From e523119e79b2bc9127bdf5c53ef58adf11a777e9 Mon Sep 17 00:00:00 2001
From: Ciheim Brown <Ciheim.Brown@ldt-4788508.gfdl.noaa.gov>
Date: Tue, 10 Dec 2024 10:09:16 -0500
Subject: [PATCH]  Doc changes

---
 .../scripts/gen_intake_gfdl_notebook.ipynb    | 4829 -----------------
 doc/generation.rst                            |   96 +-
 2 files changed, 81 insertions(+), 4844 deletions(-)
 delete mode 100644 catalogbuilder/scripts/gen_intake_gfdl_notebook.ipynb

diff --git a/catalogbuilder/scripts/gen_intake_gfdl_notebook.ipynb b/catalogbuilder/scripts/gen_intake_gfdl_notebook.ipynb
deleted file mode 100644
index ef4115d..0000000
--- a/catalogbuilder/scripts/gen_intake_gfdl_notebook.ipynb
+++ /dev/null
@@ -1,4829 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f39f9409-ee87-4431-9953-55607daba427",
-   "metadata": {},
-   "source": [
-    "This notebook was tested from a GFDL workstation.\n",
-    "This notebook is an example of using catalog builder from a notebook to generate data catalogs, a.k.a intake-esm catalogs.\n",
-    "\n",
-    "How to get here? \n",
-    "\n",
-    "Login to your workstation at GFDL.\n",
-    "module load python/3.9\n",
-    "conda activate intakebuilder \n",
-    "(For the above: Note that you can either install your own environment using the following or use an existing environment such as this: conda activate /nbhome/Aparna.Radhakrishnan/conda/envs/intakebuilder )\n",
-    "\n",
-    "conda create -n intakebuilder \n",
-    "conda install intakebuilder -c noaa-gfdl -n intakebuilder\n",
-    "\n",
-    "Now, we do a couple of things to make sure your environment is available to jupyter-lab as a kernel.\n",
-    "\n",
-    "pip install ipykernel \n",
-    "python -m ipykernel install --user --name=intakebuilder\n",
-    "\n",
-    "Now, start a jupyter-lab session from GFDL workstation: \n",
-    "\n",
-    "jupyter-lab \n",
-    "\n",
-    "This will give you the URL to the jupyter-lab session running on your localhost. Paste the URL in your web-browser (or via TigerVNC). Paste the notebook cells from this notebook, or locate the notebook from the path where you have downloaded or cloned it via git. Go to Kernel->Change Kernel-> Choose intakebuilder.\n",
-    "\n",
-    "Run the notebook and see the results! Extend it and share it with us via a github issue. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "fb3010b8-170f-4462-ad2a-457d1d5415f7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Found existing file! Overwrite? (y/n) y\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "writing..\n",
-      "JSON generated at: /home/a1r/mycatalog.json\n",
-      "CSV generated at: /home/a1r/mycatalog.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "from catalogbuilder.scripts import gen_intake_gfdl\n",
-    "import sys,os\n",
-    "\n",
-    "######USER input begins########\n",
-    "\n",
-    "#User provides the input directory for which a data catalog needs to be generated.\n",
-    "#Note that depending on the date and version of the tool, only time-series data are catalogued.\n",
-    "\n",
-    "input_path = \"/archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pdclim1850F/gfdl.ncrc5-deploy-prod-openmp/pp/\"\n",
-    "\n",
-    "#USER inputs the output path. Based on the following setting, user can expect to see /home/a1r/mycatalog.csv and /home/a1r/mycatalog.json generated as output.\n",
-    "\n",
-    "output_path = \"/home/a1r/mycatalog\"\n",
-    "\n",
-    "####END OF user input ##########\n",
-    "sys.argv = ['--INPUT_PATH', input_path, output_path]\n",
-    "\n",
-    "try:\n",
-    "      gen_intake_gfdl.main()\n",
-    "except SystemExit as e:\n",
-    "    if e.code != 0:\n",
-    "        raise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "626eaa1f-d801-4a7d-8fad-2851c9e81070",
-   "metadata": {},
-   "source": [
-    "Let's begin our analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "181913cc-4776-4b16-95d6-c6ea1b2cbdad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import intake_esm, intake\n",
-    "import matplotlib #do a pip install of tools needed in your env or from the notebook\n",
-    "from matplotlib import pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6665a48b-a335-4fc2-8130-1a4902a428b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "0f83dbc3-3dda-4a43-82e9-fb8726b2cda8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col_url = \"/home/a1r/mycatalog.json\"\n",
-    "col = intake.open_esm_datastore(col_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "344ada01-6716-4fbd-9cee-878ff815d7dd",
-   "metadata": {},
-   "source": [
-    "Explore the catalog"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "1ce0716e-6667-4aeb-8c4b-50a05643b87f",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>gfdl.ncrc5-deploy-prod-openmp</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>/archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...</td>\n",
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-       "      <td>treeFracNdlEvg</td>\n",
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-       "      <td>monthly</td>\n",
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-       "      <td>c96L65_am5f3b1r0_pdclim1850F</td>\n",
-       "      <td>monthly</td>\n",
-       "      <td>land_cmip</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>001001-001012</td>\n",
-       "      <td>1yr</td>\n",
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-       "      <td>gfdl.ncrc5-deploy-prod-openmp</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6410 rows × 17 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     activity_id  institution_id source_id                 experiment_id  \\\n",
-       "0            dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "1            dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "2            dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "3            dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "4            dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "...          ...             ...       ...                           ...   \n",
-       "6405         dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "6406         dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "6407         dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "6408         dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "6409         dev             NaN       am5  c96L65_am5f3b1r0_pdclim1850F   \n",
-       "\n",
-       "     frequency modeling_realm  table_id  member_id  grid_label  \\\n",
-       "0          3hr     atmos_cmip       NaN        NaN         NaN   \n",
-       "1          3hr     atmos_cmip       NaN        NaN         NaN   \n",
-       "2          3hr     atmos_cmip       NaN        NaN         NaN   \n",
-       "3          3hr     atmos_cmip       NaN        NaN         NaN   \n",
-       "4          3hr     atmos_cmip       NaN        NaN         NaN   \n",
-       "...        ...            ...       ...        ...         ...   \n",
-       "6405   monthly      land_cmip       NaN        NaN         NaN   \n",
-       "6406   monthly      land_cmip       NaN        NaN         NaN   \n",
-       "6407   monthly      land_cmip       NaN        NaN         NaN   \n",
-       "6408   monthly      land_cmip       NaN        NaN         NaN   \n",
-       "6409   monthly      land_cmip       NaN        NaN         NaN   \n",
-       "\n",
-       "         variable_id        temporal_subset chunk_freq  grid_label.1  \\\n",
-       "0                 pr  0002010100-0002123123        1yr           NaN   \n",
-       "1               rlut  0002010100-0002123123        1yr           NaN   \n",
-       "2                 pr  0003010100-0003123123        1yr           NaN   \n",
-       "3               rlut  0003010100-0003123123        1yr           NaN   \n",
-       "4                 pr  0004010100-0004123123        1yr           NaN   \n",
-       "...              ...                    ...        ...           ...   \n",
-       "6405  treeFracNdlDcd          001001-001012        1yr           NaN   \n",
-       "6406  treeFracNdlEvg          001001-001012        1yr           NaN   \n",
-       "6407             tsl          001001-001012        1yr           NaN   \n",
-       "6408         vegFrac          001001-001012        1yr           NaN   \n",
-       "6409       vegHeight          001001-001012        1yr           NaN   \n",
-       "\n",
-       "                           platform  dimensions cell_methods  \\\n",
-       "0     gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "1     gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "2     gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "3     gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "4     gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "...                             ...         ...          ...   \n",
-       "6405  gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "6406  gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "6407  gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "6408  gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "6409  gfdl.ncrc5-deploy-prod-openmp         NaN           ts   \n",
-       "\n",
-       "                                                   path  \n",
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-       "1     /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "2     /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "3     /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "4     /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "...                                                 ...  \n",
-       "6405  /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "6406  /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "6407  /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "6408  /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "6409  /archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pd...  \n",
-       "\n",
-       "[6410 rows x 17 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col.df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "613f8259-a92f-4be5-8268-dfbe225f0670",
-   "metadata": {},
-   "source": [
-    "Let's narrow down the search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "62acbaec-573c-47f9-83bc-015790fd7983",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expname_filter = ['c96L65_am5f3b1r0_pdclim1850F']\n",
-    "modeling_realm = \"land_cmip\"\n",
-    "frequency = \"daily\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "7fa86782-3f7b-4dbf-80af-0f035003d57f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat = col.search(experiment_id=expname_filter,frequency=frequency,modeling_realm=modeling_realm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "6fe2cf2f-e74a-4b50-a099-47c28541878d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'hflsLut', 'mrso', 'mrsos'}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(cat.df[\"variable_id\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "aa216969-e335-4448-977c-d623a62a697e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat = cat.search(variable_id=\"mrso\") #Total Soil Moisture Content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8542c4e8-07eb-48ba-b466-8e07d3405415",
-   "metadata": {},
-   "source": [
-    "dmget the files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "5227091c-5d83-4b73-a340-22e92124e1f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#for simple dmget usage, just use this !dmget {file}\n",
-    "#use following to wrap the dmget call for each path in the catalog\n",
-    "def dmgetmagic(x):\n",
-    "    cmd = 'dmget %s'% str(x) \n",
-    "    return os.system(cmd)\n",
-    "\n",
-    "#OR refer to importing dmget ,  https://github.com/aradhakrishnanGFDL/canopy-cats/tree/main/notebooks/dmget.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "5eb6b01e-4d68-48ee-904f-dd285be7dee5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dmstatus = cat.df[\"path\"].apply(dmgetmagic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "8b50305d-aac1-4df5-add1-fbc9af7773ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
-      "\t'source_id.experiment_id.frequency.modeling_realm.variable_id.chunk_freq'\n",
-      " |████████████████████████████████████████| 100.00% [1/1 00:00<00:00]\r"
-     ]
-    }
-   ],
-   "source": [
-    "dset_dict = cat.to_dataset_dict(cdf_kwargs={'chunks': {'time':5}, 'decode_times': True})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "f1c27413-e9a7-4855-b9be-1c0b9cf7f4ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "am5.c96L65_am5f3b1r0_pdclim1850F.daily.land_cmip.mrso.1yr\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k in dset_dict.keys(): \n",
-    "    print(k)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "9aae260f-87c8-4d2a-9b55-b9587c1f2309",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = dset_dict[\"am5.c96L65_am5f3b1r0_pdclim1850F.daily.land_cmip.mrso.1yr\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "c650221c-714e-4f2e-a53f-ca937c6c38ae",
-   "metadata": {},
-   "outputs": [
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-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 757MB\n",
-       "Dimensions:     (time: 3650, bnds: 2, lat: 180, lon: 288)\n",
-       "Coordinates:\n",
-       "    average_DT  (time) timedelta64[ns] 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T1  (time) object 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T2  (time) object 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "  * bnds        (bnds) float64 16B 1.0 2.0\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "    lat_bnds    (lat, bnds) float64 3kB dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "    lon_bnds    (lon, bnds) float64 5kB dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;\n",
-       "  * time        (time) object 29kB 0002-01-01 12:00:00 ... 0011-12-31 12:00:00\n",
-       "    time_bnds   (time, bnds) object 58kB dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;\n",
-       "Data variables:\n",
-       "    mrso        (time, lat, lon) float32 757MB dask.array&lt;chunksize=(5, 180, 288), meta=np.ndarray&gt;\n",
-       "Attributes: (12/18)\n",
-       "    title:                            c96L65_am5f3b1r0_pdclim1850F\n",
-       "    grid_type:                        regular\n",
-       "    grid_tile:                        N/A\n",
-       "    code_release_version:             2023.01\n",
-       "    git_hash:                         unknown githash\n",
-       "    external_variables:               land_area\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:variable_id:     mrso\n",
-       "    intake_esm_attrs:chunk_freq:      1yr\n",
-       "    intake_esm_attrs:platform:        gfdl.ncrc5-deploy-prod-openmp\n",
-       "    intake_esm_attrs:cell_methods:    ts\n",
-       "    intake_esm_attrs:_data_format_:   netcdf\n",
-       "    intake_esm_dataset_key:           am5.c96L65_am5f3b1r0_pdclim1850F.daily....</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-77f762a7-ea18-4ea9-a5cf-ad05489dd6d7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77f762a7-ea18-4ea9-a5cf-ad05489dd6d7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3650</li><li><span class='xr-has-index'>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9c145b76-4bd9-49ba-b056-382607f82e73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c145b76-4bd9-49ba-b056-382607f82e73' class='xr-section-summary' >Coordinates: <span>(10)</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>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-81b9f3cd-9f78-4209-aa28-2e666d235f58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81b9f3cd-9f78-4209-aa28-2e666d235f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1280a6c3-cc38-4a08-9656-9ed11c4ebee8' class='xr-var-data-in' type='checkbox'><label for='data-1280a6c3-cc38-4a08-9656-9ed11c4ebee8' 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>Length of average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> timedelta64[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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-32ef2c27-091f-4824-ad49-6f9e3d1f7725' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32ef2c27-091f-4824-ad49-6f9e3d1f7725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53834b0b-7ee8-4bfc-a9f1-c837cbcc947e' class='xr-var-data-in' type='checkbox'><label for='data-53834b0b-7ee8-4bfc-a9f1-c837cbcc947e' 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>Start time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object 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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-40d26f54-effb-4a2b-b4bd-ba16f1abb24f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40d26f54-effb-4a2b-b4bd-ba16f1abb24f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3064b1c0-2c04-44c5-85c5-1675ca95e0ed' class='xr-var-data-in' type='checkbox'><label for='data-3064b1c0-2c04-44c5-85c5-1675ca95e0ed' 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>End time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object 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",
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-       "  <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",
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-       "\n",
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-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bnds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-b102cb08-6f9c-494b-96af-68d8a3609f78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b102cb08-6f9c-494b-96af-68d8a3609f78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fbe110f-7dc3-4401-9c10-ddcde9058317' class='xr-var-data-in' type='checkbox'><label for='data-9fbe110f-7dc3-4401-9c10-ddcde9058317' 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>vertex number</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-dca084e6-0f71-4de0-99f7-5beac768e9e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dca084e6-0f71-4de0-99f7-5beac768e9e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0f859eb-f2b8-421e-8b01-61e3368e6013' class='xr-var-data-in' type='checkbox'><label for='data-c0f859eb-f2b8-421e-8b01-61e3368e6013' 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_N</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
-       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
-       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
-       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
-       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
-       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
-       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
-       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
-       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
-       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
-       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
-       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
-       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
-       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
-       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
-       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;</div><input id='attrs-f8606c98-c201-4ea3-978c-e6b9ce98d94e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8606c98-c201-4ea3-978c-e6b9ce98d94e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db0b3d46-6d7b-4cb6-93fe-85da2636b0c8' class='xr-var-data-in' type='checkbox'><label for='data-db0b3d46-6d7b-4cb6-93fe-85da2636b0c8' 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 bounds</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>axis :</span></dt><dd>Y</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> 2.81 kiB </td>\n",
-       "                        <td> 2.81 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (180, 2) </td>\n",
-       "                        <td> (180, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 45 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=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"25\" 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=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"12.962080\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
-       "  <text x=\"45.924160\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.924160,60.000000)\">180</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-a964d64e-7689-4623-b759-8952c8212b77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a964d64e-7689-4623-b759-8952c8212b77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68cbba46-84f5-49e5-a074-fa4cb697308c' class='xr-var-data-in' type='checkbox'><label for='data-68cbba46-84f5-49e5-a074-fa4cb697308c' 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_E</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;</div><input id='attrs-36bc9d69-fb96-4059-aadf-94ab2ac68d69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36bc9d69-fb96-4059-aadf-94ab2ac68d69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57213529-6d89-4507-951c-054c33da978d' class='xr-var-data-in' type='checkbox'><label for='data-57213529-6d89-4507-951c-054c33da978d' 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 bounds</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>axis :</span></dt><dd>X</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> 4.50 kiB </td>\n",
-       "                        <td> 4.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (288, 2) </td>\n",
-       "                        <td> (288, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 45 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=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
-       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">288</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0002-01-01 12:00:00 ... 0011-12-...</div><input id='attrs-93646069-4dd8-4504-979a-f21382857405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93646069-4dd8-4504-979a-f21382857405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f604cb77-77a2-4dfb-89e1-548c75697844' class='xr-var-data-in' type='checkbox'><label for='data-f604cb77-77a2-4dfb-89e1-548c75697844' 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>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
-       "       cftime.DatetimeNoLeap(11, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(11, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(11, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;</div><input id='attrs-d6364093-9df1-408a-8207-bca0740f3fe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6364093-9df1-408a-8207-bca0740f3fe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bda68e53-a378-422c-8eff-daf7f02fecd0' class='xr-var-data-in' type='checkbox'><label for='data-bda68e53-a378-422c-8eff-daf7f02fecd0' 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 axis boundaries</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 57.03 kiB </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650, 2) </td>\n",
-       "                        <td> (5, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 721.80 MiB </td>\n",
-       "                        <td> 0.99 MiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "       ...\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-346e8870-6779-4887-b017-d4aae705a12c' class='xr-index-data-in' type='checkbox'/><label for='index-346e8870-6779-4887-b017-d4aae705a12c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,\n",
-       "                    4.375,              5.625,              6.875,\n",
-       "                    8.125,              9.375,             10.625,\n",
-       "                   11.875,\n",
-       "       ...\n",
-       "                  348.125,            349.375,            350.625,\n",
-       "                  351.875,            353.125,            354.375,\n",
-       "                  355.625,            356.875,            358.125,\n",
-       "                  359.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1731c32-cae3-4a58-9bbb-99c3fdad557d' class='xr-index-data-in' type='checkbox'/><label for='index-a1731c32-cae3-4a58-9bbb-99c3fdad557d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0002-01-01 12:00:00, 0002-01-02 12:00:00, 0002-01-03 12:00:00,\n",
-       "             0002-01-04 12:00:00, 0002-01-05 12:00:00, 0002-01-06 12:00:00,\n",
-       "             0002-01-07 12:00:00, 0002-01-08 12:00:00, 0002-01-09 12:00:00,\n",
-       "             0002-01-10 12:00:00,\n",
-       "             ...\n",
-       "             0011-12-22 12:00:00, 0011-12-23 12:00:00, 0011-12-24 12:00:00,\n",
-       "             0011-12-25 12:00:00, 0011-12-26 12:00:00, 0011-12-27 12:00:00,\n",
-       "             0011-12-28 12:00:00, 0011-12-29 12:00:00, 0011-12-30 12:00:00,\n",
-       "             0011-12-31 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=3650, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-593f4aa0-f7b1-40dc-a2cc-b1e79101cbd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-593f4aa0-f7b1-40dc-a2cc-b1e79101cbd6' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>c96L65_am5f3b1r0_pdclim1850F</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd><dt><span>code_release_version :</span></dt><dd>2023.01</dd><dt><span>git_hash :</span></dt><dd>unknown githash</dd><dt><span>external_variables :</span></dt><dd>land_area</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;mrso&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>dev</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>am5</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>c96L65_am5f3b1r0_pdclim1850F</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>daily</dd><dt><span>intake_esm_attrs:modeling_realm :</span></dt><dd>land_cmip</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>mrso</dd><dt><span>intake_esm_attrs:chunk_freq :</span></dt><dd>1yr</dd><dt><span>intake_esm_attrs:platform :</span></dt><dd>gfdl.ncrc5-deploy-prod-openmp</dd><dt><span>intake_esm_attrs:cell_methods :</span></dt><dd>ts</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>am5.c96L65_am5f3b1r0_pdclim1850F.daily.land_cmip.mrso.1yr</dd></dl></div></li></ul></div></div>"
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-       "<xarray.Dataset> Size: 757MB\n",
-       "Dimensions:     (time: 3650, bnds: 2, lat: 180, lon: 288)\n",
-       "Coordinates:\n",
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-       "    average_T2  (time) object 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "  * bnds        (bnds) float64 16B 1.0 2.0\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "    lat_bnds    (lat, bnds) float64 3kB dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
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-       "  * time        (time) object 29kB 0002-01-01 12:00:00 ... 0011-12-31 12:00:00\n",
-       "    time_bnds   (time, bnds) object 58kB dask.array<chunksize=(5, 2), meta=np.ndarray>\n",
-       "Data variables:\n",
-       "    mrso        (time, lat, lon) float32 757MB dask.array<chunksize=(5, 180, 288), meta=np.ndarray>\n",
-       "Attributes: (12/18)\n",
-       "    title:                            c96L65_am5f3b1r0_pdclim1850F\n",
-       "    grid_type:                        regular\n",
-       "    grid_tile:                        N/A\n",
-       "    code_release_version:             2023.01\n",
-       "    git_hash:                         unknown githash\n",
-       "    external_variables:               land_area\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:variable_id:     mrso\n",
-       "    intake_esm_attrs:chunk_freq:      1yr\n",
-       "    intake_esm_attrs:platform:        gfdl.ncrc5-deploy-prod-openmp\n",
-       "    intake_esm_attrs:cell_methods:    ts\n",
-       "    intake_esm_attrs:_data_format_:   netcdf\n",
-       "    intake_esm_dataset_key:           am5.c96L65_am5f3b1r0_pdclim1850F.daily...."
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-     "execution_count": 79,
-     "metadata": {},
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-   "execution_count": 80,
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
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-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;mrso&#x27; (time: 3650, lat: 180, lon: 288)&gt; Size: 757MB\n",
-       "dask.array&lt;concatenate, shape=(3650, 180, 288), dtype=float32, chunksize=(5, 180, 288), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "    average_DT  (time) timedelta64[ns] 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T1  (time) object 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T2  (time) object 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "  * time        (time) object 29kB 0002-01-01 12:00:00 ... 0011-12-31 12:00:00\n",
-       "Attributes:\n",
-       "    units:            kg m-2\n",
-       "    long_name:        Total Soil Moisture Content\n",
-       "    cell_methods:     area: mean time: mean\n",
-       "    ocean_fillvalue:  0.0\n",
-       "    cell_measures:    area: land_area\n",
-       "    time_avg_info:    average_T1,average_T2,average_DT\n",
-       "    standard_name:    soil_moisture_content\n",
-       "    interp_method:    conserve_order1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'mrso'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3650</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-edb3954d-4304-46a0-abd0-4764acf148d6' class='xr-array-in' type='checkbox' checked><label for='section-edb3954d-4304-46a0-abd0-4764acf148d6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(5, 180, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 721.80 MiB </td>\n",
-       "                        <td> 0.99 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650, 180, 288) </td>\n",
-       "                        <td> (5, 180, 288) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 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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-       "  <text x=\"137.834311\" y=\"87.796507\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,137.834311,87.796507)\">180</text>\n",
-       "  <text x=\"35.294118\" y=\"89.710661\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.710661)\">3650</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-9b3578c7-30a4-4a28-ae7c-b73d22a61547' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b3578c7-30a4-4a28-ae7c-b73d22a61547' class='xr-section-summary' >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>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-c74f4a3f-c848-4ed3-a7b6-5388b458e93d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c74f4a3f-c848-4ed3-a7b6-5388b458e93d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd0ea5be-f033-41b9-8778-dc860ae9633a' class='xr-var-data-in' type='checkbox'><label for='data-bd0ea5be-f033-41b9-8778-dc860ae9633a' 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>Length of average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> timedelta64[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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-94650aeb-479f-4678-ab33-1dd8cd130127' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94650aeb-479f-4678-ab33-1dd8cd130127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-219f248d-8fbe-4cff-a0e1-d589119707cc' class='xr-var-data-in' type='checkbox'><label for='data-219f248d-8fbe-4cff-a0e1-d589119707cc' 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>Start time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object 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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-b9041098-4aad-44ef-9e75-30f61944beed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9041098-4aad-44ef-9e75-30f61944beed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da660594-cc46-4a60-80f2-4a2e2ca9b108' class='xr-var-data-in' type='checkbox'><label for='data-da660594-cc46-4a60-80f2-4a2e2ca9b108' 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>End time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object 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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-901b0078-c2b0-4e69-b0da-ef0300d1624b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-901b0078-c2b0-4e69-b0da-ef0300d1624b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd1dbb4-a00b-4ad3-81dc-9a5a22b44d47' class='xr-var-data-in' type='checkbox'><label for='data-bfd1dbb4-a00b-4ad3-81dc-9a5a22b44d47' 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_N</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
-       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
-       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
-       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
-       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
-       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
-       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
-       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
-       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
-       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
-       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
-       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
-       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
-       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
-       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
-       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-f934c8b1-bff3-4fda-8b20-60f79700a367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f934c8b1-bff3-4fda-8b20-60f79700a367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45039d51-613b-4ead-9609-4dced45aaa26' class='xr-var-data-in' type='checkbox'><label for='data-45039d51-613b-4ead-9609-4dced45aaa26' 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_E</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</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'>object</div><div class='xr-var-preview xr-preview'>0002-01-01 12:00:00 ... 0011-12-...</div><input id='attrs-fe8b61b0-5cb0-430c-94e1-39cc7510eeea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe8b61b0-5cb0-430c-94e1-39cc7510eeea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14b6a3e9-0017-4237-9e65-1e61342df7a0' class='xr-var-data-in' type='checkbox'><label for='data-14b6a3e9-0017-4237-9e65-1e61342df7a0' 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>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
-       "       cftime.DatetimeNoLeap(11, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(11, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(11, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34ee5c21-e792-442c-87a9-a9f56d942bb0' class='xr-section-summary-in' type='checkbox'  ><label for='section-34ee5c21-e792-442c-87a9-a9f56d942bb0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b046cbc5-3f74-4621-a0fc-74e50f0296ed' class='xr-index-data-in' type='checkbox'/><label for='index-b046cbc5-3f74-4621-a0fc-74e50f0296ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       ...\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc233807-8b29-4578-92a1-7da6ceeed522' class='xr-index-data-in' type='checkbox'/><label for='index-fc233807-8b29-4578-92a1-7da6ceeed522' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,\n",
-       "                    4.375,              5.625,              6.875,\n",
-       "                    8.125,              9.375,             10.625,\n",
-       "                   11.875,\n",
-       "       ...\n",
-       "                  348.125,            349.375,            350.625,\n",
-       "                  351.875,            353.125,            354.375,\n",
-       "                  355.625,            356.875,            358.125,\n",
-       "                  359.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-22409472-4c9b-4fde-9bad-37ebbe00ee70' class='xr-index-data-in' type='checkbox'/><label for='index-22409472-4c9b-4fde-9bad-37ebbe00ee70' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0002-01-01 12:00:00, 0002-01-02 12:00:00, 0002-01-03 12:00:00,\n",
-       "             0002-01-04 12:00:00, 0002-01-05 12:00:00, 0002-01-06 12:00:00,\n",
-       "             0002-01-07 12:00:00, 0002-01-08 12:00:00, 0002-01-09 12:00:00,\n",
-       "             0002-01-10 12:00:00,\n",
-       "             ...\n",
-       "             0011-12-22 12:00:00, 0011-12-23 12:00:00, 0011-12-24 12:00:00,\n",
-       "             0011-12-25 12:00:00, 0011-12-26 12:00:00, 0011-12-27 12:00:00,\n",
-       "             0011-12-28 12:00:00, 0011-12-29 12:00:00, 0011-12-30 12:00:00,\n",
-       "             0011-12-31 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=3650, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd19640f-9870-4aec-829f-693bc49bf9e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd19640f-9870-4aec-829f-693bc49bf9e6' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>Total Soil Moisture Content</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd><dt><span>ocean_fillvalue :</span></dt><dd>0.0</dd><dt><span>cell_measures :</span></dt><dd>area: land_area</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>soil_moisture_content</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'mrso' (time: 3650, lat: 180, lon: 288)> Size: 757MB\n",
-       "dask.array<concatenate, shape=(3650, 180, 288), dtype=float32, chunksize=(5, 180, 288), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "    average_DT  (time) timedelta64[ns] 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    average_T1  (time) object 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    average_T2  (time) object 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "  * time        (time) object 29kB 0002-01-01 12:00:00 ... 0011-12-31 12:00:00\n",
-       "Attributes:\n",
-       "    units:            kg m-2\n",
-       "    long_name:        Total Soil Moisture Content\n",
-       "    cell_methods:     area: mean time: mean\n",
-       "    ocean_fillvalue:  0.0\n",
-       "    cell_measures:    area: land_area\n",
-       "    time_avg_info:    average_T1,average_T2,average_DT\n",
-       "    standard_name:    soil_moisture_content\n",
-       "    interp_method:    conserve_order1"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds[\"mrso\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "d8e8cd0c-5502-4564-bb12-a269781415ad",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mrso = ds.mrso.isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "68b4a24c-0720-476b-8061-c42c84608e5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1502c4d3e5a0>"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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x2O+aoqKiYigybbNPW64qKkYwpRNKvS2AWb9KoyptNquHM1UeHk2qMFJP2SfuBvL0Y91pzwLwhfGXMePAoYqYX+788kpee/4jDLmZcF0tiG47NNmmRRWWseQ7JJXo0n2hsOChdPj5H0dfzqLEJMOVHABF18QJXEgbzSJvb72HnCrXs4ZP0deYDDI8VTw6NGEulxsOhlKfdxVfo/S0ehgFb/45/eLY5QA8Or6AuC50j+crlG2DbFzMWRtjZZK6Fc6jnFnB1GTdB/f2waEY00ulPWNWITEGzV+9X9r6BpyxeTbc35LfG+eHT60HwN6eoPnxIgxKlpxvWegtwph5XW0ovo8yMR0cTBVncuqUYpTcJ7fRkhId4dDlKXxVsvMAbjm0lqtbNgGiBdtqd79Y3XxOiJijk4vTJlstQoQIESJEiBDhZCAKP58Hblq8/gi/jh11/Q/zwulWjgeDszliusOukVYA3JKB74FiyTNz0+MasRmJBlMemO88yDVddbZoZ7WdS1Pbj9r+0b2X5mtmvCP4tmrK4c/tR9ImzcXjVoWiJ21n1SpdusegI1H+kNMYZrKtMMbo1ePhPlzfw8MnoYjm6MO9L1zmWr8hrMRnO+/nZZuuZ+IxKfRpevDTX51P76sPAXBuop41OOA08B8T52N7hxfVrWmVIpw4/GbDaszA72d15wgL4nkWBJVUZ9wEqxKi91lijtGhVYmHc1Uh6XukFNHLzY3SSz7MLfPs4R9W5+qH06I31BQPFZ9bnxC9X3zYYDZYV3XEm2gi+D7pgpMgzIgdsqD7btEGLa1YeKbH7CJhTLW3j6IrHvYbFgFgvGI/t62R+fiKzbO0GLO4ZfmT4CRhz+szQF1z2PlAnemy0iqz3Y1ynDGITUHHfXkZhf0H8aZlXp6oUiHPCw8Ka6tdeBHVi2bDDLW9+SZu2n0NAKrmoZsui9j0onXzWOGi4h4nn/Eino3TDtHDUYQIESJEiHCKwz8BmiM/0hwdMxTfP0qKxO8oZmZmaGho4DKuRVeM42rr43sfPeLvH+s7+7jaPV7Yt0nkqCg+C1LTtMREd/Dw+CJGJrPEYqK9sSydlv+WqNOc9Yj99OHD2orfdbjBpuVpeL4SFqv9i4W3ktPq+qrUHH8iDT9kU44HVd9m1K1QCArsavgsMxLz2CbbFy1GjTGqBllvd5Qb+FV+LUuTkqqzIjYcZrNk1QpnmvNvKGNeOcw0atOS85bZvsuru4UB+If9989bllE9OjXRhoy5JSY8lXtK/cGyMn/96Kv5k3X3AnBpatu8bXdbbfx6qh7FR4zRC4OdXwiKFc895UkXAh8h3wMtIfPouxd/jSZtThFnfz5bVPE17GA+Wr7GdycvoOzKPcXydGbt+dmRr2oTpsP1hSG4Z0rmRsU1mLWFEd35VHc9/QqITapoVahdUsUlDolWYY6cbVmMAlTPkO+K4tPVPE3KEI3U3OLO1mUHn8swzfN6+vnBNcx8bwG5HXKNmwem8Cfzciz5/HNq94RAUdG7OvBacgDMLs3SdcMunEB0ZLkakxW5bu9b91/A83fJd3ybO/lvpqenyWafroI8Maj9Tbr9yYWkMsfHHBULHi9fO/CC9ve3BRFzFCFChAgRIpziiATZJxcRc/Q0vNDM0YvNGr1tx8Bhv82tt5ZSq/QYU4etU/P7uXiTNe/33UXRK83YdS+puOawMlOPRBeaE8TVOlv02tR8fZah1LU0B5wi416MeFD0Ka54pILuxRSVpGIQm3NexlxhvTzAUBSa1VS4bK6WaS6DNOLOogKZIPsrocTw8EImady1sIObSJ+eREUN26oxPnMRV2RZl2Ycpot6dfc53LB7KwCDVnPoT6MF22SCTCcTj6Kv883JiwBoMWbntXPn2sOz9yK8MNj5xfPnM0c+oMltsrN3gqa4MDHnNu3n7Y11NnWbleODW68L3aoLMwnUoeC68BWcDotkVtgV5Qh/o9a0SWaYqnjoikdCs8Pv03ZQy3HHYkDBD2oI1sgfYyKYyyOEfS8scSHhUbvDazM6XsxDbZA5t7B9MmSRnitzBHX2KK7Y/PMXXkXjjiBrr+Jg7JVr3B0df0Frq9WgZTK4a4LaajGNoZfGueQaYeKqrs6BYgODGxYctl3fh+4/7LfngpPJHN2yqe+EMEdXrdsbMUfHgChbLUKECBEiRIgQYQ6i12ovIJ6NJaoxCnPxuSXHXk/tuWBu5pgdvHvX8FEVHzPIYcioDhNeXQfxdHfosZ+s4Ef75PP1ix8lo1ZoaZDMnHumlmEF2VSer7Cz2BaKBw/ojehKPU+i3xxFm8PqGHM0SHvsdkbsuv9SXLVxg/7GVZucVgy3XWvmaVSlv1XfYdJzAHH8TSo6BxybGT+oeYeLhRYcfxwNn+WGsFMJRTLWxl2JfCc9I8wK2WRZ5FSbBlW2bVbjNKsw68u6h1yPfJAhN+kqWL5GxZcofthp5A+3D4UOyt8fPpu394g/zQGriYNWQ6hBKbsmFdcgqYuGZUXiIMnAXdlUXJbugH9e1huOi35n17xz41w2zInGwA/Xhp9ftmgnzUbdUVxVfG5sqXs4XdP9/BnRA/+5Jvy84LrNz7udE4HR/15JlrqepliK4VY1klk5b13pGZ4abQdgy74uvmmeTyoly3xfIW7YLGoU/59kq8UDSh8Anq2ixdzDGKO5vP2ufHP4OWVa9KTzsq2vsG9GPIa0mEs8blMpy5zzXAUj7nDlejkXKb3KPaNLZFkpgecp2HZt3oNa1vCKMuf2DbSBKh1o/XkaU69fo8kr69mSR8Or0vXM1b+74BXM9ghL1rzFoHFKdITqTAGvzAvOHrmFApU22f+CD+6gS/HYPNEJwNhEBlX3+MOr7wTgvnWHO8yfDvBQwozb599G9KLoWBE9HEWIECFChAinOCLN0clF9HD0AuMvdm95xuW1jBEvmLRPX/9vl6w+bJvjhaHUWZte3Z6n01mIx5grmop/H6zX+Xprz8UoCpiaRJfbZjs5K7ufalAbrjk2y0S1nnU2N+VU2JG6TmifXY+Qe40JcopNMcjqeaK0EE3xWBqTzLGcVmcr4oqD5WuhXmfc1Sh4wq5YqFT8GFssYWkW6lOsMOpeRrOeS8GXdSddnR12G1uqieB7Cg2fdkMi/pJncsCSSL1RL1LyTMaCdh1P441ND3FxXLJdGtV6FtxBt8x3Z9azvyzH1xWbZn1yX8gkXde1kZ8ekqwYU3XJGaVQS+J4GmuzQ6wMPHSm3STVYLtafa5FD8t5mqim8AJ3ZVUR9s+7p23euKvK4RHi7EuO7sX1dKzfCEucnXgBa/fShh1UPIOSd2Ki7lWPyq3H9jWylVE2j0jW4/7vrwvXMQyXpGkxXZQx0nWX7pycI1315mVb1fB8tDNzWdWsch//MX0O33hKatW5lka2scRfrvgVICzeD5FsxLX9QyyNj/KjsbMA2D7Riu1pXN8uOqSKb4TnY8eUaPMcV8azdi29c/E9gNwH/mHHy8J+JHQ7dOJ2fJXRp+T8Kg5UFih0NMs4eL7Cwb0t/HRE+kDGJtVQCduxbQ1dl+ulfeEEScOiFGS+WY4WsrK2pzG1sZX4uGyX5NmZo9+7+0/Dz8qkiZuU/UysVZhe0gJAeqCZxq0llA3CCJ5w36Pg+tZbm0lvED3lhrtXcvFlm1mSE8+w/7P0Vywxxhl35f50H89+Tz2SR9sLWWcxwqmH6OEoQoQIESJEOMXh+mr4MPv824heqx0rooejE4S5jE+N7blh99bDHE3nTu5hu5GuIDMspVaZcNNhpJ7RysQV+4RlvNUi2Bv7zgmjIg2PEVeheU4XVVTesfv1AIwUM9iBw7T1Q52Yb7OySRidlF7lkele8lYibD9t1DPZdMUL2QvPV7Dm1I2aqzfSgrfohYCRmHVj2L4WVi+vGAbnxvcD0KebjLoF3rHrekCy4i5vEYfuBeYEFc/gW8MXADBTjfOO3nv5o6ywJVk1EdZi69agXTvIXWWpqXTIyfDj4bWMTskavi+sBYDjaGiah6ZJn1e0jqEpHuU5mW2ZQI+0QE/xtobHGQ8ySpbpJlXf4aGqMD6m4fKGVhl7F5WUWg3nwx6rjbhiszXo085iW+g/9arcRprVEu9uuwOAbVZnOH63TKxlxo7jBBl0NWbvc4vEv6VVM0Mt1ScevpJ7f3YGsclwc658ez1bZ66eoTs2wfWNO6j4ct4yqoWGH9Z769Xr2YnPhrlR+Id7z2XRwymqXp35yhpllrUKZTFtxanYwpi1pwroqkdbqpaROP+VwNzvaqCfq/luHcksb259soqrowcM5L3FZVyeEv2fhcqEnUINtDhG3OFfzvhG6M1l+yorMpJVtnW2g+/sOIdKQXRvmSdNXA8+OPRGAJoWTB/WB12b7wb/jcELwj7N1SPNVOPMWtKuqTm0rZTxsVyNiqWTL8l1V5xM4ms+WkbO8dUrtlANrrX9xUbGi2kKRTlXvdlJ2mIFfrRRWKbEHoNqq/THTbnEq+AE5O/Qf62mKV0K+5N45d7DjmXpWx4DYNc3zyLdNx3WXXzH0vto0oXx/ebwBWzd3EP7EmHiGv/7SdzZ2cPaet6YU8+tcP5CAJo3+9zZtIJUi/Rhf6GRKzqO7uR/ukA0R8dZeDZ6rXbMiB6OIkSIECFChFMc3gkoHxIJso8dkc/R0/B8fI5u2L11HhvydBhzMrUKXoLtlSCLws5iBxF/WquS1iuckZD35pfGJQtsuy2npxbB16Dhc2OfaB+eXvl9yqtHfHFFY7M1/4KqJWsYiobre9w0fiYA1zc+zD67if2W6AV2V9po0MWXZaE5QUYrY8/px7iT4RejkmmkKj5JvZ6RYqpOGJl7KFhufbu3dd4Tfr67sIKya9ARCzJ8VIutxU4eONgLQEx3+Z+9DwLQZUwRV20qgc4po1bo1iWzaLfdzH9NnB3qNJakxlmXGKAYZN9Nu0n6YxLxLzemWKCnuLsi635w63WMj2fx7WCcfIVQlqUApocWF9bhzJ4hLmzczSJT9Ay9xgT9QZZPWo0x5ZUZcSU6s32VvFf3J9LwWWzIee3UUti+y64gi2fEyaApHt2aLB9x0wzYQRV0VHqMiZBlGnez4RjsrrRx+8Hl6AFjtKbxIFm9whsaha052zQZcKTNDdUuNDw69OmwP+1aXZ+yx8mG+qiCK/3u0oXZvDh+9Jvy0dyFx3+6AoCebJ6PLvpp+PtT1S72VkWD06gXOSu+j0FHdFq7Ku3sLIq+JqHZrE4PMVSVGl55O0l7TM73hJ3i3qHFzOQDh3If4mkLN9D0OFWNjjY5zoWZ/Lx+tcUKdMWnOCuxD4COYMwBkqqM4/8beQUAjx/qZnXzQV7XLAzuuJPlmwfERXuylMT3FWIBIzU5ncY/GMfXA3f1vnzIBmmqhzZHB1a76x4tkp9bKmJ1ywg9CTkPtd9iquzzTbmHiSseX5m4JNz20ckeAPaOtJBOV5iZEOZSndE599ydNJrF4NgWhO05nko+n8QrBDeHuEtfz1jItlUcHf2Kwz3SanB+vTD8fFXHU/zPBrkn3VHu4W93vILGz8p5MjbuwiuVTpj2SEsL1aUk4pAMrrXZErQ2cckP5tdLeyG8wk6mz9EPnlhBMnN4fcXnglLB5Q1nbIt8jo4BEXMUIUKECBEinOKINEcnFxFz9DQ8V+boL3ZvmUd1avNcmX1SajXUaVi+xribCdkM29fD2ku7K214qKSDSP7C1E6atBKVQD8w7DSG7fYYE/To5bBOl4oaujtvtd2wTYBFepUGNcYBVzKext146D6dUhwqvhayUi4KE26arCp9SKpWeDxxxeXzY5czWpFoY0EiT6tZYNaVYxm36nopgJxRYnlyJPw+VwNSY3AAvjF2MbN2DDOI2OOajeXpHJgVr6NCpe67FNMdcolyqGVK6jYLkyKgOSe9l93Vdn4yIN48+ekkquaFhRZjMRs3YOl8X0HX3NArRtU8nJIB1WDcXFCden89w2fJCvERelP3BrqMKTKKjFFcdcJja9UqdGsJxjxh256yGil6sZA57DUm6Qp20agmqfo2d1ckmv3O+IUMl7LkzGDsdQs1oK+azRK2p7JxUhx+p4pJuhqEFTFVF131Qpau0Syhqy7poOZXo1EMz8vK+BCG4nJ5oq6F+djYBeHnJ6bq3kl/2P0QTdosHQEz16FVcX1C5/AmVaVRFTbg2ZgjgO6M7HNZdoyY6oTshqr4TJWTpEzp77rGYQ6UA6aoGsfUXBal5ByvTB0Mmcrt+zvxLZW5SXm+AmpQ92xRxwTXdIkPTyaYz5ng2nJ9BU3x6Q5YMUNxKHiiy8mpZTKqRT64Rv8rfy6DpUbigVt1Vq8w68q82TbVPq+Qp6Z65GJlmmLC3HooYf00U3UxtTqbWju3+wpN4fYz1bqOa24m3qt6NtOgS5tTTgrb00JGd31iH+Nulk2lnuBY6ozMgUqO32xchVqSSecHLt9+To4llrJY0JgHoD87jueroV4pZ5T49cByynvlel+0bqjet2DQ1ZcPhr/9bOhRvjbdC8A16a1Ug3H5Vv58vnPrS+j/5FMy9tOH67CeCxQtqJ2Yy8kPjXKfmDq3jZlFwfWtwRWvfQQ9uKdsPuuFq0V/Mpmj7zy+5oQwR//jzM0Rc3QMiByyI0SIECFChAgR5iB6rXYCoOFhheyLSjzIbKn504y5GUA8XXJaibWBh4/nw51BVXZV8TGwaTMkUi96MSxfJ+9KdF7yYrTqtVpGDgVPpUGRDJW0Gg/rky3SbUq+Tb7mVg2MuhXuKi0FJLLsMSYAGPENDFzMINqccFNUfBMzYJYeK/YyHWhPWvQCK1MHubrxiaBdlSZ1NtSoPFDsD+un7Si1U3Rj7Ku0hOs+OL4ojLLXNtXdnA9V6h5LABVX5w87H0Rtk8j6KwOXYgfHsqxhnIRq85ONwlSoZY3Hc8KmPNrRQ2uiSMWS8xCLC1NkFeV7aSoWakFQfKq+gmIHldYdyRTygwwlxVVw0zImZsZCV3wGJ4TN+NTBq7h82Q4uyO4GRCOzNC7ns98cZcKzmHRl3Urgzl0KWIinqp2UDNEqWb7HY5Ul/Hq87ohedgzKjoyn42rEAw2XldYZK6cpWtKeoviMF0VrYWoujYkS3UmJyFXF496hxSFL1pwuYgQR9Hm9uzEVl2/N9AIyV3+yq+5O3d86HlYu7zdHMBSPJysyvjtVmx59Ilw3Y5RCf6dvDt7HoGuG/lW14/3Wclm3eltvmMl4oNTI40PdOAFL9/IV21nVMMKGQ6JZ+cWu1ahBRte1S57k0cketuXFkXpRYoKsIddUR3uelFllfFbGoTU9yxXt29hRlHU1xQ+vw5xWolWfCefqw8UlJFWLFaZ4ImVUh9aAVbJ9hbwX46HgerE9Fc9XeGK8zqp9dMXPAHhds4uHSlKRPm0s96IqHq26aJgqnsHuSlu43awbC1lGz1fxUFiTq7OoNdbGQ+HOPUvDjDlN8UI/sbRWwVY19lVkrAcrTSS0epZo7fgBOuLTvPKszfx6Z3AiDsl5UfLSlpU3UJtqWiaVaTtRZ7SKzah3NdA2Jn0YrC7ATcmyVev2U3EP/9Pxh9mdAMSUONVgbqxJHMBNecxeLixi5uFBnKHn7uau6AaKoVO4Rq595U/GGN3YQXAZMrXG46Xn193VlyfrfldLnoL/XtXM6Q7XV+bVwXy+bUQ4NkQPRxEiRIgQIcIpDvcEZKu5UbbaMSN6ODpO/O2S1Xxoz6ZQxzPjxUP9AohWqODK94xWwUUNNUclL0aTLp4fXcYUFd8Io+6ClwhqwdfrihWCzKdtViejdgMNmugQLkvupl0TViGtmKQV6Nbk1Jb9KgXPDqvBu74atlnrR3xObbO4YjET7OeQXXe8btBKdOjTofap6MWoaEYYja9P7gv1KQvMSWy/7r6rKR5bzA4qjvRpqJQLdQum6qIqPumAETBVh13V9lA7sSA1HUbbuuJy54GlGIcCLZgClOUYDpRbme2K0ZaR8fzokp8yaDfzq0nxnNo83kF+VBg8taRBk4UX+BPhq/hm/aahpiwa0sIkvGzBTn4/9wQ7q+Kf8+2B82g367W3DMUNNS0uCrZfv4Hl3STDdo4JW/bbbBRC1i6uOtw72U9ck3mzIjNCp5mnWZP+d+jTDM3RmT1UWAxBybn+xCjLYxIZ19jJvCcMY8Uz2DnTyoGpHABj0xm8oEr85/UrMDWX9vhM2PeV7aPhuVieGQ3Zv7yXRMPDCOb1qN3AqN0QMgt77FLIZDarkvW2JtB/TXlVhlyTa5+SeX/LWCd5Sz57vsK67uHwnB4sZXETCv0N4uPz6q4nuSRV96S5LvdIWPJg3M2yoFX2MdrYwENTi9FV6U/V0XlwcjEXNwmVsDQ2Es7v+wtLSWg2LUad0VkZHw6d4kueRjyYby4KD5WW8nhBNDz7C40ULRP3DtEGpQ96fPj1rwVgZfsoWaPCgriwLzWtXynQ4QnjI3O+6MYCLybZZ9k1cHwVW61rSDrjwv45vkZPy1R4bEPVXHg9NBolXF8NfcNUfBKaFZ7DnuQUs05Qd81XSelVXr9qY7iP7z9xDuZgwEB6sPsJOc4DC3NkElXWtQir02CUmbp6mP37W8Nt2zrzMgaGRdascIg67q7EADnurFohF7CVZ8WH6Fg8QWxCrgF3dJyjQTVN0DQIMtnU1hb8nNyDvHScQk+S0dcGtQdjZZJ/VffoagZqfFTfw0mSqhVeGyNOAy33N7I+KxqpTmOK6YCRr/gGccUOs08BPr90+VH7GOF3B9HDUYQIESJEiHCKw/PVeUkvz6+NiDk6VkQPR8eJD+0RL42ZgC0adeZXlO81DjER1C6r+CYaXhjRFLwEHZpEi0XfZEe5M8xWa9Jm8VBRg2wx29dDTxSJdMZDv5qddiPbAo+eVQEzMR4yUHEMjFBXpCkuVnB9ZNVyqIup7QMI2YKLMzvCbDVDcdlWresulpijzHiJUA9kKvWsrW59CsvXaAtYkIzqMNrSwKQj41CL4EG8dgzFCRmLohej5JkctGQcF8SneHl2S7juL59cM3/SBvv0VZ/pfJLCQWFb/vjAH/P3l3yPt7ffLX3orFIIxuT7E+exfbqNPfsCfYbhi/4oyOZpayrwzj7xYuo2Jnmg2M+jU6KJycXL3DnazyOmfH9dx0auSYkL97Brc0exP/Tw8XyFslsf3xGvgcfUXgBekdrGDd23ss+WdfdbzRy0cmyyFgTj7dEVMBINmmQm9cbl3Ga0SjiHauxHsyreNTPEeVP3BvY2S7uq4oesw4iVZdpO8IvNktFHUeM7v/+len0tNCaC+lM7qx0ctHJBXTxhLPaXm7hz+zI5tqqGYsq+zaTNv53zdTykn3bg0VNjt4x2lwdnFgdjonJudm+oy4mrNnuqbewsizZnzM4yElxDPcYUJm7IHGXUMjlV2NKFhjBItX3YvsbDxSWsD7yLsmolvCbPz+xhR6WDKTsVjJnLg7NLQj3f2vggFlp4zlbFD4Ss6ZmZQb74k6swg6TJ6V6V6lCQJdohrM1AueZHpZDRK4xbsm3JiWHVrg/VJalXSQf+RDHNQfU0SgHL4/gqj4/J9VW1DCojSfSW4F4QK2MG25Vdk5RepcWcDfvroZJWy+HcOBhQjFVPZ9tMe+g/1hor8MYzHmF0hWQpbZ7oYGJUPpemEixrHQ+z5MquyXktAzTHZbzHyylmyjKeB2YbuLhtzzzm6LNL6tq1T+3bwKbAxf3x4iJG9jexvBh4r6nK4S92gvpoaiZD/op+Dl4qa1x7/qNMWjKvN/7nanwFVnSJRutINfT6Hk6Gn0ueSXcwRwzF4WAyx7QjjPjWYgdZXca2IzaN7WtsrXQf1t6phui12slFlK0WIUKECBEiRIgwBxFz9Bzxgd2SEVGLboteDFNxQq2QobihxmjQaianlUImpkPPU/GNkIHxfCXUKah4hxl8qXM8kzTFwwi+N2mzIXMAYPl6qP3Zaeew0UK9heerdOlTYfSt4YdV7fNOCivIoAPIaUVyQQQKor+Y26cVseGw3RoLdlFCqncnFY8nLGFiXF+hWSuGrEZSUbgivSX0UxpyGkMmK6eWmHBToZ6qSZslp6nce0iyhWqRN8Dq5BD/etm/8v4tUruqsL0RzwgyemZ11EkdLybfOzun+NGhs7kkJxk0PeYED8z2B+fIY7Za908yDun4KsRWCBP3nsW/CZmsESdHo17k3Eap71b1dG4bXkExqGxe8OJ8NS8ahYFqE92xKc5Lie6lW89zf6mfPeUak6SysyxjlFQtRp0s+8uSRVN2TRbEpzgvIzWseowJLolLdHvIqzDtJmg3pH9xxZrny6Mqfqg5yaoV1sYOzPHS0kKW5q7xpeze2kXDQmmnpafIdycuZFFCOIC3NWzh/sBXaspJMVhuJF+V8/J/e3/M8maF15wvjEX/I/Xxe1nDVmxfZ1uQrXZfYRm/l90UztEOY5q+RE1rZWP7esh6JlULy9fC+bk2PhgyXRvKffSbIzRrwh6kFIvdtjBMW8td9MXGaQvcrSu+wdmpvZT8wFfId8N5bigOfbFxxp1McA4NMlol1PuNu3W/F8vXaNZm+b3sprDdv3vDv3NPQc5xmzHDjlJ9nqd1K3zVMWGlGCunw6r3IN5HAO2JWdEJBZllT4x3MT7QhFqRbRUX9KKMQbXJY/Xa/WGGX3NsNvRKk+NxQ5bW9jUOlBoZLOTkvOTGQ+3SSKUBFR/LlWtoopomq1fC5cOJLKke2cfZzaLHGavKGE1Wkzi+GurTzmvcG2qpqt4z/9lYb+os10VbpOHxbe8iZpYKm5Z98vD11bi06/Z1UVig0rpItr1jcBkdrxF/pE5EX2T9v6Pvd+95ch/7n9sH0RSfZJDNm9Qs1iUHw0yt72w6FyXIBLx6xRZUxWNx4uhaqFMFHsefbXb0Og4Rno7o4ShChAgRIkQ4xeFRT6Y5njYiHBuih6NjQI0tApgMNBnNQdRZ9GJoikdv4F9T9E3GFYlEXVRUxQuzmSq+wc5qB9NO/d14jfHJaSVm3RgbphYBkDaqLE+P0mnkAYkWa++b99stJFWL309KfsYeJc9jFdlu2k2SUStkAp1KSrGw0UKGysANL5BWfYZxJxuyNq3aDEXfDLPXXF8N+zfppplw06HfSo0Nq+mUeowJ4kGkhiLjQGBM61LijuLKkEF5a/N9oR5kn90iWpKAvZoJWLdLWnYBMGZl2TErkfrC2AQVr5X/0fcIAL+Ir2bfkLTZ2jbNVCGJV5H+zJTjXLX4ybAGmYbHyoSM1712P0sbD7GsSaLFHZ2tTOxooRRUVx+wWlgaeFH9bPwMtoy184pFkkF1x+Ay0vE6a/fPWy+mMahePjqRpb15JqzlNVDIcXH7XlamZL9f2Hop1ar056Kzd7EqPhzuB4RFm8sIPhJ4AcXVGGckBjjLDDK1XC2s2VabFzutur9Ntz7FroDd2DPbzJanFobnRc1ZxIJacKrisznfwcZJ0Vv85+B6Xr1AGJP1yf2sT+4P2ct1Zp0NAdh5Tr2fr98zy1dHL+WBPX0ApNMVdjS3887OOwFoUme5NL1N9onH7bOruWnvNQD0ZSZ5ScOOUDtk+TqPlXsB2DSzgO62KXKBlslFIRewSO3GNL+ZWsFkVvQ/l6a2k/NLbK5I9lWTPksmYEHFpd5j2M8Fx+2FLBzUHbNBdHiur4bMYU4ts1qb5qzgPO2xM4zZcn3fsm8lzeki7Ym6/idtWGGmoKG7NKekv5anoSp+yLooCmgFNfQO8mMezavyADQmSqxsGEWv6QTx5rneQ5292VVoZaSYCT3Etk+1MRLPhOvpqkdLTPqwID6F7Wsh63R20+C8Nj1foTNgijxf6iHGzOA69/RwTB6b6jmi5qeGq7rW84MDUg/x5n1XsPAXPqltcq05tnPY+l5ZzpM+MMLsOpP2gDHNXb3zqPt4JnxjucyBP9tVZ8Gb59TP02Mu9oTcZ/J2AlXx6cjMde5u5FTEiSkfEj0cHSuikYoQIUKECBEiRJiDiDk6BmhK/U1thz7NjBcPGZOCm2BLeQEr48IOZLQyVhDlaYpPVi2HWWY7qx3EFZuUKVlIB6wmbhsTh+RlmXFGKllKgUPyRc27mXXibChINK4rLosC3UaTVmTcyfDD2V5A2IOaQ3dKrWIoDuOORLc/KyymJz5Ju17ztqlHbt3GFKrihezKuJslpxXDKDWuOKF/TsGLM+0maAoi9yZ9Fs9XSQZMx4SbDhmoUbuB5fHhulO44vL6zCbMrIzDL4uL63oUPS/sRxDRWKqGhsdiU7b91chKWoKMmW3lzrCmFMCHFv+CwR7RufSbo9xfWsotB8XX6LyWAVxf4cz4QNC/lHgFIVHxrB1j1yFx8K4cSNO1apT39N0JwAMzS0Mt0GvbHuPVrXVm4ZLVO/DmeBntrbbyXwPi2uu7CpOzSaZmZcyWt43RE5sImQbL0jmr54Ds0zfIKUWuSMh4/nC2m259KmRNdlfaQnZAC/xxKllhX5q1ImtMGZPttslPps9iNNCKNJsl7naWhVlyqxsO8lRaModU3ee1K59gVUI8r3rNcYpeLNTT3Dq4ggVBhk+XnpfzE9RoU8ngHUWx8OnF69j9rb7we7EY57FiD/+3LOzQ+qYDXN6wFRB2cmV8mLt80ZTtnmkmZ/Twimy9Dlotq+yhvb3sm2kK2axLkjvCmna2r+P4ariuHbCc905Ju46ncmVrLcvRo+TFwmOzfZ0ufQo7yFAbthvr9df0MhXfCDVbWbXCPifOkwEjtXF2IT1BFmFXwzQTpRQHRoVp8GyNzs4pKkUZ+7KjkooJm6qrHnHN4YFhOb+zhTiKDiQDJ/akHerG0oZFo16ss56KR58pzEvRizFUbSQWZK9Zlx2kiYPs/JdzAUg2lqhla9uextTBLMlVomNrNIq063WGxEMN9V2TToqDVo4a3dtiFhmrZkI9VZM++5wypWKKzN3L23fwb1d2sGK/jInWkMWvVkO2SNE01Aa5PmYuWUx/z9CRG3wemOtX9D+311myG8/8xbz1vrW8m3+m94ht1BiwGt6w4IIjrncy4KGEf0uOp43ni7/5m7/hQx/6EH/+53/OzTffDEClUuH//J//w/e+9z2q1SpXXnklX/rSl2hvrzPZAwMDvOtd7+I3v/kN6XSat771rXz6059G1+uPH3feeSfvf//72bJlCz09Pdx444380R/90fPu64lAxBxFiBAhQoQIpzhqr9WO99/zwYYNG/inf/on1q1bN+/3G264gZ/+9Kf84Ac/4K677mJ4eJjXve519T67LldffTWWZXH//ffz7//+7/zbv/0bH/3oR8N19u7dy9VXX83LXvYyHn/8cd73vvfxJ3/yJ9x6663Pb6BOEBTfP31coYaGhvjgBz/ILbfcQqlUYunSpXz961/nnHPOAcD3fT72sY/xta99jXw+z8UXX8yXv/xl+vv7j3kftQrIl3EtH9m7Nfy9li3iorDHaguZGg2PJn029F+ZdNNhhNWsFXBRGbYlspx00hiKG7ItMJ+VAsKMmv/YezYXde7lsXGJWEd2N5PsEm3DjWtu4dz4IHagHVik6wy7wrQ8WBZ9SZted3HeZ7Uy6UqEPe0kuTi9A4CXxKfYbuskAy3B45UFTLvJUI+RU0th/1xfnZcFVwhqrtUw7SbDLL3txXZ6ElO0B3XiuoypkE0DeHlyIPTbcH3Y72RChmrcydCqF3iiJMfxrW3nYk0GjuOugtlWxrWlLUX1+JfzvhH0tYyLwi0FuXgfyS/ifd23kVQlcvd8hS1V0dY8MtvLExPdtASszc5DLazvHOLinOicppxUeBNpN6b5yegZnN+0D4DhagM5o8xQOQfA/tlGlgXuznfuWcrb19zPrCP9jakOC8xJfjS6HoAnNy/CyEvfm88a48L2vfxqv9ScKo6luPacx9gzK2xWe6IQegw1GTK3YoGb9qrEEI8ErOFENc1D2/uIZ2VOvWX5wyyLHwz9s/5y+xsYn5I55TkKvqeiBBlUquHhH0yg98g4/MGyx1iXFKatQ5smrjr0B/qkrJrgyq4zOFaM/WQFX177bTltc27IHipDTmPoeTVQbabTnOacIOvxo3uuDfUz57QMUnKNkCV5SWY7v5leBcDP7zkbX/U58yzZrjMxzUOji5gYlPOCo3DD5b8E4OHpPkzVYUE8D4iDe1KtssQUf6pxN8ueqmTBqYpHXLHpDZiamu/PfUXxd7rt4AqaAiZzx3grzr402d3S3+w+m/1v9DHTMuesyTitC4Rl6m2YYnV2OPTaKToxnpzsDI/V8xUu6wxqk6ni+9UXGw/GT+GALV5KDVqZH61secaxz/9c7nfTxQRWyaChSc7vO5beR1K1OGjLGLXohTBjr8YQ1Ry8p90kD84s4dqmx2SMnCz/PXZmuI/SS+tauWfCBU84nJPay+JAk/na772f/i8dgBpr4HqgyhjsensXdneV/j969JjaPhn4zL6Hws8aPn/RW2eOfnDgQWYKHotWDL+gVe5rf5M+98hFJNLH97KnPOtwwzn3P6f+zs7OctZZZ/GlL32JT3ziE5x55pncfPPNTE9P09rayne+8x1e//rXA7Bt2zZWrlzJAw88wAUXXMAtt9zCNddcw/DwcMgmfeUrX+GDH/wg4+PjmKbJBz/4QX7+85+zeXNd23v99deTz+f55S9/eVzHezw4bZijqakpLr74YgzD4JZbbuGpp57i7/7u72hsrIvnPvvZz/L5z3+er3zlKzz00EOkUimuvPJKKpXKi9jzCBEiRIgQ4fhQM4E83n/PFe95z3u4+uqrueKKK+b9/uijj2Lb9rzfV6xYwcKFC3nggQcAeOCBB1i7du2812xXXnklMzMzbNmyJVzn6W1feeWVYRsvFk4bzdFnPvMZenp6+PrXvx7+1tdX1zn4vs/NN9/MjTfeyLXXXgvAN77xDdrb2/nxj3/M9ddff9L7HCFChAgRIpwIeIHz/PG2AcJGzUUsFiMWix22/ve+9z0ee+wxNmzYcNiykZERTNMkl8vN+729vZ2RkZFwnbkPRrXltWXPtM7MzAzlcplEYv5bipOF0+bh6Cc/+QlXXnklb3jDG7jrrrvo7u7m3e9+N+94xzsAeW85MjIy7wm0oaGB888/nwceeOC4Ho5qxQlBiom6vsps8ArJUFxKVowvHbwMgJZ4ifd23Q7IK7gxO8u2spg+ur5Cb/xQPa0+EEOCpDCn1GqY5p8fT/Pof5zF6EVCc//9ld+mP6D6n6p2cldpCZcmxWxQRWWBJhP72tQwmqLgBm9LbTx69Gk+fuBVAOyfaSTfIpOtueU+zjBdSsG6C41JflhYQlNWaPiSHyOOFYyBCCprQvS4auP6SijCzmhlugx5hbAyMURKrYZjNmg3z0vX320nQzGs7WtB8VYRaCaNKh4Ki2JCw//hig3sLMorj/2zjRycbMCdlr74hsefPinn9Ytrv4OpuLw0JaLlS1I7UPGoBKJmGy18VfI3e6/EHk4ypAdvlDWf+wdXcJ8mAs7WvknaUvK6YctTC8FV2Gr3AuClXLBUVDsoW6L7DPpSlNZvsCm5MVYnRVSq4VH0YlzaIq8xN2e6sGJyPhdlpxgoNVEcFmuIhp5pBkpNPLlHyoc8OaujNsmrskTSolwy8YLXib51DlpSxvbaVZu4/qwNbJkW0fWaxAH+7eDFPDkk3//qzFv5z8RZAGx/bBGqB73rRRT+kxX/yfr7/oR4IH6/MF1Pnb5p8XqeCz66Z+O87+/dvCIsnNutT4W/a3j0GuPhb8tjB9GUeoHlS9t2hfYV/bERRpwcuypy4zQUl1ZTUrITvTPMHkqyJC3zJKFa+D9pYeWtMvb58zv55afkFdyBN/ZRWF/l0uVyfDtm2+lLHQqTArr1PMnAnqHiG5hzkhY8FOKKw3UN8qqnLzYevlq2vDMYUHymmuVeMLPYAMWiNStzZ6hoUqzKXDU1h9KcUjIJzWJZbjw0ObU8Pbwf2L5G1dHZ5PbU+xH8UZMixs9s5VdLgf9UYEGS0+pJDPsCo06Q5I2aeW0lMMaseU3eNbUcy9PDQsim4rCuoS6WfvAY/3Q8eIbOg/Rz8SaxGvmDK+/l2y0X8N4L5B55z0Q/+36wBIAF5x/g3OYBNh61tZOPD/aeP+/7J/Y+wnLjcDuC0w09PT3zvn/sYx/jr//6r+f9Njg4yJ//+Z9z2223EY/H+V3DafNwtGfPHr785S/z/ve/nw9/+MNs2LCBP/uzP8M0Td761reGT6FHegKtLTsSqtUq1WpdA/T0J+oIESJEiBDhxYZ3Amqr1YKQwcHBeZqjI7FGjz76KGNjY5x11lnhb67rcvfdd/OFL3yBW2+9FcuyyOfz89ij0dFROjokYOzo6ODhhx+e1+7o6Gi4rPZ/7be562Sz2ReNNYLT6OHI8zzOOeccPvWpTwGwfv16Nm/ezFe+8hXe+ta3Pu92P/3pT/Pxj3/8yMsWi7j3TdsOhgZo3foU7pwU2JRaJaOV+evF+wDCEhkgQtSUWqUnJin4d08uo+DEaTKEmVkSHwuj4pdnttBrTLPYEJFofm2CCy/ezbUpSUFVUahJxOLxIQadDHlPItF2tc5AzfoOjUoMlFrBWhhysvx+i6RDFxrjXJ7aHvRVo+J7GDVTSK3I8uTBkMXp1vM0BcdtKgoV32ciiDTH3TQFNxGyQwDnxoWRMBTQnpYyOuGmGQyi1l7jUChwNxSLXqNIwZM+FDyTEbeBroBZyKhllsfFcK7SZHBfQz93+CI4tWdiFIsS0dw8/EouadzFRUmJmlcaFrsc+N6URH4eKqVA4AygeArUAkBHQWmp0tEsD8aq4rN5h0RWelHFTXgsWidWDTPVOBN7G8MyJb7ho1SDG1ZJ53vbzuaafhmj2weXMTPQQGe/MFZNzbPk4jK2o6U0A2NNKBkZvzPbhslbCQjKGqiNVbzABLI4myLbUaBUlvPtTid498V3AdBl5Pn6soWABAB//m/Xk8hU+fp5/wbAI+XF5EzZ5z9d+zUG7eZQnP/5qdX84Yo6Xf65JSs5Ebhp8Xra2Eb33qmjrrPPFlFxUq0y7SbD6+ml6W0hs5pSLJYYYywJrgnL1+gKbDD+YMljXHrGtrDsyCf3Xk3yuoNsvVDaXb5okIIlov7u2w6xp7mFBWfItj8bWM3DexYRPyMoyJouhCxSiuo881MbjR69EO7ngvg+djsijn53z5309k2wKRD635FfyUNDi8jGROM4lrCp7JM/QA/uzuLGffyAOVRiLmf0HaA7mQegw5wJ7zEgrHPNcHXMyvDEhOzDdjUaeG7miPkgeaJmYFsz0sy7qVCcnVQt4qpNT1C4+k/a76bom2EJoCatxCsz9dofD/LcmMX71sncvWV4Ix+/ahOv2nF1uKzrun0ArG0YZuNza/YFx/cOPBDeHzVFoeQ7IdNu+T7qSZTter4aCuePpw2AbDb7rILsl7/85Tz55Px6L29729tYsWIFH/zgB+np6cEwDG6//Xauu+46ALZv387AwAAXXnghABdeeCGf/OQnGRsbo61N3gDcdtttZLNZVq1aFa7zi1/Mt1e47bbbwjZeLJw2guzOzs5wMGtYuXIlAwNBdk3wFHqkJ9DasiPhQx/6ENPT0+G/wcHBo64bIUKECBEi/C4gk8mwZs2aef9SqRTNzc2sWbOGhoYG3v72t/P+97+f3/zmNzz66KO87W1v48ILL+SCCySr75WvfCWrVq3iLW95C0888QS33norN954I+95z3tCtup//+//zZ49e/jABz7Atm3b+NKXvsT3v/99brjhhhfz8E8f5ujiiy9m+/bt837bsWMHixbJu+y+vj46Ojq4/fbbOfPMMwF5RfbQQw/xrne966jtHk2INhcH7RwXpurRWk4t1Q0O1SoGdeYmqViHldx4ZLoXgKdG2zF0lz9b/htAjAt7TInUTMWl4Ol06KLLeXvrPaQUm1osqaKEEYznwyOlxWFa8PWNddry8cpCuo0p+oM0+qSisdacZq1ZN3+LKxKZu3iUfJfHqsJefWPkIh7duIT3Xv4rANaa45iKRMxFHwqeQcmra49qxwlwcWLPvCdtQ1EoeHUbAA0vZKQG7KYwUs+oFSZdnW2WPMDWCqouN0Xr0BubphToMvJegt6WQ7w8J8Uot1a6+MFuCTUvadxFkz4bliX5+kw3B60cP/yNRB9GQaGmZbQbPJTWClcuF6uGDWMLMTU3LBLanZrhnLPlofuhsV7WNB+k6sqlksmN0tHzZGimdqDSyERVmJj2+Ay3blvFjx4Qawm1weLc9bt4ZK8wGLGEzat6RAfyzS3n4U2ZNPcJm3F+wx6+uf98tDEZX6Nvlv+1/s7gPClM2akwpf3PLtnIU7aM/VPVbv5kxz72W8KYmIeG2fZwL99fJIzZSxu2kWmScf/skjW8UDiSRuljfWcfcd1P7dsQMoMFL06PMTGv4LGFdsTt4qrDGXEJXvpNCYJq7K7KIAmgHzE8vHhTla5Pi07o89tfRvJXcOf/vRiA6WscUHyWBCVBKr4RanpqxUpr17fnKww6mfC7obi0avVyIRVfD0sHvaPtLg5VrmZwJgeAnY+RPCTtKg4obp39BZ0nJxezpVOu97XdB3l5s8zHmhkkgVbotoMrsIPisZrqUb2tNxyT2Cv2HXGsoH6+37lzT9Cux6jdMG+dmr5LUzw69HyoBUwpFknVwg6YhiGnMbxmDVyu3DLDraufe+r6VV21eTJ82LJTSWtUgzHnrub6PgYqBV+uw912jt2lDEc6lhcCLkrIYB5PGycSn/vc51BVleuuu26eCWQNmqbxs5/9jHe9611ceOGFpFIp3vrWt3LTTTeF6/T19fHzn/+cG264gX/4h39gwYIF/PM//zNXXnnlCe3rc8Vp83B0ww03cNFFF/GpT32KN77xjTz88MN89atf5atf/SoAiqLwvve9j0984hP09/fT19fHRz7yEbq6unjNa17z4nY+QoQIESJEOA6cyNdqzxd33nnnvO/xeJwvfvGLfPGLXzzqNosWLTrstdnTcdlll7Fx46n1eHzaPByde+65/OhHP+JDH/oQN910E319fdx88828+c1vDtf5wAc+QLFY5J3vfCf5fJ5LLrmEX/7yl8ettD8nuSfUQdxbXMqtB1cyNCy6gz86+37OS+0OyxoAYfQ1aDfxhd+8gtiEfP/8H36NocDMDZhf5BUFQ/FIBQ/2rboLqEx6EqWoQC4wSxtzkzTpsywICpFOuklGHIkIt5W7sH2dbVXJkHtVehuuD3YQMRj4eAHTVfF98p4RlhRpjRVILSjwhQ0vA+D3X7aZPY7oFB4r97Kr1EZal8ja9lSKbiw0KpwLTfHIaSWaVImw52auARS8BPlA91LwEky6ac6MSSZMUvVxfdCCccgoehhst/qzWL5Pjy4ZSy9JDLF6jeic/nXoEnTFY/eE6Jq8xxuoLq3gN8p+LV1HrQYZZg0O7c2FUMdhBIyRE0Tn+2ca2T4pBW1VxWfLZAdpU6Lm/tQYLUaBxwrCWG461MXY/uCcJhwyuTIzFWlH1T0OVZIs6hB2cFFmKtTW6IaLm3GYGJCMrh9d3UKa3aTZHY7TL5gf5b9iszAJc0sY3DK8kf83uSw0nry6/Um2dnWwKzCTPCcT4z9WHP218ouBD/eeG2a3xTX7MCNUU6kXxoV6ppbnK9zYd84x7eOedTFAjvs9W+8it7LEzybExPL12QH+4Z5XcONdopP48sv/nVTAGNX6cldRzDm/uvEl/J+zb6MhyLTUFD/U7AzbjeTn6KV6zUNc1baZ2SB7rbAwzn93rJXP+xpQHAUv0ByhAY6CNy2s9a54Cy9tCjIrPQ1V8UJdXjZWCa+7JelxLkzv4tM7rwLAumUpmat2PeNY1MazQ5tmoT4ZFi0uerGwbEpWrVDxjfA+MqF4mIobmreaijunHJDFS5I7uJVjOxenM56e+XvI9RgL7l0l/5nfOJxouBw/8+M++yoRApw2D0cA11xzDddcc81RlyuKwk033TSPsosQIUKECBEiRHguOK0ejl4s/O2S1eFn7/Ye3tSzgcoCYUyqnsG4k+UpR1iIM+IDYbkEQ3HRCxofe9P3ACnkWiv2CXXPIJAoTsMnowgr4uJgoNCi1pmZW0qiDdpU6mFhbCIsTOmihJliPcZEkCUnkeawE0dVfPZYwoS06jN1zyYviYYXlj5ZmhzjsVgPq5aKFmPcTXH3rETQ39x8Pr4PRkxYJgVIJapULOnDptEuzMD/I2HYNMdLoS+Kobh4KMSCbJwFxmRYDqFRL9Jh5udERD7Tnh7qTgzNwlBkPCu+T1xRMJR69HRuTN73/yw+y13b+1Hy0h+/10JVfRQtiJVsA6dR+tfYPMtMOUbclO+2o+F6Cm6QMdeULpEyrfo59xXMICNww9QiVMUna1SC/rn8+UtFo3VWYh8f3vlajE5Ztz1dYNdoKwuaRV+jKl5YzuRb5/xrqOcA+BhH1ufMxW1r0uHnW4efkP5UXdbED/BoSQxRx+wsHzz7VvGsQQq9/mxIMtmu6X72fbwY0PDD819jS04kfriyjY/u2ch7O8Rbp+ibfOkV/x5eh66vMjcgd32VMxKiOVM1n5s3Xc5H1strgZJrhKVPCl6cWSdOp5kHxCuo3xwJfYUm3STfKQWljUyfVWfsJ6nLNTBjxxgv1s/nOW2DZILtil5s3uuPS5p3syYh13feTfJIsY940M43V3yb+AGV78xIpuHPVtUrBoDou2rwfAXL18LSR5NuOiyoHPdtDMWZl8mW92L1wtJOGjegc+O6jYbPx/eKputo2rLfNnj4WKghu5hTS4yfxD+hp8Jrtd8lRA9HESJEiBAhwimO4ykcO7eNCMeG6OHoeeCh6cWszQgr8s3t51GZSKBNC9PhxXxefckjAKS1Kq+/6r5QrzTmZni8uIhts6KFuLplE61BgdhlxhitmksthI0pKnFFC300bN/liqT4/XTpU5iKGxZSHXeyYcR9y8w6XpZ5ittnhO26Y3QZF7TsY3dRmKMZK8Yr2sRFOqbaGHO0BF/YcDnKtM5oKgfAr9Jr+PYT58lBVzR8zQ9dmnEVKoUYih4UMdU9ZguitfAtjeGYw9aYMF2q4qMoPg1JYTN0zaUnnQegOzFNSq3SoRWC4/RYoPvsCTyInrIzoR4krii06PUQv4rLU4F3kucrKIqP3iHRt6L4eJ6CPyCO4yigJ6XRmR2NaAtKZBPVcF1N8cNsNU31woKgtWXTVjw8FhWfZKABOaN5KIz4fzWzhmysws3L/wOAPVYryQVVunU51t12Kz85JNk6/z28DuMV+3nvLsmCfO+unXxh6bEXSK5hpeEy5sLCIOtRUzwpEhzMuUviFWpzqu9hGYu955We836OB5/Y+8hhvz1dTzSXMarpij6x95Fj1hg9G25avJ4b9wjbVvASzLhxugNH9wk3TckRFqlDnybvJZkJHPD/5tz/5BeT68I/Kj3mRDgf7y0uw0Vle0ncyKdjSdYkbAq2bLur2sFFvZI9t7fQRNE2yVdF7+N6KqbmkjCkrSazSFOQBTdsNdKkz9KkC4tTcOMhS3zAauLe8cUsykjfbysuIaOVuSwpmbwrdmfCjLsOvUTeM0OX+JJvMuMlyAaZgQU3wWSgKXRRWBUfxg3OR41BqmmSdnsGI04uWFelW58Kz9nH9z6Khn/CztWphIeq8TCTskPz6dIUts+RyNXGKcJvH6KHowgRIkSIEOEUh48SWogcTxsRjg2K7/sn/iX/aYyZmRkaGhpY9KlP0v//HTm18EN7NoUePxtLvdw6spLWhEQQ+6YbWd8qrNLC+CQPTPbRE7jgvqJxMwv1ST47JJkmS1LjXJeTqHq33cZliUGSgQeRioKmKNj+4XWUflrs5m+3v4KZvDABjU1FkoFGZmi0Ed9VUCcDN+Wsg5mxcAK35camIn+8+H4AFpmH2F7t5MdDksUzls9w9dIt4X4enehhYFiYGXXCxGu060yR5qOo9b6pqo9dlWdtdTSG4ij4NbdnR0G1odoa6H+SLm1t4rs0XYpzVtcB/rj9HkCyZnr0Km4QlT5pNdOtyboNqsNOO8cjZdHXtOszIfO22hyn4quMBC7A73jwrbhTJvqMHLeTc1GCemj4sHjNUKgxGhhtIpawaUoLo6IofsgcaapHQrepOnJsyxrGWRCf4kBFtB1Zo8LlWRmzS+MFnrIVfjIt7NCaxAEWm+OYYXagHmb/7LNbmHTTHLIzAKxLDhBXbD6/dPlh5/tIqGmOPDwerboMBVH9uJOl4MVZbI6F6365fykAF2+ySGsVWoNsv28t7z6mfT1fvGunZFFpik+rJufp6Zqi0NMnwId7z31B+wTw2q2HKHlmyIr0x0ZCZshGY5/Vyp6KuPmmtQqrEwfo0Oo+YSOuZHR5vorlazwyK/MxpjoMVXLMBCyj5WnogT4lrjuo+GEtNcdTmSinGJ+S8+85CisWijYsZ5YxVRdnziuQBkPYi3PS+7hnup+umPSn08zTqhdYHNQNbFar2MHcVRUYdDJh/UPL15h2k6HWyvNVCgFDltSERa3VjDwvtZtKoKcEuH96KQsTkh3bYhRo1Qthfby8l6RJnQ21OLav84nFZzzHs3Jq4D8PPDTv+3ZHDdlNVfGJKw6FOV5vb/nR/2Tw/R9henr6WR2nny9qf5P+8v6riaUPzw5+LqjO2vy/i37+gvb3twXRC8gIESJEiBAhQoQ5iF6rHQVvvOx+0puCSuHpbWEtqCZtFtvX5mWkXdS6N4wums0ifUmJ4kpujHMaB8LMofsL/TyuWvSnJaq/Ovs4j1Z6Afj56Fr+Uz+bV7c+DsBjs4u4KFP3LzEUJ9SuXJAY4LW9m0Kfo0/c9yqmp+RU+i02qu7ia0FE7oO/N0UiqKerX1LgnqllABQa4iw2x7moVXQRtEq9tx+PngnAwEArBO24aVcYFTfwClJ9UgkrZFhSMQsrGThZKz7+WDz0FXJSHrFJlfhoEDUnVfIDooHSy3B/findCYmE/6DxIfKewoQrrFjeTYVjvcI8RE4rM+3Isl+PrqBoSRSsay6f7f8hvQGTtKJrhC2VbtTJgImrqGgl6Y85DeO9aTJBJfZEyqKrYZqzm8R9uerpPDohtdVe3bWJ12c2sd0Wpmi5McWwm2BnTHRj3xk+j3NT4kC8x8mzytCws8Lq7LNbybuJMKKOK3ZY+BFgT7mV/oRkBpqKe5jfzzPBCyqzP1z1MOawLy4qrXohrKM1FzVHaO9pMdHcjKYanguD83S9VE1jVGPJSr5ZF4IGx1jLcDqRuqLnAs+v10fU8NGCz3k3ie1rpINr9kClkU4jH1anB2jTahmlCnusttAna3uhnX35RmrJlKrikzBEz9cbk2u1plVbEJ/Czan8qCgO38ajGQ483gvAsANL37iTCxvF82qo2hi6o0+7SXJGmQlbMuZmnASLG8fmONerYT20ghtjhVEgqQgjmvcc9jsZir6sO2g106TLcVU8g4pvhPcUEMbvkCPMVmd8Oqz9puExHNRkA3B9haRSRePY5++pCi9gNrfbcj/90ujlzAQaMl3xeFfXHeG6BS/BWWv2crIKTnm+Ev6dOZ42IhwbooejCBEiRIgQ4RSHi4p7nC97jnf73yVED0dHQbs5zcqUvFO3fC3MFvmv/NnsKzSRDRyTz84NsDpxgCdLwjSoiseOomRpOZ5GyTXpS0kmkdQq0liRkKyzHr1Ihy5ZJq9Zsh3L9xkOKmhf0LKPe8pL+MFBibCnKwlKgafQm5Y8gu1p/NuA1A0zhw3c+BymyFfwM4Gz9qyOF/MpLZTIuHwow6HtwoKNrU3h+6tQAuZhaCrHGZ3DPLWrrkXRdNlOOWSglfVwP15HleZUkbjuhOsOTQdajKkYZlHBM2Vdvaji6ZAYD9qsKJS6JMq0ehzUGZ39RfE90ho9Cp7JoCNap4IbZ0dFWJpJN02XPsVV2U0A9MXG+cW4OBAPF7L85Y43cF7rfgD+vu+H/LJtFd/eJ9l2Y6MN+M2BXmq5zZqWMVZnxCNJUzwa9WKYvdSl53l/q1S9H3cNht0Euy05p0nFIqVaYfXyLy/5D4wgGGtSTSY9ixFH+rul1M3bmwYoBNlCQ24DheD8AlyTe5xeQxizfU+reXWsWGXYjLpu6DgOwlAdSU/0jeU9z6ntIzFKR4o8x9wMf7Zr+zwXdKg7M5tK+ajs0IvBGv1oZQt/sG0kPIeWr4V9bdZmqXhGeCy2p/GL8bW8rl2+F71YOE+SSpUGrYQaMCb78o3M7mgkviSoa2haFCrCbG61Zf5ULJkLT5qdrGwe5aIeYW3ve2odcekOyTGP4dksBJZFeTvBjC3zZr/ahOermAGTpKuuzKkgibRVtcjPmWPjrsYyQ/bZqRmMexa7Az2Vpnhh3zXFw8ANPZDyboq8m6QxyJhr0EohcwRgu3qoVwIYtJvDzK2cdnKzIU8kDEUNM4QBckaJa5sfA6BVK2Aqbqg3LXoxepJTL0o/I7zwiB6OIkSIECFChFMc0Wu1k4vo4ego8HwljPItXwv1Ca9p3sjm5AI2zSwI1x11GlgYk7DPjalhBtK4lebx0W4e2doLwCvOeIppK8H3Nwkb9N99B9g/LeHh7/Vs5Y25h8M2i75OlzHF77VJBfovb3kJ1oT056v7X46ftaEop89UhI0BMHeZKB4EEgQSo2CnodwW1Ega1Kk2CaOzf5OwC2qXRHqG4bJh1yL0vLTrGT5eNdBWVRUS41Bplv1UXYWB8SZcS9qNpSycICr2Yx5uQkGpmVPnXIwpjYBAof3hKgOvlIi6dfkUV5y1nfaAQcl7CWxf48yYOALvtpvZWxV90v5qCxXPCLU53z1wLlVXGr20azeDpUZ641Ih/b7yYh6bWUSxKgPx3vPuoF2XfTwy28euQiuzrvTh6oYn2Gm10xVoun5VWMPdhyTDK6nbTFSSdCZFZ7K2UxQGmcA5eLedCyPJZm2Wip8NtWG57Ba2BM7kACUvNo/WHnez5D3RT2XV8nOivG8pyXabyito0EpMBxqtoWqOnedUj7kdOP4MsQ59hoIXC3VFp4PfzX+s6OAvdk+E32v6KA8FD5V/+OZrAGi//AAdyQLjgfZmxMqFjJOpuLRpBS7LbgXg+5NnE59RmD0k58JtVEkFXlq5eJl9oy24VuD2bnhspZ3OtLBMyy/dw77/XgxAuUklv6eFqQ5hAw8Uc5Rsmcea6tGaKNKoS7uerzJsN3JJkK1m4oYO3Z4vVdwPeaKfsn0Yd5vD+Wr7GvsDLWW7MY3t66F26aCVo0EvhY7utf9BdE+G4oTu2Zri4/oKlUDLlFLyz/V0nDLQaxRcoD26tvGxMMPSQ8XFY8yVuRBXLM5K7jtpfROe7zgdsqPXaseM6OEoQoQIESJEOMXh+kpo0nk8bUQ4NkQPR0fBP955JWq7RBE3nPtrVgQ1vAzF5bzkbi5MibPxU5VuVMUPl7u+ihmv+9q8tGFHGHUOVRu5tukxLgqyUH49voLGhLA2W6Y7KWTjYSbJ9koXd0/2UwmYkXSySj5wp3aLBuq0gTFTywAirA1lNfq4CY/YeJAZFgdPF+duADujYMzKym5MMrfcQqBXWTODmbKxggtIHzFJHwh2EbBAQRkpqk0GWtLGDSJueyCN3xowFqoPPtjNoouIZatYcROrTdq1GmJo/cLEvHnhw6iKH/oVPVXp5pCT4TeBxstQXH66dw0Ay1rG8fyFjBZlPC1XozUl2TZprULGqISZbB3xaVJ6lYWNognYVuxkR1ClfdNEFz2ZPLcPLwv30aCX+c6kVLu/KLMTt1nG9tejy8mXEqhB9Jj3kpR8M3QhrvhGGIm7vkqrVmQ80P88VFpCRq2wyBQ2a9zJhPX0FpgT7Kh0siwu+jPL1xl3stywW1iIzy2RWllHwof2bApZmqWxUbZWuph2hFV8rqzRM+HpjNKn9m0IfYlOhh/RC41azcQP7N5MwRP9TEqt0qrP8N63/ASAn46u46GHl/NQay8Ay7tH6TZlTi0yx9EUP9Qjru0bYufexWH7lYMpKmlhJyfIEstU8YPz73sKuuoxWZH52pIoUlgbuGU/aKKV1DAjrVCNh7rApOaQr8ZDzVHJMVkQn+Kpahcgc/DcxD4AMqpL1VcoBH5eE26CCScdskO1awWE1axtD6JByqiVkB2aixZ9Bk3xMRTpw7DdyJbZbi7LifO+qvihXu10mydHqj9YuyYBUkjWMcC56b3cV+gFjuyHF+H0RvRwFCFChAgRIpziiDRHJxfRw9FRoDRYvGq1+Ax9cculvKFfooPX5B5jxMmSUgLNSaWNpGqFzJGmeGGF8bji0KrN0KOLRmFtfBANnzVx0a1MNqa4KvDE+dbERfxmdiUvTUv0dchO8+RQJ3ZBojw17uJVhKVRbAU/6+CVZZnXUw77bW5P0LBTww2MVMsdwvqkBgIH4CykpKtUmoNt8vJ/dUsWL+aj1DLSTJ9ya3AshwAFtEB6oJUVnKFU+AbbS7kQeCApmo/bbId+L56noMzqkJZI0181y9ldQkm5qJRcI6wFFldsVHz6YqKhuDu/jGxCNBMjxQyWq7GsUZZ1xGeYtCS6btDKtGVmQoffCTfNhZld9AYpQJ3GFHfkhY1JGBaq4vO6hTL25yV381S1m31FGZD/euTs0Fnba6vS2FSkEPgp7bda+M/h9axrlEF8feMGdlrCSJmKS1x1wui7y8jToedDLdG0mwy1VeH5Cii5uGLTbUwes+6o5hJ+RmKAJbFRvrHuuWWiPR+cbizAseKzS9Zw2ZNyDa1LiFO5GbAilzTv5i2//2DIDmW0MsOB55Wm+Kh4oV7pitatbFnWiTIdCP40H208YBWzLtZMKtQGKq7CxEgM35BrzVjtsrpPnPW3J9pIxu0wg3NiJsXS9kBTpLqois+hisz7nXs7aV9bCPVzZdekw8gD0KtPYSg+j1eEVRq2GzloNYT6SVXx57haazRoJRqCTLOCF2fj7ELuHlwCwOfO+H6YybbfamGReSjcdm18kDPjAyGTVPH0UNvy0T0buWnx+ud7ak45FH2Ty7OiAzUVh4xeOWn79n0V7zgLx/pR4dljRjRSESJEiBAhQoQIcxAxR0fBm9c+zL1TonWxikboUBtXnHlOxr/f8ESYcVRDrXaR7WuMO9lw/QathO3rxAO9yjXZx9lSlYyxRr3EHaPLuGdcIrWD01k8VyXZJBFteSaOYgb7VZEoU8gDmmIltk2Kd8lkJk5RVULPFHxQLQhKJ2GMgGpLtJoYVbDTYM7Id19TUBwFJWC+FA+ebnpr1ex4VFAcQq2TmrLxXHnWVmZ0/KyNGtRh81xV6poVZLrZhkfeEo3M39//SuK5Cus6RHuTNirkjDlMmOowPCgRdGLQoLLYYnnPZgA6jTxP+RIV7ym3sjI1HLJ2U06K+2aXhjWpXBRWp4XtWZkaIabaYQT9rfGLGKuk2fHwIhkjV8HJ1FLtNKYLCdyADfjb8Svp7R6n0ZAIe9hpDPUqveo4P5leH7oMr4gdpFUrsiVglhq0evbP3mobtq/xRGkhIK7cZ6f2zvOSORo+vXgdF2+Sdh4uLuG+deazbhPhmXHnWpmPd7KcK7fMhHXX1iUGSKnVsMaYqbihVmeQJlr1Qqgjs32NsxcN8sheOafqSAzNCvR9rkJiREUPprbigZ1WcGOyvGIb2K4wUIm4jaZ5VIJ6fpf07iahybzYXWhhdDbDzA5hrzo3+Dx23xlMXSH9ffnS7dwxI1qq3tg4cdXmyaJk1jYaJRr0cqhPsz2NRl3mcck1g/uT9KHHmGR540FemZNrzUUlG4zJ2vgBDMUJtXY1TASu7LavhV5Hln98tcBOBdT0f+/cuYesWg6vUdvXcHztmTY9oXBRwvvb8bQR4dgQPRxFiBAhQoQIpzg8//g1Q15UZv6YET0cHQV3jy3FTwgTcu7S/axJiE7onlI/GbVMNYiIGrQSJS8W1hVKzcnueLTYx55SC05QifvMhkGWxQ+GupK8l+BgUKNo22wHB0Yb8QLfIMoa8fYSpiGMVVnxSWYkcqtWTMZLaZbkJAuq5JpMTkvU5mvyr0ZuZcQwGs2qXxVOEK3WNEReYPFszkgGW61wumZD0HV8LVg/iHyNaQU3DrHAINYuJnBSsqFaVfBLMQKyDTfmo7gKTouwHaris+eQ6HsUw6M6E+OxPZIBkjyo4CTBWSduu2d2D6GnpCGrSUPN6/z745JV9oa1j5EN3vmPW2lsX2PMkgi/4AibU7uZaPhzdE0OW2a7KTjC8I2UMgxN5nDbAk+XbIX2lBzoVDFBcSKJOSmXipvwOZjOcpu3AoCx5gyrksPh2C4wJ8PMu6JvMmPHw7mxODbGA7NynLcNr8DzFd60SOqQLY1P46I+Y5baXERs0QuHW1dn+Yvdcr2r+Iw72ZAt2FhaxHd/dBkAC18ywJu6NoTX/AGriRk7hqIGF9CCMvaYzEPFUbBych0A6CXJFA2mKYX7W1n4crlYNdVjcjbJFMJQ2Z7GkqxQwWnDYuDBJtr2yJwyp12yO8ukhoUNuuOP+rlw0b5gO5WEZoesE0g2Wpsp87PqGajBjSKjVWjQSqFWKKlW5zFAMhaybsmfP/dqPkA1ZmkuDMXmQ3s28enF655xzE8HlLwYBTdBJrgJVjyDOw70P8tWEU5XRA9HESJEiBAhwikO7wQIso93+98lRA9HR8HL2nfQICQEi82x0P215JloeGFEZfs6uyrtjFvC3CxNjtEU1BiyfY2SY7JjXFySHx/q5veWbOWaRsl8G3OyDFZEO7BrspnlC0Y5kM8BUK4YtDUUmK0KuxFPW7y0R/yRNk914rgaLabsZ9Y1cW2Z9JlBBcUnZG20qo9R9IhNB55DeyfwUxKuTq5vxEko1F6bqxYYRZ9YXiJEY9al2Blk25gKigeBXAHVAbUANYmMXgRPl6jY14RRCgJU3JhsWwnqTFU7HYzHpSGn3cdNeqiObKu60LDXZ8qQbJwNE0vIdktDyuIy05Mp1Ek5F78eWk5fTvQ9XfFpdpXaiWlynPtnGxmZyYbM0eZUJxOBn1NzpsjQaCNmQjrvuSqK4qMactzlwQwVNzj5PsRnlfA43QRUR5OMBfqq7XobO2fk/G5I9nFWdj+DtrBiccUOWSSACSfNPWOBpuxAI/98+de5syBMUUYr06rP95R5107JlqyxFsfKKkU4ftR0g7WxHwoy1L6z7ZwwY3PfeDM7mjrCzMomfZZdI614jsyNs5cM8GhV9Ef6wRhmQRgjACeF1EEM/la5cXACP6KybRA3nbAOGzFwghUnKwnK/VWM86ShQ2WTxIPZUEniTKk8YkjmYkeDzL0FKcmQbI8XSKgW1SDzTlW8kGlt0MssMCc4YMnczajCjhi4h41NXHFE/xL0yaWedQl1hqmGI7VxOqJWr/D1W8cA2Fbu5NLuXTx1kvYv7u3H+Vot0hwdM6KHowgRIkSIEOEUR+SQfXIRPRwdBaviw6QDbY6Hytf2XwLAX/bdyp5qG/cXpPZW0Y1xacM2fjP6UgCenOzCcoSKOaN1mKxRYWmLaINUxWdN6kCoOSp5MdakxNvkcWMB2wY7aGwUNijbWGFsOkNjWiLEIia/2i7MQSpdJWHa7JmVKG9pZpz+nlEAdk/2kN6nENiekBj3yGyfQpmWdvF9qoulnlK5WUF1hfUB0Ks+yRGb+E5pC03Dykqmla9Jdk1g2oteEn1STdvkpMDJyJfYIZXUQT/UNqkKFHqFIQLAVSgtkM/6jIriqRiFYOB9mO5TQi0TaHh7JGovt/voHsQmpd3JeAPpmLAtj2ztRamq+Gage4o7+GUdam7lZTOsazVUNPEqOrVqUb6joszoqEFmUWpIIShVhhLqpuR/targqwp2JchQ8jTOaxkAoD8+wrSb5ImCZAd1x/P0ZsfZbUlF9lvHV4e14FLNJQpenAvSATuk2JT8WO2gedO2ujbN9dV5GZIRTj5qejXfr19bnquydaYDJyPLZh0TfziB2i7am4HpHM2tMrErW2J4Zr3mYbnTQ59VcQPnejfus/egXJe+p5DMVOnOCeOTNqzQT2dlrhx6bAE8MdlN3kqhVwKdk6OQ+4Gw2ByKM3yJiXmpXAM5o4yuuMQRNmwiqAEJsDI+TMmLhe7Zw3Yji8xD2EGtsbnsj6E4GEAFYaB+G/REzwU/XCmZwfmfL8YvnzyfowgnF9HDUYQIESJEiHCKI9IcnVxED0dHwaSbYntJIv5vbDofb0Yiqp/lzqTZKPLLvcLiVGdN1FU+Pek8ALvzLYwfEKbjtoM5jLRFR5O8++9vEG3CN0cvAmDvdDPLG+X99chAE9gKkzM5ADruU2l+6yhv6pEaRSUvxpgt+oCHDy2kwayQNoQ1Gak0cHHLHtnuggL3sSrMOEuOKnBgFGdGIlhtxVImV8qxKJ74H6lOsLIPquPBbCCMyGVDPZIbU3DqxIZkrlXr/knxcTCnA0asw2f8FVUSW+PBtlLbrblP6CD35y1h9K2XJVuupuPAAxOolX2K5eu6jIZdCtWGOouTGNQZGROfIyPmg6+AUsvKCyJ9JfhfA21OGqvmAlOyLL1fwSj5oecMgFc7Vr+eAQjgJjz8nE1Hm0T1V3VuYXFQEX1jaRHfffh8li4Rz6Y1mSHiisNjBfFP2jHeSnlEqLf+9zxEfluKA5Z4OCW1qtTee0rmQ0Yrh1lQeTfF15ctJMLJQ92rTEdVvJBRsSs6tAmdqHqwfayNa9seB+A/Dp5DYkzBnRE93cxah+qYTGS9GQKSGIDUPpVSl0/TKmGVF+cmeWh7YFwWZLvVWMaORCH0WYupDobihjXuBg810lj2OXSpsEFLekaZ2iyaI62i4aR99owJI7V/ogmrovOSpaJd7EseoiHIvMq7SabdJOuDKvMVT2oGNiuzRx2j32bG6A+2jQAyDtMBjVxyTb57+yWY03KfuK77HmYK8OhJ6pPHCSgfEmmOjhnRY2SECBEiRIgQIcIcRMzRUXD75Coe3S9eNorphU7Quwst/Pq+s7n8yscAMBQPXXW594BEfaWZBK9cL66yg6Ucew41c2BYtEE96TwV32BNRjQDg7M57tqwCgCtIhldiVHZUeY798N3wNgq7/obtBK3rREtwTu23yNO20EmjaE4oUvvxakddF42za+HlgMwO9BMrrUZpoXpUPIzZAeE2ZpcoWGnISOWLvgqzPTGUBdItXq95IVsimpBfAoUN4hqcwrFhT5eQrQwasZCGRGmyJxSMFeU8M4LfJl2NqBVFKa2yzg0F33sdOAc7AnZU0t2cZJQ6iDUIKlWnSlyY8zTbbgJP3Tw1suiBakFVlpFkfWCCFu1FfwgItcsyT6LSaIbRtHHLHjUisGZMw4TqwN2zYVqTrKJQGrI9XUd4spOyVFZGhtlvyWR+S8HV3LZum1snhCd1gJjkn8dewn37ZO5YRdMlr3nIUA0Rc36LM26ROZ5N8mq+FDIWFQ8g6/21yu8Rzi5GHXECr7LyDNsNVIKJqFmupg75bM+q2Nn4DO8EgB7f5qmMZ/xC2Uyd6VLjO8SXY/igWtKNiZApVXqqxUelkzHBxfkUKqBw3yDTXEmjqEHNdAyPnlb2IuNo93MTKXoWyAMozcWJznu0tAk82hd4zD/dY7Mv0lLJdY+i7tb7hvahIIZh1KvzO0xK8PqBqlxOGo3sCYxSEaRazajVeZloFlznKB/m2qlHQ2teqH+2ZPPLipL1g+y60nRFG6b7WCJOnDS+uSfgGw1P2KOjhnRw1GECBEiRIhwisPzT8BrtShb7ZgRPRwdBWm9ghJkN6V2GqgX5QFY33iA0TMyPH5IoodD0ykMwyWTEH1ItWrwqw1rATCnNNa/bDvJwL/m/sE+MnqV87KiD+pMFlhxvkSAE9UUG3ctxCnU3WfV33TzXyPirbHrkYUYH5WJ/Y3l90sf75GsiT/uvCfcpuDF6Y5N8ceLZZ27Xr+c/RP9NOzeB4A3MUn6UYkCXbMHO6WEGWeeDp6mUMnJ99i0GjJmmu2jOrIOQCzvU25TUAJvFr3VwQoqjJe7PUqjmdANOHNQwUmA1SDLk6MOk2ulIa2ioFXqjI9Z8MnuVkKdESpYAWtjjoEx61NqC7II5xj1mnmITfvM9Nbdv81pYYwg0Ax5AQvmBNl3Qead4ilotoo5I5FyudXADUpC6Q74OjiBe3ZDc5E3LdhAjymOxcN2I2vjQr29c2nNAVwG9OXJAe6aXoEzJQew7F0Psehh2amhOFQ8I3TbXWyM4aGS0SRyv6n/tz86P5WxyBQtUFyxOeA3sa8krKe2K1nPrATMAih3CTuUdKCwCPr6JNtz8ImukJW105JtaRRkDsbHRbOUHgrqD27TKLfUNHsa7pJy6OGV0qtsn5ZrPROvojb7pA2Za77mU81ptKQk5XRt8gBja6Q/921fQnU0iblYlr3myo08Vejkje0PS98Vl5mAEu0ypogrNnts2c+3lnfzib2PhNqmuOLw4d5zT8DInl4wFAcj8E7bWe1gVcMI+zpkLsxYMfZ7TS9m9yK8gIgejiJEiBAhQoRTHFG22snFaTtSf/M3f4OiKLzvfe8Lf6tUKrznPe+hubmZdDrNddddx+jo6IvXyQgRIkSIEOEEoPZa7Xj/RTg2nJbM0YYNG/inf/on1q2bn0p6ww038POf/5wf/OAHNDQ08N73vpfXve513Hfffc95H3fuWIYaFDzt+uxD4e8//dEaNNXDcuXVVE/LFBPFVJiSv502xiZFsGk1umz6xXL+4A/uBGDlihH+ZduFdC3LA/Cy5m1sLsprs7hm83trNvNYu6ThLno4xZnpTRQ8ob1fftV2fnFwNQB7vrMeZX8C7hfK+08bennLhfIa7RcHVlEoxWnOCJW+LDfOzLWzZPafIQdw90aUabEWSIxZWEtilFvkglEciM34YSkPxQe9LPvwdIXZTgU9KDyrV3wy+2FGvDBJxGyqet0SQDE8qMi7qZmVNuaYQXpA2i116GT21C9SX62bSZZbFIxiPXXeTkNirL6epynhqz61quDGZZ+FpS7aU1poHlluk1eAsXx9WyvwvHMNUGzC0MBOi8FlqVt+8BUfjECMGvdIZsukgn2e0TYcFOWUS+ex2UU8gaTZJzSLXw6s5O/W/ACAL0+ez/7zivRTnz/7z5Pz0rF7ep7gVVM8NDw+1nc2EV581M5vVi2zyDzELUVJnPBMPxTuK8Er11Dj6ss1s29IRNaxWaX+GnoSQMENXgXrVVm/9rdKr/hh4oGT9OlqyZPU5dVZTHWwgwrQuViZrtRMuGz1mgGsVRovaZH0fA+FwdlceBxnrN3HyqykpV+W2crLs1vCZa6vompy4VV8g6IXCxMCAG7sO+d5jd1vA+pO0jr5IJX/+4NnU7YN1nVLQs2W2/oZzLe9SD2M8ELjtGOOZmdnefOb38zXvvY1Ghsbw9+np6f5l3/5F/7+7/+eyy+/nLPPPpuvf/3r3H///Tz44IMvYo8jRIgQIUKE40Otttrx/otwbDjtmKP3vOc9XH311VxxxRV84hOfCH9/9NFHsW2bK664IvxtxYoVLFy4kAceeIALLrjgOe0nuS1GZZ1MpPGfrqD1VdsAcF2VYj6BFpOoX1M82q/dyiu3i/LyocGrSXZKWq3yUAOVFp/vbJcIzBpJkumZ4aHJXgC6k9McLImxY1x32JZv58L2vQCktSo/Ongme7YIs6RaCnQLbdPdOsWhuMVFC/YBULDj/Ga0H4DiQy14SZiwJX33rtZmiLtMrpKIsH1TDt+SqNONa5SbFfxAfJwYg3KTQr2qQL3gql7yAI2i6NAxCgqeEUTSQH6gAbVB2vUKJpkn6hFopVXFM3xqIbYTV8LU+HJrkMoflOmoMUhBtQSSB/2wiC4KuIYSFrQtd3hopSD92VEotxEySYmDCkZZCu8CJMdd8n0y3VVXCugqwbH5qjBNjYuFdrIdjdIuSeV2FSgNZdCaRVTfncgTV20qQfHOq3KbqNQGEPjxpov47LVreDaMOA1ktQqtmhxMxBidWigEEzSlVhl3MnQkRYU9vGQWZVAuEDcOlRZISTY8iicMUWWhzHM754UJCzP9LqRdjCGhjmZ7ZF1Pl+WeQcgqeQmPhG6HfUmqVlhQ2fI0ktihKeTa3DBL46M0BIWwPV/l1V2bZMMuyKiVUOQPwhDV5m5ctUOjUTyxk/jRypYTMHqnPzaVhQ1eaE6EJpDXdD/JP224lImDcs9OuhDkU5wURNlqJxen1cPR9773PR577DE2bNhw2LKRkRFM0ySXy837vb29nZGRkaO2Wa1WqVbr1dBnZmaOum6ECBEiRIjwYiB6ODq5OG0ejgYHB/nzP/9zbrvtNuLx+Alr99Of/jQf//jHD/vdzhDa+M+FcW8Ga6GPkw5S2JuE6vjGctEKWf8Swwre46cumMadSqDuEBbHAAoHMuwNCtOOJdJMBIaMmc4CM4dSTBQl1bs4G8PLm+gBM+I0uBBomQam2untHyER0Dp3Dy2hmpcxaZiB5KgwIwB2VkWf0GnYK+v6pRKeLVGnma+iugZmYIaoeD6arZDcG5glVupmjQ4q8UkfvSLf8+dadHZMYWiBSaVZoRKUOxiZycLOHPEJaUevKJRbFAIZB6XWuqaoxhTVqn7oZdCsejHcuctcXc5L7awYBRUnFXxTwM764cJKK8R2+8TysoNSi0ZVMnCJHwIrC4nxYN0W8HrKzJZkfJ2hFFpVCfahU+m1yGQkRNxfamJdcpDP775cxkj1+L/9PwbgM/uvYtFH7udYsNCY5MnKAjoS08e0foQXBxNOmpIXY1EyuEi64OGFYg6bHFZwGh3U/TKxFQ9UGzKbhZkxSvXSN1pFo9qo4QWFZlMHRI8UELx4Rv2aMFvK2K6GGVKm1FPqNYcGs0wqsAdp0otk1Apa4IYaV21WxaVOiYEbaNlk2wk3xaDdTI8xEbZbKwFyw+6t4T3sdMXwB6QsU9dnj+0afCb8230vAeD3z32ccuDrMWvHObd/H5tvEZPc2BQQ1Z39rcVpozl69NFHGRsb46yzzkLXdXRd56677uLzn/88uq7T3t6OZVnk8/l5242OjtLR0XHUdj/0oQ8xPT0d/hscHHyBjyRChAgRIkR4boiy1U4uThvm6OUvfzlPPvnkvN/e9ra3sWLFCj74wQ/S09ODYRjcfvvtXHfddQBs376dgYEBLrzwwqO2G4vFiMVih/2eGAP3TInOanojENWM3lOkJSVMQrFqMvv+i6i91lfMCropbMpZHQcYee00A38tEY2vg15U0TaIZmGqLU1iUiZrMRtj1dIh9hwSesPYnaDSbRMblHBScbUwU6y8tMpEMcWTdAJw4cJ97A/E6QcmukkP1pmX9F4VOxsUlAVhjXz5rO0ZRjtnWWimWGlWiE0RGiC6Rv1CcmNB8dmAkepoz7O04RAva5SxOeRk+MqtrwAgPq7gJ8EPMsc8TY69mpPvWrWur0CTaLvW38QhHycpeiaQLDmKgW7okEu1Qaca+K7F106RNIURsz2V/NZmksNKuA+z4ONr8t3KKMTF14/8Khe9oFEJ2qm0uWBpWNNBpxIuviZxQ+PSCaqHMhQKcuAjiQzDdo5XLZC5uC4xgBvQA0syh9jJM+NDe0QPYigOi2Njz7J2hBcLP1wpWUgXb7IwFJef75NM0esWP86DHVIOxtgVp+FJPdTlgbBHRlC3GQ9KcokSy0PDbpmHAFaDsEo1ZikxDoVFAdOKiG+tINVt1o2xN7gveLvTOJ1Vfm9VPetsv9VCU1CGpkErkVLqMgEDl5Iv83qP1YaGjzEnS7KGzy1Z+TxG6cXHwMfl3qpazDsPxws1YOw3HlpAMjDczFfkHlDuCYoA53XmVFV5wRG9Vju5OG0ejjKZDGvWzBe6plIpmpubw9/f/va38/73v5+mpiay2Sx/+qd/yoUXXvicxdgRIkSIECFChN9dnDYPR8eCz33uc6iqynXXXUe1WuXKK6/kS1/60vNqSy+BtUWyEnZ/ez3+oVqxSfBsjfFdEsklhlV0t16KQh2NoQS6nIeURSxiU6iZ8X3QZ5XQ2wQVFrxcChcOTjaydV8nvhNomVI+iuaHTI2+YobyQdlJZ0eeFY2jPDS0CICSbYZZbkOLc9j5FIYEkhRWOBgTOofWSv+7xpbh79ov/bFsFBfK7bKunRKNUWwiKM8Rk7IcIBGxZ9ajs8IdbWz023jyUgmNX9a9k8RiEbO3rptl5N5u7CBKxoPUECFTo1l1zyFzRsp81LRHlSZhjdzguM1paNgttJyb0DFn6+fIu6eRgyukQ8sWH2SisYFSMKX1WQVQQwbKjdWZK7WiotpyfAC9y0YYmc5SCTylsOpvm8d2NxPrLNIYMIVl22Sw0kRPXDQou6oddBlCkb2+aQOfZr731lx8YPfmUP+R95JklMrvtJfM6YC9pRZUxWNps9COtx1cwZl9kp72xPgSctsUnGDa6BVQbT+cy6V2JSyoLF5ePkYx0PAlAZ2QzdQs8JpkLmuah0pd71h0Y1iH5IKIVcGbMHlysguARd2iHxq1Jbtyrv4IZJ7VCiPHFZvFsTEKwcU14aZPyBidCvA16Prk8WuNno6De1tYvVLu0aWKib0rQzw4h74CHC5LfcHgwwkoPBvhWHFaPxzdeeed877H43G++MUv8sUvfvHF6VCECBEiRIjwAiB6rXZycVo/HL2QcBJ1liT+ZAInCLL0InhbE6HzsuIBvmgIABIjdW8gc7cwPT0fl4hm6MMXYebnZKXsUhhfKg0rj2dI+FBtlahv8VmDHJjK4WqiF1AUn1iHiBl8X+Hu3f24ljRUyid4ANFBuLaKna5nq8VaylTjJlpVaJLx85qIrcgBoFk+pY56NlhqSMHKEWZ1mXnCDDO9IDqeWuDiqzDb6/HWRaKhOVBpRFODLD3Dou1RB70oYfP4GTGqzWCn/XCMUmIyGxTkrI9JYkwKw9Z8kIpdPlaDHExyBOxkfd1qE6gl+bLvgYUoaY85QTPVXJ2RstPgZIMwPuGiLKoSM0Q7UHEMKuNJqRwKGCNGuJ3VU0VTPRoTMvYvbdnJHWtSZJ8Q75MWoxD6HNlBx745WHdkf8xqIqWIZsHyNSrBgI47WTR9TmcjnJIYvmCGHf98LpesFjXZ+HSaA/uFidF9YYBCfZwBxTkJX8lhiAeJYaqNOGLXnN8zcn0ZgZ7OVxT8wJleVX1sT2PWDrInPY3uxZJaOWy1sWzdIGtzB8P9zDpxWgKquODFw/lYc3au1dNyUci7qdBb68HZJcDh+qPTCQs/duLZIoBYoAW1PJUt+4SlM1M2nglecH+vNkI8Sjb9rUX0cBQhQoQIESKc4oiYo5OL6OHoKFBdMGpeOz5hNpoWZEWUg5I6ehlieZ+emx4AYPTPLwrbaPz6A/Pa7P6URDkT75B13BhoPxUhTiKIKn1VorzhXy6kvKpKYEBN/NYGZoQcYsxMYxQUlFrGl+ozMRFEs1UFN+ajeHIRJHQXS/PnvWyu5IIaYhqkB+vRrGb56OV6PSg3VmfPPBOmLqhiJuUH19Zozc0yXMkB0G7OcEGnaJkWxKe46wad6W+JnfaCHw2y52099JwpdJHymVbSHxHdxrQVZ/++Vtq68nJsB3MorkL2qcA75owCTWlhbRakp9k20Up+TARLsWyVZa0Smr+ibSu3jq4iHWSWDBUayBcTWBNCQRmNVXqbZB8l28TQXKzAbyplVlHSNomUZPmUGvSwvlxr6wzTxQSmKhH2HWuEDawGg7QiNhxm/xhBFL7HqftwGbjEAyvuvJckp8qx9JsjYZZbhFMby/5kA/k7O8PvWkHmjZkXfVyNRa40glZR6t5dbp25dJKg+ErdUdkXdtQOivapNsSHhfEpxWLETZtZWy5wVfHpzYrGrenMEiuzI6Hv0awTpy8+jhUwkqN2QzgfVcXD89Xwe6teIKVWw/l4WXYbm+k/gSP124MFc/RLu7+9HoD+1nEGTRvlNskM1qqg2CdPxRM9HJ1cRHfnCBEiRIgQIUKEOYiYo6MglvfQgor0vlr3/iHIUGjYE1SrNxTmEgDt//Ds78CbvybrjLzvItHxBEiOe7R8pV4kd+T9F1HqlP3YSSXUzOhNFWIPpEL2ykcJ64+ZU2DOKMxKIhuFwSxmRyn05ohP6iELprp+oIOQaMI1JOqtteUZ9QyvaquHqnlYo6L/0Ysq06bDjmmpQD6VSIaVwg3V4dWdm/jqqyXbb09vD74K+4aF3VLf5tLnyIA2x0v0rNnJhkGpZbTgFypTfzjLzPJA5DWawgsyxdpiBRo7iyxdIv5Ag5UmdhRkEEpujD/sfigcO7XNw/Z1vrpXnG6bE0VyMWln30wTpuZgBGxQ1qxyVt9gGFVNZIosyUoa0YJ4nhajwM9W1YscA3hBXDHjJcL6VENOI18auA8vCCYzKgw6PrlgeZM2Rimorj7mptGUSHN0umBwOgdAU6aEvlbqrA3uayU5UPe6UR3JyqwF56VOSAd112J5HyehyDUHZPcoqI5PuUVWrnaD0ybXj6r5aIpPUzBfTc1h06joXs5sH8JDpezIrTulV9lbaQ3nsqG4ofbN9Qyqns66pBjbNmmiSxp2ZC7HT6Qx0G8Z9v6NsPtO2kU/INf69oE+vLhPKvhboHhQaTt5TEzEHJ1cRA9HESJEiBAhwikO31fwj/Ph5ni3/11C9HB0FHiGglIbnTmvlRUP9Epdw2MWPFxTYept4sKt+ITeJg3fFM1R6ToxoSy1qlhZwqryHTc/M8vU8ff3M/RhiWA6Pnc/hX86D4DkgylmF/r4hnSicbNKsVu20SzRDqUOyEUQm1Sxx9Mo7dKp2YU+DTtlmeISah9A2DGjGHiwIJ81CWaptoLnqpARWskxNDRg35CwQYNjXfhdQkk9FF/Eq5c8SUdWDnTP4hitzQXGdwYuv5qP3iWsSb4aJ6lbvLp/MwA/fNl5pB5sIJBiUF1f5IxmqRVV9gwyeiX0Cmo0SlzSvBuAimdg+1ro97IsfpAOdZr/r//nAGyuLODBycUAvLP3HuKKTSYQgOyzWmnVZ8JtVcXnoJUD4JzUXiacNIseFq3R/vNEiPbElETy22faqFwqhY1v3PMEI06SnsDue8iNkVRtBpyG8JyeGZMx+Yve+YamEU5ttF+7FYCdXzqf3n4533rGIn5IDzMkzQLCxAZMsjkNWrVe+0+r+pRbZWJnDvi4BiTHAmY4o2CkhMnRdRdTd9idl+tlthyDbcKkPmTpXLXkqbC22gJzkkNOZp4vUk3LltSq7Cy20VCz1jclm+0/Vhy9nNLvCsbeW9eGtn3h8PtwVm4rWA0apS4Z29gkxAcU1OCeaJR9jOrJY389lOP2OTre7X+XcEwPR01NTc+pUUVReOyxx1i0aNHz6lSECBEiRIgQIcKLhWN6OMrn89x88800NDQ867q+7/Pud78b1z29/TPE0VY+Kx7zauhYaTWMCPWKOOKaBT9Y16fYISvPvPlCst+uZ6zpZR8UJdy28D8uJH5IokXjV4/M2//U2y6k8esPkByRdcf+9CK06cAn6JCPlVVwUvV2fSXQLuTmZ8xoFSh1+Rj5Wo026SNInSfNItQ9uWagNwq2LbcQOm3rMype1cRuDM6r6lOZipPZVnsBD/GtQkFZDQrfHz2fJSskO+2alZtZnjzI5laht361fSUH8jkZZx+6UjN0BFbc737Zrxmzs3Sb4jrtojDt1PxaFGxfoxQUg+s08hQCV2vXV2nQSiw2RY8kNaQcRgIDqpIb493ddwCQd1Pcnl/FH7XeA0CPMUGHPhM6C2uKH+qIVhhjDChVzs3I+O0n0DgFmUQl2yQg2phwU8H20k6/UUVF4f2LIpbotwX9767r2tyvtOMkIDEh11M1K9o/zQruDeW6QzaArytk98vccE0FFKlXCHLdeWrAUJgOrqcyu1GYo9iUeOoAWGWDHYU2tu4V5rKvZ4x3L7qTLWXJDJ1xUoxWJJvzyUOdLGqY4tFpCVK/N3U2zddsP+Fj8luJGsHiQ2aPfNHLoDo+WiDV8tW6XvNkINIcnVwc82u166+/nra2tmNa90//9E+fd4ciRIgQIUKECPMRaY5OLo7p4cjzntt71UKh8Lw6c6pBDTwsfE2ZpztSHZ/YjIyJ4vnCxtSW+5J1BqC4PqXXnR9uZxY8jJKCEmSsKD54MdEHlF8r6x1aG3jvDMP0Wy4MGaD4lI8W1GzTKh6pYYVqLog63Xo9Jyclbt3xSdmHZkHqgBI6eNsZX6JWhDFSbR81jG6DCKla668cq7SjAArOiEyZarOwUPEpWV5pqrNV5rRPfERjb4Poka7p2EyPOUmrLvPisaYeDk2JhmL9ogMsT42G3iuTbponprr4dXEZAK2pWQ5M5QAoTSS5ZO0O1jZJ9k1ctelVxTn4ycoca2KkjtSElwoj6rhqUwyKqW0sLeItLfeFWWQpxWZsTp2pjFomGVQ2VxU4K1agWRMdUdPOWb7av5jklXt4OkacHEvM0fD7/1hw0WHrRPjtQf//fpjCm+pFrVVXCXV8IGzy3PuCVq1fe9VGBSszp6Zgn0VfYx6A3swkDw8vQhdLLDwDrFYn3M9MNY5SkOtw35Yu/r/J16DrchG3ZWbZNyQZpGv7DrAv38j5HVIbLGKN6jiSzuhIsDP1z74OiqtgzgRMYU5BO/RC9C7CqYBIkB0hQoQIESKc4oheq51cHPPD0d13331M6730pS993p05laB49awzX6tHeCCMjxOvaQXmVGgG8P2QGaqtG1jb4OmB3kipb1Nzo1Y8eYfdvCXQJMQUYa78ejtWWgk/x6Y9YtO1diE2JctKHVBp9fGCCDUhEhycZMAAVSRiBQgKy4fH4iREY1TrU7UBEuM1lkm2V62g3UOBbik752JT6n01C2APix7o3t6ltLTO0KyLgOntffdxcEEOgL7YOL84tI68Jeu+qn0TZzcNckdFmKMFqTw790t2zVXrN9ETn8IO3IBtV6dDk0FYFR9CwwtdqlXFZ9huZLBc9ydaYMoBPzjey9XZx6kEQrK8l2Cn1UFOk1D9qUoji0wJCUueRk71Qm3TpHP0Sub95ghZtUJrzUgqwm89VLteW00v+3iaErJFrqEQz89n3WvXsGsASp2ZWNA9QX/grTVYylHem0UN2F4n5RFvkYyzmGljak6YqdrUk8f1FNrScm3tn2jCTP3/7J13eBzV2bfvmdm+2lUvLrKKbVzADRts0yGAKUkgIfmSF0IJBEJiEsD0QAATCKGEFghOo+UFXkIKJEAA0zEYDAZccK+y1ay+Wm2bnZnvjzM7knCTbElu576uvbQ7MztzZnZ2dc7vPM/vSTnHa6sNs/6bsb4+7f2eTMah6YFUbucyf734HQfwRKwBrUwnp9UGlh53jo477rjtrlPsf/aKopBOp7e7nUQikUgkEsneTo87Ry0tLdtcHovFePDBB3nooYeorKzss4btaVwdJogQFSylS+aaHTuQUYcsFUcJEht0eW7Zyk9mJOlRMDxKt31l3KjVlK0oWbbztqYQKVcJbbRfuyCRb+/Hp2J4wd/QGVcU2JIZoarEShQMb6YN0F5hohaKGBo9raC3iEwrTVfxtnb6GvkbLEwXWLbJkOGHRIG9bouoN9dhW6RYLlFXKjNqDnQWCcdSwN0O/i1iP4s2D2Zk1hYqvCI+qNTTRLlHPPcpaU7IW87mVJ69rpmR3joOHymMRpqNLL599EJxDVDxKSnq0jliXTqLmOWx99Pd7bfV8KNiOf4vEd1HSBWKzlUVrxNUO0fXGravkV2rzqfq5Ntuwmv1AgJqvfPeT9orgA62hUcx6LA83DRsyjbXS/Y/VMNyMpbcUfFlT/ttdTViOCpSpEzDFeuMY3R3iN+O9EHivsr3xWhIClVyfWM+yqA4Xp+4p5WUC8XeUdrQ8GgGJ09e4rRhbXs+gwPCP2vN5iLcPvGjsmRNKRhSKfgqkXOmO8+7ZhNnWP+bI5wqApbaWV/S2yTU9czrtF/BtWXgrq/VB9NqUjnqOT3uHH01jd80TR577DFmz56Nqqo88sgjnH/++X3eQIlEIpFIDnQsnLHzbu1D0jN2KSD7n//8J7/4xS9oaGjghhtu4Gc/+xler3fnb9yHUMzODDQtZTlZW5YilJuM8GB6RBV7Jx7IFPFAIOKRLFVkOQBoepfYJBt3VGybzNHQgwqBLXbMTNpCTSp0lGQCeXCOmQqJfSZyO+N/Mllv3laLdLDTX0UxLbLWq3gXiZgZ13cbiGSJzyrRnk0qW8FtJxcm8oTvUdf4qoy/iieikPLifLu8LSImKxO/JLyWtr6GAOlWLy+uHceJZasA4dIbsE+m1N1EqafJ8SfyKAYGilP3KY+oowqF1QQpS6PU3QSIrLKMMpSJNTK71FLWFJP3F44GIDgkSslQEZ+kYlKdznFqUI32bOG83I+dumdfpgZTYrtnh9QUNeksSu1Mu+pYNttTjnLUOFeXT9vmOsn+iaV2qa2oiO+EpnfJRnV1xhhpGthhd3iiJv5G8ETF93LjR8NpmSTu8+B6N8YQg8KDhFq/pT2LId/+EoChH9vq0uGdcUQqMSLvC5uViRWb+eKzyswKFAvWPCTuyRE/76zbeCBjukG31fGGnx6BN2I5caHuDgs13RlzFNrQ3e8u3SXISE13ftaS/Q9155t08u677zJt2jTOPfdcvv3tb7Nu3Tquvvrq/a5jJJFIJBLJ3kSmfMjuPiQ9o8fK0WmnncYbb7zBhRdeyAsvvEBJyf5dnyeRp+Fy2zeSBZounscLFFwdYOTbGV4BEavjSogRhOFVnHpkhteOIeoSh6R8ZaDh6rCrabeniQ3ydGa6KSrpAOhh8TpQraDZIxhvRMQfZeKVUmEF3XbLdncoBGs6q31HhyqkAxC3FShXh5+QX0yoN4ZMAtUq0UnitVrvJWuj4uxXS0I6KI4fLRVqlOnJnICCpx0nYy7j9NuVTCwTpoJhqLy1aSQAQ3MKKQ22AvAFw/BrKaZkrQcgR4vhUTqD+jXFImGJwCbd1FidLCFpBzqN8tWyLClctw/1baDd8jkK0upUCX/bPBmC4rWmmo76U2f4aTayHEWqOh1mZXIQp2WJ+lnjvNXO8T2YlNjxRwDp42q2Os8frBTbr0jt398JydYE/vERHd8RyozlEjUZm0eL70LectPJBNVSdvxKqrPWmqXZrvkIT6TWpBirGl7ILm0jbcsXucG4c7zNUzvvxa5Ej97iPHffNgIA02th+Czk/8PupLI6MwpdCVHjLoPpBiUNWcJKTcSF2b+tqg7xIrDN/PE1WQxkuprMVhtYetw5evXVV3G5XDz33HP87W9/2+52zc3NfdIwiUQikUgkAtNSUKTP0YDR487R448/3p/t2OuIlYBmqyFavLO2mqVC2tcZRwTgjlmdI0JEHEJm2661lTLRdHpQjAgV08KyYxJay71YGgRtBUpLWrhiCskC8bqjXMQOgYhfcHcxIVeMriqSiHPI1ERL5gKqBaatHM0PoTSIuIUpl6zmE18lhXliZ01bvERGmHgbxXEsF06dNcNrz8Xb3y3TA3oQPBH7tUbnJK0F7RWWOC6ghHRysuIE3EJS82lp6uMiWCmWdjM2u54VcVErykBlmLfJqXMG0GYICSrPFSWkJsizlZwF0Uo2xEUK3zjfJnTLxcqk2M/qeBEb1hYTHiTObVJxNUtSxWJbTz0rLJXRXlFdPWJ6GeWtxWcrfD7FpMEQH3CTGaDJyOK1lnF2a7rHG034XGW53fYx/q1VJcn+T/DvIpYn+fXDiRdopErFfZ5e7yZymrhf0lVBfE0KatqODWwzccUtXHFxn6fCGsUf2e7ZYUgszKNods9cnL+Ky860SvrAyjJQ3Pt2ncu+pqs7dusF07EUnCoBkXIV0wNR23BfMeiWueaOdMkwTkN3FyvJ/kSPO0e9zUR79tln+eY3v0kwGOx1oyQSiUQikXRiWX2QrSbjx3tMv5UP+fGPf8zUqVP3We8jNQmuTMaX0ZllYrhF/EDcrsFrKSKOwPB1qkEZ0l4FT4eF9ZUEykSevS+vgukW5jpKWqhVhldzjhlosIiWZ9qjOM7VvohwwM64VqeyOjNm1DTo4U4H6+y10HqQgseODcqqtRyvldajGgldeRDmJ6IWk7cYRp+y1sl2Mf0malx1jo/VOVevJYUPkpVJmlFw5u71MBSP3UJ5WEyxNiaCDA5EOCws4orq9WxWtIv4nLAnQdTwUBUTaXF53g5adT+qfaB8dwche+iWslx4lDS1uti2IRWiNekHoN30kzDdeO0st0OC1bRP8FETCwPwnYJPmOwVDsQGcEpwHR8kBon2Wi50S2O4uxUAtyJcs0HUS3Mrae4d/CYAsU0G55ce2e3zTNuy4v+OGoLkwMX70gLMb01FbRNf1MZjdKw2kaxy0KRNrF4xBONQ8YVprg0y6AMFV8x2ntctImXiu5aYGGf04DpSs3etHZmQPcUCkiqebDvG8CdHUPDorqlR+ysZDztHDdLB09L5umvWsh4UswhO1YIBlo1kzNHA0qtstd5gyS6qRCKRSCSSfZB+6xzt67gSYhSh6mKEYLjFw3KBHhAjCC1ue5l4FEfzVHVIBVVSQZXgljSuuCVGHyakslRMt4LhF6qL6YHoEPFoLwdXXGShJfLB22bhiZj4GlV8jSqWAvFBFvFBwpE37QM9oKAHhFM1iv2whB9SrFghVqzgabcIrwVvq3ioXXw5Or47DW+bheEVGTJqGlb+dwR5I5rJG9FMuCiKlWVgZRkULIbCL0zyvrTI+9IimSfqsFmqeJhu4aadKIBDT1rO6Nx65q8azvxVw5mUu5kjstcQUFME1BSmpZAyNVKmRtrUGOpr5YyiLzij6AuOzF5De9rH541D+bxxKJsTOaiKiWoP0xrSIZrTQZrTQQo97bTrXtp1L4alMNJTT1BNElSTxEwPKdNFljtFljvFulQRHRZ0WBAzFRKWhWmpmJZKvtaObmk82zaZZ9sm81rHCOZ1HMS8joNYHh/MxlQBmwyLTcbWHf5Fk0znIZH4//UxwQ0qwQ0qocUe8j9xk/+Jm3V1hVQcVEuyNkiyNoiSkxKxiYp4mG7FcdPX3AaVWU273AbLJR5qSkHrUEnWBUjWBbAU2HzjEX13svswLT+cTssPhVO2pYk40rQP28Vc/BaqadvbyF5nuiCdJerh6SGcjOCBIqMc7e6jNzz66KOMHz+ecDhMOBxm+vTp/Pe//3XWJxIJZs6cSX5+PllZWZx11lnU19d320dVVRWnn346gUCAoqIirrnmmq3KjL3zzjsceuiheL1eRowYwRNPPLHL16mvkJ0jiUQikUj2cky7fMjuPnrD0KFD+c1vfsPChQv59NNPOeGEEzjjjDP48kthSnrllVfyn//8h+eff553332Xmpoavv3tbzvvNwyD008/nVQqxYcffsiTTz7JE088wc033+xss379ek4//XSOP/54vvjiC6644gp+9KMf8dprr/XNhdtF+i3mSCKRSCQSyb7LN77xjW6v77jjDh599FE++ugjhg4dyl/+8heeeeYZTjjhBEBktY8ZM4aPPvqIadOm8frrr7Ns2TLeeOMNiouLmThxIr/61a+47rrruPXWW/F4PMyZM4eKigp++9vfAjBmzBjmzZvH/fffz4wZMwb8nDPIztF2ULoUjNX0zpT8VMguGOuUD4F4vkpWtdigvVRzAqc9HSqumEkyRwTsWip05IopMbBNFgPiIKGNCrmrUmyZ5HHaEC/QnEBrb3NnMVktJabvDE/nKCBzTOwAw7Q/015hApkplmi6cEwpDbewHcikqqq6mNJrWS0ixodf8RHbszXULz1CpPNn2QssSJeIi1Lii/Dd3AV8v+BjANyKQcJyk6OKYNSJviq+FhYNrNZz0RQLwx7R1KeznZIgABva83DbU2qTQxvsqTCx7cZ4AVWrRXr+X9xHc0/FPyh0CW+BhnSIXwx5mfdjwnjyMP96MrHyH8QraUyHmBoQxW2r07k0p7Nw2x9yQE3x1paDABgabONHRUuoSYvA7lnDD97OFZHsCu3/HUHo1DV7uhl9ise22YgXQbzENodNqVxQOp9fNZ4GgGt5EN3fmSZueMSUTQaXumvp940/OYL0RJE9oqgWIa9O2CcO0rZy8C7tc3/G9CgYbpFEAuLzSPtxrFAyZp0g0v2tLnMtltI9TKG/6ctstUgk0m251+vdaaULwzB4/vnn6ejoYPr06SxcuBBd1znxxBOdbUaPHs2wYcOYP38+06ZNY/78+YwbN47i4mJnmxkzZvCTn/yEL7/8kkmTJjF//vxu+8hsc8UVV+zeye4m/TatVlZWhtvt3vmGEolEIpFIdojoHO1uzJHYV2lpKdnZ2c7jzjvv3O5xlyxZQlZWFl6vl0svvZR//etfjB07lrq6OjweDzk5Od22Ly4upq5OeMjV1dV16xhl1mfW7WibSCRCPB5nT9Fr5WjTpk0oisLQoUMBWLBgAc888wxjx47lkksucbZbunRp37VyD+BrNknZAy1L7UxTt1SR5u+2U3AVQyGVDbGUGF6Yrk67+USuihpUidlp/6pdqNVvx6t5IxZxuwxJsNawAzTFuni+guHrVKigM720fZgoEWLaIpOWECNPwFnmbRV/Pe0WbcMVgrY/oeFROg0tFREEalfjQEtaeNoUsqpFI2JnTSPwj20XqyyY8yE11x3hFKZVK6NU5ImDBtQUGpajxGhY5KgxRwFqN31sSBU6++o6+MrTOjg0XEWxTwy/Y4abcl9ncGpb2k/UltA2RPKYOkGoDpcNegOAdyJjAEiaLtZ6ClibEBf/j6uP4rSyZXZ7TJr0IKN8tfbxxQVpN4SkN7dlLEnbBLJmWoTXF41jaSQz6u4s0yDpG/Y39Sjz/Te8naapIy9YyL1XfJdbfvIcAHf7ZhDx5JCuF/deIq9TRc4PxncYG7LhjiMov3HbKfmxEjBtw9egL0WuP0aezy6ifNZ62lI+uGO3T3G/ITpUlANxZgJcdpKJffndsU5rhLQfTF/ne1VzYJWjvmTTpk2Ew2Hn9Y5Uo1GjRvHFF1/Q1tbG3//+d84//3zefffdgWjmHqXXytHZZ5/N22+/DYge30knncSCBQu48cYbue222/q8gRnuvPNODjvsMEKhEEVFRZx55pmsXLmy2zY9iZyXSCQSiWRfoy+z1TLZZ5nHjjpHHo+HESNGMHnyZO68804mTJjAgw8+SElJCalUitbW1m7b19fXO7VXS0pKtvofnHm9s23C4TB+v3+3rtnu0GvlaOnSpRx++OEA/O1vf+OQQw7hgw8+4PXXX+fSSy/tFoXel7z77rvMnDmTww47jHQ6zS9+8QtOPvlkli1b5rhwX3nllbz88ss8//zzZGdnc9lll/Htb3+bDz74oNfHi+erKLbCYna5SnoYtIbOGB5vyiKVrRDvogpmRh16AAIdnYVnk7lilJEpGZIKKbjtahS+Jp3GcT5nBONts/CvN2mtFI3oGGphuW3DuITC0NvnU3OtSMu1NPDYc+SJPHH8jBqUyFEJbcQpjGupdJqYWcJMMjJcLHBHFfxbOtu/MzrGJjls+EYAsj1xJ1ZoWtYaIqYPj60c+RSdDsuDhji5hOUiZktcbsVAU0xSdj2WoJokzxXl2OAKABqMLDbpokRIQzpEtitOxA7aao4GGJcn1J9lySGsTxaysFn4/puWwkh/PTXxHAAqcptZHhFfxjHhOrJcKRL2Rfq4vZK17QWMDosv6OapUfx0Fvhc1j6oW2FPSd+R6xdxaPUvjQJAfyMfW+yj/Bf7pmFh0UPbbnfJAx9SdVEBAOOLa9gSjrB6o7gni4rbHOPT7wz7jMHuVk5bK9TTe78S52apsPYBUewWQ8FyWYy4XCi8qVwTs118t7zhGOtXDqJlmHCA/cawpZiWgv65+E15YfV4yv7f4r467X0L+zfOFROKvC0aO6n8gcYuipD9+62HO98HolSTkq8NTHtxnB52ex+7i2maJJNJJk+ejNvt5s033+Sss84CYOXKlVRVVTF9urBJmD59OnfccQdbtmyhqEh8sefOnUs4HGbs2LHONq+88kq3Y8ydO9fZx56i150jXdedXuYbb7zBN7/5TUAEYtXW1vZt67rw6quvdnv9xBNPUFRUxMKFCznmmGNoa2vbaeS8RCKRSCT7InvCIfuGG27g1FNPZdiwYbS3t/PMM8/wzjvv8Nprr5Gdnc1FF13ErFmzyMvLIxwO87Of/Yzp06c7/29PPvlkxo4dy7nnnsvdd99NXV0dN910EzNnznT6EZdeeikPP/ww1157LRdeeCFvvfUWf/vb33j55Zd361x3l153jg4++GDmzJnD6aefzty5c/nVr34FQE1NDfn5+X3ewO3R1iZGQ3l5IrOqJ5HzvSHtB82O37HUzkKzpsuel7YHDEpamDdmsrYsRZQXAUjlgCuudGa9JYRhYyaDRUsouGzlqHWkj1R2Z1acaoCrwyC0WbGPq6KH7Cwzv71Dq7NNmZgjFGEw6W/sbLuWtLplWSjO+xTSflDSdlHaqMh0ixWJ1zlPzt/hNcrO7XCyavLcHXjtoCifoqMpJkaXYZYbw3ldl87BZ0tvxa42Wo0Am1PicyxwtfPi2HxAlOJ4bNM8mgxRpDakJkhYbsYERSd8aXgQtXExb35f1ddI1gYhJNrwrfGfk+fqLBJ7UsEyVsZEuZDPW4YyIaealXb5kFxXDPP4apZt5zwjRzXs8DpIdp30cXYwnK0cGQGc+3rj7UdQdtO+qR5tjy0pcS+X+luoDDRS4hfqUHMywDmDRXZnczpIzPSQo20d9VB16xFCQbbjitQ0BEe2OutH/Lx7jGA2sOXfowGIGl5MS6HITqcL+FPUzTrC2Y/SJRur6Hf713X/KqZmx2XVWE5GL4jYI2+rJQpp22TiTRVDxBllyoZoSXDF982Yo56yZcsWzjvvPGpra8nOzmb8+PG89tprnHTSSQDcf//9qKrKWWedRTKZZMaMGfz+97933q9pGi+99BI/+clPmD59OsFgkPPPP79bCE5FRQUvv/wyV155JQ8++CBDhw7lz3/+8x5N44dd6BzdddddfOtb3+Kee+7h/PPPZ8KECQD8+9//dqbb+hvTNLniiis48sgjOeSQQwB6FDm/LZLJJMlk0nn91RRHiUQikUj2OHtgXu0vf/nLDtf7fD4eeeQRHnnkke1uU1ZWttW02Vc57rjj+Pzzz3vXuH6m152j4447jsbGRiKRCLm5uc7ySy65hEAg0KeN2x4zZ85k6dKlzJs3b7f3deeddzJ79tYVHi210/tCeAN1ZqdZLkj7bLUlYaFndfqVuGKd8T6GBxKdSVmoabHO8IuhhxbXnDikVI69b1tsiaUV3B0u1LRY74l2zounQ8L+PhP3pKa7+w0pBnjaxTHSflGUNqMWYdGZEVcgbPCdwophOkuR9ICib67AFqjsv+J2Kl42iAm+KmKWkLMSuFEx8dlpHzlajFZD3CvNRhYdptcpGPtVLiw9iqvXCjfWcncDn8XL0W0Zb3i4ibq4GIknGwJMnrSWYQFR7Hakv57l8cFMzhYxUTHTy4iAiClyqQaRtI83qoVakf/17oH92+InqzuzqR4dOWKn20t6R+YzqLn2COd+tPrNaGTPUekXKqSG+H4WZwsFfGF7GasSIv5ocmA9uqURVIQE/eDGD7lk5TkAuDraSG4OkbFBMnxQmdvcJUJua4q+KeL3OhYESJouJgQ3ARD2Jai3f+O8LeK7n7SLYtdfcQTRw+IMP0f8w9pRhty+SGS4+Fu8wCSZqzm/2XoIVF3BZf+eK6blZCa7M0W2u3QwjB1bA/UtfTCt1uOAUsmu+RxZlsXChQv5wx/+QHu7kGg9Hs+AdI4uu+wyXnrpJd5++23HTgDoUeT8trjhhhtoa2tzHps2beqvpkskEolEItkH6LVytHHjRk455RSqqqpIJpOcdNJJhEIh7rrrLpLJJHPmzOmPdmJZFj/72c/417/+xTvvvENFRUW39T2JnN8W23UGVcDw2EOErl1IC9KBLvPOKQjU4qgtWsoinideeFvFaCwjisQLxfs8TWJC29cEqbDYNjHYQEkpqEn7db6CpajOey1XpzqFJRQhb5udvWZAOiDeZ6l20cSM/4ZPeCdl5thjJZ0qk7dFqEYeIbbg7oBkDgy5c/dGiBlH7EwGmoqFpig0GSKrsERroyEtFJ8O0+tkrgF2vFF3xnlaAUhZFhtcHY7qdET2Gl5JjXO2i+oe/vXRFACyh7ZxUF4jg/3ivZG0j8PCG8Q6Vxy3YuB1dS9+uD1+snqNEyNVo+fuZGvJ7uCJCBUVwHTvf/EcbzQIH64zir8gZbloM0Sqsl/T+c8mESLwmjaGaUUbOC17EQCb0tkU+kX8nKpYxHM9mC32b1YozbBA83bj5bry/qZKPO60o7bWfziY4iNFzFd1Qy5eXwplcTZgZ7wmXE5WnOlP0362/TtqWYSe3bb/2T6DLU/GCzQUA9y2q3kqWzy3kp2bfVXJtFT7t1YDKz1wSkxfOmRLdk6vO0eXX345U6ZMYdGiRd0CsL/1rW9x8cUX92njujJz5kyeeeYZXnzxRUKhkBNHlJ2djd/v71HkvEQikUgk+yJ7IlvtQKbXnaP333+fDz/8EI/H0215eXk51dXVfdawr/Loo48CIuapK48//jgXXHABsPPI+V5hQc4qWwFqM2keLYYN6SzhDZRBDyi44mJuGkR8UkbR+WrsjukSbtqZeWrD11mHiRqNYA00ThcKhRZxYWkKyS41fzKu13pUuGebbnEAT8Ry/JGMACh65zo1DR2DOrMx0kHLmXZO5iPmse3MDF+rib/TjHqXSVhuWs2AExuECvV6Np9FywA4NWexoyrVpnJ4q+YgrhkpKjC/ReVW+zu/9EgA/lQ1D8NS8CniGumWRnlQNLi+PETYneT7R4gRbZ6rg1WxYkp9QhaLGj5nlK6bGssjJWSdsrZH55PJkgModTfBdivOSXaXgjkfUrCnG9GPJI4Vg7qNiwrYnMgh3yMCWdp1H9G4+GFIRr0s1AzGBTYD4FbSBFziCx7VPITCcZI+oXp+/6CFfDDe89XDbBO/V8ejddZsc7fDxg0iKDJrlZucE9toRChHqRwYXl7neC+t3lTMyJ+JuLCxoRreeja4y9egv4mcIxSu8NPbz7bNXpXxnhNKusuuUqHqQhFyJbv4wtlksvkyP2uJPFDa+r79kr2DXneOTNPEMLYuirh582ZCoVCfNGpbWD3QA3sSOS+RSCQSyT6Hpex+QLVUjnpMrztHJ598Mg888AB//OMfAVAUhWg0yi233MJpp53W5w3cU6g6KIbtR5SyCFWJ562jRPZXxlcoHRB+F85NZ4HSJZ3BUjpvRtUQClDGBwlLxAABpMMGvmaNrFVCoTDdEB9kouSLye9USsO1RBzU1yy8N1x2PFLu4/NpvPQI5ziuBCTDYsgTaDTA0kgH7fikpOLUX0OxsFSLpB0j1ZCnUnH97mek6JZGQzrMSvvkKv0NbEgU8O4mkeV1SLDaiTMKqCmyT1vNH7ehGH2VdkvjYG8NHfZ7G4wQlX5xvbIGJfGqaSK2OhRQkwzythGzZbqAmnJ8lnRLc0bwO+NHqzaQsNyYduBZnhbbyTskkp3z0QQXEOXi9QsB+Dg2gmNz7Yy9VC5NepD1SaHqaIpJnp0qNSpYz3Nth3JqpYgy+lb4M86pMng3JtKvVCyeHT1om8fM//pK0m8Mc14PuvdDum15Dwxj3TbfO5LNZL4x3gVFpN/Ix3Vi1S6c+d6BbXmGHhS/x5mf71SegR5WMF3i+64Y4vcWwB23MNydalKsIo2pbzvLtj+QMUcDS687R7/97W+ZMWMGY8eOJZFIcPbZZ7N69WoKCgp49tln+6ONEolEIpFIJANGrztHQ4cOZdGiRfzf//0fixcvJhqNctFFF3HOOefs0SJxfY2mQzJbDCdaDladOWr/FkgUQLLA9r6IiHiejJKUyoVMsJGrw67VY89Cmi5A7cyMUHXoGCZWWh6TtoMUtJgd25SbxtPgwkwIcyNXZQfRQ0WKnKvKh69RZFYANF56hOOsrWXmzO1aau1DNNJZOI66oSpoy9j0WAqV1/W9d4mGhW5prGkXI9+aeA5fbikmmRQqT9J0sy4u1q2ektzufraFWzEJq+I9Mcvj+MU06UHK/E2UaK0AhLQEIS1Bc1qk5gXUJG12llu5r5HPexjZoiomJiqFmjAHfaZ5GrD1tLJEsivcUjHZef69FSLwxUQhS0sStxXSiO4j6BL3fF0qzCWj5pG0jXlSaPgsg2MDIn7uk0QpRy4W0vS2YpFcJ1bZ35hd58vmEjTV7P0/jwEi/PR82s6dTtu5IvYo+69bxx7Fh4jvsBpXUUwFMxMREjBQm9ykJwvnqGSbl/Rmca19jSK+NOMF5c5JkmzZ3avZC/aW4moHCLt0f7tcLn7wgx/0dVskEolEIpFsA5mtNrDsUufor3/9K3/4wx9Yt24d8+fPp6ysjPvvv5/KykrOOOOMvm7jHiEyMYniFmlciqGQDohLlciHVGnS6YEbfhfJfAXFyHhfWCi2SmN6IB3sdL12xQBVzHODUI78tZljaBj+zpptoZUuEoVgldrmRhZ4/Pb89igdIxoiXioyVtSky/Fd8kREvFLCjiNyxSFQBx32HHukEnJWisbnPr7j2mm7iqqY5GlRhgREKse8zRXEqkPgFo184IOTOOiSBT3e349WbQBgWXIQoz2dsUIR0+/4D5mWilsxyNOEH0yNnsNQdzNupdPLKBNzlBl194R2w09Ii9NuCgVvYrCKw1YZPH7QsJ28UyLpHc+N/moWpPi+DP7IxUS/iO9Jmm40TAK2evpcy1SWtxVzaK4wr50U2MDBfpHl9kEP4vh2hWRa/Bbuy/MEeaWtAERjXoy05pgZ+T1p4kBlvgg0chWaRErFd3/LwhK0BCSGi2tfEu6gptE3sA2Xys+A0WuH7EcffZRZs2Zx6qmn0tLS4mSu5ebm8sADD/R1+yQSiUQikUgGlF4rR7/73e/405/+xJlnnslvfvMbZ/mUKVO4+uqr+7RxexJPMIU7aCsNcTcdQ2xJZ2icoE9H18XrZFoFv4WrTszvq7qCHhLde8NnYngVPC2qvU7UL3OcVl2QLBCjQ9NvosZVfPVipekWGRSKKtZbloLfI2IJ4kkPHZWdWRKJYstRnLxLFeGqbatV3jZRdTq0cdtq0frfHNEnGWpdeWlsLj9c1U5FQNSR+kCpgGAaJSpuN1dRfJf3fXV5p6Hnj1ZtINvOHjsoWEdATTlxRbrpolbPJWGrRIM9LWj2sGt7ddy2xbOjB/GT1WtI2HXiQqpw187Ue7t3+MG7fC4SSU+omRbBXCa+0JXeLaxMDHLu69pEmKZ4EDNHrB/ianU8ufqL1jbxHWt/ThQdL//eon49Xn9w5rDFAKzuKCJmeMiyY7qKvSK2MNctFGgNi7cbDwKgelCKUGE7Q/ziN2dsdj2vNpQNWJvltNrA0uvO0fr165k0adJWy71eLx0dHX3SKIlEIpFIJF2QAdkDSq87RxUVFXzxxReUlXXvMb/66quMGTOmzxq2p9FUi2RCjMBcG/yky4XaEc5KEPSkaGwXgUOq10B1meghcSmDm1Q0uz5aanyCdLPPqY8WKzNQgjqegFiQSrkwOsT7FFNBTaik7XikZLGO6jNw2R19RbUYX1gLwLpIPq0ug44akYml6Aouuyx3KhvcUXDZlaTVlEX+HzrVonV3T8fIFlOhrpb+zTcpdolR2NCcVlZFvVjZtvu3q3cZHqqy/e1NW4ZzKwar48XkusSobqinGd3SCKhCUkuYbtoNER/QmCkutwNK5otUQK+Wpt30U2ifi08xcSuG49ItkQwEnTUHu9cePG5JLVPCG6n0bgGw4+56lgFa/c+D0eaJ+1wxoL3SRCsWv3MF2R2ETl2zzfepmoll7lLN8gFjWxlqXSl2i3hIX0in3fAxyK7fmDTdJCwXAbvkQFs6wMHZIs7xoPFbyHXH2JwQ9RWrYrmU5zez7ask2dfp9X/HWbNmMXPmTBKJBJZlsWDBAp599lnuvPNO/vznP/dHGyUSiUQiOcBR2Kom1S7tQ9ITet05+tGPfoTf7+emm24iFotx9tlnM3jwYB588EG+//3v90cb9wj/b/hnLE0LQ6C6/BAH54nRg0dNs6kjF7dtXlQ8KMqS+hLSJba6MdQgy2vXQUp4SAPGIWK6cWRhE6pikeMRGWhV7Tk0e8T8fTrlIu0zCITEupF5zTTEOhWOsnAL/69QZHjV52bz8OrjsDzimL4mjcxgsWOohZZQSPvFlyDtV4hcdQTRQ2xbbkvHXSviZyyNPo83ylCj5+C2zZdMS8HSVRQ7W23Yd5b0al+ZGm3aVxxauipKGiatup/LC94FYFM6yKfxzmydhOVyMtsWNJcBO64D6NfEtqsjBVTHDuMXw14GoN3yYViqVI4kewWGpXJscAUNhvitMFBx72Tu5JLVwgV7VSLO31wiRKK93Y/LZaLHhVpeX19AwzMhPF67juGaEOkCkfnpcyXID7VTu7S4X85pINDtIM12w0fc7PRLUxUTrE5FOmZ6UO115b4WVMVE9YnrOzpYyybFzxsD1Wg5rTag9KpzlE6neeaZZ5gxYwbnnHMOsViMaDRKUVFRf7VPIpFIJBKJZEDpVefI5XJx6aWXsnz5cgACgQCBQKBfGranURWTQ8I1ABT7OhWcUl8zRZ522tLC5aPIE8GlGmS7bXdbS6HS3wjAwrYyYvlupuWtB6At7WdjLJ/GhLhmrR1+huW1ALCuvoBAKMGEEnHMhOFmYkE1SVN8REfnrCJoe5tUerZwyYh5/Dd8CADVCyqdStF6XprWkOo4bYfXKuhhUFvEiNByWTBCKFkV/ZhlElIT5NiZZBVZzaymBMX2Epn8hcXCiT2Xd1uNbd9jqxKDHPVIw6LY2+5oS6WuDtp9NY7qlKPG+CReAUBLPGDXHt8+B2cJZcmrpqlJZBOzs9U0TFKWi016/o7eLpEMCO+P96ItGc9Qj/DlKXW14VN2/N0q0oRFv+YzOadSqLuftJYTTXuoahHxNIOHtTE2u45FzUMAcB/WSpZbqM8Jw0VpoJVrz3gNgIevGtn3J9bPnBRcAcDadB5N6SzazU7XprZ0AFMTCn7SdFHkEfGGATVJzPSidVGsc9wDmIQklaMBpddRdYcffjiff/55f7RFIpFIJBLJtrCUvnlIekSvY45++tOfctVVV7F582YmT55MMBjstn78+PF91rg9iQZodsxMvruDArdIB/OqOmhxahI5ADQpISoDjc773IrheO9MzK5iRXQQg9ytgJjfrgg2MtgvLnvYk2R6rpj/V4tNoobP2XZ1ooRybwOqrbYMdrc4cS4+Jc047yY+8ZUDsD4MKdEcfDVukoUGRq6ID9CDbjxtYNppb3q+gd+X6rsLtR3+PqaIH64Srr4HZ1WzoHAYsYRQX2oT2Rz+hVDMFkzUdrqvf43Zdh20U0OLeatDZEhWJ3PZkgzx42FHAXDfxvkMdzWT6tL/f7thFACFgSg7ugI/X7MS036fT9E5OWcJKVuBCqrJbs7cV65dzv3D958sTcm+R6OeRcwu7pinRSm2y8hfvfZLBrsi+Gylw43F39vHU5cWummTkeU4bU/NXcdYXzU1JUI5MiyVej2boK0WbenIorpVvO+Y0rXUxrO5f/2J9n43DtCZ9h2XlR0JwO+rPmCI1sYniXIAau0f0kxmq1sx0G313tBU3Eoa3RIqU1vKT67SNLANlwwYve4cZYKuf/7znzvLFEXBsiwURXEcsyUSiUQikfQNliUeu7sPSc/YJRPIA4GWdIBhmogj0q0gCVs5cFtpDFSnSnZYixPQkk72Q0BNOXW4kqab4YEGqlIiPqU6nsOmaA7HFa8G4JSCpc7ITbdcDHa3OnFFn0eHURpqdjIlCrWo07YO00ODEeKz+qEARMfq+DaImKJQlYW1UaX1INGeaKWBt0HD9IhvhdauUXzp8j6/XtsiZlcVX94xCJ8rjS9LqFmxtAevmql5tnPlaHu8FJnIyg6RMRNyJZgQ2sxcRHzYrLLpPLzxA8cV+/KyI4DaHu87U5Ot1NPE/cPH8PM1KwFIWG6CapKgkuq2nUSyp1h6qMHhX4jnRVo7nybDAJRo7XyRGEq+bYI2ztPEScFlrNWFEhtUk47TdpmnkVJXq/M7Y1oKa7UiItlCJamNhuloFrF/n/lKmV68nka3+K59+OA0Rlz+0YCca1/z02FH8oOV1XwWFb59YVeCbNsrDUT8UdTwAuBW04TUBE2pzhhUU+8+c9KvyJijAaXXnaOvmj9KJBKJRCLpZ/oiZkjGHPWYXneO/v3vf29zuaIo+Hw+RowYQUVFxW43bE9T5G4nWxPqQLYWc1QQ3XKxIZFPrluMLvJcUdyKQb0u5uM1NUHSrm3kVXXcisGWlBjJFfvaqYmFnfiANrV7FpZpKgxxiVicSVlV+JSU4xXUavjJsZWsmnQuyxODObRYVN9+u2YsTtKEAl0Npd35CZIBF96NYvQT3Nw316cnZKqMr35MZNWF8sQ1KymMODE74N3l/a/sKKbIKzJvXlp9CPP95Xxz0VIAPprgcuIKesqRi8XnXeKKkGM75P502JFMW5TmgY0ivuKKsjcIqkmaDDFiHOJq3eX2SyR9RSZ27/sbdbYYIQBUxaLc3Yhuq7MxU0FVLEKqyMQa4molYqvcQTXJpnQOS+OlAGRpCZJmZ422w4s3Up8tfsfWt+axJRkilha/Y2cf+wELdkMB3tPkaDGOzxZq+sZUAXlaZwaabroc5agumQ3eTn+1pOmiPpE38A2WDAi97hydeeaZToxRV7rGHR111FG88MIL5Obm9llDJRKJRCI5UFEs8djdfUh6Rq87R3PnzuXGG2/kjjvu4PDDDwdgwYIF/PKXv+Smm24iOzubH//4x1x99dX85S9/6fMGDxQb4gVkh0VV+aCadJQOt2UwJWs9zbYjbZsRwLBUx4+ozQg4ylAmDimDSzEYn1vjLG9OB52siGxXjDytw8lI0xSTz+LljPKKOJnF8WFk28rRv2vHkeeLs6FNdD7VhIrVxZRBMcC26SDZ7EMNp0hVigWG18dAO/SMvPBToLNe2eGhtc716y1Xr/3SeX7Zcxdz13efAmB+uIJDCzfx4oZxAAT+q2+3NtS2OG5JnOOzlgGQMF3U2EohQEMqi7HZ9YD4HI4JrnBG3+92jN6l85BI+oNZZdP5U9U8AJKWQokr5sTdGSj4MDHtEhKqYhG27+MOy8OmVD4L20TYxJZ4kEuHvccJQaGoVKVz+TxWDkChN8r6aB5hj4iPfHnTwRS+FaXIL+KVSv0tHB8S36V7hx88AGe9ezw8otOn6bglcWrNTnf/PFfUUY5MSxXqkY060D0NGXPUIzLCjbITv6+d0Wufo8svv5z77ruPr33ta4RCIUKhEF/72te45557uOaaazjyyCN54IEHmDt37m41TCKRSCQSiaQnPPXUU4wbNw6/34/f72f8+PH89a9/3eX99Vo5Wrt2LeFweKvl4XCYdeuEZ8/IkSNpbGzcapt9CZ+Wcvx17ts4n3V2hseGVAGGqTrKQdJ0kzDdzihCtzRWRUUGVcCVYpCvDdMOggtpCRa1Dsdj12WblLMJw5Z8GvUQATXlxACUuRvZnMrj7baxAPx3zRjcS0Sci+mBDZUJLFPsN1it4m0V7c7EG6Wz7CGCauFd5sc2eaXod/1TS60n1E0XlbDblgUcP5FdYUVyMABXfuvfzgjvkor3ydE6CNg10V7bOJrh83PYMr11p/u7cu1ydMtFqyliwDRMZ783rFvMX+qPQbWHXCumpDhmXed75x6yawqYRNJfXGx7fX2V/9s8HxOLHDXuLOuwnd87TC+jvbWcNmwVAK9ED2Kst5YlSeGQHVLjTAgI37JsV4z2tJfmpPi+5PpjxHQPqxOFgKhLuCguFKjfV33AT4f1LvZvT/LOOD9HL046rxvTIcfzTFVMdEtzfhvadR9Zttv4gCADsrfLfffdxy9/+Usuu+wyjjxS3G/z5s3j0ksvpbGxkSuvvLLX++x152jy5Mlcc801PPXUUxQWii9DQ0MD1157LYcddhgAq1evprS0tNeNkUgkEolEsg3ktNp2+d3vfsejjz7Keeed5yz75je/ycEHH8ytt946MJ2jv/zlL5xxxhkMHTrU6QBt2rSJyspKXnzxRQCi0Sg33XRTrxsjkUgkEolE0htqa2s54ogjtlp+xBFHUFvbc3+7rvS6czRq1CiWLVvG66+/zqpVq5xlJ510Eqoq5MczzzxzlxqzN7H4KLArbjCrbDqXrRHGjeWeRlRMGtJ2er67jZjpcaZdALI9QrZuSGSR5+lgYlDI0XlalE3BXHLsIrVHBlcxzCWmmr5MFbM4PoyWtJg6G++vwqvqeDVhMmjW+QnUif2bHkgWu9AiIrBb6TKiUAwwXeBtFI2PeVVQoGNwX1+hXefFsbsXEj7NvxaA6nQO+bZpXSb9fmNKqJnHDV1DVax7tmTry52Blzmnr+bmdZ01AhvMEHn2VGmDEXLMON2KsdXU3G2Vk3ar/RLJnsCNSr2ZdqbuAccexI3BSHcM7GDtea0jSVhu8uzvl0/RHXPb5R2DmL98BFNHi/lln6azsT2XjpSYoksaLhp1Md38WWIID2/8oNe2GnuS98d7mbao09w1Y8SrW27Spka1XTpqkL8NayCDsqVytF1GjBjB3/72N37xi190W/7cc88xcuSuFUbudecIQFVVTjnlFI477ji8Xu9uR4VLJBKJRCLZAbJztF1mz57N9773Pd577z0n5uiDDz7gzTff5G9/+9su7bPXnSPTNLnjjjuYM2cO9fX1rFq1isrKSn75y19SXl7ORRddtEsN2VdQMTFRHUv+aj1XFKO1MS2VYXbhx6ThojGZRcIvzNSayaLU1+Kk+gNOYdSDPfW0m37WJ4Xy8W77GPxqioakGIGZHgtXQtzZatTC1aaRDtrR15aWGfChBxQ6hoBdJ5fs1SqWAkNv33OB2H3JvcMP5pnN4lwM2pw0ZYCgItKVAUYE6jkqvIrr//ptACxToUiJONs+s/lDVHvbjWmVOqMzcbPE1YZul4u5vXLCNtux8XlRYLnsu4u72QvsC6nLkgOTJAamhWOj4VN1RmutALi1FO0WPNE8DYDGZJAvo0M4KVeYquZrHRTZKtLXcpYRP9jDuJBwlN0YL+Cg7AbyCoV5YqvuJ2Ab6KpdHWn3IT6asK1/jZlAbWFmuxZIW/o2tpMMNGeddRYff/wx999/Py+88AIAY8aMYcGCBUyatGtKf687R7fffjtPPvkkd999NxdffLGz/JBDDuGBBx7Y7ztHEolEIpEMODJbbYdMnjyZ//3f/+2z/fW6c/TUU0/xxz/+ka997WtceumlzvIJEyawYsWKPmvY3kZXo7DL1qymWhfxLJnUTtNOye86ThoebCBmeFibKAJE/IqBSto2gVwQG86JWUJ18KhJDvNtpEbPAeDz1lKiuoeWuEiXLSxrJrFcqEqKqWD4TLyNYj/xQRaqIW76aJmFGU5jbBFqVXidEJb2J9bpQnnTsGgyRYxWSEngdaUZ6RVmjW4lTUM6TG6uGM02N2WhqeLTuaLyTTalNSpdnfYLRVo7nkypFtPPH2uPs4/WsNXxq2YfQSoiRoz1L47hmQYRf/btgoV9f7ISyW6ivi3S8RsMWJgYRo4mlI+E5eYLW6n2KTork4N4v2G4eA8WR2evJEcV24bUNK22MepoTz0N2evw2EWXt2gpSrxtHBUUMaifxcvJs5X1g9xb6LBcjjHl9mwGJDtHOmRvn88++wy32824ccIE+MUXX+Txxx9n7Nix3HrrrXg8np3sYWt6bQJZXV3NiBEjtlpumia6vndIjI888gjl5eX4fD6mTp3KggUL9nSTJBKJRCLZdaw+euyH/PjHP3YSxNatW8f3vvc9AoEAzz//PNdee+0u7bPXytHYsWN5//33KSsr67b873//+y7P7fUlzz33HLNmzWLOnDlMnTqVBx54gBkzZrBy5UqKior65BgPVx3P10uWACLGSMNy3BfddM6zF7raWREfTKvuB8Cjpsl3dzilSNrSft6PHQTAYHcrCdNNzLaqj+oefFqa4TlN4jgofJkrRnmWBmbAwF0l+raKqTj3vGIAioUxVGReZX3gwvvS/ts5zFc7i0RuTiscaitHnyRKyNOinFshzv2Vr3fa/peubyKkptmY7pTUAmrKMeuMmH6OyhWlR4pXtXHb4tPJD4njVK8v4Opj/sO9b50GwP8M/5S2tFD3/m/L4UBLP52pRCJUS4Bht/QshnDNQ9O4ovi/gFBa811RamzVu9UIOOWJTBSa01kclC2U0sNC6/mso5yL898HRNZZJlvty9gQRvtr8dqljo4OraRIaydlS9RlngZGesR+PLaWru2/szmSvYBVq1YxceJEAJ5//nmOPfZYnnnmGT744AO+//3v88ADD/R6n73uHN18882cf/75VFdXY5om//znP1m5ciVPPfUUL730Uq8b0Nfcd999XHzxxfzwhz8EYM6cObz88ss89thjXH/99Xu4dRKJRCKRSPoSy7IwTdERf+ONN/j6178OQGlp6S5X6+h15+iMM87gP//5D7fddhvBYJCbb76ZQw89lP/85z+cdNJJu9SIviKVSrFw4UJuuOEGZ5mqqpx44onMnz+/T45xwtIOjgi8jNueb1+dKqEhHXJijgwUJ4PKrRhEdB9BTWQ5eNU0pqWQ5xLeIgnTTXWyM3ZJwyJuz+sX+GIEXEnWRkTZkkNya1k6XljVW6aCx1IQOhVoSTDFU/y1Com0h4rr94/stG1xU8UUAG5f/yl59rVtN110WG4KEXFDh/nq+CRR4mSvQWfRWsNSqUlnOSNdj2LgQ3de52tRcnwi1qLZyOJHYz7g/SYRcxYencCn6mgJ8Xkf4ttMuymUQZ+q00jv57Ylkv7i9TN/i24rolXpbDpML5tTeQBOGQyAunQOAH5b1S50RWh0h/gwXgnAxmQBC5rFbEEk6aM9z8e0sPAbO9jTQKvpps4Q6qxP1UnY2Z4r9HxKtDby6SxZItk1FPog5qhPWrL3MWXKFG6//XZOPPFE3n33XR599FEA1q9fT3Fx8S7tc5d8jo4++ui9srBsY2MjhmFsdTGKi4u3GyyeTCZJJjtr6UQikW1uJ5FIJBKJZO/jgQce4JxzzuGFF17gxhtvdOKi//73v2/TObsn7FLnaH/izjvvZPbs2T3e/q1DghyyxkfYdlOu17MxUJxR2MctwzmrSGQt1eg5JE0XYbfYVhQwNNBthSLP1UHMVorG+moodTVzx2YhB6pYvPPRIYx4Voy4Xrm4EMUtjhHKjuPSTDrCIlMLC2wxCiXNfq0adeWmiincvv5TAFKWxga9gA26GFqdFNiMT9XxKZ1JAvOiowD4TvanLE4OYYhbxAcZKFSl8wjbBTkNVIKK8GkpdTdR4mpjcEkrAO2Gj5TlovJqoUS+fOwEqmM5AFwy5F3GrPI6MWUBJYlP0SlxCRWq1fRQ7upsz9lDt/7SPr/5IwCWpLzOMunILcnga4BUDqx5UPgRjbj8o+1u23bedEzrA0c9DapJNMVkUmAjAKuTxdSnhdqjYpHnijLCJ2L2QmqCXFcHlZ4tgFC5rx72mrPvIa4IATu20kS41dfpYl8hLcG6pIjvzNZiNCtZLLML2P5wlagWUOJqFW1SUo4SLNkJMpV/mxiGQWtrK++99x65ud2rItxzzz1o2q6la/eoc5Sbm9tjF+zm5uZdakhfUFBQgKZp1NfXd1teX19PSUnJNt9zww03MGvWLOd1JBKRRXMlEolEsnchHbK3iaZpnHzyySxfvnyrzpHP59vOu3ZOjzpHXSO9m5qauP3225kxYwbTp08HYP78+bz22mv88pe/3OWG9AUej4fJkyfz5ptvOvXdTNPkzTff5LLLLtvme7xeL16vd5vrtoduaU58yihfLRtSBY4a5FJM6u0RVMzwUuyNMMgj6qd5VZ2k6Xay2UxLxbBjlT7tqKDD7+X4PDH99/sVx+BpUUkUiw83XBChPSJiWyKtAbAUPGOjTptiNSJjakcjyf0Zn5pmtLeORQnRsf0smc1ET7NTA2/GlxEnE/CTRDkftI1gfFY1APV6mJXtxZxdIq5dvhZ1YiZ8io4OhGxVKV9rd+IzAP4n/2OMfDFwGOmO0Gy4qLZjLzyKQYMR6tbGmrRq79fgH5s/Rrezeb4/dPp2zy1TA04qSBKgx//gokMVZrxxBVdPfxWAIwJryFcTvJ8Sqs5U/1o+tWOK3EqahOWm1RC/I/laO6cFV9Bu/659O7SCOkN8J9bqheRYcQJKZ7xfxu8NRBbcytggQGTuds3QrUrmE9BSaPZ9v8H0ctqX4vcxZnp4Z5x/16+L5IDlkEMOYd26dVRUVPTZPnvUOTr//POd52eddRa33XZbt87Gz3/+cx5++GHeeOMNrrzyyj5r3K4wa9Yszj//fKZMmcLhhx/OAw88QEdHh5O9JpFIJBLJPodUjrbL7bffztVXX82vfvUrJk+eTDAY7LY+HA73ep+9jjl67bXXuOuuu7Zafsopp+wVqfLf+973aGho4Oabb6auro6JEyfy6quv7nLE+rZ4dOQIJ/NpXbKIwe4WxzsE4MUaUXdrQl41XjXd7b2ZLDcAFLp5HrUaAWf0NXXIRt5uGcOmAqE0DAvEibYLFcm71ofpBr1MjL4CoSSpLIMDka6Za6PcafBtAqBES5KwYLBLxBU1pENk27E/U/3rcSuGc60/jZRxbsmHJCwR/xVSk04sWMT0oSkmQVWMkpuMLAxLdWInliSGkucSHki6pVHuanVGxSlLQ1Msx3m72QgQsGOZUmoajBQR+5i3r/+0W2ad1qUmVUZdzChImmJyS8Xkvrh8kn2Mood7Hk+Yvc5ED7pZHBVq6qH+DfgUg41JkQFb5m507nNVEf8KMvduoRal2XRTbaukMa3d+b6E1Tgb9Hximsie3aAXsCRWysKmoQCUh5qZFl4HiIy4FfFBRIxORUg1LTbpImMuR4s5qvvGeD6jP00zJlgDCAX3zweV9+Lq7N9Ih+ztc9ppwnfum9/8ZrcQIMuyUBQFw+j9/8ded47y8/N58cUXueqqq7otf/HFF8nPz+91A/qDyy67bLvTaBKJRCKRSPYf3n777T7fZ687R7Nnz+ZHP/oR77zzDlOnTgXg448/5tVXX+VPf/pTnzdwbyVTff3na1bSkA4TsJUFj5omac/NL2sr4ciCdRS4hD2AT9XxKIajHjWls4ganQFj5Z4GNqVEB/O0vMVwCAz1tQLwacswzKTYbyrXgsIEli5GXJV5TSS+1VkZ/kAkoyBlFL1Ww0++1oFpqzFdlaK6dIhWI8Chvg0AnFM0n9HuJtbade2Cis4nSTF3XWh/dpkaUx7F4NnRgxzlqNK7hRJNbPNlcjAGKo9uPh6AaXnrmR5c7bSxyQih2tuapoqpKE7sWo6WoESzWKaLuKivKkOz1y90FCSJpCekgipmRZw8j1A2E5Ybt5JgZt7HALSZ8EGm8KKlUeFtcOquaViE1DSarbxuSOeTsM3UEpabzak8FhriO9KYykLFYkyOSIQ5JFjNSG+ds58h7mZeahHxckeFV+FTdTakhHrVYXo7YzBRaNX9fNYu/JQOC61Hnyueu0/a2E9XaR9CTqttl2OPPZZEIsHixYvZsmWLYwi5O/S6c3TBBRcwZswYHnroIf75z38CMGbMGObNm+d0liQSiUQikfQhsnO0XV599VXOO++8bbphD9i0GsDUqVN5+umnd+Wt+x23rfw6P6ycT6MuspLGharZGBXxRzmeBMdkrSBfzVTBduFT0rjtkZKG2ekzovhY0DGcf1eJqsLTSjZQ5m9mUZuYx29PeZk+RtT7yvXE+LC2At2uDeZSd7+XvL+QZ498Y6aHtXqhs/ylsV1TPMWo9SU6l/2pah4j3a3ivZbKx20ii+eI7DVsTuU58UqZkfWaRGcM2/vjO7MdH974AUs3Dgag2N/O9OBqQqqIMzrYU4Mvk9FjeklZGjriMzQthVbTdNzVb173ue2LJeiqGkkFSdJTzEYvIU34rCUsNwlLJUcV91gKBa8d81jsijDSU4fPVrWLNQMTBdWOmRzsaqXGjj+qdDUw2NXCP5qFWjvU10KRu9M816OkWZYQvkZlnkaGuFoZ7Gux99NCvhanwxTfmXbD53iRuRSDFiNAyhT/ljan8vifoZ8A8NK740kcW9cPV2jfQcYcbZ+f/exnfPe73+Xmm2/us/jiHv3K9tY1ur29fZcaI5FIJBKJRNIb6uvrmTVrVp8mXvXYBLK2trbHVe2HDBnCF198QWVl5W41bl8g5/TVrFxYQqlPmF/maDEn0yhtqbzdPtZ5fXp4Ubf3lrraaHWLlMNSdzMGKs1Nwu+mNjubUl8zOR6hVBR6o3xQLeb4v1GxFE01nU8vevSW/j3JfYhflB8GwCGfaYz211JrxxE1vTSKbJ/wKioPNbN5apSjF3eWjbl42FFf2ZMYEBjLVTTFZFWHMBEdExRVzLuqRV25rOxIRvAZABOXNZGw3AzWxBhERcG0lSK3kiBhqXRYIo4jhUadHnJG7gE1RcIeQZtfGcOYqNywbrHz+s7K8T25NJIDjJy1CSyXj3njhwNQ4GpnUXwYJ2YtA0Rs3VifyAzzKSlazQBuuzbhWj3ARG8tGW9hDZOR7gYAdEtlo17A4aH1AIz21uBT0nyZFIppmxFA7SJRBJW0E+/3n7ZJW2XwFruFz1GlX0yJtOkis21DLJ/NiRwARoQamP/fEYROXdM3F2dfRDpkb5fvfOc7vPPOOwwfPrzP9tmjzpFlWfz5z38mKyurRzvVdX3nG0kkEolEIukZMuZouzz88MN897vf5f3332fcuHG43e5u63/+85/3ep896hwNGzasV5loJSUlWzVuf2ZFpBi/JjqEUcPnxITUdYSo6xjljKK8qs6MrKUYdoaITzEodTcBsDZVhKZY+LOSne8NZKPZ762OZZM2xH5rE9nEEh5C/k7lQ9KdpYcabJ43lk3tOQAcO3gN7WmRGfhxTRnnf/kRSTv7ZmM8n7X3T2D4lfO32s+6RBFeReeCwvfFe2MjdnrsezcIp+13YqNoNYJ8d+gYZ92Tmz5wnmtYeOyRulsxaQaq0yIOqtzd4PgjGZgYlurEJ91ZOd6pKWegdMtkky7akgzxQg++ZpPlG4Rb9fcHfUJTIsSShIhjHOJuptwlfn+qjWw0TOceK9GEmpPoEt/msxXwdtNDpWcLpS4RPtFqeyV1xbQVimYjiw/SYee71qwH+bxhCEeVCB8kv6aTsNXTmOnBq6bZFM0BIG1oNLcLx+4fjp3PmLw6NvfFhZHsdzz77LO8/vrr+Hw+3nnnnW5eR4qi9F/naMOGDb3esUQikUgkkr5BBmRvnxtvvJHZs2dz/fXXo6p9k7CyS9lqku6cVLTcyWJ6u2U0HSkxknKrJopiYZjiw3qpehyDy1s51Cc8cjQsJ2tjiLuF96KjGZwjRmxrNxWxzJOkwCc8SrLcKaYMEe7P0bSXLF+S0nArkImOkXyV1qMaGTJPjCAK3FHWRUWW2nFD17AuXkiunYGmWyrqoBibbjkCAE8b+JrFr8jzn6YpK2/g3xtEFqHvhWxi1ygMPU34rhT5otRNb3OOOW1RmndiowCoTeXQku5uY99giJG5qli81TGaco+Isxjr2UKeFmOLXQsuYbkp1MRnP6ts+3XXACpdCfQuWW4gFSQJBJ//iI23HYGrTvzGJEw3hwY3OI7Un8XLOS1rCQD5agcJy+1kjrkVg5ipkbKVpBw1hW6rQQFVJ4ekk1n5boe43wttJSmkJRx/sKZ0Fpv1POd70JQMMjpvC+W+JntfSScmc2O8gHxPlEEBsZ+ajjChYMI5nzJ/M5vZWqU6YJDTatsllUrxve99r886RtDDbDWJRCKRSCSSvZHzzz+f5557rk/3KZWjPuAPL5yCWSlUiJK8CIOzxKgpqnuI6x5n/t1Ia9ToORzmE6pDVTqbOtvnqN3006IHaIqJEVZRUQTTUvCoIu7koGANW1LCS2lVexG6qdGaFDE0soe7fSJHiQybPz15FIUF4nMZFapnZXsxK3WRffmtQV+w0F9KNEfEN0w99Usno2ZB/TDq20KYK8W1N4sgOiLNcYWrAKhJ5NLVfSXP1eGMzBdOVOArI906Q+zHrRhsSuY7tazy1Cgj3TE0r8ge6jA9JCzx9dz093H84dD/dfZxe+UExxH85nWfsy7to9QlMvFGumNsMjzMXr8Q2NppW3JgUXbzhzRfJBTRl7aM58rS19mkCxf+Sk+D47nmU5JA0qn1l7BchNQUYYRHl2lBh30//l/LVC7Nn+ccY0bWl1y97jtMyhURQWP8NY4iPtyzhdoudSdjaTeH5653KgoYqI7zdtgVZ32sgE/XDwOgpLCN4weL7DRtf5U8ekMfTKvtr5fRMAzuvvtuXnvtNcaPH79VzPN9993X633KzpFEIpFIJHs7clptuyxZsoRJk0QowdKlS7ut6xqc3Rtk56gP8ESAz4TqUDdJoWCoiBXpSHk5tHATS5qF/4duaLy4aQJnjBV+Rxv1AlbFhX9Oua+RddECDi4QOsQhoWqgc8T0eaSUIq+Yiy/yRYkkfWxuyQHgoqXLmXtIz2wWDlRGnr/QeS6icuodxW31wmLyAjEiOUK129iey3HFoiba2RVbeLdxJBs+EYrPIWes5Ecl71KkRQG4t30G2KNrgFcOzt5hOzI1+QD+Z0W1E2/RagbYZKT5R6tQhCYFNhLShBo0YVANa1NFlHsattrfbZWTuGndIjQ6fwAK1SQ1XaqgSw5s2uwEy+aqwXQM8TqeQ24lzSZbuc7TYgQV3fHZ0i1bubRfZ2qtAZybN58OS3Oy1zQsvjfoUz6MiAPVu7LJtmMw87UoqmI6DtpnD97E5lQehv3tMy2FBW3Cv+3DNZWotV7cSXEv1xoK85VyAKYUVGFaKl2/axJJhv4oPLtLMzLvv/8+P/jBD5g+fTrV1eKf+F//+lfmzZu3k3dKJBKJRCLpNVYfPSQ9otfK0T/+8Q/OPfdczjnnHD7//HOSSTF33NbWxq9//WteeeWVPm/k3k6oyqRtuOhnujxpAi4xuqnIbu7mFLstMusP9laTMyhGje3oXOhqJ2G6idlz9yubi/AVikyS5mQAn0tHT4qP7+PWCkCoCqt/L4r/jvzpx313gvs5yyancbORkXRW/n7id8cAcPFxb3Ni4QpeuUs4Y//oEiHZXl0+zd6yrdu+xi7s/pUa6a/nxbH52zzu2kQR2XasUEM6xEMjRjnrnnvq29wx7QUAvlf0Mc9tmcpBWaLq+Q9XVfH4QcOcbW+vnMAzmz8EwLAsNAXKXSLL55nNH3L20CN6diEk+yUVN4h7Y/M/DuGj6AgOzxIeQwnTTcTOpE1ZLsrdzbTbnkUNRpg8NeoMn1ckSxyn9oCaxKfoDLc92uqMLIZ7tjC5SHx/vkiWOop4vZ7NUE+z42W0Kl5CQOtUf9pNH0u2CB+mwFIfsUMSUGtn8A5qQbF/H5e2DkL92qZ+uDr7DjKVf2DptXJ0++23M2fOHP70pz91C3o68sgj+eyzz/q0cRKJRCKRSCQDTa+Vo5UrV3LMMcdstTw7O5vW1ta+aNM+R+jZj2i6Q4zOcwMJgrZyFNSSrIgUoxuas61pKTy05QQAvGqafLeIT1Ixqdez7Xl1wepEiTNX37whB2+xGJm5VJM8b5xNbhE7UBFsIlO1TcnSsczuAWgnLO3o9vqtQ7p770i2ZuTPhMt1YpGbXFfn9VuZHLRdJWjsQhempVLpFyqeV9V5ZtNhTF64yX6dptquFbVleist6YAzinYDxy2J8844ESs04rzPeByhDq1+YjL+UJJpo9dtt71d1aEnN32AexeDECX7L/HGAC+5D2bYSKH4jPVWs87O2Bzmaca0FCd7NlMjMmYrSZv1PAKqfa8qBrrlojXj0aa141YsOuwszRo9x4kpcqtpStytTvZaZn9RQ2TaPr/hUCJN4vcoywArraJV2N5uniQtcRHLWV2fywgObOVIMrD0WjkqKSlhzZqti//NmzfvgCg0K5FIJBLJgCNjjgaUXitHF198MZdffjmPPfYYiqJQU1PD/Pnzufrqq/nlL3/ZH23c61nz4DQsj10Hy1JoSoqRUK0RpjEWxK2azrampbCgvgyAQVkRDh0k1KCadC6jfDVOjayY6WVCYCNVKeHqjIJTBXu9p5Ck6aKwUmSvLWkdBIjA+KxwAp9bJ/BesdjPMfVE0z4K3CK7qsAVAaRy1FM+muACCpzX21ONQMQXxUwvzbYbcNz0kEq7WNYm4i82t+Q4nlfDaCVuuKlLipG6bqm0pIJA09b7vUBk2r1CJhNuxxlx55ce2ZNT61Pu3fAR1YZo1/3Dx+xka8meIHuJi46WPF7JEW7vpUOaHEfslKXRYXmcTLagkmKLEeKtyFgAAmoKU+scS7/TNIrhWcLd/ezc+YSUNLr925WndThu2a1GgA7TyydRMXAe4m3BrRhOrbWD8+tYZNeMjJRrkNDIKRZxeKpisWWT8EgqHNrST1dl30HGHA0svVaOrr/+es4++2y+9rWvEY1GOeaYY/jRj37Ej3/8Y372s5/1RxslEolEIpEMIHfeeSeHHXYYoVCIoqIizjzzTFauXNltm0QiwcyZM8nPzycrK4uzzjqL+vr6bttUVVVx+umnEwgEKCoq4pprriGdTnfb5p133uHQQw/F6/UyYsQInnjiif4+vZ3Sa+VIURRuvPFGrrnmGtasWUM0GmXs2LFkZR24PjtKfhLNJdQhv1snZccYpa2t+56WJbYBCLh0JzvNp6QJGAFnrj5fi5K03I7bsrc45mR85Lk6iJkeJ9PtG8WL+b/XhT9OycnLHNUIoGJBgBXRIIdmi3pujXbtLsnu0fSSyCw7dvAakqb4GrWbPpKmm1VRcf3XtOZTEOggbWcEKYqF12V0209mtG1aKiF3guaBOoE+RlUsgor0oNmbKXr4Qxp+egRVG4cDcOXXinh40rMANJtZNKRDhFSR5bjKCNBmBCj3CXVIw6TdFHFCbsXg3JIP+VejcF9fqxcSVuNsSBUCwvU6ZogYo5He7v8oNyXzGemrQ7U9kpY1F9O+0Y5zqmglpWt8Z5hwInu/aQQjf7ygfy7GvsoAKj/vvvsuM2fO5LDDDiOdTvOLX/yCk08+mWXLlhEMCnX8yiuv5OWXX+b5558nOzubyy67jG9/+9t88MEHgHCuPv300ykpKeHDDz+ktraW8847D7fbza9//WsA1q9fz+mnn86ll17K008/zZtvvsmPfvQjBg0axIwZMwbuhL/CLptAejwexo4d25dtkUgkEolEsi0G2CH71Vdf7fb6iSeeoKioiIULF3LMMcfQ1tbGX/7yF5555hlOOEEkGT3++OOMGTOGjz76iGnTpvH666+zbNky3njjDYqLi5k4cSK/+tWvuO6667j11lvxeDzMmTOHiooKfvvb3wIwZswY5s2bx/3337/3d46+/e1v93iH//znP3e5MRKJRCKRSPY+2tqEp1teXh4ACxcuRNd1TjzxRGeb0aNHM2zYMObPn8+0adOYP38+48aNo7i4czZjxowZ/OQnP+HLL79k0qRJzJ8/v9s+MttcccUV/X9SO6BHnaPs7M4AUMuy+Ne//kV2djZTpoipnIULF9La2tqrTtT+hKJZpONiyqs5GqC0pBWAxoSQHjNBuKpioSg4pSnerB3FYTkbAGGslrDcjqzdZGQRMz0U2IGNl46Z5wQxelUdA8WRpquS3YOEY8fUO9M+47Or8appZ1vJ7jN2oQu3Kj5Dw+pMmW/RgyxoLKOuTUxdJtq9HDSqgbqYKD2SG4xTHhYTZ1sAt2JSGxffraZEgIpQM0cvFqaq74/39nm779rwMe2mj1ZTpEc/PGJkn+07RzXIUdv7bH+S/iFclSaeL6bq3R+FeKVyAgBFngh5WofzO1GkdeBTdacobMaMFqA2lUPSdHNUjvgOZMxqM9P+GxIFNCbtMIuwKGKbmXpe1V5EzHA7dhetkQCWW8gZWd4kUbxOGxLHdi3pLOnLgOxIJNJtudfrxevd/m+OaZpcccUVHHnkkRxyyCEA1NXV4fF4yMnJ6bZtcXExdXV1zjZdO0aZ9Zl1O9omEokQj8fx+/dMKaQedY4ef/xx5/l1113H//t//485c+agaeJLZhgGP/3pTwmHZTyLRCKRSCR9Th9Oq5WWlnZbfMstt3Drrbdu920zZ85k6dKlB1SJsF7HHD322GPMmzfP6RgBaJrGrFmzOOKII7jnnnv6tIH7AtqaAMYQEYyq6y6WN4lesFs1sCylW3ffsuhWdHF+i0hx/dmQN3ilbSIN9ohreKCBmmQ2ZX6R2u1WDEdVAvApOltSYWddV2KvVTImLAIhq+J5DA800KILFSuT0i/ZNdY8OI2c+BrMLoVeM8pgbTSMSzNIbRSfYbBO4bPQUPKyRBHOAn8HIbusx8jFKdbHwkR1T+e+2woo8IrPZ/BHQWqmdR/d9QVbjBBthlCOvreijudGl/TofX+oEj+KPx521DbXX1i67eWSvQtXhwEF4rfbFYMX3zkcgKzhrRw/dDXHhlcA4jeqSGunVRH3SrOR5RTBBmjUsxjtF/dn0nJT6GqnXhcq6LK2EqIpoUIEXUkOCVZzqK2Q57tHENISvNcsVMtgIIkrJFL3m9qDHFTY4JhNSvqPTZs2dRMzdqQaXXbZZbz00ku89957DB061FleUlJCKpWitbW1m3pUX19PSUmJs82CBd2D6jPZbF23+WqGW319PeFweI+pRrALqfzpdJoVK1ZstXzFihWYppy6kUgkEomkr8lMq+3uAyAcDnd7bKtzZFkWl112Gf/617946623qKio6LZ+8uTJuN1u3nzzTWfZypUrqaqqYvr06QBMnz6dJUuWsGXLFmebuXPnEg6HnYSu6dOnd9tHZpvMPvYUvVaOfvjDH3LRRRexdu1aDj9cjDo+/vhjfvOb3/DDH/6wzxu4L+Cvh5TdoTZNBcM2NbMssCwFzZ5Dz6gNyyOdo3WPKlSf1ckS1kYLGJklbiJNMclxx2lLi5Hbxlgek8LCPj9bi6FbLqdo6bLoIPwnC4PI0Z96qEtEaE6K9xV4Rdp/Rl3yqnr/XIT9nNVPiPg6SzdYXDcIv1dcx0ibH7NdqD/uFg3TY+GOis/ZHQPXOyFqxovRT95BMdZHRXxYR9qLiULMVo6iCS9jC+sZ4hVmd2lTo6YfzqPd8JOyxNdeY+eDmQc3iqKlmdIQkn0b7e2F5NjP286bjq9R3Kt6cy7/bhtPeIJQNr8e/oJiLUGOaas66Swn5tGtGOho+OzfEh86biXNILe4d79RvJiVMVFMttDTzqH+DeSoIpZumZZgQyKfHI9QUyMtw5ypnkkjqjipYBkepbsHjsRmgLPVZs6cyTPPPMOLL75IKBRyYoSys7Px+/1kZ2dz0UUXMWvWLPLy8giHw/zsZz9j+vTpTJsmCnOffPLJjB07lnPPPZe7776buro6brrpJmbOnOl0yC699FIefvhhrr32Wi688ELeeust/va3v/Hyyy/v5snuHr3uHN17772UlJTw29/+ltpaUal80KBBXHPNNVx11VV93kCJRCKRSA54Brhz9OijjwJw3HHHdVv++OOPc8EFFwBw//33o6oqZ511FslkkhkzZvD73//e2VbTNF566SV+8pOfMH36dILBIOeffz633Xabs01FRQUvv/wyV155JQ8++CBDhw7lz3/+8x5N44dd6Bypqsq1117Ltdde60S8H+iB2KYbvFVCAUiWWbTbqk0oJ4ammY5ipFoiW62mXVwvRQGfJkZfrzSMY5C/jahtnqYqFpviuRR5RQZQLO2hOpkDgNev06h3mm5umd7arT0lvghlfmHetiUVRsN0Yo2qk7l9ffr7PasfnQqmHddlKSQbAqTbhTroSih47NAgJQ3poEImMdBSwPCCr0Z8zdbmFeD1iM97ZVUJx45aTVwXo3FVNVndUsBgfysABn1fOPa68qlU/X0ct094EQCPYgBF29z2W8sbmejbSLtdKDSwlyiOt6//FICbKqbs4Zbs+xgeBa/tOmq6QFvk46Oh5YAwmh3r28w4TysAE31VvB87CIDmdBCfqjsZbB2ml3ZFd2LZTggu57Ss5QDolkKH5WJFStxnC9rKOSRUQ008xz4wqK3iO1AebKI6mUuuu3uhbMmewbJ23pPy+Xw88sgjPPLII9vdpqysjFdeeWWH+znuuOP4/PPPe93G/qTXMUddycxX9jcbNmzgoosuoqKiAr/fz/Dhw7nllltIpboH7i1evJijjz4an89HaWkpd999d7+3TSKRSCSS/qYvY44kO6fXylFFRQWKsv1R7bp163arQdsiE+z9hz/8gREjRrB06VIuvvhiOjo6uPfeewHh23DyySdz4oknMmfOHJYsWcKFF15ITk4Ol1xySZ+36asERd1XVN1LZso8OtiFb0gUt10yQlXFnZnpkGuqxaKGwYCIVdrcns2InC7Zaa4EgzzCeKvOFWaItxWAPC3K0zWHk33a6q3a0ar7KQ800agLb5244SHb2+YoEfXJMND3WVD7NWkFEDE3vkIRK5FKiew/V0whUyVGM8H0gD5CxGnEom6UpIrlEVKS1eIjbon4I2+9i3etgwjliP25XQbRmJeOtBiNmyhkvS+yf6JHdwYz7i7DvrMEzxpxP14+738Yyafb3dawVOjijzWrbM8GSEr6lrw/f+g8r7/8CLQkrF4xBIB5niTefJ0cNe5sk8mOXRst5MT8ZYTsdQE7niijHH0UryRHE/d1SIujWy6WxkXqeMJwMz24mg+bRJYumsUpx3wG2HF4msK6WKbQsyw2240BnlY70Ol15+irrpW6rvP555/z6quvcs011/RVu7pxyimncMoppzivKysrWblyJY8++qjTOXr66adJpVI89thjeDweDj74YL744gvuu+++AekcSSQSiUQi2T/odefo8ssv3+byRx55hE8/3f4otK9pa2tzbMwB5s+fzzHHHIPH0+kbM2PGDO666y5aWlrIze2/WBtLg4zVUNZmi2S2UGlC61SSLWGSB4u4IZ9HR1Ggfa1oi788gmmKbS1LIeDROSFPzNW/0TTWiUcCGOJvoy0tVAfd0rapGgFsiuYQcido08W2IVeCdsPnON3uyDtnw3MTSKc0Rpz72S5dh32dqtlHOM/1bKGYKGkF1bAwA+J1sjaI5TNQbfHU9IBpf0xJHySL02Tbvi1tuoZlKmgR2yw1ywSfuFEMv4WVUmlvFaNtFAtVM/mgWqTLDsttcfyTwvMKiRzVsMvnteavhwI4n6vjjP377Weg/WtMAeVrG5yR5i0Vk3f5+H2JjDXqH4of/JDWC6YTWiXuiS+LSnApJqECcS+P9tZRkxRK5hfVQ1jXlkdltghYmlGwFK+iM9QjVO9ViUH8ed2RgHC9HhluZLCvFYDTCxfjU3SOLxTV3dtSPnJdQmVqsTNzM3GWjf190vsaUjkaUHYr5qgrp556Kv/4xz/6anc7ZM2aNfzud7/jxz/+sbOsJzbl2yKZTBKJRLo9JBKJRCLZm5AxRwNLr5Wj7fH3v/+9m5LTE66//nruuuuuHW6zfPlyRo8e7byurq7mlFNO4bvf/S4XX3zxLrW1K3feeSezZ8/erX0E6ywn7kQPKmQsYaJlJlllEUcBSKbc6LqGu1Rkjum6RipmK10xjcKDG3ijSRhjDfa3oioW6+IFznFcbqE6vDNu+66h2d4En9QPw6UJpeOcYQvQLRer45mspGS37av/eXDnCx0so8/6y/scmTJpWhJ89eI6GF6wVAU9sy6hoDW7HWVJD5kopthWMcBX66JVtbMRdQUtrmKEbBUqpaDYNfgwxWvsj99T5cX0WHQExYJVcQ8hW4HKym3arfO6avJcAKo+z+fzSZ3LR/704x2+7/7hY3bruJJ9C8UAb5sdFzk/xPJjijgpX9yvCdPFogYRj6Q3+mjbEKD1MKH4rIgP5tDgBoo0ofhsVAsYkSt0n03RHOKGm+XtwvdoUF4rdelsyjxi/fHFnQp4iSdCxPBTnwjZS9r694Qlkh3Q687RpEmTugVkW5ZFXV0dDQ0N3fwNesJVV13l+CVsj8rKSud5TU0Nxx9/PEcccQR//OMfu223PQvyzLrtccMNNzBr1izndSQS2arujEQikUgkexQ5rTag9LpzdMYZZ3TrHKmqSmFhIccdd1w3hacnFBYWUlhY2KNtq6urOf7445k8eTKPP/44qtpd4Zg+fTo33ngjuq7jdovRzty5cxk1atQO4412Vo24J4T/dz6tF4hMHksFs0soR3t9Fp48uyaaBeq6AHq5UAQsU0HzitQ2I6myZFmZUBMAK0enfHAjuT6xbVT3UBcRikQJy7bbli2xLHRTI5oQ55Sw3OiWtlX9tQyK0pk9V/69Rbtw9vs+LRdOJ5mtOJKzYnYmaWkp8Xlm1pkuUDU6ZSbFJO0XK93tCloCstaKr5XhAT3bwtUmbgjTZ+LfYjsSh0BNa6TDImBpzLFrWfXacLS42DbhcWMGxX3jUkzir1c4Lui9pdMdPU3FgjCFHjHCXzBRul5LOlENyNhreaLQGvU59Rw1xaQtImKCglUaegjW1Ynf7ix3ilJvE0NcIrtssLuVWJb4/UmbGmvaCkjbivSMfIMO08uiWBkA/i511NbH89nckYNLzWRISuWoK30xLSan1XpOrztHO6rc219UV1dz3HHHUVZWxr333ktDQ2dwakYVOvvss5k9ezYXXXQR1113HUuXLuXBBx/k/vvvH/D2SiQSiUQi2XfpdedI0zRqa2spKururNvU1ERRURGGsW2FYneYO3cua9asYc2aNd2qAkOni2d2djavv/46M2fOZPLkyRQUFHDzzTcPWBp/KiSGXGl/Z+aap1VFS6gk24UnTs5yhdYxFkZcXHYlrWAY9lDNb6C2up33qr40mz4fTPY04RvlUk0Mc+euybU1uQRz40wcJIyX6lI5ACybvO16RSFfcpvLDygsW8mxB7GKCRlDaEMFLYGjFLk6FNIhy5GnVV3FFRPrFAswcUbfrjgoloKdhCPctXPEU3+D2DaV8gGwpKGSYAdOikQqVyVSJ2IvVmsmYW+C45YIFXFHMWc7wrQUXKqBV83cC1I5knSi6hamZtcF7LAwWz3U6EJ136TnUZQnklVa3X5cHYDt35bv7aAtHWCDLuIjVUzH6T/fGyVlanyjSKjSm1N5mCiENXEv+1SddkN8B/yaTlMsSP7XVw7MCe9ryGm1AaXXnaPtWYonk8luafR9yQUXXLDT2CSA8ePH8/777/dLGyQSiUQi2WPIztGA0uPO0UMPPQSAoij8+c9/Jiurs7aXYRi89957vY452p8o+p1wm02dehhtlW5neTIf1FKR1dGY7UGNayi2WqQmVLCfKzGVdG4aLWKrSqpFoFZx6rDppoau7/zjCqzxEC9w86U9b19W1kxzKgjEtrl96NQ1u3C2+xfJbAUUnCzDtBsUMZhF1cH0gmW7m6fDllCRusQnOSpSCixXl9c6uDrAb+cJaDrYJsNoSWg7LoZ3iS0rRRXaJ6YILRIDDDPYqcC2rM0lPjTK0+3C42cIX/bq/P4+Rqi8Yxe6yNKSaF1cryWSDJ62NGmfXetPt/DVa6yLi7iiTxqGOcp1vDQNKRWPrZAalkKZt9HxUvsoUslgr4gX+lbeQpqMEOuTnbGlATXFILeITzrcV8XjLcJf7N1NIxj8rd7d2wcSCux2xcW+r9i4/9LjzlEmdseyLObMmYOmdUryHo+H8vJy5syZ0/ctlEgkEolEIhlAetw5Wr9eZMocf/zx/POf/+xXx+l9GcWE7A0ipiMZVomVAjVCHdAssDTLSXQyc9J4NwilQA9buBvdmEPtekW+FGkN2uNCwkh0eNDcOx/xKwZ4WhRiy8Xn82XOIE4tWsp6CnbyzgMXb5tFOqg4GWqmB9IB2+9FEfXTPK0iGCgdFJ5WmbAdV4ci4i8APYxQjTKJbAZk1Vi4EmJfWsrEFRf7iZSpuFYGHCXJ2wJs8NAxTGyruEy8WSIIKpkMYJgqOUFxbzT8ZzSF31jR6/PsjDvbtZglyf5NpMyDO94575LKM8n3CE+2QVkRPv+yHAB3swt9cBLNVqebk0Ea0iFK3cIxO5b2cnh+Z41N3dI4ObQUgCWJoTQbQXx2UN+zbYexcKL4wgzupSJ6wCGn1QaUXsccvf322/3RDolEIpFIJNtBpvIPLD3qHM2aNYtf/epXBIPBboaJ2+K+++7rk4btq5huxVEO0n4FNYmTzWSpYLoUdJe4Q11hHVdcKEeGTyEdMlAbhFJUWlrLkrIciIr1nqBOqsPNznDFRexMJkZm6RflLM0dzAgOzHppO2L9b0SsQ3i9yB6LCRNfFEMoQiAcsk1PZ/aat0khldP5C6OmRUwS2PFHpu0XA1hu6BikkLvK6LY9iM/I09oZA6Ckwd0OrrhY0uFyo9tK4ZARDVTX5tJm/7Kl4m565g4mkfQcT0fnfW14FTxDowxytwLw1MapeOvFvwvTBcRcZBWK7DWfliakJhw1yKOmCalC5SzRohiojMxktgXW8r+th/HHkZ3mvhLJ3kiPOkeff/45ui5u/M8++6ybCaREIpFIJJJ+Rk6rDSg96hx1nUp75513+qst+wWpkIrhtZUiRSGrCjwRcUe2jbAz09Lir1kdcGqyeZtAz7OoGL0ZgMqsRlYUFWPaGSJl313co+MXPSSy5tbeP91ZNuI8qRrtCEsVj64+R13XqbpQjzJocYV00M5e84E7ai9Pi2w1s4vAl/ZBa6X4muWsN5x4s+z1JvF8lbQd/mMGwNsMtuULuE3MlEh6qF5bCAoEC8SBxpfU0tpXJy+R2KS9whEeIBlWGJLbyufRYQCEs+O0FtpfgoBQgTJj5IgupNNlicEAFHijNKRFMF2+GuMwbwuGrZHmqS7eH797FQkOaGTnZsDodZXRCy+8kPb29q2Wd3R0cOGFF/ZJoyQSiUQikUj2FL0OyH7yySf5zW9+QygU6rY8Ho/z1FNP8dhjj/VZ4/ZFDK9CKixGSYEtJlYMDNsQJJVj4m5TsUIi8ERpcTtBJ5YbhpY1MjZbmOIkTRd63IWi7tpQYfiV83fzTA4A7Bie9nLwNSiOz1HXMnSuePdlitXdy0gxwd8gXhhe4YZtm/+SDgIuiNt1j32tKu5ol3glHRR7MG6pQp3KvNfd5HIULMNrYQRMWpqF07orb0sfnLxE0smqPx5O0Qc4v0dt49LQEWTtKqEG4U9jecQN6fHr6HEX0YS4eS0LXm86mLQpxtoe1WC5a7Cz72x1M7bxNiGl1/9yJDYyIHtg6fGdGolEsCwLy7Job2/H5/M56wzD4JVXXtmqpIhEIpFIJJI+QMYcDSg97hzl5OSgKAqKonDQQQdttV5RFGbPnt2njdsXyXliPnVXiCyoyDAVVwLsARWeFkXErqidVdyTubYHTlkHBf4OZz+jAvWoh1gsby0e0PYfSFRcJ9Q1fW4ZtR8OceK/uhQKd3yLXAn7pR2D5G6348bcYGasghXwRESGG4CeZccfZbLMQgqedts/yYJAg0FCFwf1toGepWAbC+Nts7OCgMgIIGhgpcW2K5uLyKelLy+F5AAnf6ELwwPtdsZmqDhKdGUuoRpxb2spjbg99k1VGviyUk7MkdedpjXpI+ASSTujQ3V8LUt4FpW4YngUhXfipQA8W3s4UDdg5yWR7Co97hy9/fbbWJbFCSecwD/+8Q/y8vKcdR6Ph7KyMgYPHryDPUgkEolEItkV5LTawNLjztGxxx4LCKfs0tJSVLXXsdwHDIFGcQc2TALLZWH57OARj4mlq3gDYoSVLFdQW8VHoGkmbUkfalBsGzM9aFi4Tqwa+BM4wHCftJHUX/Nxb/BtewOTTldzN2DhuGJ7I5YTG5T2KaR9XTLOVGwZW7w5UQCW/b3xtlqoaaEWgaiC7op11ncDnEy23IOaaKzJZvSIGgDG5dSyaLfPWiLpgv2PN1Eigus0S0ExFEcFTeZDslj8bqnNHhLZKoGwCJAbnbOFllSAwT4he5Z5G2k1RVWA+ZGRTPBvZG7LWACaEwECA3le+xNyWm1A6XV0XFlZGQCxWIyqqipSqVS39ePHj++blkkkEolEIgGkcjTQ9Lpz1NDQwA9/+EP++9//bnO9YRjbXH4gkbSz1dBMEcfiFdKCy6d3K2vl8ulYaWGKYxgq1c05TCvcAEB1ModFTUMIsA5J/3PQ0HpWtg8FwB3Vuo+wlE7vI0sVTtbBetN5nclkc8VMTLeCHrA//yaIFyrodmKnFge/rSpamogpymTD6VkKWrxTLTJdEBskto2uzyW7NMLwUCMAYS0OSK8YSd9h+MS97GkS0qW6JhufAqkcsd5SQY3Z61IKVouboiENgHDEHh5soMgjHLOztRhhVQTpTfJvYHWqhOXNImUzdOqaATwriWTX6fXc2BVXXEFraysff/wxfr+fV199lSeffJKRI0fy73//uz/aKJFIJBLJgY3VRw9Jj+i1cvTWW2/x4osvMmXKFFRVpaysjJNOOolwOMydd97J6aef3h/t3Kco/L1wqe644wiskAmaUBnSMTdoFh6/mLvX2z0oBWJasjS3DbdmsL6jAIBsd5zADKkaDRQuxUTNFp+FnuXHbfucdvU3AqH+BOvNbj8ymXWWpqCY4ErarxUI1lmowvQcVbdI+zPGVqAHFMdN21IhFe6MV3J1gBEWfli5Je343TrtulipYQHdp7Mlkt2hvcwiuEnBL2zWUA2RtZnJnmyabOBuEcqR4bUwvSZloc6MSa+aJmCneQ5xtdBghJ11H7dVSsWoL5AxRwNKr5Wjjo4Ox88oNzeXhgYhrY4bN47PPpNlKiQSiUQikezb9Fo5GjVqFCtXrqS8vJwJEybwhz/8gfLycubMmcOgQYP6o437LKoO4dUqbZNFH9Qd1ElFPKRiIrDEVxjDNMS69pSXQ/LrOCgohm4fNlUCsT3S7gOR1HG1FLySBcCWAg8uO75CMUFL2K7YgK/FwpWwMG3LX9PVmdFjqUItcpQkWyRK27X21HSnqqSmhIqkxMXGrjgksxUM+z3tBxko9tBlUChCnjdGyC3iOFyqjOuT9C3usihGYwjF/snROkQ2Zcb7C5+BpXZJpfR2FiAcG6ghZnppSQsH94TlJmWnXf5+43FEk15yiAzAWezfyIDsgaXXnaPLL7+c2tpaAG655RZOOeUUnn76aTweD0888URft08ikUgkEomcVhtQet05+sEPfuA8nzx5Mhs3bmTFihUMGzaMgoKCPm2cRCKRSCQSyUCz21UAA4EAhx56aF+0Zb9j2K0fsvaBaYwqE0rb+oZ8FLcJdgHHLH+Spk05ALRUB3hPy6Pm8oz8XLsHWnzgsvrJyVR4RPwcgbTjxmgpIkjalZluSFmYLsUZgbniJoF/frzd/ca+PZXoYDuQ1a/gjonP3vAq3QwfLQXcUbtYLeCr1YiXioMU+aIM8rVRnxSeAB/Xl5HHqr44bYkEgGF5LawZ6yawVAT9mzEFPdhpNeGt8mDYRZIVA5Soxuf1QwCYGNrEUE8zun1DtxoBFnZUAGBYKjmnrx7Yk9lPUSwLxdo96Wd3338g0aPO0axZs3q8w/vuu2+XGyORSCQSiWQbyGm1AaVHnaPPP/+8RztTMpUIJQ7jDl1PU8I2zFfAVefFPVKoQyndBXZpEf96F4Pvmr+nmnnAM3XEBhrtz0l1majCbQE1DemASLMHaBsN+V8o5D7Ws8+q6RANVWTk445AW6WIcPVvsUeCmbhWRTwyo/PEEJ38QeI+casGrbqfurhoRN7pfasa/WBltTiOYuBWRGP/OLKyT48h2Tu5cu1yAF5q8bJ6YwlkzE5ddkmbhF0YO6GS9ovnRn4ay4SkLv59rIsXcohvE0FVZBu0mz7y7Po63pM2DODZSCR9R486R2+//XZ/t0MikUgkEsl2kNlqA8tuxxxJdkyxr51l84YDoGcbWHlp8v8ZctaXPC3Vor2B5iOb6PhvLgBmWnXMGQf99sPd2q85Lkp6jbAISB4SR90gbBw8bYCioCU6t02FIB0Qv16lZY2cXLICgKjhJW54MK3+UWbL3aIsSUhNkLA9C65du5S7hx/SL8eT7D2MdDcDwsQxvyhCeqFIqlEMsFSFlF0YWU2BKyae+0bGyAnEnX2oism6VBGGbZunYfJa/Rixjk0Ddi77PXJabUCRnSOJRCKRSPZypHI0sMjOUT+z/vAY5eye+iAZGDIlDkI72a43lH13Mb53RdHNWNpN3jAx4l72n4MwfJAKi18rxQDDb1EyQqg4RxWtI6CJGI6WdIBlbcWoX+ufUfjtlRMAWPWnwzhpwjIAzi+Y1y/Hkuw9/HrDJzTY9WqGeFtoj/kId4j70fQoKJaF4RZqkWLhqJwdLX6OHLKeYq+IifPZAXqNdoXl98d7pWIk2eeRnSOJRCKRSPZ25LTagCI7RxJJP5M4tg6ASZ93ljL8fFIHRr0fK9tOZYtqqLpKrl8YKpmWQnVSxEC16n58WrrfS80edPEnXLPxAwBSqJyxrAm3XXn3nhfPpOJ6qYDuT4SVFHNjowFImm4qCpqoGiLi44I1FqoOftuXKzpIJXaQuAOLStqIG27H10izzG3sXdLXyGm1gaXXhWf3NMlkkokTJ6IoCl988UW3dYsXL+boo4/G5/NRWlrK3XffvWcaKZFIJBKJZJ9ln1OOrr32WgYPHsyiRYu6LY9EIpx88smceOKJzJkzhyVLlnDhhReSk5PDJZdcsodaK5F0smhS5wi7gkWsu2c6bBFpcZYGltuiJiK8jFYsHga5YqQ+vqyaIYE21vdz+2Z8GWGLIbye1ulFzG8b7mTIpYv6W7eSDDQhFU4IrgRAt1ReqTkY1f6YLUVBMS0s1c6QtAC7GHMyrbElkcUQfysALXqALyODaT2qcYDP4ABDTqsNKPtU5+i///0vr7/+Ov/4xz/473//223d008/TSqV4rHHHsPj8XDwwQfzxRdfcN9998nOkUQikUj2eeS02MCxz3SO6uvrufjii3nhhRcIBAJbrZ8/fz7HHHMMHo/HWTZjxgzuuusuWlpayM3NHcjmSiQ7pfKa+ax+YjIAB5fXEHDp1MdFzIdneBMNDdkAxI6p73fVCKDM00jCEkqWYalMClfxcq3wOsopjA5ACyQDSZ3hotUUvlsrkoM5KGcLn0dFZmW8CEKbQDHEf+OsOgt3XChH+po8mk8yWGiVAuBSTdLH1eyBM5BI+o99IubIsiwuuOACLr30UqZMmbLNberq6iguLu62LPO6rq5uu/tOJpNEIpFuD4lEIpFI9iosq28ekh6xR5Wj66+/nrvuumuH2yxfvpzXX3+d9vZ2brjhhj5vw5133sns2bP7fL8SSU8YecFCAAo+CjMutJniwjYAZn/+dafOVX9y07rusXtrU0XOc9NScZ1YBUBh/zdFMsA0GUFyNOG7laPFyHHHiQwX/zx9TQqGG6fGoGKAp12s87SD9WwB/K9w908PfNMPSGS22sCyRztHV111FRdccMEOt6msrOStt95i/vz5eL3ebuumTJnCOeecw5NPPklJSQn19fXd1mdel5SUbHf/N9xwA7NmzXJeRyIRSktLe3kmEolEIpFI9hf2aOeosLCQwsKdj0kfeughbr/9dud1TU0NM2bM4LnnnmPq1KkATJ8+nRtvvBFd13G7RdzE3LlzGTVq1A7jjbxe71adLolkoKmZFuGM1VvQ7dpmYwbVk+MRo/rtTwrvPnXpbHI0UUE9pCYocbcCoFsuTBQgqx+PLtmTlLvb0Oz0pUWmh6pYHt5mkZ3mjkLarzhKg6qDZQdhSPVhDyGz1QaUfSIge9iwYd1eZ2WJH+zhw4czdOhQAM4++2xmz57NRRddxHXXXcfSpUt58MEHuf/++we8vRKJRCKR9CWKKR67uw9Jz9gnOkc9ITs7m9dff52ZM2cyefJkCgoKuPnmm2Uav2SfQVMscjSREFAebObTRjG9m0VbvxzvhnWLcWMQs0SG5yY938lWu2PxqZR9d3G/HFeyZ/nrpg+c5+22j1XCdLOioYhUth1X1NbF3wihGqnCLJ2GiVB57fyBaq4kg1SOBpR9snNUXl6OtY2o+/Hjx/P+++/vgRZJJBKJRCLZX9gnO0cSyf5Ku+07U+ZvZIk2qF+Ocfv6TwFQFQvTUmgwRMzdm61j+bxhiDj+dlSjP1TNE+8FPIpQF84vPbJf2inpHxpMETzkU0zaTfEvIGG5GV24hYWt4v5LxDxkbRbO7QAoEPtOq3i+MmdgGywBZLbaQCM7RxKJRCKR7O30hU+R9DnqMbJzJJHsJTw8YiT/s6IWgDytgxmDlgHwFsE+2f+vN3zS7fUGPZ+GdJh3m0cBUBsLsaUmB4CcPjmiZG8kZoq4soRi0W4K1dC0VD5bX8phIzcA8GV2McloNlpSqIORwxKMOGP5HmmvRLInkJ0jiUQikUj2cuS02sAiO0cSyV7Es6P7J86oK3XpMAA+RafQFeHjz0YCMPJnH3EQG3b43h8PO6q/myfpR57f/BGb7HppDUaQDSnhM7chkQ/NSx3OcAAAJZVJREFUHhZtOAgAV4fwNtKS4n0jzvtsj7RX0gWZrTag7BO11SQSiUQikUgGCqkcSSQHGKY9JkpZGptT+Yz82Ud7uEWSgWJ1WnOe/+Szc/C4hHnRoSWb8bSopANCWghWK7hjFlpKSg17C3JabWCRnSOJRCKRSPZ2ZLbagCKn1SSSA4RflB/GJI+LQi1CoRYhZnp55eDsPd0syQCTMF0kTBffGP4l+cEO8oMdrGkrAMAVU3DFFEwPaCmLtFch7VVo/59pe7jVEsnAIpUjiUQikUj2cuS02sAiO0cSyQHEqYMn7ekmSPYwHkXEGa2MFLFhTQkARWXNuDogVC3+e2pJi7RPwf8D4buV74sRfXbPtFdiI7PVBhTZOZJIJBKJZC9HKkcDi+wcSSQSyQFEwhIO2dXt2WStEdlr7kqDWKmJOybCUL1toKYtvCdtACC6R1oqkew5ZOdIIpFIJJK9HdMSj93dh6RHyM6RRLKf8/uqDwDQsBii+fn6kMl7uEWSPYlP0QFobQugTIkBYHb4sbwmraPFNjkrVHzN2/5HOnv9QhKW21GgclSxj1sq5H3Vr8iYowFFpvJLJBKJRCLZivfee49vfOMbDB48GEVReOGFF7qttyyLm2++mUGDBuH3+znxxBNZvXp1t22am5s555xzCIfD5OTkcNFFFxGNdp+oXbx4MUcffTQ+n4/S0lLuvvvu/j61nSI7RxKJRCKR7OUodAZl7/Kjl8fs6OhgwoQJPPLII9tcf/fdd/PQQw8xZ84cPv74Y4LBIDNmzCCRSDjbnHPOOXz55ZfMnTuXl156iffee49LLrnEWR+JRDj55JMpKytj4cKF3HPPPdx666388Y9/3IWr1HfIaTWJRCI5gFDtlKXHpz9BQE0BcO2as9i8IhvL/u/ZOiXFyB9+us33fxgbydsNo/j+oAUA6JqLPDXK7PULATm91m/sAYfsU089lVNPPXU7u7J44IEHuOmmmzjjjDMAeOqppyguLuaFF17g+9//PsuXL+fVV1/lk08+YcqUKQD87ne/47TTTuPee+9l8ODBPP3006RSKR577DE8Hg8HH3wwX3zxBffdd1+3TtRAI5UjiUQikUgOICKRSLdHMpns9T7Wr19PXV0dJ554orMsOzubqVOnMn/+fADmz59PTk6O0zECOPHEE1FVlY8//tjZ5phjjsHj8TjbzJgxg5UrV9LS0rKrp7jbSOVIItnP+emwI/d0EyR7IQE1hU9JA/DbEc9zp+90VreIMiKtjVms+vNhHPSjT5ztb1/fqST91ziYmOkFwKfq1BnZ/HFk5QC2/sCjL32OSktLuy2/5ZZbuPXWW3u1r7q6OgCKi4u7LS8uLnbW1dXVUVRU1G29y+UiLy+v2zYVFRVb7SOzLjc3t1ft6itk50gikUgkkr2dPsxW27RpE+Fw2Fns9Xp3c8f7H7JzJJFIJAcIvyg/jF9v6FSDEpb4F+DB4Mohr/E7VUyR1PljVDfnUP/iGACC3hRXrBwOQNYpa1HZROXaLQDcPfyQPm3jXRs+dp4bX4n8+EX5YX16rAOVcDjcrXO0K5SUiNIz9fX1DBo0yFleX1/PxIkTnW22bNnS7X3pdJrm5mbn/SUlJdTX13fbJvM6s82eQMYcSSQSiUSyl6NYVp88+oqKigpKSkp48803nWWRSISPP/6Y6dOnAzB9+nRaW1tZuHChs81bb72FaZpMnTrV2ea9995D13Vnm7lz5zJq1Kg9NqUGUjmSSCSSA4qM+tJVQUqhkbDcnF30EQC6pdEwJMyKuFAEkqaLkX6hAByxfjU3VUzpc8Xoq3xVNTrgMe3H7u6jF0SjUdasWeO8Xr9+PV988QV5eXkMGzaMK664gttvv52RI0dSUVHBL3/5SwYPHsyZZ54JwJgxYzjllFO4+OKLmTNnDrquc9lll/H973+fwYMHA3D22Wcze/ZsLrroIq677jqWLl3Kgw8+yP3337+bJ7t7yM6RRCKRSCR7OX2h/PT2/Z9++inHH3+883rWrFkAnH/++TzxxBNce+21dHR0cMkll9Da2spRRx3Fq6++is/nc97z9NNPc9lll/G1r30NVVU566yzeOihh5z12dnZvP7668ycOZPJkydTUFDAzTffvEfT+AEUy+pDnW0/IBKJkJ2dzXGcgUtx7+nmSCQSSb/RVT0CMG2jo1bTT8T0U69nAxDSEoz21Drb3VQxhf7mq22DvS/mKG3pvMOLtLW17XYMz/bI/E865uibcbl8O3/DDkinE7z3/m392t79BakcSSQSiUSytyNrqw0osnMkkUgkByhdlZhfb/jEcc9WsRjmamaYqxnIKEVD9ljbJOwRh+wDGRnxJpFIJBKJRNIFqRxJJBKJRCo1ezl96ZAt2TmycySRSCQSyd6OnFYbUPapabWXX36ZqVOn4vf7yc3NdbwUMlRVVXH66acTCAQoKirimmuuIZ1O75nGSiQSiUQi2SfZZ5Sjf/zjH1x88cX8+te/5oQTTiCdTrN06VJnvWEYnH766ZSUlPDhhx9SW1vLeeedh9vt5te//vUebLlEIpFIJLuHYorH7u5D0jP2ic5ROp3m8ssv55577uGiiy5ylo8dO9Z5/vrrr7Ns2TLeeOMNiouLmThxIr/61a+47rrruPXWW/F4PHui6RKJRCKR7D5yWm1A2Sem1T777DOqq6tRVZVJkyYxaNAgTj311G7K0fz58xk3bhzFxcXOshkzZhCJRPjyyy+3u+9kMkkkEun2kEgkEolEcuCyT3SO1q1bB8Ctt97KTTfdxEsvvURubi7HHXcczc3Ch6Ourq5bxwhwXtfV1W1333feeSfZ2dnOo7S0tJ/OQiKRSCSSXcTqo4ekR+zRztH111+Poig7fKxYsQLTFBOlN954I2eddRaTJ0/m8ccfR1EUnn/++d1qww033EBbW5vz2LRpU1+cmkQikUgkfUamttruPiQ9Y4/GHF111VVccMEFO9ymsrKS2lpR06drjJHX66WyspKqqioASkpKWLBgQbf31tfXO+u2h9frxev17krzJRKJRCIZGGTM0YCyRztHhYWFFBYW7nS7yZMn4/V6WblyJUcddRQAuq6zYcMGysrKAJg+fTp33HEHW7ZsoaioCIC5c+cSDoe7daokEolEIpFIdsQ+ka0WDoe59NJLueWWWygtLaWsrIx77rkHgO9+97sAnHzyyYwdO5Zzzz2Xu+++m7q6Om666SZmzpwplSGJRCKR7NtYwO6m4kvhqMfsE50jgHvuuQeXy8W5555LPB5n6tSpvPXWW+Tm5gKgaRovvfQSP/nJT5g+fTrBYJDzzz+f2267bQ+3XCKRSCSS3aMvYoZkzFHP2Wc6R263m3vvvZd77713u9uUlZXxyiuvDGCrJBKJRCKR7G/sM50jiUQikUgOWCz6ICC7T1pyQCA7RxKJRCKR7O3IbLUBZZ8wgZRIJBKJRCIZKKRyJJFIJBLJ3o4JKH2wD0mPkJ0jiUQikUj2cmS22sAiO0cSiUQikeztyJijAUXGHEkkEolEIpF0QSpHEolEIpHs7UjlaECRnSOJRCKRSPZ2ZOdoQJHTahKJRCKRSCRdkMqRRCKRSCR7OzKVf0CRnSOJRCKRSPZyZCr/wCKn1SQSiUQikUi6IJUjiUQikUj2dmRA9oAiO0cSiUQikeztmBYou9m5MWXnqKfIaTWJRCKRSCSSLkjlSCKRSCSSvR05rTagyM6RRCKRSCR7PX3QOUJ2jnqK7BxJJBKJRLK3I5WjAUXGHEkkEolEIpF0QSpHEolEIpHs7ZgWuz0tJrPVeozsHEkkEolEsrdjmeKxu/uQ9Ag5rSaRSCQSiUTSBakcSSQSiUSytyMDsgcU2TmSSCQSiWRvR8YcDShyWk0ikUgkEomkC1I5kkgkEolkb0dOqw0osnMkkUgkEsnejkUfdI76pCUHBHJaTSKRSCQSiaQLUjmSSCQSiWRvR06rDSj7jHK0atUqzjjjDAoKCgiHwxx11FG8/fbb3bapqqri9NNPJxAIUFRUxDXXXEM6nd5DLZZIJBKJpI8wzb55SHrEPtM5+vrXv046neatt95i4cKFTJgwga9//evU1dUBYBgGp59+OqlUig8//JAnn3ySJ554gptvvnkPt1wikUgkkt0koxzt7kPSI/aJzlFjYyOrV6/m+uuvZ/z48YwcOZLf/OY3xGIxli5dCsDrr7/OsmXL+N///V8mTpzIqaeeyq9+9SseeeQRUqnUHj4DiUQikUgk+wr7ROcoPz+fUaNG8dRTT9HR0UE6neYPf/gDRUVFTJ48GYD58+czbtw4iouLnffNmDGDSCTCl19+ud19J5NJIpFIt4dEIpFIJHsVUjkaUPaJgGxFUXjjjTc488wzCYVCqKpKUVERr776Krm5uQDU1dV16xgBzuvM1Nu2uPPOO5k9e3b/NV4ikUgkkt1FOmQPKHtUObr++utRFGWHjxUrVmBZFjNnzqSoqIj333+fBQsWcOaZZ/KNb3yD2tra3WrDDTfcQFtbm/PYtGlTH52dRCKRSCSSfZE9qhxdddVVXHDBBTvcprKykrfeeouXXnqJlpYWwuEwAL///e+ZO3cuTz75JNdffz0lJSUsWLCg23vr6+sBKCkp2e7+vV4vXq93905EIpFIJJJ+xLJMLGv3ss129/0HEnu0c1RYWEhhYeFOt4vFYgCoanehS1VVTDs1cfr06dxxxx1s2bKFoqIiAObOnUs4HGbs2LF93HKJRCKRSAYQy9r9aTEZc9Rj9omA7OnTp5Obm8v555/PokWLWLVqFddccw3r16/n9NNPB+Dkk09m7NixnHvuuSxatIjXXnuNm266iZkzZ0plSCKRSCQSSY/ZJzpHBQUFvPrqq0SjUU444QSmTJnCvHnzePHFF5kwYQIAmqbx0ksvoWka06dP5wc/+AHnnXcet9122x5uvUQikUgku4nMVhtQ9olsNYApU6bw2muv7XCbsrIyXnnllQFqkUQikUgkA4RpgrKbMUMy5qjH7BPKkUQikUgkEslAsc8oRxKJRCKRHLBYfeBzJKfVeozsHEkkEolEspdjmSbWbk6ryVT+niM7RxKJRCKR7O1I5WhAkTFHEolEIpFIJF2QypFEIpFIJHs7pgWKVI4GCtk5kkgkEolkb8eygN1N5Zedo54ip9UkEolEIpFIuiCVI4lEIpFI9nIs08LazWk1SypHPUYqR5L/3969R0V1XX8A/w7KDM9hQGAYRJ5aHlFRsFCCFVtQMGkK2tRHaAHrwqWVihITtU1AjQaLxpBmWR9JA0kWSUiyIDZpxKIGjASJoPhEFIpgWwYiKiAEYZj9+8Nyf3N5KCTgMGR/1pq1uOecuXfvOYNs7z1zhzHG2GhH2uF5DNHevXvh6uoKExMTBAYG4uuvvx6B5EYfLo4YY4wx1kd2djaSkpKQkpKCM2fOwNfXF+Hh4WhsbNR3aCOOiyPGGGNslCMtDctjKPbs2YP4+HgsX74cPj4+2L9/P8zMzPDWW2+NUJajBxdHjDHG2Gj3iC+rdXZ2oqysDGFhYUKbkZERwsLCUFxcPBIZjiq8ILuXngVrGnR975uRMsYYG7s06ALwaBY6D8ffpJ54W1paRO0ymQwymUzUdvPmTXR3d0OpVIralUolrly58v0CMQBcHPXS1NQEADiJz/UcCWOMMUPQ2toKKyurEdm3VCqFg4MDTqqH52+ShYUFJk2aJGpLSUnBli1bhmX/YwUXR73Y2NgAAOrq6kbsza4vLS0tmDRpEm7cuAG5XK7vcIYV52aYODfDxLndR0RobW2Fo6PjiMVjYmKCmpoadHZ2Dsv+iAgSiUTU1vusEQDY2tpi3LhxaGhoELU3NDTAwcFhWGIZzbg46sXI6P4yLCsrqzH3S99DLpdzbgaIczNMnJthGmxuj+I/0SYmJjAxMRnx4+iSSqXw9/fHsWPHEBUVBQDQarU4duwYEhISHmks+sDFEWOMMcb6SEpKQmxsLGbNmoWAgACkp6ejra0Ny5cv13doI46LI8YYY4z1sWTJEnzzzTdITk6GWq3GjBkzkJeX12eR9ljExVEvMpkMKSkp/V6DNXScm2Hi3AwT52aYxnJu30VCQsIP4jJabxLiL1thjDHGGBPwTSAZY4wxxnRwccQYY4wxpoOLI8YYY4wxHVwc6di7dy9cXV1hYmKCwMBAfP311/oOaci2bNkCiUQienh5eQn9HR0dWLNmDSZMmAALCwv86le/6nOTr9HixIkTeOqpp+Do6AiJRIJPPvlE1E9ESE5OhkqlgqmpKcLCwnDt2jXRmFu3biE6OhpyuRwKhQIrVqzA3bt3H2EW/XtYbnFxcX3mMSIiQjRmtOaWmpqKH//4x7C0tIS9vT2ioqJQWVkpGjOY92FdXR2efPJJmJmZwd7eHs899xw0Gs2jTKWPweQ2d+7cPnO3atUq0ZjRmNu+ffswffp04f4+QUFBOHz4sNBvqHMGPDw3Q50zNnK4OPqf7OxsJCUlISUlBWfOnIGvry/Cw8PR2Nio79CG7LHHHkN9fb3wOHnypNC3fv16fPrpp/joo49QWFiI//73v1i0aJEeox1YW1sbfH19sXfv3n7709LS8Je//AX79+9HSUkJzM3NER4ejo6ODmFMdHQ0Ll26hPz8fHz22Wc4ceIEVq5c+ahSGNDDcgOAiIgI0Ty+//77ov7RmlthYSHWrFmDU6dOIT8/H11dXZg/fz7a2tqEMQ97H3Z3d+PJJ59EZ2cnvvrqK7z99tvIzMxEcnKyPlISDCY3AIiPjxfNXVpamtA3WnNzcnLCzp07UVZWhtLSUvz85z9HZGQkLl26BMBw5wx4eG6AYc4ZG0HEiIgoICCA1qxZI2x3d3eTo6Mjpaam6jGqoUtJSSFfX99+++7cuUPGxsb00UcfCW0VFRUEgIqLix9RhN8NAMrNzRW2tVotOTg40K5du4S2O3fukEwmo/fff5+IiC5fvkwA6PTp08KYw4cPk0Qiof/85z+PLPaH6Z0bEVFsbCxFRkYO+BxDyY2IqLGxkQBQYWEhEQ3uffj555+TkZERqdVqYcy+fftILpfTvXv3Hm0CD9A7NyKikJAQSkxMHPA5hpIbEZG1tTW9+eabY2rOevTkRjS25owNDz5zBKCzsxNlZWUICwsT2oyMjBAWFobi4mI9RvbdXLt2DY6OjnB3d0d0dDTq6uoAAGVlZejq6hLl6eXlBWdnZ4PLs6amBmq1WpSLlZUVAgMDhVyKi4uhUCgwa9YsYUxYWBiMjIxQUlLyyGMeqoKCAtjb28PT0xOrV68WvhQZMKzcmpubAfz/9xYO5n1YXFyMadOmiW42Fx4ejpaWFtH/9vWtd249srKyYGtri6lTp2Lz5s1ob28X+gwht+7ubnzwwQdoa2tDUFDQmJqz3rn1MPQ5Y8OLbwIJ4ObNm+ju7u5z10+lUokrV67oKarvJjAwEJmZmfD09ER9fT22bt2Kn/70p7h48SLUajWkUikUCoXoOUqlEmq1Wj8Bf0c98fY3Zz19arUa9vb2ov7x48fDxsZm1OcbERGBRYsWwc3NDdXV1fjjH/+IBQsWoLi4GOPGjTOY3LRaLdatW4fg4GBMnToVAAb1PlSr1f3ObU/faNBfbgDwzDPPwMXFBY6Ojjh//jw2btyIyspK5OTkABjduV24cAFBQUHo6OiAhYUFcnNz4ePjg/LycoOfs4FyAwx7ztjI4OJojFmwYIHw8/Tp0xEYGAgXFxd8+OGHMDU11WNkbCiWLl0q/Dxt2jRMnz4dHh4eKCgoQGhoqB4jG5o1a9bg4sWLonVvY8VAuemu+5o2bRpUKhVCQ0NRXV0NDw+PRx3mkHh6eqK8vBzNzc34+OOPERsbi8LCQn2HNSwGys3Hx8eg54yNDL6sBsDW1hbjxo3r88mLhoYGODg46Cmq4aFQKPCjH/0IVVVVcHBwQGdnJ+7cuSMaY4h59sT7oDlzcHDos6Beo9Hg1q1bBpevu7s7bG1tUVVVBcAwcktISMBnn32GL774Ak5OTkL7YN6HDg4O/c5tT5++DZRbfwIDAwFANHejNTepVIrJkyfD398fqamp8PX1xWuvvTYm5myg3PpjSHPGRgYXR7j/S+Pv749jx44JbVqtFseOHRNdkzZEd+/eRXV1NVQqFfz9/WFsbCzKs7KyEnV1dQaXp5ubGxwcHES5tLS0oKSkRMglKCgId+7cQVlZmTDm+PHj0Gq1wj9+huLf//43mpqaoFKpAIzu3IgICQkJyM3NxfHjx+Hm5ibqH8z7MCgoCBcuXBAVgPn5+ZDL5cKlEH14WG79KS8vBwDR3I3G3Pqj1Wpx7949g56zgfTk1h9DnjM2TPS9Iny0+OCDD0gmk1FmZiZdvnyZVq5cSQqFQvTpBEPw7LPPUkFBAdXU1FBRURGFhYWRra0tNTY2EhHRqlWryNnZmY4fP06lpaUUFBREQUFBeo66f62trXT27Fk6e/YsAaA9e/bQ2bNnqba2loiIdu7cSQqFgg4dOkTnz5+nyMhIcnNzo2+//VbYR0REBM2cOZNKSkro5MmTNGXKFFq2bJm+UhI8KLfW1lbasGEDFRcXU01NDR09epT8/PxoypQp1NHRIexjtOa2evVqsrKyooKCAqqvrxce7e3twpiHvQ81Gg1NnTqV5s+fT+Xl5ZSXl0d2dna0efNmfaQkeFhuVVVVtG3bNiotLaWamho6dOgQubu705w5c4R9jNbcNm3aRIWFhVRTU0Pnz5+nTZs2kUQioX/+859EZLhzRvTg3Ax5ztjI4eJIx+uvv07Ozs4klUopICCATp06pe+QhmzJkiWkUqlIKpXSxIkTacmSJVRVVSX0f/vtt/T73/+erK2tyczMjBYuXEj19fV6jHhgX3zxBQHo84iNjSWi+x/nf/HFF0mpVJJMJqPQ0FCqrKwU7aOpqYmWLVtGFhYWJJfLafny5dTa2qqHbMQelFt7ezvNnz+f7OzsyNjYmFxcXCg+Pr5PoT5ac+svLwCUkZEhjBnM+/D69eu0YMECMjU1JVtbW3r22Wepq6vrEWcj9rDc6urqaM6cOWRjY0MymYwmT55Mzz33HDU3N4v2Mxpz+93vfkcuLi4klUrJzs6OQkNDhcKIyHDnjOjBuRnynLGRIyEienTnqRhjjDHGRjdec8QYY4wxpoOLI8YYY4wxHVwcMcYYY4zp4OKIMcYYY0wHF0eMMcYYYzq4OGKMMcYY08HFEWOMMcaYDi6OGGOMMcZ0cHHEftDmzp2LdevWjanjxsXFISoq6nvtw9XVFRKJBBKJpM+XjerKzMyEQqH4XscaK+Li4oTX7JNPPtF3OIyx74GLI8b0ICcnBy+99JKw7erqivT0dP0F1I9t27ahvr4eVlZW+g5F7zIzM4XCR/dhYmIijHnttddQX1+vxygZY8NlvL4DYOyHyMbGRt8hPJSlpSUcHBz0HQYAoKurC8bGxnqNQS6Xo7KyUtQmkUiEn62srLiQZGyM4DNHjOm4ffs2YmJiYG1tDTMzMyxYsADXrl0T+nsuIx05cgTe3t6wsLBARESE6IyBRqPB2rVroVAoMGHCBGzcuBGxsbGiS126l9Xmzp2L2tparF+/XjgjAQBbtmzBjBkzRPGlp6fD1dVV2O7u7kZSUpJwrOeffx69vy5Rq9UiNTUVbm5uMDU1ha+vLz7++OPv9PpkZmbC2dkZZmZmWLhwIZqamvqMOXToEPz8/GBiYgJ3d3ds3boVGo1G6L9y5Qpmz54NExMT+Pj44OjRo6JLUdevX4dEIkF2djZCQkJgYmKCrKwsAMCbb74Jb29vmJiYwMvLC3/9619Fx75x4wYWL14MhUIBGxsbREZG4vr160J/QUEBAgICYG5uDoVCgeDgYNTW1g4qd4lEAgcHB9FDqVQO8RVkjBkCLo4Y0xEXF4fS0lL8/e9/R3FxMYgITzzxBLq6uoQx7e3t2L17N959912cOHECdXV12LBhg9D/5z//GVlZWcjIyEBRURFaWloeuAYlJycHTk5OwmWsoVyaeeWVV5CZmYm33noLJ0+exK1bt5Cbmysak5qainfeeQf79+/HpUuXsH79evzmN79BYWHh4F8YACUlJVixYgUSEhJQXl6On/3sZ9i+fbtozJdffomYmBgkJibi8uXLOHDgADIzM7Fjxw4A94u5qKgomJmZoaSkBAcPHsSf/vSnfo+3adMmJCYmoqKiAuHh4cjKykJycjJ27NiBiooKvPzyy3jxxRfx9ttvA7h/dik8PByWlpb48ssvUVRUJBSvnZ2d0Gg0iIqKQkhICM6fP4/i4mKsXLlSdPaHMcYAAMTYD1hISAglJiYSEdHVq1cJABUVFQn9N2/eJFNTU/rwww+JiCgjI4MAUFVVlTBm7969pFQqhW2lUkm7du0StjUaDTk7O1NkZGS/xyUicnFxoVdffVUUW0pKCvn6+oraXn31VXJxcRG2VSoVpaWlCdtdXV3k5OQkHKujo4PMzMzoq6++Eu1nxYoVtGzZsgFfl/7iWbZsGT3xxBOitiVLlpCVlZWwHRoaSi+//LJozLvvvksqlYqIiA4fPkzjx4+n+vp6oT8/P58AUG5uLhER1dTUEABKT08X7cfDw4Pee+89UdtLL71EQUFBwnE8PT1Jq9UK/ffu3SNTU1M6cuQINTU1EQAqKCgYMO+B9My7ubm56BEREdFnrG4ujDHDxGuOGPufiooKjB8/HoGBgULbhAkT4OnpiYqKCqHNzMwMHh4ewrZKpUJjYyMAoLm5GQ0NDQgICBD6x40bB39/f2i12mGNt7m5GfX19aJ4x48fj1mzZgmX1qqqqtDe3o558+aJntvZ2YmZM2cO6XgVFRVYuHChqC0oKAh5eXnC9rlz51BUVCScKQLuny3q6OhAe3s7KisrMWnSJNFaJt3XStesWbOEn9va2lBdXY0VK1YgPj5eaNdoNMI6n3PnzqGqqgqWlpai/XR0dKC6uhrz589HXFwcwsPDMW/ePISFhWHx4sVQqVSDyt/S0hJnzpwRtZmamg7quYwxw8LFEWND1HthsEQi6bPOZzgYGRn12a/u5b3BuHv3LgDgH//4ByZOnCjqk8lk3y/AAY63detWLFq0qE+f7ie7BsPc3Fy0XwB44403RMUgcL/47Bnj7+8vrE/SZWdnBwDIyMjA2rVrkZeXh+zsbLzwwgvIz8/HT37yk4fGY2RkhMmTJw8pB8aYYeLiiLH/8fb2hkajQUlJCR5//HEAQFNTEyorK+Hj4zOofVhZWUGpVOL06dOYM2cOgPtnTs6cOdNncbUuqVSK7u5uUZudnR3UajWISFgXU15eLjqWSqVCSUmJcCyNRoOysjL4+fkBAHx8fCCTyVBXV4eQkJBB5TAQb29vlJSUiNpOnTol2vbz80NlZeWARYSnpydu3LiBhoYGYTHz6dOnH3pspVIJR0dH/Otf/0J0dHS/Y/z8/JCdnQ17e3vI5fIB9zVz5kzMnDkTmzdvRlBQEN57771BFUeMsR8OLo4Y+58pU6YgMjIS8fHxOHDgACwtLbFp0yZMnDgRkZGRg97PH/7wB6SmpmLy5Mnw8vLC66+/jtu3bz9w4a+rqytOnDiBpUuXQiaTwdbWFnPnzsU333yDtLQ0PP3008jLy8Phw4dFf/gTExOxc+dOTJkyBV5eXtizZ4/opo2WlpbYsGED1q9fD61Wi9mzZ6O5uRlFRUWQy+WIjY0ddF5r165FcHAwdu/ejcjISBw5ckR0SQ0AkpOT8Ytf/ALOzs54+umnYWRkhHPnzuHixYvYvn075s2bBw8PD8TGxiItLQ2tra144YUXAOChC6O3bt2KtWvXwsrKChEREbh37x5KS0tx+/ZtJCUlITo6Grt27UJkZCS2bdsGJycn1NbWIicnB88//zy6urpw8OBB/PKXv4SjoyMqKytx7do1xMTEDCp/IoJare7Tbm9vDyMj/mwLY2OKXlc8MaZnvRdG37p1i37729+SlZUVmZqaUnh4OF29elXoz8jIEC1AJiLKzc0l3V+lrq4uSkhIILlcTtbW1rRx40b69a9/TUuXLh3wuMXFxTR9+nSSyWSife3bt48mTZpE5ubmFBMTQzt27BAtyO7q6qLExESSy+WkUCgoKSmJYmJiRIu/tVotpaenk6enJxkbG5OdnR2Fh4dTYWHhgK9LfwuyiYj+9re/kZOTE5mamtJTTz1Fu3fv7vN65OXl0eOPP06mpqYkl8spICCADh48KPRXVFRQcHAwSaVS8vLyok8//ZQAUF5eHhH9/4Lss2fP9jl+VlYWzZgxg6RSKVlbW9OcOXMoJydH6K+vr6eYmBiytbUlmUxG7u7uFB8fT83NzaRWqykqKopUKhVJpVJycXGh5ORk6u7uHvB16NGzILu/h+7iciJekM3YWCAhGoHFEowxgVarhbe3NxYvXiy6K/Zo5urqinXr1j2Sr1YpKirC7NmzUVVVJVrobqgkEglyc3O/91e4MMb0h88FMzbMamtr8cYbb+Dq1au4cOECVq9ejZqaGjzzzDP6Dm1INm7cCAsLCzQ3Nw/rfnNzc5Gfn4/r16/j6NGjWLlyJYKDgw2+MFq1ahUsLCz0HQZjbBjwmSPGhtmNGzewdOlSXLx4EUSEqVOnYufOncKiaUNQW1srfDLO3d19WNfUvPPOO9i+fTvq6upga2uLsLAwvPLKK5gwYcKwHWOoHnvssQHvlH3gwIEBF4HramxsREtLC4D7t3fQ/bQdY8ywcHHEGPvB0y0Ge1MqlX3uncQYG9u4OGKMMcYY08FrjhhjjDHGdHBxxBhjjDGmg4sjxhhjjDEdXBwxxhhjjOng4ogxxhhjTAcXR4wxxhhjOrg4YowxxhjTwcURY4wxxpiO/wPmg/QeeL/DyQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.mrso.mean(dim='time').plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "9212d429-8cd2-4ef6-a498-2fed900091d9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    00020101-00021231\n",
-       "1    00030101-00031231\n",
-       "2    00040101-00041231\n",
-       "3    00050101-00051231\n",
-       "4    00060101-00061231\n",
-       "5    00070101-00071231\n",
-       "6    00080101-00081231\n",
-       "7    00090101-00091231\n",
-       "8    00110101-00111231\n",
-       "9    00100101-00101231\n",
-       "Name: temporal_subset, dtype: object"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat.df['temporal_subset'] "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "06746aff-889b-4c67-b2d7-fb5ae821a678",
-   "metadata": {},
-   "source": [
-    "Can I please leverage CF?  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d7dadd5-7abd-4bf7-a6ca-e39d3c214b04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install cf_xarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f248b8e-2d65-469c-b41f-f1875fac7317",
-   "metadata": {},
-   "source": [
-    "#You may leverage the use of cf_xarray, xMIP etc to build your analyses from here. They all blend in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "c47d02a6-c340-45f6-8f84-f26e691358ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import cf_xarray as cfxr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "c6cb19f4-6409-4e32-9119-b0d51b42eb33",
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
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-       "\n",
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-       "\n",
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-       "  padding: 0;\n",
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-       "  padding-right: 10px;\n",
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-       "}\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",
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-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
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-       "  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",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 757MB\n",
-       "Dimensions:     (time: 3650, bnds: 2, lat: 180, lon: 288)\n",
-       "Coordinates:\n",
-       "    average_DT  (time) timedelta64[ns] 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T1  (time) object 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T2  (time) object 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "  * bnds        (bnds) float64 16B 1.0 2.0\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "    lat_bnds    (lat, bnds) float64 3kB dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "    lon_bnds    (lon, bnds) float64 5kB dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;\n",
-       "  * time        (time) object 29kB 0002-01-01 12:00:00 ... 0011-12-31 12:00:00\n",
-       "    time_bnds   (time, bnds) object 58kB dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;\n",
-       "Data variables:\n",
-       "    mrso        (time, lat, lon) float32 757MB dask.array&lt;chunksize=(5, 180, 288), meta=np.ndarray&gt;\n",
-       "Attributes: (12/18)\n",
-       "    title:                            c96L65_am5f3b1r0_pdclim1850F\n",
-       "    grid_type:                        regular\n",
-       "    grid_tile:                        N/A\n",
-       "    code_release_version:             2023.01\n",
-       "    git_hash:                         unknown githash\n",
-       "    external_variables:               land_area\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:variable_id:     mrso\n",
-       "    intake_esm_attrs:chunk_freq:      1yr\n",
-       "    intake_esm_attrs:platform:        gfdl.ncrc5-deploy-prod-openmp\n",
-       "    intake_esm_attrs:cell_methods:    ts\n",
-       "    intake_esm_attrs:_data_format_:   netcdf\n",
-       "    intake_esm_dataset_key:           am5.c96L65_am5f3b1r0_pdclim1850F.daily....</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-0efebfa1-888a-455f-9c65-87cc37829eff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0efebfa1-888a-455f-9c65-87cc37829eff' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3650</li><li><span class='xr-has-index'>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0c9e4698-1920-450b-b5ae-2318ff566ec3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c9e4698-1920-450b-b5ae-2318ff566ec3' class='xr-section-summary' >Coordinates: <span>(10)</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>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-c636037e-6aa6-42bc-a43e-f744e0620492' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c636037e-6aa6-42bc-a43e-f744e0620492' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f337d3b-14ff-4e71-a4f1-237278356c29' class='xr-var-data-in' type='checkbox'><label for='data-3f337d3b-14ff-4e71-a4f1-237278356c29' 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>Length of average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 22 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> timedelta64[ns] 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>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-e015abf1-ba65-4fe1-9453-32d4221d4409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e015abf1-ba65-4fe1-9453-32d4221d4409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d26fe1c8-c11a-4623-b750-f02d156c6511' class='xr-var-data-in' type='checkbox'><label for='data-d26fe1c8-c11a-4623-b750-f02d156c6511' 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>Start time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 22 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
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-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\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>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-34e932bf-64b8-4011-b0a2-a8b0266ea8a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34e932bf-64b8-4011-b0a2-a8b0266ea8a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eabf436e-5c50-464a-a853-1ba1095acba2' class='xr-var-data-in' type='checkbox'><label for='data-eabf436e-5c50-464a-a853-1ba1095acba2' 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>End time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 22 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object 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",
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-       "  <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",
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-       "  <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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">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'>bnds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-0e192524-0adc-4478-bed6-3c86ca9046e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e192524-0adc-4478-bed6-3c86ca9046e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3da22b05-1332-41c5-bbb5-1c221ff5361f' class='xr-var-data-in' type='checkbox'><label for='data-3da22b05-1332-41c5-bbb5-1c221ff5361f' 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>vertex number</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-e3f00aff-cadd-4653-aa7a-55f6018b1129' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3f00aff-cadd-4653-aa7a-55f6018b1129' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cfb0a16-b8d6-4eb8-a48d-463798feb3dd' class='xr-var-data-in' type='checkbox'><label for='data-6cfb0a16-b8d6-4eb8-a48d-463798feb3dd' 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_N</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
-       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
-       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
-       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
-       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
-       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
-       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
-       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
-       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
-       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
-       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
-       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
-       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
-       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
-       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
-       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;</div><input id='attrs-8ce3ba5b-3b37-4044-b5cb-605a2b4f27d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce3ba5b-3b37-4044-b5cb-605a2b4f27d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4df4f128-b9d0-49cf-b79b-bca099d71035' class='xr-var-data-in' type='checkbox'><label for='data-4df4f128-b9d0-49cf-b79b-bca099d71035' 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 bounds</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>axis :</span></dt><dd>Y</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> 2.81 kiB </td>\n",
-       "                        <td> 2.81 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (180, 2) </td>\n",
-       "                        <td> (180, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 45 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=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"25\" 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=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <!-- Text -->\n",
-       "  <text x=\"12.962080\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</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 class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-4006bda4-566a-467d-ba2d-7089d154223e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4006bda4-566a-467d-ba2d-7089d154223e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e0b6d3f-5d1c-4d62-81e3-9589cb742e33' class='xr-var-data-in' type='checkbox'><label for='data-5e0b6d3f-5d1c-4d62-81e3-9589cb742e33' 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_E</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;</div><input id='attrs-e327b825-ea23-4130-9eb2-e9e3d584b4c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e327b825-ea23-4130-9eb2-e9e3d584b4c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-964c9535-afcb-449e-b858-c0d08467b4f1' class='xr-var-data-in' type='checkbox'><label for='data-964c9535-afcb-449e-b858-c0d08467b4f1' 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 bounds</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>axis :</span></dt><dd>X</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> 4.50 kiB </td>\n",
-       "                        <td> 4.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (288, 2) </td>\n",
-       "                        <td> (288, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 45 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",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></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'>object</div><div class='xr-var-preview xr-preview'>0002-01-01 12:00:00 ... 0011-12-...</div><input id='attrs-0935985c-2a27-46f8-82e3-698979bffe52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0935985c-2a27-46f8-82e3-698979bffe52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8aab64f-fa4d-45f9-a0e2-5dc929d78265' class='xr-var-data-in' type='checkbox'><label for='data-d8aab64f-fa4d-45f9-a0e2-5dc929d78265' 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>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
-       "       cftime.DatetimeNoLeap(11, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(11, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(11, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;</div><input id='attrs-de5ea9e2-b9d3-43f7-863b-a415ea5dbfd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de5ea9e2-b9d3-43f7-863b-a415ea5dbfd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf5140fc-940f-4dcb-892f-624a61145b99' class='xr-var-data-in' type='checkbox'><label for='data-cf5140fc-940f-4dcb-892f-624a61145b99' 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 axis boundaries</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 57.03 kiB </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650, 2) </td>\n",
-       "                        <td> (5, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 22 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-07771cf6-6ada-449b-9805-6b0cd2c6b28c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07771cf6-6ada-449b-9805-6b0cd2c6b28c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mrso</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 180, 288), meta=np.ndarray&gt;</div><input id='attrs-7d04cc57-73f7-4914-9e1d-4a491955bdb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d04cc57-73f7-4914-9e1d-4a491955bdb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-819ffe08-8d02-434d-bea4-8d266fecd40e' class='xr-var-data-in' type='checkbox'><label for='data-819ffe08-8d02-434d-bea4-8d266fecd40e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>Total Soil Moisture Content</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd><dt><span>ocean_fillvalue :</span></dt><dd>0.0</dd><dt><span>cell_measures :</span></dt><dd>area: land_area</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>soil_moisture_content</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</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> 721.80 MiB </td>\n",
-       "                        <td> 0.99 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650, 180, 288) </td>\n",
-       "                        <td> (5, 180, 288) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 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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-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"105\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"117\" y1=\"70\" x2=\"117\" y2=\"105\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"80.58823529411765,70.58823529411765 117.83431083839508,70.58823529411765 117.83431083839508,105.00477897478552 80.58823529411765,105.00477897478552\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"99.211273\" y=\"125.004779\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
-       "  <text x=\"137.834311\" y=\"87.796507\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,137.834311,87.796507)\">180</text>\n",
-       "  <text x=\"35.294118\" y=\"89.710661\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.710661)\">3650</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-aff18f56-9ffe-4ebe-b8ac-1ab8ebf98857' class='xr-section-summary-in' type='checkbox'  ><label for='section-aff18f56-9ffe-4ebe-b8ac-1ab8ebf98857' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bnds</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b93667b4-329d-4871-a231-a1b29b57aa9e' class='xr-index-data-in' type='checkbox'/><label for='index-b93667b4-329d-4871-a231-a1b29b57aa9e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;bnds&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d86f0e57-41c7-4c22-8dec-47835ece4926' class='xr-index-data-in' type='checkbox'/><label for='index-d86f0e57-41c7-4c22-8dec-47835ece4926' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       ...\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1de2d6ca-922a-416f-b52b-7c2d2faeb17c' class='xr-index-data-in' type='checkbox'/><label for='index-1de2d6ca-922a-416f-b52b-7c2d2faeb17c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,\n",
-       "                    4.375,              5.625,              6.875,\n",
-       "                    8.125,              9.375,             10.625,\n",
-       "                   11.875,\n",
-       "       ...\n",
-       "                  348.125,            349.375,            350.625,\n",
-       "                  351.875,            353.125,            354.375,\n",
-       "                  355.625,            356.875,            358.125,\n",
-       "                  359.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-40facfde-daaf-4bf7-a165-e38d1107e6c4' class='xr-index-data-in' type='checkbox'/><label for='index-40facfde-daaf-4bf7-a165-e38d1107e6c4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0002-01-01 12:00:00, 0002-01-02 12:00:00, 0002-01-03 12:00:00,\n",
-       "             0002-01-04 12:00:00, 0002-01-05 12:00:00, 0002-01-06 12:00:00,\n",
-       "             0002-01-07 12:00:00, 0002-01-08 12:00:00, 0002-01-09 12:00:00,\n",
-       "             0002-01-10 12:00:00,\n",
-       "             ...\n",
-       "             0011-12-22 12:00:00, 0011-12-23 12:00:00, 0011-12-24 12:00:00,\n",
-       "             0011-12-25 12:00:00, 0011-12-26 12:00:00, 0011-12-27 12:00:00,\n",
-       "             0011-12-28 12:00:00, 0011-12-29 12:00:00, 0011-12-30 12:00:00,\n",
-       "             0011-12-31 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=3650, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-881f3abf-b68a-47a1-bae4-c0892b9b4fdd' class='xr-section-summary-in' type='checkbox'  ><label for='section-881f3abf-b68a-47a1-bae4-c0892b9b4fdd' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>c96L65_am5f3b1r0_pdclim1850F</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd><dt><span>code_release_version :</span></dt><dd>2023.01</dd><dt><span>git_hash :</span></dt><dd>unknown githash</dd><dt><span>external_variables :</span></dt><dd>land_area</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;mrso&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>dev</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>am5</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>c96L65_am5f3b1r0_pdclim1850F</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>daily</dd><dt><span>intake_esm_attrs:modeling_realm :</span></dt><dd>land_cmip</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>mrso</dd><dt><span>intake_esm_attrs:chunk_freq :</span></dt><dd>1yr</dd><dt><span>intake_esm_attrs:platform :</span></dt><dd>gfdl.ncrc5-deploy-prod-openmp</dd><dt><span>intake_esm_attrs:cell_methods :</span></dt><dd>ts</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>am5.c96L65_am5f3b1r0_pdclim1850F.daily.land_cmip.mrso.1yr</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 757MB\n",
-       "Dimensions:     (time: 3650, bnds: 2, lat: 180, lon: 288)\n",
-       "Coordinates:\n",
-       "    average_DT  (time) timedelta64[ns] 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    average_T1  (time) object 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    average_T2  (time) object 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "  * bnds        (bnds) float64 16B 1.0 2.0\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "    lat_bnds    (lat, bnds) float64 3kB dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "    lon_bnds    (lon, bnds) float64 5kB dask.array<chunksize=(288, 2), meta=np.ndarray>\n",
-       "  * time        (time) object 29kB 0002-01-01 12:00:00 ... 0011-12-31 12:00:00\n",
-       "    time_bnds   (time, bnds) object 58kB dask.array<chunksize=(5, 2), meta=np.ndarray>\n",
-       "Data variables:\n",
-       "    mrso        (time, lat, lon) float32 757MB dask.array<chunksize=(5, 180, 288), meta=np.ndarray>\n",
-       "Attributes: (12/18)\n",
-       "    title:                            c96L65_am5f3b1r0_pdclim1850F\n",
-       "    grid_type:                        regular\n",
-       "    grid_tile:                        N/A\n",
-       "    code_release_version:             2023.01\n",
-       "    git_hash:                         unknown githash\n",
-       "    external_variables:               land_area\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:variable_id:     mrso\n",
-       "    intake_esm_attrs:chunk_freq:      1yr\n",
-       "    intake_esm_attrs:platform:        gfdl.ncrc5-deploy-prod-openmp\n",
-       "    intake_esm_attrs:cell_methods:    ts\n",
-       "    intake_esm_attrs:_data_format_:   netcdf\n",
-       "    intake_esm_dataset_key:           am5.c96L65_am5f3b1r0_pdclim1850F.daily...."
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr.decode_cf(ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "0dc03c24-25b6-48f6-9c44-d8bb677244eb",
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;mrso&#x27; (time: 0, lat: 180, lon: 288)&gt; Size: 0B\n",
-       "dask.array&lt;getitem, shape=(0, 180, 288), dtype=float32, chunksize=(0, 180, 288), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "    average_DT  (time) float64 0B dask.array&lt;chunksize=(0,), meta=np.ndarray&gt;\n",
-       "    average_T1  (time) float64 0B dask.array&lt;chunksize=(0,), meta=np.ndarray&gt;\n",
-       "    average_T2  (time) float64 0B dask.array&lt;chunksize=(0,), meta=np.ndarray&gt;\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "  * time        (time) float64 0B \n",
-       "Attributes:\n",
-       "    units:            kg m-2\n",
-       "    long_name:        Total Soil Moisture Content\n",
-       "    cell_methods:     area: mean time: mean\n",
-       "    ocean_fillvalue:  0.0\n",
-       "    cell_measures:    area: land_area\n",
-       "    time_avg_info:    average_T1,average_T2,average_DT\n",
-       "    standard_name:    soil_moisture_content\n",
-       "    interp_method:    conserve_order1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'mrso'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 0</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7bcb0c4b-0a3e-46cd-b13a-8c55fc8c65d7' class='xr-array-in' type='checkbox' checked><label for='section-7bcb0c4b-0a3e-46cd-b13a-8c55fc8c65d7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(0, 180, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 0 B </td>\n",
-       "                        <td> 0 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (0, 180, 288) </td>\n",
-       "                        <td> (0, 180, 288) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 22 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",
-       "        \n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-9dc135af-cb2c-432a-b998-ed5e7d47434d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9dc135af-cb2c-432a-b998-ed5e7d47434d' class='xr-section-summary' >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>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(0,), meta=np.ndarray&gt;</div><input id='attrs-a4cb4d4c-9a9e-4663-91f0-d57660807759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4cb4d4c-9a9e-4663-91f0-d57660807759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d35238-4943-4eca-9167-98abd29c9fca' class='xr-var-data-in' type='checkbox'><label for='data-86d35238-4943-4eca-9167-98abd29c9fca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days</dd><dt><span>long_name :</span></dt><dd>Length of average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 0 B </td>\n",
-       "                        <td> 0 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (0,) </td>\n",
-       "                        <td> (0,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 22 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",
-       "        \n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(0,), meta=np.ndarray&gt;</div><input id='attrs-6d7af76b-a236-4fe3-9258-6aca3e94a4e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d7af76b-a236-4fe3-9258-6aca3e94a4e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54885b76-4143-4168-83f5-cbdba121c815' class='xr-var-data-in' type='checkbox'><label for='data-54885b76-4143-4168-83f5-cbdba121c815' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Start time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 0 B </td>\n",
-       "                        <td> 0 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (0,) </td>\n",
-       "                        <td> (0,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 22 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",
-       "        \n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(0,), meta=np.ndarray&gt;</div><input id='attrs-f15616ca-8614-4129-805d-cc8a732029f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f15616ca-8614-4129-805d-cc8a732029f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f3ec8d-24ec-4c40-accc-c2ac035dd997' class='xr-var-data-in' type='checkbox'><label for='data-b4f3ec8d-24ec-4c40-accc-c2ac035dd997' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>End time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 0 B </td>\n",
-       "                        <td> 0 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (0,) </td>\n",
-       "                        <td> (0,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 22 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",
-       "        \n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-846f55a8-e1f1-4744-96fd-c82febb2b459' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-846f55a8-e1f1-4744-96fd-c82febb2b459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa48c59d-6305-4b5b-b6c9-0e98f1995c51' class='xr-var-data-in' type='checkbox'><label for='data-aa48c59d-6305-4b5b-b6c9-0e98f1995c51' 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_N</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
-       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
-       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
-       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
-       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
-       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
-       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
-       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
-       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
-       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
-       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
-       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
-       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
-       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
-       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
-       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-c9582ce4-dc7b-4844-b02c-34fffb3314b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9582ce4-dc7b-4844-b02c-34fffb3314b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-447001a9-87d2-4d10-ba00-8696f3d53905' class='xr-var-data-in' type='checkbox'><label for='data-447001a9-87d2-4d10-ba00-8696f3d53905' 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_E</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</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'>float64</div><div class='xr-var-preview xr-preview'></div><input id='attrs-3010d033-1d74-42a3-a7ce-e07005519f71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3010d033-1d74-42a3-a7ce-e07005519f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9261f8-ba1d-454a-b182-a4813b6303ec' class='xr-var-data-in' type='checkbox'><label for='data-3b9261f8-ba1d-454a-b182-a4813b6303ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>calendar :</span></dt><dd>noleap</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([], dtype=float64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5abcd6f-6aca-49c3-9c99-9281b43cbe57' class='xr-section-summary-in' type='checkbox'  ><label for='section-f5abcd6f-6aca-49c3-9c99-9281b43cbe57' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5238b040-954e-41cc-8e4e-247efb6f5771' class='xr-index-data-in' type='checkbox'/><label for='index-5238b040-954e-41cc-8e4e-247efb6f5771' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       ...\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce118ad5-2d5e-4b61-ad9c-0f6eccd53513' class='xr-index-data-in' type='checkbox'/><label for='index-ce118ad5-2d5e-4b61-ad9c-0f6eccd53513' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,\n",
-       "                    4.375,              5.625,              6.875,\n",
-       "                    8.125,              9.375,             10.625,\n",
-       "                   11.875,\n",
-       "       ...\n",
-       "                  348.125,            349.375,            350.625,\n",
-       "                  351.875,            353.125,            354.375,\n",
-       "                  355.625,            356.875,            358.125,\n",
-       "                  359.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c97c6c3e-74e0-47fc-b64d-71ccf7c45c16' class='xr-index-data-in' type='checkbox'/><label for='index-c97c6c3e-74e0-47fc-b64d-71ccf7c45c16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efb29496-1246-4a4e-a995-35fc3fdf5e2d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efb29496-1246-4a4e-a995-35fc3fdf5e2d' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>Total Soil Moisture Content</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd><dt><span>ocean_fillvalue :</span></dt><dd>0.0</dd><dt><span>cell_measures :</span></dt><dd>area: land_area</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>soil_moisture_content</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'mrso' (time: 0, lat: 180, lon: 288)> Size: 0B\n",
-       "dask.array<getitem, shape=(0, 180, 288), dtype=float32, chunksize=(0, 180, 288), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "    average_DT  (time) float64 0B dask.array<chunksize=(0,), meta=np.ndarray>\n",
-       "    average_T1  (time) float64 0B dask.array<chunksize=(0,), meta=np.ndarray>\n",
-       "    average_T2  (time) float64 0B dask.array<chunksize=(0,), meta=np.ndarray>\n",
-       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "  * lon         (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4\n",
-       "  * time        (time) float64 0B \n",
-       "Attributes:\n",
-       "    units:            kg m-2\n",
-       "    long_name:        Total Soil Moisture Content\n",
-       "    cell_methods:     area: mean time: mean\n",
-       "    ocean_fillvalue:  0.0\n",
-       "    cell_measures:    area: land_area\n",
-       "    time_avg_info:    average_T1,average_T2,average_DT\n",
-       "    standard_name:    soil_moisture_content\n",
-       "    interp_method:    conserve_order1"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "ds.mrso.sel(time=slice(\"0002-01-01\",\"0004-01-01\"))"
-   ]
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-   "execution_count": 75,
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-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 3650)&gt; Size: 29kB\n",
-       "array([ 365.5,  366.5,  367.5, ..., 4012.5, 4013.5, 4014.5])\n",
-       "Coordinates:\n",
-       "    average_DT  (time) float64 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T1  (time) float64 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "    average_T2  (time) float64 29kB dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;\n",
-       "  * time        (time) float64 29kB 365.5 366.5 367.5 ... 4.014e+03 4.014e+03\n",
-       "Attributes:\n",
-       "    units:          days since 0001-01-01 00:00:00\n",
-       "    long_name:      time\n",
-       "    axis:           T\n",
-       "    calendar_type:  NOLEAP\n",
-       "    calendar:       noleap\n",
-       "    bounds:         time_bnds\n",
-       "    cell_methods:   time: mean</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3650</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bcb5d4d8-eaef-451f-8971-b785ea706698' class='xr-array-in' type='checkbox' checked><label for='section-bcb5d4d8-eaef-451f-8971-b785ea706698' 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>365.5 366.5 367.5 368.5 ... 4.012e+03 4.012e+03 4.014e+03 4.014e+03</span></div><div class='xr-array-data'><pre>array([ 365.5,  366.5,  367.5, ..., 4012.5, 4013.5, 4014.5])</pre></div></div></li><li class='xr-section-item'><input id='section-a859dbe9-47a7-4c51-9278-ceb6974fa72d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a859dbe9-47a7-4c51-9278-ceb6974fa72d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-e0692b46-0065-42e9-b732-0f7140fd9889' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0692b46-0065-42e9-b732-0f7140fd9889' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-982943c7-0beb-4a62-9878-f8f954fb1a51' class='xr-var-data-in' type='checkbox'><label for='data-982943c7-0beb-4a62-9878-f8f954fb1a51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days</dd><dt><span>long_name :</span></dt><dd>Length of average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 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=\"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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-e588b866-54a2-4036-8703-e032c37d6756' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e588b866-54a2-4036-8703-e032c37d6756' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3774eb85-8a4d-42c5-94ba-cc1ce7efd707' class='xr-var-data-in' type='checkbox'><label for='data-3774eb85-8a4d-42c5-94ba-cc1ce7efd707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Start time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 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=\"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=\"100\" y1=\"0\" x2=\"100\" 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3650</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5,), meta=np.ndarray&gt;</div><input id='attrs-f87f8169-7b34-4dae-85b4-8ad9eb4d0eb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f87f8169-7b34-4dae-85b4-8ad9eb4d0eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45fb4dce-9724-4432-b88b-c5b3c6b31ce4' class='xr-var-data-in' type='checkbox'><label for='data-45fb4dce-9724-4432-b88b-c5b3c6b31ce4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>End time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</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> 28.52 kiB </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3650,) </td>\n",
-       "                        <td> (5,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 730 chunks in 21 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=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
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-       "  <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",
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-       "  <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=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></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'>float64</div><div class='xr-var-preview xr-preview'>365.5 366.5 ... 4.014e+03 4.014e+03</div><input id='attrs-4355185a-b030-4b01-bb2c-8148ca7beeaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4355185a-b030-4b01-bb2c-8148ca7beeaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c07bc0-4438-4b17-a4f3-a747c0dcddc8' class='xr-var-data-in' type='checkbox'><label for='data-13c07bc0-4438-4b17-a4f3-a747c0dcddc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>calendar :</span></dt><dd>noleap</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([ 365.5,  366.5,  367.5, ..., 4012.5, 4013.5, 4014.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74814256-1381-430d-ba3f-2f220df182fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-74814256-1381-430d-ba3f-2f220df182fe' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ed81249-af0f-4f4d-8c80-018e66ef5c70' class='xr-index-data-in' type='checkbox'/><label for='index-2ed81249-af0f-4f4d-8c80-018e66ef5c70' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 365.5,  366.5,  367.5,  368.5,  369.5,  370.5,  371.5,  372.5,  373.5,\n",
-       "        374.5,\n",
-       "       ...\n",
-       "       4005.5, 4006.5, 4007.5, 4008.5, 4009.5, 4010.5, 4011.5, 4012.5, 4013.5,\n",
-       "       4014.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=3650))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d6f65a9-c283-4c38-aa41-bd52e30d1047' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d6f65a9-c283-4c38-aa41-bd52e30d1047' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>calendar :</span></dt><dd>noleap</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'time' (time: 3650)> Size: 29kB\n",
-       "array([ 365.5,  366.5,  367.5, ..., 4012.5, 4013.5, 4014.5])\n",
-       "Coordinates:\n",
-       "    average_DT  (time) float64 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    average_T1  (time) float64 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    average_T2  (time) float64 29kB dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "  * time        (time) float64 29kB 365.5 366.5 367.5 ... 4.014e+03 4.014e+03\n",
-       "Attributes:\n",
-       "    units:          days since 0001-01-01 00:00:00\n",
-       "    long_name:      time\n",
-       "    axis:           T\n",
-       "    calendar_type:  NOLEAP\n",
-       "    calendar:       noleap\n",
-       "    bounds:         time_bnds\n",
-       "    cell_methods:   time: mean"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.mrso.time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a61e9c94-5d20-44d1-9a0a-6dab48dc444c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "intakebuilder",
-   "language": "python",
-   "name": "intakebuilder"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/doc/generation.rst b/doc/generation.rst
index a90698f..c91d29c 100644
--- a/doc/generation.rst
+++ b/doc/generation.rst
@@ -126,28 +126,82 @@ From a Python script
 Do you have a python script or a notebook where you could also include steps to generate a data catalog? 
 See example `here <https://github.com/NOAA-GFDL/CatalogBuilder/blob/main/catalogbuilder/scripts/gen_intake_gfdl_runner_config.py>`_
 
-Here is another example
+Here is another example *with a custom configuration*:
 
 .. code-block:: console
 
- #!/usr/bin/env python
+   import sys, os 
+   git_package_dir = '/home/a1r/git/forkCatalogBuilder-/'
+   sys.path.append(git_package_dir)
 
- #TODO test after conda pkg is published and make changes as needed 
- from catalogbuilder.scripts import gen_intake_gfdl
- import sys
+   import catalogbuilder
+   from catalogbuilder.scripts import gen_intake_gfdl
+   ######USER input begins########
 
- input_path = "archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pdclim1850F/gfdl.ncrc5-deploy-prod-openmp/pp"
- output_path = "test"
- try:
-  gen_intake_gfdl.create_catalog(input_path,output_path)
- except:
-  sys.exit("Exception occured calling gen_intake_gfdl.create_catalog")
+   #User provides the input directory for which a data catalog needs to be generated.
 
+   input_path = "/archive/John.Krasting/fre/FMS2024.02_OM5_20240724/CM4.5v01_om5b06_piC_noBLING/gfdl.ncrc5-intel23-prod-openmp/pp/"
+   #/archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pdclim1850F/gfdl.ncrc5-deploy-prod-openmp/pp/"
+
+   #USER inputs the output path. Based on the following setting, user can expect to see /home/a1r/mycatalog.csv and /home/a1r/mycatalog.json generated as output.
+
+   output_path = "/home/a1r/tests/mycatalog-jpk-def"
+   #NOTE: If your input_path does not look like the above in general, you will need to pass a --config which is custom 
+
+   #This is an example call to run catalog builder using a yaml config file.
+   configyaml = os.path.join(git_package_dir, 'catalogbuilder/scripts/configs/config-example2.yml')
+   #input_path = "/archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pdclim1850F/gfdl.ncrc5-deploy-prod-openmp/pp"
+   #output_path = "sample-mdtf-catalog"
+
+   def create_catalog_from_config(input_path=input_path,output_path=output_path,configyaml=configyaml):
+    csv, json = gen_intake_gfdl.create_catalog(input_path=input_path,output_path=output_path,config=configyaml)
+       return(csv,json)
+
+   if __name__ == '__main__':
+       create_catalog_from_config(input_path,output_path) #,configyaml)
+
+And an example *with a default configuration*:
+
+.. code-block:: console
+
+   import sys, os 
+   git_package_dir = '/home/a1r/git/forkCatalogBuilder-/'
+   sys.path.append(git_package_dir)
+
+   import catalogbuilder
+   from catalogbuilder.scripts import gen_intake_gfdl
+   print(gen_intake_gfdl.__file__)
+
+   ######USER input begins########
+
+   #User provides the input directory for which a data catalog needs to be generated.
+
+   input_path = "/archive/a1r/fre/FMS2024.02_OM5_20240724/CM4.5v01_om5b06_piC_noBLING/gfdl.ncrc5-intel23-prod-openmp/pp/"
+   #/archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pdclim1850F/gfdl.ncrc5-deploy-prod-openmp/pp/"
+
+   #USER inputs the output path. Based on the following setting, user can expect to see /home/a1r/mycatalog.csv and /home/a1r/mycatalog.json generated as output.
+
+   output_path = "/home/a1r/tests/static-catalog"
+   #NOTE: If your input_path does not look like the above in general, you will need to pass a --config which is custom 
+    ####END OF user input ##########
+
+   #This is an example call to run catalog builder using a yaml config file.
+
+   configyaml = os.path.join(git_package_dir, 'configs/config-template.yaml')
+   #input_path = "/archive/am5/am5/am5f3b1r0/c96L65_am5f3b1r0_pdclim1850F/gfdl.ncrc5-deploy-prod-openmp/pp"
+   #output_path = "sample-mdtf-catalog"
+
+   def create_catalog_from_config(input_path=input_path,output_path=output_path): #,configyaml=configyaml):
+       csv, json = gen_intake_gfdl.create_catalog(input_path=input_path,output_path=output_path)#,verbose=True,config=configyaml)
+        return(csv,json)
+
+   if __name__ == '__main__':
+       csv,json = create_catalog_from_config(input_path,output_path)#,configyaml)
+    
 From Jupyter Notebook
 ---------------------
 
-Refer to this `notebook <https://github.com/NOAA-GFDL/CatalogBuilder/blob/main/catalogbuilder/scripts/gen_intake_gfdl_notebook.ipynb>`_ to see how you can generate catalogs from a Jupyter Notebook
-
+Refer to this `notebook <https://github.com/aradhakrishnanGFDL/canopy-cats/blob/main/notebooks/cm4_default.ipynb>`_ to see how you can generate catalogs from a Jupyter Notebook
 
 .. image:: _static/catalog_generation.png
   :alt: Screenshot of a notebook showing catalog generation
@@ -178,11 +232,23 @@ See `Flags`_ here.
 See `Fre-CLI Documentation here <https://noaa-gfdl.github.io/fre-cli/>`_
 
 
-Flags
+Arguments/Options
 _____
 
+**Input/Output paths can be passed directly to catalog builder tool through calling command**
+
+All methods of catalog builder generation support direct input/output path passing.
+
+Input path must be the 1st argument. Output path must be the 2nd.
+
+Ex. gen_intake_gfdl.py /archive/Some.User/input-path ./output_path
+
+
 .. Reference `Flags`_.
 
+- --config - Allows for catalogs to be generated with a custom configuration. Requires path to YAML configuration file. (Ex. "--config custom_config.yaml")  
 - --overwrite - Overwrite an existing catalog at the given output path
 - --append - Append (without headerlist) to an existing catalog at the given output path
-- --slow - Activates slow mode which retrieves standard_name `(or long_name) where possible. **"Standard_name" must be in your output_path_template**
+- --slow - Activates slow mode which retrieves standard_name (or long_name) where possible. **"Standard_name" must be in your output_path_template**
+- --i - Optional method for passing input path
+- --o - Optional method for passing output path