 0, 0, ..., 1, 1, 1]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]]]], shape=(1, 33, 970, 1042), dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.957 0.959 ... 0.004883 0.004883</div><input id='attrs-367f1961-54c3-4a4b-860d-38243456ce15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-367f1961-54c3-4a4b-860d-38243456ce15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f53fc5e1-8c7b-415b-bd83-c9246114930b' class='xr-var-data-in' type='checkbox'><label for='data-f53fc5e1-8c7b-415b-bd83-c9246114930b' 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>least_significant_digit :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[9.5703125e-01, 9.5898438e-01, 9.5898438e-01, ...,\n",
+       "          8.9941406e-01, 9.2187500e-01, 9.1308594e-01],\n",
+       "         [9.5800781e-01, 9.5898438e-01, 9.5898438e-01, ...,\n",
+       "          8.8671875e-01, 9.0625000e-01, 9.0527344e-01],\n",
+       "         [9.5800781e-01, 9.5898438e-01, 9.5898438e-01, ...,\n",
+       "          8.7597656e-01, 8.9355469e-01, 8.9648438e-01],\n",
+       "         ...,\n",
+       "         [5.9863281e-01, 5.9667969e-01, 5.9472656e-01, ...,\n",
+       "          9.5214844e-01, 9.5507812e-01, 9.5507812e-01],\n",
+       "         [6.0644531e-01, 6.0449219e-01, 6.0253906e-01, ...,\n",
+       "          9.5117188e-01, 9.5214844e-01, 9.5214844e-01],\n",
+       "         [6.1230469e-01, 6.1132812e-01, 6.1035156e-01, ...,\n",
+       "          9.5117188e-01, 9.5117188e-01, 9.5019531e-01]],\n",
+       "\n",
+       "        [[9.4726562e-01, 9.3945312e-01, 9.3457031e-01, ...,\n",
+       "          9.1601562e-01, 9.2382812e-01, 9.1406250e-01],\n",
+       "         [9.5214844e-01, 9.4824219e-01, 9.4238281e-01, ...,\n",
+       "          9.1699219e-01, 9.2187500e-01, 9.1210938e-01],\n",
+       "         [9.5605469e-01, 9.5312500e-01, 9.4824219e-01, ...,\n",
+       "          9.1992188e-01, 9.2089844e-01, 9.1210938e-01],\n",
+       "...\n",
+       "          7.8125000e-03, 7.8125000e-03, 7.8125000e-03],\n",
+       "         [1.9531250e-03, 1.9531250e-03, 1.9531250e-03, ...,\n",
+       "          7.8125000e-03, 7.8125000e-03, 7.8125000e-03],\n",
+       "         [1.9531250e-03, 1.9531250e-03, 1.9531250e-03, ...,\n",
+       "          7.8125000e-03, 7.8125000e-03, 7.8125000e-03]],\n",
+       "\n",
+       "        [[9.7656250e-04, 9.7656250e-04, 9.7656250e-04, ...,\n",
+       "          1.9531250e-03, 1.9531250e-03, 1.9531250e-03],\n",
+       "         [9.7656250e-04, 9.7656250e-04, 9.7656250e-04, ...,\n",
+       "          1.9531250e-03, 1.9531250e-03, 1.9531250e-03],\n",
+       "         [9.7656250e-04, 9.7656250e-04, 9.7656250e-04, ...,\n",
+       "          1.9531250e-03, 1.9531250e-03, 1.9531250e-03],\n",
+       "         ...,\n",
+       "         [9.7656250e-04, 9.7656250e-04, 9.7656250e-04, ...,\n",
+       "          4.8828125e-03, 4.8828125e-03, 4.8828125e-03],\n",
+       "         [9.7656250e-04, 9.7656250e-04, 9.7656250e-04, ...,\n",
+       "          4.8828125e-03, 4.8828125e-03, 4.8828125e-03],\n",
+       "         [9.7656250e-04, 9.7656250e-04, 9.7656250e-04, ...,\n",
+       "          4.8828125e-03, 4.8828125e-03, 4.8828125e-03]]]],\n",
+       "      shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.62 16.5 16.44 ... 48.69 48.75</div><input id='attrs-8974646b-1b5d-436b-b56e-f8438adfcc99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8974646b-1b5d-436b-b56e-f8438adfcc99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91f358b4-297c-486d-a63a-f87f6883ab6c' class='xr-var-data-in' type='checkbox'><label for='data-91f358b4-297c-486d-a63a-f87f6883ab6c' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[16.625 , 16.5   , 16.4375, ...,  0.8125,  0.1875,  0.5625],\n",
+       "         [16.625 , 16.5625, 16.5   , ...,  0.5625,  0.125 ,  0.6875],\n",
+       "         [16.6875, 16.625 , 16.5625, ...,  1.0625,  0.9375,  1.125 ],\n",
+       "         ...,\n",
+       "         [11.75  , 11.6875, 11.6875, ...,  1.5   ,  1.5   ,  1.4375],\n",
+       "         [11.75  , 11.75  , 11.75  , ...,  1.5   ,  1.4375,  1.375 ],\n",
+       "         [11.75  , 11.75  , 11.8125, ...,  1.4375,  1.375 ,  1.3125]],\n",
+       "\n",
+       "        [[22.5625, 22.5   , 22.4375, ...,  1.8125,  0.1875,  0.5625],\n",
+       "         [22.5625, 22.5   , 22.4375, ...,  1.375 ,  0.625 ,  1.3125],\n",
+       "         [22.5   , 22.5   , 22.5   , ...,  2.6875,  2.5625,  3.125 ],\n",
+       "         ...,\n",
+       "         [17.3125, 17.3125, 17.375 , ...,  1.5   ,  1.5   ,  1.4375],\n",
+       "         [17.3125, 17.3125, 17.3125, ...,  1.5   ,  1.4375,  1.375 ],\n",
+       "         [17.25  , 17.3125, 17.3125, ...,  1.4375,  1.375 ,  1.3125]],\n",
+       "\n",
+       "        [[25.375 , 25.3125, 25.25  , ...,  1.875 ,  1.    ,  2.4375],\n",
+       "         [25.4375, 25.4375, 25.375 , ...,  1.0625,  1.375 ,  2.75  ],\n",
+       "         [25.5   , 25.5   , 25.5   , ...,  2.    ,  2.5625,  3.25  ],\n",
+       "         ...,\n",
+       "...\n",
+       "         [17.3125, 17.3125, 17.3125, ..., 28.6875, 28.6875, 28.6875],\n",
+       "         [17.3125, 17.3125, 17.3125, ..., 28.75  , 28.75  , 28.75  ],\n",
+       "         [17.375 , 17.375 , 17.375 , ..., 28.8125, 28.8125, 28.8125]],\n",
+       "\n",
+       "        [[ 9.125 ,  9.125 ,  9.125 , ...,  5.8125,  5.8125,  5.75  ],\n",
+       "         [ 9.1875,  9.125 ,  9.125 , ...,  5.875 ,  5.875 ,  5.75  ],\n",
+       "         [ 9.1875,  9.125 ,  9.125 , ...,  5.9375,  5.875 ,  5.8125],\n",
+       "         ...,\n",
+       "         [17.4375, 17.4375, 17.5   , ..., 36.25  , 36.25  , 36.1875],\n",
+       "         [17.5625, 17.5625, 17.5625, ..., 36.3125, 36.25  , 36.25  ],\n",
+       "         [17.625 , 17.625 , 17.625 , ..., 36.3125, 36.3125, 36.3125]],\n",
+       "\n",
+       "        [[ 9.875 ,  9.9375, 10.    , ...,  4.625 ,  4.625 ,  4.6875],\n",
+       "         [ 9.8125,  9.875 ,  9.9375, ...,  4.5625,  4.625 ,  4.625 ],\n",
+       "         [ 9.75  ,  9.8125,  9.875 , ...,  4.5625,  4.5625,  4.625 ],\n",
+       "         ...,\n",
+       "         [14.1875, 14.25  , 14.3125, ..., 48.25  , 48.3125, 48.3125],\n",
+       "         [14.25  , 14.3125, 14.375 , ..., 48.4375, 48.5   , 48.5   ],\n",
+       "         [14.3125, 14.375 , 14.4375, ..., 48.625 , 48.6875, 48.75  ]]]],\n",
+       "      shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_from_direction</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>225.0 225.0 225.0 ... 308.0 308.0</div><input id='attrs-b91733fd-85eb-4734-be8c-5aed6a6d4ef0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b91733fd-85eb-4734-be8c-5aed6a6d4ef0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a580adf-ace2-4ff7-a818-5dad6d58daee' class='xr-var-data-in' type='checkbox'><label for='data-1a580adf-ace2-4ff7-a818-5dad6d58daee' 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>least_significant_digit :</span></dt><dd>0</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[225., 225., 225., ..., 307., 349.,  45.],\n",
+       "         [225., 225., 224., ..., 317.,  78.,  53.],\n",
+       "         [225., 225., 224., ..., 357.,  22.,  37.],\n",
+       "         ...,\n",
+       "         [ 40.,  40.,  41., ...,   9.,   9.,   9.],\n",
+       "         [ 40.,  40.,  40., ...,   6.,   6.,   6.],\n",
+       "         [ 39.,  39.,  39., ...,   4.,   4.,   2.]],\n",
+       "\n",
+       "        [[231., 231., 231., ..., 309., 349.,  45.],\n",
+       "         [231., 231., 231., ..., 323.,  76.,  54.],\n",
+       "         [231., 231., 231., ...,   1.,  32.,  46.],\n",
+       "         ...,\n",
+       "         [ 41.,  42.,  42., ...,   9.,   9.,   9.],\n",
+       "         [ 41.,  41.,  41., ...,   6.,   6.,   6.],\n",
+       "         [ 41.,  41.,  41., ...,   4.,   4.,   2.]],\n",
+       "\n",
+       "        [[240., 240., 240., ..., 307.,  49.,  63.],\n",
+       "         [240., 240., 240., ..., 328.,  74.,  69.],\n",
+       "         [240., 240., 240., ...,  10.,  44.,  58.],\n",
+       "         ...,\n",
+       "...\n",
+       "         [247., 247., 247., ..., 295., 295., 295.],\n",
+       "         [247., 247., 247., ..., 295., 295., 295.],\n",
+       "         [248., 247., 247., ..., 295., 295., 295.]],\n",
+       "\n",
+       "        [[ 74.,  74.,  74., ...,  45.,  45.,  46.],\n",
+       "         [ 75.,  74.,  74., ...,  45.,  45.,  46.],\n",
+       "         [ 75.,  74.,  74., ...,  44.,  45.,  45.],\n",
+       "         ...,\n",
+       "         [265., 265., 265., ..., 301., 301., 301.],\n",
+       "         [265., 265., 265., ..., 301., 301., 301.],\n",
+       "         [265., 265., 265., ..., 301., 301., 300.]],\n",
+       "\n",
+       "        [[ 77.,  78.,  78., ...,  13.,  13.,  13.],\n",
+       "         [ 78.,  78.,  78., ...,  13.,  13.,  13.],\n",
+       "         [ 78.,  78.,  78., ...,  13.,  13.,  13.],\n",
+       "         ...,\n",
+       "         [297., 297., 297., ..., 308., 308., 308.],\n",
+       "         [297., 297., 297., ..., 308., 308., 308.],\n",
+       "         [296., 297., 297., ..., 308., 308., 308.]]]],\n",
+       "      shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76810326-c660-41b4-8615-5aa4fdfbfb1c' class='xr-section-summary-in' type='checkbox' /><label for='section-76810326-c660-41b4-8615-5aa4fdfbfb1c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>history :</span></dt><dd>2026-03-01T13:18:47Z: StaGE Decoupler</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT54H</dd><dt><span>mosg__grid_domain :</span></dt><dd>uk_extended</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.7.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>uk_det</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>UKV Model Forecast on UK 2 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>13.8</dd><dt><span>Conventions :</span></dt><dd>CF-1.7, UKMO-1.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B 00:00:00\n",
+       "    forecast_reference_time       datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB ...\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB ...\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB ...\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-03-01T13:18:47Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_data_ukv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "59882ebb-3245-4745-9fd1-963b05c635c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_projection = cartopy.crs.LambertAzimuthalEqualArea(central_latitude=54.9, central_longitude=-2.5, false_easting=0.0, false_northing=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cfa7069-c189-4e9b-8530-a759231ce0b5",
+   "metadata": {},
+   "source": [
+    "Having loaded the data we can now visualise in the normal way. Due to the lazy loading employed by xarray, this is the point at which data is actually loaded from S3, so this step should take longer than one might expect because the data is being both loaded and plotted in this cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "aff574db-e682-4110-a973-cc4b6ca3f210",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa29934cc50>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = matplotlib.pyplot.figure(figsize=(18,6))\n",
+    "# plot temperature at 850hpa pressure level\n",
+    "ax1 = fig1.add_subplot(1,3,1,projection=cartopy.crs.OSGB() )\n",
+    "sample_data_ukv.sel({'pressure':85000})['air_temperature'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,2,projection=cartopy.crs.OSGB() )\n",
+    "\n",
+    "sample_data_ukv.sel({'pressure':85000})['relative_humidity'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,3,projection=cartopy.crs.OSGB() )\n",
+    "sample_data_ukv.sel({'pressure':85000})['wind_speed'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "111c107a-e552-4a2c-9875-08a8f756d00b",
+   "metadata": {},
+   "source": [
+    "### Loading other data - Met Office Global Analsysis Data\n",
+    "\n",
+    "We can now easily load and combine data from all the data accessors we have available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a107e66a-8ec2-4042-9102-7a37490d6b7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moglobal_accessor = pyearthtools.data.archive.MOGlobal10km(var_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c099c0fe-4785-438f-ae74-b411d07d2431",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['s3://met-office-atmospheric-model-data/global-deterministic-10km/20260301T1200Z/20260301T1200Z-PT0000H00M-temperature_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/global-deterministic-10km/20260301T1200Z/20260301T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/global-deterministic-10km/20260301T1200Z/20260301T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/global-deterministic-10km/20260301T1200Z/20260301T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc']\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_data_moglobal = moglobal_accessor[sample_time]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "67fb5a96-d769-476e-9265-b13d2dcb4478",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) 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, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --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",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\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",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\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-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\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",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > 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",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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: 4GB\n",
+       "Dimensions:                  (pressure: 37, latitude: 1920, longitude: 2560,\n",
+       "                              time: 1, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * pressure                 (pressure) float32 148B 40.0 100.0 ... 1e+05\n",
+       "  * latitude                 (latitude) float32 8kB -89.95 -89.86 ... 89.95\n",
+       "  * longitude                (longitude) float32 10kB -179.9 -179.8 ... 179.9\n",
+       "  * time                     (time) datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "    forecast_period          timedelta64[ns] 8B 00:00:00\n",
+       "    forecast_reference_time  datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature          (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    latitude_longitude       (time) int32 4B -2147483647\n",
+       "    latitude_bnds            (time, latitude, bnds) float32 15kB -90.0 ... 90.0\n",
+       "    longitude_bnds           (time, longitude, bnds) float32 20kB -180.0 ... ...\n",
+       "    flag                     (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    relative_humidity        (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    wind_speed               (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    wind_from_direction      (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-03-01T15:11:51Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT168H\n",
+       "    mosg__grid_domain:            global\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    gl_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        Global Model Forecast on Global 10 km Stand...\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0</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-5e9b075e-2f07-4697-bf1c-2218a5ca2e89' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-5e9b075e-2f07-4697-bf1c-2218a5ca2e89' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>pressure</span>: 37</li><li><span class='xr-has-index'>latitude</span>: 1920</li><li><span class='xr-has-index'>longitude</span>: 2560</li><li><span class='xr-has-index'>time</span>: 1</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-a96bc7a7-16c9-4800-bb62-1457eed75f36' class='xr-section-summary-in' type='checkbox' checked /><label for='section-a96bc7a7-16c9-4800-bb62-1457eed75f36' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(6)</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'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.0 100.0 200.0 ... 9.75e+04 1e+05</div><input id='attrs-483300c0-71f0-4ee6-a0a0-4c6ab6764bfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-483300c0-71f0-4ee6-a0a0-4c6ab6764bfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80d7ab2b-c20b-4ec0-8a64-6c048900415a' class='xr-var-data-in' type='checkbox'><label for='data-80d7ab2b-c20b-4ec0-8a64-6c048900415a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([4.00e+01, 1.00e+02, 2.00e+02, 5.00e+02, 1.00e+03, 2.00e+03, 3.00e+03,\n",
+       "       4.00e+03, 5.00e+03, 7.00e+03, 1.00e+04, 1.25e+04, 1.50e+04, 1.75e+04,\n",
+       "       2.00e+04, 2.25e+04, 2.50e+04, 2.75e+04, 3.00e+04, 3.25e+04, 3.50e+04,\n",
+       "       3.75e+04, 4.00e+04, 4.50e+04, 5.00e+04, 5.50e+04, 6.00e+04, 6.50e+04,\n",
+       "       7.00e+04, 7.50e+04, 8.00e+04, 8.50e+04, 9.00e+04, 9.25e+04, 9.50e+04,\n",
+       "       9.75e+04, 1.00e+05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.95 -89.86 ... 89.86 89.95</div><input id='attrs-93bd6c6d-7843-45e9-a963-71dc8aa20dcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93bd6c6d-7843-45e9-a963-71dc8aa20dcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92385074-3a16-4a60-a6d7-fa2604d4189c' class='xr-var-data-in' type='checkbox'><label for='data-92385074-3a16-4a60-a6d7-fa2604d4189c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>latitude_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.953125, -89.859375, -89.765625, ...,  89.765625,  89.859375,\n",
+       "        89.953125], shape=(1920,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.8 ... 179.8 179.9</div><input id='attrs-b7fad530-7eff-4c12-9de6-a980dfa1c9d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7fad530-7eff-4c12-9de6-a980dfa1c9d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29b456d8-820a-4f8d-a128-af2d889db2b9' class='xr-var-data-in' type='checkbox'><label for='data-29b456d8-820a-4f8d-a128-af2d889db2b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>longitude_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-179.92969, -179.78906, -179.64844, ...,  179.64844,  179.78906,\n",
+       "        179.92969], shape=(2560,), 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'>2026-03-01T12:00:00</div><input id='attrs-d75792fb-42c1-4f8d-ad15-2f5c7384c013' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d75792fb-42c1-4f8d-ad15-2f5c7384c013' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22042820-244b-43e8-8ed0-1545ec5d90a1' class='xr-var-data-in' type='checkbox'><label for='data-22042820-244b-43e8-8ed0-1545ec5d90a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-03-01T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-50364c22-16e4-4075-8da7-62796bbe0ec5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50364c22-16e4-4075-8da7-62796bbe0ec5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a02541e-c9c0-4d8b-b209-d3fdc24d7e3b' class='xr-var-data-in' type='checkbox'><label for='data-9a02541e-c9c0-4d8b-b209-d3fdc24d7e3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-03-01T12:00:00</div><input id='attrs-db127f0e-52fc-407f-9007-c5a167f782b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db127f0e-52fc-407f-9007-c5a167f782b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7b86603-0a65-42d4-9e73-176f23c19daf' class='xr-var-data-in' type='checkbox'><label for='data-b7b86603-0a65-42d4-9e73-176f23c19daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2026-03-01T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f079a712-9df4-48e0-adaf-d7e163c575d9' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f079a712-9df4-48e0-adaf-d7e163c575d9' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(8)</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>air_temperature</span></div><div class='xr-var-dims'>(time, pressure, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>264.0 264.0 264.0 ... 260.4 260.2</div><input id='attrs-6e1e215a-6908-46e2-b698-9d023a99c221' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e1e215a-6908-46e2-b698-9d023a99c221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4adda9c6-04a1-44fa-8064-0e235562a7bf' class='xr-var-data-in' type='checkbox'><label for='data-4adda9c6-04a1-44fa-8064-0e235562a7bf' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[264.    , 264.    , 264.    , ..., 264.    , 264.    ,\n",
+       "          264.    ],\n",
+       "         [264.    , 264.    , 264.    , ..., 264.    , 264.    ,\n",
+       "          264.    ],\n",
+       "         [264.    , 264.    , 264.    , ..., 264.    , 264.    ,\n",
+       "          264.    ],\n",
+       "         ...,\n",
+       "         [235.375 , 235.375 , 235.375 , ..., 235.375 , 235.375 ,\n",
+       "          235.375 ],\n",
+       "         [235.25  , 235.25  , 235.25  , ..., 235.25  , 235.25  ,\n",
+       "          235.25  ],\n",
+       "         [235.125 , 235.125 , 235.125 , ..., 235.125 , 235.125 ,\n",
+       "          235.125 ]],\n",
+       "\n",
+       "        [[267.125 , 267.125 , 267.125 , ..., 267.125 , 267.125 ,\n",
+       "          267.125 ],\n",
+       "         [267.125 , 267.125 , 267.125 , ..., 267.125 , 267.125 ,\n",
+       "          267.125 ],\n",
+       "         [267.1875, 267.1875, 267.1875, ..., 267.1875, 267.1875,\n",
+       "          267.1875],\n",
+       "...\n",
+       "         [261.    , 260.9375, 261.    , ..., 261.    , 260.9375,\n",
+       "          260.9375],\n",
+       "         [261.375 , 261.375 , 261.375 , ..., 261.3125, 261.375 ,\n",
+       "          261.375 ],\n",
+       "         [261.625 , 261.625 , 261.625 , ..., 261.625 , 261.625 ,\n",
+       "          261.625 ]],\n",
+       "\n",
+       "        [[254.    , 254.    , 254.    , ..., 254.125 , 254.125 ,\n",
+       "          254.125 ],\n",
+       "         [254.0625, 254.0625, 254.0625, ..., 254.0625, 254.0625,\n",
+       "          254.0625],\n",
+       "         [254.625 , 254.625 , 254.625 , ..., 254.625 , 254.625 ,\n",
+       "          254.625 ],\n",
+       "         ...,\n",
+       "         [260.3125, 260.375 , 260.375 , ..., 260.375 , 260.375 ,\n",
+       "          260.3125],\n",
+       "         [260.375 , 260.375 , 260.375 , ..., 260.375 , 260.375 ,\n",
+       "          260.375 ],\n",
+       "         [260.25  , 260.25  , 260.25  , ..., 260.375 , 260.375 ,\n",
+       "          260.25  ]]]], shape=(1, 37, 1920, 2560), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647</div><input id='attrs-3b0f13a0-47d8-4c66-b687-ecd0dee7b352' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b0f13a0-47d8-4c66-b687-ecd0dee7b352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43f8a791-d8a3-4f20-9e73-3c5053ab105a' class='xr-var-data-in' type='checkbox'><label for='data-43f8a791-d8a3-4f20-9e73-3c5053ab105a' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>earth_radius :</span></dt><dd>6371229.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bnds</span></div><div class='xr-var-dims'>(time, latitude, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -89.91 -89.91 ... 89.91 90.0</div><input id='attrs-6ee16c3c-d632-4bdd-a5b0-2a2e68669520' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ee16c3c-d632-4bdd-a5b0-2a2e68669520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec5134d4-bcbf-45f8-b4d1-eac0f66af954' class='xr-var-data-in' type='checkbox'><label for='data-ec5134d4-bcbf-45f8-b4d1-eac0f66af954' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-90.     , -89.90625],\n",
+       "        [-89.90625, -89.8125 ],\n",
+       "        [-89.8125 , -89.71875],\n",
+       "        ...,\n",
+       "        [ 89.71875,  89.8125 ],\n",
+       "        [ 89.8125 ,  89.90625],\n",
+       "        [ 89.90625,  90.     ]]], shape=(1, 1920, 2), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bnds</span></div><div class='xr-var-dims'>(time, longitude, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-8ac2e241-9568-4f07-b742-d28575d5f6bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ac2e241-9568-4f07-b742-d28575d5f6bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c712f6f-cb5b-4339-b58b-2b9e2b89e1d8' class='xr-var-data-in' type='checkbox'><label for='data-8c712f6f-cb5b-4339-b58b-2b9e2b89e1d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-180.     , -179.85938],\n",
+       "        [-179.85938, -179.71875],\n",
+       "        [-179.71875, -179.57812],\n",
+       "        ...,\n",
+       "        [ 179.57812,  179.71875],\n",
+       "        [ 179.71875,  179.85938],\n",
+       "        [ 179.85938,  180.     ]]], shape=(1, 2560, 2), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(time, pressure, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-0f41989b-5981-42f3-a4e2-4636d0c2e25a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f41989b-5981-42f3-a4e2-4636d0c2e25a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13edd501-fa49-4890-847a-ffcc33080423' class='xr-var-data-in' type='checkbox'><label for='data-13edd501-fa49-4890-847a-ffcc33080423' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         ...,\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         ...,\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         ...,\n",
+       "...\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "        [[1., 1., 1., ..., 1., 1., 1.],\n",
+       "         [1., 1., 1., ..., 1., 1., 1.],\n",
+       "         [1., 1., 1., ..., 1., 1., 1.],\n",
+       "         ...,\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "        [[1., 1., 1., ..., 1., 1., 1.],\n",
+       "         [1., 1., 1., ..., 1., 1., 1.],\n",
+       "         [1., 1., 1., ..., 1., 1., 1.],\n",
+       "         ...,\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.],\n",
+       "         [0., 0., 0., ..., 0., 0., 0.]]]],\n",
+       "      shape=(1, 37, 1920, 2560), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, pressure, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.8906 0.8926</div><input id='attrs-058d5b08-874e-434b-9754-467f80959dc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-058d5b08-874e-434b-9754-467f80959dc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9415f0f6-b34a-4a81-b09f-011a319b0332' class='xr-var-data-in' type='checkbox'><label for='data-9415f0f6-b34a-4a81-b09f-011a319b0332' 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>least_significant_digit :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         ...,\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan]],\n",
+       "\n",
+       "        [[       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "...\n",
+       "          0.91308594, 0.9140625 ],\n",
+       "         [0.87402344, 0.8623047 , 0.8623047 , ..., 0.86816406,\n",
+       "          0.8623047 , 0.8623047 ],\n",
+       "         [0.8544922 , 0.85253906, 0.85253906, ..., 0.85839844,\n",
+       "          0.8574219 , 0.8564453 ]],\n",
+       "\n",
+       "        [[0.6308594 , 0.6308594 , 0.6308594 , ..., 0.63183594,\n",
+       "          0.63183594, 0.6308594 ],\n",
+       "         [0.6279297 , 0.6279297 , 0.6279297 , ..., 0.6279297 ,\n",
+       "          0.6279297 , 0.6279297 ],\n",
+       "         [0.6308594 , 0.6308594 , 0.6308594 , ..., 0.6308594 ,\n",
+       "          0.6308594 , 0.6308594 ],\n",
+       "         ...,\n",
+       "         [0.89941406, 0.89453125, 0.8935547 , ..., 0.8955078 ,\n",
+       "          0.89453125, 0.89941406],\n",
+       "         [0.8876953 , 0.88964844, 0.8876953 , ..., 0.88671875,\n",
+       "          0.89453125, 0.8876953 ],\n",
+       "         [0.8955078 , 0.89453125, 0.89746094, ..., 0.8847656 ,\n",
+       "          0.890625  , 0.8925781 ]]]],\n",
+       "      shape=(1, 37, 1920, 2560), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, pressure, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.06 12.06 12.0 ... 12.06 12.12</div><input id='attrs-3b3bcc72-14a1-4064-9b6b-1397c26fb5e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b3bcc72-14a1-4064-9b6b-1397c26fb5e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e0f57ca-cef0-41f5-b467-053c5d75d8f3' class='xr-var-data-in' type='checkbox'><label for='data-8e0f57ca-cef0-41f5-b467-053c5d75d8f3' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[12.0625, 12.0625, 12.    , ..., 12.0625, 12.0625, 12.0625],\n",
+       "         [12.0625, 12.0625, 12.0625, ..., 12.0625, 12.0625, 12.0625],\n",
+       "         [12.0625, 12.125 , 12.125 , ..., 12.0625, 12.0625, 12.0625],\n",
+       "         ...,\n",
+       "         [ 0.4375,  0.4375,  0.4375, ...,  0.4375,  0.4375,  0.5   ],\n",
+       "         [ 0.8125,  0.8125,  0.8125, ...,  0.875 ,  0.8125,  0.8125],\n",
+       "         [ 1.1875,  1.1875,  1.1875, ...,  1.125 ,  1.1875,  1.1875]],\n",
+       "\n",
+       "        [[ 7.3125,  7.3125,  7.3125, ...,  7.25  ,  7.3125,  7.3125],\n",
+       "         [ 7.25  ,  7.25  ,  7.25  , ...,  7.3125,  7.3125,  7.25  ],\n",
+       "         [ 7.3125,  7.3125,  7.3125, ...,  7.375 ,  7.375 ,  7.3125],\n",
+       "         ...,\n",
+       "         [16.625 , 16.625 , 16.625 , ..., 16.5   , 16.5625, 16.625 ],\n",
+       "         [16.375 , 16.375 , 16.375 , ..., 16.375 , 16.375 , 16.375 ],\n",
+       "         [16.4375, 16.4375, 16.4375, ..., 16.4375, 16.4375, 16.4375]],\n",
+       "\n",
+       "        [[ 3.625 ,  3.625 ,  3.625 , ...,  3.625 ,  3.625 ,  3.625 ],\n",
+       "         [ 3.8125,  3.8125,  3.8125, ...,  3.8125,  3.8125,  3.8125],\n",
+       "         [ 3.6875,  3.6875,  3.6875, ...,  3.6875,  3.6875,  3.6875],\n",
+       "         ...,\n",
+       "...\n",
+       "         [ 9.8125,  9.8125,  9.8125, ...,  9.8125,  9.8125,  9.8125],\n",
+       "         [10.1875, 10.1875, 10.1875, ..., 10.25  , 10.25  , 10.25  ],\n",
+       "         [10.75  , 10.75  , 10.75  , ..., 10.75  , 10.75  , 10.75  ]],\n",
+       "\n",
+       "        [[ 5.5   ,  5.5   ,  5.5   , ...,  5.5   ,  5.5   ,  5.5   ],\n",
+       "         [ 5.625 ,  5.625 ,  5.625 , ...,  5.625 ,  5.625 ,  5.625 ],\n",
+       "         [ 6.125 ,  6.125 ,  6.125 , ...,  6.125 ,  6.125 ,  6.125 ],\n",
+       "         ...,\n",
+       "         [11.625 , 11.625 , 11.5625, ..., 11.5625, 11.5625, 11.625 ],\n",
+       "         [11.875 , 11.8125, 11.8125, ..., 11.8125, 11.8125, 11.875 ],\n",
+       "         [12.0625, 12.0625, 12.0625, ..., 12.0625, 12.0625, 12.0625]],\n",
+       "\n",
+       "        [[ 5.5   ,  5.5   ,  5.5   , ...,  5.5   ,  5.5   ,  5.5   ],\n",
+       "         [ 5.625 ,  5.625 ,  5.625 , ...,  5.625 ,  5.625 ,  5.625 ],\n",
+       "         [ 6.125 ,  6.125 ,  6.125 , ...,  6.125 ,  6.125 ,  6.125 ],\n",
+       "         ...,\n",
+       "         [12.125 , 12.125 , 12.125 , ..., 12.125 , 12.125 , 12.125 ],\n",
+       "         [12.    , 12.    , 11.9375, ..., 11.9375, 12.    , 12.    ],\n",
+       "         [12.125 , 12.0625, 12.0625, ..., 12.0625, 12.0625, 12.125 ]]]],\n",
+       "      shape=(1, 37, 1920, 2560), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_from_direction</span></div><div class='xr-var-dims'>(time, pressure, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>190.0 190.0 190.0 ... 323.0 323.0</div><input id='attrs-294c52cf-aacc-49bb-80fc-a61a33f26492' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-294c52cf-aacc-49bb-80fc-a61a33f26492' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65aa2f3a-225c-4f16-ba10-4f79a6ec8080' class='xr-var-data-in' type='checkbox'><label for='data-65aa2f3a-225c-4f16-ba10-4f79a6ec8080' 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>least_significant_digit :</span></dt><dd>0</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>array([[[[190., 190., 190., ..., 190., 190., 190.],\n",
+       "         [190., 190., 190., ..., 190., 190., 190.],\n",
+       "         [190., 189., 189., ..., 190., 190., 190.],\n",
+       "         ...,\n",
+       "         [221., 221., 218., ..., 211., 215., 219.],\n",
+       "         [235., 232., 232., ..., 233., 234., 234.],\n",
+       "         [239., 237., 237., ..., 234., 237., 239.]],\n",
+       "\n",
+       "        [[153., 153., 153., ..., 153., 153., 153.],\n",
+       "         [153., 153., 153., ..., 153., 153., 153.],\n",
+       "         [152., 152., 152., ..., 153., 152., 152.],\n",
+       "         ...,\n",
+       "         [ 89.,  89.,  89., ...,  88.,  89.,  89.],\n",
+       "         [ 88.,  88.,  88., ...,  88.,  88.,  88.],\n",
+       "         [ 88.,  88.,  88., ...,  87.,  87.,  87.]],\n",
+       "\n",
+       "        [[182., 182., 182., ..., 182., 182., 182.],\n",
+       "         [173., 173., 173., ..., 173., 173., 173.],\n",
+       "         [174., 174., 174., ..., 174., 174., 174.],\n",
+       "         ...,\n",
+       "...\n",
+       "         [352., 353., 353., ..., 352., 352., 352.],\n",
+       "         [353., 353., 353., ..., 353., 353., 353.],\n",
+       "         [351., 352., 352., ..., 351., 351., 351.]],\n",
+       "\n",
+       "        [[265., 265., 264., ..., 265., 265., 265.],\n",
+       "         [268., 268., 268., ..., 269., 268., 268.],\n",
+       "         [271., 271., 271., ..., 272., 271., 271.],\n",
+       "         ...,\n",
+       "         [341., 341., 341., ..., 341., 341., 341.],\n",
+       "         [338., 339., 339., ..., 338., 338., 338.],\n",
+       "         [336., 337., 337., ..., 336., 336., 336.]],\n",
+       "\n",
+       "        [[265., 265., 264., ..., 265., 265., 265.],\n",
+       "         [268., 268., 268., ..., 269., 268., 268.],\n",
+       "         [271., 271., 271., ..., 272., 271., 271.],\n",
+       "         ...,\n",
+       "         [322., 322., 322., ..., 321., 321., 322.],\n",
+       "         [322., 322., 323., ..., 322., 322., 322.],\n",
+       "         [324., 324., 324., ..., 323., 323., 323.]]]],\n",
+       "      shape=(1, 37, 1920, 2560), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc4b907b-6b06-4da5-8946-728409e91398' class='xr-section-summary-in' type='checkbox' /><label for='section-bc4b907b-6b06-4da5-8946-728409e91398' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>history :</span></dt><dd>2026-03-01T15:11:51Z: StaGE Decoupler</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT168H</dd><dt><span>mosg__grid_domain :</span></dt><dd>global</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.7.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>gl_det</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>Global Model Forecast on Global 10 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>13.8</dd><dt><span>Conventions :</span></dt><dd>CF-1.7, UKMO-1.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 4GB\n",
+       "Dimensions:                  (pressure: 37, latitude: 1920, longitude: 2560,\n",
+       "                              time: 1, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * pressure                 (pressure) float32 148B 40.0 100.0 ... 1e+05\n",
+       "  * latitude                 (latitude) float32 8kB -89.95 -89.86 ... 89.95\n",
+       "  * longitude                (longitude) float32 10kB -179.9 -179.8 ... 179.9\n",
+       "  * time                     (time) datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "    forecast_period          timedelta64[ns] 8B 00:00:00\n",
+       "    forecast_reference_time  datetime64[ns] 8B 2026-03-01T12:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature          (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    latitude_longitude       (time) int32 4B -2147483647\n",
+       "    latitude_bnds            (time, latitude, bnds) float32 15kB -90.0 ... 90.0\n",
+       "    longitude_bnds           (time, longitude, bnds) float32 20kB -180.0 ... ...\n",
+       "    flag                     (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    relative_humidity        (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    wind_speed               (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "    wind_from_direction      (time, pressure, latitude, longitude) float32 727MB ...\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-03-01T15:11:51Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT168H\n",
+       "    mosg__grid_domain:            global\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    gl_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        Global Model Forecast on Global 10 km Stand...\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_data_moglobal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f375ab1b-930f-4b9b-b6b7-94086d9b25be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa29932b0b0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = matplotlib.pyplot.figure(figsize=(18,6))\n",
+    "plot_proj = cartopy.crs.PlateCarree()\n",
+    "# plot temperature at 850hpa pressure level\n",
+    "ax1 = fig1.add_subplot(1,3,1,projection=plot_proj)\n",
+    "sample_data_moglobal.sel({'pressure':85000})['air_temperature'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,2,projection=plot_proj)\n",
+    "sample_data_moglobal.sel({'pressure':85000})['relative_humidity'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,3,projection=plot_proj)\n",
+    "sample_data_moglobal.sel({'pressure':85000})['wind_speed'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29828c03-dd5f-4fc3-a0c9-66d783019449",
+   "metadata": {},
+   "source": [
+    "Further Reading and Examples\n",
+    "* [Met Office data on AWS and other platforms](https://www.metoffice.gov.uk/services/data/external-data-channels#ASDI)\n",
+    "  * [MO Global on AWS](https://registry.opendata.aws/met-office-global-deterministic/)\n",
+    "  * [MO Global Documentation](https://www.metoffice.gov.uk/api/assets/file/global-10km-ps47-asdi-pdf-updates-20pdf?prefix=assets)\n",
+    "  * [UKV on AWS](https://registry.opendata.aws/met-office-uk-deterministic/)\n",
+    "  * [UKV Documentation](https://www.metoffice.gov.uk/api/assets/file/ukv-2km-ps47-asdi-pdf-updatespdf?prefix=assets)\n",
+    "* Other example site archive code\n",
+    "  * [NCI, Australia](https://github.com/stevehadd/PyEarthTools/tree/archive_tutorial/packages/nci_site_archive)\n",
+    "  * [JASMIN, UK](https://github.com/MetOffice/pyearthtools_jasmin/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pet_dev_nb_gpu",
+   "language": "python",
+   "name": "pet_dev_nb_gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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diff --git a/docs/notebooks/data/BuildingAnAccessor.ipynb b/docs/notebooks/data/BuildingAnAccessor.ipynb
new file mode 100644
index 00000000..7e22a924
--- /dev/null
+++ b/docs/notebooks/data/BuildingAnAccessor.ipynb
@@ -0,0 +1,8541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "05ceb7ce-88f1-4adb-92c5-6950ce766ce0",
+   "metadata": {},
+   "source": [
+    "# Building an accessor - Met Office UKV data\n",
+    "In this tutorial we will demonstrate how to build a simple data accessor for pyearthtools, which is a key building block in a PyEarthTools pipeline. For this example we will use a small sample of Forecast Analysis data from the Met Office , which is available through AWS. Although one can build an accessor which directly loads the data from S3 (which will be demonstrated towards the end of the tutorial), this notebook will also show how to create an accessor for local data sources, which may be more appropriate for an on-premise compute cluster or HPC, both for speed of data loading and where security restrictions apply."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b9a82df-84c5-44dc-bc36-02e769bf75e5",
+   "metadata": {},
+   "source": [
+    "### Load the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3479cbf1-420b-4bcf-be5f-6bfc58a85955",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "91d5f9c8-d44e-431b-9bfe-15433ef8998d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import functools\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e6015c48-eee5-4029-b3a4-c2bc86f5f0a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot\n",
+    "import cartopy\n",
+    "import numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "21c0c18c-f270-4f91-a9c9-a8371b6aac56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b738355-4348-47e6-ab89-0d59fc601005",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52411be1-b9a5-4abd-ac2a-2c58253b05cf",
+   "metadata": {},
+   "source": [
+    "### Exploring data\n",
+    "Let's start by looking at the data we are going to load.  A number of [Met Office Datasets](https://www.metoffice.gov.uk/services/data/external-data-channels) are available through third-party sources such as AWS and Snowflake. In this example we will look at loading some of UK high-resolution analysis data from AWS.\n",
+    "\n",
+    "Links:\n",
+    "* [Met Office Data on Thiurd-party platforms](Links:https://www.metoffice.gov.uk/services/data/external-data-channels)\n",
+    "* [Met Office UKV data documentation](https://www.metoffice.gov.uk/api/assets/file/ukv-2km-ps47-asdi-pdf-updatespdf?prefix=assets)\n",
+    "* [UKV on AWS Sustainable Data Initiative](https://registry.opendata.aws/met-office-uk-deterministic/)\n",
+    "* [Contents of AWS S3 bucket for MO UKV data](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html#uk-deterministic-2km/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03fee8b0-6d02-4bdf-a629-d3af7a819dd7",
+   "metadata": {},
+   "source": [
+    "We'll need to specify some arguments to interact with the S3 bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ffbfa445-050c-47eb-aca7-99f7926c05d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "open_args = {\n",
+    "    'engine':\"h5netcdf\", #\n",
+    "    'storage_options': {\"anon\": True},\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3984a111-45f2-4bf8-be6f-ca83d1511976",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mf_args = {'concat_dim' : 'time',\n",
+    "           'combine' :'nested'\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0cd7e8b-ee05-4152-8c75-95a3001cbe70",
+   "metadata": {},
+   "source": [
+    "Lets start by opening a sample of the data and visualising."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "11d4d7e8-516c-4c9b-882e-b6b645d57e6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_humidity_sample_ds = xarray.open_dataset(\n",
+    "    's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+    "    ** open_args,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "498cc779-6f42-4420-9b1c-dea821ce5c02",
+   "metadata": {},
+   "outputs": [
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+       "  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: 167MB\n",
+       "Dimensions:                       (pressure: 33, projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time       datetime64[ns] 8B ...\n",
+       "    time                          datetime64[ns] 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    relative_humidity             (pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    lambert_azimuthal_equal_area  int32 4B ...\n",
+       "    projection_y_coordinate_bnds  (projection_y_coordinate, bnds) float32 8kB ...\n",
+       "    projection_x_coordinate_bnds  (projection_x_coordinate, bnds) float32 8kB ...\n",
+       "    flag                          (pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB ...\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-06-01T13:20:41Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0</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-c9fd20a1-d615-4fc2-b7e9-bd8d1960e649' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-c9fd20a1-d615-4fc2-b7e9-bd8d1960e649' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>pressure</span>: 33</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 970</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 1042</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-8c4d36d9-e457-4de4-a3ea-c6afacf67018' class='xr-section-summary-in' type='checkbox' checked /><label for='section-8c4d36d9-e457-4de4-a3ea-c6afacf67018' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(6)</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'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-ab7c02b9-cd60-4cca-9270-c1e87db665a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab7c02b9-cd60-4cca-9270-c1e87db665a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b53fa4-2fb6-4700-a3f1-006db86f2e0e' class='xr-var-data-in' type='checkbox'><label for='data-10b53fa4-2fb6-4700-a3f1-006db86f2e0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.036e+06 -1.034e+06 ... 9.02e+05</div><input id='attrs-fa286354-440e-447a-b7f2-4650b012998b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa286354-440e-447a-b7f2-4650b012998b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a57f10d-3a04-449e-89bd-395a3f759782' class='xr-var-data-in' type='checkbox'><label for='data-4a57f10d-3a04-449e-89bd-395a3f759782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.036e+06, -1.034e+06, -1.032e+06, ...,  8.980e+05,  9.000e+05,\n",
+       "        9.020e+05], shape=(970,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.158e+06 -1.156e+06 ... 9.24e+05</div><input id='attrs-e6bc99b3-b281-4002-a955-c974a768f24b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6bc99b3-b281-4002-a955-c974a768f24b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b14d69a5-7763-4d18-afb5-3905925aac38' class='xr-var-data-in' type='checkbox'><label for='data-b14d69a5-7763-4d18-afb5-3905925aac38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.158e+06, -1.156e+06, -1.154e+06, ...,  9.200e+05,  9.220e+05,\n",
+       "        9.240e+05], shape=(1042,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2416228-8e84-49b3-a83a-a24b7f524923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2416228-8e84-49b3-a83a-a24b7f524923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cea97d2f-740c-4519-8b52-afcc3910bea5' class='xr-var-data-in' type='checkbox'><label for='data-cea97d2f-740c-4519-8b52-afcc3910bea5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-afb75665-6ee1-45e4-b202-03b9f253fee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afb75665-6ee1-45e4-b202-03b9f253fee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c14e02-c735-43d3-b410-9e2cb13746d4' class='xr-var-data-in' type='checkbox'><label for='data-07c14e02-c735-43d3-b410-9e2cb13746d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2a767df-7f85-4626-8ee4-aa528f6ff59c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2a767df-7f85-4626-8ee4-aa528f6ff59c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22835088-4146-4b2b-8cc4-c85884bad2dd' class='xr-var-data-in' type='checkbox'><label for='data-22835088-4146-4b2b-8cc4-c85884bad2dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9af55615-e46b-4dc7-bacd-d4a3d23c740c' class='xr-section-summary-in' type='checkbox' checked /><label for='section-9af55615-e46b-4dc7-bacd-d4a3d23c740c' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ff8ffdd-45f7-43a0-a5fc-bacf95b83881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ff8ffdd-45f7-43a0-a5fc-bacf95b83881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d3baac2-b000-47eb-a42d-f533440d099a' class='xr-var-data-in' type='checkbox'><label for='data-2d3baac2-b000-47eb-a42d-f533440d099a' 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>least_significant_digit :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><pre>[33354420 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_azimuthal_equal_area</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ab5ea2e-65ff-437f-8ea8-dc41e73eafcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ab5ea2e-65ff-437f-8ea8-dc41e73eafcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3148bd0-803b-4fcf-8b6d-9a29a8adacaa' class='xr-var-data-in' type='checkbox'><label for='data-e3148bd0-803b-4fcf-8b6d-9a29a8adacaa' 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>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>-2.5</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>54.9</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a393ff14-25d7-44e8-99fd-1e30e3a59ac0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a393ff14-25d7-44e8-99fd-1e30e3a59ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36099856-4b80-4e16-bbb5-70af8f99acf3' class='xr-var-data-in' type='checkbox'><label for='data-36099856-4b80-4e16-bbb5-70af8f99acf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1940 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ded62c3e-547b-4396-9c97-ca2a33a5d7b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ded62c3e-547b-4396-9c97-ca2a33a5d7b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd2bd7d-8db6-4a8e-91c5-4aad382788f7' class='xr-var-data-in' type='checkbox'><label for='data-3bd2bd7d-8db6-4a8e-91c5-4aad382788f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2084 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-775339ff-2f73-4b54-8432-31176c180649' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-775339ff-2f73-4b54-8432-31176c180649' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cb41baa-3931-4371-b913-59baf757cdee' class='xr-var-data-in' type='checkbox'><label for='data-7cb41baa-3931-4371-b913-59baf757cdee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd></dl></div><div class='xr-var-data'><pre>[33354420 values with dtype=int8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-10f935be-676d-4bd2-a769-80b7b98a80fd' class='xr-section-summary-in' type='checkbox' /><label for='section-10f935be-676d-4bd2-a769-80b7b98a80fd' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>history :</span></dt><dd>2026-06-01T13:20:41Z: StaGE Decoupler</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT54H</dd><dt><span>mosg__grid_domain :</span></dt><dd>uk_extended</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.7.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>uk_det</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>UKV Model Forecast on UK 2 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>13.8</dd><dt><span>Conventions :</span></dt><dd>CF-1.7, UKMO-1.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 167MB\n",
+       "Dimensions:                       (pressure: 33, projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time       datetime64[ns] 8B ...\n",
+       "    time                          datetime64[ns] 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    relative_humidity             (pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    lambert_azimuthal_equal_area  int32 4B ...\n",
+       "    projection_y_coordinate_bnds  (projection_y_coordinate, bnds) float32 8kB ...\n",
+       "    projection_x_coordinate_bnds  (projection_x_coordinate, bnds) float32 8kB ...\n",
+       "    flag                          (pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB ...\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-06-01T13:20:41Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_humidity_sample_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e85a4aeb-af8b-499c-9521-6639c0e71b32",
+   "metadata": {},
+   "outputs": [
+    {
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+       "  padding-left: 25px !important;\n",
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+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
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+       "\n",
+       "dl.xr-attrs {\n",
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+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
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+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
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+       "\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",
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+       "  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",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pressure&#x27; (pressure: 33)&gt; Size: 132B\n",
+       "array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * pressure                 (pressure) float32 132B 1e+05 9.75e+04 ... 1e+03\n",
+       "    forecast_period          timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time  datetime64[ns] 8B ...\n",
+       "    time                     datetime64[ns] 8B ...\n",
+       "Attributes:\n",
+       "    axis:       Z\n",
+       "    units:      Pa\n",
+       "    long_name:  pressure\n",
+       "    positive:   down</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;pressure&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>pressure</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d65e6a30-2b72-4dbe-9324-8f3f30d2671b' class='xr-array-in' type='checkbox' checked><label for='section-d65e6a30-2b72-4dbe-9324-8f3f30d2671b' 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>1e+05 9.75e+04 9.5e+04 9.25e+04 9e+04 ... 4e+03 3e+03 2e+03 1e+03</span></div><div class='xr-array-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b06f3056-23c1-4ea3-960f-faa0b2cdd758' class='xr-section-summary-in' type='checkbox' checked /><label for='section-b06f3056-23c1-4ea3-960f-faa0b2cdd758' class='xr-section-summary' title='Expand/collapse section'>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'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-d1731547-90e5-4bab-8f3e-158ae5592365' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1731547-90e5-4bab-8f3e-158ae5592365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-306366fc-1fc7-4a0d-8bc9-e8a65f478e39' class='xr-var-data-in' type='checkbox'><label for='data-306366fc-1fc7-4a0d-8bc9-e8a65f478e39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c7e6b49-8075-46e8-9330-db86d19d2c11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c7e6b49-8075-46e8-9330-db86d19d2c11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946ecaf7-1528-4245-9df2-ea4869ac325e' class='xr-var-data-in' type='checkbox'><label for='data-946ecaf7-1528-4245-9df2-ea4869ac325e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5feacc1-74e8-4edf-95ee-cff35a039ad5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5feacc1-74e8-4edf-95ee-cff35a039ad5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c26a953-26da-4a3c-b349-8610172ee00a' class='xr-var-data-in' type='checkbox'><label for='data-4c26a953-26da-4a3c-b349-8610172ee00a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-800f143e-444f-4e68-9e8a-5b629487ed0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-800f143e-444f-4e68-9e8a-5b629487ed0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-468eb3ee-b56c-495f-b1c7-ccb74769b5da' class='xr-var-data-in' type='checkbox'><label for='data-468eb3ee-b56c-495f-b1c7-ccb74769b5da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81aca44b-4164-4152-9dd1-58bf4dbfdf91' class='xr-section-summary-in' type='checkbox' checked /><label for='section-81aca44b-4164-4152-9dd1-58bf4dbfdf91' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'pressure' (pressure: 33)> Size: 132B\n",
+       "array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * pressure                 (pressure) float32 132B 1e+05 9.75e+04 ... 1e+03\n",
+       "    forecast_period          timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time  datetime64[ns] 8B ...\n",
+       "    time                     datetime64[ns] 8B ...\n",
+       "Attributes:\n",
+       "    axis:       Z\n",
+       "    units:      Pa\n",
+       "    long_name:  pressure\n",
+       "    positive:   down"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_humidity_sample_ds.pressure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe84874-6fdf-432d-8756-42aba7d97fd4",
+   "metadata": {},
+   "source": [
+    "As the UK is at a high latitude, if we used a latitude / longtidue grid, it would be distored, so we use a different projection of the surface. We specify both the projection of the data and the projection we wanted to use for the visualisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "27629ea7-fdf1-4d86-ae37-9a54b4725502",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_projection = cartopy.crs.LambertAzimuthalEqualArea(central_latitude=54.9, central_longitude=-2.5, false_easting=0.0, false_northing=0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "49958c67-0909-4ce3-b08f-1c434781e68e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f5501b6a660>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = matplotlib.pyplot.figure()\n",
+    "ax1 = fig1.add_subplot(1,1,1,projection=cartopy.crs.OSGB() )\n",
+    "ukv_humidity_sample_ds.sel({'pressure':85000})['relative_humidity'].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb50cc7c-356a-4264-96b5-cae2390935ef",
+   "metadata": {},
+   "source": [
+    "### Create a larger dataset from multiple files\n",
+    "When we interact with the data, we will likely want to use a larger sample, with more times and more varibales. The structure of the data is that each time and each variable is in it own file. So in order to build up a a dataset object with multiple variables and multiple times we have to build up a list of all the files, load them and merge them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eb9775d3-2f7a-431b-a2ba-299a776aee57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt_list = [datetime.datetime(2026,6,1,0,0) + ix1 * datetime.timedelta(hours=12) for ix1 in range(10)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6334d9da-1af0-4c6a-9dc9-c9d7c879877d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var_list = [\n",
+    "    'temperature_on_pressure_levels',\n",
+    "    'relative_humidity_on_pressure_levels',\n",
+    "    'wind_speed_on_pressure_levels',\n",
+    "    'wind_direction_on_pressure_levels',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "78a93297-405e-4a4c-95a1-12ce8c92d86d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vt_template= '{dt.year:04d}{dt.month:02d}{dt.day:02d}T{dt.hour:02d}{dt.minute:02d}Z'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "65fa05c5-7fb0-4836-92c9-d3f3a852fe95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_fname_template = '{vt_str}-PT0000H00M-{var_name}.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fe18211a-022b-42d9-bebf-9abaa1a3ad96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_path_dict = {current_var: [\n",
+    "    's3://met-office-atmospheric-model-data/uk-deterministic-2km/{vt_str}/{fname}'.format(\n",
+    "        vt_str=vt_template.format(dt=current_dt),\n",
+    "        fname=ukv_fname_template.format(vt_str=vt_template.format(dt=current_dt), var_name=current_var),\n",
+    "    )\n",
+    "    # for current_dt in dt_list for current_var in ['temperature_on_pressure_levels']\n",
+    "    for current_dt in dt_list \n",
+    "] for current_var in var_list }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "cf18bd34-8a3a-4999-8c96-c5bf4208153b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'temperature_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'],\n",
+       " 'relative_humidity_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'],\n",
+       " 'wind_speed_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'],\n",
+       " 'wind_direction_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc']}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_path_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b2f40f5-9616-46ef-9cec-3e29c46471d5",
+   "metadata": {},
+   "source": [
+    "To create the dataset, we now load all times for each variable, then merge the variables into a single dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e53d2ce3-428a-4732-a400-152a751458a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_sample_ds = xarray.merge([xarray.open_mfdataset(var_paths, ** (open_args | mf_args) ) \n",
+    "                      for current_var,var_paths in  ukv_path_dict.items()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dced4e08-267e-458c-82ad-888183bfadf4",
+   "metadata": {},
+   "outputs": [
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+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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: 6GB\n",
+       "Dimensions:                       (time: 10, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 80B 2026-06-01 ... 20...\n",
+       "    forecast_reference_time       (time) datetime64[ns] 80B 2026-06-01 ... 20...\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B 00:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "    lambert_azimuthal_equal_area  (time) int32 40B -2147483647 ... -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 78kB dask.array&lt;chunksize=(1, 970, 2), meta=np.ndarray&gt;\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 83kB dask.array&lt;chunksize=(1, 1042, 2), meta=np.ndarray&gt;\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 334MB dask.array&lt;chunksize=(1, 17, 485, 521), meta=np.ndarray&gt;\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-06-01T01:18:41Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0</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-69230b71-db79-40ea-90fc-fab455d89878' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-69230b71-db79-40ea-90fc-fab455d89878' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>pressure</span>: 33</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 970</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 1042</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-2f7588b5-624c-4885-86b1-e4ab2bf2b937' class='xr-section-summary-in' type='checkbox' checked /><label for='section-2f7588b5-624c-4885-86b1-e4ab2bf2b937' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-06-01 ... 2026-06-05T12:00:00</div><input id='attrs-618c117b-b68a-4191-ab4f-6fecbc1bfd73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-618c117b-b68a-4191-ab4f-6fecbc1bfd73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3593a70a-02ff-4553-a59c-9622fcf09726' class='xr-var-data-in' type='checkbox'><label for='data-3593a70a-02ff-4553-a59c-9622fcf09726' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-06-01T00:00:00.000000000&#x27;, &#x27;2026-06-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-02T00:00:00.000000000&#x27;, &#x27;2026-06-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-03T00:00:00.000000000&#x27;, &#x27;2026-06-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-04T00:00:00.000000000&#x27;, &#x27;2026-06-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-05T00:00:00.000000000&#x27;, &#x27;2026-06-05T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_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'>2026-06-01 ... 2026-06-05T12:00:00</div><input id='attrs-464b39b4-21c6-44fe-9298-29085564a004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-464b39b4-21c6-44fe-9298-29085564a004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28160ef4-de5c-481c-9dd3-d5c69f3151ed' class='xr-var-data-in' type='checkbox'><label for='data-28160ef4-de5c-481c-9dd3-d5c69f3151ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-06-01T00:00:00.000000000&#x27;, &#x27;2026-06-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-02T00:00:00.000000000&#x27;, &#x27;2026-06-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-03T00:00:00.000000000&#x27;, &#x27;2026-06-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-04T00:00:00.000000000&#x27;, &#x27;2026-06-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-05T00:00:00.000000000&#x27;, &#x27;2026-06-05T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-6a2aff96-2bb1-49ef-92d4-5ca22f890797' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a2aff96-2bb1-49ef-92d4-5ca22f890797' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dbbff29-0faf-4e98-8b85-876a1ae8e7fe' class='xr-var-data-in' type='checkbox'><label for='data-6dbbff29-0faf-4e98-8b85-876a1ae8e7fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.036e+06 -1.034e+06 ... 9.02e+05</div><input id='attrs-5d4e19a7-1ab3-46eb-88e8-0e86eb53077f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d4e19a7-1ab3-46eb-88e8-0e86eb53077f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4ad8a42-093b-4901-8fca-2e6971f21a70' class='xr-var-data-in' type='checkbox'><label for='data-d4ad8a42-093b-4901-8fca-2e6971f21a70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.036e+06, -1.034e+06, -1.032e+06, ...,  8.980e+05,  9.000e+05,\n",
+       "        9.020e+05], shape=(970,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.158e+06 -1.156e+06 ... 9.24e+05</div><input id='attrs-de0637b5-1ebb-4adb-b183-cc9a9fd94f2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de0637b5-1ebb-4adb-b183-cc9a9fd94f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-955afb42-fb54-46a4-9190-bd31c8706f7d' class='xr-var-data-in' type='checkbox'><label for='data-955afb42-fb54-46a4-9190-bd31c8706f7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.158e+06, -1.156e+06, -1.154e+06, ...,  9.200e+05,  9.220e+05,\n",
+       "        9.240e+05], shape=(1042,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-a2730bdb-dc9a-4658-b3ed-24049eeae021' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2730bdb-dc9a-4658-b3ed-24049eeae021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46474118-9260-42ad-987a-0b5760c6a972' class='xr-var-data-in' type='checkbox'><label for='data-46474118-9260-42ad-987a-0b5760c6a972' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9701d87f-ec10-488b-92b5-6411ed3134fc' class='xr-section-summary-in' type='checkbox' checked /><label for='section-9701d87f-ec10-488b-92b5-6411ed3134fc' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(8)</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>air_temperature</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-69339cb7-b83d-4a84-9e20-0d7ec52a1168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69339cb7-b83d-4a84-9e20-0d7ec52a1168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e8f32c0-b02f-431d-bdd8-b36bf9622883' class='xr-var-data-in' type='checkbox'><label for='data-5e8f32c0-b02f-431d-bdd8-b36bf9622883' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</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> 1.24 GiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1, 128, 128) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 23760 chunks in 31 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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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_azimuthal_equal_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647 ... -2147483647</div><input id='attrs-bb955170-a891-4e96-8d6e-6017335eb357' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb955170-a891-4e96-8d6e-6017335eb357' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946411b4-ed18-4b6d-8213-c3df96e952d1' class='xr-var-data-in' type='checkbox'><label for='data-946411b4-ed18-4b6d-8213-c3df96e952d1' 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>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>-2.5</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>54.9</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647],\n",
+       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 970, 2), meta=np.ndarray&gt;</div><input id='attrs-d2af8692-1ca9-439c-a943-bb5e2fdbac4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2af8692-1ca9-439c-a943-bb5e2fdbac4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225e3386-7830-4d79-aca6-0e145021fd66' class='xr-var-data-in' type='checkbox'><label for='data-225e3386-7830-4d79-aca6-0e145021fd66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 75.78 kiB </td>\n",
+       "                        <td> 7.58 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10, 970, 2) </td>\n",
+       "                        <td> (1, 970, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 10 chunks in 104 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1042, 2), meta=np.ndarray&gt;</div><input id='attrs-b97ae230-5176-412b-b1bb-7ec0bad5a6ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b97ae230-5176-412b-b1bb-7ec0bad5a6ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b495f997-c339-4b9d-9405-9bca6e168abd' class='xr-var-data-in' type='checkbox'><label for='data-b495f997-c339-4b9d-9405-9bca6e168abd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 81.41 kiB </td>\n",
+       "                        <td> 8.14 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10, 1042, 2) </td>\n",
+       "                        <td> (1, 1042, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 10 chunks in 104 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 17, 485, 521), meta=np.ndarray&gt;</div><input id='attrs-28f5b751-3b20-46fb-887a-13b633a484fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28f5b751-3b20-46fb-887a-13b633a484fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0660ee55-0ac2-45c4-8a5f-97e0e3163b3a' class='xr-var-data-in' type='checkbox'><label for='data-0660ee55-0ac2-45c4-8a5f-97e0e3163b3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0 1]</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> 318.09 MiB </td>\n",
+       "                        <td> 4.10 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 17, 485, 521) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 80 chunks in 102 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int8 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-153acb8f-7ad3-4501-a9f0-af29fd17fa1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-153acb8f-7ad3-4501-a9f0-af29fd17fa1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-462c941c-51db-4494-9141-e233c6704ee6' class='xr-var-data-in' type='checkbox'><label for='data-462c941c-51db-4494-9141-e233c6704ee6' 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>least_significant_digit :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.24 GiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-284052c5-b365-42b1-9df1-849227d292be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-284052c5-b365-42b1-9df1-849227d292be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-816771ca-12e3-40bb-a6c2-3a1fd15fc3e4' class='xr-var-data-in' type='checkbox'><label for='data-816771ca-12e3-40bb-a6c2-3a1fd15fc3e4' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
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+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.24 GiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 23760 chunks in 31 graph layers </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_from_direction</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-06489177-8007-4e4d-baef-577dbae6543a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06489177-8007-4e4d-baef-577dbae6543a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5388623d-4fe1-472a-9fcb-872544eb3708' class='xr-var-data-in' type='checkbox'><label for='data-5388623d-4fe1-472a-9fcb-872544eb3708' 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>least_significant_digit :</span></dt><dd>0</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <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> 1.24 GiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 1, 128, 128) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 23760 chunks in 31 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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+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 6GB\n",
+       "Dimensions:                       (time: 10, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 80B 2026-06-01 ... 20...\n",
+       "    forecast_reference_time       (time) datetime64[ns] 80B 2026-06-01 ... 20...\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B 00:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "    lambert_azimuthal_equal_area  (time) int32 40B -2147483647 ... -2147483647\n",
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+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 83kB dask.array<chunksize=(1, 1042, 2), meta=np.ndarray>\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 334MB dask.array<chunksize=(1, 17, 485, 521), meta=np.ndarray>\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 1GB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-06-01T01:18:41Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_sample_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2ea3b5ea-587b-4638-bdf6-cb0e65dca5fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "select_time = datetime.datetime(2026,6,3,12,0)"
+   ]
+  },
+  {
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+   "execution_count": 22,
+   "id": "860ad6ec-774b-48ba-9456-01e6f9574055",
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+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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.DataArray &#x27;time&#x27; (time: 10)&gt; Size: 80B\n",
+       "array([&#x27;2026-06-01T00:00:00.000000000&#x27;, &#x27;2026-06-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-02T00:00:00.000000000&#x27;, &#x27;2026-06-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-03T00:00:00.000000000&#x27;, &#x27;2026-06-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-04T00:00:00.000000000&#x27;, &#x27;2026-06-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-05T00:00:00.000000000&#x27;, &#x27;2026-06-05T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
+       "Coordinates:\n",
+       "  * time                     (time) datetime64[ns] 80B 2026-06-01 ... 2026-06...\n",
+       "    forecast_reference_time  (time) datetime64[ns] 80B 2026-06-01 ... 2026-06...\n",
+       "    forecast_period          timedelta64[ns] 8B 00:00:00\n",
+       "Attributes:\n",
+       "    standard_name:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;time&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-16663931-f02b-4e7b-a472-84a7b146a5a0' class='xr-array-in' type='checkbox' checked><label for='section-16663931-f02b-4e7b-a472-84a7b146a5a0' 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>2026-06-01 2026-06-01T12:00:00 ... 2026-06-05 2026-06-05T12:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;2026-06-01T00:00:00.000000000&#x27;, &#x27;2026-06-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-02T00:00:00.000000000&#x27;, &#x27;2026-06-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-03T00:00:00.000000000&#x27;, &#x27;2026-06-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-04T00:00:00.000000000&#x27;, &#x27;2026-06-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-05T00:00:00.000000000&#x27;, &#x27;2026-06-05T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-c1692008-019c-4123-8844-5afa6fa5940e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-c1692008-019c-4123-8844-5afa6fa5940e' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-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'>2026-06-01 ... 2026-06-05T12:00:00</div><input id='attrs-31e2f2fd-da0a-4e2a-a622-447c32645c45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31e2f2fd-da0a-4e2a-a622-447c32645c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9745ac6a-87d8-4186-87ab-a32d920cd77b' class='xr-var-data-in' type='checkbox'><label for='data-9745ac6a-87d8-4186-87ab-a32d920cd77b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-06-01T00:00:00.000000000&#x27;, &#x27;2026-06-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-02T00:00:00.000000000&#x27;, &#x27;2026-06-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-03T00:00:00.000000000&#x27;, &#x27;2026-06-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-04T00:00:00.000000000&#x27;, &#x27;2026-06-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-05T00:00:00.000000000&#x27;, &#x27;2026-06-05T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_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'>2026-06-01 ... 2026-06-05T12:00:00</div><input id='attrs-8d5d9342-c366-460d-ae8b-afedd6c141d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d5d9342-c366-460d-ae8b-afedd6c141d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd342c8a-8408-4802-9058-2b063b38df70' class='xr-var-data-in' type='checkbox'><label for='data-dd342c8a-8408-4802-9058-2b063b38df70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-06-01T00:00:00.000000000&#x27;, &#x27;2026-06-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-02T00:00:00.000000000&#x27;, &#x27;2026-06-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-03T00:00:00.000000000&#x27;, &#x27;2026-06-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-04T00:00:00.000000000&#x27;, &#x27;2026-06-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-06-05T00:00:00.000000000&#x27;, &#x27;2026-06-05T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-dbf3b5e5-6b72-4df6-b4f7-fb8071a5913d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbf3b5e5-6b72-4df6-b4f7-fb8071a5913d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3845fad-ec56-42c4-8137-004f486d5564' class='xr-var-data-in' type='checkbox'><label for='data-e3845fad-ec56-42c4-8137-004f486d5564' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5611fc88-bf03-4652-bd54-fd9a4b0c9106' class='xr-section-summary-in' type='checkbox' checked /><label for='section-5611fc88-bf03-4652-bd54-fd9a4b0c9106' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'time' (time: 10)> Size: 80B\n",
+       "array(['2026-06-01T00:00:00.000000000', '2026-06-01T12:00:00.000000000',\n",
+       "       '2026-06-02T00:00:00.000000000', '2026-06-02T12:00:00.000000000',\n",
+       "       '2026-06-03T00:00:00.000000000', '2026-06-03T12:00:00.000000000',\n",
+       "       '2026-06-04T00:00:00.000000000', '2026-06-04T12:00:00.000000000',\n",
+       "       '2026-06-05T00:00:00.000000000', '2026-06-05T12:00:00.000000000'],\n",
+       "      dtype='datetime64[ns]')\n",
+       "Coordinates:\n",
+       "  * time                     (time) datetime64[ns] 80B 2026-06-01 ... 2026-06...\n",
+       "    forecast_reference_time  (time) datetime64[ns] 80B 2026-06-01 ... 2026-06...\n",
+       "    forecast_period          timedelta64[ns] 8B 00:00:00\n",
+       "Attributes:\n",
+       "    standard_name:  time"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_sample_ds.time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "33312b30-b45f-43ac-a647-f5f4e8cfe5d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f53c580f4a0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bxMbGom/fvnj//feFX6TNnDkTH330kXDuNt1mMebPn4/4+HhUVFRgzpw5SEpKQnJyMubOnWuxqzYx+0C/zKqqKjz++ONo3ry50LWYtT6Y9+/fjwkTJiAhIQGxsbEYPnw41q9fb1Zu8+bNGDhwIJo0aYKePXsKnzH17bfffkNUVBSGDRtmNH3q1Km4du2aqGtzsdsi9nipN+SA9957jwDQrFmz6NNPP6Xs7GzasWMHtW3bllq1akWlpaVmZe+99176+OOP6fLly/T+++8TEdGPP/5IUqmU5s6dS+np6XTp0iV69dVXyd/fn9atW0dERDk5ORQeHk733HMPnTlzhq5du0bbt2+n2267jU6dOiW6DBFRdHQ0PfDAA2bbM336dGrevLnRtK5du9LAgQNp5MiR9M8//1BmZiZ9//33pNVqafz48RQVFUXfffcdZWdn0+HDh2ngwIHUunVrKikpsbnvmjdvTqGhoXYfkyZNsvs63HbbbZSYmEj79u0jrVZLeXl5dNttt1FcXBxdvHjR7vxJSUkUERFB8fHxFB0dTb1796b/9//+H2k0GqNyddnmv/76iwBQbm6u1TKvvfYaAaC0tDSz53r37k0pKSlG0zQaDfXu3ZsGDx5MOp1OWMe3335rd5v/+ecfAkCbN282e668vJwA0D333GM0vaqqikJDQ6ljx47C3+Xl5VReXk4FBQW0bds26tatG/Xq1cts3xERdezYkcaNG2e3bpbqc9ddd1F0dLSwPrVaTUREc+fOpYCAAPrggw/oypUrtGfPHurWrRvFxMTQ5cuXhWVER0fTmDFjaNSoUbR//346c+YM/frrr6RUKqlv377UtGlT+uOPP+jq1au0a9cu6tixI/Xt21dYT2VlJfXu3Zvi4uLop59+oqysLDpx4gS99tpr9PHHHxMRkU6nE+pXXl5OV65coS+++IKCgoLo3XffFery8ssvk0KhoFWrVtG1a9fozJkz9OWXX9KcOXOIqPZ1Xb9+PQGglStXmm2zGH///TcBoBkzZtCLL75IFy5coKNHj9LQoUMpPDyczpw5I5QVuw/0y7zzzjvp+eefp8zMTPrjjz/o3LlztHfvXgJAf/31l7DcoqIiatmyJaWkpNDWrVspJyeHvvnmGwoJCaHbbrtNKFdQUEBNmzalTp060e7duykrK4s++OADmjlzJgEQ9q811dXVoj5LQkNDae7cuTaX9dBDDxEAKi4uNnsuISGBhgwZYnN+R7ZF7PHCXCM3N5cA0NSpU+mOO+6gU6dO0dmzZ+mBBx4gqVRKa9eutVh2xowZdOLECdq/fz9t2bKFcnJyKDk5mbp3707//PMP5eXl0erVqykuLo7uvvtuYRkTJ06klJQU2rlzJxUUFNDx48fpzTffpEWLFjlU5oknniCZTGa2PfrP+127dgnT6nqdYc1zzz0n+j1WWFhoc1krVqwgf39/Wrx4MSmVSlKpVPT111+Tn58fffnllzbnJSJ6/vnnCQC1atWKIiIiqHXr1jRnzhy6cuWKWdm6bLNCoaD33nvP6vOZmZkEgJ577jmz5z788EMCQHv37jWavnjxYgoKCqKMjAxhHTfffLPdbSaqvU4xPSfrTZw4kfz8/Mymz5o1iwDQ0aNHSaPRCJ81paWllJ6eTo888ggFBATQ4cOHzeZ96623CABdv35dVP30lEqlcGzqz19VVVVERLR9+3YKDAykCRMmUFpaGp0/f56efPJJAkBLliwRlqE/5u+55x5atmwZXblyhT766CMiInr33XdJKpXSq6++SmfPnqXz58/Tww8/TMHBwXTw4EFhGYsWLSKpVErz58+nEydOUFZWFq1YsYKmTp0qlBFzDXP8+HEKCAig+fPn04ULFyg3N5c2btxIN998s3DNV15eTomJiTR9+nRhefptFis6OppGjx5No0aNogMHDtDly5fpjTfeIIlEYnYcit0H1q57iIhSUlJo+vTpRssVez314IMPUlBQEC1ZsoSys7Np27ZtNGLECOrTpw/17dvX7rbefffdoj5LoqOjbS5nx44dBEA4Ngw9++yzBICysrJsLsORbXH0mpfVzZAhQ6hbt240evRo2rx5s3ANGRwcbPZZaFh248aNdOnSJfrqq6+IiOiuu+6isLAw+vLLLykrK4uOHz9OI0aMoKSkJLp27RoREf3yyy8klUrp888/p5ycHLp8+TKtWLGCxo8fL6xDTBn9tfDKlSvNtsf0GjQrK4sA0LRp0+jOO++kkydP0t69e2nbtm2UlZVFSUlJ1KNHD9q+fTvl5ubSypUrKTY2lu69916b++3AgQOiz9f2zrl5eXnUrFkzGj16NGVlZZFOp6MjR45QSkoKjRgxgrRarc35jx49SgAoMTGRIiMjKSEhgcaOHUsbN240K1uXbZ4zZw717NnTZpnevXtTs2bNzKZfu3aNANATTzxhNP3s2bMUEhJCb7/9trAOAFRQUGBzPURE8+fPt3jdRlR7HAGg3377zWi6/r7v+eefJyIyuj/Iysqi7777jsLCwuill14yW+bFixcJAH3++ed262ZIf10QHR1Nd911l7A+nU5HFRUV1LFjR2ratCmtX7+ecnJy6OeffyaFQmH02uuP+TvuuIMee+wxOnv2LG3YsIGOHj1KaWlpFBERQcOHD6d9+/bR1atX6fvvvye5XE6vvPKKUI9jx45ReHg4DRgwgHbs2EF5eXn077//0rRp04T8Sq1WG13DnDp1imbPnk2BgYF07NgxYVldu3alvn370sGDB6mwsJAOHTpEzz77rJCNKJVKevzxx8nPz89oH+t0OtH7be7cuRQaGkp33XUX/fLLL5STk0Nr166l+Ph46tevn9H5QOw+0C/z7rvvpp9++omuXLki3DO+/PLLZBpRbt26lQICAmjKlCl04sQJOnfuHM2bN48kEgl98803QrkNGzaQn58f3XPPPZSRkUHp6el0991307Rp0wiA3Zxs5cqVoj9PDDMASxISEmjAgAFm0w8fPkwAbF5zO7otYo+X+uJUwGz6Zj9+/DhJJBJ64403zMq+8MILRmXLyspIoVBYDFInTpxI7dq1IyKiP/74gwBQenq61fqIKUPkeMBsKfT8+eefCQD9/vvvRtPz8vIoJCSE/vvf/9qtAwC7j1GjRtlcDlHtQXTvvfcSAPL39ycA1Lp1azpx4oTdeYmIpkyZQn/88QdlZWXR6dOnaeHChRQQEEDjxo0z+sBxZJtNA/SgoCACQCEhIVYD9Hnz5lm9IB45ciQpFAqjaQsWLKCgoCAhLHRnwJyfny+EZJ988onV5e7fv58A0M6dO82eczZgJiK65557zG42Tp06RQDomWeeMZqelZVFgYGBRhck0dHRJJPJKC8vz6jsu+++SwBo3759ZsuWSCT03XffEVHtzaq17bJn3rx5Rhc1vXv3pgkTJticR//6/P333w6vj+hGGGy6npKSEgoPDze6sRS7D/TLvPXWW83WZylgfumll4Rww9D//vc/AkBbtmwhIqIXX3yRpFIpnT171qjc448/LipgViqVoj5LAFj83DM0efJkAmDxQqNDhw7ClyvW1HVbiMyPF+Ya+tC4ZcuWVFNTY/Rc165dqW3btmZlmzdvblZ21qxZFBYWRlevXjWa/ttvvxEAOnToEBERBQcHW7wRMCSmjDMBs7PXGbbqIPY9Jubm69dff6WwsDCSSCQklUopMDCQPvvsM7vzERG988479N5779GJEycoJyeH/vjjD2rVqhVFR0cbve8c2WZLAToACgwMtBqg6891b731ltnyf/jhB7PPw5MnT5JMJjP68sBdAbNKpaKff/6Z/P39qX///jZvnFJTU2n27Nlm050NmImINm/ebPH81aNHD2rWrJnZl6UjRowghUJBFRUVRHTjeDP9vLx8+TL5+/vTY489ZjRdp9NRr1696JZbbiEiogsXLpCfn5/wpa0jTK9h9F8WlJWV2ZwvKSmJ7rrrLofXpxcdHU1BQUFC4KV35513UmhoqBBmi90H+mVauu4hMg+YxV5PnTx5kgDQa6+9ZlTuwIEDBEBUwHzbbbeJ+iyxFtDo6e95li1bZvac/prN9PrDkCu2xdY1L6ubIUOGEAD6999/jaZbem31ZQ3Ph0Q3Qjt92KxXUlJCUVFRwvF+3333UatWrWzWR0wZZwLmVq1amQW1d955JykUCrP3rj6ctBWQ6Osg5iHmmjQzM1PIAvT32BMnThQ+r205fvw4zZo1i3bu3El5eXl04MABmjRpEgGgTz/91KltthSg+/v7k1QqtRmgt2zZkrp27WpWR7VaTQCMPr9ramqob9++1KNHDyEsdFfArNPp6Pjx49SxY0cKDg62meG8+uqrZlkAkfMBs150dLTZ9YX+3nD79u1G07/++mujLER/vN10001myx08eDA1a9aMKisrjaa/9957FBAQIFxHDxgwgBITE83KidGyZUt69NFHiejGlwWGX1hbMn/+fItfyos1d+5cAmD2Bc2aNWvMzkli94F+mZZeQ0sBc5cuXahFixZmX24OGzaMoqKihC+5O3fuTB07djS6DtRqtZSamioqYNbf24h5WPrcM6TP1kxduHCBgBtfrlhT120hMj5e6pNTfTCb9svTpUsXtGnTBps2bTIra/rTjO3bt6O0tFT4KY+hUaNGISMjA7m5uWjbti2kUimefPJJ7Ny502I/JWLKOCM1NRWdO3c2mrZq1SoEBwdj4sSJRtPj4uLQrVs34eeD1ly+fBnl5eV2H/aa8dfU1GDs2LFYv349fv/9d2RnZ+Po0aNo06YNhg4dimPHjtndvj/++ANTpkxB06ZN0a5dOyxYsAD/+c9/sG7dOqxevdqpba6oqEBlZaXw0A+EUFVVZTTd0gA/ln6GI5FIjAYWOHToEN58800sXLgQbdq0sbuNztD/XCY0NBTx8fE4duwYvvzyS8ydOxcAcPHiRTz44INo06YNIiIiIJfLhZ89ODoSrjO2bNkCAJg2bZrR9KZNm2LgwIFm778BAwYgLi7OaNqqVavQsmVL9O3b12h6hw4dkJycLLyma9asQatWrTBo0CCbdVq9ejVGjx6NpKQkhIWFQS6X48svv0RWVpbwk6L27dtj06ZN+OCDDxwazMEZpp9NCoUCI0aMMNo3YveBnulnmDWbN29GmzZt0K1bN6PpM2bMAAChDps3b0aXLl2QmppqVE7s4FRBQUGiPkvKy8vx6aef2l2eRCIR9R60xNFtEXO8MNeaOHGiWV/ht99+O86cOYMrV64YTZ8wYYJZ2dWrV2Po0KFISEgwmj5q1CgAEN4v7du3xzfffIMffvjB6qjjYso4w9nrDGveffdd0e8xS915GFqyZAnuuOMOzJ07F+fPn8fFixfx5ptvYt68eXjrrbfsbttzzz2HZ555Bp06dUJiYiKmTJmC9evXo7y8HC+++KJT26wfONDwAQBqtdpsuulngLXPCgBCWY1Gg5kzZ6J9+/Z49tln7W6jMwx/7hkcHIynnnoKDz74INauXQuJRAK1Wo333nsPffr0QUxMjPB5k5mZWS/n66KiIhw5cgSTJk1CQECA0XMzZsxAaWkpDhw4YDTd9Py1bt061NTUmL2mEokEI0eOxI4dO6DVarF+/XpotVq7g0iJuYZp3749AOChhx7CgQMH3Np90U033YTY2FijaVOnTkVlZSX+/fdfAOL3gZ6l6x5LxF5P6cvddtttRuV69+6N5s2bi9lM/Pjjj6I+S8R+Jop5D1ri6LZ4+pq3MYqPj8eAAQOMpumvp0yv8WNiYnDTTTcZTVu1ahWkUqnZca1QKNCvXz+j8/WFCxfw/PPPWx2gVUwZZ0ycONFoEFQiwpo1azB8+HCz967pdYYl+kG3xTzmzJljs25nz57FoEGDEBYWhv379yMnJwfr169HWloabrnlFruD1Hbp0gXff/89Bg0ahLi4OPTu3Rt//PEH+vfvj2effRZlZWUOb7N+4EDDR01NDXQ6ndl004F6xX5WLFq0CIcPH8bXX3/ttr6iZ86cKZyv+/Xrh4SEBGzbtk0452zatAm33normjZtivDwcMjlcrzzzjsoLS0Vxjlwp82bNyM2NhZDhgwxmm56D6dnet1ZXFyMXbt2YeLEiWZdqYwaNQoajQa7d+9GYWEh9uzZg9tvv91mlytqtRrvvPMOevfubXQNc/nyZeHzNyoqCrGxsVi8eDH++OMPtw+MZ3ruuPXWWxEUFCTsG7H7wJCYvr7z8/ORlpaGKVOmmB2fM2bMQHFxMQ4dOoRr167hxIkTmDx5stGxL5VKRfcpPnnyZNGfJ+PHj7e7PGfP145ui5jjpT45FTDHx8ebTUtISEBBQYHZdNN+cvX9rj7wwAOIiIiAQqFAeHg4wsPD8fTTTwMACgoK0LFjR/zyyy+4evUqhgwZAoVCgZEjR+L3338XliWmjC3WXlhLfftevXoVKpUKTZo0Maq3/iRkadsNWRqgx9LD3iA4v/zyC7Zs2YL3339f6KOrW7duWLFiBYgIzzzzjKhtN6Xvg3Xnzp1ObbNpgK4foC8zM9NqgK7vx9FS/z0lJSWIiooS/n744YfRuXNno/68XG369OnIy8tDfn4+NBoN0tLSMHv2bAC1N4v9+vXDyZMnsXTpUpw5cwZ5eXnCB6XpSd0d9Ps7MTHR7LnExESzY9DacXzlyhVERESYvabZ2dnCMvLy8pCcnGyzPsuXL8ekSZPQuXNnbNq0CVeuXEFeXh6eeuopEJHwhc8HH3yAO++8EwsXLkSzZs3QqlUrzJs3z6zfdlew9tl0/fp14SZU7D7QE9vXd0FBgcXXJj4+HlKpVFhuYWGh1XqKJeazRC6X2x3UKDIyEkQkXPQaMn0PWuLItog9Xphr2Xp97B3rlZWVKC0txcaNG4X3iv79oj/W9ctYsWIF+vTpg9mzZyMmJgadO3fGggULjI4tMWWssXUh5ux1hjX6AUbEPGypqKjAU089hZEjR+Ktt95Cq1at0KxZMzzzzDO499578eqrryI7O9vutptq27YtOnfubHa+FrvNlgJ0hUKBN99802qAbu98DUD4vPj0009x4sQJt96s+vn5IS8vD3l5eVAqlcjLy8Pnn38u1GH27NlYuHAh5s6di0OHDiEnJwd5eXno1q1bvZyv9QMoWTtfA8bHoJ+fn9l7Vf+ajhkzxuw1Xbx4MTQaDUpLS4U+cW2ds8Vew4wYMQKfffYZDh06hL59+yIiIgITJkwQwklXEvPZJHYf6DlyvgbsX0/pX8e6nLP1g1vZe4SGhtpcjiPvQUsc2RZvuOZtjOpyvgZq3y9EhOTkZLP3y6ZNm4RlPPHEE3jxxRfx/fffo0OHDoiLi8PMmTNx8uRJYVliytgi9h67rKwMFRUVWLdundl1RrNmzSxuuyH9oNtiHqZf9pl69dVXUVBQgD///BN9+vRBbGwsxowZgyVLlmDfvn348ssvRW27af2mTJmCqqoqHDp0yOFtthSgP/DAA+jevbvNAD0yMlLUZ0VWVhbeeOMNPPvss2YNZFxp6dKlyMvLw/Xr11FVVYXNmzejX79+AID169dj9OjRSE5Oxvr163H58mXk5eVh4cKFAOrvHtvS+SAkJAQKhcLu+y83NxdEhKVLl5q9pv379xfWoR/Dyt499v3334833ngDjz/+uNE1TKdOnYT94e/vj/Xr1yMpKQnTp09HREQE+vTpg/fff9/ljXb8/f0RHR1tNE0ikSA+Pl7YN2L3gV5gYKDZl8yW2Dtf68u44nzt5+cn+vPEtEGOKbHvQUsc3RYxx0t9curKv6CgAElJSUbT8vPzLe4o047y9WV+/PFH3HzzzRaXr//WY9q0aZg2bRpyc3Oxe/dufP3115g6dSq+//57zJo1S3SZ8PBwoYWOIWstmCwNyBMVFYWoqChcvHjR4jz2DrIWLVpYHbHV0IgRI8wGKTSkH1yhe/fuRtNDQ0PRpk0bswEaxNLfBBq2BHFkm00vjPVBeUhIiNWbcH0r8TNnzqBDhw7CdCLCuXPnjL7Fz8vLQ0FBASIiIoRp+ro+/PDDeOyxx5CWloZWrVrZ3VZr/P39rdb1r7/+Qn5+Pv7++2/06NFDmC7mNXUV/c1Ffn6+xfef4cBLgPXjWKFQmH2DqKc/DqKjo2228AOAr776Cu3bt8d7771nNN10vqioKHz99df48ssvcezYMWzevBn/+9//sHbtWpw5c8ZiPZ1l6SI0Pz8f4eHhwvEqdh/oia1fZGSkxW/ZCwsLodPphNcnIiLCaj3FUKlUZid5a+6991588sknVp83fA/27t1bmF5WVobc3Fy73846si1ijxfmWrZeH9NztumxHhISgqCgIEyePBlLliyxuHz9PCkpKVi9ejWUSiUOHjyIdevW4e2338bevXuF1g1iyoSHh0OtVkOj0RjdDNo6TupynWHJ888/L6r1P1D75aq19+OlS5dQVVVldr4Gas/hX3/9Nc6cOePUgLX+/v5m52tA3DYHBgZa/FyzNXJ3q1atEBISgjNnzpg9d+bMGUilUuE8npeXB61WazbwW2VlJbZv3w65XI6XXnoJL730kogttc5aXaurq/HTTz9h3rx5Zq16s7OzzX5x4Q6G52tT+mmG52x/f3+jVn3Ajdd069ataNeuncX16AdEAmrfI9aOJUeuYR555BE88sgjuHz5Mnbu3InPP/8cI0eOxObNm60eW84Q89kkdh/oOXK+1q/P1vWU/pqzoKDA7H2en5+PmJgYu+uaOXOmzet6vaCgIJvXlJ06dYJEIrH6HjQMpyxxZFu84Zq3MarL+VpfJigoCJcvX7Z4X6r/jAkMDMSiRYvw5ptv4vTp0/jnn3/wwQcfoF+/fkhPT0ezZs1ElQkPDwcAs3vssrIyYUBwU6b1lsvlCAwMxLRp0/D555+LmsfQwYMHzQbRsub9998XGg5ZcurUKTRt2tTsfa0/h7vqHtuRbdYH6KbLszTdUOfOnfHTTz9BqVQiODhYmK7//NAPsldUVASNRoP/9//+Hz766COhnD6gbNGiBdq2bYvDhw87tM2m9F+0WfLNN98gISHBbF/U54CikZGRFj9bVSoVysrK7N5j69+fTzzxBF555RWL65DJZMIvVWxd01ZWVmL58uV4+umnMXPmTKPnsrOzjerSs2dPbNu2DWVlZdi3bx9WrlyJZ555BhkZGVav251RU1ODkpISoxwGqP3M0uc1YveBnr0vfPTEXk8ZnuOslbNn1apVuPvuu0WVXb58uTDIsyWdO3fGiRMnzKabvgctcWRbHDle6otTLZj//vtvo78zMzNx5swZDB061O68w4YNQ1BQkDAivKWH6UV2QkICpk6dinXr1iE4ONjiKOC2yrRs2RLp6elG5QsKCiyOwm3NmDFjUFhYiOPHj1uss+GHtyWmXUhYe9j7+Y3+QthwZHCg9tu9CxcumF0oi6UfhdOw24C6brM9I0aMQEhICNasWWM0fdeuXSguLjb6CcDZs2dRVFQktFjKy8vDsmXLAAAffvgh8vLy0LJlyzrVxxb9RUFYWJjR9J9++slt6zSlf3+ZjtZcVFSEPXv2iHr/jRkzBqdOncK1a9dstqAfM2YMzp07h6NHj1pdllarNdsf5eXlFkcmBmoviHr16oUXX3wRL7/8Mi5duoTMzEwAN4KPun7LZvrZpFQq8c8//xjtG7H7wFFDhw7FmTNncP78eaPp+m5n9HUYMmQI0tLSzFpwr1u3TtR6iEjUZ4lhVzXWjB8/HlKp1Ow9uHbtWuh0Ors/KXJkWxw9XphrbNiwwawl0bp169CsWTO7n5kSiQSjRo3Cjh07QEQW3y+mF9nBwcEYPHgw3nnnHdx99934559/zNZvq0zLli1BRGbnuA0bNojeZmeuMwxZ6kLC2sNWy+qEhARIJBKzbQFunMOdOWdnZ2cjLS3N6Hxd1222x8/PD+PHj8e2bduMwgStVou1a9di6NChwkXs66+/jrKyMqPzdV5eHhQKBQYNGoS8vDynf20lBhFBp9OZfd5s2bKlXn5qCwCxsbFo37491q9fb9bNxOrVqxESEoI+ffrYXMbo0aMhkUiwceNGmy3oR40aBYlEYjQCvSlnrmGaN2+OmTNnYvXq1SAio5+ph4SE1Pl8vWfPHrPWPevWrUNgYKDQ2knsPnCU2Osp/c+lTa8tTp8+bbXxhSmlUinqs6SiosLmcmJiYjBgwACsW7fO6JgqLy/Htm3bMGnSJIs/x9VzZFu84Zq3McrJyUFaWprRNP31lNhrfKVSid27d1t8r5h+sSqRSNChQwfMnTsXn3/+OSorK7F//37RZZo3bw6pVGp2j+3I+drPzw8jRozA9u3bIZFIRF1nGLLUhYS1h73PrKSkJFy9etWsq4G6nK+B2nvsgIAAIaiu6zaLMWnSJNTU1Aj393qrV6+Gv7+/0ICkS5cuKC8vR35+vtH5Wv/l7KlTp7Br16461cUefZdXhlQqFf744w+3rtfQ0KFDcfXqVbP73jVr1oCI7L7/EhIS0LVrV2zcuBHBwcFWW9DHx8ejW7duWLVqldXj0do1zIYNG1BcXGxxnvDwcIwcORKff/45hg0bZpSDhYSEQKfTGTVKcIbpuWP79u2orKwU9o3YfeCoxMREpKamYv369WbX3KtXr0ZoaCh69+6NpKQktG7d2qyeAMzeB9bU1NSI/jypqamxuaxJkyYhPz/f7DN19erVUCgUGD58uNV5HdkWZ44Xt3Okw2b9gDrTpk2jlStXkkqlorNnz1Lfvn0pNjbWqKN6fVlLncN/9NFHJJFI6D//+Q/l5uYSEdHVq1fpl19+ETqd/+677+iFF16gU6dOkVqtJrVaTd9//71Rh+BiyhDVjqwOgL744gtSq9V04cIFuv3222nQoEEWB/mzNNCeSqWim266iZKTk2nVqlVUUVFBarWaMjIyaMGCBXY7mq+oqDAavdPaQ6lU2lxOYWEhxcXFUatWrWjPnj2k1WopLy9PGJDup59+MirfqlUr6tGjh/D3L7/8Qq+88gplZGSQRqOh0tJS+uqrryg0NNRsJNC6bLN+QCb962vN22+/TX5+fvTTTz+RTqej8+fPU8eOHal79+5mA+NYW4c7BvkzlZmZSUFBQTRjxgwqKSmh0tJSeu+992jChAlWO6m3NMjf//73PwoNDbVYD0OWBvkjIpo6dSrJ5XJasWIFqVQqunjxIt18880UGhoqjDxLZH1gy5KSEmrfvj116NCBtm7dSkqlkqqrqyktLY2efvppYRCD69evU5s2bSglJYW2bt1K1dXVVFhYSEuWLBFGa33nnXdIIpHQzz//TDU1NXThwgUaNWoUjRkzhgBQeXk5EdUOKvn7778Lg/lkZWXRsGHDKD4+XuiUv7CwkAICAuipp56yO0qzJfoB+caNG0cff/wxVVZWUl5eHk2bNo0CAwOFgcgc2Qf6Zf7zzz9m67M0yF92djY1adKEevXqRenp6aRWq2nDhg0UHR1NQ4YMEbbrypUrFBERQYMHD6YLFy6QSqWiX3/9laZMmSJ6EBIxnyXl5eVUXV1td1mPPfYYhYaG0oYNG4iodhTspKQkGj16tN15HdkWscdLTU0NhYaGGg2uKHYau0E/cN+oUaPo8ccfp6KiIiotLaVXX32VANAPP/xgVvaDDz4wW87Zs2cpOjqahg0bRkeOHBFG4d67dy/NmjWLjh07RkVFRTRx4kTasmWLMDjXyZMnqXXr1jRw4EAiIlFliGrfnxERETRmzBgqKiqisrIyevvtt4WBciwN8ufsdYY1KpVK9HvMnnvuuYckEonwuaRUKmnZsmUUGBhIY8eONSr70ksvUWhoKB05coSIaq8bJk6cSDt27KDS0lJSq9W0d+9e6tGjB4WEhNDhw4ddts0KhcLuiNanT58muVxOd9xxB5WUlFBlZSXNmzePAgMDaf/+/Xb3hbsG+bNk2LBhlJSURMePH6eamhrasWMHDRgwgLp06WJ0vOlZGuQvJyeHQkND7Q6Wam2Qv1WrVpFEIqE5c+ZQQUEBlZeX07vvvksSiYTefPNNoZy1gS2JagdklMlk9MEHH1BBQQHpdDq6cuUKffXVV0YD3z311FMUEBBAixcvpsLCQqqurqadO3fSww8/TETir2EWL15Mb775Jp09e5ZqamqoqqqK3nnnHbPz3YQJE6h169ZUXFxsc99Yoz8v3n777ZSVlUVKpZK+/vprCggIMBv4Ruw+sHbdQ2Q+yB+R+OupiRMnUmRkJK1du5bUajWdPn2axo4dS127dhU1MJ5SqRT1WSJmELFdu3ZRQEAAPfnkk1RVVUUlJSU0ffp0UigUdP78ebvzi90WR65577nnHgoNDTUasFHsNHbDkCFDqGPHjnTrrbfSyZMnSaPR0IYNGygqKsrsXmLIkCEWjz2tVktjxoyhuLg4+vXXX6msrIw0Gg2dO3eO3nrrLVq8eDER1V73LV26lLKyskin01FxcTHdf//9JJPJhONITBkiohkzZlBMTAwdOHCANBoNbd26lWbNmmV1kD9L17gZGRkUFRVFt9xyCx09epQ0Gg2VlZXRv//+S3fffTedPHnS6n7TarWiz9f27ivXrVtHEomEJk6cSFeuXCGtVktHjhyhTp06UUREBF2+fFkoe+TIEQoNDTUauHj+/Pn03XffUXZ2Nul0OsrKyqJHH32UANDChQtdts1z5syhnj172twWnU4n5BxpaWlEVHvfHBQUZDYwsrV1WLu+MuXIIH+WfPnll8IgchqNhq5cuUKTJ0+mcePGEQDKysoyKm9tkD/9ALr2WBrkr7CwkJo2bUodO3akY8eOkUajoe3bt1NiYiL17NmTVCoVEdke2PLff/+l4OBgmjp1KmVkZJBWq6Xr16/T1q1badKkSVRUVERERDt37iSZTEYTJkygs2fPklarpczMTHriiScoIyODiIgGDRpEycnJdOLECaEuAwcOpI4dO9KQIUOIiOjYsWM0c+ZM2rNnD1VUVJBOp6M9e/ZQdHS00TXfzz//TABo06ZNdveNJXPnzqWQkBCaPn067dixgzQaDR0+fJhSU1OpXbt2wr28I/tg7ty5FBoaanF9lgb5+/3330kikdCjjz5KhYWFVFZWRm+99RZJJBJ65513zLb1hRdeoJKSEiouLqann36aRo0aJWpgvJqaGtGfJ6aDoptSKpXUrl076tq1K124cIG0Wi199913JJFI6JNPPrE5r6PbIuZ4Iar9DAgNDTUadFTsNEc4FTBfunSJZs6cSSEhISSVSmnYsGFmo4DauvEjIlq7di0NHjyYZDIZBQQECCNS64Og69ev0zvvvEOdOnWioKAgCgoKos6dOxt9mIgpQ1T7Ifviiy+SQqEgf39/6tGjBx06dIimT58uOmAmqj1QFi5cSK1btyaJREIhISHUoUMHeuONN4SR1uvDpUuX6N5776WEhAQKCAig8PBwGjhwIK1bt86sbFxcHLVt21b4W3+R2KlTJwoODiaJREIpKSn08ssvW7y4dXabxQbMRLWjlrdo0YL8/f0pNDSUZs6cSfn5+Xbnq8+AWb++9u3bk5+fH0VFRdFTTz1FZ8+edShg1oc7psGAKWsBs0qloldeeYWSkpJIKpWSTCajkSNHmo0cbutGq6SkhJ5++mlq1qyZMApxt27d6IMPPjAKTAoLC+nhhx+mmJgYkkqlFBcXRw8//LDw2mg0GnrmmWcoOjqa/P39qW3btrRy5Up64403jALDPXv20LRp0yg2NpYCAgIoKiqKpk+fLpxE9T799FOKi4sTjgNHPtT0YfDWrVvpxRdfpMjISJJIJNS1a1fatm2bU/vA0YCZqDaMmzRpkvDZGBMTQ0888YTZe+vYsWM0aNAgYd33338/nTt3TnTA7Eo1NTW0cOFCSkhIIH9/f4qIiKC5c+eKCs+IxG+L2ONFo9EQAKMgSuw0doNhaPzVV19RUlISSSQSat68udlnpq2Amaj2nHPPPfdQkyZNyM/PjxQKBd100020bNky0mg0pNPpaM2aNTRy5EiKiIigwMBAio+Pp0ceeUT4vBBTRm/z5s3Url07kkgkFBcXRx988IHweS82YCayf51RH1QqFb3//vvUrl07CgoKIplMRq1bt6b//ve/ZqNsz58/nwDQwYMHjbZh1KhRFBERQX5+fhQREUG33347nThxwuL6nN1mMQEzEdHBgwdp8ODBFBAQQAEBAdS7d2+Ln5HW1lFfAXN2djaNHz+egoODKSgoiIYNG0ZnzpyhgQMHig6Yz5w5QwDoqaeesrkuawEzUe3r0bt3b/Lz8yM/Pz9q27at2UjstgJmIqJly5ZR7969yd/fnwIDA6l58+Y0Z84cs3PoF198QR07diSJREJyuZyGDh1Ku3fvFp4Xcw2Tl5dHr776KrVp04ZkMhmFhIRQnz59aPny5UbrOnnyJHXv3p38/PwoNDSUBg0aZHMfmdJfo2zatInatm1Lfn5+1KRJE1q4cKHFL5nF7ANHA2ax11OVlZX0yCOPUFhYGPn7+1OvXr3o2LFjVkM+d9u8eTP17NlTeA8OHTpU+FLKHke2Rew17/Tp082u98VOYzfoX4OTJ09S3759yd/fn+RyOc2ePdvsGtLWsadWq+ntt9+mdu3akVQqpeDgYGrbti29/PLLQkOwjIwMevjhhyk5OZkCAgIoLCyMRo4cSTt37hSWI6YMEdG1a9fo1ltvJX9/fwoKCqJp06ZRSUmJQwEzUW1oOGvWLKPrjEGDBtGPP/5o1PjJ3bZt20bDhw+niIgI8vf3p7i4OJoxYwadPXvWqNzBgwcJAM2fP1+Ydu7cOXrkkUeEfRYQEEB9+/alZcuWWVyXs9ssJmAmqr3XmT17tpB/JCUl0aJFi0Q15KnPgFmn09GCBQsoJiaG/Pz8qFWrVvTjjz/Sxx9/7FDA3L17d0pISLBbX0sBMxHR5cuXafr06SSXy0kqlVJUVBTNmTPH6LrAVsBMRJSWlka33XabsM+jo6Np5MiRtHr1atLpdEK5gwcP0ujRoykoKIgCAgIoNTWVPvroI+F1v3LlCo0bN064hrn55pvp/Pnz1LdvXyEw1Gg09OOPP9JNN91EcrmcZDIZNWvWjF544QWjz4yamhq68847KSQkhGQyGYWGhlJmZqbd/aSnD4OvXbtG48aNE64vJ02aRNnZ2U7tA0cDZiKiNWvWUK9evYTrqXbt2tHSpUvNyv3www/UsmVLkkgklJCQQJ999hl9/vnnogJmV7t69SrNmDGDQkJCyN/fn1q1akVffPGF6PnFbouY44WIaOXKlWb3e2KnOcKpgFn/YaPT6aym92q1WnRAYe8bxZqaGqM3pbNlTNdVXV1tdoNXVVVltxWxfn3OtLR0NXsn3crKSrNt1KupqbH77YtpebHbrP8GSMxromfvOLC2DjEXHrYCZiIS3dqTyHif63Q6q3WwFDAPGTLE6hcYhqqrq+22ZlGpVFb3b0VFhajt0YdD9th7bQy339Z7X8xrVV1dLaqlgSFLYbDYC1Jr+0B/fFl6j+hbTNh6/+i/7Ra7bv2x5Oj7wJXqsm5HtsXe8WLp1xxip7FalkJja6+HI8eemDJi3ntiyhiuS/9+NDwHufI6oz7odDqb51B9y2lrZcR8phhyZJv1v1ASS6vVOnT9oF+H2PerrYBZqVSKau1JZL7Pq6qqjFrb6FkKmJcuXUpBQUFmN7embJ0r9LRardVjXqVSidoenU4n6n1j71pN7DWM2HVVVFRY3Ke2mIbBYo89W/vA1nVPZWWlzWsiW9dThgzXbe1Yqi91vQ8Ruy32jhf9NZvh/hM7jd1gGhrbev+JPfbE3Oe56nxtejxWVFQYnbNcfZ1RH2xtt/4+wNp5Wa1Wu+0euLq62uHPHkf3qf79KoatgFn/qzex1wuW7g9M96OlgLmkpIT8/PxENdIRc49s7Xkx9396Yq/Z7B1nYq5hbGVyhmUqKysd/hw2DYMdOfdY2we2rnv018LW2LqeMmR4zOuPQ0+ef+ryuSZ2W+wdL/rrVUv3V/amOaJOw3tLJBKrg9sFBASI7mfFXjl7A+iJLWO6LsOOxvXE9issdn3uZm+EdlsDGTm6DY6U14/C6QhH++VxZh3jx4+Hn5+f0GekniPLMdzn+r6zDA0YMABpaWlQKpVo0aKFML2qqgr79+/Hv//+a3cdMpnM4vFpyFb/XPZGI9ezd/zo2XttDJdj670vZn1itl0MsdtmrZyt48ve4BqAuMGG7B1L9c2ZvrH0HNkWe8eLpXnFTmPWWXt9HTn2xBwjYt57YsoYrsvS+9GV1xn1QSKR2Owj1drge4bPO8KRbRZ7ztBzpj9nR9fx008/4ffff8fdd9+NL774QpjuSD/5pvvc9Brvo48+wksvvQSNRmM279atW/Hoo4/aHYRRzLWIVCq1us/sve56EolE1PvG3rWa2M9qsety9HW1ROyxamsf2KqHrWthQPx7y3DddR2HpK7qeh8idlvsHS+WrtnETmPW2Xr/ufJe1VXna9N1mb4fXX2dUR9sbbe9+wBHt8GR8s68jxytj6PvV5VKJYxjYzgYqL+/v9P32KbXeFevXkWbNm0sjn2xfft2JCcn2xzEUU/MOcvatou5/9Nz5rxiaX2GrL33bWVyhmXsnQvFcOTcY20f2LrusXdNZOt6ypDhsePocegOdflcE7st9o4XS9erYqc5ok4BM2O+YtCgQUaDNrjzxmDr1q1CR/qmF/CFhYUuuRljjDHGGqqzZ88KA5m5M2x45JFHcP/99wt/G15Qf/PNN3UeaIkxxhhryBYtWoSFCxcCgM0v0esqISEBeXl5wt+GXzaPHTsWo0eP5nM2Y16AA2bWKNT1mxhH2Pp2kcNlx9h7zdq2bYvDhw/XU22YNVqtFmlpacjJyUFKSgrat29vsdzJkydx4cIFxMfHo3fv3hYvRLOzs3H48GFERERgwIABXtOShTFWf1zRykYMW63gPd1C1dc8+OCDWL58uc0yu3btQvfu3eupRswanU6Hf/75BwUFBZg+fbqoUEipVGL37t2orq5G//790aRJE6PnN2zYgJKSEqNprVu3Rq9evVxZdcaYlxH7K5y6stUKnu8VHLNhwwbcfvvtNss88cQTePPNN+upRswatVqNo0ePoqCgAKmpqWjbtq1ZmfLychw5cgQqlQpdunRBfHy8B2p6g4Qs/c7ACo1GI/wEgjHG3K2iosLm81KpFCEhIdBqtVAqlQgODvaa7msai7///htPPfUUQkJCkJSUhD179qBPnz74448/hJCooqICkydPRlpaGvr06YP09HTExcVh3bp1iIyMFJb14Ycf4sUXX0S/fv1w+fJlBAYGYsuWLUhKSvLU5vksIkJlZSVkMhlfeDPG3E6lUlnsasRQSEgIpFIpKisr4e/vz10meMBnn32G//3vf5BIJMjMzIRGo7HbBUJaWhrGjBmDyMhIRERE4NixY/j2228xdepUoUynTp0QGBiINm3aCNNGjBiBBx54wG3b0pAolUoA/MUWY8z99PfNtui/OFCr1dBoNNxIzgP++usvzJ8/H7GxsYiIiMCuXbtw00034ffffxfOFa+99hqWLl2K1q1bw9/fH3v27MFLL72EV155xWP1dqgFsyP9HTLGWF2J/TKrPluoM2M1NTVYv349WrVqBQDIz89Ht27d8N///heLFi0CUHvyy8jIQEZGBiIjI6HRaDBy5Eg8//zzWLJkCQDg1KlTePrpp/Hrr7/i9ttvh0qlwuDBg/HYY49h5cqVHts+X+UNfXozxhoPR/rs5BtVz/H398eWLVuwf/9+3HHHHaLmmTVrFgYMGIAVK1ZAIpHg7bffxv33349hw4YZtWSeNWsWnnzySTfVvGHjYJkxVl8cuW+urxbqzJyfnx/+/fdfxMTEAAByc3PRpk0bfPXVV5g3bx4AICkpCRkZGcLr+ffff2Ps2LEYPnw4BgwY4JF6Oz5KC2OMMfZ/xo8fL4TLABAbG4ubbroJx44dE6bt27cPt9xyi9BaOSAgAJMmTcLPP/8stHj75ZdfkJSUJPxkSyaT4bHHHsNff/2F0tLS+tsgxhhjrIGaPXs2WrZsKbr8yZMncfz4cTz11FNCVxpz586FRqPB6tWrjcpevHgRK1euxMGDB6FSqVxab8YYY6wxGTt2rBAuA0B8fDwUCoXRuGIPPvig0ZcFY8aMQXBwsNF9eH3jPpi9RHV1NdRqtaerwRhzk8DAQKMBKerK3Z8ZRGTWL6OYFmpKpRJ79uzBnXfeKUxLTEzEmTNnjMplZGSgsrISmZmZaNeuHU6cOIFOnToZlencuTO0Wi1Onz6Nfv361XGLGHMdPmcz1nC5+nwNuPczw9nztRgnTpwAAKPzc1hYGFq0aCE8p/fXX38hMzMTJ06cgJ+fH37++Wc+dzOP4/M1Yw2bL91jO3q+Li8vx7p161BZWYnVq1ejefPmmDNnjtXl79mzB0ql0uyeuj5xwOwFqquroQiPgVpju79Zxpjvio+Px8WLF11yAqyurkbL5nLk5WtdUDPL5HK5WR/YCxYsEEaKtubRRx+FRqPB008/LUx7/vnnMXDgQMyYMQMjR45EWloaNmzYAABC6+TS0lI0bdrUaFnR0dEAYDZwEGOeVF1djebNQ5Gfr/N0VRhjbuDK8zXg/s8MZ8/XYpSWlsLPzw/h4eFG06Ojo43OzYsXL8aoUaMgkUig0Wgwa9YsTJs2DRkZGfU2aCdjpqqrq6FQxEKtLrdfmDHmk3zpHtvR83V5eTlWrVqF0tJSHD58GHfddZfV7k2uX7+Oe++9F2PHjsXgwYNdXXXROGD2Amq1GmpNBQZ1fRr+fjzgSUNSlupcH6ilLe2P6C2G4iK5bFnuoLhofYxRb663o3Sqalz44HWo1WqXnPzUajXy8rU4fygZ4WGu7+morFyH1r2ykJWVZXRTaa811HPPPYeVK1diy5YtRiPY9ujRA+np6fj222+xc+dOpKam4rPPPsPYsWOFk6RMJjM74er/dnVLMsbqQq1WIz9fh7SD8QgLazifU6xuvi3tiPsUpzxdDSPflnZ06/K9bXudYbqPVBU1eOfm7S47XwPu/cwoLyd06Z3n8PlaLJlMBq1Wi+rqaqP9UVFRYfT36NGjhf8HBATgP//5Dzp06IBjx455rB9IxtRqNdTqcvQf8AL8/X3jHluWkWP1OVU7cYNe21qGK4ith56+Po7O5+g6xCy/NKV+joOKZvWyGq+ga15l8/muTa+6bd2aSjXWT/7R5ffYlw+3cPk9dlm5Ds17XnLofJ2YmIjly5cDAPLy8tCzZ0+EhITgzTffNF52WRnGjBmDiIgI/PLLLy6tt6M4YPYi/n4y+PtxkNKQ+AU693r6BbnmBsQvkFy2LHeoaA8oMi2HzN5cb28RHiZ1S8AsLD883KzVkjUvvvgivvzyS2zatAm9evUye75ly5Z4/fXXhb/fffddhISEIDU1FQCQkpKCffv2Gc1z+fJlADDq45kxbxEWJkGYG99/zHcsKemCIDm87nh4POw0gNr6uYO3ba8zgrT1N3i5ez4zaltFO3K+dkRKSgoA4MqVK2jTpk3tGnU6ZGdn2zw36+tSUFDg8jox5qjKtuGIvuwb9xXaTimQpWdZfs7f/n2lLD0LkLp3UDb/swVQdUg2W6/pNNP6WJqvLoz2kzRQ1P7xC6yfgFmRB5S3qJdVeV6I7V/nBIT63iCBtffYfu5ZtpPn6/j4eNxyyy3YtWuX0fTy8nKMHj0aNTU12LJli1uuBRzh+1eGjDUgpSkSlKa47gLIlctyF/02+0JdmWUvvfQSPvvsM2zcuBF9+/Y1e76yshJKpVL4u6KiAl9++SXuvfdeYWTisWPH4tixYzh37pxQ7tdff0WnTp3QrFkjagbAGPNJsyPSzKa5K9h1lKW6uYK3bF9duGvf+LL169fj0KFDAIB+/fohKioKv/32m/D85s2bUVxcjLFjxwKo/Vmu6aB+f/75J/z8/NCjR4/6qzhjNpSk+kYLZlssBc/Wwmh3c7YunqqvXsS5+huANOxSva2KNUC5ublGf2u1Whw/fhzJyTe+pKmoqMDo0aOhVquxefNmRERE1HMtzXELZsa8BAeszBe9//77eOutt/Dkk0/i0qVLuHTpEgAgMjISo0aNAlB78zlp0iTceeedkMlkWLp0KaKjo/HWW28Jyxk3bhxGjRqFcePGYd68eThz5gx+/PFHrF+/3hObxRhjLrOkpAsHmcwrHDx4EJmZmdi7dy8AYMWKFZBKpRg2bBji4uIAAE8//TSGDh2KXr16ITAwEIsXL8acOXOgVCoRGRmJd999F3PmzBEGEbp06RLuueceTJkyBc2aNcOhQ4fw9ddfY+HChUY3wox5WkmqrF4DRjH0gaszLXv18+pbD3tD2OzJANlaK2pPCrvU8FsySy+GQNfSdjcZzHG33XYbunXrhu7du6O6uhorVqxAbm6u0GUGAEyYMAHHjx/H4sWLsXHjRmF6x44d0blzZ09UmwNmxtyltJ1z/S8z5kv8/f0xffp05ObmYtWqVcL0Fi1aCAFz06ZNsXz5cixZsgTFxcV4/PHHMXPmTAQE3PhpskQiwZo1a/DVV19h//79iIiIwIEDB9C9e/f63iTGmIMaa4Bq2ILXdB/on6trK1/9/O7Yv5bq66iG8NrPjkhrEK2xxTh69Ci2bdsGAJg+fTrWrFkDAOjUqZMQMI8bNw4dOnQQ5rnvvvvQsmVL4eb2/fffx5133ik83717d6xfvx4//PADdu/ejaZNm2Lv3r3cepl5JX1LZk8Gza4IYU2X4Ylg19l1ejoIjjinahAt2lnDtmPHDvz888/Yu3cv/P39ceedd2LmzJlGg/w1a9YMsbGx2L59u9G8UqnUYwGzhIisj7LF6kVZWRkUCgWG9XiR+2BuQBwJmLn1sjFFJjWofaKtrsb5t19CaWmpS/pF0n9m5J9p7rZB/mLbXnZZfRlrSPTvv4sZCQ2iD1pvZhr6eVOQaSmQtBfYmj4vZnscCZithdymAaq1ZTkbsnrT61IXS0q6oLpCg9f7bnHp+c+dnxnl5Tq0bJfL52vGLNC/97rct8jquDieCJotBbOGgaunu5GoD3UNmG3tIzHLru+AuaG3YrbVgrlHcraoZUyOPQIAWJkv/stJTaUaq0d+4/J77OtnW7m8D+ayci0i21xo8OdrvjNijHkla4P/McYY8yxPtfb09VamS0q6CA+xZkekOdTCVsw6rD3nbFDs66+L3uyINNynOOXpajDG6hG3ZG2cvK2rlMZOHy4z38cBM2Nu4Gj3GBymMsYY8yXOhIrOBKzuWIYr2BrUz5Ew2FmOzOtooM0YY41JfYfM3tZPsCfI0rOMHmLKW/o/835Hspo6VJ7DZt/GATNjdpS2kwsPVj8aUvcYjDHWULkr5LRG35pXH4J6OmS2xNE61WUf1iVs98Z9xxhjjZUjYWtDZG+73TGIYX22Yg67VG+r8gjpxRBPV4F5CQ6YGXOAmJC5rkE0t2auxSEzY4x5P7FBpTMtY23NYxg0e0p9rt9eqO7pwfY4sGaMeStFpsorukQwDEi5FbNzGmsA7+scbcXMfBcHzIw5yN0tmTlYZYwx5q1cFSQ6Ekx7Oki2xRN1szWAoDfvK8YY8yRPh8wcKtvmqvCYQ2jP4FbMDOCAmTHGGGOM1YGzrZi5b2DGGGPeoL76YeZWzLbVdzhcX186lLeol9V4NVutmFfm96jHmjB34oCZMSdwn8yMMcYaG1uBcH2EzN7I1+tvibNhfkPcF4yxhsVWoFjfg/0xy+q7L2pPt2xvSLgVM+OAmTEXU2RUODdfA+17uaFuF2OMMWPOhMy+3DK5PrsLcUd4a2uZ3t41CWOMOcuTgaJpq2VuxWxdQ+nqglsv32CvFbP+wXwXB8yM2WErMOZWzIwxxhoLa4GjM2Ekh5fGlpR0sfpw93q9eXmMMeYO3tRqVdUhmYNmK/StmR0Nmx0t703Hg6/jVsyNGwfMjIlgL2TmoNm60hQJt2JmjLEGRh8q64PiJSVdOGR2E18LmRljzBdxyNhwcGDvvWy1Yma+jwNmxkQQEyDrg2YOmxljjDU2HBb7LluvnTOvKwfWjDFfwIEyM8THg+vYa8XMIXPDxQEzY8ztSlMkFqdzy2bGGPMdHBx6L3d2qcEhM2OsoYo4pxIejPGx4DrSiyE2g2YOmRsmDpgZYx6hD5c5ZGaMMcY8w1Z4bBgS86B/jDHGGgsOmV2H+2RuXDhgZowxxhhjdnHA2PjUpSUyt2JmjDHGmDXcirnh4YCZMREMB/mzNeAfcw63YmaMMd9gOKhfY9ZYtp+/VGCMMfeTpWd5ugoNhqsG+HNVK+awSy5ZTINh2qKZQ+aGhQNmxhzkrkH8rPVT3NCVpkga7bYzxpgvMgyZfTForWudfXGbxTDdLleEyw11XzHGGPMeqg7JLguWDXFXGfWDQ+aGgwNmxkRSZFS4rfUyB6yMMcZ8hWlo6EshoqN1NSzvq4G6Ixr69jHGGp/SFJmnq2CXuwLSxoZDZt/FIXPDwAEzYw7SB82uCpsbc7jcmLedMcZ8lemAb77UjYIjXXzoyzSGYFkMZ/cB7zvGGBOHQ2bvFHFOVaegmbvJMMYD/zVcHDAz5kGNMWBVZBL3ucwYYw2AadDsa2wFn405FG3M284YY57GIbP34pDZvbgVs+/jgJkxO0rbya0+6rTcRhguM8YYY55mGIpbClMbU8DamLaVMcZ8BXeZ4b24ywz34pDZt3HAzJgHcLjsetwqmjHGnMMhIzPl6m5B3HGM8XHLGGvoOGS2zxP7yNmQmVsxm+uRnI0eydlG045kNeWg2UdxwMyYHe4c3M8baUpLUJFxEhVn00E6naerYxd3ucEYY3Xjy91cuIKjQeXqrK5uqoljtDU6nN1dgOPrrqLyutrly2/sxwVjrOEqSZUZPRhzlKMhc1VBFq5nHoMqN8dNNfItpv0wm4bMALdm9kX+nq4AY8y7XPn6I9SUlQAA5O06IWnG/S5dfmmKBIpMcksrbnctlzHGWMMyOyLNqe4x9OHy6qyumJh83C11E2vn1xew+aNzAIDAED88vW4wwmODPFonxhjzZtbC5JJUGXd9wERz9EsJVVkRzvz5Qe0fW4DY22YgvFcfN9TMO0kvhkDXssqpefUhs6UAmnkfbsHMGBOUnTgqhMsAUJFx0nOVYYwxxtzIXl/MlhiGyp5syVyapxTCZQBQV2lxZmeBS9dhuE9c0R0Fd5PBGPMkb2+pLEvPEjWN3eALXYgQEXIPbTCaVrh2lWcq40HSiyFmrZYdwa2ZfQMHzIyJ1Bi6yig5uNvTVXCYYYtlfetogPtkZowx5hhnwkpPhcynt+ebTWvaWeGBmngOd+HBGBPL28NloDYslaVnCaEyh8u2eSpcdvRY0qmrcf3cYaNp8q7dXFijhsNeK2V9yMx9NHsvDpgZc1BDDZmJCDqNBv5hCkQNugUAED/5Tresy93dWHBXGYwxxuqLJ0JmTXXtGAmpr0wCAAQE+yGhbbhTy7I1oB+3EGaMsfpjGJqqOiT7RAvdxsSZLyq0GhUgkUDerQf8IyIBADETb3d11dzG2wYm5GDZu3EfzIw5QZFRgdJ2ck9XwyVy//wJZWmHEdAkFprCfMjbdoSmqPZntrLYBLet1x0hMLdaZowx5ghnusmwpD76ZD79zzUse+wIpH4SENWe74am5uIcgA7DY922Xk+GzEtKunBLZcZYo2IaKutbNjPfUVNdiZPLFoJ0WgTKIwEiyDt1RVVGOgBAIvWNdp76cNkwZC5v4brlSy+G4AiaGrVc7pGczSGyD/ONI5sxL9QQWjKriwpQllb7kx1NYe3PbQMiolBx9hTCu/RCUEKS29ZdH62YGWOMMbFmR6SJCjM9Nbjflk9q+1zWaQlU24AZR1bnICDYD1Pe6OyROjnKmbDaVgtrDp8ZY4zVF7EDQRafPQTSaQEA6orrAABdVSV01dWIv3OW2+pXH+qjRbPYAf2OZye6uSbMURwwM1YHvh4ya0pLAABNZ84Rpl3fvwtUUwPVtRyXrMOTQS+HzIwxxtzBEyFzeYEKvW9PRvMekZD61X5Je/jPbGiUWhRnOTc6u7ez1MKcu+1gjLlCxDmV0cMXcLcZvkFdWQL/oFCkTpwnTMv/cwUAQHX1qqeq5TKuDpkttVgWGzIz78IBM2MNnCKTjIJWbVUldKpqkFaL0iP7AADZy74EAARERqPJLbcivGsvRA4Y5tI6MMYYY75AbKtYd4fMOi2hJFcJALh85DoqitQ4+HsWLh+5Dp2WEH9bHwx5qBX6TEtGdLPQOq3LV1oCc8jMGKsLfZDsK4GyHofK3k9dfh1EOmiqylCQtgM11ZU4t/pjAEB4sw5I7HsrQjt19ZkB/tzZUtlw2dKLIVbLccjsezhgZqyOXNGK2TQEdofw8zro1Gqcf/dVZP+4FFUXzqL85FGjMqStQcWZk1AX5qMi/TjUxUVurZOr8IB+jDHGPGFi8nHh4UoalRYrF5zAu7dsh6qqBl/O3GdWpuLMVVw4UIzcjDKc2ZkPna5u1xGOhMx1CaQdDYd9JfxmjPkGXwuXDcnSs7g/Zg+yNMgfabVQFl3FqZ/fQFHGAeQd2WJWpqa6AqWXTqKmpBjlx45Aq1TWR3UdFnbpxkNM2bquSwwOmX0LD/LHmAsYhsxiBv+zNsBdXQe+04fUhssoTZFAkUlI//sjVBZdAQAosy6iptI8GJfFJaLy3Gn4hcpRnXMFFWdOoeXjLyEwqonTdfI0dwwmyBhjjLnL7u8vYv27GcLfBRfNz9fxbcKQdzIbqmA/aJRa/PTEUYx6qg2GPJji8PoMA9zZEWncOpgx1mCVpMp8NmDWt2LmgNkzLIXL1SX5OPPnB9Bpao+pqoIsaFXmXVYpi/NANWoAgCo7C5XpJ9H86RdErzvskmsH17O2jvpgbT1HsppymNwAcAtmxlxMbItmfatld7RcNl1mdXmREC7r+QXX/hwlfvKdwrSIXgMAAFqD8Jk0GpfVw9O8rT6MMca8k6dazRIR/vki02iaPFIGWagfet+ejBY9IwEAYTEyJHUMh0apFcpVV9TUa109jYNwxpg7lKTKzB6uVpeQmLvL8B5Fp/cJ4TIAkFYDP1kIZIoYJPWfIEyP7znKaD5Sq0WvQx/I1leXFfVJv15b3WQA3IrZl3DAzJgb1KXbDGdDUEvz1VRWIPvHJUhbtQgAkHzfY/BXRELerhP8Qmr7awyKT0Ls6EkAAImfHxT/FzKHpnZA/KQ7IItLcKo+YupXX+qjCxLGGGOsLjZ9eBYvd9oAZZkGQx5shaGza1sjKxKCEBIZiJCIAEx7pysAQCb3x4RXOgIA5NGBGPxAKwx9yPHWy86q73CXu8lgjLmKrfDYHWGyGI4GzvrB/jhs9ozyq+eR/uvbyE/bDpkiBm0mPwEACE9uD/+gUOi0NYjtMhTyxNrzckzHgZBI/Wr/32kQ4u+616n1ujoIFtsdhq35XVEHMThk9g3cRQZjXsjRbh2shaclB3aj8nztz2z9AoJARy+gpvQ6ZN37CAGzqiAPmrISAEBwcguEtm6HuHG3QSJxfbcSnuyugrvJYIwxc0tKutQ5vNOHjRwCOq/wUiW2L7nRcrn9sFh8cVdtv8sSiQShEYEoy1eh8HIlACChbRiSu0Sgz9/PY1Iz3u+MMeZu3ti9RmPuLkOWnuWxgP3qvr+gKskHAES27o4rO1YAAORJqdBUlaGmugKq0kLoNGpAKoVfQCC6PfQeiAgSiQTldah2fXSX4Qyx9SpvYTlUll4MwRFwNxm+jlswM+YmdR38T2yrW6tldp9B0Y5Nwp9aTTWyj60HAKiu5aLq4nlIZUHI/X0Zru/ZDgBCv8zuCJft1lfk84wxxlzHFX3u6oPlhtZlweyItHoJzdVKLb558IDRNH24DACZ+4sQGhWIo2ty8M0DBwEApKt9zhXna9PXrT622Zljhb/AYIy5g5iuMFzdstk0GFV1SG7UYbEv0H+5cPXg36gquPFa5R3ehOriXACAqrQAmqpyUI0G6csXoaogCzJ5lFBWf872VJcU7uDqVsxHsppaLcfhs/fjgJkxL2crcLX1XGHmQbNpUS26AQAqTqfh2trfQHRj/og+NyEgIspsHl/BLZQZY8w5rgjuOPxz3sWDRSjJrTaaFt38Rn+EX99/AGd2Fgh/dxmTgI4j4uqtft7E9DhraF9qMMa8mzu70ODuLnxDQdoOs2nB0YkAgHOrP0b+8W0IDKu9p/YPCUdst+Eur0N9dk3hyLLq2t2Gvb6YmffjgJkxH2A4IKDYFr7hCW0QEploNC25+zijv0mtQsLUWYjoNxj+4QpIpK79SLAW+trahtIUidsHPuRW0owx5nocMjsnpqUcKf2ijaYNm23en3LqwGhMf68r/AKkCGvimX5CvRGHzIyx+uTuAQDF4hbPnhHVphcCQhXC31L/QES06mZURl1ejLZTnkJYYgoCwyLrrW5i+1R2RwtqV3XbIb0Ywq2YfRgHzIy5UV27ybC6XBFBc0xKb3S6dT66T3sdIVG1H9Lpf39kVi73tx9Qsm8nKg8edktd65O9Vsz6fcatnRljjInl7uA8KjkED3zdB4tOjcGUNzoBALZ/dcGs3Ll/i/Drs8dxdE0OVJU1bq1TfXA2GOYvMhhjzBi3fq4/yYNuR6e7F6DtlKdqJ0gkyD243qzcmT8/wPXzR1GVb/mLgLqGvPow2fDhqmU7y1r/ymKJ7SqDeS8OmBlzo9J2ck9XAQGyUMTfcz8AQFNdDgBI6Gj+U53knrcCqO2HmXS6+qugk7gVMmOM+RZuaWpfrynJ6HBLHAov1g7mF5EQBInJ1XpUcjAik0KgrqqBVql2Sz04xGWMMc+y10JZ1SFZeOj/bmw82Yo7JCYZrUbdD53mxsCP8T1HmZVr0nEgiAgaZXl9Vs9q0Ouu8Nkw5K7rOvRdZVgLmbkVs/figJmxOihtJzd7GD7nDUpTJAhQRCCp62hhWnTLHohp3c+oXEl2Os689gwy3/sPKs9nAOAQlzHGmOu4YkDBxmDsM+2E/yd3i8S9X/Qyej4kIhDfPHgAC3tvxrn/rnTJOi29LvU1yKEzvLVejDHmStwNRt24e/8pWnRCgDxC+DuqbW8ERcUblck99DeOLZmPkz8sgK5GY/RcQxrsz5Sz26afz17IzLwTB8yMOcBamGypjLeJbzcIoU2aAwBOrl2M8ITWCO/aW3g+/+weYVj6oKRmwnRvDpkt1c2R7i+8edsYY6wh8ZVgeUlJF4uP+hSVHIKpb9Wu88Tfufjny0zM/LiH8Pz1HCXO7y0CAEQOaOP2+rgzzK3LvrVUL0+8Xowx3xZxTmW/kIc0xlbJjrIVIrt6/1k6VlLGPCT8P/3n/6L50DuNni88uRsAEBgeDal/gEvr4yhfCbRNQ2ZLuBWzd/L3dAUY8xXeGBrr6UNVw8BUoyxHRWogdGUqVOfl4NqF06gsvCw8Hxweh8rD5i2fms56BP6hrttWa4P2KTLJ6b6Q6xIMm663LvVgjDEmXn0Gf4brMgwi9dOthZOeoKnWoqpUg+DwAOSdLcPZ3YU4vfWa8Hyfacm4fPS6UXkAiBvfA3HjugM4Xt9V9hrWwm9brzNjjLmKJ8JpWXoWB88eQkRQlRUhUB6J6tJ84MQFFNCNgDuiVTejLjMCQsKhqSqDLLwJ2kx6vN7rG3bJdYPvuWr9zoTcR7KacqDsIzhgZqyBISJkH12P3FP/ALAcxPa68x1I/fzR7KEncfHDNwGJFG1ufgghEfEIpHCUwjjE9UQIa22dYsJla6E2Y4wxz7MW/rpj+Zb+tjbNEy4cLMLPTx5FVYnG4vMzP+mB9sPioBtDyD1TjnP/FiL+riEI794cIS1j67m27rGkpEudjwNveT0ZY8xTuDsN96qpqcbRc8tQ8Y/5ILwAEBLbHC1HzAIAtLhlFi5t+QGyyDi0uGUWQmKSrbZerq8Q2FOtl+vSVUZ5i9pWzLqWVRZD5q5Nr8LJxTM34YCZNSr2WiErMipsPuetrZgNw1RlSR5yT21DZN9BuL5/l1lZf1kIJFI/VF3PxdWV3wAAwuNaISKhjcXlebu6hN8cRDPGWP2w1v+yM8FgQ2qV+teb6YhMCkazbpHI2J4vTA+JDEDVdQ3iUsNQo9Zh80dnce7fQgBAkxGdERAe7KkqeyX98aU/NvTHlSvCa8ZYwxdxToWSVJmnq2HEVitlfZjMLZnrbx/kZO9DRd5FJPa9FVf3rzV7Pqxp7b309cxjuLTlBwBAdJvekCe0srtsT7c09lZiQmbmXThgZo2GmHDYsIytsNmbBYYoAEggS2iKNq++h5ryMpQc/Belxw9BW1GG5B7jkZe+HdcydkFdVfp/c9kPaL2lKwlLobCjdfOWbWGMMeY401DaUhcYviIiIRi6GsKsT3tCrdTi4O9ZSN96DRcPFqNZtwhcPnodeWfKsevbi8I8OqUaaGABsyuCYMP5OVRmjBnSd2XhbSGyM1QdkoWAubG3Wq7PgF3boglwgRDdtg9iOg2CsjgXRRn7UHR6HwBAnpCC/LQdyNm7+sY86mrRy3dXyOwr/S5bY7pfOGT2bhwwM2ZFaTu5Ucjsra2XTfnLQhCsiEX+uj9RmXkG5SeOAAASOt2M3JNbcXHvr2bzpA69V9SynQlmxbYQru+WxPr1mfZfzcGz4zIzM7FmzRrk5OQgJSUFd911F8LDw43KlJSU4JdffsH58+chl8sxdOhQDBs2zOEyjLGGwVqLZmf4WqhsqFm3CGz+6Bx+fvooTm7MAwD0u7M5Lh4sxpVjJbhyrMSofMcPZ0EWpxD+npjsuv6XubUvY6yhs9VSWf+cN7ZmNmUYMjdW9Rkul6TKEFrWAgBwbs2nkEXEoPTSSQCATBEDVWkBMtd9YTRPTOchaNJxoEPrMQ1TfT0cdpWwS0A5alsxA40nZN6xYwe2b98OlUqF3r17Y9KkSZBIjLOKM2fO4M8//8S1a9eQkJCAqVOnolUr+63m3UXqsTUzVs+caZFc2k4uPHxJyk13A4AQLgNA/tk9FsuGJ7aDqrxY9LJdFQSLCXIVmSQ8HJ1XzPoNW0NbGiiR2ffhhx/i1ltvRVZWFhISEvDrr7+iTZs2uHjxRmu7nJwcdOjQAT///DOSkpJQXV2NCRMm4Pnnn3eoDGPMt82OSBMe+r8bu4EzWyClX7QQLgNAxj/XjMr0uq2p8H8/eRBqKut/UCnGGPNVikzbn5mGYbK+pbPYwfvEBNElqTKvD6yZdfrXThYejaQBk1Bdmi+EywCgKi0Q/u8XFCr8PyyxNdQO3GML81268WDGpBdDhP8fyWpqo6TvGz16NBYuXAgiQkhICJ544gmMGjUKNTU1QpnVq1ejY8eOOH/+PFq0aIHjx4+jXbt22LZtm8fqzS2YWaPiiy2SnRESlYjuty0AiHD415cBAFq10qiMvywUNapKlF3NwMmrGUjodDOSu48VtXxPDfoHWA+XTesktn9lbrFcN6NHj8Zjjz0GPz8/AMCTTz6Jbt26YdGiRVi6dCkA4M8//0RFRQW2bNkCmaz2Ii02NhYLFizA22+/DYlEIqoMY6zhcWVLZl8UGOKP+5b2RlWJGj89fhSXj15HSa7xT2rzM29cu6Q9sAQA0Gf985jUvGEF9O5oQc2tshljphxtoWyvew19q2fTae7W2Fsxy9Kz3N6K2fR1jO08GFGpPVFx9Twubv4eUv9A6GrUwvPa6krh/xc2fg0AaD/jRQQpYtxaz8bAUhcix7MTPVGVevG///0PHTt2FP6+44470Lp1a6xbtw4TJ04EAHz22WeYMGECvv76a6Fcbm4uvvzySwwfPrze6wxwC2bWiCkyKnyyn2VlSR5O/PUeLh9cBSKd1XJ+ATL4BQaheZ/bhGnNek9CXLvBAICkriONyhdmHnRPhV3MUotmaxztl9nS/5ltbdu2FcJlAPDz80Nqairy8m60xktMTIRarUZ5ebkwraioCElJSUJwLKYMY4z5Em2NDiueP45Pp+1BSa7SajmpVAJ5lAy3L+qM0KhAAEBAkBTPbRkKAPALkCI2xfhLcXVhmdvq3ZBwuMwYsyTinMroYauMpXmYd3BlwJ6XdxS7dr6O4uJzAKx/SeAfFApFyy5QtOgkhMvtZ7woPN9y1P1G5YszDrisjo1d2CXjVswNmWG4DAAtWrRAUFCQ2T12cfGNVvI6nQ4lJSVo2tRzrbu5BTNr9BQZFT7Vmrno8jEoS/KgLMlDYqebERAcZrN8XNsBkDdphuqyAkS37I7r2em4lrETuae2G5WLSGrnxlp7L0utsblVc62yMuMAQyaTCa2LrcnKysKmTZvw2muvCdOmTJmCBQsWYPDgwejduzeuXbuGyspK/Pnnnw6VYYw1PA259fL1bCWOrb0KADi5KQ833dPSZvnoZqF4et1gHFt7Fd1uTYRaqUVsSiiuHLsOrebGF5/SoABM6XwB3E6EMcY8w17IXN/9OTfm1syuasmcnfUvamqUOJu9Ce36drJZViKRoOXI+1By4TgCw6IQGBqB2K7DkX98G7J3/2FUNiS2/vqKZt7PmftrAPjpp5+gVqsxePBgYdrixYsxb9489OnTBx07dsSxY8fQq1cvLFy40NXVFo0DZtYo+VKgbCo2tT9Kc85AIpXC36CfJ1tCo5siNLopii8fx/mdP0Ai9YO68joAICQqCVXFOQiPb+NQPVzdTYbYLi1ctS7T/+vXb2mbPNEliBjLKxIQ7IaPcWVFDYDLSE42viBasGCBzRNWZWUlpkyZgg4dOmDevHnC9OvXr2PTpk2IjIxE586dERMTg59++gn79u0Tvp0VU4YxxnxJdPMQDJzVAkfX5CClb7SoeYLDA9D/zubQqLR4o/8Wo+eimoWg+EoVWvdSwD+wYYbL3KUFY8zdTANfd7VIdndLZ32gXJ+D3TVkLVuNRMbp3xHRwna4rCeRSBCZ0g0AcH79EpRnZQAANJWlRuXkCSkurWdjYe3LGdMB/9zh94pIhEj87Bd0QFWFFgAcvr8GgGPHjmHu3Ll44YUX0L59e2H62bNnsXv3bgwbNgydO3eGUqnEtm3bcPHiRXTp4pkGHBwws0bHl8NlAAgMUaDj2CecmldZWjtwEOm0wrTUofehsigLkU3FnUy9mTuCYH3o7a0hsztlZWUhPDxc+NvWt6tVVVW49dZboVKpsG3bNgQGBgrPLVq0CFeuXMHp06eF6d26dcP999+PcePGIT4+XlQZxljDYxgmNrTWzBKJBOOeb49xz7e3X9hETfWNLrCCwvxRXV6DrmMT0KpPNKKbNY6fhzLGmCuUpsjgF2j9GtZSH8quxF1q+Ibo6DboeN9r9gtaoA+XAQkAgjRAhnZTn4W6vFh0gzDmGOnFEOjiqu0X9DKO3F8DwKlTpzBy5Ejccccd+O9//2v03H333Ydx48bh888/F6bNmDEDDz/8MPbs2ePaiovUMJs/MMYsyjm+Ufh/co9xAICC8wcQ1awLJFL3fBx4ajBAT62/IQkPDzd6WDsBKpVKjB8/HgUFBdi6dSuaNGli9Pz58+fRsWNHo9C5e/fu0Gg0uHTpkugyjLGGbXZEmsVHY5SxI1/4f/+7mgMA0tbnIqVvNCISgj1VLcYYa5DqoxsLd2usLZk9vd2Gg/wFRcUjKCIOOo0K2upKhCW29mDNGj7pZd/7wl3s/TUApKenY/jw4Zg4cSK+/PJLo3GJiAiZmZno3r270Tzdu3fH2bNn3VZ/ezhgZqwRiWreFQDgFxiM3PQdAICwmBZOL89XBsNzRcjMQbVl1dXVGD9+PPLz8/HPP/8gJsZ8lOQuXbpg3759KCoqEqatW7cOgYGBaNu2regyjDHmLquzunq6CkbCYm7ccGSllQAAkrtGuHWd9Rnm21pXQ2vJ7m2USiU2b96Mv/76C4WFhaLmycvLw8aNG7Fx40ajwXjrulzGfEVJqszrQmhXBauqDskeD2md5Q31JrpxP+wfHIbqktpfDAeGi+sai1lm6/0Wdqn+6uEpGRkZGD58OCZMmIAlS5aYDXovkUjQuXNnrF+/XjgGdTodNmzY4LHuMQDuIoOxRiVl0ExUFmXBXyZHZdEVABKEJzjW97KjHAmhXdUPc10G7muMXWHUxbPPPoutW7diypQpePXVV4XpycnJePnll4UymzZtQqdOnXDzzTfj2rVr2L17Nz755BNERkaKLsMYY+6gD5dXZ3XFxOTjHq5Nrdb9m2Do7BTs++UySq7W/gS01xTPjQruDrMj0jhMrmdpaWkYM2YMIiMjERERgWPHjuHbb7/F1KlTrc7z7rvv4rXXXsOAAQNQXV2N9PR0/P777xg2bFidlssYqzvTgNXwb0cH/avLvJ7g6nDZ2S8Q/AJkaDftOWSseBd+AbW/xIxM7QV/me+1rmXeY8yYMaioqIBUKsUjjzwiTB87diwmTJgAAPj0008xadIk9OjRA126dMGhQ4dQVlaG9evXe6raHDAz1piU5Z6FqqIYUv/aE2hAUN37o9YHwg0hlNUH3JZCZg6eLZs4caLFQfgMu8kICwvD3r17sXfvXmRmZiIsLAw//PADEhISHCrDGGuc6iOI9JZgWa9GrcX2JZmQRwei8FIlAEDqx+cgVjezZs3CgAEDsGLFCkgkErz99tu4//77MWzYMLPurQDg0KFDeOGFF7BmzRrceuutAICPP/4Yd9xxBy5cuICQkBCnlsuYr/HmfpRl6VlQdUgW/tXTTzOlL+MLIbItptvrSdm7VwIAqvKvAKgNnZl7hV0CShvwED2vvPIKNBqN2XTDe+P+/fsjMzMTu3fvxrVr13D33Xdj0KBBCAoKqs+qGuGAmTU6iowKnx/oz1lV13MAAFpNbWuoTuOfMfu5RX2xFuJa487Wzcx5t9xyC2655Ra75SQSCQYMGIABAwbUqQxjjLmat4XLAKBR1g7yV1FU27fjqKfaoEXPKLeuc0lJl0bb53VjcPLkSRw/fhyfffaZcO03d+5cLFy4EKtXr8YDDzxgNs/+/fsRFBQkhMsAMG3aNDz++OPYuHEjJk+e7NRyGatPikwVKtp7LnCpD/bCYksBtKV5rYXSzLaqwtp95h8SDv+QMDQdOMmzFWok5Fc8XQP3EXvulMvlGD16tJtrIx73wcwaJUVGhfBoTMrzL8FfJoe68joA17Rgrov67sNZvz59K2Vb67f0nK/0Oc0YYw1NYws+z+2t7cPWX1Z7qd66f8NqBbqkpAt3j+FCZWVlRg+Vyry15YkTJwAAnTp1EqaFhYWhRYsWwnOmEhMToVQqcfnyZWFaRkYGAOD48eNOL5cxb+PNLZTrwrB/ZTFdYPhauOwN9SUi6DRqyCJioSzMhn9QKCRSP09XizGP4BbMrNFrTC2aVeWFqFHVhup+ga4dhd7RFsmeYNgK2rCuhnU3bSmtn+5t28IYY6zhKrpc2y1Gjaq2JXN8mzBPVsct3ZQ0tj6Yvy3tiCBtgEuXWV2hAZCL5GTjVokLFizAwoULjaaVlpbCz88P4eHhRtOjo6NRUlJicfm33norevXqhdGjR2PevHmorq7GkiVLEBERgdLSUqeXy1h9izinstnHbkmqzGdDZm/pJsITXLnt9o4RazQV1wHSQVNRAgCQJ6a6rE6M+RpuwcwY0GhaMse2HSj8PyQy0eXLN2wZLDaQFVveVQFvaYqEu8hgjDHm1TreEi+0Xm6I9C3SrbVMb0zBsytkZWWhtLRUeLz44otmZWQyGbRaLaqrq42mV1RUWO2vMSAgALt27cK8efNw7NgxXLp0CX/++SeCg4Mhl8udXi5jnmAvQHZ2kDdf1BBCaXdsgzNfMgTIIxCR0h26mtourfxlrm3ExZgvabhXrow5qDGEzHFtByKh43AAgFbjm9/S1wfTENow3OaWzIwx5hmzI9KER0MXmyLHo7/e6I++Rq1z+zo9tV8bw+vpbuHh4UYPmcw8KEtJSQEAXLlyo9NKnU6H7OxstGrVyuqyg4KC8Oijj2LJkiX46KOPIJfLkZubi27dutVpuYx5QsQ5lc0QkUNm5mjILJFI0fKWmYZTXFshJrD3/mWexwEzYwYaQ8ic3GMcOo57Gm1vfsjTVXErR/tXNn3eUstqbv3MGGPeoTEEzfGpYXjl35vx2O8DIQttfL3acStm1+rXrx+ioqLw22+/CdM2b96M4uJijB07Vpi2fv16HDp0SPi7sLDQaDn/+9//kJiYiHHjxjm0XMa8iT6o4rDKN7k7HHfmuOg2+39oPf5RRLfr44YaMeDGF0D8vvVeje9qlTE79CFzQ+6XOTQqydNV8Ch9P8uWwmJuocwYY75DHzI31DAyJCIQIRGBnq4GawACAwOxePFizJkzB0qlEpGRkXj33XcxZ84cowH6nn76aQwdOhS9evUCADz00ENITU1Fhw4dsGXLFqxZswbr1q0Tur8Qu1zGvJW+792GGFrpB8HTB7Ky9CyzcNbwOV/grS2vJRIJwhJbe7oaDV5j+pWBL+KAmTErGtPgf6yWmJbN3IKZMca8jzcPGOdIS2tv3QbAffvYm7e5obnvvvvQsmVLrFixArm5uXj//fdx5513GpUZN24cOnToIPz9yy+/4Msvv8SOHTvQpk0bpKeno2nTpg4vlzFv1hDDZcA8jLX3t7fztfoy1thwwMyYDRwyN1yGQbH+/5b6XTadxiEzY4x5J0+HzIatqRtq9x2e3ses7oYOHYqhQ4daff5///uf0d9BQUF44okn6rxcxpj3MGzJzC2XLdO3bDf821BdWtKaLpuxhoIDZsbsMO2XmQNn3+GOMFi/PEsBNGOMMc+qzwDUWojsi+GyJ+ts6zXTT/fFfcoYY97KV1oCe7qe1lq21zUc1nfJwiEza2g4YGaMNVqmAbTh3/aCY8OgmUNmxhhrmDjYZIwxxuqfp8Nlb8bhNPNWHDAzxhot09bIjDHGfJ8zrZg91b2F4fq46wnGGGP1wbBbjPrsKkPsuhpDuMwBMWuIOGBmjDVoYloYWwuaxXSDwa2XGWPM+4gJmS0FyfUdLhv+66yGHlBzK3LGGHMdw/6X9X/XB7GhcWMIl12BWzEzb8QBM2MO0vfJzH0xNz6lKRLue5kxxnyEaTDpDf35ujME5gH4GGOMWaMPki0FuPU94J9pHWzVjTHmOzhgZsxJHDQ3fJZCZA6WGWPMN3kqWPb20NcbWgjbG+jPG+rIGGMNkWGo666g2V5w7EvBsrOthg0HDOSWx6yhknq6Aoz5otJ2cuGhD5q9jUZVietZJ6GtUXu6Kj7DsIWyIUUmcT/NjDHGnGIvHNVpCef2FKLocqVTy/f2AJsxxpjnqTok+1SQ6w1KUmXCQ6/6+jWU55xzaDmG4bLh3xHnVEYPxnwdt2BmrI68sQVzjaoKR1f8BwDQsv80xLTu6+Eaea+6dHkhpn9nxhhjjYOzXXD89uJxHF+Xi6jkYDyzYWid1u0LuDUyY4x5D3d2i+EtgbY+IK5LiFuSKkNZ9llkrvgCANBp1msICA5zenmW6sKtnJmv44CZMQd5Y6BsqrqsQPi/IqGtB2vi/UwDYmvdYlhqwWw6Xf83h86MMdb4WApObQW/+vIvrcsFALQfFueeinlQQx+AkDHGfJ21ELiuwbM3hMu2QlpnQueq/MsAAImfv0PhsmE9xK6PB/Fjvoi7yGDMQd7aJYahwNAIhEQ1RcpNdyMwNMLT1WkQxITG+nCZu9NgjDFmylrL3TumhWDmXSFYsdD7urRyVTA8OyJNeNgq4+56MMYYE8dSlxq+1M2GpXDWtLsLR8kiYhEa1wLtbn/GpfVirKHgFsyMNUCBIQp0GveUp6vRaHELZsYYY4b04amlFr0ffxB5o6CyXqslircMsuct9WCMMWZM1SEZsvQstw0S6Ax7LYCd6S4jslVXRLbqWpdqARDfeppbMTNfwy2YGbPDcEA/X+geg7mfabcYjDHGmCOsBaWOBKieCFsdbUm8pKQLtz5mjPmkxhTsWQuE9dMdbbnsLa2c7fVzzOoH7/PGg1swM2ZFaTu5xe4wOGT2LWIDYEf6TrbUbzN3i8EYY42LtUH9TANVR4Pg2RFpXhfK1rU+3PqYMeYppSky+AU61t9uYwqXAcuBsGG4LHZew1bM3sJd4aYrWheXpMoafPja0LePGeOAmTEbOExuXOoyQB+3ZGaMscbDMHA1DJrrGi47WwdfYC2QZ4yx+uLIwG6NPRizFRRbes7bgmWxTENib++WwhePS2/en8y1OGBmzAp962VXhcxEhILz+xEUHoPwuBSXLJO5Tl0C4roE04wxxrzPkWNqHD6ixgP3hkIqNf58txTsWmt1bDjN1cGqvZbTvsbX688Y8x2WQkVbfzPvVVNTjezsvUhI6AmZLLzOyxMbhnJoKg7vp8aFA2bG7FBkVNQpZCYi5KVvR9X1XBRdPIyAEAW63/YfF9aQeRqHy4wx1jDs3FWNbTtU+OTz2i+Ze3YPRI/ugcLz1sJlWwwH9rNU1pnuI2yVN3zOV0JbX6knY4wx76BSlSE7ey8KC9JRVZUPjboCqW3GO7wcT7VYFvslhr4cB7XMF/Agf4yJYKkvZrF0NWpkHVmLoouHAQCaqlJUFmdbLKvVqFB69Qy0mmrkpu9ATtpmp9fLXIf7V2aMscbhu2WVQrgMAOfO14DoxjlgdkQaZkek4SHFcfTIPIThRYdxLE2Nex8qgrJMY7Y8w7DXkYH9nGnt7CtdT9Rl4D8Oohlj7lCSKjN6iCnLPKusLAtXLm9HVVX+//2dDbW60mLZ6uoSXL9+AVqtBqfTV6Co6Gx9VrVODLt18eWW9b5cdyYet2BmzM38AmRI7nErso6sFaadWvcBes5YBL+AGxcnBef24+K+FWbzJ3QaDqnUr17q2hA52n2FYZisn8/W/Nw9BmOMNRxvvh6BNevyhL/nPnkdQUESTBwfLEy7cLEGw0fno6LC+MvHyROOGJWrK8OWz6bT6oOtMNcVAxFyn8yMMW9lGCBzMOadmjRpj4iIVigpuQAAKCu7gozTv6NL13uMyp1O/x15eYeNpqlUZYiObiP87U2thA0DZf3/Hek7nDFP4hbMjBmw1RWGIqNCeDgqrt1NkPoFGE07vmoRqq7nQlujBgAUXjxsNp9MHg0Qt56tC3cP2sfhMmOMNRwJ8X64+84Qo2kPPFyM7TurkZNTAwA4d15jFi4DQEiIe84H+lbTjvKF1r4cLjPGvJ1pi2Vfb0naUEgkUrRoOdxoWlFRBi5c2IzKimsgImi1arNwGQCCgqIsLrM+Xltb6zA8ziyF3d4QgNcFv28aPm7BzNj/0YfLpe3kRiGyfnpdBv2T+gWg3chHUXD+AEIi43H5wErUVFfg5NrFAIA2wx5E+bVMo3laD56FqOZdndoW5jwOjBljrHF77RUF4mL80K6tPx569DoA4PY7igAA2zfF4K57i43Kjx0dhB++jnZ7vcSEsa5oWSyGpdbHjqyXg2XGWH1RZKpQ0T7I09VgbhAR0QodOkxHRUUe8vKOQK0uR9aVnbh8aRvi4rrBz988kB0w8EWXDAborJJUmaig1VN9QzNWF9yCmTEb6jK4nyFNdQVCIhPRst/tCItpBQCIaz9YeP7sP18ZlQ8IDkdkM+9veeTtOCxmjDHmiMoqHSQS4MXnwjF5YghatvDDJINuL4aOLDCb56XnPHej2pg425KbMcZc1TJVbD/NzP2IdFCrKxAb1xUprUcjOfkmSCR+CAtrCgC4du0YrubsN5qnadMBosJl/bFS3y1uHVmfrx2D3M1H48AtmBn7P9a6vqjLAH8AcPngKlzL2AVZWBPI5FFI7HwLuk99XWi9LJH4gUiLYEU8Wg6YjpDIBEik/pBIOBytK+4fmTHGmFg7dlbjrvuKoNUCw4YEoVvXABzYHYcff6nCqr+URmVX/dYELZr7ITHBD1Kp95xnfKFbDEvqq+U1Y6zxKU2RwS/QvF9bR5nOx4GZ59TUVOPo0aWoKL+KiIhW8POXoV27KYiMSsWhgx8BqO1Cg0iHNm0mICw8GWFhiZBIxLevdPR1NS3v7HHmTf1B14Wl95qvbxOzjwNmxmxwNlxWV5VBXXkd0sAgXMvYhaDwWGjVSpTlnoUsNBLxHYZAoywDAIREN0WwIg4AoUZdZdZXM3MPV4fPHGYzxpjvqazS4ey5GgQGSLDkm0qEhkgRFyfFpi3V2LtfhScfC8NTz5YI5R+ZHYrKSsKefSoM7B/WIL4MXlLSxaxlsLeFvZbqx62ZGWOO0ndP4Mqgy5mgmQcRdByRDlVVhdBqVaiouIaK8qtQKFoIg/yVll5GRflVoXx8Qk8AQFlZDprEdHAoXHaGadcXrnxdLYXO3t6FhjfXjbkPB8yMmTDtg9kRuhoNLu77DUUGA/b5BQRBq6lGWHwKii8dQ3TLHghWxCGp6yjkHN+IysLLkPoFoPzaeRRmHkSLflMRm9rPVZvDbFBk1g7S5M5gWJFJKE5y2+IZYx7ybWlHBGkDRIVclvqrZZ73629VeO7lElRW1p4LmkRLcb1EhxnTQpB+ugITbg1GYKAE+3fFoe+gawCAwkIdfvuztjXzxs3VWPtnDIKDHT+H2At16/NYsbQublHMGGuo3BEyGy6XuV5VZQFOnfoFFRW5AGp/AQzUnntDQmJQXV2CJk06IDq6HYqLz6Os7AoqynNRXp4NAMjLO4y+/eYjJKSJ6HXW5+sp9ksKPr6Yt+M+mBmzo7Sd3GgAQFvyMnah+PIxtOh7GzqNm4/kHrdCq6mGRlmG61dOIjgiHkGKWABAXNubkNRlJFKH3g95THNhGZf2/QZ1Van7NqiR0YfIlpSmSFwWLttajuKi9Towxho+Dpa9z6XLNXh8/nWMGRWETeti8Psv0fDzJ+h0wBdLK5CY4IdBA2tv+FJa+ePj9yPw34UKzHkoVFjG8TQNPvmi3OF1mwa3rgpyPR0IO3KcO7oPTJ/nAJwxJpYi07z/ZXe1rhS7XMP6cEtP24gIGRl/QqtVoUvXe9Gr9zw0adIOAKG09BLU6nI0adIOEokEUqkf2rSdiOYthqNtu8nQh9AAcPDAR06t31b/3XVtsWzan7fh3/r1cqjMfAkHzIyZsNZ62TBcNi2j/7v48nFENuuC2DYDEBKViISOw9B18ssAANLVoO3NsxEYXDuwgL8sBEldRyFYEYvck1uN15V7xmXbwzyLu81grGETG3JxyOxd1m9Qwt8feP/dCPToFoihg4Owf1c8FAoJtFrg3UUK3D45RCh/x/RQPPyQHOMnFxktZ9UapemibTI8XpaUdBH+diZ09oaAtS518Ib6M8YaD9Ogzh3BHYeBrqfRVKC09BJatLwF0dFtERaWiI6d7kKrlNEAgLDwZmjfYZpQPiwsEa1ajUBp6SUANxr56HQaVCuvi16vrePFMPh1RwjszBcVjHkDDpgZE8lSqKx/6EmkfpD6Gfc8I5NHIanrKADA+Z3fmy1X6h/4f30w6xciQXBEggtrzmy1Yq4PpS05ZGasoblPccrh0JgDNe8RECABERASfONSWB4qxaoVtT+fvfu+YlQpdWbzTZkcDJnBfd9tk0LMyrhKYwiZnV2PtWCeMcZsMW0Vavi3J8M6bsVsnURSe28tlQYYTJOgWbNBkMnCcb34LK5c2WU2n0wWgcDAMOHvUHk8AgJDzco5wtXHia1l8THBfBH3wcyYHYYBsq2+mcNPl0FdUoCw2BbIP7cPxZePo+3wh1CjqkTO8Y0AgMhmXc3mCwxRoPOE56DVqKBWliE4PMb1G8F4ED7GmMcY9rfrqX52mbELF2sQESHFhYs1ePTxYix6o7Yl85vv1A7A27tnIPyk5ueMDxdH4sPFkTiVrkGbVH8EBIg/rzgahoo9PhxZrqXj0BuI7fKCB/djjLlTfQ6c5u2DtDlK7MCFjg6KWFVVAAAICAjG6fTfEBQciZYtb0H+tTSoVLXn7MjIFLP5YmI6ICamA6qrSyGV+iEw0HZXl87UzRVsHQfcrzfzNdyCmTEXyYuvhKamCgHBClza9xvKcs+i6NJRpG/8RChTnncel/b/AWVpvtn8fgEyDpfdTJFJwoMxxuqTpfDMsIsEZpur99O/e1UYMTwIfW66hkNHNPj08woseqcMW7bV3sip1ISnnr2Ov9YpodOZnzM6dghwa7jsDtaCWVfWrT7CX3f1Yc0YY0DdwkXTPnXduS5v4ExA7mgr4JLrF+DnF4jS0ivIyzuCSxe3oqgwA+npvwIAZLJwZGf9i9Ol21HUzPyXR0FBClHhsjN1cxVfPw4Y0+OAmTU6hoP2OTOvNQHBtT/BqVFVCtNU5YVQlRcKf5fkpCP/7B6cWPMOjv35Jk6uex81asf6b2Suw0EzY6yu7AVqvtLNgbdzdXAZG+OHa/latGzhBwAICZHgq+9u/Eop7YQGK/5Q4r7ZxYhNvoqOPXKxaUu10+ubHZHm0m2o6zEjpi51GbTP3fMxxpg3a0gtk63Rb6O7B6ULlIVBq1VDW3PjHFxcfE74v0pVhvz8NOQd3oi0717Gnn/fxpmMlSBqGPd59o4lDqeZN+EuMhhzUGk7ORQZFULYrP9/AAB5TAujAfuSuo5CZPOuqCy8Al2NGhVFVyALicTVk1ugriyGuhI4suI/aD3obkQ1N+8+g7kfd53BGGONz9hRQXju5VLh7wfuC8V/FyqwaWs1/P2Av9Ypce/MUEyZUTuo37VrOtx5TxGmTgnG5x9HOb1ew9BW392DpZDVUrhb1zC2vrqXENvdhV5dtsuwyw/uOoMx5irWQjtHgmN73RuI7VLCF7mya4eoqFRIpQG4cmUnACCpaX80GTsFspwuqFGWQ3c+C7qkSOQd2QStSgmVqhRXrx5AaekVdO12L2QyhUvqwRizjwNm1ujY6kdZz7Clstg+mJWl1xCsiIeqvAia6nLIwppAWZqPzJ3LoCzNE8q1HjwLyT1uRdaRtbUTSIfSq2c5YGaMsQbI0bCNuV95hQ41WkLrFH+cz6wBABQW6nD0mBLPvlgilNPqgNPH49G+641z+Lq/nW/FbEofiDaUY0S/DRz0MsYaKmv95Tran7JpWU/0/esqpnU2bNlc1+0hIlRU5EIR0QLlZdmoqVFCFS5B5bXLyFz3xY2CV4BOs15H9prvUFJyAQBQWZmHXHk+WmhcGzBb+2LA9DX1xdeSsbriLjIYc4Bp8KwPnKVHzuPkuvdRdOkINNXlUCS1h6q8ECfWvCOEy837TK5t4XzqH0S36G603KDwJvW3EYwxxjxG31UCh3CeodMRpkwvxH9eL8OVrBp07uSP6GgpZswsEsLlmwYE4pmnwrB+QzXy8rS45+4QYf6UFPe1zbB3bNh6XuwxVR+DTLo6LHfHYIeMMeYMW4OxAZZDRdN+mW0F0b7UtYaY7jAc7ZPaVHbWv0g7/h3KSi+DSIvQhBQUpu/BudUfC2U6dboLUqk/8tO2o3mLYcJ0iUSKJtfDnF63GPrts7SNjm43B9KsIeAWzIzZIKa1MwBk5R8AaWvQ5pY5qCrOQU7aJshjWiCxy0iQTouIpPaQSCQIiUhExubPcXLd+/APksMvQIbmvSdDkdjWzVvSeOi7u3Ckf2XTbjK42wzGmCvZCgz5p/31a+9+NY4e02DMqCA8MTcMc5+8jqoqHT75IAKRkVJ07xaI2Bg/VFcT/lqnxC1jC9D6/0Llt95QYOqUEDtrcI6rjgHDLiMagobWypsx1vCZtmZubMGhfntdEZbru8Xo1fsxnMnfhpILx9Ck40AoWnSCX2AwkkrjAACxgfnIO7IZZRH/93dsZzRvMRxyeXyd61CfHG0JD/jWlxKs4eMWzIyJpO972ZIQWW1/jJm7f0JCx2FoNfAOVBRcAmk1iGzaARJJbVgZFtcKnSc8j+iWPRAU1gStB838v/CZ34quosikOg/ex+EyY6y+cLhcv6Kjas+3f2+shp8/sGltDNRqYPM2FUaNCEZsTO2gf0FBEmxaF4O33lBAES7Fa6+G46H75YiI8I7ztWmLZdPw1dZxJTao9YZA11KLa9PtNizjDXVmjDVcjrTYdTZYttUq1ls4Wj9ntyU4OBoAcKHiIFqOmIWIlO4oOr0PIdFJCI1tJtQjvtdotB73MGQRsWjSpAPatrvN5eGyM6+Ju19Dbz5GWOPkHVfJjHkRfZBsGCbru8Yw7CLDUIuEm5DYZQS0aiUqi7OhiG8DALiwZzkAoLqsAKfWf4jDy19GxpYv4C8LRYfR8xAanezmrWFiuSKYZowx5t3atQ3AwX9rWzit/1sJhUKKtm38sfovJYqKtVCrCc+/XILY5BwMuSUfGzdX48dvozD3Yff+zNZZzna34qtBrKPdgDDGmKvZC47FdB3hy+x18eHK/qR79JwDeVgiyq+eh1ZdDXlCK5BOi6Kzh2rXd/EETvzwH5z84T/I3rsKTToMQOcuM+Hvbz94dSQwrkuQ6+gXBvrjx/DRWJhua2Pa9oaCA2bW6JmGxra6xTANnQ3nbevfG0GKWFz4dzmundkNANCqlcg/tw+56TtQWXQFWk011JXXUXjhkIu3gtVFaYpEeHDIzBhjDVvLFv6Y/2QYPvi4AqvWVCH9dO1Af0NG5ONkugZff1cJnQ64dFmLrf+ocDlL6+Ea22etX2ZbbLX89eaQlgcTZIx5mq3gz16QKDY09LVwzV4/04YhqyOBa9KI6VCXFSHvyGYUnKjtMuPqvjWoKsjCxU3fokZZgZrqSlQX56EsK8No+abhrre3DGe+d9wzYxwws0bJNFQ2DYtNWWvRbMjfT4Y20UOhLMlF9rG/helVxdlI7jHOqGzTbqOdrTpjjDHG6ujpx8MQESHBg49cF6bl5enQvq0/FIob3SSNGhGE7l0DPVHFOnM0JPbWUNmZennrtjDGGgdLYatp0OmrHAkArQ16aPq3tWklqTKExCQjsnUP5B//B6rSAqGMpqocLUfcK/wtkUgR3+MWuwMxGtbN1pcEru6qhINT8fSvjS+/TxorDpgZ+z+2Wi7rA2h9sGytbBNFKmJa90FIZCICgsOQ0HEYmnYfB//AYES16CaUk8mjXVp3xjylpqYG33//PSZOnIhevXph+vTp2LNnj1m53bt3Y/r06ejZsyeGDBmCBQsWoKyszKzc77//jnHjxqFnz5548sknUVJSUg9bwRhrbGQyCRa9FoGB/QMRGiLBxPHB2LQuBsHBUiz75sY5OkLRuPrkN+3T2Fvo6yS2r2lL/TR743YxxhquugTK3hxGWmudbEhs/cUsCwBiuw1DeLMOCJBHIrhJUyQPmorwZu3RQtsW0oDa8iQB/INCRa3XEyy1qrYVrIvhzceJMzhQvqGoqAivvfYahg0bhgEDBuCJJ57A1atXrZb/66+/0K5dOzz22GP1WEtz/h5dO2MeYhgQKzIqbLZetvacacisL9cS0y2WD49PRfGlYwAAnaZhnQwaEh7gzzEvvPACioqK8MADDyApKQkrV67E4MGDsXHjRtx8880AgKNHj2L48OF48skn8fzzz+PatWt48sknceTIEfz111/CshYuXIj/9//+H9577z306dMH+/fvx8svv4xPP/3UU5vHGGvApt0egmm3h5hNb9ncH+HhEpSVEfwDfPecMDsirUGFqq7oxmNJSRfuWoMx5jLOtLL0tRDNtE9lR7ZZTDnDkNRa+SBFDFLGPGg0T4lEgpJUGcIupKL00kmACEQEiaRu5+36fn3stbhuaCEyE2fs2LEYO3YsFixYAH9/f7zxxhvo378/jh49iqioKKOyOTk5ePTRR6FQKJCdne2hGtfigJkxmPetbDrNEluhtMVlt+uHmupKaKorEBbXyrmKMrdQZBIHy05atGgRAgNv/Hy8Z8+e2LdvH5YsWSIEzNu2bUNYWBjeffddoVxWVhbmzZsnXAimp6fjjTfewPLlyzF16lQAQNeuXVFTU1O/G8QYa/QSEvzw67JofPJFBR5/1DsH9xOjIYXLjjINkvX/b8z7hDHmHp7+KX9JqqzeQ0hbobD+OUfDZVtlLLX01Ws+/C5k7fod4U3big6XfSnkt/T6NuTw2RPHszfatWuX0T3277//jqioKKxbtw4zZ84Upmu1Wtx555148cUXsWnTJk9U1Qh3kcEaLWv9LusDYVtBs6X58s/tw9UTW0Cks7qe9gF90SXsZkikfnWuP3MdDpedZ3ji05NKjU8tAwcORHl5Ofbu3QsAUKvV2Lx5MwYPHixcCP7666+IiIjAlClTjOb19+fvQRljrpV2Uo3nXy7B1avWB+/r3UuG77+KRkor/gzydaYtljlkZoy5mtiB+3yVrW3Tb7vhPnDVQIdajQqZ5zfUtlC2wi9AhhbD70JUm152l+errHWr4UtBeV015PeXJab32BKJxOIXKG+88QbCwsLw6KOP1lfVbOKrZubzarTVOH1pLaRSf0SHt0aUohUC/c1/8iq2xbGevRbMhuWuhOfg0r7fAACVxTmoLr2GGrUSiZ1vhjymJVTlhQiLbYXSdr7bEsoXGAbFikzyYE0aBtM+kmUyGWQy2xcyO3fuxJYtW7BixQphWr9+/fDbb79hzJgxUCgUKCkpQd++ffHnn38KZTIyMtClSxcsW7YMS5YsgUQiQf/+/fHSSy+Z/QyIMea7vlhagV3/qjBooAzDhsjQtk1Ava5fqyXMuLsI+QU6HDioRni4BGfO1mDoEBkefzQMV7JqEBvrhx7dfHNgP3YDd4fBGHMnZ/sddjV3rbesLBuXLm5B6Pk46KJSERHRAlKpa+Ije61U9c+dO/sXsrP3QJK9GxGtuqIi7wL8AoPRtP9ESPz8UaOqQnhyO0j9ONZqSPTHh2Hr9YYSpjtzfw0A//3vfxEcHIxRo0YJ03bu3IklS5bg2LFjrq6m0/idyHze1cLjyCs+gRBZNK4WHoVU4o8mEW0QHBiB1OSRNn8qY9haWUygTDotcovSkPvvJVSX5iM0Ohmdw26BX2a+UKayKBsxIS1RHUW4fGClMN0vMAQ9pr1e536hmDilKRK3hsze0K3GX9e6IqDC9SGIplINYB+Sk5ONpi9YsAALFy60Ot/Zs2dx++2344EHHsDtt98uTD916hQeeOABPPTQQ5g+fTry8vLw7LPP4sknn8TXX38NoLZV84EDBxAUFITFixejuroaL7zwAv755x/s27cPAQH1G0IxxlyvqFiLVxaWIjpKiu07q/HKQuCmAYFQKKR48zUFmia59rI07aQav66owukzNSAC3l2kgFwuhVpTe244cUqDETfLcPcdIfjqu0r8/qdSmHfFT9EYPjTIpfVhnseBM2PMFWwNTiemT2FbvKWl5sULm1FcfA7l5Tm4cmUngoKiIJfHIS6uG2LjbP8SRExLZsOQWaOpwtWrB1GWdgUqVSmaNh2AmNhO0GiqANTeg1dfv4bIlO6ouHoe59d9ISwnqk0vNB92Z103l3kpT3RBs7awKwKUrr3Hrr2/Puzw/TUALFu2DIsXL8aKFSsQGxsLACguLsZdd92FpUuXCtO8AQfMzOeVV+VCHhyH/p0eRbW6DCcv/IH86+kAgAD/YLRIGASlqhhXTx5AaFQSwhNSIZFIrQbKVxRXUbBzH1r2n46oTA2A2iBaqbqOExf+QGlFFsJDk1BZmYPKoixswz4QbnSL0XHM4wgIrm2pHN9hKHRaDbKOrEVNdQWHy/XMXshcl4DY0+FyfcjKykJ4eLjwt61vV8+fP4/hw4dj5MiR+PLLL42eW7x4MVq1aoX33ntPmBYSEoKbb74Zzz//PNq0aYOYmBhUV1dj+fLlUCgUAICvvvoKXbt2xaFDh9C/f38Xbx1jrL6ln67tU33tyiZISvLDV99W4vU3a1tyHDuuwa6tsQgMlOCn5ZUICZZg3JhghIdb782tuFiLaS9K8Z+Zagy+6cbnk0ZDeGdxGT78tAIJ8X7o1DEAm7ZUo/+QfKP5/7tQgYcfqv1104P3y3E+swYZZzR44ZVSREV5dy9ypt08cHBqGbdiZoy5mr2wyzA4rcsggJ4OmsvLs9G8xVC0bDkCFeVXcfjwZ6iuLkZh4WnIgiKgUDRDcdFZVFReQ3R0G4SGxpktwzBovnZsG3QaFRJ6jzEqU1R0Fhmnf0NNjQoKRXOUl+fg9OnfcObMKuh0tffiYWFN0fr2pwEAOm0NqvKvQCKV4uzqj+EXFOrmPcE8oaH2xezI/TUALF++HA888AC++eYbo64k9+zZg7y8PMyfPx/z588HUDvYHwC0a9cOmzZtQrNmzdywBbZxwMzcJq/oBMqqrqJlwmAE+Ae7ZR0lbUNx9eBRhARFAwCCAsMRGdYC18svAQDO52xFufIayiqvQqkqBgCERjdD6yGzUNouEoqMCpS2k6O6vAhXNcegzcpGXtFJEHQozzmH6PAUBAaEoqq6GEVlmZAFhKF3uwcQEdYMOl0NLlzdAZ2uBvKQOFwrPoWQoCg0uSxBabva+oVGJUFTXYHKwito2m2MpU1gLmSpVbE7WzJ7QytmdwoPDzc6AVqTmZmJYcOGYfDgwfj+++/N+mCuqKhAdHS00TR9txcVFbVf9PTu3Rs//PADwsJudCOjn0dfhjHmHufOa/D1d5W4bVIwevdyXyuR75ZVAgBatfSHn58Ed04PEQLm0jIdho7MR78+gVjxhxISCfDKa6X4cHEkbh174xqisEiLQ4fVWPWXEhcv1uDYUQ2mrAVuHiZDamt/FBTqsHefGvkFWrz6YjjmPiyHn58E6zcosWlrNfr0CsThI2pcuqzFxPE3lhsf54f4OD98+kU5OncMQLcu3t1FBg9axxhj7mEYCLsi4HJlC0ydrgaXL29HQEAIkpL6u63xklJZDI2mChKJHyQSCcLCk4RxhqRSfxw9sgRNkwci68pOAEDm+fVITOqL1NRbhW40dDotqirzkZOzDzUnq5GfX3veqjyXjrDwpgARMityUVaWhaioVLRrfztksnAolcW1LaZlEVDFyVB66RSiOw4U6ib184c8oRWKzx0GiNCkXT+37APG3EHs/TUArFixAvfccw+WLl2KWbNmGT03bNgwnDhxwmjaY489BgD45JNPkJCQ4JoKO4gDZuYWV67tx5kr6wEAmholOrac5PJ1VCr/P3tnHR7Vlf7xz7hkJu5GFHenQJFSoO7U3XW3292tbLultnX91d2VCoW2FAptkeKuIULcZTKZjMvvjyETIZ5JCHA+z5OHmXOP3QmZe8/3vuf7VrJvmXd7fdyks8C7gwaz1Sskj0y9mLLqPZRVe5MCpM+8DplSTdafH5Px29voI5OpytmGe7PD16cyIBRVYDixuiFY7XXUWyswmkvQqEJIi5tNXMR4FHLvllmpVE5a/Cm+trHho1udZ/Hu35BI5YSnTvD3RyDoJJ0VmbsqGDf0ezyLzB1x6NAhn7j88ccfI5MdmcDylFNO4e9//ztr165l2rRp2Gw2nn32WaKjoxk6dCgACxYs4D//+Q8vvPAC//znP3G5XDzzzDOEh4czYYL42xEIeovsHCdnnFtJdY2bb783s31TNLoA/0bvOp0eHvhvLYuXWJg9U4VMJjlc3ljnk/fDOG9BJfkFFs47R8Oj/w3i7n/VcM2N1fztDh1fLTJTZ/RQb/Z+l4cES9DppNxwrTdyKb/Axa8rrERFyZg/V821VwUwZHCjtc7p8zWcPt8rKF92cevRTlu22lmx0sbLzwf79fx7k7aic4Xw3IiIYhYIBH1Ny+jL7kYyN+3D7XaxZ/enVFVlAKBUBhIZOdw/E25CefkeMg/+CEBs7MQjjk+d9h/W//UMBfmrkUhkTJv+EKUlW8nMXILDXo/dbsJkKsHlapy7VhtJSGg6el0sNlsttYZcJBIpGk0Yw4ZNIyJyOBKJ995Dowll0KBzfZ9X5MiZR8zB7bRTtmMluth0VMH9xx5AIPAXixYt4sorr+Ttt9/m6quvPuJ4QEAAgwcPblam03l35bUs70uEwCzwKx6Pm4LyzWTk/0Ji1GSUCh05RX8wOPF0ZDL/RgPlla7DVlfJ4FNvJTA6DQ5bXoQEDsBsq0IikVBWsxcApVzntcaQyvB4XFiN5TgsRuLCx1JQvhGA5JMuJjxlfLv2GV2lOm8nZQfWkDD2TBTqriUZFHSPtgTf3opkPpHFZYCHHnqIgoICNm3axLBhw3zlQ4YM4fvvvR7kN998M4cOHWLevHmEhIRQW1tLSkoK33//PWq194FNUFAQS5Ys4corr+SZZ57B4XAQGxvL4sWLCQ4OPhqnJhAc9+zcbee6m6oJC5Py3VfhzJxbzs/LrCy44MhEuT1h/UY7739Uz73/1HPXbY27FGw2DynJMp54JJhb76r2lV94noaYaBmjRij5bZWNl181ceH5Gp9H8j/v1nP15QHExBz5QKu71NW5uezqKsaOUfj9/PsSISy3TlORWQjOAoGgPTryD+6Mv3DD8fZE5q6Izk6nlYwD31NdncnIUdeQl/sHJSWbe0Vgzjz4IxpNGOPG3YpK1XjNjooeg0KuISdnOU6n93ocETkcuVxFSGgaABUVewgIiPbaZ1RnIpMpGTHyakJCUlodq+H8JZ38TBsoWPMtNmMVA2ZeKiwoj2OOV5uMznDDDTcglUp58sknefLJJ33lt956K3/729+O4szaRwjMAr9hsRnYd+gHqusOkRA5kYEJ8zDWl5Dl+Q2juYQQ/QC/jieVKnDZbegKbAQZGgXh+IjxxIWPZe2ulwDQqEIYO/BKtFl2Sqt247J7L4gTB92AThPBgOgpgAR7ajyA38RlgKrcHegikogZNqvNOg02HYLepz2R+USPRO4uzzzzDA8++OAR5Q3CMYBEIuHpp5/mf//7H8XFxQQGBvp8lpsyadIkDh48SEFBAVqt9ghbDYFA4B8cDg8vvFLHCy/XMXSIgo/eDSUxQU5ykozNW+x+F1i1Gu93q9HoQdnkWfOARDmb1kbz0v/VUVrq3Xr79ushzDtVQ53JzfMv1wHw1ONB3HCtjueecpOV7ewV+4o/19iornGzclkECsWxdS1oEJWFaNo5xOckEAg6gz+ErY4Eso5EZkO6CmPhQQp/+wKn08qQoQsICxuEsTafwsL1uN0upFL/PWwF7xrb4TAjbREcNnToAiyWajas9+ZUiYwcyeDBXk/YQznLAQgIiGLc+NuQSuWYTMVoNOHI5a2fX9Pzbu9zavkZeTxuanJ2ED3mVLQRCa22ERw/9HWCv/7Cpk2bcLvdR5SHh4e32ea1117rzSl1CiEwC3qMx+OhuHIbGfm/opCrGTvwKsKCUgFQKb1PPSsMGX4XmGPCR1FQvpEtB94nLX4OyTHTfcfKqvditdcCXquMBo/m3TmLAEiPn4tOEwF4BWgAux/n1iAaSyRSzNVFHFjxJgNnX49UpjiirhCXjx695c18IhEbG0tsbGyn6spksiMy57ZGZ+oIBILusf+Ag9v+VsP+Aw7+8Tc9/7hL7xNU3W5YttzCs08G+3XMYUMVyOXwxtsm3njbREVhrC/iqK7OzeNPeX2YH7wvkPPP8YrbH3zk9WsODZFyw7Xe66QuQNpr3sjyw3fEl1xZxXNPBTNl0rGzoGkqmDZ9LaKZm9P08xAis0AgaI2uJNjrbARyW+JpR+Kzy2GjeMMSKvf9RXBIKkMGX4Ba4123yuRqnE4Lxtp8gkOSO5xDV4iOGUvuod9Yt/ZxJk76e7PkfQczfgAgQBfNoMHnI5MpsdtNVFR4dw0PHXYxssPrXb0+rs0xOhMd3l70t0QipWLPatxOO7ETT+/eiQqOKfzpZX4sMHDgwC63iYtr+2+ur+jfKbIF/R6320V20Sr25f5IVOhQJg+7zScuAxRVbEUqVRAVMqydXrpOnbmETfve9r3PKfqj+bw8LiQSGROH3kRgQIxvrgAqReDhqOXmBB0w+TV6GSB+zGl4PG6MpZlYjZV+7VvQNt0RjkX0skAgON7ZsMnG/LMrcDg8LFsSwb33BPrE5cwsB3n5Li5d4N9s7GaLm7lnljfzW84vcPle15s9REdLueXGAP5+Z+NW3Kxsb4Nffozw63zaYs5sNZdfqiXjoJPfVln7ZMy28JcwfFPwLiGkCgQCwWGOpjjVmnDdnmDmtJjI+eVdqg9uIX7q+SRddKtPXAYoL9uJThdLgC7ar/MsL99N7qHffO9LS7c3O65U6tBoQhk79mZfZLLJVApASup8dDr/JBZrT3iXSKQkzb4cp8VE9cHN1CW13U9dUuOP4NjmRBKXj2VEBLOgy9gd9WQX/Y7DZcFQl4/NYSQt7hSSY08+om55zT50mkj0Wv9e/HKKVzd7P23U35u9jw0ffUTSPalUxpzxD/sSCPQFan04+shkrMZKNEEiAYFAIBAI+g6Px8N7H9azYZOd4mIXm7bYGTdGwXdfhxOgbX4t/OkXr6h6xWX+tcf4/Q8b+/Y3qsuffhDKgMTG28/oKBl7th65IH3puWBeei4YqbRvHv7J5RImjFPy2Rdmzj1L0ydjtkdrInNPxOKm/sMCEc0sEJxodFac6ihyuSsRzp0dr6FPc0UBZTv/wONyYiw8gEQiJfWMm9FFJ/vGDs60YbUaqKsrYkDSbBQK/12vPB43e/d87nsfFDSA5OQ5zeoMGbrgiHahoWnMnPVEr6+xm35Wurh0AAIThrRZX4jKAkHfIyKYBV3Cajey7eAnFFZsxmytIiJ4IFOG3daquOzxuDFZyjHWF1FWs89vc3C5HZTX7CNEn+QrM5nLO9W2L8Vl8G5tqq8uIjxtAhI/+2MJ2kfYXwgEghMZl8vDwseN3PdgLX+ttxESIuX9t0L5eXHEEeIygOyw5nvvfwx+ncfX35oJ0Eo4aYrX2uLnZZ2LDpZKJX0mLjewYaOd5CQZI4b3jg1HT+muONzSQqPlj0AgEBzvBGfajhCFDekqn2DZ9HVr73uDpv3XFWWS8d2LGLK347KbiR4zh2GX/scnLjdt07CezctdhcNh8dt8qqszAYiMHAFAbW1ep9v2dI3dGcG+6edVX5YLQNiQSehzO+6/M3UEAkHPERHMgk5jMBWwef+7qBSBTBp6M4EB7fuuZhWuBCAyeAiRwYP8Og+AiODBjB10FW63E7msf2yZaPBTbrDaKN3v9YaKSJvUZhuR5K/3EIn7BALBiYip3s2wMaXU13t49L+B3HKjrl2xdscuO4/9z0higoz7/hXo17n89IuV2BgZPy6KoM7kJkDbP7+TM7McfPOdmYUPHpmAtK/ojIDcG5G3wrdZIBAcz9SmqgjLay5QtiY4Q+eS8jWt2xXaql/1y2Ly8/9EHzeQ5FOvRqZqOyrZ4/Gwbf+HSCQyUlLm+jWCOS/3dwDS0s9i8JCLkEgkSKW9Ixd1x0+3oY3H46Fk8zK0EQloIxLbrC9E5eOPE82H+VhECMyCNnE4zSjkWjweD+U1+8ku9l50RqVd0qG4XFa9l9zStUQED2JU+iV+nZdK4fVolMtUSCUypLL+FxlcO1iHpbaMkj0riRw0FVVASLt1QQjNvUVTkVlENQsEguMRo9GNVitBLpewZ6+DNeus1Nd7OHmaittu1rfbtqTUxdXXV+N2w19/RKFW+1cATk6SodF4+9Tr+ufGOZfLwz/uNRAbI+Paq/zrQd1ZuiPstmzjD8H5puBdJ5zI/LZh5AkVyW2xWFi7di1Wq5UpU6a0m5HeYDCwbNmyVo9NmjSJ5GRvdOWyZcswGAzNjqelpTF+/Hi/zVsg6AktRammQnJrgpW/LDBa4nG7cTtsyFQaXHYrpaU7yM//E4DU029CIm37OunxuClc+x3W6hIGDTqP2LiJfp1beMTQw1HLHl+ivv5Ew++pav96zOV5pJ1xiy9hMDRaYrQnLHemjqB/0lt/kwL/IgRmQavkl64no2AZ0aEjCA8eyJ6cb33HpB1YPdTU5bEr+2sCA+JIipnOoZI1xISNQq30T1SUThNBeFA62UW/Ex06HJms/21ldTlsZK/9DGVAMPGj5neqjRCXe4/OCMsi2lkgEByLZGU7mHdWBXGxMm68Tsf/vV5HziFvEr3Q0PYFXY/Hw+nnVFBR6eKTD0L5+lsziQkyZp6s9tv87v1nILfcUcNvq6zMme2/fv3JC6/UsWGjnR++Cfe7wN4ZeiroNvVX9pfI3JITTXQ+Xtm1axennXYaISEhBAcHs2PHDj744AMuuuiiVuvX1tbyww8/NCvLzc1l48aNrFy50icw//Of/0SpVDbLen/qqacKgVlwTNA0KrI3RSyPx0P2L+9QX55H+JApSGQKyvYtRyKR4fG48HjcSNpxMC3ZvIzKfX8RMXwaankohYXriY2d2OHavD2aiuvaxKnI8v8gO+sXhg67uNt9dpbuRKNaqoopXL+Y8KEnoY9v/L7pjN9yR8kAmyIE6P6LiGLu3wiBWdAqBeWbASit3k1p9e5mx3Zlfc3UkXe12q62vogtB94HIC3uFDbvfxcAp9NKesKpXZ6Hx+NmX+5ipBI5afFzqDOXUlOXi9Vei81h5K89rxIUEI/L4yQ+YjwRwQM77rQbtLS+aHO+bjd15TkU7vgZq7GCIfNuR6bo2hegiGQWCAQCQWf5brGF2loPGo2bu/9lQNXkkvPDjxbuvtPBsKGtRyKdeV4lBYUuHrwvkLffrWfNOhuBgRJy9re/S6ktvl5k5oclFv5xl56YaCnf/WghK8ub4O+SK6tYcIEGQ62HMaMV3HazrlUv6L4kL9/JO++bePOdeu79p56pU/p2weJP0ba3o3BFksDjg6uuuoqTTjqJr7/+GolEwlNPPcV1113HrFmzWo1kHjBgAF9++WWzsmuuuYby8nJmzZp1RN9///vfe3P6AkGv0dL6oj2hubvils1YSV1hBiChfOfvvnKPx/tQuHTLMmInndlq2/Jdf1C2/Te0kQNQ6sPYuf49AMLCBqHRhHZ5LmVRJgrXfoveMojwYVOpydqGpboEl91CWdkObAEe5EYHcrmGxAHTCQiI6voJd4LOCvpOp42qjJ0Ub1yKOjiSuMlnt1m3qVjcllDckSDdmT4EfUtHf5eC/oEQmAXNcLnsbMv8FLOtCoCBCfM4WPBrszpmWxVVxhzCAlOalTucVrZlfOR7v+3gx77XIYHNExR0FqfLSnHlDgBKqnbhctubHbfaa7HaawGoNGQwZ/zCZltl/EV7wnLtYB0etxvDn8vJ3bMRh8WIOiiKQafcREBovF/GEPQNIopZIBAcSzzzgpFnnq8D4PqrdTzxtBFbi3vvt9838fJzR9o0PfRILRs3e6+pjz9l9JXPm9P9KONF35tZ9YeNlb9bkcvB4QC3G2QycLng62+9yYiW/2Zl4jglM/wYKd0V1m+08cjjtWzZ5iBAK+G/DwRy521992DX3yJtXwrVx6uFxolgk7Fnzx527tzJ66+/7rtXvv3221m4cCGLFy/m+uuv77CPuro6vvnmG/7zn/8ccb996NAhvv/+e+Lj4xk5ciQqlYgwExxbtEzyB0cKoN0Vl+tLc8le5g28UgWGoY0aQE3m1mZ1ynasImLEySi0zXf91hUepGj9jwCYy/Mwlzcm31Org7s5n0MYCw5gLDhAyZZluB12wINUrsTttGPI2dms/pChF3ZrnJ5it5vIzl5GedlO3G4nQUnDSZh+EVJF8x3MTUXg1sTjzkQ4t4Ww1eg/CJG5/yMEZkEzHC4rhjrvRWtA1EnERYzzCcxxEeMwmcuorS+k0nDwCIFZIpHgdB35Bx8VOpxgXUK35iOXaQgNTKXamENkyGAiggcTGpiMzWFCIddQXLGd8pr9GM3Fvjn0NR63i5x1X1BVuIOI9MmEp45HF57Y5Wy6nY2SFvQuQmQWCATHCs+96BWXzz1bw5mnqykucfHBx/VER0u5+EItL79q4rMvzLz4TPARSf4ysxxH9JeeJufG67ovtJ57toZVf9iYME7JrBkqbrxOR8ZBB2NGK/lusYUdO+28/V49AGPHHB17qx+XWrjp9mpGj1TwzushnDpHjS6gbyOp+2M0cNO5nAhi6/GG0Whs9l6lUh0h8O7e7d2ROHz4cF+ZXq8nKSnJd6wjvvjiC+x2O9dee+0Rx5YsWUJ2dja7d+9GJpPx+eefM3ny5K6eikDQq7S1vb494dgf2/GNRRm4bGZkKg0xE09HHRLlE5hjJp5OyaafAbDWlB0hMLvs1iP6k8jkxE48g9qBmk4Jbi2FuXhHMgVKNVKZnOCU0YQNmYRUKkeuDsBuqsGydRv5eV5v6ABdZLfPuyfYbLVs3/4udreFpKRTUE8ajyowrMN2+tzmgnJPxOWmiKjm/oOwyei/CIFZ0Iyy6r0ATBxyI0E6b/StTKrE5bYTFzEOlUJPQdkGUmJnHtFWLlOhkAegVYcyLOkcDpWsQa0MJDVudpfF1gYkEgmDEuezfs9rOFw2okKHAaCQawFIjj2Z5NiTKa7cjvvw9qLeoi0BuCzjL6pydzAy9SLUkyf5bRzB0UOIzAKBoL9TWubC7YbLL9Hy8vPeCOXpU1V88HE9I4YpeOj+IHQBUmbNVB0hLgOkJsv5DRt/LI9k1Z9Wdu5ysPDBQBLiu39reMlFWu76h4ENm+ws+S4ciUTChPHeBcCCC7QsuEDLFZcG8NmX9cjlff8dW1Li4uY7qjnrDA1vvBJyVObQQG+Iyz0RhltGJje8bq2/4zmK+VLZjl7r/6fCEcgD/LsgdtbbgN9ISGgeyPHwww+zcOHCZmW1tbXIZDICA5uLV2FhYUck6GuL9957jzPOOIOYmJhm5c899xzz5s1DIpHgcDi46qqrWLBgAQcOHECr1Xb1tASCXqO1KOXO1u8JppIcpFIFI658FIlMhsfTmB8matQstBEJyDJLCYhLP6JtQFQSANFjTyUgJpXKPWsIHTiB4JSufQ83PZfgTEiMnUpu7kp00clow+J8x+QaHbGpkSQnn0pOznKCg7u3E7mn7N//LU6Jk0Hn/o2ocj2GwM7/LhoEYH+Jyy0RUc1Hj4aHJcGZNqoGHO3ZCFoiBGZBMxxOMwAaVbCvbED0SeQU/4GxvoiEyImkJ8w9op3FVsPBguU4nPXoNUMI0EQwPOV8v8xJp4lEowqh0pBBSdUuokKHIZU0T2YQGz7GL2N1FZfbQXnmXwTHD0V9UtfF5QaxWojK/Q8hMgsEgv6M0+ldnKalNd7KTRjvjQpesdIbpXT3Xfoj2rlcHp58xsg7H9QTGChh6BA5w4cdWa87SKUSnn0ymH/db+C6m6p58vFgoqOaX6+HDlHwxCPBfhmvq7z/kfea++z/go+KuNyboqy/kvs1neOJGMX8Qe0woORoT6PLFBQUNBOOW7OnUKlUuFwurFYranWjPY3JZGr2vi327NnDpk2bWLp06RHH5s9vTGitUCj473//y9ChQ9mxYwcnnXRSV09HIDjucNnMqEIikMi818SQrEbbR5fDRmD8IIgfdES72vx9FG9YAoA2cgCB8QMJjG+ec6g7tgGGdBUx1gnk5q4k788vUepDiDM2f3AklcpISzutS/36C5OphJqaLAYNOg9VUDiU21o9x44eALSMZvY3XelbiNH+Q1hl9F+EwCxoRkTIYA6VrGbvocVEhQ4jNnw08ZHjKarYRlBA8+iIWlMhRnMxB/J+alaeEjfT7/MaMuBMth38hD053xKgDicwoHsJiPyJy2VnV/Y32OtrSDv5ym71UTtYJywx+hG1qRIhLAsEgmOC+Dg5qSlyPvionupqN/feE0hMtIzT56sZObx5Ur/iYhdbt9t58f/q2LW70Rpj4YNBrUY394SrLtfyr/sNLPnZSny8icceDvJr/93lk8/refH/TNz7Tz3Bwf6xxOiJYNwfbTKg89HJx2sU87FKYGDgEZHJLUlNTQUgPz+fgQO9ApXb7aawsJCUlJT2mgLe6OX4+PhmYnJ78wGoqKjosK5AcCIQNGA4pVt/pXDdd97oYyIZOuwSckr+RKZofMDjdjkxFWdjzN9LxZ61vnJldAyS6YMhv3PjNY9Wbl2IU6uDCI8YRmXFXrJ/eYeAa57oF6Kd2VzJrp0fERAQRXT0GKQ9nFNvRzN3lrokITL7E0O6ClqxjxEcXYTALGhGoDYWuUxNZe1BKmsPYrUbiAkbzcmj7/HVsTvqqanLY1f2V632YbZWo1L4JxqqzlyKVhWKXOa98EqQotfGdNCqd2gqBJut1ezM+gqzrYpRaRejCDn6gregZzSIykJcFggExwqXX6rl0SeM/N/rJjQaCZPGK/n4vUZ/Qrfbw18b7NxwSzWVVe5mbeVymonNPaWoyAlAXFzjreXttxz93Tl2u4f/PlrLux/Uc+1VAfzz7/65P/EX/VGo7Y9zEvScyZMnExoa6kvSB7BixQqqq6s5/fTTffV+/vlnIiMjGT9+vK/Mbrfz6aefcttttyGTNd+VUFNTg1arbRY1/d133yGTyRg7dmwvn5VAcGwQNmgCpVt/pWLPWgy5e3FGTyYubgqqaRN9daw1ZVTsWUPlvr+atZXKlTgNNdBOrqGOIjqbeta6XU6sNaUEecJwBEigAuImn9Wsn7aSHPY2lZX72bf3K1SqQEaNuhaptHNyVWc8eTtKBNgXCJFZcLwjBGZBMyQSCVEhQymq3AZAdtHvyGVqEqMak3Rsz/wMY32R7/24Qdeg00Ric5jYsPd1ak0FhOh7bohTWrWH3TnfIJOqfHYb8ZETjkoiv6ZUGjLZnbMIhVzLxCE3otdGwQFTt20uhD2GQCAQCLrDDdcG8OgT3uRezzzvTfhXWdToo7hsuZWrrq/2vX/+6WCmT1URFytj4PASNmzyz8KxpsbN7PkVVFW7+dc/9ERHSyktdR9hj9HXlJS6uP7marbvtPPsk8Fcc6XWr/cQ/hJi/S3otuef3Fk6aisE6GMPpVLJc889x80334zFYiEkJIRnnnmGm2++uVniv3/84x/MnDmzmcC8ePFiqqurue66647oNzc3l6uvvprzzz+fxMREtmzZwnvvvcfChQuP8IYWCE5UlPpQ32uHqYbsrF9Qjh2JGu860O1ysv/rp3111KExDJh5CergKIo2/Ejlvr9w1hqAkC6P3dSz1pCuIv/Pr6nJ3EJhdDKaUG/gVmDC0Gb1W7ZteczfwrPH4+bQoZXk5a4iPHwoQ4ZehFzesXVPd+fRX6KaBYLjDSEwC5phsRl84rJcpmZY8rlEhgwBDn/xl6z2icuRIUMJDIjF4bQglcopr9kHQHHVTpJipvV4Lh680VYut42dWV8AEBM+qsf9dns+h88/u+gPwoPSGZ5yPgq55qjNRyAQCAQnLi6Xh/v+U+t7f9vNOu7/V+MW+Q2bbD5xedYMFUOHKHC5PKiUUFziot7s4UCGE4vFg0bTM9FVLoc6k/ea/ewLXqH7rtuP7sPTDZtsXHdzNVIp/Lgo3Jds0J/0RGRtKQL7U2T2t3dya8kDRZTzscm1115LcnIyX3/9NSUlJbzwwgtcdtllzeqcccYZDB06tFlZaWkpDzzwAAMGHBlAMmbMGH7++Wc+/vhj1q5dS3x8POvXrxfRywJBE2rz9vpeB0QnkzjjYtTBkQA4LSYyfnjZdzxs8GRUQWG4XU4kMhlVBzcDYMnKhPCJdIaWEb3Nons9LgDqSw9RX3oIALm2e7t7uiM4txStHQ4z+/Z9RXVVJikp80gccDISSdesrLqbjLG3PZoFghMNITALmpFXug4AvTaGSUNvavblXlOXR3bR7wCMGXgl4UFpbNz3tk9wVh62xRiU0LE3W2cI1TdmrB2UeBqh+mR02ii/9N1Vqo2HyC7+HUNdPimxM0mJ7fqFTyAQCAQCf7F1m53Pv/Im5l39WyRDhzT3Xb7+Fq+4PGe2ii8/CefdD0z8+4Fa/v1ALVFR3uvXrBmqHovLAHq9lIFpCvbsc3DxhRoWXKBl6kn+F3Q7Q3Gxi1ffrOP9j+qZMF7Je2+GEhnh/0hqf4mrrYm3R7MfwfHPzJkzmTlzZpvHn3/++SPK7rzzznb7jI+P54EHHujp1ASC45bCtd8BEDPhNKLHntrsWNmOVdiNVQCMuuEZ3A4buz966Ig+IgOGHlHWHm3ZRgREJVGTtR25XE3CrIsJiEltc3dPS+G4tWjmhtddFZkDM+opLdlGbu7vuFw2Ro26htCwgR03bjF+T6OphcgsEPgPoZAJmpESOxOlPIA6cwlbDnzA/tylOJwWAIJ1jdvc7I46HE4LQ5PO9pUp5RrGDryKsKBUv8xFIpESGujtKyP/F7TqsA5a9A65JWvZmvEhLpeNsQOvIDVupk9crh2s81lciGR9xzZB2Z6jPQWBQCDoNBPGK7nnb94HuxdeWsn1N1c3s7yYO6dxa2lJiYtLFmh9ZWVlbh55KJAvPvbfdbWh768WWYiMlCGX972dVcZBB1Nnl/H1t2b+dbee774M7xVx2d+8bRjpF8G6NyOKW+tbCNoCgUDQOQbMvhyAks2/kPPr+5Tt/B2Px7v2CBzQKBzb66qRKlQkzrjEVxY2ZDLDLnsQhabrO4NaE1/VIdEodME4nVaqM7d1ul9Dusr309Y4Teu0rN/0tcfjZueO98nI+IHAwHjGT7izS+JyW+fWXfS5feeNXJckBG3B8YsQmAXNUCoCmD7qHmLDRyOVyimt3sW63a+wNeMjnC4bCZGTANh76AfW7HwBgymf+MgJAKQnzPWbuNwwl3GDrmJwojfxyP68pThdfZsp1O1xcahkLfERE5g09BbCgtKaHe+JqCwE6f6HEJkFAsGxgkQi4f5/B/Lyc8EMTJez74CDBZdVMWl6GSt/t3LxhVoAfltlY8T4Ut56x8RJU5RIpbDgAg2336L3qwj8wL2B/LkiksgIKVddX8WBDP8lEOws77xfT6Beyta/ornn74EoFCJpa09pKiz7SwgXCASCEw1dTApDLnkAfcJgXHYrxRuWsOfThRRtWIIuKhmp0vuQdv9XT5Hzy7s4rSa0EQnIVBriJp3VzMO5LdoSf1uij0tn6CUPEJQ0gtrc3VQd2OATu7tLR2O3PFZfmovBcIgRI69k+IjL0Wi67i0NnUvu1xX6MgGfEJkFxyNCYO5nWGw1ZBaswOY4OuKj2+1k+8FPKK7cgdVWi1oZjMNpptqYg8FUQErsDF9dl9vOgbyfKCzfTHzEeMKD0ntlTvGRExiUeBollTtZu/Mlyqr3dtyoh3g8bgortrBp37s4XRbiI8e1uXWoqVDcFdFYJPfrnwiRWSAQdAabzcOTzxrZtt1+1Obwwcf1/O2fBtb+ZWf6VCVmi4fsHCf33GsgNaW5C9qTz9ax8DEjCfEyHl8Y1CvzGTJYzg/fhFNv9nDynHLuvLumx4vWzrBrj51rb6zi0y/queA8DUFBJ+btbU8jijsbpdwgNAuxWSAQHAt4PB4MObuo3PcXHpfrqMzBWltB5g8vU1dwAIXWew12muso3/k7dlMNCdMu8NWtKzpI8calmCsKSJxxCTJV13L+tBRcW4v0lUhlJM+5iqABw8j/82sOfPMstsM2He311VNcNgtFG5aQu/ITVKpgwsIGdbuvzgrqXUWIzAJB9xEezP2MtbteAsBoLmbcoKv7fPzqulyq67xm/xZ7DR6PmwB1OPXWSnZmfYFa2XxRqlIEkhI7g7iIcT0e21hfTH7ZegZET0N/2Gs5s2AFuaVrSY2bzZQRt7N+z+scKllNVOiwHo/XErvDxO6c7wjRJ1Jec4A6cwnhQQNJG3gFem2M38cT9F+Csj3UporIN4FA0Db/fbyWr7+x8PxLdezZFk10lNeKoS+9cP91v8H3+v2PzKSmyMnOcVJY5GLIqNJmdaVSOOsMDa++GNJj32Wr1cO/HzBwyiw1805Vo1ZL2L3Hzqx5FUSl69jwZxS3/a2GL742c+dtOgamKzrutIs89EgtTqeHGoOb7xdbSEmWc/+/Arn5ht55eNvy99ofk9z19P9eR22bHm957g3H+ttnIhAIBI56A4dWfAiA2+kgcuSM9hv0AhW71+C01gNQk7W12bF9X/7viCR7qqAIkudegya062vQloJycdEmDDV1RAybhiIgEI/bxd7Pn8BRb2DguX8jdOB4Dq34iKqMTcROOO2I/pp6HTe1u+gsxoIMKvasQRMaTXXmVlx2KyFpY4gaNQtJedcfCPeGqNySBpG5QQBu+V4gELSOEJj7GWMGXkle6V8kxUzr87E9Hjcej5vkmJOx2Goord4NQGL0SXg8Lg7k/YTV7s1Yr9fGMHnYLX4b2+G0HrbhsFJStYvxg6/D6bKRW7oWgOyiVRSUb8bjcVFnLsXlsiOTKf02PoDdaabamE21MRugy37SIiJZIBAIThz+fbcemwVSkmVERTYukHoi8HVFIDyY6WDhg3rq6+HZF+sACAqSsHd7NMPGeMVluRycTtiwOpK0VP+JvA8urOXzr8x8/pWZa64M4IF/65k1rwKAskwTo2c6MJZ5F7iFRa5eEZi/+dZMZZUbgGuvCuCJR4JQKsWDwd56wNFRn0JYFggE/RWlLoSYCadjrsgnJHV0n4/vtNajjYgnatxcjLl7sFQVAzD8iofJ//MrjAUHcJq91/HocXOJGT+/R+M1TbhXU5NDRsb3AFTsXs3I656kaP1iHPUGAA7+8LKvXW3unlYF5tb67YrIa64sxJi/D2P+PgCGXfagz/LDENR64sCOyvqKltHMIiGgQNA+QmDuZ4QHpRHewue3ryip2sXeQ9/73idFTyMsKB2rvRaXx0VYYCo1dXlMG3U3MmnPF4sej5vckrVU1x3C5bLjdFmJCB5MheEADqeFQ8V/Nqtvd9T5Xm/a/w4ThtyAXNb1J5h5pespqtzGhMHXoZA3bjnSaSIZlHg6Gfk/AxCsT+zWeQUdMFE7WOf7V3BsIqKYBQJBe4SFynjvrY49EbtKZwRCi8XD1FnlNLhPjB2j4KH7g5BI4I23TTz/dDD33GvgxWeCOXWOmvCwnie6W73Gypvv1qPTSfjuBwtjxyjYtt2BodbNV4sszeo2iMsACy6vYtmPEYwf1/WHwvsPOLj0qiqeeyqYObPVzY59/nEYN99ezaFcFxPGKXtdXD6WEto1iL3+nHNfRuYLBAKBv4keO+eojV2wZhGGnJ0ASJVqBsy+HKU+lKqMTYQOmkh9eT7aiHiS5lyFTN7z6FyH2ciu4l/wlNdQXX0QgKio0ZSV7cBWW0HFnrXN6kvkCjxOB9bqEoo3/UzMhNMIybK3KiK3Jyzn/PoBEpmcpFOuaGYtGTFsKg5TDZX7/kKqULXpJ92RaN0XkcudQYjMAkHbnJgmdYJWCWxiAzEydQHpCacSGpjE/rwlHMj7iSpjNlKpDLlU2S1htyX5ZRvJKlqFVCKntr4Q8NpUBGpj2Zn1BUZzMYMHnElC5ERfG5XCu33IZClnw943ujWu02Wj/nB7i62G8poDWO1GAEIDU4gKHY5UqsDjcXfYV+1gne+naZlI4CcQCASC7tJRNKhaDdrDNhezZqj4cVEE06eqWL/RxqtvmLjnXgMAISFSv4jLhUVOLr26ivIKF78u9ybb3b/fyeWXavnhRwsPPVLLpAlKlnwX3qxdWqo3jmH+2RWUlR/pe9mRj69eL6GwyMUlV1ZxMNPBn6utbN1mx+PxkDxAzl23H95SfBSeBR4LEbvd8UnujA/zsXDuAoFA0F/QRjYGLQ067++Epo9DHRJFyeZfyP3tY1w2M3J1AHKVFoms59fsvFWfU5u7F4u03lcmlSqQyVTs/+opAIY2iSL2OB0ERCcDULb9Nyr3/dUsWrmz1ObuxpC9nYrdqzFXFlKbtw+3w45UriQ0fTwKXQi6w+O0pKV43F/E5Lbwp0+zEKsFxxMiglngQ6eNIjnmZA6VrPYJucWVO3C7vZngQ/RJjEq7xG/WFFW1mYQHpTNm4OWs3vE8WnUoNXW5DEs+nzpzCVa7gZiwkcikCrTqMDLyf8HmqGPayLtZu+tFLLYanC5bl8Xu5Jjp5BT/jtVe6/O8BggKiKe2vgjwEBUyzNdvnbmUrMKVpCecik4T2elxRPSyQCAQCLpCU1/f9iJGJRIJvy6NYNrscgamyVGrJRgMbp5+zrvTJzlJxpOPHRn1213W/WXHZoPvvw7n+Zfq+OobM3q9hDqjmw/eDuW9D0384y49UyapWPZjBJddXUV1jZs7b9Ox6ncbi5da+Oobc6MgTOtRtk2Fy5uCdxEfJ+fWm3S88baJk2aW+47Fx8lwujyUlnofBJ93tnc3ks3m4d4HDYwcpuC6a7p/DW762bcnpvZHH+bW6EpEc1t1jpVzFQgEgv5G5IgZVGdswuWwo9B4r4OFa78DvNHDEUOnEjvpTL+M5XY5qSs6SML0CwmISuLAoucIiE6mtGwb48bdRl7uKqQJsaj0oQy64B8UrvuBmswteNwu0s+6ncwlr1G49lsihk3t0rjBmTYmTrqbTRtfpGj94mbHtBEJmCsKAIiZ0Gj/UZWxmbrCDEbEnQt0nJiwv+FPX+a6pL5NLigQ9BZCYBYAUG+txO12khI7k2pjDjuzviI1fjb7c38EYPbY//jd81gmU+J0eiOhlIoA6syHPSNlSgYlNveeSoyaTHzEeCy2GjSqYIYnn8+eQ9/hcJq7LDBLpTKmjvgb63a/3Ky8IYo6VJ9MWc1e9uX+SFrcKezM+hKLrYbAgDh0ca0LzC3tMIS4LBAIBMc/vWFF0FFfdXVutm63c/I0FY89HMRDj9QSFS1j5Srv9fS5p4K55soAv80HICDAGyJsNnuIjJBSUemmohJSU+ScdYaGs85otJsaP07JwT0xZGY5SIiXc9bpGhYvtaDVNg8z7uxn9vB/AjmU62TZ4chp8Po6A1x5uZZPPjNzxnkVvP1aKK+/ZeLTz83Excq6JTC3FFDbElRbJvvrqH5/oaf/X4XILBAIBEfSmsCozwWPx0N96SFUQeEkz72WjO9fInfVp2jD46nJ2oYmPJ7BF/zDr3ORSGVIZHLcTjtyjfc6WF96CACtNoLhI64AwADIVVqSZl9G/JSz8bhdKAKCCEwYjNPmtb3qShSxN+I5kmHDL2Pvns+bHTNXFBAQnYylqpji9V5tQakLIf+PLwAoGzkTLX27dm76O+upsOsvoVmIzILjASEwCyivOcDOrC+QSGTMHvsAo9IvYd2uV9if+yPhQQMZPOB0v4vL4L3oSqTebUDBugTqzCWAdwtPa0ilcgI0EQDEhI8iJnxUt8fWqkM5dcIjuN1OAPYeWkxptXfBVV3nvQgXVWylqKIxy69y2GBqo5vbX/SWFUZDv0KoFggEgv5PbwjNrVFd7WLgCO/D2PffCuWWGwPYtsPOo08YCQ2R8skHoZw2V9NBL13H6fSaPauUEsaPVTYpb7tNepr3Wq5WS6gsiuv22HK5hE8/CMPh8CCXw7c/WLjljhoAPvnMTHCMmm3brYw/qczXZtbMzi+K2xJMuyOkdibquT8g/JQFAoGg98n97RMMOTsIShpByrxrSZp1OTm/vocxfz9RY+b0ki+0Bw6vseUafbMjUmnr9hsNQjRA6uk39Wj0yMgRKG56Dtwe7PUGsn96C5uxkvrSQ0ikMpwOG/m/f+HTAORyDargiB6N2VVaCsH+Enb9ITQLkVlwrCME5hMUj8fNoZI1hOgHYLFWAxARPAiJRIZKoWf84OswW6uIDhvea3Ow2o3YHEaqjYdIiplOceUONKoQQvVJvTZmS6RS75/AiNQLGJF6AfWWSuzOepRyLXsOfY/ZWoXTZSUpehoJhmgw9I23coOPs0gUeHQRif4EAkF79JVI9/ufVnbvdXDVZVpf2dgxCiQSCS88E8zE8UouvlBLYGDvpNYoLvFGDC//zcq8U9WcebqapT9beeiBQL+O097nqVB4v4svPE/Lhedpqa11s2WbnfFjlTzyRC1/rrGRl+9i0EA5jz0c1KnxOrK/aFnveBJlhcgsEAgE/sdZZ6Tmz1W4Zs7BVJ0HQHDyCACCkoaRNOcqVIFhaCMSemd8Sz0etwtjQQb6uIGknnEz2T+9RULCdCSSvkm/JZFIQQZRZTqixt+Dx+OmSF+CXKvHUlFI6bblWGu8D4WHj7gCiaL1h8J96cPckbDbFeG3ab3uiM0NbYTQLDgWEQLzCURlbSbZhauwO81Y7YYjjstlKl/G18CAGAIDYo6o0x5Ol42DBb8SoA5nQPRJHdaPDR/FwYJf2ZrxIbHhY5g97j9dGq83CNCEE4A3SdHI1IvYuPctwgLTSIs/pd123RWBRaRy/yco2xu5J4RmgUDQV9TWurny+irq6z3s3OXwlT/+pDchrVoFMdGyRoH0fAgM7JpY+PFn9axZZ+PFZ4PRBbS/6Jw+VcWwIXJu+1sNsTEyVv0awYfvtB4J1VdibFCQlFNmeT2mn3sqmCuurabG4Obj98LQ6zpeRHclyrir53KsRDILBAKBoOdULluKNT8Xe2kJbovXXqJ23Wrfcbm60bYqJHV0l/s3VxVRtu03IoZNQxeb2m5duTqA4NQxGLK3Y8zfR/KpVzPm5he8B3vZ17itxIASiZR4UxyGGBXqoAg8Hjd5qz4j7qRzCVGnYGijr95Cn9u68NtSRG5431C3O8JvW2N1BhHNLDgW6ZvHWIKjjtvjYn/uUozm4lbFZYDiyu2s3/Oaz6qiqxSUb6KoYisHC36lqja7w/pKeaOoWly53efBfLSw2GrYnb2IPTnfYrEZ2JH5BXK5hhGpF7T7xLcn4nBD25a2G31hwyHoGg1Cs0AgEPQ2L79Wx1/r7c3EZQC3N6cdVhtEJRbj/uavbomZ+QVO/vFvA98vtvDQI7Ud1o+OllFn8n4HFpe4eO3No3tdcjg8PPuikbMuqGDvPgcPP2ZkxUorb70aSmpK89iJ1nyVO/NZNa3TWv3jQTzurg1Iw49AIBCcqNQleX8qVcUY/lyF9VCOT1xuSfYv75D9y7u4HfYuj+PxuClc+x2GnJ1k/fQmDrOx3foSqRSZotHKqnDd977XhnSV76e3aK3vfFUOWzI+oGLPGkzF2eSv/prQgeOJGD69T6OUm6LP7Zx425YQ3fDjz7E6O75A0J8REcwnCAfzf8VqNzB52K0Y6vKQSpXEhI2g3HCA3dnf+OqZLOUUVWxn8ICuRS8fyPuZgvKNxISNwuYwsS93MVNH3OWzoGiJx+Mhp/h3ZFIVQQFxuD0uAtR967/UdC619YXsyPwCh7MegJKqXSjlOsYNugqFXHtEmwZhuPzgeurXF2Io2uf1ulJq0IbEkjD2TF+W4M4ihGSBQCAQrP3LxpvvmPjbHTrOPUvDl1+bue/fgRw44ODmO2rIL3D56j7/kpFbbtR1Kfnayt+t3HxHNZERUm6/RcfDjxk572wtJ09re5H38af15Be4OOcsDVu32TnvnPZ9nlvaS/hTjKytdXPDrdWsWWfD6YQZp5YDcMZ9Qzj1lOYL74bP5G3DSIr21bLjx2KyNqzBUutArVegC1My+7Y0UiaEtTpWayLziSSsdvS7E0n/BALBsYApEaRq/0eDuqwWyr75HHloGIl3/ZOq5T+jHz0OeVAwtRvXUbNqha+uMX8fptIcAhMGd7p/p7We7F/ewVyeT9yUcyjbvpKSzb+QOONioPWIWkd9LVUHNqKNSMBpMxM2eHKXzskfUbMNUcwej4fCwnVkZ/4MeKgrOghSKbqoZBKmX+jbOd0Sl8NG2Y5VWKqKMZfnIVOokak0BCWPIGrULJ9/sz/oSYQxCDsLgaAlQmA+AfB43JRU7QCgwnAArSqMqNDhSCQSokOHU2+pJKf4d1/9hKhJ7fZXayrE6bYTFpjiKyuu3E5oYApDk8/BbK1i/Z7XOZD/M0MGnNXqxcPurKfeWklU6HBGpLR9gektPB4P+/OWUGcuxVhfBECwLhGteiDFldsBGJV+CTpt1BFtG8Rlh6WO3I2LAIgaPB2ZXIXTbqY6byeG4gPEDJ1JWPI4lFqvR6WpIo/cTd9iq6sCiRSJRIpcpSFYHoNUKkc7bCRBMemEZNkBie8zqTBkYFpVhv6kk3E5bORu+AarsRyJTIEqIJiBs25AKm89MaKg8zS1wGgrWll4MgsEgt7m2x/M2O0glcAfa2z84+969DopE8areP/tUOacVuGre/ddzR9kthQEq6pd/P6njdPnq9FqvDtxfvrFgsHgYe/2KCIjpHy32MI999bw47cRxES3vmhb+5ed4GAJTz0eRER41xZ2/hBkv19s5tMvzPy5xrto1eslPPJQEP952Bt9ffstOuKuTALaHmvZ8xlkb6hi0MkRDJ8bjdXoJH9nDe9du4lx58cz4cIE4kcEIZFIsNQ6WPrUPvb/UY4ECRIZyJVSEkYGs0ljZNwYJeecuR2XXopEAlKp97qQX+Dk86/MnDpbzcb4sax45QC7l5UglUtQauScef8QkieE9vk9T1do+n+oaXR8W79HITILBIJjhaZCoj8EQXtxEfaSYlRx8dSs/h396HGoExIBCJ0zn/q9u7GXeXfoKvWh6GLT2u3PkLMLdWg06uBIAKzVpZjL84mfdgHqM6YSFOKh6uclBCYOITi59e9dY8EBAKJGzyY4ZVSXz6mnn4vdZGDnzi8x1ZVgt9cBED70JCr3/QWATK4i5bQbkMqVbfZhyN5J2bYVKPWhhA85CbfLgaO+lpLNv1B7aDeRI2cSlDQMqVyJx+Ohct9flG3/DbfD5ltjq4IjUOpCUGgDCU4ZhSY8DolU6tuV7HG7qNizFplSQ6h7HGW2A9QsWYrbYUciVxCcNJwYzxmYkjt3ve6MMN9dMVtYZQiOJYTAfAIgkUgZnX45u7K+JrvIKyQXVW5DKddRVZtJePAgX93Y8NEEqFuP5Glg76HvqbdWMjzlAmLCvBe3IF08Jks5TqcVnSaSIUlnsT/3RwK1McRHTjiiD6VcS2TwEMqq9xAWmEJcxDg/nnHHlFTtpKhia7Myu6MegykfgNjwMQTrmidf8Hg85OkLKFzyExZDKaEDvBftYWfcTUBovK9e3Mi55G9dQuGOZRRs/4nAqFQkMgW1xRkEhMYTO2IOHo8Hj9tJxd51lDp3oVYGUfzntmbjadXhWO21uN3eLdKyxX/hcbtQqPWEp07AaqykOm8HW764j4SxZxIzbJbfP6fjmfaE4tpUibDEEAgER4WHHwiioMDFi//n3dXyznv1zJ2jZu1fNtQqCaNHKdix03tduPUm7wPPtgS+L7828/BjRk6ZpeKzD8OQyyWMG6vk48/M7N7jYM5sNW+/Fsq5F1Vw0+3V/LgovFXx86ILNGzZauem22r4/uvwdufv7wjfvHwnD/y3lopKt6/MavXw2JON1h4xt087ot3BTAfLPslg9Xs5DJsTRcFOA9OvTea0fzZGj7ldHv76NJe1Hx5iy7eFhCcHEDNIz6HN1TgdbqZemcTU4HI21Edj3ZLH6hVllIVJ+XlZo7WIRAI6nYTwMCmHcr3R5c+/VIc+8g/qq+xMvDgRhVrG6vdyePfaTcQPD+LmzyYjk/cPl7qm4nHTiO+mv8cTKWpbIBCcGLQlNnc2IlWfC57kVMJOO5OqX3/GVlRIze8rCBw3EUd1FbbiQvRjJ/gE5sSZlyCVtS29OMxGDq34EIChl9yPfVQE7rhEWAJ1hQdRuacQfNJ0XAfyyV35KUMu+hdw5O5fXWwaysAwcld+ytCIBJT60A4+Cf/hdjkp3riUmqqDzcpr8/f7XifPuxZZi6R+LoeNmsytFKxZhDo4ColcgSIgmKGXPtDMpjJi2DSKNvxI7spPkCrV6OPScZgMmCsKCB04AXVIFB6PB6fZSMWeNdRzCLlGR/muP5qNp41IwFxR4HtfsuUXHPW16OMHoY1IwJCzk7Idq6jYu47YG29BnTCgU+cvRGaBQAjMJwwh+gGcNOIOzLZq7A4TOzI/9x1riG4GGJhwWrv9eDxuXG4nAPtzlxAdOgKJREJgQBzVxhwqa7OIDR9FfMQ4qmqzKKzY5hOYbQ4TDqcZnSYSi62GuMhxuNwOCso3ExM2qk07DX+TW7qOzILlgFfEHZ12CZW1WVQYDmC2VaFU6BmYMLdZG7vDxM6KnzBs2ecrq87bSeL4c5qJywAKjZ7UaZcxYMK53mjmwn243U4Sx51F5KCpSJts6xmimILH40YqlWGylFNQtok6cylqZSBlNXvRRSQRRBjh0+ZTfvAvkEiJGTYLudK7PbksI5mSvX9SuGMZuogB6CNTEHRMZ6KQm4rMrUU3i0hmgUDQGwQHS/nm8zC2bLUTEiJl8snlfPhJ/RH1Pno3lHdq249Osh3OtbPydxvLV1o5fZ6GyRO9C7tX36hjzmw1qSlynn4imKuuryY7x0laqgKn08PmrXYmTVBitXkIDpLyn/sCeXBhLXv3ORg2tONdM01F7+4KlHv3OXwWGADvvRlKQICEpb9Y+PRzMwCXvTjGF0EM4HZ7ePMdE48/ZcRu97bd+1sZQ+dEMfvW5tFjUpmEaVcnc9IVSWRvrGLnT8XUFFkYMT+G6dcm8+8hmYCeu6gHwnE6PUilYDR6+G6xmQ8/qWfMaCVffm3GrPVwzpkabrtZx979Dr7comf8+fHEjwgGYMT8aJa/dJDMdZUsey6DM+4b0q3PpDdoLULZ39YmAoFAcLwRmCchMHE2UQtGABKKN/+CYctG3/Hav9YAoA6JRt9B9LLH1Wh/lb/3F6JHXYVU4b3W1ubuJvGgBcvgAEKvvoS6/z1K9cGtxATNB8BSVYxcG4hCo8NRX8uAmZeS9fNbVOz7i9iJZ/TJrhm3007Or+9TV+gVl0MHTSQt6CRKy3ZQUuENogpOHnnE51Bflkfuqk+xG6sAsBrKUIfGkDznqiNyIAVEJzHw3Luw1lZQc3AL9WV5KHUhxEw47QjrkbiTzgGPB5BgKsmmbPtKpEqVT5AOTBiMPn4QAVFJVGVsQhsRT9jgyd4d3mPnULRhCVX7N1Dx+RcMPOdOLIMD8BcNQnFXhWYhMguOBYTAfAKhkGsIkscBXjuIhmhdAIlEhk4TiUKubrcPs7XKlyTQ5bazP28JgxLm43Y7kMtURAQP9NXVKIMxWRoXhut2vYzb4+SUcf9l3e5XAJBJlbjcdrYe/JghiWcgk6nQqIL9dMZHUlSxzScux4WPJT1hHgq5Go06lNLq3agUgYwZeBlSiRyPx4NEIqGyNpO9OT/gwUPqtCuoyNqIVKYgeeolKFRtX2zkKi2RA6cQOXBKm3WMQ7zbm4MOmA5Hfp/ZesVCUI098ljUoGlEpE0mY+XbHPz9fYbOuwNNcHQXPhFBe7QmIovoZoFA0NtIJBImjPcKwR+/H8pV11X7jul0EkwmD+lpcs7owJ5g6S+NCYeeftZIUqIcq9X7/XXFZY3Xr7g474PPerP32P+eMfLKaya++jSM9z+q59cVVvR67/fhjFPLWb40ApVKwqCBcuTyI78n/SFOlpS6mDnXew8RGSHl68/CGT7Mu9jOzHIikcBjDwdx3bllQBlKpYSSUhd3/L2GP9fYuO1mHQeVMexeVsIZ9w0hbXLbkddSmYT0k8JJP6l5ndaie28K3kVwsITrrtZx3dXeCPKXnwtp1m7cWCVXXe7mbUOwryxuaBDXvj2B9Z/lseR/+wiKVjPtmuQefUb+RNhcCAQCQfcEPFWQN5J4wMxLMGRv95VL5UrcTjtBySM67KMmZ6fvtXn/Pmo3byBowmQUoWEoIiJ9AqdUqUQaqMPl9D5BthrKObDoOYKSRhA9dg6ZP74KgEQqo3zHKqQyBUFJw1Dqw5Cr2s+f0BOKfvyEugqvuJw442JCB00gIMtBrHQ8hUV/oY8bSOLMS3A77EgVSjxuN2U7VlK65Vc0EfHETjidkq2/EpI6hpjx89odSx0UQcyE9oPiJBIpHL490celo49Lb7NuQFRjhLJX9FUSnHYB2orpFL71f+T8+j5p8pupT2vb1qM7/2966v8sEPRHhMB8guH2uNif+yNWuxGZVInb40QqkQMSUuNmt9nO43GzP3cJMpmK9PhTySz0Ji0oqdpFrakQk6UMALvTjELuvXhJJBI8Hu+WVqvdiMvtzZxrsni3CmlVYZht3qeVhro81u99HYAgXQLjBl6FTNb2l3h3ySpaCUBoYAqDk85EKpHhcFrYmfWlz4t50753fZ+LUqHDajcQFpTGsORzUdn0hJ06psfzCDpg8nk5g9fXuWWSv4bjDeUt2zQglclJn3kt+355mfytPzLolJt6PD9B+4joZYFA0Bd8+4OZJ5+pJTJCirHOjcMBcjlcukDLwHSv2NqaB+63P5j56hszC/8TyPmXeK+ztUYPp51dgVrt/f4qLm6MlpIdjgBuCKB65TXvdScuVsbGzd5FbEiwlLo6b4W5Zzb6QK/4OYIxo5pfr9sTlzsrPn/+Zb03+Aj4/utwBg1U4PF4+Oa7RnuKBxfW8tAjtUgkEB0lw2BwI9cr+faLMGacrOZtQxqzb2k/aqyr9FQ8n3L5AGqKzPzy/AFGnx2LLrTt5IpHmxMxsaFAIDj+0OVDLywrm2GrqyZv5aco9KG4LCbcTjsSmRyFOoCI4UfaODVgr6shf803hKaPQxMWi6WqGLfTQcV3X1O3bTOO6ircDjselwuJzPswWCKRgNu7xq7N8+6ulcoVGHK839XayETM5d5AstKtv1K69VcAosfN61C87Q72umoqK/YCEDv5LFJloyHLgclUyuZNLwNQV3SQ3R//F4/LiUylQSKV47SYGJA0i6Sk2RjTtISk9XyN7U+UEZHEXnUDhW/9H0XlGwlOm95qvZ5EFXc1mllEMQv6O0JgPsEordpDceUOQgNTqDYaAHB57Mwe9yAyadtbXisMGRRVNvcIVikCSU+Yy56cRb6yHZmfMXXEXYA3Ktp92E4jv3QD4PU2rrd4F7vjBl9DhSGDA3lLm/VbayrAUF/YLImgv0iKnoZCriY6dARmaxW7sr6i3lrZrI7b42R48vk4nGasDiOB2jiiQoe1ur2oQfxtTfhtj9bqtyxrKTi3h1ypISx5LGUH1nVpHgL/IxIBCgQCf1BX5+bufxpITZWTm+fwlX/+YRgTJ7QtStrtHu6514DJ5GHVHzZf+QvPBLPg8ipflPKj/zNyzlkaBiTKUR5eeNtsHvYfaBwrIV6Gww4LHwpk3hw1U2aU05IPPqpnzAudX7l3VqycNlXFP51w9RUBREZIuen2ar77wXJEvacfD0KhlJCf70Snk+I5cwIZwUoyDJ2eEosLvFYj5yTsbFbeVgK7ntp/TLo4kbUf5VKaUUfalP4rMDfQX+wyGn5PLWn5exMIBIK+pnjjUmy1lcjVATic3qCqwMShJM2+rN12JVt/pa7gAHWHk/MBRI6aiaO+lpos79rbVVeHYd2fhJx8OBhMJsftcuLxeKjK8FpyhA6aSPXBzWjC4hh49p1k/fwWpuKsZmOV7VhJ9NhTkUj9mwNAptKSkDid8LAhMGowZWs2sn//N75AswZ0MamEpo/DXm/AZbeSIB9MYGAChvT+cR1sTeRVJw5AFRuPrbiwWbm/RV4RzSw4XhAC8wmGyVKKTKpkWNK5lFTt9EX0Ol22NgVmu6Oe3TnfAqBWBmO1G5BJlUwedgtKRQB5pX9hsVXjdFkxW6twu51Ipd7oX7vTRJ25hLwyr/AZHzGB7KKVBOkSUCsDSYicgEYZzPbMT4kJG01SzFTsjnpC9Em9cv4DohvtKkzmMp+4HBqYgsVmwGKrZlDCfGLCj1zE9EQA9hdtRTEDaIKicNpMWGrL0ARF9fHMji2ECCwQCPo7uXlOzBYPV16m5czTNQwd7d39s++A8wiBuakQ+sprdZhMHmJjZBSXeCOOX3sphNkz1Tz2cBAPPVJLQryMgkIXP/xo4W936IkI90ZFlZe7WXCZ9yFwWqqMpT9bMVs8nHmahqQBcvbtiPbNY9uGKA4dcjJieMd+zB3RmoA5aYKKSYfP01TvbiYuX3axls+/MnPKLBWXXxqASiVp0lfnxe62BMum8+rO3BtoS6AOjtWgUEs5tKWatCntJ03sS1rOt6k9SH8RmVujrQcEgv7N3Xffzf79+zuueJiXXnqJwYMHd1xRIDgKWKtKUIdGE3XrTRQ88xT2umpMJVnttjGV5FCdscn7RiIBj4egpOHEjJ+Hw1xHTdY21KExWKtLqPplKYmJXoFZJdfjtNRRvnMVthrvLuKAqAHk/PIO0ePmIZHJSDvzVvJ+/5yazK2kzLsOuUaHVKHyu7gMIFOqCT/tPIIzbZBpY19Vhk9cjokZT2nZdqQKFTHj5zezowjMtPUbcbk9lJFRWHMP4XG7e+Xza0CIzILjASEwn2CEBaZRWL6FdbtfIVif6CtvsLVoDYutBrfbG9GUGDWZgwXLkEikKORaACYNvRGA/LINHCz4FavdiFYdisttRyqRU1q1x9eXVh2K2+NCJW8UScOD0zll3H+RSCRev6Tes4dqRmTIECQSGR6Pi2pjDhKJlKkj/oZW3XG23abiclejl3tKayJz0AETWmccWYBz5344OarZMej7eZ6oNHg0CwFbIBD0hJRkOSOGKfj3A7X8sboxEjkosPXvlgbxb8iH3oSAV1ym5Znn6wAYPNh7u3frTTpuuj4Ao9FD+vASao3eBaDZ7P23uNSJ5bBH843X6bDZPXg8EBHhXVBFRsioKIzF6QSFQkJiQt/cRuoCpNx9p44X/8+ETifh86/MPPO/IJ8HcndoTZRcXDCqT0RKmUJK8oQwcrfW9PpYXaGlGN7ffJnPSdjZ7KGAEJSPbdavX09qaioxMTEd1v38888xGAy9PynBCYO/hLyGSFbN6OHU/LkK64tP47J5r8Oa0Nj253A4IZ4iMoqAQUMwrPkDpS4EqVyJKjCM0Tc+h0QqZf/XT2Orbdxx63bYkcmVlGxd3qw/j9uFQuu9LkokEpJmX07ijIuRyvpW8kkccDJlZTsAKCnZgi42jdTTb+rzefgLbdpA6rZtxlVvIqQqsFfH6ozILGwyBP2ZY/OvXNAtLDYD2w5+7HtfYzxEUEACESGDkEpkbbbTa2MI1iVSZy4lNnwMeq03idyhktVEhQ4nQB0GwIDok4gNH+MTqw11+QQGxJJX+hcAOk00CrmGwIA48krXkV20yuf7LJW2PX5v4XBZAe9COkiXwOj0y1AeFs1b80RuyyPZX3RFCG6trvywudj+vCXM2T/2iMy77UU/n4j0tggsRGaBQNATLr6yit17vQ93f15mJTFBxvSpKiZPaj/a5+936vnPw7UkDZCze0s0v/1upazMzbfZZs4/R4NMJiEkRMKhAzFoNN7vqPWbvNt5f29iqXHheVryC7w2V7PmlrP6tyjUagkSiQRFz4OWm9GZyNj8Qm80tkKOz2O5KV0VQ1sTJ7srWHYnijk6Xcfq9w8xJmszE8ar+p2Y2xo9tQbxB0JUPr648847mTx5cof11q5d2wezERyvmBJBqu4dUa4uCYxbNlHzx+FdwdVVSJRKtIOHEjDppGZiYINw2PA+JG0M5QfWoQgOIWz+mSijY1CGR1Cy8ReixsxBKvdebAdf9C/cdu/12e1yYi7PIyRtLB6n9x4hYfqFyBQqVEER5P/5NcrAcPSx3vwDfSnqGtJVBGfasFgaExNHjppF7MTTkbSy1j8WopcBVPEJANT8sRL5WecdIQIfDbsMITIL+itCYD6BkLfIbuA5LK4mx7RuWN+AVCpjUOLpbNz3JmZrJaGByezOXkRp9W6yi1YxPOUCYsK8i44GcTm76Hcqaw826ychagIAqXGzyCtdh9Nl9x2rrM3E7XYRGTKYemsVBWUbSYia5BOvewOVQsfssQ9itRvQqEKQSKRHJN6DrgvL3RVy2xqvs22DDpjQqEKw2Gpwuqy+CPPW5iaimr30hggclO3p1f4FjdhsNrZv395xxcOo1WpGjx7dexMSCPyIvMVaLL/AxYvPBiOVtv+dcvMNOj78pJ4dO+0suEBLWJiUy6722l788KOFT973Xlf1eu9DyJJSF7fc4Y2kzTjoXayeNEVJUJCUEUFKhg9VUFbu8vk0FxU5+fYHC7fdrMPjgZdfrSM5Wc4F5x55zfEnb70aykP3OwkKkqLXNT5A7S/CbFctJOJHBgNecX/CeFUzIbqpNUV/peF8+/McBf2bTz75hLi4OL/XFQg6wt82BBJZ86Aej91O4PhJBAwe2up4PqGZKIJPmo7hrzUgkRAweCi5zzyOx2ajdNsKhl+xEEVAIBKJFJlKQ10SZP/nfjxuF+aKAl9/QQOGARAz8XRyV3xEQ4Zcj9tN5b516OMGog6JojZvL6bSQ8SMn99rwrMhXUUEwxh5zRO4nDaUAcG9Mk5TWvt9+lN8VUZEAlC/dzcRZ53XJzYWXU3+JxD0F4TAfAKhkGvRqEKx2BqfKtbWF1BhyCAieFC7bQM04chlarIKf2PQgNMpq9nrO5ZdtIro0BHNkuBZbIYjx5d5F58NXs/1Vm8W+grDQXZkfoZUImfaqL+zad/bOF1WdJqIXhWYwSuea9sZo7tir79oLZK6KU3F7KoUGZbNNchlaqTtJGxs2p+Iau6bSOZqsSbqFQoKCpgyZUrHFQ+TmppKVlb7fngCQX9h+lQVa/+yNyu7+Y4a3nm9YxunwJHRfPNtEVOnqFjyc6N38S+/Wqmrc/vEZYCcQ07f65JSr1VGSHDj8dRUOXv2OZBIoKbGzUkzy6k3exgzWsmXX9fz1SIL48cqel1gBoiPa7xt7a6w2VlRtDt2GS0F4vbI+su73XnEsMbrdct2/V1o7q/zEhwbpKen90pdgaAlunxoiLPqDcFOPSD5iLLSTz8g+b9PINM0ej+2FA3rkkDtSsb92zKqli1FFROHx9a4k6g6cwtRo2c3m7PH7d3N47Q0rudk6gAA5CrvddioqUUPFK1fTMWeNYQOmkhwykhylr0HQMTwab0u/MpUGmSqvvG97G3vYkvuIQA06QN7b5A2aO/cRBSzoD/Sey7lgn6JQqYmPCiduIjxvjKny9ZOCy8yqYKRaRdjslawfs9reDxuBiXMB7wezau2Po7DafXVH5Z8LiH6JNTKYCRIUSuDqTI2F3bcbifVxhx2Zn3pfe9xsnrHczhd3n5CAlN6fL5dpaX42tk2vZHwr6vCb/6WxchlKiYNvbnNhI2C1mkadSw4tkhJSaGurq7Dn507xbZqwbGFVuu9Rfvo3UZBedNmOy6X9/uqIfFayx+ALx5xMmK4kqtvqGbRdxYmT1Ry2cXehWfy4BJW/t54vZ46RcWLzwYD4HZDcpKMNetsOJ3ecfR67wO43DwXV99YRb3ZW37+xZV8tcgrXp82r4+SJ/SQBsG2qZDbMtHf4oJRvrKOkgDeFLyrW1YRmX9VsunrAp58LIhZM9Qd1u+PyfX645wExycmkwmXy3W0pyE4DumKOKfPbfxpiSzAu2YLO+1MJE08pBosLNrrM0qWTsyE0zCsW03ZV58CkDLvegCKNy4lc/3HeDyN65SUR57yvtCqUAV6k8TWFRzwzuOwwIzHQ37eSir2rAGgOmMTOb+8C4A6JBqFNqizp90tjob1RWu/m7qkxp/O9tESt8NO2Vefok5KJvLci1pt19tRxm39vxMIusP7779PUlJSp38effTRLvUvIphPIAx1+RjNxaTFzyE5ZjopsTNYs/N5ckvW+iwu2iMsMIXpI++mrGY/taYCosNGkFP8Jw6XBbfHyR/bn2TGmHtRyrVIJBJC9MnU1P0OgNVuoN5SicfjRiKREh06gtLq3WzNyG11rAlDbuj16OW26Iqw3MDRjAIOOmCiPMFBZfZmBiXM71SSwpbtT/QoZmg/krmpAN1etLMQqvsWnU7H3Llz0ek6/v8bGRnJqaee2gezEgh6jsfj4e33TAwaKOe0eWrKC2IZPq6UomIXOYecpKd5F7BNRb6m0aShoTK++yqc7TvtLPrOzNlnali7rvFh8sVXVLHoizBmHvYxPnla42LwUK4Ludyb8C8xXsHp8zR8+rmZCVPLWszR++9D9wdy1+399xrSmhDa9LNqmeSvKe1FMDftt2WkcVO7jJZRvh6Ph9/fyiJ+RBA3XBvQ2dPod9HM7Z2jQOBP5syZw0svvdQpr2aBoDUaPJhbo7PRr02jRVu2qfnjNwA0qQNJPXk2xq2bKV/0BbUb1hF26mm+Nk3HbEr02FMJGzKFqgMbkcrkKAMb18CmXTvwOJ1EX3EtEokEqVKJMjoGe2kJDfuPLNUlBCUNxzk6CtkyHeWLvmj1HAKik0mZe22zXccnIm35J7f2/6Bu2xachhpir78Fiazvc0Y1RdhmCPyB0WgkPT2diy++uMO6v/32G9XV1R3Wa4oQmE8giiq3oVToSIiciMfjobRqNwAJURM73YdUKicmbAQxYSMwmctwuCxEhQzDaC7BYqvmQO5SRqYtwOYwkVP8e7O2BlMeew8tZnjKeQxNPofosBEcyPsJq70WqVRBeGAa8ZHjcTit5JWsQz3gNNTK3n3C2h16O9lfW+O0hd1hIvuXT1DINESGDuuVuZwodEZI7o6IHHRICM+9QXR0NG+88Uan6kql0k7XFQiONmvW2igodPH1Z2HY7VBR6aK62s2M6SpSkr23bq0Jpy3LxoxSMmaUkrcNI/ng32uRK6U88h9vEsALL62iMDsWtVrC3f8y+NoolWC3w+nnVPL7r5HMnaPmls8ns/OnEtZ/lgfA+LEKLjxfy5RJKp542sivK6zMn9u3UcwdWV20FWHbVpuuiMvdnZPH42HZ8xnkbqnh/MeG805tQrvzbc3XuT/6HvfHOQkEAkFn6IpY11rdgCwHeVu2ETpoIsrwCG+9bZuQarToRo7pdH/6XB3RY04BoHS7N2Fg5EWXUf7N59Tv20P9vj3oho3AtHc39tISbyOZDFwuSjb/gjstAr18NAl3/gNLXi5lX3wMgFIfStCAYYQNnULV/o2U7lhJ3KSzkEiPz43sHUX6duX3bcnJovLnJWhS032/2/b67asoYxHNLOgpw4YN44YbbuiwnslkIjc3t0t9C4H5BKHeUklx5XYCNJH8vu1JOJzgLzp0JPFN7DK6QoAmEr02GofLypTht5FduAqdNgoAuVRJXMR4iiq2NGtTUrWL4SnnIZXIkMvUWO21yKRKEqImERc+hrLqvWQVeS+qESGDiQ0f3e1z7mu6kzivpwkB7WYjB1Z8iAsrg06/A3Vp8ySFXYnGFlHMzRHRyMcGOTk5rF69mmuuuabNOgcPHuSGG25g9erVfTcxgaAHPPI/IwBffmNmweXeBH0BWgkfvReKTNb6w6/2LAtuCt7F6rNi+e3VTM44TU1IsJQdu+w0rC8vvlBLfoGT3DwX9sO2z6WlbjZstHPWGRqCYzWUZHjndO1VAZx9pga73cOMU8sBcDg8fS4wN5xXZ+iq+Nkdcbm1MZvOz+Px8ONj+9j4VT5nPjCE8ecntNNaIDgxuOqqq9i1q/2/48zMzD6ajeBEo6eRoPpcKN+3DoepBolUSs7C+33Hoi6+AlVUdJfm0iAchqaPo2TTT7gtZhLvuZ+aP1YiDw4BQBEWTsDQ4dTv2wNNrGPqdm5DP3I0Uq0WR6U3z5F20BBCw4egjx9IwZpFmIq9dpVRo2ej0Oh7dO5Hk86KuV0VYptGppszMyj5+D3UA5KJueo6v85LIDia3Hnnnc1sd/xVtwEhMJ8gaNVhhAWmUW+tJDp0OKXV3ujllNiTu92nRCIhNW42OzI/Z3f2N0SGDPUJwjKZkqFJZxEUEEtW0e9IAJujDplUTnbRKqqNuRhM3kgot8dFbskackvW+PqOj5jQKduO/kBLEbc9sbYzgm9D287Uzd24CJfDypC5d6AODKc2uPPjCAT+pq6uDr2+/RvWuro6NBoNcnn7l5/q6mocDgdRUVHt1lMqldx0002EhIRwzjnnHHE8KyuL2bNnM2nSpI5PQCDoJ9x8g47b7qohKbHx7+ScszToArofcfT6DUamfgYLLq9i/lw1D9wbiFLpFasvvkjL+HFKbrilmjqTm9w876L1l+UWXl8RwJZFv3stMSTw2SIrH3xc36zvt14N6fa8GmgtWre1OtA1/9/OissNonJHvstN+23LoqS1sj3LS9n4VT7nPTKcCRe2Ly53NGcRMSw4XigsLCQ5OZnU1NQ265SXl/fhjASCrhGaPp7Srctx2azIlBpcdm9uAt3wxu/ozoqODQKlUhdM4KSTqPr1Z+ylJQRNmYo6Lh4AVXQMMVdeR9m3X2LJPIir3oTH6cRZVUnVb8swblyPy1QHgOVQNoUZ+5uNkX7Once0uNySBkG4s59xe/Ua+nJZzJR99Rma5FSir7wOqaJzuY2EuCzoDRwOBy6XC7W67Zwdbreb2tpagoODO7TAkXXB6qUrdRs4PvdGCI5AIpGgkGtRKXSMSL2QMQOvOFzeMy+hiOBBxISNosKQQV7p+iOOx4aPJTp0GCNSL2RU2iW43HZyiv/E5qhjePL5RIUOx+NxAc3/EGrqcrHYano0t6NJWwJva8Jza3U7EoiDDpiwGisxFO4lfswZqA8neehKHz2tLxAAZGdnc+211xIcHExsbCxRUVE8/fTTR9R75plniIyMJC4uDp1Ox4wZM9i3b1+rfW7evJno6Gji4uI6HD8+Pp4XXniBSy65hD///LPZsZycHGbNmsXgwYP57LPPuneCAsFRIDTEe3t22SVaKgpjCQmWkJjQs+t1YKCUN18N5WCmk1deM2EyNY9ISE2Rc8dtOm6+Qcfq3yIZMVzB14ss7PqxgBk3pjL71jTwgN18ZLKtLdscXY5wAJolJuxING16rCvialeT0XXku9yWqNxwrK3Ef2s/PET6SeFtisst++qI3kqy11mBHZonmhQIusPFF19MREQEzz33XJs/iYmJR3uaAkGbWIbo8EglSFNjSH7kCcKHTUUVHElgofyI5GxNE861FT3dUCd8/pl4HHaMWzZizT10RL3QWacSMGQYCXfeQ/DJs7CXlVKzcjmaoGgSZ11KQFQSnoYtSU0wZO/AabP06JyPNq15ETf9XNv6aY9mfW3bgstqIfLCSzstLgsE/mb58uXMmjWLkJAQQkNDGTVqFCtWrDii3gsvvEBYWBixsbFERkbyzjvv+GX80tJS8vLyutxOCMwnEGpVEPXWKnZlf8P2g94stf4w+U+P9ybOMllKWbvrJSy2Gg4W/MrWjI+x2g3kl21gy4EPCA9KZ9yga5g09GamjfwbMeGjGJl6EXKZCvAweditTBv5dwDqrRVs3Pd2j+d2tAk6YGom3LYl4jbUa1m/Peo2rUOmUBM6oPOLwY4QIrPXe7nhR9AxP/zwAzNnziQvL4+6ujo+++wzFi5cyJtvvumr89NPP3Hffffx4YcfYjQaqaioICAgoNXkAkajkcsvv5wFCxZ0eg533HEH9957L2effTY7duwAIDc3l1mzZpGamsqPP/7Y7lNfgaC/ERfrFZOffNbI6Ill1Bg8SKU9/06aOkXFjOnehH6Tppfxwcf1rFhp5ZTTyln+m5X3P6zn/odqMdS6+eyDMN57M5SCrFgWPWxlzh3pTLlsAABjz4nj8d3zmTxJCcBlV1ex6g9bm+O2RUsxtiPRtKmA6w+R2R8ibWfmUZ5tomBXLeMvjO90X505z94UmbsiNHfUjz/6aqtff/ct6Fsuv/xyli5ditFobLPOwIEDCQjofEJMgaCvaBAl5cHB1B/YS+Ebr1C5dx0uibNdgbMz1hxStZrICy4BoHLpDxS//xYui5niD9+hYvG32EtLqN2wjoofvyNs7ukknXIlw69cSPqZtxI2cALx084HQCKTM/qGZxkw6zIAKvasoXDd9/75AI4yXY0Ybk/Ub/Z+22YCBg9FHhjYqTm0fJAgEPiDr776ioULF1JTU4PRaOTss8/mnHPOaWYb9d1333H//ffz+eefYzabee2117j11ltZuXJlj8f/8ssvefHFF7vcTlhknEDoNdE4XRbKqvf4yuSynosuKqWeAVEnkVf2FxZbDTsyv8RkKQVgf+4SX739eUsJUEdQbthPevyphOgHUFi+GafLBkhRKgJQKfRMHnYbeaXrCAtK6/HcjiYdCcsto5m7Iu56PG5KqnYSmjQamVzZ7X5aQ/gxN9JUZBaezK1zzz33NHs/Z84c5s+fz08//cQtt9wCQEZGBqGhoZx++ukA6PV6zjvvPJ+vU9MHXbfccgvnn38+aWlpfPnll52ex8KFC6mqqmLevHl8/vnn3HDDDSQmJvLTTz+h1Wr9cKYCQd+RlipHpYJvv2+MMrI7/PMd9O979Py5xkZ9vYd/3W/wlV92dRUXna9h42a4+18G/naHjm+/t/DlvjBm3ZzGrMqtPPdLJQBxw4KQSiV881k4z75oxOWCieOVbYzYMxqsKFpLdtcV2hNr20qw15myzrBtcRGaIAVDZkV2WLel9UbDvNsTyf0ZQXxOwk6faLu4YFSX/ajbEnxbK++u13VLOxN/9i3oW3Q6HQUFBe1uw/3444/7cEYCQddRxcRRt70x75CzpvqI+9vuoBs1hqoVP+MyGjFnZlDw8nM4aw0AeNze3USW7EzsyzdjLs+nOmsbSbMvRyKVUbrVG+moCY1BIpMROnA8EqkMY8EBIoZP69G8+hPtCbtNPZU7i62kCFtxEaFz5neqvvBdFvQW7733XrP3Cxcu5Nlnn2XVqlWkp6cD8H//93+cffbZnHbaaQAsWLCAN954g9dee41TTjmlz+cMIoL5hCJYn0iDFYVS4fVe2nbwE7/0nRI3w2e3oVYGkhI7A606jCpjtq9OceV2MguXU2sqoKBsI2t2vsj+vKUADIw/FdXhOem1UQxPOf+Y8WDuDj0VcGvq8rDaa0mSDWtW7q8I5K5EUp8oiKjmzpObm9vMO/nCCy9ELpfz1FNPcejQITZs2MDrr7/OHXfc0ezm+7333iMjI4PHHnusW+O+8sornHLKKcyZM4eYmBh+/vlnEfUkOCZRKCRMnuiNNE5J9l5bn3uxjqxsR4/7njRBxblnexPyTRin5IZrA7h0gfchzDffeQXt7Bwnd/3DwJ9rbGz/sZjXFqxjxqnl1JncxA0LZOICr83DJ7ZRRN82nbg7p6PXd/+WsqnVQssff0XpdkVcbijv7thNx/J4POxYWsSo02OQK3tmc3I06M0I4Z723Z6ILCKbjx264/EoEPQnNCneoCiJUuUrq/mj5xGEUoWCyHMu9L7WaFEnpRB+hjffiHHTBl+9gtVfU3VgA8a8veSu/JQ9nz1Kbd5ekEpJnNG4WzAkbQwDZl2KNuL4TTLbNKK4O0kc67ZvRabToR042L8TEwh6SFFREXa7vdkae/PmzUyb1vyB0cknn8ymTZva7evVV19FrVa3+/Ovf/2rW/MUEcwnECqFnoEJ8yip2oVUKsPuqCMieJBf+pbL1Mwe9x+kTTydXW4HeaV/EaxLRK+NpqhiG4nRU0iMmsz2g58gkUgYED2VvNJ1VBoz0QfEEBqY7Jf59HcaxNumQnPtYF2nRd1qYw4KuZYgXQKSXhSCRTTzkdSmSk6YaOaWW1ZVKhUqlaqN2l5effVV9u7d2+ypa2JiIq+//jrXXHMNjz/+OBaLhVNOOYUHH3zQV2f//v3cd999rFmzBkUX/M5KSkq48cYbfe8dDq8AJ5fLufTSS33lsbGxvP32sW+7Izgx8Hg8/O/RIK6/pZr4OBmHcl0kDZARF+uf27Z3Xg/hrVdDkMm8D3h27bHzxddmAF59MZg7/2EgfWo4s29LI3drDcuez2D+PYNY9kIGRXuNbF5UwBvXGVCpJH4RgVuLwu1Jn63NqekY/raWaBivNRG7PNuEsczGkNntJyxtrb+G1y3LWtKbUczQuUjm7gq6De16Es3c2ahpEdXcv3jppZc477zzGDBggF/rCgR9icfjQT92PPX79+C22XDWVOOorkKT3Hbiyq4QMHQ4qY8/i6TJg5jaDetwVFUSeso86rdux4WD0FPmoklJI+/ZJwhMHAoSCca8vVQd2EDEiBmoAsP8Mp9jlc5GOpuzMtEOGtLs8+4IEcUs6ArdWV+73W5uueUW0tPTfdHKZrOZ+vp6wsOb5+KKiIjoMDluYGAgQ4cO5bbbbmuzzm+//dZuH20hBOYThL2HfsBgymfC4OsZED0F8NosSCRdjzjyeDw4XVYUck2zcmmLhIHxERNwOM1EBA9hZ9YXDEyYR0LkRA7k/0yduZTEqCkUV24HvIJptTGHlNgZgIQKw0HS4k8h/Bi3yeiIpoJyV4Tcmro8QvRJ7W69atqfiEbuPEHZng4jlfuLyHwgPxqpxv/ewm6LFYCEhOYRDg8//DALFy5ss93333/PP/7xD9544w3Gjh3rK1+yZAmXXnop3333HWeccQZGo5GrrrqKU045hU2bNiGTybjkkku44447CA4OprS01HfxLS0tJTAwsE2bC4lE0sxfWa1Wc8EFFxxRr6MLt0DQX1j1h5Wrr6/m7TdCWLvKK0q63d33YK6rc6PVSnxiMnj/bpqunYYPVXDHrTqGD1Pw6Gsu4oYFseZzJT8syeCr9wxMn6pk97ISOPy19+Pj+6j5XcVp89Qs/20VhUMUPPJQYI+2A/d2pHJ3x+iMeNvQb2t1c7fWIJVLGDA6uEvjHu3Eea2JzE2P+Zv2BOqOxmtPZG5vjJZWGwDOehvQvUWVoGt8+eWXTJ48uVOicVfqCgR9hcfpJO+5/6FJTiXmyusay91uJNJurLFdLjxOB1JV83v7lmJn2GlnYTmUDTIZNmMlCXfeg0Qup/TLT5BIZSh1IVTuWwdAxZ61VOxZS/zU87BUl2CrrWLA7EtRBgR3/YSPARqE4q4Ivg1tXBYL9tJigqdO79a4QmQ+fthZGItU6981ttvcvfU1wO23386mTZtYvXq1b0G9Ri4AAQAASURBVE3bcM/tdDqb1XU6nR3uDDr//PO57777WLBgAYFteI2bTCZyc3Pb7ac1hMB8nNMQRdwg5NbWFxERPBCgW+KyzV7H6p3PAx4C1BFMHHoDUqkCY30xAeoIFPLGP0StOpRhyedxIO9nAOIjJ3Cw4FeKK3eQHHMy+WUb0WkiiIg+CWN9MeU1+8gp/tPX/mD+MsJH3NGDsz+26IoIbLZWEaLv/E12T8TmEzGKuTMi84lAQUFBs4tOeyLtjz/+yCWXXMLLL7/M9ddf3+zYRx99xIwZMzjjjDMA71PT//73v4wbN44dO3Ywbtw4ysrKeOONN3jjjTcAsFgsuFwuRo8ezWOPPdYsSrkp0dHRLFq0qKenKhD0C35ZbuG/j9RisXp47Q0Tp831Psjtrrh87Y1VLPnZe0P7/dfhTJ+qIivbgUQiITlJ5utXKpWw8MEgHlqdSPH+9Vz4xAg2bi7htrtqmH+qmlqjG2N+HbNuSUUml/Lbq5msWWdjzTpvYr8/Vtu45CItQ4ccn9nWuxIh3FLEzi8xEBStRqnt+S13b0Vgt0Vbwm1f20/0ViSysNE4+syfPx+5vOO/jdra2j6YjUDQeRzVVVQt+wlnrYG6HVuJXHCZT/Dpjrhcf2AfJR+9C0DIzDmEzTsdl8WMo7ICVUwckiZ/J7phIwgYOpz85/6HJiUNRUgohW+8jNtuJ2jayVSu/p2Q9PFow+Mo3bYcl83SLLFf5Z61xE46s4efQP+nQfDtrFWGo6oSPB6UER3nS2gNITILOkNX1tcAd955J9988w0rV65k6NChvnKNRkNISAhlZWXN6peVlREbG9tunzqdjquvvppff/2Viy66qNU6gwYNIjQ0tKPTOQIhMB/nlFTtJLtoFVKpArfbQbUxxycwdwe5XE1DCFO9tQKTpZKD+cuorS8AID1+LkkxU331HU4LRRVbiY+YQGnVHgrKNzF4wJkUV2xDJlMyZuAVKOQaPB4P5TX7qTZmo5Br0arD0Gs7v530REOlDKS0ejdp8V03b++KFUeDsCxE5iOPnQgEBga2+VSzKUuXLmXBggW8+OKL3HrrrUccDwgIoKSkpFmZyWTyHQNvpHJT3n33XW655ZYjypvidrupr69Hr9d3OMeu1BUIjgamejdXXlvte79hkx2j0U1gYPe9jX9ZbvW93rXbzl8bbDz7Qh0As2aoePeNUIKCGvv/870cgqLV/H1KGectqGbyRCUTxitZ+LiR696dQNoU7za84XOj2byogAmBlYSESBk0UMHgQf3jlrK3xNem/bYUm1taWjStGxcrp6awlsLdBuJHBPtlLi2F5t6Mdu5sdHBf0tJWo7/NT9A5Hn/8cSorKztdvyGpkUDQH6j8aTH1+/b43lsyD6Id2H3rSVd9ve+1OfMAmoGDKfvsQ1z1JuRBwURdfAWa5BRfnfo9u3BUVxF+5rmUf/sljpoaoq+8lpL330I3aiyhF19KYJ6EsCGTqdy3HoepBleUFkVklNe+o6rbU+3XtOa93FnRVx4YBEDdjm2oE5Par9wGx5rI3PSzOpbmfSzT2fU1wF133cXnn3/OypUrGTXqyHudk08+mRUrVvDvf//bV/brr79y8sknd9j3k08+2e7xBiuOrtI/VgOCXsHpslJUvhWJRMq4QVezef+75JetZ2DC3G5FLwPIpAqmjbwbu6MepSKA7Qc/we60MGbgFRSUbyazcDl6bTRhQV7fKbuzHrfHSWXtQQorthAXMY648HEcKl6N3VHHut3/x9Cks4gMGUJU6FCiQod2MAOBx+NBJpUD3d+20ZHI3FJMPtHE5fY4UcTlzrJs2TIuuOAC7rvvPs4//3yfIKxQKAgL8/q9XXHFFcybN48nnniCiy++mLKyMv7+978zceJEBg7s/gOvnJwc5s+fT1ZWll/rCgRHg3fe8y4un3wsiE8+r2fffid/bbAxf66mg5Ztk7knhqwcJ+FhUj78pJ6XXzVx/7/0JCfLuem2Gm6+o5ovPwn3CZSK8t9wmZycdX4FOp2Ud98M5eNPvfN6/4bNjDs/ngseG0Fkqo4z7h3SZ5G0/ZWmAm9TkbepAKzReB9UKjT+T2R2tG00jjY9EZaFOH30mTNnztGegkDQLawFeVgOZaNJSUM/dgLli76gdtNfPRKY9WPGoYqLB48bt81G0btvoE4cQNScqyh+53WK3n6V1Mee8UUyO2sNAFQuW4qjopzoy65GERQMHg+mnduwlxaTfsqNKPUhRI2aCXTPPqIvaDmv7grEPa1fEwYSuRypuvv3XXDsicwN9Nf/Hycq99xzD++//z6LFi0iOjrat8bW6XTodF5t5r777mP69Ok8+eSTnHvuuXzyySfs37+fTz755KjNWwjMxzFVtTkYzcUo5Fq2ZnwEwJCks7stLjegUQWjUQVTWrWHemslcRHjCQ9Kx+6op9KQQY0pzycwa1VhJMecjNlWTZJ+APER4ymp2oXN4fVXdTjryS/bQGTIEDweN263E5lM2bMTP84prdqFwZTPmIFXtFuvo6jjrkQynyh0xhajoY4Qmr388MMPhISE8NZbb/HWW2/5yocPH+5LDnDqqaeydOlSXnrpJd5//330ej0zZszgwQcfRNrGNkKtVkt0dHSH49fW1vLqq692WK8rUVICQV/j8Xh47iXvdfGzL8zs2+/khmsDGDumZ9dDvV7KmFFKrFYPb7/rFYpvu1mPy+39/vptldfiokEoTb8vkI8+rWdAopxbb9JRV+dh0fcWX39bvyvkrPuHcEfsPupMbqBn9xPHIl0RdSurXDzxVC0LLtAQlebdPeGvqOP2IqoF7dPUYkMIzQKBoKvUblyP22LB7bBTvugL5CGhBJ/Udd/epkikUlTRMQCUff05uFyEzTsDzYBkFBGROCrKcdYaUIR5dxLpx4zDWlSARC4n4uzz0SSnUv7d177+7GWllNbsIFE/C7fDftjH2fug81gSQPtqnnVJYHh3MTK5iuCpHUd/dqa/Y+Ezbi3iW9A/+OGHH9DpdFxzzTXNyu+++27uvfdeACZPnszSpUt54oknePPNN0lNTWX58uUMGzasW2O+8sor5OXl8fzzz3d73kJgPo4JD0ojOeZkKgwHcbltDE06h9DAZL/1HxU6DIMpn4Lyjei10ei1XjEoVN84hkQiaWbjYLUb2XdoMTFhowjUxpBRsIyaulxWbH4YAJlUxayx9/VYBD9ecTjNZBT8SlTocMKDOt4q2FESwbZE5hPREqOr9JdEf0ebN998kzfffLPDeqeffjqnn356p/u97LLLuOyyy9qto1QqCQsL65TADJCa6p+M3gKBv5FIJLz+ciiff1XP7j0O7rpdx38fCPJb/2q1hC8+CWPB5ZVcdHkli78JJzxMyrxTm++EmTVDzawZjWUXXFKJ0+nhmjfH8+EtWwBYOGEFCw8f//KTMObM9n+i0c7S0o6iN8fpSjl4BeDb/+b1jX304SDCW8yzK77OrfUt8C8NQrPV5GDrUZ6LQCDwL7p86Ez8UltCW0uh0D5jNgCW7ExUCQOIveYGZNqAHs2xKeFnnYettITi994k4c57CBg6HMPq35EHh/jqyAJ0RF/cGGxk3LqJuq2biDj3Qur37sacmUHVL0uoYgkAIenjCEu53C/z87d42rKvoyHMelbvx5C9nQGzLiO4IgAqGo+1FVF9vAqzx4o4fryTnZ3dqXrz5s1j3rx5fhnT7Xbjcrl61IcQmI9jZDIlafGndMuntzNIJBIGJZ6GxVZDVuEKpFIFWnU4wbrENttUGDJAAoMST0MuU2M0l1BS1RhJEhcxVojL7ZBVuBKPx8WghPldbtueaNzZaObjXXgWyf2OLRITEzlw4MDRnoZA4BfOOUvDOWf1bFtme0w7ScWrL4Vw0201TD+lnMoqN1de1vaC2GBws2adjeeeCuaqs0qYJQ/l6hsaPaI1agmTJhzdHUd9JbS2jDruzLjrN9r4apGFl58PJjysdXuM1qKZOxPh3FfCukAgEJxItBXN2VJwC6uPRHnhJV3qoyvINBrib7qd3KcepfiDt3EaawmaMu1wFHLrmHbvQp2cStCkkwgYOpzC117y2WgAhA2c0GyOPeF4Eh/rkiAg20nBuu/QxaYTkj7uiDptnW/T8mNZbBZRzAJ/IgRmQY+QSCQMTJxPTtHvyGRKkqKnIZW2fvHzeNyUVO4kWJeIQu5dRA9POZ/UuNm43Q606jAhLreD1V5LUeV20uJmo1L2fqKyBsG5paDcVvnxQkNUckdCs4heFggExxpnn6Fh160OCgpc/OfeQMaPa1sg/uY7M243zJnljVA+4zQNhw7EkJvn5I+Q0dwZt6+vpn3UaSrodlbYff6lOkYMV3DpAm2HdRuimbsiGvelLUZbif6aWk00cCxYTbRMEig4+mRnZ4tdRoJ+QWdF5vaSyflDeJSq1URedCl127egHzOekMNR063hrDVgyT5I6KnepFxyfSAD/v0g9vIyZNoAgqv0SCQS6ro3lX6NP3yDa7K2YTdWkTLvOiSS7gUaHesi7fH00EDQfVJSUggI6NluDCEwC3pMgDqMEakXdlivoGwjtfUFBGpjMdTlE6CJQCHXoFEF9/4kjwPKqvchQUJ85Phu99FWBHJ70csNbVrWORGimaG50CxEZYFAcCwjl0tY+GDH1huFRU7uf8hr71Ba5sJq85CcJEOvlzJiuJL1hmPv9rEnUb9dFYArq1z8sdrGqy8GI5V2brHa04jknlhudIamXsXtCbNNj/V3sbnp/ITYfHS5/PLLqaur47rrruPKK68kMjLyaE9JcALTWbGwpVVCa9YCPRGbdcNGoBs2ot06HqeTovfexON0IlWrsRbko4qJRSKX+zydTYFdG9ef9GbiuKafZ3dtHfS5kJW5DX38QDShMT2aT9P/N0KwFRyLnH322RiNRg4cOMDAgQPbzJXUHiJcVNAn2Bx1ZBQsA8BoLmbzgff4Y/tTVBgycDitR3l2xwYmSykBmnDksp75XQYdMHXozdxam4a6TeufCEkCg7I9vh+BQCA4Efj7Pw2+13PPrGDitDIee9JIdo4TODY9gP0h4HaWvXsdAIwZ3X0LkZuCd3V5zn3xe+mKEHtOws5jRrhdXDCq3wvixzNffPEFF1xwAa+88grx8fGcd955LFmypMdekAJBd2lNIOyPEap1O7fhqCgHoOL7byh8/SUK33wFa34uHre75/0nNf9p7Xh7bVv244/5+BtLVTHaiLYtPruCPleIy4Jji7vuuot169YBsHfvXgYMGMCQIUOYPXs2Tqezy/0JgVnQJ6gUeiYNvZlBiacxsIl/8I7Mz9mft+QozuzYQaMKxWSpwOPp+c1CS1oKx52p30BLwVogEAgExzbvvhHKs08G8/rLIagPP9N85TUTk6aXYbb4/xp0vJGc7I3w3n/A0eO+Oisav20Y2ae2GV3hWBKafypsP1pQ0DskJyfz6KOPcujQIZYuXYpKpeKiiy4iISGB++67j4MHDx7tKQpOQDojFLYUPPtahNaPGU/0ldcRcfYF6EZ4H5LZigopfOMVTDu3+WeM3NbLm0ZutyxvzT7EH8JrS5uS1mxJuooyMAxrTWn3OxAIjlG2bdvGhg0bmDp1KgDPPfcc5513HpmZmVRXV7N48eIu9ykEZkGfERgQCx4PBw9HMjcwIPqkozSjY4tKQ+ZhOxH/JKFrTRhuKTI3FZ7bO9bQn0AgEAiOfYKDpUyeqOTF/6vD2mST0b/v0aPViFvHjlj+mxWJBBIT/GMl0p1o5v7IsSQ0C44OUqmUuXPn8uWXX/Ldd9/hdDp5+umnGTRoEPPmzWPXrmP/70BwbNFWJHN7EbltHe+NyFaJVErA4KGYszIw7W78fpXpA9EOHtbm/DpLUwG3u/PvaruOPt+e9t8Uu6kGa3UJSn1Y9zsRCI5Rtm3bxpgxY3zvV6xYwd13301aWhoXX3wxO3d2/Z7t2DPRExzTFFd5/5Omxs0mUBtLSGASMqmiU209Hg8GUz6BAbGdbnO8YKwvpra+gOEpF3Y7+UBbtPRSbi2Sub3o5qb+zMe7L7Ogf+PxePjwww9ZtGgRAD/99BNOp5Mnn3yS+++/H7lcXPIER4eGyNJjSSRc9YeVzCwnZ56u5pwzNUyeqCImpu0M9i3Jy3fidEJqyon1d2exePjsCzNzZqsYO6b7Fhmt0fT/T3+NVu4MTf2cBYKmlJWV8fHHH/PBBx+QnZ3NOeecw4033khERAQvvvgiM2bMIC8vj8DAo2gqKzjh6ImA2VQkba0ffyQEdJlM1O/bg0SpIuLMc1BGx6CKT+z0mtHtsGOuLEQXk9KsvD2BvDfE366evz/8jqszNuN2OogeN7f7nQgExyiBgYFkZ2cDXrHZbrczfPhwAGpqaoiJ6bov+Yl11y/oE5wuGw6nGZfLjsNlJa90HQZTARpVCHXmEgCyi1YxfvC1nRaK3W4Xmw+8h7G+iNHplxERPKg3T6FfUWcuZcuBD9Bro4nspfNuKgw39VvuLEJkFvQHFi5cyHvvvcfZZ5/N8uXLAZDL5ZSXl/P+++9z0003HeUZCk50epJorjtjQfuitsPhobLSjaneTZ3Jw+9/WHnzHRNJSXK27/DaOyz92UpdnYfzztF2euwlP1m49qZqxoxWsOKnEydZl9Pp4aLLK8nKdvK/x/wTDdXW77G3k/r1BUJoFjSwfPlyXnvtNX7++WfS09O5/vrrufrqqwkPD/fV+eijjxg+fDgHDx5k/PjuJ7wWCHpCTywwOkpE1zJpYEtcJhNumxW33Yajuorq5b8A4HZ4r9ceu43y774m5eH/dVpctptq2PvZYwCMvvFZJFJZl8+xpfDcV5HOPY0Ir9izhpIty4gYOQO5StOzzgSCY5B58+Zxyy23cMopp5Cdnc1VV12FRCLB7XazYsUKPv/88y73KQRmgd/weDwUVWzhQP7PzXyCVQo9WlUotfWFAEgkMgLU4chlqk7163TZ2HvoB4z1RQCEBaX5f/L9FI/Hzd5DP6BRhTB+8HXIZP6NhmpKS4uLrgrNTQVqITIL+hq3280LL7zAjh07AHwCM8BZZ53F//73PyEwC44aRyNyuaMxM7McXHRZFYVFjUm0pFKYO0fNsuWNvhiDBso5+8zOL7x+/tUrLgM8/J+gLs762OaNd0xs3mJn8aJwJk/s3D1OW7QUj1t7OHE8iMzgFZqFyHxi8/TTT5OQkMAff/zh84JsjYceeoj4+Pg+nJlA4F/aE2DbEnY9Tidl33yBadf2ZuXqAUk4qqtw1dUBoEkb6BWWZZ2zsrIZq9j3xRMA6OLSkUi9u5R6ItweKwnubLWVFK3/kYjh04ibfPZRm0dPBHmBoKcEBQWxYcMGPv30U8455xxuvvlmAA4cOMCVV17JsGGt2+y0hxCY+xHGdB2hOV3P1NhfKKvZy/68pUQEDyYhciIymRKZVIlWHYoECXll68kq/I2xA68kNDC50/3uz11Cec0+AKYMvwOppPNbdI9lXG4HmQXLqTOXMGHw9Z0W5P1NU+G5M6Jx02hmgaCvKC4uJjAwkNTUVHJycpodCw8Pp6Ki4ijNTCDof1gsHmbOLUcmlfDxe6GEhEjRBUiIipIRGSFj9VobN9xSzVlnqHn+6ZBO97tps42rrvOKywsfCmTaSUfnunU0+PlXC8+9UMe1VwX0WFyG1sXj411kBhHNfKIya9YsLrvsMlJSUo449uSTT3LxxReTkpLCxRdffBRmJxD4l9ZExfaihsu/+xrTru2Ezj0ddeIApEoVUrUaRXgELmMt5T8swnIom9irb0DSSTs4j8fjE5elciWp86/v1rkcC+Joy8/bVldN7qpPkWt0xE480+/2k11FiMyCo8nAgQN59NFHm5UNHTqUoUOHdqs/kalF0CU8HjfFlTv5fdtTrNv9Cg5nY5STy+XdnhMbPobymn0UVWzDaq/FYqvB5jCRHDOdU8Y9hMlSxorND7Ni88NUGw+1O56xvpjS6t0ATBl+OzpNBAA2Rx12R30vneXRx2avY+Petyiq2MagxNMJ1icelXm0TOQnEPRXQkNDMRgM1NTUHHGjuGLFCpKTO/9QSyA4Xsg46OC8BZVExBfx8zKLr9zt8WCzwby5agoKXbz7fj1btjmornaTcdDB1ClKMnZHc/GFWhLTigmPK+J/TxtxOj1tjlVa5uLmO2oAuP9feu64RQ+A2eLmUK4Tj6fttscyHo+He/9j4KrrqjlpipIH7++aN2x74vCx5NntL0QSwBOTpUuXUl5e3uqxH374gcrKyj6ekUDQuzQIyg3J7Nw2K5U/LSbrP/+k7JsvmtV1221I5HJUsXEYN62nft9u3BYL9rISJEoVsVffwIB77qfsmy/Iuv8f5L/yHM5aQ5tjux0OqjO3+N4Pv+oRpHIlHo8bW20lbqejU+dwLImiDZ+3sSCDA18/g8NsJPnUa5Aqem93cGdoaovSE/sVgaC/ICKYBZ3GYqthZ9ZXPh9liUSCVNoYTRwVOpT8sg3szPoCqUSJSqmjuHKb73hE8CAqDBnN+tya8aHvdVBAPBOH3gh4PZf3HvqB0urGxdWOzM+x2Gp87zWqEKaN/Ls/T7HfsD9vKU6XlUnDbvGJ6n1Ny8R/wmNZ0J/RarVcdNFFXH755dx+++14PB4yMzP59ttveeSRR/jqq6+O9hQFgj7D7fbwn4dreef9xgexWm3jg5cArZR7/qbn+Zfr+H6xheFDFSz9xYLrsFvGiGEKKqtclJQ22l298EodL7xS53v/1x+RDEz35lH4brGZv/3DgMXqFZGffLaODz+pb9Z+87ookpOOv9vOJT9Zee/Dep75XxDXXhXQ5UiojkTklon9+tLH+2ghLDNOHFwul+/hk8vlwuls3Mnp8XgoKioiOzubqKioozVFgaAZTQVBfW7PPZkB6vfvpeybL3BbzABIpM1jAENOno0lK5OSD99BFhyCOTODmj9WAiBVqVGnpGLev9dX315STO5TjRGJYfPPJGTGbAAc1VWUfPwe9rJS3/GsJa9jrijwvY8ZP7/DpHfHkrjcgMtuJXflJ+hiUkiacxUypfpoTwlo/v9IRDMLjnVEBHM/o78Kd263k51ZX+FwWogOHYFEIiU5ZnqzJH0yqYKI4HQUci0enATp4kmLm43k8H+zBnFZrQzipOF3Mirt0mZjRIYM8b3OKfnTJy5LJd4FaVNxOUAdwfCUC3rnZI8yHo+bytpMEqOnHDVxGY70ZG74v9nSa7mjdgJBX/Haa68RGhrK2WefTU5ODgMHDuTxxx/nySef5Oyzj56/mkDQ17zymol3P6jngvM0JA2QMXqUghnTm9s2nDRZyaB0OVIpyBXw9BNBxMZ4Hxrv3uvwicNvvx7Ctg1RJCc1PlBOSZYxINF7bd61x86dd9f4xOUGGtprNRIeuj/wuBSXAX5dYWHkCAXXXa3r9W22DcJyy6jn41FwPidhp4hmPgGYOnUqCoWCjRs3Mm3aNBQKhe9HqVSSnJzM5MmTGTBgwNGeqkDQDH+JgPbyMkq/+BhlVBSa1HSQSAiePrNZHXlICOqUNCQKBR67jaDJU9GNHAN4I58bxOXA8ZNIfvBRgk+e1ay9dpB3je12OCj7+rNm4jLQTFwOTBhMxIiT/XNy/QR9rvenviwXl81M3JRz+o243BoikllwLHN83u0f4zQVmfuDWOdwWsgsXIHJUkZ40EBKq3cTGTyEhMiJAJitVVQYMjhYsBzwkBA5EblMzaGS1QDIZRqiQoZQdDiaOTl2BgGacAI04Zw64ZFmY9kd9WzN+BiTpfHCFxkyhISoSdjsRjSqUAIDYvrmxI8SZmsVHo8LnSbyaE+l3UR/bT0MER7MgqOFWq3m008/5fnnn2f//v0oFApGjRqFTtc/H9wJBP7G5fLwy69WnnrOyKwZKn5dYSVQL+WlZ0OQSCRYrR6+X2xm+W9WlvxsZeJ4JQ/eF8ij/zOyY2ctABedr2HXHgcZB53o9RLOP0cLwOZ10UeM9+BCA+9+UE+DrqpSwYvPhBAfL8NQ62buKWrk8qPrbdjb7M9wMii99dvpBiG4NwTgoxXJvLhgVJ8Kv+1FMwvf5u5hsVhYu3YtVquVKVOmEB4e3ql2+fn57Nixg8TEREaPHu2Xfl9//XWMRiO33XYbd9xxRzPPR6lUSlRUFIMGDer0uQkEfUlTIbA70cz2ygpKv/wEWYAOj8uFNT+P8NPPRhnpjdg3Z2dizszA8OcqpBoNITPnYDmUTc3vv4FEgio2HkVEBKad3uR/IbNPRRagI/y0swg/7axmY5mzDlL65Sd4bDZfWdSYOYSkjsZSXYI+Lh2FtmsWT8calupSJFIZqqDOfecJBIKuIwTmfk5/EJu9XsveLTsVhgOkxM4iNW6m73he6XoKKzYDXpuLQYmnI5FIsDvrKarYitNlQR8Qw4z4f7M7ZxF6Tevb3Cy2GnZkfonZVsWghPlkFf2OVCpjaNLZyGRH1x+pL8ku/gOlPIBgXcLRnoqPrtpiNIjM/TUiX3D8kZOTw9y5c8nKyiIqKkpspxWckLz6honHnjQC8MdqGyOGK1i8KBxdgHcn0Y5ddu78h8FX/+knghgxXEl6upwrr61GIoHqGjfLf4rg2RfqCNS3vtHN7fbwwH9refeDeq6/JoDqGjffL7bw3puhzJ+r6fXz7C/8stzCrt0O7vmbvt16/hSDmyb1OxrJ/RoE366IzD0VpVsTmZv2Jyw1Os+u/2fvrMOjuPY//K5rNht3AxLctTilUMNKS93d7fb2Vu6vem9v/daFW3cqlBoVaKGCuztJiLuuy/z+WLKx3WQ3Hpj3efLAzpw5c1aS2XnnO5+zcydnnnkmYWFhGI1Gtm/fzrvvvsuiRYv8buN0Orn55pv55JNPmDZtGpWVlURFRfH11197q/bb0i/AqFGjAE8Gc1xcHBrNyfP3Q+TkxlFRzrHn/uN97DLVknDtzWjS6ie6LPr0Q1wmz/m/ceJUwk+dhct0CrlvvoqjpAh7RRlRC8/HMGoclev/QqbR+tyXJ4LjExRhEeiHDqfs5x/QDxtJyPln4QTCs+KBekHenursptK9p+By2Cje/huhKYORNIj47Al0RcVyIO9tT33vRLoGs9mM3W5vtEylUgV9XBYFcy+iqypDBcFNccV+yquPYraVIZMqcWBGp4mmb/wMYsIbzygpPy5/h/W9gOiwgd4vm4NS5+F0WimvOcr+7B9wOC2MyrgcQXBRUZONUZ+EROI5efVEcHyG02VlZPolHMn7DYVcw6iMy04quQxQVnWI5JhTkMt61q07omQW6clERkZSVlaGIAjdPhu0iEhXsnefg++WW9i8xU6Ysf6zf/01Oh78RygaTf2y8DDPMXfOWWreXRzu/V05c7aGD94J5/Kry1m7zsb5F5fxyfsRhIZK2bnbTlKCnLCwetn8wsu1vP2eif+734BOJ+G+f1bxwrPGE14uNxW6y37aTUSKlbPP1HjX+xLJHV1p3FAydwfBSuaOqHhuWK3sqz+xmjkwLr/8ciZOnMjnn3+ORCLhySef5Oqrr2bGjBl+K44feOABvv32W3bt2uWdMPf7779vdLxtS792ux23241KpSIxMRG3243VavXZVqVSicd2kW7DeMhGZbqq9YY+aFjZ7LKYqd2+DUvmEdyOepGj6ZtO1LyF3splAMHpRHA6QSKhz8NPIFV59i/T6Um67W6y/vMoErmcvP+9SvwV1xJ/+TU4a2twFJajio339mMrKqTw0w/QpPUlfOZs8t5+A93AwUSfe77f8UJwsrm9ldxdgaU0D6e1lugRp3b3ULz4e506U+4G+t6IWdAnDy+88AJPPvkkRUVFzdbdcccdvPDCC0H1JwrmXkZnSWany0Z24TpM1hJqzAWYrWXedTFhQxiRfjEh2uYVgYLgJqfYU70skUhwux2NhHBc5HCKKvaQGD2WI3m/YbGVU1mbi9laysQht6HTRCIIbvZmfUutpYTxg67Daq+msvYYozIuRyZTklu8mfjIEUilclxuByZL6Qkbk2G1VyMIAoLg6u6h+KThZ8+fOG4olZtW4IuyWaSzMBgMnHXWWTz++OM8+OCDyGQ9qzpBRKQj+eY7C7+usnIsx8madXaOz5GFXA7fL41k3FglUmlzGfPqm56/4fFxMgoL3cTF1f+enDZDjdEo4fTT1Py80srcc0uYe7aGp56t4Y5b9fzf/aEA/PSLhaeeq+bu20O44Vo9I8YVcvEFWs5doGXZt2YGDlDQP8MzP8PefQ6Sk2XeCureij+ZW1Voxe0UWozCOBHzkeuok7ldHZnR2npRMvtm9+7d7Nixg9dee80ra2+55RYeeeQRvvnmG6655ppm29TU1PDKK6/w73//2yuXAebMmdOufgGmTp3Khg0bWLduHXfeeScbNmzwO/Z169YxYcKENj1vEZH24k8u18nUlkRcmbYIy8drMbsrPGLZYvGui5gzn5BhI5GHNI+msGRn4rZZkSiUOMpKUcbGeSf/kyoU6IcOx3xgH6qEJPLfeZOwmbMp/3k5MoOBtPsfAcBZW0PhR++iCI8g9pIrKf3hG6QKJdHnXoi9qAhXTTW6QUOoSQVnTTXanQ4wRAQkIX21qXsdeoKYbCjIa1KhprISAFOMC3eDSPfWxtr0eQbz3Hp7RXBHVLSL9GzWrl3Lgw8+yLPPPsuIESNQKBSN1rfljmBRMPdCmkq6jhDOxRX7OJq/CoMuAbXSCHiygNVKI/GRI3zKZQCJRMqQPueyL/s7dhz+7Pjkf1Ppm+CZXCDC0BcJUsqrjmLUp1BUvheX23PFVi5TIQhuDhz7iYKynQztcy5aVQTr97wBwNaDH3j3E27og0YVxm9b/gXAqaMePOEqmwVB4Gj+akBCUkzP/xLtSza3JJHFimaRziQ3N5etW7fyySef8MILL5CSktJIMiclJfH111934whFRDoGu13gmhvLATjtVBWTTlHy11rPcfWqy3VMGO+/yuq+ewwczXSy+G0Ti982MWG8kg/fjiAsTIpSKWHGNDVffm3hjNlqNm6289SzNQCEGqQIgsCfa+xce1M5Z56u5u93hzDr7BJKy9ysWWcjqV8+gFdG3/dPTz7zi88ZueRCXSe/Kl3L9cad3PdLAgd+L2HhY0O8y7syF7muirk7qpmbCtyuzmVujZNRMldXVzd6rFKpUKka/y3YtWsXAEOG1H9mQ0JCSE1N9a5ryqZNm7BYLMyePZutW7eSm5tLeno6AwfWT8zdln4BFi9eTHV1NYMHD/b+3x+DBw/2u05EpDtpKN98Ve+Wr/yJ2t07CEnsjyouHlthIW6zCVViEiFDfctlAE2ffhinnUrlX7+T8/JzSHU6Yi+8DG2/DAB0AwZRvXEdyphY5GERlK/4CQCXyYTgdOK22Sh493+4rVYSb7wNa1Ym1RvXAZD5r//z7qfff57HUVlB9lOPo4iIIuWe+/0+V3/CsenjniZWXVYL5b/9gqZvOuqkwCcM9SXRfb0Gvi4yBCLpm1aL91TEauYTl127dnH++edz0003dVifomA+gWhPXrNOEwVAXMQwjub/jsNpITnmFPonn9HqttFhA5BJlWw9+D4g4Wj+alSKEEzWUkoq9xMVNgCH04zdUeuVy1KJnD92PItKYcDmqGFAytnERgwl93g1dEOG9jkPrTqcwrLdnrGqo044uWxz1LAncxllVYfpn3wmKkXvkrCBft5EuSzSWej1eq644gq/68PCwrpwNCIinYdSKaFvHzn9M+Qo5BJW/mZh6BAFX3wSQWREy5X7cbEyPvswgmmnFZOV7WLbdjv/+Gclp4xX8f6HJqKjpUyboqKg0EVJiRuAUIOEx56oZvE7tZSUuJk6WcWbr4RTVe1m124HANnHPHfdLJin4b57DNjtAm+9awLg3AW+MyF7A77ErdslcMkzOn59ZSOpo8MYMTfB7zadLZt7UnV0S9XM3SGge6JkLs4MR6rp2Pgzt8UTKZGU1HjejocffphHHnmk0bKqqipkMhkGQ2OhFRERQeXx6r6mFBcXA/DYY4+xd+9eUlJS+Ouvv5gxYwaff/45CoWiTf0CDBs2zOf/RUR6M00lszI6FolyH+qxwyj5+gukKjVxl12NbtAQf10AnjuDI8+YA26Byj9XITidFH72ITELL6B6y0YcZaXohgzHVVuN22ICtxuJQoHgcHDkkfuRabQILhcJ192EIiKSgo/fb9y/QknS7X8DIPf1lwCIOHNOozZNJaq/df7a+FvXlbKytuAoOUs+xm2xEHvhpd4q8I4aR93zaihhgxXGXfF6tDe6RKxmPjFJS0tj2bJlHdqnKJhPINpTHRqijSVUl8SBYz8CMH7QjUHFUFSb8wC80Q77sr/zrkuMHktq7CQEQaC06hBH81dTbcpDJlXiFlyM6n85EYY+CIKbfdnfe7frn3wmiVFjkUplVJvy2XX0CwDGDvR9q11vxeWys2HPYgTBzciMS4kMTe/uIbULsUpZpDswGo3cd9993T0MEZEu4YpLtTz8eDWCADffoOexh0ID3raoyO0VwjYbLF1mYekyzy27QyUKVv0SDXjiLT5ZYuKN/5lQqz3b3X17CH+/OwS5XMLDj9d4+7zmSh2336wnIUGO2eImuV8BAHfeqketPrFyU394ah/rP8lm6jV9mHlLOnKl7/iPniR/O5qGsri1aua69aJk7lxycnIaCd6m1ct1y1wuF1arFbW6XnTX1tY2etyQuuVqtZqdO3cikUjIzs5m6NChvPbaa9xxxx1t6rduvdPpDOj5hYSEiNFXIr2CphJPP2wElev+ouTrL5Dp9aTe/0gjydka1pwsAASbDcFmo+DDd7zrYi++AmV0DG6rldo9uyj5dqlnhSCgiIoi5vxLUBjDsBcXYS/wnKsrY+OIPHMe2oz+AFSs/hVXdZVnrIOHevvuCJHor4+uqoiVrDvKoe9eRReTSsqZN2OPj2hxHMGMy1/VckhWc5nbU6RsR1RMi6L5xOK0007j6aef5tFHH2XRokXNLhSHhIQQGhr4OQaIgvmEwVfWbTBVzFKJjLEDr6awbBdmW7nfSAx/pMZOQiaRU2XKo9pcgNlaCoBCriUq1HMAk0gkRBkzMOoTySvZxqHcX0iMGkOEwTNbrun4NnUcOPYjJmspRn0yu49+BUBG0hko5CfOJELVpnz2Z/+A3VnLxCG3olVHtL5RL0CUzCIiIiKdx803hDBlkoql31i4+ILgKoT7pMlZuiSSb7+3UF3j5quvPXJZIoELz6/va9BABY8/HMrkiSoefLgKpULCLTfqkcs9wvijT03etm+/Z+LgISfnnqPhrXc8y5VKeOAfvm/97S00jJ+wmZz89PwBNnx2jNl3ZjD9ur4tbtsdcRn+1tWNpzPpqTK3p46rozEYDM1ODJvSt6/nM3vs2DEyMjy32bvdbnJzc+nTp0+L2yxcuNCbr5ySksLo0aPZsmVLm/sFz4ltS7nLDREzmEV6Ix4JF4Psngeo3rAWeVh4UHIZPBK5cs0fOEpLMO2pj5xRp6SiiPTcgSxVqzGMHos6MYnyVSuo3bGNiFlnEV4ZBpWQfbT+WGQvLKDo848JnTQFqUJJ2c8/AJD8t/pojLZGXrQklANp15E4ftlM3obv0ITHEXvrzdj9XKBqOrbOkN+t9dnV0rYjJmIURfOJQ0xMDI888kizu55AnOTvpKWlzNs6ApHNEomUuMi2fQmXSKQkx55CjbmI9XteIyZ8CBGGfsSGD24WZ6GQa0mMHsOh3F/ILlpLWvwUFHItCnnzjMbc4k3e2IyYsMGkxJ7SpvH1FARBwO40YbFVYLVVsTfrW9TKEEZlXH7CyOU6RMks0pVkZ2czbdo0v+tTU1NZvXp11w1IRKSTGTpEydAhbYuLmjJJxcQJSk47s4RBA+UsnK9lwTwNqSmNvxZKJBKmTVFzyYVOnni6ms+/MnPtVXoEQWDaFBU/r7B52/65xsafa+ofH9gZ53Oiwd6EyexmcsEWso45ef9DE9vX2jj9rgwmX5nW+sZdTFOZ3VQod1ZWc8Oq5J6Uw9yQunGdDKK5JSZMmEB4eDhffPEFDz74IAArVqygvLycs846y9tu+fLlREdHM2bMGIYMGUJaWhr79+9n/vz5ADgcDo4cOcLEiROD6rcpH374ISaT54JUfn4+F154ITfccAOzZ89Gp9Oxf/9+nn32WSZOnChGaIj0GnzJO5lGQ9j0mW3qTx5iIPKMOVSs/hXT3t2ETZuJpm8/NH36NZPViqhowk+dTe2ObRR++j6aBx4lJAtCpfGUN2jnMtVS/suP3sfxV9+A8risbkhrEjIQydhVlbyC24XdVIW9qhRTcTYFm34kNHUIxgvOQdLGux8aTuLYdNLAhhMaBjopYiATCnalZK7bZ3sQRXPv5qeffuK7777jgw8+8DnJX3h4eNB9ioL5BKS7JlOrqs1l8/53USkMDE5bQLUpH6ujBp2ssTgtrTrMtoMfeh9Xmwo5lPsLNeYCQMqwvotQKUMQBDelVYcorz5KbPgQknvBxHcN8Yz/MOXVR7DYKiitOoQguJu1G5x2BaH6BB899G5EuSzSlYSFhfHPf/6z0TKn08n27dt5//33m60TETmZcTgEZs8pYdceB19+GkGfNDlZ2a5mglkQBIaOKaCyUgAgLUXOQ49V8dqbnovWN16nY85ZGlQqCTt2Olj6jZnkRDkP/9NASEhwVVrdzZGjTr5aZubIUScHDzrZtcfRrM0N1+r49z0mFld6nltXVQe3he6Y/K81Ggre7pDRJ7toViqVPPvss9xwww1YLBbCwsJ4+umnueGGGxpN0Hf33Xczffp0xowZg0Qi4eWXX+aiiy6ipqaGtLQ0lixZgsvl4vbbbw+q36akp9dHwr399ts8+OCD/OMf//AumzhxIvPnzyc9PZ1XXnmlE14REZHOwdckeO0ReRWrf6Xs5x8IGT2WsBmnYTl6GMFuR9Ikgqb0u6+pWr8GAJlOT822zRz+/BMANJEJhM2fi1SlwmUyUbtjG87qSsJOnY22b/N4xkCqjgMRpk0fd4SIdDlsVBzairk0B3t1OTV5B5u1kWv0pJ12BbXG9kXrtOV517U5meRtbxqrSD25ubmcd955XHbZZR3WpyiYT1D8VS/XLQ92EkB/CIIbq70KlSKE0qpDuAUnkcYMft/2DC63DaM+2ZuZLAgCDqe5kVyOMPRlb9YyrHZP9lOfhGnEhA/yrg8LCXym156Ew2ll19EvKKs6jEYVhs1e00wuD+93IUq57oSTy6JYFukODAYD1157rc91GRkZ7Nmzp4tHJCLS8ygrdyEByis8E/SNH6vkf++Y+HmFZ6KwHRtjSEiQIwgCEomEueeWeuUywOq/rLz+pqfiMClRxiP/DPVGZowcruTKy5rfidQbeON/tTz8eBV6vYRQg5RjOa5G66+/RsesmWrGj/NUjPdEeeuLunF2Zx60v4iKliYF7GxOltgMX1x11VWkpaXx+eefU1BQwPPPP8/FF1/cqM3ZZ5/NoEGDGj3+/fff+eCDD1i7di2zZs3i888/x2g0BtVvS+zdu5c5c+Y0Wx4REUF0dDSHDx9uUVaLiPREfAnGQMWj2+HAVVuD3BhG7e4dSLU6pAolRx/2zDcSccbZhE3zVEULgoBp7y6vXAbQDRxM0XG5DBB+3kI0KfV33+gG1P+OB/pcgpWHvqpk2yuZzaW5ZP78LnZzFcrIKOzFRY3Wy1Qa+p55HTK1zmflckuvf8Px+rtI0NK69srVliZVbAuBSN+W4lDaus9ALzyIMrr7ycjI4IsvvujQPkXBfJLSEaLZ7Xax+cC7VNXmAKBTRwKQV7LZ2yY9aTZut4sdhz+jvCaTxKjRjfoYnn4ReSVbvJML5hZvIto4gBBtbJvH1d04nBa2HvwQs7WM5JgJVJvysdgqCNHGM7zfBVjtVWze/w4maynRcQO7e7giIic8M2fO5LrrruvuYYiIdCuvvVnjnRgwOclz0rVhk927/m93hBAfL+PdD0w88ngV5y3UsH5D/foP3gknJkrGd99byc1zkZPr4o23arn1xpAufy4dydPPV/P0czXMO1uNVivlq2VmwowSnnrCyCnjVVxxbRl/rbXxxGPGRtu1Jpl70iR/3S3De2J0xslczTx9+nSmT5/ud/1zzz3XbNnIkSMZOXJku/ptiYSEBN566y1mzZqFtMFt/6tWrSIzM5PY2N57XiAiAo0FZWvYigrJ/99ruEy1SJRKBJcLXC6vQJaHR2AYMwF7STEF7/0PweVCnVLfsaZfBsapM3CUl1G7YxsA5St+Iv6q6wOKiwhGMgYqMTtCmJqKj3HkhzdR6EIJPWUKpt07QCIhZNQYouafR8XqlVT8tgJLigpVbBRN70PyNY5gK5T9rWuPHO5JorUrK69F0dz9DBs2jMLCQu69914uvPDCZnM5GI1GIiMjg+pTFMwnAQ1lckdGZ1Sb86mqzSEtbiqZBX94J+lLih6HUZ+MVh1BbskWtpZ9gMvtOUmtrM1BghSZTMWwfudTYy6kvDrT26fd0TGV1d2B02XD7Xaycd9bWGzlSCRSjhWtJ1SXyIj0S4gMTUcikXgnSVHKT8xKXzF7WaSnsXr1arTa4CZCExE50Xj7PROzZqpZs9bmrdCdOlnFvDkaRo5QsG69nXGTi8jM8qx7/yMz/frKOXzEyc036BkxVMHnS83k5tVX97pdPnfV47HbBVwueOHlGp57sQaA5T9bMYZKuf/vBq66QkeIXoogCCjkEkIifcd99IZK5t4wxu6kofw+GWVzT+Gf//wnkydPJj09nRkzZqDT6Thw4AArVqzgkUceCfoEV0SkswhGijWUdU2ziFuSeLU7tuJ2ODBOmU7ln6sBkBlCCT1lEuqEJAS3m8IlH2E5fBAEz11GbodHp6riE4g+70Ishw9hPrDP26ezsqL1AbdCeybra4+4dDlsWMryOfTNywC4BRfWtX8QMnwUYTNOQxkdA4DcEAp44kF8jdHX/oN9P1ujpefor7rX1xhOBOna9Hn5e226MntapDEffPABu3fvZvfu3TzzzDPN1ouT/IkERJ0AbG9MhkZpBCRIpTLiIoZRULaTvgmnUlmbQ0nlAaz2apQKHSmxpyCVKjicu5JqUx7xkSNJT5yFxVbBxn3/AyDc0Jf0xFmEaGOQSHpXbiOA02Vl1db/NFpm1CeTFjeVcEMfr1QGOJK3CgCNytiVQ+xSRMks0tUUFBRw1VVXNVteWFjIrl27+Oyzz7phVCIiHro7pgAgNUVOQaGLdxaHc8GlZVx2iRaNWsKLr9RQUenGahVYME/D3ber+PfT1RQWulGrJCxfFsnoUUrmn1fK+o12VCr4ekkk/TMUhIb2vuM1wHkXl7J2XX11dlKijOuu1nHFZTp02vrn9OcaOxs22RkySOGrm15Pw8+kKKE9stla62BLdw/kJCQjI4MDBw7w6quvsnnzZjIzM+nbty9r1qxhwoTeNQeLyIlNW2IifGURt4QiPBLBYUc3eCiVf65GGROLfugIandso2rtX7hqqlElJBI59xxsebnUbNmI9egRIucsIHTCJKo3rqfk268AiJy3EF3GABQRzS/SdFYkQ9O+24OpKIuDy15qtEw/bARh02c2mphQEARKln0JgExXH9cV6PPqCMnZ0r58RVE0/Wx0dEVvT5G2HTUJokjHc/XVV7NgwQK/65tWNAeCKJhPMlqSfk0nB2wtu1mlDCE1brJXmEokMo4VrUcp16JVRxBlHEBG0mykUs/HLDFqDBKJFLlMBUBe6VZvXwmRo9CpI3qlXAaQSuRo1ZGYraXEhg8lI+l0VErftw2nxk4iv3QbWw98QFzkcNRKI4lRo/22762IklmkK1Eqlc3yGSUSCTNmzOD9999n+HCxMk2k62ko7bpbMj9wr4EFi0q54NIyAH5ZacXpgFEjFTgc8OS/QunX1yNSz1uopbzCTXSUFIlEQl6ek/UbPUJ2zGgl/frKe61cBpg2WcXadXaSk2S8+FwYUyapfLYbM1rB2NFKNm2xs+jiUgb0V3DGbDWTTvHdvqfiSyQ3/SyKlc4i3Y2vyXpFRE4E/EUz+BNv+uEjqdqwhrw3PZNbOqurqPzrd5RxcUjVaiJOP5uQUWO8BUwRp5+FTFufOVyz8/g5tkSCfsiwRhW9DQm0qrrpupaqcP317W87f9vXtZXKld5lEWfMwThpKhJ5c4UlkUiIPHs+pT98Q/azT6DrPxBlbByG0eMatW/pefqquA1UegYql30J5dbG4W9ZZ9FRkSbBIkrmrsdgMLRJIreEKJhPIlqTy4H20VAy90uYSagukcqabFLiJqJS+JekCrmm0WO5VAVIAIFdR79AIdcyaegdKORqn9s3RBDc7D66FJ0mij7x0wIae2cilcqZNPS2gNrqNFGcMuQWjub/Tq2lmOKKveSVbGFkxqWEaGM6eaRdS91nRRTNIp2NXC5nxIgRXHrppc3WVVVV8dFHH/lcJyLSWfiSdd0pmUePUrJ6RTQffWpi6mQVM6b5P9YqFBJioutzGpUqMIZKqKwSWLPWTsbQQt55M5x5czR++2jIp0tMfPm1hQ/eCW9UIdxd/O1OA3+7s/Uv1FqNlC8/i+CFl2pYv8nO98stvPG/Wp54LJTrru6dx7WWPn9167pDNH+TM7xHZjWLdB52ux23241KpcLhcOB2u/22ValUje4GFBEJhtAjNmoHtn5+2ZkEI86kCgUJ199C9YZ1CE4nxqkzkEj9HzvlIY2PZ1LV8ecqCGQ98Qi6wUOJu7T5XX6+xlgRUU3BB28TcfrZaPtlBD7oFvBVrRsoztHx9Bv9fEBtjZOnIQ8Lp2rDWixZR6nauA7T3t3EXHQZMrUmqPegYbRJoFnCHU17hGtvzTcWJXPns2zZMoqKirjhhhs6tG0domA+iWko/eqqTYOd/E8ikRAdNoDosAFB7VsQBI4VbwCEhgsB/18uAexOM4VlO8kqWIPNUY1Rn9IjBHOw6DXRDOu7CACrvZptBz9k497FTBh8EzrNiZczJ1Yzi3Q2JSUlPPLIIz4lcklJCY8++qgomEW6lKYirydUh/btI+fhB0OD3u7LpVYqq4RGy1wuwU9rD4Ig8N0PVn7/08r7H5kBkMt6nyDSaaU8eJ/nNXO7BR5+vJr7/68Ku11AceEkwLeY9fV+d3dMSqB0RzWzKJdPPqZOncqGDRtYt24dd955Jxs2bPDbdt26dWJUhshJQZ2IlSqUGCcHf47rrK5qlL0MeHOaW8JSlk9V9h4KNv0IgNtm865rKbu4p6EfPBT94KEAmA8doOCDt8l/6w0Sb74DCOwCd099bm2hqbDtDeK5PVnfIq2TlZXF/v37KS0tbbXtnj17KCkpCap/UTCL+KSljOZgJXTTbQ37aiitOoz5+KSAOnUUguBmwuAbkcmUfrd1Cy7W7noZh9PsXTa073lBj6GnoVLo0ajCqbUU43RZu3s4nUbDamZROIt0JdnZ2YSHh3f3MEROcnqLXGzK/gMOfl3tOTZNm6Li9z9t/PJDFKNG+D9eA7zwci3/fqra+/h/r4WhUvU+wdwQqVTChHFKXl8MhUVuHvcRMwH+LyZ0d0xKMIiRGSKdzeLFi6murmbw4MHe//tj8ODBXTgykRONqr4qZC2s74zs4EDwF53Q1mxkt91G9dZNjZYZxk8kat7CFrdTbCti35fPeh+rklLQDRrSwhbB0x3SVhkTh0SpwlVbQ0imQIsfgg7G3+co2Epuf3K/LZ/TpvEc/vroaYJdrGrueN58803efPPNgNrecccdQfUtCmYRwLf08yWZO0IMHjj2IznF9VUKJqvnqkhB2U4So8f43a6k4oBXLg/tu4ho40CkUtnxPkrZn/U9Gcln9qqYCau9ipyijZRU7mdon0WE6hO7e0idTksXJkTxLBIsOTk5nHnmmdjtdnJycprlMDudTrKysrj//vu7aYQiIr2XjZtsnLWgvsJh23ZPDvNzL9Tw8XsRfrczmd1euXzXbXquvVrvjdxwOgXuub+S9L5ybrmx98w9YDK7WbvOzk23VTDvbDUPPeA/YiNYOdubxLOISEcxbNgwn/8XEekKelolpz/xGKiQFJxOjv33aZyVFQAoIqNwlJZQvWEtoRMmooqN97tt9o7vAFAlJhM17xzUSSnedTXbt1KzfSsZU6+its/xjOcAxtNe2rMPwe3GXlRI6fdfI5HJiL/2JiS1bRt7S3nTLUlkX7RXlDb8LATz+a3br69JBXvK5781etrva2/mkksuYfr06QG3j4qKar1RA0TBLOKlI8Se3VGLXKb2TuzXFMO+GjYel8tGfSp2Ry1mm+fk1e70Lx7LqzPZeWQJAMP7XUR02ACKyveSX7qVwWnnsG73qwiCG4utokcK5vzS7WQV/IkAxIYPISFyFFmFf5FTvBGA5JhTiI3o2CvFPZ2mMrktFfEiIgaDgWuvvZbS0lLefPNNrr322kbr1Wo1/fv3Z8aMGd00QhGRnonFImCxuAkP91/S8+bbJgAUCrjxOh0vv+Z5vOJX/3fbFBW7uPveSgBGj1Tw4H2hFBa5WHRxKddfo+fIUScffWJmyiRVjxTMmVlObru7goICF0OHKLjnTgMHDjm4/59VlFe46Z8h57mnwlAoOqYau05E90TJ3J2ZzCIiIiInG/5kpiAIuKqrkBlC/WaRm48c9srlsBmzqFi1on6ln4gMwe2mat1fmA/sBSD+quuQaXWUfP+Np5+pMyha8hEA1Re7kHZlCXAACG43Jd8uxXxwPzKtDuOU6ShjYilZ9iXW7EwkSiWxF16GMjKKmg5KoAxkkr7Wtm84iaAv/F1UaI90b2lCwbZWzIv0TqKiooKWxsEgCmaRgGia0dwUQRBYe2Qx5op84iJGMKTPOT7bHM1f5X2sSkqhct/vAMhlapJjmmer2R0mDub8TEHZjuPtVEQZ+wNQWLaT0qpD7Dj8GYLgyW6ONKa374l2MIIgkFu8kQM5PxEWkoZGZSSr4C+O5q9GKlWQkXQ6sRFDW5wc8USmqVQWq5dFgiU0NJQ777yT2tpaxo4dy/z587t7SCIiPZ633q3lvn9WoVBA5v541OrmJ6ybNttY9bsFAKNRgsVSv+7TD5pXLwuCwLsfmLj3gSrvsv8+E3a8LzurfrexfYedikrPie7jDwefBd3Z7Nxt59obyzFbBBYt1PLt9xamzy4GYP4cDffcFUL/DDlSaetyOZAq5qbre6Jkhp6ZJy5yYmG32/nXv/7Fd999R2FhIUITIbZ8+XJGjRrVTaMTORFpKNVaqlL1t76rcJlMZP7r/wCIvfgK9EOHN29jtVDx+0rvY3mDWLiQkWNQxSU028aal0PxF59iLyoEwDhlOjKtziOd13jOz2u2bwYg9JTJSJUtx2J1NW6blZJlX1GzfQuG8RNxVpRT9NmHACjCI4i7/Bo0ffshVaratZ9AhHBDAo09aekz1RGSNxhp3JuksljJ3PMRBbNIizSMyWiYodvw39D9tRw4thxzRT4AQoyx2Xq34GJ/9g/klWwhOWYCuWXbkEhg3MDr2Ljvf0SGZiCXNZ7d1+12sn7PG9gcnttsDboEhvVd5L1ya9AnUFy5j8raYwCcMuQWpJKedWX1aP5qjuavJjF6LBlJpyOTKugTP43KmhyM+iTUqp53gi0i0hvR6/WiXBYRCYA162zc90+PBHY4PNXJTfnxFwvX3VTOyBFKBMFT1fv3u0NY8qWZmhqByRObn7C99Gotj/+nPkP13cXhDBro6XzkCM+/dXL54gu0DBnsY8fdyI5dduadW0qfNDkfvxdGej8F991j4LffrYSHSRk/tn0nqU1pKacZenZmd1N5LuY1i7SXJ554gnfffZfbbruNuLi4ZlWaycnJ3TQykZOFlsRVd0065nLYKHpzsfexTKdr1sZZXU3+u2/irKwk9JTJVK37C4UxjLDpM6lY/Sv6Ic3/NtuLi8h95b/ex4bxE4mYfRYAEqkUJBIQBNwmz11LkWfN6+in1i7cDjv5772FrSCP6EUXYRg1FvBIc2dFBdqMAV0qxAP5zHSWxO1NMRcdSd3r6T5xp6/qtYiCWSRomkYbmK3l3qgHhcZAwrDZVCnqT8RyjIUUrf+JyppsBqbOI69kCzKFktiB0yj86xcACst3MqTPwkZfKA/n/eqVy6MyLifckIZEUj/7q14T7f1/RGh6o8cdQUnlQUDwVkwHg9Nl42DOL+SVbKZfwkzS4qd616mVocRGiGLZF75iMsSqZpFg2Lp1KytXriQ3Nxe32+1dHhUVxcMPP9yNIxMRaTsdWdn6+BP1FcYfvRuOTFZ/3C0scvHK6zUsftvE2WeoGTpEyRNPV/PGK2FkZ7uoqfEI4l9XWTnrDI13u63b7F65fN89IVx3tZ7Q0PrjdUJ844u/zz1l7JDnUkdmlpPf/7Rx7jkaQvSBzRLfkM8+N/Hw49X0z5Dz9eeR6LSePlQqCWfO1rSytX98iddARGxPlst1NB1ja5JZjNsQaYk//viDl19+mQULFnT3UEREegzlBzZiLssDQD9sJJo+/bzrBLeb6s0bqPhtBYIgEL1wEcVLP0c7YBDq5FSKv/4CgOotGxtN2Cc4neS/9z/AczdxzHkXooxuHC+pTk3DmnkUgPjrbkYi7zhlJLhcVG/egDolDVVsXNDb2woLKFn2Jbb8XBKuvQl1cqp3nTohCRKS2jSuYCffay8dua/eNGGfyImPKJhFWqVhJbIv9tf84f1/SEwfZA3kcunRzRxd8ylqQzTpp16DNLOUalMeYwdei2PnEY4VrSfKOIDosIFeuex2u8gp3kB24VoA+ky6mAh732b7bVjxHBaS0mx9e9mTuQyH00Rq7GTSk2YFtW1O0QbyS7aSnjSblJiJHT42ERGR5rz++uvcc889DB8+nJ07dzJhwgS2bt1KVVUV11xzTXcPT0SkzdTJu/aKxxW/Wtm81QGAVAqnnVp/HM3Nc3LW/FJMZje33aznlhv0jJxQxLVX6ThthpqZZxYTFyvl2qv0TDql/ji/fqONK64pB2D6VBV/uzOkWfVh08cdlV9cx6dLzDz/Ug333FdJQVZ8UP1nH3Ny612VnHaqildfDPPK5Y6iO6p7uytqw1dlc2ttREQAoqOjcblc3T0MkZOYYKpAu6Jq1O20k7/hBzheLBEROci7T0Fwc3jDJ9Tu3IamXwbRC8+nbPl3SDVa+k24hOwfv8NZXkbopGmEDKuP1HBWV1O6/BucFZ5jduzFl6MwhjXbtzohySuY5SH+J7T1O/Y0s/f/0kxto3WOslJKln0JQNJtd6OKD26C+5JlX+CsrCTuimsbyeX20pKI7Q2StqdEuoiIiIJZJGDq4jKaVpSGJg6iquAA2vBE4oec5m1jriggc93nRPYdS9op5wOwe8t3RIZmoFKEsGnfWwDUWooYkX4RtZZias3FuAUnB3N+BsAQl0EfWwb4OFcsqdjv/f+xovWkxU3p0Oc7KuNSNux9k6zCv5DJlKTFTWlUQd0SpdWHiTRmkBo7yed6u9OMVCJFJlX5naxBpJ6m8SwiIk0RBIEHHniAn376ibi4OM444wxWrlxJVVUV55xzDunpPSufXUQkWDpCGKb3k9OvrxyNWsJVV+iQyz3HH6dT4LqbKpDJ4K/fYoiLlfHfl2pwOgXuul3PNTeWk33MI38mTlCiVktY/HYt58zXcM75pTg8zpr/PmP0eUzbs9fR6HFVlbtRhXN7ue1mPR98bKK0zE1caj4Hd8W2OHlhQ1b9bkMqhf+9Fk5ISPMxmS1uzGaBMKO0UbV3MHS1VA20mriz9h1IG1EyizTktttu47bbbmPAgAEMHjy4u4cjItLtSGQKwtJHUZW5C0PKYEJT66uQS3b/Re2OrcRefDn6oSOwl5ZQu3sHiZMWUp2zn6r1azwN3S7UyamY9u1Bpg+h8s/V1O7aDkDknAU+5TJA7a4d3v+bDx9EGRX4XcIN5bIvlNExRJ49n9IfviHn5edJuPE2NClpAfXtspix5hwjav65aPs2/14vCAKumhqkGg1SX/lfnUxTyduamO7qqmkREYDCwkKysrICahsXF0dKSnCFnKJgFmkz3ormAaOITKufeKPq+LEqb+fPqPThpI4/F4lESsmRjViqChky4GwOHFvubS+Veg4Am/a9jdNVH6QjlSlIHX8ekjwJZms5DqeZUH39VU63UF/pYHfUYrPXoFK2PFlea9XYDTHo4pk49HbW7nqJI3m/cTT/dxQyNTKZisjQdKLDBhEWkoxEIkUQBArLd3E0fzU2ew0ut50haQt99ns4dyWZBX96nqNEjkKuRasOZ0DK2R0e89HbEYWySKAUFBSgUqmYMmUKWVlZWI7PSBYaGsqjjz7KP/7xD/7+97938yhFRLqX1BQ56/+Iabb8p1+sbNpi54dlkcTFyigtc/Hy6zVcdrGOTZsd/P6nzds2OlrGsm8tPPBQFQ88VB+38fjDoSQlyrFaBVb9YWX2TLVXyNpsjSfsem1xLff/PfiqKH+EhEjZuz2W6CTPXBAZQwuJjJAik8OYUUrOPlPD6aepvVL74CEH/3ykis1b7VRXC8ycofIplzdusnHh5WVUVwtIJBAeJiU62lPFfcWlzbMwO4qmOcytVQW3pf/eEMMhcvIwcOBA3G43Q4YMQaVSIW9yS/6qVasYO3ZsN41ORKQx/iZ968jKUYlEQvLU82Hq+Y2Wu512Cjf/ROiESeiHjgCg7OflKDQhGFOHsvujR7xtpRotgiBQ8MHbjfpQJSYTespkACyZR1GEhyMPNdbvw1p/Pl767VJCJ0xqdvHYnWZuVp0cKMbJ03A77JT/8iN5b7yMVKtDIpUgN4ahGzwU/eBhXqntttso/20FNVs24TLVIpHJ0A0Y1KxPt91G/juLsWZnAiBRqZDp9GiSU4k6Z1GXT1IYyOSAnS2XTwZ5rT/W3SPofXz22WfcddddAbW94447eOGFF4LqXxTMJwgNhWlnSrmGfbe2H3NFPraaMjZ/ch+G2HQsVYWEpwxHKpVTUnnA225w6gKf2yeNmkNMnuc23PV73sDltnHq6H8ikyo4VrSenOINjdr/seNZRqRf3GJmsq8K7JbQqSNIjZ1MVuFfCIILp9uO3Wkip3gDOcUbUMh1GLSxmG0VWGzlRBsHkhg1BrXSSHTYwGb95RZvJrPgT9LipqDXxGJ31mJ3mikq282OQ58xesCVqJUdd9J9olBXPR/s+ydy8mA2m9HrPZ+N2NhYSkpKqKmpISTEc7u+6fhEJSIiIs05muUE4OwFpQCcdqoKiQT+fncI/YcWetvdepOelGQ5P/7sOfnMSJdz8JCTCeOUXHe1R7i+8b9a/vVkNa+/HMaihVq277Rz6VVljfb33As12GwC/3e/oc1VwU2RSiWs+jmKGaeXAFBd48Zuhx9+tPLDj1YUChg90jNp4cbNdlJTZNxxSwihoVLmz2mes5yX5+T8S8tIiJfx0nMGyivclJW52brdzj33VRIZIeXsMwPPZw60ardhu9ba+xPFgeynuyWzmMks0pCHHnqImpoaXn31VZ+T/PXr18/PliIi7cPX5H49Wcy57FZcditV69dQtX4N+mEjMe3eQcqMiyndu9bbTqbSEDZthneivoZEn3chEqkUZ1UleYtfQarR0uehfyG4XBQv+wK3rfHMaXmLXyVm0UUowiOA+irl1qqVWyJs2kxqtm/FUVyE2+wZo6u2FltuDuU/L0cRFY081IgtLwfB4cAwfhKKsDDUqWnIDc3nMir67COs2ZlEn3cRSMBlMuGqqaZq3V8IgpuY8y/xTGDYwfTEOIqe/PntaGrF+V+D5rbbbuPGG28EwOl0MnPmTEaPHs0tt9xCYmIixcXFfPTRR3z00Uc89NBDQfcvCuZeTEPZ1t3STRDcVObuI2/HTySNnoettgxbTf0JpbkiH5fTRtKoOVRs3uJd3i9hJqH6BM/4d8pIjpxA6Pip7Pr2aTTGWNzlLrYd+hiX21M9lZn/B/0SZzaKx2jIziNfMGHQDeg0UX7HGkj1ckP6JsxAIddyOG8lUokMmVyBw+k5oDqcJsqqjwAwMGUuidFj/PbjdNnYl/3d8T5nNvryHGHoy+b97/DnjueIDhvIoNT5KORtn1joRKLp+9X0cXd/9kV6Hmq1milTpnDJJZdw1lln8cYbbzBxopiFLiJSR/YxJ/94sJKBAxRcuEjLG4sb/11d+ZuNfz0SitCg8HjIIAUP3Ou5AJqT4yQqUsqaVdGcekYJ/frJkcslvPJGDct/9tw9cNNtFcw7W0NWlpPiEjdNefWNWsKMUu64teU7j4Jh6BAly5dFcsOtFRQUukhMkJGb57nbyeGA9RvtAEydrOLj9yLQaPzL7dferKW2VuDWG/XMOav+eOxyCcQk53PFteWkpcp49YUwxo1V+e2nJfwJ3pbkq6/q5t5cjSyKZhGA3bt388ILLzB37tzuHorISURDEVdXcRqInPOVwdxZotHlsFGy83eqc/aTOutyCjb+2Gh97Z6daKOTCUsfxZ6PH/cuj7vuRqRKFdYiz0Xi+GtuxJafR/nKn1FGx2AryKd46ecAuC1mzM4DKBOiqNm8sdkYbHm5FH7yPok33o6Qbu+Q5yWRSkm45iZKl39D7Y5tyAyhuM0mBKfngrejpBhHSTHIZCTdeneLEwJac3Mw7duDOjUNw+jGdzo4q6qo3bGN2h3bCJ99JmHTZnaoaPb3uWnp89BaZbOISGcik8mQyTwxcitXriQkJITXXnvNu77u7t/c3FyWLl3KtddeG1T/omDuwbRWrdndYk0QBBDcCILA4d/fpzJvLwDZG7/CWl1CVL/xxAycilSmYNe3TxM3eDrRuUr2WOqroQ7n/YrdaSZ2wHkodWHklW9HY/KcZEikMkqrDlJefQSNKgyLrYLMAo9gHtrvfH7f9hQh2jhqzAUAxIYPpbB8F5sPvEdG0ulEGPpgGRobtFBuilQqJzVuEknRY4/HeQhU1eZSUnmAksr9mKyeiq+DOT9TWL4LlSIElSIEjTqMhMhRSKVyHE4L+7N/QCpVMCh1XrPKDK0qHL0mhlpLEcUV+6iszSEpeixpcVMDzn0+UamrXvaHKJxFwJMR9corr3gfv/fee97besaPH88TTzzRjaMTEel+nE4BuVzCJ0tM3PG3SgTBI5Jf+5+Jvqky/vd6OP0z5Nx6ZwVZ2S6uuVLHwUNO7/a1JjeDRxawfVMsGRkKSkpN/PizlZJSFwq5Ertd4JHHqxvt8881NhbM0/LhJ2Y2b7EjV0BVlUBqigypFB7/TzUmk8A58zUM6C/vkDkJxo1VsWlNDC4XqFQScnKd/LzCyo8/W1mzzobTCX/8ZWPOwhIS4mXExnh+FszT0CdNjtst8OXXFha/Y+KSi7SN5DKATCbhqst1vPuBicwsF/POK+XsMzQ89pCBhISu+1rtT8gGI2p7kpwWc5lPblJTU73RViIiXUVrQrnhen8SsTMkoSB4Lsraays5tOwlHGbPsfXg0hdwWk0kTTkP6YT+WDKPUvzlpyROXIBEIsVh8sRWaaOSyH3lv8RddjWqhESQSqneuA55qBGJTAaCQPFXS7Dl5aBO64M18yiV3/1J7N0XE33eRRR/+SmaIX2w7PZM9KcbNJjaHdvIf/8tjBdMQtU/Gamy9YxjaabWW+XsK05DbjAQe+FluBeej1Spwm23YT50ANO+PZj27/VUX7tcFLz3P5TRMcgMochDDKiTU9AN9GS124uLKPrsI5TRMUTNax5PGTJiFKZ9exCOR3KYdu8kfObp6AYNada2rbQ2wV5PrHIWEQE4cuQIsbGxPtfFxcVx5MiRoPsUBXMPpqeKMsHtpjJ/P4dWefKclLow7OYq+k65jPxdK7FUFpA44izihpyKRCJh3y+vI7idxA6cBpluFGlpULrd259M5slESh49l/0rXqcsaxsyhRp9VCqH9qwGwGKr8LZ3umwcyf0VgBpzAbHhQ9FpojhWtB7w5DHvPvqVp/F26Jd4WodMAFg3TpBgDEnGGJJMetIs3G4nDpeFY4XrsdgrsTlqqDblY7aVUVS+B6VcR0nVQQTBzeC0BcRFND+BUilDOGXIzQiCgNVeRVbBXxzJW02VKZ/BqfNRKjov67GnE+wFAnFCwJMTnU7HrFmzvI+TkpJYunRpN45IRKRncPCQg+dfquHLpRZCQyVUVQlcdL6W4qh4fnv9MCOGyPlqSSR6nZQVv1pZ+ZuNF58zolBI6JNW/zUxK9tTCazVSLjiUh1PPVfN8p+sFBa6mX2amlW/W5vtOzfPxdr1Nm+Gc3o/OfferePLr81s2+6Z+O/5l2p4/qUaAEaPVPDJBxFEBDhBnz/kcgl1Ea5JiXKuvUrPtVfpcToFnE5Y8qWZLVvtFBa52LDRRmaWi3c+qGXmDDVr13kez5+j4fmnjD4jPJ75j5Fn/mPE4RB4+z0Try+uZfbcEt59M7zFauamErVpNXJDmsrWlgSsr7a+lvd0RMl88nLttddy4403kpaWxqhRo7zVVSIiHUFtMoQW+l7XWiVyw8edLQ9dNgvlh7aQu8bz/VUqVyJX60ifewuHvnsVh7ma/uf+DW1kAlU6G8Vffoo+Ph1dTCoAmshELKW5mEtyADDaonCEGjFOnELt3t3Ia2rQDhiI4HBgL/XESVkzPRLZvHEvgtNF+R8/AWDZn03YeafiLKuiZvVWz7LDB7H8+6B3vLEPXIl2mP/4mkAjNKRKlfdf/eBh6AcPQ3C7EVxO7IUF1GzdjLO6CntxIeaD+6hYvZKQEaNx1lRjOXIIuTGMuCtu8DkZoW7gYPo+9iSCIGA5epiK1b9S8NG7nmrmKTM8wr2DEEWySG9j8ODB3HPPPaxZs4ZJkyZ5l+/bt4933nmHZ555Jug+RcEsEhROm5kDv72FqTTbu0yu1JAx4xq0YXEU7FkFgD46FYlEwrEt31JTdJjIvmORq7RUDQBdaX1YjiE2nX4Jp1IFqEI8uU4VObtxOe2UHFpPZe7uZmOoNuVTWO5ZrlToGdJnIRKJlJSYUyivyaSqNpfSqoPUmD3fJCprsqEDBLM/pFI5KmkI6UmzGi3PL91OduEaBEEgNXYyidGjUSlavhVYIpGgURkZmDqHKGN/dh39gr92voBRn0SILh6DNp6wkGSUipNDnral+lzMaz45OXr0KLNnz+bw4cPdPRQRkS7Fn1wE+M8z1Tz/Yo036qKqSuDVF8JYdK6G9z8q5Vc3DB6kQK+T8sdfNq67uRy5HOad7anaVauhT5qMo5keuZx3NN5bZVxc7Oab7yxIJLBho42l3zQ/kdyxy87uPQ7v46f+FcrUKWquv0bHzt0O1qy1sXGzne+Xe+T0lm0OTCaBiPCOe30aUieer7hU12iSvswsJ7fcUcHefQ7Gj1Xx+ks6Ro9StFhRXfe633jdTs6Zr+GKa8o4a0Epw4cpGDFMwfBhSsaMUjJoYOMqL18S1Z9UbSqeWxOw/tr3lCrlQBAl88nJfffdx549exg3bhxSqbTZ796aNWsYP358N41O5ETAX4SBrwzmlrb1185XJXOgwrEmFSxZRyla8h5OS/25j7HvCBImzEUi9UhQiUyONjIBp81M/jtvARA1tP4cNzRlMJbSXADSTr8adVgMDgCpFGd5GbjduGpqKF+1AsFeP3lvHbbfi3AWe4q69JOHE3beqQBEnDIP88F9WGuyqP1zO26z55hd1zYQgp0UUCKVIpEqUSeloE5K8S4X3G5Kln2BNecYckMo0YsuQj90BFJFyxXVEokEbd90NGl9KV/xE+U/L6d6/VrUySmoEhJRJSShSe2DRN41eqwzKt87q6Je5MRl8uTJXHvttUyZMoUhQ4aQkJBASUkJ27ZtY9GiRVx00UVB9ykK5l5Mdwi00swtmMtzGTD7Zvb/4slqSZ92pVcOK1SeEza3046ttpzCvb8TN2QmSSPP8o5XH5mMUheG22mj75RLqVLrMeyroSDm+IQBLgdSqYzKvH3e/cYNmUnB7l+RSOVsPfQRgtuJQZcAgkD1wPpJ8aL2K4ky9qdf4kwAHE4Lcpm6S16bpsRHjiA+ckSbt480pjNp6B3klmymqjaX/NLtZDn+RCHXMWX4Xcikrd+adCJS95n3JZ8bZpKLkvnkIjIykrKyMgRB6JDb7EVEegv+hFxZuYvnXqjhpuv1RIRL+deT1dx3TwgXLPKc4IWHeeKXqqo8t+I++Uw1aSlyvv48kpAQzzqJxFOt/PDj1bz9Rjgqled3KzfPE51hsQrMn6vh628t6HQSVCo4+0wN6zfYKCt38/GnZiQSGDVSwdZtDm+EhEQiYfhQJcOHKrn5Bs94TWY3cpnEu4+uJC1VzvJv/M/dUEfDiuOG0jYmWsYPy6JY+o2F1b9b2bDJzoefmHG74aN3wzljdvBzKrSUz9zw/W5NHvcmuVxHIGMWJfSJxbPPPktlZaXf9QMGDOi6wYicFDTNUfaVqxzIdq21bYq/bct/W4FcE0LfM6/jwNL/ApA87QIkEoknllIiQXA5EQSBisPbsOZkkz7vVvRxfbx9RA6cQOGWnwlNHUJoiidCQnfITsnxXGNVYjKm3Ts82caCgFSjRTdkGDWbPHcBF7y7GLkxDEFwII+on0hPHhKCYfQ49GlDiLxqjqe62GpHqg3uHDtYyewLiVRK9MIL2rV9xOlneZ73ts3Y8nIx/bYSwW5DP2wEsRdd3q7xdRd1n6umklmUziKt8d///pcrrriC77//nry8PMaPH88LL7zA5MmT29SfKJh7MQ2zabtCogmCQFXeflT6cAwxfYlKn0DJoQ3IVPUHCplaB0ioLT2Gy+65uhkzYHKj8QqCG7upgoThZ6BQ6xEEgfV73qB2cyGhCQOpytuHMiSSytw9AEhkCsqObsYQm05tSRZhKcMJjcvAVJZDdcGhFscc6ER5reX8dgaB7FOp0NEnfpr3cWnVYbYd/JA9mcsY2ufckzqfubXXT5TMJxcGg4GzzjqLxx9/nAcffFC8vVbkpOe31Z7qpIvO1+JyCfzrSUhMqP+9CAnxiNzdex3U1LrZscvOg/8wYDQ2Pq7s2OUgI13O/Lme4+nvf1g596IyRo5QsG27g337HeTkunAfn8fv628sLFqo4fOvLJwxS0W/fgpOO1XNgkWlWK0C/tBpe+bxLFCRKZNJWLRQy6KFnu9EJrObU6YWc+8DVWSkKxrFjXRUhEVvlMciIr6YMGFCdw9B5CSitSpkfzEZdet9yen24KytwZqdSfSgyWgiE5GrdWgiE7wFExKJBKlcidthw1ySg6kwE01EQiO5DGAuzQMgbtxZSCQSXDYLO997EAB5WDjW7EykGg2mvZ47gd0WC26zCYlKhdwQ6pkk77wxFD33CYLDiT8kUimSIOVyHQ1jM9orm9uDOiERdUIi4KmKLv3hG6rW/kl1en8MY3rf3RL+LnyIclkkEEaMGMGIESM6pK+e+W1eJGCqBui7TJ657Baq8vdjrS7BZbdiN3smEjj421s4bZ6DhVyhQipXkr/zF4789RHasHiUmvoKY894Q0AiRaZQIQgCOVu/p/b4xH/p065EG56IXKUjffpVyFU6QmP7YTdXEZU+AYlMjkprJLLPaKQyOW6Xo/lAGxDMa1P3WnbF69mw0jYYIgx9GZByNkXle8gr2doZQ+vx1EnjYC4IBNteJDhcLhfbtm3j+++/Z9++fX7b7d27l++++45Vq1ZRVVXV5n58kZuby9atW3n44YeJiopi5MiRjBkzxvtzzjnnBP28RER6M+++bwJAr5fwx182dDoJDz9ezR9/ecTzCpcnNzH7mIu0/gXY7TBjWvMTRrkMtFrPSe6mzTbOvagM8OQQ/9/9Bg4fcfLZhxGcMkFJXKyUhHgZCoWE+XM17Dvg5OEHDYQdl9YtCebeQqBSV6eV8tF74ahUcMc9FZ4KNB99Nf0JdAzByGVfuc8iIj2J2tpaKisr/f64XK7uHqJIL0Z/LLj2NamBVR/7axfsvqxZmQh2O4LbjaU0F5lSQ03uQfI3Lve2kyrVyJRqDi57kYrDWzEkNa/q9wppqQyXw8bez58CwJA8iPirrsdlNhE6fhLhs88CqRTtwEGYDx4gZtHFOEqKMYwejyolDolKiWBvfo7d0ULYnWYOOKc5kH4a/gSDRColfNYZ6IePovjrL3CUlwW1fUhW8PnLYl6zSKAcPHiQzz77jMzMTL9tzGYzv/76KytXrsRsDv53avv27bz++uusWLECgPLycnJycto0XrGCWSRg7BbPDLZR/cZTuP9Pqo5HWNSWZGE3VyFXaYlKP4Xig+sxxKYTmjCQyLRRzfqRSCSExvfn2OZvOLb5G+/ygaffilQmJ2bAZDLXfoYhph+jzn8Mwe1iy2cPYqspIzSuP9XFntksVfoIbKYKnDYz8uNV1L6quntC9aq/WIdgpadEIiEpehwVNdlkFvxBfOQIpFLx17iOptXKTV93sZq54/nxxx+566670Gq1JCQksHbtWsaNG8dXX32FVuv5vbRarSxcuJD169czefJkioqKvJMHnHfeeQH30xJ6vZ4rrrjC7/qwsLCOecIiIj2QprEJTqeAxSJgMEgoLXPzyL+qEQQwmQQ2bLIxdbKK5OFG4gcZKDtm5r5bNUwYr+LP2NEMpLG4nHmqms+/qiAyIc+77JKLtIwYpqR/uoJ/P1XNsRwX333liZi49c4Ktm6388A/DHzznYW8fBeJCTJkMk+19OhRSno6wURQtMSwIUoeeziUy64q5881dqZO9j8BYEfsryUCnVywpyMK8hOT0047jQ0bNvhdv27dOrHKWaRdNIwKCFQKtxQ10JGVoa4az0S3uthUcv5aiq26FIDKzJ3EjT0DiURKzLDp5K37hoiBp6CLTiI8fWyzfrTRychUGvZ99p9Gy1NPvQRzlAbdwCFYso+SeP2thM84DfOhA5j37UERHo5UpcaaeQTV9GgU0WHYs5vPitgRMtgXbY3O6MjxyNQaohcuwnLkIBWrVhJ9bttjOLqLuipmMRbjxGDr1q384x//IDs7m8OHD/POO++QlpbWrN1XX33FddddR3p6OjExMdx55518+umnDB06NKD9PPzwwzz99NOEhoZy4YUXMmvWLKxWK6eddho7duxArQ7ubgXRTPUgDIdqMQ1ue15wZ8uz3G3LQSIlZsAU9q943bt84Oxb0IbFAaALT0AbFodEKiNu0DR/XdFv8qVsWfJg/eOpVxASnUZZ1nYK9qxCKlOC9HgGpFRGWPIw8netRBMWh910fPKBqBQQ3FiqiwmJSm3Uf8PXobuloq99t7eatk/8NNbtfo280m0kRTf/gtFVNBX63VEl3FAi+3ufRanceTidTpYvX06fPp7b9IqLixkxYgT/+te/eOKJJwD49NNP+fXXXzly5AiJiZ7b0e68805uueUWr2AOpJ+WMBqN3HfffZ3xFEVEeg11wvCHH63s2uNgyUcRvPJa/SR/t92s5547Q1hcOQyZHIadGcdPzx1AtnACO3Wer4RN5ePC+Ro+XWJm9R+eyuf5czU884SRo5lOHn+iCrfbUyVdx/y5Gj77wsxb73gqqLOyXUyeKCe9n5x9+1u+66in0JHi9YxZaoYPU/D0c9VMmRQpZsS3E3ESwBOT9957j9raxt8hCwsLefrppxk4cGDAJ8oiIp1FZwk73WE7R3/8npCEDMwRLszF2QCow+PoN+dGbxxiWPpo8tZ9gyYinogBvi+2yNU6+pxxLYe+edm7bOCF9yOVK6n5fDnWQwdRpdYLKnVKGjKDgaLPP0Eil+MoL0OaqUWVnkTV93816jtQmSvN1HaaiO5spEoVxqmnUvbT94TNOA1FeESX7FcUwiK+qKqq4u9//zuzZs1C4WcSy61bt3LhhRfy6quvcv311wOeu3qLi4sD2sfevXt59dVX2blzJ8uXL/dWScfHxzN+/HiWLFnSYgGXL8SIjB5EdXrbJVggYq+98q+2JBOFSse+n19BKlei1HrC/x02U6N2Cm0oVfkHcDmaz05bh1ShQm2IQqENZdQF/yI8ZRjFh9Zz5M8PUemMpE1YhKWy0HtLaeqE89BFJmEqzcZxvJLa23+T206byuVg6UhJ2t6x+EOviSY2fAhH8lZhtVd3WL9toS5WpCdEUIhRGF3P3LlzvVIYIDo6msmTJ7N9+3bvMpvNhlqtJjY21rusT58+OByOoPoJFKvV2ubbekRETgQ2b7UD8PDjVfz4i5VRIz1fTMsr3I0Omfed7rlgOyJrm9++JBIJ2gmeE9Lr3h/P22+Ek5vnYu65Jezc7eDB+0KIjZFSU+sJYZ41U80Tj4Xy+5+eY3RKsgxB8FRU+0iJOOGRSCT8428G1m+08/mXlu4ejohIj2TAgAGNYq3GjBnDnDlz+P777/nmm2/8nlyLiARCbXL3CLxAYhDcDjtuhw17bQVFn36IOiUVAGtVMTUxdm+7urt1rRXNK4sbotQbAYgYOIER1z2LKiScrN8+onjHKkLThhObNhFHmadCWqpUknD1jTgrK3CZahHqvpcXCtDDj9ctSez2RHmEjp+ITKej5JuvEOoml2iFtn622hKtEUzfIr2bGTNmMHv27BYLE55++mmGDRvmlcsAiYmJjBrVPEXAFxs3bmTu3Lmkp6c3W9e/f3/27t0b9LhFwXwC0FUT/UllChzWGuRqPUPm/I3BZ92FIS6DY5u+bpQtaEwYCAi4nP4Fc2XePqzVJaSOW4hcqcFUlkPWhi+JzphI7KDpHFnzMXt/fJF9P7+CuaIAmVxJn4kXAqDQhGC3VJO57nNUIRHoI5MB3wK3La9JR+QwN+yjs6Rn/+QzkEpk7DzyOW5392TT9QSZ6+v17QnjOhGorq5u9GOz+f+drsNisbB27VqGDBniXXbZZZcxYcIEzjnnHN59912efPJJXnjhBV5//fWg+mmNoqIi5s+fj06nY8aMGYCnMnr27NkBX8kVEemtNKy6rXMxBw46+eT9CH78JorHHjLw8admNmyyezN8B/T3VC3/b1Ok335Ly1zsXXKIoWfGkTYmHJdL4IprywjRS/nw3XCefKaGBYvKGDuxiI8/81xwvv4aPf36evouK3Nx3z+ryD7m4pz5gU28e6Ixa6aKi87Xcs99lezZ2/FV3Isrh/mt6j3Rqn1PtOcj0jIGg4GoqCgOHWp5Um8Rkc7Gn7Crk4RtnWDNYfYUCtmqSggZM46EG24j8da7kGm1VPz+m7edRColJDEDU1G2374EwU3Rtl+RyBUkTJiHRCqlaPtvVGbuIvW0y5EplBz96S2yn32Civc/x2W1oIyJJeKMOQBIdXrMhw9S+ddq9JM8f2uDrUgOtnpZmqntkGznun7a25dUqST6vIswHzpAxaoVAW8XjGTuiOxukd5LW86v/fHHH38we/ZsCgsL+eabb1i7di1WqzWoPuraNxXZhw8fxmg0Bj0mMSJDJGBSxp5DZd5+4gbPQK7U4HLYUGpDqS44iK22DHVIJLWlx8jeuBQkUr/ZwGVZ2zny54cY4jIITRgIQNGBNaj0EYTG9+fAr4tR6sIJSxpMdcFB9v3yGsPm/wOVPpwBs25CrtZTlrkVa3Ux/Wdej0QqA/zL5Lrq2mClcUdU5Xam6FQq9Azrdz6b97/Lodxf6J98ZqftKxB6ShXzyYQqS4VM3XqmZ7C4jk/ElZSU1Gj5ww8/zCOPPNLitjfffDMOh4O7777bu0ytVjNx4kTefvttBEGgqKiIqKgon1dLW+qnNa699lqUSiVffvklf//73wGQy+WceeaZPPPMMzzzzDMB9yUi0pu58jIdlZVupk1RM2OamsWVwzgYWQRs5YVfjZwy3oLVKpDYNx+A0Djf8VylZS5mzC6mukZg7rWeOwzWrLWxb7+TLz4O57KrytFoJEyZpEKvk3DH3ypRKCScf66Wr5dEsm2HnehoGW+/Z2LcGCUTxnX836vegEQi4eknjOza7eDK68pYuTya0NCOqfFomq3cG3OVg6Hu+Ymi+eRg5cqVHDx4kJiYmO4eishJSkNx3FpVaFuiDtThscSOPh3B7UI3bxYSiQSZWoNMp8eSeQRGetrlb/iBmtyDGNP8/+079vvnlB/YSPyEuciUagRBoGT3n0QOmkhN3iFK9/yFNiqJkIR0SvauxfJpJXFXXodhzHikGi3afukUfvwegtNJ+EWzgc7LXW4PDcfU0RMPAugyBhA+czblv/6CKjEZXf+BAW9b9/53VwVxw/03HIMotINDmq1FGmT2cKtYPd/72nJ+7Y/i4mJ27drFhAkTGDp0KAcPHsRms7Fs2TJGjBjR6vannnoqd955J7t3724kmJctW8bHH3/Mxo0bgx6TKJh7Ca3ly9ZVcbYkUdtblRuWPJSw5PoMtEOr36WmOBNdZDK525YjU2opPboJgKFz7/HeytMUu6kcgIxTr0V6fJbb6oJDGOLSyd64lND4AWRMvxqJVIrDWsuOZU9QdGANicNPxxDrmfVeqTFQfHAtmeuW0HfKpYRE9/G5r4bPvSslc1eJVqM+iYyk0zlwbDmhuiRiIwKv9mxKS69NbxTHXVXZfyKTk5ODwWDwPlapWpZD9957L19//TUrV65sFIfx4osv8uKLL7Jr1y4SEhIAz8H09NNPJzMzE71eH1A/LWGxWFi1ahUlJSXk5+c3Wjdx4kRuuummgPoRETkRSE6S8/zT9RNbhvy8jo/uriRxSChH1pdx3z/t/LnGUzFx+y16HrqghMWVcY36WFw5jLmObRQUunn84VBumpDF4sphLP/ZilYjYdUfNsor3Py+IpqUZM/XSau1jGf/W82ihRri4mTExXkqlq+9Ssfb75n470s13H6LHpns5Msh1mgkvPu/cGaeWcytd1Xwwdvh7c5j9iVZgxGvvVlIi1nMJxZnn302W7ZsabTMbDZTW1vLgw8+SGSk/7ssREQ6k5Ym+vP12B/+1tWmSdGnne597KyuIue1F5HI5SiMYWSv/gyXzUJV1i4AUmZe6ncftqpSNBHxxAz33MVnLj6G01KLMiSM/PXfkTjpHKKGTKEmFbSrksn85T1sebmoE5MIGTYCgLCZp2PJfpOi5z8h6pbzkBtDmu2no3KW211t3GAcHS2aw2bMwnosm6IlH5N0290owsJb3SZQievrs9DROcx1E/51Vv8ibSfY8+uWUKvVbNq0id27dxMVFYXL5WLBggVcd911bNq0qdXtk5OTeeKJJxg7diwRERHIZDK+//57jhw5wkMPPRSQpG6KKJh7EIZDtchlTp+5va1JskAlc12f7ZVuDpuJ6sJDpE5YRMmh9ZRn70QVEkFM/8kkDJuFTOH/io+5shC1IQppXeVx/gHs5kpUIVHYj2yi39QrkByf4E+h1mOMH0h1wUEYXn/wlau0DJx9Cwd++x/7fn6VQWfc7pn0rwXa85w7Y6K+jiIpehwVNVkczPmZmPBB3skgugOxivnEwmAwNDoAtsT999/Pm2++yS+//MKYMWMarfv999+ZNGmSVy4DLFq0iMcee4zdu3c3mhm+pX5aoqysjJCQEDQaTTNpY7PZcLm6J0ZGRKQnsPQbC9OmqNBOiOPHZ/azokTGoIEK3ng5jKFDlH63e25zIlDIKROULK4chiAIfPCxifFjVXy/3MpF52u9chng/EVaLruqnNw8F0mJ9cufeCwUQ4iUfz9VTdYxJy88YzwpJ7tLS5Xz0nNhXHldOWvX25l0SvAnFR0tVUXJLNITuP322ykpKWm0TKfTMWTIkBbvdhIR6UqaCrrWHtcRTHyG+fBB3GYTibfeTe6r/8Uuy0dljCZ52vmE9x/v99gpuFzYqkoIS6/PXi3duxYAp7kauUZP5KBJ3nXSqYOR/q7GfPgA6sT6ikptn37EX3U9Be+/Te7fXyb5pb8h1dQfq3qCXK7btjOrqyVSKTEXXMKxF56hYtVKoheeH9T2TQWviEgdwZxft0bfvn3JyMggKioKAJlMxoIFC7jxxhtxuVzIZLJW+7j55puZPn06X3zxBbm5uURERDBv3jwmTpzYpjGJgrkH40/U+RLEDdt2VBZxS8jkKqRyFVnrvwCJlIxTrzmevdwydks15dk7SBg2u74vheeg5To+WaA6NLrRNtrwBCrz9yEI7kbyVKkNJWX0fPavfIO9P71ERNooUo5nOncUHSHjOxuJREJq7CQ27vsf2w99Qmz4UGIjhnaIaA508siGedNAs8ddgb/3Saxk7nweeOABXnvtNX7++WfGjx/fbH1SUhIrV65sdKDbt28f4JmIINB+WiIuLg6n08nOnTsJCamvthAEgddff52xY8e25amJiJwQxMXK+GSJGf7cz6gFCZz378Ck4rpPsolJ17MhaTQSPMcbuU6BMRTW5Lm8Oct1DD8uq3fvdTQSzFKphAf+YeDDT0x8/KmZ7dvtPPukkbFjTr7IjLPPVJOWKuPm2yu49modF5ynJTqq9ROAOjpDqoqSWaS7Of3001tvJCJyAtBaFancEApA7qv/RRZiYOCCu1FomlcRN6Xi6HaclhoiMuq/78qUaqRyJfbaClShUd4CLk81qwxlXBz2gvxmfWn7pmMYN4GqNX9w7KZnCZsxE+OUGR12Ybijq407IyYDQKbVYRg7nso/ViGRydAPH4UmNS3g7Zu+190pnMXq5ROTuXPn8t133yEIgvf3c9++fcTHxwckl+sYNGgQDz/8cIeMSRTMPYjqdD3hR50+1zWUeL4qnH3RmdWkUpmc9GlXUJm7l4g+o70T7bVG0b4/kErlRKef4l2mDvHc9mau9MyKa60qblSN7HZ5Ztd1O+3NKqMNcekMP+dBKvP2kbv9R+yr32XArJs65AAYSOSIv9e3qyt5Q/WJJMdM4FjRekqrDlFRm0164iwU8sBle9PnG8z4e8Lr4O/9EquqO5fnn3+e//znP9x5551kZWWRlZUFQFhYmPeE8Y477uDDDz9kzpw5nHvuuRQVFfH8889z1VVXeQVzIP20hEwm49FHH2X27NlccMEF1NbW8swzz7B06VJ2797d7NZbEZETFV+y8P57DRgMEooS0xi9MNFvu4YU7K/mwO8lnP/UsEbH1KgQNz+tcBATLeXQ4cbfWewOT377zl0Ozpzd/Piz4c8Ytmyz8+Qz1Sy8oIzVK6Lp2+fk+ioqkUh4/aVwzphXwqP/qubfT1az7vcY0lIDfx06Qqo2zTKu+7e3imaREwOTyURhYWGjycPBc6G6PbcRi4jUUSf5ukq4Bbs/Td90Is+ejyzfRNSQKQHJZYCibb9iSB6IJiLeu0wZEo7bacftcmGrLkVwuZAcl04hWSA4HNjychuNtW6ckWfNwzB6LNWbNlD24/eAhNArgiv+aI32xlt0llhuSNjUGZj27qZq/Rqq1q8h5qLL0A8e5n0dg6Gr85lFqdy7KS4u5rffPBN9CoLAhg0bUKvVpKWleQux/va3v7FkyRIWLlzI3Llz2bdvH6+88gpvv/12UPvavn0769ato1+/fsyaNYvy8nJMJlOzvOhA6L576UX8Uhd14Wt5IFQN0LdY4RxMXy0RGt+flHHnBCyXAaw1ZahCIhvlM1fk7gEgYegsVPoIcnf81GgbY/xAJFIZuTt+9tmnSh9OTP9J9J10MTVFRzCX5/psFyzBVLw2fM19XQjoCvolnkZ64iwA8kq2sGbnixRX7OvSMfjC1+exsxBFctcjl8u54IILKCgoYNmyZd6fVatWedukp6dz4MABpkyZwrp16ygpKeGtt95qdPALpJ/WuPnmm3nllVfYvHkzVquVp59+mujoaNauXUtGRkaHPm8RkZ7E4sph3p+6xw2Ji5Xxr0eMjD0vCalU0mg7f5Tnek78EgaHeped49xG9jEXC+ZquHCRlg8+NnEsp14yx0TLGDZUwYefmLDbhWZ9GgxSZkxT8/UXkYSESHjvQ1PbnnAvZ8xoJUs+igDA6YSxk4q4//8qsViav2adTVOhLFYDi3QHJSUlzJw5E71eT79+/UhPT2/0s23btu4eosgJREeLt6bCsOEEazWpwe1PIpFgnDyN+HFnodAGJpcFQcBWU4Y2srEMqj62D5lKQ8yImTjNNZTsXdNofXj8MBxlpZgPH2w+DqkUVVwCUfMWYhg/kco/Vwf+JAKgtXgLd5q50ycYDET0SlVq4i67Gm3GAACKPv2Q3NdexF5S3Ob91n0mGn4uRBks0pTS0lLv+fCiRYuoqKhg2bJlbN261dsmLCyMTZs2MX78eP744w9kMhlr167lkksuCXg/Dz/8MKeccgqPPvooP/zwAwBWq5XTTjsNq9Ua9LhPrrKRHo7hUC3I6it0A5HEvipOA4l16K64AIlUhiA0zkKtzN2DxhhLSEwauohEKvP2I7jd3tt4JDI5gtuFtbrEV5de3C4HAHZzFbqI4K+2BIK/19ZXTIQvOjM6QiZVkBo3mVB9Im63i+yitew4/BljB16LUR/Y69ER42ptMkqRE4vbb7+d22+/vdV2sbGxPPDAA+3upzXOO+88zjvvvHb3IyLSW/AnBH0t91f56muZTO45Bruc9dLz5xWeL5q33BhCVbWb/75cy+49DpKTPF8nVSowmQQKC92YzQJKpe+7idxuKC5xs26DrZVnd+Iyc4aabRti2LvPwZGjTh56rBqA/zxuDGj7phXIHUlviMwQRfiJxWOPPYbZbOaPP/4gMTGx2Z2IcXFxfrYUEQmOzhB5vrKY6yRzl4hDwY1UpkBw159jOyw1mIqziRw8CX1sKlK5ElNhJgyd6m0jV3sKvhwlJYTIPYUYvsYsOBy4amuQHFY1q9ztDAncsE93mrnTKpUDfW8U4RHEXXY1pgP7kEilFH/9BXlvvkLqA494fYWISEczaNAgPvvss1bbGY1G7rvvvjbtY+/evbz66qvs3LmT5cuXk5mZCUB8fDzjx49nyZIlXHHFFUH1KQrmXoQvOdnw/w3Xt5bT3F2YK/IJja2frMNhraW68DB9Jl0MgFylQ6k1NPpjbTdVAOCyt3IAO/5lNG/HzxgTPFXPHUlDgd/S+kDoTNkaFpIKgFYdwV87/0u1KS9gwdxeAp2MsjP26+8CS0/43It0LVu3buXw4cNoNBqGDh1Kampqdw9JRKRTaItkC1Qc5u+vRmtUENOv/u/pC59KmDlDxZDBCtZv9MjhOrkMIAiQn+85wdXp/EdV2WwCyUkytu9wsH2nnRHD/E80eCKTlCj3ZlW/+EotW7fZu3wMvVXUitnLJxZ79+7lkUceYcqUKd09FJETmC4Tvl28T3ttJS6bGX18X++y6uw9uB02YobPAECm1qLQGxttZy713PUbYm+8vCnS4/E0ZSt+JPKMOR0y5qbSuLOrlduLRC5HP3goAKEF+ZSv+BFnVSWKsPAO6b87PpsiIhs3bmTu3Lk+J9Pt378/e/fuDbpP8ZJLD6a1Slh/0QOtVcl252RnEokUgfpqqNKjmwFPzIXn3zCs1SU47RZvG2PiIAAsVS3fihKePIwBp92IuSIfc0XzCQsCpbOEZEe97oH2o1J42uUWbwq6/5Y+X4HSpRnUPi60NH3c0oUBUUKfGOzfv59x48YxevRorrzyShYuXEjfvn254oorsFgsrXcgItLLuN64s9FPsOtbQiaTILjxVhJWl1jJ2lLhFaIpx8Xypi32RttcebkOAIfTf9xDRLiMDX/GEB8n46uvxd9NgCGDFWzZ5qCouL4CzVf0SUOp2hGC1d/nozfI255eZS0SOGlpaVRUVHT3MEROUPTHPP92l8ALJnO3YbxGoHgndm+QXV6yZy1ShQq52nNMVoWEYy7OaZRvHjfmDACslS3fJRw1byHGydOo2RLcOWVTpJlan9XIPV0uN0UZHQNAzY6trbQMju6cBFDk5KUuBqPpnUOHDx/GaDQG3Z8omHsoDeVenQBrKML8VS7Xsde+jj8PvkaW/liziTK6C0Fw43bWn4i6XQ6KD65FF5HkndTPmDgYmVLDvp9fxVTmuaoqkUiJ6DMGpdbY6j6kcuXxfbXtObdWpdybkErlxEeOxGQtxeEMPj+nLQTy+nXHBQ5fv0siJx7nnnsu8fHxHDp0CLPZjNVqZdWqVaxdu5b/+7//6+7hiYj4ZPMWO2MnFfKfp6s7NIO3PfLteuNObObGcVZrP8gCIPLSMQDExkqZOlnFQ49W8f5HJhzHJ/g7dbqn0ik7u/H2TZHLQakEt6tnfEfpbu663ZO1+ekSczORDDQTy8EI4LZcYGi6z56KKJlPDO644w6eeOIJNm3ahMvV8t8OEZFgqQ18uqAWcTsc5H/wNrmLX8Ve6lvKtjV7uSHBblt3fl13NDWX5WEpzSVmxKneNuEZYzAVHiX7t49xWGoAkGtCkKt1uKyNz4t8iU6JQtFIYNcRaHxFW9t1xUR+waIbNARZSAi129snmEWhLNLdnHrqqfz444/s3r27kWBetmwZH3/8MWeeeWbQfYoRGT2Uutv8WxJhdet8CTtLVRHWqiIOrXqbIm08idHjiI8cXn+Fs4sR3C6yNn6NzVROeJLn9pJ9P7+GraaM9DlXeselCY1h4OxbOPLnh+xZ/t/jEwLqMJfloI9O89+/4KYq/wC525aj1IaiDYv32zZQOlJCdkRcSVvym5Oix5Ffuo2yqsPERgwJaJtAMrxb2761vnpCHnNrmeYivYu8vDwyMzPZunWrd6Z5mUzG1KlTefXVV7nnnnu6eYQiIr4pK3eRmeXiuRdreO9DE5dcpOWm6/VERQYX89RQBgYr3ZrGDfy6ysqf7+Yzan4iAJuX5vDHO5mMXZREeKLWm9H70Xvh3PdgFX/7RyX/frKafn3lHM10olC0HJFx5KiTl16rISvbxemz1H7bnUyMHa1k0EA5m7fa+ei29sU/+Hv/G77PgeYs94Y8ZjEuo3cyefJk1q9f733scrkYN24cUqm0WSXVmjVrGD9+fFcPUUSkMYKA5chhBLuNY8/9B92Q4RgnTUGT2gfwHXPQMI+56fKm+GrTmoR0WGrIXPkBCq0BfUwqDlM1B758DoDo4/EYABEDJiCRKcj580sqj+5AHR6H4HbhtJqQqfxLXLfNRvXWTVSt/Qvd0M79O1snk6WZ2oCrmuvadaWIlkilhI6bSPmvP+O2WZGq2vY9pu4z0PA9FqMyRLqS5ORknnjiCcaOHUtERAQymYzvv/+eI0eO8NBDDzFixIig+xQFcw/Gn2RuKhp9ibq0U86n4thOdKpIFHIte7OWUVSxh5HpF3eLSMvZ9gMlh9eTNmERhjhPxosuIhFT2TFstRWNhLA2LI7Bc/5GZc4eaoqP4rRbCE8eSmS/cT77drucHP7jAypz96ANT6Tf9KuQyoL/aAci9XsbIdo4QILTFdwtyF3xGems17o9Yxclc+8lMjIStVqNQqFotk6j0RAbG9sNoxIRaZ3TZ2lYME/Dsm8tnDNfw7sfmPjkMzPfLY0kvV/zz3Nnk7urkgcvK6P/tGjmPuiJqNKHey7aWGud3nZ14vGl58O4/ho93y23kJPjZNxYJQvmaRplMzfkkyUm7r63klCDlH8/GsrUKaJgBlCpJAweqCA3v+uqN3uaPG7PeETJ3Pt49tlnqaysDKjtgAEDOncwIiIBIFUqib3oMgrefwv9sBHYCvLJe/MVIucswDhpaotisDVp2FAsBlrZKggCh759FZfNQr+5NyFTaRDcLtThsVjLCxGcDmhwThyePhpD0gAqDm/FUpYPEikxw2cQ1m+Uz/7tpSXkv7sYZ2UF+sHDiDx7vs92rQnh1uRvW/OYuzNaQ5XkKYt3W9sumOto+Nno0skhRUSAm2++menTp/PFF1+Qm5tLREQE8+bNY+LEiW3qTxTMPRx/crkOQRCQSCRUDdBjriigfPtfKIpNhOjiEdwuai1FDE47hyTHOLYf+oSCsl3oJk/qyqcAQE3RUSL7jCGqX331Qer4c6kpOkLRgb8ISxrcqL1UKiM8ZRjhKa2fLJRlbaUydy/p067EmDSkWdVDoNS9th0pPv3JyrZUNLdlTBKJBJlUgcvtbLYukGzijqCnSFt/F2vESuYTA5VKxezZs7nzzjv5z3/+g07nyZzLzs7m3nvv5cYbb+zmEYqc7NQdrwHKy138utrGt99buOl6Pcu+9VwEdDgENv4Vw1nzS/jnI1Us+Sgy6P20Nwohe1sFcpWUS18ahVTmGe/zCwopOJDOr68epqbUxt/6HWi0/ZDBCoYMbl2GV1e7+fv9lZy7QMNzT4WhVrfteH2iotZIvDEpde9jR0vTpv0GInV7moj2hyiZexcTJkwIepunn36aRYsWkZbm/65GEZGOoO6YLbjdWLOOUrVpPbqMAViyjgJQu3M7aQ/9m7L131L28/fohwxDHmps8/4aCsVARbPTWoutspi0WVegCfMUUsg1evqeeT17Pn6MsoObiB46tdE2crWOqCGBTaZZ9vMPIAgk3/UPlJFRftu1Ry63le7ObZYcL2hxOxwd2m/DindRMot0FYMGDeLhhx/ukL5EwdzLcDmslGVuQ18CxZYijhWuIzlmAtEDzmH/ijdw2ppLwQ173+CUIbcQljyUQ0W/MqCmP+qQ4E9afeG0W5DKFAFVDDeN53A57TjtFuTqtss8t9tF2dGt6CKSCEse2uZ+GtLVFcyduT9BEI5Pqti9GZddHZURqCRuSa6Lkrl3kZuby44dO9i/fz9vvfUWqampWK1Wjh3zzOzicDh4+eWXAUhKSuLrr7/uzuGKnCSs+NVKXr4LlRKefLYGg0HC8m+ieOs9E08/58lA/PHn+oz89z8yk5oq5+EHQ7nyunI++9zEBYu0rV44rZNqLUlAs8WN2w16Xf2xuE4cNtzOpa7lF4nEK5fr1qXU5ON2CVyo2UNbvz4u+86CzQb33GUQ5bIP3O7mEZdtkaaBCOGmcRkNlzddJiLSU1i6dClTp04VBbNIh2MvLcFy6CBSnY6arZuxZB4m7pKrkIeHk/e/1wCaZe7mvvES8U9cQ+26XZR8u5SYCy5BqlQ1ahNINbI7zdxIxApuN26LmepkLYZjAcRbShtHalVHmI730/Y7YtR7qrEcOohxyvQeJ5d7BHUH606Y60oUyyJdzfLly/nuu+/Iy8sjJiaGmTNncv755yOVBh+vKwrmHk6OsRCbqZy+9gFY7VVs++JV3C7PlTIJUvTaWLIL15KwfTQSqRSlLoyBcWdgSlCCIHD4jw8AqEyUkqyZx75fXmPXt08TGt+fPtoxyMcPb/PYsjZ8RfHBtQBEZ0wkdfy5ftsq1CHYLdXYzVVUFxxCGxZPzrYfcNmtpIw9p037FwSBzHVLqCk6Qt+pl7epD190ZkyGL3HZmftzOM243Q7USqPPsXQWvqqjW5LMnT2etiBK5t6FXq/niiuuCKhtWFhYJ49G5GSkoNDFt99bmD9XQ2SElMf/U82rb9T/XRsxXMGOnQ7eec9E3RxWH74bTnWVwKiRCk6ZVgxARYWbW29Us3CBhlvvquTNt0wsOlfDVZfr0WiaS9lA5PIff9lYeEEpAGFGCev+iCEyQubdvuG20dFSHBYX5ko7h9eXsTndzoZNNt5+z8T9fw8hIaFtXx3vXhbLR/dtYeECDWmp4tdPX+TluUhKDC57u634EsgtfYZ8fc7ak/sdzH5EREREOhLB5aJm2xYUERFo0vpiOrCPgvf+510v0+uR6fSU/vgtsRd5zjENYyeg6ZeOwhhO5V+/U7trO7jcyPJCiJ67iMIlH5H9zL/RDRpC2LSZKMIjGu2ztYrUOsnstts5+vB93uWxF19BXIjv83W5SgcSKQ5TFaaibBzmKmQqLYWrPkETmUDU4Lbdtey01HLou1eRqJQYxviOqKwbc2fTVL73FJyVFQAowsK7eSQiIm1HEAQuuOACli1bxsyZM0lMTKS4uJhrrrmG119/nRUrVqBUKoPqU/yG34MxW8vYv+J1AJyj51JRsNMrlwFkMiX9k09n8/532ZP5NQ5LNf1Pu4Hoqniqkj1ibOylzyAIAtLjVzaHzv07xYfWU5a5la25HyDL0jDojNvQhMYEP8AGFVXFB9e2KJh1EYnk7fyFHV//23s1VabUkj79SrRhccHvG6guOEjZ0S30mXwJ4R1UvdzZdGRkRiDIZSokEhkWW3mH9FdHS1K4JSnb0sSUHS3au1rmi3QvRqOR++67r/WGIiKdxDU3lLNxs53PvjBzyYXaRnIZ4JYb9Lz0Wi3LvrVw+IiTiy/QcuZsjXd9aV4CdruAUuk5tr75ShiLFmr57Aszjz1RzUOPVfPo/xm45cYQ7zaByjirtb7CpqJS4OAhp1cwN2XYEAUSCfx7yq8Ibvjs+PLJV6Siu3wAiyslQcu/NyuG8uU/f2XWTDWvvyRe4PFHeLiUQ4ecjeJUoOOjH/z1FWhcRt2YOjvGQ0RERKQjqZOV1RvXU/LtVwDEX3MjBR++06idKikFTVpfyn76nqIvPkWq0RJ59nykxyeRjr34cgTXJXB8Qkr90OGkJCZRtfZPanZspXrjetQpaQyYfQsEUAHYqHrZ4UAeHoGzvAyAqg1riTvNt2CWSKVoIxPI/WspDe9W1UQm0ueMq5HKgxNDdRRuXYHTaiLx9rvaFfvRVupej+6OwWgJmdYTxecoK0UZc+LN81JXeS9WU5/YrFixgt9++41du3bRv39/7/LCwkImT57M+++/z3XXXRdUn6Jg7kGUVR1Bp4nCYqvAZCnhSP5q77qcLd81a+90WTlWtAGAGnMR2rB4kirjoEGBk0QibeiBkSlUxA2aRuzAqeTvWknejp+oytvfomAuP7aLY5u/AcFNVL/xmCsLCI0fQMrYBaSMPYdNH92DIbZfi88tbshMlDojLoed0Pj+2E0V6KNSkCnaHorvsHpuLw5P7pzbOLuzsraj9i2VyokNH0J24TpSYic2iykJdBy+CEQu1wndpmK34f8DrRLuqZXOIiIiJydFxS7+XGPD6YTX3qxh81bPBeBdux3c98+qZu1feaOWI0ecSKRgsQr87c6QZm3eMw/neqVHrkkkEjJHj+PtmTvZsMnG2QtKWfqNpZFghsYyLi/Pya13VbJlq50LFmmx2QRCQiT87Y4QSvMSOHtBCRs22dnRdzS7KxtHZXiJhGveLSNnRyV9J0RirXEQlqAhIlnXqH0wElBwg7nCwVln6JDJxGgMf1y4SMv5l5Txx582pk1t/P0oWMncUnVxS321dNGi4TadkctcNy5xsj8REZGOxKrKQTBZEXa4Ma3fTc2qLd51+W+/0ay9ed8eJDIZMo0WW+4xwmbM8srlOoR+Ns+/eISoIiycyLPnEz7zdHJe/S/W7ExsNWWoQz3xEk1FneB2U/bLcqrWr0EVn4A6ORVnRTmhE6eQ+vcHMe3bQ8EHbxOXMd3nc6rrL+30q6g8sgOF1oAqLBq3w44uJiXoc76GOCw1aCITmlVhN6W1yf2gsST2NZlfsNXJ/vbXsK+uqHrW9EtHZgil4s/VxJx3Yafuq7sQ5fKJT1ZWFnPmzGkklwFiY2O55JJLyMrKCrpPUTD3IHYe+dz7fwlS4iKHYbKUIZerKKs67HMbhVyDQq7D4TRhrsgnr3QLiVFjWqwUrcjZTe62H7FUFQKgMjTOVXK7nFTm7aUydy8uh5WKnD0YEwYguF3k7fwFlT6cimO7KMvcSsqYBaSOPw+nw4apPA9deILPcUpl8kYT/GlCo4N7cXxgqSpGplAjaUM2TGs0fN38TQ5Xhy+B6o+W3peWxtEeqRoWkkJB2Q7cbicyWduuZPuiN1QDN61iDna8YkxG76K6uppXX32VrVu3UlNT02hdfHw877zzjp8tRUSCZ8LUYu//x41RMnOGCqcTfv/T5rP9jp0OrrhUy/sfeU6Obr+7gmVfRDbLV24ox06v3spVf6/iu+WenGZ5k6Lj64072b3HwedfmSkscrF+gx2ZDC65SMsHH5vQaaU4nAJLl1l44rFQ7vt7CBs22UnZsons0WORKXwfP/uMjaDP2JZPLAOVgIsrh1GS6fl9jIzo+OP1icSE8Z5j9LFc37mZbZWnTd+rtgpYf/uvW94RwlmUzCIiIh1N/gOve/8vNejQnTIUV0U1ErUKy/aDPrdRxcZh2u35G1T512pCRo3xm0XsTjMjOF1UvbeOyrV/4jZ7MpBtAww4Gnhpl8lEzfYtWHOP4ayswJqdReiESZgP7KPyz9XIQwzU7tpB6MQpGMadQvjpZ+GorcRhqkahMwDNpZ9tsJFo3bS2vTA+EAQBa0URmvDAqnIDkcy+CEZM1z1uTRp3ZZyGVKFEnZiEs6KsS/YXKB1VeSzK5ZODAQMG8O677za7cw5g//79zJkzJ+g+RcHcgxje90J2HPHcjDpu0HUYdPHedSs2eWZ1VCtDmTL8bnYf/YqCsp1Y7VUMTJnDziNLUIVEcjj3N6LDBqGUN//jaq4ooOTQeooO/EVo/ACvYM5c+xllsf2QSD0fh6r8AzhttWiMcchVOuIGTSNxxJlIpDKcNjMypYbqwkNkrvuC3T88B4BEKid363ekjDuXmP4TO/V1As/kfiWH1hPZZ0y7rtAGSmsytS1yORh52R6ZW23KR6MKa5NcDlaw+mvfkoRvTbq3V/A2HFMwr6Molnsfc+bMIS8vj9mzZ5OSktJoXWRkx0xsKiJSR/8MGQcOurjzVj0P3mfwfjF7/D9VvPiK5+/MlnUxlJW7mX12CQD/vC+UL76yEBsrZc06O998Z2HBvPrjdZ0YM1XY2ftrEc+9WIJGI2HCOCXrN9rZss3BhZeVEhoqRSGXsP+gg+07HERHSclIlxM2JJL3H4XUFDn/vN+AQi6hvMLNvQ9Uct3NnrxAlQpsNpgyaTWzXpmOXNn2Y2ggER3XG3dy79JKoqOlzJjW9ruWTgZ27PRUwQ/I6Piv54EI10AiL5pODtiSuO5OQdxaBbaIiMjJQ9gFp1GxZCXIZCS/cBdSredY5LY7yLr8UQAizpiDccp0jjx4DwAyrZ6Q0eMw7d8DThdlP35H7KVXNZMwgsuFZU8mlV+vxnowB8OY8VRv8MxRlPvGSyhjYpFIZbhtVkwH9gGgTkxGolAQc+FlhAwb4ZnUz25DqlRRueYPyn9ZTtWaP0AipRzI2/AdGfNuxTk6Hl/UpAY2mWAgmIqysJYXED5/XsDbtCSZm65rT+xFoNt2lWS25eWiGzSkS/YVCA0/A/4yv1vLAhc5uRg2bBgul4vzzjuPG264gYSEBEpKSvjkk0/YuHEjDz30EIcPewpdjUZjQOfTomDuQYSHpjFh8M1IJFL0msZXSCcPu5Py6iyijOkAaFSeyiJVShrKIaPQ5a/CYqrA7XZQVnWYuIjGX6IL9//JsU3LkCnUJI2eS+zAaez4+glCYvqgrhGwVFTgElwIgpuItFFE9RvnMxtZrvL8wQ6Ny2Do3HuoKT6KOiQSlT6crI1Lyd70NdqwOEKiO3d258K9v+O0mTAmDurU/TSkYdyDr+W+2gfSrrMJ0cWRW7IFk6UUnSZ4ydaSZA50Mr+WZG2wIrfp6xhslXdvqLwWCZ6SkhK2bNlCXl4eRqOxu4cjchLwxaeR7Nvn5NTpqkYnnPfcaWDoYAUJ8TJSkuXERNfnIoaGSrj1Zj1PP+ep6F3ypbmRYPZU+9by5qXrMVc6OPtMNS88E8bri2vJzHZy560h/LzCSkGhC6cDEuJl3H1HCLNOVaNQSFhcOYzU42JNp/WI49gYGe+/Fc7mLXacLhg7WsnqP2xcdnUZzuf2M/f+9h9HWxLNhw47eOd9Execp/HmS4v4ZvBABTKZpwp+7BiVzzZdUZ0bjBjuTdI2kEkMRXo206ZNIzxcnFRLJDhCT5+Aql8iqrQEr1wGkCoVxD96Hc7SSnQThiDIrN516rQ+qPv0pWb7FnC5MO3djWCzIVHXby+4XBQ9+wnmbQdQRESRcPUNyHR6qjesJeKMOVhzsnHV1HjmH5JIiTj9bAwjxyDTNz73kUilyNSeORnCpkxHP3Q4sh0F6GJSEAQ3h79/g8O/vkPy4HuQqjvvQq3gdpG3dhkSqQxNv/SgtvUlmZtWFDeNy3CnmQOKtghWLHdVJbMqIRFL5hEEpxOJvOdpNV8yue5ihCiZRQDee+89Nm3axKZNm1i6dGmz9UOG1F9AueOOO3jhhRda7bPn/Sac5IRofWcha1RhJETVT4xjsXlux4gbNAOXw4pcpsLt9lS+aFWNv3hV5R/g2OZviBkwhaRRZyOVKajM3YvdVE4cM4hLHdZIugUq/GQKFcaEgd7HqeMWUlN0lOIDazpVMFflHyB3+3JiBkzBEJfRafvxue9WXptA17clqqGtxIYP5XDOSnaX/sj4pMva1Ed7K5kDqf6uGqBvsfq5JxDs74hI16FUKtFqtaJcFukyQnRSZs5ofqKn0UgaSePsY04A/u9+AxJJo/lxiYttnHlxoXQ7p91WTGKUhC9+iSEhQU5FhZsvvzYzdLCC667Wc93V/v/++BNoEomkkbA87VQ1d94WwvMvZnPW3wcgk3fMnUBNK1qrq91celU5GelyHvm/0A7Zx4mMwSDl/HO1PPVcDZdcqCMuzvdEjE0lc9P3vS2yNBipHKjkDibmwld/wW7fnogOMVajZ3D48GH279/PxIkTCQ8Px263o1AovBfxnnrqqW4eoUhvRTvU93xB6v4p0D+l2XJVbBzWvBwkcjmCy4VEpUKibHw3aPnHP2PecYjYS69CN3AwEqmU4mVfglSKYcw4Qq+c4G0bjPBUGMNgWhiW44+jo67k2HP/wbR/DyEjRgfcT7Dkb/gBc2kefU6/uk0RlL4m5wtEDvsS023dd1cSNm0mua+/SOW6vwibMr3L9x8IviIzRLksUsfVV1/NggULAmprMBgCaieG4fUCfMmsASlnM2X43bDtAAe+fYlaczFxESMAsNgqve3cLgeZ65ZgiE0nefQ8pDIFgiCQu24ZEYa+xIbXX5XwJ/gCRSKVIZUrkSp8V910BCWHN3L4jw8IjcsgefS8ZrcpdReBvnah+2u7vHpWLlMRGzGUmuJMyvu0/ZpSS+P29fw7Ssa2tn1rr6mv7YMdU9N9iBXQPY/Q0FBOOeUU3nij+WQtIiLdSUa6nHW/R3P+uVpuuLWCp56tYf4cDXq9hLz8xlm7Tz1bTVGxm4/ejSAhwfP3+n/v1lJW5ubJfxk7dFwheglqlaTD5HIddZJu334H515USkmpiw/fiSAq0rcsFWnM5ZdoEQT47Xdri+1aiyUJhraI2euNOzt0kr+O6K8zYjVEugan08lFF11Eeno68+fP5+BBTy7uU089xWOPPdbNoxM5mUi595+kPfxvKtf+Sd6bryDXG1Cn9UWw2XBbLN529t9LqFq+lsgz56IfPBSJVIq9pJjqDZ5lMl39uUZ75WfdBINSZeecY7scNnLXLKV452oSTplHaErn3yHsSyJ3hCRuTwRHsKiTU5CHhWM+sK9L9+uPtojjjopWEemdGAwGUlNTG/2Eh4djtVpJTk5utjwQRMHcw2mYHdtQisllaiy2SrYceB+zrYwwQyoRhj4ACLi97SzrNmA3V6E0Q03xUU9f+QcwW8tIjB6HRCLtsGpMQRCwmytRaEJab9wGSo9sJnPdEoyJg+k75bJOmdyvswlElra0bVviJKoG6DHoEhDcLrT7yzu1+rY9krklUdzSJIuB9Nte/FVWi/QsHnnkEW677TbS0tKYPHlyo58LLzwxZ3gW6flIJBLS+ykYN7mIpcs8J6eXXqwlIV7WqJq5utrNm2+bMJkEflttxW4XMJndvPeBidNmqklN8X2BsG5CtECpa5uX7yI6uuOPo9cbd1Je7uLsc0owmwU+/yiSvu24uHmyMWiQAoUCKivdrTdugZ4iS4OVvk1Fc7Dbi1XIvZNXXnmFw4cPk5OTw9ixY73L7777bl5++WVsNt+Tp4qItBdpprbRjyIsnIrVv1L63dcIDgfGyVNRRsd4bj9qcMyu+OM3AJw11Tgqyj3Ldv7qqV4eP7FR322hobB0VlYCIAvxf47dnqrUY6uXULZ/I/ET5hE1ZErbOzpOW56zr+rnYKiL22j4uCtQJSThtpi7dJ8t4e9z4E8kd2R+t0jv5Pbbb2fNmjUA7Nmzh5SUFAYOHMipp56K0+kMur/eZ+hOMlqSWDp1BFHGAQCUVh5gd+ZSQvVJRIfVx1bYnZ5ZbKvNBexf8Tp7v3yaI6vfx6BLQD52WIfKxtriTJzWWvQRyR3WZ0MqcnYREt2HvpMvRq7UdMo+Opo6aepLkAZTWdtwedOLDg1//FET4QaJlOo+6hb30xqBSNX2SOZgJvprr2QOVvaLkrlnIwgCF110Ef379+e8887jtNNOa/QzcWLnTz4qItIS995Tf2K46OIyiopd3H17/bLSMo9QlErhwYerGHVKIVNOLcZkFrjxWp3PPtsahfBG2VCW/OBi0EBF0Nu3xuLKYTy8IpbqaoEvPo5kzOjgJ5g9mTGbBZxOcAutt21tUr72xFN05HZ1F0E6ez9N99cWrgrd06btRNrHr7/+yv/93/+RmJjY6O5EnU5HdHQ0+/fv78bRiZyo+BOhuoGDUUR45q0p+eYrqjeuI2zaTGTa+mNxXTVz9ab1HHv+SY69+Qw1KzdhnDsZqaL+2NpUfPojJKux6Gs4tto9nr/lyphYb9uOQnC7qcreTezo2cQMn+79/WvvPoKV64G+Tj0Nt9WCoHQjuNt3UbgjqUmt/wm0vcjJydatW1m/fj2TJk0C4Nlnn+Wcc87h0KFDlJeX88033wTdp1hS0gupE122dfsoqaz7wiVh/KAbCNHGIpFIvG1SmYRx2ixAQnn2dqz79xHSdwhxg2cgk9ef9HVEzq3dXAXAodXvkjRqDrGDprWrv4a43S5qS48RkTqyw/rsChpmW3dkfm+gfdS9r1H9xpG74yfKsrYxUD6+XfsO5LPSMGu6qRwPNI85EHxlWvvLufbVdyDjCaa9SPeRn59PXl4ehYWF6PUdd+FMRKQjKCl18czxif0Ann/ayEXna1Eo6kVKnzQ5xTnxSKUS9u5z8NkXZkwmNxdfoGNLnzFsq2wuDNtapeq0uzFVOvjhRxf7yjdyxWujUag7LsLi2LZKQiJVxMeLsRjBEhUpY+ECDV98Zea2m9p3R1hPrOZtaULIhgSbidyabPc1jp5S5S0CZrMZ5fF824aCWRAESktLUXfixGYiJzdNJ54TBIHqwxtwlJUCoBs0hOjzLkSmaSxL46+9CQQBwWGn8tAfOPJLMMwcS8hpY3FL6ieZ8zX5nT/8iT5nVSUA2c/8m76nXQMxKX4ncGtIIJLYXJqL4HKijUpqts5Xfm+wBPP8O5q6/XZWPrM7zUzoueMpfOpDHLnFKJNju2yCwUCpe+8aXsAQhbJIHVu3bmXkyHq/tmLFCn788Uf69evHBRdcwI4dOzj33HOD6lOsYO4F+BJlwuZ97Dj8GQDGxEGcOuoBDLo4n5nEEokUiURCROpIBqXOI2nkWc0qgDtCeoanjmDArJswJg0mb9fKdvfXkIpjO3FYqonsO6ZD++0KunJCOH+VzDKFGo0hGnNFfruztoMdT0M6W852x+R7onDuGURGRqLRaES5LNLjsFoFBg4vxGzxlKT+viKayy/RNZLLdUilnmWDBip47KFQnnsqjNGjlEHLs9a4NW4Pt381ibPvG8jRDWVkbi4PaLtAhJzd7GTz0hxGLUho7zBPWkYMU3I004nJ3HpFVEdnIbdF6rZ1P+2pNPZHoK9HR/9OibSPyZMn89577yEIQqNzmaeffhqFQkF6eno3jk7kRMXX5HJlG76hZuVGACLnnkPspVc1k8vguRAikUphgBvj3MlE3XAOxn7TkWXpmglGX8KxTvbVib+WpF/0gkXEXX4NEoWSkj1rAnpudX22VslasvtPlCHh6OP6tNiXiG+UaZ7vOrbswm4eScs0/Bz01PezN1aw93YMBgNHjhwBPLLZbrczZIhnjraKigpCQ4OfoFsUzL2EpjEL1aZ8AOKHzSJ9+tXUDg5vJA79VYJ2poCTSCQYYvshkcpQ6Ywd0qettpzM9V9SfGAtmtAYtGHxHdJvVxNIjEVn7K+O0P21hCYMoDxrO1UFB9vdf0+Rqi3lNvt7rYMde9N9iFEZPROVSsWECRN47rnnunsoIiKNqKhwI5dDXKyUfTtiGTyobbEUHS0S7x9+hIgkz0mvMS6w2KmWZJzd7OSPt4/y3X/2YTO5GHpGXIeM82Tk1OkqbDa478GqdvUTzGemrbK3oz+XTfvu7G0biubFlcN4t2pwm/cp0nbuuusudu3axdixYzly5AhPPvkkI0eO5MEHH+Sll15C2gvnXRHpndSJwvhrbsQ4cUqrE8o3zG8OhjrZF0iUgVSlQpsxALfVilJvDKp/XwiCQE3uQXL+/JKa3AMY+wxHIu28O466o6K36T47I4JDmqlFWRGNKj6Bsve+x22ze/fVUwkmOqM76Mmv3YnI6aefztatW5k5cyYLFy7k8ssvRyKR4Ha7WbFiBWeccUbQfYoRGb2U5JjxJEWPo3qg79snu6OSs47K3H2emXRNlSjbIZqrCw+zf8Xr3sdqQ3QHjO7komFec4Iwm7LMrZQe3khoXEajNm0RpO2N/GipujnQyJamYw8k0sJfhEZrNOzbV78dGYEiEjy5ubkcOnSI77//nueee474+MYXo5KSkvj666+7aXQiJzNxcTIKsuJbPUntahZXDmP/H7sBqCq0EN237X+7nHY3L57zFxW5lgZLAwgRFvFJ/wwFd90Wwptv1/LE46GE6Nsn13xNmtfRUjjYSItg+u0KOmv8IoFhNBrZsGEDr732GqtXr6a8vJyRI0eyePHiRpP+iYh0NrH/uAxpprbDjtkdFdNgPZaN22LG5C7D7XIilclbrXz2R/6G7ynesap+gdD5x+v2TuIXLJ014V/TSBWAmPMv4dgLT2NZfgTdOQO97XpSVIaIiC9CQ0NZv349H330EfPnz+eGG24AYP/+/Vx22WUMHhz8RXdRMPdiJBJJM6HWEyop4wZPp/jAWvb+9DKDz74LhbptJ60VObuRq/VEZ5xC/s4VWKuLsZkqUOnCOnjEgeN2OZDKOn5SJH90pLSUSKTE9J/MsS3fkjx2QbdnC7cmkTtb2LZVNLfUX121syiZux69Xs+VV17pd31YWPf93RARCeZEtSuyYetE2pDZseTvrebd6zdz0fMjGHp6fdWxP+HWdGyLK4dRsL+ailwLp9+ZwcpXD+FyCBz6q5TFA7sv59ZuF5DL62NHehtXXqbj5ddrWPKFmWuv6rhjSmeK1LbGToh5yF2LxWLhr7/+wmq1csoppxAZGRnwtjt37mTv3r1MnjyZxMRE7/KffvqJysrKRm379evHmDGBRdsdOnSI9PR07r33Xu69996AxyMi0tFIJJIOl8sdgTImBv3wkdTu3kl2tYvUWVcgkUgCksxNIxEqM3diSBmM4HJSk3uA6tz9JDCvw8baFtx2B1Jl151jdyTKmFg0/TKoWrfGK5hBlMzB0p153SczGRkZPPbYY42WDRo0iEGDBnkff/nllxQWFnLrrbe22p8omE8AAp2krKvEV8Kw2UT1G8+u754hf9evpIyd36Z+7OYqtGHxxA2aTv7OFQCUZ20jbvCpHTncgHHazGz78hEEtwuA2EHTUeqMKDUGFOoQFBrPj1SuAjzxHuaKPAyx6c0yr5tS1ff/2Tvr+Lbq9Y+/49JG6r6u69zdmQ8mDBnDhsNw5164F9eLu45x+aEbMmAMxpANJjDm7tZ1q6zubdqkSX5/5KZL03jTJt3O+/XKq8k5X3lOctKT8znP+Ty2HzK6o82vIAf784pOH8CJLUs5uXcVnYbMajFXoJnM3uK0t7HP4aq9p+J8nsYPJIvZ17m99XGVHR0OF3nORPR6Pf/+979DHYaAQLsSaDZqU/tp8PzZEUy8TsmyZ/fTZ0oiYonI5Xie5qgsrAdgyEWpHN9RwYHVRez6+STj5rn3dGxrbr2rnKU/2jKqz5+lYuAAGQlxEuLjxcT/7290lBixWER1tYUNm4ykpkjo1TM8TnCTkyWMGCbnw49rueQiNVptcC0C2lLUDZdsYH+38Sb9Lt6s6eW9YQdm165dTJ8+naioKPR6PTt27OCjjz7i4osv9to3Pz+f6dOnk5+fz+LFi5kzZ07Tun/+85/I5XK6dz91h9zUqVN9FpivuuoqiouLueKKK7jyyiubjSMgIAASdQSJl11Fze6dFCz6hCLzYRKk3f0Wl61WK6baSuL6noVMpaE69yD1ZQUYyk6iinZvbRVotrQv1G7ZT+HLCwEQR6rQTR+FRKdBoo9Eotcg1Uci0UUikkmxWiw0HMvHUl2Hqn9Xmw92AASaWe7Y3nGMyP4DKf7uawx7s1D16eKyjYBARyU3N5fs7Gyf2goC82lOsLM0fUWu1hHdqR8VeftIHTgNiUzh9xhWi+32H0ujCQCRWErhgb9I7D0xJLcaVxcdaxKXAcqO78RkqGq2zI5ErsJsNPzvuZohlz7tdly7uNweyNU6UgfNIHf7ciJj04lOb34C2FYCqbPg6y+tFZl9jc1fBEE5PKmvr6e4uJi0tJYVsQUEwhVfxeLWCnfOoptYLOLpG83MvrSB7K1ldBke06y9LwKd2WQrRidTSDDVm5EqxJw8WE32tnIWDDkVb3tmqdrFZYD9B0ysWlNPVVXL24Dj48UUFZ0qprd7SyJJSe69KNvKYsIVTz+u48JLS7jrH+V8/EGM9w4B0FZCs78isz9x+PoZ2GPwZ/uu0+3lKe/NOixXX301o0eP5uuvv0YkEvH8889z/fXXM3HiRI+ZzBaLhSuuuIJ7772X+++/3+3Y99xzT0BxffXVVyxatIiFCxfy9NNPM2zYMK688kouu+wy4uMFizyB8MeVkNgWomJE3/5IIjXU7NqBerb7CzGeCrlZzeZm59himYLiPX/RaZznC032MQMVmt1lp9ZtO3gqtgYTVb9twlxV28K6QySXYTWaml5HXTKZqNkTgxJDIDhm3GoGDqHm0BYKXvqcTm/ch0TX/PxSyGb2DeE96vgIFRM6OO6Kx7nKrGzv2/bju4/BZKjixJYfAuovlsppNBqQKiNRR6dgtTSCyP9d1lhX6VIE9hel1vbDu+fUWxl+1SsMnP0IQ+e+wOBLniamy5Bmbe3iMoClscHtmO0pLttJ7jsZeUQ0NSXZLtf7u6+0137laxayY3tPRQB9GcOXGMLNouZMprCwkPPPP5+IiAgmTrT94GxsbOTss8+mqKgoxNEJCHjGlQhmLzzm+PCln7+MGiGn00A9PzywhculO/zur1Db8hUMVSa6j42lscEm2Dof4bwJjtXVFiorLR7b+Eq/vjKunKumJC+Fv1cnkLU/mZwjyfz3veZ2OY7iMoBa7fq47Pj+t5dQ3q+vnOuuimDzVmPAYzi+5877kP15W1tm+EpbxREO2dThwJ49e9i5cyf33ntvU6LG7bffjslkYunSpR77PvPMM0RGRnLLLbe4bXPs2DGWLFnC5s2baWhw/9vXFenp6Tz44IPs2bOHHTt2MGHCBF566SVSUlKYMWMGeXl5fo0nIBAogQhc7Xlbv0gkImr8JKo2b6Bm3x637ezF3JzFYJFIhFgmp7HBQGRSF0QSKRZTg1/JW5pssFosmCrKA9mEFsiSYxHJZWR88TQZnz1B+vv/JuPzJ0h7/V4k0dqmdo7iMoClPvBjY7BoEpm7m4i5ZibWeiPGXNfnHG1RaFBAINwQMpg7GB1JzIqISSW531Ryt/+Eqb6a1IHTUEcle+/Y1L8T5TnLsZhNdJ94A8WHNxDdeaBfB8CakhPs+/lNojsPoOtZVwWyGU1I5bYfHGZTfdMyq8VM4cF1lGZtBaDn2bdRfGQjVScPIVVGoonvQkq/qS7HC4W4bEcilWE2hf6g7K9tSzB8mf3Nhg5kXKHgX2iYN28ecrmcb775pinDSiqVMn36dF566SVeeumlEEcoIOAZZ0HOnUDna1amczt3QptMJuKHBQoGDreQ0eMkb7yi54rLIryObSeljw6A7K1ljLkmA6lCQmSMnM5Dor3GaMdstnLOrGKKis1s+jOB6OjWVbSP0ouprm6e/XTgkIl5t9pOiF9/WU9ZmYVFX9XR0GBlQH8Z118dgU7X8kK28/vWnp7BKpWY2lorVqs14Lu3PAmsbb0d/voyexPx/RWL7VnMp7vPc1VVVbPXCoUChaL53YO7d+8GoG/fvk3LNBoNnTt3blrnij///JP333+f7du3e4zhxx9/5OjRo+zevRuJRMKiRYsYOXKkv5vCgAEDGDBgAPfccw+3334733//PTk5OaSkpPg9loAAgPi4GrFS6VXcC0b2pK+WCK3JBtaNGUfN3l0UfPZ/xPUdS9LQ6UgUp+wYncd0fK3JBnVcJ2oLskgcNJmuM2+h5mQWcf3O8iuG2iUrKNjyKwmXzEUzyDcrHHceu5JIlU08tlhAYjv2m6vrKH7vO8xlVagGdEN3zkgqlv2FKb8YWUIM6qE90c0Y3WKswWm5bMtJbbHcV1ojAIsUNostb8K3kM0scDojCMwdGEc/W1eCmCchrb0EsIgY2z/4ity9VBdl0XfmfSgifTvhjE7rS862ZRQfWk9i7/GkDDgHgPqqErI3fkNN6QkiolLoNvF6tx7HJVlbACvlJ3ZjrKtErtZRX1WMqb4GTXyGyz5Wi5nNCx9AoYmhy+jLyd+90lbY738ndjKV7Uqq1Wrl6J+fUZ5z6upxY30NmWPmet22UIrLAPKIKOrKcj22aQ/7B3f7n7e5nfdff+MMVGQONF6BtsdgMLBq1SqKi4vJz89vtm706NHceuutIYpM4EymrTJefRWXHf9665uacuon4d3/qMBoBFc1f1wJfJo4BZ2HRPH3Z8fpPyOZUXPTAWg0Wljx5iG2LslFrpJw4VP9YIbreLdtN3LocCMAS34wcMO1kdQZLKxa3cA5U5VIpa6Pm48+Wcl7C2rYuz2Rp/5TSXGJhZQUCdt3Gpl7yakTuO9/qGsSlwFW/F7PJ/+N4a7bNa4D8rC97UlqioSaGitZx8xkdvH/Z3ughfeCTSCWGfZ+rsbydxz781CKzJosCRJF6y6cOGNusI3nbAn1+OOP88QTTzRbVllZiUQiQavVNlseExPTokCfnbKyMq644go++OAD4uPjqalx/Vvn5Zdf5pxzzkEkEmEymbj66qu55JJLOHDgAGq170JKdXU1S5YsYeHChfz++++kpqby4IMP0qNHD5/HEBBwhS/CYXsKf63xMxaJREiNtuNB8Z6/MJTm03XWbYh8vNM3KnMAOX99S315IZFJXYhMsnkGV2TvIX/jMhrrqonuOZzUUe7rKJUd3AxA5eaNRPYfhEgioS7rCLKoaGRRrs/1jSXFnHjwOWKunwUWC3VbDyDRRmAqKEWsUSOyi8uVNeQ99B7mMtuFM8POwyTcfSnJgz3/Hxicltv011eROZgZxdJYPQDGY/lEDOkZtHEFBDoSgsDcgfBVLPbFi7a9MitV+sSm52ajgcIDf9FpqG9VahWaGOK7jSB3xy/oUnqi0iXQUFvOvl/fQiJTktRrPHm7fqNg/1pS+k91fVC1WpEq1IjEEnYtfYG4zGEUHvwLsVTO0Mufczlv/p4/AGioLmX/b+8gV0eh1MZirK0gNnM4ETG2H/E1xdmU5+whqc8kGmrL0cRnEJXW1+WYzuiOWluIzM4F/toSfWofjm9eYrMg8VKA0BNtUUDSPo4vom2gRfZ8idVf0djd9w+gLABRQMA/SktL0Wg0qFSqFll+DQ0NmM2tt8kREAiE9ha0WpOt2jldQvZx23flhZerOHC177FPubMb/712E3/+Xxbj52VitVr5+l872b+qkNFXdubAmiJ+ffUgaf2Hc1fq/hb9zf9zqujdS8q/Hq7k91UNbNjUQFWVlXWr4unRvWXxvTqDhfcW2P7P9hlUgEQCUycr2bnLRNdMKfOut/1fNputPPN8FRPGKejRTYoVuPM2z8IyuH4v2/vznDpZiVgMf29oCEhgtuPKj7i9xddAxG5vbd3F7a5fqEXmtiInJ6eZcOycvWxfZjabqa+vR6lUNi2vqalp9tqRxx57jLi4OKqqqvjyyy+brC/WrVuHXq9nypQpAEybNq2pj0wm47HHHqN3797s2LGD0aNbZhk6s3LlSv773//yww8/oFKpmDNnDqtWrWLs2LEhqbsicPrhLnvWmY6SXaoeNZiabw8DUHMyi5r8ozCmm9d+1Z0h2jycwp2rOL5qEd3OvxOxREpF1k6OrfgUbaeeqGNTKd61hqguA4lISHc5jtVqQalPoD47i+OvPo+6a3eqNq1HP3Y8sTNdC9MnXrGde5f+348AKLqlYTU1YjE0EHPFqf8hlT+vx2JoIHruOTRk56OdPAyx2vX/KFe0JoPZFfb9wdv+IxKLUQ/qgWHfMaI8tuw4+5mAgL8IqkcHx5UI5ix0BVP88xe5Sku3iTdweNWHANR6yZp1Jm3wLKoLsziy5lN6T7+LnK0/IhZL6D3tTmTKSIx1VeTv+o2So5vpPHw2+tTezfpHxnWm6NDfaBIyUekTKT6yCaDJqiNn+3KqCg7Tc+qtSKRyW4wlJwBQauOJ7TKE2MxhyNW6FrGVHN2EIjKa1EHTfb5i7Eh7CsrO6JN7cNxqoerk4RaF/joCrsRcR2HaeV1r5gm0eKAj2sNCdnNbk5SURGNjI7t27UKjOSUcWa1W3nvvPYYNGxbC6ATOJOwCVnsLWY6CmqtsUV9iWbo4lsFjijCbrJSUWng9uydqvbxZf3eZqF2GxTD+xi6sePMw6YOiqKs0see3Aua+Poi+UxPJHBnDZ3ds5dmz/qDiRjX/+ocWheKUcNS/nwylEoqLLdw8L4LfVp4qypcQL2HLViO33V3G88/omTTBdqK5efOp21AfekDLoIEyJo5veRL69wYj2cfNvPtGFMOH+VZ02PHza2/vZUeiosQMHSzn91X1XDXXs22JN1xZsIRThq+vOH7H7K+h+fZ5ypjuKNvpD1qttkVmsjOZmZkAnDhxgu7dbcXBLBYLubm5dOnSxWWf7t27U1JSwvfffw/Y6hoAbN68mcjIyCaB2VU8AMXFxT7F/5///IfY2FgWLVrEjBkzkMvlPvUTEPAFS3odqG1XMX0Vmn0a18EOw3FM++u2FBA1g4ZiLC6iYu0qACpEBejxLjADiCVSMqZcw6Hv3yB/ww8kj5jFibWL0XfpT+cpV2ExGakrzuHQ928QmdyVTuMuRqGLazZGZGIG5Ue2E9VtCBZjPZWb1gMgjY7B2thI/kcLkMcnEHf+RYDt97gd7fBRyOPi0VwxBJG4+Tm01WKhes02NGcNRH+ezbbDlpnsPSs52MKyHX/2F9XAbpR+uhxLXb1foriAQDgzbNgwunbt6lNbQWAOIwwNlShkjchlwRWDQ+0DG5Xamx6Tb+Lg7wtsVhN+IJEp6DruavYuf4PDaz6moboEfWpvZErbNnUeOYfYzKHk717JoVUfEps5nE5Dz2vKyo3uPJDGhlpObFlKdPpAhlz2H/J2/cbJfauoLc3h5J7fAagtPo4msSuFB/6iIm8fABmjLnFro2ExN1J2YjcJ3ccEJC6HGoUmBqU2jsr8Ax4F5mCKtYHgSUj2ta9zf/trf8bxN5NZsMtofyQSCU8++SRnn302l156KTU1Nbz00kt899137Nmzh61bt4Y6RIHTjCNHTQwa2FKsdBRi2xpPIrKv2aKOYltKipTHN03lsUG/ASCV+3d8m3JHN45vL2fRvdvpNDAKRYSEPlMSAOg+No5//DyeLd/m8s77R/ltZT3vvBHFgH42IUmtErPo4xj+9UglO3aa2PRXAvsPNDLh7CI+XVTL5i1Gso6ZWfqjgUkTlGzfaeTRpypt23pDBPfd7T4jecnSOjqlSRg21HfRytV7GSomTVDwzvs1mExWZLLgZnO2tYWGKzHXX7sMd+O6W+btYsiZysiRI4mOjmbx4sU8/PDDAKxYsYKysjJmzDjlXbN8+XLi4+MZOnQod911F3fddVfTupqaGr799lvuuece5syZA0B5eTlqtbpZ1vR3332HRCJh8ODBPsX222+/IZP5d44gIOArDScKUXZPQyQWexQL/RWEHds7923r7FSRRELs9FmIJFLKV61A5Of3Rx2XSsroC8j961usFgvmhjqiuw9FJBIjkSvpOecfVB7fR/6mnzjwzSskj5xFbO9RTee9KSNtdySXH95Kn7mPkHH2dRxc8jp1G7YQUa3EkHUEQ9YR4s6/CHO9gZIfvwdAotURe+4F0N3kMq76A8cxl1cTOXYAcMr2oqOgHtCNUrMFw94sIob19thWyGIWCAUFBQVkZ2f71DYpKYn09HTGjBnj8/iCwBxGbNj7LjHargzu4V8xOldCWTgJXFaLGXOjEURi9Cm9/O6v0ifSddxVHF7zMVaLmbLju0gbPAuJTIFIJEITn0H3SfMoObKJ41uXUlt6gl5n345UoUYslqBP7c2JLUtt7cVikvpMoPTYNvYuf71pjpP7VpG/9w+qTh4ivvtokvpOQhHh/uaW+soizEYDkQmuBeiOgC65F2UndvpUOMh5H3PMjndc1lZF7twJ3Y7LPc3pSvQNVGQO5R0BAp657bbbiI+P57XXXqO+vp4XX3yR0aNH8/fffzdlawkIBIupM0vIOZKMStV+t28HYn/hTWRzXl9xspbE7hrkaglytdTl2O7GlEjFXPbyQD6+aQv7fi8EYP+qInpPsonM+iQVU+7oRp+pCax+dBPTzytm4ccxTVnH485SotFUodGIEIlE9O4l48brInjqP6cKmC3+rg6NRsR/P6qlbx8Z3y+OZcwoz8Lxxs1GRo2Qd9hb7SdPUvL8y9Vs3mpk9EjfMrD9pS1F9FBmgLvbV0/HLGZvyOVyXn75ZW6++WYMBgNRUVG8+OKL3Hzzzc0K/913331MmDCBoUN9K9yVnZ3NNddcw+zZs+nUqRNbtmzhww8/5IknnmjhDe2I0WjEYrGgUCiwWq3U19e7batQKDrs91cg9Jx84gPibpmNZoL7Cx6hEvo02YH7MVsa6rEYjSASoe7iW3ahI7G9R9NQUUTxnj8BKDu0BV16HwDEUjlRmQPRdupF/oYfyf3rWwyl+aSdNQeRSIQsQoc6Pp3yI9sRy5WIxGJSR1/AkWXvcXzVoqY5ytf8TuXG9Vjq6og9bzbaIcMQy2RYcCMwHzwOEgmKrqnNxOW2yk5uDc7WGeJjahSokSXGULfjUJPAHMyseQGB1vLll19y7733+tT27rvv5vXXX/dr/I6XenmaE6mOd7vOUUTzh1CJzQ01ZRxZ+xlbv3qUI2s+RpfUnbhuIwIaS5/aG3W07cDS2FBLdVFWs/UikYi4biPoM/1ujHWVHN/8/amV1uZWFBKZkj4z7yWhx1j0qbaDaOXJQxjrKuk+aR6dR1zkUVwGsJhtB0WxKLjFWoKBfT9x9XBEn9oLU11lkyWIP1T2jGx6uFvvGEuw8DRfIKJvIB7LAuFJZWUln3/+OXPmzGHdunVUVFRQXFzM0qVL6dSpE59//nmoQxQ4DXFjW9qu3KTf5VUo87b+uoid/DH/CC9OXc2rM9ZSXdrAeY/08amvM9o4JUPnnDoR3PNrQYs2ST20XPz5JMaNVXDz7eWUl1ua1tltMew886SO11/Wc+4MJXq9CLMZvvvewO23RLL8+zjGjvYsPFksVoxGK9IgZ/62JwP6yYiPF/PjT4ZQh9ICf/2U7Y+2xJc5zjRx2c51113Hb7/9RllZGfv27ePVV1/lvffea9Zm5syZbm2lZDIZl156aTPheNCgQSxfvhy5XM5ff/1FbGws69ev55FHHvEYy7hx41CpVGzcuLHpubvHxo0bW7/xAmc0spQ4t+t89dhtCwIRl+sOHyR3/ptkPfUIlX+vJeacmchiYpvWa7J9G0ckEpE45Oym1xXHdmExNzZrI5EpSDtrDp3GX0rp/vVUHN3RtM5stB+TbMfXyKQu9Jh9L/ouA5HFxSNWKCn7fQWymFjS7voH+lFjEcttF0ndCfpWYyMiqe38OhxEZfExtduHcxs76sw+1G08iOiIomm5c5twQRC+zzzuvPNODAYDBoOB6upqhg8fzq233sqePXuoqKjg0KFDPPbYY3Tp0oXHHnvM7/GFDOYwIyXWt1vJwHMmpT3jsi2KsPlCQ3Upe395E7FYij61NxZTA6mDZmCsq8JYV4HVYkab2NUve4nkPhM5vOZjAMpP7HKZDa3SJZA2+FyyNywmOr0/+pRemBpq/7fulHgvlatIH35hQNtWU3ycYxsXo9DEEunGQqMjoE3oijwimrzdK+g+8YaAM0PcZfeGwiqivfZxb98nwSaj/SkuLuaJJ57gyiuvdLnuySefdLlOQCBQHnxA4/L/pq+Zkb5kdboTyYIpjlksVm68rYw/fjtJv3MS0ScpGX5JGppYBbl7KvmlzsCIYQqiok4dr72JdwNmJLPsWVshv0PrimmobUQR0fwnp1Qu5o1Xohg5rpDHnqrkuWd0LDINxGj8ja6Zp9qKRCKuvDyCKy/333+4vNzCE/+p5Fi2mbdeC78TO18Ri0Vcfoma//uklttujiQtNfQ/3529mx1pa9sNfxGsMpozYcIEJkyY4Hb9K6+84nadQqHgyy+/bLE8NTWVhx56yK84FixYQFVVFX369Gl67o4+ffr4NbaAgCPShGgUmSlu1ztmoIY71bu2U/jl5yg7Z6Du2h1ZbByaQUMwFhZgqqxAqtFC52S3/Z3FZ6kyAmV0EvVlJ8FqperEPvQZLf9fxvQcQeXxveSuX0pEQjqyyCisZjNSlQap4lTBeFVMMhlTr/ZbOLdardSs2U7lz3+jGT+ohTdzKLKZ/d0f7PuRdshwKtetpWrTBnSjmlsLOI7pStx1XtYe+6Rg1XFmIZFIkEhsF3FWrlyJRqPh3XffbVqv0+l48sknyc3N5bvvvmPevHl+jR/6X6gCTYwbeD8KH/2XnS0K3NkCuOrjbn2wsFqtHNvwNZZGE5rkLpQf34nVamnyNraTNmQWSb0n+DyuXcyVqXUUH9lEdOdB6JJa3vYe13U4xUc2cXj1RygiY4jNtGVhmBuNLdr6i9nUwMHfFyBTaek67mrEEvdfocpMm/AQymJ+zjgKo/pDdaQPv5DDqz7k6J+f0T1yNJbB/t9eBS0zlp0L7rmKw7lvMAjkQoq/Vhn2PgIdh+PHjxMdHR3qMAROM2QXjQD2t1gezAJ/zsKYP2P6akvwxdd1LFteT/ez4sjeWk5lQT3ZW8ub1r8LDBsiZ8nXsXxaP6BZxrQ70S4iSk5SDw1leQaMtWaWPbePi55p2XapfBAP3v83Dz1WydffGZhyx1HK6iTU1QXnuPnok5X8+JOBZ5/SMXJ421hLtBd33KJhyVIDcy4v5V//1HDBLBViceiysp33K38L7bUHjt/DUMci0JL+/U99Hj/99BOXXnqpy2KDzz33HMnJyc0K+AoI+EPqc7e1ECxdEe5CW2N1FcXfLUYWHYtIJKbu0AE4dIDKv/881UgiIe22e1AkuxfUnYnuPpT8DT8iVWs4sfpLtGk9EUtbWk+ljL6AA1+/yN5Fz6BN74NFJ8NsNjYTk+0Ctr/WH4ZdRyie/x3qYb2Ivvxs7x2ChPPn7W9Gr6v2stF6tBtHU/zT95hkZejOGYlEG9HuPt0CAt44evQoiYmJLtclJSVx9OhRv8cUBOYwQiL27eNwJW65EppD5RVbdfIQVQVHbM8LDpMyYBrx3UdRVXgUiUyBXK3jxJYfyNm6jPjuo5G4OIC5wmoxAxAZ24nyE7tb3MJjRyQS02PyjVQXHCF/z+/k7fwFlT6JiBj3PnC+Up6zG7Opnj4z70OpifHYNhTCsr/CZ+eaTpjHzCVr3SI2sJNhg14Oiseduwsf7vbdYOyn3rY9mFnFzncItPV8Au7Jyclh+vTpGI1GcnJymvlIgq3ifXZ2Ng8++GCIIhQ4U/Em8PoqePkrWDuP6amvxWJt8jc+9Gcx/aYnMe3e7pTnGxhnOUJKsoTDRxq55Y5y7loUzdDZrudxHN++XKmVEQ3UlBoxNZyywHDGOnsU8zqVsXdFIb+9eQixRMTFF2l92lZPVFVZ+GGZgXvu1HDTDR3f4igqSsxXn8cwanwRN91WjlIhYsY0lfeO7YS7/dmVEN2eGc5notdyR2Tp0qVMnDjRpcD8/fffM3nyZJfrBAQCwZWwZxcK2yqbuTVey3bKV/9u811uqEcklZDwzyuQpyVg2liKRKNBrFCS99/3KFn+AynzbnU5RnXnllnMErnN70sdk0J13mFwc4exQhNNjzn/oCb/CHlbl2M5Xotu9FkBb4+jNUn1qq3IUuNJuG9uWPite/v8vQnRcedegDWigYpvV2E8lk/iA55rbIX64kao5xcIDX369OGf//wn69ata1bIb//+/fzf//0fL730kt9jCgLzaYYv2aNtjVJ7yuOq2/hr0SbasmKjO/VrWp7UewKV+Qcw1paj0iX4NK5MpSWu2yiKD68HwGp2XRwAbBYYUZ36oU/rQ31VCfIIvc9CtjusVisF+9agTeruVVwOFY6ipreLDfa2sV2GUHpsG5X5B2x+1UE8qDvP6yjMuisQGOw5HXEn+gqF+zouWq2WefPmUVJSwvvvv9/iNh6lUkmPHj2YOHFiiCIUONPxJDT7Knz5I5C5Eu/cicFisYhOnSSUllkYfmkaFzxmu0ATlaJmhj4PgAH95Dz2ZCXluYYWY7mbf0FFf8Ze3ZlvH9tNXbmJinxDi7nt44hEIroMi6HLsBgm3JSJ1WJlVPdDPm+vOz5ZWIup0crll54+J0zdusr44N0obrytnLjY8Cuj4svFkFCKvY7fDUF4Dg/MZjPW/9VKMZvNNDaeSh6xWq3k5eVx9OhREhJ8O1cQEAhXWisuA00+yxKNlpSb70DU+3/Lz4tuEgc1g4ZSu3+P11gcRWZd536UHthIVc4BACyNJrd36Sp1cSh1ccimDKGxvBxZnPv6Ub5i3lZP7aZ9xFw1vYW4bLfDsFtkBNsew1lYdRS9/SnO59zW2s1ITKeZVP+xFWujGVGWym5V7ba/8/ztgVCA8Mxm7NixzJs3j7POOou+ffuSkpJCcXEx27dv5+KLL+byyy/3e0xBYO4A+JIF6mp9qAQzRWQ0qQNnoNTFN4nLZmM9pdnbiYhJIyImFXWM7eBQmX/IZ4FZJBKRMXIOCT3GUHjgTxSaWB/6iJt5L7eG2tIc6srz6TH5pqCM11a4Em89YbVYMNZVALB54f0MmvMEMlXrb0P0VgAw0Axfd/Yard3f/RWZhSzm8ECn03HPPfdQU1PDsGHDOP/88z22r6qq4qeffgrogCkgECj+ZB+7a9saQcyTsDbozoFEbSlnwk2ZTctSN2zkh0aYOV2JRCJiQH8ZBzaUMvkOq0tbBleic69JCdw/Ioa1H2ahTfCtGqImNng2Fp9+XstFF6hJSgy/Yrytwf7+Tz+/hI8/iGbmdGVYZHvZCUfR1nmfD8cYz1TGjBnTVMBv7NixLtvMnDmT9PT09gxL4DTHlXjo+DoYmZzOYwcDzYBBNFaUox06AolKjYU6jHlF1O/PJjJ+CFKNBmVqKpXr1mAsKUYe676woV3w1mSDTBVJ9wvupvzodmoLjlGbIUEsP7XeVV8xcuTxns/hfc3arjy6DrFSjmai+1pUbem77E5g9Ud4demnXFsP2Ow/Cswfk3j51Ygkvv0mCUU2sZDFfGby2muvcc0117Bs2TLy8vIYMWIEr7/+uttjsjcEgbkD4k60cs5YDSXJ/SY3e12Rf4Dsjd8A0HnkxcR3G0lUp37kbF9GbJchSBW+/zNTRyWRMeqSoMbrCzXF2YgkUjQJmd4bhxhXwqu7LGaz0YChoqBp2fZvnkChiaXTkFnoU3v7VYjRldjqi11Ge14MCbZVhkB4EBkZ6VVcBigqKuLRRx8VBGaBoOCLz3Gg1hauxgmk6J8rT1zHuF+cls+CkafGvVG3k7gbygCYPFHBpx/GcOXlEVwzr4xt3+cydHZLuyl3sSkipEy961SthPbywC0uMXMs28xDD3Rs32VXLPyytun5tTeWodGIuP6aCO69S0NkRPhlNbtCyB4WsPPuu+9SVVXFbbfdxh133EHv3r2b1onFYhISEujRo0cIIxQ4HRAfV0Ov5lZNrgTBQMU1V2KyXbD2ZUxX/V31lUREEjvjvGbLKpaupWbtDsoifidl3m2oe/RGEhlJ8fffuLXJcIVIJCK662Ciuw6m2uGmX38zr52zo+3P3Y1jyaijYeFxlL06I1aeXsfs+gPHm57X7t3N0cf+hapLV+JmXehVnG9PhCxmgYEDBzJw4MCgjCUIzKcJroTDcKKm6Bhgy27O3rAYiUxBYq/xlJ/YjclQ5ZfAHCpqirOJiE71WNgvnHG3T0jlKnpMvokTW34gud9kLBYzZdk7OLz6I2QqDbrkXuhTe6NL6oZE5lsWmi/zBlowL9wK7fmSxRyxt74dIxIQEGgPrtPtRaOxCXrufGU9iWiuRDZvops771p3gp0/y+xjbN5yqiDu76sauPmOMj6cH01aqoSyHIPb8cKpiNqWbbZtGDa0ddZY4cjrL0Vx+z3lDB4kY8QwBes3NvDeghrmf1DDmFEKzp6iZOpkJemdwvu3SrjsKwKhZfBgW8bismXLSEpKQqUKH19xgdMLTyKaOxHYub2vYrGz3YE/MfqD6KiSmnW7bS8azeT/33xSb74T7dARVO/c7tdYEBwbD3+xWizUH81Df/649p+8jWiyK4kfimlCKYZjR4k5ezoNBSep+GsNJ157AXliEuoevYjo2Qdlp3SfilC2R8wCZyZHjhwhKysLo9HYbHmXLl2aXfj1hfD+9SngEn+FNW/Zzu2BLqUXhQf/oqHGlhV1bP3XdJ9k80o11dcQ7j8nrRYL1UXHiMkYFOpQ2gRdcg/6nXd/0+vYLkOpKTpGee5eKvL2U3J0EyKxBF1yTzoNmdXMZ7u1BLov+mJR0R5xgOdsbN2BGlyXoxQQEDhdcCWuerK6cH5+k36X3z7L7kRmX7Kq3Y0JkJoqRacTUVlp80Vdtryer7+pQ6MRUVPa4NeY3uayE2yxcd3fDSQmiklJPr3sMQCSkyUs+fqURdjZU5Rcf00EP/1cz4rf63n0yUr+/UglPXtIeeA+LbNmhpeFBoTXxQiB8EAo4CfQlljS6xAXuk+Scc4W9mSZ0BZCnKsxfZlHJBYTPXEKZSt/xdJQDw31FH3/DerMbpjrarGazR4tGVzZXwRKoEUMTevKsRoaUHY7dXeU3W/ZjqMPc1taZQQDx89NJJEQc86Mpv1Jdawr2qEjqDu4j9oD+6naspGKNX8gVqnRDhtB9KSzESt8y+JuCwsWgTMPq9XKpZdeyuLFi5FKpUic/l/ceeedfhf6EwTmDkZrszZDldmsT+lJdPoAyo7vJHXgdHJ3/IyxrhJ5RBQn965Ck5AZdidAjpSd2InJUEV0+sBQh9IuiEQiNAld0CR0odOQWdRXl1CRu4+C/X+y+8eXSeo7ieS+kxBLZCGN01+RuT2ynsMls1pAQKB9ccwu9kXcba1FgLNIFyzLgaRECV99Fsu084qJiRbTo4eUn3+rJ2ZkGhu+PMGkW7qiTz51WTgYdgfB9J0uL7ew+FsDcy5UhfXvimCSlirllhsjueXGSKqrLaxe28Cir2q5/uYyJk9U8NzTerpk+P+T391Fk2B95oLILODIjh07eO+998jKysJgaH63xIIFC/zOohIQcMSbBUAgFgF2wdm5MJs/Xs6t9X2OmjiVspW/AhBz9QxKP/uZ6ElTsTY0ULnxb/Sjz3Lb125nEazMZXdjeRq/8u8/kegjUfZ077PuLDh3JBz3K/tzdY/uRPYbiNVioSH3BDV7d1P595/U7NxO7LkXENGnX8h+vwTTh1wg/Pn111/5888/2bZtGwMHDgzKftcxzNrOcHQHapoevrRxblfZM7LpEUoSe40HQB6hRySWYDbWoUvqRmX+QawWc0hj80bezl+RKiKIiGnpPXkmoNTEkthrHP3Ou5/E3uM5ued3dv/4EhV5B0IdWpsW0vNlXG/2GJU9I6nqFl6WNQICAm2HLzYX4e4/O3SInMREMf36ykhPk1JcbGH4xWmYDGbS9+5ssQ2Ovs7e8DdT21/++3ENpWUWrr06wu++pwMajZhZM1V88Wksn30UzdaDYs6aXMiLr1ZRX2/1eRxXn6c/n7MvnA7fFYHgsHnzZkaOHInRaGT//v107dqVpKQkNmzYQEREBFqtNtQhCgh4zHJ2WeQto67Zwxec+3jrKxKL0Y+bCCIRYo0arFak+igkkRpqD+zzOp+z+BtoVrNj0UBfMZYUU719C6r+3ZplWm/LSQ37TGVX+CPKWjLqsGbWIx8fT/Rtk0l99S7kSckULPyYkx9/gKm0xOtcjhc3go3gyXxmkJuby7nnnsugQYOCdlFDyGA+zQi1iOwJuzhbemw7VosZiUyJobIIpTY27H2NozsNIH/vH1Tk7CGqU79QhxMyJFI5aYNmENtlCNkbv+PQHx8QnT6AjJGXIJH7788cLHzNZPZXjHZs701IDlXRQgEBgdMHT+JdWwqzrjKhY6Ml7N1norzCQmyMmEPrbCc73bsH93jtztIj0Ozsc6erePGVal56rZr33446Y7KYXTH9bBU7xyp47Q0Jr7xRw+Jv65j/VjRDBvvuTe3ucwlWob72ymQO1DpGoH145513ePTRR3n44YcZOXIkt9xyCyNHjmTlypVcf/31JCUlhTpEAYEmfBGX3fVzJ1K3RtCTxcaB1UrdKlvij8VgwGI0okhIDHhMb7jKVnYu8Gdf5g5ZdAyK5BQMu4/QWFqJNEbXtM7XrGVf2gVLrHbOVPe03NmH29vnK4uPJunqG6jdv5eSH5dw4vUXiZ46Df1ZE9v1d4yQuXxm0bt3bxYtWhTUMcNb1RM4ragtsx0AKvMPoNQloNYnU1OcTeZZV4Y4Mu8k959KXUU+xzZ8TURcOnLVmZ1JodIl0HPqLZRmb+f4xm85vOZjeky5uUOcyLvySPYFX4r5CfYYocVsNrfwjnJGJpORmtrxsiIEzix8FcEcxTm74Oeqr7/ZxVarldo6C8UlFkpKLfzwbSyPPXWYXhPjGTSgddZIzsKkfRtcLfcndju9esp4+zVbIbzJE5RcevGZfbIUoRbzyIM6Lpmj5q77ypl9aQmrV8ST0Tn8TwEEC40zh6ysLK677joAlEolNTW231NTpkxBpVJx6NAhevXqFcoQBc5wHIU3x4KB/grDbZEZ2lhRDkDtvj1oR4zGkHUEq8lI9ORz/B7LF9sMd1nK/mY/i8RiEq+6ntwFr1O84HsS/321x3PJQIViuwjdWqHZU9HH1noii4+pQQSRvfui7tqNspW/UvrzMkRiCfqx4wMP2g8CKWop0LHp3bs39fX1PPDAA1x22WUt7hbS6/XExsa66e0awSIjjAnE1sIucHmz1GhvrBYLBftWN702Gaowm+oBkMrb5p+XoaqY/D1/sO+XNynYt6ZVY4klUjJGXQoiMdkbvsFq9f0209MVkUhEbMZg0oddSFXBYUz11U3rQrXvBaMApjNCNnLHICsrix49enhtl56ezurVq9s+IIEzCnfWAf4KZIFYAtj7eCsq6K2dIz//Vs+xbJt1ldUKMdFiKistqLSnxOVgin9zZTvQ/raeK68r5eobSqkzWFo13qUXq7l4tooHH6sgPz+8Lbjai+7dZHzzZSyNZivLfzF47+AGV9Yojo/WjOnPPhoo3mL8qLJPm80t4B6j0Yjif8WtUlNT2blzZ9PyysrKDpHAIHBm0Zb2BK7mcsRRCDTX1lKxbm3Ta4laTWNNNYjFiOS+363iK5HHrFTnHSZ33RLyX3sd0YZjHttrsj0LzzJ9FHE3XYhh52Gq/9jStNxXmwx7u1BYargTlf2xOHGFWK4gdsZ5RPYbSPX2rX7FE+ic7ixeBE5vPv74Y9avX89LL73EkCFD6NatW7PHM8884/eY4Z++cIbiKGoFknEZbqLYnmUvY6gsbHptNhrY/9s7iGUKIuMzgjqXxdxIztYfKTz0N2KxBIvZhFiqILF3667+yZSRZIyYw+E1H1N48C8Se7ovmnAmoY5OASB32090HjmHqMMNIYnDnkHsKtPYl++MpwzlQC70hNt38HQnNjaW0tJSrFarcCIqEDLcFUULFe5sDFzZEtjb/fSzgWvmlTVbN3pCEQA3PJLCgoqYoMa4bLmBBx6qoLjEgv3abW2tFbWqeVz+8tzTev78u5Bb7yrjy89iUamE/wuREWIyu0j5dGEdUyYp6dE9sGx0T7YWwbKi8GSZEsxxBcKPK664gjlz5rB//3727t2LXq+na9euoQ5L4AzBMTvZcRm0tLhoq/lczesK9UEDuz5+tNmy8lUrAYjo3ReROLBcQnfZy6byMo78uIiak1mIpXIsjUbqinOIz3Z9Lu/oy+wpKzpSPxDNpL2UfvYziq6pKNJPWeJsy0llcFpuk4Ds+NwZxzauCFYmsyPBEmFdFddTpKRQs283VVs2oh06IihjusPTfihw+nL99ddzwQUXuF0fSP0DQWDu4DiKWOEsaik0sZgbjXQddzUWs4kDv70L2LyNJdLgXl3NWreI8pw9pA2eSUz6QHYs+Q9RacHxTY7q1I+EnmdxYvNSdEk9UOnigzJuR0YdlUTG6EvJ3vgtIomMKP20kMXiSWT2BV/8ln2Z/0zj6NGj/PDDD+Tl5ZGZmckVV1zR4oBUUVHBF198wZEjR4iMjGTChAlMnDixWRuLxcLXX3/Nxo0b0ev1zJ07l27duvkUg1arZcaMGTz99NM8/PDDXq0yBASChSvhq718ZT3hbX53MZaVW1Ao4Nmn9My9VE1S53wAIqLlPDw6B602z6+xPVldLFtu4Noby5gxTcl/ntRx/4MV1NRYiYtt/fdXrxfz/tvRXHJFCW+9V80D953Z1lZ2PpwfzXU3lTH7shI2rE1AE+lZgAj0oom/Htq+FPzz5zsVqJB8nW4vTwXUU6A1fPPNN8TFxQEwffp0Pv/8c7777juGDRvG/fffj1QqnLIKtB/uBDlf/XT9wZu47Amz0YBEo0HVuQux515I0bdfUnfI5sOsGTQ0aDECmOtqyZ3/FiKJhC7Tb8RcX8vxVYvQd+7rta8rb2ZnYq6cTkNWPkVvfEXqy3c1E8ddCcKOIrLzerso7Y62EJrbAv2Y8ZhKSyj69iskEZFE9PJ8h407kdjXCyOu+rfFRRUB36mtreWLL75gy5YtXHPNNYwaNapFmzVr1rB69WoaGhoYNmwYF1xwgc/JVlqtNuhFdIWjdZgTSEEyd6JzKAXo7hOvb3peVXjUFk9KLzoPnx3UeaoKjlB2fCeZY68gJmMwNaU5YLWgjgpecZC0wedSlr2DnG0/0nXc1YglrfOjPB2IyxxOY0MdOdt+or7fKJQKnfdOYYy7gn3uvo+e7jg43XnjjTeYP38+06dPJyUlha+++oonn3yS9evXk5Fhy2jIy8tj2LBhZGZmcuGFF1JYWMh5553HbbfdxgsvvNA01mWXXcaGDRuYN28eBw8epH///qxcuZIxY8Z4jSM3N5dt27axaNEiXn/9ddLT05uJzGlpaSxZsiT4b4DAGY0nES0YmZKBFlLzNYvU1fqr5kZw1dwIwObFrFBAQwPc8OFwvrRooMK3GDzNvaCiP9eqd/LIE5WcPUXJxx9EIxaLyDrWyIRxyqY2rX0Px4xScMVlESz4sIbZ56vomikcr7t1lfHlZzEMG1PI54vquPWm4B2zXGUdBzNjOBwu3Ai0Dc61ES688EIuvPDCEEUjIGAjVBmd3gQ98TG1LSO4XzQZ/Z5sWi5PSKTu0AHiL7qMyL7B/V9Zvvp3LPX1pN/3L3Tlek5u+QWJXIUsMsple1cFAD0hViuJvW4W+Y8voOqXDehmjHbZzpt4fDohkkqJu/ASTKWllP3+q1eBORi4E5nt6wTaj6VLl3Lrrbcybdo0PvnkE0aOHNlCYJ42bRoNDQ2MHz8etVrN3XffzXvvvcfy5cvdXpgtLi4mLy+PuLg45HI5eXl5bmOIi4sjJSXFr7gFgTlM8afwmK99vQnQ7YHVauHk3lXIVFq6T7wekSi4NuAn9/6BOiqZ6M6DAFDrkxCJpeTu+Jmu469Bpoho9RxiiZS0weeS9fcXlGRtJb7byFaPeToQ320kuTt+pqB8D50Tx4TsgkZbZhH7M3Y431EQTKZNm8Ydd9zRJObec889DBw4kGeffZYPPvgAgO+++46amhpWrlzZ5LEYHx/P448/zvPPP49IJGLFihUsXryY3bt307evLRvisssu4+6772bLli2uJ3cgMjKSa665xu36qCjXP4AFBFpLW99y74tY3Fai2zdLDDZx+f+Gk9hd47adN2HRVXxLlhrIzTPz5WcxiMW2TIshg+Qs/dHArJkqbhrTPPM50Pf5rtsj+WGZgX/8q4Kl38QFNMbpRmqKlBnnqFj8nf8CsyfbFVfLgmWZ4WoeQWzu2BiNRiwW3/zWFQqFYH8l0OaEi02ApxjsIp+zYNtYVUX19i1oh45AO3R4UOMxG+qo3Pg3+jHjkOr0VOtAfSIds/E3Tm7+maRh05t9P72Jye5Q9uiEenhvSj9dTsTIvkijfcus9JTN7I5wz162IxKJ0I0+i4LPP8JYVIg8PsFj+9ZmMXsaQ6B96dOnD/v370en0/Hpp5+6bPPKK6/Qp8+pCw+XX345Xbt25aeffuL888932WfhwoXce++93H333XTu3Jl7773XbQx33303r7/+ul9xCwJzGFHVLRKJ3Ja544/Psichy5sA3ZYCWENtOVUFhzHWViCRqZCrdZzct5ra0hN0n3hD0MXluvKTVOYfpMuYuU0HObFESvrwC8nesJjiQ+tJ7jclKHPFZg4ld+cvGCoKvTc+Q5DIlKj1yZTJyogKA2HV8XsRTNHZ17HOBHEZaFFYTyKR0K1bNwoKCpqWJScnYzQaqa6ubhKYS0tLSUlJafqu/vDDD/Tr169JXAa48sormTVrFvn5+SQnJ3uMQ6/X8+9//ztYmyUg4JWPKvugbOOvua+inDvRzdcM0uoaC5u3GNm124RIBEcTMsnaWMqmxRUMOi+FLsOj/Y7PPrcrEdBqtfLu+9VMmaSgZ49TWcV336Hh15X1/OvhCv5eneB1Dl9ITZFyxeVqFn0pnCw5MmK4nOW/GrBYrE0CvyPOn5231/4Iz+1BsLOnBYLPuHHj2Lhxo09t169fz8iRQkKHQNsS7qKa+Jgaq9mMsagQQ9YRLPUGpFodVovF5r0sE6GfNxGLPrgZp1Ub12M1m9GNGtu0TDSmO+qs3hRuX4m+ywDUsf5lOboj+uLJ1G3ahymv2KXA7CwmO2czO78ONyHZF89tZ1SdbXeEGgtPehWYg4UgMoceX2oPOIrLAJ07d0apVDY7D3fmlltu4corr0SlUiGRSLjyyivdtlWpVL4H/D8Egfk0wZ3I7ChCB2M8X8n6+0tKjm4GRMhUkZgMNYCtek/XcdegT+kV8NiuaKgt5+ifn6OIjCG688Bm60RiW2alMoh+yVarFZOhCrla8HR0RKVPoOToZpJ6j0cd5VkQbEva2gvZefzTNVu5qqqq2WuFQtEkELsjJyeH3377jSefPHXL3uzZs3n88ccZN24cw4YNo7CwkNraWr777rumNocPH26y1LBjf33kyBGvArOAQLjTFoKXqzE9CX+uLD1+XWEr6tfYCDqdiAarhPqq7QD0npLA7Kf6Bpw56EpctpitLHtuH3v3N/LcM/pm66KixIhE0KNbcH+eFhZaSEwQfNkdGTJIjskEz71YxcP/9s3Wqi1sYIKN88UWdzEKAnToWbBgQYvfGe5wPokWEDidcecRbK43kPPWqzSWlSKSShErVZhrqgEQyRWkvngbUn1wz0dqD+6nbNUKtENHINWcOu8VSSSIZDLEShUytSbgrGVoLuw3ltn+J0iiXd815Y9g3JbisqMY7Itg7NjGX+FfrFIjVqkoWf4jkf0G+hRba7OY7eM49hX8mINDIOfXvrJw4UKMRiPjxo1z20apVKJU2pJad+3aRXV1NcOHD0cmC46NnCAwhyGBCmPBFLlaO07Z8V3EZAyh05BZyFQazKZ6sjd+R1RaX6LTg3tLY1XBEbI3foPF3EiPyTciFp86iTQZqsndvpyotH5EdwrevCKRCE1CJkWH/iau20ikchU525dTmrWF/uc/iFjq/gtaV56PTKVF1tapb63EW6a7q/0tsdd4So5uJn/3SjoNvSCkAnywvgvtlfEfCLpjViRya9DHNRttY6alpTVb/vjjj/PEE0+47VdbW8vs2bPp3bs3d955Z9Py8vJyfvvtN6KioujXrx9xcXEsXLiQDRs2NJ00GgwGYmNjm41nLzpQV+f6h9uJEyc4++yzSU9P54MPPuDss892G1t6ejq//vqr+40WEAgAfwSqYHkyB7Lek1f0G8sjUOlrue6/w0joGolIJOKvT45RkW9gxgO9EEvci8ueslZdxVKRb2DFW4fYsSyf8x/vw+iRFU3rGhutPPNcFVYrLYTn1jJ2tJwvF9fx+6p6Jk9UcvCQiUnTivjgvWhmnOM+O6OszExevpl+fYNbjDgcGDJYTqc0CZ9/UcfMGSoG9JO1uJAQ6D7rb4G/YOHuYounfVQQmUNH//6CxYmAgCtcCaPiY2oMJ4/SWFZK7E0XEDl2ANI8HfV5uZSvWkHMtHORJrbeCtKO1WymavMGSn5aiqprd2JnzGq2vvbQAWr37CR+9iXU9/b/fE+T7dpKQ5GZgkiloGLpWuJuvQisVvIeeg95pwTib5vTor09i9lqtVKy8yQxfRMQS9v2grKjgOtJcHUUZFsjzIrEYnQjx1C+aiVVWzcT2W8AYrnn3yW+COC+4tj/TPFjjjwBkiD/9DMbbX/9Pb/2lR07dnD77bfz73//m169fEvm3LlzJzfccAMymYzRo0czYcIEJkyY0CrBWRCYOzDOQrQn8cvXTOZgiNRWqxWr2URkXCdkKtvVR4lMSebYua0a1xX1VcUcWPEecrWOHlNvRaVt7q+4f8V7mAxVpA6aEfS5O4+Yw96fXiV3+3LSh8/m5J7fAbBYGhEjo6G2nOrCLKI7D2wSvS1mE3uWvYIuuSc9Jt8Y9JiCiS/F7ZxRRyWRNuQ8crb+QEXuPjoNuyBgj+pgZwUHsj3OeLLdOB2zmHNycppVlvV0dbWuro5zzz2XhoYG/vjjD+QOP3yeffZZTpw4wf79+5uWDxw4kOuvv56ZM2eSmJiIVquloqKi2Zjl5eUAbqvb6nQ67rjjDvR6fdNzd+j1em+bKyDgF9fp9gLBtXryRmt8mN31bWywEJWqIrHbqWyhsddkuGzrbl5fReZF920nd3clFz3TjyEXpuJYMfDNd2r44us6nnlCxw+KQdxE8IS/S+aoWfKDgbv+Uc7mdQl88nktDQ2Ql2cGbOL2kh8MjBgmp1PaqZ/Gl11dyrbtJkrygnPrb7jxxacxzJpdwpTpxVxwnorXXtSj0bRun25PX2RXmfnu2vi7TiA0HDlyhAMHDjB69Giio6MxGo3IZC0vfggInGlYMuowV9nOO1T9MhHLZVgy6pBnRJMw9tKgz1e5YR0ly74nsv8gEi6+HJFDwTBjSTEnP1qALDYOzeBhrZ7LUbCVRKqJve5cit/9lshR/RBHqjFmn8R4vIBpz41kW04qhv3ZYDaj6psJ2ETm2q0HKHzpB+LvvJhxl8W0mMNdVnhrYgXv4m2wBNnoKdOoz82h6JsvKF+1koRL56JMS/ep7+kuBnc0/Dm/9pW9e/dy9tlnc/nll/PMM8/43O+qq67ioosuYt26daxevZrly5fz5JNPNgnOd911F7NmzfI+kAOCwBxGaA/XIJU0em3nSijzJmzZxS9v1gHBEMhMhiqsVgtytb7VY3mjMv8giET0mXlfi4xgY10lxtpy4rqORBVEeww7Sk0Myf0mk7NtOVaHIiVWq+15/q6VFB/ZQGN9DYm9x1Nblsfe5a8BIJEF5zaI9sIfcTap93hiuwzh+KYlZG9YjC6pO4pI1/6dvs4ZbAK1jrH38SQyu5urI6LVat2Ku44YDAZmzZpFcXExq1atapGJfOTIEfr06dNMdB40aBAmk4ns7GwSExPp06cPX3/9dbN++/btQywW07NnT5fzOovKngRmAYGOhLNg1hohzFtfRWExukT3/6Pd+Ts7inu+iMw1pQ3k76ti1sO9/ycun8JotLJztxG5SoL44tFu5woUsVjEC//RM2p8IVdeW8bavxqard+wycitd5aTmChmz9YkKistPPx4Jdu2m1o1b7jTo7uMPdsS+WZJHfc9UEHf3jLuudN9MUcIb2uJcI5NwDONjY1cddVVfPnll4jFYtatW8fIkSN54YUXsFgsPP7446EOUUCgTXD2DAb3ImhjcTmIREh03s8tWisq1h06gDK9M4mXX9VinfFkHgCx516ASBJYtrBz9rKjcBt51kCqV22l+P0liCNt26FOsGVnD07L5fsbPqWx1kjqK3cjS46l9u9dFL21GIA+fRtbvH+u3uNAaM172lp7CZFYTPJ1N2E8mU/Bl59R9O1XdLrnAa9zOhNIDK4sN7xtj69znykZ0Y74en7tK/v27WPSpEmcf/75vP/++35fkFWr1UydOpWpU6dSWlrKzz//zHPPPcfKlSvp06eP3wJz+6beCHikqltkkwjs+PCEtza6AzUuM5099fMkupnqayg69Dd15SexWq0u21adPAxARGwnj7EHg9qyPCKiU1uIyxW5+9j1/XOIJTISeo5ps/kTepyFOjqZ4iMbmpZJZbbbbRuNtQAYKgupLspi70+vgtVmPdB55MVtFlN74m5fkSkjSR92AQBlx3ditQbfxiEYOArFvnzfPI3h/NxOW3pBhwP19fXMmjWLoqIiVq1aRVxcXIs2/fv3Z8OGDZSWljYt++mnn5DL5U1FAi+99FKys7P5+eefAdvJ5nvvvcfZZ59NdLT/FygEBDoyN+l3NXsEgtVqZdvSPP5c14DB4Pp/cE2thW3bjVw60uA2DlevHZd7i9G+ruBwNRazle5jTl2AWlDRn+ISM1NnFLH8l3om3ea6oIldxPZ1uSs6p0v55z2aZuJycpLtxLi83HZhuKDAgtFoZdp5xXy52HbS8+0XLTOhTifkchFzL41g1AgFP/9qcLuv2AmmLYwvn117ZkQLhI63336bI0eOkJOTw7BhpzIi77vvPt566y0aGho89BYQ8A9XYtbgtFyXQqQ/wpd9jEAEzdK9heSsPMKmne6FJ8OeLBRdkhHLg+OV6omG/FzUXXu0WF6y/AcKFn2KsnMGqozMoM4pPqZGfEyNSCQi5pqZWBpMmHKLAIhMtdUKsDSaaay1eQ2YTpZQ/s0fTeJyyvgMYvo2L4LnXBSwNbG5eu4PrbWqEIlEKJJTiBo3EWNRIQ0F+e0Wg/2zac0cgc5t93+2PwROceDAASZNmsR5553HggUL/BaXq6qq+P7777nnnnsYMGAASUlJvPXWW5x77rksX77cr2xoO0IGcxihPVxDbR9li+W++t8GA2/jlmRtJWfrDwBEdepH13HX4LgbG6qKObn3DzQJmchVbe+/a6wtQ+okLteW5nBk7Weo9En0nHIzEnnL9zRYiKUyek+7k6qCoxz64wO0id0QicWYDNVUFRwFoCJvP1UFR1Bq4zA3GomITkEq978iZ7jg634nU2mI6zaKnG3LKD6yidjMocR2GYpc7VsxofYikO+R8/fE1XNHYVl7+PQVme+//35+//13Zs+ezaOPPtq0PC0tjYcffripzW+//Ubfvn2ZPHkyhYWF/PXXX7z99ttERUUBtozmJ554gosvvphzzjmHo0ePUlZWxqpVq/yKZ9u2baxcuZLc3FwsDncWxMXFCRlQAqc1zpYUF5h28PBDBXwDxESL+f2XOFJTTv3sq6m18PSzVZhMcO4MJctdjOdpHn+F74p8m4h9W+fD6HRiFlT0x1BlYuY9JrL2N/LHr3H071sDuPZvhpYZqvZYfI3nH/doufbqCCZMLeJkgYXx4xRYrVaWLjslsF90eQmHjzQyeaKCP1Y30KdP25/IhwN33xHJ3GtK6TP4JOfPUjH3EjXDhp6628pVVr19mT+F9Bz7+DKGLwhZyx2f33//nUcffZTU1NRmJ8gRERHEx8dz4MABBgwYEMIIBToyA1LzkUXImwRGZ5HKkyDsj6Bl9wMOhF1v/U3p7kJgJbHzzkc75dSFFqvVSt22g9Ru3k/0ZVNa9HUn/AWaHWqpr8dcV4ck8tT5jdVqpWrj31T8uRr9uInEnDMTkbhtchXFx9QoMpLo9PY/qfhuNZU/raPbZbbvf8H6nKZ2Fd+voSH7JAnDUynclEviqJbJbcEq9OdcpM9dIT1fxmlttm5En37I/1pDzpuvoMrshnbwUCL69kcsa24abJ8nmKJsoNvtCl99rM9UDh06xKuvvgqAxWLhk08+YcOGDYwaNYprrrkGgOnTp1NTU4NYLObWW29t6jtjxgzOO+88r3P83//9H/feey/jx4/nmWeeYeLEiURGtk5jFATmMMOfgmK+tvXVPsOXtvHdRlJ8eD31VcWUn9jN9sWPkTpwBvHdR1F4cB3HNy9Bpowk86yWt9MEm9Ls7VQVHKHLGJu3s9VqoSx7B1nrvkCpT6DLmMvbVFy2I5bI0Cbasq7sBQyzN32LSCym++QbObnnD0QiMSkDzqG+qphj67+ipuQEke2Q4R1qOo+4iOhO/Sg5upm8Xb+Ru305Pafe2vR+tSfuLp4EerHmdPRcDoTzzz/fZXV3R5sMjUbD+vXrWb9+PUePHkWj0fDpp5+SlJTUrM9jjz3GnDlz2LRpE3q9nqlTpxIR4XvBkvfee49//vOfDBgwgF27djFy5Ei2bdtGZWUlN9xwQ+AbKSAQAMEQzvyl+VwS7r9Pw0uvVlNaZmHg8EJmTFMy/+0ocnLMzLm8hKJiC48/rGV5xGA3Y/gyj3cubNzOG28XMWWSAp3OdkI6pXwbM6+uoaqwnoue6Uf/vmV+jekYiz9ZrjHREgYNlJNebiEyQsyPPxn4/gcDr7+sZ+MmI0ezGnnsIS1XXq5m0IhCXn+rmmee0AcUW0diwjgl61Yl8OXXdXy5uI7PFtZx8w0R/OcpvdvsccfnztntnmxTXC0T7C3ObOrq6ppstBwFZqvVSklJSVPFewGBYOMuwzXQzFd/BU17e+Xci+DBdwEo+e9SKpauIWnuPCRDIih++xtqN+1F2TsD7dkjgLazFNBkw/FV3yGSSonoZft9b2looPSXZVRuWEfkgEFET5nWZuIynBIVJREqFN1sBdF0XaIx1RrZ8vxqEkamYRk4jKrfNxMxvA/DHxrEtpf/ZP8n20ib2vbnmMEqmhfoZyhRqUm99W5qdu+gattmCr9ehOSXZaTd8Q+kmpY2V45CczD3G3fjOS/z5ll9Jtpk+EJkZCQDBw4E4N13321a3qnTKQ3pkUcewWRqaefmfI7tjrPOOou5c+eyZs0arrjiCsaOHcvEiROZOHEigwYNQhKABY4gMHcAvAnJrRGXfW1jMZvAavMO7nXOHRxc+T515fk0NtSRvfEbKvL2U5G7l4SeZ5E2eCZiSdtm/FitFo79/RUAebt+o7GhlvITu6kuykKb2JXuk25ELGm/3dtUXw1AVcFR6qtLKD+xm4zRl6FP7ok++ZR/rEqXwLH1X1FdlHVGCMwikQhdcg/U0SlYLGbKT+yi7PjOkAjM3qxkAsGTaH2mMGXKFKZMaZlN4YxIJGL06NGMHj3aY7vevXvTu3dvv+OwWq089NBD/PLLLyQlJTFt2jRWrlxJZWUlF154Id26dfN7TAGB1uBKLGsPEc1qtVJVZUWnE/PAfRpMRiuvv237n7T8l3pmnFfC0axGOneW8NP3cXRKk7Kgwn3M/uBu+156tZrCQgtWi4mbbi+jRzcpz71UjVIj5eaFo/5XYDAwgRn8FyjLyiwUFpl5Z341731Qw8TxCq68PIIrL29+QatfXxnr1hsDjqujkdFZyr/+qSEhQcwDD1Wy+DsDjz/i211H7t5/Xy+0BDtj3hOhuPgj4JmxY8fy8ccfc8455zQTmF988UVkMplwDBdoM4KV4Roolrp6JCf1KDKTSXr8Bk4++SEAjcUV5LzxMrLUeBoLyoi/5zIiRvRp84KXZboyyg5tASD/kw+JnjiFwm++wGo0EjNtJlHjJ7fp/M5YqmyWk7l/HKVwUw6NdSaG/Gs86vhItk24uelCQPzQFPJWZ1FXWMPg9ODYYrQ1rRF8xQoF2qEjkCelkPf+25irqmjIy0Ha0/05VLAEXF+zmJ3beOvnqn0g/s+nC8nJydxyyy0e27Q2gWrIkCEsXLgQgMOHD7N69WpWr17N66+/Tk1NDc888wx33nmnX2MKAnMY40q8cuWn3B4cWDGfmuJsNAmZJPWZRNfx19qE0kKbDUR9VTGZZ11JTOdBbR6L2VjP9m+fsone/+PElqUAxHQZQpfRlyESta+9uFytJ7H3eAoPrsNqbkSX3IPYLkOatTFUFnJk7WeIJFJi0gc2La/MFKE7Gp4exYHiuO+aTfUc+O1djIYq0ofPJr7byBBH1xxvhS99bePc3pGIvfUBxSbgOydPnkShUHDWWWeRnZ2NwWC75V2n0/Hkk0/yr3/9i/vvvz/EUQoIeKe1Bf4++qSWBx6uJD5OzF23a7j9Fg31DVbmf2A7SSstM3PVFWoe/reWCPWpY6UncdDXOJyziW/S7+K2u8r4+lvb99GsUvLd96dOFP71+0QUEdKmuVz1bwsP3nvu1HD/gxU8/nQVAD8v1TdbbzJZ+eeDFWzYZOS5p8PL1qmteeb5Kt58p4bLL1HzyINa5HIRN8lPZSX7g6/tvQnIbSUEC1nT4cO9997LmDFjGDZsGLm5uTz//PMcP36c3bt389VXXyFuw2xJgdOfnbnJiNXhlQUvPqbGFFfC8XnPAqCZMBjduWNJuP9Kit9fgqWqFrFaiUQbQcLdlyJPa+4t7IvI5q8IZ8jOIu/9t5teN1ZVULDoEwCSb7wNdZf2TxCKGNmX2q0H2PX2eoAmcdmREysOs3fBJqL7JKDppG/3GFuDu8/Rl6ze+pzj5C14B1lsHHGzLkQVgs/HH/zN/PZJxE4/cy00gonFYqGmpobq6mqqq6upra2lurqaiooKv8cSBOYwxdnTNZQZkRaLmZribNvzRiOH/vgApTae6PT+VBcepev4a4nu1K/d4rFixdLYQGR8Bp2GnIc6OoWqk4dQ6RORKiLaXVwGW3ZmpyHnEZ0+kH0/v4E2qUezOCwWM0f/WoTV0kiPyTchj9C3e4xthTeriKJD66mvLqXvufeh0iW4bRdKAslC9ufiTlW3SNjmd1gCflBXV9fkGZWYmEhxcTHV1dVoNBpEIhG1tbUhjlBAoH0yJtdtsGXcDhsq5/GnK3nq2Uruu0tDdJSYMaPkfPSBrWjdgor+YPQ9rkDFuOoa2wXUi5/vT79zkijOqsHUYCahq6ZJXHacw46j5YIvc/sT25RJSjb+mUByRj5xsWLSUpvH8d4HNXz5dR1PPqrl+mt8t+np6OTnm3nr3Roe/peWe+9qeZutN8Hf0+fkyTLDHx/tYCCIyuGHXq9n48aNvPvuu6xevZqysjIGDRrEggULmhX9ExBoL1rjp+wK50xMS0YdjVnlAMgSY6jbfojq1duIGNkXRUYyhr1ZdHrnfsQqhbshm8ZxnidQLEbbjwL9WRPQjT4LsVSG4UQ2qoxMxO1kU+O8PRJtBEkPXkPx+0uoXrW1mc/y4LRcanIr2fKfVSSO7MTAe8e2eYZ3WxDoZ1j6yzLk8Ymk3noXImn4yXrB9GsWaBs2bNjA888/z9q1a6moqKBfv35MmDCB6667jvHjxxMdHe33mOG3JwoAp0S7cLjV3i4u955+NxExaVTk7Sdvx8/k715JbOYwotJa+q+2JVK5iqFXvIgIUZP/kz6lV7vG4I7I2E70O//fKCKimi0/mrWcurJcep19OxFO1hgdPXvZm21Lec5u9Ck9w1Zcdofj9y8cvocCvqNUKjnrrLO44oormDFjBvPnz/dqzSEg0F64EuiCKXht3mLk5nkR/OdJPceyG3nxlSpeeKWa9E4SnnlC53OGtCffXUefXXdj2Z9/9n8xNDRY+cSQAkBST+8FgJ1jagtBUC4XcXhPEvX1zY/B23caeeo/Vdx0QwTXXxOJRNLxTlYDZfmvBiQSuOHa1onqvhZptONrprpzfyED+fTh/fffZ+bMmTzwwAM88MADoQ5H4AzBWUB2tlOwvw7Uj9kRV0Jbzfo9SKI0pL56N1ZjI9V/bKH821VYDA3E336R3+Jya4no3pPMp19sJlZG9u4b1DkCJfbG89HNGM3++gQ4VeePnPu+RaTVMOTf45Hrmovg4WyP0VrMtTUYjmURd+HFIRGXfRWPW9NOEKjbnry8PNLT0/nwww8DFpSdEQTmMMaTqGUXv9qj0Jih4iQisYSI6BREIhFRqb3Rp/TEWFeJXK0PyZVCsdh/w/H2QqWNa7nQagFg/2/vEJMxmMyxV7RzVKFDptJRV56H1WJp04IQ/uDr98beRijoF/4kJSXx9tunbuv7+OOPufvuu3n99dcZMWIEzz77bAijExA4haOYFmxbgIoKC/knzQwZbCuWldFZyntvRfPkY2a0GjGf1g/weXxPwrMvwrIjCoUIDD5vRrsRFdXymGT9n9684MNaFnxYS/6xZOTyM0NkTkqU0NgIBw+ZGDbUtbDhSxazu37e8MUqoy0sUwRCzxdffEHv3r1JTT19BSGB04PBabkthEu7AO1K0PS0zniiAEWXFERiMSKlHN2M0WgmDcVqNCHRRjQrzmbH1bJgEo6ZsAAisbiFTQgAFgvWBiM/zvyE+LsvJXJU+91V3RbYP19vWcwimRyxUoXxZH57hBUy7CKzqzsAABD051Zx0UUXcdFFFwV1zPD8DyLgFne38jsKYe7aBUpjgwGJTAkOlg8ikbhFlq6AezoljqWctQCIpZ6vRp9uJPQcy4Hf3qW2LDcsChsGko0siMvhT0REBNOmTWt6nZaWxnfffRfCiAQE3OMtazjQrMzyCtvFzCh9c+E0Ps52UfYmZfCyPdsqczTUGaqDBsi48HwVS5baFHFZ29YsDiumn6MkJVnCkh8MbgXmtsKfz1nIWj79GDJkCGvXruWss84KdSgCZxDtmeHqSpi21BiQ65KaiYli/ve8tOUY7sTlM6HgmTui555D4auLAJDG6kMbTBDwtYCdWC5HO2wE1Vs3E3vuBSFJ4nLeH4O5HzqPJWQydxwEgbmD4MmGwJ7J7Klta4iISaOxoZa8nb8S03kgSm182GSihjtWq5WakuOUHdvetKzz8NkhjKj90cR1RiJXUZm3PywEZncIWcoCAgKhxFdx2ZVPsV2ITUmWEB8n5pU3qlGpRPTtIyMywrfjtSsxN1CB11U/d7Ya4cTJk2YWL6lrEpdffFbXIf0cA0UsFjF5ooKVf9Tz7FNtM4dgayHgismTJ3P55ZdTXFzMqFGjiIhobtMyduxY9HpAWmPpAAD4RUlEQVR9aIITEHDCWSy2P/d0S78rMVsZ35nq7Vuo3tEDxfhkJFGaZsccV9msgrhsw2I0UbftINVrbEVuFN3SUHZLC3FU7vHH7sFXkTmiZ28q1q6iIT8PZWpg2+4qOz5Q3AnN/mTdn6n78+mEIDCHGf4KXO3hDatL7kFy3ynk7/md/N0riIhJo8+Me9p83o5OddExjm79GmNJETKlBrFUTpcxc884cV4klqBL6k557l5SBpwT6nDc2ssI4rKAgIA/fFTZh7s0+4M6pr/Cm7NYK5eLeP+daG69q4xzLyxBJIK1K+Pp1dNzGm4glgeu/HA9rXdHOIiNVVUW7v5HOT/9Uo9cDvFxYsaOUXDtVWdOgT87UyYr+XRhHYePmOjW1fV+E4hVhX1/8GYR4+oCivPcAqcfL7zwAg0NDcyfP5/58+e3WL927VqGDx8egsgEBFriLvPZWUDzZI8BoL9hPMY3TlL41efwFUTNmUTUnEmAa6HNlUDnqxh5OlH583rKv/kdS209khgdkigNCfdeHuqwPOLpwoC7z9W5nTPKTp0RK5XU7tsTsMDsLsZWFYt0s0862l04zuerLYhAx0AQmDs47SGKiUQiUgdNJ7HPBHZ8+xRiqbzN5zwdyFr3BWKtmp5TbkGTmIlIdGYJy47EdB7E4TUfU110DE18RqjD8RnnCziCCC0gIOCIryKbL7YPvnrUeuOsMQq2b0jkyf9UMv+DWnTalscef4uweYqnrURAe4ztkfH6zvwafl/VwEvP6bnwPBVaF+/ZmcKk8Ur0ehH//aiWF/6jb5M5fM1mF7KdzxzWrFkT6hAEBNziyUrDl8xUV/YYAJJIFUkPX4cxr4jcf7yJuC4yIJEtmFmo4Y4xr4jST34icsJg9LPOQp7iovZRmOMqw9fdPuRJaBZJJET2G0DV1k1EjZ+EWOG7tZW3fbYtL1zYx7Zvt7fifu7eH/ExNdSfub/XwhVBYBbwGalchVIbj1giw2q1nlG3jQaC1WohStcFbVK3UIcScvSpvYmISePY+q/oO/MfiKXhYWrp7F/uTUBuSysaAQGBjsV1ur1oNGKfRN+2soVwJ759VDuAE1HHkEgP4u3fbWsFPG8Zp4HM7Yvw6O+YnjCbrURqRFxz5ZmXseyMUininjs1PPlMFRecp2LUiOB5MbfWY1xAQEAg3PAk1G3LSXUrLjsijrAJed4K7PmS1dzRxWavgr3FVolXM3bAaSEuOy73tN3uBF/9uElU79hG6S/LiDvfe7E2f7yMW+Ot7E2g9rat3uiI+/aZgiD5hxntYXnRGkQiEZX5BzDWlIU6lLBHIldiMZtCHUZYIBJL6DLmchpqysnd8XOow2mGs4e5I5U9I10+7P0EBAQE2lMss1sM2B+eSO6txdxo5fNFzX+oO4p83sZorWWHvyyo6O9yDPtyd+vtBPpZaHVi6uutAfU9Hbn1xkiGD5Vz133l1NZZWqwP1gWTYO9//uJtfxIQEBBoLb4UEpTqbecWVRvW+T2+PRPUFadjYTSxWgmA1dgYuhhaaR8RzHHlsXHETDuXyg3rqDtyKOC5/cGeddye+5cgKHccBIFZwC9EYjH61D4oNDGhDiXsMdVVIVfrQh1G2KDSJZA6cBoF+9dSXZgV0lham30sZC8LCAj4gi8iblvNW3i4BqkU5l1ny8p1FtMCsfcIVmzO70uohb6CAjNJiZKQzR9uSCQi3notisJCC0/9p6rF+mBmvbsa29mr2dtYge47ofp+CggICDhirrEJdbqx4wMeI9wEuNaIkJ62pbG8GgBJtDbg2FpLa4XVYIvMupFjUHXpStG3X2Gpr29NaC4JNNs42CK0/WJKuO3rAs0RBOYwxFNmpKdsS3/HCoTGhjqU2o53O0pbU5kpanoAWMyNNDbUIlOF7uAXjiT2Gk9kXDpZf3+B2Rj8A6CAgIBAuOAoXAVqBdGaPr3qc4iNFaPVurfx8FWca0sRLhwySAsKzCQmCAKzI10ypDz2sJYPP65l1Zq2O1572rf89QMP9YUKAQEBAfBfJLTut91BI4uJbbbcUaR1FOrcibeOAlwohThPth2tFR3NZbaLntIoTcBjhAOBvAfO+4N9DJFYTPycyzAb6ihetsRtv/aO19X8vu6TQuG/josgMIchrrIjnYVlX4RjXzxlBYKD7mjzW2tFIhGIRFitLW8tPZMRicV0GTMXU30txze3PAC2J+6+Z/4g2GQICAh4Em/DAX+yQFsbc0fNBpVIRTQ2ChYZzlx/TQSTJii4/Z5ySsvMzdYF87NuzViu+obLd09AQKBjMDgtt+nhab03HAuX+YM11WD7m9TgsZ0roTAUdgWBEpQYJTb5ymo+vc6xA/0M7f0kg5XEXj+T6q2bqdm9o9n6YMbYXv3bssigQNsiCMwdALtQ7Oj/6muftkE4CXOF7qi1SWgWiSWo9IkUH9lIfXUpjQ11WC1mLyN0LKxWK2ZTPVZry/2h0Wig+MhGcnf+StXJw83WKTUxdB4+m5KsLZRmb2+vcD3i+F3xVTT25/soICBwZuJcAK+1t+QvqOjvV3+LpWUmdaDzh7t43Jr4+vWVsXmrkbV/1lNWZqbOcHqduALUGSwuRXSLxcravxp47sUqPltU28yLWiwW8earUZgb4Z5/VrQ43rfFPhGIOCyIzAICAoHiLBx7EpK9icwBC70i2x2wuDin8pW2EOP83RZP2x8soVOeFg9iMZU/raOxogZLnetz0fbC28UJT7h6TywZdVjNZiwNRpd9TIVlVCxdQ8UPf2IqKG22LnLcICJG9qVo6deYKsrb5KJDa4XfQIoMCnQsPJcqFQgL3IlYnkTkyp6RTUJZMEUwmUpLQ0150MY7XbBbYzgSN/ticj99n13fPwuAOjqFvjPva+/Q2oS68nwO/fEhxroKAJL7T0UqU2GxNFJTlE1F3r6mtvlAct8ppA6a3rQspssQKvL2k73xWyLjOqOIiGrnLbDh+D0REBAQ8JfWiFj+isXgn6CXEC+htNTSrEibt3iD4a0bKs/pVvW/PpKflhuYfdmpk7U/f4+nV09Za0MLOTW1Fh5+rJKFX9pO1M6ZqmTyRAU1tVYKC8y8/2Fts/b/93Et3y+ORaez5aAkJkh47WU9V19fxqcL67jmyoigxOXLvhKq/UlAQODMwBdR0LGNLwX7AkGis52rNxZXQPqp5d6yoZ2FPn9tCFzhzt6iNWMGktXtDll8NNGXTqbsy5VU/mQriqi/aCLRF08Oyvj+EIio7IyjYGu1WqlZt5Pit78BQKKLQD97IlZjI1ajieo/d9DoICqXf7uKhH/MRd2/K2C7gzp23nnkPvA2hT9+TtKA6xCJg59P2p7Cr5DJ3PEQMpjDELsdRmuFL3cZlq0ZV5fUnfKcPZRkbW1NaGcEicZ0utz3KGKlCgBdcs8QRxQcqouz2bPslSZxGSB/1wpObP2B3O3Lm4nLTev3rKTk6Jam1yKRiM4jLkIiVZC17gusltBli9m/I63NSBaEagGBM4uPKvt4bRMsf9lAOGuMAoBb7yzHYvGe3dNRhbxgxK1Uivj5hzhuv8V2DIiMFJGe3vE9mRsbrfQZWNAkLgP8uqKeBx6q5Kn/VDUTl6X/Szk5cMjEzXeUYTaf2mdmnKPimivVPPJ4JYePmIIWnztblkCKUXoaX/BmFhA4sxmQmu+TGLgtJ9WtiByIuOyrF7K0IAplp86ULVqJsbioxRjuxrbTHhYZ3sb3tt4x3tbGrj9/PMlP39z02i6wOuOYXRwMMbitsIuoFd+tbhKXAcyVtZR+tIyyhb9Qvvj3ZuIygLXBSNEbXzbLZJZEqom/fQ71+7OpXLau3bahLREymTsWgsAcxgRbHPY0rq8k9pmIPqUnuduX09ggfNndYbfKqNy+CUu9gcTzLyNt0IwQRxUc5CotIpEYdXQKGXf8m6hRpyoex2YOo/PIi+k78x8Mu+JF0gafC4hQauM4tmExNaU5TW0lchVdRl9GdWEWJ/etCsGWuCbQ74dglyEgcObhzvYiECuKYAthXTKkvPVaFMt/qefg6qKmuDzFHW44xukq1mDGfzSrkU8+r2XyRAVH9iahVnX8n8hiMZx3ru0i9+JFMfzf+9HYk5lGDpfz6INavvsqlqz9Saxfk4BKKaJTmpQ/Vjfw/EtVzcZ64hEdKSkSbr6jHKMxuLcjuxOZg7GP+rqvCwK0gMCZizthuTWipL9Zl4lXXAMWK2Urf/WYeOMsWAdbfGvLbFFPsfu6HYPTchmUfILyr1YgjlCR+urdKHuke+8YZFztG63ZXywZdcg7JwGgm3UWKc/fjqJbGgCSKA26c8cSf89lpL31DzK+eBplnwzEaiUimZSClxdiMZzy71b2TEd37ljKvlpJQ1ZewDGFE4LI3HEQLDI6EOGQISkWS0gdNJP9v77NrqUv0GXM5ehTTo/M3NagO2ptssmwi8vVRccoXrGMpD4TSdMOC2V4QUURGc3QK16gMgNEEgnx55xPZM9+6IoVqKOSm7VN7DWeshO7MdVVotTGcnjVh3Qach4AOduX02isQyQWk7t9OTHpA1FoYkKxSQICAgJ+c51uL66u07sTsjzd7u8sbgXLGqBs4gjGjV3Fwru3cdMNEVge07kUw/3Fk+ett9hbs232fsEWAxsarFx3UxlJiRL++140UmlL26uOiFgs4q3Xonj5eT0KhW2btg1KYP+BRiZNUCCRnNpOrVbMIw9qefjxSiZPVPDaW7bfnGeNVfLu+9X8sbqBlGQJR442Mv+/Ndx1mwawfSYdRZx1t+857rv1NSbgZDtHJiAgEI60xhLDURBzFm1dZfBKj+mInjqN4u+/wVRWQuIV1yLTRzW1d+fX624O53b+CMfe5nNnzRFMXMVgF3D3fbQVw94sxr1+LrnxcT6N5+tn2VpLlMFpuQHvNxFDepLx2ROIZDaJLvnxG6jbcRhl905ItM3tqeJuupDcB95GnhBDw4kCCl78DP2FE2g4dIKKH/5EolWD2Uzx+0vodMsDp4VAGwy7FoG2p+OnZ5xBhEuGpFqfSL9Z96OOSrLZG1hPv2I4geBY5M9ibiR7w2IiYlJJHTjdS8+Oh0gkRiQ5dfuwOr0LpqEpLduJxXQZfRmmhhpbAQYrHP1rIUf/WohKF09sxpCm4ofFRze1W/wCAgIC7Y03u4y2yCa+JXo3X30ew8P/0jL/g1rW/GnLcGmNIOguzkC3ob36eOKNd6o5mtXI/70fjUZz+v00tovLAKkpUqZOVjYTl+3ceH0Eo0fJ2b3HRKc0Ce8uqGH2pSXs3Wfi/vs0GAy23zhP/aeqRd9Q427/c74zwJVtRkfI5hcQEGgdbeWf7AvOAq+zLYR9nW74KFJuuoPGygrKVvzSbAxXVhu+WnAEG3vs3uwtXMVm7+Pcz1fbjMqsMg58up1e1wwmfsipc09XNhj2zPRAxGXHMdvTXsMuLgOIpFIihvZqIS4DyBKiiblyGvUHjyOLj8J4ooCCZz+m/LvVaCYNRRofDYDxeAH18uPtFr+AwOn3K/o0x+4TG2qxWa7WEd99NI0NNZgMoc+sDjcK9q3BUFVMxoiLEYk7ho+jq0KFnnAU1O2vXaHSxdNt/LVgtWCxNJIx6lL6nvtPuk+6kc4jLiJ92IUAGCoKA45dQEBAIFwJVMx1Zxvgy3iObWQyEXfdHolSCfv2m9pEXPanTbAyXoMlCB45auL1t6q545bI06KoX2sQi0W8/3Y0I4bJOZFj5sbrI1m8KIat6xO5/14tf6+Od9kvmOKso9jbHpnRjsKzgIDA6cnO3GSXy/0RH1uLu4xgZwFW1TkDdfdeGAuCdydFIAK0tz7+eC77Opc3wdpqsbLtpbVEJGvoefVgl23aSgx2FJqds5ydH+2FZsowoi6dgqmoHFlKPPH3XEandx8g9tqZJD1yHSKlHACrMXi1E8KB0yEb+3RGEJjDFF/8l0MtMhcd+htFZDQyZXhkVocLjQ115O9eSWLPs1BHu/5BczrR5DftQaDWp/Si9/S7UUTGcGz9VxxYMZ/66hIAynP2AJDQc2zbBysgICDQxjgXKPNHfHPnOewsLLsTw5wzNe0s+qqO+noYOkTeNE974hy/XWQOB1Hv2ReqSEqUcN/d2lCHEhYkJUr46IMY/nGPhrffq+HiuaV8tshWDDDrmO2Oox7dWzrsBWOfCuUYQhazgIBAKHAWyxqrq6jduwtlp/b3FXbMLvaWmRwM7BnOvhb6+2tJNaW7Chh8/zgk8uYJXIEIvIFkKLfWQiOYiEQioi6cQNJD12LKK6Lo9S8peusrrKZGRGIx1nojAPLUUxeHQ5HxLnBmIXgwhym6AzVuRWZPwrJdhG5L8dlQWUjJ0c1UFRym67irEYmF6xSO1JXnYzEbic3sWL7L7jKQg4VUrqLP9LuoLc8n669FHPrjv/ScegvJ/aYQ32MM2kTXFYAFBAQEOhrBKlBmH8vX5c6Zn9XVFlb8Uc+Dj1Qyc7qSEcMU7KxodUgB4av/tLf2wWbTViOXzlGjUp0evsvB4sH7tVx5uZo336nhwUcriY2RMHaMnCce1XLDNd5/Y7Y2U93xIoS/+0J7ZkALCAh0HMJJHHTEklGHKEuF4ehhylf/jrWxkeip00Idlkt8FSd9zXD2Jxu1/tAJJDE64oektMnn5zhme9pitBZltzQ6vfVP6nYdoejtxRS99x3xt84m9sbzUXZLa2a7Yc+aD+T9Dyf89RUXaD8EgTmMcSUme8tsbuus5vrqUnb/8GLT66i0fm06X0dEpU9EJJZQefIg6qikUIfTLjiK087FDh0RiSVExqTRfdINHFgxn70/vUrm2CuJ7iTsRwICAqcPgYijwbSusItzE6+oJ3trOUCzomzhQLgUh+vXR8Yfqxt45N9WRCJBZHYkLVXK88/oqKyycMMtZdx5WyQP/0vrcxHEUH/GroRmb/v/dbq9PNWmUQkICAi0pGrTeoq//wYAzZBhSNQtfXeDia8FAt3hrhBgIPP7iiIjicplf7FhvRx5EPTl1orUrSnoF2zEaiWRI/sCUPT2YvILy0i49zKksfoWbR3F2dZ+jqHEklEH+4VEx3BD+ETCHN2BmqaHO9rTl1kRoSeu6wgA9Km9hexlF8iUkUR3Hkjhgb+o6Gzx29u4I+Prtio1sfSZeS8RMZ04+PsHHFn7KVWFR9s4OgEBAYG2wy5cebK28KV/MBl7bUbT88GDQucv7KkwYKiZd10ku/eY2LDJGOpQwhKJRMT7b0fx1GNa3n2/hrPPLeb9/9Ywv7TlheFA9313BOsuAOe4BAQEzjzCPSNVNaUz8kRbYlJkn/b9P2UXGP0RmkMhSkYM74NEH0nlL+vbfe6OQuTIvqQ8eRPmyhpy//0OJZ/8RGNJRYt2rb3AEC5Y0jumOH46I6iDHQxPQnN7IBJLyBh1CfKIaCry9mM2NYQ0nnAlsec4jLXl1Bzc07SsMlPU9Ggr2np8b9gL//lityFTRNB90g10GnoehooCDq58n4bq0naIUkBAQKBtcJVJHIgHs7/YxTzHB8Drswt54lGbt/DqtcLx2pXoOWmCgu7dpMz/QChY7A6RSMRtN2v44dtYEhMkPPJEJX99cqxN5wxG8T3nzOVwuKAhICDQ/gxIzW963t6F2HxFGqsn5bXbAKjcvAGrxdK0ri3EXEebBH98kNsqHnscnhDJpGinjqBm7Q7M1W0rLPq6n4TjhQtFZgopz91G5Oj+1Py1k4KXF3rtI3gzCwQLQWDugHjLaG5LGhvqOPrXQoy1ZWC1svXLhzBUFoUkFldYLRaqi46Rs20ZR/78nPw9v1ORd6DZQbo9UEcnI1VEULZuVbvO29EQicQk9hpHr2l3YrWYyd35Cxbz6VXpVkBAQMBfgiGEWa1WPvq0lieergLg4rmlvLcgsN8ObZX1eSKnkb8XZvPFP3fw88sH2LEsj9ry9s0kFolEzJqh4qef68k+3tiuc3c0RgxTsOiTGKZMUrDhixMcPtLyeN2afdd5PwvEf9nbmO6WCQgInN6Eq7Bsp+H4SXLvfwuAuv17yX33dazWtq2PA75bJFjqG6jdtI/CFQspfn8Jlb9tpOFYvtd+wY4j8qyBWI0mqv/YHLS5W0s4iswSjZrY62cRe+1MjNknqV67vUUbV++3IDILtBbBg/k0wVvxv2BgtZjZ+/MbNNbXEN9jDEUH1wGEhU2G1Wolb9evFB38m8aGWqSKSJTaWCrz9mM21RMRm07PyTchkSvbNA5TQy21xccpPLiOxoZaGvNqkWzIhrh0dEetVGaKWlVMz5O/safl4Y5UriJ14HRyd/xMo9FAj0nzQh2SgICAQEhxFtZ8KernyIo3D7N6QQWXzlHxw2+NGKpMGAz+HyO8iXH29f4IgX+ua+CRxyvYu78RibSIlL46cnZW8OdHx1BpZcx9fRCc43eofmE2W9m81ciaPxt45Y1qAO5/sILFi2LbduLTgPvv1XL2ucWMGm8g92gySmXw7pxyLJDpmHEeiNDsXCgwGIK1gIBAx2NnbjLiMNbNGrJPkvfwfOQpcaiH96Zu0z4s1nqsXQxYCa3oZ66uo/iD7zFsP4TV1IgsOQ6RXGoTLM0WdOeOJebK1hUk9EVcNp0sof5ILuVfrQSg7IsV6GaORSSV+DSHXQT25yKDP8Kxox9zexWT9GWb1EN6IdFHUvzut0h0kagHdGuzeAQEQBCYOySuhOTKnpFNWc1tJTRbLRZMdVXoknvQefhskvtNwVhbgVIT+pMxq8XMyT1/IFNp6TXhOiJj0xGJxVitFqoKjnBw5ftU5O0jJmNwm8VQX13C3p9ew2yqR6bWkdBrHIX713Jg9QcMvfgZIHgCcGuF6nAkqe9kjIYqig79TX1VMUptXKhDEhAQEAgbnAumOQplroqpVZw0IFdLeOJRHX0fGUD2tnJuG+9flo3jmMHM+vxsUS179zfy4fxojg8ajDLS5g9dVVTPxzdvYd0nx1gwYmhQxEBHgdJxvMuvLuWP1Q3I5XDd1RF89Gktq9Y0UF1tQaMJ/YXzcGbwIDnfL47l4rklvDO/mn/co222vi2K+/lTqM85Fuf+AgICAnacRcRQZDmby6vBbEZ33lg0YwfScPwkYvWppKhA/XK9iba+jFV/6AR1m/ahPWckUf0mIYuJxZJRh9XUSNlXK6lc9hdRF01ErFL4HJdzDN7irF6zjeL3vgNA2TMdkVSCqaCU6jXb0E4eFtC83ggkKznUmcyuig6KlXJSX76bk8/8H6Wf/YyqX2az5EDHgn9NfTpw4T+B0CP8gj7NaMssZrFURvrwCyjP2U3+7t+RyJRExnZqs/n8QSyRIlPpiOk8EE18RtM/TpFIjC6pOwpNLNmbvsNUHzxrEavVisVsarp9qabkBGZTPb2n3cXA2Y/Qach5AFjqDVQXBd+rsKMUD/Q1TpFIRKchsxBL5JRm72jboNqQUPukCwgItD+exKtgFDuz40lYc153zj3dkcjE3H53OWdX7yBzREyLTFN39gHBjNkVZfHJpCRLOH+WqklcBtDGK+k9JYEDa4rZ/etJr7H6g9FoxWy2Ha/NZiur1jRw8w0RZO1P5qXn9My7LgKABx+tbNU8ZwpjRyu46AI1i78zuLyNu7VWGcHe/3z1YP6osk9Q5xUQEAhfQi0I2lEN7IZ6eG9KP1lO7dYDyDslIouLatWYPtle/M932ZPQrDAlA6DpPBRZzKmkMpFMimbSEAAK3/iqVbE6Y7VYsDaesqyq23kEaaye9A8eIvmJG0l86FoASj5YiqWu3qcx/bVIcW7r74WH9rZk8bQvSyJVRF9+NqbcIozHC1qsF6wyBIKJIDB3ICp7Rra5DYY3YjOHE9d1BLk7lrP/l7faxRvKE4UH/+bAivnk7PgFk6ESmVrnsl3a4JmYjQaqTh7GarW49PktPPAXB39fQGX+QY/FC82mBgr2r2Xnd8+wZdG/2bv8dWpKTqBL7IZEpqTgwFpAhEgkInXgDAD2r3iXk5KjVGaKqC3LY+tXj7Dps39QkX8gKO+Dr4SiCKA/mdZiiYyotL6UZG3Gam1f3+xgIojMAgJnFq7sLHwVydpK0NUlqrjo6X6s22Jm1PhCRuVu8Ti//bmdYFsJ7Ntv4qLLSrjrvnIic/MQx2lcbvP4G7oAsP0Hm7djncHSIr69+0ycO7uY+R/UUFJqdjvn++X9OPx3Cf+9biPJGfn06H+S+R/UIBbDudOVfPu9gYpK27Hmjlttv6++XFzHcy9V0dhopc5g4abby4hNyePhxyqC9l6cLlx4voojRxvZtv30qZ1wnW5vqENocwwGAytWrODHH3+kpKTEpz4lJSX8+uuv/PHHH5SWui7IHMi4AgLtwYDU/CYBbnBabtMjXBCJRMRePwtpnJ7Clz6nYsHadp3fLjDaC71ZjCaK319CwcsLqd5l8+6VanXN2gLIk+NQD+uNYedhzDW2rGarufkx2Wq1UvT2YoreXowxp7BFXSTH8UxF5ZR8tIzsa5/m2FVPUvjmV5iraokc2YfGkgrqdhwCQBYfhSTadufMyec/pbHMVmui+s8dZF32CCfuegVzVa3f74PzfmEXie1Ccbj5eNvj8cUqQ9UnA4kuguo1Lb2Y4dTnYL/oEM4ZzIL4Hd4IFhmnCe0laIlEIjJGXUJjQy21paE9MJvqazi+6VsAqgoOAyCVu/6HE5XWl4iYNHK2L6dg/xpqS3NQRSWRPuxCtAmZANSW5VKZf5DK/IOIpXK6jr8GfXLPU/M11FJ04C8KDvyF2VRPTMZgtAldKDq0ngMr5tNn5r10HjGHo399TlzmcHTJPYjNHErujuVgsZDz8bsok9Ooz89pGlOu0raI1RN2H2c7js9PF8uM+O4jKT22laqCI+iSuoc6HL8J9UUgAQGB8MFfkdYfv1lnP1lXYm3vyQnMfXUgH920hdq65scIVwXVnPHHXsCbOP3EM5Ws+dN28TYiQsTwoa6PWTKlhBn392T5SweYcnMyO5adRCIrYPyNmUy5vRsLKvqTlVfKho2b2LDRyCNPVHLDtRE8/4wOkch2TDSbrfz0cz3vvvk3eXurSOmj5fzH+nDyYBWPPJHDutokXn3OwvgpRTzzXBVvvx5FaoqUs8Yo+HNdA6+8Xs3CL2vRRIo5fMSWRZWQ4JvP45nExPEK0lIlfLqwliGD5S3We9s/fUGwuAguu3btYvr06URFRaHX69mxYwcfffQRF198sds+d911F99//z39+vWjoqKCHTt28Pzzz3PnnXe2alwBAYFTSPUaUp+9jay5j2Guq3VpUxAMYc2T/YE9m7l2wx6qV20FoI79tpU9wBLZsl/sNTM4se0ARa9/SUP2SSw1BpR9upBw96VItBFgtVLz104Aav7aiSwljqRHrkcapWkaw5hTSMUPf1KzbhfiCCW6c8cgjlBR8f0aCl5ZSNJD1xI5dgClHy8jYlgvxEoFURdPpuT9JTQcOsGJu15B2b0T9ftsdwybK2sQKVsekxyFWE8+yXarCXfCrXN/V7Sn9Yq7eBwtM0RSKZoJQ6hauYnouWcjlstatA9nUdkR+37aUeI90xAE5g6Eo4jsLGK1t6hltVpRRSU1ncyFAqkigsi4ztQUZzctk8hVLtuKRGLiu43i2MbFiMRiNPFdqC7KourkYdvzwqM0Ntj+SYkkUiJi0ji69jNUXbuBFcQV9dQUHwesxHUdQWLv8SgiowGISuvH3p/fYO9Pr5I2eBbq6BTy9/yONqk7crWO2MxhlB7bBiJRM3G5x+SbUEcl+7St3or7+Us4ezhHxmWg1CVQfHhDhxGY26PIpoCAQMfBV2E5mKKZOyHOUG0TSHv1aHkyYWdpzgAAzk/bGbQ4nDlnqpI/VtsE5tpaK4Z698egvucksubDLA7+WUzfsxPZ81sBO37MZ/JtXanIN5C1qayp7R23RvL2ezUcOdpIsUJPJ3ElG3ZDxcl6zhqj4M0vYhh3lgKRqAKA82Tp/PzSAdRHVFxxmZq359fwwD80dEqTcvcdkfy5roHMLlKOZjVSgC3T6rKL1dx1u8ZVqGc0YrGIq+ZG8Ppb1Tz9uA6ttu1ujHT0dRaK9QXO1VdfzejRo/n6668RiUQ8//zzXH/99UycOJHYWNc1VYYPH85rr72GRGK7yPLee+9x++23M3fuXGJiYgIeV0CgvQmnrGVX2AVkRUJSs9fONhaOGceBzuNJnFNreiNS/Ii1wXiqj9q1x7I0Vk/EkF7Ubt2PPC0BY72R+r1ZmApKESlk1G7ci0gmxWpqRDWoO8Zj+Zx86kNkafFgBXN5FQ1HcpFEa4m5chqaSUMR/08c7jdSxp/3/kTev94h6tIp1Py9m+qvd6K7ejiRo/pSsWQ1FkMDluq6JnEZoPNHjzbzGXbGlyJ83vYVVwX9QokvArZm4hAqlq6ldsMeNOMGtUNUbUeTyJzgm0WKQPshCMwdkHAQsmTKSKoKj2AxNyKWhGY3EolE9JhyMxU5ezn61+cAnNy7iqjU3i7bx3UbQWzmMBqNdRxa9SEqfSLahEx2//AC9VXFyKLj0A8bQ1L0QJTaeE5s/QGDsQaRSIREpiS53xTiug5Hpmp+kilVqOk7815ObP2xKaMaoLooC21CJp1HXIRIJKH46EbEKjXqThlEdOuFVt/6Kq6tEYmDLVoHC5FIRHy3keRsW4bJUN3i/Q437Bd+BJFZQEAgGPgqnPnaLiLKdqK2/6CJkcNdnyC6EpaDnTF6/TURjB2t4PKrSzmRY2bLViOLxDuaiZJNwqEe/rkzlvklffnzo2PsXVHA+Y/35ot/7GDvbwXIVBIGzkrmkmG13HRDJDExYhatkiOqN3NMrKHXJDVPXl7H4EEtM5iWPm/itr79+fHZfVjNRkwmeP2tal59MYoJ45TMfzuKRx6vRCKB4cPk9O8r4547w/s4FEouv1TNC69U8c2SOq6/xv0xsDWF/9pTULZ5MJ/02q4jsmfPHnbu3Mm7777blCBy++2388QTT7B06VJuuOEGl/2uvPLKZq979OiB1WqltraWmJiYgMcVEAhHXBVKc9UG2iYrVRIRSUNh8/9BrsRl5+e+ZnQ6itOu2lsy6pAdiyLtjfuo+PYPqlZsAqDm271oLu7ncsz4ey8DqxXj8QLyn/gA7TkjaMjKo+iFj2msNRI3KBljahd0M8fSWFhGxdI1aMTViAB5FxUJl02kpOd4RNLmmkLcwGSmfDyHzc+soujNr8FqpWzVr2jnDkGsVJD08HUUvb2Yhuo6ZMlxyNMT0Z0zopm47Ph5Biomd1Sct1eWGIOqXybVv2/p8AIz/E9k3i/YZYQbgsDcgfGU0dzWxHUfRfGRjVTk7SO6U+huW5RI5cRkDMJQUUDBwb+oKTpGXflJ1FFJLdo2Gg0cWfNJk52GRK7mwMr5RMal0/Ps27CMykQkEqH5n+CaOWauzyKsRKYkY+TFRHXqx5E1n2BpNGKqs/lBiSUyMkZdTNTcOSASNf34Fvkh7Nrnb61/srPFhn3McBOZY7oMIWfbT5RkbSGpz8RQh+ORyp6RgsgsICDQKoIpnjmPNX9oP2I7R/D+BzVuBea2mNcZkUjEmvghvPvmZubdUkZxiYV35tfw4AOnrKLsY1itVt58t5rnXlmJ2WhBESnl05u3IIuQcsETfek/PQlFhLSp/Z23arjzVkchshFoKS7b43jvqlIemxLLnfeVs3qtmY2bT2VpzblQzezzbXdDicUdo5huKElKlHDOVCWfLfQsMEPrLDNcWboE83tzavyO6SddVVXV7LVCoUChaP593717NwB9+/ZtWqbRaOjcuXPTOnfs3r2bvXv3kp+fz4IFC3jsscfo1KlTq8cVEAgHnLNRfRGZPRHo7fuWjDq0w0ZQvvp3YmfMQixX+DyOv/N5ay/VR9oKw2WV05CXQ8Wfq4k4vydiuayF4N1wLJ/ClxdiLqsCiZiqVVux1jXQ5YLedL98AJGpNv/mbTmRSPWRJD5wVQtRN52C/7U59b5vy0llcDpMfO8CDi3awZ73N2E1NGBtbEQklSBLiCbl6ZuxWixuM5ZbexEgWBcRWrtPBQvN5GEUvf4lxtwi5KnxoQ6n1VjSBZuMcEMQmDsAdsHK2Wc5lEJWffdIWA7VmlqiQxbFKVIHTSep32T2/vQqJ7Z8T48pt2A2GpAqbAdAq9XK/l/ewlBZiEytR5PQBavZRGyXoehSeiEWS6j8n/DrKLj6K7zqk3vSfdI8Dv3+3yYLDTuebtUJlECykF2JzMHGF9HaU+wyRQT6lJ5U5O4Le4FZQEBAIBS4EthcLRNLRKj1MnYXucjmdbLGaE2WqWMM9rFcLd/VfRh3/QG/vHqQV9/M4tqrI4iMEKFSiZBKbceFd+bX8Mxz1SjkcM40BWYzjBmlpm7SELRxymaxO87jj+CYlCTh849imHB2EZMnKputE4Rl/5hzoZrrby4jL6+RlJTgnlqcTt7L+kMmpLLgenk3mmyCeFpaWrPljz/+OE888USzZZWVlUgkErTa5vU/YmJiqKio8DjP/v37WbJkCcePH8dkMjUTk1szroBAOODOv9aOKw9ed7TWG1acrgCLBXNSFWgjAh4nGIjVSlLm3Up9Xg65775B1YpN6M4egcXYgFhuu4Bl3mog/7UF0GhG3jUVWVwUkkgVQy9NJbpXcwHT1+xw5zZiqZieVw+m0WDi8E9ZiJz8g1t7ju0qJm+x+rItzt7I4SAyRwztiUghp27rgdNCYBYIPwSBuQMSDlmSUl0UytTOGLbsgKmjQhqLHYlUjjapO0UH17Hj2ycxGarpPmke+pReFOxfg6GyEKUugX7n/rNNxF472oRMhlz2nxZzOAq7gWYMO48RDKE42NnLnmLyJ1tak5BJzrZlWK3WkHp9+4rjhaBQfz8FBATCj7YQypwFZXei7oAZyfz0wn7y8xUkJ58SuBytMYKVDerrOL0mxrP2wywmTC2itMzCxbNVvPdWNPsPmHj6uSrEYvjzjwS6ZDj+VLVVkF9Q0T8oftFKpYi/VwsnWK1l1EjbxYut201BF5jtuLr4IXgxnyInJ6eZwOucvWxfZjabqa+vR6k8dVGlpqam2WtXXHLJJVxyySUALFq0iMsuu4xevXrRt2/fVo0rIHC6YBeWW1t4LGJEH0o//ZnqP3egnzkmSNG1DmVKGlKNlrLPfqbss58RKRRkPPgEiKDgi8+g0Yx+ziSi50xq6hPtxnbCXzsKR5G2780j6DNvGNvzPZ/HB0PI9SYuO/71No6zyOxqeXsJzyKpFGX3NOqP5HhvLCAQAG2nsgkEDd2BmibRKlyEK30W6AYMpargCMbailCH00Riz3FExnXGZKgGbIUAj677gpytPwLQZdSlbsXlYAqtnubwdZ7KTJHPYq23tu7isIvUwcxodreNznN4ey+kCjVWixmrxRy02NoKx+9luHxHBQQEwoub9LuaHsEax9exBp2fjMVs5dulrb+V0N3cCyr6exTRndun9NEx7voMSstshfTSUqV89U0tk6cXYTbD5ZeoncRl92O1BrFYJGQst5LoKNtvnuoaS5vN0dZickcXqrVabbOHK4E5MzMTgBMnTjQts1gs5Obm0qVLF5/nuuyyy5BIJPz9999BHVdAoD1xtmIIROATH1M3PRyF5dZ4+kr1GlR9MqhZs93r3G2B87j27Yq78GLEqv/dGdzQgKm8lJy3X6Mh9wRibQRRF4xrk3icEUm8y1e+fpaD03KbHv7gKqPd05zuMqQ9vW5LxJFqrIaGdptP4MxCEJg7IOEiYGlqQnvbjiuU2lh6TrkFpTYOgH0/v0Fp1hYkchV9Zt5HZFy6x/7+CMDhQDD9k4MtNDvj73trNtUjEgf3VtJAcLamERAQEAgmbSFsOYu9iggpSiWsq0jwOK+/WdatEcylcjHT/tGTf/3TVkDv1Teruf3uCoxGeOIRLa+9pG+zuQWCS3W17diuULS9UO/8uQfzzoDTfX8aOXIk0dHRLF68uGnZihUrKCsrY8aMGU3Lli9fzpYtWwAoKyvDaDQ2G2fXrl2YTKYmWw5fxxUQCDechUH7a+eHO9xlK7vr46soLI3WYjWbm/q4erQ2U9ofLBl1RPToRad7HmhalvPGy5iKi1CmZ5D28l0tCvS1JiO3PcRWVwKvr/P6mnkcigxlX7AYGhDJZN4bCggEgGCREca4EpLD6fZ7s7EesGUJhxNiqYzuk+ZRcnQz+btXAtBj8k1ERKeEODL/8Fc4DkRodiUoh0vRv5qSHFT6JMSS8Po3FU7fQQEBgY5BoNmXgfRzFtxu0u+ivt7Kw/VwXpdiwPUx2x+hzpU1h72/u5jdjX/PHRp0WjEfflzD0SwzV1+h5o5bNT7HIhB6tu+0CZAD+gknrOGMXC7n5Zdf5uabb8ZgMBAVFcWLL77IzTff3MxT+b777mPChAkMHTqUrKws5s2bxwUXXEDnzp3Jzs5m/vz5zJw5k3POOcevcQUEwgV/xD5HSwNX2IVeu+jrSfz1uWBfXQNSVVSTIN1eYrKjAO5qO6RaLcnzbqVq03pqdu0AIPmGWxCXyrBom7cNpkjsbCfRFvgiFHvy4nYXn7PI3J6Zyq6wWq00HM1Fe/YIn/u09wUNfxAfb5tMfoHACS/lRqCJjiBg1RQfR6mNQywNvxMKpSaW2Mxh5O9eSXT6ACJjO4U6pBa01o/ZcRxHq4zWjufYP1gxOuLKKsNxmeaQkax1X1J2fAeJvcYHbd5AcVVk0/68I3xPBQQEQosrwbW9MyW3brcJgL16Nj9eu/K1defjHGgxPW/jflQ7gJtu2MWnC2sBePRBnd9jC7Qtni50LPqqln8/XElCgpjMLr6fVgRSULKtC/7Zxje16Ryh5rrrriMjI4Ovv/6akydP8uqrrzJ37txmbWbOnEnv3r0BGDp0KMuWLePTTz9l7dq1xMXF8fHHHzNt2jS/xxUQCCVtLe7ZBbjWCnFWi4X6IzlEjuzbrqKeq+xqV8vUmd0wFpykZtcOUm6+E3GQMmEvjN/W9HxJ0eAWom6wM4ADGc/u7RxoLN72v7YuAmg6WULBK4uwVNeh6tXZ537eLpyEEkt6+MV0piMIzB2McBK0qgqPoE3oGuow3NLYYPuHk9RnkpeW7Y8/VhTOorGjmNwWtKVNBnguTmg1mzmwbSG1uftJHz6buMzhbRqLvzh+/+ze6M7LBQQEBBwJNHM5ENz1+3NdAxERoqYMU38yo31pF0i8zgJjQ4OV226OJCpKcG/rKHz7fR13/6OCSy5Scf99Wr+9rAMRmQVaz4QJE5gwYYLb9a+88kqz16mpqTz00EOtHldAIJQEKi63t7WBqaAUc1kVyt4ZAfV3Z8PhShwM1MfZXFeHRKNF1TmwGP3BWwZ5OBDqrGRfaCyt5OR/PkIkl5P44DWo+mb61T+cRWaB8EIQmAUCJiImjfKcPaT0n4o8Qh/qcJphqq/h5J7fEYmlTX7M4YIvAq6zqGx/7bjcnVAbbIsL53laM7anbbcYGyj86VtqDu6l2/hriUrrE/A8bYE7EVkQlwUEBFqLo8jmj7ewK6sKV22KuhVRW7uVrxbXUX3OqBZzhoqb9LuwWKzc8H4Ux7Lz6NMr/O6IEnDNkqV13H53OZfOUfPmq/qACiUG4vntqk+wCgDepN/FmzW9Wj2OgIBAeOJJpAwX8Uwaq0eii6RqxSbUg3u4LRzvjDexOFhFARvyc6nesRVFYlLTMlfvm78Z40uKBgclvrbAV5/lcMVUWEbBi58BkPTwtUhjArtTLBy+HwLhjyAwhxkdSaxKH3o+2795gpztP5E59opQh9NEQ00Ze5e/jtViJmPUJUhkLStphxOuBGFXGcu+jOHO3sKVsOurUOzcty08mrWHG9n363xqy3JJvOAyxAP6UukhRqvVSn1VERV5+4mMTUcRGY2l0YhEoUamiKDRaKCuLI+ImLSw//wFBATOHFzZTbRGHPO1X8/x8WgTFNzzQCWPjTdze+LeFjGFAqvVyjXzyvj513wuu1jNBeepAh4rWCKjgHfe/28NDz9eyawZSl5/2bu4XFtn4cuv64iOEjNimAJToxWRCOal7EQkgg2bjPyl64su0fPn72lfFT5/AQGB1hBM8aw1WbdiuYyoS6dQsuB7ajfsIXK0b8dox/j9FZN97Vu9YxuFXy9EHp9AzIxZLfo64kl4vTB+W7sJys5xhHMmNATfJsNUUErug+8iVspJevQGr+Ky1WLBsOsIpoIyVP0yEStkWIyNSGN1iOUyTAWlWOrqkacnIpJIghanwOmDIDB3MMKpwFhVwREAdMk9QhzJKerK8jny52eIJTL6zLofmSr8CgW5EoG9ibbuMn8dxWN3bfxd7gvu4nWVee0cp6s+ZevXUVtygrTr70DdKcNt+5qSE5zc+wdVBUcwGw0ux4uI6YSxthxTfTUASl0CEdGpRESnoI5JJSIqBYlc6fc2u4w9TL6LAgICocGV4OVO5HJs25airquxa8uNmE1WMkfFcmv8HiB4VkjuMkrBswBeU2vh3w9X8vOv9fzf+9Gcd27g4rLjvILIGHwc39Ps440881wV866L4LmndYhErvel0jIz78yvYdlyA1nHzC7bxMeJGTlczg8/1QOrAegxLo7kXlpS+uhI7qVFl6RsMYdjJrM9tgUV/YPy+V+n28tTrRpBQEAgXHEl/IabAGm1WqnfdwxxpApFl/YvUO9YtNAxpurtWyhe+i2RfQeQcOkVTeJioJnf/ojM7fWZ+HthwJ/sZX8yuoMlMlutVkr+uxRJpIrUF+5ArHZ9/mu1WKhZt4uqFZtoOJoLZkvLRhIJEcN7U7t+d9MiRbc0FBnJyDOSbX9T4xFJBdH5TEcQmMOIqm6RePtKhougZTY1cHzzd+hTehOTMSSksVitVmqKjlF58hAn9/6BSpdAtwnXhaW47Iwn4dWfMaDtvZOdcbbN8CR0e4rNVF5GyR8/ox8+BnWnjKb2zgJ2SdZWstYtQqlLILHXOORqPRGxnbCYGmg01iGWyDDWVVJydHOTuCxTadAmZFJblkvZiZ1YzY0AKLVxxGYOJ777KKRy/0QN+0WecPkuCggIhBfuRC5PwrO9T2sFMnfC9S+vHkRuMfHlS+Jm2abeCvz5gicfXWfrD4ATOY2s/auBN9+pprDQwjuvRwVFXBaE5bbHarVy/4MVxMSIeeRBrVtxOS+vkennl1BZZeGSi9TccauMxAQJPXtIOXiokZWmTKbKj/Lbynq+/vaUMHHd1RHk5Fax89tSVr1vO8FVR8noOT6esddmkNjt1O86V4UzA7lwI1yUEBA4c3El+LkTnD2Jg85iYGvEwfq9WdT8tZPYeechS4wBfBdxg2WDAf8Tmg9JqTt8iJodW6nZvRPN4GHEnTe7ReaqfV7HGH1538KNthay27rQpDM1a7dj2JNF4oPXeBSXi9/5hpp1u1AN6EbMFdMQRyhR9euKMa8ILFZEUgkNx/Kp/mNLUz9Vv0wkukgMe7KoWrEJrFaQSlB2TUU7bRQRw3v7bO8icHohCMwdjHDJYC46tI5GYz3pI2a7PcFoL3K3/8TJvasQS+Qk9DyL1IHTEEtO+Tg2Gg1UnTxMY0MtSm0c5Tl7aaguIXXQDNRRSR5GbnscM3w94YsQHQyxOlB8ybB2hbGshJPffo5EpSZu8gy37UyGao6t/5rYzGFkjLzE4wErtssQzKYGLJZGZIqIpuVWixlDZRG1ZblUFxwhb+cv5O9ZSXy3kST3m+q30CwgICAQiNWFu3Z2cWzm7qs4P22nyzl8icdZZCvLrWPbklyefFRLp7SWP/vaU1zbtLmBOZeXUmewMna0nC8+jSWzy6mYGhutbNtuZOduE10ypGQfb+S3lfXMmqniyssjPIwsCIXBxvn9rK+38sIrVaxa08CiT2KIjHB/HL7/oUrEYtiwJoGk/2fvPMPbqNIGekbdsuXeS5zE6b0T0gsdQu+9hrawwC5Lbx8Lu5QFdtllIfSydAgBQg1JCOm998SOe++yrDrfD2FHliVZkmVbTu55Hj+2Zu7cuTMjazRn3nnftLYiok+WipMpAnQcHjeJQ09uo7zcQWqqovX7pCzLPL9/MMW76yjcUcfmRUVs/qqIQdOTOPmOgTDV83oDvUnT8r8i3jsCwfFHIKKvo7ahLERX88UyNH3TMMydCByVt94ks7vc9RSB3BGeBLGj2UzJggWYC/NRxcSScskVGMa0DSqzVlfRdOgACrUGhU5Hw0cbUWh1xN9+MgqdM0WhJ9EcaHqMzuxfV6nrbXlvxzeUuZc93bjoStnctHU/Ve99R9TUUehHD/TazrhmB42rtpN85yVETRnZZp4qPrr174jh/Yk9axr2eiOSToNCc9S1OJrNWI6UYj5cjHHjbspf+hhVSjyxZ0/HMHu8EM3HGUIwCwLG3FBFwebF6OPS0UbG9ehYSnYtpWTXMrLGnUXK0BkoFErsNgule1ZgbW7E0lRLVe4WkB0gSc67a7+j0kXSf8qlPTj6wOhJgRwMHUlzW2MDR157AYe5mcSTzkRSeS/uVJW3BZDpM/5sv05SSrUWJW1zL0sKJfq4NPRxaSTlTCRz3JmU7V1J2b6VVOdtpf+Uy4hO834CbsH1Bk+43PDpSWw2G//73//48ssvKSoqIicnhz/+8Y9MmTKltc0LL7zAggUL2i2rUCjYvn07KpXzVLRy5UpefvllDh48SFRUFLNmzeJPf/oT0dHR7ZYVCMINf+Wy629vLCoYDcA5Wdv8SjfhPo6WZew2B589sB1ZhlNPbnsTLRRSraPtcE1f8MS6vrx33WpGj1bz3hsJxMU5P8uXLG1m1Rozsgyfft5EeYUDhQIcLk9o/rLMzKUX6VGpvJ8Dg40AD3T/Hi+474+b/1DN4u+bmTFNy9gx3s/XpWV2fv6lmef/HttOLnteh9SunSRJxKToiEnRMXR2CnNuHcD2H0r49Y3D/PeyNUTeFcXddxhQq3vPdyKBQNDzdHWEamdTGxg37qF5Tx6x581sveHm6NfUTha7v3aXy96W64gWiS1bbZT940MsFWVk3HwHuuy+SJKEtaaa+o3rQJYx5R6iOS+33fU1gH7PQPRj26bQDIVQDVY0ByumfY2hs7hui2uf7ulbghl747pdlL/4EVKEFsNJE322bVi2Cd3Qvu3ksjeU0e1v9it0WnSDs9ENzibm9BMxHyqi9tuVVL6+COOaHSTdcj6qxNiAt+N4R5Zl3njjDT7//HPKyspIS0vjiiuu4Morr+zpoflE3E7oZfS0zHLYbez9ZQFaQyI5M67u0bGUH1hLwebFpI88mbThs51y2drM3p/+S8Hmb6nK3UJjeS5ZY89g9HkPMeGyv5E96TwA4rPH0HfSBT06fqBdYT5/2rtTlyOFnXjuqEhhzCEZ5ZZCHOZmACqXLKbg3VdoLi1qbeO6TbVFe4hOHYhKG7pHvzQR0WSNPYOR8+5Fa0hi75JXObJxEQ671a/lhVx2cv/997N8+XJuuOEGXnvtNQYOHMiMGTP45ZdfWttcddVVfPXVV21+lEolmZmZrXJ5y5YtzJkzh+zsbF5//XXuv/9+Pv74Y664InwKiAoEwdCSAiOYx/cXFYxulc2BLN8iB5e8fICC7bW8/EIsv8SP89jGX9zX7894WtpU5Rt5Z/5GItMNnPzSjFa5/MI/G7j0qiq+WGjii4VNzJyu5cdvkijOTWfj6hQADAaJNb8m+5TL3UlPFkXsSVasNLf+nja7nA8/MSLL7c/vy5Y7z+vzzgi+1oH7PlaqFYydl8Edn0/lnjuj+MdLDZx5XgUHD3k+XwebKkMgEBzbjMsqbP0JhM5IY0WuvvXHF+bcYspe+IjIScOJPXdm0OtzJZjcyNIhHeXPLKR5zxHSr76BiL79kCSJ5qJCCv71D+pW/0bDlk3IVhupl19N/0efot/DT6IfNASApPMvJmLMoJCM3xuB5DIOZZRwV0Qce5PILesKZhuad+cCIJvMlDzxJpVvfo29sf17wdFswbQnj8jJI4IYuXe0ORmk/PESUh+6FktxJYV/+TeNK7d5/M4g8M4zzzzDPffcw5VXXsk777zDeeed13q9Hc6ICGZBQFQf2Ya5oZKR8+4lIjqp58aRv528dZ+TPHgqGaNPbZ2+d8kCjFX5DD/jLiITstotpzUkApAx6mQUPiJmewPuUjncJDO0TwHSZoxTBpEWe6Uzj5dCSeXP35L/xr/oe8uf0CQmt7aPOSTTVF1EymAvz8R2Em1kHENOvpmyPb9RsOU76ov30X/a5UTGh3eV4XDh6aefRqPRtL4eP348a9euZcGCBcydOxeApKQkkpKOfl7s2bOH3bt38/jjj7dOW7p0KQaDgWeffbZ1WkFBAXfccQeyLPd4Kh6BoCO6Q1AFEqF7uXorT31Yyt23R3HZJZGA9+U85UruqJ2//OPAYN6evwZNpJJrX5uAzuA89771biNPP1vPH26NIu32aa3/4+N/X3/fbBVxsRLXXR3FwAFtz9eB5rn2B3/37fEa6fzx+wkcPmxjQI6KV183cuc9tVRWObjztrb1LnbscqY3iY8PrtCPr8KZC2pHcf+fozl5jo5b76xm9ikVPPFINNddE9n6/gkkF3OweZsFAsHxQ4sA9JTqwR/x7K/krf1mJerkOJL/eDHKfN91hAIRx4GkzJBlmYqvPse4ewdpV1xLRP8Bzj4sZgr//QKKCD3Zf3kYpa59WkF1QiKquHhiJk7GIQUutsOBnirw6Pqecn2/BSu0Y8+ZgaZvGurEWMx5xdR8uZzmQ4Vk/N/NbYrwWQrLwe5AN7C9MwkF+pEDyHz2Dqre/obyf39G5MY9JN54Nsqo0AWMHcv8+OOPnH/++Vx11VUAjBkzhm+//ZYff/yRm2++uYdH5x0RwSwIiKrcTUQl9yMiNrXHxmA1NXB49cdo9HEkD5pC6e7lHF71EXUl+zFWHiF12CyPchmA32+cmerKum/AXURL9HOgUdBdjWsqD29jq8uRkJRKokeOwzBsNIYhI8i++W5UMbGULPwQ2W5H/v356Ooj27GZjd6PaQiQJAWpw2Yy/My7kRRKdn//L4p3/NI6Bk+I6GUnrnK5BUUHaUzefPNNkpKSOOecc1qnTZ06lYaGBtasWQOAxWLh559/ZsaMGUIuC3oF82O3t/lxn+4vrvmXPeGvEFv8XTPGJpkrL/f9Rb6jyORgo69b+Pqvu6guMHHq3YMZX7SNdfct58efTbz3QRP9+ipJvGFKm//xlnXV1ztQayT27m8fpRoKydvZ7TreOGGilssuiWTiBC1vvhbPHbdF8ffn6tm124rD4TzPV1Ta+WaxidGjgr+B78+xHT9Ow7Kfkrn0Yj1/eaiOS66soqTUHlAfnhDvB4FA4Al3qewqnEOBw2SmaeMeDLPHtSmg5+jX1EYm+xMJ7XUdfkhp464d1G9YS/T4iagTkyhf+ClVP/9A7erfAEi94hqPchlAtlqx1VTjsFjazQt1JHFLn6Faj7cbCB2tr6vobE5vVXw00bPHEzEyh9h500l/+HosR0qp+XIZssOBLMvIskz9z+uQdJrWYpJdgTIqguQ7Lib5zksw7ThI4b0v07TtQJet71hixowZrFu3jurqagCKi4vZsmULM2eG5gmHrkJEMAsCwlRbRnzfMT06hpqCnTisZixWMzu/eQ4AhVJD5WFnZdPIBO8fxsU7lyAplBhSB3TLWENBSxSv+zToOM9xT9AyNtcx+hNdrdBoSTvvcvLf+Cf7n7wXkFDFxGKrqyGuzyhiMoZ25bAB0MemMuz0P1K0/ScKt31PU20JOdMuR5KOz3tx9fX1bV5rtVq0Wq2X1k5WrFjBkiVL+PTTTz3Ot1qtvP/++1xzzTVt5PTkyZP57LPPOP3004mJiaG2tpYTTjiBL7/8svMbIhD0AO6SyzUKsyO8SeZAxNm+A1bSUhVkZfr+qucpijNUos1Ub2XXEucN3Q/v2sKHQKReYtG3JgCGzk5GE+E50vWLr0yUlzu49OLQR7p0dvtEUTi4/8/RLF3WzMyTywFITFBQV+8gNkbBYw92Lm+++/9Ky/523eeRegXP/S2WU0/Wceefajj1rAoWf5XY+n4Xx0ggELjjLuwCLfQXqPDzVpjPHVtFDbLFinZgH6C9DA4m1UUwNGzeAED9hnXUb1gHSqUzv/LvATe6TM/BPo7mZuo3riOifw4KjQYHtm4Zb0fHL5Dj5R5FHOjyocY9cr4zaHMyiDt/FjWfLaX2y+WgUqLQ63DUG0m65XwU+uBTWvlL1JSR6IZkU/HfLyj9+3sk3Xo+hhlju3y94Yi/19ePP/44RqORjIwMUlNTKSkp4aGHHuKPf/xjdw01KIRgFviNqb4Ci6kOnaHr7nL5gz4+A318JsmDTkRnSESljUSjj+HIxq+QHQ5ifYjI6NQBNFUXIdu758TnCXfZ2pEk7mh+OKbGcKVFkPszzojMbNLOv4LGA3uIyOpH+XdfAJA2fHa3RbEqlCqyxp5BZHwGB1e8hyG5X5el5+gs0QcaUSlD/1622Z05NLOy2n6RfOyxx9qktXBn//79XHjhhdxwww1ceOGFHtt8/fXXlJeXc9NNN7WZvmvXLm644QZuuukmLrnkEkpLS7n33nu56667ePPNNzu3QQJBLyYYSSbLMqvXmMnOVvlMOQBHJd6igtEdRk8HikqrYMSpqajUCiZf3gdrs4M+Y2LZ+EUhO34oYeZN/T0ut6B2FLOnbAKg1CUytbOESpwHW1DwWEKrlXj/7QQeeqyO6VO1LFnazNLlZuadGUFGRmguL/y5KXPSHB2/fJ/MmedWcOsdNXz9RSIKheT3MRJpMgSC4wd3SRxoIbfOFvHzhmmXs1heTxdCixgwCFtjIwknnwaAJjUNe0M9lYu/Rj9oMAxx4KB9yg2FToc2I9MZGevy9Ke3QnZdSSiPTziIZneCfQ/GnjcLe4MJSaNCUimdohkwzBrnc7lQooqPJvWBa6hc8BWVry9COyATTXrPpVz1Rcwhc8hrf9hszloW/l5fv/HGG7z55pu88sorjB49mvXr1/OXv/yFIUOGcNFFF4V0bKFECGaBX8iyzJH1X6LRx5KY47saqTdcBWNnIm+jEvsw4sy7203PmXp5h8umDJlOxcH1HPztfYacfCsKRXA5AnuacIxc9oW3iGtPsj0mchyMGUddjkSCPY2yvSuJiE7utrG2EJ89moR+4ynd/SvJg6Ycl2kaCgoKiI4+GonmK3r54MGDzJkzh1NOOcVn8YE333yTmTNnMmhQ2wIgzz//PP379+e5555rnabX65k7dy733Xdfu/YCQW/Fk/BqEVyhEpeffNbExs1Wvvw4gRl+Riifk7WttaBgy+vOotYqufyF9hEqJ16ezYmXZ/tcduAANVdcpuex/6tn8iQtw4YGnnbBdV+655l23+fu7TtCSGbok6Xi/becQQeXXaLn4cfrOO2UromE8pUnPC1VybNPx3LpVVVs2Wpl/Lj2qZsEAoEAPMvjFgHaIu68FV0LROz5Hb1c20DN50uJmjkWdXKc3/13BbFTphM7ZXqbaSpDNBk33eZ8keuMpvaUpiPh1DMpfnsB1du+Jy5ndpt5XS2XvUWmh1IMd6ck90Rn1y0pFCRee2bra2V0FPa6hs4OK6hxJFw/j6ZtB6j/aX2bMR0v+Ht9/eCDD3L33Xdz3XXXATBu3Dj279/PQw89JASzoPdTlbuZ+pL9DJpzIwpl7y2Op9ZGMmD6Vez96RU2/u8vAAw7/Y9EJfbp1nH0NkEcCtxzM3cU0RxzSIbkfhiS+3XH8DyS0HcMVbmbaK4vJyImpcfG0VNER0e3OQF649ChQ8yePZsZM2bw7rvves3BXFRUxE8//cS7777bbl5jYyMJCW2fjoiPj2+dJxCEG2/XDUdnb3s+DFY2dlZSusrSqmo7Dz9Rx0XnRzBjuq51ur+pOVokc8vvUEc1B8Lfnoxh61YLM05ypmG48/YoHn0wxu/lO9rulv3eU8ftWMIQpeCfz4dGjri+n/1l1gwtsbESP/7czKb+E4Je99t1w4GSoJcXCAS9jxZ550siByor/U1rUfXed6CQSJp2bkD9B4o/uZvd8z0H0o9+4GDi5pxMzac/UfPpLyhjIpH/ex+SQtFlYrajlCf+iuaeFMeuuEtsbzdDQkHMaZND0k8wKDRqIicMpWnzXuRrzjjugrj8ub6WZZmmpiaP18bhfl18fCYWFQRM5eGNRKcO9Jl+whfhlMbBkNwPfXxG6+vujo4NpVwOp/3qD762vaPo5p4gOnUgSk0E5QfW9vRQwpbc3NxWufz++++jVHp/KuDtt98mOjqaCy64oN28uXPnsmzZMlauXAmA2WzmueeeIzU1lWHDhnXZ+AWC7qA7C8ot+cVMXZ3ME492LGI9FSV0F8quUc3dyYLaUegjFDzwl6NfwmdN950DPpC+hRwOX7zlL/eFSiVx5ukRfPSpkesij76HO/q/E+kxBAKBK90pGx0WK8a1u4g7bxbSiK7TMt6kcEsRQfdigp7meWrjTvzsk1v/1g7IYnx2cUj3py+56ms9gc5zL+TYFUUKveHrBkc4peroLJGTR2Arr6F51+GeHkpYIkkSs2fPZsGCBZSXO4MsCgsLeeedd5g7d24Pj843IoJZ4BcOmxmtIbgcOaFKjREqmqqLaa6vICZjKANnXtOrIrI9FfzrDbiO2Vv0suu2uRYKbGnfE9utUKlJGnAClYc20GfcWUi9NKVKV/LII49QUFDA+vXrGT58eOv0oUOHsnDhwtbXsizz9ttvc9VVV6HTtX+E+uabbyY3N5dTTz2VuLg46urq6N+/PwsXLvTYXiDoaa6L2cVH9jGtr71JMFeB5S1lg+vygYpP934ajQ5UKkiIb3uxGkhKhxbJ3BV5mQPhlfLh/LhgOdHREj99m8SAnM6fr7taLPtK5SDwH095wjvihmsj+d9HTaxYaWb+bJFbWSAQdA/+psNwR7bYwOFAGWdo01cLnvrsaL4/dKZooHuKDNe+6n5aB0DC1WcQc8YUoHvkcihxXU93RzaHSyR1d6Ab1g91RhINyzcTMSKnp4cTlixYsICbbrqJPn36kJKSQmlpKWeddRYvvfRSTw/NJ0IwC/xCG5WIuaEyqGXDSYjKDjsHf3sPTWQc/SZf3Cm57C2vsD/LtOCeNiKQZXsT/gpiT+16WqpHpw6gdPdyLMZatD1c4DIcefbZZ3n44YfbTXeXwg6Hg8WLF5ORkdGuLTjv1D7zzDM8/fTTFBcXEx0dTUyM/4/CCwQ9hT9FxFrwJpVDSXYfFVYrlJTayXQrtuaPBHelJ+UywPIFh1m/0cK7byR0Wi77K8sDiW721VbI5e5n5HA1MTESW7dZmDvbvxuTosifQHDss60wnQmDg7uO7Yhg5TKAIlKHIjICW3m11747WrcrHQlpb21CgaWgjKp3FxM1axzRIU6/4I9c9iaGAxHTPSmXjzckSSJiWD9Mu3N7eihhS3p6OosXL8ZkMlFeXk5qaqrPekjhghDMAr+QFApkOXxEcbDUFe+jub6C4WfchUbfcW5Zr/2ESPYG209vlM2uIt1bFHOwErorabkJYbdZum2dvYn09HTS09M7bKdUKhkyZIhf7dyr6woE4YovYRuozA0Eb30sqB3FINV6AOz2wPv1Fl3dE9htDjZ/dIgbro3klJM8y0J/ZXAgaT46K5eFWA4tLfvZ/UkAaL+vJUlCq5UwNsmt8/15LwvJLBAc+/hbpK2rci17QpIkUCqQ7Q6/1tEii91ft+BNdodaKrdEMbeJXv5xHcqYKJJuPBvp91osoSiM53483Asy+rNMIP2HK4EWmewNSBo1jmZxfd0RERERZGf7LowdTgjBLPALc2MNmghDxw3DHFNdGZJC1SYHczD4U6QuUDoSp+7pI7pqHF2Nr+1smedrfnfTUtzP3FiFPi6t29cvEAh6H97SUbgL3ECjZVv68LXM+4VOs5yU1D6fo7/F03pSuLWM7fn9g6itlTlhou9oje7OpxxMATpB8AT6XhwySM2RI23vroi0JQKBIBQRqe59dFb4OZotOBqaUMYevcb2lYLCU57kjlJmdFXEsnu/1uIKtDmZSKqu10vdIVpd5bhrLmZB6FBnJmOvqkO22ZFUIg3lsYIo8ifokPqyQzSUHSQmyAJ/4URUUl9kh42Kg+s71Y+r6AxEerrnIu7Mel37Cac0JP7ij0Suy5Ha/Hhq09UoVBoA7FZzt61TIBD0HrwV8PMlslyL67kv2xmx22Ry8PIrDfSbGM8H5jFe+/X1d3cWJPTF3Tn7iM+K4K13jTgcwZ3jWrYjVKk+WmR2IJIyHPblsYCn/e5p3+r1EsYm39GA7ohjJBAcv7jKykDFZWeFY82Xy0CpIGJk2xy0gUhhfwvwdTW6gVmYth/AWloV0n67s8CeO+FWWK9lX7j+9GYUOuc1tsMsopiPJYRgFvjEbmnm8KqPMST3JylnUk8Pp9MYkvuRmDOJ/A2LOLz6Y9a//ye2fvEkNoup28fiHonsq/idu2BtWcZ1fm/Cn/G6Rjq7S/RuTZGh0qCJjKOhTFS5FQgE/uOvOPYW5ey6nL8C7Mmn6ykusXPOo8PbLOtp+XARyd5QKiXOfWwEv60yc82N1UydXUZiRhGLvml/vnbdTm/Rqi2SOdBtdu23I7Ec6I0GgX/42ofu+3zIIBUbN1swm3vfjXeBQNB1eJOFrpKuI3EXStnYvO8Idd+sJP6iOaiT4trNDwdpHAix581EqTdQ+uz7lL3wEYcvfZiyf37S4T4LN4nri54aZ2/ZP95Q5Opbf1zRZCYD0LwnrwdGJegqhGAWeMVht5G77jNsZiP9p1zamk+pt9Nnwtk47BYqD20AQJbtARf7C2XUcKD99NaIZXd8SWZ3+e76d3dvuyRJxGePovrI1mMiD7lAIAgNb9cN7zAq2FV4drVoXPSNidffMvLogzEk94/q0nV1Fe77aMCJiUQlaPj+x2b27bcBkJra/ruIr6jiQCOOO0NLRHq4y/tjDdf9fe7ZemprZVasdD515M+xFzcBBIJjm9GZxQFHfPqSzO7CL9C+bVV1lP/nc7QDs4iZN93v5cIZhU5LwsmnYS2uxLh+FwCqBM/Fus9L3txumi/RHCrB2hsjf3u7XAbvUfaarBTU6UkY1+zooZEJugKRg1ngEVl2cGD5W9SV7Cdn2pVoDQk9PaSQ4bBZAVBqIkgZMo30EXNRKIP7Vwi17Ay2P0/5mY93QlkM0FRbhiYyNiR9CQSCY5dQR7C69+eprwW1o9j8VSGfP7SD4Sen4DhvbK+JHvBUvM09T3VyThSNVdWcd04ED9wbTf9+qjbLe+qzo3X5s18F4YU/wn5B7Shi964FIC3Vcz7H7s7ZLRAIej/uuXh9/d0Rtqo6Ch94BUkhkfyHC4+ZAC4Ae6xTIEaMzMEwZwJRJ44MuI9QFAb0Zx29hY6K+/Xm4n+OZjO2ihoiJ/b+NKyCowjBLPBIxcH11BXvY9CcG4k9BnIvu+KwOfP89J10Pgn9xnX7+l1TYbgLUE+Ru4EUxOuNRf98EawkDuU+cDjsNJQdJmP0Kc5qzwKBQOCGP7lhPc0L5NF/b20bKsx887c9jD07gwueGolC0Ts+p1q2x3W7PKW4+F9DPaecpOP1V+L96teXQHRfVzApLzrCXZALupffVpoZOVzNiOFHn0zryaKVAoGg9+FN2IVC5lW+8y2SUkHG325D5VLc71jA0WgCSSLl7stQ6HWt031JY0/7tKskc28Vsa77yNN+6UrJ3JV9N+8vQLbaiJo+pkv6F/QMx84ts2OQmL2N3bq+hoo8SnYtRZZlyvb+Rnz26GNOLgNU5W1BodISmzm8x8bgLoaDyaN8LKTJcKdlv3Qk1/3pB0KTn7q5tgyH3UJUYnan+hEIBMcmPRENWVVt58V/NZB3xMamrwqR7TJn3T+018hlV7yllJgfu528IzZ27bFx4fkRfvUTCIEet862F4Kz8/iT7mTLNgvjx2m6pG+BQHDs05FMCzTFgizLNCzfjHHjHqwVNTRt2EP8Zaccc3IZwLhhN/qxg9rI5RZc9+vC8sACvHpTSouuwlMUfXfQleszHypEitCizkjqsnUIuh8RwSwAwGYxseeHlwGoLdqHqbaU+OwxPTuoLqK+9CAxaQNRqrU9PRSf+Ire9TWvN6fLaNmuUIw9VP2Ym2oBUEdEd7ovgUBw/OEp7UNnZeXtf6xhyVIzTz1TD5QhKSAiJrBaAj1Ny37xtW9+W2VGoYCT57S/WHWnK6JUQ9GnEMudx599OD92OzabzLNldlJSfMfP+Ipevy5mF/8X1CgFAsGxSmciao3rdlHx6pcAKCKdN0u1/dJDNrZwwWEyYz5YSOJN53ht05n92J2pIFzH6CtyWBAYLUX+WnIx26rqUcUZ2jwh7FoIsDcVuRQcRQjmMKW7o5eVKi2G5P40lB/GbnH+MweblzjcMdWWkjJkak8Pwy88ieTeKI4DIZS5k0PRT23BDnTRScdUHnKBQBAa/Mn320JHKRkCyQ172cWRLFlqbn2tUHZ8XlhUMJpzsrb51X934E/+5L37rPTrq8JgCOyBu1Dm2RVRreGD+00I9/+xNess1NbKnDy3/Q0J92VELmaB4PjFmzT0JTCDFY26AS59/v6UkcKg99K692IprgBZRts3LST99VQ6C/fjG45iuatSiHjqMxTHwVUat7y2ZzfStGmPzzzdily9kMy9EJEiIwxxlcvBiub6kgNs/fKv2K3mjhsDkkJB9qTzAWiqKQYgPnt0UOsOZ0x1ZdjMjejjev7OsS9R7J4qwn1eMH0eT7inxgg2VYbssFNTsIu4rJEi/7JAIGjDdTG7AmrvTWZZrTJTZpWRunKd13QR7pwzL4KhQ47eBB55mu8LukUF4XU+95Q+wn2aLMusXmthxLDgIrNDHTncmf48pV/w91gLvNOyX1tu6Cz+3kRmhpLRI9V+FwUUCATHL8HIsyXP7+X7e37rsF1LKo1JYxuJu/RkABwNTlnWG9NjdJQapHl3HqiUqNMTu29QglYCTd0SSL+dwV0ugzMy2XywEHtNA5GThrWb5/oj6H0IwRyG1A2JavMTDA0VeViMNdQWtr0Ari87hNlY43GZiJhkUofNan2tiYwNat3hTNH2n1FHRBOTPqRHx+EuOwONUu4oPYavNr2BrhLlgfZbU7ATm9l4TN5sEQgEXYunInbe2H/AxvU3V7eZVlfWzKF1VUB7EbagdhRT7j2aw3DY3BSf/Z+TtS2sopfd8bSPfv7FzI6dVq64LLhIr1BGp3ZWLnubLvL++oc/+8hstPHFwibOPisCSZI63O8CgeD4Y3NBpseCcp6me6N24XKM63bhaLa0TpMdDozrdyHbbEB7KTfl0lTiBjvzzCpjg7u2D2cczRbqvltF1JSRKHThnYLSlXCMTvaFp9Qdvtr4058/fXYGd1Hc8nf9LxtQxkejHdQn5OsU9CxCMB+jRCZmAXBo5f+wW81U529n3y8L2PvTKxRvX+JxGUmhJGvcWa2SuWzvyu4abrdQV7Kf6rwtZIw+NWzSf7gKT08Ryy2Rt8FE4B7P0cyhiPJ22G0UbvuR6NSBRCb0zqrDAoGgZ/FHZKnVRz+TcjdVU1/ezIf3bOGZOct48/r1rfPcI15zJidw2ydTAFj9fh42iz3Eow8eV3kajMxraHDw0GO1TJuiYfZM/y9Wu0IciijX8MP1mLT8T6x46zD1jXDLjYHJG3F8BYJjn22F6SGTZxEjcwCo/vhn5++PfiLvmv+j7IWPsBwp87xMUiQzXp5H3OAkHM0WzLnFIRlLuFD9yRLsDU3EXTi3w7aByPyuxtM4wmVsweLv+F3FcldLZmgrmi35pTT+to3Ys6YhKbzrSEWu3mMEtCC8EYL5GMWQ2Lf177K9v3F41UfUFe8DwFRXQtnelVjNxnbLSZJEn/HzSBk6g/yNX9NUU9JdQ+5SLKZ6Dq38H9Fpg0gaMKmnh+MV97QOxyueBHGg+8O9j0AjuutL9tNcV0bm2DMCWk4gEAgCZeqJGgAW/d8u9v9Wwc4fS1vn/fvJWkr21XtcLnNEDDd/MJn8bbUs/e+hbhlrRwQjeV0lnyzL3HNfLRWVDl54Ni7g9ESBSO1AU1SIlBbhg/ux2Pi/w9x4rZ70dGVQfQkEAoE/GGY5nx6q/2ENzQcLqV20AtnqjFyuX7YR44bdbMpNbbecOlLDzH+fTUyWgbIXPuzWMYcCb2LYuHEP9d+vJv6yU1AnxwXcXzgK3XAcE7QdV7D5kv1JpeHeT6gjvet/Xo8yzoDhpIle2wix3HsJjzBOQUhoKDtMQ0Uuif0n0FR7VAzXlx7EYTv6GI/NbCJ/4yLyN31N+siTie8zkprCXRRu+Q4AtT4Ga1MdCrUWjT6m27cj1DQ3VHJg2dtIkkTO1MuRpPC9r9IiQb3J1JbpniKdPQnV3iipXbfDffzuEd+B9hnIMg6H88uiKO4nEAhCTVW1nXfeM3LiZC1TJmtZtcZ5ji4/2EhV/tFHCbUpMZR/t5WXPzbRf1I8Fzw1igMrK9i8qIj8rbWkDjJQdqABWYakfpE9tTl+4yttAYDFIvPgo3UsXGRiwStx9O/XtV9TPRVjdMXTPJFeofvxRwBbrTJ9s4N7v4hjKhAc24zOLEYd6byRG4w8rF+6EWQZw4yxNO890jq9ed+RNu1MOw7RsGQDypgomv9wEbLNTkzRdvZ9sJXofnE0VzVhqTcTMXpg5zaoG2mRix7l8obdlP/nc/TjhxBzxpRO990iFXtb7t2uKrrnjq9j4e+ywbYbl1UYMvEu14BKH4NC47nGhqeigND73hfHK0Iw9yLslmaMNUU47FZkmxWH3YbDYUVnSKKxIpeCzYsBaCjPJTZjaOty9aUHWv+eeOVzSJICq6mB0j2/UrTtB4q2/YBSfbTitrWpDoCUQVNRaXv33aO64n0c/O19VNpIhpx8K+qI8CqqEGyeZE9C2X1ab5TL4P+4Pe2DUKLSOmWNzdyEWhv+4kYgEHQvb9cN507DHo/zHA6ZbTus1Nc5MDXLmM3Q3CwTFSWRmKjgzHMrf2/ZwG+/JLdZ9tc3DgNw5b/GMWxuCnargz3Ly1n42E6eO3k5CpWEw+b87Cvd3wBAfFYEL19eyVuNGV2zsSHAm8RrKfBXXmHn+vnVbNpi4cXnYjn/nO75/hGIXAw2OtufooYCz3Qkl1tuEsTFKqiqdnTTqAQCQW/FXaBtLsjEWlGDrbwG2WJFttpwWKzgkNH2z8D2+UIq1xYAINsdmHbnti5b/f73ACj0Ovq+9TDgfPy/8t3vKHnqbQBKlc7ApvrcozWQ4s6b2XUb2A3IDge1C5dT89lSIicNJ+m28wN+2siTyHT0a2pNi9DTMjEQadzduZxdZa+nVBeBRiB76quj9XYGRZoGR66QxccqQjCHMbWFu6kp2IUhuR/G6iIqDq3DYTV3uFxd0R7qitpe9KYNn0Pa8Nmt0bvqCAOZY89EH5eOw2Ejoa/zcR9LUy2FW75DkhRkjDol9BvVDcgOO2X7V9NYnkt1/nZi0gaTM/1KVJqInh5aK4EW9fOGp+V6q1gOhq6UzAqF8+PRNfpfIBAIWrguZhctmcYKCm288lojfbJUyLLMBx81sf+AzeuykXoJY5Pzs2vOaeVt5vUZE8u8h4aRMcz5BJFSrWDEyakk9NFzYFUlo89IIzpFR0OlmW2LS9j9Sxlv/03FW43hUYw00Kjf3I3V3P1TDT//0ozDDos+S2TSxJ4tEhSqyGUhlztPR1HmrW20Es3NwX0fEMdEIDh+cNjsHPxsJ6YKI7GDE2le/gPFv+WBt48PxdHrqqq3vmk3O+nW84maPqb1taZPKqn3XkH9LxvR9k0jYnh/HE3NmA8XUfPFMvQThqIb0jek29SVuIpWe10jtYtX0bzvCOZ9+cRdNJfY82b6zKMbKC2SOdzprqhlb7TIXm+iORARHGhhwBaClc2SSoXD6v362tt7IBxuPAg6RgjmMKWhPJf9y94EoOLgWlTaSFIGTSWh3ziUGh0KpRqFUo2kUNJUXYTssKOJjKUqdwtIEtqoOCxN9RRs+ppBc24iNmNIu3VIkkRCv3FtpukMiQyYcXW3bGNXIMsO8tZ9TsWhDUQlZpM5+jTShs8J6Ykv1HgSwq7pLbxFJbsv11tTYnQV3tKJ+IuxuhBJUqAzJIZyWAKB4BjD4ZAZe8LRwj5KJZxyko6//zWWvtlKdDoJnVZCp5MoLbOzd5+NyZM0fPxZE81mGZ1WYtRINRdeVskZp0Uw4akTPa4nbXA0aYOjW19HJ+mYfm0/pl/bj7VdvpXB40vc/bKsmbdvWEdsViSZE1J57zFHUDl0Q40/UtPfflwRuX6Dw/14uL9+KW8IR/KLKMnoB1QH1adAIDh22VaYzoTBzieHtv1rDYe+2Nk6L7pfHOP+MoPkcRnsrspC0qiQNGqwO8is2UxkmgFThZHqXWUUN8ShG5JNzWe/0HyggKyX7kEV276wqEKnJfbMqUdf63VEjMghYkRO129sF7C5IBN7vZHiJ9/CXtOAdkAmKfdeSeT49n4hkD7DGX+iecOdrh6jvyLbXQyb84rRJLfPU+5KS3tP6TKEZA5vhGAOU5obKtq8HnvRE14fPYlKym79O31k2+qtKUOmoVD0/MVad1BbvJfiHUtoLM+j/5RLScyZ0NNDChrXyFxXUeorR/PxKJf9kcfBRjk3VhxBn5CJUt2zkXQCgSC8sVrbvl68MIkJ4zUe2/bJUtEny/nVa/4NbS9Kc/emo1ZLLKhtv9yigtGck7UtFMPtNnyJ5ZJSO39/vp5PPmti7mwd774Rg0olsaB2LPMJbSRpsNGprst0VkS6y1FBYPiKKF9QO4qCHRXIMjwwp5xgL21EFLNAcHxQn3c0VUXU9DEk3n4h/X8XcSo5uk3b4ogTGZdViKFPLIXJJxD7+/TUB68FhwNJ2f4aO9jia+GKLMvULV5F/Q9rcFhspD85H016kt/LByMEu0IgBpK/2Nsx7A1SGcJrrC2C2PV9YN6fT+yU2X69N3pLRLvgKEIwhykJfceh0ug5sPxttIbEgPMatXCsymVzYzVHNiyksSIPpSYClUZPU00xuphkBs+9iZj0wT09RL/xJI2DEaLHQwRzINvY2dQZTbXFRMb33i+EAoGge9BqJVYuTeam26rZs9eG0sc3qxZRFmhkqze57CrZehrXsbhun90u8+w/GvjgYyNKhUR8nIKqajuNRpk/32Xgj38woFJJbfoINR3JQyEXwxtfx2Z+7Hb+ld9ApF4iM+PY/M4rEAg6x+jMYsB54/eEJ04i9+vd7FqwgbR0B6P9iMJ0ne8ph3NHhLJAWlciOxyYdhyi+qOfsNfUo4jUI6mUWPJLiTxhOPFXnIY6OS6gPsMt2jTYQnnhImy94ZoqI5zG6iqIFbl6rLU1OIzNaJKTW6cFIpnD7f0kaI8QzGGKQqnCWFmAQq1l8Nz5PT2csEGWZfLWfkbFwXUAZIw6BYfdis3cRFRSXzJHn4ZSo+ugl/DEmxD1V5Qeq3LZffu7srBfC/VlhzDVlJAyaGrHjQUCwXGPUgl79to48/6hbMjuy1gPUbiuEjjUQjiU0badxXUsP/5s4oprnSkL5p2hY9AgNTU1DoxGB9dfE8X4cZ4jvUNFKPdFKCS0kNihp6TUzsKvTYwcqW69USEQCATe0MbqKFp2mKSx6QyfPxHwLg+95bh1xV957KtdOEQ92+uNlPz1LSz5ZaBQEHfBbBxGE/bGJgxzxhNzmuf0XT2NvxHSvm4S9Eb8fX+EQzRziyCWbTbq1qwEQJd19Al8bxHKQib3ToRgDjMqDq6nYMtiVJoImusriEkfgs6Q0NPDChsqD2+g4uA6NJHOu6cZo0/t4RF1LZ3NI3ws0J37oKZgJ7lrPsNmbkQfl05sxtAuX6dAIOidHCmwccsfaihtUNNUY0GlUTDy1FSPErE7pW+oo5qDldclpXb+cFcN/fspOZxr55b5UZwQwuJ9gQpfX+1FdHPvo7TMzhXXVrFnr5WICInn/xbr97Lu72NxfAWCYxtZltnw5FJK1uajilDTVNLAmLunodKpfS7XIoVDJei8SWZP6+jOqGdZlql882tslXVIWg2G2eOJu2B2wP24ikJfcjCU2xaMhPQnYr2npawvekM0vCuyLFPx7SLqN6xBtlgwjJ2AMjrG7+VF9HLvQgjmMGL7189iNdURmZiNPi6N5EFTSMqZ1NPDCguaako4sv5LGirySMyZSP8plyLLx4909ZST+Xgj2FzK7titzShU2nZpZ2SHgwPL3wZgwMxricsaEXRqGoFAcOwz6+QK9BES116tw+HQcc2VkQwcsL9du66Uyx1J066KlPbVr90uc8fdNSxZakark/huURIJ8YqQfp76u10t+6AzArGz8tFbWhRBxzSZHGjUksfI5P+82si27VZuviGS+++NxmA4WszZn2MujodAcPzw/cUfYq0z0+eUgSg0StKnZZM2rW/I+g9EmPqSzC3zu5OGXzdT+/VvWIsqSL7rUiJPGB50X66F2bxFFoezuHUl3CWzN3pKPssOB7LFikLXPpDAtrqGulW/ooiIIPOuv6BN8V3gT9C7EYI5jIjrMxJrUx0Zo09FH5fW08PpERrKc9n3y+tICgWJ/SegiYylfP9qHFYL1uYG+kw8l+RBUwCE/DsO6Ww0c13xPvb9sgClRs/wM+5q83RA2b5VgNTrcngLBIKe4bSTtZxycgRXXxHptU13RC73VIStzWLnjevWk7+1lv6T4hl4l8R9D9USE6Ngy1Yrs2dqefG5WBITQp8XNxB53lN5nTvb/nhHlmXGTCyjusbBm6/Gc868iNZ5hUU23nnPyOWX6Hnq/2I9Lu9tf4vjIBAcf6RPcT6OP+4vM1FqlGwuyCRd8k8e+isZQyEjXftwF4XB5H52pXbRCqo/+glVUiyx586kee8RLEXlWA4Xo4iOJPX+q9GPGRT84HuQYAoJ9maCzSPdlVS99z31P6whauZYEm84G4VG7UyLIctU/bAYpcFAn7v+glLv/TsztI1Sdk+dcbwd596KEMxhRNaY03tt/uBQUbjlOxw2M0kDJlFxcB0Om6V1ni46idQh03twdN3P8ZwawxfBimZ9fAYAdksT2796moi4NEac+Seaaoop3PIdSQMnC7ksEAj84r8vx7eJmnSnO9NidLc0mx+7ncfX9CV/ay39+irR1NVz0eW23+faAXjwvmgyM7rua2ZPS8KeXv+xjiRJTJ+mZdE3Jm64pZobboH7fpnFnf32M/+2GmJiJZ76P+cjtt7+14RMFggEAGPunoY68mjOf0+y1l9BHIpcvp6imD2lyPA1tkDTTFR/9BMAukF9qHx9UZt5huljQiaXXaWgp9QGXSFFu0o6hnsUs7to7omxtrwP9eMHU//DGhp/3ULjr1uIv+p0Ys+cSt13q2k6sI+0q2/oUC6741rcrwUhmcMfIZgFYUNzQyU2cxNKTQR9J19MxujTMVblY0juj6RQICmOz+rg7jI15pDsMU2Gt+kCJ2ZjDdV5W4lJH0J96QFkhx1TTQn5G7+mpmAHuphk+kw4u6eHKRAIBEHhLtO6QnC3rKOh0cHmr5wXMh+8nUD/fiqWrzCTmaFk0EAVDQ0ysbEKj+PqivGEK+7HINzHGy40Gh38usKMTgvZfZQcyXfetHhm7nIOnBvB5u02vv08AUOUosP3uaf54jgIBAJXfBX58xZV3FmZF0hxwM4gOxyYdhwCIGrmWJJvvYCYs6Zhb2wiYmg/HCYzisjOB7h5y5PrK11GMIQ6etdXZHg4y2V3emqsst2B+XARpq0H0OZkYj7kHEf1+98jN1uo+XwpMfOmETnUmXrFV4SyJ1zTrgh6B0IwC3qc0r2/UbxjCbbmRlS6KAbPnY8kSWj00Wj0I3p6eGGDr6jdFrl8vOVp9jcvc0tqDE+U7V2BQqVh2Kl/QKnSeGwjEAgE3vCUY9dXEbHOiN9QF/ALtM8pRZsYcZ6J8kONyA6ZB/8SzeBBziJJJ889eoEaG3v0HNTVIi+UsjCU+ZK7M4L9WEKWZfoOKvE6/8uvTPzrhVg250xgc21w6xByWSAQ+MJbRGhXFP1zXWcoJaHDYqVywVcYN+xBNlvQDc4m4aozAND2S29tpzR0rbhriUINhWTuTcI3EDp77EP9vgyEbf9eQ9GnOzzOq/nsFyJGDSD+slPgSPv5gchmEbXcexCCWdBjHNm4iJoj27E01ZKYM4nYzKHEpA1GqQ5dlfljAU+Rya4iuaP5xzr+SGZDSn8ScyZiMxtRqLSY6kox1TgvYFOHzSIuawSayNhuGK1AIDiW8CYku6LAnnv//ozBdX5H/Xgac8v8pcubuf2RZqoLmkjOieLsh4fx6OmV9Mnq2a+R3sR9sAIxlKLaW/5fgW8kSeK/L8fxzntGsrKUFJfYWb3GmS5txjQtZ56u47KL9UhScP9jQi4LBIKO8BVd3FUSz1e/rvKwo8hde6OJshc/xJJXimyzEXvuTHRD+qIb3AdJ4T2tV2fxJQC7Qg76EwHuK6e1P8t0By3jCoVk7kq8jW/AhSOp3V9JRFIkVbVqLHkl2CpqAYi9YDbRJ5+ApFB4THfhSkfzBb0HIZgFPYLscFC2xxk5mjV+HqlDZ4qifV5wlcTuItU1bUZLW/do5t6IuzQPNP2H+/YrlGr6T7m09XXpnhXkb1xEdNog0kfMRaUVJzSBQBAYb9cN587MwGSVP0LMVQx3dcoLX+t25X8fNVFx2MSdt0dx03WRpKXVIr5CekdIzM5x0fl6LjrfeV4+cNDK6edUoJAk7v+zgUkTRRCCQCAIjG2F6UwYXBnQMuEaLesqVj1JZ/OhQpp35aLNySDugjnoxx07tWXCPSdyMIRjwT5veMobHpURzaz/nAPAptw0Sp/7AFtlHbHnziDuwjkB+R0RpXxsIK4OBD2Cqa4MAIfNQsGmbyjbuxKVVs/gOTehjjD08OjCC3+ikT2lx+jN0cveckz7mu/PPADZYadwy3cA1Jfsp2zfSjJGnRLkSAUCgcAz/kpGb+28RSR7I5BUER21c49mXvStCYBXXmvkvf8ZUSokXvtPHLNmHE2LIfLadozYP4Hzr/80UlsrAzJXXFvFgV1HH+0O5kmBjt6nb9cNB7yn6BAIBL2P0ZnFQO9Ng9eRVHWVzqadznzL5kNFlD73Aco4A/pxQ0i8/qyQRjD3VLG1QEWsp3Qnrn30ZIE8V3p6/cHgLvxNu3MxbTsAQO3CX4maPhZNemLrfCGQjw+67jkJgcAHKm0kMelDSB48lbThs4lK7ENTdRH5m7/t6aGFLd4ikn0J1d4cxewNT9HZ3qS6ubGakl3LkOWj7SWFkkFzb0IXnQSARh/TxSMWCATHItfF7Ap4GX+lcSDSzJcsC6YAmqf5D98fzfSpWv54u4GLL9BTVe3gwsuqaG4+9s4xLXRHxPiC2lEibcbvyLLMSy83UFJibzP9ofujuf4aZ+X5uLj2ly2hTGsijoVAIOjt6McMQju4D7EXzCbm9BNRREbQsGQ9xg17Qroef2RhV6c8GJdV2ObHHV+5tDta9ljlvOTNIemnaet+jJv2srkgs/UnYkR/Em88p7WNIkI8cXQ8IiKYBT2CRh/N4Lk3tZmWu+YzjNUFPTSi3osvidybo5ih/bYFErksyzL5m76mJn8H6ohoEvuPb53nsFmxGOuIThtEQr/x7l0JBAJBl+GPEAtUmnVFzueWPhfUjuKuO7Zz1x1Hny4647QIzr2okvwCG4MGqtuNuSeimf1ZXyijvD317Ws5EeHtm+07rfz17/U8+0I9hYfSUSic53RJgs1bLcTFSrzzeoLHZQN9//dE+hmBQCAIFS0Ry55SRkQM70/GE/NbX8fLMvm3PYslrwROGN6t4+zKiFVPUrg3pZvobbhGyTssVkr//h4AWf+6B3VyfGs7W2UtAPFXnIYqTjyVfjwiIpgFYYNSo8NuNbeZ5rDbsJjqKdm9nJqCnT00stBRlyN1mfTt7TLZF+77raMc07LNxsEV71GT76xqm7f2M0y1pTRWHOHI+oXsX/oGhtQcBs26HoVS3GcTCAThR6DSKxB5GWi0Zkv7lmWio52fx43Go5/DsizzUt4Q/vxNGkv+fQCbLfyim12luT/b728bb3LZ1zzXMR3vfPp5E6fNqwDAYoHX3jBy8JCVdz8wMvuUcoqL7XzxSSLDhqq99hHsDQGBQCDobbinePAlVCVJQqHX4Wi2tJnuaLZgKa6g+pOfsRSWd9lYuwpf2x1oRLK3fo5FUb2wfFzQy47LKsRaXkPhn//VOq38X59iq22kact+Sp56m9pFK4i/4lRi500LxXAFvRBhVgQ9js3cROmeFdTk70CpPvoohaWpjq1f/F/ra5U2krisEV77kWUHNfk7sFlMVBxYy6A5N6LWRXXp2LuSFqHqSaT6Kvx3LOFJJHdU8C/mkEzR9qXU5G9nwIyriUkfzPav/s6Ob54DQKnWkTXuTFKHzUSSxD02gUAQnoRCPIY6slmWZT7+1MjCr505mSP1Rz+Lz7moktVrfml9/Y/zZnLf8INe+9q81UJFhZ2XXm7k9luiOOuMiJCNsyO8FVIMFNd9G6hAFhHNTg4dtnHbH2s49+wI/vVCLA8/VscjT9TxyBPO+fPO0PHM07EkJyk77CuYSGaBQCDorbjnFXZ/bc4roWHZJmzlNSh0R2/QNa7eQfm/Pml9LSmVaC6c43U99oYmTDsP0by/AIfRRPJtF/g1vlDkaXaXxe7S11vhP9eIW2/4yr98LMpld85L3hywcC7/1yfIFiuZL9yFvaaekiffIv+WvwOgTk8k9cFr0I8c0BXDFfQShGAW9Cj5m76mdPevKJQaotMGkDnmjNZ5Sk0EKl0UtuZGIhOzGXLSzW2WlWUH5sZqZFlGFxVPTeFuDq54r3W+xVgTdoLZHxnscNgp2PQNusgT0KWmt5nnSawey5HL0L6Aobd8yzZjI6zdT51GT+XhjQCYG6tQKNUMO/1OGiry0EUnoY9NRaH0HgUlEAgExwv+FPtr4bdVZi65shKLBcaMVvPPf8QyeNDRz9LZM3SsXuOMkPrzjzOJy9C3kaiVVXYqKhxkZiqJipQ45cyK1mWXrzB3m2B23aZAtt/b/FveiWeatgAui2wzz5dAPt7Fps0ms3K1mcZGmRUrnU+uReolKisdPP1/scycriMmRmLkCDUJ8R2LZVe6Il2MQCA4dvAmJI8lmmtMFD/xBs178lDGR2OYNY7Ys2e0ztf0TWv9O/6q04k5/cQ2yzssVmxl1SgiI1DGGah6/zsaV2xtne+vYIa2eZhDkTLDkzhuOaYdRSJbS6uo+2EN8ZeejEKnbe3PW/tQ0BXvN/c8yv5K4pblApXK9Xk11O6rQBmhxnywEEmnwVpSSeT4IaQ/MR9bTT3a7FRUKfEhLSQp6J0IwSzoMWTZQenuXwFw2C1YTY2U719FRGw6ETHJWJsbUGn02JobMVYeabNs+YG1FG75DpvZCDgLt7lzZMNXDJhxda8q4tZYmU913lbK9v6GrmQ/o87+S+u8Y10k+8LbtrdEM9d9sojSPb+2m1+weTFaQxLxfUaijYr30INAIBAcnwQaPbtthwXL70/YGo0yy1eYqahwMHSICpVK4reKBKAegIrDjcRnOi8q9+y1cuefatiy1draV0pK2wuQd943Mme2ljNO7b4o5s5SXmHn51+a+fyhIj4HLjpfj1brPFeJ6GTvrFxt5tyLKttN/9/HTTQ0OHhrQQLnzOvc+0BIZoFA4I1jSS57Eq0AA2zbad6TB4C9uh5reQ01Xy5D0ycVVUIM1uKjn8HGtTuJPXMq4BTLVe8spmHFFrA5C64qInU4jM1t1lP1/vckXHV6h+Nz9GvqVKE/b8fKU65lTxHcLch2O01bD1DzxVIsh4vR9s9g5hVJ7fp135+eoqcDef+E680MV0HtrejfwvJxWBrMLLt5IQ1HatvMk5stlL3wIX3ffBjd4D5dOVRBL0QIZkGPIUkKJl7xHPuWvk59yX6MVfkYq/K9tcZmaUKp1mJurCZv7WfEZ48hccBEJEmBqbYMm9lIZHwGVUe2UZ23hcaKPBx2q5f+wo/qI9vaRGCnDJ4SsFTuKH3EsYjhgI1Dhze0maaJjCNlyDQKNn1DU7IDoZYFAsHxhjfB5kt8+hKjf7jFQFqqkptvr+HAQRsHDtpYuMjk0qLq6F/5R6OULpjfRKTdwav/jiMrU0lenp28fBuxMQrS05RcN78agPx8ewBb1zO07J+qajvDxpS2Tk9PU6LRHG3TUR9w/OZe/mVZc7tpd94exbLl5hafERL8lcyi2J9AIDjWOKgaRfZr/TlyszN1gWnbAUzbDnhurHCpcfPdahqWbiTu0pPRDc7GYTRhLSpHttnRTxhK0X3/AaBh6Wa/BLMrXVnwrwVXoesqdn94YG2bCGxtTiZgbrdsRwQqi7tbLrvKYk9RygvLx3kUyi1t3ec1Fta1k8upk7PQpxk4vHA3kiqwJ4wExwdCMAt6FEmhIGvsGZTpY6g81FYSKtU60keehCG5P1FJ2YAz6rniwDoAMsechi7aefcxJm1Q63JqfQzVeVuIyRhKU00xNfk7MBtrSB0yA01UHKaaEiITwiuvUkNFXqtcjk4dSOqwWcRmDKEuwH6OF7nsmmpEoVAy5oLHKN39K9V1B0g7/woSyg3Yrc0UbPoGc2U5dTN9FwUUCASCcCGU0a+uuYYDae+Ns06P4OorzKxcbeZwblsbeN3VkVjGD2LQtCTUOudFR+URI+WHGrnrD1FceJ4ziumEiUeXsVhkhg1VkV9gJyVZwQcfGdm23crM6VrOPF3Hnr02srOVROoDe+SyKyKIW/ZhdbWd+bfVtE5/5qkYLr5QjyRJHcpjITDh0QejOeNUHX++v5aHH4jhpDlaJEli2a/lHDho6+nhCQQCQVjjb/oGZUwUifPPxbh2J6btbeshaAdmET13Arph/VEnxwEgW200rd+NMiGGuHNnHm08YWjrn3GXnETNJ0uImTcF41f7MDfnI6lVxJw5BdlsxWG2oE5uG9bTmSjmzkYAyw6Zg1/sbJXL0aefSPRJk9BkJLG5ILg+ezoq2VvEsSudKeTnSvzQZM7+4To2PLkUQ3Ysw2+ciFKr4uBnOwCwFJSh7ZfeQS+C4w0hmAU9TmRCFv2nXEpc1giObPgKi7GGEWf9CX1cOrIsU7b3N0p2LUOp1mKsKsRUV0p89phWuexOVGIfBsy8hsOrP6auaE/r9IoDawGQHXZGn/sgWkNCl26XryJ9rhTvXErhlsUAaPSxDJpzIwql6riRxRB45LV78T+FUkX6yLmkMxd+L4Rsa3amT9GlZgi5LBAIeg2hlsvudCaCVquVeOHZOErL7PzhrhqWrzBzwqV9WPwPp2x+aJmWzx/egSSBpcnO3uXOD+Q7bzd47E+jkfjo3QSuuqGam36XtgoFvP2ekT5ZSvIL7Nx7j4H7/hQd8FgDxZuUdpXCJxRsZPJlVVTXOAD46dskxo3V+FzevY/jHUmSmDhBy69LUtpM12ogp7+oj9AdmEwmVq5cSXNzMyeeeCKJiYkdLuNwOFi2bBkVFRVccsklSFLb72w//PADtbW1baYNGDCACRMmhHLoAsFxj6dcw55SRgBEz5mAYfZ4aj5ZQu1XzlSC/T78PySFAntjEzWfL8Ve3YCkU9P42zaQZZJ85FeOPWcG9vomaj5bCrIMkgSyTN13q3AYm1EmxJD9n3vbLdeZ6GVvhfg8iXbXNnaLndX3/0DZOqdJjpk3nYQrTvVrnd4EcjgU/vMUhdxSrM+TWA4k73JL365tNQYtU591i1aXJJBA0yeFnmZ0ZjF5PT0IQRtEFm5B2BCXNYJBs68HIHfNpzTVlFC07UfyNy7CZjHR3FAJkkRM+hAyx5zms6/4PqMYe/6jxGWNdL7OHgM45TKAStu2GI8s94yArC3c3SqXtYZEhp9xVzu5fLzI0WC201vRw7ocifyy1QA4duR1dmgCgUDQacJNMnZmPKkpSj7/KJH+/ZSs+zifuxemsH6Dmbfnb6BsfwMNFWbqy5vJHBnDRX8bRXS096+bGRkqlnyXxCcfOG/6DhqoYsQwNfkFzvP10MFtpaMsyx2es0MZvTw/djvzY7dzoWMLN95S3SqXP3g73i+53NXjOxbYut3Cxs1Wvvra1HHjENPyfxBu/59dxfbt2xkwYAB33303zzzzDH379uWzzz7zucwrr7zCwIEDufnmm7nsssuw29vnMvnzn//Ms88+y1dffdX6s23btq7aDIHguMZVgHYUTStJEvGXnkzchXMAqP9hLdaSSspe/IjGFVtxmJqxFlWiTk8k+pQTiJoy0ntfCgWJ15xB1j/vdk6QZaKmjW7N0ayKa38zuauuscdlFfrc9u3/XtMql6NmjCH+spO7ZBy9jY5Ec0fzZVlm64srQYbBqr2hHJrgGEFEMAvCiojYNDLHnE7pnl/Z+e3zAKQNn0PWuDMD7qtgy2JqCpyPcNQU7EAbFY8uOpnUYTNBgrz1X9JYnovFVI+t2Uhcn5EMmHF1u6iMrqKpupiDv33gfCFJDDnpZtQRbU/Mx4NcbtlG16hkf6KZ3aOY3dGl/14xOFnjdXnX9QsEAkFX093F3/zJB9yZ8fzzH3E88HAd7/9hM+8DyTlR3PK/yegM7pGoVZ4Wb2XdBguXXOlss3efjb7ZSk6YqOHUU3TMO1PHx58aef/DJopL7JRX2ImPU/DNl0n0zQ7N19iO9pPFInPNTdXk5jnF2mMPR3PaKcEXohNFANuSmeFMqdK/X8/kczxe5DLA1VdfzZQpU/j000+RJIm///3vXH/99cyePdtrJLNKpWLJkiWsW7eOyy67zGffd911VxeNXCAQdJSewVeUb/Rpk7EUV1D1vx+oeu87UClJvfdK9KMHBjyOkv97s/Vv0+5cNH1S0PbPIO7Sk7EUlFH96RKsRZXYaxtwmK0kXncW0SdN9NFjxwSSmuLg5zs59MVOADT900m6+Twkhfcb3a5R4YHs357CPYq5MykxAl1WkiQSx6ZRuaUEW5OVcYPaR9QHi2sqFX+i3sdlFWI1hmTVghAiBLMgrJAkifSRJ5E6bBZVuZtQqLQk9B0TVF9WUz0KlYZxFz+JpFAgSUdPLEXbfqR83yqSBp5InD4Gh81Cya6lVB7aQNKASSHaGu/y0mysYf+yN3DYnAUGolMGoI1qX4rueEqTAR1LY3+x1tdStfwnVIYYYidNg6KuX6dAIBD4oieEoj+FzjojO088Qcvyn5N5eHkWh9ZVMfXqfmgi2kvCjtJy1Nc7o4K//iKR8WM1aLVHz32HDtv4w921TDlRwwXnRpCUpOSf/2nggUdqeXtBAjqd7/NkZ2WuzSZz159rWLvOAoBaDRec0zafZEeFEwXesdtlnnm+AYDHH4np4dEc2+zcuZNt27bxyiuvtAZT3H777Tz++OMsWrSIG264weNy8+fPB2DdunU++8/NzWXhwoVkZmYyatQotFptaDdAIDjOaRGhrqLTl3h1TZ2hjNKTcucl2OuN1P+yAf24wWiz04Iah62mgcjJI0i+/UIkdVudVPTcB9iq64k6cSTKWAPN+45Q/dGP6Ib2RZPhOb1lsLiLzc0FmaTmrWTrP1e1TjNMG4Ok9H7z0rUoYDikwPAXf8Wwt2J/wVK6roDqXeVknTSA2EHOm5K9bd8JuhYhmAVhiUKpImnACZ3qw1RfgcNmwVRXiq3ZSPmBtTTVFGMzG7FbnI9hJuVMJCopG1mWsZjqyF3zKRExKa1FBTtD9EE7FlMjGv3R3JHGqgLyN35NY0Ue6ggD6oho1LooBsy4us2ygeYk7s24b2eg220x1WM1NaDS6lHroqjfvoPy779CUqvJuvY2lLoIoL1AbtnHoZbLQlgLBIJwwptkDqXw/uusAhaMCV6k7trtLO62c5cVpRLWrbfw9ntG7HYoLnFGDQ8boubhB5wCMitTyU23VfPk3+p46onYDvv3RzLXVzTzqn0kCqXE/Njt1Nc7eOKpOhZ9a6K+XmbUSDU7dlr54uNE0tP9i7T1JZc7kwu7t2JscpCfb0elgswMFYcO27j/kVrWrbfw0vOxnHFq8FHh7hxvYr++vr7Na61W207w7tjhfKpvxIgRrdMMBgN9+/ZtndcZvvnmGw4dOsSOHTtQKpV8+OGHTJ48udP9CgSCtgQq81wFoDI6krjzZgW9bltlLdgdNO87gqW4AltZNTVfLsdhMmOrqAWH84Zx/BWnIimV2OvGUvzEm5Q8/Q5ZL96FQtP5XPu2Jitb8lNR/p7xUpZlGn/dTO1XKzhcWoWmbxqWgnKiT5pI9GneP4PcxXy4RCiHA7Is01hQh91iJyLJuaP3f7iVfR9uI3VyFhMenNWj4xPHKnwRgllwzJI0YBIlO39h1+IXAVAo1cT1GUl9yQHsgDYqAZXWGYUkSRL9T7wUY2U+ZXt/67RglmWZw6s+pipvM5mjT0NSKGiqLqbqyDb0san0mXgudkszhVu/Y8CMq1vH4crxJJkDwWG1YirIRXOojsL6Ckp2LWvNrd1CXJ+RxF9yEarIKJ99dZUIFpJZIDj2ebtuOHca9nTc0A+CkY2dEZS+itl1pfD0JnrnzNay6BsVDz5a1zrtjNN0HDlio7jE+XrsaI3LvAjuuM3Af19r5MH7oonUB19SZEHtKPYsK+ODOzczbG4K2WPjKKyr48tFTTQ2ytx8QxTjxqqZf3sN118TyZTJoY3KPJbTZciyzO49Ng4ctFJd4+CV1xrJO9L2fJ3dR8lXnyUy9cTQ7ddwlcv6nUWoFJ7TdgWLzeGMrM/Kymoz/bHHHuPxxx9vM62urg6lUkl0dNuimQkJCe0K9AXK888/z6mnnookSVitVq6++mouvvhi9u7di17f/juuQCDoPkIZXaqMiSJy8giMG/dQdN9/AFCnJ6FOTcBWVQcOiJo51lkI7vf2KX+6nMI//ZOmDbuJmjo66HVvLsjEXm+k6JHXwG4n5qxpOJqaadp2APO+fCInjyDukpNoXLEVe72R+MtO8Zkao6vwJ91GONJcY6J6ZymWejMla/IpWna4zXxJpWD4jRMZfMUYFKq2+zUUUcydKQYpCB+EYBYcs6QNm0XK4KlU5W3FYqwhPns0ETHeq51KCgWxGcMoP7gWWXa0SakRCDaLifyNX1OVuwmAwm0/oFTr0EbGkTX2DFKGzsBhNbN90d9I7D8BQ3I/j/0IudyeyuU/Uv3bL8h2W+u0hP4TSB44Gbu1GUtTHYzNRpuS3jrfVfR2R/FEIZYFAkFHeJKKgUrGYKVkV8nllmWDSckxZpSGX5eksHa9mZ+WNDP1RC1zZml91kS48LwInn+xgdVrLJw8V+dxPb5ez4/djizLRP+0hk8ebEB2wJ5l5RxYWUlEjJp5c3T86Y8GsjJV3HpHNTqtxP1/bivmOtrOlvV4G4P7/GOJTZst3HBLNYVFR4XygBwVH76bgF4vUVhoIz5eyawZWjSazn/fCVep3F0UFBS0Ecee0lNotVrsdjvNzc3odEf/ZxobG9u8DobTTjtafFutVvPoo48ybNgwtm7dypQpUzrVt0AgOIq/6TFCnbKgzXrvuhRbdT2Nq7bRJ6mRPqcORBOtY3PBNR6X1WQkoUqJp2nL/k4JZktBGeWvfIGtrBqAqg9+QBGhRds3jdS/XIV+3GCatuyjacs+ku+6FIXO9w29QPI6+0NLf70tZYTdbOPgo5+yY2XbJ2FG/eFEEkamYKowYmuykjolG12c9yeNXFOyCI5fhGAWHNMolGqScvwvKqDU6HBYzZhqy9BGxaNUBxZRU5W7hdx1zmrc2ZMuIGXwFGRZbr1ItllM1ORvp+LAOmSHg0wfxQtFBHNbHBYzVct/bDe96vBGasv2EjN2EkmnneV3fyLKWCAQdJZgxayrjJ0fuz3gyOVgIp1d1+ttPKHAH9HsbRsmT9IyeVLH590FtaO4OGErAMt+bWbCOA1xcYHdFM4vsHH19dXs3G3l/HMjGHH/VLRRqtbz9Y3R21i5ysy77xv57EsTLz4XS2ys/+tw377jSS4D/PM/DW3kMsDBQzZuuq2a0aPUvPHfeJKTeqao37FIdHR0u8hkd3JycgDIz89n0KBBADgcDgoLC+nfv3/IxwNQUVER0n4FgmMVT1GvoRagoWJcViGbySR23nQGuIzPl1hVRkVg2nEIe0MTCr3WZ15kd2SHg6p3FlP/83rUaQmkP3kz2gGZbW5CW0urqF+ygZovlqIb3p/IE4YHv4F+4Gk7x7nti97CgMMrWOgmlwF2/Hcd+tQoRt85hb5n5vjdX3cI9nD93xAIwSwQtCF54IlUHFzPzm+fR6HWMmD61cRmDPFr2Yaywxxa+T/i+46hz/h5aPTOXJEtJ7+GijwOLHsLm9mIRh/LgBlXo4nwfjEg5PJRzGXF5P33eecLhYKsq29FGRmFtaaKip++wVJZRvXKpTQdOUxEehaRg0egz+6PpFS2imSxP7uehoYGDAZDu+mNjY00NjZ6XCY1NbXdNIfDgdFo9NiXQHAsEar0Ft4I5/QL3gS0v+P9lDFccWkdC940suBNIzddH8lfH49BqZQ6jGa1WRxccGklsuwsLDhlspYFtUfzQpqNNi6YX8Vvq8zodPCHW6O48rLOP+YfrscilNjtMil9iltfv/hcLFNP1FJZaeft94x8+10zq9dYuOjySgbkqDn1JB0nzdESHx94cUhX/I2gP56ZPHky8fHxfPbZZzz00EMA/Pzzz1RXV3PGGWe0tvvuu+9ITk5mwoQJfvVbU1ODXq9vEzX95ZdfolQqGTcu+GJSAsHxiKuY8yWbOxJsXS35vI3F23rjLj6J0mfe58hNT6Ppk0rqX65ElRjr17pqPllC/ZINxF95GjOvz0SpkdhccPS6rnbxKqo/+AFkGe3gPs7igz6egPK0DYHQmf0abmJ0/8fb+Pzl/QCceGY8UZfMRbbLVG4rYfdbGzEW1bPhyaXED0smaWw6adP6Et0vrsP9K6KZQ4ssy5SVlaHT6YiNje3p4fhECGaBwAV1hIHhp/+RmoKd1BTsZP+yNxk48xriskb4XM5UW8qen19BHWEgZ9rl7dJrWEz1HPz1HXTRSQyYfg+ayFif/QkZ2hbZZkfSaIlLH0bsuWeijo2nYc8OalYvx1JZ1tquubgAW20NNet+Q6GLIH7aHOKnzvFrf9osJswNlagjYtoUZhT45tChQ/z1r39l4cKF2O129Ho999xzD/fdd19rm3/+85+8/PLLbZarq6tDkiTq6+tRqZynIpPJxL333st7772HUqmkb9++vPrqq5xwQucKfgoExyPhJto6ShURDC88G8vc2Tq277Dwz1caWV8WzcXPjML1usfTehdcvZbCPDs/LU5i3BhNm3av1Yxk0f/tYvdmG599mMDM6VoUis6fk48HuQwgyzBtioZDh228/GIcs2boOJxr45PPm/h8oQn17x5/124bTU0yi74xISng1JN0vPR8LIkJHUe1mc0y+QU2zGYYMbzzBaOOFzQaDc8//zw333wzJpOJuLg4nn32WW6++eY2hf/uueceZs2a1SqYN2zYwKFDh1izZg0An376KQqFgtmzZ5OSkkJeXh7XXHMN559/Pn369GHjxo28+eabPP744+1yQwsEAv9xl3OeXve0rPRXIOpHDyTjqVto3nuEusWrKHrkNTKeuhVVvO9rrrrv11C7aAUxZ0wh9sypKDVtBbZx016q3/+emDOnEnfJSX4XEQx0v3kS/4HmWg5H2ToxuYTtwMV/yuT061KRyOUf/1Vx8NMd2JqdKSmtjRZqD1ZRuaOUna+tJyozmnF/mUny+IwO+x+bUcCGHTHYqurQZKeGpMjj8chTTz3FI488wjnnnMNXX33V08PxiRDMAoEb6ggDyYNOJGnAJA6ueI/Dqz9h5Lys1ohkTyjUWpBl0obPbieXHQ47h377AJAYMPMan1HLAs+kNGeie/Bvra+t9bUUf/I2AFFDRmI8sBvZbkeh0dL/rocxl5VQt20DlUsWA5AwbS4ADnMzzWUlWKsrsVZXUpFfibmhiuaGSuwWZ2EBSaFk+Bl3oY9LR9AxX331FbNmzeKll14iJiaGJUuWMG/ePGJiYrjlllsAeOihh1qjpcB5F3bAgAFMnz69VS4DXHTRReTl5bFx40YGDRrEvn37+Pbbb4VgFggCwF952x1F/VqYH7u9NZra1/gCHcubDaOZf9Z2SqedwMXZxXxy7zYGnJjI+PM8X8S1rF+pVjB4kKpVLruiXrSGrd/WcclzozkwOp0D9cHvo+7cx+GCSiXx1WdJbabddW8Nq9dYSE5SMH2ali8WmgB449V4UpKV/LSkmf97uo4LL61i6Y9JKBQSDofMtJJNHM618a88G3l5NnLz7OQdsVFYZEf+PcPVvfcYuO9PR79XdfQeO9657rrr6NevH59++iklJSW88MILXH755W3anHnmmQwbNqz19ZYtW1i6dCkAl1xyCV9//TUAI0aMICUlhbFjx/Ldd9/x3nvvsXLlSjIzM1mzZo2IXhYIAiCYiGN3sdmVwtm1747G6W1btP3S0fZLJ3LyCIoefIWKV78k9f6rWwvxuUdFA0hK57zY82a1W7e1vJqKV75AP34I8VecGvKCfqFOg9HTNwM8MefSZOZcmtz6eunH5ez4Tx4Ag64Yw/7/bQUgcXQakx6ZQ/nmYvZ9sIWVf1rM7AXnEzcoEQBThZG6w9UYi+tpLKzDWPT77+IG7GanqFanJpD54l1+RZcLjrJq1SreeustZs6c2dND8QshmAUCL0gKJX0nX8TOb59n+6K/ozMkotHHoomMcf7Wx4IkUXl4I/UlzkdLDClHc9jJDjtF23+ivvQgxqoCBp90s99yuSU3sIhkduK+H1SGGGImTKFu42oa9+8GhzPPo8PUxKEXnkAVFY1CpwOFgsZ9u9AmpVK/fRON+3Yh26wAqCOi0RkSiYhNIS5rOFpDAnZLM3nrPsdus3T7NvZW/vSnP7V5fdJJJ3HaaaexePHiVsHszrJlyzh8+DDvvfde67QlS5awePFiNmzY0JobcvDgwQwePLjrBi8QhIhwSkXhnirA29i6c7ze8kD7kx/a375Hn5HOgZWVfPnIDra8uZuMdBXpaQqeTleRlqYkJVnBr7+Z+eSbX2issnD7LVFt+vnhJxOfLzTx9bcmbro+kn5npLcZV7DHOFzeFz3J3XcY2LKlmsoqR6tcBjj97Ar69VVhMEjo9RI7d1v5bbWZX1eY+WKhiaJi57ndYJDom62if18V48ZG0DdbRb++Kq64poqaGke79QnJ7JtZs2Yxa9Ysr/P/8Y9/tHk9f/585s+f77PPzMxMHnzwwVAMTyA4rnCNgg1EMnuSlV0tMH2NzXU7OtoGVZyBpFvOp/Rv75J/6zOokuJQJcSwra+SiORI9MlR1B3Ip37ZJqwFZSjjDCiijhaXs1XWUvPlckzbD6CIiiDp1gsClsveUpH4s629mfOSN7OwfFzr3+6ccEY8n71dT/3hag5+tqN1etGyw/y0vxJNlAalToXD6qBo2SGqd5WR/9MBqraXAs4bAqqkOOL76kkam456ejaqlHgaf92CadfhkMrlzQWZjIw/HLL+wpGamhquvPJK3nnnHV588cWeHo5fCMEsEPhArYti2Gl3UpW7BUtTDRZjHY0VR7A0bcNmdka8RiZmkzpsJtqoRCLjj56MKnM3U7xjCQqVhoGzriM6xf/k+CDksi8kSSL1rAtJOeN8JIUC2W7HWlNF0UdvYqmuxG48mu+3uSCPoo/eRJOcRsKsU4gaOJSE2gSUqraRa1V5WyjZvRxJoSQiJqW7N+mYIi8vj/Hjx3ud/+abbzJ06FCmTp3aOm3x4sVkZ2czYcIETCYTkiR1uqq9QNCVXBezi4/sY8JWIPb0uFyjln21CSXnPj6Cq6YZ2X/ARnGJncO5dlautlBaZsduh+RkBaNOz2KEtpI/33U0x7vFInPldc6q9LffEsVjD0XzRvt6NwERTjcdeprZM3UcOZCGJDn3y3m2rTzzfD0fftLEvv22Nm0vuKSKuFiJc+bpOWdeBEOHqEiIV7S5KC0ssnH3vbUYm2RGjvD8uK3IySwQCMIZbwIzFLmTe0KOtow7kJQZaY/egGnHIWzVddir6yldU0VTeSP2ZhuSSoF+wjAihvUjasqoNueAqve/x7huFxEj+pN06wUoXeRzVxJuEcgtgrhFGPvTtuVvb8tERqt45YccFpZdhKSQsDaaKd9awsYnl2Isqsfo0nbve1tAIZEyKZOJj84hcWQqEclRKFQKNhdkIgMxQOW732HadRhN37RObG1bWo6F1dhBw17OjTfeyKWXXsrMmTOFYBYIjhW0UfGkj5zbbrrDZsVubUYd4bkQWWN5LgAjzvoTOkNil47xeKXlbrWkVKJJTKbfHQ8gyzKyxYLdZMTe1ITdZEQVFY025ehJTdkot+ur+sh2zA2VDJh5DSpN93xRCXfq69saFq1W26aIjyf+/e9/s2vXLt58802P82tra/nyyy95+umn20zPz88nOzub6667js8++wybzcaIESN45ZVXmDRpUuc2RCDoAt6uG44uquN2gtDjLUL1tuSdcHlku+l2u0xFpYOEeAVqtQ2IbTN/+w7nky2PPhjNnbcbWtfhvk5B8LTksZYkia/UY/nHM9v5xzNxWCwyNbUOamocVNc4sNvhhIkaNBrvN9m3bbey7FczV12h56LzfRdfFKJZIBCEE94EbGcLx0HoBWhXFwqMGNaPiGH92kwbm1mAtcHC9pIMFPr2gSayw4FpTx6aPqmkPXx9l4yrN0Qv+yuM/VneHUmSkH4/Z6ujtGRM60vGj9cjO2SsjRYs9c2Y65qxNpiJHZSILr7tedh1/8myTMOS9SiiIki8/qyAxuiNcBP9geLv9fV//vMf8vLy+Pjjj7traCFBCGaBIEgUKjUKlefImeoj26k4uI6kAZODkssiejl4JElC0mpRaLWoY+P9Xi4qMYu6oj1EpwzowtGFmO0HQOqCYgmyU7a4F+d57LHHePzxx70utnDhQu655x7++9//es29+L///Q+Hw8HVV1/dZrokSaxYsYKZM2dSV1eH1WrltttuY968eezfv5+YGO850AWCnkJIR9905f7xJJm9RQ0rlRKpKUqP80vL7Fx4eSWSBH+4NTR3DITM9I77/tdoJFKSlaQkd1zcr4Uxo5znvUnjfYto9/WK4yIQCHo7viKFe1KMukvuYMeypdB57aHwcu+w6u1vcdQbiZt/blD9d0RHaUDCVW76ksy+ZHIgSAoJTbQWTbSWqEz/rsskSUKbk4GkVqHJSO54gTBBu7cIlaJ9nY7OoHQ4U3D6c3194MABHnjgAb755huqqqoAMJvNAJSWlpKUlIRS6f/3pu4ktJnQBQIBAM315QAk9Bsb0HJ1OdJxL5djDsmtP12Fp/2c0H8CsuygKm9Ll623t1FQUEBdXV3rzwMPPOC17ddff82ll17KP//5T2644Qav7d58803OP/98EhIS2kzPyMhAo9Hw6KOPolQq0el0PPHEE5SXl7Nhw4aQbZNAcKxyLMqzjrZpfuz2Tkts2QGNjTL9+ipbo2yDZUHtqOOysF8oCOT9m5GhYtC0RJ57r+O2gf5fXBezK6D2AoFAEAjhKig90dmigy3TQrnNjeucn9G6oX1D1meLsPdne8MhujlUwtgbgUZDu+N+vA1zJmDacQhreU2n+vXEtsL0kPfZ1fhzfX3kyBH0ej2XXHIJY8aMYcyYMSxbtoxly5YxZswYCgoKemDk/iEEs0DwO7LDjtUcmkQ+uhjnHbqIuNDlGjoecJfKrrK5M8LZn+U1EdHo49IxVoXvB3Z3Ex0d3ebHW3qMb7/9losvvpgXX3yRW2+91Wt/W7ZsYcuWLdx0003t5s2ePRu73U5zc3PrtMZGZy5tvd73I9ACwfFMbxGagYi+FlHra5uqq+3YbM7P9RbR7Ku9t/7S0pTodHDT9Z2PXg73YxDOeNt33t43V821UrynvvU90FG/nvpxXWcoblYIBAKBP3RGuHYkOHtKYPsqOmhrtjIqITck64kcNxjtgEyUkcdnOsNg5HJnhXFniRg1EADz4aJO9eN+syIcZH8w+HN9fdJJJ1FaWtrm57TTTuO0006jtLSUvn37dv/A/USkyBAcN8iyjMNmwW5txmZuwmysoa54Lw6bBUlSUFeyH4uxBl10EjEZQ4lJH4xK68zlGBGdjFLtO/es28oCHp+nyOWYQ/JxE9EciEAO1X6py5HarFcdEY21udHHEgJ3fvjhBy644ALuv/9+zj//fEpLnVWE1Wp1uyjlN954g5ycHGbPnt2un3nz5jFmzBiuv/56Hn/8cZqbm7nnnnsYM2YMEyZM6JZtEQgCRRRy8x9/iv65trVaZSqrHNTWOqiudvDrSjP5+XZUasjNtbFytYXYWIlZM3ScNFvLkCFqHA5ISVKQkdH2621Hcttu79SmeRy/IDR425epKQpsZgevlAzjzqw9fvXTkWQWCAQCQeDIDgfWRjOWBjOWBgu1+yupWLuBjZFGHBY7RStycVjtJIxIJXVyFtXpY0ClQKFWoc5Mbq2p49+6uu4J13An2Mhl9+U8CedACgYGijImEhQK7PWhq8jXW+Xy8YAQzIJeR8Wh9QAk5XRc+Mva3EjloQ1U5W7GVFeG7Gh7FamNSkAdEYXscBCdkkN02kAayg5TfWQbZXtWtLaTJAWRiVlEpwwkefBUNProNv3YLCYsTXUoVRpkh4Py/WsAaK6vQJ3UvuCQO8eLRPaF+z5oEb+u0zsSy51Nq2FpqkUf1/setelJvvrqK+Li4njttdd47bXXWqePGDGCJUuWtL62Wq0sXbqU22+/vU0l6BaUSiXff/89Dz74IPPmzUOv1zNjxgwee+wxNJrQ5sASCELBdTG7MBh6/kGwcBZkBw5aefMdI08+FoNaLfkcq8Mhs/j7Zj75vInfVpkxGtt+nhsMEkMGqbHZZaINCl54NpbiEju/LGvmD3eb2rTtm61k6hQtF56nZ/rUtjeHLRaZ/AIbkiShj5BYvqIZqxUOHbaFZJvdt1HchAg9C2pHYSlZhUqjQBularOP/dnf4ngIBIJjlWDFm+xwUP3BD0TNHIs2u+MncC0FZdQv3Yhx3S7sNQ3kugZXSRKa7FRKVEoitVYGXjKKiKRIytYVsOe9zdhN6482jdASMbQvEaMGEn3yRCSXvLKyLGOvaUDeD9IgsFXW0rjCcypDRa4eR7+moLY9EMIxxcnC8nGcl7zZq0hume+JltzNXRnlbK9tBIcDZUznnhRryYF9vMrluLi4nh6CXwjBLOh15K7+BPBPMBds/pbKQxuI6zOKpAEnoNREoNLqUWoiUOui0EYltJNdif0nIMsyzfUVOOxWkB00VhbQUHaQsv2rqMzdTGLOBJpjHMhWK5YDuRir8j2uXxeV0E6QesJVnLr+fbyLZ/ft97Vf/JHLnqR1C831FTRVF5E6dEYwQz1uefXVV3n11Vc7bKdWq9mzx3eUWVJSEq+//nqohiYQHPOEu7z8+/MNLPrGxGWX6Bk9sv2NItfx/7bSzHXzq1Eq4YF7o0lOVrBe1R9dtIqrMvLI6a9Cp2v/2X3/n6OpqLRTXGJHqZDIO2Jj5Wozy1eY+eiTJm66LpL96v3YrTIfHLFxZHU5xqb254sBOap2Ea6d3bfhfnx6KzdGb2P8IomZU9Wt3+E6ShXTW1LJCASCY5twlWPW4krqvluNrbKOlHsu67B98ZNv4ag3En36iWgyklBERqCIjEAZGYEqMaaNTBzxu5TNOW84doudhrwakGBXbgzNe/Iw7cql6r3vaNq0B93gbGSrDXtjE6btB7FV1Hpcv7tQdvRr6lLJHI5iOVjcZXMw0cvu72Nf+8e4dicoFEQMyQ5glP6t93ji7bff7ukh+IUQzIJeR2zmcL9SUJiNNTSUO/M9ZY45nYgY/yuXSpLUpn1kQhYpg6dgbqzm8OqPqTiwBlmjQlKqMBjSSR58IrYhyeiPOKt7VuZuJiImBXWEoU2/rhLZG8e7VG4h0P3gnu7C2zQ4KvFjDsnYbRaqDm+kZPevaCLjicsa2alxCwQCQajo7YLyzNN1LF/RTFKC70rXC2pHUb7qNwDuvtPAXXc4z52NtSkADI/1nbcvKVFJUqJzHSOGqznrjAjsdpnH/1rPF181YVYUoVQrGJRm4+47DZQMGsI8w2GammQOHLSyZp2FKy6N5H1zZ7e47Tb15mMXrvy2ysyCNxsp2N7MCx8mMNuPfXwsFsAUCAS9k3CNwFTGGlDGRBIxon+HbZu2HcBRb0SVHEfiNWd22L4l8hRAqVESOygRgAhdJhFD+xF3/myaNu+j6v3vqS/cgKRSIum06McOJmJkDqrGBBzNJmS7nbq1q4ife6rXdSlynXVbuiOaubvxFYnsPq+78y57ek/bKmupX7KBuu/XEDVjTKcjmAW9AyGYBb2OgbOuBTqWjxZjLeaGSgB0hsSQrFsbFc/QU25rN70uR0IDxFh/l5nThzh/uxSXcxWm3sSnoHN4k8zQXurrdzWQt/0n6kv209xQSWzGUDLHnhlYrm2BQCDoQnrzo/4Lakcx/5ztnHNWBEql1EbytYzbNS/uS/925r+fd0ZEu3bBoFRKPPlYDE8+FuNhbAlM+11an3KSjttv+X19EaHZn4EWNGwhnI9nTyLLMm+8beSnJc0s+9XMoIEq/vNSHLNn6tq19ST2xX4VCAThRDhKZmVUBH3+e59f+ZBNuw4DEHnCCL/7d91eT9Gu+nGD0Y8b7HX5FnEcNXxkm2neRHIoRXM4RS97ksydSXPh2l9LPx3R0Xu3+UAB9T+uxbhpL5IkYZg1jvjLTgl4bILeiRDMgl6HJPmX89KQ3I+opGzUuuiAigcEQzBpLVxlqIha7h5sFhPVR7ZhaaqjtnA35oZKopL7MWDGNejjOs43FigOm5Xq/O3UFO0Oed8CgUAQzrRIPaWy/fnNk4B95qkYHnikjuxs39HOoRpbV0QYBxopK+Syd2RZZslSMzt3WTlw0MqnX5iYME7NC8/GctXleo+5/CH4/SjLMps2W3nrPVHoVyAQHJ/4e70ce9Y06r5ZiTotoePGbgQra11FcYs8dsXTNE/TAxXO4SSXO4M/0c+diXq2FFfStGUfjnoj9Us2oNBriTpxJAlXnoZC3/5mcGex1TTQ8OtmOI6LPoYrQjALjmk0+ljqSg4gOxzdKpn9nS/Ecmhxj1J2fW1urGbvz//FbKxFrYtCExnHoLk3YUjqG7L1y7IDc0M1TTXF1JcdpCp3M3aLiaikjh83EwgEgmDoLakYXKOVPc3bNl6DwwFr11k4aU7oL0Y6Gk8o9qGvbXRFpGzwjd0u88c/1fLxZ03ExUrExyt57OFo7rjV0PHCAVBVbWfXbhtbt1n47Msm9uy1kZHe84U7BQLBsUVLxOexIiuV0ZFIahXNu3OJnjOhy9bjT05lb2L5eMJVHgcqib1J50Awrt9N+cufAqCMiUQ3vB9JN52L0hC6Y+OwWLEWVWA5UoJx416aNu9DUimJmjE2ZOsQhAYhmAXHLFazkYbyXLSRsUD33N3yJpmFSO5Z7FYze39+FSSJ0efcj9YQ+B13b1jNRmoLd1NxYC1NNSU4bM4knmp9DMkDTyRpwCTUOgObPnkoZOsUCASCFnqDXG7BdayuRddkWeaDD50XkfFx3Sf4WsazoHZUt4h6X4JdcJTH/1rPZ1828d+X47jo/NBdoNrtMqvWWPjgIyOr15opLXUAoNPBKXN1/N+jMYwboyZnWGnI1ikQCI5fWnIPu4rlcEuNEQymPbnIFivKuOig+/C1H1rEsje53FE0sy/8LQTYW24GdHeuZWib4qX5QAFl//yYyInDSLrtAhQadaf6dj0+luJKGldtw7huF9biSnA4QJLQ9Esj8doziZw6CkmSaFiyvtPbJAgdQjALejV2q5mq3E3IDgcKtYbYjGGodVHYLCb2LVmA7HAwYMY1SIquf+S2BdeoWSGWw4Oi7T9hMdUx8qx7QyaXrc2NlO5eTtm+VThsFgwpOWSMOhl9XDr6uPQ2BR7tluaQrFMgEAh6Mz//0szhXBtarcSk0RtZ38cZ+fTciw28/Z6R5/4Wy7ixmm4fV0vkcaglsz+RykIut2XzVguvvt7Iow9Fh0wu2+0yXy4y8Y+XGjh4yEZqqoJLL9QzfJiaEcPV9O+nak3l0tDgCMk6BQKBoLdISk9Yy6oxbtyDpFahjIkicsIQJKUS86EiSp/9AN3w/sRfclLA/XYk2P0VwMHgb7/hfty6QioH06dss1P5+ldos9NIvuMiJGVofIt1VQ3VS3/CuGsHSBJRM8YQc9pkNNlpaLKSUeiO1ktyNIlr7HBDCGZBr6by8CaOrP8CSaFCdthQaaMwpPSnua4ci6mOoSffhi46NAX+AkXI5c7TUdoRd9zbxhySaa6voHTPCjJHnxqS94KrWAaJlCHTSeg7lojYVK85IQUCgaCnCCQNRFdG8dbWOrjs6ioAJAmQIGfyepbrncXbHnkgmuuujuySdftLMNvemXQXQi6356FH6xg+VM2tN3W+2ry7WD71ZB3PPhXD+PEaIvUiFYZAIBB4o+qdxTRt2QdKJdjtqDOT0fRJwbTtAJrMZFLvvQJJ1V4otsjZUEdqu0YqByOgj6WoZU/4I4hb2rimxQhWVo/LKmTZ64VYCsrJePrWkMhlc14JtR+swLhrB6qUeBLnn4t+zCBU8cFHygu6HyGYBb0a6fer1HGXPIndYqJk1zJMtSVoouLoP+1y9PHpPT1EQScIhaQ31ZaC7CCxf+dzhNUW7eXgivcASBkyndRhM1Fre1aICASCnuHtuuHo7OqwloTe5Ke7SG553VHxO9e0FoGi1To/z//1QiyXXKjnjbeNrFpjpLrawfN/j+Xaq9p/lnZmfd1By/gWFYwG4Jysbe3aeJsXrtvU0+zabeXPdxtQqTp3/q+qtnPR5VVs32Hl1JN1vPrvOMaM8h4d31tymQsEgt5FS5qMlr97DQqJiBH9SXv4ekx7cmlYuglreTWRJ44k/rJT2kSReiKYbXZPd9FR+gt/02MEI6SDzWncnfT0GC35JWiyUtD265xvkWWZ6o9+ou7r31ClxJN84WVEnjckZBHRgu5FCGZBr6aptgSVNhJJoUSjjyF74rk9PSRBD+KwmKlZvwpzaREJkTmYM4agNTijlpsbq9BExgbdt7mhioO/voshNYf+Uy8TYlkgEAA9J6b8EcH+TG+Z1pHM7Wxhup27LACkJCtRKiVuvjGKm2/0HaXqb9G8UBDoMXSXy74QctkzP/5s4stFJvr1VXHSbB1jx6jJzlaSd8TWqX5lWWb+bTUUF9v54eskJoz3L+3KgtpRXKbc2ql1CwQCgTu9SiwDst2OpbAc3aA+AEQM7UfE0H7dtv5QiWUILi2Ga4Tvecmbw04yuxfmC0VEckc0FtZx8POd2M02Uif3IXlCBqrkeBrX7ER2OJAUwT8V1PjrFuq+/o34y04m5qxpQiz3coRgFvRaZFmmOm8LSQNOQNGNOZb9QaTH6H4cVgtFH76JqSCPiJhU8mq2gexAF50EgKm2hOiUnID7lWUH1Ue2U7JrKSpdJAOmX4lSrQv18AUCgSAgfElKf8Ssv+I2VIL3/z6NJDOjhlkzfEc9hYquEP8d7QtPslnIZc+8/lYjDzxSx/ChKn5Z1szzLzYQH6dAlmX27gteMG/bYeHd9438+puZTz5I8FsutxyXhoagVy0QCATHBObDxdjKqjHccl7Ay7pGbAeKo19TqzxuEcOBFvFzXdYf3MfqLm/DDU/jW1g+LqBxB9q+/kgNv962CBQSGoOW3K/3gFKBKiEG2WTGVlWHOinO7/5asDc20fjbNqo/XULU9DHEnjMz4D4E4YcQzIJei8VYg83chCG5f08PpVdRs34l6uhYooaM6OmhhAzZZqP403cxFR5hyNybMaT0x2YxUV+yn4qGfeijRqOeOgGKAu87f+PXlO39jYiYFAbOvEbIZYFAcEzTImZDHTls31fCCZM0rcXU/CVchKyn/dFR9HJn5fKadWY2brYw//qo1hQjxwIffmLkgUfquO3mKJ54JBq7HTZtsfDLUjMlpXZuuiG4J4TWrDNz7kWVGAwSjz8SzdzZ4nwtEAh6lnFZha3StbdEMptzi0GpRDcgq9vX7SqZuxJ/xHIg0cDWRjN7399Cv3lDicqM6fT4AiHYVBn+tG8sqmfFnd+gjdcz899no43RsW6TnqatB2jee4S482ehSowNeMyyzU7xowuwllajHzOQxOvOCrgPQXgiBLOg12K3OKuGqnSdLwQTagItTtddyLJM+Xdftr7ue/t9aJNSenBEncNhNmPKP0zVyqU0F+aRcdmNGCTnDQeVJoL47NEoc8a0tq/LcR6bQGiqKcaQksPQU24L5dAFAoGgSwiFGPbVR7DCt77BQWKCOtghBUyoxXSg0j0Ukctff2vi9beMPPHXel79dxwXnBvRa4vJOhwyu/fY+HxhE6+81sg1V+p54pFoJElCpYITJmo5YWLnotv3H7Bht8PmNalER4sifgKBIHzoLXIZwGE0oYjUIal7ThUFKpldI54VufqAophDEbVsLG1k3wdb2ffBVtJn9GPCg7PQGEL7xFZH4wxlOg9ThZHStfnsfmsjKp2KGS+dhTbGedP2hPFNbE4+gZhTTgi6f9lqw1pcSfwVpxE7b1pIxiwID8S3L4Ggi2gRmYEKza5EkiRy/vxE6+u8/zxD9erlPTegAHGYzRgP7qNiyWKOvPEvDjzzEIX/ex17UyODZt9IujSowz4CFf/RqQMxVhXisFmDHbZAIDiGCZcIW3daivYdS3RXPmZv+Ls/Q5UW46+Px3D9Nc5o3lv+UMM5F1VibHIE1Vd343DI7Nxl5bU3Grn6hioGjSxh1inlvPO+kT/dZeC5v8WGXJbPnO68mF+6vDmk/QoEAkEwtEjlYFNGHI94EssdyWLX+Y5+TSGRy4GK2tgBCcx4eR4AxSty+fq0t6naWRpQH12FP9HZpkoj+T8fYNOzv/LDpR+x+Nz32fT3X4kdmMiMf52NLiG0UeWKCC3agVk07zsS0n4FPY+IYBYIupBwjGRWRRkY9PCzHPn8v5j35lLx09c4LGZiJ0zBbmwk79V/gOxAFR2LLi2TpoJcFEoVEX36kXr2xSi0gT1yKssypvxcGvfuQKHWYK2rQROfRNSwUX5HTzusFiqXLKZ20xpkmw1lpAF93xxiRk8gom8OmsRkkCTqaLvPQyH347KGU7TtBxorjxCdOqDT/QkEAkFX0VHeYdd5wcjanhbWPb1+133mT3E/6NyYlUqJZ5+OZeQINXffW8vqNRauvbGaB/4SzeiRam64pZpvv2tGrYZTT9axY6eVhkYHA3JUPPpgDJMnBR49VVRk47MvTRiNDiqrHei0EqecpGPaFC1qtX/fZ776uokn/1bPkXw7Wi1MGKdh/g1RTD1Ry/ixGnS6rvle1DdbxZDBKn5daebcs7v+EWuBQCDwhnvEcm9KkREq3Le3o8hib1HLLcsFI5890VXCP3lcBqd9ejk/XPwhACvu/JbxD8wkc1Z/jvywn01//xVJKRE7KBGlTkXdoWp08Xr6nDyAIdeM6/Cmq3vuZIvZwYYfq8ndaURSSBhrbTQM0JExox/a2AjgqFz2FN18XvJm3t+cw5YXV1K2tgCA6L5xpEzMZMT8SSSOSUMX33XnUv34IdR+9WubIoHuObgFvQ8hmAW9FmON8+Sg0oiLiECRVCr6nDefIx//G0tuEVXLf6R2wyriJk0H2RkdZauvpbG+FqU+CltDHQ27ttJ05DDZ8+9CHR3brk9rbQ3NxQXIdjuyww52O7aGeuq2b8RaVYEyMgq7sRFNchqNe3ZQuex7NMlpRA8fg2H4aKck9oAsy1T8/C2161eSMPMUDCPGoklM9noS9kfo1+VIfstnhdL5SLfDLiKYBQJB4LiL3K6UpO5993TEbwtV1XYKCu3ExR4bD875ksuu0cuhOtZXXR5JRYWDp5+tZ9mvZpb9WsGt86Nai+FZrfDtd80kJyuorpZZX23lrPMqeXtBPPPOjGjXX5PJweo1FhobHVhtYLXKNJtkflzSzLJfzei0EpICDAYJrUbi9beMxMcpOPMMHeeeFcHUKVpUKs/n2tVrzcy/vYYpkzW89HwcE8d3nVD2RESEhMkUPk+OCQQCQW/DkluCMqr9ucNfvIllb9LQdb4viexe+K+zcjlU0cuuRGVEc9LbF7Lkus+xm22sf/wXDo3cSfwwZ1CVbJep2VMBgKRS0FBfw67XN1B3qIoTnjgZSdH2fCnLMjV7K2gqbcBhc/CiNZIx+lyO7Gli1ddVNNXbiU5QYWqwE5kdT+3C39jy/G8kj8/g7HPVNJwUiyHuaHoy13zNTQ021jz4I6bKJiY8NJvUyVl+C+VQ3DBRaNTIVhvIHZ+zhXjuPQjBLOiV1BXvo2DTYuL6jEIXndjTw/FJOEYxN+Udouq3JTiqnR/SWdfeRvXKpVQu+x5dZjbNhUcfV7E3NR79u7Gehp1biZs8A2t1JUp9JA27tlG/YzOm/MPt1iOp1EQNHUnKmRei75uDbLejUKtxWK0YD+2jYddWqlY516uOS0AVHUPcpGnoMvuiio5BkiQqfv6G2vUrSTzpLBKmzQloO33td38lc3X+dhQqDYbkfgGtWyAQHB+4Slx/IoQ7ijJ2XTbUMrqzwtmfsbtTWmbn1jtqkCS49urgireFC655mEsPJZCaU+WzbSiorXXw5wdqaWx03vy96w9RREcr+Ntz9UyepOXgIVtr2/LytukzXny5gXlnRlBYZCPaoGDDJguff9nEdz80Y2xqf/4bP1bN83+P5byzI9BqJZRKUChgxy4ri74xsegbE+//r4n4OAVZWUpmzdBy1eWRpKYo0ekkNm+xcMGllYwZreaTDxK7vTBhQaGNLVutzL/Be22OYN7DAoFAECy9KWpZdjioXbQC4/pdJN1yfret1z3FRUf5l4MpBNhdaUoOfrGTwqWHkJQK9CmRTHxkDhueXEr+TwdQGzRYGywASEoJ2Xb0nF249DDDb6xDl6jH1mTB1mQj/6cD5P98AGNRfZt1bARik9XMvTSZmRcmktpXh80i823dBJqrmyj6NZfCpYd446Fc3npUIio7jsiMaIZcXU6D2oohTs1ZMRv547lHMJY0MP3Fs0gY0f01mRrX7SRi1AAkpbLDti3H3DUKXpGrh+ZjI3DhWEIIZkGvo65kP/uWvk506kD6Tuq+k1+o6W7xbDc1YTy4j+aSQmpWLwNAl5mNQ7ZQvvo74oZPRhERScOOTa3LaFPSMJeVOF9IEsgyUUNGULN2BRU/fd06Xd9/EKnnXU5kzmAUajUoVUgKRevjLi20Pv6iVmMYMgLDkBE4rBaMB/Ziyj9Mc0khxZ+9B4A6LgFtWiaNu7eTeNKZAcvlUFFXtJfo1IEo1aIavUAg8I2/AjeYdi1SrKcFmb/rX1A7iiu1W5l3fgXGJpn33ognMaHji4jegie57J57OVhkWebnX8zkF9h4+PE6bDaIjIT0NCVffWPigvP0/PXxGP7696MXnfHxEmYzGI0yer1EU5PMtVdFUlBoY9K0Mqy/P4QzIEfFHbdFcc68CFJTlKhUEmo1KJV4fTJo1AgNo0ZoePj+aLbtsPLDT80UFtr5z6uN/PPfjURGSkyfqmXnLivDh6n59sskNJruv7H+yzIzSiWcMtf7+VrIZYFA0NX0JqnsSvWHP1G3eBWx58wgasaYkPXbUdSpr9QZgUSsBlLcL5TRy3WHq6ndX0np2nwKfj4IgKFvLJY6M4e+3MXIW09g9zubqT9U3bpMVFYsDXk1ra/jR6SgTzOw8s/fUbGpCABVpIbM2f0Zf99MYgcmolArUKgUSCpF6/l6LXCetBm1VnJuUzIwBLg5k9qKZDb8VEPRQRNblxez9MY8lgIDx0WhVEo05jUy6/ULuH5aPlAUsgKB/mA3mjDvLyDxxrNbp/l7YyHQmwuC7kUIZkGvwmG3UrTtR/RxGQyec1M7gRluWEz1GCuOYN3fiDR5MJq4hDbzg5XMtoZ6GvfuJGb85NZ9IMsyOBxISiUOq4X6HZuxVlehiU9El5lN9apl1G/bgKTREjVkBKqYOGrXryRy4FBkq5XSRR+j0EUQPXYSERnZ6AcMRh0ThyRJyHY7Dft2Uv7dl+S9+g+UOudjU8lnXoBh6ChUUYag95FCrcEwbBSGYaOQHQ5M+bnYm00YD+zGWl1F7KSpxE+ZHXT/vvAnitlUV0bKkOldsn6BQCDwF1fZ7Es8d1daDG+R267TXn29idw8O6uWJTN4kLpdm3CiyeRg+w4re/fZyOmvYuqJGhRuj6ouqB3lNT2Gt9QYsizz0adNTD1RS3afo1+7bTaZlqCdVWssrPjNTEyMxMjhaprNcPk1ToE9bYqG/v1UrFxtxuGAKZO1vPZGI0ajzKwZWmZM0zL5BA2jR2rQaCQcDpm8Izb+dF8d9/yllpEj1FitMPVEDU88GsPokeqgi+tJksSYURrGjNIA8Mc/RFFQaGfzFgvrNlgYOULNow9G94hcBjhwwEpOfxUxMeH93VAgEAjCDWtpFfU/ryf23JnEX3JSm3mBiFs4Gi3sS7S7S8JApKF0SIelsgxTwUEkhQL9uMEo9LpWAek+VvfoZV9yuWpnGZb6ZtKmZLdOlx0yyDKSUkFTWSP5Px/AbrYR0z+e+KHJ/Hb3tzRXNqFPN5B5Ug6SJFG49BB9zxxCxZZiCn4+SFRmDIMuH0380GSSJ2aiMThrJGw8mEztwl+p/uY3fr76UxoL6wCY/NdTSJvSB6U2eF0Xm6Th5CuckclN99g4tM3IkgNpSJu2YWqwc+NTfakalAjkB9x3Z2+iWEuqQJbR9s8A/D/+7sfW0SSK+oYbQjALegWyLNNYkUf+xkU01ZQwaM4NYSmXraYGinf+gloXRWNlPrWFu36fI8E6GX2/gaRmTCK+z0hQBnexLcsyhR++gbmkkJoNqzAMGYEyMorqlUuxNdShjkvAYbFgb2pEZYjBVl8HOCVqzIQTsVZV0rh3JwCRA4aQcen1SAoFlupK6javo27rBuq3rCciuz9ZV98KSiWSUkn0sNFE9h9E5dLvqd+2kdgTphM3cWoI9tpRJIUCfd8cAAxDRoS0b290JJkVKg0Om7lbxiIQCASdoSfksqd559m28PyLDbz5jpEbro0MmVwOZfS2wyHzxttG8gts6LQSr71hxNQstzysQ3YfJZddrOeyi/VkZPj/ddl9fP/7uIm7/lwLwGUX65kxXcu7HxhZu85CfJyCxEQF+w/YSElR0FAv0/R7/uCRI1SMHa3hvf81sXK1hZgYiZ8XJ9O/n4qnn4xh0Tcm3vvAyBNPOaOYt61PISNDhUIh0b+fmi8/SeCzL038/bl6hg5R8daCeBLiQxtBPnCAmoED1MyZFR5P+OgiJIxGkX9ZIBB0P701alm22mhcu5OqdxejjI0i5owp7doEI5c7ItA0F8378mlYsRl1aiINyzZiLa5snSdp1USeMIKYIVPR9Q0+paGp0siymxcCkDatL6mTs6g/XE3ed/uQ7Q4MfWJpKKhDoVSgjFBhrja1Ljv4yrEc/HwHhUsOATDmrqkMuGgksixTua2E3EV7OPTFLvabtzHytskMvmIMmwsyUWgh/tKTiZo6CsunCzHXmpjw4GwyZvjejpaifd5kuTt6g4qR02I4OGgInDnEJRdzMHuqbQ7mYPIxKzTO71Wy2RLcAARhixDMgl5BVe4mDq/6CI0+lmGn/YHIhKxuXb8sO6g+sp3olBzUEUejdc2N1dQW7aby8GaQZSzGGux2CzhkVFo9/U68mJi0wRiHRNKwZzt1m9dyeOX/OKKJIG3YLPRnz0UisEifpoN7MZc4T96W8hKqyp0pLKJHTSAiux+WijIAYidORZOQhMNipn7HZhp2bqVu01o0SSno+w0kom8O8VNmt4p6x3gDSfFnknTSmVStXErlkm+p3biauBOORu8qdRGknHE+KWf03tQkgaKPS8NYXdTTwxAIBAKPhEshP3COpanWwoRTyjAaZZ54JJrbbo5qnQfBpygIRC4/ujKbebr9TJygbZ1mbHLw6wozXyw0sXuvFb1eYtt2K8lJCurqHVx7ZSSXXqxnyGA1j6/sQ8M32/nXfxp59oUGTj1Zx3N/iwWt5/V5S41ht8utchngo0+b+OjTJoYOUfHc32KprLJTWGTn4QeiOf0UHQ4HbNhk4bU3Glm/wcLCr02cNEdLfJyCu+800L+f82t7VKSCKy6N5IpLIzmc60yBcfWN1fzy/dFiuZIkcfEFei6+4Ph5lHT4MDX//HcjlVX2Yyodi0AgCC96q0z2RPl/Pse41pkLN/nOi1FGhf6c4atAm72xkabDB4kaMarNU7nmgnwai7dg/M9elNGRNO89gtIQgb2+Cd3gPiSeej7a6ck4mi00/LqZhp+30LhiC+qkZBJuOA39mEEex+IrennPOytaX5eszKNkZR4Aw26YgNqgpf5wNX1OHUj/c4ejjtRgqjBy8LPtlG0sYt8HW0g5IQvZIdPvrCFknTQAcJ6Lk8akkzQmHYBFp73FjlfWkj6jL+N+1xmbCzLRZKUw+aWzAtqv/splb9sbClpuKPgTte6KOj0RVErMuSXohvQFgsutLQg/hGAW9AoaK/IAGDHvz6g0wVe1DRZTbSmHfnsfjT6WMRc84pxWV8bOxS8g2+3EZgwFSUITGUP2xPNRRxjaPIJq0kjEjJ5AzOgJaLeUU1C2msK1PxDZmEva+Veg1Ptf+EibmkHSyWdhPLSfpsP7ATAMH0Pa+Zd7bK/QaNGlZ1H2zWcAqGPiSTnzAtSxce3aNvczY/1uHzVrfnVOCPIx2t6Ga5oS92jmiNg0qg5vcl9EIBAIugTXlBe+2oSbWG6hIteI0Sjz7NMxXH9N+2JrwUYh+7vMgtpRfHTPcl4pMrF3eyqJCUpkWeaSK6pYu97CwAEqJk3UcOCAjY/fT2DubKc1dj1nPzW7AGbH8dT/xbBwkYln/lHPiafWkHVfPAa3h2t85V1WKOCl52PZf8DGq6834vi9ns83XyQRG9v+KSylEk6YqOGs85yPfCbEK7j2qkhOO8Xz956iIht33uPM4agW3+gZNsQZKX/goE0IZoFA0CUcS3JZlmXMBwvQDswi9f6rQ/50cEtkq6cCbeAUinVvraX6p++wnXE2cdNnAVC/bjUVi75AitASdcJwrKVVxJ49nbgL54BSgTLv9+tmZ0wVCWPOIH7UaZhyD1Gz/BdKn3mfuAvnEHveTMZnF/sco6tozT5tMJHp0Rz4eDvNVc5xTn/xTFImHQ1s21yQiTrSKVMjkiKxNlqp3eeMpo7JiWf4TZNQatqff2S7gx2vrsPaYHEW+HO0vd4MdRFCb9HNLZHPoWJzQWZQY5dUKjTpSViKKoCjNx+EZO79iK+jgrDHajZSlbuF5EFTekQuA0TEpAJgaarF3FBFc2MVBZsXo9ZGMvysP6HW+ieIYw7J1I1NJplziRwwhOIvPuDIghdJv+RadGmZ2E0mqpb/gPHwASIys4keNQ5VdCyq6Fhn8TxAZYgmfuoc4k6cRcOubZR88T6Syve/cnNRAQDRYybRdHg/R954ifQLr0Lfd0DbhqsqKP70XaIGjyB++lwiMvoEuKd6Hx3lwdboY7Ga6pEdjrBMyyIQCI4/ujvfsi9chbgsy5R9uo20VAUX+Yic7epChWfPlnj3A/jw4ybOmRfBy680sna9hSv/NY6XLijzux9DlILmM0/k+klm/n3HAfbc9xF9bppDyjnjkSSJjPXf8toDzidcpl7dl5tHFFGLkpgYCUly/lx5mfP7wVWX6zlxZjkaDURFeT/nVNc4LXRKioIhg9RceV019/3ZwN13GFCp2i538ZVVGI0yb74azyknhUeaip4kI915UV9cYu/hkQgEgnDEVQ53JMWOJZHsjaZNe7FV1pF4w9khu8bxJhy9pdnQnzqQ6p+g6ruviZk4mYYdW6n6eTGRU0aRfPsFSMqjstaXeJQUCvQ5A9HOSaP2y+XUfL4U88FCLE9PQROtpWZ/JY2fLuXe/U3MOD+JIROjSEjTIssOJKVz2/+/vXuPb6q+/zj+Oklzbdr0Tu9cyv0iCggIiIh446IyhxMvU0Rxc7+5qdP9vGxO57zPOX8O57zfJoKIl4EiCl6QiSBQ7oJAoRdoS++XpLmd3x81NU1vSZq2KXyej0celpNz+Z409iTvfPL5Jo7sQ+LIPvSfM4wtj35OwdqDRJmbt/jyP7fawsaeyf1mD+X7ZTso31PChHvPwZTc/AP2va9vZd+S7QxfOI7a06YR27e27QexFT+2tAgsHA60urkzVdChtMaAH3+PUaYE3OVVba4TTGsWETkkYBYR6cCXr1NRsIsovRlnQy0arY70UTM63rCLKBoNluR+1JbmkfvugwCY4lLJOfPqgMLl1nr8Rg8cSr9Ft1K49GWOvPAUfWb9FFtBHtXbt2AZOrKxpcbWjU3ra6Mt6BOTiRs3iZgRp6JotURZGi9ehpTU9o8/diLWMRNQNBpc1VUUvf0a+S8/Q/ykaSRNvxBNVBTGQwYqjxWAqpJ68eVoTT0T5ne3jiZZjE7IQFU9lB3eRlL/7ptdVwhxcmorNPYPlCMlXF75oY2FvygnPe0Yx2s01Fc6efLxOGIsmhbrQteO27vv82YYeeX1eu5/sJr7H6zGalX4w52x/KaDcNk/+Pbub619PEMfGkv+i59x+J+fULu3iKTpI9j4x50MnpJEzfEG/n3LVv79w3bR0QqZGVpmnm9k0fUWkpO0ZGU2vuTOzNCibae4NjFBS/GRdLRaBVVVefjxGh57ooa16xpY/FQ8/fs17sftVvlun4tH/2Ll4jknx/W6IzExGvpma1m2vJ6fXGwKeTJDIcSJyTcQOxkCZH8eu4P8W59EdThR9Drc5dUYRwzA1EY7idaEMuGf72Ptv70uPanp54P33QWKgnnMEJIWzG4WLkPLkNo/cPb0r0dBQ/xPp2PIyaRs8Vt8uvAoEx84lw13ribW4CR7qJm3Hs9H/eGtuaLdjinZTPywFIZcMZqE4X3QxxjQWRq/3aTVtx+XnfnELNA0fqCcff4gvvnTp3x89TLG3jGVzOk5TetV7D1O4sg+DL9uHFvy49iSH9eivUQw2uu/HExvZl++2wRT5ez7oUIgobO3StmQnkHlhi9wb21o9ZvVoneSgFlElOqj+zm653OqCvcAkDRsKlq9ifjMkejN1h4bl736OHVlBUQnZpMyZBJGSyKWlP4hvXnxrZjVxSeQfd2vKVn1DsfeWwJAwpkzSD5nJh6XC1dVBc6qyh/+W4EtP4+j77xByccfoDWZcZQeQ2MwEn/GtHaPqShKU7uLqFgrWdfeRPmGzzi+9kPqD+zFMnw0tsMHqT+4D43JjEavD/q8eru2JvozJ2aiM8VQtOMTCZiFED0mEgJlX7PqtvCnp+t4+pnGKpy5F5swGRXGjoll2tQ2mhXzY1DeFVXMi+K2o6oqc/9VizEmirtvjaZPHw0XnGfEbAqsOsv/cX4vfzQAmigtfRedg2VoOgefWEXZZ7vRm7Vc889xAFxk30ZhkZvCIjcFhS6+P+jiXy/U8cxztQzoH8XRH6pq/3yvtcPXDlpt4/2KonDn7bFMn2bgV7+pYNq5JdxwXTTFJR4+/qSxjUZGhrSC8HX+uUb+9UIdO3Y5OWXkyfdaRggh/KkuNzVfbKXstQ9RbQ1oE2KJOXssUYlWLGeMCvr9bDAhs3+47K9mXWMbwpgZ4zHF9sOUMwhdQiKemPqgjuW/zpSLzNSOvZSv717Np9ctB+D3S4cz6DQLSw+OoL649sfbsVoKPz/I2htWYMmyotFqqM6rIOvcgcQNTmrtcE281c8AKWMyOPe1y9jy2Bd8/Yc1ZHx6gJhsK0e/OkzVgfKmvsyh8A98WwuQAwmFw9keA0KvYvb0ryd66HAq1q2hasOXJM28qMU6UsXcO0nALCJGZeFe9q19rtkyW2UxA6eGvydUsApyP0RnimHoub9Aq2v7jbOvqhylzdDSN2TW6HSkXvwz9EkplK75gPIvPyF+wplEWWLQJyajT0xutq29qIDqnVtR3S6iBw3DMmRE0I+PotGQOGU60QOHUPLRu5SvX4spqx8pF84lZtgpLT4xPpnVlxXgtNXQd/zJM7GhEKLntFfp2x1VwIFw2t1ccFEpxSWepmXPvVDHuo9TyBkQ2EvLrmqRsXqNnfUbHCx9I5Hp0zrfNuLirNymkBkgceowTH2T2XHj8zjq3WxfdZR/XHkc0JKWpmXc2B+3/cP/xvLvpfUUFLjR6RT699MyY3rwY5pwuoF1H6fwwEPVvPJ6HUlJWq6cb2bWBSbGjpEQ1dfHn9iZM9PIyOG6jlcWQoiTQNnrH1L90ddN/3aXVxMVH0Ps9HFB7yuQHrkdBY7e4NDjcFK+ZA2WqafS55zLm/bvIfBwubX7vdW0loxYzn52Lh/MeRVXnYNlfytg2ONXoLOA1WLAmpPYtM3IRadT+EUex3OLQFHIOHsA/S8a1u6x2zrviX82cGT1fva+soXSLYWkTsxm2IKxpE3q22KMwWqvMtnbXzmQdSJB7bFcNCYTcVOmtbmOhMy9jwTMImJUHNmB0dqHQdMWsOO9hxuX5e+g+th+rOlDemxcLoeNisPbyRo7J+BwORTxk6ZRuuaDxmPWVBFliWl1PWN6Jsb08Hyty5iaQfa1vwrLvnq7tj4Q8LidAJjj0rt7SEII0aQney77h8HrNzRw9JiHtauTufmWSnbudlJvU/n93ZW8/Wb71T5eXdWH+c2l9Zw6Whd0uBzIY9s0oV8WeM7tw641xRTurm5z/YQELf/zi9av5cGKsWh45C9xPPKXuLDs70Rls6sMG6ZDo5H2GEKI5k7GthgAdRt3YZ01maikOMpeWQnA8effJ2baWJSo4IuKvCFzsMGfbzitOWSmfkcunuo6Ek6/sOl+//U72p8/3+DWG7SOej6H++fvYdd/q2krMla0GjLPHkDm2QMCPZ1mfJ9biqLQ94LBlI2Y3mJMoU6KF86eyoGEzMEE0f7nE2hVs+pwoo22EBUbG9BxRO8gAbOIGI76SowxSZhikzn9qsdoqC2nvrwQS1LfjjfupPaqjevLC1FVT9Ahd1v7873ft/+voihExVhRPR4MqRlBHUuER2vPA291uKrKpEFCiK7XXuWyb8jcXYFzayFwUVHj38OcAVGs+ziZsnIPu/e4SIgP7Ns0nQmWOwqmN33r4Or5gU2867vPjjSFyz9IzGp8k/zYgjpAqmUjRZRWwe3q6VEIISJJbkE6GvPJORGq6nLhrqxFl55EzPRxxJwzDufR47hKK0Eb+jeEQw2ZvdsC2FfmEZUSjz4puYMtAtNWcBttbQzRB/2sez6k9w9XI/GDDf8AOdS+zZ2hRGlRPZ4O1/N+KCGVzL2DBMwiIqiqSn3lUZL6N363VFE0GGOSMMYEVgnVFqetBrfLgc4UgzYqtK+Ran7Yzu2wd2osgci+/ma05miZmKYHtQyZG38XqlsCZiFEz/CGqj3dGsNr914nmRlaos2Nb06TErVMnRJYFVRbAbHDoVJY5MZkUkjt0/a+Ogqno80KNbUdv2Fpz3v5o5sFyr4/e49/1V0eKn7Rh4wMeSkdSTQaaHC0/wG/EEL0Ft7QNNSQ0lFYCqqKPjMFRVFQ9DoMfdMw9E3r1LicpRVoyuqJUhNRB9iC3l5zyAx9NHhyG3D3q0ObF9wHw/7aqwrOGGji/Dcvx5LVc/M5RaKuCpQDfq4qCqor8E+EfYPmpjYt9p5toypaklfFose5nQ0c3bkWZ30VMak5HW8QoLryAnat/BsAGaMvIOOUc9tct61qY7fTzvED3wBQW5qHJbnz1dS+Vcu+x63KUdBZZQbVnub/XKgpOQSKgtGa0kMjEkKIllW2PRE2q6rKus8beP3NOs47J3zVYNXVHn55cwWr19ixWhUO7A6tJdGbb9VxKM/Nf792dGo8voGyb9jsG26bTRrMGfLGJpIUFrrIL3AzfKhUlAsherdQe/T6cpZUUP76RygmA/pOBsq+qtdu5vi/3gUg69e3YlAyA64ubapejjpE1aoNqLYGnEfLUPq3X1zVXhVrW4+Vb4VuTHZAwwtZqJPddaS7qop9K5q765j2vXno+wVfve77XPDUd30BoAiOBMyiR+3//BUqjuxAURRShkzGmjY4bPs+unNt088J2aNC2sex3Z9Tsn8jSTmnExvGsYnI5f0AwBs0a7RRoKrs//xlBk+7rscnnBRCRC7/6tpwh8C+++/ugHnp2/XcfmcldfUqY0/TccdtofXMa60CecPXDaxe0/gm4adz2588qC0HDrr49a2VTD5Dz+XzAt9HR49ja+GyiEzej4dv+k0F48bqGdBf3uYIIXof/8A02ODSVV7N0QdexFl0HE1sNMkLL0JjDM+EsKrH0xQuA+hTGz8QDrZdRuk/30FjMmCZPo6ohLZfT3TUHiEcQXy4dFXIHCz/oDjQ4NgbMnfnJIC27d9TNWAD1gmTAt5GWmVENnnlJXpUbWkehphEhs64EYMlISz7dDls7Fr1JA01xwHQ6AwhV58qGi2oHirydxKXOQJzfOc//W2tWtq3qrmz7P0bmn42Huq6SQlPFjEp/QGoKtxDXUUhlsSsHh6REOJk153hsrelxc7dTurqVd5ZksiZUwxha+V09x8refaFuqZ/z5llCmpcXt7P/r76r4PMDC3zf9b+120DeQwlVO5d0tN+bK3y4iu1PPCnuJ4bjBAi4rUXCPpPzNZdwnFcd1UtzqLjxMwYT+JVF4QtXK7f/j3HHny56d+xp09A0WhCC/w0Gtzl1dRt2o11zpQ2x9jeviMpXI4k/sGy77L2dGewDGAYnI1972HK1vwnqIBZRDYJmEWPqSr6Dqe9lrThZwUcLnvcrsaKUj9uZwNHd6+jqnAvqsfdFC4DeJwN7Pl4MYn9xtBnSHB/vFKHnUX54VzqK4rwuDr3ldvu4BsuB7OOBNEteXsxlx3aiiZKz7DzbsKcIJMvCiHa1tEEdOHYf3fxPY+jx9ys39BARrqWqWcG1hrD4VDR61uG0KqqcuuKVA7+eyflFR727G3ef+/Xt1Qw9UwDD95vberxHIj+/aL46yNx3Pb7SopLWu/BHMzjJ+Fy77NjlxOA+/8Yy9VXdK6fpxDixOYNJ1urFu6pcNl/HKFQ3W7qvtkFgGXyKU3BbXu9nFWPB1QVRdty/gNncTkVyz7FcaQYd01ds/uqN23E5aok7ifTMA4KrgAn5dfzyL/5CVwlFb3226Hex7I7gu7ubl/RGYFWctdv3oNpVA5JCy/Ck+rTV7kdUr0c+SRgFj2iaOenFGxdhSWlP8mDzmh3XVX1UJj7MeWHt+GoqyJnyhVoDWZUtwuPx429uoTiPV/gtNeR0O9UUD3Yq0vxuJ1N+6gtOQSqGlTArHrcHN60gvqKIqKT+pKQHd439i0nk+se3jA5kDBagKO+iujEbKKlclkIEYBImYgvXHJ3OJg3v4yoKHjgvo4nyFn7mZ2nn6ll/YYGbrrRwvnnGnE6VBocUFHp4a1l9Xz+ZRGTz9Az4XQDZS4jJd/XNm2fX+DmjTfruf8P7R/LPwB+Y0kdjz5RjV4Pf7ir+ddtg/2dSLjcOxUdbZyM99K5ZmJiemdgIYToHr4BWGdbUkQKT4OD4sffwLbzIDEzxmMc1PI8fMM/V2Ut5a9/SH3ufqISYkm6bg6qy914c7qw7TpIzSeb0MSYiR43DGdxObaKmmb7q9/6HcaRA4IKmJ3Hyih9dgUAcXPPQhvb8gPBSAj52+M7pmDGF2wYvaJkTFOo3BvCZa9AQmZXeTUx08ehS01scZ83SA4kdBaRRQJm0a1UVaUwdzVFO9aQfsp5ZJxyXodfs7VXH6doxxp0plg8bgf7P3+52f2KRkt89igyT52JMabxD5Tq8XBww5uUHdpCXMZwtAYzZQc3U7j9Y+IyR1C6/2us6UOIzxrZ6jFdDhsFW1dR+v1GADJGzUATFd5JY6wH1C5rjQHNg2T/CmX/dVtbJ5xj6mjfga7XEzxuR9h/90II0Rts/tbBZVcdZ2BOFEteTSQhoWV1k797/1zFwUMuPB54+plann6mttn9p4zS8dpLCRw5fTyKojAYyP5mI1ctKAfg5ecSuPaGcq64poypD05i6/uFeFwq0xblcFPKzlaPueyden5zWyUAl11qYvSo0L4O7A2WfSvR/cNpCZ8jl93W+KG92Ry+11ZCiBOft2o5EoLM9sbQXhDusTVw7NHXaDhYRNrd12IamdPhsWo//5ba9bloE2JxHD5G0b3PNbtfExuNdc4U4i6a2lQJ7am3k3fdAwAkz51H+WcfUfnOZ+gzU/DYHdi27ydu9hR0aUmtHnNAQy5bX/gC+57ixv3PnNzm+fkvj4TfT2cEEy77h8ldHS631x7DdwLAcFIbnGj0wb3HDrbXt+h+EjCLbqOqKvlb/sOx3Z+RNWYWaSOmB7RdyXdfoWi0DDlnEQ21ZaBoMMUmo2i0KNootDoj2qjmbyYVjYbs0y/BEJNIQvYpGGOTKT+8jcLc1RTmrkaj1VGy77/0P2MeyQMntDhmwdaVlO7fSOqws8g87UI02vAHjOEMl30FEtK2VsXsGzKHK/ANdPtIDJabpMVTtXkDeRuX0/f0Sxr7cgshRAQLNBz1vd9/m6/+28AV15QxaoSON19NDKgidPO3DvbsdfG7W2K49BITX3/jYOJ4PXq9gl6nYDIpxMV597Oj6Vjnn2vkycfjiNLC7Jkmrl8QzfMv1bHx3M/Q6hQ8bpWi3VWofzsNnaH53+BD35bz3K830m9sPHPvG8ldpx5o9fHoqIq5rcfmvfzRMtFfL5Ge3vjcuPTy4/zjyXgGDZQPh4UQresN/Xs7quL1huLuOhvHHn4VR0EJaXddi3FIdotQ1rf1x5isAr7NS6Nm3RZ0aUlkPHwTtV9uQ983jVMGV6PRadlZkok2xtSibYbGbCTjwV9St3kP0RcNxTxoCIcffYBjD70CgKLXUb95L2n3LECf1afZtqqq8tUdH+Jxuplw3wxKBk1t1h7Dvz1JW+cbCN9ANtzhqP8YIuXDiVB1Ve/ljh6XqEQr5cs+RTHoiDmr9TF4+rdsndHRxI+iZ0nALLqMt1+y01bD4U3vYqsqxlZ5lOzTLyF16JkB76e27AixaYMxx6cFNcmezhBN5ugLmv49+pK7yP/2A4zWFNJGTOfI5vc49N+laLR6Evuf1rSeqnqoLNiNdfxksgdfFPDxgtEV4XJbIW174a1/oNxVlcy9WfKM2WjN0ZR8/jFRBjNJOeObKuWFECLStBWCtrbcu8zlUnG7QatVqHppPW+v15O3qYzJZxh47aWEgHshb81tnKvgVzdaiInRBBzwPVc1mkXzfxzfg/dbSUrUsHJnNDNvH0rx97W8fvMWlvxuG1f+fQwazY/X0L3rSohO0HP9yxPQaJRWvxXVmbYl3nBZRL4Jpzc+X39/VxU331rJA3+yctqpumbPFyHEyS3Sg+WOqnhVVcXj9KDVayn5tpBjr3xGw/f5qC4P6fdchyEnsPliPHYHzmNlJF43B41BT+yM8QBYMhuPM75PJVvyLa1uaxiQgWHAD8cZA2n3LKBq1QZipo/DODibow++TNH9L5Dxl1+iS4lv2i7Hnsuh4lrO/Nss+ozPojS/97Uyai0wjfRwOVJba6Tds4CyV1dR+tx7aExGzJYRaE2BTfQsIpcEzCKsVFWltvQQxXu/ovzwNuKzR6E3WSk/vI2EvqPpO34usX06/sqOy2HjwPo3sFeV0FBbRoZPUBwqvdlKzplXNf277/if4HbYOPT1UswJ6ZisjZ+yNtSU46ivImXg0E4fs7cyHjJg798QUiVzJLe7CIb1gIrTVkPhs89hqzwGQNGOTyja8QmnX/UYitL7XhQJIU5sviGyN1Rtr+q2oNDFsuU2nlpcgyVa4Q93WXnk8RqGD4viz/daueaqaAyG9sM5VVX5v8W1vPRqHceK3QweFIXF0nGg5x/6+o5Xo1H43S2x/A74V6UJa6qJ+X89ldf+ZwtfvniQs67/8XXEoU3lDJ6S1GaIGGi47G2L0db6Ur0c+e7+YyWvvFGH3d7Yj/n8OaX848l4fjZPejgKIdoPlyMhJGxvfC67kw0f1NHwnzVUH6pg1C8nsPeDQ7iOV2KZPBrrnCnokuNbbOdfRVq+u5hvH/mCmmIbQNCT87XGNDKnWUuOtLsXUHjXYkqeXEL6fTeg6KIYk1XA/qVFaPRaksektzvGQLU1yV5XVS+3dryuet50NO5AQuNwBctdUeFcn7uf0sXLcdfZwOWm+Il/o4210v/Oe5ut114PZs0hM9jl/XikkYBZhIXH46a66DsKt6+hruwIOrOVPsOmUrx3PageMkZfQMYp57a5fVneVqqKviN95AyO7l5HxZHtqKpKYv8xqG43fYZOCfuYFUWh38R51H1YxI4PHiM+axRJ/cdgrzneuII2Crp/Dr6Q+La6CDTYDSYIDqayOdxtNnpKVY5C6Uvv4qirJOOU8yg98A0NNWUAOGorMEgVsxAiQvn2EG7Nvv1Onnmuln8vqUerhXmXmvlmk4Obbq5g+LAoPlmVgl7femCbd9jFo3+t5tK5ZgqL3Dz/Ui2797j4ySUmtBq4+sroDudWCGXsw87uw1nXD2D13/aRu/IoY+dmkD7CSsHOKlKHxLS5fUftMXxbg/i3DDnRJmw8ka1abePZF+r45SIL+7938snaxtcg+793drClEOJkFgnBMrQdLjuqGzjy8X52vLQNd2UthiHZmCeks/3p/4KikH7/omYhse9+VFVlxzMbKbfFET1hJOufXEnJt4XE9osnetwwdKmJGPqnt7ptZ2hjzPT57XwK7/0Xh298GMtZY/hq/HAcaw+gKAqKRtPmJHlthbiRWnkeSDgejrF7w2LfSf/Cpat6LEPrj4+n3k7pM8vRpScR3S+N6o++BlXFXV3VtI70Wu69JGAWneZxu9iz+mnqyvKJTspmyDmLiE0bhKJoSBk0EUddJbFpg9vcXlVVCrZ9REPNcY4f2IQmykCfIZNIHnRGl7ci0OoMDJl+PXvXPEPFke1UHGl8o6lLTCahOh5iOthBBPCfsC8Y/uFva72ZW1svGL217YbH6aDiyHayxs4hddhU0kZOp6roOzwuh4TLQoiI4h/IthcuP7W4hvv/Uk1igoZ7747l6iujibFoqKn18NnnDZx1pqHNcBlg6fJ6li63sXR5Y/XTrAuN/PEuKzOmGzscZyiBrW8AfO7Ng0mrOsaSZTWsfGQvAHqTluzRca0ex7tta5XdrR2nte0kZO4dlq+wcdqpOu7/YyyKorD/eydfrG/gp3OlelmIk93ozCJ00fqICZMDVZ1Xwdob3sFld2GZMpq4S85Cn56MqqrYp49DGxuNPju1aX3/ILOusJp9b2wDoPKdz4jJjmPUTRPpP2cYUSZds8cj3AGuISeDPr/5GcVPvkX1hxuo/nADAGlT+oHy4zHb+p34L29rvfbG3RXVy74/d1fvZf9AuStC5q7S2uNj230Id2Ut6fdejy4tiYTLZlD79U6Muv4t1m2tB7OIbBIwi047fmATdWUFDJlxI7Gpg1AUBXv1cSoKdqE3xZDQ99R2q5lc9loavFXDQN/TL2514r2uYrAkMHLO7QA46irRaLToLQmdqsDqiPWAGpY+zOEKl1sLltsKhUMJjHtjNbO9qABV9RDTZwAAiqIhLmNYD49KCCGaC6Z1Q1m5m4cfq2bhtdHc9wcrRqNCXb2HN9+qo6LSw7xLzcTGtv91wyf/r6bp5zMnG3j5ua67XvqHuxqtwqh7pzLwt05UFaqL7aTkWNDqmo85mMekvQC5o0pwERlUVWXTZgdzLzY1PRcHDdTJJH9CiGa6o7VBOO1Y/DWGeBPn/+Mi9tgbWzfadh3Evj8f04gBzcJlaFnpW/j5oWb3T358JpaM2KZ12xOOxyf69OH0/efv0Rj1OApLiUqyorWY2VoQvmO0prsC2O56DnnPxzcw9/7cW4JmX/Z9R9DGxxCV2liwpTEZiD17bOOdh1quLyFz7yIBs+g0V0MdoBLbJwdFUbBVlbBz5V9BVVE9bo7t+YKEvqeQPOgMovQtG7cX710PQJTBgjVjKEk547v5DEAbpQfAZE3p9mOHQyjBbUehb3ths++2re2nM8F3JPC4XJR+/D46sxVzXOATSwohRCSrq1NxOGDMqXqMxsYg7mdXlvH1Nw70enjy/2qZ9xMTV1wezYjhLcO5vd85cTTO48fY03T89ZG4ToXLbVUItxf6KoqCOa7xmh0drw/5uO0dy3eZhMyR7/+eqaXoqJtzzu64il4IcfLJLUhn3JDjzZb1RNjcWrVte+0fyks8xKREY0q2QD5Ur/mG4y+8j6LXUbFkDdHjRxA9cQTRE0eiaDQt9pe38odv+liNjPrlhKZwuTtpYxqDQUO/8L+fClf1ckctODr7/AhHi4+uamHR1fyrvB2FJVSv+QbLxJGtvn70DZMlVO6dpCu26LTa40cAcNiqAbBXl6K6XYycfRtDz7uJKEM0BbmryV3xIKUHNjXbtmjHpxTt/ARF0TDmsvvImTy/SyuHTzTGQ4ZuqQpuK2z2XR5IqNxbgufjn/yHhmOFDJq2AEWj7enhCCFEWOTluQA4eszdtGzHLic3/8rC9s2pXDTbxPsrbUw7r4Sbb62grt7TtN7OXU6mTC9p/HlLKqv/k8KA/qHXKXRFaOu/T/++yq2t4/33e/mjeS9/dNjHJLrWN5sa+MvD1fzmfyxMndJ7viUlhIgcY7IKQgoAfbdpax/e5W3tv63l3x5Ko+FgIfXHapuWNRw+ijbRSr+X/0Digtm4yiopeWophf+7mIa8o03rqW4P753/IjWHKxl0+SlctOpa+s8Z1hT09Ybq7Y6Eq6VHV4XLvr/zSOwfvaJkTLNbV/Kev8fhpOTvbxGVEEviz2e2ub70Xu7dpIJZhE19eREV+Ts5/v03aHQGdKZYTNY+xPbJwVFfRf7WlRzasASd0dLUakBvaZztVlU9OG016Ey9oOnxSSqQ1hi9JUBui6qqlH2xhoqvvyDlgktwj8+GA71kpkchxAmvs6FsRWXj3zOLRWHZO/Ws+8xOXZ3K0CE6khK1PP5wHA/92cpr/67jvgeqcblUFj+VAIDZ/OOHv+99YOPG6y2dGotXsH2OfXsyt7UsmP11tK5UL0euzd86uPq6csaepufO27u/Mk8IcWLxBmH+oWJ7AaH/fWELEzUKuD249NF8uaKGhkOfUvf1TgwDs1A0GqznT8R6/kTs+/M5/sL7HHvoZTIf/01jxbBGoc/4LArWHuB47tFm59Rez+NIDEJb013jDEcQ39VjjeR+zL7PKU+9neK/vYnzaBkZf/kFGlP7mYK0xei9JGAWnZYyZBK1pXns/+xFFI2WmJQBDJ3w02btMPRmKwMmzcdZX82Rze9hTRuMotGS1H8MWp2R/etewF5desIGzOHotxxuvu0tAg2Ge3uA3B7V7aZ45dtUbdlI0vQLiZtwZk8PSQghmoQj6DzrTANjT9Pxv/c0ztQ9MCeKB++3Mu8nP16vdTqF666xYDAo/Oa2ShZe62DsGD0D+kdx7HA6Q0cfZd9+Z8jn4BsCd+acfEPl9qqU2wqQ/ZdfnJULNFYye38WkWvVahs33lTBKaN0vPZiAlFRkfc6SwgROUINUHsqdFU0GuLmTqPy/S8ofvwNNNEmzOOGknjlBc3WmzRd4Zvka8i/5W9ULF9H0rWz2FqQhX7RAmI071P5VW67QalvqN4bKpvD+fsIdILBnuQNj8NdZezf07mrbMnPZJjxO4ruexdXaSWp//vzFr3D2yIhc+8kAbPA43ah0Yb+VIhLH8qpl/4RV0MdUQYzGm3rk6ooikLG6PPZs/ppao8fxhibQvHe9Rw/+C2muFQsyX1DHoMITW+adK8reRwOipa9Qt2B70i9ZD7WU0/v6SEJIQTQPCx1u1VUlZDDtLg4DR99kMyxYg/RZqXdCf0un2fmr0/WsGRZPWNO0/Hav+tZ9ZGN6mqVq66IDun4/oKpNPbfJthwOtBjnejh8onQU/qlV+v4/d2VzLrAyOKnEjCZJFwWQoRPuEJMj9ONRhd6q72En83AOnsyuNxoYsyM7VsEVLAlv/k1ePyoSqrPm0DVxxtJunYW9u8LqP1yGzWffYv1wjPaPUYkhamiOd/K5O4KhMPF+7wa6N7BRws/AlUl/b4b0Gf1CWo/3nYZEjT3HhIwn+Scthq2vv0n0kacTdaY2SHvR6ONQm+2driewdL4VVtbZTGHv1mBraoEnSmGnClXSa/bHhBI24tIPmZHExUGQnW7ObriDerzDpB5xQ1EDxzS7P6qHAWrtMkQQvQA/yDwymvL2LHLydavU9HrQwvVFEUhLbXj661Wq5CZqSW/wMWjT9Tw2BM1JMRr+NM9sZw2OrTJ9bwCqTDuSHvb+d8X6jF6exDbmt5+Tu+8V8/td1Zyw3XRPPAnK1qthMtCiI51dyXy4dX72HT/Ws5+9hISRwZWsenlG/pqoxu/YdTR+KOS41EbnNTn7qf4iTdBAeOQbOLnnRP84CNYOFt5tDXpYlfsNxzaCpkjsU3GmKwCvsmN5cv7VhIdqyPu9huISgi9lZUEzb2HBMwnOU1U45vEqqLviE7MJqFvaG/CAqU3W7GmDyVv49sAjJz9O8zx4Z9VNpJEYnsMf77tMnqLzo5XVVVshw9S/MEyHGWlpF++oEW4LALjdrvZvn07hYWF5OTkMGzYsGb3b9++nd27d7e67WWXXYZG07KK8oMPPsBms3HZZZd1yZiFiHStBYEmo0JpqYe//6OG3/02pssnxb1qfjQ33VzBJ2sbuPXmGO76fcdvDjqqMI70gFNaZESmgkIXjzxew5tL6/nJJSYevN8qk0KHyGazsX79eux2O2eccQZJSUlh2SaU/QrRHUZnFgGd+2A0WMaExiBs/9Lt6GONxGTHhbyvQAJL87ihaN5aw7GHXkGXmdLY59bQvefcXbwhcDBBbiDbdGZCP+/24QyXuzo4DndVtPf83Q0uDq/eR/6TX2OwRDHliVmYU6rZkt/5uRJOxqB569at5OXlMXDgQEaNGtXTw+mQBMwnOa3OwODp17Nv7fMU5n7U5QEzQM6UK9m37nmMMckSLvcw38rf3hgyh8JRfpzq3M1U527GWVmOPjmVvot+izE9q6eH1it9+OGH3HLLLZjNZjIyMtiwYQPjx49n+fLlmM2NF/9du3bx3nvvNdvuiy++wOPxtBogv/LKKyxatAi32y0BsxA+/vl0Av2HFvHI4zVcf62F+PiuvcZcdqmZAwdcLF1ez6Lrg2uL0V7VcFeEzMFOFujvvfzRwI8tMiI9CD8Z1NR6WLnKxltv21i/oQGTUeGxh+K4+gqzhMsh2r59OxdeeCHx8fHExcWxbds2XnrpJebNm9epbULZrxAnsj6nZ5Jz6QgOLN9F0uj0oALmMVkFAYeo3vWi4iD1d1dS/Pe3iJ971gkbLkeSLpvg0UdbAbB/ONzTVczfHk7H/t0hVj+7jdqvd6LaGrBMPoWEq2diTqkEmj+vO8sbNHvq7WHZXyRyOBzMmzePr776irFjx7Jx40Zmz57Nq6++2mpxVqSQgFlgTRtMv4nziEnu1y3HizKYGX7Bzd1yrHDyhsWBtkuI9HDZX0+Fy51tcxHIdm6bjZpd26jO3Ywt/xCK0YB5/ChSB1yOKXsASgT/kY50LpeLVatWMWDAAABKSko49dRTeeCBB3jwwQcBmD9/PvPnz2/axm63k5aWxi9+8YsWF8h9+/Zx9913c8cdd/DQQw9134kI0QsYDApLXkuipsZDfHz3/N26845Y7rwjsKoT34C3vTYYnQmCg+FfjdxWdbI3XBY9z+1W+fKrBt5aVs/KD+3U21SmTNLz1F/jmD3LRIxFrted8fOf/5xJkyaxdOlSFEXh4Ycf5rrrruPss89us+I4kG1C2a8QJ7qhV48hJjuefhe2/Q3J9oLkYANL45C+9F18R3CD7Aa+5xGpPZ+DGVdPTfwIRFwrjJr8So58tJ/i1fuoP1pDVHIc1pmTiJkyGl1aUo8+Vr3dU089xYYNG9i2bRuZmZns27eP0047jZdeeomFCxf29PDaJK/SBIpGS8qgiZjigusPdbKKtOC4N1Yde8cc7Njt/RuaBdLthcuq203td7soWvoKBx6/l+KVb6MxGEi79Coyn7qLpIWXYu43MKBwuaPfeVWO0mydSHuOdKU5c+Y0hcsAKSkpTJkyhW3btrW5zfLly6mqqmpxcWxoaODyyy/nkUceoW9fmfRTnLzaq56dOsXArAtN3Tia4PVE9a9/aO0NjSU87h32fufkvr9UMXr8MX46v4wt25zccnMMWzf24d1lycz/WXS3hMsvVY3o8mP0lJ07d5Kbm8stt9zSVAH+q1/9CqfT2eJbRsFsE8p+hTgZmJKjGfjTkUSZde0GmJ0Jl8dkFTTdwiXc++tpkRpqd1Y4wuZQ9uGotnNgxS7W3riC1ZcvYf+yHfQZl0HavdeT9fdbSZh3Drq0xg8W/R/7E+l51dVef/115s2bR2Zm42M4ePBgZs+ezeuvv97DI2ufVDBHELfzxC3xPxG47T+Ghm5Hx1XMvut3Jd0ecNP5547H1r1BdX2qHWyNPxvyDAGdg8fW0Oa67roaKr9Zj9tWj8fppP7wATz1deiS+pBw1rnEDh+N1vJDFd5BADXgx816SMXdzv3e37X3eeG2K5RnNG4HXff/tgsndMH8gy6cAFRXVzdbbjAYMBjarxi32Wxs2LCBK664os11XnjhBc4++2wGDhzYbPntt9/O0KFDufLKK3n++edDHL0Q3aOh1gXAAusuoHkw5V3mvzwQC6y7qKkJwwB7wHztNoAW4/cuh64L8BZYdzXbt6vux2va8r1Df/ipAXuts8W2vut676/RerpknKLR62/Ws3OPA5cD9u5zsmu3izgrzJll5ieXmBg9StcUVtbUdM/v4qWqEU3/X3cFl+qAMJ+KS3UAgV2vd+zYAcDIkSOblsXExNCvX7+m+/wFsk0o+xWiOznrHD09BDz19mbjyC1I/+Ene9Dj87YFGJ1ZhLPux+WjEg767btjjf2pm/Puc1TCwaD21Rbf8wtnS4NAH7e2Hq/W1mnP6MwicgvSe/T5VF/T/B2p71j87wtUR+dTvqeEgk++x93goqGqgZJvC1A9KiljMxl75zTSJmWjNUSRW5CKane0eGu6+bvGsNn7XAvnc6Cr8ouevl57PB527drFokWLmi0fNWoUTz75ZHgHFm6q6HE2m01NTU1VaYyK5CY3uZ2At9TUVNVms/WavxkWi6XFsnvvvbfDsV177bVqSkqKevTo0VbvP3DggKooivrmm282W/7++++rffv2VSsqKlRVVdXnnntO1Wq1nX2ohAg7uWbLTW4n9i2c1+vu+JsR6PX6mWeeafW6OmnSJPWaa65pdeyBbBPKfoXoDnK9lpvcTvxbb3qPHej1uqamRgVavF9evHhxxL8/lgrmCGA0Gjl06BAOR89/uiqE6Bp6vR6j0RiWfXXH3wxVVVtMotRR9fIdd9zBihUr+OSTT0hNbb3lzosvvkhCQgJz585ttnzBggXMnz+fjz76CIBNmzahqipLlixhzJgxDB48uBNnI0T4yDVbiBNbOK/X0PV/MwK9XhsMBtxuN3a7vdn51dbWtnm+gWwTyn6F6A5yvRbixNeb3mMHc72Gxuuor95wXZWAOUIYjcaIf7IIISJHpP3NuPPOO3n22Wf5+OOPGTduXKvruN1uXn75ZX7+85+3uJjOmDGD0tJS3n33XQAOHjyIqqq8++67WK1WCZhFRIm0//+EEJEtEv5m5OTkAHDkyJGma6rH46GgoKDZXArBbhPKfoXoLpHw/54QoveIhL8ZOp2OrKwsjhw50mz54cOHI/66KpP8CSGE6JS77rqLxYsXs3r1aiZMmNDmeqtXr6awsJDrr7++xX1Llixpdlu0aBEajYYlS5Zw4YUXduXwhRBCiBPexIkTSUhIYNmyZU3L1qxZQ3l5OTNnzmxatmrVKjZv3hzwNoHuVwghhBCBmTlzJitWrMDlapwbwm638/777zNr1qweHln7pIJZCCFEyJ544gkeeughfvvb35KXl0deXh4A8fHxnH/++c3WfeGFF5g8eTLDhw/vgZEKIYQQJy+9Xs/jjz/OjTfeiM1mIz4+nkcffZQbb7yx2QR9t956K9OmTWPcuHEBbRPofoUQQggRmHvuuYdx48Zx8cUXM3v2bJYtW4ZGo+G2227r6aG1S1FVVe3pQQghhOidnnrqKTZs2NBieb9+/Xj44Yeb/u10Olm4cCGXX355QBVN69at4/nnn+eNN94I63iFEEKIk9lnn33G0qVLaWhoYPr06VxxxRXNekLedtttDB8+nIULFwa8TaDrCCGEECIwRUVF/POf/+Tw4cPk5ORw0003kZSU1NPDapcEzEIIIYQQQgghhBBCCCFCIj2YhRBCCCGEEEIIIYQQQoREAmYhhBBCCCGEEEIIIYQQIZGAWQghhBBCCCGEEEIIIURIJGAWQgghhBBCCCGEEEIIERIJmIUQQgghhBBCCCGEEEKERAJmIYQQQgghhBBCCCGEECGRgFkIIYQQQgghhBBCCCFESCRgFkIIIYQQQgghhBBCCBESCZiFEEIIIYQQQgghhBBChEQCZiGEEEIIIYQQQgghhBAhkYBZCCGEEEIIIYQQQgghREgkYBZCCCGEEEIIIYQQQggRkv8HRVHYQhiMhkYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1800x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = matplotlib.pyplot.figure(figsize=(18,6))\n",
+    "# plot temperature at 850hpa pressure level\n",
+    "ax1 = fig1.add_subplot(1,3,1,projection=cartopy.crs.OSGB() )\n",
+    "ukv_sample_ds.sel({'pressure':85000, 'time': select_time})['air_temperature'].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,2,projection=cartopy.crs.OSGB() )\n",
+    "\n",
+    "ukv_sample_ds.sel({'pressure':85000, 'time': select_time})['relative_humidity'].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,3,projection=cartopy.crs.OSGB() )\n",
+    "ukv_sample_ds.sel({'pressure':85000, 'time': select_time})['wind_speed'].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba2779e-bafa-4796-87f8-386282b67bd9",
+   "metadata": {},
+   "source": [
+    "One of the advantages of interacting with the data through a PyEarthTools accessor in the form of data index class is that the logic we created above to load data from many different files and merge into a single object in memory is handled by the class. This will usually be provided by a data creator or platform manager, so the end-user doesn't need to know about the structure on disk."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f955ff8-fd5b-4fd2-8338-66c53767a625",
+   "metadata": {},
+   "source": [
+    "### Download the data\n",
+    "\n",
+    "We will now use this dataset to save out the data locally. In future we could do this through a pyearthtools class, but for now we'll do it manually, so we can demonstrate different ways of building and using an accessor for this dataset.\n",
+    "\n",
+    "We will save this data to a temporary directory for this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "529443a9-bce5-4b20-9df4-6380b0f43263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6dd6920c-ce97-4f9f-9a3b-c4ad3534fd45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/tmp/tmphi2ud3tp')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# change this path to a suitable local directory for which you have write permission\n",
+    "local_dir_obj = tempfile.TemporaryDirectory()\n",
+    "local_dir = pathlib.Path(local_dir_obj.name)\n",
+    "local_dir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "81423d81-6303-416d-a8db-061ac52ffc8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'str'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tempfile.TemporaryDirectory()as td1:\n",
+    "    print(type(td1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0486e430-9dc9-44ed-8a84-312c25bcd08d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'.nc'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pathlib.Path(ukv_path_dict[var_list[0]][1]).suffix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "23e008a8-37cf-4952-b6c2-1d0437a501c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'temperature_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-temperature_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'],\n",
+       " 'relative_humidity_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'],\n",
+       " 'wind_speed_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'],\n",
+       " 'wind_direction_on_pressure_levels': ['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T0000Z/20260601T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260601T1200Z/20260601T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T0000Z/20260602T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260602T1200Z/20260602T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T0000Z/20260603T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260603T1200Z/20260603T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T0000Z/20260604T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260604T1200Z/20260604T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T0000Z/20260605T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc',\n",
+       "  's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260605T1200Z/20260605T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc']}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_path_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a460bee2-377c-4695-873c-e2d3746740be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "temperature_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "relative_humidity_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_speed_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n",
+      "wind_direction_on_pressure_levels\n"
+     ]
+    }
+   ],
+   "source": [
+    "local_path_dict = {}\n",
+    "for var_name, path_list in ukv_path_dict.items():\n",
+    "    local_path_dict[var_name] = []\n",
+    "    for path1 in path_list:\n",
+    "        print(var_name)\n",
+    "        out_path = local_dir / (pathlib.Path(path1).stem + pathlib.Path(path1).suffix)\n",
+    "        xarray.open_dataset(path1, **open_args).to_netcdf(out_path)\n",
+    "        local_path_dict[var_name]+= [local_dir / (pathlib.Path(path1).stem + pathlib.Path(path1).suffix)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9cb2fe1f-7edf-4231-86de-11e1700fb89c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[PosixPath('/tmp/tmphi2ud3tp/20260605T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T0000Z-PT0000H00M-wind_direction_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T0000Z-PT0000H00M-wind_speed_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T0000Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260605T0000Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260604T0000Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260603T0000Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260602T0000Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'),\n",
+       " PosixPath('/tmp/tmphi2ud3tp/20260601T0000Z-PT0000H00M-temperature_on_pressure_levels.nc')]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[f1 for f1 in local_dir.iterdir()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ccced5a-af0e-4ba7-bc96-4c4b942965c1",
+   "metadata": {},
+   "source": [
+    "## Create a PyEarthTools Accessor\n",
+    "Now we will put the logic for loading the data into a PyEarthTools accessor class, so that the user doesn't need to know about the strcuture of the data on disk. To start with we will build an accessor for the netcdf files that we have downloaded. \n",
+    "\n",
+    "To create an accessor you need to implement the following:\n",
+    "* A class derived from the [Index](https://pyearthtools.readthedocs.io/en/latest/api/data/data_api.html#pyearthtools.data.indexes.Index)\n",
+    "* the `__init__` function to set up the object\n",
+    "* the `filesystem` function to supply a list of paths to load\n",
+    "* (optional) the `load` function if the loading of the data is non-standard. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "c29670ae-4e01-45dc-b2e5-4f88859477bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyearthtools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "12889c8e-2531-4d58-9f34-067ba33c69c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyearthtools.data import Petdt\n",
+    "from pyearthtools.data.indexes import ArchiveIndex\n",
+    "from pyearthtools.data.transforms import Transform, TransformCollection\n",
+    "from pyearthtools.data.archive import register_archive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ba4745b6-1620-4059-91d3-34aeb9b8d98c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MO_UKV(ArchiveIndex):\n",
+    "    \"\"\"\n",
+    "    PyEarthTools index for loading Met Office UKV data in Netcdf format.\n",
+    "    \"\"\"\n",
+    "    MO_UKV_AWS_ROOT_PATH = 's3://met-office-atmospheric-model-data/uk-deterministic-2km/'\n",
+    "    MO_UKV_AWS_DIR_TEMPLATE = MO_UKV_AWS_ROOT_PATH + '{vt_str}'\n",
+    "    MO_UKV_FNAME_TEMPLATE = '{vt_str}-PT0000H00M-{var_name}.nc'\n",
+    "    \n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        root_dir : pathlib.Path,\n",
+    "        variables: list[str] | str,\n",
+    "        *,\n",
+    "        transforms: Transform | TransformCollection | None = None,\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Init function for our data index class. This gathers together the key parameters fof the data to enable subsequent loading.\n",
+    "        \"\"\"\n",
+    "        self._root_dir = root_dir\n",
+    "        self._variables = variables\n",
+    "        super_transforms = TransformCollection([pyearthtools.data.transforms.variables.Trim(self._variables),]) + transforms\n",
+    "        \n",
+    "        # call the base class\n",
+    "        super().__init__(\n",
+    "            transforms=super_transforms,\n",
+    "        )\n",
+    "        self.record_initialisation()\n",
+    "\n",
+    "    def filesystem(\n",
+    "        self,\n",
+    "        querytime: str | Petdt,\n",
+    "    ) -> pathlib.Path | dict[str, str | pathlib.Path]:\n",
+    "        \"\"\"\n",
+    "        The `filesystem` member function is where the logic is provided of what files need to be loaded for a given request of dates and \n",
+    "        variables. This list of file is then used by PyEarthTools to provide the data to the user. If data is stored in standard formats \n",
+    "        like Netcdf, this is all that the specific class needs to implement.  Files may contain a much larger range of data in each file. \n",
+    "        PyEarthTools will handle the necessary subsetting to provide th e user only what was requested.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        querytime = Petdt(querytime)\n",
+    "        paths = []\n",
+    "        \n",
+    "        # scroll the list of requested variables and add relevant file paths for each variable\n",
+    "        for var_name in self._variables:\n",
+    "            try:\n",
+    "                current_fname = MO_UKV.MO_UKV_FNAME_TEMPLATE.format(vt_str=vt_template.format(dt=querytime), var_name=var_name)\n",
+    "                if self._root_dir is None:\n",
+    "                    current_dir = MO_UKV.MO_UKV_AWS_DIR_TEMPLATE.format(vt_str=vt_template.format(dt=querytime))\n",
+    "                    current_path = f'{current_dir}/{current_fname}'\n",
+    "                else:\n",
+    "                    current_dir = self._root_dir\n",
+    "                    current_path = current_dir / current_fname\n",
+    "                \n",
+    "                paths += [ current_path ]\n",
+    "            except KeyError:\n",
+    "                print(f'Non data for var {var_name}')\n",
+    "        \n",
+    "        print(paths)\n",
+    "        return paths\n",
+    "        \n",
+    "        return ds\n",
+    "    def __desc__(self):\n",
+    "        \"\"\"\n",
+    "        Optional function to provide additional metadata about this class.\n",
+    "        \"\"\"\n",
+    "        return {\n",
+    "            \"singleline\": \"Met Office UKV Forecast Analysis \",\n",
+    "            \"range\": \"June 2026\",\n",
+    "            \"Documentation\": \"https://registry.opendata.aws/met-office-uk-deterministic/\",\n",
+    "        }\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33eb06bc-26ee-45aa-98d0-53022559ba33",
+   "metadata": {},
+   "source": [
+    "Now we have created an accessor, we can select a set of variables and a time, and the user of rthe class doesn't need to know the details of the data storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ca229875-a072-4562-b9e7-f45adc9ca2a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_accessor = MO_UKV(local_dir, var_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e747c63f-6c73-447a-817b-4d3df23dcf3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'), PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-relative_humidity_on_pressure_levels.nc'), PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-wind_speed_on_pressure_levels.nc'), PosixPath('/tmp/tmphi2ud3tp/20260601T1200Z-PT0000H00M-wind_direction_on_pressure_levels.nc')]\n"
+     ]
+    },
+    {
+     "data": {
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+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
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+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
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+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
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+       "\n",
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+       "  padding-bottom: 4px;\n",
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+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\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-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\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",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > 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",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
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+       "  display: inline-block;\n",
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+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
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+       "  display: none;\n",
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+       ".xr-section-inline-details,\n",
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+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
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+       "\n",
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+       "\n",
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+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
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+       "\n",
+       ".xr-group-box-hline {\n",
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+       "  padding-bottom: 4px;\n",
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+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
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+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
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+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
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+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
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+       "}\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",
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+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
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+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
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+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
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+       "}\n",
+       "\n",
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+       "  font-weight: bold;\n",
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+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
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+       "\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",
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+       "}\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: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2026-06-01T12:00:00\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time       datetime64[ns] 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB dask.array&lt;chunksize=(1, 970, 2), meta=np.ndarray&gt;\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB dask.array&lt;chunksize=(1, 1042, 2), meta=np.ndarray&gt;\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0</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-01ae2d96-841d-4ef2-ad0a-4be92f66286b' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-01ae2d96-841d-4ef2-ad0a-4be92f66286b' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>pressure</span>: 33</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 970</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 1042</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-8a175634-9f9a-4a4a-957b-0de3c5c526bc' class='xr-section-summary-in' type='checkbox' checked /><label for='section-8a175634-9f9a-4a4a-957b-0de3c5c526bc' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-06-01T12:00:00</div><input id='attrs-c95c6330-b74b-49de-ba41-2c9e61e1aeb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c95c6330-b74b-49de-ba41-2c9e61e1aeb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0929afa-0feb-471d-b8fa-39096a6cd82b' class='xr-var-data-in' type='checkbox'><label for='data-d0929afa-0feb-471d-b8fa-39096a6cd82b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-06-01T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-e14729c8-08cc-4c2e-b343-51fa91de9a35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e14729c8-08cc-4c2e-b343-51fa91de9a35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84cea993-894e-4ef1-94f8-9e143cbc7ece' class='xr-var-data-in' type='checkbox'><label for='data-84cea993-894e-4ef1-94f8-9e143cbc7ece' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.036e+06 -1.034e+06 ... 9.02e+05</div><input id='attrs-60003e39-423d-432c-b307-a6f502c65693' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60003e39-423d-432c-b307-a6f502c65693' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec361f83-64c8-4d47-a32e-1969548766fd' class='xr-var-data-in' type='checkbox'><label for='data-ec361f83-64c8-4d47-a32e-1969548766fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.036e+06, -1.034e+06, -1.032e+06, ...,  8.980e+05,  9.000e+05,\n",
+       "        9.020e+05], shape=(970,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.158e+06 -1.156e+06 ... 9.24e+05</div><input id='attrs-351cb559-32cc-4e58-8722-19dd55557d01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-351cb559-32cc-4e58-8722-19dd55557d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-546a2be0-7a1e-4162-a53a-361bc96ce804' class='xr-var-data-in' type='checkbox'><label for='data-546a2be0-7a1e-4162-a53a-361bc96ce804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.158e+06, -1.156e+06, -1.154e+06, ...,  9.200e+05,  9.220e+05,\n",
+       "        9.240e+05], shape=(1042,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ef19b0a-ac0c-4503-9c04-14721e9817d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ef19b0a-ac0c-4503-9c04-14721e9817d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e64352-935b-4a8b-badc-78c9e8743159' class='xr-var-data-in' type='checkbox'><label for='data-b2e64352-935b-4a8b-badc-78c9e8743159' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e2e9817-cee3-4e8f-a9a7-91a980c3deff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e2e9817-cee3-4e8f-a9a7-91a980c3deff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a400d1-6a37-41fe-8ff0-e7e52e55ef61' class='xr-var-data-in' type='checkbox'><label for='data-22a400d1-6a37-41fe-8ff0-e7e52e55ef61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06b930de-fe9b-4579-b6db-200330a583dd' class='xr-section-summary-in' type='checkbox' checked /><label for='section-06b930de-fe9b-4579-b6db-200330a583dd' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(8)</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>air_temperature</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;</div><input id='attrs-f729e1d8-c60b-49af-b79a-a58d50430455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f729e1d8-c60b-49af-b79a-a58d50430455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbd337ed-f349-4e91-b1a7-affe8ca4d178' class='xr-var-data-in' type='checkbox'><label for='data-bbd337ed-f349-4e91-b1a7-affe8ca4d178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 127.24 MiB </td>\n",
+       "                        <td> 127.24 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "  <text x=\"253.65617842726695\" y=\"74.51030510672952\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,253.65617842726695,74.51030510672952)\">970</text>\n",
+       "  <text x=\"94.32808921363348\" y=\"141.03634257255862\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,94.32808921363348,141.03634257255862)\">33</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_azimuthal_equal_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647</div><input id='attrs-cd7b8e49-05b2-4e2b-bb80-36579ad5638f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd7b8e49-05b2-4e2b-bb80-36579ad5638f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84aa4563-75df-4a84-8b39-949589620be0' class='xr-var-data-in' type='checkbox'><label for='data-84aa4563-75df-4a84-8b39-949589620be0' 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>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>-2.5</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>54.9</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 970, 2), meta=np.ndarray&gt;</div><input id='attrs-ad3011de-b5c6-413d-bfac-2868f956bdf0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad3011de-b5c6-413d-bfac-2868f956bdf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-184fd64e-980b-49dd-ad0a-6cb2e58ae95f' class='xr-var-data-in' type='checkbox'><label for='data-184fd64e-980b-49dd-ad0a-6cb2e58ae95f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 7.58 kiB </td>\n",
+       "                        <td> 7.58 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 970, 2) </td>\n",
+       "                        <td> (1, 970, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 5 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1042, 2), meta=np.ndarray&gt;</div><input id='attrs-1a6f9b5b-f1fa-40ce-b7cc-308a8985eeca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a6f9b5b-f1fa-40ce-b7cc-308a8985eeca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-800d4e3e-2f2e-4e68-9d12-01646c582f6d' class='xr-var-data-in' type='checkbox'><label for='data-800d4e3e-2f2e-4e68-9d12-01646c582f6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.14 kiB </td>\n",
+       "                        <td> 8.14 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 1042, 2) </td>\n",
+       "                        <td> (1, 1042, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 5 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;</div><input id='attrs-4e4ccc47-16ae-4b74-ac5d-5137fe46e88d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e4ccc47-16ae-4b74-ac5d-5137fe46e88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b848dbff-e477-49d0-910e-3a6a18bea41a' class='xr-var-data-in' type='checkbox'><label for='data-b848dbff-e477-49d0-910e-3a6a18bea41a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0 1]</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> 31.81 MiB </td>\n",
+       "                        <td> 31.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int8 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
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+       "                        <th> Bytes </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 127.24 MiB </td>\n",
+       "                        <td> 127.24 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
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+       "                    <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>wind_from_direction</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 33, 970, 1042), meta=np.ndarray&gt;</div><input id='attrs-bed2caad-bd49-4ff2-8857-cab19e16d1aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed2caad-bd49-4ff2-8857-cab19e16d1aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d948d3a-3aaf-4557-ba3a-b17fb9a5026e' class='xr-var-data-in' type='checkbox'><label for='data-9d948d3a-3aaf-4557-ba3a-b17fb9a5026e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-387b1c38-535a-4643-bc9e-597b85d1caea' class='xr-section-summary-in' type='checkbox' /><label for='section-387b1c38-535a-4643-bc9e-597b85d1caea' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT54H</dd><dt><span>mosg__grid_domain :</span></dt><dd>uk_extended</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.7.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>uk_det</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>UKV Model Forecast on UK 2 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>13.8</dd><dt><span>Conventions :</span></dt><dd>CF-1.7, UKMO-1.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2026-06-01T12:00:00\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time       datetime64[ns] 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 33, 970, 1042), meta=np.ndarray>\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB dask.array<chunksize=(1, 970, 2), meta=np.ndarray>\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB dask.array<chunksize=(1, 1042, 2), meta=np.ndarray>\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB dask.array<chunksize=(1, 33, 970, 1042), meta=np.ndarray>\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 33, 970, 1042), meta=np.ndarray>\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 33, 970, 1042), meta=np.ndarray>\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 33, 970, 1042), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    ancillary_variables:          flag\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ukv_accessor['2026-06-01 12:00']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fc3b045-aec4-4487-9b78-79e11f5ff224",
+   "metadata": {},
+   "source": [
+    "### Creating an accessor for Zarr data\n",
+    "We could instead have saved the data in a format that optimised for object stores as found on cloud platforms, such as zarr. Different formats will give better performance on different platforms based on the specifics of the storage hardward and configuration, so although zarr is efficient in many cases, netcdf or another may be more appropriate in other cases.\n",
+    "\n",
+    "The ZARR format does a lot of the work of loading data from multiple files for you because of the standardised way it stores chunks of data in disk and provides a single object in memory by default. Thus for ZARR datasets we don't generally need to load to create a custom accessor, we can use the built-in `ZarrIndex` class to load our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "147d9b36-2bef-4644-b595-cd6f0a2ed82c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zarr_dir = local_dir / 'zarr_data'\n",
+    "zarr_dir.mkdir()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fec4a55-31a4-4a68-b656-ca7628821002",
+   "metadata": {},
+   "source": [
+    "We will start saving out the data a local zarr archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "758c8d1b-3e58-4619-9f99-0686de69564d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.backends.zarr.ZarrStore at 0x7f53bb4d0720>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xarray.merge([xarray.open_mfdataset(var_paths, ** ( mf_args) ) \n",
+    "                      for current_var,var_paths in  local_path_dict.items()]).to_zarr(zarr_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a63fb21-5ef5-45d2-892c-6dccda382381",
+   "metadata": {},
+   "source": [
+    "Zarr archives are sufficiently standardised in their strcuture, that we don't need to implement out own index. Instead we can make use a a standard ZarrIndex provided by PyEarthTools. This could also be used for accessing remote data. See the [Remote_Zarr_loading.ipynb](Remote_Zarr_loading.ipynb) notebook for an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1e85c061-aca8-4a2e-8e8c-ff143a8ac7a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zarr_accessor = pyearthtools.data.archive.ZarrTimeIndex(\n",
+    "    zarr_dir,\n",
+    "    variables=var_list,\n",
+    "    remote=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e0bba98a-0e81-4d50-8af6-d551be73b646",
+   "metadata": {},
+   "outputs": [
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+       "}\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: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2026-06-01T12:00:00\n",
+       "    forecast_reference_time       (time) datetime64[ns] 8B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB dask.array&lt;chunksize=(1, 17, 485, 521), meta=np.ndarray&gt;\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB dask.array&lt;chunksize=(1, 1042, 2), meta=np.ndarray&gt;\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB dask.array&lt;chunksize=(1, 970, 2), meta=np.ndarray&gt;\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-06-01T01:18:41Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8</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-1bae0020-89c6-408a-9c19-dc9bf1bcd64b' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-1bae0020-89c6-408a-9c19-dc9bf1bcd64b' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>pressure</span>: 33</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 970</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 1042</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-2cb4fd94-f270-4707-9658-b31a6c2a29d3' class='xr-section-summary-in' type='checkbox' checked /><label for='section-2cb4fd94-f270-4707-9658-b31a6c2a29d3' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-06-01T12:00:00</div><input id='attrs-bce106e6-6e14-47ce-8bc3-80017c39b13a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bce106e6-6e14-47ce-8bc3-80017c39b13a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c19747d-afd8-46d9-b67c-175cbcb94b93' class='xr-var-data-in' type='checkbox'><label for='data-8c19747d-afd8-46d9-b67c-175cbcb94b93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-06-01T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_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'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-ffbf7e96-ff81-4be6-becc-907c3f779ed8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffbf7e96-ff81-4be6-becc-907c3f779ed8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d6c26ff-015a-40c6-bfae-a3c7300b43d5' class='xr-var-data-in' type='checkbox'><label for='data-8d6c26ff-015a-40c6-bfae-a3c7300b43d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></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> 8 B </td>\n",
+       "                        <td> 8 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.0\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,60.0)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-8d3a3ef9-f84e-4804-ab46-425da4bd17ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d3a3ef9-f84e-4804-ab46-425da4bd17ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1e5c4d2-00c3-4332-971d-b28b0d982cd1' class='xr-var-data-in' type='checkbox'><label for='data-b1e5c4d2-00c3-4332-971d-b28b0d982cd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.036e+06 -1.034e+06 ... 9.02e+05</div><input id='attrs-e8904705-eb2a-416d-a77b-2299c1f16737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8904705-eb2a-416d-a77b-2299c1f16737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf13244-dfac-4da8-a1c1-3c2c57789148' class='xr-var-data-in' type='checkbox'><label for='data-aaf13244-dfac-4da8-a1c1-3c2c57789148' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-1.036e+06, -1.034e+06, -1.032e+06, ...,  8.980e+05,  9.000e+05,\n",
+       "        9.020e+05], shape=(970,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.158e+06 -1.156e+06 ... 9.24e+05</div><input id='attrs-ee485fa5-1e41-433f-8965-e7ab418c4b9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee485fa5-1e41-433f-8965-e7ab418c4b9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5b66e7d-9c0f-4687-8def-25993a13a9bc' class='xr-var-data-in' type='checkbox'><label for='data-b5b66e7d-9c0f-4687-8def-25993a13a9bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-1.158e+06, -1.156e+06, -1.154e+06, ...,  9.200e+05,  9.220e+05,\n",
+       "        9.240e+05], shape=(1042,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8fd1e37f-df5d-4d66-9b19-96e0274afb30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fd1e37f-df5d-4d66-9b19-96e0274afb30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e31ae5db-5075-44a5-b6af-56345bdbf356' class='xr-var-data-in' type='checkbox'><label for='data-e31ae5db-5075-44a5-b6af-56345bdbf356' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aeaaa2c0-cf1e-4e8c-b155-bab377006a47' class='xr-section-summary-in' type='checkbox' checked /><label for='section-aeaaa2c0-cf1e-4e8c-b155-bab377006a47' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(8)</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>air_temperature</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-552c3c06-eda1-47e3-8775-e681b145023e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-552c3c06-eda1-47e3-8775-e681b145023e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e758dd-ee9f-4a26-9942-01b78cac9510' class='xr-var-data-in' type='checkbox'><label for='data-23e758dd-ee9f-4a26-9942-01b78cac9510' 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>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</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> 127.24 MiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1, 128, 128) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2376 chunks in 4 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>flag</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 17, 485, 521), meta=np.ndarray&gt;</div><input id='attrs-9a057171-89c6-4d96-a830-b39f45c3bedc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a057171-89c6-4d96-a830-b39f45c3bedc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e27a9f-43bd-4cac-8ba1-67c38dceac2f' class='xr-var-data-in' type='checkbox'><label for='data-50e27a9f-43bd-4cac-8ba1-67c38dceac2f' 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>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0, 1]</dd><dt><span>standard_name :</span></dt><dd>status_flag</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> 31.81 MiB </td>\n",
+       "                        <td> 4.10 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 17, 485, 521) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "                        <td colspan=\"2\"> int8 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_azimuthal_equal_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2a8704f1-9c4a-4691-a4f2-2fb2714d9baa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a8704f1-9c4a-4691-a4f2-2fb2714d9baa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9be4dad3-bca6-42f7-a36b-0c96c0855e05' class='xr-var-data-in' type='checkbox'><label for='data-9be4dad3-bca6-42f7-a36b-0c96c0855e05' 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>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>54.9</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>-2.5</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1042, 2), meta=np.ndarray&gt;</div><input id='attrs-e31e469d-d6f5-4381-806a-9d4431cdc110' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e31e469d-d6f5-4381-806a-9d4431cdc110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b5087e4-467c-43b1-958e-541d99ce9a68' class='xr-var-data-in' type='checkbox'><label for='data-8b5087e4-467c-43b1-958e-541d99ce9a68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.14 kiB </td>\n",
+       "                        <td> 8.14 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 1042, 2) </td>\n",
+       "                        <td> (1, 1042, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 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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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 970, 2), meta=np.ndarray&gt;</div><input id='attrs-95533ca2-c905-4aa0-b365-2a2f67b4fb49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95533ca2-c905-4aa0-b365-2a2f67b4fb49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-595a97d3-7fe8-4bd0-a36e-f4c82593f25d' class='xr-var-data-in' type='checkbox'><label for='data-595a97d3-7fe8-4bd0-a36e-f4c82593f25d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 7.58 kiB </td>\n",
+       "                        <td> 7.58 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 970, 2) </td>\n",
+       "                        <td> (1, 970, 2) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 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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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-a8ad1d1d-ca24-4eb1-a2f8-d435a0e64b2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8ad1d1d-ca24-4eb1-a2f8-d435a0e64b2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6975bdb7-6c39-4ba0-b1ac-ef9eaa847f74' class='xr-var-data-in' type='checkbox'><label for='data-6975bdb7-6c39-4ba0-b1ac-ef9eaa847f74' 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>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>1</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> 127.24 MiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 1, 128, 128) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2376 chunks in 4 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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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_from_direction</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-0dd248e8-3389-4acc-8ab7-27d03048c48a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dd248e8-3389-4acc-8ab7-27d03048c48a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e79439f-9d1e-49e5-a385-74e271563535' class='xr-var-data-in' type='checkbox'><label for='data-7e79439f-9d1e-49e5-a385-74e271563535' 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>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 127.24 MiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 1, 128, 128) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2376 chunks in 4 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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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 128, 128), meta=np.ndarray&gt;</div><input id='attrs-904bef8a-6298-4a80-b85c-8400ac44f84c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-904bef8a-6298-4a80-b85c-8400ac44f84c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28cabb0b-2533-4ab1-9213-b4314f59f562' class='xr-var-data-in' type='checkbox'><label for='data-28cabb0b-2533-4ab1-9213-b4314f59f562' 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>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <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> 127.24 MiB </td>\n",
+       "                        <td> 64.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 33, 970, 1042) </td>\n",
+       "                        <td> (1, 1, 128, 128) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2376 chunks in 4 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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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-56f78a4f-017c-4021-a436-9b79e8524266' class='xr-section-summary-in' type='checkbox' /><label for='section-56f78a4f-017c-4021-a436-9b79e8524266' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7, UKMO-1.0</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>history :</span></dt><dd>2026-06-01T01:18:41Z: StaGE Decoupler</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT54H</dd><dt><span>mosg__grid_domain :</span></dt><dd>uk_extended</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.7.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>uk_det</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>UKV Model Forecast on UK 2 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>13.8</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2026-06-01T12:00:00\n",
+       "    forecast_reference_time       (time) datetime64[ns] 8B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    forecast_period               timedelta64[ns] 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "    flag                          (time, pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB dask.array<chunksize=(1, 17, 485, 521), meta=np.ndarray>\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB dask.array<chunksize=(1, 1042, 2), meta=np.ndarray>\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB dask.array<chunksize=(1, 970, 2), meta=np.ndarray>\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB dask.array<chunksize=(1, 1, 128, 128), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0\n",
+       "    ancillary_variables:          flag\n",
+       "    history:                      2026-06-01T01:18:41Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT54H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.8"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zarr_accessor['2026-06-01 12:00']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2361fe01-5f99-499f-aa30-8f830af4ff6e",
+   "metadata": {},
+   "source": [
+    "## Building a remote accessor\n",
+    "PyEarthTools doesn't care where we the data is coming from. Since the data is being loaded through xarray, we can make use of the fact that xarray uses the [fsspec](https://filesystem-spec.readthedocs.io/en/latest/) library to load the data as we did at the start of this notebook from within our accessor. We need to add some extra functionality to our acessor though, because the default load function options won't work with the S3 bucket. We need to pass extra arguments, so we implement the `load` function so we can call the `open_dataset` function ourselves. What this really demonstrates is that we could be loading the data from any source, such as an API or other remote source. So long as the `load` function returns an xarray dataset, then the accessor can be abstracting away the loading of data from any source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "848b07b2-cd88-41bb-90cf-f201cf0934f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MO_UKV_AWS(ArchiveIndex):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    MO_UKV_AWS_ROOT_PATH = 's3://met-office-atmospheric-model-data/uk-deterministic-2km/'\n",
+    "    MO_UKV_AWS_DIR_TEMPLATE = MO_UKV_AWS_ROOT_PATH + '{vt_str}'\n",
+    "    MO_UKV_FNAME_TEMPLATE = '{vt_str}-PT0000H00M-{var_name}.nc'\n",
+    "    \n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        variables: list[str] | str,\n",
+    "        *,\n",
+    "        transforms: Transform | TransformCollection | None = None,\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Init function for our data index class. This gathers together the key parameters fof the data to enable subsequent loading. \n",
+    "        This is the same as for the local accessor, but pointing to different paths for the files.\n",
+    "        \"\"\"\n",
+    "        self._variables = variables\n",
+    "        super_transforms = TransformCollection([pyearthtools.data.transforms.variables.Trim(self._variables),]) + transforms\n",
+    "        \n",
+    "        # call the base class\n",
+    "        super().__init__(\n",
+    "            transforms=super_transforms,\n",
+    "        )\n",
+    "        self.record_initialisation()\n",
+    "\n",
+    "    def filesystem(\n",
+    "        self,\n",
+    "        querytime: str | Petdt,\n",
+    "    ) -> pathlib.Path | dict[str, str | pathlib.Path]:\n",
+    "        \"\"\"\n",
+    "        This function provides the paths to the data as shown previously, excpet now the paths will be in the fsspec format pointing to data in S3 buckets, \n",
+    "        rather than fomratted as standard POSIX paths.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        querytime = Petdt(querytime)\n",
+    "        paths = []\n",
+    "        \n",
+    "        \n",
+    "        for var_name in self._variables:\n",
+    "            try:\n",
+    "                current_fname = MO_UKV.MO_UKV_FNAME_TEMPLATE.format(vt_str=vt_template.format(dt=querytime), var_name=var_name)\n",
+    "                current_dir = MO_UKV.MO_UKV_AWS_DIR_TEMPLATE.format(vt_str=vt_template.format(dt=querytime))\n",
+    "                current_path = f'{current_dir}/{current_fname}'\n",
+    "                paths += [ current_path ]\n",
+    "            except KeyError:\n",
+    "                print(f'No data for var {var_name}')\n",
+    "        \n",
+    "        print(paths)\n",
+    "        return paths\n",
+    "\n",
+    "    def load(self, *args, **kwargs):\n",
+    "        \"\"\"\n",
+    "        The `load` function is the main change for the index class. This is because we need to supply additional arguments to xarray to load directly \n",
+    "        from S3, so we call the load and merge functions manually here and pass the Xarray Dataset object back to the PyEarthTools framework for further\n",
+    "        processing, so as in a pipeline.\n",
+    "        \"\"\"\n",
+    "        ds = xarray.merge([xarray.open_dataset(path1, ** open_args ) for path1 in  args[0]])\n",
+    "        return ds\n",
+    "        \n",
+    "        return ds\n",
+    "    def __desc__(self):\n",
+    "        return {\n",
+    "            \"singleline\": \"Met Office UKV Forecast Analysis \",\n",
+    "            \"range\": \"June 2026\",\n",
+    "            \"Documentation\": \"https://registry.opendata.aws/met-office-uk-deterministic/\",\n",
+    "        }\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8e38bbb5-c9a9-4364-81fb-d0f03cdf5604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukv_aws_accessor = MO_UKV_AWS(var_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9fc73921-d2cf-47c6-99ef-726830f532b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['s3://met-office-atmospheric-model-data/uk-deterministic-2km/20250412T1500Z/20250412T1500Z-PT0000H00M-temperature_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/uk-deterministic-2km/20250412T1500Z/20250412T1500Z-PT0000H00M-relative_humidity_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/uk-deterministic-2km/20250412T1500Z/20250412T1500Z-PT0000H00M-wind_speed_on_pressure_levels.nc', 's3://met-office-atmospheric-model-data/uk-deterministic-2km/20250412T1500Z/20250412T1500Z-PT0000H00M-wind_direction_on_pressure_levels.nc']\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_data = ukv_aws_accessor[datetime.datetime(2025,4,12,15,0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6547cef5-f967-423b-8f5d-527cc33278bd",
+   "metadata": {},
+   "outputs": [
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+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2025-04-12T15:00:00\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    flag                          (pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB ...\n",
+       "    forecast_period               timedelta64[ns] 8B 00:00:00\n",
+       "    forecast_reference_time       datetime64[ns] 8B 2025-04-12T15:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB ...\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB ...\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "Attributes:\n",
+       "    history:                      2025-04-12T16:05:19Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT120H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.1\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0</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-67c1ed4e-4a06-46d6-8bdc-d8c377e4ec96' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-67c1ed4e-4a06-46d6-8bdc-d8c377e4ec96' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>pressure</span>: 33</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 970</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 1042</li><li><span>bnds</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-98a024fc-d260-4578-aac6-5b1fb69d4cf5' class='xr-section-summary-in' type='checkbox' checked /><label for='section-98a024fc-d260-4578-aac6-5b1fb69d4cf5' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-04-12T15:00:00</div><input id='attrs-edb30b2f-5fe0-4db0-b853-b6ebed636594' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edb30b2f-5fe0-4db0-b853-b6ebed636594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4173081-7854-40d0-b732-868741d8d4d6' class='xr-var-data-in' type='checkbox'><label for='data-b4173081-7854-40d0-b732-868741d8d4d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-04-12T15:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.75e+04 ... 2e+03 1e+03</div><input id='attrs-83da5d61-0f9d-4161-a6d7-54a8194d6e28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83da5d61-0f9d-4161-a6d7-54a8194d6e28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c7b8aa2-19b6-4f04-a2fd-2bdc85095bec' class='xr-var-data-in' type='checkbox'><label for='data-1c7b8aa2-19b6-4f04-a2fd-2bdc85095bec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  97500.,  95000.,  92500.,  90000.,  85000.,  80000.,  75000.,\n",
+       "        70000.,  65000.,  60000.,  55000.,  50000.,  45000.,  40000.,  37500.,\n",
+       "        35000.,  32500.,  30000.,  27500.,  25000.,  22500.,  20000.,  17500.,\n",
+       "        15000.,  12500.,  10000.,   7000.,   5000.,   4000.,   3000.,   2000.,\n",
+       "         1000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.036e+06 -1.034e+06 ... 9.02e+05</div><input id='attrs-e90d2555-ca6e-49c9-b198-543e3ea3340a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e90d2555-ca6e-49c9-b198-543e3ea3340a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-443a7e6e-0cfb-412b-8ef2-4eadb1a75738' class='xr-var-data-in' type='checkbox'><label for='data-443a7e6e-0cfb-412b-8ef2-4eadb1a75738' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.036e+06, -1.034e+06, -1.032e+06, ...,  8.980e+05,  9.000e+05,\n",
+       "        9.020e+05], shape=(970,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.158e+06 -1.156e+06 ... 9.24e+05</div><input id='attrs-468fc80e-07ce-4cef-b59f-9f444b86f044' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-468fc80e-07ce-4cef-b59f-9f444b86f044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e98eea18-1022-4d3c-96a6-58624a7b67fb' class='xr-var-data-in' type='checkbox'><label for='data-e98eea18-1022-4d3c-96a6-58624a7b67fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1.158e+06, -1.156e+06, -1.154e+06, ...,  9.200e+05,  9.220e+05,\n",
+       "        9.240e+05], shape=(1042,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-a6e76ace-0f2e-4062-abe0-721c6fa8534e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6e76ace-0f2e-4062-abe0-721c6fa8534e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-273374a3-15e5-43c2-b62b-d3cb5860dadd' class='xr-var-data-in' type='checkbox'><label for='data-273374a3-15e5-43c2-b62b-d3cb5860dadd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature status_flag</dd><dt><span>flag_meanings :</span></dt><dd>above_surface_pressure below_surface_pressure</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd></dl></div><div class='xr-var-data'><pre>array([[[0, 0, 0, ..., 1, 1, 1],\n",
+       "        [0, 0, 0, ..., 1, 1, 1],\n",
+       "        [0, 0, 0, ..., 1, 1, 1],\n",
+       "        ...,\n",
+       "        [1, 1, 1, ..., 1, 1, 1],\n",
+       "        [1, 1, 1, ..., 1, 1, 1],\n",
+       "        [1, 1, 1, ..., 1, 1, 1]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 1, 1, 1],\n",
+       "        [0, 0, 0, ..., 1, 1, 1],\n",
+       "        [0, 0, 0, ..., 0, 1, 1],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 1, 1, 1],\n",
+       "        [0, 0, 0, ..., 1, 1, 1],\n",
+       "        [0, 0, 0, ..., 1, 1, 1]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]]], shape=(33, 970, 1042), dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-d033b663-6a4e-4ced-9fab-741883c9cc99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d033b663-6a4e-4ced-9fab-741883c9cc99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0171f0e-5b56-464f-8741-8ab6e8e0c571' class='xr-var-data-in' type='checkbox'><label for='data-b0171f0e-5b56-464f-8741-8ab6e8e0c571' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-04-12T15:00:00</div><input id='attrs-322afe90-d97f-494d-9fcb-2edf06174eb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-322afe90-d97f-494d-9fcb-2edf06174eb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9a2003e-886a-4ce6-a3b7-c17611e17548' class='xr-var-data-in' type='checkbox'><label for='data-b9a2003e-886a-4ce6-a3b7-c17611e17548' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2025-04-12T15:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2319d1ec-4c61-437a-af27-01d170360de4' class='xr-section-summary-in' type='checkbox' checked /><label for='section-2319d1ec-4c61-437a-af27-01d170360de4' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</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>air_temperature</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>284.6 284.6 284.6 ... 226.9 226.9</div><input id='attrs-21a7ca58-52d1-4086-a9c5-c3b6cbc6b974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21a7ca58-52d1-4086-a9c5-c3b6cbc6b974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea9fd303-dc71-4de2-bcab-1ba70493a761' class='xr-var-data-in' type='checkbox'><label for='data-ea9fd303-dc71-4de2-bcab-1ba70493a761' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd></dl></div><div class='xr-var-data'><pre>array([[[[284.625 , 284.625 , 284.625 , ..., 290.5   , 290.625 ,\n",
+       "          290.8125],\n",
+       "         [284.625 , 284.625 , 284.625 , ..., 290.625 , 290.75  ,\n",
+       "          290.875 ],\n",
+       "         [284.625 , 284.625 , 284.625 , ..., 290.6875, 290.9375,\n",
+       "          291.    ],\n",
+       "         ...,\n",
+       "         [276.6875, 276.875 , 277.0625, ..., 282.75  , 282.6875,\n",
+       "          282.625 ],\n",
+       "         [276.6875, 276.875 , 277.0625, ..., 282.6875, 282.625 ,\n",
+       "          282.5625],\n",
+       "         [276.6875, 276.875 , 277.0625, ..., 282.5625, 282.5625,\n",
+       "          282.5   ]],\n",
+       "\n",
+       "        [[282.625 , 282.625 , 282.625 , ..., 289.125 , 289.1875,\n",
+       "          289.375 ],\n",
+       "         [282.5625, 282.625 , 282.625 , ..., 289.3125, 289.375 ,\n",
+       "          289.5   ],\n",
+       "         [282.5625, 282.625 , 282.625 , ..., 289.5625, 289.5625,\n",
+       "          289.6875],\n",
+       "...\n",
+       "         [220.75  , 220.75  , 220.75  , ..., 223.125 , 223.125 ,\n",
+       "          223.125 ],\n",
+       "         [220.75  , 220.75  , 220.75  , ..., 223.125 , 223.125 ,\n",
+       "          223.125 ],\n",
+       "         [220.75  , 220.75  , 220.75  , ..., 223.125 , 223.125 ,\n",
+       "          223.125 ]],\n",
+       "\n",
+       "        [[228.25  , 228.25  , 228.25  , ..., 228.5   , 228.5   ,\n",
+       "          228.5   ],\n",
+       "         [228.25  , 228.25  , 228.25  , ..., 228.5   , 228.5   ,\n",
+       "          228.5   ],\n",
+       "         [228.25  , 228.25  , 228.25  , ..., 228.5   , 228.5   ,\n",
+       "          228.5   ],\n",
+       "         ...,\n",
+       "         [223.375 , 223.375 , 223.375 , ..., 226.875 , 226.875 ,\n",
+       "          226.875 ],\n",
+       "         [223.375 , 223.375 , 223.375 , ..., 226.875 , 226.875 ,\n",
+       "          226.875 ],\n",
+       "         [223.3125, 223.3125, 223.3125, ..., 226.875 , 226.875 ,\n",
+       "          226.875 ]]]], shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_azimuthal_equal_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647</div><input id='attrs-51222306-5ea1-477b-a2b4-334986fe0c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51222306-5ea1-477b-a2b4-334986fe0c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b5fc9a3-33e0-463a-b387-f8705f663a30' class='xr-var-data-in' type='checkbox'><label for='data-1b5fc9a3-33e0-463a-b387-f8705f663a30' 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>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>-2.5</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>54.9</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.037e+06 -1.035e+06 ... 9.03e+05</div><input id='attrs-451574ed-1cbf-4303-af4f-5f8b91f1f537' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-451574ed-1cbf-4303-af4f-5f8b91f1f537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b7e33a4-a1c8-4c3f-bbff-2543ac290f59' class='xr-var-data-in' type='checkbox'><label for='data-5b7e33a4-a1c8-4c3f-bbff-2543ac290f59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.037e+06, -1.035e+06],\n",
+       "        [-1.035e+06, -1.033e+06],\n",
+       "        [-1.033e+06, -1.031e+06],\n",
+       "        ...,\n",
+       "        [ 8.970e+05,  8.990e+05],\n",
+       "        [ 8.990e+05,  9.010e+05],\n",
+       "        [ 9.010e+05,  9.030e+05]]], shape=(1, 970, 2), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(time, projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.159e+06 -1.157e+06 ... 9.25e+05</div><input id='attrs-357bbc8a-f6ee-4f72-9c7e-10658ad593a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-357bbc8a-f6ee-4f72-9c7e-10658ad593a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-687485d3-9bc9-4927-b73a-b9818d72a415' class='xr-var-data-in' type='checkbox'><label for='data-687485d3-9bc9-4927-b73a-b9818d72a415' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.159e+06, -1.157e+06],\n",
+       "        [-1.157e+06, -1.155e+06],\n",
+       "        [-1.155e+06, -1.153e+06],\n",
+       "        ...,\n",
+       "        [ 9.190e+05,  9.210e+05],\n",
+       "        [ 9.210e+05,  9.230e+05],\n",
+       "        [ 9.230e+05,  9.250e+05]]], shape=(1, 1042, 2), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6992 0.7021 0.7041 ... 0.0 0.0</div><input id='attrs-d8f7521e-bca7-4ba3-a32f-901f8fdb6e7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8f7521e-bca7-4ba3-a32f-901f8fdb6e7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbe2a42e-c9c3-48a0-b624-d46c5466e5c6' class='xr-var-data-in' type='checkbox'><label for='data-bbe2a42e-c9c3-48a0-b624-d46c5466e5c6' 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>least_significant_digit :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.69921875, 0.70214844, 0.70410156, ..., 0.6220703 ,\n",
+       "          0.6376953 , 0.63964844],\n",
+       "         [0.6972656 , 0.69921875, 0.7011719 , ..., 0.6123047 ,\n",
+       "          0.6269531 , 0.625     ],\n",
+       "         [0.69433594, 0.6972656 , 0.69921875, ..., 0.60253906,\n",
+       "          0.6152344 , 0.61035156],\n",
+       "         ...,\n",
+       "         [0.5546875 , 0.54296875, 0.5283203 , ..., 0.4970703 ,\n",
+       "          0.4892578 , 0.48046875],\n",
+       "         [0.5566406 , 0.546875  , 0.5341797 , ..., 0.5       ,\n",
+       "          0.4921875 , 0.48242188],\n",
+       "         [0.55566406, 0.546875  , 0.5341797 , ..., 0.4970703 ,\n",
+       "          0.4892578 , 0.47851562]],\n",
+       "\n",
+       "        [[0.76464844, 0.7675781 , 0.7705078 , ..., 0.62109375,\n",
+       "          0.6376953 , 0.63964844],\n",
+       "         [0.76171875, 0.76464844, 0.7675781 , ..., 0.60839844,\n",
+       "          0.6269531 , 0.625     ],\n",
+       "         [0.75878906, 0.76171875, 0.76464844, ..., 0.5957031 ,\n",
+       "          0.6142578 , 0.609375  ],\n",
+       "...\n",
+       "          0.00097656, 0.00097656],\n",
+       "         [0.00292969, 0.00292969, 0.00292969, ..., 0.00097656,\n",
+       "          0.00097656, 0.00097656],\n",
+       "         [0.00292969, 0.00292969, 0.00292969, ..., 0.00097656,\n",
+       "          0.00097656, 0.00097656]],\n",
+       "\n",
+       "        [[0.        , 0.        , 0.        , ..., 0.        ,\n",
+       "          0.        , 0.        ],\n",
+       "         [0.        , 0.        , 0.        , ..., 0.        ,\n",
+       "          0.        , 0.        ],\n",
+       "         [0.        , 0.        , 0.        , ..., 0.        ,\n",
+       "          0.        , 0.        ],\n",
+       "         ...,\n",
+       "         [0.00097656, 0.00097656, 0.00097656, ..., 0.        ,\n",
+       "          0.        , 0.        ],\n",
+       "         [0.00097656, 0.00097656, 0.00097656, ..., 0.        ,\n",
+       "          0.        , 0.        ],\n",
+       "         [0.00097656, 0.00097656, 0.00097656, ..., 0.        ,\n",
+       "          0.        , 0.        ]]]],\n",
+       "      shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.81 11.88 11.94 ... 15.81 15.88</div><input id='attrs-13286bd6-f5b9-42e1-9e06-c047b33be22c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13286bd6-f5b9-42e1-9e06-c047b33be22c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0edf06bb-d1f1-48dd-a4ae-797d845353cd' class='xr-var-data-in' type='checkbox'><label for='data-0edf06bb-d1f1-48dd-a4ae-797d845353cd' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd></dl></div><div class='xr-var-data'><pre>array([[[[11.8125, 11.875 , 11.9375, ...,  1.5625,  1.125 ,  0.8125],\n",
+       "         [11.6875, 11.75  , 11.875 , ...,  1.75  ,  1.0625,  0.625 ],\n",
+       "         [11.6875, 11.6875, 11.75  , ...,  1.9375,  1.0625,  0.4375],\n",
+       "         ...,\n",
+       "         [ 7.875 ,  8.    ,  7.75  , ...,  1.875 ,  1.875 ,  1.875 ],\n",
+       "         [ 7.8125,  8.    ,  7.75  , ...,  1.875 ,  1.875 ,  1.9375],\n",
+       "         [ 7.75  ,  7.9375,  7.75  , ...,  2.    ,  2.    ,  2.    ]],\n",
+       "\n",
+       "        [[11.625 , 11.6875, 11.75  , ...,  2.0625,  1.3125,  0.8125],\n",
+       "         [11.5625, 11.625 , 11.6875, ...,  2.75  ,  1.5   ,  0.625 ],\n",
+       "         [11.5   , 11.5625, 11.625 , ...,  3.75  ,  1.8125,  0.4375],\n",
+       "         ...,\n",
+       "         [ 9.875 , 10.125 ,  9.9375, ...,  1.875 ,  1.875 ,  1.875 ],\n",
+       "         [ 9.875 , 10.125 ,  9.9375, ...,  1.875 ,  1.875 ,  1.9375],\n",
+       "         [ 9.8125, 10.125 ,  9.9375, ...,  2.    ,  2.    ,  2.    ]],\n",
+       "\n",
+       "        [[11.125 , 11.25  , 11.375 , ...,  3.9375,  2.75  ,  1.75  ],\n",
+       "         [11.    , 11.125 , 11.25  , ...,  4.375 ,  2.625 ,  1.25  ],\n",
+       "         [10.9375, 11.0625, 11.125 , ...,  4.5   ,  2.375 ,  0.75  ],\n",
+       "         ...,\n",
+       "...\n",
+       "         [ 4.5   ,  4.5   ,  4.5   , ...,  7.625 ,  7.625 ,  7.625 ],\n",
+       "         [ 4.5   ,  4.5   ,  4.5   , ...,  7.625 ,  7.625 ,  7.625 ],\n",
+       "         [ 4.5   ,  4.5   ,  4.5   , ...,  7.625 ,  7.625 ,  7.625 ]],\n",
+       "\n",
+       "        [[ 5.375 ,  5.375 ,  5.375 , ...,  9.25  ,  9.3125,  9.375 ],\n",
+       "         [ 5.375 ,  5.375 ,  5.375 , ...,  9.25  ,  9.3125,  9.375 ],\n",
+       "         [ 5.3125,  5.3125,  5.3125, ...,  9.3125,  9.375 ,  9.375 ],\n",
+       "         ...,\n",
+       "         [ 5.875 ,  5.8125,  5.75  , ..., 11.875 , 11.875 , 11.8125],\n",
+       "         [ 5.8125,  5.8125,  5.75  , ..., 11.9375, 11.875 , 11.8125],\n",
+       "         [ 5.8125,  5.75  ,  5.75  , ..., 11.9375, 11.9375, 11.875 ]],\n",
+       "\n",
+       "        [[ 3.4375,  3.4375,  3.4375, ..., 10.875 , 10.9375, 11.    ],\n",
+       "         [ 3.375 ,  3.375 ,  3.375 , ..., 10.9375, 10.9375, 11.    ],\n",
+       "         [ 3.4375,  3.4375,  3.4375, ..., 10.9375, 11.    , 11.    ],\n",
+       "         ...,\n",
+       "         [ 8.625 ,  8.625 ,  8.625 , ..., 15.75  , 15.8125, 15.8125],\n",
+       "         [ 8.625 ,  8.625 ,  8.625 , ..., 15.75  , 15.8125, 15.875 ],\n",
+       "         [ 8.625 ,  8.625 ,  8.625 , ..., 15.8125, 15.8125, 15.875 ]]]],\n",
+       "      shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_from_direction</span></div><div class='xr-var-dims'>(time, pressure, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>336.9 336.9 336.8 ... 108.9 108.9</div><input id='attrs-6525b7b9-1005-4321-ad05-36a9f8c06d67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6525b7b9-1005-4321-ad05-36a9f8c06d67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a7c93e8-5ccc-4c9b-a30b-e35519e8e019' class='xr-var-data-in' type='checkbox'><label for='data-9a7c93e8-5ccc-4c9b-a30b-e35519e8e019' 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>least_significant_digit :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>ancillary_variables :</span></dt><dd>flag</dd><dt><span>grid_mapping :</span></dt><dd>lambert_azimuthal_equal_area</dd></dl></div><div class='xr-var-data'><pre>array([[[[336.9375, 336.875 , 336.8125, ..., 269.8125, 282.5625,\n",
+       "          302.25  ],\n",
+       "         [336.6875, 336.5   , 336.3125, ..., 268.3125, 279.5   ,\n",
+       "          304.5625],\n",
+       "         [336.25  , 336.0625, 335.8125, ..., 269.5625, 276.625 ,\n",
+       "          302.875 ],\n",
+       "         ...,\n",
+       "         [229.3125, 228.3125, 225.1875, ..., 311.5   , 311.4375,\n",
+       "          311.875 ],\n",
+       "         [231.125 , 230.375 , 228.125 , ..., 314.3125, 314.25  ,\n",
+       "          314.5   ],\n",
+       "         [233.0625, 232.5   , 231.1875, ..., 319.4375, 319.4375,\n",
+       "          318.625 ]],\n",
+       "\n",
+       "        [[337.25  , 337.1875, 337.1875, ..., 266.25  , 278.3125,\n",
+       "          302.25  ],\n",
+       "         [337.    , 336.8125, 336.625 , ..., 264.    , 273.0625,\n",
+       "          304.5625],\n",
+       "         [336.5625, 336.375 , 336.125 , ..., 264.4375, 269.1875,\n",
+       "          302.875 ],\n",
+       "...\n",
+       "         [111.3125, 111.5   , 111.5625, ...,  95.375 ,  95.8125,\n",
+       "           96.1875],\n",
+       "         [111.4375, 111.5625, 111.5625, ...,  95.5625,  95.875 ,\n",
+       "           96.1875],\n",
+       "         [111.5625, 111.5625, 111.5625, ...,  95.5625,  95.875 ,\n",
+       "           96.1875]],\n",
+       "\n",
+       "        [[127.25  , 126.0625, 124.875 , ...,  87.9375,  87.625 ,\n",
+       "           87.3125],\n",
+       "         [128.875 , 127.5625, 126.25  , ...,  87.9375,  87.625 ,\n",
+       "           87.3125],\n",
+       "         [128.25  , 127.625 , 127.    , ...,  87.9375,  87.625 ,\n",
+       "           87.3125],\n",
+       "         ...,\n",
+       "         [125.5625, 125.5625, 125.5625, ..., 109.    , 108.9375,\n",
+       "          108.9375],\n",
+       "         [125.5625, 125.5625, 125.4375, ..., 108.9375, 108.875 ,\n",
+       "          108.875 ],\n",
+       "         [125.5625, 125.5625, 125.375 , ..., 108.875 , 108.875 ,\n",
+       "          108.875 ]]]], shape=(1, 33, 970, 1042), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a7278e9-7eda-4f34-9519-7834185038bf' class='xr-section-summary-in' type='checkbox' /><label for='section-7a7278e9-7eda-4f34-9519-7834185038bf' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>2025-04-12T16:05:19Z: StaGE Decoupler</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT120H</dd><dt><span>mosg__grid_domain :</span></dt><dd>uk_extended</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.7.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>uk_det</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>UKV Model Forecast on UK 2 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>13.1</dd><dt><span>Conventions :</span></dt><dd>CF-1.7, UKMO-1.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 567MB\n",
+       "Dimensions:                       (time: 1, pressure: 33,\n",
+       "                                   projection_y_coordinate: 970,\n",
+       "                                   projection_x_coordinate: 1042, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 8B 2025-04-12T15:00:00\n",
+       "  * pressure                      (pressure) float32 132B 1e+05 ... 1e+03\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float32 4kB -1.03...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float32 4kB -1.15...\n",
+       "    flag                          (pressure, projection_y_coordinate, projection_x_coordinate) int8 33MB ...\n",
+       "    forecast_period               timedelta64[ns] 8B 00:00:00\n",
+       "    forecast_reference_time       datetime64[ns] 8B 2025-04-12T15:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    air_temperature               (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    lambert_azimuthal_equal_area  (time) int32 4B -2147483647\n",
+       "    projection_y_coordinate_bnds  (time, projection_y_coordinate, bnds) float32 8kB ...\n",
+       "    projection_x_coordinate_bnds  (time, projection_x_coordinate, bnds) float32 8kB ...\n",
+       "    relative_humidity             (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_speed                    (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "    wind_from_direction           (time, pressure, projection_y_coordinate, projection_x_coordinate) float32 133MB ...\n",
+       "Attributes:\n",
+       "    history:                      2025-04-12T16:05:19Z: StaGE Decoupler\n",
+       "    institution:                  Met Office\n",
+       "    mosg__forecast_run_duration:  PT120H\n",
+       "    mosg__grid_domain:            uk_extended\n",
+       "    mosg__grid_type:              standard\n",
+       "    mosg__grid_version:           1.7.0\n",
+       "    mosg__model_configuration:    uk_det\n",
+       "    source:                       Met Office Unified Model\n",
+       "    title:                        UKV Model Forecast on UK 2 km Standard Grid\n",
+       "    um_version:                   13.1\n",
+       "    Conventions:                  CF-1.7, UKMO-1.0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "5ce8945f-e205-459e-8425-dcb1ead0e754",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f534c2969f0>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = matplotlib.pyplot.figure(figsize=(18,6))\n",
+    "# plot temperature at 850hpa pressure level\n",
+    "ax1 = fig1.add_subplot(1,3,1,projection=cartopy.crs.OSGB() )\n",
+    "sample_data.sel({'pressure':85000})['air_temperature'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,2,projection=cartopy.crs.OSGB() )\n",
+    "\n",
+    "sample_data.sel({'pressure':85000})['relative_humidity'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1,3,3,projection=cartopy.crs.OSGB() )\n",
+    "sample_data.sel({'pressure':85000})['wind_speed'][0].plot.contourf(\n",
+    "    ax=ax1,\n",
+    "    transform=data_projection,\n",
+    ")\n",
+    "ax1.coastlines()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7fcbaa-7545-44f4-9fc1-63fab78dcad5",
+   "metadata": {},
+   "source": [
+    "### Further Reading and Examples\n",
+    "* [Met Office data on AWS and other platforms](https://www.metoffice.gov.uk/services/data/external-data-channels#ASDI)\n",
+    "  * [MO Global on AWS](https://registry.opendata.aws/met-office-global-deterministic/)\n",
+    "  * [MO Global Documentation](https://www.metoffice.gov.uk/api/assets/file/global-10km-ps47-asdi-pdf-updates-20pdf?prefix=assets)\n",
+    "  * [UKV on AWS](https://registry.opendata.aws/met-office-uk-deterministic/)\n",
+    "  * [UKV Documentation](https://www.metoffice.gov.uk/api/assets/file/ukv-2km-ps47-asdi-pdf-updatespdf?prefix=assets)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pet_dev_nb_gpu",
+   "language": "python",
+   "name": "pet_dev_nb_gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/data/Remote_Zarr_Loading.ipynb b/docs/notebooks/data/Remote_Zarr_Loading.ipynb
index 6d20de89..1472056a 100644
--- a/docs/notebooks/data/Remote_Zarr_Loading.ipynb
+++ b/docs/notebooks/data/Remote_Zarr_Loading.ipynb
@@ -1,903 +1,904 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/stevehadd/PyEarthTools/blob/ew4_tutorial_further_fixes/docs/notebooks/data/Remote_Zarr_Loading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6aaad6b9",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/stevehadd/PyEarthTools/blob/ew4_tutorial_further_fixes/docs/notebooks/data/Remote_Zarr_Loading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2_Nnfj2Dr-Zw",
+   "metadata": {
+    "id": "2_Nnfj2Dr-Zw"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install pyearthtools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98164f08-4b2f-4600-b7d5-b4cc4284aac6",
+   "metadata": {
+    "id": "98164f08-4b2f-4600-b7d5-b4cc4284aac6"
+   },
+   "source": [
+    "# Loading a Remote Zarr file - ERA5 ARCO example\n",
+    "\n",
+    "[Zarr](https://zarr.readthedocs.io/) is an increasingly popular format for storing gridded environmental science data, especially when working on cloud environments. The nature of a zarr, with consolidated metadat which enables interaction with a dataset as a unified whole, data divided into separate files called *chunks* to enable parallel read and write, which suits a massively parallel distributed file system like the object stores used by cloud providers, makes it efficient and intuive for many machine-learning platforms and workflows.\n",
+    "\n",
+    "PyEarthTools provides a data accessor class for zarr files. As PyEarthTools uses xarray for data I/O, which through fsspec supports loading data directly from remote sources, it can load data from such sources.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f644ae8b-32dd-49c8-9c14-0a32c0840ac6",
+   "metadata": {
+    "id": "f644ae8b-32dd-49c8-9c14-0a32c0840ac6"
+   },
+   "source": [
+    "### Set up imports for demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3f48f0f-6e4d-40d3-a88a-625f0d5a5b7d",
+   "metadata": {
+    "id": "c3f48f0f-6e4d-40d3-a88a-625f0d5a5b7d"
+   },
+   "outputs": [],
+   "source": [
+    "import xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e140303-a282-41e8-8424-9547f9159c26",
+   "metadata": {
+    "id": "4e140303-a282-41e8-8424-9547f9159c26"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot\n",
+    "import cartopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e4d3dc7-e81a-40c3-b604-db49bf43847e",
+   "metadata": {
+    "id": "4e4d3dc7-e81a-40c3-b604-db49bf43847e"
+   },
+   "outputs": [],
+   "source": [
+    "import pyearthtools.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab4e3a1d-770b-4cd1-a57d-c678d1af20d6",
+   "metadata": {
+    "id": "ab4e3a1d-770b-4cd1-a57d-c678d1af20d6"
+   },
+   "outputs": [],
+   "source": [
+    "import pyearthtools.pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51c6e404-a86f-4448-b9d2-37bb2b60f6cb",
+   "metadata": {
+    "id": "51c6e404-a86f-4448-b9d2-37bb2b60f6cb"
+   },
+   "source": [
+    "## Set up the accessor\n",
+    "\n",
+    "Fore this demo we will be using the copy of the [ECMWF ERA5 Reanalsyis dataset](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview) hosted by [Google Research](https://github.com/google-research/arco-era5) on the Google Cloud Storage system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ded0ad5-7561-4755-82de-deee4f4a5417",
+   "metadata": {
+    "id": "5ded0ad5-7561-4755-82de-deee4f4a5417"
+   },
+   "outputs": [],
+   "source": [
+    "remote_url = 'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2190d29c-899e-4f0d-8184-7db042c07d4a",
+   "metadata": {
+    "id": "2190d29c-899e-4f0d-8184-7db042c07d4a"
+   },
+   "outputs": [],
+   "source": [
+    "zarr_opts = {'chunks': None, 'storage_options': {'token': 'anon'}}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15ccfd12-75e7-404e-a623-065a8fc389f9",
+   "metadata": {
+    "id": "15ccfd12-75e7-404e-a623-065a8fc389f9"
+   },
+   "source": [
+    "Usually we are only interested in a subset of the data, so we can select a particular time, pressure levels and set of variables that we want to us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1760e80f-c43c-4c97-9242-69c930fed623",
+   "metadata": {
+    "id": "1760e80f-c43c-4c97-9242-69c930fed623"
+   },
+   "outputs": [],
+   "source": [
+    "select_variables = ['temperature', 'specific_humidity']\n",
+    "select_pressure_levels = [200, 500, 700, 850, 1000]\n",
+    "select_time = ('2024-01-01','2025-01-01')\n",
+    "select_region = {'latitude': (-40,-5),\n",
+    "                 'longitude': (110,155)\n",
+    "                }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "394c417d-7a85-4a8f-917c-5c32f6398437",
+   "metadata": {
+    "id": "394c417d-7a85-4a8f-917c-5c32f6398437"
+   },
+   "outputs": [],
+   "source": [
+    "select_dict = {\n",
+    "    'level': select_pressure_levels,\n",
+    "    'time': slice(*select_time),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bac39d42-cc5a-44ca-b0a1-908f61df54a4",
+   "metadata": {
+    "id": "bac39d42-cc5a-44ca-b0a1-908f61df54a4"
+   },
+   "outputs": [],
+   "source": [
+    "era5_accessor = pyearthtools.data.archive.ZarrTimeIndex(\n",
+    "       remote_url,\n",
+    "       variables=select_variables,\n",
+    "       open_kwargs=zarr_opts,\n",
+    "    remote=True,\n",
+    "    transforms=pyearthtools.data.transforms.TransformCollection([\n",
+    "        pyearthtools.data.transform.coordinates.Select(select_dict),\n",
+    "        pyearthtools.data.transform.region.Bounding(select_region['latitude'][0],select_region['latitude'][1], select_region['longitude'][0], select_region['longitude'][1])\n",
+    "    ]),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a39b7a-0a19-4db5-8363-5055c735076f",
+   "metadata": {
+    "id": "74a39b7a-0a19-4db5-8363-5055c735076f"
+   },
+   "source": [
+    "## Using the data of interest\n",
+    "We can now look at the xarray dataset through the time index function. If we do this, the xarray lazy loading paradigm means only the metadata will have been downloaded form the remote location, until we try to do some thing which requires the data. For example after exmaing the metadata, we will then plot some of the data which will take longer as the data will be transfered from the remote location. This will be quite efficient as only the chunks pertaining to the data selected (variables, time period, spatial region) will be transferred."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdfe8f43-d089-4724-aae0-d1dec734c5d2",
+   "metadata": {
+    "id": "cdfe8f43-d089-4724-aae0-d1dec734c5d2",
+    "outputId": "87cc5d10-c049-4149-b8f9-9256e8049753"
+   },
+   "outputs": [
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-        "!pip install pyearthtools"
+     "data": {
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+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
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+       ".xr-group-box-contents > input:enabled + label {\n",
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+       ".xr-array-wrap > input:focus-visible + label,\n",
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+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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: 1MB\n",
+       "Dimensions:            (time: 1, level: 5, latitude: 141, longitude: 181)\n",
+       "Coordinates:\n",
+       "  * time               (time) datetime64[ns] 8B 2024-01-23T17:00:00\n",
+       "  * level              (level) int64 40B 200 500 700 850 1000\n",
+       "  * latitude           (latitude) float32 564B -5.0 -5.25 -5.5 ... -39.75 -40.0\n",
+       "  * longitude          (longitude) float32 724B 110.0 110.2 ... 154.8 155.0\n",
+       "Data variables:\n",
+       "    specific_humidity  (time, level, latitude, longitude) float32 510kB ...\n",
+       "    temperature        (time, level, latitude, longitude) float32 510kB ...\n",
+       "Attributes:\n",
+       "    valid_time_start:       1940-01-01\n",
+       "    last_updated:           2026-06-01 03:27:53.232444+00:00\n",
+       "    valid_time_stop:        2025-12-31\n",
+       "    valid_time_stop_era5t:  2026-05-26</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-9620a655-65a9-49c9-a840-cfe344bf2829' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-9620a655-65a9-49c9-a840-cfe344bf2829' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>level</span>: 5</li><li><span class='xr-has-index'>latitude</span>: 141</li><li><span class='xr-has-index'>longitude</span>: 181</li></ul></div></li><li class='xr-section-item'><input id='section-872ed2b7-5472-4364-a5b0-dad24d4ecc18' class='xr-section-summary-in' type='checkbox' checked /><label for='section-872ed2b7-5472-4364-a5b0-dad24d4ecc18' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-23T17:00:00</div><input id='attrs-2efe8ba6-b2e9-40c1-b6e9-3c40d388a0ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2efe8ba6-b2e9-40c1-b6e9-3c40d388a0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cf1c9c-af20-4f8f-9aaa-7e0f494eebf2' class='xr-var-data-in' type='checkbox'><label for='data-f9cf1c9c-af20-4f8f-9aaa-7e0f494eebf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-23T17:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>200 500 700 850 1000</div><input id='attrs-7cfe3dac-5c1f-4fe2-82e4-139c125fc18f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cfe3dac-5c1f-4fe2-82e4-139c125fc18f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007a1c17-81fb-44c3-86a2-ca9008a111f1' class='xr-var-data-in' type='checkbox'><label for='data-007a1c17-81fb-44c3-86a2-ca9008a111f1' 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>level</dd><dt><span>units :</span></dt><dd>Hectopascal(hPa)</dd></dl></div><div class='xr-var-data'><pre>array([ 200,  500,  700,  850, 1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.0 -5.25 -5.5 ... -39.75 -40.0</div><input id='attrs-ebe64952-bab6-4d96-91fb-a4d6561040dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebe64952-bab6-4d96-91fb-a4d6561040dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e01c2f9f-7c99-4803-a84f-2a2d993ff06b' class='xr-var-data-in' type='checkbox'><label for='data-e01c2f9f-7c99-4803-a84f-2a2d993ff06b' 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></dl></div><div class='xr-var-data'><pre>array([ -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,  -6.25,  -6.5 ,  -6.75,  -7.  ,\n",
+       "        -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,  -8.5 ,  -8.75,  -9.  ,  -9.25,\n",
+       "        -9.5 ,  -9.75, -10.  , -10.25, -10.5 , -10.75, -11.  , -11.25, -11.5 ,\n",
+       "       -11.75, -12.  , -12.25, -12.5 , -12.75, -13.  , -13.25, -13.5 , -13.75,\n",
+       "       -14.  , -14.25, -14.5 , -14.75, -15.  , -15.25, -15.5 , -15.75, -16.  ,\n",
+       "       -16.25, -16.5 , -16.75, -17.  , -17.25, -17.5 , -17.75, -18.  , -18.25,\n",
+       "       -18.5 , -18.75, -19.  , -19.25, -19.5 , -19.75, -20.  , -20.25, -20.5 ,\n",
+       "       -20.75, -21.  , -21.25, -21.5 , -21.75, -22.  , -22.25, -22.5 , -22.75,\n",
+       "       -23.  , -23.25, -23.5 , -23.75, -24.  , -24.25, -24.5 , -24.75, -25.  ,\n",
+       "       -25.25, -25.5 , -25.75, -26.  , -26.25, -26.5 , -26.75, -27.  , -27.25,\n",
+       "       -27.5 , -27.75, -28.  , -28.25, -28.5 , -28.75, -29.  , -29.25, -29.5 ,\n",
+       "       -29.75, -30.  , -30.25, -30.5 , -30.75, -31.  , -31.25, -31.5 , -31.75,\n",
+       "       -32.  , -32.25, -32.5 , -32.75, -33.  , -33.25, -33.5 , -33.75, -34.  ,\n",
+       "       -34.25, -34.5 , -34.75, -35.  , -35.25, -35.5 , -35.75, -36.  , -36.25,\n",
+       "       -36.5 , -36.75, -37.  , -37.25, -37.5 , -37.75, -38.  , -38.25, -38.5 ,\n",
+       "       -38.75, -39.  , -39.25, -39.5 , -39.75, -40.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>110.0 110.2 110.5 ... 154.8 155.0</div><input id='attrs-484f27a1-31aa-4a7c-8208-66ad5e1d0379' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-484f27a1-31aa-4a7c-8208-66ad5e1d0379' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4277a4a9-2b4b-476e-bfee-5c408f9f6902' class='xr-var-data-in' type='checkbox'><label for='data-4277a4a9-2b4b-476e-bfee-5c408f9f6902' 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></dl></div><div class='xr-var-data'><pre>array([110.  , 110.25, 110.5 , 110.75, 111.  , 111.25, 111.5 , 111.75, 112.  ,\n",
+       "       112.25, 112.5 , 112.75, 113.  , 113.25, 113.5 , 113.75, 114.  , 114.25,\n",
+       "       114.5 , 114.75, 115.  , 115.25, 115.5 , 115.75, 116.  , 116.25, 116.5 ,\n",
+       "       116.75, 117.  , 117.25, 117.5 , 117.75, 118.  , 118.25, 118.5 , 118.75,\n",
+       "       119.  , 119.25, 119.5 , 119.75, 120.  , 120.25, 120.5 , 120.75, 121.  ,\n",
+       "       121.25, 121.5 , 121.75, 122.  , 122.25, 122.5 , 122.75, 123.  , 123.25,\n",
+       "       123.5 , 123.75, 124.  , 124.25, 124.5 , 124.75, 125.  , 125.25, 125.5 ,\n",
+       "       125.75, 126.  , 126.25, 126.5 , 126.75, 127.  , 127.25, 127.5 , 127.75,\n",
+       "       128.  , 128.25, 128.5 , 128.75, 129.  , 129.25, 129.5 , 129.75, 130.  ,\n",
+       "       130.25, 130.5 , 130.75, 131.  , 131.25, 131.5 , 131.75, 132.  , 132.25,\n",
+       "       132.5 , 132.75, 133.  , 133.25, 133.5 , 133.75, 134.  , 134.25, 134.5 ,\n",
+       "       134.75, 135.  , 135.25, 135.5 , 135.75, 136.  , 136.25, 136.5 , 136.75,\n",
+       "       137.  , 137.25, 137.5 , 137.75, 138.  , 138.25, 138.5 , 138.75, 139.  ,\n",
+       "       139.25, 139.5 , 139.75, 140.  , 140.25, 140.5 , 140.75, 141.  , 141.25,\n",
+       "       141.5 , 141.75, 142.  , 142.25, 142.5 , 142.75, 143.  , 143.25, 143.5 ,\n",
+       "       143.75, 144.  , 144.25, 144.5 , 144.75, 145.  , 145.25, 145.5 , 145.75,\n",
+       "       146.  , 146.25, 146.5 , 146.75, 147.  , 147.25, 147.5 , 147.75, 148.  ,\n",
+       "       148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 , 149.75, 150.  , 150.25,\n",
+       "       150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75, 152.  , 152.25, 152.5 ,\n",
+       "       152.75, 153.  , 153.25, 153.5 , 153.75, 154.  , 154.25, 154.5 , 154.75,\n",
+       "       155.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db68a417-7ddc-4d12-ab18-1ba7ce686908' class='xr-section-summary-in' type='checkbox' checked /><label for='section-db68a417-7ddc-4d12-ab18-1ba7ce686908' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d900e15-7498-48ae-8dbe-185558c89c6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d900e15-7498-48ae-8dbe-185558c89c6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ce88a3f-9a45-4bb1-a5c2-9deedebb539e' class='xr-var-data-in' type='checkbox'><label for='data-0ce88a3f-9a45-4bb1-a5c2-9deedebb539e' 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>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><pre>[127605 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4afffbf0-eaca-44ae-8330-967e7aa08f71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4afffbf0-eaca-44ae-8330-967e7aa08f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e14c2d1-9506-47c6-bc37-fa9aa8e058a2' class='xr-var-data-in' type='checkbox'><label for='data-6e14c2d1-9506-47c6-bc37-fa9aa8e058a2' 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>Temperature</dd><dt><span>short_name :</span></dt><dd>t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[127605 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec12aa4a-6873-4e4b-9627-6f8f3c8b819d' class='xr-section-summary-in' type='checkbox' checked /><label for='section-ec12aa4a-6873-4e4b-9627-6f8f3c8b819d' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>valid_time_start :</span></dt><dd>1940-01-01</dd><dt><span>last_updated :</span></dt><dd>2026-06-01 03:27:53.232444+00:00</dd><dt><span>valid_time_stop :</span></dt><dd>2025-12-31</dd><dt><span>valid_time_stop_era5t :</span></dt><dd>2026-05-26</dd></dl></div></li></ul></div></div>"
       ],
-      "metadata": {
-        "id": "2_Nnfj2Dr-Zw"
-      },
-      "id": "2_Nnfj2Dr-Zw",
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "id": "98164f08-4b2f-4600-b7d5-b4cc4284aac6",
-      "metadata": {
-        "id": "98164f08-4b2f-4600-b7d5-b4cc4284aac6"
-      },
-      "source": [
-        "# Loading a Remote Zarr file - ERA5 ARCO example\n",
-        "\n",
-        "[Zarr](https://zarr.readthedocs.io/) is an increasingly popular format for storing gridded environmental science data, especially when working on cloud environments. The nature of a zarr, with consolidated metadat which enables interaction with a dataset as a unified whole, data divided into separate files called *chunks* to enable parallel read and write, which suits a massively parallel distributed file system like the object stores used by cloud providers, makes it efficient and intuive for many machine-learning platforms and workflows.\n",
-        "\n",
-        "PyEarthTools provides a data accessor class for zarr files. As PyEarthTools uses xarray for data I/O, which through fsspec supports loading data directly from remote sources, it can load data from such sources.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f644ae8b-32dd-49c8-9c14-0a32c0840ac6",
-      "metadata": {
-        "id": "f644ae8b-32dd-49c8-9c14-0a32c0840ac6"
-      },
-      "source": [
-        "### Set up imports for demo"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "c3f48f0f-6e4d-40d3-a88a-625f0d5a5b7d",
-      "metadata": {
-        "id": "c3f48f0f-6e4d-40d3-a88a-625f0d5a5b7d"
-      },
-      "outputs": [],
-      "source": [
-        "import xarray"
+      "text/plain": [
+       "<xarray.Dataset> Size: 1MB\n",
+       "Dimensions:            (time: 1, level: 5, latitude: 141, longitude: 181)\n",
+       "Coordinates:\n",
+       "  * time               (time) datetime64[ns] 8B 2024-01-23T17:00:00\n",
+       "  * level              (level) int64 40B 200 500 700 850 1000\n",
+       "  * latitude           (latitude) float32 564B -5.0 -5.25 -5.5 ... -39.75 -40.0\n",
+       "  * longitude          (longitude) float32 724B 110.0 110.2 ... 154.8 155.0\n",
+       "Data variables:\n",
+       "    specific_humidity  (time, level, latitude, longitude) float32 510kB ...\n",
+       "    temperature        (time, level, latitude, longitude) float32 510kB ...\n",
+       "Attributes:\n",
+       "    valid_time_start:       1940-01-01\n",
+       "    last_updated:           2026-06-01 03:27:53.232444+00:00\n",
+       "    valid_time_stop:        2025-12-31\n",
+       "    valid_time_stop_era5t:  2026-05-26"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "4e140303-a282-41e8-8424-9547f9159c26",
-      "metadata": {
-        "id": "4e140303-a282-41e8-8424-9547f9159c26"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot\n",
-        "import cartopy"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "4e4d3dc7-e81a-40c3-b604-db49bf43847e",
-      "metadata": {
-        "id": "4e4d3dc7-e81a-40c3-b604-db49bf43847e"
-      },
-      "outputs": [],
-      "source": [
-        "import pyearthtools.data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ab4e3a1d-770b-4cd1-a57d-c678d1af20d6",
-      "metadata": {
-        "id": "ab4e3a1d-770b-4cd1-a57d-c678d1af20d6"
-      },
-      "outputs": [],
-      "source": [
-        "import pyearthtools.pipeline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "51c6e404-a86f-4448-b9d2-37bb2b60f6cb",
-      "metadata": {
-        "id": "51c6e404-a86f-4448-b9d2-37bb2b60f6cb"
-      },
-      "source": [
-        "## Set up the accessor\n",
-        "\n",
-        "Fore this demo we will be using the copy of the [ECMWF ERA5 Reanalsyis dataset](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview) hosted by [Google Research](https://github.com/google-research/arco-era5) on the Google Cloud Storage system."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "5ded0ad5-7561-4755-82de-deee4f4a5417",
-      "metadata": {
-        "id": "5ded0ad5-7561-4755-82de-deee4f4a5417"
-      },
-      "outputs": [],
-      "source": [
-        "remote_url = 'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "2190d29c-899e-4f0d-8184-7db042c07d4a",
-      "metadata": {
-        "id": "2190d29c-899e-4f0d-8184-7db042c07d4a"
-      },
-      "outputs": [],
-      "source": [
-        "zarr_opts = {'chunks': None, 'storage_options': {'token': 'anon'}}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "15ccfd12-75e7-404e-a623-065a8fc389f9",
-      "metadata": {
-        "id": "15ccfd12-75e7-404e-a623-065a8fc389f9"
-      },
-      "source": [
-        "Usually we are only interested in a subset of the data, so we can select a particular time, pressure levels and set of variables that we want to us"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "1760e80f-c43c-4c97-9242-69c930fed623",
-      "metadata": {
-        "id": "1760e80f-c43c-4c97-9242-69c930fed623"
-      },
-      "outputs": [],
-      "source": [
-        "select_variables = ['temperature', 'specific_humidity']\n",
-        "select_pressure_levels = [200, 500, 700, 850, 1000]\n",
-        "select_time = ('2024-01-01','2025-01-01')\n",
-        "select_region = {'latitude': (-40,-5),\n",
-        "                 'longitude': (110,155)\n",
-        "                }"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "394c417d-7a85-4a8f-917c-5c32f6398437",
-      "metadata": {
-        "id": "394c417d-7a85-4a8f-917c-5c32f6398437"
-      },
-      "outputs": [],
-      "source": [
-        "select_dict = {\n",
-        "    'level': select_pressure_levels,\n",
-        "    'time': slice(*select_time),\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "bac39d42-cc5a-44ca-b0a1-908f61df54a4",
-      "metadata": {
-        "id": "bac39d42-cc5a-44ca-b0a1-908f61df54a4"
-      },
-      "outputs": [],
-      "source": [
-        "era5_accessor = pyearthtools.data.archive.ZarrTimeIndex(\n",
-        "       remote_url,\n",
-        "       variables=select_variables,\n",
-        "       open_kwargs=zarr_opts,\n",
-        "    remote=True,\n",
-        "    transforms=pyearthtools.data.transforms.TransformCollection([\n",
-        "        pyearthtools.data.transform.coordinates.Select(select_dict),\n",
-        "        pyearthtools.data.transform.region.Bounding(select_region['latitude'][0],select_region['latitude'][1], select_region['longitude'][0], select_region['longitude'][1])\n",
-        "    ]),\n",
-        "    )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "74a39b7a-0a19-4db5-8363-5055c735076f",
-      "metadata": {
-        "id": "74a39b7a-0a19-4db5-8363-5055c735076f"
-      },
-      "source": [
-        "## Using the data of interest\n",
-        "We can now look at the xarray dataset through the time index function. If we do this, the xarray lazy loading paradigm means only the metadata will have been downloaded form the remote location, until we try to do some thing which requires the data. For example after exmaing the metadata, we will then plot some of the data which will take longer as the data will be transfered from the remote location. This will be quite efficient as only the chunks pertaining to the data selected (variables, time period, spatial region) will be transferred."
-      ]
-    },
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "era5_accessor['2024-01-23 17:00']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53cd3128-3c45-450c-a0fa-866d7fc353dc",
+   "metadata": {
+    "id": "53cd3128-3c45-450c-a0fa-866d7fc353dc",
+    "outputId": "a8642bb7-0b91-4683-bb9d-104893bc11cd"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "cdfe8f43-d089-4724-aae0-d1dec734c5d2",
-      "metadata": {
-        "id": "cdfe8f43-d089-4724-aae0-d1dec734c5d2",
-        "outputId": "87cc5d10-c049-4149-b8f9-9256e8049753"
-      },
-      "outputs": [
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-              "    --jp-content-font-color2,\n",
-              "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-              "  );\n",
-              "  --xr-font-color3: var(\n",
-              "    --jp-content-font-color3,\n",
-              "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-              "  );\n",
-              "  --xr-border-color: var(\n",
-              "    --jp-border-color2,\n",
-              "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-              "  );\n",
-              "  --xr-disabled-color: var(\n",
-              "    --jp-layout-color3,\n",
-              "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-              "  );\n",
-              "  --xr-background-color: var(\n",
-              "    --jp-layout-color0,\n",
-              "    var(--pst-color-on-background, #111111)\n",
-              "  );\n",
-              "  --xr-background-color-row-even: var(\n",
-              "    --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",
-              "  line-height: 1.6;\n",
-              "  padding-bottom: 4px;\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",
-              "}\n",
-              "\n",
-              ".xr-header {\n",
-              "  border-bottom: solid 1px var(--xr-border-color);\n",
-              "  margin-bottom: 4px;\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-obj-name {\n",
-              "  margin-left: 2px;\n",
-              "  margin-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type,\n",
-              ".xr-group-box-contents > label {\n",
-              "  color: var(--xr-font-color2);\n",
-              "  display: block;\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",
-              "  margin-block-start: 0;\n",
-              "  margin-block-end: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item > input,\n",
-              ".xr-group-box-contents > input,\n",
-              ".xr-array-wrap > input {\n",
-              "  display: block;\n",
-              "  opacity: 0;\n",
-              "  height: 0;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item > input + label,\n",
-              ".xr-var-item > input + label {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item > input:enabled + label,\n",
-              ".xr-var-item > input:enabled + label,\n",
-              ".xr-array-wrap > input:enabled + label,\n",
-              ".xr-group-box-contents > input:enabled + label {\n",
-              "  cursor: pointer;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item > input:focus-visible + label,\n",
-              ".xr-var-item > input:focus-visible + label,\n",
-              ".xr-array-wrap > input:focus-visible + label,\n",
-              ".xr-group-box-contents > input:focus-visible + label {\n",
-              "  outline: auto;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item > input:enabled + label:hover,\n",
-              ".xr-var-item > input:enabled + label:hover,\n",
-              ".xr-array-wrap > input:enabled + label:hover,\n",
-              ".xr-group-box-contents > 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",
-              "  white-space: nowrap;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary > em {\n",
-              "  font-weight: normal;\n",
-              "}\n",
-              "\n",
-              ".xr-span-grid {\n",
-              "  grid-column-end: -1;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary > span {\n",
-              "  display: inline-block;\n",
-              "  padding-left: 0.3em;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents > input:checked + label > span {\n",
-              "  display: inline-block;\n",
-              "  padding-left: 0.6em;\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",
-              ".xr-group-box-contents > label {\n",
-              "  padding-top: 4px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-inline-details {\n",
-              "  grid-column: 2 / -1;\n",
-              "}\n",
-              "\n",
-              ".xr-section-details {\n",
-              "  grid-column: 1 / -1;\n",
-              "  margin-top: 4px;\n",
-              "  margin-bottom: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in ~ .xr-section-details {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-children {\n",
-              "  display: inline-grid;\n",
-              "  grid-template-columns: 100%;\n",
-              "  grid-column: 1 / -1;\n",
-              "  padding-top: 4px;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box {\n",
-              "  display: inline-grid;\n",
-              "  grid-template-columns: 0px 30px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-vline {\n",
-              "  grid-column-start: 1;\n",
-              "  border-right: 0.2em solid;\n",
-              "  border-color: var(--xr-border-color);\n",
-              "  width: 0px;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-hline {\n",
-              "  grid-column-start: 2;\n",
-              "  grid-row-start: 1;\n",
-              "  height: 1em;\n",
-              "  width: 26px;\n",
-              "  border-bottom: 0.2em solid;\n",
-              "  border-color: var(--xr-border-color);\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents {\n",
-              "  grid-column-start: 3;\n",
-              "  padding-bottom: 4px;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents > label::before {\n",
-              "  content: \"📂\";\n",
-              "  padding-right: 0.3em;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents > input:checked + label::before {\n",
-              "  content: \"📁\";\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents > input:checked + label {\n",
-              "  padding-bottom: 0px;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-contents > input + label > span {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-group-box-ellipsis {\n",
-              "  font-size: 1.4em;\n",
-              "  font-weight: 900;\n",
-              "  color: var(--xr-font-color2);\n",
-              "  letter-spacing: 0.15em;\n",
-              "  cursor: default;\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: 1MB\n",
-              "Dimensions:            (time: 1, level: 5, latitude: 141, longitude: 181)\n",
-              "Coordinates:\n",
-              "  * time               (time) datetime64[ns] 8B 2024-01-23T17:00:00\n",
-              "  * level              (level) int64 40B 200 500 700 850 1000\n",
-              "  * latitude           (latitude) float32 564B -5.0 -5.25 -5.5 ... -39.75 -40.0\n",
-              "  * longitude          (longitude) float32 724B 110.0 110.2 ... 154.8 155.0\n",
-              "Data variables:\n",
-              "    specific_humidity  (time, level, latitude, longitude) float32 510kB ...\n",
-              "    temperature        (time, level, latitude, longitude) float32 510kB ...\n",
-              "Attributes:\n",
-              "    valid_time_start:       1940-01-01\n",
-              "    last_updated:           2026-06-01 03:27:53.232444+00:00\n",
-              "    valid_time_stop:        2025-12-31\n",
-              "    valid_time_stop_era5t:  2026-05-26</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-9620a655-65a9-49c9-a840-cfe344bf2829' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-9620a655-65a9-49c9-a840-cfe344bf2829' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>level</span>: 5</li><li><span class='xr-has-index'>latitude</span>: 141</li><li><span class='xr-has-index'>longitude</span>: 181</li></ul></div></li><li class='xr-section-item'><input id='section-872ed2b7-5472-4364-a5b0-dad24d4ecc18' class='xr-section-summary-in' type='checkbox' checked /><label for='section-872ed2b7-5472-4364-a5b0-dad24d4ecc18' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-23T17:00:00</div><input id='attrs-2efe8ba6-b2e9-40c1-b6e9-3c40d388a0ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2efe8ba6-b2e9-40c1-b6e9-3c40d388a0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cf1c9c-af20-4f8f-9aaa-7e0f494eebf2' class='xr-var-data-in' type='checkbox'><label for='data-f9cf1c9c-af20-4f8f-9aaa-7e0f494eebf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-23T17:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>200 500 700 850 1000</div><input id='attrs-7cfe3dac-5c1f-4fe2-82e4-139c125fc18f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cfe3dac-5c1f-4fe2-82e4-139c125fc18f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007a1c17-81fb-44c3-86a2-ca9008a111f1' class='xr-var-data-in' type='checkbox'><label for='data-007a1c17-81fb-44c3-86a2-ca9008a111f1' 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>level</dd><dt><span>units :</span></dt><dd>Hectopascal(hPa)</dd></dl></div><div class='xr-var-data'><pre>array([ 200,  500,  700,  850, 1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.0 -5.25 -5.5 ... -39.75 -40.0</div><input id='attrs-ebe64952-bab6-4d96-91fb-a4d6561040dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebe64952-bab6-4d96-91fb-a4d6561040dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e01c2f9f-7c99-4803-a84f-2a2d993ff06b' class='xr-var-data-in' type='checkbox'><label for='data-e01c2f9f-7c99-4803-a84f-2a2d993ff06b' 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></dl></div><div class='xr-var-data'><pre>array([ -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,  -6.25,  -6.5 ,  -6.75,  -7.  ,\n",
-              "        -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,  -8.5 ,  -8.75,  -9.  ,  -9.25,\n",
-              "        -9.5 ,  -9.75, -10.  , -10.25, -10.5 , -10.75, -11.  , -11.25, -11.5 ,\n",
-              "       -11.75, -12.  , -12.25, -12.5 , -12.75, -13.  , -13.25, -13.5 , -13.75,\n",
-              "       -14.  , -14.25, -14.5 , -14.75, -15.  , -15.25, -15.5 , -15.75, -16.  ,\n",
-              "       -16.25, -16.5 , -16.75, -17.  , -17.25, -17.5 , -17.75, -18.  , -18.25,\n",
-              "       -18.5 , -18.75, -19.  , -19.25, -19.5 , -19.75, -20.  , -20.25, -20.5 ,\n",
-              "       -20.75, -21.  , -21.25, -21.5 , -21.75, -22.  , -22.25, -22.5 , -22.75,\n",
-              "       -23.  , -23.25, -23.5 , -23.75, -24.  , -24.25, -24.5 , -24.75, -25.  ,\n",
-              "       -25.25, -25.5 , -25.75, -26.  , -26.25, -26.5 , -26.75, -27.  , -27.25,\n",
-              "       -27.5 , -27.75, -28.  , -28.25, -28.5 , -28.75, -29.  , -29.25, -29.5 ,\n",
-              "       -29.75, -30.  , -30.25, -30.5 , -30.75, -31.  , -31.25, -31.5 , -31.75,\n",
-              "       -32.  , -32.25, -32.5 , -32.75, -33.  , -33.25, -33.5 , -33.75, -34.  ,\n",
-              "       -34.25, -34.5 , -34.75, -35.  , -35.25, -35.5 , -35.75, -36.  , -36.25,\n",
-              "       -36.5 , -36.75, -37.  , -37.25, -37.5 , -37.75, -38.  , -38.25, -38.5 ,\n",
-              "       -38.75, -39.  , -39.25, -39.5 , -39.75, -40.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>110.0 110.2 110.5 ... 154.8 155.0</div><input id='attrs-484f27a1-31aa-4a7c-8208-66ad5e1d0379' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-484f27a1-31aa-4a7c-8208-66ad5e1d0379' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4277a4a9-2b4b-476e-bfee-5c408f9f6902' class='xr-var-data-in' type='checkbox'><label for='data-4277a4a9-2b4b-476e-bfee-5c408f9f6902' 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></dl></div><div class='xr-var-data'><pre>array([110.  , 110.25, 110.5 , 110.75, 111.  , 111.25, 111.5 , 111.75, 112.  ,\n",
-              "       112.25, 112.5 , 112.75, 113.  , 113.25, 113.5 , 113.75, 114.  , 114.25,\n",
-              "       114.5 , 114.75, 115.  , 115.25, 115.5 , 115.75, 116.  , 116.25, 116.5 ,\n",
-              "       116.75, 117.  , 117.25, 117.5 , 117.75, 118.  , 118.25, 118.5 , 118.75,\n",
-              "       119.  , 119.25, 119.5 , 119.75, 120.  , 120.25, 120.5 , 120.75, 121.  ,\n",
-              "       121.25, 121.5 , 121.75, 122.  , 122.25, 122.5 , 122.75, 123.  , 123.25,\n",
-              "       123.5 , 123.75, 124.  , 124.25, 124.5 , 124.75, 125.  , 125.25, 125.5 ,\n",
-              "       125.75, 126.  , 126.25, 126.5 , 126.75, 127.  , 127.25, 127.5 , 127.75,\n",
-              "       128.  , 128.25, 128.5 , 128.75, 129.  , 129.25, 129.5 , 129.75, 130.  ,\n",
-              "       130.25, 130.5 , 130.75, 131.  , 131.25, 131.5 , 131.75, 132.  , 132.25,\n",
-              "       132.5 , 132.75, 133.  , 133.25, 133.5 , 133.75, 134.  , 134.25, 134.5 ,\n",
-              "       134.75, 135.  , 135.25, 135.5 , 135.75, 136.  , 136.25, 136.5 , 136.75,\n",
-              "       137.  , 137.25, 137.5 , 137.75, 138.  , 138.25, 138.5 , 138.75, 139.  ,\n",
-              "       139.25, 139.5 , 139.75, 140.  , 140.25, 140.5 , 140.75, 141.  , 141.25,\n",
-              "       141.5 , 141.75, 142.  , 142.25, 142.5 , 142.75, 143.  , 143.25, 143.5 ,\n",
-              "       143.75, 144.  , 144.25, 144.5 , 144.75, 145.  , 145.25, 145.5 , 145.75,\n",
-              "       146.  , 146.25, 146.5 , 146.75, 147.  , 147.25, 147.5 , 147.75, 148.  ,\n",
-              "       148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 , 149.75, 150.  , 150.25,\n",
-              "       150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75, 152.  , 152.25, 152.5 ,\n",
-              "       152.75, 153.  , 153.25, 153.5 , 153.75, 154.  , 154.25, 154.5 , 154.75,\n",
-              "       155.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db68a417-7ddc-4d12-ab18-1ba7ce686908' class='xr-section-summary-in' type='checkbox' checked /><label for='section-db68a417-7ddc-4d12-ab18-1ba7ce686908' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d900e15-7498-48ae-8dbe-185558c89c6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d900e15-7498-48ae-8dbe-185558c89c6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ce88a3f-9a45-4bb1-a5c2-9deedebb539e' class='xr-var-data-in' type='checkbox'><label for='data-0ce88a3f-9a45-4bb1-a5c2-9deedebb539e' 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>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><pre>[127605 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4afffbf0-eaca-44ae-8330-967e7aa08f71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4afffbf0-eaca-44ae-8330-967e7aa08f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e14c2d1-9506-47c6-bc37-fa9aa8e058a2' class='xr-var-data-in' type='checkbox'><label for='data-6e14c2d1-9506-47c6-bc37-fa9aa8e058a2' 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>Temperature</dd><dt><span>short_name :</span></dt><dd>t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[127605 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec12aa4a-6873-4e4b-9627-6f8f3c8b819d' class='xr-section-summary-in' type='checkbox' checked /><label for='section-ec12aa4a-6873-4e4b-9627-6f8f3c8b819d' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>valid_time_start :</span></dt><dd>1940-01-01</dd><dt><span>last_updated :</span></dt><dd>2026-06-01 03:27:53.232444+00:00</dd><dt><span>valid_time_stop :</span></dt><dd>2025-12-31</dd><dt><span>valid_time_stop_era5t :</span></dt><dd>2026-05-26</dd></dl></div></li></ul></div></div>"
-            ],
-            "text/plain": [
-              "<xarray.Dataset> Size: 1MB\n",
-              "Dimensions:            (time: 1, level: 5, latitude: 141, longitude: 181)\n",
-              "Coordinates:\n",
-              "  * time               (time) datetime64[ns] 8B 2024-01-23T17:00:00\n",
-              "  * level              (level) int64 40B 200 500 700 850 1000\n",
-              "  * latitude           (latitude) float32 564B -5.0 -5.25 -5.5 ... -39.75 -40.0\n",
-              "  * longitude          (longitude) float32 724B 110.0 110.2 ... 154.8 155.0\n",
-              "Data variables:\n",
-              "    specific_humidity  (time, level, latitude, longitude) float32 510kB ...\n",
-              "    temperature        (time, level, latitude, longitude) float32 510kB ...\n",
-              "Attributes:\n",
-              "    valid_time_start:       1940-01-01\n",
-              "    last_updated:           2026-06-01 03:27:53.232444+00:00\n",
-              "    valid_time_stop:        2025-12-31\n",
-              "    valid_time_stop_era5t:  2026-05-26"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "era5_accessor['2024-01-23 17:00']"
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f105bf97590>"
       ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "53cd3128-3c45-450c-a0fa-866d7fc353dc",
-      "metadata": {
-        "id": "53cd3128-3c45-450c-a0fa-866d7fc353dc",
-        "outputId": "a8642bb7-0b91-4683-bb9d-104893bc11cd"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f105bf97590>"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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fOHr0aJPibenjkiAIgiAIN04kjgRBaBeioqIA6iyoe3X3qzlz5vDkk0+yZs0aXnjhBSRJYt26dUycOLFqvsrlXX1jda38/PxqN9b1cXNzw83NrcHzX8+vv/7K/fffz6VLl/D09AQquuMYDAaee+45br/9dhwcHOp8/1NPPcXXX3/Njh07qhJHZmZmlJeX15i3rKys6vUbERgYWGs9nQMHDlT7f0P3vUajoaCgoNqoUI1RWefq2qSHiYkJ/v7+Nepgubu71+gC2LlzZwDi4uKq9uMPP/zAhx9+SERERLUuSX5+flX/fuihh1i7di0TJ07Ew8ODcePGMXfu3GpJpOjoaGbNmlXvdjRkfU1xdZ0b+DehU1uNnt9++w1ra2tUKhWenp5VCdmmxChdqRVkDEOpX7sPbGxsAPDy8qox/er9EhUVRX5+Ps7OzrUuNyMj47rrbc5jxdXS0tKYPHkyNjY2VTXNKlX+vht7DCguLmbKlCkUFhZy4MCBaolInU7Ho48+yoIFC+jfv3+9sV3NxsYGMzOzFjku1bUuQRAEQRCaRiSOBEFoFypboKxevRpXV9caryuV/x7O3N3dGTZsGGvXruWFF17gyJEjJCQk8N5779VY3vLly+ndu3et66yrpUZdSktLyc/Pb9C8tW3D1T7//HP69OlTlTSqNG3aNFauXMnp06evW/S48sY3JyenapqbmxvJyck15k1NTQUq9ltraOi+vzp2Y/Hjjz+yePFipk+fzjPPPIOzszMKhYJ33nmnqu4PgLOzM2FhYWzdupUtW7awZcsWvv/+exYuXMgPP/zQ7OurjYODAzqdjsLCQqysrGq8fnVS4WpSLUWrhw8fXmttm6bEWJmAqWt5ramufVDb9Kv3i8FgwNnZmZ9++qnW99dXW6k5jxWV8vPzmThxInl5eezfv7/G77kyUVX5e79aamoq9vb2NVr+aDQaZs6cydmzZ9m6dWuNJO6qVauIjIxkxYoVVXXmKhUWFhIXF4ezszPm5uY1EmXff/89ixcvxs3Njd27dyNJUrVk4o0cl+palyAIgiAITSMSR4IgtAuVLRycnZ2vmzCpNG/ePB566CEiIyNZs2YN5ubmTJ06tcbyrK2tG7S8hlizZk21rk/XU9vN+dXS09Nrbe1U2ZpDp9Nd9/2V3Y2uvoHt3bs3u3fvpqCgoFoLrcouJnUlcZpbQ/e9k5MT1tbWnD9//rrLq6vlio+PDwCRkZH4+/tXTddoNMTGxtZYd0pKCsXFxdVaHVUO9+3r6wtUjEzn7+/Phg0bqq331VdfrbF+ExMTpk6dytSpUzEYDDz00EOsWLGCl19+mU6dOhEQEFDvtjVmfdfq2rUrUDHyWc+ePeudv6kaG2NsbCxAjWLIN6o1WzAFBASwY8cOhgwZ0qSWLM15rICK1jlTp07l0qVL7Nixg27dutWYx8PDAycnJ06cOFHjtWPHjtX4/RsMBhYuXMjOnTtZu3YtI0aMqPG+hIQEtFotQ4YMqfHaqlWrWLVqFRs3bmT69Ok1uuYFBwcDFcedb775hosXL1aL+0aOS3WtSxAEQRCEphGJI0EQ2oXx48djbW3NsmXLGDVqFCqVqtrrmZmZ1ZIks2bN4pFHHuGXX35h3bp1TJkypVpCICQkhICAAD744APmz59fo3XRtctraIzNVbekc+fObNu2jUuXLlV1lwL45ZdfkMvlVYmAgoIC1Gp1tZYCkiTx1ltvVcVUafbs2XzwwQd89dVXVUNml5eX8/333zNw4MAa3XNaSkP3vVwuZ/r06fz444+cOHGiRp2jyhYKlZ9rXl5etddvueUWTExM+N///seECROqEgvffvst+fn5TJ48udr8Op2OFStW8OSTTwIVCaYVK1bg5ORESEgI8G9LlKtbRxw9epTDhw9X6/aUnZ1drSvh1Z9ZZbecWbNm8cYbb7Bx48YadY4ql9/Q9dUmNDQUgBMnTrRo4qixMZ48eRIbG5tmv5mv63vQEubOncvnn3/Om2++ybJly6q9ptPpKCoqwtbWts73N+exQq/XM2/ePA4fPsymTZuqPvfazJo1ix9++IHExMSq3/vOnTu5dOkSTzzxRLV5H3nkEdasWcOKFSuYOXNmrcu77bbbak3szJgxg0mTJnHfffcxcOBAgDqTxLfeeitPPPEEn3/+OZ999hlQ8V368ssv8fDwYPDgwfXug2vVtS6tVkt0dDQ2NjbVWiVVtoy7ugtmamoq+fn5BAQE1DjfCIIgCEJHIxJHgiC0C9bW1nzxxRcsWLCAvn37ctttt+Hk5ERCQgJ//fUXQ4YMqbrpgIqWSaNGjeKjjz6isLCQefPmVVueXC7nm2++YeLEiQQHB3PXXXfh4eFBcnIyu3fvxtramj///LNRMTZn3ZJnnnmGLVu2MGzYMB5++GEcHBzYvHkzW7Zs4d57763qvnHq1Cluv/12br/9djp16kRpaSkbN27k4MGD3H///dWGYh84cCBz5szh+eefJyMjg06dOvHDDz8QFxdXo8B2S2rMvl+2bBnbtm1jxIgR3H///QQFBZGamsq6des4cOAAtra29O7dG4VCwXvvvUd+fj5qtZrRo0fj7OzM888/z+uvv86ECROYNm0akZGRfP755/Tv358777yzWlzu7u689957xMXF0blzZ9asWUNYWBhfffVV1Y3jlClT2LBhAzNmzGDy5MnExsby5Zdf0q1bN4qKiqqWde+995KTk8Po0aPx9PQkPj6eTz/9lN69e1e1tHnmmWdYv349c+bM4e677yYkJIScnBz++OMPvvzyS3r16tXg9dXG39+f7t27s2PHDu6+++7m/AiraWyM27dvZ+rUqQ1uIbR48WJ++OEHYmNjq1p+1aYyuffiiy9y2223oVKpmDp1ao26Vc1hxIgRLFmyhHfeeYewsDDGjRuHSqUiKiqKdevW8cknnzB79uw639+cx4qnnnqKP/74g6lTp5KTk8OPP/5Y7fWrv+cvvPAC69atY9SoUTz22GMUFRWxfPlyevToUa0F1H//+18+//xzQkNDMTc3r7HMGTNmYGFhQdeuXatatl3Lz8+P6dOn1xu/p6cnjz/+OMuXL0er1dK/f39+//139u/fz08//VSt22B8fHxVof3KllOVSXIfHx8WLFhw3XUlJycTFBTEokWLWLlyZdX0MWPGAFTrbvf888836HsnCIIgCB1CWwzlJgiCUJ/K4aqvHVp79+7d0vjx4yUbGxvJ1NRUCggIkBYvXiydOHGixjK+/vprCZCsrKyk0tLSWtdz+vRpaebMmZKDg4OkVqslHx8fae7cudLOnTvrjaWlHT16VJo4caLk6uoqqVQqqXPnztLbb78tabXaqnliYmKkOXPmSL6+vpKpqalkbm4uhYSESF9++WW1YcYrlZaWSk8//bTk6uoqqdVqqX///tI///zToHiuHdK8UuXw7suXL6/1fZVDumdmZlab3pB9L0mSFB8fLy1cuFBycnKS1Gq15O/vLy1durTaEPBff/215O/vLykUCgmQdu/eXfXaZ599JnXt2lVSqVSSi4uL9OCDD0q5ubnV1jFixAgpODhYOnHihBQaGiqZmppKPj4+0meffVZtPoPBIC1btkzy8fGR1Gq11KdPH2nz5s3SokWLJB8fn6r51q9fL40bN05ydnaWTExMJG9vb2nJkiVSampqteVlZ2dLDz/8sOTh4SGZmJhInp6e0qJFi6SsrKxGrU+SKoZAf/XVV6tN++ijjyRLS0uppKSkatr1Pq9rl1HXZ9eUfSJJknTx4kUJkHbs2FHrshYtWiRZWFhUmzZr1izJzMysxmdWmzfffFPy8PCQ5HJ5td/std/dyt/08ePHq72/ru2tLS5JkqSvvvpKCgkJkczMzCQrKyupR48e0rPPPiulpKTUG2tzGTFihATU+eda58+fl8aNGyeZm5tLtra20h133CGlpaVVm2fRokXXXWZ9x0JAWrp0aYO3Qa/XV32HTExMpODgYOnHH3+sMd/u3bvrjGnEiBH1rqfyu3/tcczHx6fGd7VyH7T2cV8QBEEQjJFMkhrQeV4QBEHo8Hx9fQkNDeXTTz/FzMysRVpytJWRI0eSlZVVb82h9iY/Px9/f3/ef//9akPMt5XHH3+cffv2cfLkyWotjoqLiyktLeWRRx7hzz//rNZSycXFhYULF7J8+fK2CFkQBEEQBKHDk7d1AIIgCEL78euvv+Lk5MRzzz3X1qEIDWBjY8Ozzz7L8uXLq0azayvZ2dl88803vPXWWzW6qb344os4OTnx66+/VpseHh5OaWmp+L4JgiAIgiC0IdHiSBAEQWiQgwcPUlpaCoCXlxddunRp44iaz83a4qi9uHTpEgkJCQAolUpGjhzZtgEJgiAIgiAIVUTiSBAEQejwROJIEARBEARBEGonuqoJgiAIHd6ePXtE0kgQBEEQBEFosi+++IKePXtibW2NtbU1oaGhbNmyper1srIyli5dioODA5aWlsyaNYv09PRal5WdnY2npycymYy8vLxW2oK6icSRIAiCIAiCIAiCIAjCDfD09OTdd9/l5MmTnDhxgtGjR3PrrbcSHh4OwBNPPMGff/7JunXr2Lt3LykpKcycObPWZd1zzz307NmzNcO/LtFVTRAEQRAEQRAEQRAEoZnZ29uzfPlyZs+ejZOTEz///DOzZ88GICIigqCgIA4fPsygQYOq3vPFF1+wZs0aXnnlFcaMGUNubi62trZttAUVlA2dsaysDI1G05KxCIIgCIIgCIIgCEKHZWJigqmpaVuH0eqMOd8gSVKNEWHVajVqtbrO9+j1etatW0dxcTGhoaGcPHkSrVbLLbfcUjVP165d8fb2rpY4unDhAm+88QZHjx4lJiamZTaoCRqUOCorK8PSxRl9QWFLxyMIgiAIgiAIgiAIHZKrqyuxsbEdKnlUVlaGr48F6RmGtg6lVpaWlhQVFVWb9uqrr/Laa6/VmPfcuXOEhoZW5FAsLdm4cSPdunUjLCwMExOTGi2HXFxcSEtLA6C8vJzbb7+d5cuX4+3t3f4SRxqNBn1BIR7LXkTeCl9gN7+sZl9maqxjq6/zZlffPhVgmJcvX4ybxuzffyEiO7NR7732OznfZzgzvUKZfeC95gyxSe4LGM9cnyHE5uXgZ2vPbX+s4XxmzcJuKrmCj0ZPZLCHN/P/XEtkzr/b9PLgkUztFMTc339hsKc3Tw0YyqBVX6I1GOjj4sbqKXOYun41sfm5rblpAh3zeCiOZ8ahI373AKxVZszzHsatngMp0ZfzS9w+NicfRyvp2zq0dqstftNN/f7ebMef2vZDf/tAlvVewG0Hl5Nd3jwPoq/db9fb/825j+v7nI3l83S3tGbbvMU8tWsLw718mRzQhUV/redMRlqzreP2oJ48HzqCXfExvLBvGyVabZOW4+mfw18jX+GzS3+xOfl4k5bRXPu9pc5Dj3WZxljXXjx9+jvm+QzDRmnOk6e/qzGfvkTDyTu+QKPRdKjEkUajIT3DQOQJd6ysjKsMc2GhgS79UkhMTMTa2rpqel2tjbp06UJYWBj5+fmsX7+eRYsWsXfv3gat6/nnnycoKIg777yzWWJvTg3uqgYgNzVFbtbyX2ClRd1NvprKq3shydFOtb7mEZAJNP86b3at8V1o7+b06kuaTsOlksJG769rfwcOtvaoLc1b5PfRGDJkjPDtRbFSx4wtv7FlziJ+mDmfGRt/Jqkwv9q8d/fqx4Ru3blvy+9ElRZV7QMXC0vuGziUNw/tJkFbxuN+ASRpytCrTZAD9rZ2WFtb8+bYyWyNjWJzdCT55WVtsLUdU3qa55XjYsfh1b36jUxd5wuhZbX18a2tlGDg+7S9/JUbxt0BY3kmZC4Lg8fxTfQ2tqeGYUCUo2ystrhGacqxMznaCblZ49Zz9Tqa+1h1vfgbuq7afsdKCzXW1taYWpqjVDZPV5Rrj9vXu5avOW91jdmP9R2njOX6eGzXbqgtzPF2dmZByEAe3bGZc4V5zRbfADdPlo2bwsrzp3jr0B4kpQK5UtGoZfz7fVNRamIgxKML/+SdbVI8zbVdLXUeMrM0w8TClAQpj1xFGQNcu6G2NEMvGWcLm7ZiZSXH2sgSR5UqR0qrj4mJCZ06dQIgJCSE48eP88knnzBv3jw0Gg15eXnVWh2lp6fj6uoKwK5duzh37hzr168HKrrIATg6OvLiiy/y+uuvN/NWNZzRfSotebPiEZBZ6x9BaAmmSiXjfAP5PepisyzveE4UzqY2hNgHNMvymmq212ACrdz58vI/GCSJxX/9hp2pGV+Om1Zj3mGevhxMSmBfUly16TmlJZRqtcio6Cvcxd6Ri1e1yIrKzQZguJcvbw8fy9a5ixju6dti2yQI1xLnCKEtZJTn8+6F9Sw4/DFRBSm80v02vh/0GIMdu7Z1aEIDNSYB0ZB567tubc5r2uY63tW2XWpFxbNqQyuOyTPMqRsf9rmbZ4JmYq7oeEnpLvaOxOTmcGdwbzZFXeSPyxHNtmxrEzWf3jKFY6lJvH14b5NS29d+39bE72eSez+mevRvniCNzOaU49iYWNDJ0o3taWdwVFsz3rVPW4cltAKDwUB5eTkhISGoVCp27txZ9VpkZCQJCQmEhoYC8Ntvv3HmzBnCwsIICwvjm2++AWD//v0sXbq0TeKvZFSJI3GBLtxMBrp5Yq5SsTU2qlmWdyLnMlGFKdzuM6JZltdUY916syv9LDvSzuARkElqcSEv7d9OdycXTOT/PmlSyeX0c/XgSEpijWVoDQbCMlLp7+bBQDdPghycOXdVV7ekwnxuWfM9gV99xMDVX3IpJ4uVk2dxW1CPOuMKdnDmw1ETuTUwqFocgnCjRAKpdYh9/K/44gxeOLua+4/9HwXaEpb3uYvP+z2Aq6ldW4cmNEBDEkIt1arRWI9Xna08yNUUkVmeX//MN8hGZc4nfe/j3d6L6Gnry3TPgXw94GEslXW3SGnOfWYs+7+TnQPReTm4W1pxKj2lWZc9u0swdqZmPLHr7yYlA2vbR+sTD7Ex8TBPd51Bdxuf5gjTqEQXVnQR9LN0IaowhYiCJPo5dGrjqITm9vzzz7Nv3z7i4uI4d+4czz//PHv27OGOO+7AxsaGe+65hyeffJLdu3dz8uRJ7rrrLkJDQ6sKYwcEBNC9e/eqP35+fgAEBQXh7OzclptmPIkjYznICg0nWm9d31BPX1KKCojOy2m2Zf4ct49Qxy74Wbg02zIbS2fQ46S2qTbNXKmiRKtFY/i3HkdPZ1fMVSoOpyTUupyUokIC7Rz4v3HTOJaaxJqIc9Vev5ybjdZgIL24iEV//cZvkeEsGz4OC5Wq1uX9J3Q4s7oE88mYySzo3vvGNlIQatFRjnHDPX2xM21k3xmhRYTnJ/DIya948tS3uJja8XL3uciR1f9Goc3VlRhKjnZqla6wDWll35rXcN1svLiYX/NBUkuY4BZCD1sfnj79HWN3v8JdRz7B19KZHra+N7zs9nQeCLC1J7moELVCSW5ZabMue363XmyLjSKtuKj+ma9xvX34ceQfRBYk82zQDBSyxt2mGvtno5X0FGhLsDWxAMDV1I7Eko5Z1+9mlpGRwcKFC+nSpQtjxozh+PHjbN26lbFjxwLw8ccfM2XKFGbNmsXw4cNxdXVlw4YNbRx1w7R54kgkHG4u4rP8V1cHx2qtaBqjrv24M/0MGWX53O4z/EZCuyHfxeygh60PI50rWv94BGTiYGZOdmlJtfmC7CsujFMKC/l6/HQWd++DSl5xyPG0smasbwB2pmbkl5XywNZN9T6xUsrl5JSVUlxH4cWc0lJySktatRn8zUzU+KndzX7OGurpw6ops9k3/178bFqvdcvNvE+bw9HsS7wVvoYetr4s73MXC/1G4ai2auuwhHpUJomu/nOjy7hRrZUoujbWIGsvLhQkteg6K8lkIJPJyCiraN10uTAVnUGPq6ntDS23ofvMGM6fNmpTnMwtiM3PwSBJVV0Fm8N4v0A62Tnw04UzjXpfQ753esnABxEb8bV0YbbXkEbH1hzf65b8/DLK8vExd8bexBJbE4uqVkjCzePbb78lLi6O8vJyMjIy2LFjR1XSCMDU1JT/+7//Iycnh+LiYjZs2FBV36g2I0eORJKkGiOxtYU2TRyJC8Wbk/hcK6QVFeFibtmsy9RLBtYmHGCcW+82u2k4kXOZ/RnhvNR9Lv3sK5rYersoyCotrjbfgeR4AP6YdSdjfAN4efAodt9+Dx+Nnshv0+ejNRiwMlFz/9ZN5F1T+NpEruCObr0Y4OZJsKMzn42dyq2BQSw/ur/OuD49dQRzlQq5TEaRpnkKbwpCXW7WBNLUgK7orvw2J/W2vym3sb06nRvDf8J+wMPcgQW+o1gd+iQ9bsLuHML1tVaLpebkYWaPrYlFq7U42pB4mLiidD7uey9e5o7IZDIKdaW41JM4upmOd53s7AEIS0/lXGY6Qz2b51hhZ2rGW8NuYVtsFIdrKUVQl8bs28uFqSSVZNH/JuzGdTDzIsOcu9HfIRCAmGKROBLaj+ZLPzfSzXRwFmryCMhsdxc2zS02P5fxfoHYqE2bdUSwP5KP8UCnCQxzCmZj0pFmW25jvHruF97utYDlfe7i7fC1+Fk4k6fNqzZPXH4erx7YSRd7RyKysziSksjiHn3oYu/I8dQkZMjoZO9QoyufWqFg5aRZhHp4V03LLi3h4e1/8nfMpTpjupybzaR1q+ju5MKu+Jhm3V5BqEvluay9H+88AjJRyhRM7BTA2bxY+toHEFGQWPUatGxNFoBASzfmeA/F3dyeqMIUvr68jRJ9eYuss706lBXBoawIrJRmLOu1gE9C7uOb6O1sTDpMqV4kzDuS5GindnMtbaWq6Pq60G8UKaU5JJS0bNwag44nTn3LZ/2W8L+Q+0kuzcZKacbR7LqvIerTXvZ1pUA7B/QGA7H5uexNjGVBcG8sVKo6W21DxUO7q0sOVFLIZAQ5OONva8fdPUNQyuW8uG97g2Np7L6b6RWKj4Uz7174rVHvu3p9xnpO3pF+hkX+o3ml+22E5cYSaOlGvqaEQl3zdiUUhJbQJomj9nbwFYSm+PNyBPf2DOHrCdNZsHk95Xpdg95X3++jWFdGeH4C/ew7tVniqNyg5fmwH3i1x+283mM+OoOe5Rc31jhZ/3D+dLX3vXDlQuPWwCA+GTOZ92tpQXRfr/6EuHowd9OvKOVy5DIZR1IS0RnqH640Jj+XmPzcG9w6oVJ7ujFpa+01gXT15zvAIRBrlTkZZfmU6MqJKkytdd6W2sZ5PsMIdezKyZxopnoMQC8Z+OzSXy2yrvauUFfKU6e/45HOU1jSaTzTPQcy/9CH6KSaN30dUXv7HbYXjb0hrzyHRBQk88iJFTzbbRY/hD7OyZzLRBemEV2URmRhMvHFGc0ea562mMdPfc1/us1GJVPy6rmfOZ3b8IdKMmCif2cKNeVkWV6goO58SzXBNt68M3kyMXk57E+K43hqMmYqFRP8AtHq9ZzLSic8KwMncws8razxtLLB08qavLIydsRHk96EmkFXUysU3NurH0v7DOJcZjrlej3rI89zV4++fDR6Eg9s3VQ1Apq7pRVP9R9KP1cPXC0tkSNjdXgYn5w8XPXAc1H3Pjzeb3BV3buwjFSWbN1E5jXlCWrT2OsHhUzO7T7DuSdgLL8lHOJsXlyj3n/tum/kONBS1z8xRWnsTDtLmV6DJEm81WsBGWX5PBe2kkuFzVvAXBCam0yS6i8IUlBQgI2NDV4fvYncrO4RCeojbkA6no5+8dbb2ZVfps7jQFI8S7f/WeuTnKvV9RuRIyPA0hVrE3Nii9KZ7TWESe4hTN+/rCXCbjCFTM5I5+6E5caSrSmsml7f575p5p3klJVw1981i8F9NHoi/rb2TN/wU7PHKzSeOG43jbEf+2r7XF8OnkcXaw+SS7NRy1U8fuqbOt/fXNtXGYdCJmd16BOczo1h+cWN3BcwjrneQ5mw5zX0Uv1J446sv30g/w25l8WH/0tUUWr9b+gAjP3315xa+xjd2H17dXwmciVzvIfQy9aPAEtXXM0q6qjlaYo5nx/Pmdw4zuTFciE/EalJA7w3n57SEL4YNw2AAm0J30Rv4/eko9c9Ho117c3z3WYTm5eHhcoEL+t/BxLR6PUoZDIUcjl6gwGF/N9qITmlJVirTVHK5ZzNSOP3qAusjwynQFN7i0tLlQkT/TszLbAraoWSnNISJECSoIeTC64Wlnx/7hSfnjpM4ZWu+2N8/Pl6wgz2JsZyMSsThVzOwuDeFGjK+T3qIilFBdioTbm/V390Bj0pRYU4mVvgZG7BzxfOsD4ynOi8nAa1oL/2O2mmMMFCaYq5wgQzhRqZTEZ6WR65mookmYlcSahjFxb5jaGTlRu/xO3j25jtaAwNe+B6PTdyLGjp39Z9AeNY7D+GiIIkOlu5czT7EtGFaVzOTOD1gYvIz8/H2tq6RWMwJpX5hpRIT6yt2rwMczUFhQbcuyR1uM/kWq3W4kjcfHRMxtxctDWEZaTx4LY/WDH+Vr6aMJ0Htm2iTFf7idBErmCsa2+8zB1RyOSo5Aqc1Da4m9njbeGEtcocqBjVLKU0BydTG0zkymY5sTaVXjKwM/1sjen1fe6SJKHR10yiWapMGObpy+4E0dVMaN+MqQVSQ86/JnIlw5y7sS7hIDM8B7E+8VCDl9kc2/hs0Ew8zBz4MOJ3AKIKUzBXqnExtSWltPlGprwZXb6SLPIwdxCJI6HFNbXVEVR0Ifspbi8/sRcAS6UpQdZe9LD1oaetL3f5j8FcqSa9LI9tqafZmnqa2OKmDTJyo0ICTMnVFLHw8Mfc32k8j3eZxnTPQfw38g9O5kTXmN9WZcHL3eexIy2Md8LXE3fZHh9rW3o5u6KXJPYnxlGi09Lf1YMAOwdSCgtIKiwgqTCfEp0WaxM1M/o7MNK5By+EjuDZQUPZk36exJIsopMllDI5juYWBNjac4tPAGqlksPJCaQWFWKjNkVGRVHwE2nJfHbqSI0yADvjY3h+7zZmdwlmaqeu2KjV/BB+mk9PHq7Wfe2XC2e4r1d/zFQqMkuKOZ2ewv6k+Abvt2vPN7f5DGNp4CTktYyQVq7XklaWi4OJNZYqU87nxXP/sc+IKEhu8PoaEo8xnIdrk6cpRmPQseTY50zx6M9wp2BGufRgin1vXm/r4AShFs2aOBLJIaE2xnzQbg17EmO5a8sGvpkwg5+nzOW1g7s4m1m9GF6wozNfTpqEl7kjmWX56CQ9OslAVnkBCSWZHMqKICw3hmxNIYMdg5jhOQiDZMBcoW7TxFFTrYk4x9vDx9LZzpHEwnwsVSoyS0tY0rs/ViYmfHz8+jetgtBeNHcC6XoJm+udgx/oNIHBjl1JKMnijfO/1jhuTPcchIXSlH72nbBQmnIg80KzxNSQ901068sUj/68eX5N1Q2ZUq4A4P/6PcBdRz4hT1tc53I6Okd1xdPPyhGkhI6lLboU30jy6GpFujKO50RxPCcKqGh52M3ai3FufZjmMYAFfqOIKkzh96Qj/J1yslWvd+TI0Rr05GiKePfCb2xMPMJjXabyv5D7+Sv5BMsurKs2v73aCoVMzvqEQ2glPR4BmcRHQ3xBXrX5DqckVhWV9gjIxM4eKsev3JGWxI60M9ibWDLNYyDDnYMZ4BCIbYAFBkkiu7SU1KJC/nfyML9HXSS1uJDGWBNxjjUR5647T2ZpCcuO7G3Uciu35VpWSjOWBk7iz+Tj7M8Mp0Snqapb52Jqi5upHe5m9hToStiZdoaEDjY0vbuZPWmluegkPb8nHeH3K+UnzDXG1dpGECrdcOJIJIuEhujoyaNDyQnc8eda3h85gT9m3cm22Cg+On6QpMICFvfoy6MhocQUp3LnoY/qfbq2NuEA6xIOYmtiYdQ3U9f7zNdHnuehPgP5YNQEXCwscTQzZ19SHIM9vPn6zIlGXwwJgrFrSgKpvvNrY86/jmprAqzc8LN04TafYayK3V312lCnIB4MnAhANxsv3ji/psm1Fq6Nqa7tdbOwAjLxsXDmqaAZbE4+zj+pp6pe35F2hujCNL4e+DAzvUL5LmZHk+LpCCpH2MwsL6iaZq5Q08nKDQulKYezItoqtDbREa812ip5VLnuhmhIjHrJwLn8eM7lx/NJ5J8McuzCJLcQnuo6nbv8b+GX+H1sSjraKoXgczSF2JtY0tPWl7N5cUQWJvPQiS+Z7jmIZ4Jm8HfqCcJyY6vmt1RWlPIo0v3blaupn0mOpoiVsTtZGbsTqEioGSSpqvuesX3H69pOa5U5cpmcPennOHYlOVgpqhXr+TT1HqSlf1c+Fs7E1VLfq0ArCmULxqnJiaObNWHkYeZAuUFDVrm4cW1uHT15dDojlfHrVjKtU1ce7zeYf+YuBqBcr2N94gG+urwVbQMLm0pIVX3D2yOtwcBL+7fzzIBhHE9L5lhKEguCe7Mp6iKfnBCtjYSbV1udOzenHGeiewgXC5JY0mkC5/LiOZ0bg5XSjLd63olKXnE58HHEH2xPC2u29V67vVlxbnw7cQZDPX1IK83FgERaaS4fR2yq8d7Y4nT+Tj7BTK9QVsXuFoWf65BZVpEwWuQ3imJdOf0dAuli7VH1+thdr4jR6TqAthrMoKWu7XSSngOZFziQeQEvc0fu9B3Jg50mstB3FGsTDvJb4qEWHYnqj+Rj3OLaizd6zGfxVa0ej2RFAqCWq6rNX6CtKBY9yLELCQnN+zlcW1fpRvd5c7aEvd53brrnIEp05UZR9NmYuo9X8rFwZnva6fpnFAQj0SajqhmrJ7pMY7b3EDQGHa+d+5m9GeFtHdJNp6MnjwySxO9RF9kcHclEv0DUSiWXFafJLL85uxhc7/PemxjH3sS4qv+vChcnT0FoKWG5sSSVZBFdmIZCJuf+TuN58PgX+Fo4VyWNvrq8lQ1Jh1s0Du+AHAa4VyQ1judcprOVO2+Fr6XMUPuQRRcLEpnlPRgTuRJdLXXRhIoaR6tjdzPJvR9ag66q4HBsUTpvnP9VJI06EGNPHjU1vsSSLN65sJ7vYnYw32c4C/1GMd93OBsSD7Mm4UCLPEjTSwZePfcz3w98jPd7L+b/ov7mTF4sHub2ACSXZlebP644g3UJB1nSaTxn82KbtU5PbZp6PX31/q/ts2joMuv7HEMdu3Cbz1C+urzVqFrHG0sCyVSuwtXMtkVGFBSEltKkTpQ3Y2ujCW59me09hM8u/UVqaQ7Dnbq3dUg3rZvx+9NYOoOBP6MjWR8Z3uSkUaClGxuHvUBvO79mjk4QhJtRka4MAxIrLm+lp60vIfYB+Fu6ApBVXsAPsbtaPIYSfTlvnV9DuV5LFyt3Xj77EzFFaXXOb60yp1hXJpIf9fjy8j9M2/cWi498QoG2hE1JR7nz8EdG8aS/NbX1zaAxMPZ9cCPxpZfl8XHkH8w68C4bE48wy2swvw39D493mYaz2qb+BTRSVnkhL5xZjaXKlM/7P8Dn/R5ghHN3irRlpJXm1Zj/y6gtpJbm8u3AR/l6wMPM8x5GkLUnSpmi2WODhl9PewRkVv1p6Lw3st4BDp15u+cCDmZe5Ke4xtdLag0N3RctRS8Z0Bn0VQ9uBKE9EN/WK7zNK05kXuaOuJra8VfKiTaO6ObW0VseXa2pT+BGu/bE2dSG27yHVetnb0zEZywIxsPV1I69Gec5ln0JjUGHj7kzI10qHpI0phj2jdqZfpb44kze7HkH3w18lAWHPyKjjgS6XjKgkitRyOTXHQZbqGBnYolarmKyez98LJxwN3NgbcIBfonf19ahCa2otVsetfa5PldTxBeXt/Bj3B5mew9mjtcQpnsO5J/UU6yO3VOjNdCNOJcfz/xDHzLapSdv9ryDXnZ+/N+lv2vtOltm0HLXkU8Y7NSVSe79WNJpPGqFinK9liNZkfyZcpyjWZEYrtQqag7Xa0FzI9+Bpr53mFM3Xu8xn+M5Ubx89qdm3dbmVte+a43fjlbSE1+cQbCNN38kH2vx9QlCc2h04uhmbS1yNDuSnna+BFq5czwnio2JLdtcXzCe5qLt1QCHzgAMcQrC3cze6IasNsbP1cHUnP/eMokNkRfYGNV6N8qCYAzMFCaYyk3oY+ePiVwJSITYdwLgeHbU9d/czC4XpbIm4QBPdJlWZzc1gEuFKZjIlQRauRNRkNSKEbZPCSWZ3Lrvbca69uapoOkADHAI7BCJI2M857SlG00e1bc/jeEarlBXyvcxO1kTv59bPQdxm88wJrn3Y1f6WVbF7r5ua8bG2pV+FtuLFpTpNWy9Tl0araRnb0Y4ezPCUcoUdLZyp5edH+Pd+vBBn7uILkzls6i/OZZ9qdlig7a/P5Mj456AsSz2H8Ou9LO8ce7XBtftbGttte8OZl3kVo+BvC8ejAjthGhxdMWZvDgePrGircPokIzh4qM9slCasi7hIBPdQhjn2qdq9A1jYKyfZZCjE8M8fRnm6Ut+eRm7EmLaOiRBaDVn8+JY5D+a6Z4DOZMbi+OVrh0GycDJnOhWj2eEczCnc2OqisrWZpxrb0p05WSU5bVeYO1coa6UTclHWdJpAjvSz/DBxY1tHVKLM9ZzTlur3C/13Rg3dcQpY1Gi1/BL/D5+SzzEJPd+3Ok7gtWhTxCel8ChrAgSS7Io0JZQqCtFKVNgIldSptcQUZDUqBYxja0Bp5P0XChI5EJBIr/E7yPYxpsHAyfycd972JN+juUXNxpV/Z+mCrL25JHOU+hu68MXURUtwYT67Uo/x0K/0fS3D+RIdmRbhyMI9RKJI8FodOTua015Mqgz6NFLBs7mxdLd1ruFImscY//8kgrzKdVqMVOpmN+tl0gc1aOtn2AKzeuN878yx3sIU9z7s/ziRn4c/CQAT576rkVHJ6qNuUJNX7sAPon887rzjXHtxd8pJ8hpx6NItgU7E0ssVaacy4urGsL7ZmXs5x1j0FH2kcag4/ekI/yZfIzRLj0Z7hzMfN/hWChNa50/rTSPFZf/YXtaWKv8TsLzE3j4xApGu/Tkqa7TWR36BO9d/I0DmRdbfN0twcvckXsDxnGLay8uF6by+MmvOZUrrqsaKqowhYv5iTwQOIGzebGU6DVtHZIgXFejEkduflmAuoVCEQTR+qgxdJIelUxBqV6Dtcq8rcNpF59ZXH4ei//+jTW33kY/V/e2DkcQWlWOpogVl7ey4vJWFDI56WV5rE04wPGc1umm5mHmwKvdb8PP0gVzZcW1RFpZ7nXfczgroqpbrtBwWeUFnMmNZbJ7P/5JPdXW4bSY9nDeEVqfXjKwPS2M7WlhAJgrTLBRWWCpNEUr6dEYdNibWHGbzzBe7XEbs7xCeTN8DUklzVcb6Xp2pZ8lLDeG57rN4r3ei/kz+Tj/i/zTqAcBMJErGencg/4OnXBUW+OktsHP0oXMsnyWha9jS8pJo65nZKzeu/Abn/V7gA/63M2Tp769btdtQWhrosVRB1XXxZaxtDDoiAmkxrY60hr0KOUKLJVmFOnKWjCy+rWnz+loahIv7N1GclFBW4ciCG1GLxmYuf+dVl2nn6Uzwbbe/Bq/n7jidPSSxKl6usj9mXyc8W596Wrt2W5rHF19fGzNc+z6xEO82fMOAixdiW7GWi+C0N6U6DU1WnOklObw0tl4+tj582zQTL4b+CiPn/yGCwWJrRJTjqaI58J+YIp7fx7rMpUQ+wDeDl9bNdiJWq5ioENnPM0dkMvklOk17Eg70+pd2xQyOXf738JMr1CsVeZEFCSRWppLQvFlVsXuZk/GOTQGXavGdDOJKkrlydPf8t++9/LfkPt4K3wNccUdazRMof0QiaMO6Ho3+c09KsON6ogJpMaQy+RYqkzJKM9rsxja42fz88WzbR2CIHQ45/LiyS4vJMQ+gJ/i9jSo+1nklWSRp7lDu00cXa01R7vam3GejLJ8ZnkN5v2LG1plna2pPZ57BONzOjeGe47+jw/73sP7fRaz5NjnzToqW302pxznZM5lXuo+l09D7ufvlJOYKdSEOnbBXKmmSFuGTtJjrlTzYOBE/ko+wVfRW1vlgaGDiRUvBM+hn30n1iYcZFPykVZrldWRhOcn8Pipb3i1+22sGvQETx5cgRhnTTBG8rYOQGhdTS2AePWftmAsLaGMhQwZPhZOJJVkYaU0o1DbuvVJgDb9PgiC0P5oDDo+v/Q3tioLvuz/EG6mdvW+xyDdfF0fWuvYqZcM/JVynFEuPVDIxOWeINSlRK/hubAfKNSW8mHfu7GsoyZSS0kty+WRE1/x2aW/CXXsiqe5A6tidzHv4PuM3/Mqk/e+wa373mZ17B7Gu/Xl7Z4LWuw3rZQpGOTQhf90m8XPg5+ms5U7T5/+nv+L+kskjVpQeH4CCw5/zOncGJ6+MiKmIBgbcSXRgTTXhWpbJQw6QvKoofvVUmmKhdKU7PICrFRmrV7YViSMBEFoDEe1FX8Mf4mXe8yj3KBFJVfySo/bUMtV132fj4UzADnlha0RZqtqjXPp3vTzWKvM6WPn36LraW3iHCTUp7HfkQJtCU+d/g5blQXPBM1soajqZkBiTcJ+pu17i7uP/o/VcXuqJWoKtCWsjN3Jf878QC87X57qOr3ZYwixD+CXIU/zYd+76WXrx4akQ9xx+KNWq4PX0ZUbtLxzYR0quegQJBgn8c3sIFriIqst6ja0h5HXWqN7XaGulIiCJAY7BWGpNKVI2zo1jox93wuCYJxcTO0wV6p5O3wd87yHYm9iRWcrDz7ue0/Fk/46kt+jXXqSrynmTF5c6wbcilqyi3hUUSqppTkMdwrmRM7lZlmmINwIY34ImFKaw/KLG3ij5x0cz45ic8rxtg6phtO5Mbx/cSMvBs8hoyyfVbG7mlyUWiGTY65Q42xqw0yvwUz3HMipnGheCFtFVFFqM0cuNERWeSH/jfijrcMQhFqJxFEH0Bo3+5XraI0LAmNNHl277Vf/vzHxNrQGxqmcGMa49kQlV1LUwi2OjHF/Cy3LmC/uhfYroiCJB49/waf97sfdzB5/Cxe+GfgwL539iajC6gVBTeRKRrv2ZE/GefSSAYVMjlKmoLydjTrTlIcJzflgZn/GBUa69OCjyE03tBxjIc5HQkM09XezM/0sfZM68Wy3mdiZWPBT3F6jGy3s75QTuJvZcV+ncUxw70tcUTq5mmIslGqsVeaYyJXIZXLkyJDLZBX/lsmQI0MlV2KuVGOpNMVUYVK1zGJdGcsvbmRT0lEkI9vejmZ/5oW2DkEQaiUSR0Kzaq0EkjEljxqyrS3RCqlEX4aLqS0AqaXXH9L6RhjLfhZaj0gaCc0t/8pIQDYqc2KK0njq1Hf8X78HcFBbAbCi/0Ocyo0hoTiD+OJMMsrzWdJpPPYmlmxMPEw3ay++HvgwABN2v9bq3XObQ12/q/qOsTeaRNqXGc5cn6EE23gTnp/Q6PcLQnNqzQLxTfXhxY0UaUu5v9N4CnWl/J50tK1DquGb6O0czopkknsIzmobAq3cKNaVk68tQWPQYpAkDBjQSxKSZMAgSegxoDPoKdaVU6QrpURXTrG+jDxNCRcLEsXoaIIgXJdIHAktojUuDK5dfksmOJprW66XQGrsOkp0FUPLluk1XGyh0YZE0kgQhOaQrykBwEZlAUCetpgHjn/O88GzGeHcnciCJJQyOaGOXZnlNRilXEFaaR6Pnvya6KI01gx9FoCs8oJWGU2oNTXmQci18zXkvHE2L464ogyeDZrJfcc+a9c3hx3tnNSa1znCvwxIfHF5Cz4WTkx0CzHKxBFUFFQWyWBBEFpLqyWOPMwcmO87nF62fhTpysjTFJFYks230dsoa4Vm58Y2zHxracuLjNbsvtaQ9TRlX7RU7FcnkJq6joIrT/DjizPRSfpmi62SuEDtmDrCcVFofYW6UnQGPaNcenAmL5ZcTRGFulJeOLOaWz0Gcm/AWAKt3NmXEc4zp79HLpOToymkSFeGv6Ur7mb2ADx16rubshtFU1vRNuQ8q5cMvHzuJ74Z8DCPd5nG+xc3NDlOofXU9pkaU2vrjuBkTjQPBk5EjszouqsJgiC0tkYljlJjHfHq3viRTca49OLF4DkUaEs4kHkBtUKFrcqC6Z4D8bFw4rmwH5r1QrApT+7EzVLLae0EUl0a2k2gNeO8kXUdy64Y5SKuOL25whE6uLb+jQo3Lz8LF5RyBbe49qKrtQfzDi6vem1T8lG2pJ5kkns/ngmaQVhuLFpJzzNBMzieHcVHEZuIK8pga9opLouCrbWq7zwbU5TGRxGbeD54NkW6Ur6I+qfdJeA6UsLkesdikTxqPbHF6agVKtzN7cVQ9IIgdHiNShy5+WUB6katwMfCmReD57AvM5x3wtdXK2o50KEzH/W9hzt8R/Bj3J5GLfdqzXECbQ99rts7Y93HxhhTQ2RrCnnkxFdcKkxu61CEm0B7/R0I7cMgxy5oDDriizPYnFxzpCKNQceFK10uIgqSmOLRH4Dedn7M8xnGHYc/bNV420JzJASul0DanHIcM4UJj3aZgpPahmXh69C2QGvVltCREiUNrZvYkfZJW4ktqngw52fhIhJHgiB0eC3eVe2+gHFklRewLHxdjX71R7Mv8UPMLu4LGMe5vLg2G25X3DC1HmNpfXSzOJUb3WLLFhemHYP4LQqtwcfCmZiiNO45+mmd83Sz8UZn0BNZmIxVhhlTPPqjkivJKs9vxUjbVnMNpFDXg5p1iQfJLC/gle7zsDWx4JnTK1ukq7PQNOJ4bFyyNYUUacvwMhfXQoIgCPKWXLiNypxhTt34JX5fncUYv43Zzrn8eF7vMR/bK0UzG6uhT2fq+nMzM9btEwkJQWhbHeH4JxgPXwtn4ooy6nzdVmXBLK9QIguT0Rh0HMmK5NPIzWxOPk5UQcfrntYcv826zrN7Ms7x9Onv6WPnz9NBM254PS1NXC/U1N73SXuK30ShRNMKtVgFQRCMXYu2OBruHIxMJmNPxvk659FLBl479zMrBz3Oqz1u49Vzv1CgLal63UJpykS3vky8MtxkZGEyK2N2cT4/vtpyxA1Q+2OsXdeEf4lWRzcn8bsTWpuPhROHMi/W+pqj2opP+t6PpcqUV87+DIBaoWKSewgBVm50t/HhQNaF1gzXKDRH66O6zrOncqP5b+QfPBM0kx/jdotuOIJRqes739rnLiulGSZyJbma4lZdryAIgjFq0cTRLS69OZNbMXrK9WSVF/L6uV95r/citox8FY1BR56mmBJdOW5mdihkcvZlhnMw8yKjXHqwYsBD7MsI5/Vzv7TKiGxCyxFd14yfSB7dXMRvTWht9iaWWKvMiSuuvcXRM0EzMVeqWXriy6oExgLfUQRYuQFwLPtSq8VqjG40gVRX8mhb6mme6HIr/e0DjTZx1JHOPR3t2Fzb97IlPu9etn7IZJCrKSanvJBCXWm11+XIMFOqsVCoMVeqsVCaYq0yI8DSjaFO3SjVa7hYkNjscQmCILQ3LZY48rNwoZ9DJ14790uD5j+eE8Udhz6km403diYW2KgssFCqSS3NZXfGWbLKK0Zz+z5mJ6NdevCfbrP5tN8SPoz4nZiitDq7wgnt48ZftD4ybu3hOyTUT/zGhLbga+EMUGviyM3UjsGOXXn/4oZqyQt/SxcAsssL+S5mR+sEauRuJIFU2zm2RK/hbF4c831HcLEgkYgC4xpoQZxzGq81EjEtpSGxNuZa0VFtxXNBsxjsFFRtus6gJ09b0YLI/EqyqDbFujIuFabwQtgqUkpzGrROQRCEm1mLJY7meg8lsyyf3ennGvye1LJcUstyrzuPhMTO9LMkl+bwavfb+HbgIwBkluWTUppDamkuKaU5/J1yot5lCcZD3NAaP5E8at/Eb0xoKzZX6hfWNvz7DK9BFOnK2JYaVm36n8nH6WnryzsX1tdoISA0TW033e9eWM8bPe/gy/4P8Xb4OranhbVNcNcQ55rGq+0Yf/U0Y9ynLRXTLa69eKrrdLQGPf8J+4G44gzsTSyxM7HE1sQSexNLJCSKdeWU6Mop1pVRoi+n+Kp/Z5Tl13rMEgRB6KhaJHEUZO3JJPcQVlz+p8VG64goSGLh4Y/pbuuDm5k97mZ2uJna42Zmx2CnrszwGsTTp78zuidobeHqE7MMGO3jT08nN4IdnTFVKjmWmsRf0ZFE57X+ExVxM9u+iOSRIAiNdSgrgpzyQp7ociuvna+oYyhDxiiXHsz2GsJviYcov6bb+cGsi0za+0YbRXzzujZ5lFyaw5Jjn/NGj/m8EDyHlNIcwvMT2jBC40xwtIaWbnndUc7fd/vfwj0BY9meFsZHEZuq6qYmlmS1cWSCIAjtW6MSR9M8BmBjY4NKrkBnMHAmL5YL+YnVMvLlKT68PHg2UYUprEk40OwBX00r6TmdG8Pp3Jhq061V5izvvZhPQ5bwbcx2UktzsFCaYak0xUJpit6gp0BXQp6mmKjCFJKv0wTV3cyeYU7BOKqtcFRb46C2wkpphqnCBAC5TIYM2ZW/5chlsmum1f93ib6ct86v5WBW7YVDm9ODfQbw7MDhZJYUE56VQalOywO9B/B4v8GsCDvG8mMHMEhNe8IikkAdQ0e5+LyZiN+m0JbKDVqWXVjHK91vY3XoE/wav5/xbn0ItHJnX0Y4X0dva+sQO5TUGGdu7WdDqGNXgmy88LFwwlplDsAolx5tnjgSGq+hx/ib4fxdX4JtoENndqadaXCpDEEQBKFhGpU4eiBwAiYWZmgNOkwVJpgr1SQUZ7It7TTJJdn4Wbowa+RQtHo9z59ZjV4ytFTc11WgLeHRk1/zn26zWNJpAibyis0s1Wso1pWhkMmxVpmjkMkByCkv5MOITezJqN6tztfCmc/6LcFcoSajPJ+s8gKyywtJKM6kVK9FwoBBkpCQav9bkjDU+pqh2v8HOXThnd4L+ShiE78nHWnWfXHtBcIEv85sirrIYzv/AsDH2pbZXYJ5qM9AHuwzkNSiIlaFn653ueJGtGNrjtF+BEHoOA5nRbLg8Ef8p9scHu48mWPZl3jo+BecyYtr69DaleY45i4M7s1rIWNIL8sjLDeGQ5kXiS/JIL44s81bZXT0c4qo93jjcjRFOKqt2zoMQRCEm06jEkeT97yJwqKipY1CJqeHrQ8zPEOZ6z0Ua5U5ZXoN3589zZdhx7H0rH30lNZSbtDy+vlfeefCeswUJhTpyqolsmTIsFGZE2TjxZ2+I7i/07hqiSNvc0f+F3I/2eWFzD/5YVVT15bwR9IxXuo+l2eCZpBamsPRFhxBxoDEaB9/lg0fi6OZOcO9fNHo9SQXFeBtbXvd/tziYka41s3w9FIQhNaRVV7I06e/w1ZlUVWcVmh9ld3St6ScNKrWXuJcUqGxyaPGXpvd7OftramneLvXAkIdu3I4K6KtwxEEQbhpNCpxlBLrgNzMFI+ATPSSgbDcWMJyYwEwlauIj7ZHZ6hIzlg2f6xNojHoah1xTUIiT1vM4awINAYt/wu5n+42PpzPj8dEruS93osp0Jbw2MmvWyxp5GXuyDCnbgx16kZ3Wx8AbK40F28OtV0Y3LtlI3f3CGFKpy4kFOTz6akjfHf2FKU6bS1L+JdIGhk/a5U5zwTNYGPiEU7nxrRaUceb/SK0vRO/XcHYiKRR29qfFM87R/by/KAxZJbn83vS0bYOSZxDrnG95JHYV9e3J+M8x7OjeKLLNE7mXBajLguCIDSTJhXHrvuk1TZd027UqZwYUktzmOQewvn8eOZ4D8HdzJ6FR/7bYhe4I52782bPO9AYdBzPjmL5xQ0cyowgW1NYbb4AS1duce1FYkk2W1JO3nAyIKu0hPeP7ef9Y/sbNL+46Ww/VvR/CG8LJ0a79CRfU8yOtDN8FLmpVdYtkkfCjRjk0AVvCycSS7LEE2JBaAUrwo7jamHFk8HTUcoUbEw60mblBcS5o3YN3S+V8zX0eu1m2N/1tcr6KOJ3VoU+wQLfUXwbs70VIxMEQbh5tcioau2NhMTu9POMd+vD8osbsTOxpERfTk55Yf1vboCetr7c4TsCgyRRqi9HY9Ax2qUnezPCeeP8r9d9GvJ6j/n4WboAoDPo2GYkQ+UKxudiQSKuZnaczolhoGNn+tj7t+r6RfJIaIrpngN5Jmhm1f+fOPUtx1qwu66xkiOjv0Mggxy6oJQr+D3pCHO8h9LJ0hWdZMBGZc6hrAi+vryVMsP1W4gKN6fmPr6+eWg3Krmcx7pNY7rbcFYmbGF3+rlWTSCJc0bzEfvyXwklWfwct487fEfwT+opkkuz2zokQRCEdk/e1gEYiwOZ4Tiorehm48XPcXtRyORsGPY8y3svZp73MAIt3ZAha9QyZchY4DuST0Pux1ltg0Imx9nUlkArd45nR/HuhfX1NqFNKMmkXK8lV1PEaJeeDV53c1xAiNZG7cu+jHAyy/IJsHIlriiDf1JPtXoM4jsjNIYMGQ90mgjAhN2vAeBl7tCGEbWdF4Ln8FHfexju3J1RLj34NOR+pnr0J8jGix62PnhbOHGbzzAW+49p61CFNtASSQGDJPHS/h1MWvcD0bk5vN5jPn8MfYWXgucywjkYU7mq2dd5NZHoEG5Efd+fH2J3kasp4omu01opIkEQhJubaHF0xbm8eLLLC5nuOZBl4eu55+injHTuToh9J5Z0Go9aMYVcTRGncmKIKEhCJgNvcycuF6ZSZtDUWJ69iRWjXXrib+nCqtjdfBezo0lP8ZaFr+P1HvPpYevDnozzzbGpDSISAO3PnozzrfodqYtoeWQ8jP13LCHxdvhaXgqex0+DnwLosPUoxrr2ZmXMTr6O3oa5Qs2m4S9yLi+esNwYetj60N3GB6Vcwe0+w9mUdJTUsty2DlloBa1xLI3Nz+VAUhz+tnb429oz0T2Eie4hNeZbfnFjs4z8Ks4PQnO5Xpe1coOW/4v6mzd73kGglTtRhSmtHJ0gCMLNpcUSR+1tSFEDEj/E7uTJrtPpZevHb4mH+Dl+H6vj9mAiV9LdxpsQ+06E2Hci1LEL5ko1WeUFTHQPQSGr2XCrRFfOoawIPo7cVFVAvCmKdGU8dfq7Rr3nRi/K2tPnJhgnkTwSGmp/5gUWHfkvDwVOIrIwiS0prd9Sri2pZAqW9VqAUq4guSQbW5UFb/a8A7VCxY60MNYnHgJgjtcQHu86DaVcgblS3cZRC62hNY6hViYmrL31dgJs7TmdnsK5zHRcLSwZ6O5VY979GeFNXo84HwhtYW/GeTLL8pnuOZDlFze2dTiCIAjtmmhxdJXfEg8TUZDMLK9QHgqcRD/7Tjwb9gMag45TuTGcyo2pGrrWSmlGoa60jSMWBEFo/9LKcnnl3E9tHUabmO09hAEOnXnhzCr2ZoRzp+9I+toH8MiJFZzKjamab13iQbLKC+hp60tMUXobRiy0htZKtChkchzNzInNz+WJXX+TUlRR21Ell+NtbYteMhCXn9ekB0oiWSS0hus9qNZLBjanHGee9zD+79JflOhr9hAQBEEQGkbUOLpGeH4Cb5xfw/sXNzDYKQhXU9ta5xNJI0G4PtFyrW2J/W/8pnsO5IFOE9iYdIS9V1pznM+PBypam15rd8Y5Prn05w2PrikYr+Rop1ZNuOSVl3Hn5nVYmZjwz5zFjPKuGFRBazAQnZdDXH5etbjqi68h8whCa/oz+ThqhYpbXHu3dSiCIAjtmmhxVIcyfcWoNfnakjaORBDaL9FlTRBq183aiye63MrmlON8emlz1fTzeQmU6MoZ4NCZS6ImR4fRksdJS5UJIa7u9HfzpL+rB9ZqU0q0Goq1Wkq0GnLKSnl2z1YWBffhy/HTmLJ+NVG51x+FShzXBWNyvVZH6WV5HMmKZK73UP5JPdVh6+gJgiDcKJE4qoO9iSXlei0l+vK2DkUQBEG4SVgoTbnLfwzjXPsQWZjMRxGbqg2coJP0nMqNZqBDZ36M29N2gQotriWTL97WNizq3odB7l50tXdCIZeTWVLMibRkLmZnYq5SYa4ywVJlwpQAH+Z368VnJ4/QxcGRpwcMZcnWTS0WmyC0tq+jt7Gi/0M8GzSTt8LXtnU4giAI7ZJIHNWhI3cFaG+FzQXjJlodtT7x+zVOCpmcT0Pux93Mnj0Z5/kuenuto22W63U4qq3bIEKhpbX0sdDFwpLHQkKZ27UHOaUl7E2MY+W505xISyY2v+6R+B7oPYD/DBrOttgoxvsFMtG/M1tiLrVorELj+dvYsev2e4jOzWbm77+QX16zS2tHdb1r16jCFJZdqBil2CBJ/O/Sn7V2BxYEQRDq1qKJo/acgMguL0StULXLItiV+7ypF6jt9TMTBKH5OKqt+HrAI0QWJHEhP5Ef4/Zg6MAJ9eYwxDGILtYe3H/s/wjPT6h1HnOFmqFOQXwTvb2VoxNaUmskz2d1DubVIaPRGPS8c2QvP4afoVzfsG45X4YdwyBJvBA6gojsTL4YN43T6Sl8e/Ykm6MjWzhyoaESCvMBCLBz4MxdDzP0p69IKixo46jahx1pZzCRq3i8y1T6OwSyLHwdx3Oi2josQRCEdkMUx65DWlnFk7knu96Kt3n7bC3hEZBZ9acx7xEEof0KtHNghucg7g8Yj43KvMnLmeoxAGdTG+xNrFgSOAFXM7tmjLJjmuM9hLN5cXUmjQBGOAejkivYkRbWeoEJLaK1CkWbyBV8dssUPhw9ke1xlxn9y7d8e/Zkg5NGlb46c5xPThyiq4MTX4cdp0ir4ePRkwhyaJ/XQDcjncFA568+JvpKDaqd8+7GwbTxx3m1QsFdPfry8ehJLAzug6+NbTNH2jbq+639nXKCBYc/Jr44gw/63MVUjwGtFJkgCEL71+Jd1a7X6siYWyRdLEjiw4u/s8BvFD8N7sWejPOsit1NVAsUK1XI5HSx8kCtUHEuLx6dpG/2ddS2n2+m7kMt3bqqruUb6/dX6FhM5AqW9h3EncG9cDAzR2vQoTXoGe/Wl5fOruZiQVKjl+lgYlX173K9VgwUcIP8LFzoax/AK2d/uu5849z6EJYbS0Z5fitFJjSn1j6vquRy/m/sVIZ7+fLQtj/4+wa7l/33xCGCHJyYFhjE0J++4uiCB5jVOZi3Du9pnoCFG6Yx6Jmx8WfO3v0IaqWSrybcysK/1lOs1TZ4GSvGT2eopw8XszOYHNAFE8UYDibF83vURc5lplGk1eBhaU1cQR7pxUUtuDXNr757i/SyPJ489S2Pd5nGf7rNwlFtzfcxO1oxQkEQhPapVWocXXsQv/rCypiTRxuSDvNn8jEmuIewwHckKwc9xvHsKA5nRXAyJ5roorQm1UKSIcPL3JGetr4MdOxMP/tOWF9pGZBZls9viYf4Peloi3eRM9b9XpeWuCC/0WVWvr+97cvWJuocNY2DqTn39erHqfQU9iXFUaar3oLAXKkixNWdF0NHEmBrzw/nT3NWd4rzeQnYqMx5s+cdfNH/QcLzEziZE83ahAMNruswxKkbAGqFiidPf0uxqAfRZCqZgvFufdAadOzNCK9zvlHOPRjg0JnXz/3aitEJTWUMx7QXQkcy3NuX+//5nb2JcTe8PAn46cIZxvkFMtjdG3szc85mpt3wcoXmVaAp58NjB3hqwFBCXD34ddo8Htr2J4mFdSec7UzNmB4YRE5ZKSO9/XjtwE5Wnj+NqVLJON9OLAjuzXsjxyOXyarecyApnjs3r2uNTWpW9d1bGJD4KHITmeX5PBA4kRJdGWsSDrRihIIgCO1PqxXHvt4FljEnj7SSnj+Tj/F3ygnGuPRksnt/lnSagFqhIk9TzOncGM7mxVGkLUUr6dEa9Oikiqf9pgoVtipL7EwssTWxwM7EEie1NYFW7pgr1eglAxfzE1mbcJBj2ZfQGnTM8AzlLv9bWOw/hi0pJ1mbcICEkqy23g2tzhguyBvj6niN9bsstC9qhZJvJk4n2NGFBxQDKNNpicnLJbeslCKtBj8bOzrZOSCXyQjPSmfahh+5mP1v19SM8nyWnljBFI9+TPMYyCK/0RRoS1ifeKhB63/+zCpk0KTWSkJ1T3S9lVs9B3IqJ7rWFqXmCjV3B9zCHK8h7Eg7w7a0020QpdAQxnRuCnZ0ZmFwb5Yd2dssSaNKp9NTAXikXyhJhQVsjRV1YIzR9+dOcU/PEC7lZuNtbcPe+feyKz6GLTGX2JUQQ27Zvw8fR3v78/6oCTia/dut7UByRZfZMp2OPy5H8MflCCxUKoIdXTBVKBnu5cu8oB7I4Katbrc6bg8WSlMe7TKVPG0xW1PFsbetGfM9oSB0dEYzqlpbtdxo6AFKLxnYlhbGtrQwTORKgm28CbEPoK9dAA8FTkQlr31X6iUD+ZpicjVF5GmLSS/L42DWRSIKkogsSK7x9P+9i7+x4vI/TPccxEyvUGZ4hXIo8yK/xO/jVG5Ms2yzsTKmC/IbIVohCTdKBnw8ehJd7Z2Y9fvPFGrKGeMTgK+NHbZqU2zUao6lJvHN2ROczUjjUm42Bqn6pb1CJmeEc3f62AWglwwo5YpG1WuLEAmjZmOjMudE9mUeO/V1ra9/2PduAq3c+SZ6G7/E72/l6IT6GOO5SQa8OfQWonKz+eF8897sjvcLBKCrvRPfnD1Bub75u88LN65Iq+GrM8d5rN9gJq79gf5unszr2oPloyYgSRKn0lM4kpJImV7HYyGD2ZcYx3/2bsVcpcIgSbUW1S7WajmWWnHs1xr03NurH6O8/dmV0P6uPxt6ff/l5X+wUVnwUvBczBVqNiYdaYXohNpUHmvFdbQgGCejSRxVaq1M8410l9MYdJzOjeF0bgxQMfKNQiZHKVOglCtQyRSo5Eo0Bi0F2tJGd2fL0xazMnYnP8XtYaxrb+b6DOXTfkv4v0t/83P83kYtq70wxgvzGyVOfEJTPTdwOBP8A1my9XfOZaYD8O3Zk9d9z9Xfs0nu/VjsNxoPcwfC8xKILkrjQOYFtnWwp6lmChMW+Y3GSmVGiU5DQkkG8cWZhOcnoJcMrRaHrYlFnV0E7/EfS09bXx458RWncqNbLSahfsZ8XprTtTt9Xd2Zu+lXdIbm+y6r5HIe6xfK4eQEQj282RXf/hIGHckP509zT89+3N0zhJf272BNxDmczMwZ5ePPWJ9OzO7SHUczc3YlxLB0+58V35UGVkE4mprErvgYvhg/jQ2XLrA7PobjacnVWjIZu4Ze379/cQMl+nKeDpqBrYkF38fsbIXohKvVdrwV19GCYFyMLnHUGuo6ON3IgUkvGdBLBsoNDS9OWB+tpOfv1JP8nXqSZ4JmMt93OGsS9rfqDU9rMOaL8+Ygmt0KjRHq7sUDfQbw5qHdbI+rP5Fw7Xdrod8olnSawM60s7x09kcutUBB//ZimsdAFviN4nJhKqYKFW5mQ1HI5PyRdIz3Lv7WKjEMcuhCbzv/WotiPxQ4kTt8R/JF1BaRNDIixn5OslGb8p+Bw9lwKbyqdUhzmRfUE3dLa06lp5BZUsw5Ud/IqBVrtawIO87TA4byZdgxkgoLyCwtYW3EedZGnL+hZRskiYe2/8HdPfoyv1tvbg/qCUBGcREpRYXEF+RxJiOV42nJVQ842isJiU8vbSZfU8ySwAnYqCz4JPLPJtUxFZqfuI4WBOPQqMTRrYFBbE1JQGNo2WbLbXWAMOYD05/JR5nuOZA+dv6cyLnc1uE0G2O/QBeE1vb0gKGcTEvm27MnsTZRM8LLl+FefrhYWFKu07Ez/yAX8hMpM2gp12vRXXVdO9wpmCWdJvD15W2sjBVPTD3NHbhUkMxdR/8HgIlcyR2+I1nkN4qvoreSq2n50YIme/QjrTSPnelnq02f5N6PO3xH8nHEpgbXnRJaVns5Hz0zYChKuYJ3DjdvC2QzpYpH+g7iVFoK0zoF8c6RveK2uR1YHR7Gfb36sbTvIJ7fu61Zl12m0/H56WN8fvoYHpbW9HPzwNfaFndLawLs7Bnv1wlTpYro3GzWRYbz26VwMkuKmzWG1rQqbjf52hKeDpqOu5k9H1zcKEa4NBLGfI8mCB1FoxJHbw8fy0sKOd+fO8XPF85QoClvqbhaTH0XhsZ6YIooSKZIW0YXaw+jSBw1x35qLxfpzUE0txUaqruTCx8dP8hDfQbwQO+BWKvVXC5MJb44E1crG971W1Rt/qzyAi7mJ3KxIIkpHv05lHlRJI2uMFOYYKE0xVxhQoleg8agY39GOPcGjMXdzL5VEkd/p5xktEtPBjl04Uh2ZNX0aR4DOJwVIZJGbay9nYeCHZ2Z360Xbx7aTWZpSbMu+5G+g7BRq3FwcePnC2dYEXa8WZd/tb4ubqwYP52pv60mrZ0N925MFDIZMwKDSCzI5/agnnxx+igJBS2T6EguKiA5qnpdJJVczgA3T+Z07c7j/UJ5ZsBQjqQkcik3m8SCPAo05RRpNBRpNcTl59ZaV8nYbEo+SmZ5Ps91m8VPg5/i5/i9bEsNI7k0u61D6/CM9R5NEDqKRiWOpq5fzQOhw3ii/2Ae7juIXy+e5btzJ0kpKmz2wFri4NDQC0RjPTAZMCBJbf/879ridXD9hEh7uzBvSTf63aprXxrj91VoPFu1KWqFkucHjUBr0LEx6Qi/xO2r9sQzwNIVZ1Nb1HIVZgoV3hZOBFl7cZvPMNRyFU9d+q4Nt8C4rE88yDCnYJb3uYunT39PqV5DkI0XANnlzX/eqs2F/IqRi4JtvMnTFpFVXoBCpqCHrQ9vnl/TKjEINbXX89JDfQYSl5/LqmYuiB1ga8+9vfqxPe4ykwO68NHxgzXmsVCpWNS9D1+dOXFDdZWsTdRsmHEHUNGiRajfaG9/eji5kFVaQnpxEafSU8gpK8Xe1IxlI8ZVzeduad1iiaPaaA0GDiYncDA5AWsTNVMCujDK25+hHj54du2BmUpVbf7dCTG8dWgP0Xk5rRZjUxzKimD+oQ+5P2Acd/iO5N6AcUQWJPPl5X84ln2prcPr0Iz1Hk0QOoJGJY5i83N5fu82Pjp2gIXd+7AguDeLe/Rlc3QEX4edIDw7o1mDa8uDg7EdmGTIUMtV6Nq4vlFdF9vt9SK8LTS29VFD9q2xfV+vJb4fDVOq03E0JZFCeQ5fXf6H5NKaF9fRRWlEF9Ved8RMYUKpXtPSYbYbEQXJPHn6Wz7uew/v917MqtjdPBQ4kX9STpFWltsqMZgqTDBIBu4OuIW7A26pml6gLeFkjqhr1Jra+3HI08qGCX6BvHpgJ/pmfoj0xtAxpBQVkFdWSkxeDlm1tGZ6e/hYpgd240RaSpNrK1mZmLD21tsA+PNyBHnltReNF/7V1d6R7ybNBEBnMKCUyzFIEmcz0ziTkUZSYT6eVjYMWv1lm7beKtCU8/PFs/x88d9uuUq5HHOlCisTNf3cPHhu4DB23nY3p9JSWB95ns3RkUbbe6FYV8bHkX/wRdQWBjl2YaZXKB/3vYffk47yf5c2UyLOtW3G2K95BeFm1aTi2JmlJXx4/CBfnD7GnK7dubdnP6bP6caBpHi+CjvOvqS4ZglOHBT+5WPhhFqhIrootUnvD3XswiyvIeRritmfeYGj2ZGNvsFsbxfdDqbmuFpaEpuXS4mu+YqWN5frnfiasq9vhu5wtmpTirUatM04SlB7Uq7XMe+PNU3+DEXSqKbw/ASeOvUdH/S5m/+G3EtOeSH/u/Rnq6xbIZOTXpbHtH1vY65QY6k0xVFtjb3ailM5l8kUtTNaRXs7d9Xl7h59KdCUs/5SeLMud2nfgQzx9GHxX7/x2tDRHEpOqDGPu6UV0zoFAdDV3qlJiaNOdg6sGH8rAbb2ALy8f8eNBd5BROZkcSApHk8ra25Z8z2OZuYM8fBhlLcfA9w8ySktZWd8DPnlxpeA0RkMFGjKKdCUkxxVwJboS9ziG8DsLt15c9gtvDJkFFtjL/PfE4eIzW/+ZH5zXA+VGbTsyTjPnozz3OoxkIc7T2agQyDLwteLQQ2akUdAZqOO1SJ5JAitTyY1oO9TQUEBNjY2eH30JnIz0xqvK2QyJvh35v5e/enl7MrF7Ey+PnOcPy9HNPkGsCUOBo05IBnbwWiqR3+eCZrJhsTDqOQKsssLyS4v5EJ+ApeL0uoc+SEn3p0XQ0dwZ3BvTqWlYKpU0s3RmTKdln2JcWyMusiu+GjK9fpW7W7W2BNEQ6nkcib6d2Zx9770dXWvmp5YkM+l3CwuZmfy3dmT5DTjcLLWJmrK9XrK9cbT5N7Yvr/1fdYmcgUvDh7JguDe6A0GjqUm8eyerSQX1ayHULltrXEzqFYo8be1I6u0pMUKfsplMlRyRbXvj7F9fnVxVFvzVs878bVw5kJ+Iufy4wjPSyBLU4iJXEFaaR55WuMolGqmMMHZ1Jac8kIKdS0/nPTDnSczz3soYbmx7Mk4h8agY29GOAXa5q1LI9TtZkkYQcV55tCdS/j+3Ek+rKUbWVOYKVUsHzmeKZ268smJw5zOSGHlpFnM2/QrR68khixUKu7uEcJ9vfpTptOSUJiPUiZnxsafar3q6O7ownAvH7ytbfG2tsXN0gpzpQpTpRJLlQkKuRyA/508XGt3OKF23R1d2Dx7AQ9v/5PN0ZH1v6EdcDa3YHpgNxYE98ZarebOzeuafXS2ljiXupna8ULwHPraB/Dm+TX8k3qq2dfRUTXlmN1erpcaQ1dczrEZn5Cfn4+1tXVbh9NqKvMNKZGeWFvJ2zqcagoKDbh3Sepwn8m1miVxdPWPtredH/N9RjDEKYgSXTm74mI5npbMidRkLuZkYmhA8+qWOgi058TRf7rNJsTOH3dzBxKKMzFXqrEzsUQhk5OvKeZUbgwncy5zPj+BIm0pOsmAY3EQL4SOwM3CircP7+HHC2cA8LKyYbxfIFM6daG3sxv55WX8cuEsX4YdI6+8rNZtb4nE0Y0s19PKhgXBvZkc0AW1QgHAQ9v+YPmoCfja2HEgKZ51kedJLMjD39aeQDtHOts7EOLiTnZZKRPWrqRcf+OjA87uEsw7w8eRWVrMrRt+MqrRRIzpO1zf5/xoSCgP9x3Eh8cPUKzV8mDvAZTrddy64UcKNRWtaFq7jtYYH39eHzoGTysbDJLEk7v+RimX093RBSsTNSq5nFKdjlKdlmKthvTiYtKKC7mUm0Vcfl6D1tHTyZU/Zt1JVE42Y9d+XzXdmD676wmxD+B/IfdzMucyZXotPWx9sFaZV72eqyni9oMftEqixljIkPFU11uZ4RXK4axIPM0d8DJ3BODTS5v5NX5/G0d487uZEkaV7u/Vn6cGDGHoT183y3nG29qGFeOn421tw6pzp9kcE8lrQ0ZjkCTm/VFRe6u3sytfjLsVe1MzfrwQxhenj9HZ3pGfp87ly7BjrD4fhrlKhaeVDd0cnZjeqRuB9g4UlJcTl59LQkEeSUUFlGi1lOl0FGrKeaLfYJKLCpi76dd227JUBvRxcaObowtmSiXrIs63Spe7X6fNQ2vQs2Dz+hZfV2uyMjG5sm0GZm78uUH3CQ3VUudSGTLe7nknAVau3Hbwgzof3gqN19jjd3u5XmoMkTgSiSNj1aSuatf7kYblxhKWG4uvhTOjXHowwLEz4/1HoJIrKdaVcSotjUs5WVzKySYqN4uo3OyqG8P6ln2jWqqVS2voYeNDUkk27uYOPHj8C/K0xZjIlXSz8SLErhMh9gE83mUaSrmi2vvC0lO5Z8vGak2AEwvz+ebsCb45e4IAW3vmdO3Ogu69md+tFy/s28bmKy1vm5LcUcrlDPXwoauDE5YmJmyNjWr2J0gAP06ZjalSydGUJHLLSlncoy9TArria2PHjA0/cTrj3y59p9L//XcnOwe2zV3MtE5BrIs8f0MxeFha88GoiVzMyiDI0ZkHew/gjUO7b2iZzak9NeO9s1svJCQGu3tzKj2FNw7tZsX4Wxnq6ctZWf1Ppa/ezsrvax9nNx4OGYS7hRVHUpP44fypOhM6QQ5O3NmtF4VaDcuP7meYly/fTpzJ3oRYntq1hacGDOW/YyZjkCSicrMpKC9Do9djduVJupWJGmcLC9SKikPqhawMnt69hQvZde//AFt7fpk2F4DU4tYp1NzcTuZEc6kgGX9LV3ann2Pd2YOklObgZmbHo52n4GvpgqupLYVFHSNxpJDJ+U+3WUxy78f6hIOkl+XR1dqDIm0ZP8TuZG38gbYO8abXXs/x16OUy1ncow9/REU0S9Kor4s7302cgUIux0Su4MG+A3mw70Cic3NIL6mokTO3a0VXovOZ6cz+/Zeq1p9ZyQl8cuLwlVEfB1Qts0SrZVtcFG8d3s2BpPg6azA91i/0hgprtzUzpYqL9z5WbdowTx8W/vVbi63T0cyc/m6eAAzx8MHe1KxZW023tUKNhlcO7GTttNt4YdAI3jq8p61DqpeExNrEA/xfvwcIsvbkQkFiW4d00xBd1gTBeDUqceTml4XSQt2geeOKM/g+Ziffx+zERK4kyNqLXra+BNl4MdongLt6hCCXyQDILCkmp6yUYvLJ15aQry0mT1NMdnkh5/PjuVyYiqEVs/nGdgCyUprha+nMhsRD9LUPYKJ7CL/E70Nj0FUl6r6N2Y6ZwgQ/CxeKM5xRK5TE5ufU2/IhOi+Hd4/s4+uwE7w1/BY+GzsVW7UpP1440+gL8DE+/rw9fCyuFlYA5JWV8nDfQSQXFrAtLop/YqI4npaMm/+/RdSbkswzU6rwtbHj6d1bWB8ZzoejJpJXVkpYRgqL6HPdekaXc7PZGRfNvb361Zo48rG2xdncghKdlui8nOuO+FL5hKmTnQPlOh17EmIbtR2NpZDJ6OHkwiB3b/xt7bBQmXAiLRmouBHoZGvPJycP809sVNV72ssJ9a4tGxjl7c9Ad0+e6D+EsIxU8jTF9PFXczYWpnsO5BbX3jirbdAa9BzNjmRvxnkuFiThZe6Ih5kDKrmCo9mX6NWtnEWu05nZOZjo3GziC/KZ7N+Zu3r05dUDO/nhmtGIPK2s+WnKHOzNKlrKnEpL4Yl+gzmSksiivytuBt44uJvxfp1YF3n+uiPWOJia08/VnY/HTGa4l1+diSMrExO+mjAdC5UJAB8db78JhRfOrGa29xBGOAcz0yuUfE0xuZpiXM1seeHMKqKaWJetvVHI5Lza/TbGuPYCYLb3EMr1WvZknOP/Lv1NtqZ9Jgfbk5sxaQQwyb8z7pbWfHv2xA0vq5+rBysnzSKvvBR3S2veP7qfzdGR3NerH7d17UGAnT2/TpvHIHcvfgoP4/WDu9EYqrfO/fjEQb4/d5IBbp5kl5WQXFhARklxg1qKPLHzb1ZOnsXHYybz6I7Nzdq6pDWU6bR8FXacXs6uROVmc2dwb4Z7+THM04f9SfHV5jVXVrTGyiwtJrcBiR6VXE4vZzd6Obvia2OHj7UtfjZ2eFnbABCfn8cvF85QrDW+mo036mRaxQOj14eOAWgXyaNzefHkaYoZ6tRNJI6amUgeCYJxalKLo8bSGHScyYvlTN6/N9YmciU+5k74WbrgbuaAjYk5tioLbFQWuJnZYauywF5thYlcSYG2hDO5sUQUJFOq16AxaNEYdFf+VPy7XF/9/5X/LtNrKdL924S4voPR9Q485go1g526EmztTZCNF/YmlshlMgySxN6McH5LPNQiI/X4WboAcCo3hj+Sj3FvwFhO5FwmqjCl2nyleg0XChJJTm58k+nsshIe3PYHrwwexRvDbiGnrJQzV7X0uN4+U8nlvDHsFm4P6smu+Bju+Xsj4dkZKGQyBrh5Mt4vkAl+nbmrRwipRYWsiN3M9rSwRsdYqVSnJbEgn74u7qQVFzGrSzDP7P6HzdGRvDh4FA/1GchjO/+q8/1fnTnOuum3c0/PEL49exKAEFd3Xhw0slpdpDKdlneP7GPl+dM4mJozv1tP+rq6ozdIpBYX8n+njvDkrr+xMzVja+xlkgpbptCtuVLF0wOGMrtLd6zVaoo0GqJys9AaDDw/aASSJBGZk0VmaQlfjr+VTVEXWRF2jPTiYsr0WlJjnKsl69pCfb+78KwMEgryCLC1R2PQ8XncBpYGTsLXwpkXg+cwyb0f+zPCiSpIwVRhwhjXXszzGUaBtqRa16gCbQkmciWleg3/2buVsPRUHg4JrXr6/VT/Iaw6f5rO9o6U6XSklxSxYvytFGk1PLT9T36dNo+cshIUcjk2alNkgAScz0rnfFb9Leeyy0rYGneZ7NISrExM6pzPXGVSVSA2IjuTsIzaR0lrKeZX6v3EFd/49yK1LJdPL23m00ub6WzlzkjnHnS29uDt8LUd6mJ6aeCkqqTRpqSjbE09zcWCRDQG46l9djO7WZNGADM7B3MkJZGInKwmL8Pe1IxHQ0K5o1svcstL8bSy4f2j+/gy7BgAHxzbzwS/QPILyvC2tuE/e7fy68VzdS4vr7yMbXGXGx3H4ZREHtm+mc/HTeO9EeP5z96tzT5CXEuSgGVH9lb9XyVXMC+oB5+Nncpdf/9GuV7PlIAuTPLvgo+NbdV8uWWlnExLYV9iHAmFeShlcpRyOSq5HC9rW0I9vOjn4oGZSkWpVktcQR7x+bn8FR3JhewMjqYmkd6GI6a1hh/On0YGvDZ0DAeS41v8YdyN0ksGDmdFMNQpiK+it7Z1OB2eSB4JQstrlcRRbTQGHVFFqdd9Gq2SKehm401f+wD62vkz23swJnIlJnIlKnnDQy/RlZNWlktqaS5ppbmk+uRyPKqUc5np1Zr71nbAkSGjm40Xk937Mda1N+ZKNUklWVzITyQsNwa9ZMBKacYUj37M8xnK/oxw1iYcrJYku1EWyopWXoW6Uj69tJluNl6822sRS098WWui6ka65L15aDf2Zmb895ZJPHUqrWrEiLqWaaFS8cW4Wxno7slze7ayJuLfC029JHE4JZHDKYm8fnAXvZxdua9Xf17rcTtag449GdfvKmahUqHR62utgxCbn0s/Vw+Ge/lxKDmhqvXQm4d288mYyURkZ/LFlQviax1PS+arsOO8GDoSRzNzbNSmzO/Wi7CMVB7c9geR2ZlYq9VMD+zGa0PH4G1ty+wu3ZHLZBxLTUKSJMb7dmJ6YBB3/72BDZcuNHj/NtYgdy/eHzkeJzMLvjl7gt0JsZzNTKtq6q+Uy6s1+781MIjXhozm1sCgqmlavZ7DKYkcLTrNvozz5Gja5uKzru+Ql5UNs7t0Z3GPPpiplCwLX4fGoMPOxBJ/S1dM5EpeP/cL265KNj6in8Jwp25kaQrpaesLwMLDHzPBLYQSfRlrEw6iM+j5etyTqORy/oqOJDwrg7OZaaycNIsR3n5VyyrRapm58SfG+wUCkFRQgEIuR9/IGlg2alN8bSqeEFuZmGCiqPsYlVtayicnDnFXjxC+P1ezsObV+6k5L4TMFWoW+49muucgLJSmJJZksSf9HNFFacQXZ5BQnEmZoelPsy8VpnDpmoR2RzDcKZh5PsMACM9L4IOLG1u1lazQvruiX49aoWSgm2eTC2KbKpXc3SOEB/sMQJJgXeR55nfrxVdhx/n89L/nyEKNhjcO7eb/xk7lzs3rOHBN65nmtC3uMk/t3sIHIyfgamnJw9s3k98KNYJawmenjjAvqAc2alM2zLijxuu5ZaVYqkywMzVjhJcvw718MVFULylQqCnnWGoSH504yOHkRC5mZ2CrNqNIqzGqQTdaw8rzpxnp7c97I8Yzds33FGhubKS4lk4mHMi8wET3ENxM7UhtgQfHgiAIxqRRxbEHbHyswV3VWpocGaorSSS1QomJXFWVVKr8t1qhxEyhxkltg5uZHW5mdria2uFuZo/5lWRMSlEB4ZkZxBtiSSjOJFtTgFquwsvcke62PvS3D8TWxIL0sjz+TD7OX8nHyahlCGVTuYrxbn2Z4z0EP0sXIguSWZdwgB1pZ9BKN1aEua9dAJ/2u5+5B94juTQHJ7UN/9dvCbYmFkQWJJNSmkNUYQoHMi9WSyQ1dXQCpUzB8j6LCbB0Y/q+t6vdAF29TEczc76fNBMfazuWbP2dwykNa12wYe4MNAYdT53+rs447+7Rl+cHjUAhl3M6PYWfL5xlc3Rk1UXUl+OmMcG/M+U6HePXrazWJe/pAUN5uO8gDmVeZNmF9eReSZRcvR4Z8Hi/wSztO4giTTkfHT/EjxfCajSb/27iTEb7+JNRXMS4tSurCmBam6j5ccocyvU65mz6tUHb3Rghru7c1SOEKQFdOJqSyLN7thJfkFfv+6BihLLeLm5YmZhgplThZG7BaB9/Qt290EsGNiQdZnXsbvLbaHSn5GgnbNSmzOvag8kBXejl7EqpXsOmpKP8Er8XT3NHPupzD6mlOfhaunA061JFkhYDBknCIBmY4z2Es7lx5OtKmOs9lBPZl3ns1NfV1vN4l2lM8xjApHWric7LAeCRvoNY2ncgT+7awhifAGZ1CWbp9j+xUKl4f+QEPj91FFdLS2Z2DmbkL9/U6OrpYGqOj40tvja2+FjbVfxtY4uvtS22pmZV853NSOOh7X82Wyu0G73wdVRbM81jANM9B2GuVLMu4QDn8xIY4dydgY6dcVRXFPozSAb2Z1zgk0t/kl6W1wyRdwwzPUN5Kmg6H0X8ztbU09VauQqt52ZMHA319OHHKXP4z96tSBJ0sXeki70jzuaW6CQDWr2eAk05eWWl5JaXkVtWSl5ZKTlXEhYP9R2Ik5kFP14I438nj1Ci1fD37IV4WtuQUlRISmEByUUFJBcWkF1WyutDRvPqwV38GB7W4tsW6u7F5+OmkVdexr1bNlYdp9ubLvaOrBh/K742dgDsio8hs6QYc1VFDTxzpQp3K2v8bOyIyM7k14tn+SvmEhq9Hp1BT6lOV+3a4/F+g3m832CgYjj7Ik05hRoN4VnpfHziEJE30PKsPXC1sGTf/Pt47+i+qlbhN6qlkkdWSjP+GfVajYdbQvPoqKOsieLYoji2sWq3iaMb5WFmT2crD7pYe1T9bWtiUfW6xqDjcmEqx7IvcSw7inN5cQ1+gjzAPpC5PkMJdexKTnkhG5OO8HvSkSa39PAyd+TXIc/wXNhKDmReBCpOVnO9h+Jt4YSHmT0BVm6YyJVEFiTzdfRWDmdFNnlkAjkyngqawXTPgUze80aN4bSTo51wMDVnw4z5mCqVLP77Ny5epwjwtbbOv530sjyeP7OqanmVHEzNeXPYLUwK6Mx3Z08SlZvNRP9Ahnv5kVdWypmMNPLLyzBTqnCztMIgGfjs1FG2xV2udrIIdezKC91mA7DswnoOZ0XUWBfUbLFzrfF+gawYfyufnzrK+8eqj4Y0xsefbyfOZM7vv3D8Sq2h2vxn0HDC0lOr1R661i0+ATwfOoJ/Yi4x3MuXHk6uROfl8M2ZE/x68WyztF3o2qWIWV6Dme87HIBd6WfZkXaG01dazjWFQibHVmXRqPotgx278kLwHMwVag5nRbAz/SyHMi9SZtDSx86f13rcTnJJNm+c+5X/6/8A1ipzJCT0kgE5cuQyGRISxdoyrE3M+eryVtYlHMSAhEIm540e8+lq7YWrmS0fR2xifeIhkqOdUCuUHLrz/orukmHH2BwdiZulFRklxRy+cwn7EuN4avcW3h0xjtuCerI24hyZJcX42Njha12RILIy+ff4l1ZcSHx+HnH5ecQX5Fb8nZ9HfEEeRVrNdfZA0zXmgshRbc0QxyCGOwfTz74TGoOObWmnWRW7u0ZSyEJpio+5E12tPXkqaDrlei3T9y8TQ8cL7crNmDgKsLXn56lzcbGwRG8wEFeQx6WcLFKKClDI5JgoFFirTbFTm2JnaoatqSl2ajPMVCoANkdHsvzo/moPHlwtLBnvF4i7pTUeVtZ4XvnbwcycyOxMHt/1d6slJ7ytbfhmwgxcLax4dMdm9iQad/ekuqjkcno7uxGelVFnncWRXn7c37s/gz28icvPZdX50xxISuBSbhZmShVb5izkZFoKn546wp7b7wEgv7yMv6IjKSgv57agHvx2KZw3D+1pxS1rG/8bM5nuTi6M+fW7Zmu72VIJhbVDnmVfZjifXaq7RMLNyFltw6NdphJiH8ADx78gvhm6vl9LJI46VpJCJI6MX4dNHNXGXGGCo9oGjUFHRlneDXc18DZ3ZLb3ECa590Mhk7M7/Sx/p5zkVE50o5f93cBHySzP57mwH+qMfaBDF271HEh/h0B2p5/jv5F/cObC9T+vaw+wvhbOPN9tNt1svPg44g82JB2u9X3B+lC+mjCdCWtXNqruQm9nV36feSdPn/6Ow1mRAKTHumBvasb8br24t2c/9JKBZ3b/U61+go+1LXO6dqeTrT02alNKdFryy8twMbdkiKcPL575kT0Z1esx2JlY8kK32Qx2CqJEV45O0nMoM4IXth1u1IgkQQ5OXM7NrtFlTgZcuOcxvj57go/q6EZQub0A6yLO80/sJXbGx9SYb9nwsczv1ovs0hJOpaXw44Uw9iXGNXtnF4+ATGxU5szxHsJY1954mjuSU17I70lHWRm7s84EkhwZ5ko1MmQ4qq1xMbWls7U7Uz0G4G5mz+XCVFbH7WZH2pnrrn+IYxDv9F7I4awI3rvwW7Vk6nyfETwYOIGw3Fj2ZYaz2G9MtWRuRlk+M/Yvo6+dP+/1XkxMURpvnF9Dcml2tWU8EDiBX+P3kVqay+9JR5GQSI52IsDWnn/mLEJ1pZvAvE2/cjQ1ifG+nVgxYTrj1qzkUm4WJnIFi3r0YVH3PoCM+Pxc4guuJIjyc4kryCOhIJ/S6xRhbwoZ4GNjSzcHZ+zNzLE2MUEhk2NAwmCQKv6WJKzsi1DLlZgr1Zgr1LX+7WJqi7lSjc6gJyw3lt0ZZ9mWGkaJ/vrN/kc59+CtXncSnpfAgye+aHJCURDaws2YOAKwVJngbW1DdF5ug7suqRVK1ApFo7r6VNZsbG2WKhM+HjOJ0d7+vHNkH980QxFwY9bDyYUlvfoz3i8QlUJBWEYqT+3aws7b7gZAbzCwOjwMRzNzhnv5Yq025WxGGj2dXdkee5n7tv7ethvQCvq7erBu+u28sn8nq8JP1/+GBmqJpMLbPe/EQmnK46e+afZlGys5Mr4f9BidrNwAeOLkNxzLqfvhaFM19Zje3pNHInEkEkfGqs1qHBmjEr2GhJLmO9gklGTxUcQmvrq8lakeA5jmMYDxbn3JKMtnW+pp/kk9RWxxw4aq35R0lKeCpuOstqm1q1yJXsPujHPszjjHGJdePNZlKqsGPc6bVmurWttcj6e5A/O8hzHVoz8ppTksPbGCs3lxdc4vs6+oY2Jo5DXmncG9SSnN4WjWJSyUpjzaeQrjRvfFRKGgXKdj5fnTfHH6aFWXsErxBXl8cKz6yFOVJ4ZvrR9htEuPGomjXE0Rz4StZKBDZ/wtXTFVqJjlNZhtt3figS1/VY1IVp+6WlNJwJnMNLyvjHhSm8qm679FhjOna3f6u3nUSByp5HLG+ATww/nTvHpgZ4NiuhH52hK+id7ON9Hb6WrtwXjXviz0G0WIfQCvnPuZrPKCqnk9zBxY0mk8/R0CqxWhBijWlXEoK4KvL29ljGsvXu8xnynu/Xn3wm+11t5Sy1U8Hzybw1kRPB+2qkbytI+dH+lleRzKvMjjXaZVTS/Va9AadOxOP0svWz8+7bcEgLUJB0kuzUaGjLGuvYkpSmOie192p5/j86gt1ZbtEZBJdDR0/+5TdAY9f89ZxNK+A3GJtOTZgcM4lZbCpdyKBKjGoOfrMyf4+kzL3bzIZTL8be3p7uhMd0cXeji50M3RuapFk1avp0irQavXI5PJUMhkyGUy5LKKFldaNJToyinRl1/5W0OBtpS0sjxKdeVklOeTUppNWG5sg7sk3uYzjEc6T2Fb6mmWha8TSSNBMBJFWk2dIzTWpVyva3R9nLYa4axIq2HJ1k083X8oLw0eSVcHR17Yu73GaG43i3OZ6Ty8YzOmSiVDPLx5ftAI1t16Gw9v/5PPxk5FIZezuEdfAKJys9l46QKWJmpMFAr+jrnUxtG3juNpyXx/7iSvDBlFXH4u+5LimmW5LVHzKLO8AF8Ll2ZdprGb4NaXTlZuvHvhN/7TbVaLVfS7WWvXCUJ7JRJHraBIV8Yv8fv4JX4f3ay9mODWl6ke/bnTbySXCpI5kHmREzlRhOcnoqujHtL2tNM83Hkyc32G1tscdmf6GY7nRPFi8Bw+6HMXq2N38+Xlf2qdt5etL7f5DGeoUxB5mmK+i9nBr/H76x0JKPtKcsHZwqLqhrs+tmpTpgZ05bvY7RiQuMf/Fka79OS/Jw5yKSebs5lpZJQU17+gKyovAHakneGhwIksLZvMdzHbKdVX7yZ0NPsSR7MrLrb+SDrG6z3ns3LSLO7YvJYzNzialVwmQyGrPSuuVihZENyb+Pw8ntq9hYySIuZ27VFjvnt69sPe1IzVLVxToraLpYiCZCIKktmZfpY3e97Br0OeYW/6ecLyYrFSmrHQbxQF2hLWJhwkrigdCYms8kIyy/PJKi+oSi5sSwtjgENnnguaxerQJ1hx+R9+SzyMdNXlxBiXntiZWPLZpb9qbXH3V8pJ3u51J4sDbiGtNBdXMzu+i97BdzE7qpYzxb1/1fxv9JzP7LzBlOu19HcIrJq+PTWszn1QeSP1zZkTLB81geFefpxMS+bh7ZsbtzMbyUKlYqinL4PcvSqSRA7OmF/pShKXn8v5rAw+P3WUc1nphGdl1Bi6uSWfnilkch7pPIU53kNYHbubFZe3VvvcBKE9EDcX7ZtBknj/2H4ic7N4f8R4/GzseGDrJjJLb97usmU6HTvjYziemszXE6bz4aiJLN3+B+ZKE14IHYGdqRmBdg4E2jnwy8Wz3LrhR8obOWhDe/bmoT34WNvx2dipzPr9Z6Jys+t/UxsIz09gjvcQ5noPZf2VbvM3uzxtMSW6ch7tPAWAztbuHG+BFkdNJUZYE4SWIbqqtRGlTMEgxy6Mc+1Df4dOWKvMKdGVE5Ybw7m8eEwVJjiorcjRFPJr/H7ytSUs8B3J/Z3G80nkn6xPPNSg9cz3Gc7SzpP5Jno738fsqJrube7ES93nEmzjTWzR/7N319FR3mkbx78zk4m7uxJCCO7uDqUUKHV3t61st9t2u1t5t+7uRgtVChRa3C2BkEBICHF3z/j7ByWLE5nJTJL7c07PLplH7rFHrvlJCd/lbmFNUVKrp462V9qxYeqzPLh+VatnFbt14HD+NmIcl2x5FoPJyC8T/sEXWRt49s/kVq1/NiExZShQcE3UJK6NmsL2ssM8efCb867jqFTz0bCHqG5u4pKfzr/shTw4fCy3DhzGA+tXs/q0XwJHB4fx7fzLuGn1j0R5ePPoyPF8mbqfZ7ZvOGW5lYuvIa2inIc2nNpKxpxacwL1UDtzcchIZgQNJtzFD51Rz+bSVF5K+5mGVg7266yy5/bY2SwKG8OBqizWFu/Hy96FwV7RDPSMYkvZIZ5I/qpleU+1C4O8ohniFc3EgH4oUeKudsJOqSKxMpN79n1wxj7sFCqMJiNDvGN4OP4SXOwcefHwj3ioXfBz8ODzrPVnBLCn31QqgNEh4WRUllv0xmRIQBCX9RnAvF5xuKjtya6p4kBpMSnlJaSUHQ+JLtSVxJIXP71cg3g84VJiXAN59cgv/Jy/y2L7EsJSJDTqXgb6B/LBzAUYTEauX/ljq3+c6srslSpemzqXaZEx3Lz6Jw5VlPLS5NlMCo9Co9fjYGdHcmkxd6z9lYL62gtvsJtwUatZvuBKXNX2LPjxayqaO36+Nvc5VaVQcm/veSwOH8vB6hyeP7TcIuP92BpfBze+HP0g7mpnfivYw/OHlltsXz1trCPpqiZd1WyVBEc2QImC3u7BDPXuxXDvWOLdw6jXN1GhqSPS1R+jycTde98ns76Ye3vPY0nEOHaUp/Fs6rKWGcPO5/qoqdzSawY37nyDI3UFOKsc+GTUvRhNRt5M/42d5emYMBHrFky4sx8bSpJb9YvJlmnP84/Nf/Lt4QsHP75Ozqy//EY2lB3gv4d/ZJJ/P54deA1jvnqfwvrWD6x8NidODifGZ3nh0HJWFOw5ZRkXO0euj5rCpID+mEwmAhy9eGn3Vt7bvxsnOzV9vH1JKi1q874d7ex4afJsZkXFsvjnb9h/UgsmZzs1idffSZ1Wi5+zC+/v38NLu7ecMVZS8g1383bSLt7fv+f0zZtFe06edgpVy4DU7THIK4oH4y4mwsWfRoOGA1VZ7K08yi/5u1AplEwLHMTckGEM8IwEIK+xnN3l6XyXuwWDycQIn1j2VGS0anpbJYrzfl6tcVPpZm/PBzMXMDoknPy6Gr47fJCfMg63eZY1S1342ClUXBs1meuippDTUMqzqcs4Ute6rptC2BoJjrqfABdXPp29EE9HRy7+8WvK2tAauauyUyp5f+bFjAkO54oV33GgtJgb+g/hnqGj8fpr1k6twUDvD1+1cqWdK9jVjV8WXs3RqgquXPF9h9vzWOq8OtAzksf6LibQyYsPj67hm5zNFtmPrVAplKyc+CRuaifezVjNV9kbLbavnjbWkQRHEhzZKumqZgOMmFq6DH2dvemUxzzUzrw65GaeG3gNLxxazuvpK9hVkc4/Ei7lnwmX8WDSxxfc/pfZG5gWOJDro6fy9wNfcFnEePwdPLhmxysUNB2f/vbqyEncETsbgK1lg846Bs3plAplq8dE+PuoiRhMJt7/q8ucp70reqOhTV3TLmRD6UF+yd/F/XHz6ecRgaudI2m1+dTrm7kxehpOdg78VrCHJoOGqgpHPk9JwsvRiW8uWkK8jx/X/LaMLfk5Z922r5MzUyKi6e8XSKCLK0X1dbybtJuihjo+Sd7LvJg4BvgFnRIcGUxG7JQq/Jxd+P1YOs/v3HTWbRtMJuyVKrO9DuZwri6TreGssqegsZLb97xDk0GHCRNKFHg7uDIvZDg3RE/Dy96FXRUZ/DvlO/ZVZlJ22rhdvxbsbvX+bK1ZuJOdmi/nXUqUhxc3rPqRjbnH2lWhJS541AoVw31iubXXTKJcAvgia8NZW2gJ0VVIaNQ9lTTUc8PqH/ll4VV8OGsBl//6Hc36to3Z1NXojUbuXLuCby9awutT5zFn+ed8cjCRrw8lMy8mjpenzMZepUKlUGCw0nhU1lBYX8f961by9UVLuCy+P0sPH7zwSlZwoDqb63a+xi0xM7ir91wcVGo+PWb5cSutZbxfX9zUxwPN9LpCK1dzdtJlTQjzkuDIxtXoGvln8lc8O/Aa3hx2G9n1peyvziKroQR/x3MPynwyg8nIgeps4tyCcbNz4vLw8fyUv7MlNILjA+MCvJn+G/f0nkeos+8pA4XPChrCnOChJFdnU6trwvhXSxR3v1q4wNjbI4NCWRSXwCMbf28ZqDe3oRQ7pYoId08yqyvPv4ELOPnE8PqRFXiqXYhyCaDZqOW66Kk4qez5vTCR947+3hJQFGT6EezqxpdzF+Pu4AjAAP/AswZHvb18WXvZ9cDxgbKL6uuYHd2beTFxlDc10tvblwOlxSw9reVVqJsHdsrjiXmEu+c5699RkMelffrx3v7dZ7RGclGradTp2h2NdNYJM8olgMsjxjPCp/cpn0uDyYjBZMReefxQYzQZWVOUxCfH/qSwqWPve2tY46byjsEjiPfxY+FP35Ba3r7m6uZ631QKJdGugfR1D2OQVxRjfONxVTtyuCaPm3e9SUZ921vZCdEWJ38HzXk8ksCo+ytpqOem1T+x7OIreHrsFB7btNbaJVmcxqDn/vUrWbX4Op4cM4VHN61BY9DzQ3oqP6SnolYqe1RodMK2gly+TzvI46MmsS4702bHvtIa9bydsQpnOwfmBA/r1sHRSJ+4lv9v6a55Mki2ELZBgqMuoKCpkut3vs4In95M9O/HQM9IolwD+DJrw4VX5ngLkMFeURQ2VTIpoD/OdvZ8c1rLpmdTl/F0vyu4KXo6BpORGt2pLYHmBg+jl1sQES7+uNg54qSyZ09FBnsrMgiJ+V9rkdMP7GqlkhemTuZgdQ7bdRtb/p7110mml6d3h4Ojk2mMOh5P/rLl3/ZKO7zsXSlprj5luVgvHz6fuwiD0cSlv3zLz5dcheG0KeLc7R0Y5B/E8KCQlr+tz8nkxd1b8XNy5paBw3G1t+etxJ2syTp6xgwwWTVVPLV1HUMCglmbffScNb+2dzsrF1/D02On8I8tfxLk4sYlvfsyPTKGwQHB1Gia2V9SRFJpEUklhZQ1NuDt5Iy3oxPejk54OTqjMxpIKSvhYFlJyxgA7blJc1bZ088zEgelHVqjnnJNLdkNpefssjbMuxdXRExglG8cJc3VrClK5Fh9CXX6RpxVjrjaOWKnVNJs0FGjayS9tuCsswJagjUuMsLcPLht4HA+2L+nXaGRuW6snVT2LAkfxxURE3BTO6E3GjhaX8TS3C1sLDnY6tkcheiI07+DJ/7d0c+53ED0HKnlpTyzfT0vTJzJ71kZbMzNsnZJFpdbW8MLuzbzn/HTeCtxJ3kndXE+/celnuTZHZuYEdmLe4aO5sl2zkDbWT+mbSpJYUHoKKJdAzlW37FJWDriXMdKc7wOn2WtY37oCAB6uwWfcZ1tbu0Nj6TVkRDmI8FRF7K7Ip3df80O5qxyoPm02cPO5froqUS4+FOna+L+uIvYXp5GhfbUcYV2lKdx4643eCR+IRtKD54xhXduYzkJHuHsq8xkc2kqOyuOUHuWab5PPziP9o1rCblOnqlJ8df/nmjt01HnOjFojXpKmqsJdvJmnF9ftEY9Yc6+zA8eRU5tNdetXE5pYwP1Oh1jQsKp1jQxwC+QIQHB9Pb2RalQoD1pFpO7hozircRdlDU18tw5up6dYDSZ+Dwlic9Tks67XHpVOU9s+ZP/mzSTYDd3xoVEoDUa2JSbzdLDa/BzdmFwQBDXJgzi/mFjTlm3Wa+joqkJF7Uaz7/GQMivq+VoYw4HqrNZW5REta513QGdVPa8Pex2eruHnPL35OpsHk76lHp9M+5qZ3wd3IlzC2ZJ+Dh6u4eQXlvAvw5+y7qS5B4/hfs/x0yiormRd5Ja39UOzHsxm+ARzgsDr8VV7cTPeTtZX5JMel0hGqPObPsQoiMk+BFtsfTwQWZF9ebVKXOY8f1nPWK8ozVZGfxn/DTivH1PCY5O1tNaYdRomnl3/27+NnwcHyXvJbe2c36Eao+kqmM06JsZ79fXasGRpT8bJc3VXLL5OT4YcRe1uqYLr2AGEh4JYV0SHHVRjYbzz8R0st+Lkghy8ibI0YtvsjfzTc7ZA4+CpgruS/zwrI+9nf4bxU2VjPdL4Kn+l6M3GshrLCejrpDM+mKO1hVRqqnBUanmaH0RWqMee6UdTX+FW9dETSattoCNpcf7ps8PGUmTQcuaLMtN32mvtGOSfz/mhQxnqHcvNAYddkoVpQ0NLD2czIu7t9KkP34z/e9t63lj2jwmhEWSXlnO3uICPkrey77iQrJrqujl5cPiuH4U1de2rGNO36UdZHpkL+J9/Hh+5ya+SztIg+7M/US4e+Lh4EhlcyMVTU2n1BLm5sEA/0DGxLoR7x7GHbGzuTN2NptKU/m1YBeJlcdawrsQJ28Ge8XgqnYkr6Gc3MYy7oqdQ6izL7ftfoe8xjIclGpi3IJ4st9lvDLkJlztHIlw8W/Z347yNO7d9wH7KjPN/np0VGdfTDuoVPxn/HRmRMVy59pfW/0ZMfeFzADPSF4efCMZdYX8K2WpxX8BFOJcetINrbC8RzetYdnFl/PF3MVcueJ7qpo750bVWhxUxy/PG89yLjn5vNHTwqPPU5K4of8QHhw+lvvXrbJ2OeekMxnYWZ7OOL++fJ61vtP3f6HPhLmClFJNDQu2PNfh7bRFT/vMC2FLZFY10Wa+Dm6M8ulDb/dgerkG0cstCBe7/7Uc0hh0FDZVEubsi51Shdaop07XxNMHvyWx6njI8NqQm2nUa3g8+csOnwCc7dTM6xXH5X0GEO/ni95ooKCpghAnH9zUTiRWZvJb4R42lqSgNerJz/Q963ZUCgVKhcJqTcEV0KFhnk+/CPBQOzMraCjzQ0YQ6epPQWMF6XWF+Dq4098zAqPJiMaox0llDxwfj+jR/Z+zo/zUQasm+/fnvriL2F2Rwa6KIxQ3V1PcVHVGqzVb0pkXFUEubrw382LivH14bNNafs44fMF1zBkYqRUq4j3CWBQ2hkn+/ThQncUjSZ/RLC2MhJXIRb2whF5ePiydfxklDXVcuWIZNZpma5dkMaODw/h2/mXMXfYFqRWlFzxn9KTv3BXxA3h2wnTmLv+CwxVtO5d2ZquTGYGDeKr/FVy8+VnKNbWdtl9o3eehq7fAOfk5Oqjs0BsNFxz/q6s8Z5lVTWZVs1XS4ki0Wbmmjt8K98BfkygoUBDk5IWPvRs6k4GBnlGEOHvzfe5W0mrzOVZfcsasTfZKOxro2EXfQP9ALuvTn/m94nFWq9mcl81Lu7bh49dEuLMfu8rTWVm4l4KmipZ1zncyNZhMVh100twDYNfoGvkudwvf5W5hgGckMwIHEebsR3FzFb+k7GRDyUGajTp8HdyJcPGjQd9MWu2ZU7JvKD3IhlLbnMXkbDrrAtpeqeKKvgO4d+homvQ6Fv38bavGNerohcsgryiGeMUwwDOSCBd/fB3cUCqU5DaU8Wb6b/xasButsXvPPiRsV0+6gRWd62hVBVf++j3fzl/C1/Mu5abff6Kkod7aZZndjMhe/H30RLJqqjjUitAIelYrjGVHUrh54DBemTybJb8upU7bumEbOtuO8iM06jUsChvN+0fXdNp+e8rnAGBIQDBPjplMf78AGnQ6jlVX4mpvj7NazTuJu/jq0AFrlyhEtyLBkegwEyYKmypbZslKq82/4Dpbyw5zW6+ZPBh3Md84bmZf6pkfRTulksVxCcT7+NOs11Pd3ISLvT3xPn4M8AvEz9mFgrpaPkrey/dpBymsP7UFTFf5ZaEjWvsck6uzSa7OPutj5ZraTv81rCuzUyq5NK4f9wwdRYCzKz9lHOLZHZta1XWiI5/J4d6x3NJrBgke4dRoGzhYk8Oqwr0UNVWR21jGweqcU8YRE0KI7ia9qpyrVnzPJ3MWsmLh1dy+9lcSS2xzKvC2UCoUjAuJ4L5hoxkaGMKW/Gye2baB4B5wHdNWeqOR29f8wvIFV/Dx7IX8367N7Cu2vc9Anb6JH/K2szhsLN/lbD3neJN2ChVx7iEM9IxkgGck4S5+1Oub2VKayrc5W8744fV8elJoNHmQmo9HXgHAFylJVDQ1EurmQZ1WQ5i7B/+ZMB0vRyc+T0miVnt8eA8Z60iIjpGuasIq7BQqroqcyJLwcXjau7C/6hibSw+RUpNDuaaWWMMQruw7kARffzKrK7FXqfB0cKRJryO9soLksmL2FOWzrSAX41k+wmc7MXTnE6qcCP/Hku9znLcv7828mAh3T1YcTeP1vds5VlPVqnXb+x752Ltxf5/5TAkYQGp1Lh9mrmVv5VEJiYTN6c7HWGFbfJ2ceWfGfAb5B/HU1nV8ezjZ2iW1S6ibB5fG9WNxXAIhbu4cLCvm/3ZtYWt+TpvPGT3t+zcsMIRXpswm3N2To1UVfJd2kH3Fhdw5eCQ1mmYe2rD6rOt15vWSh9qZ5eMeo7S5hh/ytnOwOhuVUoWH2pn+nhEM9Iyir0cYjip7mgxaUqtzOVpfhJ+DO+P9Ezhck8e9+z5sdXjUls9AV79uVCtU3BQznVkBI/BzduH3Y+nct24VGoMetVLJfcPGcPugETTotDy7YyPfp6W0rGvrz126qklXNVslwZGwKieVPRP9+zEtcBBDvKJxUKmB4+PtJFdn8076Kg7V5rUsf6GT4vlOBj3losrWT4idwRLv9UUxcTw7YQZ5ddU8uH41RyrLW71uW94TT7UL/TzDAQXRLgFcGTkRvcnA60dW8Efx/rYXLkQn6SnHWGEb1EolT46dwjUJg1iXk8k/Nv9BcRfpuhbp4ck9Q0azIDaeRr2OFUfTWJaWQlJpEdC+83hP/P4pgNEh4Vzepz8zo2NbBhWvbm5i0Gdvn3fdzrpW6uMeytWRkxjv1xc7parl79XaBpKrszlQncWBqizS6wpPmZl2tG8fXhp8A7fseuuU6+ALae3noLtcK5ZkBTAnujf/N3EmiSWF3Lj6RzR/zYbs5+zCwyPGsaRPfx7e8DvLjhwPj2z9uUtwJMGRrZKuasKqmgxafi9K5PeiROwUKnq5BeLn4MGB6mxqdY1nLG/rB3tbcPpFg7xm7WOnVDLAL5BxoRHMjOpFgm8Aq4+l87cNq886493ZtPa1D3D0ZGrAQCb59yPeIxSl4vgJU2PQsbY4ibfTV1Gn796zCImurSfetArr0hmN/HPLn2zKzeLZCdNZu+QGntu5kaWH/zcmn5+TM1clDCLWy4doT29KG+pZkZnGmqyMNo2N427vQJCrG0GubgS7uKEzGkkqLeJoVcWFVz5JhLsn9wwdxYLYvpQ3NfCvbRv4/shBmvX/G5euvefsnjTO0QkmYHtBLtsLcnHbYs/wwFA+mbOQf27584Lrdla3pbTafJ5I/gofezf8HD3QGw00GTQUNlWdt+Xw/qpjAIS5+LYpOOppAqJK+PWokeKGer6/+HIeHD6OtxN3UqvVUNbYwGcHE1nSpz9RHl4t60iXNSHaR4IjYTP0JgNptQWkceYAzaL9JEhqvRhPb8aFRjAuJIJRIWG42TtQq2lme0Eu/96+kZ2FF754a+3r66F2ZnLAAGYEDmKgVxQag44d5Wn8fGgXeysy0JkM1GgbMEqXNCGEOKc/czLZ/V0+/xg9iRcmzmR+r3h+Sj9EmLsH1/cbApg4WFbCvuICenn58N9Js3h+wgyOVJaTXFZMrVaDwWhErVTh5uCAm70D7vb2uKod8HBwINDVDRe1fcv+DEYjir9mYc2squDXo2l8l3bwvK2dfByd+dvIcVwa14+Kpkb+vX0DSw8nt7SMOEHOz+1Xp9WiMRwP4PaXFrdqnRPXR53xuldo69o0G63BZERj0OFt72a2GgJdXLm+/xC25edwjO71WdtdlM/Sw8ncMnAYVycM5IcjqRQ31PPIyPEAfHJw3ynLS3gkRNtJcCR6jJ74a9zZ9ISTZVve6wF+gVzSuy+zomIJcnVDazCwr7iA9/bvZmt+DillJWab4tVRqWaCfwLTAwcxwqc3AHsqj/JMylK2lKbSaLDN2WGEOB85rnZtJ45fXfl9rNVqeHTTGlZkpvH8hBm8OHkWNZpmfj16mBd3b6VG879ZXANdXJkaEcMAv0AG+AXirFajUijRGw3UabXUajXUa7UU1ddTq9VQ0lBPUUMdRfW1FNXXU9pYj6OdHcMDQ7moVx9uGTicu4eM4t39u3lr3060xv+FQSqFgmv7DeaBYWMwmEw8t2MjXx9Kbgk4Ttbdz8udwfWvgK/5LK/v+djiddHUgAE4qNRsLk01y/bGhUbw5rR5ONnZcfugESRVDeXfKd9R0lxtlu1b04lrvsc2reXl3Vu5KmEQV/UdiKvant1F+WRUllPedGYvBlt830XX9+677/Luu++SnZ0NQEJCAk8++SSzZ88GoLm5mYceeoilS5ei0WiYOXMm77zzDgEBAS3byM3N5Y477mDDhg24urpy3XXX8fzzz2NnZ93oRsY4Ej2SLV4gO9mpuSZhELuK8jjQyl/LOqo7nzAv9B7HeHrz0PBxzInpTVljAz+lH2JbQQ67iwpo0p+7K1p7X7Pxfn35W/wl+Dq4c6Aqiz+K97Oh5OA5Z1oRoiuwxWOpaJvuEBydTMHxrsY6o/GCy5qDi1rNrQOHc+fgkRyrqeKjA3tJKi3Cz8mZp8ZOobe3L98cOsBLu7dSfVKAdTpznI+7y3vYXqFu7vx52Q18mXqAZ3dsbNc2bOW66IPhd1Gnb+KhpE/atf7pn4XVl15HnVbDrb//zPRBLrw4+AYO1eRx2+63u0XL5tOfr1qpRKlQnjWkPZmtvN8nkzGOuvYYRytWrEClUhEbG4vJZOLzzz/nxRdfJCkpiYSEBO644w5WrlzJZ599hoeHB3fffTdKpZJt27YBYDAYGDRoEIGBgbz44osUFRVx7bXXcsstt/Dcc891xtM9JwmORI9lqQssBccH5IPjYzAoUeDj5IyvszO+Ts54ODhSWF9LZnUlebU1GE0mpkRE8/TYqYS5e6A3GrlyxffsLsq3SH2ns8WTprmc7T0OdnXj/mFjWNQ7gaKGel7bu40f0w+ddXa+k7X3dfKyd+WBuIuZGjiArWWHeOPICgqaKtu1LSFsTU+/Ue0OultwZC3xPn78a9xUhgWGoFQoANhTlM/TW9eTWlF63nXNdR6W9xBuGTiMx0ZOYPHP37YMNt4WtnBNpFIo2TD1WV4+/DO/FOxq93ZOfB48HRzZf8Pd/DvlO34vSgSgj3sIH4+8l7/v/4IqbT3DfWJxs3NiVeFeMurb/rpZkzk+97bwvp8gwVHXDo7OxtvbmxdffJHFixfj5+fHN998w+LFiwFIS0sjPj6eHTt2MGrUKFavXs28efMoLCxsaYX03nvv8eijj1JWVoa9vf35dmVR0lVN9FgXOkm05UQ00D+QBbF9GRkUSrSnF4526nMuqzMYUKuOz6yhNRio02rwcXJma34O161cznMTZ/DGtLks+WUpubU1ra5BnOn0LmuX9enPM+OmUqfT8u/tG/jmUPIp3QrOtY32mh00hHvjLsJgMvJk8jesKznQ7m0JYWvkJrXrO/n4Jt25O+ZwRRlLflmKq9qe/n4BOKjs2JiXdcH1bOmGtTv4JHkf82L68N9Js1jy61Kqmts2sYQtdF/ydXBHpVBS1FzVoe2ceB5RLsdvPnu7BbOmKAkTJtJqCzhSW8CsoCFMDOiHwWSkSlvPovAx/Jq/iw8z12I0mYh2DeBwbT5aY9u6/3UWcx2zbOF9F7avtrb2lH87ODjg4HDuRjUGg4Fly5bR0NDA6NGj2bdvHzqdjmnTprUs06dPH8LDw1uCox07dtC/f/9Tuq7NnDmTO+64g9TUVAYPHmz+J9ZKEhwJcQ4nn0DOdmJSKhTcPGAYl8X3J8bTm5KGetblZLL8SAo5tdUYTcebygKUNzVS3tRAeWMjjXodAS6uxHh6E+Ppjb+zC5vystlbfHxQ8Hv+/I3v5l/GDwuu5JU921h2JAV9JzW5767USiVPj53CVQmD+Dp1P8/t3NSqmdHaexHhpLLn4fhLmBk0hN8LE3kjfQU1Z5klUIiuSgIGIc6uXqdlRysmUhCWYTCZeGjDapbOv4wVi67h4Q2ru9z74aQ63qLAy97FLNvLaijhjSO/cXfvOfg7evLvlO/QGHWUNlfj43B88O179r5PSk0ui8LGcGP0NGYGDcFeaYedUkW1toH7Ez8io67QLPWYi7nPQxIe2YbPamJxNNhWRNFcrwfyCQsLO+XvTz31FE8//fQZyx88eJDRo0fT3NyMq6srP/30E3379mX//v3Y29vj6el5yvIBAQEUFx8fpqS4uPiU0OjE4ycesybbeleEsFGnN+VXAG9Om8fMqFh+yTjMU1vXsb0g94LdnU4oaainpKGe7QW5ZzxW1tjAZb9+x+OjJvLshOncOmg4n+asYn3JwfNO3Xo6ubE7Liq2iqdib2Z0SBiPblzDd2kHL7wS7Q+NIl38+c+Aqwlw9JRWRqLbkeOKEOYjN6mWcbSqgvk/fMmrU+bw7fzL+Dp1P8/v3Ey9rnUTUFg7QMhuKGVjyUHu6T2PXeXpZhkL8bvcLRQ0VfB0/yt4e9htPH7gS/p5RvBbwR4Cnby4IXoaDyR+zPe5W1lblMSl4WMp09RypDafB/ss4NUhN3H33vfJbjh/t8vOYqlzkbXfe2Hb8vLyTumqdq7WRnFxcezfv5+amhqWL1/Oddddx6ZNmzqrTIuxrQ6EQti4EyeTS/v0Y25MHHeu/ZWHNqxma35Oq0Oj1ihrbOCB9auYu/wLCrXFPDPgKh7tu6jNtZ7837ke684clWpeHHQDo0JCuGHVjxYPjWYEDuKjkfdgwsRNu96U0Eh0KxIaCWE+5j7/yvfzVIX1dVz+63c8sfkPLo7ty9rLrmdSWJS1y2q1l9J+BmDp2Ie5vdcsfP9qGdQRW8sOcceed/FxcOenCY/jZe/K7op0/p3yHUO9Y7giYgIA1boGPsxcy8/5Ozlcm8+DiR9Toanj5cE3oUTR4To6ytKfdfkuiXNxd3c/5b9zBUf29vb06tWLoUOH8vzzzzNw4EBef/11AgMD0Wq1VFdXn7J8SUkJgYGBAAQGBlJSUnLG4ycesyYJjoRoo5CYMkKDdGgMenZauPnz4YoyHtn/GS8e/pGLQoYT7tz+k1lPCYtOcFY58MqQm+jrEcaDiR+T47ivVeu19/W5IXoqT/W/go0lB7ll11vkNvaM11l0fwWZfnIh3U2d/L7Ke9x5esp52NpMwFeHDjDz+8/IqKzgs7mLeHnybFzVFx5c1trfhyptPdfvfJ1fC3ZxSeholo97jH8mXMZY33iiXALwtndFpWj7bVxGXSHX7niVFw4t552MVSRWHSOp6hjV2gbG+PU56zp1+iY+zFxLoJMn7mrnjj61Dums98Xa77/oXoxGIxqNhqFDh6JWq1m3bl3LY0eOHCE3N5fRo0cDMHr0aA4ePEhp6f9a9/3xxx+4u7vTt2/fTq/9ZNJVTYh2+LP4ADfHzODRkeP5x5Y/Lbqvgkw/1qsP8nD8QoZ6x0gg0QpqhYrXhtxMuIsf9yd+RGrN8S6BFxr8tb0X8/ZKO66ImMB3OVt4I/23dm1DCFskF8/dn7zHorsrqK/lulU/sDgugSfHTKGvjx83rP6R4oZ6a5d2XuWaWt7JWM3nWRuYHzKC+SEjmBU85JRlanWNVGsbqNLWk9tYTlZ9MYdq8jhUm4fBdPbxMev0Tawo2NPy7ycSluDt4Ma28jQm+CWQXldI8WkDc5drjg8KHOzkbZauc12BdFsT7fH3v/+d2bNnEx4eTl1dHd988w0bN25kzZo1eHh4cNNNN/Hggw/i7e2Nu7s799xzD6NHj2bUqFEAzJgxg759+3LNNdfw3//+l+LiYp544gnuuuuu8w7E3RkkOBKiHSq0dbyTsYq/JVxCkjaJ5bsqLLq/Wl0ja4oSuS/uIpaEj0OhUODr4M6Bqiw+ylzL4dp8i+6/q5kVPJR4j1Bu2/0Oh2pPbRV2rvCoIxcHkwP642LnyE/5O9u9DSFsiYQJQliG3Ihaz/IjqRwoLebTOYv46ZKruGHVD6RVlp9zeVsJDhr0zXybs5lvczYT5OiFt4MbnmoXPO3/+k/tgre9G7FuQcwIHISDSk2drokd5Wm8k7GaMs25Z+hVK1T09QhDbzRwUchwLgoZDkBxUxXFzVWoFCqUCgUp1TkA9HEPPeO6qrNY47xkK58B0XWUlpZy7bXXUlRUhIeHBwMGDGDNmjVMnz4dgFdffRWlUsmiRYvQaDTMnDmTd955p2V9lUrFb7/9xh133MHo0aNxcXHhuuuu45lnnrHWU2qhMJkuPDBLbW0tHh4ejPjpPuxcrJt0CWFL/m/QdQz3juX73K0kVR3jj/0N1GlbN/hiW4TElOGoVHNRyAgCnDwxmaBG18D0wEFEuPjxXOpy1hYnmX2/XZECBd+NfZgjdQX8M/lri+9vcdgY7uk9j50VR3h0/+cW358QliahkRCWYckbUPnetp6fswufzVmIj5Mzc5d9SUXzuWc97WqhgRIFce4hjPbtw8Kw0VRq6rh252utWtdOocJN7URf9zAGe0fjqXbBQ+2Mn6MHsW7BANy8602r/Fhpzc93Z38G9A0adl/yOjU1NacMxNzdncgbnts9GUdX22rb0lyv5/ERG3rce3I623pXhOhinjjwFXf3nsvc4GFcEzUZ42AjmfXF7MgpYW9xAfl1Nfg6uRDq5k5FcxOlDfUcraqgrKntU7M3G3Usy9t2yt++zdnMI/EL+We/JTQbtGwuSzXXU+uyIl38CXH24f8O/2DR/agVKh5PuJQZQYNZmrOFdzJWWXR/QnQGufkUwjK6WgDRnZU1NnDDqh9ZufhaXps2h+tW/mDWCU6sycfBnQBHL1ztHHFS2bOvKrPV6+pNBqq09WwrP8y28sMAPBq/iDF+8eQ2lPHP5K85Wl9kqdJtlrQ6EuI4CY6E6ACdycCrR37l1SO/EurswwDPKAZ4RjIpKpJr+w1uWU5j0OGgUh///3o9L+3ZysfJ+1p9oXKuk5bBZOSFQz/gpLLn6f5XcM++D1rG8+mp+ntGoDcaSK227OvweMKlTPTvxz+Tv2Z9SbJF9yWEpUlgJITlWPqmU76/bVfa2MB961by1bxLuXXgcN7bv/usy3WV0GBG4CAWhI5ioNfxmeMqNXX8kLedT4+tu8Ca57enMoP5oSMId/Gjn2d4jwyOhBDHSXAkhJnkN1aQ31jBqsK9AC39z+v1TZRr6nBS2ePj4MYUl8n8fdREZkT24m8bfientrpD+zVh4j+p3/PakJt5pv9VXLfzVer1zWZ4Rl3TAM9IjtYX0WzUWWwft8TMYEbQYJ448BUbSg9abD9CWJrccAoheqrtBbl8npLEbYOG83lKEk36s183dIXw6IE+F1PYVMkzKUvZVZ5utgGsN5WmkFqTS4JHOA/HL6S3WwhvHFlh0Wuss7nQ5CaW3rcQAto+j6MQolWqdQ1kN5RSrqkDoMmgJb+xgi/KlnPX3vfwd3Zl9aXXcU3CIBQd3JfWqOdfKUtxsXPgiYQlKDq8xa5JrVAx1i+e3RXpFtvH3OBhXB89lXcyVkloJLo0CY2EsDy56bRtHx7Yg5u9A5fH97d2KR3SZNBS0FjJmqIks856ZjAZeSZlKU0GLY16DTODBvPxyHuJdQ0y2z6EEF2DBEdCWEFydTY37XuZ1cV7+Pf4aaxafC1XxA/AyU7d7m2WNFfz/KFljPKN481htxLu7GvGiruGMX59cFc783uR+QcKV6DgyogJPBK/kJ/zd/F19iaz70MIcyvI9DvjvxN/F0KInq6wvo5fMg5z/7AxTAyLPOdytn7MfC9jNVMDB3BD9FSzbzu/sYJluVvRmwzcvudddCY9H4y8mxmBx4dkcFc7c1uvmXw88h7mBA01+/5PsEYIK8GvEP8jwZEQVtJk0PJK2i/cuec98utreXbCdHZcfStDAtr/K86m0lQeTPwEPwd3PhhxN972rmas2PZN8u9Pem0BOQ2lZt2uu9qZFwdfz12957I0ZwuvpP1s1u0LYQnnutGx9RsgIboLuensGv61bT17iwv4dM4irogfYO1y2mVt8X4+ylzLDdHTSPAIN/v2VxXuw13tzOKwMTyU+AnptQUsCB2Jm50Trw+5mYWhY9AbDfyj3xJCnHzMvv8T5DslhPVIcCSElR2ozuLpjA+Z8M2HpFdV8OmcRcR5t7+1UGJVJjfvegu9ycCdsXPMWKntazRo8HZwY7BXtNm26al24a2htxLvHsaDiR/z7tHVGExGs21fCEuQcEgI6+qsG1z5rndcrVbDzat/4ouUJJ6fOIOnxk7Gw8HxjOVs/bX+ImsDabX5PNP/SoZ6x5h123mN5bya9gtTAgZwU8x09lYeJcEjnN8nP02Aoye373mHzWWpNBm0lDZXm3XfpwuJKeuU75eEVEKcSgbHFsJGmPwzuXn1T3w3/zLemX4Rc5Z/gcZgaNeJq07fxPriZKYEds1fztrr25zNxLmF8Naw29hedpgXDv1Ahbau3dvzVLvwxtBb8LR35c6975m9JZMQQoiu4eq+A1kUl8Dr+3awMTfL2uUIMzMBT29bT35dLfcNG82C2L6sOJpGVk0VWTVVJJUUUaNptumBsg0mI08mf8PT/a/gjaG3kttQxv6qLA7W5LCv8iglHQx0ludtZ5BXNIGOXrx0+Cd0RgNGjGwsSSG3sYz74+aTWJmJzmQwzxO6gJPfB1sP9YToDiQ4EsKGuIXl82z6V3wy8j7+OW0w7x5d3a7t+Ni7MTGgH5tKU8xcoe1RKZSoFEq0Rj35jRXcvPstro2czG2xsxjoFcX6kuR2bTfePZT/DLgGO6WSe/Z9IKGREEL0UEMCgvjPhOkA/HPMZDbnZeNkZ0eD7uwzS0lro67ro+S9/JJxiHuGjmZkcBhL+vTD0U6N0WQiraKMHYW5VKrziHULordbCLkNZWTWF7OjPI2shhJrl09xcxV37HmXYd69mBTQn/4e4cwLGYZSoSStNp9NJSn8WrC73QNol2tqWRQ2mreG3cbWskNsLTtEbmM5jko1A7wieTt9pZmfUeuc/p2T74YQ5ifBkRA2JquhhA8z13BX7zk4qNS8mf5bm7pGBTt589qQmzGaTHyVtdFyhdqAcX7xPBC3AIPJwF1736dMU0OQoxfzQ0eQ01DaquDMzc6JCBd/vO1d8XZwJcTJhz7uofT3jOBIbQFPJH9NmaamE56NEEIIWxTn/b+b0DqthmO3PYTBaOSxTWtZdqT7/0DT05Q1NfLk1nUAKIAQNw9GBoUyOiSMmVG9CXIZQnFzFQercwh19mGCfwJ39Z7DuuJkvszewLH6YlzsHPFUu+Bl78JAzyj6eoRR1FTFqsK9ZNQXWbR+Eyb2VGawpzIDAFc7R0b6xDHRP4Fro6dwffRUVhbu5Y/i/RyszsGEqdXb/uDoGjLqChnv15ebYqZzV++55DSUklKdi73SzqKz2rbF2cJbCZOE6BiFyWS64NGitrYWDw8PRvx0H3YuDp1RlxA93oLQkTwQdzFptfl8n7uNLWWpaI36sy5rr7TDx96NId4x3NprJo16DfcnftThZsm2bHrgIJ7uf0XLv5fmbOGdjFV8MOIuXO0cuXvvB+cNfJxVDlwVOZHLI8bjqLIHjjfzLm2u5nBtPgeqsvg5fxf6TmpyLYQ5yQWyEOajVCj4fO4ihgWG4GSnZsXRNLwdnRgWGMLwL96lVqs5ZfnOaHEk33HrO/E+2ylUzA4ewnVRUwhy8j5juSaDlpTqHMJd/Ahw9GRtURIfZq6lsKmys0vGzc6JxeFjuThkBH6OHuiMeio09VRoa6nQ1LG/Kos1RYmtapHkoFQz3KcXUwMGMiNoMMVNVSza+kInPIuOac13x5rdEfUNGnZf8jo1NTW4u7tbrY7OdiJveG73ZBxdbattS3O9nsdHbOhx78npbOtdEUK0+Dl/FzkNZdwSM4NnBlxJra6RjLpCchvKyG4o5Y/i/QQ5eXFN5GQm+CegVCgxmozsqkjnPynft7sZclcQ5uzLI/ELAdAYdGwtO8RvBXvo5xFBH/dQ7tzz3jlDIzuFigWhI7k+eirOKge+y93CH8UHqNTUUatrxNiGX96EEEJ0f0aTiRtW/Ui8tx8/LbyKPt5+hHt48Gd2plVCI2Fb9CYDKwr2sLowkSHeMfg5uNOgb6ZK20C1roGipkq0Rj0qhZK5wcO4KWY63wU+TE5DGYdr8yhtrqFKW//X8vVUaxuo0tZTo2s0+2QcdfomPj32J58dW0d/zwh6uQbh4+CGj4M7AY4e3B47iztiZ7Gt7DAao44AR0/8HT3xVLvwUNInJFdnt2xLY9SxtewwW8sOszxvu1nrtCQZG0mI9pHgSAgbllR1jDv3vke4sy9TAwcS7RpIf89I5gQP47Zes3C2cyC3oYzXj6wgp6GMrIYSyjW11i7b4mYFDcFoMpFanYvOpOfJg98AMNY3HoBIF39SanJOueByVKqZGTSEqyInEujkxarCfXyc+Yd0QxNCCHFBeqORg+UlXLtyOQt792VrQTbP7dhk7bKEFZ0+ULbeZDhvVy2DycivBbtZU5TE5ID+JHiEE+cewhCvXnjZu+CgUp+xTq2ukSptPRl1RSRWZpJYlUleY3mHazdhIrk6m+TqbIZ598Jd7Ux2Qym1ukamBgxgbvAwYtyCANAa9fySv4sjtQXn3F5qTW6Ha7IGCXqFaD0JjoToAnIby/n02LqWf3vbu3J5xHiO1BawoeRgj2slo1IocVU7kuAZzm8Fe1r+vqM8jVWFe3mk70Ju6TWD7WVp6EwGnFX2jPKNw8XOka2lh3h0/+c2MYilEJYgv6AKYTnbC3LZXmDdm2T5jndtGqOO34sS+b0o8ZS/O6vs8bR3/WtsJFc87V3wtHfB196dBI9wHuxzMXZKFWXNNRQ0VaBAgd5koKCxkuyG0lZ3MTvZxSEjeaTvwpZ/G0xGChor8HVwR280sKpwH59lrevWQx8IIVpHgiMhuqBKbT3vZLRvxrXu4I/i/WiNetaVJJ8y25kRE8+mLuP73G3MCBzEMO9eGDGhMehYUbCbH/N2UtxcZcXKhRBCCNFdnN7qqCMaDVoamyrPOfaRs8qBgV6RDPGKwcveFRMmHJRq4txDmBk0mBuip/LpsT9Zlrut1T8o6v4aO/PLrA1sL08jwsWfKBd/anVN/F6UKNdMQogWEhwJIdpsoGcUUwL64652pri5ihUFezp1kMfM+mIy64vP+XhGXSEZdYWdVo8QQgjRGaS1Uc/VaNCwo/wIO8qPnPGYp9qFm2Kmc3fvuYz1i+dfB5dSoa274DZXFe0j1NmX66KnYKdU8VXWRlbodluifCFEF6e0dgFCiK5lnF9fXh96M6N9++Dn6MEVERO4KnJSp9YQ7uzLvwdcxe29ZnXqfoUQQgghTmYLYV61roGX037mvn0fEuHiz+ej72eQV1Sr1v0gcw0fHF3DxSEjWT7+MW7vNQs7hcrCFQshuhppcSSEaBVfBzeui5rKwrDRrCtO5l8p3+Jm58RPEx4n6zytf8zJXmnH4wmXMj1wEHB8itv3jv7eKfsWoiuwhRsYIXoqSw+021O/3+15XTv7tTJnl7WOSKw6xnU7XuOZAVfy0uAbuXX32xxrxTXa51nr+Sl/J5eHj+eKyAlEuQbw6P7PAVCgQK1UYTAZzT7LmxCi65AWR0KIC3Kzc+KXCU+wMGw0Kwr28MKh5cwOGsq3Y/9Go17D9vI0i9egVqj4z4CrmeCXwAuHlvPvlO9wUtnjoXa2+L6FEEKIC7FkWCGhUdvXO/m/nqRa18DDSZ9R3FTFo/ELCXP2bdV69bomipur0BsN1OmaAPBQO/PWsFvZMPVZVk18igGekRasXAhhy6TFkRDigur0Tawq3Muc4GFcFDKci0KGA7C6cB9vpv9Gja7Rovu3U6h4ZsBVDPPuxWP7P2d3ZQa+Du5oDDoWho3h02N/WnT/QgghejZfJ2fenDaPXl4+3LDqR1LKzz4z54mAx5xhhYRG5t2WpV5PW2l1BMdnbnvx8I/8d9ANLB37MGm1+WwtO0RabT7ptYWnjH+kUigZ6BnJ3b3nEecewurCfbyS9jNwfEzLQV7RZNQVEusWTKizL8nV2dZ5UkIIq5LgSAjRKs+mLuP/Dv1Igkc4Yc6+pNbkdsqU9iFO3vwjYQnxHmE8fuALdldmAOCldkGtVDE1YIAER0IIISzqnRnziffxw83egVemzOaiH75EYzCcc/mOhAg9NSg6mSUDmBPb7u6v84HqbC7a/G/G+PZhWuAgloSPw/2vVtoVmjqqtPW42jniZe+Kg0rN4Zo8bt39Nqk1uS3bSK7OJrUmlwSPcADSawus8lyEENYnwZEQotX0JgMHqrM4UJ1l0f04q+wZ7hPLaN94pgYMoEpbz717P+BgTQ5qhYp/DbiSif79KG6q4puczRatRYiuorvfBAlhTSGu7nxzKJmf0lP5fcn13Dl4FK/u3QbAuNAILontC8C+4gK+PZyMCflOtldntdoJiSkz+3tkS62OALRGPRtLU9hYmgJAoKMXvd2CiXMPwc3OiTp9E9XaBtLrCkiuzsGE6ZT1q3UN3Lr7bfwcPAhz9uVofZE1noYQwgZIcCSEsBkXhYxgasAABnlFoVbakV1fyrLcbXyVvYFGgxYFCp7odxmjfOL418FvWVeSLAM1CiGEsLhD5aUs7N2XkUGhGE0mKpqOd9F2VdvzxtS5VGuaqWpuYlFcAtMie3Hj6h+tXHHX1NmhS09pfXRCcXMVxc1VbC5LbdN6ZZoayjQ1FqpKCNEVSHAkhLAJk/z78VjfReyuSOet9JVsL0+jsKmy5XEfezdujJnGlID+PJH8FZtK23bRI0R3Z4lfz4UQxz25dR3/GD2RMHdPlvyylL3Fx7vs3DpoOC5qe2Yv/4KShnpWL74WbycnK1fbNVmzpY4cP4UQ4vwkOBJC2ITJAQNIq83ngcSPW/7mbe/KJP/+TAkcwEDPSAwmIy+n/SyhkRBCiE5V1FDH3X/+dsrfLo3rx91DRvHmvp2UNNQzOjiMeF9/rvj1OytV2XXZQvcuc4VHttZdTQghzEGCI9EjWGKWE2Fe9fomAhw9iXULxl5px2Xh45kc0A+jycSeygyeT13OlrJD1OmbrF2qEDZLfjUXwvIi3D25su9Abhk4jG8PHWgZ62hwQBBGk4nihnorVyjaS46hQghxdhIciR7lXBcDEihZX1ptAQtCR/HZqPsAKNfU8nLaz6wrTpawSIg2sMUbn/MdY22tViHOZUJoJLcNGs7Y0AhqNM28lbiTV/dsa3n8t8wjLOqdwC8Lr+K+davYkHvMitV2HbZ2DWaOY6i0OhJCdDcSHAnBqTcucqK3jhUFu9ldkY6XvQsmE+Q0lNJs1Fm7LCG6pBM3Pk52at6aPo8vUpLYlJfd6TW0ZTkJkIQts1Mq+XD2Ao5UlHP/upWsPpaBxqA/ZZnc2hoW/PQ1r0yew8ezL+GVPdt4O3HnafNUiZPZ6jWXLQbwQghhTRIcCXEaCZGsp6S5mpLmamuXIUS3sGiEN5eEjWKcXwx+Ti6dFhy197jZHW7UxoVG8Oz46WwvzOXvm9ZauxxhRt6OTjio7Hh17/bztiSq02q5dc3P3DN0NH8bMQ43ewee37mpEysV5tLRUFtaHQkhuhMJjoQ4DxkbSQjRVf1n4NUYjEY0Bh0qe22n7LOjx8qu3vpoQWw8ER6eVGmke2134+fsAkB5U8MFlzUBb+zbgcFk5P6hY/j60H5ya2Uq866qO4TaQgjRUUprFyBEV1CQ6ScXDUKILmVDSTJ1+kZWFOzGVe1ISEyZRUNwc27b0rVaioeDIwCbcrOtW4gwOz+nv4KjxsZWr/Nx8j7Kmxp5aPg4S5UlOklXPB4JIYQ5SXAkegRznfAlPBKi5/Gxd8PfwcPaZeCgVBPo6NXq5Y0mEyqFir2VR4lw8WewVzRg/hsgS4Y8XSlA8nVyZnpkLwD2lxZZuRphbr7OzgBUNLU+OGrW6/k8JYkZkb1QK+WS+2y60nVVVzoeCSGEuclZTPQY5gyPutKFjhCi/fp7RLBs3KMsH/8YUwIGdGhb7mpnPhpxN0/1uxwlijatOzVgAN+NfZgfxj/Gt2P+RpRLQMtj9ko77oydzZ2xsxnoGdnydxc7R5oMGnZVpKM16ol3D215zBw3QJ15E2XrN2xhbh78vPCqln8fkOCo2/F1cqFW04zWaGjTejsLc3FSq+nnG3DhhUWXYOvHIyGEsAQJjoRoJwmPhOj+BnlF4aBSs7P8CE/1u5xxfvFt3ka0ayB3xc7hoxF3E+zkzZSAATzd/0p87N0uuG6Ysy+vDbmZZwZcxaHaPP5x4Es0Rh1vDruVXq5BADyecCmLwsYwM2gI7wy/g38PuApve1e+yt5AhIs/9/Seh73SjgPV2Wdsvz03P9a8abLVG7b/TppJqNvxVmnLj6RQ2SxjHHU3vk7OlLWhtdEJKeWl1GiamRoZY4Gquoeuej3VmuNRV31uQghxOgmORI9i7hsOaX0kRPe2vuQgAJtLU9lSdoj/DLiaqyInnnP5Xq5BDPGKRoECZ5UD9/W+iE9H3suMoMEkV2dzx973+FfKUoZ6x7B07MNcGTHhvPt/rO8ihvvE8p+U73j8wJdsLE3hnr0fUNxUxdvDbueWmBlMDRjAm+m/cfHmZ3ky+RsGe0Xz9ZiH6OUazKaSFBaGjabJoCWtNt+sr4012VJ4tCA2ntEh4QAcrijjiS1/WrkiYQkhbu6UNtS3eT290cjKzCNc3Cu+je0MRVdhS8cj0ToKM38bY1wD8XVwN+s2hbA1Mqua6HEsMTuGTLkqRPcyNWAgl4SNQm80kFabzxDvaJ4++C23xMzgztg5GExGluZsaVl+iFcMV0dOYqRvbwAKmypxUtnjoFTz/tHf+S53KwaTEYCchlL2VGRwffRU7uo9F61Rz/K87WetY01REv08Inis72LG+yfw4dG1ZDWUcF/iR9wVO4erIyeRXlfI74WJAKwrOcCeygzujp3L3xMWk1qTS7NBy4aS5Jb9n6ytx0JbOs7ZwkxH7vYOPDFmMgC1Gg13rP2FZr3eqjUJ81MqFIwODuPzlKR2rb8mK4Mr+w4k3N2TnNpq8xbXBXT12RJb43zHI7lGtC3+Dh78NOFx8hvLWVGwh1WFe6nUtj0UVimULA4bS6CTJ0vCjw+AX6GpI6nqGJ8e+5PshlJzly6EVUlwJISZyIWBEN3DZeHjuDfuInZXpDPAM5K8hnKCnLzRmwy8e3Q1JkzcFTuHRWFjqNM1Ya+0I8o1gPTaAp5M/obS5mpmBQ+hUa9hWe42SjVnTsNdp2/izfTfcFSpuTF6Gik1uWdtEfRrwW42laYwPXAQi8LG8Paw27gv8SMy6gr57+EfefHwT5gwnbJOra6R5w4t44/i/TzSdyEGk5Hvc7edsoxKoeSS0FEovPzIrKogsaSIgvpa876Q3ZxaqeSzOYvwdTo+aPJDG1aRXVNt3aKERfT3C8DT0YnN+dntWv9YdRUAYe4ePS44Ovm6qLuHK939+XUXWuPxcD/U2Zfro6dyQ/RUluVu4+vsTdTpW9/N2FnlwL1x81r+/UPedoZ7xzItcCBhzr7cuOsNs9cuhDVJcCR6JEv9Ui0XBkJ0baN9+3B377l8lbWRd4+u5qE+C5gVNIQGfXPLMu8fXUNBUwVBjt64qh1xUKp5I/03dlektyxzsCanVfv7KPMP4txC+HjkPdy19z32V2WdsUyNrpHledtZU5TEq0Nu4o2ht3D/vo84UldwRmh0sj2VGVy341VeH3orLw++kTv2vENBUyUhTt482e9y4j3CqGxqws/ZBY1ez3M7N7W7RUVPFO3pzZDAYAD+vX0Df2RnWrkiYSkTQiOp1WjYX9K+Qc+LGuowGI2EuVl/dsbOcq5rIVtoKWhJ3f35dQfVuga2lKbS2z2Et9NXEu0ayJLwcVwSOppvcjaxLHcrjQbtBbdTp2/ih7ztLAobA9DyvwAZdYUWq18Ia5HgSAgzk/BIiK4p2MmbZ/pfydayw7x39HcAkqqOsTBsNM52DsS6BpFRX4QJEysK9phln1XaenZVpBPvEXbBwbLr9E3cn/gRrwy+ideH3sJHmWsp19TR3zOC/p4ReKpdKGmupri5iuKmanIaS9lQcpCHkz7l3eF38MqQm/itYA/XRk2hSlvPHXveZW1iEx4Ojtw/bDT/GjeVquYmfj2adtb92+JxzRo3aXZKJTcNGMqtA4ezJS+bL1P3szb7aKfWIDrX+LBIthfkYDCdO6g9H73RSINOi6u9vZkrs00XOlZ093DlXM/vxN9s8Vja07x+ZAWPJ1zKMwOuYm/FUW7c9QaXhI7i+uipXBo+lpUFe9lclsqhmrzz/kDzVvpKjtUXE+joRVZDCfsqM/Gxd6OouaoTn40QnUOCI9FjWfLCRcIjIboeO4UKR5WavZUZLReK5Zrj3bdKmqu5udcMHt3/uVn3Oca3D9dHT+XdjNWsK0m+4PL1+mbuT/yIv8Uv4J7e87BTqihorCClJof9mmMEOHoR7uzHCJ/e+Dq4My84g6cOfsODiR/z3og7uTlmBr/k7+K9o6vJSD/e+qFG08y/tm0g1NWDh4aPY/WxdHTGM8dDao/udKOkVipZ2DuBu4eMIsz9+Gv30IbVlDY2WLkyYUmuanuGBATz1NZ1HdqOUqHE2M7gqStp7Xe9u4dH59OdjotdVVFzFW9nrOLjkfcwzKcXV0dO4vlDy1mas4WroyYxN2QYV0dNolxTy7LcbfyYt4NGg+aM7WiNen7O33XK305cNwjR3UhwJISFSHgkRNeS21jGhpKDXBQygh/ydgBwqCaP3IYydEYD4/z6kuARTmpNbof3ZadQMdavD5MDBlCuqeWr7I2tXrfRoOGZlO9448hvqBRKKrR1Z11uiFc0zwy4ik9G3svjB77kll1voVbaUdBUcdYbthd3b+X3JddxWZ/+fHXowBmPd+SYZqnjYWfdeF4UE8ejoyYS6va/WXOe37lJQqMeYHRIGHZKJZvysju0HZVCgcFMgaytaut3vKdfI8l1onWVa2r4JnszXvYuTAsciAlIrMzk82PreS3tV/p5RjDBP4E7YmczxrcPDyV9QlMrurAJ0V0prV2AEN1ZT/01TYiu6lh9ySldxvQmA29nrCTGLZCCxgruiJ2NStHxU+ddsXN4buC1TA8cRFZ9Sbu2Ua1rOGdoBJBYdYwbd75Blbaed4ffwVDvmHOGRgDpVeX8lH6Ie4eOxtlO3a6aTijI9DtjP5aYzbIzzIqK5c3pF7WERrUaDTet/pH395unu6KwbRPCosiqqSK/7sxB7ttCqVS0u6ub6DrOduwTtqlcU8fbGSt59civ/Jq/m77uoTzV/3J+HP93ro6axFDvGNzsnChuqmKgVxT9PCKsXbIQViUtjoQQQvR4wU7eVGrqsFMq8bB3QYkC41/d1baVpVGhqaOwqZLBXtH8e8BVPH3w25aZWdpqhHcsSyLG8caR31hTlHjW5u/mUqqp4c697/FgnwU80e8yKkvd+JFD51z+1b3bmBkVy2tT53D72l/P6FrTml/ILTXxgDVMi4jhvZkXA1DV3MRnBxP5PCWJak3zBdYU3cXk8GjW5XR84HOTyYRKoTBDRbapp7eckbCo62rQN/N6+goAPNUu3NN7Hrf1mkWlpo6i5ir2V2WxMvV7EquOWblSIaxLgiPRo3VGH3tpiiyEbQt28mbp2IcxmUzYKVWsKz7QEhoBmDDx/tHfeTzhUqo0dYz1jefFQTfw9wOft2rmlZOFOHnzj35L2FWezve5W8876Ka5aI16ludu46KQ4dRqzh9S5dfVcs+fv/HRrAV8PPsSkkqKyK2tJre2htTyUjQG/SnHzNOPbRc6nloreGqvaZExwPFuaV+k7KdJr7NyRaIzDfIPJNTNndXH0i+88AWUNDQQ6HL+AfBF12NLxyvRcdW6Bv6d+h3PH1qO3mSwdjlC2BQJjoQQQvRoI316YzKZeDP9Nyo0dWwoPQiAr4MbeqORal0Dqwv3Ua6pZXrgIGYHD6W/ZwRvD7ud/zv8A2m1BRfch0qh5PKI8dwUPZ1yTS3PHfq+U0KjE4aoR1DZ1MjGvKwLLrsh9xgPb/ydaxIGcW2/wfg6OQPHu2itPHaED/fv4VjN8Rlj2nPTdHp4ZMs3Xo9tWstjm9ZauwxhJXNj+lDW2MDuovwOb6uwvpZg1+4ZHPXEH8ds+bglOk5CIyHOJMGREJ1AWh0JYbsGekWR11jO6qJE4txCuCt2DiN9ehPjFoTBZCSxMpM/iw+wqTSFXRXHWx6M9+uLQqHgwxF380v+Lt4/uoY6fdNZt9/XPYxH+i4k2jWQ73K28HHmHzQbO6/lSkGmH9HTvEmvqkDfysF5f0w/xI/px7u0OdupifLwYkZULJf2SWBh7768l7SHvcX5ONmpsVMqqdY0U9HUSGVTI5XNTRccy8XWbrrc7R1o1Ota/fqI7k8BzInuzapj6WaZDa2wvo4ID88Ob8fW9LRrG1s7dgkhRGeR4Ej0aHIBIITYXnaY6YGDWDv5X8DxqXR3lafzedZ6nO0cmBowkEf6LuRv8Quo0zXh7eBGk0HLzbveYkHoSG6JmcnkgAG8m7GalYV7W1oSOavsubXXLBaFjSa9rpCbd71Jel1hpz63E8e4cHdP0irad4PXqNeRWlFKakUp7yTt4q4hI7lj8AjsVaPPuU5VcxO7CvP44MBeEks69zm31X1DR/PA8LHk1FQz8duPrF2OsBGDA4IIcXNnZeYRs2wvu6aKWdGx9PcL4GBZ+wbEtzU9LTQSQoieTIIjITqJtDoSwjatLd5PSXMNvg5u5DaUkVFfdMrjKwr24G3vypSAAXioXcisLyK1Jg+9ycDyvO2sL0nmztg5/D1hMfNDRvBS2k/4O3ryYJ+LcbNz4q30lSzP247B1DmtWc4WiKuVSpoN7RvM+2Qag55X9mzj04OJONupadTrMBiNeDo64uPojLeTEz5Ozvg7u3JJbDw/XnIle4ryeX//HtblZHZi57wLUyuVPDN+GlfEDwDgx/RUK1ckbMncmDhKGurZW3zhrqit8XlKElMiovl63hIeXL+KP80w4La1yLWMEEL0PBIcCSGE6PEOVJ9/7J9KbT3L87af87H/pH7PioI9PNTnYj4ddR8A28oO83Laz5Q0V5u11tODoRM3cedrQdmg0+KiVputhqrmJqr4X9e8Wq2G3NpTpyt/O3En0yJjuHXgcD6afQlHqyp4Y98Ofj2aZrY62sPRzo4r4gdw68DhBP015szLu7fyZuLOVm8jwt0Tb0cnarUasmuqZJr1bijSw4u0ijKzdFOD49+Rq39bxmtT5vLR7Euobm6iormJvUUFbM3P5s+cYzY7+LoERUIIISQ4Ej2WdFMTQpjTgeosbtj1BrOChlCra2RL2bmnvW+vsx23WnMs0xgMOKg695RvAv7IzuSP7EyGBgZzx6CRvDFtHrOje/P45j+oaj77mFAnONrZMSsqlnqt1mytM+K8fXlnxnwi3D35JeMwSSWF/GfCdB4YPpat+TkklRZdcBtTI6J5b8bFqFUqAL5M3c8/t/xplvqE9U0Kj2JkUCjpleUs6p1g1m036HTcsuZnpkfGEOPpQ6CLK6NDwrksvj8fHdjLf3ZsNOv+OkoCIyGEECdIcCREJ5LuakJ0bwaTkZWFey2y7Y6E3S5qNQV1tWas5sJOPtYVU8ZTGQfYUNOfv8UtYtc1t5NdU8XRqkr+zDnKqmPpNOuPd6WL8/blivgBLOzdF3cHRwA+PZjIszs2dmjw6kW9E/jP+Glk11Qx8/vPyKyuxN/ZhQeHj8XbyblVrYbGhUbwzoz5/JmTyWt7t3NF/AAu7dOPF3ZuokFnm61FROt5OTrxyuTZeP81k6Cl/JGdyR8cD0PVSiUpN91Lfl3NBdbqHHKNcn4hMWVm++FRXmshRFciwZEQnUzCIyF6Lj8HD66KnMhHmWup1ze3ah1z3KRUNDUxOTyKMDcP8sx8g9qW49nG0oMkV2cxyb8/kS7+hDuH88qUObwyZQ71Wi0NOi0BLq6UNTbwZep+vks7yITQSJ4aO4VAF1fuXPtrm8ZJcrZTMz2qFwtj+zIxPIrv0w7y5NZ1NOv19PXx4/O5i9EZjVzx63cklxWfd1sjgkL5cOYCtuXncu+fv6EzGnl//x6uSRjERb36sPTwwTZUJmzR46MmolQoeGzTGl6YOBONvuPjgl1IvI8/Dio79reitZulybVJ67Sme3BrtyGEEF2FBEdCWIGER0L0THfGzmZG0GBMmHj9yIrzLtvRwMhBZcfs6FhmRsXiaGeHv4srW666hcGfvX3BbmIX0pHjV6W2nh/zd7T8225DLMODQvBwcMTd3pFDFaWsy8lsaV301aEDlDTW8+GsS3h/5sX8364tZFZXnncf40MjWNKnP9MiYnBSq9lTlM89f6xgxV8zZA32D+KzuYvIrqnmhlU/UHmB12OQfyCfzF7IvpJC7lj7C7q/aitqqGNTXjaXxw+Q4KiLi/Tw5NI+/Xhiy58sPXyQLXk5uKjtLb7fJX36UdnUyKFy614TyDVJ27UnQJLXWQjRVUlwJIQQQliIi50j7nZOuKmdGOAZyYygweiNBkZ49z7vem0NjeyUylO6cU0Ki+Lf46cR5u5BYnEhppPa6QQ4u3QoODL3jY/eL4PlR6rPu8wf2Znc/ccKHhs1kbVLrufH9EO8vm87+ad1v3O3d+CZ8VNZENuXwxVlvL5vByuOplFQ/7/lAlxc+eqiS0ktL+XGVT/i7+zCDf2HMDoknHXZmXxycB8ag6FleTulkjemzeNIZRm3/P7zKY8BfJW6n0/mLOSJ0ZN4bucmsw2mLDrXdf2GUNHUyLK04wHgyZ8ZSwl1c2dJn/68vGcrWqPhwitYiIQZHXPy63euyQuEEKKrk+BICCuRVkdCdF/2SjteGXITg72iT/n774WJ7KhI41/9ryTU2Yf8xooz1j1baOSgskNjOLPbjI+jMw8OH8Pl8QPYW1zAR8l72VWYz/uzLmZvUQHXrVzOsZoqAFQKBUaTqU1dvTpLa8YN+S3zCGuzjnJF3wHcPWQUC2LjOVpVwbGaKrJqqqhoauTWgcNxUau5f91Kfs44fPZ9ubrhorbn/3ZtZkmffjw2aiKNOi2JJUU8OHwsk8KjuOzX71qW93JwxN/ZBaVCgVqp5PTIbX3uMZ7auo4nx0wmwsOTT5L34e7giALYnJdNo43OlCX+x1Vtz6Vx/fj0tNDQ0qaEx2DCxBcp+zttnyeTaxDzk9dUCNFdSXBkQxaHJpl9m8vzB5t9m8J8JDwSonuKdPFnsFc0X2VtZE9lBnW6Jmp1jRQ1V+Fm50RxUzUfjbiHV9J+IbHqKCYTmDBRlO2Dj+Px/x/n7cuk8GgmhUcR5+3LnqJ8Vhw9QnJZEfU6Hdf1G8ylcQloDAbeSdrF6JBwPpx1SUsNj2/+g5za6pZ/m2PKeEser1oTHmmNBj5PSeL7tBQW9u5LvI8fkR5eXBLblxA3dzbnZfPoxjUUNdSdcxuZ1VWUNtTzw4IrAfjowF7+u2sLWqOBq/sO5F/jpp4S1JU1NTJ3+Zf8cdkNzIyKZdmRlDO2+XlKEjk11bw5fR7TI3u1/L1Oq+HOtb+yJT+nPS+J6CSX9umHg0rFV4cOdOp+PR0dqdVoaLJCuCjXHkIIIdpCgiMrsERAZIl9SejUOSQ8EqL7KW6uBuBwbT57K4+e8lidvonrd77Gg30u5qn+l5+64sRT/1naUM+mvGy+Tj3A9KgY/jlmUss08GWNDbyVuIuvUvdTrWmGPdsYHRzGt/MvAzglNOoqTj8WnitIatLr+Pq0m3y1Utky9tD51Giamf/jV8yIjGVPcT6HK/63z0MVZaiUSqI9vU75e2Z1JbuL8pkXE3fW4AhgY14W477+EHd7B2q1GtzsHXht6hz+PmoiW5Z/ccG6hHUogOv6DWb1sXRKGuo7dd9F9XX4Obu0fGY6i1xzCCGEaCsJjjpBZwZF5nRy3RIiWZaER0J0D3FuIfT3jCDWLRiAOn3jWZer0zfxr5SlLMvdhoe9C5VFHigUoEDR8r8FdbUcqiht6Vr2RWoS9koVcT6+BDi7siU/+4xuNTsK87j3z99QK1Vmf27WOEa1ZfDZ1oRGJxQ31PNF6pnn5qNVx7sODvALPCU4AtDo9Xg7OZ13uzWaZmo0zS3//+XdW/l2/mVMDItkU152q+sTnWdyeDSRHl48sG5Vp+97V1Ee9Votvy66mvvXrWR/6fln9jMHudYQQgjRHhIcWUhXDYvORUIky5PwSIiuJcjRi3p9M3X646PezAsezt8TFqM16sltKGN72WHSawvPu41DtXl/hSJnjnV0NlqjgYNlJRyk5JzL/Ho0rdXPobWsfWwyx/TXrVGr1bA+5xh3DB7Bj+mpLWHU8MAQJoZHcdcf558J73SRHl4A+Du7nvHY+QbUFZ1nfFgkx6orSSot6vR959bWMHf5F7w+bS4/XnIV36cd5Kmt6886npk5WPt7LIQQouuS4MjMultgdDYnnmNXD5BaM55GZ5PwSIiu4YmEJcwOHoreaGBNURLPHVqG8a+2QXM3/otGg7ZV27G1Y5Ct64wA6YWdm1l96bWsWXI9ubU1VDY3MjggmINlxazKPNLq7czv1YdnJ0zn04P7Wrq3nev4bovno57C29Gp07uonSyntprFP3/LVX0H8s8xk0ktL+XL1P1m349cWwghhOgIswdH5wtOunrQcC49ISw6m+4SINkaCY+EsG1Bjl7MDh7Kh0fXojXquav3HPZWHqWsuQYAbwc3Gs8yW1pXZYvHI0sGSOlV5dy+9hfGhkQQ7OpGqJsHebU1vLh7S6tnpJseGcMrU+aw/EgKz2zb0KrXUMIj6/BydDo+RpgV6Y1GPk9JYmJYFPN79TF7cGSL32EhhBBdS4eDo7aEJt0taOipgdHpuvL7aqsX6hIeCWG7Qpx9APijOImCpkp6uwfzeMJiluVuByDePYz8VgRHtnjsEcf9kZ3JH9mZ7Vp3bEg4b02/iN+zMnhs01qC23Ast9VzUndWp9UwKyqW5yfO4NntG6nXta61oCX8nHGIN6bNY1J4FBtzs4DWhz4nf27k+kEIIYS5tSs4ksBEXgNheRIeCWGbmg3Hp862V6oBeDble4piKrkqchJ6o4G8xnJrlmdWcgxqmyEBwXw46xK2F+TywLqVBEWXWrskcQH3r1vJlX0H8tDwcYwODmP1sXQ+PZhIaWNDp9ey4mga83vF88b02dy86y0KmlrfclG+q0IIISxJ2ZaFF4QkmyUw6cqhy+LQpC5dvyV11ddFLraEEG3RaDg+bbaznQMAOpOB94+u4dbdb3N/4kek1eZbszyzkWNj2/T18eOzOQtJLivm9jW/4h917gHMhe3Q/dVNbP6PX5JSXsoV8QP4YNaCTq8jJKaM4JgyXsz6gmptA88NvAbHv8JpIYQQwtqsNjj24tCkLtW1qauGIp2tq72vtk5aHQlhfW52TlwRMYFIV3+a9FqcVPYAOChPPYV2l8BItF2MpzdfzLuUYzVV/DPtQ3wjNdYuSbRRdk01d/+xgmkRMXw0+xIi3D3Jqa22yL7Od16v1zfz+IEv+XjkPSwMG8M3OZssUoMQQgjRFm1qcWRuXSGMkRZGPYMthzMy3oUQ1qNSKPlw5N1cGj4We6WaQCdPfB3d+STzT1Jqcq1dnsXY8jHR1oS6ufPVvEspb2zg74c+aGmRJrqmPcUF6I1GLurVx+zbDokpa9V3K6uhhDp9E/ZKmfxYCCGEbZAz0nlIYNSzyKCkQojTjffrS5izLzfufIMjdQXWLkfYGH9nF76etwSNQc8jKe9Tq2vs8DblXGQ9bvb2KICvDx3g7iEj+SIliVptx4LA9oaw9ko77JRW/X1XCCGEaCHB0VlIYNQx0l3N/KTLmhDWsTBsNAeqsnpUaCTHmtb7cNYlODrAnXvep0JbZ+1yRAc8M24q1/Y79drFYDK2a1vm+A5tKzvMRSEj+CJrA1qjvsPbE0IIITpCfso4jYRGPZst3zDJL9BCdK4olwCGevfix/wd1i5F2KB4Hz8G+gfyatovFDdXWbsc0QEPDR/Ltf0G89Lurdy25hc+PLCXn9IP0aDTtXobJ7qhmes64rOsdXiqXZgfMsIs2xNCCCE6Qloc/UUCI3GCLXcTkJZHQnSOYCdv7o2bR4Wmjo0lKdYup9PI8eXCTrxGJrt6KjR1XBw6ki1lh8y+D1s9D3UnSoWCf4+bylUJg3huxyY+OLAHgDVZGa1a35Lfl/zGCjaUHuSikBEsz9tusf0IIYQQrSHBERIaCSGEOC7YyZtro6YwO2gItbpGXkr7Cb3JYO2yxFl0drByekhQp2/i+UPLeGnwjYz2jWNH+RGL7O9sz7O1gYWET+emAN6efhHTI3vx8IbfWXak9QFxZwWsuysymBowABc7Rxr0zZ2yTyGEEOJsenxwJKGRZXT1cY5s+ddeaXUkhGWoFSo+GnE3epORdzJW8XP+LjTG1ndVEd3PhY61O8qPUNpcw2CvaLMHR62t4ULr2uq5zNqiPLyYHd2bxzataXVo1Nnn3pTqbJQKJQnuYeyubF0rKCGEEMISenRwJKGROB9bvuCW8EgI8+vtHoKHvQs37XqDtNqeMxj2CT3hmGKJ51jaXE2US4DZt2su52u51JNVNjcBUNV84ZY81vpu5DaWU61toJ9nhARHQgghrKpHBkcSGInWkvBIiJ6jn0cEzQYtGXVF1i6l03XXY4mln9do3z7084zgiQNfWXQ/wvyqNc1oDHoCnF3PuYwtfC9SanIY4Blp7TKEEEL0cD0uOJLQSAghxNmEOfuQ11je7im4uypbuDk2J0s9Hzc7J+r0TS3/tlfa8UDcfPZUZLCh9KBF9mlOtvxDiLWUNTYQ4OJyxt9t6TuRWp3LVZGTrF2GEEKIHs6qwVFnj4EjoZFoD1u+2JZWR0KYj7e9G0aTCQUKTJisXY7Fdadjh6WeS5RLACN8YpkRNJg+7qFsLTvEW+kryWss56rIifg7evC3pE8tsm9LsOXzmTWUNNQT4PK/Fke2+J0obq7GVe2Ig1ItY64JIYSwGqW1C+gsEhqJjrDFi8kT5CZACPNYUbibWLcgrouaYu1SLM6Wj2mtFRJT1vKfufV2C+a/g67nqzEPcluvWVRq6nj9yAqiXAJ4a9htDPPuxTWRk1mas4Xcxq7/WvZUJQ0N+Du7WuxzZA7V2noAPO3PbBklhBBCdJYe0VVNQiPr6Oozq53Oln+plZZHQnTcjvIjfJz5JzfFTCO9roDt5WnWLski5FhxblEuAdwUM53JAf3JbSjjyeRv2Fyags5kAOCP4v18MvJeXh96C4VNlXx2bJ2VK247Wz6XdbbSxnri/IOsXcZ5VekaAPCyd6Gkudq6xQghhOixrBYcdVagIKGREEKI1vo8az1x7iE81e8Krt/5GkXNVdYuyWwkMDo3tULFY30XMyNoEMXN1Tyb8j1ripPOGO+qSlvP3XvfJ9LFnwPVWTRL16EuKySmjGBfhc2PaVatPREcnXsQbyGEEMLSekxXNSHMwZZvvOQXZCE6zoSJ/VXHcFU7EujkZe1yzMKWu+HYiiHeMcwKHsJb6au4YttLrCrad85AoaCpgm3lh6nXX3gad1sk54rj3wl7pR3j/PqyqTTV2uWcV6W2jlpdIwM9o6xdihBCiB6sWwdH0tpIWILcgAnRfQ3wjOT22Nl8n7uVpKpjFttPZ9y8S2DUekqFAoB1JfvR/9UtTXQ/J38nxvj2wdnOgXUlB6xc1fkZTEZ+L0xkXshw7BQqa5cjhBCih7JKcNQZ3dQkNBJCCNEWAz0jeWnwDaRU5/BO+iprl9NuEhi1ndF0fBY9Rff+Pa1HOtcg6lMDB3KktoC8xnIrVdZ6PxfswsvelYn+/axdihBCiB6qRwyOLaynuw2QfYIMLipE1xfg6EmCRzi93YIZ4BnJQK8okquzeXT/Zy2DIVuCpY4dEha1n+mv/1VYtQrL6ynnrQt9F3wd3Jjgl8Bb6Ss7qaKOyWkoJb+xnASPMJtvISWEEKJ76vTgSFobCSGEsCaVQskN0VO5NmoKKoWS0uYa0usKeDL5azaWptj8YLknk7DITE60OFJ09+jIOk7/nFo7PL04ZBRao55VRfssUocl+Di4U9JcY+0yhBBC9FDdrsWRhEa2R1odCSFshZudE/8dfD193cP49Nif/JS3k+q/prvuDB09ZkhQZBlGTnRVk+DIXM73WT3xmLnOoW35XihQMD90BGuKEmnoIgOce6pdcFLZU9RUae1ShBBC9FDSmV8IIUSPcUP0VHq5BnHX3vf59Ni6Tg2NOkpCI8sx/RUcKaXFUYe1ZYytjo7H1Z71o1wD8HVwZ0PpwXbvt7MFO3kDSHAkhBA27vnnn2f48OG4ubnh7+/PggULOHLkyCnLZGZmcskll+Dn54e7uztLliyhpKTkjG2tXLmSkSNH4uTkhJeXFwsWLOikZ3F2nRocWbrVibQ2Ej2Z3FQKcX5e9q4sDBvNl9kbSKnJsXY5bSLfb8uq1NQDEOrse8Zjl4SOItLF/7zr2yvtGOHTm/CzrN9TdCQEOtcA1pbY1xCvaHRGPSnVue1a3xqCTgRHzVVWrkQIIcT5bNq0ibvuuoudO3fyxx9/oNPpmDFjBg0Nx3+obGhoYMaMGSgUCtavX8+2bdvQarVcdNFFGI3/Gyrhhx9+4JprruGGG27gwIEDbNu2jSuvvNJaTwvoRl3VJDSybdJdTQhhbREufqiVdmws6TotDUBCo86Q1VBCQWMFE/0T2F2R3vL3S8PGcn+f+VRrG7hl91sUnqXFx0DPKB7ru4hwl+Pngjkb/0WNrrHTarc2c38+Lf15H+IVQ2pNLhqjzqL7MadgJy9qdY3Ud5GudUII0VP9/vvvp/z7s88+w9/fn3379jFhwgS2bdtGdnY2SUlJuLu7A/D555/j5eXF+vXrmTZtGnq9nvvuu48XX3yRm266qWVbffv27dTncrpOa3HUHUMDIYToKtzsnLgoZDgvDb6RZ/pfydSAgTir7K1dVqeKdglEbzRYbYDZ9oTMEhp1nk2lKYz3S0D51zhHc4OHcX+f+fycvxO1UsWUgAFnrBPm7MurQ26iSlvPZ8fWAWA0mVArVNzX+yKGeffq1OfQGU5uHdTVPp8KFAzyiiKx8pi1S2mTAEdPSmVgbCGEsKra2tpT/tNoNBdcp6bm+LHb2/t4y1GNRoNCocDBwaFlGUdHR5RKJVu3bgUgMTGRgoIClEolgwcPJigoiNmzZ5OSkmKBZ9V63aLFkbQ26hqk1ZHl9i/E+cwLHs7f4hegVChJqjxGiLMPzwy4ktLmGu7b9wG5jeXWLrFTjPHrw/6qLKu1NGjrsUK+251rY2kKV0ZOpJ9nBMnV2dwfN5+tZYd4Je0X+nlEEOcecsryAY6ePN3/Cko1NTyQ+DGBTl5cHz2VBI9wfBzcWBIxjiUR47h+5+tk1BVa6VmZT3f4PMa4BuJh70JiVaa1S2mT3MZy5oYMx8XOscsM6C2EEO3xc8EA7FwcLrxgJ9I3aIANhIWFnfL3p556iqeffvqc6xmNRu6//37Gjh1Lv379ABg1ahQuLi48+uijPPfcc5hMJh577DEMBgNFRUUAHDt2/MeNp59+mldeeYXIyEhefvllJk2aRHp6eksI1dk6pcVRdwwLRPt015DPWhfU3eFCXlje1MABHKrJY8HmZ7kv8UNu2vUmS7b+Hw36Zt4adjtRLgHWLtHiPNTODPXuxdayQ1atozXf2a7YkqM7yKw7fsF24vtgwsQ4v778NvGfRLsGsK3scMuyC0JH8tXoB/G2d+Ppg9+gMerIaSjlaF0Rf09YzE0x09lfdQytUc9E/wSrPJ+z6cgYRN2Bt70rAEVNXWusoA0lB7FX2jHOL97apQghRI+Vl5dHTU1Ny39///vfz7v8XXfdRUpKCkuXLm35m5+fH8uWLWPFihW4urri4eFBdXU1Q4YMQak8Hs2cGOvoH//4B4sWLWLo0KF8+umnKBQKli1bZrkneAFdvsVRdw0iRNfT2S2PusuFvLC8AEdPdpWnU6mtb/lbQVMld+99n9eH3sKno+5lV0U6fxYfYGvZIZoMWitWaxmzgoYCsLbY+ueM801FLt9r6xnoFQXA/uosAK7c/jIDPaOIdQtid0U6iVXHfwEc6xvPw/EL+SV/F29nrDqlBcj9iR/xSPxCQpy8CXX2RWfUs6GLjal1uu70mazTNwHgaudo5UrapkxTw4GqLC4OGcmaIusfw4QQoidyd3dvGZfoQu6++25+++03Nm/eTGho6CmPzZgxg8zMTMrLy7Gzs8PT05PAwECio6MBCAoKAk4d08jBwYHo6Ghyc603sUOnzqpmbhIadU3d+X3rjAtsaY3Qczm1c0yi0uYaAhw9z/h7ta6BO/e+x1vpK/FUu/B0/ytYMfGfPNXvcsK62exQs4OHsLk01aYGLT79uyzfa+sa5RNHcVMVOQ2lAJRrallXcoD3jv7eEhrB8ZnXGvUa/nv4xzO6DVVp63k29Xtc7JxwUtnz2P4vyKwv7tTncSFt+Zx1t8/kicGlvR3crFxJ232etZ6BXlGM8e1j7VKEEEKcg8lk4u677+ann35i/fr1REVFnXNZX19fPD09Wb9+PaWlpcyfPx+AoUOH4uDgwJEjR1qW1el0ZGdnExERYfHncC4Wb3Ek3dTE2XTX8Y4srbtdxIvWSfAI55H4hfRyC2JDyUFeOvwT1bqGVq+vVCgIcPI862MN+maW521ned52ghy9mBY4kItDR/Ll6Af4Lmcrn2eto7GLt0Dyc/Ag2jWQH/N2WLuUs5LvtW0Y79+X7eVpF1zOx8GVCm3dOR+fHzISL3sXrtz+MsU2On16a1rIdsfPZWFTJbkNZVwTOemU2fO6gl0V6eyrPMpNMdNb9TkVQgjR+e666y6++eYbfvnlF9zc3CguPv7jkYeHB05OTgB8+umnxMfH4+fnx44dO7jvvvt44IEHiIuLA463bLr99tt56qmnCAsLIyIighdffBGASy+91DpPjC7c4qg7t1rpKbrre2ipi+3ueBEvWqe3WzC93IL44OgaBntF89zAa1oeC3f248l+l3FV5EQUf80GdbLLwscz1LsXnx7784L7KWqu4svsjVy5/WU+O7aexeFj+Hbsw0zy72fW59OZLgoZzlejH6RKW8/uigxrlyNsVJxbCEFO3mwsvXC3Mi97V6q15w5upwYOYFv5YZsNjU441zmlO7dqNZiMvHrkV4Z4xzDZv7+1y2mzH/N20Mc9lBAn6wyMKoQQ4vzeffddampqmDRpEkFBQS3/fffddy3LHDlyhAULFhAfH88zzzzDP/7xD1566aVTtvPiiy9y+eWXc8011zB8+HBycnJYv349Xl5enf2UWnTJMY66a+DQE514L7tb66PzjWHS3m2Jniu1Jrflf//v0A88P+haol0DiXcP5ZH4hVTrGpgeOIjBXjH86+C3LeN49POI4M7Y2XydvZGtJw3seyFao57Pstaxumgf9/W+iGcHXsNHmWv59K/pxruKKyMmcFfvuawo2M1b6StbuqkIcbrJAf2p1jawvyrrgsvW6Brpb+9y1sdCnLzp4x7KV1kbzVyhZZxoedSTzjO7K9LZU5HBVZET2dCKoNCW7Cw/gsagY4J/P77N2WztcoQQQpzGZDJdcJkXXniBF1544bzLqNVqXnrppTMCJWuyaIsjS4QBEhp1T931fe3oxXhPupgX53a0roi02nweiV/I7op0KjR1LAgdyRURE9hZcYRLt/4fDyV+Ql/3UN4adhu+Dm4sDhvDq0NuIrUml/ePrmnXfkuaq3k8+UveP/o7N8fM4IWB1+KpPvsNs62ZEzyMu3rP5bNj63jh0A8SGonzmujfjy1lqRhMxgsum99YTpCjFyrFmZdQAY7HfwnM/mucpK6gJ55nluZsId4jjAGekdYupU2ajTp2VaQzsQu3AhVCCNE1ddmuaqL7kfDo1HV64sW8ODsjJr7N2UyIsw8OKjXLc7dxSegoolwD+ClvJ1qjnt2VGdy59z081M58P/ZRHuhzMauL9vG3pE9adTN8Pl9kbeDR/Z/RzzOCz0ffz0if3mZ6ZpYxzi+eR+MX8nP+Tj7MXGvtcoSNG+IVTbiLX6tnP4t3D0NnMpw1OMqoK6RcU8tT/S9nrG88aoWK3m7B2ClU5i5bdMCuinRyGkq5JHSUtUtps82lKfT3jMDHvusN8C2EEKLr6lJd1bprsCD+5+T3uDt1X5MQSHSEn4MHV0RMoFxTS42ukaW5W4hyDWBfZSY7K/4340J2Qyl37HmXW3vN5JeCXa3qdtNaW8sOc92O13g84VJeGXITb6WvtMmuEv09Inim/1VsKTvEy4d/tnY5wsbFugXz3MBrSao6xt7Ko+dd1lllzwcj7ibKNYC30leiNxrOWKZO38TfEj/hsYTF/Hfw9S1//+zYOgkxbYgJE1Xa+rOGf7ZuW3kaRpORkb5xrCrc2+7tOCjVaIw6M1YmhBCiO+sywZGERj1Pdw2RhDhBpVAyK2gINboGtpWlYeLs/aKXhI8jyNGLvyV9Chwfg+hfKUvPumxRc9U5H+uoCm0df0v6lNt6zeTu3nMpba5hXckBi+yrve7vM5+jdUX86+C3GM/xegoBMNAzkucHXkteYzmPJH12wZZ5nvauRLkGAHB377ncGD2NlJockqqOcaAqi6P1xTip1GhNBh5K/AR/Rw+G+8RyZ+wcjtWXdMZTEq2kQEGsWzA7yjdYu5Q2q9U1kl5XyHDvXm0KjhQo6OsRxni/vkzwTyDIyZu/7//ilB8fhBBCiHPpEsGRhEaiuw6iLXq2y8LHcVfvuQBk1Zdwx553Wwa2PlmEix+HavM5VJvX2SWewYSJ947+jq+DO0/0W0JJczUpNTnWLguAwV7R9HEP5YHEj9GZzmwNIsQJQ7yieWXITSRXZ/OPA1/RaNBccJ3CpkrmbnyGGLdAXO0cCXP2Y6BnJFdFTOK2XrPOWL5W14jRZCK3oYxtZYcs8TREOwU7eeNi50hGXaG1S2mXPRUZzA0edsHl7JV2XBM5mUFeUcS5h+Bi50iVtp6tZYcpbqrm2YFXc9++j2zmGC6EEMJ22XxwJKGRONni0CQJj0S34WXvRk5DKasL93F77GyUCsUZyzgo1QzxjuHTY39aocJze+HQD4Q4+/Bkv8u4dserNNtAl4fLI8aTWVfE7op0a5cibJirnSNP9LuM5OpsHkz8BH0bQsZqXQP7KjNb/v0VoERBjFsQ4c5+NBqaadRr8LJ3JczZF18Hd5bnbbeJ74f4n95uwQCkd9XgqDKDa6Im08c9hLTagrMu42bnxP8Nuo449xB2lB/hy6wNJFdnc7A6ByMm7JV2vDrkJl4cfD137nmPrAZpFSeEEOLcbD44EuJ00vpIdBd2CiUmEwQ6eZNVX0KNrvGMZQZ6ReGksmdLqW21WNCbDPwn5Xu+GH0/d8TO5tUjv1q1njBnX8b59eXZ1GVWrUPYvgfiLsZF5cizqcvaFBqdixETGXWFXbb1Sk802CuagsYKqrT11i6lXQ5W55DfWM5Lg2/k2dRl7ChPO+XxPu4hPJFwGZ72Ltyz94OztlbVGvU8sv9z3h9+Jw/2uZh79n3QWeULIYTogmx6VEBpbSTOZ3FoknxGhM1qzbT1Ic4+FDZVEODoQa2ukakBA85Yz155fDam2rOEStZW0FTBuxmrWRw+lsFe0VatxVFlj9aoZ0pAfxyUaqvWImzXJP/+zAoewitHfqakudra5QgrGe4T26VbJmqNem7d/TYZdYW8NPgGPht1H7ODhjDUO4ZXh9zExyPvRYGCO/a8e94uzg36Zj7K/IMh3jHEuYV04jMQQgjR1dhscCSBgGgtCZCErbk2ajIrJz3Ja0NuRsGZ3c/slXaM8I4lzj2E3MZyFCgY6BXFMwOuYsXEJ7glZkbLsnrj8QF7HVS2GYb8kLeDpKpjPJ5wKe5qZ6vVkVFXyMNJnzLQM4pXh9yEt72r1WoRtsnH3o2H4y9hY8lB1hTJOaOn8nPwINzF74Kz6Nm6Gl0jDyR+zL37PqCsuYYn+l3GG0NvxcvelSeTv+aaHa+Q11h+we1sLk2hoLGCKyIndELVQgghuiqLBkftvZmXEEC0hwRIwhZEuwZyU/R0ChorGO4Te8Z0zyFOPnw88h5eHXozShTsrkhnlG8cAO8f/Z2VhftYHDYWO4WKB+Lm89zAa9Aa9WiNems8nQsyYeL51GU4qez5dOS99HEPtVoteyuPcu++Dwh38eO7sY9wbeRk7JXSI1sc91jfRRhMRl48/JO1SxFWdGIKenU3OTbsq8zk4f2fccW2l7hr73tcv/N11pUkt3pWSSMmvsreyPTAQTzT/0o8rPgDgBBCCNtlc2dNufEXHSUDaAtrGuETi8Fk5NeC3dwSMwO1UsWdMbOZHjgItdIOJ5U9hU2V3LTrTdJq81EplCRWZlKna+Lr7E3EugVzUchwboyexuLwsXyS+Se/FOyy6bE4Cv56Pv/ufxXvDr+D14/8ys/5u6xSy+HafK7Y9hLXR0/lxphpXBU5CVe1I0frirhx1xsXnHJddE8XhYxgjF88Dyd9SrWuwdrlCCuq1TWS31hOX/cw/ijeb+1yzCa3sYzcxrJ2rftrwW4a9M081GcBX45+kP8e/oGtZYfNXKEQQoiuzOLBUWtv4iUwEuYkA2gLawl39qNB38yc4KFU6xq4ImICC0JH8UPediq19WgMOtYWJ1GvbwbAYDKeMihpVn0JOqOea6Imsacig4+P/WGlZ9I2Jc3V3Ln3Pe7pPZeH4xfSzyOCFw//1PLrfmeq0zfxZvpv/JS/gycTLifBMxwve1dMptb9Ai+6l0FeUTwQN59f83ez/bRBhEXPdKgmj3iPMGuXYVPWlSSzvyqLR/ou5P8GXc/Kgr28nr6Chr/OVUIIIXo2mxjjSEIjYSny2RKd7evsTSgVStzsnHg+dTmzg4eytewQb2es4tuczfyYv6MlNDobjVHHo/s/Z3t5mtVnKmsrvcnAq0d+5emD3zIpoD8fjLiLUGcfq9XjY+9Ogmc4O8rTuGr7y63uuiG6j3BnP/5v4PUkV2fzStrP1i5H2IjDtfn0dgs+oytxT1ehrePR/Z/zbOoyJgX048vRDzDMu5e1yxJCCGEDOuWMeb6bd7mxF5YmnzHRmQqaKrhx1xu8fmQF/+p/JQaTkY8y29ZqaFdFOo/u/5ychlILVWlZfxTv55Zdb2GvtOPjEfcywqe3VerIaSiltLkGFztHmg1aq9QgrEelUPJEvyVUaGt5/MAX6EwGa5ckbMShmjwcVGpiXAOtXYpNWlW4l2t2vEpuQxmvDrmJ66KmnHWiByGEED2HVX5qOTGIsdzQi84inzfRmUqaq7k4dCR5jWXcuPONdo870ZVlNZRw0643OVybx6Pxi865nEqhJNDRCzc7J7PXUK1r4Inkr4hzC+Hd4XcQ5Ohl9n0I23VZ+Hj6uIfybOoyGiU4FCdJrytAbzQQ7y7d1c6lpLmaBxI/5pNj67g5ZjovDLoWVztHa5clhBDCSjptcGy5aRdC9CRGk4nS5hoaDRprl2I1jQYNldo6Il38T/m7AgWzggZzTdRkQpx8sFOqAKjQ1LG/KosD1cdIqsoiq74EUwe7l6XW5HL7nnf4z4Cr+WzU/eyuTOfHvB0kVR3r0HaF7RvkFcWBqixSa3IpyPQ76zIhMT0v1BWgNerJrC8m3iOMXwqsM5B/V2DCxKfH/iStNo8n+13OxyPv4ZW0X9hVkW7t0oQQQnQym5tVTQhLkhnXRGfZV3WUm6KnM8AzkuTqbGuXYxVhzr5MDxzEy6eNLXNn7GyujJzI+pJkluZsobi5GheVA73dgxnoGcU9veehVtpRo23gQHU2+6uOkVR1jKN1RW0ep6iPeyhjfPuQWJXJRSEjmBIwgCkBA7h199uk1uSa8dkKW3O4No8l4eMoPEdoBFCQ6SfhUQ91uDaP/h4R1i6jS9hRfoSbd73JY30X88qQm9hRnsbrR1aQ11hu7dKEEEJ0EgmORI8j4ZHoDF9nb2KkT28eT7iUy7e9aO1yrGJ20FDq9c2sKtzX8rcRPr2ZGzyMn/N38eLhH09ZfkPpQQAclGoSPMIZ5BXFYK9obus1CweVmnpdM9vLD/Pi4Z9oNGjwVLvw0uAb8HP04EBVNolVR/mj+MApswA9Gr+QEGcfKjR1pNXmU9RURX5jOVn1JZ3zIgirSaw8xs0xMxgcEExiSeE5l5PwqGfKqCvkopARqBRKDCajtcuxeQVNldyz7wMm+idwV+xcPhl5L8+kLGVL2SFrlyaEEKITSHAkeiQJj4SlGUxGVhcm8veExdgr7dAa9dYuqdPNCBrE+pJktEY9ES7+PNTnYoZ692JvxVE+ylx7zvU0Rh2JVZkkVmUCoFaoiPcIY5BXFFdGTOS1ITfzYNLHXBo+ljBnP34p2El/z0geiLuYO2Pn8GXWBr7M3kiAoye93UP4Z/LXrC9J7qynLWxEcnU2BXW13DFoBLet/QWj6dyt1SQ86nlKmqtRKZR427tRpqmxdjldxqbSVPZUZPCPhCW8MOg63s/4nS+yN1i7LCGEEBYm85CKHkvG3RLmplaoTvl3Rl0hBpOR78Y+wiWho854vDuLdPEnyMmbTaWpDPeO5YPhd+Hr4M6j+z/nvsQPqdLWt3pbOpOB5OpsvsjawH37PiTU2Yc3ht6Kh9qZal0972Ss5o4977Jo6/P8VrCH22Nnc0P0NEx/BQUyo1rPlJ/pyzPbNzA5IpqXJs9CpTj/rFDnGgdJdE9lzbUA+Dt6WLmSrqfRoOUfyV/xa/5uro+eau1yhBBCdAIJjoQQwgwclGq+HvMQbw27jYtChjPCpzflmlqu2v4yiZWZPNjnYr4Z+zecVfbWLrVTDPeORWvUo1Io+e/g6zlYk8PNu95iawe7NRypK+C+fR8S5x7CUO9e2Cn+13C2XFPHG+m/8V7Gam6Omc7kgAHU6hrp7RbS0acjupgTIdCarAzu/fM35veKZ/Wl1zEjsler1hPdX+lfrYz8HNytXEnXVaNroEJbZ+0yhBBCdALpqiZ6NOmyJsyll1sQIc4+hDj7MNAzEqVCic6o56e8nbyZ/hsHqrN4tO8iVD2k1dFwn1iSq7K5rddMDtXk8tj+z9GbDGbZdkZ9ERqD7pxTQ3+ZvRFnOwfujZtHcVMVI3xi+SxrnVn2LWzf6eHPqmPp5P74NY+MHM8HsxaQVFLIf3dtYUdh3nnXl65r3Vudrgm90YCvBEftNsQrhsy6ImuXIYQQohNIcCSEEO1kp1BxTdRkxvnFE+LkQ0lzNdfueJVmgw4/B3emBw7iqshJzA0ZhkqhpEpbT52+ydpld4q+HmH8kLeDucFD2Vt51Gyh0QnVugYCHD0pbKo86+MfZf7BnOBhBDp5EejkZdZ9i64npbyEa1cuZ0xIOI+MHM+38y9jc14W/921lZTysy7BZIcAAPs8SURBVA+ULgFS99bHPQQ7pYrM+mJrl9Iledu7kuAZzjMpS61dihBCiE4gXdVEjydjHYn2cLFz5MMRd3F91BSy6kv4IW87N+58g3p9M3qTgaLmKr7I3sCSbf/HdzlbOVybzxtHVli77E7jaudIjbaBPRVHmRE4GHuleX+n2Fl+BIDDNWdvNWIwGXnh0A8A7K5IN+u+Rde1vSCXBT9+zW2//0yQizu/Lb6GN6fNw93e4ZzrSPe17mmifz+qtPUkV2dbu5QuqVbXhMagw8ve1dqlCCGE6ATS4kgIpMuaaDs7hZIQZx82labyn9Tvz7lcja6Rj4/9Acc6sTgrs1OoUCvtaDZo2VSawtyQYVwSOorvcreabR+vpv1Co0HD6sJ951xmR3kaczc+I1NtizOsyT7KHzmZLOqdwD9GT+TXRVdzy+8/k1FVYe3SRCeZ6N+PrWWH5fjQTnqTgUO1eQzwiGQpW6xdjhBCCAuTFkdCCHEOChQM9IzC3+H4rDsqhbLl/9foGvnw6FqmBg4g1i3YmmXaHKe/BgCfGNCPl4fciEqh5N64i+htxtdJZzLwVvrKC3YzqdY19JjugaJtrYOMJhPLjqQw/8evaNLr+emSq5gZFdvh7QrbF+niT7iLH5tLU6xdSpeWXltAlGuAtcsQQgjRCSQ4EuIv0mVNnO6S0FG8M/x23hx2KwBXREzgpwmPs3Lik1wXNYUjdQUAzA4awmjfOIZ592JO8DCzd8vqak4ER+P8+rKv8iilzcdnL4p08bdmWUKcVW5tDYt++oZNeVm8P/NiPpy1gCiPU8fFknGOupcJ/gk06jXsrTxq7VK6tIKmCoKcvFCisHYpQgghLKxn390IcRrpsiZOUKLguugp1OuaCXX25fqoqfR2C0Zn1PNH8X6uj57KIt0YAC6LGM9lEeNb1q3TNbKlg9POd2Vq5f9mjnvtyApqtA3cEzePAzKWiLBRjXodd/2xgtXH0nls1ATWLrmeL1P38/q+HbiGnn0cLdF1TfTvx47yI2iNemuX0qUVNVWhVtrh4+BOmabG2uUIIYSwIGlxJMRppOWROKGkuRq9ycC64mSuiJyAl4Mrx+pLeO3Ir1yx7SV2V6RjNBkp19TyatovXLHtJQA81C5Wrty6Cpoq+ceBr7hk83Mcqy+mQlvH0we/paS52tqlCXFev2UeYerST/jo2Fou65vAlqtv5NKwsSikRUW3EeDoSR/3UDaXSTe1jvJQOwNQq2u0ciVCCCEsTYIjIc5CwiNhxMQLh37A096FqYEDaDZo6esexhvpx2dGK26u4j+p33P9ztfJqCvkgT4XMz1wEMA5p4jvSTaWHqRUfoEWXUxITBm+kcV8lb2Ry7a9yPqSZO7vM5+LQoZbuzRhJuP9+qIz6tnx18yMov1CnX0pba5BY9RZuxQhhBAWJsGREOcg4VHPNtgrmkVhozlck0deQzmeaheeO7SM/VVZpyyXWV/M35I+ZVNJCjfGTKO4qYpDNblWqloI0V6nj2NUpa3nxcM/sbpwH7f1moWbnZOVKhPmNNG/H3srM2nQN1u7lC4v1NmX/MZya5chhBCiE0hwJMR5SHjUMwU4evL60FsY5t0LZzsH/Bzd+Wfy16wpOvfn4dlDy/h3yndcveMVmuXXVyG6jJCYsvMOfv1uxmrslXZcFjHujMeiXAKYHzKCQV5RZzz2eN9LeXf4HTJwsA3xUDsz0CtKZlMzkxptA8FOPtKVUwghegAZHFuICzgRHsmg2T3HRP9+GExGbtz1Jg36ZlQKJQaT8bzrNOib+b0osZMqFEKcTUhMGQWZfhdcpi0qtHWsLtzHgtBRbC5NJb2uEIDRvnG8NPjGluXmb/oPFdo6AFzsHJkbMgyAICcvCpoqsVOo0JsMbdq3MK+xfn1RQI+evMCc1pUcYFH4GPp7RpAskx8IIUS3Ji2OhGilxaFJ0gKph5jk3489FRktXRkuFBoJIWzPiZZEp//XHl/nbKJO18Sno+7jhYHX0tstmJE+cQCsKz5Ara6RSm19y/Jqxf9mFnS1c+KqyIn8MP4xAhw9O/ScRMeM9+vLweocqk56r0T7JVfnUNJczbSAgdYuRQghhIVJcCREG50IkCRE6p5UCiUJHuHslIFTheiSOhIQnUtJczVX73iFZ1KWEuHiz6ej7qOvexgAUwMHsqM8jYn+/Qh39gWOt0DMbSjjl/xdaI16romcjK+DOw/HLzRrXaJt+nmEk1iVae0yug0TJtYVJzM5oD8qhdxSCCFEdyZHeSE6QAKk7sfXwR07pUpmRhNCnMJgMrKmKKklQLJXHe/tX9BYwRjfeJ4deDXfjn2YECdvdCYDV2x/if8e/pF74+bhpnbi/aO/M9o3riVcEp3L18ENbwc3jtQWWLuUbuXP4v14O7gxxCvG2qUIIYSwIBnjSAgzODk8krGQujZ/Bw/geAuDzlaQ6Wf2lhJCCPM6ESCtKUqiv0cE80KGU61tIL+pnMf6Lj5jea1RD4C98vglV4izL7kyE1Wn6+0WAtAyRpUwjyN1BdTqGunjHsKeygxrlyOEEMJCJDgSwszO1gJJwqSuo0bXiN5oYFbQEN49urrl733dw/Cwd2aHBbuwSWgkRNdysCaHgzU5AFwXNYUGfTOlzTWnLPPo/s/5aMTdTPBLoEHfzJzgoewoT7NGuT1anHsI1doGq/wo0N3lN1YQIi3phBCiW5OuakJ0gpPHRZKubbYtt7GMdzJWcXXUJCb79wdgYehoPhx59ykzKAkhxMk0Rh12ChW93ILOeOz5Q8vxsHdBpVDyfc5WK1QneruFkF4n3dQsIb+xnDBnH2uXIYQQwoKkxZEQVnCu8EhaJtmG73K3MtAzijt7z+FYQwl3954LQKNeY+XKhBC26vfCRKYFDOS94XeyqnAfdfpGVAoVrnaORLkGcLgmj6+zN7W0UBKdq7dbCH8Uyw83lpDfWMFwn1gUKDBhsnY5QgghLECCIyFsyIVaI0mw1Hm+ztnEByPu4o2ht6A3GbAzqfitcI+1yxJC2KhqXQO37XmHayInMzPo+LHaaDLRZNCQ3VDKKN84nh14DS8d/onNZalWrrZnUSmUBDp5UtBUYe1SuqWdFUe4MWYa4/36ymdbCCG6KQmOhOhCZBDuzpNak0tGXSHRroFsKDnIeL++fJz5h7XLEkLYMIPJyGdZ6/gsa90Zj3nbu/L3vot5ftC1HKrJ47Ujv5Jak2uFKnseg8lIra4RT7WLtUvpllJrckmqOsbVUZMkOBJCiG5KxjgSoouSMZMs74VDP/Bk8te42TmRVHWMen2ztUsSQnRRldp6Ht7/Gffs/QAF8N7wO7g/bj5OKntrl9YjlDRX4+/oae0yuq0vszaQ4BHOEK8Ya5cihBDCAiQ4EqIbkADJMtJq89lYmkKEi58MqiqEMIvEqkxu3f02b6avZF7IcL4a/SAXhYzAXimNwC2ptLmGAAmOLGZXRTrptQUsDh9j7VKEEEJYgFylCNGNnAiPpBtbxwQ6evHS4Buo0zdR2lyDr4M7lZp6a5clhOgmjJj4PncrW8sOcVfsHB6Jv4Tbes3kl/xd/JC3nUqtHG/MLdzFj+SqbGuX0a1lN5Ti4+Bm7TKEEEJYgLQ4EqIb6sktkFQKJTGugbjYObZ7GzdETyXCxY/S5hq87F3YUZ7Grop0M1YphBBQ2FTJP5K/4vJtL/JH8X6WhI/jh/F/5x8Jl9LPI8La5XUb/T0iCHP25feiRGuX0q05qNRojXprlyGEEMICpMWREN1YT2uBZKdQ8X+DrmOUbxwA64qTefLg123ahrPKnmmBA/kwcy1fZP0/e/cdHkWd/wH8vS29904KvYcuKCIoIjYUxMKJHfXAU+9n756nnp53ejawYq8UUVAU6b2GDiGEQHrv2WSz7fcHJiaQkGQzs9Per+fJo2x2Zz5bMvOd937mO2vFKJOIqJW8+nL8L/0nfJS5ClfFjsL0+LGYGjMC2XUl+KVgN34tSENRQ6XUZSrWZTHDUVhfgbSKE1KXomoeehMs9kapyyAiIhGw44hIA7TSfTQgMAFjwvrg9aPL8Gb6T5gUNRhXx45udR8PvRHDQ1JwXlhfRHsFn7UMg84AL4MH8szl7iqbiAgAUGtrwFenNmDGplfwt93v41BVNmYnTcR34x7BuLB+UpenSHroMCFiIFYV7oUTTqnLUTUfgwcsDqvUZRARkQjYcUSkETPi0lTfeXS8tgCNDhsMOj2+zd6EBN8I3NfnCuwqP468+jJMixuDv/W+Ap4GU/Nj1hcdxLMHvoLVaQcA1NjqUWqpxuCgHlhdtE+qp0JEGuaEE7vLM7G7PBP/PfoDnhp4PZ4ffBP+unM+jtXkS12eovTyj0Gghy+2lqZLXYqq6aFDil80tpYelboUIiISAYMjIg1Re3hUZ2vAjrJjmBA5CN9mb8I7x5ZjZEhPPDngOrx46Dvc1/tyrC06gC9OroPZbsGw4GQ83O9avDXibnyQ+St2l2cCAH7J342bky7C6LA+0EMHo96AOlsDqqxm7Co7jhX5u1BiqZL42RKRFpjtjXj+wDd4e8Td+Hfqbbhr+9so5van04aH9ES9vRGHqrKlLkXVIr2C4GfyQjqDTSIiVeKpakQao/aJs2us9fDSn+4oMtsb8eKh7zAoqAe+Hvcwqqxm/OfoUmTVFaGooRK/FOzBg3s+hIfeiDeHz8FHo+/Dv4bMRt+AOABAvE8YYn1CEekVhGS/KKQGJ+OunpPxw/gncH44TxshIvewOKx4dO8nsDnseDX1VvgYPKQuqZVAkw/GhPZBvz+2nXIyMrQn9lacgO2PrlISR5hnAACgpIGhJhGRGrHjiEij5Np9NCw4BVfGjkSOuRQLT6zu0pwUYZ7+GBfeDz/m7mi+bV/lSbx06HsEmHywqnAvzGdM3Lmv8iRu3/4mRof2xtWxo2HQ61FlNWNF3i4Eevigh28E7A4H6uwNqLM1INjDD738Y5AanIJNJUcEe95EROdS3liLh9IWYsHIv+Ifg2fh0b2fwu50SFJLuGcgkv0ikeIXhREhvTA8JAVGvQEA8MS+z7C++JAkdZ3JQ2/EkKAkvHf8V6lLUb2mU8C9ZRZqEhGRMBgcEWmY3MIjD70RTw64Dka9AZOjU7G7PBP7KrM6fNz48AG4t9dlCPLwRb29Ed9mb2z1+186cQnm7WXHsL3sWKfq1EHHSVaJNEisbs3Oboez6orw1P4v8FrqbXio7zX495ElcLhxW3R+eH881n86gj38AABmmwWHq3LwevoyrC8+hAf7XIWnB96A/J3zkSGDU5aGBifD02DC7vLjUpeiemkVJ5BdV4Jbkifi0b2fSl0OEREJjMEREcnGjT3GI9TTHzdvfR3/Sb0dl8UM71RwdGvyJCT4huOzrDVYWZCG8sZaUetkaESkHe44tbdpHZ0JkHaWZ+Dlw4vwxIDrEGjywXMHv0ajwyZ2iZgYORjPDrwBW0uP4qe8nThRW4jChko44UScTyhmJ14EH6MXvA0eeGHwLNyw+d+i19SRy2NG4GRtMY7XFkhdiurZnQ4sPPE7nh10I/oFxOFIda7UJRERkYA4xxGRxsllvqMgky9uTroI32dvQY65FCsL9mBi5KDm+YrO5cuT6wAAR6tzcaquWORKiUgr5LJ9PNPKgj14bO+nGBXWG28On4MAk4+o64vyCsbj/WdgbfEBPLHvc2wuPYKChgo44cTgoER8MGoeJkYNhh46LM/biW9ObRC1ns4IMvniwogB+DFvR8d3JkH8XrgPx2sK8Pe+06CHTupyiIhIQAyOiEgWB0fX9zgfTqcTn59cC+D0gZGv0QvjIwZ2+NjVRfuxvuggHuk3HaEe/mKXSkQaIMV2sSvr3FJ6FPfteg9xPqF4b+Rf0TcgVrS6Hu8/A9VWM1493PrUuCTfSLw69FYcrynArC3/wd/TPsLLhxfhh9ztotXSWVNihsEJYGXBbqlL0QwHnHj1yBL0DYjFdQnnS10OEREJiMEREUnO3+iN6fFjsTR3K6qtZgBAfn050ipOYGrM8E4t45Uji2Fz2vHEgOtg1BnELJeIVE4OYXpnHKnOxd073kWjw4YPRs3DY/2nI8jkK+g6En0jMCK0J/6X/hPMdgsAINorGF+c93e8M+JuFDVU4rF9n6HW1iDoervDQ2/EtXHnYUPxIVT9sU8h9zhUlY3vszfjr70uw/CQFKnLISIigTA4IiIA0h4ozUgYC6POgG9OnTGpdf5uDA9JQYRnYIfLqLKa8eLB7zA8JAXvjrwHkV5BIlVLRGomdWjU1fXn1Zfh9u1v4vWjP+LCiIH4ZtzDuKnHeMG2gSl+UQAAs92CqdHDcVvyJLw5Yg68DR7YVX4cD6UtRJ2MQiMAuLfnZQjzDMDHJ36XuhRNeifjZ+wqP46XBs9G8h+fHyIiUjYGR0QkqTifUPwlcQIW52w9a1LrtUX70WC3YlrcmE4ta0d5Bu7ZOR/BHn54a/gcBIo87wcRqYvUoVGTrtZhdzqwJHcrbtj8b6wq3Iu7e07Bkgsex5fn/R/u73Mlzgvr2+oy6T5duGS6Tnd6rpr/Db8LTw6ciRsSxuNg5Sk8uOdDPHPgK5RYqrpUq9iGBadgZo/zseD4Ss55JxG704Gn93+B/PoyvDL0FvgbvaUuiYiIuolXVSMiyST5RuLZQTeg1FKNjzJ/O+v3ZnsjluVtxzXxY/D5ybWotzd2uMyj1bm4b9f7+HD0PLw45Gb8bdf7br1cNRGRVKqsZvzn6A94//ivGB6SgpGhvXFBeH/MTDgfVocNJZZqhHr4w9NgwonaQizO2YKV+XvQ4LC2u8x1RQfxhulHZNYW4GBVNqwOu2yvLOlr9MKTA2ZiT3kmvs/eLHU5mma2N+LxfZ9j4Zi/4ckB1+GxfZ9JXRIREXUDgyMicjsPvRG3JE3ErMQLkV9fjqf2fdHugct3pzbhuvhxuDJ2FL7L3tSp5Rc2VOCp/V/gnRH34Kq40dhTfhz59RWwOe1CPg0iUhG5dBsBwKLc1G49vsZWj3XFB7Gu+CCA052do0J6I8IrEGWWGtTY6jE+fAD+3nca7ul5GVbk78LKgj04UVsIu9PRalk2px3f5ygjhHmgz5XwN3nhn7u+k224pSWFDRV44eC3+HfqbbihxwVnnY5ORETKweCIiNzKU2/Ca6m3YWBQD3yWtRafZ62F9RyBTrGlCqsK9+LmxAn4rSANlda6Tq1nb0UWNpccwcP9rgEAfHVyA97JWCHIcyAiEkt3Q6O25JrLkGve2uq2lQV7EOkVhGvixuCq2FG4occFsNitOFaTj6PVudhSehQ7yo4JXotYxocPwNSYEXjx4HcoaqiUuhz6w5bSo/giax3u7XkZIj2D8HvRPlQ21qG4ofKc+34iIpIXBkcyMCcovVP3e7+yj8iVkNbNiEsT5aClSU+/aMzrfTn6BsThb7vex4GqU5163PyMXzAmrA8eHzADj+79tNPre+Hgt+gbEIfrEsZhXHhfBkdE1CY5dRu5U1FDJRYcX4mPT/yOvgFxf/zEYkxYH1yXMA5P7fsCa4sPSF1mh4JMvnik/7XYUHwIPxfslrocOsP7mb/CCSemxozAzB7nAwDWFR3Ak/u/kLgyIiLqLAZHEupsYNTW/RkikZIMCEzAswNvQKxPKKqtZjyy95NOh0YAUNZYg5cPLcKrqbdiWtwY/JC7rVOPq7HVI6MmH4ODErGqcK+L1RMRuYeYwf25NDps2F95EvsrTzbf9uzAG/D0wOtRtqem1e1tCTD5oFrCy94/2n86AOCVw4slq4HaZ3c6sOD4SnyQ+Rt6+8fg/j5Xwag3SF0WERF1Aa+qJpGuhkZtPb67yyByh/PD++HN4Xeh1FKNB3Z/iCvX/xNpFSe6vJzNpUewJGcr/tb7CiT6RrR5n9TgZCwb/yTeGj4HvfyiAQATIwfD2+CBD9uYfJuISC6kCo3a89Kh73GoKhuvDL2l3StURnoFYfMlr+CXCc/igvD+bq7wtAkRAzE+YgBePbyk06cykzTsTgeOVOfC7rSj1togdTlERNQFDI4kIGTgwwCJ5OySqKF4achsbC1NxwN7PsTO8oxuTVD99rEVKGqoxL29Lmvz9zMTzoeH3gh/kzfeHXkvxoX1g0Gnh9VpR5WE34YTkXzJ4TQ1uYVGAGB12vH0/i/hZfDAlOhhZ/0+3DMQLw25GWabBTvLMvDC4FnwMXi6vc6JkUNwqCobG0oOuX3dWjMjLu2sH1f4Gb1Ra6sXuDoiIhITgyM3EyvkYYBEcnN+eH88NWAmVubvwTP7v0Sjw9btZVocVnyWtQbnh/dvs+sowOSNbaXpuHvHu9hRdgz/GjobD/S9CiYdW+KJSJ7kGBo1qbTWYUPxIVwWM/ys393bawr6BsQBAPoFxMOkNyLWO8St9Rl0eowM7YltpRz/iKk7IVFbChsqkBqcDB10gi2TiIjExeDIjdwR7DA8IjkI9wzEc4NuxMaSw/jX4UVwCHhZ5FWF+1DcUIUbe4w/63e55jLE+oTC4rDiqf1f4t2MX1DVWIed5ccFWz8RkVDkHBo12VtxAkm+kTDoWg8Zd5VnAgC2laXji5Nr8eDuD5FRW+DW2voFxCPA5IPtCrr6m9J0FBi5Eih9dXI9UvyjMTasr6tlERGRm3FybDdxZ6DTtC5OoE1Subvnpai3WfDSoe8FDY0AwOa047vsTbi756X4IPNXlFpqmn93qq4EEyIGAQCccOLrUxvw9akNgq6fiEgISgiNAOBQVTaMegMujUptdcWyn/N34deCPbA7HZLVNi68L6oa63CkKkeyGqjr9lWexL6KLNySNBGbS49IXQ4REXUCO45UjKevkRT6BsThspjh+CDzN5jtFlHWsSx3Oyx2G2bEj2t1e62tHj5GD3jomYkTUcekmt9IKaERAByrycdvBWm4p9dl8DV6tfqdlKGRHjpcGj0Ma4sPCv4FBZ0m5t/HZ1lrMSAoAcOCk0VbBxERCYfBkRtIHd5IvX7Slr/1vgKZNQVYkb9LtHWY7RYsy9uGa+LOQ5DJt/n2AYEJyKwtFGQ+JSIioS3KTVVUaNTk3Yxf4GP0xG3Jk6QupdmI0F6I9ArC8rwdUpdCLthWlo5j1XmYnTRR6lKIiKgTGByJTC6hjVzqIHXr6ReNIcFJWHB8pejfRH99aiMsDiteGnIzjH9Mfj04KBH7KrJEXS8RqYc7QxwlBkZNSixV+OzEGlwXPw6vpd6O6xPOR5JvpKQ1XREzAidqC3GkOlfSOtTKHd14n2WtxcjQXs2TrBMRkXwxOBKR3MIanrpGYuvxx5XODlSeEn1dFY21eGLf5+gXGI8H+16FYA8/JPiGY3/lSdHXTUTUFUoOjZp8eWo93sn4GQadDnf3nIIvxv4dP1zwBK6LHwe9m6+OFWDywQURA7A8b6db10vCWl98ENl1JfhL4gSpSyEiog5wIhCRyDmgmROUzomzSRQJvmGoaKxFja3eLes7WHUKrx1ZiicGXAeT7vTm7DAnSSUiGVFDaAScns/ou+xN+C57Ezz0RgwJSsKkqMH4W58rcEnUULx8eBGy6opEryPI5IsnBlwHHYBfC6SZo0rt3DX3lwNOLMvbjjkpl8JLb0KDw+qW9RIRUddpJjjqKMgRMkiRc2jUhOERicFTb4LZJs6E2O1Zkb8L48L74fLYEQCAQA9fFDRUuLUGIqK2qCU0OlOjw4ad5RnYWZ6BFXm78Gj/6Vg45m/4PGsdPstaA6vTLsp6R4X2xpMDroNRZ8AT+z5HpbVOlPWQ+2wuOYL7el+BEaE9samEV1gjIpIrTQRHnQlyzryPK6GKEgKjlprqZYBETbp7kGNxWOFpMAlUTef9L/0nXBgx8HQNdn5jSUSdtyg3VZQOC7WGRmc6UHUKt237H2YnTcTNSRNwUeQgvJ6+DHvKT8Ap0NXOPPRG3NNzCq7vcQG2lx7Di4e+Q1ljjSDLptbcfaXBHHMpsutKMDasH4MjIiIZU31w5GqYo7QQqDvYfURCMdsa4Wv0gh46t14euaihEjdt+Q8uihiEU3XFblsvEVFbtBIaNbE67fjoxCqsKdqPx/vPwJvD56CwvgKri/bh98J9OFaT7/Kyzw/vj7/1vgLhXoF44+iPWJSzRbBAiuRhc+kRXBI1FLojOr63REQyperJsbUU/nQXXysSwtHqHHgbPNDTP9rt6z5VV4xPsla7NbAiInUQMujRWmjUUlZdEe7e+S7+unMBtpQexdSYEVg45n58PfYhjAnt/BdUXnoTRof2xn9Sb8crQ29BjrkUt2x9A9/nbGawICJXuo2E+LxvLjmCMM8A9PaP6fayiIhIHKrtOGIQ0nXsPKLuOlyVgwZ7I4aFpHTrG2YiIlImJ5zYV5mFfZVZeCP9RwwP6YkbEi7Av1NvxTenNuGzrDVnXUDBoNOjX0A8RoT0xIjQnhgYmACT3ojsuhI8uvcTnsKkcvsrT8LmsGNgUALSa/KkLoeIiNqgyuCIoZHrOO+RdgnxraHVacf+ypMYHpyCb05tFKAqIiL3EGKuIy13G7XF7nRgR9kx7CrLwF8SJ+DmpItwRewIpFfnweF0wgEHjDoD+gfGw9fohRprPfZUZOLN9OXYVZ6BbHOp1E+B3CDQ5AOj3oCShmqpSyEionaoMjii7mP3EbkqreIEZvWYIHUZRERuxdCofQ448dnJtVievxM39hiPSK8g6HQ6GKCHBVZ8kbUOO8szcKwmH3anQ+pyNcndk2K3lOAbDgDINpdIVgMREZ0bgyNqF8MjcsWx6nz4mbwQ7RWMgoYKqcshIuo0sa6wRqeVN9binYyfpS6DBCJUWBrvEw6704E8c5kgyyMiIuGpLjjiaWrCYnikDUJ+U3689vTcRj39oxkcEZHitNwedjZEUlu3kVaft1ZJHZYm+ISjsL4CVqdd0jqIiKh9qguOSHgMj9RN6IF/qaUGFY216OUfg40lhwVdNhGRO2kxGOlKiNDefbX4uimV1KERACT4hvE0NSIimVNVcMRuI/EwPKKuOF5TgF68rC4RkaIIFSK0tRyGSdSeBJ9wbC09KnUZRER0DnqpCyDlYDCnPmIN5DNq8pEanIzrEy6Aj8FDlHUQEZFwxO48mRGX1vxD8tCd90Ko8YNRZ0CMdwg7joiIZE41wRFDDffg66weYn77+232RuwoO4Z7ek3BR6P/hh6+EaKti4iIlIUhkvTk8tr3CYiFUW9ARk2B1KUQEXXbyy+/jJEjR8Lf3x8RERGYNm0a0tNbHz9nZmbimmuuQXh4OAICAjBz5kwUFRU1//7kyZO44447kJSUBG9vb6SkpODZZ59FY2Oju59OK6oJjsh9GB5RR0otNXjmwFeYvfV1BHv4YXr8eVKXRERE7ZAyRGCI5H5yeq2HB6egztaAo9W5UpdCRNRt69evx9y5c7Ft2zasWrUKVqsVkydPRl1dHQCgrq4OkydPhk6nw5o1a7B582Y0NjbiyiuvhMPhAAAcPXoUDocD7733Hg4dOoTXX38dCxYswBNPPCHlU1PXHEfkPpzzSNncNddEgMkH3gYPZNYUumV9RESkXC0DDc6JJA45hUYAMCwkBWkVJ2B3OqQuhYjonKqrq1v929PTE56enq1uW7lyZat/f/LJJ4iIiMDu3bsxfvx4bN68GSdPnkRaWhoCAgIAAJ9++imCg4OxZs0aXHzxxZgyZQqmTJnSvIzk5GSkp6dj/vz5eO2110R6dh1TRXDEDhhpMDxSJncMxj30RjzS71pcFjMcx6rzsL74oOjrJCIi9WgKOBggqZeH3ojBQYl47/jKju9MRJpQkBUGvbeX1GW04qhvAADEx8e3uv3ZZ5/Fc889d87HVlVVAQBCQkIAABaLBTqdrlXg5OXlBb1ej02bNuHiiy9udzlNy5CKKoIjIuocdwzAfY1eeGXoLegXEId/HV6EFXm74IBT9PUSEVHXya0D5UzsQhKOUO+1UO9DjHcIPA0mpNfktbo9wScMhQ2VaHTYBFkPEZEQcnJymruEAJzVbXQmh8OBBx54AOPGjcPAgQMBAGPGjIGvry8effRRvPTSS3A6nXjsscdgt9tRUND2XG/Hjx/HW2+9JWm3EaCC4IjdRtJi15FyuGPAHeLhh/8OuwORXkG4f/eHOFh1SvR1EhGRNjBEcp0cA8LKxtNzfvT2j8X0+LGoszUgwSccQ4KT8PWpDXj72AqJKyQi+lNAQECr4Kgjc+fOxcGDB7Fp06bm28LDw/H999/j3nvvxZtvvgm9Xo8bb7wRw4YNg15/9vTTeXl5mDJlCq677jrcddddgjwPVyk+OCLpMTySP3cMsGO9Q/D6sDvhoTfirzsXIKuuqOMHERGRW8kxQHAFT2XrHDm/31VWM8w2C8aHD0BqSDIcTge2lx3D0epcXJ9wPj7KXIV6u7RXESIicsW8efOwfPlybNiwAXFxca1+N3nyZGRmZqK0tBRGoxFBQUGIiopCcnJyq/vl5+fjoosuwtixY/H++++7s/w2MTgiQTA80raeftH477A7UGdrwD0756OwoULqkoiINEvOYYHQ2IXUNiV8Bpxw4o30H/HEgOuQby5HgMkHn2etRY2tAZ+f9yBGhPTExpLDUpdJRNRpTqcT9913H5YuXYp169YhKSmp3fuGhYUBANasWYPi4mJcddVVzb/Ly8vDRRddhOHDh2PhwoVtdiO5G4MjEozU4dGZpy0yyDpNzIG0DjqMj+iPx/tfh7z6Mvzfno9Raa0TbX1ERNQ2JQQFYtN6iKTEz8CK/F3o7R+Dq+NG40RtEf419BZct+kV7CnPxL29LsOW0qO84hoRKcbcuXPx1VdfYdmyZfD390dh4ekrSwcGBsLb2xsAsHDhQvTr1w/h4eHYunUr7r//fjz44IPo0+f0sWteXh4mTJiAHj164LXXXkNJSUnz8qOiotz/pP6g6OCI8xvJj9jhUVfecwZJ4tFDhwmRgzA76SL08o/BlpIjePbA1zDbLVKXRkSkKUoMC9xBS6eyufMzIMbr+eax5Ujxj0YPnwh46k24occFWJq7DS8MnoUoryDk1ZcLvk4iIjHMnz8fADBhwoRWty9cuBC33norACA9PR2PP/44ysvLkZiYiCeffBIPPvhg831XrVqF48eP4/jx42ed5uZ0SnfBIZ2zE2uvrq5GYGAgXtpxEbz85JM1MTiSL6FCGjHeYy0FSGIM8Hr5ReP5wTehh28Etpcew6dZa7CvMkvw9RARUdsYFnWNGsMjqT4DYr2WQSZffDT6bzDq9QjzDECZpQZmuwU3bP63KOsjkitbnQU7rvkfqqqqujQRs9I15Q3x/30Bem8vqctpxVHfgJy/P6259+RM8kmBSFW623nEUFCeQjz88ErqrahqrMMd29/C0epcqUsiItIEhkWuU1v3kRo/C5XWOjyx7zPMH3kvCusrYHc68OrhxVKXRUREf1BscMRgQf66Gh7xPZW/e3peBg+9EQ/v/QSllmqpyyEiUi01hgNSmxGXpujwSO2fifSaPDyw50PMSrwQ54X1xbODbsTVG16UuiwiIoKCgyNShs6ERwyMxCH04DjOJxSXRqfirWPLGRoREYlE7eGA1JTafSSHz4U7XrP9lSexf+9JzOt9OS6KGCT6+oiIqHMYHJHoGAypw21JF6OisRY/5u2QuhQiIlWSQzigFUoJkLT6mUjxi0JGTb7UZRAR0R8YHBFRh/oFxGFKzDC8engJGh02qcshIlIdrQYEUmv5usspRJLb58Hdr02cTxhWF+5z6zqJiKh9igyO2MFC5D466PBgn6uRUZOPn9htREQkKLkFBFomhzmQ5Ph5kOI1CfbwQ0VjrdvXS0REbVNkcERE7jMlOhUDghLw150L4IBT6nKIiFRDjiGB1kl1Chs/C3/y0pvgbfBgcEREJCMMjkhTunKVNwIMOj3uTJmM1YX7sa8yS+pyiIhUg0GBvLkrQJL750CKbqMgDz8AQJXV7PZ1ExFR2/RSF0DkLgyNum5sWF9EeQfji5NrpS6FiEg15B4W0J9mxKWJ8n6JtVw1qGysRbmlBlfEjpS6FCIi+gODI9IErYVGQn1DODPhfByoPIVjvLIJEZEgGBYok1BBj5ICI6nme2pwWPFG+k+YGDkY54f3l6QGIiJqTXGnqnFibOoqrYVGQkkNTsawkBQ8uvcTqUshIiKSBVevwqaUsEguVhftw5SSVPxf32k4WHkKldY6qUsiItI0dhyRqjE0ct3tyRcjvToPm0qOSF0KEZEqMDxQl6buoXN1ESmpw6glqa8uBwD/PrIUep0Obwy/E/5Gb6nLISLSNMV1HBF1FkMj17HbiIhIOEoMDqjr+D4Lq9hShQd2f4i3RszB68PuwKN7P0VZY43UZRERaRI7jkh13q/so+nQqLvfEuqhw909p7DbiIhIAAwTSGnk0G3UJKuuCA/u+QhR3sH4ZtzDuDlxAjz0/N6biMjdGByRqmg5MBLK7KSJ6B8Yj/+l/yR1KUREisbQiKj7MmrycePm1/Bj3g7cmTIZi85/DLMTL+Lpa0REbqSoyJ4TY9O5MDTq/reEg4MScXvKxfj0xBrsq8wSqCoiIu1haERKJKduo5ZqbPV469hyLM3diht7XIhbkyfh1uRJ2Fx6BL8VpGFbaTqsTrvUZRIRqZaigiOi9jA06j5/ozeeHXgjDlaewidZq6Uuh4hIsRgaEYkj11yGfx9Zgg8zf8OU6GGYHDUU/xp6C6wOG4obqlDUUImihkpsK0vH74X7pC6XiEg1GByRojEw+lN3vyV8rP90+Bg98PzBb2B3OgSqiohIWxgaEYmvorEWX5/agK9PbUCibwRSg5MR6RWESK8gJPiG47KY4egXEI93jq2AA06pyyUiUjwGR6RIDIxOE6qlfE7KpZgQOQiP7/0MRQ2VgiyTiEhLGBgRSeNkXTFO1hW3um16/Hm4v89VCPHww/MHv5GoMiIi9WBwRIqjtdBI7PkG7kqZjFuSJ+LtYyuwoeSQqOsiIlIbBkakFnKd38gVi3O2oqKxDi8MngWzvRF7yo8ju64ENqcD/QLicEHEAJj0Rrx2ZCkKGyqkLpeISPYUExxxYmzSUmDkrsHbXSmTcWvyJLx9bAW+PrXBLeskIlI6hkVE8remaD/ij4fhuoRxmBY3utXvDlaeQoinPz4cPQ93bH+L3dZERB1QTHBE2qWlwAhwT2hk1Blwb6/LcEOPCxgaEZEsdCaMkbojgoERkbJ8mrUGn2atgb/RGwm+4TDpDcisKUSNrR6BJh98PPp+PDngOvxt9wdSl0pEJGsMjki2GBiJI9E3As8MvAEpflF4/egyLMrZ4pb1EhG1p7OBTNP93LW9ZFBEpA41tnocqspudVuV1Yx/H1mC/wy7HWNC+2BbGc9uIOnkZYYDABz1DRJXQtQ2BkckSwyNhKeDDtPjz8Nfe01FQX0F5ux4B+k1eaKvl4hIaGIFSAyKSMtmxKVJ3tXnbtvK0rGvIgt39ZzM4Igk0xQaEcmZIoIjzm+kHQyMxDE0OAlzUi7FkOAkfJe9CfMzfkGjw+aWdRMRnUt3whohDnQZFhFp24eZv+GtEXdjQsQgrCs+IHU5pEGxKSUMj0j2FBEckTYwNBKWHjqMDuuDGxIuwIjQnjhanYu/7X4fu8szRV0vEZE7dbX7iEERUfu02HW0p+IENpUcxlMDZ6JubwN2lmdIXRIRkewwOCLJMTASVoRnIC6PHYkrYkYiyjsIR6tz8djeT7Gx5LCo6yUi6iohQ5yWARLDISLqimf2f4UXh/wFr6beimf2f8kxExHRGRgckaQYGgnDoNNjTGgfXBU3CueF9YXFbsWqwr34MW87jlZzHiMikh+xwh2GRkTdo8auozO3C2c+P4vDisf2foZnBt6Afw7+C147+gN+LdjD0/rJbXi6GskdgyOShNYCI0Cc0CjSKwhXxIzEFbEjEeEViKPVuXjtyFL8XrgXZnuj4OsjIiIi9VNKeORqUNze45474MAj/a/FY/2n46G+03CsJh/zM37Bngqe5k/ii00pQc5Bf6nLIGqT7IMjToytPloKjcQKi84L64PzwwdgdGgv1Nsb8VtBGn7M24FjNfmCr4+ISGjsCiKSP7mHR2JsR66N24Pj1XvwhSUZBZZLcWl0Kv41dDbu3vEusuqKBF8f0Zmik0qRI3URRG2QfXBE6sHAqOt00CHU0w/JvlEYHdYHo0N7I8kvEjaHHQeqTuGVw0uwumgf6tldRERERAKTa3gkdvhcYjkBI+ZjQ5E3fAwLsHDM33CsOh/pNXkoqK/AppJDyDaXiloDEZGcMDgit9BKaNTVwZWX3oR4nzBEeAUhwisQkV5BiPzj/yO8ghDhGQij3gAAKG6owvaydHyY+Rt2lh9Hna1BjKdARCQqdhsRKYtcwyN3sDrqsargPvQOuBB25/kYFNQDU6KH4Z5eU/Bz/m58nLkKxZYqqcskIhIdgyMSndpDo5/yR2FQUA/U2xuR4FOPGls9aqz1sDnt7T4m1jsEMxLG4YqYkfAxegIArA4bSizVKG6oRHFDFQ5UnkLRH/+fV1+Gk3XF7npKRERERM3kFB65O3xusFdjf8VPAH7CoUpgWd4ITIsbg1uSJ2JS5GC8cmQxfi/c59aaiIjcjcERiUbtgdGi3FQk+kbgo9F/QZJf5Fm/r7c3otxSg6KGyuafUks1RoX2xvnh/VBlNeO77E3YXHIERQ2VKG+shRNOCZ4JEZH42GlEpGxyCI/ksB2xOu34PmczVuTvwkP9puH5QTdheHBPvJH+IywOa5eWpYcODo79iEgBGByRKNQaGrUcMF0anYqH+12Lgvpy3LX9bdTbGxFg8oa/yRv+Rm8EmHwQ4uGPSO8gxHiHIDU4GWGeAcg2l+DVI0vwa0EaL/NKRJogh4M9Iuo+KcMjuWxHml4Ds92Cfxz8FrvKM/F/fa/GwKAEvH50GU7UFqHSWtfhcoI9/LD8wqdxpCoHd+542w2VExG5TtbBEa+opkxqDI1aDpI89Eb8ve/VuDJ2FFbk7cJ/j/6Ahk5+w6SDjl1FRKR6cjnAIyLhyaHzSE5+zt+FI1U5eGHwLLw14m4AgNlmQa65FD/n78ayvO1tflH4aL9rAQD9AuPdWi8Rqc+PP/7Y5cdccskl8Pb27vT9ZR0ckfKoKTRqa1AU7xOGFwbPQrxPGF489D1+zt/VpWUyNCIipWD4Q0TtcXd4JLft0ZnPP6uuCLdsewNJvpGI8Q5BjE8I+gXEY17vyzGzx/n4vz0fI9tc0nz/EA8/XBAxAABQZqlGrHcI8urL3f48iEgdpk2b1qX763Q6ZGRkIDk5udOPYXBEglFLaNTeQGhS5BA81n86SixVuHP728iqK3JzZURE4pHbgRkRyRs7j1qzOx04XluA47UFzbfF+YTi5SGzMX/kvXgo7WMcqc4FAJQ31uLFQ9/j/LB+uDByIG5JmoSXDn8vVelEpAKFhYWIiIjo1H39/f27vHx9lx9B1Aa1hkYeeiMGBCbgkX7X4h+Db8KmksO4Y/tbDI2ISDVmxKUxNCIil7hj2yHX7VNn6so1l+GvOxcg21yCt0bcjVGhvZt/93P+ruZO9MtjRyDSK0isUolI5W655ZYunXb2l7/8BQEBAV1aBzuOqNvUEBo1BUaRXkEYGJiAgYE9MCAwAb0CYuChN8Jss+CVw4vxY94OiSslIuo+uR6IEZHysPPo3Gps9Xhg94f4x+Cb8O+ht+KJfZ9jc+kRAMCo0N4obqhChFcgbku+GP86vEjiaolIiRYuXNip+9XW1sLPzw/z58/v8joYHFG3KDU00kGPYI84ZJknoZd/NF4fFoOeftEI8TzdtpdnLsPBqlP4tXAPDlVlI6OmAHanQ+KqiYhcx7CIiEgaFocVT+z7HC8MnoVnB92Au3a8g1N1xXg9/UdcETMCFY21WFXIbTQRue7111/Hgw8+2O7va2pqMGXKFGzevNml5TM4IpcpLzTSIc5nMAYFXYYU/7Ew6b0AAAX15cioKcDS3G04VpOHg5XZnbqMKhGREjAwIiKxabXrqCvP2+504J8Hv8V7o+bi+UE34vZtb+Ln/F1dvtAKEVFbnnjiCYSGhmL27Nln/a6urg5TpkxBWVmZy8tncESaEOs9CBdFzUW4VzLKLTn4KHMtDlZl43hNAWps9VKXR0QkKIZFRETyY7Y34uVDi/DB6HmYEjOcoRERCebzzz/HzTffjKCgIFx11VXNt9fV1eHSSy9FSUkJ1q9f7/LyGRxRh5TXWfQnH0MwxkfehX6BFyPffBjfnfo//O8YP/ZEpF4MjYhILdS0PZudeBHOj+iPraVHUVhfgbt7XorfCtJgc9qlLo2IVGDGjBmorKzEjTfeiBUrVmDChAnNnUZFRUVYv349oqOjXV4+j6CpTUoOi4DTcxgNDb4K54XfArvTit/y/4NDVb9hUe5QqUsjIhKFmg6wiEh5tHq6Wmd4GzwwK3ECSixVuKnHhWiwN+JYTT70Oh3+uLAaEVG33XnnnSgvL8fVV1+NZcuW4ZlnnkF+fj7Wr1+PmJiYbi2bwRE1U3pY1CTRdyTOj7gdYZ5J2F+5ApuLP4HFUcPBDBGpDsMiIiJpdSYwuyRqKLyNHvi/bR+jqKHSPYURkSY98sgjKC8vx6RJk5CYmIh169YhLi6u28tlcESyZNR5wuG0wwFbp+6vgx69A8ZjZOj1CPdKQZ75IL4+eR+KGjIAgKEREakKAyMi0oJFuamq2N5FegXBYrfC4WR7ERGJ49prr231b5PJhLCwMNx///2tbl+yZIlLy2dwpHkegM4LH1TEAhD/cvPJfudhZOhM+BiD4aH3gUFnQqPDDIu9Fg2OWpj0XggwRsDbGIh6WxXSKn7A3vIfYXHUnLUsH0MQevgOR6LfSPTwHQ5vYyBO1u7E2lPvIs98QPTnQkTkbmo4gCIi0ppvTm3E5TEj8Fj/6fi/tI+lLoeIVCgwMLDVv2+88UZBl8/gSNM84RnyAYyeF2KE/mPsLPtGtDWFeCRgfOTdSPIbiey6NGRUb4DFYYbdaYWn3geeBn94GfxgdTQgw7oRtdYSRHr3wajQGzAidCaOVP2OCksuGhw1CPaIQ6LvSER69wIAFNUfw/7KFcio3ogSS+ZZ62a3EREpHQMjIiLlqrHV41+HF+M/w27H1bGjsSxvu9QlEZHKLFy4UNTlMzjSLD08g9+HwTQSdqcVsT4DsbNM+LX4GyNwXvjN6Bd4MWqsxViW8yxO1G7t1GOPVK/G9tKvMDTkavQLmIT+gZfApPdCva0KJ+t2I618CU7W7Ua9vbLdZTA0IiIlY2BERFqnltPVtpWl44fc7bivzxXIrS/F7vKzv+wkIpIrBkcaZfKdA4PnePyQ8zQmRt2HysZ8QZfvZQjAqNAbMST4SlgcdVhXNB8HKn7u9JxFTertldha8im2lnwKADDoTLA7beAlKIhI7dRwoERERH96K/0nRHkF4T+pt+OlQ9/jt8K9UpdERCpw7bXX4pNPPkFAQECn7j9r1iy8/vrriIiI6PQ6ZB0cvV/ZB3OC0qUuQ5WM3jPgsO5Fsv8Y+JlCcbhqlSDLNem8MCx0OoaHzAAA7Cj7GnvKFsPqbBBk+XanVZDlEBHJFQMjIqLW1NJ11OCw4pG9n+Cx/tPx7KAbEeEVhC9OrpO6LCJSuGXLlqGkpKRT93U6nfjpp5/wwgsvqCc4IvHYGn6E0e8BDAkehnVFC1D8x9XHXKWHEYODL8eosJvgqffFvoqfsKPsazTYqwWquOt4mhoRKY0aDoyISLs6c2l6V525XKVuL+1OB1489D2KGipxb6/L0DcgDi8d+g5me6PUpRGRQjmdTvTu3VvUdTA40ihr7Zt4P38DgkzRKLGc6MaSdOgbMBFjw2+Bvykch6tWYVvJ56ixdS7xJCKi05R6EEREJIWWQZISt58fZq7Csep8PDVwJj4YdR+e2P85TtUVS1JLXmZ48//HpnAMT6Q0a9eu7fJjYmNju3R/BkcaZnXUdys0SvIbhXHhtyHcKwXHazZjWc7TKGs8JWCFRETqp8QDHiIiOZG6G8nVTqsNJYdw5/a38NKQ2Xh/5FzM3bUAx2sLRKiwfS1Do7b+zSCJSP4uvPBC0dch++CI8xyJ4/3KPi4/NswzGROj5iLWZxBy6/bjm5P3o6D+iIDVdZ8WT1M7c0d/LhwEEMkDQyMiUhsxT1dTo2xzKebseBtvjbgbr6Xehjk73kGxpcot6+7M2JFBEhEBgF7qAkhZhgRfiRsT34Sn3h9Ls5/E99kPyS40Uru8zPA2f1xdBhFJg6EREZE4lBZcme2NeDhtIaxOO/4z7Hb4Gb2kLqldHDsSaZPsO45IHgw6Ey6LeQy9Ai5AWvkP2Fj8Aa9wJhCpd8BN6+c3SETuw9CIiNRMDl1H7rwSmxDPtbyxFv+352O8N+qveHnIbDyw50PYnQ4BqhNeXmY4x41EGqOIjqPunFZF3WfQmXBl3LNI9BuJH3Ofx7qidxkadZMcO37kVg+RWjE0IiJyD6nDq67KNpfgiX2fY1hICs4LE//4pzvhD8eMRNqiiOCIpKOHAVNjn0S8z1D8mPMsMms2S12SYskxLGqLEmokUiqGRkSkFXLZ3iktPEqrOIEccynOC+sndSkd4niRSJ5sNht+//13vPfee6ipqQEA5Ofno7a21uVlMjjSoM52cPkaQ3BV/D+Q6DsCP+U+j2yzPAYASqTEHasSayaSM7kcRBERuQu3e67ZWnIUY8P6wqQzSF0KESnMqVOnMGjQIFx99dWYO3cuSkpOdxa+8soreOihh1xermKCI56u5l79Ay/B7OQPEOGVgh9zn8XJup1Sl6RYSg5glFw7kZzw4ImISDqLclMV1Xm0In8Xgjx8Mbf35aKvq7tzFXGsSCQv999/P0aMGIGKigp4e3s3337NNddg9erVLi+Xk2NTK77GUFwS/SCS/EbhcOUqrCtaAIujRuqyFEsNO1NOgEjUPQyNiIjkQegJs8UKo47XFuCTE6txS9JEvJvxMxodNlHW0yQ2paRbY9aWj+WYkUhaGzduxJYtW+Dh4dHq9sTEROTl5bm8XMV0HAHsOhJbkCkGNyT+D+Geyfgh5yn8WvBvhkbdoIbQqImanguROzE0IiKtk9t2sKPuo6bfd+Y+YtpQfAieBhOGBieLuh6htZzTk+NHIvdzOByw2+1n3Z6bmwt/f3+Xl6u4jqP3K/tgTlC61GUo2pyg9LNCuFDPRExP+Bca7LVYnP0o6mxlElXXfUpqRVYSdh4RERGRWrTsPmpv7CjlmDKrrgjFDVUYHdobO8qOib6+7nYdtadpmRxDErnH5MmT8cYbb+D9998HAOh0OtTW1uLZZ5/F1KlTXV6uojqOSBwx3gNwXcK/UWerwPenHlJ0aCQXav2GRa3Pi0gMcvuWnYhIKnLdHsp97qPtZcdwQXh/+Bq93LI+McMdjiGJ3OO1117D5s2b0b9/fzQ0NOCmm25qPk3tlVdecXm5igyOeMqaMOJ9huLa+H/h+sTXUdGYh0WnHka9vVLqshRP7TtGtT8/IiHI9SCJiIiUY2nOVgSYfPDeyL8i1jvULesUOzziKWxE4oqPj8e+ffvw5JNP4sEHH0Rqair+9a9/IS0tDRERES4vV3GnqlF36WDwnIwbEv+OaO++KG44jhW5/0RGzSY44ZC6OCIixWNoREREQkivycNdO97Gq0NvxYej5+GRtE9woOqU6OsV67S1ls5cPk9lI+o+q9WKvn37Yvny5Zg1axZmzZol2LIV2XEEsOuoy3TeMHrPgHfYKniFvA+bw4Il2U/gy6y/4ljNBoZG1CX8poiobQyNiIhISDnmUty1422UWqpxY+J4qcsRDSfVJuo+k8mEhoYGUZat2OAIYHjUMRMMnpPgGfQmfCL2wDPoP3DYT6K+9Bosyn4Yp+p2SV2gKHjg5h7cqRO1xm0PERGJodbWgJN1xfDSe3R8Z4FI3QHEEInINXPnzsUrr7wCm80m6HIVf6oar7J2Jj30HmNg9L4KRq/LoNMHwWE9CmvtW7A1/ASnPQcAMCeIwZtY3NHeS0RERETa0Wi3IdjDvYduchnTtqxB6kCLSO527tyJ1atX47fffsOgQYPg6+vb6vdLlixxabmKD44AhkdNDJ6T4BHwNPTGJDhs2bCav4CtfhmcNvEv4UnalJcZzh04EdhtRERE4mp02OCpN7l9vXIJj5o01cLxJ1HbgoKCMH36dMGXq4rgSOt0hhR4BDwDo9cE2C0bUF/5dzise6QuS9OadmZS7WjduZNneERaxsCIiIjcocZmRoiHv9RlyAa7kIjatnDhQlGWq+g5jlpS/mlXJhh9ZsM7fC0MnhM7/Si9x1h4h/8MvTEZDeV3oqH85k6HRuzSEl9sSonbd2ZN63PneuX0TRSR2GbEpTX/EBERucOBylOI8g5ClFew29ct92CG41Ai8amq40ipp6zpjQPhGTwfOkMc4KyHwWsK7JY1nXhcP3gFvw9743ZYyu8CYBG/WHKJOzqQ5L5TJ1I6BkVERCSVvRVZcDgdSA1Owi8FFW5fv9xOWTsTO+CJTktKSoJOp2v39ydOnHBpuaoKjgDlhUd6j1HwCl4Ih+04Gipuh8n3buiNHXdP6fQx8Az5FA5bFiwV94KhkTKItdNtb0fJU9aIuodhERERyUGNrR6ZtYVIDU7BLwXSTEnB8IhI/h544IFW/7ZarUhLS8PKlSvx8MMPu7xc1QVHgLLCI4+A5+CwHUVD+V8AZz30hng47bkdPy7wnwDsaKi4DXDWiV8oCUbona6cdpDcYZNaMDAiIiK5Sas4gXFh/SStgeERkbzdf//9bd7+zjvvYNeuXS4vVzVzHJ1JCXMe6U3DYTANgLXmf4Cz/o/b+sFhO3LOxxk8J8DoNQmN1f8AHKXdqkEpAZvaCLVD68xyuPMk6hzOXUREJI5FualSl6AKeytOINYnFJFeQe3eRwcdDDrVHuIRkYsuu+wyLF682OXHq3qrIvfwyOR7Mxy2LNgbNwI4ffqZTh8Ih/XwuR4Fj4BnYLdsgb3hF/cUSorm7m+F5PwtFNGZGBYREYmP21hh7Ck/AZvDjjGhbR/jRHkF47eLnsfLQ2aLWofcv5TkWJTobIsWLUJISIjLj1flqWotyfa0NX0oDF5T0VjzKgDn6ZtMp1tPHdb2O46MvrdCZ0hEQ8W9gpUyJyhd9iGbGsm91ZdIjXjwQkRESlVjq8f+ypM4P7wfluVtP+v3F0cNgY/RE+PCxT+dTe7jWJ6yRlqVmpraanJsp9OJwsJClJSU4N1333V5uaoPjgB5hkcm7+sBOGEzf998m97UH05HJZyOgjYfo9OHw8PvftjMn8Npk9fzIWqJO2uSE4ZFROJpa3zFL6OIxLOp5DDu7jkF3gYP1NsbW/3O7nQ0/3//wASMCEmBj8ETi3O2osRSJXgtSgiPAPl3SBEJ6eqrr24VHOn1eoSHh2PChAno27evy8vVRHAEyC080sPoMwu2+mWA88+NuE4fBaejBoAOTV1ILZn8HwFgRWPNf91WqVLNiEtT/fn03AkSnRsDIyLxnGtM1fJ3DJGIhLWp5DD+1udKjArthfXFh1r9rqrxzwvmvD18DhodNuh1OgwM6oH7dr0PZxvHFwBwSdRQlFiqsLciq8v1yD08AviFJmnLc889J8pyVT3H0ZnkMngxeE6E3hgHW91nrW631S+F3hgPg9eUsx6jNw2GyWcmGmv+0ypsEop8QjVtUfNOLC8zXPYDCVInzllEJK6ujBnmBKVzjEEkoLz6cpyoLcQF4QPO+t3GksOoszXgSFUOAODajS/j0b2fIjU4GdfGj2l3mc8NuhHvjLgHvfyiXapJCeNZjklJKwwGA4qLi8+6vaysDAaDweXlaqbjqElTeCTlIMboczPsjWlw2A62ut1h3QW7ZSM8/O5HfcNKtOw68vB/GA7rUdjMX7m5WqLu4bc85E4MjIjE052xU9NjO/slXmfWJZcvBInc7deCNNyVMhmLcrbgaHVu8+01tnpctu55vDV8DnaWZ8BstyCt4gQWZ2/B3F6XI706HwerTrVaVt+AuOb/nxw9DBkZK1yqSSmdR4Aygi4iVzmdbXcWWiwWeHh4uLxczQVHTaQ6dU1nSIDRawIslQ+2+fvG2nfgHfoN9KbhcFh3AQD0HqNg8Bz/x4TYjjYfRyRnDI9IbAyMiMQl1JipvTmRXFk+T4lTFi1MI+AuX5/agAkRA/H0gOsxe9vrreY2sjsdiPMJw8qC3c23vXVsOZL9o/CvobMxZ8c7yK8vb/5dom9E8//7Gb1g0hlgddpdqksJ4RHAAInU6c033wQA6HQ6fPjhh/Dz82v+nd1ux4YNGzjHkaukCI+M3tPhdFTDVt9Omu+0nP6PsxYAoDP0gFfQfNgbd8Le8Iu7yiQSHMMjEgtDIyLxuGOcJMQ6zlyGUoKk9p67UuonadidDrxyZAk+GXM/LosejuX5O3FRxCD4mbzwU95O7CrPwMiQXgBOHztYnXY8se9zvDfyr3h7xN1459jPWF20DwCwsmAP8sxl8DV6YXbSRVg96Z9Ylrsd/zn6g0u1KSU8As4+fY3jVOqul19+GUuWLMHRo0fh7e2NsWPH4pVXXkGfPn9u0wsLC/Hwww9j1apVqKmpQZ8+ffDkk09i+vTpzfc5duwYHn74YWzevBmNjY0YPHgwXnjhBVx00UXtrvv1118HcLrjaMGCBa1OS/Pw8EBiYiIWLFjg8nPTdHAEuDs80sHoPR22hhUALG3eQ2/scfqeOj8Y/R6E0ed6OJ1VaCi/C21NmE2kJC130Nw5kxAYGhGJR8lzE8m5G6kzr6uc6+8Odh0JJ6MmH6sL92FOz0sxNWY4hgQn/fEbHTaXHMWl0cMQ4RmI4j+uplZtNeOBPR/iwT5X4R+Db8LMynF489hyHKrKxoGqU+gfEI8hwUn4OX8Xro0/D4UNFfjy5HqXalNSeNRSWzVzvEpdsX79esydOxcjR46EzWbDE088gcmTJ+Pw4cPw9fUFAMyePRuVlZX48ccfERYWhq+++gozZ87Erl27kJp6evt4xRVXoFevXlizZg28vb3xxhtv4IorrkBmZiaioqLaXHdW1unJ7S+66CIsWbIEwcHBgj43TU2O3R537ZD1phHQG+NhMy9u9z46QyIAwDtsMUy+t8HRuA0N5bMBZ4Xo9TVNYNnRlVI40SUJgZNnU3dwAmwicalpPy/luKXluMnVOtT0XpCw3s/8FRWNtaiymvH0/i+xOGcLHuo7DTodYHPYMTa89WkpRQ2VeGzfZ7hv1/vwNJjw/qi5eHLAdfA3esPLYAIAfHJiDb46uQG3JV+MMM8Al2tTS+DC8Sp1xcqVK3HrrbdiwIABGDJkCD755BNkZ2dj9+4/Tx3dsmUL7rvvPowaNQrJycl46qmnEBQU1Hyf0tJSZGRk4LHHHsPgwYPRq1cv/Otf/4LZbMbBgwfbW3WztWvXCh4aAew4auaOziOjz3Q4bNnNcxe1xWE9CFv9L7DVfwe7ZQMAm6g1tUfL34aRe/E8c+oqBkZE4lFzSNHVCbpdWbZY5gSlq2asxa4j4eSay3DLtjea/72u6AAiPAPxeP8ZMOoNGBPaBz/kbj/rcXsqMnH7tjdxeexIzO01FaNCe+OX/NMHrb5GT3yStRqXxQzDvT0vwwuHvnW5PqV2HrWF41Wqrq5u9W9PT094enqe8zFVVac7/kJCQppvGzt2LL799ltcfvnlCAoKwnfffYeGhgZMmDABABAaGoo+ffrgs88+w7Bhw+Dp6Yn33nsPERERGD58eKdqzc3NxY8//ojs7Gw0Nja2+t1///vfTi3jTAyOWhA3PPKE0etyWOsW4lynnNktq2C3rBKpBvGIORgjIgIYGBGJSc2B0ZmEHrNo6bUTCsMjcTjgxDMHvsI/Bt2ECyIG4EiLK661dd+f8nZga+lRPNzvGtycdHrulJ5+0ThWk4/3jq/EY/1nYEnuVhyqyna5JjWFRwDn7BSbX6YBBk/XLxkvBrvldD3x8fGtbn/22Wfx3HPPtfs4h8OBBx54AOPGjcPAgQObb//uu+9w/fXXIzQ0FEajET4+Pli6dCl69uwJ4PTk1r///jumTZsGf39/6PV6REREYOXKlZ3qJFq9ejWuuuoqJCcn4+jRoxg4cCBOnjwJp9OJYcOGufAKnMbg6AxihUcGr0ug0wfAVr9E8GXLiZq+ESMi+WBoRCQOLYceQnROa/n1I3lqdNjw5P4v0Ms/BkfPERw1KbVU49G9n2J8+ABEeQdjU+kRAMCKvF24M2UyxoX161ZwBKgzPGrCEEk7cnJyEBDw5+mbHXUbzZ07FwcPHsSmTZta3f7000+jsrISv//+O8LCwvDDDz9g5syZ2LhxIwYNGgSn04m5c+ciIiICGzduhLe3Nz788ENceeWV2LlzJ6Kjo8+53scffxwPPfQQnn/+efj7+2Px4sWIiIjArFmzMGXKFJefP4OjNogRHhk8xsJhz4fTflLQ5coRwyMiEgoDIyJxMPBozZUQyd2vodrGV+w6Eo/d6ehUaNTShpJDrf7tgBMVjbXwM3oJUlNTwKKmAAlgiKQlAQEBrYKjc5k3bx6WL1+ODRs2IC4urvn2zMxMvP322zh48CAGDBgAABgyZAg2btyId955BwsWLMCaNWuwfPlyVFRUNK/v3XffxapVq/Dpp5/iscceO+e6jxw5gq+//hoAYDQaUV9fDz8/P/zjH//A1VdfjXvvvdeVp8/Jsdsj9I7ZbvkVekMM9B7jBF2uXHFASkTdxdCISHi8wEXHOprImq+hcLidl7c6WwN8BQqOmqg5XGmaSJsTamuX0+nEvHnzsHTpUqxZswZJSUmtfm82mwEAen3rGMZgMMDhcJzzPnq9vvk+5+Lr69s8r1F0dDQyMzObf1daWtrFZ/Qndhydg5CdR3bLetgb98PD7z40lG8WZJlyp7ZvxojIPXggQSQ8Bh2u4esmPnYeyVedzQI/k7DBEaC+U9fa095zbCs8a7qvo75B1JpIXHPnzsVXX32FZcuWwd/fH4WFhQCAwMBAeHt7o2/fvujZsyfuvvtuvPbaawgNDcUPP/yAVatWYfny5QCA8847D8HBwbjlllvwzDPPwNvbGx988AGysrJw+eWXd1jDmDFjsGnTJvTr1w9Tp07F//3f/+HAgQNYsmQJxowZ4/JzY8dRB4QMPqy1b8HgeR70ppGCLVPuOOASjxZ2uKQdM+LSmn+ISFjcF5PccdsvT2J0HDWJTSlRdffRuZzZmcQxvXrMnz8fVVVVmDBhAqKjo5t/vv329NUJTSYTfv75Z4SHh+PKK6/E4MGD8dlnn+HTTz/F1KlTAQBhYWFYuXIlamtrMXHiRIwYMQKbNm3CsmXLMGTIkA5r+O9//4vRo0cDAJ5//nlMmjQJ3377LRITE/HRRx+5/NzYcdQJQnUe2S2r4LBmwOQ7C5bKnQJUpgxSdB7xm6vTlLIj0urAgXiwQCQmBkakJOw8kp9aWwOSjJGirkMr3UekDU5n+1dPb9KrVy8sXrz4nPcZMWIEfv311y6v3263Izc3F4MHDwZw+rS1BQsWdHk5bWHHUScJE3w4YbOs0sw8R0SdwdBIm9hdRCQuhkbqpPb3lfsFeamzNcDHcO4rRwmBY0EiYRgMBkyePBkVFRWCL5vBURcIER45LFugN0RAZ0jq+M4qovaBjhwp4dsbDhS0h4ERkfi4zyUiIZjtFvgYxQ+OAI4JiYQycOBAnDhxQvDl8lS1LuruaWtOZxUAQKfzQseNbOQKLbU5KyEcag8HCNrDwIhIfAyNSOl4ypp8dOa0GyG1HBsqeYxLJKV//vOfeOihh/DCCy9g+PDh8PX1bfX7gIAAl5bL4MjtdFIXQDKjxXO7GRppCwMjIiLqCoZHpMXxMZEQmibZvuqqq6DT/Zk9OJ1O6HQ62O12l5bL4MgF3ek60umjAABOR7mQJREpBkMjbWFoROQ+7DYiIjVheETUdWvXrhVluQyOXORqeKQ3DYDTXgqno0iEquTLXVdV47dTRPLAwIjIvRgakdqw64iAP79wZIBE1DkXXnihKMvl5Njd4EoYojcNgN16UIRqSMm00oWjleepdQyNiNyLoRERqV1sSgnHkUSdtHHjRvzlL3/B2LFjkZeXBwD4/PPPsWnTJpeXyeDIzfTGZDhtx6Uuw63YbUQAQyMt4BXTiNyPoRERiaXRYYO3wQN6Gc3R2hQgcVxJ1LbFixfj0ksvhbe3N/bs2QOLxQIAqKqqwksvveTycnmqWjd1/ZQ1J5xwbUIqUjeex01KxsCIzuSOQMNdX0zIFUMjUjueriatYzX58DJ4INEvEidqC6Uu5yxnhkccRxOdvqraggULMHv2bHzzzTfNt48bNw7//Oc/XV4ugyN3c9qh02nnZWe3EQHsNlIzBkbaJnVwMScoXbPhkdSvPRGp39HqXNidDgwIjJdlcEREZ0tPT8f48ePPuj0wMBCVlZUuL5enqgmgS4NWvS+cjjrxipEZDmy7Ro0BixqfE53G0Eib5gSlN/+QNPjaE5E71NsbkVVbhH4B8VKX0iF2GxGdFhUVhePHz54aZ9OmTUhOTnZ5udppfZEJnS4YTkeF1GWoCruN5IuhkToxMNIuOQYWWuo6kuPrT0TqdqQ6B/0D5RscMTAiau2uu+7C/fffj48//hg6nQ75+fnYunUrHnroITz99NMuL5fBkUA6M9eRTh8Fnd4HTieDI2of5zoiuWJgpG1yDi2UEh7J+TUkImrLoaocTI0ZAW+DB+rtjc2366CDE04JK3NfaCTGF6Ec65NYHnvsMTgcDkyaNAlmsxnjx4+Hp6cnHnroIdx3330uL5fBkRt5BP4TDnsx7A2rpS5FNdTabaSG8IjdRurAsIgYdnQfX0Mi13GCbGkdqDwJg06PO1IuwTvHfoYTTkyMHIxnBl6P7WXH8FPeDmwqOeLWmtwxRhZ7HNve8m11FuSIumZSO51OhyeffBIPP/wwjh8/jtraWvTv3x9+fn7dWi6DIwGdq+vI4HUljF6XoKH8LsBZ7ebKSImUGh4xMFI+hkXUREmBh1y7jpT0GhIRnelkXTHeTP8J83pfjhjvEDx34GtMjz8PueYyBHv44ZWht6LMUoPFOVvwadYaUWtRQ2BE5C4eHh7w9/eHv79/t0MjgJNju4cuGJ4Bz8NWvxx2y29SV+N2HDRrB3e2yjMjLu2sHyKA224iueLfJrnbt9mb8OjeTzEmtA8e6XctPPQmWB02zN25AE/u+xxOOHFdwjhRa3DXl6lK/NKWqCWbzYann34agYGBSExMRGJiIgIDA/HUU0/BarW6vFwGRwJr69tOz8BnAZ0BlupnJaiIlExJQYySatUyhkTUGUo8MJVjtxEg37qIiLpiS+lRvHx4ES6LGY7jNQVI8ovEnJ6XYl3xQXx9agOCPfygh06Udbs7zGF4REp233334f3338err76KtLQ0pKWl4dVXX8VHH32Ev/3tby4vl6eqiczgORFG72tgqXwQcJRKXY5k5HoKgRLI/ZQ1BkbyxWCIukqJgREREbnHqsK9CPMMwLzelyOt/ARuSrwQO8oycLAyGwBwddxoLM3dJug6pRoDN62X41xSmq+++grffPMNLrvssubbBg8ejPj4eNx4442YP3++S8tlx5EImgMSnR88Al+ErWEdbPVLpC1KpXhgLC3uTOWH3UTkKoZGRETUka9PbcBrR5YiNSQZBfXleGrgTOSYS/Bz/i7ckjQRBp1wh5dy+OJUDjUQdYWnpycSExPPuj0pKQkeHh4uL5fBkYhMfvdBpwtEY/UTUpciCzwocZ0cAxo51qRFPPWMhMDts7jYcUtEarI0dxv+l/4Tor1D4GXwwKtDb0WAyQehnv7wN3oLsg45BTZyqoWoI/PmzcMLL7wAi8XSfJvFYsGLL76IefPmubxcnqomkk+rh+HeyL/Aal4Ipz1P6nJkg6esuU4up6wxMJIeAyISAsMiIiJyRV5mOF7PTEdvUx9cGJ+EOK9IDAzywj+3rMOhoz4AfJrvq5ZxY15muGqeC6lbWloaVq9ejbi4OAwZMgQAsG/fPjQ2NmLSpEm49tprm++7ZEnnz4picCSSwcFXADoTbHWfSF2K6s2IS8Oi3FSpy1A97izlgaERdZdaAyO5fzHxfmUf1b72RGLjWE+eHl67Eo+MvgDX9xuEsnozjleUnXUfOXzpKZSWz4XjYpKroKAgTJ8+vdVt8fHx3V4ugyORDAm+CgcrVyPZwY3KmcQY3GtlQCFF1xF3jPLAwIi6i6EFkXrIPSgl9Wo5Dq21NuKZTavxbtp2vHzhZHxy+XR8cmAPXt62ARa7TcIqxSfWeNxR3yDKckk7Fi5cKMpyOceRCEI9E+FvCsexmg3cqbeDBzDyF5tSwtBIJhgaUXdxmysPHBMQkZK1F5YU1tXitp+X4JmNq3FDv0H4afpf0C9UPZ1GRMTgSBTxPkNhczQi33xI6lJkTegDGa0cXLsjzGFgJA+c8Jq6a05QOkMjIiLqts502Hx2KA1XLv4CdocDP1w7C3cMHg6dG2ojoj+VlZVh7ty56N+/P8LCwhASEtLqx1U8VU0ECb5DUVB/GDbn6ZnMOa9B+9hq7RoxT1ljaCQPDIyoO7jPkS+OCYhIzTIqynD1ki/x0Kjz8fTYi3B930FYk30CBbU1MFutyKqqwK5CXjiISCw333wzjh8/jjvuuAORkZHQ6YSJbxkcCUwHPeJ8hmBX2fetbudAsX1ChkdamesIECc8YmgkDwyNyFVa38/wywgiIuk1Oux4adt6rD6ViZl9B+Hqnv0Q4uUNT+PpQ89fszLwwpa1yK2plrhSIvXZuHEjNm3a1HxFNaEwOBJYpFdveBp8kWM++8CP4VH7ONiXHkMj6TEwIldx36IcfK9ICBwzkRJsL8jF9oLc5n8bdDpcmtQLT4+9CL9ffxve2LUVC/bukLBCIvXp27cv6uvrBV8u5zgSWJR3H9gcjSiqP9bm77mjF5+WDr4Z9qiHlj63JBzOYXQ2vh5E6sT9pPLZnU78fOIYJn3zMT49uBePjRmPB0eMlbosIlV599138eSTT2L9+vUoKytDdXV1qx9XseNIYN6GANTbq+CAXepSFIddR64R4pQ1BlDS4mCYuoLBiHLxvSMipRJyigSzzYqXt61HRUM9HhszHjk1VViUzosKEQkhKCgI1dXVmDhxYqvbnU4ndDod7HbXcgoGRwLzNPijwV5zzvvwlLX2CRUeaWmuI+DP4MeVHTpDI+kwMKLO4j6ja/hFBKkZP9ukFgv27kDf0DA8Ono8VmQeQ73NKnVJRIo3a9YsmEwmfPXVV5wcW86MOg/YnR1v9BgetY8Dftd15dsgBkbSYmhEHeE+onvkti/h+0lEdLbXdmzG1Bv64JaBqZzviEgABw8eRFpaGvr0EXYMxOBIYDZnIwy6zr2sDI/EpbWuoyad6T5iaCQdBkbawG27PDS9D3IKkIi6g59lUpvcmip8e/QA7hk6El8e3ouaxkapSyJStBEjRiAnJ4fBkdw5nDYYdB6dvj/Do7bxlLXuaxkONYVIDIykw8BIG7g9lycpAiR+FoiEp+VxnZq9tXsrruszAHcOHoHXd22RuhwiRbvvvvtw//334+GHH8agQYNgMpla/X7w4MEuLZfBkcDsTisMOlPHd2yh5UD2XAPNzt6PWuMgg4GR1BgaqR+3ycpw5vvUXpDE95OI6GxCTpDdUrG5Dp8d3Is7Bo/AJwfTUNEg/KXEibTi+uuvBwDcfvvtzbfpdDpOji03ZZZTCPSIQqRXHxQ1dH3g2dlvQ9mp1DUMj0gKDIzUjdtg5eN7SErC09RIzebv3YGb+g/BvUNH4aVt66Uuh0ixsrKyRFmuXpSlalh69TqUNpzEBRF3iL4utQ8gOKAnpZoRl8bQSOW4fSIikgb3r+pU0VCPD/fvwq2DhmFAaITU5RApVo8ePc754yoGRwJzwoFNJR8h3ncoeviOEH19ag+PhMSBBompKSzi50z9GBoREREJ790923GsvBRvXnw5vI1dm/qDiP70+eefY9y4cYiJicGpU6cAAG+88QaWLVvm8jIZHIkgq3Y7cs0HcGHk3V2e78gVag6PhD5A40E9CY1hkbYwNCIiIhJHo8OOv/2+HNF+Afjs8unoERAkdUlEijN//nz8/e9/x9SpU1FZWdk8p1FQUBDeeOMNl5fL4EgkawrfQpBHDMaE3eyW9ak5PBIaD/Kpu9hdpD1zgtIZGhERyQT3v+p1oqoCt6xYhEhfP/xy3S24ZWAqdFIXRaQgb731Fj744AM8+eSTMBgMzbePGDECBw4ccHm5DI5EUmY5iW0lX2BE6HUI8Yh3yzoZHnUeBxzkCoZF2sTAiIiI6DR3XKl3Z2Eepnz3Kb5PP4jnz5+E76fdiAvjEwVdR5i3D/Q6RlKkPllZWUhNPfuiUJ6enqirq3N5uQyORLSr7HtY7HXoF3iJ29apxvBIrIM2BgDUGewu0jaGRkRERO5ntlnx7KbVuPHHb2HSG/Dp5TPw2eUzEO3r363l9gsNx5JpN2HXLX/FN1ddL1C1RPKRlJSEvXv3nnX7ypUr0a9fP5eXy+BIRA7YcKxmPfoGTgTYZClLDAOoLQyLiKemEZHccJvUGvfR2rA1PwdXL/kCt/28BL2CQ/HrzFsxObGnS8uK8PHF0mtugo/JhE25pzAkIkrgaomk849//ANmsxl///vfMXfuXHz77bdwOp3YsWMHXnzxRTz++ON45JFHXF5+l4KjH/IGu7wirTpatRoBpgjEeg902zrV2HUkJg48qAnDIgJ4cEZE8sXtU2vcZ2vH2uwTuPS7T7CnKB//uGCSS6eZXZLYE15GE4rNdfj6yH54GowI8PAUoVoi93v++edRW1uLO++8E6+88gqeeuopmM1m3HTTTZg/fz7+97//4YYbbnB5+V3uOFqUm4pFuWefM0dty68/jKrGQvQLnOTW9aotPBJ7oMSBh7YxMCKAXUZEpAzcTpFWVTda8MauzYjy9cd5MV2fQ/brI/vx5IZVGB+fiNHRcQCAUG8focskkoTT6Wz+/1mzZiEjIwO1tbUoLCxEbm4u7rjjjm4t3+VT1Rgedd7R6jXoFXABDDqT1KXQOTA40B4GRgQwMCIi5WnabrX80Srux93PHRNkt2dvcSFyqqswqUdKlx/rcDrx5eF9WHniGGYPTIXFZsN1fdx3VgiR2HRndOL5+PggIiJCkGUbu/PgpvCIG+xzO1q1GqPDbkKy3xhk1Gx023rfr+yjqoHEnKB00TupZsSlMRTVAG6zqImatpFEpG0dbc/U1o3eEsdv2nKkrBhDI6Jdfnx6eRmGR9Xhx+NH8Ndho7G3uAC/nTwuYIVE0ujdu/dZ4dGZysvLXVp2t4KjJotyU3kgdg7ljTk4VbsHF0TciazanbA5G6Quic6h6bPMAYj6cDtFTRgYEZHWNG331BogMTxyr9iUEuRlhkuy7u/TD+KDKdfgooRkrM0+0eXHj4mJw5GyErywZR2mJPXGyOg4BkekCs8//zwCAwNFWbYgwRHA7qOOrC58E7OT38PY8NnYUPy+29bLriPXcQCiLtw2URM1bROJiOhPHLu5l1Th0aqTmdiSl42nzrsQm3JPwupwdPqxM/oMwOiYeNz+8xIYdDoEe3mjsK5GxGqJ3OeGG24Q7NS0M7k8x1F7uLFuW5U1H1tKPkNqyDWI9FLnNz3u4s6DPoYNROqh9XlAiIgA9YfnHLu5l1TzHf1j8xrEBwTh7yPHder+OgD3Dz8Pr110GRanH8Ka7BO4PKUPfEwmbM3LFrdYIjfo6BS17hI8OAJ45bX27ClfjJKGTEyO/jv0MEhdjqK5OzziIETZ+P6R2g+UiIi6Qu3bRO731e9oeSle37kZdw8dhVF/XCGtPb4mExZcejUeHDkO/96xEf+39hcAwD1DR2FDThYOl0k32TeRUFpeVU0MogRHTRggteaEA6sKXkeoZw8MDJritvWq9Vx2d+MgRJn4vhEREWkP9//uI1XX0Xv7dmJnQS5enzgV58XEt3u//06cirGxCbjzl6V4Z892AMD4uET0D4vA/LQd7iqXSFQOh0O009QAkYOjJgyP/lRiycTR6rUYHfYXGHWeUpejaFJ8W8buI2Xhe0WA+r9ZJyJyhRa2jRwHuI8U4ZHD6cSDa35GibkOX191PT6dOh39Q8+ec8nLYMSOglz8fiqz+bZ7UkdhX3EhtubnuLNkIsVyS3AEsPuopa0ln8HbGIihwVdJXYriSTXoYYBEpAxaODAiIiKSAynCo/zaGkxb+iXu/nUZ4vwDsOzav2BkVGyr++wrKcTg8Kjmfw8Oj8LY2AQs2MtuI6LOEuyqap21KDdV8wfcVdYCHKz8BSPDrseByp9hcdSJvk61XV2tJXdeae1Mrn6WGaISiU+t2zwiIiK5kupKa79mZWD1qUx8fvkMvDv5Kly+6DMUm08fY1U21MPfwwM6AE6cntsoq6oCv2ZluL1OIqVyW8dRS+w+AraXfgmDzgPDQ6+TuhSSQFPHktZDVCKxMDQiIuoYt5UkBqnmPLI5HJi3ajlsDgfmT74KJv3pQ93LU/pgX3EhnACSAoMxJbkXPti7Ew6RJxMmUhNJgqMmWg6Q6mzlSCv/AcNCroWPIVjqchRPyQOfliESgySi7lPy9oCIiEgNYlNKJAmQyhrMuPe3ZRgYHomHRl2Ay1P6YHhULN7YtQUAcNeQESirN2PxsUNur41IySQNjppoNTzaVfYd7E4rRofd5Jb1qf3qamo5WGSQRERERERqIEV4tLe4EEuPHca42ATMHjAUG3JOYmt+DsK9fTC9zwAsPLAbFrvd7XURKZnb5zhqT1N4JNcD5b4BE9HTfxw2Fn+IKmuBIMu0OGqxq+w7nBc+G3vKF6PKWijIcrVMyvmOxNLW34RWw9bOkut2hMSnlgCZiIhILc4Mj9wxB1KdtREeBgN6Bkfh3zs2AQBuHpiKRrsdXxzaJ/r6idRGNsFREzlOnj0waAouif47AKDWVoZ1Re8Ktuy08h8wNHgaLoqahx9ynhJsuVqmxvDoTAyTzia37Qa5H0MjIiIi+WsKksQMkBrtdviaTPAymnCotBgAcE2v/vjp+FFUN1pEWy+RWsniVLUzyW3uoyivvqhszMPO0m/RL3AiDDpTu/f1NYbg+h5v4PaUz3BR5DwEmmLOuWyb04LfC19Hkt8oDA2eJnDlZ1N7oKJlWj7FTWvPl87G0IiIiM4kp+MJOpuYp7E12u3Q604f6oZ5+2BweBTiAwKxPJPjBSJXyDI4aiKXjX2ueT+CPGJxonYbvAwBSPEf1+59r4l/Cf6mcGTV7kBP/3GYnvAyPPV+51x+Vu0OpJUvxQURdyLMM1no8jWJB5HQZIBE2sS/dyIiOpNcjiPo3MQKjyx2O0wGA4rratE7JAxXpPRBibkO2/NzRFkfkdrJOjgC5NF9lGPeCwDwM4Ujz3wAAwOntHvfEM8E7Cz7BmuL3sa3px6Ap8EXl8Y8DEB3znVsLP4Q5Y05mBr7BIw6TwGrP5tWuo54MHmaVruQSBv4d05ERGeS+tiBukaM8KjBZoW/yQPHK8vROzgUFyUkY9XJ47A7nYKvi0gLZB8cNZFyB1BnK0dpw0kk+KbiYOVK9PAbhgBTZJv3rbWWItlvDACg2lqElfmvIsX/PIwLv+2c67A7rfgl7yUEmCJwYeQ9gj8HreJBZWtqDJDU9nyo8/j3TUREZ2JopEyxKSWCBki/Zh2HTqeDXqfDhfFJ6BUSiszKcsGWT6Q1igmOAGm7j3LMaUjyHYkTtdvRaDdjQODkNu+3tugdJPqNRN+AiQBOn4a2vug9jAq7AcNCpp9zHeWNOVhftACDgy9Hz3OcDicErXQdUdvUGCCRtjA0IiKiMzE0oiZ5tdX4/ugB9A4ORaWlAQCwJS9b4qqIlEtRwVETKXYK+yqWw9sYiNTgq3G0ei0GBU9t85SyrNrtSK9ahwsj74GXIQAAsKd8MbaXfoULI+9GjPeAc67nQOXPyKjeiEuiH4SfMUyU56I1PMBsnxzCo+6cSieH+sn9+DdNRCQMfpFIciNk19E7advha/LA5txTmPj1RzhSJt5k3ERqp8jgCHB/91FFYw52ln2HEaHXI7NmC3yNoUjyG9XmfdcVvQu9To8LI+5uvm1P+WIAgI8xqMN1rSp4AwadB3oFjBekduKB5rm4q/vozKu+tbXeroRIDI20iX/LRETd935lH9WFRuw2ojPl19bgmU2rMaPvQDw8+gIkBwZLXRKRYik2OGrizp3EjtKvUWsrxfDQGahqLES879A272e2V2Jz8UL0D7oEgaYoAICP4fSGymyr6HA9FkcNqq1FCGxnHiWhqG3A0BEecJ6bkAFSRwFRd+thaKRN/BsmIuoeNQZGpD5Cdh19e/QA7l+9AoPCo/Db9bfhloEMGIlcYZS6ACE0hUdiH0zanY1YU/g2rk14CQAQ7ejf7n0PV/2O8yPuRP/AS7C19PPmTiOzrbJT66qxFsPfFNHdkom6rOnvqCuhrJh/ey2XzW8TiYiIukYLQRHHB+oTm1KCvMxwQZa1LOMIVp44hsfGjMfz50/CzoJcHOZpa0RdoviOo5bcsdM4VbcL6dXrAAARXilI8B3e5v1sTguOVa9Hv8BLAOiar8JmtlcCAK6IfRoXRrR/9TQvgz8s9lohS2+TFgYTLbFjofPO1S3U3W6i7tZE2sO/XSKirmF3EdGfLHY71pw60fz/RNQ1qug4askd3UfrixYg0XckPA2+6BNwIbLrdgMAIr36oMFehSprIQDgUNVvGBQ8FfE+QxDjPQBllpNodJgR5zMEvQIuaL5PqeXEWesI9ohHRvVG0Z6Dls0JSudAqosY1pCUGBoREXWMYxuicxsZHYfSejMyK8ulLoVIcVQXHDVZlJsq2sFuna0cW0s/x4TIe5BVux0AoIcBNyW9BQD4JPMOVDTmoKD+MMotORgdNgt55gMIMEUhzDMZw0Omo7IxH37GUPTwHXZWcORrDIWnwRfljTmi1E/y1nSQzAEgaR0DIyKis3F8cLYZcWk8XY06NCo6DrsKcqUug0iRVHWq2pnEvPJaWvkSfJp5F2yORnjqfeHfYiLr1JBpzf//e+EbiPbuh2jvfqhqLMCspHeQ5DcKm4o/Qq55PxJ8h521bF9jCACg1lYmSu1n0uIARAkHpEqokUgs/PwTEZ3WdMoZTz0jcp2H3oChEVHYWZgndSlEiqTq4KiJWOFRn4ALcU3Ci/hrn6WI9x0CAMg3H0K/wIvhqfcDAOSZD2B53guI8x2CisZc7Kv4CSvyXkRGzUacqtuDOJ9BMOhMrZZrsdcAADz1vqLUTfJ15sEyD55Ji/i5JyKtY1DUdTytns5lYHgkvIwm7GDHEZFLNBEcAeJ0Hx2sXNn8/5dEPwgA2FD8AXTQITXkmubfZdVux6/5ryLFfyxS/MfCqPMAAJyq2w2j3hOxPoNbLbfeXg0A8DYECFrvuWhxYKKUg1Ol1EkkBH7eiUjLGBYRiWNUdBxqGxtxuLRY6lKIFEkzwVETIcOjGlsxfsr9BwAgq3YnVuS+iIL6w9hfsQKpIde06hhKr16HL7LuRWH9EUyJfRSpwdNQZjmJMsspjI+4C0adV/N9nU4HAMDLjcEREZGU5gSlMzQiIs1iYCQMdh1Re0ZGx2JPUT7sTqfUpRApkuaCI0DY7qPjNZuQXbcXgaYoHK/ZDADYVfYdjDpTq64jACiznMSKvBexq+w7TIj6KxJ9R2JF3osI9IjG9IR/IcAUCR9DEK6IexqNdjNyzHsFqbGzOGCR3pnvAQeSpAUMjIhIy7ifFxbDI3XIywwXdHlRvn7Iqa4UdJlEWtKl4KggK0zwP2IpCRUerS+ajyCPGAwJuQoAYLZXYF/FcqSGXNvmPEWbij+G0+mAnykUZZaTWHzqUfgaQzAraT5mJ3+ACK9e+CnvBVQ0uv8cXC0NXuT+XOVeH1F3scuIiLSO+3pxzIhLY4BErRTU1iLS11/qMogUy6WOo7zMcNUESEJ0H5VasrC/YgXOC7sZIR7xANrvOgIAH2MwdDo96mwVAIDChqP4MutenKjZiqzaHfjsxF3IrtvdrZpIudhlRGrHwEgbOLkv0bnxb0N8DI+USYzjzIK6GsT6MTgiclW3TlVjgPSnzSUfo8ZajGnxL8LHEAyzvQJHqlajX+DFZ93XzxgCAKizlTffZnHU4deCf+PXgn+j3l7lch1C4ECGiMTAwEgb2gqLGCARtca/B/dheEQAUFBbjWgGR0QuE2SOI7UFSK5odJixNOcpGHRGTIt/AUadFzJrtyLIIwZBHrGt7ts7YAKcTgdqrPKd1Z8DGiISCgMjbehMOMR9CxH/DqTA8Eg5xDqmzKutQZCXN3yMJlGWT6R2RiEX1vSHHptSIuRi3a4pPOrqTqbWVoIfcp7CzB7/xeWxT+CX/H/B5mjEFbFPIat2B3LNBxDrMxAjQq/D+qL3JO8sIiISE8MibejqQfD7lX342SAiolbEbkIoqK0BAET7+SOzsryDexPRmUS5qpqauo+62oFUYjmB5XkvINFvJM6PuAPL8/6BisZcDAi6FNcmvISRoddjU/HH2FO+WKSqhaPmUwvU+ryI5ILBgDZwW0rUNfybkQ67juSn6awVdxw75tdWAwBieLoakUsE7ThqSS3dR0DXO5BO1e3G7wX/w+SYv6O6sRAr8l4EAAR7xMHhtKPKWiBarWLgt8NE1FncVmgDD36JSIlmxKUJdlVl6h53NxoU1tXC4XQixi/AreslUgvRgqMmWg2QDlWtRIApAhdE3oVqazGO1axHRWOu2CWKpukgQQ0HhTzgIRKHGrYPdG7cfhK5jn8/8sDwSHpSnJ1iczhQbK5lxxGRi0Q5Va0tajl9Dej8KWxbSz/D4cpVuDTmYcR6D3RDZeLj5ZWJqC0MjdSN230iUhOetiYdKY8JC2pr2HFEonr55ZcxcuRI+Pv7IyIiAtOmTUN6eusxcmFhIW6++WZERUXB19cXw4YNw+LFbU9jY7FYMHToUOh0Ouzdu9cNz6B9oncctaSm7iOg9RXY2tsBrSp4HX6mMFwV/xy+OfkgKhpz3FWe6No6iJD7wSMPfIiEJ/e/e+ocd20f+XkhLeL4Q37YeeR+UjcS5NfWIJodRySi9evXY+7cuRg5ciRsNhueeOIJTJ48GYcPH4avry8AYPbs2aisrMSPP/6IsLAwfPXVV5g5cyZ27dqF1NTW26RHHnkEMTEx2LdvnxRPpxW3BkdN1BYgAe2HSA7Y8FPu87i+x+u4Jv5FfH3yPlVfTa3lwIgHB0Tqx79zZeJBLBERdaS9oEepx3D5tTXoF6qes2BIflauXNnq35988gkiIiKwe/dujB8/HgCwZcsWzJ8/H6NGjQIAPPXUU3j99dexe/fuVsHRL7/8gt9++w2LFy/GL7/84r4n0Q63narWFqlTZ7E0ncrWFCY1OsxYmvMUTHovTIj8q8TVuQ9PayNSN4ZGysNtMpH78W9OvnjKWts6utKZK8dwcjjuK6it5hxH5LLq6upWPxaLpcPHVFWdbhgJCQlpvm3s2LH49ttvUV5eDofDgW+++QYNDQ2YMGFC832Kiopw11134fPPP4ePj4/gz8UVknQctaTG7qOWWnYixfkswGWxj+Fo9Wpk1e6QsCr3YycSEZE05HTQyu0/aY2c/v6IOtKVcCcvM1xxx295tTXwMpoQn++LyroG1PSyS10SnSH4uA1Gk03qMlqxWU/XEx8f3+r2Z599Fs8991y7j3M4HHjggQcwbtw4DBz453zH3333Ha6//nqEhobCaDTCx8cHS5cuRc+ePQEATqcTt956K+655x6MGDECJ0+eFPw5uULy4KiJ2gMkAPjn4QoEe6ZjTNhDyDXfAqujXuqSJMEQiUj5+LcrfzxgJZIW/waVgXMdneZqF5Fcj938Mwxn3VbVUAdcCkQHBaCyrgH+GQaGR9RpOTk5CAj4c3J1T0/Pc95/7ty5OHjwIDZt2tTq9qeffhqVlZX4/fffERYWhh9++AEzZ87Exo0bMWjQILz11luoqanB448/LsrzcJVsgqMmct4ACeHfR5bii7F/R4T3w/hf+k/Nt2u1VdadIRIHcESkBXLd1jFsJCKSn+6eQtaZYzd3nabWVljUkslw+vdOOFs9ZkB8JEaMTcAvJ44hs7Jc1BpJuQICAloFR+cyb948LF++HBs2bEBcXFzz7ZmZmXj77bdx8OBBDBgwAAAwZMgQbNy4Ee+88w4WLFiANWvWYOvWrWcFUyNGjMCsWbPw6aefCvekukB2wRGg7u6jwoYKfHD8V8zrfTlWFezF4erTV1lr65sOrYVJ7EQiInKdXAMjIq3h36LyaLHrSMgwp+WypDh+6ygwajIoIRINVhuOF5Q13zamVwI+uGc6AGBiQjKu/eErUWokbXA6nbjvvvuwdOlSrFu3DklJSa1+bzabAQB6feuppg0GAxwOBwDgzTffxD//+c/m3+Xn5+PSSy/Ft99+i9GjR4v8DNony+CoiVq7j77P3oxLoobisf7Tcfv2t2Bztt0i2bQD01qABJw96GKQRETUNiUcpHIbTlqhhL9HUhdXrnwmZgeQuyfB7mxoBACDEqJxOLcItj8O0AEgNSkGZTV1OFFUjjqLVYwSSUPmzp2Lr776CsuWLYO/vz8KCwsBAIGBgfD29kbfvn3Rs2dP3H333XjttdcQGhqKH374AatWrcLy5csBAAkJCa2W6efnBwBISUlp1b3kbrIOjgB1hkcOOPGvw4vx0ej7cFPieHyWtfac99dygNSE3UhERH9S0sEpt9mkBUr6myR16CigkcNVzMTUlcCoyaCEKKw5eLzVbXGhgSioqMHAhCi8vXIL5z2ibpk/fz4AtLpCGgAsXLgQt956K0wmE37++Wc89thjuPLKK1FbW4uePXvi008/xdSpUyWouPNkHxwB6jx17XhtAb4+tQG3Jk3CuqIDyDaXdviYRbmpmg6PmjBEIiKtUepBKbfRpGZK/buktinldDW1B0Kd4UpoFODtibjQQBzJa308WVXXgIEJUQCAXZl5gtRH2uV0Oju8T69evbB48eJOLzMxMbFTyxWbIoKjJmrrPvr4xO+YEDkIj/Sfjvt2vd9qorb2sPuotaZBW3sHJxzUEZEScFtFpBz8eyWSjiuhEQDUNjSisLIGqYkxWL77SPPteRXVzf9f32htXge7johaU1RwBKir+6jRYcOrh5fgrRFzcEXsSPyUt6PTj2X3UWscxBGRnHEbRaRs/BsmOdB6t5GroREAOJxOLNt5CDedn4p//7geDVYbAGDNweN4bNoEAEBCWBCyisub18XwiOhP+o7vIk9q2XDuqcjET3k7MbfXVIR4+HXpsUpopyUi0rL3K/to9oCTp6mRGmj5b5hITroTGjX5Ycdh+Ht74uLBvZpvK6iowc9pRwEAJkPrQ2Mh1kmkFooNjgD1hEfvHFsBg06PK2NHdfmxDI+ISAo8kOoYXyMi5WJgRCQfQgU4ueVV2J6RjWtHDWh1+6s/rMfrKzbiUE6RaOsmUjpFB0eAOsKjGls9NhQfwuQo10IghkdEJAUeVLWPrw2RcvHvl0i9Fm8/iJE949E39s9jyFB/Hzx4+QX47ek78fszd2FIYnSrx/hnGBggkeYpPjgC1BEe/VqYhkS/CPTxj3Xp8QyPiEgKPMA6G18TImVilxGR/Agd2Py27xhOllRg7qVjm2+bOqwvKuvq8egXPyMy0A+D/7jKWlu1MEAirVJFcAQoPzzaXX4c5ZYaTI52PQBieCSOOUHpnKuD6Bx4oEVn4jaTlIbbMSJtsDucePfXrZgwILk5IEpNisHOzNzmCbPXH8465zKaAiQGSaQlqgmOgNPhkVIDJLvTgXXFBzE2rG+3lsPwiNRqUW5qmz8kDzzoopb4eSAl4eeVSFtW7k1HRkEp5l12uusoITQIR/OKMXFgCjIKSpFdWtml5TFEIi1QVXDURKnhUX59OUI9/bu9HB5Mk1p0JiBiiCQfPPgiIqXhdouacByhHU4n8PbKLTivdw+MSImDh8kAu8OJC/snY+3BzG4tm91IpFaqDI4AZYZHlY218DV6waTr/kaGOz/hcFApDVc+wwyQpMc5QohICbitopY4dpAnMYOXNQczcSinCPOmjIWXyYgB8ZEI8vXG6m4GR2diiERqodrgCFBeeFRprQMABHn4CbI87gSFw8Gle3X3s8sASXpNB2X829EuvvckV/xsEhEAvPXLZgxPjkVxVR0uGdwLRVW1OJxbJNr6GCKRkqk6OAKUFR5VNDYFR76CLZMHz6Rl/PzLA0MkIpILbofoTBwraNfm9FPYk5WHhkYrdmfm4pO1u9y2bp7SRkqj+uAIUE54VNkUHJmEC44A7hBJWfh5VTeGSNrC95nkhJ9HOhPHHPTur1vRMzoMb6/cgi82pklWR1OA5JfJEInkSRPBEaCM8KjKKnzHURPuGEmr+NmXLwZI2sD3mOSAn0Miasv2jBwUVFRj8pDeUpdCJGuaCY6A0+GRnAOkensjLHar4B1HTXgATXLHz6g28YCOiMTEbQy1hWMOavL7/uO4eHAv6HU6qUshki1NBUdN5BweVTTWidJx1IQ7SdIifu7lj91H6sb3lqTCzx4RdWTNoUyEB/giJTJE6lKIZEuTwREg3/Co0lqLYIGuqtYeHkQTkVwxQFIvvq/kbvzMUXs4FqaWjuWXAAB6RYdJXAmRfGk2OALkGR5l1RZhQGCC6OvhDpO0hp95ZWGApE58T8ld+FkjUr6aXna3rKe63oLCyhoGR0TnoOngCJBfeLSp5Ah6+kcj2itY9HXxQJrkhJ9HagsDJCLqKm4z6Fw43qC2HM0rwdg+PTjPEVE7NB8cAfKaNHtHWTqqrWY82n86jDrxL8fInScRKUFTgCT3A0K510ekdvwbJCJXfLx2J/rHRWLm2MFSl0IkSwyOWpBDeGS2N+LxfZ9hSHASHus/3S3rZHhEREoi1wBJjjXJzZygdKlLICIiOktaVj4WbT2A+6eOQ0SAeBcqIlIqBkdnkEN4tLciC/88+B0uixmOu1Imu2Wdi3JTGSCR6vEzri5yCpDkUgeRlvHvkNRMDscoavf6io1oaLThsWsukroUItlhcNQGOWyYVxftwzvHVuDW5Em4MnaU29bLA2siUhqpAyQerBIRESlfdb0Fryxbh0sG98KwpFipyyGSFaPUBchVU3gUm1IiWQ1fndqAKK9gPNR3GooaKrGj7Jhb1rsoNxUz4tLcsi4iwH2BJT/X6tYywHHHKVEMjIjkg3+PRCSEDUeyAAARgTxdjaglBkcdyMsMlzQ8eiP9R8T4hOCZgdfj1m3/Q6ml2i3rZXhE7sLQiMTQ1kFkd8MkHpgSydecoHT+jRKpjH+G+BcKOpNRf/qEHKvd4fZ1E8kZg6NOkDI8csCJFw5+i0/GPIBnB96A+3d/AAecblk3wyMSG0+NJHfiQaW0ODE2EZHrYlNKZDGdhtqZjKfDKqvNLnElRPLCOY46ScoNdZXVjOcOfI0hwUm4NXmSW9fNA3sSizs/WwxAiYiIiKgjRsPpw2Obgx1HRC0xOOqCvMxwyQKkfZVZ+ChzFW5LnoRhwSluXTfDIxIaQyMiIiIiao8Up6kBgMnAjiOitvBUNRdIdera51lrkRqcjGcH3YBbtr6BSmud29bN09boTAwUiaizeJoauQPnOSKi7moOjjjHEVEr7DhykRTdRw448Y+D38Cg0+PunlPcum7gdFDAsEC7mt5/JX0OGHYSEWkLQ0oi6o66BgsAICbYX+JKiOSFHUfd5O7uo/LGWnx7aiNuS74Y7x1f6dauoybsPtIWuYRE/MwRKRMP5Mnd2HlEpGxSnaYGAMXVddh3qgCXDu2Nn9O4/yJqwo4jAbi78+iH3O1wwolpcWPcut6W5BImkLjk8D7PiEtjaERERF3CwJLawzEFdWRlWjrO75sIfy9PqUshkg0GRwJx56lrNbZ67Co/joFBCW5ZX3vkECqQeKR+fxkYEREREZG7bcvIhofRiN4xYVKXQiQbDI4E5q4Ayel0wu6UftI2Jc13Q50n9XvKwIhIHdj1QVLi549IeaQ8Ta2Jp+n0bC61DY0SV0IkHwyORCJ2eGR12hFg8hF1HV0hddBAwuF7SUREasHwiIi6KtDHCwBQZW6QuBIi+WBwJCIxu482lxzB4KBERHsFi7J8VzBwICKiJjxgJ7ngZ5GIuiLA+/TcRtX1DI6ImjA4cgMxwqN1RQdQZ2vA7KSJgi+7OxgekRD4OSIiIiExPCKizrpyeD+UVNfBbLFKXQqRbDA4chOhu48aHFa8dWw5roobhenx5wm2XCHwoJ+ISNt4kE5EcsVxqnzJYX6jv1yQivH9k/Hst79JXQqRrBilLkBrWoZHsSkl3VrWT3k70cM3Avf3uQq55jJsLzvW3fIEsyg3lRMcU7fwM9T24FbrrwkRkavmBKXj/co+UpdBRDLVLzYCf7/iAny+fg82Hj0pdTlEssLgSEJChEjvHvsZ8T5heGHwLNy9411k1RUJVV63NR30duVAlwfKROf+NtSVvyvSrpafJX5miBgeEVHbvD1MeOUvlyGjsBSvr9gkdTlEssPgSCaaQqSuBkgOOPH8ga8xf+Rf8Wrqrbhr+9uotNaJUaLLzjwIbnnw0pl2YR74uNeMuDS2cUuos689AyTqDH4+iM7G8IiUSOwrNmvdXy8dg8hAf1z/+pew2u1Sl0MkO5zjSGZc2SmY7Y24ZdlPMDk98fLQ2fDQyzsPXJSb2vzjzsdS58nlYFNr77WrfxNERETUMe4zqS2BPl6Yed4QfL5hN06WVEhdDpEsMTiSoa5MpN1037zaaty1cin6+MXhmqArNPGtRMsQiQMB4c2IS5NVgKR2WniOpB3s5iC54wTuRPIi5cTYsy44PQb7YqM8xr1EcsTgSMaaQ6E2gqS2bttbXIjv0g/i2t4DoIP2WloZIolDLgGSmt/X7j43Nb82RERiYXikLUreV2ptTO9OPp4m3HT+UCzefgCVdQ1Sl0MkWwyOFKS9EKmlZRlHEOcfgOFRsc2P0SJ2IwmP4ZG88bUhuWHXERHJBfeR1J7rzhsMHw8TPl2/W+pSiGSNwZHK7C7MQ25NNab16td8W1dOfVMrBkjCYHgkPCGfj9peGyIisbHrSP24b5Q/qU5T0+t0mD1+GH7cfRhFlbWS1ECkFAyOVMYJ4KfjR3B5Sh+Y9K3fXq2HRyQMOZy6ppYgUA3PgYhI6RgeqZca9rMcv4vH02RERKAfdmTkSF0KkewxOFKhZRlHEOzljfPjEs/6ndZ3PmoYQMiF1OERoOwASal1E3UFT1cjpWB4pD5q2M9qYdwu5aTYOt3p/zqcTslqIFIKBkcqdLS8FOnlpbi6V982f6+FndC5qGEgIRdy6D4ClPOect4tIiL5YnikfNzPUlfocDo5Ym5E1DEGRyq1MeckBodHtft7hkccUAiJ4dG5cRBLRKQMDI+USY37Wa2P1d1Br2sKjpgcEXXEKHUBJI682mrE+PlLXYasLcpNlUXgoRYz4tIkH7Q1rZ/vK5F8vF/ZhwfjpChzgtJ5mqWMST3WcAethEZSnqYGAF4epw+FG+12SesgUgJ2HKlUTaMFXkYTPA3tZ4Na2SmdixYGH1qkxm8eiYjIfRh2youWTkHj+Nx9ekeHAQCOF5ZJXAmR/DE4UqnEwGAU1NbAYred837cOTFkUDO5vLfsgCIiUh6GR/Igh/24u2hpXC51txEA9I2NQG2DBXnlVVKXQiR7DI5UqmdQCI5Xdi4919JO6ly0NDARg5xfP618U8mAiohIWAyPpKX2/XZLHI+7X5+YcKTnl3JybKJOYHCkUj2DQ3G8orzT9+fO6jStBAxaptb3mKERyRnniyElmxOUzgBJAmrbT5+L1sbhcug2AoC+seE4mlcsdRlEisDJsVUq0tcP+bXVUpehaO0NWNo6QO/u4EbpB/1KHdy1rFvs90AOk4cTEZHrOGm2+2hpf6m10EguvD1M6BEWjI/X7pK6FCJFYHCkUlUWCwI8PLv0mLzMcMSmlIhUkXqIMZg5c5lKD5KUyJ0hktCUVi8RkVIxPBIfQyN1k0u3Ue/oMOj1OqTn8diHqDMYHKlUeb0Zod4+XX5c0w6MAZK0lHBZeTUP7IQO8sR8reT8GSEiUiOGR0TK1ycmHFa7nVdUI+okBkcqVdbgWnDUhN1H8nCuwKFlYNDVYMLVsEHNYdG5dOW0xXPdX2gMjUhJ3q/sw3liSDUYHlF3abHbSE76xUbgRFE5rHa71KUQKQKDI5Uqr69HQkBgt5bB8EjeuhNOuHP+JjWT8rVhaESAsk+xJFI6hkfkKoZG0vI0GnDRwBT8uo9fZhB1FoMjlSqrN2NoZHS3l8PwSFsYEikDAwLi3yqRPDA8IlKeK0f0R7CvN77cuFfqUogUQy91ASSOsgYzwrpxqlpLeZnh/GaEiEgmGBoRyQtPwaSu0PKYWi4TY18zagDWHc5Edmml1KUQKQaDI5UqNZsR6OkFT4NwTWVa3tEREVH3sTOD1IrhkXDYVUti8jQa0C8uApuPnpK6FCJFYXCkUierKwAAPQKCBF0uu4+IiIiIzsbwiEj++sZFwGQw4EB2odSlECkKgyOVOllZCQBICgoWZfkMkIikw1OViIjkieERkbwNSYhGg9WGjIJSqUshUhQGRypV1mBGtaUByYHiBEdNGCAREbnXjLg0RZ/KwdPVSO0YHnWfkrdx1L6aXnapS8CkwT2x+0QubA6H1KUQKQqDIxXLqChD/7AIt6yL4RERkXvxwIpIvhgeEclPcmQIhiXFYun2Q1KXQqQ4DI5UbGdhHkZGx7ptfew+IiJyL6WGR+w6IiIid5s+eiDKa81YczBT6lKIFKdLwZFfpjwuoUidsyM/F1G+/kgICHTrehkgEYmP8xxRE6WfukakVuw6IpIPk8GAK0f0x4+7DsNql/6UOSKl6XLHkX+GAf4ZDJCUYFdhHhxOJ0ZHx0uyfoZHROJieERKxq4j0gKGR0TyMGlQCoJ9vbFk+0GpSyFSJJdPVWOAJH/VjRYcKSvGqOg4yWpg9xGRuBgekZIxPCItYHjkGnZSkpCmjxmE3Zm5yCqukLoUIkXq9hxHTQESgyR52lGQK2lw1IThEZF4GB5RWxblpvKzQSQTDI+IpBMXGogxvRKwiN1GRC4TfHJshkjysj0/Fz0CgxDl6yd1Kew+IhIRAwJqqeXnQe4BEruOiIi0Qarjw+mjB6La3IBV+45Jsn4iNRD1qmoMkKS3syAPAHBeTILElfyJ4RGROOQcDpD7tPc5kHOAxPCItIBdR0Tup9MBV48cgJ92H4HFxkmxiVwlanDUhAGSdMoazNhZkItr+/SXupRW2H1EJA65BgPkPh3NC9IUIMnts8LwiIiIhObtYUJ4gC/STuZLXQqRorklOGrC09ik8fWR/bggLhEJAYFSl3KWpgCJIRKRcOQWCJB8yS1EYnhEaseuI9IqqY7/vD1MAIB6i1WS9ROphVGqFTdtPGp6sWVQbCsyj+HZcRNxQ9/BeHXHRqnLaZer4VFsSonAlRAp36LcVF6RhrrkzPBIqs9PU3jEA2wiUqPYlBLNfWEqZdOAT1Nw1MjgiKg73Npx1BZ2IInPYrdhybHDuK7vQJj0kr/lgmPXElHb5NJFQu4nROgjdScSu49IrRiKErmPXqeTugQiVZBNisAASVxfH96PcB9fXJzYU+pSRMUAiag1hkfaJVTHEMMjIpIKO2eVT+rju6KqWgBAdHCApHUQKZ1sgqMmnAdJHMcqSrElLxtPjLkQAR6eUpcjOgZIRETqCI+IiIhc1WC1oaS6DrEhDI6IukN2wVFLDJCE9ci6lfD38MB/Jl4GrTRtMkAi4kG/1ik9PGLXEakNP9OkJXKYzzavvAqxIfK7SBCRksg6OGrCLiRh5NZU48E1v+CSxJ6YM3Sk1OW4FQMk0jqGR9qm9NM9eKBNRESuKq81I9TfR+oyiBRNEcFRSwyQumdt9gm8s2cbHhl1AXoHh0ldjtsxQCItY3hE3cX5jojI3ZQefJP0ooL8UVBRLXUZRIqmuOCoCQMk172+awsK62pxT6q2uo5aYnhEWsXwSLtmxKUJdrU1qTA8IiKiruoRFoxTpZWtbgs5Zmv+IaKOKTY4asIAqetsDgc+2LcTV/Xshzh/7Z7vy+4j0iqGR9qm9G/vGR4RkdLFppRIXYJmhPn7wtfLA9klFc23nRkWMUQi6pjig6MmDI+65tujB1FtacBdQ0ZIXYrkGB6RFjE80jahuo+kwvCIlIqfXdIiKSfI7hcXAQA4UVQO4OzQ6EwMj6g7Xn75ZYwcORL+/v6IiIjAtGnTkJ6e3uo+hYWFuPnmmxEVFQVfX18MGzYMixcvbnWf8vJyzJo1CwEBAQgKCsIdd9yB2tpadz6Vs6gmOALYfdQV9TYrFh7Yg+v7DkSYNyeLY3h0tqaOrM7+kPIsyk1lgKRxXQ2Q5BQ48QCclIafWdfJZbtDyjNpUApOllTgVGllp0MhhkfkqvXr12Pu3LnYtm0bVq1aBavVismTJ6Ourq75PrNnz0Z6ejp+/PFHHDhwANdeey1mzpyJtLQ/t3OzZs3CoUOHsGrVKixfvhwbNmzAnDlzpHhKzVQVHDVhgNQ5nx3aC7vTiVsHDZO6FFlg+HGaq0EQAyXlYnhETYHQuQ7O5HjgxgNxUgp+VulMPF1NfAa9DhcNSMGmdekMg8gtVq5ciVtvvRUDBgzAkCFD8MknnyA7Oxu7d+9uvs+WLVtw3333YdSoUUhOTsZTTz2FoKCg5vscOXIEK1euxIcffojRo0fj/PPPx1tvvYVvvvkG+fn5Uj01dQZHTZoCJIZIbauyNOCrw/sxe8BQ+Jk8pC5HFrQcdoj13BkoKQO7j6hJWyGSHEOjJjwgJ7njZ5RImtPVEsNDEOLng517Tnb5sQya6EzV1dWtfiwWS4ePqaqqAgCEhIQ03zZ27Fh8++23KC8vh8PhwDfffIOGhgZMmDABALB161YEBQVhxIg/p5S5+OKLodfrsX37dmGfVBcYJVuzmzWFR1KeY9uyjrZIUduH+3fhloGp+MuAoViwd4fb1y9XeZnhmvkmSKoQp631auU1l7NFuamyDgnIvZTyWXi/sg/mBKV3fEciIhlpGvdo5Qu1ml52t36h3zMqFABw4pRr48uQYzaU99bM4bIs+BzIg1HvKXUZrdgcpwOi+Pj4Vrc/++yzeO6559p9nMPhwAMPPIBx48Zh4MCBzbd/9913uP766xEaGgqj0QgfHx8sXboUPXv2BHB6DqSIiIhWyzIajQgJCUFhYaFAz6rrNPeXIJcAqS1S1FZUV4vvjh7AX1NHYfGxQygx13X8II3QQngkt4FKy3o6eu07W7va30MxNHUeKSU0IAIYHpE8sduIOkNrAZK7pESFoqyiDtU1DVKXQiqQk5ODgICA5n97ep474Jo7dy4OHjyITZs2tbr96aefRmVlJX7//XeEhYXhhx9+wMyZM7Fx40YMGjRIlNqFoLngqAkDpD/9e8cmTEnujWfGXoT7fl/ulnUqRdMOXI3hg9wHJ0LVd+Zy1PheioUBEikNwyMiUrKWYxS5j9Nc5c6uo55RoTiZXdqtZbDriJoEBAS0Co7OZd68ec2TWsfFxTXfnpmZibfffhsHDx7EgAEDAABDhgzBxo0b8c4772DBggWIiopCcXFxq+XZbDaUl5cjKipKuCfURaqe46gz5DwHkrtqq7I04MUt63Blz74YH5co+vqUSG3z8qjpuXSV2t5Ld+D8R6Qk7PAgIjWITSlR7Zdd7vpyvGdUKLJOdS84IuoKp9OJefPmYenSpVizZg2SkpJa/d5sNgMA9PrWMYzBYIDD4QAAnHfeeaisrGw1ofaaNWvgcDgwevRokZ9B+zQfHDWRe4AktqUZh7E59xT+Of5ieBmZqrdH6aGD0usXEl+LrmOA5Bp/YwRivPvDU+8ndSmawfCI5ICfQxJCU4CkthCpppe9+UcMfWPDkRQRgn2HckVZPlFb5s6diy+++AJfffUV/P39UVhYiMLCQtTX1wMA+vbti549e+Luu+/Gjh07kJmZif/85z9YtWoVpk2bBgDo168fpkyZgrvuugs7duzA5s2bMW/ePNxwww2IiYmR7LkxITiDXE9hc0ddT238HStn3oL7ho3Bv3ds6vgBGtaVuXjkgiFJ29R8OqJYeApbx3yNIegfOBm9/C9ApHev5ttrrCUobjiOXPN+5NSlocRyQsIq1a3poJ2nrhGRWqj1VLam4xshvyy/5cLhKCiqxOZtGYItk6gj8+fPB4DmK6Q1WbhwIW699VaYTCb8/PPPeOyxx3DllVeitrYWPXv2xKeffoqpU6c23//LL7/EvHnzMGnSJOj1ekyfPh1vvvmmO5/KWRgctUOMoEaIjaF/hkG08CirqgLv7tmOecPG4IeMI8ioKBNlPWoj9+BBTQMLMWlhMnShKTlA0kEPL0MAfIxB8DEEwfuP/+oAWJ0W2ByW5v/aHBbYnA2wOiywORthczQ0/86J023FBp0JEV49EeM9ALE+g5DoNxIOpw0narZhV/n3KLdkI9SzB8I8kxDl3Qfjwm+FMfJuFDdkYk/ZIqRXr4cDvPSvGDjvERGp0ZljFo73/hQZ5IdLh/bG/I/Wwe5wdnt5nOeIOsvp7Pjz1qtXLyxevPic9wkJCcFXX30lVFmC4F9AB+TYgSRmTfPTduCqXv3w4vhLcP2yb9D9Ta12yLELiYOIrmF45Bq5BEgeeh94G4JahEGB8DEEnxUO+RiD4G0IgE7X+mxtm6MRTjhg0nt1ep12pxVWhwVGnQeMeg9YHQ0oqD+KjUXv41DVb2h0mJvvW2o5gXSsBXA6aIrzGYLUkGswJfZRnB9xB9LKl+JA5c+wOHh1SyKl42lq5G6xKSWKH/cJNXH2rPNT0VBvxc+/HxCgKiICGBx1mlwDJKHraXTY8dSGVfj6qutxXd+B+O7oQUGXrxVShkhKHzRIjeGR68QOkDz0Pgj1TESYZxLCPBMR6BHdKhAy6j1a3d/htKPeXo16WyXM9kqYbRUos2TB3PzvStTbq/74b2WrkMeg84BJ7wmjzgtGvQdMOk8Y9V4w6jxg0nvBqPOEUe8Bo94LJp0n7E4rCuqPoKQhEw50vF22O604VbcLp+p2IdQzEcNCrsV54bdgdNgsHKxaibTypai2Fgn+GmoVu46ISAvUEB51l6+nB2aMGYSfVuxFfb1V6nKIVIPBURd1J0AS4/KTYoRHW/NzsDj9EJ4YcyGWZRyFxc7TJ7pDzBBJ64MDsTA86p6WE2h3N0SK9OqF1JBrEeM9AIEepy9B6nDaUdGYg4rGPBQ1HIPZXnU6HPojEGoKihrsNc2nknWV3dkIu70RQE236u+MMstJrCr4LzYXL8TQkKsxJPgKDA2+Gocqf8XG4g9hcdSKXoMWMDwiIi3Qenh07egB8PIwYsnyPYIul6erkdbx0+8iMeca6ioxall4YA+m9xmAAWHh2FNUIOiytay9HXlbIYWWd/qkHq6GSD18h2NE6Ewk+KaisjEPGTUbUdpwAqWWLJQ35sDuVN+3iGZ7BbaUfIIdpd9gUNBlOC98NpL9z8P6ovlIr14ndXmqwPCI3IGnqZHUtBoeGfQ6zLogFb+kpaOkjF+6EAmJwVE3yOn0NaFrSS8vgcVuw6DwKAZHbqDFnbvcsetIeB2FSDro0TvgQowInYkIrxQU1qdjee4LOF6z2eXOISWyORuQVrEUx2rW46LIuZga+wT6B16C1YVvodpaKHV5isfwiIhInaaPHoTYkED87eMfpS6FSHX0Hd+FOuKfYejwFLTO3EeoWoRgdThwpLQEg8OjBFkekRLlZYYz1BPJotzU5iDJqPPC0OBpuC3lE0yNfRxmWzm+P/Uwvj55HzJqNmoqNGqpzlaO5XkvYFnOMwjx7IHZye9jeMh10EP8fYnasSOExMLPFsmF1r78Oq93Ah67ZgK+27IfpevF+ZIl5Bin7yDtYseRgFqGNk2dP+4Ii9qro7vdR/tLCjE2NkGIkogUjd1H4kjwCUOY1+O4KnYUfI1eyKhZjx9zn0Op5YTUpcnKidptyDmxD2PDb8H5Ebejb+BE/F7wOooajkldmqKx84iISF5cPW7qHR2G/95yBbYdy8ZLS9cgUOC6iIjBkWikCIzaqqE74VFBbQ1CvLwFrIhIuRgeCcPP6IVJkUMwNWY4Bgb1QLXVjJUFe/DtqU0obKgAEAggVbQrsymV1VGP9UULcLRqNS6OfhA3Jr6JvRXLsLnkE1gd9R08WocQjzgEmKLQ6DCjwV6D8sYcAE53lC5rTd0hDJBICOw2IrnRwlxHkYF+ePfOacgurcRDn6+A3cF9G5EYGBypXHfCIw+DAY2Otq8EJ4d5nYjcrWnwxQCpa/TQYWRoL0yNGYELwvvDqDdge2k6nt7/JTaVHEaj4+zW76bT2BggtVbUkIGvsuYhNeQajA2/BT39z8eawrdxonZr8328DP6I8uqLaO9+iPbuh0jvPvAy+LVaTklDJraVfoHjNVvAAIndR0RESuTr6YF37pwGu8OJeR8tg9mi/AtnBB/n6XAkTwyONMDVU9dMBgNsDW3PLSKnq8oRuRu7jzqnh28ELosehinRwxDuFYis2iJ8mPkbfi1IQ1lj5y5zzwDpbE44sKd8MY7XbMLEqPtwdfzzaHTUo8ZaDL3OiGCPWACA2VaJwvqj2F32PQrrj6K8MRceei8EmKIwPHQ6rox7lgFSC+w+ou5gtxGRexn1evz3lisQHeyP2W99h5LqOresN+SYDeW9xTmEDjlmA2MjkisGRxrS1bDHt8oIq739+zM8Ii1jeHS2ME9/9A9IwIDABAwLSUH/wHhUNdbh98J9+LlgF45W57m87I6uyKZF1dYi/JDzFBJ8UhHmlQx/0+mOuK31R1FYfxRV7VyBrbwxByfrdiLWeyDGhP+FAdIZ2H1EXcHAyH1mxKW12heQtl06tDfG9umB29/9HplFZc23K3UCa6XWTdrB4EhjOhv2+GcY4DHAAKvt3PcVaiJuIiXS8qlrHnoj+vjHYkBQQnNYFOUdBAAoaqjEwcpsfHlyHTaXHIHVKez2gV1IrWWb05Bt7vprkVd/EIuzH2OA1AZ2H1FbgVDLzwMDIyJpRQb6ocrcgJ2ZuW5ft9BdRwyNSAkYHGlQR2FP0+9NBgOs9s5dBpvdR6RlWuk+ivMJxfjwgbggoj/6B8TDqDegwd6Io9X/3959h7dVnm0Av7XlIXnvkb333hsyWIGwaZkFCgTaQunHKLuFtFBmSym0FEghrJAQAmEkZIckZDmJ4yQe8d5TlrXX90ewiRM7lm1J5xzp/l2XL8fSGY/sSDrn1vO+pwzfV2fhmKEExwylqLM1B6QediH5RmcB0t66Vaix5sHlccLlccDlcUAuU0Ap00Il18DutsDkrO96BxLGACl0eBMEMSwiqZHaxNjGQR3PrdqRqHAtDGarnysiolYMjkJYRwHSmS/WKqX8vEPVOtve2dskCgXB2n00RJeG2YkjMDtxBPpHJsPmcmBvfS5eOfkFsg0lONVSBZfHu4DZn9iF1HtnB0iXpD/e5TotjnpUWo7jaNMGFJv2B6BKYTBACm4MhIikRy9wcOSrriN2G5FUMDiiTpN9tVIBexdD1bzZJkMkCiX+6D6K1+gwIDIFA3UpCFOo0WQ3odHegsa27y1odpjh7uXQIq1chbTwOKSHx2FczADMShiB5LBoNDvM2FV7HP/O/w4/1ufC6hbvVUvYhdR7rQFSnKYvwhXRkMuUUMiUUMhUcMMNp9sKp9sOrVKPZO0QZEaMw7LM51BhPoZdte+izHxY6IfgN5z/KLgwMJIeznPkPal1G3VXbGSY5DuOGBqRlDA4ok6dHqrW+9DnfC2nvgyVOFyOxKK33UcyyLA4ZRwWpYzHQF0KYtSnL6VuclphctoQrY6AWt7+5dvtccPgMLeFSk2On8OlJnsLbC4HtAo1wpRqhCs0CFOooVWoEa7UIFEThbTwOMRr9G3bq7EasKPmGLbVZiOrsVAUXUXdxRCpd+ptRehqIFqBcRd21QJ9IiZgWsLNuLrPC/iu4iUcM3wTkBqFwO6j4MDQiEi6lAo5JvZPx0e7hP2gojddRwyNSGoYHFGnVAoFHE7/nix6O465u9tjgERi0JMAaZAuFb8fejlGRffBnrqTWFO6G/nGSuS3VKLS0gjPT11FEUotolURiFFHIlp9+nvMWT9nhMef/lkVAaVcAbvbCYvTBovL3vZlddlRZW3E/oZ8lJnrUW6pR7m5Hk2OwFzWNlDO/oSaQZJvFZsOoNh0EPOT78XC1AcgkwHZTcEbHgHsPpIyhkbSxW4j7wR7t9G0wX2gD9fi28O57W4XIoxp3Wd3AiSGRiRFDI6oUyqlAmabeIeknA8DJBITbwIknTIMdwxciMvTp6LYVIN797+JQ42nOl3+dPeRFeUW7yYnVsjkkuwa8pfOTj68CZT8eeIi7UDLg81VfwcAXJjyAAAZspu+FrYkP2P3kfQwNKJgF+yhEQBcO300jpfXILeyTuhS2njTfcTAiKSMwRF1KlyjQkOLWegyeoUBEolJRwGSDDJcnDoRdw1aDLVciddzv8Lq0h98HvIwNPKO0J9mB0Nn1Oaqv8Pj8eDClPsxRD8XB+pXo8i0H+jlHFxixgCJiMQgFEKj/kmxmDW0H578dKPQpZyDwRAFMwZH1KlEfSSyCiuELsMnOP8RiUnrgd2CcWo8MPRyjIjKxNcVB/DPvA1osLcIXB2JiVTnadpS/Q+UmQ9jYtzVuCLzWdTbinGw4TMcN3wPl0eanazeYIAkbuw2omAWCqHR6MxkvHbbZSitb8LXB08IXQ5RSGFwRJ1K0Eeg2hA8J7EMj0gshsUlYPn4qbio/2CcrK/FVZ9/iMqILKHLIpFrDZGkEiDlGXcgz7gDaWEjMT7uKlyQ/DtMT7gVhxu/wOHGL2B1GYUu0W84/xGR7wndEUrCWjRmMP58/SLklFXjt+98AVsPr/xMRD3D4Ig6NHFAOjQqJWqCKDgCGB6RcEbEJWJen/6Yn9kf45NTUWxowiPbvsPqk9lweTwAfv6ksKdXY6PQsLpsnGTCIwAot2SjvCwb0eo0jI9dhklx16JvxCR8XPw7oUvzK1+HRzJ5IuSqMXDZxDc8QwrYbSRdDIy6FizdRh1dNCdSq8a9i6fjF7PG4auDx/HExxthZ2hEFHAMjugcc0f0x99vW4oT5TX4Mb9U6HJ8juERBUpCeATuGjsJF/UfgpRIHYx2G3aUFuM3m77EVwUnfwqMznXmASBDJOqI1LqPAKDJXo7NVX9Hg60EsxLvgAxyeBDcc2/5MjxShl0Btf5R2JoegNPymU+2GSoYGkkPwyLvBXNolB4bhZX3XYsIjQp//Xwr3t8hnfc8omDD4IjO8ZslM7AntwR3vvUZOjmvlTyGR+RPerUGvx47CbeOmgCH24XVJ7PxffEp7Kssg8PdvRNlhkh0PlLrPgKAJnsFlHI1IpVxMDqD//+0z+Y9kqkBAOqov8DtLIbbsb+3pRGJEkMj7wVDaNRRYBQXGY4haQm4cspIAMBlz7+H6qbzj4LgxNRE/sXgiNrRqpQYlBKP93dsDNrQqBWvuEa+plUqcevI8bhr3GSo5Aq8c/QA3szah2a7zSfbP/sAkUESAdIKj1TyMMRqMuH2uGB3W4QuJ6B6330kh8dVD7erAJro52GpvRAA37+6wm4jaWFoFFo6Co0A4NFl87FwzCAAwIvrt3cZGhGR/zE4onasDieqm4xIi9ELXUrAMECi3lLK5bhu6CjcN2EaYrVhWHX8CP5xYDdqLWa/7pdBErUSe3iUrB2KC1MeQLy2LwCgxHQINnfonQj0KjzyWAGZEnbD0whL+ArKsCvgtKz2bYFEAmJo1D1S7zbqLDSSy2SoamoGAORV1uHdrQcCWRYRdaJbwVFMvhNK1c9tgA2DmTsFo8KaRvRNjBW6jIDj8DXqLhmASwcOxe8nzUSGPgqf5+Xg5X0/oNRoEKQeDmsLbWIOj6LUyYjX9sWO6n+jzlaEGmue0CUJpqfhkcfdCJk8Cm7nCTit30AZcRuDoy6w20g6GBp1j5RDo44CozC1CrOG9sWcEf0xa1g/xESEoa7ZhKc+4cUAiMSiV8nP2WNJGSQFh8KaBkwckC50GYJg9xF5a35mf/xh8kwMi0/ExqJ83Pnt5zjZUCd0WW0YIoUmsYZHRS374PI44fDYUGTaJ3Q5gutReCT/qRNYFgaXdSPUUS8AsijAI0xQTeQrDI26J5hCI4VchmWTR2L54umI04XjZEUtPt19BNuOncLR0qqgnzaDSEp8mvScGSQxRJKuwpoGXDl1JBRyGVzu0HzFZoBEZ9MqlRiflIopKRmYm9kPYxKTsbeiFMvWrsLB6gqhyzuv1oNMBkgkFJvbhFLTIQzUzcDhxi+ELkcUuhMeyRR9oY58AA7zJ4DHCJd9L2QyORTqyXDZ+Il8R9htJA0MjULH2aHRuH6peGzZfAxOTcC6fcfwr+/2oqyBQTiRWPkt3WE3knSdqmmAWqlEaowepfWh/QLO4WuhRwagX1QM0nR6pOn06KuPwcSUNIxOSIZaoUCDxYwfK8vw0r6d2FZaJHS53cIAKTSIteso3/gD5iffC61CB6vLKHQ5ouBdeKSEJvpVeNw1sDc/CQDwuErhdpVDoZ7K4Igki6FR90mx26ijoWn9EmPwrzuWIa+yFte+vAo5ZdUCVEZE3RGwNIfdSNJRWN0AAOiXGBvywRHA7qNQEaFS4aohI3HLqPHoFxUDAHC53agyteBQTSXW5W3B3ooy5DXWQep9eAyQgp8Yw6MC4w9YkHwfBupmILvpG6HLEY3zh0dyqPVPQK4aAWv9MsDz84T7btteyNWTA1OkxLDbiIKRFEOjjqiVCjz/y4tQ1dSMO978DBa7s+uViEhwgiQ47EYSt5pmE8w2O/omxGD78UKhyxGNMz8xYYgUXBb1HYgX5i1GuEqNDQUn8cSOTSg0NKHKZITT7Ra6PL9hgESBZHY1oqBlD6Yn3IJ84w+wupqFLkk0WoOOdgGSLAbamNcgV8+AvfkJuB1H2q3jsu+FOmwpINMBHnZwkbSw26h7pBoaddRt9MAls9AvMRY3vPohQyMiCRFFYsMgSXwaTRZEhWuFLkO02IUUPC4dMAQvL7gYG4vy8cyuLag0hd4JGAOk4CK2TqMzba56DTf2ewsLku/DV+XPCl2O6LR2H8lVY6GJeQMyqGFtuBFu+65zlnXZ90AmU0ChngiXbYsA1YoTu40o2ARTaDR3RH/8YtY4PLtmM3IrxXNBESLqmlzoAjoSm+ts90WBZ7TYoAvTCF2G6OnyFOd8kXQsGzwcryy4GOvyjuPejetDMjQ6k1QPTum0q9IPiTo0AgCTswGbq17DYP0cDNHPFbocUfrCuhTauI/gcVXCUndxh6HRaQp4PC7IlAMCWh8RUVc6Oh6OjtDimWsX4vuj+fho12Gf7o/ni0T+J4nWHs6PFHgMjnqus/CI3UnikhKhw4o5C/FZ7jE8tPVbyc9b5CvsPhIHsQdAvZFr3I4Bhi2Yn3wfYtQZsLiaYHEaYHYZYHEZYHY2wepqhgfBO0y0MzHqDFye8WdUWPLxWcmTcHmiAESdMweSXDURmpjX4XEWwGn+rN193blaW7BhtxEFGyl+oNPZcfB9i2dAIZfhmdWbAlwREfmC5FIYhkiBYXe5EKFVC11GUDn7jZRBkrDuGT8FJrsdT+/azNCoA+UFCQyPAiyYw6Kzba76By5JfwyjYy6CVhEFhaz9+7nNZcKPdatwqPFzuDwOgaoMLBnkuDzjGZicDVhX+iRcHnvbfWcGIhelPoohUXNRbcnDurInYHImAkhst60zlw/VEInEi/MbeSeYQqORGUm4auooPP/FNjS0WAJcFRH5gqSTl9YQiQGSb8XpwjFpQDr+/vUPQpcS1BgkCSclQodrh47Cy/t2weQIjZPSnmB4FBihFBi1srmN+KzkobafNfIIhCmiEKaMRpgiCpkR4zAj8TaMjLkI31Y8j0rLcQGrDYy+kRMRrU7DB4XLYXO3dLpcSvgw5DRtxLeVfwO8iL07nHg7CLHbiEhYnYVG0RFavHjTJThWVo2PfTxEjYgCR5RzHHUX50PyrWunj4HT5caavdlClxJSOFdS4Nwzfgpa7Da8lx16J+zdVV6QIMlPPUlabG4TmhwVqLTk4FTLbmyt/ifeP3UXLM4mLMtYgZSw4UKX6HfDoy5EnbUQNda88y5ndNRAJlPAm9DoTMEcrATzY6PQJLX33c6OW5UKOZ7/5UXQqpW4/731QX2lWqJgFxTB0ZkYIPWOWqnANdNGY92+HDRbbEKXE9IYJPlHauTpbqO3Du+H2cluI29J7SBWSjhso2P19mJ8VvIIaqz5uCLjWUQoY8+7/J3RJzv8kgKNPBL9I6fhmOG7LpdtslcgXtO3R/sJxoAlGB8TUbD46y8uwoT+afjD/zaguqnzTsre4HkfUWAEXXDUigFSzyydOBxxunC8v4OdGGLDAMk37hk3BUa7DSvZbdRtDI8o0JweK74oewoAMD72yk6XO19AJIUAaYh+DuQyBU4YNne5bGHLj0jQ9keUKqVH+3qraUjQhC3B8jiIzhQs77WTB2Zg4ZhBeHTVt/gxv1Tocoiol4J+ciDOg+S9cI0KyxdPw1cHj6OkrknocqgTZ4ZHnBepe1Ijdbhm6Ci8uG8nu41IVFaXjQvJuY68YXO3IKtxHcbFXoF99R/B6jK2u9/bUOjO6JOiDRpGRC9CUcs+mF2NXS5b2LIPDrcVg3Qzsb/h0x7vs7tXX+vod+erQK4n2xbr35KITls4ZhBK65rw7eFcv+2DTQJEgRMyaQoDpK79at4kRGg1eOWrXUKXQl5qDZEYIHnnienzYLBZ8b/sLKFLkSxOmE1CONSwFpPirsVA3QxkN33Tdnt3gwsxhkeDdLOQHDYUa0oe8Wp5p8eKopZ96K+b1qvgCPDu6mvn+3354uptnW3/fGGS2P6G1D1XpR/iEN1OSLHbqLNu+EkD0rE7tyTA1RCRv4RcihJKAdL9l8yCVqXA69/s7nK+or4JMbhp7gSs3HoAVU3G8y5L4sMupK5d1H8wFvcfjHu++4LdRr3E8IgCzeIyoM5WiNSwEW3BkdiHn3lDKdNgdtKdKDDuRrHpgNfrVVtzMTHuWp/W0tswprshUk/2x8CISPxSY/RYNHYw+ifF4Z/f7RG6HCLykeBPTzpxZmtjsIZIi8YMQlpsFBaNGYwVn2/Ft1kdt4peMXkEHlo6F9VNRry9ZV+AqyRfYxfSuWK1YXhm1gXYUJCLDaf81zIdShge+R6Hq51fuTkbfSMmtv3c3aFWYjQp7hqEK2KwrfrNbq3XYC+DVhGJcEU0zK4m/xTXC10NPWMARBR8VAoF7r94Jq6bOQYutwffHc7F9pxCv+2Pw9SIAis4E5NuCtYuJF2YBiu3HUBytA5/u/FiTBmYgVc3nB6GplTIEalR4/eXzca8EQOwZm82nl+3DWYbOzGCBQOk05RyOV6afxEUMhme2LlJ6HKCCsMj32sdvsEA6VwVlhyMi728XVjSGkB4GyCJKbCIUiVjYtw1ONjwGQyOCq/Xi1QmYEr8DbC7LXB5pPP6LqbfPZEYSXGY2pkGpcTjxjnj8b/tB/H3r3fBYmewQxRMgisp6aVgCpBkMiBSo0FBdQNe+GI7lk0pxqNXzMPV00a3W67eaMZv/rsOW46dEqhS8rdQHsYmA/DXOYswPS0Tt274DHUWs9AlBR2GR/7B7qNzlZkOw+G2YnHqw/ii7Ek4PT8Pwfam+0hMwcUA3QxckPxbmJyN+LHuQ6/XSwkbjkvTn4DL48DHRffD5ubQcqJgIPXQCAByK2thtjlQY2hhaEQUhKSfkPhBMARIkRoN5HIZjD/NbbRmbzYOFJRhWHoiHC43nC43nC4XskurYTBbBa6WAiXUupAenjoHVwwejt9s+hK7yjlBo78wPPIPhkftmV2NWFv6R1yR8SyWZjyDdaVPwun5+f1LTMHQ2ZQyLTIjxiFJOxDJYUPRN3IS8o0/YFPlK3B4vHsPHqibiSWpD6PKehJflv0JFhEOUSPyFifI/pmUQ6MzP5x0utw4VFSOSQPS8e5W7+dsIyJpkG4yEgCxuU7Jhkf6cA0AtAVHAFBc14TiuiaBKiIxCYUupDvGTMSvx07Ckzu/x5cF0p4HRQoYHvkHh661V24+irUlj+LyzGdxecYz+Lz0iXbhkdgkaYdgcvx16BsxEUq5BiZnA+ptxfim/K843vx9t7azJPUhFBh/wDcVz8MNfppPFAykHBp1JFKjRiUvskMUlKSZigSQVLuPdGGng6OurqZGFOgQqbPLtnamJzUtGzwcf5w2F38/sBvvZfOEO1AYHvkPu49+Vm7JxtqSR3BFxnO4MnMF1pU9AatLXCcq8Zp+mJ5wMwbopqPeVoxdte+iwLi7W3MZtYpUJmBpxlOosebj28oXGBoRBYlgC43C1CqMyEjGuv3HhS6FiPxAWmmIgKR2FbYwtQoAYLFzsutQJZMB+jAtosO1iI4MQ3R4GKIjtIiOCDt92xk/R4VrEfPTd5vTieqmFpQ7jKgyGVHZYkSlyYiqlha0OOxQKxRQKxTQyBVQKRSot5iR39SAWrPpnBq6GxJ15OxtnC9IUssVuHnUODw0ZTY+On4EL+7b1ev9U/e0HggzQPI9dh/9rMKSg9UlD+HyjD/hmj4vYW3JozA6hf8/lxI2HBNir8RA3QwYHFX4uvyvONm8BR64e7Q9pUyLpRlPw+VxYn3Z03B5+J5OwSNUh6sFW2DUaly/VCgVcuzLLxW6FCLyA/EnICIkhS4khUwGAHC7e3awStKSFB2JQcnxGJAch0HJcRiYHI9+ibEI16jOWbbFakOTyYomkwVNZiuqmow4UV4Lg/n0z2qlAsnROiRF6TAkOh5zkvsiXh8BhVx+3hrK6g249Z+fosrLFmWNUoG+ibHonxSLAUmxSIyKhFalglatRJhKBbPdjqLaJhRU1eObrJOwO08HRq1BUtgYLfpFxaDBakGj1YIpqRl4aMospEbq8b9jWfjzD1u6+VskX2L3kf8wQDqt2noSHxffj2UZK3Bt31ewtvSPqLcVCVJLZvg4TEu4CanhI9BgK8WmqleQ07QRbvSui3NK/A2IUafjo6Lftl1JjoikKVgDo1bpsVFwuFwormsUuhQi8gPxJh8SIOYASfZTcOTyeASuhLorQqM+HdxERyIpKhJJ0TrE68LR0GJBWb3hp68mREeE4fLJI3DRuKGI04UDAMw2O/Kr6nGyohYbDp5ARWNzW0DUaLLAYLbC6ep+mKiQyxCvj0C4WgW70wW70wWHywWH040EfQT6J8XioaVz8bebLsYtr3/Sbh86rQb9k2LRLykW/RNPh0T9k+KQGqOHXH76/2mNoQWVjc2w2J2wOBxoMlsQqVHjwlEDccucCbh9wST8afX32FdQhtjIMNy+YDKunT4aamX7597GonzcumENCpoaevEXIF9heORfDJCAJns5Pir6La7IfA5XZDyL907dDofbEsAKZJgcdz2mJ9yECksOPi99HIUtPwLo/XtvuCIG42Ivx8GGNaizFfa+VCIKuGAPi4yDXG0f6KXE6NDYYgFPPYiCk/gSDwkSY4DUekLu4au36A1IisOdF0zGkNQEJEVHIlKrabvP7fagzmhCQ4sZsZHhSIyKbLduvdGE9QdysC+/DPlV9ahsavbLG7bL7UF1U0uH95lq7SiqbUSNoQUr770Wz//yItQ1m9AvKRYDkuKQoI9oeyzlDQacqmnAxiN5OFVdj1PVDSisaYTR2vlcXP2TYvHk1Rfgv/dcjZ3HCzG+fxpcbg/e3LgXG4/kQx+mQXREGMpijMiqqfL9g6de4dA1/wv1AMnsasT6sqdwU/9/Y2LsVdhd97+A7FevSsKcpLsxUDcdu2tXYk/dB/BFYNRqUvy1cHmcOFC/2mfbJBKbYB2uFuyBUas++misuPNCDElNQJhaia8OngjYvhsGK9tNJUJE/iWepCMIiClAkrd2HLkZHIlVUnQk7ls8HZdOGI7yBgO25pxCVZMR1YYWVDe1oNpgRG2zqV33jkapQGpsFNLjogCPB7tzS+AUyXDE7NJqPLtmM35/6WxUG1pwqroBn+05ilM1DThV3YCimgbYnN0ftnGqugG3vP4Jlk0eiV/MGoePdh3G25v3dTjxuw6KoL1KnNSx+8j/QjlAanZU47jhewyPXojdde/DlwHO2VTyMMxLugfDoi6A1WXEutIncKplj0/3EalMwOjoS7C3bhVs7o5DeyISn1AJjFq9f8nVyNBHtf386obAzi3J8IgocIRPOIJQbK5T8PCotetEq+KfWGzkMhmunzkWv1kyHRa7Ays+34LVe456NYTM5nShsKYBhTXiHIr12d5sfLY32+fb9Xi837Yuj+GRWDE8CoxQDZCOGzZidMzFSA8fhTLzEb/tZ1HKg8iMGI/t1W/haNPXcHqsPt/H1PhfwO4241DjWp9vm4h8K9TCojPd9d06PDvxAoztm4oPd2bBYPb962FXGB4RBQZTBT8RuvvoaEkVbA4nZg/rh6JaTlInJq/ccinmDO+Pj384jFc37ILJZhe6pKDD8Ei8GB4FTqgFSBWWHDTZyzE86kK/BUfjY6/EIP0sfFH2NAqM/vlkPUqVihHRi7Cj5j8Bnq+JiLojlAOjVsPiEjAqMxkbDp3A819sE6yO1vMtBkhE/nP+yyRRr8XmOgV5ETPZ7Nh5ogjzRw0M+L6pc9dMG415Iwfg/vfW47m1Wxga+VHrZI0kPuUFCTzgDqDVZePavoJdjmETBulnQyOP7HrhbkoLG4VZibdjX93HfguNgNPdRmZnIw43rvd6nUTtIMSp+/itJiL6Gd/DTrtpxDj8bd4SfHT8CB754JseXXzF1xoGKwUf9UEUrBgcBYgQAVJZvQGxkWEB3Sd1LjM+Gr+/dDY++eEINmcXCF1OSGB4JG488A68YA+RjjZugBxyjI65xKfbjVDG4uL0R1FuPopdte/4dNtny4gYixzDRrg83n2wEKaIwpWZK3BVnxcQroj2a21E/iCFrsjWsIjvW6fdMGw0npm1AG9l7cMfd2yCYaC4On1aA6Qzv4iodxgcBVggAyS704VwjRqKn66wRsJRyGV47obFqDOa8Lf124UuJ6QwPBI3HoQLJxhDJLOrEfnGXRisn+2zbcqhwMVpf4TH48GG8ufggf8+VZdBjghlLJod1V6vMz3hZgAyeODBhSkP+K02olDDsKhjywYPx59nX4h3jh7Ac3uEG57WXQyQiHqHzx6BBGIC7UOF5bh13kS8u/waPPT+1wCA2cP7IVKrRr3RjG+yTsJiF9cnBMHqV/MnYWRGEm7+xyew2B1ClxNyOOeRuLUelHPuI+F0FB5JoQugIwXG3RgaNR86ZSKMzppeb29m4u1IDhuGT4sfhNnV1PsCzyNanQa5TIEme4VXyytlWgyLugA/1n2IWtspXJ7xJ4yOvgRHmr70a51EwYoh0fnNz+yPF+YuxkfHj+DpXVva3Wcc5OKHdURBjMGRgPw9gfaOE0W45Z+f4C83LMGaB2+EWqUAPECzxYboCC0evGw2Pv/xGD7adRil9Qa/1EDA8PRE3LVwKt7evA+HiyuFLidkMTwSP06cLS7ediKJLWAqMu2Dy+PAAN10ZDV+3qttDdLNwoS4K7Gl6p+otOT0aBtn/h67+l0lak/PS1hjzfdq20Oj5kIl1+JE8xY0O6pwpPFLzEq8HQUtP8DkFOfVN4nEiqFR134zYRr2VpbhsR2bhC6FiAKMwZEI+DNAOlxUiatfeh93XDAF9UYTPt19BBa7EykxOlwzbTSunDoKv5w1HjtPFOLDXYex62QRPB6flxGSUmP0uH7mGCybMhJ5lXV447s9QpcU8hgeiR+7j6RHbN1KdrcZJaYsDNTN6FVwFKvOwMKU3+OEYUu3tnO+wK2rK90lagfAYK+Czd3S5X5i1OmYk3gXjhu+R7OjCgCws+a/GKibiZkJv8K3lS94XTNRqGNo1LUB0bEYm5SCu7/7Am6eLBCFHAZHIuKv4WstVjte/nJHu9sqG414dcMu/Ou7PVgybgiunzkWb9xxBQprGvDCum3YcaLI53UEK51WgzC1EgqFHAq5HKkxelw3YwzmjxwAk9WO1XuysXLrAVFcbYIYHkkFu4+k7ezwJNBB0gnDZixJewhRqlQYHN4N+zpTmCIal6Q/CaOzBpsqX/Z6PW+7tDrvQpLB5el6OLNSpsElaY/D6KzF95Wvtt1uc7dgV+07uDDlfhxt+goVPeySIgolDI28c9fYyag2tWBzcecXeOFwNaLgxeBIZPw9fO1sNqcLn+/Lwef7cjCmbwqWL5qGf95xBb7NysVf121FbbMpIHVIhT5Mg7sunIpBKfFIio5EUlQkwjXqc5Y7VV2PZ9dswZcHcjiPlAgxPJIGhkfBw99BUpQqFZkRY1FiyoLBUYE84w7Mc92NUdFLsLP27W5tK1KZgCsz/wKNIhyfFD8Ih8fa5Tq9mWD8zC4ks7MJ4cqYLtcZHXMJotVp+KDwnnPqO9b0LUZGL8aStEfwQeFyWF3NPa6NiAgA+uijccXg4Xh291bYXDx+IgpFDI5EKtABEnB6WNudb67BknFD8NDSOVj3fzfjuTWb8eXBEwGrQcxmDu2Lp6+5EFqVEnvySpBXWYeqJiOqm1tgttrhdLvhcLlhttlxvLyGQ/5EjuGRNHDoWnDyZZDUP3IaLk57FEq5BjaXCRsrX0KecQdyDJswInoRfqh9D254F+BHq1JxZZ+/wuPx4OOiB2BwdD0vna+uSre6bBwaHRGYnRQJrUIHq8vY6bKJ2kGotuaiwV5yzn0euPFV2Z9xQ7/XcXHao1hT8qhfrwRHJGXsNvLO9cNGo8lmxaqcI0KX0msNg5UBu8J1T4QfLRe6BKIOMTgSOSECpK8PncSuE0V46PK5ePb6xZDJZFh/4HjA9i82YWoV/nDZbFw9bTR2nijCkx9/hxp2YgUFhkfSwe6j4NadCaTP1hoa5TRtRL/IKbgk/XHkNm9DdtO3GB+7DAN105Fr3N7ldsIV0bim74uwukxYU/IwWpx1XtXrSzmG00HQKdMlSNV+2OlycZo+qLJ0/qGO0VmLr8qfxZWZf8HMxF9hR82/fV4rka/46/lEvjM6MRn7Kstgc4k3cPGWqEOjrDIvP+YgCjwGRxIR6ACp2WLDYx99C7vThT9dtxB2lwvfZuUGZN9iMq5fKp69bhHidBF45tNN+HTPUaFLIh9rHYvPAEn82H0UGrrbjWRyNiJKnYy08FEIU+rh8jjRP3IqBuvnAACi1Cld7lMGOWYn/RoKmQqrix+E2dXkVW2+VmltRLPDjCH6NLxzquMwTQY5YtUZyGn69rzbKjMfxvbqtzA3+W5UW/OQ27zVX2UTUZAbGZ+If2XtE7qMXhFzYAScDo2IxIzBkcQEMkDyeIA/rf4eaqUCf7lhCRxOFzZndz4hXjBRKxVYvng6bpkzAVnFFbjzrTUoqzcIXRb50ZmTOTJEEjcGSKGls7CmNUx5t+BWuHH6OTs8aiHmJt0ND9w4VLcWtbZC5DZv63IfSzP+hD4R47Gx8iWYXU2CdUDoVeHQq8JRYWlod/uZ8yBFqZKhlKtRbyvucnuHGtciKWwwFqY8gFprPhrtPDEhcWG3kfhl6qOg12hxrK7aq+XFNEG22MOiVgyNSAoYHElUoAIkt8eDJz7+DmqlAn+78WL89p0vgv6Ka0PTEvDc9YvRJyEar2zYife2HuBlR0MMu5CkgQFSaOvwhLOsFm8WvIa/jLkJI6KvwLPHPsXWmjHn3Y5KpsBvhk7Av/O/w8qiOgDCnciOiuoDADjSWNTh/avLxmFWwnAAQL296+AIADZVvoK08JGYGn8jvq5Y4ZM6iXqLgZF0jEpIBgAcrfUuOBKaVMIiIqmRC10A9U5srtPvL5AutwePfPANdpwoxMu3XIqpgzL9uj+hqBQK3LNoGlb99nq43G5c9/IqvLNlP0OjEKbLU7R9kXhxclM6U7W1CXfvewN76k/i2TG/xIoxN0GnDOt0eYfHhTxjBS5Km4gUbddXNPOnUdF9UGdrRqW1sdNlRkf3RZPdhPeK+ni1TafHhh/rPsQQ/RzEqoPz/ZukY3XZOIZGEjMqPgnlxmY0WC1Cl9IlKYZG7DYiqWBwFCT8HSA53W48uHID9uWX4rXbLsNdC6diSGrwnKyNzkzGJw/8ArcvmIS3v9+H61/9EHlV9UKXRSLCEEncygsSGCBRG6vbgcePfIDHDr+PMdF98bdxt0IrV3W6/ONHPoBKpsDdg5YEsMpzzUoYgYMNnQ8J18pVuCRtEr6uPADA+66NY03fweY2YZB+lk/qJOousQZG7Fjt2rS0TBysrhC6jC4xNCLyLw5VCzL+HMLmcLnwu3fX4+Er5uGm2eOxfNE0lDcYsPXYKWw9dgr7CkrhckurOydMrcR9i2fgF7PGIaesGte9vAq5lZ1fSYcIwDnhEYe0iQeHr9GZttQcRaW1EX+fcCf+POZGPJz1Hpye089XGWSIVkeg0d6CCksDNlQcwFWZ0yGHDG4E/r1sYuxA9I1MxAsn1nS6zOLU8YhQarG65Ie2286c/6gzbjjR4qhDmCLKdwUTeUGMYRF5L04bjjGJyViZ3b2rXQaSFAMjgKERSQ+DoyB15ouoL0Mkm9OFpz/dhGfXbMbE/umYN7I/5o8cgF/MGodqQws+23MUa/Zmo9rQ4rN9+svUQZl48uoLEK+PwItfbscHOw5JLvgiceioC4lhkrDO7D5iiBTaTjSX4ZHDK/HCuFvxxxFX48/HPsFFqRNxQ5/ZyIxIwMfFO/B63gbsa8jDbQMuwGB9Gk40+/6AXiGTw+Vxt/2skasQrY5Ai9MKALg6cwbyjZXIaizscH0ZZLg6Yya21xxDVQdD2boKkMwuA8IV0b18FETeY2gkfbMz+gIAtpV2/LokNKmGRkRSxOAoBPgjRHK63NiTV4I9eSVYsXYrhqcnYdmUEbh5zgT8+sIp2JZzCp/8cAQ/5BZDbFME6bQaPHjZbCybMhI/5pfizjc/QymvmEY+1tmQNgZKgccuJNrfkI+nj36EP42+ARNiByJGHYHtNcfwXVUWbuk3H0P16Xg6+yOYnFZMjh3k8+BoQdIYPDP6BlhcdhjsZoQr1dCrws9Z7i85n3W6jSlxg9E3MhF/Pd75MsDpk/WOwiOLqwlhSn33iyfqJgZGwUOn1sDucqHFYRe6lHNIOTRitxFJEYOjEOOvoWw5ZdXIKavGS1/uwMXjh+GaaaPwrzuXoazegC/256CwpgFVTS2oNhhRazDB6XZ3vVEfUirkGJySgLF9U/Cr+ZMQplbh6U834bO9R0UXbFFwY6AkHAZIoW1rzVGsyPkMS1LG45HD3+CYoQQAsK8+D38e/QusnHo/FDI5wpUan+/72j4zcaypBBurshCtjoDFZUetzYBGuwk6pRYeAC0OC/Y35He6jWsyZ+K4oRRHmoq63N+ZJ+6tIZLF2YxYdUZvHwrReTE0Ci4/VJRArVBgakoGtoqo60jKoRGRVDE4ClH+CpDMNgc+3X0En+4+gtGZybh62mj8cvY46MO0bcu43R7UGU2oNrSgqskIg8kKh8sFu/P0V+u/jRYbSuubUFTbiKomY7cDnv5JsZg9rB9mD+uHMX1ToFYq4XS5sS3nFFas3SKJ4XQUOjjcLXAYIIWuDRX7saFif7vbsg3FuGXPq7gmcyb6RiRiZeEWn+5zUGQKRkRl4uGs97CjNqdH2+gXkYQp8YPx9NEPu71u64n81HgDwjhUjfyIoVHwUMrl+PXYSbhn3BQAwICYWFEFR1LGbiOSKgZHIc5fcyEBwJGSKhwpqcLjHwMRGjWSoiORHK1DUtRP33/6OTlaB7VSAZVCDrVSCZVCDpVSAV2YBirF6ZNpm8OJkromlNQ1obi2EcU/fS+ta0J6XDTG909Fn/gYJEZF/vQVAX2YFha7A3vzSvHi+h3ILqnCyYpa2Jw8GSdpYJjkX5wHiVo1OUx4q+Bbv2x7afoU1FoN+KHuRI+3cXXmDNRaDdhcfbTH27C6x6LcYu3x+kQUOm4bNQH3T5yBHWVFOFlfh4+P9/y1x9fYbUQkDAZH1MafIZLJZsep6gacqm7weh2FXIaUGD36xEejT0IM+iSc/r5wzGCkxOigkMvbljVabDhVXY9qgwkF1cWoMbQgr7IO+wvKGBRRUOEV3fyDIRL5Q5hCjYUp4/BJyc52E2N3RAYZ+kUmIUoVjhJTLertRgBAjDoSi1PG491T37ddEa67dMowzEgYhn/lfd3hMDai3mK3UXBJiYxEs82K32/+Gg1WS7fW7WxIPrHbiKSNwRF1yF9D2brD5fagrN6AsnoDdp0sbnefSqFAelwUMuKiUNtswsmKWrg5WRGFIAZJvndmiAQwSKKeuyB5LLQKNdaX7+vw/iRtNGbED8P42AEYG9MPMerItvtsLgcUMjmUcgWsLjvWle/tcR0LksdADhm+q8pqd3tXV2Ij8gZDo+CSoYvCqaZGxIaF45ZR4/HSvl1Cl0REIsDgiM5LDAFSRxwuFwprGlBY430HE1EoYJDke+xGop66PH0KdtedQLW1qd3tw/TpuL7PbMxNGgWPx4NjhhKsK9uLg40FqLMZkREej2RtNJweF6wuB042l8PgMPe4jotSJmBvfS4a7R3P7dfZldiIusLQKHio5Qo8Nn0ubhg+Bkq5HLVmE9blHRe6LCISCXGlASRaYg2QiOj8GCT5FkMk8lZ6eByG6tNR2FKNv469GeNjBkCrUAEA5DI5ysx1ePnEOnxTeRAWV/tLXRebanxWR2Z4PEZEZ+Kxw++fdzmGR0Sh7eZR43D9sNH4697tWJ9/Ao1WK2wucc0nJOX5jThMjaSOKQB1iz/nQSIi/zszSGKI1Dsc0kbnU2M14J95GzBMn4FIpRb/K9qCRrsJAFBnM2BvXS7c8P8Q68UpE9DsMGNXXdedAxy6Rt5ip1FwiVSpcc+4Kfj0ZDb+fXh/1ysQUcjhmT/1GLuQiKSN3Ui+dXaQBDBMCmV2txMfFG0TtIYoVTguSZuEzdVHYHd7/0n9md1HMsjRL3Iy4jR9UWzajxprvr/KJYlgaBR8bh8zEeFKFV47sLtX2+HE2ETBi2f81GsMkIiCA7uRfI9hEglFDhmeGnU95DIZ3ju1udvrry4bh4tSJuDewbMQpU6Bw23FzMTbUG3Nw2fF/web2+SHqknMGBgFpxhtGH41egJWHstClanjedCIiHimTz7DAIkoeLAbyX86CpMABkrkOwqZHA8PvxITYgfi/oP/QY3N0K31U7QxuG/wJZiTNBLfVx1GteU51FrzkRkxHldkPosFKb/FhvLn/FQ9iQ0Do+B219jJkEGGNw71/MqNRBT8eIZPPscAiSj4MEjyP3YnkS8oZHL8efQvMT1+KJ7J/ggHGgq8XjcjPB6/HrgYsxNHoNlhxqOHV2JbzTEA4QBG4xeZOXC6bbC52G0UCkIhMOosyA8VSRGRuHnkWLyZtQ8NVkuvtsVhakTBTS50ARS8YnOdkr76ARF1TpenaPdF/lFekND2ReSNS9MmYWbCMDx8eCU2VR0+77IKmRxjovthUco4XJQ6Ea+MvwND9Gl46cTnuHLHX7Ct5himxQ/B74dejrmJo5BnugRKuQYHGz4L0KMhIawuGxcSoREB942fCovTif8c4YTYRL6wYsUKTJo0CTqdDomJibj88stx8uTJtvuLioogk8k6/Pr000/bbevdd9/F6NGjodVqkZiYiOXLlwf64bTDlhDyO3YgEQW/jsIjdiX51pnhETuRgodCJkeyNgYWlw1GhwUOT8+fN1q5Crf2vwAbq7Kwu+5Ep8vplGG4NG0yrsqcjiRtdNvtZeY6/OHQOxikS8NAXQomxw3G7QMuRLPDjItSJ+Dtgo0AgM/K+mF+Ii8tHYwYGIWODF0Urh06Ci/8uBNGu71X2+IHSF0zj01HeBZfN4Pdtm3bsHz5ckyaNAlOpxOPPvooFi5ciJycHERERCAjIwOVlZXt1nnrrbfwwgsvYMmSJW23vfTSS3jxxRfxwgsvYMqUKTCZTCgqKgrwo2mPZ/IUMAyQiEILwyT/YYgkbXpVOKbGDca0+KGYGj8EelV42302lwPNDjNyDKVYXboLBxtPeb3dpelTEa/Rw+52YkHSGJSYalBmqYfL44ZCJkeiNhpXpk/DRWkToZDJ8V3lIawr24v8lkq4PG64PG6MjxmAZ0bf0LbN/xZswvtFW/HO1N/g7kGnD2qdHndbwNB69TWSvlALjUK5k1OjUOCVBRehzmLGymO9ew4zNPIewyNpa25ubvezRqOBRqNpd9s333zT7ud3330XiYmJOHDgAGbPng2FQoHk5OR2y6xduxbXXHMNIiMjAQCNjY147LHHsH79eixYsKBtudGjR/vy4XQbz+Ap4BggEYWuzg4wGSj1HEMk8UvRxmCQLhWD9akYHzMAI6P7QCGT40RzGT4r/QFHGougkiuhU4UhUqlFrFqHKfGD8eqEO/C3459jXbl3k9YWmapxsKEA0+OH4dK0yR0u02hvwaqibVhbtgeN9nOvoHS4qRAVlgakhsXiwUP/xZ66XHjgwW8P/AcXJo9FhFKDZoe5bfnVZeMYHhFJzIo5CzEiPhHXfvExrE5OKxFIDI/Oz1lRBchUQpfRjtPjAABkZGS0u/3JJ5/EU089dd51DYbTF6eIjY3t8P4DBw4gKysLr7/+etttGzduhNvtRnl5OYYNGwaj0Yjp06fjxRdfPKeGQOKZOwmGARIRtWJ3km8wRBKWSqZA38gkDNKlYpAu5fT3yFREqrQAgAabEUcNxXjh+BrsrjuBOpux0219V3UIH0z/PbQK9Tn3KWRyZIYnoNRcB+cZQ9v21udib30ugNPD0TIjEpAWFgeZDHC63bC4bNjfkA+7u/MTRZfHjUezVuLNyctxbeYsnGqpRrW1CbU2A1YVb+twHXYfSR+7jULHtUNHYdngEbhv43ocrqnq1bbYbdQzDI+kqbS0FHq9vu3ns7uNzuZ2u/G73/0OM2bMwMiRIztc5u2338awYcMwffr0tttOnToFt9uN5557Dq+++iqioqLw2GOP4cILL8SRI0egVp97XBAIPGMnwTFAIqKOsDupd1pPjBgg+d8V6VOxNH0K+kUkQSlXwO1xo9RchzxjJfbUbUGesQJ5xgo0dNDh05mFyadP5JemT8bkuEGotxlRb2+GUqbEwpSxiNfoYXXZcbSpGK/lfolTLe1PAI1OC44ZSnDMUNLtx5PXUomHst7DIyOuwv+m3Y+/536F9eU/drnemeEDQyTpCLXQKJQlhEfgj9Pm4JMTR7G+4GTXK5wHQ6PeYXgkPXq9vl1w1JXly5cjOzsbO3fu7PB+i8WCVatW4fHHH293u9vthsPhwGuvvYaFCxcCAD788EMkJydjy5YtWLRoUc8fRC/wTJ1EIzbXyfCIiLrE7qTuYReSf13fZzbuHXwxvq86jM/L9iDPWIkCYyWsbkevtvtlxT7Y3A7EaXSIV+vRJyIB42P7Qy1XYWv1UeysO47+kUm4se88/HfKffjtgX/jcFORbx4UgH0Nebhx98u4b/AleHj4lZiXOAp/Pf4Zqq1NXq3PEInEKpS7jZ6esQA2lwvP7u64e9BbDI2Izu/ee+/Fl19+ie3btyM9Pb3DZVavXg2z2Yybbrqp3e0pKSkAgOHDh7fdlpCQgPj4eJSUdP/DIF/hWTqJSmv3EcAOJCLyHsMk77ALybfSwuJw96AleL9wK97I/9qn266wNOC9ws3tbpNBhgilBi1OKwDgx/pcfFqyC6+Mvx1/Gv1L3LrnVdTbOx/+1l0mpxV/yVmNLdVH8NDwK7Fy6v14KvvD816xrSNnd7QwSCKhhHJoNDu9Ly4aMBj3blwPg83a4+0wNCLqnMfjwX333Ye1a9di69at6NevX6fLvv3227jsssuQkND+dWnGjBkAgJMnT7aFTg0NDairq0OfPn38V3wXeGZOosUhbETUG2cf3DJI+hm7kHzjhr6zYbCb8PapjX7bx7zEUZgYNxADI1PQPzIZ4UoNKiwNyG4qRrahGOXmBjTaTRgfq0OfiESfBket9tbn4sbdL+OxEdfg+bE34595X+PD4u093l5HQ6MYJpG/hXJoBABLBgxGQWM9vuzFEDWGRkTnt3z5cqxatQrr1q2DTqdDVdXpYeRRUVEICwtrWy4/Px/bt2/Hhg0bztnG4MGDsXTpUvz2t7/FW2+9Bb1ej0ceeQRDhw7FvHnzAvZYzsYzchI9BkhE5AsMkjrGLqSec7pdiFJH4PL0qVhdsgtueHy6/QeHXo4rMqbhVEsV8o2V2FZzDHW2ZgzRp2FUVB/MSxoFlVwJi8uOJ4+uwsHGAp/u/0wmpxWPHv4f7hy4EPcOvhj9I5PwfM4aODy+eR6xK4nIv2al98HGop6/RjA0IuraG2+8AQCYO3duu9vfeecd3HLLLW0///e//0V6enrbHEZnW7lyJe6//35cfPHFkMvlmDNnDr755huoVMJdcU7m8Xi6PMppbm5GVFQUpi96BsqfrgxCJASGR0TkDwyRfsYAyXtyyHDf4EtwTZ+ZyDNW4OUT67yaZ0grV+GazJlodppxtKkYhS3V54ROAyKTsXLa/Xj5xDqsLv2hw+2o5UrEqnWotRng8rh98ZC8sjB5LB4efhVOGsvx6OH/obEbk373BEMk/wmVibFDvduojz4a2264Hb/6eg2+Lz7Vo22IITg6c0qLYHH2BNlOtw2bKt6EwWDo1kTMUteaN8zFUihlwoUjHXF6HNiKdSH3Nzkbz8JJUth9RET+wG6kn3EYm/fc8ODV3PX4ruoQHhh6Of456W5srMrCgYYCNNpb0GhvQbhCgyH6NPSLSEKzw4xKayPmJ43GEH0a5JBBKVfA5LQiq7EQL59Yh0prI9LC4nBr/wvQaG/B2rI9ne7f7naiytoYwEd82ndVWSgz12PF2Jvw1uTluHf/m15Pmt0TreEGAyTqiVAPjQBgTGIyAGB/VUWP1hdDaEREwuLZN0kSAyQi8qczD5IZIjFA6srx5jLc+ePruCh1Am7tvwALkkZDLpO33W9yWlHYUo1IZTpSwmJgdFpw9743UNRSjaFR6RgZ1QeXpU3Gf6bchyprI4bq02F22vBG3tcB7STqjpzmUtyx9x/4x8Rf47UJd2D5/n+hzub7+ZXOxACJuouh0Wkj45NQ0tzUo0mxGRr5x9mdRkRix7NukjQGSETkbwyR2IXkDQ88+KpiP76q2A85ZNCrwhGjjoTT40KZuR6eM4aiySFrG5qW1ViIrMZCrCvbi/uHXgalTIn/FW7F7roTsLkdQj0cr9TYDPjtgX/j9Ul34a9jb8GdP74ekKBrddk4hkc+EMzD1BgYtTcqIQnZtTXdXo+hke8xMCKp4tk2BQUGSEQUCK0H0aEaIAEMkbzhhgdNDhOaHKZO7z+b0WnBM9kf+7s0n6u0NuLRwyvx1uR7cUOf2fhf0daA7JfhUe8wNAotg2LjsTKbzxchMTAiqZN3vQiRdMTmOoNy4jwiEhddnqLtK5SVFyS0fVHoOtFcjlVF23DbgAvRJyIxYPsN5vDDn4L198bXoo5FqFSIDwtHkSHw86HRaQyNKBgwOKKgxACJiAKFIdJpDJFC239PbUKlpQGPDr8KcsgCtt9gDUHIe3zdOb8MXRQAoLTZ0K31Qv09zVcYGlGw4LgeCmocwkZEgcT5kE7jcLbQY3c7seLYavxz0l24OnMGPi7ZGbB9S23Ymrdhlz8eUzAFbQyLvJOpjwYAlHQzOKLeY2hEwYRn0xQSQilA6kmnVSj8XogCjfMhncYQKXQcNRRjdckPuHPgIuysPY5yS33A9i218MgbZ4Y8vnhsDI1CU4Y+CiaHHfVWs9frsNuo9xgaUbDh2SKFlGAMkHwxJK+zbQTT74lIKLo8RciHR60YIgW/N/O/wYyEYfjdkEvxh6x3A7pvqYRHV6Uf6naIc/by3j7OYAqLAAZGPdE3Kqbbw9So5xgYUbDiWSGFpGAIkAIxh9PZ+5Dy74tISOw+OtfZJ4AMkoKD1e3Afwo24slR16FvRCKKTN2/BHhvSCU86q1gC4S8wdCoZ4bExiOvMXDdf/4ihblLGRpRMOPk2BTSpDiJtpA1t+5bir83IjHgJNqd4+TawWNz9RHU2ZpxTeZMQfa/umxcSAYrwYqvC70zNDYBx+sDG+CGIoZGFOwYHBFBGgGSGGtkkETUMwyQzo8hkrQ5PS58VvIDFqeMR7QqQrA6xBwehUJXlC/wNaB30nV66DUa5NSzo9NfwrPKGBpRSOC4E6IziHUIm1RCma7qFNvvlUhoHMLWNc6LJE2fl+/Fzf3n4/L0qXi38HvB6giVoWvBhoGRb4xPSgUAHJd4cCTG42CGRRRqeBZH1AExBUhifLPsqe48FjH87okChQGSdxgiSUezw4xvKg9iWcY0fFC0FQ6PcP+3WzuPGCBRqLlxxFjsrShFtalF6FKCBgMjClUcqkZ0HhyCJZyzh8Hx70ChgMPXvMfhbOL3cfFOxGl0mJ88WuhSAIh76Br9jM9p3xgZn4RJKen479GDQpfSK2I5/uOQNAp1/EifyAtCdSCJ5c1SLHiVNwoF7D7qPnYiiVOJuRZ1tmakh8ULXUobdh9RqLh11HiUGQ3YWJQvdCmSxrCI6DSedRF1g5iGsFHHwRr/NhQsGCD1DEMk8VDLlYjX6FFpbRS6lHOIIUC6Kv0Qu6DOwm4j31DLFbhkwBC8sv8HuD2ebq0rps5XoT9AZWhE9DOeYRH1QCACJKHfLKWKYRIFG12eguFRDzFEEtbsxBEAgOymYoEr6ZwYAiQiXxuZkASNUokdZeJ97nVFyONgBkZE5+rW2VT40XIo5ZoulzOPTe9xQURScuabGsMJ8ers4IN/M5IKdh/1XkedDAyT/Ovy9Kk42FCAErP4f89nd/4EIkhitxH5y4TkVJgdDpxoEP9zT2wYGhF1zC9nTZ094RgoUTDzZYjEbqPAkOLvmWFXaGP3kW+dHSYxSPKdvhGJGBfTH08c+UDoUnqks1DHF4ESAyPyt0x9NAoNDXC63d1e1zjIJfhwNSkenxEFu4CegZwvwWWoRMGkpyES3yipK/78P8JQShrYfeQ/rUESA6TeW5g8Dga7Cdtqjgldik+dGfp0N0RiYESBEq5UweRwCF2G5LDbiKhzojlLOPuJyiCJgoU3VwJjYERiwCF90sLuI//h3Ei9Nz1hKHbXnYTTE7z/R70NkRgYUaCFKVWwSDQ4EuqYmKER0fmJ9mzgzCcvQyQKJgyJSGo44bh4sfvI/xgidV+8Ro9BulS8X7RV6FIChuGQb6QNqOWV1XwgTKWExSm94IjHyETiJYkjf4ZIRETi4k0nHQUOA6TA4JxI3pkcOwhujxs/1ucJXQpJDEMj3whXqtBotQhdhmSw24ioa5I70m99YjNAIiISD15hUBw4fC2wOjvJDfVASatQw+lxo9lhFroUopDkdLuhVaiELqNbOESNSNwke3TPAImISJwYIgmL3UfC6yhQCqUwqcVphVquhE4ZBqOTXQ9EgZZTX4tF/QYKXYbXOESNSPzkQhfQW+FZZUyKiYhEKjbX2fZFgaXLUwh+SWX6WXlBQruvYLavIQ9OtwuLU8YLXQpJSLA/LwLpaG0VMvXRiNJohS6lS0IeH/Acksh7QfNRMDuQiIjEjZ1IwmAHkjgFc1dSo70F22qOYVnGNKwr3wu7m8ExUSAdra0GAIxKSMLOsmKBq+kcP1Qiko6gO3JngEREJH6cXDvwGCCJXzBNvv1e4fd4c/JyPDbiWjx5dBU88AhdElE7vX1+iblDyuSwAwAiVWqBKxEvdhsRdU/QHqkzQCIikg52IwUOAyTpaD0xlWKAVNBShaePfojnxtyICssi/Cv/G6FLohDn6+fR2dsTU5A0JeX0+c+BqnKBK+kcu42IpCXoj87Ds8oYHhERSQhDpMBggCQdUg2QdtTm4B+5X+E3Qy6FThWGl06sg8vjFrosEqm0AbU+DV8C/Xzxdf29MSg2HlUmI2ot4ryyodChEbuNiLovJI7I2X1ERCRNDJH8jwGSdEgxQPq4ZCdanFb837BlSNbG4PEjH8DssgldFgUhMTwvWmsQOkBSyGSwu/iaTkS+I/mrqnUHr8BGRCRdvEKbf7VehY1XYhM/qV2Z7auK/fj9of9iZFQfvDHpbiRqooQuiYJE2oDati8xEbqeDF0UmqxWQWvoDN/DiaQpJD++ZQcSEZG0sRPJv9iFJA3lBQmCn6B6a39DPu7a90+8MO4W/HvKvfi/Q+/ipFG886+QMKTy/9kbgR66FqXRYkR8IgZEx+KCvgPxj4O7A7ZvKWETAVHPhPTRNgMkIiLp4xXa/OfM7iOGSOIkpeFrhaZq3PHj6/jr2Jvx+qS78NTRVdhZe1zosoj8IpCh0XXDRuHx6fMQoVLD7nIhp74Gn548FrD9e0vobiOGRkQ9x6NrMEAiIgom7EbyD4ZI5AuN9hbct/8tPDHyWqwYcxNeO/klPi3dJXRZRD4VqNAoRhuGv81bjAV9BuDD40fwVtY+lDQ3weXxBGT/3SF0aEREvcMj6jOcmUIzRCIikj52I/kHQyTqDZvbgceOfIB7Bi3B74ZeBpfHjTVlHFZDwSFQodGI+ES8uWgpwpQq/OrrNfi++FRA9itV7DYi6h0eQXeCXUhERMGHQZLvMUQSBynNdwQAHnjwet4GqOVK3Dv4YhxsLECRqUbosoh6LJBD064aMgLPzroQJxrqcPd3H6GixRiwffcEu42IpI9HzF1ggEREFLw4rM23zr4iG4Mk6so/877GhNiBeGLkdbjzx9fh9PD/DElPIEIjvVqDCclpuGvsJExJzcCHx4/gqZ3fw+YS93NGDKERu42Ieo9HyV7iMDYiouDGbiTfYzdSYEmt6wg4PWztmeyP8O/J9+JXAy7Am/nfCl0SUbe0hkYahQJ6tQY6tQZ6jQZ6tfan7xroNdqfvp95/+l/uz0eWJ1OWJwOWH76bnU6YXU64PR40FcfjcGx8UiJ1AEAsmoqcduGNdhc4p+haWd/AEBEBDA46hGGSEREwY/dSL7FECkwpBge5Ror8J+C73DnwEXYU3cSh5uKhC6JqFMqmQLjYgdgRFQmMuV9MXxqIhLCIqBRdvw+4XK7YbTb0Gy3odl2+rvRbsOppkYY7TbIZDKEKZXQKpUIU6qgU6uREB4BrUIJtUKB0mYD1uQeQ25jPU7W1+JEQ12AH3HPsduIKHjwSLiXGCIREQU/diP5FkMk/2rtgJBSgPRB0TZMix+Kx0dei7v2vYE6W7PQJYU0tVyJibEDcbSpGEanRehyBKdThmFa/FDMShyOqXFDEK7UoN5ixrG6GnyZfxKVJuNPoZC1LRxq/dnkcAhdPhFRr/HI14cYIhERhQZ2I/kOQyT/kVL3kRse/Cn7Y7w5eTk+mvEHfFi0HauKt8HisgtdWkhRy5W4LG0ybuw3D/EaPZodZrx7ajNWl+6Cy+MWuryAkkGGeUmjcEX6VIyO7gulXIEcQyn+V7QFaw5W4aSEOn+85cthamLoNiIi3+HRrp9wUm0iotDAEMl3OLm270kpPKq0NuL6H/6GG/vOxS/6zsFl6ZPxn4KN+Kp8H9zwCF1eUFPLlbg0bRJu7DsPsRodvqs8hLVle7AkZTyWD74Ik+IG4fEj74dEkCeHDPOTR+OWfgvQLzIJBxry8fLJddhZexx1tuaAXj2NeofD1Ih8h0e4fsYAiYgodDBE8i12I/mGlIaumZxW/Cv/G6wt24NfD1yEh4dfiaszpmNz9VFUWhtQaWlEpaUR9bZmhkk+oJIpcOlPHUZxPwVG7xVuRqn5dDfNMUMJttQcxV/G3IwXx92G3x38D+zu4OwkUcjkWJA0Gjf3W4C+kYnYXXcCK3JW45ihpG0ZhkbeYbcRUfCReTyeLt91m5ubERUVhQtSfw2lXBOIuoIaQyQiotDCEMm3GCL1jhQCpFZD9Wm4fcBCDNGlIVaja7vd4XaiytqEKksjKiwNKGipxNcVB2F22QSsVjpi1JG4MHksruszC/EaPTZWZuG9wu9RYu54+NVwfQb+PvFO7K47icePfABPkIV2AyNT8PjIazFQl4JdtcfxzqlNON7cvlsl2EOjYBumJtVuI6fbhk0Vb8JgMECv1wtdTsC05g1zsRRKmUroctpxehzYinUh9zc5G49kBcC5kIiIQgs7kXyr9QSHAVLPSKkD6URzOR489A4AQCtXITksBilhMUjRxp7+HhaLIfo0XJQ6AbcPWIg1pbvxackuNDlMAlcuTgMjU3DHwIWYFj8UHo8Hm6uP4J1T36PEfP7/CznNpXjl5Bd4ePhVGBfTHwcbCwJUsX/JIcMNfefg9gEXoshUg1/t/TtONEszcBALMYRGROR7PHoVGEMkIqLQ0npQzQCp9ziUrXekFCABgNXtQJGpBkWmmnPui9focW3mLFyTORPX95mNL8v34b+nNjFAOssbk+5GuPL06IHvqw9DLVdiXtIofFC0DU7P+Z9DQ/XpaLAZkW0oDkSpfpcaFovHRlyDUdF98EHRNrxdsBGOTn4H7DbyjlhCI6l2GxGJGY9aRYTzIRERhQ52IfkWu5B67syTYqmESGerszXj9byvsLJwM5ZlTMO1mTMxKW4Q7j/4NqqsjUKXJxq37HkFMxKGY2RUJvpGJKLJYcJt/S/A7MQR+N2B/8DotHS4XpI2GktSJuCdU5vazXGkkMmRGhYLhUyOMnN9l+GTWFycOhG/G3IZmhwm3Lv/TRxuKhK6JCIiUeORqgixC4mIKLQwRPIddiH1jtRDJKPTgvcKN2NjVRZeHv8rvDn5Hjxw8G0UtFR1uo5eFY4kbTRMTiuMDgtMTmvQTrxdbmnAJyU78QlOX27+krRJGKxLQ2pYLPSqsA6Do2hVBP427lY02I1YW7an7fYpcYPx59G/bOtgcrpdyG+pxMNZK1FrMwTqIXWLQibH/UMuwxUZ07C+fB9eO/kFzCFwpbjzYbcREXmDR6cixxCJiCi0METyHXYh9Y6UQ6QKSwPu2vcGXhx3G16feBceynq3w66S4foMPD/uFsSoI9vdXmdrxvryH7G2dA/q7cYAVe1/UapwXJA8BmOi+2NEVAaSw2LwVfl+/DNvQ4fD+obq0/Ds6JugUSixfP+baHFaAQAp2hg8PeoGHDeUYkvNUShlCsRrdFicOgG/H7oUDx9eGeiH1qUoVTj+PPqXGBXdByuOrcaXFfu8Wi+Yh6kxNCIib/GIVELOfjFkkEREFNwYIvmGLk/B8KiXpBgiNdpbcO/+N/GXsTfh5fG345nsj7GtJrvtimDT44fiT6N/gZPN5Xgo6z1oFSrolGGIVIZhsC4V12bOwi/7zsWmqsP4uGQn8owVAj+inhsX0x9XZ87A9PihkEGGbEMxttQcxdbq7LY5i27uNx/XZM7Ed1WHoJIpoFWoMT9pNPJbKvHHw++36yJ6cNgVsLudGBvbHxPiBrbdfsxQglmJI9AvIgmFpuqAP87O9ItIwl/H3oxwpQa/OfBvHOHQtKDD0IjIv3gUKmHsRiIiCh0MkXqH3Ue+I6UQyeyy4cFD7+CJkdfh2TG/RLPDjDJzPVqcFkyIHYgdNcfwdPZH7ebtafVWwbe4JG0Srs6YgSWpE3CwoQCHGk+hb0Qi+kcmIz08DgaHGZWWRmQbivF52R6UmesFeJSdGxvTD7/qfyHGxw5AnrEC/8j9ChursmBwmM9ZdrAuFdHqCFyTORNFLTVocVqwpmw33sz7pm3S6HCFBjf1m4ep8UOwoeIALkqdgIez3sPsxBG4IHksRkRlwu1xY1r8ENEERzMThuHJkdej3FKP+w68hWprk9frstuoa2LpNiIi/5J5PJ4uB3E3NzcjKioKF6T+Gkq5JhB1US8wRCIiCg0MkHqOAZLviTlEkkGGsTH9MCa6HxK1UYhWRSC/pRLvnvq+y/mMFDI5ZiWMwDWZM9AnIhGFpmqcaqlCqakOelU4UsNiMTV+CKLVEdhbl4vPSn/ArrrjAXpkHRsT3Q+3DzgdGJ1sLsd/T23Eztqua7oodSIeGLoUVZZGPHfsU+Q0lwIAwhVqXJUxA9f1mQWtQo0Pi7fjhKEMT4++ARqFClaXHcWmGgzRp8PmcuDlk19gffmP/n6YXfpl37n49cBF2FGTgz8d+xiWbs5nFKzBka9CI0AcwVEwdRs53TZsqngTBoMBer1e6HICpjVvmIulUMpUQpfTjtPjwFasC7m/ydkYHAU5hkhERKGBIVL3MTzyHzGHSP6glisxP2k0lmVMw4ioTBxtKsZrJ9e3BS+BrOMPw67ARakTkdtcjrdPbcLO2pxubaNvRCKeHHkdBuvT8E3FQRSZqnFdn9kIV2qwvuxHHG8uw4395iI1LBYnmsuxo/YYsptK8NqEO/Bh8Xa8c+p72NwO/zxAL6nlSjw8/EosShmPd05twtsFm9qGKHqLoVHXGBr5HoMjBkdixeAoxDBIIiIKfgyRuocBkv+EWoAEAONjBuA3Qy7BIF0qvqs8hH/lf9Ot4VE9Fa/R4bkxN2FgZAr+dnwtNlQe6PG25D9dce3OgYsQqdTiy/J9WFm4BRckj8HywRefs/y2mmwMiEzGDT+8CJfH3ZuH0WvxGh1WjLkZAyKT8eyxT/B99ZEebScYgyNfhkaA8MFRsIVGAIMjBkfixSPLEMN5kYiIgh/nQ+oezn/kP1KaD8lXDjYW4LY9r+Gi1Im4c+AifDj9QXxUvAMbq7L8Nu/PcH0GVoy9CW6PB/fsfwMnmst7tT03PPii/Ed8V3kIaoUKzT/NiRSj1uFwYyFMTissLgcWJI8GAAzVpSFWo8O9gy7Ge4WbO7xCWyAM1afjL2NuggfA3fvewEljz34PDI3ELxhDIyIxY8cRAWCIREQUChgidY3hkf+FSoAEnJ4b6Jd95+HaPjOhVahRZ2vGvvo87GvIw4GGfNTZjL3ex+KU8fi/YcuQa6zAo4dXosHe4oPKvaOSKTAzYTjyjZWYlzQKv+g7FzIZ8HbBRnxasqvL+aN86YLkMXh0+NXIN1bikcMrUW/v2e82GEMjILi6jYI5NGLHETuOxIrBEbXDAImIKPgxQOoaAyT/C6UASS1XYkx0P0yKG4hJsYMwWJ8GANhcfQQvHv+8Rx06Cpkcdw9aguv7zMb68n148fjatqufCUWvCsev+l+AZRnTUGKqg8FhgtPjQo3VgPyWSnxSvLPXYZIcMsRqdEjSRrd9DYhMxpLUCfi64gCeP76mw6vkeYOhkfeECo6COTQCGBwxOBIvBkfUKYZIRETBjyHS+TFA8r9QCpBaRasiMCtxBO4auBgeePB8zhpsrz3m9fqDIlPwmyGXYnR0X/w990usLv3Bj9V23+jovrg0bRJkkEEtVyJBG4WRUZn4V/43+KBoW6frJWtjcHHqRIyL7Y8ycz0MdhMUcjni1D8HRfEaPZTyn4MQs9OGamsT1pfvw8clO3pcM0Mj7zE08h8GRwyOxIpHi9QpzodERBT8Wk8AGCB1jPMf+V/rCXsoBUhNDhPWl/+IXbU5+MOwZVgx9iacaC5DqbkOZeY67K3LRbah5JwrgWWGx+PmfguwMGUsSs11uP/g2zjYWCDQo+jckaYiHGkqanfbPYOW4PYBC7G77iROtVR1uN7fJ96JKFU49jfkY5AuBRFKLTweD+rtRlRZG3G4qRDVVgOqrU2osTah2tqEFqc1AI9ImoJtXiMiEg6PEskrDJGIiIIbJ9Q+PwZI/heKAVKDvQWPHF6JBUmjMTluMFLDYjExdiBu7X8B6m1G7KjNwc7aY0jRxmJxyniMiM5Evc2Ivx3/HF9W7BP8Cmbd8Z+CjZgaNwSPDL8Kd/74+jmhGACEKdTYU5eLp7M/bHts0aoIv0+2HYzdRv4KjdhtRBSaeGRI3db6ws0AiYgoOLELqXNnnowxRPKPUAyQvq8+0nbZeDlkGBGVidmJIzEncQQuT58Cp9uFPfUn8djh97Gr7niP5/ARkt3txEsn1+H1iXfhguQx2FiVhdkJI3Btn5nIMZTh28qD+FfeN/jDsCvw/Nhb8NTRD/G7IZdhcep4fF62By8cX+vzmoIxMAKCr9OIoRGR8DjHEfUaAyQiouDHEKlzDJD8L5RCpLNlhieg2WEW7BL3vrZizE0Ypk9Hg70FQ/RpONJUhIzweMSoI/G342tRaq7DX8feDLfHA4VMjh/rczErcQTmbnrUp5N/MzTqPnYb+R/nOOIcR2IlF7oAkr7wrLKQekEnIgpFsblOQS+/LGa6PEXbF/lHeUFC0J7od6XEXBs0oREAvJG3ARqFCnW2Ztx/8G3cve8NLN3+LD4p3okHh12BZG0MHs5aiTxjBe7Z/y98VrobABCv8d0JW7D+X2JoRET+wo8PyWc4DxIRUfDjMLbz41A2/wrFYWzBpsRchyVbn253m8vjxqu566GUK/DQ8GX4w6F3cc/+fwEA+kQkAgAStVGotDb2at/BGhgBDI2IyL941Ed+wXmQiIiCGyfT7trZJ3IMknxHSgFSR2GFFOoWwksn1iElLAaPjLgKN+1+GQaHGc0OMwAgUhnWq20zNCIi6jke6ZFfMUAiIgp+7ELyDruRfO/MMEDoMKY7wcTZywpdu1h44MGKY6uxctr9eHDYFXj8yAdonY5VJe9ZOBLMgRHg/9CI3UZEBDA4ogBhgEREFPwYIHmPIZLvBTJE8nUYIaYATGj1diPeyv8W/zd8GVbpt2Fp+hQ43E7kGiu6vS2GRr3D0IiIWvHIjgKKARIRUfBjgNQ9DJF8z5fDwwIdPjBEAnbVHUejvQX/mXIfbC4HXj25HhWWBq/XD/bACGBoRESBxSM6EgQDJCKi4McAqftaTwYZIPmeFMOEUB3SVmdrxrIdKzAhdgAKW2pQ5eWk2FL8G3dXIOYzYmhERGfjkRwJigESEVHwY4DUfexCoo6EUpBkdzuxu+6kV8uGQmAEMDQiIuHwCI5EgQESEVHw45XYeoZdSNSZUL9iGwMj32JoRESd4VEbiUp4VhnDIyKiEMAupO5jgETe6CxMCZZAKVTCIiBwgRHA0IiIzo9HayQ67D4iIgodDJC6jwES9YQ3gYuYwyUGRv7D0IiIusKjNBItdh8REYUOBkjdxwCJfK01nBFLgMSwyP8YGhGRN3h0RqLG7iMiotDCAKn7GCCRrwkdIDEwCgyGRkTkLbnQBRB5g28wREShJTbXKdhJjVTp8hSCnoRS8Al0gFNekBAyoZHQz1e+vhJRd/DjPJIMDl0jIgo97EDqPnYghZ6hsfEYn5SKEw11OFhd4dNtB6L7KFTCIkDYDqNWQoZG/DCYSJp4FEaSwqFrREShiQFS9zFACh3/uPBSDIyJAwB8fPwont29Fc12m0/3UV6Q4PPwiIFR4DE0IqKe4NEXSRK7j4iIQhMDpO5jgBTc0iL1yNBH4eldm2F1OvHI1DnQqTW4Z+MXPt+Xr8IjBkbCYGhERD3Foy6SLHYfERGFLgZI3ccAKfiMiE/Eh5deizqzGRuLClBmNCA5Qocbho/22z57Ex5JNTDK1EehtNkATzfWYWD0M4ZGRNLHybFJ8vhmREQUujiJdveJ6YSWeufXYyah1tyCJZ++hzKjAQCQXVeFhPAIJEdE+m2/3Q2ApDzp9S0jx2H7DXfgu2tuxbzM/l6tI6bnmNCvjzxOJwoODI4oKPBNiYgotLUGSEKfJEmF0Fd0It+IVGuQ39TQbj6j4/Wnu4HSdVF+3bc3YZCUAyMAWNJ/MJ6YMR8fHz+KKrMR/158OZb0H9zp8mJ7Xgn9esjjc6Lgwf5uChocukZERACHsXUHh69Jn1rRPqgIU6oAAHKZLCD7l3Iw1JkwpQq3jR6P34yfhi/yj+Phbd9CJpPh5fkX4bUFF+MetwsbiwralhdTWNSKoRER+RI7jijo8I2KiIgADmPrjtZOCTGeAFPHtEolpqSmY19lebvb+0XFAABKmw1ClBUU/jZvMf4weRaO1dXg31n74QHg9njwwOYN+LYoH69feBnm/zRsTYzPGaEnweaxOIWqFStWYNKkSdDpdEhMTMTll1+OkydPtt1fVFQEmUzW4denn34KADh8+DCuv/56ZGRkICwsDMOGDcOrr74q1ENqw+CIghLfsIiIqBWHsXUPAyRpSI3QIUKlRq3Z1O72I7VVcLrdWNRvoECVSd/7x7JgtNswPjkVa5bdgMTwCACAy+PB777/CpuLC/DGostwV/JEJOgjBK62PaFDI6JQtm3bNixfvhx79uzBxo0b4XA4sHDhQphMp1+nMzIyUFlZ2e7r6aefRmRkJJYsWQIAOHDgABITE/H+++/j2LFj+OMf/4hHHnkE//jHP4R8aJB5PJ4uLxDQ3NyMqKgoXJD6ayjlmkDUReQTHLZGRETnw+FsXeMwNvF6c9FSjE1MwbwP34bZ6Wi7/dOl18HscODmDZ8JWJ20xYeFIzVSj1WXXoM3s37E3w/uabtPJZfjxfGLcdG4oVAq5PjucB7+7/2v4HJ357prvidUaMTAyHecbhs2VbwJg8EAvV4vdDkB05o3zMVSKGUqoctpx+lxYCvW9ehvUltbi8TERGzbtg2zZ8/ucJlx48Zh/PjxePvttzvdzvLly3H8+HFs3ry5W/v3pW4dLVkrSqFNzfBXLUQ+pz5YAPOoNKHLICIikdIfa/9z40AGSWcLzz79vWUAAySx+c+Pu/D2kmVIUqhQYDQCAEbEJ2FAWCSeP7IdbotV4Aqlq8ZiRUNTEyymFjjNlna/S1WBAg8fWYdnP/4aN84ej5vmTEDfmAjkVtQJUmtM/unASIjYKPxouSD7DVZOt13oEgTlhAMQNn89hxOnQ/nm5uZ2t2s0Gmg052+qMRhODxmOjY3t8P4DBw4gKysLr7/+epfb6WwbgeJVx5HVakW/fv1QVVUViJqIiIiIiIiIQk5ycjIKCwuh1WqFLiVgxJ43REZGoqWlpd1tTz75JJ566qlO13G73bjsssvQ1NSEnTt3drjMPffcg61btyInJ6fT7fzwww+YM2cOvvrqKyxcuLBH9fuCVx+rabVaFBYWwm4P7QSUiIiIiIiIyF/UanVIhUaA+PMGj8cD2VlXquyq22j58uXIzs7uNDSyWCxYtWoVHn/88U63kZ2djaVLl+LJJ58UNDQCujFUTavVhtx/YCIiIiIiIiLyr2DKG+699158+eWX2L59O9LTO55zd/Xq1TCbzbjppps6vD8nJwcLFizAnXfeiccee8yf5XrFq6FqRERERERERETUMY/Hg/vuuw9r167F1q1bMWjQoE6XnTt3LuLj47F69epz7jt27Bjmz5+Pm2++Gc8//7w/S/YaZ4AkIiIiIiIiIuqF5cuXY9WqVVi3bh10Ol3bnE1RUVEICwtrWy4/Px/bt2/Hhg0bztlGdnY25s+fj0WLFuGBBx5o24ZCoUBCQkJgHkgH2HFERERERERERNQLZ8+D1Oqdd97BLbfc0vbzo48+ivfffx9FRUWQy+Xtln3qqafw9NNPn7ONPn36oKioyJfldguDIyIiIiIiIiIi6pC860WIiIiIiIiIiCgUMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIOMTgiIiIiIiIiIqIO/T/SozlKrKw5/QAAAABJRU5ErkJggg==",
-            "text/plain": [
-              "<Figure size 1600x1000 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig1 = matplotlib.pyplot.figure(figsize=(16,10))\n",
-        "ax1=fig1.add_subplot(1,1,1,projection=cartopy.crs.PlateCarree())\n",
-        "era5_accessor['2024-01-23 17:00']['temperature'].sel(level=850)[0].plot.contourf(ax=ax1)\n",
-        "ax1.coastlines(color='w')"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1000 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "pet_nb_cpu",
-      "language": "python",
-      "name": "pet_nb_cpu"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.12.10"
-    },
-    "colab": {
-      "provenance": [],
-      "include_colab_link": true
-    }
+   ],
+   "source": [
+    "fig1 = matplotlib.pyplot.figure(figsize=(16,10))\n",
+    "ax1=fig1.add_subplot(1,1,1,projection=cartopy.crs.PlateCarree())\n",
+    "era5_accessor['2024-01-23 17:00']['temperature'].sel(level=850)[0].plot.contourf(ax=ax1)\n",
+    "ax1.coastlines(color='w')"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "pet_dev_nb_gpu",
+   "language": "python",
+   "name": "pet_dev_nb_gpu"
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
\ No newline at end of file
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/data/Untitled.ipynb b/docs/notebooks/data/Untitled.ipynb
new file mode 100644
index 00000000..c472344e
--- /dev/null
+++ b/docs/notebooks/data/Untitled.ipynb
@@ -0,0 +1,3811 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "634976da-3998-4fd7-baa8-94607636c4b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea0bb0a7-5108-4b19-aa6f-cedc7c7b77cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_4034/1346181214.py:1: FutureWarning: In a future version, xarray will not decode the variable 'forecast_period' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n",
+      "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n",
+      "To opt-in to future behavior, set `decode_timedelta=False`.\n",
+      "  xarray.open_dataset(\n"
+     ]
+    }
+   ],
+   "source": [
+    "xarray.open_dataset(    \n",
+    "    's3://met-office-atmospheric-model-data/uk-deterministic-2km/20260505T0000Z/20260505T0000Z-PT0000H00M-temperature_on_pressure_levels.nc', \n",
+    "    engine='h5netcdf', \n",
+    "    storage_options={'anon':True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "390203e5-f2fe-43d1-8e79-2b8cf9b1d120",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "met-office-atmospheric-model-data/uk-deterministic/20260505T0000Z/20260505T0000Z-PT0000H00M-temperature_on_pressure_levels.nc",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m xarray.open_dataset(\n\u001b[32m      2\u001b[39m     \u001b[33m's3://met-office-atmospheric-model-data/uk-deterministic/20260505T0000Z/20260505T0000Z-PT0000H00M-temperature_on_pressure_levels.nc'\u001b[39m,\n\u001b[32m      3\u001b[39m     engine=\u001b[33m'h5netcdf'\u001b[39m,\n\u001b[32m      4\u001b[39m     storage_options={\u001b[33m'anon'\u001b[39m:\u001b[38;5;28;01mTrue\u001b[39;00m})\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/api.py:607\u001b[39m, in \u001b[36mopen_dataset\u001b[39m\u001b[34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, create_default_indexes, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[39m\n\u001b[32m    595\u001b[39m decoders = _resolve_decoders_kwargs(\n\u001b[32m    596\u001b[39m     decode_cf,\n\u001b[32m    597\u001b[39m     open_backend_dataset_parameters=backend.open_dataset_parameters,\n\u001b[32m   (...)\u001b[39m\u001b[32m    603\u001b[39m     decode_coords=decode_coords,\n\u001b[32m    604\u001b[39m )\n\u001b[32m    606\u001b[39m overwrite_encoded_chunks = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33moverwrite_encoded_chunks\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m607\u001b[39m backend_ds = \u001b[30;43mbackend\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mopen_dataset\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    608\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfilename_or_obj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    609\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdrop_variables\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdrop_variables\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    610\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mdecoders\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    611\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    612\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    613\u001b[39m ds = _dataset_from_backend_dataset(\n\u001b[32m    614\u001b[39m     backend_ds,\n\u001b[32m    615\u001b[39m     filename_or_obj,\n\u001b[32m   (...)\u001b[39m\u001b[32m    626\u001b[39m     **kwargs,\n\u001b[32m    627\u001b[39m )\n\u001b[32m    628\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/h5netcdf_.py:540\u001b[39m, in \u001b[36mH5netcdfBackendEntrypoint.open_dataset\u001b[39m\u001b[34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, format, group, lock, invalid_netcdf, phony_dims, decode_vlen_strings, driver, driver_kwds, storage_options)\u001b[39m\n\u001b[32m    537\u001b[39m emit_phony_dims_warning, phony_dims = _check_phony_dims(phony_dims)\n\u001b[32m    539\u001b[39m filename_or_obj = _normalize_filename_or_obj(filename_or_obj)\n\u001b[32m--> \u001b[39m\u001b[32m540\u001b[39m store = \u001b[30;43mH5NetCDFStore\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    541\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfilename_or_obj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    542\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mformat\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mformat\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    543\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mgroup\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgroup\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    544\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mlock\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlock\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    545\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43minvalid_netcdf\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43minvalid_netcdf\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    546\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mphony_dims\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mphony_dims\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    547\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdecode_vlen_strings\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdecode_vlen_strings\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    548\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdriver\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdriver\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    549\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdriver_kwds\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdriver_kwds\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    550\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    551\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    553\u001b[39m store_entrypoint = StoreBackendEntrypoint()\n\u001b[32m    555\u001b[39m ds = store_entrypoint.open_dataset(\n\u001b[32m    556\u001b[39m     store,\n\u001b[32m    557\u001b[39m     mask_and_scale=mask_and_scale,\n\u001b[32m   (...)\u001b[39m\u001b[32m    563\u001b[39m     decode_timedelta=decode_timedelta,\n\u001b[32m    564\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/h5netcdf_.py:191\u001b[39m, in \u001b[36mH5NetCDFStore.open\u001b[39m\u001b[34m(cls, filename, mode, format, group, lock, autoclose, invalid_netcdf, phony_dims, decode_vlen_strings, driver, driver_kwds, storage_options)\u001b[39m\n\u001b[32m    189\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m is_remote_uri(filename) \u001b[38;5;129;01mand\u001b[39;00m driver \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    190\u001b[39m     mode_ = \u001b[33m\"\u001b[39m\u001b[33mrb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mode == \u001b[33m\"\u001b[39m\u001b[33mr\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m mode\n\u001b[32m--> \u001b[39m\u001b[32m191\u001b[39m     filename = \u001b[30;43m_open_remote_file\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    192\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmode_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\n\u001b[32m    193\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    195\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, BytesIOProxy):\n\u001b[32m    196\u001b[39m     source = filename\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/common.py:223\u001b[39m, in \u001b[36m_open_remote_file\u001b[39m\u001b[34m(file, mode, storage_options)\u001b[39m\n\u001b[32m    218\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mfsspec\u001b[39;00m\n\u001b[32m    220\u001b[39m fs, _, paths = fsspec.get_fs_token_paths(\n\u001b[32m    221\u001b[39m     file, mode=mode, storage_options=storage_options\n\u001b[32m    222\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m223\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpaths\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/spec.py:1405\u001b[39m, in \u001b[36mAbstractFileSystem.open\u001b[39m\u001b[34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[39m\n\u001b[32m   1403\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1404\u001b[39m     ac = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33mautocommit\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._intrans)\n\u001b[32m-> \u001b[39m\u001b[32m1405\u001b[39m     f = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_open\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1406\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1407\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1408\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1409\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mac\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1410\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1411\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1412\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1414\u001b[39m         \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mfsspec\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompression\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m compr\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/s3fs/core.py:829\u001b[39m, in \u001b[36mS3FileSystem._open\u001b[39m\u001b[34m(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, size, requester_pays, cache_options, **kwargs)\u001b[39m\n\u001b[32m    826\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m cache_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    827\u001b[39m     cache_type = \u001b[38;5;28mself\u001b[39m.default_cache_type\n\u001b[32m--> \u001b[39m\u001b[32m829\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mS3File\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    830\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    831\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    832\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    833\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    834\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43macl\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43macl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    835\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mversion_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mversion_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    836\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfill_cache\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfill_cache\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    837\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43ms3_additional_kwargs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mkw\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    838\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    839\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    840\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mrequester_pays\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrequester_pays\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    841\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    842\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    843\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/s3fs/core.py:2564\u001b[39m, in \u001b[36mS3File.__init__\u001b[39m\u001b[34m(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays, cache_options, size)\u001b[39m\n\u001b[32m   2562\u001b[39m         \u001b[38;5;28mself\u001b[39m.details = s3.info(path)\n\u001b[32m   2563\u001b[39m         \u001b[38;5;28mself\u001b[39m.version_id = \u001b[38;5;28mself\u001b[39m.details.get(\u001b[33m\"\u001b[39m\u001b[33mVersionId\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2564\u001b[39m \u001b[30;43msuper\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m__init__\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   2565\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43ms3\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2566\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2567\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2568\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2569\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2570\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2571\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2572\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2573\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;28mself\u001b[39m.s3 = \u001b[38;5;28mself\u001b[39m.fs  \u001b[38;5;66;03m# compatibility\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when not using autocommit we want to have transactional state to manage\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/spec.py:1979\u001b[39m, in \u001b[36mAbstractBufferedFile.__init__\u001b[39m\u001b[34m(self, fs, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\u001b[39m\n\u001b[32m   1977\u001b[39m         \u001b[38;5;28mself\u001b[39m.size = size\n\u001b[32m   1978\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1979\u001b[39m         \u001b[38;5;28mself\u001b[39m.size = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdetails\u001b[39;49m[\u001b[33m\"\u001b[39m\u001b[33msize\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1980\u001b[39m     \u001b[38;5;28mself\u001b[39m.cache = caches[cache_type](\n\u001b[32m   1981\u001b[39m         \u001b[38;5;28mself\u001b[39m.blocksize, \u001b[38;5;28mself\u001b[39m._fetch_range, \u001b[38;5;28mself\u001b[39m.size, **cache_options\n\u001b[32m   1982\u001b[39m     )\n\u001b[32m   1983\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/spec.py:1992\u001b[39m, in \u001b[36mAbstractBufferedFile.details\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1989\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m   1990\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdetails\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m   1991\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._details \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1992\u001b[39m         \u001b[38;5;28mself\u001b[39m._details = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43minfo\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1993\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._details\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/asyn.py:118\u001b[39m, in \u001b[36msync_wrapper.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    115\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m    116\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args, **kwargs):\n\u001b[32m    117\u001b[39m     \u001b[38;5;28mself\u001b[39m = obj \u001b[38;5;129;01mor\u001b[39;00m args[\u001b[32m0\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43msync\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mloop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/asyn.py:103\u001b[39m, in \u001b[36msync\u001b[39m\u001b[34m(loop, func, timeout, *args, **kwargs)\u001b[39m\n\u001b[32m    101\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m FSTimeoutError \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mreturn_result\u001b[39;00m\n\u001b[32m    102\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[32m    104\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    105\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m return_result\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/asyn.py:56\u001b[39m, in \u001b[36m_runner\u001b[39m\u001b[34m(event, coro, result, timeout)\u001b[39m\n\u001b[32m     54\u001b[39m     coro = asyncio.wait_for(coro, timeout=timeout)\n\u001b[32m     55\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m     result[\u001b[32m0\u001b[39m] = \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[32m     57\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m     58\u001b[39m     result[\u001b[32m0\u001b[39m] = ex\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/s3fs/core.py:1799\u001b[39m, in \u001b[36mS3FileSystem._info\u001b[39m\u001b[34m(self, path, bucket, key, refresh, version_id)\u001b[39m\n\u001b[32m   1787\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m   1788\u001b[39m         out.get(\u001b[33m\"\u001b[39m\u001b[33mKeyCount\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m0\u001b[39m) > \u001b[32m0\u001b[39m\n\u001b[32m   1789\u001b[39m         \u001b[38;5;129;01mor\u001b[39;00m out.get(\u001b[33m\"\u001b[39m\u001b[33mContents\u001b[39m\u001b[33m\"\u001b[39m, [])\n\u001b[32m   1790\u001b[39m         \u001b[38;5;129;01mor\u001b[39;00m out.get(\u001b[33m\"\u001b[39m\u001b[33mCommonPrefixes\u001b[39m\u001b[33m\"\u001b[39m, [])\n\u001b[32m   1791\u001b[39m     ):\n\u001b[32m   1792\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m   1793\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mname\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m/\u001b[39m\u001b[33m\"\u001b[39m.join([bucket, key]),\n\u001b[32m   1794\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mtype\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mdirectory\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   1795\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33msize\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m0\u001b[39m,\n\u001b[32m   1796\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mStorageClass\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mDIRECTORY\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   1797\u001b[39m         }\n\u001b[32m-> \u001b[39m\u001b[32m1799\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(path)\n\u001b[32m   1800\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ClientError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m   1801\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m translate_boto_error(e, set_cause=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: met-office-atmospheric-model-data/uk-deterministic/20260505T0000Z/20260505T0000Z-PT0000H00M-temperature_on_pressure_levels.nc"
+     ]
+    }
+   ],
+   "source": [
+    "xarray.open_dataset(\n",
+    "    's3://met-office-atmospheric-model-data/uk-deterministic/20260505T0000Z/20260505T0000Z-PT0000H00M-temperature_on_pressure_levels.nc', \n",
+    "    engine='h5netcdf', \n",
+    "    storage_options={'anon':True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2d3c77c0-b924-42f7-a2ff-b539a5f62d48",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "met-office-atmospheric-model-data/uk-deterministic//20260301T1200Z/20260301T1200Z-PT0000H00M-temperature_on_pressure_levels.nc",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m xarray.open_dataset(\u001b[33m's3://met-office-atmospheric-model-data/uk-deterministic//20260301T1200Z/20260301T1200Z-PT0000H00M-temperature_on_pressure_levels.nc'\u001b[39m, engine=\u001b[33m'h5netcdf'\u001b[39m, storage_options={\u001b[33m'anon'\u001b[39m:\u001b[38;5;28;01mTrue\u001b[39;00m})\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/api.py:607\u001b[39m, in \u001b[36mopen_dataset\u001b[39m\u001b[34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, create_default_indexes, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[39m\n\u001b[32m    595\u001b[39m decoders = _resolve_decoders_kwargs(\n\u001b[32m    596\u001b[39m     decode_cf,\n\u001b[32m    597\u001b[39m     open_backend_dataset_parameters=backend.open_dataset_parameters,\n\u001b[32m   (...)\u001b[39m\u001b[32m    603\u001b[39m     decode_coords=decode_coords,\n\u001b[32m    604\u001b[39m )\n\u001b[32m    606\u001b[39m overwrite_encoded_chunks = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33moverwrite_encoded_chunks\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m607\u001b[39m backend_ds = \u001b[30;43mbackend\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mopen_dataset\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    608\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfilename_or_obj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    609\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdrop_variables\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdrop_variables\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    610\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mdecoders\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    611\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    612\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    613\u001b[39m ds = _dataset_from_backend_dataset(\n\u001b[32m    614\u001b[39m     backend_ds,\n\u001b[32m    615\u001b[39m     filename_or_obj,\n\u001b[32m   (...)\u001b[39m\u001b[32m    626\u001b[39m     **kwargs,\n\u001b[32m    627\u001b[39m )\n\u001b[32m    628\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/h5netcdf_.py:540\u001b[39m, in \u001b[36mH5netcdfBackendEntrypoint.open_dataset\u001b[39m\u001b[34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, format, group, lock, invalid_netcdf, phony_dims, decode_vlen_strings, driver, driver_kwds, storage_options)\u001b[39m\n\u001b[32m    537\u001b[39m emit_phony_dims_warning, phony_dims = _check_phony_dims(phony_dims)\n\u001b[32m    539\u001b[39m filename_or_obj = _normalize_filename_or_obj(filename_or_obj)\n\u001b[32m--> \u001b[39m\u001b[32m540\u001b[39m store = \u001b[30;43mH5NetCDFStore\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    541\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfilename_or_obj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    542\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mformat\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mformat\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    543\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mgroup\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgroup\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    544\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mlock\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlock\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    545\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43minvalid_netcdf\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43minvalid_netcdf\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    546\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mphony_dims\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mphony_dims\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    547\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdecode_vlen_strings\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdecode_vlen_strings\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    548\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdriver\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdriver\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    549\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdriver_kwds\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdriver_kwds\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    550\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    551\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    553\u001b[39m store_entrypoint = StoreBackendEntrypoint()\n\u001b[32m    555\u001b[39m ds = store_entrypoint.open_dataset(\n\u001b[32m    556\u001b[39m     store,\n\u001b[32m    557\u001b[39m     mask_and_scale=mask_and_scale,\n\u001b[32m   (...)\u001b[39m\u001b[32m    563\u001b[39m     decode_timedelta=decode_timedelta,\n\u001b[32m    564\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/h5netcdf_.py:191\u001b[39m, in \u001b[36mH5NetCDFStore.open\u001b[39m\u001b[34m(cls, filename, mode, format, group, lock, autoclose, invalid_netcdf, phony_dims, decode_vlen_strings, driver, driver_kwds, storage_options)\u001b[39m\n\u001b[32m    189\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m is_remote_uri(filename) \u001b[38;5;129;01mand\u001b[39;00m driver \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    190\u001b[39m     mode_ = \u001b[33m\"\u001b[39m\u001b[33mrb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mode == \u001b[33m\"\u001b[39m\u001b[33mr\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m mode\n\u001b[32m--> \u001b[39m\u001b[32m191\u001b[39m     filename = \u001b[30;43m_open_remote_file\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    192\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmode_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\n\u001b[32m    193\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    195\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, BytesIOProxy):\n\u001b[32m    196\u001b[39m     source = filename\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/xarray/backends/common.py:223\u001b[39m, in \u001b[36m_open_remote_file\u001b[39m\u001b[34m(file, mode, storage_options)\u001b[39m\n\u001b[32m    218\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mfsspec\u001b[39;00m\n\u001b[32m    220\u001b[39m fs, _, paths = fsspec.get_fs_token_paths(\n\u001b[32m    221\u001b[39m     file, mode=mode, storage_options=storage_options\n\u001b[32m    222\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m223\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpaths\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/spec.py:1405\u001b[39m, in \u001b[36mAbstractFileSystem.open\u001b[39m\u001b[34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[39m\n\u001b[32m   1403\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1404\u001b[39m     ac = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33mautocommit\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._intrans)\n\u001b[32m-> \u001b[39m\u001b[32m1405\u001b[39m     f = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_open\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1406\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1407\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1408\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1409\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mac\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1410\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1411\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1412\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1414\u001b[39m         \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mfsspec\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompression\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m compr\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/s3fs/core.py:829\u001b[39m, in \u001b[36mS3FileSystem._open\u001b[39m\u001b[34m(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, size, requester_pays, cache_options, **kwargs)\u001b[39m\n\u001b[32m    826\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m cache_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    827\u001b[39m     cache_type = \u001b[38;5;28mself\u001b[39m.default_cache_type\n\u001b[32m--> \u001b[39m\u001b[32m829\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mS3File\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    830\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    831\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    832\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    833\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    834\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43macl\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43macl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    835\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mversion_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mversion_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    836\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfill_cache\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfill_cache\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    837\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43ms3_additional_kwargs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mkw\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    838\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    839\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    840\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mrequester_pays\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrequester_pays\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    841\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    842\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    843\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/s3fs/core.py:2564\u001b[39m, in \u001b[36mS3File.__init__\u001b[39m\u001b[34m(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays, cache_options, size)\u001b[39m\n\u001b[32m   2562\u001b[39m         \u001b[38;5;28mself\u001b[39m.details = s3.info(path)\n\u001b[32m   2563\u001b[39m         \u001b[38;5;28mself\u001b[39m.version_id = \u001b[38;5;28mself\u001b[39m.details.get(\u001b[33m\"\u001b[39m\u001b[33mVersionId\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2564\u001b[39m \u001b[30;43msuper\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m__init__\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   2565\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43ms3\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2566\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2567\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2568\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mblock_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2569\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mautocommit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2570\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2571\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2572\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msize\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   2573\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;28mself\u001b[39m.s3 = \u001b[38;5;28mself\u001b[39m.fs  \u001b[38;5;66;03m# compatibility\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when not using autocommit we want to have transactional state to manage\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/spec.py:1979\u001b[39m, in \u001b[36mAbstractBufferedFile.__init__\u001b[39m\u001b[34m(self, fs, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\u001b[39m\n\u001b[32m   1977\u001b[39m         \u001b[38;5;28mself\u001b[39m.size = size\n\u001b[32m   1978\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1979\u001b[39m         \u001b[38;5;28mself\u001b[39m.size = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdetails\u001b[39;49m[\u001b[33m\"\u001b[39m\u001b[33msize\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1980\u001b[39m     \u001b[38;5;28mself\u001b[39m.cache = caches[cache_type](\n\u001b[32m   1981\u001b[39m         \u001b[38;5;28mself\u001b[39m.blocksize, \u001b[38;5;28mself\u001b[39m._fetch_range, \u001b[38;5;28mself\u001b[39m.size, **cache_options\n\u001b[32m   1982\u001b[39m     )\n\u001b[32m   1983\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/spec.py:1992\u001b[39m, in \u001b[36mAbstractBufferedFile.details\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1989\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m   1990\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdetails\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m   1991\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._details \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1992\u001b[39m         \u001b[38;5;28mself\u001b[39m._details = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43minfo\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1993\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._details\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/asyn.py:118\u001b[39m, in \u001b[36msync_wrapper.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    115\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m    116\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args, **kwargs):\n\u001b[32m    117\u001b[39m     \u001b[38;5;28mself\u001b[39m = obj \u001b[38;5;129;01mor\u001b[39;00m args[\u001b[32m0\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43msync\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mloop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/asyn.py:103\u001b[39m, in \u001b[36msync\u001b[39m\u001b[34m(loop, func, timeout, *args, **kwargs)\u001b[39m\n\u001b[32m    101\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m FSTimeoutError \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mreturn_result\u001b[39;00m\n\u001b[32m    102\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[32m    104\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    105\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m return_result\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/fsspec/asyn.py:56\u001b[39m, in \u001b[36m_runner\u001b[39m\u001b[34m(event, coro, result, timeout)\u001b[39m\n\u001b[32m     54\u001b[39m     coro = asyncio.wait_for(coro, timeout=timeout)\n\u001b[32m     55\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m     result[\u001b[32m0\u001b[39m] = \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[32m     57\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m     58\u001b[39m     result[\u001b[32m0\u001b[39m] = ex\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/gws/ssde/j25b/mohc_shared/users/shaddad/venv/pet_dev_nb_gpu/lib/python3.12/site-packages/s3fs/core.py:1799\u001b[39m, in \u001b[36mS3FileSystem._info\u001b[39m\u001b[34m(self, path, bucket, key, refresh, version_id)\u001b[39m\n\u001b[32m   1787\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m   1788\u001b[39m         out.get(\u001b[33m\"\u001b[39m\u001b[33mKeyCount\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m0\u001b[39m) > \u001b[32m0\u001b[39m\n\u001b[32m   1789\u001b[39m         \u001b[38;5;129;01mor\u001b[39;00m out.get(\u001b[33m\"\u001b[39m\u001b[33mContents\u001b[39m\u001b[33m\"\u001b[39m, [])\n\u001b[32m   1790\u001b[39m         \u001b[38;5;129;01mor\u001b[39;00m out.get(\u001b[33m\"\u001b[39m\u001b[33mCommonPrefixes\u001b[39m\u001b[33m\"\u001b[39m, [])\n\u001b[32m   1791\u001b[39m     ):\n\u001b[32m   1792\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m   1793\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mname\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m/\u001b[39m\u001b[33m\"\u001b[39m.join([bucket, key]),\n\u001b[32m   1794\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mtype\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mdirectory\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   1795\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33msize\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m0\u001b[39m,\n\u001b[32m   1796\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mStorageClass\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mDIRECTORY\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   1797\u001b[39m         }\n\u001b[32m-> \u001b[39m\u001b[32m1799\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(path)\n\u001b[32m   1800\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ClientError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m   1801\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m translate_boto_error(e, set_cause=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: met-office-atmospheric-model-data/uk-deterministic//20260301T1200Z/20260301T1200Z-PT0000H00M-temperature_on_pressure_levels.nc"
+     ]
+    }
+   ],
+   "source": [
+    "xarray.open_dataset('s3://met-office-atmospheric-model-data/uk-deterministic//20260301T1200Z/20260301T1200Z-PT0000H00M-temperature_on_pressure_levels.nc', engine='h5netcdf', storage_options={'anon':True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "16cb73cc-b5ed-49b9-9666-928525baecc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "\n",
+      "KeyboardInterrupt\n",
+      "\n",
+      "\n",
+      "KeyboardInterrupt\n",
+      "\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n"
+     ]
+    }
+   ],
+   "source": [
+    "xarray.open_zarr('gs://gcp-public-data-arco-era5/ar/1959-2022-6h-1440x721.zarr')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4ebebc53-61ab-43d5-8c64-4ded44694c0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "/tmp/ipykernel_1786/3347581234.py:1: FutureWarning: In a future version, xarray will not decode the variable 'step' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n",
+      "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n",
+      "To opt-in to future behavior, set `decode_timedelta=False`.\n",
+      "  single_reanalysis = xarray.open_zarr(\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n",
+      "Could not determine bucket type for bucket name gcp-public-data-arco-era5: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information., falling back to GCSFileSystem\n"
+     ]
+    },
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+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) 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, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --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",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\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",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\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-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\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",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > 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",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\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",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\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: 31TB\n",
+       "Dimensions:              (time: 374016, values: 542080)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 3MB 1979-01-01 ... 2021-08-31T...\n",
+       "    valid_time           (time) datetime64[ns] 3MB dask.array&lt;chunksize=(48,), meta=np.ndarray&gt;\n",
+       "    latitude             (values) float64 4MB dask.array&lt;chunksize=(542080,), meta=np.ndarray&gt;\n",
+       "    longitude            (values) float64 4MB dask.array&lt;chunksize=(542080,), meta=np.ndarray&gt;\n",
+       "    depthBelowLandLayer  float64 8B ...\n",
+       "    entireAtmosphere     float64 8B ...\n",
+       "    number               int64 8B ...\n",
+       "    step                 timedelta64[ns] 8B ...\n",
+       "    surface              float64 8B ...\n",
+       "Dimensions without coordinates: values\n",
+       "Data variables: (12/38)\n",
+       "    cape                 (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    d2m                  (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    hcc                  (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    istl1                (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    istl2                (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    istl3                (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    ...                   ...\n",
+       "    tsn                  (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    u10                  (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    u100                 (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    v10                  (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    v100                 (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "    z                    (time, values) float32 811GB dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:               CF-1.7\n",
+       "    GRIB_centre:               ecmf\n",
+       "    GRIB_centreDescription:    European Centre for Medium-Range Weather Forec...\n",
+       "    GRIB_edition:              1\n",
+       "    GRIB_subCentre:            0\n",
+       "    history:                   2022-09-23T18:56 GRIB to CDM+CF via cfgrib-0.9...\n",
+       "    institution:               European Centre for Medium-Range Weather Forec...\n",
+       "    pangeo-forge:inputs_hash:  5f4378143e9f42402424280b63472752da3aa79179b53b...\n",
+       "    pangeo-forge:recipe_hash:  0c3415923e347ce9dac9dc5c6d209525f4d45d799bd25b...\n",
+       "    pangeo-forge:version:      0.9.1</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-5fd6082a-31c7-4301-b97c-6c45a50fa992' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-5fd6082a-31c7-4301-b97c-6c45a50fa992' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 374016</li><li><span>values</span>: 542080</li></ul></div></li><li class='xr-section-item'><input id='section-383a195a-33e0-40e7-ac34-1c37d6af143a' class='xr-section-summary-in' type='checkbox' checked /><label for='section-383a195a-33e0-40e7-ac34-1c37d6af143a' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-08-31T23:00:00</div><input id='attrs-ea042028-7ca0-4c00-aa36-4d76d9e1dfc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea042028-7ca0-4c00-aa36-4d76d9e1dfc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8af0db2d-e82a-4fe5-8412-d6a28054cbc1' class='xr-var-data-in' type='checkbox'><label for='data-8af0db2d-e82a-4fe5-8412-d6a28054cbc1' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;1979-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-08-31T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-08-31T22:00:00.000000000&#x27;, &#x27;2021-08-31T23:00:00.000000000&#x27;],\n",
+       "      shape=(374016,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_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'>dask.array&lt;chunksize=(48,), meta=np.ndarray&gt;</div><input id='attrs-16a0cd1f-bde5-4aec-b62b-7f58feff8e42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16a0cd1f-bde5-4aec-b62b-7f58feff8e42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96dc7681-375c-4b80-8feb-9aefb187f58a' class='xr-var-data-in' type='checkbox'><label for='data-96dc7681-375c-4b80-8feb-9aefb187f58a' 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>time</dd><dt><span>standard_name :</span></dt><dd>time</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> 2.85 MiB </td>\n",
+       "                        <td> 384 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016,) </td>\n",
+       "                        <td> (48,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
+       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
+       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
+       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >374016</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(542080,), meta=np.ndarray&gt;</div><input id='attrs-4fb3b197-8617-488e-bbe1-ca86f833c86c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fb3b197-8617-488e-bbe1-ca86f833c86c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95ec7743-016d-4e32-a0e2-8e87413844ae' class='xr-var-data-in' type='checkbox'><label for='data-95ec7743-016d-4e32-a0e2-8e87413844ae' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.14 MiB </td>\n",
+       "                        <td> 4.14 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (542080,) </td>\n",
+       "                        <td> (542080,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(542080,), meta=np.ndarray&gt;</div><input id='attrs-fb898e26-95ed-4b12-a615-5bdf63f37778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb898e26-95ed-4b12-a615-5bdf63f37778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5b2d007-61fe-4663-9eda-3ddb329759ad' class='xr-var-data-in' type='checkbox'><label for='data-f5b2d007-61fe-4663-9eda-3ddb329759ad' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.14 MiB </td>\n",
+       "                        <td> 4.14 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (542080,) </td>\n",
+       "                        <td> (542080,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depthBelowLandLayer</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da99c2af-65f2-401b-b0ea-dadd0307630a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da99c2af-65f2-401b-b0ea-dadd0307630a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48542652-7487-41be-a3cf-f27cb89c107d' class='xr-var-data-in' type='checkbox'><label for='data-48542652-7487-41be-a3cf-f27cb89c107d' 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>soil depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>entireAtmosphere</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d7d35370-87d6-457f-bca8-7d8aafdb4452' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7d35370-87d6-457f-bca8-7d8aafdb4452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da6f989e-3a93-47c3-a74a-6a9fdb939548' class='xr-var-data-in' type='checkbox'><label for='data-da6f989e-3a93-47c3-a74a-6a9fdb939548' 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>original GRIB coordinate for key: level(entireAtmosphere)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ed337ce-c95d-46d3-9050-2437cf2a1ad1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ed337ce-c95d-46d3-9050-2437cf2a1ad1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abca923a-05f0-48eb-8a9c-3f0b4ce07d8c' class='xr-var-data-in' type='checkbox'><label for='data-abca923a-05f0-48eb-8a9c-3f0b4ce07d8c' 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>ensemble member numerical id</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35dc03e3-6685-4506-abbe-c5816393d302' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35dc03e3-6685-4506-abbe-c5816393d302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc3c68e4-1f8a-4941-b925-2f610183a8e9' class='xr-var-data-in' type='checkbox'><label for='data-fc3c68e4-1f8a-4941-b925-2f610183a8e9' 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>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bbc59eeb-ddb6-4317-adf6-e72ef699c665' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbc59eeb-ddb6-4317-adf6-e72ef699c665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-520a0d44-92f2-4f3d-ba18-5bdd416fa941' class='xr-var-data-in' type='checkbox'><label for='data-520a0d44-92f2-4f3d-ba18-5bdd416fa941' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f817958-1c51-41bd-8326-b4966e0488f7' class='xr-section-summary-in' type='checkbox' /><label for='section-6f817958-1c51-41bd-8326-b4966e0488f7' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(38)</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>cape</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-dc0dfc62-d5e4-470b-b6e5-cc1f4247dd9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc0dfc62-d5e4-470b-b6e5-cc1f4247dd9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67582b49-82cf-40cc-a5f6-e2ccd7b6796c' class='xr-var-data-in' type='checkbox'><label for='data-67582b49-82cf-40cc-a5f6-e2ccd7b6796c' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>J kg**-1</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"47\" x2=\"120\" y2=\"47\" />\n",
+       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n",
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+       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-b9df9604-8661-4683-86b6-1e16b4f37004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9df9604-8661-4683-86b6-1e16b4f37004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f61646cd-f0d8-4564-813f-49e6580dcc12' class='xr-var-data-in' type='checkbox'><label for='data-f61646cd-f0d8-4564-813f-49e6580dcc12' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-7bdfd657-3e14-434c-bc7c-d4ada9140a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bdfd657-3e14-434c-bc7c-d4ada9140a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcb645f7-e32f-490f-a33e-a15c45e2c615' class='xr-var-data-in' type='checkbox'><label for='data-dcb645f7-e32f-490f-a33e-a15c45e2c615' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>(0 - 1)</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-049ef07d-5b57-4c7d-b298-eddcbb6361df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-049ef07d-5b57-4c7d-b298-eddcbb6361df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cec533f-d27f-4964-98fb-ef8e4b6b8e79' class='xr-var-data-in' type='checkbox'><label for='data-1cec533f-d27f-4964-98fb-ef8e4b6b8e79' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-777d74e2-79a5-4bce-a307-6fee0b0039f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-777d74e2-79a5-4bce-a307-6fee0b0039f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2a4434-8153-4960-9357-7e0fbc82560b' class='xr-var-data-in' type='checkbox'><label for='data-0f2a4434-8153-4960-9357-7e0fbc82560b' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-b5242dcd-b314-4caf-8424-c672495ca000' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5242dcd-b314-4caf-8424-c672495ca000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05f241b3-b472-4a30-91f3-dbc196ff4330' class='xr-var-data-in' type='checkbox'><label for='data-05f241b3-b472-4a30-91f3-dbc196ff4330' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-011d18e9-5f0e-4c4f-ae82-928ac2e37c78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-011d18e9-5f0e-4c4f-ae82-928ac2e37c78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db2c614e-6362-40bd-959d-de8e739235ca' class='xr-var-data-in' type='checkbox'><label for='data-db2c614e-6362-40bd-959d-de8e739235ca' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-9fb319b3-b98b-4e5d-9cce-ab7b0c852fdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fb319b3-b98b-4e5d-9cce-ab7b0c852fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d437147-0b52-4ce7-8087-7115813d3221' class='xr-var-data-in' type='checkbox'><label for='data-3d437147-0b52-4ce7-8087-7115813d3221' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>(0 - 1)</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-29166ef9-ea21-427f-bae2-d2c32e5ce6b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29166ef9-ea21-427f-bae2-d2c32e5ce6b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30c6c05d-e1ff-448a-a89e-50d21ea5100e' class='xr-var-data-in' type='checkbox'><label for='data-30c6c05d-e1ff-448a-a89e-50d21ea5100e' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>(0 - 1)</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-f7406f11-ab43-4c2f-a24f-281d2f7c3355' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7406f11-ab43-4c2f-a24f-281d2f7c3355' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b292de3-cbbf-404f-bc22-ee9d632d9fe0' class='xr-var-data-in' type='checkbox'><label for='data-0b292de3-cbbf-404f-bc22-ee9d632d9fe0' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 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1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p79.162</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-8cc7cf79-55fb-46a0-9a08-50ff2d24e0c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cc7cf79-55fb-46a0-9a08-50ff2d24e0c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-615e34aa-bc58-4e5e-8b65-3a53a2bf5c81' class='xr-var-data-in' type='checkbox'><label for='data-615e34aa-bc58-4e5e-8b65-3a53a2bf5c81' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>p79.162</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.785</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.785</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.719</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Vertical integral of divergence of cloud liquid water flux</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>162079</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 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+       "    <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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p80.162</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-be5e5810-d793-40dc-b951-65a42dbf77ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be5e5810-d793-40dc-b951-65a42dbf77ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bcca1f7-420b-463a-994c-53278cb87500' class='xr-var-data-in' type='checkbox'><label for='data-5bcca1f7-420b-463a-994c-53278cb87500' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>p80.162</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.785</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.785</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.719</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Vertical integral of divergence of cloud frozen water flux</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>162080</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 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1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>viiwd</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>entireAtmosphere</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical integral of divergence of cloud frozen water flux</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>siconc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-ad014c86-d6b1-4a13-a498-cb79331ae10d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad014c86-d6b1-4a13-a498-cb79331ae10d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79e1bba7-5e2a-43af-a84e-cdfb6d0bd08d' class='xr-var-data-in' type='checkbox'><label for='data-79e1bba7-5e2a-43af-a84e-cdfb6d0bd08d' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-8f3801e9-0c80-4943-bf53-bf0e342382fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f3801e9-0c80-4943-bf53-bf0e342382fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68146c0-e5d1-4b50-83d6-bba64d0b81ad' class='xr-var-data-in' type='checkbox'><label for='data-c68146c0-e5d1-4b50-83d6-bba64d0b81ad' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-e7cef20a-7ad3-4e54-969c-8d84d64a6427' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7cef20a-7ad3-4e54-969c-8d84d64a6427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a3d6aaf-5d96-4231-9364-d4d0ea12feec' class='xr-var-data-in' type='checkbox'><label for='data-7a3d6aaf-5d96-4231-9364-d4d0ea12feec' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sp</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Surface pressure</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>134</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>sp</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Surface pressure</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-83fb82c4-7e51-47a1-9ed8-a36547a4e397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83fb82c4-7e51-47a1-9ed8-a36547a4e397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de9608e7-c305-4176-8061-b1b72f297201' class='xr-var-data-in' type='checkbox'><label for='data-de9608e7-c305-4176-8061-b1b72f297201' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-279f8f3f-d56c-4abb-a2c2-d10151370971' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-279f8f3f-d56c-4abb-a2c2-d10151370971' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7698e6-0a98-4d24-b3b4-7c6336cf61d4' class='xr-var-data-in' type='checkbox'><label for='data-9b7698e6-0a98-4d24-b3b4-7c6336cf61d4' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-bfc69cec-f4fc-4cd2-902f-9d49def725f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfc69cec-f4fc-4cd2-902f-9d49def725f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da07f94-0c88-4f25-9297-d6d992a6f901' class='xr-var-data-in' type='checkbox'><label for='data-8da07f94-0c88-4f25-9297-d6d992a6f901' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-de0a476c-8625-47ef-91d7-848a91cef52f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de0a476c-8625-47ef-91d7-848a91cef52f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45b0318a-f6c7-463b-aac3-0da3e204cb8b' class='xr-var-data-in' type='checkbox'><label for='data-45b0318a-f6c7-463b-aac3-0da3e204cb8b' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-9118a12f-5021-45d2-824d-38a9ce90b800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9118a12f-5021-45d2-824d-38a9ce90b800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fb1f155-12cb-4c59-9bcc-683c2b5954c9' class='xr-var-data-in' type='checkbox'><label for='data-6fb1f155-12cb-4c59-9bcc-683c2b5954c9' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n",
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+       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-4fed667a-c33c-43e1-a633-9ed950bfaa24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fed667a-c33c-43e1-a633-9ed950bfaa24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67436e23-1eaa-43a9-8295-02152dfcbc7f' class='xr-var-data-in' type='checkbox'><label for='data-67436e23-1eaa-43a9-8295-02152dfcbc7f' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-9db2a3d0-5b58-49db-b6e4-5b7cc35fc7ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9db2a3d0-5b58-49db-b6e4-5b7cc35fc7ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e66b75-e048-436b-8223-afaa74d4e165' class='xr-var-data-in' type='checkbox'><label for='data-10e66b75-e048-436b-8223-afaa74d4e165' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-d9756137-2af6-4492-8af7-9c86c6202d46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9756137-2af6-4492-8af7-9c86c6202d46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8caf9908-54ce-41c4-9432-4b726ab5043e' class='xr-var-data-in' type='checkbox'><label for='data-8caf9908-54ce-41c4-9432-4b726ab5043e' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-3bfa0e53-28a4-4472-b5c0-6af103e61aea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bfa0e53-28a4-4472-b5c0-6af103e61aea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6481d466-b02e-48a1-9b95-28b10984a765' class='xr-var-data-in' type='checkbox'><label for='data-6481d466-b02e-48a1-9b95-28b10984a765' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-c228a208-5ec8-4af3-80e5-789f8b0c108b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c228a208-5ec8-4af3-80e5-789f8b0c108b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8880ff05-2a45-4241-afd5-bd17a7d6680a' class='xr-var-data-in' type='checkbox'><label for='data-8880ff05-2a45-4241-afd5-bd17a7d6680a' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-d01751c6-75f6-4571-8a1c-5e958ccc5b9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d01751c6-75f6-4571-8a1c-5e958ccc5b9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd23db5-48d0-43a2-bf7a-167dad66dd3b' class='xr-var-data-in' type='checkbox'><label for='data-3bd23db5-48d0-43a2-bf7a-167dad66dd3b' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
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+       "  <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n",
+       "  <line x1=\"0\" y1=\"78\" x2=\"120\" y2=\"78\" />\n",
+       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-0a323c2c-1e9e-4da1-ab69-ad3e834aaae7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a323c2c-1e9e-4da1-ab69-ad3e834aaae7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80d5a3d4-fdf1-4e74-b541-8a805d1c12a2' class='xr-var-data-in' type='checkbox'><label for='data-80d5a3d4-fdf1-4e74-b541-8a805d1c12a2' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-3a7fe99e-e286-4de7-886c-47a8ecf0a63b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a7fe99e-e286-4de7-886c-47a8ecf0a63b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92f282dd-a51c-46cc-9e94-8743250820bc' class='xr-var-data-in' type='checkbox'><label for='data-92f282dd-a51c-46cc-9e94-8743250820bc' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-d58de040-0371-439b-ad59-bc53e2ac0dbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d58de040-0371-439b-ad59-bc53e2ac0dbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f45afe0-faee-4479-9321-eab813f910bc' class='xr-var-data-in' type='checkbox'><label for='data-0f45afe0-faee-4479-9321-eab813f910bc' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-df20b071-4d51-4726-bf72-7f2d369aa132' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df20b071-4d51-4726-bf72-7f2d369aa132' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1b0d3e9-6143-48fd-b605-48785310a741' class='xr-var-data-in' type='checkbox'><label for='data-a1b0d3e9-6143-48fd-b605-48785310a741' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-8dbff3f8-8ae3-4c18-abd3-bddf40d6aaa8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dbff3f8-8ae3-4c18-abd3-bddf40d6aaa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a266c8ee-8374-4c1d-89f3-98a38d774d4a' class='xr-var-data-in' type='checkbox'><label for='data-a266c8ee-8374-4c1d-89f3-98a38d774d4a' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-ae5df4a1-99f4-4ef9-b3b9-3a655873fe43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae5df4a1-99f4-4ef9-b3b9-3a655873fe43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82cbab55-d863-4be2-8be6-0c6f049683a3' class='xr-var-data-in' type='checkbox'><label for='data-82cbab55-d863-4be2-8be6-0c6f049683a3' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column water vapour</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 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+       "    <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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-e903b495-8d72-49ea-ae6f-5c017f7ede37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e903b495-8d72-49ea-ae6f-5c017f7ede37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2465e92c-68bc-49a2-a879-975a373f34b7' class='xr-var-data-in' type='checkbox'><label for='data-2465e92c-68bc-49a2-a879-975a373f34b7' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 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1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u10</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-24a1997d-7571-4adf-b46c-8f81422d8f98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24a1997d-7571-4adf-b46c-8f81422d8f98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae592642-5b4e-436c-bfc6-5da10d6e9d24' class='xr-var-data-in' type='checkbox'><label for='data-ae592642-5b4e-436c-bfc6-5da10d6e9d24' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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+       "    <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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u100</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-1cbca8d0-bafd-438f-ba9e-00d75c2c0ae8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cbca8d0-bafd-438f-ba9e-00d75c2c0ae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc4ac92a-c866-45cc-bc6d-53602a7dd214' class='xr-var-data-in' type='checkbox'><label for='data-cc4ac92a-c866-45cc-bc6d-53602a7dd214' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u100</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>100 metre U wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228246</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>100u</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>100 metre U wind component</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m s**-1</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-f92bbe14-d267-467a-bf52-6ecbc9683a88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f92bbe14-d267-467a-bf52-6ecbc9683a88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b30f838d-249d-49a0-8d5f-1a01fb23b82f' class='xr-var-data-in' type='checkbox'><label for='data-b30f838d-249d-49a0-8d5f-1a01fb23b82f' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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+       "    <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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v100</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-829d93ef-d611-4c26-adab-965d79987f14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-829d93ef-d611-4c26-adab-965d79987f14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-841a43f4-9dc5-4b38-85a9-febc8de5e4b7' class='xr-var-data-in' type='checkbox'><label for='data-841a43f4-9dc5-4b38-85a9-febc8de5e4b7' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v100</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>100 metre V wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228247</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>100v</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>100 metre V wind component</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m s**-1</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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 542080), meta=np.ndarray&gt;</div><input id='attrs-2fa00e17-3857-4421-9004-65b478c348bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fa00e17-3857-4421-9004-65b478c348bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa48a784-52da-4249-bf06-c413311a3d31' class='xr-var-data-in' type='checkbox'><label for='data-fa48a784-52da-4249-bf06-c413311a3d31' 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>GRIB_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 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+       "    <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> 755.29 GiB </td>\n",
+       "                        <td> 99.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (374016, 542080) </td>\n",
+       "                        <td> (48, 542080) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7792 chunks in 2 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=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,82.79574970484062 0.0,82.79574970484062\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"102.79574970484062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n",
+       "  <text x=\"140.0\" y=\"41.39787485242031\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,41.39787485242031)\">374016</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ae0294b-0000-49c3-8305-04ee7f89da9c' class='xr-section-summary-in' type='checkbox' /><label for='section-5ae0294b-0000-49c3-8305-04ee7f89da9c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>2022-09-23T18:56 GRIB to CDM+CF via cfgrib-0.9.10.1/ecCodes-2.20.0 with {&quot;source&quot;: &quot;tmp/tmp2vvg8mzi.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>pangeo-forge:inputs_hash :</span></dt><dd>5f4378143e9f42402424280b63472752da3aa79179b53b71401ee0ecf2fdc24e</dd><dt><span>pangeo-forge:recipe_hash :</span></dt><dd>0c3415923e347ce9dac9dc5c6d209525f4d45d799bd25b04bf2adf398bfc83bb</dd><dt><span>pangeo-forge:version :</span></dt><dd>0.9.1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 31TB\n",
+       "Dimensions:              (time: 374016, values: 542080)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 3MB 1979-01-01 ... 2021-08-31T...\n",
+       "    valid_time           (time) datetime64[ns] 3MB dask.array<chunksize=(48,), meta=np.ndarray>\n",
+       "    latitude             (values) float64 4MB dask.array<chunksize=(542080,), meta=np.ndarray>\n",
+       "    longitude            (values) float64 4MB dask.array<chunksize=(542080,), meta=np.ndarray>\n",
+       "    depthBelowLandLayer  float64 8B ...\n",
+       "    entireAtmosphere     float64 8B ...\n",
+       "    number               int64 8B ...\n",
+       "    step                 timedelta64[ns] 8B ...\n",
+       "    surface              float64 8B ...\n",
+       "Dimensions without coordinates: values\n",
+       "Data variables: (12/38)\n",
+       "    cape                 (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    d2m                  (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    hcc                  (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    istl1                (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    istl2                (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    istl3                (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    ...                   ...\n",
+       "    tsn                  (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    u10                  (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    u100                 (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    v10                  (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    v100                 (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "    z                    (time, values) float32 811GB dask.array<chunksize=(48, 542080), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:               CF-1.7\n",
+       "    GRIB_centre:               ecmf\n",
+       "    GRIB_centreDescription:    European Centre for Medium-Range Weather Forec...\n",
+       "    GRIB_edition:              1\n",
+       "    GRIB_subCentre:            0\n",
+       "    history:                   2022-09-23T18:56 GRIB to CDM+CF via cfgrib-0.9...\n",
+       "    institution:               European Centre for Medium-Range Weather Forec...\n",
+       "    pangeo-forge:inputs_hash:  5f4378143e9f42402424280b63472752da3aa79179b53b...\n",
+       "    pangeo-forge:recipe_hash:  0c3415923e347ce9dac9dc5c6d209525f4d45d799bd25b...\n",
+       "    pangeo-forge:version:      0.9.1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "single_reanalysis = xarray.open_zarr(\n",
+    "    \"gs://gcp-public-data-arco-era5/co/single-level-reanalysis.zarr\",\n",
+    "    chunks={'time': 48}, \n",
+    "    consolidated=True,\n",
+    ")\n",
+    "single_reanalysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6ef0e5f-7d7a-4e5f-83bf-97e8efaced50",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pet_dev_nb_gpu",
+   "language": "python",
+   "name": "pet_dev_nb_gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/data/untitled.txt b/docs/notebooks/data/untitled.txt
new file mode 100644
index 00000000..886b7131
--- /dev/null
+++ b/docs/notebooks/data/untitled.txt
@@ -0,0 +1 @@
+xarray.merge([xarray.open_dataset(path1, ** open_args ) for path1 in  args[0]()])
\ No newline at end of file
diff --git a/packages/data/src/pyearthtools/data/archive/__init__.py b/packages/data/src/pyearthtools/data/archive/__init__.py
index 73529dcc..75c759de 100644
--- a/packages/data/src/pyearthtools/data/archive/__init__.py
+++ b/packages/data/src/pyearthtools/data/archive/__init__.py
@@ -48,7 +48,6 @@
 
 import sys
 
-
 __all__ = [
     "set_root",
     "reset_root",
@@ -61,11 +60,12 @@
     "ZarrTimeIndex",
 ]
 
+
 def print_indexes(show_projects: bool):
-    '''
+    """
     Args:
         show_projects: if true, show project codes (if applicable to the Index).
-    '''
+    """
     current_module = sys.modules[__name__]
     for obj_name in dir(current_module):
 
@@ -73,10 +73,11 @@ def print_indexes(show_projects: bool):
 
         if isinstance(obj_reference, type):
             if issubclass(obj_reference, pyearthtools.data.indexes.Index):
-                project_code = getattr(obj_reference, 'project_code', None)
-                print(f'{obj_reference} is a registered Index in the archive as {__name__}.{obj_name}')
+                project_code = getattr(obj_reference, "project_code", None)
+                print(f"{obj_reference} is a registered Index in the archive as {__name__}.{obj_name}")
                 if project_code and show_projects:
-                    print(f'{obj_reference} requires access to {project_code}')
+                    print(f"{obj_reference} requires access to {project_code}")
+
 
 def get_indexes():
 
diff --git a/packages/nci_site_archive/src/site_archive_nci/_Himawari.py b/packages/nci_site_archive/src/site_archive_nci/_Himawari.py
index 0992eff3..6d675e32 100644
--- a/packages/nci_site_archive/src/site_archive_nci/_Himawari.py
+++ b/packages/nci_site_archive/src/site_archive_nci/_Himawari.py
@@ -90,7 +90,7 @@ def check_resolution(bands: list[str]):
 class Himawari(ArchiveIndex):
     """Index into Himawari 8/9 satellite data"""
 
-    project_code="rv74"
+    project_code = "rv74"
 
     @property
     def _desc_(self):
diff --git a/packages/third_party/aws_site_archive/MANIFEST.in b/packages/third_party/aws_site_archive/MANIFEST.in
new file mode 100644
index 00000000..9ae8ee03
--- /dev/null
+++ b/packages/third_party/aws_site_archive/MANIFEST.in
@@ -0,0 +1,8 @@
+include README.md
+include LICENSE
+include MANIFEST.in
+recursive-include src *.py
+recursive-include notebooks *.ipynb *.md
+recursive-include scripts *.py *.sh
+recursive-include deploy *.sh *.py *.yaml *.json
+
diff --git a/packages/third_party/aws_site_archive/README.md b/packages/third_party/aws_site_archive/README.md
new file mode 100644
index 00000000..b4a7207a
--- /dev/null
+++ b/packages/third_party/aws_site_archive/README.md
@@ -0,0 +1,27 @@
+# AWS Data Accessors
+
+This package is for providing accessors for accessing data stored in public S3 buckets on AWS. 
+
+The indices provided through the site archive load the data directly from AWS S3 buckets, so loading of data will be restricted by network speed if accessing outside AWS. It would be recommnded to consider using a [Cache operator](https://pyearthtools.readthedocs.io/en/latest/api/pipeline/pipeline_api.html#pyearthtools.pipeline.modifications.Cache) to store a local copy of the normalised data to speed up loading of data as part of a machine learning training pipeline.
+
+### Usage
+The following is a python snippet that demonstrates how to access Met Office UKV data on AWS through the site archive.
+
+```
+import datetime
+import site_archive_aws
+import pyearthtools.data.archive
+
+var_list = [
+    'temperature_on_pressure_levels',
+    'relative_humidity_on_pressure_levels',
+    'wind_speed_on_pressure_levels',
+    'wind_direction_on_pressure_levels',
+]
+
+ukv_accessor = pyearthtools.data.archive.MOUKV(var_list)
+
+sample_time = datetime.datetime(2026,3,1,12,0)
+
+sample_time = datetime.datetime(2026,3,1,12,0)
+```
\ No newline at end of file
diff --git a/packages/third_party/aws_site_archive/pyproject.toml b/packages/third_party/aws_site_archive/pyproject.toml
new file mode 100644
index 00000000..efb78086
--- /dev/null
+++ b/packages/third_party/aws_site_archive/pyproject.toml
@@ -0,0 +1,55 @@
+[build-system]
+requires = ["setuptools>=61", "setuptools-scm>=8.0"]
+
+[project]
+name = "PyEarthTools-archive-AWS"
+description = "PyEarthTools archive for Met Office and partner datasets available via the AWS Sustainable Data Initiative (ASDI)"
+requires-python = ">=3.11, <3.14"
+keywords = ["pyearthtools", "aws", "met-office", "nwp", "weather"]
+maintainers = [
+  {email = "stephen.haddad@metoffice.gov.uk", name = "Stephen Haddad"}
+]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+    "Topic :: Scientific/Engineering :: Atmospheric Science",
+]
+dependencies = [
+  "pyearthtools-data>=0.1.0",
+  # AWS / cloud access
+  "s3fs>=2024.1.0",
+  "fsspec>=2024.1.0",
+  ]
+  
+dynamic = ["version", "readme"]
+
+[project.optional-dependencies]
+dev = [
+  "pytest>=7.0",
+  "pytest-cov",
+  "black",
+  "isort",
+  "mypy",
+]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.setuptools.dynamic]
+readme = {file = ["README.md"], content-type = "text/markdown"}
+
+[project.urls]
+homepage = "https://pyearthtools.readthedocs.io/en/latest/"
+documentation = "https://pyearthtools.readthedocs.io/en/latest/"
+repository = "https://github.com/ACCESS-Community-Hub/PyEarthTools"
+
+[tool.isort]
+profile = "black"
+
+[tool.black]
+line-length = 120
+
+[tool.mypy]
+warn_return_any = true
+warn_unused_configs = true
diff --git a/packages/third_party/aws_site_archive/requirements-dev.txt b/packages/third_party/aws_site_archive/requirements-dev.txt
new file mode 100644
index 00000000..94050590
--- /dev/null
+++ b/packages/third_party/aws_site_archive/requirements-dev.txt
@@ -0,0 +1,19 @@
+# Install base requirements first
+-r requirements.txt
+
+# Development and testing
+pytest>=7.0
+pytest-cov
+black>=23.0
+isort>=5.12
+mypy>=1.0
+
+# Notebook execution
+jupyter>=1.0
+nbformat>=5.0
+nbconvert>=7.0
+
+# AWS SageMaker (optional, for running examples on SageMaker)
+sagemaker>=2.200.0
+boto3>=1.34.0
+
diff --git a/packages/third_party/aws_site_archive/requirements.txt b/packages/third_party/aws_site_archive/requirements.txt
new file mode 100644
index 00000000..1a6153a6
--- /dev/null
+++ b/packages/third_party/aws_site_archive/requirements.txt
@@ -0,0 +1,22 @@
+# Core AWS/cloud access dependencies
+s3fs>=2024.1.0
+fsspec>=2024.1.0
+
+# Data reading
+xarray>=2024.1.0
+zarr>=2.16.0
+cfgrib>=0.9.10
+eccodes>=1.6.0
+
+# Scientific stack
+numpy>=1.24.0
+pandas>=2.0.0
+scipy>=1.11.0
+
+# Visualisation
+matplotlib>=3.7.0
+cartopy>=0.22.0
+
+# PyEarthTools core
+pyearthtools-data>=0.1.0
+
diff --git a/packages/third_party/aws_site_archive/src/site_archive_aws/__init__.py b/packages/third_party/aws_site_archive/src/site_archive_aws/__init__.py
new file mode 100644
index 00000000..e36ed192
--- /dev/null
+++ b/packages/third_party/aws_site_archive/src/site_archive_aws/__init__.py
@@ -0,0 +1,46 @@
+# Copyright Commonwealth of Australia, Bureau of Meteorology 2026.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from pyearthtools.data.archive import (
+    register_archive,
+    load_root_directories_from_config,
+)
+
+ROOT_DIRECTORIES = {
+    "MOGlobal10km_AWS": "s3://met-office-atmospheric-model-data/global-deterministic-10km",
+    "MOUKV_AWS": "s3://met-office-atmospheric-model-data/uk-deterministic-2km",
+}
+
+register_archive("ROOT_DIRECTORIES")(ROOT_DIRECTORIES)
+
+# Make 'aws' a registered archive namespace so pyearthtools can find it
+import site_archive_aws  # noqa: E402
+
+from site_archive_aws.mo_global_10km import MOGlobal10km  # noqa: E402, F401
+from site_archive_aws.mo_ukv import MOUKV  # noqa: E402, F401
+
+register_archive("aws")(site_archive_aws)
+
+try:
+    # Generated by setuptools-scm at install time; not in version control.
+    from ._version import __version__  # type: ignore
+except ImportError:
+    __version__ = "999"
+
+__all__ = [
+    "MOGlobal10km",
+    "MOUKV",
+]
diff --git a/packages/third_party/aws_site_archive/src/site_archive_aws/mo_global_10km.py b/packages/third_party/aws_site_archive/src/site_archive_aws/mo_global_10km.py
new file mode 100644
index 00000000..aab80598
--- /dev/null
+++ b/packages/third_party/aws_site_archive/src/site_archive_aws/mo_global_10km.py
@@ -0,0 +1,106 @@
+# Copyright Commonwealth of Australia, Bureau of Meteorology 2026.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pathlib
+
+import xarray
+
+import pyearthtools
+import pyearthtools.data
+
+from pyearthtools.data import Petdt
+from pyearthtools.data.indexes import ArchiveIndex
+from pyearthtools.data.transforms import Transform, TransformCollection
+from pyearthtools.data.archive import register_archive
+
+
+@register_archive("MOGlobal10km", sample_kwargs=dict(variables=["temperature_on_pressure_levels"]))
+class MOGlobal10km(ArchiveIndex):
+    """
+    Data Accessor for accessing Met Office data through the AWS Sustainable Data initiative.
+    """
+
+    MO_GLOBAL_AWS_DIR_TEMPLATE = "{root_dir}/{vt_str}"
+    MO_GLOBAL_FNAME_TEMPLATE = "{vt_str}-PT0000H00M-{var_name}.nc"
+
+    def __init__(
+        self,
+        variables: list[str],
+        *,
+        transforms: Transform | TransformCollection | None = None,
+    ):
+        """
+        Init function for Met Office Global 10km model accessor class.
+        """
+        self._variables = variables
+        self._open_args = {
+            "engine": "h5netcdf",  #
+            "storage_options": {"anon": True},
+        }
+        self._mf_args = {"concat_dim": "time", "combine": "nested"}
+
+        super_transforms = (
+            TransformCollection(
+                [
+                    pyearthtools.data.transforms.variables.Trim(self._variables),
+                ]
+            )
+            + transforms
+        )
+
+        # call the base class
+        super().__init__(
+            transforms=super_transforms,
+        )
+        self.record_initialisation()
+
+    def filesystem(
+        self,
+        querytime: str | Petdt,
+    ) -> pathlib.Path | dict[str, str | pathlib.Path]:
+
+        querytime = Petdt(querytime)
+        paths = []
+        vt_template = "{dt.year:04d}{dt.month:02d}{dt.day:02d}T{dt.hour:02d}{dt.minute:02d}Z"
+        _fname_template = "{vt_str}-PT0000H00M-{var_name}.nc"
+        root_dir = self.ROOT_DIRECTORIES["MOGlobal10km_AWS"]
+
+        for var_name in self._variables:
+            try:
+                current_fname = MOGlobal10km.MO_GLOBAL_FNAME_TEMPLATE.format(
+                    vt_str=vt_template.format(dt=querytime), var_name=var_name
+                )
+                current_dir = MOGlobal10km.MO_GLOBAL_AWS_DIR_TEMPLATE.format(
+                    root_dir=root_dir, vt_str=vt_template.format(dt=querytime)
+                )
+                current_path = f"{current_dir}/{current_fname}"
+                paths += [current_path]
+            except KeyError:
+                print(f"No data for var {var_name}")
+
+        print(paths)
+        return paths
+
+    def load(self, *args, **kwargs):
+        ds = xarray.merge([xarray.open_dataset(path1, **self._open_args) for path1 in args[0]])
+        return ds
+
+        return ds
+
+    def _desc_(self):
+        return {
+            "singleline": "Met Office 10km Global Determinsitic Forecast Analysis ",
+            "range": "June 2026",
+            "Documentation": "https://registry.opendata.aws/met-office-global-deterministic/",
+        }
diff --git a/packages/third_party/aws_site_archive/src/site_archive_aws/mo_ukv.py b/packages/third_party/aws_site_archive/src/site_archive_aws/mo_ukv.py
new file mode 100644
index 00000000..cd47e97a
--- /dev/null
+++ b/packages/third_party/aws_site_archive/src/site_archive_aws/mo_ukv.py
@@ -0,0 +1,105 @@
+# Copyright Commonwealth of Australia, Bureau of Meteorology 2026.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pathlib
+
+import xarray
+
+import pyearthtools
+import pyearthtools.data
+
+from pyearthtools.data import Petdt
+from pyearthtools.data.indexes import ArchiveIndex
+from pyearthtools.data.transforms import Transform, TransformCollection
+from pyearthtools.data.archive import register_archive
+
+
+@register_archive("MOUKV", sample_kwargs=dict(variables=["temperature_on_pressure_levels"]))
+class MOUKV(ArchiveIndex):
+    """
+    Data Accessor for accessing Met Office UKV model data through the AWS Sustainable Data initiative.
+    """
+
+    MO_UKV_AWS_DIR_TEMPLATE = "{root_dir}/{vt_str}"
+    MO_UKV_FNAME_TEMPLATE = "{vt_str}-PT0000H00M-{var_name}.nc"
+
+    def __init__(
+        self,
+        variables: list[str],
+        *,
+        transforms: Transform | TransformCollection | None = None,
+    ):
+        """
+        Init function for Met Office UKV model data accessor.
+        """
+        self._variables = variables
+
+        self._open_args = {
+            "engine": "h5netcdf",  #
+            "storage_options": {"anon": True},
+        }
+        self._mf_args = {"concat_dim": "time", "combine": "nested"}
+        super_transforms = (
+            TransformCollection(
+                [
+                    pyearthtools.data.transforms.variables.Trim(self._variables),
+                ]
+            )
+            + transforms
+        )
+
+        # call the base class
+        super().__init__(
+            transforms=super_transforms,
+        )
+        self.record_initialisation()
+
+    def filesystem(
+        self,
+        querytime: str | Petdt,
+    ) -> pathlib.Path | dict[str, str | pathlib.Path]:
+
+        if not self._variables:
+            raise pyearthtools.data.DataNotFoundError("No variables specified to load.")
+
+        querytime = Petdt(querytime)
+        paths = []
+
+        vt_template = "{dt.year:04d}{dt.month:02d}{dt.day:02d}T{dt.hour:02d}{dt.minute:02d}Z"
+        root_dir = self.ROOT_DIRECTORIES["MOUKV_AWS"]
+        for var_name in self._variables:
+            try:
+                current_fname = MOUKV.MO_UKV_FNAME_TEMPLATE.format(
+                    vt_str=vt_template.format(dt=querytime), var_name=var_name
+                )
+                current_dir = MOUKV.MO_UKV_AWS_DIR_TEMPLATE.format(
+                    root_dir=root_dir, vt_str=vt_template.format(dt=querytime)
+                )
+                current_path = f"{current_dir}/{current_fname}"
+                paths += [current_path]
+            except KeyError:
+                print(f"No data for var {var_name}")
+        print(paths)
+        return paths
+
+    def load(self, *args, **kwargs):
+        ds = xarray.merge([xarray.open_dataset(path1, **self._open_args) for path1 in args[0]])
+        return ds
+
+    def _desc_(self):
+        return {
+            "singleline": "Met Office UKV Forecast Analysis ",
+            "range": "June 2026",
+            "Documentation": "https://registry.opendata.aws/met-office-uk-deterministic/",
+        }
diff --git a/packages/third_party/aws_site_archive/tests/test_moglobal.py b/packages/third_party/aws_site_archive/tests/test_moglobal.py
new file mode 100644
index 00000000..2268a308
--- /dev/null
+++ b/packages/third_party/aws_site_archive/tests/test_moglobal.py
@@ -0,0 +1,92 @@
+import pytest
+
+import datetime
+import random
+
+import site_archive_aws
+import pyearthtools.data
+
+from pyearthtools.data import Petdt
+
+VAR_LIST = [
+    "temperature_on_pressure_levels",
+    "relative_humidity_on_pressure_levels",
+    "wind_speed_on_pressure_levels",
+    "wind_direction_on_pressure_levels",
+]
+
+
+def _setup_index(var_list):
+    moglobal_index = pyearthtools.data.archive.MOGlobal10km(var_list)
+    return moglobal_index
+
+
+def _get_random_time():
+    current_dt = datetime.datetime.now()
+    previous_midnight = datetime.datetime(
+        current_dt.year,
+        current_dt.month,
+        current_dt.day,
+        0,
+        0,
+    )
+    # select a date within the coverage of data, which is the past 2 years
+    select_dt = previous_midnight - datetime.timedelta(hours=6 * random.randrange(2800))
+    return select_dt
+
+
+def test_moglobal_filepaths():
+    """
+    Test the filpaths functions.
+    """
+    var_list = VAR_LIST
+    ukv_accessor = _setup_index(var_list)
+
+    select_dt = Petdt(_get_random_time())
+
+    file_list = ukv_accessor.filesystem(select_dt)
+
+    assert len(var_list) == len(file_list)
+    assert all([v1 in str(f1) for v1, f1 in zip(var_list, file_list)])
+
+
+# @pytest.mark.slow
+@pytest.mark.noci
+def test_moglobal_load():
+    """
+    Integration Test of actually retrieving data.
+    """
+    moglobal_index = _setup_index(VAR_LIST)
+    select_dt = _get_random_time()
+    moglobal_ds = moglobal_index[select_dt]
+
+    # check the size of the data loaded
+    assert moglobal_ds["air_temperature"].shape == (1, 37, 1920, 2560)
+
+@pytest.mark.xfail(raises=pyearthtools.data.DataNotFoundError)
+def test_moglobal_novar():
+    """
+    Test that retrieval fails locally if no variables are specified.
+    """
+    moglobal_index = _setup_index([])
+    moglobal_ds = moglobal_index[_get_random_time()]
+
+
+@pytest.mark.noci
+@pytest.mark.xfail(raises=pyearthtools.data.DataNotFoundError)
+def test_moglobal_invalid_time():
+    """
+    Test that retrieval fails if an invalid time is given (data is hourly)
+    """
+    moglobal_index = _setup_index(VAR_LIST)
+    moglobal_index[_get_random_time() + datetime.timedelta(seconds=600)]
+
+
+@pytest.mark.noci
+@pytest.mark.xfail(raises=pyearthtools.data.DataNotFoundError)
+def test_moglobal_invalid_var():
+    """
+    Test that retrieval fails if an invalid variable name is given.
+    """
+    moglobal_index = _setup_index(["nonsense_name"])
+    moglobal_index[_get_random_time()]
diff --git a/packages/third_party/aws_site_archive/tests/test_ukv.py b/packages/third_party/aws_site_archive/tests/test_ukv.py
new file mode 100644
index 00000000..e46076cb
--- /dev/null
+++ b/packages/third_party/aws_site_archive/tests/test_ukv.py
@@ -0,0 +1,91 @@
+import pytest
+
+import datetime
+import random
+
+import site_archive_aws
+import pyearthtools.data
+
+from pyearthtools.data import Petdt
+
+VAR_LIST = [
+    "temperature_on_pressure_levels",
+    "relative_humidity_on_pressure_levels",
+    "wind_speed_on_pressure_levels",
+    "wind_direction_on_pressure_levels",
+]
+
+
+def _setup_index(var_list):
+    ukv_accessor = pyearthtools.data.archive.MOUKV(var_list)
+    return ukv_accessor
+
+
+def _get_random_time():
+    current_dt = datetime.datetime.now()
+    previous_midnight = datetime.datetime(
+        current_dt.year,
+        current_dt.month,
+        current_dt.day,
+        0,
+        0,
+    )
+    # select a date within the coverage of data, which is the past 2 years
+    select_dt = previous_midnight - datetime.timedelta(hours=6 * random.randrange(2800))
+    return select_dt
+
+
+def test_ukv_filepaths():
+    """
+    Test the filpaths functions.
+    """
+    var_list = VAR_LIST
+    ukv_accessor = _setup_index(var_list)
+
+    select_dt = Petdt(_get_random_time())
+
+    file_list = ukv_accessor.filesystem(select_dt)
+
+    assert len(var_list) == len(file_list)
+    assert all([v1 in str(f1) for v1, f1 in zip(var_list, file_list)])
+
+@pytest.mark.noci
+def test_ukv_load():
+    """
+    Integration Test of actually retrieving data.
+    """
+    ukv_index = _setup_index(VAR_LIST)
+    select_dt = _get_random_time()
+    ukv_ds = ukv_index[select_dt]
+
+    # check the size of the data loaded
+    assert ukv_ds["air_temperature"].shape == (1, 33, 970, 1042)
+
+
+@pytest.mark.xfail(raises=pyearthtools.data.DataNotFoundError)
+def test_ukv_novar():
+    """
+    Test that retrieval fails locally if no variables are specified.
+    """
+    ukv_index = _setup_index([])
+    ukv_ds = ukv_index[_get_random_time()]
+
+
+@pytest.mark.noci
+@pytest.mark.xfail(raises=pyearthtools.data.DataNotFoundError)
+def test_ukv_invalid_time():
+    """
+    Test that retrieval fails if an invalid time is given (data is hourly)
+    """
+    ukv_index = _setup_index(VAR_LIST)
+    ukv_index[_get_random_time() + datetime.timedelta(seconds=600)]
+
+
+@pytest.mark.noci
+@pytest.mark.xfail(raises=pyearthtools.data.DataNotFoundError)
+def test_ukv_invalid_var():
+    """
+    Test that retrieval fails if an invalid variable name is given.
+    """
+    ukv_index = _setup_index(["nonsense_name"])
+    ukv_index[_get_random_time()]
diff --git a/requirements_cicd.txt b/requirements_cicd.txt
index 1d7fc87e..24ec9095 100644
--- a/requirements_cicd.txt
+++ b/requirements_cicd.txt
@@ -5,4 +5,5 @@
 -e packages/tutorial
 -e packages/zoo
 -e packages/bundled_models/fourcastnext
+-e packages/third_party/aws_site_archive
 -e .[test]